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morphen.py
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morphen.py
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"""
..___|**_
.|||||||||*+@+*__*++.
_||||. .*+;].,#_
_|||*_ _ .@@@#@.
_|||||_ .@##@#| _||_
Morphen |****_ .@.,/\..@_.
#///#+++*| . .@@@;#.,.\@.
.||__|**|||||*||*+@#];_. ;,;_
Geferson Lucatelli +\*_.__|**#
|.. .]]
;@ @.*.
#| _;]];|.
]_ _+;]@.
_/_ |]\| . _
...._@* __ ..... ]]+ .. _
.. . . .. .|.|_ ..
"""
__version__ = '0.3.1alpha-1'
__codename__ = 'Pelicoto'
__author__ = 'Geferson Lucatelli'
__coauthors__ = ('Javier Moldon, Rob Beswick, '
'Fabricio Ferrari, Leonardo Ferreira')
__email__ = 'geferson.lucatelli@postgrad.manchester.ac.uk'
__date__ = '2024 03 25'
# print(__doc__)
import argparse
import os
import sys
import matplotlib as mpl
import logging
from matplotlib import use as mpluse
# sys.path.append("/mirror/scratch/lucatelli/app/miniconda3/envs/casa6/lib/python3.8/site-packages/")
sys.path.append('libs/')
sys.path.append('analysis_scripts/')
# import sys
import libs as mlibs
import analysisUtils as au
from analysisUtils import *
import coloredlogs
class config():
"""
Configuration Class to specify basic parameters.
"""
def reset_rc_params():
"""
Global configuration for matplotlib.pyplot
"""
mpl.rcParams.update({'font.size': 16,
"text.usetex": False, #
"font.family": "sans-serif",
'mathtext.fontset': 'stix',
"font.family": "sans",
'font.weight': 'medium',
'font.family': 'STIXGeneral',
'xtick.labelsize': 16,
'figure.figsize': (6, 4),
'ytick.labelsize': 16,
'axes.labelsize': 16,
'xtick.major.width': 1,
'ytick.major.width': 1,
'axes.linewidth': 1.5,
'axes.edgecolor':'orange',
'lines.linewidth': 2,
'legend.fontsize': 14,
'grid.linestyle': '--',
# 'grid.color':'black',
'axes.grid.which': 'major',
'axes.grid.axis': 'both',
'axes.spines.right': True,
'axes.grid': True,
'axes.titlesize' : 16,
'legend.framealpha': 1.0
})
pass
reset_rc_params()
sigma=3
mask_iterations = 1
show_plots = True
ext = '.jpg'
log_file_name = 'logfile.log'
if "--noshow" in sys.argv:
mpluse('Agg')
def __init__(self):
print("Initializing Morphen")
# class _logging_():
# def __init__(self,log_file_name):
# self.log_file_name = log_file_name.replace('.fits','.log')
# self.start_log()
#
#
# def start_log(self):
# self.logger = logging.getLogger(__name__)
# # Set the log level
# self.logger.setLevel(logging.DEBUG)
# # Fancy format
# log_format = "%(asctime)s - %(levelname)s - %(message)s"
# # Use colored logs to add color to the log messages
# coloredlogs.install(level='DEBUG', logger=self.logger, fmt=log_format)
# # coloredlogs.install(level='CALC', logger=logger, fmt=log_format)
# # config.log_file_name = config.file_name('.fits', '.log')
# file_handler = logging.FileHandler(self.log_file_name)
# file_handler.setLevel(logging.DEBUG)
# file_handler.setFormatter(logging.Formatter(log_format))
# self.logger.addHandler(file_handler)
# self.logger.info("Initializing Logging!")
# config.loger_file = True
class _logging_():
logger = logging.getLogger(__name__)
# Set the log level
logger.setLevel(logging.DEBUG)
# Fancy format
log_format = "%(asctime)s - %(levelname)s - %(message)s"
# Use colored logs to add color to the log messages
coloredlogs.install(level='DEBUG', logger=logger, fmt=log_format)
# coloredlogs.install(level='CALC', logger=logger, fmt=log_format)
try:
print("# Removing previous log file.")
os.system(f"rm -r {config.log_file_name}")
except:
pass
file_handler = logging.FileHandler(config.log_file_name)
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter(log_format))
logger.addHandler(file_handler)
def __init__(self):
logger.info("Initializing Logging!")
