adding logic for matplotlib versions

Author: kujaku11

mtpy/imaging/mtplot_tools/plot_settings.py

@@ -12,6 +12,7 @@
 
 import matplotlib.colors as colors
 import matplotlib.colorbar as mcb
+from matplotlib import __version__ as matplotlib_version
 
 from . import MTEllipse, MTArrows
 
@@ -350,7 +351,13 @@ def det_error_bar_properties(self):
 
     @property
     def font_dict(self):
-        return {"fontsize": self.font_size + 2, "fontweight": self.font_weight}
+        if int(matplotlib_version[2]) < 9:
+            return {"size": self.font_size + 2, "weight": self.font_weight}
+        else:
+            return {
+                "fontsize": self.font_size + 2,
+                "fontweight": self.font_weight,
+            }
 
     def make_pt_cb(self, ax):
         cmap = mtcl.cmapdict[self.ellipse_cmap]

mtpy/modeling/simpeg/recipes/inversion_1d.py

@@ -53,6 +53,7 @@ def __init__(self, mt_dataframe=None, **kwargs):
         self.rho_initial = 100
         self.rho_reference = 100
         self.output_dict = None
+        self.max_iterations = 40
 
         for key, value in kwargs.items():
             setattr(self, key, value)
@@ -192,12 +193,35 @@ def cull_from_difference(self, sub_df, max_diff_res=1.0, max_diff_phase=10):
             np.where(np.log10(abs(np.diff(sub_df.res))) > max_diff_res)[0] + 1
         ] = 0
 
-        self._sub_df = sub_df.loc[(sub_df != 0).all(axis=1)]
+        return sub_df.loc[(sub_df != 0).all(axis=1)]
 
-    def cull_from_interpolated(
-        self,
-    ):
-        pass
+    def cull_from_interpolated(self, sub_df, tolerance=0.1, s_factor=2):
+        """
+        create a cubic spline as a smooth version of the data and then
+        find points a certain distance away to remove.
+
+        :param : DESCRIPTION
+        :type : TYPE
+        :return: DESCRIPTION
+        :rtype: TYPE
+
+        """
+
+        from scipy import interpolate
+
+        spline_res = interpolate.splrep(
+            sub_df.period,
+            sub_df.resistivity,
+            s=s_factor * len(sub_df.period),
+        )
+
+        bad_res = np.where(
+            abs(
+                interpolate.splev(sub_df.period, spline_res)
+                - sub_df.resistivity
+            )
+            > tolerance
+        )
 
     def cull_from_model(self, iteration):
         """
@@ -244,7 +268,7 @@ def run_fixed_layer_inversion(
 
         # Cull the data
         if cull_from_difference:
-            self.cull_from_difference(self._sub_df)
+            self._sub_df = self.cull_from_difference(self._sub_df)
 
         source_list = []
         for freq in self.frequencies:
@@ -316,7 +340,7 @@ def run_fixed_layer_inversion(
             reg.norms = [p_s, p_z]
             # Reach target misfit for L2 solution, then use IRLS until model stops changing.
             IRLS = directives.Update_IRLS(
-                max_irls_iterations=40, minGNiter=1, f_min_change=1e-5
+                max_irls_iterations=maxIter, minGNiter=1, f_min_change=1e-5
             )
 
             # The directives are defined as a list.