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wistful/pympris | pympris/Player.py | Player.SetPosition | def SetPosition(self, track_id, position):
"""Sets the current track position in microseconds.
:param str track_id: The currently playing track's identifier.
:param int position: Track position in microseconds.
This must be between 0 and <track_length>.
If ... | python | def SetPosition(self, track_id, position):
"""Sets the current track position in microseconds.
:param str track_id: The currently playing track's identifier.
:param int position: Track position in microseconds.
This must be between 0 and <track_length>.
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inveniosoftware-contrib/json-merger | json_merger/comparator.py | BaseComparator.process_lists | def process_lists(self):
"""Do any preprocessing of the lists."""
for l1_idx, obj1 in enumerate(self.l1):
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if self.equal(obj1, obj2):
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"""Do any preprocessing of the lists."""
for l1_idx, obj1 in enumerate(self.l1):
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inveniosoftware-contrib/json-merger | json_merger/comparator.py | BaseComparator.get_matches | def get_matches(self, src, src_idx):
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ldomic/lintools | lintools/data.py | Data.find_the_closest_atoms | def find_the_closest_atoms(self,topology):
"""
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"""
# The measurements are made to ligand molecule without hydrogen atoms (ligand_noH) because the
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This function defines the ligand atoms that are closest to the residues that will be plotted
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ldomic/lintools | lintools/data.py | Data.load_data | def load_data(self, topology, mol_file, ligand_name, offset=0):
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ldomic/lintools | lintools/data.py | Data.analyse_topology | def analyse_topology(self,topology, cutoff=3.5):
"""
In case user wants to analyse only a single topology file, this process will determine the residues
that should be plotted and find the ligand atoms closest to these residues.
"""
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In case user wants to analyse only a single topology file, this process will determine the residues
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robinandeer/puzzle | puzzle/utils/headers.py | get_header | def get_header(vcf_file_path):
"""Parse the header and return a header object
Args:
vcf_file_path(str): Path to vcf
Returns:
head: A HeaderParser object
"""
logger.info("Parsing header of file {0}".format(vcf_file_path))
head = HeaderParser()
handle = get_vc... | python | def get_header(vcf_file_path):
"""Parse the header and return a header object
Args:
vcf_file_path(str): Path to vcf
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head: A HeaderParser object
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DarkEnergySurvey/ugali | ugali/analysis/kernel.py | EllipticalKernel.sample_lonlat | def sample_lonlat(self, n):
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"""
Sample 2D distribution of points in lon, lat
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# From http://en.wikipedia.org/wiki/Ellipse#General_parametric_form
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alphagov/performanceplatform-collector | performanceplatform/collector/ga/plugins/aggregate.py | group | def group(iterable, key):
"""
groupby which sorts the input, discards the key and returns the output
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for _, grouped in groupby(sorted(iterable, key=key), key=key):
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alphagov/performanceplatform-collector | performanceplatform/collector/ga/plugins/aggregate.py | aggregate_count | def aggregate_count(keyname):
"""
Straightforward sum of the given keyname.
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def inner(docs):
return sum(doc[keyname] for doc in docs)
return keyname, inner | python | def aggregate_count(keyname):
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Straightforward sum of the given keyname.
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alphagov/performanceplatform-collector | performanceplatform/collector/ga/plugins/aggregate.py | aggregate_rate | def aggregate_rate(rate_key, count_key):
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Compute an aggregate rate for `rate_key` weighted according to
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alphagov/performanceplatform-collector | performanceplatform/collector/ga/plugins/aggregate.py | make_aggregate | def make_aggregate(docs, aggregations):
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Given `docs` and `aggregations` return a single document with the
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MasterKale/django-cra-helper | cra_helper/templatetags/cra_helper_tags.py | json | def json(value):
'''
Sanitize the JSON string using the Bleach HTML tag remover
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Sanitize the JSON string using the Bleach HTML tag remover
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cfusting/fastgp | fastgp/algorithms/afpo.py | find_pareto_front | def find_pareto_front(population):
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pareto_front = set(range(len(population)))
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... | python | def find_pareto_front(population):
"""Finds a subset of nondominated individuals in a given list
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_abs | def fn_abs(self, value):
"""
Return the absolute value of a number.
:param value: The number.
:return: The absolute value of the number.
"""
if is_ndarray(value):
return numpy.absolute(value)
else:
return abs(value) | python | def fn_abs(self, value):
"""
Return the absolute value of a number.
:param value: The number.
:return: The absolute value of the number.
"""
if is_ndarray(value):
return numpy.absolute(value)
else:
return abs(value) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_get_mask | def fn_get_mask(self, value):
"""
Return an array mask.
:param value: The array.
:return: The array mask.
"""
value = self._to_ndarray(value)
if numpy.ma.is_masked(value):
return value.mask
else:
return numpy.zeros(value.shape).a... | python | def fn_get_mask(self, value):
"""
Return an array mask.