class read_data():
"""
Read Input Data
"""
def __init__(self, filename=None,residualname=None,psfname=None,
imagelist = [],residuallist=[],):
self.filename = filename
self.residualname = residualname
self.psfname = psfname
self.print_names()
self.get_data()
def print_names(self):
if self.filename != None:
print('++>> Image File:', os.path.basename(self.filename))
if self.residualname != None:
print('++>> Residual File:', os.path.basename(self.residualname))
elif self.residualname == None:
print('-->> No Residual File was provided.')
if self.psfname != None:
print('++>> PSF File:', os.path.basename(self.psfname))
elif self.psfname == None:
print('-->> No PSF File was provided.')
def get_data(self):
self.image_data_2D = None
self.residual_data_2D = None
self.psf_data_2D = None
self.rms_img = None
self.rms_res = None
if self.filename != None:
self.image_data_2D = mlibs.ctn(self.filename)
self.rms_img = mlibs.mad_std(self.image_data_2D)
if self.residualname != None:
self.residual_data_2D = mlibs.ctn(self.residualname)
self.rms_res = mlibs.mad_std(self.residual_data_2D)
if self.psfname != None:
self.psf_data_2D = mlibs.ctn(self.psfname)
class radio_image_analysis():
def __init__(self, input_data,z = None,do_petro=False,
# logger=None,
crop=False,box_size=256,
apply_mask=True,mask=None,dilation_size = None,
sigma_level=3, sigma_mask=6,vmin_factor=3,last_level=3,
results=None,mask_component=None,
npixels=128,kernel_size=21,fwhm=81,
SAVE=True, show_figure=True):
self.input_data = input_data
# self.logger = logger
self.crop = crop
self.box_size = box_size
self.do_petro = do_petro
self.apply_mask = apply_mask
self.dilation_size = dilation_size
self.sigma_level = sigma_level
self.sigma_mask = sigma_mask
self.mask = mask
self.mask_component = mask_component
self.vmin_factor = vmin_factor
self.last_level = last_level
self.npixels = npixels
self.kernel_size = kernel_size
self.fwhm = fwhm
self.z = z
self.results = results
self.SAVE = SAVE
self.show_figure = show_figure
self.image_properties()
def image_properties(self):
try:
self.cell_size = mlibs.get_cell_size(self.input_data.filename)
except:
# print('!! WARNING !! Setting cellsize/pixelsize to unity.')
_logging_.logger.warning("Setting cellsize/pixelsize to unity.")
self.cell_size = 1
_logging_.logger.info("Computing image level statistics.")
if self.z is None:
_logging_.logger.warning("The redshift of the source was not specified."
"Conversions to physical units will not be "
"performed.")
