Skip to content

GitLab

Commit 9888cc7e authored by Médéric Boquien's avatar Médéric Boquien
Browse files

Move the plotting functions out of the analysis modules. Plots are now... 

Move the plotting functions out of the analysis modules. Plots are now generated using an external script named pcigale-plots. This script is ready for multiprocessing but it is not yet activated. Finally, we can now save the reduced χ² values (which is required to plot them).
parent abdf6e97
Hide whitespace changes
Inline Side-by-side
pcigale/analysis_modules/pdf_analysis/__init__.py
View file @ 9888cc7e
......@@ -25,16 +25,14 @@ reduced χ²) is given for each observation.
import os
import numpy as np
import matplotlib
from numpy import newaxis
from collections import OrderedDict
from datetime import datetime
from progressbar import ProgressBar
from matplotlib import pyplot as plt
from astropy.table import Table, Column
from ...utils import read_table
from .. import AnalysisModule, complete_obs_table
from .utils import gen_pdf, gen_best_sed_fig
from .utils import gen_pdf
from ...warehouse import SedWarehouse
from ...data import Database
......@@ -51,9 +49,6 @@ RESULT_FILE = "analysis_results.fits"
BEST_MODEL_FILE = "best_models.fits"
# Directory where the output files are stored
OUT_DIR = "out/"
# Wavelength limits (restframe) when plotting the best SED.
PLOT_L_MIN = 91
PLOT_L_MAX = 1e6
# Number of points in the PDF
PDF_NB_POINTS = 1000
......@@ -73,28 +68,16 @@ class PdfAnalysis(AnalysisModule):
"If true, save the best SED for each observation to a file.",
False
)),
("plot_best_sed", (
("save_chi2", (
"boolean",
"If true, for each observation save a plot of the best SED "
"and the observed fluxes.",
False
)),
("plot_chi2_distribution", (
"boolean",
"If true, for each observation and each analysed variable "
"plot the value vs reduced chi-square distribution.",
"If true, for each observation and each analysed variable save "
"the reduced chi².",
False
)),
("save_pdf", (
"boolean",
"If true, for each observation and each analysed variable "
"save the probability density function.",
False
)),
("plot_pdf", (
"boolean",
"If true, for each observation and each analysed variable "
"plot the probability density function.",
"If true, for each observation and each analysed variable save "
"the probability density function.",
False
)),
("storage_type", (
......@@ -128,9 +111,6 @@ class PdfAnalysis(AnalysisModule):
"""
# To be sure matplotlib will not display the interactive window.
matplotlib.interactive(0)
# Rename the output directory if it exists
if os.path.exists(OUT_DIR):
new_name = datetime.now().strftime("%Y%m%d%H%M") + "_" + OUT_DIR
......@@ -144,11 +124,8 @@ class PdfAnalysis(AnalysisModule):
# Get the parameters
analysed_variables = parameters["analysed_variables"]
save_best_sed = (parameters["save_best_sed"].lower() == "true")
plot_best_sed = (parameters["plot_best_sed"].lower() == "true")
plot_chi2_distribution = (
parameters["plot_chi2_distribution"].lower() == "true")
save_chi2 = (parameters["save_chi2"].lower() == "true")
save_pdf = (parameters["save_pdf"].lower() == "true")
plot_pdf = (parameters["plot_pdf"].lower() == "true")
# Get the needed filters in the pcigale database. We use an ordered
# dictionary because we need the keys to always be returned in the
......@@ -253,6 +230,8 @@ class PdfAnalysis(AnalysisModule):
best_chi2_all = np.empty_like(best_idx_all)
best_chi2_red_all = np.empty_like(best_idx_all)
normalisation_factors_all = np.empty_like(best_idx_all)
best_fluxes = np.empty((len(obs_table), len(filters)))
best_variables_all = [None]*len(obs_table)
......@@ -370,10 +349,11 @@ class PdfAnalysis(AnalysisModule):
best_chi2_all[idx_obs] = chi_squares[best_index]
best_chi2_red_all[idx_obs] = reduced_chi_squares[best_index]
best_variables_all[idx_obs] = list(model_info_obs[best_index])
best_fluxes[idx_obs, :] = model_fluxes_obs[best_index, :]
if plot_best_sed or save_best_sed:
if save_best_sed:
print("Plotting/saving the best models...")
