Plots with Self-Defined Colormaps¶
This tutorial shows how to use the module tropy.plotting_tools.colormaps to make plots with the colormaps provided by this module.
Import Libraries¶
[1]:
%matplotlib inline
# standard libs
import numpy as np
import scipy.ndimage
# plotting and mapping
import pylab as plt
import seaborn as sns
sns.set_context('talk')
plt.rcParams['figure.figsize'] = (8, 6)
# the own tropy lib
import tropy.plotting_tools.colormaps
import tropy.plotting_tools.shaded
Making Example Data¶
We make some random example data for plotting.
[2]:
nrow, ncol = 180, 200
x = np.linspace(0, 1, ncol)
y = np.linspace(0, 1, nrow)
r = 4 * np.random.randn( nrow, ncol )
# smoothing
r_sm = scipy.ndimage.gaussian_filter(r, 2 )
Colormap based on Colorname¶
We take the random data and test different colormap options.
[3]:
cmap = tropy.plotting_tools.colormaps.colorname_based_cmap( 'darkgreen')
plt.pcolormesh(x, y, r_sm, cmap = cmap)
plt.colorbar()
[3]:
<matplotlib.colorbar.Colorbar at 0x7f484e2bdc10>
[4]:
cmap = tropy.plotting_tools.colormaps.colorname_based_cmap( 'darkgreen',
start_col = 'black',
final_col = 'gold')
plt.pcolormesh(x, y, r_sm, cmap = cmap)
plt.colorbar()
[4]:
<matplotlib.colorbar.Colorbar at 0x7f484e1b8f50>
Hmm, don’t know if you need this … let’s look at the next functions.
“Nice” Colormaps¶
We loop over the different colormap choices.
[5]:
help( tropy.plotting_tools.colormaps.nice_cmaps )
Help on function nice_cmaps in module tropy.plotting_tools.colormaps:
nice_cmaps(cmap_name)
Some pre-defined "nice" colormaps.
Parameters
----------
cmap_name : str
name of a pre-defined colormap.
* 'red2blue_disc' : a discrete red to blue
transistion
* 'red2blue' : a contineous red to blue
transistion
* 'white-green-orange' : a green-based transition
from white to orange
* 'white-blue-green-orange' : a blue-based transition
from white to orange
* 'white-purple-orange' : a purple-based transistion
from white to orange
* 'ocean-clouds' : for clouds over ocean
* 'land-clouds' : for clouds over land
Returns
-------
cmap : matplotlib colormap object
resulting colormap
[6]:
colormap_names = [ 'red2blue_disc', # a discrete red to blue transistion
'red2blue' , # a continuous red to blue transistion
'white-green-orange', # a green-based transition from white to orange
'white-blue-green-orange', # a blue-based transition from white to orange
'white-purple-orange', # a purple-based transistion from white to orange
'ocean-clouds', # for clouds over ocean
'land-clouds' # for clouds over land
]
Another Rainbow¶
[7]:
fig, axs = plt.subplots(nrows = 1, ncols = 2, figsize = (16,6))
axs = axs.flatten()
for i, colmap in enumerate( colormap_names[0:2] ):
cmap = tropy.plotting_tools.colormaps.nice_cmaps( colmap )
plt.sca( axs[i] )
plt.title( colmap )
plt.pcolormesh(x, y, r_sm, cmap = cmap)
plt.colorbar()
Starting from “nowhere”¶
The colormaps start with white and are useful for positive-definite fields, like PDFs.
[8]:
fig, axs = plt.subplots(nrows = 3, ncols = 1, figsize = (8,18))
axs = axs.flatten()
plt.subplots_adjust( wspace = 0.4 )
for i, colmap in enumerate( colormap_names[2: 5] ):
cmap = tropy.plotting_tools.colormaps.nice_cmaps( colmap )
plt.sca( axs[i] )
plt.title( colmap )
plt.pcolormesh(x, y, r_sm, cmap = cmap, vmin = -0.2, vmax = 1.)
plt.colorbar()
Clouds¶
The colormaps are for clouds over different undergrounds - to mimick the visible satellite composites.
