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{
"filename": "main_create_masterstamps_catalogue.py",
"repo_name": "torluca/morphofit",
"repo_path": "morphofit_extracted/morphofit-master/main_functions/main_create_masterstamps_catalogue.py",
"type": "Python"
}
|
#! /usr/bin/env python
# Copyright (C) 2019,2020 ETH Zurich, Institute for Particle Physics and Astrophysics
# Copyright (C) 2021 University Observatory, Ludwig-Maximilians-Universitaet Muenchen
# Author: Luca Tortorelli
# System imports
from __future__ import (print_function, division, absolute_import,
unicode_literals)
# External modules
import glob
import os
import argparse
import subprocess
import h5py
from astropy.table import Table, vstack
import numpy as np
# morphofit imports
from morphofit.utils import get_logger
from morphofit.catalogue_managing import combine_properties # , delete_repeating_sources
from morphofit.catalogue_managing import match_with_source_galaxies_catalogue
logger = get_logger(__file__)
def create_masterstamps_catalogue(args, root_target_fields, root_stamps_target_fields, telescope_name,
target_field_name, waveband_combinations, indices_target_galaxies,
stamp_index_combinations, psf_image_type_combinations, sigma_image_type_combinations,
background_estimate_method_combinations, wavebands,
source_galaxies_catalogue, temp_dir):
"""
:param args:
:param root_target_fields:
:param root_stamps_target_fields:
:param telescope_name:
:param target_field_name:
:param waveband_combinations:
:param indices_target_galaxies:
:param stamp_index_combinations:
:param psf_image_type_combinations:
:param sigma_image_type_combinations:
:param background_estimate_method_combinations:
:param wavebands:
:param source_galaxies_catalogue:
:param temp_dir:
:return:
"""
galfit_properties_mastertable = Table()
for i in range(len(waveband_combinations)):
try:
subprocess.run(['cp', os.path.join(root_stamps_target_fields, 'stamp{}_{}_{}_{}/{}_{}_{}_{}_{}_{}_{}.fits'
.format(stamp_index_combinations[i],
psf_image_type_combinations[i],
sigma_image_type_combinations[i],
background_estimate_method_combinations[i], telescope_name,
target_field_name, waveband_combinations[i],
stamp_index_combinations[i],
psf_image_type_combinations[i], sigma_image_type_combinations[i],
background_estimate_method_combinations[i])), temp_dir])
best_fit_properties_table_filename = os.path.join(temp_dir, '{}_{}_{}_{}_{}_{}_{}.fits'
.format(telescope_name,
target_field_name,
waveband_combinations[i],
stamp_index_combinations[i],
psf_image_type_combinations[i],
sigma_image_type_combinations[i],
background_estimate_method_combinations[i]))
best_fit_properties_table = Table.read(best_fit_properties_table_filename, format='fits', memmap=True)
galfit_properties_mastertable = vstack([galfit_properties_mastertable, best_fit_properties_table])
subprocess.run(['rm', best_fit_properties_table_filename])
except Exception as exception:
logger.info(exception)
logger.info('Missing {}_{}_{}_{}_{}_{}_{}.fits table'.format(telescope_name, target_field_name,
waveband_combinations[i],
stamp_index_combinations[i],
psf_image_type_combinations[i],
sigma_image_type_combinations[i],
background_estimate_method_combinations[i]))
pass
galfit_properties_mastertable_filename = os.path.join(temp_dir, '{}_{}_{}'
.format(telescope_name, target_field_name,
args.galfit_properties_mastertable_suffix))
galfit_properties_mastertable.write(galfit_properties_mastertable_filename, format='fits', overwrite=True)
subprocess.run(['cp', galfit_properties_mastertable_filename, root_stamps_target_fields])
multiband_tables = []
for stamp_number in indices_target_galaxies:
stamp_number_mask = np.where(galfit_properties_mastertable['STAMP_INDEX'] == str(stamp_number))
try:
table = combine_properties(galfit_properties_mastertable[stamp_number_mask], wavebands, telescope_name,
target_field_name, args.galaxy_ids_key, args.light_profiles_key,
args.galaxy_components_key, fit_kind='stamps', index=stamp_number)
table = match_with_source_galaxies_catalogue(table, source_galaxies_catalogue,
args.source_galaxies_catalogue_id_key)
table.write(os.path.join(temp_dir, '{}_{}_stamp{}.cat'.format(telescope_name, target_field_name,
stamp_number)), format='fits', overwrite=True)
multiband_tables.append(table)
except Exception as e:
logger.info(e)
multiband_table = vstack(multiband_tables, join_type='exact')
multiband_table_filename = os.path.join(temp_dir, '{}_{}_stamps_orig.cat'.format(telescope_name, target_field_name))
multiband_table.write(multiband_table_filename, format='fits', overwrite=True)
subprocess.run(['cp', multiband_table_filename, root_target_fields])
# multiband_table = delete_repeating_sources(multiband_table, wavebands)
# multiband_table_filename = os.path.join(temp_dir, '{}_{}_stamps.cat'.format(telescope_name, target_field_name))
# multiband_table.write(multiband_table_filename, format='fits', overwrite=True)
subprocess.run(['cp', multiband_table_filename, root_target_fields])
subprocess.run(['cp'] + glob.glob(os.path.join(temp_dir, '*stamp*.cat')) + [root_stamps_target_fields])
def main(indices, args):
"""
:param indices:
:param args:
:return:
"""
args = setup(args)
for index in indices:
logger.info('=============================== running on index={}'.format(index))
if args.local_or_cluster == 'cluster':
temp_dir = os.environ['TMPDIR']
elif args.local_or_cluster == 'local':
temp_dir = os.path.join(args.temp_dir_path, 'tmp_index{:06d}'.format(index))
os.makedirs(temp_dir, exist_ok=False)
else:
raise KeyError
h5pytable_filename = os.path.join(args.h5pytable_folder, args.h5pytable_filename)
subprocess.run(['cp', h5pytable_filename, temp_dir])
h5pytable_filename = os.path.join(temp_dir, args.h5pytable_filename)
h5table = h5py.File(h5pytable_filename, 'r')
grp = h5table['{}'.format(index)]
source_galaxies_catalogue_filename = grp['source_galaxies_catalogues'][()].decode('utf8')
subprocess.run(['cp', source_galaxies_catalogue_filename, temp_dir])
source_galaxies_catalogue = Table.read(os.path.join(temp_dir,
os.path.basename(source_galaxies_catalogue_filename)),
format='fits')
root_target_fields = grp['root_target_fields'][()].decode('utf8')
root_stamps_target_fields = grp['root_stamps_target_fields'][()].decode('utf8')
telescope_name = grp['telescope_name'][()].decode('utf8')
target_field_name = grp['target_field_names'][()].decode('utf8')
waveband_combinations = [name.decode('utf8') for name in grp['waveband_combinations'][()]]
indices_target_galaxies = [name.decode('utf8') for name in grp['indices_target_galaxies'][()]]
stamp_index_combinations = [name.decode('utf8') for name in grp['stamp_index_combinations'][()]]
psf_image_type_combinations = [name.decode('utf8')
for name in grp['psf_image_type_combinations'][()]]
sigma_image_type_combinations = [name.decode('utf8')
for name in grp['sigma_image_type_combinations'][()]]
background_estimate_method_combinations = [name.decode('utf8')
for name in
grp['background_estimate_method_combinations'][()]]
wavebands = [name.decode('utf8') for name in grp['wavebands'][()]]
h5table.close()
create_masterstamps_catalogue(args, root_target_fields, root_stamps_target_fields, telescope_name,
target_field_name, waveband_combinations, indices_target_galaxies,
stamp_index_combinations, psf_image_type_combinations,
sigma_image_type_combinations, background_estimate_method_combinations,
wavebands, source_galaxies_catalogue, temp_dir)
if args.local_or_cluster == 'local':
subprocess.run(['rm', '-rf', temp_dir])
yield index
def check_missing(indices, args):
"""
:param indices:
:param args:
:return:
"""
list_missing = []
args = setup(args)
for index in indices:
current_is_missing = False
h5pytable_filename = os.path.join(args.h5pytable_folder, args.h5pytable_filename)
h5table = h5py.File(h5pytable_filename, 'r')
grp = h5table['{}'.format(index)]
root_stamps_target_fields = grp['root_stamps_target_fields'][()].decode('utf8')
telescope_name = grp['telescope_name'][()].decode('utf8')
target_field_name = grp['target_field_names'][()].decode('utf8')
h5table.close()
try:
table = Table.read(os.path.join(root_stamps_target_fields, '{}_{}_stamps_orig.cat'
.format(telescope_name, target_field_name)), format='fits')
print(len(table))
except Exception as errmsg:
logger.error('error opening catalogue: errmsg: %s' % errmsg)
current_is_missing = True
if current_is_missing:
list_missing.append(index)
logger.info('%d catalogue missing' % index)
else:
logger.debug('%d tile all OK' % index)
n_missing = len(list_missing)
logger.info('found missing %d' % n_missing)
logger.info(str(list_missing))
return list_missing
def setup(args):
"""
:param args:
:return:
"""
cwd = os.getcwd()
description = "Create master catalogue of GALFIT run on stamps around target galaxies in Target Fields"
parser = argparse.ArgumentParser(description=description, add_help=True)
parser.add_argument('--h5pytable_folder', type=str, action='store', default=cwd,
help='h5py table folder')
parser.add_argument('--h5pytable_filename', type=str, action='store', default='table_masterstamps.h5',
help='h5py table filename')
parser.add_argument('--temp_dir_path', type=str, action='store', default=cwd,
help='temporary folder where to make calculations locally, used only if --local_or_cluster'
' is set to local')
parser.add_argument('--galfit_properties_mastertable_suffix', type=str, action='store', default='mastertable.fits',
help='Filename suffix for the master table of the run on stamps')
parser.add_argument('--source_galaxies_catalogue_id_key', type=str, action='store', default='NUMBER',
help='Catalogue header keywords for id')
parser.add_argument('--galaxy_ids_key', type=str, action='store', default='NUMBER',
help='Catalogue header key of the galaxy Ids')
parser.add_argument('--light_profiles_key', type=str, action='store', default='LIGHT_PROFILE',
help='Catalogue header key of the galaxy light profiles')
parser.add_argument('--galaxy_components_key', type=str, action='store', default='COMPONENT_NUMBER',
help='Catalogue header key of the galaxy components')
parser.add_argument('--local_or_cluster', type=str, action='store', default='local',
help='system type: local machine or hpc')
args = parser.parse_args(args)
return args
|
torlucaREPO_NAMEmorphofitPATH_START.@morphofit_extracted@morphofit-master@main_functions@main_create_masterstamps_catalogue.py@.PATH_END.py
|
{
"filename": "_imputation.py",
"repo_name": "rapidsai/cuml",
"repo_path": "cuml_extracted/cuml-main/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_imputation.py",
"type": "Python"
}
|
# Original authors from Sckit-Learn:
# Nicolas Tresegnie <nicolas.tresegnie@gmail.com>
# Sergey Feldman <sergeyfeldman@gmail.com>
# License: BSD 3 clause
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above do not endorse or promote this production.
from ....internals import _deprecate_pos_args
from ....common.array_descriptor import CumlArrayDescriptor
from ....internals.array_sparse import SparseCumlArray
from ..utils.validation import FLOAT_DTYPES
from ..utils.validation import check_is_fitted
from cuml.internals.mixins import AllowNaNTagMixin, SparseInputTagMixin, \
StringInputTagMixin
from ..utils.skl_dependencies import BaseEstimator, TransformerMixin
from ....thirdparty_adapters import (_get_mask,
_masked_column_median,
_masked_column_mean,
_masked_column_mode)
from cuml.internals.safe_imports import gpu_only_import_from
import cuml
from cuml.internals.safe_imports import gpu_only_import
import numbers
import warnings
from cuml.internals.safe_imports import cpu_only_import
numpy = cpu_only_import('numpy')
np = gpu_only_import('cupy')
sparse = gpu_only_import_from('cupyx.scipy', 'sparse')
def is_scalar_nan(x):
return bool(isinstance(x, numbers.Real) and np.isnan(x))
def _check_inputs_dtype(X, missing_values):
if (X.dtype.kind in ("f", "i", "u") and
not isinstance(missing_values, numbers.Real)):
raise ValueError("'X' and 'missing_values' types are expected to be"
" both numerical. Got X.dtype={} and "
" type(missing_values)={}."
.format(X.dtype, type(missing_values)))
def _get_elem_at_rank(rank, data, n_negative, n_zeros):
"""Find the value in data augmented with n_zeros for the given rank"""
if rank < n_negative:
return data[rank]
if rank - n_negative < n_zeros:
return 0
return data[rank - n_zeros]
def _get_median(data, n_zeros):
"""Compute the median of data with n_zeros additional zeros.
This function is used to support sparse matrices; it modifies data in-place
"""
n_elems = len(data) + n_zeros
if not n_elems:
return np.nan
n_negative = (data < 0).sum()
middle, is_odd = divmod(n_elems, 2)
data = np.sort(data)
if is_odd:
return _get_elem_at_rank(middle, data,
n_negative, n_zeros)
elm1 = _get_elem_at_rank(middle - 1, data,
n_negative, n_zeros)
elm2 = _get_elem_at_rank(middle, data,
n_negative, n_zeros)
return (elm1 + elm2) / 2.
def _most_frequent(array, extra_value, n_repeat):
"""Compute the most frequent value in a 1d array extended with
[extra_value] * n_repeat, where extra_value is assumed to be not part
of the array."""
values, counts = np.unique(array,
return_counts=True)
most_frequent_count = counts.max()
if most_frequent_count > n_repeat:
value = values[counts == most_frequent_count].min()
elif n_repeat > most_frequent_count:
value = extra_value
else:
value = min(extra_value, values[counts == most_frequent_count].min())
return value
class _BaseImputer(TransformerMixin):
"""Base class for all imputers.
It adds automatically support for `add_indicator`.
"""
def __init__(self, *, missing_values=np.nan, add_indicator=False):
self.missing_values = missing_values
self.add_indicator = add_indicator
def _fit_indicator(self, X):
"""Fit a MissingIndicator."""
if self.add_indicator:
with cuml.using_output_type("cupy"):
self.indicator_ = MissingIndicator(
missing_values=self.missing_values, error_on_new=False
)
self.indicator_.fit(X)
else:
self.indicator_ = None
def _transform_indicator(self, X):
"""Compute the indicator mask.'
Note that X must be the original data as passed to the imputer before
any imputation, since imputation may be done inplace in some cases.
"""
if self.add_indicator:
if not hasattr(self, 'indicator_'):
raise ValueError(
"Make sure to call _fit_indicator before "
"_transform_indicator"
)
return self.indicator_.transform(X)
def _concatenate_indicator(self, X_imputed, X_indicator):
"""Concatenate indicator mask with the imputed data."""
if not self.add_indicator:
return X_imputed
hstack = sparse.hstack if sparse.issparse(X_imputed) else np.hstack
if X_indicator is None:
raise ValueError(
"Data from the missing indicator are not provided. Call "
"_fit_indicator and _transform_indicator in the imputer "
"implementation."
)
return hstack((X_imputed, X_indicator))
def _more_tags(self):
return {'allow_nan': is_scalar_nan(self.missing_values)}
class SimpleImputer(_BaseImputer, BaseEstimator,
SparseInputTagMixin, AllowNaNTagMixin):
"""Imputation transformer for completing missing values.
Parameters
----------
missing_values : number, string, np.nan (default) or None
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For pandas' dataframes with
nullable integer dtypes with missing values, `missing_values`
should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.
strategy : string, default='mean'
The imputation strategy.
- If "mean", then replace missing values using the mean along
each column. Can only be used with numeric data.
- If "median", then replace missing values using the median along
each column. Can only be used with numeric data.
- If "most_frequent", then replace missing using the most frequent
value along each column. Can be used with strings or numeric data.
- If "constant", then replace missing values with fill_value. Can be
used with strings or numeric data.
strategy="constant" for fixed value imputation.
fill_value : string or numerical value, default=None
When strategy == "constant", fill_value is used to replace all
occurrences of missing_values.
If left to the default, fill_value will be 0 when imputing numerical
data and "missing_value" for strings or object data types.
verbose : integer, default=0
Controls the verbosity of the imputer.
copy : boolean, default=True
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, in the following cases,
a new copy will always be made, even if `copy=False`:
- If X is not an array of floating values;
- If X is encoded as a CSR matrix;
- If add_indicator=True.
add_indicator : boolean, default=False
If True, a :class:`MissingIndicator` transform will stack onto output
of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won't appear on
the missing indicator even if there are missing values at
transform/test time.
Attributes
----------
statistics_ : array of shape (n_features,)
The imputation fill value for each feature.
Computing statistics can result in `np.nan` values.
During :meth:`transform`, features corresponding to `np.nan`
statistics will be discarded.
See also
--------
IterativeImputer : Multivariate imputation of missing values.
Examples
--------
>>> import cupy as cp
>>> from cuml.preprocessing import SimpleImputer
>>> imp_mean = SimpleImputer(missing_values=cp.nan, strategy='mean')
>>> imp_mean.fit(cp.asarray([[7, 2, 3], [4, cp.nan, 6], [10, 5, 9]]))
SimpleImputer()
>>> X = [[cp.nan, 2, 3], [4, cp.nan, 6], [10, cp.nan, 9]]
>>> print(imp_mean.transform(cp.asarray(X)))
[[ 7. 2. 3. ]
[ 4. 3.5 6. ]
[10. 3.5 9. ]]
Notes
-----
Columns which only contained missing values at :meth:`fit` are discarded
upon :meth:`transform` if strategy is not "constant".
"""
statistics_ = CumlArrayDescriptor()
@_deprecate_pos_args(version="21.06")
def __init__(self, *, missing_values=np.nan, strategy="mean",
fill_value=None, copy=True, add_indicator=False):
super().__init__(
missing_values=missing_values,
add_indicator=add_indicator
)
self.strategy = strategy
self.fill_value = fill_value
self.copy = copy
def get_param_names(self):
return super().get_param_names() + [
"strategy",
"fill_value",
"verbose",
"copy"
]
def _validate_input(self, X, in_fit):
allowed_strategies = ["mean", "median", "most_frequent", "constant"]
if self.strategy not in allowed_strategies:
raise ValueError("Can only use these strategies: {0} "
" got strategy={1}".format(allowed_strategies,
self.strategy))
if self.strategy in ("most_frequent", "constant"):
dtype = None
else:
dtype = FLOAT_DTYPES
if not is_scalar_nan(self.missing_values):
force_all_finite = True
else:
force_all_finite = "allow-nan"
try:
X = self._validate_data(X, reset=in_fit,
accept_sparse='csc', dtype=dtype,
force_all_finite=force_all_finite,
copy=self.copy)
except ValueError as ve:
if "could not convert" in str(ve):
new_ve = ValueError("Cannot use {} strategy with non-numeric "
"data:\n{}".format(self.strategy, ve))
raise new_ve from None
else:
raise ve
_check_inputs_dtype(X, self.missing_values)
if X.dtype.kind not in ("i", "u", "f", "O"):
raise ValueError("SimpleImputer does not support data with dtype "
"{0}. Please provide either a numeric array (with"
" a floating point or integer dtype) or "
"categorical data represented either as an array "
"with integer dtype or an array of string values "
"with an object dtype.".format(X.dtype))
return X
def fit(self, X, y=None) -> "SimpleImputer":
"""Fit the imputer on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
Returns
-------
self : SimpleImputer
"""
if type(X) is list:
X = np.asarray(X)
X = self._validate_input(X, in_fit=True)
super()._fit_indicator(X)
# default fill_value is 0 for numerical input and "missing_value"
# otherwise
if self.fill_value is None:
if X.dtype.kind in ("i", "u", "f"):
fill_value = 0
else:
fill_value = "missing_value"
else:
fill_value = self.fill_value
# fill_value should be numerical in case of numerical input
if (self.strategy == "constant" and
X.dtype.kind in ("i", "u", "f") and
not isinstance(fill_value, numbers.Real)):
raise ValueError("'fill_value'={0} is invalid. Expected a "
"numerical value when imputing numerical "
"data".format(fill_value))
if sparse.issparse(X):
# missing_values = 0 not allowed with sparse data as it would
# force densification
if self.missing_values == 0:
raise ValueError("Imputation not possible when missing_values "
"== 0 and input is sparse. Provide a dense "
"array instead.")
else:
self.statistics_ = self._sparse_fit(X,
self.strategy,
self.missing_values,
fill_value)
else:
self.statistics_ = self._dense_fit(X,
self.strategy,
self.missing_values,
fill_value)
return self
def _sparse_fit(self, X, strategy, missing_values, fill_value):
"""Fit the transformer on sparse data."""
mask_data = _get_mask(X.data, missing_values)
n_implicit_zeros = X.shape[0] - np.diff(X.indptr)
statistics = np.empty(X.shape[1])
if strategy == "constant":
# for constant strategy, self.statistcs_ is used to store
# fill_value in each column
statistics.fill(fill_value)
else:
for i in range(X.shape[1]):
column = X.data[X.indptr[i]:X.indptr[i + 1]]
mask_column = mask_data[X.indptr[i]:X.indptr[i + 1]]
column = column[~mask_column]
# combine explicit and implicit zeros
mask_zeros = _get_mask(column, 0)
column = column[~mask_zeros]
n_explicit_zeros = mask_zeros.sum()
n_zeros = n_implicit_zeros[i] + n_explicit_zeros
if strategy == "mean":
s = column.size + n_zeros
statistics[i] = np.nan if s == 0 else column.sum() / s
elif strategy == "median":
statistics[i] = _get_median(column,
n_zeros)
elif strategy == "most_frequent":
statistics[i] = _most_frequent(column,
0,
n_zeros)
return statistics
def _dense_fit(self, X, strategy, missing_values, fill_value):
"""Fit the transformer on dense data."""
# Mean
if strategy == "mean":
return _masked_column_mean(X, missing_values)
# Median
elif strategy == "median":
return _masked_column_median(X, missing_values)
# Most frequent
elif strategy == "most_frequent":
return _masked_column_mode(X, missing_values)
# Constant
elif strategy == "constant":
return np.full(X.shape[1], fill_value, dtype=X.dtype)
def transform(self, X) -> SparseCumlArray:
"""Impute all missing values in X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete.
"""
check_is_fitted(self)
X = self._validate_input(X, in_fit=False)
X_indicator = super()._transform_indicator(X)
statistics = self.statistics_
if X.shape[1] != statistics.shape[0]:
raise ValueError("X has %d features per sample, expected %d"
% (X.shape[1], self.statistics_.shape[0]))
# Delete the invalid columns if strategy is not constant
if self.strategy == "constant":
valid_statistics = statistics
else:
# same as np.isnan but also works for object dtypes
invalid_mask = _get_mask(statistics, np.nan)
valid_mask = np.logical_not(invalid_mask)
valid_statistics = statistics[valid_mask]
valid_statistics_indexes = np.flatnonzero(valid_mask)
if invalid_mask.any():
missing = np.arange(X.shape[1])[invalid_mask]
if self.verbose:
warnings.warn("Deleting features without "
"observed values: %s" % missing)
X = X[:, valid_statistics_indexes]
# Do actual imputation
if sparse.issparse(X):
if self.missing_values == 0:
raise ValueError("Imputation not possible when missing_values "
"== 0 and input is sparse. Provide a dense "
"array instead.")
else:
mask = _get_mask(X.data, self.missing_values)
indexes = np.repeat(
np.arange(len(X.indptr) - 1, dtype=int),
np.diff(X.indptr).tolist())[mask]
X.data[mask] = valid_statistics[indexes].astype(X.dtype,
copy=False)
else:
mask = _get_mask(X, self.missing_values)
if self.strategy == "constant":
X[mask] = valid_statistics[0]
else:
for i, vi in enumerate(valid_statistics_indexes):
feature_idxs = np.flatnonzero(mask[:, vi])
X[feature_idxs, vi] = valid_statistics[i]
X = super()._concatenate_indicator(X, X_indicator)
return X
class MissingIndicator(TransformerMixin,
BaseEstimator,
AllowNaNTagMixin,
SparseInputTagMixin,
StringInputTagMixin):
"""Binary indicators for missing values.
Note that this component typically should not be used in a vanilla
:class:`Pipeline` consisting of transformers and a classifier, but rather
could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`.
Parameters
----------
missing_values : number, string, np.nan (default) or None
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For pandas' dataframes with
nullable integer dtypes with missing values, `missing_values`
should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.
features : str, default=None
Whether the imputer mask should represent all or a subset of
features.
- If "missing-only" (default), the imputer mask will only represent
features containing missing values during fit time.
- If "all", the imputer mask will represent all features.
sparse : boolean or "auto", default=None
Whether the imputer mask format should be sparse or dense.
- If "auto" (default), the imputer mask will be of same type as
input.
- If True, the imputer mask will be a sparse matrix.
- If False, the imputer mask will be a numpy array.
error_on_new : boolean, default=None
If True (default), transform will raise an error when there are
features with missing values in transform that have no missing values
in fit. This is applicable only when ``features="missing-only"``.
Attributes
----------
features_ : ndarray, shape (n_missing_features,) or (n_features,)
The features indices which will be returned when calling ``transform``.
They are computed during ``fit``. For ``features='all'``, it is
to ``range(n_features)``.
Examples
--------
>>> import numpy as np
>>> from sklearn.impute import MissingIndicator
>>> X1 = np.array([[np.nan, 1, 3],
... [4, 0, np.nan],
... [8, 1, 0]])
>>> X2 = np.array([[5, 1, np.nan],
... [np.nan, 2, 3],
... [2, 4, 0]])
>>> indicator = MissingIndicator()
>>> indicator.fit(X1)
MissingIndicator()
>>> X2_tr = indicator.transform(X2)
>>> X2_tr
array([[False, True],
[ True, False],
[False, False]])
"""
features_ = CumlArrayDescriptor()
@_deprecate_pos_args(version="21.06")
def __init__(self, *, missing_values=np.nan, features="missing-only",
sparse="auto", error_on_new=True):
self.missing_values = missing_values
self.features = features
self.sparse = sparse
self.error_on_new = error_on_new
def get_param_names(self):
return super().get_param_names() + [
"missing_values",
"features",
"sparse",
"error_on_new"
]
def _get_missing_features_info(self, X):
"""Compute the imputer mask and the indices of the features
containing missing values.
Parameters
----------
X : {ndarray or sparse matrix}, shape (n_samples, n_features)
The input data with missing values. Note that ``X`` has been
checked in ``fit`` and ``transform`` before to call this function.
Returns
-------
imputer_mask : {ndarray or sparse matrix}, shape \
(n_samples, n_features)
The imputer mask of the original data.
features_with_missing : ndarray, shape (n_features_with_missing)
The features containing missing values.
"""
if sparse.issparse(X):
mask = _get_mask(X.data, self.missing_values)
# The imputer mask will be constructed with the same sparse format
# as X.
sparse_constructor = (sparse.csr_matrix if X.format == 'csr'
else sparse.csc_matrix)
imputer_mask = sparse_constructor(
(mask, X.indices.copy(), X.indptr.copy()),
shape=X.shape, dtype=np.float32)
# temporarily switch to using float32 as
# cupy cannot operate with bool as of now
if self.features == 'missing-only':
n_missing = imputer_mask.sum(axis=0)
if self.sparse is False:
imputer_mask = imputer_mask.toarray()
elif imputer_mask.format == 'csr':
imputer_mask = imputer_mask.tocsc()
else:
imputer_mask = _get_mask(X, self.missing_values)
if self.features == 'missing-only':
n_missing = imputer_mask.sum(axis=0)
if self.sparse is True:
imputer_mask = sparse.csc_matrix(imputer_mask)
if self.features == 'all':
features_indices = np.arange(X.shape[1])
else:
features_indices = np.flatnonzero(n_missing)
return imputer_mask, features_indices
def _validate_input(self, X, in_fit):
if not is_scalar_nan(self.missing_values):
force_all_finite = True
else:
force_all_finite = "allow-nan"
X = self._validate_data(X, reset=in_fit,
accept_sparse=('csc', 'csr'), dtype=None,
force_all_finite=force_all_finite)
_check_inputs_dtype(X, self.missing_values)
if X.dtype.kind not in ("i", "u", "f", "O"):
raise ValueError("MissingIndicator does not support data with "
"dtype {0}. Please provide either a numeric array"
" (with a floating point or integer dtype) or "
"categorical data represented either as an array "
"with integer dtype or an array of string values "
"with an object dtype.".format(X.dtype))
if sparse.issparse(X) and self.missing_values == 0:
# missing_values = 0 not allowed with sparse data as it would
# force densification
raise ValueError("Sparse input with missing_values=0 is "
"not supported. Provide a dense "
"array instead.")
return X
def _fit(self, X, y=None):
"""Fit the transformer on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
Returns
-------
imputer_mask : {ndarray or sparse matrix}, shape (n_samples, \
n_features)
The imputer mask of the original data.
"""
X = self._validate_input(X, in_fit=True)
self._n_features = X.shape[1]
if self.features not in ('missing-only', 'all'):
raise ValueError("'features' has to be either 'missing-only' or "
"'all'. Got {} instead.".format(self.features))
if not ((isinstance(self.sparse, str) and
self.sparse == "auto") or isinstance(self.sparse, bool)):
raise ValueError("'sparse' has to be a boolean or 'auto'. "
"Got {!r} instead.".format(self.sparse))
missing_features_info = self._get_missing_features_info(X)
self.features_ = missing_features_info[1]
return missing_features_info[0]
def fit(self, X, y=None) -> "MissingIndicator":
"""Fit the transformer on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
Returns
-------
self : object
Returns self.
"""
self._fit(X, y)
return self
def transform(self, X) -> SparseCumlArray:
"""Generate missing values indicator for X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete.
Returns
-------
Xt : {ndarray or sparse matrix}, shape (n_samples, n_features) \
or (n_samples, n_features_with_missing)
The missing indicator for input data. The data type of ``Xt``
will be boolean.
"""
check_is_fitted(self)
X = self._validate_input(X, in_fit=False)
if X.shape[1] != self._n_features:
raise ValueError("X has a different number of features "
"than during fitting.")
imputer_mask, features = self._get_missing_features_info(X)
if self.features == "missing-only":
with cuml.using_output_type("numpy"):
np_features = np.asnumpy(features)
features_diff_fit_trans = numpy.setdiff1d(np_features,
self.features_)
if (self.error_on_new and features_diff_fit_trans.size > 0):
raise ValueError("The features {} have missing values "
"in transform but have no missing values "
"in fit.".format(features_diff_fit_trans))
if self.features_.size < self._n_features:
imputer_mask = imputer_mask[:, self.features_]
return imputer_mask
def fit_transform(self, X, y=None) -> SparseCumlArray:
"""Generate missing values indicator for X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete.
Returns
-------
Xt : {ndarray or sparse matrix}, shape (n_samples, n_features) \
or (n_samples, n_features_with_missing)
The missing indicator for input data. The data type of ``Xt``
will be boolean.