print("Saving the best models...")
with SedWarehouse(cache_type=parameters["storage_type"]) as \
sed_warehouse:
......@@ -383,44 +363,10 @@ class PdfAnalysis(AnalysisModule):
np.array(creation_modules_params)[w_models][best_index]
)
best_lambda = sed.wavelength_grid
best_fnu = sed.fnu * normalisation_factors[best_index]
if save_best_sed:
sed.to_votable(
OUT_DIR + "{}_best_model.xml".format(obs['id']),
mass=normalisation_factors[best_index]
)
if plot_best_sed:
plot_mask = (
(best_lambda >= PLOT_L_MIN * (1 + obs["redshift"]))
&
(best_lambda <= PLOT_L_MAX * (1 + obs["redshift"]))
)
figure = gen_best_sed_fig(
best_lambda[plot_mask],
best_fnu[plot_mask],
[f.effective_wavelength for f in filters.values()],
norm_model_fluxes[best_index, :],
[obs[f] for f in filters]
)
if figure is None:
print("Can not plot best model for observation "
"{}!".format(obs['id']))
else:
figure.suptitle(
u"Best model for {} - red-chi² = {}".format(
obs['id'],
reduced_chi_squares[best_index]
)
)
figure.savefig(OUT_DIR + "{}_best_model.pdf".format
(obs['id']))
plt.close(figure)
sed.to_votable(
OUT_DIR + "{}_best_model.xml".format(obs['id']),
mass=normalisation_factors[best_index]
)
##################################################################
# Probability Density Functions #
......@@ -430,8 +376,7 @@ class PdfAnalysis(AnalysisModule):
# parameters using a weighted kernel density estimation. This part
# should definitely be improved as we simulate the weight by adding
# as many value as their probability * 100.
if save_pdf or plot_pdf:
if save_pdf:
print("Computing the probability density functions...")
......@@ -447,8 +392,7 @@ class PdfAnalysis(AnalysisModule):
# TODO: use logging
print("Can not compute PDF for observation <{}> and "
"variable <{}>.".format(obs['id'], var_name))
if save_pdf and pdf_prob is not None:
else:
table = Table((
Column(pdf_grid, name=var_name),
Column(pdf_prob, name="probability density")
......@@ -456,38 +400,17 @@ class PdfAnalysis(AnalysisModule):
table.write(OUT_DIR + "{}_{}_pdf.fits".format(
obs['id'], var_name))
if plot_pdf and pdf_prob is not None:
figure = plt.figure()
ax = figure.add_subplot(111)
ax.plot(pdf_grid, pdf_prob)
ax.set_xlabel(var_name)
ax.set_ylabel("Probability density")
figure.savefig(OUT_DIR + "{}_{}_pdf.pdf".format(
obs['id'], var_name))
plt.close(figure)
if save_chi2:
##################################################################
# Reduced-chisquares plots #
##################################################################
if plot_chi2_distribution:
print("Plotting the reduced chi squares distributions...")
print("Saving the chi²...")
for var_index, var_name in enumerate(analysed_variables):
values = model_variables_obs[:, var_index]
figure = plt.figure()
ax = figure.add_subplot(111)
ax.plot(values, reduced_chi_squares, "ob")
ax.set_xlabel(var_name)
ax.set_ylabel("reduced chi-square")
figure.suptitle("Reduced chi-square distribution of {} "
"values for {}".format(obs['id'],
var_name))
figure.savefig(OUT_DIR + "{}_{}_chisquares.pdf".format(
obs['id'], var_name))
plt.close(figure)
table = Table((
Column(model_variables_obs[:, var_index],
name=var_name),
Column(reduced_chi_squares, name="chi2")))
table.write(OUT_DIR + "{}_{}_chi2.fits".format(obs['id'],
var_name))
# Create and save the result table.
result_table = Table()
......@@ -534,6 +457,11 @@ class PdfAnalysis(AnalysisModule):
column *= normalisation_factors_all
best_model_table.add_column(column)
for index, name in enumerate(filters):
column = Column(best_fluxes[:, index] * normalisation_factors_all,
name=name, unit='mJy')
best_model_table.add_column(column)
best_model_table.write(OUT_DIR + BEST_MODEL_FILE)
......
