def contour_plot(fisher,
fiducials,
fname,
name='',
add_marker=False,
latex=True):
from chainconsumer import ChainConsumer
mean = [fiducials[key] for key in fisher.params]
cov = np.linalg.inv(fisher.values)
parameters = [latex_mapping[x]
for x in fisher.params] if latex else fisher.params
c = ChainConsumer()
c.add_covariance(mean, cov, parameters=parameters, name=name, shade=False)
if add_marker:
c.add_marker(mean,
parameters=parameters,
marker_style="*",
marker_size=100,
color="r",
name='')
c.configure(usetex=False, serif=False, sigma2d=True, sigmas=[1])
fig = c.plotter.plot()
fig.set_size_inches(3 + fig.get_size_inches())
fig.savefig(fname)
def contour_plot_multiple(fisher_list,fiducials,fname,name_list,add_marker=False,latex=True):
from chainconsumer import ChainConsumer
c = ChainConsumer()
means_list=[]
covs_list=[]
parameters_list=[]
for i in range(len(fisher_list)):
means_list.append([fiducials[key] for key in fisher_list[i].params])
covs_list.append(np.linalg.inv(fisher_list[i].values))
parameters_list.append([latex_mapping[x] for x in fisher_list[i].params] if latex else fisher_list[i].params)
c.add_covariance(means_list[i], covs_list[i], parameters=parameters_list[i], name=name_list[i], shade=True)
if add_marker: c.add_marker(mean1, parameters=parameters1, marker_style="*", marker_size=100, color="r",name='')
c.configure(usetex=False, serif=False,sigma2d=True,sigmas=[0,1,2])
fig = c.plotter.plot()
fig.set_size_inches(3 + fig.get_size_inches())
fig.savefig(fname)
def test_marker(self):
loc = [0, 1, 2]
c = ChainConsumer()
c.add_marker(loc)
assert np.all(np.equal(loc, c.chains[0].chain[0, :]))
===============================
Sometimes you want to compare your data to a Fisher matrix projection,
or you just have some Gaussian you want also drawn.
Or maybe its just a random point you want to put on the plot.
It's all easy to do.
"""
# -*- coding: utf-8 -*-
from chainconsumer import ChainConsumer
mean = [1, 5]
cov = [[1, 1], [1, 3]]
parameters = ["a", "b"]
c = ChainConsumer()
c.add_covariance(mean, cov, parameters=parameters, name="Cov")
c.add_marker(mean,
parameters=parameters,
name="Marker!",
marker_style="*",
marker_size=100,
color="r")
c.configure(usetex=False, serif=False)
fig = c.plotter.plot()
fig.set_size_inches(
3 + fig.get_size_inches()) # Resize fig for doco. You don't need this.
params_dict, display=False, figname=figname)
# Add the chain or MAP to the Chainconsumer plots
extra.pop("realisation", None)
if realisation == "mean":
fitname = data[0]["name"].replace(" ", "_")
stats[fitname] = []
output[fitname] = []
c.add_chain(df,
weights=weight,
**extra,
plot_contour=True,
plot_point=False,
show_as_1d_prior=False)
else:
c.add_marker(params, **extra)
# Compute some summary statistics and add them to a dictionary
mean, cov = weighted_avg_and_cov(
df[[
"$\\alpha_\\parallel$",
"$\\alpha_\\perp$",
]],
weight,
axis=0,
)
corr = cov[1, 0] / np.sqrt(cov[0, 0] * cov[1, 1])
stats[fitname].append([
mean[0], mean[1],
np.sqrt(cov[0, 0]),
