matplotlib 3.8: fix get_cmap deprecation

Author: emanuel-schmid

climada/engine/forecast.py

@@ -31,6 +31,7 @@
 from matplotlib.ticker import PercentFormatter, ScalarFormatter
 from matplotlib.colors import ListedColormap, BoundaryNorm
 import cartopy.crs as ccrs
+from matplotlib import colormaps as cm
 import pyproj
 import shapely
 from cartopy.io import shapereader
@@ -88,7 +89,7 @@
 warnprob_colors_extended = np.repeat(warnprob_colors, 10, axis=0)
 CMAP_WARNPROB = ListedColormap(warnprob_colors_extended)
 # colors for impact forecast
-color_map_pre = plt.get_cmap("plasma", 90)
+color_map_pre = cm.get_cmap("plasma").resampled(90)
 impact_colors = color_map_pre(np.linspace(0, 1, 90))
 white_extended = np.repeat([[255 / 255, 255 / 255, 255 / 255, 1]], 10, axis=0)
 impact_colors_extended = np.append(white_extended, impact_colors, axis=0)

climada/engine/unsequa/unc_output.py

@@ -33,6 +33,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib as mpl
+from matplotlib import colormaps as cm
 
 from climada import CONFIG
 
@@ -1034,7 +1035,7 @@ def plot_sensitivity_map(self, salib_si='S1', **kwargs):
             if len(n) > 0 :
                 n = n[0]
                 cmap = mpl.colors.ListedColormap(
-                    plt.get_cmap(MAP_CMAP).colors[:len(labels)]
+                    cm.get_cmap(MAP_CMAP).colors[:len(labels)]
                     )
                 colors = list(cmap.colors)
                 colors[n] = tuple(np.repeat(0.93, 3))

climada/entity/measures/measure_set.py

@@ -26,7 +26,7 @@
 import logging
 from typing import Optional, List
 
-import matplotlib.pyplot as plt
+from  matplotlib import colormaps as cm
 import numpy as np
 import pandas as pd
 import xlsxwriter
@@ -310,7 +310,7 @@ def check(self):
         ValueError
         """
         for key_haz, meas_dict in self._data.items():
-            def_color = plt.cm.get_cmap('Greys', len(meas_dict))
+            def_color = cm.get_cmap('Greys').resampled(len(meas_dict))
             for i_meas, (name, meas) in enumerate(meas_dict.items()):
                 if (name != meas.name) | (name == ''):
                     raise ValueError("Wrong Measure.name: %s != %s."

climada/util/plot.py

@@ -36,6 +36,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib as mpl
+from matplotlib import colormaps as cm
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from shapely.geometry import box
 import cartopy.crs as ccrs
@@ -440,10 +441,10 @@ def geo_scatter_categorical(array_sub, geo_coord, var_name, title,
             if cmap_name in ['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',
                     'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c']:
                 cmap = mpl.colors.ListedColormap(
-                    mpl.cm.get_cmap(cmap_name).colors[:array_sub_n]
+                    cm.get_cmap(cmap_name).colors[:array_sub_n]
                 )
             else:
-                cmap = mpl.cm.get_cmap(cmap_arg, array_sub_n)
+                cmap = cm.get_cmap(cmap_arg).resampled(array_sub_n)
         elif isinstance(cmap_arg, mpl.colors.ListedColormap):
             # If a user brings their own colormap it's probably qualitative
             cmap_name = 'defined by the user'
@@ -456,7 +457,7 @@ def geo_scatter_categorical(array_sub, geo_coord, var_name, title,
         # default qualitative colormap
         cmap_name = CMAP_CAT
         cmap = mpl.colors.ListedColormap(
-            mpl.cm.get_cmap(cmap_name).colors[:array_sub_n]
+            cm.get_cmap(cmap_name).colors[:array_sub_n]
         )
 
     if array_sub_n > cmap.N:

climada/util/test/test_plot.py

@@ -23,6 +23,7 @@
 import cartopy
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib import colormaps as cm
 import cartopy.crs as ccrs
 
 import climada.util.plot as u_plot
@@ -67,7 +68,7 @@ def test_geo_scatter_categorical(self):
                                   1: 'int',
                                   2.0: 'float',
                                   'a': 'string'},
-                        pop_name=False, cmap=plt.get_cmap('Set1'))
+                        pop_name=False, cmap=cm.get_cmap('Set1'))
         plt.close()
 
         #test colormap warning

script/applications/eca_san_salvador/functions_ss.py

@@ -22,6 +22,7 @@
 import contextily as ctx
 import geopandas as gpd
 import matplotlib.patches as patches
+from matplotlib import colormaps as cm
 from shapely import wkt
 
 def plot_salvador_ma():
@@ -110,11 +111,11 @@ def plot_exposure_ss(exposures, point=None):
         ax.set_ylim(1536680.51725147, 1539512.429812354)
     else:
         # create new map for viviendas
-        cmap_viv = plt.cm.get_cmap('autumn', 4)
+        cmap_viv = cm.get_cmap('autumn').resampled(4)
         cmap_viv = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap',
             [cmap_viv(i) for i in range(cmap_viv.N)], cmap_viv.N)
         # create new map for aups
-        cmap_aup = plt.cm.get_cmap('winter', 4)
+        cmap_aup = cm.get_cmap('winter').resampled(4)
         cmap_aup = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap',
             [cmap_aup(i) for i in range(cmap_aup.N)], cmap_aup.N)