pcigale/analysis_modules/pdf_analysis/utils.py
View file @ 9888cc7e
......@@ -7,10 +7,6 @@
import numpy as np
from scipy.stats import gaussian_kde
from scipy.linalg import LinAlgError
from matplotlib import pyplot as plt
from copy import deepcopy
from ...sed.cosmology import cosmology
from ...creation_modules import get_module as get_creation_module
def gen_pdf(values, probabilities, grid):
......@@ -65,44 +61,3 @@ def gen_pdf(values, probabilities, grid):
result = None
return result
def gen_best_sed_fig(wave, fnu, filters_wave, filters_model, filters_obs):
"""Generate a figure for plotting the best models
Parameters
----------
wave : array-like of floats
The wavelength grid of the model spectrum.
fnu : array-like of floats
The Fnu spectrum of the model at each wavelength.
filters_wave : array-like of floats
The effective wavelengths of the various filters.
filters_model : array-like of floats
The model fluxes in each filter.
filters_obs : array-like of floats
The observed fluxes in each filter.
Returns
-------
A matplotlib.plt.figure.
"""
try:
figure = plt.figure()
ax = figure.add_subplot(111)
ax.loglog(wave, fnu, "-b", label="Model spectrum")
ax.loglog(filters_wave, filters_model, "ob", label="Model fluxes")
ax.loglog(filters_wave, filters_obs, "or", label="Observation fluxes")
ax.set_xlabel("Wavelength [nm]")
ax.set_ylabel("Flux [mJy]")
ax.legend(loc=0)
return figure
except ValueError:
# If the SED can't be plot in x and y logarithm scaled, that means
# that we have either negative wavelength or flux and that something
# has gone wrong.
return None
pcigale_plots/__init__.py 0 → 100644
View file @ 9888cc7e
# -*- coding: utf-8 -*-
# Copyright (C) 2013 Centre de données Astrophysiques de Marseille
# Copyright (C) 2013-2014 Yannick Roehlly
# Copyright (C) 2013 Institute of Astronomy
# Licensed under the CeCILL-v2 licence - see Licence_CeCILL_V2-en.txt
# Author: Yannick Roehlly & Médéric Boquien
__version__ = "0.1-alpha"
import argparse
from astropy.table import Table
from itertools import product, repeat
from collections import OrderedDict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import multiprocessing as mp
import numpy as np
import os
from pcigale.data import Database
from pcigale.utils import read_table
from pcigale.session.configuration import Configuration
# Name of the file containing the best models information
BEST_MODEL_FILE = "best_models.fits"
# Directory where the output files are stored
OUT_DIR = "out/"
# Wavelength limits (restframe) when plotting the best SED.
PLOT_L_MIN = 91.
PLOT_L_MAX = 1e6
def _chi2_worker(obj_name, var_name):
"""Plot the reduced χ² associated with a given analysed variable
Parameters
----------
obj_name: string
Name of the object.
var_name: string
Name of the analysed variable..
"""
if os.path.isfile(OUT_DIR + "{}_{}_chi2.fits".format(obj_name, var_name)):
chi2 = Table.read(OUT_DIR + "{}_{}_chi2.fits".format(obj_name,
var_name))
figure = plt.figure()
ax = figure.add_subplot(111)
ax.scatter(chi2[var_name], chi2['chi2'], color='k')
ax.set_xlabel(var_name)
ax.set_ylabel("Reduced $\chi^2$")
ax.minorticks_on()
figure.suptitle("Reduced $\chi^2$ distribution of {} values for {}."
.format(obj_name, var_name))
figure.savefig(OUT_DIR + "{}_{}_chi2.pdf".format(obj_name, var_name))
plt.close(figure)
else:
print("No chi² found for {}. No plot created.".format(obj_name))
def _pdf_worker(obj_name, var_name):
"""Plot the PDF associated with a given analysed variable
Parameters
----------
obj_name: string
Name of the object.
var_name: string
Name of the analysed variable..
