def main():
"""Main function for calling summarize_clsim_table as a script"""
t0 = time()
args = parse_args()
kwargs = vars(args)
table_fpaths = []
for fpath in kwargs.pop('table-fpaths'):
table_fpaths.extend(glob(expand(fpath)))
for fpath in table_fpaths:
kwargs['table_fpath'] = fpath
summarize_clsim_table(**kwargs)
total_time = time() - t0
if len(table_fpaths) > 1:
avg = np.round(total_time / len(table_fpaths), 3)
wstderr('Average time to summarize tables: {} s/table\n'.format(avg))
def summarize_clsim_table(table_fpath,
table=None,
save_summary=True,
outdir=None):
"""
Parameters
----------
table_fpath : string
Path to table (or just the table's filename if `outdir` is specified)
table : mapping, optional
If the table has already been loaded, it can be passed here to avoid
re-loading the table.
save_summary : bool
Whether to save the table summary to disk.
outdir : string, optional
If `save_summary` is True, write the summary to this directory. If
`outdir` is not specified and `save_summary` is True, the summary will
be written to the same directory that contains `table_fpath`.
Returns
-------
table
See `load_clsim_table` for details of the data structure
summary : OrderedDict
"""
t_start = time()
if save_summary:
from pisa.utils.jsons import from_json, to_json
table_fpath = expand(table_fpath)
srcdir, clsim_fname = dirname(table_fpath), basename(table_fpath)
invalid_fname = False
try:
fname_info = interpret_clsim_table_fname(clsim_fname)
except ValueError:
invalid_fname = True
fname_info = {}
if outdir is None:
outdir = srcdir
outdir = expand(outdir)
mkdir(outdir)
if invalid_fname:
metapath = None
else:
metaname = (CLSIM_TABLE_METANAME_PROTO[-1].format(
hash_val=fname_info['hash_val']))
metapath = join(outdir, metaname)
if metapath and isfile(metapath):
meta = from_json(metapath)
else:
meta = dict()
if table is None:
table = load_clsim_table(table_fpath)
summary = OrderedDict()
for key in table.keys():
if key == 'table':
continue
summary[key] = table[key]
if fname_info:
for key in ('hash_val', 'string', 'depth_idx', 'seed'):
summary[key] = fname_info[key]
# TODO: Add hole ice info when added to tray_kw_to_hash
if meta:
summary['n_events'] = meta['tray_kw_to_hash']['NEvents']
summary['ice_model'] = meta['tray_kw_to_hash']['IceModel']
summary['tilt'] = not meta['tray_kw_to_hash']['DisableTilt']
for key, val in meta.items():
if key.endswith('_binning_kw'):
summary[key] = val
elif 'fname_version' in fname_info and fname_info['fname_version'] == 1:
summary['n_events'] = fname_info['n_events']
summary['ice_model'] = 'spice_mie'
summary['tilt'] = False
summary['r_binning_kw'] = dict(min=0.0, max=400.0, n_bins=200, power=2)
summary['costheta_binning_kw'] = dict(min=-1, max=1, n_bins=40)
summary['t_binning_kw'] = dict(min=0.0, max=3000.0, n_bins=300)
summary['costhetadir_binning_kw'] = dict(min=-1, max=1, n_bins=20)
summary['deltaphidir_binning_kw'] = dict(min=0.0, max=np.pi, n_bins=20)
# Save marginal distributions and info to file
norm = (
1 / table['n_photons'] /
(SPEED_OF_LIGHT_M_PER_NS / table['phase_refractive_index'] *
np.mean(np.diff(table['t_bin_edges'])))
#* table['angular_acceptance_fract']
* (len(table['costheta_bin_edges']) - 1))
summary['norm'] = norm
dim_names = ('r', 'costheta', 't', 'costhetadir', 'deltaphidir')
n_dims = len(table['table_shape'])
assert n_dims == len(dim_names)
# Apply norm to underflow and overflow so magnitudes can be compared
# relative to plotted marginal distributions
for flow, idx in product(('underflow', 'overflow'), iter(range(n_dims))):
summary[flow][idx] = summary[flow][idx] * norm
wstderr('Finding marginal distributions...\n')
wstderr(' masking off zeros in table...')
t0 = time()
nonzero_table = np.ma.masked_equal(table['table'], 0)
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3, 3)))
t0_marg = time()
summary['dimensions'] = OrderedDict()
for keep_axis, ax_name in zip(tuple(range(n_dims)), dim_names):
remove_axes = list(range(n_dims))
remove_axes.pop(keep_axis)
remove_axes = tuple(remove_axes)
axis = OrderedDict()
wstderr(' mean across non-{} axes...'.format(ax_name))
t0 = time()
axis['mean'] = norm * np.asarray(
np.mean(table['table'], axis=remove_axes))
wstderr(' ({} s)\n'.format(np.round(time() - t0, 3)))
wstderr(' median across non-{} axes...'.format(ax_name))
t0 = time()
axis['median'] = norm * np.asarray(
np.ma.median(nonzero_table, axis=remove_axes))
wstderr(' ({} s)\n'.format(np.round(time() - t0, 3)))
wstderr(' max across non-{} axes...'.format(ax_name))
t0 = time()
axis['max'] = norm * np.asarray(
np.max(table['table'], axis=remove_axes))
wstderr(' ({} s)\n'.format(np.round(time() - t0, 3)))
summary['dimensions'][ax_name] = axis
wstderr(' Total time to find marginal distributions: {} s\n'.format(
np.round(time() - t0_marg, 3)))
if save_summary:
ext = None
base_fname = clsim_fname
while ext not in ('', '.fits'):
base_fname, ext = splitext(base_fname)
ext = ext.lower()
outfpath = join(outdir, base_fname + '_summary.json.bz2')
to_json(summary, outfpath)
print('saved summary to "{}"'.format(outfpath))
wstderr('Time to summarize table: {} s\n'.format(
np.round(time() - t_start, 3)))
return table, summary
def load_ckv_table_compr(fpath, mmap):
"""Load a Cherenkov table from disk.
