andrashann · October 17, 2020 13:49 · Oct 17, 2020
diff --git a/cmap_map.py b/cmap_map.py
@@ -0,0 +1,35 @@
+import matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+
+#https://scipy-cookbook.readthedocs.io/items/Matplotlib_ColormapTransformations.html
+
+def cmap_map(function, cmap):
+    """ Applies function (which should operate on vectors of shape 3: [r, g, b]), on colormap cmap.
+    This routine will break any discontinuous points in a colormap.
+    """
+    cdict = cmap._segmentdata
+    step_dict = {}
+    # Firt get the list of points where the segments start or end
+    for key in ('red', 'green', 'blue'):
+        step_dict[key] = list(map(lambda x: x[0], cdict[key]))
+    step_list = sum(step_dict.values(), [])
+    step_list = np.array(list(set(step_list)))
+    # Then compute the LUT, and apply the function to the LUT
+    reduced_cmap = lambda step : np.array(cmap(step)[0:3])
+    old_LUT = np.array(list(map(reduced_cmap, step_list)))
+    new_LUT = np.array(list(map(function, old_LUT)))
+    # Now try to make a minimal segment definition of the new LUT
+    cdict = {}
+    for i, key in enumerate(['red','green','blue']):
+        this_cdict = {}
+        for j, step in enumerate(step_list):
+            if step in step_dict[key]:
+                this_cdict[step] = new_LUT[j, i]
+            elif new_LUT[j,i] != old_LUT[j, i]:
+                this_cdict[step] = new_LUT[j, i]
+        colorvector = list(map(lambda x: x + (x[1], ), this_cdict.items()))
+        colorvector.sort()
+        cdict[key] = colorvector
+
+    return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
diff --git a/new_york_distancing_space.ipynb b/new_york_distancing_space.ipynb