custom colormap WIP

1 files changed, 27 insertions, 2 deletions

diff --git a/test/cross.py b/test/cross.py
index de6b9be..6194630 100755
--- a/test/cross.py
+++ b/test/cross.py
@@ -1,10 +1,32 @@
 #!/usr/bin/env python3
 
 import matplotlib.pyplot as plt
+import matplotlib as mpl
+from matplotlib.colors import LinearSegmentedColormap
 
 import xarray as xr
 from metpy.interpolate import cross_section
 
+clcov_cmap = {
+    'red': (
+        (0.0, 0.0, 0.0),
+        (0.1, 1.0, 1.0),
+        (1.0, 0.2, 0.2),
+    ),
+    'green': (
+        (0.0, 0.0, 0.0),
+        (0.1, 1.0, 1.0),
+        (1.0, 0.2, 0.2),
+    ),
+    'blue': (
+        (0.0, 1.0, 1.0),
+        (0.1, 1.0, 1.0),
+        (1.0, 0.2, 0.2),
+    ),
+}
+
+mpl.colormaps.register(LinearSegmentedColormap('clcov', clcov_cmap))
+
 data = xr.load_dataset('dwd_icon-eu/combined.grib2', engine='cfgrib')
 lat, lon = (47.96, 11.99)
 data = data.sel(latitude=lat, longitude = lon, method='nearest')
@@ -14,13 +36,16 @@ print(data)
 # start figure and set axis
 fig, ax = plt.subplots(figsize=(5, 5))
 
+ax.set_ylabel('Pressure level [hPa]')
+
 #clc = ax.plot(data.step.values.astype('float64'), data.isobaricInhPa, data.ccl.transpose())
 #clc = ax.imshow(data.ccl.transpose(), extent=(data.step.values.astype(float).min(), data.step.values.astype(float).max(), data.isobaricInhPa.min(), data.isobaricInhPa.max()), aspect='auto', cmap='Blues_r', vmin=0, vmax=100)
 #plt.colorbar(clc, label='clcov')
-clc = ax.contourf(data.step.values.astype('float64'), data.isobaricInhPa, data.ccl.transpose(), cmap='Blues_r', vmin=0, vmax=100, levels=9)
+# Blues_r
+clc = ax.contourf(data.step.values.astype('float64'), data.isobaricInhPa, data.ccl.transpose(), cmap='clcov', vmin=0, vmax=100, levels=9)
 
 # use Format parameter for n/8
-plt.colorbar(clc, label='clcov', extendfrac=None, ticks=[100*n/8 for n in range(9)], format=lambda x,_: f'{int(x/12.5)}/8')
+plt.colorbar(clc, label='cloudcov', extendfrac=None, ticks=[100*n/8 for n in range(9)], format=lambda x,_: f'{int(x/12.5)}/8')
 
 
 cf = ax.contour(data.step.values.astype('float64'), data.isobaricInhPa, data.t.metpy.convert_units('degC').transpose())