def load_gen_data(self):
logging.debug('Loading generator level sample')
unfold_pipeline_cfg = self.params['unfold_pipeline_cfg'].value
if isinstance(unfold_pipeline_cfg, str):
pipeline_cfg = from_file(unfold_pipeline_cfg)
pipeline_hash = pipeline_cfg
sa_cfg = from_file(
pipeline_cfg.get('stage.data', 'param.data_sample_config'))
template_maker = Pipeline(pipeline_cfg)
elif isinstance(unfold_pipeline_cfg, Pipeline):
pipeline_hash = unfold_pipeline_cfg.state_hash
sa_cfg = from_file(
unfold_pipeline_cfg.params['data_sample_config'].value)
template_maker = unfold_pipeline_cfg
gen_cfg = from_file(sa_cfg.get('neutrinos|gen_lvl', 'gen_cfg_file'))
this_hash = hash_obj([gen_cfg, pipeline_hash, self.output_str],
full_hash=self.full_hash)
if self.gen_data_hash == this_hash:
return self._gen_data
full_gen_data = template_maker.get_outputs()
if not isinstance(full_gen_data, Data):
raise AssertionError(
'Output of pipeline is not a Data object, instead is type '
'{0}'.format(type(full_gen_data)))
trans_data = full_gen_data.transform_groups(self.output_str)
gen_data = trans_data[self.output_str]
self._gen_data = gen_data
self.gen_data_hash = this_hash
return gen_data
def __load(self, fname):
try:
data, meta = from_file(fname, return_attrs=True)
except TypeError:
data = from_file(fname)
meta = None
return data, meta
def load_discr_sys(self, sys_list):
"""Load the fit results from the file and make some check
compatibility"""
self.fit_results = from_file(self.params['fit_results_file'].value)
if not set(self.input_names) == set(self.fit_results['map_names']):
for name in self.input_names:
if not name in self.fit_results['map_names']:
#check if there is somethingi uniquely compatible
compatible_names = [
mapname in name
for mapname in self.fit_results['map_names']
]
if sum(compatible_names) == 1:
# compatible
compatible_name = self.fit_results['map_names'][
compatible_names.index(True)]
self.fit_results[name] = self.fit_results[
compatible_name]
logging.warning(
'Substituting hyperplane parameterization %s for %s'
% (compatible_name, name))
else:
logging.error('No compatible map for %s found!' % name)
assert set(sys_list) == set(self.fit_results['sys_list'])
self.sys_list = self.fit_results['sys_list']
def _compute_outputs(self, inputs=None):
"""Apply basic cuts and compute histograms for output channels."""
logging.debug('Entering sample._compute_outputs')
self.config = from_file(self.params['data_sample_config'].value)
name = self.config.get('general', 'name')
logging.trace('{0} sample sample_hash = '
'{1}'.format(name, self.sample_hash))
self.load_sample_events()
if self.params['keep_criteria'].value is not None:
# TODO(shivesh)
raise NotImplementedError(
'needs check to make sure this works in a DistributionMaker'
)
self._data.applyCut(self.params['keep_criteria'].value)
self._data.update_hash()
if self.output_events:
return self._data
outputs = []
if self.neutrinos:
trans_nu_data = self._data.transform_groups(
self._output_nu_groups
)
for fig in trans_nu_data.keys():
outputs.append(trans_nu_data.histogram(
kinds = fig,
binning = self.output_binning,
weights_col = 'pisa_weight',
errors = True,
name = str(NuFlavIntGroup(fig)),
))
if self.muons:
outputs.append(self._data.histogram(
kinds = 'muons',
binning = self.output_binning,
weights_col = 'pisa_weight',
errors = True,
name = 'muons',
tex = r'\rm{muons}'
))
if self.noise:
outputs.append(self._data.histogram(
kinds = 'noise',
binning = self.output_binning,
weights_col = 'pisa_weight',
errors = True,
name = 'noise',
tex = r'\rm{noise}'
))
name = self.config.get('general', 'name')
return MapSet(maps=outputs, name=name)
def __init__(self, prem_file, detector_depth=1., prop_height=2.):
# Load earth model
if prem_file is not None:
self.using_earth_model = True
prem = from_file(prem_file, as_array=True)
self.rhos = prem[..., 1][::-1].astype(FTYPE)
self.radii = prem[..., 0][::-1].astype(FTYPE)
r_earth = prem[-1][0]
self.default_elec_frac = 0.5
n_prem = len(self.radii) - 1
self.max_layers = 2 * n_prem + 1
else:
self.using_earth_model = False
r_earth = 6371.0 #If no Earth model provided, use a standard Earth radius value
# Set some other
self.r_detector = r_earth - detector_depth
self.prop_height = prop_height
self.detector_depth = detector_depth
self.min_detector_depth = 1.0e-3 # <-- Why? // [km] so min is ~ 1 m
# Some additional handling of the Earth model
if self.using_earth_model:
# Change outermost radius to a bit underground, where the detector
if self.detector_depth >= self.min_detector_depth:
self.radii[0] -= detector_depth
self.max_layers += 1
# Compute coszen limit
self.computeMinLengthToLayers()
def sampleHypercube(n_dim, n_samp, rand_set_id=0, crit='m', iterations=5,
rdata_dir='~/cowen/data/random'):
"""Load (if file exists) or generate samples from within hypercube using
Latin hypercube sampling
Requires pyDOE to generate new samples.
"""
fname = samplesFilename(n_dim=n_dim,
n_samp=n_samp,
rand_set_id=rand_set_id,
crit=crit,
iterations=iterations)
rdata_dir = os.path.expandvars(os.path.expanduser(rdata_dir))
fpath = os.path.join(rdata_dir, fname)
if os.path.exists(fpath):
samps = fileio.from_file(fpath)
else:
logging.info('File not found. Generating new set of samples & saving'
' result to "%s"', fpath)
import pyDOE
mkdir(rdata_dir)
# Set a deterministic random state based upon the critical hypercube
# sampling parameters specified
n_bad_seeds(n_dim, n_samp, rand_set_id)
samps = pyDOE.lhs(n=n_dim, samples=n_samp, criterion=crit,
iterations=iterations)
fileio.to_file(samps, fpath)
return samps
def save(self, fpath, ver=None, **kwargs):
"""Save cross sections (and the energy specification) to a file at
`fpath`."""
if ver is None:
if self._ver is None:
raise ValueError(
'Either a ver must be specified in call to `save` or it '
'must have been set prior to the invocation of `save`.'
)
ver = self._ver
else:
assert ver == self._ver
try:
fpath = find_resource(fpath)
except IOError:
pass
fpath = os.path.expandvars(os.path.expanduser(fpath))
all_xs = {}
# Get any existing data from file
if os.path.exists(fpath):
all_xs = from_file(fpath)
# Validate existing data by instantiating objects from each
for v, d in all_xs.items():
CrossSections(ver=v, energy=d['energy'], xsec=d['xsec'])
if ver in all_xs:
logging.warning('Overwriting existing version "' + ver +
'" in file ' + fpath)
all_xs[ver] = {'xsec':self, 'energy':self.energy}
to_file(all_xs, fpath, **kwargs)
def _get_reco_kernels(self, kernelfile=None, **kwargs):
for reco_scale in ['e_reco_scale', 'cz_reco_scale']:
if reco_scale in kwargs:
if not kwargs[reco_scale]==1:
raise ValueError('%s = %.2f not valid for RecoServiceKernelFile!'
%(reco_scale, kwargs[reco_scale]))
if not kernelfile in [self.kernelfile, None]:
logging.info('Reconstruction from non-default kernel file %s!'%kernelfile)
return fileio.from_file(find_resource(kernelfile))
if not hasattr(self, 'kernels'):
logging.info('Using file %s for default reconstruction'%(kernelfile))
self.kernels = fileio.from_file(find_resource(kernelfile))
return self.kernels
def load(fpath, ver=None, **kwargs):
"""Load cross sections from a file locatable and readable by the PISA
from_file command. If `ver` is provided, it is used to index into the
top level of the loaded dictionary"""
all_xsec = from_file(fpath, **kwargs)
if ver not in all_xsec:
raise ValueError('Version "%s" not found. Valid versions in file'
'"%s" are: %s' % (ver, fpath, all_xsec.keys()))
return all_xsec[ver]['energy'], all_xsec[ver]['xsec']
def load_noise_events(config, dataset):
name = config.get('general', 'name')
weight = config.get('noise', 'weight')
weight_units = config.get('noise', 'weight_units')
sys_list = split(config.get('noise', 'sys_list'))
base_prefix = config.get('noise', 'baseprefix')
keep_keys = split(config.get('noise', 'keep_keys'))
aliases = config.items('noise%saliases' % SEP)
if base_prefix == 'None':
base_prefix = ''
if dataset == 'nominal':
paths = []
for sys in sys_list:
ev_sys = 'noise%s%s' % (SEP, sys)
nominal = config.get(ev_sys, 'nominal')
ev_sys_nom = ev_sys + SEP + nominal
paths.append(config.get(ev_sys_nom, 'file_path'))
if len(set(paths)) > 1:
raise AssertionError(
'Choice of nominal file is ambigous. Nominal '
'choice of systematic parameters must coincide '
'with one and only one file. Options found are: '
'{0}'.format(paths))
file_path = paths[0]
else:
file_path = config.get(dataset, 'file_path')
logging.info('Extracting noise dataset "{0}" from sample '
'"{1}"'.format(dataset, name))
noise = from_file(file_path)
sample.strip_keys(keep_keys, noise)
if weight == 'None' or weight == '1':
noise['sample_weight'] = np.ones(noise['weights'].shape)
elif weight == '0':
noise['sample_weight'] = np.zeros(noise['weights'].shape)
else:
noise['sample_weight'] = noise[weight] * ureg(weight_units)
noise['pisa_weight'] = deepcopy(noise['sample_weight'])
for alias, expr in aliases:
if alias in noise:
logging.warning(
'Overwriting Data key {0} with aliased expression '
'{1}'.format(alias, expr))
noise[alias] = eval(re.sub(r'\<(.*?)\>', r"noise['\1']", expr))
noise_dict = {'noise': noise}
return Data(noise_dict,
metadata={
'name': name,
'noise_sample': dataset
})
def load_pid_energy_param(source):
"""Load pid energy-dependent parameterisation from file or dictionary.