[9]:
fig, axs = plt.subplots(nrows = 1, ncols = 2, figsize = (16,6))
axs = axs.flatten()
for i, colmap in enumerate( colormap_names[-2:] ):
cmap = tropy.plotting_tools.colormaps.nice_cmaps( colmap )
plt.sca( axs[i] )
plt.title( colmap )
plt.pcolormesh(x, y, r_sm, cmap = cmap, vmin = -3., vmax = 1.)
plt.colorbar()
Colormaps for Satellite Data¶
The function enhanced_colormap is for 10.8 um brightness temperatures from satellite (e.g. for Meteosat SEVIRI).
We scale our random data in the typical range (200 K, 300 K)
[10]:
help( tropy.plotting_tools.colormaps.enhanced_colormap)
Help on function enhanced_colormap in module tropy.plotting_tools.colormaps:
enhanced_colormap(vmin=200.0, vmed=240.0, vmax=300.0)
Standard color enhancement used for infrared satellite brightness
temperatures (BTs) in the atmospheric window around 10.8 um.
Parameters
----------
vmin : float, optional
minimum BT
vmed : float, optional
BT where transition between gray-scale and rainbow
colors is done
vmax : float, optional
maximum BT
Returns
-------
mymap : matplotlib colormap object
resulting colormap
[11]:
b = scipy.ndimage.gaussian_filter( r_sm, 5)
bn = (b - b.min()) / (b.max() - b.min())
[12]:
bt108 = 100 * bn + 200
[13]:
cmap = tropy.plotting_tools.colormaps.enhanced_colormap( )
plt.pcolormesh(x, y, bt108, cmap = cmap, vmin = 200, vmax = 300)
plt.colorbar()
[13]:
<matplotlib.colorbar.Colorbar at 0x7f484c60f0d0>
The function enhanced_wv62_cmap is for the brightness temperature in the water vapor channel (e.g. for Meteosat SEVIRI).
We scale our random data in the typical range (200 K, 260 K)
[14]:
help(tropy.plotting_tools.colormaps.enhanced_wv62_cmap)
Help on function enhanced_wv62_cmap in module tropy.plotting_tools.colormaps:
enhanced_wv62_cmap(vmin=200.0, vmed1=230.0, vmed2=240.0, vmax=260.0)
Color enhancement (non-standard - more artistic) used for infrared satellite
brightness temperatures (BTs) for water vapor channels.
Parameters
----------
vmin : float, optional
minimum BT
vmed1 : float, optional
BT where first transition between brownish colors
and gray-scale is done
vmed2 : float, optional
BT where second transition between gray-scale
and rainbow colors is done
vmax : float, optional
maximum BT
Returns
-------
mymap : matplotlib colormap object
resulting colormap
[15]:
bt062 = 70 * bn + 200
[16]:
cmap = tropy.plotting_tools.colormaps.enhanced_wv62_cmap( )
plt.pcolormesh(x, y, bt062, cmap = cmap, vmin = 200, vmax = 260)
plt.colorbar()
[16]:
<matplotlib.colorbar.Colorbar at 0x7f484c92e210>
Colormap for Radar Data¶
This colormap is taken from the DWD website to plot rain accumulations.
[17]:
help( tropy.plotting_tools.colormaps.dwd_sfmap )
Help on function dwd_sfmap in module tropy.plotting_tools.colormaps:
dwd_sfmap()
Colormap for DWD Radolan SF product for observed daily rain
accumulations.
Returns
-------
rrlevs : list
rain rate levels (in mm)
cmap : matplotlib colormap object
SF colormap
[18]:
rr = 600 * b**3
rr = np.ma.masked_less(rr, 0.1)
[19]:
rrlevs, cmap = tropy.plotting_tools.colormaps.dwd_sfmap( )
tropy.plotting_tools.shaded.shaded(x, y, rr, cmap = cmap, levels = rrlevs)
plt.colorbar()
[19]:
<matplotlib.colorbar.Colorbar at 0x7f484c6bf590>
Summary¶
The module tropy.plotting_tools.colormaps provides you some self-defined colormaps, esp.
- for satellite data
- positive-definite fields (like pdfs) which start from zero (white)