From 23f3f62636b30bfd6c7fe54db1dc8ac1f0c40a6f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 16 Feb 2024 20:00:30 -0500
Subject: [PATCH 001/266] FEAT: trying a vertical 1d solver

---
 edu/examples/Advection/euler_vertical.cpp | 737 ++++++++++++++++++++++
 1 file changed, 737 insertions(+)
 create mode 100644 edu/examples/Advection/euler_vertical.cpp

diff --git a/edu/examples/Advection/euler_vertical.cpp b/edu/examples/Advection/euler_vertical.cpp
new file mode 100644
index 00000000..65a0a429
--- /dev/null
+++ b/edu/examples/Advection/euler_vertical.cpp
@@ -0,0 +1,737 @@
+
+// g++ -o euler1dv.exe -I/usr/local/include euler_vertical.cpp
+
+/// The armadillo library is to allow the use of 3d cubes and other
+/// array types, with array math built in. This eliminates loops!
+#include <armadillo>
+
+/// This is used for timing and the random seed generator:
+#include <chrono>
+
+// Types
+// Precision compile-time aliasing
+//#ifdef AETHER_USE_PRECISION_DOUBLE
+/// Precision type chosen to be `double` through `AETHER_USE_PRECISION_DOUBLE`
+using precision_t = double;
+//#else
+/// Precision type compile-time default to float.
+//using precision_t = float;
+//#endif
+
+/// Armadillo type vector (single column) with compile-time precision.
+using arma_vec = arma::Col<precision_t>;
+/// Armadillo type matrix (two dimension) with compile-time precision.
+using arma_mat = arma::Mat<precision_t>;
+/// Armadillo type cube (three dimension) with compile-time precision.
+using arma_cube = arma::Cube<precision_t>;
+
+#include <fstream>
+
+const precision_t t0 = 1000.0;
+const precision_t mass = 16.0 * 1.67e-27;
+const precision_t r0 = 1.0e19 * mass;
+const precision_t kb = 1.38e-23;
+const precision_t gravity = -kb * t0 / mass / 100000.0;
+
+// ---------------------------------------------------------
+// grid stretched creation
+// ---------------------------------------------------------
+
+arma_vec init_stretched_grid(int64_t nPts, int64_t nGCs) {
+
+  precision_t dx = 1.0;
+  arma_vec x(nPts + nGCs * 2);
+
+  precision_t factor = 1.0;
+  precision_t i2pi = 2.0 * 3.1415927 / (nPts-1);
+
+  x(nGCs) = 0.0;
+  
+  for (int64_t i = 1; i < nPts + nGCs; i++) {
+    x(i + nGCs) = x(i - 1 + nGCs) + dx  + factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  for (int64_t i = -1; i >= -nGCs; i--) {
+    x(i + nGCs) = x(i + 1 + nGCs) - dx  - factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  precision_t maxX = x(nPts + nGCs - 1);
+  x = 100.0 * x / maxX;
+  
+  return x;
+}
+
+// ---------------------------------------------------------
+// grid creation
+// ---------------------------------------------------------
+
+arma_vec init_grid(int64_t nPts, int64_t nGCs) {
+
+  precision_t dx = 1.0 / nPts;
+  arma_vec x(nPts + nGCs * 2);
+
+  // uniform grid:
+  for (int64_t i = -nGCs; i < nPts + nGCs; i++) {
+    x(i + nGCs) = i * dx;
+  }
+  // stretch to be 100 km:
+  x = x * 200.0 * 1000.0;
+
+  return x;
+}
+
+// ---------------------------------------------------------
+// bin edges
+// ---------------------------------------------------------
+
+arma_vec calc_bin_edges(arma_vec centers) {
+
+  int64_t nPts = centers.n_elem;
+  arma_vec edges(nPts+1);
+
+  precision_t dc = centers(1) - centers(0);
+
+  edges(0) = centers(0) - dc / 2.0;
+  edges(1) = centers(0) + dc / 2.0;
+  for (int64_t i = 2; i < nPts + 1; i++)
+    edges(i) = 2 * centers(i - 1) - edges(i - 1);
+  
+  return edges;
+}
+
+// ---------------------------------------------------------
+// bin widths
+// ---------------------------------------------------------
+
+arma_vec calc_bin_widths(arma_vec edges) {
+
+  int64_t nPts = edges.n_elem - 1;
+  arma_vec widths(nPts);
+
+  for (int64_t i = 0; i < nPts; i++)
+    widths(i) = edges(i + 1) - edges(i);
+  
+  return widths;
+}
+
+// ---------------------------------------------------------
+// initial rho
+// ---------------------------------------------------------
+
+arma_vec init_rho(int64_t nPts, arma_vec x) {
+
+  arma_vec rho(nPts);
+  precision_t h, dx;
+  rho(0) = r0;
+  for (int64_t i = 1; i < nPts; i++) {
+    // t = 100:
+    h = kb * t0 / mass / abs(gravity);
+    dx = x(i) - x(i-1);
+    rho(i) = rho(i-1) * exp( - dx / h);
+//std::cout << "i, rho : " << i
+//	      << " " << rho(i)
+//	      << " " << dx
+//	      << " " << h << "\n";
+  }
+
+  return rho;
+}
+
+// ---------------------------------------------------------
+// set BCs
+// ---------------------------------------------------------
+
+void set_bcs(int64_t nPts, int64_t nGCs,
+	     arma_vec x,
+	     arma_vec &rho,
+	     arma_vec &vel,
+	     arma_vec &temp) {
+
+  precision_t h, dx;
+  // Lower BC on rho:
+  rho(0) = r0;
+  vel(0) = 0.0; //vel(nGCs);
+  temp(0) = t0;
+  for (int64_t i = 1; i < nGCs; i++) {
+    h = kb * t0 / mass / abs(gravity);
+    dx = x(i) - x(i-1);
+    rho(i) = rho(i-1) * exp( - dx / h);
+    vel(i) = 0.0; //vel(nGCs);
+    temp(i) = t0;
+  }
+  // Upper BC on rho:
+  for (int64_t i = nPts + nGCs; i < nPts + 2 * nGCs; i++) {
+    h = kb * temp(i) / mass / abs(gravity);
+    dx = x(i) - x(i-1);
+    temp(i) = temp(i-1);
+    rho(i) = temp(i-1) / temp(i) * rho(i-1) * exp( - dx / h);
+    //if (vel(i-1) >= 0.0) {
+    vel(i) = vel(i-1);
+      //} else {
+      //vel(i) = 0.0;
+      //}
+  }
+
+  return;
+}
+
+// ---------------------------------------------------------
+// initial velocity
+// ---------------------------------------------------------
+
+arma_vec init_vel(int64_t nPts) {
+  arma_vec vel(nPts);
+  // all cells positive to right:
+  vel.zeros();
+  return vel;
+}
+
+// ---------------------------------------------------------
+// initial temp (e)
+// ---------------------------------------------------------
+
+arma_vec init_temp(int64_t nPts) {
+  arma_vec temp(nPts);
+  temp.ones();
+  temp = temp * t0;
+  return temp;
+}
+
+// ---------------------------------------------------------
+// exchange messages
+// ---------------------------------------------------------
+
+void exchange(arma_vec &values, int64_t nPts, int64_t nGCs) {
+
+  int64_t iEnd = nPts + 2 * nGCs;
+  // this is a periodic BC:
+  for (int64_t i = 0; i < nGCs; i++) {
+    values(i) = values(iEnd - 2 * nGCs + i);
+    values(iEnd - nGCs + i) = values(nGCs + i);
+  }
+}
+
+// ---------------------------------------------------------
+//
+// ---------------------------------------------------------
+
+arma_vec limiter_mc(arma_vec left,
+		    arma_vec right,
+		    int64_t nPts,
+		    int64_t nGCs) {
+
+  precision_t beta = 0.8;
+
+  arma_vec s = left % right;
+  arma_vec combined = (left + right) * 0.5;
+
+  left = left * beta;
+  right = right * beta;
+  arma_vec limited = left;
+  
+  for (int64_t i = 1; i < nPts + 2 * nGCs - 1; i++) {
+    if (s(i) < 0) {
+      // Sign < 0 means opposite signed left and right:
+      limited(i) = 0.0;
+    } else {
+      if (left(i) > 0 && right(i) > 0) {
+	if (right(i) < limited(i))
+	  limited(i) = right(i);
+	if (combined(i) < limited(i))
+	  limited(i) = combined(i);
+      } else {
+	if (right(i) > limited(i))
+	  limited(i) = right(i);
+	if (combined(i) > limited(i))
+	  limited(i) = combined(i);
+      }
+    }
+
+  }
+  return limited;
+}
+
+// ---------------------------------------------------------
+// calc gradients at centers
+//   - values and x defined at centers
+// ---------------------------------------------------------
+
+arma_vec calc_grad(arma_vec values,
+		   arma_vec x,
+		   int64_t nPts,
+		   int64_t nGCs) {
+
+  arma_vec gradients = values * 0.0;
+  arma_vec gradL = values * 0.0;
+  arma_vec gradR = values * 0.0;
+
+  precision_t factor1 = 0.625;
+  precision_t factor2 = 0.0416667;
+  precision_t h;
+
+  int64_t i;
+  
+  i = nGCs - 1;
+  h = 2.0 / (x(i+1) - x(i));
+  gradR(i) = h * (factor1 * (values(i+1) - values(i)) -
+		  factor2 * (values(i+2) - values(i-1)));
+  gradL(i) = (values(i) - values(i-1)) / (x(i) - x(i-1));
+  
+  for (i = nGCs; i < nPts + nGCs; i++) {
+    h = 2.0 / (x(i) - x(i-1));
+    gradL(i) = h * (factor1 * (values(i) - values(i-1)) -
+		    factor2 * (values(i+1) - values(i-2)));
+    h = 2.0 / (x(i+1) - x(i));
+    gradR(i) = h * (factor1 * (values(i+1) - values(i)) -
+		    factor2 * (values(i+2) - values(i-1)));
+  }
+  i = nPts + nGCs;
+  h = 2.0 / (x(i) - x(i-1));
+  gradL(i) = h * (factor1 * (values(i) - values(i-1)) -
+		  factor2 * (values(i+1) - values(i-2)));
+  gradR(i) = (values(i+1) - values(i)) / (x(i+1) - x(i));
+  
+  gradients = limiter_mc(gradL, gradR, nPts, nGCs);
+
+  return gradients;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_left(arma_vec values,
+			   arma_vec gradients,
+			   arma_vec x_centers,
+			   arma_vec x_edges,
+			   int64_t nPts,
+			   int64_t nGCs) {
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++)
+    projected(i + 1) = values(i) +
+      gradients(i) * (x_edges(i + 1) - x_centers(i));
+
+  return projected;
+}
+
+
+// ---------------------------------------------------------
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_right(arma_vec values,
+			    arma_vec gradients,
+			    arma_vec x_centers,
+			    arma_vec x_edges,
+			    int64_t nPts,
+			    int64_t nGCs) {
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++)
+    projected(i) = values(i) +
+      gradients(i) * (x_edges(i) - x_centers(i));
+
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Limiter on values
+//   projected is assumed to be on the edge between the
+//   i-1 and i cell (i-1/2)
+//   limited is returned at edges
+// ---------------------------------------------------------
+
+arma_vec limiter_value(arma_vec projected,
+		       arma_vec values,
+		       int64_t nPts,
+		       int64_t nGCs) {
+  
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec limited = projected;
+
+  precision_t mini, maxi;
+  
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
+
+    mini = values(i-1);
+    if (values(i) < mini)
+      mini = values(i);
+    maxi = values(i-1);
+    if (values(i) > maxi)
+      maxi = values(i);
+
+    if (limited(i) < mini)
+      limited(i) = mini;
+    if (limited(i) > maxi)
+      limited(i) = maxi;
+    
+  }
+  return limited;
+}
+
+
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_left_new(arma_vec values,
+			       arma_vec x_centers,
+			       arma_vec x_edges,
+			       int64_t nPts,
+			       int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  projected.zeros();
+
+  precision_t dxei, dxci, dxcip1, r;
+  
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i + 1) = values(i) + 
+      0.5 * dxei * (values(i) - values(i - 1)) / dxci + 
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
+  }
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_right_new(arma_vec values,
+				arma_vec x_centers,
+				arma_vec x_edges,
+				int64_t nPts,
+				int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  precision_t dxei, dxci, dxcip1, r;
+  
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i) = values(i) - 
+      0.5 * dxei * (values(i + 1) - values(i)) / dxcip1 +
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
+  }
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
+}
+
+// ---------------------------------------------------------
+// gudonov upwind scheme
+// ---------------------------------------------------------
+
+arma_vec gudonov(arma_vec valL,
+		 arma_vec valR,
+		 arma_vec velL,
+		 arma_vec velR,
+		 int64_t nPts,
+		 int64_t nGCs) {
+  
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec flux = velL * 0.0;
+  arma_vec vel = (velL + velR)/2.0;
+  
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
+    if (vel(i) > 0)
+      flux(i) = valR(i) * vel(i);
+    else
+      flux(i) = valL(i) * vel(i);
+  }
+  return flux;
+}
+
+// ---------------------------------------------------------
+// gudonov upwind scheme
+// ---------------------------------------------------------
+
+arma_vec rusanov(arma_vec valL,
+		 arma_vec valR,
+		 arma_vec velL,
+		 arma_vec velR,
+		 arma_vec widths,
+		 int64_t nPts,
+		 int64_t nGCs) {
+  
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec ws = abs((velL + velR)/2.0) + 1.;
+  arma_vec fluxL = valL % velL;
+  arma_vec fluxR = valR % velR;
+  arma_vec valDiff = valL - valR;
+  arma_vec flux = (fluxL + fluxR) / 2.0;
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++)
+    flux(i) = flux(i) - ws(i)/2 * valDiff(i);
+    
+  return flux;
+}
+
+// ---------------------------------------------------------
+//
+// ---------------------------------------------------------
+
+void output(arma_vec values,
+	    std::string filename,
+	    bool DoAppend,
+	    int64_t nPts,
+	    int64_t nGCs) {
+
+  std::ofstream outfile;
+  if (DoAppend)
+    outfile.open(filename, std::ios_base::app);
+  else
+    outfile.open(filename);
+  
+  int64_t i;
+  for (i = 0; i < nPts + 2 * nGCs ; i++) {
+    outfile << values(i) << " ";
+  }
+  outfile << "\n";
+  outfile.close();
+    
+}
+
+
+// ---------------------------------------------------------
+//
+// ---------------------------------------------------------
+
+
+// ---------------------------------------------------------
+// main code
+// ---------------------------------------------------------
+
+int main() {
+
+  precision_t timeMax = 0.1;
+  precision_t time = 0.0;
+  
+  precision_t gamma = 5.0/3.0;
+  precision_t KoM = kb/mass;
+  //gamma = kb/mass;
+
+  int64_t iStep;
+  
+  int64_t nPts = 100, i;
+  int64_t nGCs = 2;
+  int64_t nPtsTotal = nGCs + nPts + nGCs;
+
+  arma_vec x = init_grid(nPts, nGCs);
+  //arma_vec x = init_stretched_grid(nPts, nGCs);
+  arma_vec edges = calc_bin_edges(x);
+  arma_vec widths = calc_bin_widths(edges);
+
+  precision_t dt = 0.00001 * x(nPts + nGCs - 1) / nPts;
+  int64_t nSteps = 100.0 / dt;
+  
+  // std::cout << "dt : " << dt << "; nSteps: " << nSteps << "\n";
+
+  // state variables:
+  arma_vec rho = init_rho(nPtsTotal, x);
+  arma_vec grad_rho;
+  arma_vec rhoL;
+  arma_vec rhoR;
+
+  arma_vec vel = init_vel(nPtsTotal);
+  arma_vec grad_vel;
+  arma_vec velL;
+  arma_vec velR;
+
+  // temp is "e" (not E):
+  arma_vec temp = init_temp(nPtsTotal);
+  arma_vec grad_temp;
+  arma_vec tempL, tempR;
+
+  arma_vec eq1Flux, eq1FluxL, eq1FluxR;
+  arma_vec eq2Flux, eq2FluxL, eq2FluxR;
+  arma_vec eq3Flux, eq3FluxL, eq3FluxR;
+  arma_vec wsL, wsR, ws;
+  arma_vec dtAll;
+
+  arma_vec diff;
+
+//  exchange(rho, nPts, nGCs);
+//  exchange(vel, nPts, nGCs);
+//  exchange(temp, nPts, nGCs);
+
+  arma_vec momentum = rho % vel;
+  arma_vec grad_momenum, momentumL, momentumR;
+
+  arma_vec totalE = rho % temp * KoM + 0.5 * rho % vel % vel;
+  arma_vec grad_totalE, totaleL, totaleR;
+  
+  output(rho, "rho.txt", false, nPts, nGCs);
+  output(vel, "vel.txt", false, nPts, nGCs);
+  output(temp, "temp.txt", false, nPts, nGCs);
+  output(totalE, "totale.txt", false, nPts, nGCs);
+  output(x, "x.txt", false, nPts, nGCs);
+
+  iStep = 0;
+  while (time < timeMax) {
+
+    std::cout << "iStep = " << iStep
+	      << "; time =  " << time
+	      << "; vel =  " << vel(80) << "\n";
+
+    // -----------------------------------
+    // Rho
+
+    grad_rho = calc_grad(rho, x, nPts, nGCs);
+
+    // Right side of edge from left
+    rhoR = project_from_left_new(rho,
+			     x, edges,
+			     nPts, nGCs);
+    
+    // Left side of edge from left
+    rhoL = project_from_right_new(rho,
+			      x, edges,
+			      nPts, nGCs);
+
+    // -----------------------------------
+    // vel
+
+    grad_vel = calc_grad(vel, x, nPts, nGCs);
+    // Right side of edge from left
+    velR = project_from_left_new(vel,
+			     x, edges,
+			     nPts, nGCs);
+    // Left side of edge from left
+    velL = project_from_right_new(vel,
+			      x, edges,
+			      nPts, nGCs);
+
+    // -----------------------------------
+    // temp
+
+    grad_temp = calc_grad(temp, x, nPts, nGCs);
+    // Right side of edge from left
+    tempR = project_from_left_new(temp,
+			      x, edges,
+			      nPts, nGCs);
+    // Left side of edge from left
+    tempL = project_from_right_new(temp,
+			       x, edges,
+			       nPts, nGCs);
+
+    // eq 1 = rho
+    // eq 2 = rho * vel (momentum)
+    // eq 3 = E --> rho * (temp + 0.5 * vel^2) (totalE) 
+    
+    // Calculate fluxes of different terms at the edges:
+    eq1FluxL = rhoL % velL;
+    eq1FluxR = rhoR % velR;
+
+    momentumL = eq1FluxL;
+    momentumR = eq1FluxR;
+    totaleL = rhoL % tempL * KoM + 0.5 * rhoL % velL % velL;
+    totaleR = rhoR % tempR * KoM + 0.5 * rhoR % velR % velR;
+
+    //eq2FluxL = rhoL % (velL % velL + (gamma-1) * tempL);
+    //eq2FluxR = rhoR % (velR % velR + (gamma-1) * tempR);
+    eq2FluxL = rhoL % (velL % velL + KoM * tempL);
+    eq2FluxR = rhoR % (velR % velR + KoM * tempR);
+
+    //eq3FluxL = rhoL % velL % (0.5 * velL % velL + gamma * tempL);
+    //eq3FluxR = rhoR % velR % (0.5 * velR % velR + gamma * tempR);
+    eq3FluxL = rhoL % velL % (0.5 * velL % velL + gamma * tempL * KoM);
+    eq3FluxR = rhoR % velR % (0.5 * velR % velR + gamma * tempR * KoM);
+
+    // Calculate the wave speed for the diffusive flux:
+    //wsL = abs(velL) + sqrt(gamma * (gamma-1.0) * tempL);
+    //wsR = abs(velR) + sqrt(gamma * (gamma-1.0) * tempR);
+    wsL = abs(velL) + sqrt(gamma * KoM * tempL);
+    wsR = abs(velR) + sqrt(gamma * KoM * tempR);
+    ws = wsR;
+    dt = 0.0;
+    for (i = 1; i < nPts + nGCs*2 - 1; i++) {
+      if (wsR(i) > ws(i)) ws(i) = wsR(i);
+      if (widths(i) / ws(i) > dt)
+	dt = widths(i) / ws(i);
+    }
+    dt = dt * 0.001;
+    time = time + dt;
+    
+    // Calculate average flux at the edges:
+    diff = rhoR - rhoL;
+    eq1Flux = (eq1FluxL + eq1FluxR) / 2 + 0.5 * ws % diff;
+    diff = momentumR - momentumL;
+    eq2Flux = (eq2FluxL + eq2FluxR) / 2 + 0.5 * ws % diff;
+    diff = totaleR - totaleL;
+    eq3Flux = (eq3FluxL + eq3FluxR) / 2 + 0.5 * ws % diff;
+    
+    // Update values:
+    for (i = nGCs; i < nPts + nGCs*2 - 1; i++) {
+      rho(i) = rho(i) - dt / widths(i) * (eq1Flux(i+1) - eq1Flux(i));
+      momentum(i) = momentum(i) - dt / widths(i) * (eq2Flux(i+1) - eq2Flux(i))
+	+ gravity * rho(i) * dt;
+      totalE(i) = totalE(i) - dt / widths(i) * (eq3Flux(i+1) - eq3Flux(i));
+    }
+
+    //exchange(rho, nPts, nGCs);
+    //exchange(momentum, nPts, nGCs);
+    //exchange(totalE, nPts, nGCs);
+    vel = momentum / rho;
+    temp = (totalE / rho - 0.5 * vel % vel) / KoM;
+    
+    set_bcs(nPts, nGCs, x, rho, vel, temp);
+
+    output(rho, "rho.txt", true, nPts, nGCs);
+    output(vel, "vel.txt", true, nPts, nGCs);
+    output(temp, "temp.txt", true, nPts, nGCs);
+    output(totalE, "totale.txt", true, nPts, nGCs);
+    //output(rhoL, "rhor.txt", false, nPts, nGCs);
+    //output(rhoR, "rhol.txt", false, nPts, nGCs);
+    iStep++;
+    
+  }
+  
+  return 0;
+}

From 0e23bec51b73cfae8ba0ab52e1a513fd646aefdd Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 16 Feb 2024 20:02:43 -0500
Subject: [PATCH 002/266] FEAT: use only variables needed from header

---
 edu/examples/Advection/advect.cpp | 223 ++++++++++++++++++++++++------
 1 file changed, 184 insertions(+), 39 deletions(-)

diff --git a/edu/examples/Advection/advect.cpp b/edu/examples/Advection/advect.cpp
index 8fe26261..76fac277 100644
--- a/edu/examples/Advection/advect.cpp
+++ b/edu/examples/Advection/advect.cpp
@@ -1,7 +1,31 @@
 
 // g++ -I/usr/local/include -I/Users/ridley/Software/Json/json/include main.cpp
+// g++ -I/usr/local/include -o advect1d advect.cpp
+
+/// The armadillo library is to allow the use of 3d cubes and other
+/// array types, with array math built in. This eliminates loops!
+#include <armadillo>
+
+/// This is used for timing and the random seed generator:
+#include <chrono>
+
+// Types
+// Precision compile-time aliasing
+#ifdef AETHER_USE_PRECISION_DOUBLE
+/// Precision type chosen to be `double` through `AETHER_USE_PRECISION_DOUBLE`
+using precision_t = double;
+#else
+/// Precision type compile-time default to float.
+using precision_t = float;
+#endif
+
+/// Armadillo type vector (single column) with compile-time precision.
+using arma_vec = arma::Col<precision_t>;
+/// Armadillo type matrix (two dimension) with compile-time precision.
+using arma_mat = arma::Mat<precision_t>;
+/// Armadillo type cube (three dimension) with compile-time precision.
+using arma_cube = arma::Cube<precision_t>;
 
-#include "../../../include/aether.h"
 #include <fstream>
 
 // ---------------------------------------------------------
@@ -17,10 +41,40 @@ arma_vec init_grid(int64_t nPts, int64_t nGCs) {
   for (int64_t i = -nGCs; i < nPts + nGCs; i++) {
     x(i + nGCs) = i * dx;
   }
+  precision_t maxX = x(nPts + nGCs - 1);
+  x = 100.0 * x / maxX;
 
   return x;
 }
 
+// ---------------------------------------------------------
+// grid stretched creation
+// ---------------------------------------------------------
+
+arma_vec init_stretched_grid(int64_t nPts, int64_t nGCs) {
+
+  precision_t dx = 1.0;
+  arma_vec x(nPts + nGCs * 2);
+
+  precision_t factor = 1.0;
+  precision_t i2pi = 2.0 * 3.1415927 / (nPts-1);
+
+  x(nGCs) = 0.0;
+  
+  for (int64_t i = 1; i < nPts + nGCs; i++) {
+    x(i + nGCs) = x(i - 1 + nGCs) + dx  + factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  for (int64_t i = -1; i >= -nGCs; i--) {
+    x(i + nGCs) = x(i + 1 + nGCs) - dx  - factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  precision_t maxX = x(nPts + nGCs - 1);
+  x = 100.0 * x / maxX;
+  
+  return x;
+}
+
 // ---------------------------------------------------------
 // bin edges
 // ---------------------------------------------------------
@@ -80,6 +134,7 @@ arma_vec init_vel(int64_t nPts) {
   arma_vec vel(nPts);
   // all cells positive to right:
   vel.ones();
+  vel = -1.0 * vel;
   return vel;
 }
 
@@ -182,6 +237,43 @@ arma_vec calc_grad(arma_vec values,
   return gradients;
 }
 
+// ---------------------------------------------------------
+// Limiter on values
+//   projected is assumed to be on the edge between the
+//   i-1 and i cell (i-1/2)
+//   limited is returned at edges
+// ---------------------------------------------------------
+
+arma_vec limiter_value(arma_vec projected,
+		       arma_vec values,
+		       int64_t nPts,
+		       int64_t nGCs) {
+  
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec limited = projected;
+
+  precision_t mini, maxi;
+  
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
+
+    mini = values(i-1);
+    if (values(i) < mini)
+      mini = values(i);
+    maxi = values(i-1);
+    if (values(i) > maxi)
+      maxi = values(i);
+
+    if (limited(i) < mini)
+      limited(i) = mini;
+    if (limited(i) > maxi)
+      limited(i) = maxi;
+    
+  }
+  return limited;
+}
+
 // ---------------------------------------------------------
 // Project gradients + values to the right face, from the left
 //   returned values are on the i - 1/2 edges
@@ -209,6 +301,42 @@ arma_vec project_from_left(arma_vec values,
   return projected;
 }
 
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_left_new(arma_vec values,
+			       arma_vec x_centers,
+			       arma_vec x_edges,
+			       int64_t nPts,
+			       int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  projected.zeros();
+
+  precision_t dxei, dxci, dxcip1, r;
+  
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i + 1) = values(i) + 
+      0.5 * dxei * (values(i) - values(i - 1)) / dxci + 
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
+  }
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
+}
+
 
 // ---------------------------------------------------------
 // Project gradients + values to the left face, from the right
@@ -238,40 +366,39 @@ arma_vec project_from_right(arma_vec values,
 }
 
 // ---------------------------------------------------------
-// Limiter on values
-//   projected is assumed to be on the edge between the
-//   i-1 and i cell (i-1/2)
-//   limited is returned at edges
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
 // ---------------------------------------------------------
 
-arma_vec limiter_value(arma_vec projected,
-		       arma_vec values,
-		       int64_t nPts,
-		       int64_t nGCs) {
-  
-  int64_t iStart = 0;
-  int64_t iEnd = nPts + 2 * nGCs;
-
-  arma_vec limited = projected;
+arma_vec project_from_right_new(arma_vec values,
+				arma_vec x_centers,
+				arma_vec x_edges,
+				int64_t nPts,
+				int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
 
-  precision_t mini, maxi;
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  precision_t dxei, dxci, dxcip1, r;
   
-  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
-
-    mini = values(i-1);
-    if (values(i) < mini)
-      mini = values(i);
-    maxi = values(i-1);
-    if (values(i) > maxi)
-      maxi = values(i);
+  projected.zeros();
 
-    if (limited(i) < mini)
-      limited(i) = mini;
-    if (limited(i) > maxi)
-      limited(i) = maxi;
-    
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i) = values(i) - 
+      0.5 * dxei * (values(i + 1) - values(i)) / dxcip1 +
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
   }
-  return limited;
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
 }
 
 // ---------------------------------------------------------
@@ -363,19 +490,21 @@ void output(arma_vec values,
 
 int main() {
 
-  precision_t dt = 0.01;
-
-  int64_t nSteps = 3;
-  int64_t iStep;
-  
-  int64_t nPts = 60;
+  int64_t nPts = 200;
   int64_t nGCs = 2;
   int64_t nPtsTotal = nGCs + nPts + nGCs;
 
   arma_vec x = init_grid(nPts, nGCs);
+  //arma_vec x = init_stretched_grid(nPts, nGCs);
   arma_vec edges = calc_bin_edges(x);
   arma_vec widths = calc_bin_widths(edges);
 
+  precision_t dt = 0.1 * x(nPts + nGCs - 1) / nPts;
+  precision_t time = 0.0;
+
+  int64_t nSteps = x(nPts + nGCs - 1) / dt;
+  int64_t iStep;
+    
   arma_vec grad_rho;
   arma_vec rhoL;
   arma_vec rhoR;
@@ -392,19 +521,29 @@ int main() {
   exchange(vel, nPts, nGCs);
 
   output(rho, "rho.txt", false, nPts, nGCs);
+  output(x, "x.txt", false, nPts, nGCs);
   
   for (iStep = 0; iStep < nSteps; iStep++) {
+
+    std::cout << "iStep = " << iStep << "; time =  " << time << "\n";
+    time = time + dt;
   
     grad_rho = calc_grad(rho, x, nPts, nGCs);
 
     // Right side of edge from left
-    rhoR = project_from_left(rho, grad_rho,
+//rhoR = project_from_left(rho, grad_rho,
+//			     x, edges,
+//			     nPts, nGCs);
+    rhoR = project_from_left_new(rho,
 			     x, edges,
 			     nPts, nGCs);
     //rhoR = limiter_value(rhoR, rho, nPts, nGCs);
 
     // Left side of edge from left
-    rhoL = project_from_right(rho, grad_rho,
+//    rhoL = project_from_right(rho, grad_rho,
+//			      x, edges,
+//			      nPts, nGCs);
+    rhoL = project_from_right_new(rho,
 			      x, edges,
 			      nPts, nGCs);
     //rhoL = limiter_value(rhoL, rho, nPts, nGCs);
@@ -413,13 +552,19 @@ int main() {
     grad_vel = calc_grad(vel, x, nPts, nGCs);
 
     // Right side of edge from left
-    velR = project_from_left(vel, grad_vel,
+//    velR = project_from_left(vel, grad_vel,
+//			     x, edges,
+//			     nPts, nGCs);
+    velR = project_from_left_new(vel,
 			     x, edges,
 			     nPts, nGCs);
     //velR = limiter_value(velR, vel, nPts, nGCs);
 
     // Left side of edge from left
-    velL = project_from_right(vel, grad_vel,
+//    velL = project_from_right(vel, grad_vel,
+//			      x, edges,
+//			      nPts, nGCs);
+    velL = project_from_right_new(vel,
 			      x, edges,
 			      nPts, nGCs);
     //velL = limiter_value(velL, vel, nPts, nGCs);

From 8c883062bf2e0d76f6c13e02ba6e0b10e6c62063 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 16 Feb 2024 20:03:15 -0500
Subject: [PATCH 003/266] FEAT: stretched grid

---
 edu/examples/Advection/euler.cpp | 159 ++++++++++++++++++++++++++++---
 1 file changed, 145 insertions(+), 14 deletions(-)

diff --git a/edu/examples/Advection/euler.cpp b/edu/examples/Advection/euler.cpp
index 5a2ad1e4..d3a7f652 100644
--- a/edu/examples/Advection/euler.cpp
+++ b/edu/examples/Advection/euler.cpp
@@ -1,9 +1,60 @@
 
-// g++ -I/usr/local/include -I/Users/ridley/Software/Json/json/include main.cpp
+// g++ -o euler1d.exe -I/usr/local/include euler.cpp
+
+/// The armadillo library is to allow the use of 3d cubes and other
+/// array types, with array math built in. This eliminates loops!
+#include <armadillo>
+
+/// This is used for timing and the random seed generator:
+#include <chrono>
+
+// Types
+// Precision compile-time aliasing
+#ifdef AETHER_USE_PRECISION_DOUBLE
+/// Precision type chosen to be `double` through `AETHER_USE_PRECISION_DOUBLE`
+using precision_t = double;
+#else
+/// Precision type compile-time default to float.
+using precision_t = float;
+#endif
+
+/// Armadillo type vector (single column) with compile-time precision.
+using arma_vec = arma::Col<precision_t>;
+/// Armadillo type matrix (two dimension) with compile-time precision.
+using arma_mat = arma::Mat<precision_t>;
+/// Armadillo type cube (three dimension) with compile-time precision.
+using arma_cube = arma::Cube<precision_t>;
 
-#include "../../../include/aether.h"
 #include <fstream>
 
+// ---------------------------------------------------------
+// grid stretched creation
+// ---------------------------------------------------------
+
+arma_vec init_stretched_grid(int64_t nPts, int64_t nGCs) {
+
+  precision_t dx = 1.0;
+  arma_vec x(nPts + nGCs * 2);
+
+  precision_t factor = 1.0;
+  precision_t i2pi = 2.0 * 3.1415927 / (nPts-1);
+
+  x(nGCs) = 0.0;
+  
+  for (int64_t i = 1; i < nPts + nGCs; i++) {
+    x(i + nGCs) = x(i - 1 + nGCs) + dx  + factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  for (int64_t i = -1; i >= -nGCs; i--) {
+    x(i + nGCs) = x(i + 1 + nGCs) - dx  - factor * (1 + cos(i * i2pi));
+    std::cout << "i : " << i << " " << cos(i * i2pi) << "\n";
+  }
+  precision_t maxX = x(nPts + nGCs - 1);
+  x = 100.0 * x / maxX;
+  
+  return x;
+}
+
 // ---------------------------------------------------------
 // grid creation
 // ---------------------------------------------------------
@@ -17,6 +68,7 @@ arma_vec init_grid(int64_t nPts, int64_t nGCs) {
   for (int64_t i = -nGCs; i < nPts + nGCs; i++) {
     x(i + nGCs) = i * dx;
   }
+  x = x * 100.0;
 
   return x;
 }
@@ -63,7 +115,7 @@ arma_vec init_rho(int64_t nPts) {
 
   arma_vec rho(nPts);
   for (int64_t i = 0; i < nPts; i++) {
-    if (i > (1 * nPts)/4 && i < 3 * nPts / 4)
+    if (i > nPts/2 - 3 && i < nPts / 2 + 3)
       rho(i) = 2.2;
     else
       rho(i) = 2.0;
@@ -285,6 +337,79 @@ arma_vec limiter_value(arma_vec projected,
   return limited;
 }
 
+
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_left_new(arma_vec values,
+			       arma_vec x_centers,
+			       arma_vec x_edges,
+			       int64_t nPts,
+			       int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  projected.zeros();
+
+  precision_t dxei, dxci, dxcip1, r;
+  
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i + 1) = values(i) + 
+      0.5 * dxei * (values(i) - values(i - 1)) / dxci + 
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
+  }
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_vec project_from_right_new(arma_vec values,
+				arma_vec x_centers,
+				arma_vec x_edges,
+				int64_t nPts,
+				int64_t nGCs) {
+  int64_t iStart = 1;
+  int64_t iEnd = nPts + 2 * nGCs - 1;
+
+  // Define at edges:
+  arma_vec projected(nPts + 2 * nGCs + 1);
+  precision_t dxei, dxci, dxcip1, r;
+  
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t i = iStart; i < iEnd; i++) {
+    dxei = x_edges(i + 1) - x_edges(i);
+    dxci = x_centers(i) - x_centers(i - 1);
+    dxcip1 = x_centers(i + 1) - x_centers(i);
+    r = dxcip1 / dxci;
+    projected(i) = values(i) - 
+      0.5 * dxei * (values(i + 1) - values(i)) / dxcip1 +
+      0.125 * dxei * dxei * (values(i + 1) + r * values(i - 1) - (1 + r) * values(i)) / (dxci * dxcip1);
+  }
+
+  projected = limiter_value(projected, values, nPts, nGCs);
+  
+  return projected;
+}
+
 // ---------------------------------------------------------
 // gudonov upwind scheme
 // ---------------------------------------------------------
@@ -374,20 +499,24 @@ void output(arma_vec values,
 
 int main() {
 
-  precision_t dt = 0.0005;
+  precision_t time = 0.0;
+  
   precision_t gamma = 5.0/3.0;
 
-  int64_t nSteps = 100;
   int64_t iStep;
   
-  int64_t nPts = 60;
+  int64_t nPts = 100;
   int64_t nGCs = 2;
   int64_t nPtsTotal = nGCs + nPts + nGCs;
 
-  arma_vec x = init_grid(nPts, nGCs);
+  //arma_vec x = init_grid(nPts, nGCs);
+  arma_vec x = init_stretched_grid(nPts, nGCs);
   arma_vec edges = calc_bin_edges(x);
   arma_vec widths = calc_bin_widths(edges);
 
+  precision_t dt = 0.01 * x(nPts + nGCs - 1) / nPts;
+  int64_t nSteps = 10.0 / dt;
+
   // state variables:
   arma_vec rho = init_rho(nPtsTotal);
   arma_vec grad_rho;
@@ -424,10 +553,12 @@ int main() {
   output(rho, "rho.txt", false, nPts, nGCs);
   output(vel, "vel.txt", false, nPts, nGCs);
   output(temp, "temp.txt", false, nPts, nGCs);
+  output(x, "x.txt", false, nPts, nGCs);
   
   for (iStep = 0; iStep < nSteps; iStep++) {
 
-    std::cout << "step : " << iStep << "\n";
+    std::cout << "iStep = " << iStep << "; time =  " << time << "\n";
+    time = time + dt;
     
     // -----------------------------------
     // Rho
@@ -435,12 +566,12 @@ int main() {
     grad_rho = calc_grad(rho, x, nPts, nGCs);
 
     // Right side of edge from left
-    rhoR = project_from_left(rho, grad_rho,
+    rhoR = project_from_left_new(rho,
 			     x, edges,
 			     nPts, nGCs);
     
     // Left side of edge from left
-    rhoL = project_from_right(rho, grad_rho,
+    rhoL = project_from_right_new(rho,
 			      x, edges,
 			      nPts, nGCs);
 
@@ -449,11 +580,11 @@ int main() {
 
     grad_vel = calc_grad(vel, x, nPts, nGCs);
     // Right side of edge from left
-    velR = project_from_left(vel, grad_vel,
+    velR = project_from_left_new(vel,
 			     x, edges,
 			     nPts, nGCs);
     // Left side of edge from left
-    velL = project_from_right(vel, grad_vel,
+    velL = project_from_right_new(vel,
 			      x, edges,
 			      nPts, nGCs);
 
@@ -462,11 +593,11 @@ int main() {
 
     grad_temp = calc_grad(temp, x, nPts, nGCs);
     // Right side of edge from left
-    tempR = project_from_left(temp, grad_temp,
+    tempR = project_from_left_new(temp,
 			      x, edges,
 			      nPts, nGCs);
     // Left side of edge from left
-    tempL = project_from_right(temp, grad_temp,
+    tempL = project_from_right_new(temp,
 			       x, edges,
 			       nPts, nGCs);
 

From ada2f9325ff8651fb1cbbdd51fcdc8f3ff7c963d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 16 Feb 2024 20:03:49 -0500
Subject: [PATCH 004/266] FEAT: mods for plotting

---
 edu/examples/Advection/plot.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/edu/examples/Advection/plot.py b/edu/examples/Advection/plot.py
index 3dc8e6d9..a922e766 100755
--- a/edu/examples/Advection/plot.py
+++ b/edu/examples/Advection/plot.py
@@ -33,8 +33,11 @@ def read_file(file):
 
     return values
 
-def plot_data(values, fileout):
+def plot_data(values, fileout, x = []):
 
+    if (len(x) == 0):
+        nPts = len(values[0])
+        x = np.arange(0, nPts)
     fig = plt.figure(figsize = (10,10))
     ax = fig.add_subplot(111)
     n = len(values)-1
@@ -42,12 +45,13 @@ def plot_data(values, fileout):
         nSkip = int(n / 10)
     else:
         nSkip = 1
+        if (n == 0):
+            n = 1
     for i, v in enumerate(values):
         per = 0.1 + 0.9 * float(i) / float(n)
         if (i % nSkip == 0):
-            ax.plot(v, alpha = per)
-
-    ax.plot(values[-1])
+            print('Plotting... ', i, values[i][10])
+            ax.plot(x, values[i])
     fig.savefig(fileout)
     plt.close()
         
@@ -61,6 +65,7 @@ def plot_data(values, fileout):
     fileout = fileout[0]
 
 values = read_file(filein)
-
-plot_data(values, fileout)
+x = read_file('x.txt')
+x = x[0]
+plot_data(values, fileout, x = x)
 

From 0b69f4d7563f7fb88eaa0a22c22006ce8e19b749 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 17:18:50 -0400
Subject: [PATCH 005/266] FEAT: Update binary search `search_altitudes`

- Make it into a general function (not just altitude)
- Handles ooblate planets now!
- Optional argument to not search ghost cells
---
 include/grid.h                    |  3 ---
 src/solver_grid_interpolation.cpp | 27 +++++++++++++++------------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index fed807e9..ce777925 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -531,9 +531,6 @@ class Grid
     bool in_grid;
   };
 
-  // Return the index of the last element that has altitude smaller than or euqal to the input
-  uint64_t search_altitude(const precision_t alt_in) const;
-
   // Calculate the range of a spherical grid
   void get_sphere_grid_range(struct sphere_range &sr) const;
   // Calculate the range of a cubesphere grid
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 39bb2dc3..a98f9b50 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -72,22 +72,23 @@ int64_t get_cube_surface_number(const arma_vec &point_in) {
 // Helper variables / function ends. The following are all member functions of Grid class
 
 // --------------------------------------------------------------------------
-// Return the index of the last element that has altitude smaller than or euqal to the input
+// Return the index of the last element that has a value smaller than or equal to the input
+// - Optional argument (nGCs=0) since we cannot see grid info.
 // --------------------------------------------------------------------------
 
-uint64_t Grid::search_altitude(const precision_t alt_in) const {
+uint64_t binary_search_array(precision_t val_in, arma_vec ref_arr, int64_t nGCs = 0) {
   // Copy from std::upper_bound. Can't directly use it
   // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
   uint64_t first, last, len;
   first = nGCs;
-  last = nAlts - nGCs;
+  last = ref_arr.size();
   len = last - first;
 
   while (len > 0) {
     uint64_t half = len >> 1;
     uint64_t mid = first + half;
 
-    if (geoAlt_scgc(0, 0, mid) > alt_in)
+    if (ref_arr(mid) > val_in)
       len = half;
 
     else {
@@ -227,10 +228,12 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 
   // The altitude may not be linearly spaced, so use binary search to find
   // the first element smaller than or equal to the altitude of the give point
-  // Implemented in search_altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+  // Implemented in binary_search_array
+  // - since Alt can be latitude-dependent, this needs to be done last.
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
+                  coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;
@@ -302,10 +305,10 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   coef.iCol = static_cast<uint64_t>(col_frac_index);
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
-  // Use binary search to find the index for altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+  // Use binary search to find the index for altitude (handles oblate planets)
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;

From 235e527ec61d5f0a4f8aa18f95313e66846d3fdf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 19:20:39 -0400
Subject: [PATCH 006/266] BUG: Complete the change from MagLat to MagInvLat.

MagLat is not defined on the geo grid (yet)

> But invLat is made for both the geo grid & mag grid. And included in 'ions'/'bfield' outputs
---
 include/grid.h    | 3 +--
 src/fill_grid.cpp | 4 ++--
 src/output.cpp    | 4 ++++
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index ce777925..f6a660ec 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -69,8 +69,7 @@ class Grid
   // These define the magnetic grid:
   // Armidillo Cube Versions:
   arma_cube magLon_scgc, magX_scgc;
-  // The magnetic latitude and altitude need to be defined better. This should be the angle between
-  // magnetic equator and the point, but sometimes it is invariant latitude.
+  // The magnetic latitude is the angle between the magnetic equator and the point.
   arma_cube magLat_scgc, magY_scgc;
   // This is often just the altitude....
   arma_cube magAlt_scgc, magZ_scgc;
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 2e0b2418..183017cb 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -125,7 +125,7 @@ void Grid::calc_mlt() {
   // calculated and converted to an hour.
 
   for (int iZ = 0; iZ < nZ; iZ++) {
-    dlat_north = 1.0 - (cPI / 2.0 - magLat_scgc.slice(iZ)) / cPI;
+    dlat_north = 1.0 - (cPI / 2.0 - magInvLat_scgc.slice(iZ)) / cPI;
     x_blend = dlat_north * mag_pole_north_gse[0](0, 0, iZ) +
               (1.0 - dlat_north) * mag_pole_south_gse[0](0, 0, iZ);
     y_blend = dlat_north * mag_pole_north_gse[1](0, 0, iZ) +
@@ -171,7 +171,7 @@ void Grid::fill_grid_bfield(Planets planet) {
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
           // This is Invariant Latitude
-          magLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
+          // (regular magnetic latitude is not defined yet for the geo grid)
           magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
           magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
 
diff --git a/src/output.cpp b/src/output.cpp
index 2a16a8c0..61115d97 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -260,6 +260,10 @@ bool output(const Neutrals &neutrals,
                                                     "Magnetic Longitude",
                                                     "degrees",
                                                     grid.magLon_scgc * cRtoD);
+        AllOutputContainers[iOutput].store_variable("invLat",
+                                                      "Magnetic Invariant Latitude",
+                                                      "degrees",
+                                                      grid.magInvLat_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",

From c578a436ab2f0e573de3a4ab15463396b60b4583 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 19:53:25 -0400
Subject: [PATCH 007/266] BUG: do not fill dipole grid's mag coords in
 fill_grid_bfield

ideally this is refactored, but whatever. The way this was being done kept messing with the order of points.
---
 src/fill_grid.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 183017cb..5dd2f98e 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -172,8 +172,12 @@ void Grid::fill_grid_bfield(Planets planet) {
                                    planet);
           // This is Invariant Latitude
           // (regular magnetic latitude is not defined yet for the geo grid)
+          // - magLat is not used by the geo grid, so not defined
           magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
-          magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
+
+          // init_mag grid already initialized magLon
+          if (iGridShape_ != iDipole_)
+            magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
 
           bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 

From 464998fbbcbdccf7e49c78add316aa8df93b3cc8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 10 Mar 2025 22:05:25 -0400
Subject: [PATCH 008/266] FEAT: Add methods to do dipole interpolation

---
 include/grid.h                    | 32 +++++++++++++
 src/solver_grid_interpolation.cpp | 80 +++++++++++++++++++++++++++++++
 2 files changed, 112 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index f6a660ec..874dcb94 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -433,6 +433,20 @@ class Grid
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
                                const std::vector<precision_t> &Alts);
+
+  /**
+   * \brief Set the interpolation coefficients for the dipole grid
+   * \param Lons The longitude of points
+   * \param Lats The latitude of points
+   * \param Alts The altitude of points
+   * \pre Lons, Lats and Alts have the same size
+   * \return true if the function succeeds, false if the instance is not a
+   *         mag grid or the size of Lons, Lats and Alts are not the same.
+   */
+  bool set_dipole_interpolation_coefs(const std::vector<precision_t> &Lons,
+                                      const std::vector<precision_t> &Lats,
+                                      const std::vector<precision_t> &Alts);
+  
   /**
    * \brief Create a map of geographic locations to data and do the interpolation
    * \param data The value at the positions of geoLon, geoLat, and geoAlt
@@ -511,6 +525,17 @@ class Grid
     bool col_min_exclusive;
     bool col_max_exclusive;
   };
+  // The struct representing the range of a dipole grid (in magnetic coordinates)
+  struct dipole_range{
+    precision_t lon_min;
+    precision_t lon_max;
+    precision_t dLon;
+    precision_t lat_min;
+    precision_t lat_max;
+    precision_t dLat;
+    precision_t alt_min;
+    precision_t alt_max;
+  };
 
   // The index and coefficient used for interpolation
   // Each point is processed by the function set_interpolation_coefs and stored
@@ -534,6 +559,8 @@ class Grid
   void get_sphere_grid_range(struct sphere_range &sr) const;
   // Calculate the range of a cubesphere grid
   void get_cubesphere_grid_range(struct cubesphere_range &cr) const;
+  // Calculate the range of a dipole grid
+  void get_dipole_grid_range(struct dipole_range &dr) const;
 
   // Helper function for set_interpolation_coefs
   void set_interp_coef_sphere(const sphere_range &sr,
@@ -544,6 +571,11 @@ class Grid
                                   const precision_t lon_in,
                                   const precision_t lat_in,
                                   const precision_t alt_in);
+  // (note these are magnetic coordinates)
+  void set_interp_coef_dipole(const dipole_range &dr,
+                              const precision_t lon_in,
+                              const precision_t lat_in,
+                              const precision_t alt_in);
 
   // Processed interpolation coefficients
   std::vector<struct interp_coef_t> interp_coefs;
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index a98f9b50..a974de13 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -180,6 +180,28 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
   }
 }
 
+// --------------------------------------------------------------------------
+// Get the range of a Dipole grid
+// --------------------------------------------------------------------------
+
+void Grid::get_dipole_grid_range(struct dipole_range &dr) const {
+  // Retrieve the range and delta of longitude, latitude and altitude
+  // ** Note these are magnetic coordinates. Not labeled for consistency **
+  dr.lon_min = magLon_Corner(nGCs, nGCs, nGCs);
+  dr.lon_max = magLon_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  dr.lat_min = magLat_Corner(nGCs, nGCs, nGCs);
+  dr.lat_max = magLat_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  // magAlt and geoAlt are the same, doesn't matter which we use:
+  dr.alt_min = geoAlt_scgc(nGCs, nGCs, nGCs);
+  dr.alt_max = geoAlt_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+
+  // MagLon steps are uniform:
+  dr.dLon = magLon_Corner(1, 0, 0) - magLon_Corner(0, 0, 0);
+}
+
+
 // --------------------------------------------------------------------------
 // Set interpolation coefficients helper function for spherical grid
 // Almost the copy of interp_sphere_linear_helper
@@ -315,6 +337,64 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   interp_coefs.push_back(coef);
 }
 
+void Grid::set_interp_coef_dipole(const dipole_range &dr,
+                                  const precision_t lon_in,
+                                  const precision_t lat_in,
+                                  const precision_t alt_in) {
+
+  // The structure which will be put into the interp_coefs. Initialize in_grid to be false
+  struct interp_coef_t coef;
+  coef.in_grid = false;
+
+  // Determine whether the point is inside this grid
+  // Treat north pole specially because latitude is inclusive for both -cPI/2 and cPI/2
+  if (lon_in < dr.lon_min || lon_in >= dr.lon_max || lat_in < dr.lat_min
+      || lat_in > dr.lat_max || (lat_in == dr.lat_max && dr.lat_max != cPI / 2)
+      || alt_in < dr.alt_min || alt_in > dr.alt_max) {
+    interp_coefs.push_back(coef);
+    return;
+  }
+
+  // ASSUMPTION: LONGITUDE IS LINEARLY SPACED, nGCs >= 1
+  // For the cell containing it, directly calculate its x index
+  // Find y & z indices using binary search
+
+  // The number of dLon between the innermost ghost cell and the given point
+  coef.rRow = (lon_in - dr.lon_min) / dr.dLon + 0.5;
+  // Take the integer part
+  coef.iRow = static_cast<uint64_t>(coef.rRow);
+  // Calculate the fractional part, which is the ratio for Longitude
+  coef.rRow -= coef.iRow;
+  // The actual x-axis index of the bottom-left of the cube used for interpolation
+  coef.iRow += nGCs - 1;
+
+
+  // Different from the sphere, latitude & altitude are not evenly spaced.
+  // Use binary search for both.
+
+  // Lat needs to be done a little different because it could be increasing or
+  // decreasing (depending on the hemisphere we're in). Take the absolute value!
+  coef.iCol = binary_search_array(abs(lat_in),
+                                  abs(magInvLat_scgc.tube(coef.iRow, coef.iCol)), nGCs);
+
+  // need alt index to find lat coef
+  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
+
+  // then we can do the ratios:
+  coef.rCol = (lat_in - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (magLat_scgc(coef.iRow, coef.iCol + 1, coef.iAlt)
+                 - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt));
+
+  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1)
+                 - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
+
+  // Put the coefficient into the vector
+  coef.in_grid = true;
+  interp_coefs.push_back(coef);
+}
+
 // --------------------------------------------------------------------------
 // Set the interpolation coefficients
 // --------------------------------------------------------------------------

From a10c7945f9681e103e98237fdd089bf80b51f3f9 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 13 Mar 2025 06:55:10 -0400
Subject: [PATCH 009/266] FEAT: pass latitudes from one grid to the next

---
 src/grid_match.cpp | 168 ++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 159 insertions(+), 9 deletions(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 25da6188..39639dab 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -3,6 +3,65 @@
 
 #include "aether.h"
 
+// -----------------------------------------------------------------------------
+// Send arrays of variables to other processors on the given grid.
+// -----------------------------------------------------------------------------
+
+bool exchange_information(int64_t *nPointsToPass,
+                          std::vector<precision_t *> varToSend,
+                          int64_t *nPointsToReceive,
+                          std::vector<precision_t *> varToReceive) {
+
+  int64_t jNode, iPt, iTag, iProcTo, iProcFrom;
+  std::vector<MPI_Request> requests(nGrids);
+
+  // Here we send the message into the wind:
+  //   - if it is the same processor, just copy the information
+  //   - if it is a different processor, send the data
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    if (jNode == iGrid) {
+      for (iPt = 0; iPt < nPointsToPass[jNode]; iPt ++) {
+        varToReceive[jNode][iPt] = varToSend[jNode][iPt];
+      }
+    } else {
+      iProcTo = iMember * nGrids + jNode;
+      // iTag is a unique id allowing all processors to
+      // communicate asynchronously
+      iTag = iProc * 10000 + iProcTo;
+      MPI_Isend(varToSend[jNode],
+                nPointsToPass[jNode] * sizeof(precision_t),
+                MPI_BYTE,
+                iProcTo,
+                iTag,
+                aether_comm,
+                &requests[jNode]);
+    }
+  }
+
+  // Wait for everyone to get the information that was sent:
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    if (jNode != iGrid)
+      MPI_Wait(&requests[jNode], MPI_STATUS_IGNORE);
+
+  // Receive it into the receiving array:
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    if (jNode != iGrid) {
+      iProcFrom = iMember * nGrids + jNode;
+      // Rebuid the unique id:
+      iTag = iProcFrom * 10000 + iProc;
+      MPI_Recv(varToReceive[jNode],
+               nPointsToReceive[jNode] * sizeof(precision_t),
+               MPI_BYTE,
+               jNode,
+               iTag,
+               aether_comm,
+               MPI_STATUS_IGNORE);
+    }
+
+  MPI_Barrier(aether_comm);
+  return true;
+}
+
 bool grid_match(Grid gGrid, 
                 Grid mGrid, 
                 Quadtree gQuadtree,
@@ -17,8 +76,16 @@ bool grid_match(Grid gGrid,
   precision_t lon, lat;
   precision_t normX, normY, normZ;
   arma_vec norms(3);
-  int64_t iNode;
+  int64_t jNode, kNode;
+  int64_t *nPointsToPass = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+  int64_t *nPointsDummy = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    nPointsToPass[jNode] = 0;
 
+  // This is not the most efficient way to do this, but the first pass, let's
+  // just count how many points we need to send to the other processors:
   for (iX = mGCs; iX < mnX - mGCs; iX++) {
     for (iY = mGCs; iY < mnY - mGCs; iY++) {
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
@@ -28,19 +95,102 @@ bool grid_match(Grid gGrid,
           norms(0) = lon / cPI;
           norms(1) = lat / cPI;
           norms(2) = 0.0;
-          iNode = gQuadtree.find_point(norms);
+          jNode = gQuadtree.find_point(norms);
         } else {
           norms = sphere_to_cube(lon, lat);
-          iNode = gQuadtree.find_point(norms);
+          jNode = gQuadtree.find_point(norms);
+        }
+        if (jNode < 0 || jNode >= nGrids) {
+            std::cout << "out of bounds!!! " << jNode << "\n";
         }
-        std::cout << "lon, lat, node: " << lon*cRtoD << " "
-        << lat*cRtoD << " "
-        << norms(0) << " "
-        << norms(1) << " "
-        << norms(2) << " "
-        << iNode << "\n";
+        nPointsToPass[jNode] = nPointsToPass[jNode]+1;
+        /* std::cout << "lon, lat, node: " << lon*cRtoD << " "
+            << lat*cRtoD << " "
+            << norms(0) << " "
+            << norms(1) << " "
+            << norms(2) << " "
+            << jNode << " "
+            << iProc << " "
+            << nPoints[jNode] << "\n"; */
       }
     }
   }
+  std::cout << "made it here: " << iProc << "\n";
+  MPI_Barrier(aether_comm);
+
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    std::cout << "nPtsToPass : " << iProc << " " << nPointsToPass[jNode] << "\n";
+
+  std::cout << "sending number of points :\n";
+
+  // This section sends the number of points that need to be transfered to each processor.
+  // Then the processor saves the number of points, so it can be remembered, and both the
+  // sender and receiver will have the information.
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    if (jNode == iGrid) {
+      for (kNode = 0; kNode < nGrids ; kNode++)
+        nPointsDummy[kNode] = nPointsToPass[kNode];
+    }
+    MPI_Bcast(nPointsDummy, nGrids, MPI_INT64_T, jNode, aether_comm);
+    nPointsToReceive[jNode] = nPointsDummy[iGrid];
+  }
+
+  MPI_Barrier(aether_comm);
+
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    std::cout << "nPtsToReceive : " << iProc << " " << jNode << " " << nPointsToReceive[jNode] << "\n";
+    MPI_Barrier(aether_comm);
+  }
+
+  //  Now we need to create an array of send points and an array of receive points.
+  std::vector<precision_t *> latsToPass(nGrids);
+  std::vector<precision_t *> lonsToPass(nGrids);
+  std::vector<precision_t *> altsToPass(nGrids);
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    latsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
+    lonsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
+    altsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
+  }
+
+  std::vector<precision_t *> latsToInterTo(nGrids);
+  std::vector<precision_t *> lonsToInterTo(nGrids);
+  std::vector<precision_t *> altsToInterTo(nGrids);
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    latsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
+    lonsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
+    altsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
+  }
+
+  // now, the second pass, let's store the information so we can pass it:
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    nPointsToPass[jNode] = 0;
+  for (iX = mGCs; iX < mnX - mGCs; iX++) {
+    for (iY = mGCs; iY < mnY - mGCs; iY++) {
+      for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
+        lon = mGrid.geoLon_scgc(iX, iY, iZ);
+        lat = mGrid.geoLat_scgc(iX, iY, iZ);
+        if (gGrid.iGridShape_ == gGrid.iSphere_) {
+          norms(0) = lon / cPI;
+          norms(1) = lat / cPI;
+          norms(2) = 0.0;
+          jNode = gQuadtree.find_point(norms);
+        } else {
+          norms = sphere_to_cube(lon, lat);
+          jNode = gQuadtree.find_point(norms);
+        }
+        latsToPass[jNode][nPointsToPass[jNode]] = lat;
+        lonsToPass[jNode][nPointsToPass[jNode]] = lon;
+        altsToPass[jNode][nPointsToPass[jNode]] = mGrid.geoAlt_scgc(iX, iY, iZ);
+        nPointsToPass[jNode] = nPointsToPass[jNode]+1;
+      }
+    }
+  }
+  bool didWork;
+  didWork = exchange_information(nPointsToPass,
+                                 latsToPass,
+                                 nPointsToReceive,
+                                 latsToInterTo);
+
+
   return true;
 }

From 955bcf93ab5986b8de67e2bf90a3353c3a2676ba Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 14 Mar 2025 12:34:51 -0400
Subject: [PATCH 010/266] FEAT: Inputs for new dipole grid

---
 share/run/UA/inputs/defaults.json |  2 ++
 share/run/aether.json             |  8 +++-----
 src/init_mag_grid.cpp             | 21 ++++++---------------
 src/inputs.cpp                    | 27 +++++++++++++--------------
 4 files changed, 24 insertions(+), 34 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 861e4f81..e5ab817c 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -83,6 +83,8 @@
         "Shape" : "dipole",
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
+		"LatRange" : [85.0, 360.0],
+		"AltRange" : [0.0, 360.0],
         "nLonsPerBlock" : 22,
         "nAlts" : 50,
         "MinAlt" : 80.0,
diff --git a/share/run/aether.json b/share/run/aether.json
index 76bef500..ea5387e2 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -26,15 +26,13 @@
         "IsUniformAlt" : false},
     
     "ionGrid": {
-        "dAltStretch": 0.6,
-        "LatStretch": 1,
         "Shape": "dipole",
         "nLonsPerBlock": 36,
         "nLatsPerBlock" : 18,
         "nAlts":36,
-        "LatMax":88,
-        "MinAlt":100.0,
-        "MinApex":150.0
+        "LatRange":[10, 80],
+        "AltRange":[80.0, 1000],
+		"LonRange" : [0.0, 360.0]
         },
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index d9b56927..1cb64f1a 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -634,33 +634,24 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   int64_t nGCs = get_nGCs();
 
   // Get inputs:
+
+  precision_t max_lat = grid_input.lat_min;
+  precision_t max_lat = grid_input.lat_max;
+
   precision_t min_alt = grid_input.alt_min * cKMtoM;
-  precision_t LatStretch = grid_input.LatStretch;
-  precision_t Gamma = grid_input.FieldLineStretch;
-  precision_t min_apex = grid_input.min_apex * cKMtoM;
-  precision_t max_lat = grid_input.max_blat;
+  precision_t max_alt = grid_input.alt_max * cKMtoM;
 
   // Normalize inputs to planet radius... (update when earth is oblate)
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
-  precision_t min_apex_re = (min_apex + planetRadius) / planetRadius;
-
-  if (LatStretch != 1) {
-    report.error("LatStretch values =/= 1 are not yet supported!");
-    DidWork = false;
-  }
+  precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
   }
 
-  if (min_alt >= min_apex) {
-    report.error("min_apex must be more than min_alt");
-    DidWork = false;
-  }
-
   // Get some coordinates and sizes in normalized coordinates:
   arma_vec lower_left_norm = quadtree_ion.get_vect("LL"); // origin
   arma_vec size_right_norm = quadtree_ion.get_vect("SR"); // lon_lims
diff --git a/src/inputs.cpp b/src/inputs.cpp
index f8351ca9..c35d8637 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -452,22 +452,22 @@ Inputs::grid_input_struct Inputs::get_grid_inputs(std::string gridtype) {
   grid_specs.lon_min = min_max[0] * cDtoR;
   grid_specs.lon_max = min_max[1] * cDtoR;
 
-  grid_specs.alt_min = check_settings_pt(gridtype, "MinAlt");
+  min_max = get_setting_intarr(gridtype, "LatRange");
+  grid_specs.lat_min = min_max[0] * cDtoR;
+  grid_specs.lat_max = min_max[1] * cDtoR;
+
   // The rest of the settings are different for mag/geo grids,
   // First take the magnetic options, then "else" should be (cube-)sphere
-
+  // - This checks if "dipole" is in shape, to account for the # of root nodes.
   if (grid_specs.shape.find("dipole") != std::string::npos) {
-    // Latitude range (base of field line) is specified with max lat & min apex.
-    grid_specs.max_blat = check_settings_pt(gridtype, "LatMax") * cDtoR;
-    grid_specs.min_apex = check_settings_pt(gridtype, "MinApex");
-    // stretch the baselatitudes (not yet implemented)
-    grid_specs.LatStretch = check_settings_pt(gridtype, "LatStretch");
-    // controls the spacing of points along field line, <<1 for more pts at low alts
-    grid_specs.FieldLineStretch = check_settings_pt(gridtype, "dAltStretch");
+    // Invariant latitude range (of real corners) is specified with max/min.
+    // max alt of open field lines, and min alt, is set in AltRange
+    min_max = get_setting_intarr(gridtype, "AltRange");
+    grid_specs.alt_min = min_max[0];
+    grid_specs.alt_max = min_max[1];
+
   } else {
-    min_max = get_setting_intarr(gridtype, "LatRange");
-    grid_specs.lat_min = min_max[0] * cDtoR;
-    grid_specs.lat_max = min_max[1] * cDtoR;
+    grid_specs.alt_min = check_settings_pt(gridtype, "MinAlt");
     grid_specs.alt_file = check_settings_str(gridtype, "AltFile");
     grid_specs.IsUniformAlt = get_setting_bool(gridtype, "IsUniformAlt");
 
@@ -551,9 +551,8 @@ precision_t Inputs::get_dt_output(int iOutput) {
 
   if (iOutput < nOutputs)
     value = settings.at("Outputs").at("dt").at(iOutput);
-  else{
+  else
     report.error("Output Error; more output types than dt's provided.");
-  }
 
   return value;
 }

From c5688465468ac75e1e5a9792ae4160a899c128cf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 14 Mar 2025 14:10:12 -0400
Subject: [PATCH 011/266] FEAT: Quadtree for new dipole - 4 & 6 root nodes

---
 include/dipole.h                  | 126 +++++++++---------------------
 include/quadtree.h                |   2 +
 share/run/UA/inputs/defaults.json |   2 +-
 share/run/aether.json             |   2 +-
 src/quadtree.cpp                  |  38 ++++-----
 5 files changed, 58 insertions(+), 112 deletions(-)

diff --git a/include/dipole.h b/include/dipole.h
index c8aed455..9ad2f8a4 100644
--- a/include/dipole.h
+++ b/include/dipole.h
@@ -8,119 +8,71 @@
 #include <memory>
 
 /*************************************************
- * \brief A namespace with all (1-root) dipole grid logic.
+ * \brief A namespace with all (4-root) dipole grid logic.
  *************************************************/
-namespace Dipole {
-
-  /// The normalized origins of each face of the cube (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0}
-  };
-
-  /// Normalized right steps in cube
-  static const arma_mat RIGHTS = {
-				  {2.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in cube
-  static const arma_mat UPS = {
-			       {0.0, 1.0, 0.0}
-  };
+namespace Dipole4 {
 
+/// The normalized origins of each node (i.e. corner)
+static const arma_mat ORIGINS = {
+  { 0.0,  -0.5, 0.0},
+  { 0.0, -0.25, 0.0},
+  { 0.0,   0.0, 0.0},
+  { 0.0,  0.25, 0.0}
 };
 
-/*************************************************
- * \brief A namespace with all (2-root) dipole grid logic.
- *************************************************/
-namespace Dipole2 {
-
-  /// The normalized origins of each face of the cube (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0},
-           { 0.0,  0.0, 0.0}
-  };
-
-  /// Normalized right steps in cube
-  static const arma_mat RIGHTS = {
-				  {2.0, 0.0, 0.0},
-				  {2.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in cube
-  static const arma_mat UPS = {
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0}
-  };
-
+/// Normalized right steps in node
+static const arma_mat RIGHTS = {
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0}
 };
 
-/*************************************************
- * \brief A namespace with all (4-root) dipole grid logic.
- *************************************************/
-namespace Dipole4 {
-
-  /// The normalized origins of each node (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0},
-				   { 1.0, -0.5, 0.0},
-				   { 1.0,  0.0, 0.0},
-           { 0.0,  0.0, 0.0}
-  };
-
-  /// Normalized right steps in node
-  static const arma_mat RIGHTS = {
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0},
-				  {1.0, 0.0, 0.0}
-  };
-
-  /// Normalized up steps in node
-  static const arma_mat UPS = {
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0},
-			       {0.0, 0.5, 0.0}
-  };
+/// Normalized up steps in node
+static const arma_mat UPS = {
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0}
+};
 
 };
 
 
 /*************************************************
  * \brief A namespace with all (6-root) dipole grid logic.
- * This is the same as the Sphere6
  *************************************************/
 
 namespace Dipole6 {
 
 /// The normalized origins of each face of the cube (i.e. corner)
 static const arma_mat ORIGINS = {
-    {    0.0, -0.5, 0.0},
-    {2.0/3.0, -0.5, 0.0},
-    {4.0/3.0, -0.5, 0.0},
-    {    0.0, 0.0, 0.0},
-    {2.0/3.0, 0.0, 0.0},
-    {4.0/3.0, 0.0, 0.0}
+  {    0.0, -0.5, 0.0},
+  {    0.0, -1.0 / 3.0, 0.0},
+  {    0.0, -1.0 / 6.0, 0.0},
+  {    0.0, 0.0, 0.0},
+  {    0.0, 1.0 / 6.0, 0.0},
+  {    0.0, 1.0 / 3.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat RIGHTS = {
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0}
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0},
+  { 2.0,  0.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat UPS = {
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0}
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0},
+  { 0.0, 1.0 / 6.0, 0.0}
 };
 
 }
diff --git a/include/quadtree.h b/include/quadtree.h
index e63ec1d5..18b584c7 100644
--- a/include/quadtree.h
+++ b/include/quadtree.h
@@ -181,6 +181,8 @@ class Quadtree {
   bool IsSphere = false;
   /// Defines whether the quadtree is a cubesphere or not:
   bool IsCubeSphere = false;
+  /// Defines whether the quadtree is a dipole or not:
+  bool IsDipole = false;
   
 };
 
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index e5ab817c..09ade2c6 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -80,7 +80,7 @@
         "AltFile" : ""},
 
     "ionGrid" : {
-        "Shape" : "dipole",
+        "Shape" : "dipole4",
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
 		"LatRange" : [85.0, 360.0],
diff --git a/share/run/aether.json b/share/run/aether.json
index ea5387e2..96f1f08e 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -26,7 +26,7 @@
         "IsUniformAlt" : false},
     
     "ionGrid": {
-        "Shape": "dipole",
+        "Shape": "dipole4",
         "nLonsPerBlock": 36,
         "nLatsPerBlock" : 18,
         "nAlts":36,
diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 6e5f6cf6..0e2aae65 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -8,8 +8,8 @@
 //   - Cubesphere, this has 6 root nodes (2 polar, 4 equatorial)
 //   - Sphere, this has 1 root node (whole grid)
 //   - Sphere6, this is a spherical grid, but has 6 root nodes (2 lats, 3 lons)
-//   - Dipole, which may be the same as Sphere
 //   - Dipole4, which has 4 root nodes (4 lats, 1 lon)
+//   - Dipole6, which has 4 root nodes (6 lats, 1 lon)
 
 #include "aether.h"
 
@@ -22,11 +22,8 @@ Quadtree::Quadtree(std::string shape) {
   if (shape == "sphere")
     nRootNodes = 1;
 
-  if (shape == "dipole")
-    nRootNodes = 1;
-
-  if (shape == "dipole2")
-    nRootNodes = 2;
+  if (shape == "dipole4")
+    nRootNodes = 4;
 
   if (shape == "dipole6")
     nRootNodes = 6;
@@ -66,25 +63,19 @@ void Quadtree::build(std::string gridtype) {
     IsSphere = true;
   }
 
-  if (grid_input.shape == "dipole") {
-    origins = Dipole::ORIGINS;
-    rights = Dipole::RIGHTS;
-    ups = Dipole::UPS;
-    IsSphere = true;
-  }
-
-  if (grid_input.shape == "dipole2") {
-    origins = Dipole2::ORIGINS;
-    rights = Dipole2::RIGHTS;
-    ups = Dipole2::UPS;
-    IsSphere = true;
+  if (grid_input.shape == "dipole4") {
+    origins = Dipole4::ORIGINS;
+    rights = Dipole4::RIGHTS;
+    ups = Dipole4::UPS;
+    IsDipole = true;
   }
 
   if (grid_input.shape == "dipole6") {
     origins = Dipole6::ORIGINS;
     rights = Dipole6::RIGHTS;
     ups = Dipole6::UPS;
-    IsSphere = true;
+    IsDipole = true;
+
   }
 
   arma_vec o(3), r(3), u(3);
@@ -112,10 +103,11 @@ void Quadtree::build(std::string gridtype) {
   // restrict the domain.  This will only work for the spherical
   // grid so far:
 
-  if (grid_input.lon_min > 0.0 ||
-      grid_input.lon_max < 2.0 * cPI ||
-      grid_input.lat_min > -cPI / 2.0 ||
-      grid_input.lat_max < cPI / 2.0) {
+  if ((grid_input.lon_min > 0.0 ||
+       grid_input.lon_max < 2.0 * cPI ||
+       grid_input.lat_min > -cPI / 2.0 ||
+       grid_input.lat_max < cPI / 2.0)
+      && (IsSphere)) {
     // We are dealing with less than the whole Earth...
     origins(0) = grid_input.lon_min / cPI;
     origins(1) = grid_input.lat_min / cPI;

From 3027485ee1d33948ea82fe581d6a1615945ef4fc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 17 Mar 2025 16:07:17 -0400
Subject: [PATCH 012/266] FEAT/DOC: Update Python script to match new dipole
 grid

- Does not have final pole ghost cells yet...
---
 edu/examples/Dipole/dipole.py | 479 +++++++++++++++++-----------------
 1 file changed, 235 insertions(+), 244 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index f8bae674..a50a3093 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -6,122 +6,153 @@
 # Set to None or '' to not save, just show (pan/zoom capabilities).
 fig_save_path = None
 
+# Number of lats/alts (without ghost cells)
+nLatsPerBlock_in = 12 
+nAltsPerBlock_in = 12 
 
-nf = 100  # number of field lines
-nz = 200  # number of grid cells on each field line
-alt_min = 90  # in km, min altitude
+# in degrees, where to begin & end grid between (90,0)
+max_blat = 85
+min_blat = 12
 
-# in degrees, start/end latitudes of field lines. between (90,0)
-max_blat = 89.9
-min_blat = 2.25
+# In km (above surface)
+min_alt = 80
+max_alt = 800
 
-gams = 0.1
-# point distribution along field lines.
-# higher puts more pts at high altitudes.
+# Number of "blocks" to simulate - i.e. # of processors in Aether run (>4 & even)
+nBlocks = 6
 
-baselat_spacing_factor = 6  # exponential factor for spacing baselats (uses cos() too)
+nGCs = 2
 
 # consts:
-Re = 6371  # in km
-
-
-# leave empty for "auto" (aaron b choose), or put in custom limits here.
-# [left, right, bottom, top]
-limits_left_plot = None  # default is whole image
-limits_right_plot = None  # default is [-0.1, 2.0, -0.3, 1.75]
-
-
-# ------------------------------------------------------------------------
-# Main code is here:
-# ------------------------------------------------------------------------
-
-####        Useful Functions:          ####
-
-
-def calc_baselats(blat_min, blat_max, n_f, factor):
-    """Lay down base latitudes
-
-    Args:
-        blat_min (float): min latitude to start (positive), in degrees.
-        blat_max (float): max latitude in degrees, positiv & less than 90.0
-        nf (int): Number of field lines. Must be even!
-        factor (float): Factor to use to space latitudes.
-
-    Returns:
-        numpy array: starting latitudes
-
-    Notes: follows very similar approach to doi:10.1029/2000JA000035
-    - Not exactly exponential, but exponential and a cosine!
-    - Spaced according to B-field strength.
-
-    """
-    # Space out base latitudes:
-
-    baselats = np.linspace(
-        np.cos(np.deg2rad(blat_min ** (1 / factor))),
-        np.cos(np.deg2rad(blat_max ** (1 / factor))),
-        num=n_f,
-    )
-
-    # baselats are in southern hemisphere
-    baselats = -1 * np.flip(np.rad2deg(np.arccos(baselats)) ** factor)
-
-    return baselats
-
-
-def calc_q(alt, theta, re=None, is_alt=False, isnt_re=False):
-    """Calculate q (distance along field line) for a point
-
-    Args:
-        alt (float, array-like): altitude (kn or Re)
-        theta (float or array-like): magnetic latitude (in degrees).
-            0 at mag equator (measured from North Pole).
-        re (float): radius of earth in km. only used if alt is altitude and/or in km
-        is_alt (bool): altitude is altitude? False means it's radius. Default is False
-        isnt_re (bool): altitude is in km? False means altitude has units [Re]. Default is False
-
-    Returns:
-        (array or float): q-value for the given (alt, theta) point.
-
-    Notes:
-    - make sure theta has units degrees and is measured from north pole
-    - See <doi:10.1029/2000JA000035> for more information.
-    - is_alt & isnt_re are from debugging & aren't used anymore.
-
-    """
-
-    if is_alt:  # convert altitude to radius
-        alt = alt + re
-    if isnt_re:  # convert km to re
-        alt = alt / re
-
-    return np.cos(np.deg2rad(90 - theta)) / (alt**2)
-
-
-def calc_p(alt, theta, re=None, is_alt=False, isnt_re=False):
-    """Calculate p-value (l-shell) for a given altitude & latitude
-
-    Args:
-        alt (float): altitude (in km or Re), (above surface or from origin)
-        theta (float): latitude, in degrees
-        re (float): earth radius in km. optional.
-        is_alt (bool): is alt altitude? if it's radius, set to False (default).
-        isnt_re (bool): altitude is in km? False (default) means altitude has units [Re].
-
-    Returns:
-        (array or float): p-value. Same as l-Shell, in Re.
-
-    Notes:
-    - make sure theta has units degrees and is measured from north pole
-    - See <doi:10.1029/2000JA000035> for more information.
-    - is_alt & isnt_re are from debugging & aren't used anymore.
-
-    """
-    if is_alt:
-        alt = alt + re
-    if isnt_re:
-        alt = alt / re
-    return alt / (np.sin(np.deg2rad(90 - theta)) ** 2)
+Re_KM = 6371  # in km
+cPI = np.pi
+
+
+########                  ~~~~~~~~~~~~~~~~~~~~                  ########
+# OUTLINE:
+# - constants & inputs (above)
+# - def main (the function to create the grid)
+# - def the ploting function
+# - def all conversions
+# - call main:
+#   - make the grid as Aether would
+#   - call the plotting script
+
+def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock, nAltsPerBlock):
+
+    pcenters = np.zeros([len(origins), nLatsPerBlock, nAltsPerBlock])
+    qcenters = np.zeros([len(origins), nLatsPerBlock, nAltsPerBlock])
+
+    # Loop through QT's origins
+    for n, origin in enumerate(origins):
+        close_this_block = False
+        isSouth = False
+        # If we're in south hemisphere, flip everything. WIll be undone later.
+        if origin < -0.01:
+            isSouth = True
+            origin  = -1*origin - extent
+
+        lat0 = (2*(lat_max - lat_min))*origin    
+        dlat = extent * ((lat_max - lat_min)*2) / (nLatsPerBlock - nGCs)
+        
+        # Put latitudes down evenly (centers & corners)
+        # - This forms the invariant latitudes which field lines must pass thru
+        lat1d = []
+        lat1d_co = []   
+        pcenters1d = np.empty(nLatsPerBlock)
+        qs = np.empty((nLatsPerBlock, nAltsPerBlock))
+        pcenters2d = np.empty((nLatsPerBlock, nAltsPerBlock))
+        
+
+        for i in range(nLatsPerBlock):
+            lat1d.append(lat0 + (i - nGCs + 0.5) * dlat + lat_min)
+            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+            # Easier to save later if we get this:
+            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+
+        for i in range(nLatsPerBlock+1):
+            lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
+
+        pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
+
+        ## Determine if field lines should close. There are two conditions:
+        # - If the lowest l-shell in this block < altMin
+        if np.min(pcorners) < alt_maxRE: 
+            close_this_block = True
+        # - Or if we are touching the qeuator
+        if origin < 0.01: # NH equator
+            close_this_block + True
+        if np.abs(extent + origin) < 0.01: # SH equator
+            close_this_block = True
+
+        ## Setting up the q-values...
+
+        # The idea here is that we wither want the field line to close (wrap over equator)
+        # or to have its boundaries entirely within min/max alt. 
+        # We do not want field lines ending before max_alt, and vice-versa
+        # Q_max is obtained from the minimum altitude point on the highest latitude field line
+        q_max_center = rp2q(alt_minRE, pcenters1d[-1])
+        if close_this_block:
+            q_min_center = 0 # if the block is closed, q_min = 0. This is the equator!
+        else:
+            # If open, q_min is the highest altitude point on the lowest latitude field line
+            q_min_center = rp2q(alt_maxRE, pcenters1d[0])
+
+        
+        delQ = (q_max_center - q_min_center) / (nAltsPerBlock - nGCs*2) 
+        for iAlt in range(nAltsPerBlock):
+            qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
+        
+        # If we were in South hemisphere, multiply by -1
+        # (and reverse so the points are in the same order as NH)
+        if isSouth:
+            qs = -1*np.flip(qs)
+        qcenters[n,:] = qs
+        pcenters[n,:] = pcenters2d
+
+    return qcenters, pcenters
+
+
+####        Useful Functions for conversions:          ####
+
+def rt2q(r, t):
+    return np.cos(cPI/2 - t)/r**2
+
+def rt2p(r, t):
+    return r/(np.sin(cPI/2 - t)**2)
+
+def rt2qp(r, t):
+    q = rt2q(r, t)
+    p = rt2p(r, t)
+    return q, p
+
+def rt2xy(r, t):
+    x = r*np.cos(t)
+    y = r*np.sin(t)
+    return x, y
+
+
+def rq2t(r,q):
+    return np.arcsin(q * r**2)
+def rp2t(r, p):
+    return np.arccos(np.sqrt(r/p))
+
+def rp2q(r, p):
+    return np.sqrt((1-r/p)/r**4)
+
+def qp2xy(q, p):
+    r_ = qp_solve(q, p)
+    t_ = rq2t(r_, q)
+    return rt2xy(r_, t_)
+
+def tp2r(t, p):
+    return p * (np.cos(t)**2)
+
+def alt2r(alt, re):
+    return (alt + re)/re
+
+def r2alt(r, re):
+    return r*re - re
 
 
 def qp_solve(q, p):
@@ -145,149 +176,109 @@ def qp_solve(q, p):
     return new_r
 
 
-####        The main stuff:          ####
-
-
-def calc_exp_grid(nf0, nz0, altminre, baselats, pvals, gamma):
-    """Exponential Grid laydown, keeps grid parallel & perpendicular to B
-
-    Args:
-        nf0 (int): number of field lines
-        nz0 (int): number of points along each field line. MUST BE EVEN!
-        altminre (float): min altitude (in Re from center of earth) to trace from
-        baselats (list/array): latitudes to start at (from calc_baselats)
-        pvals (flaot): p-values (dipole coords, so it's L-shells) for all field lines
-        gamma (float): factor used to space points along field line.
-            Lower values increase point density @ low altitudes
-
-    Returns:
-        [np.array, np.array]: (nf, nz) dimensionsl arrays of:
-            - Latitudes (in deg)
-            - Radii (in re) of grid
-
-    ** This can be vectorized & shortened A LOT.
-    Left in this state for readability and in case things need to be changed.
-
+def make_plot(qs, ps, alt_min_RE, Re_km=6371,
+              abs_bot = False # Take the absolute value of latitude on bottom plot?
+              ):
+
+    rs = qp_solve(qs, ps)
+    ts = rq2t(rs, qs)
+    
+    fig = plt.figure(figsize=(8,11))
+    
+    gs = plt.GridSpec(8,9)
+    
+    ax0 = fig.add_subplot(gs[:5,:3])
+    for x,y in zip(*qp2xy(qs, ps)):
+        ax0.scatter(x,y, s=5)
+    
+    xlim, ylim = ax0.get_xlim(), ax0.get_ylim()
+    
+    circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7)
+    ax0.add_patch(circle1)
+    
+    ax0.set_ylim(ylim)
+    ax0.set_xlim(xlim)
+    ax0.set_aspect(1)
+    ax0.set_title('in Re:')
+    
+    
+    ax1 = fig.add_subplot(gs[:2,4:])
+    counts, _, _ = ax1.hist(rs.flatten(), bins=60)
+    ax1.vlines(alt_min_RE, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
+    ax1.set_title(f"{np.sum(rs < alt_min_RE) / np.prod(rs.shape)*100:.2f}% of points below min_alt\n"
+                 f"{np.sum(rs < 1) / np.prod(rs.shape)*100:.2f}% of points below 0 Re")
+    ax1.set_xlabel('Each cell altitude in Re')
+    ax1.set_ylabel('bin count')
+    
+    
+    ax1p2 = fig.add_subplot(gs[2,4:])
+    alt_min_KM = r2alt(alt_min_RE, Re_km)
+    counts, bins, _ = ax1p2.hist(r2alt(rs, Re_km).flatten(), bins=200)
+    ax1p2.vlines(alt_min_KM, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
+    ax1p2.set_xlim(-100, 1000)
+    ax1p2.set_xlabel('altitude in km')
+
+    another_hist_ax = fig.add_subplot(gs[3:5, 4:])
+    another_hist_ax.hist(np.rad2deg(ts.flatten()), bins=90)
+    another_hist_ax.set_xlabel('Magnetic Latitude (deg)')
+    # another_hist_ax.set_xlabel(
+
+    
+    ax2 = fig.add_subplot(gs[5:,:])
+    for x,y in zip(np.rad2deg(ts), r2alt(rs, Re_km)):
+        if abs_bot:
+            ax2.scatter(np.abs(x),y)
+        else:
+            ax2.scatter(x,y)
+            
+    ax2.hlines(100, 0 if abs_bot else -90, 90, color='k', alpha=.8)
+    ax2.set_ylim(0,1500)
+    ax2.set_xlabel('Magnetic Latitude (deg)')
+    ax2.set_ylabel('Altitude (km)')
+    
+    
+    
+    plt.tight_layout()
+
+    return fig
+
+
+def generate_sym_quadtree(nBlocks):
     """
+    Makes the latitude portion of the quadtree
+    input: nBlocks
+    outputs:
+        origins (normed y-coordinate of lower-left)
+        extent (size_up_norm)
+    """
+    origins = np.linspace(-0.5, 0.5, num=nBlocks, endpoint=False)
+    extent = 1/nBlocks
 
-    lats_2d = []
-    rs_2d = []
-
-    nzh = int(nz0 / 2)
-
-    for f_iter in range(nf0):
-        # q in sh & nh
-        q_S = calc_q(altminre, baselats[f_iter], is_alt=False, isnt_re=False)
-        q_N = calc_q(altminre, -baselats[f_iter], is_alt=False, isnt_re=False)
-
-        # linear spacing - made it really readable, could be done "cleaner"
-        delqp = (q_N - q_S) / nz0
-        qp0 = []
-        for i in range(nz0):
-            qp0.append(q_S + i * delqp)
-
-        # exp grid laydown, (same for all calls here, speed not an issue though)
-        delqp = altminre * delqp
-        f00s = []
-        for i in range(nz0):
-            f00s.append(gamma + (1 - gamma) * np.exp(-(((i - nzh) / (nz0 / 10)) ** 2)))
-
-        # spacing according to sinh function
-        ft = []
-        for i in range(nz0):
-            fb0 = (1 - f00s[i]) / np.exp(-q_S / delqp - 1)
-            fa = f00s[i] - fb0
-            ft.append(fa + fb0 * np.exp(-(qp0[i] - q_S) / delqp))
-
-        # q values, from south -> equator
-        qpnew = []
-        for i in range(nzh):
-            delq = qp0[i] - q_S
-            qpnew.append(q_S + ft[i] * delq)
-
-        # qpnew is from south-equator. extend it to north pole,
-        # so *-1 & reverse order so it is ascending
-        qpnew.extend(np.flip(np.array(qpnew)) * -1)
-
-        ilats = []
-        irs = []
-
-        for i in range(nz0):
-            # use qpsolve to get r from (q,p)
-            irs.append(qp_solve(qpnew[i], pvals[f_iter]))
-            # Use dipole equations to get lat from q and r
-            # q = cos(theta)/r**2
-            ilats.append(np.rad2deg(np.arcsin(qpnew[i] * irs[-1] ** 2)))
-
-        # Put into 2-D; lists easier & then return numpy
-        lats_2d.append(ilats)
-        rs_2d.append(irs)
-
-    return np.array(lats_2d), np.array(rs_2d)
-
-
-####        Actual computation:          ####
-
-
-AltMinRe = (alt_min + Re) / Re  # alt min in Re
-
-# baselats
-baselats = calc_baselats(min_blat, max_blat, nf, baselat_spacing_factor)
-
-# l-shells
-pvals = calc_p(AltMinRe, baselats)
-
-# take those, make lats & radii
-lats, rs = calc_exp_grid(nf, nz, AltMinRe, baselats, pvals, gams)
-
-
-print("making plot")
-
-# change variables in case anyone wants to make different plots
-xs = lats[:, :]
-ys = rs[:, :]
+    return origins, extent
 
 
-fig, ax = plt.subplots(1, 2, figsize=(7, 7))
 
-# scatter points, same color is same field line
-for x, y in zip(xs, ys):
-    ax[0].scatter(y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)))
-    ax[1].scatter(y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)))
+# ------------------------------------------------------------------------
+# Main code is here:
+# ------------------------------------------------------------------------
+if __name__ == "__main__":
 
-# change variables again, overwrite previous xs, ys
-# Take every 4th field line so it's more clear to see things
-xs = lats[:, :-8:4]
-ys = rs[:, :-8:4]
-# black dashed lines, same nz value at different nf's
-for x, y in zip(xs.T, ys.T):
-    ax[0].plot(
-        y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)), linestyle="--", color="k"
-    )
-    ax[1].plot(
-        y * np.cos(np.deg2rad(x)), y * np.sin(np.deg2rad(x)), linestyle="--", color="k"
-    )
+    alt_maxRE = alt2r(max_alt, Re_KM)
+    alt_minRE = alt2r(min_alt, Re_KM)
+    
+    nLatsPerBlock = nLatsPerBlock_in + nGCs*2
+    nAltsPerBlock = nAltsPerBlock_in + nGCs*2
 
-# make square-ish
-ax[0].set_aspect(1)
-ax[1].set_aspect(1)
+    origins, extent = generate_sym_quadtree(nBlocks)
 
-# custom limits?
-if limits_left_plot:
-    ax[1].set_xlim(limits_left_plot[0], limits_left_plot[1])
-    ax[1].set_ylim(limits_left_plot[2], limits_left_plot[3])
+    qs, ps = main(alt_minRE, alt_maxRE, min_blat, max_blat, origins, extent, nLatsPerBlock, nAltsPerBlock)
 
+    # if we want r&theta now:
+    # rs = qp_solve(qs, ps)
+    # ts = rq2t(rs, qs)
+    # Otherwise the plotting function does it:
 
-if limits_right_plot:
-    ax[1].set_xlim(limits_right_plot[0], limits_right_plot[1])
-    ax[1].set_ylim(limits_right_plot[2], limits_right_plot[3])
-else:
-    ax[1].set_xlim(-0.1, 2)
-    ax[1].set_ylim(-0.3, 1.75)
+    fig = make_plot(qs, ps, alt_minRE, Re_KM, abs_bot=False)
 
-# save or show:
-if fig_save_path:
-    plt.savefig(fig_save_path)
-else:
     plt.show()
-plt.close("all")
+

From defd757f4d0ded903fa9fc0e0ea8b5ef50e1c2e2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 17 Mar 2025 16:53:05 -0400
Subject: [PATCH 013/266] FEAT: Put pole-most points into python script

---
 edu/examples/Dipole/dipole.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index a50a3093..fd6d8fee 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -3,8 +3,7 @@
 
 ####        Set inputs          ####
 
-# Set to None or '' to not save, just show (pan/zoom capabilities).
-fig_save_path = None
+# To save plot, change last line of the code to save it, otherwise it is just 'show'n
 
 # Number of lats/alts (without ghost cells)
 nLatsPerBlock_in = 12 
@@ -32,11 +31,11 @@
 # OUTLINE:
 # - constants & inputs (above)
 # - def main (the function to create the grid)
-# - def the ploting function
 # - def all conversions
-# - call main:
+# - def the ploting function
+# - Run script:
 #   - make the grid as Aether would
-#   - call the plotting script
+#   - call the plotting function
 
 def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock, nAltsPerBlock):
 
@@ -62,17 +61,23 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
         pcenters1d = np.empty(nLatsPerBlock)
         qs = np.empty((nLatsPerBlock, nAltsPerBlock))
         pcenters2d = np.empty((nLatsPerBlock, nAltsPerBlock))
-        
 
         for i in range(nLatsPerBlock):
             lat1d.append(lat0 + (i - nGCs + 0.5) * dlat + lat_min)
-            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+
+        # IF touching pole, put last ghost cell at 89.9 degrees & the 2nd to last 1/2 way there.
+        if origin + extent > 0.49:
+            lat1d[-1] = 89.9
+            lat1d[-2] = (lat1d[-1] + lat1d[-2]) /2
+
+        for i in range(nLatsPerBlock):
+            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
             # Easier to save later if we get this:
-            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[-1]))**2)
+            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
 
-        for i in range(nLatsPerBlock+1):
+        # Corners (only used here to determins if we need to close >1 block per hemisphere)
+        for i in range(nLatsPerBlock+1): 
             lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
-
         pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
 
         ## Determine if field lines should close. There are two conditions:
@@ -276,6 +281,7 @@ def generate_sym_quadtree(nBlocks):
     # if we want r&theta now:
     # rs = qp_solve(qs, ps)
     # ts = rq2t(rs, qs)
+
     # Otherwise the plotting function does it:
 
     fig = make_plot(qs, ps, alt_minRE, Re_KM, abs_bot=False)

From ff16298288922b641afafb223e2d8e157a146a64 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 12:24:19 -0400
Subject: [PATCH 014/266] STY: more comments, formatting

---
 edu/examples/Dipole/dipole.py | 44 +++++++++++++++++++----------------
 1 file changed, 24 insertions(+), 20 deletions(-)

diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index fd6d8fee..71bd85f8 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -3,13 +3,14 @@
 
 ####        Set inputs          ####
 
-# To save plot, change last line of the code to save it, otherwise it is just 'show'n
+# To save plot, change last line of the code, otherwise it is just shown
 
 # Number of lats/alts (without ghost cells)
 nLatsPerBlock_in = 12 
 nAltsPerBlock_in = 12 
 
 # in degrees, where to begin & end grid between (90,0)
+# Grid is mirrored across N/S hemisphere
 max_blat = 85
 min_blat = 12
 
@@ -17,7 +18,7 @@
 min_alt = 80
 max_alt = 800
 
-# Number of "blocks" to simulate - i.e. # of processors in Aether run (>4 & even)
+# Number of "blocks" to simulate - i.e. # of processors in Aether run (must be >4 & even)
 nBlocks = 6
 
 nGCs = 2
@@ -71,20 +72,20 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             lat1d[-2] = (lat1d[-1] + lat1d[-2]) /2
 
         for i in range(nLatsPerBlock):
-            pcenters1d[i] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
+            pcenters1d[i] = alt_minRE / (np.sin(cPI/2 - np.deg2rad(lat1d[i]))**2)
             # Easier to save later if we get this:
-            pcenters2d[i, :] = alt_minRE / (np.sin(np.pi/2 - np.deg2rad(lat1d[i]))**2)
+            pcenters2d[i, :] = alt_minRE / (np.sin(cPI/2 - np.deg2rad(lat1d[i]))**2)
 
-        # Corners (only used here to determins if we need to close >1 block per hemisphere)
+        # Corners (only used here to determine if we need to close > 1 block per hemisphere)
         for i in range(nLatsPerBlock+1): 
             lat1d_co.append(lat0 + (i - nGCs) * dlat + lat_min)
-        pcorners = alt_minRE / np.sin(np.pi/2 - np.deg2rad(lat1d_co)) **2
+        pcorners = alt_minRE / np.sin(cPI/2 - np.deg2rad(lat1d_co)) **2
 
         ## Determine if field lines should close. There are two conditions:
         # - If the lowest l-shell in this block < altMin
         if np.min(pcorners) < alt_maxRE: 
             close_this_block = True
-        # - Or if we are touching the qeuator
+        # - Or if we are touching the equator
         if origin < 0.01: # NH equator
             close_this_block + True
         if np.abs(extent + origin) < 0.01: # SH equator
@@ -92,10 +93,11 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
 
         ## Setting up the q-values...
 
-        # The idea here is that we wither want the field line to close (wrap over equator)
+        # The idea here is that we either want the field line to close (wrap over equator)
         # or to have its boundaries entirely within min/max alt. 
-        # We do not want field lines ending before max_alt, and vice-versa
-        # Q_max is obtained from the minimum altitude point on the highest latitude field line
+        # We do not want field lines ending before max_alt, and vice-versa.
+        # By definition, q=0 at the equator and +/- infinity at the N/S poles, so:
+        # - Q_max is obtained from the minimum altitude point on the highest latitude field line
         q_max_center = rp2q(alt_minRE, pcenters1d[-1])
         if close_this_block:
             q_min_center = 0 # if the block is closed, q_min = 0. This is the equator!
@@ -109,7 +111,7 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
         
         # If we were in South hemisphere, multiply by -1
-        # (and reverse so the points are in the same order as NH)
+        # (and reverse so the points are ascending in q, same order as NH)
         if isSouth:
             qs = -1*np.flip(qs)
         qcenters[n,:] = qs
@@ -119,6 +121,14 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
 
 
 ####        Useful Functions for conversions:          ####
+####        - Not all used... 
+####        - Format: in2out, in as few letters as necessary
+####          example: cart to geo "xy2rt": (x,y) --> (r, theta)
+
+## NOTE: theta for the dipole coordinate system is defined as co-latitude, not latitude
+## Thus, we do (cPI-theta) for rt2(q/p).
+## Then things are kept as-is, until conversion back to spherical when 
+## colatitude is again considered
 
 def rt2q(r, t):
     return np.cos(cPI/2 - t)/r**2
@@ -205,8 +215,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     ax0.set_xlim(xlim)
     ax0.set_aspect(1)
     ax0.set_title('in Re:')
-    
-    
+
     ax1 = fig.add_subplot(gs[:2,4:])
     counts, _, _ = ax1.hist(rs.flatten(), bins=60)
     ax1.vlines(alt_min_RE, 0, max(counts)*1.1, linestyle = '--', alpha=.7, color='k')
@@ -214,8 +223,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
                  f"{np.sum(rs < 1) / np.prod(rs.shape)*100:.2f}% of points below 0 Re")
     ax1.set_xlabel('Each cell altitude in Re')
     ax1.set_ylabel('bin count')
-    
-    
+
     ax1p2 = fig.add_subplot(gs[2,4:])
     alt_min_KM = r2alt(alt_min_RE, Re_km)
     counts, bins, _ = ax1p2.hist(r2alt(rs, Re_km).flatten(), bins=200)
@@ -226,9 +234,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     another_hist_ax = fig.add_subplot(gs[3:5, 4:])
     another_hist_ax.hist(np.rad2deg(ts.flatten()), bins=90)
     another_hist_ax.set_xlabel('Magnetic Latitude (deg)')
-    # another_hist_ax.set_xlabel(
 
-    
     ax2 = fig.add_subplot(gs[5:,:])
     for x,y in zip(np.rad2deg(ts), r2alt(rs, Re_km)):
         if abs_bot:
@@ -240,9 +246,7 @@ def make_plot(qs, ps, alt_min_RE, Re_km=6371,
     ax2.set_ylim(0,1500)
     ax2.set_xlabel('Magnetic Latitude (deg)')
     ax2.set_ylabel('Altitude (km)')
-    
-    
-    
+
     plt.tight_layout()
 
     return fig

From 3f8465fdb6399a7f30a3abf9fe5a95f80aba3df8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 20:06:09 -0400
Subject: [PATCH 015/266] FEAT: New dipole grid is in place. Kinda.

Here's what I know:
- it doesn't run
- nan's in density and velocity, i think
- maybe in chapman integrals?
- k-coord is not right (should be alt above equator, traced along dipole -maybe)
- altitude is always negative???

But the rest is good! LOL
---
 src/init_mag_grid.cpp | 699 ++++++++++++------------------------------
 1 file changed, 192 insertions(+), 507 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1cb64f1a..8a4df97a 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -10,6 +10,7 @@
 // or with approach from (Swisdak, 2006), who solved it analytically:
 //  https://arxiv.org/pdf/physics/0606044
 //
+// Overloaded for single conversions & arma_cubes
 // ----------------------------------------------------------------------
 
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
@@ -31,7 +32,7 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   // now that r is determined we can solve for theta
   // theta = asin(sqrt(r/p));
   theta = acos(q * pow(r, 2.0));
-  // Then make sure its the correct sign & direction
+  // Then make sure its the correct sign & direction (not colatitude)
   theta = cPI / 2 - theta;
 
   return {r, theta};
@@ -57,367 +58,6 @@ std::pair<arma_cube, arma_cube> qp_to_r_theta(arma_cube q, arma_cube p) {
   return {r, theta};
 }
 
-// ----------------------------------------------------------------------
-// The general idea here is to make the physical cells within the
-// upper and lower limits.  The cell EDGES will be these limits, so
-// that the cell CENTERS (which this function calculates) will be
-// 1/2 dlat away from these locations.
-// The two limits coming in are the lowest northern latitude field line
-// and the highest northern latitude field line (i.e., they are both
-// positive values and are over half the domain.)
-// If the block is touching the equator boundaries or the polar
-// boundaries, then these ghost cells extend beyond these boundaries and
-// the EDGES go to [-88, -1, 1, or 88] degrees latitude, depending on
-// the boundary. This function deals with CENTERS, though, so the
-// centers are selected so the the edges will be correct when calculated
-// down stream.
-// If we are running on 1 processor only, then this is all thrown out
-// the window and code puts the centers are the +/- upper_lim. It first
-// builds the southern hemisphere with nLats/2 points, then mirrors them.
-// ----------------------------------------------------------------------
-
-arma_vec Grid::baselat_spacing(precision_t extent,
-                               precision_t origin,
-                               precision_t upper_lim,
-                               precision_t lower_lim,
-                               precision_t spacing_factor) {
-  std::string function = "Grid::baselat_spacing";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  if (report.test_verbose(3))
-    std::cout << "inputs : " << iProc << " " <<  extent << " " << origin << " "
-              << lower_lim * cRtoD << " " << lower_lim * cRtoD << "\n";
-
-  // intermediate latitude values
-  precision_t lat_low, lat_high, lat_low0, lat_high0;
-  // intermediate calculation values
-  precision_t dlat, bb, aa, ang0, angq, nLats_here, extent_here;
-  precision_t dlat0, dlatLower, dLatUpper;
-
-  // Now we can allocate the return array,
-  arma_vec Lats(nLats);
-
-  // Noting the special case of 1 root node & 1 processor...
-  bool DO_FLIPBACK = false;
-
-  int64_t iStart, iEnd;
-
-  if (extent > 0.5) {
-    // This is when running on 1 processor:
-    DO_FLIPBACK = true;
-    nLats_here = nLats / 2;
-    extent_here = 0.5;
-    iStart = 0;
-    iEnd = nLats_here;
-  } else {
-    // Span only physical cells with extent:
-    nLats_here = nLats - 2 * nGCs;
-    extent_here = extent;
-    // Want to fill in only physical cells, then do ghostcells later:
-    iStart = nGCs;
-    iEnd = nLats - nGCs;
-  }
-
-  // get the upper & lower latitude bounds for our division of the quadree
-  if (origin < 0) {
-    // negative origin == Southern hemisphere: lat_high <=> lat_low
-    lat_low, lat_high = -upper_lim, -lower_lim;
-    lat_low0 = lat_low;
-    lat_low = -lower_lim + (upper_lim - lower_lim) * (origin / 0.5);
-    lat_high = lat_low + (upper_lim - lower_lim) * (extent_here / 0.5);
-  } else {
-    // Northern hemisphere:
-    lat_low, lat_high = lower_lim, upper_lim;
-    lat_low0 = lower_lim;
-    lat_low = lower_lim + (upper_lim - lower_lim) * (origin / 0.5);
-    lat_high = lat_low + (upper_lim - lower_lim) * (extent_here / 0.5);
-  }
-
-  if (report.test_verbose(3))
-    std::cout << "lat_low, lat_high : "
-              << lat_low*cRtoD << " " << lat_high*cRtoD << " " << lower_lim << " " <<
-              upper_lim << "\n";
-
-  // normalized spacing in latitude
-  // NOTE: spacing factor != 1 will not work yet. but framework is here...
-  bb = (lat_high - lat_low) / (pow(lat_high, spacing_factor) - pow(lat_low,
-                               spacing_factor));
-  aa = lat_high - bb * pow(lat_high, spacing_factor);
-  dlat = (lat_high - lat_low) / (nLats_here);
-  // Save dlat so that we can use it in ghostcells if they are interior:
-  dlat0 = dlat;
-
-  if (!HasYdim) {
-    // edge case for 1-D (or no latitudinal extent, really)
-    // In 1-D, the base latitudes will be 1/2 way between LatMax & minApex,
-    // dlat is adjustable if it doesn't suit your needs.
-    DO_FLIPBACK = false;
-    dlat = 1.0 * cDtoR;
-    nLats_here = nLats + 1;
-  }
-
-  // Fill in physical cell centers:
-  for (int64_t j = iStart; j < iEnd; j++) {
-    ang0 = lat_low + (float(j - iStart) + 0.5) * dlat;
-    angq = aa + bb * pow(ang0, spacing_factor);
-    Lats[j] = angq;
-  }
-
-  if (DO_FLIPBACK) {
-    // In the flipback case (single processor, global sim), we want baselats
-    // to be strictly increasing, same as geo grid!
-    // remember : nLats_here = nLats / 2
-    for (int64_t j = 0; j < nLats_here; j++)
-      // mirror south to north:
-      Lats[j + nLats_here] = -1 * Lats[nLats_here - j - 1];
-  } else {
-    // Here we are filling ghostcells, first the lower GCs, then the upper GCs.
-    // If they are interior GCs, use the default dlat.  If they are exterior GCs
-    // (i.e., poleward of max lat or equatorward of min lat), then adjust the dlat
-    // to force the last cell edges to be at [-89.9, -1, 1, 89.9] depending on cells.
-    // Do the lower ghostcells:
-    // If the GCs are interior, leave dlat alone.
-    dlat = dlat0;
-
-    // South polar region:
-    if (fabs( fabs(lat_low) - fabs(upper_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near south pole!\n";
-
-      dlat = (89.9 * cDtoR + lat_low) / nGCs;
-    }
-
-    // North equatorial region:
-    if (fabs( fabs(lat_low) - fabs(lower_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near northern equator!\n";
-
-      dlat = (lat_low - 1.0 * cDtoR) / nGCs;
-    }
-
-    // The user may not want to go all the way to the pole or the equator.
-    // if we are very close to the pole or equator, then the calculated dlat
-    // will be small so we don't hit either.  If we are far enough away from
-    // either, we can just leave dlat alone.
-    if (dlat > dlat0)
-      dlat = dlat0;
-
-    // Fill in GCs:
-    for (int64_t j = 0; j < iStart; j++) {
-      ang0 = lat_low + (float(j - iStart) + 0.5) * dlat;
-      angq = aa + bb * pow(ang0, spacing_factor);
-      Lats[j] = angq;
-    }
-
-    // Do the upper ghostcells:
-    // If the GCs are interior, leave dlat alone.
-    dlat = dlat0;
-
-    // North polar region:
-    if (lat_high == upper_lim) {
-      if (report.test_verbose(2))
-        std::cout << "Near north pole!\n";
-
-      dlat = (89.9 * cDtoR - lat_high) / nGCs;
-    }
-
-    // South equatorial region:
-    if (fabs( fabs(lat_high) - fabs(lower_lim)) < 0.001) {
-      if (report.test_verbose(2))
-        std::cout << "Near southern equator!\n";
-
-      dlat = -(1.0 * cDtoR + lat_high) / nGCs;
-    }
-
-    // The user may not want to go all the way to the pole or the equator.
-    // if we are very close to the pole or equator, then the calculated dlat
-    // will be small so we don't hit either.  If we are far enough away from
-    // either, we can just leave dlat alone.
-    if (dlat > dlat0)
-      dlat = dlat0;
-
-    // Fill in the GCs:
-    for (int64_t j = iEnd; j < nLats; j++) {
-      ang0 = lat_high + (float(j - iEnd) + 0.5) * dlat;
-      angq = aa + bb * pow(ang0, spacing_factor);
-      Lats[j] = angq;
-    }
-  }
-
-  if (report.test_verbose(3))
-    std::cout << "Lats from baselat_spacing :\n" << Lats * cRtoD << "\n";
-
-  report.exit(function);
-  return Lats;
-}
-
-// // Gravity vectors in the dipole basis
-// void calc_dipole_gravity(Planets planet){
-
-// // rhat = -(2*cos/(del)) qhat + (sin/(del)) phat
-
-
-// }
-
-
-// === SPACING ALONG FIELD LINE === //
-// Coordinates along the field line to begin modeling
-// - Created in dipole (p,q) coordinates, stored as magnetic coords
-// - North & south hemisphere base-latitudes, shouldn't be *too* hard to support offset
-//   dipole and/or oblate Earth.
-// isCorner is a bool, if false then the p's and q's are stored for later (p,q cell centers).
-// Field line filling only needs to be redone for the "down" edges, left is the same p,q
-// and then for "lower", we just shift the p,q after
-
-void Grid::fill_field_lines(arma_vec baseLatsLoc,
-                            precision_t min_altRe, precision_t Gamma,
-                            Planets planet,
-                            bool isCorner = false) {
-
-  std::string function = "Grid::fill_field_lines";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  precision_t q_Start, delqp;
-
-  // allocate & calculate some things outside of the main loop
-  // - mostly just factors to make the code easier to read
-  precision_t qp0, fb0, ft, delq, qp2, fa, fb, term0, term1, term2, term3;
-  // exp_q_dist is the fraction of total q-distance to step for each pt along field line
-  arma_vec exp_q_dist(nAlts);
-
-  // corners/edges have one more lat dimension...
-  int64_t nLatLoc = baseLatsLoc.n_elem;
-
-  // temp holding of results from q,p -> r,theta conversion:
-  std:: pair<precision_t, precision_t> r_theta;
-  report.print(3, " calculating lshells!");
-
-  // Find L-Shell for each baseLat
-  // using L=R/sin2(theta), where theta is from north pole
-  arma_vec Lshells(nLatLoc);
-
-  for (int64_t iLat = 0; iLat < nLatLoc; iLat++)
-    Lshells(iLat) = (min_altRe) / pow(sin(cPI / 2 - baseLatsLoc(iLat)), 2.0);
-
-  report.print(3, "lshells calculated!");
-
-  if (!isCorner) {
-    for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-      for (int64_t iLat = 0; iLat < nLatLoc; iLat ++) {
-        for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++) {
-          magP_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-          j_center_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-        }
-      }
-    }
-  } else {
-    for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-      for (int64_t iLat = 0; iLat < nLatLoc; iLat ++) {
-        for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++) {
-          magP_Down(iLon, iLat, iAlt) = Lshells(iLat);
-          j_edge_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-          j_corner_scgc(iLon, iLat, iAlt) = Lshells(iLat);
-        }
-      }
-    }
-  }
-
-  report.print(3, "dipole p-values stored for later.");
-
-  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++)
-    exp_q_dist(iAlt) = Gamma + (1 - Gamma) * exp(-pow(((iAlt - nAlts) /
-                                                       (nAlts / 5.0)), 2.0));
-
-  report.print(3, "expQ");
-
-  // This is wrong (same lat everywhere), but get_radius doesnt support oblate earth yet.
-  precision_t planetRadius = planet.get_radius(0.0);
-
-  // mag alts and lats:
-  arma_mat bAlts(nLatLoc, nAlts), bLats(nLatLoc, nAlts);
-
-  if (report.test_verbose(3))
-    std::cout << "Setting min alt (actually r in Re) : "
-              << min_altRe << " "
-              << planetRadius << " "
-              << (min_altRe - 1.0) * planetRadius / 1000.0 << "\n";
-
-  for (int iLat = 0; iLat < nLatLoc; iLat++) {
-    q_Start = -cos(cPI / 2 + baseLatsLoc(iLat)) / pow(min_altRe, 2.0);
-
-    // calculate const stride in dipole coords, same as sami2/3 (huba & joyce 2000)
-    // Note this is not the:
-    // ==  >>   sinh(gamma*qi)/sinh(gamma*q_S)  <<  ==
-    // but a different formula where the spacing is more easily controlled.
-    // Doesn't have any lat/lon dependence so won't work for offset dipoles
-    delqp = (-q_Start) / (nAlts + 1);
-    delqp = min_altRe * delqp;
-
-    for (int iAlt = 0; iAlt < nAlts; iAlt++) {
-      qp0 = q_Start + iAlt * (delqp);
-      fb0 = (1 - exp_q_dist(iAlt)) / exp(-q_Start / delqp - 1);
-      ft = exp_q_dist(iAlt) - fb0 + fb0 * exp(-(qp0 - q_Start) / delqp);
-      delq = qp0 - q_Start;
-
-      // Q value at this point:
-      qp2 = q_Start + ft * delq;
-
-      if (isCorner) {
-        // save the q for the "down" case:
-        for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-          magQ_Down(iLon, iLat, iAlt) = qp2;
-
-          if (iLat < nLats)
-            k_edge_scgc(iLon, iLat, iAlt) = qp2;
-
-          k_corner_scgc(iLon, iLat, iAlt) = qp2;
-        }
-      } else {
-        for (int64_t iLon = 0; iLon < nLons; iLon ++) {
-          magQ_scgc(iLon, iLat, iAlt) = qp2;
-          k_center_scgc(iLon, iLat, iAlt) = qp2;
-        }
-
-        r_theta = qp_to_r_theta(qp2, Lshells(iLat));
-        bAlts(iLat, iAlt) = r_theta.first;
-        bLats(iLat, iAlt) = r_theta.second;
-      }
-    }
-  }
-
-  report.print(3, "QP-rtheta done!");
-
-  if (isCorner) { // we don't need the rest, yet
-    report.exit(function);
-    return;
-  }
-
-  arma_vec rNorm1d(nAlts), lat1dAlong(nAlts);
-
-  // rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
-
-  for (int64_t iLat = 0; iLat < nLatLoc; iLat++) {
-    for (int64_t iLon = 0; iLon < nLons; iLon++) {
-      // Not currently used. Dipole isn't offset. Leaving just in case.
-      // Lon = magPhi_scgc(iLon, iLat, 1);
-
-      for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
-        lat1dAlong(iAlt) = bLats(iLat, iAlt);
-        rNorm1d(iAlt) = bAlts(iLat, iAlt);
-      }
-
-      // Lay things down in the same order as the geo grid.
-      //centers only
-      magAlt_scgc.tube(iLon, iLat) = rNorm1d * planetRadius;
-      magLat_scgc.tube(iLon, iLat) = lat1dAlong;
-    }
-  }
-
-  report.exit(function);
-  return;
-}
 
 ////////////////////////////////////////////
 // convert cell coordinates to geographic //
@@ -455,96 +95,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   return llr;
 }
 
-// Use magP and magQ to make alt edges:
-// This does the heavy lifting for the edges & corners of the dipole grid.
-// These will be 1/2 way btwn each q point, which is pretty close to evenly spaced.
-// They will not, however, line up from one field line to the next.
-// It's not going to be *too* hard to get the corners to line up, but it messes with the
-// orthogonality too much for me to figure out right now.
-void Grid::dipole_alt_edges(Planets planet, precision_t min_altRe) {
-
-  std::string function = "Grid::dipole_alt_edges";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  // P-coordinates will be the same along alt coord, we saved p-vals when we made them
-  // in the fill field line function.
-  precision_t pTmp;
-
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++) {
-      pTmp = magP_Down(iLon, iLat, 0);
-
-      for (int64_t iAlt = 0; iAlt < nAlts; iAlt ++)
-        magP_Corner(iLon, iLat, iAlt) = pTmp;
-    }
-  }
-
-  // Here are some shortcuts that exploit the symmetry.
-  // This is done by each coord so cases like offset dipoles or oblate planets are easier later
-
-  // first, use the fact that p is the same along each field line (alt)
-  for (int64_t iLon = 0; iLon < nLons + 1; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magP_Corner(iLon, iLat, nAlts) = magP_Corner(iLon, iLat, nAlts - 1);
-  }
-
-  // And final step, use the longitude symmetry.
-  // It's fine, until the dipole is offset. then the entire fill_field_lines needs to be redone.
-  for (int64_t iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magP_Corner(nLons, iLat, iAlt) = magP_Corner(nLons - 1, iLat, iAlt);
-  }
-
-  // For q-coord we'll avg q_down (from different baseLat) above and below the point...
-  // May need to change the dipole spacing func's to get this working exactly though.
-  // With how the field line pts are currently put in, this ends up being quite a hassle.
-  // Not to mention, there would be a corner at q=0 (so r=A_LOT).
-  // Top and bottom-most corners take the same q-step as the previous cell.
-  precision_t qTmp;
-
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++) {
-      for (int64_t iAlt = 1; iAlt < nAlts; iAlt ++)
-        magQ_Corner(iLon, iLat, iAlt) = (magQ_Down(iLon, iLat,
-                                                   iAlt - 1) + magQ_Down(iLon, iLat, iAlt)) / 2;
-
-      magQ_Corner(iLon, iLat, 0) = (2 * magQ_Corner(iLon, iLat, 1) - magQ_Corner(iLon,
-                                    iLat, 2));
-    }
-  }
-
-  // for last (alt) corner, take the same step as the prev corner to the highest center.
-  // this will force the highest corner to be above the last center
-  for (int64_t iLon = 0; iLon < nLons; iLon++) {
-    for (int64_t iLat = 0; iLat < nLats; iLat++) {
-      qTmp = 2 * magQ_Corner(iLon, iLat, nAlts - 1) - magQ_Corner(iLon, iLat,
-                                                                  nAlts - 2);
-      magQ_Corner(iLon, iLat, nAlts) = qTmp;
-    }
-  }
-
-  // last lon corner, copy previous. It's the same!
-  for (int64_t iAlt = 0; iAlt < nAlts + 1; iAlt ++) {
-    for (int64_t iLat = 0; iLat < nLats + 1; iLat++)
-      magQ_Corner(nLons, iLat, iAlt) = magQ_Corner(nLons - 1, iLat, iAlt);
-  }
-
-  // Now we have (p,q) coords corners, convert to lon/lat/alt and we r off to the races
-  std::pair <arma_cube, arma_cube> rtheta;
-  precision_t planetRadius;
-  rtheta = qp_to_r_theta(magQ_Corner, magP_Corner);
-  magLat_Corner = rtheta.second;
-
-  // Change if the dipole is offset and/or planet is oblate:
-  planetRadius =  planet.get_radius(magLat_scgc.at(1));
-  magAlt_Corner = rtheta.first * planetRadius;
-
-  report.exit(function);
-  return;
-}
-
-
 // -----------------------------------------------------------------------
 // Convert XyzDipole to XyzGeo
 //
@@ -618,15 +168,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsGeoGrid = false;
   IsMagGrid = true;
   IsCubeSphereGrid = false;
+  IsDipole = true;
 
-  report.print(0, "Creating inter-node connections Grid");
+  // report.print(0, "Creating inter-node connections Grid");
 
   //if (!Is0D & !Is1Dz)
   //  create_sphere_connection(quadtree_ion);
 
   report.print(0, "Creating Dipole Grid");
 
-  report.print(3, "Getting mgrid_inputs inputs in dipole grid");
+  report.print(3, "Getting grid inputs for dipole grid");
 
   Inputs::grid_input_struct grid_input = input.get_grid_inputs("ionGrid");
 
@@ -635,7 +186,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // Get inputs:
 
-  precision_t max_lat = grid_input.lat_min;
+  precision_t min_lat = grid_input.lat_min;
   precision_t max_lat = grid_input.lat_max;
 
   precision_t min_alt = grid_input.alt_min * cKMtoM;
@@ -647,6 +198,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
   precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
+  magLat_scgc.zeros();
+  magInvLat_scgc.zeros();
+  magLon_scgc.zeros();
+  magAlt_scgc.zeros();
+  magLon_Corner.zeros();
+  magLat_Corner.zeros();
+  magAlt_Corner.zeros();
+
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
@@ -655,8 +214,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Get some coordinates and sizes in normalized coordinates:
   arma_vec lower_left_norm = quadtree_ion.get_vect("LL"); // origin
   arma_vec size_right_norm = quadtree_ion.get_vect("SR"); // lon_lims
-  arma_vec size_up_norm = quadtree_ion.get_vect("SU");    //[1] = lat_lims
-  report.print(3, "Initializing (dipole) longitudes");
+  arma_vec size_up_norm = quadtree_ion.get_vect("SU");    // lat_extent
+  report.print(3, "Got all settings. Initializing longitudes.");
 
   precision_t dlon = size_right_norm(0) * cPI / (nLons - 2 * nGCs);
   precision_t lon0 = lower_left_norm(0) * cPI;
@@ -672,7 +231,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Dimension iterators
   int64_t iLon, iLat, iAlt;
 
-  // Longitudes (symmetric, for now):
+  /////////////////
+  // Longitudes: //
+  /////////////////
+
   // - Make a 1d vector
   // - copy it into the 3d cube
   for (iLon = 0; iLon < nLons; iLon++) {
@@ -680,7 +242,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lon1dLeft(iLon) = lon0 + (iLon - nGCs) * dlon; // corners
   }
 
-
   lon1dLeft(nLons) = lon0 + (nLons - nGCs) * dlon;
 
   for (iLat = 0; iLat < nLats; iLat++) {
@@ -691,78 +252,201 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-    }
-  }
 
-  for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-    for (iLat = 0; iLat < nLats + 1; iLat++) {
-      // Corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
   }
 
+  if (magLon_scgc.has_nan())
+    report.error("NAN IN MAGLON");
+
+  // for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+  //   for (iLat = 0; iLat < nLats + 1; iLat++) {
+  //     // Corners
+  //     magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+  //     i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+  //   }
+  // }
+
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
   // Latitudes: //
   ////////////////
 
-  // min_lat calculated from min_apex
-  precision_t min_lat = acos(sqrt(1 / min_apex_re));
+  // Invariant latitude is evenly spaced across each block.
+  // Latitude limits are adjusted here, not in quadtree
+
+  // - From the quadtree, we see the origin & extent of this block
+  // - That is normalized, without any influence from settings
+  // - Scale it with the latitude limits provided by the user
+  // - Put invariant latitudes down, linearly, between this range.
+
+  // This has to be done differently in the north & south hemisphere.
+  // So note if we are in the southern hemisphere and invert it afterwards.
+
+  bool isSouth = false;
+  precision_t lat_origin = lower_left_norm(1);
 
-  // latitude of field line base:
-  // todo: needs support for variable stretching. it's like, halfway there.
+  if (lat_origin < -0.01) { // handles some imprecision
+    isSouth = true;
+    lat_origin = -1.0 * lat_origin - size_up_norm(1);
+  }
 
-  if (report.test_verbose(2))
-    std::cout << "computing baselats : " << max_lat* cRtoD  << " " << min_lat* cRtoD
-              << "\n";
+  precision_t lat0 = 2.0 * (max_lat - min_lat) * lat_origin;
+  precision_t dlat = 2.0 * size_up_norm(1) * (max_lat -  min_lat) /
+                     (nLats - nGCs);
 
-  arma_vec baseLats = baselat_spacing(size_up_norm(1), lower_left_norm(1),
-                                      max_lat, min_lat, 1.0);
+  arma_vec lat1d(nLats);
+  arma_vec lat1dDown(nLats + 1);
 
-  if (report.test_verbose(2))
-    std::cout << "baselats : " << baseLats * cRtoD << "\n";
+  for (iLat = 0; iLat < nLats; iLat++) {
+    lat1d(iLat) = lat0 + (iLat - nGCs + 0.5) * dlat + min_lat; // centers
+    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners
+  }
 
-  // downward sides (latitude shifted by 1/2 step):
-  // TODO: This only works for linear latitude spacing, which is all that's supported right now.
-  // When the exponential spacing (or something else) is fixed, this needs updating.
-  precision_t dlat;
-  dlat = baseLats(1) - baseLats(0);
+  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
 
-  // put one cell halfway btwn each base latitude, leave 1st and last cell for now...
-  for (int64_t iLat = 1; iLat < nLats; iLat ++)
-    baseLats_down(iLat) = (baseLats(iLat - 1) + baseLats(iLat)) / 2.0;
+  // At the pole:
+  // - put last ghost cell's corner at 89.9 degrees latitude
+  // - put 2nd to last corner 1/2 way between 89.9 and the last real corner
+  // - evenly space the ghost cells between these.
 
-  // Put in 1st and last cell. Done this way so it's easier to put in supercell or something else
-  baseLats_down(0) = baseLats(0) * 1.5 - baseLats(1) * 0.5;
-  baseLats_down(nLats) = baseLats(nLats - 1) * 1.5 - baseLats(nLats - 2) * 0.5;
+  if (lat_origin + size_up_norm(1) > 0.49) { // touching the pole
+    lat1dDown(nLats) = 89.9 * 180.0 / cPI;
+    lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
+    lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
+    lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
+    // lat1d(-1) = (lat1dDown(-1) + lat1dDown(-2)) / 2.0;
+  }
 
-  if (report.test_verbose(2))
-    std::cout << "baselats_down : " << baseLats_down * cRtoD << "\n";
+  // l-shells of centers (nLats)
+  arma_vec Pcenters = min_alt_re / pow(sin(cPI / 2 - lat1d), 2);
 
-  report.print(3, "baselats done!");
+  // l-shells of corners
+  arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
-  // latitude & altitude of points on field lines (2D)
-  // Cell centers
-  fill_field_lines(baseLats, min_alt_re, Gamma, planet);
-  // Corners (final bool argument) tells function to place stuff in the corner.
-  // This is only down for the "down" edges, where the base latitudes are different.
-  fill_field_lines(baseLats_down, min_alt_re, Gamma, planet, true);
 
-  // The baseLats are the Invariant Latitudes of the grid, so we can just fill in all of the
-  // points with these values
-  for (iAlt = 0; iAlt < nAlts; iAlt++)
-    for (iLat = 0; iLat < nLats; iLat++)
-      for (iLon = 0; iLon < nLons; iLon++)
-        magInvLat_scgc(iLon, iLat, iAlt) = baseLats(iLat);
+  if (isSouth){ // so the values are increasing:
+    lat1d = -1*reverse(lat1d);
+  }
 
-  report.print(4, "Field-aligned Edges");
-  dipole_alt_edges(planet, min_alt_re);
 
-  report.print(3,
-               "Done generating symmetric latitude & altitude spacing in dipole.");
+  for (iLon = 0; iLon < nLons; iLon++) {
+    for (iAlt = 0; iAlt < nAlts; iAlt++) {
+      magInvLat_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = lat1d;
+      magP_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = Pcenters;
+      magP_Down.subcube(iLon, 0, iAlt, iLon, nLats, iAlt) = Pcorners;
+    }
+  }
+
+  report.print(3, "Done initializing invariant latitudes");
+
+  ////////////////
+  // Altitudes: //
+  ////////////////
+
+  // - Trace each field line from q_min to q_max. Identical for all field lines within this block.
+  // - Obtain the minimum "altitude" (q) from the highest latitude
+  // field line on each block
+  // - Obtain the maximum "altitude" from the lowest latitude *open* field line.
+  // (closed field lines are treated differently)
+  //  - In other words, since we are tracing from q_min to q_max, use the highest field
+  //    line to get q_min and the lowest for q_max. This forces all field lines to
+  //    start & end within the bounds.
+  // - Evenly space all points' "altitude" linear across these two values
+  // - Altitude here refers to the dipole q-coordinate - cos(magLat)/r^2
+  // - Blocks touching a pole or the equator are treated differently
+
+  // Field lines close if:
+  // - touching the (magnetic) equator
+  // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
+
+  precision_t q_min = 0; // q=0 at equator (for closed blocks)
+  bool close_this_block = false;
+
+  if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
+      || (abs(lat_origin) < 0.01)) // equator, with some imprecision
+    close_this_block = true;
+
+  std::cout << "closing block?: " << close_this_block << "\n";
+
+  if (!close_this_block)
+    // invLats are still all in North Hemisphere & increasing.
+    // Use minimum p & alt to solve for q
+    // q = sqrt((1-r/p)/r^4)
+    // TODO: use nGCs or 0 to index Pcorners??
+    q_min = pow(((1 - min_alt_re / Pcorners(nGCs)) / pow(min_alt_re, 4.0)), 0.5);
+
+  // Trace each field line up to q_max, obtained from the lowest field line in the block
+  precision_t q_max = pow(((1 - max_alt_re / Pcorners(nLats)) / pow(max_alt_re,
+                           4.0)), 0.5);
+
+  precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);
+
+  arma_vec magQ1d(nAlts);
+  arma_vec magQ_corner_1d(nAlts + 1);
+
+  for (iAlt = 0; iAlt < nAlts; iAlt ++) {
+    magQ1d(iAlt) = q_min + (iAlt - nGCs + 0.5) * delQ;
+    magQ_corner_1d(iAlt) = q_min + (iAlt - nGCs) * delQ;
+  }
+
+  magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
+
+  if (isSouth) {
+    magQ1d = -1.0 * reverse(magQ1d);
+    magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
+  }
+
+  report.print(3, "Made q's");
+  ////////////////////////////
+  // That is the grid made. //
+  ////////////////////////////
+
+
+  std::pair<precision_t, precision_t> rtheta, rtheta_corner;
+  precision_t altitude;
+  arma_vec tmp(nLons), tmp2(nLons+1);
+
+  // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
+  for (iLat = 0; iLat < nLats; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts; iAlt++) {
+
+      // this is for cell centers:
+      rtheta = qp_to_r_theta(magQ1d(iAlt), Pcenters(iLat));
+      tmp.zeros();
+      tmp += rtheta.first;
+      magAlt_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      k_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      tmp.zeros();
+      tmp += rtheta.second;
+      magLat_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+      j_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
+
+      // this is for cell corners:
+      rtheta_corner = qp_to_r_theta(magQ_corner_1d(iAlt), Pcorners(iLat));
+      tmp2.zeros();
+      tmp2 += rtheta_corner.first;
+      magAlt_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+      tmp2.zeros();
+      tmp2 += rtheta_corner.second;
+      magLat_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+    }
+  }
 
+  if (magLat_scgc.has_nan())
+    report.error("NAN IN MAGLAT");
+    
+  if (magLat_Corner.has_nan() || magLat_Corner.has_inf())
+  report.error("NAN IN MAGLAT_C");
+  
+  if (magAlt_Corner.has_nan())
+    report.error("NAN IN MAGALT_CORNER");
+
+  report.print(4,"got my mlats");
+  
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 
@@ -797,7 +481,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   arma_cube bm = sqrt(br % br + bt % bt);
   // Latitudinal direction of radial:
   arma_cube s = sign(magLat_scgc);
-  // s.elem(find(s == 0)).ones();
+  s.elem(find(s == 0)).ones();
 
   rad_unit_vcgc[1] = bt / bm % s;
   rad_unit_vcgc[2] = -br / bm;
@@ -824,7 +508,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // put back into altitude. we've been carrying around radius:
-  magAlt_scgc = magAlt_scgc - planetRadius;
+  // magAlt_scgc = magAlt_scgc - planetRadius;
+  // this breaks things more???
 
   report.exit(function);
   return DidWork;

From d64be6bd6755dd44fe852ca4914d98d5efa49521 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 18 Mar 2025 22:17:31 -0400
Subject: [PATCH 016/266] BUG: Quadtree was dividing dipole into (nonexistent)
 fourths. Everything died

---
 include/sphere.h | 31 +++++++++++++++++++++++++++++++
 src/quadtree.cpp | 10 ++++++++++
 2 files changed, 41 insertions(+)

diff --git a/include/sphere.h b/include/sphere.h
index 4f3902a0..32de5fdf 100644
--- a/include/sphere.h
+++ b/include/sphere.h
@@ -29,6 +29,37 @@ namespace Sphere {
 
 };
 
+/*************************************************
+ * \brief A namespace with all (4-root) sphere grid logic.
+ *************************************************/
+namespace Sphere4 {
+
+  /// The normalized origins of each node (i.e. corner)
+  static const arma_mat ORIGINS = {
+    { 0.0,  -0.5, 0.0},
+    { 0.0, -0.25, 0.0},
+    { 0.0,   0.0, 0.0},
+    { 0.0,  0.25, 0.0}
+  };
+  
+  /// Normalized right steps in node
+  static const arma_mat RIGHTS = {
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0},
+    {2.0, 0.0, 0.0}
+  };
+  
+  /// Normalized up steps in node
+  static const arma_mat UPS = {
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0},
+    {0.0, 0.25, 0.0}
+  };
+  
+  };
+
 /*************************************************
  * \brief A namespace with all (6-root) sphere grid logic.
  *************************************************/
diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 0e2aae65..ff5d45a2 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -22,6 +22,9 @@ Quadtree::Quadtree(std::string shape) {
   if (shape == "sphere")
     nRootNodes = 1;
 
+    if (shape == "sphere4")
+    nRootNodes = 4;
+
   if (shape == "dipole4")
     nRootNodes = 4;
 
@@ -63,6 +66,13 @@ void Quadtree::build(std::string gridtype) {
     IsSphere = true;
   }
 
+  if (grid_input.shape == "sphere4") {
+    origins = Sphere4::ORIGINS;
+    rights = Sphere4::RIGHTS;
+    ups = Sphere4::UPS;
+    IsSphere = true;
+  }
+
   if (grid_input.shape == "dipole4") {
     origins = Dipole4::ORIGINS;
     rights = Dipole4::RIGHTS;

From 553e6e6a136ec92e878db09f3626471cbe62146a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 12:43:14 -0400
Subject: [PATCH 017/266] FEAT: Orthogonal dipole grid is working

- clean up comments, debugging, etc.
- fix issue with quadtree values being abs()
---
 src/init_mag_grid.cpp | 51 ++++++++++---------------------------------
 1 file changed, 12 insertions(+), 39 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8a4df97a..1652f089 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -198,14 +198,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
   precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
-  magLat_scgc.zeros();
-  magInvLat_scgc.zeros();
-  magLon_scgc.zeros();
-  magAlt_scgc.zeros();
-  magLon_Corner.zeros();
-  magLat_Corner.zeros();
-  magAlt_Corner.zeros();
-
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;
@@ -261,14 +253,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   if (magLon_scgc.has_nan())
     report.error("NAN IN MAGLON");
 
-  // for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
-  //   for (iLat = 0; iLat < nLats + 1; iLat++) {
-  //     // Corners
-  //     magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-  //     i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-  //   }
-  // }
-
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
@@ -313,24 +297,24 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - put 2nd to last corner 1/2 way between 89.9 and the last real corner
   // - evenly space the ghost cells between these.
 
-  if (lat_origin + size_up_norm(1) > 0.49) { // touching the pole
-    lat1dDown(nLats) = 89.9 * 180.0 / cPI;
+  // Check if we're touching the pole, need to look at original quadtree values
+  if ((abs(lower_left_norm(1) + size_up_norm(1)) > 0.49) // north pole
+      || (lower_left_norm(1) < -0.49)) { // south pole
+    lat1dDown(nLats) = 89.9 * cDtoR;
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
     lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
     lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
-    // lat1d(-1) = (lat1dDown(-1) + lat1dDown(-2)) / 2.0;
   }
 
-  // l-shells of centers (nLats)
+  // l-shells of centers
   arma_vec Pcenters = min_alt_re / pow(sin(cPI / 2 - lat1d), 2);
 
   // l-shells of corners
   arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
 
-  if (isSouth){ // so the values are increasing:
-    lat1d = -1*reverse(lat1d);
-  }
+  if (isSouth)  // so the values are increasing:
+    lat1d = -1 * reverse(lat1d);
 
 
   for (iLon = 0; iLon < nLons; iLon++) {
@@ -367,11 +351,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   bool close_this_block = false;
 
   if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-      || (abs(lat_origin) < 0.01)) // equator, with some imprecision
+      || (abs(lat_origin - size_up_norm(1)) < 0.01)) // equator, with some imprecision
     close_this_block = true;
 
-  std::cout << "closing block?: " << close_this_block << "\n";
-
   if (!close_this_block)
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
@@ -400,7 +382,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
   }
 
-  report.print(3, "Made q's");
   ////////////////////////////
   // That is the grid made. //
   ////////////////////////////
@@ -408,7 +389,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   std::pair<precision_t, precision_t> rtheta, rtheta_corner;
   precision_t altitude;
-  arma_vec tmp(nLons), tmp2(nLons+1);
+  arma_vec tmp(nLons), tmp2(nLons + 1);
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
   for (iLat = 0; iLat < nLats; iLat ++) {
@@ -436,17 +417,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     }
   }
 
-  if (magLat_scgc.has_nan())
-    report.error("NAN IN MAGLAT");
-    
-  if (magLat_Corner.has_nan() || magLat_Corner.has_inf())
-  report.error("NAN IN MAGLAT_C");
-  
-  if (magAlt_Corner.has_nan())
-    report.error("NAN IN MAGALT_CORNER");
-
-  report.print(4,"got my mlats");
-  
+  // magAlt is in units of planet radius
+  magAlt_scgc *= planetRadius;
+
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 

From 4f453673a79c42ffed721b11a027dbb2a59fc172 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 12:43:51 -0400
Subject: [PATCH 018/266] FEAT: Show which cubes are not all_finite in stdout

---
 src/tools.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tools.cpp b/src/tools.cpp
index dc2f621f..d041bf49 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -654,7 +654,7 @@ bool all_finite(arma_cube cube, std::string name) {
       "," + std::to_string(loc[1]) +
       "," + std::to_string(loc[2]) + ")";
     int size = locations.size();
-    std::cout << "all_finite : " << cube(loc[0], loc[1], loc[2]) << "\n";
+    std::cout << "all_finite ("<<name<<"): " << cube(loc[0], loc[1], loc[2]) << "\n";
     std::string error_message =
       std::to_string(size) +
       " Nonfinite values exist in " + name +

From 704cdc731bbe853d5d69eb5abbd7bb6dccaa2df9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 17:31:27 -0400
Subject: [PATCH 019/266] DOC: Start updating dipole writeup. Committing to see
 if github likes latex in md

---
 doc/internals/grid.md | 50 +++++++++++++++++++++++++++++++++++++------
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index ad91f315..8f1b1d38 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -114,12 +114,50 @@ bottom of the field-line. Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
-The latitudinal spacing is determined by the `LatStretch` factor in the settings.
-The base latitudes are then scaled in such a way that **higher** `LatStretch` leads to
-more points near the equator, 1.0 is roughly linear, and then values less than 1.0 will
-distribute more points near the poles. The exact spacing is calculated where the
-difference between successive values is proportional to:
-`cos(lat_max)^(1/LatStretch)`. Using an even number of latitudes is required.
+The dipole grid requires >4 root nodes which ensures the coordinates are 
+mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
+compatibility with the neutral grid being a sphere or cubesphere. In both cases,
+each node the entire longitude range and given a portion of the latitude range.
+So in the case of `dipole4`, the four nodes are each given 1/4 of the available
+latitudes and all of the longitudes.
+
+The dipole grid is evenly spaced in **invariant latitude** (where the field line
+passes the minumum altitude) and **q** (the dipole coordinate
+specifying how far along the field line a point lies). Q is dimensionless and defined 
+to be -infinity at the south pole, +infinity at the north pole, and 0 at the
+magnetic equator. The equations for p (L-shell) and q are taken as the following,
+where r is the distance from the origin and $\theta$ is *colatitude*:
+
+$$
+p = \frac{r}{\sin^2\theta}
+
+\newline
+\newline
+
+q = \frac{\cos{\theta}}{r^2}
+$$
+
+
+
+these are the steps taken:
+
+1. Receive latitude range of this block from the quadtree. This will look 
+something like `lower_left_norm=(0.0, -0.5, 0.0)` and `size_up_norm=(0, 0.25, 0)`
+for the node nearest the south pole in dipole4. From this, determine if we are
+in the southern hemisphere. If we are, everything will be done as if it was the north
+hemisphere and then reversed & negated at the end.
+2. Store the latitude (j) component of `lower_left_norm` as `lat_origin`. If this
+node is in the southern hemisphere, store the top of the node's extent as lat_origin.
+3. Scale this node's portion of the quadtree to be limited by the user-provided
+`lat_range`. These for the invariant latitudes, which are evenly spaced between 
+the latitude range provided and dictate where each field line passes through the minimum
+altitude provided.
+4. Determine if this node will have closed or open field lines. There are two conditions:
+   - If the node is touching the equator
+   - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
+5. If the field line closes, 
+
+
 
 Along the `k` dimension, field lines terminate after a specified number of points.
 When using the Dipole grid option, there is not an option to set the maximum altitude.

From 07c42c27face840cc0df251cb2a48f33fdb3822e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 17:33:44 -0400
Subject: [PATCH 020/266] doc: testing github markdown again

---
 doc/internals/grid.md | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 8f1b1d38..aab1b5cd 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -128,14 +128,9 @@ to be -infinity at the south pole, +infinity at the north pole, and 0 at the
 magnetic equator. The equations for p (L-shell) and q are taken as the following,
 where r is the distance from the origin and $\theta$ is *colatitude*:
 
-$$
-p = \frac{r}{\sin^2\theta}
+$p = \frac{r}{\sin^2\theta}$
 
-\newline
-\newline
-
-q = \frac{\cos{\theta}}{r^2}
-$$
+$q = \frac{\cos{\theta}}{r^2}$
 
 
 

From 79ec550b59e6294195ef2e14e34d019f22744340 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 18:53:30 -0400
Subject: [PATCH 021/266] BUGS: Fix mismatch max/min & clear up bool logic in
 init_mag_grid q-limits

---
 src/init_mag_grid.cpp | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1652f089..8e8cb260 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -347,22 +347,25 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - touching the (magnetic) equator
   // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
 
-  precision_t q_min = 0; // q=0 at equator (for closed blocks)
+  precision_t q_min;
   bool close_this_block = false;
 
-  if ((Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-      || (abs(lat_origin - size_up_norm(1)) < 0.01)) // equator, with some imprecision
+  if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
+    close_this_block = true;
+  
+  if(lat_origin < 0.01) // equator, with some imprecision
     close_this_block = true;
 
-  if (!close_this_block)
+  if (close_this_block)
+    q_min = 0; // q=0 at equator (for closed blocks)
+  else
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    // TODO: use nGCs or 0 to index Pcorners??
-    q_min = pow(((1 - min_alt_re / Pcorners(nGCs)) / pow(min_alt_re, 4.0)), 0.5);
+  q_min = pow(((1 - max_alt_re / Pcorners(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - max_alt_re / Pcorners(nLats)) / pow(max_alt_re,
+  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats)) / pow(min_alt_re,
                            4.0)), 0.5);
 
   precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);

From dd9d4e199ea8878d7f4ad92d5eeddfcf7f3f0f5f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 19 Mar 2025 18:58:51 -0400
Subject: [PATCH 022/266] DOC: explain how the dipole grid is made.

---
 doc/internals/grid.md | 79 ++++++++++++++++++++++++++++---------------
 1 file changed, 51 insertions(+), 28 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index aab1b5cd..00a8dca6 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -18,19 +18,21 @@ again, 16 processors are needed, etc. However, in the altitude/radial direction,
 the number of points that are specified in the aether.json is unchanged, as it
 does not rely on the number of processors used.
 
-- [Grid Types Explained](#grid-types-explained)
-- [Grid Shapes Explained](#grid-shapes-explained)
-  - [TL;DR](#tldr)
-  - [The Sphere Grid](#the-sphere-grid)
-  - [The Cubesphere Grid](#the-cubesphere-grid)
-  - [The Dipole Grid](#the-dipole-grid)
-  - [Root Nodes](#root-nodes)
-    - [Sphere](#sphere)
-    - [Cubesphere](#cubesphere)
-    - [Specifying Root Nodes](#specifying-root-nodes)
-- [Specifying the Grid](#specifying-the-grid)
-  - [Horizontal Resolution](#horizontal-resolution)
-  - [Vertical Resolution](#vertical-resolution)
+- [Grids in Aether](#grids-in-aether)
+  - [Grid Types Explained](#grid-types-explained)
+  - [Grid Shapes Explained](#grid-shapes-explained)
+    - [TL;DR](#tldr)
+    - [The Sphere Grid](#the-sphere-grid)
+    - [The Cubesphere Grid](#the-cubesphere-grid)
+    - [The Dipole Grid](#the-dipole-grid)
+      - [Inputs:](#inputs)
+    - [Root Nodes](#root-nodes)
+      - [Sphere](#sphere)
+      - [Cubesphere](#cubesphere)
+      - [Specifying Root Nodes](#specifying-root-nodes)
+  - [Specifying the Grid](#specifying-the-grid)
+    - [Horizontal Resolution](#horizontal-resolution)
+    - [Vertical Resolution](#vertical-resolution)
 
 ## Grid Types Explained
 
@@ -110,7 +112,9 @@ scale-height.
 
 The dipole grid is aligned with the magnetic field. The `k` dimension is along
 the fieldline, `i` is magnetic longitude, and `j` is roughly latitude for the
-bottom of the field-line. Each fieldline starts at the lowest modeled altitude
+bottom of the field-line. 
+
+Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
@@ -133,8 +137,7 @@ $p = \frac{r}{\sin^2\theta}$
 $q = \frac{\cos{\theta}}{r^2}$
 
 
-
-these are the steps taken:
+Here is how the dipole grid is generated:
 
 1. Receive latitude range of this block from the quadtree. This will look 
 something like `lower_left_norm=(0.0, -0.5, 0.0)` and `size_up_norm=(0, 0.25, 0)`
@@ -147,24 +150,44 @@ node is in the southern hemisphere, store the top of the node's extent as lat_or
 `lat_range`. These for the invariant latitudes, which are evenly spaced between 
 the latitude range provided and dictate where each field line passes through the minimum
 altitude provided.
+   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude. Add
+another corner 1/2 way between this point and the last "real" corner, and put
+cell centers between these corners.
 4. Determine if this node will have closed or open field lines. There are two conditions:
    - If the node is touching the equator
    - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
-5. If the field line closes, 
+5. Determine the limits & values of the q-coordinate for all points along all field
+lines on this node. The q-values on each node are identical, and the p-value is
+constant along each field line (by definition). To solve for q, use the p-values
+from step 3 and the altitude, as described below. Use $q=\sqrt{(1-r/p)/r^4}$.
+   - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
+magnetic equator and two ghost cell centers across the equator for message passing.
+   - If the field line does not close, $q_{min}$ is calculated from the highest
+altitude point on the lowest latitude field line. This is the point farthest
+from the planet on the most equatorward field line (and since q=0 at the
+equator, it has the lowest allowed q-value).
+   - The maximum q-value is solved for identically in open & closed blocks with
+the lower altitude limit and the highest latitude field line. The point closest
+to the planet on the highest latitude field line has the highest allowed q-value
+(q=$\pm$infinity at the poles).
+6. We now have p (step 3) and q (step 5) for all points on the grid. From this
+we solve for `(r, \theta)`, and any other coordinates we need.
+
+See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
+experiment with the available options.
 
+#### Inputs:
 
+- ***Shape***: either `dipole4` or `dipole6`. Cannot (yet) be run on a single core.
+- ***nLonsPerBlock***: number of magnetic longitudes
+- ***nLatsPerBlock***: number of field lines (invariant latitudes)
+- ***nAlts***: Number of points along each field line. A number of these will 
+be discarded for being at too low of altitude.
+- ***AltRange***: [`min_alt`, `max_alt`] - the altitude (in km) range to bound 
+cells by. 
+- ***LatRange***: [`min_lat`, `max_lat`] - the limits on invariant latitudes 
+(in degrees). Sets the limits on the latitudes where field lines cross `min_alt`.
 
-Along the `k` dimension, field lines terminate after a specified number of points.
-When using the Dipole grid option, there is not an option to set the maximum altitude.
-Rather, points are laid down from the pole towards the equator, stopping when the field
-line reaches the equator. Ghost cells are then used to pass information across the
-equator. The spacing of points along each field line is the same as
-[(Huba, Joyce & Fedder, 2000)](https://doi.org/10.1029/2000JA000035).
-Using an even number of points along the `k` dimension (`nAlts`) means that no points
-will lie on the magnetic equator and thus the field lines from the high latitude
-regions will not reach beyond the plasmasphere. See
-[the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
-experiment with the available options.
 
 ### Root Nodes
 

From c00e905335bf1731cb8685b85c217bf6df38af38 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 09:59:57 -0400
Subject: [PATCH 023/266] sty/doc: Change grid.md formatting to make it
 prettier?

---
 doc/internals/grid.md | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 00a8dca6..9a530d58 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -128,14 +128,19 @@ latitudes and all of the longitudes.
 The dipole grid is evenly spaced in **invariant latitude** (where the field line
 passes the minumum altitude) and **q** (the dipole coordinate
 specifying how far along the field line a point lies). Q is dimensionless and defined 
-to be -infinity at the south pole, +infinity at the north pole, and 0 at the
-magnetic equator. The equations for p (L-shell) and q are taken as the following,
+to be $-\infty$ at the south pole, $+\infty$ at the north pole, and 0 at the
+magnetic equator. The equations for p (L-shell) and q are the following,
 where r is the distance from the origin and $\theta$ is *colatitude*:
 
-$p = \frac{r}{\sin^2\theta}$
+```math
+p = \frac{r}{\sin^2\theta}
 
-$q = \frac{\cos{\theta}}{r^2}$
+```
+
+```math
+q = \frac{\cos{\theta}}{r^2}
 
+```
 
 Here is how the dipole grid is generated:
 
@@ -159,7 +164,7 @@ cell centers between these corners.
 5. Determine the limits & values of the q-coordinate for all points along all field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below. Use $q=\sqrt{(1-r/p)/r^4}$.
+from step 3 and the altitude, as described below. Use $q=\sqrt{(1-\frac{r}{p})/r^4}$.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest
@@ -170,28 +175,28 @@ equator, it has the lowest allowed q-value).
 the lower altitude limit and the highest latitude field line. The point closest
 to the planet on the highest latitude field line has the highest allowed q-value
 (q=$\pm$infinity at the poles).
-6. We now have p (step 3) and q (step 5) for all points on the grid. From this
-we solve for `(r, \theta)`, and any other coordinates we need.
+6. We now have `p` (step 3) and `q` (step 5) for all points on the grid. From this
+we solve for $(r, \theta)$, and any other coordinates we need.
 
 See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
 experiment with the available options.
 
 #### Inputs:
 
-- ***Shape***: either `dipole4` or `dipole6`. Cannot (yet) be run on a single core.
+- ***Shape***: either `dipole4` or `dipole6`. Cannot be run on a single core.
 - ***nLonsPerBlock***: number of magnetic longitudes
 - ***nLatsPerBlock***: number of field lines (invariant latitudes)
 - ***nAlts***: Number of points along each field line. A number of these will 
 be discarded for being at too low of altitude.
-- ***AltRange***: [`min_alt`, `max_alt`] - the altitude (in km) range to bound 
+- ***AltRange***: (`min_alt`, `max_alt`) - the altitude (in km) range to bound 
 cells by. 
-- ***LatRange***: [`min_lat`, `max_lat`] - the limits on invariant latitudes 
+- ***LatRange***: (`min_lat`, `max_lat`) - the limits on invariant latitudes 
 (in degrees). Sets the limits on the latitudes where field lines cross `min_alt`.
 
 
 ### Root Nodes
 
->This document uses the words "block" and "node" somewhat
+> This document uses the words "block" and "node" somewhat
 interchangably. Technically, a "block" is single (`i, j, k`) grid, while a
 "node" can be multiple "blocks" that make up a section of the globe.
 

From bc0a2763d4960b9fdc7d477dd03ba8100612d9be Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 10:08:49 -0400
Subject: [PATCH 024/266] sty: Does github let me number equations?

---
 doc/internals/grid.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 9a530d58..9fba4e49 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -134,12 +134,12 @@ where r is the distance from the origin and $\theta$ is *colatitude*:
 
 ```math
 p = \frac{r}{\sin^2\theta}
-
+\tag{1}
 ```
 
 ```math
 q = \frac{\cos{\theta}}{r^2}
-
+\tag{2}
 ```
 
 Here is how the dipole grid is generated:
@@ -155,16 +155,17 @@ node is in the southern hemisphere, store the top of the node's extent as lat_or
 `lat_range`. These for the invariant latitudes, which are evenly spaced between 
 the latitude range provided and dictate where each field line passes through the minimum
 altitude provided.
-   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude. Add
+   - At the poles, put the last corner at $89.9^\circ$ magnetic latitude, or
+$0.1^\circ$ and $179.9^\circ$ magnetic ***co***latitude. Add
 another corner 1/2 way between this point and the last "real" corner, and put
 cell centers between these corners.
 4. Determine if this node will have closed or open field lines. There are two conditions:
    - If the node is touching the equator
    - If the lowest L-shell is below the maximum altitude. This is rare, but prevents unexpected behavior.
-5. Determine the limits & values of the q-coordinate for all points along all field
+5. Determine the limits, then values, of the q-coordinate for all points along each field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below. Use $q=\sqrt{(1-\frac{r}{p})/r^4}$.
+from step 3 and the altitude, as described below and Equation 3.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest

From 0d1d3518ee8f8794ccedb920541e68b2d9024ba0 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 10:15:36 -0400
Subject: [PATCH 025/266] STY: it does not. revert previous commit, fix eq
 format, change some wording. done w this file for now

---
 doc/internals/grid.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 9fba4e49..92da6fdb 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -134,12 +134,10 @@ where r is the distance from the origin and $\theta$ is *colatitude*:
 
 ```math
 p = \frac{r}{\sin^2\theta}
-\tag{1}
 ```
 
 ```math
 q = \frac{\cos{\theta}}{r^2}
-\tag{2}
 ```
 
 Here is how the dipole grid is generated:
@@ -165,7 +163,7 @@ cell centers between these corners.
 5. Determine the limits, then values, of the q-coordinate for all points along each field
 lines on this node. The q-values on each node are identical, and the p-value is
 constant along each field line (by definition). To solve for q, use the p-values
-from step 3 and the altitude, as described below and Equation 3.
+from step 3 and the altitude, as described below and $q=\sqrt{(1-r/p)/r^4}$.
    - If the field line closes, $q_{min}=0$. There will be a corner/edge at the 
 magnetic equator and two ghost cell centers across the equator for message passing.
    - If the field line does not close, $q_{min}$ is calculated from the highest
@@ -179,8 +177,8 @@ to the planet on the highest latitude field line has the highest allowed q-value
 6. We now have `p` (step 3) and `q` (step 5) for all points on the grid. From this
 we solve for $(r, \theta)$, and any other coordinates we need.
 
-See [the dipole script in edu/examples](../../edu/examples/Dipole/dipole.py) to
-experiment with the available options.
+See [edu/examples/Dipole](../../edu/examples/Dipole) for more detailed information
+and to experiment with the available options in a Python script.
 
 #### Inputs:
 

From 5b405a8fdc4b81fdae25c972ca6910a6631029a1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:15:27 -0400
Subject: [PATCH 026/266] DOC: Decide what the dipole (i,j,k) coords are!

Need to finish implementing
---
 doc/internals/coordinates.md | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/doc/internals/coordinates.md b/doc/internals/coordinates.md
index 418e0412..515857b2 100644
--- a/doc/internals/coordinates.md
+++ b/doc/internals/coordinates.md
@@ -20,15 +20,16 @@
 
 ## Dipole Coordinates
 - Longitude (radians) - radians east of the meridian that contains the north magnetic pole and north rotation axis
-- P (?) - Identifies the field line, related to L-shell
-- Q (meters?) - The distance along the field line from some reference point, related to magnetic latitude.
+- P (meters) - Identifies the field line. This is the same as L-shell and is constant along wach field line.
+- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole.
 
 ## More Dipole Coordinates
 - L-shell (Planetary Radii) - The distance from the planet's center at which the magnetic field encounters the dipole's equatorial plane
-- Magnetic Latitude (radians) - angle between the dipole's equatorial plane and the point.
-- Invariant Latitude (degrees) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet.  This is constant along the field-line and is related to the L-Shell.
+- Magnetic Latitude (radians) - angle between the dipole's equatorial plane and a point.
+- Invariant Latitude (radians) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet ([specified in the inputs](../internals/grid.md#inputs)).  This is constant along the field-line and is related to the L-Shell.
 - Magnetic Local Time (hours) - Angle between the sun, the north magnetic pole, and the point. Explicitly, this is done in PSE XY coordinates, ignoring the Z coorinate.
 
+> The dipole `(i,j,k)` coordinates are (magnetic longitude, magnetic latitude, radius).
 
 # Coordinates in Aether
 
@@ -46,12 +47,15 @@ Because Aether considers gravity to be a function of radius and explicitly inclu
 
 ## i, j, k Coordinates
 
-As described in the grid.md file, Aether uses a logical '(i, j, k)' 3D grid structure.  Therefore, we refer to the 'primary' coordinates as the ijk coordinate system.  What this means is that the i-coordinate is in the i-direction, the j-coordinate is in the j-direction, and the k-coordinate is in the k-direction.  
+As described in the grid.md file, Aether uses a logical `(i, j, k)` 3D grid structure.  Therefore, we refer to the 'primary' coordinates as the ijk coordinate system.  What this means is that the i-coordinate is in the i-direction, the j-coordinate is in the j-direction, and the k-coordinate is in the k-direction.  
 
 For the (perfectly) spherical grid, the i-coordinate is longitude, the j-coordinate is latitude, and the k-coordinate is radius.
 
 For the Cubedsphere grid, the i-coordinate is RIGHT, the j-coodinate is UP, and the k-coordinate is radius.  Each face of the cubedsphere has the same coordinate system, but only with reference to that face.  This means that if each face is looked at independently, the lower left corner is at (about) i = -45, j = -45 deg, while the upper right corner is at i = +45, j = +45 deg. Radius is treated the same as in a spherical grid.
 
+For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is magnetic latitude, and the k-coordinate is radius. The dipole is orthogonal to a dipolar magnetic field.
+
+
 Should the official coordinates be in the native coordinates (which could be different for each system), or should the coordinates be in meters, such that when gradients are taken, they are in /m?
 
 Maybe we could have:
@@ -69,6 +73,6 @@ Locations:
 All locations should be described in the following coordinates:
 - i, j, k
 - lon, lat, radius (+alt)
-- magnetic lon, invariant lat?
+- magnetic lon, invariant lat (only the dipole magnetic grid then has magnetic latitude)
 
 

From 42701eefc85893c79cef906b67d0ad8e0053f4d9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:22:57 -0400
Subject: [PATCH 027/266] MAINT: Clean up unused/outdated bits. clear up
 comments around magnetic coordinates

---
 include/bfield.h        |  4 ----
 include/grid.h          | 19 ++++---------------
 include/init_mag_grid.h |  7 -------
 src/dipole.cpp          | 24 ------------------------
 src/init_mag_grid.cpp   | 21 ---------------------
 5 files changed, 4 insertions(+), 71 deletions(-)

diff --git a/include/bfield.h b/include/bfield.h
index 91a09ff3..df64fac8 100644
--- a/include/bfield.h
+++ b/include/bfield.h
@@ -10,10 +10,6 @@ struct bfield_info_type {
   precision_t lat;
 };
 
-precision_t get_lshell(precision_t lat, precision_t rNorm);
-arma_vec get_lat_from_r_and_lshell(arma_vec r, precision_t lshell);
-precision_t get_lat_from_r_and_lshell(precision_t r, precision_t lshell);
-
 arma_vec get_magnetic_pole(int IsNorth,
 		       Planets planet);
 
diff --git a/include/grid.h b/include/grid.h
index 874dcb94..3e32b600 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -66,16 +66,16 @@ class Grid
   arma_cube g11_upper_Down, g12_upper_Down, g21_upper_Down, g22_upper_Down;
   arma_cube sqrt_g_Down;
 
-  // These define the magnetic grid:
-  // Armidillo Cube Versions:
-  arma_cube magLon_scgc, magX_scgc;
+  // These define the magnetic grid (only defined for a dipole grid):
   // The magnetic latitude is the angle between the magnetic equator and the point.
   arma_cube magLat_scgc, magY_scgc;
-  // This is often just the altitude....
+  // This is the same as radius.
   arma_cube magAlt_scgc, magZ_scgc;
+  // These exist for all grid types:
   // Invariant latitude is the magnetic latitude that the field line hits at the lowest altitude.
   // This is basically the L-shell, but models want it expressed as latitude and not L-shell.
   arma_cube magInvLat_scgc;
+  arma_cube magLon_scgc, magX_scgc;
   // This is the angle from the sun, to the magnetic pole to the point.
   arma_cube magLocalTime_scgc;
 
@@ -98,17 +98,6 @@ class Grid
   arma_cube magAlt_Down;
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
-
-  //For easier interpolation:
-  arma_vec baseLats_down;
-
-  // these need to be stored in (p,q) coords for a bit, its messy:
-  arma_cube magP_Down;
-  arma_cube magP_Below;
-  arma_cube magQ_Down;
-  arma_cube magQ_Below;
-  arma_cube magP_Corner;
-  arma_cube magQ_Corner;
   
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/include/init_mag_grid.h b/include/init_mag_grid.h
index a9493aaa..b1af1f4c 100644
--- a/include/init_mag_grid.h
+++ b/include/init_mag_grid.h
@@ -17,13 +17,6 @@ bool init_dipole_grid(Grid &mGrid, Planets planet);
 // return (r,theta)
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q, precision_t p);
 
-// Take limits & specs of field line, fill it with points.
-// std:: tuple <arma_mat, arma_mat, arma_vec> Grid::fill_field_lines (arma_vec lShells, 
-//                                                 int64_t nAlts,
-//                                                 int64_t nLats,
-//                                                 precision_t Gamma);
-
-
 // convert mag to geographic
 std::vector <arma_cube> mag_to_geo(arma_cube magLon, 
                                    arma_cube magLat,
diff --git a/src/dipole.cpp b/src/dipole.cpp
index d97c66f3..2887e547 100644
--- a/src/dipole.cpp
+++ b/src/dipole.cpp
@@ -6,30 +6,6 @@
 
 #include "aether.h"
 
-// -----------------------------------------------------------------------------
-// get the l-shell given latitude (in radians) and normalized radius
-// -----------------------------------------------------------------------------
-
-precision_t get_lshell(precision_t lat, precision_t rNorm) {
-  precision_t cosLat = cos(lat);
-  precision_t lshell = rNorm / (cosLat * cosLat);
-  return lshell;
-}
-
-precision_t get_lat_from_r_and_lshell(precision_t r, precision_t lshell) {
-  precision_t cosLat = sqrt(r / lshell);
-
-  if (cosLat < -1.0)
-    cosLat = -1.0;
-
-  if (cosLat > 1.0)
-    cosLat = 1.0;
-
-  precision_t lat = acos(cosLat);
-  return lat;
-}
-
-
 
 // -----------------------------------------------------------------------------
 // Calculate a tilted offset dipole field given the planetary
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8e8cb260..977b0b27 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -38,27 +38,6 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   return {r, theta};
 }
 
-std::pair<arma_cube, arma_cube> qp_to_r_theta(arma_cube q, arma_cube p) {
-  // return quanties
-  arma_cube r, theta;
-  // Intermediate quantities:
-  arma_cube term0, term1, term2, term3;
-
-  term0 = 256.0 / 27.0 * (q % q) % (p % p % p % p);
-  term1 = pow((1.0 + sqrt(1.0 + term0)), 2.0 / 3.0);
-  term2 = pow(term0, 1.0 / 3.0);
-  term3 = 0.5 * pow(((term1 % term1 + term1 % term2 + term2 % term2) / term1),
-                    3.0 / 2.0);
-
-  r = p % (4.0 * term3) / ((1.0 + term3) % (1.0 + sqrt(2.0 * term3 - 1.0)));
-
-  // now that r is determined we can solve for theta
-  theta = asin(q % (r % r));
-
-  return {r, theta};
-}
-
-
 ////////////////////////////////////////////
 // convert cell coordinates to geographic //
 ////////////////////////////////////////////

From 66e5763f5e3428648869d412539b42adf271f084 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 11:23:50 -0400
Subject: [PATCH 028/266] Do not re-define magnetic coords for dipole grid in
 fill_grid

- The dipole bfield still needs work...
---
 src/fill_grid.cpp | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 5dd2f98e..330a6992 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -170,15 +170,14 @@ void Grid::fill_grid_bfield(Planets planet) {
 
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
-          // This is Invariant Latitude
-          // (regular magnetic latitude is not defined yet for the geo grid)
-          // - magLat is not used by the geo grid, so not defined
-          magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
-
-          // init_mag grid already initialized magLon
-          if (iGridShape_ != iDipole_)
+          // Magnetic coordinates:
+          // init_mag grid already initializes magLon & magInvLat
+          // #TODO: make sure the bfield is correct for the dipole 
+          // - maybe Dot product the B_vec with (ijk)_vec?
+          if (iGridShape_ != iDipole_){
+            magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
-
+          }
           bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 
           for (iDim = 0; iDim < 3; iDim++) {

From 9251259ad923c6f571e28c13e08c3b1bf2898fad Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 19:03:18 -0400
Subject: [PATCH 029/266] BUG: Rewrite init_mag_grid again. Fix order of
 points, add in (i,j,k), corners, edges, etc.