Parameters
----------
source : string or mapping
If string, interprete as resource location of the file; if mapping, use
directly.
Returns
-------
pid_energy_param_dict : OrderedDict
Keys are `NuFlavIntGroup`s and values are callables of one arg.
"""
# Get the original dict
if isinstance(source, str):
orig_dict = from_file(source)
elif isinstance(source, Mapping):
orig_dict = source
else:
raise TypeError('`source` must either be string or mapping; got %s'
' instead.' % type(source))
# Build dict with flavintgroups as keys; subdict with signatures as keys
# and callables as values
pid_energy_param_dict = OrderedDict()
for flavintgroup_str, subdict in orig_dict.items():
flavintgroup = NuFlavIntGroup(flavintgroup_str)
pid_energy_param_dict[flavintgroup] = OrderedDict()
for signature, sig_param_spec in subdict.items():
if isinstance(sig_param_spec, str):
sig_param_func = eval(sig_param_spec)
if not callable(sig_param_func):
raise ValueError(
'Group %s PID signature %s param spec "%s" does'
' not evaluate to a callable.'
% (flavintgroup_str, signature, sig_param_spec)
)
elif callable(sig_param_spec):
sig_param_func = sig_param_spec
else:
raise TypeError(
'Group %s PID signature %s parameterization is a "%s"'
' but must be a string or callable.'
% (flavintgroup_str, signature, type(sig_param_spec))
)
pid_energy_param_dict[flavintgroup][signature] = sig_param_func
return pid_energy_param_dict
def load_discr_sys(self, pnames):
"""Load the fit results from the file and make some check
compatibility"""
self.fit_results = {}
for pname in pnames:
self.fit_results[pname] = from_file(self.params[pname +
'_file'].value)
if sorted(self.input_names) != sorted(
self.fit_results[pname]['map_names']):
raise ValueError(
"Expected input names - %s. Got %s." %
(self.input_names, self.fit_results[pname]['map_names']))
self.pnames = pnames
def earth_model(YeI, YeO, YeM, PREM_file='osc/nuSQuIDS_PREM.dat'): # pylint: disable=invalid-name
"""Return a `nuSQUIDSpy.EarthAtm` object with
user-defined electron fractions. Note that a
temporary Earth model file is produced (over-
written) each time this function is executed.
Parameters
----------
YeI, YeO, YeM : float
electron fractions in Earth's inner core,
outer core, and mantle
(defined by spherical shells with radii of
1121.5, 3480.0, and 6371.0 km)
PREM_file : str
path to nuSQuIDS PREM Earth Model file whose
electron fractions will be modified
Returns
-------
earth_atm : nuSQUIDSpy.EarthAtm
can be passed to `Set_EarthModel` method of
a nuSQuIDs propagator object
"""
logging.debug("Regenerating nuSQuIDS Earth Model with electron"
" fractions: YeI=%s, YeO=%s, YeM=%s" % (YeI, YeO, YeM))
earth_radius = 6371.0 # km
# radii at which main transitions occur according to PREM
transition_radii = np.array([1121.5, 3480.0, earth_radius]) # km
fname_tmp = os.path.join(CACHE_DIR, "nuSQuIDS_PREM_TMP.dat")
PREM_file = from_file(fname=PREM_file, as_array=True)
for i, (r, _, _) in enumerate(PREM_file):
# r is fraction of total radius
current_radius = r*earth_radius
if current_radius <= transition_radii[0]:
# inner core region
Ye_new = YeI
elif current_radius <= transition_radii[1]:
# outer core region
Ye_new = YeO
elif current_radius <= transition_radii[2]:
# mantle region
Ye_new = YeM
# update electron fraction
PREM_file[i][2] = Ye_new
# make temporary file
np.savetxt(fname=fname_tmp, X=PREM_file)
# create and return the Earth model from file
earth_atm = nsq.EarthAtm(fname_tmp)
return earth_atm
def stability_test(func, func_kw, ref_path, ignore_fails=False, define_as_ref=False):
"""basic stability test of a Numba CPUDispatcher function (i.e., function
compiled via @jit / @njit)"""
func_name = func.py_func.__name__
logging.info("stability testing `%s`", func_name)
ref_path = expand(ref_path)
test = execute_func(func=func, func_kw=func_kw)
if define_as_ref:
to_file(test, ref_path)
# Even when we define the test case as ref, round-trip to/from file to
# ensure that doesn't corrupt the values
ref = from_file(ref_path)
check(test=test, ref=ref, label=func_name, ignore_fails=ignore_fails)
return test, ref
def __init__(self, prem_file, detector_depth=1., prop_height=2.):
# Load earth model
if prem_file is not None:
self.using_earth_model = True
prem = from_file(prem_file, as_array=True)
# The following radii and densities are extracted in reverse order
# w.r.t the file. The first elements of the arrays below corresponds
# the Earth's surface, and the following numbers go deeper toward the
# planet's core
self.rhos = prem[..., 1][::-1].astype(FTYPE)
self.radii = prem[..., 0][::-1].astype(FTYPE)
r_earth = prem[-1][0]
self.default_elec_frac = 0.5
# Add an external layer corresponding to the atmosphere / production boundary
self.radii = np.concatenate(
(np.array([r_earth + prop_height]), self.radii))
self.rhos = np.concatenate((np.ones(1, dtype=FTYPE), self.rhos))
self.max_layers = 2 * (len(self.radii))
else:
self.using_earth_model = False
r_earth = 6371.0 #If no Earth model provided, use a standard Earth radius value
#
# Make some checks about the input production height and detector depth
#
assert detector_depth > 0, 'ERROR: detector depth must be a positive value'
assert detector_depth <= r_earth, 'ERROR: detector depth is deeper than one Earth radius!'
assert prop_height >= 0, 'ERROR: neutrino production height must be positive'
# Set some other
self.r_detector = r_earth - detector_depth
self.prop_height = prop_height
self.detector_depth = detector_depth
if self.using_earth_model:
# Compute the coszen_limits
self.computeMinLengthToLayers()
def __init__(self, run_settings, detector=None):
super().__init__()
if isinstance(run_settings, str):
rsd = fileio.from_file(resources.find_resource(run_settings))
elif isinstance(run_settings, dict):
rsd = run_settings
else:
raise TypeError('Unhandled run_settings type passed in arg: ' +
type(run_settings))
if detector:
detector = str(detector).strip()
self.detector = detector
# Determine how deeply nested runs are in the dict to allow for
# user to specify a dict that has multiple detectors in it OR
# a dict with just a single detector in it
if 'flavints' in rsd.values()[0]:
runs_d = rsd
elif 'flavints' in rsd.values()[0].values()[0]:
if self.detector is None:
if len(rsd) == 1:
runs_d = rsd.values()[0]
else:
raise ValueError('Must specify which detector; detectors '
'found: ' + str(rsd.keys()))
else:
runs_d = rsd[self.detector.strip()]
else:
raise Exception('dict must either be 3 levels: '
'{DET:{RUN:{...}}}; or 2 levels: {RUN:{...}}')
# Force run numbers to be strings (JSON files cannot have an int as
# a key, so it is a string upon import, and it's safest to keep it as
# a string considering how non-standardized naming is in IceCube) and
# convert actual run settings dict to MCSimRunSettings instances
runs_d = {str(k): MCSimRunSettings(v) for k, v in runs_d.items()}
# Save the runs_d to this object instance, which behaves like a dict
self.update(runs_d)
def load_from_nu_file(events_file, all_flavints, weight, weight_units,
keep_keys, aliases):
flav_fidg = FlavIntDataGroup(flavint_groups=all_flavints)
events = from_file(events_file)
sample.strip_keys(keep_keys, events)
nu_mask = events['ptype'] > 0
nubar_mask = events['ptype'] < 0
cc_mask = events['interaction'] == 1
nc_mask = events['interaction'] == 2
if weight == 'None' or weight == '1':
events['sample_weight'] = \
np.ones(events['ptype'].shape) * ureg.dimensionless
elif weight == '0':
events['sample_weight'] = \
np.zeros(events['ptype'].shape) * ureg.dimensionless
else:
events['sample_weight'] = events[weight] * \
ureg(weight_units)
events['pisa_weight'] = deepcopy(events['sample_weight'])
for alias, expr in aliases:
if alias in events:
logging.warning(
'Overwriting Data key {0} with aliased expression '
'{1}'.format(alias, expr))
events[alias] = eval(re.sub(r'\<(.*?)\>', r"events['\1']", expr))
for flavint in all_flavints:
i_mask = cc_mask if flavint.cc else nc_mask
t_mask = nu_mask if flavint.particle else nubar_mask
flav_fidg[flavint] = {
var: events[var][i_mask & t_mask]
for var in events.iterkeys()
}
return flav_fidg
def parse_fit_config(fit_cfg):
"""Perform sanity checks on and parse fit configuration file.
Parameters
----------
fit_cfg : str
path to a fit configuration file
Returns
-------
fit_cfg : PISAConfigParser
parsed fit configuration
sys_list : list of str
parsed names of systematic parameters
units_list : list of str
units corresponding to each discrete systematic
combine_regex : list of str
each string is a regular expression for combining pipeline outputs; see
:func:`pisa.core.map.MapSet.combine_regex` for details.
"""
fit_cfg = from_file(fit_cfg)
no_ws_section_map = {s.strip(): s for s in fit_cfg.sections()}
if GENERAL_SECTION_NAME not in no_ws_section_map.values():
raise KeyError('Fit config is missing the "%s" section!' %
GENERAL_SECTION_NAME)
general_section = fit_cfg[GENERAL_SECTION_NAME]
if SYS_LIST_OPTION not in general_section:
raise KeyError(
"Fit config has to specify systematic parameters as"
' "%s" option in "%s" section (comma-separated list of names).' %
(SYS_LIST_OPTION, GENERAL_SECTION_NAME))
sys_list = [s.strip() for s in general_section[SYS_LIST_OPTION].split(",")]
if UNITS_OPTION in general_section:
units_list = []
units_specs = (general_section[UNITS_OPTION].replace(
UNITS_SPECIFIER, "").split(","))
for units_spec in units_specs:
# Make sure units are interpret-able by Pint
try:
ureg.Unit(units_spec)
except:
logging.error(
'Unit "%s" specified by "%s" option in "general" section is not'
"interpret-able by Pint",
units_spec,
UNITS_OPTION,
)
raise
units_list.append(units_spec)
else:
units_list = ["dimensionless" for s in sys_list]
logging.warn(
"No %s option found in %s section; assuming systematic parameters are"
" dimensionless",
UNITS_OPTION,
GENERAL_SECTION_NAME,
)
if len(units_list) != len(sys_list):
raise ValueError(
'{} units specified by "{}" option but {} systematics specified by "{}"'
"option; must be same number of each.".format(
len(units_list), UNITS_OPTION, len(sys_list), SYS_LIST_OPTION))
logging.info(
"Found systematic parameters %s",
["{} ({})".format(s, u) for s, u in zip(sys_list, units_list)],
)
combine_regex = general_section.get(COMBINE_REGEX_OPTION, None)
if combine_regex:
try:
combine_regex = literal_eval(combine_regex)
except (SyntaxError, ValueError):
logging.warn(
'Deprecated syntax for "combine_re" (make into a Python-evaluatable'
"sequence of strings instead) :: combine_regex = %s",
combine_regex,
)
combine_regex = [r.strip() for r in combine_regex.split(",")]
if APPLY_ALL_SECTION_NAME in no_ws_section_map:
apply_all_section = fit_cfg[no_ws_section_map[APPLY_ALL_SECTION_NAME]]
for no_ws_sname, sname in no_ws_section_map.items():
if not (no_ws_sname.startswith(NOMINAL_SET_PFX)
or no_ws_sname.startswith(SYS_SET_PFX)):
continue
sys_set_section = fit_cfg[sname]
for option, val in apply_all_section.items():
sys_set_section[option] = val
return fit_cfg, sys_list, units_list, combine_regex
def parse_args(command, description):
"""Parse command line args.
Returns
-------
init_args_d : dict
"""
assert command in [discrete_hypo_test, inj_param_scan, systematics_tests]
parser = ArgumentParser(
description=description,
formatter_class=ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
'-d',
'--logdir',
required=True,
metavar='DIR',
type=str,
help='Directory into which to store results and metadata.')
parser.add_argument('--min-settings',
type=str,
metavar='MINIMIZER_CFG',
default=None,
help='''Minimizer settings config file.''')
parser.add_argument(
'--min-method',
type=str,
default=None,
choices=('l-bfgs-b', 'slsqp'),
help='''Name of minimizer to use. Note that this takes precedence over
the minimizer method specified via the --min-settings config
file.''')
parser.add_argument(
'--min-opt',
type=str,
metavar='OPTION:VALUE',
nargs='+',
default=None,
help='''Minimizer option:value pair(s) (can specify multiple).
Values specified here override any of the same name in the config file
specified by --min-settings''')
parser.add_argument(
'--no-octant-check',
action='store_true',
help='''Disable fitting hypotheses in theta23 octant opposite initial
octant.''')
parser.add_argument(
'--ordering-check',
action='store_true',
help='''Fit both ordering hypotheses. This should only be flagged if
the ordering is NOT the discrete hypothesis being tested''')
parser.add_argument(
'--shared-params',
type=str,
default=None,
action='append',
help='''Shared parameters for multi detector analysis (repeat for
multiple). The values of these parameters are kept the same in all
detectors that contain the param.''')
if command == discrete_hypo_test:
# Data cannot be data for MC studies e.g. injected parameter scans so
# these arguments are redundant there.
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'--data-is-data',
action='store_true',
help='''Data pipeline is based upon actual, measured data. The
naming scheme for stored results is chosen accordingly.''')
group.add_argument(
'--data-is-mc',
action='store_true',
help='''Data pipeline is based upon Monte Carlo simulation, and not
actual data. The naming scheme for stored results is chosen
accordingly. If this is selected, --fluctuate-data is forced off.'''
)
# For the MC tests (injected parameter scan, systematic tests etc.) you
# must have the same pipeline for h0, h1 and data. So this argument is
# instead replaced with a generic pipeline argument.
parser.add_argument(
'--h0-pipeline',
required=True,
type=str,
action='append',
metavar='PIPELINE_CFG',
help='''Settings for the generation of hypothesis h0
distributions; repeat this argument to specify multiple
pipelines.''')
else:
assert command in [inj_param_scan, systematics_tests]
parser.add_argument(
'--pipeline',
required=True,
type=str,
action='append',
metavar='PIPELINE_CFG',
help='''Settings for the generation of h0, h1 and data
distributions; repeat this argument to specify multiple
pipelines.''')
parser.add_argument(
'--h0-param-selections',
type=str,
default=None,
metavar='PARAM_SELECTOR_LIST',
help='''Comma-separated (no spaces) list of param selectors to apply to
hypothesis h0's distribution maker's pipelines.''')
parser.add_argument('--h0-name',
type=str,
metavar='NAME',
default=None,
help='''Name for hypothesis h0. E.g., "NO" for normal
ordering in the neutrino mass ordering analysis. Note that the name
here has no bearing on the actual process, so it's important that you
be careful to use a name that appropriately identifies the
hypothesis.''')
# For the MC tests (injected parameter scan, systematic tests etc.) you
# must have the same pipeline for h0, h1 and data. So this argument is
# hidden.
if command not in (inj_param_scan, systematics_tests):
parser.add_argument(
'--h1-pipeline',
type=str,
action='append',
default=None,
metavar='PIPELINE_CFG',
help='''Settings for the generation of hypothesis h1 distributions;
repeat this argument to specify multiple pipelines. If omitted, the
same settings as specified for --h0-pipeline are used to generate
hypothesis h1 distributions (and so you have to use the
--h1-param-selections argument to generate a hypotheses distinct
from hypothesis h0 but still use h0's distribution maker).''')
parser.add_argument(
'--h1-param-selections',
type=str,
default=None,
metavar='PARAM_SELECTOR_LIST',
help='''Comma-separated (no spaces) list of param selectors to apply to
hypothesis h1 distribution maker's pipelines.''')
parser.add_argument('--h1-name',
type=str,
metavar='NAME',
default=None,
help='''Name for hypothesis h1. E.g., "IO" for inverted
ordering in the neutrino mass ordering analysis. Note that the name
here has no bearing on the actual process, so it's important that you
be careful to use a name that appropriately identifies the
hypothesis.''')
# For the MC tests (injected parameter scan, systematic tests etc.) you
# must have the same pipeline for h0, h1 and data. So this argument is
# hidden.
if command not in (inj_param_scan, systematics_tests):
parser.add_argument(
'--data-pipeline',
type=str,
action='append',
default=None,
metavar='PIPELINE_CFG',
help='''Settings for the generation of "data" distributions; repeat
this argument to specify multiple pipelines. If omitted, the same
settings as specified for --h0-pipeline are used to generate data
distributions (i.e., data is assumed to come from hypothesis h0.'''
)
parser.add_argument(
'--data-param-selections',
type=str,
default=None,
metavar='PARAM_SELECTOR_LIST',
help='''Comma-separated list of param selectors to apply to the data
distribution maker's pipelines. If neither --data-pipeline nor
--data-param-selections are specified, *both* are copied from
--h0-pipeline and --h0-param-selections, respectively. However,
if --data-pipeline is specified while --data-param-selections is not,
then the param selections in the pipeline config file(s) specified are
used to produce data distributions.''')
parser.add_argument(
'--data-name',
type=str,
metavar='NAME',
default=None,
help='''Name for the data. E.g., "NO" for normal ordering in the
neutrino mass ordering analysis. Note that the name here has no bearing
on the actual process, so it's important that you be careful to use a
name that appropriately identifies the hypothesis.''')
# For the injected parameter scan and systematic studies, only the Asimov
# analysis should be used, so these arguments are not needed.
if command not in (inj_param_scan, systematics_tests):
parser.add_argument(
'--fluctuate-data',
action='store_true',
help='''Apply fluctuations to the data distribution. This should
*not* be set for analyzing "real" (measured) data, and it is common
to not use this feature even for Monte Carlo analysis. Note that if
this is not set, --num-data-trials and --data-start-ind are forced
to 1 and 0, respectively.''')
parser.add_argument(
'--fluctuate-fid',
action='store_true',
help='''Apply fluctuations to the fiducaial distributions. If this
flag is not set, --num-fid-trials and --fid-start-ind are forced to
1 and 0, respectively.''')
parser.add_argument(
'--metric',
type=str,
required=True,
metavar='METRIC',
action='append',
choices=sorted(ALL_METRICS),
help='''Name of metric(s) to use for optimizing the fit. Must be one of
%s. Repeat this argument if you want to use different metrics for
different detectors. If only one metric is specified, all detectors will
use the same. Otherwise you have to specify one metric for each detector
(even if two use the same) and pay attention to the order.''' %
(ALL_METRICS, ))
parser.add_argument(
'--other-metric',
type=str,
default=None,
metavar='METRIC',
action='append',
choices=['all'] + sorted(ALL_METRICS),
help='''Name of another metric to evaluate at the best-fit point. Must
be either 'all' or one of %s. Repeat this argument (or use 'all') to
specify multiple metrics.''' % (ALL_METRICS, ))
if command not in (inj_param_scan, systematics_tests):
parser.add_argument(
'--num-data-trials',
type=int,
default=1,
help='''When performing Monte Carlo analysis, set to > 1 to produce
multiple pseudodata distributions from the data distribution maker's
Asimov distribution. This is overridden if --fluctuate-data is not
set (since each data distribution will be identical if it is not
fluctuated). This is typically left at 1 (i.e., the Asimov
distribution is assumed to be representative.''')
parser.add_argument('--data-start-ind',
type=int,
default=0,
help='''Fluctated data set index.''')
parser.add_argument(
'--num-fid-trials',
type=int,
default=1,
help='''Number of fiducial pseudodata trials to run. In our
experience, it takes ~10^3-10^5 fiducial psuedodata trials to
achieve low uncertainties on the resulting significance, though
that exact number will vary based upon the details of an
analysis.''')
parser.add_argument('--fid-start-ind',
type=int,
default=0,
help='''Fluctated fiducial data index.''')
# A blind analysis only makes sense when the possibility of actually
# analysing data is available.
if command not in (inj_param_scan, systematics_tests):
parser.add_argument(
'--blind',
action='store_true',
help='''Blinded analysis. Do not show parameter values or store to
logfiles.''')
parser.add_argument(
'--allow-dirty',
action='store_true',
help='''Warning: Use with caution. (Allow for run despite dirty
repository.)''')
parser.add_argument(
'--allow-no-git-info',
action='store_true',
help='''*** DANGER! Use with extreme caution! (Allow for run despite
complete inability to track provenance of code.)''')
parser.add_argument(
'--no-min-history',
action='store_true',
help='''Do not store minimizer history (steps). This behavior is also
enforced if --blind is specified.''')
# Add in the arguments specific to the injected parameter scan.
if command == inj_param_scan:
parser.add_argument(
'--param_name',
type=str,
metavar='NAME',
required=True,
help='''Name of param to scan over. This must be in the config
files defined above. One exception is that you can define this as
`sin2theta23` and it will be interpreted not as theta23 values but
as the square of the sine of theta23 values instead.''')
parser.add_argument(
'--inj_vals',
type=str,
required=True,
help='''List of values to inject as true points in the parameter
defined above. Must be something that numpy can interpret. In this
script, numpy is imported as np so please use np in your string. An
example would be np.linspace(0.35,0.65,31).''')
parser.add_argument(
'--inj_units',
type=str,
required=True,
help='''A string to be able to deal with the units in the parameter
scan and make sure that they match those in the config files. Even
if the parameter is dimensionless this must be stated.''')
parser.add_argument(
'--use-inj-prior',
action='store_true',
help='''Generally, one should not use a prior on the parameter of
interest here since the Asimov analysis breaks down with the use of
non-central prior i.e. injecting a truth that differs from the
centre of the prior. Flag this to force the prior to be left on.'''
)
# Add in the arguments specific to the systematic tests.
if command == systematics_tests:
parser.add_argument(
'--inject_wrong',
action='store_true',
help='''Inject a parameter to some systematically wrong value.
This will be either +/- 1 sigma or +/- 10%% if such a definition
is impossible. By default this parameter will be fixed unless
the fit_wrong argument is also flagged.''')
parser.add_argument(
'--fit_wrong',
action='store_true',
help='''In the case of injecting a systematically wrong hypothesis
setting this argument will get the minimiser to try correct for it.
If inject_wrong is set to false then this must also be set to
false or else the script will fail.''')
parser.add_argument(
'--only_syst',
default=None,
type=str,
action='append',
metavar='PARAM_NAME',
help='''Specify the name of one of the systematics in the file to
run the test for this systematic. Repeat this argument to specify
multiple systematics. If none are provided, the test will be run
over all systematics in the pipeline.''')
parser.add_argument(
'--skip_baseline',
action='store_true',
help='''Skip the baseline systematic test i.e. the one where none
of them are fixed and/or modified. In most cases you will want this
for comparison but if you are only interested in the effect of
shifting certain systematics then this step can be skipped.''')
parser.add_argument(
'--pprint',
action='store_true',
help='''Live-updating one-line vew of metric and parameter values. (The
latter are not displayed if --blind is specified.)''')
parser.add_argument('-v',
action='count',
default=None,
help='set verbosity level')
args = parser.parse_args(sys.argv[2:])
assert args.min_settings is not None or args.min_method is not None
init_args_d = vars(args)
set_verbosity(init_args_d.pop('v'))
min_settings_from_file = init_args_d.pop('min_settings')
minimizer = init_args_d.pop('min_method')
min_opt = init_args_d.pop('min_opt')
# TODO: put this datastructure remnant from PISA 2 out of its misery...
minimizer_settings = dict(method=dict(value='', desc='no desc'),
options=dict(value=dict(), desc=dict()))
if min_settings_from_file is not None:
minimizer_settings.update(from_file(min_settings_from_file))
if minimizer is not None:
minimizer_settings['method'] = dict(value=minimizer, desc='no desc')
if min_opt is not None:
for opt_val_str in min_opt:
opt, val_str = [s.strip() for s in opt_val_str.split(':')]
try:
val = int(val_str)
except ValueError:
try:
val = float(val_str)
except ValueError:
val = val_str
minimizer_settings['options']['value'][opt] = val
minimizer_settings['options']['desc'][opt] = 'no desc'
init_args_d['minimizer_settings'] = minimizer_settings
init_args_d['check_octant'] = not init_args_d.pop('no_octant_check')
init_args_d['check_ordering'] = init_args_d.pop('ordering_check')
if command not in (inj_param_scan, systematics_tests):
init_args_d['data_is_data'] = not init_args_d.pop('data_is_mc')
else:
init_args_d['data_is_data'] = False
init_args_d['fluctuate_data'] = False
init_args_d['fluctuate_fid'] = False
init_args_d['store_minimizer_history'] = (
not init_args_d.pop('no_min_history'))
other_metrics = init_args_d.pop('other_metric')
if other_metrics is not None:
other_metrics = [s.strip().lower() for s in other_metrics]
if 'all' in other_metrics:
other_metrics = sorted(ALL_METRICS)
for m in init_args_d['metric']:
if m in other_metrics:
other_metrics.remove(m)
if not other_metrics:
other_metrics = None
else:
logging.info('Will evaluate other metrics %s', other_metrics)
init_args_d['other_metrics'] = other_metrics
return init_args_d
def parse_pipeline_config(config):
"""Parse pipeline config.
Parameters
----------
config : string or ConfigParser
Returns
-------
stage_dicts : OrderedDict
Keys are (stage_name, service_name) tuples and values are OrderedDicts
with keys the argnames and values the arguments' values. Some known arg
values are parsed out fully into Python objects, while the rest remain
as strings that must be used or parsed elsewhere.
"""
# Note: imports placed here to avoid circular imports
from pisa.core.binning import MultiDimBinning, OneDimBinning
from pisa.core.param import ParamSelector
if isinstance(config, basestring):
config = from_file(config)
elif isinstance(config, PISAConfigParser):
pass
else:
raise TypeError(
'`config` must either be a string or PISAConfigParser. Got %s '
'instead.' % type(config))
if not config.has_section('binning'):
raise NoSectionError(
"Could not find 'binning'. Only found sections: %s" %
config.sections())
# Create binning objects
binning_dict = {}
for name, value in config['binning'].items():
if name.endswith('.order'):
order = split(config.get('binning', name))
binning, _ = split(name, sep='.')
bins = []
for bin_name in order:
try:
def_raw = config.get('binning', binning + '.' + bin_name)
except:
dims_defined = [
split(dim, sep='.')[1]
for dim in config['binning'].keys()
if dim.startswith(binning +
'.') and not dim.endswith('.order')
]
logging.error(
"Failed to find definition of '%s' dimension of '%s'"
" binning entry. Only found definition(s) of: %s",
bin_name, binning, dims_defined)
del dims_defined
raise
try:
kwargs = eval(def_raw) # pylint: disable=eval-used
except:
logging.error(
"Failed to evaluate definition of '%s' dimension of"
" '%s' binning entry:\n'%s'", bin_name, binning,
def_raw)
raise
try:
bins.append(OneDimBinning(bin_name, **kwargs))
except:
logging.error(
"Failed to instantiate new `OneDimBinning` from '%s'"
" dimension of '%s' binning entry with definition:\n"
"'%s'\n", bin_name, binning, kwargs)
raise
binning_dict[binning] = MultiDimBinning(bins)
# Pipeline section
section = 'pipeline'
# Get and parse the order of the stages (and which services implement them)
order = [split(x, STAGE_SEP) for x in split(config.get(section, 'order'))]
param_selections = []
if config.has_option(section, 'param_selections'):
param_selections = split(config.get(section, 'param_selections'))
detector_name = None
if config.has_option(section, 'detector_name'):
detector_name = config.get(section, 'detector_name')
# Parse [stage.<stage_name>] sections and store to stage_dicts
stage_dicts = OrderedDict()
for stage, service in order:
old_section_header = 'stage%s%s' % (STAGE_SEP, stage)
new_section_header = '%s%s%s' % (stage, STAGE_SEP, service)
if config.has_section(old_section_header):
logging.warning(
'"%s" is an old-style section header, in the future use "%s"' %
(old_section_header, new_section_header))
section = old_section_header
elif config.has_section(new_section_header):
section = new_section_header
else:
raise IOError(
'missing section in cfg for stage "%s" service "%s"' %
(stage, service))
# Instantiate dict to store args to pass to this stage
service_kwargs = OrderedDict()
param_selector = ParamSelector(selections=param_selections)
service_kwargs['params'] = param_selector
n_params = 0
for fullname in config.options(section):
try:
value = config.get(section, fullname)
except:
logging.error(
'Unable to obtain value of option "%s" in section "%s".' %
(fullname, section))
raise
# See if this matches a param specification
param_match = PARAM_RE.match(fullname)
if param_match is not None:
n_params += 1
param_match_dict = param_match.groupdict()
param_subfields = param_match_dict['subfields'].split('.')
# Figure out what the dotted fields represent...
infodict = interpret_param_subfields(subfields=param_subfields)
# If field is an attr, skip since these are located manually
if infodict['attr'] is not None:
continue
# Check if this param already exists in a previous stage; if
# so, make sure there are no specs for this param, but just a
# link to previous the param object that is already
# instantiated.
for kw in stage_dicts.values():
# Stage did not get a `params` argument from config
if not kw.has_key('params'):
continue
# Retrieve the param from the ParamSelector
try:
param = kw['params'].get(name=infodict['pname'],
selector=infodict['selector'])
except KeyError:
continue
# Make sure there are no other specs (in this section) for
# the param defined defined in previous section
for a in PARAM_ATTRS:
if config.has_option(section, '%s.%s' % (fullname, a)):
raise ValueError("Parameter spec. '%s' of '%s' "
"found in section '%s', but "
"parameter exists in previous "
"stage!" % (a, fullname, section))
break
# Param *not* found in a previous stage (i.e., no explicit
# `break` encountered in `for` loop above); therefore must
# instantiate it.
else:
param = parse_param(config=config,
section=section,
selector=infodict['selector'],
fullname=fullname,
pname=infodict['pname'],
value=value)
param_selector.update(param, selector=infodict['selector'])
# If it's not a param spec but contains 'binning', assume it's a
# binning spec for CAKE stages
elif 'binning' in fullname:
service_kwargs[fullname] = binning_dict[value]
# it's gonna be a PI stage
elif '_specs' in fullname:
value = parse_string_literal(value)
# is it None?
if value is None:
service_kwargs[fullname] = value
# is it evts?
elif value in ['evnts', 'events']:
service_kwargs[fullname] = 'events'
# so it gotta be a binning
else:
service_kwargs[fullname] = binning_dict[value]
# it's a list on in/output names list
elif fullname.endswith('_names'):
value = split(value)
service_kwargs[fullname] = value
# Otherwise it's some other stage instantiation argument; identify
# this by its full name and try to interpret and instantiate a
# Python object using the string
else:
try:
value = parse_quantity(value)
value = value.nominal_value * value.units
except ValueError:
value = parse_string_literal(value)
service_kwargs[fullname] = value
# If no params actually specified in config, remove 'params' from the
# service's keyword args
if n_params == 0:
service_kwargs.pop('params')
# Store the service's kwargs to the stage_dicts
stage_dicts[(stage, service)] = service_kwargs
stage_dicts['detector_name'] = detector_name
return stage_dicts
def parse_param(config, section, selector, fullname, pname, value):
"""Parse a param specification from a PISA config file.
Note that if the param sepcification does not include ``fixed``,
``prior``, and/or ``range``, the defaults for these are:
``fixed = True``, ``prior = None``, and ``range = None``.
If a prior is specified explicitly via ``.prior``, this takes precendence,
but if no ``.prior`` is specified and the param's value is parsed to be a
:class:`uncertainties.AffineScalarFunc` (i.e. have `std_dev` attribute), a
Gaussian prior is constructed from that and then the AffineScalarFunc is
stripped out of the param's value (such that it is just a
:class:`~pint.quantity.Quantity`).
Parameters
----------
config : pisa.utils.config_parser.PISAConfigParser
section : string
selector : string or None
fullname : string
pname : string
value : string
Returns
-------
param : pisa.core.param.Param
"""
# Note: imports placed here to avoid circular imports
from pisa.core.param import Param
from pisa.core.prior import Prior
kwargs = dict(name=pname, is_fixed=True, prior=None, range=None)
try:
value = parse_quantity(value)
kwargs['value'] = value.nominal_value * value.units
except ValueError:
value = parse_string_literal(value)
kwargs['value'] = value
# Search for explicit attr specifications
if config.has_option(section, fullname + '.fixed'):
kwargs['is_fixed'] = config.getboolean(section, fullname + '.fixed')
if config.has_option(section, fullname + '.unique_id'):
kwargs['unique_id'] = config.get(section, fullname + '.unique_id')
if config.has_option(section, fullname + '.range'):
range_ = config.get(section, fullname + '.range')
# Note: `nominal` and `sigma` are called out in the `range_` string
if 'nominal' in range_:
nominal = value.n * value.units # pylint: disable=unused-variable
if 'sigma' in range_:
sigma = value.s * value.units # pylint: disable=unused-variable
range_ = range_.replace('[', 'np.array([')
range_ = range_.replace(']', '])')
# Strip out uncertainties from value itself (as we will rely on the
# prior from here on out)
kwargs['range'] = eval(range_).to(value.units) # pylint: disable=eval-used
if config.has_option(section, fullname + '.prior'):
prior = str(config.get(section, fullname + '.prior')).strip().lower()
if prior == 'uniform':
kwargs['prior'] = Prior(kind='uniform')
elif prior == 'jeffreys':
kwargs['prior'] = Prior(kind='jeffreys',
A=kwargs['range'][0],
B=kwargs['range'][1])
elif prior == 'spline':
priorname = pname
if selector is not None:
priorname += '_' + selector
data = config.get(section, fullname + '.prior.data')
data = from_file(data)
data = data[priorname]
knots = ureg.Quantity(np.asarray(data['knots']), data['units'])
knots = knots.to(value.units)
coeffs = np.asarray(data['coeffs'])
deg = data['deg']
kwargs['prior'] = Prior(kind='spline',
knots=knots,
coeffs=coeffs,
deg=deg)
elif prior == 'none':
kwargs['prior'] = None
elif 'gauss' in prior:
raise Exception('Please use new style +/- notation for gaussian'
' priors in config')
else:
raise Exception('Prior type unknown')
elif hasattr(value, 'std_dev') and value.std_dev != 0:
kwargs['prior'] = Prior(kind='gaussian',
mean=value.nominal_value * value.units,
stddev=value.std_dev * value.units)
# Strip out any uncertainties from value itself (an explicit ``.prior``
# specification takes precedence over this)
if hasattr(value, 'std_dev'):
value = value.nominal_value * value.units
try:
param = Param(**kwargs)
except:
logging.error('Failed to instantiate new Param object with kwargs %s',
kwargs)
raise
return param
p_name, value = args.set_param_data.split("=")
print("set param ", p_name, "to ", value, "for data")
value = parse_quantity(value)
data_fixed_param = {p_name: value.n}
value = value.n * value.units
prm = data_maker.params[p_name]
prm.value = value
data_maker.update_params(prm)
data_maker.params.fix(p_name)
analysis = Analysis(data_maker=data_maker,
template_maker=template_maker,
metric=args.metric,
blind=args.blind)
analysis.minimizer_settings = from_file(args.minimizer_settings)
analysis.pseudodata_method = args.pseudo_data
#analysis.randomize_free_params()
results = []
for i in range(args.num_trials):
logging.info('Running trial %i' % i)
np.random.seed()
analysis.generate_psudodata()
if args.function == 'profile':
if args.mode == 'H0':
results.append(
analysis.profile(args.var, [0.] * ureg.dimensionless,
def test_CrossSections(outdir=None):
"""Unit tests for CrossSections class"""
from shutil import rmtree
from tempfile import mkdtemp
remove_dir = False
if outdir is None:
remove_dir = True
outdir = mkdtemp()
try:
# "Standard" location of cross sections file in PISA; retrieve 2.6.4 for
# testing purposes
pisa_xs_file = 'cross_sections/cross_sections.json'
xs = CrossSections(ver='genie_2.6.4', xsec=pisa_xs_file)
# Location of the root file to use (not included in PISA at the moment)
test_dir = expand(os.path.join('/tmp', 'pisa_tests', 'cross_sections'))
#root_xs_file = os.path.join(test_dir, 'genie_2.6.4_simplified.root')
root_xs_file = find_resource(os.path.join(
#'tests', 'data', 'xsec', 'genie_2.6.4_simplified.root'
'cross_sections', 'genie_xsec_H2O.root'
))
# Make sure that the XS newly-imported from ROOT match those stored in
# PISA
if os.path.isfile(root_xs_file):
xs_from_root = CrossSections.new_from_root(root_xs_file,
ver='genie_2.6.4')
logging.info('Found and loaded ROOT source cross sections file %s',
root_xs_file)
#assert xs_from_root.allclose(xs, rtol=1e-7)
# Check XS ratio for numu_cc to numu_cc + numu_nc (user must inspect)
kg0 = NuFlavIntGroup('numu_cc')
kg1 = NuFlavIntGroup('numu_nc')
logging.info(
r'\int_1^80 xs(numu_cc) E^{-1} dE = %e',
xs.get_xs_ratio_integral(kg0, None, e_range=[1, 80], gamma=1)
)
logging.info(
'(int E^{-gamma} * (sigma_numu_cc)/int(sigma_(numu_cc+numu_nc)) dE)'
' / (int E^{-gamma} dE) = %e',
xs.get_xs_ratio_integral(kg0, kg0+kg1, e_range=[1, 80], gamma=1,
average=True)
)
# Check that XS ratio for numu_cc+numu_nc to the same is 1.0
int_val = xs.get_xs_ratio_integral(kg0+kg1, kg0+kg1, e_range=[1, 80],
gamma=1, average=True)
if not recursiveEquality(int_val, 1):
raise ValueError('Integral of nc + cc should be 1.0; get %e'
' instead.' % int_val)
# Check via plot that the
# Plot all cross sections stored in PISA xs file
try:
alldata = from_file(pisa_xs_file)
xs_versions = alldata.keys()
for ver in xs_versions:
xs = CrossSections(ver=ver, xsec=pisa_xs_file)
xs.plot(save=os.path.join(
outdir, 'pisa_' + ver + '_nuxCCNC_H2O_cross_sections.pdf'
))
except ImportError as exc:
logging.debug('Could not plot; possible that matplotlib not'
'installed. ImportError: %s', exc)
finally:
if remove_dir:
rmtree(outdir)
def load_interpolated_hypersurfaces(input_file):
'''
Load a set of interpolated hypersurfaces from a file.
Analogously to "load_hypersurfaces", this function returns a
collection with a HypersurfaceInterpolator object for each Map.
Parameters
----------
input_file : str
A JSON input file as produced by fit_hypersurfaces if interpolation params
were given. It has the form::
{
interpolation_param_spec = {
'param1': {"values": [val1_1, val1_2, ...], "scales_log": True/False}
'param2': {"values": [val2_1, val2_2, ...], "scales_log": True/False}
...
