# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from distutils.version import LooseVersion, StrictVersion
import pkg_resources
import os,re
import sys
from copy import deepcopy
import configparser
from astropy.io.fits import Header
import astropy.io.ascii as asciitable
from astropy.table import Table, Column
import math
import numpy as np
np.random.seed(seed=1234)
# LOCAL IMPORTS
from .__version__ import __version__
from .math_utils import merge_where_nan, median_clip, safe_exp
from .instruments import *
from .hyperspectral_cube import HyperspectralCube as HyperCube
from .string_stdout import StringStdOut
from .model_class import Model
from .model_sersic3d import ModelSersic
from .galaxy_parameters import GalaxyParameters, GalaxyParametersError
from .plot_utilities import Plots
from .mcmc import MCMC
from .galpak3d_utils import _save_to_file,_read_file, _read_instrument, _read_model
#will be removed
DiskModel = ModelSersic #for backward compatibility
DefaultModel = ModelSersic
OII = {'wave': [3726.2, 3728.9], 'ratio':[0.8,1.0]}
# LOGGING CONFIGURATION
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('GalPaK')
# OPTIONAL IMPORTS
try:
import bottleneck as bn
except ImportError:
logger.info(" bottleneck (optional) not installed, performances will be degraded")
import numpy as bn
try:
import pyfftw
except ImportError:
logger.info(" PyFFTW (optional) not installed, performances will be degraded")
try:
import mpdaf
logger.info("Found MPDAF version %s" % (mpdaf.__version__))
mpdaf_there=True
except ImportError:
mpdaf_there=False
logger.warning(" MPDAF (optional) not installed / not required")
try:
import emcee
emcee_there=True
logger.info("Found EMCEE version %s" % (emcee.__version__))
logger.warning("EMCEE tested for version > 3.0")
except ImportError:
emcee_there=False
logger.warning(" EMCEE (optional) not installed / not required. So option use_emcee is disabled")
try:
import dynesty
dynesty_there=True
logger.info("Found Dynesty version %s \n EXPERIMENTAL and UNSUPPORTED" % (dynesty.__version__))
except ImportError:
dynesty_there=False
logger.warning(" Dynesty (optional) not installed / not required. ")
try:
import pymultinest
multinest_there=True
logger.info("Found PyMultinest version %s" % (pkg_resources.get_distribution("pymultinest").version))
except ImportError:
multinest_there=False
logger.warning(" PyMultinest (optional) not installed / not required. ")
try:
import corner
except ImportError:
logger.info("corner (optional) not installed, corner plots will be disabled")
#Python3 compatibility
try:
basestring
except NameError:
basestring = str
if sys.version>=LooseVersion('2.7') and sys.version<LooseVersion('3.5'):
try:
reload # Python 2.7
except NameError:
try:
from importlib import reload # Python 3.4+
except ImportError:
from imp import reload # Python 3.0 - 3.3
reload(sys)
sys.setdefaultencoding('utf-8')
[docs]class GalPaK3D(Plots, MCMC):
"""
GalPaK3D is a tool to extract Galaxy Parameters and Kinematics from
3-Dimensional data, using reverse deconvolution with Bayesian analysis
Markov Chain Monte Carlo. (random walk)
cube: HyperspectralCube|string
The actual data on which we'll work ; it should contain only one galaxy.
Can be a HyperspectralCube object, a string filename to a FITS file, or
even MPDAF's ``mpdaf.obj.Cube``.
seeing: float
Aka the Point Spread Function's Full Width Half Maximum.
This convenience parameter, when provided, will override the FWHM value
of the instrument's PSF.
instrument: Instrument
The instrument configuration to use when simulating convolution.
The default is :class:`MUSE <galpak.MUSE>`.
crval3: float
A value for the cube's header's CRVAL3 when it is missing.
You should update your cube's header.
crpix3: float
A value for the cube's header's CRPIX3 when it is missing.
You should update your cube's header.
cunit3: float
A value for the cube's header's CUNIT3 when it is missing.
You should update your cube's header.
cdelt3: float
A value for the cube's header's CDELT3 when it is missing.
You should update your cube's header.
cunit1: float
A value for the cube's header's CUNIT1 (&2) when it is missing.
You should update your cube's header.
force_header_update: bool
Set to True to force the update of the above header cards,
when their values are not missing.
Note: These will not be saved into the FITS file. (if the cube is one)
"""
logger.info(' Running galpak ' + __version__)
def __init__(self, cube, variance=None, model=None,
seeing=None, instrument=None, quiet=False,
crval3=None, crpix3=None, cunit3=None, cdelt3=None, ctype3=None, cunit1=None,
force_header_update=False):
# DEVS : If you change the signature above,
# remember to update the run() in api.py
# Prepare output attributes
self.acceptance_rate = 100.
self.galaxy = GalaxyParameters()
self.stdev = GalaxyParameters()
self.chain = None
self.sub_chain = None
self.psf3d = None
self.convolved_cube = None
self.deconvolved_cube = None
self.residuals_cube = None
self.residuals_map = None
self.variance_cube = None
#true intrinsic maps
self.true_flux_map = None
self.true_velocity_map = None
self.true_disp_map = None
self.error_maps = False
self.true_flux_map_error = None
self.true_velocity_map_error = None
self.true_disp_map_error = None
#observed maps
self.obs_flux_map = None
self.obs_velocity_map = None
self.obs_disp_map = None
self.max_iterations = None
self.method = None
self.chain_fraction = None
self.percentile = None
self.initial_parameters = None
self.min_boundaries = None
self.max_boundaries = None
self.known_parameters = None
self.random_scale = None
self.reduce_chi = None
self.chi_stat = 'gaussian'
self.chi_at_p = None
self.best_chisq = None
self.stats = None
self.BIC = None
self.DIC = None
self.mcmc_method = None
self.mcmc_sampling = None
#self.redshift = None
# Assign the logger to a property for convenience, and set verbosity
self.logger = logger
self.version = __version__
self.config = configparser.RawConfigParser()
self.model = None
if quiet:
self._set_verbose(None)
self.verbose=None
else:
self._set_verbose(True)
# Set up the input data cube
if isinstance(cube, basestring):
self.logger.info('Reading cube from %s' % (cube))
cube = HyperCube.from_file(cube, verbose=not quiet)
elif isinstance(cube, HyperCube):
self.logger.info('Provided cube is a HyperSpectral Object')
elif mpdaf_there:
if isinstance(cube, mpdaf.obj.Cube):
self.logger.info('Provided Cube is a mpdaf Cube object')
cube = HyperCube.from_mpdaf(cube, verbose=not quiet)
else:
raise TypeError("Provided cube is not a HyperspectralCube "
"nor mpdaf's Cube")
else:
raise TypeError("Provided cube is not a HyperspectralCube ")
if cube.is_empty():
raise ValueError("Provided cube is empty")
self.cube = cube
if not self.cube.has_header():
self.logger.info("Reading hyperspectral cube without header. "
"Creating a minimal one.")
self.cube.header = Header()
# GalPaK needs a sane Cube
#self.cube.sanitize()
# Set up the variance
if variance is not None:
self.logger.info('Using user-provided variance input')
if isinstance(variance, basestring):
self.logger.info("Read provided variance cube %s into HyperCube." % (variance))
variance_cube = HyperCube.from_file(variance)
elif isinstance(variance, HyperCube):
variance_cube = variance
if variance_cube.data is None:
self.logger.warning("Provided variance cube is empty.")
