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auto_selfcal_wsclean.py
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auto_selfcal_wsclean.py
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"""
..___|**_
.|||||||||*+@+*__*++.
_||||. .*+;].,#_
Morphen _|||*_ _ .@@@#@.
_|||||_ .@##@#| _||_
Radio Self-Calibration |****_ .@.,/\..@_.
Module #///#+++*| . .@@@;#.,.\@.
.||__|**|||||*||*+@#];_. ;,;_
Geferson Lucatelli +\*_.__|**#
|.. .]]
;@ @.*.
#| _;]];|.
]_ _+;]@.
_/_ |]\| . _
...._@* __ ..... ]]+ .. _
.. . . .. .|.|_ ..
This module consists of performing interferometric imaging with wsclean and
running CASA's task gaincal for self-calibration.
It was tested for VLA (L,S,C,X,Ku) and eMERLIN (C band) observations.
Faint sources or higher-frequency observations (e.g. < 10 mJy)
may not work well. So, more experiments are required for
K and Ka VLA bands and eMERLIN fainter sources.
The user is advised to run the code in an interactive session (ipython),
step-by-step, and check the results of each step.
Check the config.py file at:
https://github.com/lucatelli/morphen/blob/main/selfcal/config.py
Note that the pure automated self-calibration is still experimental,
but showed to be good in most cases.
Check https://github.com/lucatelli/morphen/blob/main/selfcal/README.md for more information.
"""
__version__ = 0.3
__author__ = 'Geferson Lucatelli'
__email__ = '[email protected]'
__date__ = '2024 16 01'
print(__doc__)
import os
import sys
sys.path.append('../libs/')
import libs as mlibs
import glob
import pandas as pd
from casatasks import *
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from scipy.optimize import curve_fit
try:
import casatools
from casatasks import *
except:
print('Not importing casatools. '
'Maybe you are inside inside CASA? '
'Or check your modular installation?')
pass
from casaplotms import plotms
from casaviewer.imview import imview
msmd = casatools.msmetadata()
ms = casatools.ms()
tb = casatools.table()
# import config_combined as cf
import config as cf
from importlib import reload
reload(cf)
mlibs.reset_rc_params()
FIELD = cf.FIELD
ANTENNAS = cf.ANTENNAS
refantmode = cf.refantmode
SPWS = cf.SPWS
minblperant = cf.minblperant
cell_sizes_JVLA = cf.cell_sizes_JVLA
cell_sizes_eMERLIN = cf.cell_sizes_eMERLIN
taper_sizes_eMERLIN = cf.taper_sizes_eMERLIN
taper_sizes_JVLA = cf.taper_sizes_JVLA
receiver = cf.receiver
cell_size = cf.cell_size
taper_size = cf.taper_size
nc = cf.nc
negative_arg = cf.negative_arg
solnorm = cf.solnorm
combine = cf.combine
outlierfile = cf.outlierfile #deprecated, it was only used in CASA
quiet = cf.quiet
run_mode = cf.run_mode
path = cf.path
vis_list = cf.vis_list
steps = cf.steps
refant = cf.refant
instrument = cf.instrument
init_parameters = cf.init_parameters
global_parameters = cf.global_parameters
params_very_faint = cf.params_very_faint
params_faint = cf.params_faint
params_standard_1 = cf.params_standard_1
params_standard_2 = cf.params_standard_2
params_bright = cf.params_bright
params_trial_2 = cf.params_trial_2
do_additional_images = cf.do_additional_images
def select_parameters(total_flux,snr=None):
if total_flux < 10:
params = params_very_faint.copy()
elif 10 <= total_flux < 20:
params = params_faint.copy()
elif 20 <= total_flux < 50:
params = params_standard_1.copy()
elif 50 <= total_flux < 100:
params = params_standard_2.copy()
else: # total_flux >= 100
params = params_bright.copy()
return params
def get_spwids(vis):
lobs = listobs(vis=vis)
extract_spwids = {key: lobs[key] for key in lobs if 'scan_' in key}
unique_spwids = set()
for key in extract_spwids:
nested_dict = extract_spwids[key]
for inner_key in nested_dict:
spwids = nested_dict[inner_key]['SpwIds']
# Convert the array to a sorted tuple and add to the set
unique_spwids.add(tuple(sorted(spwids)))
# Convert the set of tuples back to a sorted list of lists
unique_spwids_lists = sorted([list(t) for t in unique_spwids])
# Flatten the list and then convert to a set to get unique elements
unique_elements = set(element for sublist in unique_spwids_lists for element in sublist)
# Convert the set back to a list and sort it
unique_elements_sorted = sorted(list(unique_elements))
return unique_elements_sorted
def get_spwmap(vis):
lobs = listobs(vis=vis)
extract_spwids = {key: lobs[key] for key in lobs if 'scan_' in key}
unique_spwids = set()
for key in extract_spwids:
nested_dict = extract_spwids[key]
for inner_key in nested_dict:
spwids = nested_dict[inner_key]['SpwIds']
# Convert the array to a sorted tuple and add to the set
unique_spwids.add(tuple(sorted(spwids)))
# Convert the set of tuples back to a sorted list of lists
unique_spwids_lists = sorted([list(t) for t in unique_spwids])
counts = {}
for lst in unique_spwids_lists:
if lst[0] not in counts:
counts[lst[0]] = len(lst)
# Construct the spwmap
spwmap_i = [item for item, count in counts.items() for _ in range(count)]
spwmap = [spwmap_i[:len(get_spwids(g_vis))]]
return spwmap
def print_table(data):
"""
Print a simple dictionary as a user-friendly readable table to the terminal.