:param value: The array.
:return: The array mask.
"""
value = self._to_ndarray(value)
if numpy.ma.is_masked(value):
return value.mask
else:
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_min | def fn_min(self, a, axis=None):
"""
Return the minimum of an array, ignoring any NaNs.
:param a: The array.
:return: The minimum value of the array.
"""
return numpy.nanmin(self._to_ndarray(a), axis=axis) | python | def fn_min(self, a, axis=None):
"""
Return the minimum of an array, ignoring any NaNs.
:param a: The array.
:return: The minimum value of the array.
"""
return numpy.nanmin(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_max | def fn_max(self, a, axis=None):
"""
Return the maximum of an array, ignoring any NaNs.
:param a: The array.
:return: The maximum value of the array
"""
return numpy.nanmax(self._to_ndarray(a), axis=axis) | python | def fn_max(self, a, axis=None):
"""
Return the maximum of an array, ignoring any NaNs.
:param a: The array.
:return: The maximum value of the array
"""
return numpy.nanmax(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_median | def fn_median(self, a, axis=None):
"""
Compute the median of an array, ignoring NaNs.
:param a: The array.
:return: The median value of the array.
"""
return numpy.nanmedian(self._to_ndarray(a), axis=axis) | python | def fn_median(self, a, axis=None):
"""
Compute the median of an array, ignoring NaNs.
:param a: The array.
:return: The median value of the array.
"""
return numpy.nanmedian(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_mean | def fn_mean(self, a, axis=None):
"""
Compute the arithmetic mean of an array, ignoring NaNs.
:param a: The array.
:return: The arithmetic mean of the array.
"""
return numpy.nanmean(self._to_ndarray(a), axis=axis) | python | def fn_mean(self, a, axis=None):
"""
Compute the arithmetic mean of an array, ignoring NaNs.
:param a: The array.
:return: The arithmetic mean of the array.
"""
return numpy.nanmean(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_std | def fn_std(self, a, axis=None):
"""
Compute the standard deviation of an array, ignoring NaNs.
:param a: The array.
:return: The standard deviation of the array.
"""
return numpy.nanstd(self._to_ndarray(a), axis=axis) | python | def fn_std(self, a, axis=None):
"""
Compute the standard deviation of an array, ignoring NaNs.
:param a: The array.
:return: The standard deviation of the array.
"""
return numpy.nanstd(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_var | def fn_var(self, a, axis=None):
"""
Compute the variance of an array, ignoring NaNs.
:param a: The array.
:return: The variance of the array.
"""
return numpy.nanvar(self._to_ndarray(a), axis=axis) | python | def fn_var(self, a, axis=None):
"""
Compute the variance of an array, ignoring NaNs.
:param a: The array.
:return: The variance of the array.
"""
return numpy.nanvar(self._to_ndarray(a), axis=axis) | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_ceil | def fn_ceil(self, value):
"""
Return the ceiling of a number.
:param value: The number.
:return: The ceiling of the number.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return numpy.ceil(self._to_ndarray(value))
else:
ret... | python | def fn_ceil(self, value):
"""
Return the ceiling of a number.
:param value: The number.
:return: The ceiling of the number.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return numpy.ceil(self._to_ndarray(value))
else:
ret... | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_int | def fn_int(self, value):
"""
Return the value cast to an int.
:param value: The number.
:return: The number as an int.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return self._to_ndarray(value).astype('int')
else:
return... | python | def fn_int(self, value):
"""
Return the value cast to an int.
:param value: The number.
:return: The number as an int.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return self._to_ndarray(value).astype('int')
else:
return... | [
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consbio/ncdjango | ncdjango/geoprocessing/evaluation.py | Parser.fn_float | def fn_float(self, value):
"""
Return the value cast to a float.
:param value: The number.
:return: The number as a float.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return self._to_ndarray(value).astype('float')
else:
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"""
Return the value cast to a float.
:param value: The number.
:return: The number as a float.
"""
if is_ndarray(value) or isinstance(value, (list, tuple)):
return self._to_ndarray(value).astype('float')
else:
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totalgood/pugnlp | src/pugnlp/tutil.py | make_datetime | def make_datetime(dt, date_parser=parse_date):
"""Coerce a datetime or string into datetime.datetime object
Arguments:
dt (str or datetime.datetime or atetime.time or numpy.Timestamp): time or date
to be coerced into a `datetime.date` object
Returns:
datetime.time: Time of day portion ... | python | def make_datetime(dt, date_parser=parse_date):
"""Coerce a datetime or string into datetime.datetime object
Arguments:
dt (str or datetime.datetime or atetime.time or numpy.Timestamp): time or date
to be coerced into a `datetime.date` object
Returns:
datetime.time: Time of day portion ... | [
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totalgood/pugnlp | src/pugnlp/tutil.py | quantize_datetime | def quantize_datetime(dt, resolution=None):
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Also works with a `datetime.timetuple` or `time.struct_time` or a 1to9-tuple of ints or floats.
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>... | python | def quantize_datetime(dt, resolution=None):
"""Quantize a datetime to integer years, months, days, hours, minutes, seconds or microseconds
Also works with a `datetime.timetuple` or `time.struct_time` or a 1to9-tuple of ints or floats.
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totalgood/pugnlp | src/pugnlp/tutil.py | timetag_str | def timetag_str(dt=None, sep='-', filler='0', resolution=6):
"""Generate a date-time tag suitable for appending to a file name.
>>> timetag_str(resolution=3) == '-'.join('{0:02d}'.format(i) for i in
... tuple(datetime.datetime.now().timetuple()[:3]))
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>>> timetag_str(datetime.dateti... | python | def timetag_str(dt=None, sep='-', filler='0', resolution=6):
"""Generate a date-time tag suitable for appending to a file name.
>>> timetag_str(resolution=3) == '-'.join('{0:02d}'.format(i) for i in
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totalgood/pugnlp | src/pugnlp/tutil.py | make_tz_aware | def make_tz_aware(dt, tz='UTC', is_dst=None):
"""Add timezone information to a datetime object, only if it is naive.
>>> make_tz_aware(datetime.datetime(2001, 9, 8, 7, 6))
datetime.datetime(2001, 9, 8, 7, 6, tzinfo=<UTC>)
>>> make_tz_aware(['2010-01-01'], 'PST')
[datetime.datetime(2010, 1, 1, 0, 0,... | python | def make_tz_aware(dt, tz='UTC', is_dst=None):
"""Add timezone information to a datetime object, only if it is naive.
>>> make_tz_aware(datetime.datetime(2001, 9, 8, 7, 6))
datetime.datetime(2001, 9, 8, 7, 6, tzinfo=<UTC>)
>>> make_tz_aware(['2010-01-01'], 'PST')
[datetime.datetime(2010, 1, 1, 0, 0,... | [
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hootnot/postcode-api-wrapper | postcodepy/typedefs.py | translate_addresstype | def translate_addresstype(f):
"""decorator to translate the addressType field.
translate the value of the addressType field of the API response into a
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"""
@wraps(f)
def wr(r, pc):
at = r["addressType"]
try:
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"""decorator to translate the addressType field.
translate the value of the addressType field of the API response into a
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"""
@wraps(f)
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consbio/ncdjango | ncdjango/interfaces/data/classify.py | quantile | def quantile(data, num_breaks):
"""
Calculate quantile breaks.
Arguments:
data -- Array of values to classify.
num_breaks -- Number of breaks to perform.
"""
def scipy_mquantiles(a, prob=list([.25,.5,.75]), alphap=.4, betap=.4, axis=None, limit=()):
""" function copied from scipy 0... | python | def quantile(data, num_breaks):
"""
Calculate quantile breaks.
Arguments:
data -- Array of values to classify.
num_breaks -- Number of breaks to perform.
"""
def scipy_mquantiles(a, prob=list([.25,.5,.75]), alphap=.4, betap=.4, axis=None, limit=()):
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Arguments:
data -- Array of values to classify.
num_breaks -- Number of breaks to perform.
"""
step = (numpy.amax(data) - numpy.amin(data)) / num_breaks
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DarkEnergySurvey/ugali | ugali/utils/fileio.py | add_column | def add_column(filename,column,formula,force=False):
""" Add a column to a FITS file.
ADW: Could this be replaced by a ftool?
"""
columns = parse_formula(formula)
logger.info("Running file: %s"%filename)
logger.debug(" Reading columns: %s"%columns)
data = fitsio.read(filename,columns=colum... | python | def add_column(filename,column,formula,force=False):
""" Add a column to a FITS file.
ADW: Could this be replaced by a ftool?