'paramN': {"values": [valN_1, valN_2, ...], "scales_log": True/False}
},
'hs_fits': [
<list of dicts where keys are map names such as 'nue_cc' and values
are hypersurface states>
]
}
Returns
-------
collections.OrderedDict
dictionary with a :obj:`HypersurfaceInterpolator` for each map
'''
assert isinstance(input_file, str)
logging.info(f"Loading interpolated hypersurfaces from file: {input_file}")
# Load the data from the file
input_data = from_file(input_file)
# check the file contents
assert set(['interpolation_param_spec',
'hs_fits']).issubset(set(input_data.keys())), 'missing keys'
# input_data['hs_fits'] is a list of dicts, each dict contains "param_values"
# and "hs_fit"
map_names = None
logging.info("Reading file complete, generating hypersurfaces...")
for hs_fit_dict in input_data['hs_fits']:
# this is still not the actual Hypersurface, but a dict with the (linked)
# maps and the HS fit for the map...
hs_state_maps = hs_fit_dict["hs_fit"]
if map_names is None:
map_names = list(hs_state_maps.keys())
else:
assert set(map_names) == set(
hs_state_maps.keys()), "inconsistent maps"
# When data is recovered from JSON, the object states are not automatically
# converted to the corresponding objects, so we need to do it manually here.
for map_name in map_names:
hs_state_maps[map_name] = Hypersurface.from_state(
hs_state_maps[map_name])
logging.info(f"Read hypersurface maps: {map_names}")
# Now we have a list of dicts where the map names are on the lower level.
# We need to convert this into a dict of HypersurfaceInterpolator objects.
output = collections.OrderedDict()
for m in map_names:
hs_fits = [{
"param_values": fd["param_values"],
"hs_fit": fd['hs_fit'][m]
} for fd in input_data['hs_fits']]
output[m] = HypersurfaceInterpolator(
input_data['interpolation_param_spec'], hs_fits)
return output
def load_and_modify_pipeline_cfg(fit_cfg, section):
"""Load and modify the pipeline config file as specified in that section of the fit
config.
Parameters
----------
fit_cfg : pisa.utils.config_parser.PISAConfigParser
any subclass of :class:`configparser.RawConfigParser` should work as well
section : str
name of the section to extract from the `fit_cfg`
Returns
-------
pipeline_cfg : pisa.utils.config_parser.PISAConfigParser
pipeline config
pipeline_cfg_path : str
path to the pipeline config as it is specified in the fit config
"""
pipeline_cfg_path = fit_cfg.get(section, SYS_SET_OPTION)
other_options = fit_cfg.options(section)
other_options.remove(SYS_SET_OPTION)
pipeline_cfg = from_file(pipeline_cfg_path)
# Get a no-whitespace version of the section names
section_map = {s.strip(): s for s in pipeline_cfg.sections()}
for option in other_options:
set_match = SET_OPTION_RE.match(option)
remove_match = REMOVE_OPTION_RE.match(
option) if not set_match else None
if set_match:
section_spec, set_option = set_match.groups()
no_ws_section_spec = section_spec.strip()
set_option = set_option.strip()
if no_ws_section_spec not in section_map:
logging.debug(
'Adding section [%s] to in-memory copy of pipeline config "%s"',
section_spec,
pipeline_cfg_path,
)
pipeline_cfg.add_section(section_spec)
section_map[no_ws_section_spec] = section_spec
if set_option:
set_value = fit_cfg.get(section, option).strip()
logging.debug(
'Setting section [%s] option "%s = %s" in in-memory'
' copy of pipeline config "%s"',
section_spec,
set_option,
set_value,
pipeline_cfg_path,
)
pipeline_cfg.set(section_map[no_ws_section_spec], set_option,
set_value)
elif remove_match:
section_spec, remove_option = remove_match.groups()
no_ws_section_spec = section_spec.strip()
remove_option = remove_option.strip()
if no_ws_section_spec in section_map:
if remove_option:
logging.debug(
'Removing section [%s] option "%s" from in-memory copy of'
' pipeline config "%s"',
section_spec,
remove_option,
pipeline_cfg_path,
)
pipeline_cfg.remove_option(section_map[no_ws_section_spec],
remove_option)
else:
logging.debug(
"Removing section [%s] from in-memory copy of pipeline config"
' "%s"',
section_spec,
pipeline_cfg_path,
)
pipeline_cfg.remove_section(
section_map[no_ws_section_spec])
else:
logging.warn(
"Told to remove section [%s] but section does not exist in"
' pipline config "%s"',
section_spec,
pipeline_cfg_path,
)
else:
raise ValueError(
"Unhandled option in fit config: {}".format(option))
return pipeline_cfg, pipeline_cfg_path
def load_neutrino_events(config, dataset):
nu_data = []
if dataset == 'neutrinos%sgen_lvl' % SEP:
gen_cfg = from_file(config.get(dataset, 'gen_cfg_file'))
name = gen_cfg.get('general', 'name')
datadir = gen_cfg.get('general', 'datadir')
event_types = split(gen_cfg.get('general', 'event_type'))
weights = split(gen_cfg.get('general', 'weights'))
weight_units = gen_cfg.get('general', 'weight_units')
keep_keys = split(gen_cfg.get('general', 'keep_keys'))
aliases = gen_cfg.items('aliases')
logging.info('Extracting neutrino dataset "{0}" from generator '
'level sample "{1}"'.format(dataset, name))
for idx, flav in enumerate(event_types):
fig = NuFlavIntGroup(flav)
all_flavints = fig.flavints
events_file = datadir + gen_cfg.get(flav, 'filename')
flav_fidg = sample.load_from_nu_file(
events_file, all_flavints, weights[idx], weight_units,
keep_keys, aliases
)
nu_data.append(flav_fidg)
else:
name = config.get('general', 'name')
flavours = split(config.get('neutrinos', 'flavours'))
weights = split(config.get('neutrinos', 'weights'))
weight_units = config.get('neutrinos', 'weight_units')
sys_list = split(config.get('neutrinos', 'sys_list'))
base_prefix = config.get('neutrinos', 'baseprefix')
keep_keys = split(config.get('neutrinos', 'keep_keys'))
aliases = config.items('neutrinos%saliases' % SEP)
logging.info('Extracting neutrino dataset "{0}" from sample '
'"{1}"'.format(dataset, name))
if base_prefix == 'None':
base_prefix = ''
for idx, flav in enumerate(flavours):
f = int(flav)
all_flavints = NuFlavIntGroup(f, -f).flavints
if dataset == 'nominal':
prefixes = []
for sys in sys_list:
ev_sys = 'neutrinos%s%s' % (SEP, sys)
nominal = config.get(ev_sys, 'nominal')
ev_sys_nom = ev_sys + SEP + nominal
prefixes.append(config.get(ev_sys_nom, 'file_prefix'))
if len(set(prefixes)) > 1:
raise AssertionError(
'Choice of nominal file is ambigous. Nominal '
'choice of systematic parameters must coincide '
'with one and only one file. Options found are: '
'{0}'.format(prefixes)
)
file_prefix = flav + prefixes[0]
else:
file_prefix = flav + config.get(dataset, 'file_prefix')
events_file = path.join( config.get('general', 'datadir'), base_prefix + file_prefix )
flav_fidg = sample.load_from_nu_file(
events_file, all_flavints, weights[idx], weight_units,
keep_keys, aliases
)
nu_data.append(flav_fidg)
nu_data = Data(
reduce(add, nu_data),
metadata={'name': name, 'sample': dataset}
)
return nu_data
def main():
args = parse_args()
set_verbosity(args.v)
if args.plot:
import matplotlib as mpl
mpl.use('pdf')
import matplotlib.pyplot as plt
from pisa.utils.plotter import Plotter
cfg = from_file(args.fit_settings)
sys_list = cfg.get('general', 'sys_list').replace(' ', '').split(',')
stop_idx = cfg.getint('general', 'stop_after_stage')
for sys in sys_list:
# Parse info for given systematic
nominal = cfg.getfloat(sys, 'nominal')
degree = cfg.getint(sys, 'degree')
force_through_nominal = cfg.getboolean(sys, 'force_through_nominal')
runs = eval(cfg.get(sys, 'runs'))
#print "runs ", runs
smooth = cfg.get(sys, 'smooth')
x_values = np.array(sorted(runs))
# Build fit function
if force_through_nominal:
function = "lambda x, *p: np.polynomial.polynomial.polyval(x, [1.] + list(p))"
else:
function = "lambda x, *p: np.polynomial.polynomial.polyval(x, list(p))"
# Add free parameter for constant term
degree += 1
fit_fun = eval(function)
# Instantiate template maker
template_maker = Pipeline(args.template_settings)
if not args.set_param == '':
for one_set_param in args.set_param:
p_name, value = one_set_param.split("=")
#print "p_name,value= ", p_name, " ", value
value = parse_quantity(value)
value = value.n * value.units
param = template_maker.params[p_name]
#print "old ", p_name, "value = ", param.value
param.value = value
#print "new ", p_name, "value = ", param.value
template_maker.update_params(param)
inputs = {}
map_names = None
# Get sys templates
for run in runs:
for key, val in cfg.items('%s:%s'%(sys, run)):
if key.startswith('param.'):
_, pname = key.split('.')