- All appears OK except some of the corners. IDK why. Need to commit this and move on. Will revisit & document
---
 src/init_mag_grid.cpp | 181 +++++++++++++++++++++++++++++++-----------
 1 file changed, 133 insertions(+), 48 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 977b0b27..6a6fb987 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -10,7 +10,6 @@
 // or with approach from (Swisdak, 2006), who solved it analytically:
 //  https://arxiv.org/pdf/physics/0606044
 //
-// Overloaded for single conversions & arma_cubes
 // ----------------------------------------------------------------------
 
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
@@ -223,7 +222,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
-
+      // corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
@@ -266,7 +265,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   for (iLat = 0; iLat < nLats; iLat++) {
     lat1d(iLat) = lat0 + (iLat - nGCs + 0.5) * dlat + min_lat; // centers
-    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners
+    lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners & edges
   }
 
   lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
@@ -291,19 +290,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // l-shells of corners
   arma_vec Pcorners = min_alt_re / pow(sin(cPI / 2 - lat1dDown), 2);
 
-
-  if (isSouth)  // so the values are increasing:
-    lat1d = -1 * reverse(lat1d);
-
-
-  for (iLon = 0; iLon < nLons; iLon++) {
-    for (iAlt = 0; iAlt < nAlts; iAlt++) {
-      magInvLat_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = lat1d;
-      magP_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = Pcenters;
-      magP_Down.subcube(iLon, 0, iAlt, iLon, nLats, iAlt) = Pcorners;
-    }
-  }
-
   report.print(3, "Done initializing invariant latitudes");
 
   ////////////////
@@ -331,8 +317,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
     close_this_block = true;
-  
-  if(lat_origin < 0.01) // equator, with some imprecision
+
+  if (lat_origin < 0.01) // equator, with some imprecision
     close_this_block = true;
 
   if (close_this_block)
@@ -341,12 +327,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-  q_min = pow(((1 - max_alt_re / Pcorners(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats)) / pow(min_alt_re,
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats-nGCs)) / pow(min_alt_re,
                            4.0)), 0.5);
 
+  // Counter-intuitive, but the maximum value of q is actually where we start
+  // (lowest altitude), since q=0 at equator.
   precision_t delQ = (q_max - q_min) / (nAlts - nGCs * 2.0);
 
   arma_vec magQ1d(nAlts);
@@ -359,48 +347,145 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
 
-  if (isSouth) {
-    magQ1d = -1.0 * reverse(magQ1d);
-    magQ_corner_1d = -1.0 * reverse(magQ_corner_1d);
-  }
+  report.print(3,
+               "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
   ////////////////////////////
+  // Now to store everything....
+  // It's all done at the end to make things more simple earlier, but that makes this part messier.
 
-
-  std::pair<precision_t, precision_t> rtheta, rtheta_corner;
-  precision_t altitude;
-  arma_vec tmp(nLons), tmp2(nLons + 1);
+  // temp holding names:
+  std::pair<precision_t, precision_t> rtheta, rtheta_edge;
+  precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
+              pcenter, pedge, qcenter, qedge;
+  // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
+  // Currently the grid is symmetric in longitude.
+  // Interte through centers & edges first, then do corners afterwards
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
+      // We have to reverse & negate things; want latitudes from south->north
+      // and altitude low->high. Altitude is in the correct direction, so change how we 
+      // access values in the latitude dimension.
+
+      if (isSouth) {
+        qcenter = magQ1d(iAlt);
+        pcenter = Pcenters(nLats - iLat - 1);
+
+        qedge = magQ_corner_1d(iAlt);
+        pedge = Pcorners(nLats - iLat - 1);
+
+        invLat = lat1d(nLats - iLat - 1) * -1;
+        invLat_edge = lat1dDown(nLats - iLat - 1) * -1;
+
+        rtheta = qp_to_r_theta(qcenter, pcenter);
+        rtheta_edge = qp_to_r_theta(qcenter, pcenter);
 
-      // this is for cell centers:
-      rtheta = qp_to_r_theta(magQ1d(iAlt), Pcenters(iLat));
-      tmp.zeros();
-      tmp += rtheta.first;
-      magAlt_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      k_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      tmp.zeros();
-      tmp += rtheta.second;
-      magLat_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-      j_center_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = tmp;
-
-      // this is for cell corners:
-      rtheta_corner = qp_to_r_theta(magQ_corner_1d(iAlt), Pcorners(iLat));
-      tmp2.zeros();
-      tmp2 += rtheta_corner.first;
-      magAlt_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
-      tmp2.zeros();
-      tmp2 += rtheta_corner.second;
-      magLat_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = tmp2;
+        // Flip hemisphere of latitude & q (cannot be done before qp_to_rtheta)
+        radius = rtheta.first;
+        theta = rtheta.second * -1.0;
+        qcenter *= -1.0;
+
+        radius_edge = rtheta_edge.first;
+        theta_edge = rtheta.second * -1.0;
+        qedge *= 1.0;
+      } else {
+        qcenter = magQ1d(iAlt);
+        pcenter = Pcenters(iLat);
+
+        qedge = magQ_corner_1d(iAlt);
+        pedge = Pcorners(iLat);
+
+        invLat = lat1d(iLat);
+        invLat_edge = lat1dDown(iLat);
+
+        rtheta = qp_to_r_theta(qcenter, pcenter);
+        rtheta_edge = qp_to_r_theta(qedge, pedge);
+
+        radius = rtheta.first;
+        theta = rtheta.second;
+
+        radius_edge = rtheta_edge.first;
+        theta_edge = rtheta_edge.second;
+      }
+
+      for (iLon = 0; iLon < nLons; iLon ++){
+        magLat_scgc(iLon, iLat, iAlt) = theta;
+        j_center_scgc(iLon, iLat, iAlt) = theta;
+
+        magLat_Down(iLon, iLat, iAlt) = theta_edge;
+        j_edge_scgc(iLon, iLat, iAlt) = theta_edge;
+
+        magAlt_scgc(iLon, iLat, iAlt) = radius;
+        k_center_scgc(iLon, iLat, iAlt) = radius;
+
+        magAlt_Below(iLon, iLat, iAlt) = radius_edge;
+        k_edge_scgc(iLon, iLat, iAlt) = radius_edge;
+
+        // extra coordinates
+        magP_scgc(iLon, iLat, iAlt) = pcenter;
+        magQ_scgc(iLon, iLat, iAlt) = qcenter;
+        magInvLat_scgc(iLon, iLat, iAlt) = invLat;
+      }
+    }
+  }
+
+  report.print(3, "Centers are in");
+
+  precision_t radius_corner, theta_corner, invLat_corner,
+              pcorner, qcorner;
+
+  for (iLat = 0; iLat < nLats + 1; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+
+      // Same process as the centers & edges (above)
+      if (isSouth) {
+        qcorner = magQ_corner_1d(iAlt);
+        pcorner = Pcorners(nLats - iLat);
+        invLat_corner = lat1dDown(nLats - iLat) * -1;
+        rtheta = qp_to_r_theta(qcorner, pcorner);
+
+        radius_corner = rtheta.first;
+        theta_corner = rtheta.second * -1;
+        qcorner *= -1;
+      } else {
+        qcorner = magQ_corner_1d(iAlt);
+        pcorner = Pcorners(iLat);
+        invLat_corner = lat1dDown(iLat);
+        rtheta = qp_to_r_theta(qcorner, pcorner);
+        radius_corner = rtheta.first;
+        theta_corner = rtheta.second;
+      }
+      for (iLon = 0; iLon < nLons + 1; iLon ++){
+        magLat_Corner(iLon, iLat, iAlt) = theta_corner;
+        j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
+
+        magAlt_Corner(iLon, iLat, iAlt) = radius_corner;
+        k_corner_scgc(iLon, iLat, iAlt) = radius_corner;
+
+        magP_Corner(iLon, iLat, iAlt) = pcorner;
+        magQ_Corner(iLon, iLat, iAlt) = qcorner;
+        magInvLat_Corner(iLon, iLat, iAlt) = invLat_corner;
+      }
     }
   }
 
-  // magAlt is in units of planet radius
+  report.print(3, "Corners done too");
+
+  // all distances, so far, are in units of planet radii, turn into meters.
+  // Except for Q, leave that dimensionless.
   magAlt_scgc *= planetRadius;
+  k_center_scgc *= planetRadius;
+  magAlt_Below *= planetRadius;
+  k_edge_scgc *= planetRadius;
+  magP_scgc *= planetRadius;
+  magAlt_Corner *= planetRadius;
+  k_corner_scgc *= planetRadius;
+  magP_Corner *= planetRadius;
+
 
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);

From 112ce907ce42b5865ba54f0a560142a0d3c3f67d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 20 Mar 2025 19:10:33 -0400
Subject: [PATCH 030/266] STY: astyle, and make sure grids are addressed
 consistently

snuck in a few variables from previous commit too
---
 include/grid.h        |  4 ++++
 src/fill_grid.cpp     |  2 +-
 src/grid.cpp          |  9 ++-------
 src/init_mag_grid.cpp | 12 +++++++-----
 4 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 3e32b600..40129d70 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -99,6 +99,10 @@ class Grid
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
   
+  arma_cube magP_Corner;
+  arma_cube magQ_Corner;
+  arma_cube magInvLat_Corner;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 330a6992..deb2b181 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -174,7 +174,7 @@ void Grid::fill_grid_bfield(Planets planet) {
           // init_mag grid already initializes magLon & magInvLat
           // #TODO: make sure the bfield is correct for the dipole 
           // - maybe Dot product the B_vec with (ijk)_vec?
-          if (iGridShape_ != iDipole_){
+          if (IsGeoGrid){
             magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
           }
diff --git a/src/grid.cpp b/src/grid.cpp
index 9ed462c3..4e5c9934 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -65,7 +65,7 @@ Grid::Grid(std::string gridtype) {
   if (grid_input.nZ == 1)
     HasZdim = false;
 
-  if (mklower(grid_input.shape) == "sphere")
+  if (mklower(grid_input.shape).find("sphere") != std::string::npos)
     iGridShape_ = iSphere_;
 
   if (mklower(grid_input.shape) == "cubesphere")
@@ -179,14 +179,9 @@ Grid::Grid(std::string gridtype) {
   magLat_Corner.set_size(nX + 1, nY + 1, nZ + 1);
   magAlt_Corner.set_size(nX + 1, nY + 1, nZ + 1);
 
-  magP_Down.set_size(nX, nY + 1, nZ);
-  magP_Below.set_size(nX, nY, nZ + 1);
-  magQ_Down.set_size(nX, nY + 1, nZ);
-  magQ_Below.set_size(nX, nY, nZ + 1);
   magP_Corner.set_size(nX + 1, nY + 1, nZ + 1);
   magQ_Corner.set_size(nX + 1, nY + 1, nZ + 1);
-
-  baseLats_down.set_size(nY + 1);
+  magInvLat_Corner.set_size(nX + 1, nY + 1, nZ + 1);
 
   radius_scgc.set_size(nX, nY, nZ);
   radius2_scgc.set_size(nX, nY, nZ);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6a6fb987..569ad0f8 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -330,8 +330,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats-nGCs)) / pow(min_alt_re,
-                           4.0)), 0.5);
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - nGCs)) / pow(
+                             min_alt_re,
+                             4.0)), 0.5);
 
   // Counter-intuitive, but the maximum value of q is actually where we start
   // (lowest altitude), since q=0 at equator.
@@ -368,7 +369,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. Altitude is in the correct direction, so change how we 
+      // and altitude low->high. Altitude is in the correct direction, so change how we
       // access values in the latitude dimension.
 
       if (isSouth) {
@@ -412,7 +413,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
         theta_edge = rtheta_edge.second;
       }
 
-      for (iLon = 0; iLon < nLons; iLon ++){
+      for (iLon = 0; iLon < nLons; iLon ++) {
         magLat_scgc(iLon, iLat, iAlt) = theta;
         j_center_scgc(iLon, iLat, iAlt) = theta;
 
@@ -459,7 +460,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
         radius_corner = rtheta.first;
         theta_corner = rtheta.second;
       }
-      for (iLon = 0; iLon < nLons + 1; iLon ++){
+
+      for (iLon = 0; iLon < nLons + 1; iLon ++) {
         magLat_Corner(iLon, iLat, iAlt) = theta_corner;
         j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
 

From 1ae6be7e9bbc5f07b0b8143d2cd490c10ce4db20 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 21 Mar 2025 17:27:44 -0400
Subject: [PATCH 031/266] BUG/FEAT: Change & correct how dipole grid spacing is
 done

---
 src/grid_spacing.cpp | 229 ++++++++-----------------------------------
 1 file changed, 41 insertions(+), 188 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index ea3e4d74..09a4926f 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -23,6 +23,24 @@ void Grid::calc_grid_spacing(Planets planet) {
   report.print(3, "ending calc_grid_spacing");
 }
 
+void Grid::calc_dipole_grid_spacing(Planets planet) {
+
+  report.print(3, "starting calc_grid_spacing");
+
+  calc_i_grid_spacing();
+  calc_j_grid_spacing();
+  calc_k_grid_spacing();
+
+  report.print(3, "ending calc_grid_spacing");
+}
+
+
+// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
+// Note the cos->sin, since magLat is latitude, not colatitude.
+inline arma_cube delTc(arma_cube theta) {
+  return (sqrt(3 * sin(theta) % sin(theta) + 1));
+}
+
 // ---------------------------------------
 // Grid spacing for altitude:
 // ---------------------------------------
@@ -130,6 +148,17 @@ void Grid::calc_k_grid_spacing() {
   dr_edge.slice(iZ) =
     radius_scgc.slice(iZ) - radius_scgc.slice(iZ - 1);
 
+  // For the sphere & cubesphere, k is in meters:
+  if (iGridShape_ == iSphere_ || iGridShape_  == iCubesphere_){
+    dk_center_m_scgc = dk_center_scgc;
+    dk_edge_m = dk_edge;
+  }
+  // This needs to be turned into a distance for the dipole:
+  if (iGridShape_ == iDipole_){
+    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
+    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTc(magLat_scgc);
+  }
+
   // For a stretched grid, calculate some useful quantities:
   // lower is defined for the current cell, which
   // means that upper(iZ) is lower(iZ+1)
@@ -145,10 +174,6 @@ void Grid::calc_k_grid_spacing() {
   dk_ratio_sq = dk_ratio % dk_ratio;
   dk_one_minus_r2 = 1.0 - dk_ratio_sq;
 
-  // k is in meters:
-  dk_edge_m = dk_edge;
-  dk_center_m_scgc = dk_center_scgc;
-
   report.print(4, "ending calc_k_grid_spacing");
   return;
 }
@@ -220,17 +245,19 @@ void Grid::calc_i_grid_spacing() {
   di_center_m_scgc = di_center_scgc % radius_scgc;
   di_edge_m = di_edge % radius_scgc;
 
-  // If the shape is a sphere, then the first coordinate is longitude.  The physical
-  // distance needs to be changed by the cos of the latitude, which is the j coordinate.
+  // If the shape is a sphere or dipole, then the first coordinate is longitude.  
+  // The physical distance needs to be changed by the cos of the latitude, 
+  // which is the j coordinate in the sphere (different for dipole).
   if (iGridShape_ == iSphere_) {
     di_center_m_scgc = di_center_m_scgc % abs(cos(j_center_scgc));
     // edge is in-line with the j center
     di_edge_m = di_edge_m % abs(cos(j_center_scgc));
   }
-
-  // Need a similar thing for the dipole grid here!
+  // Dipole will use cos(geoLat):
   if (iGridShape_ == iDipole_) {
-    // do something here!
+    di_center_m_scgc = di_center_m_scgc % abs(cos(geoLat_scgc));
+    // edge is in-line with the j center
+    di_edge_m = di_edge_m % abs(cos(geoLat_scgc));
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -316,7 +343,11 @@ void Grid::calc_j_grid_spacing() {
     dj_edge_m = dj_edge % radius_scgc;
   }
 
-  // Need to do something for the dipole grid?
+  // Dipole will have different scaling...
+  if (iGridShape_ == iSphere_ || iGridShape_ == iCubesphere_) {
+    dj_center_m_scgc = dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_edge_m = dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+  }
 
   // For a stretched grid, calculate some useful quantities:
   // egde is defined for the current cell, which
@@ -335,181 +366,3 @@ void Grid::calc_j_grid_spacing() {
 
   report.print(4, "ending calc_j_grid_spacing");
 }
-
-// -----------------------------------------------------------------------------
-//  Calaculate Grid Spacing for Dipole Grid
-// -----------------------------------------------------------------------------
-
-void Grid::calc_dipole_grid_spacing(Planets planet) {
-
-  int64_t iLon, iLat, iAlt;
-
-  report.print(3, "starting calc_dipole_grid_spacing");
-
-  // This is close, but may need to be adjusted later.
-  // These quantities are obtained from integrating the scale factor (h)
-  // The along-field-line distance (alt) should be right, but the lat distance
-  // is the shortest distance from a point to the adjacent field line, not the adjacent cell.
-
-  report.print(3, "starting alt");
-  calc_alt_dipole_grid_spacing();
-  report.print(3, "starting lat");
-  calc_lat_dipole_grid_spacing();
-  report.print(3, "starting long");
-  calc_long_dipole_grid_spacing();
-
-  calc_i_grid_spacing();
-
-  std::vector<arma_cube> lon_lat_radius;
-  lon_lat_radius.push_back(geoLon_scgc);
-  lon_lat_radius.push_back(geoLat_scgc);
-  lon_lat_radius.push_back(radius_scgc);
-  std::vector<arma_cube> xyz;
-
-  xyz = transform_llr_to_xyz_3d(lon_lat_radius);
-  geoX_scgc = xyz[0];
-  geoY_scgc = xyz[0];
-  geoZ_scgc = xyz[0];
-
-  report.print(3, "ending calc_dipole_grid_spacing");
-}
-
-// for sanity (only marginally helpful):
-inline arma_mat delTm(arma_mat theta) {
-  return (sqrt(3 * cos(theta) % cos(theta) + 1));
-}
-inline arma_cube delTc(arma_cube theta) {
-  return (sqrt(3 * cos(theta) % cos(theta) + 1));
-}
-
-// -----------------------------------------------------------------------------
-// Grid spacing for altitude:
-//   - Dipole grid needs to be handled differently!
-// -----------------------------------------------------------------------------
-
-void Grid::calc_alt_dipole_grid_spacing() {
-
-  int64_t iAlt;
-  precision_t planetRadius;
-
-  for (iAlt = 1; iAlt < nAlts - 1; iAlt++) {
-
-    dalt_center_scgc.slice(iAlt) =
-      abs(magAlt_scgc.slice(iAlt + 1) % sin(magLat_scgc.slice(iAlt + 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt + 1)))
-          - magAlt_scgc.slice(iAlt - 1) % sin(magLat_scgc.slice(iAlt - 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt - 1)))) * 2;
-    dk_center_scgc.slice(iAlt) = dalt_center_scgc.slice(iAlt);
-
-    dalt_lower_scgc.slice(iAlt) =
-      abs(magAlt_scgc.slice(iAlt) % sin(magLat_scgc.slice(iAlt))
-          % (1 / delTm(magLat_scgc.slice(iAlt)))
-          - magAlt_scgc.slice(iAlt - 1) % sin(magLat_scgc.slice(iAlt - 1))
-          % (1 / delTm(magLat_scgc.slice(iAlt - 1)))) * 2;
-    dk_edge.slice(iAlt) = dalt_lower_scgc.slice(iAlt);
-
-    dr_edge.slice(iAlt) =
-      radius_scgc.slice(iAlt) - radius_scgc.slice(iAlt - 1);
-  }
-
-  dalt_center_scgc.slice(0) = dalt_center_scgc.slice(1);
-  dalt_center_scgc.slice(nAlts - 1) = dalt_center_scgc.slice(nAlts - 2);
-  dk_center_scgc.slice(0) = dalt_center_scgc.slice(0);
-  dk_center_scgc.slice(nAlts - 1) = dalt_center_scgc.slice(nAlts - 2);
-
-  dalt_lower_scgc.slice(0) = dalt_lower_scgc.slice(1);
-  dr_edge.slice(0) = dr_edge.slice(1);
-  dk_edge.slice(0) = dalt_lower_scgc.slice(1);
-  iAlt = nAlts - 1;
-  dalt_lower_scgc.slice(iAlt) =
-    magAlt_scgc.slice(iAlt) - magAlt_scgc.slice(iAlt - 1);
-  dk_edge.slice(iAlt) = dalt_lower_scgc.slice(iAlt);
-  dr_edge.slice(iAlt) =
-    radius_scgc.slice(iAlt) - radius_scgc.slice(iAlt - 1);
-
-  // For a stretched grid, calculate some useful quantities:
-  // lower is defined for the current cell, which
-  // means that upper(iAlt) is lower(iAlt+1)
-  // ratio = upper / lower
-  for (iAlt = 0; iAlt < nAlts - 1; iAlt++) {
-    dalt_ratio_scgc.slice(iAlt) =
-      dalt_lower_scgc.slice(iAlt + 1) / dalt_lower_scgc.slice(iAlt);
-    dk_ratio.slice(iAlt) =
-      dk_edge.slice(iAlt + 1) / dk_edge.slice(iAlt);
-  }
-
-  iAlt = nAlts - 1;
-  dalt_ratio_scgc.slice(iAlt) = dalt_ratio_scgc.slice(iAlt - 1);
-  dk_ratio.slice(iAlt) = dk_ratio.slice(iAlt - 1);
-
-  // Need the square of the ratio:
-  dalt_ratio_sq_scgc = dalt_ratio_scgc % dalt_ratio_scgc;
-  dk_ratio_sq = dk_ratio % dk_ratio;
-  dk_one_minus_r2 = 1.0 - dk_ratio_sq;
-
-  // k is in meters:
-  dk_edge_m = dk_edge;
-  dk_center_m_scgc = dk_center_scgc;
-}
-
-// ---------------------------------------
-// Grid spacing for latitude:
-// Again, different for the dipole...
-//  - uhoh, might not be right. not actually perpendicular to q-p, but no way around that, i think.
-// ---------------------------------------
-
-void Grid::calc_lat_dipole_grid_spacing() {
-
-  int64_t iLat;
-
-  for (iLat = 1; iLat < nLats - 1; iLat++) {
-    dlat_center_scgc.col(iLat) =
-      abs(magAlt_scgc.col(iLat + 1) % sin(magLat_scgc.col(iLat + 1))
-          % (1 / delTc(magLat_scgc.col(iLat + 1)))
-          - magAlt_scgc.col(iLat - 1) % sin(magLat_scgc.col(iLat - 1))
-          % (1 / delTc(magLat_scgc.col(iLat - 1)))) * 2;
-  }
-
-  // Bottom (one sided):
-  iLat = 0;
-  dlat_center_scgc.col(iLat) =
-    geoLat_scgc.col(iLat + 1) - geoLat_scgc.col(iLat);
-  // Top (one sided):
-  iLat = nLats - 1;
-  dlat_center_scgc.col(iLat) =
-    geoLat_scgc.col(iLat) - geoLat_scgc.col(iLat - 1);
-
-  // Make this into a distance:
-  dlat_center_dist_scgc = dlat_center_scgc % radius_scgc;
-  dj_center_scgc = dlat_center_scgc;
-  dj_center_m_scgc = dlat_center_dist_scgc;
-}
-
-// ---------------------------------------
-// Grid spacing for longitude:
-// ---------------------------------------
-
-void Grid::calc_long_dipole_grid_spacing() {
-
-  int64_t iLon;
-
-  for (iLon = 1; iLon < nLons - 1; iLon++)
-    dlon_center_scgc.row(iLon) =
-      (magLon_scgc.row(iLon + 1) - magLon_scgc.row(iLon - 1)) / 2.0;
-
-  // this might be fine for the dipole, if it works for the geo grid...
-
-  // Bottom (one sided):
-  iLon = 0;
-  dlon_center_scgc.row(iLon) =
-    magLon_scgc.row(iLon + 1) - magLon_scgc.row(iLon);
-  // Top (one sided):
-  iLon = nLons - 1;
-  dlon_center_scgc.row(iLon) =
-    magLon_scgc.row(iLon) - magLon_scgc.row(iLon - 1);
-
-  // Make this into a distance:
-  dlon_center_dist_scgc =
-    // dlon_center_scgc % radius_scgc % abs(cos(geoLat_scgc));
-    dlon_center_scgc % magAlt_scgc % cos(magLat_scgc);
-}

From c6ddab2532434b51fe4ee13cb5d7460b5bd7eef1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sat, 22 Mar 2025 13:47:18 -0400
Subject: [PATCH 032/266] FEAT: Add generic k-direction gradient

---
 src/solver_gradients.cpp | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 6843eaa3..5434b7fb 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -321,6 +321,44 @@ arma_cube calc_gradient_lat(arma_cube value, Grid grid) {
   return calc_gradient2o_j(value, grid);
 }
 
+// --------------------------------------------------------------------------
+// Calculate the 2nd order gradient in the native k direction
+//   - these formulas assume that the grid is uniform.
+// --------------------------------------------------------------------------
+
+arma_cube calc_gradient2o_k(arma_cube value, Grid grid) {
+
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
+  int64_t iZ;
+
+  arma_cube gradient(nX, nY, nZ);
+  gradient.zeros();
+
+  if (grid.get_HasZdim()) {
+    // Interior:
+    for (iZ = 1; iZ < nZ - 1; iZ++)
+      gradient.slice(iZ) =
+                (value.slice(iZ + 1) - value.slice(iZ - 1)) /
+                (2 * grid.dk_center_m_scgc.slice(iZ));
+
+    // Lower (one sided):
+    iZ = 0;
+    gradient.slice(iZ) =
+              (value.slice(iZ + 1) - value.slice(iZ)) /
+              grid.dk_center_m_scgc.slice(iZ);
+
+    // Upper (one sided):
+    iZ = nZ - 1;
+    gradient.slice(iZ) =
+              (value.slice(iZ) - value.slice(iZ - 1)) /
+              grid.dk_center_m_scgc.slice(iZ);
+  }
+
+  return gradient;
+}
+
 // --------------------------------------------------------------------------
 // Calculate the gradient in the altitudinal direction
 // --------------------------------------------------------------------------

From 59d32e822691ae7b2a80c04a09e31c7f4ffdffca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sat, 22 Mar 2025 14:00:04 -0400
Subject: [PATCH 033/266] FEAT: Dipole gradient. Might be redundant...

- The gradient in the dipole grid is, I think, the same as the spherical grid. Except the k-direction
- We can do away with this separate method and just put an if statement around that in calc_gradient_vector?
---
 include/solvers.h        |  1 +
 src/solver_gradients.cpp | 26 ++++++++++++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/include/solvers.h b/include/solvers.h
index 02e805e7..ca59515c 100644
--- a/include/solvers.h
+++ b/include/solvers.h
@@ -87,6 +87,7 @@ arma_cube calc_gradient_lat(arma_cube value, Grid grid);
 arma_cube calc_gradient_alt(arma_cube value, Grid grid);
 std::vector<arma_cube> calc_gradient_vector(arma_cube value_scgc, Grid grid);
 std::vector<arma_cube> calc_gradient_cubesphere(arma_cube value, Grid grid);
+std::vector<arma_cube> calc_gradient_dipole(arma_cube value, Grid grid);
 arma_cube calc_gradient_alt_4th(arma_cube value, Grid grid);
 arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt);
 
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 5434b7fb..49867725 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -20,6 +20,8 @@ std::vector<arma_cube> calc_gradient_vector(arma_cube value_scgc, Grid grid) {
 
   if (grid.iGridShape_ == grid.iCubesphere_)
     gradient_vcgc = calc_gradient_cubesphere(value_scgc, grid);
+  else if (grid.iGridShape_ == grid.iDipole_)
+    gradient_vcgc = calc_gradient_dipole(value_scgc, grid);
   else {
 
     report.print(4, "Going into calc_gradient_lon");
@@ -453,6 +455,30 @@ arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt) {
   return valueOut;
 }
 
+// --------------------------------------------------------------------------
+// Calculate the gradient on the dipole grid
+// - This is identical to the spherical grid, except the k/alt direction.
+// --------------------------------------------------------------------------
+std::vector<arma_cube> calc_gradient_dipole(arma_cube value_scgc, Grid grid) {
+
+  std::vector<arma_cube> gradient_vcgc;
+
+  report.print(3, "Calculating dipole griadient");
+
+  report.print(4, "Going into calc_gradient_lon");
+  gradient_vcgc.push_back(calc_gradient2o_i(value_scgc, grid));
+
+
+  report.print(4, "Going into calc_gradient_lat");
+  gradient_vcgc.push_back(calc_gradient2o_j(value_scgc, grid));
+
+
+  report.print(4, "Going into calc_gradient_alt");
+  gradient_vcgc.push_back(calc_gradient2o_k(value_scgc, grid));
+
+  return gradient_vcgc;
+}
+
 // --------------------------------------------------------------------------
 // Calculate the gradient in cubesphere spatial discretization
 // --------------------------------------------------------------------------

From f61f2a5c4ac0856812d6fa273d78955c667fcf68 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 10:27:42 -0400
Subject: [PATCH 034/266] MAINT: Remove some unused func's and variables from
 mag grid things

---
 include/grid.h        | 24 -------------------
 src/grid.cpp          |  1 -
 src/init_mag_grid.cpp | 54 -------------------------------------------
 3 files changed, 79 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 40129d70..e0cd3f77 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -83,7 +83,6 @@ class Grid
   // Phi => Longitude
   // P   => L-shell
   // Q   => Distance along field line
-  arma_cube magPhi_scgc;
   arma_cube magP_scgc;
   arma_cube magQ_scgc;
 
@@ -317,32 +316,9 @@ class Grid
   void calc_cent_acc(Planets planet);
 
   // Make mag-field grid:
-  void convert_dipole_geo_xyz(Planets planet, precision_t XyzDipole[3],
-                              precision_t XyzGeo[3]);
-
   bool init_dipole_grid(Quadtree quadtree_ion, Planets planet);
   // Support functions:
   void calc_dipole_grid_spacing(Planets planet);
-  void calc_alt_dipole_grid_spacing();
-  void calc_lat_dipole_grid_spacing();
-  void calc_long_dipole_grid_spacing();
-  void fill_field_lines(arma_vec baseLats, precision_t min_altRe,
-                        precision_t Gamma, Planets planet,
-                        bool isCorner);
-  void dipole_alt_edges(Planets planet, precision_t min_altRe);
-  // get the latitude spacing given the quadtree start & size, and the latitude limits
-  // extent: quadtree up
-  // origin: quadtree origin
-  // upper_lim: upper latitude limit (input)
-  // lower_lim: lower latitude limit (from min_apex)
-  // nLats: number of latitudes (nY)
-  // spacing_factor: (not supported yet), so always 1.0. Will adjust baselat spacing, eventually.
-  arma_vec baselat_spacing(precision_t extent,
-                          precision_t origin,
-                          precision_t upper_lim,
-                          precision_t lower_lim,
-                          // int16_t nLats,
-                          precision_t spacing_factor);
 
   // Update ghost cells with values from other processors
   void exchange(arma_cube &data, const bool pole_inverse);
diff --git a/src/grid.cpp b/src/grid.cpp
index 4e5c9934..3e99f8cc 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -157,7 +157,6 @@ Grid::Grid(std::string gridtype) {
   magAlt_scgc.set_size(nX, nY, nZ);
   magInvLat_scgc.set_size(nX, nY, nZ);
 
-  magPhi_scgc.set_size(nX, nY, nZ);
   magP_scgc.set_size(nX, nY, nZ);
   magQ_scgc.set_size(nX, nY, nZ);
 
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 569ad0f8..bfe4776b 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -73,60 +73,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   return llr;
 }
 
-// -----------------------------------------------------------------------
-// Convert XyzDipole to XyzGeo
-//
-// -----------------------------------------------------------------------
-
-void Grid::convert_dipole_geo_xyz(Planets planet, precision_t XyzDipole[3],
-                                  precision_t XyzGeo[3]) {
-
-  std::string function = "Grid::convert_dipole_geo_xyz";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  precision_t XyzRemoveShift[3];
-  precision_t XyzRemoveTilt[3];
-  precision_t XyzRemoveRot[3];
-
-  // get planetary parameters
-  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
-  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
-  precision_t radius = planet.get_radius(0.0);
-
-
-  // get the dipole shift, but normalize it to equatorial radius
-  precision_t dipole_center[3];
-  std::vector<precision_t> temp_dipole_center = planet.get_dipole_center();
-
-  if ((temp_dipole_center[0] != 0) or (temp_dipole_center[1] != 0) or
-      (temp_dipole_center[2] != 0)) {
-    report.print(0,
-                 "Dipole center != 0, but that is not supported yet. Setting to 0!");
-    temp_dipole_center = {0, 0, 0};
-
-  }
-
-  transform_float_vector_to_array(temp_dipole_center, dipole_center);
-
-  dipole_center[0] = dipole_center[0] / radius;
-  dipole_center[1] = dipole_center[1] / radius;
-  dipole_center[2] = dipole_center[2] / radius;
-
-  // Remove Tilt
-  transform_rot_y(XyzDipole, magnetic_pole_tilt, XyzRemoveTilt);
-
-  // Remove Rot
-  transform_rot_z(XyzRemoveTilt, magnetic_pole_rotation, XyzRemoveRot);
-
-  // Remove Shift
-  vector_add(XyzRemoveRot, dipole_center, XyzGeo);
-
-  report.exit(function);
-  return;
-
-}
-
 // ----------------------------------------------------------------------
 // Initialize the dipole grid.
 // - inputs (min_apex, min_alt, LatStretch, FieldLineStretch, max_lat_dipole)

From b06b008b1a7b3d413f7a4526a55539cd252b3d54 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 10:28:51 -0400
Subject: [PATCH 035/266] FEAT: Add bones for converting offset dipole to geo
 (grid)

---
 src/init_mag_grid.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index bfe4776b..83720da4 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -55,16 +55,22 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 
   precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
   precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
 
   // Reverse our dipole rotations:
   xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
-  // offset dipole (not yet implemented):
-  // std::vector<precision_t> dipole_center = planet.get_dipole_center();
-  // xyz_geo[0] = xyzRot2[0] + dipole_center[0];
-  // xyz_geo[1] = xyzRot2[1] + dipole_center[1];
-  // xyz_geo[2] = xyzRot2[2] + dipole_center[2];
+  // offset dipole (not fully suported yet, so will be zero. 
+  if ((dipole_center[0] != 0) || (dipole_center[1] != 0) ||
+    (dipole_center[2] != 0)) {
+  report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
+  dipole_center = {0, 0, 0};
+  }
+
+  xyz_geo[0] = xyzRot2[0] + dipole_center[0];
+  xyz_geo[1] = xyzRot2[1] + dipole_center[1];
+  xyz_geo[2] = xyzRot2[2] + dipole_center[2];
 
   // transform back to lon, lat, radius:
   llr = transform_xyz_to_llr_3d(xyzRot2);

From 699ce3be4c4f493de693c2e0a4a9b3d875a30bcc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 11:11:17 -0400
Subject: [PATCH 036/266] FEAT/STY: Overload get_radius to accept floats &
 cubes

Two calls were replaced. Do we want to switch everything to cubes from floats?
---
 include/planets.h     |  3 ++-
 src/fill_grid.cpp     |  8 +-------
 src/init_mag_grid.cpp |  2 +-
 src/planets.cpp       | 13 +++++++++++++
 4 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/include/planets.h b/include/planets.h
index a512daac..f6ba44e9 100644
--- a/include/planets.h
+++ b/include/planets.h
@@ -61,11 +61,12 @@ class Planets {
   /**********************************************************************
      \brief Returns radius of the planet, which can be a function of latitude
 
-     currently, this ignores the latitude, but should be implemented.
+     Overloaded for floats & cubes, much easier to call this on a cube.
 
      \param latitude the latitude to get the radius at.
    **/
   precision_t get_radius(precision_t latitude);
+  arma_cube get_radius(arma_cube latitude);
 
   /**********************************************************************
      \brief Returns the longitude offset to convert from longitude to local time
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index deb2b181..0fb4c2bb 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -215,16 +215,10 @@ void Grid::fill_grid_radius(Planets planet) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  int64_t iLon, iLat, iAlt;
-
   // This generalizes things so that radius could be a function of all
   // three dimensions.  The Cubesphere has different latitudes in the first
   // and second dimensions.
-  for (iLon = 0; iLon < nLons; iLon++)
-    for (iLat = 0; iLat < nLats; iLat++)
-      for (iAlt = 0; iAlt < nAlts; iAlt++)
-        radius_scgc(iLon, iLat, iAlt) =
-          planet.get_radius(geoLat_scgc(iLon, iLat, iAlt));
+  radius_scgc = planet.get_radius(geoLat_scgc);
 
   radius_scgc = radius_scgc + geoAlt_scgc;
   radius2_scgc = radius_scgc % radius_scgc;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 83720da4..b20118be 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -446,7 +446,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   geoLon_scgc = llr[0];
   geoLat_scgc = llr[1];
-  geoAlt_scgc = llr[2] - planetRadius;
+  geoAlt_scgc = llr[2] - planet.get_radius(geoLat_scgc);
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
diff --git a/src/planets.cpp b/src/planets.cpp
index 0528cfd7..1216d787 100644
--- a/src/planets.cpp
+++ b/src/planets.cpp
@@ -66,6 +66,7 @@ precision_t Planets::get_cos_dec(Times time) {
 
 // -----------------------------------------------------------------------------
 // Get the radius of the planet as a function of latitude (meters)
+// - Overloaded to support passing cubes, avoids loops in the calling function.
 // -----------------------------------------------------------------------------
 
 precision_t Planets::get_radius(precision_t latitude) {
@@ -75,6 +76,18 @@ precision_t Planets::get_radius(precision_t latitude) {
     return planet.radius;
 }
 
+arma_cube Planets::get_radius(arma_cube latitude) {
+  if (input.get_do_lat_dependent_radius())
+    return planet.polar_radius + (planet.delta_radius * cos(latitude));
+  else{
+    arma_cube radius(latitude.n_rows,
+                    latitude.n_cols,
+                    latitude.n_slices);
+    radius = planet.radius;
+    return radius;
+  }
+}
+
 // -----------------------------------------------------------------------------
 // Get the rotation of the dipole (in radians, rotation in longitude)
 // -----------------------------------------------------------------------------

From 77698d43f0a59fe51fc2961cbc6c14d3cde4b14c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 23 Mar 2025 11:27:26 -0400
Subject: [PATCH 037/266] BUG: Fix dipole grid's radius calculations

---
 src/init_mag_grid.cpp | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index b20118be..123fc177 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -123,6 +123,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t max_alt = grid_input.alt_max * cKMtoM;
 
   // Normalize inputs to planet radius... (update when earth is oblate)
+  // Here we are using the equatorial radius.
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
@@ -458,8 +459,13 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
-  // Calculate the radius, of planet
-  fill_grid_radius(planet);
+  // Fill grid radius, radius2, radius2i
+  // fill_grid_radius uses radius of the planet & geo_alt
+  // That would be redundant here since we already know the radius (magAlt)
+  // This is NOT yet offset: to offset do magAlt + dipole_cnter_m
+  radius_scgc = magAlt_scgc;
+  radius2_scgc = radius_scgc % radius_scgc;
+  radius2i_scgc = 1.0 / radius2_scgc;
 
   // Figure out what direction is radial:
   rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
@@ -470,15 +476,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  arma_cube br = 2 * sin(abs(magLat_scgc));
-  arma_cube bt = cos(magLat_scgc);
-  arma_cube bm = sqrt(br % br + bt % bt);
-  // Latitudinal direction of radial:
-  arma_cube s = sign(magLat_scgc);
-  s.elem(find(s == 0)).ones();
-
-  rad_unit_vcgc[1] = bt / bm % s;
-  rad_unit_vcgc[2] = -br / bm;
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[2] = -2* sin(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From f0b231c7dfb9376baca29079fc76a942430ce2fa Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 25 Mar 2025 16:27:30 -0400
Subject: [PATCH 038/266] Revert "FEAT/STY: Overload get_radius to accept
 floats & cubes"

This reverts commit 699ce3be4c4f493de693c2e0a4a9b3d875a30bcc.
---
 include/planets.h     |  3 +--
 src/fill_grid.cpp     |  8 +++++++-
 src/init_mag_grid.cpp |  2 +-
 src/planets.cpp       | 13 -------------
 4 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/include/planets.h b/include/planets.h
index f6ba44e9..a512daac 100644
--- a/include/planets.h
+++ b/include/planets.h
@@ -61,12 +61,11 @@ class Planets {
   /**********************************************************************
      \brief Returns radius of the planet, which can be a function of latitude
 
-     Overloaded for floats & cubes, much easier to call this on a cube.
+     currently, this ignores the latitude, but should be implemented.
 
      \param latitude the latitude to get the radius at.
    **/
   precision_t get_radius(precision_t latitude);
-  arma_cube get_radius(arma_cube latitude);
 
   /**********************************************************************
      \brief Returns the longitude offset to convert from longitude to local time
diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 0fb4c2bb..deb2b181 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -215,10 +215,16 @@ void Grid::fill_grid_radius(Planets planet) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
+  int64_t iLon, iLat, iAlt;
+
   // This generalizes things so that radius could be a function of all
   // three dimensions.  The Cubesphere has different latitudes in the first
   // and second dimensions.
-  radius_scgc = planet.get_radius(geoLat_scgc);
+  for (iLon = 0; iLon < nLons; iLon++)
+    for (iLat = 0; iLat < nLats; iLat++)
+      for (iAlt = 0; iAlt < nAlts; iAlt++)
+        radius_scgc(iLon, iLat, iAlt) =
+          planet.get_radius(geoLat_scgc(iLon, iLat, iAlt));
 
   radius_scgc = radius_scgc + geoAlt_scgc;
   radius2_scgc = radius_scgc % radius_scgc;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 123fc177..e10f3b80 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -447,7 +447,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   geoLon_scgc = llr[0];
   geoLat_scgc = llr[1];
-  geoAlt_scgc = llr[2] - planet.get_radius(geoLat_scgc);
+  geoAlt_scgc = llr[2] - planetRadius;
   report.print(4,
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
diff --git a/src/planets.cpp b/src/planets.cpp
index 1216d787..0528cfd7 100644
--- a/src/planets.cpp
+++ b/src/planets.cpp
@@ -66,7 +66,6 @@ precision_t Planets::get_cos_dec(Times time) {
 
 // -----------------------------------------------------------------------------
 // Get the radius of the planet as a function of latitude (meters)
-// - Overloaded to support passing cubes, avoids loops in the calling function.
 // -----------------------------------------------------------------------------
 
 precision_t Planets::get_radius(precision_t latitude) {
@@ -76,18 +75,6 @@ precision_t Planets::get_radius(precision_t latitude) {
     return planet.radius;
 }
 
-arma_cube Planets::get_radius(arma_cube latitude) {
-  if (input.get_do_lat_dependent_radius())
-    return planet.polar_radius + (planet.delta_radius * cos(latitude));
-  else{
-    arma_cube radius(latitude.n_rows,
-                    latitude.n_cols,
-                    latitude.n_slices);
-    radius = planet.radius;
-    return radius;
-  }
-}
-
 // -----------------------------------------------------------------------------
 // Get the rotation of the dipole (in radians, rotation in longitude)
 // -----------------------------------------------------------------------------

From 34f7eb1077209383feecd48c669eaae1c4aadb45 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 25 Mar 2025 16:40:28 -0400
Subject: [PATCH 039/266] BUGS: Fix type issues in mag_to_geo & clarify
 reporting in solver_gradients. The code is stable.

---
 src/init_mag_grid.cpp    | 12 ++++++------
 src/solver_gradients.cpp |  6 +++---
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index e10f3b80..67be3a25 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -62,15 +62,15 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
   // offset dipole (not fully suported yet, so will be zero. 
-  if ((dipole_center[0] != 0) || (dipole_center[1] != 0) ||
-    (dipole_center[2] != 0)) {
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
   report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-  dipole_center = {0, 0, 0};
+  dipole_center = {0.0, 0.0, 0.0};
   }
 
-  xyz_geo[0] = xyzRot2[0] + dipole_center[0];
-  xyz_geo[1] = xyzRot2[1] + dipole_center[1];
-  xyz_geo[2] = xyzRot2[2] + dipole_center[2];
+  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
 
   // transform back to lon, lat, radius:
   llr = transform_xyz_to_llr_3d(xyzRot2);
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 49867725..464239cd 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -465,15 +465,15 @@ std::vector<arma_cube> calc_gradient_dipole(arma_cube value_scgc, Grid grid) {
 
   report.print(3, "Calculating dipole griadient");
 
-  report.print(4, "Going into calc_gradient_lon");
+  report.print(4, "Going into calc_gradient_i (dipole)");
   gradient_vcgc.push_back(calc_gradient2o_i(value_scgc, grid));
 
 
-  report.print(4, "Going into calc_gradient_lat");
+  report.print(4, "Going into calc_gradient_j (dipole)");
   gradient_vcgc.push_back(calc_gradient2o_j(value_scgc, grid));
 
 
-  report.print(4, "Going into calc_gradient_alt");
+  report.print(4, "Going into calc_gradient_K (DIPOLE)");
   gradient_vcgc.push_back(calc_gradient2o_k(value_scgc, grid));
 
   return gradient_vcgc;

From dd595f5579b633d40f936fb13f94b2b7a2673952 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 31 Mar 2025 14:33:01 -0400
Subject: [PATCH 040/266] BUG: Fix order of pts in mag grid (k-direction) &
 start changing to dipole p,q for i,j from lon,lat

---
 src/init_mag_grid.cpp | 56 ++++++++++++++++++++++++-------------------
 1 file changed, 32 insertions(+), 24 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 67be3a25..506c0310 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -314,6 +314,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   std::pair<precision_t, precision_t> rtheta, rtheta_edge;
   precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
               pcenter, pedge, qcenter, qedge;
+  int64_t iAlt2;
   // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
@@ -322,8 +323,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. Altitude is in the correct direction, so change how we
-      // access values in the latitude dimension.
+      // and altitude low->high. 
+      // - Altitude is in the reverse direction in both hemispheres.
+      // - Latitude is reversed in southern hemisphere.
+      iAlt2 = nAlts - iAlt - 1;
 
       if (isSouth) {
         qcenter = magQ1d(iAlt);
@@ -367,22 +370,22 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       }
 
       for (iLon = 0; iLon < nLons; iLon ++) {
-        magLat_scgc(iLon, iLat, iAlt) = theta;
-        j_center_scgc(iLon, iLat, iAlt) = theta;
+        magLat_scgc(iLon, iLat, iAlt2) = theta;
+        magLat_Down(iLon, iLat, iAlt2) = theta_edge;
 
-        magLat_Down(iLon, iLat, iAlt) = theta_edge;
-        j_edge_scgc(iLon, iLat, iAlt) = theta_edge;
+        magAlt_scgc(iLon, iLat, iAlt2) = radius;
+        magAlt_Below(iLon, iLat, iAlt2) = radius_edge;
 
-        magAlt_scgc(iLon, iLat, iAlt) = radius;
-        k_center_scgc(iLon, iLat, iAlt) = radius;
+        magInvLat_scgc(iLon, iLat, iAlt2) = invLat;
 
-        magAlt_Below(iLon, iLat, iAlt) = radius_edge;
-        k_edge_scgc(iLon, iLat, iAlt) = radius_edge;
+        magP_scgc(iLon, iLat, iAlt2) = pcenter;
+        j_center_scgc(iLon, iLat, iAlt2) = pcenter;
+        j_edge_scgc(iLon, iLat, iAlt2) = pedge;
+
+        magQ_scgc(iLon, iLat, iAlt2) = qcenter;
+        k_center_scgc(iLon, iLat, iAlt2) = qcenter;
+        k_edge_scgc(iLon, iLat, iAlt2) = qedge;
 
-        // extra coordinates
-        magP_scgc(iLon, iLat, iAlt) = pcenter;
-        magQ_scgc(iLon, iLat, iAlt) = qcenter;
-        magInvLat_scgc(iLon, iLat, iAlt) = invLat;
       }
     }
   }
@@ -394,6 +397,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   for (iLat = 0; iLat < nLats + 1; iLat ++) {
     for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
+      iAlt2 = nAlts - iAlt;
 
       // Same process as the centers & edges (above)
       if (isSouth) {
@@ -415,15 +419,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       }
 
       for (iLon = 0; iLon < nLons + 1; iLon ++) {
-        magLat_Corner(iLon, iLat, iAlt) = theta_corner;
-        j_corner_scgc(iLon, iLat, iAlt) = theta_corner;
+        magLat_Corner(iLon, iLat, iAlt2) = theta_corner;
+        magAlt_Corner(iLon, iLat, iAlt2) = radius_corner;
+
+        magInvLat_Corner(iLon, iLat, iAlt2) = invLat_corner;
 
-        magAlt_Corner(iLon, iLat, iAlt) = radius_corner;
-        k_corner_scgc(iLon, iLat, iAlt) = radius_corner;
+        magP_Corner(iLon, iLat, iAlt2) = pcorner;
+        j_corner_scgc(iLon, iLat, iAlt2) = pcorner;
 
-        magP_Corner(iLon, iLat, iAlt) = pcorner;
-        magQ_Corner(iLon, iLat, iAlt) = qcorner;
-        magInvLat_Corner(iLon, iLat, iAlt) = invLat_corner;
+        magQ_Corner(iLon, iLat, iAlt2) = qcorner;
+        k_corner_scgc(iLon, iLat, iAlt2) = qcorner;
       }
     }
   }
@@ -433,15 +438,18 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
   magAlt_scgc *= planetRadius;
-  k_center_scgc *= planetRadius;
   magAlt_Below *= planetRadius;
-  k_edge_scgc *= planetRadius;
   magP_scgc *= planetRadius;
   magAlt_Corner *= planetRadius;
-  k_corner_scgc *= planetRadius;
   magP_Corner *= planetRadius;
+  magQ_Corner *= planetRadius;
+  magQ_scgc *= planetRadius;
 
+  k_center_scgc *= planetRadius;
+  k_edge_scgc *= planetRadius;
+  k_corner_scgc *= planetRadius;
 
+  // Convert to geographic, rotating and (maybe) shifting the dipole grid.
   std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
                                            planet);
 

From dc4045afa41103bde3bc24fdc755cd2aa665aeb3 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 14:29:43 -0400
Subject: [PATCH 041/266] sty: clean up error reporting around offset dipole

---
 src/init_mag_grid.cpp | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 506c0310..a390631d 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -61,11 +61,14 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
   xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
 
-  // offset dipole (not fully suported yet, so will be zero. 
+  // offset dipole (not fully suported yet, so will be zero)
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
-  report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-  dipole_center = {0.0, 0.0, 0.0};
+    
+    if (iProc == 0) // only one error
+      report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
+    
+    dipole_center = {0.0, 0.0, 0.0};
   }
 
   xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);

From 2df83cb7ff8ce88b1e055ea92f4a812d70976ab6 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 16:26:10 -0400
Subject: [PATCH 042/266] BUG/STY: Fix some missing points in dipole grid,
 formatting

---
 src/init_mag_grid.cpp | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index a390631d..bc94f593 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -64,10 +64,10 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   // offset dipole (not fully suported yet, so will be zero)
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
-    
+
     if (iProc == 0) // only one error
       report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-    
+
     dipole_center = {0.0, 0.0, 0.0};
   }
 
@@ -178,15 +178,17 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       // left edges
       magLon_Left.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_edge_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
+    }
+  }
+
+  for (iLat = 0; iLat < nLats + 1; iLat ++) {
+    for (iAlt = 0; iAlt < nAlts + 1; iAlt++) {
       // corners
       magLon_Corner.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
       i_corner_scgc.subcube(0, iLat, iAlt, nLons, iLat, iAlt) = lon1dLeft;
     }
   }
 
-  if (magLon_scgc.has_nan())
-    report.error("NAN IN MAGLON");
-
   report.print(3, "Done initializing longitudes, moving to latitude");
 
   ////////////////
@@ -224,7 +226,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lat1dDown(iLat) = lat0 + (iLat - nGCs) * dlat + min_lat; // corners & edges
   }
 
-  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat; // last corner
+  lat1dDown(nLats) = lat0 + (nLats - nGCs) * dlat + min_lat; // last corner
 
   // At the pole:
   // - put last ghost cell's corner at 89.9 degrees latitude
@@ -302,7 +304,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     magQ_corner_1d(iAlt) = q_min + (iAlt - nGCs) * delQ;
   }
 
-  magQ_corner_1d(nAlts) = q_min - nGCs * delQ;
+  magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
   report.print(3,
                "Done generating points for magnetic grid. Plugging everything in");
@@ -318,7 +320,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t radius, radius_edge, theta, theta_edge, invLat, invLat_edge,
               pcenter, pedge, qcenter, qedge;
   int64_t iAlt2;
-  // we need to turn single floats into vectors/cubes:
 
   // We can solve for (r, theta) for each point on the (q,p) grid. Do that & store:
   // Currently the grid is symmetric in longitude.
@@ -326,7 +327,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   for (iLat = 0; iLat < nLats; iLat ++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       // We have to reverse & negate things; want latitudes from south->north
-      // and altitude low->high. 
+      // and altitude low->high.
       // - Altitude is in the reverse direction in both hemispheres.
       // - Latitude is reversed in southern hemisphere.
       iAlt2 = nAlts - iAlt - 1;
@@ -406,12 +407,12 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
       if (isSouth) {
         qcorner = magQ_corner_1d(iAlt);
         pcorner = Pcorners(nLats - iLat);
-        invLat_corner = lat1dDown(nLats - iLat) * -1;
+        invLat_corner = lat1dDown(nLats - iLat) * -1.0;
         rtheta = qp_to_r_theta(qcorner, pcorner);
 
         radius_corner = rtheta.first;
-        theta_corner = rtheta.second * -1;
-        qcorner *= -1;
+        theta_corner = rtheta.second * -1.0;
+        qcorner *= -1.0;
       } else {
         qcorner = magQ_corner_1d(iAlt);
         pcorner = Pcorners(iLat);
@@ -487,8 +488,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
-  rad_unit_vcgc[2] = -2* sin(magLat_scgc) / pow(1+ 3* sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From fd005d50fffcf191bd974cf42b288a5574f62e7e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 17:42:41 -0400
Subject: [PATCH 043/266] FEAT: Check for non-physical cells on geo & mag grids

---
 include/grid.h        | 4 ++++
 src/init_geo_grid.cpp | 4 ++++
 src/init_mag_grid.cpp | 5 +++++
 3 files changed, 13 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index e0cd3f77..23de18ab 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -102,6 +102,10 @@ class Grid
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
 
+  // Dipole grid has cells below the surface of earth. These variables hold masks
+  // to either access those cells or ignore them (with .elem()).
+  arma::uvec isTooLowCell, isPhysicalCell;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 78b75acb..7d3d611b 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -150,6 +150,10 @@ void Grid::create_altitudes(Planets planet) {
     }
   }
 
+  // All cells on the geographic grid *should* be ok
+  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isPhysicalCell = find(geoAlt_scgc > 0.0);
+
   report.exit(function);
   return;
 }
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index bc94f593..a4596c37 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -505,6 +505,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   calc_dipole_grid_spacing(planet);
 
+
+  // Generate mask for physicsl cells
+  isTooLowCell = find(magAlt_scgc < min_alt_re);
+  isPhysicalCell = find(magAlt_scgc > min_alt_re);
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From 88fc85a99c94c94336e889a2c044f5f806283262 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 3 Apr 2025 19:29:27 -0400
Subject: [PATCH 044/266] BUG: Catch errors making any kind of mag grid

---
 src/main/main.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 9d0a0a3f..eba6d868 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -92,8 +92,6 @@ int main() {
 
     if (mGrid.iGridShape_ == mGrid.iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
-      if (!didWork)
-        throw std::string("init_dipole_grid failed!");
     } else {
       std::cout << "Making Spherical Magnetic Grid\n";
       mGrid.set_IsDipole(false);
@@ -101,6 +99,10 @@ int main() {
       mGrid.set_IsGeoGrid(false);
     }
 
+    if (!didWork)
+    throw std::string("Initializing magneitic grid failed!");
+
+
     didWork = grid_match(gGrid, mGrid, quadtree, quadtree_ion);
 
     // Initialize Neutrals on geographic grid:

From 471f5f582151237ce0301d1247ed0613007e8d67 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 4 Apr 2025 18:09:24 -0400
Subject: [PATCH 045/266] BUG: Fix radius vs alt units & geo/mag lat on dipole
 grid

---
 src/grid_spacing.cpp  | 16 ++++++++--------
 src/init_mag_grid.cpp | 16 ++++++++--------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 09a4926f..56142840 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -155,8 +155,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
-    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTc(magLat_scgc);
+    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
+    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTc(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -253,11 +253,11 @@ void Grid::calc_i_grid_spacing() {
     // edge is in-line with the j center
     di_edge_m = di_edge_m % abs(cos(j_center_scgc));
   }
-  // Dipole will use cos(geoLat):
+  // Dipole will use cos(magLat)
   if (iGridShape_ == iDipole_) {
-    di_center_m_scgc = di_center_m_scgc % abs(cos(geoLat_scgc));
+    di_center_m_scgc = di_center_m_scgc % abs(cos(magLat_scgc));
     // edge is in-line with the j center
-    di_edge_m = di_edge_m % abs(cos(geoLat_scgc));
+    di_edge_m = di_edge_m % abs(cos(magLat_scgc));
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -344,9 +344,9 @@ void Grid::calc_j_grid_spacing() {
   }
 
   // Dipole will have different scaling...
-  if (iGridShape_ == iSphere_ || iGridShape_ == iCubesphere_) {
-    dj_center_m_scgc = dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
-    dj_edge_m = dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+  if (iGridShape_ == iDipole_) {
+    dj_center_m_scgc = magAlt_scgc % dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_edge_m = magAlt_scgc % dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index a4596c37..5b6d0579 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -441,10 +441,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
-  magAlt_scgc *= planetRadius;
-  magAlt_Below *= planetRadius;
+  magAlt_scgc;
+  magAlt_Below;
+  magAlt_Corner;
   magP_scgc *= planetRadius;
-  magAlt_Corner *= planetRadius;
   magP_Corner *= planetRadius;
   magQ_Corner *= planetRadius;
   magQ_scgc *= planetRadius;
@@ -454,7 +454,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   k_corner_scgc *= planetRadius;
 
   // Convert to geographic, rotating and (maybe) shifting the dipole grid.
-  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc,
+  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc * planetRadius,
                                            planet);
 
   geoLon_scgc = llr[0];
@@ -464,7 +464,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
                "Done dipole -> geographic transformations for the dipole grid centers.");
 
   std::vector <arma_cube> llr_corner = mag_to_geo(magLon_Corner, magLat_Corner,
-                                                  magAlt_Corner, planet);
+                                                  magAlt_Corner * planetRadius, planet);
   geoLon_Corner = llr_corner[0];
   geoLat_Corner = llr_corner[1];
   geoAlt_Corner = llr_corner[2] - planetRadius;
@@ -475,7 +475,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // fill_grid_radius uses radius of the planet & geo_alt
   // That would be redundant here since we already know the radius (magAlt)
   // This is NOT yet offset: to offset do magAlt + dipole_cnter_m
-  radius_scgc = magAlt_scgc;
+  radius_scgc = magAlt_scgc * planetRadius;
   radius2_scgc = radius_scgc % radius_scgc;
   radius2i_scgc = 1.0 / radius2_scgc;
 
@@ -507,8 +507,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // Generate mask for physicsl cells
-  isTooLowCell = find(magAlt_scgc < min_alt_re);
-  isPhysicalCell = find(magAlt_scgc > min_alt_re);
+  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isPhysicalCell = find(geoAlt_scgc > 0.0);
 
   report.print(4, "Done altitude spacing for the dipole grid.");
 

From 31040728cbda0da5462c50a3af7f5124ac086ec9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 15:49:02 -0400
Subject: [PATCH 046/266] STY: Minor changes to dipole things

---
 include/grid.h        | 2 +-
 src/init_mag_grid.cpp | 5 +----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index 23de18ab..122a8af1 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -331,6 +331,7 @@ class Grid
 
   bool IsLatLonGrid;
   bool IsCubeSphereGrid;
+  bool IsDipole;
   bool DoesTouchNorthPole;
   bool DoesTouchSouthPole;
   /// The processor to the East/Right/X+:
@@ -435,7 +436,6 @@ class Grid
   bool HasBField;
   bool IsExperimental;
   bool IsMagGrid;
-  bool IsDipole = false;
   std::string gridType;
 
   int64_t nX, nLons;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 5b6d0579..3050565f 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -65,9 +65,6 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
   if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
       (dipole_center[2] != 0.0)) {
 
-    if (iProc == 0) // only one error
-      report.error("Dipole center != 0, but that is not supported yet. Setting to 0!");
-
     dipole_center = {0.0, 0.0, 0.0};
   }
 
@@ -507,7 +504,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
 
   // Generate mask for physicsl cells
-  isTooLowCell = find(geoAlt_scgc < 0.0);
+  isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 70796dad7ae27d670491f4071f3c9c30772efaf4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 15:51:51 -0400
Subject: [PATCH 047/266] BUG: Fix dipole gradients!

---
 include/tools.h      |  3 +++
 src/dipole.cpp       |  6 ++++++
 src/grid_spacing.cpp | 15 ++++-----------
 3 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/include/tools.h b/include/tools.h
index 20b1948a..5c83ed34 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -293,5 +293,8 @@ std::vector<int> indef_vector(arma_cube cube);
 // --------------------------------------------------------------------------
 
 arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
+// Used for dipole gradients & distances. 
+// Probably needs to be moved.
+arma_cube delTheta(arma_cube magLat);
 
 #endif  // INCLUDE_TOOLS_H_
diff --git a/src/dipole.cpp b/src/dipole.cpp
index 2887e547..553da188 100644
--- a/src/dipole.cpp
+++ b/src/dipole.cpp
@@ -114,3 +114,9 @@ bfield_info_type get_dipole(precision_t lon,
 
   return bfield_info;
 }
+
+// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
+// Note the cos->sin, since magLat is latitude, not colatitude.
+arma_cube delTheta(arma_cube magLat) {
+  return (sqrt(3 * sin(magLat) % sin(magLat) + 1));
+}
\ No newline at end of file
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 56142840..04ff6e12 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -34,13 +34,6 @@ void Grid::calc_dipole_grid_spacing(Planets planet) {
   report.print(3, "ending calc_grid_spacing");
 }
 
-
-// This is the del value from (Swisdak, 2006) & others. Used in Dipole distance calc's.
-// Note the cos->sin, since magLat is latitude, not colatitude.
-inline arma_cube delTc(arma_cube theta) {
-  return (sqrt(3 * sin(theta) % sin(theta) + 1));
-}
-
 // ---------------------------------------
 // Grid spacing for altitude:
 // ---------------------------------------
@@ -155,8 +148,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTc(magLat_scgc);
-    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTc(magLat_scgc);
+    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
+    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
@@ -345,8 +338,8 @@ void Grid::calc_j_grid_spacing() {
 
   // Dipole will have different scaling...
   if (iGridShape_ == iDipole_) {
-    dj_center_m_scgc = magAlt_scgc % dj_center_scgc % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
-    dj_edge_m = magAlt_scgc % dj_edge % pow(sin(magLat_scgc), 3) / delTc(magLat_scgc);
+    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
+    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 4abac089ba706055e5121754f4a32fc6139055e5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 16:57:45 -0400
Subject: [PATCH 048/266] STY: Use scale height func in chapman integrals

---
 src/calc_neutral_derived.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/calc_neutral_derived.cpp b/src/calc_neutral_derived.cpp
index 541dbb77..1b1ea783 100644
--- a/src/calc_neutral_derived.cpp
+++ b/src/calc_neutral_derived.cpp
@@ -572,9 +572,7 @@ void Neutrals::calc_chapman(Grid grid) {
 
   for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
-    species[iSpecies].scale_height_scgc =
-      cKB * temperature_scgc /
-      (species[iSpecies].mass * grid.gravity_mag_scgc);
+    calc_scale_height(grid);
 
     xp3d = grid.radius_scgc / species[iSpecies].scale_height_scgc;
     y3d = sqrt(0.5 * xp3d) % abs(grid.cos_sza_scgc);

From fd13016585c3885a79698e19d45611ecc06f15c2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 17:20:27 -0400
Subject: [PATCH 049/266] FEAT: Add cube with boolean "UseThisCell" for dipole
 grid. default=true

---
 include/grid.h        | 1 +
 src/grid.cpp          | 3 +++
 src/init_mag_grid.cpp | 1 +
 3 files changed, 5 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index 122a8af1..f421acd5 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -105,6 +105,7 @@ class Grid
   // Dipole grid has cells below the surface of earth. These variables hold masks
   // to either access those cells or ignore them (with .elem()).
   arma::uvec isTooLowCell, isPhysicalCell;
+  arma_cube UseThisCell; // (bool values whether altitude is valid)
 
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/src/grid.cpp b/src/grid.cpp
index 3e99f8cc..89a4315b 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -270,6 +270,9 @@ Grid::Grid(std::string gridtype) {
   HasBField = 0;
   IsExperimental = false;
 
+  UseThisCell.set_size(nX, nY, nZ);
+  UseThisCell.fill(true);
+
   cent_acc_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
   for (int i = 0; i < 3; i++)
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 3050565f..778a952e 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -506,6 +506,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Generate mask for physicsl cells
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
+  UseThisCell.elem(isTooLowCell).fill(false);
 
   report.print(4, "Done altitude spacing for the dipole grid.");
 

From 6b5ba696d9ce9ee18549a778f2de01a36521fa1c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:25:17 -0400
Subject: [PATCH 050/266] SPEED: speed up calc_efield a tiny bit

---
 src/calc_ion_drift.cpp | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index ab1c6acb..ef3160cf 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -10,10 +10,7 @@
 void Ions::calc_efield(Grid grid) {
 
   // efield = - grad(potential)
-  efield_vcgc = calc_gradient_vector(potential_scgc, grid);
-
-  for (int64_t iComp = 0; iComp < 3; iComp++)
-    efield_vcgc[iComp] = -efield_vcgc[iComp];
+  efield_vcgc = calc_gradient_vector(-1.0 * potential_scgc, grid);
 
   // Remove component along b-field (should be zero, anyways!)
   arma_cube edotb = dot_product(efield_vcgc, grid.bfield_unit_vcgc);
@@ -28,12 +25,16 @@ void Ions::calc_efield(Grid grid) {
 // --------------------------------------------------------------------------
 
 void Ions::calc_exb_drift(Grid grid) {
+  std::string function = "Ions::calc_exb";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
   arma_cube bmag2 =
     (grid.bfield_mag_scgc) % (grid.bfield_mag_scgc);
   exb_vcgc = cross_product(efield_vcgc, grid.bfield_vcgc);
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
     exb_vcgc[iComp] = exb_vcgc[iComp] / bmag2;
+  report.exit(function);
 }
 
 // --------------------------------------------------------------------------
@@ -42,6 +43,10 @@ void Ions::calc_exb_drift(Grid grid) {
 
 std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     Grid grid) {
+
+      std::string function = "Ions::elec_ion_pressure_gradient";
+      static int iFunction = -1;
+      report.enter(function, iFunction);
   std::vector<arma_cube> pressure_gradient_vcgc;
   arma_cube total_pressure_scgc;
 
@@ -57,6 +62,7 @@ std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     cKB;
 
   pressure_gradient_vcgc = calc_gradient_vector(total_pressure_scgc, grid);
+  report.exit(function);
 
   return pressure_gradient_vcgc;
 }

From 218c7a56ffe96a981bfb8f036645e8ca22bf3636 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:28:53 -0400
Subject: [PATCH 051/266] STY: remove std namespace from init_mag_grid

---
 src/init_mag_grid.cpp | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 778a952e..4cfdfd08 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -87,10 +87,9 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 // ----------------------------------------------------------------------
 bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
-  using namespace std;
   bool DidWork = true;
 
-  string function = "Grid::init_dipole_grid";
+  std::string function = "Grid::init_dipole_grid";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
@@ -514,11 +513,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   fill_grid_bfield(planet);
   report.print(4, "Done filling dipole grid with b-field!");
 
-
-  // put back into altitude. we've been carrying around radius:
-  // magAlt_scgc = magAlt_scgc - planetRadius;
-  // this breaks things more???
-
   report.exit(function);
   return DidWork;
 }

From a4724ffd31266920ca9a93751abaffb3fb0ae365 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 7 Apr 2025 19:35:40 -0400
Subject: [PATCH 052/266] STY/Bugs: Get chapman integrals working on dipole,
 clean up code a bit.

---
 src/calc_neutral_derived.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/calc_neutral_derived.cpp b/src/calc_neutral_derived.cpp
index 1b1ea783..d6802d93 100644
--- a/src/calc_neutral_derived.cpp
+++ b/src/calc_neutral_derived.cpp
@@ -583,8 +583,8 @@ void Neutrals::calc_chapman(Grid grid) {
       species[iSpecies].density_scgc.slice(iAlt) %
       species[iSpecies].scale_height_scgc.slice(iAlt);
 
-    species[iSpecies].rho_alt_int_scgc.slice(iAlt) = integral3d.slice(
-                                                       iAlt) * species[iSpecies].mass;
+    species[iSpecies].rho_alt_int_scgc.slice(iAlt) = integral3d.slice(iAlt)
+                                                     * species[iSpecies].mass;
 
     for (iAlt = nAlts - 2; iAlt >= 0; iAlt--) {
       // dr is used here instead of dalt, since we only want the radial integration, while
@@ -619,14 +619,16 @@ void Neutrals::calc_chapman(Grid grid) {
         integral1d = integral3d.tube(iLon, iLat);
         log_int1d = log_int3d.tube(iLon, iLat);
         xp1d = xp3d.tube(iLon, iLat);
-        y1d = y3d.tube(iLon, iLat);
+        // y1d = y3d.tube(iLon, iLat);
         erfcy1d = erfcy3d.tube(iLon, iLat);
         radius1d = grid.radius_scgc.tube(iLon, iLat);
-        H1d = species[iSpecies].scale_height_scgc.tube(iLon, iLat);
+        // H1d = species[iSpecies].scale_height_scgc.tube(iLon, iLat);
 
         for (iAlt = nGCs; iAlt < nAlts; iAlt++) {
+          if (!grid.UseThisCell(iLon, iLat, iAlt))
+            continue; // masks off cells below surface of earth, not the best implementation.
           // This is on the dayside:
-          if (sza1d(iAlt) < cPI / 2 || sza1d(iAlt) > 3 * cPI / 2) {
+          else if (sza1d(iAlt) < cPI / 2 || sza1d(iAlt) > 3 * cPI / 2) {
             species[iSpecies].chapman_scgc(iLon, iLat, iAlt) =
               integral1d(iAlt) * sqrt(0.5 * cPI * xp1d(iAlt)) * erfcy1d(iAlt);
           } else {

From f97eb6f01a1e8785ea2f557c43bf521f8910f1db Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 13:09:58 -0400
Subject: [PATCH 053/266] DOC: add explainer on ninja builds, correct some info
 on installing & running

---
 doc/installation/dependencies.md | 17 +++++++-----
 doc/usage/running_aether.md      | 46 ++++++++++++++++++++++++++++----
 2 files changed, 51 insertions(+), 12 deletions(-)

diff --git a/doc/installation/dependencies.md b/doc/installation/dependencies.md
index c02dcd5a..c0d41a6f 100644
--- a/doc/installation/dependencies.md
+++ b/doc/installation/dependencies.md
@@ -21,7 +21,7 @@ If a path is printed, `cmake` is installed. To check the version, run `cmake
 The layout of this page is as follows:
 
 - [Installing Dependencies](#installing-dependencies)
-  - [Install gcc](#install-gcc)
+  - [Install C++ Compiler](#install-gcc)
   - [Install cmake](#install-cmake)
   - [Install JSON libraries](#install-json-libraries)
   - [Install Armadillo (and boost)](#install-armadillo-and-boost)
@@ -29,20 +29,23 @@ The layout of this page is as follows:
 
 ## Install gcc
 
-This comes installed by default on Ubuntu. On MacOS this can be installed, for
+On MacOS this can be installed, for
 example, using:
 
 ```bash
 sudo port install gcc11
 ```
 
-> As development began, gcc11 was the latest version; there are newer versions
-> of `gcc` available now (latest version is gcc14), which have not yet been
-> validated.
+On Ubuntu, `gcc` (the C-compiler) is pre-installed, but the C++ compiler is not. This 
+can be installed with (subsituting your machine's package manager command):
+
+``bash
+sudo apt install g++
+```
 
 ## Install cmake
 
-Aether uses [CMake](https://cmake.org/) instead of `make`. If you don't have it
+Aether uses [CMake](https://cmake.org/) instead of GNU make. If you don't have it
 installed, you need it.
 
 For MacOS, this can be installed with:
@@ -57,7 +60,7 @@ For Ubuntu/Debian Linux:
 sudo apt install cmake
 ```
 
-This can be done on RedHat using yum also.
+This can be done on RedHat using `yum` also.
 
 ## Install JSON libraries
 
diff --git a/doc/usage/running_aether.md b/doc/usage/running_aether.md
index af7a9260..2811afa6 100644
--- a/doc/usage/running_aether.md
+++ b/doc/usage/running_aether.md
@@ -18,17 +18,18 @@ cp -R share/run ./run.first_run
 This creates the directory where you will do your run.  In that directory is a
 link to the aether executable and an input file called aether.json.
 
-You can then run the executable from the directory you created.
+You can then run the executable from the directory you created. This uses four cores,
+which is the minimum for the dipole grid.
 
 ```bash
 cd run.first_run
-./aether
+mpirun -np 4 ./aether
 ```
 
 You should see something like:
 