"""
columns = parse_formula(formula)
logger.info("Running file: %s"%filename)
logger.debug(" Reading columns: %s"%columns)
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DarkEnergySurvey/ugali | ugali/utils/fileio.py | load_files | def load_files(filenames,multiproc=False,**kwargs):
""" Load a set of FITS files with kwargs. """
filenames = np.atleast_1d(filenames)
logger.debug("Loading %s files..."%len(filenames))
kwargs = [dict(filename=f,**kwargs) for f in filenames]
if multiproc:
from multiprocessing import Pool
... | python | def load_files(filenames,multiproc=False,**kwargs):
""" Load a set of FITS files with kwargs. """
filenames = np.atleast_1d(filenames)
logger.debug("Loading %s files..."%len(filenames))
kwargs = [dict(filename=f,**kwargs) for f in filenames]
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DarkEnergySurvey/ugali | ugali/scratch/simulation/survey_selection_function.py | surveySelectionFunction.applyFracdet | def applyFracdet(self, lon, lat):
"""
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"""
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fracdet_core = meanFracdet(self.m_fracdet, lon, lat, np.tile(0.1, len(lon)))
fracdet_wide = mean... | python | def applyFracdet(self, lon, lat):
"""
We want to enforce minimum fracdet for a satellite to be considered detectable
True is passes fracdet cut
"""
self.loadFracdet()
fracdet_core = meanFracdet(self.m_fracdet, lon, lat, np.tile(0.1, len(lon)))
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DarkEnergySurvey/ugali | ugali/scratch/simulation/survey_selection_function.py | surveySelectionFunction.applyHotspot | def applyHotspot(self, lon, lat):
"""
Exclude objects that are too close to hotspot
True if passes hotspot cut
"""
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"""
Exclude objects that are too close to hotspot
True if passes hotspot cut
"""
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DarkEnergySurvey/ugali | ugali/scratch/simulation/survey_selection_function.py | surveySelectionFunction.predict | def predict(self, lon, lat, **kwargs):
"""
distance, abs_mag, r_physical
"""
assert self.classifier is not None, 'ERROR'
pred = np.zeros(len(lon))
cut_geometry, flags_geometry = self.applyGeometry(lon, lat)
x_test = []
for key, operation ... | python | def predict(self, lon, lat, **kwargs):
"""
distance, abs_mag, r_physical
"""
assert self.classifier is not None, 'ERROR'
pred = np.zeros(len(lon))
cut_geometry, flags_geometry = self.applyGeometry(lon, lat)
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DarkEnergySurvey/ugali | ugali/candidate/associate.py | catalogFactory | def catalogFactory(name, **kwargs):
"""
Factory for various catalogs.
"""
fn = lambda member: inspect.isclass(member) and member.__module__==__name__
catalogs = odict(inspect.getmembers(sys.modules[__name__], fn))
if name not in list(catalogs.keys()):
msg = "%s not found in catalogs:\n ... | python | def catalogFactory(name, **kwargs):
"""
Factory for various catalogs.
"""
fn = lambda member: inspect.isclass(member) and member.__module__==__name__
catalogs = odict(inspect.getmembers(sys.modules[__name__], fn))
if name not in list(catalogs.keys()):
msg = "%s not found in catalogs:\n ... | [
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DarkEnergySurvey/ugali | ugali/analysis/results.py | write_results | def write_results(filename,config,srcfile,samples):
""" Package everything nicely """
results = createResults(config,srcfile,samples=samples)
results.write(filename) | python | def write_results(filename,config,srcfile,samples):
""" Package everything nicely """
results = createResults(config,srcfile,samples=samples)
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DarkEnergySurvey/ugali | ugali/analysis/results.py | Results.estimate | def estimate(self,param,burn=None,clip=10.0,alpha=0.32):
""" Estimate parameter value and uncertainties """
# FIXME: Need to add age and metallicity to composite isochrone params (currently properties)
if param not in list(self.samples.names) + list(self.source.params) + ['age','metallicity']:
... | python | def estimate(self,param,burn=None,clip=10.0,alpha=0.32):
""" Estimate parameter value and uncertainties """
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if param not in list(self.samples.names) + list(self.source.params) + ['age','metallicity']:
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DarkEnergySurvey/ugali | ugali/analysis/results.py | Results.estimate_params | def estimate_params(self,burn=None,clip=10.0,alpha=0.32):
""" Estimate all source parameters """
mle = self.get_mle()
out = odict()
for param in mle.keys():
out[param] = self.estimate(param,burn=burn,clip=clip,alpha=alpha)
return out | python | def estimate_params(self,burn=None,clip=10.0,alpha=0.32):
""" Estimate all source parameters """
mle = self.get_mle()
out = odict()
for param in mle.keys():
out[param] = self.estimate(param,burn=burn,clip=clip,alpha=alpha)
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DarkEnergySurvey/ugali | ugali/analysis/results.py | Results.estimate_position_angle | def estimate_position_angle(self,param='position_angle',burn=None,clip=10.0,alpha=0.32):
""" Estimate the position angle from the posterior dealing
with periodicity.
"""
# Transform so peak in the middle of the distribution
pa = self.samples.get(param,burn=burn,clip=clip)
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""" Estimate the position angle from the posterior dealing
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"""
# Transform so peak in the middle of the distribution
pa = self.samples.get(param,burn=burn,clip=clip)
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alphagov/performanceplatform-collector | performanceplatform/collector/webtrends/keymetrics.py | Collector.date_range_for_webtrends | def date_range_for_webtrends(cls, start_at=None, end_at=None):
"""
Get the start and end formatted for query
or the last hour if none specified.
