def test_time_formatting(self):
mpc_time = "2000 01 01.000001"
iso_time = "2000-01-01 00:00:00.0864"
t1 = Time(mpc_time, format='mpc', scale='utc', precision=6)
t2 = Time(iso_time, format='iso', scale='utc', precision=6)
t3 = t2.replicate(format='mpc')
t3.precision = 6
self.assertEquals(mpc_time, str(t1))
self.assertEquals(t2.jd, t1.jd)
self.assertEquals(mpc_time, str(t3))
def test_time_formatting(self):
mpc_time = "2000 01 01.000001"
iso_time = "2000-01-01 00:00:00.0864"
t1 = Time(mpc_time, format='mpc', scale='utc', precision=6)
t2 = Time(iso_time, format='iso', scale='utc', precision=6)
t3 = t2.replicate(format='mpc')
t3.precision = 6
self.assertEquals(mpc_time, str(t1))
self.assertEquals(t2.jd, t1.jd)
self.assertEquals(mpc_time, str(t3))
def _get_time(self):
# Replicate packet time for each sample
base_times = Time(
list(
chain(*[[
scet_to_datetime(
f'{self["scet_coarse"][i]}:{self["scet_fine"][i]}')
] * n for i, n in enumerate(self['num_samples'])])))
# For each sample generate sample number and multiply by duration and apply unit
start_delta = np.hstack([
(np.arange(ns) * it)
for ns, it in self[['num_samples', 'integration_time']]
])
# hstack op loses unit
start_delta = start_delta.value * self['integration_time'].unit
duration = np.hstack([
np.ones(num_sample) * int_time for num_sample, int_time in self[
['num_samples', 'integration_time']]
])
duration = duration.value * self['integration_time'].unit
# TODO Write out and simplify
end_delta = start_delta + duration
# Add the delta time to base times and convert to relative from start time
times = base_times + start_delta + (end_delta - start_delta) / 2
# times -= times[0]
return times, duration
def from_fits(cls, fitspath):
header = fits.getheader(fitspath)
control = QTable.read(fitspath, hdu='CONTROL')
data = QTable.read(fitspath, hdu='DATA')
obs_beg = Time(header['DATE_OBS'])
data['time'] = (data['time'] + obs_beg)
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
# Header
control = Control()
scet_coarse = packets['NIX00445']
scet_fine = packets['NIX00446']
start_times = Time([
scet_to_datetime(f'{scet_coarse[i]}:{scet_fine[i]}')
for i in range(len(scet_coarse))
])
control['summing_value'] = packets['NIX00088']
control['averaging_value'] = packets['NIX00490']
control['index'] = range(len(control))
delta_time = ((control['summing_value'] * control['averaging_value']) /
1000.0)
samples = packets['NIX00089']
offsets = [
delta_time[i] * 0.5 * np.arange(ns) * u.s
for i, ns in enumerate(samples)
]
time = Time(
np.hstack(
[start_times[i] + offsets[i] for i in range(len(offsets))]))
timedel = np.hstack(offsets).value * u.s
# Data
try:
data = Data()
data['time'] = time
data['timedel'] = timedel
data['cha_diode0'] = packets['NIX00090']
data['cha_diode1'] = packets['NIX00091']
data['chb_diode0'] = packets['NIX00092']
data['chb_diode1'] = packets['NIX00093']
data['control_index'] = np.hstack(
[np.full(ns, i) for i, ns in enumerate(samples)])
except ValueError as e:
logger.warning(e)
return None
return cls(control=control, data=data)
def observable2dict(nrm, multi=False, display=False):
""" Convert nrm data in an Observable loaded with `ObservablesFromText` into
a dictionary compatible with oifits.save and oifits.show function.
