Added reader for HST STIS data

chromatic/rainbows/actions/normalization.py

@@ -44,12 +44,20 @@ def normalize(self, axis="wavelength", percentile=50):
     # create an empty copy
     new = self._create_copy()
 
+    # shortcut for the first letter of the axis
+    a = axis.lower()[0]
+
     # (ignore nan warnings)
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
 
-        if axis.lower()[0] == "w":
-            normalization = np.nanpercentile(new.flux, percentile, axis=self.timeaxis)
+        # get fluxes, with not-OK replaced with nans
+        flux_for_normalizing = new.get_ok_data()
+        negative_normalization_message = ""
+        if a == "w":
+            normalization = np.nanpercentile(
+                flux_for_normalizing, percentile, axis=self.timeaxis
+            )
             for k in self._keys_that_respond_to_math:
                 new.fluxlike[k] = new.get(k) / normalization[:, np.newaxis]
             try:
@@ -58,8 +66,11 @@ def normalize(self, axis="wavelength", percentile=50):
                 )
             except ValueError:
                 pass
-        elif axis.lower()[0] == "t":
-            normalization = np.nanpercentile(self.flux, percentile, axis=self.waveaxis)
+
+        elif a == "t":
+            normalization = np.nanpercentile(
+                flux_for_normalizing, percentile, axis=self.waveaxis
+            )
             for k in self._keys_that_respond_to_math:
                 new.fluxlike[k] = new.get(k) / normalization[np.newaxis, :]
             try:
@@ -69,6 +80,33 @@ def normalize(self, axis="wavelength", percentile=50):
             except ValueError:
                 pass
 
+    if a in "wt":
+        thing = {"w": "wavelengths", "t": "times"}[a]
+        fix = {
+            "w": """
+                ok = rainbow.get_median_spectrum() > 0
+                rainbow[ok, :].normalize()
+        """,
+            "t": """
+                ok = rainbow.get_median_lightcurve() > 0
+                rainbow[:, ok].normalize()
+        """,
+        }[a]
+        if np.any(normalization < 0):
+            cheerfully_suggest(
+                f"""
+            There are {np.sum(normalization < 0)} negative {thing} that
+            are going into the normalization of this Rainbow. If you're
+            not expecting negative fluxes, it may be useful to trim them
+            away with something like:
+
+            {fix}
+
+            Otherwise, watch out that your fluxes and uncertainties may
+            potentially have flipped sign!
+            """
+            )
+
     # append the history entry to the new Rainbow
     new._record_history_entry(h)
 

chromatic/rainbows/get/__init__.py

@@ -1,2 +1,3 @@
 from .timelike import *
 from .wavelike import *
+from .fluxlike import *

chromatic/rainbows/get/fluxlike/__init__.py

@@ -0,0 +1 @@
+from .subset import *

chromatic/rainbows/get/fluxlike/subset.py

@@ -0,0 +1,41 @@
+from ....imports import *
+
+__all__ = [
+    "get_ok_data",
+]
+
+
+def get_ok_data(
+    self,
+    y="flux",
+    minimum_acceptable_ok=1,
+):
+    """
+    A small wrapper to get the good data as a 2D array.
+
+    Extract fluxes as 2D array, marking data that are not `ok`
+    either as nan or by inflating uncertainties to infinity.
+
+    Parameters
+    ----------
+    y : array
+        The desired quantity (default is `flux`)
+    minimum_acceptable_ok : float, optional
+        The smallest value of `ok` that will still be included.
+        (1 for perfect data, 1e-10 for everything but terrible data, 0 for all data)
+
+    Returns
+    -------
+    flux : array
+        The fluxes, but with not-OK data replaced with nan.
+    """
+    # get 1D array of what to keep
+    ok = self.ok >= minimum_acceptable_ok
+
+    # create copy of flux array
+    y = self.get(y) * 1
+
+    # set bad values to nan
+    y[ok == False] = np.nan
+
+    return y

chromatic/rainbows/get/timelike/median_lightcurve.py

@@ -14,4 +14,4 @@ def get_median_lightcurve(self):
     """
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        return np.nanmedian(self.flux, axis=0)
+        return np.nanmedian(self.get_ok_data(), axis=0)

chromatic/rainbows/get/wavelike/median_spectrum.py

@@ -14,4 +14,4 @@ def get_median_spectrum(self):
     """
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        return np.nanmedian(self.flux, axis=1)
+        return np.nanmedian(self.get_ok_data(), axis=1)

chromatic/rainbows/rainbow.py

@@ -817,8 +817,9 @@ def _validate_core_dictionaries(self):
 
         # make sure 2D arrays are uniquely named from 1D
         for k in tuple(self.fluxlike.keys()):
-            if (k in self.wavelike) or (k in self.timelike):
-                self.fluxlike[f"{k}_2d"] = self.fluxlike.pop(k)
+            if k is not "ok":
+                if (k in self.wavelike) or (k in self.timelike):
+                    self.fluxlike[f"{k}_2d"] = self.fluxlike.pop(k)
 
         if "ok" in self.fluxlike:
             is_nan = np.isnan(self.fluxlike["flux"])
@@ -1005,7 +1006,7 @@ def __repr__(self):
         get_ok_data_for_wavelength,
     )
 
-    # import summary statistics for each wavelength
+    # import summary statistics for each time
     from .get.timelike import (
         get_average_lightcurve,
         get_median_lightcurve,
@@ -1015,6 +1016,11 @@ def __repr__(self):
         set_times_from_astropy,
     )
 
+    # import 2D summary statistics
+    from .get.fluxlike import (
+        get_ok_data,
+    )
+
     # import visualizations that can act on Rainbows
     from .visualizations import (
         imshow,

chromatic/rainbows/readers/__init__.py

@@ -16,6 +16,7 @@
 # some particular instruments
 from .nres import *
 from .atoca import *
+from .hst import *
 
 # some individual-ish folks
 from .espinoza import *