Unlike reports, this does not aggregate periods
and so it is possible to just query a range and parse out the
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"""
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DarkEnergySurvey/ugali | ugali/utils/shell.py | get_ugali_dir | def get_ugali_dir():
"""Get the path to the ugali data directory from the environment"""
dirname = os.getenv('UGALIDIR')
# Get the HOME directory
if not dirname:
dirname=os.path.join(os.getenv('HOME'),'.ugali')
if not os.path.exists(dirname):
from ugali.utils.logger import logger
... | python | def get_ugali_dir():
"""Get the path to the ugali data directory from the environment"""
dirname = os.getenv('UGALIDIR')
# Get the HOME directory
if not dirname:
dirname=os.path.join(os.getenv('HOME'),'.ugali')
if not os.path.exists(dirname):
from ugali.utils.logger import logger
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DarkEnergySurvey/ugali | ugali/utils/shell.py | get_iso_dir | def get_iso_dir():
"""Get the ugali isochrone directory."""
dirname = os.path.join(get_ugali_dir(),'isochrones')
if not os.path.exists(dirname):
from ugali.utils.logger import logger
msg = "Isochrone directory not found:\n%s"%dirname
logger.warning(msg)
return dirname | python | def get_iso_dir():
"""Get the ugali isochrone directory."""
dirname = os.path.join(get_ugali_dir(),'isochrones')
if not os.path.exists(dirname):
from ugali.utils.logger import logger
msg = "Isochrone directory not found:\n%s"%dirname
logger.warning(msg)
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warrenspe/hconf | hconf/Config.py | ConfigManager.registerParser | def registerParser(self, parser):
"""
Registers a parser to parse configuration inputs.
"""
if not isinstance(parser, Subparser):
raise TypeError("%s is not an instance of a subparser." % parser)
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Registers a parser to parse configuration inputs.
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"""
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warrenspe/hconf | hconf/Config.py | ConfigManager._cast | def _cast(self):
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if configDict['cast'] is not None:
configValue = getattr(self._config, configName)
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"""
Iterates through our parsed configuration options and cast any options with marked cast types.
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juju/theblues | theblues/jimm.py | JIMM.list_models | def list_models(self, macaroons):
""" Get the logged in user's models from the JIMM controller.
@param macaroons The discharged JIMM macaroons.
@return The json decoded list of environments.
"""
return make_request("{}model".format(self.url), timeout=self.timeout,
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""" Get the logged in user's models from the JIMM controller.
@param macaroons The discharged JIMM macaroons.
@return The json decoded list of environments.
"""
return make_request("{}model".format(self.url), timeout=self.timeout,
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accraze/python-markov-novel | src/markov_novel/novel.py | Novel.write | def write(self, novel_title='novel', filetype='txt'):
"""
Composes chapters
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"""
self._compose_chapters()
self._write_to_file(novel_title, filetype) | python | def write(self, novel_title='novel', filetype='txt'):
"""
Composes chapters
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accraze/python-markov-novel | src/markov_novel/novel.py | Novel._compose_chapters | def _compose_chapters(self):
"""
Creates a chapters
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chapter_num = count + 1
c = Chapter(self.markov, chapter_num)
self.chapters.append(c) | python | def _compose_chapters(self):
"""
Creates a chapters
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"""
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chapter_num = count + 1
c = Chapter(self.markov, chapter_num)
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joeyespo/path-and-address | path_and_address/validation.py | valid_address | def valid_address(address):
"""
Determines whether the specified address string is valid.
"""
if not address:
return False
components = str(address).split(':')
if len(components) > 2 or not valid_hostname(components[0]):
return False
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"""
Determines whether the specified address string is valid.
"""
if not address:
return False
components = str(address).split(':')
if len(components) > 2 or not valid_hostname(components[0]):
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joeyespo/path-and-address | path_and_address/validation.py | valid_hostname | def valid_hostname(host):
"""
Returns whether the specified string is a valid hostname.
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if len(host) > 255:
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host = host[:-1]
return all(_hostname_re.match(c) for c in host.split('.')) | python | def valid_hostname(host):
"""
Returns whether the specified string is a valid hostname.
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DarkEnergySurvey/ugali | ugali/analysis/imf.py | IMF.sample | def sample(self, n, mass_min=0.1, mass_max=10., steps=10000, seed=None):
"""
Sample initial mass values between mass_min and mass_max,
following the IMF distribution.
ADW: Should this be `sample` or `simulate`?
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-----------
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"""
Sample initial mass values between mass_min and mass_max,
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DarkEnergySurvey/ugali | ugali/analysis/imf.py | Kroupa2001.pdf | def pdf(cls, mass, log_mode=True):
""" PDF for the Kroupa IMF.
