def from_packets(cls, packets, eng_packets):
# Header
control = Control.from_packets(packets)
# Control
control['samples_per_variance'] = np.array(packets.get('NIX00279'),
np.ubyte)
control['pixel_mask'] = _get_pixel_mask(packets)
control['detector_mask'] = _get_detector_mask(packets)
control['compression_scheme_variance_skm'] = _get_compression_scheme(
packets, 'NIXD0118', 'NIXD0119', 'NIXD0120')
energy_masks = np.array([[
bool(int(x)) for x in format(packets.get('NIX00282')[i], '032b')
] for i in range(len(packets.get('NIX00282')))])
control['energy_bin_mask'] = energy_masks
control['num_energies'] = 1
control['num_samples'] = packets.get('NIX00280')
time, duration = control._get_time()
# Map a given entry back to the control info through index
control_indices = np.hstack([
np.full(ns, cind) for ns, cind in control[['num_samples', 'index']]
])
vs, vk, vm = control['compression_scheme_variance_skm'][0]
variance, variance_var = decompress(packets.get('NIX00281'),
s=vs,
k=vk,
m=vm,
return_variance=True)
# Data
data = Data()
data['time'] = time
data['timedel'] = duration
data['control_index'] = control_indices
data['variance'] = variance
data['variance_err'] = np.sqrt(variance_var)
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
control = Control.from_packets(packets)
control['integration_time'] = (
np.array(packets['NIX00122'], np.uint32) + 1) * 0.1 * u.s
# control['obs_beg'] = control['obs_utc']
# control['.obs_end'] = control['obs_beg'] + timedelta(seconds=control['duration'].astype('float'))
# control['.obs_avg'] = control['obs_beg'] + (control['obs_end'] - control['obs_beg']) / 2
# Control
control['quiet_time'] = np.array(packets['NIX00123'], np.uint16)
control['live_time'] = np.array(packets['NIX00124'], np.uint32)
control['average_temperature'] = np.array(packets['NIX00125'],
np.uint16)
control['detector_mask'] = _get_detector_mask(packets)
control['pixel_mask'] = _get_pixel_mask(packets)
control['subspectrum_mask'] = _get_sub_spectrum_mask(packets)
control['compression_scheme_counts_skm'] = _get_compression_scheme(
packets, 'NIXD0126', 'NIXD0127', 'NIXD0128')
subspec_data = {}
j = 129
for subspec, i in enumerate(range(300, 308)):
subspec_data[subspec + 1] = {
'num_points': packets.get(f'NIXD0{j}')[0],
'num_summed_channel': packets.get(f'NIXD0{j + 1}')[0],
'lowest_channel': packets.get(f'NIXD0{j + 2}')[0]
}
j += 3
control['num_samples'] = np.array(packets.get('NIX00159'), np.uint16)
# control.remove_column('index')
# control = unique(control)
# control['index'] = np.arange(len(control))
control['subspec_num_points'] = np.array(
[v['num_points'] for v in subspec_data.values()]).reshape(1, -1)
control['subspec_num_summed_channel'] = np.array([
v['num_summed_channel'] for v in subspec_data.values()
]).reshape(1, -1)
control['subspec_lowest_channel'] = np.array([
v['lowest_channel'] for v in subspec_data.values()
]).reshape(1, -1)
subspec_index = np.argwhere(
control['subspectrum_mask'][0].flatten() == 1)
num_sub_spectra = control['subspectrum_mask'].sum(axis=1)
sub_channels = [
np.arange(control['subspec_num_points'][0, index] + 1) *
(control['subspec_num_summed_channel'][0, index] + 1) +
control['subspec_lowest_channel'][0, index]
for index in subspec_index
]
channels = list(chain(*[ch.tolist() for ch in sub_channels]))
control['num_channels'] = len(channels)
# Data
data = Data()
data['control_index'] = [0]
data['time'] = (Time(
scet_to_datetime(f"{control['scet_coarse'][0]}"
f":{control['scet_fine'][0]}")) +
control['integration_time'][0] / 2).reshape(1)
data['timedel'] = control['integration_time'][0]
# data['detector_id'] = np.array(packets.get('NIXD0155'), np.ubyte)
# data['pixel_id'] = np.array(packets.get('NIXD0156'), np.ubyte)
# data['subspec_id'] = np.array(packets.get('NIXD0157'), np.ubyte)
num_spec_points = np.array(packets.get('NIX00146'))