else:
self.logger.info("Saving variance into varianceCube")
if variance_cube.filename == None:
variance_cube.filename = 'Variance_cube_from_user'
elif isinstance(variance, float):
variance_cube = HyperCube(variance)
variance_cube.filename = 'Variance_float_from_user={:.2e}'.format(variance)
elif not (isinstance(variance, float) or isinstance(variance, HyperCube) or isinstance(variance, basestring)):
raise TypeError("Provided variance is not a string nor HyperCube nor a float")
else:
variance_cube = HyperCube(self.cube.var, filename='Variance_from_cube_extension')
# Set up the instrument's context
if instrument is None:
instrument = MUSE()
self.logger.warning('Using the MUSE instrument per default. '
'You should specify your own instrument.')
if isinstance(instrument, basestring):
#read config
instrument = _read_instrument(instrument)
#raise ValueError("Instrument needs to be an instance of "
# "Instrument, not a string.")
if not isinstance(instrument, Instrument):
raise ValueError("Instrument needs to be an instance of Instrument")
self.instrument = instrument
## Set default cube specs from instrument when missing from headers
# as we don't want to rely on the input fits having properly set headers.
# So, we aggregate in the cube our own specs (in " and µm) for our personal use :
# - xy_step
# - z_step
# - z_central
# 1. Patch up the HyperCube's missing values
try:
self.cube.patch(
crval3=crval3, crpix3=crpix3, cunit3=cunit3,
cdelt3=cdelt3, ctype3=ctype3, cunit1=cunit1, force=force_header_update
)
except ValueError:
raise ValueError("The cube already has one of the header cards "
"you're trying to provide. "
"Use force_header_update=True to override.")
self.logger.debug('Header after patch : %s' % self.cube)
#set xy_step z_step and z_central
# 2. Set cube metadata from the the instrument if header is incomplete
self.cube.defaults_from_instrument(instrument=instrument)
# 3. Initialize steps xy_steps z_steps z_cunnit and z_central
self.cube.initialize_self_cube()
# 4. Calibrate the instrument with the cube
self.instrument.use_pixelsize_from_cube(self.cube)
self.logger.debug('z central : %4.e' % (self.cube.z_central) )
# Override the PSF FWHM (aka. seeing) if provided
if seeing is not None and self.instrument.psf is not None:
try:
self.instrument.psf.fwhm = seeing
except AttributeError:
raise IOError("You provided a seeing but your instrument's PSF has no FWHM.")
# Handle the variance, when provided, or generate one
variance_data = variance_cube.data
if variance_data is not None:
self.logger.info("Replacing 0s in the variance cube by 1e12")
variance_data = np.where(variance_data == 0.0, 1e12, variance_data)
else:
# Clip data, and collect standard deviation sigma
self.logger.warning("No variance provided. Estimating Variance from edge statistics")
clipped_data, clip_sigma, __ = median_clip(self.cube.data[:, 2:-4, 2:4], 2.5)#yband at x[2:4]
self.logger.info("Computed stdev from the edges: sigma=%.e" % clip_sigma)
# Adjust stdev margin if it is zero, as we'll divide with it later on
if not np.isfinite(clip_sigma):
clipped_data, clip_sigma, __ = median_clip(self.cube.data, 2.5)
self.logger.info("reComputing stdev from the whole cube: sigma=%.e" % clip_sigma)
if np.size(clip_sigma) == 1 and clip_sigma == 0:
clip_sigma = 1e-20
variance_data = clip_sigma ** 2 *np.ones_like(self.cube.data)
self.logger.info('Variance estimated is %s ' % (str(clip_sigma**2)))
# Save the variance cube
variance_cube.data = variance_data
self.variance_cube = variance_cube # cube of sigma^2
self.error_cube = np.sqrt(self.variance_cube.data) # cube of sigmas
# Provide the user with some feedback
self.logger.info("Setting up with the following setup :\n%s" % self.instrument)
# 5. Init model
# Set up the model context
# Set up the model context
if isinstance(model, basestring)==True:
model = _read_model(deepcopy(model))
if model is not None:
self._init_model(model)
self.logger.info("Setting up the model : %s" % (self.model.__name__()))
self.logger.info("Model setup :\n%s" % (self.model) )
def _init_model(self, model):
# Set up the simulation model
if model is not None:
self.model = model
self.logger.info("Init boundaries from model '%s'" %(self.model.__name__()))
self.model_dict = self.model.__dict__
#important:
self.model.pixscale = self.cube.xy_step
# Compute a flux estimation
# fixme: add weighted sum with variance if present
self.flux_est = bn.nansum(self.cube.data)
if self.flux_est < 0:
self.logger.warning(
"WARNING: Initial flux (%4.2e) is <0 -- "
"likely wrong, will recompute it ignoring <0 values"
% self.flux_est
)
self.flux_est = np.sum(np.where(self.cube.data > 0, self.cube.data, 0))
self.logger.warning('Initial flux is now %4.2e' % self.flux_est)
self.logger.info('TIP: use `initial_parameters=` to set the flux')
else:
self.logger.info('Initial flux is %4.2e' % self.flux_est)
# Default boundaries
self.min_boundaries = self.model.min_boundaries(self)
self.max_boundaries = self.model.max_boundaries(self)
# Default initial parameters
self.initial_parameters = (self.max_boundaries + self.min_boundaries) / 2.
#set known parameters to ones
self.known_parameters = self.model.Parameters()
[docs] def run_mcmc(self, max_iterations=15000,
method_chain='last',
last_chain_fraction=60,
percentile=95,
model=None,
chi_stat='gaussian',
mcmc_method='galpak',
mcmc_sampling=None,
min_boundaries=None,
max_boundaries=None,
known_parameters=None,
initial_parameters=None,
random_scale=None,
min_acceptance_rate=10,
verbose=True,
emcee_nwalkers=30,
**kwargs):
# DEVS : If you change the signature above,
# remember to update the run() in api.py
"""
Main method_chain of GalPak, computes and returns the galaxy parameters
as a :class:`GalaxyParameters <galpak.GalaxyParameters>` object
using reverse deconvolution with a MCMC.
Also fills up the following attributes :
- chain
- psf3d
- deconvolved_cube
- convolved_cube
- residuals_cube (Data-Model in units of sigma)
- residuals_map (average of data-model in units of sigma or = 1/N_z.
Sum_z Residuals_cube. sqrt(Nz) )
- acceptance_rate
- galaxy (same object as returned value)
with Vmax forced to be positive [and 180 added to PA]
- stdev (also available as galaxy.stdev)
- true_flux_map
- true_velocity_map
- true_disp_map
Stops iteration if acceptance rate drops below ``min_acceptance_rate`` %
or when ``max_iterations`` are reached.
max_iterations: int
Maximum number of useful iterations.
method_chain: 'chi_sorted' | 'chi_min' | 'last' | 'MAP'
Method used to determine the best parameters from the chain.
- 'last' (default) : mean of the last_chain_fraction(%) last parameters of the chain
- 'chi_sorted' : mean of the last_chain_fraction(%) best fit parameters of the chain
- 'chi_min' : mean of last_chain_fraction(%) of the chain around the min chi
- 'MAP': Parameters at Maximum At Posteriori, i.e. at chi_min
last_chain_fraction: int
Last Chain fraction (in %) used to compute the best parameters.