"""
imputer_mask = self._fit(X, y)
if self.features_.size < self._n_features:
imputer_mask = imputer_mask[:, self.features_]
return imputer_mask
|
rapidsaiREPO_NAMEcumlPATH_START.@cuml_extracted@cuml-main@python@cuml@cuml@_thirdparty@sklearn@preprocessing@_imputation.py@.PATH_END.py
|
{
"filename": "m31_feature_analysis.ipynb",
"repo_name": "snad-space/zwad",
"repo_path": "zwad_extracted/zwad-master/notebooks/m31_feature_analysis.ipynb",
"type": "Jupyter Notebook"
}
|
```python
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
import warnings
warnings.filterwarnings("ignore")
```
```python
# M31
m31_oid = np.memmap('../data/oid_m31.dat', mode='r', dtype=np.uint64)
m31_names = open('../data/feature_m31.name').read().split()
#x = np.memmap('feature_m31.dat', mode='r', dtype=np.float32, shape=(oid.size, len(names)))
# OR
m31_dtype = [(name, np.float32) for name in m31_names]
m31_x = np.memmap('../data/feature_m31.dat', mode='r', dtype=m31_dtype, shape=m31_oid.shape)
```
```python
for i in m31_names:
fig, ax = plt.subplots(figsize=(6, 4))
plt.hist(m31_x['{}'.format(i)], histtype='step', color='darkorange')
ax.set_title('{}'.format(i))
ax.set_ylabel('Counts')
ax.set_xlabel('Value')
ax.set_yscale('log')
```
```python
m31_x2 = np.memmap('../data/feature_m31.dat', mode='r', dtype=np.float32, shape=(m31_oid.size, len(m31_names)))
m31 = pd.DataFrame(m31_x2, index=m31_oid, columns=m31_names)
m31
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>amplitude</th>
<th>beyond_1_std</th>
<th>beyond_2_std</th>
<th>cusum</th>
<th>eta</th>
<th>eta_e</th>
<th>inter_percentile_range_25</th>
<th>inter_percentile_range_10</th>
<th>kurtosis</th>
<th>linear_fit_slope</th>
<th>...</th>
<th>periodogram_cusum</th>
<th>periodogram_eta</th>
<th>periodogram_inter_percentile_range_25</th>
<th>periodogram_standard_deviation</th>
<th>periodogram_percent_amplitude</th>
<th>chi2</th>
<th>skew</th>
<th>standard_deviation</th>
<th>stetson_K</th>
<th>weighted_mean</th>
</tr>
</thead>
<tbody>
<tr>
<th>695211400017839</th>
<td>0.699500</td>
<td>0.227941</td>
<td>0.036765</td>
<td>0.128972</td>
<td>1.576097</td>
<td>4.729054e+09</td>
<td>0.212000</td>
<td>0.450199</td>
<td>3.944156</td>
<td>-0.001640</td>
<td>...</td>
<td>0.148243</td>
<td>0.038084</td>
<td>0.959692</td>
<td>1.045485</td>
<td>9.733143</td>
<td>1.391858</td>
<td>-1.312442</td>
<td>0.202145</td>
<td>0.664184</td>
<td>20.516939</td>
</tr>
<tr>
<th>695211400043887</th>
<td>0.443000</td>
<td>0.288889</td>
<td>0.044444</td>
<td>0.179944</td>
<td>1.524735</td>
<td>3.644123e+09</td>
<td>0.204000</td>
<td>0.400000</td>
<td>0.133404</td>
<td>-0.000005</td>
<td>...</td>
<td>0.156987</td>
<td>0.032495</td>
<td>0.875076</td>
<td>0.984689</td>
<td>10.104938</td>
<td>0.548229</td>
<td>-0.357512</td>
<td>0.163288</td>
<td>0.792986</td>
<td>20.698317</td>
</tr>
<tr>
<th>695211400043454</th>
<td>0.589499</td>
<td>0.280000</td>
<td>0.032000</td>
<td>0.191169</td>
<td>1.652675</td>
<td>2.317022e+09</td>
<td>0.204500</td>
<td>0.484001</td>
<td>1.439840</td>
<td>0.000048</td>
<td>...</td>
<td>0.144973</td>
<td>0.031337</td>
<td>0.856762</td>
<td>0.939969</td>
<td>7.261847</td>
<td>0.791332</td>
<td>-0.746378</td>
<td>0.190502</td>
<td>0.728758</td>
<td>20.749649</td>
</tr>
<tr>
<th>695211400042791</th>
<td>0.604000</td>
<td>0.261745</td>
<td>0.053691</td>
<td>0.158801</td>
<td>1.574722</td>
<td>1.996893e+09</td>
<td>0.203499</td>
<td>0.433001</td>
<td>1.735631</td>
<td>0.000804</td>
<td>...</td>
<td>0.159723</td>
<td>0.033665</td>
<td>0.761747</td>
<td>0.886971</td>
<td>8.016976</td>
<td>0.915853</td>
<td>-0.816090</td>
<td>0.178804</td>
<td>0.737000</td>
<td>20.493862</td>
</tr>
<tr>
<th>695211400016239</th>
<td>0.825500</td>
<td>0.196203</td>
<td>0.025316</td>
<td>0.085341</td>
<td>1.951849</td>
<td>2.571876e+09</td>
<td>0.155001</td>
<td>0.323599</td>
<td>18.212532</td>
<td>-0.002264</td>
<td>...</td>
<td>0.176922</td>
<td>0.049399</td>
<td>0.618860</td>
<td>0.638475</td>
<td>5.355614</td>
<td>1.734685</td>
<td>-2.598536</td>
<td>0.162091</td>
<td>0.504324</td>
<td>20.329548</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>695211200027762</th>
<td>0.137500</td>
<td>0.300000</td>
<td>0.032000</td>
<td>0.076794</td>
<td>1.583493</td>
<td>6.722693e+09</td>
<td>0.053001</td>
<td>0.105000</td>
<td>0.686157</td>
<td>-0.000049</td>
<td>...</td>
<td>0.172669</td>
<td>0.028054</td>
<td>0.722513</td>
<td>0.826720</td>
<td>9.868394</td>
<td>1.089929</td>
<td>-0.198571</td>
<td>0.041880</td>
<td>0.782368</td>
<td>18.673153</td>
</tr>
<tr>
<th>695211200001880</th>
<td>0.049500</td>
<td>0.283465</td>
<td>0.055118</td>
<td>0.151784</td>
<td>1.451552</td>
<td>2.788117e+09</td>
<td>0.018000</td>
<td>0.037000</td>
<td>0.999901</td>
<td>-0.000003</td>
<td>...</td>
<td>0.165232</td>
<td>0.017666</td>
<td>0.749936</td>
<td>0.936336</td>
<td>12.354101</td>
<td>1.967448</td>
<td>0.421042</td>
<td>0.015429</td>
<td>0.763788</td>
<td>14.979458</td>
</tr>
<tr>
<th>695211200027621</th>
<td>0.073500</td>
<td>0.274590</td>
<td>0.049180</td>
<td>0.083840</td>
<td>1.638867</td>
<td>3.939941e+09</td>
<td>0.033501</td>
<td>0.057198</td>
<td>0.447904</td>
<td>-0.000009</td>
<td>...</td>
<td>0.165867</td>
<td>0.023609</td>
<td>0.749228</td>
<td>0.822715</td>
<td>9.576206</td>
<td>1.469046</td>
<td>-0.069856</td>
<td>0.024325</td>
<td>0.793072</td>
<td>17.515738</td>
</tr>
<tr>
<th>695211200002462</th>
<td>0.044000</td>
<td>0.311024</td>
<td>0.074803</td>
<td>0.152871</td>
<td>1.594203</td>
<td>2.863704e+09</td>
<td>0.020000</td>
<td>0.041000</td>
<td>0.126569</td>
<td>0.000034</td>
<td>...</td>
<td>0.174980</td>
<td>0.017528</td>
<td>0.722896</td>
<td>0.866926</td>
<td>11.782808</td>
<td>1.851398</td>
<td>0.099805</td>
<td>0.016328</td>
<td>0.791217</td>
<td>15.804447</td>
</tr>
<tr>
<th>695211200070946</th>
<td>0.195500</td>
<td>0.228346</td>
<td>0.055118</td>
<td>0.080264</td>
<td>2.166719</td>
<td>3.407947e+09</td>
<td>0.051250</td>
<td>0.126400</td>
<td>2.358748</td>
<td>-0.000295</td>
<td>...</td>
<td>0.157390</td>
<td>0.064581</td>
<td>0.539544</td>
<td>0.521232</td>
<td>3.815655</td>
<td>1.502394</td>
<td>-0.244482</td>
<td>0.055769</td>
<td>0.713932</td>
<td>18.852880</td>
</tr>
</tbody>
</table>
<p>57546 rows × 42 columns</p>
</div>
# Amplitude
```python
m31_lg_amplitude = m31[m31.amplitude >= 2]
m31_lg_amplitude
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>amplitude</th>
<th>beyond_1_std</th>
<th>beyond_2_std</th>
<th>cusum</th>
<th>eta</th>
<th>eta_e</th>
<th>inter_percentile_range_25</th>
<th>inter_percentile_range_10</th>
<th>kurtosis</th>
<th>linear_fit_slope</th>
<th>...</th>
<th>periodogram_cusum</th>
<th>periodogram_eta</th>
<th>periodogram_inter_percentile_range_25</th>
<th>periodogram_standard_deviation</th>
<th>periodogram_percent_amplitude</th>
<th>chi2</th>
<th>skew</th>
<th>standard_deviation</th>
<th>stetson_K</th>
<th>weighted_mean</th>
</tr>
</thead>
<tbody>
<tr>
<th>695211400034403</th>
<td>2.380000</td>
<td>0.293706</td>
<td>0.048951</td>
<td>0.210137</td>
<td>1.039928</td>
<td>1.205708e+09</td>
<td>1.702999</td>
<td>2.813601</td>
<td>-0.436671</td>
<td>-0.000443</td>
<td>...</td>
<td>0.118531</td>
<td>0.011458</td>
<td>0.861847</td>
<td>1.873147</td>
<td>28.882809</td>
<td>1568.272339</td>
<td>0.410859</td>
<td>1.097088</td>
<td>0.875042</td>
<td>16.457632</td>
</tr>
<tr>
<th>695211400117334</th>
<td>2.017000</td>
<td>0.304000</td>
<td>0.040000</td>
<td>0.225917</td>
<td>1.219688</td>
<td>1.659339e+09</td>
<td>1.389252</td>
<td>2.385000</td>
<td>0.346864</td>
<td>-0.001382</td>
<td>...</td>
<td>0.152174</td>
<td>0.012302</td>
<td>0.955405</td>
<td>1.669254</td>
<td>18.446514</td>
<td>564.353943</td>
<td>0.899401</td>
<td>0.931375</td>
<td>0.892021</td>
<td>17.024920</td>
</tr>
<tr>
<th>695211400132953</th>
<td>2.092999</td>
<td>0.148515</td>
<td>0.039604</td>
<td>0.175341</td>
<td>2.005198</td>
<td>2.937219e+09</td>
<td>0.511499</td>
<td>1.030001</td>
<td>8.822386</td>
<td>-0.002927</td>
<td>...</td>
<td>0.205521</td>
<td>0.038477</td>
<td>0.676035</td>
<td>0.729360</td>
<td>5.612421</td>
<td>653.334839</td>
<td>-1.979900</td>
<td>0.534341</td>
<td>0.637040</td>
<td>17.629156</td>
</tr>
<tr>
<th>695211400134621</th>
<td>2.072000</td>
<td>0.222222</td>
<td>0.042735</td>
<td>0.205176</td>
<td>1.286780</td>
<td>3.154132e+08</td>
<td>0.816750</td>
<td>1.311199</td>
<td>2.749617</td>
<td>-0.003801</td>
<td>...</td>
<td>0.182031</td>
<td>0.043424</td>
<td>0.997531</td>
<td>0.959020</td>
<td>6.650954</td>
<td>1211.347778</td>
<td>-1.063906</td>
<td>0.625971</td>
<td>0.733490</td>
<td>16.854887</td>
</tr>
<tr>
<th>695211400133905</th>
<td>2.008500</td>
<td>0.264706</td>
<td>0.051471</td>
<td>0.201054</td>
<td>1.111610</td>
<td>3.095530e+09</td>
<td>0.829000</td>
<td>1.321501</td>
<td>1.778120</td>
<td>-0.005151</td>
<td>...</td>
<td>0.171246</td>
<td>0.045952</td>
<td>0.944676</td>
<td>0.977172</td>
<td>6.589044</td>
<td>711.201538</td>
<td>-0.784495</td>
<td>0.604325</td>
<td>0.738324</td>
<td>17.322025</td>
</tr>
<tr>
<th>695211400128356</th>
<td>2.158500</td>
<td>0.248366</td>
<td>0.052288</td>
<td>0.215560</td>
<td>1.261481</td>
<td>2.453895e+09</td>
<td>0.997000</td>
<td>1.685200</td>
<td>2.329020</td>
<td>-0.009636</td>
<td>...</td>
<td>0.173038</td>
<td>0.036671</td>
<td>0.993792</td>
<td>1.058148</td>
<td>11.425224</td>
<td>398.763824</td>
<td>-1.321880</td>
<td>0.732896</td>
<td>0.740169</td>
<td>17.948902</td>
</tr>
<tr>
<th>695211400134257</th>
<td>2.344000</td>
<td>0.248408</td>
<td>0.050955</td>
<td>0.208204</td>
<td>1.311461</td>
<td>2.778140e+09</td>
<td>1.039499</td>
<td>1.677399</td>
<td>2.156704</td>
<td>-0.009567</td>
<td>...</td>
<td>0.164955</td>
<td>0.038528</td>
<td>0.964947</td>
<td>1.051983</td>
<td>11.262469</td>
<td>391.997070</td>
<td>-1.228385</td>
<td>0.736809</td>
<td>0.735943</td>
<td>17.962700</td>
</tr>
<tr>
<th>695211400102351</th>
<td>2.362000</td>
<td>0.396694</td>
<td>0.024793</td>
<td>0.155299</td>
<td>1.549895</td>
<td>2.136628e+09</td>
<td>1.404751</td>
<td>3.065599</td>
<td>-0.579718</td>
<td>0.001458</td>
<td>...</td>
<td>0.144106</td>
<td>0.022466</td>
<td>0.992880</td>
<td>0.968951</td>
<td>8.050076</td>
<td>747.957153</td>
<td>0.321721</td>
<td>1.132575</td>
<td>0.900563</td>
<td>17.092886</td>
</tr>
<tr>
<th>695211400124577</th>
<td>2.044500</td>
<td>0.358025</td>
<td>0.006173</td>
<td>0.203197</td>
<td>1.588497</td>
<td>2.624290e+09</td>
<td>1.459000</td>
<td>3.120001</td>
<td>-0.784893</td>
<td>-0.003234</td>
<td>...</td>
<td>0.112881</td>
<td>0.019039</td>
<td>0.824700</td>
<td>0.893768</td>
<td>9.279490</td>
<td>1209.481201</td>
<td>0.397880</td>
<td>1.082259</td>
<td>0.903058</td>
<td>16.626255</td>
</tr>
<tr>
<th>695211400133827</th>
<td>2.180000</td>
<td>0.370000</td>
<td>0.020000</td>
<td>0.268216</td>
<td>0.931083</td>
<td>6.939510e+07</td>
<td>1.393997</td>
<td>2.275499</td>
<td>-0.315073</td>
<td>0.000728</td>
<td>...</td>
<td>0.161987</td>
<td>0.015354</td>
<td>0.857513</td>
<td>1.317902</td>
<td>11.432131</td>
<td>657.535339</td>
<td>0.559455</td>
<td>0.887947</td>
<td>0.889247</td>
<td>16.999174</td>
</tr>
<tr>
<th>695211400128409</th>
<td>2.162000</td>
<td>0.207407</td>
<td>0.029630</td>
<td>0.180046</td>
<td>1.236907</td>
<td>3.553011e+08</td>
<td>0.638000</td>
<td>1.479000</td>
<td>4.264343</td>
<td>-0.013022</td>
<td>...</td>
<td>0.183828</td>
<td>0.037394</td>
<td>0.786550</td>
<td>1.048196</td>
<td>10.671906</td>
<td>480.207489</td>
<td>-1.672038</td>
<td>0.656025</td>
<td>0.735810</td>
<td>17.875925</td>
</tr>
<tr>
<th>695211400132782</th>
<td>2.141500</td>
<td>0.238938</td>
<td>0.035398</td>
<td>0.157207</td>
<td>1.676215</td>
<td>5.774614e+08</td>
<td>0.713251</td>
<td>1.586000</td>
<td>3.660393</td>
<td>-0.012453</td>
<td>...</td>
<td>0.157894</td>
<td>0.041225</td>
<td>0.815148</td>
<td>0.955798</td>
<td>7.516850</td>
<td>520.624390</td>
<td>-1.171845</td>
<td>0.659780</td>
<td>0.696312</td>
<td>17.840738</td>
</tr>
<tr>
<th>695211400130264</th>
<td>2.164500</td>
<td>0.272727</td>
<td>0.060606</td>
<td>0.207770</td>
<td>1.506238</td>
<td>2.802569e+09</td>
<td>0.785500</td>
<td>1.908998</td>
<td>1.623971</td>
<td>-0.001941</td>
<td>...</td>
<td>0.124233</td>
<td>0.040835</td>
<td>0.791734</td>
<td>1.004123</td>
<td>11.761590</td>
<td>696.563477</td>
<td>-1.113008</td>
<td>0.751903</td>
<td>0.808963</td>
<td>17.505594</td>
</tr>
<tr>
<th>695211400134262</th>
<td>2.084000</td>
<td>0.338462</td>
<td>0.030769</td>
<td>0.189452</td>
<td>1.651187</td>
<td>2.282841e+09</td>
<td>1.155001</td>
<td>1.839499</td>
<td>0.040601</td>
<td>-0.002277</td>
<td>...</td>
<td>0.135150</td>
<td>0.036971</td>
<td>0.928898</td>
<td>0.934759</td>
<td>8.900330</td>
<td>559.752625</td>
<td>-0.374571</td>
<td>0.740374</td>
<td>0.777658</td>
<td>17.455154</td>
</tr>
<tr>
<th>695211400051367</th>
<td>2.062000</td>
<td>0.179245</td>
<td>0.061321</td>
<td>0.178532</td>
<td>1.331189</td>
<td>8.182753e+08</td>
<td>0.391500</td>
<td>1.088400</td>
<td>8.286645</td>
<td>-0.009949</td>
<td>...</td>
<td>0.184710</td>
<td>0.035392</td>
<td>0.789023</td>
<td>0.994109</td>
<td>15.490909</td>
<td>417.975677</td>
<td>-2.318994</td>
<td>0.522621</td>
<td>0.708856</td>
<td>18.014046</td>
</tr>
<tr>
<th>695211400134464</th>
<td>2.041500</td>
<td>0.197368</td>
<td>0.046053</td>
<td>0.143319</td>
<td>1.513368</td>
<td>1.686746e+09</td>
<td>0.528999</td>
<td>1.248800</td>
<td>6.524868</td>
<td>-0.013050</td>
<td>...</td>
<td>0.179425</td>
<td>0.029010</td>
<td>0.790380</td>
<td>0.881608</td>
<td>7.325937</td>
<td>470.570404</td>
<td>-2.034039</td>
<td>0.581614</td>
<td>0.724141</td>
<td>17.936180</td>
</tr>
<tr>
<th>695211400000352</th>
<td>2.400000</td>
<td>0.288288</td>
<td>0.045045</td>
<td>0.160094</td>
<td>1.602353</td>
<td>9.396195e+08</td>
<td>1.299751</td>
<td>2.475200</td>
<td>0.269096</td>
<td>-0.001185</td>
<td>...</td>
<td>0.143338</td>
<td>0.017688</td>
<td>0.736078</td>
<td>1.019456</td>
<td>9.457602</td>
<td>1488.709473</td>
<td>0.583173</td>
<td>0.983108</td>
<td>0.864887</td>
<td>16.493820</td>
</tr>
<tr>
<th>695211400053697</th>
<td>2.212500</td>
<td>0.310000</td>
<td>0.080000</td>
<td>0.229030</td>
<td>1.349776</td>
<td>4.898475e+08</td>
<td>1.032000</td>
<td>2.337502</td>
<td>0.153038</td>
<td>0.000263</td>
<td>...</td>
<td>0.182627</td>
<td>0.026980</td>
<td>1.074533</td>
<td>0.876942</td>
<td>4.537641</td>
<td>1371.101685</td>
<td>0.280188</td>
<td>0.917519</td>
<td>0.851317</td>
<td>16.688284</td>
</tr>
<tr>
<th>695211400028274</th>
<td>2.113000</td>
<td>0.318519</td>
<td>0.044444</td>
<td>0.155424</td>
<td>1.620563</td>
<td>1.274164e+09</td>
<td>1.290501</td>
<td>2.348999</td>
<td>-0.431219</td>
<td>-0.001148</td>
<td>...</td>
<td>0.158261</td>
<td>0.011947</td>
<td>1.023619</td>
<td>1.243340</td>
<td>12.130541</td>
<td>1059.956909</td>
<td>-0.033797</td>
<td>0.905951</td>
<td>0.876673</td>
<td>16.931715</td>
</tr>
</tbody>
</table>
<p>19 rows × 42 columns</p>
</div>
### Basically everything is an artifact at these high amplitudes :'(
### But they mostly have average beyond_1_std. So let's see about obj with high amplitude and high beyond_1_std
### will also have to explore smaller amplitudes (but still in tail of distribution)
# Amplitude + Beyond 1 std
```python
m31_lg_amp_and_1std = m31[(m31.amplitude >= .5) & (m31.beyond_1_std >= .4) ]
m31_lg_amp_and_1std
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>amplitude</th>
<th>beyond_1_std</th>
<th>beyond_2_std</th>
<th>cusum</th>
<th>eta</th>
<th>eta_e</th>
<th>inter_percentile_range_25</th>
<th>inter_percentile_range_10</th>
<th>kurtosis</th>
<th>linear_fit_slope</th>
<th>...</th>
<th>periodogram_cusum</th>
<th>periodogram_eta</th>
<th>periodogram_inter_percentile_range_25</th>
<th>periodogram_standard_deviation</th>
<th>periodogram_percent_amplitude</th>
<th>chi2</th>
<th>skew</th>
<th>standard_deviation</th>
<th>stetson_K</th>
<th>weighted_mean</th>
</tr>
</thead>
<tbody>
<tr>
<th>695211100011822</th>
<td>0.548000</td>
<td>0.448649</td>
<td>0.000000</td>
<td>0.212565</td>
<td>0.411408</td>
<td>4.491793e+08</td>
<td>0.582249</td>
<td>0.851000</td>
<td>-1.350367</td>
<td>-0.000462</td>
<td>...</td>
<td>0.167682</td>
<td>0.026004</td>
<td>0.996828</td>
<td>1.919208</td>
<td>65.052376</td>
<td>16.704514</td>
<td>-0.198973</td>
<td>0.315431</td>
<td>0.907555</td>
<td>19.153934</td>
</tr>
<tr>
<th>695211100004173</th>
<td>0.767501</td>
<td>0.430939</td>
<td>0.005525</td>
<td>0.187364</td>
<td>0.483873</td>
<td>7.955822e+08</td>
<td>0.658751</td>
<td>0.944201</td>
<td>-1.243901</td>
<td>-0.000503</td>
<td>...</td>
<td>0.140833</td>
<td>0.024017</td>
<td>1.186760</td>
<td>2.537382</td>
<td>62.822670</td>
<td>10.997559</td>
<td>-0.026965</td>
<td>0.360303</td>
<td>0.897825</td>
<td>19.507534</td>
</tr>
<tr>
<th>695211100068775</th>
<td>0.532001</td>
<td>0.411111</td>
<td>0.005556</td>
<td>0.215667</td>
<td>0.613142</td>
<td>6.792539e+08</td>
<td>0.400999</td>
<td>0.608500</td>
<td>-0.890289</td>
<td>-0.001074</td>
<td>...</td>
<td>0.127151</td>
<td>0.025795</td>
<td>1.065006</td>
<td>2.402177</td>
<td>63.986897</td>
<td>6.307240</td>
<td>-0.149432</td>
<td>0.229186</td>
<td>0.879984</td>
<td>19.444675</td>
</tr>
<tr>
<th>695211100023670</th>
<td>0.599501</td>
<td>0.409722</td>
<td>0.027778</td>
<td>0.231675</td>
<td>0.603006</td>
<td>1.607585e+09</td>
<td>0.567001</td>
<td>0.828899</td>
<td>-1.035711</td>
<td>-0.001269</td>
<td>...</td>
<td>0.153948</td>
<td>0.020290</td>
<td>1.127080</td>
<td>2.133416</td>
<td>55.921345</td>
<td>3.442677</td>
<td>0.377472</td>
<td>0.312781</td>
<td>0.852283</td>
<td>20.036995</td>
</tr>
<tr>
<th>695211100144667</th>
<td>0.994500</td>
<td>0.412281</td>
<td>0.008772</td>
<td>0.197129</td>
<td>1.683427</td>
<td>1.865964e+09</td>
<td>0.838001</td>
<td>1.321201</td>
<td>-0.986911</td>
<td>-0.000536</td>
<td>...</td>
<td>0.154853</td>
<td>0.044020</td>
<td>0.929398</td>
<td>0.948260</td>
<td>7.338324</td>
<td>67.946686</td>
<td>-0.422264</td>
<td>0.498351</td>
<td>0.898137</td>
<td>18.716841</td>
</tr>
<tr>
<th>695211100038567</th>
<td>0.710501</td>
<td>0.412791</td>
<td>0.005814</td>
<td>0.203079</td>
<td>0.463803</td>
<td>4.132107e+08</td>
<td>0.576500</td>
<td>0.953001</td>
<td>-1.178914</td>
<td>-0.000606</td>
<td>...</td>
<td>0.144832</td>
<td>0.023146</td>
<td>1.203996</td>
<td>2.239781</td>
<td>57.148846</td>
<td>9.157421</td>
<td>-0.035361</td>
<td>0.356240</td>
<td>0.868917</td>
<td>19.621275</td>
</tr>
<tr>
<th>695211100012076</th>
<td>0.580500</td>
<td>0.413613</td>
<td>0.020942</td>
<td>0.312670</td>
<td>0.325494</td>
<td>3.542713e+08</td>
<td>0.431250</td>
<td>0.715799</td>
<td>-0.824989</td>
<td>0.000049</td>
<td>...</td>
<td>0.137749</td>
<td>0.018524</td>
<td>0.826184</td>
<td>1.972319</td>
<td>65.323799</td>
<td>16.756071</td>
<td>0.252223</td>
<td>0.268829</td>
<td>0.858947</td>
<td>18.897766</td>
</tr>
<tr>
<th>695211100035741</th>
<td>0.773499</td>
<td>0.406504</td>
<td>0.008130</td>
<td>0.162203</td>
<td>1.468791</td>
<td>1.611173e+09</td>
<td>0.576748</td>
<td>0.975000</td>
<td>-0.857616</td>
<td>0.000626</td>
<td>...</td>
<td>0.147720</td>
<td>0.031864</td>
<td>0.851318</td>
<td>0.974419</td>
<td>9.040049</td>
<td>6.322053</td>
<td>0.284647</td>
<td>0.356673</td>
<td>0.878299</td>
<td>19.795219</td>
</tr>
<tr>
<th>695211300018884</th>
<td>0.674000</td>
<td>0.401575</td>
<td>0.023622</td>
<td>0.399688</td>
<td>0.250500</td>
<td>6.431188e+08</td>
<td>0.471251</td>
<td>0.859999</td>
<td>-0.860290</td>
<td>0.006443</td>
<td>...</td>
<td>0.124966</td>
<td>0.042434</td>
<td>1.018644</td>
<td>1.860740</td>
<td>47.187836</td>
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<tr>
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<tr>
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<tr>
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<tr>
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<tr>
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<tr>
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<tr>
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<tr>
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<tr>
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</tr>
</tbody>
</table>
<p>37 rows × 42 columns</p>
</div>
### mostly Cepheids... combining features may be best way to discover true anomalies
### interesting: 695211300018884 , 695211400009049 ...
# Linear Trend
```python
m31_lg_linear_trend = m31[m31.linear_trend >= .009]
m31_lg_linear_trend
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>amplitude</th>
<th>beyond_1_std</th>
<th>beyond_2_std</th>
<th>cusum</th>
<th>eta</th>
<th>eta_e</th>
<th>inter_percentile_range_25</th>
<th>inter_percentile_range_10</th>
<th>kurtosis</th>
<th>linear_fit_slope</th>
<th>...</th>
<th>periodogram_cusum</th>
<th>periodogram_eta</th>
<th>periodogram_inter_percentile_range_25</th>
<th>periodogram_standard_deviation</th>
<th>periodogram_percent_amplitude</th>
<th>chi2</th>
<th>skew</th>
<th>standard_deviation</th>
<th>stetson_K</th>
<th>weighted_mean</th>
</tr>
</thead>
<tbody>
<tr>
<th>695211100022045</th>
<td>1.3060</td>
<td>0.281818</td>
<td>0.054545</td>
<td>0.372344</td>
<td>0.192495</td>
<td>127563440.0</td>
<td>0.508999</td>
<td>1.586000</td>
<td>0.093616</td>
<td>0.015123</td>
<td>...</td>
<td>0.121819</td>
<td>0.027250</td>
<td>0.942317</td>
<td>2.215710</td>
<td>28.834238</td>
<td>27.740152</td>
<td>-1.034228</td>
<td>0.584109</td>
<td>0.925093</td>
<td>19.562902</td>
</tr>
<tr>
<th>695211400009049</th>
<td>0.7635</td>
<td>0.428571</td>
<td>0.000000</td>
<td>0.438763</td>
<td>0.194513</td>
<td>72289328.0</td>
<td>0.808002</td>
<td>1.165998</td>
<td>-1.336752</td>
<td>0.011980</td>
<td>...</td>
<td>0.093476</td>
<td>0.017952</td>
<td>1.078872</td>
<td>3.831684</td>
<td>49.701527</td>
<td>5.552580</td>
<td>-0.282411</td>
<td>0.444718</td>
<td>0.910447</td>
<td>20.274681</td>
</tr>
<tr>
<th>695211400121607</th>
<td>1.0440</td>
<td>0.361111</td>
<td>0.000000</td>
<td>0.435610</td>
<td>0.365767</td>
<td>738136128.0</td>
<td>0.948000</td>
<td>1.390501</td>
<td>-1.225616</td>
<td>0.010255</td>
<td>...</td>
<td>0.136348</td>
<td>0.009870</td>
<td>0.953997</td>
<td>3.646029</td>
<td>42.746563</td>
<td>8.932074</td>
<td>0.155026</td>
<td>0.562205</td>
<td>0.877161</td>
<td>20.012375</td>
</tr>
<tr>
<th>695211400014745</th>
<td>0.8095</td>
<td>0.360360</td>
<td>0.036036</td>
<td>0.391412</td>
<td>0.462021</td>
<td>77281928.0</td>
<td>0.470499</td>
<td>0.872601</td>
<td>-0.365091</td>
<td>0.009002</td>
<td>...</td>
<td>0.118469</td>
<td>0.041838</td>
<td>0.853858</td>
<td>2.484790</td>
<td>37.225822</td>
<td>2.174049</td>
<td>0.356900</td>
<td>0.337592</td>
<td>0.830490</td>
<td>20.601290</td>
</tr>
<tr>
<th>695211400025927</th>
<td>0.7015</td>
<td>0.297030</td>
<td>0.039604</td>
<td>0.333839</td>
<td>0.780629</td>
<td>337253696.0</td>
<td>0.323002</td>
<td>0.626799</td>
<td>0.552218</td>
<td>0.014561</td>
<td>...</td>
<td>0.140121</td>
<td>0.027622</td>
<td>0.623367</td>
<td>1.225854</td>
<td>27.272932</td>
<td>1.195005</td>
<td>0.324640</td>
<td>0.242561</td>
<td>0.791279</td>
<td>20.601425</td>
</tr>
<tr>
<th>695211400027347</th>
<td>0.9670</td>
<td>0.366972</td>
<td>0.018349</td>
<td>0.430793</td>
<td>0.301353</td>
<td>587397312.0</td>
<td>0.823999</td>
<td>1.278200</td>
<td>-1.090405</td>
<td>0.011836</td>
<td>...</td>
<td>0.105503</td>
<td>0.013193</td>
<td>0.861321</td>
<td>3.319987</td>
<td>42.995911</td>
<td>6.291706</td>
<td>-0.195676</td>
<td>0.489083</td>
<td>0.861518</td>
<td>20.341236</td>
</tr>
<tr>
<th>695211200036829</th>
<td>0.9775</td>
<td>0.282486</td>
<td>0.039548</td>
<td>0.387356</td>
<td>0.229426</td>
<td>239532144.0</td>
<td>0.463999</td>
<td>0.867199</td>
<td>0.861531</td>
<td>0.008735</td>
<td>...</td>
<td>0.097481</td>
<td>0.033426</td>
<td>0.952237</td>
<td>2.477490</td>
<td>63.367161</td>
<td>5.250046</td>
<td>0.710169</td>
<td>0.354064</td>
<td>0.817896</td>
<td>20.095215</td>
</tr>
<tr>
<th>695211200075348</th>
<td>1.1080</td>
<td>0.458333</td>
<td>0.013889</td>
<td>0.400897</td>
<td>0.117357</td>
<td>23674476.0</td>
<td>1.024000</td>
<td>1.484999</td>
<td>-1.029066</td>
<td>0.017021</td>
<td>...</td>
<td>0.101488</td>
<td>0.048847</td>
<td>0.894277</td>
<td>2.820131</td>
<td>63.978363</td>
<td>12.322401</td>
<td>0.163349</td>
<td>0.557800</td>
<td>0.902715</td>
<td>20.001472</td>
</tr>
<tr>
<th>695211200028179</th>
<td>1.0400</td>
<td>0.288462</td>
<td>0.038462</td>
<td>0.402731</td>
<td>0.426862</td>
<td>158537280.0</td>
<td>0.493000</td>
<td>0.822899</td>
<td>0.424714</td>
<td>0.011650</td>
<td>...</td>
<td>0.133496</td>
<td>0.032916</td>
<td>0.767359</td>
<td>1.748228</td>
<td>33.011219</td>
<td>3.210102</td>
<td>0.000096</td>
<td>0.343939</td>
<td>0.714015</td>
<td>20.775995</td>
</tr>
</tbody>
</table>
<p>9 rows × 42 columns</p>
</div>
### all interesting... but most interesting: 695211100022045 -- transient ; 695211200036829 ; 695211200075348
```python
```
|
snad-spaceREPO_NAMEzwadPATH_START.@zwad_extracted@zwad-master@notebooks@m31_feature_analysis.ipynb@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/box/marker/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._symbol import SymbolValidator
from ._size import SizeValidator
from ._outliercolor import OutliercolorValidator
from ._opacity import OpacityValidator
from ._line import LineValidator
from ._color import ColorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._symbol.SymbolValidator",
"._size.SizeValidator",
"._outliercolor.OutliercolorValidator",
"._opacity.OpacityValidator",
"._line.LineValidator",
"._color.ColorValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@box@marker@__init__.py@.PATH_END.py
|
{
"filename": "_weight.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/choroplethmap/colorbar/title/font/_weight.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WeightValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self,
plotly_name="weight",
parent_name="choroplethmap.colorbar.title.font",
**kwargs,
):
super(WeightValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
extras=kwargs.pop("extras", ["normal", "bold"]),
max=kwargs.pop("max", 1000),
min=kwargs.pop("min", 1),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@choroplethmap@colorbar@title@font@_weight.py@.PATH_END.py
|
{
"filename": "test_inmemory.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/unit_tests/docstore/test_inmemory.py",
"type": "Python"
}
|
"""Test in memory docstore."""
import pytest
from langchain_core.documents import Document
from langchain_community.docstore.in_memory import InMemoryDocstore
def test_document_found() -> None:
"""Test document found."""
_dict = {"foo": Document(page_content="bar")}
docstore = InMemoryDocstore(_dict)
output = docstore.search("foo")
assert isinstance(output, Document)
assert output.page_content == "bar"
def test_document_not_found() -> None:
"""Test when document is not found."""
_dict = {"foo": Document(page_content="bar")}
docstore = InMemoryDocstore(_dict)
output = docstore.search("bar")
assert output == "ID bar not found."
def test_adding_document() -> None:
"""Test that documents are added correctly."""
_dict = {"foo": Document(page_content="bar")}
docstore = InMemoryDocstore(_dict)
new_dict = {"bar": Document(page_content="foo")}
docstore.add(new_dict)
# Test that you can find new document.
foo_output = docstore.search("bar")
assert isinstance(foo_output, Document)
assert foo_output.page_content == "foo"
# Test that old document is the same.
bar_output = docstore.search("foo")
assert isinstance(bar_output, Document)
assert bar_output.page_content == "bar"
def test_adding_document_already_exists() -> None:
"""Test that error is raised if document id already exists."""
_dict = {"foo": Document(page_content="bar")}
docstore = InMemoryDocstore(_dict)
new_dict = {"foo": Document(page_content="foo")}
# Test that error is raised.
with pytest.raises(ValueError):
docstore.add(new_dict)
# Test that old document is the same.
bar_output = docstore.search("foo")
assert isinstance(bar_output, Document)
assert bar_output.page_content == "bar"
def test_default_dict_value_in_constructor() -> None:
"""Test proper functioning if no _dict is provided to the constructor."""
docstore = InMemoryDocstore()
docstore.add({"foo": Document(page_content="bar")})
output = docstore.search("foo")
assert isinstance(output, Document)
assert output.page_content == "bar"
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@unit_tests@docstore@test_inmemory.py@.PATH_END.py
|
{
"filename": "test_pytrees.py",
"repo_name": "rhayes777/PyAutoFit",
"repo_path": "PyAutoFit_extracted/PyAutoFit-main/test_autofit/jax/test_pytrees.py",
"type": "Python"
}
|
import numpy as np
import pytest
from autofit.jax_wrapper import numpy as jnp
import autofit as af
jax = pytest.importorskip("jax")
def recreate(o):
flatten_func, unflatten_func = jax._src.tree_util._registry[type(o)]
children, aux_data = flatten_func(o)
return unflatten_func(aux_data, children)
@pytest.fixture(name="gaussian")
def make_gaussian():
return af.Gaussian(centre=1.0, sigma=1.0, normalization=1.0)
@pytest.fixture(autouse=True)
def patch_np(monkeypatch):
monkeypatch.setattr(af.example.model, "np", jnp)
def classic(gaussian, size=1000):
return list(map(gaussian.f, np.arange(size)))
def vmapped(gaussian, size=1000):
f = jax.vmap(gaussian.f)
return list(f(np.arange(size)))
def test_gaussian_prior():
prior = af.GaussianPrior(mean=1.0, sigma=1.0)
new = recreate(prior)
assert new.mean == prior.mean
assert new.sigma == prior.sigma
assert new.id == prior.id
assert new.lower_limit == prior.lower_limit
assert new.upper_limit == prior.upper_limit
@pytest.fixture(name="model")
def _model():
return af.Model(
af.Gaussian,
centre=af.GaussianPrior(mean=1.0, sigma=1.0),
normalization=af.GaussianPrior(mean=1.0, sigma=1.0, lower_limit=0.0),
sigma=af.GaussianPrior(mean=1.0, sigma=1.0, lower_limit=0.0),
)
def test_model(model):
new = recreate(model)
assert new.cls == af.Gaussian
centre = new.centre
assert centre.mean == model.centre.mean
assert centre.sigma == model.centre.sigma
assert centre.id == model.centre.id
def test_instance(model):
instance = model.instance_from_prior_medians()
new = recreate(instance)
assert isinstance(new, af.Gaussian)
assert new.centre == instance.centre
assert new.normalization == instance.normalization
assert new.sigma == instance.sigma
def test_uniform_prior():
prior = af.UniformPrior(lower_limit=0.0, upper_limit=1.0)
new = recreate(prior)
assert new.lower_limit == prior.lower_limit
assert new.upper_limit == prior.upper_limit
assert new.id == prior.id
def test_model_instance(model):
collection = af.Collection(gaussian=model)
instance = collection.instance_from_prior_medians()
new = recreate(instance)
assert isinstance(new, af.ModelInstance)
assert isinstance(new.gaussian, af.Gaussian)
def test_collection(model):
collection = af.Collection(gaussian=model)
new = recreate(collection)
assert isinstance(new, af.Collection)
assert isinstance(new.gaussian, af.Model)
assert new.gaussian.cls == af.Gaussian
centre = new.gaussian.centre
assert centre.mean == model.centre.mean
assert centre.sigma == model.centre.sigma
assert centre.id == model.centre.id
class KwargClass:
"""
@DynamicAttrs
"""
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def test_kwargs():
model = af.Model(KwargClass, a=1, b=2)
instance = model.instance_from_prior_medians()
assert instance.a == 1
assert instance.b == 2
new = recreate(instance)
assert new.a == instance.a
assert new.b == instance.b
|
rhayes777REPO_NAMEPyAutoFitPATH_START.@PyAutoFit_extracted@PyAutoFit-main@test_autofit@jax@test_pytrees.py@.PATH_END.py
|
{
"filename": "fluence_flux.py",
"repo_name": "HeRTA/FRBSTATS",
"repo_path": "FRBSTATS_extracted/FRBSTATS-main/figs/fluence_flux.py",
"type": "Python"
}
|
from csv import reader
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
### Set MPL plot parameters
# Selectable SVG text
plt.rcParams['svg.fonttype'] = 'none'
# Use TeX
plt.rcParams['text.usetex'] = True
# Set figsize
plt.rcParams["figure.figsize"] = (26,20)
plt.rcParams["figure.dpi"] = 300
# Set xtick size
plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 2
plt.rcParams['xtick.minor.size'] = 10
plt.rcParams['xtick.minor.width'] = 2
# Set ytick size
plt.rcParams['ytick.major.size'] = 20
plt.rcParams['ytick.major.width'] = 2
plt.rcParams['ytick.minor.size'] = 10
plt.rcParams['ytick.minor.width'] = 2
# Hide secondary spines
plt.rcParams['axes.spines.top'] = False
plt.rcParams['axes.spines.right'] = False
### Load data
# Initiate empty parameter lists
dm = []
fluence = []
flux = []
# Read FRBSTATS CSV catalogue
with open('../catalogue.csv', 'r') as read_obj:
csv_reader = reader(read_obj)
header = next(csv_reader)
# Skip header
if header != None:
for row in csv_reader:
dm.append(row[9])
fluence.append(row[12])
flux.append(row[10])
### Pre-process data
# Pick out incompatible rows
idx_mask = set()
for idx, val in enumerate(dm):
try:
dm[idx] = float(val)
except ValueError:
idx_mask.add(idx)
for idx, val in enumerate(fluence):
try:
fluence[idx] = float(val)
except ValueError:
idx_mask.add(idx)
for idx, val in enumerate(flux):
try:
flux[idx] = float(val)
except ValueError:
idx_mask.add(idx)
# Dump rows with missing data
for idx in sorted(idx_mask, reverse=True):
del dm[idx]
del fluence[idx]
del flux[idx]
### Initiate plot
# Apply grid
plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=1)
# Scatter plot
plt.scatter(flux, fluence, c=dm, s=500, alpha=0.7, edgecolor='black', linewidth=2, cmap='plasma', zorder=10)
# Set colorbar
cbar = plt.colorbar()
cbar.set_label(r'$\mathrm{Dispersion \ Measure \ }\Bigg[\mathrm{pc \ cm}^{-3}\Bigg]$', fontsize=52)
cbar.ax.tick_params(labelsize=42)
# Remove alpha colorbar component
cbar.set_alpha(1)
cbar.draw_all()
# Set axis labels & figure title
plt.xlabel(r'$\mathrm{Peak \ Flux \ Density \ [Jy]}$', fontsize=52)
plt.ylabel(r'$\mathrm{Burst \ Fluence \ [Jy \ ms]}$', fontsize=52)
plt.title(r'$\mathrm{FRB \ Fluence-Flux \ Distribution}$', fontsize=72, y=1.01)
# Set log-log scaling
plt.xscale('log')
plt.yscale('log')
# Set ylim
#plt.xlim(10**-1,10**4)
#plt.ylim(10**-2,10**3)
# Set tick size
plt.xticks(fontsize=42, y=-0.005)
plt.yticks(fontsize=42)
plt.tight_layout()
# Save data to a scalable format
plt.savefig('fluence_flux.svg', format='svg')
plt.savefig('fluence_flux.pdf')
plt.savefig('fluence_flux.png')
|
HeRTAREPO_NAMEFRBSTATSPATH_START.@FRBSTATS_extracted@FRBSTATS-main@figs@fluence_flux.py@.PATH_END.py
|
{
"filename": "fakes_and_scores.ipynb",
"repo_name": "snad-space/zwad",
"repo_path": "zwad_extracted/zwad-master/notebooks/fakes_and_scores.ipynb",
"type": "Jupyter Notebook"
}
|
```python
%load_ext autoreload
%autoreload 2
# Comment to make figures visible in html version of the notebook
# %matplotlib notebook
```
```python
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
from functools import lru_cache, reduce
from zwad.ad import ZtfAnomalyDetector
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['pgf.rcfonts'] = True
matplotlib.rcParams['font.size'] = 14
algos_for_fields = {
'm31': ['iso', 'gmm', 'svm', 'lof'],
'deep': ['iso', 'gmm', 'svm'],
'disk': ['iso', 'gmm'],
}
jobs = '4'
data_dir = os.path.join('..', 'data')
fake_dir = os.path.join(data_dir, 'fakes')
scalings = ['std', 'pca', 'pca15', 'pca24', 'norm']
styles = {'iso': '--', 'gmm': '-', 'svm': '-.', 'lof': ':', 'union': ':'}
colors = {
'iso': (230/255, 159/255, 0),
'gmm': (86/255, 180/255, 233/255),
'svm': (0, 158/255, 115/255),
'lof': (213/255, 94/255, 0),
'union': (204/255, 121/255, 167/255),
}
algo_paper_names = {
'iso': 'IF',
'gmm': 'GMM',
'svm': 'O-SVM',
'lof': 'LOF',
'union': 'union',
}
field_paper_names = {
'm31': r'\textsc{M\,31}',
'deep': r'\textsc{Deep}',
'disk': r'\textsc{Disk}',
}
```
# Contents
* [Code](#Code)
* [Generating score files with different scalings](#Generating-score-files-with-scalings)
* [Compare fake detection with different scalings](#Compare-fake-detection-with-scalings)
* [Generating score files for different fields](#Generating-score-files-for-different-fields)
* [Plot the fake detection curves for fields](#Plot-the-fake-detection-curves-for-fields)
* [Plot the cumulative score distributions](#Plot-the-cumulative-score-distributions)
# Code
```python
@lru_cache()
def load_fake_names(fake_filename):
"""
Just load the fake names, in tuple.