Parameters
----------
fpath : string
Path to directory containing the table's .npy files.
mmap : bool
Whether to memory map the table (if it's stored in a directory
containing .npy files).
Returns
-------
table : OrderedDict
Items are
- 'n_photons' :
- 'group_refractive_index' :
- 'phase_refractive_index' :
- 'r_bin_edges' :
- 'costheta_bin_edges' :
- 't_bin_edges' :
- 'costhetadir_bin_edges' :
- 'deltaphidir_bin_edges' :
- 'ckv_template_map' : np.ndarray
- 't_indep_ckv_table' : np.ndarray (if available)
"""
fpath = expand(fpath)
table = OrderedDict()
if mmap:
mmap_mode = 'r'
else:
mmap_mode = None
if DEBUG:
wstderr('Loading ckv table from {} ...\n'.format(fpath))
if isfile(fpath):
assert basename(fpath) == 'ckv_table.npy'
fpath = dirname(fpath)
t0 = time()
indir = fpath
for key in CKV_TABLE_KEYS + ['t_indep_ckv_table']:
fpath = join(indir, key + '.npy')
if DEBUG:
wstderr(' loading {} from "{}" ...'.format(key, fpath))
t1 = time()
if isfile(fpath):
table[key] = np.load(fpath)
elif key != 't_indep_ckv_table':
raise ValueError(
'Could not find file "{}" for loading table key "{}"'
.format(fpath, key)
)
if DEBUG:
wstderr(' ({} ms)\n'.format(np.round((time() - t1)*1e3, 3)))
if DEBUG:
wstderr(' Total time to load: {} s\n'.format(np.round(time() - t0, 3)))
return table
def load_clsim_table_minimal(fpath, mmap=False, include_overflow=False):
"""Load a CLSim table from disk (optionally compressed with zstd).
Similar to the `load_clsim_table` function but the full table, including
under/overflow bins, is kept and no normalization or further processing is
performed on the table data besides populating the ouptput OrderedDict.
Parameters
----------
fpath : string
Path to file to be loaded. If the file has extension 'zst', 'zstd', or
'zstandard', the file will be decompressed using the `python-zstandard`
Python library before passing to `fits` for interpreting.
mmap : bool, optional
Whether to memory map the table
include_overflow : bool, optional
By default, overflow bins (if present) are removed
Returns
-------
table : OrderedDict
"""
t0 = time()
table = OrderedDict()
fpath = expand(fpath)
if DEBUG:
wstderr('Loading table from {} ...\n'.format(fpath))
if isdir(fpath):
indir = fpath
if mmap:
mmap_mode = 'r'
else:
mmap_mode = None
for rel_fpath in listdir(indir):
key, ext = splitext(rel_fpath)
abs_fpath = join(indir, rel_fpath)
if not (isfile(abs_fpath) and ext == '.npy'):
continue
if DEBUG:
wstderr(' loading {} from "{}" ...'.format(key, abs_fpath))
t1 = time()
val = np.load(abs_fpath, mmap_mode=mmap_mode)
# Pull "small" things (less than 10 MiB) into memory so we don't
# have too many file handles open due to memory mapping
if mmap and val.nbytes < 10 * 1024**2:
val = np.copy(val)
table[key] = val
if DEBUG:
wstderr(' ({} ms)\n'.format(np.round((time() - t1) * 1e3, 3)))
elif isfile(fpath):
from astropy.io import fits
fobj = get_decompressd_fobj(fpath)
pf_table = None
try:
pf_table = fits.open(fobj, mode='readonly', memmap=mmap)
header = pf_table[0].header # pylint: disable=no-member
table['table_shape'] = np.array(pf_table[0].data.shape, dtype=int) # pylint: disable=no-member
table['group_refractive_index'] = set_explicit_dtype(
force_little_endian(header['_i3_n_group']))
table['phase_refractive_index'] = set_explicit_dtype(
force_little_endian(header['_i3_n_phase']))
n_dims = len(table['table_shape'])
new_style = False
axnames = [None] * n_dims
binning = [None] * n_dims
for key in header.keys():
if not key.startswith('_i3_ax_'):
continue
new_style = True
axnum = header[key]
axname = key[len('_i3_ax_'):]
be0 = header['_i3_{}_min'.format(axname)]
be1 = header['_i3_{}_max'.format(axname)]
n_bins = header['_i3_{}_n_bins'.format(axname)]
power = header.get('_i3_{}_power'.format(axname), 1)
bin_edges = force_little_endian(pf_table[axnum + 1].data) # pylint: disable=no-member
assert np.isclose(bin_edges[0],
be0), '%f .. %f' % (be0, bin_edges[0])
assert np.isclose(bin_edges[-1],
be1), '%f .. %f' % (be1, bin_edges[-1])
assert len(bin_edges) == n_bins + 1, '%d vs. %d' % (
len(bin_edges), n_bins + 1)
assert np.allclose(
bin_edges,
powerspace(start=be0,
stop=be1,
num=n_bins + 1,
power=power),
)
axnames[axnum] = axname
binning[axnum] = bin_edges
if not new_style:
if n_dims == 5:
axnames = [
'r', 'costheta', 't', 'costhetadir', 'deltaphidir'
]
elif n_dims == 6:
axnames = [
'r', 'costheta', 'phi', 't', 'costhetadir',
'deltaphidir'
]
else:
raise NotImplementedError(
'{}-dimensional table not handled for old-style CLSim'
' tables'.format(n_dims))
binning = [
force_little_endian(pf_table[i + 1].data).flat
for i in range(len(axnames))
] # pylint: disable=no-member
for axnum, (axname, bin_edges) in enumerate(zip(axnames, binning)):
assert axname is not None, 'missing axis %d name' % axnum
assert bin_edges is not None, 'missing axis %d binning' % axnum
dtype = np.dtype([(axname, np.float64, dim.size)
for axname, dim in zip(axnames, binning)])
table['binning'] = np.array(tuple(binning), dtype=dtype)
for keyroot in GENERIC_KEYS:
keyname = '_i3_' + keyroot