# _logging_.file_handler.info("Computing image level statistics.")
# self.image_level_statistics = \
# mlibs.level_statistics(img=self.input_data.filename,
# cell_size=self.cell_size, crop=self.crop,
# sigma = self.sigma_level,
# apply_mask=self.apply_mask,
# results=self.results, SAVE=self.SAVE,
# ext=config.ext,
# show_figure=config.show_plots)
#
# _logging_.logger.info("Computing image properties.")
# self.levels, self.fluxes, self.agrow, self.plt_image, \
# self.omask, self.mask, self.results_im_props = \
# mlibs.compute_image_properties(img=self.input_data.filename,
# cell_size=self.cell_size,
# residual=self.input_data.residualname,
# sigma_mask=self.sigma_mask,
# dilation_size=self.dilation_size,
# crop=self.crop,
# iterations=config.mask_iterations,
# box_size=self.box_size,
# last_level=self.last_level,
# mask=self.mask,
# apply_mask=self.apply_mask,
# vmin_factor=self.vmin_factor,
# results=self.results,
# show_figure=self.show_figure,
# logger=_logging_.logger)
#
# self.img_stats = \
# mlibs.get_image_statistics(imagename=self.input_data.filename,
# residual_name=self.input_data.residualname,
# cell_size=self.cell_size,
# mask_component=None,
# mask=self.mask,
# region='', dic_data=None,
# sigma_mask=self.sigma_mask,
# apply_mask=self.apply_mask,
# fracX=0.15, fracY=0.15)
self.image_measures, self.mask, self.omask = \
mlibs.measures(imagename=self.input_data.filename,
residualname=self.input_data.residualname,
z=self.z,
mask_component=self.mask_component,
sigma_mask=self.sigma_mask,
last_level=self.last_level,
vmin_factor=self.vmin_factor,
plot_catalog=True,
data_2D=self.input_data.image_data_2D,
npixels=self.npixels,fwhm=self.fwhm,
kernel_size=self.kernel_size,
dilation_size=self.dilation_size,
main_feature_index=0,
results_final={},
crop=self.crop, box_size=self.box_size,
iterations=config.mask_iterations,
fracX=0.15, fracY=0.15,
deblend=False, bkg_sub=False,
bkg_to_sub=None, rms=None,
do_petro=self.do_petro,
apply_mask=self.apply_mask,
do_PLOT=True, SAVE=self.SAVE,
show_figure=self.show_figure,
mask=self.mask,
do_measurements='all',
compute_A=True,
add_save_name='',logger=_logging_.logger)
class source_extraction():
"""
Source extraction class, responsible to find relevant regions of emission.
For now, only SEP is implemented. Soon, other algorithms will be added
in this class as alternatives for source extraction. These are:
- PyBDSF
- AstroDendro
- Photutils
"""
def __init__(self, input_data,z=0.05,ids_to_add=[1],
crop=False, box_size=256,
apply_mask=False, mask=None, dilation_size = None,
sigma_level=6, sigma_mask=6, vmin_factor=3, mask_component=None,
bwf=1, bhf=1, fwf=1, fhf=1,
segmentation_map = True, filter_type='matched',
deblend_nthresh=25, deblend_cont=1e-8,
clean_param=0.5, clean=True,
minarea_factor = 1.0,
sort_by='flux', # sort detected source by flux
sigma=6, # min rms to search for sources
ell_size_factor=2.0, # unstable, please inspect!
obs_type = 'radio', algorithm='SEP',
show_detection=False,show_petro_plots=False,
SAVE=True, show_figure=True,dry_run = False):
"""
Parameters
----------
input_data : str
Path to the image.
z : float
Redshift of the source.
ids_to_add : list
List of ids to be added to the catalogue.
crop : bool
If True, crop the image.
box_size : int
Size of the box to be cropped.
apply_mask : bool
If True, apply the mask to the image.
mask : array
Mask to be applied to the image.
dilation_size : int
Size of the binary dilation kernel.
sigma_level : float
Sigma level for detection.
sigma_mask : float
Sigma level for the mask.
vmin_factor : float
Factor to be multiplied by the standard deviation to set the minimum
value for the imshow plot.
mask_component : array
Mask to be applied to the image.
bwf : int
Box width fraction in terms of the beam size
for the background estimation.
bhf : int
Box height fraction in terms of the beam size
for the background estimation.
fwf : int
Filter width fraction in terms of the beam size
for the background estimation.
fhf : int
Filter height fraction in terms of the beam size
for the background estimation.
segmentation_map : bool
If True, returns the segmentation map.
filter_type : str
Type of filter to be used.
deblend_nthresh : int
Number of thresholds for deblending.
deblend_cont : float
Minimum contrast ratio for deblending.
clean_param : float
Cleaning parameter.
clean : bool
If True, clean the image.
minarea_factor : float
Factor to be multiplied by the minimum area for detection.
Default is 1.0, i.e. one restoring beam size. Any structure smaller
than one beam size will not be detected. This is critical if you have
oversampled data.
sort_by : str
Sort the output by flux or area.
sigma : float
Sigma level for detection.
ell_size_factor : int
Size factor of the ellipse to be drawn in the detected structures.
show_detection : bool
If True, show the detection plot.
show_petro_plots : bool
If True, show the petrosian plots.
SAVE : bool
If True, save plots.
show_figure : bool
If True, show the figure.
dry_run : bool
If True, do not compute source properties. In a first run, use True
to inspect how well the source detection was.