nrm: an ObservablesFromText object, treated as a target if nrm_c=None
multi: Bool. If true, do not take mean or median of slices
(preserve separate integrations)
"""
info4oif = nrm.info4oif_dict
ctrs_inst = info4oif['ctrs_inst']
t = Time('%s-%s-%s' %
(info4oif['year'], info4oif['month'], info4oif['day']), format='fits')
ins = info4oif['telname']
filt = info4oif['filt']
wl, e_wl = oifits.GetWavelength(ins, filt)
bls = nrm.bls
# Index 0 and 1 reversed to get the good u-v coverage (same fft)
ucoord = bls[:, 1]
vcoord = bls[:, 0]
D = 6.5 # Primary mirror display
theta = np.linspace(0, 2*np.pi, 100)
x = D/2. * np.cos(theta) # Primary mirror display
y = D/2. * np.sin(theta)
bl_vis = ((ucoord**2 + vcoord**2)**0.5)
tuv = nrm.tuv
v1coord = tuv[:, 0, 0]
u1coord = tuv[:, 0, 1]
v2coord = tuv[:, 1, 0]
u2coord = tuv[:, 1, 1]
u3coord = -(u1coord+u2coord)
v3coord = -(v1coord+v2coord)
bl_cp = []
n_bispect = len(v1coord)
for k in range(n_bispect):
B1 = np.sqrt(u1coord[k] ** 2 + v1coord[k] ** 2)
B2 = np.sqrt(u2coord[k] ** 2 + v2coord[k] ** 2)
B3 = np.sqrt(u3coord[k] ** 2 + v3coord[k] ** 2)
bl_cp.append(np.max([B1, B2, B3])) # rad-1
bl_cp = np.array(bl_cp)
flagVis = [False] * nrm.nbl
flagT3 = [False] * nrm.ncp
if multi == True:
nrmd2c = populate_NRM(nrm, method='multi') # RAC 2021
else:
nrmd2c = populate_NRM(nrm, method='med')
dct = {'OI_VIS2': {'VIS2DATA': nrmd2c.vis2,
'VIS2ERR': nrmd2c.e_vis2,
'UCOORD': ucoord,
'VCOORD': vcoord,
'STA_INDEX': nrm.bholes,
'MJD': t.mjd,
'INT_TIME': info4oif['itime'],
'TIME': 0,
'TARGET_ID': 1,
'FLAG': flagVis,
'BL': bl_vis
},
'OI_VIS': {'TARGET_ID': 1,
'TIME': 0,
'MJD': t.mjd,
'INT_TIME': info4oif['itime'],
'VISAMP': nrmd2c.visamp,
'VISAMPERR': nrmd2c.e_visamp,
'VISPHI': nrmd2c.visphi,
'VISPHIERR': nrmd2c.e_visphi,
'UCOORD': ucoord,
'VCOORD': vcoord,
'STA_INDEX': nrm.bholes,
'FLAG': flagVis,
'BL': bl_vis
},
'OI_T3': {'TARGET_ID': 1,
'TIME': 0,
'MJD': t.mjd,
'INT_TIME': info4oif['itime'],
'T3PHI': nrmd2c.cp,
'T3PHIERR': nrmd2c.e_cp,
'T3AMP': nrmd2c.cpamp,
'T3AMPERR': nrmd2c.e_cp,
'U1COORD': u1coord,
'V1COORD': v1coord,
'U2COORD': u2coord,
'V2COORD': v2coord,
'STA_INDEX': nrm.tholes,
'FLAG': flagT3,
'BL': bl_cp
},
'OI_WAVELENGTH': {'EFF_WAVE': wl,
'EFF_BAND': e_wl
},
'info': {'TARGET': info4oif['objname'],
'CALIB': info4oif['objname'],
'OBJECT': info4oif['objname'],
'FILT': info4oif['filt'],
'INSTRUME': info4oif['instrument'],
'ARRNAME': info4oif['arrname'],
'MASK': info4oif['arrname'], # oifits.py looks for dct.info['MASK']
'MJD': t.mjd,
'DATE-OBS': t.fits,
'TELESCOP': info4oif['telname'],
'OBSERVER': 'UNKNOWN',
'INSMODE': info4oif['pupil'],
'PSCALE': info4oif['pscale_mas'],
'STAXY': info4oif['ctrs_inst'], # as-built mask hole coords
'ISZ': 77, # size of the image needed (or fov)
'NFILE': 0,
'PA': info4oif['pa'],
'CTRS_EQT':info4oif['ctrs_eqt'] # mask hole coords rotated to equatotial
}
}
if display:
plt.figure(figsize=(14.2, 7))
plt.subplot(1, 2, 1)
# Index 0 and 1 reversed to get the good u-v coverage (same fft)
#lt.scatter(ctrs[:, 1], ctrs[:, 0], s=2e3, c='', edgecolors='navy')