if keyname in header:
val = force_little_endian(header[keyname])
if keyroot in (
't_is_residual_time',
'disable_tilt',
'disable_anisotropy',
):
val = np.bool8(val)
else:
val = set_explicit_dtype(val)
table[keyroot] = val
# Get string values from keys that have a prefix preceded by the
# value all in the key (I3 software had issues saving strings as
# values in the header "dict" so the workaround was to store the
# string value in this way)
for infix in INFIX_KEYS:
keyroot = '_i3_' + infix + '_'
for keyname in header.keys():
if not keyname.startswith(keyroot):
continue
val = keyname[len(keyroot):]
table[infix] = np.string0(val)
if include_overflow:
slicer = (slice(None), ) * n_dims
else:
slicer = (slice(1, -1), ) * n_dims
table['table'] = force_little_endian(pf_table[0].data[slicer]) # pylint: disable=no-member
wstderr(' (load took {} s)\n'.format(np.round(time() - t0, 3)))
except:
wstderr('ERROR: Failed to load "{}"\n'.format(fpath))
raise
finally:
del pf_table
if hasattr(fobj, 'close'):
fobj.close()
del fobj
else: # fpath is neither dir nor file
raise ValueError('Table does not exist at path "{}"'.format(fpath))
if 'step_length' not in table:
table['step_length'] = 1
if 't_is_residual_time' not in table:
table['t_is_residual_time'] = True
if DEBUG:
wstderr(' Total time to load: {} s\n'.format(np.round(time() - t0,
3)))
return table
def load_clsim_table(fpath, angular_acceptance_fract, quantum_efficiency):
"""Load a CLSim table from disk (optionally compressed with zstd).
Parameters
----------
fpath : string
Path to file to be loaded. If the file has extension 'zst', 'zstd', or
'zstandard', the file will be decompressed using the `python-zstandard`
Python library before passing to `fits` for interpreting.
Returns
-------
table : OrderedDict
Items include
- 'table_shape' : tuple of int
- 'table' : np.ndarray
- 't_indep_table' : np.ndarray
- 'n_photons' :
- 'group_refractive_index' :
- 'phase_refractive_index' :
If the table is 5D, items also include
- 'r_bin_edges' :
- 'costheta_bin_edges' :
- 't_bin_edges' :
- 'costhetadir_bin_edges' :
- 'deltaphidir_bin_edges' :
- 'table_norm'
"""
table = OrderedDict()
assert isfile(fpath)
table = load_clsim_table_minimal(fpath=fpath, include_overflow=True)
if 'is_normed' not in table:
table['is_normed'] = False
is_normed = table['is_normed']
if not is_normed:
table['table_norm'] = get_table_norm(
angular_acceptance_fract=angular_acceptance_fract,
quantum_efficiency=quantum_efficiency,
step_length=table['step_length'],
**{k: table[k]
for k in TABLE_NORM_KEYS if k != 'step_length'})
table[
't_indep_table_norm'] = quantum_efficiency * angular_acceptance_fract
wstderr('Interpreting table...\n')
t0 = time()
n_dims = len(table['table_shape'])
# Cut off first and last bin in each dimension (underflow and
# overflow bins)
slice_wo_overflow = (slice(1, -1), ) * n_dims
wstderr(' slicing to remove underflow/overflow bins...')
t0 = time()
table_wo_overflow = table['table'][slice_wo_overflow]
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3)))
wstderr(' slicing and summarizing underflow and overflow...')
is_normed = table['is_normed']
if not is_normed:
table['table_norm'] = get_table_norm(
angular_acceptance_fract=angular_acceptance_fract,
quantum_efficiency=quantum_efficiency,
step_length=table['step_length'],
**{k: table[k]
for k in TABLE_NORM_KEYS if k != 'step_length'})
table[
't_indep_table_norm'] = quantum_efficiency * angular_acceptance_fract
wstderr('Interpreting table...\n')
t0 = time()
n_dims = len(table['table_shape'])
# Cut off first and last bin in each dimension (underflow and
# overflow bins)
slice_wo_overflow = (slice(1, -1), ) * n_dims
wstderr(' slicing to remove underflow/overflow bins...')
t0 = time()
table_wo_overflow = table['table'][slice_wo_overflow]
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3)))
wstderr(' slicing and summarizing underflow and overflow...')
t0 = time()
underflow, overflow = [], []
for n in range(n_dims):
sl = tuple([slice(1, -1)] * n + [0] + [slice(1, -1)] *
(n_dims - 1 - n))
underflow.append(table['table'][sl].sum())
sl = tuple([slice(1, -1)] * n + [-1] + [slice(1, -1)] *
(n_dims - 1 - n))
overflow.append(table['table'][sl].sum())
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3)))
table['table'] = table_wo_overflow
table['underflow'] = np.array(underflow)
table['overflow'] = np.array(overflow)
return table
def get_all_stats(
outdir,
min_pulses_per_event,
overwrite=False,
only_sets=None,
processes=None,
verbosity=0,
):
"""Get stats for all data and MC sets.
Parameters
----------
outdir : string
min_pulses_per_event : int >= 0
overwrite : bool, optional
Whether to overwrite any existing stats files
only_sets : string, iterable thereof, or None, optional
If specified, string(s) must be keys of `MC_NAME_DIRINFOS` and/or
`DATA_NAME_DIRINFOS` dicts.
processes : None or int > 0, optional
verbosity : int >= 0, optional
Returns
-------
stats : OrderedDict
Keys are dataset names and values are OrderedDicts containing the stats
for the corresponding datasets.