 ```bash
-run.first_run% ./aether
+run.first_run% mpirun -np 4 ./aether
 > Need to NOT adjust F10.7, but that isn't included yet!!!
 > Writing file : 3DALL_20110320_000000
 > Writing file : 3DBFI_20110320_000000
@@ -59,6 +60,16 @@ The successful end of this will show a timing summary, similar to:
     timing_total (s) : 2.94398
 ```
 
+### Running in 1D
+
+If you want to quickly run Aether to test if things are working, there are a few changes
+that need to be made to run in one dimension. 
+
+1. Change the input file's, `aether.json`, value for the neutral and ion grids **both**
+to `"sphere"`. No number!
+2. Run the code with `./aether`. This will not use MPI, however armadillo may use
+multiple OpenMP processes for math, so be careful on cluster login nodes.
+
 ## Output Files
 
 Aether outputs to a subdirectory called UA/output. At this time, all processors
@@ -127,11 +138,36 @@ model.  This file is in UA/inputs/defaults.json.
 This is a json file that sets all of the defaults within Aether.  This file
 should never be modified!
 
-### For Developers
+## For Developers
 
-Within Aether, the inputs.cpp file has a large handful of of get_ routines to
+Within Aether, the inputs.cpp file has a large handful of of `get_` routines to
 get the values of the settings that the user has set.
 
+To speedup builds, it can be faster to use Ninja instead of GNU make. Ninja 
+automatically parallelizes to fit your machine, can re-run cmake for small changes, and
+has other small differences from GNU make. To use ninja for builds:
+
+1. Ensure it is installed. This can be done with conda or your system's package manager
+(note on Ubuntu it is called "ninja-build)
+2. Clear the GNU make build pecs from `build`. This is most easily done by removing the
+`CMakeCache.txt` file, but you can remove the entire contents of the build directory.
+3. Tell cmake to generate build scripts for Ninja. From `Aether/build/`, run:
+`cmake -GNinja [any options] ../`.
+4. Build with `ninja`. This will use as many cores asz your system has.
+
+The dfevelopment process can be further sped up since Ninja can change directories
+before compiling. This is useful, for example, to not need to cd out of run when testing
+changes. From `Aether/run/`, you can compile and run the code with the one-liner:
+
+```bash
+ninja -C ../build && mpirun -np 4 ./aether
+```
+
+The `-C` flag specifies which directory to move to before building. Obviously, change it
+if yours is different. When changing header files, Ninja often catches the change and
+will re-run cmake automatically. If not, you will need to remove `CMakeCache.txt` and
+re-run cmake (again using the `-GNinja` flag).
+
 ## aether.json file
 
 The file aether.json is read in AFTER the defaults file and these settings

From 90c576951bd9a52dbd3ebd603dafe4d960060a42 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 15:49:44 -0400
Subject: [PATCH 054/266] DOC: Improve dipole docs. add .ipynb with plots, etc.

---
 .gitignore                                    |   1 +
 doc/internals/coordinates.md                  |   6 +-
 edu/examples/Dipole/MoreDipoleInfo.ipynb      | 334 ++++++++++++++++++
 edu/examples/Dipole/dipole.py                 |   8 +-
 .../Dipole/plots/ghost-cells-dipole.png       | Bin 0 -> 101955 bytes
 .../Dipole/plots/order-of-cells-dipole.png    | Bin 0 -> 109324 bytes
 .../Dipole/plots/q-p-dipole-global-plot.png   | Bin 0 -> 71055 bytes
 7 files changed, 343 insertions(+), 6 deletions(-)
 create mode 100644 edu/examples/Dipole/MoreDipoleInfo.ipynb
 create mode 100644 edu/examples/Dipole/plots/ghost-cells-dipole.png
 create mode 100644 edu/examples/Dipole/plots/order-of-cells-dipole.png
 create mode 100644 edu/examples/Dipole/plots/q-p-dipole-global-plot.png

diff --git a/.gitignore b/.gitignore
index 12bc3ad8..6c8fc705 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ doc/generated
 
 # Python extras
 __pycache__
+.ipynb_checkpoints
 
 # autogenerated files
 tags
diff --git a/doc/internals/coordinates.md b/doc/internals/coordinates.md
index 515857b2..d33d1984 100644
--- a/doc/internals/coordinates.md
+++ b/doc/internals/coordinates.md
@@ -21,7 +21,7 @@
 ## Dipole Coordinates
 - Longitude (radians) - radians east of the meridian that contains the north magnetic pole and north rotation axis
 - P (meters) - Identifies the field line. This is the same as L-shell and is constant along wach field line.
-- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole.
+- Q (dimensionless) - parameterizes the distance along the field line, related to magnetic latitude & radius. This varies along the field line, but the values are idential for all field lines within each node. q=0 at the equator, and approaches positive (negative) infinity as theta points towards the north (south) pole. Thus, q values will be negative in the southern hemisphere and the change in q "upwards" will be negative in the northern hemisphere. See [../../edu/examples/Dipole](../../edu/examples/Dipole) for more information.
 
 ## More Dipole Coordinates
 - L-shell (Planetary Radii) - The distance from the planet's center at which the magnetic field encounters the dipole's equatorial plane
@@ -29,7 +29,7 @@
 - Invariant Latitude (radians) - angle between the dipole's equatorial plane and the point at which the field-line passes through a reference radius of the planet ([specified in the inputs](../internals/grid.md#inputs)).  This is constant along the field-line and is related to the L-Shell.
 - Magnetic Local Time (hours) - Angle between the sun, the north magnetic pole, and the point. Explicitly, this is done in PSE XY coordinates, ignoring the Z coorinate.
 
-> The dipole `(i,j,k)` coordinates are (magnetic longitude, magnetic latitude, radius).
+> The dipole `(i,j,k)` coordinates are (magnetic longitude, p, q).
 
 # Coordinates in Aether
 
@@ -53,7 +53,7 @@ For the (perfectly) spherical grid, the i-coordinate is longitude, the j-coordin
 
 For the Cubedsphere grid, the i-coordinate is RIGHT, the j-coodinate is UP, and the k-coordinate is radius.  Each face of the cubedsphere has the same coordinate system, but only with reference to that face.  This means that if each face is looked at independently, the lower left corner is at (about) i = -45, j = -45 deg, while the upper right corner is at i = +45, j = +45 deg. Radius is treated the same as in a spherical grid.
 
-For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is magnetic latitude, and the k-coordinate is radius. The dipole is orthogonal to a dipolar magnetic field.
+For the dipole coordinate system, the i-coordinate is magnetic longitude, the j-coordinate is L-shell, and the k-coordinate is Q: a dimensionless parameter, normalized to the planet radius, representing diatance along a magnetic field line. The dipole is orthogonal to a dipolar magnetic field.
 
 
 Should the official coordinates be in the native coordinates (which could be different for each system), or should the coordinates be in meters, such that when gradients are taken, they are in /m?
diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
new file mode 100644
index 00000000..7ff0ff7a
--- /dev/null
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dipole # use the coordinate transforms from dipole.py\n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "\n",
+    "save_figs = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "175f59e3-7801-4b34-ac69-1a91231b9786",
+   "metadata": {},
+   "source": [
+    "# Dipole Information continued\n",
+    "\n",
+    "This document has more information on the dipole coordinate system. Some things are impossible to convey in just words...\n",
+    "\n",
+    "\n",
+    "First, a primer on the dipole coordinates and some plots of how they look in 2D space:\n",
+    "\n",
+    "## Dipole (p,q) coordinates\n",
+    "\n",
+    "The magnetic coordinates in Aether are orthogonal to a dipolar magnetic field. The `p` coordinate is the same as L-shell, and `q` paramaterizes the displacement along the magnetic field line. In terms of radius and ***co***latitude, `p` and `q` are calculated as\n",
+    "\n",
+    "$$\n",
+    "p = \\frac{r}{\\sin^2{\\theta}}\n",
+    "\\tag{1}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "q = \\frac{\\cos{\\theta}}{r^2}\n",
+    "\\tag{2}\n",
+    "$$\n",
+    "\n",
+    "Here is a quick plot of `p` and `q` in 2D space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "54ce013a-a7a3-4e5a-8fde-c15276486ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make r, theta\n",
+    "rs = np.linspace(.95, 2.5, num=100)\n",
+    "thetas = np.deg2rad(np.linspace(-88,  88, num=100))\n",
+    "# turn that into x,y for plotting\n",
+    "xs, ys = dipole.rt2xy(*np.meshgrid(rs, thetas.copy()))\n",
+    "qs, ps = dipole.rt2qp(*np.meshgrid(rs, thetas))\n",
+    "\n",
+    "# then the plotting:\n",
+    "fig, axs = plt.subplots(2,2,figsize=(6,5), sharey='row', height_ratios=[.05, 1])\n",
+    "\n",
+    "qc = axs[1,0].pcolor(xs, ys, qs, vmin = -0.8, vmax=0.8, cmap='RdBu_r')\n",
+    "fig.colorbar(qc, cax=axs[0,0], orientation='horizontal', label ='Q-values (dimensionless)')\n",
+    "\n",
+    "pc = axs[1,1].pcolor(xs, ys, ps, vmin = 1, vmax=8, cmap='rainbow')\n",
+    "fig.colorbar(pc, cax=axs[0,1], orientation='horizontal', label ='P-values (Planet Radii)')\n",
+    "\n",
+    "circle0 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=-2)\n",
+    "axs[1, 0].add_patch(circle0)\n",
+    "axs[1, 0].set_xlim(-0.1, 2.6)\n",
+    "axs[1, 0].set_ylim(-2.6, 2.6)\n",
+    "axs[1, 0].set_aspect(1);\n",
+    "\n",
+    "circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=-2)\n",
+    "axs[1, 1].add_patch(circle1)\n",
+    "axs[1, 1].set_xlim(-0.1, 2.6)\n",
+    "axs[1, 1].set_ylim(-2.6, 2.6)\n",
+    "axs[1, 1].set_aspect(1);\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig('plots/q-p-dipole-global-plot.png')\n",
+    "\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29767ef6-0901-48fc-962c-ea57c44c2506",
+   "metadata": {},
+   "source": [
+    "In Aether, both p and q are stored as native coordinate for the dipole grid, normalized to the planet radius in meters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc6301bc-92ed-432c-86f0-857ddaacecce",
+   "metadata": {},
+   "source": [
+    "## Aether's p,q Values\n",
+    "\n",
+    "First, let's get some dipole coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a190f87-6708-43af-94c7-8cdbf6a2cacf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use four blocks:\n",
+    "origins, extent = dipole.generate_sym_quadtree(4)\n",
+    "\n",
+    "# call dipole.main, which returns q & p\n",
+    "# These values are a bit unreasonable, but it makes the plots more clear.\n",
+    "qs, ps = dipole.main(alt_minRE=1.01, alt_maxRE=1.3,\n",
+    "                     lat_min=15, lat_max=80,\n",
+    "                     origins=origins, extent=extent, \n",
+    "                     nLatsPerBlock=14, nAltsPerBlock=18)\n",
+    "\n",
+    "# get cartesian & spherical coords:\n",
+    "rs = dipole.qp_solve(qs, ps) #radius\n",
+    "ts = np.rad2deg(dipole.rq2t(rs, qs)) #theta (magLat)\n",
+    "\n",
+    "xs, ys = dipole.qp2xy(qs, ps)\n",
+    "\n",
+    "# also want the altitude:\n",
+    "alts = dipole.r2alt(rs, 6371) #(in km)\n",
+    "\n",
+    "n_y = 14\n",
+    "n_z = 18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a8b44e8-896d-488b-baab-a37abbf00da2",
+   "metadata": {},
+   "source": [
+    "### Ghost Cells\n",
+    "\n",
+    "The dipole grid has 2 ghost cells in each block (or on each node), similar to the spherical grid.\n",
+    "\n",
+    "In the longitudinal direction, these behave as expected.\n",
+    "\n",
+    "The latitudinal ghost cells take one of two forms:\n",
+    "- If the node is touching the pole, the final field line is traced from 89$^\\circ$ magnetic latitude. This ghost cell will form a supercell.\n",
+    "- Othwerwise, nothing special happens and the final two field lines take the same step in invariant latitude as the rest. This means that boundaries between nodes will overlap. So the invariant latitudes of interior field lines will be identical, but the cell locations will not (since the q-values are different).\n",
+    "\n",
+    "The altitudinal ghost cells also take two forms:\n",
+    "- If the node is touching the equator, the last physical cell lies in the same hemisphere as the rest of the cells on that node. The two ghost cells in the altitudinal direction continue to take the same step in q as the previous, thus wrap over the magnetic equator. These ghost cells will line up with the ghost cells from the equator-most node on the opposite hemisphere.\n",
+    "- Othwerwise, the same step in q is taken and there is nothing special.\n",
+    "\n",
+    "\n",
+    "An image for clarity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ae05677-85d0-48a6-b878-daabb15be856",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=(8,6))\n",
+    "\n",
+    "for n in range(4):\n",
+    "    axs.scatter(ts[n], alts[n], s=12)\n",
+    "\n",
+    "# Doing this manually (not looping) since each node is different:\n",
+    "# (interior) latitude ghost cells:\n",
+    "axs.scatter(0,-1000, marker='x', color='k', s=105, label='latitude ghost cells') # only to get label\n",
+    "axs.scatter(ts[0, -2:, :], alts[0, -2:, :], marker='x', alpha = 0.7, s=105, color='blue')\n",
+    "axs.scatter(ts[1,  :2, :], alts[1, :2, :], marker='x', alpha = 0.7, s=105, color='orange')\n",
+    "axs.scatter(ts[2, -2:, :], alts[2, -2:, :], marker='x', alpha = 0.7, s=105, color='green')\n",
+    "axs.scatter(ts[3,  :2, :], alts[3,  :2, :], marker='x', alpha = 0.7, s=105, color='red')\n",
+    "\n",
+    "axs.scatter(0,-1000, marker='+', color='k', s=105, label='altitude ghost cells')\n",
+    "axs.scatter(ts[1, :, -2:], alts[1, :, -2:], marker='+', alpha = 0.7, s=125, color='orange')\n",
+    "axs.scatter(ts[2, :, -2:], alts[2, :, -2:], marker='+', alpha = 0.7, s=125, color='green')\n",
+    "\n",
+    "axs.scatter(0,-1000, marker='D', color='k', s=105, label='pole ghost cells')\n",
+    "axs.scatter(ts[0, :2, :], alts[0, :2, :], marker='D', alpha = 0.7, s=100, color='blue')\n",
+    "axs.scatter(ts[3, -2:, :], alts[3, -2:, :], marker='D', alpha = 0.7, s=100, color='red')\n",
+    "\n",
+    "axs.legend()\n",
+    "\n",
+    "axs.set_ylim(-100, 6000)\n",
+    "\n",
+    "fig.suptitle(\"Latitude & Altitude Ghost Cells\")\n",
+    "axs.set_xlabel(\"Magnetic Latitude (deg)\")\n",
+    "axs.set_ylabel(\"Altitude (km)\")\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig(\"plots/ghost-cells-dipole.png\")\n",
+    "\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b944a0-ac97-4063-b89a-41d4e78da2da",
+   "metadata": {},
+   "source": [
+    "(The equator-most ghost cells are not shown)\n",
+    "\n",
+    "\n",
+    "### Order of points\n",
+    "\n",
+    "The order of points in the dipole grid is the same as the spherical grid. Most simply, the field lines are arranged in the order of increasing invariant latitude & from lowest to highest altitude.\n",
+    "\n",
+    "Where this gets complicated is that the actual values are not necessarily increasing. For example, the altitude of the ghost cells at the ends of field lines touching the equator is not increasing anymore, since the field lines begin curving back towards the center of the planet. Further, since q=0 at the equator the k-coordinate of all points in the southern hemisphere (grid cells with negative values of magnetic latitude) will have negative k-coordinates. \n",
+    "\n",
+    "To help illustrate this, here is a plot where the color of each point is its index along the j or k axis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "42aa573f-d79a-400c-950d-b6259b4683d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1,2, figsize=(10,6))\n",
+    "\n",
+    "# The weird indexing and adding/subtracting just makes the plot look better\n",
+    "# Ignore some ghost cells, make colorbar clip extrema, etc.\n",
+    "\n",
+    "for i in range(4):\n",
+    "    for j in range(n_y):\n",
+    "        ax0 = axs[0].scatter(xs[i, j, :-2], ys[i, j, :-2], s=12, \n",
+    "                             c=range(n_z-2), vmin=0, vmax=n_z-3,\n",
+    "                             cmap='plasma') # distinct colormap\n",
+    "\n",
+    "    for j in range(2, n_z-2):\n",
+    "        ax1 = axs[1].scatter(xs[i, :, j], ys[i, :, j], s=12, \n",
+    "                             c=range(n_y), vmin=1, vmax=n_y-2, \n",
+    "                             zorder=4-i) # reverse order points are drawn in\n",
+    "\n",
+    "circle0 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=0)\n",
+    "circle1 = plt.Circle((0, 0), 1, color='k', alpha = .7, zorder=0)\n",
+    "axs[0].add_patch(circle0)\n",
+    "axs[1].add_patch(circle1)\n",
+    "axs[0].set_aspect(1);\n",
+    "axs[1].set_aspect(1);\n",
+    "fig.colorbar(ax0, ax=axs[0], orientation='horizontal', label='altitude (k) index of each cell')\n",
+    "fig.colorbar(ax1, ax=axs[1], orientation='horizontal', label='latitude (j) index of each cell')\n",
+    "\n",
+    "fig.suptitle(\"Indices of dipole grid cells\")\n",
+    "\n",
+    "if save_figs:\n",
+    "    fig.savefig(\"plots/order-of-cells-dipole.png\")\n",
+    "    \n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "025f5263-732e-41f2-9a5d-fae07190afe3",
+   "metadata": {},
+   "source": [
+    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the sign of the d_k distances may not be intuitive. \n",
+    "\n",
+    "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2ca9881-8114-4b63-86ca-68b1575ea59d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:mypy]",
+   "language": "python",
+   "name": "conda-env-mypy-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index 71bd85f8..676a0b3e 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -111,10 +111,12 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             qs[:, iAlt] = ((q_min_center + (iAlt - nGCs + 0.5) * delQ))
         
         # If we were in South hemisphere, multiply by -1
-        # (and reverse so the points are ascending in q, same order as NH)
+        # And put data in the same order as we get back from Aether
         if isSouth:
-            qs = -1*np.flip(qs)
-        qcenters[n,:] = qs
+            qs = -1.0*qs
+            pcenters2d = np.flip(pcenters2d, axis=0)
+
+        qcenters[n,:] = np.flip(qs, axis=1)
         pcenters[n,:] = pcenters2d
 
     return qcenters, pcenters
diff --git a/edu/examples/Dipole/plots/ghost-cells-dipole.png b/edu/examples/Dipole/plots/ghost-cells-dipole.png
new file mode 100644
index 0000000000000000000000000000000000000000..c77bd3e36baf1e2f3a77c954be150b2f0534e1cc
GIT binary patch
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HcmV?d00001


From ed941f810ee28ad089c7e15982dc9adc422888bf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 16:17:29 -0400
Subject: [PATCH 055/266] BUG: Fix units in dipole dk_*_meters

---
 src/grid_spacing.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 04ff6e12..f1a46625 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,8 +148,8 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    dk_center_m_scgc = pow(radius_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
-    dk_edge_m = pow(radius_scgc, 3) % dk_edge / delTheta(magLat_scgc);
+    dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
+    dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 1f475791e8c99aad46a8f04154bafd310cb109ac Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 18:59:23 -0400
Subject: [PATCH 056/266] STY: Limits outputs from grid_match to high verbosity

---
 src/grid_match.cpp | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 25da6188..3a3d5775 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -3,8 +3,8 @@
 
 #include "aether.h"
 
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+bool grid_match(Grid gGrid,
+                Grid mGrid,
                 Quadtree gQuadtree,
                 Quadtree mQuadtree) {
 
@@ -33,14 +33,16 @@ bool grid_match(Grid gGrid,
           norms = sphere_to_cube(lon, lat);
           iNode = gQuadtree.find_point(norms);
         }
-        std::cout << "lon, lat, node: " << lon*cRtoD << " "
-        << lat*cRtoD << " "
-        << norms(0) << " "
-        << norms(1) << " "
-        << norms(2) << " "
-        << iNode << "\n";
+        if (report.test_verbose(6))
+          std::cout << "lon, lat, node: " << lon*cRtoD << " "
+                    << lat*cRtoD << " "
+                    << norms(0) << " "
+                    << norms(1) << " "
+                    << norms(2) << " "
+                    << iNode << "\n";
       }
     }
   }
+
   return true;
 }

From f3e7f81f542767cd7be6a2da83dad0d09f0a462a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:16:28 -0400
Subject: [PATCH 057/266] BUG: Fix init_mag_grid gravity unit vector

---
 src/init_mag_grid.cpp | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 4cfdfd08..2612db20 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -437,9 +437,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   // all distances, so far, are in units of planet radii, turn into meters.
   // Except for Q, leave that dimensionless.
-  magAlt_scgc;
-  magAlt_Below;
-  magAlt_Corner;
   magP_scgc *= planetRadius;
   magP_Corner *= planetRadius;
   magQ_Corner *= planetRadius;
@@ -484,8 +481,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(1 + 3 * sin(magLat_scgc), 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
@@ -506,7 +503,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
   UseThisCell.elem(isTooLowCell).fill(false);
-
+  
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From aae064fb9300259554a399044da53e3e6fd506f5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:18:26 -0400
Subject: [PATCH 058/266] BUG: Ensure dk is negative for the dipole grid.

Dipole grid's dk's may not be decreasing
---
 src/grid_spacing.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index f1a46625..1a2fe168 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,6 +148,11 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
+    // the dk's may not be negative (like the sphere). make sure they are
+    // this gets rid of SO many errors...
+    dk_center_scgc = -1.0 * abs(dk_center_scgc);
+    dk_edge = -1.0 * abs(dk_edge);
+    dr_edge = -1.0 * abs(dr_edge);
     dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
     dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }

From 2274a46abd7c48c8d4e8047185b51d060f034c94 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 8 Apr 2025 21:24:29 -0400
Subject: [PATCH 059/266] BUG: Remove need to do altitude gradient. use k only.

change all d_alt_lower - dk_edge_m
and d_alt_denter - dk_center_m
---
 src/calc_dt.cpp                     |  2 +-
 src/calc_ion_temperature.cpp        |  4 ++--
 src/grid_spacing.cpp                |  2 +-
 src/ions_bcs.cpp                    |  2 +-
 src/neutrals_bcs.cpp                |  4 ++--
 src/neutrals_energy.cpp             |  2 +-
 src/neutrals_momentum_viscosity.cpp |  2 +-
 src/solver_gradients.cpp            |  5 ++---
 src/solver_vertical_rusanov.cpp     | 24 ++++++++++++------------
 9 files changed, 23 insertions(+), 24 deletions(-)

diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index dc51d5b4..f949532f 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -141,7 +141,7 @@ precision_t calc_dt_vertical(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   precision_t dt;
 
   if (grid.get_nZ(false) > 1) {
-    arma_cube dtz = grid.dalt_center_scgc / cMax_vcgc[2];
+    arma_cube dtz = grid.dk_center_m_scgc / cMax_vcgc[2];
     dt = dtz.min();
   } else
     dt = 1e32;
diff --git a/src/calc_ion_temperature.cpp b/src/calc_ion_temperature.cpp
index 8db03280..0989c136 100644
--- a/src/calc_ion_temperature.cpp
+++ b/src/calc_ion_temperature.cpp
@@ -84,7 +84,7 @@ void Ions::calc_ion_temperature(Neutrals neutrals, Grid grid,
         lambda1d(1) = lambda1d(2);
         lambda1d(0) = lambda1d(2);
         front1d  = 3.0 / 2.0 * cKB * density_scgc.tube(iLon, iLat);
-        dalt1d   = grid.dalt_lower_scgc.tube(iLon, iLat);
+        dalt1d   = grid.dk_edge_m.tube(iLon, iLat);
         sources1d = (heating_neutral_friction_scgc.tube(iLon, iLat) +
                      heating_neutral_heat_transfer_scgc.tube(iLon, iLat));
         sources1d = sources1d / front1d;
@@ -122,7 +122,7 @@ void Ions::calc_ion_temperature(Neutrals neutrals, Grid grid,
           lambda1d(1) = lambda1d(2);
           lambda1d(0) = lambda1d(2);
           front1d  = 3.0 / 2.0 * cKB * species[iIon].density_scgc.tube(iLon, iLat);
-          dalt1d   = grid.dalt_lower_scgc.tube(iLon, iLat);
+          dalt1d   = grid.dk_edge_m.tube(iLon, iLat);
           sources1d = (species[iIon].heating_neutral_friction_scgc.tube(iLon, iLat) +
                        species[iIon].heating_neutral_heat_transfer_scgc.tube(iLon, iLat));
           sources1d = sources1d / front1d;
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 1a2fe168..943fd3cd 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -12,7 +12,7 @@ void Grid::calc_grid_spacing(Planets planet) {
 
   report.print(3, "starting calc_grid_spacing");
 
-  calc_alt_grid_spacing();
+  // calc_alt_grid_spacing();
   calc_lat_grid_spacing();
   calc_long_grid_spacing();
 
diff --git a/src/ions_bcs.cpp b/src/ions_bcs.cpp
index 29d4cc89..c17757a7 100644
--- a/src/ions_bcs.cpp
+++ b/src/ions_bcs.cpp
@@ -91,7 +91,7 @@ bool Ions::set_upper_bcs(Grid grid) {
         species[iSpecies].temperature_scgc.slice(iAlt) /
         species[iSpecies].temperature_scgc.slice(iAlt - 1) %
         species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dalt_lower_scgc.slice(iAlt) / h);
+        exp(-grid.dk_edge_m.slice(iAlt) / h);
       species[iSpecies].velocity_vcgc[2].slice(iAlt).zeros();
     }
   }
diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index e9308e23..c4cab8fb 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -93,7 +93,7 @@ bool Neutrals::set_upper_bcs(Grid grid) {
       h = species[iSpecies].scale_height_scgc.slice(iAlt);
       species[iSpecies].density_scgc.slice(iAlt) =
         species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dalt_lower_scgc.slice(iAlt) / h);
+        exp(-grid.dk_edge_m.slice(iAlt) / h);
     }
   }
 
@@ -216,7 +216,7 @@ bool Neutrals::set_lower_bcs(Grid grid,
         temperature_scgc.slice(iAlt + 1) /
         temperature_scgc.slice(iAlt) %
         species[iSpecies].density_scgc.slice(iAlt + 1) %
-        exp(grid.dalt_lower_scgc.slice(iAlt) / sh_ave);
+        exp(grid.dk_edge_m.slice(iAlt) / sh_ave);
     }
 
     for (iAlt = nGCs - 1; iAlt >= 0; iAlt--) {
diff --git a/src/neutrals_energy.cpp b/src/neutrals_energy.cpp
index 893ab86e..7e812dd6 100644
--- a/src/neutrals_energy.cpp
+++ b/src/neutrals_energy.cpp
@@ -60,7 +60,7 @@ void Neutrals::update_temperature(Grid grid, Times time) {
         //temp1d = temp1d + dt * sources1d;
         //sources1d.zeros();
 
-        dalt1d = grid.dalt_lower_scgc.tube(iLon, iLat);
+        dalt1d = grid.dk_edge_m.tube(iLon, iLat);
         conduction1d.zeros();
 
         conduction1d = solver_conduction(temp1d,
diff --git a/src/neutrals_momentum_viscosity.cpp b/src/neutrals_momentum_viscosity.cpp
index 4efaaaee..2baa68f3 100644
--- a/src/neutrals_momentum_viscosity.cpp
+++ b/src/neutrals_momentum_viscosity.cpp
@@ -64,7 +64,7 @@ void Neutrals::update_horizontal_velocity(Grid grid, Times time) {
           lambda1d = lambda3d.tube(iLon, iLat);
           rhor21d = rhor23d.tube(iLon, iLat);
           sources1d.zeros();
-          dalt1d = grid.dalt_lower_scgc.tube(iLon, iLat);
+          dalt1d = grid.dk_edge_m.tube(iLon, iLat);
           visc1d.zeros();
 
           visc1d = solver_conduction(vel1d,
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 464239cd..6773f30e 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -448,10 +448,9 @@ arma_mat project_onesided_alt_3rd(arma_cube value, Grid grid, int64_t iAlt) {
       grid.MeshCoef1s3rdp5.slice(iAlt) % value.slice(iAlt + 5);
   */
   gradient = (value.slice(iAlt + 2) - value.slice(iAlt + 1)) /
-             grid.dalt_lower_scgc.slice(iAlt + 2);
+             grid.dk_edge_m.slice(iAlt + 2);
 
-  valueOut = value.slice(iAlt + 1) - gradient % grid.dalt_lower_scgc.slice(
-               iAlt + 1);
+  valueOut = value.slice(iAlt + 1) - gradient % grid.dk_edge_m.slice(iAlt + 1);
   return valueOut;
 }
 
diff --git a/src/solver_vertical_rusanov.cpp b/src/solver_vertical_rusanov.cpp
index 8027cd94..50e376d8 100644
--- a/src/solver_vertical_rusanov.cpp
+++ b/src/solver_vertical_rusanov.cpp
@@ -36,12 +36,12 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Only do calculation on physical cells
   for (iZ = nGCs; iZ < nZs - nGCs; iZ++) {
-    ida = 2.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+    ida = 2.0 / grid.dk_edge_m.slice(iZ + 1);
     dVarUp = ida %
              (factor1 * (inVar.slice(iZ + 1) - inVar.slice(iZ)) -
               factor2 * (inVar.slice(iZ + 2) - inVar.slice(iZ - 1)));
 
-    ida = 2.0 / grid.dalt_lower_scgc.slice(iZ);
+    ida = 2.0 / grid.dk_edge_m.slice(iZ);
     dVarDown = ida %
                (factor1 * (inVar.slice(iZ) - inVar.slice(iZ - 1)) -
                 factor2 * (inVar.slice(iZ + 1) - inVar.slice(iZ - 2)));
@@ -55,9 +55,9 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Ghostcell closest to the bottom physical cell:
   iZ = nGCs - 1;
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ + 1);
   dVarUp = ida % (inVar.slice(iZ + 1) - inVar.slice(iZ));
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ);
   dVarDown = ida % (inVar.slice(iZ) - inVar.slice(iZ - 1));
 
   for (iX = nGCs; iX < nXs - nGCs; iX++)
@@ -67,9 +67,9 @@ void calc_facevalues_alts_rusanov(Grid &grid,
 
   // Ghostcell closest to the top physical cell:
   iZ = nZs - nGCs;
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ + 1);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ + 1);
   dVarUp = ida % (inVar.slice(iZ + 1) - inVar.slice(iZ));
-  ida = 1.0 / grid.dalt_lower_scgc.slice(iZ);
+  ida = 1.0 / grid.dk_edge_m.slice(iZ);
   dVarDown = ida % (inVar.slice(iZ) - inVar.slice(iZ - 1));
 
   for (iX = nGCs; iX < nXs - nGCs; iX++)
@@ -80,10 +80,10 @@ void calc_facevalues_alts_rusanov(Grid &grid,
   for (iZ = nGCs; iZ < nZs - nGCs + 1; iZ++) {
     outLeft.slice(iZ) =
       inVar.slice(iZ - 1) +
-      0.5 * dVarLimited.slice(iZ - 1) % grid.dalt_lower_scgc.slice(iZ);
+      0.5 * dVarLimited.slice(iZ - 1) % grid.dk_edge_m.slice(iZ);
     outRight.slice(iZ) =
       inVar.slice(iZ) -
-      0.5 * dVarLimited.slice(iZ) % grid.dalt_lower_scgc.slice(iZ);
+      0.5 * dVarLimited.slice(iZ) % grid.dk_edge_m.slice(iZ);
   }
 
   /*
@@ -94,7 +94,7 @@ void calc_facevalues_alts_rusanov(Grid &grid,
         << inVar(7,19,19) << " "
         << inVar(7,19,20) << " "
         << dVarLimited(7,19,18) << " "
-        << grid.dalt_lower_scgc(7,19,17) << " "
+        << grid.dk_edge_m(7,19,17) << " "
         << outRight(7, 19, 17) << " "
         << outRight(7, 19, 18) << " "
         << outLeft(7, 19, 17) << " "
@@ -142,7 +142,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
     outGrad.slice(iZ) = 0.5 *
                         (varLeft.slice(iZ + 1) + varRight.slice(iZ + 1) -
                          varLeft.slice(iZ) - varRight.slice(iZ)) /
-                        grid.dalt_center_scgc.slice(iZ);
+                        grid.dk_center_m_scgc.slice(iZ);
 
   /*
   if (iProc == 11)
@@ -151,7 +151,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
         << varLeft(7, 19, 18) << " "
         << varRight(7, 19, 17) << " "
         << varRight(7, 19, 18) << " "
-        << grid.dalt_center_scgc(7, 19, 17) << " "
+        << grid.dk_edge_m(7, 19, 17) << " "
         << outGrad(7, 19, 17) << "\n";
   */
   for (iZ = nGCs; iZ < nZs - nGCs + 1; iZ++) {
@@ -177,7 +177,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
   for (iZ = nGCs; iZ < nZs - nGCs; iZ++)
     outDiff.slice(iZ) =
       (diffFlux.slice(iZ + 1) - diffFlux.slice(iZ)) /
-      grid.dalt_center_scgc.slice(iZ);
+      grid.dk_center_m_scgc.slice(iZ);
 
   report.exit(function);
   return;

From 224a5d44e2da3ef5882641b32ff716a2ce7a1d30 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:47:31 -0400
Subject: [PATCH 060/266] BUG: um, dk needs to be POSITIVE for dipole.
 nan_checks pass now

---
 src/grid_spacing.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 943fd3cd..1e88cc1c 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -148,11 +148,11 @@ void Grid::calc_k_grid_spacing() {
   }
   // This needs to be turned into a distance for the dipole:
   if (iGridShape_ == iDipole_){
-    // the dk's may not be negative (like the sphere). make sure they are
+    // the dk's may be negative (not allowed). make sure they are positive
     // this gets rid of SO many errors...
-    dk_center_scgc = -1.0 * abs(dk_center_scgc);
-    dk_edge = -1.0 * abs(dk_edge);
-    dr_edge = -1.0 * abs(dr_edge);
+    dk_center_scgc = abs(dk_center_scgc);
+    dk_edge = abs(dk_edge);
+    dr_edge =  abs(dr_edge);
     dk_center_m_scgc = pow(magAlt_scgc, 3) % dk_center_scgc / delTheta(magLat_scgc);
     dk_edge_m = pow(magAlt_scgc, 3) % dk_edge / delTheta(magLat_scgc);
   }

From 7bb99ed767ff76a887b1d4f0db1dd164f27c2edf Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:50:23 -0400
Subject: [PATCH 061/266] DOC: Update dipole ipynb w prev commit change

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 7ff0ff7a..1e8b9ca1 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
    "metadata": {},
    "outputs": [],
@@ -12,7 +12,7 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.colors import LinearSegmentedColormap\n",
     "\n",
-    "save_figs = True"
+    "save_figs = False"
    ]
   },
   {
@@ -237,6 +237,8 @@
     "\n",
     "Where this gets complicated is that the actual values are not necessarily increasing. For example, the altitude of the ghost cells at the ends of field lines touching the equator is not increasing anymore, since the field lines begin curving back towards the center of the planet. Further, since q=0 at the equator the k-coordinate of all points in the southern hemisphere (grid cells with negative values of magnetic latitude) will have negative k-coordinates. \n",
     "\n",
+    "The vertical solver does not like negative dk values, so all dk are made positive.\n",
+    "\n",
     "To help illustrate this, here is a plot where the color of each point is its index along the j or k axis:"
    ]
   },
@@ -296,7 +298,7 @@
    "id": "025f5263-732e-41f2-9a5d-fae07190afe3",
    "metadata": {},
    "source": [
-    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the sign of the d_k distances may not be intuitive. \n",
+    "Since altitude is negative in the southern hemisphere and the k-coordinate is decreasing in the northern hemisphere, the actual sign of the d_k distances may not be intuitive. Internally, we take the absolute value of dk.\n",
     "\n",
     "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
    ]

From d88cd134f8e06df84e19ccf4043b06dbde7df4ca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 14:52:59 -0400
Subject: [PATCH 062/266] FEAT: Remove need to do lat/lon gradients, instead we
 can use j&i

---
 src/calc_dt.cpp      | 4 ++--
 src/grid_spacing.cpp | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index f949532f..beb0de18 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -41,11 +41,11 @@ precision_t calc_dt_sphere(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   arma_cube dtCube;
 
   // Longitudinal Direction:
-  dtCube = grid.dlon_center_dist_scgc / cMax_vcgc[0];
+  dtCube = grid.di_center_m_scgc / cMax_vcgc[0];
   dta(0) = dtCube.min();
 
   // Latitudinal Direction:
-  dtCube = grid.dlat_center_dist_scgc / cMax_vcgc[1];
+  dtCube = grid.dj_center_m_scgc / cMax_vcgc[1];
   dta(1) = dtCube.min();
 
   // Vertical Direction:
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index 1e88cc1c..d7fd245f 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -13,8 +13,8 @@ void Grid::calc_grid_spacing(Planets planet) {
   report.print(3, "starting calc_grid_spacing");
 
   // calc_alt_grid_spacing();
-  calc_lat_grid_spacing();
-  calc_long_grid_spacing();
+  // calc_lat_grid_spacing();
+  // calc_long_grid_spacing();
 
   calc_i_grid_spacing();
   calc_j_grid_spacing();

From 0a3e91faba2b004c1b631805614cc27f9b70e564 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 16:57:52 -0400
Subject: [PATCH 063/266] STY: Alter some verbosity when running

---
 src/exchange_messages.cpp | 14 +++++++-------
 src/main/main.cpp         |  3 ++-
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index bd5964ab..befbdb7f 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -966,7 +966,7 @@ bool exchange_one_var(Grid &grid,
   precision_t oneSign = 1.0;
 
   for (int iDir = 0; iDir < 4; iDir++) {
-    if (report.test_verbose(2))
+    if (report.test_verbose(4))
       std::cout << "packing one var : " << iDir << " " << iProc
                 << " " << grid.interchangesOneVar[iDir].iProc_to
                 << " " << grid.interchangesOneVar[iDir].iTag << "\n";
@@ -980,20 +980,20 @@ bool exchange_one_var(Grid &grid,
     // Current PE is the sender, so check if receiver exists:
     if (grid.interchangesOneVar[iDir].iProc_to > -1) {
       iP = 0;
-      report.print(2, "Packing Border");
+      report.print(4, "Packing Border");
       DidWork = pack_border(var_scgc,
                             grid.interchangesOneVar[iDir].buffer,
                             &iP,
                             nG,
                             iDir);
-      report.print(2, "Done Packing Border");
+      report.print(4, "Done Packing Border");
     }
   }
 
   // Send all faces asynchronously:
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
-      report.print(2, "Sending one face");
+      report.print(4, "Sending one face");
       DidWork = grid.send_one_var_one_face(iDir);
     }
   }
@@ -1001,7 +1001,7 @@ bool exchange_one_var(Grid &grid,
   // Receive all faces asynchronously:
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
-      report.print(2, "Receiving one face");
+      report.print(4, "Receiving one face");
       DidWork = grid.receive_one_var_one_face(iDir);
     }
   }
@@ -1016,7 +1016,7 @@ bool exchange_one_var(Grid &grid,
   for (int iDir = 0; iDir < 4; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       iP = 0;
-      report.print(2, "Unpacking Border");
+      report.print(4, "Unpacking Border");
       DidWork = unpack_border(var_to_pass,
                               grid.interchangesOneVar[iDir].rbuffer,
                               &iP,
@@ -1025,7 +1025,7 @@ bool exchange_one_var(Grid &grid,
                               grid.interchangesOneVar[iDir].DoReverseX,
                               grid.interchangesOneVar[iDir].DoReverseY,
                               grid.interchangesOneVar[iDir].XbecomesY);
-      report.print(2, "Done Unpacking Border");
+      report.print(4, "Done Unpacking Border");
     }
   }
 
diff --git a/src/main/main.cpp b/src/main/main.cpp
index eba6d868..64d25548 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -256,6 +256,8 @@ int main() {
 
     } // End of outer time loop - done with run!
 
+    report.report_errors();
+
     report.exit(function);
     report.times();
 
@@ -268,7 +270,6 @@ int main() {
     }
   }
 
-
   // End parallel tasks:
   iErr = MPI_Finalize();
   return iErr;

From 03e521ea340b5ca55283dcb1f1cebd71d7ec432d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 9 Apr 2025 17:59:06 -0400
Subject: [PATCH 064/266] BUG: Gravity & b-field on dipole, fixed

---
 src/fill_grid.cpp     | 30 +++++++++++++++++++++---------
 src/init_mag_grid.cpp | 18 ++++++++++++------
 2 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index deb2b181..11018b76 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -170,21 +170,18 @@ void Grid::fill_grid_bfield(Planets planet) {
 
           bfield_info = get_bfield(lon, lat, alt, DoDebug,
                                    planet);
+
           // Magnetic coordinates:
           // init_mag grid already initializes magLon & magInvLat
-          // #TODO: make sure the bfield is correct for the dipole 
-          // - maybe Dot product the B_vec with (ijk)_vec?
-          if (IsGeoGrid){
+          if (IsGeoGrid) {
             magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
           }
-          bfield_mag_scgc(iLon, iLat, iAlt) = 0.0;
 
           for (iDim = 0; iDim < 3; iDim++) {
             bfield_vcgc[iDim](iLon, iLat, iAlt) = bfield_info.b[iDim] * cNTtoT;
-            bfield_mag_scgc(iLon, iLat, iAlt) =
-              bfield_mag_scgc(iLon, iLat, iAlt) +
-              bfield_vcgc[iDim](iLon, iLat, iAlt) * bfield_vcgc[iDim](iLon, iLat, iAlt);
+            bfield_mag_scgc(iLon, iLat, iAlt) += pow(bfield_vcgc[iDim](iLon, iLat, iAlt),
+                                                     2);
           }
 
           bfield_mag_scgc(iLon, iLat, iAlt) =
@@ -193,8 +190,23 @@ void Grid::fill_grid_bfield(Planets planet) {
       }
     }
 
-    for (iDim = 0; iDim < 3; iDim++)
-      bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);
+    // Now we modify the dipole's magnetic field to account for any imprecision.
+    // Take the bfield_mag and put it into the third component (b-hat = k-hat)
+    if (IsDipole) {
+      bfield_vcgc[2] = bfield_mag_scgc;
+      bfield_vcgc[1].zeros();
+      bfield_vcgc[0].zeros();
+
+      bfield_unit_vcgc[0].zeros();
+      bfield_unit_vcgc[1].zeros();
+      bfield_unit_vcgc[2].ones();
+
+      bfield_unit_vcgc[2] % sign(magInvLat_scgc * -1.0);
+
+      // slight complication -
+    } else
+      for (iDim = 0; iDim < 3; iDim++)
+        bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);
 
     int IsNorth = 1, IsSouth = 0;
     mag_pole_north_ll = get_magnetic_pole(IsNorth, planet);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 2612db20..c910dbd6 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -447,7 +447,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   k_corner_scgc *= planetRadius;
 
   // Convert to geographic, rotating and (maybe) shifting the dipole grid.
-  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc, magAlt_scgc * planetRadius,
+  std::vector <arma_cube> llr = mag_to_geo(magLon_scgc, magLat_scgc,
+                                           magAlt_scgc * planetRadius,
                                            planet);
 
   geoLon_scgc = llr[0];
@@ -473,6 +474,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   radius2i_scgc = 1.0 / radius2_scgc;
 
   // Figure out what direction is radial:
+  // This is all in the dipole's i,j,k coordinate system...
   rad_unit_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
   gravity_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
@@ -482,11 +484,12 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   }
 
   rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)),
+                                                 0.5);
 
   precision_t mu = planet.get_mu();
-  gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
-  gravity_vcgc[2] = mu * rad_unit_vcgc[2] % radius2i_scgc;
+  gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
+  gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
   gravity_mag_scgc = sqrt(
@@ -499,11 +502,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   calc_dipole_grid_spacing(planet);
 
 
-  // Generate mask for physicsl cells
+  //////////////////////////////////////
+  // Generate mask for physicsl cells //
+  //////////////////////////////////////
+
   isTooLowCell = find(geoAlt_scgc <= 0.0);
   isPhysicalCell = find(geoAlt_scgc > 0.0);
   UseThisCell.elem(isTooLowCell).fill(false);
-  
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From 18c674012df1472dc90a7efac9eedaae4c85d387 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Apr 2025 11:58:14 -0400
Subject: [PATCH 065/266] FEAT: Create function to read armadillo cubes into
 Python. Contains instructions :)

is debugged too, btw. LMK if you find issues though.
---
 srcPython/read_armadillo.py | 127 ++++++++++++++++++++++++++++++++++++
 1 file changed, 127 insertions(+)
 create mode 100644 srcPython/read_armadillo.py

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
new file mode 100644
index 00000000..03d45e81
--- /dev/null
+++ b/srcPython/read_armadillo.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+""" 
+Routines to read Armadillo objects
+==================================
+
+
+This is just a couple of functions which will read Armadillo exports into Python.
+Directly exporting Armadillo cubes can be very useful during development and/or
+debugging, but is not suitable for production runs.
+
+To export an Armadillo cube from Aether:
+(substitute the cube name & file name. This does support multiple processors)
+
+grid.geoLon_scgc.save("geoLon_" + tostr(iProc, 3) + ".txt", arma_ascii));
+
+Notes:
+- tostr() is defined in src/tools.cpp of Aether & zero-pads an int to return a str.
+- This will rewrite the existing file each time it is called.
+- Output is to the same directory the executable is called from.
+- This uses the arma_ascii format, which is way less efficient than HDF5 or binary. 
+  I have found this format is the easiest to work with, but your mileage may vary.
+- The several python armadillo implementations look abandoned and/or did not work for me.
+- See the armadillo documentation for more information on saving cubes or other data types:
+  https://arma.sourceforge.net/docs.html#save_load_mat
+
+"""
+
+import numpy as np
+from glob import glob
+import os, errno
+
+def check_file_inputs(files):
+    """ Make sorted list of files (that exist) from a str or list
+
+    Inputs
+    ------
+        files (str or list) Can be list of files, single file, directory, or a pattern to glob
+
+    Returns
+    -------
+        list: sorted list of files that so indeed exist
+    
+    """
+    print(type(files))
+    if isinstance(files, str): # Probably need to glob
+        print(type(files))
+        if "*" in files: # Definitely need to glob
+            files2read = np.sort(glob(files))
+        elif os.path.isfile(files):
+            files2read = [files] # Single file needs to be made into list
+        elif os.path.isdir(files):
+            # We were given a directory. Read all .txt files without log in name
+            files_ = np.sort(glob(os.path.join(files, "*.txt")))
+            files2read = [f for f in files_ if "log" not in f]
+        else: # pretty error message from stack overflow
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), *files)
+        
+        if len(files2read) == 0:
+            raise ValueError(
+                f"Could not find any armadillo cubes from '{files}'."
+                " Check path or provide files.\n")
+
+        return files2read
+
+    # Sort list & check if all files exist. error if not.
+    try: # attempt to handle anything listlike (np arrays, dict keys, etc.)
+        files2read = [f for f in np.sort(files) if os.path.isfile(f)]
+        if len(files2read) != len(files):
+            bad_files = [f for f in files if f not in files2read]
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), *bad_files)
+
+    except: # Not expected types
+        raise TypeError("Need list or str. Could not handle type: " + type(files))
+    
+    return files2read
+
+
+def cube2np(files2read):
+    """ Read armadillo cubes from .txt files, automatically globs and/or input.
+        return np array of shape (nFiles, n_x, n_y, n_x)
+
+    Inputs
+    ------
+        files (str or list-like): either path to files or list of files. If it's a str,
+            the pattern is globbed & sorted, or the directory's .txt files are sorted.
+            If it's list-like, the list is sorted.
+
+    Outputs
+    -------
+        np.array of shape (nFiles, n_x, n_y, n_z) & dtype float. If we are only reading
+            one file, return shape is just (n_x, n_y, n_z)
+
+    Usage
+    -----
+
+    lons = cube2np("../run/geolon_*.txt")
+    lons = cube2np(np.sort(glob.glob("../run/geolon_*.txt")))
+
+    """
+
+    # Sanitize input
+    files2read = check_file_inputs(files2read)
+
+    out = [] # output holder
+    for thisf in files2read:
+        with open(thisf, 'r') as f:
+            _ = f.readline() # first line is a header, not needed
+            shape = f.readline().strip() # next line holds the shape of the cube
+            shape = shape.split(' ')
+            if len(shape) != 3:
+                raise ValueError(
+                    f"File ({thisf}) does not appear to be an armadillo cube.\n"
+                    f"Found shape: {shape}")
+            shape = np.array(shape, dtype=int) # convert shape to np array of int's
+            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
+            for i in range(int(shape[0])): # n_x
+                for j in range(int(shape[2])): # n_z
+                    # each line is n_y long. Convert it to a python list & retain it
+                    l = f.readline().strip().replace('  ',',').split(',')
+                    ls[i,:, j] = l # n_y
+        out.append(ls) # speed not a huge issue, work with lists
+
+    # remove 0th dimension if we only are reading one file
+    if len(files2read) == 1:
+        out = out[0] 
+
+    return np.array(out)
\ No newline at end of file

From 58e2f21625eaade63549712c714b312a2b5c61a4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 14 Apr 2025 12:35:52 -0400
Subject: [PATCH 066/266] FEAT: Better handling of alt gc's. And add mat of
 first ghost cell.

---
 include/grid.h        | 10 +++++++---
 src/grid.cpp          |  3 +++
 src/init_geo_grid.cpp | 16 ++++++++++++++--
 src/init_mag_grid.cpp | 15 +++++++++++++--
 4 files changed, 37 insertions(+), 7 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index f421acd5..f4ef10bb 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -102,10 +102,14 @@ class Grid
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
 
-  // Dipole grid has cells below the surface of earth. These variables hold masks
-  // to either access those cells or ignore them (with .elem()).
+  // Masks to either access the non-physical (ghost) cells, or ignore them - use with
+  // .elem()). Together they *should* hold the indices of all cells.
   arma::uvec isTooLowCell, isPhysicalCell;
-  arma_cube UseThisCell; // (bool values whether altitude is valid)
+  // (bool values whether altitude is valid)
+  arma_cube UseThisCell;
+  // Matrices whose elements denote the altitude index of the interiormost ghost cell
+  // in the k-up and k-down direction (altitude for geo grids, q for dipole).
+  arma_mat first_lower_gc, first_upper_gc;
 
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
diff --git a/src/grid.cpp b/src/grid.cpp
index 89a4315b..e435fba7 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -12,6 +12,7 @@
 Grid::Grid(std::string gridtype) {
 
   // At this point, we only need 2 ghostcells.  Hardcode this:
+  // This is also (kinda?) set in sizes.h for the geo & mag grid independently
   nGCs = 2;
 
   Inputs::grid_input_struct grid_input = input.get_grid_inputs(gridtype);
@@ -272,6 +273,8 @@ Grid::Grid(std::string gridtype) {
 
   UseThisCell.set_size(nX, nY, nZ);
   UseThisCell.fill(true);
+  first_lower_gc.set_size(nX, nY);
+  first_upper_gc.set_size(nX, nY);
 
   cent_acc_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 7d3d611b..95138d5b 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -151,8 +151,20 @@ void Grid::create_altitudes(Planets planet) {
   }
 
   // All cells on the geographic grid *should* be ok
-  isTooLowCell = find(geoAlt_scgc < 0.0);
-  isPhysicalCell = find(geoAlt_scgc > 0.0);
+  isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);
+  isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
+  // get the ghost cell indices on each lat/lon point. 
+  // may be redundant can fill lower with nGCs-1, but this is here for now
+  arma::uvec theGCs;
+  for (iLon=0; iLon<nLons; iLon++){
+    for (iLat = 0; iLat<nLats; iLat++){
+      // find *last* cell below alt_min
+      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
+      // Get the last element if the col-vec
+      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+    }
+  }
+  first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.exit(function);
   return;
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index c910dbd6..5bb3d267 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -506,10 +506,21 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // Generate mask for physicsl cells //
   //////////////////////////////////////
 
-  isTooLowCell = find(geoAlt_scgc <= 0.0);
-  isPhysicalCell = find(geoAlt_scgc > 0.0);
+  isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);
+  isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
+  arma::uvec theGCs;
+  for (iLon=0; iLon<nLons; iLon++){
+    for (iLat = 0; iLat<nLats; iLat++){
+      // find *last* cell below alt_min
+      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
+      // Get the last element if the col-vec
+      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+    }
+  }
+  first_upper_gc.fill(nAlts - nGCs * 2 - 1);
+
   report.print(4, "Done altitude spacing for the dipole grid.");
 
   // Calculate magnetic field and magnetic coordinates:

From e0db896334d230b5184c0e19a46632ce590fd7ca Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 14 Apr 2025 18:22:05 -0400
Subject: [PATCH 067/266] FEAT: Add func to read armadillo mats

---
 srcPython/read_armadillo.py | 64 +++++++++++++++++++++++++++++++++----
 1 file changed, 58 insertions(+), 6 deletions(-)

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
index 03d45e81..4c75c0b8 100644
--- a/srcPython/read_armadillo.py
+++ b/srcPython/read_armadillo.py
@@ -41,9 +41,8 @@ def check_file_inputs(files):
         list: sorted list of files that so indeed exist
     
     """
-    print(type(files))
+    
     if isinstance(files, str): # Probably need to glob
-        print(type(files))
         if "*" in files: # Definitely need to glob
             files2read = np.sort(glob(files))
         elif os.path.isfile(files):
@@ -76,8 +75,8 @@ def check_file_inputs(files):
 
 
 def cube2np(files2read):
-    """ Read armadillo cubes from .txt files, automatically globs and/or input.
-        return np array of shape (nFiles, n_x, n_y, n_x)
+    """ Read armadillo cubes from .txt files, automatically globs input.
+        return np array of shape (nFiles, n_x, n_y, n_z)
 
     Inputs
     ------
@@ -118,10 +117,63 @@ def cube2np(files2read):
                     # each line is n_y long. Convert it to a python list & retain it
                     l = f.readline().strip().replace('  ',',').split(',')
                     ls[i,:, j] = l # n_y
-        out.append(ls) # speed not a huge issue, work with lists
+        out.append(ls.T) # speed not a huge issue, work with lists
+
+    # remove 0th dimension if we only are reading one file
+    if len(files2read) == 1:
+        out = out[0] 
+
+    return np.array(out)
+
+
+
+
+def mat2np(files2read):
+    """ Read armadillo matrices from .txt files, automatically globs input.
+        return np array of shape (nFiles, n_x, n_y)
+
+    Inputs
+    ------
+        files (str or list-like): either path to files or list of files. If it's a str,
+            the pattern is globbed & sorted, or the directory's .txt files are sorted.
+            If it's list-like, the list is sorted.
+
+    Outputs
+    -------
+        np.array of shape (nFiles, n_x, n_y) & dtype float. If we are only reading
+            one file, return shape is just (n_x, n_y)
+
+    Usage
+    -----
+
+    lons = mat2np("../run/geolon_*.txt")
+    lons = mat2np(np.sort(glob.glob("../run/geolon_*.txt")))
+
+    """
+
+    # Sanitize input
+    files2read = check_file_inputs(files2read)
+
+    out = [] # output holder
+    for thisf in files2read:
+        with open(thisf, 'r') as f:
+            _ = f.readline() # first line is a header, not needed
+            shape = f.readline().strip() # next line holds the shape of the cube
+            shape = shape.split(' ')
+            if len(shape) != 2:
+                raise ValueError(
+                    f"File ({thisf}) does not appear to be an armadillo matrix.\n"
+                    f"Found shape: {shape}")
+            shape = np.array(shape, dtype=int) # convert shape to np array of int's
+            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
+            for i in range(int(shape[0])): # n_x
+                # each line is n_y long. Convert it to a python list & retain it
+                l = f.readline().strip().replace('  ',',').split(',')
+                ls[i,:] = l # n_y
+        out.append(ls.T) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:
         out = out[0] 
 
-    return np.array(out)
\ No newline at end of file
+    return np.array(out)

From 6bb4d6d60ada41ea57c08d740afcee410d2bb2f6 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 13:38:26 -0400
Subject: [PATCH 068/266] bug??: Make sure the altitude limits & ghost cells on
 the dipole grid are correct

---
 src/init_mag_grid.cpp | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 5bb3d267..dcf1a288 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -121,7 +121,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt = grid_input.alt_min * cKMtoM;
   precision_t max_alt = grid_input.alt_max * cKMtoM;
 
-  // Normalize inputs to planet radius... (update when earth is oblate)
+  // Normalize inputs to planet radius... (update one day to support oblate Planet)
   // Here we are using the equatorial radius.
   precision_t planetRadius = planet.get_radius(0.0);
   // Altitude to begin modeling, normalized to planet radius
@@ -281,10 +281,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    q_min = pow(((1 - max_alt_re / Pcenters(nGCs)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcorners(0)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - nGCs)) / pow(
+  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats -1)) / pow(
                              min_alt_re,
                              4.0)), 0.5);
 
@@ -302,8 +302,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
-  report.print(3,
-               "Done generating points for magnetic grid. Plugging everything in");
+  report.print(3, "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
@@ -510,15 +509,20 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
-  arma::uvec theGCs;
   for (iLon=0; iLon<nLons; iLon++){
     for (iLat = 0; iLat<nLats; iLat++){
       // find *last* cell below alt_min
-      theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
-      // Get the last element if the col-vec
-      first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
+      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) 
+                                        < grid_input.alt_min * cKMtoM).max();
     }
   }
+  if (first_lower_gc.min() < nGCs-1 || first_lower_gc.max() > nAlts-nGCs-1){
+    report.error("Invalid magnetic grid!! Either:");
+    report.error(" - Lowest latitude field line is entirely below min_alt");
+    report.error(" - Highest altitude field line is above min_alt");
+    report.error("This should not happen. Something is terribly wrong. Goodbye.");
+    return false;
+  }
   first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 94fcdea310342a830fd30ac5937d7a8b43d443dc Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 18:20:17 -0400
Subject: [PATCH 069/266] FEAT: Get vertical neutral (lower) bc's working with
 new dipole grid

---
 src/advance.cpp      |   4 +-
 src/neutrals_bcs.cpp | 157 +++++++++++++++++++++++--------------------
 src/neutrals_ics.cpp |  22 +++---
 3 files changed, 98 insertions(+), 85 deletions(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index 023a73d5..c474636e 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -106,8 +106,8 @@ bool advance(Planets &planet,
   if (didWork)
     didWork = ions.set_bcs(gGrid, time, indices);
 
-  //if (didWork)
-  //  didWork = neutralsMag.set_bcs(mGrid, time, indices);
+  if (didWork)
+    didWork = neutralsMag.set_bcs(mGrid, time, indices);
 
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: set bcs");
 
diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index c4cab8fb..8f91fb7c 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -92,8 +92,8 @@ bool Neutrals::set_upper_bcs(Grid grid) {
 
       h = species[iSpecies].scale_height_scgc.slice(iAlt);
       species[iSpecies].density_scgc.slice(iAlt) =
-        species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dk_edge_m.slice(iAlt) / h);
+                                      species[iSpecies].density_scgc.slice(iAlt - 1) %
+                                      exp(-grid.dk_edge_m.slice(iAlt) / h);
     }
   }
 
@@ -118,6 +118,8 @@ bool Neutrals::set_lower_bcs(Grid grid,
   json bcs = input.get_boundary_condition_types();
   int64_t nGCs = grid.get_nGCs();
   int64_t iSpecies, iAlt, iDir;
+  int64_t nLats = grid.get_nLats();
+  int64_t nLons = grid.get_nLons();
 
   std::string bcsType = mklower(bcs["type"]);
 
@@ -125,7 +127,10 @@ bool Neutrals::set_lower_bcs(Grid grid,
   // MSIS BCs - only works if FORTRAN is enabled!
   //-----------------------------------------------
 
-  if (bcsType == "msis") {
+  // ALB changes to lower BCs only really work now for dipole grid. Don't use msis
+  // if we are handed a dipole grid.
+
+  if (bcsType == "msis" && !grid.IsDipole) {
 
     report.print(2, "Using MSIS for Boundary Conditions");
 
@@ -168,78 +173,84 @@ bool Neutrals::set_lower_bcs(Grid grid,
           std::cout << "  Found in MSIS!\n";
 
         species[iSpecies].density_scgc.slice(0) =
-          msis.get_mat(species[iSpecies].cName);
+                                        msis.get_mat(species[iSpecies].cName);
       } else {
         if (report.test_verbose(3))
           std::cout << "  NOT Found in MSIS - setting constant\n";
 
         species[iSpecies].density_scgc.slice(0).
-        fill(species[iSpecies].lower_bc_density);
+                                      fill(species[iSpecies].lower_bc_density);
       }
 
     }
 
   } // type == Msis
 
+  precision_t sh_ave;
+
   //-----------------------------------------------
-  // Planet BCs - set to fixed constant values.
+  // Fill the lower+ ghost cells
   //-----------------------------------------------
+  // - Planet BCs are in here too, can be refactored out
+  // - Dipole grid must use planet BCs, for now.
+  // - This kind-of assumes nGCs=2, so may need to be updated.
+  // - If the first_lower_gc is at iAlt = 1, this may cause issues.
+  // - The equator-most (j-hat) grid cell will be entirely below min_alt!
+  for (int iLon = nGCs; iLon < nLons - nGCs; iLon++) {
+    for (int iLat = nGCs; iLat < nLats - nGCs; iLat++) {
+      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
-  if (bcsType == "planet") {
-
-    report.print(2, "setting lower bcs to planet");
-
-    // Set the lower boundary condition in the last ghost cell:
-    for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-      species[iSpecies].density_scgc.slice(nGCs - 1).
-      fill(species[iSpecies].lower_bc_density);
-    }
-
-    temperature_scgc.slice(nGCs - 1).fill(initial_temperatures[0]);
-    didWork = true;
-  }
-
-  // fill the second+ grid cells with the bottom temperature:
-  for (iAlt = nGCs - 2; iAlt >= 0; iAlt--)
-    temperature_scgc.slice(iAlt) = temperature_scgc.slice(iAlt + 1);
-
-  arma_mat sh_ave;
-
-  // fill the lower ghost cells with a hydrostatic solution:
-  for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-    for (iAlt = nGCs - 2; iAlt >= 0; iAlt--) {
-      sh_ave =
-        (species[iSpecies].scale_height_scgc.slice(iAlt) +
-         species[iSpecies].scale_height_scgc.slice(iAlt + 1)) / 2;
-
-      species[iSpecies].density_scgc.slice(iAlt) =
-        temperature_scgc.slice(iAlt + 1) /
-        temperature_scgc.slice(iAlt) %
-        species[iSpecies].density_scgc.slice(iAlt + 1) %
-        exp(grid.dk_edge_m.slice(iAlt) / sh_ave);
-    }
-
-    for (iAlt = nGCs - 1; iAlt >= 0; iAlt--) {
-      //std::cout << "before project : " << iAlt << " " << iSpecies << " "
-      //  << species[iSpecies].velocity_vcgc[2](10,10,2) << "\n";
-      species[iSpecies].velocity_vcgc[2].slice(iAlt) =
-        species[iSpecies].velocity_vcgc[2].slice(iAlt + 1);
-      //project_onesided_alt_3rd(species[iSpecies].velocity_vcgc[2], grid, iAlt);
+        // k-index of 1st lower ghost cell is not constant on the dipole grid.
+        // On the latlon grid with nGCS=2, this will be 1
+        iAlt = grid.first_lower_gc(iLon, iLat);
+
+        //-----------------------------------------------
+        // Planet BCs - set to fixed constant values.
+        //-----------------------------------------------
+        if (bcsType == "planet" || grid.IsDipole) {
+
+          // Fill all lower ghost cells density with lower boundary condition:
+          species[iSpecies].density_scgc.subcube(iLon, iLat, 0,
+                                                 iLon, iLat, iAlt - 1).fill(
+                                                   species[iSpecies].lower_bc_density);
+          // only fill 1st GC with lower temperature
+          temperature_scgc(iLon, iLat, iAlt) = initial_temperatures[0];
+        }  // planet bc type
+
+        // Set all lower ghost cells to bottom temperature:
+        temperature_scgc.subcube(iLon, iLat, 0, iLon, iLat, iAlt - 1).fill(
+                          temperature_scgc(iLon, iLat, iAlt));
+
+
+        // 1st ghost cell density is filled with a hydrostatic solution.
+        sh_ave = (species[iSpecies].scale_height_scgc(iLon, iLat, iAlt)
+                  + species[iSpecies].scale_height_scgc(iLon, iLat, iAlt + 1)) / 2;
+
+        species[iSpecies].density_scgc(iLon, iLat, iAlt) =
+          temperature_scgc(iLon, iLat, iAlt + 1)
+          / temperature_scgc(iLon, iLat, iAlt)
+          * species[iSpecies].density_scgc(iLon, iLat, iAlt + 1)
+          * exp(-grid.dk_edge_m(iLon, iLat, iAlt) / sh_ave);
+
+        // Vertical velocities: (In GITM this projected down with mesh coeffs)
+        // Take lowest physical cell's vertical velocity and project it down nGCs cells.
+        // All "GCs" lower than that have 0 vertical velocity since they're nonphysical.
+        species[iSpecies].velocity_vcgc[2].subcube(
+          iLon, iLat, iAlt - 1, size(1, 1, nGCs)).fill(
+            species[iSpecies].velocity_vcgc[2](iLon, iLat, iAlt + 1));
+        //project_onesided_alt_3rd(species[iSpecies].velocity_vcgc[2], grid, iAlt);
+
+        if (iAlt > nGCs - 1) { // Fill all lower GCs w/ zero vertical velocity
+          species[iSpecies].velocity_vcgc[2].subcube(iLon, iLat, 0,
+                                                     size(1, 1, iAlt - 1)).zeros();
+
+        }
+      }
     }
   }
 
-  // Force vertical velocities to be zero in the ghost cells:
-  for (iDir = 0; iDir < 2; iDir++) {
-    for (iAlt = 0; iAlt < nGCs; iAlt++) {
-      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-        // species velocity:
-        species[iSpecies].velocity_vcgc[iDir].slice(iAlt).zeros();
-      }
+  didWork = true;
 
-      // bulk velocity:
-      //velocity_vcgc[iDir].slice(iAlt).zeros();
-    }
-  }
 
   calc_bulk_velocity();
 
@@ -281,8 +292,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX - 1, iY) -
-          temperature_scgc.tube(iX - 2, iY);
+                          2 * temperature_scgc.tube(iX - 1, iY) -
+                          temperature_scgc.tube(iX - 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -291,8 +302,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
-            species[iSpecies].density_scgc.tube(iX - 2, iY);
+                                          2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
+                                          species[iSpecies].density_scgc.tube(iX - 2, iY);
       }
     }
   }
@@ -303,8 +314,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX + 1, iY) -
-          temperature_scgc.tube(iX + 2, iY);
+                          2 * temperature_scgc.tube(iX + 1, iY) -
+                          temperature_scgc.tube(iX + 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -313,8 +324,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
-            species[iSpecies].density_scgc.tube(iX + 2, iY);
+                                          2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
+                                          species[iSpecies].density_scgc.tube(iX + 2, iY);
       }
     }
   }
@@ -325,8 +336,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nX - nGCs; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX, iY - 1) -
-          temperature_scgc.tube(iX, iY - 2);
+                          2 * temperature_scgc.tube(iX, iY - 1) -
+                          temperature_scgc.tube(iX, iY - 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -335,8 +346,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
-            species[iSpecies].density_scgc.tube(iX, iY - 2);
+                                          2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
+                                          species[iSpecies].density_scgc.tube(iX, iY - 2);
       }
     }
   }
@@ -347,8 +358,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nGCs - 1; iY >= 0; iY--) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-          2 * temperature_scgc.tube(iX, iY + 1) -
-          temperature_scgc.tube(iX, iY + 2);
+                          2 * temperature_scgc.tube(iX, iY + 1) -
+                          temperature_scgc.tube(iX, iY + 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -357,8 +368,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-            2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
-            species[iSpecies].density_scgc.tube(iX, iY + 2);
+                                          2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
+                                          species[iSpecies].density_scgc.tube(iX, iY + 2);
       }
     }
   }
diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 225eeb19..88bc01da 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -99,7 +99,7 @@ bool Neutrals::initial_conditions(Grid grid,
               std::cout << "  NOT Found in MSIS - setting constant\n";
 
             species[iSpecies].density_scgc.slice(0).
-            fill(species[iSpecies].lower_bc_density);
+                                          fill(species[iSpecies].lower_bc_density);
             fill_with_hydrostatic(iSpecies, 1, nAlts, grid);
           }
 
@@ -129,8 +129,6 @@ bool Neutrals::initial_conditions(Grid grid,
       arma_vec alt1d(nAlts);
       arma_vec temp1d(nAlts);
 
-      arma_mat H2d(nLons, nLats);
-
       if (nInitial_temps > 0) {
         for (iLon = 0; iLon < nLons; iLon++) {
           for (iLat = 0; iLat < nLats; iLat++) {
@@ -155,7 +153,7 @@ bool Neutrals::initial_conditions(Grid grid,
                     iA++;
 
                   iA--;
-                  // alt will be between iA and iA+1:
+                  // alt will be between iA and iA+1
                   r = (alt - initial_altitudes[iA]) /
                       (initial_altitudes[iA + 1] - initial_altitudes[iA]);
                   temp1d[iAlt] =
@@ -169,14 +167,18 @@ bool Neutrals::initial_conditions(Grid grid,
           }
         }
       } else
-        temp1d = 200.0;
+        temperature_scgc.fill(200.0);
 
       // Make the initial condition in the lower ghost cells to be consistent
-      // with the actual lowwer BC:
-
-      for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-        species[iSpecies].density_scgc.slice(0).
-        fill(species[iSpecies].lower_bc_density);
+      // with the actual lower BC:
+      for (iLon = 0; iLon < nLons; iLon ++) {
+        for (iLat = 0; iLat < nLats; iLat++) {
+          for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+            species[iSpecies].density_scgc.subcube(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)).fill(
+                species[iSpecies].lower_bc_density);
+          }
+        }
       }
 
       report.print(2, "Calculating scale height");

From 392dc09afaa3b61a01daa8250c7c0c5d6ad7a024 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 15 Apr 2025 19:26:33 -0400
Subject: [PATCH 070/266] BUG: ammend prev commit: IC's and BCs were missing
 some important cells.

---
 src/neutrals_bcs.cpp | 4 ++--
 src/neutrals_ics.cpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 8f91fb7c..985594c6 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -196,8 +196,8 @@ bool Neutrals::set_lower_bcs(Grid grid,
   // - This kind-of assumes nGCs=2, so may need to be updated.
   // - If the first_lower_gc is at iAlt = 1, this may cause issues.
   // - The equator-most (j-hat) grid cell will be entirely below min_alt!
-  for (int iLon = nGCs; iLon < nLons - nGCs; iLon++) {
-    for (int iLat = nGCs; iLat < nLats - nGCs; iLat++) {
+  for (int iLon = 0; iLon < nLons; iLon++) {
+    for (int iLat = 0; iLat < nLats; iLat++) {
       for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
         // k-index of 1st lower ghost cell is not constant on the dipole grid.
diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 88bc01da..a1430d96 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -175,7 +175,7 @@ bool Neutrals::initial_conditions(Grid grid,
         for (iLat = 0; iLat < nLats; iLat++) {
           for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
             species[iSpecies].density_scgc.subcube(
-              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)).fill(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)+1).fill(
                 species[iSpecies].lower_bc_density);
           }
         }

From 89d766c8571d5f352488ecc762bc1654e36fd6ad Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 10:27:03 -0400
Subject: [PATCH 071/266] DOC: Some plots of the dipole corners & their
 connections

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb      |  79 +++++++++++++++---
 .../plots/highlatitude_corner_connections.png | Bin 0 -> 625233 bytes
 .../plots/midlatitude_corner_connections.png  | Bin 0 -> 353691 bytes
 3 files changed, 66 insertions(+), 13 deletions(-)
 create mode 100644 edu/examples/Dipole/plots/highlatitude_corner_connections.png
 create mode 100644 edu/examples/Dipole/plots/midlatitude_corner_connections.png

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 1e8b9ca1..6d93769d 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d3792d6f-1f9b-4364-a1a8-f5d37dabdf41",
    "metadata": {},
    "outputs": [],
@@ -112,7 +112,7 @@
    "id": "cc6301bc-92ed-432c-86f0-857ddaacecce",
    "metadata": {},
    "source": [
-    "## Aether's p,q Values\n",
+    "# Aether's p,q Values on the Dipole Grid\n",
     "\n",
     "First, let's get some dipole coordinates:"
    ]
@@ -152,7 +152,7 @@
    "id": "7a8b44e8-896d-488b-baab-a37abbf00da2",
    "metadata": {},
    "source": [
-    "### Ghost Cells\n",
+    "## Ghost Cells\n",
     "\n",
     "The dipole grid has 2 ghost cells in each block (or on each node), similar to the spherical grid.\n",
     "\n",
@@ -223,15 +223,76 @@
     "plt.show();"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "97273c1d-6c47-4509-b639-f8f53a5c2de0",
+   "metadata": {},
+   "source": [
+    "## Corners and cell centers\n",
+    "\n",
+    "The corners and centers look a little confusing. They are not automatically generated by the python script, so these plots were made using actual model outputs. \n",
+    "\n",
+    "The parameters used in this run are:\n",
+    "\n",
+    "```json\n",
+    "\n",
+    "    \"ionGrid\": {\n",
+    "        \"Shape\": \"dipole4\",\n",
+    "        \"nLonsPerBlock\": 14,\n",
+    "        \"nLatsPerBlock\" : 20,\n",
+    "        \"nAlts\":30,\n",
+    "        \"AltRange\":[80,1500],\n",
+    "        \"LatRange\":[7, 87]\n",
+    "        },\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Some notes:\n",
+    "- Cell centers are the larger dots, and the corners are the smaller dots.\n",
+    "- In each panel, dashed lines are drawn between the $\\hat{j}$ and/or $\\hat{k}$ corners for clarity.\n",
+    "- Horizontal, solid, black lines are drawn at 0 and 80 km (min_alt)\n",
+    "- This will be mirrored in the southern hemisphere\n",
+    "\n",
+    "First, a look at the midlatitudes:\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "94817fae-2328-4a62-986d-bf5f4c049ff5.png": {
+     "image/png": 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vsBIlCx5j9GvYkFJqgFlKsQqCUFCl1MS+lGJdrVZfK9nrjIdZJUtbVRmMh5tE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tvtOJ3O9cVQQItxmdSBz2OWoCp3ajJgXqQMH2f4+Q+jTL2Iz+crfu+wEsmLYrEYg4ODnD9/HpvN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ksINxNB15JgCAPiUtmhKP/9xlH533WSY6PSTf2Y6+hsOlw+F8fOHyMcCyNKIhExgiiJhGNh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\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C4/FA0zRSB9bMzAxUVcXQ0BDJBvPBiQdx7T3XkEwm8b6ffB9Zoe1augXWWz/BarXWVdCy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aZAb29vzVbg5eB2uzEwMNBwWW+hUEChUADHcVucZ2azWXOGlkUTvGi7TXbg0NN9kIFaG3Rd+DGE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aUl+Hw+EgfWlStXAFR1uowu8svLywiFQggGg4bLDaLRKC5fuoTlzA/xvuF/AqdzbMG2/dVq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\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paQnDw8PgOA52ux3btm1TfS9EUcTo6KiiFpNWzpDJZDAxMYHp6WnF7zpfvGh+fh4TExOK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\nTkSuQmCC\n"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "945adde8-905b-4009-aee0-db0963977a75",
+   "metadata": {},
+   "source": [
+    "![midlatitude_corner_connections.png](attachment:94817fae-2328-4a62-986d-bf5f4c049ff5.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "f2d75913-95bf-43e3-8611-47a2a8eb4c28.png": {
+     "image/png": 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PErkMGJhYE5pXN2u6/lQbaMliyDmEvUf3omuiC9W51Whe3QwX50Ltv8zciTe1iVchM2sG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g8ZGRmYM2cOdYC+3+/HyMgIlSg1PDyMxsZGWK1WzJ8/nzg+MzOTWqBgcTNlZWVh6dKlTAKg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\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YwLFzIFqSXTejDj3X9WABFuDQikNUHIjnefh8Pni9Xqr3kN0TPp8PoihqzoFMJhNMJhPV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\n0XIgh8MBt9uN7OzsEeefscJ/TjgBy8BJDwCjdzF3/PX6M9ejs2c3Ltl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dBkMD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zY4qcyaMkJjY2OJt40WvF4vBgcHqTlNQ0MDBgcHqYuaWK1WNDQ0mMpMoc1aJ/MPpedYJKLO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\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Vfw/FYjH09/fDbrdj6dKlOf+mJGC1t7cjkUhIxF3LvDAWi+PNN4HTTpuLDGqNTyQSkgkv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\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sNpvqu1kikUAsFgPDMHIwhIxlGEaXAz3xhKWI5+dyOQwMDMjbIjCZTLKnot6aPDTE5Anv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4tEilFkKhEERRNGSunkql5CwjpSCvVW744IPNAAT8/Oc1uOoqp9yaWkusuPLKiPxSOBVM5Xs+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spKKisrM3DE7NmzMwTdi4uLWbFihW7aXUNDg+oELCoqUkXa9WzKlClUVVWRTCZ5/MXHjTGQ\ny4VUK3HicSearhMFBQVMmzZNvWZGJqrkig30EgOGwufzUVxcrM5JTqeTRYsW5bSTJAm/35+zyder\nsmmEgWbMyNTWNdP1zH4WtFUUs/sVmHffvn3s37+fadOmGUZglZWV0dhYzp135nP88QIDSSi6VCBk\nEJxOSKcrWL8+xs03F6lRVXrk0V13RZg2rYDa2toMgXA9XPPee2H8fj9z587NwDZ6bffvD5GXl8e0\nadPUir9GGKi0NMnu3TJQy89/PpvLLjPW1kql4L//24uSb+3jiiuKueIKMwzkZvr06UiSxLvvvgsY\nYyCn05lz70KhkDoXZBMAZu88jFezTCQSzNQwL1Zr9OBgHVOmjGtfVVRUGO5lxkX7ze2cc/ycc45C\nlq9da93eTKogmUzS1dUFKHNEtrC8mdnBkQrOSaMsJGmUokn2xeGtrm97+8T29rIML78s84UvjJCf\nn6fOs1OnTjUl4t9v+2fAPx86AmvNyxtJ+pSgHPF8JWS4+M8/ZM8lZ1FXnTspNHe8wqyfHwtj6Q96\nVQrBQshuqJlZG2cpeoL5sOG1DQCs/eRaRuIjzCidwbmLzmWwfdBWKVc9UwW9UyjJnx7N51l2OLz2\n9717n2lk0P3b788Bju+39YR7uO/d+2gJttBQ0sCaZWtISkooyaZTN3HJk5cYhpjbNasJw05anais\nY3WcP3b9kdnVs0nvNBbNnVs315IIdLvdtib6/Px8XU9xtvn9/oxF0ey4ZgSj1qZNm8aUKVNseSYW\nLFhALBazRWAJUGbUNhwOE4lEqKqqYmQkj0ceKaWvr5D584XnT0SkKB7Pl1/WAjsZJbRPAW+plMxH\nPnKQ3/7WT0lJCRdfrLxnelFVo6MR+vuxFVIrUlzsEBZCkDi7Gqoe0EskEhx9dJw333SwbFkhX/2q\n4lXZvDnXYzp+vkIUVUkffPNNuOAC44UsmUyq4zfbOGTb0NAQsixbesz0TIRWl5eXZ8x1DQ3G5Fw6\nrdwbUKrujYyMIMuyrdS5ydhkondCoZBaicjoPT3UqKBIpJfbbx/iuuscKGml9sVR7V5fu7Z5s8zZ\nZ/eyYcNBPv95F4sWLVI3vH9P8kqbJqBXlr219R83EssIA1357p3s+ciaDJDvdruZNWsWbd1/4TO/\nOE8JtqjWx0DZlba0diB4gMUPLFaq8iXgS49/CYrhL5f8hVfbXlWjo77Q8AVGB0bVzb4gKZPJJKFQ\nyNSLq+Ict3IMokCRPv7RjrGgoIBQKEQoFLJcIx0OB2Vl4w5GKwy0tW0rV6y4wtb85XA4WLFihW1s\nVldXp/u5HgZy+pxQZA8DOZ1Oy+ugxUB6VbdFFcFs05ItHo/HNHJDpIm+0vEKMytmGgrySw6J+VPn\nW25O7a4jxcXFtlLjy8rKbG2ICwsLTaMGtbZgwQLS6bQlxvT5fCxevJhEImH5vDidTmbMmGHoVJUk\nScUMis5VEQ8/7GB4OMDs2VoMFABmAAF+/3tBOiZRZAfSCPkBSYpx3XV9HH98lAsvLEEUVNcjj3p7\nw8Tj1rhGVErUCpkbmdDAA3sYaGBghM98Bnbt8rNggaJ/VV9vFDEGiURk7LxdCI1RKww0PDyMJEn4\nfL4JVVIWFZBLS0sntGcTkVtAzrtsZ42WJImDBw+STqctCd9Dsclglf7+ftLpNIFAwHCeOhQMpIjD\nt3LZZXGUZ9o7IXF4q+srEqjsZhU8/3yab32rg1tv7WflyiK1qmA28Z9KpVSHwKE6XUEhJ8vLy3X7\nmgz+CYfDpFIpAoHAhLIwDsU+dARWQl92gWQC7tn6Da5Z+aBaMUdYddlCZX4Ojn1Qp/lc249JhUA1\nlS3Lvn3MtzM8YXXF+qBEWFtbG7FYjLq6OiJEcoDKAyc9wPn3nq9EP5bre9qsTOgumIWnSpJES7DF\n0CvmxMmBoQm62w/Rtu7dyqpHVuWE8m9ZvQX5JuXiX7ziA8wvOUR7ZNcjnLXlLH5+ys/xOD056RYO\nHHicHtYsW/N3HOXhNbveBCvwqzUzQCrLsuodf/llP+ee6yOZLM0AIQ88AONeRi8qMwzAfJRtoHMM\n4IxyzDHdtLa6MjxtesCpr8+fEQ5vZLIsU1JSQjgctkV2idQCq35BKf8LCokmFsTqamOP6dVXh7nt\ntjTK0hDgvvsU4Ga2kDmdQfUYE1lYBQCbaPRVMplUI7eyAc6aNco9NQKnCrE4ql4X4b2djPX39xMM\nBqmtrc25b5P1XhUWFjJv3jwSiYTu83+oXrFoNEpHR8eYOPxUNm0KcMkl9vUU7FxfO6aE8SeBZiDM\njTfCjTcWsGePxLx51qkN77f9PVJRD5cZYqCogoHWnfd4zobloyuOU4IOwiiSeT6gMBMDmRFY1YHq\ncYm9BAoRVpybxgao+Er0U1ZWRm9vLwMDAxQVFbF3714cDgezZs2if7Q/AwPd94X7WLNpDQwDEvzu\nrN9Z4h8Ryah9R9PptKF3u6GhQS0uYYWB2sPtE0qdOFTH4v9hoH9+s5O6BQqJZSXLIPozW0NHR0dV\ncuXZZz2sWlVDMlmjg4GSKC+xFlf4gMUIKlyZ50f4xCd66e8fzlg/9TBQPF5MJBIhLy9PN01WmCzL\nlJeXq5F/IipJz2KxGLIsq1GByWTSFCuKtd7v95NMJqmu9hjiny1bFMLrootSKFH3DksM1NiYJBLp\nVXGcmSWTSWRZxuPxIMuyGrmud/+EDq2ejEcwGFSLHxQWFqptPR4Pa9Y4Tdfoc85JcvBgD8lkkvz8\nfFMyN5lMqimEeveutbUVh8NBTU0NDocjo60ZVjnhBON+a2tr8Xq9KhktxuB2u3G73bb6FW31TKku\nOQSk+fGP67j6amv8U1tbS1VVFW632xIDrVrlIJWynucVDDSK4vUJsHZtLWvX5tHYKDNrVu7vBwcH\naW9vp6ysLCdiczJmNr9MBv90dnYSDoeZOXPmpDVlJ2ofOgLLybjXUWuuNDT3tqvhs9kaET//6EVc\ntvt/VB2l7CqFWnM4HLpesP857X+46IGLxvuYBLkUjUaJRCJs27ONi569KAeoXL7wcgBuOe4W1r29\nTtfT1tSkaFNNnz5d9yXu7u6mq6uLyspKQ72tt99+G3e3olGgJ/WfDqfx9HhoaWmhoaFBt4/du3cT\nj8eZNWuW7mY7kUiwa9cuPB6Pbmg2KFEaHR0dJNwJVj26SjeU/4xNZ/D7Vb9n/vT5hhvSrq4uRkZG\nqKqqMny5ent7CYVClJeXGy5EwWCQYDBIUVGRIWiIx+P09PSolb2yTU2FCAEOuOx3l4ELvE4vKTml\n3m+P08NvTv0NzlEnESKm5EY4HEaWZfx+vylAikajGekARmYGIP6ZLBaLIUkSwaCLc8/16YKQ88+H\nX/zCyaWXliGmxPXr4bbbxCLp1E0LtDKRjmFlDofDMlxca0uWLGF0dNRWKmYgEKCmpiZnoTIKt3/q\nKYXI27ChmBtvHNfKMlvITj3VXPtBz0TEBUws7RDGo68KCgpyzsuMnNuyRUmpPHCgh9dfh5NOKp20\nJ0tUyYvH4xQWFmY8D4cavWPkrT7UftPpNM3NzciyzMqVJdxwgzI3TURTys71tWOFhVGgEWXT5ASm\nARW8T8FwE7bDnSr5QZohBkopGEhoBWVjoI1HncUNv3tYyazIg3uPuTqnUrPWsjHQfV+4jzU/W6OQ\nYDF47IzHbOGf8vJyent7GR4eJpVKqdEVW/du5ezHzs7AQE6HE5yw7rR13LLjFlKyvvh2Z2enWvBG\nD6g3NTURCoUMwXYkEmHv3r3kDeYZRgalB9N4ej309/frRgpIksQ777yDy+ViyZIlumSZ2JQUFxcb\n4qgDBw4QiURwFjhVofMcDHTPGTyz6hkWNCygsrJSd+1uamoinU4zbdo01bGTn5+fETHS0tKCJEnU\n1dWp60s0GmVkZASfz0dpaSldXV0kEgkqKipy1sFYLEZfXx/RaBS/369Wec225qFmZv1glpL14ILL\ntl0G6GOg+066D0fUwa7WXWrKpR4RGAwG8Xg8+P1+mpqaSCQSzJ49O2OOlySJ0dFRVf8KoLm5mWg0\nmiFALIoHZV/HlpYWQqEQ06ZNs1zv2traGBoaspUG1dnZSV9fH9XV1TkO9mwT+L2iosISN+zcuZM/\n/OEPeDweliw51nD9OP982Lgxyg03dKB482cZYqA774zg80VpaekkLy9PN1VS2LRp0wiFQrz33nvk\n5eUZ4nyfz0dDQwPRaJS3334bj8djKPmRn5/P8uXLGRkZUd+v5cuXG45BEGPt7e309/dz5JFHmkou\n3HVXBNjP179eyw9/OI/f/95pioE2bnyHj350n61iMDt37iSdTrN48WKi0ahKUOmt+bt37yaZTLJg\nwYKcqC6hkSki0Pfu3Us8HmfevHlUVxeYrtHB4H4eeOA9Pv/5GhoaGkxJ9aamJiKRCLNmzcp53uPx\nuDqOiooKOjo6CIVCY9GAZaZY5Q9/aMPtDlJfX6/7bmgzTjo6OhgcHBybt6pM+/3Tnw7icPQzZcoU\n3fTHaDRKe3s7n/kMvPmmG1luobOz1jDaVZiWELPCQEuX1pHhpTHsc4BxDOQB5gKF2MzafF/tnwX/\n/PPvULPMSAEolYSSvHJCoRBbX1/Pqhf+K0MjQlR6vXTOMfyCV3WrFAoA98z+Z7jwmQtzyKVL51wK\nwG3H38Y3/vcbk0plkySJgegAFz53IUlPMgeo/OyvP+O5859j9pTZrD01N9lYlmVVUNmInDIrD639\n/uS5J3NP5z05YvQOHHgcHlMxelA2TMLTOZnvYbwc8yO7HzEO5U8meWz7Y9w45UbDfqLRKOFw2HSB\nCYfDBINB08iWSCTCwMCAqddrdHSUvr4+AoEAVVVVOUD/jIVnjB0QxTU+xkPs/upuHtv9WIZorhyW\naW1tpbS01DSlT+g8WbHfe/fuRZIkFi1aZEqA7NixA0mSWLhwoWm7xsZGYrEY06dPN/VEh8NhOjs7\nKSwstFws+vv7icfjlJaWWoZjDw8PI8syBQUFuqBdRF89+2w+iUQKWU6jeBUVEyDkj3/0AzNUXavl\ny/UBzuholP5+Y72tD8IcDoft45uljOp5TL/4xSouuKAMSZL4xjfgyivNF7LmZvjb3wpZujQ5ISJK\neB4DgYAtL7PWtKBJz8zAaTKZ5OGHB1m3DkpKqtX0h4na4OAg8Xgct9udQ1JOxnsVi8V09U4OtV+t\ntba2Eo/H8Xq9hptlO2Z2fe2Y4pRo4fbbJa67Lg+YjZ3KRXbscImuH+5UyQ/SDDFQDEqKyolGo/oY\naABwwBXLPs3dfa/Q1zfIwMCAbvr6tn3bWL1ldS655IErPnkFd793N/19/bbG6/f7aWhooLi4WMUC\nA9EBznr6LHXNFxhIlmW8Ti+fnvlprj3pWl0HQTKZJBgMmq5bVhhIiL6ftuA07mq9SzcyyO1wc/Kc\nk4lEIiqRrV0DRXEKbcpYJBKho6NDTd0UFez0tCuFrk5bWxu1tbU81WJS7CWR5N6X7uXSoy/F7/fr\nkkYixQOUKIT+/n7q6uoy1pKRkRGSyWTG5k+s3UI3bHh4mEgkonuMZDJJb28vwWCQkpISHA4HxcXF\n+hhIyNA4Ufd7ehgo1B2itbWVYDBIaWkpqVQqJ+JGkiSampoAWLZsGdFoVNWG0hJYsViMPXv24Ha7\nWbZsGaA4UkX1PW27Xbt2qal8woSovrbtu+++i8fjYc6cORnjkmU5o0KhIGkrKip010qBh0HBcy6X\nS7fYktBoE20HBgbweDwUFBTkPM9ut1tNzdu2LZ9EIo4su9Bu/cT68Ze/BIA81q1Lc8stxhiotzdC\nT08+06dPt5S2gMxKfYerrdPpVMlGq7ZFRUUEAgF1XyQE+PXwD8CVV87miCP6cLlc3HqrxNVXO00x\nUEcHlJWVsHhxnqVjU1uJ0CoC3ahSXzKZVKPKxPUX1020NVujv//9MLfemiQ/38nRR5vfv+x+tSZ0\nqoqLi9XCCaKtFVZ5/HHFGaftNxwOk5+fbxqlZ6fflSv1x6t14JWUlOD1eunt7Z1wASE4dAykENbd\nfOc78J3vFKJgoEJbGMhqvHYxUCgUYnR0lEAgkPPc/rPgnw8dgeV2KOui9hY7AHcKlteezoGWtzhn\n+3+RkDM1IpCV3x4x42TkL/9Bt++SkhIicoQ1m9eQdOaSSz9/8+c8d/5zLGhYwA0n3ZDz+1gsxoED\nB/B6vcwy2DnJssxT+57SrWAoI5NMJ3l6/9NcM+0aw98LsyKojL4XwGvvwF62nLUlJ2Td4/Tw36f8\nN2X5ZabRPtoy1HqmLe1rZKLNwchBY5FP2UVnqNNWP2ahxnbGY1UaOruNUcj/tz75Lb73yPeUHziV\naL2ZpTNzNMU6hjoA82qGYK+6oBYom/WXSqXUe2d13FgsRjwet0wNFASinaiuwcFBQiFFpNOKqOnu\n7iYcDjNjxgxdIBCJRNXKgi7XAKlUB4podYPaxuWCgoLxRVFEpOzdu5dTTpGZPn26Oo5du5SKPFZA\nJR6PZ5RCN7NIJEJ+fv7fTawx27RjtlrIQiE477xaNm+uRYPzLa2oqIja2lpbUWRaC4fD6vNmRpjp\ngVMlZLsPZeYPsGZNgDVrsFV9T2si+ko5TnXOfZuM96q1tZVoNEpDQ4PheR2KV2x4eJihoSEcDgcz\nZ860ncZiZEbg344NDQ2NeZ6LUUhj14TSGI3scIqOHq5Uyb+HGWKguIKBWjveYdVLOhho7LefXnEe\n3/r4b+jq6qK1tRWfz6dWVCsvL6cv0sfqLatzIoFkWcbj9HD8ouNZdeQqXQfa4OAgPT09lJSUZESa\niI2YWDuf2vcUKTllioGOXHikrp6oHr4ZHR1VN/rV1dXq+mY05+7YsYMDBw4wUjzCI6se0cVAP/rc\njyjLLyMcDhOLxUin0zkEFmTiH4fDkbEOmmEkp9OJy+UinU6TTCbpCHeYYqD+WP+YxlE853tBpgAZ\nEdjZlQj1cFJ2WzMMJD4Tae5mGOj6j1/PD/b9QA0Z3HqePgYaSAzgcDjUtUKPwBJjEgLyQldNSzRp\nz0GLk8S117YV1zB7/RZ/i7biGHrRWtn9hsNhRkZGdB19oq3A3mJjrYdpxDMr2ra2tiLLMkuWLMnB\na16vl1gszt69bhIJPy5XO6nUMFAPjDuAXC7w+1Pcf38fJSXJsegWiffee4/TTvMza9YsnE4n6XSa\n9va4OieY4TMR6WZFSkmSpOqdTobsEum+Zo5w7XuurSBpZD6fD1FV3goDxWIevvOdGmbPnscYJ2pr\nzNXV1bjdbkMCy4g80otA1yO7stfo5mZBZCjE2U03lXPTTU5T/GNEoiUSCZWAE/O4tq0VVunoyOw3\nlUrR2NiI0+lk7ty5GbhwIv2aCaiLiHnhwBMEnB0bGRlhZGSEgoICNRJtshgonU6rDtyKiplAIXfe\nmc9Xv/rBYqChoSH6+vp0JTD+WfDPP8aO6TDarz99PV7HuMa5E/A64Ef/djnFhVN57r2fk5R15apI\nyfDHfb817Luuro5Xgq+QcpkDK7OIo2g0alixBsbE9cIHcTn1NxguFLLGipwCawLLaBMjSRIvNL/A\nVc9eRSwVo/WaVjYct4FLj7iUDcdtoO3aNo6tP9a0D7AmsKy+17aZXjrdOJQ/nWZK4RRbBNbhJKes\n2gQTQTXkX5IlklISSZZIpBPc+uqtANx07E3gxDBaTwAuMyJJlLi2amdU7nmy7WRZzik1bWRGJaEP\nta1VtcJHH41y1VUwOppPOi0qCmb2m0rJTJ2aGXEpyzKRSCSjyp0AWmAdgdXe3s67776rgg0jS6fT\n7Nmzh3feeScHbGebLMvs3LlTTfOwslAoxPDwsG61KLu2Zo2yYOlNaZIktDNg9WqlTXOz4gHauFGJ\n3tq4Ufk72/Ly8qirq5uw/pWoPFRTUzNhwq+qSgZEVEimdsdEbGhoSC3xrBcBMlHv1eDgoJo2ZUaM\nHopXrLi4mOnTpzNt2jRb6a/vpzU0NDB16lSuuGIWsuzi4osVkLRy5eT71KZXSpIC4CRpPLVA7xk0\nM5Em4PUqVbc8HuVfr3diqZJ/DzPCQN896gKKC6ey9c3b9TGQpGCgF977BXV1dZSWliLLspqO5XQ6\naWho4JXgK4aRQCkpxSsHX2HBggW6EcOJRIJoNKpLssD4hqYr3IXLYY6B2tvb2b59uzonq6ehQ2CJ\ntH4RvWnlxPN4PPy18698detXDTHQp+o/BYy/s9m4Tg/fiHVSROaINka4xOfzkU6nicfjNJQ1mGKg\nupI6NeJG73thRgRWKpVSr792PALrCIxhl8CSJImh+JAhBvrRGz8CB1z5b1dC2hoDaQmsbBPPkziv\nbKIpu50Wr+i1NcI1WqJJnKc4bjbuN2qrh2u0ZFc8Hlc1avVwlbZfoQdlJOzsdDp5++0k99yTYnTU\nTzotntFsDJRi6lTl3ov7G41GSaVSjI6OZkQQinNwu92G2EKWZfbu3cs777yj9meEWUKhELt372bP\nnj05pFS2RSIRdu3axcGDB3NIKT3Trq1GZIyeaYk0Kwz09NNK2/PPlywxkDYCq6CggPr6ekPsqm2r\ntdLSUmpqajLkSYzaak3BOTHGK2uXaz7XN6Nr1t3drVbyFPOftq210Hlmv52dnSoJnP3MT6RIT3a/\nWqutraWysjLHgWfneQiHw/T09KiRb2Y2NDREU1OTWuUx21wuF7Nnz2bGjBlceeVsZLmaK68sssRA\nVvqJhxMD/bPgn8NCYL366qv8x3/8h1oG+Yknnsj4XpZlvvOd71BXV0d+fj6f/vSn2blzZ0abeDzO\n1772NTWv/tRTT6Wjo2PCYznhqBtpveI9Niw9mUunL2bD0pNpu3IHn17+VQC6Qj3owiInuMohETB/\nQIWop565vC76pX7DCUlMLmYPoizL1BXUkca4Kt2UwimTjq6C8QVVr03zUDN5/5nHuhfXgQNWb1lN\nzQ9rWLlgJXedfBfXf+J6qgJVlh5MIRQPh0ZgCVBx9tKz8Tg9OMi8dg4cuHFz8tyTbfXzQZJcT+x7\nwhDoSymJqz92NauXrka+SWblAv2Zyw6Ble19NDI7UVpgDN6M2jmdTkvBdbuklKi0AcaklLBEIqGm\noGb329ysAI5rr/UAHrZs8SPLYuEe71fxKEQ56qid7NixQ/18dHRUBYbiOkSjUVWA0yoyTYAmq3MQ\n7bxer2W0ViQSIRaLMTw8bIu86e7uprGx0ZJEE9bc3MzevXvVMYH5QqbEbgTRxnC8+aZS4WftWvjF\nL5R/6+th2zZbQ7A0t9tNVVWVpU6InuXnS2zaVIoC3pUop8mkrQnPXVVVle6cYwR49bxX6XRaXeeE\neKmRTaRfPbOryXa4LZlMcvDgQfVvJX2j+rBUyhVmJ71yoibSBDZsgEsvVf5ta/vHKSFtZEYY6OOL\nlNDSg+EufQyUr2CgkE/xrDc0NOD3+1XvuFivTTFQnoKBjNLwzQrhjIyMsGfPHnp7e5lSPMWYrJEU\nDJROp0mlUjmbCj0MJDRmYrFYRiSyEQb6t//5N+78652QssZAom87BJaWaBBjyW6jNZ/Ppzqozl9+\nvi4GIg1uh5sT5pxgGIGlPY62inI2gaVtI0y0FdFGeiSX9vycTqdK0G3Zu8UUA52//HxOnHciIzeO\n6GIgSZLUcQmnkcAHWsvGSVYElnaenQiBld3WDiklIqVEWz1MkE1KGfWZ3dbMgdfcDA0NTjZvVs7h\nt791IcvifmdjoH6OOGI/fX196rkJvWCtw0N85vf7VckNPRMRiQ6HQx2bdk+gNYE3/H6/JSk1MjLC\n6OiomnJv1lZEp+3du5fR0VFLkkeWZd57772MuU6JlDLDQLGx/2SES8AMA5ml5GWbEXnk8/mYMmVK\nRqS2HXIuEFDOQ6kuXQi4LfGPXr+JREJ1BGhxmLatFVZZuXK8bSQSUfubPn16ztowkX7PGFNn0bsO\nTqeT6dOn6xJuVjYRrBKPxwkGg+q7AsozrsXheXl5E3beCjMa7+HGQP8M+OewEFiRSIRly5Zx5513\n6n5/2223cfvtt3PnnXfyv//7v9TU1HD88cerIr4A11xzDY8//jgPPfQQf/rTnwiHw5xyyikTjh54\n4vX1VFcs5vrTt3HXF9/j+tO3UVU+LhxYW1itTw05QHLCtBJ9vRhQJr7pRcaRQFK+xPKFyw3zws3A\nm7B0Ok1VQRVuh1uXrLHSnrJDYJm1yaim6Mj6PGucYE1OmbWxSxi93v46NUU1bFm9Ba/Li9PhxOP0\n4HQ48bq8/PC4H1KWX2bYT3b4vFWbQ43AEmCqM9JpDPTH0h6tiB87BJYeKDNrd7gIrPcjqkrr0bQi\naQR4y8vLy62KpT6uM4ClKKRFDLcbHA5/Bgj51a+ilJVljs0KvFmdg9A9sUtgWZWZBtT50k71QUmS\n1L7tVMmSZZnh4WHC4XDOdddbyNrb4cEHR4AmYA9ARrUeMw9QR0eHql32QZrL5aKkZBqwiE2blOcl\nkbAXMSYsGAwSi8VwuVyGBSMm4r3q6uoimUzi8/motggFm4xXrKenx/YaOpHrYNcikQi7d++mq6uL\nnsPRoYGJ1AI9OxTRUZEmcNddyr//KJ5HMzPCQGLdryusNcVA00sUJX2n08msWbPweDwqkZBOp80x\nUKGCgbRkg4jagHEMpDe3p9NpQqEQwWCQqoAxBnI7FIeV0GCyQ2C53W51fg+Hw6ZR6NWBalSGL531\nedZ4QVkjRAUuLbliRE6JcYh1wmgcom0qleLPLX+mtqhWHwM5vdx2/G3UltbicDh0I7CycYsegWWE\nbdxuNw6HQxV+F+M1i14TBFZHpMMYA0ku+kb7cLvdhtHHWuecWQRWtnPOiMAySyHUzpMCh1hFYAms\npIdrtGNIJBJqVJUeTrNLSmW3FZhEr62ynASAWhSRsejYuHw4na6M9eOuu0apqHCp9w7Go620eEeL\ni9rb22lvb9clhMRvtdWPQZ880mIgKwJLtBUYyCzlMBKJIEkSbrc7gxwzIrCi0SiJRIJwOKy+A6Kt\nEQbatKkPaEGpRiBZYqCBAQepVIq2traMeVHPJptOaWYORx5Qz/r1UwFZTVmzEzEmrKenR9Wd1eJW\nLSFkhVUqK8fbtre3A4oWmB4Onmy/oLwjRjpXentoKww0Gcw6MDDAvn37aG1tzXAOgzK/iRT0Q7X3\nAwP9o+Ofw6KBdeKJJ3LiiSfqfifLMnfccQff+ta3WDkWH/frX/+a6upqHnzwQb785S8zPDzMpk2b\nuP/++znuuOMAeOCBB5g2bRovvPACJ5xwgu2xfOm1e/jS2/+fvfOOk6Qq1/+3Ovd0T46bZmdzJpnx\nqgiCCsj1wrKkZSUIInAlSFgULt5rIAcVvXq5+1MXVNwFVFAkiYqyihdkCbvLhpmdnKd7ejqH6vr9\nUXNqqrsr9OwOmPb5fPgA3WeqT1dX1XnO877v836H9gt/w8K5xxTOJZ+nKTgb14Tq+198KboccMq7\nrjI99uuvv06uP4cz60RxKdNu91uOgPXs/me58bkb+fy/fp77/nJfie/C5rM2c9icw0zLQKYjYDmd\nTsNuij8+9cecdf9Zmrxp1E3RrgxxOuWBVmN+ufuXXP2rq6lsruS8951H15VdPPDaAwUmn727exFt\nda0+B8wFLD3RsTIRnI7ItaBhAfI+kyhyTo0iW4lOiqJMSzArV8Aqd5yd2FTuOD2xtxtrR97KHRsI\nwGOPwSmnaKO55x44/HA3L7/stjVmNyJvTU1NVFdX2xIK/d/aRW7eKgFLkDe3212WwCg2daKLUzGM\n6v0jkXEA7rijimuvLa9j4aWXJhgaGmJkZITDDjtsWl5MPT09+P1+6urqDtgvTDX4VP/7ggvUDKz5\n88v3TaqqqqK1tVXLzjNDOSafqVRKSzOfN29eWVG+6ZiHDg8P09vby+joKCtXrrQ8/kz6RykKPPUU\nvOtdIbq7u8jn8/j9/ml1qZwu/l5MR98OfPqp7/DpP32HfZ99jkXzPjz1hqLwavvPOOW4q7nvud9r\nHlh6uBxw2nummsN4PB7NnFqWZV555RXyA3lcksu4uYuOA01MTLBv3z48Hg+rVq3SRBAw5kA1NTU4\nHA7+2PNHvrnnm1x/5vXc+6d7SzjQ99Z/jzWta/D7/UQiEaLRaIGvjRkHqqysJJVKaePFGCMO9D+f\n/B8u/vbFqoA16c9UzIH05X9er5dUKkUymdTWajN+4/P5mJiYKBCwzHiJ2+3mTz1/4r6/3Mfy9y3n\njMPOKOFApy0+jVBPSPtOVhlY4nP0G3TRcdgsyCdJEm63m0wmo61tVnzE4XBoXGlB/QLLssdZ1bM0\nvyoj6LmNmShVPE7/HWa6hNAsA6ucUj8oL6vqQDKwjNbsQAAeesjFmWeCeiEnuOceOProCp5/vnD9\nGBlJEIm4tZLVfD5vGMRbsGBBSfZzsVE+FApYxaJUsaAlxgoO5HA4yOfzJTxLUZQSvmQ2FqaEbRHA\nsxOEIpGINj6ZTJLNZgvGGnGgiYkI4ODaawPccYdiy4Eef9zB8cerWWROp5Ply5cbzgVKM4Ty+Txd\nXV3U1dWVNFAoN5vo1FNh3z6J8XH4939XaGy0XvtXrSo97qxZs3A4HCWB0eI5WHGV7m517NjYmOZp\nOtemBXE5x+3rK1xXurq6CIfDJJNJ5s+fb3lcq/PwzneWn4GlrnPwm9/k+chHehkeVlUwo6ZU4XCY\nnp4e2yZdoN7jzc3NpnujvzYHamlpIZvNvq0Nrt5yE/f9+/czODjICSecoL3m9Xr50Ic+xLZt2/jM\nZz7Dyy+/TDabLRgze/ZsVq9ezbZt26YlYAnRpbluZcHLDQ0N9CWf5MtdP+X6d72He9tfnOrAg0rc\n/vv4f2fxgneZHvrp9qe58bkbufqUq/nWK98qIVYPr3uYpoC5RGkVfewId7DoG5PG7rPhrv+7C4CN\n79/IRHpCE2usjg/lCViVlZW43W6e7XyW9b9YX2KuefkRl4MP7vj4HVz74rWG3gQi6mgmhkiSRFVV\nleU83G43gUDAcKHWzkcIcML5vzif858+n/bPtReYfObzeUa8IxoJM4Isy7jdbkujR5ExU5w+r4cg\nLZIk2ZbqbevZxvqj1/PVP3/VsIORW1Ez6coRwgSBtPo8sM+YmukSQivyZjTO7XbbChbTEbCsoo+g\nLkTAZGfBBNkszJtXwYc/XDhu167SzCoj8gb23xUKyZsVFEXRPsdOwBKp1lCegFVM3uygJ2/lQFEU\n3ve+CC+9BEuXVnPNNfYdC/fvh7GxENu2wYknVk9LvEqn05rYU1VVZSvCFmNsbEwznhXQewaoprXq\n6yJa2tVV6g/hcDjKLsOzMvlUFHjwwW6OOEKhtrbGsKPXgRxXIJFIaKWJjY2NluLVgZwHK2zdCmec\n0c+ttw7wkY+o/lsLFiw4aON4K/y9mI6+LUgAOXjtpUH27XyKxsZGmpqaeLVvM3f2P8LysQYePv4m\n1j7z5UIO5FY50ILWdxQcTjxfc7kcz3Y8yxee/QLXrzMWl/QcKBgMaiVtooOclY3C/vH9HL3laHX+\nQbjthdsAaw7kdru1LC/xXDTjQMFgkJGREaLRKPX19ciyzC/3/pIzHjmjhANdsvIS8MHnP/B57uq5\nq4QDiU5WsizjdDrx+/2agCWeoW63m8rKypL1SZ+BJQQDo/W9I9zBojsXqUmuEpz50zM586dnlnCg\nWCxG3BvH5XIRj8e1bnbFPi/6VvCi7F90eRZ+RmZcQwhY+rXcDEKofLH3Rc775Hnc/Lubjbs45l18\nZPFHNHHUCHphyu12F4igehQH59xut5Y5ZjVOjPV4PNr5UhTFlCuJgJD4e6sgnggG+Xw+W1HK5XIR\nCATweDy2HMjlcmldlwUnMOdLPqCRa65xc+edKgdqba0oWD9UY3bV07G2trbkt9bzIn1JoMiYssuq\ngqlOe8W/h96WQZzruro6LVtND31GlZhDXV1dybUuIDiQeC7U1tYamu0Xj6+qqsLn85HNZi2v82Qy\nyQc+kOapp6pYurSN//xPN9dcY82BhodrePnlPv7lX9y2JWQ1NTX4fD7tvITDYUKhkGEH0Orqarxe\nr2WwcnBwkNraWqqrq/F4PPj9ftu1/5VXqmhqchVcAy6Xy7CzdVVVFU6ns2CsGVeprKxElvM8+mgf\nH/qQ6odqdq4rKyuRJKmAu5kdNxgM0tzcrD3rReMao47VYmwgELA9D//3f+r/l5uB9fTTMl/8Yhe3\n3lrFRz6iin5G3denU5po1C1Qj+lyoJm0cACmxWFnCm+5gKXv1qRHc3MzXV1d2hiPx1PSfam5uVn7\n+2Kk0+mCSJM+jfzxE24iUDFFcjp6f8uir38YJgA/3LbvRQA2LnkvE+kYC2rms/adX2JsWCIUCjF3\n7tyCqFylp5Lbtt0Gk1O5+493g7uUWMWH47z88svMmzfPsLzEKvpYnJ4ucOMHbyyJ/FkhGAxy1FFH\nWd5os2fPZig2xPofrS/pJJSRM9y3/T4GvzpIc7CZaz5mvFMyeoDp4fV6WbJkieUYK08W7XzUmbw+\nCYfDwapVq7CC1+vlsMMOsxzj8/k48sgjLbNr3G43Rx11lK3Z9puON/ncS5+jeXEzD697mLVb1pYQ\n/Z9c8BOOnn+0pRDmcrlYunSp7efV1tbi8XhsxZX6+nr8fr+tQl5dXY3b7bYVkTweD4FAwHacLMtl\nzQ/Kz+oCLKOPUJhtc9xxapZV8Vz1ZRF6rwa7Y1uh3KwqPSGz+77TGQsHLmCVuwjF43FNNBbfs5wI\n0EMPhbjuOqisrMOkEashhH/AgYhX+Xyenp4eZFlm2bJl2nzL8QzQkyS7TkfTwZYtChddFOT22xN8\n7nPWkcfporhdtFmpo8B0z4MZ1C6PedSSijAbNwI0s2/fHJzOmSVLxRClBWvXFkZQ3e6/LdPRmYQp\nB/LCN46+iGzWycjICG+8+Tuu+d0tkAYC8OnffBdq4cW1m3h+z6PsH+9iQc18Pr7iOlLxANFolIaG\nhlIO9LvboBtIov63q5QDDXUM8Ze//IXly5dTUVFBY2OjVj4qTOHBggNVoApYKVRrPYc1B6qqqmJs\nbIxIJKJtVGfNmmVYjqv3wVq2bBmjyVHOuPcMQw70nZ3fYdt12/DIHjau3ViyAZIkqaCTtN/v1yL9\nAjU1NYZZh0IAcbvdplkB2vnwoFaAOVHT5aRSDhQMBlk92QJ2YGBAE3r0qKurK9kwt7a2FhiFNzQ0\n0NDQYMiBhDeN1+ultbXVkictXryY/4v9Hzf8/AYWdS8y5UA//syPOXbesQQCAVMeVF1dzZIlS7QN\nrJn34ezZs7WW8GDMLRVFYe7cuVonMoHa2tqCvUc+n6epqYlMJlOyqa6srCzgm36/XzsvxaioqGDF\nihWAusexWrt9Ph/Lly9HURS2b9+uHdsIbrebZcuWkcvlePXVVy3HnnGGhzPOUBMAzjtvB6lUKafR\n85+FCxcWBNasvDnb2to00VMPfRaZ+D0WmKR/GHEls3vCKAN93rx5hmP15ZWCA1ll+ORyOU0MFPzX\nDsL/a+HChbS1tQH2HCiTqeGGG6q59VaJ97yn1njgJIrXbeETZSTG2AXVYrEYfX19DAwMcPjhh2vi\n4H//t/Xa/8tf1nPNNfWTr1nzH6NnjBlqa2t58slKrrlG4a67Elx1lfkCbfYcNUJ1dTXV1dUkEgk6\nOjoA9Xc3En6qqqq0a+OOO6zPw8MPS7pqDnOoHCgLdAEeNm6sAdpob7f+rWfCTuOfkQO95QKWQPGF\nX85mwGrMLbfcwn/+538avpfJFdaTNtetVAMRoiXPJG785M81oSuRSDA2vAugpO1vLj8pIBRdY8XE\nqkPpsPw+oIoSIlpQnLq++ZOb2fCzKZnUqHQvmUySz+fxer2mC4skSbbndvOrm03NNbP5LA+89kBJ\nO+O3EwFPgMfOfIxTHpp6ahidj5mGXWmSVTZUQRadG8545AwAXrzwRZ7vfr6g7NEuk07MpZxsG7/f\nX1bGUrkLTEtLi+0YUDcK5ZhpV1VVsWbNmrIe0kuWLCGTyZSVrbFo0SKSyWRZIlNtbS0ul6tEoDEy\na5dlmerqajKZjEZ0Y7EYw8PDVFVVGRIIAX1av10G1ltVPpjL5TTyVs54fbS1XMFLkLfq6mrtWWMV\nAXK54Npro6jF207OP7+a88/HsoWzgKIomoBlde7NEAqFkGW5JAPLriWz3jMgEonQ09NzQJ0T9VAJ\nDqgGg7O57roWrrvOupX1dKAosHlzF2vWpPF6PRqxtsJ0zoMVVM0ggWrsL6G2aq+nzMfJQWM65ZX/\nCDDlQLUwZ0ENx645gaGhITq7grANlf9UAHHABasWnMy7J43dQfU26Y2rWXumHCiNyoMmgLpSDjSo\nDBbwtqamJoaGhojH40SjUa3RiCkHWreZDd/doKaEpeHxC0rXfOHT5/f7NQGr2AfLaB33eDx4PB7y\n+TyZTMaWAz0z+AwXH3FxWV07GxoaqKurKytIU1lZyZo1a2zHBTwBHjt7ehxoOs0tzDaFRueueI01\n40nFHGjdw+uAA+dALperrDWpsrLSdq2TJMlWzAe1RM9MGCmGXdmPQEtLCy0tLWV4FEkcccQRJSKb\n2TxXrFhBKpUqq6S+paWFeDxe8lvqvT2bm5s1E/7iYNHIyAjxeJz6+noqKytxOBxatp8eQgiy2qMI\nvFUcKBqNoihKQbacFcTzo6KioizxCqY4kP4+MuNAoK6vP/6x2iBj48ZKNm50l73up9Np7VwdCP8Y\nGRnR/lZ/rUxn7e/v7ycejzNnzpyD6mQ8xYFcwAI+//k8n/+8NGMcKJeT2by5g3e+U81uL+eetzsP\n3d3qf9vdvyoHGkeLFrEMqDDNYJ9OQFQ087BqmjUdDtTY2Khl7s0ERPav3+8v+x46WLzlApbYDA8O\nDhYsrsPDw1qUrKWlhUwmQzgcLoiEDA8Pc/TRRxse94YbbuDqq6/W/n9iYoJ58+YRuSFSsuAFKpp4\n7OM3csozX9FeK87SSiaTdHd3kyLF+TvOL4nKFUAyJhJ2Hlf6SE8xQZQVGUmRYAy+dtzX+ML2LxiW\n7vX29jIxMcGCBQsOeCOVz+e1TkJG38+Bg/3hA3S9PUAY+VBk82oN2KZTNnHhYxeatln+W4FZFt2q\nplW8e+673+bZ/O2h3Id1uRk2Pp+vrGwkKIy26GFkzO52uwui66ASrXA4DFiLKJIk0dbWVuCFYoaa\nmhrT6G0xRNp8OeRN3wGxnIVEZF+Jkp9yoBewBKwiQA8+CKefHp4cWYO+Q8Qdd6gEoq1NXWyLF/to\nNKqJmgeSpixKD4ujlNPxDBgYGCCdTpNIJA5KwColMg6T1w8M//u/w1x8cZhbb5W4/PKFZf2eM+Wd\noPrOBTnllIWoKSOV0+7yODSkZoRZXQ9WKKe88h8F5XCgmpoali1bxmPBGznlV1+BKJCEbx11Cb09\nYdrapgjs+Pg43d3djMZHOf3500s5kAIEgQiQgp984ie2HMjlclFfX8/IyAhDQ0MsXrxYy4Qw5EAZ\nCZJwxVFX8PX2rxuu+Xv27EFRFNasWaO1cS9XeF+xYgVOpxNFUWw50JA8VHYwZyYI+yEO9I+PcjiQ\nUVdls3EVFRVlZ4nX19cbNpjScyC9kFR8T42PjzMxMUEgEKCyshKn06l1gtbD5/MZejoa7Y9aWloM\n7998Pl8SiPf5fKTT6YI5KoqiCeb6scVm72KsOG6x4FdsoWA1FlR7D32QMJfLTXbXdRhyIJeLScP0\nMSCHyoHU9cpszRPeXpIkadlXZhno+Xy+wAZFj1wup3HXxsbGgrFtbU7LtX/+/DzZrOqJNjw8rIko\nRrCagx7qei5P/uNAyBBm67wQSe26rIux9923l6uuinPnnRVccYV5hqv+uG1tLsvzsHx5PStXVtny\nqUAAHnigmXPPbUQN4FXMGAcKh8N0dnZSVVVlWd1ULgeazv6pHPT29hKLxVi0aNFb6neqx1suYC1Y\nsICWlhaeeeYZjjzySECN+P/ud7/jtttUj4N3vOMduN1unnnmGdatUyM2AwMDvPHGG9x+++2Gx/V6\nvdNSDrOymmq/6b3nceGfvl+SpSXLMvF4nKe7njaMygl8ZOFHeDb2rCGRKMekHVSicvpWA4Iogzvr\n5p3170S52fjz7TyuJiYmGBsbIxAImCrP27dvxzU4adppcBh5QsY95Kavr8+wVFBRFP7yl7/gdDpZ\ns2aN4U09NDTEwMAA9fX1ptGsN998k2w2y5u5Nzn3l+eW+FD8+JM/5rVTX8Pj8Ziej/Hxcfr7+6mq\nqjJNER4ZGSEUClFXV2eaajsyMkIkEqG+vr6klFUgFAoRiUSoqamhtrbWkHBuOWUL6zavU1P/K8yF\nzr6+Pjwej6U/TSQSIZvNEgwGTR80iqIQCoXweDwEg0HTY2WzWdLpNF6v15JsizbZdoS83Gv9bx2B\nQIBZs2bZPkvK9bVyOBymXUiLMZ0FxKrcthg1NTWsWrXKtvRUwOv1UlNTU5Y4BmoJTjqd1nzu9DCL\nADU2Knz96+NccQWIuuCbboKVK+2NwwV5OxDz9lgsRjKZNPxdyvUMiEajxONxJEmy7RRoB683x3e+\ns59LLpmFqgYwbYJjhKnyPdUwdOPGuWzcGCgrqnmw/lHCCNrj8Uz6ztVM+s4J0l4eZtJI/p8B0+FA\nWTkNLtj08fO48DffJ51JEovF2LVrF3PmzKGxsZF0Oq1yoAELDuSCDyz/AL8f+T39vf0oRxZmyRut\nC83Nzdr6mkwmVe8VMw6UBZfTxfvnv5+b199cshaLDSugbWiKjZCHh4e1TJHi55PL5SIWi7F79258\nIZ+5wfiojHvYzfj4uCEZj0aj7N27l0AgwLJlywyP0d7eTiwWo7W11ZBT5HI5duzYgdPppMPVwbqH\n15VwoO+d8D1eP+11KioqSN9gnJXT19fHxMQEzc3NmgG1w+EoWKu6urpIp9PMnj1bEwAymYyWFVdb\nW0t3dze5XI5Zs2aVZHSn02nC4TBDQ0PU1NTQ2NhIRUWFIQf6/rHf57zN54EXaDSvJAiHwzidTi0L\n2qjMbGRkBIfDQXV1NYqi0N3dTT6fL9jACXN5r9eriTmyLNPe3o4sy1oZXyqV0qoXio3E9+zZQy6X\nY+XKlZoYYbZZ3rlzp1aObhds27lzJ9lslhUrVpQ1NpPJsHz5cltesGvXLq0U1k7A+sUvfkEkEuG4\n444zFGRFdnplZSW7du0ikUiwdOnSEj5QHOzr7OwkHA7T1NRUMNbj8ZTsPXbv3m24uS3uZAeqQB2N\nRlm4cGHBfdPa2loy93379jExMUFbW1vB+j5nzhxqa2sLfuf29nYikQjz588vCUIGg0EymYwWINu/\nfz/hcJjW1lZD3iUEr0AgQH9/P6FQSLOOMeNAv/99irVr96Omr87l8cfhuefM17w1a3oYHR1l1qxZ\nthnovb29jIyMGHotjY6OoigKgUCAiooKenp6GB4epqWlhQ0b5liu/Sec0M9rrw1pIqFVM5aBgQEG\nBwdpamqyzGCU5Qluv30n112nAHOAVksONDIyQl9fHw0NDZYl1yoH6gZeBqq55poPc801LlMONDo6\nSm9vL3V1dWzYsMDyPJx/vgu/39r32OVyTWYGNwAn8L//C5/+dHkcqBwj+fe9z/44/2yYEQErFoux\nb98+7f/379/P9u3bqauro7W1lSuvvJKvfe1rLFmyhCVLlvC1r32NiooKzj77bECN4l944YV8/vOf\np76+nrq6Oq655hrWrFmjdSU8WJz6gdtRPqCKYRd89Hsl7+fzeZR8np0Df8TR5CBPaVTOJbmo8dWQ\nubq0Nh7K39Sbpa6jQE7J8cS+Jzj+X443/FsrE1RQF+lQKISiKKY+XIqicNLSk/hO33eMOwmhGoxb\nGZ6Lf5ttKI1Si4uRyWQYjAyy/pn1ZJ3ZEh+KM7ecyePHP86sGvO0+EwmQzKZtFzwk0mVrFulKcfj\ncSKRiOWYWCxGKBTC6/Xyh+E/lESPb/rNTXxm+WcgAbceeysb/7LRUOjMZDIMDQ3ZprQL0t/a2mr6\n/bLZLJ2dnUiSxFFHHWV6rEgkQldXl616Pzo6Sl9fH/X19ZblRyIaUFtba+pvAOr19sYbb+D1elm0\naJFlBCMUCmkbBrssl1AoRDabpbq6uqzOhqI7RjEpNSq/1JcOChhlav0tYzqRFbPsNKtjr1q1Suuk\nUwyjCFAmk0VRXECe//3fSj79abjlFnWBtjIOl2VZy/Y6kPJBkTovSHrxPMvxDBgYGNA+/2AzLQYG\nBibLFXJs2rRi2iKPGVRdzQGsQI3yNulet//bA/VOyOfztLe3a5upU0/1FHR5LBczbSR/CIUo5j+Z\nTIb9+/cTjUb53e9+R1NTExUVFSj5PK/2/A6H34QDOVzMap7F9tO3k8vlGBoqzFIy4kBer5fa2loi\nkQiJRAK/32/OgQDZJfNy6mVOrz295D19CYcZ94hGo4yPj5uu5YJD/euKf+VbXd8yNhjHxUlLTiKb\nzRIOhwkGgwX3vizLJeUkwouroaGBqqoqcrmcaRBhcHCQ3t5exsbGcAadrPvNOkMvrvMePY8fvPcH\nVDgqcDqdhhu4VCpFIpFAlmXC4TDd3d3U1NQUZBHH43HNfkL/WmdnJ4FAQPt9MpmMoUifzWbp6+uj\nt7eXuXPnUltba5hBd9NvbuLcWefCGFzzsWu4c+BOQw6USCQYGBjQDM4lSTLkEX19fciyzKpVq3C5\nXNpaoC9TjUajdHZ2UllZydKlSwH12hAlZ8Lke3BwkLGxMebMmVNwzTocDs1MPJfL0d3dbSp0AFrW\nUWdnp1ZSZcbjcrkckUiE1157jcbGRssNuCzLDAwM4PF4mD9/vmWwTGTE1NTUMG/ePMvMFNFRcGJi\ngqamppL7RvgGgRoAD4fDNDc3F4hS6XSaXC6nZX2BGlTTm4xbwa4D4EyPlSSp5PxZHbc4QCiuLbM5\nNDQ04Pf7yefzhEKhkrFGHCiZzAAeLrsswLe+5WB4GC691HzN27ZNnW80GiWbzVpmoJt1IVQUReNA\n4vuJ86Aoiu3a39go0dcnEwqFaGxstCxRLrcToiq4R4Act902m+uvn57IYwb1sVUBtE3+O6B73Xq+\nB8OB0uk0e/bswefzsWjRIk47zaFxoAsvtP5O+nXS3lDf+ljThVgT/H7/QZWE/jUxIwLWSy+9xId1\nrb1EWvunPvUpvv/973PdddeRTCa59NJLCYfDvOc97+Hpp58ueEDec889uFwu1q1bRzKZ5LjjjuP7\n3//+W9q5qBh/fvNHvDyyD8kk0UH2ySyevdiUONkJWKFQiOHhYZ557RkcGBBEBZySk/5ov+kc7TKw\n7N4XxGv32G62rttaEvVzO9x868RvUec3z3bQf4adyGX1+8myzC/3/JIsJj4UuSxP7H2Ci997sekx\nzFo/G40pp+NfOWPC6bBh9DgjZ/jOn7/DU+ufYkXbCq7/xPWWx7GLyBW3hj7QMfpx5XYgtDteOp02\n7BRjNE4QPrt7WZTpiTkaRXebg+pqNDo6SjQaxel04vP5bMcODw/bRoZAvSZff/11XC4Xq1evxul0\nap2awF7AGhkZ0dL6rYTsWCxGOp2msrKyrN9ORHf+VjDd9GOPx8MVV6zksstyuFwSoRBs3GhvHJ7L\n5bQU/emKh/rUebMNhp1nQDQa46mnohx9tFR2OZEZ0uk0IyMjHHssRCJzqKqanshjBbV8D045xQWo\n1/10MrsOxD8qn8+zb98+LYvDSPgtFzNlJP/XhKLAU0/BRz+qRm7/luHxeFi6dCn79u0jnU7T3d3N\n4OAgO3p+yR9ju5BKkx1AAtkrs2TuEubOnUtnZycDAwPU1dVpv7vZJmPu3Lm0trYyMDDAT//yU3aH\ndxuX701yoN6JXsPj6DeJ+uerLMvEYjGqq6ttOVBXVxd79uxh1D/K1tO3logwboebu064izp/nSag\nFGd4CH6j/wyxfvl8Pqqqqmw5kMhk/e3Qb829uHJZftvzWz6x8BMFhv16iM/Re4sVjzXiSeI3E2ub\nFZcSvEh4JYbSIVMO9IPtP+C+E+9j5aKV3H7R7YbroOBAonujoiglHEEfBPV4PBrfFEKTmJMRB5Ik\nCafTiSzL5HI5rRtm8TgBp9NJLpdDlmXbLsziuIlEgnw+b8lrRJmd/jtbjY1Go4yMjGjVBGa8xuFw\nMDo6itfrZfbs2bhcLtOxbrebUCjEm2++SVNTk2V5TyQSUa1UJkVRIQjqOz6L37Ourg6n01lwvYis\nvuLu4kbiUSgU0nxe9edQjNUHwEX1QDHebmFMD7HpFxzD7rjr11fxoQ+tpL+/nxtuyPOjH1mveY8+\nKvFv/6YeV9hHmD3T9KKUHhMTExp/FNlsxUKT1do/MOAgFArz4osyp53mM61OMTquEUKhEIlEgg99\nyMnJJ9fQ3Kxw3XUWJ63M44LKdTZvltiwwYco0bTiQMXPJavzkEgkiEQieL3eguC6EK8ymYzmCTcd\nnl5RUcHs2bPx+Xz87/9aXw9bt0qcdFLZh7aFyKgVZbwHC0WBbdtmxsesXMyIgHXMMcdYXlySJPGl\nL32JL33pS6ZjfD4f3/zmN/nmN785E1OaFjp6f8uiez8MHYC3xKsdUKNynnoPV514lemCZSdgpdNp\nfvbaz3hmzzNINQZjFJAVmTnV5h3+9OnzRihH4Hq241lu+PUNbFmyha4ru3jgtQcKzDVjQzEtvdsI\n5YpTVmNEjXl/rF8lD5RGKp2Kk75oX1ni1NsxRhCQR3Y/Yk44s6rodtgS866H5Yhl+nFWm8LpCE7T\nGWcndAkiazdOGJpPp6ug3+83je4+vO5hTl56MolEgm0921i+fLnt2Hg8zraebZw1/yxgihS2j7Yz\n2zebC959AXNrVcIoiJq+jl+85vP5LK95sREURqxWAtbo6ChjY2Om7XX16OzsJBaLsXDhQtvacpHF\nVl9fX5ZfVCwWK2hh/VZD3F/lmoeKbqYH0qVFGOG6XC5L8cvKM+D//b8BrrwSvvWtet7xjgMTZwT6\n+/tRFGXaGW92SCaTk1mGKrE6kPI9mJ5/VLF4tWTJkrKMeM0wU0byf01s3QpnnAFbtqiR1L91SJLE\nkiVLqKqq4tGff5drn7hZ9chaYsKB3BKeBg+fO+Fz1AfqGR0dJR6PE4vFNFJvxoHEuvPo649y5c+v\n5PSjTzcu35vkQK01raTTaSKRSEGZvT4DXb+xef3117VyMTsOlMlk2Na1jfv23mfKgQb2DZDL5QgE\nAkxMTJBIJAwFLP16IDJ5xTpmxYF8Ph+yLJPJZBiID5h6cTkVJ0PJoQIBphiCuzidzhJRqniMkYCV\nzWaRZVk7b0a8xO12oyiKNvahnQ+ZcqBcPscfev7AYcsOI5vNGnIOMT+xpsqyrGWZFI9xOp3ab+ly\nuchms2Sz2RIBq3gNc7lcmoDl9Xotg3gul2qpIawWzMaJsZlMhkQigdfrteQ24ncrp3uwCJbl83l8\nPp8lr5mfm8/2we2a/YHVWK/Ly47BHSxevFhbBwUH2je0j7mVc7nw3Rcyu3q29t30AiEYWygYCU0i\nGy4YDBaU1hqN7evrI5PJaM+g4rHielQUhZ07d+JwOFi5cmXBvIyEpt7eXmRZpqmpqSC73kyUikQi\nJZ0wzQQhI0xnrCRJuFwu1YOv03rN6+lRn2/BYJB58+bZ7rGh9LuJe6q+vl6bp5EgZLb2K4rCr34V\n4hvfqKS1dRaTzU4t52A2T0VR6O9XkzOamprIZrNlnzM7iMB3LqeOvfVWhY0bp5/ZZXYeEokE/f39\nVFdXa2tdOp1m9+7dZLNZfD4fS5cuxe12E4vFGBkZwe/32wY99RUgdtdDV1fpfP+W8OSTcNVVUFkJ\n55339nzm29aF8G8ZzXUr1WzDFYWvS0i4HC4tKvfwuoctO6cEAgGtQ0BxNORD8z/Ee+55D8SACkw9\ntlySi39d/q+mn2FXQmhF3jrCHSy6axGMANJUh5j2z7WzsHZKNo3IEdNjgHH00WyM2YZfEKrZwdnk\nI3lDxiznZeZUznlbxSk7fyiA3livJeHsi/ZZHqecrCnhK2M3brqZVeWOsyNb5Y4TQpcYZ5UpJUSp\n2rm1ptHdtVvWsu+yfTy550lu+PUNeOZ4uOKpK0zHdl3Zxc/f+Dkbn9lI3bw6qsaqNKLnSDqQwzJf\neeIrPHrZo5owBoWZVuX6X4lxFRUVtlGYcrvv5PN54vG41lHHDuPj44TDYfx+f1kCVldXF6lUisWL\nF5c1PhQKEQ6HaWhoKNtQXXiK6M9JOcbhhRkt009pCQQCrF692jbybQTVTyGJ6lchcdllLVx2WXld\nE42QSCS0UgMjX8EDNS9XFIX9+/eTTCZ5//uzKIr6RzOV2WUEvXjldDpZvHjxQYlXMHNG8m8X8vk8\n6XSaVCrFnj1p3vveJoSh5KSN54x1VXqr0dzczJnrLuDS39+srsNeIAX4rDmQKIcSzyVFUTSvIofD\nUfCsr/RUctu222AUyMLWN7YKC7hCKCoH+rcV/8auXbuQZbnAXNoogCdJEsFgkEgkwsTEhC0Heuf/\neycMAJXmHKgv3weoBs1CwNJDfMaBClii/CidTjO3Zi7yiIkXlywzt3aulsmTz+dLvpee3wiuIIQb\nIeKI86bnQG63W8to0gdujM6b/rVcLkdPrMeSA42lxjSxyYi/6PmWXmgyGqP/e3FM/VgzPuVyubTS\nN0VRLHmXOC/Cj8uq27TT6SSfz5NKpWx96IQgpM9YNuNA2WxWE6XCGfMs/7Vb1nLNgmu484U7qW6u\nZlVsleXYz9R+hge2P0BjayOneU4rELscEQdyXOYrz3yFRz79CDWeGtLpNLIsF/AdI16UzWaJxWIF\n94YZrykWjzKZDJlMRrt3rcbG43Htui/+TYxEqbGxMXK5XInnpZHQlMvl2LdvH5Ikcdhhh2nXgVUJ\nYbFAZlduKL6v2+0umK/dmjd/vlqK9pvfKJx7rjUHMhOPRHdU/evlzBcEBwqhmq27ueCCWi64wHxd\nsxOwRkZGSKfTuN1umpqa6OvrKxhrxoHsjpvJZOjs7ERRFD784TpeeglqahSuNy6AKZnvgcBMvBLv\nhUIhqqqqppW1b389HPB03xLIskwqleLNN1O8851T19L551N2h/GDxSEBi8kuhR8t7FK4+UNXMeSZ\nXRCVa6ywNlG26q7jknSnWtL/5xRBdDlc3Hb8bTQEzL1eplNCWLxQnrbytCmhSPfnxZ1jjMiZHjOR\ngSVIVUtVC65+l6UXl9XnvJ0ZWJlMhm0922irazM3f82potvBlivqo49W338mSwhFe3G7cVB+BpZe\nwLKKEn50wUd5au9T3PDrG3gx96JpdDcjZ5h3+zwIAS645IlLDD9XjG25rQXUJnSsf3x9wb2XT6vC\nadaZ1cSuWCzGtp5trJ29Vhsny7Khp0IxyhW69FFeu7GiTMHtdtsKWIqiaO2gy8nwyWQymv9IuQKE\naGRQUVFRtoA1MDDA6OhogVdIOcbhP/jBBOef72PLFs9BZbQciG+VKh75gaVAAnVXf+A+TH196oa4\nrq6uJBvsYMzLBwYGSCaTuFyug+qOWC7y+Tx79+4lFovhdDpZsmTJjKSfH6yR/IFgOiV/wlcplUqR\nSqUKMlzUx3kVqu/GFP6ePLtqa+by2Gdv5JQtX4EkEIb/PfXfCQdbCziQPoBX/DySJInFixcDpRwo\nl58UHCZQs7wCQBA8Dg85JTfFk5wqB2oKNqEoCmNjYwV+VmYBvKqqKkMBy5ADiSVep5foOZDIDoep\njbjwSBKfa5WBlU6ntY5cxWMEPB4PsiyzfXA7p3zoFL7Z/k1jLy7JxSkrTsGdm8o2Kj7veu4iNvmi\n7F0IPuJ8FPNGj8ejmfeD9bNSiF1/7Pkj8+fMN+ZAeTXw2FLVUvDZxdDzFrfbTTqdLhlrxG0EP9ML\nWGbZ5WKs3gLArAW9GCvEGK/Xa7rBFaKULMu4XC5Lzqjv1GfHgXbu3smdL9xJZWMlL3tfNuVAaTnN\nV3/7VQBuev4mbmq/CQnJdOw3tn0DgLtfvJu7//NuPA6PdmzBgTIOVex65H2PkEqleLn3ZU70n6gd\nS9wP+mf9+Pg4PT09BINBzS/VjAOJe0AcR98pufiaLBalhJeZUZOZ4rHJZFILmJXjgSXM2P1+v2EG\nVrHIoygKo6OjyLKsCWTlZGDt27ePbDarzUlRFNs1b906ia1bo/zXf9Xg91tn9VrNofj8lluSp65f\njcAsoAFBnsvxlCqG8HcDDH20rDjQe99rPd/Ozk7y+bzWEGBsbGxamV3THasGrfYYilf6seVAZOE6\nHA42bPBaXg9nnikxeSvMGBQFnntOYf16ew40NjZGLBbTSt/F81qN1zj4a/CffzgBKx6PH5BvVjQW\nhQx8+93ncOmff4gzk+ez7/qs9r4sy7zwwgsAHH744aYC0lBsiLU/XKsZVwqfqwwZHDkH+WxeJU4Z\nuP8T9zMcH9bI1SfaPkFkQO3UIxaCYlRVVWmROyOTdNF165c7fsnlv7+8YKG88akbufKoK7n3l/eq\nQlYGtp6+FbIQz059XjQaJZVKkUwmDRf7aDSqGTibzXNiYkIj+0Zj4vE4T+x8gpt/fzNXnXIV3/q/\nb5X4UHzj2G/gx691mTGCmIuehBmNURRF820qRi6X04hLJpMxJF2yLPOLN37Bzb+9ma+f93VcOZeh\nOakz4+TYecdaznl8fFwzFrc6f8lkEq/Xazqm3GPl83lN2LAaJ35zkfZuVrIgIm+gnjur+Y2NjfHb\nfb/luOBxhvdFmjSnPnAq2UQWwoATfvjKD02P55AcyAkZsupYLFKEC8a6Uf+tR2LyNQUyqQybXtwE\nI/DFp7+It8bLOVXnMBQb4se7f0zneCeto62cc/g5WsZYMYaHh0kmk0iSZHlOwuGwZp4oBD4zDA0N\nlXUdAFo5j2gTbzd+ZGSEZDJJIBCwnQeo19HQ0JDWpdLu+AJ9fX2aobH4m2AQHnwQzjmn1DTzrrug\nuVkBdgI51q1bCgR4/fXyM3Gi0ahlV85ysGULrFvnQE0VibN1q/p6mV9bQywW05o2VFdXF5y3oSHV\noFPoIYIvp9Nw2mmwa5c5EUgkEuzfvx9FUZg/f74W1X4roCjw7LNwzDE5zVRWmESXex1Ywep6ePBB\n1cdiBj6mAI88Ap/6FHz/+zInnpjWiJn4d1tbmyYWjIyMaOUPAk6nc9Kg3MuDDyZZv15BGMfORGfJ\nA8VBcSA/3LzkE/zn9sdxpPN89v1THCgSifCHV/5ARUVFSee9RCJBKpWirq7OlAMBKu/IAROw9cKt\nHDXrKB564yGNAx3beCy5iRypVIpgMEgymaSvr0/zXslms9p/66870c0ulUppfkY/e/1nfPrJT5dw\noEvfeSnf3v9t9dmfgK1nFXIgWZa1LCrBG4RZs7geBO8o5jciO0g8t4ESwVPguY7nuPN3d1I7u5YH\nT36Qcx49p4QD3fbB2/DjR5bUOYVCoYLAgfBiArRNRS6X08YqiqJxQo/HU3KvirFjY2O2a1c6neaF\n/S/w3X3f5ZsXf9OYA2XBmXPywdYPkk6nGR8fN+SQkUiEXC6n8a1kMsn4+HiBCCW4TSAQ0OakHyuC\nZ5FIRCtv1M9dNPiZmJjQ/s7n8xl+v3Q6TTKZZGRkRMuWsToPokO06F5uhmQySTQaZcfYDtpWtply\noE/84BPQD+Tg5uduht2qF5xJoYaaIZkD8kBmkuuYBFXJoibRTI7NCNKkoHKgyf/OpDI8vvNxRnpG\neLTvUVb9yyrWrl7LUGyIR3ofYX9oP22hNs4+7Gyag81aAEx06ZVlWcsyhsL7U3DLWCxGPB7XuFIw\nGCw5f8Vjxb1UX19vOjYejxeMraqqKsmaFKX24rigNlMQ463mKxCNRonFYprYJYywjcYKiIwcYYAv\nxjY0xC050Jo1IWAfMMa6dWqGrxkHSiQSBXMQmftGop+YrzhnVrj//iQXXeRFJdvWHEjMweg5Mzg4\nSDQaxev14vf7teeNx+OhoyNuyYFeeCFOKpU0vCdHRkYYHh7G4XCwYMEC7VlXDj8Vv53L5bIdm0gk\nSCSSvPiii9NOmyAej+N2u5k7d24J7xLHtdofC4hmWMFgkCVLllhyoNraHPl8YFrc2+47PfFEkptv\nTiDLET7+8VQBB8rlcgXrvOh2q4fL5SIY9PGtb3m57DI/4AH8bxv/kZS/1YLKaWJiYqLsjIBDOIRD\nOIRDOIRDOISZxKZNChdeqIpjp55qPVZwlkgkMiOeaIc40CEcwiEcwiEcwiH89aCUxX/g4DnQP1wG\n1iEcwiEcwiEcwiEcwtuNCy54a/3HDuEQDuEQDuEQDuEQ/hbxdqZE/cMJWP39/TPa4Ukgm83yxhtv\nAHDkkUeqpUWv/5iuSBfzq+dz1pqzeOi5h/jiM1+EetRMumJMwGWrL+Pq46+m+QAKRBVFIZlM4nA4\nLP1wrnryKr63/XtTvhM6uBwuzj/ifO752D3T/vwDhdG5ern/Zc1AFdRSxo8v+fjbNicr/L3N9+2G\noig82/EshzUdxopvrzAspXQ73GTz5ZlnS5LEmavO5Mev/5j7TryPy391OZ9/3+f55p+/WVJS8cNT\nf8hHF31UM1B1Op38au+vDMsvfnjqDzlm7jFaKvV4dpwV35qcbxIYRy0tbACP08M9H7mHy7ZeppZz\nC0uoKJBGLe+umJrvVz78FeZWzeVTP/sUmz+5mffXv1/rUrLQwrkwn8/z6quvArB69WpLz5FYLMbe\nvXtxuVysWbPG8hzm83lee+01FEVh5cqVtt5kExMTtLe3l3VsUH/zN954g1wux+LFiw1T043w+uuv\nk8vlWLBggW0HRQFZlrnvvjf44hfz3HPPMq66qoKrr4b77itNrf7hD+Hjutswn8+zY8eOaX+mHkND\nQ/T39+Pz+VixYqqzxxNPwPr19nMQUBRFM7Q2w733ql4PRou+JMFXvgJXXFH4ejabZceOHSiKQltb\nm2Fr66EhWLHCuAuPx2NdmqhHLKbQ0rIfiACtqIubeny7FPGZmsPBIp1OMzIywtjYGA0NDcyZM4cn\nnhBm63HAx9atTsPf8O8RM8mBQqEQ3d3dzJo1C4/HU1DuoF8nZ3lncZh0GM+8/Azf3ftd9VIxwih8\nesWnWbdqHcFgkNWrV1veH6FQiK6uLrxeLytXrtTKzvSG5QKiHMPr9XJ/9/2WHOjsxWdzxZorqK2t\n1Xz5zJBIJEin0wQCAVuvSdFGvbh0uZhTNAebuejxi7T3/9b4xCEOZI2Z5kAC9338Pi5/4nJ+eNoP\n8Tg9przmhIUnaCV4gCUH+peWfyGZTNLY2MhwfHiKA0VQSwgDQJXKgW49+laufvRq1f5RcKCxyX9X\nou1rJEnihqNuwBvzcvMfb2bzBZs53Hs4ExMTBV6XRhDcw+PxsGrVKsvzMTg4yMDAADU1NSyw8Q+I\nRqPs27evbE4zMDDA4OBgWccGtfTuzTffxOFwsGbNGttGPaCuPTt37kSSJFavXm3pl1b8XTZt2seN\nNzr51rfWcNllUtkcKJFIsHv3biRJYtWqVQfkAdre3s7ExAQNDQ3MmzdPe326HEiWZdty9gPhQOJZ\n73A4WL58uSHfnSn+EYnkmDPn/4BuYCWwRju+EQcaHx9n//79BAIBqquXWs7hxRfHicXUsUuXLrWc\nh6Iomm1QudeRQCwWY3h4mEgkwsKFC6muruanP01y7rkJVG+zGrZudRwUB0omkwXNQ94O/MMJWIFA\nYEZMZYuRzWa1jhPP9T5XYlB64ws3wiDqhthDiYAlIeFp9vAf6//DtJPhxMQEIyMjBINBQ4Erm81q\nD6ajjjrKdK5LWpaQd5t09kNm6aylb8k5MoKRaeWX//Rlrn7f1eCBTads4sLHLsThLTVd/Gvg722+\nfw1s2bGFM356BuvXrCfnyqnl8UWQJZnzDz+f723/nvaay+FCzsslRrUep4d7T7mXH531IwAu+5fL\nALj2mGtL2puLe0cvoKw9Yi0fWPwB07ENDWpThDrqeGT9I6zdspZMOoPT60T2y3j8Hh5e97BK6hqm\nfmOn5ESemPSVEPc1qjfFF//wRfV/PLDhiQ2Qh9c+/RoL6xbaXhfvfe97icfjtuU+brebJUuWAPZm\n7+l0mvr6enK5XFlm3qJTYUNDQ1nXcSwW04zkm5uby/KW0v/NrFmzyiJ8oPqmffSjXv71X32sWtXI\nWWepHViyWZXkCP/ebFYlU11dU0QkFArhdrsJBALMnj172h5YwjPG7/fT1tamnZuhITj33PLmIDA8\nPMzg4CBz5841/U0+/Wn48peNjTs9HvV9o5/nsMMOY3x8XGscUoxHHpmaYzFyOXj0UeN20cUIh3u5\n++4MV19dAdQBAR5/HGz2/DM6hwNFNBplaGhIM+r1er0oikIgEEBw6k2bAlx4ITgcfz3PqpnGTHKg\neDyOz+cjHA5TV1eH3++noqLCkAMpYwr0orJKA51HQsIzz8NXL/oqw/uHNc8m/b0xNDQ06Q+jdjn1\n+XyMjIwgSZJmON7d3U1lZWUJ4ff5fHi9XqqqqliSseZAa5asseRQehzsuTTiFA7J8TfLJw5xIHu8\nFRyo+6pumgJNGv8BLHmNXqS240ACwWBwigNNTHKgQBEHmqP7jXFMdZr0oe0YnZKTr/35a6pvqeBA\nMmy/cDtzZs0xFXrF87eurq7A1Nxs7KxZs7Rnjt3YXC5HbW2t5Vh9UEns6WbNmmU4vjgANTExoXV3\nLg7gmQWrotEofr+fqqoqje/ZNeIC1dvpYx/zc/bZdcyfX8FppzksOVBnp0JTk6KZzPv9furq6gwD\nePomFUbikvDUFRxICGCjo05LDtTZqdDYWHjcjo4O0uk08+fPL2heo5/Dpz/ttORAF16o4PWqjZTE\ncQOBgNZoQb9+CJFHkiQeecRpyT8eeUThqqusBaF8Pk9v717uvLOSa65ZhSpgWXMgce4qKip45JGA\n5RwefzzLiSf68fv9ts/S8fFx2tvbCQQCLF++3HIsqOciFAoxPDys+cH5/X7tHqyoUD9v0yZmhAP1\n9vYSi8XweDyGQdW3Av9wAtZbBfGAerr9ab60+0tTXTwM2giLTmdWLaiNkEqlGB8fN910mXXg0aO3\ntxdf3Icz50RxKqWd/RJuDs8fTldXl9YGWw9Zltm+fTtOp5PDDz/c8LO6uroIh8PMnj3bMNIiMgTG\nM+Oc/rRxe9+7nrqL35z2G1bMXoFys3HO4a5duwBYuHChocKeSqXo6OjA5/OZZr2Ew2GGh4eprq42\nbWk6OjrK+Pg4OU/OtB3xXc/exYvnvMjSuUu54GbjGpFIJEIkEqGystL0BhbZAD6fTxNWjCCMCWtq\nakwfrslkkkQioT0szRCNRpEkCb/fbxkNicfjWlcgKG33/KH5H+I9m96jGoI64MHXHzQ9llNy0jeh\ndl4TZOjao6/l7j/eXRIlNLsvmoPNXHN0eTvccseevPRkuq7s4nsvfY+9A3tZ2LiQi953kfb54loc\nS4xx/bPXTxm/6/iYoVmqAxY2LyTgsV8BXC5XWV41Ho+n7ExNr9fL8uXLy+qoAmonmGAwaJtRICAW\nPZ/PV7YoND4+DkB1dXXZ4hWgGcEKYrJ58xRpKpyT+voDD0wJIcPDasvJhoaGAzJwD4VCZLNZ3G53\nATGazhxgqutOLpczbLYh0NysdtpZu7Y0qvnww+YkqbKy0jILrrNTPZZRp2ynE/bvN/1TDcPDwwwN\nDU0SsDY2bQpy4YXG0cS3ag4HglAopJnzClRXV9PU1KRt+k49deq3PFTyZ46mpiZSqRQjIyN0dnbi\ndrv53cDvuOGVG0o5UCUq95FRM1z95hxIrpfp7+/XhDGBeDzO+Pi4dm07nU6WLl1KRUUFDodDe74Z\nPU+cTif19fV0dXWpHAgnCgYcaNzNEcoRjI2NaZ3E9BgfH6ejo4PKykotgFCMnTt3ks1mWbJkieHa\nGw6H6e7uJu1Mc/rPSjmFoii4x90szC5k/Mpxw4y5dDrN3r178Xg8LF26VOteGwgEtGfb2NgYw8PD\n1NTUaN29hAifyWSor6+nr6+PeDxOc3NzybqTz+e1bIaamhoclQ5TDnTnE3fyxCeeYHFwsSlnGx5W\nhcmqqiqtI2Qxz5yYmCAWixEMBqmqqiKTyWhdWkUmjCzLDA8P4/V6C66PdDpNT08PDoeDhQsXMjEx\ngSzLhtlxqVSK7u5uHA4HDQ0NeDweLQhdjGQyyf79+8nlcqxatUrjSYYc6NvvUbsgu+05UNdwFwzB\nV4//Kl987YuWHKhCqeD111/H4/Fo5slmvCYWi9HR0YHH49E2s2Zj+/v7+cMf/kAwGOTEE0/UONCm\nP29i3+A+ls1dxvnvOJ+mQBPRaJRX/vUVfD4ft3/kdq7/1fXqQSQKdouyIqsNdEZRG/Q2AE5YPGux\nIaeYmJhg3759+P1+VqxYgdfrNc0Sj0QitLe3U1FRwfLlyy25bSQSYd++fdqmvrq62pQDFY9dvHix\nqW9g8VhQeZvX6y0RhYzGCoTDYQBtP1COCJHP5xkfHycajWrZn48/vtySf9x/f4QTT2zH5/NphuKN\njY2m58FqDoJD1dTUkEql2LFjB4FAoOw5iOPG43Ht+xffc8VzsOJAXm+EV18tna/R99Mft7NzuSX/\n2L3b+Lh6dHZ2Tjas8gLL2bTJZ8uBqqurOeKII5AkyZYDdXUdeJMhMyiKwuDgICMjIwUdaOvq6mhq\natK65f4jcKBDAlaZECnDX3juC0izSlvWFmPzJzczFB+yjIYYfQaYC1TlKPcPvfQQ1/7qWq46+Sq+\n/eq3SxbK7578Xer8daafITZb+XzecozVpkxs2n6+8+emrYCzuSw/2/EzlrUuMzmKSir0rauLITrL\nWCGdThOLxSxLquLxOJFIhJ/1/sx8vqksP/rzj9jYsNH0OLFYjJGREQBTASuVSjE0NERFRYWlgNXX\n10c+n6eystJUwIpEIvT19VFXV2eZAt3T00MymWTx4sWmwomiKOzevRtFUVizZg1P7X+qJArrkibn\nMTT5R02YPkFkReYIzxHceeqdzJ8/XxP9rnjPFSVRwgqlgl27dlFZWWmaUSIgWrnW1tbalsmIbpqV\nlZUFJbfNwWY2HlP4O4quLRUVFTidTjYcvoEbn76RjJIpIG8iWvrdk7/LeT87TxOrHz/r8bLEq7ca\n5Yo2TqdzWuV1lZWV0xLIQN38ut1uSwJaDFmWtQ6XYuNSrhCSSCSIx+NIkmR5b1lhaGhIm7v+XE5X\njFGFn5ytUA1w8slqBtcDD6jHWbAANmwoFa9EZ5tyRMe2NpUEGkGW7bs5ihbpAOeeO4drr1V/i+kQ\nnYOdw4FCdAFyOBzU19fT1NRkWXJ/CNaYN2+e1lFu6x+38o2ubyA1GnAgCbX0KAXEYPNZ5hyooaGB\nioqKkme4kUAlyqTAPognyzI/+b+fcMOvb+D606/n3j/dW8KBvv6xr1Prq8XhcJDP55FluaDMJp/P\nawFLgXg8TjQapbKykkAgoHVVNZpHX18fnZ2dyLLME4NPmHKKXDbHpmc2cXH6Yt7xjneUPCdzuVxB\n5+TXX38dRVFYvXq1xmfS6TSJRKIker9nzx5A3UwlEgmi0aihWAfqJq29vZ22tjae6njKnAMlsmx+\nbjOXHn8pixYtMuSh4XBY41vDw8NIkkRra2vBeZqYmGBoaIjm5mbt9xdd2gSPSafT9Pf3lwQSQOU9\n4rNFWcz8+fMNn7PRyb7zIgvzyCOPNL12xsbG6O3tRZIk1qxZY5iJ5pJcalBrGM1+wAy5fI4V+RV8\n5pjPsHj+Yr7wb18AjDmQElPYu3cvoVDItqRVCJLxeNw2MDQ2NqZ1c9Rf483BZr5w7BcKxmYyGSYm\nJhgeHsbv97PhyA3c+Isb1a6FuiVHcKC7PnEXl/+/y7X3rDiQJEkFWTdWEEK11R5DPxYoOK7Z7yte\nF2PdbrfpPVE8FlTBpLGxsYQDGY0VaG1tJRwOa1zLaL7FEB0dvV4vHo8HRVFs+Ud3t3pckVnv9/sL\nnpt6iDkYcblsNsvYmFoz2tLSQm4yfaicOQgxRhy3t7cXgPr6ek00ERDnTIy14kCRyNTYaDRKIBAw\nve71x7XjH/PnF86hGH19fYTDYSRJ4uKLF/H5z6scwo4D6TPF7Odgfazi45Y7bnx8XAvENjU10dDQ\nULKPFN0ofT7ftDj63xIOCVhloCPcwaJ7FqmeOV7MxSsF3jn7nbyUe4mAJ8A1hxdGQ/r6+kgmkzQ3\nNxtGz62ii0bv66NDlZ5Kbtt2m1rGCNzzp3vAAxvfv5GJ9IS2UGbCGYaGhkw/oxyRTCwsZtk84v2B\n+ABOyWmYpeZUnPRF+yxTN8X3NRsjHq5W9bZCgbaqAxfH6Yv3HfB89Z9ltcEU0RGrMbIsFyyydsey\n8zsqZ1wmk9HEwnAmbBiFzSpZnHknMpNPZKd5Srzb4ebjbR8nk8kUXEtGUcKRkRESiURZtfqRSIRw\nOIzP57MVsMbGxhgbG2P27NladNoM8XicPXv24PF4WLNmDc3BZr534vc47/vnkXPncDkLswgGBwZh\nEO4+7W6ufu5q+nv7mWiZsJyToihadKi5udnyHhPt2SsrK20FC3Gv2HkNzASmk9U0nQwyAafTyWGH\nHaa1XIbyhRDR4re2tvaAfB8mJiY04aM4ujcdMSabzWpC2Jw5c8o6Z83N1uV0iqKwf/9+0uk0ixYt\nsr32N2xQfSWM0vLdbvV9M8TjcfZPKnINDQ2mmat2OJg5lItEIsHw8DANDQ0aYW9qasLj8dDQ0PC2\n3BP/6JAkCWrhI7d/RPUErDDhQArgh9V1q3nD+wYV7oqSZ/2+ffsAaGtrMwym2AlUxfykhAP99jbo\nAXJw2x9uA6mUA411j5FIJAiHw+zfv5+ampqCDG4jfjM6Osro6CgtLS0EAgFLDpRKpYjFYkiSRF/M\nmlOMxEdwOBykUilDAQum+I3X69XanItnoxEHkiQJt9utZWxZ8SSHw4HL5SKXy5HNZumN9ZrO1yW5\nGE2M4nK5yGazhnxCcCCxYRWlXfrncTEnE/MSooXT6TTlSWJsPp8nn89b8ikxVvhlut1u0zXX5XKR\nyWSQZRmfz8dQbMiUAzlwkGfq/JhyIMXNx5d8nPGB8YLPMuJA+4f3k0wmbTN2Qb0W4/G4Jr5aob+/\nn5GRETKZjHbezTA2Nsabb77Jjh07mDdvHh8IfoBvH/9tPrv1s+Q8uZJMyj079kASbjjuBm7ZcQuD\nA4Mk5yVLxAqYul/j8Tjt7e1UV1ebBnb0JXuRSIRgMGj6HBdj9fzVDOWIR+WMLf4MK0GouJy7WLgx\nQmVlJWvWrGF0dJSBgQHy+XzZYowoWTTLvrKbw/DwMIqiEAwGCQQCGqcqRxBqa5s6biQSIRaL4XA4\nmD17dllzMONAYmwmk2Hfvn243W6WLl1qeM/rfxs7/nH22RKTRQIlGBkZYXBQ3UzPnz8fr9fL0NAQ\nLpfLVPQ0gt0cPvUpL05nS9mVEFD6u0UiEUZGRliwYIF2n8yePRtZlqmtrTW9J0SmelNT0yEB6x8Z\nzYFmtcbd5rqVJIn3z3s/L6x/wfCCFFE8sxugXPImSVJJdEgzKxXX9uQhbvzgjQVRke6xbsBefLIi\n/uUKWHNr5iKPGT/xZFlmTuUc02MI0iVJkinpEGOs5jodkautrg15n/V8rTbG5Yhl5YwRpMzlclmK\nHOWIYXpCZDUulUqxrWcbH178YTa/utk0Citn1GP91/H/xX/s+A/TlPgta7dQGVdFWrvsh1QqVdY4\nQMu4MyJJxdDXfRe/LkoYxINbjNU/yP9l1r/wi7N/wfNjzxN2hwuyCPY59vHSRS8xb9481i5fq0WC\nrYSFRCJBJBIhHo/bCmrhcJj+/n7b7DpQs2U6Ozupq6ujra3NciyokTF9WYUdUqkUbrf7bRMDirPD\nyhFCFAW2b2/hgx+sxuE4sLRsIToZCR/TEWME6QwEAgdkIl8MRYEf/WiAZcuSuN2ussjGgZYmAlrp\nT1VVFa2tZm7c9jiYORhBUeCpp+CEExQmJiKaXxKoa6MQsLxe7wE1STkEc8yumg0tqDxIQSsRLIbk\nlDjhXSfw+rmvGx5HZMLoSbh+42kWxBsbG9OyGsX7phwojsp/soCnlAON5NUsab/fr2UM6WEUxNOv\nE/pMEqP12e/3k8/nyeVytNa0Iveac4rZtbNxOp3aGlj8PkzxG72AJWDGb7xeL9lslkwmY8uB3G63\nNt8FdQuMS+TzIOdlWqpbyhKwPB5PgTCm5zvFvMXhcOB0OpFlmWw2WyBgFfMkp9OpZfOIDDX9sYrH\nijm5XC7TjBQxNpPJ8OrQqxztPNqSAymy+tq/H/3vfDP0Ta79F2MOtPnEzTRkG4i741pGnxm3F9m1\nPp/Ptrohl8vhdDpxu92WY2VZJpPJ4Ha78Xg8BWPD4TCyLFNVVaWdu0QioTVHEL/j+1rexy/O/gUv\nJl5kMDdYwIFeDL/IA598gMWLF3Pue84lkUiQSqUMuZn4LWKxmPbfdgLWyMgI+/bto7W11VSQ0YTs\noSG2b9/O3LlzbcfKskx7ezvBYJDGxkbDe7hYlBJWHUa/n1UGltlx7cZ6PB6qqqoYGBhAURRb/nHO\nORJjY9DVNY+jj24lGDSvBjATsPL5vFZBItZP/XkoRxAKh9XjiJJgEUwqdw5m81UU+NnPejnppBYC\nAY8tb1UUxZZ/NDbC+LjxHIT9xezZs6mvr2diYoLe3l78fr+tgCWqbDweD7NmzbKcQ2urD5hjew6m\nvhf8/vewdGmesbFRrWQb1DVSZG+WY1Hyj4BDAlYZCHgCPHbmY5zy0CmmYyQkXD4X645cZ5tBZbaI\n2WVgiYdeKBUy9FVQD6JNyDClVyxiB5OBZWUAqP+MU1eeyn3779PmKSAh4cLFSUtPOighrRxxqlwB\na1vPNs55zzl87aWvGc9XUec7U+LUwWZpTfdYVtFHgK2vbeVzv/oc3/jkN+jMd5pHYRUXn1z5Sc46\n4ixuWnsTYJwSH5SC7Nq1C5fLZduRQpB3O1Eqn89rD2u7sYqiaMct3vCPj48zMDBAfX29JvgIAUsf\nLUskEtRX1HP1qqtLyhji8bg2Xng22Zkwio2SFZEWEKUPYuzQkOrD1NmpZgNt2DBlGj4xMYGiKGV1\n/hAkJZ/Pl2202NnZSSKRYOHChWULMl1dXQSDQWpra8v2vzIj+OUIIVu2wBlnwJYtfk4/vayPK0Fr\nayvDw8OG4ke5YkwqlWJ0dBTAthy2XDz4YIINGwa59Va4+OLWsju8lFuaWAzRca6mpuaAfMRmYg5G\n+MlPZM46a5Q77xzmmGPU55okSWV1kjuEg0PAE+Cx9Y9xyv9OciCjWIMELp+LM448w/AY+o2CuK6G\nhoYYGhqira2Nqqoq0yCeLMskk0ni8TiBQICx5BinP2zCgdyoPlwZePxTpRxIfEYwGESSJE3oEeuo\nEb/RC1j6zacRPxECVjqdZt3h67hr510lnIK8mtH00aUfxel0FohSAkYZWEBZApb4LnYZWDBVsrWt\ncxvnn3o+X/r9l0zne9zi43A4HBqv0KM4c9ztdmsClh5GPMnlcmkdrMCa2wgBLZVKaZ9ntkkWmVUe\nj8cyA93hcPDbjt9y5wt3svDwhXSmLTgQLo5ZeAwnrziZuz58F26325ADEYfu7m7tc2VZNvwNBFdx\nOBza9zDr3iYCeD6fj1wup4mpRmusfqwQCMXYkZERotEobW1t2oY8Ho9rPk9ibDKZpL6ino3v3Vjw\ne6VSKRRFIZ1Ok0wmtXmbcSAxv2g0SnV1tSUHEtej4FiiWsWIA9XUTB3XLiCnzwLL5XJEo1HTdUMv\nSsmyrHUfNOrqZ5SBlclktACkPqhpJ9zoOZBe7LLnHxI/+QnccINCfX2VJQcyE9wcDgeLFy8mFApp\nAoh+vuXMIRxW+bXw2zXL3p6ugPWzn4X46lfj+P0OrrjCvO5uOqWJ0ag5t1m8eLHWtXi6yGQympH+\nrFmzZowDSZLEk09muemmQSKRNMceO7VHbmhomJFg6d8bDglYZUK0wxWG1C6Hi7ySLzRi/OzDHL30\naNNjTCfDygjipnx8z+OG0SHxvx9Z+BGejT9r2NrXTqCaTgaW1TEUReHN0JtsPX1riY+A2+HmrhPu\nos5fZ5uBdbDiVDljntj9BBuf2Uj9/HoeXvcwa7esLZnvrcfdSp2/7m3NwLITsKyij8VjBIkyNWZX\nA+N87pnPQbUq2hlBzqqZaMWeUsUp8ULUmU5WVTmZWqJUzq5ETD+2+PwYZVsZvSZIYjEpE5sCYYxv\nJH4ZoVjAMhOl8vm8NrayspLHH4fTTy8kDTfdpJKGk0+eErvsysrEHPL5PG63u6wstmw2WyDWlYN4\nPM7o6CihUGha3UgGBgaIRCLMmjWrZDE2IwGxmBoBhBzgYt1kl/f2djDp62AKr9db0DK6GHZERFHg\npz+NsGSJQk2NNUkvBx0dsGiRAnQCChs31rJxY+20vptdaaJAcXBlOmnyMzUHM0ydhzeB1OSxXLzw\nQgPvelfTAZWLHsL0kc1nIWjBgXxuHt7wMO9e+m4URSEcDpNMJpkzR40w6zcr+tKfbDbL6OgoVVVV\npkG8mpoaenp6NH+lx3Y9Zs6B3PDe2e/lT5k/WXIg8QwU3nl68aB4DiL7IpfLaeuVw+Ew5Gp+v59c\nLsefu/7MBz7wAUNO4XK4uO3422ipadFKCItR3DLdSMAya6suxupFHrP7xOFw8Oe+P3PfzvtY/M7F\nxvNlcr4V6obUqBxNvOZ0OnE4HLjdbq2jmR5G/MbtdmtdKfXHMuI2ojxSrEtWmepC7JJlWTuWKQd6\nU/2bS391KVRZc6CmBjWzRJRHGnGg7rFujSOAuYAlyt/038NOwAoGg1qWiCzLht9f8BJ9NkYmk8Hn\n85XwnWw2q2Wq6TOyqqurSzLoQF3jFUVhbGyMfD7PEUccoXk2GUHMT2QwWnGgujoHyWRS86bz+Xym\nHOihhxw0N2dIp9NIkmQrjAHaM6S6utp0r6UXj/QBQqN7yCirKhwOMzY2RiaTKeBldhlYe/fuxeFw\nMHfu3LLFmFgM1K+tAIotB7ISj4LBYME5nI4gFI+rmVLPPTfBiSeq4o3Z/q7cwJi69qdRTedcXH/9\nXK6/3mv73fQox54BKBCCi31Upyu4lTuHfD5PNpvVuuyaQT0PGaAd8HDddVWAjz//uYkjj6yfVpMk\nq3n+veGQgFUmTl1xqtZ55YIjL2AoNmTbsrYYB5uB5fV6mTVrFmO7x4yjQ4paj1/jq0G+1nhRsxOw\nZsoD69mOZ7lh2w1suWgLXVd2lZyrgX1qhy4zYcmMmOlxsAJWR7iDRfcu0nzDzvnZOeCAFy98kee7\nn9fme/aqsxnYN2D5WSKlHd6eDKxcLmcZfRTQi1ympqSAsLUS7aDdDnfJBkFCwp13c9LSk2x9t8Tn\n2olSomygnLHlZmqBefmg/j1B3mRZNszWMivH0/+9aDQgIpdW0AtYVqLUBz8Y54UXFD74QTeRiI/T\nT59K2xa8J5NRI2Fvvpnit7/NcPTRUlnZWqKEp9wUYzE+EAiULRSIzjPT7T4oCJ8ZSTAiAaqmFgP2\noNZ4z9fGvhWwIkNbt8LZZzezeXOQ008/+HJL9TsMo9ZruYBW3eszi66uLjKZDIsWLSq7VNTqOptJ\nqMeUUGvYBoFmoI7DD3dwSLt6+1DMgQajg/zglR/QHe0u4UDpdFrzUauvr8fn8xlmYNXX1zM8PKx2\nAdatacUcyePxaH4yfr+foeyQKQdyep1UeavYfuZ2Dl9xeMn30HOcQCCgCVhCbDfKwHI4HPh8PpLJ\npBYwsOJpf+j+A3e+cCeLjlrEZz78mRIOdPrS0xntGtV4jpGAVWyRcCAZWELkcTgchvPtCHew7L5l\n0AsEYd3D6u63mAOd0noK0aGotoZZCVhinRD/1o8VWUP6943GluNtJcQcq3VXCFi5XA6v12vNgcSl\nNHnpmXEgV97FMQuOsS3hK+YrYg5m40QHOSsfLL2FQjQa1RoqGa3NerFLZGCJsktZlpEkSeNcgtME\ng0HtOhEZOUbQZ2sJ4dEq6OFwOLTPBZU7mXGgn/zEgdeb4NVX4T3vqWBoCFMOdMYZEg88kGD7doWj\njqqY6h5psDbV1095a9XX11sG/PRZVUIoNMtwMcpoKu4+WDzWiONkMhnt2TJ//nzteHY+USoHGgH2\noq6NU2Ot5jsdMaZcr6pnn4Wbb57LvHnzOOooc45Z7hzU79CHKs5VAI261w/8uMVjZVlm9+7dVFVV\nGWbOG+3Z7fhPOXMQ3rt+v5+VK1eajlOPG0DltwqwGKhm5Uo4AO3qLUFDQwPV1dVl7c9mCocErAOE\nWctaK9gJWK2trQXKezF8Ph+zZs3CEXBM+T0UfIDa/W1O5RxTciU26Gaih8vloqqqytJnRZiYmopC\ndy+CBOCbIkTtn2tnYe2UZB7yhnA6nZabJZ/PZ5sSbFeiJkmSZmpajOZAs0paxOmePGWrmlbx7rnv\n1sYlk0kGGMDlcll2RBSfd7Cm8mb+D2ZjyvHJmshNmJqSuhwujQjjUktPAcNMtLs+ombO2Yk1gpSV\nO87j8dhunKfjfyXGFl/HItKof0+MFb4ddhCbgoqKirKzqpLJFM8/n+P973cQjVZYilL/9V9Rrr8e\n7ruvkkQCy9bF1147wcMPw733BnnHOxy22VrClLOcbC2Y8gKYTk29GXmzgmj77nA4pvVZgQDcf/8o\nF12kRh8BHn9cfb1cgaWvr49UKsWsWbMOyMxSjY5N/f+GDQE2bDiwLDA93O4M99zTz1VXgeqT4NK+\n20xiYGCAsbExJEkikUgYNhgpht11NhMIhdQ1orq6msceg1NOqQfqAOktOQ+HUD4SiQRj3WP8a9O/\nsvyDpS3IfT4fNTU1jI+PMzg4SFtbm6GAVVFRQUVFBYlEglAoxMqVK01LiWtqaojFYvzf8P+xdPZS\n5DcNNvkKyE6ZlqoWLVuqeL2oqqrSMlwCgQAjIyPaMx3UNauysrJk7RIBi2QySWVlpeF61RHuYNE3\nFkEH4IJLHruES56/hPbPtRfwxWQySaIigcPh0Ay5izNdnE6nZj4u5gWFApbgUMVz0WdgWWUoNQea\nVf4jgT6ZrZgDjYyMECWqCR5GJYTlCFjiv4u5lFh3hcBhJ2A5HI6CddsMojQRIJKNmDenwYnskNUA\nngUHcikubjvuNhodjbZrqOA2VVVVuN1uS/8rUO8Z/Xytxvr9fgKBgGW3cH0Qr6GhQRNE9AE48bf6\n1yorK20N4kUHxPr6eqLRKIqiaBnaxuuuKqDu2RPkqKMCDA9Lphxo3ToHl1wi8Y1vVLJ0aZDxcSsO\n5OR//gd+/etKFi2qZMUK87VpyxYHS5ZU4Ha7URTFkmcIHiJJkhbEMxOwhGen3mxcPE+K/0aMNbof\nRdVCZWWlVn5bU1Njy4sDAbjnnjBXXVWBuj6q5yAWg29/u5T/uFwuamtrC467d+9efD4fLS0tJc+g\nujrzShkBlQO5Jj/fyQUX1HLBBeYcyOVyUVdXZ8u3c7kIt98e57rrqoHl2nczW/vFccsJtooOpy6X\ni46ODq2JQnNzs+nfa1VQFvznmGOmn9lkJHbl83kGBwdpaGggEPDw2GN+Tjnl/YgN698aB5rJrP1y\ncUjAmiHIsswbb7yhteA1WlTsMqz07xWnOW84fAPNwWa27tzKpu2bjDufOCXcs9xc82/mwppI5zdD\nVVWV7cK8ZMkS7b+L53naytNUobjoxmoOFO4cly8vJb161NTU2Nb0zp8/n/k2fUhXr15tOtcNh29Q\nfT0eOkWLvhn5hvn9fo466ijLBd3j8XDEEUdMCUEmWLVqFZlMxlLYmTNnjm09v9frZcmSJbZmkKKF\n7f+8+j/mxux5GQJwx7F3cO0L15KRM5y64lTDzDl5QtZar1rB5/MVmKSbQXT9sRO6oLTDkRWsDNzF\n/PRp7VAodlllB+pL6vQZSlaL2uBgjM99Du69t4JMRjIlZOk0XH+9Kopdfrkqirlcxq2L83l4+GE1\nYnfllVVceSV4PFNkr1gY2707zXPPpTj6aEm7x61Ennw+r0UEy62vTyQSByRECfI2Hc8sUK+f8XFV\nMPvWtxq47DL1+5YrsMiyzMjICLIsH5DfAYjzlULd/biLXj9wuFwuAoEmIM6mTQ1ceKH63WYKigJb\nt46xcGE/kqQGUMoRr6wi4mvXqiUGB/Pds9ksXV1dRCIR3G43q1atIptVCfSmTdKMnwcrvF1ZZn9v\ncLvdZDIZ8vk8o6OjNDQ0MDExwf79+wkEAixevJiWlhbGx8cJhUIFnaiKuVF9fb0qiE2a0Gr+WEXr\n9RnLz1Azu399A/99wX/jdrhLvZp84Jnt4aLjLwKmunLpsUinNovAgyiJkiSJ5uZmQx+8iooKxsbG\nUBSFpUuXaq/r5zm7cvJ71qJyoMllrZj/+P1+VqxYAcD+/fs13x89Zs+eXXDevF4vTU1NWjabJEmm\nUXu/3691z9Lf00Yc6NGLHuXUH56qZV8bcaDGxkbq6+stbQuE34/gJOI86tdRn8/H6tWrS7jU3Llq\n1ob47ZcsWaKVuxWjra0NSZLIZDKa8bgZFi1aRENDA6lUiu+9+T1rDjQX7jjuDq590ZwDnbXyLJSY\nmm1v1TBFURQqKytJpVIsXbrUUgAQ5UMiQG0FPQfS83Cjz9cH8Y4//njtPWGwbWahsGDBArLZrCm3\n1B97+fLlWmdROw7U2dnCl7/sZs6caiYmzEWpTAa+8Y1GIM/FF9cA5hxIUSR+/esGoIbPfraaz37W\nnAOtW+fgL39pors7wZIlFdp1Y/yMd7J48WKi0Sjj4+OTa7GxWuB0OgueKSKAJ4QoPVwuV8FYPcbG\nxgA0v1WrsXpEo1Gy2Twwn/vvP4yLLoIXXij1qpriP+6CrquxWIyJiQmi0WiJZ5XH47FtIgRiTcyg\nZgg5il4vRbnHVYXaZqCVTZvm2K79Xq+3rOOKsW1tC9i8uYtVqyZwOtWMQyPxSr9m2fGfnTvV/z/Q\nckNQ16POzk6tG/mSJUtQb32JTZuYUQ5kNc+/df5zSMCaIehLyOxKBO1qT43SnG987kYy2YwqtDgg\nx+RnIZW0t51bOzPmwXYwmudNv7mJmz54E19+/stT4wwI0dsNs7le/b6rAdj0SdXXw8gzA+wzqwDb\njDKYMje1gsdj32XD6XSWlUVTUVHB7wd+T1/KvJ23y+Hiog9dxDUfu4ZrPjYlfhpmGZb5M86aNcuW\njIEqmK5ataqsY7a1tVl6FOmxaNEiEolECQE2EqtEpE1/zvfs2UMmk2Hx4sUlvgrV1dVa9H54eIRt\n2+DjHw8YLmrpNHziE6DWaDq58kprUUoNg4tsAHXjYa6bKqh97afGWmVrXXNNRJet5SwrWyufz+P1\nestOCz6Q8kHhlwOUmOWX83kf/nCeN97wsWpVkEsvVRfd+fPLE1jGxsY0EfVAO7cEAvDtb3dx6aVx\nYAFQOyPRMYfDwUUXzeEidS/OBRcc3PGKsXlzlPPO6+LWW2H9+payBbzNm62vswceOHDPq9HRUXp7\ne7USl6amJhwOB6eeOvV5M30ezPB2ZJn9vcLtdjN79mx6e3vp6+ujpqZG62QnxIlAIEBlZSXRaJSh\noSHNMLmY/9TV1dHb20sikSCRSKjlRQYdBq979joYAGT47COfhQB4HB5ySm4qS9ipcqDFVYvp7+83\nLHXTw+v10tDQgN/vt+wSB6q4HgwGC56FhiVpDhe52qlAlh3/KXfDJUlS2eufy+UquZ8tOZBvytvM\njAM5HA7bdUDPkYz4kiRJhsGq4rXC5/OZBsnEb1QOTwKVY/xx5I90R7tNOZDb5eaiY8rkQDW2H4kk\nSWX/rmaCqRHWrFmjeVXZff6aNWtIJBIl59uIA7W0tBAMBrXqi1wux+7duzXBUf/75PN5mpqaSKVS\nTExEeeWVPHPnYppZPsWB1HKHSy6x40CJybEuRLcIcw6UQPW/dCDIqdXa9IUvRPj5z+E731HLr+ye\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msyqv6DG4SPJqSVpjRaNmeF28Zgpu4nQ6tedGOp3WNn5CaBNjBMSzPp1O23IgIVxks1nm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\n"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c93cd416-bf6e-4998-8cb4-a63d5b5fdaf2",
+   "metadata": {},
+   "source": [
+    "And then a look at the high latitude region. Note the limits on the axis are slightly different. \n",
+    "\n",
+    "![highlatitude_corner_connections.png](attachment:f2d75913-95bf-43e3-8611-47a2a8eb4c28.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "34b944a0-ac97-4063-b89a-41d4e78da2da",
    "metadata": {},
    "source": [
-    "(The equator-most ghost cells are not shown)\n",
     "\n",
     "\n",
-    "### Order of points\n",
+    "## Order of points\n",
     "\n",
     "The order of points in the dipole grid is the same as the spherical grid. Most simply, the field lines are arranged in the order of increasing invariant latitude & from lowest to highest altitude.\n",
     "\n",
@@ -302,14 +363,6 @@
     "\n",
     "If there are any other questions, contact Aaron B and this file can be updated with more plots :)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2ca9881-8114-4b63-86ca-68b1575ea59d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/edu/examples/Dipole/plots/highlatitude_corner_connections.png b/edu/examples/Dipole/plots/highlatitude_corner_connections.png
new file mode 100644
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z@UiVwUandREmXZtn-vUCeifuO5mUSGp%&bb9M`^IBxVpSXl@b8`8VMt>;N()(D{BH
zcwZ@1z{OHQbb6QI-yGok%H>yh@ds4~CyK;E8aJ4M$JW&gV($Yp1;o$?ywg`j!DsYR
z(RA9S+Uvs`x;7*}WI<Ztr@EC)K0~DWq0>+_A!`&C%PXE=yZ$RR5Pibz(?t&Y9^{}$
zQ}l@wLU~Hu8na=Wbr@I`KuioG$9jib2_B^R2U}HX@-hh@(BFT|dBFqAJ!tf9*W4dX
zIH2|8^QA%9<PB0<q)&))c|t*RZQ7qC=OtP~+=G>Aj)^*umkYBAJHJ(Mi%p+ocXxNR
zO4@lU^sl)|!T8@Rw7d?xrTy9}iB<lkt4j*9FrLs4#cRu#;s)3XWZo4Q--D*<*WKL(
z7bkn9#EyG86qgs<Yb;W|qvz|$7T814_AmF%f?p{Ogo|i6Z8hk>d)Gln*j%^Fl(>sS
z(8jr*JGh_S9<Y|qDrxmF(TFt&?3a--FaOqJc;pEKR#4MFPx(RQl#kokVW2PQ2-4;U
z&24y1jh_66hxp51%!<#-)jR#)uA=I~4E}lPmDm5-4L!Nt^51^yp8-nN9fVo)4|f&4
z1E^I0yp|<mU;StJ^KZA-?AZT&FxUSd5603CjOtHQXpsAff<I>rjP>*MF5mhW#&7=&

literal 0
HcmV?d00001


From 0dc3e6c12b3111961b6fe807830d5c8a0c33ac93 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 10:27:43 -0400
Subject: [PATCH 072/266] BUG/STY: Astyle! And fixing init_mag_grid to work
 better for hi-res

---
 src/init_mag_grid.cpp | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index dcf1a288..6983f761 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -281,10 +281,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     // invLats are still all in North Hemisphere & increasing.
     // Use minimum p & alt to solve for q
     // q = sqrt((1-r/p)/r^4)
-    q_min = pow(((1 - max_alt_re / Pcorners(0)) / pow(max_alt_re, 4.0)), 0.5);
+    q_min = pow(((1 - max_alt_re / Pcenters(0)) / pow(max_alt_re, 4.0)), 0.5);
 
   // Trace each field line up to q_max, obtained from the lowest field line in the block
-  precision_t q_max = pow(((1 - min_alt_re / Pcorners(nLats -1)) / pow(
+  precision_t q_max = pow(((1 - min_alt_re / Pcenters(nLats - 1)) / pow(
                              min_alt_re,
                              4.0)), 0.5);
 
@@ -302,7 +302,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   magQ_corner_1d(nAlts) = q_min + (nAlts - nGCs) * delQ;
 
-  report.print(3, "Done generating points for magnetic grid. Plugging everything in");
+  report.print(3,
+               "Done generating points for magnetic grid. Plugging everything in");
 
   ////////////////////////////
   // That is the grid made. //
@@ -509,20 +510,22 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
   UseThisCell.elem(isTooLowCell).fill(false);
 
-  for (iLon=0; iLon<nLons; iLon++){
-    for (iLat = 0; iLat<nLats; iLat++){
+  for (iLon = 0; iLon < nLons; iLon++) {
+    for (iLat = 0; iLat < nLats; iLat++) {
       // find *last* cell below alt_min
-      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) 
-                                        < grid_input.alt_min * cKMtoM).max();
+      first_lower_gc(iLon, iLat) = find(geoAlt_scgc.tube(iLon, iLat) < min_alt).max();
     }
   }
-  if (first_lower_gc.min() < nGCs-1 || first_lower_gc.max() > nAlts-nGCs-1){
+
+  if (first_lower_gc.min() < nGCs - 1 ||
+      first_lower_gc.max() > nAlts - nGCs - 1) {
     report.error("Invalid magnetic grid!! Either:");
     report.error(" - Lowest latitude field line is entirely below min_alt");
     report.error(" - Highest altitude field line is above min_alt");
     report.error("This should not happen. Something is terribly wrong. Goodbye.");
     return false;
   }
+
   first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.print(4, "Done altitude spacing for the dipole grid.");

From 862b40a7ad363eaed937ea7794679ec46d38b2c9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 17 Apr 2025 12:57:39 -0400
Subject: [PATCH 073/266] BUG: Use radial distance for BC's, not k-distance

---
 src/neutrals_bcs.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 985594c6..7d93ef56 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -230,7 +230,7 @@ bool Neutrals::set_lower_bcs(Grid grid,
           temperature_scgc(iLon, iLat, iAlt + 1)
           / temperature_scgc(iLon, iLat, iAlt)
           * species[iSpecies].density_scgc(iLon, iLat, iAlt + 1)
-          * exp(-grid.dk_edge_m(iLon, iLat, iAlt) / sh_ave);
+          * exp(-grid.dr_edge(iLon, iLat, iAlt) / sh_ave);
 
         // Vertical velocities: (In GITM this projected down with mesh coeffs)
         // Take lowest physical cell's vertical velocity and project it down nGCs cells.

From bf53dfe5389e03c86a8187205722ddb414c9f517 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:38:50 -0400
Subject: [PATCH 074/266] BUG: something is wrong with gravity!

---
 src/init_mag_grid.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6983f761..3816f867 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -492,10 +492,13 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
-  gravity_mag_scgc = sqrt(
-                       gravity_vcgc[0] % gravity_vcgc[0] +
-                       gravity_vcgc[1] % gravity_vcgc[1] +
-                       gravity_vcgc[2] % gravity_vcgc[2]);
+
+  gravity_mag_scgc = mu / pow(radius_scgc, 2);
+  
+  //gravity_mag_scgc = sqrt(
+  //                     gravity_vcgc[0] % gravity_vcgc[0] +
+  //                     gravity_vcgc[1] % gravity_vcgc[1] +
+  //                     gravity_vcgc[2] % gravity_vcgc[2]);
 
   report.print(4, "Done gravity calculations for the dipole grid.");
 

From 6d25699b43e25c32ae6e1b30d3a34da701ffcb33 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:08 -0400
Subject: [PATCH 075/266] BUG: need to start at the first physical cell

---
 src/neutrals.cpp | 58 ++++++++++++++++++++++++++++++++----------------
 1 file changed, 39 insertions(+), 19 deletions(-)

diff --git a/src/neutrals.cpp b/src/neutrals.cpp
index 471cbb67..823b9828 100644
--- a/src/neutrals.cpp
+++ b/src/neutrals.cpp
@@ -255,17 +255,27 @@ void Neutrals::fill_with_hydrostatic(int64_t iStart,
 
   int64_t iNeutral, iSpecies;
 
+  int64_t iX, iY, iZ;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iFirst;
+
   for (iNeutral = 0; iNeutral < nSpeciesAdvect; iNeutral++) {
     iSpecies = species_to_advect[iNeutral];
-
-    // Integrate with hydrostatic equilibrium up:
-    for (int iAlt = iStart; iAlt < iEnd; iAlt++) {
-      species[iSpecies].density_scgc.slice(iAlt) =
-        temperature_scgc.slice(iAlt - 1) /
-        temperature_scgc.slice(iAlt) %
-        species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dr_edge.slice(iAlt) /
-            species[iSpecies].scale_height_scgc.slice(iAlt));
+    for (iX = nGCs; iX < nX - nGCs; iX++) {
+      for (iY = nGCs; iY < nY - nGCs; iY++) {
+        iFirst = grid.first_lower_gc(iX, iY) + iStart;
+        // Integrate with hydrostatic equilibrium up:
+        for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
+          species[iSpecies].density_scgc(iX, iY, iAlt) =
+            temperature_scgc(iX, iY, iAlt - 1) /
+            temperature_scgc(iX, iY, iAlt) *
+            species[iSpecies].density_scgc(iX, iY, iAlt - 1) *
+            exp(-grid.dr_edge(iX, iY, iAlt) /
+                species[iSpecies].scale_height_scgc(iX, iY, iAlt));
+        }
+      }
     }
   }
 
@@ -283,14 +293,25 @@ void Neutrals::fill_with_hydrostatic(int64_t iSpecies,
                                      int64_t iEnd,
                                      Grid grid) {
 
-  // Integrate with hydrostatic equilibrium up:
-  for (int iAlt = iStart; iAlt < iEnd; iAlt++) {
-    species[iSpecies].density_scgc.slice(iAlt) =
-      temperature_scgc.slice(iAlt - 1) /
-      temperature_scgc.slice(iAlt) %
-      species[iSpecies].density_scgc.slice(iAlt - 1) %
-      exp(-grid.dr_edge.slice(iAlt) /
-          species[iSpecies].scale_height_scgc.slice(iAlt));
+  int64_t iX, iY, iZ;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iFirst;
+
+  for (iX = nGCs; iX < nX - nGCs; iX++) {
+    for (iY = nGCs; iY < nY - nGCs; iY++) {
+      iFirst = grid.first_lower_gc(iX, iY) + iStart;
+      // Integrate with hydrostatic equilibrium up:I
+      for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
+        species[iSpecies].density_scgc(iX, iY, iAlt) =
+          temperature_scgc(iX, iY, iAlt - 1) /
+          temperature_scgc(iX, iY, iAlt) *
+          species[iSpecies].density_scgc(iX, iY, iAlt - 1) *
+          exp(-grid.dr_edge(iX, iY, iAlt) /
+              species[iSpecies].scale_height_scgc(iX, iY, iAlt));
+      }
+    }
   }
 
   calc_mass_density();
@@ -466,8 +487,7 @@ bool Neutrals::restart_file(std::string dir, std::string cGridtype,
         else
           RestartContainer.store_variable(cName,
                                           velocity_unit,
-                                          species[iSpecies].
-                                          velocity_vcgc[iDir]);
+                                          species[iSpecies].velocity_vcgc[iDir]);
       }
     }
 

From f80320ee736f536e21e217d791b8e5ba1f73f0f5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:19 -0400
Subject: [PATCH 076/266] BUG: first GC + nGCs - 1 = 2, I think

---
 src/neutrals_ics.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index a1430d96..8da58901 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -99,7 +99,7 @@ bool Neutrals::initial_conditions(Grid grid,
               std::cout << "  NOT Found in MSIS - setting constant\n";
 
             species[iSpecies].density_scgc.slice(0).
-                                          fill(species[iSpecies].lower_bc_density);
+            fill(species[iSpecies].lower_bc_density);
             fill_with_hydrostatic(iSpecies, 1, nAlts, grid);
           }
 
@@ -174,9 +174,11 @@ bool Neutrals::initial_conditions(Grid grid,
       for (iLon = 0; iLon < nLons; iLon ++) {
         for (iLat = 0; iLat < nLats; iLat++) {
           for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+
             species[iSpecies].density_scgc.subcube(
-              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat)+1).fill(
+              iLon, iLat, 0, iLon, iLat, grid.first_lower_gc(iLon, iLat) + 1).fill(
                 species[iSpecies].lower_bc_density);
+
           }
         }
       }
@@ -188,7 +190,7 @@ bool Neutrals::initial_conditions(Grid grid,
       report.print(2, "Filling with hydrostatic");
 
       for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
-        fill_with_hydrostatic(iSpecies, nGCs, nAlts, grid);
+       fill_with_hydrostatic(iSpecies, nGCs - 1, nAlts, grid);
     } // type = planet
   }
 

From f4c319f008ca60d2e3fc3d81eab3506130061a93 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 07:39:30 -0400
Subject: [PATCH 077/266] FEAT: output magnitude of gravity

---
 src/output.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/output.cpp b/src/output.cpp
index 61115d97..c9fa6dca 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -89,7 +89,8 @@ bool output(const Neutrals &neutrals,
 
     // make sure the output dt is set correctly. Otherwise these errors aren't caught correctly.
     precision_t dt_output = input.get_dt_output(iOutput);
-    if (dt_output == 0.0){
+
+    if (dt_output == 0.0) {
       report.exit(function);
       return false;
     }
@@ -243,6 +244,9 @@ bool output(const Neutrals &neutrals,
         AllOutputContainers[iOutput].store_variable("Gvertical",
                                                     "m/s^2",
                                                     grid.gravity_vcgc[2]);
+        AllOutputContainers[iOutput].store_variable("Gmag",
+                                                    "m/s^2",
+                                                    grid.gravity_mag_scgc);
         AllOutputContainers[iOutput].store_variable("Gpotential",
                                                     "m^2/s^2",
                                                     grid.gravity_potential_scgc);
@@ -261,9 +265,9 @@ bool output(const Neutrals &neutrals,
                                                     "degrees",
                                                     grid.magLon_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("invLat",
-                                                      "Magnetic Invariant Latitude",
-                                                      "degrees",
-                                                      grid.magInvLat_scgc * cRtoD);
+                                                    "Magnetic Invariant Latitude",
+                                                    "degrees",
+                                                    grid.magInvLat_scgc * cRtoD);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",

From 9ea7544d89f0259d80b1ebeeba864f4f83076f2b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 08:06:36 -0400
Subject: [PATCH 078/266] BUG: need sphere4 to go along with dipole4

---
 share/run/aether.json | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 96f1f08e..56439570 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -8,17 +8,12 @@
         "iFunctionVerbose" : {
             "Grid::create_altitudes": 0},	
         "dt" : 10.0,
-        "check_for_nans" : false
-    },
+        "check_for_nans" : false},
 
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
-    "GeoBlockSize" : {
-        "nLons" : 18,
-        "nLats" : 18,
-        "nAlts" : 50},
-
     "neuGrid" : {
+        "Shape": "sphere4",
 	"nLonsPerBlock" : 24,
 	"nLatsPerBlock" : 22,
 	"nAlts" : 40,
@@ -28,12 +23,11 @@
     "ionGrid": {
         "Shape": "dipole4",
         "nLonsPerBlock": 36,
-        "nLatsPerBlock" : 18,
-        "nAlts":36,
-        "LatRange":[10, 80],
-        "AltRange":[80.0, 1000],
-		"LonRange" : [0.0, 360.0]
-        },
+        "nLatsPerBlock": 18,
+        "nAlts": 50,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+	"LonRange": [0.0, 360.0] },
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
 

From ae3ce01c9e1aba839afc495d04436ca3bc75325f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 23 Apr 2025 08:42:51 -0400
Subject: [PATCH 079/266] BUG: to capture e-region, need 100 points

---
 share/run/aether.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 56439570..d10243a7 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -24,7 +24,7 @@
         "Shape": "dipole4",
         "nLonsPerBlock": 36,
         "nLatsPerBlock": 18,
-        "nAlts": 50,
+        "nAlts": 100,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
 	"LonRange": [0.0, 360.0] },

From 3bcd3fa5ad93634e08928416d248f1f5c71669dd Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:40:04 -0400
Subject: [PATCH 080/266] STY: Astyle, yo

---
 src/init_mag_grid.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 3816f867..7deebd46 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -145,7 +145,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   arma_vec lon1dLeft(nLons + 1);
 
-  // if we are not doing anything in the lon direction, then set dlon to
+  // If we are not doing anything in the lon direction, then set dlon to
   // something reasonable:
   if (!HasXdim)
     dlon = 1.0 * cDtoR;
@@ -158,7 +158,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   /////////////////
 
   // - Make a 1d vector
-  // - copy it into the 3d cube
+  // - Copy it into the 3d cube
   for (iLon = 0; iLon < nLons; iLon++) {
     lon1d(iLon) = lon0 + (iLon - nGCs + 0.5) * dlon;
     lon1dLeft(iLon) = lon0 + (iLon - nGCs) * dlon; // corners
@@ -504,9 +504,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   calc_dipole_grid_spacing(planet);
 
-
   //////////////////////////////////////
-  // Generate mask for physicsl cells //
+  // Generate mask for physical cells //
   //////////////////////////////////////
 
   isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);

From e12e25e9486f46dfc23d26418ca76d9b738116b5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:41:01 -0400
Subject: [PATCH 081/266] FEAT: Make ortho dipole4 the default ionGrid in
 settings files

---
 share/run/UA/inputs/defaults.json | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 09ade2c6..f3c5b6e0 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -79,19 +79,14 @@
 	"IsUniformAlt" : true,
         "AltFile" : ""},
 
-    "ionGrid" : {
-        "Shape" : "dipole4",
-        "nLatsPerBlock" : 18,
-		"LonRange" : [0.0, 360.0],
-		"LatRange" : [85.0, 360.0],
-		"AltRange" : [0.0, 360.0],
-        "nLonsPerBlock" : 22,
-        "nAlts" : 50,
-        "MinAlt" : 80.0,
-        "MinApex" : 120.0,
-        "LatMax":88.0,
-        "LatStretch":1.0,
-        "dAltStretch" : 0.6},
+	"ionGrid": {
+		"Shape": "dipole4",
+		"nLonsPerBlock": 14,
+		"nLatsPerBlock" : 20,
+		"nAlts":30,
+		"AltRange":[80,1500],
+		"LatRange":[7, 87]
+		},
 
     "Oblate" : {
 	"isOblate" : false,

From 7b8f0de78f78b02ef8387a3d243e5eb5549ff9cb Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:44:50 -0400
Subject: [PATCH 082/266] STY: Formatting of share/run/UA/inputs/defaults.json

---
 share/run/UA/inputs/defaults.json | 126 +++++++++++++++---------------
 1 file changed, 64 insertions(+), 62 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index f3c5b6e0..4573571a 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -1,38 +1,40 @@
 
 {
     "Debug" : {
-	"iVerbose" : 0,
-	"doInheritVerbose" : false,
-	"dt" : 60.0,
-	"TimingPercent" : 1.0,
-	"iTimingDepth" : 5,
-	"iProc" : 0,
-        "iFunctionVerbose" : {
-            "Grid::create_altitudes": 0},	
-	"check_for_nans" : false,
-	"nan_test" : {
+		"iVerbose" : 0,
+		"doInheritVerbose" : false,
+		"dt" : 60.0,
+		"TimingPercent" : 1.0,
+		"iTimingDepth" : 5,
+		"iProc" : 0,
+		"iFunctionVerbose" : {
+			"Grid::create_altitudes": 0},	
+		"check_for_nans" : false,
+		"nan_test" : {
             "insert" : false,
-            "variable" : "temperature_scgc"} },
+            "variable" : "temperature_scgc"} 
+		},
 
     "InitialConditions" : {
-	"type" : "Planet"},    
+		"type" : "Planet"},    
 
     "BoundaryConditions" : {
-	"type" : "Planet"},
+		"type" : "Planet"},
 
     "Advection" : {
-	"Neutrals" : {
-	    "Vertical" : "rusanov",
-	    "Horizontal" : "default",
-	    "useBulkWinds" : true,
-	    "useImplicitFriction" : true},
-	"Ions" : {
-	    "Along" : "rusanov",
-	    "Across" : "default"} },
+		"Neutrals" : {
+			"Vertical" : "rusanov",
+			"Horizontal" : "default",
+			"useBulkWinds" : true,
+			"useImplicitFriction" : true},
+		"Ions" : {
+			"Along" : "rusanov",
+			"Across" : "default"}
+		},
     
     "Student" : {
-	"name" : "",
-	"is" : false },
+		"name" : "",
+		"is" : false },
 
     "Planet" : {
         "name" : "earth",
@@ -41,42 +43,42 @@
     "BField" : "dipole",
 
     "Electrodynamics" : {
-	"Potential" : "weimer",
-	"DiffuseAurora" : "fta",
-	"Dir" : "UA/inputs/ext/ie/",
-	"File" : ""},
+		"Potential" : "weimer",
+		"DiffuseAurora" : "fta",
+		"Dir" : "UA/inputs/ext/ie/",
+		"File" : ""},
 
     "Euv" : {
-	"doUse" : true,
-	"Model" : "euvac",
-	"File" : "UA/inputs/euv.csv",
-	"IncludePhotoElectrons" : true,
-	"HeatingEfficiency" : 0.05,
-	"dt" : 60.0},
+		"doUse" : true,
+		"Model" : "euvac",
+		"File" : "UA/inputs/euv.csv",
+		"IncludePhotoElectrons" : true,
+		"HeatingEfficiency" : 0.05,
+		"dt" : 60.0},
 
     "DoCalcBulkIonTemp" : false,
     
     "Eddy" : {
-	"Coefficient" : 50.0,
-	"BottomPressure" : 0.01,
-	"TopPressure" : 0.005,
-	"UseInEnergy": true,
-	"UseInMomentum": true},
+		"Coefficient" : 50.0,
+		"BottomPressure" : 0.01,
+		"TopPressure" : 0.005,
+		"UseInEnergy": true,
+		"UseInMomentum": true},
 
     "StartTime" : [2011, 3, 20, 0, 0, 0],
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
     "neuGrid" : {
         "Shape" : "sphere",
-	"LatRange" : [-90.0, 90.0],
+		"LatRange" : [-90.0, 90.0],
         "nLatsPerBlock" : 18,
-	"LonRange" : [0.0, 360.0],
+		"LonRange" : [0.0, 360.0],
         "nLonsPerBlock" : 20,
         "nAlts" : 40,
-	"MinAlt" : 100.0,
-	"dAltkm" : 5.0,
+		"MinAlt" : 100.0,
+		"dAltkm" : 5.0,
         "dAltScale" : 0.25,
-	"IsUniformAlt" : true,
+		"IsUniformAlt" : true,
         "AltFile" : ""},
 
 	"ionGrid": {
@@ -85,23 +87,23 @@
 		"nLatsPerBlock" : 20,
 		"nAlts":30,
 		"AltRange":[80,1500],
-		"LatRange":[7, 87]
-		},
+		"LatRange":[7, 87]},
 
     "Oblate" : {
-	"isOblate" : false,
+		"isOblate" : false,
         "isJ2" : false},
 
     "Ensembles" : {
         "nMembers" : 1},
 
     "Sources" : {
-	"Grid" : {
-		"Coriolis" : true,
-		"Cent_acc": true },
-	"Neutrals" : {
-	    "NO_cool" : false,
-	    "O_cool": false } },
+		"Grid" : {
+			"Coriolis" : true,
+			"Cent_acc": true },
+		"Neutrals" : {
+		    "NO_cool" : false,
+	    	"O_cool": false } 
+		},
     
     "Seed" : 0,
 
@@ -115,18 +117,18 @@
     "OmniwebFile" : ["UA/inputs/omni_20110319.txt"],
     
     "Logfile" : {
-	"name" : ["UA/output/log_geo.txt", "UA/output/log_mag.txt"],
-	"append" : false,
-	"dt" : 10.0,
-	"species" : ["O2", "O2+"]},
+		"name" : ["UA/output/log_geo.txt", "UA/output/log_mag.txt"],
+		"append" : false,
+		"dt" : 10.0,
+		"species" : ["O2", "O2+"]},
 
     "Outputs" : {
-	"type" : ["states", "grid"],
-	"dt" : [900, -1]},
+		"type" : ["states", "grid"],
+		"dt" : [900, -1]},
 
     "Restart" : {
-	"do" : false,
-	"OutDir" : "UA/restartOut",
-	"InDir" : "UA/restartIn",
-	"dt" : 3600.0}
+		"do" : false,
+		"OutDir" : "UA/restartOut",
+		"InDir" : "UA/restartIn",
+		"dt" : 3600.0}
 }

From 6d5a5cc4588d36da5c8e04ae501b2e186e284589 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 18 Apr 2025 15:46:33 -0400
Subject: [PATCH 083/266] FEAT: Make sphere4 the default neutral shape. This
 will require mpi!

---
 share/run/UA/inputs/defaults.json | 2 +-
 share/run/aether.json             | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 4573571a..557a06bf 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -69,7 +69,7 @@
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 
     "neuGrid" : {
-        "Shape" : "sphere",
+        "Shape" : "sphere4",
 		"LatRange" : [-90.0, 90.0],
         "nLatsPerBlock" : 18,
 		"LonRange" : [0.0, 360.0],
diff --git a/share/run/aether.json b/share/run/aether.json
index d10243a7..65ac4f7e 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -14,9 +14,9 @@
 
     "neuGrid" : {
         "Shape": "sphere4",
-	"nLonsPerBlock" : 24,
-	"nLatsPerBlock" : 22,
-	"nAlts" : 40,
+        "nLonsPerBlock" : 24,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 40,
         "dAltScale" : 0.25,
         "IsUniformAlt" : false},
     
@@ -35,6 +35,7 @@
         "Potential" : "Weimer05",
         "DiffuseAurora" : "fta",
         "File": "UA/inputs/b20110320n_omni.bin"},
+        
     "Outputs" : {
         "type" : ["states", "grid"],
         "dt" : [900, -1] },

From 99cb393d6c1dedf131d22c883f0d36dbfc282b1a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 13:26:40 -0400
Subject: [PATCH 084/266] FEAT: Framework for adding tests in at runtime

---
 CMakeLists.txt                    | 3 ++-
 include/aether.h                  | 3 +++
 include/inputs.h                  | 6 ++++++
 share/run/UA/inputs/defaults.json | 5 ++++-
 src/inputs.cpp                    | 8 ++++++++
 src/main/main.cpp                 | 3 +++
 6 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a479a1fe..b37b2c65 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,6 +18,7 @@ endif()
 
 # Directory variables
 file(GLOB SRC_FILES ${PROJECT_SOURCE_DIR}/src/*.cpp)
+file(GLOB TEST_FILES ${PROJECT_SOURCE_DIR}/srcTest/*.cpp)
 set(MAIN_DIR ${PROJECT_SOURCE_DIR}/src/main)
 set(TESTS_DIR ${PROJECT_SOURCE_DIR}/tests)
 set(OUT_DIR ${PROJECT_SOURCE_DIR}/src/output)
@@ -34,7 +35,7 @@ elseif(TEST_EXCHANGE)
 elseif(TEST_GRADIENT)
   add_executable(aether ${SRC_FILES} ${MSIS_FILES} ${MAIN_DIR}/main_test_gradient.cpp)
 else()
-  add_executable(aether ${SRC_FILES} ${MSIS_FILES} ${IE_FILES} ${MAIN_DIR}/main.cpp)
+  add_executable(aether ${SRC_FILES} ${TEST_FILES} ${MSIS_FILES} ${IE_FILES} ${MAIN_DIR}/main.cpp)
 endif()
 
 if(USE_DOUBLE_PRECISION)
diff --git a/include/aether.h b/include/aether.h
index 28c6a4b5..ec51bdc2 100644
--- a/include/aether.h
+++ b/include/aether.h
@@ -143,4 +143,7 @@ using json = nlohmann::json;
 // not commented
 #include "external_msis.h"
 
+// To hold all the test functions
+#include "test.h"
+
 #endif  // INCLUDE_AETHER_H_
diff --git a/include/inputs.h b/include/inputs.h
index 4abc9167..db266aac 100644
--- a/include/inputs.h
+++ b/include/inputs.h
@@ -562,6 +562,12 @@ class Inputs {
      \param 
    **/
   std::vector<precision_t> get_satellite_dts();
+
+    /**********************************************************************
+     \brief returns settings["
+     \param 
+   **/
+  json get_tests();
   
   // General get_setting functions with error checks:
   
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 557a06bf..f86b7c47 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -130,5 +130,8 @@
 		"do" : false,
 		"OutDir" : "UA/restartOut",
 		"InDir" : "UA/restartIn",
-		"dt" : 3600.0}
+		"dt" : 3600.0},
+
+	"DoTests": {
+				"exit_on_fail": true}
 }
diff --git a/src/inputs.cpp b/src/inputs.cpp
index c35d8637..921afd41 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -1163,6 +1163,14 @@ bool Inputs::get_advection_neutrals_implicitfriction() {
   return get_setting_bool("Advection", "Neutrals", "useImplicitFriction");
 }
 
+// -----------------------------------------------------------------------
+// See what tests are requested
+// -----------------------------------------------------------------------
+
+json Inputs::get_tests() {
+  return get_setting_json("DoTests");
+}
+
 // --------------------------------------------------------------------------
 // check to see if class is ok
 // --------------------------------------------------------------------------
diff --git a/src/main/main.cpp b/src/main/main.cpp
index 64d25548..d620f28c 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -31,6 +31,9 @@ int main() {
       report.print(-1, "Hello " +
                    input.get_student_name() + " - welcome to Aether!");
 
+    // Find out what tests we are running:
+    json tests = input.get_tests();
+
     // For now, the number of processors and blocks are set by the 
     // neutral grid shape, since this could be sphere (1 root) or
     // cubesphere (6 root)

From 79a4c851aa8942d1d1205d3ae5bb6dc1b0aa209e Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 13:27:51 -0400
Subject: [PATCH 085/266] FEAT: Create (semi-functional) gradient test

---
 include/test.h                    |  17 ++
 share/run/UA/inputs/defaults.json |   2 +-
 src/main/main.cpp                 |   4 +
 srcTest/test_gradient.cpp         | 260 ++++++++++++++++++++++++++++++
 4 files changed, 282 insertions(+), 1 deletion(-)
 create mode 100644 include/test.h
 create mode 100644 srcTest/test_gradient.cpp

diff --git a/include/test.h b/include/test.h
new file mode 100644
index 00000000..ea74ab86
--- /dev/null
+++ b/include/test.h
@@ -0,0 +1,17 @@
+// Copyright 2020, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+#ifndef INCLUDE_TEST_H_
+#define INCLUDE_TEST_H_
+
+#include "aether.h"
+
+
+// Gradient tests
+// Cubesphere is not done nor tested
+bool test_gradient(Planets planet, Quadtree quadtree, json test_config, Grid gGrid, Grid mGrid);
+bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid);
+bool test_gradient_ijk(Planets planet, Grid grid);
+
+
+#endif
\ No newline at end of file
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index f86b7c47..1b4138ab 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -132,6 +132,6 @@
 		"InDir" : "UA/restartIn",
 		"dt" : 3600.0},
 
-	"DoTests": {
+	"DoTests": {"test_gradient": false,
 				"exit_on_fail": true}
 }
diff --git a/src/main/main.cpp b/src/main/main.cpp
index d620f28c..0ec17726 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -116,6 +116,10 @@ int main() {
     Ions ions(gGrid, planet);
     Ions ionsMag(mGrid, planet);
 
+    if (tests["test_gradient"]) {
+      test_gradient(planet, quadtree, tests, gGrid, mGrid);
+      }
+
     // -----------------------------------------------------------------
     // This is a unit test for checking for nans and infinities.
     // Is simply adds nans and infinities in a few places, then
diff --git a/srcTest/test_gradient.cpp b/srcTest/test_gradient.cpp
new file mode 100644
index 00000000..6270e35a
--- /dev/null
+++ b/srcTest/test_gradient.cpp
@@ -0,0 +1,260 @@
+
+// Copyright 2020, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+#include <iostream>
+
+#include "aether.h"
+
+bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
+                   Grid gGrid, Grid mGrid) {
+  std::string function = "test_gradient";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  bool didWork;
+
+  report.print(2, "Testing neutral grid");
+
+  if (gGrid.IsCubeSphereGrid)
+    didWork = test_gradient_cubesphere(planet, quadtree, gGrid);
+
+  if (gGrid.IsDipole || gGrid.IsLatLonGrid)
+    didWork = test_gradient_ijk(planet, gGrid);
+
+  MPI_Barrier(aether_comm);
+
+  if (!didWork && test_config["exit_on_fail"])
+    throw std::string("Gradient test failed - neutral grid");
+
+  report.print(2, "Testing ion grid");
+
+  if (mGrid.IsCubeSphereGrid) // it's technically possible...
+    didWork = test_gradient_cubesphere(planet, quadtree, mGrid);
+
+  if (mGrid.IsDipole || mGrid.IsLatLonGrid)
+    didWork = test_gradient_ijk(planet, mGrid);
+
+  if (!didWork && test_config["exit_on_fail"])
+    throw std::string("Gradient test failed - ion grid");
+
+
+
+  report.exit(function);
+
+  return didWork;
+}
+
+bool test_gradient_ijk(Planets planet, Grid grid) {
+
+  std::string function = "test_gradient_dipole";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  int64_t nIs = grid.get_nX();
+  int64_t nJs = grid.get_nY();
+  int64_t nKs = grid.get_nZ();
+  int64_t nGCs = grid.get_nGCs();
+
+  int64_t nX, nY, nZ;
+  precision_t tol = 1e-3;
+  bool didWork = true;
+
+  arma_cube predicted_gradient, true_gradient;
+  arma::uvec err_points;
+
+  arma_cube gradient_error;
+  gradient_error.set_size(nIs, nJs, nKs);
+  gradient_error.zeros();
+
+  int64_t nCellsTot = nX * nY * nZ;
+  int64_t nCellsNGCs = (nX - 2 * nGCs) * (nY - 2 * nGCs) * (nZ - 2 * nGCs);
+
+  report.print(2, "Beginning i-gradient");
+
+  /////////////////////////////////////////////////////////////
+  // Test the gradient in i-direction, d/dx(sin x) = cos(x)  //
+  /////////////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_i(sin(grid.i_center_scgc), grid);
+  true_gradient = cos(grid.i_center_scgc) / (grid.di_center_m_scgc);
+
+  gradient_error = abs(predicted_gradient - true_gradient) / abs(true_gradient);
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs,
+                                               nGCs, // don't look at ghost cells
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = all_finite(predicted_gradient, "Gradient_4o_i");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in i-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+  report.print(2, "Beginning j-gradient");
+
+  /////////////////////////////////////////////////////////////
+  // Test the gradient in j-direction, d/dx(cos x) = -sin(x) //
+  /////////////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_j(cos(grid.j_center_scgc), grid);
+  true_gradient = -1.0 * sin(grid.j_center_scgc) / grid.dj_center_m_scgc;
+
+  gradient_error = predicted_gradient - true_gradient;
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_j");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in j-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+
+  report.print(2, "Beginning k-gradient");
+
+  //////////////////////////////////////////////////////
+  // Test the gradient in k-direction, d/dx(x^2) = 2x //
+  //////////////////////////////////////////////////////
+
+  predicted_gradient = calc_gradient2o_k(grid.radius2_scgc, grid);
+  true_gradient = 2.0 * grid.radius_scgc / grid.dk_center_m_scgc;
+
+  gradient_error = (predicted_gradient - true_gradient);
+  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
+                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
+                    > tol);
+
+  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_k");
+
+  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
+            << ") => Points in k-gradient, above tol: " <<
+            100.0 * err_points.n_elem / predicted_gradient.n_elem
+            << "% (" << err_points.n_elem << ", " <<
+            predicted_gradient.n_elem << ")\n";
+
+  if (err_points.n_elem > true_gradient.n_elem * tol)
+    didWork = false;
+
+  report.exit(function);
+
+  return didWork;
+}
+
+
+// This is non-functional. 
+// Taken from src/main/main_test_gradient.cpp with enough edits to compile.
+bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid) {
+
+
+  std::string function = "test_gradient_cubesphere";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Set tolerance limit
+  precision_t tol = 1e-5;
+
+  // Print current side number
+  std::string side_num = std::to_string(quadtree.iSide + 1);
+  std::cout << "Initiating Test 1 for Side Number (1-based index): " << side_num
+            << std::endl;
+
+  /**
+    * Extract some test data generated by Aether Model
+    */
+
+  // Cell center coordinates
+  arma_mat aether_lon_cc = grid.geoLon_scgc.slice(0);
+  arma_mat aether_lat_cc = grid.geoLat_scgc.slice(0);
+
+  int64_t nXs = grid.get_nY();
+  int64_t nYs = grid.get_nX();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+
+  // Test scalar field and gradients
+  arma_cube scgc(nXs, nYs, nAlts);
+  arma_cube grad_lon_analytical(nXs, nYs, nAlts);
+  arma_cube grad_lat_analytical(nXs, nYs, nAlts);
+
+  // Radius Information
+  precision_t planet_R = planet.get_radius(0);
+  // radius of planet + altitude
+  // just pick alt at (0,0) loction
+  arma_vec R_Alts = grid.geoAlt_scgc.tube(0, 0) + planet_R;
+
+  for (int iAlt = 0; iAlt < nAlts; iAlt++) {
+    arma_mat curr_scalar(nXs, nYs, arma::fill::zeros); // setup zero mat
+    arma_mat curr_grad_lon(nXs, nYs);
+    arma_mat curr_grad_lat(nXs, nYs);
+    precision_t A = 1;
+    precision_t B = 1;
+
+    for (int j = 0; j < nYs; j++) {
+      for (int i = 0; i < nXs; i++) {
+        precision_t curr_lat = aether_lat_cc(i, j);
+        precision_t curr_lon = aether_lon_cc(i, j);
+
+        curr_scalar(i, j) = std::sin(curr_lat);
+        curr_grad_lon(i, j) = 0.;
+        curr_grad_lat(i, j) = std::cos(
+                                curr_lat); // Assume R=1, we will scale the numerical result
+      }
+    }
+
+    scgc.slice(iAlt) = curr_scalar;
+    grad_lon_analytical.slice(iAlt) = curr_grad_lon;
+    grad_lat_analytical.slice(iAlt) = curr_grad_lat;
+  }
+
+  std::vector<arma_cube> test_res = calc_gradient_cubesphere(scgc, grid);
+
+  // Perform Tests
+  for (int iAlt = 0; iAlt < nAlts; iAlt++) {
+    arma_mat curr_grad_lon = grad_lon_analytical.slice(iAlt);
+    arma_mat curr_grad_lat = grad_lat_analytical.slice(iAlt);
+    arma_mat curr_numgrad_lon = test_res[0].slice(iAlt);
+    arma_mat curr_numgrad_lat = test_res[1].slice(iAlt);
+
+
+    // Evaluate actual cells only
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        if (std::abs(curr_grad_lat(i, j) - curr_numgrad_lat(i,
+                                                            j) * R_Alts(iAlt)) > 1e-4) { // For float precision
+          std::cout << "Found Incorrect latitudinal gradient for face " + side_num +
+                    ", test f = sin(lat)" << std::endl;
+          std::cout << std::abs(curr_grad_lat(i, j) - curr_numgrad_lat(i,
+                                j)* R_Alts(iAlt)) << std::endl;
+          std::cout << iAlt << std::endl;
+          goto endloop1;
+        }
+
+        if (std::abs(curr_grad_lon(i, j) - curr_numgrad_lon(i,
+                                                            j) * R_Alts(iAlt)) > 1e-4) { // For float precision
+          std::cout << "Found Incorrect longitudinal gradient for face " + side_num +
+                    ", test f = sin(lat)" << std::endl;
+          goto endloop1;
+        }
+      }
+    }
+  }
+
+endloop1:
+
+  report.exit(function);
+  report.times();
+
+  return false;
+}
\ No newline at end of file

From 042302ff7fa4cc237258e88faa2827c592d0d283 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Mon, 21 Apr 2025 14:20:04 -0400
Subject: [PATCH 086/266] DOC: Fix MacOS install instructions to use g++ over
 clang

---
 doc/installation/dependencies.md | 20 ++++++++++++--------
 doc/installation/installation.md |  6 +++---
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/doc/installation/dependencies.md b/doc/installation/dependencies.md
index c0d41a6f..eabdf3a1 100644
--- a/doc/installation/dependencies.md
+++ b/doc/installation/dependencies.md
@@ -21,25 +21,29 @@ If a path is printed, `cmake` is installed. To check the version, run `cmake
 The layout of this page is as follows:
 
 - [Installing Dependencies](#installing-dependencies)
-  - [Install C++ Compiler](#install-gcc)
+  - [Install Compiler](#install-compiler)
   - [Install cmake](#install-cmake)
   - [Install JSON libraries](#install-json-libraries)
   - [Install Armadillo (and boost)](#install-armadillo-and-boost)
   - [Install NetCDF (optional)](#install-netcdf-optional)
 
-## Install gcc
+## Install Compiler
 
-On MacOS this can be installed, for
-example, using:
+On MacOS this can be installed, for example, using:
 
 ```bash
-sudo port install gcc11
+sudo port install g++
 ```
 
+> NOTE: On Macos, cmake will default to using clang instead of gcc.
+You need to set an environmental variable to tell cmake to use g++ instead. 
+Do this with `export CXX=/opt/local/bin/g++`,
+and replace the path if it is placed somewhere different.
+
 On Ubuntu, `gcc` (the C-compiler) is pre-installed, but the C++ compiler is not. This 
 can be installed with (subsituting your machine's package manager command):
 
-``bash
+```bash
 sudo apt install g++
 ```
 
@@ -103,7 +107,7 @@ sudo port install lapack
 sudo port install OpenBLAS
 sudo port install boost
 sudo port install armadillo
-sudo port install openmpi-bin libopenmpi-dev
+sudo port install openmpi
  ```
 
 ## Install NetCDF (optional)
@@ -122,7 +126,7 @@ sudo port install netcdf-cxx4
 If you want the gcc version of netcdf, then:
 
 ```bash
-sudo port install netcdf-cxx4 +gcc10
+sudo port install netcdf-cxx4
 ```
 
 On Ubuntu, gcc is the default compiler, it seems like you can probably just do:
diff --git a/doc/installation/installation.md b/doc/installation/installation.md
index bec10985..850d79fb 100644
--- a/doc/installation/installation.md
+++ b/doc/installation/installation.md
@@ -97,10 +97,10 @@ Here `FLAG` is a flag name and `VALUE` is the desired value (note the `-D`).  A
 more complete discussion of the available compilation flags can be found on the
 [Compilation Options](build_opts.md) page.
 
-If your default compiler isn't a GCC compiler, you will likely need to specify
-the desired GCC compiler at this step using:
+> If your default compiler isn't a GCC compiler, you will likely need to specify
+the desired GCC compiler at this step using the environmental variable `$CXX`, or:
 
-```bash
+> ```bash
 cmake -DCMAKE_CXX_COMPILER=<gcc or mpi executable with full path>
 ```
 

From 3f3fa44a752da0227101afdb2586f2de8ee85e29 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 10:12:58 -0400
Subject: [PATCH 087/266] FEAT: Add a globbed run* to .gitignore - supports any
 name of rundir's

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 6c8fc705..7269e4bc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,7 +9,7 @@
 
 # Run and build directories
 run/
-run.test/
+run*/
 build/
 
 # Doxygen generated files

From 5ef672ecaf998afe551e4a0b6f2507bfec05d980 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 11:30:25 -0400
Subject: [PATCH 088/266] FEAT: Post processor improvements! Including:

- NetCDF/hdf5 python packages are optional. matplotlib is still mandatory though
- Look in pwd & pwd/UA/output (lets you postproc from run/)
- Add arg to set path to output files
---
 srcPython/postAether.py | 43 +++++++++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index fcdea2e1..2a08fffb 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -8,8 +8,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import matplotlib.cm as cm
-from netCDF4 import Dataset
-from h5py import File
 import argparse
 import os
 import json
@@ -36,6 +34,9 @@ def parse_args():
     parser.add_argument('-oned', \
                         help='strip 1d files of ghostcells and store in one file', \
                         action="store_true")
+    parser.add_argument('-dir', default=None, type=str,
+                        help="Directory to find Aether files in. Will look in current"
+                        " directory & $PWD/UA/output/")
 
     args = parser.parse_args()
 
@@ -544,6 +545,13 @@ def get_base_files():
             IsFound, item = if_unique(ensembleFiles, fileInfo['ensembleFile'])
             if (IsFound):
                 filesInfo[i]['ensembleMembers'] = ensembleCounter[item]
+                
+    if len(filesInfo) == 0:
+        try:
+            os.chdir("UA/output")
+            get_base_files()
+        except:
+            print("No input files found!!")
     
     return filesInfo
 
@@ -948,18 +956,33 @@ def write_and_plot_data(dataToWrite,
 # main code
 #----------------------------------------------------------------------------
 
-if __name__ == '__main__':  # main code block
+def main(args):
 
-    args = parse_args()
     isVerbose = args.v
+    
+    if args.dir:
+        if isVerbose:
+            print("changing directory to: ", args.dir)
+        os.chdir(args.dir)
 
     filesInfo = get_base_files()
+    
+    if len(filesInfo) == 0:
+        return
 
     iVar = 3
     iAlt = args.alt
 
     output_netcdf = False if args.hdf5 else True
-
+    
+    if filesInfo[0]['isNetCDF']:
+        try:
+            from netCDF4 import Dataset
+            from h5py import File
+        except InputError:
+            raise InputError(
+              "Attempting to postprocess NetCDF files, but NetCDF is not installed for Python")    
+    
     for iFile, fileInfo in enumerate(filesInfo):
         coreFile = fileInfo['coreFile']
         isNetCDF = fileInfo['isNetCDF']
@@ -1004,4 +1027,12 @@ def write_and_plot_data(dataToWrite,
                     if (isVerbose):
                         print('    ', command)
                     os.system(command)
-    
+
+# call main:
+if __name__ == '__main__':  
+
+    args = parse_args()
+
+    # This allows code to cleanly exit on error
+    main(args)
+    
\ No newline at end of file

From 30fa3dd3423967b51fd3387acf17256548fcfff8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 11:59:18 -0400
Subject: [PATCH 089/266] BUG: Whoops prev commit on postprocessor was missing
 two pieces

---
 srcPython/postAether.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index 2a08fffb..f4e0455c 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -12,6 +12,11 @@
 import os
 import json
 from struct import unpack
+try:
+    from netCDF4 import Dataset
+    from h5py import File
+except InputError:
+    print("NetCDF and/or h5py not found")
 
 # ----------------------------------------------------------------------
 # Function to parse input arguments
@@ -549,7 +554,7 @@ def get_base_files():
     if len(filesInfo) == 0:
         try:
             os.chdir("UA/output")
-            get_base_files()
+            filesInfo = get_base_files()
         except:
             print("No input files found!!")
     
@@ -974,15 +979,7 @@ def main(args):
     iAlt = args.alt
 
     output_netcdf = False if args.hdf5 else True
-    
-    if filesInfo[0]['isNetCDF']:
-        try:
-            from netCDF4 import Dataset
-            from h5py import File
-        except InputError:
-            raise InputError(
-              "Attempting to postprocess NetCDF files, but NetCDF is not installed for Python")    
-    
+
     for iFile, fileInfo in enumerate(filesInfo):
         coreFile = fileInfo['coreFile']
         isNetCDF = fileInfo['isNetCDF']

From febb59b4b7d28341205ca729486eb1804805321f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 23 Apr 2025 12:36:40 -0400
Subject: [PATCH 090/266] BUG: Fix radial unit vector typos in init_mag_grid

- Sign was wrong
- should have been sin^2, not just sin in denominator
---
 src/init_mag_grid.cpp | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 7deebd46..4b324266 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -483,9 +483,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = -2 * sin(magLat_scgc) / pow(abs(1 + 3 * sin(magLat_scgc)),
-                                                 0.5);
+  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+                                                % sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = 2.0 * sin(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+                                                      % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
@@ -494,11 +495,6 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   gravity_potential_scgc.zeros();
 
   gravity_mag_scgc = mu / pow(radius_scgc, 2);
-  
-  //gravity_mag_scgc = sqrt(
-  //                     gravity_vcgc[0] % gravity_vcgc[0] +
-  //                     gravity_vcgc[1] % gravity_vcgc[1] +
-  //                     gravity_vcgc[2] % gravity_vcgc[2]);
 
   report.print(4, "Done gravity calculations for the dipole grid.");
 

From fa4c0bb71125a86b93950645e655f2b57f850546 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:49:54 -0400
Subject: [PATCH 091/266] DOC: added a couple of comments

---
 src/calc_ion_drift.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index ef3160cf..6f1c0cad 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -34,6 +34,7 @@ void Ions::calc_exb_drift(Grid grid) {
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
     exb_vcgc[iComp] = exb_vcgc[iComp] / bmag2;
+
   report.exit(function);
 }
 
@@ -44,9 +45,9 @@ void Ions::calc_exb_drift(Grid grid) {
 std::vector<arma_cube> Ions::calc_ion_electron_pressure_gradient(int64_t iIon,
     Grid grid) {
 
-      std::string function = "Ions::elec_ion_pressure_gradient";
-      static int iFunction = -1;
-      report.enter(function, iFunction);
+  std::string function = "Ions::elec_ion_pressure_gradient";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
   std::vector<arma_cube> pressure_gradient_vcgc;
   arma_cube total_pressure_scgc;
 
@@ -201,6 +202,9 @@ void Ions::calc_ion_drift(Neutrals neutrals,
           species[iIon].par_velocity_vcgc[iComp] =
             (species[iIon].par_velocity_vcgc[iComp] + a_par[iComp] * dt) /
             (1 + nuin_sum * dt);
+
+          // These need to change, since they are dependent on the
+          // grid. Closed, dipole fieldlines should NOT do this!!!
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 1).zeros();
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 2).zeros();
           species[iIon].par_velocity_vcgc[iComp].slice(nZ - 3) =
@@ -238,6 +242,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   }  // for iIon
 
+  // This is the mass weighted total bulk velocity:
   for (int64_t iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp] = velocity_vcgc[iComp] / sum_rho;
 

From b699a5ea65561e34c265b0e366173bdac793ebf5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:54:04 -0400
Subject: [PATCH 092/266] BUG: change sign of b-field in north

---
 src/fill_grid.cpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index 11018b76..faaff08c 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -193,15 +193,13 @@ void Grid::fill_grid_bfield(Planets planet) {
     // Now we modify the dipole's magnetic field to account for any imprecision.
     // Take the bfield_mag and put it into the third component (b-hat = k-hat)
     if (IsDipole) {
-      bfield_vcgc[2] = bfield_mag_scgc;
+      bfield_vcgc[2] = bfield_mag_scgc % sign(magInvLat_scgc * -1.0);
       bfield_vcgc[1].zeros();
       bfield_vcgc[0].zeros();
 
       bfield_unit_vcgc[0].zeros();
       bfield_unit_vcgc[1].zeros();
-      bfield_unit_vcgc[2].ones();
-
-      bfield_unit_vcgc[2] % sign(magInvLat_scgc * -1.0);
+      bfield_unit_vcgc[2] = 1.0 * sign(magInvLat_scgc * -1.0);
 
       // slight complication -
     } else

From 5a5204e4724a4c9be4c8aeb3cefd3ad5afb5a016 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 07:54:49 -0400
Subject: [PATCH 093/266] BUG: still issues with gravity direction

---
 src/init_mag_grid.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 4b324266..478b418c 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -483,14 +483,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     gravity_vcgc[iV].zeros();
   }
 
-  rad_unit_vcgc[1] = cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+  // Gravity should be negative in the k direction, since the grid switches directions.
+  // j direction should switch signs when crossing the equator (+ in south, - in north)  
+  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
                                                 % sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = 2.0 * sin(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
+  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc)) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
                                                       % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
-  gravity_vcgc[1] = - mu * rad_unit_vcgc[1] % radius2i_scgc;
-  gravity_vcgc[2] = - mu * rad_unit_vcgc[2] % radius2i_scgc;
+  gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;
+  gravity_vcgc[2] = mu * rad_unit_vcgc[2] % radius2i_scgc;
   gravity_potential_scgc.set_size(nX, nY, nAlts);
   gravity_potential_scgc.zeros();
 

From 7f3274369bf443cffd5e861d149d5a52faf9830c Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:48:16 -0400
Subject: [PATCH 094/266] FEAT: output grid spacing

---
 src/output.cpp | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/src/output.cpp b/src/output.cpp
index c9fa6dca..fc1e7767 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -28,6 +28,9 @@ std::string get_filename_from_type(std::string type_output) {
 
   if (type_output == "bfield")
     filename = "3DBF";
+    
+  if (type_output == "delta")
+    filename = "3DDE";
 
   if (type_output == "moment")
     filename = "3DMO";
@@ -255,6 +258,22 @@ bool output(const Neutrals &neutrals,
                                                     grid.radius_scgc);
       }
 
+      if (type_output == "delta") {
+        AllOutputContainers[iOutput].store_variable("dim",
+                                                    "di Center m",
+                                                    "m",
+                                                    grid.di_center_m_scgc);
+        AllOutputContainers[iOutput].store_variable("djm",
+                                                    "dj Center m",
+                                                    "m",
+                                                    grid.dj_center_m_scgc);
+        AllOutputContainers[iOutput].store_variable("dkm",
+                                                    "dk Center m",
+                                                    "m",
+                                                    grid.dk_center_m_scgc);
+
+      }
+
       if (type_output == "bfield" || type_output == "ions") {
         AllOutputContainers[iOutput].store_variable("mlat",
                                                     "Magnetic Latitude",

From cdc8bacbcb937e36895834510c593d5b5c2774b3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:49:17 -0400
Subject: [PATCH 095/266] BUG: spacing should have times -1 in south

---
 src/grid_spacing.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index d7fd245f..ad6d5b45 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -343,8 +343,8 @@ void Grid::calc_j_grid_spacing() {
 
   // Dipole will have different scaling...
   if (iGridShape_ == iDipole_) {
-    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
-    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc);
+    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
+    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:

From 0bb93cdd85dae976a6ef09e468e11df6c5995661 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:49:52 -0400
Subject: [PATCH 096/266] STY: removed a random comment

---
 src/fill_grid.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index faaff08c..e98e0603 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -200,8 +200,6 @@ void Grid::fill_grid_bfield(Planets planet) {
       bfield_unit_vcgc[0].zeros();
       bfield_unit_vcgc[1].zeros();
       bfield_unit_vcgc[2] = 1.0 * sign(magInvLat_scgc * -1.0);
-
-      // slight complication -
     } else
       for (iDim = 0; iDim < 3; iDim++)
         bfield_unit_vcgc[iDim] = bfield_vcgc[iDim] / (bfield_mag_scgc + 1e-32);

From 551525371442699c355832e66954725db449e8ce Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 24 Apr 2025 20:50:50 -0400
Subject: [PATCH 097/266] STY: iDim -> iComp + remove int64_t everywhere

---
 src/calc_ion_drift.cpp | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index 6f1c0cad..ec64aec6 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -101,7 +101,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
   std::vector<arma_cube> total_acc = make_cube_vector(nX, nY, nZ, 3);
   std::vector<arma_cube> efield_acc = make_cube_vector(nX, nY, nZ, 3);
 
-  int64_t iIon, iNeutral, iDim;
+  int64_t iIon, iNeutral, iComp;
 
   std::vector<arma_cube> grad_Pi_plus_Pe;
   arma_cube rho, nuin, nuin_sum, Nie, sum_rho;
@@ -115,7 +115,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   fill_electrons();
 
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp].zeros();
 
   std::vector<arma_cube> a_par = make_cube_vector(nX, nY, nZ, 3);
@@ -124,7 +124,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   for (iIon = 0; iIon < nSpecies; iIon++) {
 
-    for (int64_t iComp = 0; iComp < 3; iComp++)
+    for (iComp = 0; iComp < 3; iComp++)
       species[iIon].perp_velocity_vcgc[iComp].zeros();
 
     if (species[iIon].DoAdvect) {
@@ -139,16 +139,16 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
       // This is assuming that the 3rd dim is radial.
       // Want actual gravity for 3rd dim
-      for (iDim = 0; iDim < 3; iDim ++) {
-        gravity_vcgc[iDim] = grid.gravity_vcgc[iDim];
-        grad_Pi_plus_Pe[iDim] = grad_Pi_plus_Pe[iDim] / rho;
-        efield_acc[iDim] = Nie % efield_vcgc[iDim] / rho;
+      for (iComp = 0; iComp < 3; iComp ++) {
+        gravity_vcgc[iComp] = grid.gravity_vcgc[iComp];
+        grad_Pi_plus_Pe[iComp] = grad_Pi_plus_Pe[iComp] / rho;
+        efield_acc[iComp] = Nie % efield_vcgc[iComp] / rho;
       }
 
       // Neutral Wind Forcing:
       report.print(5, "neutral winds");
 
-      for (int64_t iComp = 0; iComp < 3; iComp++)
+      for (iComp = 0; iComp < 3; iComp++)
         wind_acc[iComp].zeros();
 
       nuin_sum.zeros();
@@ -157,14 +157,14 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         nuin = species[iIon].nu_ion_neutral_vcgc[iNeutral];
         nuin_sum = nuin_sum + species[iIon].nu_ion_neutral_vcgc[iNeutral];
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           wind_acc[iComp] = wind_acc[iComp] +
                             nuin % neutrals.velocity_vcgc[iComp];
         }
       }
 
       // Total Forcing (sum everything - this is A_s):
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         total_acc[iComp] =
           - grad_Pi_plus_Pe[iComp]
           + gravity_vcgc[iComp]
@@ -176,7 +176,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         // With a Planetary Magnetic field
         arma_cube a_dot_b = dot_product(total_acc, grid.bfield_unit_vcgc);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = a_dot_b % grid.bfield_unit_vcgc[iComp];
           a_perp[iComp] = total_acc[iComp] - a_par[iComp];
         }
@@ -190,7 +190,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
           Nie % Nie % grid.bfield_mag_scgc % grid.bfield_mag_scgc;
         bottom.clamp(1e-32, 1e32);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           // I redefined A to be an acceleration instead of a force, which
           // then changes the definition of top
           top = rho % nuin % a_perp[iComp] + Nie % a_x_b[iComp];
@@ -214,7 +214,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         }
       } else {
         // No Planetary Magnetic field
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = total_acc[iComp];
           // Steady state:
           //species[iIon].par_velocity_vcgc[iComp] =
@@ -230,9 +230,9 @@ void Ions::calc_ion_drift(Neutrals neutrals,
       // Calculate the mass-weighted average total velocity
       sum_rho = sum_rho + rho;
 
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         species[iIon].velocity_vcgc[iComp] =
-          //species[iIon].perp_velocity_vcgc[iComp] +
+          species[iIon].perp_velocity_vcgc[iComp] +
           species[iIon].par_velocity_vcgc[iComp];
         velocity_vcgc[iComp] = velocity_vcgc[iComp] +
                                rho % (species[iIon].velocity_vcgc[iComp]);
@@ -243,7 +243,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
   }  // for iIon
 
   // This is the mass weighted total bulk velocity:
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp] = velocity_vcgc[iComp] / sum_rho;
 
   report.exit(function);

From 3866b608549ca0624b765cc2b828cefe09887d1c Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Fri, 25 Apr 2025 12:54:49 -0400
Subject: [PATCH 098/266] FEAT: Add isClosed to all grids & doesTouch(S/N) pole
 to dipole

---
 include/grid.h        |  3 +++
 src/grid.cpp          |  5 +++++
 src/init_mag_grid.cpp | 12 ++++++++----
 3 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index f4ef10bb..c11eb4e6 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -111,6 +111,9 @@ class Grid
   // in the k-up and k-down direction (altitude for geo grids, q for dipole).
   arma_mat first_lower_gc, first_upper_gc;
 
+  // Whether to close field lines on dipole grid (Always false for geo grids)
+  bool IsClosed;
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
diff --git a/src/grid.cpp b/src/grid.cpp
index e435fba7..168a0999 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -271,6 +271,11 @@ Grid::Grid(std::string gridtype) {
   HasBField = 0;
   IsExperimental = false;
 
+  // Spatial info defaults
+  IsClosed = false;
+  DoesTouchNorthPole = false;
+  DoesTouchSouthPole = false;
+
   UseThisCell.set_size(nX, nY, nZ);
   UseThisCell.fill(true);
   first_lower_gc.set_size(nX, nY);
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 478b418c..c47ecb2f 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -236,6 +236,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;
     lat1d(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 1)) / 2.0;
     lat1d(nLats - 2) = (lat1dDown(nLats - 1) + lat1dDown(nLats - 2)) / 2.0;
+
+    if (sign(lower_left_norm(1)) > 0)
+      DoesTouchNorthPole = true;
+    else
+      DoesTouchSouthPole = true;
   }
 
   // l-shells of centers
@@ -267,15 +272,14 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - minimum Lshell in this block is < max_alt (the q-value would be undefined)
 
   precision_t q_min;
-  bool close_this_block = false;
 
   if (Pcorners.min() < max_alt_re) // invalid q's - Lshell < max_alt
-    close_this_block = true;
+    IsClosed = true;
 
   if (lat_origin < 0.01) // equator, with some imprecision
-    close_this_block = true;
+    IsClosed = true;
 
-  if (close_this_block)
+  if (IsClosed)
     q_min = 0; // q=0 at equator (for closed blocks)
   else
     // invLats are still all in North Hemisphere & increasing.

From db2d8fbbcfb66216f7ad6c312226ccf57223253a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 14:48:00 -0400
Subject: [PATCH 099/266] DOC: Update docs & default input files to be
 consistent & correct

---
 README.md                         | 25 +++++----
 doc/README.md                     |  1 +
 doc/internals/grid.md             | 85 +++++++++++++++----------------
 share/run/UA/inputs/defaults.json | 15 +++---
 share/run/aether.json             |  2 +-
 5 files changed, 67 insertions(+), 61 deletions(-)

diff --git a/README.md b/README.md
index d6fd6ebe..ab60df80 100644
--- a/README.md
+++ b/README.md
@@ -19,14 +19,14 @@ developed.
 
 ## Contents
 
-- [Aether](#aether)
-  - [Contents](#contents)
-  - [Quick Start](#quick-start)
-    - [Dependencies](#dependencies)
-    - [Getting the Code](#getting-the-code)
-    - [Compiling \& Running](#compiling--running)
-  - [Code Manual](#code-manual)
-  - [Further Documentation](#further-documentation)
+- [Contents](#contents)
+- [Quick Start](#quick-start)
+  - [Dependencies](#dependencies)
+  - [Getting the Code](#getting-the-code)
+  - [Compiling \& Running](#compiling--running)
+  - [Inputs](#inputs)
+- [Code Manual](#code-manual)
+- [Further Documentation](#further-documentation)
 
 ## Quick Start
 
@@ -141,9 +141,16 @@ structure like this:
 cd ..
 cp -R share/run ./run.test
 cd run.test
-./aether
+mpirun -np 4 ./aether
 ```
 
+This will run the default configuration of Aether, which requires four MPI processors.
+The default grids are `sphere4` and `dipole4` for the neutrals and ions (respectively),
+which both require four "root nodes", or MPI workers. To read more about root nodes and
+the available grids, please see [this page in the documentation](doc/internals/grid.md).
+
+### Inputs
+
 There are essentially two input files that specify the settings in the code.
 When you are in a run directory, they are:
 
diff --git a/doc/README.md b/doc/README.md
index d1601e3d..e51ddd53 100644
--- a/doc/README.md
+++ b/doc/README.md
@@ -32,6 +32,7 @@ Contents within `doc`:
   - [Ensembles](internals/ensembles.md)
   - [Indices](internals/indices.md)
   - [The Grid](internals/grid.md)
+  - [Coordinate Systems](internals/coordinates.md)
 - [Doxyfile](Doxyfile)
 - [README (this page)](README.md)
 - [Citations](citations.md)
diff --git a/doc/internals/grid.md b/doc/internals/grid.md
index 92da6fdb..4be0269f 100644
--- a/doc/internals/grid.md
+++ b/doc/internals/grid.md
@@ -18,21 +18,21 @@ again, 16 processors are needed, etc. However, in the altitude/radial direction,
 the number of points that are specified in the aether.json is unchanged, as it
 does not rely on the number of processors used.
 
-- [Grids in Aether](#grids-in-aether)
-  - [Grid Types Explained](#grid-types-explained)
-  - [Grid Shapes Explained](#grid-shapes-explained)
-    - [TL;DR](#tldr)
-    - [The Sphere Grid](#the-sphere-grid)
-    - [The Cubesphere Grid](#the-cubesphere-grid)
-    - [The Dipole Grid](#the-dipole-grid)
-      - [Inputs:](#inputs)
-    - [Root Nodes](#root-nodes)
-      - [Sphere](#sphere)
-      - [Cubesphere](#cubesphere)
-      - [Specifying Root Nodes](#specifying-root-nodes)
-  - [Specifying the Grid](#specifying-the-grid)
-    - [Horizontal Resolution](#horizontal-resolution)
-    - [Vertical Resolution](#vertical-resolution)
+- [Grid Types Explained](#grid-types-explained)
+- [Grid Shapes Explained](#grid-shapes-explained)
+  - [TL;DR](#tldr)
+  - [The Sphere Grid](#the-sphere-grid)
+  - [The Cubesphere Grid](#the-cubesphere-grid)
+  - [The Dipole Grid](#the-dipole-grid)
+    - [Inputs:](#inputs)
+  - [Root Nodes](#root-nodes)
+    - [Sphere](#sphere)
+    - [Cubesphere](#cubesphere)
+    - [Dipole](#dipole)
+    - [Specifying Root Nodes](#specifying-root-nodes)
+- [Specifying the Grid](#specifying-the-grid)
+  - [Horizontal Resolution](#horizontal-resolution)
+  - [Vertical Resolution](#vertical-resolution)
 
 ## Grid Types Explained
 
@@ -77,8 +77,8 @@ system can simulate a sub-region of the Earth if desired.
 
 The user needs to specify the shape of the grid, which specifies the grid shape
 and the number of root nodes. Shapes include: `sphere` (1 root node), `sphere6`
-(6 root nodes), `cubesphere` (6 root nodes), `dipole` (1 root node), `dipole4`
-(4 root nodes), and `dipole6` (6 root nodes).
+(6 root nodes), `cubesphere` (6 root nodes),`dipole4` (4 root nodes), and
+`dipole6` (6 root nodes).
 
 ### The Sphere Grid
 
@@ -118,13 +118,6 @@ Each fieldline starts at the lowest modeled altitude
 and curves towards the equator. In the northern hemisphere, this means that the
 fieldlines curve south, while in the southern hemisphere they curve north.
 
-The dipole grid requires >4 root nodes which ensures the coordinates are 
-mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
-compatibility with the neutral grid being a sphere or cubesphere. In both cases,
-each node the entire longitude range and given a portion of the latitude range.
-So in the case of `dipole4`, the four nodes are each given 1/4 of the available
-latitudes and all of the longitudes.
-
 The dipole grid is evenly spaced in **invariant latitude** (where the field line
 passes the minumum altitude) and **q** (the dipole coordinate
 specifying how far along the field line a point lies). Q is dimensionless and defined 
@@ -236,6 +229,20 @@ left-right direction and the up-down direction. For a cubesphere grid, the
 number of processors that can be used to specify the grid are then: 6, 24 (6
 \* 4), 96 (6 \* 4^2), 384 (6 \* 4^3), etc.
 
+#### Dipole
+
+The dipole grid requires >4 root nodes to ensure the coordinates are 
+mutually orthogonal. The available shapes are `dipole4` and `dipole6`, for 
+compatibility with the neutral grid being a sphere or cubesphere. In both cases,
+each root node covers the entire longitude range and given a portion of the latitude 
+range. So in the case of `dipole4`, the four nodes each cover 1/4 of the available
+latitudes and all of the longitudes. The available latitudes are scaled to the latitude
+limits specified in the input file, so the divisions will not be at $\pm45^\circ$ and
+$0^\circ$ latitude, rather will be offset to evenly divide the entire range across the
+blocks. Dividing the root nodes works identically to the spherical grid, for example
+`dipole4` can be used with 16 MPI tasks and each root node is divided into four blocks,
+forming a 2x2 grid.
+
 #### Specifying Root Nodes
 
 The root nodes indicate the span of the grid that they cover. This is done in a
@@ -365,27 +372,17 @@ this is the number of points along the dipole flux tube.
 ```
 
 ```json
-  "ionGrid" : {
-    "Shape" : "dipole",
-    "LatRange" : [-90.0, 90.0],
-    "nLatsPerBlock" : 18,
-    "LonRange" : [0.0, 360.0],
-    "nLonsPerBlock" : 36,
-    "nAlts" : 200,
-        "MinAlt" : 80.0,
-        "MinApex" : 120.0,
-        "MaxAlt" : 5000.0},
+    "ionGrid": {
+        "Shape": "dipole4",
+        "nLonsPerBlock": 36,
+        "nLatsPerBlock": 18,
+        "nAlts": 100,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+	      "LonRange": [0.0, 360.0]},
 ```
 
 The dipole grid has both open field-lines and closed field-lines. The closed
 field-lines are near the equator, while the open field-lines are near the poles.
-The variable `MaxAlt` sets where this differentiation occurs - if the apex
-height of the field-line is above this altitude, then it is open. All
-field-lines in Aether start at the `MinAlt` and rise along a dipolar shape until
-they either encounter the equatorial plane or `MaxAlt`. In the south, these
-field-lines tilt towards the north (from `MinAlt` to `MaxAlt`) and in the north,
-the field-lines tilt towards the south (from `MinAlt` to `MaxAlt`).
-
-- The spacing is uniform in longitude.
-- The spacing along the field-line has non-uniform spacing.
-- The spacing in latitude is non-uniform.
+The variable `MaxAlt` sets where the differentiation occurs - if the apex
+height of all field-lines on this block are above this altitude, then it is open. 
\ No newline at end of file
diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 1b4138ab..91afec57 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -81,13 +81,14 @@
 		"IsUniformAlt" : true,
         "AltFile" : ""},
 
-	"ionGrid": {
-		"Shape": "dipole4",
-		"nLonsPerBlock": 14,
-		"nLatsPerBlock" : 20,
-		"nAlts":30,
-		"AltRange":[80,1500],
-		"LatRange":[7, 87]},
+    "ionGrid": {
+        "Shape": "dipole4",
+        "nLonsPerBlock": 36,
+        "nLatsPerBlock": 18,
+        "nAlts": 100,
+        "LatRange": [10, 80],
+        "AltRange": [80.0, 1000],
+        "LonRange": [0.0, 360.0]},
 
     "Oblate" : {
 		"isOblate" : false,
diff --git a/share/run/aether.json b/share/run/aether.json
index 65ac4f7e..65780a75 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -27,7 +27,7 @@
         "nAlts": 100,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
-	"LonRange": [0.0, 360.0] },
+        "LonRange": [0.0, 360.0]},
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
 

From beba721299f1d758433f6d9b80d8a590de48401a Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 16:46:08 -0400
Subject: [PATCH 100/266] DOC: Add section on OpenMP & toc to running_aether.md

I did some digging. Parallelization is weird.
---
 doc/usage/running_aether.md | 43 +++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/doc/usage/running_aether.md b/doc/usage/running_aether.md
index 2811afa6..7702d8e9 100644
--- a/doc/usage/running_aether.md
+++ b/doc/usage/running_aether.md
@@ -4,6 +4,22 @@ This document assumes you have already downloaded and built the Aether model. If
 not, you should return to [one](../../README.md) of
 [these](../installation/installation.md) pages before continuing.
 
+- [The first run](#the-first-run)
+  - [Running in 1D](#running-in-1d)
+  - [Using OpenMP](#using-openmp)
+- [Output Files](#output-files)
+  - [Blocks](#blocks)
+  - [Ensembles](#ensembles)
+  - [Post processing](#post-processing)
+- [Input Files](#input-files)
+- [defaults.json file](#defaultsjson-file)
+- [For Developers](#for-developers)
+- [aether.json file](#aetherjson-file)
+- [planet.in file](#planetin-file)
+- [orbits.csv file](#orbitscsv-file)
+- [chemistry file](#chemistry-file)
+
+
 ## The first run
 
 Once you have compiled you can run Aether. To remember which runs you're doing,
@@ -70,6 +86,33 @@ to `"sphere"`. No number!
 2. Run the code with `./aether`. This will not use MPI, however armadillo may use
 multiple OpenMP processes for math, so be careful on cluster login nodes.
 