cs, ck, cm = control['compression_scheme_counts_skm'][0]
counts, counts_var = decompress(packets.get('NIX00158'),
s=cs,
k=ck,
m=cm,
return_variance=True)
counts_rebinned = np.apply_along_axis(
rebin_proportional, 1, counts.reshape(-1, len(channels)), channels,
np.arange(1025))
counts_var_rebinned = np.apply_along_axis(
rebin_proportional, 1, counts_var.reshape(-1, len(channels)),
channels, np.arange(1025))
dids = np.array(packets.get('NIXD0155'),
np.ubyte).reshape(-1, num_sub_spectra[0])[:, 0]
pids = np.array(packets.get('NIXD0156'),
np.ubyte).reshape(-1, num_sub_spectra[0])[:, 0]
full_counts = np.zeros((32, 12, 1024))
full_counts[dids, pids] = counts_rebinned
full_counts_var = np.zeros((32, 12, 1024))
full_counts_var[dids, pids] = counts_var_rebinned
data['counts'] = full_counts.reshape((1, *full_counts.shape))
data['counts_err'] = np.sqrt(full_counts_var).reshape(
(1, *full_counts_var.shape))
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
# Header
control = Control.from_packets(packets)
# Control
control['pixel_mask'] = _get_pixel_mask(packets)
control['compression_scheme_spectra_skm'] = _get_compression_scheme(
packets, 'NIXD0115', 'NIXD0116', 'NIXD0117')
control['compression_scheme_triggers_skm'] = _get_compression_scheme(
packets, 'NIXD0112', 'NIXD0113', 'NIXD0114')
# Fixed for spectra
num_energies = 32
control['num_energies'] = num_energies
control['num_samples'] = np.array(packets['NIX00089'])
# Due to the way packets are split up full contiguous block of detector 1-32 are not always
# down-linked to the ground so need to pad the array to write to table and later fits
total_samples = control['num_samples'].sum()
full, partial = divmod(total_samples, 32)
pad_after = 0
if partial != 0:
pad_after = 32 - partial
control_indices = np.pad(np.hstack([
np.full(ns, cind) for ns, cind in control[['num_samples', 'index']]
]), (0, pad_after),
constant_values=-1)
control_indices = control_indices.reshape(-1, 32)
duration, time = cls._get_time(control, num_energies, packets,
pad_after)
# sample x detector x energy
# counts = np.array([eng_packets.get('NIX00{}'.format(i)) for i in range(452, 484)],
# np.uint32).T * u.ct
ss, sk, sm = control['compression_scheme_spectra_skm'][0]
counts, counts_var = zip(*[
decompress(packets.get('NIX00{}'.format(i)),
s=ss,
k=sk,
m=sm,
return_variance=True) for i in range(452, 484)
])
counts = np.vstack(counts).T
counts_var = np.vstack(counts_var).T
counts = np.pad(counts, ((0, pad_after), (0, 0)), constant_values=0)
counts_var = np.pad(counts_var, ((0, pad_after), (0, 0)),
constant_values=0)
ts, tk, tm = control['compression_scheme_triggers_skm'][0]
triggers, triggers_var = decompress(packets.get('NIX00484'),
s=ts,
k=tk,
m=tm,
return_variance=True)
triggers = np.pad(triggers, (0, pad_after), constant_values=0)
triggers_var = np.pad(triggers_var, (0, pad_after), constant_values=0)
detector_index = np.pad(np.array(packets.get('NIX00100'), np.int16),
(0, pad_after),
constant_values=-1)
num_integrations = np.pad(np.array(packets.get('NIX00485'), np.uint16),
(0, pad_after),
constant_values=0)
# Data
data = Data()
data['control_index'] = control_indices[:, 0]
data['time'] = time[:, 0]
data['timedel'] = duration[:, 0]
data['detector_index'] = detector_index.reshape(-1, 32) * u.ct
data['spectra'] = counts.reshape(-1, 32, num_energies) * u.ct
data['spectra_err'] = np.sqrt(counts_var.reshape(-1, 32, num_energies))
data['triggers'] = triggers.reshape(-1, num_energies)
data['triggers_err'] = np.sqrt(triggers_var.reshape(-1, num_energies))
data['num_integrations'] = num_integrations.reshape(-1, num_energies)
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
control = Control.from_packets(packets)
# Control
control['energy_bin_mask'] = _get_energy_bins(packets, 'NIX00266',
'NIXD0111')