"""
outdir = expand(outdir)
if isinstance(only_sets, string_types):
only_sets = [only_sets]
to_process = chain.from_iterable(
[MC_NAME_DIRINFOS.items(),
DATA_NAME_DIRINFOS.items()])
if only_sets is not None:
only_sets = [s.split("/") for s in only_sets]
new_to_process = []
for set_name, subsets_list in to_process:
new_subsets_list = []
for only_set in only_sets:
if set_name != only_set[0]:
continue
if len(only_set) == 1:
new_subsets_list = subsets_list
break
else:
for subset in subsets_list:
if subset["id"] == only_set[1]:
new_subsets_list.append(subset)
if len(new_subsets_list) > 0:
new_to_process.append((set_name, new_subsets_list))
to_process = new_to_process #((key, val) for key, val in to_process if key in only_sets)
print(to_process)
mkdir(outdir)
stats = OrderedDict()
for name, dirinfos in to_process:
t0 = time.time()
this_stats = OrderedDict()
for dirinfo in dirinfos:
augmented_name = "{}.{}".format(name, dirinfo["id"])
outfile = join(outdir, "stats_{}.npz".format(augmented_name))
if isfile(outfile) and not overwrite:
contents = OrderedDict([(k, v)
for k, v in np.load(outfile).items()])
if verbosity >= 1:
wstderr(
'loaded stats for set "{}" from file "{}" ({} sec)\n'.
format(augmented_name, outfile,
time.time() - t0))
else:
contents = get_stats(
min_pulses_per_event=min_pulses_per_event,
dirinfo=dirinfo,
processes=processes,
verbosity=verbosity,
)
#np.savez_compressed(outfile, **contents)
np.savez(outfile, **contents)
if verbosity >= 1:
wstderr('saved stats for set "{}" to file "{}" ({} sec)\n'.
format(name, outfile,
time.time() - t0))
if name == "data":
stats[dirinfo["id"]] = contents
else:
for key, vals in contents.items():
if key not in this_stats:
this_stats[key] = []
this_stats[key].append(vals)
del contents
if name != "data":
stats[name] = OrderedDict([(k, np.concatenate(v))
for k, v in this_stats.items()])
return stats
def combine_tdi_tiles(
source_dir,
dest_dir,
table_hash,
gcd,
bin_edges_file,
tile_spec_file,
):
"""Combine individual time-independent tiles (one produced per DOM) into a single
TDI table.
Parameters
----------
source_dir : str
dest_dir : str
bin_edges_file : str
tile_spec_file : str
"""
source_dir = expand(source_dir)
dest_dir = expand(dest_dir)
gcd = expand(gcd)
bin_edges_file = expand(bin_edges_file)
tile_spec_file = expand(tile_spec_file)
mkdir(dest_dir)
assert isdir(source_dir)
assert isfile(bin_edges_file)
assert isfile(tile_spec_file)
gcd = extract_gcd(gcd)
bin_edges = load_pickle(bin_edges_file)
x_edges = bin_edges['x']
y_edges = bin_edges['y']
z_edges = bin_edges['z']
ctdir_edges = bin_edges['costhetadir']
phidir_edges = bin_edges['phidir']
n_x = len(x_edges) - 1
n_y = len(y_edges) - 1
n_z = len(z_edges) - 1
n_ctdir = len(ctdir_edges) - 1
n_phidir = len(phidir_edges) - 1
n_dir_bins = n_ctdir * n_phidir
x_bw = (x_edges.max() - x_edges.min()) / n_x
y_bw = (y_edges.max() - y_edges.min()) / n_y
z_bw = (z_edges.max() - z_edges.min()) / n_z
bin_vol = x_bw * y_bw * z_bw
ctdir_min = ctdir_edges.min()
ctdir_max = ctdir_edges.max()
phidir_min = phidir_edges.min()
phidir_max = phidir_edges.max()
with open(tile_spec_file, 'r') as f:
tile_specs = [l.strip() for l in f.readlines()]
table = np.zeros(shape=(n_x, n_y, n_z, n_ctdir, n_phidir),
dtype=np.float32)
# Slice all table dimensions to exclude {under,over}flow bins
central_slice = (slice(1, -1), ) * 5
angsens_model = None
ice_model = None
disable_tilt = None
disable_anisotropy = None
n_phase = None
n_group = None
tiles_info = []
for tile_spec in tile_specs:
info = None
try:
fields = tile_spec.split()
info = OrderedDict()
info['tbl_idx'] = int(fields[0])
info['string'] = int(fields[1])
info['dom'] = int(fields[2])
info['seed'] = int(fields[3])
info['n_events'] = int(fields[4])
info['x_min'] = float(fields[5])
info['x_max'] = float(fields[6])
info['n_x'] = int(fields[7])
info['y_min'] = float(fields[8])
info['y_max'] = float(fields[9])
info['n_y'] = int(fields[10])
info['z_min'] = float(fields[11])
info['z_max'] = float(fields[12])
info['n_z'] = int(fields[13])
info['n_ctdir'] = int(fields[14])
info['n_phidir'] = int(fields[15])
tiles_info.append(info)
tile_fpath = glob(
join(
source_dir, 'clsim_table_set'
'_{table_hash}'
'_tile_{tbl_idx}'
'_string_{string}'
'_dom_{dom}'
'_seed_{seed}'
'_n_{n_events}'
'.fits'.format(table_hash=table_hash, **info)))[0]
try:
fits_table = fits.open(tile_fpath,
mode='readonly',
memmap=True)
except:
wstderr('Failed on tile_fpath "{}"'.format(tile_fpath))
raise
primary = fits_table[0]
header = primary.header # pylint: disable=no-member
keys = header.keys()
this_gcd_i3_md5 = extract_meta_from_keys(keys, '_i3_gcd_i3_md5_')
assert this_gcd_i3_md5 == gcd['source_gcd_i3_md5'], \
'this: {}, ref: {}'.format(this_gcd_i3_md5, gcd['source_gcd_i3_md5'])
this_angsens_model = extract_meta_from_keys(keys, '_i3_angsens_')
if angsens_model is None:
angsens_model = this_angsens_model
