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)
control.add_data('compression_scheme_counts_skm',
_get_compression_scheme(packets, 'NIX00263'))
control.add_data('compression_scheme_triggers_skm',
_get_compression_scheme(packets, 'NIX00242'))
data = Data()
try:
data['delta_time'] = packets.get_value('NIX00441')
data.add_meta(name='delta_time', nix='NIX00441', packets=packets)
except AttributeError:
data['delta_time'] = packets.get_value('NIX00404')
data.add_meta(name='delta_time', nix='NIX00404', packets=packets)
data['control_index'] = np.full(len(data['delta_time']), 0)
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.add_basic(name='rcr',
nix='NIX00401',
attr='value',
packets=packets)
data.add_data('pixel_mask1', _get_pixel_mask(packets, 'NIXD0407'))
data.add_data('pixel_mask2', _get_pixel_mask(packets, 'NIXD0444'))
data.add_data('pixel_mask3', _get_pixel_mask(packets, 'NIXD0445'))
data.add_data('pixel_mask4', _get_pixel_mask(packets, 'NIXD0446'))
data.add_data('pixel_mask5', _get_pixel_mask(packets, 'NIXD0447'))
data.add_data('detector_masks', _get_detector_mask(packets))
data['detector_masks'] = fix_detector_mask(control,
data['detector_masks'])
# NIX00405 in BSD is 1 indexed
data['integration_time'] = packets.get_value('NIX00405')
data.add_meta(name='integration_time', nix='NIX00405', packets=packets)
triggers = []
triggers_var = []
for i in range(242, 258):
triggers.extend(packets.get_value(f'NIX00{i}'))
triggers_var.extend(packets.get_value(f'NIX00{i}', attr='error'))
data['triggers'] = np.array(triggers).reshape(-1, 16)
data['triggers'].meta = {
'NIXS': [f'NIX00{i}' for i in range(242, 258)]
} # ,
# 'PCF_CURTX': [packets.get(f'NIX00{i}')[0].idb_info.PCF_CURTX
# for i in range(242, 258)]}
data['triggers_err'] = np.sqrt(triggers_var).reshape(-1, 16)
tids = np.searchsorted(data['delta_time'], unique_times)
data = data[tids]
# sum(packets.get_value('NIX00258'))
# Data
e_low = np.array(packets.get_value('NIXD0016'))
e_high = np.array(packets.get_value('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.get_value('NIX00261')).reshape(
unique_times.size, -1)
data.add_meta(name='flux', nix='NIX00261', packets=packets)
num_detectors = packets.get_value('NIX00262')[0]
data['detector_id'] = np.array(packets.get_value('NIX00100')).reshape(
unique_times.size, -1, num_detectors)
data['real'] = packets.get_value('NIX00263').reshape(
unique_times.size, num_detectors, -1)
data.add_meta(name='real', nix='NIX00263', packets=packets)
data['real_err'] = np.sqrt(
packets.get_value('NIX00263',
attr='error').reshape(unique_times.size,
num_detectors, -1))
data.add_meta(name='real_err', nix='NIX00263', packets=packets)
data['imaginary'] = packets.get_value('NIX00264').reshape(
unique_times.size, num_detectors, -1)
data.add_meta(name='imaginary', nix='NIX00264', packets=packets)
data['imaginary_err'] = np.sqrt(
packets.get_value('NIX00264',
attr='error').reshape(unique_times.size,
num_detectors, -1))
data.add_meta(name='imaginary', nix='NIX00264', packets=packets)
data['time'] = (control["time_stamp"][0] + data['delta_time'] +
data['integration_time'] / 2)
data['timedel'] = SCETimeDelta(data['integration_time'])
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)