"""
import tableprint
rows = []
for key, value in data.items():
if isinstance(value, list):
# Convert list to string
value = ', '.join(map(str, value))
rows.append((key, value))
headers = ["Parameter", "Value"]
tableprint.table(rows, headers)
pass
def report_flag(summary, axis):
for id, stats in summary[axis].items():
print('%s %s: %5.1f percent flagged' % (
axis, id, 100. * stats['flagged'] / stats['total']))
pass
def compute_flux_density(imagename, residualname, mask=None):
beam_area = mlibs.beam_area2(imagename)
image_data = mlibs.ctn(imagename)
if mask is None:
rms = mlibs.mad_std(mlibs.ctn(residualname))
_, mask = mlibs.mask_dilation(imagename, show_figure=False, PLOT=True, rms=rms, sigma=6,
iterations=2)
total_flux_density = mlibs.np.nansum(image_data * mask) / beam_area
# compute error in flux density
data_res = mlibs.ctn(residualname)
res_error_rms = mlibs.np.sqrt(mlibs.np.nansum(
(abs(data_res * mask - mlibs.np.nanmean(data_res * mask))) ** 2 * mlibs.np.nansum(
mask))) / beam_area
# res_error_rms = 3 * mlibs.np.nansum(data_res * mask)/beam_area
print('-----------------------------------------------------------------')
print('Estimate of flux error (based on rms of '
'residual x area): ')
print('Flux Density = ', total_flux_density * 1000, '+/-',
res_error_rms * 1000, 'mJy')
print('Fractional error flux = ', abs(res_error_rms) / total_flux_density)
print('-----------------------------------------------------------------')
# print(f"Flux Density = {total_flux_density*1000:.2f} mJy")
return (total_flux_density, abs(res_error_rms))
def compute_image_stats(path,
image_list,
image_statistics,
prefix='',
sigma=None,
selfcal_step=None):
"""
This function will compute statistics of a cleaned image from a wsclean run
at a given self-cal step (provided an image prefix). It will also store
associated model and residual images.
Parameters
----------
path : str
Path to the image files.
image_list : list
List to store the image names of each self-cal step.
image_statistics : dict
Dictionary to store the statistics of the images at a given self-cal step.
It can be an existing dictionary.
prefix : str
Prefix of the image files.
"""
file_list = glob.glob(f"{path}*{prefix}*MFS-image.fits")
file_list.sort(key=os.path.getmtime, reverse=False)
try:
image_list[prefix] = file_list[-1]
except:
image_list[prefix] = file_list
image_list[prefix+'_residual'] = image_list[prefix].replace(
'MFS-image.fits', 'MFS-residual.fits')
image_list[prefix+'_model'] = image_list[prefix].replace(
'MFS-image.fits', 'MFS-model.fits')
if sigma is None:
if (selfcal_step == 'test_image') or (selfcal_step == 'p0'):
"""
We must be more conservative when creating masks to compute the total flux density
before self-calibration. The image may contain artifacts above the default sigma
threshold of 6.0, and may lead to overestimation of the total flux density.
An alternative sigma is 8. Note that the mask dilation is a very powerful approach and
very sensitive to the sigma threshold. A sigma of 8 results large differences in
relation to a sigma of 6.
