fix a bug, extend the readme

README.md

@@ -6,6 +6,10 @@
 
 # Scientific color maps 
 
+## Blog post
+
+[Scicomap Medium blog post (free)](https://towardsdatascience.com/your-colour-map-is-bad-heres-how-to-fix-it-lessons-learnt-from-the-event-horizon-telescope-b82523f09469)
+
 ## Installation
 
 ```shell
@@ -97,7 +101,7 @@ import scicomap as sc
 import matplotlib.pyplot as plt
 
 # the thing that should not be
-ugly_jet = plt.get_cmap("YlOrBr")
+ugly_jet = plt.get_cmap("jet")
 sc_map =  sc.ScicoMiscellaneous(cmap=ugly_jet)
 f=sc_map.assess_cmap(figsize=(22,10))
 ```
@@ -219,6 +223,74 @@ which render as
 <td align="left"><img src="pics/vanimo-fixed-examples.png" width="1000"/></td>
 
 
+# Use with matplotlib
+
+## Use a corrected colormap in a matplotlib figure
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import scicomap as sc
+from scicomap.utils import _fn_with_roots
+
+# load the color map
+div_map = sc.ScicoDiverging(cmap='watermelon')
+
+# correct the colormap
+div_map.unif_sym_cmap(lift=15, 
+                      bitonic=False, 
+                      diffuse=True)
+
+# get the fixed color map
+fixed_cmap = div_map.get_mpl_color_map()
+print(type(fixed_cmap))
+
+# use it as you like
+im = _fn_with_roots()
+norm = mpl.colors.CenteredNorm()
+divnorm = mpl.colors.TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1.25)
+fig = plt.figure(figsize=(3,3), facecolor="white")
+ax = fig.add_subplot(1, 1, 1, facecolor="white")
+pos = ax.imshow(im, cmap=fixed_cmap, aspect="auto", norm=divnorm)
+fig.colorbar(pos, ax=ax);
+```
+<td align="left"><img src="pics/mpl.png" width="250"/></td>
+
+## Correct a matplotlib colormap
+
+
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import scicomap as sc
+from scicomap.utils import _fn_with_roots
+
+# load the color map
+mpl_cmap_obj = plt.get_cmap("PRGn")
+div_map = sc.ScicoDiverging(cmap=mpl_cmap_obj)
+
+# correct the colormap
+div_map.unif_sym_cmap(lift=None, 
+                      bitonic=False, 
+                      diffuse=True)
+
+# get the fixed color map
+fixed_cmap = div_map.get_mpl_color_map()
+print(type(fixed_cmap))
+
+# use it as you like
+im = _fn_with_roots()
+norm = mpl.colors.CenteredNorm()
+divnorm = mpl.colors.TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1.25)
+fig = plt.figure(figsize=(3,3), facecolor="white")
+ax = fig.add_subplot(1, 1, 1, facecolor="white")
+pos = ax.imshow(im, cmap=fixed_cmap, aspect="auto", norm=divnorm)
+fig.colorbar(pos, ax=ax);
+```
+
+<td align="left"><img src="pics/mpl2.png" width="250"/></td>
+
 # Comparing color maps
 
 You can easily compare, raw or corrected, color maps using a picture of your choice
@@ -353,6 +425,11 @@ sc.plot_colormap(ctype='qualitative',
 
 # Changes log
 
+### 0.3
+
+ - Add a section "how to use with matplotlib"
+ - [Bug] Center diverging color map in examples
+
 ### 0.2
 
  - [Bug] Fix typo in chart titles

scicomap/__init__.py

@@ -3,7 +3,7 @@
 from . import cmath
 from . import cblind
 
-__version__ = "0.2"
+__version__ = "0.3"
 __all__ = ["scicomap", "datasets", "cmath", "cblind", "utils"]
 
 # bound to upper level

scicomap/scicomap.py

@@ -577,7 +577,8 @@ def draw_example(self, facecolor="black", figsize=(20, 20)):
                              arr_3d=[(xpyr, ypyr, zpyr), (per_x, per_z, per_z)],
                              figsize=figsize,
                              facecolor=facecolor,
-                             cname=self.cname)
+                             cname=self.cname,
+                             norm=True)
 
         return fig
 
@@ -747,7 +748,8 @@ def draw_example(self, facecolor="black", figsize=(20, 20)):
                              arr_3d=[(xpyr, ypyr, zpyr), (per_x, per_z, per_z)],
                              figsize=figsize,
                              facecolor=facecolor,
-                             cname=self.cname)
+                             cname=self.cname,
+                             norm=True)
 
         return fig
 

scicomap/utils.py

@@ -1,5 +1,6 @@
 import itertools
 import numpy as np
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 from matplotlib.colors import ListedColormap
@@ -151,7 +152,7 @@ def _plot_e_field(ax, cmap, n_neg_charges=2, facecolor="black", title=False):
     return ax
 
 
-def _plot_examples(color_map, images, arr_3d, figsize, facecolor, cname, cblind=True):
+def _plot_examples(color_map, images, arr_3d, figsize, facecolor, cname, cblind=True, norm=False):
     """Create the figure based on the provided images, continuous colormaps"""
     fig = plt.figure(figsize=figsize, facecolor=facecolor)
     n_images = len(images)
@@ -167,6 +168,7 @@ def _plot_examples(color_map, images, arr_3d, figsize, facecolor, cname, cblind=
     idx_3d = 0
     n_rows = len(c_maps)
     n_cols = len(images)
+
     for c_map, im in itertools.product(c_maps, images):
 
         if isinstance(im, str) and ('3D' in im):
@@ -186,8 +188,9 @@ def _plot_examples(color_map, images, arr_3d, figsize, facecolor, cname, cblind=
             ax = fig.add_subplot(n_rows, n_cols, axi, facecolor=facecolor)
             ax = _plot_complex_arg(ax, c_map, facecolor, title=axi <= n_cols)
         else:
+            cmap_norm = mpl.colors.CenteredNorm() if norm else None
             ax = fig.add_subplot(n_rows, n_cols, axi, facecolor=facecolor)
-            ax.imshow(im, cmap=c_map, aspect="auto")
+            ax.imshow(im, cmap=c_map, aspect="auto", norm=cmap_norm)
             ax.get_xaxis().set_visible(False)
             ax.get_yaxis().set_visible(False)
             ax.set_title(sub_title[axi - 1], color=title_color, fontsize=16, loc="left")

setup.py

@@ -27,7 +27,7 @@
 
 setup(
     name="scicomap",
-    version="0.2",
+    version="0.3",
     description="Scientific color maps",
     long_description=README,
     long_description_content_type="text/markdown",