control['compression_scheme_background_skm'] = _get_compression_scheme(
packets, 'NIXD0108', 'NIXD0109', 'NIXD0110')
control['compression_scheme_triggers_skm'] = _get_compression_scheme(
packets, 'NIXD0112', 'NIXD0113', 'NIXD0114')
control['num_energies'] = _get_num_energies(packets)
control['num_samples'] = np.array(
packets['NIX00277'])[np.cumsum(control['num_energies']) - 1]
time, duration = control._get_time()
# Map a given entry back to the control info through index
control_indices = np.hstack([
np.full(ns, cind) for ns, cind in control[['num_samples', 'index']]
])
# Data
bs, bk, bm = control['compression_scheme_background_skm'][0]
counts, counts_var = decompress(packets['NIX00278'],
s=bs,
k=bk,
m=bm,
return_variance=True)
flat_indices = np.hstack((0, np.cumsum([*control['num_samples']]) *
control['num_energies'])).astype(int)
counts_reformed = [
np.array(counts[flat_indices[i]:flat_indices[i + 1]]).reshape(
n_eng, n_sam)
for i, (
n_sam,
n_eng) in enumerate(control[['num_samples', 'num_energies']])
]
counts_var_reformed = [
np.array(counts_var[flat_indices[i]:flat_indices[i + 1]]).reshape(
n_eng, n_sam)
for i, (
n_sam,
n_eng) in enumerate(control[['num_samples', 'num_energies']])
]
counts = np.hstack(counts_reformed).T
counts_var = np.hstack(counts_var_reformed).T
ts, tk, tm = control['compression_scheme_triggers_skm'][0]
triggers, triggers_var = decompress(packets['NIX00274'],
s=ts,
k=tk,
m=tm,
return_variance=True)
data = Data()
data['control_index'] = control_indices
data['time'] = time
data['timedel'] = duration
data['background'] = counts * u.ct
data['background_err'] = np.sqrt(counts_var) * u.ct
data['triggers'] = triggers
data['triggers_err'] = np.sqrt(triggers_var)
return cls(control=control, data=data)
def from_packets(cls, packets, eng_packets):
control = Control.from_packets(packets)
control['detector_mask'] = _get_detector_mask(packets)
control['pixel_mask'] = _get_pixel_mask(packets)
control['energy_bin_edge_mask'] = _get_energy_bins(
packets, 'NIX00266', 'NIXD0107')
control['compression_scheme_counts_skm'] = \
_get_compression_scheme(packets, 'NIXD0101', 'NIXD0102', 'NIXD0103')
control['compression_scheme_triggers_skm'] = \
_get_compression_scheme(packets, 'NIXD0104', 'NIXD0105', 'NIXD0106')
control['num_energies'] = _get_num_energies(packets)
control['num_samples'] = np.array(
packets['NIX00271'])[np.cumsum(control['num_energies']) - 1]
time, duration = control._get_time()
# Map a given entry back to the control info through index
control_indices = np.hstack([
np.full(ns, cind) for ns, cind in control[['num_samples', 'index']]
])
cs, ck, cm = control['compression_scheme_counts_skm'][0]
counts, counts_var = decompress(packets['NIX00272'],
s=cs,
k=ck,
m=cm,
return_variance=True)
ts, tk, tm = control['compression_scheme_triggers_skm'][0]
triggers, triggers_var = decompress(packets['NIX00274'],
s=ts,
k=tk,
m=tm,
return_variance=True)
flat_indices = np.hstack((0, np.cumsum([*control['num_samples']]) *
control['num_energies'])).astype(int)
counts_reformed = [
np.array(counts[flat_indices[i]:flat_indices[i + 1]]).reshape(
n_eng, n_sam)
for i, (
n_sam,
n_eng) in enumerate(control[['num_samples', 'num_energies']])
]
counts_var_reformed = [
np.array(counts_var[flat_indices[i]:flat_indices[i + 1]]).reshape(
n_eng, n_sam)
for i, (
n_sam,
n_eng) in enumerate(control[['num_samples', 'num_energies']])
]
counts = np.hstack(counts_reformed).T
counts_var = np.hstack(counts_var_reformed).T
data = Data()
data['control_index'] = control_indices
data['time'] = time
data['timedel'] = duration
data['triggers'] = triggers
data['triggers_err'] = np.sqrt(triggers_var)
data['rcr'] = packets['NIX00276']
data['counts'] = counts * u.ct
data['counts_err'] = np.sqrt(counts_var) * u.ct
return cls(control=control, data=data)
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)