This is a very small python package used to make histograms in python, and optionally plot them with matplotlib. The underlying histogramming tool is from numpy. This provides a very handy wrappers around them to easily make common histograms in 1d and 2d and plot with nice labels.
$ pip install easyhistYou can import the library and make histograms and plot them(optionally)
import numpy as np
import matplotlib.pyplot as plt
import easyhist as eh
# initialize data
x = np.random.normal(0,1,10000)
h = eh.Hist1D(x,bins='auto')The returned histogram object has a handy plot method which uses matplotlib to plot the histogram.
fig,ax = plt.subplots(1,1,figsize=(12,6))
h.plot(ax)
The histogram comes by default with error bars. Different keyword parameters can be passed to customise the histogram.
fig,ax= plt.subplots(1,1,figsize=(12,6))
h.plot(ax,steps=True,ebar=False,filled=True)
Axes labels and titles can be passed to the plot function.
fig,ax= plt.subplots(1,1,figsize=(12,6))
h.plot(ax,steps=True,ebar=False,filled=True,xlabel='x (unit)',ylabel='y(unit)',title='Test')
Many times we have to fit gaussian to the histogram. An easy fit_normal method is provided with fits normal_distribution to the dateset.
h_fited = h.fit_normal()Since the fitted object is an instance of Hist1D we can use the plot method as above to plot.
fig,ax= plt.subplots(1,1,figsize=(12,6))
h_fited.plot(ax,steps=True,ebar=False,filled=True,xlabel='x (unit)',ylabel='y(unit)',title='Test')
The library naturally has Hist2D class for 2D histogram.
y = np.random.normal(2,3,10000)
h2d = eh.Hist2D((x,y),bins=200)We can similarly plot the histogram.
fig,ax= plt.subplots(1,1,figsize=(12,6))
h2d.plot(ax,steps=True,ebar=False,filled=True,xlabel='x (unit)',ylabel='y(unit)',title='Test',cbarlabel='Colorbar',aspect='auto',cmin=1)
There are a lot of othe nice useful features which can be found in the documentation.