如何标准化 python 中的二维直方图？

Question

I'm trying to plot a 2d histogram. The histogram is basically a galaxy and I have the points of each luminous point. I have plotted the histogram but it's not properly normalized, as the values of the colorbar should go from 0 to 1. How can I fix this?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import kde

fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(20, 8))

data1 = pd.read_csv('mydata.txt', sep='\s+', header=None)

az1 = data1[0]
el1 = data1[1]

nbins = 250
hist1 = axes[0].hist2d(az1, el1, bins=nbins, cmap='magma', density=True)
 
fig.colorbar(hist1[3], ax = axes)

Answer 1

I tried with the function hist2D but I didn't find a way to normalized the result with it. So what I suggest is using the hitrogram from the numpy modul: np.nistogram2d where you can extract the result and then normalized the output before display it. Here an example with random numbers:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import kde

fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(20, 8))

# data1 = pd.read_csv('mydata.txt', sep='\s+', header=None)
N=10000
az1 = np.random.random(N)
el1 = np.random.random(N)

nbins = 250
hist1 = axes[0].hist2d(az1, el1, bins=nbins, cmap='magma', density=True)
fig.colorbar(hist1[3], ax = axes)


H, xedges, yedges = np.histogram2d(el1, az1, bins=(nbins, nbins),density=True )
# H_normalized = H/float(az1.shape[0]) # the integral over the histogrm is 1
H_normalized = H/H.max((0,1)) # the max value of the histogrm is 1
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]

im = axes[1].imshow(H_normalized, extent=extent, cmap='magma', interpolation='none',origin ='lower')
fig.colorbar(im, ax=axes[1])
plt.show()