Defaults to 60.
model = DefaultModel()
see class DiskModel or ModelSersic
chi_stat: 'gaussian' [default] | 'Mighell' | 'Neyman' | 'Cstat' | 'Pearson'
The chi2 statitics
https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/XSappendixStatistics.html
- 'gaussian' (default): Sum (D - M)^2 / e
- 'Neyman' Sum (D - M )^2 / max(D,1)
- 'Mighell' Sum (D + min(D,1) - M)^2 / (D+1) Mighell http://adsabs.harvard.edu/abs/1999ApJ...518..380M
- 'Cstat' Sum ( M - D + D * log(D/M) ) Cash statistique Humphrey 2009, http://adsabs.harvard.edu/abs/2009ApJ...693..822H
- 'Pearson' Sum ( M - D )^2 / M Pearson statistic Humphrey 2009, http://adsabs.harvard.edu/abs/2009ApJ...693..822H
mcmc_method: 'galpak' [default] | 'emcee_walkers'| 'emcee_MH' | 'dynesty' | 'multinest'
The MCMC method.
- galpak: for the original MCMC algorithm using Cauchy proposal distribution
- emcee_MH: emcee Metropolis Hasting
- emcee_walkers: emcee multi-Walkers algorithms with Moves if version>=3.0
- dynesty: unsupported
- multinest: using Importance Nested Sampling w/ pyMultinest
- pymc3: to be implemented
mcmc_sampling: None [default] | 'Cauchy' | 'AdaptiveCauchy' | 'Normal' | 'DE' | 'walkers'
The sampling proposal distribution for MCMC_methods [galpak, emcee]
- 'Cauchy' default when mcmc_method = 'galpak' or 'emcee_MH'
requires tuning random_scale
- 'AdaptiveCauchy' for mcmc_method = 'galpak [using last 500 or 750 iterations]
- 'Normal' Gaussian sampling for 'galpak' or 'emcee_MH'
- 'walkers' (=StretchMove) default when mcmc_method ='emcee_walkers'
min_boundaries: ndarray|GalaxyParameters
The galaxy parameters will never be less than these values.
Will override the default minimum boundaries for the parameters.
If any of these values are NaN, they will be replaced by the default ones.
max_boundaries: ndarray|GalaxyParameters
The galaxy parameters will never be more than these values.
Will override the default minimum boundaries for the parameters.
If any of these values are NaN, they will be replaced by the default ones.
known_parameters: ndarray|GalaxyParameters
All set parameters in this array will be skipped in the MCMC,
the algorithm will not try to guess them.
initial_parameters: ndarray|GalaxyParameters
The initial galaxy parameters of the MCMC chain.
If None, will use the inital parameters provided by the model.
The galaxy parameters not initialized by the model or by this
parameter will be set to the mean of the boundaries.
random_scale: float
Scale the amplitude of the MCMC sampling by these values.
This is an important parameter to adjust for reasonable acceptance rate.
The acceptance rate should be around 30-50%.
If the acceptance rate is <20-30% (too low), decrease random_scale
IF the acceptance rate is >50-60% (too high), increase random_scale
verbose: boolean
Set to True to output a detailed log of the process.
The run is faster when this is left to False.
"""
# DEVS : If you change the signature above,
# remember to update the run() in api.py
#re initialize psf
self.instrument.psf3d_fft = None
# Save the parameters (the animation uses them)
self.max_iterations = max_iterations
self.method = method_chain
self.chain_fraction = last_chain_fraction
self.chi_stat = chi_stat
self.percentile = percentile
self.verbose = verbose
#save intermediate model maps
self.error_maps = None #will be set later
self.chain_flux_map = []
self.chain_velocity_map = []
self.chain_dispersion_map = []
# Set up the simulation model
if model is None:
if self.model is not None:
self.logger.warning("Model already specified: '%s'" % (self.model.__name__()))
else:
self.model = DefaultModel()
self.logger.warning("Will use default model '%s'" %(self.model.__name__()))
self.logger.info("Model setup :\n%s" % (self.model) )
elif model is not None:
if isinstance(model, basestring):
#read from config file
self.model = _read_model(model)
self.logger.info("Model set to %s from file: %s" % (self.model.__name__(), self.model) )
elif isinstance(model, Model):
self.logger.warning("Model was already set")
self.model = model
self.logger.info("Model set to %s : %s" % (self.model.__name__(), self.model))
else:
raise ValueError
self._init_model(self.model)
#For backwards compatibility:
## will be removed in the future
#if self.model.line is None:
# self.model.line = self.line
#else:
# self.line = self.model.line
#save attribute
#For backwards compatibility:
## will be removed in the future
## if not None, computes Mdyn(Re)
#if self.model.redshift is not None:
# self.redshift = self.model.redshift
# Sanitize data and set arbitrary big stdev where NaNs are
#cube_data = self.cube.data
#cube_data = np.nan_to_num(cube_data)
# Set verbosity
self._set_verbose(verbose)
dim_p = np.size(GalaxyParameters())
# In fraction of boundary space, an arbitrarily
# small value for closeness to boundaries
self.eps = 0.003
# Merge provided boundaries (if any) with default boundaries
if isinstance(min_boundaries, basestring):
min_boundaries = self._read_params(deepcopy(min_boundaries),'MIN')
if isinstance(max_boundaries, basestring):
max_boundaries = self._read_params(deepcopy(max_boundaries),'MAX')
if min_boundaries is not None:
min_boundaries = deepcopy(min_boundaries)
merge_where_nan(min_boundaries, self.min_boundaries) #this returns a ndarray
self.min_boundaries = GalaxyParameters().from_ndarray(min_boundaries)
if max_boundaries is not None:
max_boundaries = deepcopy(max_boundaries)
merge_where_nan(max_boundaries, self.max_boundaries) #this returns a ndarray
self.max_boundaries = GalaxyParameters().from_ndarray(max_boundaries)
bug_boundaries = self.min_boundaries > self.max_boundaries
if bug_boundaries.any():
self.logger.debug("Min Boundaries : %s", self.min_boundaries)
self.logger.debug("Max Boundaries : %s", self.max_boundaries)
raise ValueError("Boundaries are WRONG, because min > max")
# Default initial parameters
self.initial_parameters = self.model.initial_parameters(self)
# Create initial galaxy parameters using mean and provided values
mean_parameters = (self.max_boundaries + self.min_boundaries) / 2.
#complete default values with mean parameters
merge_where_nan(self.initial_parameters, mean_parameters)
self.logger.info("Default Param_init : %s", self.initial_parameters)
# read initial parameter from config file
if isinstance(initial_parameters, basestring):
initial_parameters = self._read_params(deepcopy(initial_parameters), 'INIT')
# Merge provided initial parameters (if any) with the defaults
if initial_parameters is not None:
#complete input with default values
template = self.model.Parameters()
merge_where_nan(template, initial_parameters)
merge_where_nan(template, self.initial_parameters)
self.initial_parameters = template
self.logger.info("Initial parameters : %s", self.initial_parameters)
# By default, try to guess all parameters
should_guess_flags = np.ones(dim_p) # 0: we know it / 1: try to guess
#if using input image
# @fixme; this is unused
if self.model_dict['flux_profile'] == 'user' and known_parameters is None:
raise self.logger.error(
"With an input image it is advised to freeze "
"the `inclination`, using `known_parameters=`.")