+### Using OpenMP
+
+> This section is mostly a placeholder. Everything is correct, but has little
+> effect on Aether's speed. This is only really a concern on laptops with low core counts
+> or shared systems.
+
+Armadillo contains several optimizations which utilize OpenMP for parallelization beyond
+the block decomposition on the entire sphere. Thus, runs on 4 MPI processors can benefit
+from devoting additional processors to OpenMP parallelization.
+
+The number of OpenMP tasks Armadillo is able to utilize can be set before compiling or
+at runtime. To change this *before* compiling, change the value of`ARMA_OPENMP_THREADS`
+in the [Armadillo config.hpp file](../../share/include/armadillo_bits/config.hpp#173)
+from 8. This will require re-compiling & possibly re-running `cmake`. The more flexible 
+option is to use a variable at runtime:
+
+The easier way to set the number of OpenMP threads is to use the variable 
+`OMP_NUM_THREADS`. This can be set before running the executable with 
+`export OMP_NUM_THREADS=2`, or at runtime with:
+
+```bash
+OMP_NUM_THREADS=2 mpirun -np 4 ./aether
+```
+At this stage in development, there is not much speedup available from OpenMP. For
+example, the change in runtime from the default value of 8 (from Armadillo) and 1
+(disabling OpenMP) in a 10-minute run is about one minute, or about 10%.
+
 ## Output Files
 
 Aether outputs to a subdirectory called UA/output. At this time, all processors

From b6eccccae5b2ddef8a7f89fb8221723199cf8dc9 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Sun, 27 Apr 2025 16:46:44 -0400
Subject: [PATCH 101/266] BUG: Make check_for_nans TRUE be default. there are
 some gremlins

---
 share/run/aether.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index 65780a75..cc3e330d 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -8,7 +8,7 @@
         "iFunctionVerbose" : {
             "Grid::create_altitudes": 0},	
         "dt" : 10.0,
-        "check_for_nans" : false},
+        "check_for_nans" : true},
 
     "EndTime" : [2011, 3, 20, 0, 10, 0],
 

From fc39fa0dc3636adfa02630f1310acf30ac94032f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:04:36 -0400
Subject: [PATCH 102/266] FEAT: added ability to message pass

---
 include/grid.h | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/include/grid.h b/include/grid.h
index c11eb4e6..ebee7cc9 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -267,6 +267,7 @@ class Grid
 
   void set_IsDipole(bool value);
   bool get_IsDipole();
+  bool get_IsClosed();
 
   int64_t get_nPointsInGrid();
 
@@ -327,6 +328,8 @@ class Grid
   void report_grid_boundaries();
   void calc_cent_acc(Planets planet);
 
+  void create_dipole_connection(Quadtree quadtree);
+
   // Make mag-field grid:
   bool init_dipole_grid(Quadtree quadtree_ion, Planets planet);
   // Support functions:
@@ -350,17 +353,26 @@ class Grid
   int iProcYp;
   /// The processor to the South/Down/Y-:
   int iProcYm;
+  // This is special, since message passing in the z direction will only be
+  // between closed magnetic field lines, so we don't need a +/- (p/m):
+  int iProcZ;
 
+  bool isExchangeInitialized = false;
+  
   arma_vec edge_Xp;
   arma_vec edge_Yp;
   arma_vec edge_Xm;
   arma_vec edge_Ym;
+  // again, z will only be in one
+  arma_vec edge_Z;
 
   int64_t iRoot;
   int64_t iRootXp;
   int64_t iRootXm;
   int64_t iRootYp;
   int64_t iRootYm;
+  // again, z will only be in one
+  int64_t iRootZ;
 
   struct messages_struct
   {

From 989984c83121127defce1704bb9c0039f89d6ec1 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:05:50 -0400
Subject: [PATCH 103/266] FEAT: message passing on multiple grids + iDir = 4

---
 src/exchange_messages.cpp | 127 +++++++++++++++++++++++++++++++-------
 1 file changed, 103 insertions(+), 24 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index befbdb7f..8e0a41be 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -105,6 +105,7 @@ bool Ions::exchange_old(Grid &grid) {
 //     1 - top
 //     2 - left
 //     3 - bottom
+//     4 - vertical (k direction) for closed field lines
 //
 // cells (assume gc = 2):
 // 0  1 | 2 3 4 ... n-gc*2 n-gc-1 | n-gc n-1
@@ -124,6 +125,7 @@ bool pack_border(const arma_cube &value,
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
+  int64_t iZstart, iZend;
 
   // ----------------------------
   // left / right message passing
@@ -159,8 +161,22 @@ bool pack_border(const arma_cube &value,
     }
   }
 
+  // ----------------------------
+  // k-dir (only top)
+  if (iDir == 4) {
+    iXstart = nG;
+    iXend = nX - nG;
+    iYstart = nG;
+    iYend = nY - nG;
+    iZstart = nZ - nG;
+    iZend = nZ;
+  } else {
+    iZstart = nG;
+    iZend = nZ - nG;
+  }
+
   try {
-    for (int64_t iZ = nG; iZ < nZ - nG; iZ++) {
+    for (int64_t iZ = iZstart; iZ < iZend; iZ++) {
       for (int64_t iY = iYstart; iY < iYend; iY++) {
         for (int64_t iX = iXstart; iX < iXend; iX++) {
           packed[*iCounter] = value(iX, iY, iZ);
@@ -186,6 +202,7 @@ bool pack_border(const arma_cube &value,
 //     1 - top
 //     2 - left
 //     3 - bottom
+//     4 - k-dir top
 //   DoReverseX and DoReverseY are because packing always happens from
 //     lower left to upper right, while face we are unpacking too may
 //     have a different (left - right and up - down) geometry
@@ -210,7 +227,8 @@ bool unpack_border(arma_cube &value,
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
-  int64_t xInc = 1, yInc = 1;
+  int64_t iZstart, iZend;
+  int64_t xInc = 1, yInc = 1, zInc = 1;
 
   int64_t iXOff = 0;
   int64_t nCx = nX - 2 * nG;
@@ -261,10 +279,26 @@ bool unpack_border(arma_cube &value,
     }
   }
 
+  if (iDir == 4) {
+    iXstart = nG;
+    iXend = nX - nG;
+    iYstart = nG;
+    iYend = nY - nG;
+    // need to reverse direction, since packing is from the bottom up,
+    // which means unpacking should be from the top down
+    iZend = nZ - nG;
+    iZstart = nZ;
+    zInc = -1;
+  }  else {
+    iZstart = nG;
+    iZend = nZ - nG;
+  }
+
   try {
     int64_t iXp, iYp;
 
-    for (int64_t iZ = nG; iZ < nZ - nG; iZ++) {
+    for (int64_t iZ = iZstart; iZ < iZend; iZ += zInc) {
+
       if (XbecomesY) {
         for (int64_t iX = iXstart; iX < iXend; iX += xInc) {
           iXp = iX;
@@ -311,7 +345,6 @@ bool unpack_border(arma_cube &value,
   } catch (...) {
     DidWork = false;
   }
-
   return DidWork;
 }
 
@@ -440,6 +473,12 @@ bool Grid::send_one_var_one_face(int64_t iFace) {
 
   bool DidWork = true;
 
+  if (report.test_verbose(4))
+    std::cout << "in send_one_var_one_face : " << iFace << " from: " <<
+    iProc << " to: " <<
+    interchangesOneVar[iFace].iProc_to << " tag: " <<
+    interchangesOneVar[iFace].iTag << "\n";
+
   MPI_Isend(interchangesOneVar[iFace].buffer,
             interchangesOneVar[iFace].iSizeTotal,
             MPI_BYTE,
@@ -480,6 +519,13 @@ bool Grid::receive_one_var_one_face(int64_t iFace) {
 
   bool DidWork = true;
 
+  if (report.test_verbose(4))
+    std::cout << "in receive_one_var_one_face : " << iFace << " from: " <<
+    iProc << " to: " << 
+    interchangesOneVar[iFace].iProc_to << " tag: " <<
+    interchangesOneVar[iFace].iTag << "\n";
+
+
   MPI_Recv(interchangesOneVar[iFace].rbuffer,
            interchangesOneVar[iFace].iSizeTotal,
            MPI_BYTE,
@@ -508,6 +554,7 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
 
   int64_t nPtsX = nGCs * (nY - nGCs * 2) * (nZ - nGCs * 2);
   int64_t nPtsY = nGCs * (nX - nGCs * 2) * (nZ - nGCs * 2);
+  int64_t nPtsZ = nGCs * (nX - nGCs * 2) * (nY - nGCs * 2);
 
   new_inter.iFace = iDir;
   new_inter.DoReverseX = DoReverseX;
@@ -515,16 +562,24 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
   new_inter.IsPole = IsPole;
   new_inter.XbecomesY = XbecomesY;
 
+  // Along i axis (left or right):
   if (iDir == 0 || iDir == 2) {
     new_inter.iSizeTotal = nVars * nPtsX * sizeof(precision_t);
     new_inter.index.set_size(nGCs, nY);
     new_inter.ratio.set_size(nGCs, nY);
-  } else {
+  }
+
+  // Along j axis (up or down):
+  if (iDir == 1 || iDir == 3) {
     new_inter.iSizeTotal = nVars * nPtsY * sizeof(precision_t);
     new_inter.index.set_size(nGCs, nX);
     new_inter.ratio.set_size(nGCs, nX);
   }
 
+  // Along K axis (up only for now):
+  if (iDir == 4)
+    new_inter.iSizeTotal = nVars * nPtsZ * sizeof(precision_t);
+
   new_inter.buffer = static_cast<precision_t*>(malloc(new_inter.iSizeTotal));
   new_inter.rbuffer = static_cast<precision_t*>(malloc(new_inter.iSizeTotal));
 
@@ -538,7 +593,6 @@ Grid::messages_struct Grid::make_new_interconnection(int64_t iDir,
   return new_inter;
 }
 
-
 /*
 // -----------------------------------------------------------------------------
 // Exchange messages for the NEUTRALS:
@@ -772,6 +826,15 @@ bool Neutrals::exchange_really_old(Grid &grid) {
 
 // -----------------------------------------------------------------------------
 // Initialize interfaces between horizontal sides on a grid
+// Directions:
+//  0 = + i (right)
+//  1 = + j (up)
+//  2 = - i (left)
+//  3 = - j (down)
+//  4 = + k (vertical) - only along closed dipole field lines
+// For the cubesphere grid:
+//  iRoot = 4 is the south polar region
+//  iRoot = 5 is the north polar region
 // -----------------------------------------------------------------------------
 
 bool exchange_sides_init(Grid &grid, int64_t nVarsToPass) {
@@ -911,6 +974,24 @@ bool exchange_sides_init(Grid &grid, int64_t nVarsToPass) {
                                   ReverseY,
                                   XbecomesY));
 
+  if (grid.get_IsDipole() && grid.get_IsClosed()) {
+    // This operates in the N/S (j or Y) direction:
+    ReverseX = false;
+    ReverseY = false;
+    IsPole = false;
+    XbecomesY = false;
+    grid.interchangesOneVar.push_back(
+      grid.make_new_interconnection(4,
+                                    nVarsToPass,
+                                    grid.iProcZ,
+                                    grid.edge_Z,
+                                    IsPole,
+                                    ReverseX,
+                                    ReverseY,
+                                    XbecomesY));
+
+  }
+
   report.exit(function);
   return DidWork;
 }
@@ -942,30 +1023,31 @@ bool exchange_one_var(Grid &grid,
 
   bool DidWork = true;
 
-  int iTag, iDir;
+  int iTag, iDir, nDir;
   int iSpecies;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
+  static int64_t nX = grid.get_nX();
+  static int64_t nY = grid.get_nY();
+  static int64_t nZ = grid.get_nZ();
   static int64_t nG = grid.get_nGCs();
-  static int64_t nPtsX = nG * (nY - nG * 2) * (nZ - nG * 2);
-  static int64_t nPtsY = nG * (nX - nG * 2) * (nZ - nG * 2);
-  static bool IsFirstTime = true;
   static arma_cube var_scgc;
 
   static int64_t nVarsToPass = 1;
 
-  if (IsFirstTime) {
+  if (!grid.isExchangeInitialized) {
     DidWork = exchange_sides_init(grid, nVarsToPass);
-    var_scgc.set_size(nX, nY, nX);
-    IsFirstTime = false;
+    grid.isExchangeInitialized = true;
   }
+  var_scgc.set_size(nX, nY, nZ);
 
   int64_t iP;
   precision_t oneSign = 1.0;
 
-  for (int iDir = 0; iDir < 4; iDir++) {
+  nDir = 4;
+  if (grid.get_IsDipole() && grid.get_IsClosed())
+    nDir++;
+
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (report.test_verbose(4))
       std::cout << "packing one var : " << iDir << " " << iProc
                 << " " << grid.interchangesOneVar[iDir].iProc_to
@@ -991,7 +1073,7 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Send all faces asynchronously:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       report.print(4, "Sending one face");
       DidWork = grid.send_one_var_one_face(iDir);
@@ -999,7 +1081,7 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Receive all faces asynchronously:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       report.print(4, "Receiving one face");
       DidWork = grid.receive_one_var_one_face(iDir);
@@ -1007,13 +1089,13 @@ bool exchange_one_var(Grid &grid,
   }
 
   // Wait for messages to get there:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0)
       MPI_Wait(&grid.interchangesOneVar[iDir].requests, MPI_STATUS_IGNORE);
   }
 
   // Unpack all faces:
-  for (int iDir = 0; iDir < 4; iDir++) {
+  for (int iDir = 0; iDir < nDir; iDir++) {
     if (grid.interchangesOneVar[iDir].iProc_to >= 0) {
       iP = 0;
       report.print(4, "Unpacking Border");
@@ -1029,9 +1111,6 @@ bool exchange_one_var(Grid &grid,
     }
   }
 
-  // Wait for all processors to be done.
-  MPI_Barrier(aether_comm);
-
   // If this is a cubesphere grid, interpolate ghostcells to their proper location
   //if (grid.IsCubeSphereGrid & grid.gcInterpolationSet) {
   //  report.print(3, "Interpolating Ghostcells to Proper Location");

From 1c2ef28a463b1e3336f2a16d61b1dd82bdcbaa64 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:06:30 -0400
Subject: [PATCH 104/266] FEAT: gotta know if dipole and closed!

---
 src/grid.cpp | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/grid.cpp b/src/grid.cpp
index 168a0999..93270b37 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -520,6 +520,23 @@ void Grid::set_IsDipole(bool value) {
   IsDipole = value;
 }
 
+// --------------------------------------------------------------------------
+// Get whether the grid is a dipole grid
+// --------------------------------------------------------------------------
+
+bool Grid::get_IsDipole() {
+  return IsDipole;
+}
+
+// --------------------------------------------------------------------------
+// Get whether the dipole grid is closed (true) or open (false)
+// --------------------------------------------------------------------------
+
+bool Grid::get_IsClosed() {
+  return IsClosed;
+}
+
+
 // --------------------------------------------------------------------------
 // Get total number of grid points
 // --------------------------------------------------------------------------

From 9c9ac140e84313cd38e2af3c7c8570e9a204e0fe Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:07:08 -0400
Subject: [PATCH 105/266] FEAT: dipole quadtree is like sphere quadtree

---
 src/quadtree.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index ff5d45a2..35ebb2e5 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -514,19 +514,18 @@ int64_t Quadtree::find_point(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
-
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+  if (IsDipole)
+    wrap_point = wrap_point_sphere(point);
 
   int64_t iNode = -1;
 
   for (int64_t iRoot = 0; iRoot < nRootNodes; iRoot++) {
     iNode = find_point(wrap_point, root_nodes[iRoot]);
-
     if (iNode > -1)
       break;
   }
-
   return iNode;
 }
 
@@ -542,9 +541,10 @@ int64_t Quadtree::find_root(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
-
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+  if (IsDipole)
+    wrap_point = wrap_point_cubesphere(point);
 
   int64_t iNode = -1, iRoot;
 

From 1f0622b3ad0782fdf2f47f1329cd5b22ce1d7de1 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:07:58 -0400
Subject: [PATCH 106/266] FEAT: connectivity for dipole grid

---
 src/init_mag_grid.cpp | 128 ++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 124 insertions(+), 4 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index c47ecb2f..6add43e5 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -5,6 +5,127 @@
 
 #include "aether.h"
 
+// ----------------------------------------------------------------------
+// Create connectivity between the nodes for message passing for dipole
+//  (this looks a lot like sphere, since they are very related)
+// ----------------------------------------------------------------------
+
+void Grid::create_dipole_connection(Quadtree quadtree) {
+
+  std::string function = "Grid::create_dipole_connection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  IsLatLonGrid = true;
+
+  // Get some coordinates and sizes in normalized coordinates:
+  arma_vec lower_left_norm = quadtree.get_vect("LL");
+  arma_vec middle_norm = quadtree.get_vect("MID");
+  arma_vec size_right_norm = quadtree.get_vect("SR");
+  arma_vec size_up_norm = quadtree.get_vect("SU");
+
+  // Move to the next block in 4 directions:
+  arma_vec down_norm = middle_norm - 0.51 * size_up_norm;
+  arma_vec up_norm = middle_norm + 0.51 * size_up_norm;
+  arma_vec left_norm = middle_norm - 0.51 * size_right_norm;
+  arma_vec right_norm = middle_norm + 0.51 * size_right_norm;
+
+  // The first component could wrap around:
+  right_norm(0) = fmod(right_norm(0), quadtree.limit_high(0));
+  left_norm(0) = fmod((left_norm(0) + quadtree.limit_high(0)),
+                      quadtree.limit_high(0));
+
+  // These should be the exact edge of the face.
+  // The from and to processors should get these in the same place,
+  // so they can be used to match which processor to send / receive info
+  edge_Xp = middle_norm + size_right_norm / 2.0;
+  // wrap in longitude:
+  edge_Xp(0) = fmod(edge_Xp(0), quadtree.limit_high(0));
+  edge_Xm = middle_norm - size_right_norm / 2.0;
+  edge_Yp = middle_norm + size_up_norm / 2.0;
+  edge_Ym = middle_norm - size_up_norm / 2.0;
+  // by default, edge_Z isn't even an edge, since most processors should 
+  // not exchange messages in the Z direction.
+  edge_Z = middle_norm;
+
+  iProcYm = quadtree.find_point(down_norm) + iMember * nGrids;
+  iProcYp = quadtree.find_point(up_norm) + iMember * nGrids;
+  iProcXm = quadtree.find_point(left_norm) + iMember * nGrids;
+  iProcXp = quadtree.find_point(right_norm) + iMember * nGrids;
+  iProcZ = iProc;
+
+  iRoot = quadtree.find_root(middle_norm);
+  iRootYm = quadtree.find_root(down_norm);
+  iRootYp = quadtree.find_root(up_norm);
+  iRootXm = quadtree.find_root(left_norm);
+  iRootXp = quadtree.find_root(right_norm);
+  iRootZ = iRoot;
+
+  // If we are a closed field-line, then we want to exchange messages
+  // along the Z direction, which turns out to be the same processor
+  // as the Y direction, so just take that one:
+  IsClosed = false;
+  if ((middle_norm(1) < 0) && (up_norm(1) > 0)) {
+    // We are in the south and need to pass to the north:
+    iRootZ = iRootYp;
+    iProcZ = iProcYp;
+    edge_Z = edge_Yp;
+    // To make the point unique, we need to alter the edge location
+    // otherwise the message passing will get confused. Since the
+    // points are "higher" than the other edges, let's just add some
+    // to the 3rd dimension:
+    edge_Z(2) = 5.0;
+    IsClosed = true;
+  }
+  if ((middle_norm(1) > 0) && (down_norm(1) < 0)) {
+    // We are in the north and need to pass to the south:
+    iRootZ = iRootYm;
+    iProcZ = iProcYm;
+    edge_Z = edge_Ym;
+    // See note above...
+    edge_Z(2) = 5.0;
+    IsClosed = true;
+  }
+
+  // Check if touching South Pole:
+  if (lower_left_norm(1) == quadtree.limit_low(1)) {
+    DoesTouchSouthPole = true;
+
+    // edges need to be adjusted to deal with longitudes, since the
+    // pole will 180deg different for the from and to processors
+    if (edge_Ym(0) < 1.0)
+      edge_Ym(0) += 0.5;
+    else
+      edge_Ym(0) -= 0.5;
+  }
+
+  // Check if touching North Pole:
+  if (lower_left_norm(1) + size_up_norm(1) == quadtree.limit_high(1)) {
+    DoesTouchNorthPole = true;
+
+    // edge need to be adjusted to deal with longitudes, since the
+    // pole will 180deg different for the from and to processors
+    if (edge_Yp(0) < 1.0)
+      edge_Yp(0) += 0.5;
+    else
+      edge_Yp(0) -= 0.5;
+  }
+
+  if (report.test_verbose(2))
+    std::cout << "connectivity : "
+              << "  iProc : " << iProc << "\n"
+              << "  isnorth : " << DoesTouchNorthPole << "\n"
+              << "  issouth : " << DoesTouchSouthPole << "\n"
+              << "  iProcYm : " << iProcYm << "\n"
+              << "  iProcYp : " << iProcYp << "\n"
+              << "  iProcXm : " << iProcXm << "\n"
+              << "  iProcXp : " << iProcXp << "\n";
+
+  report.exit(function);
+  return;
+}
+
+
 // ----------------------------------------------------------------------
 // Routine to convert p and q to r and theta. Can be solved iteratively,
 // or with approach from (Swisdak, 2006), who solved it analytically:
@@ -99,10 +220,9 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsCubeSphereGrid = false;
   IsDipole = true;
 
-  // report.print(0, "Creating inter-node connections Grid");
-
-  //if (!Is0D & !Is1Dz)
-  //  create_sphere_connection(quadtree_ion);
+  report.print(0, "Creating inter-node dipole connections");
+  if (!Is0D & !Is1Dz)
+    create_dipole_connection(quadtree_ion);
 
   report.print(0, "Creating Dipole Grid");
 

From 12ba82c8f08778739bb1d96965737b06c0bc0f2d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 30 Apr 2025 10:08:31 -0400
Subject: [PATCH 107/266] FEAT: exchange messages for ion grid

---
 src/advance.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/advance.cpp b/src/advance.cpp
index c474636e..b85395ae 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -127,6 +127,7 @@ bool advance(Planets &planet,
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
     neutrals.exchange_old(gGrid);
     ions.exchange_old(gGrid);
+    ionsMag.exchange_old(mGrid);
     advect(gGrid, time, neutrals);
   }
 

From 0bb1649e7e157fce367a70b45c29d829a7a2ae8f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 30 Apr 2025 11:16:30 -0400
Subject: [PATCH 108/266] BUG: row/col switched in solver_gradients 4o_j

---
 src/solver_gradients.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 6773f30e..ef1c06c7 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -264,7 +264,7 @@ arma_cube calc_gradient4o_j(arma_cube value, Grid grid) {
     iY = 0;
     gradient.col(iY) =
       (value.col(iY + 1) - value.col(iY)) /
-      grid.dj_center_m_scgc.row(iY);
+      grid.dj_center_m_scgc.col(iY);
 
     // Upper (one sided):
     iY = nY - 1;

From 4961430013ba9fd7bfb5b1d44e95c5f9908eb8b2 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 09:42:05 -0400
Subject: [PATCH 109/266] BUG: init_mag_grid wasn't correctly identifying if
 its touching the pole

---
 src/init_mag_grid.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 6add43e5..8de44039 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -350,7 +350,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   // - evenly space the ghost cells between these.
 
   // Check if we're touching the pole, need to look at original quadtree values
-  if ((abs(lower_left_norm(1) + size_up_norm(1)) > 0.49) // north pole
+  if ((lower_left_norm(1) + size_up_norm(1) > 0.49) // north pole
       || (lower_left_norm(1) < -0.49)) { // south pole
     lat1dDown(nLats) = 89.9 * cDtoR;
     lat1dDown(nLats - 1) = (lat1dDown(nLats) + lat1dDown(nLats - 2)) / 2.0;

From c740361928b267312c102ca99e302bf9c3cc8c2f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 09:43:05 -0400
Subject: [PATCH 110/266] STY: astyle, yo

---
 src/init_mag_grid.cpp | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8de44039..f5c3f584 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -44,7 +44,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
   edge_Xm = middle_norm - size_right_norm / 2.0;
   edge_Yp = middle_norm + size_up_norm / 2.0;
   edge_Ym = middle_norm - size_up_norm / 2.0;
-  // by default, edge_Z isn't even an edge, since most processors should 
+  // by default, edge_Z isn't even an edge, since most processors should
   // not exchange messages in the Z direction.
   edge_Z = middle_norm;
 
@@ -65,6 +65,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
   // along the Z direction, which turns out to be the same processor
   // as the Y direction, so just take that one:
   IsClosed = false;
+
   if ((middle_norm(1) < 0) && (up_norm(1) > 0)) {
     // We are in the south and need to pass to the north:
     iRootZ = iRootYp;
@@ -77,6 +78,7 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
     edge_Z(2) = 5.0;
     IsClosed = true;
   }
+
   if ((middle_norm(1) > 0) && (down_norm(1) < 0)) {
     // We are in the north and need to pass to the south:
     iRootZ = iRootYm;
@@ -221,6 +223,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsDipole = true;
 
   report.print(0, "Creating inter-node dipole connections");
+
   if (!Is0D & !Is1Dz)
     create_dipole_connection(quadtree_ion);
 
@@ -608,11 +611,11 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   }
 
   // Gravity should be negative in the k direction, since the grid switches directions.
-  // j direction should switch signs when crossing the equator (+ in south, - in north)  
-  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
-                                                % sin(magLat_scgc)), 0.5);
-  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc)) / pow(abs(1.0 + 3.0 * sin(magLat_scgc)
-                                                      % sin(magLat_scgc)), 0.5);
+  // j direction should switch signs when crossing the equator (+ in south, - in north)
+  rad_unit_vcgc[1] = sign(magLat_scgc) % cos(magLat_scgc)
+                     / pow(abs(1.0 + 3.0 * sin(magLat_scgc) % sin(magLat_scgc)), 0.5);
+  rad_unit_vcgc[2] = - 2.0 * abs(sin(magLat_scgc))
+                     / pow(abs(1.0 + 3.0 * sin( magLat_scgc) % sin(magLat_scgc)), 0.5);
 
   precision_t mu = planet.get_mu();
   gravity_vcgc[1] = mu * rad_unit_vcgc[1] % radius2i_scgc;

From eb9e29b5cbce1de3e49a96623d6b3e80cf5a082d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 8 May 2025 13:49:22 -0400
Subject: [PATCH 111/266] BUG: Dipole missing factor of 2 in number of GCs

---
 edu/examples/Dipole/MoreDipoleInfo.ipynb | 12 ++++++------
 edu/examples/Dipole/dipole.py            |  2 +-
 src/init_mag_grid.cpp                    |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/edu/examples/Dipole/MoreDipoleInfo.ipynb b/edu/examples/Dipole/MoreDipoleInfo.ipynb
index 6d93769d..547388a1 100644
--- a/edu/examples/Dipole/MoreDipoleInfo.ipynb
+++ b/edu/examples/Dipole/MoreDipoleInfo.ipynb
@@ -130,7 +130,7 @@
     "# call dipole.main, which returns q & p\n",
     "# These values are a bit unreasonable, but it makes the plots more clear.\n",
     "qs, ps = dipole.main(alt_minRE=1.01, alt_maxRE=1.3,\n",
-    "                     lat_min=15, lat_max=80,\n",
+    "                     lat_min=15, lat_max=84,\n",
     "                     origins=origins, extent=extent, \n",
     "                     nLatsPerBlock=14, nAltsPerBlock=18)\n",
     "\n",
@@ -178,7 +178,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -311,7 +311,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 4 Axes>"
       ]
@@ -367,9 +367,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:mypy]",
+   "display_name": "Python [conda env:interactive]",
    "language": "python",
-   "name": "conda-env-mypy-py"
+   "name": "conda-env-interactive-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -381,7 +381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/edu/examples/Dipole/dipole.py b/edu/examples/Dipole/dipole.py
index 676a0b3e..f188a3ea 100755
--- a/edu/examples/Dipole/dipole.py
+++ b/edu/examples/Dipole/dipole.py
@@ -53,7 +53,7 @@ def main(alt_minRE, alt_maxRE, lat_min, lat_max, origins, extent, nLatsPerBlock,
             origin  = -1*origin - extent
 
         lat0 = (2*(lat_max - lat_min))*origin    
-        dlat = extent * ((lat_max - lat_min)*2) / (nLatsPerBlock - nGCs)
+        dlat = extent * (2*(lat_max - lat_min)) / (nLatsPerBlock - nGCs*2)
         
         # Put latitudes down evenly (centers & corners)
         # - This forms the invariant latitudes which field lines must pass thru
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index f5c3f584..1bdfd0c2 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -335,7 +335,7 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
 
   precision_t lat0 = 2.0 * (max_lat - min_lat) * lat_origin;
   precision_t dlat = 2.0 * size_up_norm(1) * (max_lat -  min_lat) /
-                     (nLats - nGCs);
+                     (nLats - nGCs * 2);
 
   arma_vec lat1d(nLats);
   arma_vec lat1dDown(nLats + 1);

From d86b038bc0e7e6f0a0c688b6cb7f2aa232647400 Mon Sep 17 00:00:00 2001
From: Aaron James Ridley <ridley@umich.edu>
Date: Tue, 13 May 2025 15:23:29 -0400
Subject: [PATCH 112/266] DOC: describe using astyle within vscode

---
 CONTRIBUTING.md | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 07ced9ce..6f8bcef8 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -34,29 +34,20 @@ on WSL with Ubuntu, the command seems to be:
 
 astyle --options=.astylerc src/*.cpp
 
-#### Linting
+Astyle can also be used within VSCode as an extension. Install the "Astyle" extension (author: Chieh Yu).  Then go to settings (the "Manage" wheel on the bottom left, then click on "settings"). Under "Extensions", click on "Astyle Configuration".  Within that, you can set the location of the astylerc file. Click on "Edit in settings.json". Modify the file so it has the following line:
+    "[cpp]": {
+        "editor.defaultFormatter": "chiehyu.vscode-astyle",
+        "editor.formatOnSave": true
+    },
+    "astyle.astylerc": "${workspaceRoot}/.astylerc"
 
-For *C++* code make sure to use a static code checker like
-[cpplint](https://github.com/cpplint/cpplint) to check the code for
-any style issues before submitting.  For *Python*,
-[flake8](https://flake8.pycqa.org/en/latest/) is a good option.  Both
-of these may by installed using pip.
+This should then allow you to format the code using the defaults that we have set in the astyle file. This can be done with (Ctrl-Shift-I for Linux, Alt-Shift-F for Windows) and upon saving the file - this reformats the whole file to put it into the astyle format.
 
-To install `cpplint`
+#### Linting
 
-```sh
-# depending on your system one of these lines applies
-pip install --user cpplint
-pip install cpplint
-python3 -m pip cpplint
-python3 -m pip --user cpplint
-```
+For *Python*,
+[flake8](https://flake8.pycqa.org/en/latest/) is a good option.  This may by installed using pip.
 
-Using a linter in an editor is a good supplement, but not a replacement for the
-static linters. The linter in the 'atom' editor requires that you install the
-`linter` and `gcc-linter` packages. Atom also has additional packages
-`whitespaces` and `tabs-to-spaces` to automatically remove whitespaces at the
-end of the lines, and convert tabs to spaces.
 
 ## Commit Styling
 

From d8c29af5ff73cd3ce328b717d7fdee3421e0b7f6 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:00:48 -0400
Subject: [PATCH 113/266] BUG: need a communicator for members of same ensemble

---
 include/parallel.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/parallel.h b/include/parallel.h
index 6da886c5..11372be3 100644
--- a/include/parallel.h
+++ b/include/parallel.h
@@ -30,6 +30,7 @@ extern std::string cGrid;
 
 /// communicator for all of aether
 extern MPI_Comm aether_comm;
+extern MPI_Comm aether_member_comm;
 
 /**********************************************************************
   \brief initialize mpi and figure out ensembles and grid blocks

From fe71babaf326c466d4eaeea6fc749d7f93bec290 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:02:54 -0400
Subject: [PATCH 114/266] BUG: static causes issues for second grid

---
 src/exchange_messages.cpp | 138 +++++++++++++++++++++++++++++---------
 1 file changed, 107 insertions(+), 31 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index 8e0a41be..895bb854 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -36,6 +36,58 @@ bool Neutrals::exchange_old(Grid &grid) {
   return DidWork;
 }
 
+void average_value_at_pole(Grid &grid, arma_cube &value, arma_cube &velocity,
+                           int64_t iLast, int64_t iPole,
+                           bool doesTouchPole) {
+  // Now let's deal with the poles:
+  // north pole first:
+  int64_t iX, nX = grid.get_nX();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t iY, iInc = 1;
+
+  if (iPole < iLast)
+    iInc = -1;
+
+  double weight, sumValue, sumWeight, totalValue, totalWeight, poleValue;
+
+  for (iZ = nGCs; iZ < nZ - nGCs; iZ++) {
+
+    sumValue = 0.0;
+    sumWeight = 0.0;
+
+    if (doesTouchPole) {
+      for (iX = nGCs; iX < nX - nGCs; iX++) {
+        // determine the weight based on the northward velocity:
+        weight = iInc * velocity(iX, iLast, iZ) / 1000.0 + 1.0;
+
+        if (weight < 0.01)
+          weight = 0.01;
+
+        sumValue = sumValue + value(iX, iLast, iZ) * weight;
+        sumWeight = sumWeight + weight;
+      }
+    }
+
+    MPI_Allreduce(&sumValue, &totalValue, 1, MPI_DOUBLE, MPI_SUM,
+                  aether_comm);
+    MPI_Allreduce(&sumWeight, &totalWeight, 1, MPI_DOUBLE, MPI_SUM,
+                  aether_comm);
+    poleValue = totalValue / totalWeight;
+
+    //if (iZ == 10)
+    //std::cout << "pole value : " << poleValue << " " << totalValue << " " << totalWeight << " "
+    //<< iProc << " " << doesTouchPole << "\n";
+
+    if (doesTouchPole) {
+      for (iX = nGCs; iX < nX - nGCs; iX++)
+        for (iY = iLast + iInc; iY == iPole; iY += iInc)
+          value(iX, iY, iZ) = poleValue;
+    }
+  }
+
+}
+
 // -----------------------------------------------------------------------------
 // This is the main exchange messages for the neutrals.
 //   We are exchanging densities, temperatures, and velocities
@@ -53,7 +105,7 @@ bool Ions::exchange_old(Grid &grid) {
   for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
     DidWork = exchange_one_var(grid, species[iSpecies].density_scgc, false);
 
-  DidWork = exchange_one_var(grid, temperature_scgc, false);
+  //DidWork = exchange_one_var(grid, temperature_scgc, false);
   DidWork = exchange_one_var(grid, electron_temperature_scgc, false);
 
   // velocity components:
@@ -64,6 +116,25 @@ bool Ions::exchange_old(Grid &grid) {
   // don't reverse vertical across the pole:
   DidWork = exchange_one_var(grid, velocity_vcgc[2], false);
 
+  int64_t iPole = grid.get_nY() - 1;
+  int64_t iLast = iPole - nGCs;
+
+  average_value_at_pole(grid, temperature_scgc, velocity_vcgc[1],
+                        iLast, iPole, grid.DoesTouchNorthPole);
+
+  for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
+    average_value_at_pole(grid, species[iSpecies].density_scgc, velocity_vcgc[1],
+                          iLast, iPole, grid.DoesTouchNorthPole);
+
+  iPole = 0;
+  iLast = nGCs;
+  average_value_at_pole(grid, temperature_scgc, velocity_vcgc[1],
+                        iLast, iPole, grid.DoesTouchSouthPole);
+
+  for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
+    average_value_at_pole(grid, species[iSpecies].density_scgc, velocity_vcgc[1],
+                          iLast, iPole, grid.DoesTouchSouthPole);
+
   report.exit(function);
   return DidWork;
 }
@@ -119,9 +190,9 @@ bool pack_border(const arma_cube &value,
                  int iDir) {
 
   bool DidWork = true;
-  static int64_t nX = value.n_rows;
-  static int64_t nY = value.n_cols;
-  static int64_t nZ = value.n_slices;
+  int64_t nX = value.n_rows;
+  int64_t nY = value.n_cols;
+  int64_t nZ = value.n_slices;
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
@@ -221,9 +292,9 @@ bool unpack_border(arma_cube &value,
                    bool XbecomesY) {
 
   bool DidWork = true;
-  static int64_t nX = value.n_rows;
-  static int64_t nY = value.n_cols;
-  static int64_t nZ = value.n_slices;
+  int64_t nX = value.n_rows;
+  int64_t nY = value.n_cols;
+  int64_t nZ = value.n_slices;
 
   int64_t iXstart, iXend;
   int64_t iYstart, iYend;
@@ -345,6 +416,7 @@ bool unpack_border(arma_cube &value,
   } catch (...) {
     DidWork = false;
   }
+
   return DidWork;
 }
 
@@ -394,7 +466,7 @@ bool pack_one_var_on_one_face(arma_cube var_scgc,
                               int iDirToPass,
                               Grid &grid) {
 
-  static int nG = grid.get_nGCs();
+  int nG = grid.get_nGCs();
   int iDir = grid.interchangesOneVar[iDirToPass].iFace;
   int iReceiver = grid.interchangesOneVar[iDirToPass].iProc_to;
   precision_t *buffer = grid.interchangesOneVar[iDirToPass].buffer;
@@ -475,9 +547,9 @@ bool Grid::send_one_var_one_face(int64_t iFace) {
 
   if (report.test_verbose(4))
     std::cout << "in send_one_var_one_face : " << iFace << " from: " <<
-    iProc << " to: " <<
-    interchangesOneVar[iFace].iProc_to << " tag: " <<
-    interchangesOneVar[iFace].iTag << "\n";
+              iProc << " to: " <<
+              interchangesOneVar[iFace].iProc_to << " tag: " <<
+              interchangesOneVar[iFace].iTag << "\n";
 
   MPI_Isend(interchangesOneVar[iFace].buffer,
             interchangesOneVar[iFace].iSizeTotal,
@@ -521,9 +593,9 @@ bool Grid::receive_one_var_one_face(int64_t iFace) {
 
   if (report.test_verbose(4))
     std::cout << "in receive_one_var_one_face : " << iFace << " from: " <<
-    iProc << " to: " << 
-    interchangesOneVar[iFace].iProc_to << " tag: " <<
-    interchangesOneVar[iFace].iTag << "\n";
+              iProc << " to: " <<
+              interchangesOneVar[iFace].iProc_to << " tag: " <<
+              interchangesOneVar[iFace].iTag << "\n";
 
 
   MPI_Recv(interchangesOneVar[iFace].rbuffer,
@@ -1026,24 +1098,28 @@ bool exchange_one_var(Grid &grid,
   int iTag, iDir, nDir;
   int iSpecies;
 
-  static int64_t nX = grid.get_nX();
-  static int64_t nY = grid.get_nY();
-  static int64_t nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
-  static arma_cube var_scgc;
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
+  arma_cube var_scgc;
 
-  static int64_t nVarsToPass = 1;
+  int64_t nVarsToPass = 1;
 
   if (!grid.isExchangeInitialized) {
     DidWork = exchange_sides_init(grid, nVarsToPass);
     grid.isExchangeInitialized = true;
+    std::cout << "initializing : " << grid.get_gridtype() << " " << nZ << " " <<
+              iProc << "\n";
   }
+
   var_scgc.set_size(nX, nY, nZ);
 
   int64_t iP;
   precision_t oneSign = 1.0;
 
   nDir = 4;
+
   if (grid.get_IsDipole() && grid.get_IsClosed())
     nDir++;
 
@@ -1139,10 +1215,10 @@ bool test_ghostcell_interpolation(Grid &grid) {
 
   bool didWork = true;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
+  int64_t iX, nX = grid.get_nX();
+  int64_t iY, nY = grid.get_nY();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
   int64_t iStart, iEnd, jStart, jEnd, iDir;
 
   // Check the latitudes and longitudes to make sure that they map to
@@ -1252,9 +1328,9 @@ arma_cube interpolate_ghostcells(arma_cube varIn, Grid &grid) {
   bool didWork = true;
 
   int64_t iDir;
-  static int64_t iX, ix_, nX = grid.get_nX();
-  static int64_t iY, iy_, nY = grid.get_nY();
-  static int64_t iG, nG = grid.get_nGCs();
+  int64_t iX, ix_, nX = grid.get_nX();
+  int64_t iY, iy_, nY = grid.get_nY();
+  int64_t iG, nG = grid.get_nGCs();
   precision_t r_;
   arma_cube varOut = varIn;
 
@@ -1322,10 +1398,10 @@ bool find_ghostcell_interpolation_coefs(Grid &grid) {
 
   bool didWork = true;
 
-  static int64_t iX, nX = grid.get_nX();
-  static int64_t iY, nY = grid.get_nY();
-  static int64_t iZ, nZ = grid.get_nZ();
-  static int64_t nG = grid.get_nGCs();
+  int64_t iX, nX = grid.get_nX();
+  int64_t iY, nY = grid.get_nY();
+  int64_t iZ, nZ = grid.get_nZ();
+  int64_t nG = grid.get_nGCs();
 
   // Test to see if the longitudes are the same as the original
   arma_cube yOther = grid.refy_angle * cRtoD;

From 165582890d29ed1a3fc8961876b3333d4033496b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:03:55 -0400
Subject: [PATCH 115/266] FEAT: new communicator for ensemble

---
 src/init_parallel.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/init_parallel.cpp b/src/init_parallel.cpp
index c940148a..e89751d2 100644
--- a/src/init_parallel.cpp
+++ b/src/init_parallel.cpp
@@ -22,6 +22,7 @@ std::string cMember;
 std::string cGrid;
 
 MPI_Comm aether_comm;
+MPI_Comm aether_member_comm;
 
 bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion) {
 
@@ -71,6 +72,11 @@ bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion) {
     iMember = iProc / nGrids;
     iGrid = iProc % nGrids;
 
+    // Need a communicator for each ensemble member, this allows
+    // communication between all blocks in one ensemble member without
+    // the others getting the messages:
+    MPI_Comm_split(aether_comm, iMember, iGrid, &aether_member_comm);
+
     if (report.test_verbose(2))
       std::cout << "iProc : " << iProc
                 << "; iMember : " << iMember

From 5fe11f9af853b870389ae468dd67712107423d38 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 14 May 2025 07:05:16 -0400
Subject: [PATCH 116/266] FEAT: sync mean across ensemble member

---
 src/tools.cpp | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/tools.cpp b/src/tools.cpp
index d041bf49..c62b3172 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -238,6 +238,23 @@ precision_t sync_mean_across_all_procs(precision_t value) {
   return global_value;
 }
 
+// ----------------------------------------------------------------------------
+// Calculate the average value across all processors
+//   - this is the same as sync_mean_across_all_procs, but is limited to
+//     processors in a given member
+// ----------------------------------------------------------------------------
+
+precision_t sync_mean_across_member(precision_t value) {
+  precision_t global_value;
+  double vSend, vReceive;
+  double nSend, nReceive;
+  vSend = value;
+  nSend = 1.0;
+  MPI_Allreduce(&vSend, &vReceive, 1, MPI_DOUBLE, MPI_SUM, aether_member_comm);
+  MPI_Allreduce(&nSend, &nReceive, 1, MPI_DOUBLE, MPI_SUM, aether_member_comm);
+  global_value = vReceive / nReceive;
+  return global_value;
+}
 // ----------------------------------------------------------------------------
 // Generate a vector of normally distributed random doubles
 // ----------------------------------------------------------------------------

From 243b38debb42cc2dde9a9dab9a8a515f9bece2ff Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 19:57:08 -0400
Subject: [PATCH 117/266] FEAT: get grid to grid message passing to work

---
 src/grid_match.cpp | 164 +++++++++++++++++++++++++++++++++++++++------
 1 file changed, 145 insertions(+), 19 deletions(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 39639dab..ecb62c92 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -62,11 +62,27 @@ bool exchange_information(int64_t *nPointsToPass,
   return true;
 }
 
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+// -----------------------------------------------------------------------------
+// This function:
+//   on the requesting information side:
+//     - figures out which processor each point of the other grid is on
+//     - counts the points for each processor
+//     - exchanges how many points to pass for each processor
+//     - makes lists of coordinates to send to each processor
+//     - sends those lists
+//   on the interpolator side:
+//     - builds interpolators for the requested information
+// -----------------------------------------------------------------------------
+
+bool grid_match(Grid &gGrid, 
+                Grid &mGrid, 
                 Quadtree gQuadtree,
                 Quadtree mQuadtree) {
 
+  std::string function = "grid_match";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
   // Let's do magnetic to geographic first:
 
   int64_t iX, mnX = mGrid.get_nX();
@@ -86,6 +102,7 @@ bool grid_match(Grid gGrid,
 
   // This is not the most efficient way to do this, but the first pass, let's
   // just count how many points we need to send to the other processors:
+  mGrid.gridToGridMap.set_size(mnX, mnY, mnZ);
   for (iX = mGCs; iX < mnX - mGCs; iX++) {
     for (iY = mGCs; iY < mnY - mGCs; iY++) {
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
@@ -103,6 +120,7 @@ bool grid_match(Grid gGrid,
         if (jNode < 0 || jNode >= nGrids) {
             std::cout << "out of bounds!!! " << jNode << "\n";
         }
+        mGrid.gridToGridMap(iX, iY, iZ) = jNode;
         nPointsToPass[jNode] = nPointsToPass[jNode]+1;
         /* std::cout << "lon, lat, node: " << lon*cRtoD << " "
             << lat*cRtoD << " "
@@ -115,13 +133,13 @@ bool grid_match(Grid gGrid,
       }
     }
   }
-  std::cout << "made it here: " << iProc << "\n";
   MPI_Barrier(aether_comm);
 
-  for (jNode = 0; jNode < nGrids ; jNode++)
-    std::cout << "nPtsToPass : " << iProc << " " << nPointsToPass[jNode] << "\n";
-
-  std::cout << "sending number of points :\n";
+  if (report.test_verbose(3)) {
+    for (jNode = 0; jNode < nGrids ; jNode++)
+      std::cout << "nPtsToPass : " << iProc << " " << nPointsToPass[jNode] << "\n";
+    std::cout << "sending number of points :\n";
+  }
 
   // This section sends the number of points that need to be transfered to each processor.
   // Then the processor saves the number of points, so it can be remembered, and both the
@@ -135,27 +153,23 @@ bool grid_match(Grid gGrid,
     nPointsToReceive[jNode] = nPointsDummy[iGrid];
   }
 
-  MPI_Barrier(aether_comm);
-
-  for (jNode = 0; jNode < nGrids ; jNode++) {
-    std::cout << "nPtsToReceive : " << iProc << " " << jNode << " " << nPointsToReceive[jNode] << "\n";
-    MPI_Barrier(aether_comm);
+  if (report.test_verbose(3)) {
+    for (jNode = 0; jNode < nGrids ; jNode++) {
+      std::cout << "nPtsToReceive : " << iProc << " " << jNode << " " << nPointsToReceive[jNode] << "\n";
+    }
   }
 
   //  Now we need to create an array of send points and an array of receive points.
   std::vector<precision_t *> latsToPass(nGrids);
   std::vector<precision_t *> lonsToPass(nGrids);
   std::vector<precision_t *> altsToPass(nGrids);
-  for (jNode = 0; jNode < nGrids ; jNode++) {
-    latsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-    lonsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-    altsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-  }
-
   std::vector<precision_t *> latsToInterTo(nGrids);
   std::vector<precision_t *> lonsToInterTo(nGrids);
   std::vector<precision_t *> altsToInterTo(nGrids);
   for (jNode = 0; jNode < nGrids ; jNode++) {
+    latsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
+    lonsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
+    altsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
     latsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
     lonsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
     altsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
@@ -186,11 +200,123 @@ bool grid_match(Grid gGrid,
     }
   }
   bool didWork;
+  // Pass first coordinate (lons)
+  didWork = exchange_information(nPointsToPass,
+                                 lonsToPass,
+                                 nPointsToReceive,
+                                 lonsToInterTo);
+  // Pass second coordinate (lats)
   didWork = exchange_information(nPointsToPass,
                                  latsToPass,
                                  nPointsToReceive,
                                  latsToInterTo);
+  // Pass third coordinate (alts):
+  didWork = exchange_information(nPointsToPass,
+                                 altsToPass,
+                                 nPointsToReceive,
+                                 altsToInterTo);
 
+  if (report.test_verbose(2)) {
+    for (jNode = 0; jNode < nGrids ; jNode++) {
+      std::cout << "Received the following points from iGrid = " << jNode << "\n";
+      std::cout << " -> points received : " << nPointsToReceive[jNode] << "\n";
+      for (int64_t iPt = 0; iPt < nPointsToReceive[jNode]; iPt++) 
+        std::cout << "  -> " << iPt << " "
+                    << lonsToInterTo[jNode][iPt] << " "
+                    << latsToInterTo[jNode][iPt] << " "
+                    << altsToInterTo[jNode][iPt] << "\n";
+    }
+  }
 
-  return true;
+  struct grid_to_grid_t oneGrid;
+
+  int64_t nPts;
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    // These are backwards now, since we will switch sender and reciever:
+    oneGrid.nPts = nPointsToReceive[jNode];
+    oneGrid.nPtsReceive = nPointsToPass[jNode];
+    oneGrid.iProcTo = iMember * nGrids + jNode;
+    if (report.test_verbose(2))
+      std::cout << "Making interpolation coefficients for : " << jNode 
+        << "; points : " << oneGrid.nPts << "\n";
+    if (oneGrid.nPts > 0) {
+      // Interpolation function takes vectors, 
+      // so transfer these arrays to vectors:
+      std::vector<precision_t> Lons(oneGrid.nPts);
+      std::vector<precision_t> Lats(oneGrid.nPts);
+      std::vector<precision_t> Alts(oneGrid.nPts);
+      for (int64_t iPt = 0; iPt < oneGrid.nPts; iPt++) {
+        Lons[iPt] = lonsToInterTo[jNode][iPt];
+        Lats[iPt] = latsToInterTo[jNode][iPt];
+        Alts[iPt] = altsToInterTo[jNode][iPt];
+      }
+      oneGrid.interpCoefs = gGrid.get_interpolation_coefs(Lons, Lats, Alts);
+    }
+    gGrid.gridToGridCoefs.push_back(oneGrid);
+  }
+
+  report.exit(function);
+  return didWork;
 }
+
+bool get_data_from_other_grid(Grid &gGrid, 
+                              Grid &mGrid,
+                              arma_cube &gData,
+                              arma_cube &mData) {
+
+  std::string function = "get_data_from_other_grid";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  int64_t jNode, iPt;
+  std::vector<precision_t *> dataToSend(nGrids);
+  std::vector<precision_t *> dataToReceive(nGrids);
+  int64_t *nPointsToSend = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+
+  for (jNode = 0; jNode < nGrids ; jNode++) {
+    if (report.test_verbose(2))
+      std::cout << "nPts : " << jNode << " " << gGrid.gridToGridCoefs[jNode].nPts << "\n";
+    nPointsToSend[jNode] = gGrid.gridToGridCoefs[jNode].nPts;
+    nPointsToReceive[jNode] = gGrid.gridToGridCoefs[jNode].nPtsReceive;
+    dataToSend[jNode] = static_cast<precision_t*>(malloc(gGrid.gridToGridCoefs[jNode].nPts * sizeof(precision_t)));
+    dataToReceive[jNode] = static_cast<precision_t*>(malloc(gGrid.gridToGridCoefs[jNode].nPtsReceive * sizeof(precision_t)));
+    std::vector<precision_t> values = gGrid.get_interpolation_values(gData, gGrid.gridToGridCoefs[jNode].interpCoefs);
+    
+    for (iPt = 0; iPt < gGrid.gridToGridCoefs[jNode].nPts; iPt++) {
+      dataToSend[jNode][iPt] = values[iPt];
+      if (report.test_verbose(2))
+        std::cout << "datatosend : " << iPt << " " << dataToSend[jNode][iPt] << "\n";
+    }
+  }
+  bool didWork = exchange_information(nPointsToSend,
+                                      dataToSend,
+                                      nPointsToReceive,
+                                      dataToReceive);
+  int64_t iX, mnX = mGrid.get_nX();
+  int64_t iY, mnY = mGrid.get_nY();
+  int64_t iZ, mnZ = mGrid.get_nZ();
+  int64_t mGCs = mGrid.get_nGCs();
+  std::vector<int64_t> iCounter(nGrids);
+  for (jNode = 0; jNode < nGrids ; jNode++)
+    iCounter[jNode] = 0;
+
+  for (iX = mGCs; iX < mnX - mGCs; iX++) {
+    for (iY = mGCs; iY < mnY - mGCs; iY++) {
+      for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
+        jNode = mGrid.gridToGridMap(iX, iY, iZ);
+        if (report.test_verbose(2)) {
+          std::cout << "unpacking point : " << iX << " " << iY << " " << iZ << " " << jNode << " " 
+          << iCounter[jNode] << " " << dataToReceive[jNode][iCounter[jNode]] << "\n";
+        }
+
+        mData(iX, iY, iZ) = dataToReceive[jNode][iCounter[jNode]];
+        iCounter[jNode] = iCounter[jNode]+1;
+      }
+    }
+  }
+
+  report.exit(function);
+  return true;
+
+}
\ No newline at end of file

From fc6f18b497d14aaefb209ba379a923d005811690 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 19:57:50 -0400
Subject: [PATCH 118/266] FEAT: needed for grid to grid MP

---
 include/calc_grid_derived.h | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/include/calc_grid_derived.h b/include/calc_grid_derived.h
index 28d5237f..046fbd46 100644
--- a/include/calc_grid_derived.h
+++ b/include/calc_grid_derived.h
@@ -19,9 +19,14 @@ arma_vec calc_bin_widths(arma_vec centers);
 // ----------------------------------------------------------------------------
 // A helper function for mapping grids
 // ----------------------------------------------------------------------------
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+bool grid_match(Grid &gGrid, 
+                Grid &mGrid, 
                 Quadtree gQuadtree,
                 Quadtree mQuadtree);
 
+bool get_data_from_other_grid(Grid &gGrid, 
+                              Grid &mGrid,
+                              arma_cube &gData,
+                              arma_cube &mData);
+
 #endif  // INCLUDE_CALC_GRID_DERIVED_H_

From 406642dfaa91ca37d0a031a9e3296da51a03aef7 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 19:58:44 -0400
Subject: [PATCH 119/266] FEAT: needed for grid to grid MP

---
 include/grid.h | 80 +++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 59 insertions(+), 21 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index fed807e9..b56fead2 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -11,6 +11,33 @@
 // Grid class
 // ----------------------------------------------------------------------------
 
+  struct interp_coef_t
+  {
+    // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
+    uint64_t iRow;
+    uint64_t iCol;
+    uint64_t iAlt;
+    // The coefficients along row, column and altitude
+    precision_t rRow;
+    precision_t rCol;
+    precision_t rAlt;
+    // Whether the point is within this grid or not
+    bool in_grid;
+    // If this is set to true:
+    bool above_grid, below_grid;
+    // do interpolation in lat and lon, but extrapolate in altitude
+  };
+
+  struct grid_to_grid_t {
+    int64_t iProcTo;
+    int64_t nPts;
+    int64_t nPtsReceive;
+    std::vector<struct interp_coef_t> interpCoefs;
+    std::vector<precision_t *> valueToSend;
+    std::vector<precision_t *> valueToReceive;
+  };
+
+
 class Grid
 {
 
@@ -20,13 +47,24 @@ class Grid
   const int iDipole_ = 3;
   int iGridShape_ = -1;
 
-  // Armidillo Cube Versions:
+  // The index and coefficient used for interpolation
+  // Each point is processed by the function set_interpolation_coefs and stored
+  // in the form of this structure.
+  // If the point is out of the grid, in_grid = false and all other members are undefined
+
+  std::vector<struct grid_to_grid_t> gridToGridCoefs;
+  arma::Cube<int> gridToGridMap;
+
+  // Armadillo Cube Versions:
   // Cell Center Coordinates
   arma_cube geoLon_scgc, geoX_scgc;
   arma_cube geoLat_scgc, geoY_scgc;
   arma_cube geoAlt_scgc, geoZ_scgc;
   arma_cube geoLocalTime_scgc;
 
+  // This is an array for testing things:
+  arma_cube test_scgc;
+
   // Reference coordinate
   arma_cube refx_scgc, refy_scgc;
 
@@ -434,6 +472,22 @@ class Grid
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
                                const std::vector<precision_t> &Alts);
+
+  /**
+   * \brief Set the interpolation coefficients
+   * \param Lons The longitude of points
+   * \param Lats The latitude of points
+   * \param Alts The altitude of points
+   * \pre This instance is an geo grid
+   * \pre Lons, Lats and Alts have the same size
+   * \return list of interpolation coefficients
+   */
+  
+  std::vector<struct interp_coef_t> get_interpolation_coefs(
+                                    const std::vector<precision_t> &Lons,
+                                    const std::vector<precision_t> &Lats,
+                                    const std::vector<precision_t> &Alts);
+  
   /**
    * \brief Create a map of geographic locations to data and do the interpolation
    * \param data The value at the positions of geoLon, geoLat, and geoAlt
@@ -443,6 +497,8 @@ class Grid
    *         an empty vector if the data is not the same size as the geo grid.
    */
   std::vector<precision_t> get_interpolation_values(const arma_cube &data) const;
+  std::vector<precision_t> get_interpolation_values(arma_cube data,
+                                                    std::vector<struct interp_coef_t> coefArray);
 
 private:
   bool IsGeoGrid;
@@ -513,24 +569,6 @@ class Grid
     bool col_max_exclusive;
   };
 
-  // The index and coefficient used for interpolation
-  // Each point is processed by the function set_interpolation_coefs and stored
-  // in the form of this structure.
-  // If the point is out of the grid, in_grid = false and all other members are undefined
-  struct interp_coef_t
-  {
-    // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
-    uint64_t iRow;
-    uint64_t iCol;
-    uint64_t iAlt;
-    // The coefficients along row, column and altitude
-    precision_t rRow;
-    precision_t rCol;
-    precision_t rAlt;
-    // Whether the point is within this grid or not
-    bool in_grid;
-  };
-
   // Return the index of the last element that has altitude smaller than or euqal to the input
   uint64_t search_altitude(const precision_t alt_in) const;
 
@@ -540,11 +578,11 @@ class Grid
   void get_cubesphere_grid_range(struct cubesphere_range &cr) const;
 
   // Helper function for set_interpolation_coefs
-  void set_interp_coef_sphere(const sphere_range &sr,
+  struct interp_coef_t get_interp_coef_sphere(const sphere_range &sr,
                               const precision_t lon_in,
                               const precision_t lat_in,
                               const precision_t alt_in);
-  void set_interp_coef_cubesphere(const cubesphere_range &cr,
+  struct interp_coef_t get_interp_coef_cubesphere(const cubesphere_range &cr,
                                   const precision_t lon_in,
                                   const precision_t lat_in,
                                   const precision_t alt_in);

From b365ed045fd52c6482f79156b4a3f13812209848 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 19:59:30 -0400
Subject: [PATCH 120/266] FEAT: testing grid to grid MP

---
 src/advance.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/advance.cpp b/src/advance.cpp
index 023a73d5..c6252c40 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -46,6 +46,11 @@ bool advance(Planets &planet,
     didWork = neutralsMag.check_for_nonfinites("Top of Advance - ion grid");
   }
 
+  // here we are going to grab stuff from the neutral grid and put it on the
+  // ion grid
+  didWork = get_data_from_other_grid(gGrid, mGrid, neutrals.temperature_scgc, mGrid.test_scgc);
+
+
   gGrid.calc_sza(planet, time);
   mGrid.calc_sza(planet, time);
 

From e9c01dea0f36fd321b6977f02f0b20955a3674d8 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 20:00:02 -0400
Subject: [PATCH 121/266] FEAT: testing grid to grid MP

---
 src/grid.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/grid.cpp b/src/grid.cpp
index 9ed462c3..eb9f8798 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -80,6 +80,9 @@ Grid::Grid(std::string gridtype) {
   geoAlt_scgc.set_size(nX, nY, nZ);
   geoLocalTime_scgc.set_size(nX, nY, nZ);
 
+  test_scgc.set_size(nX, nY, nZ);
+  test_scgc.zeros();
+
   refx_scgc.set_size(nX, nY, nZ);
   refy_scgc.set_size(nX, nY, nZ);
   refx_angle.set_size(nX, nY, nZ);

From 7bde45e5c566d58ef7efeda5f2c8be5e2d3c3d17 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 20:00:43 -0400
Subject: [PATCH 122/266] STY: remove space

---
 src/main/main.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 9d0a0a3f..946e293e 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -100,7 +100,6 @@ int main() {
       didWork = mGrid.init_geo_grid(quadtree, planet);
       mGrid.set_IsGeoGrid(false);
     }
-
     didWork = grid_match(gGrid, mGrid, quadtree, quadtree_ion);
 
     // Initialize Neutrals on geographic grid:

From 21c934dd277c30ee0881d9c381225606b3d18295 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 20:01:27 -0400
Subject: [PATCH 123/266] FEAT: make an output file for test results

---
 src/output.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/output.cpp b/src/output.cpp
index 2a16a8c0..97a85a1c 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -41,6 +41,9 @@ std::string get_filename_from_type(std::string type_output) {
   if (type_output == "therm")
     filename = "3DTH";
 
+  if (type_output == "test")
+    filename = "3DTE";
+
   return filename;
 
 }
@@ -153,7 +156,6 @@ bool output(const Neutrals &neutrals,
           store_variable("density_" + neutrals.species[iSpecies].cName,
                          neutrals.density_unit,
                          neutrals.species[iSpecies].density_scgc);
-
       // Neutral Temperature:
       if (type_output == "neutrals" ||
           type_output == "states")
@@ -322,6 +324,13 @@ bool output(const Neutrals &neutrals,
                                                     grid.cent_acc_vcgc[2]);
       }
 
+      // Neutral Temperature:
+      if (type_output == "test")
+        AllOutputContainers[iOutput].
+        store_variable("test_grid",
+                       "none",
+                       grid.test_scgc);
+
       // ------------------------------------------------------------
       // Set output file names
 

From b7b2449d6ec2f8e394d939101906e54981334f03 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 20 May 2025 20:05:06 -0400
Subject: [PATCH 124/266] FEAT: allow above and below grid interpolation

---
 src/solver_grid_interpolation.cpp | 183 +++++++++++++++++++++++++-----
 1 file changed, 155 insertions(+), 28 deletions(-)

diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 39bb2dc3..29379f34 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -184,7 +184,8 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
 // Almost the copy of interp_sphere_linear_helper
 // --------------------------------------------------------------------------
 
-void Grid::set_interp_coef_sphere(const sphere_range &sr,
+struct interp_coef_t Grid::get_interp_coef_sphere(
+                                  const sphere_range &sr,
                                   const precision_t lon_in,
                                   const precision_t lat_in,
                                   const precision_t alt_in) {
@@ -200,13 +201,15 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 
   // Determine whether the point is inside this grid
   // Treat north pole specially because latitude is inclusive for both -cPI/2 and cPI/2
+  // Dpn't check for altitude here!
   if (lon_in < sr.lon_min || lon_in >= sr.lon_max || lat_in < sr.lat_min
-      || lat_in > sr.lat_max || (lat_in == sr.lat_max && sr.lat_max != cPI / 2)
-      || alt_in < sr.alt_min || alt_in > sr.alt_max) {
-    interp_coefs.push_back(coef);
-    return;
+      || lat_in > sr.lat_max || (lat_in == sr.lat_max && sr.lat_max != cPI / 2)) {
+    return coef;
   }
 
+  // This point is in the grid!
+  coef.in_grid = true;
+
   // ASSUMPTION: LONGITUDE AND LATITUDE ARE LINEARLY SPACED, nGCs >= 1
   // For the cell containing it, directly calculate its x and y index
   // Find its z index using binary search
@@ -228,13 +231,27 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
   // The altitude may not be linearly spaced, so use binary search to find
   // the first element smaller than or equal to the altitude of the give point
   // Implemented in search_altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
 
-  // Put the coefficient into the vector
-  coef.in_grid = true;
-  interp_coefs.push_back(coef);
+  if (alt_in < sr.alt_min) {
+    coef.iAlt = nGCs;
+    coef.rAlt = alt_in - sr.alt_min;
+    coef.below_grid = true;
+    coef.above_grid = false;
+  } else {
+    if (alt_in > sr.alt_max) {
+      coef.iAlt = nAlts - nGCs;
+      coef.rAlt = alt_in - sr.alt_max;
+      coef.below_grid = false;
+      coef.above_grid = true;
+    } else {
+      coef.iAlt = search_altitude(alt_in);
+      coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
+                  / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+      coef.below_grid = false;
+      coef.above_grid = false;
+    }
+  }
+  return coef;
 }
 
 // --------------------------------------------------------------------------
@@ -242,7 +259,7 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 // Almost the copy of interp_cubesphere_linear_helper
 // --------------------------------------------------------------------------
 
-void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
+struct interp_coef_t Grid::get_interp_coef_cubesphere(const cubesphere_range &cr,
                                       const precision_t lon_in,
                                       const precision_t lat_in,
                                       const precision_t alt_in) {
@@ -259,8 +276,7 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
 
   // Determine whether the projection point is on the surface of the grid
   if (surface_in != cr.surface_number) {
-    interp_coefs.push_back(coef);
-    return;
+    return coef;
   }
 
   // Calculate the theoretical fractional row index and column index
@@ -280,12 +296,13 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
       || row_frac_index > row_index_max || (row_frac_index == row_index_max &&
                                             cr.row_max_exclusive)
       || col_frac_index > col_index_max || (col_frac_index == col_index_max &&
-                                            cr.col_max_exclusive)
-      || alt_in < cr.alt_min || alt_in > cr.alt_max) {
-    interp_coefs.push_back(coef);
-    return;
+                                            cr.col_max_exclusive)) {
+    return coef;
   }
 
+  // This point is in the grid!
+  coef.in_grid = true;
+
   // Get the real integer index and the interpolation coefficient
   uint64_t row_index, col_index, alt_index;
   precision_t rRow, rCol, rAlt;
@@ -303,13 +320,27 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
   // Use binary search to find the index for altitude
-  coef.iAlt = search_altitude(alt_in);
-  coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-              / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
 
-  // Put the coefficient into the vector
-  coef.in_grid = true;
-  interp_coefs.push_back(coef);
+  if (alt_in < cr.alt_min) {
+    coef.iAlt = nGCs;
+    coef.rAlt = 0.0;
+    coef.below_grid = true;
+    coef.above_grid = false;
+  } else {
+    if (alt_in > cr.alt_max) {
+      coef.iAlt = nAlts - nGCs;
+      coef.rAlt = 0.0;
+      coef.below_grid = false;
+      coef.above_grid = true;
+    } else {
+      coef.iAlt = search_altitude(alt_in);
+      coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
+                  / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+      coef.below_grid = false;
+      coef.above_grid = false;
+    }
+  }
+  return coef;
 }
 
 // --------------------------------------------------------------------------
@@ -319,6 +350,8 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
 bool Grid::set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                    const std::vector<precision_t> &Lats,
                                    const std::vector<precision_t> &Alts) {
+
+  struct interp_coef_t coef;
   // If this is not a geo grid, return false
   if (!IsGeoGrid)
     return false;
@@ -337,21 +370,84 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &Lons,
     get_cubesphere_grid_range(cr);
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_cubesphere(cr, Lons[i], Lats[i], Alts[i]);
+    for (size_t i = 0; i < Lons.size(); ++i) {
+      coef = get_interp_coef_cubesphere(cr, Lons[i], Lats[i], Alts[i]);
+      interp_coefs.push_back(coef);
+    }
+
   } else {
     // Calculate the range of the grid
     struct sphere_range sr;
     get_sphere_grid_range(sr);
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_sphere(sr, Lons[i], Lats[i], Alts[i]);
+    for (size_t i = 0; i < Lons.size(); ++i) {
+      coef = get_interp_coef_sphere(sr, Lons[i], Lats[i], Alts[i]);
+      interp_coefs.push_back(coef);
+    }
   }
 
   return true;
 }
 
+// --------------------------------------------------------------------------
+// Set the interpolation coefficients 
+//    (v2 - return a list of interpolation coefficients)
+// --------------------------------------------------------------------------
+
+std::vector<struct interp_coef_t> Grid::get_interpolation_coefs(
+                                    const std::vector<precision_t> &Lons,
+                                    const std::vector<precision_t> &Lats,
+                                    const std::vector<precision_t> &Alts) {
+
+  int64_t nPts = Lons.size(), iPt;
+  std::vector<struct interp_coef_t> listOfCoefs;
+  struct interp_coef_t singleCoef;
+  bool isBad = false;
+
+  // If this is not a geo grid, return false
+  if (!IsGeoGrid)
+    isBad = true;
+
+  // If the size of Lons, Lats and Alts are not the same, return false
+  if (Lons.size() != Lats.size() || Lats.size() != Alts.size())
+    isBad = true;
+
+  if (isBad) {
+    for (iPt = 0; iPt < nPts; ++iPt) {
+      // Put the coefficient into the vector
+      singleCoef.in_grid = false;
+      listOfCoefs.push_back(singleCoef);
+    }
+    return listOfCoefs;
+  }
+
+  // Handle according to whether it is cubesphere or not
+  if (IsCubeSphereGrid) {
+    // Calculate the range of the grid
+    struct cubesphere_range cr;
+    get_cubesphere_grid_range(cr);
+
+    // Calculate the index and coefficients for each point
+    for (iPt = 0; iPt < nPts; ++iPt) {
+      singleCoef = get_interp_coef_cubesphere(cr, Lons[iPt], Lats[iPt], Alts[iPt]);
+      listOfCoefs.push_back(singleCoef);
+    }
+  } else {
+    // Calculate the range of the grid
+    struct sphere_range sr;
+    get_sphere_grid_range(sr);
+
+    // Calculate the index and coefficients for each point
+    for (iPt = 0; iPt < nPts; ++iPt) {
+      singleCoef = get_interp_coef_sphere(sr, Lons[iPt], Lats[iPt], Alts[iPt]);
+      listOfCoefs.push_back(singleCoef);
+    }      
+  }
+
+  return listOfCoefs;
+}
+
 // --------------------------------------------------------------------------
 // Do the interpolation based on the coefficients stored in interp_coefs
 // --------------------------------------------------------------------------
@@ -381,3 +477,34 @@ std::vector<precision_t> Grid::get_interpolation_values(
 
   return ans;
 }
+
+// --------------------------------------------------------------------------
+// Do the interpolation based on the coefficients passed in
+// --------------------------------------------------------------------------
+
+std::vector<precision_t> Grid::get_interpolation_values(arma_cube data,
+                                                        std::vector<struct interp_coef_t> coefArray ) {
+  std::vector<precision_t> ans;
+
+  // If the size of data is not the same as the size of grid, return an empty vector
+  if (data.n_rows != nLons || data.n_cols != nLats || data.n_slices != nAlts)
+    return ans;
+
+  for (auto &it : coefArray) {
+    // Do interpolation if in_grid = true. Push cNinf otherwise
+    if (it.in_grid) {
+      ans.push_back(interpolate_unit_cube(
+                      data.subcube(it.iRow, it.iCol, it.iAlt, unit_cube_size),
+                      it.rRow,
+                      it.rCol,
+                      it.rAlt
+                    ));
+      // Add std::cout if needed here
+      // std::cout << "iProc = " << iProc << " interpolates the point successfully\n";
+    } else
+      ans.push_back(cNinf);
+  }
+
+  return ans;
+}
+

From ab076ec80aa56574bc1a1a0ed4c7796f65640170 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 22 May 2025 20:46:10 -0400
Subject: [PATCH 125/266] BUG: Fix read_armadillo.py, indexing was
 weird/inconsistent

---
 srcPython/read_armadillo.py | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/srcPython/read_armadillo.py b/srcPython/read_armadillo.py
index 4c75c0b8..9f9427ed 100644
--- a/srcPython/read_armadillo.py
+++ b/srcPython/read_armadillo.py
@@ -111,13 +111,10 @@ def cube2np(files2read):
                     f"File ({thisf}) does not appear to be an armadillo cube.\n"
                     f"Found shape: {shape}")
             shape = np.array(shape, dtype=int) # convert shape to np array of int's
-            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
-            for i in range(int(shape[0])): # n_x
-                for j in range(int(shape[2])): # n_z
-                    # each line is n_y long. Convert it to a python list & retain it
-                    l = f.readline().strip().replace('  ',',').split(',')
-                    ls[i,:, j] = l # n_y
-        out.append(ls.T) # speed not a huge issue, work with lists
+        # Read in cube, transform shape, use same indexing as arma does
+        one_cube = np.loadtxt(thisf, skiprows=2, ).reshape(shape)
+        
+        out.append(one_cube) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:
@@ -165,12 +162,10 @@ def mat2np(files2read):
                     f"File ({thisf}) does not appear to be an armadillo matrix.\n"
                     f"Found shape: {shape}")
             shape = np.array(shape, dtype=int) # convert shape to np array of int's
-            ls = np.zeros(shape) # holder for this file's outputs, dtype is float
-            for i in range(int(shape[0])): # n_x
-                # each line is n_y long. Convert it to a python list & retain it
-                l = f.readline().strip().replace('  ',',').split(',')
-                ls[i,:] = l # n_y
-        out.append(ls.T) # speed not a huge issue, work with lists
+        
+        one_mat = np.loadtxt(thisf, skiprows=2).reshape(shape)
+
+        out.append(ls) # speed not a huge issue, work with lists
 
     # remove 0th dimension if we only are reading one file
     if len(files2read) == 1:

From f0f534195d2edf7d1047240995e5dde17bc5a311 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Tue, 1 Jul 2025 14:36:12 -0400
Subject: [PATCH 126/266] Create fism.py (direct from D Brandt)

---
 srcPython/fism.py | 383 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 383 insertions(+)
 create mode 100644 srcPython/fism.py

diff --git a/srcPython/fism.py b/srcPython/fism.py
new file mode 100644
index 00000000..dd158880
--- /dev/null
+++ b/srcPython/fism.py
@@ -0,0 +1,383 @@
+#!/usr/bin/env python
+
+# Authors of this code:
+# Daniel A. Brandt, Ph.D., Michigan Tech Research Institute, daabrand@mtu.edu
+# Aaron J. Ridley, Ph.D., University of Michigan, ridley@umich.edu
+
+# This file contains a suite of tools that do the following:
+# 1. Download FISM2 data for a time period the user desires.
+# 2. Rebin that data into the binning scheme the user desires (i.e. EUVAC-37, NEUVAC-59, or SOLOMON).
+# 3. Outputs a FISM2 file with the rebinnined irradiances in the desired bins (for use by euv.cpp)
+
+# Top-level imports:
+import numpy as np
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+import matplotlib
+matplotlib.use('Qt5Agg')
+import pathlib
+import os
+from urllib.request import urlretrieve
+from netCDF4 import Dataset
+import scipy.integrate as integ
+
+# Directory management:
+here = pathlib.Path(__file__).parent.resolve()
+
+# Physical constants:
+h = 6.62607015e-34 # Planck's constant in SI units of J s
+c = 299792458 # Speed of light in m s^-1
+
+# Helper Functions:
+def getFism(dateStart, dateEnd, stanBands=False):
+    """
+    Download FISM2 daily data, either in the Standard Bands or not. Default is to download the data NOT in the
+    Standard Bands. Note that this function downloads the data to a pre-determined location. The download is also
+    'smart'. If the data already exists (over the exact same time period). It is NOT overwitten. If the requested data
+    covers times OUTSIDE the time period of existing data, the NEW data is simply appended or prepended to a file
+    containing the existing data.
+    Args:
+        dateStart: str
+            The start date in 'YYYY-MM-DD' format.
+        dateEnd: str
+            The end date in 'YYYY-MM-DD' format.
+        stanBands: bool
+            If True, downloads FISM2 data in the STAN BANDS. Default is False.
+    Returns:
+        fismFile: str
+            The location of the downloaded FISM data.
+    """
+    dateStartDatetime = datetime.strptime(dateStart, "%Y-%m-%d")
+    dateEndDatetime = datetime.strptime(dateEnd, "%Y-%m-%d")
+
+    # Helper function to manage the obtaining of data, given a URL:
+    def urlObtain(url, fname):
+        if os.path.isfile(fname) == True:
+            print('File already exists (loading in data) '+str(fname))
+        else:
+            urlretrieve(url, fname)
+
+    if stanBands:
+        URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
+        fname = 'FISM2_daily_stan_bands.nc'
+        saveLoc = here.joinpath('../../tmp/')
+        if os.path.exists(saveLoc) == False:
+            os.makedirs(saveLoc)
+        urlObtain(URL, saveLoc.joinpath(fname))
+        datetimes, wavelengths, irradiance, uncertainties = readFism(saveLoc.joinpath(fname), stanBands=True)
+    else:
+        URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_hr_data/daily_data.nc'
+        fname = 'FISM2_daily_bands.nc'
+        saveLoc = here.joinpath('../../tmp/')
+        if os.path.exists(saveLoc) == False:
+            os.makedirs(saveLoc)
+        urlObtain(URL, saveLoc.joinpath(fname))
+        datetimes, wavelengths, irradiance, uncertainties = readFism(saveLoc.joinpath(fname))
+
+    # Subset the data in time, and save the subset data to a relative path:
+    subset_inds = np.where((datetimes >= dateStartDatetime) & (datetimes <= dateEndDatetime))[0]
+    subset_times = datetimes[subset_inds]
+    subset_irradiance = irradiance[subset_inds, :]
+    subset_uncertainty = uncertainties[subset_inds, :]
+
+    return subset_times, wavelengths, subset_irradiance, subset_uncertainty
+
+def rebin(fism_out, binning_scheme='EUVAC', zero=True):
+    """
+    Takes the output of getFism and rebins the data into whatever format the user desires.
+    Args:
+        fism_out: arraylike
+            The output of getFism. Contains 4 elements: (1) datetime values for the FISM2 spectra, (2) the wavelengths
+            of the spectrum, (3) the actual FISM2 irradiance spectra, and (4) uncertainties on the FISM2 irradiances (is
+            non-NaN only for FISM2 data not in the Standard Bands).
+        binning_scheme: str
+            Determines the binning scheme to be used. Valid arguments include the following:
+            'EUVAC': Uses the 37 wavelength band scheme described in Richards, et al. 1994; doi.org/10.1029/94JA00518
+            'NEUVAC': Uses the 59 wavelength band scheme described in Brandt and Ridley, 2024; doi.org/10.1029/2024SW004043
+            'HFG': Uses the 23 wavelength band scheme described in Solomon and Qian, 2005; https://doi.org/10.1029/2005JA011160
+            'SOLOMON': Same situation as for argument 'HFG'.
+            NOTE: If 'HFG' or 'SOLOMON' is chosen, the values of fism_out must correspond to getFism being run with the
+            argument stanBands = True. If this IS NOT the case, an error will be thrown.
+        zero: bool
+            Controls whether singular (bright) wavelength lines are set to a value of zero after they are extracted.
+            Default is True.
+    Returns:
+        fism2_file: str
+            The location of the rebinned FISM data.
+        fism2_data: arraylike
+            Contains 3 elements: (a) a list of datetimes for the data and (b) the rebinned FISM2 data.
+    """
+    # Unpack the contents of fism_out:
+    datetimes, wavelengths, irradiance, uncertainties = fism_out
+
+    # Get the native wavelength resolution of the input data:
+    # nativeResolution = np.concatenate((np.diff(wavelengths), np.array([np.diff(wavelengths)[-1]])), axis=0)
+    nativeWavelengths = wavelengths.copy()
+
+    if binning_scheme != 'HFG' and binning_scheme != 'SOLOMON':
+        if binning_scheme == 'EUVAC':
+            # Grab the euv_37.csv file:
+            fileStr = str(here.joinpath('euv_files/euv_37.csv'))
+            bin_bounds = read_euv_csv_file(fileStr)
+            tag = '_37'
+        elif binning_scheme == 'NEUVAC':
+            # Grab the euv_59.csv file:
+            fileStr = str(here.joinpath('euv_files/euv_59.csv'))
+            bin_bounds = read_euv_csv_file(fileStr)
+            tag = '_59'
+        else:
+            raise FileNotFoundError('The .csv files for specifying bin boundaries cannot be found!!')
+
+        # Perform the rebinning!
+        shorts = bin_bounds['short'] / 10.
+        longs = bin_bounds['long'] / 10.
+        newWaves = 0.5 * (shorts + longs)
+
+        # Instantiate the new data array:
+        if len(irradiance.shape) < 2:
+            fism2_data = np.zeros((1, newWaves.shape[0]))
+        else:
+            fism2_data = np.zeros((irradiance.shape[0], newWaves.shape[0]))
+
+        # First go through all the wavelengths that are singular
+        myData = irradiance
+        for iWave, short in enumerate(shorts):
+            long = longs[iWave]
+            if (long == short):
+                i = np.argmin(np.abs(wavelengths - short))
+                i2 = np.argmin(np.abs(nativeWavelengths - short))
+                try:
+                    fism2_data[:, iWave] = myData[:, i] * (nativeWavelengths[i2 + 1] - nativeWavelengths[i2])
+                except:
+                    fism2_data[:, iWave] = myData[i] * (nativeWavelengths[i2 + 1] - nativeWavelengths[i2])
+                if zero == True:
+                    # Zero out bin so we don't double count it.
+                    try:
+                        myData[:, i] = np.zeros_like(myData[:, i])
+                    except:
+                        myData[i] = 0.0
+
+        # Then go through the ranges
+        for iWave, short in enumerate(shorts):
+            long = longs[iWave]
+            if (long != short):
+                d1 = np.abs(wavelengths - short)
+                iStart = np.argmin(d1)
+                d2 = np.abs(wavelengths - long)
+                iEnd = np.argmin(d2)
+                wave_int = 0.0
+                # For wavelengths at or below 0.2 nm, just compute the sum:
+                if long <= 0.2:
+                    for i in range(iStart + 1, iEnd + 1):
+                        fism2_data[:, iWave] += myData[:, i] * \
+                                             (wavelengths[i + 1] - wavelengths[i])
+                        wave_int += (wavelengths[i + 1] - wavelengths[i])
+                else:
+                    # For issues computing the sum, integrate instead:
+                    try:
+                        fism2_data[:, iWave] = integ.trapezoid(myData[:, iStart:iEnd], wavelengths[iStart:iEnd], axis=1)
+                    except:
+                        fism2_data[:, iWave] = integ.trapezoid(myData[iStart:iEnd], wavelengths[iStart:iEnd])
+
+                    # # Plotting for a sanity check:
+                    # plt.figure()
+                    # plt.plot(wavelengths[iStart:iEnd], myData[0, iStart:iEnd], marker='o')
+                    # plt.scatter(newWaves, fism2_data[0, :])
+                    # plt.show()
+
+    elif binning_scheme == 'HFG' or binning_scheme == 'SOLOMON':
+        # Determine whether the supplied data already conforms to the Solomon and Qian binning scheme.
+        tag = '_solomon'
+        if fism_out[2].shape[1] != 23:
+            raise ValueError("Incorrect dimensions for element 3 of argument 'fism_out'. Dimensions must be (n,23), "
+                             "resulting from running function 'getFism' with argument 'stanBands'=True. ")
+        # Should the data confirm to the proper dimensions, there is no rebinning step that needs to be done. Simply
+        # continue.
+        fism2_data = fism_out[2]
+    else:
+        # If the input irradiance data DOES NOT conform to the Solomon and Qian binning scheme, throw an error.
+        raise ValueError("Invalid value for argument 'binning_scheme'. Must be 'EUVAC', 'NEUVAC', 'HFG', or 'SOLOMON'.")
+
+    # Save the rebinned data to a relative path (outside the package directory) in the form of a .txt file:
+    fism2_file = here.joinpath('../../tmp/fism2_file'+tag+'.txt')
+    saveFism(fism2_data, datetimes, fism2_file)
+
+    return fism2_file, fism2_data
+
+def saveFism(data, times, filename):
+    """
+    Takes (rebinned) FISM2 data and saves it a .txt file at a user-defined location.
+    Args:
+        data: numpy.ndarray
+            Irradiance data in a nxm array where the first dimension corresponds to observations (the spectrum number)
+            and the second dimension corresponds to wavelengths.
+        times: numpy.ndarray
+            The time values at which each spectrum is recorded.
+        filename: str
+            The desired location where the data will be saved.
+    Returns:
+        Nothing. Simply saves a file.
+    """
+    # A helper function for working with integers:
+    def numStr(num):
+        if int(num) < 10:
+            return ' ' + str(int(num))
+        else:
+            return str(int(num))
+
+    # Define a helper function for opening a file to write the data, in such a way as to include parent directories if
+    # needed:
+    def safe_open_w(path):
+        ''' Open "path" for writing, creating any parent directories as needed.
+        (https://stackoverflow.com/questions/23793987/write-a-file-to-a-directory-that-doesnt-exist)
+        '''
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        return open(path, 'w')
+
+    # Open the new file and begin writing, line by line:
+    with safe_open_w(str(filename)) as output:
+        # Write the header information:
+        output.write("#START\n")
+        # Write the irradiances themselves:
+        firstLine = ['%.6g' % (element) for element in data[0, :]]
+        firstLine_joined = ' '.join(firstLine)
+        # The first line should always be a duplicate of the first line of data, but starting at UTC=00:00 of the first date:
+        output.write(' ' + str(times[0].year) + ' ' + numStr(times[0].month) + ' ' + numStr(
+            times[0].day) + '  0  0  0 ' + firstLine_joined + '\n')
+        # The rest of the lines can be straight from the data:
+        for i in range(data.shape[0]):
+            currentLine_joined = ' '.join(['%.6g' % (element) for element in data[i, :]])
+            output.writelines(' ' + str(times[i].year) + ' ' + numStr(times[i].month) + ' ' + numStr(
+                times[i].day) + ' ' + numStr(times[i].hour) + '  0  0 ' + currentLine_joined + '\n')
+        # The last line should occur 12 hours from the last datapoint, but have duplicate values there:
+        lastLine_joined = ' '.join(['%.6g' % (element) for element in data[-1, :]])
+        lastTime = times[-1] + timedelta(hours=12)
+        output.write(' ' + str(lastTime.year) + ' ' + numStr(lastTime.month) + ' ' + numStr(
+            lastTime.day) + '  0  0  0 ' + lastLine_joined + '\n')
+
+    print('FISM2 data saved to: ')
+    os.system('readlink -f '+str(filename))
+    return
+
+def readFism(fism_file, stanBands=False):
+    """
+    Load in spectrum data from a FISM2 file.
+    Args:
+        fism_file: str
+            The location of a FISM2 NETCDF4 file.
+        stanBands: bool
+            If True, expects the data to be read in to be in the STAN BANDS. Default is False.
+    Returns:
+        datetimes: numpy.ndarray
+            An array of datetimes for reach FISM2 spectrum.
+        wavelengths: numpy.ndarray
+            A one-dimensional array of wavelengths at which there are irradiance values.
+        irradiances: numpy.ndarray
+            A two-dimensional array of irradiance values in each wavelength bin.
+        uncertainties: numpy.ndarray
+            A two-dimensional array of irradiance uncertainty values at each bin. Only returned if stanBands is False.
+    """
+    fism2Data = Dataset(fism_file)
+    wavelengths = np.asarray(fism2Data.variables['wavelength'])
+    if stanBands:
+        flux = np.asarray(fism2Data.variables['ssi']) # photons/cm^2/second
+        pFlux = flux * 1e4 # photons/m^2/second
+        # Convert fluxes to irradiances:
+        irradiance = np.zeros_like(flux)
+        for i in range(flux.shape[1]):
+            irradiance[:, i] = spectralIrradiance(pFlux[:, i], wavelengths[i] * 10.)# W/m^2
+        uncertainties = np.full_like(irradiance, np.nan)
+    else:
+        irradiance = np.asarray(fism2Data.variables['irradiance'])
+        uncertainties = np.asarray(fism2Data.variables['uncertainty'])
+    dates = fism2Data.variables['date']
+    datetimes = []
+    for j in range(len(dates)):
+        year = dates[j][:4]
+        day = dates[j][4:]
+        currentDatetime = datetime(int(year), 1, 1) + timedelta(int(day) -1 ) + timedelta(hours=12)
+        datetimes.append(currentDatetime)
+    datetimes = np.asarray(datetimes)
+
+    return datetimes, wavelengths, irradiance, uncertainties
+
+def spectralIrradiance(photonFlux, wavelength):
+    """
+    Convert the photon flux to the corresponding spectral irradiance, given a specific wavelength.
+    Args:
+        photonFlux: numpy.ndarray, float, or int
+            Photon flux in units of photons s^-1 m^-2. For a singular wavelength, units are in photons m^-2.
+        wavelength: wavelength: float
+            A specific wavelength in Angstroms.
+    Returns:
+        irradiance: numpy.ndarray or float
+            The corresponding spectral irradiance in units of W/m^2/nm.
+    """
+    photonEnergy = (h*c) / (wavelength*1e-10) # Convert the wavelength in the denominator to meters.
+    irradiance= photonFlux * photonEnergy
+    return irradiance
+
+def read_euv_csv_file(file):
+    """
+    Originally written by Aaron J. Ridley, within the file 'fism2_process.py':
+    https://github.com/aaronjridley/EUV/blob/main/fism2_process.py
+
+    This file reads in binning data from a CSV file that specifies bin boundaries and cross sections for either the
+    EUVAC model or the NEUVAC model.
+    Args:
+        file: str
+            The location of the .csv file to be read.
+    Returns:
+        wavelengths: numpy.ndarray
+            The wavelength bin boundaries for either the EUVAC model or the NEUVAC model.
+    """
+    fpin = open(file, 'r')
+
+    iFound = 0
+    afac = []
+    f74113 = []
+    for line in fpin:
+        aline = line.split(',')
+        s = aline[-1].strip().split('.')[0]
+        if (aline[0].strip() == "Short"):
+            if (s.isnumeric()):
+                short = np.asarray(aline[5:], dtype=float)
+            else:
+                short = np.asarray(aline[5:-1], dtype=float)
+            iFound += 1
+        if (aline[0].strip() == "Long"):
+            if (s.isnumeric()):
+                long = np.asarray(aline[5:], dtype=float)
+            else:
+                long = np.asarray(aline[5:-1], dtype=float)
+        if (aline[0].strip() == "F74113"):
+            if (s.isnumeric()):
+                f74113 = np.asarray(aline[5:], dtype=float)
+            else:
+                f74113 = np.asarray(aline[5:-1], dtype=float)
+            iFound += 1
+        if (aline[0].strip() == "AFAC"):
+            if (s.isnumeric()):
+                afac = np.asarray(aline[5:], dtype=float)
+            else:
+                afac = np.asarray(aline[5:-1], dtype=float)
+            iFound += 1
+    # Save and convert from Angstroms to nm (FISM is in nm)
+    wavelengths = {'short': short / 10.0,
+                   'long': long / 10.0,
+                   'afac': afac,
+                   'f74113': f74113}
+    return wavelengths
+
+# Execution (testing):
+if __name__ == '__main__':
+    # Download some FISM2 data for the time period stated by the user.
+    dateStart = '2015-08-13'
+    dateEnd = '2015-08-19'
+    # fismOut = getFism(dateStart, dateEnd)
+    # rebinnedFismFile, rebinnedFismData = rebin(fismOut, binning_scheme='EUVAC')
+    # rebinnedFismFile_N, rebinnedFismData_N = rebin(fismOut, binning_scheme='NEUVAC')
+
+    fismOut_S = getFism(dateStart, dateEnd, stanBands=True)
+    rebinnedFismFile_S, rebinnedFismData_S = rebin(fismOut_S, binning_scheme='SOLOMON')
+

From a650409f0b368625111edc10951985fd5a6c11a8 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 2 Jul 2025 10:24:10 -0400
Subject: [PATCH 127/266] FEAT: Read in FISM data

---
 include/euv.h    | 27 ++++++++++++++++++++++
 include/inputs.h |  6 +++++
 src/euv.cpp      | 59 ++++++++++++++++++++++++++++++++++++++++++++++++
 src/inputs.cpp   | 11 ++++++++-
 4 files changed, 102 insertions(+), 1 deletion(-)

diff --git a/include/euv.h b/include/euv.h
index 138e74f3..686be2f8 100644
--- a/include/euv.h
+++ b/include/euv.h
@@ -93,6 +93,11 @@ class Euv {
 
   /// NEUVAC model intercept:
   std::vector<float> neuvac_int;
+
+  // To avoid having to start from 0 each iteration:
+  int fism_prev_index = 0;
+  // Declare this so it is not passed between function:
+  index_file_output_struct fismData;
   
   // --------------------------------------------------------------------
   // Functions:
@@ -115,6 +120,17 @@ class Euv {
      \param indices Need the F107 and F107a
    **/
  bool solomon_hfg(Times time, Indices indices);
+
+  /**********************************************************************
+     \brief returns the FISM spectrum for a given time
+
+    Unlike the other EUV models ([N]EUVAC, Solomon, etc.), the soectrum
+    is read from a file. These data are stored in fismData
+
+     \param time The times within the model (dt is needed)
+   **/
+
+ bool fism(Times time);
   
   /**********************************************************************
      \brief Compute the EUV spectrum given F107 and F107a (new version)
@@ -161,6 +177,17 @@ class Euv {
    **/
   bool read_file();
 
+  /**********************************************************************
+     \brief Read in the FISM  file
+
+     Read in the CSV file with FISM data. This can be made with
+      srcPython/fism.py. The data are read into a index_file_output_struct,
+      where each row is one time, and each col is a "variable". These should
+      match the number of bins in the provided EUV file.
+   **/
+ index_file_output_struct read_fism(std::string fism_filename);
+
+
   /**********************************************************************
      \brief Interprets the EUV CSV rows and returns the relevant row
 
diff --git a/include/inputs.h b/include/inputs.h
index c753d874..9436fbca 100644
--- a/include/inputs.h
+++ b/include/inputs.h
@@ -224,6 +224,12 @@ class Inputs {
    **/
   std::string get_euv_file();
   
+  /**********************************************************************
+     \brief returns settings["
+     \param 
+   **/
+  std::string get_euv_fismfile();
+  
   /**********************************************************************
      \brief returns settings["
      \param 
diff --git a/src/euv.cpp b/src/euv.cpp
index e97a47a0..ddcb709b 100644
--- a/src/euv.cpp
+++ b/src/euv.cpp
@@ -44,6 +44,11 @@ Euv::Euv() {
         }
       }
 
+      // read in FISM data - does not need to be "slotted"
+      if (input.get_euv_model() == "fism"){
+        fismData = read_fism(input.get_euv_fismfile());
+      }
+
       // Slot the EUVAC model coefficients:
       if (input.get_euv_model() == "euvac") {
         IsOk = slot_euv("F74113", "", euvac_f74113);
@@ -167,6 +172,60 @@ bool Euv::read_file() {
   return DidWork;
 }
 
+// -------------------------------------------------------------------------------
+// Read in FISM data. FISM files are created with srcPython/fism.py,
+// and the data are read in to an index_file_output_struct.
+// Inside the struct, we have time & each of the "variables" correspond to a 
+// FISM bin. This number of bins should match the number of bins in the EUV file
+// -------------------------------------------------------------------------------
+
+index_file_output_struct Euv::read_fism(std::string fism_filename){
+
+  std::ifstream fismfstream;
+  fismfstream.open(fism_filename);
+  std::vector<std::vector<std::string>>  fism_file;
+  fism_file = read_csv(fismfstream);
+
+  index_file_output_struct fism_contents;
+
+  // one row per time
+  fism_contents.nTimes = fism_file.size();
+  // first six cols are the YYYY,MM,DD,HH,mm,ss (no ms)
+  // the rest are the binned fism data
+  fism_contents.nVars = fism_file[0].size() - 6;
+
+  // check that the user provided the correct EUV file
+  // The number of bins in euv file should match the number of fism bins ("nVars")
+  if (fism_contents.nVars != nWavelengths){
+    report.error("Number of FISM wavelengths does not match the EUV file provided!");
+    report.error("Either change EUV file or check your FISM file is correct.");
+    IsOk = false;
+  }
+
+  std::vector<int> itime(7, 0);
+  std::vector<std::vector<float>> values; // holds all values
+  std::vector<float> values_tmp(fism_contents.nVars); // holds values in each row
+  
+  for (int iLine=0; iLine < fism_file.size(); iLine ++){
+
+    itime[0] = stoi(fism_file[iLine][0]);
+    itime[1] = stoi(fism_file[iLine][1]);
+    itime[2] = stoi(fism_file[iLine][2]);
+    itime[3] = stoi(fism_file[iLine][3]);
+    itime[4] = stoi(fism_file[iLine][4]);
+    itime[5] = stoi(fism_file[iLine][5]);
+    itime[6] = 0; // 0 ms
+    fism_contents.times.push_back(time_int_to_real(itime));
+    for (int iVar=0;iVar<fism_contents.nVars;iVar++)
+      values_tmp[iVar] = stof(fism_file[iLine][iVar + 6]);
+    values.push_back(values_tmp);
+  }
+
+  fism_contents.values = values;
+
+  return fism_contents;
+}
+
 // ---------------------------------------------------------------------------
 // Match rows in EUV file to different types of things, such as cross
 // sections and spectra
diff --git a/src/inputs.cpp b/src/inputs.cpp
index 703c0517..318faa92 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -915,13 +915,22 @@ std::string Inputs::get_diffuse_auroral_model() {
 }
 
 // -----------------------------------------------------------------------
-// Return the EUV model used (EUVAC only option now)
+// Return the EUV model used (EUVAC, NEUVAC, FISM, etc.)
 // -----------------------------------------------------------------------
 
 std::string Inputs::get_euv_model() {
   return mklower(check_settings_str("Euv", "Model"));
 }
 
+
+// -----------------------------------------------------------------------
+// Return the FISM data file
+// -----------------------------------------------------------------------
+
+std::string Inputs::get_euv_fismfile(){
+  return get_setting_str("Euv", "fismFile");
+}
+
 // -----------------------------------------------------------------------
 // Return the heating efficiency of the neutrals for EUV
 // -----------------------------------------------------------------------

From c8b09cd316762b102dca16c4554c612d5620d647 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 2 Jul 2025 11:23:47 -0400
Subject: [PATCH 128/266] FEAT: Get stored FISM, make it available as EUV model

---
 include/euv.h    |  8 ++++---
 src/calc_euv.cpp |  3 +++
 src/euv.cpp      | 61 +++++++++++++++++++++++++++++++++++++++++-------
 3 files changed, 60 insertions(+), 12 deletions(-)

diff --git a/include/euv.h b/include/euv.h
index 686be2f8..0006dfc5 100644
--- a/include/euv.h
+++ b/include/euv.h
@@ -124,13 +124,15 @@ class Euv {
   /**********************************************************************
      \brief returns the FISM spectrum for a given time
 
-    Unlike the other EUV models ([N]EUVAC, Solomon, etc.), the soectrum
-    is read from a file. These data are stored in fismData
+    Unlike the other EUV models ([N]EUVAC, Solomon, etc.), the spectrum
+    is read from a file (stored in fismData). This does the same thing
+    as get_index, however FISM is not stored in Indices since it can
+    have variable # of bins
 
      \param time The times within the model (dt is needed)
    **/
 
- bool fism(Times time);
+ bool get_fism(Times time);
   
   /**********************************************************************
      \brief Compute the EUV spectrum given F107 and F107a (new version)
diff --git a/src/calc_euv.cpp b/src/calc_euv.cpp
index 2c971f2d..35683130 100644
--- a/src/calc_euv.cpp
+++ b/src/calc_euv.cpp
@@ -49,6 +49,9 @@ bool calc_euv(Planets planet,
       didWork = euv.neuvac(time, indices);
     else if (euvModel == "hfg")
       didWork = euv.solomon_hfg(time, indices);
+    else if (euvModel == "fism"){
+      didWork = euv.get_fism(time);
+    }
 
     if (didWork)
       euv.scale_from_1au(planet, time);
diff --git a/src/euv.cpp b/src/euv.cpp
index ddcb709b..7df04c8c 100644
--- a/src/euv.cpp
+++ b/src/euv.cpp
@@ -44,10 +44,9 @@ Euv::Euv() {
         }
       }
 
-      // read in FISM data - does not need to be "slotted"
-      if (input.get_euv_model() == "fism"){
+      // Read in FISM data - does not need to be "slotted"
+      if (input.get_euv_model() == "fism")
         fismData = read_fism(input.get_euv_fismfile());
-      }
 
       // Slot the EUVAC model coefficients:
       if (input.get_euv_model() == "euvac") {
@@ -175,11 +174,11 @@ bool Euv::read_file() {
 // -------------------------------------------------------------------------------
 // Read in FISM data. FISM files are created with srcPython/fism.py,
 // and the data are read in to an index_file_output_struct.
-// Inside the struct, we have time & each of the "variables" correspond to a 
+// Inside the struct, we have time & each of the "variables" correspond to a
 // FISM bin. This number of bins should match the number of bins in the EUV file
 // -------------------------------------------------------------------------------
 
-index_file_output_struct Euv::read_fism(std::string fism_filename){
+index_file_output_struct Euv::read_fism(std::string fism_filename) {
 
   std::ifstream fismfstream;
   fismfstream.open(fism_filename);
@@ -196,7 +195,7 @@ index_file_output_struct Euv::read_fism(std::string fism_filename){
 
   // check that the user provided the correct EUV file
   // The number of bins in euv file should match the number of fism bins ("nVars")
-  if (fism_contents.nVars != nWavelengths){
+  if (fism_contents.nVars != nWavelengths) {
     report.error("Number of FISM wavelengths does not match the EUV file provided!");
     report.error("Either change EUV file or check your FISM file is correct.");
     IsOk = false;
@@ -205,8 +204,8 @@ index_file_output_struct Euv::read_fism(std::string fism_filename){
   std::vector<int> itime(7, 0);
   std::vector<std::vector<float>> values; // holds all values
   std::vector<float> values_tmp(fism_contents.nVars); // holds values in each row
-  
-  for (int iLine=0; iLine < fism_file.size(); iLine ++){
+
+  for (int iLine = 0; iLine < fism_file.size(); iLine ++) {
 
     itime[0] = stoi(fism_file[iLine][0]);
     itime[1] = stoi(fism_file[iLine][1]);
@@ -216,8 +215,10 @@ index_file_output_struct Euv::read_fism(std::string fism_filename){
     itime[5] = stoi(fism_file[iLine][5]);
     itime[6] = 0; // 0 ms
     fism_contents.times.push_back(time_int_to_real(itime));
-    for (int iVar=0;iVar<fism_contents.nVars;iVar++)
+
+    for (int iVar = 0; iVar < fism_contents.nVars; iVar++)
       values_tmp[iVar] = stof(fism_file[iLine][iVar + 6]);
+
     values.push_back(values_tmp);
   }
 
@@ -430,6 +431,48 @@ bool Euv::euvac(Times time,
   return didWork;
 }
 
+// --------------------------------------------------------------------------
+// From the FISM file, interpolate the nearest 2 data to the current time
+// --------------------------------------------------------------------------
+
+bool Euv::get_fism(Times time) {
+  // This is functionally similar to get_indices, however we do not store FISM in
+  // the Indices class since it has variable number of bins.
+
+  double time_now = time.get_current();
+
+  if (fism_prev_index == 0) {
+    // This is probably the first time we're "running" fism.
+    // Make sure the file covers the entire time range of the run.
+    double end_time = time.get_end();
+    std::cout << "first time in fism\n";
+
+    if (time_now < fismData.times[0] && end_time > fismData.times[-1]) {
+      report.error("FISM data does not cover the entire time range!");
+      report.error("Please check that your FISM file is correct.");
+      IsOk = false;
+    }
+  }
+
+  // Get the index prior to the current time
+  while (fismData.times[fism_prev_index + 1] <= time_now)
+    fism_prev_index ++;
+
+  // Determine time-interpolation weighting factor
+  precision_t dt_fism;
+  dt_fism = fismData.times[fism_prev_index + 1] - fismData.times[fism_prev_index];
+  precision_t x = (time_now - fismData.times[fism_prev_index]) / dt_fism;
+  std::cout << x << "\n";
+
+  // store the wavelength:
+  for (int iWave = 0; iWave < nWavelengths; iWave ++)
+    wavelengths_intensity_1au[iWave] =
+      (1.0 - x) * fismData.values[fism_prev_index][iWave]
+      + x * fismData.values[fism_prev_index + 1][iWave];
+
+  return IsOk;
+}
+
 // --------------------------------------------------------------------------
 // Calculate EUVAC
 // --------------------------------------------------------------------------

From f7e381ed0e032c9f996f6146cc48517202c53d44 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 2 Jul 2025 11:26:14 -0400
Subject: [PATCH 129/266] FEAT: Python script to download & rebin FISM
 automatically

---
 srcPython/fism.py | 92 ++++++++++++++++++++++++++---------------------
 1 file changed, 51 insertions(+), 41 deletions(-)

diff --git a/srcPython/fism.py b/srcPython/fism.py
index dd158880..92752878 100644
--- a/srcPython/fism.py
+++ b/srcPython/fism.py
@@ -10,11 +10,9 @@
 # 3. Outputs a FISM2 file with the rebinnined irradiances in the desired bins (for use by euv.cpp)
 
 # Top-level imports:
+import argparse
 import numpy as np
-import matplotlib.pyplot as plt
 from datetime import datetime, timedelta
-import matplotlib
-matplotlib.use('Qt5Agg')
 import pathlib
 import os
 from urllib.request import urlretrieve
@@ -23,13 +21,14 @@
 
 # Directory management:
 here = pathlib.Path(__file__).parent.resolve()
+saveLoc = here.joinpath('.')
 
 # Physical constants:
 h = 6.62607015e-34 # Planck's constant in SI units of J s
 c = 299792458 # Speed of light in m s^-1
 
 # Helper Functions:
-def getFism(dateStart, dateEnd, stanBands=False):
+def getFism(dateStart, dateEnd, saveLoc, stanBands=False):
     """
     Download FISM2 daily data, either in the Standard Bands or not. Default is to download the data NOT in the
     Standard Bands. Note that this function downloads the data to a pre-determined location. The download is also
@@ -41,6 +40,8 @@ def getFism(dateStart, dateEnd, stanBands=False):
             The start date in 'YYYY-MM-DD' format.
         dateEnd: str
             The end date in 'YYYY-MM-DD' format.
+        saveLoc: str/path
+            The path to save data to
         stanBands: bool
             If True, downloads FISM2 data in the STAN BANDS. Default is False.
     Returns:
@@ -56,40 +57,32 @@ def urlObtain(url, fname):
             print('File already exists (loading in data) '+str(fname))
         else:
             urlretrieve(url, fname)
+    
+    URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
+    fname = 'FISM2_daily.nc'
+    urlObtain(URL, fname)
 
     if stanBands:
-        URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
-        fname = 'FISM2_daily_stan_bands.nc'
-        saveLoc = here.joinpath('../../tmp/')
-        if os.path.exists(saveLoc) == False:
-            os.makedirs(saveLoc)
-        urlObtain(URL, saveLoc.joinpath(fname))
-        datetimes, wavelengths, irradiance, uncertainties = readFism(saveLoc.joinpath(fname), stanBands=True)
+        datetimes, wavelengths, irradiance = readFism(fname, stanBands=True)
     else:
-        URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_hr_data/daily_data.nc'
-        fname = 'FISM2_daily_bands.nc'
-        saveLoc = here.joinpath('../../tmp/')
-        if os.path.exists(saveLoc) == False:
-            os.makedirs(saveLoc)
-        urlObtain(URL, saveLoc.joinpath(fname))
-        datetimes, wavelengths, irradiance, uncertainties = readFism(saveLoc.joinpath(fname))
+        datetimes, wavelengths, irradiance = readFism(fname, stanBands=False)
 
     # Subset the data in time, and save the subset data to a relative path:
     subset_inds = np.where((datetimes >= dateStartDatetime) & (datetimes <= dateEndDatetime))[0]
     subset_times = datetimes[subset_inds]
     subset_irradiance = irradiance[subset_inds, :]
-    subset_uncertainty = uncertainties[subset_inds, :]
 
-    return subset_times, wavelengths, subset_irradiance, subset_uncertainty
+    return subset_times, wavelengths, subset_irradiance
 
-def rebin(fism_out, binning_scheme='EUVAC', zero=True):
+def rebin(fism_out, saveLoc, binning_scheme='EUVAC', zero=True):
     """
     Takes the output of getFism and rebins the data into whatever format the user desires.
     Args:
         fism_out: arraylike
             The output of getFism. Contains 4 elements: (1) datetime values for the FISM2 spectra, (2) the wavelengths
-            of the spectrum, (3) the actual FISM2 irradiance spectra, and (4) uncertainties on the FISM2 irradiances (is
-            non-NaN only for FISM2 data not in the Standard Bands).
+            of the spectrum, (3) the actual FISM2 irradiance spectra.
+        saveLoc: path
+            Path to save data files to.
         binning_scheme: str
             Determines the binning scheme to be used. Valid arguments include the following:
             'EUVAC': Uses the 37 wavelength band scheme described in Richards, et al. 1994; doi.org/10.1029/94JA00518
@@ -108,7 +101,7 @@ def rebin(fism_out, binning_scheme='EUVAC', zero=True):
             Contains 3 elements: (a) a list of datetimes for the data and (b) the rebinned FISM2 data.
     """
     # Unpack the contents of fism_out:
-    datetimes, wavelengths, irradiance, uncertainties = fism_out
+    datetimes, wavelengths, irradiance = fism_out
 
     # Get the native wavelength resolution of the input data:
     # nativeResolution = np.concatenate((np.diff(wavelengths), np.array([np.diff(wavelengths)[-1]])), axis=0)
@@ -117,12 +110,12 @@ def rebin(fism_out, binning_scheme='EUVAC', zero=True):
     if binning_scheme != 'HFG' and binning_scheme != 'SOLOMON':
         if binning_scheme == 'EUVAC':
             # Grab the euv_37.csv file:
-            fileStr = str(here.joinpath('euv_files/euv_37.csv'))
+            fileStr = str(saveLoc.joinpath('euv.csv'))
             bin_bounds = read_euv_csv_file(fileStr)
             tag = '_37'
         elif binning_scheme == 'NEUVAC':
             # Grab the euv_59.csv file:
-            fileStr = str(here.joinpath('euv_files/euv_59.csv'))
+            fileStr = str(saveLoc.joinpath('euv_59.csv'))
             bin_bounds = read_euv_csv_file(fileStr)
             tag = '_59'
         else:
@@ -179,12 +172,6 @@ def rebin(fism_out, binning_scheme='EUVAC', zero=True):
                     except:
                         fism2_data[:, iWave] = integ.trapezoid(myData[iStart:iEnd], wavelengths[iStart:iEnd])
 
-                    # # Plotting for a sanity check:
-                    # plt.figure()
-                    # plt.plot(wavelengths[iStart:iEnd], myData[0, iStart:iEnd], marker='o')
-                    # plt.scatter(newWaves, fism2_data[0, :])
-                    # plt.show()
-
     elif binning_scheme == 'HFG' or binning_scheme == 'SOLOMON':
         # Determine whether the supplied data already conforms to the Solomon and Qian binning scheme.
         tag = '_solomon'
@@ -199,7 +186,7 @@ def rebin(fism_out, binning_scheme='EUVAC', zero=True):
         raise ValueError("Invalid value for argument 'binning_scheme'. Must be 'EUVAC', 'NEUVAC', 'HFG', or 'SOLOMON'.")
 
     # Save the rebinned data to a relative path (outside the package directory) in the form of a .txt file:
-    fism2_file = here.joinpath('../../tmp/fism2_file'+tag+'.txt')
+    fism2_file = saveLoc.joinpath('fism2_file'+tag+'.txt')
     saveFism(fism2_data, datetimes, fism2_file)
 
     return fism2_file, fism2_data
@@ -286,10 +273,8 @@ def readFism(fism_file, stanBands=False):
         irradiance = np.zeros_like(flux)
         for i in range(flux.shape[1]):
             irradiance[:, i] = spectralIrradiance(pFlux[:, i], wavelengths[i] * 10.)# W/m^2
-        uncertainties = np.full_like(irradiance, np.nan)
     else:
-        irradiance = np.asarray(fism2Data.variables['irradiance'])
-        uncertainties = np.asarray(fism2Data.variables['uncertainty'])
+        irradiance = np.asarray(fism2Data.variables['ssi'])
     dates = fism2Data.variables['date']
     datetimes = []
     for j in range(len(dates)):
@@ -299,7 +284,7 @@ def readFism(fism_file, stanBands=False):
         datetimes.append(currentDatetime)
     datetimes = np.asarray(datetimes)
 
-    return datetimes, wavelengths, irradiance, uncertainties
+    return datetimes, wavelengths, irradiance
 
 def spectralIrradiance(photonFlux, wavelength):
     """
@@ -369,15 +354,40 @@ def read_euv_csv_file(file):
                    'f74113': f74113}
     return wavelengths
 
+
+def get_args():
+
+    parser = argparse.ArgumentParser(description = 'Create FISM input data')
+    parser.add_argument('start',
+                        help='Start date (format YYYYMMDD)',
+                        type=str)
+    parser.add_argument('end',
+                        help='End date (format YYYYMMDD)',
+                        type=str)
+    parser.add_argument('-b', '--binning',
+                        help="Binning scheme to use. Can be [solomon,neuvac,euvac] "
+                        "(case insensitive)",
+                        type=str)
+
+    args = parser.parse_args()
+
+    return args
+
 # Execution (testing):
 if __name__ == '__main__':
     # Download some FISM2 data for the time period stated by the user.
-    dateStart = '2015-08-13'
-    dateEnd = '2015-08-19'
+
+    args = get_args()
+
     # fismOut = getFism(dateStart, dateEnd)
     # rebinnedFismFile, rebinnedFismData = rebin(fismOut, binning_scheme='EUVAC')
     # rebinnedFismFile_N, rebinnedFismData_N = rebin(fismOut, binning_scheme='NEUVAC')
 
-    fismOut_S = getFism(dateStart, dateEnd, stanBands=True)
-    rebinnedFismFile_S, rebinnedFismData_S = rebin(fismOut_S, binning_scheme='SOLOMON')
+    fismOut_S = getFism(dateStart, dateEnd, saveLoc, stanBands=True)
+    rebinnedFismFile_S, rebinnedFismData_S = rebin(fismOut_S, saveLoc, binning_scheme='SOLOMON')
+
+    fismOut_N = getFism(dateStart, dateEnd, saveLoc, stanBands=False)
+    rebinnedFismFile_N, rebinnedFismData_N = rebin(fismOut_N, saveLoc, binning_scheme='NEUVAC')
 
+    fismOut_E = getFism(dateStart, dateEnd, saveLoc, stanBands=True)
+    rebinnedFismFile_E, rebinnedFismData_E = rebin(fismOut_E, saveLoc, binning_scheme='EUVAC')

From bb8cfb618492ab6a84713af566d61f4359f4b015 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 2 Jul 2025 11:42:43 -0400
Subject: [PATCH 130/266] STY: Clean up fism code: add reporting & remove
 debugging cout's

---
 src/euv.cpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/src/euv.cpp b/src/euv.cpp
index 7df04c8c..e43f9a45 100644
--- a/src/euv.cpp
+++ b/src/euv.cpp
@@ -438,8 +438,13 @@ bool Euv::euvac(Times time,
 bool Euv::get_fism(Times time) {
   // This is functionally similar to get_indices, however we do not store FISM in
   // the Indices class since it has variable number of bins.
+  
+  std::string function = "Euv::get_fism";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
 
   double time_now = time.get_current();
+  bool didWork = true;
 
   if (fism_prev_index == 0) {
     // This is probably the first time we're "running" fism.
@@ -450,7 +455,7 @@ bool Euv::get_fism(Times time) {
     if (time_now < fismData.times[0] && end_time > fismData.times[-1]) {
       report.error("FISM data does not cover the entire time range!");
       report.error("Please check that your FISM file is correct.");
-      IsOk = false;
+      didWork = false;
     }
   }
 
@@ -462,7 +467,6 @@ bool Euv::get_fism(Times time) {
   precision_t dt_fism;
   dt_fism = fismData.times[fism_prev_index + 1] - fismData.times[fism_prev_index];
   precision_t x = (time_now - fismData.times[fism_prev_index]) / dt_fism;
-  std::cout << x << "\n";
 
   // store the wavelength:
   for (int iWave = 0; iWave < nWavelengths; iWave ++)
@@ -470,7 +474,8 @@ bool Euv::get_fism(Times time) {
       (1.0 - x) * fismData.values[fism_prev_index][iWave]
       + x * fismData.values[fism_prev_index + 1][iWave];
 
-  return IsOk;
+  report.exit(function);
+  return didWork;
 }
 
 // --------------------------------------------------------------------------

From 8044ddd53c2711b311c04c0169694e69b7c46427 Mon Sep 17 00:00:00 2001
From: "Daniel A. Brandt" <daabrand@mtu.edu>
Date: Thu, 3 Jul 2025 16:39:43 -0400
Subject: [PATCH 131/266] Major updates to fism.py: Errors regarding rebinning
 and case sensitivity resolved.

---
 srcPython/fism.py | 257 +++++++++++++++++++++++++++-------------------
 1 file changed, 150 insertions(+), 107 deletions(-)

diff --git a/srcPython/fism.py b/srcPython/fism.py
index 92752878..a64ae886 100644
--- a/srcPython/fism.py
+++ b/srcPython/fism.py
@@ -2,6 +2,7 @@
 
 # Authors of this code:
 # Daniel A. Brandt, Ph.D., Michigan Tech Research Institute, daabrand@mtu.edu
+# Aaron L. Bukowski, Ph.D., University of Michigan, abukowski@umich.edu
 # Aaron J. Ridley, Ph.D., University of Michigan, ridley@umich.edu
 
 # This file contains a suite of tools that do the following:
@@ -14,83 +15,137 @@
 import numpy as np
 from datetime import datetime, timedelta
 import pathlib
-import os
-from urllib.request import urlretrieve
+import os, sys
+import pooch
 from netCDF4 import Dataset
 import scipy.integrate as integ
 
 # Directory management:
 here = pathlib.Path(__file__).parent.resolve()
-saveLoc = here.joinpath('.')
+euvDir = here.parent.joinpath('share/run/UA/inputs')
 
 # Physical constants:
 h = 6.62607015e-34 # Planck's constant in SI units of J s
 c = 299792458 # Speed of light in m s^-1
 
 # Helper Functions:
-def getFism(dateStart, dateEnd, saveLoc, stanBands=False):
+def getFism2(dateStart, dateEnd, source, downloadDir=None):
     """
-    Download FISM2 daily data, either in the Standard Bands or not. Default is to download the data NOT in the
-    Standard Bands. Note that this function downloads the data to a pre-determined location. The download is also
-    'smart'. If the data already exists (over the exact same time period). It is NOT overwitten. If the requested data
-    covers times OUTSIDE the time period of existing data, the NEW data is simply appended or prepended to a file
-    containing the existing data.
-    Args:
-        dateStart: str
-            The start date in 'YYYY-MM-DD' format.
-        dateEnd: str
-            The end date in 'YYYY-MM-DD' format.
-        saveLoc: str/path
-            The path to save data to
-        stanBands: bool
-            If True, downloads FISM2 data in the STAN BANDS. Default is False.
-    Returns:
-        fismFile: str
-            The location of the downloaded FISM data.
+    Given a starting date and an ending date, automatically download irradiance data from LISIRD for a specific source,
+    including FISM2 daily or FISM2 in the Standard Bands.
+    :param dateStart: str
+        The starting date for the data in YYYY-MM-DD format.
+    :param dateEnd: str
+        The ending date for the data in YYYY-MM-DD format.
+    :param source: str
+        The type of data to be obtained. Valid inputs are:
+        - FISM2 (for daily averages of FISM2 data)
+        - FISM2S (for daily averages of FISM2 standard bands, according to Solomon and Qian 2005)
+    :return times: ndarray
+        Datetime values for each spectrum.
+    :return wavelengths: ndarray
+        Wavelength bins (bin boundaries) for the spectral data.
+    :return irradiance: ndarray
+        A 2D array where each row is a spectrum at a particular time, and the columns are wavelength bands.
     """
+    # Converting the input time strings to datetimes:
     dateStartDatetime = datetime.strptime(dateStart, "%Y-%m-%d")
     dateEndDatetime = datetime.strptime(dateEnd, "%Y-%m-%d")
 
-    # Helper function to manage the obtaining of data, given a URL:
-    def urlObtain(url, fname):
-        if os.path.isfile(fname) == True:
-            print('File already exists (loading in data) '+str(fname))
-        else:
-            urlretrieve(url, fname)
-    
-    URL = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
-    fname = 'FISM2_daily.nc'
-    urlObtain(URL, fname)
-
-    if stanBands:
-        datetimes, wavelengths, irradiance = readFism(fname, stanBands=True)
+    # Check if the user has asked for a source that can be obtained:
+    validSources = ['FISM2', 'FISM2S']
+    if source not in validSources:
+        raise ValueError("Variable 'source' must be either 'FISM2' or 'FISM2S.")
+
+    # If the download directory is not specified, set it to the top directory that the package is in:
+    if downloadDir is None:
+        downloadDir = os.getcwd()
+
+    # Download the most recent file for the corresponding source and read it in:
+    if source == 'FISM2':
+        url = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_hr_data/daily_data.nc'
+        fname = 'FISM2_daily_data.nc'
+        urlObtain(url, loc=downloadDir, fname=fname) # hash='dbee404e1c75689b47691b8a4a733236bb66abbdc0f01b8cbd8236f69fe9d469'
+        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(downloadDir + '/' + fname)
     else:
-        datetimes, wavelengths, irradiance = readFism(fname, stanBands=False)
+        url = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
+        fname = 'FISM2_daily_bands.nc'
+        urlObtain(url, loc=downloadDir, fname=fname) # hash='27e3183f8ad6b289de191a63d3feada64c9d3f6b2973315ceda4a42c41638465'
+        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(downloadDir + '/' + fname, bands=True)
+
+    # Subset the data according to user demands:
+    validInds = np.where((datetimes >= dateStartDatetime) & (datetimes <= dateEndDatetime))[0]
+    times = datetimes[validInds]
+    if source == 'FISM2S':
+        irradiance = irradiance[-1, validInds, :]
+    else:
+        irradiance = irradiance[validInds, :]
 
-    # Subset the data in time, and save the subset data to a relative path:
-    subset_inds = np.where((datetimes >= dateStartDatetime) & (datetimes <= dateEndDatetime))[0]
-    subset_times = datetimes[subset_inds]
-    subset_irradiance = irradiance[subset_inds, :]
+    # Return the resulting data:
+    return times, wavelengths, irradiance
 
-    return subset_times, wavelengths, subset_irradiance
+def obtainFism2(myFism2File, bands=False):
+    """
+    Load in spectrum data from a FISM2 file.
+    :param myFism2File: str
+        The location of the NETCDF4 file.
+    :param bands: bool
+        If True, loads in the data segmented into the Solomon and Qian 2005 standard bands.
+    :return datetimes: ndarray
+        An array of datetimes for each TIMED/SEE spectra.
+    :return wavelengths: ndarray
+        A one-dimensional array of wavelengths at which there are irradiance values.
+    :return irradiances: ndarray
+        A two-dimensional array of irradiance values at each time.
+    :return uncertainties: ndarray
+        A two-dimensional array of irradiance uncertainty values at each time.
+    """
+    fism2Data = Dataset(myFism2File)
+    wavelengths = np.asarray(fism2Data.variables["wavelength"])
+    if bands == True:  # STANDARD BANDS
+        flux = np.asarray(fism2Data.variables["ssi"])  # photons/cm2/second
+        # bandwidths = np.asarray(fism2Data.variables['band_width'])
+        pFlux = flux * 1.0e4  # photons/m2/second
+        # Convert fluxes to irradiances:
+        irr = np.zeros_like(flux)
+        for i in range(flux.shape[1]):
+            irr[:, i] = spectralIrradiance(pFlux[:, i], wavelengths[i] * 10.0)  # W/m^2
+        irradiance = np.array([flux, irr])
+        uncertainties = np.full_like(irradiance, fill_value=np.nan) # TODO: Replace with an estimation of uncertainty
+    else:  # NATIVE DATA
+        irradiance = np.asarray(fism2Data.variables["irradiance"])  # W/m^2/nm
+        uncertainties = np.asarray(fism2Data.variables["uncertainty"])
+    dates = fism2Data.variables["date"]
+    datetimes = []
+    for i in range(len(dates)):
+        year = dates[i][:4]
+        day = dates[i][4:]
+        currentDatetime = (
+            datetime(int(year), 1, 1)
+            + timedelta(int(day) - 1)
+            + timedelta(hours=12)
+        )
+        datetimes.append(currentDatetime)
+    datetimes = np.asarray(datetimes)
+    return datetimes, wavelengths, irradiance, uncertainties
 
-def rebin(fism_out, saveLoc, binning_scheme='EUVAC', zero=True):
+def rebin(fism_out, saveLoc=os.getcwd(), binning_scheme='EUVAC', zero=True):
     """
     Takes the output of getFism and rebins the data into whatever format the user desires.
     Args:
         fism_out: arraylike
-            The output of getFism. Contains 4 elements: (1) datetime values for the FISM2 spectra, (2) the wavelengths
+            The output of getFism2. Contains 4 elements: (1) datetime values for the FISM2 spectra, (2) the wavelengths
             of the spectrum, (3) the actual FISM2 irradiance spectra.
         saveLoc: path
-            Path to save data files to.
+            Path to save data files to. Defaults to the current working directory.
         binning_scheme: str
             Determines the binning scheme to be used. Valid arguments include the following:
-            'EUVAC': Uses the 37 wavelength band scheme described in Richards, et al. 1994; doi.org/10.1029/94JA00518
-            'NEUVAC': Uses the 59 wavelength band scheme described in Brandt and Ridley, 2024; doi.org/10.1029/2024SW004043
+            'EUVAC' or 'Euvac' or 'euvac': Uses the 37 wavelength band scheme described in Richards, et al. 1994; doi.org/10.1029/94JA00518
+            'NEUVAC' or 'Neuvac' or 'neuvac': Uses the 59 wavelength band scheme described in Brandt and Ridley, 2024; doi.org/10.1029/2024SW004043
             'HFG': Uses the 23 wavelength band scheme described in Solomon and Qian, 2005; https://doi.org/10.1029/2005JA011160
-            'SOLOMON': Same situation as for argument 'HFG'.
-            NOTE: If 'HFG' or 'SOLOMON' is chosen, the values of fism_out must correspond to getFism being run with the
-            argument stanBands = True. If this IS NOT the case, an error will be thrown.
+            'SOLOMON' or 'Solomon' or 'solomon': Same situation as for argument 'HFG'.
+            NOTE: If 'HFG' or 'SOLOMON' is chosen, the values of fism_out must correspond to getFism2 being run with the
+            argument source='FISM2'. If this IS NOT the case, an error will be thrown.
         zero: bool
             Controls whether singular (bright) wavelength lines are set to a value of zero after they are extracted.
             Default is True.
@@ -107,15 +162,15 @@ def rebin(fism_out, saveLoc, binning_scheme='EUVAC', zero=True):
     # nativeResolution = np.concatenate((np.diff(wavelengths), np.array([np.diff(wavelengths)[-1]])), axis=0)
     nativeWavelengths = wavelengths.copy()
 
-    if binning_scheme != 'HFG' and binning_scheme != 'SOLOMON':
-        if binning_scheme == 'EUVAC':
+    if binning_scheme != 'HFG' and binning_scheme != 'SOLOMON' and binning_scheme != 'Solomon' and binning_scheme != 'solomon':
+        if binning_scheme == 'EUVAC' or binning_scheme == 'Euvac' or binning_scheme == 'euvac':
             # Grab the euv_37.csv file:
-            fileStr = str(saveLoc.joinpath('euv.csv'))
+            fileStr = str(euvDir.joinpath('euv.csv'))
             bin_bounds = read_euv_csv_file(fileStr)
             tag = '_37'
-        elif binning_scheme == 'NEUVAC':
+        elif binning_scheme == 'NEUVAC' or binning_scheme == 'Neuvac' or binning_scheme == 'neuvac':
             # Grab the euv_59.csv file:
-            fileStr = str(saveLoc.joinpath('euv_59.csv'))
+            fileStr = str(euvDir.joinpath('euv_59.csv'))
             bin_bounds = read_euv_csv_file(fileStr)
             tag = '_59'
         else:
@@ -172,7 +227,7 @@ def rebin(fism_out, saveLoc, binning_scheme='EUVAC', zero=True):
                     except:
                         fism2_data[:, iWave] = integ.trapezoid(myData[iStart:iEnd], wavelengths[iStart:iEnd])
 
-    elif binning_scheme == 'HFG' or binning_scheme == 'SOLOMON':
+    elif binning_scheme == 'HFG' or binning_scheme == 'SOLOMON' or binning_scheme == 'Solomon' or binning_scheme == 'solomon':
         # Determine whether the supplied data already conforms to the Solomon and Qian binning scheme.
         tag = '_solomon'
         if fism_out[2].shape[1] != 23:
@@ -186,7 +241,7 @@ def rebin(fism_out, saveLoc, binning_scheme='EUVAC', zero=True):
         raise ValueError("Invalid value for argument 'binning_scheme'. Must be 'EUVAC', 'NEUVAC', 'HFG', or 'SOLOMON'.")
 
     # Save the rebinned data to a relative path (outside the package directory) in the form of a .txt file:
-    fism2_file = saveLoc.joinpath('fism2_file'+tag+'.txt')
+    fism2_file = pathlib.Path(os.getcwd()).joinpath('fism2_file'+tag+'.txt')
     saveFism(fism2_data, datetimes, fism2_file)
 
     return fism2_file, fism2_data
@@ -246,46 +301,6 @@ def safe_open_w(path):
     os.system('readlink -f '+str(filename))
     return
 
-def readFism(fism_file, stanBands=False):
-    """
-    Load in spectrum data from a FISM2 file.
-    Args:
-        fism_file: str
-            The location of a FISM2 NETCDF4 file.
-        stanBands: bool
-            If True, expects the data to be read in to be in the STAN BANDS. Default is False.
-    Returns:
-        datetimes: numpy.ndarray
-            An array of datetimes for reach FISM2 spectrum.
-        wavelengths: numpy.ndarray
-            A one-dimensional array of wavelengths at which there are irradiance values.
-        irradiances: numpy.ndarray
-            A two-dimensional array of irradiance values in each wavelength bin.
-        uncertainties: numpy.ndarray
-            A two-dimensional array of irradiance uncertainty values at each bin. Only returned if stanBands is False.
-    """
-    fism2Data = Dataset(fism_file)
-    wavelengths = np.asarray(fism2Data.variables['wavelength'])
-    if stanBands:
-        flux = np.asarray(fism2Data.variables['ssi']) # photons/cm^2/second
-        pFlux = flux * 1e4 # photons/m^2/second
-        # Convert fluxes to irradiances:
-        irradiance = np.zeros_like(flux)
-        for i in range(flux.shape[1]):
-            irradiance[:, i] = spectralIrradiance(pFlux[:, i], wavelengths[i] * 10.)# W/m^2
-    else:
-        irradiance = np.asarray(fism2Data.variables['ssi'])
-    dates = fism2Data.variables['date']
-    datetimes = []
-    for j in range(len(dates)):
-        year = dates[j][:4]
-        day = dates[j][4:]
-        currentDatetime = datetime(int(year), 1, 1) + timedelta(int(day) -1 ) + timedelta(hours=12)
-        datetimes.append(currentDatetime)
-    datetimes = np.asarray(datetimes)
-
-    return datetimes, wavelengths, irradiance
-
 def spectralIrradiance(photonFlux, wavelength):
     """
     Convert the photon flux to the corresponding spectral irradiance, given a specific wavelength.
@@ -354,6 +369,28 @@ def read_euv_csv_file(file):
                    'f74113': f74113}
     return wavelengths
 
+def urlObtain(URL, loc=None, fname=None, hash=None):
+    """
+    Helper function that uses Pooch to download files to a location specified by the user.
+    :param URL: str
+        The location of a file to be downloaded.
+    :param loc: str
+        The place the file will be downloaded.
+    :param fname: str
+        The name the file will have once it is downloaded.
+    :param hash: str
+        A known hash (checksum) of the file. Will be used to verify the download or check if an existing file needs to
+        be updated.
+    :return:
+    """
+    if loc is None:
+        loc = os.getcwd()
+    if os.path.isfile(str(loc) + '/' + fname) is False:
+        fname_loc = pooch.retrieve(url=URL, known_hash=hash, fname=fname, path=loc)
+    else:
+        fname_loc = str(loc) + '/' + fname
+
+    return fname_loc
 
 def get_args():
 
@@ -378,16 +415,22 @@ def get_args():
     # Download some FISM2 data for the time period stated by the user.
 
     args = get_args()
+    dateStart = args.start
+    dateEnd = args.end
+    binning_scheme = args.binning
+
+    if binning_scheme == 'HFG' or binning_scheme == 'SOLOMON' or binning_scheme == 'Solomon' or binning_scheme == 'solomon':
+        # SOLOMON (STAN BANDS; b23)
+        fism2_out_23 = getFism2(dateStart, dateEnd, 'FISM2S', downloadDir=None)
+        fism2_file_23, fism2_data_23 = rebin(fism2_out_23, binning_scheme=binning_scheme)
+    else:
+        fism2_out_raw = getFism2(dateStart, dateEnd, 'FISM2', downloadDir=None)
+        if binning_scheme == 'NEUVAC' or binning_scheme == 'Neuvac' or binning_scheme == 'neuvac':
+            # NEUVAC BINS (b59)
+            fism2_file_59, fism2_data_59 = rebin(fism2_out_raw, binning_scheme=binning_scheme, zero=True)
+        else:
+            # EUVAC BINS (b37)
+            fism2_file_37, fism2_data_37 = rebin(fism2_out_raw, binning_scheme=binning_scheme, zero=True)
 
-    # fismOut = getFism(dateStart, dateEnd)
-    # rebinnedFismFile, rebinnedFismData = rebin(fismOut, binning_scheme='EUVAC')
-    # rebinnedFismFile_N, rebinnedFismData_N = rebin(fismOut, binning_scheme='NEUVAC')
-
-    fismOut_S = getFism(dateStart, dateEnd, saveLoc, stanBands=True)
-    rebinnedFismFile_S, rebinnedFismData_S = rebin(fismOut_S, saveLoc, binning_scheme='SOLOMON')
-
-    fismOut_N = getFism(dateStart, dateEnd, saveLoc, stanBands=False)
-    rebinnedFismFile_N, rebinnedFismData_N = rebin(fismOut_N, saveLoc, binning_scheme='NEUVAC')
-
-    fismOut_E = getFism(dateStart, dateEnd, saveLoc, stanBands=True)
-    rebinnedFismFile_E, rebinnedFismData_E = rebin(fismOut_E, saveLoc, binning_scheme='EUVAC')
+    # Exit with a zero error code:
+    sys.exit(0)

From 2606ba8333f3a59a5ae48978ebe2d673889e2684 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Jul 2025 16:17:58 -0400
Subject: [PATCH 132/266] bugs: fism csv file needs to be comma separated,
 without any #start

---
 srcPython/fism.py | 33 +++++++++++++++++----------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/srcPython/fism.py b/srcPython/fism.py
index a64ae886..d5d55495 100644
--- a/srcPython/fism.py
+++ b/srcPython/fism.py
@@ -49,8 +49,12 @@ def getFism2(dateStart, dateEnd, source, downloadDir=None):
         A 2D array where each row is a spectrum at a particular time, and the columns are wavelength bands.
     """
     # Converting the input time strings to datetimes:
-    dateStartDatetime = datetime.strptime(dateStart, "%Y-%m-%d")
-    dateEndDatetime = datetime.strptime(dateEnd, "%Y-%m-%d")
+    try:
+        dateStartDatetime = datetime.strptime(dateStart, "%Y-%m-%d")
+        dateEndDatetime = datetime.strptime(dateEnd, "%Y-%m-%d")
+    except:
+        dateStartDatetime = datetime.strptime(dateStart, "%Y%m%d")
+        dateEndDatetime = datetime.strptime(dateEnd, "%Y%m%d")
 
     # Check if the user has asked for a source that can be obtained:
     validSources = ['FISM2', 'FISM2S']
@@ -262,10 +266,7 @@ def saveFism(data, times, filename):
     """
     # A helper function for working with integers:
     def numStr(num):
-        if int(num) < 10:
-            return ' ' + str(int(num))
-        else:
-            return str(int(num))
+        return ',' + str(int(num))
 
     # Define a helper function for opening a file to write the data, in such a way as to include parent directories if
     # needed:
@@ -279,23 +280,23 @@ def safe_open_w(path):
     # Open the new file and begin writing, line by line:
     with safe_open_w(str(filename)) as output:
         # Write the header information:
-        output.write("#START\n")
+        # output.write("#START\n")
         # Write the irradiances themselves:
         firstLine = ['%.6g' % (element) for element in data[0, :]]
-        firstLine_joined = ' '.join(firstLine)
+        firstLine_joined = ','.join(firstLine)
         # The first line should always be a duplicate of the first line of data, but starting at UTC=00:00 of the first date:
-        output.write(' ' + str(times[0].year) + ' ' + numStr(times[0].month) + ' ' + numStr(
-            times[0].day) + '  0  0  0 ' + firstLine_joined + '\n')
+        output.write(str(times[0].year) + numStr(times[0].month) + numStr(
+            times[0].day) + ',0,0,0,' + firstLine_joined + '\n')
         # The rest of the lines can be straight from the data:
         for i in range(data.shape[0]):
-            currentLine_joined = ' '.join(['%.6g' % (element) for element in data[i, :]])
-            output.writelines(' ' + str(times[i].year) + ' ' + numStr(times[i].month) + ' ' + numStr(
-                times[i].day) + ' ' + numStr(times[i].hour) + '  0  0 ' + currentLine_joined + '\n')
+            currentLine_joined = ','.join(['%.6g' % (element) for element in data[i, :]])
+            output.writelines(str(times[i].year) + numStr(times[i].month) + numStr(
+                times[i].day) + numStr(times[i].hour) + ',0,0,' + currentLine_joined + '\n')
         # The last line should occur 12 hours from the last datapoint, but have duplicate values there:
-        lastLine_joined = ' '.join(['%.6g' % (element) for element in data[-1, :]])
+        lastLine_joined = ','.join(['%.6g' % (element) for element in data[-1, :]])
         lastTime = times[-1] + timedelta(hours=12)
-        output.write(' ' + str(lastTime.year) + ' ' + numStr(lastTime.month) + ' ' + numStr(
-            lastTime.day) + '  0  0  0 ' + lastLine_joined + '\n')
+        output.write(str(lastTime.year) + numStr(lastTime.month) + numStr(
+            lastTime.day) + ',0,0,0,' + lastLine_joined + '\n')
 
     print('FISM2 data saved to: ')
     os.system('readlink -f '+str(filename))

From a3bd162f6ac3229d42857ea517c0f700f8115bad Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Jul 2025 16:45:19 -0400
Subject: [PATCH 133/266] maint: clean debug info, add fism.md with info on new
 fism stuff

---
 doc/internals/fism.md | 30 ++++++++++++++++++++++++++++++
 src/euv.cpp           |  1 -
 2 files changed, 30 insertions(+), 1 deletion(-)
 create mode 100644 doc/internals/fism.md

diff --git a/doc/internals/fism.md b/doc/internals/fism.md
new file mode 100644
index 00000000..1133396e
--- /dev/null
+++ b/doc/internals/fism.md
@@ -0,0 +1,30 @@
+# FISM
+
+FISM can be used as a EUV model.
+
+Needs the FISM files automatically made by `srcPython/fism.py`.
+
+FISM contains the binned flux already, however Aether must still be provided with an
+EUV csv file containing the cross-sections.
+
+The different fism models available in fism.py contain different numbers of bins, so
+the euv file must be different.
+
+|  Model  |  number of bins  | euv file |
+|  :---  |  :-------------: | -------: |
+| HFG    | 23  | euv_solomon.csv |
+| Solomon | 23  | euv_solomon.csv |
+| NEUVAC  | 37/59   | euv.csv / euv_59.csv |
+| EUVAC  |  37  | euv.csv |
+
+The input format, when using fism data:
+
+    "Euv" : {
+        "doUse" : true,
+        "Model" : "fism",
+        "File" : "UA/inputs/euv_59.csv",
+        "fismFile": "fism2_file_59.txt",
+        "IncludePhotoElectrons" : true,
+        "HeatingEfficiency" : 0.05,
+        "dt" : 60.0
+    },
diff --git a/src/euv.cpp b/src/euv.cpp
index e43f9a45..a5457351 100644
--- a/src/euv.cpp
+++ b/src/euv.cpp
@@ -450,7 +450,6 @@ bool Euv::get_fism(Times time) {
     // This is probably the first time we're "running" fism.
     // Make sure the file covers the entire time range of the run.
     double end_time = time.get_end();
-    std::cout << "first time in fism\n";
 
     if (time_now < fismData.times[0] && end_time > fismData.times[-1]) {
       report.error("FISM data does not cover the entire time range!");

From 201649bf4302722e89f9f02a3d9e115b4f232a10 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Jul 2025 16:46:16 -0400
Subject: [PATCH 134/266]  bug: set default download location & euv model in
 fism.py

---
 srcPython/fism.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/srcPython/fism.py b/srcPython/fism.py
index d5d55495..d4cc8ecb 100644
--- a/srcPython/fism.py
+++ b/srcPython/fism.py
@@ -70,12 +70,12 @@ def getFism2(dateStart, dateEnd, source, downloadDir=None):
         url = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_hr_data/daily_data.nc'
         fname = 'FISM2_daily_data.nc'
         urlObtain(url, loc=downloadDir, fname=fname) # hash='dbee404e1c75689b47691b8a4a733236bb66abbdc0f01b8cbd8236f69fe9d469'
-        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(downloadDir + '/' + fname)
+        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(os.path.join(downloadDir, fname))
     else:
         url = 'https://lasp.colorado.edu/eve/data_access/eve_data/fism/daily_bands/daily_bands.nc'
         fname = 'FISM2_daily_bands.nc'
         urlObtain(url, loc=downloadDir, fname=fname) # hash='27e3183f8ad6b289de191a63d3feada64c9d3f6b2973315ceda4a42c41638465'
-        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(downloadDir + '/' + fname, bands=True)
+        datetimes, wavelengths, irradiance, uncertainties = obtainFism2(os.path.join(downloadDir, fname), bands=True)
 
     # Subset the data according to user demands:
     validInds = np.where((datetimes >= dateStartDatetime) & (datetimes <= dateEndDatetime))[0]
@@ -405,7 +405,7 @@ def get_args():
     parser.add_argument('-b', '--binning',
                         help="Binning scheme to use. Can be [solomon,neuvac,euvac] "
                         "(case insensitive)",
-                        type=str)
+                        type=str, default="neuvac")
 
     args = parser.parse_args()
 
@@ -422,10 +422,10 @@ def get_args():
 
     if binning_scheme == 'HFG' or binning_scheme == 'SOLOMON' or binning_scheme == 'Solomon' or binning_scheme == 'solomon':
         # SOLOMON (STAN BANDS; b23)
-        fism2_out_23 = getFism2(dateStart, dateEnd, 'FISM2S', downloadDir=None)
+        fism2_out_23 = getFism2(dateStart, dateEnd, 'FISM2S', downloadDir=here)
         fism2_file_23, fism2_data_23 = rebin(fism2_out_23, binning_scheme=binning_scheme)
     else:
-        fism2_out_raw = getFism2(dateStart, dateEnd, 'FISM2', downloadDir=None)
+        fism2_out_raw = getFism2(dateStart, dateEnd, 'FISM2', downloadDir=here)
         if binning_scheme == 'NEUVAC' or binning_scheme == 'Neuvac' or binning_scheme == 'neuvac':
             # NEUVAC BINS (b59)
             fism2_file_59, fism2_data_59 = rebin(fism2_out_raw, binning_scheme=binning_scheme, zero=True)

From 226ed39b121bc9e217ab37205637024124164ee5 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Thu, 10 Jul 2025 16:47:15 -0400
Subject: [PATCH 135/266] git: add fism files to gitignore

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 12bc3ad8..ec439684 100644
--- a/.gitignore
+++ b/.gitignore
@@ -38,3 +38,7 @@ _deps
 #vs code settings
 .vscode
 .DS_Store
+
+# fism files
+srcPython/FISM*.nc
+srcPython/fism*.txt
\ No newline at end of file

From 91a3a1b1d408a462f0c2f5eac2b860af94e8b7f2 Mon Sep 17 00:00:00 2001
From: "Daniel A. Brandt" <daabrand@mtu.edu>
Date: Mon, 14 Jul 2025 15:39:48 -0400
Subject: [PATCH 136/266] Added neuvac.py within srcPython; generates a .txt
 file for irradiances from the NEUVAC model, in the same manner as fism.py.

---
 src/euv.cpp                                   |   3 +-
 srcPython/.idea/.gitignore                    |   3 +
 .../inspectionProfiles/profiles_settings.xml  |   6 +
 srcPython/.idea/misc.xml                      |   4 +
 srcPython/.idea/modules.xml                   |   8 +
 srcPython/.idea/srcPython.iml                 |  12 +
 srcPython/.idea/vcs.xml                       |   6 +
 srcPython/fism.py                             |  10 +-
 srcPython/neuvac.py                           | 475 ++++++++++++++++++
 9 files changed, 521 insertions(+), 6 deletions(-)
 create mode 100644 srcPython/.idea/.gitignore
 create mode 100644 srcPython/.idea/inspectionProfiles/profiles_settings.xml
 create mode 100644 srcPython/.idea/misc.xml
 create mode 100644 srcPython/.idea/modules.xml
 create mode 100644 srcPython/.idea/srcPython.iml
 create mode 100644 srcPython/.idea/vcs.xml
 create mode 100644 srcPython/neuvac.py

diff --git a/src/euv.cpp b/src/euv.cpp
index e43f9a45..d22086c5 100644
--- a/src/euv.cpp
+++ b/src/euv.cpp
@@ -47,7 +47,8 @@ Euv::Euv() {
       // Read in FISM data - does not need to be "slotted"
       if (input.get_euv_model() == "fism")
         fismData = read_fism(input.get_euv_fismfile());
-
+      // Read in NEUVAC data - also does not need to be "slotted"
+      
       // Slot the EUVAC model coefficients:
       if (input.get_euv_model() == "euvac") {
         IsOk = slot_euv("F74113", "", euvac_f74113);
diff --git a/srcPython/.idea/.gitignore b/srcPython/.idea/.gitignore
new file mode 100644
index 00000000..26d33521
--- /dev/null
+++ b/srcPython/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/srcPython/.idea/inspectionProfiles/profiles_settings.xml b/srcPython/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 00000000..105ce2da
--- /dev/null
+++ b/srcPython/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/srcPython/.idea/misc.xml b/srcPython/.idea/misc.xml
new file mode 100644
index 00000000..76a040b9
--- /dev/null
+++ b/srcPython/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (solarVenv)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/srcPython/.idea/modules.xml b/srcPython/.idea/modules.xml
new file mode 100644
index 00000000..c7044f5d
--- /dev/null
+++ b/srcPython/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/srcPython.iml" filepath="$PROJECT_DIR$/.idea/srcPython.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/srcPython/.idea/srcPython.iml b/srcPython/.idea/srcPython.iml
new file mode 100644
index 00000000..410274b1
--- /dev/null
+++ b/srcPython/.idea/srcPython.iml
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (solarVenv)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="NUMPY" />
+    <option name="myDocStringFormat" value="NumPy" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/srcPython/.idea/vcs.xml b/srcPython/.idea/vcs.xml
new file mode 100644
index 00000000..6c0b8635
--- /dev/null
+++ b/srcPython/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/srcPython/fism.py b/srcPython/fism.py
index d5d55495..0bb7e788 100644
--- a/srcPython/fism.py
+++ b/srcPython/fism.py
@@ -276,7 +276,7 @@ def safe_open_w(path):
         '''
         os.makedirs(os.path.dirname(path), exist_ok=True)
         return open(path, 'w')
-
+    
     # Open the new file and begin writing, line by line:
     with safe_open_w(str(filename)) as output:
         # Write the header information:
@@ -298,7 +298,7 @@ def safe_open_w(path):
         output.write(str(lastTime.year) + numStr(lastTime.month) + numStr(
             lastTime.day) + ',0,0,0,' + lastLine_joined + '\n')
 
-    print('FISM2 data saved to: ')
+    print('Irradiance data saved to: ')
     os.system('readlink -f '+str(filename))
     return
 
@@ -405,7 +405,7 @@ def get_args():
     parser.add_argument('-b', '--binning',
                         help="Binning scheme to use. Can be [solomon,neuvac,euvac] "
                         "(case insensitive)",
-                        type=str)
+                        type=str, default="neuvac")
 
     args = parser.parse_args()
 
@@ -422,10 +422,10 @@ def get_args():
 
     if binning_scheme == 'HFG' or binning_scheme == 'SOLOMON' or binning_scheme == 'Solomon' or binning_scheme == 'solomon':
         # SOLOMON (STAN BANDS; b23)
-        fism2_out_23 = getFism2(dateStart, dateEnd, 'FISM2S', downloadDir=None)
+        fism2_out_23 = getFism2(dateStart, dateEnd, 'FISM2S', downloadDir=here)
         fism2_file_23, fism2_data_23 = rebin(fism2_out_23, binning_scheme=binning_scheme)
     else:
-        fism2_out_raw = getFism2(dateStart, dateEnd, 'FISM2', downloadDir=None)
+        fism2_out_raw = getFism2(dateStart, dateEnd, 'FISM2', downloadDir=here)
         if binning_scheme == 'NEUVAC' or binning_scheme == 'Neuvac' or binning_scheme == 'neuvac':
             # NEUVAC BINS (b59)
             fism2_file_59, fism2_data_59 = rebin(fism2_out_raw, binning_scheme=binning_scheme, zero=True)
diff --git a/srcPython/neuvac.py b/srcPython/neuvac.py
new file mode 100644
index 00000000..3fb0dadb
--- /dev/null
+++ b/srcPython/neuvac.py
@@ -0,0 +1,475 @@
+#!/usr/bin/env python
+
+# Authors of this code:
+# Daniel A. Brandt, Ph.D., Michigan Tech Research Institute, daabrand@mtu.edu
+
+# This file contains a suite of tools that do the following:
+# 1 - Obtain F10.7 data between any dates of the user's choosing.
+# 2 - Generate NEUVAC irradiances between any two dates of the user's choosing.
+# 3 - Output the NEUVAC irradiances to a .csv file to be used by Aether, either in the b37 or b59 bins.
+
+# Top-level imports:
+import argparse
+import numpy as np
+from datetime import datetime
+import pathlib
+from pathlib import Path
+import pandas as pd
+from scipy.interpolate import CubicSpline
+import urllib.request, pickle
+from fism import saveFism
+import os, sys
+import pooch
+
+# Directory management:
+here = pathlib.Path(__file__).parent.resolve()
+euvDir = here.parent.joinpath('share/run/UA/inputs')
+
+# Physical constants:
+h = 6.62607015e-34 # Planck's constant in SI units of J s
+c = 299792458 # Speed of light in m s^-1
+
+# Global variable(s):
+# Waves Table (coefficients for the old NEUVAC Model):
+# Format: 0-Min, 1-Max, 2-S_1i, 3-S_Ai, 4-S_Di, 5-I_i, 6-Pi, 7-Ai
+waveTable = np.array([
+    [1700.00, 1750.00, 1.31491e-06, 6.71054e-06, 5.78034e-07, 0.00355128, 1.05517, 0.901612],
+    [1650.00, 1700.00, 5.19285e-07, 2.62376e-06, 3.08447e-07, 0.00218156, 1.06245, 0.964892],
+    [1600.00, 1650.00, 3.85348e-07, 1.73851e-06, 3.34911e-07, 0.00115310, 1.07246, 0.959562],
+    [1550.00, 1600.00, 2.96220e-07, 1.29250e-06, 2.61812e-07, 0.000814814, 1.04567, 0.967804],
+    [1500.00, 1550.00, 2.35326e-07, 1.21123e-06, 2.27793e-07, 0.000566574, 1.13520, 0.970257],
+    [1450.00, 1500.00, 1.86793e-07, 5.96399e-07, 1.48283e-07, 0.000331058, 1.01564, 0.940506],
+    [1400.00, 1450.00, 1.96396e-07, 5.84154e-07, 1.82438e-07, 0.000207013, 1.67546, 0.945697],
+    [1350.00, 1400.00, 1.04362e-07, 5.02422e-07, 1.45100e-07, 0.000153277, 1.04246, 0.992749],
+    [1300.00, 1350.00, 1.74403e-07, 6.32214e-07, 4.03009e-07, 0.000311075, 1.00964, 1.09381],
+    [1250.00, 1300.00, 7.12738e-08, 2.44220e-07, 9.56532e-08, 9.68823e-05, 1.15737, 1.01121],
+    [1200.00, 1250.00, 8.74335e-06, 5.02272e-05, 1.32536e-05, 0.00263307, 1.46273, 0.987493],
+    [1215.67, 1215.67, 6.43713e-06, 5.16823e-05, 1.11399e-05, 0.00247063, 1.26340, 0.998295],
+    [1150.00, 1200.00, 1.15468e-07, 2.74916e-07, 1.65125e-07, 0.000105178, 1.66887, 1.00997],
+    [1100.00, 1150.00, 7.71861e-08, 2.15061e-07, 1.44227e-07, 5.16157e-05, 0.971988, 1.05634],
+    [1050.00, 1100.00, 5.84127e-08, 3.08808e-07, 1.25160e-07, 4.65227e-05, 1.58808, 1.05327],
+    [1000.00, 1050.00, 2.23073e-07, 6.92710e-07, 5.19444e-07, 5.44992e-05, 0.449052, 1.10271],
+    [1031.91, 1031.91, 6.18723e-08, 1.21679e-07, 2.28527e-07, 3.14905e-05, 1.42684, 1.17863],
+    [1025.72, 1025.72, 1.61504e-07, 4.38856e-07, 2.79663e-07, 1.06365e-05, 1.09262, 1.05186],
+    [950.00, 1000.00, 1.70358e-07, 5.20531e-07, 3.86006e-07, 3.34989e-05, 0.491283, 1.09676],
+    [977.02, 977.02, 1.51857e-07, 5.60743e-07, 2.74541e-07, 6.71100e-06, 1.44918, 1.04869],
+    [900.00, 950.00, 7.27646e-08, 4.53511e-07, 1.91513e-07, 3.93851e-05, 1.21476, 1.06473],
+    [850.00, 900.00, 1.45264e-07, 2.82927e-07, 4.22856e-07, 4.83494e-05, 1.15579, 1.14948],
+    [800.00, 850.00, 6.69560e-08, 1.26613e-07, 1.76066e-07, 3.69687e-05, 1.14722, 1.12832],
+    [750.00, 800.00, 3.22816e-08, 7.81757e-08, 6.32959e-08, 4.42679e-05, 0.969748, 1.06692],
+    [789.36, 789.36, 1.19733e-08, 2.53334e-08, 1.58546e-08, 1.25539e-05, 1.48302, 1.00982],
+    [770.41, 770.41, 7.33597e-09, 2.10650e-08, 1.63125e-08, 8.88041e-06, 1.18634, 1.06584],
+    [765.15, 765.15, 4.85967e-09, 1.05567e-08, 5.42104e-09, 1.15262e-05, 1.17912, 1.03352],
+    [700.00, 750.00, 1.85139e-08, 3.63837e-08, 3.29576e-08, 1.72134e-05, 1.25328, 1.06364],
+    [703.36, 703.36, 5.34708e-09, 9.65120e-09, 4.54419e-09, 8.80278e-06, 1.51207, 0.972520],
+    [650.00, 700.00, 1.79851e-08, 6.39605e-08, 1.86000e-08, 1.41950e-05, 1.11181, 0.945801],
+    [600.00, 650.00, 1.52595e-07, 5.29641e-07, 1.41837e-07, 3.96165e-05, 1.00554, 0.949913],
+    [629.73, 629.73, 4.96048e-08, 2.46454e-07, 3.12902e-08, 1.59200e-05, 1.01611, 0.846628],
+    [609.76, 609.76, 2.80641e-08, 3.24530e-07, 1.81554e-08, 1.68460e-06, 0.973085, 0.793355],
+    [550.00, 600.00, 1.12234e-07, 6.29889e-07, 1.56092e-07, 2.79143e-05, 0.961457, 0.970150],
+    [584.33, 584.33, 7.91646e-08, 3.05430e-07, 5.14430e-08, 1.70372e-05, 0.844250, 0.881026],
+    [554.31, 554.31, 2.47485e-08, 2.68042e-07, 5.40951e-08, 1.16226e-06, 1.08699, 1.01483],
+    [500.00, 550.00, 1.12037e-07, 7.84515e-07, 6.32364e-08, 4.55230e-06, 1.13480, 0.816868],
+    [450.00, 500.00, 1.10016e-07, 3.96192e-07, 7.37101e-08, 2.62692e-05, 1.15344, 0.865234],
+    [465.22, 465.22, 9.60010e-09, 1.75358e-08, 6.91440e-11, 1.45142e-05, 1.62256, -0.203971],
+    [400.00, 450.00, 5.15555e-08, 2.89821e-07, 3.85807e-08, 1.64207e-05, 1.36652, 0.893190],
+    [350.00, 400.00, 3.91955e-07, 1.43942e-06, 3.16713e-07, -2.36108e-06, 1.05819, 0.910235],
+    [368.07, 368.07, 1.38855e-07, 7.21254e-07, 1.01814e-07, 8.71098e-07, 1.26707, 0.890513],
+    [300.00, 350.00, 1.35439e-06, 1.09238e-05, 8.24308e-07, 4.35250e-05, 1.22619, 0.816515],
+    [303.78, 303.78, 7.43959e-07, 5.94012e-06, 4.05188e-07, 9.23799e-05, 1.32976, 0.796970],
+    [303.31, 303.31, 5.25977e-07, 7.87164e-06, 3.07932e-07, 7.87468e-05, 0.945961, 0.759694],
+    [250.00, 300.00, 9.10710e-07, 3.91586e-06, 1.20177e-06, -9.64301e-06, 1.07360, 0.958369],
+    [284.15, 284.15, 8.67633e-07, 6.00671e-06, 3.97664e-07, -0.000107230, 1.20608, 0.773950],
+    [256.30, 256.30, 6.44996e-08, 4.12637e-07, 1.05193e-07, 6.61853e-06, 1.48670, 1.03265],
+    [200.00, 250.00, 4.83013e-07, 1.18898e-06, 8.94772e-07, 5.34779e-05, 1.04532, 1.07888],
+    [150.00, 200.00, 7.13305e-07, 2.47623e-06, 9.78936e-07, 0.000261230, 1.47374, 1.01156],
+    [100.00, 150.00, 4.03676e-08, 2.28270e-07, 4.43965e-08, 2.16162e-05, 1.09062, 0.970310],
+    [50.00, 100.00, 1.69769e-07, 6.93618e-07, 2.89457e-07, 2.03013e-05, 1.07887, 1.06022],
+    [32.00, 50.00, 1.23478e-07, 4.43644e-07, 1.75749e-07, -1.34567e-05, 1.27409, 1.01254],
+    [23.00, 32.00, 6.10174e-08, 2.34313e-07, 1.10591e-07, -1.22729e-05, 0.699812, 1.04841],
+    [16.00, 23.00, 2.23866e-07, 7.97533e-07, 3.03563e-07, -5.62012e-05, 0.706360, 0.987835],
+    [8.00, 16.00, 3.10773e-07, 1.22767e-06, 3.74797e-07, -8.41459e-05, 1.39529, 0.963859],
+    [4.00, 8.00, 1.17378e-08, 7.13970e-08, 1.38839e-08, -3.63146e-06, 0.811119, 0.920702],
+    [2.00, 4.00, 3.97985e-09, 4.12085e-08, 4.71914e-09, -1.86099e-06, 1.15214, 0.916686],
+    [1.00, 2.00, 3.52498e-09, 1.57342e-08, 4.03741e-09, -8.84488e-07, 0.951714, 0.943490]
+    ])
+
+# Helper Functions:
+def rollingAverage(myData, window_length=1, impute_edges=True, center=True):
+    """
+    Using pandas, compute a rolling average of over 'data' using a window length of 'windowlength'. Sets the leading and
+    trailing windows to the values of the original data.
+    :param myData: arraylike
+        The data over which to compute the rolling average.
+    :param window_length: int
+        The size of the window over which to average.
+    :param impute_edges: bool
+        A boolean determining whether the edges will be interpolated. Default is True.
+    :param center: bool
+        A boolean determining whether the centered average will be used.
+    :return: rolled, arraylike
+        The rolling average data.
+    """
+    myDataframe = pd.DataFrame(data=myData, columns=['Var'])
+    myDataframe['Rolling'] = myDataframe['Var'].rolling(window=window_length, center=center).mean()
+    firstValidIndex = myDataframe['Rolling'].first_valid_index()
+    lastValidIndex = myDataframe['Rolling'].last_valid_index()
+    if impute_edges == True:
+        # Sample x-axis:
+        sampleXaxis = np.linspace(0, window_length, window_length)
+        middleIndex = int(0.5*window_length)
+        # Use cubic interpolation to fill the gaps on the edges:
+        leadingEdgeStartingVal = myDataframe['Var'][:window_length].values[0]
+        leadingEndingVal = myDataframe['Rolling'][firstValidIndex]
+        leadingEdgeMiddleVal = np.mean([leadingEdgeStartingVal, leadingEndingVal])
+        leadingSpline = CubicSpline([sampleXaxis[0], sampleXaxis[middleIndex], sampleXaxis[-1]],
+                                    [leadingEdgeStartingVal, leadingEdgeMiddleVal, leadingEndingVal])
+        leadingImputedValues = leadingSpline(sampleXaxis)
+
+        trailingEdgeStartingVal = myDataframe['Rolling'][lastValidIndex]
+        trailingEndingVal = myDataframe['Var'].values[-1]
+        trailingEdgeMiddleVal = np.mean([trailingEdgeStartingVal, trailingEndingVal])
+        trailingSpline = CubicSpline([sampleXaxis[0], sampleXaxis[middleIndex], sampleXaxis[-1]],
+                                    [trailingEdgeStartingVal, trailingEdgeMiddleVal, trailingEndingVal])
+        trailingImputedValues = trailingSpline(sampleXaxis)
+        # Ingest the imputed values:
+        myDataframe['Rolling'][:window_length] = leadingImputedValues
+        myDataframe['Rolling'][-window_length:] = trailingImputedValues
+    else:
+        myDataframe['Rolling'][:window_length] = myDataframe['Var'][:window_length]
+        myDataframe['Rolling'][-window_length:] = myDataframe['Var'][-window_length:]
+    rolled = myDataframe['Rolling'].values
+    return rolled
+
+def readCLS(filename):
+    """
+    Load in flare-corrected, Sun-Earth distance adjusted flux values recorded by the Collecte Localisation Satellites
+    (CLS).
+    :param filename: str
+        The location of the data file.
+    :return times: list
+        The datetimes for each data value.
+    :return data: ndarray
+        The solar flux data for F30, F15, F10.7, F8, and F3.2.
+    """
+    times = []
+    precisionVals = []
+    with open(filename, 'r') as myFile:
+        allLines = myFile.readlines()
+        data = np.zeros((len(allLines)-25, 5))
+        i = 0
+        j = 0
+        for line in allLines:
+            if i >= 25:
+                elements = line.split()
+                data[j, :] = np.array([float(elements[5]), float(elements[9]), float(elements[13]), float(elements[17]), float(elements[21])])
+                times.append( datetime(int(elements[0]), int(elements[1]), int(elements[2]), 12) )
+                precisionVals.append( [float(elements[6]), float(elements[10]), float(elements[14]), float(elements[18]), float(elements[22])] )
+                j += 1
+            i += 1
+    # Print the precision:
+    # print('Mean precision values...')
+    # print('F30: '+str(np.nanmean([element[0] for element in precisionVals]))+' sfu') # 6
+    # print('F15: ' + str(np.nanmean([element[1] for element in precisionVals])) + ' sfu') # 8
+    # print('F10.7: ' + str(np.nanmean([element[2] for element in precisionVals])) + ' sfu') # 13
+    # print('F8: ' + str(np.nanmean([element[3] for element in precisionVals])) + ' sfu') # 12
+    # print('F3.2: ' + str(np.nanmean([element[4] for element in precisionVals])) + ' sfu') # 11
+    return times, data
+
+def getCLSF107(dateStart, dateEnd, truncate=True):
+    """
+    Obtains Sun-Earth distance adjusted, flare-corrected F10.7 data from Collecte Localisation Satellites. Downloads the
+    most recent measurements to a file. Reads the file and extracts the F10.7 values between two dates. Note that if the
+    ending date is less than or equal to the last date in the version of the file that has already been downloaded, the
+    file IS NOT re-downloaded, but simply parsed. Otherwise, the file is redownloaded.
+    :param dateStart: str
+        The starting date in YYYYMMDD format.
+    :param dateEnd: str
+        The ending date in YYYYMMDD format.
+    :param truncate: bool
+        Controls whether to truncate the data to exclude the most recent 81 days. Defaults is True.
+    """
+    dateStart = dateStart[:4]+'-'+dateStart[4:6]+'-'+dateStart[6:]
+    dateEnd = dateEnd[:4]+'-'+dateEnd[4:6]+'-'+dateEnd[6:]
+    dateTimeStart = datetime.strptime(dateStart, '%Y-%m-%d')
+    dateTimeEnd = datetime.strptime(dateEnd, '%Y-%m-%d')
+    fname = euvDir.joinpath("radio_flux_adjusted_observation.txt")
+    if fname.exists():
+        # Read in the file:
+        times, data = readCLS(fname)
+        # Check if the ending date exceeds the ending date in the file. If so, redownloading the file:
+        if times[-1] > dateTimeEnd:
+            out = urllib.request.urlretrieve(
+                'ftp://ftpsedr.cls.fr/pub/previsol/solarflux/observation/radio_flux_adjusted_observation.txt', fname)
+        times, data = readCLS(fname)
+    else:
+        # Download the file:
+        out = urllib.request.urlretrieve('ftp://ftpsedr.cls.fr/pub/previsol/solarflux/observation/radio_flux_adjusted_observation.txt', fname)
+        times, data = readCLS(fname)
+
+    # Compute the 81-day (centered) averaged F10.7 and 54-day averaged (:
+    F107 = data[:, 2]
+    F107A = rollingAverage(F107, window_length=81, impute_edges=True)
+    F107B = rollingAverage(F107, window_length=54, impute_edges=True, center=False)
+    print(f'\n\nlen(F107) = {len(F107)}, len(F107B) = {len(F107B)}, len(F107B) = {len(F107B)}')
+    # Extract the values in the desired time range:
+    goodInds = np.where((np.asarray(times) >= dateTimeStart) & (np.asarray(times) <= dateTimeEnd))[0]
+    # Truncation:
+    if truncate and len(goodInds) >= 2*81:
+        goodInds = goodInds[:-81]
+    return np.asarray(times)[goodInds], np.asarray(F107)[goodInds], np.asarray(F107A)[goodInds], np.asarray(F107B)[goodInds]
+
+def mycorrelate2d(df, normalized=False):
+    """
+    Compute the correlation matrix from 2D data, where each row is cross correlated with the others.
+    This function handles NaN values by ignoring them.
+    :param df: ndarray
+        A 2D array of dimensions n x m.
+    :param normalized: bool
+        Determines whether the resulting correlation matrix is normalized. Default is False.
+    :returns ccm: ndarray
+        The [normalized] cross-correlation matrix.
+    Source: https://stackoverflow.com/questions/54292947/basics-of-normalizing-cross-correlation-with-a-view-to-comparing-signals
+    """
+    # Initialize cross correlation matrix with zeros
+    ccm = np.zeros((df.shape[1], df.shape[1]))
+    # Fill in each entry of the matrix one-by-one:
+    for i in range(df.shape[1]):
+        outer_row = df[:, i]
+        for j in range(df.shape[1]):
+            inner_row = df[:, j]
+            goodInds = np.logical_and(~np.isnan(outer_row), ~np.isnan(inner_row))
+            if (not normalized):
+                x = np.correlate(outer_row[goodInds], inner_row[goodInds])
+            else:
+                x = get_cc(outer_row[goodInds], inner_row[goodInds])
+                # a = (inner_row - np.mean(inner_row)) / (np.std(inner_row) * len(inner_row))
+                # b = (outer_row - np.mean(outer_row)) / (np.std(outer_row) )
+                # x = np.correlate(a, b)
+            ccm[i, j] = x
+    return ccm
+
+def get_cc(array1, array2, normalize=True):
+    """
+    Compute the cross-correlation of two 1D arrays of the same length.
+    :param array1: ndarray
+        A 1D array of length n.
+    :param array2: ndarray
+        A 1D array of length n.
+    :return c: float
+        The normalized correlation of the two arrays.
+    """
+    if normalize:
+        a = (array1 - np.mean(array1)) / (np.std(array1) * len(array1))
+        b = (array2 - np.mean(array2)) / (np.std(array2))
+        c = np.correlate(a, b)
+    else:
+        c = np.correlate(array1, array2)
+    return c
+
+def loadPickle(pickleFilename):
+    """
+    Given the name of a (pre-existing) pickle file, load its contents.
+    :param: pickleFilename, str
+        A string with the location/name of the filename.
+    :return: var
+        The loaded data.
+    """
+    with open(pickleFilename, 'rb') as pickleFile:
+        var = pickle.load(pickleFile)
+    return var
+
+# CORE NEUVAC FUNCTIONS:
+def irrFunc(F107input, A, B, C, D, E, F):
+    F107, F107A = F107input
+    return A * (F107 ** B) + C * (F107A ** D) + E * (F107A - F107) + F
+
+def neuvacEUV(f107, f107b, bands=None, tableFile=None, statsFiles=None):
+    """
+    Use a parametric model to compute solar flux in the 59 conventional wavelength bands used by Aether/GITM. Capable
+    of returning perturbed irradiance values that are perturbed according to the variations of in the intensity of each
+    bin
+    :param f107: ndarray
+        F10.7 values.
+    :param f107b: ndarray
+        81-day center-averaged F10.7 values; must be the same length as f107.
+    :param bands: str
+        If None or 'NEUVAC', returns irradiances in the GITM Bands. If 'EUVAC', returns them in the 37 bands used by
+        EUVAC. If 'SOLOMON', returns them in the 22 bands used by Solomon and Qian.
+    :param tableFile: str
+        Corresponds to the .txt file holding the NEUVAC coefficients most recently-generated by fitNeuvac.py. If
+        not given, simply uses the table file corresponding to the selected bin structure. Default is None.
+    :param statsFiles: Bool
+        Determines whether data for uncertainty quantification is exploited. Involves usage of a list containing
+        2 elements where the first element is a file containing the 59x59 correlation matrix and the second
+        element is a file containing the 1x59 standard deviation values for NEUVAC. NOT REQUIRED.
+    :return euvIrradiance: ndarray
+        A nxm ndarray where n is the number of EUV irradiance values and m is the number of wavelength bands.
+    :return perturbedEuvIrradiance: ndarray
+        A nxm ndarray where n is the number of EUV irradiance values perturbed due to inherent uncertainty and m is the
+        number of wavelength bands.
+    :return savedPerts: ndarray
+        A nxm ndarray of the perturbations (time series of the NEUVAC+Perturbation - NEUVAC)
+    :return cc2: ndarray
+        A mxm ndarray of the correlation matrix between each wavelength's time-series of the NEUVAC+Perturbation -
+        NEUVAC.
+    """
+    if type(f107) != np.ndarray:
+        f107 = np.asarray([f107])
+        f107b = np.asarray([f107b])
+        if bands == 'SOLOMON':
+            solarFlux = np.zeros((1, 22))
+        else:
+            solarFlux = np.zeros((1, waveTable.shape[0]))
+    else:
+        if bands == 'SOLOMON':
+            solarFlux = np.zeros((len(f107), 22))
+        else:
+            solarFlux = np.zeros((len(f107), waveTable.shape[0]))
+    euvIrradiance = np.zeros_like(solarFlux)
+    perturbedEuvIrradiance = np.zeros_like(solarFlux)
+    # Gather the model parameters:
+    if tableFile is None:
+        if bands == 'SOLOMON':
+            tableFile = euvDir.joinpath('neuvac_table_stan_bands.txt') #'../data/neuvac_table_stan_bands.txt'
+        else:
+            tableFile = euvDir.joinpath('neuvac_table.txt') #'../data/neuvac_table.txt'
+    neuvacTable = []
+    with open(here.parent.joinpath(tableFile)) as neuvacFile: # open(tableFile)
+        contents = neuvacFile.readlines()
+        i = 0
+        for line in contents:
+            if i > 17:
+                neuvacTable.append([float(element) for element in line.split(' ')])
+            i+=1
+    neuvacTable = np.asarray(neuvacTable)
+
+    # If no stats file is provided, simply return the base model output (using the required table file):
+    if not statsFiles:
+        # Loop across the F10.7 (and F10.7A) values:
+        for i in range(len(f107)):
+            k = 0
+            for j in (range(solarFlux.shape[1])):
+                irrRes = irrFunc([f107[i], f107b[i]], *neuvacTable[j, 2:])
+                if irrRes < 0:
+                    irrRes = 0
+                    euvIrradiance[i, k] = irrRes
+                else:
+                    euvIrradiance[i, k] = irrRes
+                k += 1
+        if bands == 'EUVAC':  # Returns values ONLY for those corresponding to the wavelengths used by EUVAC
+            return euvIrradiance[:, 7:44], None, None, None
+        else:
+            return euvIrradiance, None, None, None
+    else:
+        # Include statistical data for calculating uncertainties via perturbations:
+        if bands == 'NEUVAC':
+            statsFiles = [euvDir.joinpath('corMat.pkl'), euvDir.joinpath('sigma_NEUVAC.pkl')]
+        elif bands == 'EUVAC':
+            statsFiles = [euvDir.joinpath('corMatEUVAC.pkl'), euvDir.joinpath('sigma_EUVAC.pkl')]
+        else:
+            statsFiles = [euvDir.joinpath('corMatStanBands.pkl'), euvDir.joinpath('sigma_NEUVAC_StanBands.pkl')]
+        corMatFile = statsFiles[0]
+        corMat = loadPickle(corMatFile)
+        sigmaFile = statsFiles[1]
+        STDNeuvacResids = loadPickle(sigmaFile)
+        # Loop across the F10.7 (and F10.7A) values:
+        nTimes = len(f107)
+        nWaves = solarFlux.shape[1]
+        savedPerts = np.zeros((nTimes, nWaves))
+        for i in range(len(f107)):
+            # Loop across the wavelengths (59 conventional wavelengths):
+            k = 0
+            P_n = []
+            for j in (range(solarFlux.shape[1])):
+                # Percentage perturbation:
+                P_j = np.random.normal(0, 1.0)
+                P_n.append(P_j)
+                P_1 = P_n[0]
+                # Normalized Correlated Perturbation:
+                if bands == 'SOLOMON':
+                    if j < 5:
+                        C_j1 = corMat[0, j] # 3 # Only consider correlation with the third wavelength bin of the SOLOMON bins!
+                    else:
+                        C_j1 = corMat[5, j] # Only consider correlation with the fifth wavelength bin of the SOLOMON bins!
+                else:
+                    if j < 7:
+                        # Only consider correlation with the third wavelength bin (of the NEUVAC bins!) when bands are below 8.
+                        C_j1 = corMat[0, j] # 2
+                    else:
+                        # Only consider correlation with the first wavelength bin (of the EUVAC bins!) when bands are above 8.
+                        C_j1 = corMat[7, j]
+                N_j = C_j1 * P_1 + (1.0 - C_j1) * P_j
+                # Actual Normalized Correlated Perturbation:
+                A_j = STDNeuvacResids[j] * N_j
+                irrRes = irrFunc([f107[i], f107b[i]], *neuvacTable[j, 2:])
+                if irrRes < 0:
+                    irrRes = 0
+                    euvIrradiance[i, k] = irrRes
+                    if irrRes + A_j < 0:
+                        perturbedEuvIrradiance[i, k] = 0
+                    else:
+                        perturbedEuvIrradiance[i, k] = irrRes + A_j
+                else:
+                    euvIrradiance[i, k] = irrRes
+                    perturbedEuvIrradiance[i, k] = irrRes + A_j
+                savedPerts[i, j] = A_j
+                k += 1
+
+        # Generate a correlation matrix of the perturbations (to compare to the input correlation matrix as a sanity check):
+        cc2 = mycorrelate2d(savedPerts, normalized=True)
+
+        if bands == 'EUVAC':  # Returns values ONLY for those corresponding to the wavelengths used by EUVAC
+            return euvIrradiance[:, 7:44], perturbedEuvIrradiance[:, 7:44], savedPerts, cc2
+        else:
+            return euvIrradiance, perturbedEuvIrradiance, savedPerts, cc2
+
+# -----------------------------------------------------------------------------------------------------------------------------------------
+# Argument Parsing Function:
+def get_args():
+
+    parser = argparse.ArgumentParser(description = 'Create NEUVAC input data')
+    parser.add_argument('start',
+                        help='Start date (format YYYYMMDD)',
+                        type=str)
+    parser.add_argument('end',
+                        help='End date (format YYYYMMDD)',
+                        type=str)
+    parser.add_argument('-b', '--binning',
+                        help="Binning scheme to use. Can be [solomon,neuvac,euvac] "
+                        "(case insensitive)",
+                        type=str, default="neuvac")
+    args = parser.parse_args()
+
+    return args
+# -----------------------------------------------------------------------------------------------------------------------------------------
+
+# Example Execution:
+
+# python neuvac.py 20110319 20110321 -b euvac
+
+args = get_args()
+dateStart = args.start
+dateEnd = args.end
+binning_scheme = args.binning
+
+# Load F10.7 data (from Collecte Localisation Satellites):
+times, F107, F107A, F107B = getCLSF107(dateStart, dateEnd)
+
+# Generate NEUVAC Irradiance from that F10.7 data:
+if binning_scheme == 'HFG' or binning_scheme == 'SOLOMON' or binning_scheme == 'Solomon' or binning_scheme == 'solomon':
+    # SOLOMON (STAN BANDS; b23)
+    irradiance, _, _, _ = neuvacEUV(F107, F107B, bands='SOLOMON')
+else:
+    if binning_scheme == 'NEUVAC' or binning_scheme == 'Neuvac' or binning_scheme == 'neuvac':
+        # NEUVAC BINS (b59)
+        irradiance, _, _, _ = neuvacEUV(F107, F107B, bands='NEUVAC')
+    else:
+        # EUVAC BINS (b37)
+        irradiance, _, _, _ = neuvacEUV(F107, F107B, bands='EUVAC')
+
+# Save the NEUVAC Irradiance to a file that Aether can use:
+tag = str(irradiance.shape[1])
+fname = os.getcwd() + '/neuvac_file_'+tag+'.txt'
+saveFism(irradiance, times, fname)
+

From 46e85edd46845c2f67c2927ad4f7d681bca89402 Mon Sep 17 00:00:00 2001
From: "Daniel A. Brandt" <daabrand@mtu.edu>
Date: Mon, 14 Jul 2025 17:27:05 -0400
Subject: [PATCH 137/266] Added version 2 of the euv_59.csv file; contains
 updated NEUVAC coefficients.

