py-sciplot¶
This is the documentation of py-sciplot, scientific plotting tools for python matplotlib.
Note
This project is in development.
Sciplot offers additional features to matplotlib for making scientific plots, such as histograms and graphs.
One of the major features is the builtin hist function for plotting multiple histograms and graphs on the same
x-axis with the same binning.
This package is intended to be used alongside with matplotlib.
Example¶
Simple fit and plot of a Gaussian Distribution:
import matplotlib.pyplot as plt
import sciplot as sp
plt.style.use('sciplot')
import numpy as np
simulation_data = np.append(np.random.random_sample(100)*7 - 3.5, np.random.normal(0, 0.5, 100))
data = np.append(np.random.random_sample(100)*7 - 3.5, np.random.normal(0, 0.4, 100))
sp.errorhist(simulation_data, bins=30, color='black', label='Pseudo Simulation')
sp.hist(data, fill=True, style=1, label='Pseudo Data')
plt.legend()
sp.labels('E', "Events", "GeV", 1)
sp.save("example.png")
The produced output will look like this:
Another feature is the automaitc creation of stacked histograms:
import matplotlib.pyplot as plt
import sciplot as sp
import pandas as pd
import numpy as np
plt.style.use('sciplot_ticks')
# Create some pseudo data
nb = 5000
ns = 3000
df = dict({'mass': np.append(np.random.random_sample(nb)*7 - 3.5, np.random.normal(0, 0.5, ns))})
df['exp'] = np.random.randint(0, 6, ns+nb)
df = pd.DataFrame(df)
# Automatic creation of a stacked plot of 'mass' split by values of 'exp'
sp.stacked(df, "mass", 'exp', bins=50,)
sp.xlim()
sp.labels('$\Delta M$', "Events", "GeV", 0)
sp.save("stacked_plot.png")
The produced output will look like this:
Installation¶
This package will be made available for pip.