# Flag parameters that we manually specified and don't need to guess
if isinstance(known_parameters, basestring):
known_parameters = self._read_params(deepcopy(known_parameters), 'KNOWN')
if known_parameters is not None:
if len(known_parameters) != dim_p:
raise ValueError("The `known_parameters=` must be an array of "
"length %d, or even better an instance of `%s`"
% (dim_p, self.model.parameters_class()))
else:
merge_where_nan(self.known_parameters, known_parameters)
self.logger.info("Using known parameters: %s", self.known_parameters)
# Freeze the known parameters by flagging them as not-to-guess
for idx in range(dim_p):
parameter = self.known_parameters[idx]
if not math.isnan(parameter):
should_guess_flags[idx] = 0
sign = math.copysign(1., parameter)
self.min_boundaries[idx] = parameter * (1 - self.eps * sign) - self.eps
self.max_boundaries[idx] = parameter * (1 + self.eps * sign) + self.eps
self.initial_parameters[idx] = parameter
# The setup is finished, let's dump some information
self.logger.info("Min Boundary: %s", self.min_boundaries)
self.logger.info("Max Boundary: %s", self.max_boundaries)
# The setup is done, we can now start the MCMC loop.
if isinstance(random_scale, basestring):
random_scale = self._read_params(deepcopy(random_scale), 'RSCALE')
self.random_scale = random_scale
random_amplitude = self._init_sampling_scale(random_scale, should_guess_flags)
# Zero random amplitude where parameters are known
random_amplitude = random_amplitude * should_guess_flags
self.random_amplitude = random_amplitude
self.logger.info("Starting with χ² = %f", self.compute_chi(self.initial_parameters) / self.Ndegree)
## The actual MCMC #####################################################
#self.error_maps = save_error_maps #save intermediate model maps
#set walkers
if mcmc_method == 'galpak' and mcmc_sampling is None:
mcmc_sampling = 'Cauchy'
#if mcmc_method == 'emcee_MH' and mcmc_sampling is None:
# mcmc_sampling = 'Cauchy'
if mcmc_method == 'emcee_walkers' and mcmc_sampling is None:
mcmc_sampling = 'walkers'
self.mcmc_method = mcmc_method
self.mcmc_sampling = mcmc_sampling
#burnin = np.int(0.15*self.max_iterations)
if mcmc_method == 'galpak':
chain = self.myMCMC(max_iterations, random_amplitude, sampling_method=mcmc_sampling, min_acceptance_rate=min_acceptance_rate)
elif mcmc_method == 'emcee_MH' and emcee_there:
#DEFAULT EMCEE parameters for EnsembleSampler(EMCEE)
pass
elif mcmc_method == 'emcee_walkers' and emcee_there:
#DEFAULT EMCEE parameters for EnsembleSampler(EMCEE)
#kwargs_sampler = {
# 'pool':None, \
# 'backend':None,\
# 'vectorize':False,\
# 'blobs_dtype':None,\
# 'postargs':None,\
# 'threads': 1,
# }
emcee_threads = 4
kwargs_emcee ={
'store': True, \
'tune': True, \
'thin': 30
}
#update default parameters
kwargs_emcee.update(kwargs)
#@fixme: need to accept parallelize
pos0 = np.array([self.initial_parameters * (1+1e-3*np.random.randn(dim_p)) for i in range(emcee_nwalkers) ])
self.logger.critical("Running EMCEE with %d walkers on %d iterations" % (emcee_nwalkers, self.max_iterations))
if LooseVersion(emcee.__version__)<LooseVersion('3.0'):
raise Exception("EMCEE version not supported ", emcee.__version__)
#EMCE version3
if mcmc_sampling == 'Cauchy':
from .mcmc import CauchyMove
myMove=CauchyMove(self.random_amplitude.as_vector()**2)
self.logger.critical("Running EMCEE Walkers with Cauchy Sampling")
elif mcmc_sampling == 'Normal':
from emcee.moves import GaussianMove
myMove=GaussianMove(self.random_amplitude.as_vector()**2)
self.logger.info("Random Ampl : %s", self.random_amplitude.as_vector())
self.logger.critical("Running EMCEE Walkers with Gaussian Sampling")
elif mcmc_sampling == 'DE':
from emcee.moves import DEMove
myMove=DEMove()
self.logger.critical("Running EMCEE Walkers with DE Sampling")
elif mcmc_sampling == 'Snooker':
from emcee.moves import DESnookerMove
myMove=DESnookerMove()
self.logger.critical("Running EMCEE Walkers with Snooker Sampling")
elif mcmc_sampling == 'walkers':
myMove = None #default StretchMove from EMCEE
self.logger.critical("Running EMCEE Walkers with default Stretch Sampling")
elif mcmc_sampling == 'walkersCauchy':
from .mcmc import CauchyMove
#50/50 StretchMove and CauchyMove
myMove = [ (emcee.moves.StretchMove(),0.6), (CauchyMove(self.random_amplitude.as_vector()**2),0.4)]
self.logger.critical("Running EMCEE Walkers with 60/40 StretchMove() & Cauchy Sampling")
else:
raise Exception("mcmc_sampling not valid. Options are ", self.SAMPLING_VALID)
kwargs_emcee.update(kwargs)
#Multiprocessing
#try:
# from multiprocessing import Pool
# self.logger.info("Running EMCEE with multiprocessing")
# with Pool() as pool:
# self.sampler = emcee.EnsembleSampler(emcee_nwalkers, dim_p, self, moves=myMove, pool=pool)
# if self.verbose is not True:
# self.sampler.run_mcmc(pos0, self.max_iterations, progress=True, **kwargs_emcee)
# else:
# for state in self.sampler.sample(pos0, iterations=self.max_iterations, **kwargs_emcee):
# for k, r in enumerate(state.coords):
# print("%d %s log L=%f" % (self.sampler.iteration, self.model.Parameters().from_ndarray(r), \
# state.log_prob[k]))
#except ImportError:
self.logger.info(" Running EMCEE with 4 threads")
self.sampler = emcee.EnsembleSampler(emcee_nwalkers, dim_p, self, moves=myMove, threads=4)
if self.verbose is not True:
self.sampler.run_mcmc(pos0, self.max_iterations, progress=True, **kwargs_emcee)
else:
for state in self.sampler.sample(pos0, iterations=self.max_iterations, **kwargs_emcee):
for k,r in enumerate(state.coords):
print("%d %s log L=%f" %(self.sampler.iteration, self.model.Parameters().from_ndarray(r),\
state.log_prob[k]))
self.sampler.__dict__['kwargs'] = kwargs_emcee
self.acceptance_rate = self.sampler.acceptance_fraction
self.logger.info("EMCEE MH: Acceptance: %s " % (self.sampler.acceptance_fraction))
#self.logger.info("EMCEE MH: Naccepted states ", (self.sampler.naccepted))
#flat_chain = self.sampler.get_chain(discard=burnin, flat=True)
chain_data = self.sampler.flatchain
chain = Table(chain_data, names=self.model.Parameters().names)
lnprob = self.sampler.flatlnprobability
chain.add_column(Column(-2*lnprob / self.Ndegree), name='reduced_chi')
#using dynesty
elif mcmc_method == 'dynesty' and dynesty_there:
# "Dynamic" nested sampling.
#
nlive = 500
self.sampler = dynesty.DynamicNestedSampler(self.loglike, self.ptform, dim_p \
, bound='single' #to force posterior weights
, sample='unif' #unif/hscale
)
kwargs_dynesty = {'nlive_init': 30 , 'nlive_batch': 200 }
kwargs_dynesty.update(kwargs)
self.logger.critical('EXPERIMENTAL Running Dynesty with ', kwargs_dynesty)
self.sampler.run_nested(wt_kwargs={'pfrac': 0.9} #posterior based
, maxiter = self.max_iterations
, **kwargs_dynesty
)
self.dresults = self.sampler.results
chain_data = self.dresults.samples
chain = Table(chain_data, names=self.galaxy.names)
lnprob = self.dresults.logz
chain.add_column(Column(-2*lnprob / self.Ndegree), name='reduced_chi')
#using pymultinest
elif mcmc_method == 'multinest' and multinest_there:
"""
run(LogLikelihood, Prior, n_dims, n_params=None, n_clustering_params=None,
wrapped_params=None, importance_nested_sampling=True, multimodal=True,
const_efficiency_mode=False, n_live_points=400, evidence_tolerance=0.5,
sampling_efficiency=0.8, n_iter_before_update=100, null_log_evidence=-1e+90,
max_modes=100, mode_tolerance=-1e+90, outputfiles_basename=u'chains/1-',
seed=-1, verbose=False, resume=True, context=0, write_output=True,
log_zero=-1e+100, max_iter=0, init_MPI=True, dump_callback=None)
"""
if 'outpath' not in kwargs.keys():
outpath = './pymulti'
if os.path.isdir(outpath) is False:
os.mkdir(outpath)
output = outpath + '/out'
else:
outpath = kwargs['outpath']
if os.path.isdir(outpath) is False:
os.mkdir(outpath)
output = outpath + '/out'
kwargs.pop('outpath')
#default parameters
kwargs_multi={'n_live_points': 200, \
'evidence_tolerance':0.5, \
'n_iter_before_update' : 200, \
'const_efficiency_mode' : False, \
'sampling_efficiency':0.8, \
'resume' : False}
kwargs_multi.update(kwargs)
self.logger.critical("Running MultiNest with ", kwargs_multi)
self.logger.info(" Multinest, ignoring max_iteration")
pymultinest.run(self.pyloglike, self.pycube, n_dims= dim_p, \
max_iter=0, verbose=self.verbose, \
outputfiles_basename=output, **kwargs_multi)
# create analyzer object
#embedded in solve
analyzer = pymultinest.Analyzer(dim_p, outputfiles_basename = output)
# get a dictionary containing information about
# the logZ and its errors
# the individual modes and their parameters
# quantiles of the parameter posteriors
data = analyzer.get_data()[:,:-1]
#print(data.shape)
stats = analyzer.get_mode_stats()
#lnZ = stats['evidence']
# iterate through the "posterior chain"
#for params in a.get_equal_weighted_posterior():
# print(params)
samples = analyzer.get_equal_weighted_posterior()
#print(chain_data.shape)
chain = Table(samples[:,:-1], names=self.galaxy.names)
lnprob = samples[:,-1]
chain.add_column(Column(-2*lnprob / self.Ndegree), name='reduced_chi')
# get the best fit (highest likelihood) point
#bestfit_params = stats['modes'][0]['mean']
#bestfit_params = stats['modes'][0]['maximum']
#bestfit_params = stats['modes'][0]['maximum a posterior']
#OR
#bestfit_params = stats.get_best_fit()['parameters']
self.sampler = dict(samples = samples, stats=stats, \
kwargs=kwargs_multi
)
#clean
os.system('rm -rf {}/'.format(outpath))
elif mcmc_method == 'pymc3':
raise NotImplementedError
elif mcmc_method == 'pynuts':
raise NotImplementedError
else:
raise Exception("method_mcmc %s not valid. Used of of %s" % (mcmc_method, self.MCMC_VALID))
# Store chain
self.logger.info("self.chain : full Markov chain")
#good_idx = np.where(chain['reduced_chi']!=0)
#self.chain = Table(chain[good_idx])
# Sanitize the chain if vel <0
self.model.sanitize_chain(chain)
self.chain = chain
# Store PSF 3D, which may not be defined
try:
self.psf3d = HyperCube(self.instrument.psf3d)
except AttributeError:
pass
# Extract Galaxy Parameters from chain, and store them
self.logger.info("Extracting best parameters (medians) from chain")
self.best_parameters_from_chain(method_chain, last_fraction=last_chain_fraction, percentile=percentile)
# Create output cubes
self.logger.info("self.convolved_cube : simulated convolved cube from found galaxy parameters")
self.convolved_cube = self.create_convolved_cube(self.galaxy, self.cube.shape)
self.convolved_cube.header = self.cube.header
self.logger.info("self.deconvolved_cube : deconvolved cube from found galaxy parameters")
self.deconvolved_cube = self.create_clean_cube(self.galaxy, self.cube.shape, final=True)
self.deconvolved_cube.header = self.cube.header
self.logger.info("self.residuals_cube : diff between actual data and convolved cube, scaled by stdev margin")
self.residuals_cube = (self.cube - self.convolved_cube) / self.error_cube # * np.mean(variance_cube)
self.residuals_cube.header = self.cube.header
#compute observed maps
#_ = self._make_moment_maps(self.convolved_cube, mask=True) # make moment maps with cube convolved with 3DPSF
_ = self._make_maps_Epinat(self.convolved_cube, mask=True)
# Average of residuals, normalized to sigma_mu
nz = self.residuals_cube.shape[0]
self.residuals_map = (self.residuals_cube.data.sum(0) / nz) * np.sqrt(nz)
# Compute the χ²
self.compute_stats()
self.logger.info("χ² at best param: %f", self.chi_at_p)
self.logger.info("Best min χ², %f ", self.best_chisq)
self.logger.info("BIC (full) : %f ", self.BIC)
self.logger.info("DIC : %f ", self.DIC)
# Show a plot of the chain if verbose, to draw attention to the chain
if verbose:
# Sometimes, when the number of iterations is low, plotting fails.
# It is a complex issue with matplotlib, so we're 'try'-wrapping it.
try:
self.plot_mcmc(adapt_range='5stdev')
except:
pass
try:
self.plot_geweke()
except:
pass
return self.galaxy
def best_parameters_from_chain(self, method_chain='last', last_fraction=60, percentile=95):
"""
Computes best fit galaxy parameters from chain, using medians from a specified method_chain.
method_chain: string 'last' | 'chi_sorted' | 'chi_min' | 'MAP'
The method to use to extract the fittest parameters from the chain.
'last' (default) : mean of the last_fraction(%) last parameters of the chain
'chi_sorted' : mean of the last_fraction(%) best fit parameters of the chain
'chi_min' : mean of last_fraction(%) of the chain around the min chi
'MAP': Parameters at Maximum At Posteriori, i.e. at chi_min
last_fraction: float % (60 as default)
Fraction of the end of the chain used in determining the parameters.
percentile: float % (95 as default)
None: the method to use to compute the errors on the parameter is the standard deviation of the median
float: the percentile (the 68th, or 95th percentile) to be used for the errors on the parameters
#fixme: in which case returns the lower and upper values.
Returns the galaxy parameters and the stdev
"""
self.method = method_chain
self.chain_fraction = last_fraction
self.percentile = percentile
if self.chain is None:
raise RuntimeError("No chain! Run .run_mcmc() first.")