"""
sigma = 8.0
else:
sigma = 6.0
level_stats = mlibs.level_statistics(image_list[prefix],sigma=sigma)
image_stats = mlibs.get_image_statistics(imagename=image_list[prefix],
dic_data=level_stats,
sigma_mask=sigma)
img_props = mlibs.compute_image_properties(image_list[prefix],
image_list[prefix+'_residual'],
results = image_stats,
sigma_mask = sigma,
show_figure=False)[-1]
image_statistics[prefix] = img_props
try:
centre = mlibs.nd.maximum_position(mlibs.ctn(image_list[prefix]))[::-1]
# centre = (int(image_statistics[prefix]['y0']),int(image_statistics[prefix]['x0']))
fig = mlibs.plt.figure(figsize=(16, 8))
ax0 = fig.add_subplot(1, 2, 2)
ax0 = mlibs.eimshow(imagename = image_list[prefix],
center=centre,
projection = 'offset',plot_colorbar=True,
rms=mlibs.mad_std(mlibs.ctn(image_list[prefix + '_residual'])),
# crop=True,box_size=300,
figsize=(8, 8), ax=ax0,fig=fig,
crop=True, box_size=int(4 * image_statistics[prefix]['C95radii']),
# save_name=image_list[prefix].replace('.fits', '_map'),
add_beam=True)
ax0.set_title(f'Radio Map')
ax1 = fig.add_subplot(1, 2, 1)
ax1 = mlibs.eimshow(imagename = image_list[prefix + '_residual'],
center=centre,
projection='offset',
vmin_factor=-3.0, vmax_factor=0.99,
add_contours=False,
figsize=(8, 8), ax=ax1,fig=fig,
crop=True, box_size=int(4 * image_statistics[prefix]['C95radii']),
save_name=image_list[prefix].replace('.fits', '_map'),
plot_title = f'Residual Map', plot_colorbar=False,
add_beam=False)
except:
print('--==>> Error on plotting radio map.')
pass
try:
sub_band_images = glob.glob(
image_list[prefix].replace('-MFS-image.fits', '') + '-????-image.fits')
sub_band_residuals = glob.glob(
image_list[prefix + '_residual'].replace('-MFS-residual.fits',
'') + '-????-residual.fits')
_FLUXES = []
_FLUXES_err = []
for i in range(len(sub_band_images)):
# flux_density, flux_density_err = mlibs.compute_flux_density(sub_band_images[i],
# sub_band_residuals[i],
# mask=None)
img_props = mlibs.compute_image_properties(sub_band_images[i],
sub_band_residuals[i],
sigma_mask=sigma,
last_level=1.5,
mask=None, verbose=1,
save_csv = True,
show_figure=False)[-1]
# flux_density,flux_density_err = img_props['total_flux_levels'], img_props['flux_error_res_3']
flux_density, flux_density_err = img_props['total_flux_mask'], img_props['flux_error_res_3']
print('Flux density = ', flux_density)
_FLUXES.append(flux_density)
_FLUXES_err.append(flux_density_err)
FLUXES = mlibs.np.asarray(_FLUXES)
FLUXES_err = mlibs.np.asarray(_FLUXES_err)
freqlist = mlibs.getfreqs(sub_band_images)
mini, result_1 = mlibs.do_fit_spec_RC_linear(freqlist,FLUXES*1000,FLUXES_err*1000,
basename_save=image_list[prefix],
title_text=r'Sub-band Images',
plot_errors_shade = True,do_mcmc_fit=True,
verbose=2)
# plt.figure(figsize=(8, 6))
# plt.errorbar(freqlist / 1e9, FLUXES * 1000,
# yerr=FLUXES_err * 1000,
# fmt='o',
# # label='Observed data',
# color='k', ecolor='gray', alpha=0.5)
# plt.xlabel('Frequency [GHz]')
# plt.ylabel('Flux Density [mJy]')
# plt.ylim(0,)
# plt.title('Sub-Band Images')
# plt.savefig(image_list[prefix].replace('-MFS-image.fits', '_freq_flux.jpg'), dpi=300,
# bbox_inches='tight')
except:
print('--==>> Some error found in the sub-bands images.')
