def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = QLProduct.from_levelb(
levelb, parent=parent, NIX00405_offset=QLNIX00405_off)
control.add_data('energy_bin_edge_mask',
_get_energy_bins(packets, 'NIX00266', 'NIXD0111'))
control.add_basic(name='num_energies', nix='NIX00270', packets=packets)
control['num_samples'] = np.array(packets.get_value(
'NIX00277')).flatten()[np.cumsum(control['num_energies']) - 1]
control['num_samples'].meta = {'NIXS': 'NIX00277'}
time, duration, scet_timerange = 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']]
])
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00278'))
counts = np.array(packets.get_value('NIX00278')).reshape(
control['num_energies'][0], control['num_samples'].sum())
counts_var = np.array(packets.get_value('NIX00278', attr="error")).\
reshape(control['num_energies'][0], control['num_samples'].sum())
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00274'))
triggers = packets.get_value('NIX00274').T
triggers_var = packets.get_value('NIX00274', attr="error").T
data = Data()
data['control_index'] = control_indices
data['time'] = time
data['timedel'] = duration
data.add_meta(name='timedel', nix='NIX00405', packets=packets)
data['triggers'] = triggers.astype(get_min_uint(triggers))
data.add_meta(name='triggers', nix='NIX00274', packets=packets)
data['triggers_err'] = np.float32(np.sqrt(triggers_var))
data['counts'] = (counts.T * u.ct).astype(get_min_uint(counts))
data.add_meta(name='counts', nix='NIX00278', packets=packets)
data['counts_err'] = np.float32(np.sqrt(counts_var).T * u.ct)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
def from_levelb(cls, levelb, parent):
packets, idb_versions = GenericProduct.getLeveL0Packets(levelb)
control = Control()
control['scet_coarse'] = packets.get('scet_coarse')
control['scet_fine'] = packets.get('scet_fine')
control['integration_time'] = 0
control['index'] = np.arange(len(control)).astype(
get_min_uint(len(control)))
control['raw_file'] = levelb.control['raw_file']
control['packet'] = levelb.control['packet']
control['parent'] = parent
# Create array of times as dt from date_obs
times = SCETime(control['scet_coarse'], control['scet_fine'])
# Data
data = Data()
data['time'] = times
data['timedel'] = SCETimeDelta(0, 0)
reshape_nixs = {'NIX00103', 'NIX00104'}
reshape = False
if reshape_nixs.intersection(packets.data[0].__dict__.keys()):
reshape = True
for nix, param in packets.data[0].__dict__.items():
name = param.idb_info.get_product_attribute_name()
data.add_basic(name=name,
nix=nix,
attr='value',
packets=packets,
reshape=reshape)
data['control_index'] = np.arange(len(control)).astype(
get_min_uint(len(control)))
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions,
packets=packets)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = QLProduct.from_levelb(
levelb, parent=parent, NIX00405_offset=QLNIX00405_off)
# Control
control['samples_per_variance'] = np.array(
packets.get_value('NIX00279'), np.ubyte)
control.add_meta(name='samples_per_variance',
nix='NIX00279',
packets=packets)
control.add_data('pixel_mask', _get_pixel_mask(packets))
control.add_data('detector_mask', _get_detector_mask(packets))
control.add_data('compression_scheme_variance_skm',
_get_compression_scheme(packets, 'NIX00281'))
energy_masks = np.array([[
bool(int(x))
for x in format(packets.get_value('NIX00282')[i], '032b')
] for i in range(len(packets.get_value('NIX00282')))])
control['energy_bin_mask'] = energy_masks
control.add_meta(name='energy_bin_mask',
nix='NIX00282',
packets=packets)
control['num_energies'] = 1
control.add_basic(name='num_samples', nix='NIX00280', packets=packets)
time, duration, scet_timerange = 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']]
])
variance = packets.get_value('NIX00281').T
variance_var = packets.get_value('NIX00281', attr='error').T
# Data
data = Data()
data['time'] = time
data['timedel'] = duration
data.add_meta(name='timedel', nix='NIX00405', packets=packets)
data['control_index'] = control_indices
data['variance'] = variance.astype(get_min_uint(variance))
data.add_meta(name='variance', nix='NIX00281', packets=packets)
data['variance_err'] = np.float32(np.sqrt(variance_var))
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
def from_packets(cls, packets, NIX00405_offset=0):
"""
Common generator method the create and prepare the control table.