---
 share/run/UA/inputs/euv_59_v2.csv | 43 +++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 share/run/UA/inputs/euv_59_v2.csv

diff --git a/share/run/UA/inputs/euv_59_v2.csv b/share/run/UA/inputs/euv_59_v2.csv
new file mode 100644
index 00000000..7f807952
--- /dev/null
+++ b/share/run/UA/inputs/euv_59_v2.csv
@@ -0,0 +1,43 @@
+ Short,,wave,1.0000E+00,Angstroms, 1.0000E+00, 2.0000E+00, 4.0000E+00, 8.0000E+00, 1.6000E+01, 2.3000E+01, 3.2000E+01, 5.0000E+01, 1.0000E+02, 1.5000E+02, 2.0000E+02, 2.5630E+02, 2.8415E+02, 2.5000E+02, 3.0331E+02, 3.0378E+02, 3.0000E+02, 3.6807E+02, 3.5000E+02, 4.0000E+02, 4.6522E+02, 4.5000E+02, 5.0000E+02, 5.5437E+02, 5.8433E+02, 5.5000E+02, 6.0976E+02, 6.2973E+02, 6.0000E+02, 6.5000E+02, 7.0331E+02, 7.0000E+02, 7.6515E+02, 7.7041E+02, 7.8936E+02, 7.5000E+02, 8.0000E+02, 8.5000E+02, 9.0000E+02, 9.7702E+02, 9.5000E+02, 1.0257E+03, 1.0319E+03, 1.0000E+03, 1.0500E+03, 1.1000E+03, 1.1500E+03, 1.2157E+03, 1.2000E+03, 1.2500E+03, 1.3000E+03, 1.3500E+03, 1.4000E+03, 1.4500E+03, 1.5000E+03, 1.5500E+03, 1.6000E+03, 1.6500E+03, 1.7000E+03, from GITM
+ Long,,wave,1.0000E+00,Angstroms, 2.0000E+00, 4.0000E+00, 8.0000E+00, 1.6000E+01, 2.3000E+01, 3.2000E+01, 5.0000E+01, 1.0000E+02, 1.5000E+02, 2.0000E+02, 2.5000E+02, 2.5630E+02, 2.8415E+02, 3.0000E+02, 3.0331E+02, 3.0378E+02, 3.5000E+02, 3.6807E+02, 4.0000E+02, 4.5000E+02, 4.6522E+02, 5.0000E+02, 5.5000E+02, 5.5437E+02, 5.8433E+02, 6.0000E+02, 6.0976E+02, 6.2973E+02, 6.5000E+02, 7.0000E+02, 7.0331E+02, 7.5000E+02, 7.6515E+02, 7.7041E+02, 7.8936E+02, 8.0000E+02, 8.5000E+02, 9.0000E+02, 9.5000E+02, 9.7702E+02, 1.0000E+03, 1.0257E+03, 1.0319E+03, 1.0500E+03, 1.1000E+03, 1.1500E+03, 1.2000E+03, 1.2157E+03, 1.2500E+03, 1.3000E+03, 1.3500E+03, 1.4000E+03, 1.4500E+03, 1.5000E+03, 1.5500E+03, 1.6000E+03, 1.6500E+03, 1.7000E+03, 1.7500E+03, from GITM
+ F74113,,,1.0000E+09,/cm2/s, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.2000E+00, 4.5000E-01, 4.8000E+00, 3.1000E+00, 4.6000E-01, 2.1000E-01, 1.6790E+00, 8.0000E-01, 6.9000E+00, 9.6500E-01, 6.5000E-01, 3.1400E-01, 3.8300E-01, 2.9000E-01, 2.8500E-01, 4.5200E-01, 7.2000E-01, 1.2700E+00, 3.5700E-01, 5.3000E-01, 1.5900E+00, 3.4200E-01, 2.3000E-01, 3.6000E-01, 1.4100E-01, 1.7000E-01, 2.6000E-01, 7.0200E-01, 7.5800E-01, 1.6250E+00, 3.5370E+00, 3.0000E+00, 4.4000E+00, 1.4750E+00, 3.5000E+00, 2.1000E+00, 2.4670E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ AFAC,,,1.0000E+00,, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.0017E-02, 7.1250E-03, 1.3375E-02, 1.9450E-02, 2.7750E-03, 1.3768E-01, 2.6467E-02, 2.5000E-02, 3.3333E-03, 2.2450E-02, 6.5917E-03, 3.6542E-02, 7.4083E-03, 7.4917E-03, 2.0225E-02, 8.7583E-03, 3.2667E-03, 5.1583E-03, 3.6583E-03, 1.6175E-02, 3.3250E-03, 1.1800E-02, 4.2667E-03, 3.0417E-03, 4.7500E-03, 3.8500E-03, 1.2808E-02, 3.2750E-03, 4.7667E-03, 4.8167E-03, 5.6750E-03, 4.9833E-03, 3.9417E-03, 4.4167E-03, 5.1833E-03, 5.2833E-03, 4.3750E-03, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ NEUV_S1,,,1.0000e+00,slope, 4.7721e-01, 4.8836e-01, 5.1112e-01, 4.6218e-01, 4.4626e-01, 4.5621e-01, 4.6444e-01, 2.5497e-01, 4.5348e-01, 2.7143e-01, 1.0616e+00, 4.3086e-01, 4.8995e-01, 4.3046e-01, 1.9877e-01, 4.5349e-01, 5.1979e-01, 4.5590e-01, 4.0438e-01, 4.4541e-01, 4.5721e-01, 5.2010e-01, 4.5938e-01, 4.5614e-01, 4.9421e-01, 4.6293e-01, 4.6222e-01, 4.5820e-01, 4.4724e-01, 4.5370e-01, 4.5756e-01, 4.6456e-01, 4.5873e-01, 4.5651e-01, 4.5443e-01, 4.4956e-01, 4.1966e-01, 5.3686e-01, 4.3159e-01, 3.6366e-01, 4.7233e-01, 4.7194e-01, 4.5386e-01, 4.5133e-01, 4.6587e-01, 4.4840e-01, 5.3661e-01, 1.0786e-01, 2.6182e-01, 4.4807e-01, 4.2671e-01, 4.9602e-01, 4.4946e-01, 5.0319e-01, 4.3481e-01, 4.9051e-01, 1.5122e-01, 3.8279e-01, 7.0315e-01,from GITM
+ NEUV_S2,,,1.0000e+00,slope, -4.7721e-01, -4.8836e-01, -5.1112e-01, -4.6218e-01, -4.4626e-01, -4.5620e-01, -4.6443e-01, -2.5496e-01, -4.5348e-01, -2.7142e-01, -1.0616e+00, -4.3086e-01, -4.8995e-01, -4.3045e-01, -1.9877e-01, -4.5349e-01, -5.1978e-01, -4.5590e-01, -4.0438e-01, -4.4540e-01, -4.5721e-01, -5.2010e-01, -4.5938e-01, -4.5614e-01, -4.9421e-01, -4.6293e-01, -4.6222e-01, -4.5820e-01, -4.4724e-01, -4.5370e-01, -4.5756e-01, -4.6456e-01, -4.5873e-01, -4.5651e-01, -4.5443e-01, -4.4956e-01, -4.1966e-01, -5.3685e-01, -4.3158e-01, -3.6366e-01, -4.7233e-01, -4.7194e-01, -4.5386e-01, -4.5133e-01, -4.6587e-01, -4.4840e-01, -5.3661e-01, -1.0781e-01, -2.6182e-01, -4.4807e-01, -4.2671e-01, -4.9602e-01, -4.4946e-01, -5.0318e-01, -4.3481e-01, -4.9051e-01, -1.5121e-01, -3.8278e-01, -7.0314e-01,from GITM
+ NEUV_S3,,,1.0000e+00,slope, 4.7721e-01, 4.8836e-01, 5.1112e-01, 4.6218e-01, 4.4626e-01, 4.5621e-01, 4.6444e-01, 2.5496e-01, 4.5348e-01, 2.7143e-01, 1.0616e+00, 4.3086e-01, 4.8995e-01, 4.3045e-01, 1.9877e-01, 4.5349e-01, 5.1979e-01, 4.5590e-01, 4.0438e-01, 4.4541e-01, 4.5721e-01, 5.2010e-01, 4.5938e-01, 4.5614e-01, 4.9421e-01, 4.6293e-01, 4.6222e-01, 4.5820e-01, 4.4724e-01, 4.5370e-01, 4.5756e-01, 4.6456e-01, 4.5873e-01, 4.5651e-01, 4.5443e-01, 4.4956e-01, 4.1966e-01, 5.3685e-01, 4.3158e-01, 3.6366e-01, 4.7233e-01, 4.7194e-01, 4.5386e-01, 4.5133e-01, 4.6587e-01, 4.4840e-01, 5.3661e-01, 1.0783e-01, 2.6182e-01, 4.4807e-01, 4.2671e-01, 4.9602e-01, 4.4946e-01, 5.0319e-01, 4.3481e-01, 4.9051e-01, 1.5121e-01, 3.8278e-01, 7.0314e-01,from GITM
+ NEUV_l1,,,1.0000e+00,ints, 6.3336e-12, -1.7586e-09, -5.8173e-07, -2.7526e-05, -7.9475e-05, -2.5214e-05, -7.7868e-05, -1.8471e-04, -1.0315e-05, -4.5460e-06, -1.7387e-04, -2.8295e-06, -9.3578e-05, -8.0510e-05, 2.5654e-04, 9.7116e-07, -4.2748e-05, 1.8657e-05, -4.2105e-05, 1.1967e-05, 1.3408e-05, -3.2775e-06, 7.3782e-06, 2.1466e-05, 2.6201e-05, 8.4018e-06, -1.1750e-06, 4.4671e-05, 2.3090e-07, 1.1496e-05, 1.0248e-05, 1.1219e-05, 1.0442e-05, 1.1598e-05, 1.0931e-05, 3.5491e-05, 5.7380e-05, 7.2432e-05, 6.6057e-05, 9.0619e-05, 3.0050e-05, 3.3145e-05, 2.8926e-05, 3.9467e-05, 3.6713e-05, 4.8318e-05, 1.5926e-04, 3.6572e-03, 3.2218e-04, 8.4120e-05, 2.6237e-04, 1.4758e-04, 1.9098e-04, 2.9104e-04, 4.6908e-04, 7.1953e-04, 1.0507e-03, 1.9508e-03, 3.2611e-03,from GITM
+ NEUV_P1,,,1.0000e+00,powers, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 9.9997e-01, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,from GITM
+ NEUV_P2,,,1.0000e+00,powers, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,from GITM
+ O2,O2,abs,1.0000E-22,m2, 4.0000E-04, 4.0000E-03, 2.4000E-02, 1.4000E-01, 1.0200E+00, 1.0000E-01, 3.2000E-01, 1.1800E+00, 4.0000E+00, 7.1000E+00, 1.0600E+01, 1.3200E+01, 1.5700E+01, 1.5100E+01, 1.6800E+01, 1.6800E+01, 1.7190E+01, 1.8400E+01, 1.8170E+01, 1.9390E+01, 2.0400E+01, 2.1590E+01, 2.4060E+01, 2.5590E+01, 2.2000E+01, 2.5040E+01, 2.6100E+01, 2.5800E+01, 2.6020E+01, 2.6270E+01, 2.5000E+01, 2.9050E+01, 2.1960E+01, 2.5180E+01, 2.6660E+01, 2.7090E+01, 2.0870E+01, 9.8500E+00, 1.5540E+01, 4.0000E+00, 1.6530E+01, 1.6000E+00, 1.0000E+00, 1.1000E+00, 1.0000E+00, 1.0000E-01, 3.0000E-01, 1.0000E-02, 3.0000E+00, 3.0000E-01, 2.2000E+00, 1.2000E+01, 1.5000E+01, 1.3000E+01, 1.0000E+01, 6.0000E+00, 3.4000E+00, 1.5000E+00, 5.0000E-01, from GITM
+ O,O,abs,1.0000E-22,m2, 2.0000E-04, 2.0000E-03, 1.2000E-02, 7.0000E-02, 5.1000E-01, 5.0000E-02, 1.6000E-01, 5.9000E-01, 1.6000E+00, 2.9000E+00, 5.3000E+00, 6.0500E+00, 7.1300E+00, 6.6100E+00, 7.6800E+00, 7.7000E+00, 8.6700E+00, 9.9500E+00, 9.6400E+00, 1.1210E+01, 1.1250E+01, 1.1640E+01, 1.1910E+01, 1.2130E+01, 1.2170E+01, 1.1900E+01, 1.2230E+01, 1.2220E+01, 1.2210E+01, 1.0040E+01, 1.1350E+01, 8.0000E+00, 4.1800E+00, 4.1800E+00, 4.2800E+00, 4.2300E+00, 4.3800E+00, 4.1800E+00, 2.1200E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N2,N2,abs,1.0000E-22,m2, 3.0000E-04, 3.0000E-03, 1.5000E-02, 9.0000E-02, 4.8000E-01, 1.1600E+00, 2.4000E-01, 6.0000E-01, 1.9000E+00, 4.4000E+00, 8.0000E+00, 9.7000E+00, 1.0600E+01, 1.0300E+01, 1.1600E+01, 1.1600E+01, 1.3000E+01, 1.8000E+01, 1.7510E+01, 2.1070E+01, 2.1800E+01, 2.1850E+01, 2.4530E+01, 2.4690E+01, 2.3200E+01, 2.2380E+01, 2.3100E+01, 2.3200E+01, 2.3220E+01, 2.9750E+01, 2.6300E+01, 3.0940E+01, 3.5460E+01, 2.6880E+01, 1.9260E+01, 3.0710E+01, 1.5050E+01, 4.6630E+01, 1.6990E+01, 7.0000E-01, 3.6160E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CO2,CO2,abs,1.0000E-22,m2, 3.5800E-03, 3.5800E-03, 3.5800E-03, 3.5800E-01, 3.5800E-01, 3.5800E-01, 3.5800E-01, 1.5500E+00, 4.6200E+00, 9.0900E+00, 1.4360E+01, 1.6510E+01, 1.9020E+01, 1.7520E+01, 2.1490E+01, 2.1590E+01, 2.3570E+01, 2.5270E+01, 2.4870E+01, 2.8270E+01, 2.9530E+01, 3.0250E+01, 3.1490E+01, 3.3200E+01, 3.4200E+01, 3.4910E+01, 3.5300E+01, 3.4300E+01, 3.4450E+01, 3.3700E+01, 2.3520E+01, 3.2830E+01, 9.3840E+01, 6.1940E+01, 2.6490E+01, 3.9830E+01, 1.3980E+01, 4.4670E+01, 5.2080E+01, 4.2870E+01, 5.0310E+01, 1.5100E+01, 1.4200E+01, 1.8240E+01, 1.7400E+01, 4.0800E+01, 8.8200E-01, 4.9600E-02, 8.1000E-02, 3.7300E-01, 7.3900E-01, 6.0700E-01, 5.2400E-01, 5.4400E-01, 4.3100E-01, 2.5800E-01, 1.2600E-01, 4.8000E-02, 1.6000E-02, from GITM
+ CO,CO,abs,1.0000E-22,m2, 4.1700E-03, 4.1700E-03, 4.1700E-03, 4.1700E-01, 4.1700E-01, 4.1700E-01, 4.1700E-01, 8.7000E-01, 2.3900E+00, 4.6700E+00, 7.0100E+00, 8.6100E+00, 1.0540E+01, 9.4200E+00, 1.1870E+01, 1.1900E+01, 1.3440E+01, 1.5260E+01, 1.4960E+01, 1.7960E+01, 2.0170E+01, 2.0570E+01, 2.1090E+01, 2.1620E+01, 2.2000E+01, 2.1910E+01, 2.2100E+01, 2.2030E+01, 2.1920E+01, 2.1040E+01, 2.3850E+01, 2.5200E+01, 2.6280E+01, 1.5260E+01, 3.3130E+01, 2.0540E+01, 2.2610E+01, 3.6980E+01, 5.0320E+01, 2.8500E+01, 5.2830E+01, 1.3900E+00, 1.3900E+00, 8.5700E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CH4,CH4,abs,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.6430E-01, 4.2100E-01, 4.5350E-01, 2.0400E-01, 5.9300E-01, 1.4960E+00, 2.7940E+00, 3.8570E+00, 5.0530E+00, 4.3600E+00, 6.0330E+00, 6.0590E+00, 7.8290E+00, 1.0165E+01, 9.7760E+00, 1.4701E+01, 1.8770E+01, 2.1449E+01, 2.4644E+01, 2.7924E+01, 3.1052E+01, 3.0697E+01, 3.3178E+01, 3.5276E+01, 3.4990E+01, 3.9280E+01, 4.1069E+01, 4.2927E+01, 4.5458E+01, 4.5716E+01, 4.6472E+01, 4.5921E+01, 4.8327E+01, 4.8968E+01, 4.8001E+01, 4.1154E+01, 3.8192E+01, 3.2700E+01, 3.0121E+01, 2.9108E+01, 2.8400E+01, 1.8000E+01, 1.9200E+01, 1.7860E+01, 1.8318E+01, 1.9068E+01, 1.2826E+01, 3.2898E+00, 1.2600E-01, 7.9900E-04, 1.4000E-05, 7.0000E-06, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ H2,H2,abs,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.0000E-02, 8.0000E-02, 2.1000E-01, 4.3000E-01, 6.0000E-01, 8.4000E-01, 7.3000E-01, 1.0200E+00, 1.0200E+00, 1.4200E+00, 1.9400E+00, 1.9000E+00, 3.0300E+00, 3.8700E+00, 4.5000E+00, 5.3600E+00, 6.1700E+00, 7.0200E+00, 6.8600E+00, 7.8100E+00, 8.4600E+00, 8.4500E+00, 9.9000E+00, 1.0730E+01, 1.1370E+01, 1.0760E+01, 8.6400E+00, 7.3400E+00, 8.7500E+00, 8.2500E+00, 4.8000E-01, 1.9000E-01, 0.0000E+00, 5.0000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ HCN,HCN,abs,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ He,He,abs,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.4400E-01, 4.7900E-01, 1.1570E+00, 1.6010E+00, 2.1210E+00, 2.5950E+00, 2.3200E+00, 2.9530E+00, 2.9620E+00, 3.5440E+00, 4.2680E+00, 4.1420E+00, 5.4470E+00, 6.5630E+00, 7.2080E+00, 9.5800E-01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ NO,NO,abs,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.4000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.4000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.0000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O2,O2+,ion,1.0000E-22,m2, 4.0000E-04, 4.0000E-03, 2.4000E-02, 1.4000E-01, 1.0200E+00, 1.0000E-01, 3.2000E-01, 1.1800E+00, 4.0000E+00, 7.1000E+00, 1.0600E+01, 1.3200E+01, 1.5700E+01, 1.5100E+01, 1.6800E+01, 1.6800E+01, 1.7190E+01, 1.8400E+01, 1.8170E+01, 1.9390E+01, 2.0400E+01, 2.1590E+01, 2.4060E+01, 2.5590E+01, 2.2000E+01, 2.5040E+01, 2.6100E+01, 2.5800E+01, 2.5940E+01, 2.2050E+01, 2.3000E+01, 2.3810E+01, 8.5900E+00, 9.6900E+00, 1.1050E+01, 9.3900E+00, 6.1200E+00, 4.6900E+00, 9.3400E+00, 2.5000E+00, 1.2220E+01, 1.0000E+00, 0.0000E+00, 2.7000E-01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+,ion,1.0000E-22,m2, 6.0000E-05, 6.0000E-04, 4.0000E-03, 2.0000E-02, 1.5000E-02, 1.5000E-02, 5.0000E-02, 1.8000E-01, 4.6000E-01, 7.8000E-01, 1.3800E+00, 1.5100E+00, 1.7800E+00, 1.6500E+00, 1.9200E+00, 1.9300E+00, 2.2500E+00, 2.5900E+00, 2.5100E+00, 3.0300E+00, 3.1500E+00, 3.2600E+00, 3.4500E+00, 3.5200E+00, 3.5300E+00, 3.4500E+00, 3.6700E+00, 3.7900E+00, 3.7800E+00, 4.0100E+00, 4.9100E+00, 4.2000E+00, 4.1800E+00, 4.1800E+00, 4.2800E+00, 4.2300E+00, 4.3800E+00, 4.1800E+00, 2.1200E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+2D,ion,1.0000E-22,m2, 6.0000E-05, 6.0000E-04, 4.0000E-03, 2.0000E-02, 1.5000E-02, 1.5000E-02, 5.0000E-02, 1.9000E-01, 5.1000E-01, 9.9000E-01, 1.8600E+00, 2.1200E+00, 2.6400E+00, 2.3800E+00, 2.8400E+00, 2.8500E+00, 3.4700E+00, 3.9800E+00, 3.8600E+00, 4.7100E+00, 5.0600E+00, 5.2400E+00, 5.3600E+00, 5.4600E+00, 5.4800E+00, 5.3600E+00, 5.5000E+00, 5.5000E+00, 5.4900E+00, 5.5200E+00, 6.4400E+00, 3.8000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+2P,ion,1.0000E-22,m2, 7.0000E-05, 7.0000E-04, 4.0000E-03, 3.0000E-02, 2.0000E-02, 2.0000E-02, 6.0000E-02, 2.2000E-01, 6.2000E-01, 1.1300E+00, 2.0700E+00, 2.4200E+00, 2.7100E+00, 2.5800E+00, 2.9200E+00, 2.9300E+00, 2.9500E+00, 3.3800E+00, 3.2800E+00, 3.4800E+00, 3.0400E+00, 3.1400E+00, 3.1000E+00, 3.1500E+00, 3.1600E+00, 3.0900E+00, 3.0600E+00, 2.9300E+00, 2.9300E+00, 5.0000E-01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N2,N2+,ion,1.0000E-22,m2, 3.0000E-04, 3.0000E-03, 1.5000E-02, 9.0000E-02, 4.8000E-01, 1.1600E+00, 2.4000E-01, 6.0000E-01, 1.9000E+00, 4.4000E+00, 8.0000E+00, 9.7000E+00, 1.0600E+01, 1.0300E+01, 1.1600E+01, 1.1600E+01, 1.3000E+01, 1.8000E+01, 1.7510E+01, 2.1070E+01, 2.1800E+01, 2.1850E+01, 2.4530E+01, 2.4690E+01, 2.3200E+01, 2.2380E+01, 2.3100E+01, 2.3200E+01, 2.3220E+01, 2.5060E+01, 2.3000E+01, 2.3200E+01, 2.3770E+01, 1.8390E+01, 1.0180E+01, 1.6750E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N,N+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 1.0000E-01, 5.0000E-01, 1.0000E+00, 1.0000E+00, 1.0000E+00, 2.0000E+00, 2.5000E+00, 3.5000E+00, 4.0000E+00, 5.0000E+00, 5.0000E+00, 6.0000E+00, 6.0000E+00, 6.5000E+00, 8.0000E+00, 7.0000E+00, 1.0000E+01, 1.0000E+01, 1.0000E+01, 1.1000E+01, 1.1500E+01, 1.2000E+01, 1.1000E+01, 1.2000E+01, 1.2000E+01, 1.2000E+01, 1.2000E+01, 1.2000E+01, 1.1000E+01, 1.1000E+01, 1.1000E+01, 1.0000E+01, 1.0000E+01, 1.0000E+01, 1.0000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CO2,CO2+,ion,1.0000E-22,m2, 1.5500E+00, 1.5500E+00, 1.5500E+00, 1.5500E+00, 1.5500E+00, 1.5500E+00, 1.5500E+00, 1.5500E+00, 4.6200E+00, 9.0900E+00, 1.4320E+01, 1.6110E+01, 1.8600E+01, 1.7140E+01, 2.1390E+01, 2.1440E+01, 2.3630E+01, 2.5560E+01, 2.5520E+01, 2.7170E+01, 2.8760E+01, 3.0680E+01, 3.2602E+01, 3.3210E+01, 3.3860E+01, 3.4960E+01, 3.5300E+01, 3.4300E+01, 3.4570E+01, 3.2290E+01, 2.0860E+01, 2.7490E+01, 8.6320E+01, 5.1770E+01, 2.1680E+01, 3.4090E+01, 1.0930E+01, 7.1400E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CO,CO+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CH4,CH4+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.0000E-01, 5.9000E-01, 1.5000E+00, 2.7900E+00, 3.8600E+00, 5.0500E+00, 4.3600E+00, 6.0300E+00, 6.0600E+00, 7.8300E+00, 1.0170E+01, 9.7800E+00, 1.4700E+01, 1.8770E+01, 2.1450E+01, 2.4640E+01, 2.7920E+01, 3.1050E+01, 3.0700E+01, 3.3180E+01, 3.5280E+01, 3.4990E+01, 3.9280E+01, 4.1070E+01, 4.2930E+01, 4.4800E+01, 4.4800E+01, 4.4610E+01, 4.4690E+01, 4.0280E+01, 2.5530E+01, 1.3860E+01, 1.4000E-01, 4.8000E-01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ H2,H2+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.0000E-02, 8.0000E-02, 2.0000E-01, 4.0000E-01, 5.5000E-01, 7.5000E-01, 6.5000E-01, 9.0000E-01, 9.0000E-01, 1.3000E+00, 1.7800E+00, 1.7400E+00, 2.8900E+00, 3.7800E+00, 4.0500E+00, 5.2500E+00, 6.0500E+00, 6.9000E+00, 6.7400E+00, 7.6700E+00, 8.3000E+00, 8.2900E+00, 9.7000E+00, 1.0730E+01, 9.7600E+00, 8.6200E+00, 7.0700E+00, 5.0700E+00, 6.6300E+00, 9.0000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ HCN,HCN+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ NO,NO+,ion,1.0000E-22,m2, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.4000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.4000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 1.0000E+01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 2.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O2,O2+,pei,1.0000E+00,, 1.3469E+02, 1.3469E+02, 3.2212E+01, 1.3309E+01, 3.9615E+01, 3.9615E+01, 2.8340E+00, 1.0920E+00, 1.0920E+00, 4.1600E-01, 1.8400E-01, 1.8400E-01, 1.8400E-01, 1.8400E-01, 9.0000E-02, 9.0000E-02, 2.4000E-02, 2.4000E-02, 2.4000E-02, 2.4000E-02, 2.4000E-02, 2.4000E-02, 2.4000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O2,O+,pei,1.0000E+00,, 7.6136E+01, 7.6136E+01, 1.7944E+01, 6.9810E+00, 2.0338E+01, 2.0338E+01, 1.4370E+00, 5.2100E-01, 5.2100E-01, 1.6300E-01, 5.2000E-02, 5.2000E-02, 5.2000E-02, 5.2000E-02, 1.4000E-02, 1.4000E-02, 1.0000E-03, 1.0000E-03, 1.0000E-03, 1.0000E-03, 1.0000E-03, 1.0000E-03, 1.0000E-03, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N2,N2+,pei,1.0000E+00,, 2.6399E+02, 2.6399E+02, 6.2570E+01, 2.5213E+01, 8.5400E+00, 8.5400E+00, 6.1420E+00, 2.2880E+00, 2.2880E+00, 7.8600E-01, 3.2400E-01, 3.2400E-01, 3.2400E-01, 3.2400E-01, 1.6900E-01, 1.6900E-01, 3.1000E-02, 3.1000E-02, 3.1000E-02, 3.1000E-02, 3.1000E-02, 3.1000E-02, 3.1000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N2,N+,pei,1.0000E+00,, 7.8674E+01, 7.8674E+01, 1.8310E+01, 6.9480E+00, 2.2950E+00, 2.2950E+00, 1.6470E+00, 1.6470E+00, 5.7100E-01, 1.4600E-01, 3.7000E-02, 3.7000E-02, 3.7000E-02, 3.7000E-02, 8.0000E-03, 8.0000E-03, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CH4,CH4+,pei,1.0000E+00,, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ CH4,CH3+,pei,1.0000E+00,, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+,pei,1.0000E+00,, 8.1240E+01, 8.1240E+01, 1.8896E+01, 9.4250E+00, 2.8622E+01, 2.8622E+01, 2.0190E+00, 9.0200E-01, 9.0200E-01, 4.7000E-01, 3.2500E-01, 3.2500E-01, 3.2500E-01, 3.2500E-01, 2.0900E-01, 2.0900E-01, 8.4000E-02, 8.4000E-02, 8.4000E-02, 8.4000E-02, 8.4000E-02, 8.4000E-02, 8.4000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+2D,pei,1.0000E+00,, 8.8526E+01, 8.8526E+01, 2.0691E+01, 9.3650E+00, 2.8199E+01, 2.8199E+01, 1.9620E+00, 8.5300E-01, 8.5300E-01, 4.1800E-01, 2.5300E-01, 2.5300E-01, 2.5300E-01, 2.5300E-01, 1.4800E-01, 1.4800E-01, 3.4000E-02, 3.4000E-02, 3.4000E-02, 3.4000E-02, 3.4000E-02, 3.4000E-02, 3.4000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O,O+2P,pei,1.0000E+00,, 4.7358E+01, 4.7358E+01, 1.1007E+01, 4.7720E+00, 1.4556E+01, 1.4556E+01, 1.0140E+00, 4.3600E-01, 4.3600E-01, 2.0300E-01, 1.1600E-01, 1.1600E-01, 1.1600E-01, 1.1600E-01, 6.1000E-02, 6.1000E-02, 9.0000E-03, 9.0000E-03, 9.0000E-03, 9.0000E-03, 9.0000E-03, 9.0000E-03, 9.0000E-03, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ N2,N_4S,ped,1.0000E+00,, 2.4500E+02, 2.4500E+02, 5.2052E+01, 2.5255E+01, 9.0490E+00, 9.0490E+00, 6.5320E+00, 2.9090E+00, 2.9090E+00, 1.3710E+00, 7.6400E-01, 7.6400E-01, 7.6400E-01, 7.6400E-01, 5.1500E-01, 5.1500E-01, 1.5700E-01, 1.5700E-01, 1.5700E-01, 1.5700E-01, 1.5700E-01, 1.5700E-01, 1.5700E-01, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM
+ O2,O,ped,1.0000E+00,, 8.7864E+01, 8.7864E+01, 2.0318E+01, 1.7821E+01, 5.6969E+01, 5.6969E+01, 4.1130E+00, 2.0410E+00, 2.0410E+00, 1.2710E+00, 9.9600E-01, 9.9600E-01, 9.9600E-01, 9.9600E-01, 7.6200E-01, 7.6200E-01, 6.5300E-01, 6.5300E-01, 6.5300E-01, 6.5300E-01, 6.5300E-01, 6.5300E-01, 6.5300E-01, 1.1000E-02, 1.1000E-02, 1.1000E-02, 1.1000E-02, 1.1000E-02, 1.1000E-02, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, from GITM

From 592aebfa4a7e06a4157df7ea9d6a203fb4df5a72 Mon Sep 17 00:00:00 2001
From: "Daniel A. Brandt" <daabrand@mtu.edu>
Date: Tue, 15 Jul 2025 09:16:31 -0400
Subject: [PATCH 138/266] Added additional information to doc/internals/fism.md
 to explain how users can execute fism.py.

---
 doc/internals/fism.md | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/doc/internals/fism.md b/doc/internals/fism.md
index 1133396e..5b1322e8 100644
--- a/doc/internals/fism.md
+++ b/doc/internals/fism.md
@@ -1,10 +1,10 @@
-# FISM
+# FISM-2
 
-FISM can be used as a EUV model.
+FISM-2 can be used as a EUV model.
 
-Needs the FISM files automatically made by `srcPython/fism.py`.
+Needs the FISM-2 files automatically made by `srcPython/fism.py`.
 
-FISM contains the binned flux already, however Aether must still be provided with an
+FISM-2 contains the binned flux already, however Aether must still be provided with an
 EUV csv file containing the cross-sections.
 
 The different fism models available in fism.py contain different numbers of bins, so
@@ -28,3 +28,15 @@ The input format, when using fism data:
         "HeatingEfficiency" : 0.05,
         "dt" : 60.0
     },
+
+To generate FISM-2 irradiances between two dates, one may call fism.py like so:
+
+srcPython/fism.py 20110319 20110321 -b neuvac
+
+Note that the optional argument '-b' defaults to the binning scheme of the NEUVAC model,
+which employs 59 bins. To use the 37 bins of the EUVAC model, the argument should be
+'euvac', while to use the 23 bins used by the HFG model, the argument should be 'solomon'.
+
+fism.py should always be run before Aether is run using the FISM-2 model. Even though the
+entire FISM-2 irradiances are stored in the repository, Aether will need a separate .csv
+file containing the temporal subset of FISM-2 irradiances in the desired binning scheme.

From 516782fe302a0162cbe656f0b0d0f8afda0e935e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:50:57 -0400
Subject: [PATCH 139/266] Note that adjusted means to 1AU and not to Earth

---
 srcPython/neuvac.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/srcPython/neuvac.py b/srcPython/neuvac.py
index 3fb0dadb..9134e899 100644
--- a/srcPython/neuvac.py
+++ b/srcPython/neuvac.py
@@ -178,10 +178,13 @@ def readCLS(filename):
 
 def getCLSF107(dateStart, dateEnd, truncate=True):
     """
-    Obtains Sun-Earth distance adjusted, flare-corrected F10.7 data from Collecte Localisation Satellites. Downloads the
-    most recent measurements to a file. Reads the file and extracts the F10.7 values between two dates. Note that if the
-    ending date is less than or equal to the last date in the version of the file that has already been downloaded, the
-    file IS NOT re-downloaded, but simply parsed. Otherwise, the file is redownloaded.
+    Obtains flare-corrected F10.7 data from Collecte Localisation Satellites. The "adjusted" here means that it has been
+    adjusted from measurements from Earth to 1AU. (Aether/GITM need measurements at 1AU, so they can adjust to the 
+    proper sun-planet distance, where planet can be Earth, Venus, Mars, etc.) A description of the data is
+    provided here: https://spaceweather.cls.fr/services/radioflux/. 
+    Downloads the most recent measurements to a file. Reads the file and extracts the F10.7 values between two dates. 
+    Note that if the ending date is less than or equal to the last date in the version of the file that has already 
+    been downloaded, the file IS NOT re-downloaded, but simply parsed. Otherwise, the file is redownloaded.
     :param dateStart: str
         The starting date in YYYYMMDD format.
     :param dateEnd: str

From 7fdbc00572c68c42b340d9ea3215eb8a4b1a50bb Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:52:39 -0400
Subject: [PATCH 140/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/.gitignore | 3 ---
 1 file changed, 3 deletions(-)
 delete mode 100644 srcPython/.idea/.gitignore

diff --git a/srcPython/.idea/.gitignore b/srcPython/.idea/.gitignore
deleted file mode 100644
index 26d33521..00000000
--- a/srcPython/.idea/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml

From d39113ba75ef3f2e2f673a03bbe23ba8622e7b16 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:53:14 -0400
Subject: [PATCH 141/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/inspectionProfiles/profiles_settings.xml | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 srcPython/.idea/inspectionProfiles/profiles_settings.xml

diff --git a/srcPython/.idea/inspectionProfiles/profiles_settings.xml b/srcPython/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2da..00000000
--- a/srcPython/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file

From 87e48289f7d5463b22e2da50d269696ce315b7a4 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:53:31 -0400
Subject: [PATCH 142/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/misc.xml | 4 ----
 1 file changed, 4 deletions(-)
 delete mode 100644 srcPython/.idea/misc.xml

diff --git a/srcPython/.idea/misc.xml b/srcPython/.idea/misc.xml
deleted file mode 100644
index 76a040b9..00000000
--- a/srcPython/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (solarVenv)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file

From 86bfcb7c182d2f5c3ba06a236691781c222bd5b2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:53:50 -0400
Subject: [PATCH 143/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/vcs.xml | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 srcPython/.idea/vcs.xml

diff --git a/srcPython/.idea/vcs.xml b/srcPython/.idea/vcs.xml
deleted file mode 100644
index 6c0b8635..00000000
--- a/srcPython/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
-  </component>
-</project>
\ No newline at end of file

From 32f5efe623a5f64946976ff9dbf9bb6fcfe16d93 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:54:27 -0400
Subject: [PATCH 144/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/modules.xml | 8 --------
 1 file changed, 8 deletions(-)
 delete mode 100644 srcPython/.idea/modules.xml

diff --git a/srcPython/.idea/modules.xml b/srcPython/.idea/modules.xml
deleted file mode 100644
index c7044f5d..00000000
--- a/srcPython/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/srcPython.iml" filepath="$PROJECT_DIR$/.idea/srcPython.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file

From ce2b86aa70be0ab5434e05261cf74598ef575512 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 07:54:46 -0400
Subject: [PATCH 145/266] This file is a custom setup file for an editor, maybe

---
 srcPython/.idea/srcPython.iml | 12 ------------
 1 file changed, 12 deletions(-)
 delete mode 100644 srcPython/.idea/srcPython.iml

diff --git a/srcPython/.idea/srcPython.iml b/srcPython/.idea/srcPython.iml
deleted file mode 100644
index 410274b1..00000000
--- a/srcPython/.idea/srcPython.iml
+++ /dev/null
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.10 (solarVenv)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="NUMPY" />
-    <option name="myDocStringFormat" value="NumPy" />
-  </component>
-</module>
\ No newline at end of file

From 63dafb45caa19ed957a83697773ea47e60ce3874 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 08:10:12 -0400
Subject: [PATCH 146/266] BUG: shouldnt have so much output

---
 src/grid_match.cpp | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 25da6188..4cb3fea0 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -3,8 +3,8 @@
 
 #include "aether.h"
 
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+bool grid_match(Grid gGrid,
+                Grid mGrid,
                 Quadtree gQuadtree,
                 Quadtree mQuadtree) {
 
@@ -24,6 +24,7 @@ bool grid_match(Grid gGrid,
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
         lon = mGrid.geoLon_scgc(iX, iY, iZ);
         lat = mGrid.geoLat_scgc(iX, iY, iZ);
+
         if (gGrid.iGridShape_ == gGrid.iSphere_) {
           norms(0) = lon / cPI;
           norms(1) = lat / cPI;
@@ -33,14 +34,9 @@ bool grid_match(Grid gGrid,
           norms = sphere_to_cube(lon, lat);
           iNode = gQuadtree.find_point(norms);
         }
-        std::cout << "lon, lat, node: " << lon*cRtoD << " "
-        << lat*cRtoD << " "
-        << norms(0) << " "
-        << norms(1) << " "
-        << norms(2) << " "
-        << iNode << "\n";
       }
     }
   }
+
   return true;
 }

From a22076165d6f9d159236a87720c2bc1bda3f2630 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 17 Jul 2025 11:55:28 -0400
Subject: [PATCH 147/266] I hate warnings

---
 ext/IE/mh86.f | 126 +++++++++++++++++++++++++++-----------------------
 1 file changed, 69 insertions(+), 57 deletions(-)

diff --git a/ext/IE/mh86.f b/ext/IE/mh86.f
index 35785c53..87548c6a 100644
--- a/ext/IE/mh86.f
+++ b/ext/IE/mh86.f
@@ -105,14 +105,12 @@ SUBROUTINE MHEMODL (RMLAT,RMLT,HP,BY,BZ,MODL,ET,EP,EPOT)
 
       TIME = RMLTLON/15.
       TIME = RMLT
-   20 IF (TIME .GT. TMX) THEN
-	TIME = TIME - 24.
-	GO TO 20
-      ENDIF
-   30 IF (TIME .LT. TMN) THEN
-	TIME = TIME + 24.
-	GO TO 30
-      ENDIF
+      do while (time > tmx)
+        time = time - 24.0
+      enddo
+      do while (time < tmn)
+        time = time + 24.0
+      enddo
 
       H = SIGN (1.,RMLAT)
       RMLA = MIN(MAX(ABS(RMLAT),RMLAMN(MODL)), RMLAMX(MODL))
@@ -255,8 +253,9 @@ SUBROUTINE MHINIT (MODL,IUN,IPR,ISTAT)
       ENDIF
 
       READ (IUN,*) NLABS
-      DO 10 I=1,NLABS
-   10 READ (IUN,'(A)',END=998,ERR=999) LABELS
+      DO I=1,NLABS
+        READ (IUN,'(A)',END=998,ERR=999) LABELS
+      enddo
 
       READ (IUN,*,END=998,ERR=999) KFIT
       IF (KFIT .NE. 2) THEN
@@ -297,8 +296,9 @@ SUBROUTINE MHINIT (MODL,IUN,IPR,ISTAT)
 
       IF (MODL .EQ. 2) THEN
 C          Reverse the sign of BETA for MHS to conform to MHI convention
-	DO 20 I=1,NBETA
-   20   BETA(I,NDX,MODL) = -BETA(I,NDX,MODL)
+	  DO I=1,NBETA
+          BETA(I,NDX,MODL) = -BETA(I,NDX,MODL)
+        enddo
       ENDIF
       ENDDO
 C  30 IF (IPR .EQ. 1) WRITE (6,'(''MHINIT: Read ''A,'' pars: I MODL BY B
@@ -392,10 +392,11 @@ SUBROUTINE GETFIT (NDX,MODL,TIME,RMLA,DFIT,ESFIT,EEFIT,ISTAT)
       DFIT = 0.0
       DDXFIT = 0.
       DDYFIT = 0.
-      DO 30 I=1,NBETA
-      DFIT   = DFIT   + BETA(I,NDX,MODL)*F(1,1,I)
-      DDXFIT = DDXFIT + BETA(I,NDX,MODL)*F(2,1,I)/Y
-   30 DDYFIT = DDYFIT + BETA(I,NDX,MODL)*F(1,2,I)
+      DO I=1,NBETA
+        DFIT   = DFIT   + BETA(I,NDX,MODL)*F(1,1,I)
+        DDXFIT = DDXFIT + BETA(I,NDX,MODL)*F(2,1,I)/Y
+        DDYFIT = DDYFIT + BETA(I,NDX,MODL)*F(1,2,I)
+      enddo 
       DDYFIT = -DDYFIT
       DFIT   = -DFIT/1000.
       ESFIT  = -DDYFIT
@@ -422,10 +423,11 @@ SUBROUTINE BASPRC (TX,TY,NX,NY,KX,KY,X,Y,F)
 
       NDERIV = MIN0 (3,KX-1,KY-1)
       N = NX*NY
-      DO 10 I=1,N
-      DO 10 J=1,9
-   10 F(J,I) = 0.
-
+      DO I=1,N
+        DO J=1,9
+          F(J,I) = 0.
+        enddo
+      enddo
       XP = MOD (X,TX(NX+1))
       CALL INTERV (TX, NX+KX, XP, ILEFTX, MFLAG)
       YP = Y
@@ -449,24 +451,25 @@ SUBROUTINE BASPRC (TX,TY,NX,NY,KX,KY,X,Y,F)
       CALL BSPLVD (TY, KY, YP, ILEFTY, A, VALY, NDERIV)
       NPX = NX - (KX-1)
 
-      DO 20 MX=1,KX
-      IX = LFTMKX + MX
-      IF (IX .GE. NX-(KX-2)) IX = IX-NX+(KX-1)
-
-      DO 30 MY=1,KY
-      IY = LFTMKY + MY - 1
-      IF (IY .GT. 0) THEN
-	DO 40 JX=1,3
-	LX = MX + KX*(JX-1)
-	DO 50 JY=1,3
-	J = JX + (JY-1)*3
-	LY = MY + KY*(JY-1)
-	I = IX + (IY-1)*NPX
-   50   F(J,I) = VALX(LX)*VALY(LY)
-   40   CONTINUE
-      ENDIF
-   30 CONTINUE
-   20 CONTINUE
+      DO MX=1,KX
+        IX = LFTMKX + MX
+        IF (IX .GE. NX-(KX-2)) IX = IX-NX+(KX-1)
+
+        DO MY=1,KY
+          IY = LFTMKY + MY - 1
+          IF (IY .GT. 0) THEN
+	      DO JX=1,3
+	        LX = MX + KX*(JX-1)
+	        DO JY=1,3
+	          J = JX + (JY-1)*3
+	          LY = MY + KY*(JY-1)
+	          I = IX + (IY-1)*NPX
+                F(J,I) = VALX(LX)*VALY(LY)
+              enddo
+            enddo
+          ENDIF
+        enddo
+      enddo
 
       RETURN
       END
@@ -631,14 +634,15 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C     ORDER  BEFORE  BSPLVB  IS CALLED TO PUT VALUES FOR THE NEXT
 C     HIGHER ORDER ON TOP OF IT.
       IDERIV = MHIGH
-      DO 15 M=2,MHIGH
+      DO M=2,MHIGH
          JP1MID = 1
-         DO 11 J=IDERIV,K
+         DO J=IDERIV,K
             DBIATX(J,IDERIV) = DBIATX(JP1MID,1)
-   11       JP1MID = JP1MID + 1
+            JP1MID = JP1MID + 1
+         enddo
          IDERIV = IDERIV - 1
          CALL BSPLVB(T,KP1-IDERIV,2,X,LEFT,DBIATX)
-   15    CONTINUE
+      enddo
 C
 C     AT THIS POINT,  B(LEFT-K+I, K+1-J)(X) IS IN  DBIATX(I,J) FOR
 C     I=J,...,K AND J=1,...,MHIGH ('=' NDERIV). IN PARTICULAR, THE
@@ -647,15 +651,17 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C     RATE THEIR B-REPR. BY DIFFERENCING, THEN EVALUATE AT  X.
 C
       JLOW = 1
-      DO 20 I=1,K
-         DO 19 J=JLOW,K
-   19       A(J,I) = 0.
+      DO I=1,K
+         DO J=JLOW,K
+           A(J,I) = 0.
+         enddo
          JLOW = I
-   20    A(I,I) = 1.
+         A(I,I) = 1.
+      enddo
 C     AT THIS POINT, A(.,J) CONTAINS THE B-COEFFS FOR THE J-TH OF THE
 C     K  B-SPLINES OF INTEREST HERE.
 C
-      DO 40 M=2,MHIGH
+      DO M=2,MHIGH
          KP1MM = KP1 - M
          FKP1MM = FLOAT(KP1MM)
          IL = LEFT
@@ -665,14 +671,16 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C        B-SPLINES FROM THOSE FOR PRECEDING DERIVATIVE BY DIFFERENCING
 C        AND STORE AGAIN IN  A(.,J) . THE FACT THAT  A(I,J) = 0  FOR
 C        I .LT. J  IS USED.
-         DO 25 LDUMMY=1,KP1MM
+         DO LDUMMY=1,KP1MM
             FACTOR = FKP1MM/(T(IL+KP1MM) - T(IL))
 C           THE ASSUMPTION THAT T(LEFT).LT.T(LEFT+1) MAKES DENOMINATOR
 C           IN  FACTOR  NONZERO.
-            DO 24 J=1,I
-   24          A(I,J) = (A(I,J) - A(I-1,J))*FACTOR
+            DO J=1,I
+              A(I,J) = (A(I,J) - A(I-1,J))*FACTOR
+            enddo
             IL = IL - 1
-   25       I = I - 1
+            I = I - 1
+         enddo
 C
 C        FOR I=1,...,K, COMBINE B-COEFFS A(.,I) WITH B-SPLINE VALUES
 C        STORED IN DBIATX(.,M) TO GET VALUE OF  (M-1)ST  DERIVATIVE OF
@@ -681,12 +689,15 @@ SUBROUTINE BSPLVD ( T, K, X, LEFT, A, DBIATX, NDERIV )
 C        OF ORDER M THERE IS SAFE SINCE THE REMAINING B-SPLINE DERIVAT-
 C        IVES OF THE SAME ORDER DO NOT USE THIS VALUE DUE TO THE FACT
 C        THAT  A(J,I) = 0  FOR J .LT. I .
-   30    DO 40 I=1,K
+   30    DO I=1,K
             SUM = 0.
             JLOW = MAX0(I,M)
-            DO 35 J=JLOW,K
-   35          SUM = A(J,I)*DBIATX(J,M) + SUM
-   40       DBIATX(I,M) = SUM
+            DO J=JLOW,K
+              SUM = A(J,I)*DBIATX(J,M) + SUM
+            enddo
+            DBIATX(I,M) = SUM
+         enddo
+       enddo
    99                                   RETURN
       END
 
@@ -772,10 +783,11 @@ SUBROUTINE BSPLVB ( T, JHIGH, INDEX, X, LEFT, BIATX )
          DELTAR(J) = T(LEFT+J) - X
          DELTAL(J) = X - T(LEFT+1-J)
          SAVED = 0.
-         DO 26 I=1,J
+         DO I=1,J
             TERM = BIATX(I)/(DELTAR(I) + DELTAL(JP1-I))
             BIATX(I) = SAVED + DELTAR(I)*TERM
-   26       SAVED = DELTAL(JP1-I)*TERM
+            SAVED = DELTAL(JP1-I)*TERM
+         enddo
          BIATX(JP1) = SAVED
          J = JP1
          IF (J .LT. JHIGH)              GO TO 20

From ff07fa4fa5cf39f20e78fb162e6b4d0bb7049e3d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:09:35 -0400
Subject: [PATCH 148/266] STY: tabs

---
 include/cubesphere.h | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/include/cubesphere.h b/include/cubesphere.h
index e99036a6..ecca2aed 100644
--- a/include/cubesphere.h
+++ b/include/cubesphere.h
@@ -15,32 +15,32 @@ namespace CubeSphere {
 
 /// The normalized origins of each face of the cube (i.e. corner)
 static const arma_mat ORIGINS = {
-    {-1.0, -1.0, -1.0},
-    { 1.0, -1.0, -1.0},
-    { 1.0,  1.0, -1.0},
-    {-1.0,  1.0, -1.0},
-    {-1.0, -1.0, -1.0},
-    { 1.0, -1.0,  1.0}
+  {-1.0, -1.0, -1.0},
+  { 1.0, -1.0, -1.0},
+  { 1.0,  1.0, -1.0},
+  {-1.0,  1.0, -1.0},
+  {-1.0, -1.0, -1.0},
+  { 1.0, -1.0,  1.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat RIGHTS = {
-		{ 2.0,  0.0, 0.0},
-		{ 0.0,  2.0, 0.0},
-		{-2.0,  0.0, 0.0},
-		{ 0.0, -2.0, 0.0},
-		{ 0.0,  2.0, 0.0},
-		{ 0.0,  2.0, 0.0}
+  { 2.0,  0.0, 0.0},
+  { 0.0,  2.0, 0.0},
+  {-2.0,  0.0, 0.0},
+  { 0.0, -2.0, 0.0},
+  { 0.0,  2.0, 0.0},
+  { 0.0,  2.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat UPS = {
-		{ 0.0, 0.0, 2.0},
-		{ 0.0, 0.0, 2.0},
-		{ 0.0, 0.0, 2.0},
-		{ 0.0, 0.0, 2.0},
-		{ 2.0, 0.0, 0.0},
-		{-2.0, 0.0, 0.0}
+  { 0.0, 0.0, 2.0},
+  { 0.0, 0.0, 2.0},
+  { 0.0, 0.0, 2.0},
+  { 0.0, 0.0, 2.0},
+  { 2.0, 0.0, 0.0},
+  {-2.0, 0.0, 0.0}
 };
 
 } // CubeSphere::

From 9c22a68dd91ea1518c5c50d11e0a881e0d87a259 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:10:41 -0400
Subject: [PATCH 149/266] STY: spaces and tabs

---
 include/inputs.h | 431 ++++++++++++++++++++++++-----------------------
 1 file changed, 216 insertions(+), 215 deletions(-)

diff --git a/include/inputs.h b/include/inputs.h
index c753d874..ad8964f7 100644
--- a/include/inputs.h
+++ b/include/inputs.h
@@ -19,7 +19,7 @@
 
 class Inputs {
 
-public:
+ public:
 
   int iVerbose;
   int iVerboseProc;
@@ -45,7 +45,7 @@ class Inputs {
     // - "cubesphere", sets grid.iGridShape_ = iCubesphere_
     // - "dipole", sets grid.iGridShape_ = iDipole_
     std::string shape;
-    
+
     // Minimum altitude to simulate:
     precision_t alt_min;
     // Some grids allow the specification of the maximum altitude:
@@ -61,7 +61,7 @@ class Inputs {
     bool IsUniformAlt;
 
     // Only needed for Mag Field grid:
-    // min_apex (not used) and LatStretch is used 
+    // min_apex (not used) and LatStretch is used
     // as lat = min_lat + dlat where dlat = acos(cos(lat^stretch))^(1/stretch)
     precision_t min_apex, LatStretch, FieldLineStretch, max_blat;
 
@@ -75,41 +75,41 @@ class Inputs {
 
 
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   Inputs() {}
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   Inputs(Times &time);
 
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   int read(Times &time);
 
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool read_inputs_json(Times &time);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool set_verbose(json in);
-  
+
   // --------------------------------------------------------------------
   // get functions:
   //  - These functions offer access to specific parts of the settings json.
   //  - They call the general functions that check whether the key(s) exists.
   //  - If the key does not exist, an error flag (in report) is set.
-  //  - 
+  //  -
   // --------------------------------------------------------------------
 
   // ---------------------
@@ -121,13 +121,13 @@ class Inputs {
      \param none
    **/
   int get_verbose();
-  
+
   /**********************************************************************
      \brief returns settings["Debug"]["iProc"]
      \param none
    **/
   int get_verbose_proc();
-  
+
   /**********************************************************************
      \brief returns settings["Debug"]["dt"]
      \param none
@@ -144,13 +144,13 @@ class Inputs {
      \param none
    **/
   precision_t get_n_outputs();
-  
+
   /**********************************************************************
      \brief returns settings["Outputs"]["dt"][iOutput]
      \param iOutput int specifying which output file type to report on
    **/
   precision_t get_dt_output(int iOutput);
-  
+
   /**********************************************************************
      \brief returns settings["Outputs"]["type"][iOutput]
      \param iOutput int specifying which output file type to report on
@@ -162,7 +162,7 @@ class Inputs {
      \param none
    **/
   precision_t get_dt_euv();
-  
+
   /**********************************************************************
      \brief returns settings["Euv"]["IncludePhotoElectrons"]
      \param none
@@ -175,263 +175,263 @@ class Inputs {
      \param none
    **/
   std::string get_diffuse_auroral_model();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_potential_model();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_electrodynamics_dir();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_electrodynamics_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_electrodynamics_north_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_electrodynamics_south_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_euv_heating_eff_neutrals();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_euv_model();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_euv_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_euv_douse();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_aurora_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_chemistry_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_indices_lookup_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::vector<std::string> get_omniweb_files();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_number_of_omniweb_files();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_f107_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_planet();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_planetary_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_planet_species_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_collision_file();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_do_calc_bulk_ion_temp();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_eddy_coef();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_eddy_bottom();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_eddy_top();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_use_eddy_momentum();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_use_eddy_energy();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_bfield_type();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_do_restart();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_restartout_dir();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_restartin_dir();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_dt_write_restarts();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_original_seed();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_updated_seed();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   void set_seed(int seed);
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool write_restart();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
-  json get_perturb_values(); 
-  
+  json get_perturb_values();
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_do_lat_dependent_radius();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_do_J2();
 
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_check_for_nans();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_nan_test();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_nan_test_variable();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_is_cubesphere();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_NO_cooling();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_O_cooling();
 
@@ -474,53 +474,54 @@ class Inputs {
 
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_use_centripetal();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_use_coriolis();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_cent_acc();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_student_name();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_is_student();
-  
-  
+
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   json get_initial_condition_types();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   json get_boundary_condition_types();
 
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_advection_neutrals_vertical();
+  std::string get_advection_neutrals_horizontal();
   bool get_advection_neutrals_bulkwinds();
   bool get_advection_neutrals_implicitfriction();
 
@@ -528,227 +529,227 @@ class Inputs {
 
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_nLons(std::string gridtype);
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_nLats(std::string gridtype);
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_nAlts(std::string gridtype);
 
   /**********************************************************************
      \brief returns settings[gridtype, "shape"]
-     \param 
+     \param
    **/
   std::string get_grid_shape(std::string gridtype);
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   int get_nMembers();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_logfile();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::string get_logfile(int64_t iLog);
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::vector<std::string> get_species_vector();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   bool get_logfile_append();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   precision_t get_logfile_dt();
 
   // Satellites
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::vector<std::string> get_satellite_files();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::vector<std::string> get_satellite_names();
-  
+
   /**********************************************************************
      \brief returns settings["
-     \param 
+     \param
    **/
   std::vector<precision_t> get_satellite_dts();
-  
+
   // General get_setting functions with error checks:
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::string get_setting_str(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::string get_setting_str(std::string key1, std::string key2);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::string get_setting_str(std::string key1,
                               std::string key2,
                               std::string key3);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   json get_setting_json(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   json get_setting_json(std::string key1, std::string key2);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool get_setting_bool(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool get_setting_bool(std::string key1, std::string key2);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool get_setting_bool(std::string key1, std::string key2, std::string key3);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   precision_t get_setting_float(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   precision_t get_setting_float(std::string key1, std::string key2);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   int64_t get_setting_int(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   int64_t get_setting_int(std::string key1, std::string key2);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::vector<int> get_setting_intarr(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::vector<int> get_setting_intarr(std::string key1, std::string key2);
 
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::vector<int> get_setting_timearr(std::string key1);
 
   // Check settings functions:
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool check_settings(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   bool check_settings(std::string key1, std::string key2);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::string check_settings_str(std::string key1);
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   std::string check_settings_str(std::string key1, std::string key2);
 
-  
+
   /**********************************************************************
-     \brief 
-     \param 
+     \brief
+     \param
    **/
   precision_t check_settings_pt(std::string key1, std::string key2);
-  
+
   /**********************************************************************
      \brief Check to see if internal state of class is ok
    **/
   bool is_ok();
-  
-private:
+
+ private:
 
   // This is the main variable that contains all of the settings in Aether:
   json settings;
-  
+
   // These are a bunch of misc strings that should go away:
   std::string euv_file = "UA/inputs/euv.csv";
   std::string aurora_file = "UA/inputs/aurora_earth.csv";
@@ -778,7 +779,7 @@ class Inputs {
   std::string restart_in_directory = "UA/restartIn";
 
   bool DoRestart;
-  
+
   precision_t dt_euv;
   precision_t dt_report;
 
@@ -787,7 +788,7 @@ class Inputs {
   int nAltsGeo;
 
   int updated_seed;
-  
+
   /// An internal variable to hold the state of the class
   bool isOk;
 

From 8b4310aa6c402025bb71e7eb3525658028f26ef4 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:11:56 -0400
Subject: [PATCH 150/266] FEAT: added variables for calc_ion_v

---
 include/ions.h | 69 ++++++++++++++++++++++++++++++--------------------
 1 file changed, 42 insertions(+), 27 deletions(-)

diff --git a/include/ions.h b/include/ions.h
index 05ab7bbe..b43a5f89 100644
--- a/include/ions.h
+++ b/include/ions.h
@@ -11,13 +11,13 @@
  * \class Ions
  *
  * \brief Defines the ion states
- * 
+ *
  * The Ion class defines the ion states as well as a bunch
- * of derived states and source/loss terms.  
+ * of derived states and source/loss terms.
  *
  * \author Aaron Ridley
  *
- * \date 2021/03/28 
+ * \date 2021/03/28
  *
  **************************************************************/
 
@@ -29,7 +29,7 @@ class Ions {
   // species of ion.  We will then have a vector of these species.
 
   int64_t nSpecies = 8;
-  
+
   struct species_chars {
 
     /// Name of the species
@@ -68,7 +68,7 @@ class Ions {
 
     /// Ion - Electron collision frequencies:
     std::vector<precision_t> nu_ion_electron;
-    
+
     // Sources and Losses:
 
     /// Number density of species (/m3)
@@ -187,12 +187,25 @@ class Ions {
   /// Average energy of diffuse electron aurora (keV, tbc):
   arma_mat avee;
 
+  // Some variables that we are going to use in calc_ion_v:
+  std::vector<arma_cube> gravity_vcgc;
+  std::vector<arma_cube> wind_acc;
+  std::vector<arma_cube> total_acc;
+  std::vector<arma_cube> efield_acc;
+  std::vector<arma_cube> a_par;
+  std::vector<arma_cube> a_perp;
+  std::vector<arma_cube> a_x_b;
+  std::vector<arma_cube> grad_Pi_plus_Pe;
+  arma_cube rho, nuin, nuin_sum, Nie, sum_rho;
+  arma_cube top, bottom;
+
+
   /// Number of species to advect:
   int nSpeciesAdvect;
-      
+
   /// IDs of species to advect:
   std::vector<int> species_to_advect;
-    
+
   // names and units
   const std::string density_name = "Neutral Bulk Density";
   const std::string density_unit = "/m3";
@@ -207,7 +220,7 @@ class Ions {
 
   const std::string potential_name = "Potential";
   const std::string potential_unit = "Volts";
-  
+
   // --------------------------------------------------------------------
   // Functions:
 
@@ -225,11 +238,11 @@ class Ions {
   species_chars create_species(Grid grid);
 
   /**********************************************************************
-     \brief 
+     \brief
      \param planet contains information about the species to simulate
    **/
   int read_planet_file(Planets planet);
-  
+
   /**********************************************************************
      \brief Initialize the ion temperature (to the neutral temperature)
      \param neutrals the neutral class to grab the temperature from
@@ -325,16 +338,16 @@ class Ions {
      \param dt the delta-t for the current time
    **/
   void calc_ion_drift(Neutrals neutrals,
-		      Grid grid,
-		      precision_t dt);
-  
+                      Grid grid,
+                      precision_t dt);
+
   /**********************************************************************
      \brief Calculate the ion + electron pressure gradient
      \param iIon which ion to act upon
      \param grid this is the grid to solve the equation on
    **/
   std::vector<arma_cube> calc_ion_electron_pressure_gradient(int64_t iIon,
-							     Grid grid);
+                                                             Grid grid);
 
   /**********************************************************************
      \brief Calculates the ion temperature(s) on the given grid
@@ -356,7 +369,7 @@ class Ions {
   /// @brief Calculate epsilon
   /// @details intermediate variable used in photoelectron & ionization heating
   /// From (Smithro & Solomon, 2008).
-  /// @param neutrals 
+  /// @param neutrals
   /// @return epsilon
   **/
   arma_cube calc_epsilon(Neutrals &neutrals);
@@ -366,17 +379,17 @@ class Ions {
     \details Based on (Swartz & Nisbet, 1972) & (Smithro & Solomon, 2008)
             Uses equations 9-12 from (Zhu & Ridley, 2016)
             https://doi.org/10.1016/j.jastp.2016.01.005
-    \param epsilon 
-    \return Qphe 
+    \param epsilon
+    \return Qphe
   **/
   arma_cube calc_photoelectron_heating(arma_cube epsilon);
 
   /**********************************************************************
     \brief Calculates auroral heating
-    \details NOTE: in GITM this is solved separately for ion precipitation & auroral 
+    \details NOTE: in GITM this is solved separately for ion precipitation & auroral
         ionization. In Aether these are both in ions.species[iIon].ionization_scgc...
-    \param epsilon 
-    \return Qaurora 
+    \param epsilon
+    \return Qaurora
   **/
   arma_cube calc_ionization_heating(arma_cube epsilon);
 
@@ -394,26 +407,28 @@ class Ions {
   /**********************************************************************
     \brief Calculates electron-neutral elastic collisional heating
     \details From Schunk and Nagy 2009
-    \param neutrals 
+    \param neutrals
     \return vector<Qencp, Qencm, Qenc_v>
   **/
-  std::vector<arma_cube> calc_electron_neutral_elastic_collisions(Neutrals &neutrals);
+  std::vector<arma_cube> calc_electron_neutral_elastic_collisions(
+    Neutrals &neutrals);
 
   /**********************************************************************
     \brief Calculates the electron-neutral inelastic collisional heating
     \details From Schunk and Nagy 2009 pages 277, 282.
     This includes N2, O2 rotation, fine structure, O(1D) exitation & vibration, N2 vibration.
     See equation 15 from (Zhu, Ridley, Deng, 2016) https://doi.org/10.1016/j.jastp.2016.01.005
-    \param neutrals 
+    \param neutrals
     \return vector<Qencp, Qencm, Qenc_v>
   **/
-  std::vector<arma_cube> calc_electron_neutral_inelastic_collisions(Neutrals &neutrals);
+  std::vector<arma_cube> calc_electron_neutral_inelastic_collisions(
+    Neutrals &neutrals);
 
   /**********************************************************************
     \brief Calculate the thermoelectric current (same at all altitudes)
     \details Use eq. 6 of https://doi.org/10.1016/j.jastp.2016.01.005
     - Since we do not know e- parallel velocity, the dipole needs to do it this way too.
-    \param grid 
+    \param grid
     \return arma_mat JParaAlt
   **/
   arma_mat calc_thermoelectric_current(Grid &grid);
@@ -422,7 +437,7 @@ class Ions {
      \brief Check all of the variables for nonfinites, such as nans
      \param none
    **/
-  bool check_for_nonfinites();
+  bool check_for_nonfinites(std::string location);
 
   /**********************************************************************
      \brief Run through a test of an arma_cube to see if it contains nans
@@ -445,7 +460,7 @@ class Ions {
   bool exchange_old(Grid &grid);
 
   /**********************************************************************
-     \brief Vertical advection solver - Rusanov 
+     \brief Vertical advection solver - Rusanov
      \param grid The grid to define the neutrals on
      \param time contains information about the current time
    **/

From a3cecb6274ce5717c0edc246fc10e261ecfc5512 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:12:49 -0400
Subject: [PATCH 151/266] FEAT: working on adding horizontal cubesphere solver

---
 include/neutrals.h | 240 ++++++++++++++++++++++++++++++++++-----------
 1 file changed, 184 insertions(+), 56 deletions(-)

diff --git a/include/neutrals.h b/include/neutrals.h
index d7ac5321..1ee222b6 100644
--- a/include/neutrals.h
+++ b/include/neutrals.h
@@ -11,7 +11,7 @@
  * \class Neutrals
  *
  * \brief Defines the neutral states
- * 
+ *
  * The Neutrals class defines the neutrals states as well as a bunch
  * of derived states and source/loss terms.  The initial temperature
  * structure as well as the lower boundary densities can be set
@@ -19,7 +19,7 @@
  *
  * \author Aaron Ridley
  *
- * \date 2021/03/28 
+ * \date 2021/03/28
  *
  **************************************************************/
 
@@ -52,7 +52,7 @@ class Neutrals {
     /// Number density of species (/m3)
     arma_cube density_scgc;
     arma_cube newDensity_scgc;
-    
+
     /// Velocity of each species (m/s). For all below:
     /// Index 0 = longitudinal component of velocity
     /// Index 1 = latitudinal
@@ -65,14 +65,14 @@ class Neutrals {
 
     /// Coefficient for the friction term (sum of friction coefs with others)
     arma_cube neutral_friction_coef;
-      
+
     /// Acceleration of each species based on Eddy contribution.
     /// Only in vertical direction.
     arma_cube acc_eddy;
-      
+
     /// Acceleration of each species due to ion drag.
     std::vector<arma_cube> acc_ion_drag;
-            
+
     /// concentration (density of species / total density)
     arma_cube concentration_scgc;
     // mass concentration (mass * density of species / rho)
@@ -104,7 +104,7 @@ class Neutrals {
     std::vector<int> iEuvPeiId_;
     /// Which ion species results from the ionization?
     std::vector<int> iEuvPeiSpecies_;
-    
+
     int nAuroraIonSpecies;
     std::vector<int> iAuroraIonSpecies_;
     float Aurora_Coef;
@@ -127,7 +127,7 @@ class Neutrals {
 
     /// Chemistry source rate (/m3/s)
     arma_cube sources_scgc;
-    
+
     /// Chemistry loss rate (/m3/s)
     arma_cube losses_scgc;
 
@@ -145,7 +145,7 @@ class Neutrals {
 
   /// sound speed + abs(bulk velocity (m/s))
   std::vector<arma_cube> cMax_vcgc;
-  
+
   /// bunk temperature (K)
   arma_cube temperature_scgc;
   arma_cube newTemperature_scgc;
@@ -177,7 +177,7 @@ class Neutrals {
   /// Viscosity
   arma_cube viscosity_scgc;
 
-  /// O cooling 
+  /// O cooling
   arma_cube O_cool_scgc;
 
   /// NO cooling
@@ -225,10 +225,10 @@ class Neutrals {
   std::vector<double> initial_altitudes;
   std::vector<double> initial_temperatures;
   int64_t nInitial_temps = 0;
-    
+
   /// Number of species to advect:
   int nSpeciesAdvect;
-    
+
   /// IDs of species to advect:
   std::vector<int> species_to_advect;
 
@@ -257,9 +257,9 @@ class Neutrals {
      \param indices used to help set initial conditions
    **/
   Neutrals(Grid grid,
-	   Planets planet,
-	   Times time,
-	   Indices indices);
+           Planets planet,
+           Times time,
+           Indices indices);
 
   /**********************************************************************
      \brief Creates the variables within the species_chars structure
@@ -270,7 +270,7 @@ class Neutrals {
   /**********************************************************************
      \brief Read in the planet-specific file
 
-     This file specifies the species to model, their masses, 
+     This file specifies the species to model, their masses,
      diffusion coefficients and all of the other things needed
      for specifying the neutrals.
 
@@ -285,8 +285,8 @@ class Neutrals {
      \param indices used to help set initial conditions
    **/
   bool initial_conditions(Grid grid,
-			  Times time,
-			  Indices indices);
+                          Times time,
+                          Indices indices);
 
   /**********************************************************************
      \brief temporary function to set neutral densities with in the model
@@ -301,14 +301,14 @@ class Neutrals {
      \param grid The grid to define the neutrals on
    **/
   void fill_with_hydrostatic(int64_t iStart,
-			     int64_t iEnd,
-			     Grid grid);
+                             int64_t iEnd,
+                             Grid grid);
 
   void fill_with_hydrostatic(int64_t iSpecies,
-			     int64_t iStart,
-			     int64_t iEnd,
-			     Grid grid);
-  
+                             int64_t iStart,
+                             int64_t iEnd,
+                             Grid grid);
+
   /**********************************************************************
      \brief Limit the density to a floor and a ceiling
    **/
@@ -324,38 +324,38 @@ class Neutrals {
      \param grid The grid to define the neutrals on
    **/
   void calc_scale_height(Grid grid);
-  
+
   /**********************************************************************
      \brief Calculate the viscosity coefficient
    **/
   void calc_viscosity();
-  
+
   /**********************************************************************
      \brief Calculate the eddy diffusion coefficient in valid pressure
    **/
   void calc_kappa_eddy();
-  
+
   /**********************************************************************
      \brief Calculate the concentration for each species (species ndensity / total ndensity)
    **/
   void calc_concentration();
 
   /**********************************************************************
-     \brief Calculate the density of each species from the mass concentration 
+     \brief Calculate the density of each species from the mass concentration
             for each species and rho (ndensity = con * rho / mass)
    **/
   void calc_density_from_mass_concentration();
-  
+
   /**********************************************************************
      \brief Calculate the bulk mean major mass
    **/
   void calc_mean_major_mass();
-    
+
   /**********************************************************************
      \brief Calculate the mean pressure
    **/
   void calc_pressure();
-    
+
   /**********************************************************************
      \brief Calculate bulk velocity
    **/
@@ -421,8 +421,8 @@ class Neutrals {
      \param indices used to help set initial conditions
    **/
   bool set_bcs(Grid grid,
-	       Times time,
-	       Indices indices);
+               Times time,
+               Indices indices);
 
   /**********************************************************************
      \brief Set lower boundary conditions for the neutrals
@@ -431,8 +431,8 @@ class Neutrals {
      \param indices used to help set initial conditions
    **/
   bool set_lower_bcs(Grid grid,
-		     Times time,
-		     Indices indices);
+                     Times time,
+                     Indices indices);
 
   /**********************************************************************
      \brief Set upper boundary conditions for the neutrals
@@ -448,7 +448,7 @@ class Neutrals {
      \param grid The grid to define the neutrals on
   **/
   bool set_horizontal_bcs(int64_t iDir, Grid grid);
-  
+
   /**********************************************************************
      \brief Get the species ID number (int) given the species name (string)
      \param name string holding the species name (e.g., "O+")
@@ -470,7 +470,7 @@ class Neutrals {
      \param dir directory to write restart files
      \param DoRead read the restart files if true, write if false
    **/
-  bool restart_file(std::string dir, std::string cGridtype, bool DoRead);  
+  bool restart_file(std::string dir, std::string cGridtype, bool DoRead);
 
   /**********************************************************************
      \brief Exchange messages between processors
@@ -482,9 +482,9 @@ class Neutrals {
   /**********************************************************************
    \brief add eddy contributions to vertical acceleration
    \param grid The grid to define the neutrals on
-  **/ 
+  **/
   void vertical_momentum_eddy(Grid &grid);
-  
+
   /**********************************************************************
      \brief Exchange one face for the NEUTRALS
 
@@ -503,29 +503,29 @@ class Neutrals {
    **/
 
   bool exchange_one_face(int iReceiver, int iSender,
-			 precision_t *buffer,
-			 int64_t iTotalSize,
-			 int nG, int iDir);
+                         precision_t *buffer,
+                         int64_t iTotalSize,
+                         int nG, int iDir);
 
   bool pack_one_face(int iReceiver,
-		     precision_t *buffer,
-		     int nG, int iDir,
-		     bool IsPole);
+                     precision_t *buffer,
+                     int nG, int iDir,
+                     bool IsPole);
   bool unpack_one_face(int iSender,
-		       precision_t *buffer,
-		       int nG, int iDir,
-		       bool DoReverseX,
-		       bool DoReverseY,
-		       bool XbecomesY);
+                       precision_t *buffer,
+                       int nG, int iDir,
+                       bool DoReverseX,
+                       bool DoReverseY,
+                       bool XbecomesY);
 
   /**********************************************************************
-     \brief Vertical advection solver - Rusanov 
+     \brief Vertical advection solver - Rusanov
      \param grid The grid to define the neutrals on
      \param time contains information about the current time
    **/
   void solver_vertical_rusanov(Grid grid,
-			       Times time);
-  
+                               Times time);
+
   /**********************************************************************
      \brief Call the correct vertical advection scheme
      \param grid The grid to define the neutrals on
@@ -542,18 +542,146 @@ class Neutrals {
      \param vels updated velocity, which acts as a source term for the implicit solve
    **/
   arma_vec calc_friction_one_cell(int64_t iLong, int64_t iLat, int64_t iAlt,
-				   precision_t dt, arma_vec &vels);
+                                  precision_t dt, arma_vec &vels);
 
   /**********************************************************************
      \brief Calculate the neutral friction in all cells (calls one_cell above)
      \param dt time step
    **/
-  void calc_neutral_friction_implicit(precision_t dt);  
+  void calc_neutral_friction_implicit(precision_t dt);
 
   /**********************************************************************
      \brief Calculate the neutral friction coefficients for semi-implicit solver
    **/
-  void calc_neutral_friction_coefs();    
+  void calc_neutral_friction_coefs();
+
+  /**********************************************************************
+     \brief Residuals for **fluid motion** horizontally with Rusanov
+     \brief It actually updates the weighted residuals (-1/Area*R) for efficiency
+
+     \param grid
+     \param time
+     \param states
+   **/
+  std::vector<arma_mat> residual_horizontal_rusanov(std::vector<arma_mat>& states,
+                                                    Grid& grid, Times& time, int64_t iAlt);
+
+  /**********************************************************************
+     \brief Solves for **fluid motion** horizontally with RK4
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_RK4(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Solves for **fluid motion** horizontally with RK1
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_RK1(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Call the correct horizontal advection scheme with CE eqn
+     \param grid The grid to define the neutrals on
+     \param time contains information about the current time
+   **/
+  bool advect_horizontal(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Solves for fluid motion (pure advect) horizontally with Rusanov
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_rusanov_advection(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Solves for fluid motion (pure advect) horizontally with RK1
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_RK1_advection(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Solves for fluid motion (pure advect) horizontally with RK2
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_RK2_advection(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Solves for fluid motion (pure advect) horizontally with RK4
+
+     \param grid
+     \param time
+     \param report
+   **/
+  void solver_horizontal_RK4_advection(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Residuals for fluid motion (pure advect) horizontally with HLLE
+     \brief It actually updates the weighted residuals (-1/Area*R) for efficiency
+
+     \param grid
+     \param time
+     \param states
+   **/
+  std::vector<arma_mat> residual_horizontal_hlle_advection(
+    std::vector<arma_mat>& states, Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Residuals for fluid motion (pure advect) horizontally with Rusanov
+     \brief It actually updates the weighted residuals (-1/Area*R) for efficiency
+
+     \param grid
+     \param time
+     \param states
+   **/
+  std::vector<arma_mat> residual_horizontal_rusanov_advection(
+    std::vector<arma_mat>& states, Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Call the horizontal advection scheme with only advection
+     \param grid The grid to define the neutrals on
+     \param time contains information about the current time
+   **/
+  bool advect_horizontal_advection(Grid& grid, Times& time);
+
+  /**********************************************************************
+     \brief Setup initial condition for the cosine bell test
+     \brief For advection test
+     \param grid The grid to define the neutrals on
+     \param time contains information about the current time
+     \param indices used to help set initial conditions
+     \param planet planet data for extracting the radius
+   **/
+  bool cosine_bell_ic(Grid grid,
+                      Times time,
+                      Indices indices,
+                      Planets planet);
+
+  /**********************************************************************
+     \brief Setup initial condition for the blob test
+     \brief For Actual Cubesphere fluid solver
+     \param grid The grid to define the neutrals on
+     \param time contains information about the current time
+     \param indices used to help set initial conditions
+     \param planet planet data for extracting the radius
+   **/
+  bool blob_ic(Grid grid,
+               Times time,
+               Indices indices,
+               Planets planet);
+
 };
 
 #endif  // INCLUDE_NEUTRALS_H_

From 5fe512c5af3ccce96d7474a717407eef25647cfd Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:14:46 -0400
Subject: [PATCH 152/266] STY: tabs and spaces + horizontal cubesphere

---
 include/solvers.h | 122 ++++++++++++++++++++++++++++++----------------
 1 file changed, 79 insertions(+), 43 deletions(-)

diff --git a/include/solvers.h b/include/solvers.h
index 4e5ebb6f..dd4afe4f 100644
--- a/include/solvers.h
+++ b/include/solvers.h
@@ -21,32 +21,68 @@ struct projection_struct {
   arma_mat grad_edge_DU;
 };
 
-arma_vec limiter_mc(arma_vec &left, arma_vec &right, int64_t nPts, int64_t nGCs);
+arma_vec limiter_mc(arma_vec &left, arma_vec &right, int64_t nPts,
+                    int64_t nGCs);
 arma_vec calc_grad_1d(arma_vec &values,
-		      						arma_vec &x,
-		      						int64_t nPts,
-		      						int64_t nGCs);
+                      arma_vec &x,
+                      int64_t nPts,
+                      int64_t nGCs);
 arma_mat calc_grad(arma_mat values, arma_mat x, int64_t nGCs, bool DoX);
 
+
+projection_struct project_to_edges(arma_mat &values,
+                                   arma_mat &x_centers, arma_mat &x_edges,
+                                   arma_mat &y_centers, arma_mat &y_edges,
+                                   int64_t nGCs);
+
+namespace Cubesphere_tools {
+/*
+struct projection_struct {
+  arma_mat gradLR;
+  arma_mat gradDU;
+  arma_mat R;
+  arma_mat L;
+  arma_mat U;
+  arma_mat D;
+};
+*/
+arma_vec limiter_mc(arma_vec &left, arma_vec &right, int64_t nPts,
+                    int64_t nGCs);
+void print(arma_vec values);
+arma_vec calc_grad_1d(arma_vec &values, arma_vec &x, int64_t nPts,
+                      int64_t nGCs);
+arma_mat calc_grad(arma_mat values, arma_mat x, int64_t nGCs, bool DoX);
+
+arma_mat project_from_left(arma_mat values, arma_mat gradients,
+                           arma_mat x_centers, arma_mat x_edges, int64_t nGCs);
+arma_mat project_from_right(arma_mat values, arma_mat gradients,
+                            arma_mat x_centers, arma_mat x_edges, int64_t nGCs);
+arma_vec limiter_value(arma_vec projected, arma_vec values, int64_t nPts,
+                       int64_t nGCs);
+//projection_struct project_to_edges(arma_mat &values, arma_mat &x_centers,
+//                                   arma_mat &x_edges, arma_mat &y_centers, arma_mat &y_edges, int64_t nGCs);
+}
+
+
 void advect(Grid &grid,
             Times &time,
             Neutrals &neutrals);
 
 arma_vec solver_conduction(
-			   arma_vec value,
-			   arma_vec lambda,
-			   arma_vec front,
-			   arma_vec source,
-			   arma_vec dx,
-			   precision_t dt,
-			   int64_t nGCs,
-			   bool return_diff = false,
-			   arma_vec source2 = arma_vec());
+  arma_vec value,
+  arma_vec lambda,
+  arma_vec front,
+  arma_vec source,
+  arma_vec dx,
+  precision_t dt,
+  int64_t nGCs,
+  bool return_diff = false,
+  arma_vec source2 = arma_vec());
 
 arma_cube solver_chemistry(arma_cube density,
-			   arma_cube source,
-			   arma_cube loss,
-			   precision_t dt);
+                           arma_cube source,
+                           arma_cube loss,
+                           precision_t dt);
 
 std::vector<arma_cube> coriolis(std::vector<arma_cube> velocity,
                                 precision_t rotation_rate,
@@ -56,32 +92,32 @@ std::vector<arma_cube> coriolis(std::vector<arma_cube> velocity,
 /// or an interpolated value should be used.
 const int iPrevious_ = 1;
 const int iNext_ = 2;
-const int iClosest_ = 3; 
+const int iClosest_ = 3;
 const int iInterp_ = 4;
 
 double interpolate_1d(double outX,
-		      std::vector<double> inXs,
-		      std::vector<double> inValues);
+                      std::vector<double> inXs,
+                      std::vector<double> inValues);
 
 double interpolate_1d_get_index_doubles(double intime,
-					std::vector<double> times);
+                                        std::vector<double> times);
 
 // Overloading the interpolation function:
 double interpolate_1d_w_index(std::vector<double> values,
-			      double interpolation_index,
-			      int interpolation_type);
+                              double interpolation_index,
+                              int interpolation_type);
 double interpolate_1d_w_index(std::vector<float> values,
-			      double interpolation_index,
-			      int interpolation_type);
+                              double interpolation_index,
+                              int interpolation_type);
 double interpolate_1d_w_index(std::vector<float> values,
-			      float interpolation_index,
-			      int interpolation_type);
+                              float interpolation_index,
+                              int interpolation_type);
 double interpolate_1d_w_index(arma_vec values,
-			      double interpolation_index,
-			      int interpolation_type);
+                              double interpolation_index,
+                              int interpolation_type);
 fmat interpolate_1d_w_index(std::vector<fmat> values,
-			    double interpolation_index,
-			    int interpolation_type);
+                            double interpolation_index,
+                            int interpolation_type);
 
 arma_cube calc_gradient_lon(arma_cube value, Grid grid);
 arma_cube calc_gradient_lat(arma_cube value, Grid grid);
@@ -113,18 +149,18 @@ precision_t interpolate_unit_cube(const arma_cube &data,
                                   const precision_t zRatio);
 
 precision_t limiter_mc(precision_t dUp,
-		       precision_t dDown,
-		       precision_t beta);
-
-
-  /**********************************************************************
-     \brief Calculate dt (cell size / cMax) in each direction, and take min
-     \param dt returns the neutral time-step
-     \param grid The grid to define the neutrals on
-   **/
-  precision_t calc_dt(Grid grid, std::vector<arma_cube> cMax_vcgc);
-  precision_t calc_dt_sphere(Grid grid, std::vector<arma_cube> cMax_vcgc);  
-  precision_t calc_dt_cubesphere(Grid grid, std::vector<arma_cube> cMax_vcgc);  
-  precision_t calc_dt_vertical(Grid grid, std::vector<arma_cube> cMax_vcgc);  
+                       precision_t dDown,
+                       precision_t beta);
+
+
+/**********************************************************************
+   \brief Calculate dt (cell size / cMax) in each direction, and take min
+   \param dt returns the neutral time-step
+   \param grid The grid to define the neutrals on
+ **/
+precision_t calc_dt(Grid grid, std::vector<arma_cube> cMax_vcgc);
+precision_t calc_dt_sphere(Grid grid, std::vector<arma_cube> cMax_vcgc);
+precision_t calc_dt_cubesphere(Grid grid, std::vector<arma_cube> cMax_vcgc);
+precision_t calc_dt_vertical(Grid grid, std::vector<arma_cube> cMax_vcgc);
 
 #endif  // INCLUDE_SOLVERS_H_

From 2fd3d917783d14fa090dbc4a9fb71872c436866e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:17:39 -0400
Subject: [PATCH 153/266] FEAT: optimizing

---
 src/calc_ion_drift.cpp | 44 ++++++++++++++----------------------------
 1 file changed, 15 insertions(+), 29 deletions(-)

diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index ab1c6acb..bff5df9a 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -16,7 +16,8 @@ void Ions::calc_efield(Grid grid) {
     efield_vcgc[iComp] = -efield_vcgc[iComp];
 
   // Remove component along b-field (should be zero, anyways!)
-  arma_cube edotb = dot_product(efield_vcgc, grid.bfield_unit_vcgc);
+  arma_cube edotb;
+  edotb = dot_product(efield_vcgc, grid.bfield_unit_vcgc);
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
     efield_vcgc[iComp] =
@@ -28,8 +29,8 @@ void Ions::calc_efield(Grid grid) {
 // --------------------------------------------------------------------------
 
 void Ions::calc_exb_drift(Grid grid) {
-  arma_cube bmag2 =
-    (grid.bfield_mag_scgc) % (grid.bfield_mag_scgc);
+  arma_cube bmag2;
+  bmag2 = (grid.bfield_mag_scgc) % (grid.bfield_mag_scgc);
   exb_vcgc = cross_product(efield_vcgc, grid.bfield_vcgc);
 
   for (int64_t iComp = 0; iComp < 3; iComp++)
@@ -89,35 +90,20 @@ void Ions::calc_ion_drift(Neutrals neutrals,
   report.print(5, "going into calc_exb_drift");
   calc_exb_drift(grid);
 
-  std::vector<arma_cube> gravity_vcgc = make_cube_vector(nX, nY, nZ, 3);
-  std::vector<arma_cube> wind_acc = make_cube_vector(nX, nY, nZ, 3);
-  std::vector<arma_cube> total_acc = make_cube_vector(nX, nY, nZ, 3);
-  std::vector<arma_cube> efield_acc = make_cube_vector(nX, nY, nZ, 3);
-
   int64_t iIon, iNeutral, iDim;
+  int64_t iComp;
 
-  std::vector<arma_cube> grad_Pi_plus_Pe;
-  arma_cube rho, nuin, nuin_sum, Nie, sum_rho;
-  arma_cube top, bottom;
-
-  nuin_sum.set_size(nX, nY, nZ);
   nuin_sum.zeros();
-
-  sum_rho.set_size(nX, nY, nZ);
   sum_rho.zeros();
 
   fill_electrons();
 
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp].zeros();
 
-  std::vector<arma_cube> a_par = make_cube_vector(nX, nY, nZ, 3);
-  std::vector<arma_cube> a_perp = make_cube_vector(nX, nY, nZ, 3);
-  std::vector<arma_cube> a_x_b;
-
   for (iIon = 0; iIon < nSpecies; iIon++) {
 
-    for (int64_t iComp = 0; iComp < 3; iComp++)
+    for (iComp = 0; iComp < 3; iComp++)
       species[iIon].perp_velocity_vcgc[iComp].zeros();
 
     if (species[iIon].DoAdvect) {
@@ -141,7 +127,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
       // Neutral Wind Forcing:
       report.print(5, "neutral winds");
 
-      for (int64_t iComp = 0; iComp < 3; iComp++)
+      for (iComp = 0; iComp < 3; iComp++)
         wind_acc[iComp].zeros();
 
       nuin_sum.zeros();
@@ -150,14 +136,14 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         nuin = species[iIon].nu_ion_neutral_vcgc[iNeutral];
         nuin_sum = nuin_sum + species[iIon].nu_ion_neutral_vcgc[iNeutral];
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           wind_acc[iComp] = wind_acc[iComp] +
                             nuin % neutrals.velocity_vcgc[iComp];
         }
       }
 
       // Total Forcing (sum everything - this is A_s):
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         total_acc[iComp] =
           - grad_Pi_plus_Pe[iComp]
           + gravity_vcgc[iComp]
@@ -169,7 +155,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         // With a Planetary Magnetic field
         arma_cube a_dot_b = dot_product(total_acc, grid.bfield_unit_vcgc);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = a_dot_b % grid.bfield_unit_vcgc[iComp];
           a_perp[iComp] = total_acc[iComp] - a_par[iComp];
         }
@@ -183,7 +169,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
           Nie % Nie % grid.bfield_mag_scgc % grid.bfield_mag_scgc;
         bottom.clamp(1e-32, 1e32);
 
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           // I redefined A to be an acceleration instead of a force, which
           // then changes the definition of top
           top = rho % nuin % a_perp[iComp] + Nie % a_x_b[iComp];
@@ -204,7 +190,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
         }
       } else {
         // No Planetary Magnetic field
-        for (int64_t iComp = 0; iComp < 3; iComp++) {
+        for (iComp = 0; iComp < 3; iComp++) {
           a_par[iComp] = total_acc[iComp];
           // Steady state:
           //species[iIon].par_velocity_vcgc[iComp] =
@@ -220,7 +206,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
       // Calculate the mass-weighted average total velocity
       sum_rho = sum_rho + rho;
 
-      for (int64_t iComp = 0; iComp < 3; iComp++) {
+      for (iComp = 0; iComp < 3; iComp++) {
         species[iIon].velocity_vcgc[iComp] =
           //species[iIon].perp_velocity_vcgc[iComp] +
           species[iIon].par_velocity_vcgc[iComp];
@@ -232,7 +218,7 @@ void Ions::calc_ion_drift(Neutrals neutrals,
 
   }  // for iIon
 
-  for (int64_t iComp = 0; iComp < 3; iComp++)
+  for (iComp = 0; iComp < 3; iComp++)
     velocity_vcgc[iComp] = velocity_vcgc[iComp] / sum_rho;
 
   report.exit(function);

From bb59b824e334d19f930665a1a8c537699cb757dd Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:18:43 -0400
Subject: [PATCH 154/266] FEAT: advection neutrals horizontal

---
 src/inputs.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/inputs.cpp b/src/inputs.cpp
index 703c0517..c8e385d3 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -551,9 +551,8 @@ precision_t Inputs::get_dt_output(int iOutput) {
 
   if (iOutput < nOutputs)
     value = settings.at("Outputs").at("dt").at(iOutput);
-  else{
+  else
     report.error("Output Error; more output types than dt's provided.");
-  }
 
   return value;
 }
@@ -779,7 +778,8 @@ bool Inputs::get_do_ionization_heating() {
 // -----------------------------------------------------------------------
 
 bool Inputs::get_do_electron_ion_collisional_heating() {
-  return get_setting_bool("Sources", "Ions", "IncludeElectronIonCollisionalHeating");
+  return get_setting_bool("Sources", "Ions",
+                          "IncludeElectronIonCollisionalHeating");
 }
 
 // -----------------------------------------------------------------------
@@ -787,7 +787,8 @@ bool Inputs::get_do_electron_ion_collisional_heating() {
 // -----------------------------------------------------------------------
 
 bool Inputs::get_do_electron_neutral_elastic_collisional_heating() {
-  return get_setting_bool("Sources", "Ions", "IncludeElectronNeutralElasticCollisionalHeating");
+  return get_setting_bool("Sources", "Ions",
+                          "IncludeElectronNeutralElasticCollisionalHeating");
 }
 
 // -----------------------------------------------------------------------
@@ -795,7 +796,8 @@ bool Inputs::get_do_electron_neutral_elastic_collisional_heating() {
 // -----------------------------------------------------------------------
 
 bool Inputs::get_do_electron_neutral_inelastic_collisional_heating() {
-  return get_setting_bool("Sources", "Ions", "IncludeElectronNeutralInelasticCollisionalHeating");
+  return get_setting_bool("Sources", "Ions",
+                          "IncludeElectronNeutralInelasticCollisionalHeating");
 }
 
 // -----------------------------------------------------------------------
@@ -1200,6 +1202,10 @@ std::string Inputs::get_advection_neutrals_vertical() {
   return get_setting_str("Advection", "Neutrals", "Vertical");
 }
 
+std::string Inputs::get_advection_neutrals_horizontal() {
+  return get_setting_str("Advection", "Neutrals", "Horizontal");
+}
+
 std::string Inputs::get_advection_ions_along() {
   return get_setting_str("Advection", "Ions", "Along");
 }

From 4038fe38f3c2f2641549c3469dc6877ab73e526c Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:19:28 -0400
Subject: [PATCH 155/266] FEAT: optimizing

---
 src/ions.cpp | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/src/ions.cpp b/src/ions.cpp
index 075c8df0..bb372a33 100644
--- a/src/ions.cpp
+++ b/src/ions.cpp
@@ -107,6 +107,25 @@ Ions::Ions(Grid grid, Planets planet) {
   velocity_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
   cMax_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 
+  // Some variables that will be used in calc_ion_v:
+  gravity_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
+  wind_acc = make_cube_vector(nLons, nLats, nAlts, 3);
+  total_acc = make_cube_vector(nLons, nLats, nAlts, 3);
+  efield_acc = make_cube_vector(nLons, nLats, nAlts, 3);
+  a_par = make_cube_vector(nLons, nLats, nAlts, 3);
+  a_perp = make_cube_vector(nLons, nLats, nAlts, 3);
+  a_x_b = make_cube_vector(nLons, nLats, nAlts, 3);
+  grad_Pi_plus_Pe = make_cube_vector(nLons, nLats, nAlts, 3);
+  rho.set_size(nLons, nLats, nAlts);
+  nuin.set_size(nLons, nLats, nAlts);
+  nuin_sum.set_size(nLons, nLats, nAlts);
+  Nie.set_size(nLons, nLats, nAlts);
+  sum_rho.set_size(nLons, nLats, nAlts);
+  top.set_size(nLons, nLats, nAlts);
+  bottom.set_size(nLons, nLats, nAlts);
+
+
+
   Cv_scgc.set_size(nLons, nLats, nAlts);
   Cv_scgc.zeros();
   lambda.set_size(nLons, nLats, nAlts);
@@ -260,7 +279,7 @@ void Ions::nan_test(std::string variable) {
 // Checks for nans and +/- infinities in density, temp, and velocity
 //----------------------------------------------------------------------
 
-bool Ions::check_for_nonfinites() {
+bool Ions::check_for_nonfinites(std::string location) {
   bool didWork = true;
 
   if (!all_finite(density_scgc, "density_scgc") ||
@@ -269,7 +288,7 @@ bool Ions::check_for_nonfinites() {
     didWork = false;
 
   if (!didWork)
-    throw std::string("Check for nonfinites failed!!!\n");
+    report.error("ions are nan from location : " + location);
 
   return didWork;
 }
@@ -495,7 +514,7 @@ void Ions::fill_electrons() {
 // Will return nSpecies for electrons
 //----------------------------------------------------------------------
 
-int Ions::get_species_id(const std::string &name) const{
+int Ions::get_species_id(const std::string &name) const {
 
   std::string function = "Ions::get_species_id";
   static int iFunction = -1;

From 066f57a1ecb849cfeb141cdb18c99d1d52c88d61 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:20:06 -0400
Subject: [PATCH 156/266] STY: spaces and tabs

---
 src/main/main.cpp | 34 +++++++++++++++++++++++++++-------
 1 file changed, 27 insertions(+), 7 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 9d0a0a3f..c290c84b 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -9,7 +9,7 @@
 // -----------------------------------------------------------------------------
 
 int main() {
-  
+
   int iErr = 0;
   std::string sError;
   bool didWork = true;
@@ -24,6 +24,7 @@ int main() {
   try {
     // Create inputs (reading the input file):
     input = Inputs(time);
+
     if (!input.is_ok())
       throw std::string("input initialization failed!");
 
@@ -31,32 +32,37 @@ int main() {
       report.print(-1, "Hello " +
                    input.get_student_name() + " - welcome to Aether!");
 
-    // For now, the number of processors and blocks are set by the 
+    // For now, the number of processors and blocks are set by the
     // neutral grid shape, since this could be sphere (1 root) or
     // cubesphere (6 root)
     Quadtree quadtree(input.get_grid_shape("neuGrid"));
     Quadtree quadtree_ion(input.get_grid_shape("ionGrid"));
+
     if (!quadtree.is_ok())
       throw std::string("quadtree initialization failed!");
 
     // Initialize MPI and parallel aspects of the code:
     didWork = init_parallel(quadtree, quadtree_ion);
+
     if (!didWork)
       throw std::string("init_parallel failed!");
 
     // Everything should be set for the inputs now, so write a restart file:
     didWork = input.write_restart();
+
     if (!didWork)
       throw std::string("input.write_restart failed!");
 
     // Initialize the EUV system:
     Euv euv;
+
     if (!euv.is_ok())
       throw std::string("EUV initialization failed!");
 
     // Initialize the planet:
     Planets planet;
     MPI_Barrier(aether_comm);
+
     if (!planet.is_ok())
       throw std::string("planet initialization failed!");
 
@@ -64,6 +70,7 @@ int main() {
     Indices indices;
     didWork = read_and_store_indices(indices);
     MPI_Barrier(aether_comm);
+
     if (!didWork)
       throw std::string("read_and_store_indices failed!");
 
@@ -74,6 +81,7 @@ int main() {
     Grid gGrid("neuGrid");
     didWork = gGrid.init_geo_grid(quadtree, planet);
     MPI_Barrier(aether_comm);
+
     if (!didWork)
       throw std::string("init_geo_grid failed!");
 
@@ -92,6 +100,7 @@ int main() {
 
     if (mGrid.iGridShape_ == mGrid.iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
+
       if (!didWork)
         throw std::string("init_dipole_grid failed!");
     } else {
@@ -124,9 +133,12 @@ int main() {
 
     if (input.get_check_for_nans()) {
       didWork = neutrals.check_for_nonfinites("After Inputs");
+
       if (!didWork)
         throw std::string("NaNs found in Neutrals in Initialize!\n");
-      didWork = ions.check_for_nonfinites();
+
+      didWork = ions.check_for_nonfinites("NaNs found in Ions in Initialize!\n");
+
       if (!didWork)
         throw std::string("NaNs found in Ions in Initialize!\n");
     }
@@ -152,9 +164,12 @@ int main() {
     // Initialize electrodynamics and check if electrodynamics times
     // works with input time
     Electrodynamics electrodynamics(time);
+
     if (!electrodynamics.is_ok())
       throw std::string("electrodynamics on geo grid initialization failed!");
+
     Electrodynamics electrodynamicsMag(time);
+
     if (!electrodynamicsMag.is_ok())
       throw std::string("electrodynamics on mag grid initialization failed!");
 
@@ -172,6 +187,7 @@ int main() {
       didWork = output(neutrals, ions, gGrid, time, planet);
       didWork = output(neutralsMag, ionsMag, mGrid, time, planet);
     }
+
     if (!didWork)
       throw std::string("Initial output failed!");
 
@@ -232,14 +248,18 @@ int main() {
       if (!time.check_time_gate(input.get_dt_write_restarts())) {
         report.print(3, "Writing restart files");
 
-        didWork = neutrals.restart_file(input.get_restartout_dir(), gGrid.get_gridtype(), DoWrite);
-        didWork = neutralsMag.restart_file(input.get_restartout_dir(), mGrid.get_gridtype(), DoWrite);
+        didWork = neutrals.restart_file(input.get_restartout_dir(),
+                                        gGrid.get_gridtype(), DoWrite);
+        didWork = neutralsMag.restart_file(input.get_restartout_dir(),
+                                           mGrid.get_gridtype(), DoWrite);
 
         if (!didWork)
           throw std::string("Writing Restart for Neutrals Failed!!!\n");
 
-        didWork = ions.restart_file(input.get_restartout_dir(), gGrid.get_gridtype(), DoWrite);
-        didWork = ionsMag.restart_file(input.get_restartout_dir(), mGrid.get_gridtype(), DoWrite);
+        didWork = ions.restart_file(input.get_restartout_dir(), gGrid.get_gridtype(),
+                                    DoWrite);
+        didWork = ionsMag.restart_file(input.get_restartout_dir(), mGrid.get_gridtype(),
+                                       DoWrite);
 
         if (!didWork)
           throw std::string("Writing Restart for Ions Failed!!!\n");

From afb783b8376dd722554f32ada74e90da716f6e18 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:21:03 -0400
Subject: [PATCH 157/266] FEAT: allow horizontal advection

---
 src/neutrals_advect.cpp | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index a80dc274..8316cbeb 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -33,3 +33,41 @@ bool Neutrals::advect_vertical(Grid grid, Times time) {
   return didWork;
 }
 
+bool Neutrals::advect_horizontal(Grid & grid, Times & time) {
+  bool didWork = true;
+
+  std::string function = "Neutrals::advance_horizontal";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  if (input.get_advection_neutrals_horizontal() == "advect_test")
+    solver_horizontal_RK4_advection(grid, time);
+  else if (input.get_advection_neutrals_horizontal() == "fv") {
+    solver_horizontal_RK1(grid, time);
+
+    std::cout << "after rk1 : " << velocity_vcgc[0](20, 13, 10) << "\n";
+  } else {
+    std::cout << "Horizontal solver not found!\n";
+    std::cout << "  ==> Requested : "
+              << input.get_advection_neutrals_horizontal()
+              << "\n";
+    didWork = false;
+  }
+
+  report.exit(function);
+  return didWork;
+}
+
+bool Neutrals::advect_horizontal_advection(Grid & grid, Times & time) {
+  bool didWork = true;
+
+  std::string function = "Neutrals::advance_horizontal_advection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  //solver_horizontal_rusanov_advection(grid, time);
+  solver_horizontal_RK4_advection(grid, time);
+
+  report.exit(function);
+  return didWork;
+}

From 2ac8e4f15c737fa8a76e10f25ef39c1e307d4e3a Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:21:49 -0400
Subject: [PATCH 158/266] FEAT: add bell test ICs for advection

---
 src/neutrals_ics.cpp | 216 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 216 insertions(+)

diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 225eeb19..7b9613ba 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -200,5 +200,221 @@ bool Neutrals::initial_conditions(Grid grid,
   return didWork;
 }
 
+bool Neutrals::cosine_bell_ic(Grid grid,
+                              Times time,
+                              Indices indices,
+                              Planets planet) {
+  std::string function = "Neutrals::cosine_bell_ic";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Planet.get_radius() takes in latitude
+  // but at current stage is unimplemented
+  // Anyway, we use equator radius as assumption for CubeSphere
+  // CubeSphere must be a perfect sphere!!
+  precision_t planet_R = planet.get_radius(0);
+
+  // radius of planet + altitude
+  // just pick alt at (0,0) loction
+  arma_vec R_Alts = grid.geoAlt_scgc.tube(0, 0) + planet_R;
+
+
+  /** Get a bunch of constants for setting up the ic **/
+  precision_t R = R_Alts(2); // select R in the middle
+  //7.37128e+06 meters Earth radius + 1000km height
+  //std::cout << R << std::endl;
+
+  // Determine flow direction
+  // 0 - Equatorial
+  // pi/2 - Meridional
+  // pi/4 - NE direction
+  precision_t alpha_0 = 0.;
+
+  // Scaling factor for physical velocity
+  // 12 day period in miliseconds
+  precision_t u_0 = cTWOPI * R / (12.*24.*60.*60.);
+
+  // Radius of the cosine bell
+  precision_t r_0 = R / 3.;
+
+  // Center of the cosine bell
+  precision_t lon_0 = 7.*cPI / 4. + cPI / 8;
+  precision_t lat_0 = 0.;
+
+  // Maximum height for the cosine bell
+  precision_t h_0 = 1000.;
+
+  // Some grid dimensions and coordinates
+  int64_t nLats = grid.get_nLats();
+  int64_t nLons = grid.get_nLons();
+  int64_t nAlts = grid.get_nAlts();
+  arma_mat lat_grid = grid.geoLat_scgc.slice(2);
+  arma_mat lon_grid = grid.geoLon_scgc.slice(2);
+
+  // Calculate for physical velocity for every altitude
+  // First we prepare velocities for one slice
+  arma_mat slice_u = velocity_vcgc[0].slice(2);
+  arma_mat slice_v = velocity_vcgc[1].slice(2);
+
+  // Fill velocities in one slice
+  for (int64_t iLat = 0; iLat < nLats; iLat++) {
+    for (int64_t iLon = 0; iLon < nLons; iLon++) {
+      precision_t curr_lat = lat_grid(iLon, iLat);
+      precision_t curr_lon = lon_grid(iLon, iLat);
+      slice_u(iLon, iLat) = u_0 * (cos(alpha_0) * cos(curr_lat) + sin(alpha_0) * cos(
+                                     curr_lon) * sin(curr_lat));
+      slice_v(iLon, iLat) = -u_0 * sin(alpha_0) * sin(curr_lon);
+    }
+  }
+
+  // Update this slice of velocity to all slices (for completeness)
+  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
+    velocity_vcgc[0].slice(iAlt) = slice_u;
+    velocity_vcgc[1].slice(iAlt) = slice_v;
+  }
+
+  // Calculate the cosine bell or rho_scgc
+  // First, again take a slice
+  arma_mat slice_rho = rho_scgc.slice(2);
+
+  // Fill rho in one slice
+  for (int64_t iLat = 0; iLat < nLats; iLat++) {
+    for (int64_t iLon = 0; iLon < nLons; iLon++) {
+      precision_t curr_lat = lat_grid(iLon, iLat);
+      precision_t curr_lon = lon_grid(iLon, iLat);
+
+      // Calculate great circle distance
+      precision_t dlon_2 = (curr_lon - lon_0) / 2.0;
+      precision_t dlat_2 = (curr_lat - lat_0) / 2.0;
+
+      precision_t r_d = 2.0 * R * asin(sqrt(sin(dlat_2) * sin(dlat_2) + sin(
+                                              dlon_2) * sin(dlon_2) * cos(curr_lat) * cos(lat_0)));
+
+      if (r_d < r_0)
+        slice_rho(iLon, iLat) = (h_0 / 2) * (1 + cos(cPI * r_d / r_0));
+
+      else
+        slice_rho(iLon, iLat) = 0.;
+    }
+  }
+
+  // Update this slice of rho to all slices (for completeness)
+  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
+    rho_scgc.slice(iAlt) = slice_rho;
+
+    // Do zero concentration conversion
+    for (int64_t iSpec = 0; iSpec < nSpecies; iSpec++)
+      species[iSpec].density_scgc.slice(iAlt) = slice_rho / species[iSpec].mass;
+  }
+
+  // Add some velocity pertubation
+  //std::cout <<  velocity_vcgc[0].slice(2) << std::endl;
+  //std::cout << rho_scgc.slice(2) << std::endl;
+
+  return 1;
+}
+
+bool Neutrals::blob_ic(Grid grid,
+                       Times time,
+                       Indices indices,
+                       Planets planet) {
+  std::string function = "Neutrals::blob_ic";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Planet.get_radius() takes in latitude
+  // but at current stage is unimplemented
+  // Anyway, we use equator radius as assumption for CubeSphere
+  // CubeSphere must be a perfect sphere!!
+  precision_t planet_R = planet.get_radius(0);
+
+  // radius of planet + altitude
+  // just pick alt at (0,0) loction
+  arma_vec R_Alts = grid.geoAlt_scgc.tube(0, 0) + planet_R;
+
+
+  /** Get a bunch of constants for setting up the ic **/
+  precision_t R = R_Alts(2); // select R in the middle
+  //7.37128e+06 meters Earth radius + 1000km height
+  //std::cout << R << std::endl;
+
+  // Radius of the blob
+  // Hardcoded
+  precision_t r_0 = 111321 * 10;
+
+  // Center of the blob
+  precision_t lon_0 = 7.*cPI / 4. - cPI / 8.;
+  precision_t lat_0 = 0.;
+
+  // Some grid dimensions and coordinates
+  int64_t nLats = grid.get_nLats();
+  int64_t nLons = grid.get_nLons();
+  int64_t nAlts = grid.get_nAlts();
+  arma_mat lat_grid = grid.geoLat_scgc.slice(2);
+  arma_mat lon_grid = grid.geoLon_scgc.slice(2);
+
+  // Calculate for physical velocity for every altitude
+  // First we prepare velocities for one slice
+  arma_mat slice_u = velocity_vcgc[0].slice(2);
+  arma_mat slice_v = velocity_vcgc[1].slice(2);
+
+  // Fill velocities in one slice
+  for (int64_t iLat = 0; iLat < nLats; iLat++) {
+    for (int64_t iLon = 0; iLon < nLons; iLon++) {
+      slice_u(iLon, iLat) = 0.;
+      slice_v(iLon, iLat) = 0.;
+    }
+  }
+
+  // Update this slice of velocity to all slices (for completeness)
+  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
+    velocity_vcgc[0].slice(iAlt) = slice_u;
+    velocity_vcgc[1].slice(iAlt) = slice_v;
+  }
+
+  // Calculate the cosine bell or rho_scgc
+  // First, again take a slice
+  arma_mat slice_rho = rho_scgc.slice(2);
+
+  // Fill rho in one slice
+  for (int64_t iLat = 0; iLat < nLats; iLat++) {
+    for (int64_t iLon = 0; iLon < nLons; iLon++) {
+      precision_t curr_lat = lat_grid(iLon, iLat);
+      precision_t curr_lon = lon_grid(iLon, iLat);
+
+      // Calculate great circle distance
+      precision_t dlon_2 = (curr_lon - lon_0) / 2.0;
+      precision_t dlat_2 = (curr_lat - lat_0) / 2.0;
+
+      precision_t r_d = 2.0 * R * asin(sqrt(sin(dlat_2) * sin(dlat_2) + sin(
+                                              dlon_2) * sin(dlon_2) * cos(curr_lat) * cos(lat_0)));
+
+      if (r_d < r_0)
+        slice_rho(iLon, iLat) = 5e-12;
+
+      else
+        slice_rho(iLon, iLat) = 1e-12;
+    }
+  }
+
+  // Update this slice of rho to all slices (for completeness)
+  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++) {
+    rho_scgc.slice(iAlt) = slice_rho;
+
+    // Do zero concentration conversion
+    for (int64_t iSpec = 0; iSpec < nSpecies; iSpec++)
+      species[iSpec].density_scgc.slice(iAlt) = slice_rho / species[iSpec].mass;
+  }
+
+  // Temperature setup
+  for (int64_t iAlt = 0; iAlt < nAlts; iAlt++)
+    temperature_scgc.slice(iAlt) = 600.*arma_mat(nLons, nLats, fill::ones);
+
+  // Add some velocity pertubation
+  //std::cout <<  velocity_vcgc[0].slice(2) << std::endl;
+  //std::cout << rho_scgc.slice(2) << std::endl;
+
+  return 1;
+}
 
 

From ed1632a443ab7a10624cb880ee513b6f4a2f7ad2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:22:40 -0400
Subject: [PATCH 159/266] FEAT: horizontal solver for cubesphere

---
 src/solver_horizontal_cubesphere.cpp | 861 ++++++++++++++++++++++++++-
 1 file changed, 860 insertions(+), 1 deletion(-)

diff --git a/src/solver_horizontal_cubesphere.cpp b/src/solver_horizontal_cubesphere.cpp
index ebce8b2c..88d491b9 100644
--- a/src/solver_horizontal_cubesphere.cpp
+++ b/src/solver_horizontal_cubesphere.cpp
@@ -3,4 +3,863 @@
 
 // Initial version: F. Cheng, July 2023
 
-#include "../include/aether.h"
\ No newline at end of file
+#include "aether.h"
+
+// using namespace Cubesphere_tools;
+
+std::vector<arma_mat> Neutrals::residual_horizontal_rusanov(
+  std::vector<arma_mat>& states,
+  Grid& grid,
+  Times& time,
+  int64_t iAlt) {
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+
+  /** Extract Grid Features **/
+  arma_mat x = grid.refx_scgc.slice(iAlt);
+  arma_mat xEdges = grid.refx_Left.slice(iAlt);
+  arma_mat y = grid.refy_scgc.slice(iAlt);
+  arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+  // Get reference grid dimensions (Assume dx = dy and equidistant)
+  arma_vec x_vec = x.col(0);
+  precision_t dx = x_vec(1) - x_vec(0);
+  precision_t area = dx * dx;
+  arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+  /** States preprocessing **/
+  /* MASS DENSITY */
+  arma_mat rho = states[0];
+
+  /* VELOCITY */
+  // Get contravariant velocity
+  //arma_mat xVel = states[1]; // u^1
+  //arma_mat yVel = states[2]; // u^2
+
+  // Generate contravriant momentum
+  arma_mat xMomentum = states[1]; // x1momentum
+  arma_mat yMomentum = states[2]; // x2momentum
+
+  // Resolve to contravariant velocity
+  arma_mat xVel = xMomentum / rho; // u^1
+  arma_mat yVel = yMomentum / rho; // u^2
+
+  // Generate velocity magnitude squared
+  arma_mat vel2 = xVel % xVel + yVel % yVel;
+
+  /* TEMP and ENERGY */
+  // Generate total energy (rhoE) (TODO: Verify)
+  arma_mat rhoE = states[3];
+
+  /** Advancing **/
+  /* Initialize projection constructs and storages */
+  projection_struct rhoP;
+  projection_struct xMomentumP;
+  projection_struct yMomentumP;
+  projection_struct rhoEP;
+  projection_struct gammaP;
+  projection_struct tempP;
+  projection_struct numberDensityP;
+
+  // They are all pure scalar fields without sqrt(g)
+  arma_mat rhoL, rhoR, rhoD, rhoU;
+  arma_mat xVelL, xVelR, xVelD, xVelU;
+  arma_mat yVelL, yVelR, yVelD, yVelU;
+  arma_mat totalEL, totalER, totalED, totalEU;
+
+  arma_mat velL2, velR2, velD2, velU2;
+  arma_mat internaleL, internaleR, internaleD, internaleU;
+  arma_mat pressureL, pressureR, pressureD, pressureU;
+
+  /** Initialize Flux and Wave Speed Storages */
+  arma_mat eq1FluxLR, eq1FluxDU;
+  arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+
+  arma_mat eq2FluxLR, eq2FluxDU;
+  arma_mat eq2FluxL, eq2FluxR, eq2FluxD, eq2FluxU;
+
+  arma_mat eq3FluxLR, eq3FluxDU;
+  arma_mat eq3FluxL, eq3FluxR, eq3FluxD, eq3FluxU;
+
+  arma_mat eq4FluxLR, eq4FluxDU;
+  arma_mat eq4FluxL, eq4FluxR, eq4FluxD, eq4FluxU;
+
+  arma_mat wsL, wsR, wsD, wsU, wsLR, wsDU;
+
+  arma_mat diff; // for Riemann Solver
+
+  /* Projection */
+  rhoP = project_to_edges(rho, x, xEdges, y, yEdges, nGCs);
+  xMomentumP = project_to_edges(xMomentum, x, xEdges, y, yEdges, nGCs);
+  yMomentumP = project_to_edges(yMomentum, x, xEdges, y, yEdges, nGCs);
+  rhoEP = project_to_edges(rhoE, x, xEdges, y, yEdges, nGCs);
+  // Also need to project gamma and temp - these should be passed, since
+  // they need to be updated for the RK4 scheme:
+  gammaP = project_to_edges(gamma_scgc.slice(iAlt), x, xEdges, y, yEdges, nGCs);
+  tempP = project_to_edges(temperature_scgc.slice(iAlt), x, xEdges, y, yEdges,
+                           nGCs);
+  numberDensityP = project_to_edges(density_scgc.slice(iAlt), x, xEdges, y,
+                                    yEdges,
+                                    nGCs);
+
+  // Resolve Scalar Fields into rho, xVel, yVel, and totalE (without rho)
+  rhoL = rhoP.L;
+  rhoR = rhoP.R;
+  rhoD = rhoP.D;
+  rhoU = rhoP.U;
+
+  xVelL = xMomentumP.L / rhoL;
+  xVelR = xMomentumP.R / rhoR;
+  xVelD = xMomentumP.D / rhoD;
+  xVelU = xMomentumP.U / rhoU;
+
+  yVelL = yMomentumP.L / rhoL;
+  yVelR = yMomentumP.R / rhoR;
+  yVelD = yMomentumP.D / rhoD;
+  yVelU = yMomentumP.U / rhoU;
+
+  totalEL = rhoEP.L / rhoL;
+  totalER = rhoEP.R / rhoR;
+  totalED = rhoEP.D / rhoD;
+  totalEU = rhoEP.U / rhoU;
+
+  velL2 = xVelL % xVelL + yVelL % yVelL;
+  velR2 = xVelR % xVelR + yVelR % yVelR;
+  velD2 = xVelD % xVelD + yVelD % yVelD;
+  velU2 = xVelU % xVelU + yVelU % yVelU;
+
+  internaleL = totalEL - 0.5 * velL2;
+  internaleR = totalER - 0.5 * velR2;
+  internaleD = totalED - 0.5 * velD2;
+  internaleU = totalEU - 0.5 * velU2;
+
+  //pressureL = (gammaP.L - 1) % (rhoP.L % internaleL);
+  //pressureR = (gammaP.R - 1) % (rhoP.R % internaleR);
+  //pressureD = (gammaP.D - 1) % (rhoP.D % internaleD);
+  //pressureU = (gammaP.U - 1) % (rhoP.U % internaleU);
+
+  pressureL = cKB * (numberDensityP.L % tempP.L);
+  pressureR = cKB * (numberDensityP.R % tempP.R);
+  pressureD = cKB * (numberDensityP.D % tempP.D);
+  pressureU = cKB * (numberDensityP.U % tempP.U);
+
+  /* Calculate Edge Fluxes */
+  // Note that dot product between normal vector at edge and flux vector
+  // resolves into a pure one component flux or either hat{x} or hat{y}
+  // Flux calculated from the left of the edge
+  eq1FluxL = rhoL % xVelL % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the right of the edge
+  eq1FluxR = rhoR % xVelR % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the down of the edge
+  eq1FluxD = rhoD % yVelD % grid.sqrt_g_Down.slice(iAlt);
+  // Flux calculated from the up of the edge
+  eq1FluxU = rhoU % yVelU % grid.sqrt_g_Down.slice(iAlt);
+  /*
+    eq2FluxL = (rhoL % xVelL % xVelL +
+                pressureL % grid.g11_upper_Left.slice(iAlt)) %
+               grid.sqrt_g_Left.slice(iAlt);
+    eq2FluxR = (rhoR % xVelR % xVelR +
+                pressureR % grid.g11_upper_Left.slice(iAlt)) %
+               grid.sqrt_g_Left.slice(iAlt);
+    eq2FluxD = (rhoD % yVelD % xVelD +
+                pressureD % grid.g12_upper_Down.slice(iAlt)) %
+               grid.sqrt_g_Down.slice(iAlt);
+    eq2FluxU = (rhoU % yVelU % xVelU +
+                pressureU % grid.g12_upper_Down.slice(iAlt)) %
+               grid.sqrt_g_Down.slice(iAlt);
+  */
+  eq2FluxL = (rhoL % xVelL % xVelL +
+              pressureL) %
+             grid.sqrt_g_Left.slice(iAlt);
+  eq2FluxR = (rhoR % xVelR % xVelR +
+              pressureR) %
+             grid.sqrt_g_Left.slice(iAlt);
+  eq2FluxD = (rhoD % yVelD % xVelD +
+              pressureD) %
+             grid.sqrt_g_Down.slice(iAlt);
+  eq2FluxU = (rhoU % yVelU % xVelU +
+              pressureU) %
+             grid.sqrt_g_Down.slice(iAlt);
+  /*
+    eq3FluxL = (rhoL % xVelL % yVelL +
+                pressureL % grid.g21_upper_Left.slice(iAlt)) %
+               grid.sqrt_g_Left.slice(iAlt);
+    eq3FluxR = (rhoR % xVelR % yVelR +
+                pressureR % grid.g21_upper_Left.slice(iAlt)) %
+               grid.sqrt_g_Left.slice(iAlt);
+    eq3FluxD = (rhoD % yVelD % yVelD +
+                pressureD % grid.g22_upper_Down.slice(iAlt)) %
+               grid.sqrt_g_Down.slice(iAlt);
+    eq3FluxU = (rhoU % yVelU % yVelU +
+                pressureU % grid.g22_upper_Down.slice(iAlt)) %
+               grid.sqrt_g_Down.slice(iAlt);
+  */
+  eq3FluxL = (rhoL % xVelL % yVelL +
+              pressureL) %
+             grid.sqrt_g_Left.slice(iAlt);
+  eq3FluxR = (rhoR % xVelR % yVelR +
+              pressureR) %
+             grid.sqrt_g_Left.slice(iAlt);
+  eq3FluxD = (rhoD % yVelD % yVelD +
+              pressureD) %
+             grid.sqrt_g_Down.slice(iAlt);
+  eq3FluxU = (rhoU % yVelU % yVelU +
+              pressureU) %
+             grid.sqrt_g_Down.slice(iAlt);
+
+  eq4FluxL = (rhoEP.L + pressureL) % xVelL % grid.sqrt_g_Left.slice(iAlt);
+  eq4FluxR = (rhoEP.R + pressureR) % xVelR % grid.sqrt_g_Left.slice(iAlt);
+  eq4FluxD = (rhoEP.D + pressureD) % yVelD % grid.sqrt_g_Down.slice(iAlt);
+  eq4FluxU = (rhoEP.U + pressureU) % yVelU % grid.sqrt_g_Down.slice(iAlt);
+
+  /* Wave Speed Calculation */
+  wsL = sqrt(velL2) + sqrt(gammaP.L % (gammaP.L - 1.) % tempP.L);
+  wsR = sqrt(velR2) + sqrt(gammaP.R % (gammaP.R - 1.) % tempP.R);
+  wsD = sqrt(velD2) + sqrt(gammaP.D % (gammaP.D - 1.) % tempP.D);
+  wsU = sqrt(velU2) + sqrt(gammaP.U % (gammaP.U - 1.) % tempP.U);
+
+  //wsL = abs(xVelL) + sqrt(gammaP.L % (gammaP.L - 1.) % internaleL);
+  //wsR = abs(xVelR) + sqrt(gammaP.R % (gammaP.R - 1.) % internaleR);
+  //wsD = abs(yVelD) + sqrt(gammaP.D % (gammaP.D - 1.) % internaleD);
+  //wsU = abs(yVelU) + sqrt(gammaP.U % (gammaP.U - 1.) % internaleU);
+
+  // Find the maximum wave speed
+  wsLR = wsR;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL(i, j) > wsLR(i, j))
+        wsLR(i, j) = wsL(i, j);
+    }
+  }
+
+  wsDU = wsD;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU(i, j) > wsDU(i, j))
+        wsDU(i, j) = wsU(i, j);
+    }
+  }
+
+  /* Calculate average flux at the edges (Rusanov Flux) */
+  /* Why is it + instead of - for the state difference?
+   * Because the projection actually works backwards
+   * Left states are actually right
+   * Right states are actually left
+   * Due to the convention in the past codes
+   * We keep it this way for consistency
+   */
+
+  // State difference, need to add sqrt(g)
+  diff = (rhoR - rhoL) % grid.sqrt_g_Left.slice(iAlt);
+  eq1FluxLR = (eq1FluxL + eq1FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoU - rhoD) % grid.sqrt_g_Down.slice(iAlt);
+  eq1FluxDU = (eq1FluxD + eq1FluxU) / 2 + 0.5 * wsDU % diff;
+
+  if (iAlt == -1) {
+    std::cout << "in solver: " << iProc << " " <<
+              wsDU(13, 23) << " " <<
+              wsU(13, 23) << " " <<
+              wsD(13, 23) << " " <<
+              gammaP.D(13, 23) << " " <<
+              internaleD(13, 23) << " " <<
+              gammaP.U(14, 23) << " " <<
+              internaleU(13, 23) << " " <<
+              diff(13, 22) << "\n";
+  }
+
+  diff = (rhoR % xVelR - rhoL % xVelL) % grid.sqrt_g_Left.slice(iAlt);
+  eq2FluxLR = (eq2FluxL + eq2FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoU % xVelU - rhoD % xVelD) % grid.sqrt_g_Down.slice(iAlt);
+  eq2FluxDU = (eq2FluxD + eq2FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (rhoR % yVelR - rhoL % yVelL) % grid.sqrt_g_Left.slice(iAlt);
+  eq3FluxLR = (eq3FluxL + eq3FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoU % yVelU - rhoD % yVelD) % grid.sqrt_g_Down.slice(iAlt);
+  eq3FluxDU = (eq3FluxD + eq3FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (rhoR % totalER - rhoL % totalEL) % grid.sqrt_g_Left.slice(iAlt);
+  eq4FluxLR = (eq4FluxL + eq4FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoU % totalEU - rhoD % totalED) % grid.sqrt_g_Down.slice(iAlt);
+  eq4FluxDU = (eq4FluxD + eq4FluxU) / 2 + 0.5 * wsDU % diff;
+
+  // Setup residual storage for return
+  arma_mat eq1_residual(nXs, nYs, fill::zeros);
+  arma_mat eq2_residual(nXs, nYs, fill::zeros);
+  arma_mat eq3_residual(nXs, nYs, fill::zeros);
+  arma_mat eq4_residual(nXs, nYs, fill::zeros);
+
+  // State Update
+  // Note the ghost cells WILL NOT BE UPDATED
+  for (int j = nGCs; j < nYs - nGCs; j++) {
+    for (int i = nGCs; i < nXs - nGCs; i++) {
+      precision_t rhoResidual_ij = dx * eq1FluxLR(i + 1, j) -
+                                   dx * eq1FluxLR(i, j) +
+                                   dx * eq1FluxDU(i, j + 1) -
+                                   dx * eq1FluxDU(i, j);
+      eq1_residual(i, j) = -1 / area * rhoResidual_ij;
+      precision_t xMomentumResidual_ij = dx * eq2FluxLR(i + 1, j) -
+                                         dx * eq2FluxLR(i, j) +
+                                         dx * eq2FluxDU(i, j + 1) -
+                                         dx * eq2FluxDU(i, j);
+      eq2_residual(i, j) = -1 / area * xMomentumResidual_ij;
+      precision_t yMomentumResidual_ij = dx * eq3FluxLR(i + 1, j) -
+                                         dx * eq3FluxLR(i, j) +
+                                         dx * eq3FluxDU(i, j + 1) -
+                                         dx * eq3FluxDU(i, j);
+      eq3_residual(i, j) = -1 / area * yMomentumResidual_ij;
+      precision_t rhoEResidual_ij = dx * eq4FluxLR(i + 1, j) -
+                                    dx * eq4FluxLR(i, j) +
+                                    dx * eq4FluxDU(i, j + 1) -
+                                    dx * eq4FluxDU(i, j);
+      eq4_residual(i, j) = -1 / area * rhoEResidual_ij;
+    }
+  }
+
+  if (iAlt == -1) {
+    std::cout << "in solver2: " << iProc << " " <<
+              eq1_residual(13, 22) << " " <<
+              eq2_residual(13, 22) << " " <<
+              eq3_residual(13, 22) << " " <<
+              eq4_residual(13, 22) << " " <<
+              area << "\n";
+  }
+
+
+  // Setup return vector
+  std::vector<arma_mat> return_vector;
+  return_vector.push_back(eq1_residual);
+  return_vector.push_back(eq2_residual);
+  return_vector.push_back(eq3_residual);
+  return_vector.push_back(eq4_residual);
+
+  return return_vector;
+}
+
+void Neutrals::solver_horizontal_RK1(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK1";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+  arma_mat x(nXs, nYs), y(nXs, nYs);
+  arma_mat jacobian(nXs, nYs), rho(nXs, nYs), rhoE(nXs, nYs), vel2(nXs, nYs);
+  arma_mat uVel(nXs, nYs), vVel(nXs, nYs), xVel(nXs, nYs), yVel(nXs, nYs);
+  arma_mat xMomentum(nXs, nYs), yMomentum(nXs, nYs);
+  arma_mat xMomentum_0(nXs, nYs), yMomentum_0(nXs, nYs);
+  arma_mat rho_0(nXs, nYs), rhoE_0(nXs, nYs);
+  arma_mat f_0_eq1(nXs, nYs), f_0_eq2(nXs, nYs);
+  arma_mat f_0_eq3(nXs, nYs), f_0_eq4(nXs, nYs);
+
+  calc_concentration();
+
+  // Advance for bulk calculation first, calculate for every altitude
+
+  std::cout << "nAlts : " << nAlts << "\n";
+
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    std::cout << "iAlt : " << iAlt << "\n";
+    /** Extract Grid Features **/
+    x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    uVel = velocity_vcgc[0].slice(iAlt);
+    vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+           grid.A12_inv_scgc.slice(iAlt); // u^1
+    yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+           grid.A22_inv_scgc.slice(iAlt); // u^2
+    vel2 = xVel % xVel + yVel % yVel;
+    // Generate contravriant momentum (no sqrt(g))
+    xMomentum = rho % xVel; // x1momentum
+    yMomentum = rho % yVel; // x2momentum
+
+    /* TEMP and ENERGY */
+    // Generate total energy (rhoE (no sqrt(g)))
+    // (TODO: Verify units)
+    rhoE = rho % (temperature_scgc.slice(iAlt) % Cv_scgc.slice(
+                    iAlt) + 0.5 * vel2);
+
+
+    if (iAlt == -1) {
+      std::cout << "before solve: " << iProc << " " <<  temperature_scgc(13, 22,
+                2) << " " <<
+                rhoE(13, 22) << " " << xVel(13, 22) << " " << yVel(13, 22) << " " <<
+                rho_scgc(13, 22, 2) << "\n";
+    }
+
+    if (iAlt == -2) {
+      std::cout << "before solve: " <<
+                temperature_scgc(13, 22, 2) << " " <<
+                xVel(13, 22) << " " << yVel(13, 22) << " " <<
+                rho_scgc(13, 22, 2) << "\n";
+    }
+
+
+    /** Advancing with RK4 **/
+    // Setup Containers
+    rho_0 = rho;
+    xMomentum_0 = xMomentum;
+    yMomentum_0 = yMomentum;
+    rhoE_0 = rhoE;
+
+    // FIRST (1) STEP, Compute F_0-> State_1
+    // Pass in state vector
+    std::vector<arma_mat> state_0;
+    state_0.push_back(rho_0);
+    state_0.push_back(xMomentum_0);
+    state_0.push_back(yMomentum_0);
+    state_0.push_back(rhoE_0);
+    std::vector<arma_mat> f_0_vec = residual_horizontal_rusanov(state_0, grid, time,
+                                                                iAlt);
+    // Extract Gradients
+    f_0_eq1 = f_0_vec[0];
+    f_0_eq2 = f_0_vec[1];
+    f_0_eq3 = f_0_vec[2];
+    f_0_eq4 = f_0_vec[3];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        rho(i, j) = rho_0(i, j) + dt * f_0_eq1(i, j) / jacobian(i, j);
+        xMomentum(i, j) = xMomentum_0(i, j) - dt * f_0_eq2(i, j) / jacobian(i, j);
+        yMomentum(i, j) = yMomentum_0(i, j) - dt * f_0_eq3(i, j) / jacobian(i, j);
+        rhoE(i, j) = rhoE_0(i, j) + dt * f_0_eq4(i, j) / jacobian(i, j);
+      }
+    }
+
+    if (iAlt == -1) {
+      std::cout << "after solve: " << iProc << " " <<
+                dt << " " <<
+                f_0_eq1(13, 22) << " " <<
+                f_0_eq2(13, 22) << " " <<
+                f_0_eq3(13, 22) << " " <<
+                f_0_eq4(13, 22) << " " <<
+                rhoE(13, 22) << " " << xMomentum(13, 22) << " " << yMomentum(13, 22) << " " <<
+                rho_scgc(13, 22, 2) << "\n";
+    }
+
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    xVel = xMomentum / rho; // u^1
+    yVel = yMomentum / rho; // u^2
+    vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+
+    if (iProc == 0 && iAlt == -2) {
+      std::cout << "a11 : " << grid.A11_scgc(13, 20, 2) << " "
+                << grid.A12_scgc(13, 20, 2) << "\n";
+      std::cout << "inv : " << grid.A11_inv_scgc(13, 20, 2) << " "
+                << grid.A12_inv_scgc(13, 20, 2) << "\n";
+    }
+
+    velocity_vcgc[0].slice(iAlt) = xVel % grid.A11_scgc.slice(
+                                     iAlt) + yVel % grid.A12_scgc.slice(iAlt);
+    velocity_vcgc[1].slice(iAlt) =
+      xVel % grid.A21_scgc.slice(iAlt) +
+      yVel % grid.A22_scgc.slice(iAlt);
+
+    /* Update temperature */
+    temperature_scgc.slice(iAlt) = (rhoE / rho - 0.5 * vel2) / Cv_scgc.slice(iAlt);
+
+
+    //if (iAlt == 10) {
+    std::cout << "after solve: " << iAlt << " " <<
+              temperature_scgc(13, 22, 10) << " " <<
+              velocity_vcgc[0](13, 22, 10) << " " << velocity_vcgc[1](13, 22, 10) << " " <<
+              rho_scgc(13, 22, 10) << "\n";
+    //}
+
+    calc_density_from_mass_concentration();
+    //assign_bulk_velocity();
+
+  }
+
+  report.exit(function);
+  return;
+}
+
+/*
+void Neutrals::solver_horizontal_RK1(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK1";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+  iAlt = 0;
+
+    arma_mat x(nXs, nYs), y(nXs, nYs);
+    arma_mat jacobian(nXs, nYs), rho(nXs, nYs), rhoE(nXs, nYs), vel2(nXs, nYs);
+    arma_mat uVel(nXs, nYs), vVel(nXs, nYs), xVel(nXs, nYs), yVel(nXs, nYs);
+    arma_mat xMomentum(nXs, nYs), yMomentum(nXs, nYs);
+    arma_mat xMomentum_0(nXs, nYs), yMomentum_0(nXs, nYs);
+    arma_mat rho_0(nXs, nYs), rhoE_0(nXs, nYs);
+    arma_mat f_0_eq1(nXs, nYs), f_0_eq2(nXs, nYs);
+    arma_mat f_0_eq3(nXs, nYs), f_0_eq4(nXs, nYs);
+
+// Advance for bulk calculation first, calculate for every altitude
+for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+  // Extract Grid Features
+arma_mat x = grid.refx_scgc.slice(iAlt);
+arma_mat xEdges = grid.refx_Left.slice(iAlt);
+arma_mat y = grid.refy_scgc.slice(iAlt);
+arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+// Get reference grid dimensions (Assume dx = dy and equidistant)
+arma_vec x_vec = x.col(0);
+precision_t dx = x_vec(1) - x_vec(0);
+precision_t area = dx * dx;
+arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+// States preprocessing
+// MASS DENSITY
+arma_mat rho = rho_scgc.slice(iAlt);
+
+// VELOCITY
+// Get spherical velocity
+arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+// Convert to contravariant (reference) velocity
+arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                grid.A12_inv_scgc.slice(iAlt); // u^1
+arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                grid.A22_inv_scgc.slice(iAlt); // u^2
+arma_mat vel2 = xVel % xVel + yVel % yVel;
+// Generate contravriant momentum (no sqrt(g))
+arma_mat xMomentum = rho % xVel; // x1momentum
+arma_mat yMomentum = rho % yVel; // x2momentum
+
+// TEMP and ENERGY
+// Generate total energy (rhoE (no sqrt(g)))
+// (TODO: Verify units)
+arma_mat rhoE = rho % (temperature_scgc.slice(iAlt) %
+                       Cv_scgc.slice(iAlt) + 0.5 * vel2);
+
+// Advancing with RK4
+// Setup Containers
+arma_mat rho_0 = rho;
+arma_mat xMomentum_0 = xMomentum;
+arma_mat yMomentum_0 = yMomentum;
+arma_mat rhoE_0 = rhoE;
+
+// FIRST (1) STEP, Compute F_0-> State_1
+// Pass in state vector
+std::vector<arma_mat> state_0;
+state_0.push_back(rho_0);
+state_0.push_back(xMomentum_0);
+state_0.push_back(yMomentum_0);
+state_0.push_back(rhoE_0);
+std::vector<arma_mat> f_0_vec = residual_horizontal_rusanov(state_0, grid, time,
+                                                            iAlt);
+// Extract Gradients
+arma_mat f_0_eq1 = f_0_vec[0];
+arma_mat f_0_eq2 = f_0_vec[1];
+arma_mat f_0_eq3 = f_0_vec[2];
+arma_mat f_0_eq4 = f_0_vec[3];
+
+// Update Bulk Scalars and Contravariant velocity
+// Euler State Update
+for (int j = nGCs; j < nYs - nGCs; j++) {
+  for (int i = nGCs; i < nXs - nGCs; i++) {
+    rho(i, j) = rho_0(i, j) + dt * f_0_eq1(i, j) / jacobian(i, j);
+    xMomentum(i, j) = xMomentum_0(i, j) - dt * f_0_eq2(i, j) / jacobian(i, j);
+    yMomentum(i, j) = yMomentum_0(i, j) - dt * f_0_eq3(i, j) / jacobian(i, j);
+    rhoE(i, j) = rhoE_0(i, j) + dt * f_0_eq4(i, j) / jacobian(i, j);
+  }
+}
+
+// Re-derive Spherical Velocity and Bulk States
+// Density
+rho_scgc.slice(iAlt) = rho;
+
+// Bulk Velocity
+xVel = xMomentum / rho; // u^1
+yVel = yMomentum / rho; // u^2
+vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+velocity_vcgc[0].slice(iAlt) = xVel % grid.A11_scgc.slice(
+                                 iAlt) + yVel % grid.A12_scgc.slice(iAlt);
+velocity_vcgc[1].slice(iAlt) = xVel % grid.A21_scgc.slice(
+                                 iAlt) + yVel % grid.A22_scgc.slice(iAlt);
+
+// Update specie number density and velocity
+for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+  //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].mass_concentration_scgc.slice(iAlt);
+  species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+  species[iSpec].velocity_vcgc[0].slice(iAlt) = velocity_vcgc[0].slice(iAlt);
+  species[iSpec].velocity_vcgc[1].slice(iAlt) = velocity_vcgc[1].slice(iAlt);
+}
+
+// Update temperature
+temperature_scgc.slice(iAlt) = (rhoE / rho - 0.5 * vel2) / Cv_scgc.slice(iAlt);
+
+report.exit(function);
+return;
+}
+
+*/
+
+void Neutrals::solver_horizontal_RK4(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK4";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt() / 10;
+
+  // Advance for bulk calculation first, calculate for every altitude
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    /** Extract Grid Features **/
+    arma_mat x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    arma_mat y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    arma_mat rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+    arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                    grid.A12_inv_scgc.slice(iAlt); // u^1
+    arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                    grid.A22_inv_scgc.slice(iAlt); // u^2
+    arma_mat vel2 = xVel % xVel + yVel % yVel;
+    // Generate contravriant momentum (no sqrt(g))
+    arma_mat xMomentum = rho % xVel; // x1momentum
+    arma_mat yMomentum = rho % yVel; // x2momentum
+
+    /* TEMP and ENERGY */
+    // Generate total energy (rhoE (no sqrt(g)))
+    // (TODO: Verify units)
+    //arma_mat rhoE = rho % (temperature_scgc.slice(iAlt) % Cv_scgc.slice(
+    //                         iAlt) + 0.5 * vel2);
+    arma_mat rhoE = rho % (temperature_scgc.slice(iAlt) %
+                           Cv_scgc.slice(iAlt) +
+                           0.5 * vel2);
+
+    /** Advancing with RK4 **/
+    // Setup Containers
+    arma_mat rho_0 = rho;
+    arma_mat rho_1(nXs, nYs, fill::zeros); // corresponding f_1
+    arma_mat rho_2(nXs, nYs, fill::zeros); // corresponding f_2
+    arma_mat rho_3(nXs, nYs, fill::zeros); // corresponding f_3
+
+    arma_mat xMomentum_0 = xMomentum;
+    arma_mat xMomentum_1(nXs, nYs, fill::zeros); // corresponding f_1
+    arma_mat xMomentum_2(nXs, nYs, fill::zeros); // corresponding f_2
+    arma_mat xMomentum_3(nXs, nYs, fill::zeros); // corresponding f_3
+
+    arma_mat yMomentum_0 = yMomentum;
+    arma_mat yMomentum_1(nXs, nYs, fill::zeros); // corresponding f_1
+    arma_mat yMomentum_2(nXs, nYs, fill::zeros); // corresponding f_2
+    arma_mat yMomentum_3(nXs, nYs, fill::zeros); // corresponding f_3
+
+    arma_mat rhoE_0 = rhoE;
+    arma_mat rhoE_1(nXs, nYs, fill::zeros); // corresponding f_1
+    arma_mat rhoE_2(nXs, nYs, fill::zeros); // corresponding f_2
+    arma_mat rhoE_3(nXs, nYs, fill::zeros); // corresponding f_3
+
+    // FIRST (1) STEP, Compute F_0-> State_1
+    // Pass in state vector
+    std::vector<arma_mat> state_0;
+    state_0.push_back(rho_0);
+    state_0.push_back(xMomentum_0);
+    state_0.push_back(yMomentum_0);
+    state_0.push_back(rhoE_0);
+    std::vector<arma_mat> f_0_vec = residual_horizontal_rusanov(state_0, grid, time,
+                                                                iAlt);
+    // Extract Gradients
+    arma_mat f_0_eq1 = f_0_vec[0];
+    arma_mat f_0_eq2 = f_0_vec[1];
+    arma_mat f_0_eq3 = f_0_vec[2];
+    arma_mat f_0_eq4 = f_0_vec[3];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        rho_1(i, j) = rho_0(i, j) + 0.5 * dt * f_0_eq1(i, j) / jacobian(i, j);
+        xMomentum_1(i, j) = xMomentum_0(i, j) + 0.5 * dt * f_0_eq2(i, j) / jacobian(i,
+                            j);
+        yMomentum_1(i, j) = yMomentum_0(i, j) + 0.5 * dt * f_0_eq3(i, j) / jacobian(i,
+                            j);
+        rhoE_1(i, j) = rhoE_0(i, j) + 0.5 * dt * f_0_eq4(i, j) / jacobian(i, j);
+      }
+    }
+
+    // SECOND (2) STEP, Compute F_1-> State_2
+    // Pass in state vector
+    std::vector<arma_mat> state_1;
+    state_1.push_back(rho_1);
+    state_1.push_back(xMomentum_1);
+    state_1.push_back(yMomentum_1);
+    state_1.push_back(rhoE_1);
+    std::vector<arma_mat> f_1_vec = residual_horizontal_rusanov(state_1, grid, time,
+                                                                iAlt);
+    // Extract Gradients
+    arma_mat f_1_eq1 = f_1_vec[0];
+    arma_mat f_1_eq2 = f_1_vec[1];
+    arma_mat f_1_eq3 = f_1_vec[2];
+    arma_mat f_1_eq4 = f_1_vec[3];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        rho_2(i, j) = rho_0(i, j) + 0.5 * dt * f_1_eq1(i, j) / jacobian(i, j);
+        xMomentum_2(i, j) = xMomentum_0(i, j) + 0.5 * dt * f_1_eq2(i, j) / jacobian(i,
+                            j);
+        yMomentum_2(i, j) = yMomentum_0(i, j) + 0.5 * dt * f_1_eq3(i, j) / jacobian(i,
+                            j);
+        rhoE_2(i, j) = rhoE_0(i, j) + 0.5 * dt * f_1_eq4(i, j) / jacobian(i, j);
+      }
+    }
+
+    // THIRD (3) STEP, Compute F_2-> State_3
+    // Pass in state vector
+    std::vector<arma_mat> state_2;
+    state_2.push_back(rho_2);
+    state_2.push_back(xMomentum_2);
+    state_2.push_back(yMomentum_2);
+    state_2.push_back(rhoE_2);
+    std::vector<arma_mat> f_2_vec = residual_horizontal_rusanov(state_2, grid, time,
+                                                                iAlt);
+    // Extract Gradients
+    arma_mat f_2_eq1 = f_2_vec[0];
+    arma_mat f_2_eq2 = f_2_vec[1];
+    arma_mat f_2_eq3 = f_2_vec[2];
+    arma_mat f_2_eq4 = f_2_vec[3];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        rho_3(i, j) = rho_0(i, j) + dt * f_2_eq1(i, j) / jacobian(i, j);
+        xMomentum_3(i, j) = xMomentum_0(i, j) + dt * f_2_eq2(i, j) / jacobian(i, j);
+        yMomentum_3(i, j) = yMomentum_0(i, j) + dt * f_2_eq3(i, j) / jacobian(i, j);
+        rhoE_3(i, j) = rhoE_0(i, j) + dt * f_2_eq4(i, j) / jacobian(i, j);
+      }
+    }
+
+    // FOURTH (4) STEP, Compute F_3
+    // Pass in state vector
+    std::vector<arma_mat> state_3;
+    state_3.push_back(rho_3);
+    state_3.push_back(xMomentum_3);
+    state_3.push_back(yMomentum_3);
+    state_3.push_back(rhoE_3);
+    std::vector<arma_mat> f_3_vec = residual_horizontal_rusanov(state_3, grid, time,
+                                                                iAlt);
+    // Extract Gradients
+    arma_mat f_3_eq1 = f_3_vec[0];
+    arma_mat f_3_eq2 = f_3_vec[1];
+    arma_mat f_3_eq3 = f_3_vec[2];
+    arma_mat f_3_eq4 = f_3_vec[3];
+
+    // Summing all steps for final update
+    arma_mat f_sum_eq1 = f_0_eq1 + 2 * f_1_eq1 + 2 * f_2_eq1 + f_3_eq1;
+    arma_mat f_sum_eq2 = f_0_eq2 + 2 * f_1_eq2 + 2 * f_2_eq2 + f_3_eq2;
+    arma_mat f_sum_eq3 = f_0_eq3 + 2 * f_1_eq3 + 2 * f_2_eq3 + f_3_eq3;
+    arma_mat f_sum_eq4 = f_0_eq4 + 2 * f_1_eq4 + 2 * f_2_eq4 + f_3_eq4;
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        rho(i, j) = rho(i, j) + dt / 6 * f_sum_eq1(i, j) / jacobian(i, j);
+        xMomentum(i, j) = xMomentum(i, j) + dt / 6 * f_sum_eq2(i, j) / jacobian(i, j);
+        yMomentum(i, j) = yMomentum(i, j) + dt / 6 * f_sum_eq3(i, j) / jacobian(i, j);
+        rhoE(i, j) = rhoE(i, j) + dt / 6 * f_sum_eq4(i, j) / jacobian(i, j);
+      }
+    }
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    xVel = xMomentum / rho; // u^1
+    yVel = yMomentum / rho; // u^2
+    vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+    velocity_vcgc[0].slice(iAlt) = xVel % grid.A11_scgc.slice(
+                                     iAlt) + yVel % grid.A12_scgc.slice(iAlt);
+    velocity_vcgc[1].slice(iAlt) = xVel % grid.A21_scgc.slice(
+                                     iAlt) + yVel % grid.A22_scgc.slice(iAlt);
+
+    /* Update specie number density and velocity */
+    for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+      //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].concentration_scgc.slice(iAlt);
+      species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+      species[iSpec].velocity_vcgc[0].slice(iAlt) = velocity_vcgc[0].slice(iAlt);
+      species[iSpec].velocity_vcgc[1].slice(iAlt) = velocity_vcgc[1].slice(iAlt);
+    }
+
+    /* Update temperature */
+    temperature_scgc.slice(iAlt) = (rhoE / rho - 0.5 * vel2) / Cv_scgc.slice(iAlt);
+
+    report.exit(function);
+    return;
+  }
+}

From 3084f94a5a13863e83cfaa347eee6ec206e11e2d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:24:25 -0400
Subject: [PATCH 160/266] FEAT: cubesphere tools for solvers

---
 src/cubesphere_tools.cpp | 300 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 300 insertions(+)
 create mode 100644 src/cubesphere_tools.cpp

diff --git a/src/cubesphere_tools.cpp b/src/cubesphere_tools.cpp
new file mode 100644
index 00000000..d94011bb
--- /dev/null
+++ b/src/cubesphere_tools.cpp
@@ -0,0 +1,300 @@
+// Copyright 2024, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+// Initial version: F. Cheng, Feb 2024
+
+#include "aether.h"
+
+arma_vec Cubesphere_tools::limiter_mc(arma_vec &left,
+                                      arma_vec &right,
+                                      int64_t nPts,
+                                      int64_t nGCs) {
+
+  precision_t beta = 0.8;
+
+  arma_vec s = left % right;
+  arma_vec combined = (left + right) * 0.5;
+
+  left = left * beta;
+  right = right * beta;
+  arma_vec limited = left;
+
+  for (int64_t i = 1; i < nPts + 2 * nGCs - 1; i++) {
+    if (s(i) < 0) {
+      // Sign < 0 means opposite signed left and right:
+      limited(i) = 0.0;
+    } else {
+      if (left(i) > 0 && right(i) > 0) {
+        if (right(i) < limited(i))
+          limited(i) = right(i);
+
+        if (combined(i) < limited(i))
+          limited(i) = combined(i);
+      } else {
+        if (right(i) > limited(i))
+          limited(i) = right(i);
+
+        if (combined(i) > limited(i))
+          limited(i) = combined(i);
+      }
+    }
+  }
+
+  return limited;
+}
+
+void Cubesphere_tools::print(arma_vec values) {
+  int64_t nP = values.n_elem;
+
+  for (int64_t i = 0; i < nP; i++)
+    std::cout << values(i) << " ";
+
+  std::cout << "\n";
+}
+
+// ---------------------------------------------------------
+// calc gradients at centers
+//   - values and x defined at centers
+// ---------------------------------------------------------
+
+arma_vec Cubesphere_tools::calc_grad_1d(arma_vec &values,
+                                        arma_vec &x,
+                                        int64_t nPts,
+                                        int64_t nGCs) {
+
+  arma_vec gradients = values * 0.0;
+  arma_vec gradL = values * 0.0;
+  arma_vec gradR = values * 0.0;
+
+  precision_t factor1 = 0.625;
+  precision_t factor2 = 0.0416667;
+  precision_t h;
+
+  int64_t i;
+  arma_vec hv = values * 0.0;
+
+  i = nGCs - 1;
+  h = 2.0 / (x(i + 1) - x(i));
+  gradR(i) = h * (factor1 * (values(i + 1) - values(i)) -
+                  factor2 * (values(i + 2) - values(i - 1)));
+  gradL(i) = (values(i) - values(i - 1)) / (x(i) - x(i - 1));
+
+  // This is attempting to vectorize the problem, but it seems to be slower?
+  //  int64_t iS = nGCs;
+  //  int64_t iE = nPts + nGCs - 1;
+  //  hv.rows(iS, iE) = 2.0 / (x.rows(iS, iE) - x.rows(iS-1, iE-1));
+  //  gradL.rows(iS, iE) = hv.rows(iS,iE) % (factor1 * (values.rows(iS, iE) -
+  //                values.rows(iS-1, iE-1)) -
+  //           factor2 * (values.rows(iS+1, iE+1) -
+  //                values.rows(iS-2, iE-2)));
+  //  hv.rows(iS, iE) = 2.0 / (x.rows(iS+1, iE+1) - x.rows(iS, iE));
+  //  gradR.rows(iS, iE) = hv.rows(iS,iE) % (factor1 * (values.rows(iS+1, iE+1) -
+  //                values.rows(iS, iE)) -
+  //           factor2 * (values.rows(iS+2, iE+2) -
+  //                values.rows(iS-1, iE-1)));
+
+  for (i = nGCs; i < nPts + nGCs; i++) {
+    h = 2.0 / (x(i) - x(i - 1));
+    gradL(i) = h * (factor1 * (values(i) - values(i - 1)) -
+                    factor2 * (values(i + 1) - values(i - 2)));
+    h = 2.0 / (x(i + 1) - x(i));
+    gradR(i) = h * (factor1 * (values(i + 1) - values(i)) -
+                    factor2 * (values(i + 2) - values(i - 1)));
+  }
+
+  i = nPts + nGCs;
+  h = 2.0 / (x(i) - x(i - 1));
+  gradL(i) = h * (factor1 * (values(i) - values(i - 1)) -
+                  factor2 * (values(i + 1) - values(i - 2)));
+  gradR(i) = (values(i + 1) - values(i)) / (x(i + 1) - x(i));
+
+  gradients = Cubesphere_tools::limiter_mc(gradL, gradR, nPts, nGCs);
+
+  return gradients;
+}
+
+// ---------------------------------------------------------
+// calc gradients at centers for 2d matrices
+//   - values and x defined at centers
+// ---------------------------------------------------------
+
+arma_mat Cubesphere_tools::calc_grad(arma_mat values,
+                                     arma_mat x,
+                                     int64_t nGCs,
+                                     bool DoX) {
+
+  arma_mat v2d, x2d;
+
+  if (DoX) {
+    v2d = values;
+    x2d = x;
+  } else {
+    v2d = values.t();
+    x2d = x.t();
+  }
+
+  int64_t nX = v2d.n_rows;
+  int64_t nY = v2d.n_cols;
+  arma_mat grad2d = v2d * 0.0;
+
+  int64_t nPts = nX - 2 * nGCs;
+  arma_vec values1d(nX);
+  arma_vec x1d(nX);
+
+  for (int64_t j = 1; j < nY - 1; j++) {
+    values1d = v2d.col(j);
+    x1d = x2d.col(j);
+    grad2d.col(j) = calc_grad_1d(values1d, x1d, nPts, nGCs);
+  }
+
+  arma_mat gradients;
+
+  if (DoX)
+    gradients = grad2d;
+  else
+    gradients = grad2d.t();
+
+  return gradients;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_mat Cubesphere_tools::project_from_left(arma_mat values,
+                                             arma_mat gradients,
+                                             arma_mat x_centers,
+                                             arma_mat x_edges,
+                                             int64_t nGCs) {
+
+  int64_t nX = values.n_rows;
+  int64_t nY = values.n_cols;
+
+  // Define at edges:
+  arma_mat projected(nX + 1, nY);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t j = 0; j < nY; j++) {
+    for (int64_t i = 1; i < nX - 1; i++) {
+      projected(i + 1, j) = values(i, j) +
+                            gradients(i, j) * (x_edges(i + 1, j) - x_centers(i, j));
+    }
+
+    projected(1, j) = projected(2, j);
+    projected(0, j) = projected(1, j);
+    projected(nX, j) = projected(nX - 1, j);
+  }
+
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_mat Cubesphere_tools::project_from_right(arma_mat values,
+                                              arma_mat gradients,
+                                              arma_mat x_centers,
+                                              arma_mat x_edges,
+                                              int64_t nGCs) {
+  int64_t nX = values.n_rows;
+  int64_t nY = values.n_cols;
+
+  // Define at edges:
+  arma_mat projected(nX + 1, nY);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t j = 0; j < nY; j++) {
+    for (int64_t i = 1; i < nX - 1; i++) {
+      projected(i, j) = values(i, j) +
+                        gradients(i, j) * (x_edges(i, j) - x_centers(i, j));
+    }
+
+    projected(0, j) = projected(1, j);
+    projected(nX - 1, j) = projected(nX - 2, j);
+    projected(nX, j) = projected(nX - 1, j);
+  }
+
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Limiter on values
+//   projected is assumed to be on the edge between the
+//   i-1 and i cell (i-1/2)
+//   limited is returned at edges
+// ---------------------------------------------------------
+
+arma_vec Cubesphere_tools::limiter_value(arma_vec projected,
+                                         arma_vec values,
+                                         int64_t nPts,
+                                         int64_t nGCs) {
+
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec limited = projected;
+
+  precision_t mini, maxi;
+
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
+
+    mini = values(i - 1);
+
+    if (values(i) < mini)
+      mini = values(i);
+
+    maxi = values(i - 1);
+
+    if (values(i) > maxi)
+      maxi = values(i);
+
+    if (limited(i) < mini)
+      limited(i) = mini;
+
+    if (limited(i) > maxi)
+      limited(i) = maxi;
+  }
+
+  return limited;
+}
+
+// // ---------------------------------------------------------
+// // take gradients and project to all edges
+// // ---------------------------------------------------------
+
+// projection_struct Cubesphere_tools::project_to_edges(arma_mat &values,
+//                                                      arma_mat &x_centers, arma_mat &x_edges,
+//                                                      arma_mat &y_centers, arma_mat &y_edges,
+//                                                      int64_t nGCs) {
+
+//   int64_t nX = values.n_rows;
+//   int64_t nY = values.n_cols;
+
+//   projection_struct proj;
+
+//   proj.gradLR = calc_grad(values, x_centers, nGCs, true);
+//   proj.gradDU = calc_grad(values.t(), y_centers.t(), nGCs, true).t();
+
+//   proj.R = project_from_left(values, proj.gradLR,
+//                              x_centers, x_edges, nGCs);
+//   // Left side of edge from left
+//   proj.L = project_from_right(values, proj.gradLR,
+//                               x_centers, x_edges, nGCs);
+//   // Up side of edge from down (left)
+//   proj.U = project_from_left(values.t(), proj.gradDU.t(),
+//                              y_centers.t(), y_edges.t(), nGCs)
+//            .t();
+//   // Down side of edge from up (right)
+//   proj.D = project_from_right(values.t(), proj.gradDU.t(),
+//                               y_centers.t(), y_edges.t(), nGCs)
+//            .t();
+
+//   return proj;
+// }