pass
return(image_statistics,image_list)
def create_mask(imagename,rms_mask,sigma_mask,mask_grow_iterations,PLOT=False):
valid_sigma_mask = sigma_mask
while True:
mask_valid = mlibs.mask_dilation(imagename,
PLOT=PLOT,
rms=rms_mask,
dilation_size = None,
sigma=valid_sigma_mask,
iterations=mask_grow_iterations)[1]
if mask_valid.sum() > 0:
break
print(' ++>> No mask found with sigma_mask:',valid_sigma_mask)
print(' ++>> Reducing sigma_mask by 2 until valid mask is found...')
valid_sigma_mask = valid_sigma_mask - 2.0
if valid_sigma_mask <= 6:
print("Reached minimum sigma threshold without finding a valid mask.")
break
mask_valid = mlibs.mask_dilation(imagename,
PLOT=PLOT,
rms=rms_mask,
dilation_size=1,
sigma=valid_sigma_mask-1,
iterations=mask_grow_iterations)[1]
mask = mask_valid
mask_wslclean = mask * 1.0 # mask in wsclean is inverted
mask_name = imagename.replace('.fits', '') + '_mask.fits'
mlibs.pf.writeto(mask_name, mask_wslclean, overwrite=True)
return(mask_name)
def sinusoidal_function(t, A, omega, phi, offset):
"""
###############
*** TESTING ***
###############
"""
return A * np.sin(omega * t + phi) + offset
def fit_function_and_estimate_coherence(time, phase,PLOT=False):
"""
###############
*** TESTING ***
###############
"""
# Initial guess for the parameters: amplitude, angular frequency, phase shift, and offset
guess_amplitude = (np.max(phase) - np.min(phase)) / 2
# guess_omega = 2 * np.pi / np.ptp(time) # Assume a period equal to the range of time
# guess_amplitude = np.nanmean(phase)
guess_omega = 2 * np.pi / 120. # Assume a period equal to the range of time
guess_phi = phase[0]
guess_offset = np.mean(phase)
p0 = [guess_amplitude, guess_omega, guess_phi, guess_offset]
# Curve fitting
try:
params, covariance = curve_fit(sinusoidal_function, time, phase, p0=p0)
except RuntimeError as e:
print(f"Curve fitting failed: {e}")
return None, None
# Calculate the fitted curve
fitted_phase = sinusoidal_function(time, *params)
if PLOT is True:
# Plot the original data and the fitted curve
plt.figure(figsize=(10, 5))
plt.scatter(time, phase, label='Original Data')
plt.plot(time, fitted_phase, label='Fitted Curve', color='red')
plt.legend()
plt.xlabel('Time (seconds)')
plt.ylabel('Phase (degrees)')
plt.title('Phase Data and Fitted Curve')
plt.show()
# Analyze the residuals
residuals = phase - fitted_phase
residual_std = np.std(residuals)
# Use the period of the fitted sinusoid as an estimate for coherence time
estimated_coherence_time = 2 * np.pi / params[1]
return estimated_coherence_time, residual_std
def estimate_local_coherence_time(time, phase,PLOT=False):
"""
###############
*** TESTING ***
###############
"""
# Normalize phase
phase_normalized = phase - np.mean(phase)
# Calculate the autocorrelation of the phase signal
autocorr = np.correlate(phase_normalized, phase_normalized, mode='full')
autocorr = autocorr[
autocorr.size // 2:] # Take the second half of the autocorrelation
autocorr /= np.max(autocorr) # Normalize the autocorrelation
# Find the first point where the autocorrelation drops below 0.5 after the peak
half_max_index = np.where(autocorr < 0.5)[0][0] if np.any(autocorr < 0.5) else len(
autocorr) - 1
# The coherence time is the time corresponding to the half-max index
coherence_time = time[half_max_index] - time[0] if half_max_index < len(time) else \
time[-1] - time[0]
if PLOT is True:
# Plot the autocorrelation
plt.figure(figsize=(10, 5))
time_delays = time[:autocorr.size] - time[0]
plt.plot(time_delays, autocorr)
plt.axvline(x=time_delays[half_max_index], color='r', linestyle='--',
label='Local Coherence Time')
plt.title('Autocorrelation of Phase Signal')
plt.xlabel('Time Delay (seconds)')
plt.ylabel('Autocorrelation')
plt.legend()
plt.grid(True)
plt.show()
return coherence_time
def table_phase_time0(vis,gain_table,spw,intent='*TARGET*',ant = None):
"""
###############
*** TESTING ***
###############
"""
