I made a function to get the hex code given to a set of data as follows:
from matplotlib import cm, colors
def get_color(series_data, cmap='Reds'):
color_map = cm.get_cmap(cmap, 20)
f = lambda x: colors.rgb2hex(color_map(x/series_data.max())[:3])
return series_data.apply(f)
The cm.get_cmap(cmap, 20) generates a matplotlib.colors.LinearSegmentedColormap object that is ranged from the minimum value of the input series_data to its maximum.
I cannot see how could I define the color limits for the data to be evaluated. For instance, what if I wanted to set constant color limits, defining as the minimum the value 0 and the maximum 100? How could I do that within my function?
I tried to substitute series_data.max() to 100 to control the max equivalent color (max), but I couldn't control the cmin.
The parameter of color_map needs to be scaled to the [0.,1.) range. For instance, if the minimum (maximum) color value needs to be obtained for the lo (hi) value:
from matplotlib import cm, colors
import pandas as pd
def get_color(series_data, cmap='Reds', lo=None, hi=None):
if lo is None:
lo = series_data.min()
if hi is None:
hi = series_data.max()
if lo == hi:
raise Exception('Invalid range.')
color_map = cm.get_cmap(cmap, 20)
f = lambda x: colors.rgb2hex(color_map((x-lo)/(hi-lo))[:3])
return series_data.apply(f)
s = pd.Series(np.linspace(0,3,16))
colz = get_color(s, lo=1, hi=2)
for x, c in zip(s, colz):
print('{:.2f} {}'.format(x,c))
The sample output is
0.00 #fff5f0
0.20 #fff5f0
0.40 #fff5f0
0.60 #fff5f0
0.80 #fff5f0
1.00 #fff5f0
1.20 #fdc7b0
1.40 #fc8363
1.60 #ed392b
1.80 #af1117
2.00 #67000d
2.20 #67000d
2.40 #67000d
2.60 #67000d
2.80 #67000d
3.00 #67000d