"""
self.input_data = input_data
self.z = z
self.ids_to_add = ids_to_add
self.crop = crop
self.box_size = box_size
self.apply_mask = apply_mask
self.mask = mask
self.dilation_size = dilation_size
self.sigma_level = sigma_level
self.sigma_mask = sigma_mask
self.sigma = sigma
self.minarea_factor = minarea_factor
self.ell_size_factor = ell_size_factor
self.vmin_factor = vmin_factor
self.mask_component = mask_component
self.algorithm = algorithm
# self.bw = bw
# self.bh = bh
# self.fw = fw
# self.fh = fh
# if (bw == None) & (bw == None) & (bw == None) & (bw == None):
try:
self.bspx, self.aO, self.bO = \
mlibs.get_beam_size_px(self.input_data.filename)
print(self.bspx)
self.bw = self.aO / bwf
self.bh = self.bO / bhf
self.fw = self.aO / fwf
self.fh = self.bO / fhf
except:
self.bw = int((input_data.image_data_2D.shape[0]*0.2)/bwf)
self.bh = int((input_data.image_data_2D.shape[1]*0.2)/bhf)
self.fw = int((input_data.image_data_2D.shape[0]*0.1)/fwf)
self.fh = int((input_data.image_data_2D.shape[1]*0.1)/fhf)
try:
self.minarea = mlibs.beam_area2(self.input_data.filename)
except:
self.minarea = self.input_data.image_data_2D.shape[0]/30
# self.bw, self.bh, self.fw, self.fh = bw, bh, fw, fh
self.segmentation_map = segmentation_map
self.filter_type = filter_type
self.deblend_nthresh = deblend_nthresh
self.deblend_cont = deblend_cont
self.clean_param = clean_param
self.clean = clean
self.sort_by = sort_by
self.SAVE = SAVE
self.show_figure = show_figure
self.show_detection = show_detection
self.show_petro_plots = show_petro_plots
self.obs_type = obs_type
if dry_run is True:
self.show_detection = True
self.get_sources()
if dry_run is not True:
self.contruct_source_properties()
def get_sources(self):
if self.algorithm == 'SEP':
self.masks, self.indices, self.bkg, self.seg_maps, self.objects = \
mlibs.sep_source_ext(self.input_data.filename,
bw=self.bw, bh=self.bh, fw=self.fw, fh=self.fh,
# filtering options for source detection
minarea=self.minarea,
segmentation_map=self.segmentation_map,
filter_type=self.filter_type, mask=self.mask,
deblend_nthresh=self.deblend_nthresh,
deblend_cont=self.deblend_cont,
clean_param=self.clean_param,
clean=self.clean,
sort_by=self.sort_by,
minarea_factor=self.minarea_factor,
dilation_size=self.dilation_size,
sigma=self.sigma,sigma_mask=self.sigma_mask,
ell_size_factor=self.ell_size_factor,
apply_mask=self.apply_mask,
show_detection=self.show_detection)
if self.algorithm == 'PF':
self.masks, self.indices, self.bkg, self.seg_maps, self.objects = \
mlibs.phot_source_ext(self.input_data.filename,
bw=self.bw, bh=self.bh, fw=self.fw, fh=self.fh,
# filtering options for source detection
segmentation_map=self.segmentation_map,
filter_type=self.filter_type, mask=self.mask,
deblend_nthresh=self.deblend_nthresh,
deblend_cont=self.deblend_cont,
clean_param=self.clean_param,
clean=self.clean,
sort_by=self.sort_by,
sigma=self.sigma,sigma_mask=self.sigma_mask,
dilation_size=self.dilation_size,
minarea=self.minarea,
minarea_factor = self.minarea_factor,
ell_size_factor=self.ell_size_factor,
apply_mask=self.apply_mask,
show_detection=self.show_detection)
def contruct_source_properties(self):
(self.sources_photometries, self.n_components,self.n_IDs,
self.psf_name, self.mask, self.bkg) = \
mlibs.prepare_fit(self.input_data.filename,
self.input_data.residualname,
z=self.z,ids_to_add = self.ids_to_add,
bw=self.bw, bh=self.bh, fw=self.fw, fh=self.fh,
sigma=self.sigma, sigma_mask=self.sigma_mask,
apply_mask=self.apply_mask,
deblend_nthresh=self.deblend_nthresh,
ell_size_factor=self.ell_size_factor,
minarea=self.minarea,
minarea_factor=self.minarea_factor,
deblend_cont=self.deblend_cont,
clean_param=self.clean_param,
obs_type=self.obs_type,algorithm=self.algorithm,
show_petro_plots=self.show_petro_plots)
class evaluate_source_structure():
"""
This will be designed to evaluate the souce structure
in order to check its complexity and compute how many model
components will be required to perform the multi-sersic fitting.
Also, this will compute basic source morphology, in order to
quantify which component represents a compact or a extended
structure.
"""
pass
class sersic_multifit_radio():
"""
Multi-Sersic Fitting Decomposition.
Perform a semi-automated and robust multi-sersic image decomposition.
It supports GPU-acceleration using Jax. If no GPU is present, Jax still
will benefit from CPU parallel processing. Do not worry, you do not have
to change anything, Jax will automatically detect wheter you are runnin on
CPU or GPU.
Basic principles:
- run a source extraction, to identify relevant emission.
- compute basic properties for each identified region, such as
size, intensity, shape and orientation
- uses that information to construct an object and prepare the
settings to start the fit
- compute statistics of the fit
- if asked, calculates the relative fluxes of each component.
To improve:
- run an MCMC on model parameters (optional, takes time)
To-do:
- automated evaluation of which components is compact (unresolved)
and which components is extended
- automated evaluation of which structure cannot be modelled by a
single function. For example, a spiral galaxy is reconized as a single
source, but it can not be modelled by a single function: we require to
model the bulge/bar/disk, for example.
"""
def __init__(self, input_data, SE, aspect=None,
which_residual='shuffled',
fix_geometry = True,
comp_ids = [],
fix_n = None,
fix_value_n = None, dr_fix = None,fix_x0_y0=None,
sigma=6.0, use_mask_for_fit=False,mask_for_fit=None,
mask=None,
tr_solver = "exact",
convolution_mode='GPU',method1='least_squares',
self_bkg = False, bkg_rms_map = None,
method2='least_squares',z = 0.01,
verbose=0):
"""
Parameters
----------
input_data : object
Input data object. See read_data class.
SE : object
Source extraction object.
aspect : float
Aspect ratio of the image.
which_residual : str
Which residual to use for the fitting.
fix_geometry : bool
If True, fix the geometry of the components.
comp_ids : list
List of component IDs to be fitted.
fix_n : list
List of booleans to fix the Sersic index.
fix_value_n : list
List of values to fix the Sersic index.
dr_fix : list
List of values to fix the dr parameter.
sigma : float
Sigma level for detection.
tr_solver : str
Solver for the trust region problem.
convolution_mode : str
Convolution mode.
method1 : str
Method for the first pass of the fit.
method2 : str
Method for the second pass of the fit.
z : float
Redshift of the source.
Examples:
fix_n = [True, True, True,True, True, True,True, True, True]
fix_value_n=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
dr_fix = [3, 3, 100, 100, 10, 10, 10, 10, 10]
"""
self.input_data = input_data
self.SE = SE
self.use_mask_for_fit = use_mask_for_fit
if self.use_mask_for_fit == True:
if mask_for_fit is None:
_logging_.logger.info(f" ++>> Using a mask for fitting was requested, "
f"but no mask was provided. Using the mask from the source "
f"extraction object (SE.mask).")
self.mask_for_fit = self.SE.mask
else:
_logging_.logger.info(f" ++>> Using the provided mask for fitting.")
self.mask_for_fit = mask_for_fit
else:
_logging_.logger.info(f" ++>> Fitting without a mask.")