# msmd = casatools.msmetadata()
# ms = casatools.ms()
# tb = casatools.table()
msmd.open(vis)
scans = msmd.scansforintent(intent).tolist()
msmd.close()
tb.open(gain_table)
time, spwid, gain, antennae = tb.getcol('TIME'), tb.getcol(
'SPECTRAL_WINDOW_ID'), tb.getcol('CPARAM'), tb.getcol('ANTENNA1')
tb.close()
unique_spwids = np.unique(spwid)
msmd.open(vis)
scans = msmd.scansforintent(intent).tolist()
msmd.close()
# mstool = casatools.ms
# myms = mstool()
# myms.open(vis)
#
# # if spw is None:
#
# myms.selectinit(datadescid=unique_spwids[0])
# myms.close()
# compute co-time per spw for all scans (use all antennas)
co_times_spw = np.zeros(len(unique_spwids))
co_times_spw[:] = np.nan
for i in range(len(unique_spwids)):
spw = unique_spwids[i]
match = spwid == spw
if ant is not None:
match &= antennae == int(ant)
# fig = plt.gcf()
# ax1 = plt.subplot(3, 1, 1)
timee = time[match] - time[match][0]
phase = np.mean((np.angle(gain[:, 0, match].T) * 180 / np.pi), axis=1)
# ax1.scatter(timee, phase, marker='.', s=1)
coherence_time, residual_std = fit_function_and_estimate_coherence(timee, phase)
co_times_spw[i] = coherence_time
# coherence_time = estimate_local_coherence_time(timee, phase)
if coherence_time == 0.0:
co_times_spw[i] = np.nan
else:
co_times_spw[i] = coherence_time
print(f"Estimated Coherence Time for spw {spw} : {coherence_time} seconds")
# print(f"Residual Standard Deviation: {residual_std} degrees")
if np.isnan(np.nanmean(co_times_spw)) == True:
solint = np.inf
else:
solint = int(np.nanmean(co_times_spw))
return(solint)
def table_phase_time(vis,gain_table,spw,intent='*TARGET*',ant = None):
"""
###############
*** TESTING ***
###############
"""
# msmd = casatools.msmetadata()
# ms = casatools.ms()
# tb = casatools.table()
msmd.open(vis)
scans = msmd.scansforintent(intent).tolist()
msmd.close()
tb.open(gain_table)
time, spwid, gain, antennae = tb.getcol('TIME'), tb.getcol(
'SPECTRAL_WINDOW_ID'), tb.getcol('CPARAM'), tb.getcol('ANTENNA1')
tb.close()
unique_spwids = np.unique(spwid)
msmd.open(vis)
scans = msmd.scansforintent(intent).tolist()
msmd.close()
mstool = casatools.ms
myms = mstool()
myms.open(vis)
# if spw is None:
myms.selectinit(datadescid=unique_spwids[0])
myms.close()
# compute co-time per spw for all scans (use all antennas)
co_times_spw = np.zeros(len(unique_spwids))
co_times_spw[:] = np.nan
for i in range(len(unique_spwids)):
spw = unique_spwids[i]
match = spwid == spw
if ant is not None:
match &= antennae == int(ant)
# fig = plt.gcf()
# ax1 = plt.subplot(3, 1, 1)
timee = time[match] - time[match][0]
phase = np.mean((np.angle(gain[:, 0, match].T) * 180 / np.pi), axis=1)
# ax1.scatter(timee, phase, marker='.', s=1)
# coherence_time, residual_std = fit_function_and_estimate_coherence(timee, phase)
# co_times_spw[i] = coherence_time
coherence_time = estimate_local_coherence_time(timee, phase)
if coherence_time == 0.0:
co_times_spw[i] = np.nan
else:
co_times_spw[i] = coherence_time
print(f"Estimated Coherence Time for spw {spw} : {coherence_time} seconds")
# print(f"Residual Standard Deviation: {residual_std} degrees")
if np.isnan(np.nanmean(co_times_spw)) == True:
solint = np.inf
else:
solint = int(np.nanmean(co_times_spw))
return(solint)
def plot_visibilities(g_vis, name, with_DATA=False, with_MODEL=False,
with_CORRECTED=False):
if with_MODEL == True:
plotms(vis=g_vis, xaxis='UVwave', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline',
ydatacolumn='model', avgchannel='32', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_uvwave_amp_model.jpg')
plotms(vis=g_vis, xaxis='freq', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS, coloraxis='scan',
ydatacolumn='model', avgchannel='', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_freq_amp_model.jpg')
if with_DATA == True:
plotms(vis=g_vis, xaxis='UVwave', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline',
ydatacolumn='data', avgchannel='32', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