# Data correction for Vmax
#vmax_sign = (self.chain['maximum_velocity'] < 0)
#pa_correction = np.where(vmax_sign, self.chain['pa'] + 180., self.chain['pa'])
#pa_correction = np.where(pa_correction > 180, pa_correction - 360, pa_correction)
#self.chain['pa'] = pa_correction
#self.chain['maximum_velocity'] = np.abs(self.chain['maximum_velocity'])
cols = [ Column(data=np.cos(np.radians(self.chain['pa'])), name='cospa'),
Column(data=np.cos(np.radians(self.chain['pa']*2)),name='cos2pa'),
Column(data=np.sin(np.radians(self.chain['pa'])), name='sinpa'),
Column(data=np.sin(np.radians(self.chain['pa']*2)),name='sin2pa')
]
chain_full = self.chain.copy()
chain_full.add_columns(list(cols))
#extract subchain
chain_size = np.size(chain_full)
n = int(chain_size * last_fraction / 100.) # number of samples (last_fraction(%) of total)
idx = chain_full.argsort('reduced_chi')
self.chain.idxsorted = idx
if method_chain == 'chi_min' or method_chain == 'MAP':
min_chi_index = self._get_min_chi_index()
xmin = np.max([0, min_chi_index - n // 2])
xmax = np.min([chain_size, min_chi_index + n // 2])
sub_chain = chain_full[xmin: xmax]
elif method_chain == 'last':
sub_chain = chain_full[-n:]
xmin = chain_size - n
xmax = chain_size
elif method_chain == 'chi_sorted':
sub_chain = chain_full[idx][:n]
sub_idx = idx[:n]
xmin = 0
xmax = n
else:
raise ValueError("Unsupported `method_chain` '%s'"
% method_chain)
#compute error model maps
#@fixme
# if self.error_maps:
# if method_chain == 'chi_min' or method_chain =='MAP':
# tmp_f = np.array(self.chain_flux_map)[min_chi_index - n // 2: min_chi_index + n // 2]
# tmp_v = np.array(self.chain_velocity_map)[min_chi_index - n // 2: min_chi_index + n // 2]
# tmp_s = np.array(self.chain_dispersion_map)[min_chi_index - n // 2: min_chi_index + n // 2]
# elif method_chain == 'last':
# tmp_f = np.array(self.chain_flux_map)[-n:]
# tmp_v = np.array(self.chain_velocity_map)[-n:]
# tmp_s = np.array(self.chain_dispersion_map)[-n:]
# elif method_chain == 'chi_sorted':
# tmp_f = np.array(self.chain_flux_map)[sub_idx]
# tmp_v = np.array(self.chain_velocity_map)[sub_idx]
# tmp_s = np.array(self.chain_dispersion_map)[sub_idx]
# self.true_flux_map_error = HyperCube(
# np.percentile(tmp_f, 50 + percentile/2., axis=0) -
# np.percentile(tmp_f, 50 - percentile/2., axis=0)
# )
# self.true_velocity_map_error = HyperCube(
# np.percentile(tmp_v, 50 + percentile/2., axis=0) -
# np.percentile(tmp_v, 50 - percentile/2., axis=0)
# )
# self.true_disp_map_error = HyperCube(
# np.percentile(tmp_s, 50 + percentile/2., axis=0) -
# np.percentile(tmp_s, 50 - percentile/2., axis=0)
# )
# Compute best_parameters
parameter_names = list(self.model.Parameters().names)
if self.method == 'MAP':
tmp_chain = np.array(chain_full[parameter_names].as_array().tolist())
best_parameter = tmp_chain[idx[0]]
else:
tmp_chain = np.array(sub_chain[parameter_names].as_array().tolist()) #convert astropy Table into array
best_parameter = np.median(tmp_chain, axis=0)
# Compute errors to parameters
sigma_parameter = np.std(tmp_chain, axis=0)
#handle PA edges at +/-180
#following https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Circular_Data_Analysis.pdf
cos1=np.median(sub_chain['cospa'])
sin1=np.median(sub_chain['sinpa'])
invtan = np.arctan2(sin1,cos1)
pa_circular_best = np.degrees(invtan) # over -180;180
#save
pa_idx = sub_chain[parameter_names].index_column('pa')
best_parameter[pa_idx] = np.where(pa_circular_best>0, pa_circular_best, pa_circular_best+360)
r1 = np.sqrt(sub_chain['cospa'].sum()**2+sub_chain['sinpa'].sum()**2)
r1 = r1/np.size(sub_chain)
pa_circular_std = np.sqrt(-2.*np.log(r1))
cos2=np.median(sub_chain['cos2pa'])
sin2=np.median(sub_chain['sin2pa'])
#r2 = np.sqrt(sub_chain['cos2pa']**2+sub_chain['sin2pa']**2)
#r2 = np.median(r2)
invtan2= np.arctan2(sin2,cos2)
angle2= np.degrees(invtan2)
## this is??
#pa_circular_dispersion = (1-angle2)/(2*r1**2)
#print "pa disp %.3f " % (pa_circular_dispersion)
#center chain['pa']
sub_chain_pa = sub_chain['pa'] - best_parameter[pa_idx]
#sub_chain_pa = np.where(sub_chain_pa > 180, sub_chain_pa - 360, sub_chain_pa)
#sub_chain_pa = np.where(sub_chain_pa < -180, sub_chain_pa + 360, sub_chain_pa)
std_pa = np.std(sub_chain_pa)
#This is identical to pa_circular_std
#print "pa sigma %.3f" % (std_pa)
#print "pa stdev %.3f " % (np.degrees(pa_circular_std))
sigma_parameter[pa_idx] = std_pa
#add back centering offset
sub_chain['pa'] = sub_chain_pa + best_parameter[pa_idx]
#keep full set
chain_names = deepcopy(parameter_names)
chain_names.append('reduced_chi')
self.sub_chain = sub_chain[chain_names]
self.chain = chain_full[chain_names]
self.chain.xmin = xmin
self.chain.xmax = xmax
# self.sub_chain = sub_chain[parameter_names] #will be used for correlation plots
#min in sub_chain:
#self.best_chisq = np.min(self.sub_chain['reduced_chi'])
#min absolute
self.best_chisq = self.chain[idx[0]]['reduced_chi']
#Save
self.galaxy = GalaxyParameters.from_ndarray(best_parameter)
self.galaxy.stdev = GalaxyParametersError.from_ndarray(sigma_parameter)
self.stdev = self.galaxy.stdev
# Compute percentiles
if percentile is not None:
self.logger.info('Setting %2d percentiles ' % (percentile))
error_parameter_upper = np.percentile(self.sub_chain[parameter_names].as_array().tolist(), 50. + percentile/2., axis=0)
error_parameter_lower = np.percentile(self.sub_chain[parameter_names].as_array().tolist(), 50. - percentile/2., axis=0)
self.galaxy.upper = GalaxyParametersError.from_ndarray(error_parameter_upper)
self.galaxy.lower = GalaxyParametersError.from_ndarray(error_parameter_lower)
self.galaxy.ICpercentile = percentile
# Store galaxy parameters and stdev
self.logger.info("self.galaxy : fittest parameters : %s", repr(self.galaxy))
self.logger.info("self.stdev : parameters stdev : %s", str(self.stdev))
return None
def compute_stats(self, snr_min=0.02):
"""
snr_min : float [default = 0.02]
minimum snr to compute BIC restricted over pixels with snr > snr_min
compute stats (BIC, DIC, AIC)
"""
dim_data = np.size(self.cube.data)
self.chi_at_p = self.compute_chi(self.galaxy) / self.Ndegree
self.convolved_cube = self.create_convolved_cube(self.galaxy, self.cube.shape)
snr = (self.convolved_cube.data) / np.median(self.variance_cube.data)**0.5
good = np.ones_like(snr)
good[snr<snr_min] = 0
dim_good = good.sum() #count only pixels with snr >snr_min and above
Nd_good = (dim_good - self.dim_p_free - 1) # degree of freedom
# like = -0.5*np.nansum(self.variance_chi)-0.5*self.compute_chi(params)
#BIC = -2 * like(theta)
self.BIC = self.chi_at_p * self.Ndegree + self.dim_p_free * np.log(dim_data)
#AIC = -2 * like + 2 dim_p
self.AIC = self.chi_at_p * self.Ndegree + 2 * self.dim_p_free
#DIC
if self.method != 'chi_sorted':
log_Lp = -0.5 * self.sub_chain['reduced_chi'] * self.Ndegree
if np.isfinite(log_Lp).all():
pD = 2 * np.var(log_Lp)
else:
pD = 2 * np.nanvar(log_Lp[np.isfinite(log_Lp)==True])
# P = 2 * (logp_max-np.mean(self.lnp))
#pD = 2 * -0.5 * (self.chi_at_p - np.mean(self.sub_chain['reduced_chi'])) * self.Ndegree
#DIC = -2 * like + 2 pd
self.DIC = self.chi_at_p * self.Ndegree + 2 * pD
else:
self.DIC = 0
pD=0
self.stats = Table(np.array(['%.8f' % (self.best_chisq), '%.8f' % (self.chi_at_p),
'%.2f' % (self.BIC), self.Ndegree,
'%.2f' % (self.AIC), self.dim_p_free,
'%.2f' % (pD),
'%.2f' % (self.DIC),
np.max(snr)]
), \
names=['best_chi2', 'chi2_at_p', 'BIC', 'Ndegree', \
'AIC', 'k', \
'pD', 'DIC', 'SNRmax'])
if self.mcmc_method is 'multinest':
evidence = self.sampler['stats']['global evidence']
self.stats.add_column(evidence*-2,name='log Z')
return self.stats
[docs] def create_clean_cube(self, galaxy, shape, final=False):
"""
Creates a cube containing a clean simulation of a galaxy according to
the provided model.
