Envt fall2025

environment.yml

@@ -8,7 +8,7 @@ dependencies:
   - python=3.11.0
   - cartopy
   - numba
-  - scipy<1.16
+  - scipy
   - pyyaml
   - requests
   - scikit-learn
@@ -18,8 +18,8 @@ dependencies:
   - tqdm
   - wget
   - pip
-  - pytest
   - pip:
+    - pytest
     - pyhht
     - dill
     - '-e .'

pyleoclim/core/correns.py

@@ -202,7 +202,7 @@ def plot(self, figsize=[4, 4], title=None, ax=None, savefig_settings=None, hist_
 
         # put everything into a dataframe to be able to use seaborn
 
-        data = np.empty((len(self.r),3)); data[:] = np.NaN
+        data = np.empty((len(self.r),3)); data[:] = np.nan
         col  = [f'p < {self.alpha} (w/ FDR)',f'p < {self.alpha} (w/o FDR)', f'p ≥ {self.alpha}']
         data[self.signif_fdr,0] = r_signif_fdr        
         data[self.signif, 1] = r_signif

pyleoclim/utils/spectral.py

@@ -1025,10 +1025,10 @@ def cwt_psd(ys, ts, freq=None, freq_method='log', freq_kwargs=None,scale = None,
 def beta_estimation(psd, freq, fmin=None, fmax=None, logf_binning_step='max', verbose=False):
     ''' Estimate the scaling exponent of a power spectral density.
 
-    Models the spectrum as :math: `S(f) \propto 1/f^{\beta}`. For instance:
-    - :math: `\beta = 0` corresponds to white noise
-    - :math: `\beta = 1` corresponds to pink noise
-    - :math: `\beta = 2` corresponds to red noise (Brownian motion)
+    Models the spectrum as :math: `S(f) \\propto 1/f^{\\beta}`. For instance:
+    - :math: `\\beta = 0` corresponds to white noise
+    - :math: `\\beta = 1` corresponds to pink noise
+    - :math: `\\beta = 2` corresponds to red noise (Brownian motion)
 
 
     Parameters
@@ -1170,7 +1170,7 @@ def beta2Hurst(beta):
     Parameters
     ----------
     beta : float
-        the estimated slope of a power spectral density :math: `S(f) \propto 1/f^{\beta}`
+        the estimated slope of a power spectral density :math: `S(f) \\propto 1/f^{\\beta}`
 
     Returns
     -------

pyleoclim/utils/tsutils.py

@@ -1316,7 +1316,7 @@ def gaussianize(ys):
 
     # Create a blank copy of the array.
     yg = copy.deepcopy(ys)
-    yg[:] = np.NAN
+    yg[:] = np.nan
 
     nz = np.logical_not(np.isnan(ys))
     index = np.argsort(ys[nz])

pyproject.toml

@@ -25,17 +25,15 @@ dependencies = [
   "seaborn>=0.13.0",
   "scikit-learn>=0.24.2",
   "pathos>=0.2.8",
-  "tqdm>=4.61.2",
-  "tftb>=0.1.3",
+  "tqdm",
   "wget>=3.2",
   "numba>=0.56",
   "nitime>=0.9",
   "tabulate>=0.8.9",
-  "Unidecode>=1.1.1",
   "cartopy>=0.22.0",
   "pyyaml",
   "beautifulsoup4",
-  "scipy<1.16.0",
+  "scipy>=1.15.0",
   "requests",
   "dill",
 ]