# plotrange=[-1,-1,-1,0.3],
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_uvwave_amp_data.jpg')
plotms(vis=g_vis, xaxis='freq', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline',avgscan=True,
ydatacolumn='data', avgchannel='16', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_freq_amp_data.jpg')
if with_CORRECTED == True:
plotms(vis=g_vis, xaxis='UVwave', yaxis='amp',
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline', avgantenna=False,
ydatacolumn='corrected-model', avgchannel='32', avgtime='360',
correlation='LL', plotrange=[0, 0, 0, 0],
width=1000, height=440, showgui=False, overwrite=True, dpi=1200, highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_uvwave_amp_corrected-model.jpg')
plotms(vis=g_vis, xaxis='UVwave', yaxis='amp',
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline', avgantenna=False,
ydatacolumn='corrected/model', avgchannel='64', avgtime='360',
correlation='LL',
width=1000, height=440, showgui=False, overwrite=True, dpi=1200, highres=True,
plotrange=[0, 0, 0, 5],
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_uvwave_amp_corrected_div_model.jpg')
plotms(vis=g_vis, xaxis='UVwave', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS,coloraxis='baseline',
# plotrange=[-1,-1,0,0.3],
ydatacolumn='corrected', avgchannel='32', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_uvwave_amp_corrected.jpg')
plotms(vis=g_vis, xaxis='freq', yaxis='amp', avgantenna=False,
antenna=ANTENNAS, spw=SPWS, coloraxis='baseline',
ydatacolumn='corrected', avgchannel='16', avgtime='360',
correlation='LL',plotrange=[0,0,0,0],
width=1000, height=440, showgui=False, overwrite=True,dpi=1200,highres=True,
plotfile=os.path.dirname(
g_vis) + '/selfcal/plots/' + name + '_freq_amp_corrected.jpg')
pass
def start_image(g_name, n_interaction, imsize='2048', imsizey=None, cell='0.05asec',
robust=0.0,
base_name=None,
nsigma_automask = '7.0',nsigma_autothreshold='0.1',
delmodel=False, niter=600,
opt_args = '',quiet=True,shift=None,
nc=4,negative_arg='negative',
PLOT=False, datacolumn='DATA',mask=None,
savemodel=True, uvtaper=[""]):
'''
Wsclean wrapper function. It calls wslcean from the command line with some
predifined arguments. This initial step runs on the DATA column and creates
the initial model which is used to calculate the initial complex self-gains.
'''
g_vis = g_name + '.ms'
if imsizey is None:
imsizey = imsize
if base_name is None:
base_name = str(n_interaction)+'_start_image_'
else:
base_name = base_name
os.system("export OPENBLAS_NUM_THREADS=1 && python imaging_with_wsclean.py --f " +
g_name + " --sx "
+ str(imsize) + " --sy " + str(imsizey) + " --niter "
+ str(niter) + " --data " + datacolumn + " --cellsize " + cell
+ ' --nsigma_automask ' + nsigma_automask + ' --mask '+str(mask)
+ ' --nsigma_autothreshold ' + nsigma_autothreshold
# +' --opt_args '+ opt_args
+ ' --quiet ' + str(quiet)
+ ' --nc ' + str(nc) + ' --negative_arg ' + negative_arg
+ ' --shift ' + str(shift)
+ " --r " + str(robust) + " --t "+str(uvtaper)
+ " --update_model " + str(savemodel) + " --save_basename " + base_name)
if PLOT == True:
plot_visibilities(g_vis=g_vis, name=base_name,
with_MODEL=True, with_DATA=False, with_CORRECTED=True)
pass
def get_tb_data(table, param):
tb.open(table)
param_data = tb.getcol(param).ravel()
tb.close()
return (param_data)
def make_plot_snr(caltable, cut_off, plot_snr=True, bins=50, density=True,
save_fig=False):
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
snr = get_tb_data(caltable, 'SNR')
plt.figure(figsize=(3, 3))
if plot_snr:
plt.hist(snr, bins=bins, density=density, histtype='step')
# plt.legend( loc='upper right' )
plt.xlabel('SNR')
# plt.semilogy()
# plt.semilogx()
plt.axvline(x=3, color='k', linestyle='--')
plt.axvline(x=cut_off, color='r', linestyle='--')
plt.grid()
if save_fig == True:
plt.savefig(caltable.replace('.tb', '.jpg'), dpi=300, bbox_inches='tight')
# plt.show()
plt.clf()
