def setUpClass(cls):
# Load data from a JSON file
np.seterr(all='raise')
fname = 'test_data/test_data.json'
logger.info("Getting Data from file: {}".format(fname))
with open(fname, 'r') as json_file:
calib_info = json.load(json_file)
info = calib_info['info']
ant_pos = calib_info['ant_pos']
config = settings.from_api_json(info['info'], ant_pos)
flag_list = []
gains_json = calib_info['gains']
gains = np.asarray(gains_json['gain'])
phase_offsets = np.asarray(gains_json['phase_offset'])
config = settings.from_api_json(info['info'], ant_pos)
measurements = []
for d in calib_info['data']:
vis_json, source_json = d
cv, timestamp = api_imaging.vis_calibrated(vis_json, config, gains,
phase_offsets,
flag_list)
src_list = elaz.from_json(source_json, 0.0)
cls.disko = DiSkO.from_cal_vis(cv)
cls.nside = 16
cls.sphere = HealpixSphere(cls.nside)
cls.to = TelescopeOperator(cls.disko, cls.sphere)
def setUpClass(cls):
# Load data from a JSON file
fname = 'test_data/test_data.json'
logger.info("Getting Data from file: {}".format(fname))
with open(fname, 'r') as json_file:
calib_info = json.load(json_file)
info = calib_info['info']
cls.ant_pos = np.array(calib_info['ant_pos'])
config = settings.from_api_json(info['info'], cls.ant_pos)
flag_list = []
gains_json = calib_info['gains']
gains = np.asarray(gains_json['gain'])
phase_offsets = np.asarray(gains_json['phase_offset'])
#config = settings.from_api_json(info['info'], cls.ant_pos)
measurements = []
for d in calib_info['data']:
vis_json, source_json = d
cv, _timestamp = api_imaging.vis_calibrated(
vis_json, config, gains, phase_offsets, flag_list)
cls.disko = DiSkO.from_cal_vis(cv)
cls.nside = 16
cls.sphere = HealpixSphere(cls.nside)
res_deg = 4.0
cls.subsphere = HealpixSubSphere.from_resolution(resolution=res_deg *
60.0,
theta=np.radians(0.0),
phi=0.0,
radius=np.radians(89))
cls.adaptive_sphere = AdaptiveMeshSphere.from_resolution(
res_arcmin=20,
res_arcmax=res_deg * 60,
theta=np.radians(0.0),
phi=0.0,
radius=np.radians(10))
cls.gamma = cls.disko.make_gamma(cls.sphere)
cls.subgamma = cls.disko.make_gamma(cls.subsphere)
def test_uv_equal(self):
shutil.rmtree(TEST_MS, ignore_errors=True)
ms_from_json(TEST_MS, self.json_data, pol2=False)
u_arr, v_arr, w_arr, frequency, cv_vis, hdr, timestamp = disko.read_ms(
TEST_MS, num_vis=276, res_arcmin=120)
logger.info("U shape: {}".format(u_arr.shape))
info = self.json_data['info']
ant_pos = self.json_data['ant_pos']
config = settings.from_api_json(info['info'], ant_pos)
gains_json = self.json_data['gains']
gains = np.asarray(gains_json['gain'])
phase_offsets = np.asarray(gains_json['phase_offset'])
cal_vis, timestamp = api_imaging.vis_calibrated(
self.json_data['data'][0][0], config, gains, phase_offsets, [])
c = cal_vis.get_config()
ant_p = np.asarray(c.get_antenna_positions())
# We need to get the vis array to be correct for the full set of u,v,w points (baselines),
# including the -u,-v, -w points.
baselines, u_arr2, v_arr2, w_arr2 = disko.get_all_uvw(ant_p)
self.assertAlmostEqual(np.max(u_arr, axis=0), np.max(u_arr2, axis=0))
logger.info("U2 shape {}".format(u_arr2.shape))
for i in range(u_arr.shape[0]):
a = u_arr[i]
b = u_arr2[i]
self.assertAlmostEqual(a, b)
a = v_arr[i]
b = v_arr2[i]
self.assertAlmostEqual(a, b)
def ms_from_json(ms_name, json_data, pol2):
info = json_data['info']
ant_pos = json_data['ant_pos']
config = settings.from_api_json(info['info'], ant_pos)
gains = json_data['gains']['gain']
phases = json_data['gains']['phase_offset']
for d in json_data['data']: # TODO deal with multiple observations in the JSON file later.