"""
if os.path.isfile(OUT_DIR + "{}_{}_pdf.fits".format(obj_name, var_name)):
pdf = Table.read(OUT_DIR + "{}_{}_pdf.fits".format(obj_name, var_name))
figure = plt.figure()
ax = figure.add_subplot(111)
ax.plot(pdf[var_name], pdf['probability density'], color='k')
ax.set_xlabel(var_name)
ax.set_ylabel("Probability density")
ax.minorticks_on()
figure.savefig(OUT_DIR + "{}_{}_pdf.pdf".format(obj_name, var_name))
plt.close(figure)
else:
print("No PDF found for {}. No plot created.".format(obj_name))
def _sed_worker(obs, mod, filters):
"""Plot the best SED with the associated fluxes in bands
Parameters
----------
obs: Table row
Data from the input file regarding one object.
mod: Table row
Data from the best model of one object.
filters: ordered dictionary of Filter objects
The observed fluxes in each filter.
"""
if os.path.isfile(OUT_DIR + "{}_best_model.xml".format(obs['id'])):
sed = Table.read(OUT_DIR + "{}_best_model.xml".format(obs['id']),
table_id="Fnu")
filters_wl = np.array([filt.effective_wavelength
for filt in filters.values()])
obs_fluxes = np.array([obs[filt] for filt in filters.keys()])
mod_fluxes = np.array([mod[filt] for filt in filters.keys()])
figure = plt.figure()
ax = figure.add_subplot(111)
ax.loglog(sed['wavelength'], sed['F_nu'], label="Model spectrum",
color='k')
ax.scatter(filters_wl, obs_fluxes, marker='o', color='r',
label="Model fluxes")
ax.scatter(filters_wl, mod_fluxes, marker='o', color='b',
label="Observed fluxed")
ax.set_xlim(PLOT_L_MIN * (1. + obs['redshift']),
PLOT_L_MAX * (1. + obs['redshift']))
ax.set_xlabel("Wavelength [nm]")
ax.set_ylabel("Flux [mJy]")
ax.legend(loc=0)
figure.suptitle("Best model for {}. Reduced $\chi^2$={}".format(
obs['id'],
np.round(mod['reduced_chi_square'], decimals=2)))
figure.savefig(OUT_DIR + "{}_best_model.pdf".format(obs['id']))
plt.close(figure)
else:
print("No SED found for {}. No plot created.".format())
def chi2(config):
"""Plot the χ² values of analysed variables.
"""
input_data = read_table(config.configuration['data_file'])
chi2_vars = (config.configuration['analysis_method_params']
['analysed_variables'])
with mp.Pool(1) as pool:
items = product(input_data['id'], chi2_vars)
pool.starmap(_chi2_worker, items)
pool.close()
pool.join()
def pdf(config):
"""Plot the PDF of analysed variables.
"""
input_data = read_table(config.configuration['data_file'])
pdf_vars = (config.configuration['analysis_method_params']
['analysed_variables'])
with mp.Pool(1) as pool:
items = product(input_data['id'], pdf_vars)
pool.starmap(_pdf_worker, items)
pool.close()
pool.join()
def sed(config):
"""Plot the best SED with associated observed and modelled fluxes.
"""
obs = read_table(config.configuration['data_file'])
mod = Table.read(OUT_DIR + BEST_MODEL_FILE)
with Database() as base:
filters = OrderedDict([(name, base.get_filter(name))
for name in config.configuration['column_list']
if not name.endswith('_err')])
with mp.Pool(1) as pool:
pool.starmap(_sed_worker, zip(obs, mod, repeat(filters)))
pool.close()
pool.join()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--conf-file', dest='config_file',
help="Alternative configuration file to use.")
subparsers = parser.add_subparsers(help="List of commands")
pdf_parser = subparsers.add_parser('pdf', help=pdf.__doc__)
pdf_parser.set_defaults(parser='pdf')
chi2_parser = subparsers.add_parser('chi2', help=chi2.__doc__)
chi2_parser.set_defaults(parser='chi2')
sed_parser = subparsers.add_parser('sed', help=sed.__doc__)
sed_parser.set_defaults(parser='sed')
args = parser.parse_args()
if args.config_file:
config = Configuration(args.config_file)
else:
config = Configuration()
if args.parser == 'chi2':
chi2(config)
elif args.parser == 'pdf':
pdf(config)
elif args.parser == 'sed':
sed(config)
setup.py 100644 → 100755
View file @ 9888cc7e
......@@ -17,7 +17,7 @@ class custom_build(build):
build.run(self)
entry_points = {
'console_scripts': ['pcigale = pcigale:main']
'console_scripts': ['pcigale = pcigale:main', 'pcigale-plots = pcigale_plots:main']
}
setup(
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment