#Show plots inline, and load main getdist plot module and samples class
%matplotlib inline
from __future__ import print_function
from getdist import plots, MCSamples
import getdist, IPython
print('Version: ',getdist.__version__)
Version: 0.2.2
#Get some random samples for demonstration:
#make random covariance, then independent samples from Gaussian
import numpy as np
ndim = 4
nsamp = 10000
np.random.seed(10)
A = np.random.rand(ndim,ndim)
cov = np.dot(A, A.T)
samps = np.random.multivariate_normal([0]*ndim, cov, size=nsamp)
A = np.random.rand(ndim,ndim)
cov = np.dot(A, A.T)
samps2 = np.random.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)
#Triangle plot
g = plots.getSubplotPlotter()
g.triangle_plot([samples, samples2], filled=True)
#Here we are using inline plots, but if you wanted to export to file you'd just to e.g.
#g.export('output_file.pdf')
#1D marginalized plot
g = plots.getSinglePlotter(width_inch=4)
g.plot_1d(samples, 'x2')
#1D marginalized comparison plot
g = plots.getSinglePlotter(width_inch=3)
g.plot_1d([samples, samples2], 'x1')
#1D normalized comparison plot
g = plots.getSinglePlotter(width_inch=4)
g.plot_1d([samples, samples2], 'x1', normalized=True)
#2D line contour comparison plot with extra bands and markers
g = plots.getSinglePlotter()
g.plot_2d([samples, samples2], 'x1', 'x2')
g.add_x_marker(0)
g.add_y_bands(0, 1)
#Filled 2D comparison plot with legend
g = plots.getSinglePlotter(width_inch=4, ratio=1)
g.plot_2d([samples, samples2], 'x1', 'x2', filled=True)
g.add_legend(['sim 1', 'sim 2'], colored_text=True);
#Shaded 2D comparison plot
g = plots.getSinglePlotter(width_inch=4)
g.plot_2d([samples, samples2], 'x1', 'x2', shaded=True);
#Customized 2D filled comparison plot
g = plots.getSinglePlotter(width_inch=6, ratio=3 / 5.)
g.settings.legend_fontsize = 10
g.plot_2d([samples, samples2], 'x1', 'x2', filled=True,
colors=['green', ('#F7BAA6', '#E03424')], lims=[-4, 7, -5, 5])
g.add_legend(['Sim ', 'Sim 2'], legend_loc='upper right');
#Change the contours levels for marge stats and plots
#(note you need a lot of samples for 99% confidence contours to be accurate)
g = plots.getSinglePlotter()
samples.updateSettings({'contours': [0.68, 0.95, 0.99]})
g.settings.num_plot_contours = 3
g.plot_2d(samples, 'x1', 'x2');
#2D scatter (3D) plot
g = plots.getSinglePlotter(width_inch=5)
g.plot_3d(samples, ['x1', 'x2', 'x3'])
#Multiple 1D subplots
g = plots.getSubplotPlotter(width_inch=5)
g.settings.axes_fontsize = 9
g.settings.legend_fontsize = 10
g.plots_1d(samples, ['x0', 'x1', 'x2', 'x3'], nx=2);
#Multiple 2D subplots
g = plots.getSubplotPlotter(subplot_size=2.5)
g.plots_2d(samples, param_pairs=[['x0', 'x1'], ['x2', 'x3']],
nx=2, filled=True);
#Customized triangle plot
g = plots.getSubplotPlotter()
g.settings.figure_legend_frame = False
g.settings.alpha_filled_add=0.4
legends = ['Simulation', 'Simulation 2']
g.triangle_plot([samples, samples2], ['x0', 'x1', 'x2'],
filled_compare=True,
legend_labels=legends,
legend_loc='upper right',
line_args=[{'ls':'--', 'color':'green'},
{'lw':2, 'color':'darkblue'}],
contour_colors=['green','darkblue'])
#3D (scatter) triangle plot
g = plots.getSubplotPlotter(width_inch=6)
g.settings.axes_fontsize = 9
g.settings.legend_fontsize = 11
g.triangle_plot([samples, samples2], ['x1', 'x2', 'x3'],
plot_3d_with_param='x0', legend_labels=legends)
#You can reference g.subplots for manual tweaking,
#e.g. let's add a vertical axis line in the first column
for ax in g.subplots[:,0]:
ax.axvline(0, color='gray', ls='--')
IPython.display.display(g.fig)
#Rectangle 2D comparison plots
g = plots.getSubplotPlotter()
g.settings.figure_legend_frame = False
g.rectangle_plot(['x0', 'x1'], ['x2', 'x3'],
roots=[samples, samples2], filled=True,
plot_texts=[['Test Label', None], ['Test 2', None]]);
#Example of how to handle boundaries (samples are restricted to x0 >-0.5)
cut_samps = samps[samps[:,0]>-0.5,:]
cut_samples = MCSamples(samples=cut_samps, names = names, labels = labels,