vis_json, source_json = d
cal_vis, timestamp = api_imaging.vis_calibrated(vis_json, config, gains, phases, [])
src_list = source_json
if pol2:
pol_feeds = [ 'RR', 'LL' ]
else:
pol_feeds = [ 'RR' ]
vis_data, baselines = cal_vis.get_all_visibility()
vis_array = np.array(vis_data, dtype=np.complex64)
ms_create(ms_table_name=ms_name, info = info['info'],
ant_pos = ant_pos,
vis_array = vis_array, baselines=baselines, timestamps=timestamp,
pol_feeds=pol_feeds, sources=src_list)
config = api_handler.get_config(api)
mode = api.get('mode/current')
if mode['mode'] != 'vis':
print("ERROR: Telescope must be in visibility mode to allow imaging. Set via the web API")
gains = api.get('calibration/gain')
visibility_data = api.get('imaging/vis')
'''
STEP 2: Apply Calibration to the visiblilties
'''
print("Apply Calibration Data")
cv, timestamp = api_imaging.vis_calibrated(visibility_data, config, gains['gain'], gains['phase_offset'], flag_list=[])
'''
STEP 3: Perform the imaging
'''
print("Generate Dirty Image")
n_bin = 2**9 # Image resolution
cal_ift, cal_extent, n_fft, bin_width = api_imaging.image_from_calibrated_vis(cv, nw=n_bin/4, num_bin=n_bin)
# Take the absolute value
img = np.abs(cal_ift)
# Scale it to multiples of the image standard deviation
sd = np.std(img)
scaled_image = img/sd
'info': info,
'ant_pos': ant_pos,
'gains': gains_json,
'data': [[vis_json, src_json]]
}
info = json_data['info']
ant_pos = json_data['ant_pos']
config = settings.from_api_json(info['info'], ant_pos)
gains = json_data['gains']['gain']
phases = json_data['gains']['phase_offset']
for d in json_data[
'data']: # TODO deal with multiple observations in the JSON file later.
vis_json, source_json = d
cv, timestamp = api_imaging.vis_calibrated(vis_json, config, gains,
phases, [])
src_list = source_json
# FIXME. These appear to be weird polarization codes (or ID's). WHY oh WHY are they called corr?
# We use RR antennas, so this should be either RR or I?. I'm using FITS code 1 for this 'I'
corr_types = [[MS_STOKES_ENUMS['I']]]
ms_create(ms_table_name=ARGS.ms,
info=info['info'],
ant_pos=ant_pos,
cal_vis=cv,
timestamps=timestamp,
corr_types=corr_types,
sources=src_list)
config = api_handler.get_config(api)
mode = api.get('mode/current')
if mode['mode'] != 'vis':
print("ERROR: Telescope must be in visibility mode to allow imaging. Set via the web API")
gains = api.get('calibration/gain')
visibility_data = api.get('imaging/vis')
'''
STEP 2: Apply Calibration to the visiblilties
'''
print("Apply Calibration Data")
cv, timestamp = api_imaging.vis_calibrated(visibility_data, config, gains['gain'], gains['phase_offset'], flag_list=[])
'''
STEP 3: Perform the imaging
'''
print("Generate Dirty Image")
n_bin = 2**9 # Image resolution
cal_ift, cal_extent, n_fft, bin_width = api_imaging.image_from_calibrated_vis(cv, nw=n_bin/4, num_bin=n_bin)
# Take the absolute value
img = np.abs(cal_ift)
# Scale it to multiples of the image standard deviation
sd = np.std(img)
scaled_image = img/sd