"""
return tuple(pd.read_csv(fake_filename, index_col=0)['0'])
def fake_indices(scores, fake_names):
"""
Calculate fake indices
Parameters
----------
scores: Scores of all the objects, including fakes at the end.
fake_names: Tuple of fake object names.
Return
------
Table with 'name' and 'order' columns, sorted by 'order'.
"""
fake_n = len(fake_names)
index = np.argsort(scores)
fake_index = np.argsort(index)[-fake_n:] # Guess what's going on here ;)
fake_table = pd.DataFrame({'order': fake_index, 'name': fake_names})
return fake_table.sort_values(by='order').reset_index(drop=True)
def union_fakes(algo_to_fakes):
"""
Union the different algorithms' fake detection curves to one
"""
order = []
for fake_table in algo_to_fakes.values():
order.append(fake_table.sort_values(by='name')['order'].to_numpy())
name = sorted(fake_table['name'])
min_order = np.array(order).min(axis=0)
table = pd.DataFrame({'order': min_order, 'name': name})
table = table.sort_values(by='order').reset_index(drop=True)
return table
def make_fake_tables(scores_dir, fake_dir, field, algos):
"""
Read the score files with fakes. Read the fake descriptions.
Make the tables with score oderings.
"""
fake_tables = {}
for algo in algos:
scores = np.memmap(os.path.join(scores_dir, 'score_{}_{}_fake.dat'.format(field, algo)), dtype=np.float64)
fake_names = load_fake_names(os.path.join(fake_dir, 'fakes_{}_fake.csv'.format(field)))
fake_tables[algo] = fake_indices(scores, fake_names)
fake_tables['union'] = union_fakes(fake_tables)
return fake_tables
```
# Generating score files with scalings
```python
# Generate the score files for m31 field with different scalings
field = 'm31'
for scaling in scalings:
# Put score files in different directories for future comparison
scores_dir = os.path.join(data_dir, 'scores_' + scaling)
os.makedirs(scores_dir, exist_ok=True)
for algo in algos_for_fields[field]:
real_args = ['--oid', os.path.join(data_dir, 'oid_{}.dat'.format(field)),
'--feature', os.path.join(data_dir, 'feature_{}.dat'.format(field)),]
fake_args = ['--oid', os.path.join(fake_dir, 'oid_{}_fake.dat'.format(field)),
'--feature', os.path.join(fake_dir, 'feature_{}_fake.dat'.format(field)),]
score_file = os.path.join(scores_dir, 'score_{}_{}_fake.dat'.format(field, algo))
score_args = ['--output', score_file]
if os.path.exists(score_file):
continue
args = ['--jobs', jobs, '--scale', scaling, '--classifier', algo]
args += real_args + fake_args + score_args
ZtfAnomalyDetector(args).run()
```
# Compare fake detection with scalings
```python
# Plot the fake curves
field = 'm31'
oid_len = os.stat(os.path.join(data_dir, 'oid_m31.dat')).st_size // 8
fig1, ax = plt.subplots(nrows=len(scalings), sharex=True, figsize=(8, 10))
fig2, bx = plt.subplots(figsize=(8, 3))
for i, scaling in enumerate(scalings):
scores_dir = os.path.join(data_dir, 'scores_' + scaling)
fake_tables = make_fake_tables(scores_dir, fake_dir, field, algos_for_fields[field])
for algo, fake_table in fake_tables.items():
ax[i].plot(fake_table['order'] + 1, np.arange(len(fake_table)) + 1, label=algo)
ax[i].set(title='{}, {}'.format(field, scaling), ylabel='Number of fakes')
ax[i].set(xscale='log', xlim=[1, oid_len])
ax[i].legend(loc='lower right')
ax[i].grid()
fake_table = fake_tables['union']
bx.plot(fake_table['order'] + 1, np.arange(len(fake_table)) + 1, label=scaling)
# display(pd.DataFrame({k: v['name'] for k, v in algo_to_fakes.items()}))
ax[-1].set(xlabel='Number of outliers')
fig1.tight_layout()
bx.set(title='m31', ylabel='Number of fakes', xlabel='Number of outliers', xscale='log')
bx.legend(loc='lower right')
bx.grid()
fig2.tight_layout()
```
# Generating score files for different fields
## Score files may be downloaded
```shell
cd ../data
wget "http://sai.snad.space/ztf/scores.tar.gz" -O - | tar -zxf -
```
## Or they may be generated
```python
# Generate the score files for m31 field with different scalings
# WARNING: that may be a long run
scaling = 'std'
scores_dir = os.path.join(data_dir, 'scores')
os.makedirs(scores_dir, exist_ok=True)
for field in algos_for_fields.keys():
for algo in algos_for_fields[field]:
real_args = ['--oid', os.path.join(data_dir, 'oid_{}.dat'.format(field)),
'--feature', os.path.join(data_dir, 'feature_{}.dat'.format(field)),]
fake_args = ['--oid', os.path.join(fake_dir, 'oid_{}_fake.dat'.format(field)),
'--feature', os.path.join(fake_dir, 'feature_{}_fake.dat'.format(field)),]
score_file = os.path.join(scores_dir, 'score_{}_{}_fake.dat'.format(field, algo))
score_args = ['--output', score_file]
if os.path.exists(score_file):
continue
args = ['--jobs', jobs, '--scale', scaling, '--classifier', algo]
args += real_args + fake_args + score_args
ZtfAnomalyDetector(args).run()
```
# Plot the fake detection curves for fields
```python
# Plot the fake curves for fields
scores_dir = os.path.join(data_dir, 'scores')
for field in algos_for_fields.keys():
oid_len = os.stat(os.path.join(data_dir, 'oid_{}.dat'.format(field))).st_size // 8
fake_tables = make_fake_tables(scores_dir, fake_dir, field, algos_for_fields[field])
fig, ax = plt.subplots(figsize=(7, 3))
for algo, fake_table in fake_tables.items():
ax.plot(fake_table['order'] + 1, np.arange(len(fake_table)) + 1,
label=algo_paper_names[algo],
lw=3, ls=styles[algo], color=colors[algo])
ax.set(title=field_paper_names[field], ylabel='Number of fakes', xlabel='Number of outliers')
ax.set(xscale='log', xlim=[1, 10**np.ceil(np.log10(oid_len))], ylim=[0, 16])
ax.vlines(oid_len, 0, 18, ls='--', lw=2, color='black')
ax.grid()
ax.legend(loc='lower right', bbox_to_anchor=(0.9, 0))
fig.tight_layout()
display(pd.DataFrame({k: v['name'] for k, v in fake_tables.items()}))
display(fake_tables['union'])
plt.savefig('../figs/fakes/{}_fakes.pdf'.format(field))
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>iso</th>
<th>gmm</th>
<th>svm</th>
<th>lof</th>
<th>union</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>step_inverse_noise</td>
<td>Gaia16aye_format_r</td>
<td>step_noise</td>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>1</th>
<td>step_noise</td>
<td>ZTF18abaqxrt_format_r</td>
<td>step_inverse_noise</td>
<td>ZTF18abaqxrt_format_r</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>2</th>
<td>Gaia16aye_3_format_r</td>
<td>step_noise</td>
<td>Gaia16aye_3_format_r</td>
<td>flat</td>
<td>step_noise</td>
</tr>
<tr>
<th>3</th>
<td>kilonova170817_format_r</td>
<td>step_inverse_noise</td>
<td>kilonova170817_format_r</td>
<td>step_noise</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>4</th>
<td>Gaia16aye_2_format_r</td>
<td>Gaia16aye_3_format_r</td>
<td>ZTF18abaqxrt_format_r</td>
<td>step_inverse_noise</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>5</th>
<td>ZTF18abaqxrt_format_r</td>
<td>delta_inverse_noise</td>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_3_format_r</td>
<td>flat</td>
</tr>
<tr>
<th>6</th>
<td>Gaia16aye_format_r</td>
<td>kilonova170817_format_r</td>
<td>Gaia16aye_2_format_r</td>
<td>delta_noise</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>ZTF18aaztjyd_format_r</td>
<td>delta_noise</td>
<td>flat</td>
<td>delta_inverse_noise</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>8</th>
<td>ZTF18ablruzq_format_r</td>
<td>Gaia16aye_2_format_r</td>
<td>delta_noise</td>
<td>kilonova170817_format_r</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>9</th>
<td>ZTF18acskgwu_format_r</td>
<td>flat</td>
<td>delta_inverse_noise</td>
<td>Gaia16aye_2_format_r</td>
<td>delta_noise</td>
</tr>
<tr>
<th>10</th>
<td>delta_inverse_noise</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>11</th>
<td>delta_noise</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>12</th>
<td>flat</td>
<td>ZTF18ablruzq_format_r</td>
<td>ZTF18ablruzq_format_r</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>13</th>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>ZTF18ablruzq_format_r</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>14</th>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>order</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>1</th>
<td>0</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>2</th>
<td>0</td>
<td>step_noise</td>
</tr>
<tr>
<th>3</th>
<td>1</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>4</th>
<td>2</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>5</th>
<td>2</td>
<td>flat</td>
</tr>
<tr>
<th>6</th>
<td>3</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>6</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>8</th>
<td>8</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>9</th>
<td>8</td>
<td>delta_noise</td>
</tr>
<tr>
<th>10</th>
<td>22</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>11</th>
<td>23</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>12</th>
<td>31</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>13</th>
<td>407</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>14</th>
<td>3629</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>iso</th>
<th>gmm</th>
<th>svm</th>
<th>union</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>step_noise</td>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_2_format_r</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>1</th>
<td>step_inverse_noise</td>
<td>ZTF18abaqxrt_format_r</td>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>2</th>
<td>Gaia16aye_3_format_r</td>
<td>step_inverse_noise</td>
<td>ZTF18abaqxrt_format_r</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>3</th>
<td>Gaia16aye_2_format_r</td>
<td>step_noise</td>
<td>Gaia16aye_3_format_r</td>
<td>step_noise</td>
</tr>
<tr>
<th>4</th>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_2_format_r</td>
<td>kilonova170817_format_r</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>5</th>
<td>kilonova170817_format_r</td>
<td>Gaia16aye_3_format_r</td>
<td>step_inverse_noise</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>6</th>
<td>ZTF18aaztjyd_format_r</td>
<td>kilonova170817_format_r</td>
<td>step_noise</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>ZTF18abaqxrt_format_r</td>
<td>delta_inverse_noise</td>
<td>flat</td>
<td>flat</td>
</tr>
<tr>
<th>8</th>
<td>delta_noise</td>
<td>delta_noise</td>
<td>delta_inverse_noise</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>9</th>
<td>delta_inverse_noise</td>
<td>flat</td>
<td>delta_noise</td>
<td>delta_noise</td>
</tr>
<tr>
<th>10</th>
<td>ZTF18ablruzq_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>11</th>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18ablruzq_format_r</td>
<td>ZTF18ablruzq_format_r</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>12</th>
<td>flat</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>13</th>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>14</th>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>order</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>1</th>
<td>0</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>3</th>
<td>1</td>
<td>step_noise</td>
</tr>
<tr>
<th>4</th>
<td>3</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>5</th>
<td>4</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>6</th>
<td>7</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>13</td>
<td>flat</td>
</tr>
<tr>
<th>8</th>
<td>15</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>9</th>
<td>24</td>
<td>delta_noise</td>
</tr>
<tr>
<th>10</th>
<td>28</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>11</th>
<td>458</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>12</th>
<td>699</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>13</th>
<td>2584</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>14</th>
<td>5825</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>iso</th>
<th>gmm</th>
<th>union</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>step_noise</td>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>1</th>
<td>step_inverse_noise</td>
<td>step_noise</td>
<td>step_noise</td>
</tr>
<tr>
<th>2</th>
<td>Gaia16aye_format_r</td>
<td>Gaia16aye_3_format_r</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>3</th>
<td>Gaia16aye_2_format_r</td>
<td>step_inverse_noise</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>4</th>
<td>kilonova170817_format_r</td>
<td>ZTF18abaqxrt_format_r</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>5</th>
<td>Gaia16aye_3_format_r</td>
<td>Gaia16aye_2_format_r</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>6</th>
<td>ZTF18aaztjyd_format_r</td>
<td>kilonova170817_format_r</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>ZTF18acskgwu_format_r</td>
<td>delta_inverse_noise</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>8</th>
<td>ZTF18abaqxrt_format_r</td>
<td>delta_noise</td>
<td>delta_noise</td>
</tr>
<tr>
<th>9</th>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>ZTF18aaztjyd_format_r</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>10</th>
<td>ZTF18ablruzq_format_r</td>
<td>ZTF18acskgwu_format_r</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>11</th>
<td>delta_noise</td>
<td>flat</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>12</th>
<td>delta_inverse_noise</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>13</th>
<td>flat</td>
<td>ZTF18ablruzq_format_r</td>
<td>flat</td>
</tr>
<tr>
<th>14</th>
<td>flat_noise</td>
<td>flat_noise</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>order</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2</td>
<td>Gaia16aye_format_r</td>
</tr>
<tr>
<th>1</th>
<td>17</td>
<td>step_noise</td>
</tr>
<tr>
<th>2</th>
<td>27</td>
<td>Gaia16aye_3_format_r</td>
</tr>
<tr>
<th>3</th>
<td>52</td>
<td>step_inverse_noise</td>
</tr>
<tr>
<th>4</th>
<td>64</td>
<td>ZTF18abaqxrt_format_r</td>
</tr>
<tr>
<th>5</th>
<td>73</td>
<td>Gaia16aye_2_format_r</td>
</tr>
<tr>
<th>6</th>
<td>82</td>
<td>kilonova170817_format_r</td>
</tr>
<tr>
<th>7</th>
<td>88</td>
<td>delta_inverse_noise</td>
</tr>
<tr>
<th>8</th>
<td>139</td>
<td>delta_noise</td>
</tr>
<tr>
<th>9</th>
<td>222</td>
<td>ZTF18aaztjyd_format_r</td>
</tr>
<tr>
<th>10</th>
<td>477</td>
<td>ZTF18acskgwu_format_r</td>
</tr>
<tr>
<th>11</th>
<td>812</td>
<td>OGLE-LMC-CEP-0227_format_V</td>
</tr>
<tr>
<th>12</th>
<td>862</td>
<td>ZTF18ablruzq_format_r</td>
</tr>
<tr>
<th>13</th>
<td>1190</td>
<td>flat</td>
</tr>
<tr>
<th>14</th>
<td>301471</td>
<td>flat_noise</td>
</tr>
</tbody>
</table>
</div>
# Plot the cumulative score distributions
```python
for field in algos_for_fields:
algo_to_fakes = {}
n = len(algos_for_fields[field])
fig, ax = plt.subplots(n, 1, sharex=True, figsize=(8, 2 * n))
for i, algo in enumerate(algos_for_fields[field]):
scores = np.memmap('../data/scores/score_{}_{}_fake.dat'.format(field, algo), dtype=np.float64)
ax[i].plot(np.arange(len(scores)), np.sort(scores), label=algo)
ax[i].set(ylabel='object score')
ax[i].legend(loc='upper left')
ax[i].grid()
ax[-1].set(xlabel='object number')
ax[0].set(title=field)
ax[0].set(xscale='log', xlim=[1, oid_len])
fig.tight_layout()
```
|
snad-spaceREPO_NAMEzwadPATH_START.@zwad_extracted@zwad-master@notebooks@fakes_and_scores.ipynb@.PATH_END.py
|
{
"filename": "broadband_poly.py",
"repo_name": "andreicuceu/vega",
"repo_path": "vega_extracted/vega-master/vega/broadband_poly.py",
"type": "Python"
}
|
import numpy as np
class BroadbandPolynomials:
""" Class for computing broadband polynomials. """
def __init__(self, bb_input, cf_name, model_coordinates, dist_model_coordinates):
self.model_coordinates = model_coordinates
self.dist_model_coordinates = dist_model_coordinates
self.bb_terms = {
'pre-add': [],
'pre-mul': [],
'post-add': [],
'post-mul': []
}
for i, bb in enumerate(bb_input.values()):
bb = bb.split()
# Check if the bb setup is valid
if len(bb) not in [5, 6]:
raise ValueError(
f'Broadband setup must have 5 or 6 elements. Got {len(bb)} elements')
if bb[0] not in ['add', 'mul']:
raise ValueError(f'Broadband type must be either "add" or "mul". Got {bb[0]}')
if bb[1] not in ['pre', 'post']:
raise ValueError(f'Broadband position must be either "pre" or "post". Got {bb[1]}')
if bb[2] not in ['rp,rt', 'r,mu']:
raise ValueError(
f'Broadband coordinates must be either "rp,rt" or "r,mu". Got {bb[2]}')
if len(bb[3].split(':')) != 3:
raise ValueError(
f'Broadband coordinates must be in the format "min:max:step". Got {bb[3]}')
if len(bb[4].split(':')) != 3:
raise ValueError(
f'Broadband coordinates must be in the format "min:max:step". Got {bb[4]}')
if len(bb) > 5 and bb[5] != 'broadband_sky':
raise ValueError(
'If passing six elements in the broadband config, '
f'the sixth element must be "broadband_sky". Got {bb[5]}'
)
# Initialize the broadband config
r1_min, r1_max, dr1 = bb[3].split(':')
r2_min, r2_max, dr2 = bb[4].split(':')
if len(bb) > 5:
name = f'BB-{cf_name}-{i}-{bb[5]}'
else:
name = f'BB-{cf_name}-{i} {bb[0]} {bb[1]} {bb[2]}'
# Create the broadband term dictionary
bb_term = {
'name': name,
'func': 'broadband' if len(bb) == 5 else bb[5],
'coordinates': bb[2],
'r1_config': (int(r1_min), int(r1_max), int(dr1)),
'r2_config': (int(r2_min), int(r2_max), int(dr2))
}
self.bb_terms[f'{bb[1]}-{bb[0]}'] += [bb_term]
def compute(self, params, pos_type):
assert pos_type in list(self.bb_terms.keys())
if 'pre' in pos_type:
coordinates = self.model_coordinates
else:
coordinates = self.dist_model_coordinates
bb_poly_total = None
for bb_term in self.bb_terms[pos_type]:
if bb_term['func'] == 'broadband':
bb_poly = self._compute_broadband(bb_term, params, coordinates)
elif bb_term['func'] == 'broadband_sky':
bb_poly = self._compute_broadband_sky(bb_term['name'], params, coordinates)
else:
raise ValueError(f'Broadband function {bb_term["func"]} not supported')
if bb_poly_total is None:
bb_poly_total = 1 + bb_poly if 'mul' in pos_type else bb_poly
elif 'mul' in pos_type:
bb_poly_total *= 1 + bb_poly
else:
bb_poly_total += bb_poly
if bb_poly_total is None:
bb_poly_total = 1 if 'mul' in pos_type else 0
return bb_poly_total
@staticmethod
def _compute_broadband_sky(bb_term_name, params, coordinates):
"""Compute sky broadband term.
Calculates a Gaussian broadband in rp,rt for the sky residuals.
Parameters
----------
bb_term : dict
broadband term config
params : dict
Computation parameters
Returns
-------
1d Array
Output broadband
"""
scale = params[bb_term_name + '-scale-sky']
sigma = params[bb_term_name + '-sigma-sky']
corr = scale / (sigma * np.sqrt(2. * np.pi))
corr *= np.exp(-0.5 * (coordinates.rt_grid / sigma)**2)
w = (coordinates.rp_grid >= 0.) & (coordinates.rp_grid < coordinates.rp_binsize)
corr[~w] = 0.
return corr
@staticmethod
def _compute_broadband(bb_term, params, coordinates):
"""Compute broadband term.
Calculates a power-law broadband in r and mu or rp,rt.
Parameters
----------
bb_term : dict
broadband term config
params : dict
Computation parameters
Returns
-------
1d Array
Output broadband
"""
if bb_term['coordinates'] == 'r,mu':
r1 = coordinates.r_grid / 100.
r2 = coordinates.mu_grid
elif bb_term['coordinates'] == 'rp,rt':
r1 = coordinates.r_grid / 100. * coordinates.mu_grid
r2 = coordinates.r_grid / 100. * np.sqrt(1 - coordinates.mu_grid**2)
else:
raise ValueError(f'Coordinates {bb_term["coordinates"]} not supported')
r1_min, r1_max, dr1 = bb_term['r1_config']
r2_min, r2_max, dr2 = bb_term['r2_config']
r1_powers = np.arange(r1_min, r1_max + 1, dr1)
r2_powers = np.arange(r2_min, r2_max + 1, dr2)
bb_params = []
for i in r1_powers:
for j in r2_powers:
bb_params.append(params[f'{bb_term["name"]} ({i},{j})'])
# the first dimension of bb_params is that of r1 power indices
# the second dimension of bb_params is that of r2 power indices
bb_params = np.array(bb_params).reshape(r1_max - r1_min + 1, -1)
# we are summing 3D array along 2 dimensions.
# first dimension is the data array (2500 for the standard rp rt grid of 50x50)
# second dimension is the first dimension of bb_params = r1 power indices
# third dimension is the sectond dimension of bb_params = r2 power indices
# dimensions addressed in an array get the indices ':'
# dimensions not addressed in an array get the indices 'None'
# we sum over the second and third dimensions which are powers of r
corr = (bb_params[None, :, :] * r1[:, None, None]**r1_powers[None, :, None]
* r2[:, None, None]**r2_powers[None, None, :]).sum(axis=(1, 2))
return corr
|
andreicuceuREPO_NAMEvegaPATH_START.@vega_extracted@vega-master@vega@broadband_poly.py@.PATH_END.py
|
{
"filename": "jwstpipe1p1p0_ramp_fit.py",
"repo_name": "chriswillott/jwst",
"repo_path": "jwst_extracted/jwst-master/columnjump/columnjump/jwstpipe1p1p0_ramp_fit.py",
"type": "Python"
}
|
#! /usr/bin/env python
#
# ramp_fit.py - calculate weighted mean of slope, based on Massimo
# Robberto's "On the Optimal Strategy to fit MULTIACCUM
# ramps in the presence of cosmic rays."
# (JWST-STScI-0001490,SM-12; 07/25/08). The derivation
# is a generalization for >1 cosmic rays, calculating
# the slope and variance of the slope for each section
# of the ramp (in between cosmic rays). The intervals are
# determined from the input data quality arrays.
#
# Note:
# In this module, comments on the 'first group','second group', etc are
# 1-based, unless noted otherwise.
import time
import logging
import numpy as np
from multiprocessing.pool import Pool as Pool
import multiprocessing
import warnings
from jwst import datamodels
from jwst.datamodels import dqflags
from jwst.lib import pipe_utils
#from . import gls_fit # used only if algorithm is "GLS"
from . import jwstpipe1p1p0_utils
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
DO_NOT_USE = dqflags.group['DO_NOT_USE']
JUMP_DET = dqflags.group['JUMP_DET']
BUFSIZE = 1024 * 300000 # 300Mb cache size for data section
def ramp_fit(model, buffsize, save_opt, readnoise_model, gain_model,
algorithm, weighting, max_cores):
"""
Calculate the count rate for each pixel in all data cube sections and all
integrations, equal to the slope for all sections (intervals between
cosmic rays) of the pixel's ramp divided by the effective integration time.
The weighting parameter must currently be set to 'optim', to use the optimal
weighting (paper by Fixsen, ref. TBA) will be used in the fitting; this is
currently the only supported weighting scheme.
Parameters
----------
model : data model
input data model, assumed to be of type RampModel
buffsize : int
size of data section (buffer) in bytes
save_opt : boolean
calculate optional fitting results
readnoise_model : instance of data Model
readnoise for all pixels
gain_model : instance of gain model
gain for all pixels
algorithm : string
'OLS' specifies that ordinary least squares should be used;
'GLS' specifies that generalized least squares should be used.
weighting : string
'optimal' specifies that optimal weighting should be used;
currently the only weighting supported.
max_cores : string
Number of cores to use for multiprocessing. If set to 'none' (the default), then no multiprocessing will be done.
The other allowable values are 'quarter', 'half', and 'all'. This is the fraction of cores to use for multi-proc.
The total number of cores includes the SMT cores (Hyper Threading for Intel).
Returns
-------
new_model : Data Model object
DM object containing a rate image averaged over all integrations in
the exposure
int_model : Data Model object or None
DM object containing rate images for each integration in the exposure
opt_model : RampFitOutputModel object or None
DM object containing optional OLS-specific ramp fitting data for the
exposure
gls_opt_model : GLS_RampFitModel object or None
Object containing optional GLS-specific ramp fitting data for the
exposure
"""
if algorithm.upper() == "GLS":
new_model, int_model, gls_opt_model = gls_ramp_fit(model,
buffsize, save_opt,
readnoise_model, gain_model, max_cores)
opt_model = None
else:
# Get readnoise array for calculation of variance of noiseless ramps, and
# gain array in case optimal weighting is to be done
frames_per_group = model.meta.exposure.nframes
readnoise_2d, gain_2d = jwstpipe1p1p0_utils.get_ref_subs(model, readnoise_model,
gain_model, frames_per_group)
new_model, int_model, opt_model = \
ols_ramp_fit_multi(model, buffsize, save_opt, readnoise_2d,
gain_2d, weighting, max_cores)
gls_opt_model = None
# Update data units in output models
if new_model is not None:
new_model.meta.bunit_data = 'DN/s'
new_model.meta.bunit_err = 'DN/s'
if int_model is not None:
int_model.meta.bunit_data = 'DN/s'
int_model.meta.bunit_err = 'DN/s'
return new_model, int_model, opt_model, gls_opt_model
def ols_ramp_fit_multi(input_model, buffsize, save_opt, readnoise_2d, gain_2d,
weighting, max_cores):
"""
Setup the inputs to ols_ramp_fit with and without multiprocessing. The inputs will be sliced into the
number of cores that are being used for multiprocessing. Because the data models cannot be pickled, only
numpy arrays are passed and returned as parameters to ols_ramp_fit.
Parameters
----------
input_model : data model
input data model, assumed to be of type RampModel
buffsize : int
size of data section (buffer) in bytes (not used)
save_opt : boolean
calculate optional fitting results
readnoise_model : instance of data Model
readnoise for all pixels
gain_model : instance of gain model
gain for all pixels
algorithm : string
'OLS' specifies that ordinary least squares should be used;
'GLS' specifies that generalized least squares should be used.
weighting : string
'optimal' specifies that optimal weighting should be used;
currently the only weighting supported.
max_cores : string
Number of cores to use for multiprocessing. If set to 'none' (the default), then no multiprocessing will be done.
The other allowable values are 'quarter', 'half', and 'all'. This is the fraction of cores to use for multi-proc.
The total number of cores includes the SMT cores (Hyper Threading for Intel).
Returns
-------
new_model : Data Model object
DM object containing a rate image averaged over all integrations in
the exposure
int_model : Data Model object or None
DM object containing rate images for each integration in the exposure
opt_model : RampFitOutputModel object or None
DM object containing optional OLS-specific ramp fitting data for the
exposure
gls_opt_model : GLS_RampFitModel object or None
Object containing optional GLS-specific ramp fitting data for the
exposure
"""
# Determine number of slices to use for multi-processor computations
if max_cores == 'none':
number_slices = 1
else:
num_cores = multiprocessing.cpu_count()
log.debug(f'Found {num_cores} possible cores to use for ramp fitting')
if max_cores == 'quarter':
number_slices = num_cores // 4 or 1
elif max_cores == 'half':
number_slices = num_cores // 2 or 1
elif max_cores == 'all':
number_slices = num_cores
else:
number_slices = 1
# Copy the int_times table for TSO data
if pipe_utils.is_tso(input_model) and hasattr(input_model, 'int_times'):
int_times = input_model.int_times
else:
int_times = None
total_rows = input_model.data.shape[2]
total_cols = input_model.data.shape[3]
number_of_integrations = input_model.data.shape[0]
# Call ramp fitting for the single processor (1 data slice) case
if number_slices == 1:
max_segments, max_CRs = calc_num_seg(input_model.groupdq, number_of_integrations)
log.debug(f"Max segments={max_segments}")
int_model, opt_model, out_model = create_output_models(input_model,
number_of_integrations, save_opt, total_cols, total_rows,
max_segments, max_CRs)
out_model.data, out_model.dq, out_model.var_poisson, out_model.var_rnoise, out_model.err,\
int_data, int_dq, int_var_poisson, int_var_rnoise, int_err,\
dummy, opt_slope, opt_sigslope, opt_var_poisson, opt_var_rnoise, \
opt_yint, opt_sigyint, opt_pedestal, opt_weights, opt_crmag,\
actual_segments, actual_CRs = \
ols_ramp_fit(input_model.data, input_model.err, input_model.groupdq, input_model.pixeldq,
buffsize, save_opt, readnoise_2d, gain_2d, weighting,
input_model.meta.instrument.name, input_model.meta.exposure.frame_time,
input_model.meta.exposure.ngroups, input_model.meta.exposure.group_time,
input_model.meta.exposure.groupgap, input_model.meta.exposure.nframes,
input_model.meta.exposure.drop_frames1, int_times)
# Populate the rateints output model
int_model.data = int_data
int_model.dq = int_dq
int_model.var_poisson = int_var_poisson
int_model.var_rnoise = int_var_rnoise
int_model.err = int_err
int_model.int_times = int_times
# Populate the optional output model
if save_opt:
opt_model.slope = opt_slope
opt_model.sigslope = opt_sigslope
opt_model.var_poisson = opt_var_poisson
opt_model.var_rnoise = opt_var_rnoise
opt_model.yint = opt_yint
opt_model.sigyint = opt_sigyint
opt_model.pedestal = opt_pedestal
opt_model.weights = opt_weights
opt_model.crmag = opt_crmag
return out_model, int_model, opt_model
# Call ramp fitting for multi-processor (multiple data slices) case
else:
log.debug(f'number of processes being used is {number_slices}')
rows_per_slice = round(total_rows / number_slices)
pool = Pool(processes=number_slices)
slices = []
# Populate the first n-1 slices
for i in range(number_slices - 1):
start_row = i * rows_per_slice
stop_row = (i + 1) * rows_per_slice
readnoise_slice = readnoise_2d[start_row: stop_row, :]
gain_slice = gain_2d[start_row: stop_row, :]
data_slice = input_model.data[:,:,start_row: stop_row, :].copy()
err_slice = input_model.err[:, :, start_row: stop_row, :].copy()
groupdq_slice = input_model.groupdq[:, :, start_row: stop_row, :].copy()
pixeldq_slice = input_model.pixeldq[ start_row: stop_row, :].copy()
slices.insert(i, (data_slice, err_slice, groupdq_slice, pixeldq_slice, buffsize, save_opt, readnoise_slice,
gain_slice, weighting,
input_model.meta.instrument.name, input_model.meta.exposure.frame_time,
input_model.meta.exposure.ngroups, input_model.meta.exposure.group_time,
input_model.meta.exposure.groupgap, input_model.meta.exposure.nframes,
input_model.meta.exposure.drop_frames1, int_times))
# last slice gets the rest
start_row = (number_slices - 1) * rows_per_slice
readnoise_slice = readnoise_2d[start_row: total_rows, :]
gain_slice = gain_2d[start_row: total_rows, :]
data_slice = input_model.data[:, :, start_row: total_rows, :].copy()
err_slice = input_model.err[:, :, start_row: total_rows, :].copy()
groupdq_slice = input_model.groupdq[:, :, start_row: total_rows, :].copy()
pixeldq_slice = input_model.pixeldq[start_row: total_rows, :].copy()
slices.insert(number_slices - 1, (data_slice, err_slice, groupdq_slice, pixeldq_slice, buffsize, save_opt,
readnoise_slice, gain_slice, weighting,
input_model.meta.instrument.name, input_model.meta.exposure.frame_time,
input_model.meta.exposure.ngroups, input_model.meta.exposure.group_time,
input_model.meta.exposure.groupgap, input_model.meta.exposure.nframes,
input_model.meta.exposure.drop_frames1, int_times))
# Start up the processes for each slice
log.debug("Creating %d processes for ramp fitting " % number_slices)
real_results = pool.starmap(ols_ramp_fit, slices)
pool.close()
pool.join()
k = 0
log.debug("All processes complete")
# Create new model for the primary output.
actual_segments = real_results[0][20]
actual_CRs = real_results[0][21]
int_model, opt_model, out_model = create_output_models(input_model,
number_of_integrations, save_opt, total_cols, total_rows,
actual_segments, actual_CRs)
int_model.int_times = int_times
# iterate over the number of slices and place the results into the output models
for resultslice in real_results:
start_row = k * rows_per_slice
if len(real_results) == k + 1: # last result
out_model.data[start_row: total_rows, :] = resultslice[0]
out_model.dq[start_row:total_rows, :] = resultslice[1]
out_model.var_poisson[start_row:total_rows, :] = resultslice[2]
out_model.var_rnoise[start_row:total_rows, :] = resultslice[3]
out_model.err[start_row:total_rows, :] = resultslice[4]
if resultslice[5] is not None: #Integration results exist
int_model.data[: , start_row:total_rows, :] = resultslice[5]
int_model.dq[:, start_row:total_rows, :] = resultslice[6]
int_model.var_poisson[:, start_row:total_rows, :] = resultslice[7]
int_model.var_rnoise[:, start_row:total_rows, :] = resultslice[8]
int_model.err[:, start_row:total_rows, :] = resultslice[9]
if resultslice[11] is not None: #Optional results exist
opt_model.slope[:, :, start_row:total_rows, :] = resultslice[11]
opt_model.sigslope[:, :, start_row:total_rows, :] = resultslice[12]
opt_model.var_poisson[:,:, start_row:total_rows, :] = resultslice[13]
opt_model.var_rnoise[:, :, start_row:total_rows, :] = resultslice[14]
opt_model.yint[:, :, start_row:total_rows, :] = resultslice[15]
opt_model.sigyint[:, :, start_row:total_rows, :] = resultslice[16]
opt_model.pedestal[:, start_row:total_rows, :] = resultslice[17]
opt_model.weights[:, :, start_row:total_rows, :] = resultslice[18]
opt_model.crmag[:, :, start_row:total_rows, :] = resultslice[19]
else: #all but last slice
stop_row = (k + 1) * rows_per_slice
out_model.data[start_row: stop_row, :] = resultslice[0]
out_model.dq[start_row: stop_row, :] = resultslice[1]
out_model.var_poisson[ start_row: stop_row, :] = resultslice[2]
out_model.var_rnoise[ start_row: stop_row, :] = resultslice[3]
out_model.err[start_row: stop_row, :] = resultslice[4]
if resultslice[5] is not None: #Multiple integration results exist
int_model.data[:, start_row: stop_row, :] = resultslice[5]
int_model.dq[:, start_row: stop_row, :] = resultslice[6]
int_model.var_poisson[:, start_row: stop_row, :] = resultslice[7]
int_model.var_rnoise[:, start_row: stop_row, :] = resultslice[8]
int_model.err[:, start_row: stop_row, :] = resultslice[9]
if resultslice[11] is not None: #Optional Results exist
opt_model.slope[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[11]
opt_model.sigslope[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[12]
opt_model.var_poisson[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[13]
opt_model.var_rnoise[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[14]
opt_model.yint[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[15]
opt_model.sigyint[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[16]
opt_model.pedestal[:, start_row: (k + 1) *rows_per_slice, :] = resultslice[17]
opt_model.weights[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[18]
opt_model.crmag[:, :, start_row: (k + 1) *rows_per_slice, :] = resultslice[19]
k = k + 1
return out_model, int_model, opt_model
def create_output_models(input_model, number_of_integrations, save_opt, total_cols, total_rows,
actual_segments, actual_CRs):
"""
Create_output_models is used to make blank output models to hold the results from the OLS
ramp fitting.