self.mask_for_fit = None
if mask is None:
self.mask = self.SE.mask
else:
self.mask = mask
self.aspect = aspect
self.comp_ids = comp_ids
self.fix_geometry = fix_geometry
self.convolution_mode = convolution_mode
self.method1 = method1
self.method2 = method2
self.tr_solver = tr_solver
self.z = z
self.which_residual = which_residual
self.sigma = sigma
self.self_bkg = self_bkg
self.bkg_rms_map = bkg_rms_map
self.verbose = verbose
if fix_n is None:
self.fix_n = [True] * self.SE.n_components
else:
self.fix_n = fix_n
if fix_value_n is None:
self.fix_value_n = [0.5] * self.SE.n_components
else:
self.fix_value_n = fix_value_n
if dr_fix is None:
self.dr_fix = [10] * self.SE.n_components
else:
self.dr_fix = dr_fix
if fix_x0_y0 is None:
self.fix_x0_y0 = [True] * self.SE.n_components
if fix_geometry is None:
self.fix_geometry = [True] * self.SE.n_components
else:
self.fix_geometry = fix_geometry
self.__sersic_radio()
def __sersic_radio(self):
(self.results_fit, self.result_mini, self.mini, self.lmfit_results,
self.lmfit_results_1st_pass,
self.errors_fit, self.models, self.data_properties,
self.results_compact_conv_morpho,
self.results_compact_deconv_morpho, self.results_ext_conv_morpho,
self.results_ext_deconv_morpho,
self.components_deconv_props, self.components_conv_props,
self.image_results_conv, self.image_results_deconv,self.bkg_images,
self.class_resuts, self.compact_model) = \
mlibs.run_image_fitting(imagelist=[self.input_data.filename],
residuallist=[self.input_data.residualname],
aspect=self.aspect,
which_residual=self.which_residual,
comp_ids=self.comp_ids,# which IDs refers to compact components?
sources_photometries=self.SE.sources_photometries,
n_components=self.SE.n_components,
z=self.z,
convolution_mode=self.convolution_mode,
method1=self.method1,
method2=self.method2,
mask=self.mask,
use_mask_for_fit=self.use_mask_for_fit,
mask_for_fit=self.mask_for_fit,
bkg_rms_map=self.bkg_rms_map,
self_bkg=self.self_bkg,
save_name_append='',
fix_n=self.fix_n,
tr_solver = self.tr_solver,
fix_value_n=self.fix_value_n,
fix_geometry=self.fix_geometry, # unstable if False
dr_fix=self.dr_fix,
fix_x0_y0 = self.fix_x0_y0,
sigma=self.sigma,
logger=_logging_.logger,verbose=self.verbose)
# compute sizes
try:
self.pix_to_pc = \
mlibs.pixsize_to_pc(z=self.z,
cell_size=mlibs.get_cell_size(self.input_data.filename))
self.size_unit = ' pc'
except:
self.pix_to_pc = 1
self.size_unit = ' px'
self.beam_size_px = self.results_fit['beam_size_px']
self.beam_size_pc = self.beam_size_px * self.pix_to_pc
# 50% core-compact/unresolved deconvolved radii
self.C50comp_radii_deconv = \
self.results_compact_deconv_morpho['C50radii'] * self.pix_to_pc
# 50% core-compact/unresolved convolved radii
self.C50comp_radii_conv = \
self.results_compact_conv_morpho['C50radii'] * self.pix_to_pc
# 95% core-compact/unresolved deconvolved radii
self.C95comp_radii_deconv = \
self.results_compact_deconv_morpho['C95radii'] * self.pix_to_pc
# 95% core-compact/unresolved convolved radii
self.C95comp_radii_conv = \
self.results_compact_conv_morpho['C95radii'] * self.pix_to_pc
# 50% core-compact/unresolved deconvolved radii
self.C50ext_radii_deconv = \
self.results_ext_deconv_morpho['C50radii'] * self.pix_to_pc
# 50% core-compact/unresolved convolved radii
self.C50ext_radii_conv = \
self.results_ext_conv_morpho['C50radii'] * self.pix_to_pc
# 95% core-compact/unresolved deconvolved radii
self.C95ext_radii_deconv = \
self.results_ext_deconv_morpho['C95radii'] * self.pix_to_pc
# 95% core-compact/unresolved convolved radii
self.C95ext_radii_conv = \
self.results_ext_conv_morpho['C95radii'] * self.pix_to_pc
# Rn main core-compact/unresolved component (ID1)
self.Rn_comp = self.results_fit['f1_Rn'] * self.pix_to_pc
