def _apply_sig_clip(data,
e_data,
sig_thres=2,
ymin=0,
ymax=1.2,
var='V2',
display=False):
""" Apply the sigma-clipping on the dataset and plot some diagnostic plots. """
filtered_data = sigma_clip(data, sigma=sig_thres, axis=0)
n_files = data.shape[0]
n_pts = data.shape[1] # baselines or bs number
mn_data_clip, std_data_clip = [], []
for i in range(n_pts):
cond = filtered_data[:, i].mask
data_clip = data[:, i][~cond]
e_data_clip = e_data[:, i][~cond]
cmn, std = wtmn(data_clip, weights=e_data_clip)
mn_data_clip.append(cmn)
std_data_clip.append(std)
data_med = np.median(data, axis=0)
if var == 'V2':
ylabel = r'Raw V$^2$'
xlabel = '# baselines'
else:
ylabel = r'Raw closure Phases $\Phi$ [deg]'
xlabel = '# bispectrum'
ymin = -ymax
if display:
plt.figure()
plt.title('CALIBRATOR')
if n_files != 1:
plt.plot(data[0], color='grey', alpha=.2, label='Data vs. files')
plt.plot(data[1:, :].T, color='grey', alpha=.2)
plt.plot(data_med, 'g--', lw=1, label='Median value')
for i in range(n_files):
data_ind = data[i, :]
cond = filtered_data[i, :].mask
x = np.arange(len(data_ind))
bad = data_ind[cond]
x_bad = x[cond]
if i == 0:
plt.plot(x_bad,
bad,
'rx',
ms=3,
label=r'Rejected points (>%i$\sigma$)' % sig_thres)
else:
plt.plot(x_bad, bad, 'rx', ms=3)
plt.legend(loc='best', fontsize=9)
plt.ylim(ymin, ymax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tight_layout()
return np.array(mn_data_clip), np.array(std_data_clip)
def average_calib_files(list_nrm, sig_thres=2, display=False):
""" Average NRM data extracted from multiple calibrator files. Additionaly,
perform sigma-clipping to reject suspicious dataset.
Parameters:
-----------
`list_nrm` : {list}
List of classes containing extracted NRM data (see bispect.py) of multiple calibrator files,\n
`sig_thres` : {float}
Threshold of the sigma clipping (default: 2-sigma around the median is used),\n
"""
nfiles = len(list_nrm)
l_pa = np.zeros(nfiles)
cp_vs_file, e_cp_vs_file = [], []
vis2_vs_file, e_vis2_vs_file = [], []
# Fill array containing each vis2 and cp across files.
for n in range(nfiles):
nrm = list_nrm[n]
hdu = fits.open(nrm.infos.filename)
hdr = hdu[0].header
try:
# todo: Check parallactic angle param of a real NIRISS header.
l_pa[n] = hdr['PARANG']
except KeyError:
l_pa[n] = 0
cp = nrm.cp
e_cp = nrm.e_cp
vis2 = nrm.vis2
e_vis2 = nrm.e_vis2
cp_vs_file.append(cp)
e_cp_vs_file.append(e_cp)
vis2_vs_file.append(vis2)
e_vis2_vs_file.append(e_vis2)
bl = list_nrm[0].bl
cp_vs_file = np.array(cp_vs_file)
e_cp_vs_file = np.array(e_cp_vs_file)
vis2_vs_file = np.array(vis2_vs_file)
e_vis2_vs_file = np.array(e_vis2_vs_file)
zero_uncer = (e_vis2_vs_file == 0)
e_vis2_vs_file[zero_uncer] = np.max(e_vis2_vs_file)
cmn_vis2, std_vis2 = wtmn(vis2_vs_file, e_vis2_vs_file)
cmn_cp, std_cp = wtmn(cp_vs_file, e_cp_vs_file)
# Apply sigma clipping on the averages
cmn_vis2_clip, std_vis2_clip = _apply_sig_clip(vis2_vs_file,
e_vis2_vs_file,
sig_thres=sig_thres,
var='V2',
display=display)
cmn_cp_clip, std_cp_clip = _apply_sig_clip(cp_vs_file,
e_cp_vs_file,
sig_thres=sig_thres,
ymax=10,
var='CP',
display=display)
res = {
'f_v2_clip': np.array(cmn_vis2_clip),
'f_v2': np.array(cmn_vis2),
'std_vis2_clip': np.array(std_vis2_clip),
'std_vis2': np.array(std_vis2),
'f_cp_clip': np.array(cmn_cp_clip),
'f_cp': np.array(cmn_cp),
'std_cp_clip': np.array(std_cp_clip),
'std_cp': np.array(std_cp),
'bl': bl,
'pa': l_pa
}
return dict2class(res)
def _apply_sig_clip(data,
e_data,
sig_thres=2,
ymin=0,
ymax=1.2,
var="V2",
display=False):
"""Apply the sigma-clipping on the dataset and plot some diagnostic plots."""
import matplotlib.pyplot as plt
from astropy.stats import sigma_clip
filtered_data = sigma_clip(data, sigma=sig_thres, axis=0)
n_files = data.shape[0]
n_pts = data.shape[1] # baselines or bs number
mn_data_clip, std_data_clip = [], []
for i in range(n_pts):
cond = filtered_data[:, i].mask
data_clip = data[:, i][~cond]
e_data_clip = e_data[:, i][~cond]
cmn, std = wtmn(data_clip, weights=e_data_clip)
mn_data_clip.append(cmn)
std_data_clip.append(std)
data_med = np.median(data, axis=0)
if var == "V2":
ylabel = r"Raw V$^2$"
xlabel = "# baselines"
else:
ylabel = r"Raw closure Phases $\Phi$ [deg]"
xlabel = "# bispectrum"
ymin = -ymax
if display:
plt.figure()
plt.title("CALIBRATOR")
if n_files != 1:
plt.plot(data[0], color="grey", alpha=0.2, label="Data vs. files")
plt.plot(data[1:, :].T, color="grey", alpha=0.2)
plt.plot(data_med, "g--", lw=1, label="Median value")
for i in range(n_files):
data_ind = data[i, :]
cond = filtered_data[i, :].mask
x = np.arange(len(data_ind))
bad = data_ind[cond]
x_bad = x[cond]
if i == 0:
plt.plot(
x_bad,
bad,
"rx",
ms=3,
label=r"Rejected points (>%i$\sigma$)" % sig_thres,
)
else:
plt.plot(x_bad, bad, "rx", ms=3)
plt.legend(loc="best", fontsize=9)
plt.ylim(ymin, ymax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tight_layout()
return np.array(mn_data_clip), np.array(std_data_clip)