Parameters
----------
packets : `PacketSequence`
The set of packets of same data type for what the data product will be created.
NIX00405_offset : int, optional
NIX00405 (integration time) is used in two ways (X * 0.1s) or ((X+1) * 0.1s),
by default 0
Returns
-------
`Control`
The Control object for the data product.
"""
# Header
control = cls()
# self.energy_bin_mask = None
# self.samples = None
control.add_basic(name='scet_coarse',
nix='NIX00445',
packets=packets,
dtype=np.uint32)
# Not all QL data have fine time in TM default to 0 if no present
try:
control.add_basic(name='scet_fine',
nix='NIX00446',
packets=packets)
except AttributeError:
control['scet_fine'] = np.zeros_like(control['scet_coarse'],
np.uint32)
try:
control['integration_time'] = (packets.get_value('NIX00405') +
(NIX00405_offset * u.s))
control.add_meta(name='integration_time',
nix='NIX00405',
packets=packets)
except AttributeError:
control['integration_time'] = np.zeros_like(
control['scet_coarse'], np.float) * u.s
# control = unique(control)
control['index'] = np.arange(len(control)).astype(
get_min_uint(len(control)))
return control
def from_levelb(cls, levelb, *, parent=''):
"""Converts level binary science packets to a L1 product.
Parameters
----------
levelb : `stixcore.products.levelb.binary.LevelB`
The binary level product.
parent : `str`, optional
The parent data file name the binary packed comes from, by default ''
NIX00405_offset : int, optional
[description], by default 0
Returns
-------
tuple (packets, idb_versions, control)
the converted packets
all used IDB versions and time periods
initialized control table
"""
packets, idb_versions = GenericProduct.getLeveL0Packets(levelb)
control = ControlSci.from_packets(packets)
# control.remove_column('num_structures')
control['index'] = np.ubyte(0)
control['packet'] = levelb.control['packet'].reshape(1, -1)
control['packet'].dtype = get_min_uint(control['packet'])
control['raw_file'] = np.unique(levelb.control['raw_file']).reshape(
1, -1)
control['parent'] = parent
if len(control) != 1:
raise ValueError(
'Creating a science product form packets from multiple products'
)
return packets, idb_versions, control
def from_levelb(cls, levelb, parent=''):
packets, idb_versions = GenericProduct.getLeveL0Packets(levelb)
control = Control()
control['scet_coarse'] = packets.get('scet_coarse')
control['scet_fine'] = packets.get('scet_fine')
control['index'] = np.arange(len(control)).astype(get_min_uint(len(control)))
# When the packets are parsed empty packets are dropped but in LB we don't parse so this
# is not known need to compare control and levelb.control and only use matching rows
if len(levelb.control) > len(control):
matching_index = np.argwhere(
np.in1d(levelb.control['scet_coarse'], np.array(packets.get('scet_coarse'))))
control['raw_file'] = levelb.control['raw_file'][matching_index].reshape(-1)
control['packet'] = levelb.control['packet'][matching_index].reshape(-1)
else:
control['raw_file'] = levelb.control['raw_file'].reshape(-1)
control['packet'] = levelb.control['packet'].reshape(-1)
control['parent'] = parent
tmp = Data()
tmp.add_basic(name='ubsd_counter', nix='NIX00285', packets=packets, dtype=np.uint32)
tmp.add_basic(name='pald_counter', nix='NIX00286', packets=packets, dtype=np.uint32)
tmp.add_basic(name='num_flares', nix='NIX00294', packets=packets, dtype=np.uint16)
colnames = ['start_scet_coarse', 'end_scet_coarse', 'highest_flareflag', 'tm_byte_volume',
'average_z_loc', 'average_y_loc', 'processing_mask']
flares = Data()
if tmp['num_flares'].sum() > 0:
flares.add_basic(name='start_scet_coarse', nix='NIX00287', packets=packets)
flares.add_basic(name='end_scet_coarse', nix='NIX00288', packets=packets)
flares.add_basic(name='highest_flareflag', nix='NIX00289', packets=packets,
dtype=np.byte)
flares.add_basic(name='tm_byte_volume', nix='NIX00290', packets=packets, dtype=np.byte)
flares.add_basic(name='average_z_loc', nix='NIX00291', packets=packets, dtype=np.byte)
flares.add_basic(name='average_y_loc', nix='NIX00292', packets=packets, dtype=np.byte)
flares.add_basic(name='processing_mask', nix='NIX00293', packets=packets, dtype=np.byte)
tmp_data = defaultdict(list)
start = 0
for i, (ubsd, pald, n_flares) in enumerate(tmp):
end = start + n_flares
if n_flares == 0:
tmp_data['control_index'].append(i)
tmp_data['coarse'].append(control['scet_coarse'][i])
tmp_data['fine'].append(control['scet_fine'][i])
tmp_data['ubsd'].append(ubsd)
tmp_data['pald'].append(pald)
for name in colnames:
tmp_data[name].append(0)
else:
tmp_data['control_index'].append([i] * n_flares)
tmp_data['coarse'].append([control['scet_coarse'][i]] * n_flares)
tmp_data['fine'].append([control['scet_fine'][i]] * n_flares)
ubsd['ubsd']([ubsd] * n_flares)
pald['pald'].append([pald] * n_flares)
for name in colnames:
tmp_data[name].extend(flares[name][start:end])
start = end
data = Data(tmp_data)
data['time'] = SCETime(tmp_data['coarse'], tmp_data['fine'])
data['timedel'] = SCETimeDelta(np.full(len(data), 0), np.full(len(data), 0))
data.remove_columns(['coarse', 'fine'])
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions,
packets=packets)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = QLProduct.from_levelb(levelb, parent=parent)
# Control
control.add_basic(name='integration_time', nix='NIX00122', packets=packets,
dtype=np.uint32, attr='value')
control.add_basic(name='quiet_time', nix='NIX00123', packets=packets,
dtype=np.uint16, attr='value')
control.add_basic(name='live_time', nix='NIX00124', packets=packets,
dtype=np.uint32, attr='value')
control.add_basic(name='average_temperature', nix='NIX00125', packets=packets,
dtype=np.uint16, attr='value')
control.add_data('detector_mask', _get_detector_mask(packets))
control.add_data('pixel_mask', _get_pixel_mask(packets))
control.add_data('subspectrum_mask', _get_sub_spectrum_mask(packets))
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00158'))
subspec_data = {}
j = 129
for subspec, i in enumerate(range(300, 308)):
subspec_data[subspec+1] = {'num_points': packets.get_value(f'NIXD0{j}'),
'num_summed_channel': packets.get_value(f'NIXD0{j + 1}'),
'lowest_channel': packets.get_value(f'NIXD0{j + 2}')}
j += 3
control.add_basic(name='num_samples', nix='NIX00159', packets=packets, dtype=np.uint16)
control['subspec_num_points'] = (
np.vstack([v['num_points'] for v in subspec_data.values()]).T + 1).astype(np.uint16)
control['subspec_num_summed_channel'] = (np.vstack(
[v['num_summed_channel'] for v in subspec_data.values()]).T + 1).astype(np.uint16)
control['subspec_lowest_channel'] = (
np.vstack([v['lowest_channel'] for v in subspec_data.values()]).T).astype(np.uint16)
channels = []
for i, subspectrum_mask in enumerate(control['subspectrum_mask']):
subspec_index = np.argwhere(subspectrum_mask == 1)
sub_channels = [np.arange(control['subspec_num_points'][i, index])
* (control['subspec_num_summed_channel'][i, index])
+ control['subspec_lowest_channel'][i, index] for index in
subspec_index]
channels.append(list(chain(*[ch.tolist() for ch in sub_channels])))
control['num_channels'] = [len(c) for c in channels]
duration = SCETimeDelta(packets.get_value('NIX00122').astype(np.uint32))
time = SCETime(control['scet_coarse'], control['scet_fine']) + duration / 2
dids = packets.get_value('NIXD0155')
pids = packets.get_value('NIXD0156')
ssids = packets.get_value('NIXD0157')
num_spec_points = packets.get_value('NIX00146')
unique_times, unique_time_indices = np.unique(time.as_float(), return_index=True)
unique_times_lookup = {k: v for k, v in zip(unique_times, np.arange(unique_times.size))}
# should really do the other way make a smaller lookup rather than repeating many many times
tids = np.hstack([[unique_times_lookup[t.as_float()]] * n
for t, n in zip(time, control['num_samples'])])
c_in = list(chain.from_iterable([repeat(c, n)
for c, n in zip(channels, control['num_samples'])]))
counts = packets.get_value('NIX00158')
counts_var = packets.get_value('NIX00158', attr='error')
c_out = np.arange(1025)
start = 0
count_map = defaultdict(list)
counts_var_map = defaultdict(list)