galaxy: GalaxyParameters
The parameters upon which the simulated galaxy will be built.
shape: Tuple of 3
The 3D (z, y, x) shape of the resulting cube.
Eg: (21, 21, 21)
Returns a HyperspectralCube
"""
# Create radial velocities and radial dispersions
#flux_cube, vz, vz_map, s_map, sig_map, sigz_disk_map, sig_intr = \
# self.model._compute_galaxy_model(galaxy, shape)
## normalize to real flux
#flux_map = flux_cube.sum(0)
#flux_map = galaxy.flux * flux_map / flux_map.sum()
if self.model is None:
raise AssertionError(" Model is undefined. Please define model first")
modelcube, flux_map, vz_map, s_map = self.model._create_cube(galaxy, shape,\
self.instrument.z_step_kms, zo=galaxy['z'])
#@fixme: currently records all calculations..
# if self.error_maps:
# self.chain_flux_map.append(flux_map)
# self.chain_velocity_map.append(vz_map)
# self.chain_dispersion_map.append(s_map)
# This is too expensive ! We create Cubes on each iteration...
# There are ways to optimize this, as only the last one is used.
if final is True:
self.true_flux_map = HyperCube(flux_map)
self.true_velocity_map = HyperCube(vz_map)
self.true_disp_map = HyperCube(s_map)
#modelcube = self.model._create_cube(shape, flux_map, vz_map, s_map,
# self.instrument.z_step_kms, zo=galaxy.z)
#is this used?
modelcube.xy_step = self.cube.xy_step
modelcube.z_step = self.cube.z_step
modelcube.z_central = self.cube.z_central
modelcube.z_cunit = self.cube.z_cunit
return modelcube
[docs] def create_convolved_cube(self, galaxy, shape):
"""
Creates a cube containing a convolved simulation of a galaxy according
to the provided model.
The convolution is done by the instrument you provided upon
instantiation of this class.
galaxy: GalaxyParameters
The parameters upon which the simulated galaxy will be built.
shape: Tuple of 3
The 3D (Z, Y, X) shape of the resulting cube.
Eg: (21, 21, 21)
Returns a HyperspectralCube
"""
clean_cube = self.create_clean_cube(galaxy, shape)
return self.instrument.convolve(clean_cube)
def import_chain(self, filepath, compute_best_params=False, method_chain='last'):
"""
Imports the chain stored in a .dat file so that you may plot.
compute_best_parameters False[default] | True
if True will use 'last' method and 60%
use best_parameters_from_chain method to customize
"""
with open(filepath, 'r') as chain_data:
self.chain = asciitable.read(chain_data.read(), Reader=asciitable.FixedWidth)
self.model.sanitize_chain(self.chain)
if compute_best_params is True:
self.best_parameters_from_chain(method_chain=method_chain, last_fraction=60, percentile=95)
NO_CHAIN_ERROR = "No chain to plot! Run .run_mcmc() or .import_chain() " \
"first."
[docs] def save(self, name, overwrite=False):
"""
Saves the results of the MCMC to files :
- <name>_galaxy_parameters.txt
A plain text representation of the parameters of the galaxy.
- <name>_galaxy_parameters.dat
A table representation of the parameters of the galaxy.
- <name>_chain.dat
A table representation of the Markov Chain.
Each line holds one set of galaxy parameters and its associated reduced chi.
- <name>_run_parameters.txt
A plain text representation of the run_parameters.
- <name>_instrument.txt
A plain text representation of the instrument parameters.
- <name>_convolved_cube.fits
A FITS file containing the PSF-convolved result cube.
- <name>_deconvolved_cube.fits
A FITS file containing the pre-convolution clean cube.
- <name>_residuals_cube.fits
A FITS file containing the diff between input data and simulation.
- <name>_3Dkernel.fits
A FITS file containing the 3D kernel used
- <name>_true_flux_map.fits
A FITS file containing the true flux map [intrinsic]
- <name>_true_vel_map.fits
A FITS file containing the true velocity map [intrinsic]
- <name>_true_sig_map.fits
A FITS file containing the true dispersion map [intrinsic]
- <name>_obs_flux_map.fits
A FITS file containing the observed flux map [intrinsic]
- <name>_obs_vel_map.fits
A FITS file containing the observed velocity map [intrinsic]
- <name>_obs_sig_map.fits
A FITS file containing the observed dispersion map [intrinsic]
- <name>_images.pdf/png
A PNG image generated by the ``plot_images`` method.
Note: the overwrite option is always true for this file.
- <name>_mcmc.pdf/png
A PNG image generated by the ``plot_mcmc`` method.
Note: the overwrite option is always true for this file.
- <name>_true_maps.pdf/png
A PNG image generated by the ``plot_true_vfield`` method.
- <name>_obs_maps.pdf/png
The observed maps generated by the ``plot_obs_vfield`` method.
- <name>_model.txt
The model configuration
- <name>_instrument.txt
The instrument configuration
- <name>_geweke.pdf/png
The geweke diagnostics plot
- <name>_galaxy_parameters_convergence.dat
The convergence of each parameter based on the geweke diagnostics
- <name>_corner.pdf/png
The corner plot for the MCMC chain. Requires
- <name>_stats.dat
A ascii file containing the BIC/DIC etc criteria
The .dat files can be easily read using astropy.table and its ``ascii_fixedwidth`` format : ::
Table.read('example.chain.dat', format='ascii.fixed_width')
.. warning::
The generated files are not compressed and may take up a lot of disk
space.
name: string
An absolute or relative name that will be used as prefix for the
save files.
Eg: 'my_run', or '/home/me/science/my_run'.
overwrite: bool
When set to true, will OVERWRITE existing files.