Normalization is set over the mass range from 0.1 Msun to 100 Msun
"""
log_mass = np.log10(mass)
# From Eq 2
mb = mbreak = [0.08, 0.5] # Msun
a = alpha = [0.3, 1.3, 2.3] # alpha
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""" PDF for the Kroupa IMF.
Normalization is set over the mass range from 0.1 Msun to 100 Msun
"""
log_mass = np.log10(mass)
# From Eq 2
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DarkEnergySurvey/ugali | ugali/analysis/imf.py | Salpeter1955.pdf | def pdf(cls, mass, log_mode=True):
""" PDF for the Salpeter IMF.
Value of 'a' is set to normalize the IMF to 1 between 0.1 and 100 Msun
"""
alpha = 2.35
a = 0.060285569480482866
dn_dm = a * mass**(-alpha)
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# Number per logarithmic mass ... | python | def pdf(cls, mass, log_mode=True):
""" PDF for the Salpeter IMF.
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"""
alpha = 2.35
a = 0.060285569480482866
dn_dm = a * mass**(-alpha)
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warrenspe/hconf | hconf/subparsers/_subparser.py | ConfigFileSubparser._getConfigFile | def _getConfigFile(self, config):
"""
Retrieves a file descriptor to a configuration file to process.
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Outputs: An open file descriptor to the configuration file to parse in read mode if successful, else None.
"""
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"""
Retrieves a file descriptor to a configuration file to process.
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schlamar/latexmk.py | latexmake.py | _count_citations | def _count_citations(aux_file):
'''
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@return: defaultdict(int) - {citation_name: number, ...}
'''
counter = defaultdict(int)
with open(aux_file) as fobj:
content = fobj.read()
for match in CITE_PATTERN.finditer(content):
name = match.grou... | python | def _count_citations(aux_file):
'''
Counts the citations in an aux-file.
@return: defaultdict(int) - {citation_name: number, ...}
'''
counter = defaultdict(int)
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schlamar/latexmk.py | latexmake.py | LatexMaker._setup_logger | def _setup_logger(self):
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log.addHandler(handler)
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log.setLevel(logging.INFO)
return log | python | def _setup_logger(self):
'''Set up a logger.'''
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schlamar/latexmk.py | latexmake.py | LatexMaker._parse_texlipse_config | def _parse_texlipse_config(self):
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# If Eclipse's workspace refresh, the
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# so try again after short sleep if
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Read the project name from the texlipse
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schlamar/latexmk.py | latexmake.py | LatexMaker._read_latex_files | def _read_latex_files(self):
'''
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schlamar/latexmk.py | latexmake.py | LatexMaker.read_glossaries | def read_glossaries(self):
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pattern = r'\\@newglossary\{(.*)\}\{.*\}\{(.*)\}\{(.*)\}'
for match in re... | python | def read_glossaries(self):
'''
Read all existing glossaries in the main aux-file.
'''
filename = '%s.aux' % self.project_name
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schlamar/latexmk.py | latexmake.py | LatexMaker.check_errors | def check_errors(self):
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Check if errors occured during a latex run by
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'''
errors = ERROR_PATTTERN.findall(self.out)
# "errors" is a list of tuples
if errors:
self.log.error('! Errors occurred:')
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'''
Check if errors occured during a latex run by
scanning the output.
'''
errors = ERROR_PATTTERN.findall(self.out)
# "errors" is a list of tuples
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schlamar/latexmk.py | latexmake.py | LatexMaker.generate_citation_counter | def generate_citation_counter(self):
'''
Generate dictionary with the number of citations in all
included files. If this changes after the first latex run,
you have to run "bibtex".
'''
cite_counter = dict()
filename = '%s.aux' % self.project_name
with ope... | python | def generate_citation_counter(self):
'''
Generate dictionary with the number of citations in all
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cite_counter = dict()
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schlamar/latexmk.py | latexmake.py | LatexMaker.latex_run | def latex_run(self):
'''
Start latex run.
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Popen... | python | def latex_run(self):
'''
Start latex run.
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self.log.info('Running %s...' % self.latex_cmd)
cmd = [self.latex_cmd]
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schlamar/latexmk.py | latexmake.py | LatexMaker.bibtex_run | def bibtex_run(self):
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Start bibtex run.
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Start bibtex run.
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schlamar/latexmk.py | latexmake.py | LatexMaker.makeindex_runs | def makeindex_runs(self, gloss_files):
'''
Check for each glossary if it has to be regenerated
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@return: True if "makeindex" was called.
'''
gloss_changed = False
for gloss in self.glossaries:
make_gloss = False
ext_i, ext... | python | def makeindex_runs(self, gloss_files):
'''
Check for each glossary if it has to be regenerated
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schlamar/latexmk.py | latexmake.py | LatexMaker.open_preview | def open_preview(self):
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Try to open a preview of the generated document.