# self.Rn_comp_err = self.results_fit['f1_Rn'][0].stderr * self.pix_to_pc
mlibs.print_logger_header(title="Core-Compact Component Sizes",
logger=_logging_.logger)
_logging_.logger.info(f" >=> Beam Size = "
f"{self.beam_size_px[0]:.2f} px")
_logging_.logger.info(f" >=> Beam Size = "
f"{self.beam_size_pc[0]:.2f} {self.size_unit}")
_logging_.logger.info(f" >=> Rn Main Compact = "
f"{self.Rn_comp[0]:.2f} {self.size_unit}")
_logging_.logger.info(f" >=> C50 Compact Deconv Radii = "
f"{self.C50comp_radii_deconv[0]:.2f} {self.size_unit}")
_logging_.logger.info(f" >=> C50 Compact Conv Radii = "
f"{self.C50comp_radii_conv[0]:.2f} {self.size_unit}")
_logging_.logger.info(f" >=> C95 Compact Deconv Radii = "
f"{self.C95comp_radii_deconv[0]:.2f} {self.size_unit}")
_logging_.logger.info(f" >=> C95 Compact Conv Radii = "
f"{self.C95comp_radii_conv[0]:.2f} {self.size_unit}")
mlibs.print_logger_header(title="Extended Component Sizes",
logger=_logging_.logger)
_logging_.logger.info(f" >=> C50 Extended Deconv Radii = "
f"{self.C50ext_radii_deconv[0]:.2f} {self.size_unit} "
f"[flagged={self.results_ext_deconv_morpho['flag50'][0]}]")
_logging_.logger.info(f" >=> C50 Extended Conv Radii = "
f"{self.C50ext_radii_conv[0]:.2f} {self.size_unit} "
f"[flagged={self.results_ext_conv_morpho['flag50'][0]}]")
_logging_.logger.info(f" >=> C95 Extended Deconv Radii = "
f"{self.C95ext_radii_deconv[0]:.2f} {self.size_unit}"
f"[flagged={self.results_ext_deconv_morpho['flag9095'][0]}]")
_logging_.logger.info(f" >=> C95 Extended Conv Radii = "
f"{self.C95ext_radii_conv[0]:.2f} {self.size_unit}"
f"[flagged={self.results_ext_conv_morpho['flag9095'][0]}]")
class sersic_multifit_general():
"""
Multi-Sersic Fitting Decomposition.
Perform a semi-automated and robust multi-sersic image decomposition.
It supports GPU-acceleration using Jax. If no GPU is present, Jax still
will benefit from CPU parallel processing. Do not worry, you do not have
to change anything, Jax will automatically detect wheter you are running on
CPU or GPU.
This class it to help in modelling optical data, but is pure experimental.
Major milestones:
- improve source detection
- improve background estimation
- improve PSF modelling, especially for JWST data.
"""
def __init__(self, input_data, SE,
fix_geometry = True,
comp_ids = ['1'],
fix_n = None,
fix_value_n = None, dr_fix = None,
constrained=True, self_bkg=False,
sigma=6.0, use_mask_for_fit=False,mask_for_fit=None,
bkg_rms_map = None,
loss='cauchy', tr_solver='exact',
regularize=True, f_scale=1.0, ftol=1e-10,
xtol=1e-10, gtol=1e-10,
init_params=0.2, final_params=5.0,
which_residual = 'user',
convolution_mode='GPU',method1='least_squares',
method2='least_squares',z = 0.01,
save_name_append = ''):
self.input_data = input_data
self.SE = SE
if use_mask_for_fit == True:
if mask_for_fit is None:
self.mask_for_fit = self.SE.mask
else:
self.mask_for_fit = mask_for_fit
else:
self.mask_for_fit = None
self.comp_ids = comp_ids
self.fix_geometry = fix_geometry
self.convolution_mode = convolution_mode
self.constrained = constrained
self.method1 = method1
self.method2 = method2
self.init_params = init_params
self.final_params = final_params
self.tr_solver = tr_solver
self.regularize = regularize
self.f_scale = f_scale
self.ftol = ftol
self.xtol = xtol
self.gtol = gtol
self.loss = loss
self.z = z
self.sigma = sigma
self.which_residual = which_residual
self.self_bkg = self_bkg
self.bkg_rms_map = bkg_rms_map
if self.bkg_rms_map is not None:
self.rms_map = self.bkg_rms_map
else:
if self.self_bkg == True:
self.rms_map = self.SE.bkg
else:
self.rms_map = None
if fix_n is None:
self.fix_n = [False] * self.SE.n_components
else:
self.fix_n = fix_n
if fix_value_n is None:
self.fix_value_n = [1.5] * self.SE.n_components
else:
self.fix_value_n = fix_value_n
if dr_fix is None:
self.dr_fix = [10] * self.SE.n_components
