Unequal width binned histogram in python

Question

I have an array with probability values stored in it. Some values are 0. I need to plot a histogram such that there are equal number of elements in each bin. I tried using matplotlibs hist function but that lets me decide number of bins. How do I go about plotting this?(Normal plot and hist work but its not what is needed)

I have 10000 entries. Only 200 have values greater than 0 and lie between 0.0005 and 0.2. This distribution isnt even as 0.2 only one element has whereas 2000 approx have value 0.0005. So plotting it was an issue as the bins had to be of unequal width with equal number of elements

Answer 1

这项任务对我来说没有多大意义，但是下面的代码确实做了，我理解为要做的事情。

我还认为代码的最后几行是你真正想要做的。使用不同的箱宽来改善可视化（但不要针对每个箱内等量样品的分布）！我使用了astroml's hist with method='blocks'（astropy supports this too）

代码

# Python 3 -> beware the // operator!

import numpy as np
import matplotlib.pyplot as plt
from astroML import plotting as amlp

N_VALUES = 1000
N_BINS = 100

# Create fake data
prob_array = np.random.randn(N_VALUES)
prob_array /= np.max(np.abs(prob_array),axis=0)  # scale a bit

# Sort array
prob_array = np.sort(prob_array)

# Calculate bin-borders,
bin_borders = [np.amin(prob_array)] + [prob_array[(N_VALUES // N_BINS) * i] for i in range(1, N_BINS)] + [np.amax(prob_array)]

print('SAMPLES: ', prob_array)
print('BIN-BORDERS: ', bin_borders)

# Plot hist
counts, x, y = plt.hist(prob_array, bins=bin_borders)
plt.xlim(bin_borders[0], bin_borders[-1] + 1e-2)
print('COUNTS: ', counts)
plt.show()


# And this is, what i think, what you really want

fig, (ax1, ax2) = plt.subplots(2)
left_blob = np.random.randn(N_VALUES/10) + 3
right_blob = np.random.randn(N_VALUES) + 110
both = np.hstack((left_blob, right_blob))  # data is hard to visualize with equal bin-widths

ax1.hist(both)
amlp.hist(both, bins='blocks', ax=ax2)
plt.show()

输出