plt.scatter(ctrs[:, 1], ctrs[:, 0], s=2e3, edgecolors='navy')
#lt.scatter(-1000, 1000, s=5e1, c='',
plt.scatter(-1000, 1000, s=5e1,
edgecolors='navy', label='Aperture mask')
plt.plot(x, y, '--', color='gray', label='Primary mirror equivalent')
plt.xlabel('Aperture x-coordinate [m]')
plt.ylabel('Aperture y-coordinate [m]')
plt.legend(fontsize=8)
plt.axis([-4., 4., -4., 4.])
plt.subplot(1, 2, 2)
#lt.scatter(ucoord, vcoord, s=1e2, c='', edgecolors='navy')
plt.scatter(ucoord, vcoord, s=1e2, edgecolors='navy')
#lt.scatter(-ucoord, -vcoord, s=1e2, c='', edgecolors='crimson')
plt.scatter(-ucoord, -vcoord, s=1e2, edgecolors='crimson')
plt.plot(0, 0, 'k+')
plt.axis((D, -D, -D, D))
plt.xlabel('Fourier u-coordinate [m]')
plt.ylabel('Fourier v-coordinate [m]')
plt.tight_layout()
Plot_observables(nrm, display=display)
#if nrm_c: Plot_observables(nrm_c=display) # Plot calibrated or single object raw oifits data
return dct
def to_time(self):
return Time(self.to_datetime())
def from_packets(cls, packets, eng_packets):
# Control
control = Control.from_packets(packets)
control['pixel_mask'] = np.unique(_get_pixel_mask(packets), axis=0)
control['detector_mask'] = np.unique(_get_detector_mask(packets),
axis=0)
control['rcr'] = np.unique(packets['NIX00401']).astype(np.int16)
control['index'] = range(len(control))
e_min = np.array(packets['NIXD0442'])
e_max = np.array(packets['NIXD0443'])
energy_unit = np.array(packets['NIXD0019']) + 1
num_times = np.array(packets['NIX00089'])
total_num_times = num_times.sum()
cs, ck, cm = control['compression_scheme_counts_skm'][0]
counts, counts_var = decompress(packets['NIX00268'],
s=cs,
k=ck,
m=cm,
return_variance=True)
counts = counts.reshape(total_num_times, -1)
counts_var = counts_var.reshape(total_num_times, -1)
full_counts = np.zeros((total_num_times, 32))
full_counts_var = np.zeros((total_num_times, 32))
cids = [
np.arange(emin, emax + 1, eunit)
for (emin, emax, eunit) in zip(e_min, e_max, energy_unit)
]
control['energy_bin_mask'] = np.full((1, 32), False, np.ubyte)
control['energy_bin_mask'][:, cids] = True
dl_energies = np.array([[ENERGY_CHANNELS[ch]['e_lower']
for ch in chs] +
[ENERGY_CHANNELS[chs[-1]]['e_upper']]
for chs in cids][0])
sci_energies = np.hstack(
[[ENERGY_CHANNELS[ch]['e_lower'] for ch in range(32)],
ENERGY_CHANNELS[31]['e_upper']])
ind = 0
for nt in num_times:
e_ch_start = 0
e_ch_end = counts.shape[1]
if dl_energies[0] == 0:
full_counts[ind:ind + nt, 0] = counts[ind:ind + nt, 0]
full_counts_var[ind:ind + nt, 0] = counts_var[ind:ind + nt, 0]
e_ch_start = 1
if dl_energies[-1] == np.inf:
full_counts[ind:ind + nt, -1] = counts[ind:ind + nt, -1]
full_counts_var[ind:ind + nt, -1] = counts[ind:ind + nt, -1]
e_ch_end -= 1
torebin = np.where((dl_energies >= 4.0) & (dl_energies <= 150.0))
full_counts[ind:ind + nt, 1:-1] = np.apply_along_axis(
rebin_proportional, 1, counts[ind:ind + nt,
e_ch_start:e_ch_end],
dl_energies[torebin], sci_energies[1:-1])
full_counts_var[ind:ind + nt, 1:-1] = np.apply_along_axis(
rebin_proportional, 1, counts_var[ind:ind + nt,
e_ch_start:e_ch_end],
dl_energies[torebin], sci_energies[1:-1])
ind += nt
if counts.sum() != full_counts.sum():
raise ValueError(
'Original and reformatted count totals do not match')
delta_time = (np.array(packets['NIX00441'], np.uint16)) * 0.1 * u.s
closing_time_offset = (np.array(packets['NIX00269'],
np.uint16)) * 0.1 * u.s
# TODO incorporate into main loop above
centers = []
deltas = []
last = 0
for i, nt in enumerate(num_times):
edge = np.hstack([
delta_time[last:last + nt],
delta_time[last + nt - 1] + closing_time_offset[i]
])
delta = np.diff(edge)
center = edge[:-1] + delta / 2
centers.append(center)
deltas.append(delta)
last = last + nt
centers = np.hstack(centers)
deltas = np.hstack(deltas)
# Data
data = Data()
data['time'] = Time(scet_to_datetime(f'{int(control["time_stamp"][0])}:0')) \
+ centers
data['timedel'] = deltas
ts, tk, tm = control['compression_scheme_triggers_skm'][0]
triggers, triggers_var = decompress(packets['NIX00267'],
s=ts,
k=tk,
m=tm,
return_variance=True)
data['triggers'] = triggers
data['triggers_err'] = np.sqrt(triggers_var)
data['counts'] = full_counts * u.ct
data['counts_err'] = np.sqrt(full_counts_var) * u.ct
data['control_index'] = 0
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
# Control
control = Control.from_packets(packets)
control.remove_column('num_structures')
control = unique(control)
if len(control) != 1:
raise ValueError()
control['index'] = range(len(control))
data = Data()
data['control_index'] = np.full(len(packets['NIX00441']), 0)
data['delta_time'] = (np.array(packets['NIX00441'],
np.uint16)) * 0.1 * u.s
unique_times = np.unique(data['delta_time'])
# time = np.array([])
# for dt in set(self.delta_time):
# i, = np.where(self.delta_time == dt)
# nt = sum(np.array(packets['NIX00258'])[i])
# time = np.append(time, np.repeat(dt, nt))
# self.time = time
data['rcr'] = packets['NIX00401']
data['pixel_mask1'] = _get_pixel_mask(packets, 'NIXD0407')
data['pixel_mask2'] = _get_pixel_mask(packets, 'NIXD0444')
data['pixel_mask3'] = _get_pixel_mask(packets, 'NIXD0445')
data['pixel_mask4'] = _get_pixel_mask(packets, 'NIXD0446')
data['pixel_mask5'] = _get_pixel_mask(packets, 'NIXD0447')
data['detector_masks'] = _get_detector_mask(packets)
data['integration_time'] = (np.array(packets['NIX00405'])) * 0.1
ts, tk, tm = control['compression_scheme_triggers_skm'][0]
triggers, triggers_var = decompress(
[packets[f'NIX00{i}'] for i in range(242, 258)],
s=ts,
k=tk,
m=tm,
return_variance=True)
data['triggers'] = triggers.T
data['triggers_err'] = np.sqrt(triggers_var).T
tids = np.searchsorted(data['delta_time'], unique_times)
data = data[tids]
num_energy_groups = sum(packets['NIX00258'])
# Data
vis = np.zeros((unique_times.size, 32, 32), dtype=complex)
vis_err = np.zeros((unique_times.size, 32, 32), dtype=complex)
e_low = np.array(packets['NIXD0016'])
e_high = np.array(packets['NIXD0017'])
# TODO create energy bin mask
control['energy_bin_mask'] = np.full((1, 32), False, np.ubyte)
all_energies = set(np.hstack([e_low, e_high]))
control['energy_bin_mask'][:, list(all_energies)] = True
data['flux'] = np.array(packets['NIX00261']).reshape(
unique_times.size, -1)
num_detectors = packets['NIX00262'][0]
detector_id = np.array(packets['NIX00100']).reshape(
unique_times.size, -1, num_detectors)
# vis[:, detector_id[0], e_low.reshape(unique_times.size, -1)[0]] = (
# np.array(packets['NIX00263']) + np.array(packets['NIX00264'])
# * 1j).reshape(unique_times.size, num_detectors, -1)
ds, dk, dm = control['compression_scheme_counts_skm'][0]
real, real_var = decompress(packets['NIX00263'],
s=ds,
k=dk,
m=dm,
return_variance=True)
imaginary, imaginary_var = decompress(packets['NIX00264'],
s=ds,
k=dk,
m=dm,
return_variance=True)
mesh = np.ix_(np.arange(unique_times.size), detector_id[0][0],
e_low.reshape(unique_times.size, -1)[0])
vis[mesh] = (real + imaginary * 1j).reshape(unique_times.size,
num_detectors, -1)
# TODO this doesn't seem correct prob need combine in a better
vis_err[mesh] = (np.sqrt(real_var) +
np.sqrt(imaginary_var) * 1j).reshape(
unique_times.size, num_detectors, -1)
data['visibility'] = vis
data['visibility_err'] = vis_err
data['time'] = Time(scet_to_datetime(f'{int(control["time_stamp"][0])}:0')) \
+ data['delta_time'] + data['integration_time'] / 2
data['timedel'] = data['integration_time']
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
# Control
ssid = packets['SSID'][0]
control = Control.from_packets(packets)
control.remove_column('num_structures')
control = unique(control)
if len(control) != 1:
raise ValueError(
'Creating a science product form packets from multiple products'
)
control['index'] = 0
data = Data()
data['delta_time'] = (np.array(packets['NIX00441'],
np.int32)) * 0.1 * u.s
unique_times = np.unique(data['delta_time'])
data['rcr'] = np.array(packets['NIX00401'], np.ubyte)
data['num_pixel_sets'] = np.array(packets['NIX00442'], np.ubyte)
pixel_masks = _get_pixel_mask(packets, 'NIXD0407')
pixel_masks = pixel_masks.reshape(-1, data['num_pixel_sets'][0], 12)
if ssid == 21 and data['num_pixel_sets'][0] != 12:
pixel_masks = np.pad(pixel_masks,
((0, 0), (0, 12 - data['num_pixel_sets'][0]),
(0, 0)))
data['pixel_masks'] = pixel_masks
data['detector_masks'] = _get_detector_mask(packets)
data['integration_time'] = (np.array(packets.get('NIX00405'),
np.uint16)) * 0.1 * u.s
# TODO change once FSW fixed
ts, tk, tm = control['compression_scheme_counts_skm'][0]
triggers, triggers_var = decompress(
[packets.get(f'NIX00{i}') for i in range(242, 258)],
s=ts,
k=tk,
m=tm,
return_variance=True)
data['triggers'] = triggers.T
data['triggers_err'] = np.sqrt(triggers_var).T
data['num_energy_groups'] = np.array(packets['NIX00258'], np.ubyte)
tmp = dict()
tmp['e_low'] = np.array(packets['NIXD0016'], np.ubyte)
tmp['e_high'] = np.array(packets['NIXD0017'], np.ubyte)
tmp['num_data_elements'] = np.array(packets['NIX00259'])
unique_energies_low = np.unique(tmp['e_low'])
unique_energies_high = np.unique(tmp['e_high'])
# counts = np.array(eng_packets['NIX00260'], np.uint32)
cs, ck, cm = control['compression_scheme_counts_skm'][0]
counts, counts_var = decompress(packets.get('NIX00260'),
s=cs,
k=ck,
m=cm,
return_variance=True)
counts = counts.reshape(unique_times.size, unique_energies_low.size,
data['detector_masks'][0].sum(),
data['num_pixel_sets'][0].sum())
counts_var = counts_var.reshape(unique_times.size,
unique_energies_low.size,
data['detector_masks'][0].sum(),
data['num_pixel_sets'][0].sum())
# t x e x d x p -> t x d x p x e
counts = counts.transpose((0, 2, 3, 1))
counts_var = np.sqrt(counts_var.transpose((0, 2, 3, 1)))
if ssid == 21:
out_counts = np.zeros((unique_times.size, 32, 12, 32))
out_var = np.zeros((unique_times.size, 32, 12, 32))
elif ssid == 22:
out_counts = np.zeros((unique_times.size, 32, 4, 32))
out_var = np.zeros((unique_times.size, 32, 4, 32))
# energy_index = 0
# count_index = 0
# for i, time in enumerate(unique_times):
# inds = np.where(data['delta_time'] == time)
# cur_num_energies = data['num_energy_groups'][inds].astype(int).sum()
# low = np.unique(tmp['e_low'][energy_index:energy_index+cur_num_energies])
# high = np.unique(tmp['e_high'][energy_index:energy_index + cur_num_energies])
# cur_num_energies = low.size
# num_counts = tmp['num_data_elements'][energy_index:energy_index+cur_num_energies].sum()
# cur_counts = counts[count_index:count_index+num_counts]
# count_index += num_counts
# pids = data[inds[0][0]]['pixel_masks']
# dids = np.where(data[inds[0][0]]['detector_masks'] == True)
# cids = np.full(32, False)
# cids[low] = True
#
# if ssid == 21:
# cur_counts = cur_counts.reshape(cur_num_energies, dids[0].size, pids.sum())
# elif ssid == 22:
# cur_counts = cur_counts.reshape(cur_num_energies, dids[0].size, 4)
#
dl_energies = np.array([
[ENERGY_CHANNELS[lch]['e_lower'], ENERGY_CHANNELS[hch]['e_upper']]
for lch, hch in zip(unique_energies_low, unique_energies_high)
]).reshape(-1)
dl_energies = np.unique(dl_energies)
sci_energies = np.hstack(
[[ENERGY_CHANNELS[ch]['e_lower'] for ch in range(32)],
ENERGY_CHANNELS[31]['e_upper']])
# If there is any onboard summing of energy channels rebin back to standard sci channels
if (unique_energies_high - unique_energies_low).sum() > 0:
rebinned_counts = np.zeros((*counts.shape[:-1], 32))
rebinned_counts_var = np.zeros((*counts_var.shape[:-1], 32))
e_ch_start = 0
e_ch_end = counts.shape[-1]
if dl_energies[0] == 0.0:
rebinned_counts[..., 0] = counts[..., 0]
rebinned_counts_var[..., 0] = counts_var[..., 0]
e_ch_start += 1
elif dl_energies[-1] == np.inf:
rebinned_counts[..., -1] = counts[..., -1]
rebinned_counts_var[..., -1] = counts_var[..., -1]
e_ch_end -= 1
torebin = np.where((dl_energies >= 4.0) & (dl_energies <= 150.0))
rebinned_counts[..., 1:-1] = np.apply_along_axis(
rebin_proportional, -1,
counts[...,
e_ch_start:e_ch_end].reshape(-1, e_ch_end - e_ch_start),
dl_energies[torebin], sci_energies[1:-1]).reshape(
(*counts.shape[:-1], 30))
rebinned_counts_var[..., 1:-1] = np.apply_along_axis(
rebin_proportional, -1,
counts_var[..., e_ch_start:e_ch_end].reshape(
-1, e_ch_end - e_ch_start), dl_energies[torebin],