_, avg_angsens = load_angsens_model(angsens_model)
else:
assert this_angsens_model == angsens_model
this_table_hash = extract_meta_from_keys(keys, '_i3_hash_')
assert this_table_hash == table_hash
this_ice_model = extract_meta_from_keys(keys, '_i3_ice_')
if ice_model is None:
ice_model = this_ice_model
else:
assert this_ice_model == ice_model
this_disable_anisotropy = header['_i3_disable_anisotropy']
if disable_anisotropy is None:
disable_anisotropy = this_disable_anisotropy
else:
assert this_disable_anisotropy == disable_anisotropy
this_disable_tilt = header['_i3_disable_tilt']
if disable_tilt is None:
disable_tilt = this_disable_tilt
else:
assert this_disable_tilt == disable_tilt
this_n_phase = header['_i3_n_phase']
if n_phase is None:
n_phase = this_n_phase
else:
assert this_n_phase == n_phase
this_n_group = header['_i3_n_group']
if n_group is None:
n_group = this_n_group
else:
assert this_n_group == n_group
assert info['n_ctdir'] == n_ctdir
assert info['n_phidir'] == n_phidir
assert np.isclose(header['_i3_costhetadir_min'], ctdir_min)
assert np.isclose(header['_i3_costhetadir_max'], ctdir_max)
assert np.isclose(header['_i3_phidir_min'], phidir_min)
assert np.isclose(header['_i3_phidir_max'], phidir_max)
n_photons = header['_i3_n_photons']
n_dir_bins = info['n_ctdir'] * info['n_phidir']
this_x_bw = (info['x_max'] - info['x_min']) / info['n_x']
this_y_bw = (info['y_max'] - info['y_min']) / info['n_y']
this_z_bw = (info['z_max'] - info['z_min']) / info['n_z']
assert this_x_bw == x_bw
assert this_y_bw == y_bw
assert this_z_bw == z_bw
assert np.any(np.isclose(info['x_min'], x_edges))
assert np.any(np.isclose(info['x_max'], x_edges))
assert np.any(np.isclose(info['y_min'], y_edges))
assert np.any(np.isclose(info['y_max'], y_edges))
assert np.any(np.isclose(info['z_min'], z_edges))
assert np.any(np.isclose(info['z_max'], z_edges))
quantum_efficiency = 0.25 * gcd['rde'][info['string'] - 1,
info['dom'] - 1]
norm = n_dir_bins * quantum_efficiency * avg_angsens / (n_photons *
bin_vol)
if np.isnan(norm):
print('\nTile {} norm is nan!'.format(info['tbl_idx']))
print(' quantum_efficiency = {}, n_photons = {}'.format(
quantum_efficiency, n_photons))
elif norm == 0:
print('\nTile {} norm is 0'.format(info['tbl_idx']))
x_start = np.digitize(info['x_min'] + x_bw / 2, x_edges) - 1
x_stop = np.digitize(info['x_max'] - x_bw / 2, x_edges)
y_start = np.digitize(info['y_min'] + y_bw / 2, y_edges) - 1
y_stop = np.digitize(info['y_max'] - y_bw / 2, y_edges)
z_start = np.digitize(info['z_min'] + z_bw / 2, z_edges) - 1
z_stop = np.digitize(info['z_max'] - z_bw / 2, z_edges)
# NOTE: comparison excludes norm = 0 _and_ norm = NaN
if norm > 0:
assert not np.isnan(norm)
table[x_start:x_stop, y_start:y_stop,
z_start:z_stop, :, :] += (
norm * primary.data[central_slice] # pylint: disable=no-member
)
except:
wstderr('Failed on tile_spec {}'.format(tile_spec))
if info is not None:
wstderr('Info:\n{}'.format(info))
raise
wstderr('.')
wstderr('\n')
metadata = OrderedDict()
metadata['table_hash'] = table_hash
metadata['disable_tilt'] = disable_tilt
metadata['disable_anisotropy'] = disable_anisotropy
metadata['gcd'] = gcd
metadata['angsens_model'] = angsens_model
metadata['ice_model'] = ice_model
metadata['n_phase'] = n_phase
metadata['n_group'] = n_group
metadata['tiles_info'] = tiles_info
outdir = join(
dest_dir, 'tdi_table_{}_tilt_{}_anisotropy_{}'.format(
table_hash,
'off' if disable_tilt else 'on',
'off' if disable_anisotropy else 'on',
))
mkdir(outdir)
name = 'tdi_table.npy'
outfpath = join(outdir, name)
wstdout('saving table to "{}"\n'.format(outfpath))
np.save(outfpath, table)
#outfpath = join(outdir, 'tdi_bin_edges.json')
#wstdout('saving bin edges to "{}"\n'.format(outfpath))
#json.dump(
# bin_edges,
# file(outfpath, 'w'),
# sort_keys=False,
# indent=2,
#)
outfpath = join(outdir, 'tdi_bin_edges.pkl')
wstdout('saving bin edges to "{}"\n'.format(outfpath))
pickle.dump(
bin_edges,
open(outfpath, 'wb'),
protocol=pickle.HIGHEST_PROTOCOL,
)
#outfpath = join(outdir, 'tdi_metadata.json')
#wstdout('saving metadata to "{}"\n'.format(outfpath))
#json.dump(
# metadata,
# file(outfpath, 'w'),
# sort_keys=False,
# indent=2,
#)
outfpath = join(outdir, 'tdi_metadata.pkl')
wstdout('saving metadata to "{}"\n'.format(outfpath))
pickle.dump(
metadata,
open(outfpath, 'wb'),
protocol=pickle.HIGHEST_PROTOCOL,
)
def get_stats(dirinfo, min_pulses_per_event, processes=None, verbosity=0):
"""
Parameters
----------
dirinfo : dict
Must contain keys / vals
"id" : string
"path" : string
"n_files" : int
min_pulses_per_event : int >= 0
processes : None or int > 0, optional
verbosity : int >= 0
"""
if isinstance(dirinfo, string_types):
dirinfo = [dirinfo]
elif isinstance(dirinfo, Mapping):
dirinfo = [dirinfo]
#pulses_filter = fixed_charge_filter
#pulses_filter = quantize_min_q_filter
#pulses_filter = irregular_quantize_min_q_filter