plt.close()
print('P(<=' + str(cut_off) + ') = {0} ({1})'.format(
stats.percentileofscore(snr, cut_off), ''))
def calibration_table_plot(table, stage='selfcal',
table_type='gain_phase', kind='',
xaxis='time', yaxis='phase',
fields=['']):
if not os.path.exists(os.path.dirname(table) + '/plots/' + stage):
os.makedirs(os.path.dirname(table) + '/plots/' + stage)
if yaxis == 'phase':
plotrange = [-1, -1, -180, 180]
else:
plotrange = [-1, -1, -1, -1]
if fields == '':
plotms(vis=table, xaxis=xaxis, yaxis=yaxis, field='',
gridcols=1, gridrows=1, coloraxis='spw', antenna='', plotrange=plotrange,
width=1000, height=400, dpi=600, overwrite=True, showgui=True,
# correlation='LL,RR',
plotfile=os.path.dirname(
table) + '/plots/' + stage + '/' + table_type + '_' + xaxis + '_' + yaxis + '_field_' + str(
'all') + '.jpg')
plotms(vis=table, xaxis=xaxis, yaxis=yaxis, field='',avgbaseline=True,
gridcols=1, gridrows=1, coloraxis='spw', antenna='', plotrange=plotrange,
width=1000, height=400, dpi=600, overwrite=True, showgui=True,
# correlation='LL,RR',
plotfile=os.path.dirname(
table) + '/plots/' + stage + '/' + table_type + '_' + xaxis + '_' +
yaxis + '_avgbaseline_field_' + str(
'all') + '.jpg')
else:
for FIELD in fields:
plotms(vis=table, xaxis=xaxis, yaxis=yaxis, field=FIELD,
# gridcols=4,gridrows=4,coloraxis='spw',antenna='',iteraxis='antenna',
# width=2048,height=1280,dpi=256,overwrite=True,showgui=False,
gridcols=1, gridrows=1, coloraxis='spw', antenna='',
plotrange=plotrange,
width=1000, height=400, dpi=600, overwrite=True, showgui=False,
# correlation='LL,RR',
plotfile=os.path.dirname(
table) + '/plots/' + stage + '/' + table_type + '_' + xaxis + '_' + yaxis + '_field_' + str(
FIELD) + '.jpg')
pass
def check_solutions(g_name, field, cut_off=2.0, minsnr=2.0, n_interaction=0, uvrange='',
solnorm=solnorm, combine='', calmode='p', gaintype='G',solint_factor=1.0,
interp = '',spwmap = [],
gain_tables_selfcal=[''], special_name='',refant = '', minblperant=4,
return_solution_stats=False):
g_vis = g_name + '.ms'
minsnr = minsnr
solint_template = np.asarray([24,48,96,192,384])
solints = solint_template * solint_factor
caltable_int = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_int_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_1 = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_'+
str(int(solints[0]))+'_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_2 = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_'+
str(int(solints[1]))+'_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_3 = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_'+
str(int(solints[2]))+'_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_4 = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_'+
str(int(solints[3]))+'_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_5 = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_'+
str(int(solints[4]))+'_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
caltable_inf = (os.path.dirname(g_name) + '/selfcal/selfcal_test_' + str(
n_interaction) + '_' + os.path.basename(g_name) + '_solint_inf_minsnr_' + str(
minsnr) + '_calmode' + calmode + '_combine' + combine + '_gtype_' +
gaintype + special_name + '.tb')
if not os.path.exists(caltable_int):
print('>> Performing test-gaincal for solint=int...')
gaincal(vis=g_vis, caltable=caltable_int, solint='int', refant=refant,
interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_1):
print('>> Performing test-gaincal for solint='+str(solints[0])+'s...')
gaincal(vis=g_vis, caltable=caltable_1, solint=str(solints[0])+'s',
refant=refant,interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_2):
print('>> Performing test-gaincal for solint='+str(solints[1])+'s...')
gaincal(vis=g_vis, caltable=caltable_2, solint=str(solints[1])+'s',
refant=refant,interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_3):
print('>> Performing test-gaincal for solint='+str(solints[2])+'s...')