sci_energies[1:-1]).reshape((*counts_var.shape[:-1], 30))
energy_indices = np.full(32, True)
energy_indices[[0, -1]] = False
ix = np.ix_(np.full(unique_times.size, True),
data['detector_masks'][0].astype(bool),
np.ones(data['num_pixel_sets'][0], dtype=bool),
np.full(32, True))
out_counts[ix] = rebinned_counts
out_var[ix] = rebinned_counts_var
else:
energy_indices = np.full(32, False)
energy_indices[unique_energies_low.min(
):unique_energies_high.max() + 1] = True
ix = np.ix_(np.full(unique_times.size,
True), data['detector_masks'][0].astype(bool),
np.ones(data['num_pixel_sets'][0], dtype=bool),
energy_indices)
out_counts[ix] = counts
out_var[ix] = counts_var
# if (high - low).sum() > 0:
# raise NotImplementedError()
# #full_counts = rebin_proportional(dl_energies, cur_counts, sci_energies)
#
# dids2 = data[inds[0][0]]['detector_masks']
# cids2 = np.full(32, False)
# cids2[low] = True
# tids2 = time == unique_times
#
# if ssid == 21:
# out_counts[np.ix_(tids2, cids2, dids2, pids)] = cur_counts
# elif ssid == 22:
# out_counts[np.ix_(tids2, cids2, dids2)] = cur_counts
if counts.sum() != out_counts.sum():
import ipdb
ipdb.set_trace()
raise ValueError(
'Original and reformatted count totals do not match')
control['energy_bin_mask'] = np.full((1, 32), False, np.ubyte)
all_energies = set(np.hstack([tmp['e_low'], tmp['e_high']]))
control['energy_bin_mask'][:, list(all_energies)] = True
# time x energy x detector x pixel
# counts = np.array(
# eng_packets['NIX00260'], np.uint16).reshape(unique_times.size, num_energies,
# num_detectors, num_pixels)
# time x channel x detector x pixel need to transpose to time x detector x pixel x channel
sub_index = np.searchsorted(data['delta_time'], unique_times)
data = data[sub_index]
data['time'] = Time(scet_to_datetime(f'{int(control["time_stamp"][0])}:0')) \
+ data['delta_time'] + data['integration_time'] / 2
data['timedel'] = data['integration_time']
data['counts'] = out_counts * u.ct
data['counts_err'] = out_var * u.ct
data['control_index'] = control['index'][0]
data.remove_columns(['delta_time', 'integration_time'])
data = data['time', 'timedel', 'rcr', 'pixel_masks', 'detector_masks',
'num_pixel_sets', 'num_energy_groups', 'triggers',
'triggers_err', 'counts', 'counts_err']
data['control_index'] = 0
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
control = Control.from_packets(packets)
control.remove_column('num_structures')
control = unique(control)
if len(control) != 1:
raise ValueError(
'Creating a science product form packets from multiple products'
)
control['index'] = 0
data = Data()
data['start_time'] = (np.array(packets.get('NIX00404'),
np.uint16)) * 0.1 * u.s
data['rcr'] = np.array(packets.get('NIX00401')[0], np.ubyte)
data['integration_time'] = (np.array(
packets.get('NIX00405')[0], np.int16)) * 0.1 * u.s
data['pixel_masks'] = _get_pixel_mask(packets, 'NIXD0407')
data['detector_masks'] = _get_detector_mask(packets)
data['triggers'] = np.array(
[packets.get(f'NIX00{i}') for i in range(408, 424)], np.int64).T