From a4cae5f26537d56e61a6e153134dc56950000804 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 13 Aug 2025 14:24:54 -0400
Subject: [PATCH 161/266] FEAT: advection only with cubesphere

---
 ...solver_horizontal_cubesphere_advection.cpp | 993 ++++++++++++++++++
 1 file changed, 993 insertions(+)
 create mode 100644 src/solver_horizontal_cubesphere_advection.cpp

diff --git a/src/solver_horizontal_cubesphere_advection.cpp b/src/solver_horizontal_cubesphere_advection.cpp
new file mode 100644
index 00000000..e96b1e88
--- /dev/null
+++ b/src/solver_horizontal_cubesphere_advection.cpp
@@ -0,0 +1,993 @@
+// Copyright 2023, the Aether Development Team (see doc/dev_team.md for members)
+// Full license can be found in License.md
+
+// Initial version: F. Cheng, July 2023
+
+#include "aether.h"
+
+using namespace Cubesphere_tools;
+
+// DOES NOT WORK WELL
+std::vector<arma_mat> Neutrals::residual_horizontal_hlle_advection(
+  std::vector<arma_mat>& states, Grid& grid, Times& time) {
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  /** Extract Grid Features **/
+  arma_mat x = grid.refx_scgc.slice(iAlt);
+  arma_mat xEdges = grid.refx_Left.slice(iAlt);
+  arma_mat y = grid.refy_scgc.slice(iAlt);
+  arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+  // Get reference grid dimensions (Assume dx = dy and equidistant)
+  arma_vec x_vec = x.col(0);
+  precision_t dx = x_vec(1) - x_vec(0);
+  precision_t area = dx * dx;
+  arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+  /** State/Velocity extraction **/
+  /* MASS DENSITY */
+  arma_mat rho = states[0];
+
+  /* VELOCITY */
+  // Convert to contravariant (reference) velocity
+  arma_mat xVel = states[1]; // u^1
+  arma_mat yVel = states[2]; // u^2
+
+  // Generate velocity magnitude squared
+  arma_mat vel2 = xVel % xVel + yVel % yVel;
+
+  /** Advancing **/
+  /* Initialize projection constructs and storages */
+  projection_struct rhoP;
+  projection_struct xVelP;
+  projection_struct yVelP;
+
+  // They are all pure scalar fields without sqrt(g)
+  arma_mat rhoL, rhoR, rhoD, rhoU;
+  arma_mat xVelL, xVelR, xVelD, xVelU;
+  arma_mat yVelL, yVelR, yVelD, yVelU;
+
+  arma_mat velL2, velR2, velD2, velU2;
+
+  /** Initialize Flux and Wave Speed Storages */
+  arma_mat eq1FluxLR_left, eq1FluxDU_down;
+  arma_mat eq1FluxLR_right, eq1FluxDU_upper;
+  arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+
+  arma_mat wsL, wsR, wsD, wsU;
+  arma_mat wsL_min, wsL_max, wsR_min, wsR_max;
+  arma_mat wsD_min, wsD_max, wsU_min, wsU_max;
+  arma_mat wsLR_max, wsDU_max, wsLR_min, wsDU_min;
+
+  arma_mat diff; // for Riemann Solver
+
+  /* Projection */
+  rhoP = project_to_edges(rho, x, xEdges, y, yEdges, nGCs);
+  xVelP = project_to_edges(xVel, x, xEdges, y, yEdges, nGCs);
+  yVelP = project_to_edges(yVel, x, xEdges, y, yEdges, nGCs);
+
+  // Resolve Scalar Fields into rho, xVel, yVel, and totalE (without rho)
+  rhoL = rhoP.L;
+  rhoR = rhoP.R;
+  rhoD = rhoP.D;
+  rhoU = rhoP.U;
+
+  xVelL = xVelP.L;
+  xVelR = xVelP.R;
+  xVelD = xVelP.D;
+  xVelU = xVelP.U;
+
+  yVelL = yVelP.L;
+  yVelR = yVelP.R;
+  yVelD = yVelP.D;
+  yVelU = yVelP.U;
+
+  //velL2 = xVelL % xVelL + yVelL % yVelL;
+  //velR2 = xVelR % xVelR + yVelR % yVelR;
+  //velD2 = xVelD % xVelD + yVelD % yVelD;
+  //velU2 = xVelU % xVelU + yVelU % yVelU;
+
+  /* Calculate Edge Fluxes */
+  // Note that dot product between normal vector at edge and flux vector
+  // resolves into a pure one component flux or either hat{x} or hat{y}
+
+  // Flux calculated from the left of the edge
+  eq1FluxL = rhoL % xVelL % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the right of the edge
+  eq1FluxR = rhoR % xVelR % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the down of the edge
+  eq1FluxD = rhoD % yVelD % grid.sqrt_g_Down.slice(iAlt);
+  // Flux calculated from the up of the edge
+  eq1FluxU = rhoU % yVelU % grid.sqrt_g_Down.slice(iAlt);
+
+  /* Wave Speed Calculation (Left/Down) */
+  wsL = xVelL;
+  wsR = xVelR;
+  wsD = yVelD;
+  wsU = yVelU;
+
+  wsL_max = wsL;
+  wsL_min = wsL;
+  wsR_max = wsR;
+  wsR_min = wsR;
+  wsD_max = wsD;
+  wsD_min = wsD;
+  wsU_max = wsU;
+  wsU_min = wsU;
+
+  // Process wave speeds from each direction first
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+
+      if (wsL(i, j) > 0.)
+        wsL_min(i, j) = 0.;
+
+      else
+        wsL_max(i, j) = 0.;
+
+      if (wsR(i, j) > 0.)
+        wsR_min(i, j) = 0.;
+
+      else
+        wsR_max(i, j) = 0.;
+    }
+  }
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsD(i, j) > 0.)
+        wsD_min(i, j) = 0.;
+
+      else
+        wsD_max(i, j) = 0.;
+
+      if (wsU(i, j) > 0.)
+        wsU_min(i, j) = 0.;
+
+      else
+        wsU_max(i, j) = 0.;
+    }
+  }
+
+  // Process edge wave speeds
+  wsLR_max = wsR_max;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL_max(i, j) > wsLR_max(i, j))
+        wsLR_max(i, j) = wsL_max(i, j);
+    }
+  }
+
+  wsDU_max = wsD_max;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU_max(i, j) > wsDU_max(i, j))
+        wsDU_max(i, j) = wsU_max(i, j);
+    }
+  }
+
+  wsLR_min = wsR_min;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL_min(i, j) < wsLR_min(i, j))
+        wsLR_min(i, j) = wsL_min(i, j);
+    }
+  }
+
+  wsDU_min = wsD_min;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU_min(i, j) < wsDU_min(i, j))
+        wsDU_min(i, j) = wsU_min(i, j);
+    }
+  }
+
+  /* Calculate average flux at the edges (HLLE Flux) */
+  arma_mat wsLR_sum = wsLR_max + wsLR_min;
+  arma_mat wsLR_diff = wsLR_max - wsLR_min;
+  diff = (rhoR - rhoL) % grid.sqrt_g_Left.slice(
+           iAlt); // State difference, need to add sqrt(g)
+  eq1FluxLR_left = 0.5 * (eq1FluxL + eq1FluxR) + 0.5 * (wsLR_sum / wsLR_diff) %
+                   (eq1FluxR - eq1FluxL) - (wsLR_max % wsLR_min) / wsLR_diff % diff;
+
+  arma_mat wsDU_sum = wsDU_max + wsDU_min;
+  arma_mat wsDU_diff = wsDU_max - wsDU_min;
+  diff = (rhoU - rhoD) % grid.sqrt_g_Down.slice(iAlt);
+  eq1FluxDU_down = 0.5 * (eq1FluxU + eq1FluxD) + 0.5 * (wsDU_sum / wsDU_diff) %
+                   (eq1FluxD - eq1FluxU) - (wsDU_max % wsDU_min) / wsDU_diff % diff;
+
+  /* Wave Speed Calculation (Right/Up) */
+  wsL = -xVelL;
+  wsR = -xVelR;
+  wsD = -yVelD;
+  wsU = -yVelU;
+
+  wsL_max = wsL;
+  wsL_min = wsL;
+  wsR_max = wsR;
+  wsR_min = wsR;
+  wsD_max = wsD;
+  wsD_min = wsD;
+  wsU_max = wsU;
+  wsU_min = wsU;
+
+  // Process wave speeds from each direction first
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+
+      if (wsL(i, j) > 0.)
+        wsL_min(i, j) = 0.;
+
+      else
+        wsL_max(i, j) = 0.;
+
+      if (wsR(i, j) > 0.)
+        wsR_min(i, j) = 0.;
+
+      else
+        wsR_max(i, j) = 0.;
+    }
+  }
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsD(i, j) > 0.)
+        wsD_min(i, j) = 0.;
+
+      else
+        wsD_max(i, j) = 0.;
+
+      if (wsU(i, j) > 0.)
+        wsU_min(i, j) = 0.;
+
+      else
+        wsU_max(i, j) = 0.;
+    }
+  }
+
+  // Process edge wave speeds
+  wsLR_max = wsR_max;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL_max(i, j) > wsLR_max(i, j))
+        wsLR_max(i, j) = wsL_max(i, j);
+    }
+  }
+
+  wsDU_max = wsD_max;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU_max(i, j) > wsDU_max(i, j))
+        wsDU_max(i, j) = wsU_max(i, j);
+    }
+  }
+
+  wsLR_min = wsR_min;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL_min(i, j) < wsLR_min(i, j))
+        wsLR_min(i, j) = wsL_min(i, j);
+    }
+  }
+
+  wsDU_min = wsD_min;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU_min(i, j) < wsDU_min(i, j))
+        wsDU_min(i, j) = wsU_min(i, j);
+    }
+  }
+
+  /* Calculate average flux at the edges (HLLE Flux) */
+  wsLR_sum = wsLR_max + wsLR_min;
+  wsLR_diff = wsLR_max - wsLR_min;
+  diff = (rhoR - rhoL) % grid.sqrt_g_Left.slice(
+           iAlt); // State difference, need to add sqrt(g)
+  eq1FluxLR_right = 0.5 * (eq1FluxL + eq1FluxR) + 0.5 * (wsLR_sum / wsLR_diff) %
+                    (eq1FluxR - eq1FluxL) - (wsLR_max % wsLR_min) / wsLR_diff % diff;
+
+  wsDU_sum = wsDU_max + wsDU_min;
+  wsDU_diff = wsDU_max - wsDU_min;
+  diff = (rhoU - rhoD) % grid.sqrt_g_Down.slice(iAlt);
+  eq1FluxDU_upper = 0.5 * (eq1FluxU + eq1FluxD) + 0.5 * (wsDU_sum / wsDU_diff) %
+                    (eq1FluxD - eq1FluxU) - (wsDU_max % wsDU_min) / wsDU_diff % diff;
+
+
+  // Setup residual storage for return
+  arma_mat eq1_residual(nXs, nYs, fill::zeros);
+
+  // State Update
+  // Note the ghost cells WILL NOT BE UPDATED
+  for (int j = nGCs; j < nYs - nGCs; j++) {
+    for (int i = nGCs; i < nXs - nGCs; i++) {
+      precision_t rhoResidual_ij = dx * eq1FluxLR_right(i + 1, j) -
+                                   dx * eq1FluxLR_left(i, j) +
+                                   dx * eq1FluxDU_upper(i, j + 1) -
+                                   dx * eq1FluxDU_down(i, j);
+      eq1_residual(i, j) = -1 / area * rhoResidual_ij;
+    }
+  }
+
+  // Setup return vector
+  std::vector<arma_mat> return_vector;
+  return_vector.push_back(eq1_residual);
+
+  return return_vector;
+}
+
+// WORKS, but diffusive
+std::vector<arma_mat> Neutrals::residual_horizontal_rusanov_advection(
+  std::vector<arma_mat>& states, Grid& grid, Times& time) {
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  /** Extract Grid Features **/
+  arma_mat x = grid.refx_scgc.slice(iAlt);
+  arma_mat xEdges = grid.refx_Left.slice(iAlt);
+  arma_mat y = grid.refy_scgc.slice(iAlt);
+  arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+  // Get reference grid dimensions (Assume dx = dy and equidistant)
+  arma_vec x_vec = x.col(0);
+  precision_t dx = x_vec(1) - x_vec(0);
+  precision_t area = dx * dx;
+  arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+  /** State/Velocity extraction **/
+  /* MASS DENSITY */
+  arma_mat rho = states[0];
+
+  /* VELOCITY */
+  // Convert to contravariant (reference) velocity
+  arma_mat xVel = states[1]; // u^1
+  arma_mat yVel = states[2]; // u^2
+
+  // Generate velocity magnitude squared
+  arma_mat vel2 = xVel % xVel + yVel % yVel;
+
+  /** Advancing **/
+  /* Initialize projection constructs and storages */
+  projection_struct rhoP;
+  projection_struct xVelP;
+  projection_struct yVelP;
+
+  // They are all pure scalar fields without sqrt(g)
+  arma_mat rhoL, rhoR, rhoD, rhoU;
+  arma_mat xVelL, xVelR, xVelD, xVelU;
+  arma_mat yVelL, yVelR, yVelD, yVelU;
+
+  arma_mat velL2, velR2, velD2, velU2;
+
+  /** Initialize Flux and Wave Speed Storages */
+  arma_mat eq1FluxLR, eq1FluxDU;
+  arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+
+  arma_mat wsL, wsR, wsD, wsU, wsLR, wsDU;
+
+  arma_mat diff; // for Riemann Solver
+
+  /* Projection */
+  rhoP = project_to_edges(rho, x, xEdges, y, yEdges, nGCs);
+  xVelP = project_to_edges(xVel, x, xEdges, y, yEdges, nGCs);
+  yVelP = project_to_edges(yVel, x, xEdges, y, yEdges, nGCs);
+
+  // Resolve Scalar Fields into rho, xVel, yVel, and totalE (without rho)
+  rhoL = rhoP.L;
+  rhoR = rhoP.R;
+  rhoD = rhoP.D;
+  rhoU = rhoP.U;
+
+  xVelL = xVelP.L;
+  xVelR = xVelP.R;
+  xVelD = xVelP.D;
+  xVelU = xVelP.U;
+
+  yVelL = yVelP.L;
+  yVelR = yVelP.R;
+  yVelD = yVelP.D;
+  yVelU = yVelP.U;
+
+  velL2 = xVelL % xVelL + yVelL % yVelL;
+  velR2 = xVelR % xVelR + yVelR % yVelR;
+  velD2 = xVelD % xVelD + yVelD % yVelD;
+  velU2 = xVelU % xVelU + yVelU % yVelU;
+
+  /* Calculate Edge Fluxes */
+  // Note that dot product between normal vector at edge and flux vector
+  // resolves into a pure one component flux or either hat{x} or hat{y}
+
+  // Flux calculated from the left of the edge
+  eq1FluxL = rhoL % xVelL % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the right of the edge
+  eq1FluxR = rhoR % xVelR % grid.sqrt_g_Left.slice(iAlt);
+  // Flux calculated from the down of the edge
+  eq1FluxD = rhoD % yVelD % grid.sqrt_g_Down.slice(iAlt);
+  // Flux calculated from the up of the edge
+  eq1FluxU = rhoU % yVelU % grid.sqrt_g_Down.slice(iAlt);
+
+  /* Wave Speed Calculation */
+  wsL = sqrt(velL2);
+  wsR = sqrt(velR2);
+  wsD = sqrt(velD2);
+  wsU = sqrt(velU2);
+
+  wsLR = wsR;
+
+  for (int i = 0; i < nXs + 1; i++) {
+    for (int j = 0; j < nYs; j++) {
+      if (wsL(i, j) > wsLR(i, j))
+        wsLR(i, j) = wsL(i, j);
+    }
+  }
+
+  wsDU = wsD;
+
+  for (int i = 0; i < nXs; i++) {
+    for (int j = 0; j < nYs + 1; j++) {
+      if (wsU(i, j) > wsDU(i, j))
+        wsDU(i, j) = wsU(i, j);
+    }
+  }
+
+  /* Calculate average flux at the edges (Rusanov Flux) */
+  diff = (rhoR - rhoL) % grid.sqrt_g_Left.slice(
+           iAlt); // State difference, need to add sqrt(g)
+  eq1FluxLR = (eq1FluxL + eq1FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoU - rhoD) % grid.sqrt_g_Down.slice(iAlt);
+  eq1FluxDU = (eq1FluxD + eq1FluxU) / 2 + 0.5 * wsDU % diff;
+
+  // Setup residual storage for return
+  arma_mat eq1_residual(nXs, nYs, fill::zeros);
+
+  // State Update
+  // Note the ghost cells WILL NOT BE UPDATED
+  for (int j = nGCs; j < nYs - nGCs; j++) {
+    for (int i = nGCs; i < nXs - nGCs; i++) {
+      precision_t rhoResidual_ij = dx * eq1FluxLR(i + 1, j) -
+                                   dx * eq1FluxLR(i, j) +
+                                   dx * eq1FluxDU(i, j + 1) -
+                                   dx * eq1FluxDU(i, j);
+      eq1_residual(i, j) = -1 / area * rhoResidual_ij;
+    }
+  }
+
+  // Setup return vector
+  std::vector<arma_mat> return_vector;
+  return_vector.push_back(eq1_residual);
+
+  return return_vector;
+}
+
+void Neutrals::solver_horizontal_rusanov_advection(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_rusanov_advection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+
+  // Advance for bulk calculation first, calculate for every altitude
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    /** Extract Grid Features **/
+    arma_mat x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    arma_mat y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    arma_mat rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+    arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                    grid.A12_inv_scgc.slice(iAlt); // u^1
+    arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                    grid.A22_inv_scgc.slice(iAlt); // u^2
+
+    // Generate velocity magnitude squared
+    arma_mat vel2 = xVel % xVel + yVel % yVel;
+
+    /** Advancing **/
+    /* Initialize projection constructs and storages */
+    projection_struct rhoP;
+    projection_struct xVelP;
+    projection_struct yVelP;
+
+    // They are all pure scalar fields without sqrt(g)
+    arma_mat rhoL, rhoR, rhoD, rhoU;
+    arma_mat xVelL, xVelR, xVelD, xVelU;
+    arma_mat yVelL, yVelR, yVelD, yVelU;
+
+    arma_mat velL2, velR2, velD2, velU2;
+
+    /** Initialize Flux and Wave Speed Storages */
+    arma_mat eq1FluxLR, eq1FluxDU;
+    arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+
+    arma_mat wsL, wsR, wsD, wsU, wsLR, wsDU;
+
+    arma_mat diff; // for Riemann Solver
+
+    /* Projection */
+    rhoP = project_to_edges(rho, x, xEdges, y, yEdges, nGCs);
+    xVelP = project_to_edges(xVel, x, xEdges, y, yEdges, nGCs);
+    yVelP = project_to_edges(yVel, x, xEdges, y, yEdges, nGCs);
+
+    // Resolve Scalar Fields into rho, xVel, yVel, and totalE (without rho)
+    rhoL = rhoP.L;
+    rhoR = rhoP.R;
+    rhoD = rhoP.D;
+    rhoU = rhoP.U;
+
+    xVelL = xVelP.L;
+    xVelR = xVelP.R;
+    xVelD = xVelP.D;
+    xVelU = xVelP.U;
+
+    yVelL = yVelP.L;
+    yVelR = yVelP.R;
+    yVelD = yVelP.D;
+    yVelU = yVelP.U;
+
+    velL2 = xVelL % xVelL + yVelL % yVelL;
+    velR2 = xVelR % xVelR + yVelR % yVelR;
+    velD2 = xVelD % xVelD + yVelD % yVelD;
+    velU2 = xVelU % xVelU + yVelU % yVelU;
+
+
+    /* Calculate Edge Fluxes */
+    // Note that dot product between normal vector at edge and flux vector
+    // resolves into a pure one component flux or either hat{x} or hat{y}
+
+    // Flux calculated from the left of the edge
+    eq1FluxL = rhoL % xVelL % grid.sqrt_g_Left.slice(iAlt);
+    // Flux calculated from the right of the edge
+    eq1FluxR = rhoR % xVelR % grid.sqrt_g_Left.slice(iAlt);
+    // Flux calculated from the down of the edge
+    eq1FluxD = rhoD % yVelD % grid.sqrt_g_Down.slice(iAlt);
+    // Flux calculated from the up of the edge
+    eq1FluxU = rhoU % yVelU % grid.sqrt_g_Down.slice(iAlt);
+
+    /* Wave Speed Calculation */
+    wsL = sqrt(velL2);
+    wsR = sqrt(velR2);
+    wsD = sqrt(velD2);
+    wsU = sqrt(velU2);
+
+    wsLR = wsR;
+
+    for (int i = 0; i < nXs + 1; i++) {
+      for (int j = 0; j < nYs; j++) {
+        if (wsL(i, j) > wsLR(i, j))
+          wsLR(i, j) = wsL(i, j);
+      }
+    }
+
+    wsDU = wsD;
+
+    for (int i = 0; i < nXs; i++) {
+      for (int j = 0; j < nYs + 1; j++) {
+        if (wsU(i, j) > wsDU(i, j))
+          wsDU(i, j) = wsU(i, j);
+      }
+    }
+
+    /* Calculate average flux at the edges (Rusanov Flux) */
+    diff = (rhoR - rhoL) % grid.sqrt_g_Left.slice(
+             iAlt); // State difference, need to add sqrt(g)
+    eq1FluxLR = (eq1FluxL + eq1FluxR) / 2 + 0.5 * wsLR % diff;
+    diff = (rhoU - rhoD) % grid.sqrt_g_Down.slice(iAlt);
+    eq1FluxDU = (eq1FluxD + eq1FluxU) / 2 + 0.5 * wsDU % diff;
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++) {
+        precision_t rhoResidual_ij = dx * eq1FluxLR(i + 1, j) -
+                                     dx * eq1FluxLR(i, j) +
+                                     dx * eq1FluxDU(i, j + 1) -
+                                     dx * eq1FluxDU(i, j);
+        rho(i, j) = rho(i, j) - dt / area / jacobian(i, j) * rhoResidual_ij;
+      }
+    }
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    //vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+    //velocity_vcgc[0].slice(iAlt) = xVel%grid.A11_scgc.slice(iAlt) + yVel%grid.A12_scgc.slice(iAlt);
+    //velocity_vcgc[1].slice(iAlt) = xVel%grid.A21_scgc.slice(iAlt) + yVel%grid.A22_scgc.slice(iAlt);
+
+    /* Update specie density */
+    for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+      //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].concentration_scgc.slice(iAlt);
+      species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+    }
+
+
+    report.exit(function);
+    return;
+  }
+}
+
+void Neutrals::solver_horizontal_RK1_advection(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK1_advection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+  // Advance for bulk calculation first, calculate for every altitude
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    /** Extract Grid Features **/
+    arma_mat x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    arma_mat y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    arma_mat rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+    arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                    grid.A12_inv_scgc.slice(iAlt); // u^1
+    arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                    grid.A22_inv_scgc.slice(iAlt); // u^2
+
+    /** Advancing with RK4 **/
+    // Setup Containers
+    arma_mat rho_0 = rho;
+
+    // FIRST (1) STEP, Compute F_0-> State_1
+    // Pass in state vector
+    std::vector<arma_mat> state_0;
+    state_0.push_back(rho_0);
+    state_0.push_back(xVel);
+    state_0.push_back(yVel);
+    std::vector<arma_mat> f_0_vec = residual_horizontal_rusanov_advection(state_0,
+                                    grid, time);
+
+    // Extract Gradients
+    arma_mat f_0_eq1 = f_0_vec[0];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho(i, j) = rho_0(i, j) + dt * f_0_eq1(i, j) / jacobian(i, j);
+    }
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    //vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+    //velocity_vcgc[0].slice(iAlt) = xVel%grid.A11_scgc.slice(iAlt) + yVel%grid.A12_scgc.slice(iAlt);
+    //velocity_vcgc[1].slice(iAlt) = xVel%grid.A21_scgc.slice(iAlt) + yVel%grid.A22_scgc.slice(iAlt);
+
+    /* Update specie density */
+    for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+      //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].concentration_scgc.slice(iAlt);
+      species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+    }
+
+
+    report.exit(function);
+    return;
+  }
+}
+
+void Neutrals::solver_horizontal_RK2_advection(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK2_advection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+  // Advance for bulk calculation first, calculate for every altitude
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    /** Extract Grid Features **/
+    arma_mat x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    arma_mat y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    arma_mat rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+    arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                    grid.A12_inv_scgc.slice(iAlt); // u^1
+    arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                    grid.A22_inv_scgc.slice(iAlt); // u^2
+
+    /** Advancing with RK4 **/
+    // Setup Containers
+    arma_mat rho_0 = rho;
+    arma_mat rho_1(nXs, nYs, fill::zeros); // corresponding f_1
+
+    // FIRST (1) STEP, Compute F_0-> State_1
+    // Pass in state vector
+    std::vector<arma_mat> state_0;
+    state_0.push_back(rho_0);
+    state_0.push_back(xVel);
+    state_0.push_back(yVel);
+    std::vector<arma_mat> f_0_vec = residual_horizontal_hlle_advection(state_0,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_0_eq1 = f_0_vec[0];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho_1(i, j) = rho_0(i, j) + dt * f_0_eq1(i, j) / jacobian(i, j);
+    }
+
+    // SECOND (2) STEP, Compute F_1-> State_2
+    // Pass in state vector
+    std::vector<arma_mat> state_1;
+    state_1.push_back(rho_1);
+    state_1.push_back(xVel);
+    state_1.push_back(yVel);
+    std::vector<arma_mat> f_1_vec = residual_horizontal_hlle_advection(state_1,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_1_eq1 = f_1_vec[0];
+
+    // Summing all steps for final update
+    arma_mat f_sum_eq1 = f_0_eq1 + f_1_eq1;
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho(i, j) = rho(i, j) + 0.5 * dt * f_sum_eq1(i, j) / jacobian(i, j);
+    }
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    //vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+    //velocity_vcgc[0].slice(iAlt) = xVel%grid.A11_scgc.slice(iAlt) + yVel%grid.A12_scgc.slice(iAlt);
+    //velocity_vcgc[1].slice(iAlt) = xVel%grid.A21_scgc.slice(iAlt) + yVel%grid.A22_scgc.slice(iAlt);
+
+    /* Update specie density */
+    for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+      //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].concentration_scgc.slice(iAlt);
+      species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+    }
+
+
+    report.exit(function);
+    return;
+  }
+}
+
+void Neutrals::solver_horizontal_RK4_advection(Grid& grid, Times& time) {
+  // Function Reporting
+  std::string function = "Neutrals::solver_horizontal_RK4_advection";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  // Dimensions of Spatial Discretization
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+  int iAlt, iSpec;
+
+  // Time Discretization (TODO: change dt calculation method)
+  precision_t dt = time.get_dt();
+
+  // Advance for bulk calculation first, calculate for every altitude
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+    /** Extract Grid Features **/
+    arma_mat x = grid.refx_scgc.slice(iAlt);
+    arma_mat xEdges = grid.refx_Left.slice(iAlt);
+    arma_mat y = grid.refy_scgc.slice(iAlt);
+    arma_mat yEdges = grid.refy_Down.slice(iAlt);
+
+    // Get reference grid dimensions (Assume dx = dy and equidistant)
+    arma_vec x_vec = x.col(0);
+    precision_t dx = x_vec(1) - x_vec(0);
+    precision_t area = dx * dx;
+    arma_mat jacobian = grid.sqrt_g_scgc.slice(iAlt);
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    arma_mat rho = rho_scgc.slice(iAlt);
+
+    /* VELOCITY */
+    // Get spherical velocity
+    arma_mat uVel = velocity_vcgc[0].slice(iAlt);
+    arma_mat vVel = velocity_vcgc[1].slice(iAlt);
+    // Convert to contravariant (reference) velocity
+    arma_mat xVel = uVel % grid.A11_inv_scgc.slice(iAlt) + vVel %
+                    grid.A12_inv_scgc.slice(iAlt); // u^1
+    arma_mat yVel = uVel % grid.A21_inv_scgc.slice(iAlt) + vVel %
+                    grid.A22_inv_scgc.slice(iAlt); // u^2
+
+    /** Advancing with RK4 **/
+    // Setup Containers
+    arma_mat rho_0 = rho;
+    arma_mat rho_1(nXs, nYs, fill::zeros); // corresponding f_1
+    arma_mat rho_2(nXs, nYs, fill::zeros); // corresponding f_2
+    arma_mat rho_3(nXs, nYs, fill::zeros); // corresponding f_3
+
+    // FIRST (1) STEP, Compute F_0-> State_1
+    // Pass in state vector
+    std::vector<arma_mat> state_0;
+    state_0.push_back(rho_0);
+    state_0.push_back(xVel);
+    state_0.push_back(yVel);
+    std::vector<arma_mat> f_0_vec = residual_horizontal_rusanov_advection(state_0,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_0_eq1 = f_0_vec[0];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho_1(i, j) = rho_0(i, j) + 0.5 * dt * f_0_eq1(i, j) / jacobian(i, j);
+    }
+
+    // SECOND (2) STEP, Compute F_1-> State_2
+    // Pass in state vector
+    std::vector<arma_mat> state_1;
+    state_1.push_back(rho_1);
+    state_1.push_back(xVel);
+    state_1.push_back(yVel);
+    std::vector<arma_mat> f_1_vec = residual_horizontal_rusanov_advection(state_1,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_1_eq1 = f_1_vec[0];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho_2(i, j) = rho_0(i, j) + 0.5 * dt * f_1_eq1(i, j) / jacobian(i, j);
+    }
+
+    // THIRD (3) STEP, Compute F_2-> State_3
+    // Pass in state vector
+    std::vector<arma_mat> state_2;
+    state_2.push_back(rho_2);
+    state_2.push_back(xVel);
+    state_2.push_back(yVel);
+    std::vector<arma_mat> f_2_vec = residual_horizontal_rusanov_advection(state_2,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_2_eq1 = f_2_vec[0];
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho_3(i, j) = rho_0(i, j) + dt * f_2_eq1(i, j) / jacobian(i, j);
+    }
+
+    // FOURTH (4) STEP, Compute F_3
+    // Pass in state vector
+    std::vector<arma_mat> state_3;
+    state_3.push_back(rho_3);
+    state_3.push_back(xVel);
+    state_3.push_back(yVel);
+    std::vector<arma_mat> f_3_vec = residual_horizontal_rusanov_advection(state_3,
+                                    grid, time);
+    // Extract Gradients
+    arma_mat f_3_eq1 = f_3_vec[0];
+
+    // Summing all steps for final update
+    arma_mat f_sum_eq1 = f_0_eq1 + 2 * f_1_eq1 + 2 * f_2_eq1 + f_3_eq1;
+
+    /* Update Bulk Scalars and Contravariant velocity */
+    // Euler State Update
+    for (int j = nGCs; j < nYs - nGCs; j++) {
+      for (int i = nGCs; i < nXs - nGCs; i++)
+        rho(i, j) = rho(i, j) + dt / 6 * f_sum_eq1(i, j) / jacobian(i, j);
+    }
+
+    /* Re-derive Spherical Velocity and Bulk States */
+    // Density
+    rho_scgc.slice(iAlt) = rho;
+
+    // Bulk Velocity
+    //vel2 = xVel % xVel + yVel % yVel; // Squared Magnitude of Contravariant
+    //velocity_vcgc[0].slice(iAlt) = xVel%grid.A11_scgc.slice(iAlt) + yVel%grid.A12_scgc.slice(iAlt);
+    //velocity_vcgc[1].slice(iAlt) = xVel%grid.A21_scgc.slice(iAlt) + yVel%grid.A22_scgc.slice(iAlt);
+
+    /* Update specie density */
+    for (iSpec = 0; iSpec < nSpecies; iSpec++) {
+      //species[iSpec].density_scgc.slice(iAlt) = rho % species[iSpec].concentration_scgc.slice(iAlt);
+      species[iSpec].density_scgc.slice(iAlt) = rho / species[iSpec].mass;
+    }
+
+
+    report.exit(function);
+    return;
+  }
+}
\ No newline at end of file

From e1172caec844fc71f82ebdf416ad8d8d2a7ca515 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 14:57:45 -0400
Subject: [PATCH 162/266] BUG: Typo in input/importError in postaether

---
 srcPython/postAether.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index f4e0455c..b3523cab 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -15,7 +15,7 @@
 try:
     from netCDF4 import Dataset
     from h5py import File
-except InputError:
+except ImportError:
     print("NetCDF and/or h5py not found")
 
 # ----------------------------------------------------------------------

From c039efd0d31cd2b4f3de2915f84e1cd5196ac37d Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 16:21:25 -0400
Subject: [PATCH 163/266] FEAT: Dipole interpolation coefficients

---
 include/grid.h                    |   4 +-
 src/solver_grid_interpolation.cpp | 128 +++++++++++++++++++++++++-----
 2 files changed, 111 insertions(+), 21 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index ebee7cc9..cde1a92c 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -426,7 +426,9 @@ class Grid
    */
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
-                               const std::vector<precision_t> &Alts);
+                               const std::vector<precision_t> &Alts,
+                               bool areLocsGeo=true,
+                               bool areLocsIJK=true);
 
   /**
    * \brief Set the interpolation coefficients for the dipole grid
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index a974de13..2c6ea23c 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -399,39 +399,127 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 // Set the interpolation coefficients
 // --------------------------------------------------------------------------
 
-bool Grid::set_interpolation_coefs(const std::vector<precision_t> &Lons,
-                                   const std::vector<precision_t> &Lats,
-                                   const std::vector<precision_t> &Alts) {
-  // If this is not a geo grid, return false
-  if (!IsGeoGrid)
-    return false;
+bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
+                                   const std::vector<precision_t> &j_coords,
+                                   const std::vector<precision_t> &k_coords,
+                                   bool areLocsGeo,// geo or mag?
+                                   bool areLocsIJK // Are locs in 'native' coords?
+                                  ) {
+  /*
+  Inputs:
+    i_coord: longitude, either geo or mag (depends if areLocsGeo)
+    j_coord:
+      - Latitude if geographic
+      - Invariant latitude if magnetic (areLocsGeo = false)
+      - L-shell / dipole 'p': if magnetic and (areLocsIJK = false)
+    k_coord:
+      - Altitude/radius if NOT areLocsIJK
+      - distance along field line, or dipole 'q' if areLocsIJK
+  */
+
+  std::string function = "Grid::set_interpolation_coefs";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
 
   // If the size of Lons, Lats and Alts are not the same, return false
-  if (Lons.size() != Lats.size() || Lats.size() != Alts.size())
+  if (i_coords.size() != j_coords.size() || j_coords.size() != k_coords.size()) {
+    report.error("Length of i,j,k vectors do not match!");
     return false;
+  }
 
   // Clear the previous interpolation coefficients
   interp_coefs.clear();
 
-  // Handle according to whether it is cubesphere or not
-  if (IsCubeSphereGrid) {
-    // Calculate the range of the grid
-    struct cubesphere_range cr;
-    get_cubesphere_grid_range(cr);
+  if (IsGeoGrid) {
+    // Handle according to whether it is cubesphere or not
+    if (IsCubeSphereGrid) {
+      // Calculate the range of the grid
+      struct cubesphere_range cr;
+      get_cubesphere_grid_range(cr);
+
+      // Calculate the index and coefficients for each point
+      for (size_t i = 0; i < i_coords.size(); ++i)
+        set_interp_coef_cubesphere(cr, i_coords[i], j_coords[i], k_coords[i]);
+    } else if (IsLatLonGrid) {
+      // Calculate the range of the grid
+      struct sphere_range sr;
+      get_sphere_grid_range(sr);
+
+      // Calculate the index and coefficients for each point
+      for (size_t i = 0; i < i_coords.size(); ++i)
+        set_interp_coef_sphere(sr, i_coords[i], j_coords[i], k_coords[i]);
+    }
+  }
+
+  else { // IsDipole
 
-    // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_cubesphere(cr, Lons[i], Lats[i], Alts[i]);
-  } else {
     // Calculate the range of the grid
-    struct sphere_range sr;
-    get_sphere_grid_range(sr);
+    struct dipole_range dr;
+    get_dipole_grid_range(dr);
+
+    Planets planet;
+
+    // make holders for dipole coordinates.
+    int64_t iLoc, nPts = i_coords.size();
+    std::vector<precision_t> mlon(nPts), p_coord(nPts), q_coord(nPts), dipijk(3);
+
+    // these are the magnetic coordinates. A temporary step!
+    // this is a vector of cubes with shape (nPts, 1, 1) - avoids having to overload things
+    std::vector<arma_cube> magCoords;
+
+    if (areLocsGeo) {
+      arma_cube cubeCoord;
+      cubeCoord = vec2cube(i_coords);
+      magCoords.push_back(cubeCoord);
+      cubeCoord = vec2cube(j_coords);
+      magCoords.push_back(cubeCoord);
+      cubeCoord = vec2cube(k_coords);
+      magCoords.push_back(cubeCoord);
+
+      magCoords = geo_to_mag(magCoords[0], magCoords[1], magCoords[2], planet);
+      // for (iLoc = 0; iLoc < nPts; iLoc++) {
+      //   magCoords = geo_to_mag(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc], planet);
+      //   mlon[iLoc] = magCoords[0];
+      //   p_coord[iLoc] = magCoords[1];
+      //   q_coord[iLoc] = magCoords[2];
+    }
+
+    else {
+      magCoords = {vec2cube(i_coords),vec2cube(j_coords), vec2cube(k_coords)};
+    }
+
+    // std::vector<precision_t> dipcoords = geo_to_mag(i_coords[0], j_coords[0], k_coords[0], planet);
+    std::vector<precision_t> dipCoords;
+    if (!areLocsIJK) {
+      std::vector<precision_t> planet_radii(nPts);
+      for (iLoc = 0; iLoc < nPts; iLoc++) {
+        // Convert from mag->dipole coordinates.
+        if (areLocsGeo){ // we were given the geo-latitude
+          planet_radii[iLoc] = planet.get_radius(j_coords[iLoc]);
+        } else{
+          // equatorial radius :(
+          planet_radii[iLoc] = planet.get_radius(0.0);
+        }
+        dipCoords = mag_to_ijk(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc], planet_radii[iLoc]);
+        mlon[iLoc] = dipCoords[0];
+        p_coord[iLoc] = dipCoords[1];
+        q_coord[iLoc] = dipCoords[2];
+      }
+    } else {
+      // just save the values
+      for (iLoc = 0; iLoc < nPts; iLoc++) {
+        mlon[iLoc] = i_coords[iLoc];
+        p_coord[iLoc] = j_coords[iLoc];
+        q_coord[iLoc] = k_coords[iLoc];
+      }
+    }
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < Lons.size(); ++i)
-      set_interp_coef_sphere(sr, Lons[i], Lats[i], Alts[i]);
+    for (size_t i = 0; i < i_coords.size(); ++i)
+      set_interp_coef_dipole(dr, mlon[i], p_coord[i], q_coord[i]);
   }
 
+  report.exit(function);
   return true;
 }
 

From fea307eddf87877b573fd9c87899cba58abed2c1 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:04:28 -0400
Subject: [PATCH 164/266] FEAT: Conversions btwn mag/geo (moved one out of
 init_mag_grid)

---
 include/tools.h       |  28 ++++++++++
 src/init_mag_grid.cpp |  42 ---------------
 src/tools.cpp         | 121 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 149 insertions(+), 42 deletions(-)

diff --git a/include/tools.h b/include/tools.h
index 5c83ed34..fd899c48 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -297,4 +297,32 @@ arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
 // Probably needs to be moved.
 arma_cube delTheta(arma_cube magLat);
 
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
+                                   arma_cube magAlt,
+                                   Planets planet);
+
+////////////////////////////////////////////
+// convert mag coordinates to dipole ijk  //
+////////////////////////////////////////////
+std::vector<precision_t> mag_to_ijk(precision_t mlon,
+                                    precision_t mlat, 
+                                    precision_t radius,
+                                    precision_t planet_radius);
+
+
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector<arma_cube> geo_to_mag(arma_cube glon,
+                                    arma_cube glat, 
+                                    arma_cube radius,
+																	Planets &planet) ;
+
+
+arma_cube vec2cube(std::vector<precision_t> ivec);
+
+
 #endif  // INCLUDE_TOOLS_H_
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 1bdfd0c2..8b8e0836 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -160,48 +160,6 @@ std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q,
   return {r, theta};
 }
 
-////////////////////////////////////////////
-// convert cell coordinates to geographic //
-////////////////////////////////////////////
-std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
-                                   arma_cube magAlt,
-                                   Planets planet) {
-  std::string function = "Grid::mag_to_geo";
-  static int iFunction = -1;
-  report.enter(function, iFunction);
-
-  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
-  llr.push_back(magLon);
-  llr.push_back(magLat);
-  llr.push_back(magAlt);
-  xyz_mag = transform_llr_to_xyz_3d(llr);
-
-  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
-  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
-  std::vector<precision_t> dipole_center = planet.get_dipole_center();
-
-  // Reverse our dipole rotations:
-  xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
-  xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
-
-  // offset dipole (not fully suported yet, so will be zero)
-  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
-      (dipole_center[2] != 0.0)) {
-
-    dipole_center = {0.0, 0.0, 0.0};
-  }
-
-  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
-  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
-  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
-
-  // transform back to lon, lat, radius:
-  llr = transform_xyz_to_llr_3d(xyzRot2);
-
-  report.exit(function);
-  return llr;
-}
-
 // ----------------------------------------------------------------------
 // Initialize the dipole grid.
 // - inputs (min_apex, min_alt, LatStretch, FieldLineStretch, max_lat_dipole)
diff --git a/src/tools.cpp b/src/tools.cpp
index c62b3172..4ced3b73 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -856,3 +856,124 @@ arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in) {
 
   return ans;
 }
+
+////////////////////////////////////////////
+// convert cell coordinates to geographic //
+////////////////////////////////////////////
+std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
+                                   arma_cube magAlt,
+                                   Planets planet) {
+  std::string function = "Grid::mag_to_geo";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
+  llr.push_back(magLon);
+  llr.push_back(magLat);
+  llr.push_back(magAlt);
+  xyz_mag = transform_llr_to_xyz_3d(llr);
+
+  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
+  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
+
+  // Reverse our dipole rotations:
+  xyzRot1 = rotate_around_y_3d(xyz_mag, magnetic_pole_tilt);
+  xyzRot2 = rotate_around_z_3d(xyzRot1, magnetic_pole_rotation);
+
+  // offset dipole (not fully suported yet, so will be zero)
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
+
+    dipole_center = {0.0, 0.0, 0.0};
+  }
+
+  xyz_geo.push_back(xyzRot2[0] + dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] + dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] + dipole_center[2]);
+
+  // transform back to lon, lat, radius:
+  llr = transform_xyz_to_llr_3d(xyzRot2);
+
+  report.exit(function);
+  return llr;
+}
+
+////////////////////////////////////////////
+//  convert cell coordinates to magnetic  //
+////////////////////////////////////////////
+
+std::vector<arma_cube> geo_to_mag(arma_cube glon,
+                                    arma_cube glat, 
+                                    arma_cube radius,
+                                    Planets &planet) {
+
+    std::string function = "Grid::geo_to_gmag";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  std::vector<arma_cube> llr, xyz_mag, xyz_geo, xyzRot1, xyzRot2;
+  llr.push_back(glon);
+  llr.push_back(glat);
+  llr.push_back(radius);
+  xyz_mag = transform_llr_to_xyz_3d(llr);
+
+  precision_t magnetic_pole_rotation = planet.get_dipole_rotation();
+  precision_t magnetic_pole_tilt = planet.get_dipole_tilt();
+  std::vector<precision_t> dipole_center = planet.get_dipole_center();
+
+  // Reverse our dipole rotations:
+  xyzRot1 = rotate_around_z_3d(xyz_mag, -magnetic_pole_tilt);
+  xyzRot2 = rotate_around_y_3d(xyzRot1, -magnetic_pole_rotation);
+
+  // offset dipole (not fully suported yet, so will be zero)
+  if ((dipole_center[0] != 0.0) || (dipole_center[1] != 0.0) ||
+      (dipole_center[2] != 0.0)) {
+
+    dipole_center = {0.0, 0.0, 0.0};
+  }
+
+  xyz_geo.push_back(xyzRot2[0] - dipole_center[0]);
+  xyz_geo.push_back(xyzRot2[1] - dipole_center[1]);
+  xyz_geo.push_back(xyzRot2[2] - dipole_center[2]);
+
+  // transform back to lon, lat, radius:
+  llr = transform_xyz_to_llr_3d(xyzRot2);
+
+  report.exit(function);
+  return llr;
+}
+
+
+std::vector<precision_t> mag_to_ijk(precision_t mlon,
+                                    precision_t mLat, 
+                                    precision_t radius,
+                                    precision_t planet_radius) {
+
+  precision_t i_lon, j_p, k_q;
+  
+  // precision_t planet_radius = planet.get_radius();
+
+  i_lon = mlon;
+  j_p = radius / planet_radius / pow(cos(mLat), 2);
+  k_q = sin(mLat) / pow(radius / planet_radius, 2.);
+
+  return {i_lon, j_p, k_q};
+}
+
+// -----------------------------------------------------------------------
+// Transform a flat vector into a 1D cube (avoids having to overload everything)
+// - this is overloaded for one vec/cube
+// -----------------------------------------------------------------------
+
+arma_cube vec2cube(std::vector<precision_t> ivec) {
+  arma_cube outvec;
+  int sizei = ivec.size();
+  arma_cube I;
+
+  I.set_size(sizei, 1, 1);
+  for (int i =0; i<sizei; i++){
+    I[i] = ivec[i];
+  }
+  return I;
+}
\ No newline at end of file

From d310131a8455b77a8ffdae5c4e934de0da747d6f Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:23:25 -0400
Subject: [PATCH 165/266] add thermoelectric heating=false to defaults.json

---
 share/run/UA/inputs/defaults.json | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 78086e2c..fd7bfc84 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -112,7 +112,8 @@
 		  "IncludeIonizationHeating": true,
 		  "IncludeElectronIonCollisionalHeating":true,
 		  "IncludeElectronNeutralElasticCollisionalHeating":true,
-		  "IncludeElectronNeutralInelasticCollisionalHeating":true}},
+		  "IncludeElectronNeutralInelasticCollisionalHeating":true,
+			"IncludeThermoelectricHeating": false}},
     
     "Seed" : 0,
 

From 6d5ae64b5a33f2ad3b3c8a70a13985cf09da8384 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 17:25:11 -0400
Subject: [PATCH 166/266] sty: rename binary > bisect search

---
 src/solver_grid_interpolation.cpp | 38 +++++++++++++++++--------------
 1 file changed, 21 insertions(+), 17 deletions(-)

diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 2c6ea23c..409ffd3d 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -76,7 +76,8 @@ int64_t get_cube_surface_number(const arma_vec &point_in) {
 // - Optional argument (nGCs=0) since we cannot see grid info.
 // --------------------------------------------------------------------------
 
-uint64_t binary_search_array(precision_t val_in, arma_vec ref_arr, int64_t nGCs = 0) {
+uint64_t bisect_search_array(precision_t val_in, arma_vec ref_arr,
+                             int64_t nGCs = 0) {
   // Copy from std::upper_bound. Can't directly use it
   // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
   uint64_t first, last, len;
@@ -186,16 +187,16 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
 
 void Grid::get_dipole_grid_range(struct dipole_range &dr) const {
   // Retrieve the range and delta of longitude, latitude and altitude
-  // ** Note these are magnetic coordinates. Not labeled for consistency **
-  dr.lon_min = magLon_Corner(nGCs, nGCs, nGCs);
-  dr.lon_max = magLon_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  // ** Note the max/min are magnetic coordinates.  **
+  dr.lon_min = i_corner_scgc(nGCs, nGCs, nGCs);
+  dr.lon_max = i_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
-  dr.lat_min = magLat_Corner(nGCs, nGCs, nGCs);
-  dr.lat_max = magLat_Corner(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  dr.lat_min = j_corner_scgc(nGCs, nGCs, nGCs);
+  dr.lat_max = j_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
   // magAlt and geoAlt are the same, doesn't matter which we use:
-  dr.alt_min = geoAlt_scgc(nGCs, nGCs, nGCs);
-  dr.alt_max = geoAlt_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
+  dr.alt_min = k_corner_scgc(nGCs, nGCs, nGCs);
+  dr.alt_max = k_corner_scgc(nLons - nGCs, nLats - nGCs, nAlts - nGCs);
 
   // MagLon steps are uniform:
   dr.dLon = magLon_Corner(1, 0, 0) - magLon_Corner(0, 0, 0);
@@ -250,9 +251,10 @@ void Grid::set_interp_coef_sphere(const sphere_range &sr,
 
   // The altitude may not be linearly spaced, so use binary search to find
   // the first element smaller than or equal to the altitude of the give point
-  // Implemented in binary_search_array
+  // Implemented in bisect_search_array
   // - since Alt can be latitude-dependent, this needs to be done last.
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.iAlt = bisect_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
   coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
               / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
                   coef.iCol, coef.iAlt));
@@ -328,9 +330,11 @@ void Grid::set_interp_coef_cubesphere(const cubesphere_range &cr,
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
   // Use binary search to find the index for altitude (handles oblate planets)
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol), nGCs);
+  coef.iAlt = bisect_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+                                  nGCs);
   coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
-              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
+              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
+                  coef.iCol, coef.iAlt));
 
   // Put the coefficient into the vector
   coef.in_grid = true;
@@ -357,7 +361,7 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 
   // ASSUMPTION: LONGITUDE IS LINEARLY SPACED, nGCs >= 1
   // For the cell containing it, directly calculate its x index
-  // Find y & z indices using binary search
+  // Find y & z indices using a bisecting search
 
   // The number of dLon between the innermost ghost cell and the given point
   coef.rRow = (lon_in - dr.lon_min) / dr.dLon + 0.5;
@@ -370,15 +374,15 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
 
 
   // Different from the sphere, latitude & altitude are not evenly spaced.
-  // Use binary search for both.
+  // Use the bisect search function for both.
 
   // Lat needs to be done a little different because it could be increasing or
   // decreasing (depending on the hemisphere we're in). Take the absolute value!
-  coef.iCol = binary_search_array(abs(lat_in),
-                                  abs(magInvLat_scgc.tube(coef.iRow, coef.iCol)), nGCs);
+  coef.iCol = bisect_search_array(abs(lat_in),
+                                  abs(j_center_scgc.tube(coef.iRow, coef.iCol)), nGCs);
 
   // need alt index to find lat coef
-  coef.iAlt = binary_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
+  coef.iAlt = bisect_search_array(alt_in, k_center_scgc.tube(coef.iRow, coef.iCol),
                                   nGCs);
 
   // then we can do the ratios:

From cfd09cf0323265bb18fdfc188a4a9701fa7c8e17 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:41:04 -0400
Subject: [PATCH 167/266] test: Some stuff for the gradient test on the dipole
 grid. it doesnt work.

Hopefully this code can either help someone (me) firgure it out again later, or be deleted.

To revert this commit we need to remove the debug flag from the call to test_gradient_ijk. NBD.
---
 include/test.h            |   2 +-
 src/main/main.cpp         |   9 +-
 src/solver_gradients.cpp  |   7 +-
 srcTest/test_gradient.cpp | 284 +++++++++++++++++++++++++-------------
 4 files changed, 196 insertions(+), 106 deletions(-)

diff --git a/include/test.h b/include/test.h
index ea74ab86..d6f9b287 100644
--- a/include/test.h
+++ b/include/test.h
@@ -11,7 +11,7 @@
 // Cubesphere is not done nor tested
 bool test_gradient(Planets planet, Quadtree quadtree, json test_config, Grid gGrid, Grid mGrid);
 bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid);
-bool test_gradient_ijk(Planets planet, Grid grid);
+bool test_gradient_ijk(Planets planet, Grid grid, bool debug);
 
 
 #endif
\ No newline at end of file
diff --git a/src/main/main.cpp b/src/main/main.cpp
index 0ec17726..f5e6957a 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -12,7 +12,7 @@ int main() {
   
   int iErr = 0;
   std::string sError;
-  bool didWork = true;
+  bool didWork = true, testsPassing=true;
 
   Times time;
 
@@ -117,8 +117,11 @@ int main() {
     Ions ionsMag(mGrid, planet);
 
     if (tests["test_gradient"]) {
-      test_gradient(planet, quadtree, tests, gGrid, mGrid);
-      }
+      testsPassing = test_gradient(planet, quadtree, tests, gGrid, mGrid);
+    }
+
+    if (!testsPassing && tests["exit_on_fail"])
+      throw std::string("Cannot continue!!");
 
     // -----------------------------------------------------------------
     // This is a unit test for checking for nans and infinities.
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index ef1c06c7..761b17e5 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -367,10 +367,9 @@ arma_cube calc_gradient2o_k(arma_cube value, Grid grid) {
 
 arma_cube calc_gradient_alt(arma_cube value, Grid grid) {
 
-  int64_t nX = grid.get_nLons();
-  int64_t nY = grid.get_nLats();
-  int64_t nZ = grid.get_nAlts();
-  int64_t nGCs = grid.get_nGCs();
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
   int64_t iK;
 
   arma_cube gradient(nX, nY, nZ);
diff --git a/srcTest/test_gradient.cpp b/srcTest/test_gradient.cpp
index 6270e35a..cc0e546f 100644
--- a/srcTest/test_gradient.cpp
+++ b/srcTest/test_gradient.cpp
@@ -6,23 +6,94 @@
 
 #include "aether.h"
 
+
+// Modularize the test function so it's easy to change
+std::vector<arma_cube> test_func(Grid grid, Planets planet, bool debug) {
+
+  // one element for each coord; compatibility w/ doing individual functions
+  // out_vals has 6 elements:
+  // - first 3 are the function & last 3 are expected gradient
+  std::vector<arma_cube> out_vals;
+  arma_cube one_elem, i_coords, j_coords, k_coords;
+
+  if (grid.IsLatLonGrid) {
+    i_coords = grid.geoLon_scgc;
+    j_coords = grid.geoLat_scgc;
+    k_coords = grid.radius_scgc;
+
+    // use the func cos(i) * sin(j) * r^2
+    one_elem = cos(i_coords) % sin(j_coords) % k_coords % k_coords;
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+
+    // The true gradient values:
+    out_vals.push_back(-600.0 * sin(i_coords) % tan(j_coords) % k_coords);
+    out_vals.push_back(cos(i_coords) % cos(j_coords) % k_coords);
+    out_vals.push_back(2.0 * cos(i_coords) % sin(j_coords) % k_coords);
+  }
+
+  if (grid.IsDipole) {
+    std::cout<<"I AMN DIPOLE\n";
+    precision_t planetRadius = planet.get_radius(0.0);
+    i_coords = grid.magLon_scgc;
+    j_coords = grid.magP_scgc;
+    k_coords = grid.magQ_scgc;
+
+    // use the func cos(i) * sin(j) * r^2
+    // one_elem = cos(grid.magLon_scgc) % sin(grid.magLat_scgc) % grid.radius_scgc % grid.radius_scgc;
+    one_elem = i_coords % i_coords + j_coords % j_coords + k_coords % k_coords;
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+    out_vals.push_back(one_elem);
+
+    arma_cube delT = pow(1 + 3.0 * cos(cPI/2. - grid.magLat_scgc) % cos(cPI/2. - grid.magLat_scgc),
+                         0.5);
+
+    // arma_cube r = grid.radius_scgc / planetRadius;
+    // // The true gradient values:
+    // out_vals.push_back(-k_coords % k_coords % sin(i_coords) % j_coords % j_coords
+    //                    / (r % pow(cos(grid.magLat_scgc), 2.0))); // mayB sin
+    // out_vals.push_back(2.0 * delT % j_coords % j_coords % cos(i_coords) % j_coords
+    //                    / pow(cos(grid.magLat_scgc), 3.0)); // mayb sin???
+    // out_vals.push_back(2.0 * delT % k_coords % cos(i_coords) % j_coords % j_coords
+    //                    / pow(r, 3.0));
+
+    out_vals.push_back(2.0 * i_coords
+                       / (grid.radius_scgc % cos(grid.magLat_scgc))); // mayB sin
+    out_vals.push_back(2.0 * j_coords
+                       % delT / pow(cos(grid.magLat_scgc), 3.0)); // mayb sin???
+    out_vals.push_back(2.0 * k_coords
+                       % delT / pow(grid.radius_scgc, 3.0));
+
+  }
+
+  if (debug) {
+    std::string numproc = tostr(iProc, 2);
+    std::string gridshape = "gridshape-" + tostr(grid.iGridShape_, 2);
+    i_coords.save(gridshape + "_proc-" + numproc + "_i_center.txt", arma_ascii);
+    j_coords.save(gridshape + "_proc-" + numproc + "_j_center.txt", arma_ascii);
+    k_coords.save(gridshape + "_proc-" + numproc + "_k_center.txt", arma_ascii);
+  }
+
+  return out_vals;
+}
+
 bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
                    Grid gGrid, Grid mGrid) {
   std::string function = "test_gradient";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  bool didWork;
+  bool didWork = true;
+  bool debug = test_config["dump_debug_cubes"];
 
   report.print(2, "Testing neutral grid");
 
-  if (gGrid.IsCubeSphereGrid)
-    didWork = test_gradient_cubesphere(planet, quadtree, gGrid);
-
   if (gGrid.IsDipole || gGrid.IsLatLonGrid)
-    didWork = test_gradient_ijk(planet, gGrid);
-
-  MPI_Barrier(aether_comm);
+    didWork = didWork && test_gradient_ijk(planet, gGrid, debug);
+  else
+    report.error("Cubesphere gradient test not built yet sorry");
 
   if (!didWork && test_config["exit_on_fail"])
     throw std::string("Gradient test failed - neutral grid");
@@ -30,122 +101,139 @@ bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
   report.print(2, "Testing ion grid");
 
   if (mGrid.IsCubeSphereGrid) // it's technically possible...
-    didWork = test_gradient_cubesphere(planet, quadtree, mGrid);
+    didWork = didWork && test_gradient_cubesphere(planet, quadtree, mGrid);
 
   if (mGrid.IsDipole || mGrid.IsLatLonGrid)
-    didWork = test_gradient_ijk(planet, mGrid);
-
-  if (!didWork && test_config["exit_on_fail"])
-    throw std::string("Gradient test failed - ion grid");
-
+    didWork = didWork && test_gradient_ijk(planet, mGrid, debug);
 
+  // if (!didWork && test_config["exit_on_fail"])
+  //   throw std::string("Gradient test failed - ion grid");
 
   report.exit(function);
 
   return didWork;
 }
 
-bool test_gradient_ijk(Planets planet, Grid grid) {
+void send_message(std::string Message, int nGood, int nBad) {
+  std::string newMessage;
+  newMessage = "iProc: " + tostr(iProc, 2) + " " + Message;
+  printf("%s has FAILED! (%i/%i); or (%f) perc\n", newMessage.data(), nBad, nGood,
+         100.*nBad / nGood);
+  return;
+}
+
 
-  std::string function = "test_gradient_dipole";
+bool test_gradient_ijk(Planets planet, Grid grid, bool debug) {
+
+  std::string function = "test_gradient_ijk";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  int64_t nIs = grid.get_nX();
-  int64_t nJs = grid.get_nY();
-  int64_t nKs = grid.get_nZ();
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t nZ = grid.get_nZ();
   int64_t nGCs = grid.get_nGCs();
 
-  int64_t nX, nY, nZ;
-  precision_t tol = 1e-3;
-  bool didWork = true;
-
-  arma_cube predicted_gradient, true_gradient;
-  arma::uvec err_points;
+  // numbers of grid points without ghost cells:
+  int64_t nI, nJ, nK;
+  nI = nX - 2 * nGCs;
+  nJ = nY - 2 * nGCs;
+  nK = nZ - 2 * nGCs;
 
-  arma_cube gradient_error;
-  gradient_error.set_size(nIs, nJs, nKs);
-  gradient_error.zeros();
+  bool didWork = true;
 
   int64_t nCellsTot = nX * nY * nZ;
-  int64_t nCellsNGCs = (nX - 2 * nGCs) * (nY - 2 * nGCs) * (nZ - 2 * nGCs);
-
-  report.print(2, "Beginning i-gradient");
-
-  /////////////////////////////////////////////////////////////
-  // Test the gradient in i-direction, d/dx(sin x) = cos(x)  //
-  /////////////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_i(sin(grid.i_center_scgc), grid);
-  true_gradient = cos(grid.i_center_scgc) / (grid.di_center_m_scgc);
-
-  gradient_error = abs(predicted_gradient - true_gradient) / abs(true_gradient);
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs,
-                                               nGCs, // don't look at ghost cells
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = all_finite(predicted_gradient, "Gradient_4o_i");
-
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in i-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
+  int64_t nCellsNGCs = nI * nJ * nK;
+
+  report.print(2, "Beginning gradient test");
+
+  std::vector<arma_cube> tmp, func_values, true_gradient, predicted_gradient;
+  tmp = test_func(grid, planet, debug);
+
+  func_values.push_back(tmp[0]);
+  func_values.push_back(tmp[1]);
+  func_values.push_back(tmp[2]);
+  true_gradient.push_back(tmp[3]);
+  true_gradient.push_back(tmp[4]);
+  true_gradient.push_back(tmp[5]);
+
+  if (grid.IsDipole) {
+    predicted_gradient.push_back(calc_gradient2o_i(func_values[0], grid));
+    predicted_gradient.push_back(calc_gradient2o_j(func_values[1], grid));
+    predicted_gradient.push_back(calc_gradient2o_k(func_values[2], grid));
+  } else {
+    predicted_gradient.push_back(calc_gradient2o_i(func_values[0], grid));
+    predicted_gradient.push_back(calc_gradient2o_j(func_values[1], grid));
+    predicted_gradient.push_back(calc_gradient2o_k(func_values[2], grid));
+  }
 
-  if (err_points.n_elem > true_gradient.n_elem * tol)
+  arma::uvec bad_is, bad_js, bad_ks;
+
+  // Look for values > 5% different from expected
+  bad_is = find(abs(
+                  (predicted_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[0].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+  bad_js = find(abs(
+                  (predicted_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[1].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+  bad_ks = find(abs(
+                  (predicted_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)
+                   - true_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2))
+                  / true_gradient[2].subcube(2, 2, 2, nI + 2, nJ + 2, nK + 2)) > 0.25);
+
+  // ghost cells are hard; if more than 1% of *real* cells are out of spec, the test fails.
+  if (bad_is.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_i:", nCellsNGCs, bad_is.n_elem);
     didWork = false;
+  }
 
-  report.print(2, "Beginning j-gradient");
-
-  /////////////////////////////////////////////////////////////
-  // Test the gradient in j-direction, d/dx(cos x) = -sin(x) //
-  /////////////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_j(cos(grid.j_center_scgc), grid);
-  true_gradient = -1.0 * sin(grid.j_center_scgc) / grid.dj_center_m_scgc;
-
-  gradient_error = predicted_gradient - true_gradient;
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_j");
-
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in j-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
-
-  if (err_points.n_elem > true_gradient.n_elem * tol)
+  if (bad_js.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_j:", nCellsNGCs, bad_js.n_elem);
     didWork = false;
+  }
 
+  if (bad_ks.n_elem > 0.1 * nCellsNGCs) {
+    send_message("grad_k:", nCellsNGCs, bad_ks.n_elem);
+    didWork = false;
+  }
 
-  report.print(2, "Beginning k-gradient");
-
-  //////////////////////////////////////////////////////
-  // Test the gradient in k-direction, d/dx(x^2) = 2x //
-  //////////////////////////////////////////////////////
-
-  predicted_gradient = calc_gradient2o_k(grid.radius2_scgc, grid);
-  true_gradient = 2.0 * grid.radius_scgc / grid.dk_center_m_scgc;
-
-  gradient_error = (predicted_gradient - true_gradient);
-  err_points = find(abs(gradient_error.subcube(nGCs, nGCs, nGCs,
-                                               size(nIs - 2 * nGCs, nJs - 2 * nGCs, nKs - 2 * nGCs)))
-                    > tol);
-
-  didWork = didWork && all_finite(predicted_gradient, "Gradient_2o_k");
+  // Output if requested:
+  std::string numproc = tostr(iProc, 2);
+
+  if (debug) {
+    std::string numproc = tostr(iProc, 2);
+    std::string gridshape = "gridshape-" + tostr(grid.iGridShape_, 2) + "_iproc-";
+    grid.di_center_m_scgc.save(gridshape + numproc + "_di_center_m.txt",
+                               arma_ascii);
+    grid.dj_center_m_scgc.save(gridshape + numproc + "_dj_center_m.txt",
+                               arma_ascii);
+    grid.dk_center_m_scgc.save(gridshape + numproc + "_dk_center_m.txt",
+                               arma_ascii);
+
+    func_values[0].save(gridshape + numproc + "_testfunc.txt", arma_ascii);
+    true_gradient[0].save(gridshape + numproc + "_actual_grad_i.txt",
+                          arma_ascii);
+    true_gradient[1].save(gridshape + numproc + "_actual_grad_j.txt",
+                          arma_ascii);
+    true_gradient[2].save(gridshape + numproc + "_actual_grad_k.txt",
+                          arma_ascii);
+
+    predicted_gradient[0].save(gridshape + numproc + "_i-predicted-grad.txt",
+                               arma_ascii);
+    predicted_gradient[1].save(gridshape + numproc + "_j-predicted-grad.txt",
+                               arma_ascii);
+    predicted_gradient[2].save(gridshape + numproc + "_k-predicted-grad.txt",
+                               arma_ascii);
+  }
 
-  std::cout << "(iproc " << iProc << ", gridtype: " << grid.get_gridtype()
-            << ") => Points in k-gradient, above tol: " <<
-            100.0 * err_points.n_elem / predicted_gradient.n_elem
-            << "% (" << err_points.n_elem << ", " <<
-            predicted_gradient.n_elem << ")\n";
+  // For completeness, check for non-finites
+  didWork = didWork && all_finite(true_gradient, "TRUE GRADIENT");
+  didWork = didWork && all_finite(func_values, "FUNCTION");
+  didWork = didWork && all_finite(predicted_gradient, "AETHER'S GRADIENT");
 
-  if (err_points.n_elem > true_gradient.n_elem * tol)
-    didWork = false;
+  report.report_errors();
 
   report.exit(function);
 
@@ -153,7 +241,7 @@ bool test_gradient_ijk(Planets planet, Grid grid) {
 }
 
 
-// This is non-functional. 
+// This is non-functional.
 // Taken from src/main/main_test_gradient.cpp with enough edits to compile.
 bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid) {
 

From 61ed7792715a27cc269aff2fba9c8556744fd6b4 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:42:12 -0400
Subject: [PATCH 168/266] STY: ASTYLE, YO

---
 include/grid.h           | 30 +++++++++--------------
 include/tools.h          | 53 +++++++++++++++++++++-------------------
 src/output.cpp           |  2 +-
 src/solver_gradients.cpp |  2 +-
 4 files changed, 42 insertions(+), 45 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index cde1a92c..9bb98b27 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -11,10 +11,9 @@
 // Grid class
 // ----------------------------------------------------------------------------
 
-class Grid
-{
+class Grid {
 
-public:
+ public:
   const int iSphere_ = 1;
   const int iCubesphere_ = 2;
   const int iDipole_ = 3;
@@ -97,7 +96,7 @@ class Grid
   arma_cube magAlt_Down;
   arma_cube magAlt_Below;
   arma_cube magAlt_Corner;
-  
+
   arma_cube magP_Corner;
   arma_cube magQ_Corner;
   arma_cube magInvLat_Corner;
@@ -358,7 +357,7 @@ class Grid
   int iProcZ;
 
   bool isExchangeInitialized = false;
-  
+
   arma_vec edge_Xp;
   arma_vec edge_Yp;
   arma_vec edge_Xm;
@@ -374,8 +373,7 @@ class Grid
   // again, z will only be in one
   int64_t iRootZ;
 
-  struct messages_struct
-  {
+  struct messages_struct {
     int64_t iFace;
     int64_t iProc_to;
     int64_t iSizeTotal;
@@ -442,7 +440,7 @@ class Grid
   bool set_dipole_interpolation_coefs(const std::vector<precision_t> &Lons,
                                       const std::vector<precision_t> &Lats,
                                       const std::vector<precision_t> &Alts);
-  
+
   /**
    * \brief Create a map of geographic locations to data and do the interpolation
    * \param data The value at the positions of geoLon, geoLat, and geoAlt
@@ -453,7 +451,7 @@ class Grid
    */
   std::vector<precision_t> get_interpolation_values(const arma_cube &data) const;
 
-private:
+ private:
   bool IsGeoGrid;
   bool HasBField;
   bool IsExperimental;
@@ -481,8 +479,7 @@ class Grid
 
   // interpolation members
   // The struct representing the range of a spherical grid
-  struct sphere_range
-  {
+  struct sphere_range {
     precision_t lon_min;
     precision_t lon_max;
     precision_t dLon;
@@ -493,8 +490,7 @@ class Grid
     precision_t alt_max;
   };
   // The struct representing the range of a cubesphere grid
-  struct cubesphere_range
-  {
+  struct cubesphere_range {
     // The minimum value and delta change of row and col
     // We don't use row_max and col_max because they are not promised to be
     // greater than min, for example the right norm of suface 2 expands along
@@ -521,7 +517,7 @@ class Grid
     bool col_max_exclusive;
   };
   // The struct representing the range of a dipole grid (in magnetic coordinates)
-  struct dipole_range{
+  struct dipole_range {
     precision_t lon_min;
     precision_t lon_max;
     precision_t dLon;
@@ -536,8 +532,7 @@ class Grid
   // Each point is processed by the function set_interpolation_coefs and stored
   // in the form of this structure.
   // If the point is out of the grid, in_grid = false and all other members are undefined
-  struct interp_coef_t
-  {
+  struct interp_coef_t {
     // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
     uint64_t iRow;
     uint64_t iCol;
@@ -578,8 +573,7 @@ class Grid
   // Initialize connections between processors
   void init_connection();
   // Used for message exchange
-  struct idx2d_t
-  {
+  struct idx2d_t {
     // Index of row and column
     int64_t ilon;
     int64_t ilat;
diff --git a/include/tools.h b/include/tools.h
index fd899c48..e8200476 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -8,11 +8,11 @@
 // Structure for a 2x2 matrix for a cubesphere:
 // ----------------------------------------------------------------------------
 
-struct mat_2x2{
-    arma_mat A11; 
-    arma_mat A12; 
-    arma_mat A21; 
-    arma_mat A22;
+struct mat_2x2 {
+  arma_mat A11;
+  arma_mat A12;
+  arma_mat A21;
+  arma_mat A22;
 };
 
 // -----------------------------------------------------------------------------
@@ -23,9 +23,9 @@ struct mat_2x2{
 // -----------------------------------------------------------------------------
 
 bool find_interpolation_coefficients(arma_vec inX,
-				     arma_vec outX,
-				     arma_vec &outIndex,
-				     arma_vec &outRatio);
+                                     arma_vec outX,
+                                     arma_vec &outIndex,
+                                     arma_vec &outRatio);
 
 // -----------------------------------------------------------------------------
 // This takes the index and ratio determined in the above function and
@@ -33,8 +33,8 @@ bool find_interpolation_coefficients(arma_vec inX,
 // -----------------------------------------------------------------------------
 
 arma_vec interpolate1d(arma_vec inY,
-		       arma_vec &index,
-		       arma_vec &ratio);
+                       arma_vec &index,
+                       arma_vec &ratio);
 
 // -----------------------------------------------------------------------------
 // Set all of the ghost cells to a constant value that is fed in.
@@ -42,8 +42,8 @@ arma_vec interpolate1d(arma_vec inY,
 // -----------------------------------------------------------------------------
 
 void set_gcs_to_value(arma_cube &var_scgc,
-		      precision_t value,
-		      int64_t nGCs);
+                      precision_t value,
+                      int64_t nGCs);
 
 // ----------------------------------------------------------------------------
 // Fix corners in an arma cube
@@ -101,39 +101,39 @@ precision_t sync_mean_across_all_procs(precision_t value);
 // ----------------------------------------------------------------------
 
 std::vector<double> get_normal_random_vect(double mean,
-					   double std,
-					   int64_t nValues,
-					   int seed);
+                                           double std,
+                                           int64_t nValues,
+                                           int seed);
 
 // ----------------------------------------------------------------------
 // Generate a vector of uniformly distributed random unsigned ints
 // ----------------------------------------------------------------------
 
 std::vector<unsigned int> get_random_unsigned_vect(int64_t nValues,
-						   int seed);
+                                                   int seed);
 
 // ----------------------------------------------------------------------
 // Make a vector of arma cubes:
 // ----------------------------------------------------------------------
 
 std::vector<arma_cube> make_cube_vector(int64_t nLons,
-					int64_t nLats,
-					int64_t nAlts,
-					int64_t nComps);
+                                        int64_t nLats,
+                                        int64_t nAlts,
+                                        int64_t nComps);
 
 // ----------------------------------------------------------------------
 // Take the dot product between two armadilo cubes
 // ----------------------------------------------------------------------
 
 arma_cube dot_product(std::vector<arma_cube> vec1,
-		      std::vector<arma_cube> vec2);
+                      std::vector<arma_cube> vec2);
 
 // ----------------------------------------------------------------------
 // Take the cross product between two arma cubes
 // ----------------------------------------------------------------------
 
 std::vector<arma_cube> cross_product(std::vector<arma_cube> vec1,
-				     std::vector<arma_cube> vec2);
+                                     std::vector<arma_cube> vec2);
 
 // ----------------------------------------------------------------------
 // Convert an armadillo vector to a c++ vector
@@ -220,7 +220,8 @@ bool is_approx_equal(arma_vec &vec1, arma_vec &vec2, precision_t tol);
 //-------------------------------------------------------------
 // Overload col vector function with row vec
 //-------------------------------------------------------------
-bool is_approx_equal(Row<precision_t> &vec1, Row<precision_t> &vec2, precision_t tol);
+bool is_approx_equal(Row<precision_t> &vec1, Row<precision_t> &vec2,
+                     precision_t tol);
 
 //-------------------------------------------------------------
 // Checks whether a vector is constant (all values the same)
@@ -234,7 +235,8 @@ bool is_approx_constant(arma_vec &vec, precision_t tol);
 // u and v are spherical velocities
 // u1 and u2 are contravariant velocities
 // --------------------------------------------------------------------------
-void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2, mat_2x2 &A_inv_mat);
+void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2,
+                 mat_2x2 &A_inv_mat);
 
 // --------------------------------------------------------------------------
 // Convert spherical vector (velocities) to reference (contravariant) vector
@@ -242,7 +244,8 @@ void sphvect2ref(arma_mat& u, arma_mat& v, arma_mat& u1, arma_mat& u2, mat_2x2 &
 // u and v are spherical velocities
 // u1 and u2 are contravariant velocities
 // --------------------------------------------------------------------------
-void refvect2sph(arma_mat &u1, arma_mat &u2, arma_mat &u, arma_mat &v, mat_2x2 &A_mat);
+void refvect2sph(arma_mat &u1, arma_mat &u2, arma_mat &u, arma_mat &v,
+                 mat_2x2 &A_mat);
 
 //-----------------------------------------------------------------------
 // Checks if armacube(s) has all finite values, if not, adds them to
@@ -293,7 +296,7 @@ std::vector<int> indef_vector(arma_cube cube);
 // --------------------------------------------------------------------------
 
 arma_vec sphere_to_cube(precision_t lon_in, precision_t lat_in);
-// Used for dipole gradients & distances. 
+// Used for dipole gradients & distances.
 // Probably needs to be moved.
 arma_cube delTheta(arma_cube magLat);
 
diff --git a/src/output.cpp b/src/output.cpp
index fc1e7767..205701a7 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -28,7 +28,7 @@ std::string get_filename_from_type(std::string type_output) {
 
   if (type_output == "bfield")
     filename = "3DBF";
-    
+
   if (type_output == "delta")
     filename = "3DDE";
 
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 761b17e5..b67617fb 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -73,7 +73,7 @@ arma_cube calc_gradient2o_i(arma_cube value, Grid grid) {
     for (iX = 1; iX < nX - 1; iX++)
       gradient.row(iX) =
         (value.row(iX + 1) - value.row(iX - 1)) /
-        (2 * grid.di_center_m_scgc.row(iX));
+        (2.0 * grid.di_center_m_scgc.row(iX));
 
     // Lower (one sided):
     iX = 0;

From 85f9c8722f106e872c57cd4c8e5f4b9fe0ef6e90 Mon Sep 17 00:00:00 2001
From: Aaron Bukowski <abukowski21@gmail.com>
Date: Wed, 13 Aug 2025 18:42:26 -0400
Subject: [PATCH 169/266] add radius to bfield output

---
 src/output.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/output.cpp b/src/output.cpp
index 205701a7..f4adf0d9 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -287,6 +287,9 @@ bool output(const Neutrals &neutrals,
                                                     "Magnetic Invariant Latitude",
                                                     "degrees",
                                                     grid.magInvLat_scgc * cRtoD);
+        AllOutputContainers[iOutput].store_variable("radius",
+                                                    "m",
+                                                    grid.radius_scgc);
         AllOutputContainers[iOutput].store_variable("mlt",
                                                     "Magnetic Local Time",
                                                     "hours",

From e4ee36fad147388c228f5f4b10fd9465ece28ec8 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:08:27 -0400
Subject: [PATCH 170/266] FEAT: added equal-angle terms for cubesphere

---
 include/grid.h | 93 +++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 73 insertions(+), 20 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index fed807e9..c9c82a1e 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -7,14 +7,47 @@
 #include <unordered_map>
 #include "mpi.h"
 
+// ----------------------------------------------------------------------------
+// This structure needs to be defined outside of the grid, since we can just
+// pass this stuff to the solver.
+// ----------------------------------------------------------------------------
+
+struct cubesphere_chars {
+  // For convenience, store the grid size:
+  int64_t nXt, nYt, nGCs;
+  int64_t iXfirst_, iXlast_;
+  int64_t iYfirst_, iYlast_;
+
+  // These are for Ronchi et al., JCP 124, 93-114, 1996
+  arma_mat X, Y, Z, C, D, d;
+  // These are the only things that depend on altitude:
+  arma_cube dlx, dln, dS;
+  // In theory, the radius is just a 1D vector:
+  arma_vec R;
+  // xi is the LR direction
+  // nu is the UD direction
+  arma_mat xi, nu;
+  // for the equal-angle grid, we can just use these:
+  precision_t dxi, dnu;
+  arma_mat Apn, Apx, Atn, Atx;
+  arma_mat Axt, Axp, Ant, Anp;
+
+  // These are for computing normals to the cell edges (horizontal)
+  arma_mat nXiLon;
+  arma_mat nXiLat;
+  arma_mat nNuLon;
+  arma_mat nNuLat;
+  arma_mat lat, lon;
+};
+
+
 // ----------------------------------------------------------------------------
 // Grid class
 // ----------------------------------------------------------------------------
 
-class Grid
-{
+class Grid {
 
-public:
+ public:
   const int iSphere_ = 1;
   const int iCubesphere_ = 2;
   const int iDipole_ = 3;
@@ -66,6 +99,8 @@ class Grid
   arma_cube g11_upper_Down, g12_upper_Down, g21_upper_Down, g22_upper_Down;
   arma_cube sqrt_g_Down;
 
+  cubesphere_chars cubeC, cubeL, cubeD;
+
   // These define the magnetic grid:
   // Armidillo Cube Versions:
   arma_cube magLon_scgc, magX_scgc;
@@ -110,7 +145,7 @@ class Grid
   arma_cube magQ_Below;
   arma_cube magP_Corner;
   arma_cube magQ_Corner;
-  
+
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent
   arma_vec mag_pole_north_ll;
@@ -317,6 +352,29 @@ class Grid
   void create_sphere_grid(Quadtree quadtree);
   void create_cubesphere_connection(Quadtree quadtree);
   void create_cubesphere_grid(Quadtree quadtree);
+
+  // These two go together, since one builds the angles and the
+  // other scales by the radius:
+  void init_cubesphere_grid(Quadtree quadtree,
+                            arma_vec dr,
+                            arma_vec du,
+                            arma_vec ll,
+                            precision_t left_off,
+                            precision_t down_off,
+                            cubesphere_chars &cubeX);
+  void scale_cube_by_radius(cubesphere_chars &cubeX);
+
+  void convert_vector_xn_to_ll(arma_mat aXi,
+                               arma_mat aNu,
+                               arma_mat &aLon,
+                               arma_mat &aLat,
+                               cubesphere_chars grid);
+  void convert_vector_ll_to_xn(arma_mat aLon,
+                               arma_mat aLat,
+                               arma_mat &aXi,
+                               arma_mat &aNu,
+                               cubesphere_chars grid);
+
   void create_altitudes(Planets planet);
   void fill_grid_bfield(Planets planet);
   bool read_restart(std::string dir);
@@ -346,11 +404,11 @@ class Grid
   // nLats: number of latitudes (nY)
   // spacing_factor: (not supported yet), so always 1.0. Will adjust baselat spacing, eventually.
   arma_vec baselat_spacing(precision_t extent,
-                          precision_t origin,
-                          precision_t upper_lim,
-                          precision_t lower_lim,
-                          // int16_t nLats,
-                          precision_t spacing_factor);
+                           precision_t origin,
+                           precision_t upper_lim,
+                           precision_t lower_lim,
+                           // int16_t nLats,
+                           precision_t spacing_factor);
 
   // Update ghost cells with values from other processors
   void exchange(arma_cube &data, const bool pole_inverse);
@@ -381,8 +439,7 @@ class Grid
   int64_t iRootYp;
   int64_t iRootYm;
 
-  struct messages_struct
-  {
+  struct messages_struct {
     int64_t iFace;
     int64_t iProc_to;
     int64_t iSizeTotal;
@@ -444,7 +501,7 @@ class Grid
    */
   std::vector<precision_t> get_interpolation_values(const arma_cube &data) const;
 
-private:
+ private:
   bool IsGeoGrid;
   bool HasBField;
   bool IsExperimental;
@@ -473,8 +530,7 @@ class Grid
 
   // interpolation members
   // The struct representing the range of a spherical grid
-  struct sphere_range
-  {
+  struct sphere_range {
     precision_t lon_min;
     precision_t lon_max;
     precision_t dLon;
@@ -485,8 +541,7 @@ class Grid
     precision_t alt_max;
   };
   // The struct representing the range of a cubesphere grid
-  struct cubesphere_range
-  {
+  struct cubesphere_range {
     // The minimum value and delta change of row and col
     // We don't use row_max and col_max because they are not promised to be
     // greater than min, for example the right norm of suface 2 expands along
@@ -517,8 +572,7 @@ class Grid
   // Each point is processed by the function set_interpolation_coefs and stored
   // in the form of this structure.
   // If the point is out of the grid, in_grid = false and all other members are undefined
-  struct interp_coef_t
-  {
+  struct interp_coef_t {
     // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
     uint64_t iRow;
     uint64_t iCol;
@@ -555,8 +609,7 @@ class Grid
   // Initialize connections between processors
   void init_connection();
   // Used for message exchange
-  struct idx2d_t
-  {
+  struct idx2d_t {
     // Index of row and column
     int64_t ilon;
     int64_t ilat;

From 60d61ad937b0584c58f68a06bd796e49317908da Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:09:06 -0400
Subject: [PATCH 171/266] FEAT: added rochi cubesphere solver

---
 include/neutrals.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/neutrals.h b/include/neutrals.h
index 1ee222b6..61686eab 100644
--- a/include/neutrals.h
+++ b/include/neutrals.h
@@ -583,6 +583,7 @@ class Neutrals {
      \param report
    **/
   void solver_horizontal_RK1(Grid& grid, Times& time);
+  void solver_horizontal_RK1_rochi(Grid& grid, Times& time);
 
   /**********************************************************************
      \brief Call the correct horizontal advection scheme with CE eqn

From b9d0fa1e473353bb9710276c9b68194615d64e5b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:11:20 -0400
Subject: [PATCH 172/266] commented out some things that dont work yet

---
 src/advance.cpp | 23 +++++++++++++++--------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index ba1af5f8..db69c24f 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -74,7 +74,6 @@ bool advance(Planets &planet,
 
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: After extras");
 
-
   ions.fill_electrons();
   ions.calc_sound_speed();
   ions.calc_cMax();
@@ -111,7 +110,6 @@ bool advance(Planets &planet,
 
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: set bcs");
 
-
   // advect in the 3rd dimension (vertical), but only if we have it:
   if (gGrid.get_nAlts(false) > 1) {
     neutrals.advect_vertical(gGrid, time);
@@ -119,7 +117,11 @@ bool advance(Planets &planet,
     if (didWork & input.get_check_for_nans())
       didWork = neutrals.check_for_nonfinites("After Vertical Neutral Advection");
 
-    ions.advect_vertical(gGrid, time);
+    // ajr - ions.advect_vertical(gGrid, time);
+
+    if (didWork & input.get_check_for_nans())
+      didWork = ions.check_for_nonfinites("After Vertical Ion Advection");
+
   }
 
   // advect in the 1st and 2nd dimensions (horizontal), but only if
@@ -127,12 +129,17 @@ bool advance(Planets &planet,
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
     neutrals.exchange_old(gGrid);
     ions.exchange_old(gGrid);
-    advect(gGrid, time, neutrals);
+    neutrals.advect_horizontal(gGrid, time);
   }
 
-  if (didWork & input.get_check_for_nans()) {
+  if (input.get_check_for_nans()) {
     didWork = neutrals.check_for_nonfinites("Geo Grid: After Horizontal Advection");
     didWork = neutralsMag.check_for_nonfinites("Ion Grid: After Horizontal Advection");
+
+    if (!didWork) {
+      report.exit(function);
+      return didWork;
+    }
   }
 
   // ------------------------------------
@@ -199,10 +206,10 @@ bool advance(Planets &planet,
       didWork = neutralsMag.check_for_nonfinites("Ion Grid: After Add Sources");
     }
 
-    ions.calc_ion_temperature(neutrals, gGrid, time);
-    // ions.calc_electron_temperature(neutrals, gGrid, time);
+    //ions.calc_ion_temperature(neutrals, gGrid, time);
+    //ions.calc_electron_temperature(neutrals, gGrid, time);
     //ionsMag.calc_ion_temperature(neutralsMag, mGrid, time);
-    ionsMag.calc_electron_temperature(neutralsMag, mGrid, time);
+    //ionsMag.calc_electron_temperature(neutralsMag, mGrid, time);
 
     if (didWork & input.get_check_for_nans())
       didWork = neutrals.check_for_nonfinites("After Vertical Advection");

From e4af8a44309bfbe1da1385bddb7231e35590dff8 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:12:11 -0400
Subject: [PATCH 173/266] FEAT: added equal-angular cubesphere grid

---
 src/grid_cubesphere.cpp | 450 ++++++++++++++++++++++++++++++++++++++--
 1 file changed, 427 insertions(+), 23 deletions(-)

diff --git a/src/grid_cubesphere.cpp b/src/grid_cubesphere.cpp
index d5c6742d..4be8c25c 100644
--- a/src/grid_cubesphere.cpp
+++ b/src/grid_cubesphere.cpp
@@ -72,6 +72,404 @@ void Grid::create_cubesphere_connection(Quadtree quadtree) {
   return;
 }
 
+
+// ----------------------------------------------------------------------
+// This function takes the normalized coordinates and makes latitude
+// and longitude arrays from them.  It can do this for the corners or
+// edges, depending on the offset.
+// ----------------------------------------------------------------------
+
+void Grid::init_cubesphere_grid(Quadtree quadtree,
+                                arma_vec dr,
+                                arma_vec du,
+                                arma_vec ll,
+                                precision_t left_off,
+                                precision_t down_off,
+                                cubesphere_chars &cubeX) {
+
+  std::string function = "Grid::init_cubesphere_grid";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  precision_t dnu, dxi, nu0, xi0;
+
+  // The du, dr, and ll were meant to be used on the cube
+  // and not really on the equal-angle grid.  So, we probably
+  // want to rethink these...
+  if (quadtree.iSide == 0) {
+    dnu = du[2];
+    dxi = dr[0];
+    nu0 = ll[0];
+    xi0 = ll[2];
+  }
+
+  if (quadtree.iSide == 1) {
+    dnu = du[2];
+    dxi = dr[1];
+    nu0 = ll[1];
+    xi0 = ll[2];
+  }
+
+  if (quadtree.iSide == 2) {
+    dnu = du[2];
+    dxi = -dr[0];
+    nu0 = -ll[0];
+    xi0 = ll[2];
+  }
+
+  if (quadtree.iSide == 3) {
+    dnu = du[2];
+    dxi = -dr[1];
+    nu0 = -ll[1];
+    xi0 = ll[2];
+  }
+
+  if (quadtree.iSide == 4) {
+    dnu = du[0];
+    dxi = dr[1];
+    nu0 = ll[1];
+    xi0 = ll[0];
+  }
+
+  if (quadtree.iSide == 5) {
+    dnu = -du[0];
+    dxi = dr[1];
+    nu0 = ll[1];
+    xi0 = -ll[0];
+  }
+
+  // Normalized from -1 to 1 -> -pi/4 to pi/4
+  dnu = dnu * cPI / 4.0;
+  dxi = dxi * cPI / 4.0;
+  nu0 = nu0 * cPI / 4.0;
+  xi0 = xi0 * cPI / 4.0;
+
+  cubeX.dnu = dnu;
+  cubeX.dxi = dxi;
+
+  int64_t iDU, iLR;
+  precision_t iD, iL;
+  int64_t nXp = nX, nYp = nY;
+
+  // If we are shifting the grid over and doing edges, we
+  // need to increase the number of points by 1 in that
+  // direction:
+  if (left_off < cSmall)
+    nXp++;
+
+  if (down_off < cSmall)
+    nYp++;
+
+  // These are convenient for the solver:
+  cubeX.nXt = nXp;
+  cubeX.nYt = nYp;
+  cubeX.nGCs = nGCs;
+  cubeX.iXfirst_ = nGCs;
+  cubeX.iXlast_ = nXp - nGCs;
+  cubeX.iYfirst_ = nGCs;
+  cubeX.iYlast_ = nYp - nGCs;
+
+  // these are coordinates:
+  cubeX.lat.resize(nXp, nYp);
+  cubeX.lon.resize(nXp, nYp);
+  cubeX.nu.resize(nXp, nYp);
+  cubeX.xi.resize(nXp, nYp);
+
+  cubeX.X.resize(nXp, nYp);
+  cubeX.Y.resize(nXp, nYp);
+  cubeX.Z.resize(nXp, nYp);
+  cubeX.C.resize(nXp, nYp);
+  cubeX.D.resize(nXp, nYp);
+  cubeX.d.resize(nXp, nYp);
+
+  // These are dependent on radius,
+  // but that is not included at this time:
+  cubeX.dlx.resize(nXp, nYp, nZ);
+  cubeX.dln.resize(nXp, nYp, nZ);
+  cubeX.dS.resize(nXp, nYp, nZ);
+  cubeX.R.resize(nZ);
+
+  // These are matricies for rotating vectors:
+  cubeX.Apn.resize(nXp, nYp);
+  cubeX.Apx.resize(nXp, nYp);
+  cubeX.Atn.resize(nXp, nYp);
+  cubeX.Atx.resize(nXp, nYp);
+  cubeX.Axt.resize(nXp, nYp);
+  cubeX.Axp.resize(nXp, nYp);
+  cubeX.Ant.resize(nXp, nYp);
+  cubeX.Anp.resize(nXp, nYp);
+
+  // These are for computing normals to the cell edges (horizontal)
+  cubeX.nXiLon.resize(nXp, nYp);
+  cubeX.nXiLat.resize(nXp, nYp);
+  cubeX.nNuLon.resize(nXp, nYp);
+  cubeX.nNuLat.resize(nXp, nYp);
+
+  precision_t det, dmo, latp, lonp;
+
+  // Loop through each point and derive the coordinate
+  for (iDU = 0; iDU < nY; iDU++) {
+    for (iLR = 0; iLR < nX; iLR++) {
+
+      // the offsets are so we can find cell centers, edges, and corners
+      iD = iDU - nGCs + down_off;
+      iL = iLR - nGCs + left_off;
+
+      // Define local coordinates:
+      // Xi is LR (x), Nu is UD (y)
+      cubeX.nu(iLR, iDU) = (nu0 + dnu * iD);
+      cubeX.xi(iLR, iDU) = (xi0 + dxi * iL);
+
+      cubeX.X(iLR, iDU) = tan(cubeX.xi(iLR, iDU));
+      cubeX.Y(iLR, iDU) = tan(cubeX.nu(iLR, iDU));
+
+      // Transformation from 3D Cartesian to LatLong
+      // lonp = std::atan2(y_cart, x_cart) + cPI/2.0;
+      if (quadtree.iSide == 0) {
+        lonp = std::atan(cubeX.X(iLR, iDU));
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(1.0 / cubeX.Y(iLR, iDU) / std::cos(lonp));
+      }
+
+      if (quadtree.iSide == 1) {
+        lonp = std::atan(-1.0 / cubeX.X(iLR, iDU));
+
+        if (lonp < 0)
+          lonp = cPI + lonp;
+
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(1.0 / cubeX.Y(iLR, iDU) / std::sin(lonp));
+      }
+
+      if (quadtree.iSide == 2) {
+        lonp = std::atan(cubeX.X(iLR, iDU)) + cPI;
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(-1.0 / cubeX.Y(iLR, iDU) / std::cos(lonp));
+      }
+
+      if (quadtree.iSide == 3) {
+        lonp = std::atan(-1.0 / cubeX.X(iLR, iDU));
+
+        if (lonp > 0)
+          lonp = lonp + cPI;
+        else
+          lonp = 2 * cPI + lonp;
+
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(-1.0 / cubeX.Y(iLR, iDU) / std::sin(lonp));
+      }
+
+      if (quadtree.iSide == 4) {
+        lonp = std::atan2(cubeX.X(iLR, iDU), cubeX.Y(iLR, iDU));
+        latp = std::atan2(-cubeX.Y(iLR, iDU), cos(lonp) );
+      }
+
+      if (quadtree.iSide == 5) {
+        lonp = std::atan2(-cubeX.X(iLR, iDU), cubeX.Y(iLR, iDU));
+        latp = -std::atan2(-cubeX.Y(iLR, iDU), cos(lonp) );
+      }
+
+      if (latp > 0)
+        latp = cPI / 2 - latp;
+      else
+        latp = -(cPI / 2 + latp);
+
+      if (lonp > cTWOPI)
+        lonp = lonp - cTWOPI;
+
+      if (lonp < 0.0)
+        lonp = lonp + cTWOPI;
+
+      // Fill Computed coords
+      cubeX.lat(iLR, iDU) = latp;
+      cubeX.lon(iLR, iDU) = lonp;
+
+      cubeX.d(iLR, iDU) =
+        1 +
+        cubeX.X(iLR, iDU) * cubeX.X(iLR, iDU) +
+        cubeX.Y(iLR, iDU) * cubeX.Y(iLR, iDU);
+
+      cubeX.C(iLR, iDU) =
+        sqrt(1 + cubeX.X(iLR, iDU) * cubeX.X(iLR, iDU));
+      cubeX.D(iLR, iDU) =
+        sqrt(1 + cubeX.Y(iLR, iDU) * cubeX.Y(iLR, iDU));
+
+      if (quadtree.iSide < 4) {
+        cubeX.Axt(iLR, iDU) = 0.0;
+        cubeX.Axp(iLR, iDU) =
+          cubeX.C(iLR, iDU) * cubeX.D(iLR, iDU) /
+          sqrt(cubeX.d(iLR, iDU));
+        cubeX.Ant(iLR, iDU) = -1.0;
+        cubeX.Anp(iLR, iDU) =
+          cubeX.X(iLR, iDU) * cubeX.Y(iLR, iDU) /
+          sqrt(cubeX.d(iLR, iDU));
+      } else {
+        if (cubeX.d(iLR, iDU) < 1.0001)
+          cubeX.d(iLR, iDU) = 1.0001;
+
+        dmo = 1.0 / std::sqrt(cubeX.d(iLR, iDU) - 1);
+
+        if (quadtree.iSide == 4) {
+          cubeX.Axt(iLR, iDU) =
+            - dmo * cubeX.D(iLR, iDU) * cubeX.X(iLR, iDU);
+          cubeX.Axp(iLR, iDU) =
+            dmo * cubeX.D(iLR, iDU) * cubeX.Y(iLR, iDU) /
+            sqrt(cubeX.d(iLR, iDU));
+          cubeX.Ant(iLR, iDU) =
+            - dmo * cubeX.C(iLR, iDU) * cubeX.Y(iLR, iDU);
+          cubeX.Anp(iLR, iDU) =
+            - dmo * cubeX.C(iLR, iDU) * cubeX.X(iLR, iDU) /
+            sqrt(cubeX.d(iLR, iDU));
+
+        } else {
+          // iFace == 5
+          cubeX.Axt(iLR, iDU) =
+            dmo * cubeX.D(iLR, iDU) * cubeX.X(iLR, iDU);
+          cubeX.Axp(iLR, iDU) =
+            - dmo * cubeX.D(iLR, iDU) *
+            cubeX.Y(iLR, iDU) /
+            sqrt(cubeX.d(iLR, iDU));
+          cubeX.Ant(iLR, iDU) =
+            dmo * cubeX.C(iLR, iDU) * cubeX.Y(iLR, iDU);
+          cubeX.Anp(iLR, iDU) =
+            dmo * cubeX.C(iLR, iDU) *
+            cubeX.X(iLR, iDU) /
+            sqrt(cubeX.d(iLR, iDU));
+        }
+      }
+
+      // Calculate inverse of matrix for calculating Ax and An from At and Ap:
+      det = 1.0 / (cubeX.Axt(iLR, iDU) * cubeX.Anp(iLR, iDU) -
+                   cubeX.Axp(iLR, iDU) * cubeX.Ant(iLR, iDU));
+
+      cubeX.Atx(iLR, iDU) = det * cubeX.Anp(iLR, iDU);
+      cubeX.Atn(iLR, iDU) = - det * cubeX.Axp(iLR, iDU);
+      cubeX.Apx(iLR, iDU) = - det * cubeX.Ant(iLR, iDU);
+      cubeX.Apn(iLR, iDU) = det * cubeX.Axt(iLR, iDU);
+
+      // These (dlx and dln) need to be multiplied by radius
+      cubeX.dlx(iLR, iDU, 0) =
+        cubeX.D(iLR, iDU) * dxi /
+        cubeX.d(iLR, iDU) /
+        (cos(cubeX.xi(iLR, iDU)) * cos(cubeX.xi(iLR, iDU)));
+      cubeX.dln(iLR, iDU, 0) =
+        cubeX.C(iLR, iDU) * dnu /
+        cubeX.d(iLR, iDU) /
+        (cos(cubeX.nu(iLR, iDU)) * cos(cubeX.nu(iLR, iDU)));
+
+      // Need to multiply dS * radius ^ 2
+      cubeX.dS(iLR, iDU, 0) =
+        dxi * dnu /
+        (sqrt(cubeX.d(iLR, iDU) * cubeX.d(iLR, iDU) * cubeX.d(iLR, iDU)) *
+         cos(cubeX.xi(iLR, iDU)) * cos(cubeX.xi(iLR, iDU)) *
+         cos(cubeX.nu(iLR, iDU)) * cos(cubeX.nu(iLR, iDU)));
+
+    }
+  }
+
+  // Calculate norms given the values above:
+  arma_mat e1Lat, e1Lon, e2Lat, e2Lon, m, one, zero;
+  m.resize(nXp, nYp);
+  one.resize(nXp, nYp);
+  one.fill(1.0);
+  zero.resize(nXp, nYp);
+  zero.fill(0.0);
+
+  // define e1 as the LR (xi) direction:
+  e1Lat.resize(nXp, nYp);
+  e1Lon.resize(nXp, nYp);
+  convert_vector_xn_to_ll(one, zero, e1Lon, e1Lat, cubeX);
+  m = sqrt(e1Lon % e1Lon + e1Lat % e1Lat);
+
+  // Rotate by 90 deg (CCW) to get the norm:
+  cubeX.nNuLon = -e1Lat / m;
+  cubeX.nNuLat = e1Lon / m;
+
+  // define e2 as the DU (nu) direction:
+  e2Lat.resize(nXp, nYp);
+  e2Lon.resize(nXp, nYp);
+  convert_vector_xn_to_ll(zero, one, e2Lon, e2Lat, cubeX);
+  m = sqrt(e2Lon % e2Lon + e2Lat % e2Lat);
+  // Rotate by 90 deg (CW) to get the norm:
+  cubeX.nXiLon = e2Lat / m;
+  cubeX.nXiLat = -e2Lon / m;
+
+  report.exit(function);
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+void Grid::convert_vector_xn_to_ll(arma_mat aXi,
+                                   arma_mat aNu,
+                                   arma_mat &aLon,
+                                   arma_mat &aLat,
+                                   cubesphere_chars grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  aLat = -(grid.Atx % aXi + grid.Atn % aNu);
+  aLon = grid.Apx % aXi + grid.Apn % aNu;
+
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+void Grid::convert_vector_ll_to_xn(arma_mat aLon,
+                                   arma_mat aLat,
+                                   arma_mat &aXi,
+                                   arma_mat &aNu,
+                                   cubesphere_chars grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  aXi = -grid.Axt % aLat + grid.Axp % aLon;
+  aNu = -grid.Ant % aLat + grid.Anp % aLon;
+  return;
+}
+
+
+
+// ----------------------------------------------------------------------
+// This function scales the deltas in the grid by the radius
+//   - This assumes that radius is not dependent on lat / lon!!!
+// ----------------------------------------------------------------------
+
+void Grid::scale_cube_by_radius(cubesphere_chars &cubeX) {
+
+  int64_t iZ;
+
+  for (iZ = 1; iZ < nZ; iZ++) {
+    cubeX.R(iZ) = radius_scgc(nGCs, nGCs, iZ);
+    // These are distances:
+    cubeX.dlx.slice(iZ) =
+      cubeX.dlx.slice(0) * cubeX.R(iZ);
+    cubeX.dln.slice(iZ) =
+      cubeX.dln.slice(0) * cubeX.R(iZ);
+    // This is an area:
+    cubeX.dS.slice(iZ) =
+      cubeX.dS.slice(0) * cubeX.R(iZ) * cubeX.R(iZ);
+  }
+
+  // Lastly, scale the 0th slice
+  iZ = 0;
+  cubeX.R(iZ) = radius_scgc(nGCs, nGCs, iZ);
+  cubeX.dlx.slice(iZ) =
+    cubeX.dlx.slice(0) * cubeX.R(iZ);
+  cubeX.dln.slice(iZ) =
+    cubeX.dln.slice(0) * cubeX.R(iZ);
+  // This is an area:
+  cubeX.dS.slice(iZ) =
+    cubeX.dS.slice(0) * cubeX.R(iZ) * cubeX.R(iZ);
+
+  return;
+}
+
 // ----------------------------------------------------------------------
 // This function takes the normalized coordinates and makes latitude
 // and longitude arrays from them.  It can do this for the corners or
@@ -85,10 +483,10 @@ void fill_cubesphere_lat_lon_from_norms(Quadtree quadtree,
                                         int64_t nGCs,
                                         precision_t left_off,
                                         precision_t down_off,
-                                        arma_mat &lat2d,
-                                        arma_mat &lon2d,
-                                        arma_mat &refx,
-                                        arma_mat &refy) {
+                                        arma_mat & lat2d,
+                                        arma_mat & lon2d,
+                                        arma_mat & refx,
+                                        arma_mat & refy) {
 
   int64_t nX = lat2d.n_rows;
   int64_t nY = lat2d.n_cols;
@@ -172,25 +570,25 @@ void fill_cubesphere_lat_lon_from_norms(Quadtree quadtree,
 // generate transformation and metric tensors
 // ----------------------------------------------------------------------
 void transformation_metrics(Quadtree quadtree,
-                            arma_mat &lat2d,
-                            arma_mat &lon2d,
-                            arma_mat &refx,
-                            arma_mat &refy,
-                            arma_mat &A11,
-                            arma_mat &A12,
-                            arma_mat &A21,
-                            arma_mat &A22,
-                            arma_mat &A11_inv,
-                            arma_mat &A12_inv,
-                            arma_mat &A21_inv,
-                            arma_mat &A22_inv,
-                            arma_mat &g11_upper,
-                            arma_mat &g12_upper,
-                            arma_mat &g21_upper,
-                            arma_mat &g22_upper,
-                            arma_mat &sqrt_g,
-                            arma_mat &refx_angle,
-                            arma_mat &refy_angle) {
+                            arma_mat & lat2d,
+                            arma_mat & lon2d,
+                            arma_mat & refx,
+                            arma_mat & refy,
+                            arma_mat & A11,
+                            arma_mat & A12,
+                            arma_mat & A21,
+                            arma_mat & A22,
+                            arma_mat & A11_inv,
+                            arma_mat & A12_inv,
+                            arma_mat & A21_inv,
+                            arma_mat & A22_inv,
+                            arma_mat & g11_upper,
+                            arma_mat & g12_upper,
+                            arma_mat & g21_upper,
+                            arma_mat & g22_upper,
+                            arma_mat & sqrt_g,
+                            arma_mat & refx_angle,
+                            arma_mat & refy_angle) {
 
   int64_t nX = lat2d.n_rows;
   int64_t nY = lat2d.n_cols;
@@ -343,6 +741,12 @@ void Grid::create_cubesphere_grid(Quadtree quadtree) {
   du = size_up_norm / (nLats - 2 * nGCs);
   ll = lower_left_norm;
 
+  // This function builds the equal-angle grid, but doesn't
+  // scale them with altitude, since that has not been created, yet:
+  init_cubesphere_grid(quadtree, dr, du, ll, 0.5, 0.5, cubeC);
+  init_cubesphere_grid(quadtree, dr, du, ll, 0.0, 0.5, cubeL);
+  init_cubesphere_grid(quadtree, dr, du, ll, 0.5, 0.0, cubeD);
+
   int64_t iAlt, iLon, iLat;
 
   // ---------------------------------------------

From 8f183f9864cb28ea0a0ae95c213bc6585adedaa3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:13:18 -0400
Subject: [PATCH 174/266] FEAT: added equal-angular cubesphere grid

---
 src/init_geo_grid.cpp | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 78b75acb..0dbae8db 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -190,12 +190,10 @@ bool Grid::init_geo_grid(Quadtree quadtree,
   //  report.print(1, "Restarting! Reading grid files!");
   //  DidWork = read_restart(input.get_restartin_dir());
   //} else {
-  if (iGridShape_ == iCubesphere_) {
-    //if (input.get_do_restart())
-    //  report.print(0, "Not restarting the grid - it is too complicated!");
-
+  if (iGridShape_ == iCubesphere_)
     create_cubesphere_grid(quadtree);
-  } else
+
+  else
     create_sphere_grid(quadtree);
 
   //MPI_Barrier(aether_comm);
@@ -211,8 +209,15 @@ bool Grid::init_geo_grid(Quadtree quadtree,
 
   // Correct the reference grid with correct length scale:
   // (with R = actual radius)
-  if (iGridShape_ == iCubesphere_)
+  if (iGridShape_ == iCubesphere_) {
     correct_xy_grid(planet);
+    // New functions for equal-angular grid (center, left, down):
+    report.print(3, "Scaling Cube by Radius");
+    scale_cube_by_radius(cubeC);
+    scale_cube_by_radius(cubeL);
+    scale_cube_by_radius(cubeD);
+    report.print(3, "Done Scaling Cube by Radius");
+  }
 
   if (IsMagGrid) {
     report.print(0, "--> Grid is Magnetic, so rotating");

From 04a87ef1d769c813f258de41d68a66601657881f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:14:03 -0400
Subject: [PATCH 175/266] FEAT: equal-angular solver is default

---
 src/neutrals_advect.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index 8316cbeb..465afad3 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -42,11 +42,10 @@ bool Neutrals::advect_horizontal(Grid & grid, Times & time) {
 
   if (input.get_advection_neutrals_horizontal() == "advect_test")
     solver_horizontal_RK4_advection(grid, time);
-  else if (input.get_advection_neutrals_horizontal() == "fv") {
-    solver_horizontal_RK1(grid, time);
+  else if (input.get_advection_neutrals_horizontal() == "fv")
+    solver_horizontal_RK1_rochi(grid, time);
 
-    std::cout << "after rk1 : " << velocity_vcgc[0](20, 13, 10) << "\n";
-  } else {
+  else {
     std::cout << "Horizontal solver not found!\n";
     std::cout << "  ==> Requested : "
               << input.get_advection_neutrals_horizontal()

From 2faca6d235372b90a5bb1afec77406dcf8270f43 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:14:42 -0400
Subject: [PATCH 176/266] FEAT: added, and commented out, blob for testing

---
 src/neutrals_ics.cpp | 41 +++++++++++++++++++++++++++++++++++++----
 1 file changed, 37 insertions(+), 4 deletions(-)

diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 7b9613ba..c6a0c281 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -35,6 +35,8 @@ bool Neutrals::initial_conditions(Grid grid,
   precision_t alt, r;
   int64_t nAlts = grid.get_nZ(true);
   int64_t nGCs = grid.get_nGCs();
+  int64_t nLons = grid.get_nLons();
+  int64_t nLats = grid.get_nLats();
 
   report.print(3, "Creating Neutrals initial_condition");
 
@@ -120,10 +122,6 @@ bool Neutrals::initial_conditions(Grid grid,
       // temperature profile in the planet.in file.
       // ---------------------------------------------------------------------
 
-      int64_t nLons = grid.get_nLons();
-      int64_t nLats = grid.get_nLats();
-      int64_t nAlts = grid.get_nAlts();
-
       // Let's assume that the altitudes are not dependent on lat/lon:
 
       arma_vec alt1d(nAlts);
@@ -190,6 +188,41 @@ bool Neutrals::initial_conditions(Grid grid,
     } // type = planet
   }
 
+  /*
+  This section is for putting an initial blob into the simulation
+  to test the advection solver.
+  precision_t lon_0 = 0.0;
+  precision_t lat_0 = 0.0;
+  precision_t r_0 = 150.0 * 1000.0 * 10.0;
+
+  for (iAlt = 0; iAlt < nAlts; iAlt++) {
+
+    for (int64_t iLat = 0; iLat < nLats; iLat++) {
+      for (int64_t iLon = 0; iLon < nLons; iLon++) {
+        precision_t curr_lat = grid.geoLat_scgc(iLon, iLat, iAlt);
+        precision_t curr_lon = grid.geoLon_scgc(iLon, iLat, iAlt);
+        precision_t R = grid.radius_scgc(iLon, iLat, iAlt);
+
+        // Calculate great circle distance
+        precision_t dlon_2 = (curr_lon - lon_0) / 2.0;
+        precision_t dlat_2 = (curr_lat - lat_0) / 2.0;
+
+        precision_t r_d = 2.0 * R * asin(sqrt(sin(dlat_2) * sin(dlat_2) + sin(
+                                                dlon_2) * sin(dlon_2) * cos(curr_lat) * cos(lat_0)));
+
+        if (r_d < r_0) {
+          for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+            species[iSpecies].density_scgc(iLon, iLat,
+                                           iAlt) = species[iSpecies].density_scgc(iLon, iLat, iAlt) * 10.;
+            std::cout << "increasing density!\n";
+          }
+        }
+      }
+    }
+  }
+  */
+
+
   // ensure that the densities are all within bounds:
   clamp_density();
 

From c2ba58d828d2435f12c44422a72546627d7c6e39 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:15:28 -0400
Subject: [PATCH 177/266] removed a bunch of commented out code

---
 src/solver_vertical_rusanov.cpp | 75 +--------------------------------
 1 file changed, 2 insertions(+), 73 deletions(-)

diff --git a/src/solver_vertical_rusanov.cpp b/src/solver_vertical_rusanov.cpp
index 8027cd94..8bac9c04 100644
--- a/src/solver_vertical_rusanov.cpp
+++ b/src/solver_vertical_rusanov.cpp
@@ -86,23 +86,6 @@ void calc_facevalues_alts_rusanov(Grid &grid,
       0.5 * dVarLimited.slice(iZ) % grid.dalt_lower_scgc.slice(iZ);
   }
 
-  /*
-  if (iProc == 11)
-    std::cout << "facevalues : "
-        << inVar(7,19,17) << " "
-        << inVar(7,19,18) << " "
-        << inVar(7,19,19) << " "
-        << inVar(7,19,20) << " "
-        << dVarLimited(7,19,18) << " "
-        << grid.dalt_lower_scgc(7,19,17) << " "
-        << outRight(7, 19, 17) << " "
-        << outRight(7, 19, 18) << " "
-        << outLeft(7, 19, 17) << " "
-        << outLeft(7, 19, 18) << " "
-        << dVarUp(7, 19) << " "
-        << dVarDown(7, 19) << "\n";
-  */
-
   return;
 }
 
@@ -144,16 +127,6 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
                          varLeft.slice(iZ) - varRight.slice(iZ)) /
                         grid.dalt_center_scgc.slice(iZ);
 
-  /*
-  if (iProc == 11)
-    std::cout << "calc_grad : "
-        << varLeft(7, 19, 17) << " "
-        << varLeft(7, 19, 18) << " "
-        << varRight(7, 19, 17) << " "
-        << varRight(7, 19, 18) << " "
-        << grid.dalt_center_scgc(7, 19, 17) << " "
-        << outGrad(7, 19, 17) << "\n";
-  */
   for (iZ = nGCs; iZ < nZs - nGCs + 1; iZ++) {
     for (iX = nGCs; iX < nXs - nGCs; iX++)
       for (iY = nGCs; iY < nYs - nGCs; iY++) {
@@ -164,11 +137,7 @@ void calc_grad_and_diff_alts_rusanov(Grid &grid,
 
         diffFlux(iX, iY, iZ) =
           0.5 * cMaxLocal * (varRight(iX, iY, iZ) - varLeft(iX, iY, iZ));
-        //if (iZ <= 10 && iX == 4 && iY == 4)
-        //  std::cout << "diff flux : " << diffFlux(iX, iY, iZ)
-        //      << " " << cMaxLocal
-        //      << " " << varRight(iX, iY, iZ)
-        //      << " " << varLeft(iX, iY, iZ) << "\n";
+
       }
   }
 
@@ -311,17 +280,7 @@ void Neutrals::solver_vertical_rusanov(Grid grid,
                 species[iSpecies].velocity_vcgc[2] % gradLogN_s[iSpecies])
         + dt * diffLogN_s[iSpecies];
       species[iSpecies].newDensity_scgc = exp(log_s);
-      /*
-            std::cout << iSpecies << " " << log_s(2,2,19) << " "
-            << dt << " "
-            << divVertVel_s[iSpecies](2,2,19) << " "
-            << species[iSpecies].velocity_vcgc[2](2,2,19) << " "
-            << gradLogN_s[iSpecies](2,2,19) << " "
-            << species[iSpecies].velocity_vcgc[2](2,2,19) * gradLogN_s[iSpecies](2,2,19) << " "
-            << diffLogN_s[iSpecies](2,2,19) << " "
-            << species[iSpecies].density_scgc(2,2,19) << " "
-            << species[iSpecies].newDensity_scgc(2,2,19) << "\n";
-      */
+
       accTotal =
         dt * grid.gravity_vcgc[2]
         - dt * temperature_scgc % gradLogN_s[iSpecies] * cKB / mass
@@ -386,36 +345,6 @@ void Neutrals::solver_vertical_rusanov(Grid grid,
         }
       }
 
-  bool doPrintThis = false;
-
-  if (doPrintThis) {
-    iX = 2;
-    iY = 2;
-    iSpecies = 0;
-    mass = species[iSpecies].mass;
-
-    for (int iAlt = 19; iAlt < 20; iAlt++) {
-      std::cout << iAlt << " "
-                << log(species[iSpecies].density_scgc(iX, iY, iAlt)) << " "
-                << temperature_scgc(iX, iY, iAlt) << " "
-                << species[iSpecies].velocity_vcgc[2](iX, iY, iAlt) << " "
-                << temperature_scgc(iX, iY, iAlt) * gradLogN_s[iSpecies](iX, iY,
-                    iAlt) * cKB / mass << " "
-                << gradTemp(iX, iY, iAlt) * cKB / mass << " "
-                << grid.gravity_vcgc[2](iX, iY, iAlt) << "\n";
-    }
-  }
-
-  //calc_neutral_friction();
-  /*
-    for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-      if (species[iSpecies].DoAdvect) {
-          species[iSpecies].velocity_vcgc[2] =
-            species[iSpecies].velocity_vcgc[2] + dt *
-            species[iSpecies].acc_neutral_friction[2];
-      }
-    }
-  */
   calc_mass_density();
   // Calculate bulk vertical winds:
   velocity_vcgc[2].zeros();

From 3e7cff874a13133f6c5da62a56cecaae1e9743a3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:16:32 -0400
Subject: [PATCH 178/266] FEAT: added equal-angle cubesphere solver

---
 src/solver_horizontal_cubesphere.cpp | 365 ++++++++++++++++++++++++++-
 1 file changed, 358 insertions(+), 7 deletions(-)

diff --git a/src/solver_horizontal_cubesphere.cpp b/src/solver_horizontal_cubesphere.cpp
index 88d491b9..98868b1a 100644
--- a/src/solver_horizontal_cubesphere.cpp
+++ b/src/solver_horizontal_cubesphere.cpp
@@ -2,15 +2,310 @@
 // Full license can be found in License.md
 
 // Initial version: F. Cheng, July 2023
+// Moved to new solver: August 2025
 
 #include "aether.h"
 
+// ---------------------------------------------------------
+// Update States
+// ---------------------------------------------------------
+
+void update_states_cubesphere(arma_mat rho,
+                              arma_mat &xVel,
+                              arma_mat &yVel,
+                              arma_mat &temp,
+                              arma_mat &drhodt,
+                              arma_mat &dlonVeldt,
+                              arma_mat &dlatVeldt,
+                              arma_mat &dtempdt,
+                              cubesphere_chars gridC,
+                              cubesphere_chars gridL,
+                              cubesphere_chars gridD,
+                              precision_t dt,
+                              int64_t iZ) {
+
+  arma_mat xMomentum, yMomentum;
+  arma_mat rhoE, energy, vel2;
+
+  precision_t cv = 1500.0;
+
+  if (report.test_verbose(2))
+    std::cout << "  --> update_states\n";
+
+  // Derived variables:
+  xMomentum = rho % xVel; // x1momentum, pure scalar field
+  yMomentum = rho % yVel; // y1momentum, pure scalar field
+  rhoE = rho % temp;
+
+  vel2 = xVel % xVel + yVel % yVel;
+  //energy = rho % (0.5 * vel2 + cv * temp);
+  energy = cv * rho % temp;
+
+  /** Initialize projection constructs */
+  static projection_struct rhoP;
+  static projection_struct xMomentumP, xVelP;
+  static projection_struct yMomentumP, yVelP;
+  static projection_struct energyP;
+  static projection_struct tempP;
+
+  // They are all pure scalar fields without sqrt(g)
+  static arma_mat totaleL, totaleR, totaleD, totaleU;
+  static arma_mat velL2, velR2, velD2, velU2;
+  static arma_mat pressureL, pressureR, pressureD, pressureU;
+
+  arma_mat dxVeldt = xVel * 0.0;
+  arma_mat dyVeldt = yVel * 0.0;
+
+  dlonVeldt = dxVeldt * 0.0 + 1;
+  dlatVeldt = dyVeldt * 0.0 + 1;
+
+  static arma_mat velNormL, velNormR, velNormU, velNormD;
+
+  /** Initialize Flux and Wave Speed Storages */
+  static arma_mat eq1FluxLR, eq1FluxDU;
+  static arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+  static arma_mat eq2FluxLR, eq2FluxDU;
+  static arma_mat eq2FluxL, eq2FluxR, eq2FluxD, eq2FluxU;
+  static arma_mat eq3FluxLR, eq3FluxDU;
+  static arma_mat eq3FluxL, eq3FluxR, eq3FluxD, eq3FluxU;
+  static arma_mat eq4FluxLR, eq4FluxDU;
+  static arma_mat eq4FluxL, eq4FluxR, eq4FluxD, eq4FluxU;
+
+  arma_mat wsL, wsR, wsD, wsU, wsLR, wsDU;
+
+  arma_mat diff; // for Riemann Solver
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Projecting\n";
+
+  rhoP = project_to_edges(rho, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                          gridC.nGCs);
+  // project the lon / lat velocities to the edges:
+  xVelP = project_to_edges(xVel, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+  yVelP = project_to_edges(yVel, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+  xMomentumP = project_to_edges(xMomentum, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                                gridC.nGCs);
+  yMomentumP = project_to_edges(yMomentum, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                                gridC.nGCs);
+  energyP = project_to_edges(energy, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                             gridC.nGCs);
+  tempP = project_to_edges(temp, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Derived values\n";
+
+  velL2 = (xVelP.L % xVelP.L + yVelP.L % yVelP.L);
+  velR2 = (xVelP.R % xVelP.R + yVelP.R % yVelP.R);
+  velD2 = (xVelP.D % xVelP.D + yVelP.D % yVelP.D);
+  velU2 = (xVelP.U % xVelP.U + yVelP.U % yVelP.U);
+
+  precision_t k = 1.38e-23;
+  // let's be Oxygen:
+  precision_t mass = 16.0 * 1.67e-27;
+  pressureL = k / mass * (rhoP.L % tempP.L);
+  pressureR = k / mass * (rhoP.R % tempP.R);
+  pressureD = k / mass * (rhoP.D % tempP.D);
+  pressureU = k / mass * (rhoP.U % tempP.U);
+
+  arma_mat pressureLR = (pressureL + pressureR) / 2;
+  arma_mat pressureDU = (pressureD + pressureU) / 2;
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Normal Velocities\n";
+
+  // Calculate the normal velocity at the boundaries:
+  velNormL = xVelP.L % gridL.nXiLon + yVelP.L % gridL.nXiLat;
+  velNormR = xVelP.R % gridL.nXiLon + yVelP.R % gridL.nXiLat;
+  velNormU = xVelP.U % gridD.nNuLon + yVelP.U % gridD.nNuLat;
+  velNormD = xVelP.D % gridD.nNuLon + yVelP.D % gridD.nNuLat;
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Fluxes eq 1\n";
+
+  // Flux calculated from the left of the edge
+  eq1FluxL = rhoP.L % velNormL;
+  // Flux calculated from the right of the edge
+  eq1FluxR = rhoP.R % velNormR;
+  // Flux calculated from the down of the edge
+  eq1FluxD = rhoP.D % velNormD;
+  // Flux calculated from the up of the edge
+  eq1FluxU = rhoP.U % velNormU;
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Fluxes eq 2\n";
+
+  eq2FluxL = (xMomentumP.L % velNormL);
+  eq2FluxR = (xMomentumP.R % velNormR);
+  eq2FluxD = (xMomentumP.D % velNormD);
+  eq2FluxU = (xMomentumP.U % velNormU);
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Fluxes eq 3\n";
+
+  eq3FluxL = (yMomentumP.L % velNormL);
+  eq3FluxR = (yMomentumP.R % velNormR);
+  eq3FluxD = (yMomentumP.D % velNormD);
+  eq3FluxU = (yMomentumP.U % velNormU);
+
+  eq4FluxL = energyP.L % velNormL;
+  eq4FluxR = energyP.R % velNormR;
+  eq4FluxD = energyP.D % velNormD;
+  eq4FluxU = energyP.U % velNormU;
+
+  // ------------------------------------------------
+  // Calculate the wave speed for the diffusive flux:
+  // In Reference velocities
+  if (report.test_verbose(3))
+    std::cout << "  ---> Diffusive Fluxes\n";
+
+  precision_t cGamma = 5.0 / 3.0;
+
+  wsL.resize(gridC.nXt + 1, gridC.nYt);
+  wsR.resize(gridC.nXt + 1, gridC.nYt);
+  wsD.resize(gridC.nXt, gridC.nYt + 1);
+  wsU.resize(gridC.nXt, gridC.nYt + 1);
+
+  wsL.zeros();
+  wsR.zeros();
+  wsU.zeros();
+  wsD.zeros();
+
+  for (int64_t i = 0; i < gridC.nXt; i++) {
+    for (int64_t j = 0; j < gridC.nYt; j++) {
+      wsL(i, j) = sqrt(velL2(i, j)) + sqrt(cGamma * (cGamma - 1) * tempP.L(i, j));
+      wsR(i, j) = sqrt(velR2(i, j)) + sqrt(cGamma * (cGamma - 1) * tempP.R(i, j));
+      wsD(i, j) = sqrt(velD2(i, j)) + sqrt(cGamma * (cGamma - 1) * tempP.D(i, j));
+      wsU(i, j) = sqrt(velU2(i, j)) + sqrt(cGamma * (cGamma - 1) * tempP.U(i, j));
+    }
+  }
+
+  wsLR = wsR;
+
+  for (int64_t i = 0; i < gridC.nXt; i++) {
+    for (int64_t j = 0; j < gridC.nYt; j++) {
+      if (wsL(i, j) > wsLR(i, j))
+        wsLR(i, j) = wsL(i, j);
+    }
+  }
+
+  wsDU = wsD;
+
+  for (int64_t i = 0; i < gridC.nXt; i++) {
+    for (int64_t j = 0; j < gridC.nYt; j++) {
+      if (wsU(i, j) > wsDU(i, j))
+        wsDU(i, j) = wsU(i, j);
+    }
+  }
+
+  // ------------------------------------------------
+  // Calculate average flux at the edges (Rusanov Flux):
+
+  if (report.test_verbose(3))
+    std::cout << "  ---> Averaging fluxes at edges\n";
+
+  diff = (rhoP.R - rhoP.L);
+  eq1FluxLR = (eq1FluxL + eq1FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoP.U - rhoP.D);
+  eq1FluxDU = (eq1FluxD + eq1FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (xMomentumP.R - xMomentumP.L);
+  eq2FluxLR = (eq2FluxL + eq2FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (xMomentumP.U - xMomentumP.D);
+  eq2FluxDU = (eq2FluxD + eq2FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (yMomentumP.R - yMomentumP.L);
+  eq3FluxLR = (eq3FluxL + eq3FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (yMomentumP.U - yMomentumP.D);
+  eq3FluxDU = (eq3FluxD + eq3FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (energyP.R - energyP.L);
+  eq4FluxLR = (eq4FluxL + eq4FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (energyP.U - energyP.D);
+  eq4FluxDU = (eq4FluxD + eq4FluxU) / 2 + 0.5 * wsDU % diff;
+
+  // ------------------------------------------------
+  // Update values:
+  if (report.test_verbose(3))
+    std::cout << "  ---> Updating equations of state\n";
+
+  precision_t dpdx, dpdn, pp, pm;
+
+  arma_mat ax(gridC.nXt, gridC.nYt), an(gridC.nXt, gridC.nYt);
+
+  ax.zeros();
+  an.zeros();
+  arma_mat dedt(gridC.nXt, gridC.nYt);
+  dedt.zeros();
+
+  arma_mat rhoNew = rho;
+
+  // Only deal with inner cell
+  for (int64_t j = gridC.iYfirst_; j < gridC.iYlast_; j++) {
+    for (int64_t i = gridC.iXfirst_; i < gridC.iXlast_; i++) {
+      precision_t rhoResidual_ij = (gridL.dln(i + 1, j, iZ) * eq1FluxLR(i + 1, j) -
+                                    gridL.dln(i, j, iZ) * eq1FluxLR(i, j) +
+                                    gridD.dlx(i, j + 1, iZ) * eq1FluxDU(i, j + 1) -
+                                    gridD.dlx(i, j, iZ) * eq1FluxDU(i, j));
+      drhodt(i, j) = rhoResidual_ij / gridC.dS(i, j, iZ);
+
+      rhoNew(i, j) = rho(i, j) + dt * drhodt(i, j);
+
+      precision_t xMomentumResidual_ij = (gridL.dln(i + 1, j, iZ) * eq2FluxLR(i + 1,
+                                          j) -
+                                          gridL.dln(i, j, iZ) * eq2FluxLR(i, j) +
+                                          gridD.dlx(i, j + 1, iZ) * eq2FluxDU(i, j + 1) -
+                                          gridD.dlx(i, j, iZ) * eq2FluxDU(i, j));
+      dxVeldt(i, j) = xMomentumResidual_ij / gridC.dS(i, j, iZ) / rhoNew(i, j);
+
+      precision_t yMomentumResidual_ij = (gridL.dln(i + 1, j, iZ) * eq3FluxLR(i + 1,
+                                          j) -
+                                          gridL.dln(i, j, iZ) * eq3FluxLR(i, j) +
+                                          gridD.dlx(i, j + 1, iZ) * eq3FluxDU(i, j + 1) -
+                                          gridD.dlx(i, j, iZ) * eq3FluxDU(i, j));
+      dyVeldt(i, j) = yMomentumResidual_ij / gridC.dS(i, j, iZ) / rhoNew(i, j);
+
+      // Calculate the gradient in the potential in the cubesphere
+      // coordinate system:
+      dpdx = 1 / gridC.R(iZ) * gridC.D(i, j) *
+             (pressureLR(i + 1, j) - pressureLR(i, j)) / gridC.dxi;
+      dpdn = 1 / gridC.R(iZ) * gridC.X(i, j) * gridC.Y(i, j) /
+             gridC.D(i, j) *
+             (pressureDU(i, j + 1) - pressureDU(i, j)) / gridC.dnu;
+      ax(i, j) = (dpdx + dpdn) / rhoNew(i, j);
+
+      dpdx = 1 / gridC.R(iZ) * gridC.X(i, j) * gridC.Y(i, j) /
+             gridC.C(i, j) * (pressureLR(i + 1, j) - pressureLR(i, j)) / gridC.dxi;
+      dpdn = 1 / gridC.R(iZ) * gridC.C(i, j) *
+             (pressureDU(i, j + 1) - pressureDU(i, j)) / gridC.dnu;
+      an(i, j) = (dpdx + dpdn) / rhoNew(i, j);
+
+      precision_t energyResidual_ij = (gridL.dln(i + 1, j, iZ) * eq4FluxLR(i + 1, j) -
+                                       gridL.dln(i, j, iZ) * eq4FluxLR(i, j) +
+                                       gridD.dlx(i, j + 1, iZ) * eq4FluxDU(i, j + 1) -
+                                       gridD.dlx(i, j, iZ) * eq4FluxDU(i, j));
+      dedt(i, j) = energyResidual_ij / gridC.dS(i, j, iZ);
+
+    }
+  }
+
+  // lat is negative because of the Rochi definition of theta:
+  dlatVeldt = dyVeldt - (ax % gridC.Atx + an % gridC.Atn);
+  dlonVeldt = dxVeldt + ax % gridC.Apx + an % gridC.Apn;
+  dtempdt = dedt / rhoNew / cv;
+
+  return;
+}
+
+
 // using namespace Cubesphere_tools;
 
 std::vector<arma_mat> Neutrals::residual_horizontal_rusanov(
   std::vector<arma_mat>& states,
-  Grid& grid,
-  Times& time,
+  Grid & grid,
+  Times & time,
   int64_t iAlt) {
 
   // Dimensions of Spatial Discretization
@@ -341,7 +636,66 @@ std::vector<arma_mat> Neutrals::residual_horizontal_rusanov(
   return return_vector;
 }
 
-void Neutrals::solver_horizontal_RK1(Grid& grid, Times& time) {
+
+//--------------------------------------------------------------------
+// New solver using Rochi
+//--------------------------------------------------------------------
+
+void Neutrals::solver_horizontal_RK1_rochi(Grid & grid, Times & time) {
+
+  std::string function = "Neutrals::solver_horizontal_RK1_rochi";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  precision_t dt = time.get_dt();
+
+  int64_t nXs = grid.get_nX();
+  int64_t nYs = grid.get_nY();
+  int64_t nGCs = grid.get_nGCs();
+  int64_t nAlts = grid.get_nAlts();
+
+  calc_concentration();
+
+  arma_mat temp(nXs, nYs), rho(nXs, nYs), vLon(nXs, nYs), vLat(nXs, nYs);
+
+  arma_mat k1rho(nXs, nYs);
+  arma_mat k1vLon(nXs, nYs), k1vLat(nXs, nYs);
+  arma_mat k1temp(nXs, nYs);
+
+  int64_t iAlt;
+
+  for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
+
+    /** States preprocessing **/
+    /* MASS DENSITY */
+    rho = rho_scgc.slice(iAlt);
+    vLon = velocity_vcgc[0].slice(iAlt);
+    vLat = velocity_vcgc[1].slice(iAlt);
+    temp = temperature_scgc.slice(iAlt);
+
+    // k1 - start at t0, go to t+1/2 to figure out slope at t0 (k1)
+    update_states_cubesphere(
+      rho, vLon, vLat, temp,
+      k1rho, k1vLon, k1vLat, k1temp,
+      grid.cubeC, grid.cubeL, grid.cubeD, dt, iAlt);
+    // Take full step using k1:
+    rho_scgc.slice(iAlt) = rho - k1rho * dt;
+    velocity_vcgc[0].slice(iAlt) = vLon - k1vLon * dt;
+    velocity_vcgc[1].slice(iAlt) = vLat - k1vLat * dt;
+    temperature_scgc.slice(iAlt) = temp - k1temp * dt;
+
+  }
+
+
+  calc_density_from_mass_concentration();
+
+  report.exit(function);
+  return;
+
+}
+
+
+void Neutrals::solver_horizontal_RK1(Grid & grid, Times & time) {
   // Function Reporting
   std::string function = "Neutrals::solver_horizontal_RK1";
   static int iFunction = -1;
@@ -370,10 +724,7 @@ void Neutrals::solver_horizontal_RK1(Grid& grid, Times& time) {
 
   // Advance for bulk calculation first, calculate for every altitude
 
-  std::cout << "nAlts : " << nAlts << "\n";
-
   for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
-    std::cout << "iAlt : " << iAlt << "\n";
     /** Extract Grid Features **/
     x = grid.refx_scgc.slice(iAlt);
     arma_mat xEdges = grid.refx_Left.slice(iAlt);
@@ -642,7 +993,7 @@ return;
 
 */
 
-void Neutrals::solver_horizontal_RK4(Grid& grid, Times& time) {
+void Neutrals::solver_horizontal_RK4(Grid & grid, Times & time) {
   // Function Reporting
   std::string function = "Neutrals::solver_horizontal_RK4";
   static int iFunction = -1;

From 7f765e3977b5d68ca081a7bd8b5dfb2f1b92fcf4 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:17:24 -0400
Subject: [PATCH 179/266] tiny changes

---
 edu/examples/Advection/cubesphere_test.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/edu/examples/Advection/cubesphere_test.cpp b/edu/examples/Advection/cubesphere_test.cpp
index f201e00b..f23176aa 100644
--- a/edu/examples/Advection/cubesphere_test.cpp
+++ b/edu/examples/Advection/cubesphere_test.cpp
@@ -97,7 +97,6 @@ void output(arma_mat &values,
     outfile << nX << " " << nY << "\n";
   }
   outfile << values;
-  outfile << "----";
   outfile.close();
 }
 
@@ -801,7 +800,7 @@ arma_mat init_rho(arma_mat &x,
 
   r = sqrt((x - 0.0) % (x - 0.0) + (y - 0.0) % (y - 0.0));
   rho.fill(1.0);
-  // rho.elem( find( r < 0.25)).fill(2.2);
+  rho.elem( find( r < 0.25)).fill(2.2);
   // rho.elem( find( r < 0.25)) = 2.25 - r.elem( find( r < 0.25));
 
   return rho;
@@ -857,7 +856,7 @@ int main()
 {
   precision_t dt = 0.0001;        // Time Step
   precision_t current_time = 0.0; // Initial Time 0
-  precision_t total_time = 2.0;   // Total simulation time
+  precision_t total_time = 0.1;   // Total simulation time
   precision_t cfl = 0.1;         // CFL Number
   precision_t gamma = 5.0 / 3.0;  // Specific ratio of heat
 
@@ -1274,4 +1273,4 @@ int main()
   output(xVelSph_output, "xvel_sph.txt", true);
   output(yVelSph_output, "yvel_sph.txt", true);
   return 0;
-}
\ No newline at end of file
+}

From 25284aa880975a86bd20c0a7c3511629b5a2573d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 21 Aug 2025 08:19:09 -0400
Subject: [PATCH 180/266] FEAT: 2d toy code for equal-angle cubesphere

---
 .../Advection/cubesphere_equal_angle.cpp      | 1757 +++++++++++++++++
 1 file changed, 1757 insertions(+)
 create mode 100644 edu/examples/Advection/cubesphere_equal_angle.cpp