pulses_filter = pulse_integration_filter
#pulses_filter = None
#emax = 100
emax = np.inf
pool = Pool(processes=processes)
results = []
for root_dirinfo in dirinfo:
root_dir = expand(root_dirinfo["path"])
n_files = root_dirinfo["n_files"]
for dirpath, dirs_, files in walk(root_dir, followlinks=True):
dirs_.sort(key=nsort_key_func)
if "events.npy" in files:
results.append(
pool.apply_async(
process_dir,
tuple(),
dict(
dirpath=dirpath,
n_files=n_files,
pulses_filter=pulses_filter,
emax=emax,
min_pulses_per_event=min_pulses_per_event,
verbosity=verbosity,
),
))
pool.close()
pool.join()
stats = deepcopy(STATS_PROTO)
for result in results:
result = result.get()
for key in result.keys():
stats[key].extend(result[key])
# Concatenate and cull
new_stats = OrderedDict()
for stat_name in stats.keys():
vals = stats[stat_name]
if len(vals) == 0:
if verbosity >= 1:
wstderr('Not using stat "{}" for dirs {}\n'.format(
stat_name, dirinfo))
elif np.isscalar(vals[0]):
new_stats[stat_name] = np.array(vals)
else:
new_stats[stat_name] = np.concatenate(vals)
stats = new_stats
return stats
def process_dir(
dirpath,
n_files,
min_pulses_per_event,
pulses_filter,
emax,
verbosity=0,
):
"""
Parameters
----------
dirpath : string
n_files : int > 0
min_pulses_per_event : int >= 0
pulses_filter : None or callable, optional
emax : 0 <= scalar <= np.inf
verbosity : int >= 0
Returns
-------
stats : OrderedDict
Keys are taken from STATS_PROTO, values are numpy arrays
"""
stats = deepcopy(STATS_PROTO)
events = np.load(join(dirpath, "events.npy"), mmap_mode="r")
if len(events) == 0:
return stats
mask_vals = deepcopy(events["L5_oscNext_bool"])
if np.count_nonzero(mask_vals) == 0:
return stats
if verbosity >= 2:
wstderr(".")
if isfile(join(dirpath, "truth.npy")):
truth = np.load(join(dirpath, "truth.npy"), mmap_mode="r")
weights = truth["weight"]
use_weights = True
else:
weights = np.ones(shape=len(events))
use_weights = False
if np.isfinite(emax) and emax > 0:
recos = np.load(
join(dirpath, "recos", "retro_crs_prefit.npy"),
mmap_mode="r",
)
with np.errstate(invalid='ignore'):
mask_vals &= recos["energy"]["median"] <= emax
if np.count_nonzero(mask_vals) == 0:
return stats
pulses = load_pickle(
join(dirpath, "pulses", "{}.pkl".format(PULSE_SERIES_NAME)))
for mask_val, event_pulses, weight in zip(mask_vals, pulses, weights):
if not mask_val:
continue
if callable(pulses_filter):
event_pulses = pulses_filter(event_pulses)
if len(event_pulses) == 0:
continue
if use_weights:
normed_weight = weight / n_files
# qtot is sum of charge of all hits on all DOMs
event_pulses_ = []
tmp_hits_per_dom = []
tmp_charge_per_dom = []
tmp_time_diffs_within_dom = []
tmp_weight_per_dom = []
for omkey, dom_pulses in event_pulses:
event_pulses_.append(dom_pulses)
tmp_hits_per_dom.append(len(dom_pulses))
tmp_charge_per_dom.append(dom_pulses["charge"].sum())
#stats["time_diffs_between_hits"].append(
# np.concatenate([[0.], np.diff(np.sort(dom_pulses["time"]))])
#)
tmp_time_diffs_within_dom.append(dom_pulses["time"] -
dom_pulses["time"].min())
if use_weights:
tmp_weight_per_dom.append(normed_weight)
event_pulses = np.concatenate(event_pulses_)
# TODO: move min_pulses_per_event before qmin processing
# TODO: small-pulse agglomeration filter
if len(event_pulses) < min_pulses_per_event:
continue
stats["doms_per_event"].append(len(event_pulses))
stats["hits_per_dom"].extend(tmp_hits_per_dom)
stats["charge_per_dom"].extend(tmp_charge_per_dom)
stats["time_diffs_within_dom"].extend(tmp_time_diffs_within_dom)
if use_weights:
stats["weight_per_dom"].extend(tmp_weight_per_dom)
charge = event_pulses["charge"]
stats["charge_per_hit"].append(charge)
stats["charge_per_event"].append(charge.sum())
stats["hits_per_event"].append(len(event_pulses))
stats["time_diffs_within_event"].append(event_pulses["time"] -
event_pulses["time"].min())
if use_weights:
stats["weight_per_event"].append(normed_weight)
stats["weight_per_hit"].append(
np.full(shape=len(event_pulses), fill_value=normed_weight))
return stats
def combine_tables(table_fpaths, outdir=None, overwrite=False):
"""Combine multiple tables together into a single table.
All tables specified must have the same binnings defined. Tables should
also be produced using different random seeds (if all else besides
n_photons is equal); if corresponding metadata files can be found in the
same directories as the CLSim tables, this will be enforced prior to
loading and combining the actual tables together.
Parameters
----------
table_fpaths : string or iterable thereof
Each string is glob-expanded
outdir : string, optional
Directory to which to save the combined table; if not specified, the
resulting table will be returned but not saved to disk.
overwrite : bool
Overwrite an existing table. If a table is found at the output path and
`overwrite` is False, the function simply returns without raising an
exception.