for tid, did, pid, ssid, nps, cin in zip(tids, dids, pids, ssids, num_spec_points, c_in):
end = start + nps
logger.debug('%d, %d, %d, %d, %d, %d', tid, did, pid, ssid, nps, end)
count_map[tid, did, pid].append(counts[start:end])
counts_var_map[tid, did, pid].append(counts_var[start:end])
start = end
full_counts = np.zeros((unique_times.size, 32, 12, 1024))
full_counts_var = np.zeros((unique_times.size, 32, 12, 1024))
for tid, did, pid in count_map.keys():
cur_counts = count_map[tid, did, pid]
cur_counts_var = counts_var_map[tid, did, pid]
counts_rebinned = rebin_proportional(np.hstack(cur_counts), cin, c_out)
counts_var_rebinned = rebin_proportional(np.hstack(cur_counts_var), cin, c_out)
full_counts[tid, did, pid] = counts_rebinned
full_counts_var[tid, did, pid] = counts_var_rebinned
control = control[unique_time_indices]
control['index'] = np.arange(len(control))
# Data
data = Data()
data['time'] = time[unique_time_indices]
data['timedel'] = duration[unique_time_indices]
data.add_meta(name='timedel', nix='NIX00122', packets=packets)
data['counts'] = (full_counts*u.ct).astype(get_min_uint(full_counts))
data.add_meta(name='counts', nix='NIX00158', packets=packets)
data['counts_err'] = (np.sqrt(full_counts_var)*u.ct).astype(np.float32)
data['control_index'] = np.arange(len(control)).astype(np.uint16)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions,
packets=packets)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = QLProduct.from_levelb(levelb, parent=parent,
NIX00405_offset=QLNIX00405_off)
control.add_data('pixel_mask', _get_pixel_mask(packets))
control.add_data('compression_scheme_spectra_skm',
_get_compression_scheme(packets, 'NIX00452'))
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00484'))
# Fixed for spectra
num_energies = np.unique(packets.get_value('NIX00100')).size
control['num_energies'] = num_energies
control.add_meta(name='num_energies', nix='NIX00100', packets=packets)
control.add_basic(name='num_samples', nix='NIX00089', packets=packets)
# TODO Handel NIX00089 value of zero ie valid packet with no data
# 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, scet_timerange = 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
counts = []
counts_var = []
for i in range(452, 484):
counts.append(packets.get_value('NIX00{}'.format(i)))
counts_var.append(packets.get_value('NIX00{}'.format(i), attr='error'))
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)
triggers = packets.get_value('NIX00484').T.reshape(-1)
triggers_var = packets.get_value('NIX00484', attr='error').T.reshape(-1)
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_value('NIX00100'), np.int16), (0, pad_after),
constant_values=-1)
num_integrations = np.pad(np.array(packets.get_value('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.add_meta(name='timedel', nix='NIX00405', packets=packets)
data['detector_index'] = detector_index.reshape(-1, 32).astype(np.ubyte)
data.add_meta(name='detector_index', nix='NIX00100', packets=packets)
data['spectra'] = (counts.reshape(-1, 32, num_energies) * u.ct).astype(get_min_uint(counts))
# data['spectra'].meta = {'NIXS': [f'NIX00{i}' for i in range(452, 484)],
# 'PCF_CURTX': [packets.get(f'NIX00{i}')[0].idb_info.PCF_CURTX
# for i in range(452, 484)]}
data['spectra'].meta = {'NIXS': 'NIX00452',
'PCF_CURTX': packets.get('NIX00452')[0].idb_info.PCF_CURTX}
data['spectra_err'] = np.float32(np.sqrt(counts_var.reshape(-1, 32, num_energies)))
data['triggers'] = triggers.reshape(-1, num_energies).astype(get_min_uint(triggers))
data.add_meta(name='triggers', nix='NIX00484', packets=packets)
data['triggers_err'] = np.float32(np.sqrt(triggers_var.reshape(-1, num_energies)))
data['num_integrations'] = num_integrations.reshape(-1, num_energies).astype(np.ubyte)
data.add_meta(name='num_integrations', nix='NIX00485', packets=packets)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions,
packets=packets)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = QLProduct.from_levelb(levelb, parent=parent,
NIX00405_offset=QLNIX00405_off)