Parameters
----------
input_model : DataModel
The input ramp model
number_of_integrations : int
The number of integration in the input model
save_opt : Boolean
Whether to save the optional outputs
total_cols : int
The number of columns in the input image
total_rows : int
The number of rows in the input image
actual_segments : int
The largest number of segments in the integration resulting from cosmic rays
actual_CRs : int
The largest number of cosmic rays jumps found in any integration
Returns
------------
int_model : DataModel
The per integration output model
opt_model : DataModel
The optional output model
out_model : RampFitOutputModel
The standard rate output model
"""
imshape = (total_rows, total_cols)
out_model = datamodels.ImageModel(data=np.zeros(imshape, dtype=np.float32),
dq=np.zeros(imshape, dtype=np.uint32),
var_poisson=np.zeros(imshape, dtype=np.float32),
var_rnoise=np.zeros(imshape, dtype=np.float32),
err=np.zeros(imshape, dtype=np.float32))
# ... and add all keys from input
out_model.update(input_model)
# create per integrations model
int_model = datamodels.CubeModel(
data=np.zeros((number_of_integrations,) + imshape, dtype=np.float32),
dq=np.zeros((number_of_integrations,) + imshape, dtype=np.uint32),
var_poisson=np.zeros((number_of_integrations,) + imshape, dtype=np.float32),
var_rnoise=np.zeros((number_of_integrations,) + imshape, dtype=np.float32),
err=np.zeros((number_of_integrations,) + imshape, dtype=np.float32))
int_model.int_times = None
int_model.update(input_model) # ... and add all keys from input
# Create model for the optional output
if save_opt:
opt_model = datamodels.RampFitOutputModel(
slope=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
yint=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
sigyint=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
sigslope=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
weights=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
firstf_int=np.zeros((number_of_integrations,) + imshape, dtype=np.float32),
pedestal=np.zeros((number_of_integrations,) + imshape, dtype=np.float32),
crmag=np.zeros((number_of_integrations,) + (actual_CRs,) + imshape, dtype=np.float32),
var_poisson=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
var_rnoise=np.zeros((number_of_integrations,) + (actual_segments,) + imshape, dtype=np.float32),
)
opt_model.meta.filename = input_model.meta.filename
opt_model.update(input_model) # ... and add all keys from input
else:
opt_model = None
return int_model, opt_model, out_model
def ols_ramp_fit(data, err, groupdq, inpixeldq, buffsize, save_opt, readnoise_2d, gain_2d,
weighting, instrume, frame_time, ngroups, group_time, groupgap, nframes,
dropframes1, int_times):
"""
Fit a ramp using ordinary least squares. Calculate the count rate for each
pixel in all data cube sections and all integrations, equal to the weighted
slope for all sections (intervals between cosmic rays) of the pixel's ramp
divided by the effective integration time.
Parameters
----------
data : The input 4-D array with ramp data (num_integrations, num_groups, num_rows, num_cols)
The input ramp data
err : The input 4-D error that matches the ramp data
groupdq : The input 4-D group DQ flags
inpixeldq : The input 2-D pixel DQ flags
buffsize : int
The working buffer size
save_opt : Boolean
Whether to return the optional output model
readnoise_2d : 2D float32
The read noise of each pixel
gain_2d : 2D float32
The gain of each pixel
weighting : string
'optimal' is the only valid value
instrume : string
Instrument name
frame_time : float32
The time to read one frame.
ngroups : int
The number of groups in each integration
group_time : float32
The time to read one group.
groupgap : int
The number of frames that are not included in the group average
nframes : int
The number of frames that are included in the group average
dropframes1 :
The number of frames dropped at the beginning of every integration
int_times : None
Not used
Returns
-------
new_model.data : 2-D float32
The output final rate of each pixel
new_model.dq : 2-D DQflag
The output pixel dq for each pixel
new_model.var_poisson : 2-D float32
The variance in each pixel due to Poisson noise
new_model.var_rnoise : 2-D float32
The variance in each piel due to read noise
new_model.err : 2-D float32
The output total variance for each pixel
int_data : 3-D float32
The rate for each pixel in each integration
int_dq : 3-D float32
The pixel dq flag for each integration
int_var_poisson : 3-D float32
The variance of the rate for each integration due to Poisson noise
int_var_rnoise : 3-D float32
The variance of the rate for each integration due to read noise
int_err : 3-D float32
The total variance of the rate for each integration
int_int_times : 3-D
The total time for each integration
opt_slope : 4-D float32
The rate of each segment in each integration
opt_sigslope : 4-D float32
The total variance of the rate for each pixel in each segment of each integration
opt_var_poisson : 4-D float32
The Poisson variance of the rate for each pixel in each segment of each integration
opt_var_rnoise : 4-D float32
The read noise variance of the rate for each pixel in each segment of each integration
opt_yint : 4-D float32
The y-intercept for each pixel in each segment of each integration
opt_sigyint : 4-D float32
The variance for each pixel in each segment of each integration
opt_pedestal : 4-D float32
The zero point for each pixel in each segment of each integration
opt_weights : 4-D float32
The weight of each pixel to use in combining the segments
opt_crmag : 4-D float32
The magnitude of each CR in each integration
actual_segments : int
The actual maximum number of segments in any integration
actual_CRs : int
The actual maximum number of CRs in any integration
"""
tstart = time.time()
# Get needed sizes and shapes
n_int = data.shape[0]
nreads = data.shape[1]
nrows = data.shape[2]
ncols = data.shape[3]
imshape = (nrows, ncols)
cubeshape = (nreads,) + imshape
# Save original shapes for writing to log file, as these may change for MIRI
orig_nreads = nreads
orig_cubeshape = cubeshape
if (dropframes1 is None): # set to default if missing
dropframes1 = 0
log.debug('Missing keyword DRPFRMS1, so setting to default value of 0')
# For MIRI datasets having >1 group, if all pixels in the final group are
# flagged as DO_NOT_USE, resize the input model arrays to exclude the
# final group. Similarly, if leading groups 1 though N have all pixels
# flagged as DO_NOT_USE, those groups will be ignored by ramp fitting, and
# the input model arrays will be resized appropriately. If all pixels in
# all groups are flagged, return None for the models.
if (instrume == 'MIRI' and nreads > 1):
first_gdq = groupdq[:,0,:,:]
num_bad_slices = 0 # number of initial groups that are all DO_NOT_USE
while (np.all(np.bitwise_and( first_gdq, dqflags.group['DO_NOT_USE']))):
num_bad_slices += 1
nreads -= 1
ngroups -= 1
# Check if there are remaining groups before accessing data
if ngroups < 1 : # no usable data
log.error('1. All groups have all pixels flagged as DO_NOT_USE,')
log.error(' so will not process this dataset.')
return None, None, None
data = data[:,1:,:,:]
err = err[:,1:,:,:]
groupdq = groupdq[:,1:,:,:]
cubeshape = (nreads,) + imshape
# Where the initial group of the just-truncated data is a cosmic ray,
# remove the JUMP_DET flag from the group dq for those pixels so
# that those groups will be included in the fit.
wh_cr = np.where( np.bitwise_and(groupdq[:,0,:,:],
dqflags.group['JUMP_DET']) != 0 )
num_cr_1st = len(wh_cr[0])
for ii in range(num_cr_1st):
groupdq[ wh_cr[0][ii], 0, wh_cr[1][ii],
wh_cr[2][ii]] -= dqflags.group['JUMP_DET']
first_gdq = groupdq[:,0,:,:]
log.info('Number of leading groups that are flagged as DO_NOT_USE: %s', num_bad_slices)
# If all groups were flagged, the final group would have been picked up
# in the while loop above, ngroups would have been set to 0, and Nones
# would have been returned. If execution has gotten here, there must
# be at least 1 remaining group that is not all flagged.
last_gdq = groupdq[:,-1,:,:]
if np.all(np.bitwise_and( last_gdq, dqflags.group['DO_NOT_USE'] )):
nreads -= 1
ngroups -= 1
# Check if there are remaining groups before accessing data
if ngroups < 1 : # no usable data
log.error('2. All groups have all pixels flagged as DO_NOT_USE,')
log.error(' so will not process this dataset.')
return None, None, None
data = data[:,:-1,:,:]
err = err[:,:-1,:,:]
groupdq = groupdq[:,:-1,:,:]
cubeshape = (nreads,)+imshape
log.info('MIRI dataset has all pixels in the final group flagged as DO_NOT_USE.')
# Next block is to satisfy github issue 1681:
# "MIRI FirstFrame and LastFrame minimum number of groups"
if (ngroups < 2):
log.warning('MIRI datasets require at least 2 groups/integration')
log.warning('(NGROUPS), so will not process this dataset.')
return None, None, None
if (ngroups == 1):
log.warning('Dataset has NGROUPS=1, so count rates for each integration')
log.warning('will be calculated as the value of that 1 group divided by')
log.warning('the group exposure time.')
# Calculate effective integration time (once EFFINTIM has been populated
# and accessible, will use that instead), and other keywords that will
# needed if the pedestal calculation is requested. Note 'nframes'
# is the number of given by the NFRAMES keyword, and is the number of
# frames averaged on-board for a group, i.e., it does not include the
# groupgap.
effintim = (nframes + groupgap) * frame_time
# Get GROUP DQ and ERR arrays from input file
gdq_cube = groupdq
gdq_cube_shape = gdq_cube.shape
# If all the pixels have their initial groups flagged as saturated, the DQ
# in the primary and integration-specific output products are updated,
# the other arrays in all output products are populated with zeros, and
# the output products are returned to ramp_fit(). If the initial group of
# a ramp is saturated, it is assumed that all groups are saturated.
first_gdq = groupdq[:,0,:,:]
if np.all(np.bitwise_and( first_gdq, dqflags.group['SATURATED'] )):
new_model, int_model, opt_model = jwstpipe1p1p0_utils.do_all_sat(inpixeldq, groupdq, imshape, n_int, save_opt )
if (save_opt):
actual_segments = 0
actual_CRs = 0
return new_model.data, new_model.dq, new_model.var_poisson, new_model.var_rnoise, new_model.err, \
int_model.data, int_model.dq, int_model.var_poisson, int_model.var_rnoise, int_model.err, int_model.int_times, \
opt_model.slope, opt_model.sigslope, opt_model.var_poisson, opt_model.var_rnoise, opt_model.yint, opt_model.sigyint, \
opt_model.pedestal, opt_model.weights, opt_model.crmag, actual_segments, actual_CRs
# Get max number of segments fit in all integrations
max_seg, num_CRs = calc_num_seg(gdq_cube, n_int)
del gdq_cube
f_max_seg = 0 # final number to use, usually overwritten by actual value
(dq_int, median_diffs_2d, num_seg_per_int, sat_0th_group_int) =\
jwstpipe1p1p0_utils.alloc_arrays_1(n_int, imshape)
opt_res = jwstpipe1p1p0_utils.OptRes(n_int, imshape, max_seg, nreads, save_opt)
# Get Pixel DQ array from input file. The incoming RampModel has uint32
# PIXELDQ, but ramp fitting will update this array here by flagging
# the 2D PIXELDQ locations where the ramp data has been previously
# flagged as jump-detected or saturated. These additional bit values
# require this local variable to be uint16, and it will be used as the
# (uint16) PIXELDQ in the outgoing ImageModel.
pixeldq = inpixeldq.copy()
pixeldq = jwstpipe1p1p0_utils.reset_bad_gain( pixeldq, gain_2d ) # Flag bad pixels in gain
# In this 'First Pass' over the data, loop over integrations and data
# sections to calculate the estimated median slopes, which will be used
# to calculate the variances. This is the same method to estimate slopes
# as is done in the jump detection step, except here CR-affected and
# saturated groups have already been flagged. The actual, fit, slopes for
# each segment are also calculated here.
# Loop over data integrations:
for num_int in range(0, n_int):
# Loop over data sections
for rlo in range(0, cubeshape[1], nrows):
rhi = rlo + nrows
if rhi > cubeshape[1]:
rhi = cubeshape[1]
data_sect = np.float32(data[num_int,: , :, :])
#dt = np.dtype(data_sect)
# Skip data section if it is all NaNs
if np.all(np.isnan( data_sect)):
log.error('Current data section is all nans, so not processing the section.')
continue
# first frame section for 1st group of current integration
ff_sect = data[ num_int, 0, rlo:rhi, :]
# Get appropriate sections
gdq_sect = groupdq[num_int,:,:,:]
rn_sect = readnoise_2d[rlo:rhi, :]
gain_sect = gain_2d[rlo:rhi, :]
# Reset all saturated groups in the input data array to NaN
where_sat = np.where( np.bitwise_and(gdq_sect,
dqflags.group['SATURATED']) != 0)
data_sect[ where_sat ] = np.NaN
del where_sat
# Compute the first differences of all groups
first_diffs_sect = np.diff(data_sect, axis=0)
# If the dataset has only 1 group/integ, assume the 'previous group'
# is all zeros, so just use data as the difference
if (first_diffs_sect.shape[0] == 0):
first_diffs_sect = data_sect.copy()
else:
# Similarly, for datasets having >1 group/integ and having
# single-group segments, just use the data as the difference
wh_nan = np.where( np.isnan( first_diffs_sect[0,:,:]) )
if (len(wh_nan[0]) > 0):
first_diffs_sect[0,:,:][wh_nan] = data_sect[0,:,:][wh_nan]
del wh_nan
i_group,i_yy,i_xx, = np.where( np.bitwise_and(gdq_sect[1:,:,:],
dqflags.group['JUMP_DET']) != 0)
# Mask all the first differences that are affected by a CR,
# starting at group 1. The purpose of starting at index 1 is
# to shift all the indices down by 1, so they line up with the
# indices in first_diffs.
first_diffs_sect[ i_group-1, i_yy, i_xx ] = np.NaN
del i_group, i_yy, i_xx
# Check for pixels in which there is good data in 0th group, but
# all first_diffs for this ramp are NaN because there are too
# few good groups past the 0th. Due to the shortage of good
# data, the first_diffs will be set here equal to the data in
# the 0th group.
wh_min = np.where( np.logical_and(np.isnan(first_diffs_sect)
.all(axis=0), np.isfinite( data_sect[0,:,:])))
if len(wh_min[0] > 0):
first_diffs_sect[0,:,:][wh_min] = data_sect[0,:,:][wh_min]
del wh_min
# All first differences affected by saturation and CRs have been set
# to NaN, so compute the median of all non-NaN first differences.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "All-NaN.*", RuntimeWarning)
nan_med = np.nanmedian(first_diffs_sect, axis=0)
nan_med[np.isnan(nan_med)] = 0. # if all first_diffs_sect are nans
median_diffs_2d[ rlo:rhi, : ] += nan_med
# Calculate the slope of each segment
# note that the name "opt_res", which stands for "optional results",
# is deceiving; this in fact contains all the per-integration and
# per-segment results that will eventually be used to compute the
# final slopes, sigmas, etc. for the main (non-optional) products
t_dq_cube, inv_var, opt_res, f_max_seg, num_seg = \
calc_slope(data_sect, gdq_sect, frame_time, opt_res, save_opt,
rn_sect, gain_sect, max_seg, ngroups, weighting,
f_max_seg)
del gain_sect
# Populate 3D num_seg { integ, y, x } with 2D num_seg for this data
# section (y,x) and integration (num_int)
sect_shape = data_sect.shape[-2:]
num_seg_per_int[num_int, rlo:rhi, :] = num_seg.reshape(sect_shape)
# Populate integ-spec slice which is set if 0th group has SAT
wh_sat0 = np.where( np.bitwise_and(gdq_sect[0,:,:],
dqflags.group['SATURATED']))
if ( len(wh_sat0[0] ) > 0):
sat_0th_group_int[num_int, rlo:rhi, :][ wh_sat0 ] = 1
del wh_sat0
pixeldq_sect = pixeldq[rlo:rhi, :].copy()
dq_int[num_int, rlo:rhi, :] = \
dq_compress_sect(t_dq_cube, pixeldq_sect).copy()
del t_dq_cube
# Loop over the segments and copy the reshaped 2D segment-specific
# results for the current data section to the 4D output arrays.
opt_res.reshape_res(num_int, rlo, rhi, sect_shape, ff_sect, save_opt)
if save_opt:
# Calculate difference between each slice and the previous slice
# as approximation to cosmic ray amplitude for those pixels
# having their DQ set for cosmic rays
data_diff = data_sect - jwstpipe1p1p0_utils.shift_z(data_sect, -1)
dq_cr = np.bitwise_and(dqflags.group['JUMP_DET'], gdq_sect)
opt_res.cr_mag_seg[num_int, :, rlo:rhi, :] = \
data_diff * (dq_cr != 0)
del data_diff
del data_sect
del ff_sect
del gdq_sect
if pixeldq_sect is not None:
del pixeldq_sect
# Compute the final 2D array of differences; create rate array
median_diffs_2d /= n_int
med_rates = median_diffs_2d/group_time
del median_diffs_2d
del first_diffs_sect
(var_p3, var_r3, var_p4, var_r4, var_both4, var_both3,
inv_var_both4, s_inv_var_p3, s_inv_var_r3, s_inv_var_both3,
segs_4) = jwstpipe1p1p0_utils.alloc_arrays_2(n_int, imshape, max_seg)
# In this 'Second Pass' over the data, loop over integrations and data
# sections to calculate the variances of the slope using the estimated
# median slopes from the 'First Pass'. These variances are due to Poisson
# noise only, read noise only, and the combination of Poisson noise and
# read noise. The integration-specific variances are 3D arrays, and the
# segment-specific variances are 4D arrays . The naming convention for
# the arrays:
# 'var': a variance
# 'p3': intermediate 3D array for variance due to Poisson noise
# 'r4': intermediate 4D array for variance due to read noise
# 'both4': intermediate 4D array for combined variance due to both
# Poisson and read noise
# 'inv_<X>': intermediate array = 1/<X>
# 's_inv_<X>': intermediate array = 1/<X>, summed over integrations
# Loop over data integrations
for num_int in range(n_int):
# Loop over data sections
for rlo in range(0, cubeshape[1], nrows):
rhi = rlo + nrows
if rhi > cubeshape[1]:
rhi = cubeshape[1]
gdq_sect = groupdq[num_int, :, rlo:rhi, :]
rn_sect = readnoise_2d[rlo:rhi, :]
gain_sect = gain_2d[rlo:rhi, :]
# Calculate results needed to compute the variance arrays
den_r3, den_p3, num_r3, segs_beg_3 = \
jwstpipe1p1p0_utils.calc_slope_vars( rn_sect, gain_sect, gdq_sect,
group_time, max_seg )
segs_4[num_int, :, rlo:rhi, :] = segs_beg_3
# Suppress harmless arithmetic warnings for now
warnings.filterwarnings("ignore", ".*invalid value.*",
RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*",
RuntimeWarning)
var_p4[num_int, :, rlo:rhi, :] = den_p3 * med_rates[ rlo:rhi, :]
# Find the segment variance due to read noise and convert back to DN
var_r4[num_int, :, rlo:rhi, :] = num_r3 * den_r3/gain_sect**2
# Reset the warnings filter to its original state
warnings.resetwarnings()
del den_r3, den_p3, num_r3, segs_beg_3
del gain_sect
del gdq_sect
# The next 4 statements zero out entries for non-existing segments, and
# set the variances for segments having negative slopes (the segment
# variance is proportional to the median estimated slope) to
# outrageously large values so that they will have negligible
# contributions.
var_p4[num_int,:,:,:] *= ( segs_4[num_int,:,:,:] > 0)
# Suppress, then re-enable harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
var_p4[var_p4 <= 0.] = jwstpipe1p1p0_utils.LARGE_VARIANCE
var_r4[num_int,:,:,:] *= ( segs_4[num_int,:,:,:] > 0)
var_r4[var_r4 <= 0.] = jwstpipe1p1p0_utils.LARGE_VARIANCE
# The sums of inverses of the variances are needed for later
# variance calculations.
s_inv_var_p3[num_int, :, :] = (1./var_p4[num_int, :, :, :]).sum(axis=0)
var_p3[num_int, :, :] = 1./ s_inv_var_p3[num_int, :, :]
s_inv_var_r3[num_int, :, :] = (1./var_r4[num_int, :, :, :]).sum(axis=0)
var_r3[num_int, :, :] = 1./ s_inv_var_r3[num_int, :, :]
# Huge variances correspond to non-existing segments, so are reset to 0
# to nullify their contribution.
var_p3[var_p3 > 0.1 * jwstpipe1p1p0_utils.LARGE_VARIANCE] = 0.
warnings.resetwarnings()
var_both4[num_int,:,:,:] = var_r4[num_int,:,:,:] + var_p4[num_int,:,:,:]
inv_var_both4[num_int, :, :, :] = 1./var_both4[num_int, :, :, :]
# Want to retain values in the 4D arrays only for the segments that each
# pixel has, so will zero out values for the higher indices. Creating
# and manipulating intermediate arrays (views, such as var_p4_int
# will zero out the appropriate indices in var_p4 and var_r4.)
# Extract the slice of 4D arrays for the current integration
var_p4_int = var_p4[num_int,:,:,:] # [ segment, y, x ]
inv_var_both4_int = inv_var_both4[num_int,:,:,:]
# Zero out non-existing segments
var_p4_int *= ( segs_4[num_int,:,:,:] > 0)
inv_var_both4_int *= ( segs_4[num_int,:,:,:] > 0)
# reshape these arrays to simplify masking [ segment, 1D pixel ]
var_p4_int2 = var_p4_int.reshape(( var_p4_int.shape[0],
var_p4_int.shape[1]*var_p4_int.shape[2]))
s_inv_var_both3[num_int,:,:] = (inv_var_both4[num_int,:,:,:]).sum(axis=0)
# Suppress, then re-enable harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
var_both3[num_int, :, :] = 1./s_inv_var_both3[num_int, :, :]
warnings.resetwarnings()
del var_p4_int
del var_p4_int2
del gain_2d
var_p4 *= ( segs_4[:,:,:,:] > 0) # Zero out non-existing segments
var_r4 *= ( segs_4[:,:,:,:] > 0)
# Delete lots of arrays no longer needed
if inv_var_both4_int is not None:
del inv_var_both4_int
if med_rates is not None:
del med_rates
if num_seg_per_int is not None:
del num_seg_per_int
if readnoise_2d is not None:
del readnoise_2d
if rn_sect is not None:
del rn_sect
if segs_4 is not None:
del segs_4
# Now that the segment-specific and integration-specific variances have
# been calculated, the segment-specific, integration-specific, and
# overall slopes will be calculated. The integration-specific slope is
# calculated as a weighted average of the segments in the integration:
# slope_int = sum_over_segs(slope_seg/var_seg)/ sum_over_segs(1/var_seg)
# The overall slope is calculated as a weighted average of the segments in
# all integrations:
# slope = sum_over_integs_and_segs(slope_seg/var_seg)/
# sum_over_integs_and_segs(1/var_seg)
slope_by_var4 = opt_res.slope_seg.copy()/var_both4
del var_both4
s_slope_by_var3 = slope_by_var4.sum(axis=1) # sum over segments (not integs)
s_slope_by_var2 = s_slope_by_var3.sum(axis=0) # sum over integrations
s_inv_var_both2 = s_inv_var_both3.sum(axis=0)
# Compute the 'dataset-averaged' slope
# Suppress, then re-enable harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
slope_dataset2 = s_slope_by_var2/s_inv_var_both2
warnings.resetwarnings()
del s_inv_var_both2, s_slope_by_var2, s_slope_by_var3, slope_by_var4
del s_inv_var_both3
# Replace nans in slope_dataset2 with 0 (for non-existing segments)
slope_dataset2[np.isnan(slope_dataset2)] = 0.
# Compute the integration-specific slope
the_num = (opt_res.slope_seg * inv_var_both4).sum(axis=1)
the_den = (inv_var_both4).sum(axis=1)
# Suppress, then re-enable harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
slope_int = the_num/the_den
warnings.resetwarnings()
del the_num, the_den
# Clean up ramps that are SAT on their initial groups; set ramp parameters
# for variances and slope so they will not contribute
var_p3, var_both3, slope_int, dq_int = jwstpipe1p1p0_utils.fix_sat_ramps(
sat_0th_group_int, var_p3, var_both3, slope_int, dq_int)
if sat_0th_group_int is not None:
del sat_0th_group_int
# Loop over data integrations to calculate integration-specific pedestal
if save_opt:
dq_slice = np.zeros((gdq_cube_shape[2],gdq_cube_shape[3]),
dtype=np.uint32)
for num_int in range(0, n_int):
dq_slice = groupdq[num_int, 0, :, :]
opt_res.ped_int[ num_int, :, : ] = \
jwstpipe1p1p0_utils.calc_pedestal(num_int, slope_int, opt_res.firstf_int,
dq_slice, nframes, groupgap, dropframes1)
del dq_slice
# Collect optional results for output
if save_opt:
gdq_cube = groupdq
opt_res.shrink_crmag(n_int, gdq_cube, imshape, nreads)
del gdq_cube
# Some contributions to these vars may be NaN as they are from ramps
# having PIXELDQ=DO_NOT_USE
var_p4[ np.isnan( var_p4 )] = 0.
var_r4[ np.isnan( var_r4 )] = 0.
# Truncate results at the maximum number of segments found
opt_res.slope_seg = opt_res.slope_seg[:,:f_max_seg,:,:]
opt_res.sigslope_seg = opt_res.sigslope_seg[:,:f_max_seg,:,:]
opt_res.yint_seg = opt_res.yint_seg[:,:f_max_seg,:,:]
opt_res.sigyint_seg = opt_res.sigyint_seg[:,:f_max_seg,:,:]
opt_res.weights = (inv_var_both4[:,:f_max_seg,:,:])**2.
opt_res.var_p_seg = var_p4[:,:f_max_seg,:,:]
opt_res.var_r_seg = var_r4[:,:f_max_seg,:,:]
opt_model = opt_res.output_optional(effintim)
else:
opt_model = None
if inv_var_both4 is not None:
del inv_var_both4
if var_p4 is not None:
del var_p4
if var_r4 is not None:
del var_r4
if inv_var is not None:
del inv_var
if pixeldq is not None:
del pixeldq
# Output integration-specific results to separate file
int_model = jwstpipe1p1p0_utils.output_integ(slope_int, dq_int, effintim,
var_p3, var_r3, var_both3, int_times)
if opt_res is not None:
del opt_res
if slope_int is not None:
del slope_int
del var_p3
del var_r3
del var_both3
if int_times is not None:
del int_times
# Divide slopes by total (summed over all integrations) effective
# integration time to give count rates.
c_rates = slope_dataset2 / effintim
# Compress all integration's dq arrays to create 2D PIXELDDQ array for
# primary output
final_pixeldq = dq_compress_final(dq_int, n_int)
if dq_int is not None:
del dq_int
tstop = time.time()
log_stats(c_rates)
log.debug('Instrument: %s', instrume)
log.debug('Number of pixels in 2D array: %d', nrows * ncols)
log.debug('Shape of 2D image: (%d, %d)' %(imshape))
log.debug('Shape of data cube: (%d, %d, %d)' %(orig_cubeshape))
log.debug('Buffer size (bytes): %d', buffsize)
log.debug('Number of rows per buffer: %d', nrows)
log.info('Number of groups per integration: %d', orig_nreads)
log.info('Number of integrations: %d', n_int)
log.debug('The execution time in seconds: %f', tstop - tstart)
# Compute the 2D variances due to Poisson and read noise
var_p2 = 1/(s_inv_var_p3.sum(axis=0))
var_r2 = 1/(s_inv_var_r3.sum(axis=0))
# Huge variances correspond to non-existing segments, so are reset to 0
# to nullify their contribution.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "invalid value.*", RuntimeWarning)
var_p2[var_p2 > 0.1 * jwstpipe1p1p0_utils.LARGE_VARIANCE] = 0.
var_r2[var_r2 > 0.1 * jwstpipe1p1p0_utils.LARGE_VARIANCE] = 0.
# Some contributions to these vars may be NaN as they are from ramps
# having PIXELDQ=DO_NOT_USE
var_p2[ np.isnan( var_p2 )] = 0.
var_r2[ np.isnan( var_r2 )] = 0.
# Suppress, then re-enable, harmless arithmetic warning
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
err_tot = np.sqrt(var_p2 + var_r2)
warnings.resetwarnings()
del s_inv_var_p3
del s_inv_var_r3
# Create new model for the primary output.
new_model = datamodels.ImageModel(data=c_rates.astype(np.float32),
dq=final_pixeldq.astype(np.uint32),
var_poisson=var_p2.astype(np.float32),
var_rnoise=var_r2.astype(np.float32),
err=err_tot.astype(np.float32))
if int_model is not None:
int_data = int_model.data.copy()
int_dq = int_model.dq.copy()
int_var_poisson = int_model.var_poisson.copy()
int_var_rnoise = int_model.var_rnoise.copy()
int_err = int_model.err.copy()
int_int_times = int_model.int_times.copy()
else:
int_data = None
int_dq = None
int_var_poisson = None
int_var_rnoise = None
int_err = None
int_int_times = None
if opt_model is not None:
opt_slope = opt_model.slope.copy()
opt_sigslope = opt_model.sigslope.copy()
opt_var_poisson = opt_model.var_poisson.copy()
opt_var_rnoise = opt_model.var_rnoise.copy()
opt_yint = opt_model.yint.copy()
opt_sigyint = opt_model.sigyint.copy()
opt_pedestal = opt_model.pedestal.copy()
opt_weights = opt_model.weights.copy()
opt_crmag = opt_model.crmag.copy()
actual_segments = opt_slope.shape[1]
actual_CRs = opt_crmag.shape[1]
else:
opt_slope = None
opt_sigslope = None
opt_var_poisson = None
opt_var_rnoise = None
opt_yint = None
opt_sigyint = None
opt_pedestal = None
opt_weights = None
opt_crmag = None
actual_segments = 0
actual_CRs = 0
return new_model.data, new_model.dq, new_model.var_poisson, new_model.var_rnoise, new_model.err, \
int_data, int_dq, int_var_poisson, int_var_rnoise, int_err, int_int_times, \
opt_slope, opt_sigslope, opt_var_poisson, opt_var_rnoise, opt_yint, opt_sigyint, \
opt_pedestal, opt_weights, opt_crmag, actual_segments, actual_CRs
def gls_ramp_fit(input_model, buffsize, save_opt,
readnoise_model, gain_model, max_cores):
"""Fit a ramp using generalized least squares.
Extended Summary
----------------
Calculate the count rate for each pixel in the data ramp, for every
integration. Generalized least squares is used for fitting the ramp
in order to take into account the correlation between reads. If the
input file contains multiple integrations, a second output file will
be written, containing per-integration count rates.
One additional file can optionally be written (if save_opt is True),
containing per-integration data.
Parameters
----------
model : data model
Input data model, assumed to be of type RampModel.
buffsize : int
Size of data section (buffer) in bytes.
save_opt : boolean
Calculate optional fitting results.
readnoise_model : instance of data Model
Readnoise for all pixels.
gain_model : instance of gain model
Gain for all pixels.
Returns
-------
new_model : Data Model object
DM object containing a rate image averaged over all integrations in
the exposure.
int_model : Data Model object or None
DM object containing rate images for each integration in the exposure,
or None if there is only one integration.
gls_opt_model : GLS_RampFitModel object or None
Object containing optional GLS-specific ramp fitting data for the
exposure; this will be None if save_opt is False.
"""
if max_cores == 'none':
number_slices = 1
else:
num_cores = multiprocessing.cpu_count()
log.info("Found %d possible cores to use for ramp fitting " % num_cores)
if max_cores == 'quarter':
number_slices = num_cores // 4 or 1
elif max_cores == 'half':
number_slices = num_cores // 2 or 1
elif max_cores == 'all':
number_slices = num_cores
else:
number_slices = 1
# Get needed sizes and shapes
nreads, npix, imshape, cubeshape, n_int, instrume, frame_time, ngroups, \
group_time = jwstpipe1p1p0_utils.get_dataset_info(input_model)
(group_time, frames_per_group, saturated_flag, jump_flag) = \
jwstpipe1p1p0_utils.get_more_info(input_model)
# Get readnoise array for calculation of variance of noiseless ramps, and
# gain array in case optimal weighting is to be done
# KDG - not sure what this means and no optimal weigting in GLS
readnoise_2d, gain_2d = jwstpipe1p1p0_utils.get_ref_subs(input_model, readnoise_model,
gain_model, frames_per_group)
# Flag any bad pixels in the gain
pixeldq = jwstpipe1p1p0_utils.reset_bad_gain(input_model.pixeldq, gain_2d)
log.info("number of processes being used is %d" % number_slices)
total_rows = input_model.data.shape[2]
tstart = time.time()
# Determine the maximum number of cosmic ray hits for any pixel.
max_num_cr = -1 # invalid initial value
for num_int in range(n_int):
i_max_num_cr = jwstpipe1p1p0_utils.get_max_num_cr(input_model.groupdq[num_int,:,:,:], jump_flag)
max_num_cr = max(max_num_cr, i_max_num_cr)
# Calculate effective integration time (once EFFINTIM has been populated
# and accessible, will use that instead), and other keywords that will
# needed if the pedestal calculation is requested. Note 'nframes'
# is the number of given by the NFRAMES keyword, and is the number of
# frames averaged on-board for a group, i.e., it does not include the
# groupgap.
effintim, nframes, groupgap, dropframes1= jwstpipe1p1p0_utils.get_efftim_ped(input_model)
if number_slices ==1:
rows_per_slice = total_rows
slopes, slope_int, slope_err_int, pixeldq_sect, dq_int, sum_weight, \
intercept_int, intercept_err_int, pedestal_int, ampl_int, ampl_err_int = \
gls_fit_all_integrations(frame_time, gain_2d, input_model.groupdq,
group_time, jump_flag, max_num_cr, input_model.data, \
input_model.err, nframes, pixeldq, readnoise_2d, \
saturated_flag, save_opt)
else:
rows_per_slice = round(total_rows / number_slices)
pool = Pool(processes=number_slices)
slices = []
slopes = np.zeros(imshape, dtype=np.float32)
sum_weight = np.zeros(imshape, dtype=np.float32)
# For multiple-integration datasets, will output integration-specific
# results to separate file named <basename> + '_rateints.fits'.
# Even if there's only one integration, the output results will be
# saved in these arrays.
slope_int = np.zeros((n_int,) + imshape, dtype=np.float32)
slope_err_int = np.zeros((n_int,) + imshape, dtype=np.float32)
dq_int = np.zeros((n_int,) + imshape, dtype=np.uint32)
out_pixeldq = np.zeros(imshape, dtype=np.uint32)
if save_opt:
# Create arrays for the fitted values of zero-point intercept and
# cosmic-ray amplitudes, and their errors.
intercept_int = np.zeros((n_int,) + imshape, dtype=np.float32)
intercept_err_int = np.zeros((n_int,) + imshape, dtype=np.float32)
# The pedestal is the extrapolation of the first group back to zero
# time, for each integration.
pedestal_int = np.zeros((n_int,) + imshape, dtype=np.float32)
# If there are no cosmic rays, set the last axis length to 1.
shape_ampl = (n_int, imshape[0], imshape[1], max(1, max_num_cr))
ampl_int = np.zeros(shape_ampl, dtype=np.float32)
ampl_err_int = np.zeros(shape_ampl, dtype=np.float32)
##Loop over number of processes
for i in range(number_slices - 1):
start_row = i * rows_per_slice
stop_row = (i + 1) * rows_per_slice
readnoise_slice = readnoise_2d[start_row: stop_row, :]
gain_slice = gain_2d[start_row: stop_row, :]
data_slice = input_model.data[:,:,start_row: stop_row, :].copy()
err_slice = input_model.err[:, :, start_row: stop_row, :].copy()
groupdq_slice = input_model.groupdq[:, :, start_row: stop_row, :].copy()
pixeldq_slice = pixeldq[ start_row: stop_row, :].copy()
slices.insert(i, (frame_time, gain_slice, groupdq_slice, group_time,
jump_flag, max_num_cr, data_slice, err_slice, frames_per_group, pixeldq_slice,
readnoise_slice, saturated_flag, save_opt))
#The last slice takes the remainder of the rows
start_row = (number_slices - 1) * rows_per_slice
readnoise_slice = readnoise_2d[start_row: total_rows, :]
gain_slice = gain_2d[start_row: total_rows, :]
data_slice = input_model.data[:, :, start_row: total_rows, :].copy()
err_slice = input_model.err[:, :, start_row: total_rows, :].copy()
groupdq_slice = input_model.groupdq[:, :, start_row: total_rows, :].copy()
pixeldq_slice = input_model.pixeldq[start_row: total_rows, :].copy()
slices.insert(number_slices - 1, (frame_time, gain_slice, groupdq_slice, group_time,
jump_flag, max_num_cr, data_slice, err_slice, frames_per_group, pixeldq_slice,
readnoise_slice, saturated_flag, save_opt))
log.debug("Creating %d processes for ramp fitting " % number_slices)
real_results = pool.starmap(gls_fit_all_integrations, slices)
pool.close()
pool.join()
k = 0
log.debug("All processes complete")
for resultslice in real_results:
start_row = k * rows_per_slice
if len(real_results) == k + 1: # last result
slopes[start_row:total_rows, :] = resultslice[0]
slope_int[:, start_row:total_rows, :] = resultslice[1]
slope_err_int[:, start_row:total_rows, :] = resultslice[2]
out_pixeldq[start_row:total_rows, :] = resultslice[3]
if resultslice[4] is not None:
dq_int[:, start_row:total_rows, :] = resultslice[4]#nint > 1
sum_weight[start_row:total_rows, :] = resultslice[5] #nint > 1
if resultslice[6] is not None:
intercept_int[:, start_row: total_rows, :] = resultslice[6] # optional
intercept_err_int[:, start_row:total_rows, :] = resultslice[7] # optional
pedestal_int[:, start_row: total_rows, :] = resultslice[8] # optional
ampl_int[:, start_row:total_rows, :] = resultslice[9] # optional
ampl_err_int[:, start_row: total_rows, :] = resultslice[10] # optional
else:
stop_row = (k + 1) * rows_per_slice
slopes[start_row:stop_row, :] = resultslice[0]
slope_int[:, start_row:stop_row, :] = resultslice[1]
slope_err_int[:, start_row:stop_row, :] = resultslice[2]
out_pixeldq[start_row:stop_row, :] = resultslice[3]
if resultslice[4] is not None:
dq_int[:, start_row:stop_row, :] = resultslice[4] # nint > 1
sum_weight[start_row:stop_row, :] = resultslice[5] # nint > 1
if resultslice[6] is not None:
intercept_int[:, start_row: stop_row, :] = resultslice[6] # optional
intercept_err_int[:, start_row:stop_row, :] = resultslice[7] # optional
pedestal_int[:, start_row: stop_row, :] = resultslice[8] # optional
ampl_int[:, start_row:stop_row, :] = resultslice[9] # optional
ampl_err_int[:, start_row: stop_row, :] = resultslice[10] # optional
k = k + 1
# Average the slopes over all integrations.
if n_int > 1:
sum_weight = np.where(sum_weight <= 0., 1., sum_weight)
recip_sum_weight = 1. / sum_weight
slopes *= recip_sum_weight
gls_err = np.sqrt(recip_sum_weight)
# Convert back from electrons to DN.
slope_int /= gain_2d
slope_err_int /= gain_2d
if n_int > 1:
slopes /= gain_2d
gls_err /= gain_2d
if save_opt:
intercept_int /= gain_2d
intercept_err_int /= gain_2d
pedestal_int /= gain_2d
gain_shape = gain_2d.shape
gain_4d = gain_2d.reshape((1, gain_shape[0], gain_shape[1], 1))
ampl_int /= gain_4d
ampl_err_int /= gain_4d
del gain_4d
del gain_2d
# Compress all integration's dq arrays to create 2D PIXELDDQ array for
# primary output
final_pixeldq = dq_compress_final(dq_int, n_int)
int_model = jwstpipe1p1p0_utils.gls_output_integ(input_model, slope_int, slope_err_int, dq_int)
if save_opt: # collect optional results for output
# Get the zero-point intercepts and the cosmic-ray amplitudes for
# each integration (even if there's only one integration).
gls_opt_model = jwstpipe1p1p0_utils.gls_output_optional(input_model,
intercept_int, intercept_err_int,
pedestal_int,
ampl_int, ampl_err_int)
else:
gls_opt_model = None
tstop = time.time()
if n_int > 1:
log_stats(slopes)
else:
log_stats(slope_int[0])
log.debug('Instrument: %s' % instrume)
log.debug('Number of pixels in 2D array: %d' % npix)
log.debug('Shape of 2D image: (%d, %d)' % imshape)
log.debug('Shape of data cube: (%d, %d, %d)' % cubeshape)
log.debug('Buffer size (bytes): %d' % buffsize)
log.debug('Number of rows per slice: %d' % rows_per_slice)
log.info('Number of groups per integration: %d' % nreads)
log.info('Number of integrations: %d' % n_int)
log.debug('The execution time in seconds: %f' % (tstop - tstart,))
# Create new model...
if n_int > 1:
new_model = datamodels.ImageModel(data=slopes.astype(np.float32),
dq=final_pixeldq, err=gls_err.astype(np.float32))
else:
new_model = datamodels.ImageModel(data=slope_int[0], dq=final_pixeldq,
err=slope_err_int[0])
new_model.update(input_model) # ... and add all keys from input
return new_model, int_model, gls_opt_model
def gls_fit_all_integrations(frame_time, gain_2d, gdq_cube,
group_time, jump_flag, max_num_cr, data_sect, input_var_sect,
nframes_used, pixeldq, readnoise_2d, saturated_flag, save_opt):
"""
This method will fit the rate for all pixels and all integrations using the Generalized Least
Squares (GLS) method.
Parameters
----------
frame_time : float32
The time to read one frame
gain_2d : 2D float32
The gain in electrons per DN for each pixel
gdq_cube : 4-D DQ Flags
The group dq flag values for all groups in the exposure
group_time : float32
The time to read one group
jump_flag : DQ flag
The DQ value to mark a jump
max_num_cr : int
The largest number of cosmic rays found in any integration
data_sect : 4-D float32
The input ramp cube with the sample values for each group of each integration for each pixel
input_var_sect: 4-D float32
The input variance for each group of each integration for each pixel
nframes_used : int
The number of frames used to form each group average
pixel_dq : 2-D DQ flags
The pixel DQ flags for all pixels
readnoise_2d : 2-D float32
The read noise for each pixel
saturated_flag : DQ flag
The DQ flag value to mark saturation
save_opt : boolean
Set to true to return the optional output model
Returns
--------
slopes : 2-D float32
The output rate for each pixel
slope_int : 2-D float32
The output y-intercept for each pixel
slope_var_sect : 2-D float32
The variance of the rate for each pixel
pixeldq_sect : 2-D DQ flag
The pixel dq for each pixel
dq_int : 3-D DQ flag
The pixel dq for each integration for each pixel
sum_weight : 2-D float32
The sum of the weights for each pixel
intercept_int : 3-D float32
The y-intercept for each integration for each pixel
intercept_err_int : 3-D float32
The uncertainty of the y-intercept for each pixel of each integration
pedestal_int : 3-D float32
The pedestal value for each integration for each pixel
ampl_int : 3-D float32
The amplitude of each cosmic ray for each pixel
ampl_err_int :
The variance of the amplitude of each cosmic ray for each pixel
"""
number_ints = data_sect.shape[0]
number_rows = data_sect.shape[2]
number_cols = data_sect.shape[3]
imshape = (data_sect.shape[2], data_sect.shape[3])
slope_int = np.zeros((number_ints, number_rows, number_cols), dtype=np.float32)
slope_err_int = np.zeros((number_ints, number_rows, number_cols), dtype=np.float32)
dq_int = np.zeros((number_ints, number_rows, number_cols), dtype=np.uint32)
temp_dq = np.zeros((number_rows, number_cols), dtype=np.uint32)
slopes = np.zeros((number_rows, number_cols), dtype=np.float32)
sum_weight = np.zeros((number_rows, number_cols), dtype=np.float32)
if save_opt:
# Create arrays for the fitted values of zero-point intercept and
# cosmic-ray amplitudes, and their errors.
intercept_int = np.zeros((number_ints,) + imshape, dtype=np.float32)
intercept_err_int = np.zeros((number_ints,) + imshape, dtype=np.float32)
# The pedestal is the extrapolation of the first group back to zero
# time, for each integration.
pedestal_int = np.zeros((number_ints,) + imshape, dtype=np.float32)
# The first group, for calculating the pedestal. (This only needs
# to be nrows high, but we don't have nrows yet. xxx)
first_group = np.zeros(imshape, dtype=np.float32)
# If there are no cosmic rays, set the last axis length to 1.
shape_ampl = (number_ints, imshape[0], imshape[1], max(1, max_num_cr))
ampl_int = np.zeros(shape_ampl, dtype=np.float32)
ampl_err_int = np.zeros(shape_ampl, dtype=np.float32)
else:
intercept_int = None
intercept_err_int = None
pedestal_int = None
first_group = None
shape_ampl = None
ampl_int = None
ampl_err_int = None
# loop over data integrations
for num_int in range(number_ints):
if save_opt:
first_group[:, :] = 0. # re-use this for each integration
# We'll propagate error estimates from previous steps to the
# current step by using the variance.
input_var_sect = input_var_sect ** 2
# Convert the data section from DN to electrons.
data_sect *= gain_2d
if save_opt:
first_group[:, :] = data_sect[num_int, 0, :, :].copy()
(intercept_sect, intercept_var_sect,
slope_sect, slope_var_sect,
cr_sect, cr_var_sect) = \
gls_fit.determine_slope(data_sect[num_int,:,:,:], input_var_sect[num_int,:,:,:],
gdq_cube[num_int,:,:,:], readnoise_2d, gain_2d,
frame_time, group_time,
nframes_used, max_num_cr,
saturated_flag, jump_flag)
slope_int[num_int, :, :] = slope_sect.copy()
v_mask = (slope_var_sect <= 0.)
if v_mask.any():
# Replace negative or zero variances with a large value.
slope_var_sect[v_mask] = jwstpipe1p1p0_utils.LARGE_VARIANCE
# Also set a flag in the pixel dq array.
temp_dq[:, :][v_mask] = dqflags.pixel['UNRELIABLE_SLOPE']
del v_mask
# If a pixel was flagged (by an earlier step) as saturated in
# the first group, flag the pixel as bad.
# Note: save s_mask until after the call to jwstpipe1p1p0_utils.gls_pedestal.
s_mask = (gdq_cube[0] == saturated_flag)
if s_mask.any():
temp_dq[:, :][s_mask] = dqflags.pixel['UNRELIABLE_SLOPE']
slope_err_int[num_int, :, :] = np.sqrt(slope_var_sect)
# We need to take a weighted average if (and only if) number_ints > 1.
# Accumulate sum of slopes and sum of weights.
if number_ints > 1:
weight = 1. / slope_var_sect
slopes[:, :] += (slope_sect * weight)
sum_weight[:, :] += weight
if save_opt:
# Save the intercepts and cosmic-ray amplitudes for the
# current integration.
intercept_int[num_int, :, :] = intercept_sect.copy()
intercept_err_int[num_int, :, :] = \
np.sqrt(np.abs(intercept_var_sect))
pedestal_int[num_int, :, :] = \
jwstpipe1p1p0_utils.gls_pedestal(first_group[:, :],
slope_int[num_int, :, :],
s_mask,
frame_time, nframes_used)
ampl_int[num_int, :, :, :] = cr_sect.copy()
ampl_err_int[num_int, :, :, :] = \
np.sqrt(np.abs(cr_var_sect))
# Compress 4D->2D dq arrays for saturated and jump-detected
# pixels
pixeldq_sect = pixeldq[:, :].copy()
dq_int[num_int, :, :] = \
dq_compress_sect(gdq_cube[num_int,:,:,:], pixeldq_sect).copy()
dq_int[num_int, :, :] |= temp_dq
temp_dq[:, :] = 0 # initialize for next integration
return slopes, slope_int, slope_var_sect, pixeldq_sect, dq_int, sum_weight, \
intercept_int, intercept_err_int, pedestal_int, ampl_int, ampl_err_int
def calc_power(snr):
"""
Using the given SNR, calculate the weighting exponent, which is from
`Fixsen, D.J., Offenberg, J.D., Hanisch, R.J., Mather, J.C, Nieto,
Santisteban, M.A., Sengupta, R., & Stockman, H.S., 2000, PASP, 112, 1350`.
Parameters
----------
snr : float32, 1D array
signal-to-noise for the ramp segments
Returns
-------
pow_wt.ravel() : float32, 1D array
weighting exponent
"""
pow_wt = snr.copy() * 0.0
pow_wt[np.where(snr > 5.)] = 0.4
pow_wt[np.where(snr > 10.)] = 1.0
pow_wt[np.where(snr > 20.)] = 3.0
pow_wt[np.where(snr > 50.)] = 6.0
pow_wt[np.where(snr > 100.)] = 10.0
return pow_wt.ravel()
def interpolate_power(snr):
pow_wt = snr.copy() * 0.0
pow_wt[np.where(snr > 5.)] = ((snr[snr>5]-5)/(10 - 5)) * 0.6 + 0.4
pow_wt[np.where(snr > 10.)] = ((snr[snr>10]-10)/(20 - 10)) * 2.0 + 1.0
pow_wt[np.where(snr > 20.)] = ((snr[snr>20]-20))/(50 - 20) * 3.0 + 3.0
pow_wt[np.where(snr > 50.)] = ((snr[snr>50] - 50))/(100 - 50) * 4.0 + 6.0
pow_wt[np.where(snr > 100.)] = 10.0
return pow_wt.ravel()
def dq_compress_final(dq_int, n_int):
"""
Combine the integration-specific dq arrays (which have already been
compressed and combined with the PIXELDQ array) to create the dq array
of the primary output product.
Parameters
----------
dq_int : uint16, 3D array
cube of combined dq arrays for all data sections in a single integration
n_int : int
total number of integrations in data set
Returns
-------
f_dq : uint16, 2D array
combination of all integration's pixeldq arrays
"""
f_dq = dq_int[0, :, :]
for jj in range(1, n_int):
f_dq = np.bitwise_or(f_dq, dq_int[jj, :, :])
return f_dq
def dq_compress_sect(gdq_sect, pixeldq_sect):
"""
Get ramp locations where the data has been flagged as saturated in the 4D
GROUPDQ array for the current data section, find the corresponding image
locations, and set the SATURATED flag in those locations in the PIXELDQ
array. Similarly, get the ramp locations where the data has been flagged as
a jump detection in the 4D GROUPDQ array, find the corresponding image
locations, and set the COSMIC_BEFORE flag in those locations in the PIXELDQ
array. These modifications to the section of the PIXELDQ array are not used
to flag groups for any computations; they are used only in the integration-
specific output.
Parameters
----------
gdq_sect : int (uint8), 3D array
cube of GROUPDQ array for a data section
pixeldq_sect : int, 2D array
dq array of data section of input model
Returns
-------
pixeldq_sect : int, 2D array
dq array of data section updated with saturated and jump-detected flags
"""
sat_loc_r = np.bitwise_and(gdq_sect, dqflags.group['SATURATED'])
sat_loc_im = np.where(sat_loc_r.sum(axis=0) > 0)
pixeldq_sect[sat_loc_im] = np.bitwise_or(pixeldq_sect[sat_loc_im],
dqflags.pixel['SATURATED'])
cr_loc_r = np.bitwise_and(gdq_sect, dqflags.group['JUMP_DET'])
cr_loc_im = np.where(cr_loc_r.sum(axis=0) > 0)
pixeldq_sect[cr_loc_im] = np.bitwise_or(pixeldq_sect[cr_loc_im],
dqflags.pixel['JUMP_DET'])
return pixeldq_sect
def calc_nrows(model, buffsize, cubeshape, nreads):
"""
Calculate the number of rows per data section to process.
Parameters
----------
model : instance of Data Model
DM object for input
buffsize : int
size of data section (buffer) in bytes
cubeshape : (int, int, int) tuple
shape of input dataset
nreads : int
number of reads in input dataset
Returns
-------
nrows : int
number of rows in buffer of data section
"""
bitpix = model.data.dtype.itemsize
bytepix = int(abs(bitpix) / 8)
if bytepix < 1:
bytepix = 1
nrows = int(buffsize / (bytepix * cubeshape[2] * nreads))
if nrows < 1:
nrows = 1
if nrows > cubeshape[1]:
nrows = cubeshape[1]
return nrows
def calc_slope(data_sect, gdq_sect, frame_time, opt_res, save_opt, rn_sect,
gain_sect, i_max_seg, ngroups, weighting, f_max_seg):
"""
Compute the slope of each segment for each pixel in the data cube section
for the current integration. Each segment has its slope fit in fit_lines();
that slope and other quantities from the fit are added to the 'optional
result' object by append_arr() from the appropriate 'CASE' (type of segment)
in fit_next_segment().
Parameters
----------
data_sect : float, 3D array
section of input data cube array
gdq_sect : int, 3D array
section of GROUPDQ data quality array
frame_time : float
integration time
opt_res : OptRes object
Contains all quantities derived from fitting all segments in all
pixels in all integrations, which will eventually be used to compute
per-integration and per-exposure quantities for all pixels. It's
also used to populate the optional product, when requested.
save_opt : boolean
save optional fitting results
rn_sect : float, 2D array
read noise values for all pixels in data section
gain_sect : float, 2D array
gain values for all pixels in data section
i_max_seg : int
used for size of initial allocation of arrays for optional results;
maximum possible number of segments within the ramp, based on the
number of CR flags
ngroups : int
number of groups per integration
weighting : string
'optimal' specifies that optimal weighting should be used; currently
the only weighting supported.
f_max_seg : int
actual maximum number of segments within a ramp, based on the fitting
of all ramps; later used when truncating arrays before output.
Returns
-------
gdq_sect : int, 3D array
data quality flags for pixels in section
inv_var : float, 1D array
values of 1/variance for good pixels
opt_res : OptRes object
contains all quantities related to fitting for use in computing final
slopes, variances, etc. and is used to populate the optional output
f_max_seg : int
actual maximum number of segments within a ramp, updated here based on
fitting ramps in the current data section; later used when truncating
arrays before output.
num_seg : int, 1D array
numbers of segments for good pixels
"""
nreads, asize2, asize1 = data_sect.shape
npix = asize2 * asize1 # number of pixels in section of 2D array
all_pix = np.arange(npix)
arange_nreads_col = np.arange(nreads)[:, np.newaxis]
start = np.zeros(npix, dtype=np.int32) # lowest channel in fit
# Highest channel in fit initialized to last read
end = np.zeros(npix, dtype=np.int32) + (nreads - 1)
pixel_done = (end < 0) # False until processing is done
inv_var = np.zeros(npix, dtype=np.float32) # inverse of fit variance
num_seg = np.zeros(npix, dtype=np.int32) # number of segments per pixel
# End stack array - endpoints for each pixel
# initialize with nreads for each pixel; set 1st channel to 0
end_st = np.zeros((nreads + 1, npix), dtype=np.int32)
end_st[0, :] = nreads - 1
# end_heads is initially a tuple populated with every pixel that is
# either saturated or contains a cosmic ray based on the input DQ
# array, so is sized to accomodate the maximum possible number of
# pixels flagged. It is later compressed to be an array denoting
# the number of endpoints per pixel.
end_heads = np.ones(npix * nreads, dtype=np.int32)
# Create nominal 2D ERR array, which is 1st slice of
# avged_data_cube * readtime
err_2d_array = data_sect[0, :, :] * frame_time
# Suppress, then re-enable, harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
err_2d_array[err_2d_array < 0] = 0
warnings.resetwarnings()
# Frames >= start and <= end will be masked. However, the first channel
# to be included in fit will be the read in which a cosmic ray has
# been flagged
mask_2d = ((arange_nreads_col >= start[np.newaxis, :]) &
(arange_nreads_col <= end[np.newaxis, :]))
end = 0 # array no longer needed
# Section of GROUPDQ dq section, excluding bad dq values in mask
gdq_sect_r = np.reshape(gdq_sect, (nreads, npix))
mask_2d[gdq_sect_r != 0] = False # saturated or CR-affected
mask_2d_init = mask_2d.copy() # initial flags for entire ramp
wh_f = np.where(np.logical_not(mask_2d))
these_p = wh_f[1] # coordinates of pixels flagged as False
these_r = wh_f[0] # reads of pixels flagged as False
del wh_f
# Populate end_st to contain the set of end points for each pixel.
# Populate end_heads to initially include every pixel that is either
# saturated or contains a cosmic ray. Skips the duplicated final group
# for saturated pixels. Saturated pixels resulting in a contiguous set
# of intervals of length 1 will later be flagged as too short
# to fit well.
for ii, val in enumerate(these_p):
if (these_r[ii] != (nreads - 1)):
end_st[end_heads[these_p[ii]], these_p[ii]] = these_r[ii]
end_heads[these_p[ii]] += 1
# Sort and reverse array to handle the order that saturated pixels
# were added
end_st.sort(axis=0)
end_st = end_st[::-1]
# Reformat to designate the number of endpoints per pixel; compress
# to specify number of groups per pixel
end_heads = (end_st > 0).sum(axis=0)
# Create object to hold optional results
opt_res.init_2d(npix, i_max_seg, save_opt)
# LS fit until 'nreads' iterations or all pixels in
# section have been processed
for iter_num in range(nreads):
if pixel_done.all():
break
# frames >= start and <= end_st will be included in fit
mask_2d = ((arange_nreads_col >= start) &
(arange_nreads_col <
(end_st[end_heads[all_pix] - 1, all_pix] + 1)))
mask_2d[gdq_sect_r != 0] = False # RE-exclude bad group dq values
# for all pixels, update arrays, summing slope and variance
f_max_seg, num_seg = \
fit_next_segment(start, end_st, end_heads, pixel_done, data_sect,
mask_2d, mask_2d_init, inv_var, num_seg, opt_res, save_opt,
rn_sect, gain_sect, ngroups, weighting, f_max_seg)
if f_max_seg is None:
f_max_seg = 1
arange_nreads_col = 0
all_pix = 0
return gdq_sect, inv_var, opt_res, f_max_seg, num_seg
def fit_next_segment(start, end_st, end_heads, pixel_done, data_sect, mask_2d,
mask_2d_init, inv_var, num_seg, opt_res, save_opt, rn_sect,
gain_sect, ngroups, weighting, f_max_seg):
"""
Call routine to LS fit masked data for a single segment for all pixels in
data section. Then categorize each pixel's fitting interval based on
interval length, and whether the interval is at the end of the array.
Update the start array, the end stack array, the end_heads array which
contains the number of endpoints. For pixels in which the fitting intervals
are long enough, the resulting slope and variance are added to the
appropriate stack arrays. The first channel to fit in a segment is either
the first group in the ramp, or a group in which a cosmic ray has been
flagged.
Parameters
----------
start : int, 1D array
lowest channel in fit
end_st : int, 2D array
stack array of endpoints
end_heads : int, 1D array
number of endpoints for each pixel
pixel_done : boolean, 1D array
whether each pixel's calculations are completed
data_sect : float, 3D array
data cube section
mask_2d : bool, 2D array
delineates which channels to fit for each pixel
mask_2d_init : bool, 2D array
copy of intial mask_2d
inv_var : float, 1D array
values of 1/variance for good pixels
num_seg : int, 1D array
numbers of segments for good pixels
opt_res : OptRes object
all fitting quantities, used to compute final results
and to populate optional output product
save_opt : boolean
save optional fitting results
rn_sect : float, 2D array
read noise values for all pixels in data section
gain_sect : float, 2D array
gain values for all pixels in data section
ngroups : int
number of groups per integration
weighting : string
'optimal' specifies that optimal weighting should be used; currently
the only weighting supported.
f_max_seg : int
actual maximum number of segments within a ramp, updated here based on
fitting ramps in the current data section; later used when truncating
arrays before output.
Returns
-------
f_max_seg : int
actual maximum number of segments within a ramp, updated here based on
fitting ramps in the current data section; later used when truncating
arrays before output.
num_seg : int, 1D array
numbers of segments for good pixels
"""
nreads, asize2, asize1 = data_sect.shape # Note: nreads is a scalar here
all_pix = np.arange(asize2 * asize1)
ramp_mask_sum = mask_2d_init.sum(axis=0)
# Compute fit quantities for the next segment of all pixels
# Each returned array below is 1D, for all npix pixels for current segment
slope, intercept, variance, sig_intercept, sig_slope = fit_lines(data_sect,
mask_2d, rn_sect, gain_sect, ngroups, weighting)
end_locs = end_st[end_heads[all_pix] - 1, all_pix]
# Set the fitting interval length; for a segment having >1 groups, this is
# the number of groups-1
l_interval = end_locs - start
wh_done = (start == -1) # done pixels
l_interval[wh_done] = 0 # set interval lengths for done pixels to 0
# Create array to set when each good pixel is classified for the current
# semiramp (to enable unclassified pixels to have their arrays updated)
got_case = np.zeros((asize1*asize2), dtype=bool)
# CASE A) Long enough (semiramp has >2 groups), at end of ramp
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of ends to 0
# - add slopes and variances to running sums
# For segments of this type, the final good group is the final group in the
# ramp, and the variable `l_interval` used below is equal to the number of
# the segment's groups minus 1.
wh_check = np.where((l_interval>1) & (end_locs==nreads-1) & (~pixel_done))
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
start[these_pix] = -1 # all processing for this pixel is completed
end_st[end_heads[these_pix] - 1, these_pix] = 0
end_heads[these_pix] = 0
pixel_done[these_pix] = True # all processing for pixel is completed
got_case[ these_pix ] = True
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "invalid value.*", RuntimeWarning)
g_pix = these_pix[variance[these_pix] > 0.] # good pixels
if (len(g_pix) > 0):
inv_var[g_pix] += 1.0 / variance[g_pix]
# Append results to arrays
opt_res.append_arr(num_seg, g_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[g_pix] += 1
f_max_seg = max(f_max_seg, num_seg.max())
# CASE B) Long enough (semiramp has >2 groups ), not at array end (meaning
# final group for this semiramp is not final group of the whole ramp)
# - remove current end from end stack
# - decrement number of ends
# - add slopes and variances to running sums
# For segments of this type, the final good group in the segment is a CR
# and/or SAT and is not the final group in the ramp, and the variable
# `l_interval` used below is equal to the number of the segment's groups.
wh_check = np.where((l_interval > 2) & (end_locs != nreads - 1) & ~pixel_done)
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
got_case[ these_pix ] = True
start[these_pix] = end_locs[these_pix]
end_st[end_heads[these_pix] - 1, these_pix] = 0
end_heads[these_pix] -= 1
end_heads[end_heads < 0.] = 0.
g_pix = these_pix[variance[these_pix] > 0.] # good pixels
if (len(g_pix) > 0):
inv_var[g_pix] += 1.0 / variance[g_pix]
# Append results to arrays
opt_res.append_arr(num_seg, g_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[g_pix] += 1
f_max_seg = max(f_max_seg, num_seg.max())
# If there are pixels with no later good groups, update stack
# arrays accordingly
c_mask_2d_init = mask_2d_init.copy()
# create array: 0...nreads-1 in a column for each pixel
arr_ind_all = np.array( [np.arange(nreads),] *
c_mask_2d_init.shape[1]).transpose()
wh_c_start_all = np.zeros( c_mask_2d_init.shape[1], dtype=np.uint8)
wh_c_start_all[ g_pix ] = start[ g_pix ]
# set to False all groups before start group
c_mask_2d_init[ arr_ind_all < wh_c_start_all ] = False
# select pixels having all groups False from start to ramp end
wh_rest_false = np.where( c_mask_2d_init.sum(axis=0) == 0)
if(len(wh_rest_false[0]) > 0):
pix_rest_false = wh_rest_false[0]
start[ pix_rest_false ] = -1
end_st[ end_heads[ pix_rest_false ] - 1, pix_rest_false ] = 0
end_heads[ pix_rest_false ] = 0
pixel_done[ pix_rest_false ] = True # all processing is complete
# CASE C) - dataset has NGROUPS=1 ; so special fitting is done for all pixels
# and all intervals are at the end of the array.
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of ends to 0
# - add slopes and variances to running sums
# - set pixel_done to True to designate all fitting done
if (ngroups == 1):
start[all_pix] = -1
end_st[end_heads[all_pix] - 1, all_pix] = 0
end_heads[all_pix] = 0
pixel_done[all_pix] = True
wh_check = np.where(mask_2d_init[0, :] & (ramp_mask_sum == 1))
if(len(wh_check[0]) > 0):
g_pix = wh_check[0]
# Ignore all pixels having no good groups (so the single group is bad)
if (len(g_pix) > 0):
inv_var[g_pix] += 1.0 / variance[g_pix]
# Append results to arrays
opt_res.append_arr(num_seg, g_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[g_pix] = 1
return 1, num_seg
# CASE D) - dataset has NGROUPS=2, so special fitting is done for all pixels.
# All segments are at the end of the array.
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of ends to 0
# - add slopes and variances to running sums
# - set pixel_done to True to designate all fitting done
if (ngroups == 2):
start[all_pix] = -1
end_st[end_heads[all_pix] - 1, all_pix] = 0
end_heads[all_pix] = 0
pixel_done[all_pix] = True
g_pix = all_pix[variance[all_pix] > 0.]
if (len(g_pix) > 0):
inv_var[g_pix] += 1.0 / variance[g_pix]
opt_res.append_arr(num_seg, g_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[g_pix] = 1
return 1, num_seg
# CASE E) - interval too short to fit normally (only 2 good groups)
# At end of array, NGROUPS>1, but exclude NGROUPS==2 datasets
# as they are covered in CASE D.
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of ends to 0
# - add slopes and variances to running sums
# - set pixel_done to True to designate all fitting done
# For segments of this type, the final good group is the final group in the
# ramp, and the variable `l_interval` used below = 1, and the number of
# groups in the segment = 2
wh_check = np.where((l_interval == 1) & (end_locs == nreads - 1) &
(nreads > 1) & (ngroups != 2) & (~pixel_done))
# Require that pixels to be processed here have at least 1 good group out
# of the final 2 groups (these ramps have 2 groups and are at the end of
# the array).
wh_list = []
if(len(wh_check[0]) > 0):
num_wh = len(wh_check[0])
for ii in range( num_wh ): # locate pixels with at least 1 good group
this_pix = wh_check[0][ii]
sum_final_2 = mask_2d_init[start[this_pix]:, this_pix].sum()
if sum_final_2 > 0:
wh_list.append( wh_check[0][ii] ) # add to list to be fit
if len(wh_list) > 0:
these_pix = np.asarray( wh_list )
got_case[ these_pix ] = True
start[these_pix] = -1
end_st[end_heads[these_pix] - 1, these_pix] = 0
end_heads[these_pix] = 0
pixel_done[these_pix] = True
g_pix = these_pix[variance[these_pix] > 0.] # good pixels
if (len(g_pix) > 0):
inv_var[g_pix] += 1.0 / variance[g_pix]
# Append results to arrays
opt_res.append_arr(num_seg, g_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[g_pix] += 1
f_max_seg = max(f_max_seg, num_seg.max())
# CASE F) - full-length ramp has 2 good groups not at array end
# - use the 2 good reads to get the slope
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of end to 0
# - add slopes and variances to running sums
# - set pixel_done to True to designate all fitting done
# For segments of this type, the final good group in the segment is
# followed by a group that is flagged as a CR and/or SAT and is not the
# final group in the ramp, and the variable `l_interval` used below is
# equal to 2, which is the number of the segment's groups.
# Copy mask, as will modify when calculating the number of later good groups
c_mask_2d_init = mask_2d_init.copy()
wh_check = np.where((l_interval == 2) & ( ngroups >2 ) &
(end_locs != nreads - 1) & ~pixel_done)
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
got_case[ these_pix ] = True
# Suppress, then re-enable, harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
inv_var[these_pix] += 1.0 / variance[these_pix]
warnings.resetwarnings()
# create array: 0...nreads-1 in a column for each pixel
arr_ind_all = np.array([np.arange(nreads),] *
c_mask_2d_init.shape[1]).transpose()
wh_c_start_all = np.zeros( mask_2d_init.shape[1], dtype=np.uint8)
wh_c_start_all[ these_pix ] = start[ these_pix]
# set to False all groups before start group
c_mask_2d_init[ arr_ind_all < wh_c_start_all ] = 0
tot_good_groups = c_mask_2d_init.sum(axis=0 )
# Select pixels having at least 2 later good groups (these later good
# groups are a segment whose slope will be calculated)
wh_more = np.where( tot_good_groups[these_pix] > 1 )
pix_more = these_pix[ wh_more ]
start[ pix_more ] = end_locs[ pix_more ]
end_st[ end_heads[ pix_more ] - 1, pix_more ] = 0
end_heads[ pix_more ] -= 1
# Select pixels having less than 2 later good groups (these later good
# groups will not be used)
wh_only = np.where( tot_good_groups[these_pix] <= 1 )
pix_only = these_pix[ wh_only ]
start[ pix_only ] = -1
end_st[ end_heads[ pix_only ] - 1, pix_only ] = 0
end_heads[ pix_only ] = 0
pixel_done[ pix_only ] = True # all processing for pixel is completed
end_heads[(end_heads < 0.)] = 0.
# Append results to arrays
opt_res.append_arr(num_seg, these_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[these_pix] += 1
f_max_seg = max(f_max_seg, num_seg.max())
# CASE G) - full-length ramp has a good group on 0th group of the entire ramp,
# and no later good groups. Will use single good group data as the slope.
# - set start to -1 to designate all fitting done
# - remove current end from end stack
# - set number of end to 0
# - add slopes and variances to running sums
# - set pixel_done to True to designate all fitting done
wh_check = np.where(mask_2d_init[0, :] & ~mask_2d_init[1, :] &
(ramp_mask_sum == 1) & ~pixel_done)
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
got_case[ these_pix ] = True
start[these_pix] = -1
end_st[end_heads[these_pix] - 1, these_pix] = 0
end_heads[these_pix] = 0
pixel_done[these_pix] = True # all processing for pixel is completed
inv_var[these_pix] += 1.0 / variance[these_pix]
# Append results to arrays
opt_res.append_arr(num_seg, these_pix, intercept, slope,
sig_intercept, sig_slope, inv_var, save_opt)
num_seg[these_pix] += 1
f_max_seg = max(f_max_seg, num_seg.max())
# CASE H) - the segment has a good 0th group and a bad 1st group. For the
# data from the 0th good group of this segment to possibly be used as a
# slope, that group must necessarily be the 0th group of the entire ramp.
# It is possible to have a single 'good' group segment after the 0th group
# of the ramp; in that case the 0th group and the 1st group would both have
# to be CRs, and the data of the 0th group would not be included as a slope.
# For a good 0th group in a ramp followed by a bad 1st group there must be
# good groups later in the segment because if there were not, the segment
# would already have be classified as CASE G. In this situation, since
# there are later good groups in the segment, those later good groups will
# be used in the slope computation, and the 0th good group will not be.
# As a result, for all instances of these types of segments, the data in the
# initial good group will not be used in the slope calculation, but the
# arrays for the indices for the ramp (end_st, etc) are appropriately
# adjusted.
# - increment start array
# - remove current end from end stack
# - decrement number of ends
wh_check = np.where(mask_2d_init[0, :] & ~mask_2d_init[1, :] & ~pixel_done &
(end_locs==1) & (start==0))
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
got_case[ these_pix ] = True
start[ these_pix ] += 1
start[ start > nreads-1 ] = nreads - 1 # to keep at max level
end_st[ end_heads[ these_pix ] - 1, these_pix ] = 0
end_heads[ these_pix ] -= 1
end_heads [end_heads < 0. ] = 0.
# CASE OTHER) - all other types of segments not covered earlier. No segments
# handled here have adequate data, but the stack arrays are updated.
# - increment start array
# - remove current end from end stack
# - decrement number of ends
wh_check = np.asarray( np.where( ~pixel_done & ~got_case ))
if(len(wh_check[0]) > 0):
these_pix = wh_check[0]
start[ these_pix ] += 1
start[ start > nreads-1 ] = nreads -1 # to keep at max level
end_st[end_heads[these_pix] - 1, these_pix] = 0
end_heads[these_pix] -= 1
end_heads[end_heads < 0.] = 0.
return f_max_seg, num_seg
def fit_lines(data, mask_2d, rn_sect, gain_sect, ngroups, weighting):
"""
Do linear least squares fit to data cube in this integration for a single
segment for all pixels. In addition to applying the mask due to identified
cosmic rays, the data is also masked to exclude intervals that are too short
to fit well. The first channel to fit in a segment is either the first group
in the ramp, or a group in which a cosmic ray has been flagged.