diff --git a/edu/examples/Advection/cubesphere_equal_angle.cpp b/edu/examples/Advection/cubesphere_equal_angle.cpp
new file mode 100644
index 00000000..3c3687e4
--- /dev/null
+++ b/edu/examples/Advection/cubesphere_equal_angle.cpp
@@ -0,0 +1,1757 @@
+/*
+  This is an example of a second order 2D solver for the Euler equations.
+
+  to compile:
+  g++ -I/usr/local/include -I/Users/ridley/Software/Json/json/include -o cubesphere2d cubesphere2d.cpp
+
+*/
+
+#include <armadillo>
+#include <fstream>
+
+using precision_t = double;
+
+/// Armadillo type vector (single column) with compile-time precision.
+using arma_vec = arma::Col<precision_t>;
+/// Armadillo type matrix (two dimension) with compile-time precision.
+using arma_mat = arma::Mat<precision_t>;
+/// Armadillo type cube (three dimension) with compile-time precision.
+using arma_cube = arma::Cube<precision_t>;
+
+precision_t cPI = 3.141592653589793;
+precision_t cTWOPI = 2.0 * cPI;
+precision_t cRtoD = 180.0 / cPI;
+precision_t cPIdiv2 = cPI / 2;
+precision_t cGamma = 5.0 / 3.0;  // Specific ratio of heat
+precision_t cKb = 1.38e-23;
+precision_t mmm = 16.0 * 1.67e-27;
+
+// ---------------------------------------------------------
+// A couple of global variables
+// ---------------------------------------------------------
+
+int64_t verbose = 1;
+
+struct projection_struct {
+  arma_mat gradLR;
+  arma_mat gradDU;
+  arma_mat R;
+  arma_mat L;
+  arma_mat U;
+  arma_mat D;
+};
+
+struct grid_struct {
+
+  // sizes:
+  int64_t nXt, nYt, nGCs;
+  int64_t iXfirst_, iXlast_;
+  int64_t iYfirst_, iYlast_;
+
+  // Positions:
+  arma_mat lon;
+  arma_mat lat;
+
+  // These are for Ronchi et al., JCP 124, 93-114, 1996
+  arma_mat X, Y, Z, C, D, d;
+  arma_mat dlx, dln, dS;
+  // xi is the LR direction
+  // nu is the UD direction
+  arma_mat xi, nu;
+  arma_mat x, y, r;
+  arma_mat Apn, Apx, Atn, Atx;
+  arma_mat Axt, Axp, Ant, Anp;
+  precision_t dxi, dnu, R;
+  arma_mat alpha;
+  arma_mat sinAlpha;
+
+  arma_mat nXiLon;
+  arma_mat nXiLat;
+  arma_mat nNuLon;
+  arma_mat nNuLat;
+  // These are eq28 of Nair (g lower ij):
+  arma_mat gl11, gl12, gl21, gl22;
+  // These are eq29 of Nair (g upper ij):
+  arma_mat sqrtg;
+  arma_mat gu11, gu12, gu21, gu22;
+  // These are eq32 of Nair (sphere-to-cube):
+  arma_mat s2c11, s2c12, s2c21, s2c22;
+  arma_mat c2s11, c2s12, c2s21, c2s22;
+};
+
+// ---------------------------------------------------------
+//
+// ---------------------------------------------------------
+
+precision_t calc_dt(arma_mat dx,
+                    arma_mat dy,
+                    arma_mat &wsLR,
+                    arma_mat &wsDU,
+                    int64_t nGCs) {
+
+  if (verbose > 2)
+    std::cout << "  --> calc_dt\n";
+
+  int64_t nX = wsLR.n_rows;
+  int64_t nY = wsLR.n_cols;
+
+  precision_t wsX, wsY, dtX, dtY, dt;
+
+  dt = 1e32;
+
+  for (int64_t j = nGCs; j < nY - nGCs; j++) {
+    for (int64_t i = nGCs; i < nX - nGCs; i++) {
+      wsX = (wsLR(i + 1, j) + wsLR(i, j)) / 2;
+      dtX = dx(i, j) / wsX;
+      wsY = (wsDU(i, j + 1) + wsDU(i, j)) / 2;
+      dtY = dy(i, j) / wsY;
+
+      if (dtX < dt)
+        dt = dtX;
+
+      if (dtY < dt)
+        dt = dtY;
+    }
+  }
+
+  return dt;
+}
+
+/**
+ * Output function
+ *
+ * @param values Values
+ * @param filename FileName
+ * @param DoAppend
+ */
+void output(arma_mat &values,
+            std::string filename,
+            bool DoAppend) {
+
+  std::ofstream outfile;
+
+  if (DoAppend)
+    outfile.open(filename, std::ios_base::app);
+  else {
+    outfile.open(filename);
+    int64_t nX = values.n_rows;
+    int64_t nY = values.n_cols;
+    outfile << nX << " " << nY << "\n";
+  }
+
+  outfile << values;
+  outfile.close();
+}
+
+/**
+ * Transform spherical coordinates to 3D Cartesian
+ *
+ * doi: 10.1016/j.jcp.2007.07.022
+ * Section 3, Eqn (23)
+ *
+ * @return dh Great Circle Distance between two points
+ */
+arma_vec sph2cart(precision_t lon,
+                  precision_t lat,
+                  precision_t r) {
+  arma_vec xyz(3);
+  xyz(0) = r * std::cos(lat) * std::cos(lon);
+  xyz(1) = r * std::cos(lat) * std::sin(lon);
+  xyz(2) = r * std::sin(lat);
+  return xyz;
+}
+
+grid_struct init_grid_equidistant(int iFace,
+                                  int64_t nX,
+                                  int64_t nY,
+                                  int64_t nGCs,
+                                  precision_t R,
+                                  precision_t xOff,
+                                  precision_t yOff) {
+
+  double a = R / std::sqrt(3);
+
+  grid_struct grid;
+  int64_t nXt = nX + 2 * nGCs;
+  int64_t nYt = nY + 2 * nGCs;
+
+  grid.nXt = nXt;
+  grid.nYt = nYt;
+  grid.nGCs = nGCs;
+  grid.iXfirst_ = nGCs;
+  grid.iYfirst_ = nGCs;
+  grid.iXlast_ = nX + nGCs;
+  grid.iYlast_ = nY + nGCs;
+
+  // Positions:
+  grid.lon.resize(nXt, nYt);
+  grid.lat.resize(nXt, nYt);
+
+  grid.x.resize(nXt, nYt);
+  grid.y.resize(nXt, nYt);
+  grid.r.resize(nXt, nYt);
+  grid.xi.resize(nXt, nYt);
+  grid.nu.resize(nXt, nYt);
+  grid.X.resize(nXt, nYt);
+  grid.Y.resize(nXt, nYt);
+  grid.Z.resize(nXt, nYt);
+  grid.C.resize(nXt, nYt);
+  grid.D.resize(nXt, nYt);
+  grid.d.resize(nXt, nYt);
+  grid.dlx.resize(nXt, nYt);
+  grid.dln.resize(nXt, nYt);
+  grid.dS.resize(nXt, nYt);
+
+  grid.s2c11.resize(nXt, nYt);
+  grid.s2c12.resize(nXt, nYt);
+  grid.s2c21.resize(nXt, nYt);
+  grid.s2c22.resize(nXt, nYt);
+  grid.c2s11.resize(nXt, nYt);
+  grid.c2s12.resize(nXt, nYt);
+  grid.c2s21.resize(nXt, nYt);
+  grid.c2s22.resize(nXt, nYt);
+
+  grid.gl11.resize(nXt, nYt);
+  grid.gl12.resize(nXt, nYt);
+  grid.gl21.resize(nXt, nYt);
+  grid.gl22.resize(nXt, nYt);
+  grid.gu11.resize(nXt, nYt);
+  grid.gu12.resize(nXt, nYt);
+  grid.gu21.resize(nXt, nYt);
+  grid.gu22.resize(nXt, nYt);
+  grid.sqrtg.resize(nXt, nYt);
+
+  double iD, iL, x, y, theta, phi, r;
+
+  double dx = 2 * a / nX;
+  double dy = 2 * a / nY;
+  double r3, r4;
+  double R2 = R * R;
+
+  // Loop through each point and derive the coordinate
+  // DU is y-direction (down-up)
+  // LR is x-direction (left-right)
+  for (int iDU = 0; iDU < nYt; iDU++) {
+    for (int iLR = 0; iLR < nXt; iLR++) {
+
+      // the offsets are so we can find cell centers, edges, and corners
+      // Centers assume Off = 0.5, which edges assume Off = 0
+      double iD = iDU - nGCs + yOff;
+      double iL = iLR - nGCs + xOff;
+
+      x = -a + iL * dx;
+      y = -a + iD * dy;
+      phi = std::atan(x / a);
+      // y = a * tan(theta) * sec(phi) => y * cos(phi) = a * tan(theta)
+      theta = std::atan(y * std::cos(phi) / a);
+      //std::cout << "Grid creation : "
+      //          << iDU << " "
+      //          << iLR << " "
+      //          << y << " "
+      //          << x << " "
+      //          << theta * cRtoD << " "
+      //          << phi * cRtoD << "\n";
+      grid.lon(iLR, iDU) = phi;
+      grid.lat(iLR, iDU) = theta;
+      grid.X(iLR, iDU) = R * std::cos(theta) * std::cos(phi);
+      grid.Y(iLR, iDU) = R * std::cos(theta) * std::sin(phi);
+      grid.Z(iLR, iDU) = R * std::sin(theta);
+      grid.r(iLR, iDU) = std::sqrt(a * a + x * x + y * y);
+
+      // Equation 28 of Nair:
+      r4 = r * r * r * r;
+      grid.gl11(iLR, iDU) = R2 / r4 * (a * a + y * y);
+      grid.gl12(iLR, iDU) = - R2 / r4 * (x * y);
+      grid.gl21(iLR, iDU) = - R2 / r4 * (x * y);
+      grid.gl22(iLR, iDU) = R2 / r4 * (a * a + x * x);
+      // Equation 29 of Nair:
+      r3 = r * r * r;
+      grid.sqrtg(iLR, iDU) = R2 * a / r3;
+      grid.gu11(iLR, iDU) = grid.gl22(iLR, iDU) / grid.sqrtg(iLR, iDU);
+      grid.gu12(iLR, iDU) = - grid.gl12(iLR, iDU) / grid.sqrtg(iLR, iDU);
+      grid.gu21(iLR, iDU) = - grid.gl21(iLR, iDU) / grid.sqrtg(iLR, iDU);
+      grid.gu22(iLR, iDU) = grid.gl11(iLR, iDU) / grid.sqrtg(iLR, iDU);
+
+      grid.s2c11(iLR, iDU) = a / (R * std::cos(theta) * std::cos(phi)) *
+                             (1 / std::cos(theta));
+      grid.s2c12(iLR, iDU) = 0.0;
+      grid.s2c21(iLR, iDU) = a / (R * std::cos(theta) * std::cos(phi)) *
+                             (std::tan(theta) * std::tan(phi));
+      grid.s2c22(iLR, iDU) = a / (R * std::cos(theta) * std::cos(phi)) *
+                             (1 / std::cos(phi));
+      grid.c2s11(iLR, iDU) = (R * std::cos(theta) * std::cos(phi)) / a * std::cos(
+                               theta);
+      grid.c2s12(iLR, iDU) = 0.0;
+      grid.c2s21(iLR, iDU) = -(R * std::cos(theta) * std::cos(phi)) / a *
+                             std::sin(theta) * std::sin(phi);
+      grid.c2s22(iLR, iDU) = (R * std::cos(theta) * std::cos(phi)) / a * std::cos(
+                               phi);
+
+    }
+  }
+
+  //xxx
+  return grid;
+}
+
+
+grid_struct init_grid(int iFace,
+                      int64_t nX, int64_t nY, int64_t nGCs,
+                      precision_t R,
+                      precision_t xOff, precision_t yOff) {
+
+  grid_struct grid;
+  int64_t nXt = nX + 2 * nGCs;
+  int64_t nYt = nY + 2 * nGCs;
+
+  grid.nXt = nXt;
+  grid.nYt = nYt;
+  grid.nGCs = nGCs;
+  grid.iXfirst_ = nGCs;
+  grid.iYfirst_ = nGCs;
+  grid.iXlast_ = nX + nGCs;
+  grid.iYlast_ = nY + nGCs;
+
+  // Positions:
+  grid.lon.resize(nXt, nYt);
+  grid.lat.resize(nXt, nYt);
+
+  grid.xi.resize(nXt, nYt);
+  grid.nu.resize(nXt, nYt);
+  grid.X.resize(nXt, nYt);
+  grid.Y.resize(nXt, nYt);
+  grid.C.resize(nXt, nYt);
+  grid.D.resize(nXt, nYt);
+  grid.d.resize(nXt, nYt);
+  grid.dlx.resize(nXt, nYt);
+  grid.dln.resize(nXt, nYt);
+  grid.dS.resize(nXt, nYt);
+
+  grid.Axt.resize(nXt, nYt);
+  grid.Axp.resize(nXt, nYt);
+  grid.Ant.resize(nXt, nYt);
+  grid.Anp.resize(nXt, nYt);
+
+  grid.Apn.resize(nXt, nYt);
+  grid.Apx.resize(nXt, nYt);
+  grid.Atn.resize(nXt, nYt);
+  grid.Atx.resize(nXt, nYt);
+
+  precision_t fortyfive = cPI / 4.0;
+  // Xi is LR (x), Nu is UD (y)
+  precision_t dxi = 2.0 * fortyfive / (nX - 1 + xOff * 2);
+  precision_t dnu = 2.0 * fortyfive / (nY - 1 + yOff * 2);
+
+  grid.dxi = dxi;
+  grid.dnu = dnu;
+  grid.R = R;
+
+  precision_t latp, lonp;
+
+  // Loop through each point and derive the coordinate
+
+  precision_t total_area = 0.0, det, dmo;
+
+  for (int iDU = 0; iDU < nYt; iDU++) {
+    for (int iLR = 0; iLR < nXt; iLR++) {
+
+      // the offsets are so we can find cell centers, edges, and corners
+      double iD = iDU - nGCs + yOff;
+      double iL = iLR - nGCs + xOff;
+
+      // Define local coordinates:
+      // Xi is LR (x), Nu is UD (y)
+      grid.nu(iLR, iDU) = (-fortyfive + dnu * iD);
+      grid.xi(iLR, iDU) = (-fortyfive + dxi * iL);
+
+      grid.X(iLR, iDU) = tan(grid.xi(iLR, iDU));
+      grid.Y(iLR, iDU) = tan(grid.nu(iLR, iDU));
+
+      // Transformation from 3D Cartesian to LatLong
+      // lonp = std::atan2(y_cart, x_cart) + cPI/2.0;
+      if (iFace == 0) {
+        lonp = std::atan(grid.X(iLR, iDU));
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(1.0 / grid.Y(iLR, iDU) / std::cos(lonp));
+      }
+
+      if (iFace == 1) {
+        lonp = std::atan(-1.0 / grid.X(iLR, iDU));
+
+        if (lonp < 0)
+          lonp = cPI + lonp;
+
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(1.0 / grid.Y(iLR, iDU) / std::sin(lonp));
+      }
+
+      if (iFace == 2) {
+        lonp = std::atan(grid.X(iLR, iDU)) + cPI;
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(-1.0 / grid.Y(iLR, iDU) / std::cos(lonp));
+      }
+
+      if (iFace == 3) {
+        lonp = std::atan(-1.0 / grid.X(iLR, iDU));
+
+        if (lonp > 0)
+          lonp = lonp + cPI;
+        else
+          lonp = 2 * cPI + lonp;
+
+        // Theta in Ronchi is from the north pole, so lat is 90 - theta
+        latp = std::atan(-1.0 / grid.Y(iLR, iDU) / std::sin(lonp));
+      }
+
+      if (iFace == 4) {
+        lonp = std::atan2(grid.X(iLR, iDU), grid.Y(iLR, iDU));
+        latp = std::atan2(-grid.Y(iLR, iDU), cos(lonp) );
+      }
+
+      if (iFace == 5) {
+        lonp = std::atan2(-grid.X(iLR, iDU), grid.Y(iLR, iDU));
+        latp = -std::atan2(-grid.Y(iLR, iDU), cos(lonp) );
+      }
+
+      if (latp > 0)
+        latp = cPI / 2 - latp;
+      else
+        latp = -(cPI / 2 + latp);
+
+      // Fill Computed coords
+      grid.lat(iLR, iDU) = latp;
+      grid.lon(iLR, iDU) = lonp;
+
+      grid.d(iLR, iDU) =
+        1 +
+        grid.X(iLR, iDU) * grid.X(iLR, iDU) +
+        grid.Y(iLR, iDU) * grid.Y(iLR, iDU);
+      grid.C(iLR, iDU) = sqrt(1 +
+                              grid.X(iLR, iDU) * grid.X(iLR, iDU));
+      grid.D(iLR, iDU) = sqrt(1 +
+                              grid.Y(iLR, iDU) * grid.Y(iLR, iDU));
+
+      if (iFace < 4) {
+        grid.Axt(iLR, iDU) = 0.0;
+        grid.Axp(iLR, iDU) = grid.C(iLR, iDU) * grid.D(iLR, iDU) /
+                             sqrt(grid.d(iLR, iDU));
+        grid.Ant(iLR, iDU) = -1.0;
+        grid.Anp(iLR, iDU) = grid.X(iLR, iDU) * grid.Y(iLR, iDU) /
+                             sqrt(grid.d(iLR, iDU));
+      } else {
+        dmo = 1.0 / std::sqrt(grid.d(iLR, iDU) - 1);
+
+        //if (dmo > 100.0)
+        //  dmo = 100.0;
+
+        if (iFace == 4) {
+          grid.Axt(iLR, iDU) = - dmo * grid.D(iLR, iDU) * grid.X(iLR, iDU);
+          grid.Axp(iLR, iDU) = dmo * grid.D(iLR, iDU) * grid.Y(iLR, iDU) /
+                               sqrt(grid.d(iLR, iDU));
+          grid.Ant(iLR, iDU) = - dmo * grid.C(iLR, iDU) * grid.Y(iLR, iDU);
+          grid.Anp(iLR, iDU) = - dmo * grid.C(iLR, iDU) * grid.X(iLR, iDU) /
+                               sqrt(grid.d(iLR, iDU));
+
+        } else {
+          // iFace == 5
+          grid.Axt(iLR, iDU) = dmo * grid.D(iLR, iDU) * grid.X(iLR, iDU);
+          grid.Axp(iLR, iDU) = - dmo * grid.D(iLR, iDU) * grid.Y(iLR, iDU) /
+                               sqrt(grid.d(iLR, iDU));
+          grid.Ant(iLR, iDU) = dmo * grid.C(iLR, iDU) * grid.Y(iLR, iDU);
+          grid.Anp(iLR, iDU) = dmo * grid.C(iLR, iDU) * grid.X(iLR, iDU) /
+                               sqrt(grid.d(iLR, iDU));
+        }
+      }
+
+      // Calculate inverse of matrix for calculating Ax and An from At and Ap:
+      det = 1.0 / (grid.Axt(iLR, iDU) * grid.Anp(iLR, iDU) -
+                   grid.Axp(iLR, iDU) * grid.Ant(iLR, iDU));
+
+      grid.Atx(iLR, iDU) = det * grid.Anp(iLR, iDU);
+      grid.Atn(iLR, iDU) = - det * grid.Axp(iLR, iDU);
+      grid.Apx(iLR, iDU) = - det * grid.Ant(iLR, iDU);
+      //grid.Apx(iLR, iDU) = grid.d(iLR, iDU) / (grid.C(iLR, iDU) * grid.D(iLR, iDU));
+      grid.Apn(iLR, iDU) = det * grid.Axt(iLR, iDU);
+
+      grid.dlx(iLR, iDU) =
+        R * grid.D(iLR, iDU) * dxi /
+        grid.d(iLR, iDU) /
+        (cos(grid.xi(iLR, iDU)) * cos(grid.xi(iLR, iDU)));
+      grid.dln(iLR, iDU) =
+        R * grid.C(iLR, iDU) * dnu /
+        grid.d(iLR, iDU) /
+        (cos(grid.nu(iLR, iDU)) * cos(grid.nu(iLR, iDU)));
+
+      //grid.dS(iLR, iDU) = R * R * dxi * dnu /
+      //                    (sqrt(grid.d(iLR, iDU) * grid.d(iLR, iDU) * grid.d(iLR, iDU))) *
+      //                    grid.C(iLR, iDU) * grid.C(iLR, iDU) *
+      //                    grid.D(iLR, iDU) * grid.D(iLR, iDU);
+      grid.dS(iLR, iDU) = R * R * dxi * dnu /
+                          (sqrt(grid.d(iLR, iDU) * grid.d(iLR, iDU) * grid.d(iLR, iDU)) *
+                           cos(grid.xi(iLR, iDU)) * cos(grid.xi(iLR, iDU)) *
+                           cos(grid.nu(iLR, iDU)) * cos(grid.nu(iLR, iDU)));
+      //grid.dS(iLR, iDU) = R * R * dxi * dnu *
+      //                    grid.C(iLR, iDU) * grid.D(iLR, iDU) /
+      //                    (grid.d(iLR, iDU) * grid.d(iLR, iDU) *
+      //                     cos(grid.xi(iLR, iDU)) * cos(grid.xi(iLR, iDU)) *
+      //                     cos(grid.nu(iLR, iDU)) * cos(grid.nu(iLR, iDU)));
+
+      if (iLR > 2 && iLR < nXt - 3 &&
+          iDU > 2 && iDU < nYt - 3)
+        total_area = total_area + grid.dS(iLR, iDU);
+    }
+  }
+
+  std::cout << "Total Area : " << total_area << "; expected : " << 4 * cPI * R *
+            R / 6.0 << "\n";
+
+  return grid;
+}
+
+/**
+ * Calculate Great Circle Distance
+ *
+ * doi: 10.1016/j.jcp.2007.07.022
+ * Section 3, Eqn (23)
+ *
+ * @return dh Great Circle Distance between two points
+ */
+precision_t calc_great_circle(precision_t lon1,
+                              precision_t lon2,
+                              precision_t lat1,
+                              precision_t lat2) {
+
+  precision_t dlon_2 = (lon2 - lon1) / 2.0;
+  precision_t dlat_2 = (lat2 - lat1) / 2.0;
+
+  precision_t dh = 2.0 * std::asin(std::sqrt(std::sin(dlat_2) * std::sin(dlat_2) +
+                                             std::sin(dlon_2) * std::sin(dlon_2) * std::cos(lat1) * std::cos(lat2)));
+
+  return dh;
+}
+
+// ---------------------------------------------------------
+// Angle between three points on a sphere
+// ---------------------------------------------------------
+precision_t calc_angle_given_three_lon_lat(precision_t p1_lon,
+                                           precision_t p1_lat,
+                                           precision_t p2_lon,
+                                           precision_t p2_lat,
+                                           precision_t p3_lon,
+                                           precision_t p3_lat,
+                                           precision_t R) {
+
+  arma_vec p1 = sph2cart(p1_lon, p1_lat, R);
+  arma_vec p2 = sph2cart(p2_lon, p2_lat, R);
+  arma_vec p3 = sph2cart(p3_lon, p3_lat, R);
+  arma_vec d1 = p1 - p2;
+  arma_vec d2 = p3 - p2;
+  precision_t n1 = std::sqrt(d1(0) * d1(0) +
+                             d1(1) * d1(1) +
+                             d1(2) * d1(2));
+  precision_t n2 = std::sqrt(d2(0) * d2(0) +
+                             d2(1) * d2(1) +
+                             d2(2) * d2(2));
+  d1 = d1 / n1;
+  d2 = d2 / n2;
+  precision_t angle = std::acos(d1(0) * d2(0) +
+                                d1(1) * d2(1) +
+                                d1(2) * d2(2));
+
+  return angle;
+}
+
+// ---------------------------------------------------------
+// bin edges
+// ---------------------------------------------------------
+
+arma_vec calc_bin_edges(arma_vec centers) {
+
+  int64_t nPts = centers.n_elem;
+  arma_vec edges(nPts + 1);
+
+  precision_t dc = centers(1) - centers(0);
+
+  edges(0) = centers(0) - dc / 2.0;
+  edges(1) = centers(0) + dc / 2.0;
+
+  for (int64_t i = 2; i < nPts + 1; i++)
+    edges(i) = 2 * centers(i - 1) - edges(i - 1);
+
+  return edges;
+}
+
+// ---------------------------------------------------------
+// bin edges
+// ---------------------------------------------------------
+
+arma_mat calc_bin_edges(arma_mat centers, bool DoX) {
+
+  // X is first dimension (row), Y is second dimension (col)
+
+  int64_t nX = centers.n_rows;
+  int64_t nY = centers.n_cols;
+  arma_mat edges;
+  arma_vec centers1d;
+
+  if (DoX) {
+    if (verbose > 2)
+      std::cout << "  --> x\n";
+
+    edges.resize(nX + 1, nY);
+
+    for (int64_t j = 0; j < nY; j++) {
+      centers1d = centers.col(j);
+      edges.col(j) = calc_bin_edges(centers1d);
+    }
+  } else {
+    if (verbose > 2)
+      std::cout << "  --> y\n";
+
+    edges.resize(nX, nY + 1);
+
+    for (int64_t i = 0; i < nX; i++) {
+      centers1d = centers.row(i).as_col();
+      edges.row(i) = calc_bin_edges(centers1d).as_row();
+    }
+  }
+
+  return edges;
+}
+
+// ---------------------------------------------------------
+// bin widths
+// ---------------------------------------------------------
+
+arma_vec calc_bin_widths(arma_vec edges) {
+
+  int64_t nPts = edges.n_elem - 1;
+  arma_vec widths(nPts);
+
+  for (int64_t i = 0; i < nPts; i++)
+    widths(i) = edges(i + 1) - edges(i);
+
+  return widths;
+}
+
+// ---------------------------------------------------------
+// bin widths 2d
+// ---------------------------------------------------------
+
+arma_mat calc_bin_widths(arma_mat edges, bool DoX) {
+
+  int64_t nX = edges.n_rows;
+  int64_t nY = edges.n_cols;
+
+  arma_mat widths;
+  arma_vec edges1d;
+
+  if (DoX) {
+    if (verbose > 2)
+      std::cout << "  --> x\n";
+
+    nX--;
+    widths.resize(nX, nY);
+
+    for (int64_t j = 0; j < nY; j++) {
+      edges1d = edges.col(j);
+      widths.col(j) = calc_bin_widths(edges1d);
+    }
+  } else {
+    if (verbose > 2)
+      std::cout << "  --> y\n";
+
+    nY--;
+    widths.resize(nX, nY);
+
+    for (int64_t i = 0; i < nX; i++) {
+      edges1d = edges.row(i).as_col();
+      widths.row(i) = calc_bin_widths(edges1d).as_row();
+    }
+  }
+
+  return widths;
+}
+
+/**SOME PROJECTION AND GRADIENT CODE **/
+// ---------------------------------------------------------
+//
+// ---------------------------------------------------------
+
+arma_vec limiter_mc(arma_vec &left,
+                    arma_vec &right,
+                    int64_t nPts,
+                    int64_t nGCs) {
+
+  precision_t beta = 0.8;
+
+  arma_vec s = left % right;
+  arma_vec combined = (left + right) * 0.5;
+
+  left = left * beta;
+  right = right * beta;
+  arma_vec limited = left;
+
+  for (int64_t i = 1; i < nPts + 2 * nGCs - 1; i++) {
+    if (s(i) < 0) {
+      // Sign < 0 means opposite signed left and right:
+      limited(i) = 0.0;
+    } else {
+      if (left(i) > 0 && right(i) > 0) {
+        if (right(i) < limited(i))
+          limited(i) = right(i);
+
+        if (combined(i) < limited(i))
+          limited(i) = combined(i);
+      } else {
+        if (right(i) > limited(i))
+          limited(i) = right(i);
+
+        if (combined(i) > limited(i))
+          limited(i) = combined(i);
+      }
+    }
+  }
+
+  return limited;
+}
+
+void print(arma_vec values) {
+  int64_t nP = values.n_elem;
+
+  for (int64_t i = 0; i < nP; i++)
+    std::cout << values(i) << " ";
+
+  std::cout << "\n";
+}
+
+// ---------------------------------------------------------
+// calc gradients at centers
+//   - values and x defined at centers
+// ---------------------------------------------------------
+
+arma_vec calc_grad_1d(arma_vec &values,
+                      arma_vec &x,
+                      int64_t nPts,
+                      int64_t nGCs) {
+
+  arma_vec gradients = values * 0.0;
+  arma_vec gradL = values * 0.0;
+  arma_vec gradR = values * 0.0;
+
+  precision_t factor1 = 0.625;
+  precision_t factor2 = 0.0416667;
+  precision_t h;
+
+  int64_t i;
+  arma_vec hv = values * 0.0;
+
+  i = nGCs - 1;
+  h = 2.0 / (x(i + 1) - x(i));
+  gradR(i) = h * (factor1 * (values(i + 1) - values(i)) -
+                  factor2 * (values(i + 2) - values(i - 1)));
+  gradL(i) = (values(i) - values(i - 1)) / (x(i) - x(i - 1));
+
+  // This is attempting to vectorize the problem, but it seems to be slower?
+  //  int64_t iS = nGCs;
+  //  int64_t iE = nPts + nGCs - 1;
+  //  hv.rows(iS, iE) = 2.0 / (x.rows(iS, iE) - x.rows(iS-1, iE-1));
+  //  gradL.rows(iS, iE) = hv.rows(iS,iE) % (factor1 * (values.rows(iS, iE) -
+  //                values.rows(iS-1, iE-1)) -
+  //           factor2 * (values.rows(iS+1, iE+1) -
+  //                values.rows(iS-2, iE-2)));
+  //  hv.rows(iS, iE) = 2.0 / (x.rows(iS+1, iE+1) - x.rows(iS, iE));
+  //  gradR.rows(iS, iE) = hv.rows(iS,iE) % (factor1 * (values.rows(iS+1, iE+1) -
+  //                values.rows(iS, iE)) -
+  //           factor2 * (values.rows(iS+2, iE+2) -
+  //                values.rows(iS-1, iE-1)));
+
+  for (i = nGCs; i < nPts + nGCs; i++) {
+    h = 2.0 / (x(i) - x(i - 1));
+    gradL(i) = h * (factor1 * (values(i) - values(i - 1)) -
+                    factor2 * (values(i + 1) - values(i - 2)));
+    h = 2.0 / (x(i + 1) - x(i));
+    gradR(i) = h * (factor1 * (values(i + 1) - values(i)) -
+                    factor2 * (values(i + 2) - values(i - 1)));
+  }
+
+  i = nPts + nGCs;
+  h = 2.0 / (x(i) - x(i - 1));
+  gradL(i) = h * (factor1 * (values(i) - values(i - 1)) -
+                  factor2 * (values(i + 1) - values(i - 2)));
+  gradR(i) = (values(i + 1) - values(i)) / (x(i + 1) - x(i));
+
+  gradients = limiter_mc(gradL, gradR, nPts, nGCs);
+
+  return gradients;
+}
+
+// ---------------------------------------------------------
+// calc gradients at centers for 2d matrices
+//   - values and x defined at centers
+// ---------------------------------------------------------
+
+arma_mat calc_grad(arma_mat values,
+                   arma_mat x,
+                   int64_t nGCs,
+                   bool DoX) {
+
+  arma_mat v2d, x2d;
+
+  if (DoX) {
+    v2d = values;
+    x2d = x;
+  } else {
+    v2d = values.t();
+    x2d = x.t();
+  }
+
+  int64_t nX = v2d.n_rows;
+  int64_t nY = v2d.n_cols;
+  arma_mat grad2d = v2d * 0.0;
+
+  int64_t nPts = nX - 2 * nGCs;
+  arma_vec values1d(nX);
+  arma_vec x1d(nX);
+
+  for (int64_t j = 1; j < nY - 1; j++) {
+    values1d = v2d.col(j);
+    x1d = x2d.col(j);
+    grad2d.col(j) = calc_grad_1d(values1d, x1d, nPts, nGCs);
+  }
+
+  arma_mat gradients;
+
+  if (DoX)
+    gradients = grad2d;
+  else
+    gradients = grad2d.t();
+
+  return gradients;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the right face, from the left
+//   returned values are on the i - 1/2 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_mat project_from_left(arma_mat values,
+                           arma_mat gradients,
+                           arma_mat x_centers,
+                           arma_mat x_edges,
+                           int64_t nGCs) {
+
+  int64_t nX = values.n_rows;
+  int64_t nY = values.n_cols;
+
+  // Define at edges:
+  arma_mat projected(nX + 1, nY);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t j = 0; j < nY; j++) {
+    for (int64_t i = 1; i < nX - 1; i++) {
+      projected(i + 1, j) = values(i, j) +
+                            gradients(i, j) * (x_edges(i + 1, j) - x_centers(i, j));
+    }
+
+    projected(1, j) = projected(2, j);
+    projected(0, j) = projected(1, j);
+    projected(nX, j) = projected(nX - 1, j);
+  }
+
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Project gradients + values to the left face, from the right
+//   returned values are on the i - 1 edges
+//     (between i-1 and i cell center)
+// ---------------------------------------------------------
+
+arma_mat project_from_right(arma_mat values,
+                            arma_mat gradients,
+                            arma_mat x_centers,
+                            arma_mat x_edges,
+                            int64_t nGCs) {
+  int64_t nX = values.n_rows;
+  int64_t nY = values.n_cols;
+
+  // Define at edges:
+  arma_mat projected(nX + 1, nY);
+  projected.zeros();
+
+  // no gradient in the 0 or iEnd cells
+  for (int64_t j = 0; j < nY; j++) {
+    for (int64_t i = 1; i < nX - 1; i++) {
+      projected(i, j) = values(i, j) +
+                        gradients(i, j) * (x_edges(i, j) - x_centers(i, j));
+    }
+
+    projected(0, j) = projected(1, j);
+    projected(nX - 1, j) = projected(nX - 2, j);
+    projected(nX, j) = projected(nX - 1, j);
+  }
+
+  return projected;
+}
+
+// ---------------------------------------------------------
+// Limiter on values
+//   projected is assumed to be on the edge between the
+//   i-1 and i cell (i-1/2)
+//   limited is returned at edges
+// ---------------------------------------------------------
+
+arma_vec limiter_value(arma_vec projected,
+                       arma_vec values,
+                       int64_t nPts,
+                       int64_t nGCs) {
+
+  int64_t iStart = 0;
+  int64_t iEnd = nPts + 2 * nGCs;
+
+  arma_vec limited = projected;
+
+  precision_t mini, maxi;
+
+  for (int64_t i = iStart + 1; i < iEnd - 1; i++) {
+
+    mini = values(i - 1);
+
+    if (values(i) < mini)
+      mini = values(i);
+
+    maxi = values(i - 1);
+
+    if (values(i) > maxi)
+      maxi = values(i);
+
+    if (limited(i) < mini)
+      limited(i) = mini;
+
+    if (limited(i) > maxi)
+      limited(i) = maxi;
+  }
+
+  return limited;
+}
+
+// ---------------------------------------------------------
+// take gradients and project to all edges
+// ---------------------------------------------------------
+
+projection_struct project_to_edges(arma_mat &values,
+                                   arma_mat &x_centers, arma_mat &x_edges,
+                                   arma_mat &y_centers, arma_mat &y_edges,
+                                   int64_t nGCs) {
+
+  int64_t nX = values.n_rows;
+  int64_t nY = values.n_cols;
+
+  projection_struct proj;
+
+  proj.gradLR = calc_grad(values, x_centers, nGCs, true);
+  proj.gradDU = calc_grad(values.t(), y_centers.t(), nGCs, true).t();
+
+  proj.R = project_from_left(values, proj.gradLR,
+                             x_centers, x_edges, nGCs);
+  // Left side of edge from left
+  proj.L = project_from_right(values, proj.gradLR,
+                              x_centers, x_edges, nGCs);
+  // Up side of edge from down (left)
+  proj.U = project_from_left(values.t(), proj.gradDU.t(),
+                             y_centers.t(), y_edges.t(), nGCs)
+           .t();
+  // Down side of edge from up (right)
+  proj.D = project_from_right(values.t(), proj.gradDU.t(),
+                              y_centers.t(), y_edges.t(), nGCs)
+           .t();
+
+  return proj;
+}
+
+/**** SOME INITIALIZATION FUNCTION, NOT CORE ****/
+// ---------------------------------------------------------
+// initial rho: initialize the whole field to be 2.0
+// ---------------------------------------------------------
+arma_mat init_rho(arma_mat &x,
+                  arma_mat &y) {
+
+  int64_t nX = x.n_rows;
+  int64_t nY = x.n_cols;
+
+  arma_mat rho(nX, nY);
+  arma_mat r;
+
+  r = sqrt((x - 0.0) % (x - 0.0) + (y - 0.0) % (y - 0.0));
+  rho.fill(1.0);
+  rho.elem( find( r < 0.25)).fill(1.2);
+
+  return rho;
+}
+
+// ---------------------------------------------------------
+// initial velocity: initialize zero velocity
+// ---------------------------------------------------------
+
+arma_mat init_vel(arma_mat &x,
+                  arma_mat &y) {
+  int64_t nX = x.n_rows;
+  int64_t nY = x.n_cols;
+  arma_mat vel(nX, nY);
+  // all cells positive to right:
+  vel.zeros();
+  vel.fill(0.5);
+  return vel;
+}
+
+
+// ---------------------------------------------------------
+// initial values
+// ---------------------------------------------------------
+
+arma_mat init_value(arma_mat &x,
+                    arma_mat &y,
+                    precision_t inVal) {
+  int64_t nX = x.n_rows;
+  int64_t nY = x.n_cols;
+  arma_mat val(nX, nY);
+  val.fill(inVal);
+  return val;
+}
+
+arma_mat init_vel2(arma_mat &x,
+                   arma_mat &y) {
+  int64_t nX = x.n_rows;
+  int64_t nY = x.n_cols;
+  arma_mat vel(nX, nY);
+  // all cells positive to right:
+  vel.zeros();
+  //vel.fill(1.0);
+  return vel;
+}
+
+// ---------------------------------------------------------
+// initial temp (E): constant total energy
+// THIS IS NOT e but E, the total energy
+// ---------------------------------------------------------
+
+arma_mat init_temp(arma_mat &x,
+                   arma_mat &y) {
+  int64_t nX = x.n_rows;
+  int64_t nY = x.n_cols;
+
+  arma_mat temp(nX, nY);
+  temp.fill(100.0);
+  return temp;
+}
+
+// ---------------------------------------------------------
+// Calculate the max speed in the x and y directions
+// ---------------------------------------------------------
+
+void calc_cmax(arma_mat &xVel,
+               arma_mat &yVel,
+               arma_mat &temp,
+               arma_mat &xMax,
+               arma_mat &yMax) {
+
+  if (verbose > 2)
+    std::cout << "  --> calc_max\n";
+
+  arma_mat xVel2, yVel2;
+
+  xVel2 = xVel % xVel;
+  yVel2 = yVel % yVel;
+  xMax = sqrt(xVel2) + sqrt(cKb / mmm * temp);
+  yMax = sqrt(yVel2) + sqrt(cKb / mmm * temp);
+
+  return;
+}
+
+
+// ---------------------------------------------------------
+// Set Boundary Conditions
+// ---------------------------------------------------------
+
+void set_bcs(arma_mat &rho,
+             arma_mat &xVel,
+             arma_mat &yVel,
+             arma_mat &temp,
+             grid_struct gridC) {
+
+  if (verbose > 2)
+    std::cout << "  --> set_bcs\n";
+
+  // ------------------------------------------------
+  // Exchange messages (set BCs, really):
+  for (int64_t i = gridC.iXfirst_; i < gridC.iXlast_; i++) {
+    for (int64_t j = 0; j < gridC.nGCs; j++) {
+      // bottom bc:
+      rho(i, gridC.iYfirst_ - 1 - j) = rho(i, gridC.iYlast_ - 1 - j);
+      // top bc:
+      rho(i, gridC.iYlast_ + j) = rho(i, gridC.iYfirst_ + j);
+    }
+  }
+
+  for (int64_t j = gridC.iYfirst_; j < gridC.iYlast_; j++) {
+    for (int64_t i = 0; i < gridC.nGCs; i++) {
+      // left bc:
+      rho(gridC.iXfirst_ - 1 - i, j) = rho(gridC.iXlast_ - 1 - i, j);
+      // right bc:
+      rho(gridC.iXlast_ + i, j) = rho(gridC.iXfirst_ + i, j);
+    }
+  }
+
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+void convert_vector_ll_to_xn(arma_mat aLon,
+                             arma_mat aLat,
+                             arma_mat &aXi,
+                             arma_mat &aNu,
+                             grid_struct grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  aXi = -grid.Axt % aLat + grid.Axp % aLon;
+  aNu = -grid.Ant % aLat + grid.Anp % aLon;
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from sphere to cube
+//   -> Using equation (32) of Nair:
+// ---------------------------------------------------------
+
+void convert_vector_sphere_to_cube(arma_mat u,
+                                   arma_mat v,
+                                   arma_mat &u1,
+                                   arma_mat &u2,
+                                   grid_struct grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  u1 = grid.s2c11 % u + grid.s2c12 % v;
+  u2 = grid.s2c21 % u + grid.s2c22 % v;
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector cube to sphere
+//   -> Using equation (32) of Nair:
+// ---------------------------------------------------------
+
+void convert_vector_cube_to_sphere(arma_mat u1,
+                                   arma_mat u2,
+                                   arma_mat &u,
+                                   arma_mat &v,
+                                   grid_struct grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  u = grid.c2s11 % u1 + grid.c2s12 % u2;
+  v = grid.c2s21 % u1 + grid.c2s22 % u2;
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+void convert_vector_xn_to_ll(arma_mat aXi,
+                             arma_mat aNu,
+                             arma_mat &aLon,
+                             arma_mat &aLat,
+                             grid_struct grid) {
+
+  // Ronchi defines aPhi = aLon, aTheta = -aLat
+  aLat = -(grid.Atx % aXi + grid.Atn % aNu);
+  aLon = grid.Apx % aXi + grid.Apn % aNu;
+
+  return;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+arma_mat calc_angle_between_coords(grid_struct grid) {
+
+  arma_mat e1Lat, e1Lon, e2Lat, e2Lon, m, one, zero;
+  arma_mat e1dote2, alpha;
+
+  m.resize(grid.nXt, grid.nYt);
+  one.resize(grid.nXt, grid.nYt);
+  one.fill(1.0);
+  zero.resize(grid.nXt, grid.nYt);
+  zero.fill(0.0);
+
+  // define e1 as the LR (xi) direction:
+  e1Lat.resize(grid.nXt, grid.nYt);
+  e1Lon.resize(grid.nXt, grid.nYt);
+  convert_vector_xn_to_ll(one, zero, e1Lon, e1Lat, grid);
+  m = sqrt(e1Lon % e1Lon + e1Lat % e1Lat);
+  e1Lon = e1Lon / m;
+  e1Lat = e1Lat / m;
+
+  // define e2 as the DU (nu) direction:
+  e2Lat.resize(grid.nXt, grid.nYt);
+  e2Lon.resize(grid.nXt, grid.nYt);
+  convert_vector_xn_to_ll(zero, one, e2Lon, e2Lat, grid);
+  m = sqrt(e2Lon % e2Lon + e2Lat % e2Lat);
+  e2Lon = e2Lon / m;
+  e2Lat = e2Lat / m;
+
+  alpha = acos(e1Lat % e2Lat + e1Lon % e2Lon);
+
+  return alpha;
+}
+
+// ---------------------------------------------------------
+// Convert vector from Alat, Alon to Axi, Anu
+//   -> Using equation (7) of Ronchi et al:
+// ---------------------------------------------------------
+
+void calc_norms(grid_struct &grid) {
+
+  arma_mat e1Lat, e1Lon, e2Lat, e2Lon, m, one, zero;
+
+  grid.nXiLon.resize(grid.nXt, grid.nYt);
+  grid.nXiLat.resize(grid.nXt, grid.nYt);
+  grid.nNuLon.resize(grid.nXt, grid.nYt);
+  grid.nNuLat.resize(grid.nXt, grid.nYt);
+
+  m.resize(grid.nXt, grid.nYt);
+  one.resize(grid.nXt, grid.nYt);
+  one.fill(1.0);
+  zero.resize(grid.nXt, grid.nYt);
+  zero.fill(0.0);
+
+  // define e1 as the LR (xi) direction:
+  e1Lat.resize(grid.nXt, grid.nYt);
+  e1Lon.resize(grid.nXt, grid.nYt);
+  convert_vector_xn_to_ll(one, zero, e1Lon, e1Lat, grid);
+  m = sqrt(e1Lon % e1Lon + e1Lat % e1Lat);
+
+  // Rotate by 90 deg (CCW) to get the norm:
+  grid.nNuLon = -e1Lat / m;
+  grid.nNuLat = e1Lon / m;
+
+  // define e2 as the DU (nu) direction:
+  e2Lat.resize(grid.nXt, grid.nYt);
+  e2Lon.resize(grid.nXt, grid.nYt);
+  convert_vector_xn_to_ll(zero, one, e2Lon, e2Lat, grid);
+  m = sqrt(e2Lon % e2Lon + e2Lat % e2Lat);
+  // Rotate by 90 deg (CW) to get the norm:
+  grid.nXiLon = e2Lat / m;
+  grid.nXiLat = -e2Lon / m;
+
+  return;
+}
+
+
+// ---------------------------------------------------------
+// Update States
+// ---------------------------------------------------------
+
+void update_states(arma_mat rho,
+                   arma_mat &xVel,
+                   arma_mat &yVel,
+                   arma_mat &temp,
+                   arma_mat &drhodt,
+                   arma_mat &dlonVeldt,
+                   arma_mat &dlatVeldt,
+                   arma_mat &dtempdt,
+                   grid_struct gridC,
+                   grid_struct gridL,
+                   grid_struct gridD,
+                   precision_t dt) {
+
+  arma_mat xMomentum, yMomentum;
+  arma_mat rhoE, energy, vel2;
+
+  precision_t cv = 1500.0;
+
+  if (verbose > 2)
+    std::cout << "  --> update_states\n";
+
+  // Derived variables:
+  xMomentum = rho % xVel; // x1momentum, pure scalar field
+  yMomentum = rho % yVel; // y1momentum, pure scalar field
+  rhoE = rho % temp;
+
+  vel2 = xVel % xVel + yVel % yVel;
+  //energy = rho % (0.5 * vel2 + cv * temp);
+  energy = cv * rho % temp;
+
+  /** Initialize projection constructs */
+  static projection_struct rhoP;
+  static projection_struct xMomentumP, xVelP;
+  static projection_struct yMomentumP, yVelP;
+  static projection_struct energyP;
+  static projection_struct tempP;
+
+  // They are all pure scalar fields without sqrt(g)
+  static arma_mat totaleL, totaleR, totaleD, totaleU;
+  static arma_mat velL2, velR2, velD2, velU2;
+  static arma_mat pressureL, pressureR, pressureD, pressureU;
+
+  arma_mat dxVeldt = xVel * 0.0;
+  arma_mat dyVeldt = yVel * 0.0;
+
+  dlonVeldt = dxVeldt * 0.0 + 1;
+  dlatVeldt = dyVeldt * 0.0 + 1;
+
+  static arma_mat velNormL, velNormR, velNormU, velNormD;
+
+  /** Initialize Flux and Wave Speed Storages */
+  static arma_mat eq1FluxLR, eq1FluxDU;
+  static arma_mat eq1FluxL, eq1FluxR, eq1FluxD, eq1FluxU;
+  static arma_mat eq2FluxLR, eq2FluxDU;
+  static arma_mat eq2FluxL, eq2FluxR, eq2FluxD, eq2FluxU;
+  static arma_mat eq3FluxLR, eq3FluxDU;
+  static arma_mat eq3FluxL, eq3FluxR, eq3FluxD, eq3FluxU;
+  static arma_mat eq4FluxLR, eq4FluxDU;
+  static arma_mat eq4FluxL, eq4FluxR, eq4FluxD, eq4FluxU;
+
+  arma_mat wsL, wsR, wsD, wsU, wsLR, wsDU;
+
+  arma_mat diff; // for Riemann Solver
+
+  if (verbose > 3)
+    std::cout << "  ---> Projecting\n";
+
+  rhoP = project_to_edges(rho, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                          gridC.nGCs);
+  // project the lon / lat velocities to the edges:
+  xVelP = project_to_edges(xVel, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+  yVelP = project_to_edges(yVel, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+  xMomentumP = project_to_edges(xMomentum, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                                gridC.nGCs);
+  yMomentumP = project_to_edges(yMomentum, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                                gridC.nGCs);
+  energyP = project_to_edges(energy, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                             gridC.nGCs);
+  tempP = project_to_edges(temp, gridC.xi, gridL.xi, gridC.nu, gridD.nu,
+                           gridC.nGCs);
+
+  if (verbose > 3)
+    std::cout << "  ---> Derived values\n";
+
+  velL2 = (xVelP.L % xVelP.L + yVelP.L % yVelP.L);
+  velR2 = (xVelP.R % xVelP.R + yVelP.R % yVelP.R);
+  velD2 = (xVelP.D % xVelP.D + yVelP.D % yVelP.D);
+  velU2 = (xVelP.U % xVelP.U + yVelP.U % yVelP.U);
+
+  precision_t k = 1.38e-23;
+  // let's be Oxygen:
+  precision_t mass = 16.0 * 1.67e-27;
+  pressureL = k / mass * (rhoP.L % tempP.L);
+  pressureR = k / mass * (rhoP.R % tempP.R);
+  pressureD = k / mass * (rhoP.D % tempP.D);
+  pressureU = k / mass * (rhoP.U % tempP.U);
+
+  arma_mat pressureLR = (pressureL + pressureR) / 2;
+  arma_mat pressureDU = (pressureD + pressureU) / 2;
+
+  if (verbose > 3)
+    std::cout << "  ---> Normal Velocities\n";
+
+  // Calculate the normal velocity at the boundaries:
+  velNormL = xVelP.L % gridL.nXiLon + yVelP.L % gridL.nXiLat;
+  velNormR = xVelP.R % gridL.nXiLon + yVelP.R % gridL.nXiLat;
+  velNormU = xVelP.U % gridD.nNuLon + yVelP.U % gridD.nNuLat;
+  velNormD = xVelP.D % gridD.nNuLon + yVelP.D % gridD.nNuLat;
+
+  if (verbose > 3)
+    std::cout << "  ---> Fluxes eq 1\n";
+
+  // Flux calculated from the left of the edge
+  eq1FluxL = rhoP.L % velNormL;
+  // Flux calculated from the right of the edge
+  eq1FluxR = rhoP.R % velNormR;
+  // Flux calculated from the down of the edge
+  eq1FluxD = rhoP.D % velNormD;
+  // Flux calculated from the up of the edge
+  eq1FluxU = rhoP.U % velNormU;
+
+  if (verbose > 3)
+    std::cout << "  ---> Fluxes eq 2\n";
+
+  eq2FluxL = (xMomentumP.L % velNormL);
+  eq2FluxR = (xMomentumP.R % velNormR);
+  eq2FluxD = (xMomentumP.D % velNormD);
+  eq2FluxU = (xMomentumP.U % velNormU);
+
+  if (verbose > 3)
+    std::cout << "  ---> Fluxes eq 3\n";
+
+  eq3FluxL = (yMomentumP.L % velNormL);
+  eq3FluxR = (yMomentumP.R % velNormR);
+  eq3FluxD = (yMomentumP.D % velNormD);
+  eq3FluxU = (yMomentumP.U % velNormU);
+
+  eq4FluxL = energyP.L % velNormL;
+  eq4FluxR = energyP.R % velNormR;
+  eq4FluxD = energyP.D % velNormD;
+  eq4FluxU = energyP.U % velNormU;
+
+  // ------------------------------------------------
+  // Calculate the wave speed for the diffusive flux:
+  // In Reference velocities
+  if (verbose > 3)
+    std::cout << "  ---> Diffusive Fluxes\n";
+
+  wsL = sqrt(velL2) + sqrt(cGamma * (cGamma - 1) * tempP.L);
+  wsR = sqrt(velR2) + sqrt(cGamma * (cGamma - 1) * tempP.R);
+  wsD = sqrt(velD2) + sqrt(cGamma * (cGamma - 1) * tempP.D);
+  wsU = sqrt(velU2) + sqrt(cGamma * (cGamma - 1) * tempP.U);
+
+  wsLR = wsR;
+
+  for (int64_t i = 0; i < gridC.nXt + 1; i++) {
+    for (int64_t j = 0; j < gridC.nYt; j++) {
+      if (wsL(i, j) > wsLR(i, j))
+        wsLR(i, j) = wsL(i, j);
+    }
+  }
+
+  wsDU = wsD;
+
+  for (int64_t i = 0; i < gridC.nXt; i++) {
+    for (int64_t j = 0; j < gridC.nYt + 1; j++) {
+      if (wsU(i, j) > wsDU(i, j))
+        wsDU(i, j) = wsU(i, j);
+    }
+  }
+
+  // ------------------------------------------------
+  // Calculate average flux at the edges (Rusanov Flux):
+
+  if (verbose > 3)
+    std::cout << "  ---> Averaging fluxes at edges\n";
+
+  diff = (rhoP.R - rhoP.L);
+  eq1FluxLR = (eq1FluxL + eq1FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (rhoP.U - rhoP.D);
+  eq1FluxDU = (eq1FluxD + eq1FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (xMomentumP.R - xMomentumP.L);
+  eq2FluxLR = (eq2FluxL + eq2FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (xMomentumP.U - xMomentumP.D);
+  eq2FluxDU = (eq2FluxD + eq2FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (yMomentumP.R - yMomentumP.L);
+  eq3FluxLR = (eq3FluxL + eq3FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (yMomentumP.U - yMomentumP.D);
+  eq3FluxDU = (eq3FluxD + eq3FluxU) / 2 + 0.5 * wsDU % diff;
+
+  diff = (energyP.R - energyP.L);
+  eq4FluxLR = (eq4FluxL + eq4FluxR) / 2 + 0.5 * wsLR % diff;
+  diff = (energyP.U - energyP.D);
+  eq4FluxDU = (eq4FluxD + eq4FluxU) / 2 + 0.5 * wsDU % diff;
+
+  // ------------------------------------------------
+  // Update values:
+  if (verbose > 3)
+    std::cout << "  ---> Updating equations of state\n";
+
+  precision_t dpdx, dpdn, pp, pm;
+
+  arma_mat ax, an;
+
+  ax = xVel * 0.0;
+  an = yVel * 0.0;
+  arma_mat dedt = xVel * 0.0;
+
+  arma_mat rhoNew = rho;
+
+  // Only deal with inner cell
+  for (int64_t j = gridC.iYfirst_; j < gridC.iYlast_; j++) {
+    for (int64_t i = gridC.iXfirst_; i < gridC.iXlast_; i++) {
+      precision_t rhoResidual_ij = (gridL.dln(i + 1, j) * eq1FluxLR(i + 1, j) -
+                                    gridL.dln(i, j) * eq1FluxLR(i, j) +
+                                    gridD.dlx(i, j + 1) * eq1FluxDU(i, j + 1) -
+                                    gridD.dlx(i, j) * eq1FluxDU(i, j));
+      drhodt(i, j) = rhoResidual_ij / gridC.dS(i, j);
+
+      rhoNew(i, j) = rho(i, j) + dt * drhodt(i, j);
+
+      precision_t xMomentumResidual_ij = (gridL.dln(i + 1, j) * eq2FluxLR(i + 1, j) -
+                                          gridL.dln(i, j) * eq2FluxLR(i, j) +
+                                          gridD.dlx(i, j + 1) * eq2FluxDU(i, j + 1) -
+                                          gridD.dlx(i, j) * eq2FluxDU(i, j));
+      dxVeldt(i, j) = xMomentumResidual_ij / gridC.dS(i, j) / rhoNew(i, j);
+
+      precision_t yMomentumResidual_ij = (gridL.dln(i + 1, j) * eq3FluxLR(i + 1, j) -
+                                          gridL.dln(i, j) * eq3FluxLR(i, j) +
+                                          gridD.dlx(i, j + 1) * eq3FluxDU(i, j + 1) -
+                                          gridD.dlx(i, j) * eq3FluxDU(i, j));
+      dyVeldt(i, j) = yMomentumResidual_ij / gridC.dS(i, j) / rhoNew(i, j);
+
+      // Calculate the gradient in the potential in the cubesphere
+      // coordinate system:
+      dpdx = 1 / gridC.R * gridC.D(i, j) *
+             (pressureLR(i + 1, j) - pressureLR(i, j)) / gridC.dxi;
+      dpdn = 1 / gridC.R * gridC.X(i, j) * gridC.Y(i, j) /
+             gridC.D(i, j) *
+             (pressureDU(i, j + 1) - pressureDU(i, j)) / gridC.dnu;
+      ax(i, j) = (dpdx + dpdn) / rhoNew(i, j);
+
+      dpdx = 1 / gridC.R * gridC.X(i, j) * gridC.Y(i, j) /
+             gridC.C(i, j) * (pressureLR(i + 1, j) - pressureLR(i, j)) / gridC.dxi;
+      dpdn = 1 / gridC.R * gridC.C(i, j) *
+             (pressureDU(i, j + 1) - pressureDU(i, j)) / gridC.dnu;
+      an(i, j) = (dpdx + dpdn) / rhoNew(i, j);
+
+      precision_t energyResidual_ij = (gridL.dln(i + 1, j) * eq4FluxLR(i + 1, j) -
+                                       gridL.dln(i, j) * eq4FluxLR(i, j) +
+                                       gridD.dlx(i, j + 1) * eq4FluxDU(i, j + 1) -
+                                       gridD.dlx(i, j) * eq4FluxDU(i, j));
+      dedt(i, j) = energyResidual_ij / gridC.dS(i, j);
+
+    }
+  }
+
+  dlatVeldt = dyVeldt - (ax % gridC.Atx + an % gridC.Atn);
+  dlonVeldt = dxVeldt + ax % gridC.Apx + an % gridC.Apn;
+  dtempdt = dedt / rhoNew / cv;
+
+  return;
+}
+
+
+// ---------------------------------------------------------
+// Main Code!
+// ---------------------------------------------------------
+
+int main() {
+  precision_t dt;        // Time Step
+  precision_t current_time = 0.0; // Initial Time 0
+  precision_t total_time = 100.0;   // Total simulation time
+  precision_t cfl = 0.75;
+  int iFace = 5;
+
+  precision_t dtOut = total_time / 50.0; // Output Interval
+  precision_t dtReport = total_time / 200.0; // Output Interval
+
+  int64_t iStep;    // Iterator of Time Step
+
+  int64_t nX = 50;  // Number of x grid cells
+  int64_t nY = 50; // Number of y grid cells
+  int64_t nGCs = 2; // Number of ghost cells
+
+  // Radius of Sphere
+  precision_t R = 10000.;
+
+  if (verbose > 0)
+    std::cout << "> generating cubesphere cell center and metrics\n";
+
+  grid_struct gridC_eq = init_grid_equidistant(iFace, nX, nY, nGCs, R, 0.5, 0.5);
+
+  output(gridC_eq.lat, "eq_lat.txt", false);
+  output(gridC_eq.lon, "eq_lon.txt", false);
+  output(gridC_eq.r, "eq_r.txt", false);
+
+  grid_struct gridC = init_grid(iFace, nX, nY, nGCs, R, 0.5, 0.5);
+  grid_struct gridL = init_grid(iFace, nX + 1, nY, nGCs, R, 0.0, 0.5);
+  grid_struct gridD = init_grid(iFace, nX, nY + 1, nGCs, R, 0.5, 0.0);
+
+  gridC.alpha = calc_angle_between_coords(gridC);
+  gridL.alpha = calc_angle_between_coords(gridL);
+  gridD.alpha = calc_angle_between_coords(gridD);
+
+  gridC.sinAlpha = sin(gridC.alpha);
+  gridL.sinAlpha = sin(gridL.alpha);
+  gridD.sinAlpha = sin(gridD.alpha);
+
+  calc_norms(gridC);
+  calc_norms(gridL);
+  calc_norms(gridD);
+
+  /** State Initialization */
+  /// Initialize Density
+  if (verbose > 0)
+    std::cout << "> initializing rho\n";
+
+  arma_mat rho = init_rho(gridC.xi, gridC.nu); // rho, pure scalar field
+
+  /// Initialize Velocity and Momentum
+  if (verbose > 0)
+    std::cout << "> initializing vel\n";
+
+  // Initialize spherical velocity
+  // Supposed to be Longitudinal Velocity:
+  arma_mat vLon = init_value(gridC.xi, gridC.nu, 0.0);
+  // Supposed to be Latitudinal Velocity:
+  arma_mat vLat = init_value(gridC.xi, gridC.nu, 0.0);
+
+  arma_mat vXi, vNu;
+  convert_vector_ll_to_xn(vLon, vLat, vXi, vNu, gridC);
+
+
+  /// Initialize total energy
+  if (verbose > 0)
+    std::cout << "> initializing energy\n";
+
+  arma_mat temp = init_temp(gridC.xi, gridC.nu);
+
+  /** Output some pre-simulation results */
+  if (verbose > 0)
+    std::cout << "-> outputting\n";
+
+  output(gridC.xi, "xi.txt", false);
+  output(gridC.dS, "dS.txt", false);
+  output(gridC.d, "d.txt", false);
+  output(gridC.nu, "nu.txt", false);
+  output(gridC.lat, "lat.txt", false);
+  output(gridC.lon, "lon.txt", false);
+  output(gridC.alpha, "alpha.txt", false);
+  output(rho, "rho.txt", false);
+  output(vLon, "vLon.txt", false);
+  output(vLat, "vLat.txt", false);
+  output(vXi, "vXi.txt", false);
+  output(vNu, "vNu.txt", false);
+  output(temp, "temp.txt", false);
+  iStep = 0;
+
+  arma_mat xMax, yMax;
+
+  arma_mat drhodt;
+  arma_mat dxVeldt, dyVeldt;
+  arma_mat dtempdt;
+
+  drhodt.resize(gridC.nXt, gridC.nYt);
+  dxVeldt.resize(gridC.nXt, gridC.nYt);
+  dyVeldt.resize(gridC.nXt, gridC.nYt);
+  dtempdt.resize(gridC.nXt, gridC.nYt);
+
+  arma_mat k1rho, k2rho, k3rho, k4rho, rhoInterK1, rhoInterK2, rhoInterK3;
+  k1rho.resize(gridC.nXt, gridC.nYt);
+  k2rho.resize(gridC.nXt, gridC.nYt);
+  k3rho.resize(gridC.nXt, gridC.nYt);
+  k4rho.resize(gridC.nXt, gridC.nYt);
+  rhoInterK1.resize(gridC.nXt, gridC.nYt);
+  rhoInterK2.resize(gridC.nXt, gridC.nYt);
+  rhoInterK3.resize(gridC.nXt, gridC.nYt);
+
+  arma_mat k1vLon, k2vLon, k3vLon, k4vLon, vLonInterK1, vLonInterK2, vLonInterK3;
+  k1vLon.resize(gridC.nXt, gridC.nYt);
+  k2vLon.resize(gridC.nXt, gridC.nYt);
+  k3vLon.resize(gridC.nXt, gridC.nYt);
+  k4vLon.resize(gridC.nXt, gridC.nYt);
+  vLonInterK1.resize(gridC.nXt, gridC.nYt);
+  vLonInterK2.resize(gridC.nXt, gridC.nYt);
+  vLonInterK3.resize(gridC.nXt, gridC.nYt);
+
+  arma_mat k1vLat, k2vLat, k3vLat, k4vLat, vLatInterK1, vLatInterK2, vLatInterK3;
+  k1vLat.resize(gridC.nXt, gridC.nYt);
+  k2vLat.resize(gridC.nXt, gridC.nYt);
+  k3vLat.resize(gridC.nXt, gridC.nYt);
+  k4vLat.resize(gridC.nXt, gridC.nYt);
+  vLatInterK1.resize(gridC.nXt, gridC.nYt);
+  vLatInterK2.resize(gridC.nXt, gridC.nYt);
+  vLatInterK3.resize(gridC.nXt, gridC.nYt);
+
+  arma_mat k1temp, k2temp, k3temp, k4temp, tempInterK1, tempInterK2, tempInterK3;
+  k1temp.resize(gridC.nXt, gridC.nYt);
+  k2temp.resize(gridC.nXt, gridC.nYt);
+  k3temp.resize(gridC.nXt, gridC.nYt);
+  k4temp.resize(gridC.nXt, gridC.nYt);
+  tempInterK1.resize(gridC.nXt, gridC.nYt);
+  tempInterK2.resize(gridC.nXt, gridC.nYt);
+  tempInterK3.resize(gridC.nXt, gridC.nYt);
+
+  while (current_time < total_time) {
+
+    if (int((current_time - dt) / dtReport) != int((current_time ) / dtReport)) {
+      std::cout << "step : " << iStep << "; time : " << current_time << "\n";
+      arma_vec amin, amax;
+      precision_t mini, maxi;
+      amin = rho.min();
+      mini = amin.min();
+      amax = max(rho, 1);
+      maxi = arma::max(amax);
+      std::cout << "  -> min/max (rho) : " << mini << " / " << maxi << "\n";
+      amin = temp.min();
+      mini = amin.min();
+      amax = max(temp, 1);
+      maxi = arma::max(amax);
+      std::cout << "  -> min/max (temp) : " << mini << " / " << maxi << "\n";
+    }
+
+    calc_cmax(vLon, vLat, temp, xMax, yMax);
+    dt = calc_dt(gridC.dlx, gridC.dln, xMax, yMax, gridC.nGCs);
+    dt = cfl * dt;
+    std::cout << " dt: " << dt << "\n";
+
+    // k1 - start at t0, go to t+1/2 to figure out slope at t0 (k1)
+    update_states(rho, vLon, vLat, temp,
+                  k1rho, k1vLon, k1vLat, k1temp,
+                  gridC, gridL, gridD, dt / 2);
+    // Take 1/2 step to figure out t+1/2 values, using k1:
+    rhoInterK1 = rho + k1rho * dt / 2;
+    vLonInterK1 = vLon + k1vLon * dt / 2;
+    vLatInterK1 = vLat + k1vLat * dt / 2;
+    tempInterK1 = temp + k1temp * dt / 2;
+    set_bcs(rhoInterK1, vLonInterK1, vLatInterK1, tempInterK1, gridC);
+
+    // k2 - start at t+1/2, go to t+1 to figure out slope at t+1/2 (k2)
+    update_states(rhoInterK1, vLonInterK1, vLatInterK1, tempInterK1,
+                  k2rho, k2vLon, k2vLat, k2temp,
+                  gridC, gridL, gridD, dt / 2);
+    // Take 1/2 step to figure out t+1/2 values, using k2:
+    rhoInterK2 = rho + k2rho * dt / 2;
+    vLonInterK2 = vLon + k2vLon * dt / 2;
+    vLatInterK2 = vLat + k2vLat * dt / 2;
+    tempInterK2 = temp + k2temp * dt / 2;
+    set_bcs(rhoInterK2, vLonInterK2, vLatInterK2, tempInterK2, gridC);
+
+    // k3 - start at t+1/2, go to t+1 to figure out slope at t+1/2 (k3)
+    update_states(rhoInterK2, vLonInterK2, vLatInterK2, tempInterK2,
+                  k3rho, k3vLon, k3vLat, k3temp,
+                  gridC, gridL, gridD, dt / 2);
+    // Take full step to figure out k4, using k3 slope:
+    rhoInterK3 = rho + k3rho * dt;
+    vLonInterK3 = vLon + k3vLon * dt;
+    vLatInterK3 = vLat + k3vLat * dt;
+    tempInterK3 = temp + k3rho * dt;
+    set_bcs(rhoInterK3, vLonInterK3, vLatInterK3, tempInterK3, gridC);
+
+    // k4 - start at t+1, go to t+2 to figure out slope at t+1 (k4)
+    update_states(rhoInterK3, vLonInterK3, vLatInterK3, tempInterK3,
+                  k4rho, k4vLon, k4vLat, k4temp,
+                  gridC, gridL, gridD, dt);
+
+    rho = rho - dt / 6 * (k1rho + 2 * k2rho + 2 * k3rho + k4rho);
+    vLon = vLon - dt / 6 * (k1vLon + 2 * k2vLon + 2 * k3vLon + k4vLon);
+    vLat = vLat - dt / 6 * (k1vLat + 2 * k2vLat + 2 * k3vLat + k4vLat);
+    temp = temp - dt / 6 * (k1temp + 2 * k2temp + 2 * k3temp + k4temp);
+    set_bcs(rho, vLon, vLat, temp, gridC);
+
+    iStep++;
+    current_time += dt;
+
+    if (verbose > 3)
+      std::cout << "  ---> Outputing\n";
+
+    if (int((current_time - dt) / dtOut) != int((current_time ) / dtOut)) {
+      std::cout << "> Outputing at time : " << current_time << "\n";
+      output(rho, "rho.txt", true);
+      output(vLon, "vLon.txt", true);
+      output(vLat, "vLat.txt", true);
+      output(temp, "temp.txt", true);
+    }
+  }
+
+  return 0;
+}

From d7b9cb9877077085cf47eec59183576ff577f611 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 22 Aug 2025 15:05:30 -0400
Subject: [PATCH 181/266] STY: spacing, BUG: removed variables

---
 include/grid.h | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index da702265..daae63d9 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -100,10 +100,7 @@ class Grid {
   arma_cube sqrt_g_Down;
 
   cubesphere_chars cubeC, cubeL, cubeD;
-  
-  // These define the magnetic grid:
-  // Armidillo Cube Versions:
-  arma_cube magLon_scgc, magX_scgc;
+
   // The magnetic latitude and altitude need to be defined better. This should be the angle between
   // magnetic equator and the point, but sometimes it is invariant latitude.
   // These define the magnetic grid (only defined for a dipole grid):
@@ -510,8 +507,8 @@ class Grid {
   bool set_interpolation_coefs(const std::vector<precision_t> &Lons,
                                const std::vector<precision_t> &Lats,
                                const std::vector<precision_t> &Alts,
-                               bool areLocsGeo=true,
-                               bool areLocsIJK=true);
+                               bool areLocsGeo = true,
+                               bool areLocsIJK = true);
 
   /**
    * \brief Set the interpolation coefficients for the dipole grid

From 180a6a2e333cb4018f531689bab4473cc585afa4 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 22 Aug 2025 15:06:04 -0400
Subject: [PATCH 182/266] Moved advection solver into neutral class

---
 src/solver_advection.cpp | 48 +++++++++++++++++++---------------------
 1 file changed, 23 insertions(+), 25 deletions(-)

diff --git a/src/solver_advection.cpp b/src/solver_advection.cpp
index 1e69c7bc..48e88688 100644
--- a/src/solver_advection.cpp
+++ b/src/solver_advection.cpp
@@ -282,9 +282,7 @@ precision_t calc_dt(arma_mat &xWidth,
 //
 // ---------------------------------------------------------
 
-void advect(Grid &grid,
-            Times &time,
-            Neutrals &neutrals) {
+void Neutrals::advect_sphere(Grid &grid, Times &time) {
 
   std::string function = "advect";
   static int iFunction = -1;
@@ -337,9 +335,9 @@ void advect(Grid &grid,
   arma_mat gamma2d;
 
   // These are all needed by the solver:
-  neutrals.calc_mass_density();
-  neutrals.calc_mean_major_mass();
-  neutrals.calc_specific_heat();
+  calc_mass_density();
+  calc_mean_major_mass();
+  calc_specific_heat();
 
   arma_mat t_to_e;
 
@@ -350,14 +348,14 @@ void advect(Grid &grid,
     if (report.test_verbose(3))
       std::cout << "Advection: Working with iAlt: " << iAlt << "\n";
 
-    xVel = neutrals.velocity_vcgc[0].slice(iAlt);
-    yVel = neutrals.velocity_vcgc[1].slice(iAlt);
-    rho = neutrals.rho_scgc.slice(iAlt);
+    xVel = velocity_vcgc[0].slice(iAlt);
+    yVel = velocity_vcgc[1].slice(iAlt);
+    rho = rho_scgc.slice(iAlt);
     // this is "e", or temperature expressed as an energy
-    gamma2d = neutrals.gamma_scgc.slice(iAlt);
-    t_to_e = 1.0 / (gamma2d - 1.0) * cKB / neutrals.mean_major_mass_scgc.slice(
+    gamma2d = gamma_scgc.slice(iAlt);
+    t_to_e = 1.0 / (gamma2d - 1.0) * cKB / mean_major_mass_scgc.slice(
                iAlt);
-    temp = t_to_e % neutrals.temperature_scgc.slice(iAlt);
+    temp = t_to_e % temperature_scgc.slice(iAlt);
 
     // ------------------------------------------------
     // Calculate derived equations (at cell centers - these will be updated)
@@ -542,8 +540,8 @@ void advect(Grid &grid,
     xVel = xMomentum / rho;
     yVel = yMomentum / rho;
 
-    neutrals.velocity_vcgc[0].slice(iAlt) = xVel;
-    neutrals.velocity_vcgc[1].slice(iAlt) = yVel;
+    velocity_vcgc[0].slice(iAlt) = xVel;
+    velocity_vcgc[1].slice(iAlt) = yVel;
     temp = (totalE / rho - 0.5 * (xVel % xVel + yVel % yVel)) / t_to_e;
     temp.clamp(200, 2000);
 
@@ -555,35 +553,35 @@ void advect(Grid &grid,
         //if (cos(grid.geoLat_scgc(i,j,iAlt)) < 0.2) {
         //  fac = fac * (0.2 - cos(grid.geoLat_scgc(i,j,iAlt)));
         //}
-        //dm = (1.0 - fac) * neutrals.temperature_scgc(i,j,iAlt);
-        //dp = (1.0 + fac) * neutrals.temperature_scgc(i,j,iAlt);
+        //dm = (1.0 - fac) * temperature_scgc(i,j,iAlt);
+        //dp = (1.0 + fac) * temperature_scgc(i,j,iAlt);
         //if (temp(i,j) < dm) temp(i,j) = dm;
         //if (temp(i,j) > dp) temp(i,j) = dp;
-        neutrals.temperature_scgc(i, j, iAlt) = temp(i, j);
+        temperature_scgc(i, j, iAlt) = temp(i, j);
 
-        //dm = (1.0 - fac) * neutrals.rho_scgc(i,j,iAlt);
-        //dp = (1.0 + fac) * neutrals.rho_scgc(i,j,iAlt);
+        //dm = (1.0 - fac) * rho_scgc(i,j,iAlt);
+        //dp = (1.0 + fac) * rho_scgc(i,j,iAlt);
         //if (rho(i,j) < dm) rho(i,j) = dm;
         //if (rho(i,j) > dp) rho(i,j) = dp;
-        neutrals.rho_scgc(i, j, iAlt) = rho(i, j);
+        rho_scgc(i, j, iAlt) = rho(i, j);
       }
     }
 
     if (report.test_verbose(3) && iAlt == 8) {
-      std::cout << "end t : " << neutrals.temperature_scgc.slice(
-                  iAlt).min() << " " << neutrals.temperature_scgc.slice(iAlt).max() << "\n";
+      std::cout << "end t : " << temperature_scgc.slice(
+                  iAlt).min() << " " << temperature_scgc.slice(iAlt).max() << "\n";
       std::cout << "end temp : " << temp.min() << " " << temp.max() << "\n";
       std::cout << "end xVel : " << xVel.min() << " " << xVel.max() << "\n";
       std::cout << "end yVel : " << yVel.min() << " " << yVel.max() << "\n";
     }
   }
 
-  neutrals.calc_density_from_mass_concentration();
+  calc_density_from_mass_concentration();
 
   // Assign bulk horizontal velocity to all species:
-  for (int64_t iSpecies = 0; iSpecies < neutrals.nSpecies; iSpecies++)
+  for (int64_t iSpecies = 0; iSpecies < nSpecies; iSpecies++)
     for (int64_t iDir = 0; iDir < 2; iDir++)
-      neutrals.species[iSpecies].velocity_vcgc[iDir] = neutrals.velocity_vcgc[iDir];
+      species[iSpecies].velocity_vcgc[iDir] = velocity_vcgc[iDir];
 
   report.exit(function);
   return;

From 007ce26f8c8373b6a706e0a152665190adbd2112 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 22 Aug 2025 15:06:29 -0400
Subject: [PATCH 183/266] Moved advection solver into neutral class

---
 include/neutrals.h      |  5 +----
 src/neutrals_advect.cpp | 25 ++++++++++++++-----------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/include/neutrals.h b/include/neutrals.h
index 61686eab..34f67e34 100644
--- a/include/neutrals.h
+++ b/include/neutrals.h
@@ -597,16 +597,15 @@ class Neutrals {
 
      \param grid
      \param time
-     \param report
    **/
   void solver_horizontal_rusanov_advection(Grid& grid, Times& time);
+  void advect_sphere(Grid &grid, Times &time);
 
   /**********************************************************************
      \brief Solves for fluid motion (pure advect) horizontally with RK1
 
      \param grid
      \param time
-     \param report
    **/
   void solver_horizontal_RK1_advection(Grid& grid, Times& time);
 
@@ -615,7 +614,6 @@ class Neutrals {
 
      \param grid
      \param time
-     \param report
    **/
   void solver_horizontal_RK2_advection(Grid& grid, Times& time);
 
@@ -624,7 +622,6 @@ class Neutrals {
 
      \param grid
      \param time
-     \param report
    **/
   void solver_horizontal_RK4_advection(Grid& grid, Times& time);
 
diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index 465afad3..f2b11652 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -40,18 +40,21 @@ bool Neutrals::advect_horizontal(Grid & grid, Times & time) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  if (input.get_advection_neutrals_horizontal() == "advect_test")
-    solver_horizontal_RK4_advection(grid, time);
-  else if (input.get_advection_neutrals_horizontal() == "fv")
-    solver_horizontal_RK1_rochi(grid, time);
+  if (grid.iGridShape_ == grid.iCubesphere_) {
+    if (input.get_advection_neutrals_horizontal() == "advect_test")
+      solver_horizontal_RK4_advection(grid, time);
+    else if (input.get_advection_neutrals_horizontal() == "fv")
+      solver_horizontal_RK1_rochi(grid, time);
+    else {
+      std::cout << "Horizontal solver not found!\n";
+      std::cout << "  ==> Requested : "
+                << input.get_advection_neutrals_horizontal()
+                << "\n";
+      didWork = false;
+    }
+  } else
+    advect_sphere(grid, time);
 
-  else {
-    std::cout << "Horizontal solver not found!\n";
-    std::cout << "  ==> Requested : "
-              << input.get_advection_neutrals_horizontal()
-              << "\n";
-    didWork = false;
-  }
 
   report.exit(function);
   return didWork;

From 58ef38038cdc6603eeb85e04d12a749777b191d3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 22 Aug 2025 15:06:52 -0400
Subject: [PATCH 184/266] remove cout

---
 src/exchange_messages.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index 895bb854..09f5ed1e 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -1109,8 +1109,6 @@ bool exchange_one_var(Grid &grid,
   if (!grid.isExchangeInitialized) {
     DidWork = exchange_sides_init(grid, nVarsToPass);
     grid.isExchangeInitialized = true;
-    std::cout << "initializing : " << grid.get_gridtype() << " " << nZ << " " <<
-              iProc << "\n";
   }
 
   var_scgc.set_size(nX, nY, nZ);

From dc68f6f3e5c0242a85b5bf05fbcb9857b4a09fb2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Fri, 22 Aug 2025 15:07:16 -0400
Subject: [PATCH 185/266] test run should be much faster

---
 share/run/UA/inputs/defaults.json |  2 +-
 share/run/aether.json             | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index fd7bfc84..91ce86a8 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -24,7 +24,7 @@
     "Advection" : {
 		"Neutrals" : {
 			"Vertical" : "rusanov",
-			"Horizontal" : "default",
+			"Horizontal" : "fv",
 			"useBulkWinds" : true,
 			"useImplicitFriction" : true},
 		"Ions" : {
diff --git a/share/run/aether.json b/share/run/aether.json
index cc3e330d..b192b5b5 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -10,21 +10,21 @@
         "dt" : 10.0,
         "check_for_nans" : true},
 
-    "EndTime" : [2011, 3, 20, 0, 10, 0],
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
 
     "neuGrid" : {
         "Shape": "sphere4",
-        "nLonsPerBlock" : 24,
-        "nLatsPerBlock" : 22,
+        "nLonsPerBlock" : 20,
+        "nLatsPerBlock" : 18,
         "nAlts" : 40,
-        "dAltScale" : 0.25,
+        "dAltScale" : 0.3,
         "IsUniformAlt" : false},
     
     "ionGrid": {
         "Shape": "dipole4",
-        "nLonsPerBlock": 36,
+        "nLonsPerBlock": 24,
         "nLatsPerBlock": 18,
-        "nAlts": 100,
+        "nAlts": 50,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
         "LonRange": [0.0, 360.0]},

From a9faa80d942d7f23a8ab63c915e2f2253f45fa26 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:08:50 -0400
Subject: [PATCH 186/266] tabs and adding in display cube and matrix

---
 include/tools.h | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/include/tools.h b/include/tools.h
index e8200476..2283c1b7 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -64,6 +64,8 @@ std::string add_cmember(std::string inString);
 // ----------------------------------------------------------------------
 
 void display_vector(arma_vec vec);
+void display_matrix(std::string, arma_mat mat);
+void display_cube(std::string, arma_cube values);
 
 // ----------------------------------------------------------------------
 // Display an armadillo vector with a strong name in front
@@ -311,7 +313,7 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 // convert mag coordinates to dipole ijk  //
 ////////////////////////////////////////////
 std::vector<precision_t> mag_to_ijk(precision_t mlon,
-                                    precision_t mlat, 
+                                    precision_t mlat,
                                     precision_t radius,
                                     precision_t planet_radius);
 
@@ -320,9 +322,9 @@ std::vector<precision_t> mag_to_ijk(precision_t mlon,
 // convert cell coordinates to geographic //
 ////////////////////////////////////////////
 std::vector<arma_cube> geo_to_mag(arma_cube glon,
-                                    arma_cube glat, 
-                                    arma_cube radius,
-																	Planets &planet) ;
+                                  arma_cube glat,
+                                  arma_cube radius,
+                                  Planets &planet) ;
 
 
 arma_cube vec2cube(std::vector<precision_t> ivec);

From be8d2070dc96ebe56c6c1fdf59ee204822e25376 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:09:33 -0400
Subject: [PATCH 187/266] add in things needed for sphere

---
 share/run/UA/inputs/defaults.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 91ce86a8..5aad82bb 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -86,6 +86,10 @@
         "nLonsPerBlock": 36,
         "nLatsPerBlock": 18,
         "nAlts": 100,
+	"AltFile": "",
+        "dAltScale" : 0.25,
+        "IsUniformAlt" : false,	
+	"MinAlt": 80.0,
         "LatRange": [10, 80],
         "AltRange": [80.0, 1000],
         "LonRange": [0.0, 360.0]},

From efbfc8e16b7f7ca0fafd75dee8d0a340eeaeac2f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:10:08 -0400
Subject: [PATCH 188/266] turn back on some terms

---
 src/add_sources.cpp | 71 ++++++++++++++++++++++++---------------------
 1 file changed, 38 insertions(+), 33 deletions(-)

diff --git a/src/add_sources.cpp b/src/add_sources.cpp
index b9eaf2a9..40786512 100644
--- a/src/add_sources.cpp
+++ b/src/add_sources.cpp
@@ -21,7 +21,7 @@ void Neutrals::add_sources(Times time, Planets planet, Grid grid) {
   heating_sources_total = heating_euv_scgc
                           + heating_chemical_scgc
                           + heating_ion_friction_scgc
-                          //+ heating_ion_heat_transfer_scgc
+                          + heating_ion_heat_transfer_scgc
                           - O_cool_scgc
                           - NO_cool_scgc;
 
@@ -32,11 +32,11 @@ void Neutrals::add_sources(Times time, Planets planet, Grid grid) {
   int64_t iDir, iSpec, iSpecies;
   double tSim = time.get_simulation_time();
 
-  // Horizontal winds use bulk winds:
-  if (input.get_use_coriolis())
-    acc_coriolis = coriolis(velocity_vcgc, planet.get_omega(), grid.geoLat_scgc);
-
   /*
+    // Horizontal winds use bulk winds:
+    if (input.get_use_coriolis())
+      acc_coriolis = coriolis(velocity_vcgc, planet.get_omega(), grid.geoLat_scgc);
+
     // Vertical winds use species winds:
     for (iSpec = 0; iSpec < nSpeciesAdvect; iSpec++) {
       // Pick out the advected neutral species:
@@ -48,62 +48,64 @@ void Neutrals::add_sources(Times time, Planets planet, Grid grid) {
       advected_neutral.velocity_vcgc[iDir] =
         advected_neutral.velocity_vcgc[iDir] +
         dt * (ramp * grid.cent_acc_vcgc[iDir] +
-        ramp * acc_coriolis[iDir] +
-        advected_neutral.acc_neutral_friction[iDir] / 4.0 +
-        advected_neutral.acc_ion_drag[iDir] +
-        advected_neutral.acc_eddy);
+              ramp * acc_coriolis[iDir] +
+              advected_neutral.acc_neutral_friction[iDir] / 4.0 +
+              advected_neutral.acc_ion_drag[iDir] +
+              advected_neutral.acc_eddy);
     }
 
     calc_mass_density();
     // Calculate bulk vertical winds:
     velocity_vcgc[2].zeros();
+
     for (iSpecies = 0; iSpecies < nSpecies; iSpecies++)
       if (species[iSpecies].DoAdvect) {
         velocity_vcgc[2] = velocity_vcgc[2] +
-          species[iSpecies].mass * species[iSpecies].density_scgc %
-          species[iSpecies].velocity_vcgc[2] / rho_scgc;
+                           species[iSpecies].mass * species[iSpecies].density_scgc %
+                           species[iSpecies].velocity_vcgc[2] / rho_scgc;
       }
 
-    */
-
-  // Add Velocity sources to bulk winds:
-  for (iDir = 0; iDir < 2; iDir++) {
-    velocity_vcgc[iDir] =
-      velocity_vcgc[iDir] + dt * (
-        grid.cent_acc_vcgc[iDir] +
-        acc_coriolis[iDir] +
-        acc_ion_collisions[iDir]);
-    acc_sources_total[iDir].zeros();
-  }
 
-  // Apply Viscosity:
-  update_horizontal_velocity(grid, time);
+    // Add Velocity sources to bulk winds:
+    for (iDir = 0; iDir < 2; iDir++) {
+      velocity_vcgc[iDir] =
+        velocity_vcgc[iDir] + dt * (
+          grid.cent_acc_vcgc[iDir] +
+          acc_coriolis[iDir] +
+          acc_ion_collisions[iDir]);
+      acc_sources_total[iDir].zeros();
+    }
 
-  // Assign bulk horizontal velocity to all species:
-  for (iSpecies = 0; iSpecies < nSpecies; iSpecies++)
-    for (iDir = 0; iDir < 2; iDir++)
-      species[iSpecies].velocity_vcgc[iDir] = velocity_vcgc[iDir];
+    // Apply Viscosity:
+    update_horizontal_velocity(grid, time);
 
-  /*
+    // Assign bulk horizontal velocity to all species:
+    for (iSpecies = 0; iSpecies < nSpecies; iSpecies++)
+      for (iDir = 0; iDir < 2; iDir++)
+        species[iSpecies].velocity_vcgc[iDir] = velocity_vcgc[iDir];
+  */
   // If we only consider the bulk winds in the horizontal direction:
   if (input.get_advection_neutrals_bulkwinds()) {
     // Calculate Coriolis:
     if (input.get_use_coriolis())
       acc_coriolis = coriolis(velocity_vcgc, planet.get_omega(), grid.geoLat_scgc);
+
     // Add Velocity sources to bulk winds:
     for (int iDir = 0; iDir < 3; iDir++) {
       velocity_vcgc[iDir] = velocity_vcgc[iDir] + dt * (
-        grid.cent_acc_vcgc[iDir] +
-        acc_coriolis[iDir] +
-        acc_ion_collisions[iDir]);
+                              grid.cent_acc_vcgc[iDir] +
+                              acc_coriolis[iDir] +
+                              acc_ion_collisions[iDir]);
       acc_sources_total[iDir].zeros();
     }
+
     // Apply Viscosity:
     update_horizontal_velocity(grid, time);
   } else {
     for (int64_t iSpec = 0; iSpec < nSpeciesAdvect; iSpec++) {
       // Pick out the advected neutral species:
       species_chars & advected_neutral = species[species_to_advect[iSpec]];
+
       // Calculate Coriolis:
       if (input.get_use_coriolis())
         acc_coriolis = coriolis(advected_neutral.velocity_vcgc,
@@ -119,6 +121,7 @@ void Neutrals::add_sources(Times time, Planets planet, Grid grid) {
                 acc_coriolis[iDir] +
                 advected_neutral.acc_neutral_friction[iDir] / 4.0 +
                 advected_neutral.acc_ion_drag[iDir]);
+
         // eddy acceleration is only in the vertical direction:
         if (iDir == 2)
           advected_neutral.velocity_vcgc[iDir] =
@@ -126,10 +129,12 @@ void Neutrals::add_sources(Times time, Planets planet, Grid grid) {
             dt * advected_neutral.acc_eddy;
       }
     }
+
     calc_bulk_velocity();
   }
+
   assign_bulk_velocity();
-  */
+
   report.exit(function);
   return;
 }

From 2449592c81cbc06b89dcf758d2383a0ddbc1173a Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:10:52 -0400
Subject: [PATCH 189/266] fill corners augmenting and display cube/matrix

---
 src/tools.cpp | 88 +++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 78 insertions(+), 10 deletions(-)

diff --git a/src/tools.cpp b/src/tools.cpp
index 4ced3b73..efc6fcc2 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -121,7 +121,44 @@ void fill_corners(arma_cube &values, int64_t nGCs) {
 
   int64_t nXs = values.n_rows, iX;
   int64_t nYs = values.n_cols, iY;
-  int64_t iGCx, iGCy;
+  int64_t nZs = values.n_slices, iZ;
+  int64_t iGCx, iGCy, iGCz;
+
+  // Bottom:
+  for (iGCz == 0; iGCz < nGCs; iGCz++) {
+    for (iGCx = 0; iGCx < nGCs; iGCx++) {
+      for (iY = 0; iY < nYs; iY++) {
+        // Bottom:
+        values(iGCx, iY, iGCz) =
+          values(nGCs, iY, nGCs);
+        values(nXs - iGCx - 1, iY, iGCz) =
+          values(nXs - nGCs - 1, iY, nGCs);
+        // top:
+        values(iGCx, iY, nZs - iGCz - 1) =
+          values(nGCs, iY, nZs - nGCs - 1);
+        values(nXs - iGCx - 1, iY, nZs - iGCz - 1) =
+          values(nXs - nGCs - 1, iY, nZs - nGCs - 1);
+      }
+    }
+  }
+
+  for (iGCz = 0; iGCz < nGCs; iGCz++) {
+    for (iGCy = 0; iGCy < nGCs; iGCy++) {
+      for (iX = 0; iX < nXs; iX++) {
+        // Bottoms:
+        values(iX, iGCy, iGCz) =
+          values(iX, nGCs, nGCs);
+        values(iX, nYs - iGCy - 1, iGCz) =
+          values(iX, nYs - nGCs - 1, nGCs);
+        // tops:
+        values(iX, iGCy, nZs - iGCz - 1) =
+          values(iX, nGCs, nZs - nGCs - 1);
+        values(iX, nYs - iGCy - 1, nZs - iGCz - 1) =
+          values(iX, nYs - nGCs - 1, nZs - nGCs - 1);
+
+      }
+    }
+  }
 
   for (iGCx = 0; iGCx < nGCs; iGCx++) {
     for (iGCy = 0; iGCy < nGCs; iGCy++) {
@@ -159,6 +196,35 @@ void display_vector(arma_vec vec) {
   std::cout << "\n";
 }
 
+// ----------------------------------------------------------------------------
+// Neatly display an armadillo matrix with a name
+// ----------------------------------------------------------------------------
+
+void display_cube(std::string name, arma_cube values) {
+  std::cout << name << " ";
+
+  for (int64_t i = 0; i < values.n_slices; i++) {
+    std::cout << "Slice : " << i << ":\n";
+    display_matrix(" ", values.slice(i));
+  }
+
+}
+
+// ----------------------------------------------------------------------------
+// Neatly display an armadillo matrix with a name
+// ----------------------------------------------------------------------------
+
+void display_matrix(std::string name, arma_mat mat) {
+  std::cout << name << "\n";
+
+  for (int64_t i = 0; i < mat.n_cols; i++)
+    display_vector(" ", mat.col(i));
+
+  std::cout << "\n";
+}
+
+
+
 // ----------------------------------------------------------------------------
 // Neatly display an armadillo vector with a name
 // ----------------------------------------------------------------------------
@@ -671,7 +737,8 @@ bool all_finite(arma_cube cube, std::string name) {
       "," + std::to_string(loc[1]) +
       "," + std::to_string(loc[2]) + ")";
     int size = locations.size();
-    std::cout << "all_finite ("<<name<<"): " << cube(loc[0], loc[1], loc[2]) << "\n";
+    std::cout << "all_finite (" << name << "): " << cube(loc[0], loc[1],
+                                                         loc[2]) << "\n";
     std::string error_message =
       std::to_string(size) +
       " Nonfinite values exist in " + name +
@@ -904,11 +971,11 @@ std::vector <arma_cube> mag_to_geo(arma_cube magLon, arma_cube magLat,
 ////////////////////////////////////////////
 
 std::vector<arma_cube> geo_to_mag(arma_cube glon,
-                                    arma_cube glat, 
-                                    arma_cube radius,
-                                    Planets &planet) {
+                                  arma_cube glat,
+                                  arma_cube radius,
+                                  Planets &planet) {
 
-    std::string function = "Grid::geo_to_gmag";
+  std::string function = "Grid::geo_to_gmag";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
@@ -946,12 +1013,12 @@ std::vector<arma_cube> geo_to_mag(arma_cube glon,
 
 
 std::vector<precision_t> mag_to_ijk(precision_t mlon,
-                                    precision_t mLat, 
+                                    precision_t mLat,
                                     precision_t radius,
                                     precision_t planet_radius) {
 
   precision_t i_lon, j_p, k_q;
-  
+
   // precision_t planet_radius = planet.get_radius();
 
   i_lon = mlon;
@@ -972,8 +1039,9 @@ arma_cube vec2cube(std::vector<precision_t> ivec) {
   arma_cube I;
 
   I.set_size(sizei, 1, 1);
-  for (int i =0; i<sizei; i++){
+
+  for (int i = 0; i < sizei; i++)
     I[i] = ivec[i];
-  }
+
   return I;
 }
\ No newline at end of file

From c0a9ab6508c90c4a36aee070e7b255b71549a04d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:11:45 -0400
Subject: [PATCH 190/266] more infinity checks

---
 src/advance.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index 96230431..523e3e59 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -88,6 +88,9 @@ bool advance(Planets &planet,
   precision_t dtIon = calc_dt(gGrid, ions.cMax_vcgc);
   time.calc_dt(dtNeutral, dtIon);
 
+  if (report.test_verbose(1))
+    std::cout << "dt in advance : " << time.get_dt() << "\n";
+
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: after calc dt");
 
   // ------------------------------------
@@ -129,9 +132,11 @@ bool advance(Planets &planet,
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
     neutrals.exchange_old(gGrid);
     ions.exchange_old(gGrid);
+
+    didWork = neutrals.check_for_nonfinites("Geo Grid: Before Horizontal Advection");
     neutrals.advect_horizontal(gGrid, time);
+    didWork = neutrals.check_for_nonfinites("Geo Grid: After Horizontal Advection");
     ionsMag.exchange_old(mGrid);
-    //advect(gGrid, time, neutrals);
   }
 
   if (input.get_check_for_nans()) {

From ec083d115821f8b719ea9ae8fe48328655d5a213 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:12:08 -0400
Subject: [PATCH 191/266] removed comments

---
 src/solver_horizontal_cubesphere.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/solver_horizontal_cubesphere.cpp b/src/solver_horizontal_cubesphere.cpp
index 98868b1a..26192564 100644
--- a/src/solver_horizontal_cubesphere.cpp
+++ b/src/solver_horizontal_cubesphere.cpp
@@ -666,8 +666,6 @@ void Neutrals::solver_horizontal_RK1_rochi(Grid & grid, Times & time) {
 
   for (iAlt = nGCs; iAlt < nAlts - nGCs; iAlt++) {
 
-    /** States preprocessing **/
-    /* MASS DENSITY */
     rho = rho_scgc.slice(iAlt);
     vLon = velocity_vcgc[0].slice(iAlt);
     vLat = velocity_vcgc[1].slice(iAlt);
@@ -683,10 +681,8 @@ void Neutrals::solver_horizontal_RK1_rochi(Grid & grid, Times & time) {
     velocity_vcgc[0].slice(iAlt) = vLon - k1vLon * dt;
     velocity_vcgc[1].slice(iAlt) = vLat - k1vLat * dt;
     temperature_scgc.slice(iAlt) = temp - k1temp * dt;
-
   }
 
-
   calc_density_from_mass_concentration();
 
   report.exit(function);

From 557d6440171bbff8a9b27cac462fa353229356e2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:12:43 -0400
Subject: [PATCH 192/266] tabbifying

---
 src/solver_gradients.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index b67617fb..73754c10 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -342,20 +342,20 @@ arma_cube calc_gradient2o_k(arma_cube value, Grid grid) {
     // Interior:
     for (iZ = 1; iZ < nZ - 1; iZ++)
       gradient.slice(iZ) =
-                (value.slice(iZ + 1) - value.slice(iZ - 1)) /
-                (2 * grid.dk_center_m_scgc.slice(iZ));
+        (value.slice(iZ + 1) - value.slice(iZ - 1)) /
+        (2 * grid.dk_center_m_scgc.slice(iZ));
 
     // Lower (one sided):
     iZ = 0;
     gradient.slice(iZ) =
-              (value.slice(iZ + 1) - value.slice(iZ)) /
-              grid.dk_center_m_scgc.slice(iZ);
+      (value.slice(iZ + 1) - value.slice(iZ)) /
+      grid.dk_center_m_scgc.slice(iZ);
 
     // Upper (one sided):
     iZ = nZ - 1;
     gradient.slice(iZ) =
-              (value.slice(iZ) - value.slice(iZ - 1)) /
-              grid.dk_center_m_scgc.slice(iZ);
+      (value.slice(iZ) - value.slice(iZ - 1)) /
+      grid.dk_center_m_scgc.slice(iZ);
   }
 
   return gradient;

From 8581c2befdf02f011df11c531829ab2553cd3071 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:13:19 -0400
Subject: [PATCH 193/266] crashout smoothly if wrong shape is given

---
 src/quadtree.cpp | 39 +++++++++++++++++++++++++++++++--------
 1 file changed, 31 insertions(+), 8 deletions(-)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 35ebb2e5..aa61b98e 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -15,21 +15,38 @@
 
 int64_t iProcQuery = -1;
 
-Quadtree::Quadtree(std::string shape) {
-  if (shape == "cubesphere")
+Quadtree::Quadtree(std::string shapeInput) {
+  IsOk = false;
+  std::string shape = mklower(shapeInput);
+
+  if (shape == "cubesphere") {
     nRootNodes = 6;
+    IsOk = true;
+  }
 
-  if (shape == "sphere")
+  if (shape == "sphere") {
     nRootNodes = 1;
+    IsOk = true;
+  }
 
-    if (shape == "sphere4")
+  if (shape == "sphere4") {
     nRootNodes = 4;
+    IsOk = true;
+  }
 
-  if (shape == "dipole4")
+  if (shape == "dipole4") {
     nRootNodes = 4;
+    IsOk = true;
+  }
 
-  if (shape == "dipole6")
+  if (shape == "dipole6") {
     nRootNodes = 6;
+    IsOk = true;
+  }
+
+  if (!IsOk)
+    report.error("quadtree shape not found : " + shape);
+
 }
 
 // --------------------------------------------------------------------------
@@ -504,7 +521,7 @@ arma_vec Quadtree::wrap_point_cubesphere(arma_vec point) {
 
 // --------------------------------------------------------------------------
 // This is the starting point for determining which node a point
-// on the sphere is located.  The point needs to be in normalized 
+// on the sphere is located.  The point needs to be in normalized
 // coordinates.
 // --------------------------------------------------------------------------
 
@@ -514,8 +531,10 @@ int64_t Quadtree::find_point(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
+
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+
   if (IsDipole)
     wrap_point = wrap_point_sphere(point);
 
@@ -523,15 +542,17 @@ int64_t Quadtree::find_point(arma_vec point) {
 
   for (int64_t iRoot = 0; iRoot < nRootNodes; iRoot++) {
     iNode = find_point(wrap_point, root_nodes[iRoot]);
+
     if (iNode > -1)
       break;
   }
+
   return iNode;
 }
 
 // --------------------------------------------------------------------------
 // This is the starting point for determining which root a point
-// on the sphere is located.  The point needs to be in normalized 
+// on the sphere is located.  The point needs to be in normalized
 // coordinates.
 // --------------------------------------------------------------------------
 
@@ -541,8 +562,10 @@ int64_t Quadtree::find_root(arma_vec point) {
 
   if (IsSphere)
     wrap_point = wrap_point_sphere(point);
+
   if (IsCubeSphere)
     wrap_point = wrap_point_cubesphere(point);
+
   if (IsDipole)
     wrap_point = wrap_point_cubesphere(point);
 

From c336dd480c7a6cd779e735f241534a65e2c6f878 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:13:43 -0400
Subject: [PATCH 194/266] added space

---
 src/neutrals_advect.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index f2b11652..7efaa231 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -45,6 +45,7 @@ bool Neutrals::advect_horizontal(Grid & grid, Times & time) {
       solver_horizontal_RK4_advection(grid, time);
     else if (input.get_advection_neutrals_horizontal() == "fv")
       solver_horizontal_RK1_rochi(grid, time);
+
     else {
       std::cout << "Horizontal solver not found!\n";
       std::cout << "  ==> Requested : "

From b090979e568382cbffdb7ddf3bfdb3bfb54e5cef Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:14:14 -0400
Subject: [PATCH 195/266] crashout smoother in general

---
 src/main/main.cpp | 38 ++++++++++++++++++++------------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index fcb62ce0..32f3ac0c 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -12,7 +12,7 @@ int main() {
 
   int iErr = 0;
   std::string sError;
-  bool didWork = true, testsPassing=true;
+  bool didWork = true, testsPassing = true;
 
   Times time;
 
@@ -35,14 +35,18 @@ int main() {
     // Find out what tests we are running:
     json tests = input.get_tests();
 
-    // For now, the number of processors and blocks are set by the 
+    // For now, the number of processors and blocks are set by the
     // neutral grid shape, since this could be sphere (1 root) or
     // cubesphere (6 root)
     Quadtree quadtree(input.get_grid_shape("neuGrid"));
-    Quadtree quadtree_ion(input.get_grid_shape("ionGrid"));
 
     if (!quadtree.is_ok())
-      throw std::string("quadtree initialization failed!");
+      throw std::string("quadtree for neutrals initialization failed!");
+
+    Quadtree quadtree_ion(input.get_grid_shape("ionGrid"));
+
+    if (!quadtree_ion.is_ok())
+      throw std::string("quadtree for ions initialization failed!");
 
     // Initialize MPI and parallel aspects of the code:
     didWork = init_parallel(quadtree, quadtree_ion);
@@ -103,6 +107,7 @@ int main() {
 
     if (mGrid.iGridShape_ == mGrid.iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
+
       if (!didWork)
         throw std::string("init_dipole_grid failed!");
     } else {
@@ -113,7 +118,7 @@ int main() {
     }
 
     if (!didWork)
-    throw std::string("Initializing magneitic grid failed!");
+      throw std::string("Initializing magneitic grid failed!");
 
 
     didWork = grid_match(gGrid, mGrid, quadtree, quadtree_ion);
@@ -126,9 +131,8 @@ int main() {
     Ions ions(gGrid, planet);
     Ions ionsMag(mGrid, planet);
 
-    if (tests["test_gradient"]) {
+    if (tests["test_gradient"])
       testsPassing = test_gradient(planet, quadtree, tests, gGrid, mGrid);
-    }
 
     if (!testsPassing && tests["exit_on_fail"])
       throw std::string("Cannot continue!!");
@@ -287,21 +291,19 @@ int main() {
 
     } // End of outer time loop - done with run!
 
-    report.report_errors();
-
-    report.exit(function);
     report.times();
 
   } catch (std::string error) {
-    report.report_errors();
-
-    if (iProc == 0) {
-      std::cout << error << "\n";
-      std::cout << "---- Must Exit! ----\n";
-    }
+    report.error(error);
   }
 
-  // End parallel tasks:
-  iErr = MPI_Finalize();
+  report.exit(function);
+  report.report_errors();
+
+  if (nProcs > 0)
+    // End parallel tasks:
+    iErr = MPI_Finalize();
+
   return iErr;
+
 }

From 998993fa6032bb262c0d6a513b3f7227d49c8d91 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:15:06 -0400
Subject: [PATCH 196/266] check to make sure nX = nY for cubesphere

---
 src/grid.cpp | 32 ++++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/src/grid.cpp b/src/grid.cpp
index 93270b37..cbe9f409 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -19,6 +19,28 @@ Grid::Grid(std::string gridtype) {
 
   gridType = gridtype;
 
+  if (mklower(grid_input.shape).find("sphere") != std::string::npos)
+    iGridShape_ = iSphere_;
+
+  if (mklower(grid_input.shape) == "cubesphere")
+    iGridShape_ = iCubesphere_;
+
+  //lowercase, check for any number of dipole, so dipole2 matches & dipole does too
+  if (mklower(grid_input.shape).find("dipole") != std::string::npos)
+    iGridShape_ = iDipole_;
+
+  if (iGridShape_ == iCubesphere_) {
+    if (grid_input.nX > grid_input.nY) {
+      report.error("Cubesphere grid: nX > nY, reducing nX");
+      grid_input.nX = grid_input.nY;
+    }
+
+    if (grid_input.nY > grid_input.nX) {
+      report.error("Cubesphere grid: nY > nX, reducing nY");
+      grid_input.nY = grid_input.nX;
+    }
+  }
+
   nX = grid_input.nX + nGCs * 2;
   nLons = nX;
   nY = grid_input.nY + nGCs * 2;
@@ -66,16 +88,6 @@ Grid::Grid(std::string gridtype) {
   if (grid_input.nZ == 1)
     HasZdim = false;
 
-  if (mklower(grid_input.shape).find("sphere") != std::string::npos)
-    iGridShape_ = iSphere_;
-
-  if (mklower(grid_input.shape) == "cubesphere")
-    iGridShape_ = iCubesphere_;
-
-  //lowercase, check for any number of dipole, so dipole2 matches & dipole does too
-  if (mklower(grid_input.shape).find("dipole") != std::string::npos)
-    iGridShape_ = iDipole_;
-
   geoLon_scgc.set_size(nX, nY, nZ);
   geoLat_scgc.set_size(nX, nY, nZ);
   geoAlt_scgc.set_size(nX, nY, nZ);

From c32d8081d0fe20641dbfb0d0f963fd41a17aca0e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:15:53 -0400
Subject: [PATCH 197/266] simplify dtx and dtx

---
 src/calc_dt.cpp | 34 ++++++++++++++++++++++------------
 1 file changed, 22 insertions(+), 12 deletions(-)

diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index beb0de18..96658471 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -92,22 +92,31 @@ precision_t calc_dt_cubesphere(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   arma_mat dummy_1(nXs, nYs, fill::ones);
 
   // Loop through altitudes
+
   for (int iAlt = 0; iAlt < nAlts; iAlt++) {
     // Conver cMax to contravariant velocity first
-    arma_mat u1 = sqrt(
-                    cMax_vcgc[0].slice(iAlt) % grid.A11_inv_scgc.slice(iAlt) %
-                    cMax_vcgc[0].slice(iAlt) % grid.A11_inv_scgc.slice(iAlt) +
-                    cMax_vcgc[1].slice(iAlt) % grid.A12_inv_scgc.slice(iAlt) %
-                    cMax_vcgc[1].slice(iAlt) % grid.A12_inv_scgc.slice(iAlt));
-    arma_mat u2 = sqrt(
-                    cMax_vcgc[0].slice(iAlt) % grid.A21_inv_scgc.slice(iAlt) %
-                    cMax_vcgc[0].slice(iAlt) % grid.A21_inv_scgc.slice(iAlt) +
-                    cMax_vcgc[1].slice(iAlt) % grid.A22_inv_scgc.slice(iAlt) %
-                    cMax_vcgc[1].slice(iAlt) % grid.A22_inv_scgc.slice(iAlt));
-    dtx.slice(iAlt) = grid.drefx(iAlt) * dummy_1 / u1;
-    dty.slice(iAlt) = grid.drefy(iAlt) * dummy_1 / u2;
+    //arma_mat u1 = sqrt(
+    //                cMax_vcgc[0].slice(iAlt) % grid.A11_inv_scgc.slice(iAlt) %
+    //                cMax_vcgc[0].slice(iAlt) % grid.A11_inv_scgc.slice(iAlt) +
+    //                cMax_vcgc[1].slice(iAlt) % grid.A12_inv_scgc.slice(iAlt) %
+    //                cMax_vcgc[1].slice(iAlt) % grid.A12_inv_scgc.slice(iAlt));
+    //arma_mat u2 = sqrt(
+    //                cMax_vcgc[0].slice(iAlt) % grid.A21_inv_scgc.slice(iAlt) %
+    //                cMax_vcgc[0].slice(iAlt) % grid.A21_inv_scgc.slice(iAlt) +
+    //                cMax_vcgc[1].slice(iAlt) % grid.A22_inv_scgc.slice(iAlt) %
+    //                cMax_vcgc[1].slice(iAlt) % grid.A22_inv_scgc.slice(iAlt));
+    dtx.slice(iAlt) = grid.cubeC.dlx.slice(iAlt) / cMax_vcgc[0].slice(iAlt);
+    dty.slice(iAlt) = grid.cubeC.dln.slice(iAlt) / cMax_vcgc[1].slice(iAlt);
+    //dty.slice(iAlt) = grid.drefy(iAlt) * dummy_1 / u2;
   }
 
+  //if (iProc == 0)
+  //  display_cube("dtx : ", dtx);
+
+  //if (iProc == 0)
+  //  display_cube("dty : ", dty);
+
+
   // Take minimum dts in each direction:
   dta(0) = dtx.min();
   dta(1) = dty.min();
@@ -116,6 +125,7 @@ precision_t calc_dt_cubesphere(Grid grid, std::vector<arma_cube> cMax_vcgc) {
   // Set a minimum dt:
   dta(3) = 10.0;
   // Take the minimum of all directions:
+
   dt = dta.min();
 
   if (report.test_verbose(3))

From fabbb08d19ef10ec6ca94528cc3bf7937e267cc2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Sat, 23 Aug 2025 12:16:48 -0400
Subject: [PATCH 198/266] turn back on interpolation + fill corners

---
 src/exchange_messages.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/exchange_messages.cpp b/src/exchange_messages.cpp
index 09f5ed1e..518ca540 100644
--- a/src/exchange_messages.cpp
+++ b/src/exchange_messages.cpp
@@ -105,7 +105,7 @@ bool Ions::exchange_old(Grid &grid) {
   for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
     DidWork = exchange_one_var(grid, species[iSpecies].density_scgc, false);
 
-  //DidWork = exchange_one_var(grid, temperature_scgc, false);
+  DidWork = exchange_one_var(grid, temperature_scgc, false);
   DidWork = exchange_one_var(grid, electron_temperature_scgc, false);
 
   // velocity components:
@@ -1186,14 +1186,14 @@ bool exchange_one_var(Grid &grid,
   }
 
   // If this is a cubesphere grid, interpolate ghostcells to their proper location
-  //if (grid.IsCubeSphereGrid & grid.gcInterpolationSet) {
-  //  report.print(3, "Interpolating Ghostcells to Proper Location");
-  //  var_scgc = interpolate_ghostcells(var_to_pass, grid);
-  //  var_to_pass = var_scgc;
-  //}
+  if (grid.IsCubeSphereGrid & grid.gcInterpolationSet) {
+    report.print(3, "Interpolating Ghostcells to Proper Location");
+    var_scgc = interpolate_ghostcells(var_to_pass, grid);
+    var_to_pass = var_scgc;
+  }
 
   // Now we fill in the corners so that we don't have zero values there:
-  //fill_corners(var_to_pass, nG);
+  fill_corners(var_to_pass, nG);
 
   report.exit(function);
   return DidWork;

From 28c0fdb53beffc76b11bcbcf57ac784e8ef9ebca Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 10:54:59 -0400
Subject: [PATCH 199/266] FEAT: consolidate blocks into one grid if possible

---
 srcPython/postAether.py | 222 +++++++++++++++++++++++++++++++++++++---
 1 file changed, 210 insertions(+), 12 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index b3523cab..59b04e42 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -39,6 +39,9 @@ def parse_args():
     parser.add_argument('-oned', \
                         help='strip 1d files of ghostcells and store in one file', \
                         action="store_true")
+    parser.add_argument('-combine', \
+                        help='combine all of the blocks into a single block (spherical only)', \
+                        action="store_true")
     parser.add_argument('-dir', default=None, type=str,
                         help="Directory to find Aether files in. Will look in current"
                         " directory & $PWD/UA/output/")
@@ -728,23 +731,28 @@ def get_sizes(allBlockData):
 # Write a NetCDF file from the data
 #----------------------------------------------------------------------------
 
-def write_netcdf(allBlockData, fileName, isVerbose = True):
+def write_netcdf(allBlockData, fileName, \
+                 isVerbose = True, \
+                 isConsolidated = False):
 
     if (isVerbose):
         print('    Outputting file : ', fileName)
     ncfile = Dataset(fileName, 'w')
 
-    nBlocks = len(allBlockData)
-    nLons, nLats, nZ = get_sizes(allBlockData)
+    if (not isConsolidated):
+        oneBlock = allBlockData[0]
+        nBlocks = len(allBlockData)
+        nLons, nLats, nZ = get_sizes(allBlockData)
+        block_dim = ncfile.createDimension('block', None)
+    else:
+        oneBlock = allBlockData
+        nLons, nLats, nZ = np.shape(allBlockData[0])
 
     lon_dim = ncfile.createDimension('lon', nLons)
     lat_dim = ncfile.createDimension('lat', nLats)
     z_dim = ncfile.createDimension('z', nZ)
-    block_dim = ncfile.createDimension('block', None)
     time_dim = ncfile.createDimension('time', None)
 
-    oneBlock = allBlockData[0]
-
     time_out = ncfile.createVariable('time', np.float64, ('time',))
     time_out[0] = datetime_to_epoch(oneBlock["time"])
         
@@ -760,14 +768,22 @@ def write_netcdf(allBlockData, fileName, isVerbose = True):
         else:
             longName = v
         unitName = oneBlock['units'][iV]
-        allNetCDFVars.append(ncfile.createVariable(v, np.float32, \
+        if (isConsolidated):
+            allNetCDFVars.append(ncfile.createVariable(v, np.float32, \
+                                                   ('lon', 'lat', 'z')))
+        else:
+            allNetCDFVars.append(ncfile.createVariable(v, np.float32, \
                                                    ('block', 'lon', 'lat', 'z')))
         allNetCDFVars[-1].units = unitName
         allNetCDFVars[-1].long_name = longName
 
-        for iB, oneBlock in enumerate(allBlockData):
-            tmp = np.asarray(oneBlock[iV])
-            allNetCDFVars[-1][iB,:,:,:] = tmp
+        if (isConsolidated):
+            tmp = np.asarray(allBlockData[iV])
+            allNetCDFVars[-1][:,:,:] = tmp
+        else:
+            for iB, oneBlock in enumerate(allBlockData):
+                tmp = np.asarray(oneBlock[iV])
+                allNetCDFVars[-1][iB,:,:,:] = tmp
         
     ncfile.close()
 
@@ -927,6 +943,168 @@ def calc_std_of_ensembles(filesInfo,
 
     return stdData
 
+#----------------------------------------------------------------------------
+# Test to see if grid is uniform:
+#----------------------------------------------------------------------------
+
+def calc_if_uniform_grid(dataToWrite):
+
+    nBlocks = len(dataToWrite)
+
+    # Let's figure out if we have a uniform horizontal grid:
+    isUniform = True
+
+    for iBlock in range(nBlocks):
+        # Assume first 3 variables are lon, lat, alt:
+        longitude = dataToWrite[iBlock][0]
+        latitude = dataToWrite[iBlock][1]
+
+        if (iBlock == 0):
+            dLon = longitude[1, 0, 0] - longitude[0, 0, 0]
+            dLat = latitude[0, 1, 0] - latitude[0, 0, 0]
+        else:
+            dLonT = longitude[1, 0, 0] - longitude[0, 0, 0]
+            dLatT = latitude[0, 1, 0] - latitude[0, 0, 0]
+            if (np.abs(dLat - dLatT) > dLat/1000.0):
+                isUniform = False
+            if (np.abs(dLon - dLonT) > dLon/1000.0):
+                isUniform = False
+
+    return isUniform
+
+
+#----------------------------------------------------------------------------
+# Figure out number of ghostcells
+#   - Simple method works if grid touches the south pole
+#----------------------------------------------------------------------------
+
+def calc_ghostcells(dataToWrite):
+
+    nGCs = -1
+    # ---------------------------------------------
+    # Try simple method first:
+    # Assume first 3 variables of first block are lon, lat, alt:
+    lon1d = dataToWrite[0][0][:, 0, 0]
+    lat1d = dataToWrite[0][1][0, :, 0]
+
+    nGCs = 0
+    print(lat1d)
+    while (lat1d[nGCs] < -90.0):
+        # Checking to see how many points are below south pole:
+        nGCs = nGCs + 1
+
+    if (nGCs < 0):
+        # Didn't find GCs, test longitude
+        while (lon1d[nGCs] < 0):
+            # Checking to see how many points are below south pole:
+            nGCs = nGCs + 1
+
+    return nGCs
+
+
+#----------------------------------------------------------------------------
+# Figure out number of blocks in lat and lon
+#   - Assume a quadtree!!!
+#----------------------------------------------------------------------------
+
+def calc_blocks(dataToWrite, iLon_ = 0, iLat_ = 1):
+
+    nBlocksLon = 1
+    nBlocksLat = 1
+
+    nBlocksTotal = len(dataToWrite)
+
+    # Brute force method:
+    # assume that all longitude blocks will have same latitude
+    # assume that all latitude blocks will have same longitude
+
+    # assume var 0 is longitude and var 1 is latitude
+    iBlock = 0
+    testLon = dataToWrite[iBlock][iLon_][0, 0, 0]
+    testLat = dataToWrite[iBlock][iLat_][0, 0, 0]
+
+    iBlock = 1
+    while (iBlock < nBlocksTotal):
+        if (np.abs(dataToWrite[iBlock][iLon_][0, 0, 0] - testLon) > 0.001):
+            nBlocksLon = nBlocksLon + 1
+        if (np.abs(dataToWrite[iBlock][iLat_][0, 0, 0] - testLat) > 0.001):
+            nBlocksLat = nBlocksLat + 1
+        iBlock = iBlock + 1
+
+    print(' -> nBlocksLon, nBlocksLat : ', nBlocksLon, nBlocksLat)
+
+    if (not (nBlocksLat * nBlocksLon == nBlocksTotal)):
+        print("Finding nBlocksLat and nBlocksLon didn't work!")
+        print("Tell Aaron to fix this!")
+        nBlocksLat = -1
+        nBlocksLon = -1
+
+    return nBlocksLon, nBlocksLat
+
+#----------------------------------------------------------------------------
+# Consolidate Blocks
+#----------------------------------------------------------------------------
+
+def consolidate_blocks(originalData, iLon_ = 0, iLat_ = 1):
+
+    nBlocksLon, nBlocksLat = calc_blocks(originalData)
+    nGCs = calc_ghostcells(originalData)
+    nLons, nLats, nAlts = np.shape(originalData[0][0])
+    nLons = nLons - 2 * nGCs
+    nLats = nLats - 2 * nGCs
+    nVars = len(originalData[0])
+    nLonsTotal = nLons * nBlocksLon + 2 * nGCs
+    nLatsTotal = nLats * nBlocksLat + 2 * nGCs
+    Lat0 = originalData[0][iLat_][nGCs, nGCs, nGCs]
+    Lon0 = originalData[0][iLon_][nGCs, nGCs, nGCs]
+    dLat = originalData[0][iLat_][nGCs, nGCs + 1, nGCs] - Lat0
+    dLon = originalData[0][iLon_][nGCs + 1, nGCs, nGCs] - Lon0
+    nBlocks = len(originalData)
+
+    consolidatedData = {}
+    for key in originalData[0].keys():
+        if (isinstance(key, str)):
+            consolidatedData[key] = originalData[0][key]
+        else:
+            # need to move data over
+            data = np.zeros((nLonsTotal, nLatsTotal, nAlts))
+            for iBlock in range(nBlocks):
+                # interior points:
+                iLatS = int((originalData[iBlock][iLat_][nGCs, nGCs, nGCs] - Lat0)/dLat) + nGCs
+                iLatE = iLatS + nLats
+                iLonS = int((originalData[iBlock][iLon_][nGCs, nGCs, nGCs] - Lon0)/dLon) + nGCs
+                iLonE = iLonS + nLons
+                iLonSO = nGCs
+                iLonEO = nGCs + nLons
+                iLatSO = nGCs
+                iLatEO = iLatSO + nLats
+                #print(iLonS, iLonE, nLonsTotal, ' -> ', iLonSO, iLonEO, nLons)
+                #print(iLatS, iLatE, nLatsTotal, ' -> ', iLatSO, iLatEO, nLats)
+                #print(nGCs)
+                data[iLonS:iLonE, iLatS:iLatE, 0:nAlts] = \
+                    originalData[iBlock][key][iLonSO:iLonEO, iLatSO:iLatEO, 0:nAlts]
+
+                # Lat down edge:
+                if (iLatS == nGCs):
+                    data[iLonS-nGCs:iLonE+nGCs, 0:nGCs, :] = \
+                        originalData[iBlock][key][0:nLons+2*nGCs, 0:nGCs, :]
+                # Lon Left edge:
+                if (iLonS == nGCs):
+                    data[0:nGCs, iLatS-nGCs:iLatE+nGCs, :] = \
+                        originalData[iBlock][key][0:nGCs, 0:nLats+2*nGCs, :]
+                # Lat up edge
+                if (iLatE == nLatsTotal - nGCs):
+                    data[iLonS-nGCs:iLonE+nGCs, iLatE:iLatE+nGCs, :] = \
+                        originalData[iBlock][key][0:nLons+2*nGCs, nLats:nLats+nGCs, :]
+                # long right edge
+                if (iLonE == nLonsTotal - nGCs):
+                    data[iLonE:iLonE+nGCs, iLatS-nGCs:iLatE+nGCs, :] = \
+                        originalData[iBlock][key][nLons:nLons+nGCs, 0:nLats+2*nGCs, :]
+                consolidatedData[key] = data
+
+    print(consolidatedData.keys())
+
+    return consolidatedData
 
 #----------------------------------------------------------------------------
 # write and plot data
@@ -940,10 +1118,30 @@ def write_and_plot_data(dataToWrite,
                         output_netcdf,
                         isVerbose = True):
 
+    # We want to figure out whether we can combine our blocks into
+    # a single block and just write that out - it is much easier to
+    # deal with in this case!
+
+    canConsolidateBlocks = False
+    print(np.shape(dataToWrite))
+    print('keys : ', dataToWrite[0].keys())
+    print('shape of variable : ', np.shape(dataToWrite[0][0]))
+    isUniform = calc_if_uniform_grid(dataToWrite)
+    print(' -> isUniform : ', isUniform)
+
+    if (isUniform):
+        nBlocksLon, nBlocksLat = calc_blocks(dataToWrite)
+        if (nBlocksLon > 0):
+            canConsolidateBlocks = True
+
+    print('can consolidate blocks: ', canConsolidateBlocks)
+    if (canConsolidateBlocks):
+        dataToWrite = consolidate_blocks(dataToWrite)
+
     if output_netcdf:
         netcdfFile = fileStart + fileAddon + '.nc'
         print('  --> Outputting nc file : ', netcdfFile)
-        write_netcdf(dataToWrite, netcdfFile, isVerbose = isVerbose)
+        write_netcdf(dataToWrite, netcdfFile, isVerbose = isVerbose, isConsolidated = canConsolidateBlocks)
     else:
         hdf5File = fileStart + fileAddon + '.hdf5'
         print('  --> Outputting hdf5 file : ', hdf5File)
@@ -1032,4 +1230,4 @@ def main(args):
 
     # This allows code to cleanly exit on error
     main(args)
-    
\ No newline at end of file
+    

From e2051991e3b3443d3ebd345b2c6e0d8b92b9113f Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 11:12:13 -0400
Subject: [PATCH 200/266] FEAT: recovered from an old branch

---
 doc/internals/interpolation.md | 46 ++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 doc/internals/interpolation.md

diff --git a/doc/internals/interpolation.md b/doc/internals/interpolation.md
new file mode 100644
index 00000000..4eb0e08d
--- /dev/null
+++ b/doc/internals/interpolation.md
@@ -0,0 +1,46 @@
+
+# How to use the built in interpolator in Aether
+
+Before attempting interpolation, you need to have the geogrid, some
+grids where you want to perform the interpolation (the size of the
+grid should match exactly with the geogrid, and the values in the grid
+should represent the quantity at the center of cells in the geogrid),
+and a set of points at which you want to interpolate the values.
+
+First, place the longitude, latitude, and altitude of the points into
+three different vectors. For example, if you have points p1 (lon 180,
+lat 0, alt 10000), p2 (lon 90, lat -30, alt 15000), p3 (lon 270, lat
+40, alt 10000), and p4 (lon 0, lat 0, alt 15000), then you should
+create three vectors as follows:
+
+```bash
+std::vector<precision_t> Lons = {180, 90, 270, 0};
+std::vector<precision_t> Lats = {0, -30, 40, 0};
+std::vector<precision_t> Alts = {10000, 15000, 10000, 15000};
+```
+Second, set the interpolation coefficients. Continuing with the
+previous example, assuming you have a geogrid named "geo_grid," you
+should call:
+
+```bash
+geo_grid.set_interpolation_coefs(Lons, Lats, Alts);
+```
+
+All subsequent calls to geo_grid.get_interpolation_values will use
+these coefficients until geo_grid.set_interpolation_coefs is called
+again (and the coefficients are updated to the newly set ones).
+
+Finally, obtain the values at the set of points. Continuing with the
+previous example, if you want to perform interpolation on "arma_cube
+data1" and "arma_cube data2" with the set of points, you can execute:
+
+```bash
+std::vector<precision_t> ans1 = geo_grid.get_interpolation_values(data1);
+std::vector<precision_t> ans2 = geo_grid.get_interpolation_values(data2);
+```
+
+The first element in "ans1" (i.e., ans1[0]) represents the
+interpolated value for the quantity "data1" at point p1 (lon 180, lat
+0, alt 10000), and the second element is the result for point p2, and
+so on. This is the same for "ans2", except that it is for the quantity
+"data2".

From 1d9b1706f04d5fb80ee52d3551a4f839f27e9991 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 17:19:53 -0400
Subject: [PATCH 201/266] STY: removed some print statements

---
 srcPython/postAether.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index 59b04e42..694b2ad4 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -988,7 +988,6 @@ def calc_ghostcells(dataToWrite):
     lat1d = dataToWrite[0][1][0, :, 0]
 
     nGCs = 0
-    print(lat1d)
     while (lat1d[nGCs] < -90.0):
         # Checking to see how many points are below south pole:
         nGCs = nGCs + 1
@@ -1031,8 +1030,6 @@ def calc_blocks(dataToWrite, iLon_ = 0, iLat_ = 1):
             nBlocksLat = nBlocksLat + 1
         iBlock = iBlock + 1
 
-    print(' -> nBlocksLon, nBlocksLat : ', nBlocksLon, nBlocksLat)
-
     if (not (nBlocksLat * nBlocksLon == nBlocksTotal)):
         print("Finding nBlocksLat and nBlocksLon didn't work!")
         print("Tell Aaron to fix this!")
@@ -1102,8 +1099,6 @@ def consolidate_blocks(originalData, iLon_ = 0, iLat_ = 1):
                         originalData[iBlock][key][nLons:nLons+nGCs, 0:nLats+2*nGCs, :]
                 consolidatedData[key] = data
 
-    print(consolidatedData.keys())
-
     return consolidatedData
 
 #----------------------------------------------------------------------------
@@ -1123,18 +1118,14 @@ def write_and_plot_data(dataToWrite,
     # deal with in this case!
 
     canConsolidateBlocks = False
-    print(np.shape(dataToWrite))
-    print('keys : ', dataToWrite[0].keys())
-    print('shape of variable : ', np.shape(dataToWrite[0][0]))
     isUniform = calc_if_uniform_grid(dataToWrite)
-    print(' -> isUniform : ', isUniform)
 
     if (isUniform):
         nBlocksLon, nBlocksLat = calc_blocks(dataToWrite)
         if (nBlocksLon > 0):
             canConsolidateBlocks = True
 
-    print('can consolidate blocks: ', canConsolidateBlocks)
+    print('  -> can consolidate blocks: ', canConsolidateBlocks)
     if (canConsolidateBlocks):
         dataToWrite = consolidate_blocks(dataToWrite)
 

From f5b12223269c6a844ecea795ed224fcdaaa5d2ad Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 17:20:54 -0400
Subject: [PATCH 202/266] FEAT: switch back on some features on dipole grid

---
 src/advance.cpp | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index 523e3e59..2c781f02 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -111,6 +111,9 @@ bool advance(Planets &planet,
   if (didWork)
     didWork = neutralsMag.set_bcs(mGrid, time, indices);
 
+  if (didWork)
+    didWork = ionsMag.set_bcs(mGrid, time, indices);
+
   didWork = neutralsMag.check_for_nonfinites("Ion Grid: set bcs");
 
   // advect in the 3rd dimension (vertical), but only if we have it:
@@ -127,6 +130,21 @@ bool advance(Planets &planet,
 
   }
 
+  // advect in the 3rd dimension (vertical), but only if we have it:
+  if (mGrid.get_nAlts(false) > 1) {
+    neutralsMag.advect_vertical(mGrid, time);
+
+    if (didWork & input.get_check_for_nans())
+      didWork = neutralsMag.check_for_nonfinites("After Vertical Neutral Advection");
+
+    // ajr - ionsMag.advect_vertical(mGrid, time);
+
+    if (didWork & input.get_check_for_nans())
+      didWork = ionsMag.check_for_nonfinites("After Vertical Ion Advection");
+
+  }
+
+
   // advect in the 1st and 2nd dimensions (horizontal), but only if
   // we have those dimensions:
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
@@ -137,6 +155,7 @@ bool advance(Planets &planet,
     neutrals.advect_horizontal(gGrid, time);
     didWork = neutrals.check_for_nonfinites("Geo Grid: After Horizontal Advection");
     ionsMag.exchange_old(mGrid);
+    //neutralsMag.exchange_old(mGrid);
   }
 
   if (input.get_check_for_nans()) {
@@ -206,7 +225,7 @@ bool advance(Planets &planet,
     calc_ion_collisions(neutrals, ions);
 
     neutrals.add_sources(time, planet, gGrid);
-    //neutralsMag.add_sources(time, planet, mGrid);
+    neutralsMag.add_sources(time, planet, mGrid);
 
     if (didWork & input.get_check_for_nans()) {
       didWork = neutrals.check_for_nonfinites("Geo Grid: After Add Sources");

From 91b95b300447fcb380de89b7bcc1963aa0b59ba0 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 17:21:24 -0400
Subject: [PATCH 203/266] STY: spaces

---
 src/neutrals.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/neutrals.cpp b/src/neutrals.cpp
index 28993f14..b6cc83c4 100644
--- a/src/neutrals.cpp
+++ b/src/neutrals.cpp
@@ -263,9 +263,11 @@ void Neutrals::fill_with_hydrostatic(int64_t iStart,
 
   for (iNeutral = 0; iNeutral < nSpeciesAdvect; iNeutral++) {
     iSpecies = species_to_advect[iNeutral];
+
     for (iX = nGCs; iX < nX - nGCs; iX++) {
       for (iY = nGCs; iY < nY - nGCs; iY++) {
         iFirst = grid.first_lower_gc(iX, iY) + iStart;
+
         // Integrate with hydrostatic equilibrium up:
         for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
           species[iSpecies].density_scgc(iX, iY, iAlt) =
@@ -274,6 +276,7 @@ void Neutrals::fill_with_hydrostatic(int64_t iStart,
             species[iSpecies].density_scgc(iX, iY, iAlt - 1) *
             exp(-grid.dr_edge(iX, iY, iAlt) /
                 species[iSpecies].scale_height_scgc(iX, iY, iAlt));
+
         }
       }
     }
@@ -302,6 +305,7 @@ void Neutrals::fill_with_hydrostatic(int64_t iSpecies,
   for (iX = nGCs; iX < nX - nGCs; iX++) {
     for (iY = nGCs; iY < nY - nGCs; iY++) {
       iFirst = grid.first_lower_gc(iX, iY) + iStart;
+
       // Integrate with hydrostatic equilibrium up:I
       for (int iAlt = iFirst; iAlt < iEnd; iAlt++) {
         species[iSpecies].density_scgc(iX, iY, iAlt) =

From 84ddaf1d88651cb99a8df7f2facfbfbf3130a6b7 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 17:22:16 -0400
Subject: [PATCH 204/266] BUG: start with 0th cell

---
 src/neutrals_advect.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index 7efaa231..dcdd67cd 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -17,8 +17,10 @@ bool Neutrals::advect_vertical(Grid grid, Times time) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  if (input.get_advection_neutrals_vertical() == "hydro")
-    fill_with_hydrostatic(1, grid.get_nZ(), grid);
+  if (grid.get_IsDipole() ||
+      input.get_advection_neutrals_vertical() == "hydro")
+    fill_with_hydrostatic(0, grid.get_nZ(), grid);
+
   else if (input.get_advection_neutrals_vertical() == "rusanov")
     solver_vertical_rusanov(grid, time);
   else {

From 445248053a3191e40941ff74b5ad820dd2e9edf8 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 17:23:53 -0400
Subject: [PATCH 205/266] STY: spacing + setting lower temp BC

---
 src/neutrals_bcs.cpp | 61 +++++++++++++++++++++-----------------------
 1 file changed, 29 insertions(+), 32 deletions(-)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 7d93ef56..77368a39 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -92,8 +92,8 @@ bool Neutrals::set_upper_bcs(Grid grid) {
 
       h = species[iSpecies].scale_height_scgc.slice(iAlt);
       species[iSpecies].density_scgc.slice(iAlt) =
-                                      species[iSpecies].density_scgc.slice(iAlt - 1) %
-                                      exp(-grid.dk_edge_m.slice(iAlt) / h);
+        species[iSpecies].density_scgc.slice(iAlt - 1) %
+        exp(-grid.dk_edge_m.slice(iAlt) / h);
     }
   }
 
@@ -173,13 +173,13 @@ bool Neutrals::set_lower_bcs(Grid grid,
           std::cout << "  Found in MSIS!\n";
 
         species[iSpecies].density_scgc.slice(0) =
-                                        msis.get_mat(species[iSpecies].cName);
+          msis.get_mat(species[iSpecies].cName);
       } else {
         if (report.test_verbose(3))
           std::cout << "  NOT Found in MSIS - setting constant\n";
 
         species[iSpecies].density_scgc.slice(0).
-                                      fill(species[iSpecies].lower_bc_density);
+        fill(species[iSpecies].lower_bc_density);
       }
 
     }
@@ -198,11 +198,16 @@ bool Neutrals::set_lower_bcs(Grid grid,
   // - The equator-most (j-hat) grid cell will be entirely below min_alt!
   for (int iLon = 0; iLon < nLons; iLon++) {
     for (int iLat = 0; iLat < nLats; iLat++) {
-      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
-        // k-index of 1st lower ghost cell is not constant on the dipole grid.
-        // On the latlon grid with nGCS=2, this will be 1
-        iAlt = grid.first_lower_gc(iLon, iLat);
+      // k-index of 1st lower ghost cell is not constant on the dipole grid.
+      // On the latlon grid with nGCS=2, this will be 1
+      iAlt = grid.first_lower_gc(iLon, iLat);
+      temperature_scgc(iLon, iLat, iAlt) = initial_temperatures[0];
+      // Set all lower ghost cells to bottom temperature:
+      temperature_scgc.subcube(iLon, iLat, 0, iLon, iLat, iAlt - 1).fill(
+        temperature_scgc(iLon, iLat, iAlt));
+
+      for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
         //-----------------------------------------------
         // Planet BCs - set to fixed constant values.
@@ -213,15 +218,8 @@ bool Neutrals::set_lower_bcs(Grid grid,
           species[iSpecies].density_scgc.subcube(iLon, iLat, 0,
                                                  iLon, iLat, iAlt - 1).fill(
                                                    species[iSpecies].lower_bc_density);
-          // only fill 1st GC with lower temperature
-          temperature_scgc(iLon, iLat, iAlt) = initial_temperatures[0];
         }  // planet bc type
 
-        // Set all lower ghost cells to bottom temperature:
-        temperature_scgc.subcube(iLon, iLat, 0, iLon, iLat, iAlt - 1).fill(
-                          temperature_scgc(iLon, iLat, iAlt));
-
-
         // 1st ghost cell density is filled with a hydrostatic solution.
         sh_ave = (species[iSpecies].scale_height_scgc(iLon, iLat, iAlt)
                   + species[iSpecies].scale_height_scgc(iLon, iLat, iAlt + 1)) / 2;
@@ -251,7 +249,6 @@ bool Neutrals::set_lower_bcs(Grid grid,
 
   didWork = true;
 
-
   calc_bulk_velocity();
 
   if (!didWork) {
@@ -292,8 +289,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-                          2 * temperature_scgc.tube(iX - 1, iY) -
-                          temperature_scgc.tube(iX - 2, iY);
+          2 * temperature_scgc.tube(iX - 1, iY) -
+          temperature_scgc.tube(iX - 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -302,8 +299,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-                                          2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
-                                          species[iSpecies].density_scgc.tube(iX - 2, iY);
+            2 * species[iSpecies].density_scgc.tube(iX - 1, iY) -
+            species[iSpecies].density_scgc.tube(iX - 2, iY);
       }
     }
   }
@@ -314,8 +311,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = 0; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-                          2 * temperature_scgc.tube(iX + 1, iY) -
-                          temperature_scgc.tube(iX + 2, iY);
+          2 * temperature_scgc.tube(iX + 1, iY) -
+          temperature_scgc.tube(iX + 2, iY);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -324,8 +321,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-                                          2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
-                                          species[iSpecies].density_scgc.tube(iX + 2, iY);
+            2 * species[iSpecies].density_scgc.tube(iX + 1, iY) -
+            species[iSpecies].density_scgc.tube(iX + 2, iY);
       }
     }
   }
@@ -336,8 +333,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nX - nGCs; iY < nY; iY++) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-                          2 * temperature_scgc.tube(iX, iY - 1) -
-                          temperature_scgc.tube(iX, iY - 2);
+          2 * temperature_scgc.tube(iX, iY - 1) -
+          temperature_scgc.tube(iX, iY - 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -346,8 +343,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-                                          2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
-                                          species[iSpecies].density_scgc.tube(iX, iY - 2);
+            2 * species[iSpecies].density_scgc.tube(iX, iY - 1) -
+            species[iSpecies].density_scgc.tube(iX, iY - 2);
       }
     }
   }
@@ -358,8 +355,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
       for (iY = nGCs - 1; iY >= 0; iY--) {
         // Constant Gradient for Temperature:
         temperature_scgc.tube(iX, iY) =
-                          2 * temperature_scgc.tube(iX, iY + 1) -
-                          temperature_scgc.tube(iX, iY + 2);
+          2 * temperature_scgc.tube(iX, iY + 1) -
+          temperature_scgc.tube(iX, iY + 2);
 
         // Constant Value for Velocity:
         for (iV = 0; iV < 3; iV++)
@@ -368,8 +365,8 @@ bool Neutrals::set_horizontal_bcs(int64_t iDir, Grid grid) {
         // Constant Gradient for densities:
         for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
           species[iSpecies].density_scgc.tube(iX, iY) =
-                                          2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
-                                          species[iSpecies].density_scgc.tube(iX, iY + 2);
+            2 * species[iSpecies].density_scgc.tube(iX, iY + 1) -
+            species[iSpecies].density_scgc.tube(iX, iY + 2);
       }
     }
   }

From f2dac4d5b4300eb6de76a8b8cd93bcbc2318786e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 18:48:46 -0400
Subject: [PATCH 206/266] FEAT: fill ghostcells with values to allow good math

---
 include/tools.h |  1 +
 src/tools.cpp   | 33 +++++++++++++++++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/include/tools.h b/include/tools.h
index 2283c1b7..24a7fbe5 100644
--- a/include/tools.h
+++ b/include/tools.h
@@ -50,6 +50,7 @@ void set_gcs_to_value(arma_cube &var_scgc,
 //   - basically fill in the corners with values near them
 // ----------------------------------------------------------------------------
 
+void fill_horizontal_ghostcels(arma_cube &values, int64_t nGCs);
 void fill_corners(arma_cube &values, int64_t nGCs);
 
 // -----------------------------------------------------------------------------
diff --git a/src/tools.cpp b/src/tools.cpp
index efc6fcc2..9601d868 100644
--- a/src/tools.cpp
+++ b/src/tools.cpp
@@ -111,6 +111,39 @@ arma_vec interpolate1d(arma_vec inY,
   return outY;
 }
 
+// ----------------------------------------------------------------------------
+// Fix corners in an arma cube
+//   - basically fill in the corners with values near them
+// ----------------------------------------------------------------------------
+
+void fill_horizontal_ghostcels(arma_cube &values, int64_t nGCs) {
+
+  int64_t nXs = values.n_rows, iX;
+  int64_t nYs = values.n_cols, iY;
+  int64_t nZs = values.n_slices, iZ;
+  int64_t iGCx, iGCy, iGCz;
+
+  for (iGCx = 0; iGCx < nGCs; iGCx++) {
+    for (iY = 0; iY < nYs; iY++) {
+      // Bottom:
+      values.tube(iGCx, iY) = values.tube(nGCs, iY);
+      values.tube(nXs - iGCx - 1, iY) = values.tube(nXs - nGCs - 1, iY);
+    }
+  }
+
+  for (iX = 0; iX < nXs; iX++) {
+    for (iGCy = 0; iGCy < nGCs; iGCy++) {
+      // Bottom:
+      values.tube(iX, iGCy) = values.tube(iX, nGCs);
+      values.tube(iX, nYs - iGCy - 1) = values.tube(iX, nYs - nGCs - 1);
+    }
+  }
+
+  //fill_corners(values, nGCs);
+
+  return;
+
+}
 
 // ----------------------------------------------------------------------------
 // Fix corners in an arma cube

From 42e39f3118b28518178e8532cbaef080702e1de5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 18:49:01 -0400
Subject: [PATCH 207/266] FEAT: fill ghostcells with values to allow good math

---
 src/advance.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index 2c781f02..375e24eb 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -144,7 +144,6 @@ bool advance(Planets &planet,
 
   }
 
-
   // advect in the 1st and 2nd dimensions (horizontal), but only if
   // we have those dimensions:
   if (gGrid.get_HasXdim() || gGrid.get_HasYdim()) {
@@ -155,6 +154,12 @@ bool advance(Planets &planet,
     neutrals.advect_horizontal(gGrid, time);
     didWork = neutrals.check_for_nonfinites("Geo Grid: After Horizontal Advection");
     ionsMag.exchange_old(mGrid);
+    fill_horizontal_ghostcels(neutralsMag.temperature_scgc, mGrid.get_nGCs());
+
+    //for (int iSpecies = 0; iSpecies < neutralsMag.nSpecies; iSpecies++)
+    //  fill_horizontal_ghostcels(neutralsMag.species[iSpecies].density_scgc,
+    //                            mGrid.get_nGCs());
+
     //neutralsMag.exchange_old(mGrid);
   }
 

From ea9b0c868cffb11ba4d4c014e26a61db3d1fce35 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 20:43:56 -0400
Subject: [PATCH 208/266] FEAT: finally, a formatter for json files

---
 srcPython/format_json.py | 56 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100755 srcPython/format_json.py

diff --git a/srcPython/format_json.py b/srcPython/format_json.py
new file mode 100755
index 00000000..2b4b6960
--- /dev/null
+++ b/srcPython/format_json.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+import os
+import argparse
+import json
+
+# ----------------------------------------------------------------------------
+# Get arguments as inputs into the code
+#-----------------------------------------------------------------------------
+
+def get_args():
+
+    parser = argparse.ArgumentParser(
+        description = 'reformat json files')
+    
+    # Get the files to plot:
+    parser.add_argument('filelist', nargs='+', \
+                        help = 'list of files for formatting')
+
+    args = parser.parse_args()
+
+    return args
+
+# ----------------------------------------------------------------------
+# do system command
+# ----------------------------------------------------------------------
+
+def run_command(command, verbose = False):
+    if (verbose):
+        print(" -> Running Command : ")
+        print("    ", command)
+    os.system(command)
+    return True
+
+# Needed to run main script as the default executable from the command line
+if __name__ == '__main__':
+
+    # Get the input arguments
+    args = get_args()
+    filelist = args.filelist
+
+    for file in filelist:
+
+        fileSave = file + '.orig'
+        command = 'mv ' + file + ' ' + fileSave
+        run_command(command, verbose = True)
+        
+        with open(fileSave, 'r') as handle:
+            print('-> Reading : ', fileSave)
+            parsed = json.load(handle)        
+
+            fpOut = open(file, 'w')
+            print('-> Writing : ', file)
+            json.dump(parsed, fpOut, indent=4)
+            fpOut.close()
+

From 0a65a33f4f91ff17d3868a6e4a772e1d383032b6 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 20:44:19 -0400
Subject: [PATCH 209/266] STY: formatted

---
 share/run/UA/inputs/defaults.json | 330 +++++++++++++++++-------------
 1 file changed, 185 insertions(+), 145 deletions(-)

diff --git a/share/run/UA/inputs/defaults.json b/share/run/UA/inputs/defaults.json
index 5aad82bb..5e61f466 100644
--- a/share/run/UA/inputs/defaults.json
+++ b/share/run/UA/inputs/defaults.json
@@ -1,151 +1,191 @@
-
 {
-    "Debug" : {
-		"iVerbose" : 0,
-		"doInheritVerbose" : false,
-		"dt" : 60.0,
-		"TimingPercent" : 1.0,
-		"iTimingDepth" : 5,
-		"iProc" : 0,
-		"iFunctionVerbose" : {
-			"Grid::create_altitudes": 0},	
-		"check_for_nans" : false,
-		"nan_test" : {
-            "insert" : false,
-            "variable" : "temperature_scgc"} 
-		},
-
-    "InitialConditions" : {
-		"type" : "Planet"},    
-
-    "BoundaryConditions" : {
-		"type" : "Planet"},
-
-    "Advection" : {
-		"Neutrals" : {
-			"Vertical" : "rusanov",
-			"Horizontal" : "fv",
-			"useBulkWinds" : true,
-			"useImplicitFriction" : true},
-		"Ions" : {
-			"Along" : "rusanov",
-			"Across" : "default"}
-		},
-    
-    "Student" : {
-		"name" : "",
-		"is" : false },
-
-    "Planet" : {
-        "name" : "earth",
-        "file": "UA/inputs/earth.in"},
-
-    "BField" : "dipole",
-
-    "Electrodynamics" : {
-		"Potential" : "weimer",
-		"DiffuseAurora" : "fta",
-		"Dir" : "UA/inputs/ext/ie/",
-		"File" : ""},
-
-    "Euv" : {
-		"doUse" : true,
-		"Model" : "euvac",
-		"File" : "UA/inputs/euv.csv",
-		"IncludePhotoElectrons" : true,
-		"HeatingEfficiency" : 0.05,
-		"dt" : 60.0},
-
-    "DoCalcBulkIonTemp" : false,
-    
-    "Eddy" : {
-		"Coefficient" : 50.0,
-		"BottomPressure" : 0.01,
-		"TopPressure" : 0.005,
-		"UseInEnergy": true,
-		"UseInMomentum": true},
-
-    "StartTime" : [2011, 3, 20, 0, 0, 0],
-    "EndTime" : [2011, 3, 20, 0, 10, 0],
-
-    "neuGrid" : {
-        "Shape" : "sphere4",
-		"LatRange" : [-90.0, 90.0],
-        "nLatsPerBlock" : 18,
-		"LonRange" : [0.0, 360.0],
-        "nLonsPerBlock" : 20,
-        "nAlts" : 40,
-		"MinAlt" : 100.0,
-		"dAltkm" : 5.0,
-        "dAltScale" : 0.25,
-		"IsUniformAlt" : true,
-        "AltFile" : ""},
-
+    "Debug": {
+        "iVerbose": 0,
+        "doInheritVerbose": false,
+        "dt": 60.0,
+        "TimingPercent": 1.0,
+        "iTimingDepth": 5,
+        "iProc": 0,
+        "iFunctionVerbose": {
+            "Grid::create_altitudes": 0
+        },
+        "check_for_nans": false,
+        "nan_test": {
+            "insert": false,
+            "variable": "temperature_scgc"
+        }
+    },
+    "InitialConditions": {
+        "type": "Planet"
+    },
+    "BoundaryConditions": {
+        "type": "Planet"
+    },
+    "Advection": {
+        "Neutrals": {
+            "Vertical": "rusanov",
+            "Horizontal": "fv",
+            "useBulkWinds": true,
+            "useImplicitFriction": true
+        },
+        "Ions": {
+            "Along": "rusanov",
+            "Across": "default"
+        }
+    },
+    "Student": {
+        "name": "",
+        "is": false
+    },
+    "Planet": {
+        "name": "earth",
+        "file": "UA/inputs/earth.in"
+    },
+    "BField": "dipole",
+    "Electrodynamics": {
+        "Potential": "weimer",
+        "DiffuseAurora": "fta",
+        "Dir": "UA/inputs/ext/ie/",
+        "File": ""
+    },
+    "Euv": {
+        "doUse": true,
+        "Model": "euvac",
+        "File": "UA/inputs/euv.csv",
+        "IncludePhotoElectrons": true,
+        "HeatingEfficiency": 0.05,
+        "dt": 60.0
+    },
+    "DoCalcBulkIonTemp": false,
+    "Eddy": {
+        "Coefficient": 50.0,
+        "BottomPressure": 0.01,
+        "TopPressure": 0.005,
+        "UseInEnergy": true,
+        "UseInMomentum": true
+    },
+    "StartTime": [
+        2011,
+        3,
+        20,
+        0,
+        0,
+        0
+    ],
+    "EndTime": [
+        2011,
+        3,
+        20,
+        0,
+        10,
+        0
+    ],
+    "neuGrid": {
+        "Shape": "sphere4",
+        "LatRange": [
+            -90.0,
+            90.0
+        ],
+        "nLatsPerBlock": 18,
+        "LonRange": [
+            0.0,
+            360.0
+        ],
+        "nLonsPerBlock": 20,
+        "nAlts": 40,
+        "MinAlt": 100.0,
+        "dAltkm": 5.0,
+        "dAltScale": 0.25,
+        "IsUniformAlt": true,
+        "AltFile": ""
+    },
     "ionGrid": {
         "Shape": "dipole4",
         "nLonsPerBlock": 36,
         "nLatsPerBlock": 18,
         "nAlts": 100,
-	"AltFile": "",
-        "dAltScale" : 0.25,
-        "IsUniformAlt" : false,	
-	"MinAlt": 80.0,
-        "LatRange": [10, 80],
-        "AltRange": [80.0, 1000],
-        "LonRange": [0.0, 360.0]},
-
-    "Oblate" : {
-		"isOblate" : false,
-        "isJ2" : false},
-
-    "Ensembles" : {
-        "nMembers" : 1},
-
-    "Sources" : {
-
-	  "Grid" : {
-		  "Coriolis" : true,
-		  "Cent_acc": true },
-      
-	  "Neutrals" : {
-	    "NO_cool" : false,
-	    "O_cool": false },
-      
-	  "Ions" : {
-		  "IncludePhotoElectronHeating": true,
-		  "IncludeIonizationHeating": true,
-		  "IncludeElectronIonCollisionalHeating":true,
-		  "IncludeElectronNeutralElasticCollisionalHeating":true,
-		  "IncludeElectronNeutralInelasticCollisionalHeating":true,
-			"IncludeThermoelectricHeating": false}},
-    
-    "Seed" : 0,
-
-    "F107File" : "UA/inputs/f107.txt",
-    "ChemistryFile" : "UA/inputs/chemistry_earth_richards.csv",
-    "CollisionsFile" : "UA/inputs/ion_neutral_collision_frequencies.csv",
-    "PlanetCharacteristicsFile" : "UA/inputs/orbits.csv",
-    "AuroraFile" : "UA/inputs/aurora_earth.csv",
-    "IndicesLookupFile" : "UA/inputs/indices_lookup.json",
-
-    "OmniwebFile" : ["UA/inputs/omni_20110319.txt"],
-    
-    "Logfile" : {
-		"name" : ["UA/output/log_geo.txt", "UA/output/log_mag.txt"],
-		"append" : false,
-		"dt" : 10.0,
-		"species" : ["O2", "O2+"]},
-
-    "Outputs" : {
-		"type" : ["states", "grid"],
-		"dt" : [900, -1]},
-
-    "Restart" : {
-		"do" : false,
-		"OutDir" : "UA/restartOut",
-		"InDir" : "UA/restartIn",
-		"dt" : 3600.0},
-
-	"DoTests": {"test_gradient": false,
-				"exit_on_fail": true}
-}
+        "AltFile": "",
+        "dAltScale": 0.25,
+        "IsUniformAlt": false,
+        "MinAlt": 80.0,
+        "LatRange": [
+            10,
+            80
+        ],
+        "AltRange": [
+            80.0,
+            1000
+        ],
+        "LonRange": [
+            0.0,
+            360.0
+        ]
+    },
+    "Oblate": {
+        "isOblate": false,
+        "isJ2": false
+    },
+    "Ensembles": {
+        "nMembers": 1
+    },
+    "Sources": {
+        "Grid": {
+            "Coriolis": true,
+            "Cent_acc": true
+        },
+        "Neutrals": {
+            "NO_cool": false,
+            "O_cool": false
+        },
+        "Ions": {
+            "IncludePhotoElectronHeating": true,
+            "IncludeIonizationHeating": true,
+            "IncludeElectronIonCollisionalHeating": true,
+            "IncludeElectronNeutralElasticCollisionalHeating": true,
+            "IncludeElectronNeutralInelasticCollisionalHeating": true,
+            "IncludeThermoelectricHeating": false
+        }
+    },
+    "Seed": 0,
+    "F107File": "UA/inputs/f107.txt",
+    "ChemistryFile": "UA/inputs/chemistry_earth_richards.csv",
+    "CollisionsFile": "UA/inputs/ion_neutral_collision_frequencies.csv",
+    "PlanetCharacteristicsFile": "UA/inputs/orbits.csv",
+    "AuroraFile": "UA/inputs/aurora_earth.csv",
+    "IndicesLookupFile": "UA/inputs/indices_lookup.json",
+    "OmniwebFile": [
+        "UA/inputs/omni_20110319.txt"
+    ],
+    "Logfile": {
+        "name": [
+            "UA/output/log_geo.txt",
+            "UA/output/log_mag.txt"
+        ],
+        "append": false,
+        "dt": 10.0,
+        "species": [
+            "O2",
+            "O2+"
+        ]
+    },
+    "Outputs": {
+        "type": [
+            "states",
+            "grid"
+        ],
+        "dt": [
+            900,
+            -1
+        ]
+    },
+    "Restart": {
+        "do": false,
+        "OutDir": "UA/restartOut",
+        "InDir": "UA/restartIn",
+        "dt": 3600.0
+    },
+    "DoTests": {
+        "test_gradient": false,
+        "exit_on_fail": true
+    }
+}
\ No newline at end of file

From bb451e786e5173875f60d117aa2a5d948d933241 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 3 Sep 2025 21:20:17 -0400
Subject: [PATCH 210/266] BUG: need to exit main function before reporting time

---
 src/main/main.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 32f3ac0c..54e8316f 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -291,13 +291,12 @@ int main() {
 
     } // End of outer time loop - done with run!
 
-    report.times();
-
   } catch (std::string error) {
     report.error(error);
   }
 
   report.exit(function);
+  report.times();
   report.report_errors();
 
   if (nProcs > 0)

From 73ba4255ae6a732d671d619a6b16395158ab443d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 13:34:40 -0400
Subject: [PATCH 211/266] FEAT: set vertical BCs on the closed field lines

---
 include/grid.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/grid.h b/include/grid.h
index daae63d9..c4187111 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -150,6 +150,7 @@ class Grid {
 
   // Whether to close field lines on dipole grid (Always false for geo grids)
   bool IsClosed;
+  bool setNorthAsDown, setSouthAsDown;
 
   // These are the locations of the magnetic poles:
   //  ll -> lat, lon, radius independent

From dd6671343da89d4ae8a1456105ad98b774615fe2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 13:35:31 -0400
Subject: [PATCH 212/266] FIX: set some vertical BCs below the grid

---
 src/advance.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/advance.cpp b/src/advance.cpp
index 375e24eb..f56e6aea 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -155,6 +155,7 @@ bool advance(Planets &planet,
     didWork = neutrals.check_for_nonfinites("Geo Grid: After Horizontal Advection");
     ionsMag.exchange_old(mGrid);
     fill_horizontal_ghostcels(neutralsMag.temperature_scgc, mGrid.get_nGCs());
+    neutralsMag.set_lower_bcs(mGrid, time, indices);
 
     //for (int iSpecies = 0; iSpecies < neutralsMag.nSpecies; iSpecies++)
     //  fill_horizontal_ghostcels(neutralsMag.species[iSpecies].density_scgc,
@@ -221,6 +222,12 @@ bool advance(Planets &planet,
     chemistry.calc_chemistry(neutrals, ions, time, gGrid);
     chemistryMag.calc_chemistry(neutralsMag, ionsMag, time, mGrid);
 
+    // We could have some weird results in the non-physical cells,
+    // so correct them
+    if (mGrid.IsDipole)
+      didWork = ionsMag.set_bcs(mGrid, time, indices);
+
+
     if (input.get_O_cooling())
       neutrals.calc_O_cool();
 

From d658e2f00ceddf40d3b64642450a5f405a0923ae Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 14:57:53 -0400
Subject: [PATCH 213/266] BUG: need dipole stuff so that variables are
 initialized

---
 src/init_geo_grid.cpp | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index d61fb623..ed333537 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -153,17 +153,19 @@ void Grid::create_altitudes(Planets planet) {
   // All cells on the geographic grid *should* be ok
   isTooLowCell = find(geoAlt_scgc < grid_input.alt_min * cKMtoM);
   isPhysicalCell = find(geoAlt_scgc >= grid_input.alt_min * cKMtoM);
-  // get the ghost cell indices on each lat/lon point. 
+  // get the ghost cell indices on each lat/lon point.
   // may be redundant can fill lower with nGCs-1, but this is here for now
   arma::uvec theGCs;
-  for (iLon=0; iLon<nLons; iLon++){
-    for (iLat = 0; iLat<nLats; iLat++){
+
+  for (iLon = 0; iLon < nLons; iLon++) {
+    for (iLat = 0; iLat < nLats; iLat++) {
       // find *last* cell below alt_min
       theGCs = find(geoAlt_scgc.tube(iLon, iLat) < grid_input.alt_min * cKMtoM);
       // Get the last element if the col-vec
       first_lower_gc(iLon, iLat) = theGCs(theGCs.n_elem - 1);
     }
   }
+
   first_upper_gc.fill(nAlts - nGCs * 2 - 1);
 
   report.exit(function);
@@ -278,6 +280,11 @@ bool Grid::init_geo_grid(Quadtree quadtree,
                                        input.get_student_name(),
                                        4, "");
 
+  // The dipole grid has some variables that need to be set:
+  IsClosed = false;
+  setNorthAsDown = false;
+  setSouthAsDown = false;
+
   report.exit(function);
   return DidWork;
 }

From 8f31f0d0011a79290666a07495a36d48b49682ce Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 14:59:11 -0400
Subject: [PATCH 214/266] FEAT: know which horizontal cells to set as BCs

---
 src/init_mag_grid.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8b8e0836..8fc17d2d 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -65,6 +65,8 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
   // along the Z direction, which turns out to be the same processor
   // as the Y direction, so just take that one:
   IsClosed = false;
+  setNorthAsDown = false;
+  setSouthAsDown = false;
 
   if ((middle_norm(1) < 0) && (up_norm(1) > 0)) {
     // We are in the south and need to pass to the north:
@@ -76,7 +78,9 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
     // points are "higher" than the other edges, let's just add some
     // to the 3rd dimension:
     edge_Z(2) = 5.0;
+    // Let set_BCs know which side to use as BCs
     IsClosed = true;
+    setNorthAsDown = true;
   }
 
   if ((middle_norm(1) > 0) && (down_norm(1) < 0)) {
@@ -86,7 +90,9 @@ void Grid::create_dipole_connection(Quadtree quadtree) {
     edge_Z = edge_Ym;
     // See note above...
     edge_Z(2) = 5.0;
+    // Let set_BCs know which side to use as BCs
     IsClosed = true;
+    setSouthAsDown = true;
   }
 
   // Check if touching South Pole:

From f27299c3344ab860663394f73684ccfe4dede0a2 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 15:00:01 -0400
Subject: [PATCH 215/266] FEAT: working on BCs for closed fieldlines

---
 src/ions_bcs.cpp | 162 +++++++++++++++++++++++++++++++++--------------
 1 file changed, 115 insertions(+), 47 deletions(-)

diff --git a/src/ions_bcs.cpp b/src/ions_bcs.cpp
index c17757a7..0c793853 100644
--- a/src/ions_bcs.cpp
+++ b/src/ions_bcs.cpp
@@ -6,14 +6,9 @@
 #include "aether.h"
 
 // -----------------------------------------------------------------------------
-//  Set initial conditions for the neutrals.
-//    Two methods implemented so far:
-//      - Planet: Use fixed density values in the planet.in file and the
-//                temperature profile to set the densities and temperature.
-//                Densities are filled with hydrostatic solution.
-//      - Msis: Use NRL MSIS to set the densities and temperatures.  If the
-//              densities are not found, then set to density in planet.in
-//              file and fill with hydrostatic.
+// Set boundary conditions for the ions.
+// The dipolar grid is fundamentally different than the sphere/cubesphere grids.
+// We need to treat them differently.
 // -----------------------------------------------------------------------------
 
 //----------------------------------------------------------------------
@@ -67,33 +62,41 @@ bool Ions::set_upper_bcs(Grid grid) {
   arma_mat h;
   arma_mat aveT;
 
-  for (iAlt = nAlts - nGCs; iAlt < nAlts; iAlt++) {
-    // Bulk Quantities:
-    // Constant gradient (ignoring grid spacing...)
-    temperature_scgc.slice(iAlt) =
-      2 * temperature_scgc.slice(iAlt - 1) - temperature_scgc.slice(iAlt - 2);
+  // If we are on the dipole grid and our field-lines are closed, then
+  // we don't want to set upper boundary conditions, since we will
+  // be message passing them.
 
-    // For each species:
-    for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+  if (!grid.IsClosed) {
+
+    for (iAlt = nAlts - nGCs; iAlt < nAlts; iAlt++) {
+      // Bulk Quantities:
       // Constant gradient (ignoring grid spacing...)
-      species[iSpecies].temperature_scgc.slice(iAlt) =
-        2 * species[iSpecies].temperature_scgc.slice(iAlt - 1) -
-        species[iSpecies].temperature_scgc.slice(iAlt - 2);
-
-      aveT = (species[iSpecies].temperature_scgc.slice(iAlt) +
-              electron_temperature_scgc.slice(iAlt));
-      // Calculate scale height for the species:
-      h = cKB / species[iSpecies].mass *
+      temperature_scgc.slice(iAlt) =
+        2 * temperature_scgc.slice(iAlt - 1) - temperature_scgc.slice(iAlt - 2);
+
+      // For each species:
+      for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+        // Constant gradient (ignoring grid spacing...)
+        species[iSpecies].temperature_scgc.slice(iAlt) =
+          2 * species[iSpecies].temperature_scgc.slice(iAlt - 1) -
+          species[iSpecies].temperature_scgc.slice(iAlt - 2);
+
+        aveT = (species[iSpecies].temperature_scgc.slice(iAlt) +
+                electron_temperature_scgc.slice(iAlt));
+        // Calculate scale height for the species:
+        h = cKB / species[iSpecies].mass *
+            species[iSpecies].temperature_scgc.slice(iAlt) /
+            abs(grid.gravity_vcgc[2].slice(iAlt));
+        // Assume each species falls of with (modified) hydrostatic:
+        species[iSpecies].density_scgc.slice(iAlt) =
           species[iSpecies].temperature_scgc.slice(iAlt) /
-          abs(grid.gravity_vcgc[2].slice(iAlt));
-      // Assume each species falls of with (modified) hydrostatic:
-      species[iSpecies].density_scgc.slice(iAlt) =
-        species[iSpecies].temperature_scgc.slice(iAlt) /
-        species[iSpecies].temperature_scgc.slice(iAlt - 1) %
-        species[iSpecies].density_scgc.slice(iAlt - 1) %
-        exp(-grid.dk_edge_m.slice(iAlt) / h);
-      species[iSpecies].velocity_vcgc[2].slice(iAlt).zeros();
+          species[iSpecies].temperature_scgc.slice(iAlt - 1) %
+          species[iSpecies].density_scgc.slice(iAlt - 1) %
+          exp(-grid.dk_edge_m.slice(iAlt) / h);
+        species[iSpecies].velocity_vcgc[2].slice(iAlt).zeros();
+      }
     }
+
   }
 
   report.exit(function);
@@ -114,26 +117,91 @@ bool Ions::set_lower_bcs(Grid grid, Times time, Indices indices) {
 
   int64_t nAlts = grid.get_nZ();
   int64_t nX = grid.get_nX(), iX;
-  int64_t nY = grid.get_nY(), iY;
+  int64_t nY = grid.get_nY(), iY, iYs, iYe;
   int64_t nGCs = grid.get_nGCs();
-  int64_t iAlt;
+  int64_t iAlt, iFirst;
   arma_mat h;
   arma_mat aveT;
 
-  for (iAlt = nGCs - 1; iAlt >= 0; iAlt--) {
-    // Bulk Quantities:
-    temperature_scgc.slice(iAlt) = temperature_scgc.slice(iAlt + 1);
-
-    // For each species:
-    for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
-      // assign all species temperatures the bulk temperature:
-      species[iSpecies].temperature_scgc.slice(iAlt) =
-        temperature_scgc.slice(iAlt);
-      // Assume each species falls off a bit.
-      // this BC shouldn't matter, since the bottom of the code
-      // should be in chemical equalibrium:
-      species[iSpecies].density_scgc.slice(iAlt) =
-        0.95 * species[iSpecies].density_scgc.slice(iAlt + 1);
+  // This is true for all grids:
+  for (iX = 0; iX < nX; iX++) {
+    for (int iY = 0; iY < nY; iY++) {
+      iFirst = grid.first_lower_gc(iX, iY);
+
+      for (iAlt = iFirst; iAlt >= 0; iAlt--) {
+        // Bulk Quantities:
+        temperature_scgc.slice(iAlt) = temperature_scgc.slice(iFirst + 1);
+
+        // For each species:
+        for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+          // assign all species temperatures the bulk temperature:
+          species[iSpecies].temperature_scgc.slice(iAlt) =
+            temperature_scgc.slice(iAlt);
+          // Assume each species falls off a bit.
+          // this BC shouldn't matter, since the bottom of the code
+          // should be in chemical equalibrium:
+          species[iSpecies].density_scgc.slice(iAlt) =
+            0.95 * species[iSpecies].density_scgc.slice(iFirst + 1);
+        }
+      }
+    }
+  }
+
+  // This section is for the dipole grid.  If the field-lines are
+  // closed, then we will treat the N/S ghostcells as LOWER boundaries.
+  // If thr grid is in the south, then treat the north bounday as the
+  // lower boundary.  If the grid is in the north, treat the south boundary
+  // as the lower boundary.
+  // Because we are expecting to be chemically dominant, the lower BCs don't
+  // matter as much for the ions.  We really just want to fill them with some
+  // reasonable values.
+
+  if (grid.setNorthAsDown) {
+    // First physical cell:
+    iFirst = nY - nGCs - 2;
+    iYs = nY - nGCs - 1;
+    iYe = nY;
+  }
+
+  if (grid.setSouthAsDown) {
+    // First physical cell:
+    iFirst = nGCs;
+    iYs = 0;
+    iYe = nGCs;
+  }
+
+  if (grid.setNorthAsDown || grid.setSouthAsDown) {
+
+    for (iX = 0; iX < nX; iX++) {
+      for (int iY = iYs; iY < iYe; iY++) {
+        // Bulk Quantities:
+        temperature_scgc.tube(iX, iY) = temperature_scgc.tube(iX, iFirst);
+
+        // For each species:
+        for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+          // assign all species temperatures the bulk temperature:
+          species[iSpecies].temperature_scgc.tube(iX, iY) =
+            temperature_scgc.tube(iX, iFirst);
+          // Assume each species falls off a bit.
+          // this BC shouldn't matter, since the bottom of the code
+          // should be in chemical equalibrium:
+          species[iSpecies].density_scgc.tube(iX, iY) =
+            0.95 * species[iSpecies].density_scgc.tube(iX, iFirst);
+        }
+
+        for (iAlt = 0; iAlt <= grid.first_lower_gc(iX, iY); iAlt++) {
+          //std::cout << "ion bcs, dipole, setnorth iAlt : "
+          //          << iAlt << " "
+          //          << grid.first_lower_gc(iX, iFirst) << " "
+          //          << temperature_scgc(iX, iFirst, grid.first_lower_gc(iX, iFirst) + 1) << " "
+          //          << temperature_scgc(iX, iFirst, iAlt) << " "
+          //          << grid.geoAlt_scgc(iX, iFirst, grid.first_lower_gc(iX,
+          //                              iFirst) + 1) / 1000.0 << "\n";
+          temperature_scgc(iX, iY, iAlt) =
+            temperature_scgc(iX, iFirst, grid.first_lower_gc(iX, iFirst) + 1);
+        }
+
+      }
     }
   }
 

From 913f2a27870a2da77a6cc91337ceb2938c02a9ee Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 15:00:42 -0400
Subject: [PATCH 216/266] FEAT: setting horizontal BCs for closed field lines

---
 src/neutrals_bcs.cpp | 52 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 77368a39..59990c51 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -247,6 +247,58 @@ bool Neutrals::set_lower_bcs(Grid grid,
     }
   }
 
+  // This section is for the dipole grid.  If the field-lines are
+  // closed, then we will treat the N/S ghostcells as LOWER boundaries.
+  // If thr grid is in the south, then treat the north bounday as the
+  // lower boundary.  If the grid is in the north, treat the south boundary
+  // as the lower boundary.
+  // Because we are expecting to be chemically dominant, the lower BCs don't
+  // matter as much for the ions.  We really just want to fill them with some
+  // reasonable values.
+
+  int64_t nX = grid.get_nX();
+  int64_t nY = grid.get_nY();
+  int64_t iX, iY, iYs, iYe, iFirst;
+
+  if (grid.setNorthAsDown) {
+    // First physical cell:
+    iFirst = nY - nGCs - 1;
+    iYs = nY - nGCs;
+    iYe = nY;
+  }
+
+  if (grid.setSouthAsDown) {
+    // First physical cell:
+    iFirst = nGCs;
+    iYs = 0;
+    iYe = nGCs;
+  }
+
+  if (grid.setNorthAsDown || grid.setSouthAsDown) {
+
+    for (iX = 0; iX < nX; iX++) {
+      for (int iY = iYs; iY < iYe; iY++) {
+        // Bulk Quantities:
+        temperature_scgc.tube(iX, iY) = temperature_scgc.tube(iX, iFirst);
+
+        // For each species:
+        for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
+          // Assume each species falls off a bit.
+          // this BC shouldn't matter, since we are not going to do
+          // horizontal advection on neutrals:
+          species[iSpecies].density_scgc.tube(iX, iY) =
+            0.95 * species[iSpecies].density_scgc.tube(iX, iFirst);
+        }
+
+        for (iAlt = 0; iAlt <= grid.first_lower_gc(iX, iY); iAlt++) {
+          temperature_scgc(iX, iY, iAlt) =
+            temperature_scgc(iX, iFirst, grid.first_lower_gc(iX, iFirst));
+        }
+
+      }
+    }
+  }
+
   didWork = true;
 
   calc_bulk_velocity();

From 389789097007827fcbe5d35e4fdb44e1c2359372 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 4 Sep 2025 15:01:26 -0400
Subject: [PATCH 217/266] comment and spacing

---
 src/neutrals_ics.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/neutrals_ics.cpp b/src/neutrals_ics.cpp
index 7cefe289..1f8b7c18 100644
--- a/src/neutrals_ics.cpp
+++ b/src/neutrals_ics.cpp
@@ -122,7 +122,7 @@ bool Neutrals::initial_conditions(Grid grid,
       // temperature profile in the planet.in file.
       // ---------------------------------------------------------------------
 
-      // Let's assume that the altitudes are not dependent on lat/lon:
+      // Let's assume that the altitudes are dependent on lat/lon:
 
       arma_vec alt1d(nAlts);
       arma_vec temp1d(nAlts);
@@ -188,7 +188,7 @@ bool Neutrals::initial_conditions(Grid grid,
       report.print(2, "Filling with hydrostatic");
 
       for (int iSpecies = 0; iSpecies < nSpecies; iSpecies++)
-       fill_with_hydrostatic(iSpecies, nGCs - 1, nAlts, grid);
+        fill_with_hydrostatic(iSpecies, nGCs - 1, nAlts, grid);
     } // type = planet
   }
 

From be2a6b15b577885e21c9d7e220cca8146b0022ea Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:16:36 -0400
Subject: [PATCH 218/266] get to work with cubesphere

---
 tests/restart_ensembles/aether.json.whole | 33 ++++++++++++-----------
 tests/restart_ensembles/run_all.sh        | 10 +++++--
 2 files changed, 26 insertions(+), 17 deletions(-)

diff --git a/tests/restart_ensembles/aether.json.whole b/tests/restart_ensembles/aether.json.whole
index 5cb6453a..c0a74992 100644
--- a/tests/restart_ensembles/aether.json.whole
+++ b/tests/restart_ensembles/aether.json.whole
@@ -1,7 +1,7 @@
 
 {
     "Ensembles" : {
-	"nMembers" : 5},
+	"nMembers" : 3},
 
     "Perturb": {
 	"f107" : { "Mean" : 1.0,
@@ -17,23 +17,26 @@
 	"iVerbose" : 0},
 
     "StartTime" : [2011, 3, 20, 0, 0, 0],
-    "EndTime" : [2011, 3, 20, 0, 10, 0],
+    "EndTime" : [2011, 3, 20, 0, 6, 0],
 
     "neuGrid" : {
-	"nLons" : 12,
-	"nLats" : 12,
-	"nAlts" : 30},
-
+        "Shape": "cubesphere",
+        "nLonsPerBlock" : 20,
+        "nLatsPerBlock" : 20,
+        "nAlts" : 30,
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
     "ionGrid": {
-        "dAltStretch": 0.2,
-        "LatStretch": 1,
-        "Shape": "dipole",
-        "nLonsPerBlock": 18,
-        "nLatsPerBlock" : 18,
-        "nAlts":36,
-        "LatMax":88,
-        "MinAlt": 80.0,
-        "MinApex": 125.0},
+        "Shape": "cubesphere",
+        "nLonsPerBlock": 16,
+        "nLatsPerBlock": 16,
+        "nAlts": 30,
+        "LatRange": [-90, 90],
+        "AltRange": [100.0, 1000],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
 
     "InitialConditions" : {
 	"type" : "msis"},
diff --git a/tests/restart_ensembles/run_all.sh b/tests/restart_ensembles/run_all.sh
index 6d2c6d49..636549b8 100755
--- a/tests/restart_ensembles/run_all.sh
+++ b/tests/restart_ensembles/run_all.sh
@@ -1,11 +1,17 @@
 #!/bin/sh
 
-NPROC=5
+# cubesphere has 6 blocks:
+NBLOCKS=6
+# run with 3 members:
+NMEMBERS=2
+# run for a total of 180s
 TOTALTIME=180
+# this is the mpi command
 MPI=/usr/bin/mpirun
+# stop this many times
 NTIMES=2
 
 # include -dowhole to run the whole simulation as comparison:
 
-../../srcPython/run_restarts.py -totaltime=${TOTALTIME} -mpi=${MPI} -rundir=../../share/run -ensembles=${NPROC} -restarts=${NTIMES}
+../../srcPython/run_restarts.py -totaltime=${TOTALTIME} -mpi=${MPI} -rundir=../../share/run -ensembles=${NMEMBERS} -restarts=${NTIMES} -blocks=${NBLOCKS}
 

From 4d467bcb5870438e33b3b60cabb6e8ac90d167cd Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:17:51 -0400
Subject: [PATCH 219/266] BUG: need to write restart files for grid

---
 src/init_geo_grid.cpp | 2 ++
 src/init_mag_grid.cpp | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index ed333537..29e0fafd 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -275,6 +275,8 @@ bool Grid::init_geo_grid(Quadtree quadtree,
   // Calculate magnetic field and magnetic coordinates:
   fill_grid_bfield(planet);
 
+  write_restart(input.get_restartout_dir());
+
   // Throw a little message for students:
   report.student_checker_function_name(input.get_is_student(),
                                        input.get_student_name(),
diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 8fc17d2d..19185638 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -625,6 +625,8 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   fill_grid_bfield(planet);
   report.print(4, "Done filling dipole grid with b-field!");
 
+  write_restart(input.get_restartout_dir());
+
   report.exit(function);
   return DidWork;
 }

From bf98af986757cea384f5a8d4234d4cc216368b14 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:18:35 -0400
Subject: [PATCH 220/266] BUG: dont have every processor write

---
 src/time.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/time.cpp b/src/time.cpp
index 6accc347..4463d04e 100644
--- a/src/time.cpp
+++ b/src/time.cpp
@@ -58,8 +58,11 @@ bool Times::restart_file(std::string dir, bool DoRead) {
     iStep--;
     dt = 0;
     increment_time();
-    std::cout << "Restarted time, Current time : ";
-    display_itime(iCurrent);
+
+    if (report.test_verbose(0)) {
+      std::cout << "Restarted time, Current time : ";
+      display_itime(iCurrent);
+    }
   } else {
     restart_time_json = { {"currenttime", current},
       {"istep", iStep}

From 63ef60d92ef3677804d7af221ddc91cccaf89609 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:19:21 -0400
Subject: [PATCH 221/266] allow control of perturbing restarts

---
 src/indices.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/indices.cpp b/src/indices.cpp
index 4dedf81d..2ed8eb12 100644
--- a/src/indices.cpp
+++ b/src/indices.cpp
@@ -143,7 +143,7 @@ bool read_and_store_indices(Indices &indices) {
 bool Indices::perturb() {
   bool DidWork = true;
   bool DoReport = false;
-  int64_t iDebug = 2;
+  int64_t iDebug = 0;
 
   json perturb_values = input.get_perturb_values();
 
@@ -152,7 +152,7 @@ bool Indices::perturb() {
     for (auto it = perturb_values.begin(); it != perturb_values.end(); ++it) {
       std::string name = it.key();
 
-      if (name != "Chemistry") {
+      if (name != "Chemistry" && name != "restart_control") {
 
         if (report.test_verbose(iDebug)) {
           std::cout << "Perturbing Index : " << name << "\n";

From c9a3dde78d15e5f212e96f314ee5e5a3515e2d7a Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:19:44 -0400
Subject: [PATCH 222/266] allow control of perturbing restarts

---
 src/read_input_file.cpp | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/src/read_input_file.cpp b/src/read_input_file.cpp
index 389e0878..66a5e883 100644
--- a/src/read_input_file.cpp
+++ b/src/read_input_file.cpp
@@ -24,6 +24,9 @@ bool Inputs::read_inputs_json(Times &time) {
   json defaults;
   json user_inputs;
 
+  // allow changing of perturbations during the restart process:
+  json perturbations;
+
   isOk = true;
 
   // Set the default values first:
@@ -54,6 +57,9 @@ bool Inputs::read_inputs_json(Times &time) {
         // if they really want:
         restart_inputs["Logfile"]["append"] = true;
         settings.merge_patch(restart_inputs);
+
+        if (restart_inputs.contains("Perturb"))
+          perturbations["Perturb"] = restart_inputs["Perturb"];
       }
     }
   }
@@ -62,6 +68,21 @@ bool Inputs::read_inputs_json(Times &time) {
   // settings, with the default/restart settings being the default:
   settings.merge_patch(user_inputs);
 
+  // There are perturbations in the restart files:
+  if (perturbations.contains("Perturb"))
+
+    // If the user wants the restart perturbations to overwrite the
+    // aether.json perturbations, then do it:
+    if (user_inputs.contains("Perturb")) {
+      if (user_inputs["Perturb"].contains("restart_control"))
+        if (user_inputs["Perturb"]["restart_control"])
+          settings.merge_patch(perturbations);
+    } else
+      // if there are perturbations in the restart files, but none
+      // in the user files, then push the restart perturbations into
+      // the settings to make them consistent
+      settings.merge_patch(perturbations);
+
   //change planet file to the one specified on aether.json:
   if (isOk)
     settings["PlanetSpeciesFile"] = get_setting_str("Planet", "file");

From 2371fb26f998f3cc2febc5d10127f3daea22b767 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 16 Sep 2025 20:53:57 -0400
Subject: [PATCH 223/266] FEAT: update to latest code

---
 tests/restarts/aether.first.json | 30 ++++++++++++++++++++----------
 tests/restarts/aether.whole.json | 30 ++++++++++++++++++++----------
 2 files changed, 40 insertions(+), 20 deletions(-)

diff --git a/tests/restarts/aether.first.json b/tests/restarts/aether.first.json
index 9fec0780..d8ae5f16 100644
--- a/tests/restarts/aether.first.json
+++ b/tests/restarts/aether.first.json
@@ -6,21 +6,23 @@
 	"dt" : 10.0},
 
     "neuGrid" : {
+	"Shape" : "sphere",
 	"nLons" : 12,
 	"nLats" : 12,
-	"nAlts" : 30},
-
+	"nAlts" : 30,
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
     "ionGrid": {
-        "dAltStretch": 0.2,
-        "LatStretch": 1,
-        "Shape": "dipole",
+        "Shape": "sphere",
         "nLonsPerBlock": 18,
         "nLatsPerBlock" : 18,
         "nAlts":36,
-        "LatMax":88,
-        "MinAlt": 80.0,
-        "MinApex": 125.0
-        },
+        "LatRange": [-90, 90],
+        "AltRange": [100.0, 1000],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
     
     "StartTime" : [2011, 3, 20, 0, 0, 0],
     "EndTime" : [2011, 3, 20, 0, 5, 0],
@@ -30,6 +32,12 @@
         "DiffuseAurora" : "fta",
         "File": "UA/inputs/b20110320n_omni.bin"},
     
+    "InitialConditions" : {
+	"type" : "msis"},
+
+    "BoundaryConditions" : {
+	"type" : "msis"},
+
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
     
     "Outputs" : {
@@ -38,6 +46,8 @@
 
     "Restart" : {
 	"do" : false,
-	"dt" : 900.0}
+	"dt" : 900.0},
+
+    "PlanetFile" : "UA/inputs/earth.in"
 
 }
diff --git a/tests/restarts/aether.whole.json b/tests/restarts/aether.whole.json
index 53b964d9..f421033d 100644
--- a/tests/restarts/aether.whole.json
+++ b/tests/restarts/aether.whole.json
@@ -6,21 +6,23 @@
 	"dt" : 10.0},
 
     "neuGrid" : {
+	"Shape" : "sphere",
 	"nLons" : 12,
 	"nLats" : 12,
-	"nAlts" : 30},
-
+	"nAlts" : 30,
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
     "ionGrid": {
-        "dAltStretch": 0.2,
-        "LatStretch": 1,
-        "Shape": "dipole",
+        "Shape": "sphere",
         "nLonsPerBlock": 18,
         "nLatsPerBlock" : 18,
         "nAlts":36,
-        "LatMax":88,
-        "MinAlt": 80.0,
-        "MinApex": 125.0
-        },
+        "LatRange": [-90, 90],
+        "AltRange": [100.0, 1000],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
     
     "StartTime" : [2011, 3, 20, 0, 0, 0],
     "EndTime" : [2011, 3, 20, 0, 10, 0],
@@ -30,6 +32,12 @@
         "DiffuseAurora" : "fta",
         "File": "UA/inputs/b20110320n_omni.bin"},
     
+    "InitialConditions" : {
+	"type" : "msis"},
+
+    "BoundaryConditions" : {
+	"type" : "msis"},
+
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
     
     "Outputs" : {
@@ -38,6 +46,8 @@
 
     "Restart" : {
 	"do" : false,
-	"dt" : 900.0}
+	"dt" : 900.0},
+
+    "PlanetFile" : "UA/inputs/earth.in"
 
 }

From e3f6a07f59d9adacd36288daeb0b75b9d8df09f0 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 17 Sep 2025 11:42:18 -0400
Subject: [PATCH 224/266] STY: fewer couts

---
 src/main/main.cpp     | 2 +-
 src/output_netcdf.cpp | 6 ++++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 54e8316f..377e91a5 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -111,7 +111,7 @@ int main() {
       if (!didWork)
         throw std::string("init_dipole_grid failed!");
     } else {
-      std::cout << "Making Spherical Magnetic Grid\n";
+      report.print(0, "Making Spherical Magnetic Grid\n");
       mGrid.set_IsDipole(false);
       didWork = mGrid.init_geo_grid(quadtree, planet);
       mGrid.set_IsGeoGrid(false);
diff --git a/src/output_netcdf.cpp b/src/output_netcdf.cpp
index 4a30ef9e..0e2501bf 100644
--- a/src/output_netcdf.cpp
+++ b/src/output_netcdf.cpp
@@ -72,8 +72,10 @@ bool OutputContainer::read_container_netcdf() {
   std::string UNITS = "units";
 
   try {
-    std::cout << "Reading NetCDF file into container : "
-              << whole_filename << "\n";
+    if (report.test_verbose(0))
+      std::cout << "Reading NetCDF file into container : "
+                << whole_filename << "\n";
+
     NcFile ncdf_file_in(whole_filename, NcFile::read);
 
     std::multimap<std::string, NcVar> variables_in_file;

From 02feed7a78e1a052169949e5e8208ab9da05c88b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:01:08 -0400
Subject: [PATCH 225/266] FEAT: write y,m,d,h,m,s,ms to restart file

---
 src/time.cpp | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/src/time.cpp b/src/time.cpp
index 4463d04e..e15d4a0d 100644
--- a/src/time.cpp
+++ b/src/time.cpp
@@ -64,8 +64,16 @@ bool Times::restart_file(std::string dir, bool DoRead) {
       display_itime(iCurrent);
     }
   } else {
-    restart_time_json = { {"currenttime", current},
-      {"istep", iStep}
+    restart_time_json = {
+      {"currenttime", current},
+      {"istep", iStep},
+      {"year", year},
+      {"month", month},
+      {"day", day},
+      {"hour", hour},
+      {"minute", minute},
+      {"second", second},
+      {"millisecond", milli},
     };
     DidWork = write_json(filename, restart_time_json);
   }

From a02941824fee2082d0483961595c2f78276aae53 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:01:57 -0400
Subject: [PATCH 226/266] FEAT: write current indices for restarting

---
 src/main/main.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 377e91a5..49454b02 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -197,6 +197,14 @@ int main() {
 
       if (!didWork)
         throw std::string("Reading Restart for time Failed!!!\n");
+
+      didWork = indices.restart_file(input.get_restartin_dir(),
+                                     true,
+                                     time.get_current());
+
+      if (!didWork)
+        throw std::string("Reading Restart for Indices Failed!!!\n");
+
     }
 
     // This is for the initial output.  If it is not a restart, this will go:

From a5cf9e1369b9ec8ff5798e9daca45ccc0766a321 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:02:36 -0400
Subject: [PATCH 227/266] FEAT: read indices for restart

---
 src/advance.cpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/advance.cpp b/src/advance.cpp
index f56e6aea..5d39bb6e 100644
--- a/src/advance.cpp
+++ b/src/advance.cpp
@@ -46,6 +46,8 @@ bool advance(Planets &planet,
     didWork = neutralsMag.check_for_nonfinites("Top of Advance - ion grid");
   }
 
+  json dummy = indices.get_all_indices(time.get_current());
+
   gGrid.calc_sza(planet, time);
   mGrid.calc_sza(planet, time);
 
@@ -258,13 +260,16 @@ bool advance(Planets &planet,
 
     if (time.check_time_gate(input.get_dt_write_restarts())) {
       report.print(3, "Writing restart files");
-      neutrals.restart_file(input.get_restartout_dir(), gGrid.get_gridtype(),
+      neutrals.restart_file(input.get_restartout_dir(),
+                            gGrid.get_gridtype(),
                             DoWrite);
-      neutralsMag.restart_file(input.get_restartout_dir(), mGrid.get_gridtype(),
+      neutralsMag.restart_file(input.get_restartout_dir(),
+                               mGrid.get_gridtype(),
                                DoWrite);
       ions.restart_file(input.get_restartout_dir(), gGrid.get_gridtype(), DoWrite);
       ionsMag.restart_file(input.get_restartout_dir(), mGrid.get_gridtype(), DoWrite);
       time.restart_file(input.get_restartout_dir(), DoWrite);
+      indices.restart_file(input.get_restartout_dir(), DoWrite, time.get_current());
     }
   }
 

From ae6f79b5ba89b93c55c8cb21121221d5275c05f7 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:03:56 -0400
Subject: [PATCH 228/266] FEAT: if restart process changes indices, reperturb

---
 src/indices.cpp | 176 ++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 171 insertions(+), 5 deletions(-)

diff --git a/src/indices.cpp b/src/indices.cpp
index 2ed8eb12..b26e2b5a 100644
--- a/src/indices.cpp
+++ b/src/indices.cpp
@@ -20,6 +20,9 @@ Indices::Indices() {
   index_time_pair single_index;
   single_index.nValues = 0;
   single_index.name = "";
+  single_index.didPerturb = false;
+  single_index.isAddPerturb = false;
+  single_index.isConstantPerturb = false;
 
   std::string lookup_file = input.get_indices_lookup_file();
   indices_lookup = read_json(lookup_file);
@@ -181,6 +184,76 @@ bool Indices::perturb() {
 // Perturb a specific index in the way the user requested
 // ----------------------------------------------------------------------
 
+void Indices::reperturb_index(int iIndex,
+                              precision_t unperturbedValue,
+                              precision_t perturbedValue,
+                              precision_t newValue) {
+
+  int64_t nValues = all_indices_arrays[iIndex].nValues;
+
+  if (all_indices_arrays[iIndex].didPerturb &&
+      all_indices_arrays[iIndex].isConstantPerturb) {
+    precision_t perturb;
+
+    if (all_indices_arrays[iIndex].isAddPerturb) {
+      // constant, non-normalized value:
+      perturb = newValue - unperturbedValue;
+
+      if (iGrid == 0)
+        std::cout << "  -> New Added Perturb : " << perturb << "\n";
+
+      for (int64_t iValue = 0; iValue < nValues; iValue++) {
+        all_indices_arrays[iIndex].values[iValue] =
+          all_indices_arrays[iIndex].originals[iValue] + perturb;
+      }
+
+    } else {
+      // constant, normalized value:
+      perturb = newValue / unperturbedValue;
+
+      if (iGrid == 0)
+        std::cout << "  -> New (normalized) Multiplied Perturb : "
+                  << perturb << "\n";
+
+      for (int64_t iValue = 0; iValue < nValues; iValue++) {
+
+        all_indices_arrays[iIndex].values[iValue] =
+          all_indices_arrays[iIndex].originals[iValue] * perturb;
+      }
+    }
+
+  } else {
+    std::string mess = "Reperturb index: don't know how to ";
+    mess = mess + "handle non-perturb or nonconstant perturb";
+    report.error(mess);
+  }
+
+}
+
+// ----------------------------------------------------------------------
+// Perturb a specific index in the way the user requested
+/*
+The way this code works is that you can perturb things in different ways.
+Multiply by a constant value:
+  - if the mean is 1.0, then the perturbed value will be unbiased
+  - if the mean is above or below 1.0, it will be biased.
+  - the standard deviation is normalized to 1, so it is a percentage
+    of the value.
+  - This will come up with a value that you multiply all of the values by,
+    like 0.843 or 1.203.
+Multiply by a non-constant value:
+  - same as above, but each value will have a different random number
+    instead of a single (constant) value
+Add a constant value:
+  - the mean and standard deviation are NOT normalized.
+  - a single value then derived given the mean and the standard dev.
+  - an unbiased value would have a mean = 0
+Add a non-constant value:
+  - same as above, but each value will have a different random number
+    instead of a single (constant) value
+*/
+// ----------------------------------------------------------------------
+
 void Indices::perturb_index(int iIndex, int seed,
                             json style, bool DoReport) {
 
@@ -191,6 +264,8 @@ void Indices::perturb_index(int iIndex, int seed,
   bool add = true;
   bool constant = false;
 
+  all_indices_arrays[iIndex].didPerturb = true;
+
   if (style.contains("Mean"))
     mean = style["Mean"];
 
@@ -200,15 +275,21 @@ void Indices::perturb_index(int iIndex, int seed,
     std = standard_deviation(all_indices_arrays[iIndex].values);
 
   // Add or Multiply the random values
-  if (style.contains("Add"))
+  if (style.contains("Add")) {
     add = style["Add"];
 
+    if (add)
+      all_indices_arrays[iIndex].isAddPerturb = true;
+  }
+
   // Only one value for all elements or individual values for elements
   if (style.contains("Constant"))
     constant = style["Constant"];
 
-  if (constant)
+  if (constant) {
     nV = 1;
+    all_indices_arrays[iIndex].isConstantPerturb = true;
+  }
 
   std::vector<double> perturbations = get_normal_random_vect(mean,
                                                              std,
@@ -220,6 +301,9 @@ void Indices::perturb_index(int iIndex, int seed,
     if (!constant)
       iV = iValue;
 
+    all_indices_arrays[iIndex].originals.push_back(
+      all_indices_arrays[iIndex].values[iValue]);
+
     if (add) {
       if (DoReport && iValue == 0)
         std::cout << "  ==> Adding " << perturbations[iV] << "\n";
@@ -316,7 +400,8 @@ precision_t Indices:: get_f107a(double time) {
 // This is the general function for getting an index
 // ----------------------------------------------------------------------
 
-precision_t Indices::get_index(double time, int index) {
+precision_t Indices::get_index(double time, int index,
+                               bool useNonperturbed /* = false */) {
 
   int64_t iLow, iMid, iHigh;
 
@@ -353,8 +438,14 @@ precision_t Indices::get_index(double time, int index) {
                all_indices_arrays[index].times[iMid]);
   precision_t x = (time - all_indices_arrays[index].times[iMid]) / dt;
 
-  precision_t value = (1.0 - x) * all_indices_arrays[index].values[iMid] +
-                      x * all_indices_arrays[index].values[iMid + 1];
+  precision_t value;
+
+  if (useNonperturbed)
+    value = (1.0 - x) * all_indices_arrays[index].originals[iMid] +
+            x * all_indices_arrays[index].originals[iMid + 1];
+  else
+    value = (1.0 - x) * all_indices_arrays[index].values[iMid] +
+            x * all_indices_arrays[index].values[iMid + 1];
 
   return value;
 }
@@ -417,6 +508,81 @@ bool Indices::set_index(int index,
   return DidWork;
 }
 
+json Indices::get_all_indices(double time) {
+  json outputJson;
+
+  int64_t iIndex;
+  precision_t value;
+
+  for (iIndex = 0; iIndex < nIndices; iIndex++) {
+    if (all_indices_arrays[iIndex].nValues > 0) {
+      value = get_index(time, iIndex);
+      outputJson[all_indices_arrays[iIndex].name] = value;
+    }
+  }
+
+  return outputJson;
+}
+
+// -----------------------------------------------------------------------------
+// This is for restarting the code. Either write or read the time.
+// -----------------------------------------------------------------------------
+
+bool Indices::restart_file(std::string dir, bool DoRead, double time) {
+
+  std::string filename;
+  bool DidWork = true;
+  filename = dir + "/indices_" + cMember + ".json";
+
+  json restart_indices_json, original_indices_json;
+
+  if (DoRead) {
+    restart_indices_json = read_json(filename);
+
+    if (report.test_verbose(1)) {
+      std::cout << "Restarted indices, Current time : ";
+      std::cout << std::setw(2) << restart_indices_json << "\n";
+    }
+
+    original_indices_json = get_all_indices(time);
+    precision_t orig, rest, unperturbed;
+
+    for (auto it = original_indices_json.begin();
+         it != original_indices_json.end(); ++it) {
+      std::string name = it.key();
+      orig = original_indices_json[name];
+      rest = restart_indices_json[name];
+
+      if (abs(orig - rest) > cSmall) {
+        int iIndex = lookup_index_id(name);
+        unperturbed = get_index(time, iIndex, true);
+
+        if (iGrid == 0)
+          std::cout << "  -> Index was altered during restart : "
+                    << name << " -> index number: "
+                    << iIndex << "; orig, rest, un "
+                    << orig << " "
+                    << rest << " "
+                    << unperturbed << " "
+                    << all_indices_arrays[iIndex].isAddPerturb << "\n";
+
+        reperturb_index(iIndex, unperturbed, orig, rest);
+
+      }
+    }
+
+
+  } else {
+    restart_indices_json = get_all_indices(time);
+
+    if (iGrid == 0)
+      DidWork = write_json(filename, restart_indices_json);
+  }
+
+  return DidWork;
+}
+
+
 // ----------------------------------------------------------------------
 // Dump the contents of an index_file_output_struct
 // ----------------------------------------------------------------------

From 320946e300d0807277476c5bb6da67d27fb20e1d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:04:06 -0400
Subject: [PATCH 229/266] FEAT: if restart process changes indices, reperturb

---
 include/indices.h | 69 +++++++++++++++++++++++++++++++----------------
 1 file changed, 46 insertions(+), 23 deletions(-)

diff --git a/include/indices.h b/include/indices.h
index 0c58b216..1e1b6ba0 100644
--- a/include/indices.h
+++ b/include/indices.h
@@ -20,7 +20,7 @@
 
  * \author Aaron Ridley
  *
- * \date 2021/04/16 
+ * \date 2021/04/16
  **************************************************************/
 
 #include <vector>
@@ -34,10 +34,10 @@ struct index_file_output_struct {
 
   /// number of times read in:
   int64_t nTimes;
-  
+
   /// array of times that correspond to the values:
   std::vector<double> times;
-  
+
   /// number of variables read in:
   int nVars;
 
@@ -62,9 +62,9 @@ void print_index_file_output_struct(index_file_output_struct contents);
 
 class Indices {
 
-// -----------------------------------------------------------------------
-// Public functions and variables
-// -----------------------------------------------------------------------
+  // -----------------------------------------------------------------------
+  // Public functions and variables
+  // -----------------------------------------------------------------------
 
  public:
 
@@ -88,10 +88,10 @@ class Indices {
   /**************************************************************
    \brief a series of functions that return the internal index number
 
-   In order to keep track of which index is which, the class uses 
+   In order to keep track of which index is which, the class uses
    constants. These functions return these constants. The user doesn't
    really need to know about the constants, but they have to get the
-   constant (when reading the file, for example) and then provide that 
+   constant (when reading the file, for example) and then provide that
    to the set index function. Conversely, we could create a bunch of
    set_ functions (such as the set_f107 function below). We figured
    that this minor inconvience is easier than making a bunch of set_
@@ -112,13 +112,17 @@ class Indices {
   int get_au_index_id();
   int get_al_index_id();
 
+  json get_all_indices(double time);
+  bool restart_file(std::string dir, bool DoRead, double time);
+
+
   /**************************************************************
    \brief Return the indices index of the variable name
    \param name the name of the variable to find the index for
    **/
-  
+
   int lookup_index_id(std::string name);
-  
+
   /**************************************************************
    \brief This function sets the f107, does an 81 day ave, sets f107a too
    \param f107_contents contents from the f107 file (time, f107, etc.)
@@ -134,9 +138,9 @@ class Indices {
    \param missing value for missing data
    **/
   bool set_index(int index_id,
-		 std::vector<double> time,
-		 std::vector<float> values,
-		 precision_t missing);
+                 std::vector<double> time,
+                 std::vector<float> values,
+                 precision_t missing);
 
   /**************************************************************
    \brief set the index array into the indices class
@@ -146,10 +150,10 @@ class Indices {
    \param missing value for missing data
    **/
   bool set_index(std::string index_name,
-		 std::vector<double> timearray,
-		 std::vector<float> indexarray,
-		 precision_t missing);
-  
+                 std::vector<double> timearray,
+                 std::vector<float> indexarray,
+                 precision_t missing);
+
   /**************************************************************
    \brief Perturbs the indices requested by user input
    **/
@@ -164,30 +168,44 @@ class Indices {
    **/
   void perturb_index(int iIndex, int seed, json style, bool DoReport);
 
+  /**************************************************************
+   \brief Re-Perturbs the specific indices based on old values and the new value
+   \param iIndex which index to perturb
+   \param unperturbedValue unperturbed value (value read in at start))
+   \param perturbedValue value that the code has now
+   \param newValue value that the restart index file contains
+   **/
+
+  void reperturb_index(int iIndex,
+                       precision_t unperturbedValue,
+                       precision_t perturbedValue,
+                       precision_t newValue);
+
+
   /**************************************************************
    \brief The general function that returns the index value at the time
    \param time the time in seconds that the index is requested at
    \param the index to return (i.e., one of the constants defined above)
    **/
-  precision_t get_index(double time, int index);
+  precision_t get_index(double time, int index, bool useNonperturbed = false);
 
   /**************************************************************
    * \brief Get the name of the indices at the specified index
    * \param iIndex which index to get name
    * \return The string of name if the function succeeds, empty string if iIndex is out of range
    **/
-  std::string get_name(int iIndex); 
+  std::string get_name(int iIndex);
 
   /**************************************************************
    \brief Return the number of the indices vector
    **/
   int all_indices_array_size();
 
-// -----------------------------------------------------------------------
-// Private functions and variables
-// -----------------------------------------------------------------------
+  // -----------------------------------------------------------------------
+  // Private functions and variables
+  // -----------------------------------------------------------------------
 
-private:
+ private:
 
   /// structure containing information about the specific index:
   struct index_time_pair {
@@ -197,12 +215,17 @@ class Indices {
 
     /// a vector of values for the index:
     std::vector<precision_t> values;
+    std::vector<precision_t> originals;
 
     /// a vector of times for the values:
     std::vector<double> times;
 
     /// the name of the index as a string:
     std::string name;
+
+    bool didPerturb;
+    bool isAddPerturb;
+    bool isConstantPerturb;
   };
 
   /// the vector that contains all of the indices vectors:

From 7cc4d92d3c84cf963b28c8393f2596c246096d2d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 18 Sep 2025 13:04:34 -0400
Subject: [PATCH 230/266] FEAT: add perturbations to test

---
 tests/restart_ensembles/aether.json.whole | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/restart_ensembles/aether.json.whole b/tests/restart_ensembles/aether.json.whole
index c0a74992..797526d5 100644
--- a/tests/restart_ensembles/aether.json.whole
+++ b/tests/restart_ensembles/aether.json.whole
@@ -19,6 +19,12 @@
     "StartTime" : [2011, 3, 20, 0, 0, 0],
     "EndTime" : [2011, 3, 20, 0, 6, 0],
 
+    "Perturb": {
+	"f107" : { "Mean" : 1.0,
+		   "Std" : 0.10,
+		   "Add" : false,
+		   "Constant" : true}},
+		   
     "neuGrid" : {
         "Shape": "cubesphere",
         "nLonsPerBlock" : 20,

From f24c149ad30b178a502327963f1737dda6c7b01d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 23 Sep 2025 21:01:40 -0400
Subject: [PATCH 231/266] BUG: need the altitude of the lower BC from planet

---
 include/grid.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/grid.h b/include/grid.h
index c4187111..d0715bf2 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -147,6 +147,7 @@ class Grid {
   // Matrices whose elements denote the altitude index of the interiormost ghost cell
   // in the k-up and k-down direction (altitude for geo grids, q for dipole).
   arma_mat first_lower_gc, first_upper_gc;
+  precision_t altitude_lower_bc;
 
   // Whether to close field lines on dipole grid (Always false for geo grids)
   bool IsClosed;

From e4965bb93a7b0336025b19a0538af2c686e113ea Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 23 Sep 2025 21:01:57 -0400
Subject: [PATCH 232/266] BUG: need the altitude of the lower BC from planet

---
 include/planets.h | 37 +++++++++++++++++++++++--------------
 1 file changed, 23 insertions(+), 14 deletions(-)

diff --git a/include/planets.h b/include/planets.h
index a512daac..25ab1aa8 100644
--- a/include/planets.h
+++ b/include/planets.h
@@ -15,11 +15,11 @@
 
 class Planets {
 
-// -----------------------------------------------------------------------
-// Public functions and variables
-// -----------------------------------------------------------------------
+  // -----------------------------------------------------------------------
+  // Public functions and variables
+  // -----------------------------------------------------------------------
 
-public:
+ public:
 
   // --------------------------------------------------------------------
   // Functions:
@@ -110,7 +110,7 @@ class Planets {
   precision_t get_dipole_strength();
 
   /**********************************************************************
-     \brief Returns omega (rotation rate) of the planet 
+     \brief Returns omega (rotation rate) of the planet
    **/
   precision_t get_omega();
 
@@ -118,27 +118,32 @@ class Planets {
      \brief returns neutrals json for neutral density BCs
    **/
   json get_neutrals();
-  
+
   /**********************************************************************
      \brief returns neutral temperature json for temperature ICs
    **/
   json get_temperatures();
-  
+
   /**********************************************************************
      \brief returns ions json for ion density characteristics
    **/
   json get_ions();
-  
+
+  /**********************************************************************
+     \brief returns altitude of the density boundary condition:
+   **/
+  precision_t get_altitude_of_bc();
+
   /**********************************************************************
      \brief Check to see if internal state of class is ok
    **/
-  
+
   bool is_ok();
 
-// -----------------------------------------------------------------------
-// Private functions and variables
-// -----------------------------------------------------------------------
-  
+  // -----------------------------------------------------------------------
+  // Private functions and variables
+  // -----------------------------------------------------------------------
+
  private:
 
   /// A structure to describe the planetary characteristics for each planet
@@ -250,10 +255,14 @@ class Planets {
 
   /// Information about the initial temperature of the planet
   json temperatures;
-  
+
   /// Information about the ions of the planet
   json ions;
 
+  /// This is needed to specify at what altitude the densities
+  /// are specified:
+  precision_t altitude_of_bc;
+
   /// An internal variable to hold the state of the class
   bool IsOk;
 

From 1cd92cb5f2ad1432088ff9273c58c7e2a8b4f83d Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 23 Sep 2025 21:02:32 -0400
Subject: [PATCH 233/266] BUG: need the altitude of the lower BC from planet

---
 src/planets.cpp | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/src/planets.cpp b/src/planets.cpp
index 0528cfd7..200786b7 100644
--- a/src/planets.cpp
+++ b/src/planets.cpp
@@ -502,6 +502,14 @@ json Planets::get_ions() {
   return ions;
 }
 
+// --------------------------------------------------------------------------
+// returns altitude of the density boundary condition
+// --------------------------------------------------------------------------
+
+precision_t Planets::get_altitude_of_bc() {
+  return altitude_of_bc;
+}
+
 // -----------------------------------------------------------------------------
 // Read in the planet specific file that describes the species
 // -----------------------------------------------------------------------------
@@ -544,6 +552,17 @@ bool Planets::read_planet_specific_file() {
           std::cout << neutrals << "\n";
       } // #neutrals
 
+      if (hash == "#altitude_of_bc") {
+        report.print(iDebug, "Found #altitude_of_bc!");
+        altitude_of_bc = read_float(infile_ptr, "#altitude_of_bc");
+        // Units read in = km
+        // Units needed in code = m:
+        altitude_of_bc = altitude_of_bc * 1000.0;
+
+        if (report.test_verbose(iDebug))
+          std::cout << altitude_of_bc << "\n";
+      } // #altitude_of_bc
+
       if (hash == "#temperature") {
         report.print(iDebug, "Found #temperatures!");
         std::vector<std::vector<std::string>> lines = read_csv(infile_ptr);

From ce96914fdb751016ad07af3605a0d9034be1b4c6 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Tue, 23 Sep 2025 21:03:07 -0400
Subject: [PATCH 234/266] FEAT: added sphere6 as a shape

---
 src/quadtree.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index aa61b98e..9918abaf 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -34,6 +34,11 @@ Quadtree::Quadtree(std::string shapeInput) {
     IsOk = true;
   }
 
+  if (shape == "sphere6") {
+    nRootNodes = 6;
+    IsOk = true;
+  }
+
   if (shape == "dipole4") {
     nRootNodes = 4;
     IsOk = true;

From f6bf8296f0ffc53deb305fb231933e94151bd64e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 06:59:49 -0400
Subject: [PATCH 235/266] BUG: need the altitude of the lower BC from planet

---
 include/neutrals.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/neutrals.h b/include/neutrals.h
index 34f67e34..e51b3452 100644
--- a/include/neutrals.h
+++ b/include/neutrals.h
@@ -225,6 +225,7 @@ class Neutrals {
   std::vector<double> initial_altitudes;
   std::vector<double> initial_temperatures;
   int64_t nInitial_temps = 0;
+  precision_t altitude_of_bc;
 
   /// Number of species to advect:
   int nSpeciesAdvect;

From 827d175c3640ded014ad8442ea0ed2a11c1235bf Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:01:13 -0400
Subject: [PATCH 236/266] FEAT: prep for physical cell only calcs

---
 src/solver_chemistry.cpp | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/solver_chemistry.cpp b/src/solver_chemistry.cpp
index 3c9fd21d..ce40ca00 100644
--- a/src/solver_chemistry.cpp
+++ b/src/solver_chemistry.cpp
@@ -12,8 +12,20 @@ arma_cube solver_chemistry(arma_cube density,
                            arma_cube source,
                            arma_cube loss,
                            precision_t dt) {
-  arma_cube normalized_loss = loss / (density + 1e-6);
+  arma_cube normalized_loss;
+  normalized_loss = loss / (density + 1e-6);
   arma_cube new_density = (density + dt * source) /
                           (1.0 + dt * normalized_loss);
   return new_density;
 }
+
+arma_mat solver_chemistry(arma_mat density,
+                          arma_mat source,
+                          arma_mat loss,
+                          precision_t dt) {
+  arma_mat normalized_loss;
+  normalized_loss = loss / (density + 1e-6);
+  arma_mat new_density = (density + dt * source) /
+                         (1.0 + dt * normalized_loss);
+  return new_density;
+}

From 60c8086f7ffb6239be2d72719806c5f1e90682aa Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:01:31 -0400
Subject: [PATCH 237/266] FEAT: prep for physical cell only calcs

---
 include/solvers.h | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/include/solvers.h b/include/solvers.h
index d7d2412a..53385833 100644
--- a/include/solvers.h
+++ b/include/solvers.h
@@ -64,10 +64,6 @@ arma_vec limiter_value(arma_vec projected, arma_vec values, int64_t nPts,
 }
 
 
-void advect(Grid &grid,
-            Times &time,
-            Neutrals &neutrals);
-
 arma_vec solver_conduction(
   arma_vec value,
   arma_vec lambda,
@@ -84,6 +80,11 @@ arma_cube solver_chemistry(arma_cube density,
                            arma_cube loss,
                            precision_t dt);
 
+arma_mat solver_chemistry(arma_mat density,
+                          arma_mat source,
+                          arma_mat loss,
+                          precision_t dt);
+
 std::vector<arma_cube> coriolis(std::vector<arma_cube> velocity,
                                 precision_t rotation_rate,
                                 arma_cube lat_scgc);

From 653650c8bc559890840f838cd44069364b55e8a9 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:02:44 -0400
Subject: [PATCH 238/266] BUG: need the altitude of the lower BC from planet

---
 share/run/UA/inputs/earth.in | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/share/run/UA/inputs/earth.in b/share/run/UA/inputs/earth.in
index 6a968640..812097d7 100644
--- a/share/run/UA/inputs/earth.in
+++ b/share/run/UA/inputs/earth.in
@@ -25,6 +25,8 @@ BC is a density that is used in the lowest boundary cell if you
   In this example file, the values are from 96.87 km Jan 1, 2013
   O_1D is made up.
 