ranges={'x0':(-0.5, None)})
g = plots.getSubplotPlotter()
g.plots_1d(cut_samples,nx=4);
g = plots.getSinglePlotter(width_inch=4, ratio=1)
g.plot_2d(cut_samples, 'x0', 'x1', filled=True);
#Add and plot a new derived parameter
#getParms gets p so that p.x0, p.x1.. are numpy vectors of sample values
p = samples.getParams()
samples.addDerived((5+p.x2)** 2, name='z', label='z_d')
g = plots.getSubplotPlotter()
g.plots_2d(samples,'x1',['x2','z'], nx=2);
#Many other things you can do besides plot, e.g. get latex
print(cut_samples.getInlineLatex('x0',limit=2))
print(samples.getInlineLatex('x0',limit=2))
x_0 < 2.31 x_0 = 0.0^{+2.5}_{-2.5}
print(samples.getInlineLatex('x1',limit=1))
x_1 = 0.00\pm 0.96
print(samples.getTable().tableTex())
\begin{tabular} { l c} Parameter & 95\% limits\\ \hline {\boldmath$x_0 $} & $0.0^{+2.5}_{-2.5} $\\ {\boldmath$x_1 $} & $0.0^{+1.9}_{-1.9} $\\ {\boldmath$x_2 $} & $0.0^{+2.3}_{-2.3} $\\ {\boldmath$x_3 $} & $0.0^{+2.2}_{-2.2} $\\ $z_d $ & $26^{+20}_{-20} $\\ \hline \end{tabular}
print(samples.PCA(['x1','x2']))
PCA for parameters: 2 :x_1 3 :x_2 Correlation matrix for reduced parameters x1 : 1.0000 0.8487 x2 : 0.8487 1.0000 e-values of correlation matrix PC 1: 0.1513 PC 2: 1.8487 e-vectors 2: -0.7071 0.7071 3: 0.7071 0.7071 Principle components PC1 (e-value: 0.151292) [0.958949] (x_1-0.004343)/1.000000) [-0.958949] (x_2-0.000594)/-1.229803) = -0.000000 +- 0.527496 PC2 (e-value: 1.848708) [0.958949] (x_1-0.004343)/1.000000) [0.958949] (x_2-0.000594)/1.229803) = 0.000000 +- 1.843932 Correlations of principle components 1 2 PC 1 1.000 0.000 PC 2 0.000 1.000 1 0.077 0.921 (x_0) 2 0.275 0.961 (x_1) 3 -0.275 0.961 (x_2) 4 -0.353 0.807 (x_3) 5 -0.270 0.948 (z_d)
stats = cut_samples.getMargeStats()
lims0 = stats.parWithName('x0').limits
lims1 = stats.parWithName('x1').limits
for conf, lim0, lim1 in zip(samples.contours,lims0, lims1):
print('x0 %s%% lower: %.3f upper: %.3f (%s)'%(conf, lim0.lower, lim0.upper, lim0.limitType()))
print('x1 %s%% lower: %.3f upper: %.3f (%s)'%(conf, lim1.lower, lim1.upper, lim1.limitType()))
x0 0.68% lower: -0.500 upper: 1.030 (one tail upper limit) x1 0.68% lower: -0.307 upper: 1.082 (two tail) x0 0.95% lower: -0.500 upper: 2.308 (one tail upper limit) x1 0.95% lower: -0.807 upper: 1.946 (two tail) x0 0.99% lower: -0.500 upper: 3.149 (one tail upper limit) x1 0.99% lower: -1.034 upper: 2.671 (two tail)
#Save to file
import tempfile, os
tempdir = os.path.join(tempfile.gettempdir(),'testchaindir')
os.mkdirs(tempdir)
rootname = os.path.join(tempdir, 'testchain')
samples.saveAsText(rootname)
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-28-24a9264274ec> in <module>() 2 import tempfile, os 3 tempdir = os.path.join(tempfile.gettempdir(),'testchaindir') ----> 4 os.mkdirs(tempdir) 5 rootname = os.path.join(tempdir, 'testchain') 6 samples.saveAsText(rootname) AttributeError: 'module' object has no attribute 'mkdirs'
#Load from file
from getdist import loadMCSamples
readsamps = loadMCSamples(rootname)
#Make plots from chain files, loading automatically as needed
g = plots.getSinglePlotter(chain_dir=tempdir, width_inch=4)
g.plot_2d('testchain', 'x1', 'x2', shaded=True);
#Custom settings for all loaded chains can be set as follows
g = plots.getSinglePlotter(chain_dir=tempdir,
analysis_settings={'ignore_rows': 0.2, 'contours':[0.2, 0.4, 0.6, 0.8]});
g.settings.num_plot_contours = 4
g.plot_2d('testchain', 'x1', 'x2', filled=False);
#Chains can be loaded by searching in multiple directories, give list as chain_dir
#(not chain names must be unique)
#make second test chain in new temp dir
temp2 = os.path.join(tempdir,'chaindir2')
cut_samples.saveAsText(os.path.join(temp2, 'testchain2'), make_dirs=True)
#Plot from chain files
g = plots.getSinglePlotter(chain_dir=[tempdir, temp2])
g.plot_2d(['testchain','testchain2'], 'x1', 'x2', filled=True);
#cleanup test files
import shutil
shutil.rmtree(tempdir)