else:
self.dr_fix = dr_fix
if fix_geometry is None:
self.fix_geometry = [True] * self.SE.n_components
else:
self.fix_geometry = fix_geometry
self.save_name_append = save_name_append
self.__sersic_general()
def __sersic_general(self):
(self.result_mini, self.mini, self.result_1, self.result_extra,
self.model_dict, self.image_results_conv, self.image_results_deconv, self.bkg_images,
self.smodel2D, self.model_temp) = \
mlibs.do_fit2D(imagename=self.input_data.filename,
residualname=self.input_data.residualname,
init_constraints=self.SE.sources_photometries,
psf_name=self.input_data.psfname,
params_values_init_IMFIT=None,
ncomponents=self.SE.n_components,
constrained=self.constrained,
self_bkg=self.self_bkg,
rms_map = self.rms_map,
which_residual=self.which_residual,
mask_region = self.mask_for_fit,
# rms_map=self.SE.bkg,
# rms_map=None,
fix_n=self.fix_n,
fix_value_n=self.fix_value_n,
dr_fix=self.dr_fix,
convolution_mode=self.convolution_mode,
fix_geometry=self.fix_geometry, workers=-1,
method1=self.method1,
method2=self.method2,
init_params=self.init_params,
final_params=self.final_params,
loss=self.loss, tr_solver=self.tr_solver,
regularize=self.regularize, f_scale=self.f_scale,
ftol=self.ftol,
xtol=self.xtol, gtol=self.gtol,
save_name_append=self.save_name_append,
logger=_logging_.logger)
all_comps_ids = np.arange(1, self.SE.n_components + 1).astype('str')
mask_compact_ids = np.isin(all_comps_ids, np.asarray(self.comp_ids))
ext_ids = list(all_comps_ids[~mask_compact_ids])
special_name = ''
compact_model = 0
extended_model = 0
compact_model_deconv = 0
extended_model_deconv = 0
if self.input_data.rms_res == None:
rms_std_res = self.input_data.rms_img
else:
rms_std_res = self.input_data.rms_res
for lc in self.comp_ids:
compact_model = (compact_model +
self.model_dict['model_c' + lc + '_conv'])
compact_model_deconv = (compact_model_deconv +
self.model_dict['model_c' + lc])
# if ext_ids is not None:
if ext_ids == []:
extended_model = 0
extended_model_deconv = 0
nfunctions = 1
else:
for le in ext_ids:
extended_model = (extended_model +
self.model_dict['model_c' + le + '_conv'])
extended_model_deconv = (extended_model_deconv +
self.model_dict['model_c' + le])
nfunctions = None
self.decomposition_results = mlibs.plot_decomp_results(imagename=self.input_data.filename,
compact=compact_model,
extended_model=extended_model,
rms=rms_std_res,
nfunctions=nfunctions,
obs_type=self.SE.obs_type,
special_name=special_name)
mlibs.plot_fit_results(self.input_data.filename,
self.model_dict,
self.image_results_conv,
self.SE.sources_photometries,
bkg_image=self.bkg_images[-1],
crop=False, box_size=200,
obs_type=self.SE.obs_type,
vmax_factor=0.3, vmin_factor=1.0)
mlibs.plot_slices(data_2D=self.input_data.image_data_2D,
model_dict=self.model_dict,
image_results_conv=self.image_results_conv[-2],
Rp_props=self.SE.sources_photometries,
residual_2D=None)
self.parameter_results = self.result_mini.params.valuesdict().copy()
try:
for param in self.result_mini.params.valuesdict().keys():
self.parameter_results[param+'_err'] = self.result_mini.params[param].stderr
except:
pass
self.parameter_results['#imagename'] = os.path.basename(self.input_data.filename)
self.results_fit = {**self.parameter_results, **self.decomposition_results}
# self.results_fit.append(all_results)
pass
class morphometry():
"""
Core functionalities from Morfometryka.
Morfometryka is not publically available yet,
so these functions will be added in a later stage.
"""
def __init__(self, input_data, aspect=None):
self.input_data = input_data
def _concentration(self):
pass
def _asymetry(self):
pass
def _momentum(self):
pass
def _sigma_psi(self):
pass
def _entropy(self):
"""
This will be provided here soon, since it was my Master Thesis research.
"""
pass
def _kurvature(self):