control.add_data('detector_mask', _get_detector_mask(packets))
control.add_data('pixel_mask', _get_pixel_mask(packets))
control.add_data('energy_bin_edge_mask', _get_energy_bins(packets, 'NIX00266', 'NIXD0107'))
control.add_basic(name='num_energies', nix='NIX00270', packets=packets)
control['num_samples'] = np.array(packets.get_value('NIX00271')).flatten()[
np.cumsum(control['num_energies']) - 1]
control.add_meta(name='num_samples', nix='NIX00270', packets=packets)
time, duration, scet_timerange = 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']]])
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00272'))
counts_flat = packets.get_value('NIX00272')
counts_var_flat = packets.get_value('NIX00272', attr='error')
flat_indices = np.hstack((0, np.cumsum([*control['num_samples']]) *
control['num_energies'])).astype(int)
counts = np.hstack([
counts_flat[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 = np.hstack([
counts_var_flat[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']])])
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00274'))
triggers = packets.get_value('NIX00274').T
triggers_var = packets.get_value('NIX00274', attr="error").T
data = Data()
data['control_index'] = control_indices
data['time'] = time
data['timedel'] = duration
data.add_meta(name='timedel', nix='NIX00405', packets=packets)
data['triggers'] = triggers.astype(get_min_uint(triggers))
data.add_meta(name='triggers', nix='NIX00274', packets=packets)
data['triggers_err'] = np.float32(np.sqrt(triggers_var))
data['rcr'] = np.hstack(packets.get_value('NIX00276')).flatten().astype(np.ubyte)
data.add_meta(name='rcr', nix='NIX00276', packets=packets)
data['counts'] = (counts.T*u.ct).astype(get_min_uint(counts))
data.add_meta(name='counts', nix='NIX00272', packets=packets)
data['counts_err'] = np.float32(np.sqrt(counts_var).T * u.ct)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions,
packets=packets)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions = GenericProduct.getLeveL0Packets(levelb)
if len(packets.data) == 0:
logger.warning('No data all packets empty %s', levelb)
return None
control = ControlSci()
scet_coarse = packets.get_value('NIX00445')
scet_fine = packets.get_value('NIX00446')
control.add_basic(name='summing_value',
nix='NIX00088',
packets=packets,
dtype=np.uint8)
control.add_basic(name='samples',
nix='NIX00089',
packets=packets,
dtype=np.uint16)
try:
control.add_basic(name='averaging_value',
nix='NIX00490',
packets=packets,
dtype=np.uint16)
except AttributeError:
control['averaging_value'] = np.uint16(1)
try:
control.add_basic(name='request_id',
nix='NIX00037',
packets=packets,
dtype=np.uint32)
except AttributeError:
control['request_id'] = np.uint32(0)
control['raw_file'] = np.unique(levelb.control['raw_file']).reshape(
1, -1)
control['packet'] = levelb.control['packet'].reshape(1, -1)
control['parent'] = parent
control['index'] = np.arange(len(control)).astype(
get_min_uint(len(control)))
delta_time = ((control['summing_value'] * control['averaging_value']) /
1000.0) * u.s
samples = packets.get_value('NIX00089')
offsets = SCETimeDelta(
np.concatenate([
delta_time[i] * np.arange(0, ns)
for i, ns in enumerate(samples)
]))
timedel = SCETimeDelta(
np.concatenate(
[delta_time[i] * np.ones(ns) for i, ns in enumerate(samples)]))
ctimes = np.concatenate(
[np.full(ns, scet_coarse[i]) for i, ns in enumerate(samples)])
ftimes = np.concatenate(
[np.full(ns, scet_fine[i]) for i, ns in enumerate(samples)])
starts = SCETime(ctimes, ftimes)
time = starts + offsets
# Data
try:
data = Data()
data['time'] = time
data['timedel'] = timedel
data.add_basic(name='cha_diode0',
nix='NIX00090',
packets=packets,
dtype=np.uint16)
data.add_basic(name='cha_diode1',
nix='NIX00091',
packets=packets,
dtype=np.uint16)
data.add_basic(name='chb_diode0',
nix='NIX00092',
packets=packets,
dtype=np.uint16)
data.add_basic(name='chb_diode1',
nix='NIX00093',
packets=packets,
dtype=np.uint16)
data['control_index'] = np.hstack(
[np.full(ns, i) for i, ns in enumerate(samples)])
except ValueError as e:
logger.warning(e)
raise e
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = ScienceProduct.from_levelb(
levelb, parent=parent)
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00268'))
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00267'))
control['pixel_masks'] = np.unique(_get_pixel_mask(packets)[0], axis=0)
control.add_meta(name='pixel_masks', nix='NIXD0407', packets=packets)
control['detector_masks'] = np.unique(_get_detector_mask(packets)[0],
axis=0)
control['dectecor_masks'] = fix_detector_mask(
control, control['detector_masks'])
control.add_meta(name='detector_masks',
nix='NIX00407',
packets=packets)
raw_rcr = packets.get_value('NIX00401', attr='value')
e_min = np.array(packets.get_value('NIXD0442'))
e_max = np.array(packets.get_value('NIXD0443'))
energy_unit = np.array(packets.get_value('NIXD0019')) + 1
num_times = np.array(packets.get_value('NIX00089'))
total_num_times = num_times.sum()
rcr = np.hstack([
np.full(nt, rcr) for rcr, nt in zip(raw_rcr, num_times)
]).astype(np.ubyte)
counts = np.array(packets.get_value('NIX00268'))
counts_var = np.array(packets.get_value('NIX00268', attr='error'))
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')
try:
delta_time = packets.get_value('NIX00441')
except AttributeError:
delta_time = packets.get_value('NIX00404')
closing_time_offset = packets.get_value('NIX00269')
# 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)
deltas = SCETimeDelta(deltas)
# Data
data = Data()
data['time'] = control['time_stamp'][0] + centers
data['timedel'] = deltas
data['timedel'].meta = {'NIXS': ['NIX00441', 'NIX00269']}
data.add_basic(name='triggers', nix='NIX00267', packets=packets)
data['triggers'] = data['triggers'].astype(
get_min_uint(data['triggers']))
data['rcr'] = rcr
data.add_meta(name='rcr', nix='NIX00401', packets=packets)
data.add_basic(name='triggers_err',
nix='NIX00267',
attr='error',
packets=packets)
data['triggers_err'] = np.float32(data['triggers_err'])
data['counts'] = (full_counts * u.ct).astype(
get_min_uint(full_counts))[..., :e_max.max() + 1]
data.add_meta(name='counts', nix='NIX00268', packets=packets)
data['counts_err'] = np.float32(np.sqrt(full_counts_var) *
u.ct)[..., :e_max.max() + 1]
data['control_index'] = np.ubyte(0)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = ScienceProduct.from_levelb(
levelb, parent=parent)
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00260'))
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00242'))
data = Data()
try:
data['delta_time'] = np.uint32(
packets.get_value('NIX00441').to(u.ds))
data.add_meta(name='delta_time', nix='NIX00441', packets=packets)
except AttributeError:
data['delta_time'] = np.uint32(
packets.get_value('NIX00404').to(u.ds))
data.add_meta(name='delta_time', nix='NIX00404', packets=packets)
unique_times = np.unique(data['delta_time'])
data.add_basic(name='rcr',
nix='NIX00401',
attr='value',
packets=packets,
dtype=np.ubyte)
data['num_pixel_sets'] = np.atleast_1d(
_get_unique(packets, 'NIX00442', np.ubyte))
data.add_meta(name='num_pixel_sets', nix='NIX00442', packets=packets)
pixel_masks, pm_meta = _get_pixel_mask(packets, 'NIXD0407')
pixel_masks = pixel_masks.reshape(-1, data['num_pixel_sets'][0], 12)
if packets.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.add_data('pixel_masks', (pixel_masks, pm_meta))
data.add_data('detector_masks', _get_detector_mask(packets))
# NIX00405 in BSD is 1 indexed
data['integration_time'] = SCETimeDelta(packets.get_value('NIX00405'))
data.add_meta(name='integration_time', nix='NIX00405', packets=packets)
triggers = np.array(
[packets.get_value(f'NIX00{i}') for i in range(242, 258)])
triggers_var = np.array([
packets.get_value(f'NIX00{i}', attr='error')
for i in range(242, 258)
])
data['triggers'] = triggers.T.astype(get_min_uint(triggers))
data['triggers'].meta = {
'NIXS': [f'NIX00{i}' for i in range(242, 258)]
}
data['triggers_err'] = np.float32(np.sqrt(triggers_var).T)
data.add_basic(name='num_energy_groups',
nix='NIX00258',
packets=packets,
dtype=np.ubyte)
tmp = dict()