data['num_samples'] = np.array(packets.get('NIX00406'), np.int16)
num_detectors = 32
num_energies = 32
num_pixels = 12
# Data
tmp = dict()
tmp['pixel_id'] = np.array(packets.get('NIXD0158'), np.ubyte)
tmp['detector_id'] = np.array(packets.get('NIXD0153'), np.ubyte)
tmp['channel'] = np.array(packets.get('NIXD0154'), np.ubyte)
tmp['continuation_bits'] = packets.get('NIXD0159', np.ubyte)
control['energy_bin_mask'] = np.full((1, 32), False, np.ubyte)
all_energies = set(tmp['channel'])
control['energy_bin_mask'][:, list(all_energies)] = True
# Find contiguous time indices
unique_times = np.unique(data['start_time'])
time_indices = np.searchsorted(unique_times, data['start_time'])
# Create full count array 0s are not send down, if cb = 0 1 count, for cb 1 just extract
# and for cb 2 extract and sum
raw_counts = packets.get('NIX00065')
counts_1d = []
raw_count_index = 0
for cb in tmp['continuation_bits']:
if cb == 0:
counts_1d.append(1)
elif cb == 1:
cur_count = raw_counts[raw_count_index]
counts_1d.append(cur_count)
raw_count_index += cb
elif cb == 2:
cur_count = raw_counts[raw_count_index:(raw_count_index + cb)]
combined_count = int.from_bytes(
(cur_count[0] + 1).to_bytes(2, 'big') +
cur_count[1].to_bytes(1, 'big'), 'big')
counts_1d.append(combined_count)
raw_count_index += cb
else:
raise ValueError(
f'Continuation bits value of {cb} not allowed (0, 1, 2)')
counts_1d = np.array(counts_1d, np.uint16)
# raw_counts = counts_1d
end_inds = np.cumsum(data['num_samples'])
start_inds = np.hstack([0, end_inds[:-1]])
dd = [(tmp['pixel_id'][s:e], tmp['detector_id'][s:e],
tmp['channel'][s:e], counts_1d[s:e])
for s, e in zip(start_inds.astype(int), end_inds)]
counts = np.zeros(
(len(unique_times), num_detectors, num_pixels, num_energies),
np.uint32)
for i, (pid, did, cid, cc) in enumerate(dd):
counts[time_indices[i], did, pid, cid] = cc
# Create final count array with 4 dimensions: unique times, 32 det, 32 energies, 12 pixels
# for i in range(self.num_samples):
# tid = np.argwhere(self.raw_counts == unique_times)
# start_index = 0
# for i, time_index in enumerate(time_indices):
# end_index = np.uint32(start_index + np.sum(data['num_samples'][time_index]))
#
# for did, cid, pid in zip(tmp['detector_id'], tmp['channel'], tmp['pixel_id']):
# index_1d = ((tmp['detector_id'] == did) & (tmp['channel'] == cid)
# & (tmp['pixel_id'] == pid))
# cur_count = counts_1d[start_index:end_index][index_1d[start_index:end_index]]
# # If we have a count assign it other wise do nothing as 0
# if cur_count:
# counts[i, did, cid, pid] = cur_count[0]
#
# start_index = end_index
sub_index = np.searchsorted(data['start_time'], unique_times)
data = data[sub_index]
data['time'] = Time(scet_to_datetime(f'{int(control["time_stamp"][0])}:0'))\
+ data['start_time'] + data['integration_time']/2
data['timedel'] = data['integration_time']
data['counts'] = counts * u.ct
data['control_index'] = control['index'][0]
data.remove_columns(['start_time', 'integration_time', 'num_samples'])
return cls(control=control, data=data)