gaincal(vis=g_vis, caltable=caltable_3, solint=str(solints[2])+'s',
refant=refant,interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_4):
print('>> Performing test-gaincal for solint='+str(solints[3])+'s...')
gaincal(vis=g_vis, caltable=caltable_4, solint=str(solints[3])+'s',
refant=refant,interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_5):
print('>> Performing test-gaincal for solint='+str(solints[4])+'s...')
gaincal(vis=g_vis, caltable=caltable_5, solint=str(solints[4])+'s',
refant=refant,interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
if not os.path.exists(caltable_inf):
print('>> Performing test-gaincal for solint=inf...')
gaincal(vis=g_vis, caltable=caltable_inf, solint='inf', refant=refant,
interp=interp,spwmap = spwmap,
solnorm=solnorm, combine=combine, minblperant=minblperant,
calmode=calmode, gaintype=gaintype, minsnr=minsnr, uvrange=uvrange,
gaintable=gain_tables_selfcal)
def make_plot_check(cut_off=cut_off, return_solution_stats=False):
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
snr_int = get_tb_data(caltable_int, 'SNR')
# snr_5 = get_tb_data(caltable_5,'SNR')
snr_1 = get_tb_data(caltable_1, 'SNR')
snr_2 = get_tb_data(caltable_2, 'SNR')
snr_3 = get_tb_data(caltable_3, 'SNR')
snr_4 = get_tb_data(caltable_4, 'SNR')
snr_5 = get_tb_data(caltable_5, 'SNR')
snr_inf = get_tb_data(caltable_inf, 'SNR')
plt.figure()
plt.hist(snr_int, bins=50, density=True, histtype='step',
label='int')
plt.hist(snr_1, bins=50, density=True, histtype='step',
label=str(solints[0])+' seconds')
plt.hist(snr_2, bins=50, density=True, histtype='step',
label=str(solints[1])+' seconds')
plt.hist(snr_3, bins=50, density=True, histtype='step',
label=str(solints[2])+' seconds')
plt.hist(snr_4, bins=50, density=True, histtype='step',
label=str(solints[3])+' seconds')
plt.hist(snr_5, bins=50, density=True, histtype='step',
label=str(solints[4])+' seconds')
plt.hist(snr_inf, bins=50, density=True, histtype='step',
label='inf')
plt.legend(loc='upper right')
plt.xlabel('SNR')
# plt.semilogx()
plt.savefig(os.path.dirname(g_name) + '/selfcal/plots/' + str(n_interaction) +
'_' + os.path.basename(
g_name) + '_calmode' + calmode + '_combine' + combine + '_gtype_' + gaintype
+ special_name + '_gain_solutions_comparisons_norm.pdf')
# plt.clf()
# plt.close()
# plt.show()
plt.figure()
plt.figure()
plt.hist(snr_int, bins=50, density=False, histtype='step',
label='int')
plt.hist(snr_1, bins=50, density=False, histtype='step',
label=str(solints[0])+' seconds')
plt.hist(snr_2, bins=50, density=False, histtype='step',
label=str(solints[1])+' seconds')
plt.hist(snr_3, bins=50, density=False, histtype='step',
label=str(solints[2])+' seconds')
plt.hist(snr_4, bins=50, density=False, histtype='step',
label=str(solints[3])+' seconds')
plt.hist(snr_5, bins=50, density=False, histtype='step',
label=str(solints[4])+' seconds')
plt.hist(snr_inf, bins=50, density=False, histtype='step',
label='inf')
plt.legend(loc='upper right')
plt.xlabel('SNR')
# plt.semilogx()
plt.savefig(os.path.dirname(g_name) + '/selfcal/plots/' + str(n_interaction) +
'_' + os.path.basename(g_name) + '_calmode' + calmode + '_combine' + combine +
'_gtype_' + gaintype + special_name +
'_gain_solutions_comparisons.pdf')
print('P(<=' + str(cut_off) + ') = {0} ({1})'.format(
stats.percentileofscore(snr_int, cut_off), 'int'))
print('P(<=' + str(cut_off) + ') = {0} ({1})'.format(
stats.percentileofscore(snr_1, cut_off), str(solints[0])+' s'))
print('P(<=' + str(cut_off) + ') = {0} ({1})'.format(
stats.percentileofscore(snr_2, cut_off), str(solints[1])+' s'))
print('P(<=' + str(cut_off) + ') = {0} ({1})'.format(
stats.percentileofscore(snr_3, cut_off), str(solints[2])+' s'))