Parameters
----------
data : float, 3D array
array of values for current data section
mask_2d : boolean, 2D array
delineates which channels to fit for each pixel
rn_sect : float, 2D array
read noise values for all pixels in data section
gain_sect : float, 2D array
gain values for all pixels in data section
ngroups : int
number of groups per integration
weighting : string
'optimal' specifies that optimal weighting should be used; currently
the only weighting supported.
Returns
-------
Note - all of these pertain to a single segment (hence '_s')
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section (for a single segment)
"""
# To ensure that the first channel to be fit is the cosmic-ray-affected
# group, the channel previous to each channel masked as good is
# also masked as good. This is only for the local purpose of setting
# the first channel, and will not propagate beyond this current function
# call.
c_mask_2d = mask_2d.copy()
wh_mask_2d = np.where(c_mask_2d)
c_mask_2d[np.maximum(wh_mask_2d[0] - 1, 0), wh_mask_2d[1]] = True
del wh_mask_2d
# num of reads/pixel unmasked
nreads_1d = c_mask_2d.astype(np.int16).sum(axis=0)
npix = c_mask_2d.shape[1]
slope_s = np.zeros(npix, dtype=np.float32)
variance_s = np.zeros(npix, dtype=np.float32)
intercept_s = np.zeros(npix, dtype=np.float32)
sig_intercept_s = np.zeros(npix, dtype=np.float32)
sig_slope_s = np.zeros(npix, dtype=np.float32)
# Calculate slopes etc. for datasets having either 1 or 2 groups per
# integration, and return
if (ngroups == 1): # process all pixels in 1 group/integration dataset
slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s = \
fit_1_group(slope_s, intercept_s, variance_s, sig_intercept_s,
sig_slope_s, npix, data, c_mask_2d)
return slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s
if (ngroups == 2): # process all pixels in 2 group/integration dataset
rn_sect_1d = rn_sect.reshape(npix)
slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s = \
fit_2_group(slope_s, intercept_s, variance_s, sig_intercept_s,
sig_slope_s, npix, data, c_mask_2d, rn_sect_1d)
return slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s
# reshape data_masked
data_masked = data * np.reshape(c_mask_2d, data.shape)
data_masked = np.reshape(data_masked, (data_masked.shape[0], npix))
# For datasets having >2 groups/integration, for any semiramp in which the
# 0th group is good and the 1st group is bad, determine whether or not to
# use the 0th group.
wh_pix_1r = np.where(c_mask_2d[0,:] & (np.logical_not(c_mask_2d[1,:])))
if (len(wh_pix_1r[0]) > 0 ):
slope_s, intercept_s, variance_s, sig_intercept_s, \
sig_slope_s = fit_single_read(slope_s, intercept_s,
variance_s, sig_intercept_s, sig_slope_s, npix, data, wh_pix_1r)
del wh_pix_1r
# For datasets having >2 groups/integrations, for any semiramp in which only
# the 0th and 1st group are good, set slope, etc
wh_pix_2r = np.where( c_mask_2d.sum(axis=0) ==2) # ramps with 2 good groups
slope_s, intercept_s, variance_s, sig_slope_s, sig_intercept_s = \
fit_double_read( c_mask_2d, wh_pix_2r, data_masked, slope_s, intercept_s,
variance_s, sig_slope_s, sig_intercept_s, rn_sect)
del wh_pix_2r
# Select ramps having >2 good groups
wh_pix_to_use = np.where(c_mask_2d.sum(axis=0) > 2)
good_pix = wh_pix_to_use[0] # Ramps with >2 good groups
data_masked = data_masked[:, good_pix]
del wh_pix_to_use
xvalues = np.arange(data_masked.shape[0])[:, np.newaxis] * c_mask_2d
xvalues = xvalues[:, good_pix] # set to those pixels to be used
c_mask_2d = c_mask_2d[:, good_pix]
nreads_1d = nreads_1d[good_pix]
if weighting.lower() == 'optimal': # fit using optimal weighting
# get sums from optimal weighting
sumx, sumxx, sumxy, sumy, nreads_wtd, xvalues = calc_opt_sums( rn_sect,
gain_sect, data_masked, c_mask_2d, xvalues, good_pix )
slope, intercept, sig_slope, sig_intercept = calc_opt_fit( nreads_wtd,
sumxx, sumx, sumxy, sumy)
variance = sig_slope**2. # variance due to fit values
elif weighting.lower() == 'unweighted': # fit using unweighted weighting
# get sums from unweighted weighting
sumx, sumxx, sumxy, sumy =\
calc_unwtd_sums(data_masked, xvalues)
slope, intercept, sig_slope, sig_intercept, line_fit =\
calc_unwtd_fit(xvalues, nreads_1d, sumxx, sumx, sumxy, sumy)
denominator = nreads_1d * sumxx - sumx**2
# In case this branch is ever used again, disable, and then re-enable
# harmless arithmetic warrnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
variance = nreads_1d / denominator
warnings.resetwarnings()
denominator = 0
else: # unsupported weighting type specified
log.error('FATAL ERROR: unsupported weighting type specified.')
slope_s[good_pix] = slope
variance_s[good_pix] = variance
intercept_s[good_pix] = intercept
sig_intercept_s[good_pix] = sig_intercept
sig_slope_s[good_pix] = sig_slope
return slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s
def fit_single_read(slope_s, intercept_s, variance_s, sig_intercept_s,
sig_slope_s, npix, data, wh_pix_1r):
"""
For datasets having >2 groups/integrations, for any semiramp in which the
0th group is good and the 1st group is either SAT or CR, set slope, etc.
Parameters
----------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
npix : int
number of pixels in 2D array
data : float
array of values for current data section
wh_pix_1r : tuple
locations of pixels whose only good group is the 0th group
Returns
-------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
"""
data0_slice = data[0, :, :].reshape(npix)
slope_s[wh_pix_1r] = data0_slice[wh_pix_1r]
# The following arrays will have values correctly calculated later; for
# now they are just place-holders
variance_s[wh_pix_1r] = jwstpipe1p1p0_utils.LARGE_VARIANCE
sig_slope_s[wh_pix_1r] = 0.
intercept_s[wh_pix_1r] = 0.
sig_intercept_s[wh_pix_1r] = 0.
return slope_s, intercept_s, variance_s, sig_slope_s, sig_intercept_s
def fit_double_read(mask_2d, wh_pix_2r, data_masked, slope_s, intercept_s,
variance_s, sig_slope_s, sig_intercept_s, rn_sect):
"""
Process all semi-ramps having exactly 2 good groups. May need to optimize
later to remove loop over pixels.
Parameters
----------
mask_2d : bool, 2D array
delineates which channels to fit for each pixel
wh_pix_2r : tuple
locations of pixels whose only good groups are the 0th and the 1st
data_masked : float, 2D array
masked values for all pixels in data section
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
rn_sect : float, 2D array
read noise values for all pixels in data section
Returns
-------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
"""
rn_sect_flattened = rn_sect.flatten()
for ff in range(len(wh_pix_2r[0])): # loop over the pixels
pixel_ff = wh_pix_2r[0][ff] # pixel index (1d)
rn = rn_sect_flattened[pixel_ff] # read noise for this pixel
read_nums = np.where( mask_2d[:,pixel_ff])
second_read = read_nums[0][1]
data_ramp = data_masked[:, pixel_ff] * mask_2d[:, pixel_ff]
data_semi = data_ramp[ mask_2d[:, pixel_ff]] # picks only the 2
diff_data = data_semi[1] - data_semi[0]
slope_s[ pixel_ff ] = diff_data
intercept_s[ pixel_ff ] = data_semi[1]*(1.- second_read) + \
data_semi[0]*second_read # by geometry
variance_s[ pixel_ff ] = 2.0 * rn * rn
sig_slope_s[pixel_ff] = np.sqrt(2) * rn
sig_intercept_s[ pixel_ff ] = np.sqrt(2) * rn
return slope_s, intercept_s, variance_s, sig_slope_s, sig_intercept_s
def calc_unwtd_fit(xvalues, nreads_1d, sumxx, sumx, sumxy, sumy):
"""
Do linear least squares fit to data cube in this integration, using
unweighted fits to the segments. Currently not supported.
Parameters
----------
xvalues : int, 1D array
indices of valid pixel values for all groups
nreads_1d : int, 1D array
number of reads in an integration
sumxx : float
sum of squares of xvalues
sumx : float
sum of xvalues
sumxy : float
sum of product of xvalues and data
sumy : float
sum of data
Returns
-------
slope : float, 1D array
weighted slope for current iteration's pixels for data section
intercept : float, 1D array
y-intercepts from fit for data section
sig_slope : float, 1D array
sigma of slopes from fit for data section
sig_intercept : float, 1D array
sigma of y-intercepts from fit for data section
line_fit : float, 1D array
values of fit using slope and intercept
"""
denominator = nreads_1d * sumxx - sumx**2
# In case this branch is ever used again, suppress, and then re-enable
# harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
slope = (nreads_1d * sumxy - sumx * sumy) / denominator
intercept = (sumxx * sumy - sumx * sumxy) / denominator
sig_intercept = (sumxx / denominator)**0.5
sig_slope = (nreads_1d / denominator)**0.5
warnings.resetwarnings()
line_fit = (slope * xvalues) + intercept
return slope, intercept, sig_slope, sig_intercept, line_fit
def calc_opt_fit(nreads_wtd, sumxx, sumx, sumxy, sumy):
"""
Do linear least squares fit to data cube in this integration for a single
semi-ramp for all pixels, using optimally weighted fits to the semi_ramps.
The weighting uses the formulation by Fixsen (Fixsen et al, PASP, 112, 1350).
Note - these weights, sigmas, and variances pertain only to the fitting, and
the variances are *NOT* the variances of the slope due to noise.
Parameters
----------
nreads_wtd : float, 1D array
sum of product of data and optimal weight
sumxx : float, 1D array
sum of squares of xvalues
sumx : float, 1D array
sum of xvalues
sumxy : float, 1D array
sum of product of xvalues and data
sumy : float, 1D array
sum of data
Returns
-------
slope : float, 1D array
weighted slope for current iteration's pixels for data section
intercept : float, 1D array
y-intercepts from fit for data section
sig_slope : float, 1D array
sigma of slopes from fit for data section
sig_intercept : float, 1D array
sigma of y-intercepts from fit for data section
"""
denominator = nreads_wtd * sumxx - sumx**2
# Suppress, and then re-enable harmless arithmetic warnings
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
slope = (nreads_wtd * sumxy - sumx * sumy) / denominator
intercept = (sumxx * sumy - sumx * sumxy) / denominator
sig_intercept = (sumxx / denominator)**0.5
sig_slope = (nreads_wtd / denominator)**0.5 # STD of the slope's fit
warnings.resetwarnings()
return slope, intercept, sig_slope, sig_intercept
def fit_1_group(slope_s, intercept_s, variance_s, sig_intercept_s,
sig_slope_s, npix, data, mask_2d):
"""
This function sets the fitting arrays for datasets having only 1 group
per integration.
Parameters
----------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
npix : int
number of pixels in 2d array
data : float
array of values for current data section
mask_2d : bool, 2D array
delineates which channels to fit for each pixel
Returns
-------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
"""
# For pixels not saturated, recalculate the slope as the value of the SCI
# data in that group, which will later be divided by the group exposure
# time to give the count rate. Recalculate other fit quantities to be
# benign.
slope_s = data[0, :, :].reshape(npix)
# The following arrays will have values correctly calculated later; for
# now they are just place-holders
variance_s = np.zeros(npix, dtype=np.float32) + jwstpipe1p1p0_utils.LARGE_VARIANCE
sig_slope_s = slope_s * 0.
intercept_s = slope_s * 0.
sig_intercept_s = slope_s * 0.
# For saturated pixels, overwrite slope with benign values.
wh_sat0 = np.where(np.logical_not(mask_2d[0, :]))
if (len(wh_sat0[0]) > 0):
sat_pix = wh_sat0[0]
slope_s[sat_pix] = 0.
return slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s
def fit_2_group(slope_s, intercept_s, variance_s, sig_intercept_s,
sig_slope_s, npix, data, mask_2d, rn_sect_1d):
"""
This function sets the fitting arrays for datasets having only 2 groups
per integration.
Parameters
----------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
npix : int
number of pixels in 2d array
data : float
array of values for current data section
mask_2d : bool, 2D array
delineates which channels to fit for each pixel
rn_sect_1d : float, 1D array
read noise values for all pixels in data section
Returns
-------
slope_s : float, 1D array
weighted slope for current iteration's pixels for data section
intercept_s : float, 1D array
y-intercepts from fit for data section
variance_s : float, 1D array
variance of residuals for fit for data section
sig_intercept_s : float, 1D array
sigma of y-intercepts from fit for data section
sig_slope_s : float, 1D array
sigma of slopes from fit for data section
"""
# For pixels saturated on the first group, overwrite fit values with
# benign values to be recalculated later.
wh_sat0 = np.where(np.logical_not(mask_2d[0, :]))
if (len(wh_sat0[0]) > 0):
sat_pix = wh_sat0[0]
slope_s[sat_pix] = 0.
variance_s[sat_pix] = 0.
sig_slope_s[sat_pix] = 0.
intercept_s[sat_pix] = 0.
sig_intercept_s[sat_pix] = 0.
del wh_sat0
# For pixels saturated on the second group, recalculate the slope as
# the value of the SCI data in the first group, which will later be
# divided by the group exposure time to give the count rate, and
# recalculate the other fit quantities to be benign. Note: these pixels
# will already have been handled earlier (for intervals of arbitrary
# length) in this function, but are being included here to explicitly
# cover all possibilities for pixels in datasets with ngroups=2. Will
# later consider refactoring.
wh_sat1 = np.where((mask_2d[:, :].sum(axis=0) == 1) & mask_2d[0, :])
if (len(wh_sat1[0]) > 0):
data0_slice = data[0, :, :].reshape(npix)
slope_s[wh_sat1] = data0_slice[wh_sat1]
# set variance non-zero because calling function uses variance=0 to
# throw out bad results; this is not bad
variance_s[wh_sat1] = 1.
sig_slope_s[wh_sat1] = 0.
intercept_s[wh_sat1] = 0.
sig_intercept_s[wh_sat1] = 0.
del wh_sat1
# For pixels with no saturated values, recalculate the slope as the
# difference between the values of the second and first groups (1-based),
# which will later be divided by the group exposure time to give the count
# rate, and recalculate other fit quantities to be benign.
wh_sat_no = np.where(mask_2d[:, :].sum(axis=0) == 2)
if (len(wh_sat_no[0]) > 0):
data0_slice = data[0, :, :].reshape(npix)
data1_slice = data[1, :, :].reshape(npix)
slope_s[wh_sat_no] = data1_slice[wh_sat_no] - data0_slice[wh_sat_no]
sig_slope_s[wh_sat_no] = np.sqrt(2) * rn_sect_1d[wh_sat_no]
intercept_s[wh_sat_no] = data0_slice[wh_sat_no] -\
data1_slice[wh_sat_no] # by geometry
sig_intercept_s[wh_sat_no] = np.sqrt(2) * rn_sect_1d[wh_sat_no]
variance_s[wh_sat_no] = np.sqrt(2) * rn_sect_1d[wh_sat_no]
del wh_sat_no
return slope_s, intercept_s, variance_s, sig_intercept_s, sig_slope_s
def calc_num_seg(gdq, n_int):
"""
Calculate the maximum number of segments that will be be fit within an
integration, calculated over all pixels and all integrations. This value
is based on the locations of cosmic ray-affected pixels in all of the ramps,
and will be used to allocate arrays used for the optional output product.
Parameters
----------
gdq : float, 3D array
cube of GROUPDQ array for a data
n_int : int
total number of integrations in data set
Return:
-------
int(max_cr) +1 : int
maxmimum number of segments; n CRS implies n+1 segments
"""
max_cr = 0 # max number of CRS for all integrations
# For all 2d pixels, get max number of CRs or DO_NOT_USE flags along their
# ramps, to use as a surrogate for the number of segments along the ramps
# Note that we only care about flags that are NOT in the first or last groups,
# because exclusion of a first or last group won't result in an additional segment.
max_cr = np.count_nonzero(np.bitwise_and(gdq[:, 1:-1], JUMP_DET | DO_NOT_USE), axis=1).max()
# Do not want to return a value > the number of groups, which can occur if
# this is a MIRI dataset in which the first or last group was flagged as
# DO_NOT_USE and also flagged as a jump.
max_num_seg = int(max_cr) + 1 # n CRS implies n+1 segments
if (max_num_seg > gdq.shape[1]):
max_num_seg = gdq.shape[1]
return max_num_seg, max_cr
def calc_unwtd_sums(data_masked, xvalues):
"""
Calculate the sums needed to determine the slope and intercept (and sigma
of each) using an unweighted fit. Unweighted fitting currently not
supported.
Parameters
----------
data_masked : float, 2D array
masked values for all pixels in data section
xvalues : int, 1D array
indices of valid pixel values for all groups
Return:
-------
sumx : float
sum of xvalues
sumxx : float
sum of squares of xvalues
sumxy : float
sum of product of xvalues and data
sumy : float
sum of data
"""
sumx = xvalues.sum(axis=0)
sumxx = (xvalues**2).sum(axis=0)
sumy = (np.reshape(data_masked.sum(axis=0), sumx.shape))
sumxy = (xvalues * np.reshape(data_masked, xvalues.shape)).sum(axis=0)
return sumx, sumxx, sumxy, sumy
def calc_opt_sums(rn_sect, gain_sect, data_masked, mask_2d, xvalues, good_pix):
"""
Calculate the sums needed to determine the slope and intercept (and sigma of
each) using the optimal weights. For each good pixel's segment, from the
initial and final indices and the corresponding number of counts, calculate
the SNR. From the SNR, calculate the weighting exponent using the formulation
by Fixsen (Fixsen et al, PASP, 112, 1350). Using this exponent and the gain
and the readnoise, the weights are calculated from which the sums are
calculated.
Parameters
----------
rn_sect : float, 2D array
read noise values for all pixels in data section
gain_sect : float, 2D array
gain values for all pixels in data section
data_masked : float, 2D array
masked values for all pixels in data section
mask_2d : bool, 2D array
delineates which channels to fit for each pixel
xvalues : int, 2D array
indices of valid pixel values for all groups
good_pix : int, 1D array
indices of pixels having valid data for all groups
Return:
-------
sumx : float
sum of xvalues
sumxx : float
sum of squares of xvalues
sumxy : float
sum of product of xvalues and data
sumy : float
sum of data
nreads_wtd : float, 1D array
sum of optimal weights
xvalues : int, 2D array
rolled up indices of valid pixel values for all groups
"""
c_mask_2d = mask_2d.copy() # copy the mask to prevent propagation
rn_sect = np.float32(rn_sect)
# Return 'empty' sums if there is no more data to fit
if (data_masked.size == 0):
return np.array([]), np.array([]), np.array([]), np.array([]),\
np.array([]), np.array([])
# get initial group for each good pixel for this semiramp
fnz = np.argmax(c_mask_2d, axis=0)
# For those pixels that are all False, set to sentinel value of -1
fnz[c_mask_2d.sum(axis=0) == 0] = -1
mask_2d_sum = c_mask_2d.sum(axis=0) # number of valid groups/pixel
# get final valid group for each pixel for this semiramp
ind_lastnz = fnz + mask_2d_sum - 1
# get SCI value of initial good group for semiramp
data_zero = data_masked[fnz, range(data_masked.shape[1])]
# get SCI value of final good group for semiramp
data_final = data_masked[(ind_lastnz), range(data_masked.shape[1])]
data_diff = data_final - data_zero # correctly does *NOT* have nans
ind_lastnz = 0
# Use the readnoise and gain for good pixels only
rn_sect_rav = rn_sect.flatten()[ good_pix ]
rn_2_r = rn_sect_rav * rn_sect_rav
gain_sect_r = gain_sect.flatten()[ good_pix ]
# Calculate the sigma for nonzero gain values
sigma_ir = data_final.copy() * 0.0
numer_ir = data_final.copy() * 0.0
# Calculate the SNR for pixels from the readnoise, the gain, and the
# difference between the last and first reads for pixels where this results
# in a positive SNR. Otherwise set the SNR to 0.
sqrt_arg = rn_2_r + data_diff * gain_sect_r
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "invalid value.*", RuntimeWarning)
wh_pos = np.where((sqrt_arg >= 0.) & (gain_sect_r != 0.))
numer_ir[wh_pos] = np.sqrt(rn_2_r[wh_pos] + \
data_diff[wh_pos] * gain_sect_r[wh_pos])
sigma_ir[wh_pos] = numer_ir[wh_pos] / gain_sect_r[wh_pos]
snr = data_diff * 0.
snr[wh_pos] = data_diff[wh_pos] / sigma_ir[wh_pos]
snr[np.isnan(snr)] = 0.0
snr[snr < 0.] = 0.0
del wh_pos
gain_sect_r = 0
numer_ir = 0
data_diff = 0
sigma_ir = 0
power_wt_r = calc_power(snr) # Get the interpolated power for this SNR
# Make array of number of good groups, and exponents for each pixel
num_nz = (data_masked != 0.).sum(0) # number of nonzero groups per pixel
nrd_data_a = num_nz.copy()
num_nz = 0
nrd_prime = (nrd_data_a - 1) / 2.
nrd_data_a = 0
# Calculate inverse read noise^2 for use in weights
# Suppress, then re-enable, harmless arithmetic warning
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
invrdns2_r = 1./rn_2_r
warnings.resetwarnings()
rn_sect = 0
fnz = 0
# Set optimal weights for each group of each pixel;
# for all pixels at once, loop over the groups
wt_h = np.zeros(data_masked.shape, dtype=np.float32)
for jj_rd in range(data_masked.shape[0]):
wt_h[jj_rd, :] = \
abs((abs(jj_rd - nrd_prime) / nrd_prime) ** power_wt_r) * invrdns2_r
wt_h[np.isnan(wt_h)] = 0.
wt_h[np.isinf(wt_h)] = 0.
# For all pixels, 'roll' up the leading zeros such that the 0th group of
# each pixel is the lowest nonzero group for that pixel
wh_m2d_f = np.logical_not(c_mask_2d[0, :]) # ramps with initial group False
while (wh_m2d_f.sum() > 0):
data_masked[:, wh_m2d_f] = np.roll(data_masked[:, wh_m2d_f], -1, axis=0)
c_mask_2d[:, wh_m2d_f] = np.roll(c_mask_2d[:, wh_m2d_f], -1, axis=0)
xvalues[:, wh_m2d_f] = np.roll(xvalues[:, wh_m2d_f], -1, axis=0)
wh_m2d_f = np.logical_not(c_mask_2d[0, :])
# Create weighted sums for Poisson noise and read noise
nreads_wtd = (wt_h * c_mask_2d).sum(axis=0) # using optimal weights
sumx = (xvalues * wt_h).sum(axis=0)
sumxx = (xvalues**2 * wt_h).sum(axis=0)
c_data_masked = data_masked.copy()
c_data_masked[np.isnan(c_data_masked)] = 0.
sumy = (np.reshape((c_data_masked * wt_h).sum(axis=0), sumx.shape))
sumxy = (xvalues * wt_h * np.reshape(c_data_masked, xvalues.shape)).sum(axis=0)
return sumx, sumxx, sumxy, sumy, nreads_wtd, xvalues
def log_stats(c_rates):
"""
Optionally log statistics of detected cosmic rays
Parameters
----------
c_rates : float, 2D array
weighted count rate
Returns
-------
None
"""
wh_c_0 = np.where(c_rates == 0.) # insuff data or no signal
log.debug('The number of pixels having insufficient data')
log.debug('due to excessive CRs or saturation %d:', len(wh_c_0[0]))
log.debug('Count rates - min, mean, max, std: %f, %f, %f, %f'
% (c_rates.min(), c_rates.mean(), c_rates.max(), c_rates.std()))
|
chriswillottREPO_NAMEjwstPATH_START.@jwst_extracted@jwst-master@columnjump@columnjump@jwstpipe1p1p0_ramp_fit.py@.PATH_END.py
|
{
"filename": "mkCat_tar4ang.py",
"repo_name": "desihub/LSS",
"repo_path": "LSS_extracted/LSS-main/scripts/mkCat_tar4ang.py",
"type": "Python"
}
|
'''
one executable to create catalogs for given target type meant for angular clustering
'''
#standard python
import sys
import os
import shutil
import unittest
from datetime import datetime
import json
import numpy as np
import fitsio
import glob
import argparse
from astropy.table import Table,join,unique,vstack
from matplotlib import pyplot as plt
#sys.path.append('../py')
#from this package
import LSS.imaging.select_samples as ss
parser = argparse.ArgumentParser()
parser.add_argument("--type", help="tracer type to be selected")
parser.add_argument("--tarver", help="version of targeting",default='0.57.0')
parser.add_argument("--survey", help="e.g., sv1 or main",default='sv3')
parser.add_argument("--basedir", help="base directory for output, default is CSCRATCH",default=os.environ['CSCRATCH'])
parser.add_argument("--version", help="catalog version; use 'test' unless you know what you are doing!",default='test')
args = parser.parse_args()
type = args.type
tarver = args.tarver
version = args.version
basedir = args.basedir
survey = args.survey
if survey == 'main':
tp = 'DESI_TARGET'
sw = ''
if survey == 'sv1':
tp = 'SV1_DESI_TARGET'
sw = 'sv1'
if survey == 'sv3':
tp = 'SV3_DESI_TARGET'
sw = 'sv3'
outdir = basedir+'/tarcat/v'+version+'/tv'+tarver+'/'
if not os.path.exists( basedir+'/tarcat'):
os.mkdir(basedir+'/tarcat')
print('created '+basedir+'/tarcat')
if not os.path.exists( basedir+'/tarcat/v'+version):
os.mkdir(basedir+'/tarcat/v'+version)
print('created '+basedir+'/tarcat/v'+version)
if not os.path.exists(outdir):
os.mkdir(outdir)
print('created '+outdir)
dirsweeps = '/global/project/projectdirs/cosmo/data/legacysurvey/dr9/south/sweep/9.0/'
dirsweepn = '/global/project/projectdirs/cosmo/data/legacysurvey/dr9/north/sweep/9.0/'
targroot = '/project/projectdirs/desi/target/catalogs/dr9/'+tarver+'/targets/'+survey+'/resolve/'
ranroot = '/global/cfs/cdirs/desi/target/catalogs/dr9/0.49.0/randoms/resolve/randoms-1-'
nran = 10
sfs = glob.glob(dirsweeps+'sweep*')
sfn = glob.glob(dirsweepn+'sweep*')
elgandlrgbits = [1,5,6,7,8,9,11,12,13] #these get used to veto imaging area; combination of bits applied to ELGs and LRGs in DR8 targeting
mkbsamp = True #make the base sample
domaskd = True #mask data based on mask bits above
domaskr = True #mask randoms
'test'
print('type being used for bright/dark '+type[:3])
#columns to select from target sample
keys = ['RA', 'DEC', 'BRICKID', 'BRICKNAME','MORPHTYPE','DCHISQ','FLUX_G', 'FLUX_R', 'FLUX_Z','FLUX_W1','FLUX_W2','MW_TRANSMISSION_G', 'MW_TRANSMISSION_R', 'MW_TRANSMISSION_Z', 'MW_TRANSMISSION_W1', 'MW_TRANSMISSION_W2','FLUX_IVAR_G', 'FLUX_IVAR_R', 'FLUX_IVAR_Z','NOBS_G', 'NOBS_R', 'NOBS_Z','PSFDEPTH_G', 'PSFDEPTH_R', 'PSFDEPTH_Z', 'GALDEPTH_G', 'GALDEPTH_R',\
'GALDEPTH_Z','FIBERFLUX_G', 'FIBERFLUX_R', 'FIBERFLUX_Z', 'FIBERTOTFLUX_G', 'FIBERTOTFLUX_R', 'FIBERTOTFLUX_Z',\
'MASKBITS', 'EBV', 'PHOTSYS','TARGETID',tp,'SHAPE_R']
if mkbsamp: #concatenate target files for given type, with column selection hardcoded
prog = 'dark'
if type[:3] == 'BGS':
prog = 'bright'
ss.gather_targets(type,targroot,outdir,tarver,survey,prog,keys=keys)
if domaskd:
dd = fitsio.read(outdir+type+sw +'targetsDR9v'+tarver.strip('.')+'.fits' )
dd = ss.mask(dd,elgandlrgbits)
outf = outdir+type+sw +'targetsDR9v'+tarver.strip('.')+'_masked.fits'
fitsio.write(outf,dd,clobber=True)
print('wrote to '+outf)
if domaskr:
for ii in range(0,nran):
rr = fitsio.read(ranroot+str(ii)+'.fits',columns=['RA','DEC','BRICKID','PHOTSYS','NOBS_G','NOBS_R','NOBS_Z','MASKBITS'])
#need to restrict columns on line above otherwise run out of memory
rr = ss.mask(rr,elgandlrgbits)
outf = outdir+'randomsDR9v'+tarver.strip('.')+'_'+str(ii)+'_masked.fits'
fitsio.write(outf,rr,clobber=True)
print('wrote to '+outf)
|
desihubREPO_NAMELSSPATH_START.@LSS_extracted@LSS-main@scripts@mkCat_tar4ang.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "sczesla/PyAstronomy",
"repo_path": "PyAstronomy_extracted/PyAstronomy-master/src/pyTiming/__init__.py",
"type": "Python"
}
|
from . import pyPDM
from . import pyPeriod
from .stringlength import *
|
sczeslaREPO_NAMEPyAstronomyPATH_START.@PyAstronomy_extracted@PyAstronomy-master@src@pyTiming@__init__.py@.PATH_END.py
|
{
"filename": "plot_wedge.py",
"repo_name": "desihub/LSS",
"repo_path": "LSS_extracted/LSS-main/scripts/plotting/plot_wedge.py",
"type": "Python"
}
|
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import fitsio
from astropy.table import join,Table
import healpy as hp
from LSS.tabulated_cosmo import TabulatedDESI
cosmo = TabulatedDESI()
dis_dc = cosmo.comoving_radial_distance
outdir = '/global/cfs/cdirs/desi/survey/catalogs/main/LSS/daily/LSScats/plots/'
zcol = 'Z_not4clus'
#ram = 130
#rax = 220
ram = 0
rax = 360
ra0 = (ram+rax)/2.
decm = -0.5
decx = .5
zmin = 0
zmax = 3.5
#plt.figure()
fig, ax = plt.subplots(dpi=1000)
ax.set_aspect('equal')
ax.patch.set_facecolor('black')
#ax.patch.set_alpha(1)
msdic = {'QSO':.24,'ELG':.21,'LRG':.21,'BGS_ANY':.1}
tps = ['QSO','LRG','BGS_ANY','ELG']
cl = ['y','r','lime','b']
zordl = [2,5,3,1]
for tp,c,zo in zip(tps,cl,zordl):
cols = ['RA','DEC',zcol,'ZWARN','DELTACHI2','LOCATION_ASSIGNED']
if tp == 'ELG':
cols.append('o2c')
zmin = 0.6
dt = fitsio.read('/global/cfs/cdirs/desi/survey/catalogs/main/LSS/daily/LSScats/test/'+tp+'_full.dat.fits',columns=cols)
sel = dt['RA'] > ram
sel &= dt['RA'] < rax
sel &= dt['DEC'] > decm
sel &= dt['DEC'] < decx
#sel &= dt[zcol] < zmax
#sel &= dt[zcol] > zmin
dt = dt[sel]
wz = dt['ZWARN']*0 == 0
wz &= dt['ZWARN'] != 1.e20
wz &= dt['ZWARN'] != 999999
wz &= dt['LOCATION_ASSIGNED'] == 1
if tp == 'QSO':
#good redshifts are currently just the ones that should have been defined in the QSO file when merged in full
wg = dt[zcol]*0 == 0
wg &= dt[zcol] != 999999
wg &= dt[zcol] != 1.e20
if tp[:3] == 'ELG':
wg = dt['o2c'] > 0.9
if tp == 'LRG':
# Custom DELTACHI2 vs z cut from Rongpu
#wg = dt['ZWARN'] == 0
#drz = (10**(3 - 3.5*dt[zcol]))
#mask_bad = (drz>30) & (dt['DELTACHI2']<30)
#mask_bad |= (drz<30) & (dt['DELTACHI2']<drz)
#mask_bad |= (dt['DELTACHI2']<10)
#wg &= dt[zcol]<1.4
#wg &= (~mask_bad)
wg = dt['DELTACHI2'] > 15
wg &= dt['ZWARN'] == 0
wg &= dt[zcol]<1.5
if tp[:3] == 'BGS':
wg = dt['DELTACHI2'] > 40
print(tp+':')
print('# of good obs: '+str(len(dt[wz])))
print('# of good z: '+str(len(dt[wz&wg])))
print('completeness: '+str(round(len(dt[wz])/len(dt),3)))
dt = dt[wg&wz]
sel = dt[zcol] < zmax
sel &= dt[zcol] > zmin
dt = dt[sel]
r = dis_dc(dt[zcol])
th = (90-dt['DEC'])*np.pi/180.