Returns
-------
combined_table
"""
t_start = time()
# Get all input table filepaths, including glob expansion
orig_table_fpaths = deepcopy(table_fpaths)
if isinstance(table_fpaths, string_types):
table_fpaths = [table_fpaths]
table_fpaths_tmp = []
for fpath in table_fpaths:
table_fpaths_tmp.extend(glob(expand(fpath)))
table_fpaths = sorted(table_fpaths_tmp, key=nsort_key_func)
if not table_fpaths:
raise ValueError(
"Found no tables given `table_fpaths` = {}".format(orig_table_fpaths)
)
wstderr(
'Found {} tables to combine:\n {}\n'.format(
len(table_fpaths), '\n '.join(table_fpaths)
)
)
# Create the output directory
if outdir is not None:
outdir = expand(outdir)
mkdir(outdir)
# Combine the tables
combined_table = None
table_keys = None
for fpath in table_fpaths:
table = load_clsim_table_minimal(fpath, mmap=True)
base = basename(fpath)
rootname, ext = splitext(base)
if ext.lstrip('.') in COMPR_EXTENSIONS:
base = rootname
if 'source_tables' not in table:
table['source_tables'] = np.array([base], dtype=np.string0)
if combined_table is None:
combined_table = table
table_keys = set(table.keys())
# Formulate output file paths and check if they exist (do on first
# table to avoid finding out we are going to overwrite a file
# before loading all the source tables)
if outdir is not None:
output_fpaths = OrderedDict(
(
(k, join(outdir, k + '.npy'))
for k in sorted(table_keys.difference(NO_WRITE_KEYS))
)
)
if not overwrite:
for fp in output_fpaths.values():
if isfile(fp):
raise IOError(
'File at {} already exists, NOT overwriting'.format(fp)
)
wstderr(
'Output files will be written to:\n {}\n'.format(
'\n '.join(output_fpaths.values())
)
)
continue
# Make sure keys are the same
new_table_keys = set(table.keys())
missing_keys = sorted(
table_keys
.difference(new_table_keys)
.difference(NO_VALIDATE_KEYS)
)
additional_keys = sorted(
new_table_keys
.difference(table_keys)
.difference(NO_VALIDATE_KEYS)
)
if missing_keys or additional_keys:
raise ValueError(
'Table is missing keys {} and/or has additional keys {}'.format(
missing_keys, additional_keys
)
)
# Validate keys that should be equal
for key in sorted(table_keys.difference(NO_VALIDATE_KEYS)):
if not np.array_equal(table[key], combined_table[key]):
raise ValueError('Unequal "{}" in file {}'.format(key, fpath))
# Add values from keys that should be summed
for key in SUM_KEYS:
if key not in table:
continue
combined_table[key] += table[key]
# Concatenate and sort new source table(s) in source_tables array
combined_table['source_tables'] = np.sort(
np.concatenate([combined_table['source_tables'], table['source_tables']])
)
# Make sure to clear table from memory since these can be quite large
del table
# Save the data to npy files on disk (in a sub-directory for all of this
# table's files)
if outdir is not None:
wstderr('Writing files:\n')
len_longest_fpath = np.max([len(p) for p in output_fpaths.values()])
for key in sorted(table_keys.difference(NO_WRITE_KEYS)):
fpath = output_fpaths[key]
wstderr(' {} ...'.format(fpath.ljust(len_longest_fpath)))
t0 = time()
np.save(fpath, combined_table[key])
wstderr(' ({:12.3f} s)\n'.format(time() - t0))
wstderr(
'Total time to combine tables: {} s\n'.format(np.round(time() - t_start, 3))
)
return combined_table
def load_clsim_table_minimal(fpath, step_length=None, mmap=False):
"""Load a CLSim table from disk (optionally compressed with zstd).
Similar to the `load_clsim_table` function but the full table, including
under/overflow bins, is kept and no normalization or further processing is
performed on the table data besides populating the ouptput OrderedDict.
Parameters
----------
fpath : string
Path to file to be loaded. If the file has extension 'zst', 'zstd', or
'zstandard', the file will be decompressed using the `python-zstandard`
Python library before passing to `pyfits` for interpreting.
mmap : bool, optional
Whether to memory map the table (if it's stored in a directory
containing .npy files).
Returns
-------
table : OrderedDict
Items include
- 'table_shape' : tuple of int
- 'table' : np.ndarray
- 't_indep_table' : np.ndarray (if available)
- 'n_photons' :
- 'phase_refractive_index' :
- 'r_bin_edges' :
- 'costheta_bin_edges' :
- 't_bin_edges' :
- 'costhetadir_bin_edges' :
- 'deltaphidir_bin_edges' :
"""
table = OrderedDict()
fpath = expand(fpath)
if DEBUG:
wstderr('Loading table from {} ...\n'.format(fpath))
if isdir(fpath):
t0 = time()
indir = fpath
if mmap:
mmap_mode = 'r'
else:
mmap_mode = None
for key in MY_CLSIM_TABLE_KEYS + ['t_indep_table']:
fpath = join(indir, key + '.npy')
if DEBUG:
wstderr(' loading {} from "{}" ...'.format(key, fpath))
t1 = time()
if isfile(fpath):
table[key] = np.load(fpath, mmap_mode=mmap_mode)
elif key != 't_indep_table':
raise ValueError(
'Could not find file "{}" for loading table key "{}"'
.format(fpath, key)
)
if DEBUG:
wstderr(' ({} ms)\n'.format(np.round((time() - t1)*1e3, 3)))
if step_length is not None and 'step_length' in table:
assert step_length == table['step_length']
if DEBUG:
wstderr(' Total time to load: {} s\n'.format(np.round(time() - t0, 3)))
return table
if not isfile(fpath):
raise ValueError('Table does not exist at path "{}"'.format(fpath))
if mmap:
print('WARNING: Cannot memory map a fits or compressed fits file;'
' ignoring `mmap=True`.')