param = template_maker.params[pname]
try:
value = parse_quantity(val)
param.value = value.n * value.units
except ValueError:
value = parse_string_literal(val)
param.value = value
param.set_nominal_to_current_value()
template_maker.update_params(param)
# Retreive maps
template = template_maker.get_outputs(idx=stop_idx)
if map_names is None: map_names = [m.name for m in template]
inputs[run] = {}
for m in template:
inputs[run][m.name] = m.hist
# Numpy acrobatics:
arrays = {}
for name in map_names:
arrays[name] = []
for x in x_values:
arrays[name].append(
inputs[x][name] / unp.nominal_values(inputs[nominal][name])
)
a = np.array(arrays[name])
arrays[name] = np.rollaxis(a, 0, len(a.shape))
# Shift to get deltas
x_values -= nominal
# Binning object (assuming they're all the same)
binning = template.maps[0].binning
shape = [d.num_bins for d in binning] + [degree]
shape_small = [d.num_bins for d in binning]
outputs = {}
errors = {}
for name in map_names:
# Now actualy perform some fits
outputs[name] = np.ones(shape)
errors[name] = np.ones(shape)
for idx in np.ndindex(*shape_small):
y_values = unp.nominal_values(arrays[name][idx])
y_sigma = unp.std_devs(arrays[name][idx])
if np.any(y_sigma):
popt, pcov = curve_fit(fit_fun, x_values, y_values,
sigma=y_sigma, p0=np.ones(degree))
else:
popt, pcov = curve_fit(fit_fun, x_values, y_values,
p0=np.ones(degree))
perr = np.sqrt(np.diag(pcov))
for k, p in enumerate(popt):
outputs[name][idx][k] = p
errors[name][idx][k] = perr[k]
# TODO(philippeller): the below block of code will fail
# Maybe plot
#if args.plot:
# fig_num = i + nx * j
# if fig_num == 0:
# fig = plt.figure(num=1, figsize=( 4*nx, 4*ny))
# subplot_idx = nx*(ny-1-j)+ i + 1
# plt.subplot(ny, nx, subplot_idx)
# #plt.snameter(x_values, y_values, color=plt_colors[name])
# plt.gca().errorbar(x_values, y_values, yerr=y_sigma,
# fmt='o', color=plt_colors[name],
# ecolor=plt_colors[name],
# mec=plt_colors[name])
# # Plot nominal point again in black
# plt.snameter([0.0], [1.0], color='k')
# f_values = fit_fun(x_values, *popt)
# fun_plot, = plt.plot(x_values, f_values,
# color=plt_colors[name])
# plt.ylim(np.min(unp.nominal_values(arrays[name]))*0.9,
# np.max(unp.nominal_values(arrays[name]))*1.1)
# if i > 0:
# plt.setp(plt.gca().get_yticklabels(), visible=False)
# if j > 0:
# plt.setp(plt.gca().get_xticklabels(), visible=False)
if smooth == 'gauss':
for name in map_names:
for d in range(degree):
outputs[name][...,d] = gaussian_filter(outputs[name][...,d],sigma=1)
if smooth == 'gauss_pid':
for name in map_names:
split_idx = binning.names.index('pid')
tot = len(binning)-1
for d in range(degree):
for p in range(len(binning['pid'])):
outputs[name][...,p,d] = gaussian_filter(
np.swapaxes(outputs[name], split_idx, tot)[...,p,d],
sigma=1
)
outputs[name] = np.swapaxes(outputs[name], split_idx, tot)
# Save the raw ones anyway
outputs['pname'] = sys
outputs['nominal'] = nominal
outputs['function'] = function
outputs['map_names'] = map_names
outputs['binning_hash'] = binning.hash
to_file(outputs, '%s/%s_sysfits_%s_%s.json'%(args.out_dir, sys,
args.tag, smooth))
if args.plot:
for d in range(degree):
maps = []
for name in map_names:
maps.append(Map(name='%s_raw'%name, hist=outputs[name][...,d],
binning=binning))
maps = MapSet(maps)
my_plotter = Plotter(
stamp='',
outdir=args.out_dir,
fmt='pdf',
log=False,
label=''
)
my_plotter.plot_2d_array(
maps,
fname='%s_%s_%s_%s'%(sys, args.tag, d, smooth),
)
def load_events_file(self, events_file, variable_mapping=None):
"""Fill this events container from an input HDF5 file filled with event
data Optionally can provide a variable mapping so select a subset of
variables, rename them, etc.
Parameters
----------
events_file : string or mapping
If string, interpret as a path and load file at that path; the
loaded object should be a mapping. If already a mapping, take and
interpret events from that.
variable_mapping : mapping, optional
If specified, should be a mapping where the keys are the
destination variable names and the items are either the source
variable names or an iterable of source variables names. In the
latter case, each of the specified source variables will become a
column vector in the destination array.
"""
# Validate `events_file`
if not isinstance(events_file, (str, Mapping)):
raise TypeError(
"`events_file` must be either string or mapping; got (%s)"
% type(events_file)
)
# Validate `variable_mapping`
if variable_mapping is not None:
if not isinstance(variable_mapping, Mapping):
raise TypeError("'variable_mapping' must be a mapping (e.g., dict)")
for dst, src in variable_mapping.items():
if not isinstance(dst, str):
raise TypeError("`variable_mapping` 'dst' (key) must be a string")
if isinstance(src, str):
pass # Nothing to do
elif isinstance(src, Iterable):
for v in src:
if not isinstance(v, str):
raise TypeError(
"`variable_mapping` 'src' (value) has at least"
" one element that is not a string"
)
else:
raise TypeError(
"`variable_mapping` 'src' (value) must be a string or"
" an iterable of strings"
)
if isinstance(events_file, str):
input_data = from_file(events_file)
if not isinstance(input_data, Mapping):
raise TypeError(
'Contents loaded from "%s" must be a mapping; got: %s'
% (events_file, type(input_data))
)
else: # isinstance(events_file, Mapping)
input_data = events_file
# Events and EventsPi objects have attr `metadata`
metadata = getattr(input_data, 'metadata', None)
# HDF files have attr `attrs` attached, if present (see pisa.utils.hdf)
if not metadata:
metadata = getattr(input_data, 'attrs', None)
if metadata:
if not isinstance(metadata, Mapping):
raise TypeError(
"metadata or attrs expected to be a Mapping, but got {}".format(
type(metadata)
)
)
# TODO: events.py calls `tolist` method on all values that have
# that method (e.g., convert numpy arrays to lists). Why? Is this
# necessary? Should we do that here, too?
self.metadata.update(metadata)
#
# Re-format inputs
#
# The following is intended to re-format input data into the desired
# format. This is required to handle various inout cases and to ensure
# backwards compatibility with older input file formats.
# Convert to the required event keys, e.g. "numu_cc", "nutaubar_nc", etc.
if self.neutrinos:
input_data = split_nu_events_by_flavor_and_interaction(input_data)
# The value for each category should itself be a dict of the event
# variables, where each entry is has a variable name as the key and an
# np.array filled once per event as the value.
#
# For backwards compatibility, convert to this format from known older
# formats first
if self.neutrinos:
for key, cat_dict in input_data.items():
if not isinstance(cat_dict, Mapping):
raise Exception(
"'%s' input data is not a mapping, unknown format (%s)"
% (key, type(cat_dict))
)
for var_key, var_data in cat_dict.items():
if not isinstance(var_data, np.ndarray):
raise Exception(
"'%s/%s' input data is not a numpy array, unknown"
" format (%s)" % (key, var_key, type(var_data))
)
# Ensure backwards compatibility with the old style "oppo" flux
# variables
if self.neutrinos:
fix_oppo_flux(input_data)
#
# Load the event data
#
# Should be organised under a single layer of keys, each representing
# some category of input data
# Loop over the input types
for data_key in input_data.keys():
if data_key in self:
raise ValueError(
"Key '%s' has already been added to this data structure"
)
self[data_key] = OrderedDict()
# Loop through variable mapping
# If none provided, just use all variables and keep the input names
if variable_mapping is None:
variable_mapping_to_use = tuple(
zip(input_data[data_key].keys(), input_data[data_key].keys())
)
else:
variable_mapping_to_use = variable_mapping.items()
# Get the array data (stacking if multiple input variables defined)
# and check the variable exists in the input data
for var_dst, var_src in variable_mapping_to_use:
# TODO What about non-float data? Use dtype...
array_data = None
if isinstance(var_src, str):
var_src = [var_src]
array_data_to_stack = []
for var in var_src:
if var in input_data[data_key]:
array_data_to_stack.append(
input_data[data_key][var].astype(FTYPE)
)
else:
raise KeyError(
"Variable '%s' cannot be found for '%s' events"
% (var, data_key)
)
# Note `squeeze` removes the extraneous 2nd dim in case of a
# single `src`
array_data = np.squeeze(np.stack(array_data_to_stack, axis=1))
# Add each array to the event
# TODO Memory copies?
if array_data is None:
raise ValueError(
"Cannot find source variable(s) '%s' for '%s'"
% (var_src, data_key)
)
else:
# Down sample events if required
if self.fraction_events_to_keep is not None:
rand = np.random.RandomState(123456) # Enforce same sample each time
num_events_to_keep = int(np.round(self.fraction_events_to_keep*float(array_data.size)))
array_data = rand.choice(array_data, size=num_events_to_keep, replace=False)
# Add to array
self[data_key][var_dst] = array_data
def reweight(self):
"""Main rewighting function."""
this_hash = hash_obj([self.weight_hash, self.params.values_hash],
full_hash=self.full_hash)
if this_hash == self.fit_hash:
return
fit_coeffs = self.calculate_fit_coeffs()
sample_config = from_file(self.params['discr_sys_sample_config'].value)
degree = int(self.params['poly_degree'].value)
force_through_nominal = self.params['force_through_nominal'].value
if force_through_nominal:
def fit_func(vals, *poly_coeffs):
return np.polynomial.polynomial.polyval(
vals, [1.] + list(poly_coeffs))
else:
def fit_func(vals, *poly_coeffs):
return np.polynomial.polynomial.polyval(
vals, list(poly_coeffs))
# add free param for constant term
degree += 1
def parse(string):
return string.replace(' ', '').split(',')
if self.neutrinos:
sys_list = parse(sample_config.get('neutrinos', 'sys_list'))
for fig in self._data.keys():
self._data[fig]['fit_weight'] = \
deepcopy(self._data[fig]['weight_weight'])
for sys in sys_list:
nominal = sample_config.get('neutrinos|' + sys, 'nominal')
for fig in self._data.keys():
fit_map = unp.nominal_values(fit_coeffs[sys][fig].hist)
if self.params['smoothing'].value == 'gauss':
# TODO(shivesh): new MapSet functions?
for d in range(degree):
fit_map[..., d] = gaussian_filter(fit_map[..., d],
sigma=1)
shape = self.fit_binning.shape
transform = np.ones(shape)
sys_offset = self.params['nu_' +
sys].value.m - float(nominal)
for idx in np.ndindex(shape):
transform[idx] *= fit_func(sys_offset, *fit_map[idx])
hist_idxs = self._data.digitize(
kinds=fig,
binning=self.fit_binning,
)
# Discrete systematics reweighting
# TODO(shivesh): speedup this
for idx, wght in enumerate(
np.nditer(self._data[fig]['fit_weight'],
op_flags=['readwrite'])):
idx_slice = tuple(hist_idxs[idx])
if shape[0] == 0 or shape[1] == 0 or \
idx_slice[0] > shape[0] or idx_slice[1] > shape[1]:
# Outside binning range
wght *= 0
else:
wght *= transform[tuple([x - 1
for x in idx_slice])]
for fig in self._data.keys():
self._data[fig]['pisa_weight'] = \
deepcopy(self._data[fig]['fit_weight'])
if self.muons:
sys_list = parse(sample_config.get('muons', 'sys_list'))
self._data['muons']['fit_weight'] = \
deepcopy(self._data['muons']['weight_weight'])
for sys in sys_list:
fit_map = unp.nominal_values(fit_coeffs[sys]['muons'].hist)
if self.params['smoothing'].value == 'gauss':
# TODO(shivesh): new MapSet functions?
for d in range(degree):
fit_map[..., d] = gaussian_filter(fit_map[..., d],
sigma=1)
shape = self.fit_binning.shape
transform = np.ones(shape)
for idx in np.ndindex(shape):
transform[idx] *= fit_func(self.params['mu_' + sys].value,
*fit_map[idx])
hist_idxs = self._data.digitize(
kinds='muons',
binning=self.fit_binning,
)
# Discrete systematics reweighting
for idx, wght in enumerate(self._data['muons']['fit_weight']):
idx_slice = tuple(hist_idxs[idx])
if shape[0] == 0 or shape[1] == 0 or \
idx_slice[0] > shape[0] or idx_slice[1] > shape[1]:
# Outside binning range
wght *= 0
else:
wght *= transform[tuple([x - 1 for x in idx_slice])]
self._data['muons']['pisa_weight'] = \
deepcopy(self._data['muons']['fit_weight'])
self.fit_hash = this_hash
self._data.metadata['fit_hash'] = self.fit_hash
self._data.update_hash()
def fromFile(cls, filename):
"""
Load a Fisher matrix from a json file
"""
return cls(**from_file(filename))
def _calculate_fit_coeffs(data,
params,
fit_binning,
nu_params=None,
mu_params=None):
"""
Calculate the fit coefficients for each systematic, flavint,
bin for a polynomial.