"""
if self.chain is None:
raise RuntimeError("Nothing to save! Run .run_mcmc() first.")
filename = '%s_galaxy_parameters.txt' % name
_save_to_file(filename, self.galaxy.long_info(), overwrite)
filename = '%s_galaxy_parameters.dat' % name
_save_to_file(filename, self.galaxy.structured_info(), overwrite)
filename = '%s_chain.dat' % name
_save_to_file(filename, self._chain_as_asciitable(), overwrite)
filename = '%s_stats.dat' % name
self.stats.write(filename, format='ascii.fixed_width', overwrite=overwrite)
filename = '%s_run_parameters.txt' % name
_save_to_file(filename, self.__str__(), overwrite)
filename = '%s_instrument.txt' % name
_save_to_file(filename, self.instrument.__str__(), overwrite)
filename = '%s_model.txt' % name
_save_to_file(filename, self.model.__str__(), overwrite)
filename = '%s_convolved_cube.fits' % name
self.convolved_cube.write_to(filename, overwrite)
filename = '%s_deconvolved_cube.fits' % name
self.deconvolved_cube.write_to(filename, overwrite)
filename = '%s_residuals_cube.fits' % name
self.residuals_cube.write_to(filename, overwrite)
filename = '%s_3Dkernel.fits' % name
self.psf3d.write_to(filename, overwrite)
filename = '%s_obs_flux_map.fits' % name
self.obs_flux_map.write_to(filename, overwrite)
filename = '%s_obs_vel_map.fits' % name
self.obs_velocity_map.write_to(filename, overwrite)
filename = '%s_obs_disp_map.fits' % name
self.obs_disp_map.write_to(filename, overwrite)
filename = '%s_true_flux_map.fits' % name
self.true_flux_map.write_to(filename, overwrite)
filename = '%s_true_vel_map.fits' % name
self.true_velocity_map.write_to(filename, overwrite)
filename = '%s_true_disp_map.fits' % name
self.true_disp_map.write_to(filename, overwrite)
filename = '%s_rotcurve' % name
self.model.plot_vprofile(self.galaxy,chain=self.sub_chain,filename=filename + '.png')
self.model.plot_vprofile(self.galaxy,chain=self.sub_chain,filename=filename + '.pdf')
#for quick display only
filename = '%s_true_maps' % name
self.plot_true_vfield(filename + '.png')
self.plot_true_vfield(filename + '.pdf')
filename = '%s_obs_maps' % name
self.plot_obs_vfield(filename + '.png')
self.plot_obs_vfield(filename + '.pdf')
#@fixme
# if self.error_maps:
# filename = '%s_true_flux_map_error.fits' % name
# self.true_flux_map_error.write_to(filename, overwrite)
# filename = '%s_true_vel_map_error.fits' % name
# self.true_velocity_map_error.write_to(filename, overwrite)
# filename = '%s_true_disp_map_error.fits' % name
# self.true_disp_map_error.write_to(filename, overwrite)
filename = '%s_images' % name
self.plot_images(filename + '.png')
self.plot_images(filename + '.pdf')
filename = '%s_mcmc' % name
self.plot_mcmc(filename + '.png', method='last')
self.plot_mcmc(filename + '.pdf', method='last')
#Deprecicated
# filename = '%s_correlations' % name
#self.plot_correlations(filename + '.png')
#self.plot_correlations(filename + '.pdf')
try:
filename = '%s_corner' % name
self.corner=self.plot_corner(filename + '.png',nsigma=4)
_ = self.plot_corner(filename + '.pdf',nsigma=4)
except:
self.corner=False
self.logger.warning("plot corner failed ")
filename = '%s_geweke' % name
self.plot_geweke(filename + '.png')
self.plot_geweke(filename + '.pdf')
filename = '%s_galaxy_parameters_convergence.dat' % name
self.convergence.write(filename, format='ascii.fixed_width', overwrite=overwrite)
self.logger.info("Saved files in %s" % os.getcwd())
#fixme: to do
# def read_files(self, name):
def __str__(self):
"""
Return information about this run in a multiline string.
"""
return """
galpak_version = %s
input_cube = %s
Var_cube = %s
%s
mcmc_method = %s
mcmc_sampling = %s
iterations = %s
random_scale = %s
parameters method = %s, chain_fraction: %s,
CI percentile: %s,
%s
min_boundaries = %s
max_boundaries = %s
known_parameters = %s
initial_parameters = %s
final_parameters = \n %s
best_chi2 = %s
median_chi2 = %s
BIC = %s
acceptance_rate = %s
""" % (
self.version,
self.cube.filename,
self.variance_cube.filename,
self.instrument, self.mcmc_method, self.mcmc_sampling, self.max_iterations, self.random_scale,
self.method,
self.chain_fraction, self.percentile,
self.model,
self.min_boundaries, self.max_boundaries,
self.known_parameters, self.initial_parameters,
self.galaxy.structured_info(),
self.best_chisq, self.chi_at_p,
self.BIC,
self.acceptance_rate
)
def _chain_as_asciitable(self):
"""
Exports the chain as an `asciitable`.
See the public API `import_chain()` for the reverse operation of loading
the chain from an `asciitable` file.
"""
out = StringStdOut()
asciitable.write(self.chain,
output=out,
Writer=asciitable.FixedWidth,
names=self.chain.dtype.names)
return out.content
def _get_min_chi_index(self):
"""
Gets the index in the chain of the parameters with the minimal chi.
"""
if self.chain is None:
raise RuntimeError("No chain! Run `run_mcmc()` first.")
idx = self.chain.idxsorted[0]
return idx
#############################################
#
# Private methods, modeling
#
#############################################
def _set_verbose(self, verbose):
"""
Update the logger's status
"""
self.logger.disabled=False
if verbose is True:
#self.logger.setLevel('INFO')
if self.model is not None:
self.model.logger.disabled = False
np.seterr(all='warn')
elif verbose is False:
#self.logger.setLevel('DEBUG')
if self.model is not None:
self.model.logger.disabled = True
np.seterr(all='ignore')
elif verbose is None:
self.logger.disabled=True
if self.model is not None:
self.model.logger.disabled = True
else:
raise ValueError("verbose should be None | True | False")
def _read_params(self, file_config, type):
"""
sets random scale from config file
:return: ModelParameters
"""
if file_config is not None:
if os.path.isfile(file_config):
config = configparser.RawConfigParser()
config.read(file_config)
else:
raise ValueError("Read params: Config file %s not present" % (file_config))
else:
raise ValueError("Parameter file not defined")
if self.config.has_section(type):
config = self.config[type]
else:
self.logger.warning("Read params: Config file has no %s section" % (type))
par = self.model.parameters_class()()
for r in list(config.keys()):
if r in par.names:
par[r] = eval(config[r])
else:
self.logger.warning("Config %s has keys not used for this model" % (type))
return par
def _init_sampling_scale(self, random_scale, should_guess_flags):
dim_d = np.size(self.cube.data)
dim_p = len(self.initial_parameters)
# Tweak the random amplitude vector (Kp coeff, as pid)
# that we can document Model.setup_random_amplitude() adequately
random_amplitude = np.sqrt(
(self.min_boundaries - self.max_boundaries) ** 2 / 12.
) * dim_p / dim_d
# Let the model adjust the random amplitude of the parameter jump
self.model.setup_random_amplitude(random_amplitude)
# Scale MCMC if needed // allowing vectors
if random_scale is not None:
if np.size(random_scale) != 1:
merge_where_nan(random_scale, np.ones_like(random_amplitude))
random_amplitude = random_amplitude * random_scale
# Zero random amplitude where parameters are known
random_amplitude = random_amplitude * should_guess_flags
return random_amplitude
