def plot_all_files(df_all):
output_file = "all_biascor_results.png"
logging.info(f"Plotting all fits to {output_file}")
c = ChainConsumer()
labels = [r"$\Omega_m$", "$w$", r"$\sigma_{int}$"]
data = []
for name, df in df_all.groupby("name"):
means = [df["omm"].mean(), df["w"].mean(), df["sigint"].mean()]
if df.shape[0] < 2:
name2 = name + " (showing mean error)"
cov = np.diag(
[df["omm_sig"].mean()**2, df["w_sig"].mean()**2, 0.01**2])
else:
name2 = name + " (showing scatter error)"
cov = np.diag(
[df["omm"].std()**2, df["w"].std()**2, df["sigint"].std()**2])
c.add_covariance(means,
cov,
parameters=labels,
name=name2.replace("_", "\\_"))
data.append([name, df["w"].mean(), df["w"].std(), df["w_sig"].mean()])
wdf = pd.DataFrame(
data, columns=["name", "mean_w", "scatter_mean_w", "mean_std_w"])
wdf.to_csv(output_file.replace(".png", ".csv"),
index=False,
float_format="%0.4f")
c.configure(statistics="mean")
c.plotter.plot_summary(errorbar=True, filename=output_file)
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 test_covariance(self):
mean = [0, 1]
cov = [[1, 1], [1, 2.5]]
c = ChainConsumer()
c.add_covariance(mean, cov)
mean_obs = np.mean(c.chains[0].chain, axis=0)
cov_obs = np.cov(c.chains[0].chain.T)
assert np.all(np.isclose(mean, mean_obs, atol=1e-2))
assert np.all(np.isclose(cov, cov_obs, atol=1e-2))
def plot_single_file(source_file, df):
logging.info(f"Plotting single file {source_file}")
name = os.path.basename(os.path.dirname(os.path.dirname(source_file)))
output_file = name + ".png"
logging.info(f"Creating wfit plot output to {output_file}")
c = ChainConsumer()
labels = [r"$\Omega_m$", "$w$", r"$\sigma_{int}$"]
for index, row in df.iterrows():
means = [row["OM"], row["w"], row["sigint"]]
cov = np.diag([row["OM_sig"] ** 2, row["wsig_marg"] ** 2, 0.01 ** 2])
c.add_covariance(means, cov, parameters=labels, name=f"Realisation {index}")
c.plotter.plot_summary(errorbar=True, filename=output_file)
del c
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)
===============================
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.