"""
logging.debug('Calculating fit coefficients')
config = from_file(params['discr_sys_sample_config'].value)
degree = int(params['poly_degree'].value)
force_through_nominal = params['force_through_nominal'].value
if force_through_nominal:
def fit_func(vals, *poly_coeffs):
return np.polynomial.polynomial.polyval(
vals, [1.] + list(poly_coeffs))
else:
def fit_func(vals, *poly_coeffs):
return np.polynomial.polynomial.polyval(
vals, list(poly_coeffs))
# add free param for constant term
degree += 1
template_maker = Pipeline(params['pipeline_config'].value)
dataset_param = template_maker.params['dataset']
def parse(string):
return string.replace(' ', '').split(',')
sys_fit_coeffs = OrderedDict()
if nu_params is not None:
sys_list = parse(config.get('neutrinos', 'sys_list'))
nu_params = deepcopy(map(lambda x: x[3:], nu_params))
if set(nu_params) != set(sys_list):
raise AssertionError(
'Systematics list listed in the sample config file does '
'not match the params in the pipeline config file\n {0} '
'!= {1}'.format(set(nu_params), set(sys_list)))
for sys in sys_list:
ev_sys = 'neutrinos|' + sys
runs = parse(config.get(ev_sys, 'runs')[1:-1])
nominal = config.get(ev_sys, 'nominal')
mapset_dict = OrderedDict()
flavint_groups = None
for run in runs:
logging.info('Loading run {0} of systematic '
'{1}'.format(run, sys))
dataset_param.value = ev_sys + '|' + run
template_maker.update_params(dataset_param)
template = template_maker.get_outputs(
idx=int(params['stop_after_stage'].m))
if not isinstance(template, Data):
raise AssertionError(
'Template output is not a Data object, instead is '
'type {0}'.format(type(template)))
if flavint_groups is None:
flavint_groups = template.flavint_groups
else:
if set(flavint_groups) != set(template.flavint_groups):
raise AssertionError(
'Mismatch of flavint_groups - ({0}) does not '
'match flavint_groups '
'({1})'.format(flavint_groups,
template.flavint_groups))
outputs = []
for fig in template.keys():
outputs.append(
template.histogram(kinds=fig,
binning=fit_binning,
weights_col='pisa_weight',
errors=False,
name=str(NuFlavIntGroup(fig))))
mapset_dict[run] = MapSet(outputs, name=run)
nom_mapset = mapset_dict[nominal]
fracdiff_mapset_dict = OrderedDict()
for run in runs:
mapset = []
for flavintg_map in mapset_dict[run]:
# TODO(shivesh): error propagation?
flavintg = flavintg_map.name
mask = ~(nom_mapset[flavintg].hist == 0.)
div = np.zeros(flavintg_map.shape)
with np.errstate(divide='ignore', invalid='ignore'):
div[mask] = \
unp.nominal_values(flavintg_map.hist[mask]) /\
unp.nominal_values(nom_mapset[flavintg].hist[mask])
mapset.append(
Map(name=flavintg,
binning=flavintg_map.binning,
hist=div))
fracdiff_mapset_dict[run] = MapSet(mapset)
delta_runs = np.array([float(x)
for x in runs]) - float(nominal)
coeff_binning = OneDimBinning(name='coeff',
num_bins=degree,
is_lin=True,
domain=[-1, 1])
combined_binning = fit_binning + coeff_binning
params_mapset = []
for fig in template.keys():
# TODO(shivesh): Fix numpy warning on this line
pvals_hist = np.empty(map(int, combined_binning.shape),
dtype=object)
hists = [
fracdiff_mapset_dict[run][fig].hist for run in runs
]
zip_hists = np.dstack(hists)
for idx in np.ndindex(fit_binning.shape):
y_values = []
y_sigma = []
for run in fracdiff_mapset_dict:
y_values.append(
unp.nominal_values(
fracdiff_mapset_dict[run][fig].hist[idx]))
y_sigma.append(
unp.std_devs(
fracdiff_mapset_dict[run][fig].hist[idx]))
if np.any(y_sigma):
popt, pcov = curve_fit(fit_func,
delta_runs,
y_values,
sigma=y_sigma,
p0=np.ones(degree))
else:
popt, pcov = curve_fit(fit_func,
delta_runs,
y_values,
p0=np.ones(degree))
# perr = np.sqrt(np.diag(pcov))
# pvals = unp.uarray(popt, perr)
pvals_hist[idx] = popt
pvals_hist = np.array(pvals_hist.tolist())
params_mapset.append(
Map(name=fig,
binning=combined_binning,
hist=pvals_hist))
params_mapset = MapSet(params_mapset, name=sys)
if sys in sys_fit_coeffs:
sys_fit_coeffs[sys] = MapSet(
[sys_fit_coeffs[sys], params_mapset])
else:
sys_fit_coeffs[sys] = params_mapset
if mu_params is not None:
sys_list = parse(config.get('muons', 'sys_list'))
mu_params = deepcopy(map(lambda x: x[3:], mu_params))
if set(mu_params) != set(sys_list):
raise AssertionError(
'Systematics list listed in the sample config file does '
'not match the params in the pipeline config file\n {0} '
'!= {1}'.format(set(mu_params), set(sys_list)))
for sys in sys_list:
ev_sys = 'muons|' + sys
runs = parse(config.get(ev_sys, 'runs')[1:-1])
nominal = config.get(ev_sys, 'nominal')
map_dict = OrderedDict()
flavint_groups = None
for run in runs:
logging.info('Loading run {0} of systematic '
'{1}'.format(run, sys))
dataset_param.value = ev_sys + '|' + run
template_maker.update_params(dataset_param)
template = template_maker.get_outputs(
idx=int(params['stop_after_stage'].m))
if not isinstance(template, Data):
raise AssertionError(
'Template output is not a Data object, instead is '
'type {0}'.format(type(template)))
if not template.contains_muons:
raise AssertionError(
'Template output does not contain muons')
output = template.histogram(
kinds='muons',
binning=fit_binning,
# NOTE: weights cancel in fraction
weights_col=None,
errors=False,
name='muons')
map_dict[run] = output
nom_map = map_dict[nominal]
fracdiff_map_dict = OrderedDict()
for run in runs:
mask = ~(nom_map.hist == 0.)
div = np.zeros(nom_map.shape)
with np.errstate(divide='ignore', invalid='ignore'):
div[mask] = \
unp.nominal_values(map_dict[run].hist[mask]) /\
unp.nominal_values(nom_map.hist[mask])
fracdiff_map_dict[run] = Map(name='muons',
binning=nom_map.binning,
hist=div)
delta_runs = np.array([float(x)
for x in runs]) - float(nominal)
coeff_binning = OneDimBinning(name='coeff',
num_bins=degree,
is_lin=True,
domain=[-1, 1])
combined_binning = fit_binning + coeff_binning
pvals_hist = np.empty(map(int, combined_binning.shape),
dtype=object)
hists = [fracdiff_map_dict[run].hist for run in runs]
zip_hists = np.dstack(hists)
for idx in np.ndindex(fit_binning.shape):
y_values = []
y_sigma = []
for run in fracdiff_mapset_dict:
y_values.append(
unp.nominal_values(
fracdiff_mapset_dict[run][fig].hist[idx]))
y_sigma.append(
unp.std_devs(
fracdiff_mapset_dict[run][fig].hist[idx]))
if np.any(y_sigma):
popt, pcov = curve_fit(fit_func,
delta_runs,
y_values,
sigma=y_sigma,
p0=np.ones(degree))
else:
popt, pcov = curve_fit(fit_func,
delta_runs,
y_values,
p0=np.ones(degree))
# perr = np.sqrt(np.diag(pcov))
# pvals = unp.uarray(popt, perr)
pvals_hist[idx] = popt
pvals_hist = np.array(pvals_hist.tolist())
params_map = Map(name='muons',
binning=combined_binning,
hist=pvals_hist)
if sys in sys_fit_coeffs:
sys_fit_coeffs[sys] = MapSet(
[sys_fit_coeffs[sys], params_map])
else:
sys_fit_coeffs[sys] = params_map
return sys_fit_coeffs
'--infile',
type=str,
required=True
)
parser.add_argument(
'--outfile',
type=str,
required=True
)
args = parser.parse_args()
import sys, os, re, traceback, time, warnings, itertools
import copy
#from pisa.utils import utils as putils
from pisa.utils.fileio import from_file, to_file
from pisa.utils import params as ppars
from pisa.utils import utils as putils
ts0 = from_file(args.infile)
ts1 = copy.deepcopy(ts0)
for paramname, param in sorted(ts0['params'].iteritems()):
new_prior = ppars.Prior.from_param(param)
if new_prior is None:
continue
print 'Converting prior for param `' + paramname + '`'
new_param = copy.deepcopy(param)
new_param.update(new_prior.build_dict())
ts1['params'][paramname] = new_param
to_file(ts1, args.outfile)