import pyfits
t0 = time()
fobj = get_decompressd_fobj(fpath)
try:
pf_table = pyfits.open(fobj)
table['table_shape'] = pf_table[0].data.shape # pylint: disable=no-member
table['n_photons'] = force_little_endian(
pf_table[0].header['_i3_n_photons'] # pylint: disable=no-member
)
table['group_refractive_index'] = force_little_endian(
pf_table[0].header['_i3_n_group'] # pylint: disable=no-member
)
table['phase_refractive_index'] = force_little_endian(
pf_table[0].header['_i3_n_phase'] # pylint: disable=no-member
)
if step_length is not None:
table['step_length'] = step_length
n_dims = len(table['table_shape'])
if n_dims == 5:
# Space-time dimensions
table['r_bin_edges'] = force_little_endian(
pf_table[1].data # meters # pylint: disable=no-member
)
table['costheta_bin_edges'] = force_little_endian(
pf_table[2].data # pylint: disable=no-member
)
table['t_bin_edges'] = force_little_endian(
pf_table[3].data # nanoseconds # pylint: disable=no-member
)
# Photon directionality
table['costhetadir_bin_edges'] = force_little_endian(
pf_table[4].data # pylint: disable=no-member
)
table['deltaphidir_bin_edges'] = force_little_endian(
pf_table[5].data # pylint: disable=no-member
)
else:
raise NotImplementedError(
'{}-dimensional table not handled'.format(n_dims)
)
table['table'] = force_little_endian(pf_table[0].data) # pylint: disable=no-member
wstderr(' (load took {} s)\n'.format(np.round(time() - t0, 3)))
finally:
del pf_table
if hasattr(fobj, 'close'):
fobj.close()
del fobj
return table
def combine_clsim_tables(table_fpaths,
outdir=None,
overwrite=False,
step_length=1.0):
"""Combine multiple CLSim-produced tables together into a single table.
All tables specified must have the same binnings defined. Tables should
also be produced using different random seeds; if corresponding metadata
files can be found in the same directories as the CLSim tables, this will
be enforced prior to loading and combining the actual tables together.
Parameters
----------
table_fpaths : string or iterable thereof
Each string is glob-expanded
outdir : string, optional
Directory to which to save the combined table; if not specified, the
resulting table will be returned but not saved to disk.
overwrite : bool
Overwrite an existing table. If a table is found at the output path and
`overwrite` is False, the function simply returns.
step_length : float > 0 in units of meters
Needed for computing the normalization to apply to the `table` in order
to generate the `t_indep_table` (if the latter doesn't already exist).
Note that normalization constants due to `n_photons`,
`quantum_efficiency`, and `angular_acceptance_fract` as well as
normalization depending (only) upon radial bin (i.e 1/r^2 geometric
factor) are _not_ applied to the tables. The _only_ normalization
applied (and _only_ to `t_indep_table`) is the multiple-counting factor
that is a function of `step_length` and whichever of the time or radial
bin dimensions is smaller.
Returns
-------
combined_table
"""
t_start = time()
# Get all input table filepaths, including glob expansion
if isinstance(table_fpaths, basestring):
table_fpaths = [table_fpaths]
table_fpaths_tmp = []
for fpath in table_fpaths:
table_fpaths_tmp.extend(glob(expand(fpath)))
table_fpaths = sorted(table_fpaths_tmp)
wstderr('Found {} tables to combine:\n {}\n'.format(
len(table_fpaths), '\n '.join(table_fpaths)))
# Formulate output filenames and check if they exist
output_fpaths = None
if outdir is not None:
outdir = expand(outdir)
mkdir(outdir)
output_fpaths = OrderedDict(
((k, join(outdir, k + '.npy')) for k in ALL_KEYS))
output_fpaths['source_tables'] = join(outdir, 'source_tables.txt')
if not overwrite:
for fpath in output_fpaths:
if isfile(fpath):
raise IOError('File {} exists'.format(fpath))
wstderr('Output files will be written to:\n {}\n'.format('\n '.join(
output_fpaths.values())))
# Combine the tables
combined_table = None
for fpath in table_fpaths:
table = load_clsim_table_minimal(fpath,
step_length=step_length,
mmap=True)
if combined_table is None:
combined_table = table
continue
if set(table.keys()) != set(SUM_KEYS + VALIDATE_KEYS):
raise ValueError(
'Table keys {} do not match expected keys {}'.format(
sorted(table.keys()), sorted(ALL_KEYS)))
for key in VALIDATE_KEYS:
if not np.array_equal(table[key], combined_table[key]):
raise ValueError('Unequal {} in file {}'.format(key, fpath))
for key in SUM_KEYS:
combined_table[key] += table[key]
del table
# Force quantum_efficiency and angular_acceptance_fract to 1 (these should
# be handled by the user at the time the table is used to represent a
# particular or subgroup of DOMs)
t_indep_table, _ = generate_time_indep_table(table=table,
quantum_efficiency=1,
angular_acceptance_fract=1)
table['t_indep_table'] = t_indep_table
# Save the data to npy files on disk (in a sub-directory for all of this
# table's files)
if outdir is not None:
basenames = []
for fpath in table_fpaths:
base = basename(fpath)
rootname, ext = splitext(base)
if ext.lstrip('.') in COMPR_EXTENSIONS:
base = rootname
basenames.append(base)
wstderr('Writing files:\n')
for key in ALL_KEYS:
fpath = output_fpaths[key]
wstderr(' {} ...'.format(fpath))
t0 = time()
np.save(fpath, combined_table[key])
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3, 3)))
fpath = output_fpaths['source_tables']
wstderr(' {} ...'.format(fpath))
t0 = time()
with open(fpath, 'w') as fobj:
fobj.write('\n'.join(sorted(basenames)))
wstderr(' ({} ms)\n'.format(np.round((time() - t0) * 1e3, 3)))
wstderr('Total time to combine tables: {} s\n'.format(
np.round(time() - t_start, 3)))
return combined_table