phi = (dt['RA']-ra0)*np.pi/180
x = r*np.cos(phi)*np.sin(th)
y = r*np.sin(phi)*np.sin(th)
z = r*np.cos(th)
ax.plot(x,y,'s',color=c,zorder=zo,ms=msdic[tp],lw=0,mew=0,)
if tp == 'QSO':
sel = dt[zcol] > 2.1
ax.plot(x[sel],y[sel],'s',color='white',zorder=zo,ms=msdic[tp],lw=0,mew=0)
#plt.show()
del dt
print(tp+' done')
#plt.axis('off')
for spine in ax.spines.values():
spine.set_visible(False)
ax.tick_params(bottom=False, labelbottom=False,
left=False, labelleft=False)
plt.savefig('/global/cfs/cdirs/desi/survey/catalogs/main/LSS/daily/LSScats/plots/wedge_all.png')
plt.show()
|
desihubREPO_NAMELSSPATH_START.@LSS_extracted@LSS-main@scripts@plotting@plot_wedge.py@.PATH_END.py
|
{
"filename": "_name.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/layout/shape/_name.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class NameValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="name", parent_name="layout.shape", **kwargs):
super(NameValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@layout@shape@_name.py@.PATH_END.py
|
{
"filename": "test_esa_hubble_remote.py",
"repo_name": "astropy/astroquery",
"repo_path": "astroquery_extracted/astroquery-main/astroquery/esa/hubble/tests/test_esa_hubble_remote.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
=================
eHST Remote Tests
=================
European Space Astronomy Centre (ESAC)
European Space Agency (ESA)
"""
import tempfile
import os
import numpy as np
import pytest
from astroquery.esa.hubble import ESAHubble
from astropy import coordinates
esa_hubble = ESAHubble()
def data_path(filename):
data_dir = os.path.join(os.path.dirname(__file__), 'data')
return os.path.join(data_dir, filename)
def create_temp_folder():
return tempfile.TemporaryDirectory()
def remove_last_job():
jobs = esa_hubble._tap.list_async_jobs()
if len(jobs) > 0:
esa_hubble._tap.remove_jobs(jobs[-1].jobid)
@pytest.mark.remote_data
class TestEsaHubbleRemoteData:
obs_query = "select top 2050 a.observation_id from ehst.archive a"
top_obs_query = "select top 100 a.observation_id from ehst.archive a"
hst_query = "select top 50 a.observation_id from ehst.archive " \
"a where a.collection='HST'"
top_artifact_query = "select a.artifact_id, a.observation_id from ehst.artifact a " \
"where a.observation_id = 'iexn02e9q'"
temp_folder = create_temp_folder()
temp_folder_for_fits = create_temp_folder()
def test_query_tap_async(self):
result = esa_hubble.query_tap(query=self.top_obs_query, async_job=True)
assert len(result) > 10
assert "observation_id" in result.keys()
remove_last_job()
def test_download_product(self):
result = esa_hubble.query_tap(query=self.hst_query)
observation_id = np.random.choice((result['observation_id']))
temp_file = os.path.join(self.temp_folder.name, observation_id)
esa_hubble.download_product(observation_id=observation_id,
filename=temp_file)
possible_values = [os.path.exists(temp_file + '.jpg'),
os.path.exists(temp_file + '.zip'),
os.path.exists(temp_file + 'fits.gz')]
assert any([os.path.exists(f) for f in possible_values])
def test_get_artifact(self):
result = esa_hubble.query_tap(query=self.top_artifact_query)
assert "artifact_id" in result.keys()
artifact_id = np.random.choice(result["artifact_id"])
temp_file = os.path.join(self.temp_folder.name, artifact_id)
esa_hubble.get_artifact(artifact_id=artifact_id, filename=temp_file)
possible_values = [os.path.exists(temp_file),
os.path.exists(temp_file + '.zip'),
os.path.exists(temp_file + 'fits.gz')]
assert any([os.path.exists(f) for f in possible_values])
def test_cone_search(self):
c = coordinates.SkyCoord("00h42m44.51s +41d16m08.45s", frame='icrs')
compressed_temp_file = os.path.join(self.temp_folder.name, "cone_search_m31_5.vot.gz")
# open & extracting the file
table = esa_hubble.cone_search(coordinates=c, radius=7, filename=compressed_temp_file, verbose=True)
assert 'observation_id' in table.columns
assert len(table) > 0
remove_last_job()
# tests for get_related_members
def test_hst_composite_to_hst_simple(self):
result = esa_hubble.get_member_observations('jdrz0c010')
assert result == ['jdrz0cjxq', 'jdrz0cjyq']
def test_hst_simple_to_hst_composite(self):
result = esa_hubble.get_member_observations(observation_id='hst_12069_b2_acs_wfc_f775w_jbf6b2')
assert 'hst_12069_b2_acs_wfc_f775w_jbf6b2cf' in result
def test_hap_composite_to_hap_simple(self):
result = esa_hubble.get_member_observations(observation_id='hst_15446_4v_acs_wfc_f606w_jdrz4v')
assert result == ['hst_15446_4v_acs_wfc_f606w_jdrz4vkv', 'hst_15446_4v_acs_wfc_f606w_jdrz4vkw']
def test_hap_simple_to_hap_composite(self):
result = esa_hubble.get_member_observations(observation_id='hst_16316_71_acs_sbc_f150lp_jec071i9')
assert result == ['hst_16316_71_acs_sbc_f150lp_jec071']
def test_hap_simple_to_hst_simple(self):
result = esa_hubble.get_hap_hst_link(observation_id='hst_16316_71_acs_sbc_f150lp_jec071i9')
assert result == ['jec071i9q']
def test_hst_simple_to_hap_simple(self):
result = esa_hubble.get_hap_hst_link(observation_id='jec071i9q')
assert result == ['hst_16316_71_acs_sbc_f150lp_jec071i9']
def test_query_target(self):
compressed_temp_file = os.path.join(self.temp_folder.name, "m31_query.xml.gz")
table = esa_hubble.query_target(name="m3", filename=compressed_temp_file)
assert 'observation_id' in table.columns
def test_retrieve_observations_from_program(self):
results = esa_hubble.get_observations_from_program(program=5773)
assert 'u2lx0507t' in results['observation_id']
def test_retrieve_fits_from_program(self):
esa_hubble.download_files_from_program(program=5410,
instrument_name='WFPC2',
obs_collection='HLA',
filters=['F814W/F450W'],
folder=str(self.temp_folder_for_fits.name))
assert len(os.listdir(self.temp_folder_for_fits.name)) > 0
def test_get_datalabs_path_image(self):
result = esa_hubble.get_datalabs_path(filename='ib4x04ivq_flt.jpg', default_volume=None)
assert result == '/data/user/hub_hstdata_i/i/b4x/04/ib4x04ivq_flt.jpg'
def test_get_datalabs_path_fits(self):
result = esa_hubble.get_datalabs_path(filename='ib4x04ivq_flt.fits', default_volume=None)
assert result == '/data/user/hub_hstdata_i/i/b4x/04/ib4x04ivq_flt.fits.gz'
|
astropyREPO_NAMEastroqueryPATH_START.@astroquery_extracted@astroquery-main@astroquery@esa@hubble@tests@test_esa_hubble_remote.py@.PATH_END.py
|
{
"filename": "generate_breaking_files.ipynb",
"repo_name": "pynucastro/pynucastro",
"repo_path": "pynucastro_extracted/pynucastro-main/pynucastro/library/tabular/suzuki/generate_breaking_files.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import scipy.constants as scp
import itertools
import os
import re
```
```python
eV_to_J, _, _ = scp.physical_constants['electron volt-joule relationship']
MeV_to_eV = 1.0e6
J_to_erg = 1.0e7
MeV_to_erg = MeV_to_eV * eV_to_J * J_to_erg
```
```python
files = ['A21_OFNeNaMg_ScrExp.odat',
'A20_OFNeNaMg_ScrExp.odat',
'A19_OFNeNa_ScrExp.odat',
'A18_OFNe_ScrExp.odat',
'A17_FO_ScrExp.odat',
'A22_FNeNaMg_ScrExp.odat',
'A23_FNeNaMgAl_ScrExp.odat',
'A24_NeNaMgAlSi_ScrExp.odat',
'A25_NeNaMgAlSi_ScrExp.odat',
'A26_NaMgAlSi_ScrExp.odat',
'A27_NaMgAlSiP_ScrExp.odat',
'A28_NaMgAlSiPS_ScrExp.odat']
```
```python
def weak_decay_read(file):
headers = []
data = []
desc = []
with open(file) as f:
_ = f.readline()
while (l := f.readline()):
if l == "\n":
continue
headers.append([])
if l.startswith('!'):
headers[-1].append(l.rstrip('\n'))
while (l := f.readline().rstrip('\n')):
if l.startswith('!'):
headers[-1].append(l)
# sections are separated by empty lines
data.append(np.genfromtxt(itertools.takewhile(lambda x: x.rstrip('\n'), f), autostrip=True))
if not headers[-1]:
headers.pop(-1)
data.pop(-1)
for head, chunk in zip(headers, data):
for line in head:
if line.startswith('!'):
#print(line)
if re.search('e-capture', line):
out_str = 'e-capture'
elif re.search('beta-decay', line):
out_str = 'beta-decay'
else:
out_str = None
elements = re.findall('[0-9]{1,2}[A-Za-z]{1,2}', line)
el1 = elements[0].lower()
el2 = elements[1].lower()
break
else:
continue
if out_str=='e-capture':
name=f"{el1}-{el2}_electroncapture.dat"
elif out_str=='beta-decay':
name=f"{el1}-{el2}_betadecay.dat"
else:
name=None
if (out_str=='e-capture' or out_str=='beta-decay'):
head[-2] = '!Log(rhoY) Log(T) mu dQ Vs Log(e-cap-rate) nu-energy-loss gamma-energy'
head[-1] = '!Log(g/cm^3) Log(K) erg erg erg Log(1/s) Log(erg/s) Log(erg/s)'
desc.append((name, head, chunk))
return desc
```
```python
def weak_decay_write(desc):
for output_file in desc:
name, head, chunk = output_file
with open(name, "w") as output:
rho, temp, mu, dq, vs, rate, nu_energy_loss, gamma_energy = chunk.T
mu *= MeV_to_erg
dq *= MeV_to_erg
vs *= MeV_to_erg
rate = rate
nu_energy_loss = nu_energy_loss + np.log10(MeV_to_erg)
gamma_energy = gamma_energy + np.log10(MeV_to_erg)
for line in head:
output.write(line)
output.write("\n")
for i in range(len(rho)):
output.write(f"{rho[i]:>.2f} {temp[i]:>17.2f} {mu[i]:>24.5e} {dq[i]:>12.5e} {vs[i]:>12.5e} {rate[i]:>13.5e} {nu_energy_loss[i]:16.5e} {gamma_energy[i]:16.5e}\n")
```
```python
for file in files:
desc = weak_decay_read(file)
weak_decay_write(desc)
```
```python
for file in files:
try:
desc = weak_decay_read(file)
except:
print(file)
```
|
pynucastroREPO_NAMEpynucastroPATH_START.@pynucastro_extracted@pynucastro-main@pynucastro@library@tabular@suzuki@generate_breaking_files.ipynb@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "astromer-science/main-code",
"repo_path": "main-code_extracted/main-code-main/presentation/scripts/__init__.py",
"type": "Python"
}
|
astromer-scienceREPO_NAMEmain-codePATH_START.@main-code_extracted@main-code-main@presentation@scripts@__init__.py@.PATH_END.py
|
|
{
"filename": "_text.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/choropleth/colorbar/title/_text.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="text", parent_name="choropleth.colorbar.title", **kwargs
):
super(TextValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@choropleth@colorbar@title@_text.py@.PATH_END.py
|
{
"filename": "test_input_types.py",
"repo_name": "e-koch/FilFinder",
"repo_path": "FilFinder_extracted/FilFinder-master/fil_finder/tests/test_input_types.py",
"type": "Python"
}
|
# Licensed under an MIT open source license - see LICENSE
import pytest
import numpy as np
import numpy.testing as npt
import astropy.units as u
from astropy.io.fits import PrimaryHDU
try:
from spectral_cube import Projection, Slice
SPECTRALCUBE_INSTALL = True
except ImportError:
SPECTRALCUBE_INSTALL = False
from ..io_funcs import input_data
from ._testing_data import *
def test_array_input():
output = input_data(img)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.dimensionless_unscaled
def test_array_input_withheader():
new_hdr = hdr.copy()
new_hdr['BUNIT'] = 'K'
output = input_data(img, header=new_hdr)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
npt.assert_equal(new_hdr, output["header"])
def test_quantity_input():
quant = img * u.K
output = input_data(quant)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
def test_quantity_input_withheader():
quant = img * u.K
# Give the header a different BUNIT. Always use the unit
# attached to the Quantity object
new_hdr = hdr.copy()
new_hdr['BUNIT'] = 'Jy/beam'
output = input_data(quant, header=new_hdr)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
# The header should now have K set
assert output['header']['BUNIT'] == 'K'
def test_HDU_input():
hdu = PrimaryHDU(img, header=hdr)
output = input_data(hdu)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.dimensionless_unscaled
npt.assert_equal(hdr, output["header"])
def test_HDU_input_withbunit():
hdr['BUNIT'] = 'K'
hdu = PrimaryHDU(img, header=hdr)
output = input_data(hdu)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
npt.assert_equal(hdr, output["header"])
@pytest.mark.skipif("not SPECTRALCUBE_INSTALL")
def test_SC_inputs():
hdr['BUNIT'] = 'K'
hdu = PrimaryHDU(img, header=hdr)
proj = Projection.from_hdu(hdu)
output = input_data(proj)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
npt.assert_equal(proj.header, output["header"])
slic = Slice.from_hdu(hdu)
output = input_data(slic)
npt.assert_equal(img, output["data"].value)
assert output['data'].unit == u.K
npt.assert_equal(slic.header, output["header"])
def test_3D_input():
with pytest.raises(TypeError):
input_data(np.ones((3, ) * 3))
def test_3D_squeezable_input():
output = input_data(np.ones((3, 3, 1)))
npt.assert_equal(np.ones((3, 3)), output["data"].value)
assert output['data'].unit == u.dimensionless_unscaled
|
e-kochREPO_NAMEFilFinderPATH_START.@FilFinder_extracted@FilFinder-master@fil_finder@tests@test_input_types.py@.PATH_END.py
|
{
"filename": "_tickformatstop.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/choroplethmap/colorbar/_tickformatstop.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Tickformatstop(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "choroplethmap.colorbar"
_path_str = "choroplethmap.colorbar.tickformatstop"
_valid_props = {"dtickrange", "enabled", "name", "templateitemname", "value"}
# dtickrange
# ----------
@property
def dtickrange(self):
"""
range [*min*, *max*], where "min", "max" - dtick values which
describe some zoom level, it is possible to omit "min" or "max"
value by passing "null"
The 'dtickrange' property is an info array that may be specified as:
* a list or tuple of 2 elements where:
(0) The 'dtickrange[0]' property accepts values of any type
(1) The 'dtickrange[1]' property accepts values of any type
Returns
-------
list
"""
return self["dtickrange"]
@dtickrange.setter
def dtickrange(self, val):
self["dtickrange"] = val
# enabled
# -------
@property
def enabled(self):
"""
Determines whether or not this stop is used. If `false`, this
stop is ignored even within its `dtickrange`.
The 'enabled' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["enabled"]
@enabled.setter
def enabled(self, val):
self["enabled"] = val
# name
# ----
@property
def name(self):
"""
When used in a template, named items are created in the output
figure in addition to any items the figure already has in this
array. You can modify these items in the output figure by
making your own item with `templateitemname` matching this
`name` alongside your modifications (including `visible: false`
or `enabled: false` to hide it). Has no effect outside of a
template.
The 'name' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["name"]
@name.setter
def name(self, val):
self["name"] = val
# templateitemname
# ----------------
@property
def templateitemname(self):
"""
Used to refer to a named item in this array in the template.
Named items from the template will be created even without a
matching item in the input figure, but you can modify one by
making an item with `templateitemname` matching its `name`,
alongside your modifications (including `visible: false` or
`enabled: false` to hide it). If there is no template or no
matching item, this item will be hidden unless you explicitly
show it with `visible: true`.
The 'templateitemname' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["templateitemname"]
@templateitemname.setter
def templateitemname(self, val):
self["templateitemname"] = val
# value
# -----
@property
def value(self):
"""
string - dtickformat for described zoom level, the same as
"tickformat"
The 'value' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["value"]
@value.setter
def value(self, val):
self["value"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
dtickrange
range [*min*, *max*], where "min", "max" - dtick values
which describe some zoom level, it is possible to omit
"min" or "max" value by passing "null"
enabled
Determines whether or not this stop is used. If
`false`, this stop is ignored even within its
`dtickrange`.
name
When used in a template, named items are created in the
output figure in addition to any items the figure
already has in this array. You can modify these items
in the output figure by making your own item with
`templateitemname` matching this `name` alongside your
modifications (including `visible: false` or `enabled:
false` to hide it). Has no effect outside of a
template.
templateitemname
Used to refer to a named item in this array in the
template. Named items from the template will be created
even without a matching item in the input figure, but
you can modify one by making an item with
`templateitemname` matching its `name`, alongside your
modifications (including `visible: false` or `enabled:
false` to hide it). If there is no template or no
matching item, this item will be hidden unless you
explicitly show it with `visible: true`.
value
string - dtickformat for described zoom level, the same
as "tickformat"
"""
def __init__(
self,
arg=None,
dtickrange=None,
enabled=None,
name=None,
templateitemname=None,
value=None,
**kwargs,
):
"""
Construct a new Tickformatstop object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.choroplethmap.
colorbar.Tickformatstop`
dtickrange
range [*min*, *max*], where "min", "max" - dtick values
which describe some zoom level, it is possible to omit
"min" or "max" value by passing "null"
enabled
Determines whether or not this stop is used. If
`false`, this stop is ignored even within its
`dtickrange`.
name
When used in a template, named items are created in the
output figure in addition to any items the figure
already has in this array. You can modify these items
in the output figure by making your own item with
`templateitemname` matching this `name` alongside your
modifications (including `visible: false` or `enabled:
false` to hide it). Has no effect outside of a
template.
templateitemname
Used to refer to a named item in this array in the
template. Named items from the template will be created
even without a matching item in the input figure, but
you can modify one by making an item with
`templateitemname` matching its `name`, alongside your
modifications (including `visible: false` or `enabled:
false` to hide it). If there is no template or no
matching item, this item will be hidden unless you
explicitly show it with `visible: true`.
value
string - dtickformat for described zoom level, the same
as "tickformat"
Returns
-------
Tickformatstop
"""
super(Tickformatstop, self).__init__("tickformatstops")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.choroplethmap.colorbar.Tickformatstop
constructor must be a dict or
an instance of :class:`plotly.graph_objs.choroplethmap.colorbar.Tickformatstop`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("dtickrange", None)
_v = dtickrange if dtickrange is not None else _v
if _v is not None:
self["dtickrange"] = _v
_v = arg.pop("enabled", None)
_v = enabled if enabled is not None else _v
if _v is not None:
self["enabled"] = _v
_v = arg.pop("name", None)
_v = name if name is not None else _v
if _v is not None:
self["name"] = _v
_v = arg.pop("templateitemname", None)
_v = templateitemname if templateitemname is not None else _v
if _v is not None:
self["templateitemname"] = _v
_v = arg.pop("value", None)
_v = value if value is not None else _v
if _v is not None:
self["value"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@choroplethmap@colorbar@_tickformatstop.py@.PATH_END.py
|
{
"filename": "_text.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/legendgrouptitle/_text.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="text", parent_name="scattersmith.legendgrouptitle", **kwargs
):
super(TextValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@legendgrouptitle@_text.py@.PATH_END.py
|
{
"filename": "_legend.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/carpet/_legend.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LegendValidator(_plotly_utils.basevalidators.SubplotidValidator):
def __init__(self, plotly_name="legend", parent_name="carpet", **kwargs):
super(LegendValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
dflt=kwargs.pop("dflt", "legend"),
edit_type=kwargs.pop("edit_type", "style"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@carpet@_legend.py@.PATH_END.py
|
{
"filename": "mp_procoli_functions.py",
"repo_name": "tkarwal/procoli",
"repo_path": "procoli_extracted/procoli-main/src/procoli/mp_procoli_functions.py",
"type": "Python"
}
|
import os
from copy import deepcopy
from glob import glob
from subprocess import run
from time import time
import re
import numpy as np
from getdist import mcsamples
import procoli.procoli_io as pio
from procoli.procoli_errors import (ParamDifferenceError, GlobalMLDifferenceError,
LogParamUpdateError, ExperimentNotFoundError)
class lkl_prof:
"""
Class for profiling likelihoods from MontePython MCMC chains.
Parameters:
- chains_dir (str): Directory containing MCMC chains and log.param, OR containing .covmat, .bestfit and log.param files.
- prof_param (str): The parameter for which the likelihood will be profiled as recognised by MontePython in the log.param.
- prof_incr (float, optional): Increment for profiling prof_param.
- prof_min (float, optional): Minimum value of prof_param for profiling.
- prof_max (float, optional): Maximum value of prof_param for profiling.
- info_root (str, optional): Information root for the chains directory. Defaults to the last part of the chains directory path.
Provide this if your .covmat and .bestit files have a different filename than the name of the parent directory.
- processes (int, optional): Number of parallel processes to use. Defaults to 5.
- R_minus_1_wanted (float, optional): The target R-1 value for chains. Defaults to 0.05. If this R-1 is not attained,
the code prints a warning, but continues anyway.
- mcmc_chain_settings (dict, optional): Settings for MCMC chains as understood by GetDist mcsamples.loadMCSamples.
Defaults to {'ignore_rows': 0.3}.
- jump_fac (list, optional): List of jump factors for profile-likelihood simulated-annealing temperature ladder.
Defaults to [0.15, 0.1, 0.05].
- temp (list, optional): List of temperature values for profile-likelihood simulated-annealing temperature ladder.
Defaults to [0.1, 0.005, 0.001].
- global_jump_fac (list, optional): List of jump factors for global minimum temperature ladder.
Defaults to [1, 0.8, 0.5, 0.2, 0.1, 0.05].
- global_min_temp (list, optional): List of minimum temperatures for global minimum temperature ladder.
Defaults to [0.3333, 0.25, 0.2, 0.1, 0.005, 0.001].
Attributes:
- chains_dir (str): Directory containing MCMC chains.
- info_root (str): Information root for the chains directory.
- processes (int): Number of parallel processes to use.
- R_minus_1_wanted (float): The target R-1 value.
- mcmc_chain_settings (dict): Settings for MCMC chains.
- mcmc_chains (None or list): Placeholder for storing MCMC chains.
- prof_param (str): The parameter for which the likelihood will be profiled.
- prof_incr (float or None): Increment for profiling.
- prof_min (float or None): Minimum value for profiling.
- prof_max (float or None): Maximum value for profiling.
- jump_fac (list): List of jump factors for local temperature ladder.
- temp (list): List of temperature values for local temperature ladder.
- global_jump_fac (list): List of jump factors for global temperature ladder.
- global_min_temp (list): List of minimum temperatures for global temperature ladder.
- covmat_file (str): Full path to the covariance matrix file.
Example:
```
profiler = lkl_prof(chains_dir='path/to/chains', prof_param='theta', processes=4)
```
"""
def __init__(self, chains_dir, prof_param, info_root=None, processes=5,
R_minus_1_wanted=0.05, mcmc_chain_settings={'ignore_rows' : 0.3},
prof_incr=None, prof_min=None, prof_max=None,
jump_fac=[0.15, 0.1, 0.05], temp=[0.1, 0.005, 0.001],
global_jump_fac=[1, 0.8, 0.5, 0.2, 0.1, 0.05],
global_min_temp=[0.3333, 0.25, 0.2, 0.1, 0.005, 0.001]
):
chains_full_path = os.path.abspath(chains_dir)
self.chains_dir = chains_full_path + '/'
if info_root is None:
info_root = [x for x in chains_full_path.split('/') if x][-1]
self.info_root = info_root
self.processes = processes
self.R_minus_1_wanted = R_minus_1_wanted
self.mcmc_chain_settings = mcmc_chain_settings
self.mcmc_chains = None
self.prof_param = prof_param
self.prof_incr = prof_incr
self.prof_min = prof_min
self.prof_max = prof_max
self.jump_fac = jump_fac
self.temp = temp
self.global_min_jump_fac = global_jump_fac
self.global_min_temp = global_min_temp
self.covmat_file = f'{self.chains_dir}{self.info_root}.covmat'
def set_jump_fac(self, jump_fac):
"""
Setter function for the jump factor for the likelihood profile
:jump_fac: A list of jump factors
:return: Nothing
"""
self.jump_fac = jump_fac
def set_temp(self, temp):
"""
Setter function for the jump factor for the likelihood profile
:temp: A list of temperatures
:return: Nothing
"""
self.temp = temp
def set_global_jump_fac(self, global_jump_fac):
"""
Setter function for the jump factor for the global mimimum
:global_jump_fac: A list of jump factors
:return: Nothing
"""
self.global_min_jump_fac = global_jump_fac
def set_global_temp(self, global_min_temp):
"""
Setter function for the jump factor for the global mimimum
:global_min_temp: A list of temperatures
:return: Nothing
"""
self.global_min_temp = global_min_temp
def check_mcmc_chains(self, read_all_chains=False):
"""
Check if mcmc chains chains exist.
If read_all_chains = False
This explicitly uses the longest chain root in the folder.
That is, if the self.chains_dir contains files with the roots:
1993-10-05_500_
1993-10-05_5000000_
1991-08-15_1000000_
The code will pick out the longest chain root name, so 1993-10-05_5000000_
If read_all_chains = True
This sets up an MCMCSamples instance using the longest chain
It then replaces the chains in that instance with all the chains in the
folder
No duplication of chains occurs.
:read_all_chains: boolean for whether to read all the chains in the chains
directory
:return: True if files found, else False
"""
max_steps_in_chain = str( max( [ int(i[len(self.chains_dir)+11:-7]) for i in
glob(f'{self.chains_dir}*__1.txt') ] ) )
for file_root in glob(f'{self.chains_dir}*__1.txt'):
if max_steps_in_chain in file_root:
self.chain_root = file_root[len(self.chains_dir):-6]
print('check_mcmc_chains: Looking for files: '\
f'{self.chains_dir}{self.chain_root}')
try:
self.mcmc_chains = mcsamples.loadMCSamples(self.chains_dir+self.chain_root,
settings=self.mcmc_chain_settings)
self.covmat_file = self.chains_dir+self.info_root+'.covmat'
except OSError:
return False
if read_all_chains is True:
chain_root_list = glob(f'{self.chains_dir}*__*.txt')
print("check_mcmc_chains: Reading all chains:")
for chain_x in chain_root_list:
print(chain_x)
try:
self.mcmc_chains.readChains(chain_root_list)
except OSError:
return False
return True
def run_mcmc(self, N_steps=30000):
"""
Run MCMC chains
Requires the folder chains_dir to already be popualted with a log.param file
:N_steps: number of steps to take for the MCMC
:return: True if files found, else False
"""
with open(f'{self.chains_dir}log.param', 'r'):
pass
try:
with open(f'{self.chains_dir}{self.info_roo}.bestfit', 'r'):
pass
bf_exists = True
except FileNotFoundError:
bf_exists = False
try:
with open(f'{self.chains_dir}{self.info_root}.covmat', 'r'):
pass
covmat_exists = True
except FileNotFoundError:
covmat_exists = False
if (bf_exists and covmat_exists):
run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -b {bf} -c {covmat} -N {steps} '\
'--update 50 --superupdate 20'.format(
procs=self.processes,
param=self.chains_dir+'log.param',
output=self.chains_dir,
bf=self.chains_dir+self.info_root+'.bestfit',
covmat=self.chains_dir+self.info_root+'.covmat',
steps=N_steps
)
elif bf_exists:
run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -b {bf} -N {steps} --update 50 --superupdate 20'.format(
procs=self.processes,
param=self.chains_dir+'log.param',
output=self.chains_dir,
bf=self.chains_dir+self.info_root+'.bestfit',
steps=N_steps
)
elif covmat_exists:
run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -c {covmat} -N {steps} --update 50 --superupdate 20'.format(
procs=self.processes,
param=self.chains_dir+'log.param',
output=self.chains_dir,
covmat=self.chains_dir+self.info_root+'.covmat',
steps=N_steps
)
else:
run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -N {steps} --update 50 --superupdate 20'.format(
procs=self.processes,
param=self.chains_dir+'log.param',
output=self.chains_dir,
steps=N_steps
)
run(run_command, shell=True)
return True
def check_mcmc_convergence(self, mcmc_chains=None):
"""
Check if MCMC converged
:mcmc_chains: getdist MCSamples instance
:return: True if MCMC chains have converged to the desired R-1,
default is R-1=0.05. Else False
"""
if mcmc_chains is None:
mcmc_chains=self.mcmc_chains
current_R_minus_1 = mcmc_chains.getGelmanRubin()
if current_R_minus_1 < self.R_minus_1_wanted:
print("check_mcmc_convergence: Chains converged sufficiently. '\
'Current R-1 = {:.3f} satisfies R-1 wanted = {:.3f}. '\
'\nMove on to checking minimum.".format(current_R_minus_1,
self.R_minus_1_wanted))
return True
else:
print("check_mcmc_convergence: Chains not converged. '\
'Current R-1 = {:.3f} while R-1 wanted = {:.3f}. '\
'\nResume MCMC. ".format(current_R_minus_1,self.R_minus_1_wanted))
return False
def mcmc(self):
"""
Check MCMC and run if needed
/!\ THIS FUNCTION DOESN'T ACTUALLY LEAD TO CONVERGENCE. NEEDS IMPROVEMENT.
:return: True once finished
"""
if not self.check_mcmc_chains(read_all_chains=True):
self.run_mcmc()
while not self.check_mcmc_convergence():
run(f'mpirun -np 1 MontePython.py info {self.chains_dir} '\
'--keep-non-markovian --noplot --want-covmat --minimal', shell=True)
self.run_mcmc(N_steps=50000)
self.check_mcmc_chains(read_all_chains=True)
return True
def check_global_min_has_lower_loglike(self, existing_min):
"""
Check the negative log likelihoods of the global minimum bestfit and the
info root bestfit to see which is better (lower negative log likelihood)
:existing_min: True if global minimum was run and relevant files are accesible.
Else False
:return: If a global bestfit already exists with a lower
log likehood than the info root bestfit
"""
global_path = 'global_min/global_min'
global_min_is_better = False
if existing_min:
if os.path.exists(f'{self.chains_dir}{global_path}.bestfit'):
global_min_point = pio.get_MP_bf_dict(f'{self.chains_dir}{global_path}.bestfit')
info_root_point = pio.get_MP_bf_dict(f'{self.chains_dir}{self.info_root}.bestfit')
if info_root_point['-logLike'] != global_min_point['-logLike']:
print('check_global_min: WARNING!!!: global_min folder found with '\
'a global_min.bestfit that is different from '\
f'{self.info_root}.bestfit. Code will use the better '\
'chi^2 of the two going forward.')
if info_root_point['-logLike'] >= global_min_point['-logLike']:
_ = pio.file_copy(f'{self.chains_dir}{global_path}.bestfit',
f'{self.chains_dir}{self.info_root}.bestfit')
global_min_is_better = True
print(f'check_global_min: WARNING!!!: global_min folder found '\
'with a global_min.bestfit that was found to be as good '\
f'or a better chi^2 than the {self.info_root}.bestfit file. '\
f'Code will replace the {self.info_root}.bestfit and '\
f'{self.info_root}.log files with ones from the '\
'global_min/global_min.bestfit and .log going forward.')
return global_min_is_better
def check_global_min(self, mcmc_chains=None):
"""
Check for .bestfit file. This does not necessarily indicate a global
minimum run!!!
It only indicates that there exists a file storing some bf in the
'info_root' file.
This also resets the info_root to the current directory name to avoid
errors later in the code.
:mcmc_chains: getdist MCSamples instance
:return: True if global minimum was run and relevant files are accesible.
Else False, If a global bestfit already exists with a lower
log likehood than the info root bestfit
"""
if mcmc_chains is None:
mcmc_chains=self.mcmc_chains
global_min_exists = False
existing_min = False
try:
# TODO can probably check if it exists with the os module
pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}.bestfit')
print(f'check_global_min: Found minimum with file name {self.info_root}')
pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}.covmat')
print(f'check_global_min: Found covmat with file name {self.info_root}')
new_info_root = [x for x in self.chains_dir.split('/') if x][-1]
if self.info_root != new_info_root:
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.bestfit',
f'{self.chains_dir}{new_info_root}.bestfit')
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.covmat',
f'{self.chains_dir}{new_info_root}.covmat')
try:
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.log',
f'{self.chains_dir}{new_info_root}.log')
except FileNotFoundError:
self.make_log_file( bf_file=f'{self.chains_dir}{self.info_root}.bestfit',
output_loc=self.chains_dir
)
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.log',
f'{self.chains_dir}{new_info_root}.log')
self.info_root = new_info_root
else:
if not os.path.exists(f'{self.chains_dir}{self.info_root}.log'):
self.make_log_file( bf_file=f'{self.chains_dir}{self.info_root}.bestfit',
output_loc=self.chains_dir
)
global_min_exists = True
existing_min = True
except OSError:
try:
new_info_root = [x for x in self.chains_dir.split('/') if x][-1]
# TODO can we run montepython with mpirun directly from python?
run(f'mpirun -np 1 MontePython.py info {self.chains_dir} '\
'--keep-non-markovian --noplot --want-covmat --minimal',
shell=True, check=True)
# TODO can probably check if it exists with the module
pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}.bestfit')
# TODO why change the info root?
self.info_root = new_info_root
print('check_global_min: Found minimum with file name '\
f'{self.info_root}')
global_min_exists = True
except OSError:
print('check_global_min: Cannot run MP info for global minimum. '\
'Something went wrong. Either provide chains or provide .bestfit and .covmat file.')
global_min_exists = False
global_min_is_better = self.check_global_min_has_lower_loglike(existing_min)
return global_min_exists, global_min_is_better
def global_min(self, run_glob_min=None, N_min_steps=4000, run_minuit=False):
"""
Check global minizer, run if wanted (default False), then write if not
already written
So:
1) Load / create the global minimum file.
2) Check if the previous global minimum bestfit is
better than the info_root bestfit
3) If we want a global min run, run the minimizer
4) grab the global minimizer results
5) check if we have a file with prof lkl values.
* If yes, check that it has the same parameters and in the right order.
Proceed.
* If no file, start it and write the first line as param names. Proceed.
* If file yes, but parameters don't match, then print an error. Stop.
6) check if global minimum params have already been written (first line of file)
* If parameters are written, check that they match global minimum.
Don't write them again
* If parameters are written but don't match, spit out error.
* If no params written, add this current ML values for all parameters
in append mode
:run_glob_min: Boolean for whether to run a global minimizer.
If True or False are given then choose to run the minimzer accordingly
If no value is given then let check_global_min decide by checking
if the global min has already been run and has a better bestfit
:N_min_steps: The number of steps the minimizer should use for each run
:run_minuit: Flag for the minimizer to use minuit
:return: global maximum lkl dictionary
"""
# check to see if the global min exists already
# and decide to run the minimizer accordingly
global_min_exists, global_min_is_better = self.check_global_min()
if run_glob_min is None:
if global_min_is_better:
run_glob_min = False
else:
run_glob_min = True
if run_glob_min:
pio.make_path(f'{self.chains_dir}global_min', exist_ok=True)
_ = pio.file_copy(f'{self.chains_dir}log.param',
f'{self.chains_dir}global_min/log.param')
self.run_minimizer(min_folder='global_min', N_steps=N_min_steps,
run_minuit=run_minuit,
jump_fac=self.global_min_jump_fac,
temp=self.global_min_temp)
_ = pio.file_copy(f'{self.chains_dir}global_min/global_min.bestfit',
f'{self.chains_dir}{self.info_root}.bestfit')
_ = pio.file_copy(f'{self.chains_dir}global_min/global_min.log',
f'{self.chains_dir}{self.info_root}.log')
param_names, param_ML, MLs = self.read_minimum(extension='')
# Additional code to get chi2 per experiment
MLs_and_chi2 = self.update_MLs_chi2_per_exp(MLs)
self.param_order = [key for key in MLs_and_chi2]
self.global_ML = deepcopy(MLs_and_chi2)
# self.param_order = param_names.tolist()
extension = '_lkl_profile.txt'
extension = self.pn_ext(extension)
try:
self.match_param_names(self.param_order)
except FileNotFoundError:
print('global_min: File not found. Starting a new file now: '\
f'{self.chains_dir}{self.info_root}{extension}\n')
with open(f'{self.chains_dir}{self.info_root}{extension}', 'w') as lkl_txt:
lkl_txt.write('#')
for param_recorded in self.param_order:
lkl_txt.write(f'\t {param_recorded}')
lkl_txt.write("\n")
lkl_prof_table = pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}{extension}')
# TODO param order should inherit from file header, param order not matching
# should never cause the code to fail
if lkl_prof_table.shape!=(0,):
if not self.match_param_line(self.global_ML, loc=0):
raise GlobalMLDifferenceError(f'{self.chains_dir}{self.info_root}')
else:
self.write_MLs(MLs_and_chi2)
return self.global_ML
def pn_ext(self, extension):
"""
Prefix the file extension string input with
the sign of the profile lkl parameter,
and its name to track files correctly.
:extension: A string of the file name extension, eg. "_good_pupper"
:return: String of extension prefixed with the sign and name of the
profile lkl parameter "_+height_good_pupper"
"""
if len(extension)>0:
if self.prof_incr > 0:
extension = '_+'+self.prof_param+extension
if self.prof_incr < 0:
extension = '_-'+self.prof_param+extension
return extension
def read_minimum(self, extension='_lkl_prof'):
"""
Read minimum file and save parameter names list, parameter values list
and MLs dictionary
Also update the dictionary object self.MLs
:extension: The extension of the life type being read in. Leave this as is, the
rest of the code assumes the same naming conventions. Otherwise, specify to
read a specific file, but know that this will update the self.MLs dict too.
:return: List of parameter names, list of parameter ML values,
dictionary of {'param_names': param_ML_value}
"""
prefix_extension = self.pn_ext(extension)
# TODO can probably make this a single read to dict
param_ML = pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}{prefix_extension}.bestfit')
param_names = pio.read_header_as_list(f'{self.chains_dir}{self.info_root}{prefix_extension}.bestfit')
MLs = dict(zip(param_names, param_ML))
with open(f'{self.chains_dir}{self.info_root}{prefix_extension}.log') as log_file:
last_line = log_file.readlines()[-1]
neg_logLike = float(last_line.split(":")[-1])
MLs['-logLike'] = neg_logLike
param_names = np.append(param_names, '-logLike')
param_ML = np.append(param_ML, MLs['-logLike'])
self.MLs = MLs
# TODO do we want to remove param_ML from the output?
# It's never used as an output
return param_names, param_ML, MLs
def read_lkl_output(self, extension='_lkl_profile.txt', loc=-1):
"""
Read (default = last) line of lkl prof output file into list
:extension: Leave this alone, thank you.
:loc: integer location of line in file to read. Default is last line
:return: Dict of parameters
"""
prefix_extension = self.pn_ext(extension)
lkl_prof_table = pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}{prefix_extension}')
try:
lkl_prof_table.shape[1] # check that lkl_prof_table has multiple rows
lkl_prof_table = lkl_prof_table[loc, :]
except IndexError:
pass
self.param_names = pio.read_header_as_list(f'{self.chains_dir}{self.info_root}{prefix_extension}')
MLs = dict(zip(self.param_names, lkl_prof_table))
return MLs
def write_MLs(self, MLs=None, extension='_lkl_profile.txt'):
"""
Write params from MLs dict into txt file in append mode
Note that to write, we use self.param_order, not self.param_names.
This is because the global param_names list is the one that has the
correct order.
:extension: Leave it alone, thank you.
:return: new length of the saved lkl profile table
"""
if MLs is None:
MLs = self.MLs
prefix_extension = self.pn_ext(extension)
with open(f'{self.chains_dir}{self.info_root}{prefix_extension}', 'a') as lkl_txt:
for param in self.param_order:
lkl_txt.write(f'\t {str(MLs[param])}')
lkl_txt.write('\n')
lkl_prof_table = pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}{prefix_extension}')
return lkl_prof_table.shape
def match_param_names(self, param_names, extension='_lkl_profile.txt'):
"""
Check that param names match in target file and MLs dictionary
Matches combination, not permutation.
:param_names: List of param_names to check against the file
:extension: Leave it alone, thank you.
