Revision 972d1c00176ad1b021d31c925127d8d1cc914f79 authored by Nicholas Kern on 05 March 2018, 01:30:38 UTC, committed by Bryna Hazelton on 05 March 2018, 20:48:15 UTC
1 parent 2a9d6dc
fhd_cal.py
from scipy.io.idl import readsav
import os
import numpy as np
import warnings
from uvcal import UVCal
import utils as uvutils
from fhd import get_fhd_history
class FHDCal(UVCal):
"""
Defines a FHD-specific subclass of UVCal for reading FHD calibration save files.
This class should not be interacted with directly, instead use the read_fhd_cal
method on the UVCal class.
"""
def read_fhd_cal(self, cal_file, obs_file, settings_file=None, raw=True,
extra_history=None, run_check=True, check_extra=True,
run_check_acceptability=True):
"""
Read data from an FHD cal.sav file.
Args:
cal_file: The cal.sav file to read from.
obs_file: The obs.sav file to read from.
settings_file: The settings_file to read from. Optional, but very
useful for provenance.
raw: Option to use the raw (per antenna, per frequency) solution or
to use the fitted (polynomial over phase/amplitude) solution.
Default is True (meaning use the raw solutions).
extra_history: Optional string or list of strings to add to the
object's history parameter. Default is None.
run_check: Option to check for the existence and proper shapes of
parameters after reading in the file. Default is True.
check_extra: Option to check optional parameters as well as required
ones. Default is True.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
"""
this_dict = readsav(cal_file, python_dict=True)
cal_data = this_dict['cal']
this_dict = readsav(obs_file, python_dict=True)
obs_data = this_dict['obs']
self.Nspws = 1
self.spw_array = np.array([0])
self.Nfreqs = int(cal_data['n_freq'][0])
self.freq_array = np.zeros((self.Nspws, len(cal_data['freq'][0])), dtype=np.float_)
self.freq_array[0, :] = cal_data['freq'][0]
self.channel_width = float(np.mean(np.diff(self.freq_array)))
# FHD only calculates one calibration over all the times.
# cal_data.n_times gives the number of times that goes into that one
# calibration, UVCal.Ntimes gives the number of separate calibrations
# along the time axis.
self.Ntimes = 1
time_array = obs_data['baseline_info'][0]['jdate'][0]
self.integration_time = np.round(np.mean(np.diff(time_array)) * 24 * 3600, 2)
self.time_array = np.array([np.mean(time_array)])
self.Njones = int(cal_data['n_pol'][0])
# FHD only has the diagonal elements (jxx, jyy) and if there's only one
# present it must be jxx
if self.Njones == 1:
self.jones_array = np.array([-5])
else:
self.jones_array = np.array([-5, -6])
self.telescope_name = obs_data['instrument'][0]
self.Nants_data = int(cal_data['n_tile'][0])
self.Nants_telescope = int(cal_data['n_tile'][0])
self.antenna_names = cal_data['tile_names'][0].tolist()
self.antenna_numbers = np.arange(self.Nants_telescope)
self.ant_array = np.arange(self.Nants_data)
self.set_sky()
self.sky_field = 'phase center (RA, Dec): ({ra}, {dec})'.format(
ra=obs_data['orig_phasera'][0], dec=obs_data['orig_phasedec'][0])
self.sky_catalog = cal_data['skymodel'][0]['catalog_name'][0]
self.ref_antenna_name = cal_data['ref_antenna_name'][0]
self.Nsources = int(cal_data['skymodel'][0]['n_sources'][0])
self.baseline_range = [float(cal_data['min_cal_baseline'][0]),
float(cal_data['max_cal_baseline'][0])]
galaxy_model = cal_data['skymodel'][0]['galaxy_model'][0]
if galaxy_model == 0:
galaxy_model = None
else:
galaxy_model = 'gsm'
diffuse_model = cal_data['skymodel'][0]['diffuse_model'][0]
if diffuse_model == '':
diffuse_model = None
else:
diffuse_model = os.path.basename(diffuse_model)
if galaxy_model is not None:
if diffuse_model is not None:
self.diffuse_model = galaxy_model + ' + ' + diffuse_model
else:
self.diffuse_model = galaxy_model
elif diffuse_model is not None:
self.diffuse_model = diffuse_model
self.gain_convention = 'divide'
self.x_orientation = 'east'
self.set_gain()
fit_gain_array_in = cal_data['gain'][0]
fit_gain_array = np.zeros(self._gain_array.expected_shape(self), dtype=np.complex_)
for jones_i, arr in enumerate(fit_gain_array_in):
fit_gain_array[:, 0, :, 0, jones_i] = arr
if raw:
res_gain_array_in = cal_data['gain_residual'][0]
res_gain_array = np.zeros(self._gain_array.expected_shape(self), dtype=np.complex_)
for jones_i, arr in enumerate(fit_gain_array_in):
res_gain_array[:, 0, :, 0, jones_i] = arr
self.gain_array = fit_gain_array + res_gain_array
else:
self.gain_array = fit_gain_array
# FHD doesn't really have a chi^2 measure. What is has is a convergence measure.
# The solution converged well if this is less than the convergence
# threshold ('conv_thresh' in extra_keywords).
self.quality_array = np.zeros_like(self.gain_array, dtype=np.float)
convergence = cal_data['convergence'][0]
for jones_i, arr in enumerate(convergence):
self.quality_array[:, 0, :, 0, jones_i] = arr
# array of used frequencies (1: used, 0: flagged)
freq_use = obs_data['baseline_info'][0]['freq_use'][0]
# array of used antennas (1: used, 0: flagged)
ant_use = obs_data['baseline_info'][0]['tile_use'][0]
# array of used times (1: used, 0: flagged)
time_use = obs_data['baseline_info'][0]['time_use'][0]
time_array_use = time_array[np.where(time_use > 0)]
self.time_range = [np.min(time_array_use), np.max(time_array_use)]
# Currently this can't include the times because the flag array
# dimensions has to match the gain array dimensions. This is somewhat artificial...
self.flag_array = np.zeros_like(self.gain_array, dtype=np.bool)
flagged_ants = np.where(ant_use == 0)[0]
for ant in flagged_ants:
self.flag_array[ant, :] = 1
flagged_freqs = np.where(freq_use == 0)[0]
for freq in flagged_freqs:
self.flag_array[:, :, freq] = 1
# currently don't have branch info. may change in future.
self.git_origin_cal = 'https://github.com/EoRImaging/FHD'
self.git_hash_cal = obs_data['code_version'][0]
self.extra_keywords['autoscal'] = \
'[' + ', '.join(str(d) for d in cal_data['auto_scale'][0]) + ']'
self.extra_keywords['nvis_cal'] = cal_data['n_vis_cal'][0]
self.extra_keywords['time_avg'] = cal_data['time_avg'][0]
self.extra_keywords['cvgthres'] = cal_data['conv_thresh'][0]
if 'DELAYS' in obs_data.dtype.names:
if obs_data['delays'][0] is not None:
self.extra_keywords['delays'] = \
'[' + ', '.join(str(int(d)) for d in obs_data['delays'][0]) + ']'
if not raw:
self.extra_keywords['polyfit'] = cal_data['polyfit'][0]
self.extra_keywords['bandpass'] = cal_data['bandpass'][0]
self.extra_keywords['mode_fit'] = cal_data['mode_fit'][0]
self.extra_keywords['amp_deg'] = cal_data['amp_degree'][0]
self.extra_keywords['phse_deg'] = cal_data['phase_degree'][0]
if settings_file is not None:
self.history, self.observer = get_fhd_history(settings_file, return_user=True)
else:
warnings.warn('No settings file, history will be incomplete')
self.history = ''
if extra_history is not None:
if isinstance(extra_history, (list, tuple)):
self.history += '\n' + '\n'.join(extra_history)
else:
self.history += '\n' + extra_history
if not uvutils.check_history_version(self.history, self.pyuvdata_version_str):
if self.history.endswith('\n'):
self.history += self.pyuvdata_version_str
else:
self.history += '\n' + self.pyuvdata_version_str
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
![swh spinner](/static/img/swh-spinner.gif)
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