In [5]:
# Triangle plot
g = plots.get_subplot_plotter()
g.triangle_plot([samples, samples2], filled=True)
Demonstrates the types of plot you can make with GetDist and how to make them. You can also run this notebook online.
# Show plots inline, and load main getdist plot module and samples class
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import sys, os
sys.path.insert(0,os.path.realpath(os.path.join(os.getcwd(),'..')))
from getdist import plots, MCSamples
import getdist
import matplotlib.pyplot as plt
import IPython
print('GetDist Version: %s, Matplotlib version: %s'%(getdist.__version__, plt.matplotlib.__version__))
# matplotlib 2 may not work very well without usetex on, can uncomment
# plt.rcParams['text.usetex']=True
GetDist Version: 1.4.3, Matplotlib version: 3.7.1
# use this here *after* the above (instead of matplotlib inline) to use interactive plots
# %matplotlib notebook
# Get some random samples for demonstration:
# make random covariance, then independent samples from Gaussian
import numpy as np
ndim = 4
nsamp = 10000
random_state = np.random.default_rng(10) # seed random generator
A = random_state.random((ndim,ndim))
cov = np.dot(A, A.T)
samps = random_state.multivariate_normal([0]*ndim, cov, size=nsamp)
A = random_state.random((ndim,ndim))
cov = np.dot(A, A.T)
samps2 = random_state.multivariate_normal([0]*ndim, cov, size=nsamp)
# Get the getdist MCSamples objects for the samples, specifying same parameter
# names and labels; if not specified weights are assumed to all be unity
names = ["x%s"%i for i in range(ndim)]
labels = ["x_%s"%i for i in range(ndim)]
samples = MCSamples(samples=samps,names = names, labels = labels)
samples2 = MCSamples(samples=samps2,names = names, labels = labels, label='Second set')
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# Triangle plot
g = plots.get_subplot_plotter()
g.triangle_plot([samples, samples2], filled=True)
# Here we are using inline plots, but if you wanted to export to file you'd just do e.g.
# g.export('output_file.pdf')
# 1D marginalized plot
g = plots.get_single_plotter(width_inch=4)
g.plot_1d(samples, 'x2')
# 1D marginalized comparison plot
g = plots.get_single_plotter(width_inch=3)
g.plot_1d([samples, samples2], 'x1')
# 1D normalized comparison plot
g = plots.get_single_plotter(width_inch=4)
g.plot_1d([samples, samples2], 'x1', normalized=True)
# 2D line contour comparison plot with extra bands and markers
g = plots.get_single_plotter()
g.plot_2d([samples, samples2], 'x1', 'x2')
g.add_x_marker(0)
g.add_y_bands(0, 1)