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self.log.info('Opening preview...')
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ext = 'pdf'
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filename = '%s.%s' % (self.proj... | python | def open_preview(self):
'''
Try to open a preview of the generated document.
Currently only supported on Windows.
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schlamar/latexmk.py | latexmake.py | LatexMaker.need_latex_rerun | def need_latex_rerun(self):
'''
Test for all rerun patterns if they match the output.
'''
for pattern in LATEX_RERUN_PATTERNS:
if pattern.search(self.out):
return True
return False | python | def need_latex_rerun(self):
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Test for all rerun patterns if they match the output.
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schlamar/latexmk.py | latexmake.py | LatexMaker.run | def run(self):
'''Run the LaTeX compilation.'''
# store files
self.old_dir = []
if self.opt.clean:
self.old_dir = os.listdir('.')
cite_counter, toc_file, gloss_files = self._read_latex_files()
self.latex_run()
self.read_glossaries()
gloss_ch... | python | def run(self):
'''Run the LaTeX compilation.'''
# store files
self.old_dir = []
if self.opt.clean:
self.old_dir = os.listdir('.')
cite_counter, toc_file, gloss_files = self._read_latex_files()
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DarkEnergySurvey/ugali | ugali/analysis/farm.py | Farm.command | def command(self, outfile, configfile, pix):
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Generate the command for running the likelihood scan.
"""
params = dict(script=self.config['scan']['script'],
config=configfile, outfile=outfile,
nside=self.nside_likelihood, pix=pix,
... | python | def command(self, outfile, configfile, pix):
"""
Generate the command for running the likelihood scan.
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params = dict(script=self.config['scan']['script'],
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coords : Array of target locations in Galactic coordinates.
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Genida/django-appsettings | src/appsettings/__init__.py | AppSettings.check | def check(cls):
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return None
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Class method to check every settings.
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DarkEnergySurvey/ugali | ugali/analysis/source.py | Source.set_model | def set_model(self, name, model):
""" Set a model.
Parameters
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name : name of the model -- e.g., richness, kernel, isochrone, etc.
model : the model instance
Returns
-------
None
"""
# Careful to not use `hasattr`
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Parameters
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name : name of the model -- e.g., richness, kernel, isochrone, etc.
model : the model instance
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DarkEnergySurvey/ugali | ugali/analysis/source.py | Source.set_params | def set_params(self,**kwargs):
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for key,value in list(kwargs.items()):
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""" Set the parameter values """
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DarkEnergySurvey/ugali | ugali/analysis/source.py | Source.get_free_params | def get_free_params(self):
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totalgood/pugnlp | src/pugnlp/regexes.py | iter_finds | def iter_finds(regex_obj, s):
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DarkEnergySurvey/ugali | ugali/isochrone/composite.py | CompositeIsochrone.composite_decorator | def composite_decorator(func):
"""
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"""
Decorator for wrapping functions that calculate a weighted sum
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | mergeCatalogs | def mergeCatalogs(catalog_list):
"""
Merge a list of Catalogs.
Parameters:
-----------
catalog_list : List of Catalog objects.
Returns:
--------
catalog : Combined Catalog object
"""
# Check the columns
for c in catalog_list:
if c.data.dtype.names != catalog_l... | python | def mergeCatalogs(catalog_list):
"""
Merge a list of Catalogs.
Parameters:
-----------
catalog_list : List of Catalog objects.
Returns:
--------
catalog : Combined Catalog object
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog.applyCut | def applyCut(self, cut):
"""
Return a new catalog which is a subset of objects selected
using the input cut array.
NOTE: This is really a *selection* (i.e., objects are retained if the value of 'cut' is True)
"""
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"""
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog.bootstrap | def bootstrap(self, mc_bit=0x10, seed=None):
"""
Return a random catalog by boostrapping the colors of the objects in the current catalog.
"""
if seed is not None: np.random.seed(seed)
data = copy.deepcopy(self.data)
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... | python | def bootstrap(self, mc_bit=0x10, seed=None):
"""
Return a random catalog by boostrapping the colors of the objects in the current catalog.
"""
if seed is not None: np.random.seed(seed)
data = copy.deepcopy(self.data)
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog.project | def project(self, projector = None):
"""
Project coordinates on sphere to image plane using Projector class.
"""
msg = "'%s.project': ADW 2018-05-05"%self.__class__.__name__
DeprecationWarning(msg)
if projector is None:
try:
self.projector = ug... | python | def project(self, projector = None):
"""
Project coordinates on sphere to image plane using Projector class.
"""
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DeprecationWarning(msg)
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog.spatialBin | def spatialBin(self, roi):
"""
Calculate indices of ROI pixels corresponding to object locations.