tmp['e_low'] = np.array(packets.get_value('NIXD0016'), np.ubyte)
tmp['e_high'] = np.array(packets.get_value('NIXD0017'), np.ubyte)
tmp['num_data_elements'] = np.array(packets.get_value('NIX00259'))
unique_energies_low = np.unique(tmp['e_low'])
unique_energies_high = np.unique(tmp['e_high'])
counts = np.array(packets.get_value('NIX00260'))
counts_var = np.array(packets.get_value('NIX00260', attr='error'))
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))
out_counts = None
out_var = None
counts_var = np.sqrt(counts_var.transpose((0, 2, 3, 1)))
if packets.ssid == 21:
out_counts = np.zeros((unique_times.size, 32, 12, 32))
out_var = np.zeros((unique_times.size, 32, 12, 32))
elif packets.ssid == 22:
out_counts = np.zeros((unique_times.size, 32, 12, 32))
out_var = np.zeros((unique_times.size, 32, 12, 32))
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 counts.sum() != out_counts.sum():
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
# only fix here as data is send so needed for extraction but will be all zeros
data['detector_masks'] = fix_detector_mask(control,
data['detector_masks'])
sub_index = np.searchsorted(data['delta_time'], unique_times)
data = data[sub_index]
data['time'] = (control['time_stamp'][0] + data['delta_time'] +
data['integration_time'] / 2)
data['timedel'] = data['integration_time']
data['counts'] = \
(out_counts * u.ct).astype(get_min_uint(out_counts))[..., :tmp['e_high'].max()+1]
data.add_meta(name='counts', nix='NIX00260', packets=packets)
data['counts_err'] = np.float32(out_var *
u.ct)[..., :tmp['e_high'].max() + 1]
data['control_index'] = control['index'][0]
data = data['time', 'timedel', 'rcr', 'pixel_masks', 'detector_masks',
'num_pixel_sets', 'num_energy_groups', 'triggers',
'triggers_err', 'counts', 'counts_err']
data['control_index'] = np.ubyte(0)
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
def from_levelb(cls, levelb, parent=''):
packets, idb_versions, control = ScienceProduct.from_levelb(
levelb, parent=parent)
data = Data()
data['start_time'] = packets.get_value('NIX00404').astype(np.uint32)
data.add_meta(name='start_time', nix='NIX00404', packets=packets)
data.add_basic(name='rcr',
nix='NIX00401',
attr='value',
packets=packets,
dtype=np.ubyte)
# NIX00405 in BSD is 1 indexed
data['integration_time'] = packets.get_value('NIX00405').astype(
np.uint16)
data.add_meta(name='integration_time', nix='NIX00405', packets=packets)
data.add_data('pixel_masks', _get_pixel_mask(packets, 'NIXD0407'))
data.add_data('detector_masks', _get_detector_mask(packets))
data['triggers'] = np.array(
[packets.get_value(f'NIX00{i}') for i in range(408, 424)]).T
data['triggers'].dtype = get_min_uint(data['triggers'])
data['triggers'].meta = {
'NIXS': [f'NIX00{i}' for i in range(408, 424)]
}
data.add_basic(name='num_samples',
nix='NIX00406',
packets=packets,
dtype=np.uint16)
num_detectors = 32
num_energies = 32
num_pixels = 12
# Data
tmp = dict()
tmp['pixel_id'] = np.array(packets.get_value('NIXD0158'), np.ubyte)
tmp['detector_id'] = np.array(packets.get_value('NIXD0153'), np.ubyte)
tmp['channel'] = np.array(packets.get_value('NIXD0154'), np.ubyte)
tmp['continuation_bits'] = np.array(packets.get_value('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'])
counts_1d = packets.get_value('NIX00065')
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
data['detector_masks'] = fix_detector_mask(control,
data['detector_masks'])
sub_index = np.searchsorted(data['start_time'], unique_times)
data = data[sub_index]
data['time'] = control["time_stamp"][0] \
+ data['start_time'] + data['integration_time'] / 2
data['timedel'] = SCETimeDelta(data['integration_time'])
data['counts'] = (counts * u.ct).astype(get_min_uint(counts))
# data.add_meta(name='counts', nix='NIX00065', packets=packets)
data['control_index'] = control['index'][0]
data.remove_columns(['start_time', 'integration_time', 'num_samples'])
return cls(service_type=packets.service_type,
service_subtype=packets.service_subtype,
ssid=packets.ssid,
control=control,
data=data,
idb_versions=idb_versions)