+#ALTITUDE_OF_BC
+96.87
 
 #NEUTRALS
 name, mass, vibration, thermal_cond, thermal_exp, advect, BC

From 52b77838a795f876d63c5b8c7679f81c05844fc1 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:03:11 -0400
Subject: [PATCH 239/266] BUG: should be FLOAT

---
 src/file_input.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/file_input.cpp b/src/file_input.cpp
index 29e3d67a..667f8e03 100644
--- a/src/file_input.cpp
+++ b/src/file_input.cpp
@@ -170,7 +170,7 @@ precision_t read_float(std::ifstream &file_ptr, std::string hash) {
     line = strip_string_end(line);
 
     try {
-      output = stoi(line);
+      output = stof(line);
     } catch (...) {
       std::cout << "Issue in read_float!\n";
       std::cout << "In hash: ";

From 4e08694f92a8164a6aae0170cf5697bba4c96a0b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:04:12 -0400
Subject: [PATCH 240/266] set geo grid before init_grid

---
 src/main/main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 49454b02..ab93c1ec 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -113,8 +113,8 @@ int main() {
     } else {
       report.print(0, "Making Spherical Magnetic Grid\n");
       mGrid.set_IsDipole(false);
-      didWork = mGrid.init_geo_grid(quadtree, planet);
       mGrid.set_IsGeoGrid(false);
+      didWork = mGrid.init_geo_grid(quadtree, planet);
     }
 
     if (!didWork)

From f89490ee486a6615d5eb2be3a151c9f885f01194 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:05:03 -0400
Subject: [PATCH 241/266] BUG: scale the density to real altitude

---
 src/neutrals_bcs.cpp | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/src/neutrals_bcs.cpp b/src/neutrals_bcs.cpp
index 59990c51..ecb87da2 100644
--- a/src/neutrals_bcs.cpp
+++ b/src/neutrals_bcs.cpp
@@ -207,8 +207,23 @@ bool Neutrals::set_lower_bcs(Grid grid,
       temperature_scgc.subcube(iLon, iLat, 0, iLon, iLat, iAlt - 1).fill(
         temperature_scgc(iLon, iLat, iAlt));
 
+      precision_t t = temperature_scgc(iLon, iLat, 0);
+      precision_t g = abs(grid.gravity_vcgc[2](iLon, iLat, iAlt));
+
+      precision_t alt1 = grid.geoAlt_scgc(iLon, iLat, iAlt);
+      precision_t alt0 = grid.altitude_lower_bc;
+      precision_t dz = alt1 - alt0;
+
       for (iSpecies = 0; iSpecies < nSpecies; iSpecies++) {
 
+        precision_t m = mean_major_mass_scgc(iLon, iLat, iAlt);
+
+        //if (m == 0)
+        m = species[iSpecies].mass;
+
+        precision_t h = cKB * t / (m * g);
+        precision_t factor = exp(-dz / h);
+
         //-----------------------------------------------
         // Planet BCs - set to fixed constant values.
         //-----------------------------------------------
@@ -217,6 +232,7 @@ bool Neutrals::set_lower_bcs(Grid grid,
           // Fill all lower ghost cells density with lower boundary condition:
           species[iSpecies].density_scgc.subcube(iLon, iLat, 0,
                                                  iLon, iLat, iAlt - 1).fill(
+                                                   factor *
                                                    species[iSpecies].lower_bc_density);
         }  // planet bc type
 

From 910c4e814286eca72010b206bcce7fa0c4551f85 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:06:18 -0400
Subject: [PATCH 242/266] BUG: get the correct grid settings

---
 src/init_geo_grid.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 29e0fafd..d14f1a0f 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -20,7 +20,9 @@ void Grid::create_altitudes(Planets planet) {
 
   arma_vec alt1d(nAlts);
 
-  Inputs::grid_input_struct grid_input = input.get_grid_inputs("neuGrid");
+  Inputs::grid_input_struct grid_input;
+
+  grid_input = input.get_grid_inputs(gridType);
 
   if (grid_input.IsUniformAlt) {
     for (iAlt = 0; iAlt < nAlts; iAlt++)
@@ -217,6 +219,9 @@ bool Grid::init_geo_grid(Quadtree quadtree,
   //MPI_Barrier(aether_comm);
   create_altitudes(planet);
 
+  // set the altitude of the lower boundary values:
+  altitude_lower_bc = planet.get_altitude_of_bc();
+
   init_connection();
 
   //DidWork = write_restart(input.get_restartout_dir());

From 6368921f7382abedd0845d0b596172bbdd285d53 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 07:06:42 -0400
Subject: [PATCH 243/266] BUG: need the altitude of the lower BC from planet

---
 src/init_mag_grid.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 19185638..0daf3043 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -215,6 +215,10 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   precision_t min_alt_re = (min_alt + planetRadius) / planetRadius;
   precision_t max_alt_re = (max_alt + planetRadius) / planetRadius;
 
+  // set the altitude of the lower boundary from the planet file
+  //   -- this is used for setting densities hydrostatically.
+  altitude_lower_bc = planet.get_altitude_of_bc();
+
   if (nAlts % 2 != 0) {
     report.error("nAlts must be even!");
     DidWork = false;

From 39b2cc5494e28adaabf35bda49b3ca16e10a89aa Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 10:12:11 -0400
Subject: [PATCH 244/266] FEAT: define grid shapes and type in constants

---
 include/constants.h               | 20 ++++++++++++++++----
 include/grid.h                    |  3 +++
 src/calc_dt.cpp                   |  9 +--------
 src/calc_electron_temperature.cpp |  2 +-
 src/calc_neutral_derived.cpp      |  2 +-
 src/init_parallel.cpp             |  5 ++---
 src/neutrals_advect.cpp           |  2 +-
 src/output.cpp                    |  2 +-
 src/solver_gradients.cpp          |  4 ++--
 9 files changed, 28 insertions(+), 21 deletions(-)

diff --git a/include/constants.h b/include/constants.h
index 474eb8f4..16183e9d 100644
--- a/include/constants.h
+++ b/include/constants.h
@@ -6,6 +6,18 @@
 
 #include <vector>
 
+// -------------------------------------------------------------------------
+// Define some constants for the code so that all functions understand
+// stuff
+// These are not physical constants, but are useful references
+// -------------------------------------------------------------------------
+
+const int iSphere_ = 1;
+const int iCubesphere_ = 2;
+const int iDipole_ = 3;
+const std::string neutralType_ = "neuGrid";
+const std::string ionType_ = "ionGrid";
+
 // -------------------------------------------------------------------------
 // Physical Constants
 //   - Naming standards:
@@ -65,7 +77,7 @@ const double cJULIAN2000 = 2451545.0;
 // -------------------------------------------------------------------------
 
 const precision_t cPI = 3.141592653589793;
-const precision_t cTWOPI = 2*cPI;
+const precision_t cTWOPI = 2 * cPI;
 
 // -------------------------------------------------------------------------
 // Conversion Constants:
@@ -75,8 +87,8 @@ const precision_t cTWOPI = 2*cPI;
 //   - Names are all UPPER CASE otherwise
 // -------------------------------------------------------------------------
 
-const precision_t cDtoR = cPI/180.0;
-const precision_t cRtoD = 180.0/cPI;
+const precision_t cDtoR = cPI / 180.0;
+const precision_t cRtoD = 180.0 / cPI;
 
 // -------------------------------------------------------------------------
 // converting time between seconds and other units of time:
@@ -99,7 +111,7 @@ const double cMtoS = 60.0;
 const double cStoM = 1.0 / cMtoS;
 
 // MilliSeconds <-> Seconds:
-const double cMStoS = 1.0/1000.0;
+const double cMStoS = 1.0 / 1000.0;
 const double cStoMS = 1000.0;
 
 // -------------------------------------------------------------------------
diff --git a/include/grid.h b/include/grid.h
index d0715bf2..c3826dfd 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -53,6 +53,9 @@ class Grid {
   const int iDipole_ = 3;
   int iGridShape_ = -1;
 
+  const std::string neutralType_ = "neuGrid";
+  const std::string ionType_ = "ionGrid";
+
   // Armidillo Cube Versions:
   // Cell Center Coordinates
   arma_cube geoLon_scgc, geoX_scgc;
diff --git a/src/calc_dt.cpp b/src/calc_dt.cpp
index 96658471..1ab838bd 100644
--- a/src/calc_dt.cpp
+++ b/src/calc_dt.cpp
@@ -16,7 +16,7 @@ precision_t calc_dt(Grid grid, std::vector<arma_cube> cMax_vcgc) {
 
   precision_t dt;
 
-  if (grid.iGridShape_ == grid.iCubesphere_)
+  if (grid.iGridShape_ == iCubesphere_)
     dt = calc_dt_cubesphere(grid, cMax_vcgc);
   else
     dt = calc_dt_sphere(grid, cMax_vcgc);
@@ -110,13 +110,6 @@ precision_t calc_dt_cubesphere(Grid grid, std::vector<arma_cube> cMax_vcgc) {
     //dty.slice(iAlt) = grid.drefy(iAlt) * dummy_1 / u2;
   }
 
-  //if (iProc == 0)
-  //  display_cube("dtx : ", dtx);
-
-  //if (iProc == 0)
-  //  display_cube("dty : ", dty);
-
-
   // Take minimum dts in each direction:
   dta(0) = dtx.min();
   dta(1) = dty.min();
diff --git a/src/calc_electron_temperature.cpp b/src/calc_electron_temperature.cpp
index a5a00552..5b459982 100644
--- a/src/calc_electron_temperature.cpp
+++ b/src/calc_electron_temperature.cpp
@@ -616,7 +616,7 @@ arma_mat Ions::calc_thermoelectric_current(Grid &grid) {
 
   // with the dipole, the field-aligned current is in the k^ direction
   // But we do not solve for e- velocity (and exb is 0 parallel to B), so we cannot do this:
-  // if (grid.iGridShape_ == grid.iDipole_){
+  // if (grid.iGridShape_ == iDipole_){
   //   for (int64_t iAlt = 0; iAlt < ions.density_scgc.n_slices; iAlt++){
   //     JParallel += (ions.density_scgc.slice(iAlt) * cE % (ions.velocity_vcgc[2].slice(iAlt) - ions.exb_vcgc[2].slice(iAlt)))
   //                  * grid.dalt_center_scgc[iAlt];
diff --git a/src/calc_neutral_derived.cpp b/src/calc_neutral_derived.cpp
index d6802d93..d4959b8b 100644
--- a/src/calc_neutral_derived.cpp
+++ b/src/calc_neutral_derived.cpp
@@ -407,7 +407,7 @@ precision_t Neutrals::calc_dt(Grid grid) {
 
   precision_t dt;
 
-  if (grid.iGridShape_ == grid.iCubesphere_)
+  if (grid.iGridShape_ == iCubesphere_)
     dt = calc_dt_cubesphere(grid);
   else {
     int iDir;
diff --git a/src/init_parallel.cpp b/src/init_parallel.cpp
index e89751d2..4da0c6a1 100644
--- a/src/init_parallel.cpp
+++ b/src/init_parallel.cpp
@@ -105,9 +105,8 @@ bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion) {
     if (report.test_verbose(2))
       std::cout << "seed : " << seed << "\n";
 
-    quadtree.build("neuGrid");
-    // #TODO
-    quadtree_ion.build("ionGrid");
+    quadtree.build(neutralType_);
+    quadtree_ion.build(ionType_);
 
   } else {
     if (iProc == 0) {
diff --git a/src/neutrals_advect.cpp b/src/neutrals_advect.cpp
index dcdd67cd..613d4130 100644
--- a/src/neutrals_advect.cpp
+++ b/src/neutrals_advect.cpp
@@ -42,7 +42,7 @@ bool Neutrals::advect_horizontal(Grid & grid, Times & time) {
   static int iFunction = -1;
   report.enter(function, iFunction);
 
-  if (grid.iGridShape_ == grid.iCubesphere_) {
+  if (grid.iGridShape_ == iCubesphere_) {
     if (input.get_advection_neutrals_horizontal() == "advect_test")
       solver_horizontal_RK4_advection(grid, time);
     else if (input.get_advection_neutrals_horizontal() == "fv")
diff --git a/src/output.cpp b/src/output.cpp
index f4adf0d9..08ba1dd4 100644
--- a/src/output.cpp
+++ b/src/output.cpp
@@ -362,7 +362,7 @@ bool output(const Neutrals &neutrals,
         report.error("File output type not found!");
         didWork = false;
       } else {
-        if (grid.get_IsGeoGrid())
+        if (grid.get_gridtype() == neutralType_)
           filename = filename + "G_";
         else
           filename = filename + "M_";
diff --git a/src/solver_gradients.cpp b/src/solver_gradients.cpp
index 73754c10..6a0d0ac2 100644
--- a/src/solver_gradients.cpp
+++ b/src/solver_gradients.cpp
@@ -18,9 +18,9 @@ std::vector<arma_cube> calc_gradient_vector(arma_cube value_scgc, Grid grid) {
     display_vector("gradient, value : ", value_scgc.tube(9, 9));
   }
 
-  if (grid.iGridShape_ == grid.iCubesphere_)
+  if (grid.iGridShape_ == iCubesphere_)
     gradient_vcgc = calc_gradient_cubesphere(value_scgc, grid);
-  else if (grid.iGridShape_ == grid.iDipole_)
+  else if (grid.iGridShape_ == iDipole_)
     gradient_vcgc = calc_gradient_dipole(value_scgc, grid);
   else {
 

From a6f24887c61f887da6cc025f284d67ae8e5dd510 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 10:12:44 -0400
Subject: [PATCH 245/266] FEAT: define grid shapes and type in constants

---
 src/main/main.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index ab93c1ec..7ed004b3 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -84,8 +84,8 @@ int main() {
     // Perturb the inputs if user has asked for this
     indices.perturb();
 
-    // Initialize Geographic grid:
-    Grid gGrid("neuGrid");
+    // Initialize neutral grid:
+    Grid gGrid(neutralType_);
     didWork = gGrid.init_geo_grid(quadtree, planet);
     MPI_Barrier(aether_comm);
 
@@ -102,8 +102,8 @@ int main() {
     if (input.get_cent_acc())
       gGrid.calc_cent_acc(planet);
 
-    // Initialize Magnetic grid:
-    Grid mGrid("ionGrid");
+    // Initialize ion grid:
+    Grid mGrid(ionType_);
 
     if (mGrid.iGridShape_ == mGrid.iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
@@ -111,10 +111,10 @@ int main() {
       if (!didWork)
         throw std::string("init_dipole_grid failed!");
     } else {
-      report.print(0, "Making Spherical Magnetic Grid\n");
+      report.print(1, "Making Spherical Magnetic Grid\n");
       mGrid.set_IsDipole(false);
-      mGrid.set_IsGeoGrid(false);
       didWork = mGrid.init_geo_grid(quadtree, planet);
+      mGrid.set_IsGeoGrid(false);
     }
 
     if (!didWork)

From ccced609a643ae30bd634d6808d569aea6523a44 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 10:13:13 -0400
Subject: [PATCH 246/266] STY: more precise output

---
 src/init_geo_grid.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index d14f1a0f..7f1b27bf 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -188,17 +188,17 @@ bool Grid::init_geo_grid(Quadtree quadtree,
   report.enter(function, iFunction);
   bool DidWork = true;
 
-  IsGeoGrid = 1;
+  IsGeoGrid = true;
 
   if (iGridShape_ == iCubesphere_) {
-    report.print(0, "Creating Cubesphere Grid");
+    report.print(0, "Creating Cubesphere Grid for : " + gridType);
 
     if (!Is0D & !Is1Dz)
       create_cubesphere_connection(quadtree);
 
     IsCubeSphereGrid = true;
   } else {
-    report.print(0, "Creating Spherical Grid");
+    report.print(0, "Creating Spherical Grid for : " + gridType);
 
     if (!Is0D & !Is1Dz)
       create_sphere_connection(quadtree);

From 449ed7241cc5ce34af74ede130a446edef3228f5 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 10:13:18 -0400
Subject: [PATCH 247/266] STY: more precise output

---
 src/init_mag_grid.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/init_mag_grid.cpp b/src/init_mag_grid.cpp
index 0daf3043..be4bf6c1 100644
--- a/src/init_mag_grid.cpp
+++ b/src/init_mag_grid.cpp
@@ -186,16 +186,16 @@ bool Grid::init_dipole_grid(Quadtree quadtree_ion, Planets planet) {
   IsCubeSphereGrid = false;
   IsDipole = true;
 
-  report.print(0, "Creating inter-node dipole connections");
+  report.print(0, "Creating inter-node dipole connections for: " + gridType);
 
   if (!Is0D & !Is1Dz)
     create_dipole_connection(quadtree_ion);
 
-  report.print(0, "Creating Dipole Grid");
+  report.print(0, "Creating Dipole Grid for: " + gridType);
 
   report.print(3, "Getting grid inputs for dipole grid");
 
-  Inputs::grid_input_struct grid_input = input.get_grid_inputs("ionGrid");
+  Inputs::grid_input_struct grid_input = input.get_grid_inputs(gridType);
 
   // Number of ghost cells:
   int64_t nGCs = get_nGCs();

From 1ed1318e6f86712ae5cab2e9f96d90c627067df3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:33:58 -0400
Subject: [PATCH 248/266] moved to constants

---
 include/grid.h | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index c3826dfd..bc1cd15b 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -48,14 +48,7 @@ struct cubesphere_chars {
 class Grid {
 
  public:
-  const int iSphere_ = 1;
-  const int iCubesphere_ = 2;
-  const int iDipole_ = 3;
   int iGridShape_ = -1;
-
-  const std::string neutralType_ = "neuGrid";
-  const std::string ionType_ = "ionGrid";
-
   // Armidillo Cube Versions:
   // Cell Center Coordinates
   arma_cube geoLon_scgc, geoX_scgc;

From 43c1eed58f20216e525b6c0186ebdfd55d93cf55 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:34:23 -0400
Subject: [PATCH 249/266] changed sphere6

---
 include/sphere.h | 110 +++++++++++++++++++++++------------------------
 1 file changed, 55 insertions(+), 55 deletions(-)

diff --git a/include/sphere.h b/include/sphere.h
index 32de5fdf..b2483752 100644
--- a/include/sphere.h
+++ b/include/sphere.h
@@ -12,20 +12,20 @@
  *************************************************/
 namespace Sphere {
 
-  /// The normalized origins of each face of the cube (i.e. corner)
-  static const arma_mat ORIGINS = {
-				   { 0.0, -0.5, 0.0}
-  };
+/// The normalized origins of each face of the cube (i.e. corner)
+static const arma_mat ORIGINS = {
+  { 0.0, -0.5, 0.0}
+};
 
-  /// Normalized right steps in cube
-  static const arma_mat RIGHTS = {
-				  {2.0, 0.0, 0.0}
-  };
+/// Normalized right steps in cube
+static const arma_mat RIGHTS = {
+  {2.0, 0.0, 0.0}
+};
 
-  /// Normalized right steps in cube
-  static const arma_mat UPS = {
-			       {0.0, 1.0, 0.0}
-  };
+/// Normalized right steps in cube
+static const arma_mat UPS = {
+  {0.0, 1.0, 0.0}
+};
 
 };
 
@@ -34,31 +34,31 @@ namespace Sphere {
  *************************************************/
 namespace Sphere4 {
 
-  /// The normalized origins of each node (i.e. corner)
-  static const arma_mat ORIGINS = {
-    { 0.0,  -0.5, 0.0},
-    { 0.0, -0.25, 0.0},
-    { 0.0,   0.0, 0.0},
-    { 0.0,  0.25, 0.0}
-  };
-  
-  /// Normalized right steps in node
-  static const arma_mat RIGHTS = {
-    {2.0, 0.0, 0.0},
-    {2.0, 0.0, 0.0},
-    {2.0, 0.0, 0.0},
-    {2.0, 0.0, 0.0}
-  };
-  
-  /// Normalized up steps in node
-  static const arma_mat UPS = {
-    {0.0, 0.25, 0.0},
-    {0.0, 0.25, 0.0},
-    {0.0, 0.25, 0.0},
-    {0.0, 0.25, 0.0}
-  };
-  
-  };
+/// The normalized origins of each node (i.e. corner)
+static const arma_mat ORIGINS = {
+  { 0.0,  -0.5, 0.0},
+  { 0.0, -0.25, 0.0},
+  { 0.0,   0.0, 0.0},
+  { 0.0,  0.25, 0.0}
+};
+
+/// Normalized right steps in node
+static const arma_mat RIGHTS = {
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0},
+  {2.0, 0.0, 0.0}
+};
+
+/// Normalized up steps in node
+static const arma_mat UPS = {
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0},
+  {0.0, 0.25, 0.0}
+};
+
+};
 
 /*************************************************
  * \brief A namespace with all (6-root) sphere grid logic.
@@ -67,32 +67,32 @@ namespace Sphere6 {
 
 /// The normalized origins of each face of the cube (i.e. corner)
 static const arma_mat ORIGINS = {
-    {    0.0, -0.5, 0.0},
-    {2.0/3.0, -0.5, 0.0},
-    {4.0/3.0, -0.5, 0.0},
-    {    0.0, 0.0, 0.0},
-    {2.0/3.0, 0.0, 0.0},
-    {4.0/3.0, 0.0, 0.0}
+  {0.0, -0.5, 0.0},
+  {1.0, -0.5, 0.0},
+  {0.0, -0.5 + 1.0 / 3.0, 0.0},
+  {1.0, -0.5 + 1.0 / 3.0, 0.0},
+  {0.0, -0.5 + 2.0 / 3.0, 0.0},
+  {1.0, -0.5 + 2.0 / 3.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat RIGHTS = {
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0},
-		{ 2.0/3.0,  0.0, 0.0}
+  { 1.0,  0.0, 0.0},
+  { 1.0,  0.0, 0.0},
+  { 1.0,  0.0, 0.0},
+  { 1.0,  0.0, 0.0},
+  { 1.0,  0.0, 0.0},
+  { 1.0,  0.0, 0.0}
 };
 
 /// Normalized right steps in cube
 static const arma_mat UPS = {
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0},
-		{ 0.0, 0.5, 0.0}
+  { 0.0, 1.0 / 3.0, 0.0},
+  { 0.0, 1.0 / 3.0, 0.0},
+  { 0.0, 1.0 / 3.0, 0.0},
+  { 0.0, 1.0 / 3.0, 0.0},
+  { 0.0, 1.0 / 3.0, 0.0},
+  { 0.0, 1.0 / 3.0, 0.0}
 };
 
 } // CubeSphere::

From 879b87a051140034e633d889c59be3f9a2733f53 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:35:20 -0400
Subject: [PATCH 250/266] use gridshape instead of booleans

---
 src/fill_grid.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/fill_grid.cpp b/src/fill_grid.cpp
index e98e0603..3006b827 100644
--- a/src/fill_grid.cpp
+++ b/src/fill_grid.cpp
@@ -173,7 +173,7 @@ void Grid::fill_grid_bfield(Planets planet) {
 
           // Magnetic coordinates:
           // init_mag grid already initializes magLon & magInvLat
-          if (IsGeoGrid) {
+          if (iGridShape_ != iDipole_) {
             magInvLat_scgc(iLon, iLat, iAlt) = bfield_info.lat;
             magLon_scgc(iLon, iLat, iAlt) = bfield_info.lon;
           }
@@ -192,7 +192,7 @@ void Grid::fill_grid_bfield(Planets planet) {
 
     // Now we modify the dipole's magnetic field to account for any imprecision.
     // Take the bfield_mag and put it into the third component (b-hat = k-hat)
-    if (IsDipole) {
+    if (iGridShape_ == iDipole_) {
       bfield_vcgc[2] = bfield_mag_scgc % sign(magInvLat_scgc * -1.0);
       bfield_vcgc[1].zeros();
       bfield_vcgc[0].zeros();
@@ -228,6 +228,7 @@ void Grid::fill_grid_radius(Planets planet) {
   // This generalizes things so that radius could be a function of all
   // three dimensions.  The Cubesphere has different latitudes in the first
   // and second dimensions.
+
   for (iLon = 0; iLon < nLons; iLon++)
     for (iLat = 0; iLat < nLats; iLat++)
       for (iAlt = 0; iAlt < nAlts; iAlt++)

From 3ad28434b7655900e3ca866cd21c70135deb13dc Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:36:03 -0400
Subject: [PATCH 251/266] set lower alt bc and report stuff

---
 src/grid.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/grid.cpp b/src/grid.cpp
index cbe9f409..974acf44 100644
--- a/src/grid.cpp
+++ b/src/grid.cpp
@@ -32,11 +32,15 @@ Grid::Grid(std::string gridtype) {
   if (iGridShape_ == iCubesphere_) {
     if (grid_input.nX > grid_input.nY) {
       report.error("Cubesphere grid: nX > nY, reducing nX");
+      report.print(0, gridType +
+                   ": Cubesphere selected, but nX /= nY, reducing nX");
       grid_input.nX = grid_input.nY;
     }
 
     if (grid_input.nY > grid_input.nX) {
       report.error("Cubesphere grid: nY > nX, reducing nY");
+      report.print(0, gridType +
+                   ": Cubesphere selected, but nX /= nY, reducing nY");
       grid_input.nY = grid_input.nX;
     }
   }
@@ -292,6 +296,7 @@ Grid::Grid(std::string gridtype) {
   UseThisCell.fill(true);
   first_lower_gc.set_size(nX, nY);
   first_upper_gc.set_size(nX, nY);
+  altitude_lower_bc = 0.0;
 
   cent_acc_vcgc = make_cube_vector(nLons, nLats, nAlts, 3);
 

From 1eb2a6b8891730673d95ce25279f2b6328a99604 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:37:09 -0400
Subject: [PATCH 252/266] clean up and use gridshape

---
 src/solver_grid_interpolation.cpp | 61 ++++++++++++++++++-------------
 1 file changed, 36 insertions(+), 25 deletions(-)

diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 409ffd3d..e5e294bb 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -382,7 +382,8 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
                                   abs(j_center_scgc.tube(coef.iRow, coef.iCol)), nGCs);
 
   // need alt index to find lat coef
-  coef.iAlt = bisect_search_array(alt_in, k_center_scgc.tube(coef.iRow, coef.iCol),
+  coef.iAlt = bisect_search_array(alt_in, k_center_scgc.tube(coef.iRow,
+                                                             coef.iCol),
                                   nGCs);
 
   // then we can do the ratios:
@@ -425,6 +426,8 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
   static int iFunction = -1;
   report.enter(function, iFunction);
 
+  report.print(1, "interpolation gridtype : " + gridType);
+
   // If the size of Lons, Lats and Alts are not the same, return false
   if (i_coords.size() != j_coords.size() || j_coords.size() != k_coords.size()) {
     report.error("Length of i,j,k vectors do not match!");
@@ -434,28 +437,31 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
   // Clear the previous interpolation coefficients
   interp_coefs.clear();
 
-  if (IsGeoGrid) {
-    // Handle according to whether it is cubesphere or not
-    if (IsCubeSphereGrid) {
-      // Calculate the range of the grid
-      struct cubesphere_range cr;
-      get_cubesphere_grid_range(cr);
-
-      // Calculate the index and coefficients for each point
-      for (size_t i = 0; i < i_coords.size(); ++i)
-        set_interp_coef_cubesphere(cr, i_coords[i], j_coords[i], k_coords[i]);
-    } else if (IsLatLonGrid) {
-      // Calculate the range of the grid
-      struct sphere_range sr;
-      get_sphere_grid_range(sr);
-
-      // Calculate the index and coefficients for each point
-      for (size_t i = 0; i < i_coords.size(); ++i)
-        set_interp_coef_sphere(sr, i_coords[i], j_coords[i], k_coords[i]);
-    }
+  if (iGridShape_ == iCubesphere_) {
+    report.print(1, "interpolation grid is cubesphere");
+
+    // Calculate the range of the grid
+    struct cubesphere_range cr;
+    get_cubesphere_grid_range(cr);
+
+    // Calculate the index and coefficients for each point
+    for (size_t i = 0; i < i_coords.size(); ++i)
+      set_interp_coef_cubesphere(cr, i_coords[i], j_coords[i], k_coords[i]);
   }
 
-  else { // IsDipole
+  if (iGridShape_ == iSphere_) {
+    report.print(1, "interpolation grid is sphere");
+
+    struct sphere_range sr;
+    get_sphere_grid_range(sr);
+
+    // Calculate the index and coefficients for each point
+    for (size_t i = 0; i < i_coords.size(); ++i)
+      set_interp_coef_sphere(sr, i_coords[i], j_coords[i], k_coords[i]);
+  }
+
+  if (iGridShape_ == iDipole_) { // IsDipole
+    report.print(1, "interpolation grid is dipole");
 
     // Calculate the range of the grid
     struct dipole_range dr;
@@ -489,22 +495,27 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
     }
 
     else {
-      magCoords = {vec2cube(i_coords),vec2cube(j_coords), vec2cube(k_coords)};
+      magCoords = {vec2cube(i_coords), vec2cube(j_coords), vec2cube(k_coords)};
     }
 
     // std::vector<precision_t> dipcoords = geo_to_mag(i_coords[0], j_coords[0], k_coords[0], planet);
     std::vector<precision_t> dipCoords;
+
     if (!areLocsIJK) {
       std::vector<precision_t> planet_radii(nPts);
+
       for (iLoc = 0; iLoc < nPts; iLoc++) {
         // Convert from mag->dipole coordinates.
-        if (areLocsGeo){ // we were given the geo-latitude
+        if (areLocsGeo)  // we were given the geo-latitude
           planet_radii[iLoc] = planet.get_radius(j_coords[iLoc]);
-        } else{
+
+        else {
           // equatorial radius :(
           planet_radii[iLoc] = planet.get_radius(0.0);
         }
-        dipCoords = mag_to_ijk(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc], planet_radii[iLoc]);
+
+        dipCoords = mag_to_ijk(i_coords[iLoc], j_coords[iLoc], k_coords[iLoc],
+                               planet_radii[iLoc]);
         mlon[iLoc] = dipCoords[0];
         p_coord[iLoc] = dipCoords[1];
         q_coord[iLoc] = dipCoords[2];

From 167ce81bf62d06c2e6214afd6bd22bbf80a07710 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:37:44 -0400
Subject: [PATCH 253/266] use constants for shapes

---
 src/grid_match.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 1df5ce97..2497a13a 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -25,7 +25,7 @@ bool grid_match(Grid gGrid,
         lon = mGrid.geoLon_scgc(iX, iY, iZ);
         lat = mGrid.geoLat_scgc(iX, iY, iZ);
 
-        if (gGrid.iGridShape_ == gGrid.iSphere_) {
+        if (gGrid.iGridShape_ == iSphere_) {
           norms(0) = lon / cPI;
           norms(1) = lat / cPI;
           norms(2) = 0.0;
@@ -34,6 +34,7 @@ bool grid_match(Grid gGrid,
           norms = sphere_to_cube(lon, lat);
           iNode = gQuadtree.find_point(norms);
         }
+
         if (report.test_verbose(6))
           std::cout << "lon, lat, node: " << lon*cRtoD << " "
                     << lat*cRtoD << " "

From 1464c8510e26ec9b3f9b356a3adf79f9de9c3af3 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:38:18 -0400
Subject: [PATCH 254/266] FEAT: output lats and lons verbose=1

---
 src/grid_sphere.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/grid_sphere.cpp b/src/grid_sphere.cpp
index 8f940ff9..d3bb2120 100644
--- a/src/grid_sphere.cpp
+++ b/src/grid_sphere.cpp
@@ -124,6 +124,11 @@ void Grid::create_sphere_grid(Quadtree quadtree) {
   for (iLon = 0; iLon < nLons; iLon++)
     lon1d(iLon) = lon0 + (iLon - nGCs + 0.5) * dlon;
 
+  if (report.test_verbose(1)) {
+    std::cout << function << ": " << lon0 << " " << dlon << "\n";
+    display_vector("in function " + function + " lon1d : ", lon1d * cRtoD);
+  }
+
   for (iLat = 0; iLat < nLats; iLat++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       geoLon_scgc.subcube(0, iLat, iAlt, nLons - 1, iLat, iAlt) = lon1d;
@@ -146,6 +151,12 @@ void Grid::create_sphere_grid(Quadtree quadtree) {
   for (iLat = 0; iLat < nLats; iLat++)
     lat1d(iLat) = lat0 + (iLat - nGCs + 0.5) * dlat;
 
+  if (report.test_verbose(1)) {
+    std::cout << function << ": " << lat0 << " " << dlat << "\n";
+
+    display_vector("in function " + function + " lat1d : ", lat1d * cRtoD);
+  }
+
   for (iLon = 0; iLon < nLons; iLon++) {
     for (iAlt = 0; iAlt < nAlts; iAlt++) {
       geoLat_scgc.subcube(iLon, 0, iAlt, iLon, nLats - 1, iAlt) = lat1d;

From a3196bbbf48508345fab61efdb153b32ceae486e Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:38:55 -0400
Subject: [PATCH 255/266] FEAT: use gridshape

---
 src/init_geo_grid.cpp | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/init_geo_grid.cpp b/src/init_geo_grid.cpp
index 7f1b27bf..ddb19d2c 100644
--- a/src/init_geo_grid.cpp
+++ b/src/init_geo_grid.cpp
@@ -235,14 +235,14 @@ bool Grid::init_geo_grid(Quadtree quadtree,
   if (iGridShape_ == iCubesphere_) {
     correct_xy_grid(planet);
     // New functions for equal-angular grid (center, left, down):
-    report.print(3, "Scaling Cube by Radius");
+    report.print(2, "Scaling Cube by Radius");
     scale_cube_by_radius(cubeC);
     scale_cube_by_radius(cubeL);
     scale_cube_by_radius(cubeD);
-    report.print(3, "Done Scaling Cube by Radius");
+    report.print(2, "Done Scaling Cube by Radius");
   }
 
-  if (IsMagGrid) {
+  if (gridType == ionType_) {
     report.print(0, "--> Grid is Magnetic, so rotating");
     std::vector<arma_cube> llr, xyz, xyzRot1, xyzRot2;
     llr.push_back(geoLon_scgc);
@@ -269,14 +269,12 @@ bool Grid::init_geo_grid(Quadtree quadtree,
 
   // Calculate PFPC coordinates (i.e., XYZ from LLR)
   calc_xyz(planet);
-
   // Calculate grid spacing
   calc_grid_spacing(planet);
   //calculate radial unit vector (for spherical or oblate planet)
   calc_rad_unit(planet);
   // Calculate gravity (including J2 term, if desired)
   calc_gravity(planet);
-
   // Calculate magnetic field and magnetic coordinates:
   fill_grid_bfield(planet);
 

From 35afea5faf8000c2ffe61545e030fb21eac51519 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:39:30 -0400
Subject: [PATCH 256/266] FEAT: check of latrange is set correctly for dipole

---
 src/inputs.cpp | 23 +++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/src/inputs.cpp b/src/inputs.cpp
index d4303538..21dc05e0 100644
--- a/src/inputs.cpp
+++ b/src/inputs.cpp
@@ -107,7 +107,7 @@ std::string dummy_string = "unknown";
 
 bool Inputs::check_settings(std::string key1,
                             std::string key2) {
-  if (report.test_verbose(5))
+  if (report.test_verbose(10))
     std::cout << "checking setting : "
               << key1 << " and "
               << key2 << "\n";
@@ -132,7 +132,7 @@ bool Inputs::check_settings(std::string key1,
 // 1 key:
 
 bool Inputs::check_settings(std::string key1) {
-  if (report.test_verbose(5))
+  if (report.test_verbose(10))
     std::cout << "checking setting : " << key1 << "\n";
 
   // try to find the keys first
@@ -466,6 +466,25 @@ Inputs::grid_input_struct Inputs::get_grid_inputs(std::string gridtype) {
     grid_specs.alt_min = min_max[0];
     grid_specs.alt_max = min_max[1];
 
+    precision_t minDipoleLat = 10.0 * cDtoR;
+    precision_t maxDipoleLat = 80.0 * cDtoR;
+
+    if (grid_specs.lat_min < minDipoleLat) {
+      grid_specs.lat_min = minDipoleLat;
+      report.print(0, "Error in setting min lat for " +
+                   grid_specs.shape +
+                   " - moving to 10 deg");
+      report.error("Setting min dipole lat to 10.0");
+    }
+
+    if (grid_specs.lat_max > maxDipoleLat) {
+      grid_specs.lat_max = maxDipoleLat;
+      report.print(0, "Error in setting max lat for " +
+                   grid_specs.shape +
+                   " - moving to 80 deg");
+      report.error("Setting max dipole lat to 80.0");
+    }
+
   } else {
     grid_specs.alt_min = check_settings_pt(gridtype, "MinAlt");
     grid_specs.alt_file = check_settings_str(gridtype, "AltFile");

From bd3093cab0806ef15d18f6597d7b26f4c51ccf2a Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:40:12 -0400
Subject: [PATCH 257/266] BUG: quadtree wrong for ion geo grid

---
 src/main/main.cpp | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/main/main.cpp b/src/main/main.cpp
index 7ed004b3..e3405588 100644
--- a/src/main/main.cpp
+++ b/src/main/main.cpp
@@ -38,12 +38,12 @@ int main() {
     // For now, the number of processors and blocks are set by the
     // neutral grid shape, since this could be sphere (1 root) or
     // cubesphere (6 root)
-    Quadtree quadtree(input.get_grid_shape("neuGrid"));
+    Quadtree quadtree(input.get_grid_shape(neutralType_));
 
     if (!quadtree.is_ok())
       throw std::string("quadtree for neutrals initialization failed!");
 
-    Quadtree quadtree_ion(input.get_grid_shape("ionGrid"));
+    Quadtree quadtree_ion(input.get_grid_shape(ionType_));
 
     if (!quadtree_ion.is_ok())
       throw std::string("quadtree for ions initialization failed!");
@@ -105,7 +105,7 @@ int main() {
     // Initialize ion grid:
     Grid mGrid(ionType_);
 
-    if (mGrid.iGridShape_ == mGrid.iDipole_) {
+    if (mGrid.iGridShape_ == iDipole_) {
       didWork = mGrid.init_dipole_grid(quadtree_ion, planet);
 
       if (!didWork)
@@ -113,14 +113,13 @@ int main() {
     } else {
       report.print(1, "Making Spherical Magnetic Grid\n");
       mGrid.set_IsDipole(false);
-      didWork = mGrid.init_geo_grid(quadtree, planet);
+      didWork = mGrid.init_geo_grid(quadtree_ion, planet);
       mGrid.set_IsGeoGrid(false);
     }
 
     if (!didWork)
       throw std::string("Initializing magneitic grid failed!");
 
-
     didWork = grid_match(gGrid, mGrid, quadtree, quadtree_ion);
 
     // Initialize Neutrals on geographic grid:

From c6ad5e49252e6616d1e6ed840b7bd38fd6a3857b Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:41:01 -0400
Subject: [PATCH 258/266] define radius

---
 src/solver_advection.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/solver_advection.cpp b/src/solver_advection.cpp
index 48e88688..8da03682 100644
--- a/src/solver_advection.cpp
+++ b/src/solver_advection.cpp
@@ -284,7 +284,7 @@ precision_t calc_dt(arma_mat &xWidth,
 
 void Neutrals::advect_sphere(Grid &grid, Times &time) {
 
-  std::string function = "advect";
+  std::string function = "advect_sphere";
   static int iFunction = -1;
   report.enter(function, iFunction);
 
@@ -369,10 +369,12 @@ void Neutrals::advect_sphere(Grid &grid, Times &time) {
     xMomentum = rho % xVel;
     yMomentum = rho % yVel;
 
-    x = grid.x_Center.slice(iAlt) * grid.radius_scgc(1, 1, iAlt);
-    y = grid.y_Center.slice(iAlt) * grid.radius_scgc(1, 1, iAlt);
-    xEdges = grid.x_Left.slice(iAlt) * grid.radius_scgc(1, 1, iAlt);
-    yEdges = grid.y_Down.slice(iAlt) * grid.radius_scgc(1, 1, iAlt);
+    precision_t radius = grid.radius_scgc(1, 1, iAlt);
+
+    x = grid.x_Center.slice(iAlt) * radius;
+    y = grid.y_Center.slice(iAlt) * radius;
+    xEdges = grid.x_Left.slice(iAlt) * radius;
+    yEdges = grid.y_Down.slice(iAlt) * radius;
 
     rhoP = project_to_edges(rho, x, xEdges, y, yEdges, nGCs);
     xVelP = project_to_edges(xVel, x, xEdges, y, yEdges, nGCs);

From 326b61ab0d9733762ff41e840944bbda90387f14 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:41:42 -0400
Subject: [PATCH 259/266] BUG: report error if shape not found

---
 src/quadtree.cpp | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index 9918abaf..bcc2c707 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -68,6 +68,12 @@ bool Quadtree::is_ok() {
 
 void Quadtree::build(std::string gridtype) {
 
+  std::string function = "Quadtree::build";
+  static int iFunction = -1;
+  report.enter(function, iFunction);
+
+  IsOk = false;
+
   arma_mat origins;
   arma_mat rights;
   arma_mat ups;
@@ -79,6 +85,7 @@ void Quadtree::build(std::string gridtype) {
     rights = CubeSphere::RIGHTS;
     ups = CubeSphere::UPS;
     IsCubeSphere = true;
+    IsOk = true;
   }
 
   if (grid_input.shape == "sphere") {
@@ -86,6 +93,7 @@ void Quadtree::build(std::string gridtype) {
     rights = Sphere::RIGHTS;
     ups = Sphere::UPS;
     IsSphere = true;
+    IsOk = true;
   }
 
   if (grid_input.shape == "sphere4") {
@@ -93,6 +101,15 @@ void Quadtree::build(std::string gridtype) {
     rights = Sphere4::RIGHTS;
     ups = Sphere4::UPS;
     IsSphere = true;
+    IsOk = true;
+  }
+
+  if (grid_input.shape == "sphere6") {
+    origins = Sphere6::ORIGINS;
+    rights = Sphere6::RIGHTS;
+    ups = Sphere6::UPS;
+    IsSphere = true;
+    IsOk = true;
   }
 
   if (grid_input.shape == "dipole4") {
@@ -100,6 +117,7 @@ void Quadtree::build(std::string gridtype) {
     rights = Dipole4::RIGHTS;
     ups = Dipole4::UPS;
     IsDipole = true;
+    IsOk = true;
   }
 
   if (grid_input.shape == "dipole6") {
@@ -107,7 +125,13 @@ void Quadtree::build(std::string gridtype) {
     rights = Dipole6::RIGHTS;
     ups = Dipole6::UPS;
     IsDipole = true;
+    IsOk = true;
+  }
 
+  if (!IsOk) {
+    report.error("quadtree shape not found (in build): " + grid_input.shape);
+    report.exit(function);
+    return;
   }
 
   arma_vec o(3), r(3), u(3);
@@ -168,6 +192,9 @@ void Quadtree::build(std::string gridtype) {
     tmp = new_node(o, r, u, iP, iDepth, iNode);
     root_nodes.push_back(tmp);
   }
+
+  report.exit(function);
+  return;
 }
 
 // --------------------------------------------------------------------------

From aa7405d5449146a0b11f09b99c8a94d428b69f7c Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:42:30 -0400
Subject: [PATCH 260/266] BUG: works with sphere6 type now

---
 srcPython/postAether.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/srcPython/postAether.py b/srcPython/postAether.py
index 694b2ad4..a938ffb2 100755
--- a/srcPython/postAether.py
+++ b/srcPython/postAether.py
@@ -1024,9 +1024,11 @@ def calc_blocks(dataToWrite, iLon_ = 0, iLat_ = 1):
 
     iBlock = 1
     while (iBlock < nBlocksTotal):
-        if (np.abs(dataToWrite[iBlock][iLon_][0, 0, 0] - testLon) > 0.001):
+        if ((np.abs(dataToWrite[iBlock][iLon_][0, 0, 0] - testLon) > 0.1) and
+            (np.abs(dataToWrite[iBlock][iLat_][0, 0, 0] - testLat) < 0.1)):
             nBlocksLon = nBlocksLon + 1
-        if (np.abs(dataToWrite[iBlock][iLat_][0, 0, 0] - testLat) > 0.001):
+        if ((np.abs(dataToWrite[iBlock][iLat_][0, 0, 0] - testLat) > 0.1) and
+            (np.abs(dataToWrite[iBlock][iLon_][0, 0, 0] - testLon) < 0.1)):
             nBlocksLat = nBlocksLat + 1
         iBlock = iBlock + 1
 
@@ -1064,20 +1066,20 @@ def consolidate_blocks(originalData, iLon_ = 0, iLat_ = 1):
             consolidatedData[key] = originalData[0][key]
         else:
             # need to move data over
+            #print('variable : ', key)
             data = np.zeros((nLonsTotal, nLatsTotal, nAlts))
             for iBlock in range(nBlocks):
                 # interior points:
-                iLatS = int((originalData[iBlock][iLat_][nGCs, nGCs, nGCs] - Lat0)/dLat) + nGCs
+                iLatS = int(round((originalData[iBlock][iLat_][nGCs, nGCs, nGCs] - Lat0)/dLat)) + nGCs
                 iLatE = iLatS + nLats
-                iLonS = int((originalData[iBlock][iLon_][nGCs, nGCs, nGCs] - Lon0)/dLon) + nGCs
+                iLonS = int(round((originalData[iBlock][iLon_][nGCs, nGCs, nGCs] - Lon0)/dLon)) + nGCs
                 iLonE = iLonS + nLons
                 iLonSO = nGCs
                 iLonEO = nGCs + nLons
                 iLatSO = nGCs
                 iLatEO = iLatSO + nLats
-                #print(iLonS, iLonE, nLonsTotal, ' -> ', iLonSO, iLonEO, nLons)
-                #print(iLatS, iLatE, nLatsTotal, ' -> ', iLatSO, iLatEO, nLats)
-                #print(nGCs)
+                #print('lons : ', iLonS, iLonE, nLonsTotal, ' -> ', iLonSO, iLonEO, nLons)
+                #print('lats : ', iLatS, iLatE, nLatsTotal, ' -> ', iLatSO, iLatEO, nLats)
                 data[iLonS:iLonE, iLatS:iLatE, 0:nAlts] = \
                     originalData[iBlock][key][iLonSO:iLonEO, iLatSO:iLatEO, 0:nAlts]
 

From 3f5d6f7c30afa89c4eafed5e029cc6d6436af102 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 24 Sep 2025 16:43:39 -0400
Subject: [PATCH 261/266] FEAT: test all combos of grid shapes

---
 tests/grid_shapes/aether.json.whole           | 42 +++++++++
 tests/grid_shapes/aether_cube_cube.json       | 42 +++++++++
 tests/grid_shapes/aether_cube_dipole6.json    | 42 +++++++++
 tests/grid_shapes/aether_cube_sphere6.json    | 42 +++++++++
 tests/grid_shapes/aether_sphere4_dipole4.json | 42 +++++++++
 tests/grid_shapes/aether_sphere4_sphere4.json | 42 +++++++++
 tests/grid_shapes/aether_sphere6_dipole6.json | 42 +++++++++
 tests/grid_shapes/aether_sphere6_sphere6.json | 42 +++++++++
 tests/grid_shapes/aether_sphere_sphere.json   | 42 +++++++++
 tests/grid_shapes/run_test.sh                 | 92 +++++++++++++++++++
 10 files changed, 470 insertions(+)
 create mode 100644 tests/grid_shapes/aether.json.whole
 create mode 100644 tests/grid_shapes/aether_cube_cube.json
 create mode 100644 tests/grid_shapes/aether_cube_dipole6.json
 create mode 100644 tests/grid_shapes/aether_cube_sphere6.json
 create mode 100644 tests/grid_shapes/aether_sphere4_dipole4.json
 create mode 100644 tests/grid_shapes/aether_sphere4_sphere4.json
 create mode 100644 tests/grid_shapes/aether_sphere6_dipole6.json
 create mode 100644 tests/grid_shapes/aether_sphere6_sphere6.json
 create mode 100644 tests/grid_shapes/aether_sphere_sphere.json
 create mode 100755 tests/grid_shapes/run_test.sh

diff --git a/tests/grid_shapes/aether.json.whole b/tests/grid_shapes/aether.json.whole
new file mode 100644
index 00000000..84033c7b
--- /dev/null
+++ b/tests/grid_shapes/aether.json.whole
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "cubesphere",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "sphere6",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90.0, 90.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_cube_cube.json b/tests/grid_shapes/aether_cube_cube.json
new file mode 100644
index 00000000..b8e19691
--- /dev/null
+++ b/tests/grid_shapes/aether_cube_cube.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "cubesphere",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "cubesphere",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 32,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_cube_dipole6.json b/tests/grid_shapes/aether_cube_dipole6.json
new file mode 100644
index 00000000..696d0352
--- /dev/null
+++ b/tests/grid_shapes/aether_cube_dipole6.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "cubesphere",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "dipole6",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [10.0, 80.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_cube_sphere6.json b/tests/grid_shapes/aether_cube_sphere6.json
new file mode 100644
index 00000000..84033c7b
--- /dev/null
+++ b/tests/grid_shapes/aether_cube_sphere6.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "cubesphere",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "sphere6",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90.0, 90.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_sphere4_dipole4.json b/tests/grid_shapes/aether_sphere4_dipole4.json
new file mode 100644
index 00000000..19b55f43
--- /dev/null
+++ b/tests/grid_shapes/aether_sphere4_dipole4.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "sphere4",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "dipole4",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [10.0, 80.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_sphere4_sphere4.json b/tests/grid_shapes/aether_sphere4_sphere4.json
new file mode 100644
index 00000000..b402b0f5
--- /dev/null
+++ b/tests/grid_shapes/aether_sphere4_sphere4.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "sphere4",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "sphere4",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_sphere6_dipole6.json b/tests/grid_shapes/aether_sphere6_dipole6.json
new file mode 100644
index 00000000..c0bac693
--- /dev/null
+++ b/tests/grid_shapes/aether_sphere6_dipole6.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "sphere6",
+        "nLonsPerBlock" : 26,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "dipole6",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [10.0, 80.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_sphere6_sphere6.json b/tests/grid_shapes/aether_sphere6_sphere6.json
new file mode 100644
index 00000000..3b8a1c68
--- /dev/null
+++ b/tests/grid_shapes/aether_sphere6_sphere6.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "sphere6",
+        "nLonsPerBlock" : 22,
+        "nLatsPerBlock" : 22,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "sphere6",
+        "nLonsPerBlock": 32,
+        "nLatsPerBlock": 16,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90.0, 90.0],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/aether_sphere_sphere.json b/tests/grid_shapes/aether_sphere_sphere.json
new file mode 100644
index 00000000..935880b3
--- /dev/null
+++ b/tests/grid_shapes/aether_sphere_sphere.json
@@ -0,0 +1,42 @@
+
+{
+    "Debug" : {
+        "dt": 1.0,
+        "TimingPercent": 10.0,
+	"iVerbose" : 0},
+
+    "EndTime" : [2011, 3, 20, 0, 1, 0],
+
+    "neuGrid" : {
+        "Shape": "sphere",
+        "nLonsPerBlock" : 32,
+        "nLatsPerBlock" : 16,
+        "nAlts" : 30,
+	"MinAlt": 95,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+    
+    "ionGrid": {
+        "Shape": "sphere",
+        "nLonsPerBlock": 42,
+        "nLatsPerBlock": 22,
+        "nAlts": 40,
+	"MinAlt": 80,
+        "LatRange": [-90, 90],
+        "LonRange": [0.0, 360.0],
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
+
+    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
+    
+    "Electrodynamics" : {
+        "Potential" : "Weimer05",
+        "DiffuseAurora" : "fta"},
+    
+    "Outputs" : {
+	"type" : ["states"],
+	"dt" : [60] }
+
+}
diff --git a/tests/grid_shapes/run_test.sh b/tests/grid_shapes/run_test.sh
new file mode 100755
index 00000000..4c87f3d0
--- /dev/null
+++ b/tests/grid_shapes/run_test.sh
@@ -0,0 +1,92 @@
+#!/bin/sh
+
+RUN=sphere_sphere
+PE=1
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=sphere_sphere
+PE=4
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=sphere4_sphere4
+PE=4
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=sphere6_sphere6
+PE=6
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=cube_cube
+PE=6
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=cube_sphere6
+PE=6
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=sphere4_dipole4
+PE=4
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=cube_dipole6
+PE=6
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+
+RUN=sphere6_dipole6
+PE=6
+rm -rf ./run.${RUN}
+cp -R ../../share/run ./run.${RUN}
+cd run.${RUN}
+cp ../aether_${RUN}.json ./aether.json
+mpirun -np ${PE} ./aether
+../../../srcPython/postAether.py -rm
+cd ..
+

From dfcc27d2224f70d1adc0c43ff29727f4dc64dddf Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 26 Nov 2025 15:40:27 -0500
Subject: [PATCH 262/266] DOC: added more comments

---
 src/quadtree.cpp | 33 +++++++++++++++++++++++++++------
 1 file changed, 27 insertions(+), 6 deletions(-)

diff --git a/src/quadtree.cpp b/src/quadtree.cpp
index bcc2c707..832e5a78 100644
--- a/src/quadtree.cpp
+++ b/src/quadtree.cpp
@@ -1,12 +1,17 @@
 // Copyright 2024, the Aether Development Team (see doc/dev_team.md for members)
 // Full license can be found in License.md
 
-// Need to allow more types of grids.  We have two axes of grids, really:
-//   - Neutral
-//   - Ion
-// Within each of those, we can have several types of grids:
+// The way that the quadtree works is that you start with a given number of
+// root nodes.  When the user wants higher resolution, they as for 4 times
+// more processors, and then each root node is broken into 4.  If 16 times
+// more processors are asked for, then those 4 nodes are each broken into
+// 4 more nodes.  This goes on for as many processors as the user would like,
+// but the processors has to equal nRootNodes * 4^depth
+//
+// The following grid shapes are suppored at this time:
 //   - Cubesphere, this has 6 root nodes (2 polar, 4 equatorial)
 //   - Sphere, this has 1 root node (whole grid)
+//   - Sphere4, this is a spherical grid, but has 4 root nodes (2 lats, 2 lons)
 //   - Sphere6, this is a spherical grid, but has 6 root nodes (2 lats, 3 lons)
 //   - Dipole4, which has 4 root nodes (4 lats, 1 lon)
 //   - Dipole6, which has 4 root nodes (6 lats, 1 lon)
@@ -80,6 +85,10 @@ void Quadtree::build(std::string gridtype) {
 
   Inputs::grid_input_struct grid_input = input.get_grid_inputs(gridtype);
 
+  // Here we are taking the shape and getting the sizes and positions
+  // of the root nodes.  These are defined in the different header files
+  // such as sphere.h, cubesphere.h, and dipole.h
+
   if (grid_input.shape == "cubesphere") {
     origins = CubeSphere::ORIGINS;
     rights = CubeSphere::RIGHTS;
@@ -128,6 +137,7 @@ void Quadtree::build(std::string gridtype) {
     IsOk = true;
   }
 
+  // If we can't find the shape, then there is a big problem
   if (!IsOk) {
     report.error("quadtree shape not found (in build): " + grid_input.shape);
     report.exit(function);
@@ -158,6 +168,9 @@ void Quadtree::build(std::string gridtype) {
   // Before we build the quadtree, we need to allow the user to
   // restrict the domain.  This will only work for the spherical
   // grid so far:
+  // (as programmed, this should work ok for the sphere and
+  //  dipole shapes, but will never work for cubesphere. For the
+  // cubesphere grid, it is much more complicated.)
 
   if ((grid_input.lon_min > 0.0 ||
        grid_input.lon_max < 2.0 * cPI ||
@@ -414,7 +427,14 @@ int64_t Quadtree::find_point(arma_vec point, Quadtree::qtnode node) {
 }
 
 // --------------------------------------------------------------------------
-//
+// This takes a normalized point, figures out if it is beyond the limits
+// of the root node, and if it is, then moves the coordinates onto the other
+// node.
+//  This is pretty much useful for the CubeSphere, since when you go over
+// the edge of one side, you are technically then on another side.  This can
+// happen on the sphere also, when you go across the 0/360 line (or 0/2 line
+// in normalized coordinates).  If can aslo happen at the poles when you go
+// over the pole.
 // --------------------------------------------------------------------------
 
 arma_vec Quadtree::wrap_point_sphere(arma_vec point) {
@@ -467,7 +487,8 @@ arma_vec Quadtree::wrap_point_sphere(arma_vec point) {
 }
 
 // --------------------------------------------------------------------------
-//
+// Well, ok - the above wrap_point seems to only work for the sphere and
+// dipole shape, which this is specially designed for the cubesphere
 // --------------------------------------------------------------------------
 
 arma_vec Quadtree::wrap_point_cubesphere(arma_vec point) {

From ce0ab93bb82a11d7545ba95afe2783446831d443 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 26 Nov 2025 15:43:47 -0500
Subject: [PATCH 263/266] updated file

---
 tests/restart_cubesphere/aether.whole.json | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tests/restart_cubesphere/aether.whole.json b/tests/restart_cubesphere/aether.whole.json
index 8a70fef8..7e683f29 100644
--- a/tests/restart_cubesphere/aether.whole.json
+++ b/tests/restart_cubesphere/aether.whole.json
@@ -15,8 +15,6 @@
     "Electrodynamics" : {
 	"File" : "UA/inputs/b20110320n_omni.bin"},
     
-    "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
-    
     "CubeSphere" : {
 	"is" : true},
 

From a0ee54ecd0fc8957994b5e80c268fc7edb6a47ea Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 26 Nov 2025 15:58:34 -0500
Subject: [PATCH 264/266] STY: astyle, yo

---
 include/advance.h             | 30 +++++++-------
 include/aurora.h              | 20 +++++-----
 include/bfield.h              |  6 +--
 include/calc_euv.h            | 16 ++++----
 include/calc_grid_derived.h   |  6 +--
 include/chemistry.h           | 12 +++---
 include/collisions.h          |  2 +-
 include/electrodynamics.h     | 74 +++++++++++++++++------------------
 include/euv.h                 | 46 +++++++++++-----------
 include/external_msis.h       | 24 ++++++------
 include/file_input.h          |  8 ++--
 include/init_mag_grid.h       |  4 +-
 include/inputs.h              | 10 ++---
 include/logfile.h             | 18 ++++-----
 include/output.h              | 46 +++++++++++-----------
 include/parallel.h            | 30 +++++++-------
 include/quadtree.h            | 40 +++++++++----------
 include/read_collision_file.h | 18 ++++-----
 include/read_indices_files.h  |  6 +--
 include/report.h              | 22 +++++------
 include/sizes.h               |  2 +-
 include/test.h                |  3 +-
 include/times.h               | 22 +++++------
 include/transform.h           | 23 ++++++-----
 src/calc_ion_drift.cpp        |  4 +-
 src/calc_ion_temperature.cpp  | 15 ++++---
 src/chemistry.cpp             | 38 ++++++++++--------
 src/grid_spacing.cpp          | 16 +++++---
 src/solver_conduction.cpp     |  7 ++--
 29 files changed, 291 insertions(+), 277 deletions(-)

diff --git a/include/advance.h b/include/advance.h
index 0f906b8c..1f287c4b 100644
--- a/include/advance.h
+++ b/include/advance.h
@@ -33,20 +33,20 @@
 
 
 bool advance(Planets &planet,
-	     Grid &gGrid,
-	     Grid &mGrid,
-	     Times &time,
-	     Euv &euv,
-	     Neutrals &neutrals,
-	     Neutrals &neutralsMag,
-	     Ions &ions,
-	     Ions &ionsMag,
-	     Chemistry &chemistry,
-	     Chemistry &chemistryMag,
-	     Electrodynamics &electrodynamics,
-	     Electrodynamics &electrodynamicsMag,
-	     Indices &indices,
-	     Logfile &logfile,
-	     Logfile &logfileMag);
+             Grid &gGrid,
+             Grid &mGrid,
+             Times &time,
+             Euv &euv,
+             Neutrals &neutrals,
+             Neutrals &neutralsMag,
+             Ions &ions,
+             Ions &ionsMag,
+             Chemistry &chemistry,
+             Chemistry &chemistryMag,
+             Electrodynamics &electrodynamics,
+             Electrodynamics &electrodynamicsMag,
+             Indices &indices,
+             Logfile &logfile,
+             Logfile &logfileMag);
 
 #endif // INCLUDE_ADVANCE_H_
diff --git a/include/aurora.h b/include/aurora.h
index 75889cc9..85cb0873 100644
--- a/include/aurora.h
+++ b/include/aurora.h
@@ -12,16 +12,16 @@
  **/
 
 void read_aurora(Neutrals &neutrals,
-		 Ions &ions);
+                 Ions &ions);
 
 arma_vec calculate_fang(float eflux,  // in ergs/cm2/s
-			float avee,   // in keV
-			float Ebin,   // eV
-			arma_vec rhoH,
-			std::vector<float> Ci,
-			float dE,     // eV
-			arma_vec H,
-			bool DoDebug);
+                        float avee,   // in keV
+                        float Ebin,   // eV
+                        arma_vec rhoH,
+                        std::vector<float> Ci,
+                        float dE,     // eV
+                        arma_vec H,
+                        bool DoDebug);
 
 /**********************************************************************
   * brief Read in a file containing information about splitting ionization
@@ -32,7 +32,7 @@ arma_vec calculate_fang(float eflux,  // in ergs/cm2/s
  **/
 
 void calc_aurora(Grid grid,
-		 Neutrals &neutrals,
-		 Ions &ions);
+                 Neutrals &neutrals,
+                 Ions &ions);
 
 #endif  // INCLUDE_AURORA_H_
diff --git a/include/bfield.h b/include/bfield.h
index df64fac8..a7535f2c 100644
--- a/include/bfield.h
+++ b/include/bfield.h
@@ -11,18 +11,18 @@ struct bfield_info_type {
 };
 
 arma_vec get_magnetic_pole(int IsNorth,
-		       Planets planet);
+                           Planets planet);
 
 bfield_info_type get_bfield(precision_t lon,
                             precision_t lat,
                             precision_t alt,
-			    bool DoDebug,
+                            bool DoDebug,
                             Planets planet);
 
 bfield_info_type get_dipole(precision_t lon,
                             precision_t lat,
                             precision_t alt,
-			    bool DoDebug,
+                            bool DoDebug,
                             Planets planet);
 
 #endif // INCLUDE_BFIELD_H_
diff --git a/include/calc_euv.h b/include/calc_euv.h
index 561930a2..83b3509c 100644
--- a/include/calc_euv.h
+++ b/include/calc_euv.h
@@ -21,16 +21,16 @@
 // -------------------------------------------------------------------------
 
 bool calc_euv(Planets planet,
-	      Grid grid,
-	      Times time,
-	      Euv &euv,
-	      Neutrals &neutrals,
-	      Ions &ions,
-	      Indices indices);
+              Grid grid,
+              Times time,
+              Euv &euv,
+              Neutrals &neutrals,
+              Ions &ions,
+              Indices indices);
 
 void calc_ionization_heating(Euv euv,
-			     Neutrals &neutrals,
-			     Ions &ions);
+                             Neutrals &neutrals,
+                             Ions &ions);
 
 
 #endif  // INCLUDE_CALC_EUV_H_
diff --git a/include/calc_grid_derived.h b/include/calc_grid_derived.h
index 28d5237f..98c22be0 100644
--- a/include/calc_grid_derived.h
+++ b/include/calc_grid_derived.h
@@ -7,7 +7,7 @@
 #include <vector>
 
 // ----------------------------------------------------------------------------
-// 
+//
 // ----------------------------------------------------------------------------
 
 std::vector<precision_t> calc_bin_edges(std::vector<precision_t> centers);
@@ -19,8 +19,8 @@ arma_vec calc_bin_widths(arma_vec centers);
 // ----------------------------------------------------------------------------
 // A helper function for mapping grids
 // ----------------------------------------------------------------------------
-bool grid_match(Grid gGrid, 
-                Grid mGrid, 
+bool grid_match(Grid gGrid,
+                Grid mGrid,
                 Quadtree gQuadtree,
                 Quadtree mQuadtree);
 
diff --git a/include/chemistry.h b/include/chemistry.h
index 118d0189..eef015c5 100644
--- a/include/chemistry.h
+++ b/include/chemistry.h
@@ -72,7 +72,7 @@ class Chemistry {
     /// type of formula to use for reaction rate:
     int type;
     /// name of the reaction
-    std::string name;    
+    std::string name;
 
   };
 
@@ -93,12 +93,12 @@ class Chemistry {
                              Ions &ions);
 
  private:
-  bool search(std::string name, 
-              json &headers, 
+  bool search(std::string name,
+              json &headers,
               std::vector<std::string> &error);
 
-  bool check_chemistry_file(json &headers, 
-                            std::vector<std::vector<std::string>> csv, 
+  bool check_chemistry_file(json &headers,
+                            std::vector<std::vector<std::string>> csv,
                             Report &report);
 
   int read_chemistry_file(Neutrals neutrals,
@@ -107,7 +107,7 @@ class Chemistry {
   reaction_type interpret_reaction_line(const Neutrals &neutrals,
                                         const Ions &ions,
                                         const std::vector<std::string> &line,
-					const json &headers);
+                                        const json &headers);
 
   void find_species_id(const std::string &name,
                        const Neutrals &neutrals,
diff --git a/include/collisions.h b/include/collisions.h
index 1a59a19c..98b1173c 100644
--- a/include/collisions.h
+++ b/include/collisions.h
@@ -11,6 +11,6 @@
 #include "ions.h"
 
 void calc_ion_neutral_coll_freq(Neutrals &neutrals,
-                 Ions &ions);
+                                Ions &ions);
 
 #endif  // INCLUDE_COLLISIONS_H_
diff --git a/include/electrodynamics.h b/include/electrodynamics.h
index 13a631ab..40cd5555 100644
--- a/include/electrodynamics.h
+++ b/include/electrodynamics.h
@@ -54,7 +54,7 @@ class Electrodynamics {
 
      This does the following:
      - initialize all variables to missing values
-     - read in file if it exists 
+     - read in file if it exists
    **/
   Electrodynamics(Times time);
 
@@ -66,12 +66,12 @@ class Electrodynamics {
      \param time need current time
      \param ions Going to set the potential and aurora
    **/
-  
+
   bool update(Planets planet,
-	      Grid gGrid,
-	      Times time,
-        Indices &indices,
-        Ions &ions);
+              Grid gGrid,
+              Times time,
+              Indices &indices,
+              Ions &ions);
 
 
   /**************************************************************
@@ -85,7 +85,7 @@ class Electrodynamics {
    **/
 
   bool check_times(double inputStartTime, double inputEndTime);
-  
+
   /**************************************************************
      \brief used in advance.cpp to get potential, eflux, avee
 
@@ -95,9 +95,9 @@ class Electrodynamics {
    **/
 
   std::tuple<arma_cube,
-	     arma_mat,
-	     arma_mat> get_electrodynamics(arma_cube magLat,
-					   arma_cube magLocalTime);
+      arma_mat,
+      arma_mat> get_electrodynamics(arma_cube magLat,
+                                    arma_cube magLocalTime);
 
   /**************************************************************
      \brief Gets interpolation indices
@@ -105,7 +105,7 @@ class Electrodynamics {
      Performs 2d interpolation over search vector to get indices
 
      \param vals the 2d array that needs indices
-     \param search The vector of values to interpolate over  
+     \param search The vector of values to interpolate over
    **/
 
   arma_mat get_interpolation_indices(arma_mat vals, arma_vec search);
@@ -121,7 +121,7 @@ class Electrodynamics {
 
      \param time the time requested.
    **/
-  
+
   void set_time(double time);
 
   /**************************************************************
@@ -195,7 +195,7 @@ class Electrodynamics {
      \param value Value to assign to Kp index
    **/
   void set_kp(precision_t value);
-  
+
   /**************************************************************
      \brief Get 2D electric potential on specified grid
 
@@ -209,7 +209,7 @@ class Electrodynamics {
        with the potentials in the grids
    **/
   arma_cube get_potential(arma_cube magLat,
-			  arma_cube magLocalTime);
+                          arma_cube magLocalTime);
 
   /**************************************************************
      \brief Get 2D electron energy flux on specified grid
@@ -266,13 +266,13 @@ class Electrodynamics {
        with the ion avee in the grids
    **/
   arma_mat get_ion_avee();
-  
+
   /**********************************************************************
      \brief Check to see if internal state of class is ok
    **/
-  
+
   bool is_ok();
-  
+
  private:
 
   /// This is the interpolation method for time:
@@ -284,7 +284,7 @@ class Electrodynamics {
   /// Use the next value:
   const int iNext_ = 2;
   // Use the closest value:
-  const int iClosest_ = 3; 
+  const int iClosest_ = 3;
   /// Interpolate:
   const int iInterp_ = 4;
 
@@ -307,7 +307,7 @@ class Electrodynamics {
   /// A 2d array of magnetic local times needed. Can set interpolation
   /// coefficients in all of the grids when this is called:
   arma_mat mlts_needed;
-  
+
   /// These are all indices that may be needed by sub-models:
   precision_t imf_bx_needed;
   precision_t imf_by_needed;
@@ -340,11 +340,11 @@ class Electrodynamics {
   /// If we don't read in an electrodynamics file, then this should be
   /// set to an auroral model to use.  Need to add model types.
   std::string auroral_model_to_use;
-  
+
   /// Set the interpolation indices as a float. For each interpolation index,
-  /// the integer portion is the current index, and the decimal part is the 
+  /// the integer portion is the current index, and the decimal part is the
   /// percentage of the distance between the current index and the next
-  /// index.  For example, a distance midway between index 45 and 46 
+  /// index.  For example, a distance midway between index 45 and 46
   /// would give an interpolation index of 45.5.
   /// For time, we are assuming that all grids have the same times or that
   /// there are no overlaps in time, I think.
@@ -378,22 +378,22 @@ class Electrodynamics {
 
     /// Potential at current time:
     arma_mat potential_current;
-    
+
     /// Vector of 2d electron energy flux (in ergs/cm2/s):
     std::vector<arma_mat> energy_flux;
     /// Said energy flux at the current time:
     arma_mat energy_flux_current;
-    
+
     /// Vector of 2d electron average energy (in keV):
     std::vector<arma_mat> average_energy;
     /// Average energy at current time:
     arma_mat average_energy_current;
-    
+
     /// Vector of 2d ion energy flux (in ergs/cm2/s):
     std::vector<arma_mat> ion_energy_flux;
     /// ion energy flux at current time:
     arma_mat ion_energy_flux_current;
-    
+
     /// Vector of 2d ion average energy (in keV):
     std::vector<arma_mat> ion_average_energy;
     /// ion average energy at current time:
@@ -401,7 +401,7 @@ class Electrodynamics {
 
     /// Set to 1 if ion precipitation is included, else set to 0:
     int DoesIncludeIonPrecip;
-    
+
     /// This sets the priority of the grid. The higher the number, the
     /// more important it is, so it should overwrite any regions of
     /// a lower priority grid. For example, you could have a global
@@ -419,28 +419,28 @@ class Electrodynamics {
     /// is outside of the mlt range of the grid, then the
     /// interpolation index should be set to -1:
     arma_mat mlts_indices;
-    
+
   };
-  
+
   /// As described above, a structure containing the grid-based
   /// values of electrodynamics as a function of time.  This is
   /// vector, because we can have nested grids, or, in theory, the
   /// grid could change as a function of time. You can then search
   /// for the apropriate grid in space and time.
   std::vector<input_electrodynamics_struct> input_electrodynamics;
-  
+
   /// Because each grid has a priority, we need to go through them in
   /// priority order, this is the sorted indices list, so that
   /// grid_order[0] points to the input_electrodynamics with the
   /// lowest priority, grid_order[1] points to the 2nd lowest, etc.
   std::vector<int> grid_order;
-  
+
   /// Number of input grids for electrodynamics:
   int nElectrodynamicsGrids;
-  
+
   /// An internal variable to hold the state of the class
   bool IsOk;
-  
+
   /**************************************************************
      \brief Reads a netcdf file that has the electrodynamics specification
 
@@ -469,13 +469,13 @@ class Electrodynamics {
      grids, so that the values are overwritten. To keep it
      "functional", we pass in the last round of values and those are
      moved into the output values and then the overlapping region is
-     overwritten (e.g., in the get_potential function, the 
+     overwritten (e.g., in the get_potential function, the
      grids need to be cycled through calling get_values with the
      potential on that grid and the interpolation indices for the grid.
 
      \param values_current the pot/eflux/avee/etc from
      input_electrodynamics grid
- 
+
      \param lats_indices the interpolation indices for the current
      grid latitudes
 
@@ -483,8 +483,8 @@ class Electrodynamics {
      grid mlts
 
      \param values_old the output of this function for the last grid
-  **/  
-  
+  **/
+
   arma_mat get_values(arma_mat matToInterpolateOn, int rows, int cols);
 
   void set_all_indices_for_ie(Times time, Indices &indices);
diff --git a/include/euv.h b/include/euv.h
index 138e74f3..a9dd826d 100644
--- a/include/euv.h
+++ b/include/euv.h
@@ -8,7 +8,7 @@
  * \class Euv
  *
  * \brief Defines the Extreme Ultraviolet radiation above the atmosphere
- * 
+ *
  * The Euv class defines the EUV environment above the atmosphere. It
  * does this through the use of a CSV file that contains a bunch of
  * information. Namely:
@@ -18,7 +18,7 @@
  *
  * \author Aaron Ridley
  *
- * \date 2021/03/28 
+ * \date 2021/03/28
  *
  **************************************************************/
 
@@ -27,12 +27,12 @@
 
 class Euv {
 
-public:
+ public:
 
   /// whether to actuall use euv at all:
   bool doUse;
-  
-  /// number of wavelengths in spectrum: 
+
+  /// number of wavelengths in spectrum:
   int nWavelengths;
 
   // number of lines in the EUV CSV file:
@@ -59,10 +59,10 @@ class Euv {
 
   /// EUV Spectrum, lower wavelength of the bins:
   std::vector<float> wavelengths_short;
-  
+
   /// EUV Spectrum, upper wavelength of the bins:
   std::vector<float> wavelengths_long;
-  
+
   /// EUV Spectrum, energy of bin:
   std::vector<float> wavelengths_energy;
 
@@ -81,7 +81,7 @@ class Euv {
   std::vector<float> solomon_hfg_c1;
   std::vector<float> solomon_hfg_c2;
   std::vector<float> solomon_hfg_fref;
-  
+
   /// NEUVAC model linear coefficients (1-3):
   std::vector<float> neuvac_s1;
   std::vector<float> neuvac_s2;
@@ -93,7 +93,7 @@ class Euv {
 
   /// NEUVAC model intercept:
   std::vector<float> neuvac_int;
-  
+
   // --------------------------------------------------------------------
   // Functions:
 
@@ -109,13 +109,13 @@ class Euv {
    **/
   bool euvac(Times time, Indices indices);
 
- /**********************************************************************
-     \brief Compute the EUV spectrum given F107 and F107a
-     \param time The times within the model (dt is needed)
-     \param indices Need the F107 and F107a
-   **/
- bool solomon_hfg(Times time, Indices indices);
-  
+  /**********************************************************************
+      \brief Compute the EUV spectrum given F107 and F107a
+      \param time The times within the model (dt is needed)
+      \param indices Need the F107 and F107a
+    **/
+  bool solomon_hfg(Times time, Indices indices);
+
   /**********************************************************************
      \brief Compute the EUV spectrum given F107 and F107a (new version)
      \param time The times within the model (dt is needed)
@@ -135,7 +135,7 @@ class Euv {
 
      Reads through each row in the EUV CSV file and figures out whether
      the row is abs, ion, diss, and then figures out which neutral it is
-     acting on and which neutral or ion results from the action 
+     acting on and which neutral or ion results from the action
      (e.g. O + photon -> O+, identifies O as ionization "loss" and
      O+ as an ionization "source")
 
@@ -147,16 +147,16 @@ class Euv {
   /**********************************************************************
      \brief Check to see if internal state of class is ok
    **/
-  
+
   bool is_ok();
-  
-private:
+
+ private:
 
   /**********************************************************************
      \brief Read in the EUV CSV file
 
      Read in the EUV CSV file that describes all of the wavelengths and
-     cross sections (and any other EUV - related things that are a 
+     cross sections (and any other EUV - related things that are a
      function of wavelength)
    **/
   bool read_file();
@@ -172,8 +172,8 @@ class Euv {
      \return values The values in the CSV row that matches the item (and item2)
    **/
   bool slot_euv(std::string item,
-		std::string item2,
-		std::vector<float> &values);
+                std::string item2,
+                std::vector<float> &values);
 
   /// An internal variable to hold the state of the class
   bool IsOk;
diff --git a/include/external_msis.h b/include/external_msis.h
index d6285456..764557c5 100644
--- a/include/external_msis.h
+++ b/include/external_msis.h
@@ -8,13 +8,13 @@
  * \class Msis
  *
  * \brief create an interface to the msis model
- * 
+ *
  * MSIS is a neutral model of the atmosphere, written in
- * fortran and provided by NRL. 
+ * fortran and provided by NRL.
  *
  * \author Aaron Ridley
  *
- * \date 2023/04/30 
+ * \date 2023/04/30
  *
  **************************************************************/
 
@@ -27,20 +27,20 @@ class Msis {
   bool set_f107(precision_t f107in, precision_t f107ain);
   bool set_ap(precision_t apin);
   bool set_locations(arma_vec longitude,
-		     arma_vec latitude,
-		     arma_vec altitude);
+                     arma_vec latitude,
+                     arma_vec altitude);
   bool set_locations(arma_mat longitude,
-		     arma_mat latitude,
-		     arma_mat altitude);
+                     arma_mat latitude,
+                     arma_mat altitude);
   bool set_locations(arma_cube longitude,
-		     arma_cube latitude,
-		     arma_cube altitude);
+                     arma_cube latitude,
+                     arma_cube altitude);
   arma_vec get_vec(std::string value);
   arma_mat get_mat(std::string value);
   arma_cube get_cube(std::string value);
   bool is_valid_species(std::string value);
   bool is_ok();
-  
+
  private:
 
   int iYear, iDay;
@@ -54,10 +54,10 @@ class Msis {
   arma_cube altKm;
   std::vector<arma_cube> msis_results;
 
-  bool didChange = true;  
+  bool didChange = true;
   json value_lookup;
   bool isCompiled;
-  
+
   bool reset_interface_variable_sizes();
   bool reset_results();
 };
diff --git a/include/file_input.h b/include/file_input.h
index 460a6fe6..ee127f7e 100644
--- a/include/file_input.h
+++ b/include/file_input.h
@@ -31,9 +31,9 @@ std::vector<std::vector<std::string>> read_csv(std::ifstream &file_ptr);
  \param csvLines a matrix of strings
  **/
 json put_csv_in_json_w_name(std::vector<std::vector<std::string>>
-			    csvLines);
+                            csvLines);
 json put_csv_in_json_wo_name(std::vector<std::vector<std::string>>
-			     csvLines);
+                             csvLines);
 
 
 /**************************************************************
@@ -46,8 +46,8 @@ std::vector<std::vector<std::string>> read_ssv(std::ifstream &file_ptr);
 /**************************************************************
  \brief Reads either a comma-separated time or series of lines describing time
 
-  format is either 
-  y, m, d, h, m, s, ms 
+  format is either
+  y, m, d, h, m, s, ms
   or
   y
   m
diff --git a/include/init_mag_grid.h b/include/init_mag_grid.h
index b1af1f4c..415978a1 100644
--- a/include/init_mag_grid.h
+++ b/include/init_mag_grid.h
@@ -13,12 +13,12 @@ bool init_dipole_grid(Grid &mGrid, Planets planet);
 
 // Analytic solution to get from q,p dipole coords to r,theta
 // q coordinate along b-field line
-// p l-shell 
+// p l-shell
 // return (r,theta)
 std::pair<precision_t, precision_t> qp_to_r_theta(precision_t q, precision_t p);
 
 // convert mag to geographic
-std::vector <arma_cube> mag_to_geo(arma_cube magLon, 
+std::vector <arma_cube> mag_to_geo(arma_cube magLon,
                                    arma_cube magLat,
                                    arma_cube magAlt,
                                    Planets planet);
diff --git a/include/inputs.h b/include/inputs.h
index ce273f76..7c48dbf1 100644
--- a/include/inputs.h
+++ b/include/inputs.h
@@ -607,12 +607,12 @@ class Inputs {
    **/
   std::vector<precision_t> get_satellite_dts();
 
-    /**********************************************************************
-     \brief returns settings["
-     \param 
-   **/
+  /**********************************************************************
+   \brief returns settings["
+   \param
+  **/
   json get_tests();
-  
+
   // General get_setting functions with error checks:
 
   /**********************************************************************
diff --git a/include/logfile.h b/include/logfile.h
index 1fe19d5d..f413a209 100644
--- a/include/logfile.h
+++ b/include/logfile.h
@@ -5,7 +5,7 @@
 #define INCLUDE_LOGFILE_H_
 
 /**************************************************************
- * 
+ *
  * logfile.h:
  *
  *    Write the logfile
@@ -19,7 +19,7 @@
 /**
  * The class Satellite is used to track the satellites
  * Given any time, the user can obtain the geographic location of the satellite
- * 
+ *
  * ASSUMPTION : The satellite csv layout is the same as the following
  * year   mon   day   hr    min   sec   lon      lat      alt   x   y    z    vx     vy     vz
  * (int)  (int) (int) (int) (int) (int) (degree) (degree) (km) (km) (km) (km) (km/s) (km/s) (km/s)
@@ -27,11 +27,11 @@
 
 class Satellite {
 
-public:
+ public:
 
   /**
    * \brief Initialize the satellite class
-   *        The name of the satellite is not allowed to have any characters which can 
+   *        The name of the satellite is not allowed to have any characters which can
    *        terminate the read of a string including white space' ', endline'\n', and '\t'
    *        Different satellites must have different names (not only input file names)
    * \param csv_in The path to the satellite csv file
@@ -69,7 +69,7 @@ class Satellite {
   // DEBUG
   void print();
 
-private:
+ private:
 
   // The name of the satellite
   std::string name;
@@ -93,7 +93,7 @@ class Satellite {
 
 class Logfile {
 
-public:
+ public:
 
   /**
    * \brief Initialize the Logfile.
@@ -101,7 +101,7 @@ class Logfile {
    *    every dt time.
    */
   Logfile(Indices &indices, int64_t iLog);
-  
+
   /**
    * \brief Close the file stream if not append
    */
@@ -117,7 +117,7 @@ class Logfile {
                      Grid &gGrid,
                      Times &time);
 
-private:
+ private:
 
   // The name of logfile
   std::string logfileName;
@@ -133,7 +133,7 @@ class Logfile {
   bool doAppend;
 
   // A randomly chosen point for test
-  std::vector<precision_t> lla {2,2,2};
+  std::vector<precision_t> lla {2, 2, 2};
 };
 
 #endif  // INCLUDE_LOGFILE_H_
diff --git a/include/output.h b/include/output.h
index d6503984..2dc3d7cd 100644
--- a/include/output.h
+++ b/include/output.h
@@ -9,7 +9,7 @@
 /**************************************************************
  * \class Output
  * \brief A containing to allow storage of variables for output
- * 
+ *
  * Writing output is a multi-step process now:
  *  1. Create a container to store the variables you want to output
  *  2. Define the variables to output within the container
@@ -17,13 +17,13 @@
  *  4. Write the output
  *
  * \author Aaron Ridley
- * \date 2021/10/21 
+ * \date 2021/10/21
  **************************************************************/
 
 class OutputContainer {
 
  public:
-  
+
   /**********************************************************************
      \brief initialize the output container
    **/
@@ -63,8 +63,8 @@ class OutputContainer {
      \param value the array of the data to output
    **/
   void store_variable(std::string name,
-		      std::string unit,
-		      arma_cube value);
+                      std::string unit,
+                      arma_cube value);
 
   /**********************************************************************
      \brief store a variable to the list of variables to output
@@ -74,9 +74,9 @@ class OutputContainer {
      \param value the array of the data to output
    **/
   void store_variable(std::string name,
-		      std::string long_name,
-		      std::string unit,
-		      arma_cube value);
+                      std::string long_name,
+                      std::string unit,
+                      arma_cube value);
 
   /**********************************************************************
      \brief Get an arma_cube from the Container
@@ -129,12 +129,12 @@ class OutputContainer {
      \brief write a file with the information in the container
    **/
   bool write();
-  
+
   /**********************************************************************
      \brief write a json header file with the information in the container
    **/
   bool write_container_header();
-  
+
   /**********************************************************************
      \brief write a binary file with the information in the container
    **/
@@ -149,27 +149,27 @@ class OutputContainer {
      \brief write a netcdf file with the information in the container
    **/
   bool write_container_netcdf();
-  
+
   /**********************************************************************
      \brief read from a file an load into the container
    **/
   bool read();
-  
+
   /**********************************************************************
      \brief display information contained in the container
    **/
   void display();
-  
+
   /**********************************************************************
      \brief read a netcdf file - put the information in the container
    **/
   bool read_container_netcdf();
-  
+
   /**********************************************************************
-     \brief clears the vector of variables 
+     \brief clears the vector of variables
    **/
   void clear_variables();
-  
+
  private:
 
   /// User can set the directory for output
@@ -206,7 +206,7 @@ class OutputContainer {
 
   /// The frequency of the output for this particular container:
   float dt_output;
-  
+
   /// This is to allow the user to select different output formats
   int output_type;
 
@@ -214,7 +214,7 @@ class OutputContainer {
   const int binary_type = 0;
   const int netcdf_type = 1;
   const int hdf5_type = 2;
-  
+
 };
 
 /**********************************************************************
@@ -238,12 +238,12 @@ class OutputContainer {
 **/
 
 bool output(const Neutrals &neutrals,
-	    const Ions &ions,
-	    Grid &grid,
-	    Times time,
-	    const Planets &planet);
+            const Ions &ions,
+            Grid &grid,
+            Times time,
+            const Planets &planet);
 
 void output_binary_3d(std::ofstream &binary,
-		      arma_cube value);
+                      arma_cube value);
 
 #endif  // INCLUDE_OUTPUT_H_
diff --git a/include/parallel.h b/include/parallel.h
index 11372be3..1a363569 100644
--- a/include/parallel.h
+++ b/include/parallel.h
@@ -4,7 +4,7 @@
 #ifndef INCLUDE_PARALLEL_H_
 #define INCLUDE_PARALLEL_H_
 
-/// Need MPI (message passing interface) to do parallel stuff: 
+/// Need MPI (message passing interface) to do parallel stuff:
 #include "mpi.h"
 
 /// number of processors in whole simulation
@@ -51,10 +51,10 @@ bool init_parallel(Quadtree &quadtree, Quadtree &quadtree_ion);
 **/
 
 bool pack_border(const arma_cube &value,
-		 precision_t *packed,
-		 int64_t *iCounter,
-		 int64_t nG,
-		 int iDir);
+                 precision_t *packed,
+                 int64_t *iCounter,
+                 int64_t nG,
+                 int iDir);
 
 /**********************************************************************
   \brief Unpack variable buffer after message pass
@@ -72,16 +72,16 @@ bool pack_border(const arma_cube &value,
 **/
 
 bool unpack_border(arma_cube &value,
-		   precision_t *packed,
-		   int64_t *iCounter,
-		   int64_t nG,
-		   int iDir,
-		   bool DoReverseX,
-		   bool DoReverseY,
-		   bool XbecomesY);
+                   precision_t *packed,
+                   int64_t *iCounter,
+                   int64_t nG,
+                   int iDir,
+                   bool DoReverseX,
+                   bool DoReverseY,
+                   bool XbecomesY);
 
 /**********************************************************************
-  \brief initialize the grid variables to set up ghostcell message passing 
+  \brief initialize the grid variables to set up ghostcell message passing
   \param grid the grid to set up message passing on
   \param nVarsToPass how many variables to pass
 **/
@@ -96,8 +96,8 @@ bool exchange_sides_init(Grid &grid, int64_t nVarsToPass);
 **/
 
 bool exchange_one_var(Grid &grid,
-		      arma_cube &var_to_pass,
-		      bool doReverseSignAcrossPole);
+                      arma_cube &var_to_pass,
+                      bool doReverseSignAcrossPole);
 
 /**********************************************************************
   \brief test the exchange messages one var function
diff --git a/include/quadtree.h b/include/quadtree.h
index 18b584c7..d098efcc 100644
--- a/include/quadtree.h
+++ b/include/quadtree.h
@@ -9,14 +9,14 @@
 /**************************************************************
  * \class Quadtree
  *
- * \brief Defines the quadtree for blocks 
- * 
+ * \brief Defines the quadtree for blocks
+ *
  * Aether is logically an i, j, k grid structure.  Aether does domain
  * decomposition on the (i, j) coordinates and each processor works on
  * the full domain of the k dimension.  (e.g., nn spherical
  * coordinates, i = longitude, j = latitude, and k = altitude.)  The
  * quadtree takes the (i, j) dimensions and makes blocks out of them.
- * 
+ *
  * In the quadtree there are a number of root nodes, which are then
  * divided into 2 x 2 blocks.  Each of those can then be subdivided
  * into 2 x 2 blocks.  Each block resides on a separate processor.
@@ -32,13 +32,13 @@
  *
  * \author Aaron Ridley
  *
- * \date 2022/07/05 
+ * \date 2022/07/05
  *
  **************************************************************/
 
 class Quadtree {
 
-public:
+ public:
 
   /// number of blocks in each direction:
   const uint64_t nLR = 2;
@@ -86,7 +86,7 @@ class Quadtree {
 
   /// Number of root nodes:
   int64_t nRootNodes;
-  
+
   /// The quadtree root nodes:
   std::vector<struct qtnode> root_nodes;
 
@@ -98,7 +98,7 @@ class Quadtree {
   arma_vec limit_high = {0.0, 0.0, 0.0};
   /// For the given processor, the side that it is on:
   uint64_t iSide = -1;
-  
+
   /**********************************************************************
      \brief Initializes the quadtree
    **/
@@ -107,7 +107,7 @@ class Quadtree {
   /**********************************************************************
      \brief Builds the quadtree
    **/
-  void build(std::string gridtype); 
+  void build(std::string gridtype);
 
   /**********************************************************************
      \brief Makes a new node on the quadtree, recursively
@@ -119,18 +119,18 @@ class Quadtree {
      \param iSide basically the root node, or the side of the cubesphere
    **/
   qtnode new_node(arma_vec lower_left_norm_in,
-		  arma_vec size_right_norm_in,
-		  arma_vec size_up_norm_in,
-		  uint64_t &iProc_in_out,
-		  uint64_t depth_in,
-		  uint64_t iSide);
+                  arma_vec size_right_norm_in,
+                  arma_vec size_up_norm_in,
+                  uint64_t &iProc_in_out,
+                  uint64_t depth_in,
+                  uint64_t iSide);
 
   /**********************************************************************
      \brief Get different vectors from the node
      \param node which node to get the vector from
      \param which defines the vector to get:
                   LL = lower left;
-                  SR = size in the right/left direction; 
+                  SR = size in the right/left direction;
                   SU = size in the up/down direction;
                   MID = mid point of the node;
    **/
@@ -154,26 +154,26 @@ class Quadtree {
   int64_t find_root(arma_vec point);
 
   /**********************************************************************
-     \brief If the point is outside of the normalized limits of the 
+     \brief If the point is outside of the normalized limits of the
             quadtree, this tries to put the point back into the domain
      \param point the x, y, z normalized coordinate of the point.
    **/
   arma_vec wrap_point_sphere(arma_vec point);
 
   /**********************************************************************
-     \brief If the point is outside of the normalized limits of the 
+     \brief If the point is outside of the normalized limits of the
             quadtree, this tries to put the point back into the domain
      \param point the x, y, z normalized coordinate of the point.
    **/
   arma_vec wrap_point_cubesphere(arma_vec point);
-  
+
   /**********************************************************************
      \brief Check to see if internal state of class is ok
    **/
-  
+
   bool is_ok();
 
-private:
+ private:
 
   /// Defines whether the quadtree state is ok:
   bool IsOk = true;
@@ -183,7 +183,7 @@ class Quadtree {
   bool IsCubeSphere = false;
   /// Defines whether the quadtree is a dipole or not:
   bool IsDipole = false;
-  
+
 };
 
 #endif  // INCLUDE_QUADTREE_H_
diff --git a/include/read_collision_file.h b/include/read_collision_file.h
index 64a9fb91..a11d1c85 100644
--- a/include/read_collision_file.h
+++ b/include/read_collision_file.h
@@ -7,27 +7,27 @@
 #include "../include/aether.h"
 
 void read_collision_file(Neutrals &neutrals,
-			 Ions &ions);
+                         Ions &ions);
 
 void parse_nu_in_table(std::vector<std::vector<std::string>> csv,
-		       Neutrals &neutrals,
-		       Ions &ions);
+                       Neutrals &neutrals,
+                       Ions &ions);
 
 void parse_resonant_nu_in_table(std::vector<std::vector<std::string>> csv,
-				Neutrals &neutrals,
-				Ions &ions);
+                                Neutrals &neutrals,
+                                Ions &ions);
 
 void parse_bst_in_table(std::vector<std::vector<std::string>> csv,
-			Neutrals &neutrals,
-			Ions &ions);
+                        Neutrals &neutrals,
+                        Ions &ions);
 
 void parse_diffexp_in_table(std::vector<std::vector<std::string>> csv,
-			    Neutrals &neutrals);
+                            Neutrals &neutrals);
 
 void parse_diff0_in_table(std::vector<std::vector<std::string>> csv,
                           Neutrals &neutrals);
 
 void check_collision_frequncies(Ions ions,
-				Neutrals neutrals);
+                                Neutrals neutrals);
 
 #endif  // INCLUDE_COLLISION_FILE_H_
diff --git a/include/read_indices_files.h b/include/read_indices_files.h
index bd20e2f1..52bd6449 100644
--- a/include/read_indices_files.h
+++ b/include/read_indices_files.h
@@ -17,7 +17,7 @@
 /**********************************************************************
   \brief Reads in all of the indices files and stores them in Indices
 
-  This function goes through all of the input indices files and 
+  This function goes through all of the input indices files and
   reads in the files, then stores the values into the Indices class.
   At this point, it can read in the following file types:
   1. NGDC F10.7 files.
@@ -32,7 +32,7 @@ bool read_and_store_indices(Indices &indices);
   \param f107_file the f10.7 file to read in
 **/
 index_file_output_struct read_f107_file(std::string f107_file,
-					Indices indices);
+                                        Indices indices);
 
 /**********************************************************************
   \brief Read the OMNIWeb file format and store in the index_file struct
@@ -40,7 +40,7 @@ index_file_output_struct read_f107_file(std::string f107_file,
   \param indices needed to get the indices index for each variable
 **/
 index_file_output_struct read_omni_file(std::string omni_file,
-					Indices indices);
+                                        Indices indices);
 
 /**********************************************************************
   \brief This code compares a string to return the variable index
diff --git a/include/report.h b/include/report.h
index e29696eb..156a434d 100644
--- a/include/report.h
+++ b/include/report.h
@@ -28,11 +28,11 @@
 
 class Report {
 
-// -----------------------------------------------------------------------
-// Public functions and variables
-// -----------------------------------------------------------------------
+  // -----------------------------------------------------------------------
+  // Public functions and variables
+  // -----------------------------------------------------------------------
 
-public:
+ public:
 
   // Functions:
 
@@ -146,9 +146,9 @@ class Report {
    \param cFunctionName
    **/
   void student_checker_function_name(bool isStudent,
-				     std::string cStudentName,
-				     int iFunctionNumber,
-				     std::string cFunctionName);
+                                     std::string cStudentName,
+                                     int iFunctionNumber,
+                                     std::string cFunctionName);
 
   /**************************************************************
    \brief Starts timer and reports when entering a function, if applicable
@@ -179,10 +179,10 @@ class Report {
    **/
   void times();
 
-// -----------------------------------------------------------------------
-// Private functions and variables
-// -----------------------------------------------------------------------
-private:
+  // -----------------------------------------------------------------------
+  // Private functions and variables
+  // -----------------------------------------------------------------------
+ private:
 
   /// global verbose level of the code
   int iVerbose;
diff --git a/include/sizes.h b/include/sizes.h
index ff10b6b2..5a02b9cb 100644
--- a/include/sizes.h
+++ b/include/sizes.h
@@ -7,7 +7,7 @@
 // This is the file that defines the number of grid points in each
 // direction.  The entire code is based on these numbers, so you need
 // to recompile if you change these numbers.
-// 
+//
 // These are temporary and will eventually be removed.
 
 // This is for the geographic grid:
diff --git a/include/test.h b/include/test.h
index d6f9b287..3b021a68 100644
--- a/include/test.h
+++ b/include/test.h
@@ -9,7 +9,8 @@
 
 // Gradient tests
 // Cubesphere is not done nor tested
-bool test_gradient(Planets planet, Quadtree quadtree, json test_config, Grid gGrid, Grid mGrid);
+bool test_gradient(Planets planet, Quadtree quadtree, json test_config,
+                   Grid gGrid, Grid mGrid);
 bool test_gradient_cubesphere(Planets planet, Quadtree quadtree, Grid grid);
 bool test_gradient_ijk(Planets planet, Grid grid, bool debug);
 
diff --git a/include/times.h b/include/times.h
index f082f31e..12b891e0 100644
--- a/include/times.h
+++ b/include/times.h
@@ -5,7 +5,7 @@
 #define INCLUDE_TIMES_H_
 
 /**************************************************************
- * 
+ *
  * times.h:
  *
  *   Functions that are assocuated with keeping track of time in
@@ -20,7 +20,7 @@
 
 class Times {
 
-public:
+ public:
 
   /**************************************************************
      \brief Initialize the Times class
@@ -61,7 +61,7 @@ class Times {
      \brief Sets the start, restart, and current times.
 
      This sets the start time, restart time, and current time to
-   the input time, and initializes iStep and dt, then calls 
+   the input time, and initializes iStep and dt, then calls
    increment_time, which derives a bunch of other variables.
 
    \param itime year, month, day, hour, minute, second, millisecond vector
@@ -161,21 +161,21 @@ class Times {
      \brief Get the current time as an array
    **/
   std::vector<int> get_iCurrent();
-  
+
   /**************************************************************
      \brief Get the current simulation time (sec since start)
    **/
   double get_simulation_time();
-  
+
   /**********************************************************************
      \brief Read / Write restart files for time
      \param dir directory to write restart files
      \param DoRead read the restart files if true, write if false
    **/
-  bool restart_file(std::string dir, bool DoRead);  
+  bool restart_file(std::string dir, bool DoRead);
+
+ private:
 
-private:
-  
   // -------------------------------------------------------------
   // These variables are for keeping track of the time. All in seconds
   // since reference time (except where noted).
@@ -212,7 +212,7 @@ class Times {
 
   /// Universal time in hours
   precision_t ut;
-  
+
   /// in weird JPL units
   precision_t orbittime;
 
@@ -227,13 +227,13 @@ class Times {
 
   /// This is day of year (and NOT real Julian Day!)
   int jDay;
-  
+
   /// This is Julian day
   double julian_day;
 
   /// represented as YYMMDD
   std::string sYMD;
-  
+
   /// represented as HHMMSS
   std::string sHMS;
 
diff --git a/include/transform.h b/include/transform.h
index fe27f452..91b275cf 100644
--- a/include/transform.h
+++ b/include/transform.h
@@ -16,15 +16,15 @@ std::string mkupper(std::string inString);
 void copy_cube_to_array(arma_cube cube_in,
                         precision_t *array_out);
 void copy_mat_to_array(arma_mat mat_in,
-                        precision_t *array_out,
-                        bool isFortran);
+                       precision_t *array_out,
+                       bool isFortran);
 void copy_array_to_mat(precision_t *array_in,
                        arma_mat &mat_out,
                        bool isFortran);
 
 void copy_vector_to_array(std::vector<precision_t> vector_in,
-			  int64_t nElements,
-			  precision_t *array_out);
+                          int64_t nElements,
+                          precision_t *array_out);
 
 // This is needed when sending strings to Fortran.
 // We do this by copying the ascii numbers into an integer array,
@@ -34,13 +34,18 @@ int* copy_string_to_int(std::string inString);
 arma_cube calc_magnitude(std::vector<arma_cube> xyz);
 std::vector<arma_cube> transform_llr_to_xyz_3d(std::vector<arma_cube> llr);
 std::vector<arma_cube> transform_xyz_to_llr_3d(std::vector<arma_cube> xyz);
-std::vector<arma_cube> rotate_around_x_3d(std::vector<arma_cube> XYZ_in, precision_t angle);
-std::vector<arma_cube> rotate_around_y_3d(std::vector<arma_cube> XYZ_in, precision_t angle);
-std::vector<arma_cube> rotate_around_z_3d(std::vector<arma_cube> XYZ_in, precision_t angle);
+std::vector<arma_cube> rotate_around_x_3d(std::vector<arma_cube> XYZ_in,
+                                          precision_t angle);
+std::vector<arma_cube> rotate_around_y_3d(std::vector<arma_cube> XYZ_in,
+                                          precision_t angle);
+std::vector<arma_cube> rotate_around_z_3d(std::vector<arma_cube> XYZ_in,
+                                          precision_t angle);
 
 void transform_llr_to_xyz(precision_t llr_in[3], precision_t xyz_out[3]);
-void transform_rot_z(precision_t xyz_in[3], precision_t angle_in, precision_t xyz_out[3]);
-void transform_rot_y(precision_t xyz_in[3], precision_t angle_in, precision_t xyz_out[3]);
+void transform_rot_z(precision_t xyz_in[3], precision_t angle_in,
+                     precision_t xyz_out[3]);
+void transform_rot_y(precision_t xyz_in[3], precision_t angle_in,
+                     precision_t xyz_out[3]);
 void transform_float_vector_to_array(std::vector<precision_t> input,
                                      precision_t output[3]);
 
diff --git a/src/calc_ion_drift.cpp b/src/calc_ion_drift.cpp
index 0cc9821a..19970f1a 100644
--- a/src/calc_ion_drift.cpp
+++ b/src/calc_ion_drift.cpp
@@ -26,11 +26,11 @@ void Ions::calc_efield(Grid grid) {
 // --------------------------------------------------------------------------
 
 void Ions::calc_exb_drift(Grid grid) {
-  
+
   std::string function = "Ions::calc_exb";
   static int iFunction = -1;
   report.enter(function, iFunction);
-  
+
   arma_cube bmag2 =
     (grid.bfield_mag_scgc) % (grid.bfield_mag_scgc);
   exb_vcgc = cross_product(efield_vcgc, grid.bfield_vcgc);
diff --git a/src/calc_ion_temperature.cpp b/src/calc_ion_temperature.cpp
index 42c5f5b1..72f6df5b 100644
--- a/src/calc_ion_temperature.cpp
+++ b/src/calc_ion_temperature.cpp
@@ -27,18 +27,17 @@ void Ions::init_ion_temperature(Neutrals neutrals, Grid grid) {
 
   temperature_scgc = neutrals.temperature_scgc;
 
-  // For electron temperature, we need to check if some species are present or not. 
+  // For electron temperature, we need to check if some species are present or not.
   // Do this check now & warn if needed:
   if ((neutrals.get_species_id("O") == -1)
-      || (neutrals.get_species_id("O2") == -1) 
-      ||(neutrals.get_species_id("N2") == -1)){
-    if (input.get_do_photoelectron_heating() 
+      || (neutrals.get_species_id("O2") == -1)
+      || (neutrals.get_species_id("N2") == -1)) {
+    if (input.get_do_photoelectron_heating()
         || input.get_do_ionization_heating()
-        || input.get_do_electron_neutral_elastic_collisional_heating()) {
-    report.error("Your electron temperature sources require neutral O, O2, and N2 to be present.");
-    }
+        || input.get_do_electron_neutral_elastic_collisional_heating())
+      report.error("Your electron temperature sources require neutral O, O2, and N2 to be present.");
   }
-  
+
   return;
 }
 
diff --git a/src/chemistry.cpp b/src/chemistry.cpp
index a7713c2f..9eb2ebf7 100644
--- a/src/chemistry.cpp
+++ b/src/chemistry.cpp
@@ -25,7 +25,8 @@ Chemistry::Chemistry(Neutrals neutrals,
 
   std::string function = "Chemistry::Chemistry"; //record current function
   static int iFunction = -1; //usually -1 for report function
-  report.enter(function, iFunction); //keeps track of functions for: verbose levels, etc.
+  report.enter(function,
+               iFunction); //keeps track of functions for: verbose levels, etc.
 
   if (read_chemistry_file(neutrals, ions) > 0) { //searching for valid chem file
     report.print(0, "Could not read chemistry file!");
@@ -529,10 +530,11 @@ int Chemistry::read_chemistry_file(Neutrals neutrals,
 // Interpret a comma separated line of the chemical reaction file
 // -----------------------------------------------------------------------------
 
-Chemistry::reaction_type Chemistry::interpret_reaction_line(const Neutrals &neutrals,
-                                                            const Ions &ions,
-                                                            const std::vector<std::string> &line,
-                                                            const json &headers) {
+Chemistry::reaction_type Chemistry::interpret_reaction_line(
+  const Neutrals &neutrals,
+  const Ions &ions,
+  const std::vector<std::string> &line,
+  const json &headers) {
 
   std::string function = "Chemistry::interpret_reaction_line";
   static int iFunction = -1;
@@ -647,13 +649,15 @@ void Chemistry::find_species_id(const std::string &name,
   int iSpecies;
   IsNeutral = false;
 
-  id_ = neutrals.get_species_id(name); //from earth.in, starts at 0 w/ first species under "#NEUTRALS",(neutrals.cpp)
+  id_ = neutrals.get_species_id(
+          name); //from earth.in, starts at 0 w/ first species under "#NEUTRALS",(neutrals.cpp)
 
   if (id_ > -1)
     IsNeutral = true;
 
   else
-    id_ = ions.get_species_id(name);//from earth.in, starts at 0 w/ first species under "#IONS",(ions.cpp)
+    id_ = ions.get_species_id(
+            name);//from earth.in, starts at 0 w/ first species under "#IONS",(ions.cpp)
 
   report.exit(function);
   return;
@@ -671,23 +675,23 @@ void Chemistry::display_reaction(Chemistry::reaction_type reaction) {
   std::cout << "Number of Sources : " << reaction.nSources << "\n";
 
   for (i = 0; i < reaction.nLosses; i++) // First line for reaction
-    if (i < reaction.nLosses - 1) {//
+    if (i < reaction.nLosses - 1)  //
       std::cout << reaction.losses_names[i] << " + ";
-    } else {//
+
+    else  //
       std::cout << reaction.losses_names[i] << "  ->  ";
-    }
 
   for (i = 0; i < reaction.nSources; i++)
-    if (i < reaction.nSources - 1) {//
+    if (i < reaction.nSources - 1)  //
       std::cout << reaction.sources_names[i] << " + ";
-    } else {//
+
+    else  //
       std::cout << reaction.sources_names[i] << " (RR : " << reaction.rate << ")\n";
-    }
 
   for (i = 0; i < reaction.nLosses; i++)//Second line for reaction
     if (i < reaction.nLosses - 1) {//
       std::cout << reaction.losses_ids[i]
-              << "(" << reaction.losses_IsNeutral[i] << ")" << " + ";
+                << "(" << reaction.losses_IsNeutral[i] << ")" << " + ";
     } else {//
       std::cout << reaction.losses_ids[i]
                 << "(" << reaction.losses_IsNeutral[i] << ")" << "  ->  ";
@@ -696,11 +700,11 @@ void Chemistry::display_reaction(Chemistry::reaction_type reaction) {
   for (i = 0; i < reaction.nSources; i++)
     if (i < reaction.nSources - 1) {//
       std::cout << reaction.sources_ids[i]
-              << "(" << reaction.sources_IsNeutral[i] << ")" << " + ";
+                << "(" << reaction.sources_IsNeutral[i] << ")" << " + ";
     } else {//
       std::cout << reaction.sources_ids[i]
-              << "(" << reaction.sources_IsNeutral[i]
-              << ")" << " (RR : " << reaction.rate << ")\n";
+                << "(" << reaction.sources_IsNeutral[i]
+                << ")" << " (RR : " << reaction.rate << ")\n";
     }
 
 
diff --git a/src/grid_spacing.cpp b/src/grid_spacing.cpp
index ad6d5b45..f1a55c92 100644
--- a/src/grid_spacing.cpp
+++ b/src/grid_spacing.cpp
@@ -142,12 +142,13 @@ void Grid::calc_k_grid_spacing() {
     radius_scgc.slice(iZ) - radius_scgc.slice(iZ - 1);
 
   // For the sphere & cubesphere, k is in meters:
-  if (iGridShape_ == iSphere_ || iGridShape_  == iCubesphere_){
+  if (iGridShape_ == iSphere_ || iGridShape_  == iCubesphere_) {
     dk_center_m_scgc = dk_center_scgc;
     dk_edge_m = dk_edge;
   }
+
   // This needs to be turned into a distance for the dipole:
-  if (iGridShape_ == iDipole_){
+  if (iGridShape_ == iDipole_) {
     // the dk's may be negative (not allowed). make sure they are positive
     // this gets rid of SO many errors...
     dk_center_scgc = abs(dk_center_scgc);
@@ -243,14 +244,15 @@ void Grid::calc_i_grid_spacing() {
   di_center_m_scgc = di_center_scgc % radius_scgc;
   di_edge_m = di_edge % radius_scgc;
 
-  // If the shape is a sphere or dipole, then the first coordinate is longitude.  
-  // The physical distance needs to be changed by the cos of the latitude, 
+  // If the shape is a sphere or dipole, then the first coordinate is longitude.
+  // The physical distance needs to be changed by the cos of the latitude,
   // which is the j coordinate in the sphere (different for dipole).
   if (iGridShape_ == iSphere_) {
     di_center_m_scgc = di_center_m_scgc % abs(cos(j_center_scgc));
     // edge is in-line with the j center
     di_edge_m = di_edge_m % abs(cos(j_center_scgc));
   }
+
   // Dipole will use cos(magLat)
   if (iGridShape_ == iDipole_) {
     di_center_m_scgc = di_center_m_scgc % abs(cos(magLat_scgc));
@@ -343,8 +345,10 @@ void Grid::calc_j_grid_spacing() {
 
   // Dipole will have different scaling...
   if (iGridShape_ == iDipole_) {
-    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
-    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc), 3) / delTheta(magLat_scgc) % sign(magLat_scgc);
+    dj_center_m_scgc = radius_scgc % dj_center_scgc % pow(cos(magLat_scgc),
+                                                          3) / delTheta(magLat_scgc) % sign(magLat_scgc);
+    dj_edge_m = radius_scgc % dj_edge % pow(cos(magLat_scgc),
+                                            3) / delTheta(magLat_scgc) % sign(magLat_scgc);
   }
 
   // For a stretched grid, calculate some useful quantities:
diff --git a/src/solver_conduction.cpp b/src/solver_conduction.cpp
index ff353a11..e6de684a 100644
--- a/src/solver_conduction.cpp
+++ b/src/solver_conduction.cpp
@@ -29,7 +29,7 @@ arma_vec solver_conduction(arma_vec value,
                            int64_t nGCs,
                            bool return_diff, // (optional) False by default (return new `value`)
                            arma_vec source2 // (optional) Sources dependent on `value`
-                           ) {
+                          ) {
 
   int64_t nPts = value.n_elem;
 
@@ -52,7 +52,7 @@ arma_vec solver_conduction(arma_vec value,
   conduction.zeros();
 
   // If source2 is not given, set it to zero:
-  if (source2.n_elem == 0){
+  if (source2.n_elem == 0) {
     source2.set_size(source.n_elem);
     source2.zeros();
   }
@@ -64,7 +64,8 @@ arma_vec solver_conduction(arma_vec value,
 
   arma_vec a = di / du22 % r - dl / du12 % r % r;
   arma_vec c = di / du22 + dl / du12;
-  arma_vec b = -1.0 / m - di / du22 % (1.0 + r) - dl / du12 % (1.0 - r % r) + source2 % front * dt;
+  arma_vec b = -1.0 / m - di / du22 % (1.0 + r) - dl / du12 %
+               (1.0 - r % r) + source2 % front * dt;
   arma_vec d = -1.0 * (value / m + source % front * dt);
 
   // Lower BCs (fixed value):

From 2cd37a5d86b4495a2f819df867406c731bc96371 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Wed, 26 Nov 2025 16:28:25 -0500
Subject: [PATCH 265/266] BUG: need default to run on 1 processor

---
 share/run/aether.json | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/share/run/aether.json b/share/run/aether.json
index b192b5b5..6325cc69 100644
--- a/share/run/aether.json
+++ b/share/run/aether.json
@@ -13,7 +13,7 @@
     "EndTime" : [2011, 3, 20, 0, 1, 0],
 
     "neuGrid" : {
-        "Shape": "sphere4",
+        "Shape": "sphere",
         "nLonsPerBlock" : 20,
         "nLatsPerBlock" : 18,
         "nAlts" : 40,
@@ -21,13 +21,12 @@
         "IsUniformAlt" : false},
     
     "ionGrid": {
-        "Shape": "dipole4",
+        "Shape": "sphere",
         "nLonsPerBlock": 24,
-        "nLatsPerBlock": 18,
+        "nLatsPerBlock": 22,
         "nAlts": 50,
-        "LatRange": [10, 80],
-        "AltRange": [80.0, 1000],
-        "LonRange": [0.0, 360.0]},
+        "dAltScale" : 0.3,
+        "IsUniformAlt" : false},
 
     "OmniwebFiles" : ["UA/inputs/omni_20110319.txt"],
 

From 5cd781caf258f92c02bf432341d7eda69dc92183 Mon Sep 17 00:00:00 2001
From: Aaron Ridley <ridley@umich.edu>
Date: Thu, 27 Nov 2025 13:24:00 -0500
Subject: [PATCH 266/266] BUG: proper merge between the Aarons codes

---
 include/grid.h                    | 101 +++++++-------
 src/grid_match.cpp                | 114 +++++++++------
 src/solver_grid_interpolation.cpp | 225 +++++++++++++++++++++---------
 3 files changed, 282 insertions(+), 158 deletions(-)

diff --git a/include/grid.h b/include/grid.h
index a5f4c0b1..0f7a79c7 100644
--- a/include/grid.h
+++ b/include/grid.h
@@ -45,30 +45,31 @@ struct cubesphere_chars {
 // Grid class
 // ----------------------------------------------------------------------------
 
-  struct interp_coef_t {
-    // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
-    uint64_t iRow;
-    uint64_t iCol;
-    uint64_t iAlt;
-    // The coefficients along row, column and altitude
-    precision_t rRow;
-    precision_t rCol;
-    precision_t rAlt;
-    // Whether the point is within this grid or not
-    bool in_grid;
-    // If this is set to true:
-    bool above_grid, below_grid;
-    // do interpolation in lat and lon, but extrapolate in altitude
-  };
+struct interp_coef_t {
+  // The point is inside the cube of:
+  // [iRow, iRow+1], [iCol, iCol+1] [iAlt, iAlt+1]
+  uint64_t iRow;
+  uint64_t iCol;
+  uint64_t iAlt;
+  // The coefficients along row, column and altitude
+  precision_t rRow;
+  precision_t rCol;
+  precision_t rAlt;
+  // Whether the point is within this grid or not
+  bool in_grid;
+  // If this is set to true:
+  bool above_grid, below_grid;
+  // do interpolation in lat and lon, but extrapolate in altitude
+};
 
-  struct grid_to_grid_t {
-    int64_t iProcTo;
-    int64_t nPts;
-    int64_t nPtsReceive;
-    std::vector<struct interp_coef_t> interpCoefs;
-    std::vector<precision_t *> valueToSend;
-    std::vector<precision_t *> valueToReceive;
-  };
+struct grid_to_grid_t {
+  int64_t iProcTo;
+  int64_t nPts;
+  int64_t nPtsReceive;
+  std::vector<struct interp_coef_t> interpCoefs;
+  std::vector<precision_t *> valueToSend;
+  std::vector<precision_t *> valueToReceive;
+};
 
 class Grid {
  public:
@@ -552,12 +553,12 @@ class Grid {
    * \pre Lons, Lats and Alts have the same size
    * \return list of interpolation coefficients
    */
-  
+
   std::vector<struct interp_coef_t> get_interpolation_coefs(
-                                    const std::vector<precision_t> &Lons,
-                                    const std::vector<precision_t> &Lats,
-                                    const std::vector<precision_t> &Alts);
-  
+    const std::vector<precision_t> &Lons,
+    const std::vector<precision_t> &Lats,
+    const std::vector<precision_t> &Alts);
+
   /**
    * \brief Set the interpolation coefficients for the dipole grid
    * \param Lons The longitude of points
@@ -666,18 +667,18 @@ class Grid {
   // Each point is processed by the function set_interpolation_coefs and stored
   // in the form of this structure.
   // If the point is out of the grid, in_grid = false and all other members are undefined
-  struct interp_coef_t {
-    // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
-    uint64_t iRow;
-    uint64_t iCol;
-    uint64_t iAlt;
-    // The coefficients along row, column and altitude
-    precision_t rRow;
-    precision_t rCol;
-    precision_t rAlt;
-    // Whether the point is within this grid or not
-    bool in_grid;
-  };
+  //struct interp_coef_t {
+  //  // The point is inside the cube of [iRow, iRow+1], [iCol, iCol+1], [iAlt, iAlt+1]
+  //  uint64_t iRow;
+  //  uint64_t iCol;
+  //  uint64_t iAlt;
+  //  // The coefficients along row, column and altitude
+  //  precision_t rRow;
+  //  precision_t rCol;
+  //  precision_t rAlt;
+  //  // Whether the point is within this grid or not
+  //  bool in_grid;
+  //};
 
   // Calculate the range of a spherical grid
   void get_sphere_grid_range(struct sphere_range &sr) const;
@@ -688,18 +689,18 @@ class Grid {
 
   // Helper function for set_interpolation_coefs
   struct interp_coef_t get_interp_coef_sphere(const sphere_range &sr,
-                              const precision_t lon_in,
-                              const precision_t lat_in,
-                              const precision_t alt_in);
+                                              const precision_t lon_in,
+                                              const precision_t lat_in,
+                                              const precision_t alt_in);
   struct interp_coef_t get_interp_coef_cubesphere(const cubesphere_range &cr,
-                                  const precision_t lon_in,
-                                  const precision_t lat_in,
-                                  const precision_t alt_in);
+                                                  const precision_t lon_in,
+                                                  const precision_t lat_in,
+                                                  const precision_t alt_in);
   // (note these are magnetic coordinates)
-  void set_interp_coef_dipole(const dipole_range &dr,
-                              const precision_t lon_in,
-                              const precision_t lat_in,
-                              const precision_t alt_in);
+  struct interp_coef_t get_interp_coef_dipole(const dipole_range &dr,
+					      const precision_t lon_in,
+					      const precision_t lat_in,
+					      const precision_t alt_in);
 
   // Processed interpolation coefficients
   std::vector<struct interp_coef_t> interp_coefs;
diff --git a/src/grid_match.cpp b/src/grid_match.cpp
index 908baf8b..28d1c2b1 100644
--- a/src/grid_match.cpp
+++ b/src/grid_match.cpp
@@ -20,9 +20,8 @@ bool exchange_information(int64_t *nPointsToPass,
   //   - if it is a different processor, send the data
   for (jNode = 0; jNode < nGrids ; jNode++) {
     if (jNode == iGrid) {
-      for (iPt = 0; iPt < nPointsToPass[jNode]; iPt ++) {
+      for (iPt = 0; iPt < nPointsToPass[jNode]; iPt ++)
         varToReceive[jNode][iPt] = varToSend[jNode][iPt];
-      }
     } else {
       iProcTo = iMember * nGrids + jNode;
       // iTag is a unique id allowing all processors to
@@ -74,8 +73,8 @@ bool exchange_information(int64_t *nPointsToPass,
 //     - builds interpolators for the requested information
 // -----------------------------------------------------------------------------
 
-bool grid_match(Grid &gGrid, 
-                Grid &mGrid, 
+bool grid_match(Grid &gGrid,
+                Grid &mGrid,
                 Quadtree gQuadtree,
                 Quadtree mQuadtree) {
 
@@ -93,8 +92,10 @@ bool grid_match(Grid &gGrid,
   precision_t normX, normY, normZ;
   arma_vec norms(3);
   int64_t jNode, kNode;
-  int64_t *nPointsToPass = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
-  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+  int64_t *nPointsToPass = static_cast<int64_t*>(malloc(nGrids * sizeof(
+                                                          int64_t)));
+  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(
+                                                             int64_t)));
   int64_t *nPointsDummy = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
 
   for (jNode = 0; jNode < nGrids ; jNode++)
@@ -103,6 +104,7 @@ bool grid_match(Grid &gGrid,
   // This is not the most efficient way to do this, but the first pass, let's
   // just count how many points we need to send to the other processors:
   mGrid.gridToGridMap.set_size(mnX, mnY, mnZ);
+
   for (iX = mGCs; iX < mnX - mGCs; iX++) {
     for (iY = mGCs; iY < mnY - mGCs; iY++) {
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
@@ -118,11 +120,12 @@ bool grid_match(Grid &gGrid,
           norms = sphere_to_cube(lon, lat);
           jNode = gQuadtree.find_point(norms);
         }
-        if (jNode < 0 || jNode >= nGrids) {
-            std::cout << "out of bounds!!! " << jNode << "\n";
-        }
+
+        if (jNode < 0 || jNode >= nGrids)
+          std::cout << "out of bounds!!! " << jNode << "\n";
+
         mGrid.gridToGridMap(iX, iY, iZ) = jNode;
-        nPointsToPass[jNode] = nPointsToPass[jNode]+1;
+        nPointsToPass[jNode] = nPointsToPass[jNode] + 1;
         /* std::cout << "lon, lat, node: " << lon*cRtoD << " "
             << lat*cRtoD << " "
             << norms(0) << " "
@@ -134,11 +137,13 @@ bool grid_match(Grid &gGrid,
       }
     }
   }
+
   MPI_Barrier(aether_comm);
 
   if (report.test_verbose(3)) {
     for (jNode = 0; jNode < nGrids ; jNode++)
       std::cout << "nPtsToPass : " << iProc << " " << nPointsToPass[jNode] << "\n";
+
     std::cout << "sending number of points :\n";
   }
 
@@ -150,14 +155,15 @@ bool grid_match(Grid &gGrid,
       for (kNode = 0; kNode < nGrids ; kNode++)
         nPointsDummy[kNode] = nPointsToPass[kNode];
     }
+
     MPI_Bcast(nPointsDummy, nGrids, MPI_INT64_T, jNode, aether_comm);
     nPointsToReceive[jNode] = nPointsDummy[iGrid];
   }
 
   if (report.test_verbose(3)) {
-    for (jNode = 0; jNode < nGrids ; jNode++) {
-      std::cout << "nPtsToReceive : " << iProc << " " << jNode << " " << nPointsToReceive[jNode] << "\n";
-    }
+    for (jNode = 0; jNode < nGrids ; jNode++)
+      std::cout << "nPtsToReceive : " << iProc << " " << jNode << " " <<
+                nPointsToReceive[jNode] << "\n";
   }
 
   //  Now we need to create an array of send points and an array of receive points.
@@ -167,24 +173,33 @@ bool grid_match(Grid &gGrid,
   std::vector<precision_t *> latsToInterTo(nGrids);
   std::vector<precision_t *> lonsToInterTo(nGrids);
   std::vector<precision_t *> altsToInterTo(nGrids);
+
   for (jNode = 0; jNode < nGrids ; jNode++) {
-    latsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-    lonsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-    altsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] * sizeof(precision_t)));
-    latsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
-    lonsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
-    altsToInterTo[jNode] = static_cast<precision_t*>(malloc(nPointsToReceive[jNode] * sizeof(precision_t)));
+    latsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] *
+                                                         sizeof(precision_t)));
+    lonsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] *
+                                                         sizeof(precision_t)));
+    altsToPass[jNode] = static_cast<precision_t*>(malloc(nPointsToPass[jNode] *
+                                                         sizeof(precision_t)));
+    latsToInterTo[jNode] = static_cast<precision_t*>(malloc(
+                                                       nPointsToReceive[jNode] * sizeof(precision_t)));
+    lonsToInterTo[jNode] = static_cast<precision_t*>(malloc(
+                                                       nPointsToReceive[jNode] * sizeof(precision_t)));
+    altsToInterTo[jNode] = static_cast<precision_t*>(malloc(
+                                                       nPointsToReceive[jNode] * sizeof(precision_t)));
   }
 
   // now, the second pass, let's store the information so we can pass it:
   for (jNode = 0; jNode < nGrids ; jNode++)
     nPointsToPass[jNode] = 0;
+
   for (iX = mGCs; iX < mnX - mGCs; iX++) {
     for (iY = mGCs; iY < mnY - mGCs; iY++) {
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
         lon = mGrid.geoLon_scgc(iX, iY, iZ);
         lat = mGrid.geoLat_scgc(iX, iY, iZ);
-        if (gGrid.iGridShape_ == gGrid.iSphere_) {
+
+        if (gGrid.iGridShape_ == iSphere_) {
           norms(0) = lon / cPI;
           norms(1) = lat / cPI;
           norms(2) = 0.0;
@@ -193,13 +208,15 @@ bool grid_match(Grid &gGrid,
           norms = sphere_to_cube(lon, lat);
           jNode = gQuadtree.find_point(norms);
         }
+
         latsToPass[jNode][nPointsToPass[jNode]] = lat;
         lonsToPass[jNode][nPointsToPass[jNode]] = lon;
         altsToPass[jNode][nPointsToPass[jNode]] = mGrid.geoAlt_scgc(iX, iY, iZ);
-        nPointsToPass[jNode] = nPointsToPass[jNode]+1;
+        nPointsToPass[jNode] = nPointsToPass[jNode] + 1;
       }
     }
   }
+
   bool didWork;
   // Pass first coordinate (lons)
   didWork = exchange_information(nPointsToPass,
@@ -221,38 +238,45 @@ bool grid_match(Grid &gGrid,
     for (jNode = 0; jNode < nGrids ; jNode++) {
       std::cout << "Received the following points from iGrid = " << jNode << "\n";
       std::cout << " -> points received : " << nPointsToReceive[jNode] << "\n";
-      for (int64_t iPt = 0; iPt < nPointsToReceive[jNode]; iPt++) 
+
+      for (int64_t iPt = 0; iPt < nPointsToReceive[jNode]; iPt++)
         std::cout << "  -> " << iPt << " "
-                    << lonsToInterTo[jNode][iPt] << " "
-                    << latsToInterTo[jNode][iPt] << " "
-                    << altsToInterTo[jNode][iPt] << "\n";
+                  << lonsToInterTo[jNode][iPt] << " "
+                  << latsToInterTo[jNode][iPt] << " "
+                  << altsToInterTo[jNode][iPt] << "\n";
     }
   }
 
   struct grid_to_grid_t oneGrid;
 
   int64_t nPts;
+
   for (jNode = 0; jNode < nGrids ; jNode++) {
     // These are backwards now, since we will switch sender and reciever:
     oneGrid.nPts = nPointsToReceive[jNode];
     oneGrid.nPtsReceive = nPointsToPass[jNode];
     oneGrid.iProcTo = iMember * nGrids + jNode;
+
     if (report.test_verbose(2))
-      std::cout << "Making interpolation coefficients for : " << jNode 
-        << "; points : " << oneGrid.nPts << "\n";
+      std::cout << "Making interpolation coefficients for : " << jNode
+                << "; points : " << oneGrid.nPts << "\n";
+
     if (oneGrid.nPts > 0) {
-      // Interpolation function takes vectors, 
+      // Interpolation function takes vectors,
       // so transfer these arrays to vectors:
       std::vector<precision_t> Lons(oneGrid.nPts);
       std::vector<precision_t> Lats(oneGrid.nPts);
       std::vector<precision_t> Alts(oneGrid.nPts);
+
       for (int64_t iPt = 0; iPt < oneGrid.nPts; iPt++) {
         Lons[iPt] = lonsToInterTo[jNode][iPt];
         Lats[iPt] = latsToInterTo[jNode][iPt];
         Alts[iPt] = altsToInterTo[jNode][iPt];
       }
+
       oneGrid.interpCoefs = gGrid.get_interpolation_coefs(Lons, Lats, Alts);
     }
+
     gGrid.gridToGridCoefs.push_back(oneGrid);
   }
 
@@ -260,7 +284,7 @@ bool grid_match(Grid &gGrid,
   return didWork;
 }
 
-bool get_data_from_other_grid(Grid &gGrid, 
+bool get_data_from_other_grid(Grid &gGrid,
                               Grid &mGrid,
                               arma_cube &gData,
                               arma_cube &mData) {
@@ -272,24 +296,33 @@ bool get_data_from_other_grid(Grid &gGrid,
   int64_t jNode, iPt;
   std::vector<precision_t *> dataToSend(nGrids);
   std::vector<precision_t *> dataToReceive(nGrids);
-  int64_t *nPointsToSend = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
-  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(int64_t)));
+  int64_t *nPointsToSend = static_cast<int64_t*>(malloc(nGrids * sizeof(
+                                                          int64_t)));
+  int64_t *nPointsToReceive = static_cast<int64_t*>(malloc(nGrids * sizeof(
+                                                             int64_t)));
 
   for (jNode = 0; jNode < nGrids ; jNode++) {
     if (report.test_verbose(2))
-      std::cout << "nPts : " << jNode << " " << gGrid.gridToGridCoefs[jNode].nPts << "\n";
+      std::cout << "nPts : " << jNode << " " << gGrid.gridToGridCoefs[jNode].nPts <<
+                "\n";
+
     nPointsToSend[jNode] = gGrid.gridToGridCoefs[jNode].nPts;
     nPointsToReceive[jNode] = gGrid.gridToGridCoefs[jNode].nPtsReceive;
-    dataToSend[jNode] = static_cast<precision_t*>(malloc(gGrid.gridToGridCoefs[jNode].nPts * sizeof(precision_t)));
-    dataToReceive[jNode] = static_cast<precision_t*>(malloc(gGrid.gridToGridCoefs[jNode].nPtsReceive * sizeof(precision_t)));
-    std::vector<precision_t> values = gGrid.get_interpolation_values(gData, gGrid.gridToGridCoefs[jNode].interpCoefs);
-    
+    dataToSend[jNode] = static_cast<precision_t*>(malloc(
+                                                    gGrid.gridToGridCoefs[jNode].nPts * sizeof(precision_t)));
+    dataToReceive[jNode] = static_cast<precision_t*>(malloc(
+                                                       gGrid.gridToGridCoefs[jNode].nPtsReceive * sizeof(precision_t)));
+    std::vector<precision_t> values = gGrid.get_interpolation_values(gData,
+                                      gGrid.gridToGridCoefs[jNode].interpCoefs);
+
     for (iPt = 0; iPt < gGrid.gridToGridCoefs[jNode].nPts; iPt++) {
       dataToSend[jNode][iPt] = values[iPt];
+
       if (report.test_verbose(2))
         std::cout << "datatosend : " << iPt << " " << dataToSend[jNode][iPt] << "\n";
     }
   }
+
   bool didWork = exchange_information(nPointsToSend,
                                       dataToSend,
                                       nPointsToReceive,
@@ -299,6 +332,7 @@ bool get_data_from_other_grid(Grid &gGrid,
   int64_t iZ, mnZ = mGrid.get_nZ();
   int64_t mGCs = mGrid.get_nGCs();
   std::vector<int64_t> iCounter(nGrids);
+
   for (jNode = 0; jNode < nGrids ; jNode++)
     iCounter[jNode] = 0;
 
@@ -306,13 +340,15 @@ bool get_data_from_other_grid(Grid &gGrid,
     for (iY = mGCs; iY < mnY - mGCs; iY++) {
       for (iZ = mGCs; iZ < mnZ - mGCs; iZ++) {
         jNode = mGrid.gridToGridMap(iX, iY, iZ);
+
         if (report.test_verbose(2)) {
-          std::cout << "unpacking point : " << iX << " " << iY << " " << iZ << " " << jNode << " " 
-          << iCounter[jNode] << " " << dataToReceive[jNode][iCounter[jNode]] << "\n";
+          std::cout << "unpacking point : " << iX << " " << iY << " " << iZ << " " <<
+                    jNode << " "
+                    << iCounter[jNode] << " " << dataToReceive[jNode][iCounter[jNode]] << "\n";
         }
 
         mData(iX, iY, iZ) = dataToReceive[jNode][iCounter[jNode]];
-        iCounter[jNode] = iCounter[jNode]+1;
+        iCounter[jNode] = iCounter[jNode] + 1;
       }
     }
   }
diff --git a/src/solver_grid_interpolation.cpp b/src/solver_grid_interpolation.cpp
index 0ec38266..e0cba3ca 100644
--- a/src/solver_grid_interpolation.cpp
+++ b/src/solver_grid_interpolation.cpp
@@ -3,7 +3,9 @@
 
 #include "aether.h"
 
-// Hepler varialbes / function begins. These are only used inside this cpp file and neither declared nor visible in any other file
+// Hepler variables / function begins.
+// These are only used inside this cpp file and neither declared
+// nor visible in any other file
 
 // The size of a 2*2*2 arma cube
 const arma::SizeCube unit_cube_size = arma::size(2, 2, 2);
@@ -69,17 +71,48 @@ int64_t get_cube_surface_number(const arma_vec &point_in) {
   }
 }
 
-// Helper variables / function ends. The following are all member functions of Grid class
+// Helper variables / function ends. The following are all member
+// functions of Grid class
 
 // --------------------------------------------------------------------------
-// Return the index of the last element that has a value smaller than or equal to the input
+// Return the index of the last element that has altitude smaller than
+// or equal to the input
+// --------------------------------------------------------------------------
+
+uint64_t Grid::search_altitude(const precision_t alt_in) const {
+  // Copy from std::upper_bound. Can't directly use it
+  // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
+  uint64_t first, last, len;
+  first = nGCs;
+  last = nAlts - nGCs;
+  len = last - first;
+
+  while (len > 0) {
+    uint64_t half = len >> 1;
+    uint64_t mid = first + half;
+
+    if (geoAlt_scgc(0, 0, mid) > alt_in)
+      len = half;
+
+    else {
+      first = mid + 1;
+      len = len - half - 1;
+    }
+  }
+
+  return first - 1;
+}
+
+// --------------------------------------------------------------------------
+// Return the index of the last element that has a value smaller than
+//   or equal to the input
 // - Optional argument (nGCs=0) since we cannot see grid info.
 // --------------------------------------------------------------------------
 
+// this replaces the above
+
 uint64_t bisect_search_array(precision_t val_in, arma_vec ref_arr,
                              int64_t nGCs = 0) {
-  // Copy from std::upper_bound. Can't directly use it
-  // mainly because geoAlt_scgc(0, 0, *) can't be formed as an iterator
   uint64_t first, last, len;
   first = nGCs;
   last = ref_arr.size();
@@ -181,6 +214,7 @@ void Grid::get_cubesphere_grid_range(struct cubesphere_range &cr) const {
   }
 }
 
+
 // --------------------------------------------------------------------------
 // Get the range of a Dipole grid
 // --------------------------------------------------------------------------
@@ -208,24 +242,27 @@ void Grid::get_dipole_grid_range(struct dipole_range &dr) const {
 // Almost the copy of interp_sphere_linear_helper
 // --------------------------------------------------------------------------
 
-struct interp_coef_t Grid::get_interp_coef_sphere(
-                                  const sphere_range &sr,
-                                  const precision_t lon_in,
-                                  const precision_t lat_in,
-                                  const precision_t alt_in) {
+
+struct interp_coef_t Grid::get_interp_coef_sphere(const sphere_range &sr,
+						  const precision_t lon_in,
+						  const precision_t lat_in,
+						  const precision_t alt_in) {
+
   // WARNING: IF WE ARE DEALING WITH LESS THAN THE WHOLE EARTH, THEN ALL THE POINTS WITH
   // LONGITUDE = geo_grid_input.lon_max = settings["GeoGrid"]["MaxLon"]
   // OR LATITUDE = geo_grid_input.lat_max = settings["GeoGrid"]["MaxLat"]
   // ARE EXCLUDED.
   // TO FIX IT, EACH GRID SHOULD BE ABLE TO ACCESS THE MaxLon and MaxLat
 
-  // The structure which will be put into the interp_coefs. Initialize in_grid to be false
+  // The structure which will be put into the interp_coefs.
+  // Initialize in_grid to be false
   struct interp_coef_t coef;
   coef.in_grid = false;
 
   // Determine whether the point is inside this grid
-  // Treat north pole specially because latitude is inclusive for both -cPI/2 and cPI/2
-  // Dpn't check for altitude here!
+  // Treat north pole specially because latitude is inclusive for
+  //   both -cPI/2 and cPI/2
+  // Don't check for altitude here!
   if (lon_in < sr.lon_min || lon_in >= sr.lon_max || lat_in < sr.lat_min
       || lat_in > sr.lat_max || (lat_in == sr.lat_max && sr.lat_max != cPI / 2)) {
     return coef;
@@ -268,9 +305,13 @@ struct interp_coef_t Grid::get_interp_coef_sphere(
       coef.below_grid = false;
       coef.above_grid = true;
     } else {
-      coef.iAlt = search_altitude(alt_in);
-      coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-                  / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+      coef.iAlt = bisect_search_array(alt_in,
+				      geoAlt_scgc.tube(coef.iRow, coef.iCol),
+				      nGCs);
+      coef.rAlt =
+	(alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+	/ (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) -
+	   geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
       coef.below_grid = false;
       coef.above_grid = false;
     }
@@ -343,36 +384,63 @@ struct interp_coef_t Grid::get_interp_coef_cubesphere(const cubesphere_range &cr
   coef.iCol = static_cast<uint64_t>(col_frac_index);
   coef.rCol = col_frac_index - coef.iCol;
   coef.iCol += nGCs - 1;
-  // Use binary search to find the index for altitude (handles oblate planets)
-  coef.iAlt = bisect_search_array(alt_in, geoAlt_scgc.tube(coef.iRow, coef.iCol),
-                                  nGCs);
-  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
-              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) - geoAlt_scgc(coef.iRow,
-                  coef.iCol, coef.iAlt));
 
-  // Put the coefficient into the vector
-  coef.in_grid = true;
-  interp_coefs.push_back(coef);
+
+  // The altitude may not be linearly spaced, so use binary search to find
+  // the first element smaller than or equal to the altitude of the give point
+  // Implemented in search_altitude
+
+  if (alt_in < cr.alt_min) {
+    coef.iAlt = nGCs;
+    coef.rAlt = alt_in - cr.alt_min;
+    coef.below_grid = true;
+    coef.above_grid = false;
+  } else {
+    if (alt_in > cr.alt_max) {
+      coef.iAlt = nAlts - nGCs;
+      coef.rAlt = alt_in - cr.alt_max;
+      coef.below_grid = false;
+      coef.above_grid = true;
+    } else {
+      coef.iAlt = bisect_search_array(alt_in,
+				      geoAlt_scgc.tube(coef.iRow, coef.iCol),
+				      nGCs);
+      coef.rAlt =
+	(alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+	/ (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) -
+	   geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
+      coef.below_grid = false;
+      coef.above_grid = false;
+    }
+  }
+  return coef;
 }
 
-void Grid::set_interp_coef_dipole(const dipole_range &dr,
-                                  const precision_t lon_in,
-                                  const precision_t lat_in,
-                                  const precision_t alt_in) {
 
-  // The structure which will be put into the interp_coefs. Initialize in_grid to be false
+struct interp_coef_t Grid::get_interp_coef_dipole(const dipole_range &dr,
+						  const precision_t lon_in,
+						  const precision_t lat_in,
+						  const precision_t alt_in) {
+
+  // The structure which will be put into the interp_coefs. Initialize
+  // in_grid to be false
   struct interp_coef_t coef;
   coef.in_grid = false;
 
-  // Determine whether the point is inside this grid
-  // Treat north pole specially because latitude is inclusive for both -cPI/2 and cPI/2
-  if (lon_in < dr.lon_min || lon_in >= dr.lon_max || lat_in < dr.lat_min
-      || lat_in > dr.lat_max || (lat_in == dr.lat_max && dr.lat_max != cPI / 2)
+  // Determine whether the point is inside this grid Treat north pole
+  // specially because latitude is inclusive for both -cPI/2 and cPI/2
+  if (lon_in < dr.lon_min ||
+      lon_in >= dr.lon_max ||
+      lat_in < dr.lat_min  ||
+      lat_in > dr.lat_max ||
+      (lat_in == dr.lat_max && dr.lat_max != cPI / 2)
       || alt_in < dr.alt_min || alt_in > dr.alt_max) {
-    interp_coefs.push_back(coef);
-    return;
+    return coef;
   }
 
+  // Put the coefficient into the vector
+  coef.in_grid = true;
+  
   // ASSUMPTION: LONGITUDE IS LINEARLY SPACED, nGCs >= 1
   // For the cell containing it, directly calculate its x index
   // Find y & z indices using a bisecting search
@@ -383,10 +451,10 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
   coef.iRow = static_cast<uint64_t>(coef.rRow);
   // Calculate the fractional part, which is the ratio for Longitude
   coef.rRow -= coef.iRow;
-  // The actual x-axis index of the bottom-left of the cube used for interpolation
+  // The actual x-axis index of the bottom-left of the cube used for
+  // interpolation
   coef.iRow += nGCs - 1;
 
-
   // Different from the sphere, latitude & altitude are not evenly spaced.
   // Use the bisect search function for both.
 
@@ -395,42 +463,44 @@ void Grid::set_interp_coef_dipole(const dipole_range &dr,
   coef.iCol = bisect_search_array(abs(lat_in),
                                   abs(j_center_scgc.tube(coef.iRow, coef.iCol)), nGCs);
 
-  // need alt index to find lat coef
-  coef.iAlt = bisect_search_array(alt_in, k_center_scgc.tube(coef.iRow,
-                                                             coef.iCol),
-                                  nGCs);
-
-  // then we can do the ratios:
-  coef.rCol = (lat_in - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt))
-              / (magLat_scgc(coef.iRow, coef.iCol + 1, coef.iAlt)
-                 - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt));
-
-  coef.rAlt = (alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
-              / (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1)
-                 - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
-
-  if (alt_in < cr.alt_min) {
+  // Use binary search to find the index for altitude
+  if (alt_in < dr.alt_min) {
     coef.iAlt = nGCs;
     coef.rAlt = 0.0;
     coef.below_grid = true;
     coef.above_grid = false;
   } else {
-    if (alt_in > cr.alt_max) {
+    if (alt_in > dr.alt_max) {
       coef.iAlt = nAlts - nGCs;
       coef.rAlt = 0.0;
       coef.below_grid = false;
       coef.above_grid = true;
     } else {
-      coef.iAlt = search_altitude(alt_in);
-      coef.rAlt = (alt_in - geoAlt_scgc(0, 0, coef.iAlt))
-                  / (geoAlt_scgc(0, 0, coef.iAlt + 1) - geoAlt_scgc(0, 0, coef.iAlt));
+      // Use binary search to find the index for altitude (handles
+      // oblate planets)
+
+      // need alt index to find lat coef
+      coef.iAlt = bisect_search_array(alt_in,
+				      k_center_scgc.tube(coef.iRow, coef.iCol),
+				      nGCs);
+      // then we can do the ratios:
+      coef.rCol =
+	(lat_in - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt))
+	/ (magLat_scgc(coef.iRow, coef.iCol + 1, coef.iAlt)
+	   - magLat_scgc(coef.iRow, coef.iCol, coef.iAlt));
+      coef.rAlt =
+	(alt_in - geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt))
+	/ (geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt + 1) -
+	   geoAlt_scgc(coef.iRow, coef.iCol, coef.iAlt));
       coef.below_grid = false;
       coef.above_grid = false;
     }
   }
+
   return coef;
 }
 
+
 // --------------------------------------------------------------------------
 // Set the interpolation coefficients
 // --------------------------------------------------------------------------
@@ -458,9 +528,12 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
   report.enter(function, iFunction);
 
   report.print(1, "interpolation gridtype : " + gridType);
+  
+  struct interp_coef_t coef;
 
   // If the size of Lons, Lats and Alts are not the same, return false
-  if (i_coords.size() != j_coords.size() || j_coords.size() != k_coords.size()) {
+  if (i_coords.size() != j_coords.size() ||
+      j_coords.size() != k_coords.size()) {
     report.error("Length of i,j,k vectors do not match!");
     return false;
   }
@@ -468,16 +541,22 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
   // Clear the previous interpolation coefficients
   interp_coefs.clear();
 
-  if (iGridShape_ == iCubesphere_) {
-    report.print(1, "interpolation grid is cubesphere");
-
+  // ---------------------------------------------------
+  // Cubesphere
+  if (IsCubeSphereGrid) {
     // Calculate the range of the grid
     struct cubesphere_range cr;
     get_cubesphere_grid_range(cr);
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < i_coords.size(); ++i)
-      set_interp_coef_cubesphere(cr, i_coords[i], j_coords[i], k_coords[i]);
+    for (size_t i = 0; i < i_coords.size(); ++i) {
+      coef = get_interp_coef_cubesphere(cr,
+					i_coords[i],
+					j_coords[i],
+					k_coords[i]);
+      interp_coefs.push_back(coef);
+    }
+
   }
 
   if (iGridShape_ == iSphere_) {
@@ -487,8 +566,13 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
     get_sphere_grid_range(sr);
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < i_coords.size(); ++i)
-      set_interp_coef_sphere(sr, i_coords[i], j_coords[i], k_coords[i]);
+    for (size_t i = 0; i < i_coords.size(); ++i) {
+      coef = get_interp_coef_sphere(sr,
+				    i_coords[i],
+				    j_coords[i],
+				    k_coords[i]);
+      interp_coefs.push_back(coef);
+    }
   }
 
   if (iGridShape_ == iDipole_) { // IsDipole
@@ -504,8 +588,9 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
     int64_t iLoc, nPts = i_coords.size();
     std::vector<precision_t> mlon(nPts), p_coord(nPts), q_coord(nPts), dipijk(3);
 
-    // these are the magnetic coordinates. A temporary step!
-    // this is a vector of cubes with shape (nPts, 1, 1) - avoids having to overload things
+    // these are the magnetic coordinates. A temporary step!  this is
+    // a vector of cubes with shape (nPts, 1, 1) - avoids having to
+    // overload things
     std::vector<arma_cube> magCoords;
 
     if (areLocsGeo) {
@@ -561,8 +646,10 @@ bool Grid::set_interpolation_coefs(const std::vector<precision_t> &i_coords,
     }
 
     // Calculate the index and coefficients for each point
-    for (size_t i = 0; i < i_coords.size(); ++i)
-      set_interp_coef_dipole(dr, mlon[i], p_coord[i], q_coord[i]);
+    for (size_t i = 0; i < i_coords.size(); ++i) {
+      coef = get_interp_coef_dipole(dr, mlon[i], p_coord[i], q_coord[i]);
+      interp_coefs.push_back(coef);
+    }
   }
 
   report.exit(function);