"""
if hasattr(self,'pixel_roi_index') and hasattr(self,'pixel'):
logger.warning('Catalog alread spatially binned')
return
# ADW: Not safe to set index = -1... | python | def spatialBin(self, roi):
"""
Calculate indices of ROI pixels corresponding to object locations.
"""
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logger.warning('Catalog alread spatially binned')
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog.write | def write(self, outfile, clobber=True, **kwargs):
"""
Write the current object catalog to FITS file.
Parameters:
-----------
filename : the FITS file to write.
clobber : remove existing file
kwargs : passed to fitsio.write
Returns:
--------
... | python | def write(self, outfile, clobber=True, **kwargs):
"""
Write the current object catalog to FITS file.
Parameters:
-----------
filename : the FITS file to write.
clobber : remove existing file
kwargs : passed to fitsio.write
Returns:
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog._parse | def _parse(self, roi=None, filenames=None):
"""
Parse catalog FITS files into recarray.
Parameters:
-----------
roi : The region of interest; if 'roi=None', read all catalog files
Returns:
--------
None
"""
if (roi is not None) an... | python | def _parse(self, roi=None, filenames=None):
"""
Parse catalog FITS files into recarray.
Parameters:
-----------
roi : The region of interest; if 'roi=None', read all catalog files
Returns:
--------
None
"""
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DarkEnergySurvey/ugali | ugali/observation/catalog.py | Catalog._defineVariables | def _defineVariables(self):
"""
Helper funtion to define pertinent variables from catalog data.
ADW (20170627): This has largely been replaced by properties.
"""
logger.info('Catalog contains %i objects'%(len(self.data)))
mc_source_id_field = self.config['catalog']['mc_... | python | def _defineVariables(self):
"""
Helper funtion to define pertinent variables from catalog data.
ADW (20170627): This has largely been replaced by properties.
"""
logger.info('Catalog contains %i objects'%(len(self.data)))
mc_source_id_field = self.config['catalog']['mc_... | [
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consbio/ncdjango | ncdjango/geoprocessing/workflow.py | Workflow.add_node | def add_node(self, node_id, task, inputs):
"""
Adds a node to the workflow.
:param node_id: A unique identifier for the new node.
:param task: The task to run.
:param inputs: A mapping of inputs from workflow inputs, or outputs from other nodes. The format should be
... | python | def add_node(self, node_id, task, inputs):
"""
Adds a node to the workflow.
:param node_id: A unique identifier for the new node.
:param task: The task to run.
:param inputs: A mapping of inputs from workflow inputs, or outputs from other nodes. The format should be
... | [
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consbio/ncdjango | ncdjango/geoprocessing/workflow.py | Workflow.map_output | def map_output(self, node_id, node_output_name, parameter_name):
"""
Maps the output from a node to a workflow output.
:param node_id: The id of the node to map from.
:param node_output_name: The output parameter name for the node task to map to the workflow output.
:param param... | python | def map_output(self, node_id, node_output_name, parameter_name):
"""
Maps the output from a node to a workflow output.
:param node_id: The id of the node to map from.
:param node_output_name: The output parameter name for the node task to map to the workflow output.
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consbio/ncdjango | ncdjango/geoprocessing/workflow.py | Workflow.to_json | def to_json(self, indent=None):
"""Serialize this workflow to JSON"""
inputs = ParameterCollection(self.inputs)
d = {
'meta': {
'name': self.name,
'description': self.description
},
'inputs': [],
'workflow': [],
... | python | def to_json(self, indent=None):
"""Serialize this workflow to JSON"""
inputs = ParameterCollection(self.inputs)
d = {
'meta': {
'name': self.name,
'description': self.description
},
'inputs': [],
'workflow': [],
... | [
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consbio/ncdjango | ncdjango/geoprocessing/workflow.py | Workflow.from_json | def from_json(cls, text):
"""Return a new workflow, deserialized from a JSON string"""
d = json.loads(text)
meta = d.get('meta', {})
workflow = cls(name=meta.get('name'), description=meta.get('description'))
for workflow_input in d.get('inputs', []):
parameter_cls ... | python | def from_json(cls, text):
"""Return a new workflow, deserialized from a JSON string"""
d = json.loads(text)
meta = d.get('meta', {})
workflow = cls(name=meta.get('name'), description=meta.get('description'))
for workflow_input in d.get('inputs', []):
parameter_cls ... | [
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MasterKale/django-cra-helper | cra_helper/management/commands/runserver.py | Command.get_handler | def get_handler(self, *args, **options):
'''
Return the static files serving handler wrapping the default handler,
if static files should be served. Otherwise return the default handler.
'''
handler = super().get_handler(*args, **options)
use_static_handler = options['use... | python | def get_handler(self, *args, **options):
'''
Return the static files serving handler wrapping the default handler,
if static files should be served. Otherwise return the default handler.
'''
handler = super().get_handler(*args, **options)
use_static_handler = options['use... | [
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