:return: True if param_names match, else False
"""
prefix_extension = self.pn_ext(extension)
params_recorded = pio.read_header_as_list(f'{self.chains_dir}{self.info_root}{prefix_extension}')
# TODO this can probably be combined so you don't have to compare them
# against each other twice
mismatched_params = [param for param in param_names
if param not in params_recorded]
if not mismatched_params:
param_names_in_rec = True
else:
print("\nmatch_param_names: Params don't match. \nPassed param_names has "\
"the following params that are not expected by params recorded:")
print(mismatched_params)
param_names_in_rec = False
mismatched_params = [param for param in params_recorded
if param not in param_names]
if not mismatched_params:
rec_params_in_param_names = True
else:
print("\nmatch_param_names: Params don't match. \nRecorded params expected "\
"that are absent in passed param_names: ")
print(mismatched_params)
rec_params_in_param_names = False
if (param_names_in_rec and rec_params_in_param_names):
print("match_param_names: Params match - the recorded params contain the "\
"same params as param_names passed. ")
self.param_order = params_recorded
return True
else:
raise ParamDifferenceError(f'{self.chains_dir}{self.info_root}{prefix_extension}')
def match_param_line(self, MLs, param_names=None, extension='_lkl_profile.txt',
loc=-1):
"""
Check if specified (default: last) location in lkl_prof output file matches
current MLs
:param_names: list of parameter names in the same order as that printed in
the file.
This is usually the global param_order list. Note: LIST not array!
:MLs: dictionary of {'param_name': ML_value }
:extension: Leave it alone, thank you.
:loc: integer location of row in file to check, default is the last line
:return: True if match, else False
"""
prefix_extension = self.pn_ext(extension)
if param_names is None:
# param_names=self.param_order
try:
param_names = [i for i in self.param_order if i not in self.likelihoods]
param_names.remove('Total')
except AttributeError:
pass
except ValueError:
pass
# print('match_param_line: checking file '\
# f'{self.chains_dir}{self.info_root}{prefix_extension}')
lkl_prof_table = pio.load_mp_info_files(f'{self.chains_dir}{self.info_root}{prefix_extension}')
if lkl_prof_table.size==0:
print("match_param_line: File empty ")
return False
else:
try:
lkl_prof_table.shape[1] # check that lkl_prof_table has multiple rows
if False in [ lkl_prof_table[loc, param_names.index(param)]
== MLs[param] for param in param_names ]:
return False
else:
return True
except IndexError:
print("match_param_line: Only one entry in file, checking that entry ")
if False in [ lkl_prof_table[param_names.index(param)]
== MLs[param] for param in param_names ]:
return False
else:
return True
def update_neg_log_likelhood_chains(self, path, temp, chain_length,
previous_chains=[]):
"""
Update the chain files to show the correct negative log likelihood
when using the temperature parameter
Ignore chain files that have already been updated
:path: Path to the directory with the chain files
:temp: Float of the temperature parameter used
:chain_length: Int of how many points are in each chain file
:previous_chains: List of the previous chain files that have been updated
The dfault is an empty list
:return: All chain files that have been updated previously,
or that needed no updates if temp == 1 for that step.
"""
chains = glob(f'{path}*_{chain_length}__*.txt')
if temp != 1:
for chain in chains:
if chain not in previous_chains:
# read in the current chain, update the negative log likelihood
# and then save the new data back to the original file
chain_file = pio.load_mp_info_files(chain)
row_size = chain_file.shape[1]
frmt_list = ['%.4g', '%.6g'] + ['%.6e']*(row_size-2)
chain_file[:,1] = chain_file[:,1]*temp
pio.save_mp_info_files(chain, chain_file, fmt=frmt_list,
delimiter='\t')
return chains
def run_minimizer(self, min_folder="lkl_prof", prev_bf=None, N_steps=3000,
run_minuit=False, jump_fac=None, temp=None):
"""
Run minimizer as described in 2107.10291, by incrementally running a finer MCMC
with a more discrening lklfactor that increases preference for moving towards
higher likelihoods
and taking smaller jumps between points so we sample a finer grid in parameter
space.
This can be modified as wanted, changing step sizes per rung, lkl factor and
jumping factors.
You would need to change this function, then import the class and run a lkl
prof as usual.
Default:
N_steps = input parameter, same for all rungs
lklfactor = 10, 200, 1000
jumping factor (-f) = 0.5, 0.1, 0.05
Requires the folder min_folder to already be popualted with a log.param file
:min_folder: Folder to run minimizer in
:prev_bf: Starting best-fit file.
This is the MAP of the MCMC chains for the global bf run,
and the previous point for the lkl prof.
The function init_lkl_prof takes care of designating this,
set interanlly.
:N_steps: Number of steps each minimizer rung takes, same for all rungs
:return: True
"""
if min_folder=="lkl_prof":
min_folder += self.pn_ext('/')
# Prep the command
if not min_folder:
min_folder = '.'
elif min_folder[-1] == '/':
min_folder = min_folder[:-1]
if not prev_bf:
prev_bf = self.info_root
elif '.bestfit' in prev_bf:
prev_bf = prev_bf[:-8]
# TODO clean this up
# Check if jumping factors and lkl factors are defined, otherwise set to
# lkl profile run defaults
if jump_fac is None:
jump_fac = self.jump_fac
if temp is None:
temp = self.temp
if len(jump_fac) != len(temp):
jump_fac = [0.15, 0.1, 0.05]
# TODO: DEFAULTS HARD CODED HERE.
# Move to init as defaults that the user should not know about
# and cannot change.
temp = [0.1, 0.005, 0.001]
print('!!!!!!!!!\n!!!!!!!!!\n!!!!!!!!!')
print('Error in run_minimizer: Lists passed for jumping factor and lkl '\
'factor are of different lengths. \n'\
'Setting to defaults!!! \n'\
f'jumping factor list = {jump_fac} \n'\
f'temperature list = {temp}')
print("!!!!!!!!!\n!!!!!!!!!\n!!!!!!!!!")
##### First rung #####
# MCMC
mp_run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -b {bf} -c {covmat} -N {steps} -f {f} '\
'-T {temp}'.format(
procs=self.processes,
param=self.chains_dir+min_folder+'/log.param',
output=self.chains_dir+min_folder+'/',
bf=self.chains_dir+prev_bf+'.bestfit',
covmat=self.chains_dir+self.info_root+'.covmat',
steps=N_steps,
f = jump_fac[0],
temp = temp[0]
)
previous_chains = glob(f'{self.chains_dir}{min_folder}/*_{N_steps}__*.txt')
run(mp_run_command, shell=True, check=True)
previous_chains = self.update_neg_log_likelhood_chains(f'{self.chains_dir}{min_folder}/',
temp[0], N_steps, previous_chains=previous_chains)
# analyse
mp_info_command = 'mpirun -np 1 MontePython.py info {folder} '\
'--keep-non-markovian --noplot --minimal'.format(
folder=self.chains_dir+min_folder+'/'
)
run(mp_info_command, shell=True)
# print output
if min_folder=='.':
prev_bf = [x for x in str(os.getcwd()).split('/') if x][-1]
# switch to current directory as bf root,
# ensures that we're using the most recent file
else:
prev_bf = min_folder+'/'+min_folder
# switch to most recently produced bf file
# in the minimizer directory as bf root
# set new minimum
new_min_point = pio.get_MP_bf_dict(f'{self.chains_dir}{prev_bf}.bestfit')
print('\n\n------------------> After minimizer rung 1, -logL minimized to '\
'{logL} \n\n'.format(logL=new_min_point['-logLike']))
##### Loop over other rungs #####
num_itrs = len(jump_fac)
for i in range(1,num_itrs):
# MCMC
run_command = 'mpirun -np {procs} MontePython.py run -p {param} '\
'-o {output} -b {bf} -c {covmat} -N {steps} -f {f} '\
'-T {temp}'.format(
procs=self.processes,
param=self.chains_dir+min_folder+'/log.param',
output=self.chains_dir+min_folder+'/',
bf=self.chains_dir+prev_bf+'.bestfit',
covmat=self.chains_dir+self.info_root+'.covmat',
steps=N_steps,
f = jump_fac[i],
temp = temp[i]
)
run(run_command, shell=True)
previous_chains = self.update_neg_log_likelhood_chains(f'{self.chains_dir}{min_folder}/',
temp[i], N_steps, previous_chains=previous_chains)
# analyse
run_command = 'mpirun -np 1 MontePython.py info {folder} '\
'--keep-non-markovian --noplot --minimal'.format(
folder=self.chains_dir+min_folder+'/'
)
run(run_command, shell=True)
# set new minimum
new_min_point = pio.get_MP_bf_dict(self.chains_dir+prev_bf+'.bestfit')
print('\n\n------------------> After minimizer rung {ith}, '\
'-logL minimized to {logL} \n\n'.format(
ith=i+1, logL=new_min_point['-logLike']))
##### Bonus step: run Minuit minimizer #####
if run_minuit:
# NOTE: this will only work well if the minimizer outputs results that
# have correctly scaled params
# MP rescales some params: omega_b (1e-2), A_s (1e-9)
# and a couple of PLC params
# So this output needs to scale them back to normal,
# with omega_b of O(0.01), etc.
# TK will update MP to do this correctly.
# Fix already there, need to turn print into replacement
# Run minuit minimizer
run_command = 'mpirun -np 1 MontePython.py run -p {param} -o {output} '\
'-b {bf} -c {covmat} --minimize'.format(
param=self.chains_dir+min_folder+'/log.param',
output=self.chains_dir+min_folder+'/',
bf=self.chains_dir+prev_bf+'.bestfit',
covmat=self.chains_dir+self.info_root+'.covmat'
)
run(run_command, shell=True)
# run a fake MCMC point at this minimum
# Here, we need to run a fake chain at this minimized point first
# in order to create the .bestfit and .log files that play well with
# the rest of the code.
run_command = 'mpirun -np 1 MontePython.py run -p {param} -o {output} '\
'-b {bf} -c {covmat} -N {steps} -f {f}'.format(
param=self.chains_dir+min_folder+'/log.param',
output=self.chains_dir+min_folder+'/',
bf=self.chains_dir+min_folder+'/results.minimized',
covmat=self.chains_dir+self.info_root+'.covmat',
steps=1,
f = 0
)
run(run_command, shell=True)
# analyse
run_command = 'mpirun -np 1 MontePython.py info {folder} '\
'--keep-non-markovian --noplot --minimal'.format(
folder=self.chains_dir+min_folder+'/'
)
run(run_command, shell=True)
# update and print minimum
new_min_point = pio.get_MP_bf_dict(f'{self.chains_dir}{prev_bf}.bestfit')
print('\n\n------------------> After Minuit run, -logL minimized to '\
'{logL} \n\n'.format(logL=new_min_point['-logLike']))
return True
def first_jump_fac_less_than_prof_incr(self):
"""
Checks if the first element of the 'jump_fac' sequence in the simulated-annealing
minimizer is less than the absolute value of the ratio between the step size
in the profile parameter 'prof_incr', and its 1sigma error from the covariance
matrix.
:return: True if j < \Delta \theta_i / \sigma_i
"""
covmat_header = pio.read_header_as_list(f'{self.chains_dir}{self.info_root}.covmat')
covmat = np.loadtxt(f'{self.chains_dir}{self.info_root}.covmat')
prof_param_index = covmat_header.index(self.prof_param)
param_1sigma = covmat[prof_param_index,prof_param_index]**0.5
is_j_less_than_incr = (self.jump_fac[0] < np.abs(self.prof_incr/param_1sigma) )
# we want j < \Delta \theta_i / \sigma_i
if not is_j_less_than_incr:
print(' ___\n // \\\\\n // ! \\\\\n//_____\\\\\n')
print('Warning: Increments in profile parameter are smaller than '\
'the first jumping factor in simulated annealing sequence. \n'\
'Ideally, we want first jumping factor < '\
'(increment in profile parameter)/(error in profile parameter) \n'\
'Currently we have j > Delta theta_i/ sigma_i with \n'\
f'{self.jump_fac[0]} > {np.abs(self.prof_incr)}/{param_1sigma} = {np.abs(self.prof_incr/param_1sigma)} \n'\
'This will result in a poor profile. \n'\
'Either increase the profile increment prof_incr, \n'\
'or decrease the first jumping factor in the simulated annealing sequence list jump_fac\n'\
)
return is_j_less_than_incr
def init_lkl_prof(self, lkl_dir = "lkl_prof"):
"""
Initialise profile lkl yaml:
1) create profile lkl folder and copy the global log.param into it
2) read in last .bestfit file and set that as the current MLs dictionary,
as self.MLs
this updates the location in prof_param that we're at for running prof lkls.
Under MP, decided to do this instead of updating to the last line of the
lkl output file
3) copy global bf into prof lkl output bf if the file doesn't exist
:lkl_dir: Leave the extension alone, thank you.
:return: the current lkl prof_param value
"""
global_lp = f'{self.chains_dir}log.param'
full_lkl_dir = f'{self.chains_dir}{lkl_dir}{self.pn_ext("/")}'
pio.make_path(full_lkl_dir, exist_ok=True)
_ = pio.file_copy(global_lp, full_lkl_dir)
try:
self.read_minimum()
except OSError:
# the lkl prof bf and lof files don't exist
# copy global bf
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.bestfit',
f'{self.chains_dir}{self.info_root}{self.pn_ext("_lkl_prof")}.bestfit')
# copy global log
_ = pio.file_copy(f'{self.chains_dir}{self.info_root}.log',
f'{self.chains_dir}{self.info_root}{self.pn_ext("_lkl_prof")}.log')
# now this should work
self.read_minimum()
# # /!\ Used to be initialised to last entry of lkl prof txt file
# self.MLs = self.read_lkl_output()
# # Copy last lkl profile txt point into the bestfit file:
# lkl_prof_header = pio.read_header_as_list(self.info_root+self.pn_ext('_lkl_profile.txt'))
# update_bf_to_last_point = self.info_root+self.pn_ext("_lkl_prof")+".bestfit"
# with open(update_bf_to_last_point, 'w') as lkl_txt:
# lkl_txt.write("# ")
# lkl_txt.write((", ").join(lkl_prof_header))
# lkl_txt.write("\n")
# lkl_txt.write(str(self.MLs[lkl_prof_header[0]]))
# for param in lkl_prof_header[1:]:
# lkl_txt.write(" "+str(self.MLs[param]) )
_ = self.first_jump_fac_less_than_prof_incr()
return self.MLs[self.prof_param]
def increment_update_logparam(self, lkl_dir = "lkl_prof"):
"""
Update log.param value for prof_param to next increment = current + increment
:lkl_dir: Leave the extension alone, thank you.
:return: new value of prof_param that the log.param was updated to,
string of the line containing the prof_param in the log.param
"""
extension = self.pn_ext('/')
lkl_lp = f'{self.chains_dir}{lkl_dir}{extension}log.param'
with open(lkl_lp, 'r') as f:
lkl_lp_lines = f.readlines()
line_modified = False
lp_prof_param_string = f"'{self.prof_param}'"
with open(lkl_lp, 'w') as f:
for line in lkl_lp_lines:
if lp_prof_param_string in line:
# print("Original: \t"+line)
prof_param_lp_line = line.split('=')
prof_param_lp_data = prof_param_lp_line[1].split(',')
updated_prof_param = self.MLs[self.prof_param]+self.prof_incr
prof_param_lp_data[0] = f'[{updated_prof_param/float(prof_param_lp_data[4])}'
prof_param_lp_data[3] = '0.'
prof_param_lp_data_str = ','.join(prof_param_lp_data)
prof_param_lp_line = f'{prof_param_lp_line[0]} '\
f'= {prof_param_lp_data_str}'
# print("Modified: \t"+line)
f.write(prof_param_lp_line)
line_modified = True
else:
f.write(line)
if line_modified is False:
raise LogParamUpdateError(self.prof_param, lkl_lp)
return updated_prof_param, prof_param_lp_line
def get_prof_param_value_from_lp(self, lp_dir = "lkl_prof"):
"""
Get current value of the prof lkl parameter from the lop param file
:lp_dir: directory of the log.param file to read. Default set for using
function internally.
:return: 'mean' of prof lkl parameter in the log.param as float
"""
if lp_dir:
lp_dir += self.pn_ext('/')
lp_file = f'{lp_dir}log.param'
with open(lp_file, 'r') as f:
lkl_lp_lines = f.readlines()
lp_prof_param_string = "'"+self.prof_param+"'"
for line in lkl_lp_lines:
if lp_prof_param_string in line:
prof_param_line = line
prof_param_line = prof_param_line.split("=")
prof_param_line = prof_param_line[1].split(",")
prof_param_line = prof_param_line[0].strip()[1:]
prof_param_value = float(prof_param_line)
break
return prof_param_value
def get_experiments(self):
"""
Extracts a list of likelihoods from the log.param file in the specified
chains directory.
Returns:
list or None: A list of likelihoods if found, otherwise None.
Raises:
FileNotFoundError: If the log.param file is not found in the specified
chains directory.
Example:
>>> profile = lkl_prof(chains_dir='/path/to/chains/', ...)
>>> likelihoods = profile.get_experiments()
>>> print(likelihoods)
['Planck_highl_TTTEEE', 'Planck_lowl_EE', 'Planck_lowl_TT']
"""
# Read the text file
lp_file_content = pio.read_file(f'{self.chains_dir}log.param')
# Define the pattern for finding likelihoods
experiments_line = re.compile(r"data\.experiments=\[([^\]]+)\]")
# Use regular expression to find the likelihoods
match = experiments_line.search(lp_file_content)
if match:
# Extract the likelihoods from the matched group
likelihoods_str = match.group(1)
# Split the likelihoods string into a list
likelihoods = [lkl.strip(' ').strip("'")
for lkl in likelihoods_str.split(',')]
# Print the extracted likelihoods
# print("Likelihoods of interest:")
# for lkl in likelihoods:
# print(lkl)
else:
likelihoods = None
print(f'Error in get_experiments: No likelihoods found in the file ' \
f'{self.chains_dir}log.param.')
self.likelihoods = likelihoods
return likelihoods
def MP_run_chi2_per_exp_at_point(self, output_dir, param_point_bf_file):
"""
Run MontePython to calculate and display chi^2 values for each likelihood
at a specific parameter point.
Args:
output_dir (str): The directory to store the output files from the MontePython run.
param_point_bf_file (str): The file containing the best-fit parameter point
for which chi^2 values will be calculated.
Returns:
str: The output string from the MontePython run,
containing effective chi^2 values for different likelihoods.
Example:
>>> output_directory = '/path/to/output/'
>>> best_fit_param_file = '/path/to/best_fit.bestfit'
>>> chi2_output_str = profile.MP_run_chi2_per_exp_at_point(output_directory, best_fit_param_file)
>>> print(chi2_output_str)
"... -> for Planck_highl_TTTEEE : ... chi2eff= 123.45 ... -> for Planck_lowl_EE : ... chi2eff= 67.89 ..."
"""
mp_run_command = 'mpirun -np 1 MontePython.py -N 1 -f 0 --display-each-chi2 '\
f'-o {output_dir} -p {self.chains_dir}log.param -b {param_point_bf_file}'
captured_output = run(mp_run_command, shell=True, check=True,
capture_output=True).stdout
# Turn our b-string into a normal string
chi2_per_exp_output = captured_output.decode('utf-8')
return chi2_per_exp_output
def get_chi2_per_exp_dict(self, chi2_per_exp_output, likelihoods=None):
"""
Extract and return chi^2 values for each likelihood from a given output string.
This method parses the output string generated by a
MontePython --display-each-chi2
run to extract effective chi^2 values for different likelihoods.
Args:
likelihoods (list, optional): A list of likelihood names.
Defaults to None, in which case it uses the likelihoods attribute of
the class instance.
chi2_per_exp_output (str): The output string from a
MontePython --display-each-chi2
run containing effective chi^2 values for different likelihoods.
Returns:
dict: A dictionary where keys are likelihood names and values are
corresponding chi^2 values.
Raises:
ExperimentNotFoundError: If the effective chi^2 value is not found
for a given likelihood or 'Total'.
Example:
>>> profile = lkl_prof(chains_dir='/path/to/chains/', ...)
>>> likelihood_list = profile.get_experiments()
>>> chi2_output_str = "... -> for Planck_highl_TTTEEE : ... chi2eff= 123.45
... -> for Planck_lowl_EE : ... chi2eff= 67.89 ..."
>>> chi2_dict = profile.get_chi2_per_exp_dict(chi2_output_str)
>>> print(chi2_dict)
{'Planck_highl_TTTEEE': 2400.00, 'Planck_lowl_EE': 400.00,
'Planck_lowl_TT': 25.00}
"""
# get any likelihood cross references for MP
# where the log.param and output refer to the same likelihood
# but with different names
exp_crosslist = pio.get_experiment_crosslist()
if likelihoods is None:
likelihoods = self.likelihoods
# Initialize a dictionary to store chi2eff values for each likelihood
chi2eff_values = {}
# Iterate over likelihoods
for lkl in likelihoods:
# Define the reg expression pattern for finding chi2eff value
pattern = re.compile(fr"-> for {lkl} : .* chi2eff= ([0-9.-]+(?:[eE][+-]?[0-9]+)?)")
# Use regular expression to find the chi2eff value
match = pattern.search(chi2_per_exp_output)
if match:
# Convert the matched value to float and store in the dictionary
chi2eff_values[lkl] = float(match.group(1))
# if the experiment is missing from the output, check the MP experiment
# crosslistings for any name swaps and repeat the above
else:
if lkl in exp_crosslist:
lkl_cross = exp_crosslist[lkl]
pattern = re.compile(fr"-> for {lkl_cross} : .* chi2eff= ([0-9.-]+)")
match = pattern.search(chi2_per_exp_output)
if match:
chi2eff_values[lkl] = float(match.group(1))
else:
raise ExperimentNotFoundError(lkl_cross)
else:
raise ExperimentNotFoundError(lkl)
# Total chi2
pattern = re.compile(r"-> Total:.*chi2eff= ([0-9.-]+)")
# Use regular expression to find the chi2eff value
match = pattern.search(chi2_per_exp_output)
if match:
# Convert the matched value to float and store in the dictionary
chi2eff_values['Total'] = float(match.group(1))
else:
raise ExperimentNotFoundError('Total')
return chi2eff_values
def update_MLs_chi2_per_exp(self, param_point):
"""
Update a parameter point with corresponding chi^2 values for
each likelihood using MontePython.
We run MontePython at the param_point dictionary, get chi2 per
experiment and output a dictionary of the param and chi2 values.
Args:
param_point (dict): A dictionary representing a point in the parameter space.
Returns:
dict: A dictionary containing the original parameter point along with the
chi^2 values for each likelihood.
Example:
>>> parameter_point = {'param1': 1.0, 'param2': 2.0, 'param3': 3.0}
>>> updated_point = update_MLs_chi2_per_exp(parameter_point)
>>> print(updated_point)
{'param1': 1.0, 'param2': 2.0, 'param3': 3.0, 'Planck_highl_TTTEEE': 2400.00, 'Planck_lowl_EE': 400.00, 'Planck_lowl_TT': 25.00}
"""
# set likelihoods variable from log.param
self.get_experiments()
# set location for running this point
save_output_bf_loc = f'{self.chains_dir}chi2_per_exp/'
pio.make_path(save_output_bf_loc)
# set bf file for running file
save_output_bf_file = save_output_bf_loc+'chi2_per_exp.bestfit'
# Write this point to a MP style .bestfit file
pio.write_bf_dict_to_file(param_point, save_output_bf_file)
# Run MP at this point and get chi2 values from output as dict
chi2_per_exp_output = self.MP_run_chi2_per_exp_at_point(output_dir=save_output_bf_loc, param_point_bf_file=save_output_bf_file)
# print(chi2_per_exp_output)
chi2eff_values = self.get_chi2_per_exp_dict(chi2_per_exp_output)
# new dictionary with passed parameter points and chi2eff values
params_and_chi2s = deepcopy(param_point)
params_and_chi2s.update(chi2eff_values)
return params_and_chi2s
def make_log_file(self, bf_file, output_loc=None, output_log_file=None):
"""
Generate a .log file containing the minimum of -logLike for a given .bestfit file
using MontePython, following the usual syntax of MP.
This function sets the likelihoods variable from log.param, runs MontePython at the
specified parameter point given by bf_file, obtains chi^2 values for each experiment,
and appends the total -logLike to a .log file.
Args:
bf_file (str): File path to the MontePython best-fit parameter file.
output_loc (str, optional): Output location for the .log file. Defaults to
self.chains_dir.
Returns:
str: File path to the generated .log file.
Example:
>>> log_file_path = make_log_file('my_procoli.bestfit')
>>> print(log_file_path)
'my_procoli.log'
"""
# set output defaults
if not output_loc:
output_loc = self.chains_dir
if not output_log_file:
output_log_file = f'{bf_file[:-8]}.log'
# set likelihoods variable from log.param
self.get_experiments()
# Run MP at this point and get chi2 values from output as dict
chi2_per_exp_output = self.MP_run_chi2_per_exp_at_point(
output_dir=output_loc,
param_point_bf_file=bf_file
)
# print(chi2_per_exp_output)
chi2eff_values = self.get_chi2_per_exp_dict(chi2_per_exp_output)
# only relevant line to add to .log file
save_loglike_in_log = f"--> Minimum of -logLike : {chi2eff_values['Total']/2}"
# save the .log file
pio.save_file( output_log_file,
lines=save_loglike_in_log
)
return output_log_file
def update_and_save_min_output(self, extension='_lkl_prof'):
"""
Function to add the profile lkl param to the output bf file,
by copying the file to the main folder with this addition.
/!\ File naming scheme hard-coded within this function.
:extension: Leave it alone, thank you.
:return: current value of the prof lkl param as a float
"""
prefix_extension = self.pn_ext(extension)
pn_ext = self.pn_ext('/')
min_output_bf = f'{self.chains_dir}lkl_prof{pn_ext}'\
f'lkl_prof{pn_ext[:-1]}.bestfit'
bf_lines = pio.readlines_file(min_output_bf)
bf_lines[0] = f'{bf_lines[0][:-1]}, {self.prof_param}\n'
bf_lines[1] = f'{bf_lines[1][:-1]} {self.current_prof_param}\n'
save_output_bf = f'{self.chains_dir}{self.info_root}{prefix_extension}.bestfit'
pio.save_file(save_output_bf, bf_lines)
from_file = f'{self.chains_dir}lkl_prof{pn_ext}lkl_prof{pn_ext[:-1]}.log'
to_file = f'{self.chains_dir}{self.info_root}{prefix_extension}.log'
_ = pio.file_copy(from_file, to_file)
return self.current_prof_param
def run_lkl_prof(self, time_mins=False, N_min_steps=3000, run_minuit=False):
"""
Run the likelihood profile loop.
Initialise time-keeping file if wanted.
While we are within the bounds of the profile param we want to explore:
1) check if the point we are currently at i.e. param_ML and MLs, matches the
last entry in the lkl_prof table.
- if it does, the last minimum was run and saved successfully.
- if not, check if a minimum file exists.
- if it does, read it in and save it in the lkl prof txt. minimum run
successfully.
2) check if minimum was run and saved.
- if yes, increment the prof lkl param and update the log.param,
remove all previous chains and analysis files created from the previous
increment.
Remember, this increment means prof_param = current_MLs + increment.
So we are simply one increment away from the most recent .bestfit file.
- With this setup, we really shouldn't wind up in a mininmum-not-saved
regime.
But in this case, grab the current value of prof_param so we have it,
and still remove files from any previous run.
3) run the minimizer, pointing to the bf file (usually the one we just wrote)
as starting bf
4) save minimizer output + value of the prof param into a new bf in the
main folder.
Finally, outside the loop, save the output of the last minimizer.
:time_mins: boolean for whether you want to time each minimiser increment or not
:N_min_steps: Number of steps to run per rung of the minimizer
:return: the value of the profile lkl parameter at the end of this loop
"""
_ = self.first_jump_fac_less_than_prof_incr()
if time_mins is True:
time_extension = self.pn_ext('_time_stamps.txt')
with open(f'{self.chains_dir}{self.info_root}{time_extension}', 'a') as lkl_txt:
lkl_txt.write("#")
lkl_txt.write(f' {self.prof_param} \t step_size \t minimizer_time ')
lkl_txt.write("\n")
while ((self.MLs[self.prof_param] <= self.prof_max)
and (self.MLs[self.prof_param] >= self.prof_min)):
last_entry_matches_current_params = self.match_param_line(self.MLs)
if not last_entry_matches_current_params:
param_names, param_ML, self.MLs = self.read_minimum()
# read_min updates self.MLs
MLs_and_chi2 = self.update_MLs_chi2_per_exp(self.MLs)
self.write_MLs(MLs_and_chi2)
print(f'run_lkl_prof: -----> Minimizer run successfully for '\
f'{self.prof_param} = {self.MLs[self.prof_param]}')
# TODO see about re-writing this function to not need to go line by
# line somehow. I don't know if there is anything better
self.current_prof_param, _ = self.increment_update_logparam()
# break out of the loop if the parameter is outside it's range
if not ((self.current_prof_param <= self.prof_max)
and (self.current_prof_param >= self.prof_min)):
break
pn_ext_str = self.pn_ext('/')
rm_chain_path = f'{self.chains_dir}lkl_prof{pn_ext_str}20*'
rm_info_path = f'{self.chains_dir}lkl_prof{pn_ext_str}'\
f'lkl_prof{pn_ext_str[:-1]}*'
pio.rm_files_wildcared(rm_chain_path)
pio.rm_files_wildcared(rm_info_path)
time_start = time()
print(f'run_lkl_prof: -----> Running point {self.prof_param} '\
f'= {self.current_prof_param}')
self.run_minimizer(prev_bf=self.info_root+self.pn_ext("_lkl_prof"),
min_folder="lkl_prof" + self.pn_ext('/')[:-1],
N_steps=N_min_steps,
run_minuit=run_minuit)
self.update_and_save_min_output()
time_end = time()
time_taken = time_end - time_start
if time_mins is True:
with open(f'{self.chains_dir}{self.info_root}{time_extension}', 'a') as lkl_txt:
lkl_txt.write(f'{self.current_prof_param:.4g} '\
f'\t {self.prof_incr:.2g} \t {time_taken:.2f} \n')
print(f'run_lkl_prof: Time taken for minimizer '\
f'= {time_taken:.2f}')
param_names, param_ML, self.MLs = self.read_minimum()
# prof_incr *= 2. # Readjust prof lkl increment if wanted by copying this
# function and adding such a line
# outside loop now
# TODO do we need this? Does it every actually need to run this?
# Could it be before the loop begins and then put at the end of the loop,
# but still insdie it
last_entry_matches_current_params = self.match_param_line(self.MLs)
if not last_entry_matches_current_params:
param_names, param_ML, self.MLs = self.read_minimum()
MLs_and_chi2 = self.update_MLs_chi2_per_exp(self.MLs)
self.write_MLs(MLs_and_chi2)
self.write_MLs(self.MLs)
print(f'run_lkl_prof: -----> Minimizer run successfully for '\
f'{self.prof_param} = {self.MLs[self.prof_param]}')
return self.MLs[self.prof_param]
def full_lkl_prof_array(self):
"""
Combine positive and negative increment files into one array
But first check that they have the same param order.
:return: full likelihood profile array
"""
pos_filename = f'{self.chains_dir}{self.info_root}_+'\
f'{self.prof_param}_lkl_profile.txt'
neg_filename = f'{self.chains_dir}{self.info_root}_-'\
f'{self.prof_param}_lkl_profile.txt'
try:
pos_header = pio.read_header_as_list(pos_filename)
all_MLs_p = pio.load_mp_info_files(pos_filename)
pos_file = True
except FileNotFoundError:
pos_file = False
try:
neg_header = pio.read_header_as_list(neg_filename)
all_MLs_n = pio.load_mp_info_files(neg_filename)
if pos_file is True:
if pos_header==neg_header:
all_MLs = np.concatenate( (np.flip(all_MLs_n, 0),all_MLs_p) )
else:
print('full_lkl_prof_array: the positive and negative files either '\
'have different parameters '\
'or have them in different orders. \n'\
'Either way, this function cannot correctly combine them. ')
return 0
else:
all_MLs = np.flip(all_MLs_n, 0)
except FileNotFoundError:
if pos_file is True:
all_MLs = all_MLs_p
else:
print('full_lkl_prof_array: could not find files '\
f'\n{pos_filename} \n{neg_filename} ')
return all_MLs
def full_lkl_prof_dict(self):
"""
Combine positive and negative increment files into one dictionary with
keys = param names
values = 1D array of profile likelihood values
:return: full likelihood profile dictionary
"""
full_prof_dict = {}
full_lkl_prof_array = self.full_lkl_prof_array()
try:
pos_filename = f'{self.chains_dir}{self.info_root}_+'\
f'{self.prof_param}_lkl_profile.txt'
lkl_prof_header = pio.read_header_as_list(pos_filename)
except FileNotFoundError:
neg_filename = f'{self.chains_dir}{self.info_root}_-'\
f'{self.prof_param}_lkl_profile.txt'
lkl_prof_header = pio.read_header_as_list(neg_filename)
for param_num in range(len(lkl_prof_header)):
full_prof_dict[lkl_prof_header[param_num]] = full_lkl_prof_array[:,param_num]
# # Commented out following. Using file header to get param order
# for param_num in range(len(self.param_order)):
# full_prof_dict[self.param_order[param_num]] = full_lkl_prof_array[:,param_num]
return full_prof_dict
def sum_params(self, params_to_sum):
"""
Sum list of params and return array of summed params.
Useful for adding up chi^2's post profile lkl run
:params_to_sum: list of parameter names that you want to sum.
:return: array of summed parameters
"""
prof_lkl = self.full_lkl_prof_dict()
param_vectors = [prof_lkl[param] for param in params_to_sum]
param_stack = np.stack(param_vectors, axis=0)
summed_params = param_stack.sum(axis=0)
return summed_params
|
tkarwalREPO_NAMEprocoliPATH_START.@procoli_extracted@procoli-main@src@procoli@mp_procoli_functions.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "wfirst-cgi/emccd_detect",
"repo_path": "emccd_detect_extracted/emccd_detect-master/arcticpy_folder/build/lib/arcticpy/__init__.py",
"type": "Python"
}
|
from autoarray.instruments import acs
from arcticpy.main import add_cti, remove_cti, model_for_HST_ACS
from arcticpy.roe import (
ROE,
ROEChargeInjection,
ROETrapPumping,
)
from arcticpy.ccd import CCD, CCDPhase
from arcticpy.traps import (
Trap,
TrapInstantCapture,
TrapLifetimeContinuumAbstract,
TrapLogNormalLifetimeContinuum,
)
from arcticpy.trap_managers import (
AllTrapManager,
TrapManager,
TrapManagerTrackTime,
TrapManagerInstantCapture,
)
|
wfirst-cgiREPO_NAMEemccd_detectPATH_START.@emccd_detect_extracted@emccd_detect-master@arcticpy_folder@build@lib@arcticpy@__init__.py@.PATH_END.py
|
{
"filename": "svi_flow_guide.ipynb",
"repo_name": "pyro-ppl/pyro",
"repo_path": "pyro_extracted/pyro-master/tutorial/source/svi_flow_guide.ipynb",
"type": "Jupyter Notebook"
}
|
# SVI with a Normalizing Flow guide
Thanks to their expressiveness, normalizing flows (see [normalizing flow introduction](normalizing_flows_intro.ipynb)) are great guide candidates for stochastic variational inference (SVI). This notebook demonstrates how to perform amortized SVI with a normalizing flow as guide.
> In this notebook we use [Zuko](https://zuko.readthedocs.io/) to implement normalizing flows, but similar results can be obtained with other PyTorch-based flow libraries.
```python
import pyro
import torch
import zuko # pip install zuko
from corner import corner, overplot_points # pip install corner
from pyro.contrib.zuko import ZukoToPyro
from pyro.optim import ClippedAdam
from pyro.infer import SVI, Trace_ELBO
from torch import Tensor
```
## Model
We define a simple non-linear model $p(x | z)$ with a standard Gaussian prior $p(z)$ over the latent variables $z$.
```python
prior = pyro.distributions.Normal(torch.zeros(3), torch.ones(3)).to_event(1)
def likelihood(z: Tensor):
mu = z[..., :2]
rho = z[..., 2].tanh() * 0.99
cov = 1e-2 * torch.stack([
torch.ones_like(rho), rho,
rho, torch.ones_like(rho),
], dim=-1).unflatten(-1, (2, 2))
return pyro.distributions.MultivariateNormal(mu, cov)
def model(x: Tensor):
with pyro.plate("data", x.shape[1]):
z = pyro.sample("z", prior)
with pyro.plate("obs", 5):
pyro.sample("x", likelihood(z), obs=x)
```
We sample 64 reference latent variables and observations $(z^*, x^*)$. In practice, $z^*$ is unknown, and $x^*$ is your data.
```python
z_star = prior.sample((64,))
x_star = likelihood(z_star).sample((5,))
```
## Guide
We define the guide $q_\phi(z | x)$ with a normalizing flow. We choose a conditional [neural spline flow](https://arxiv.org/abs/1906.04032) borrowed from the [Zuko](https://zuko.readthedocs.io/) library. Because Zuko distributions are very similar to Pyro distributions, a thin wrapper (`ZukoToPyro`) is sufficient to make Zuko and Pyro 100% compatible.
```python
flow = zuko.flows.NSF(features=3, context=10, transforms=1, hidden_features=(256, 256))
flow.transform = flow.transform.inv # inverse autoregressive flow (IAF) are fast to sample from
def guide(x: Tensor):
pyro.module("flow", flow)
with pyro.plate("data", x.shape[1]): # amortized
pyro.sample("z", ZukoToPyro(flow(x.transpose(0, 1).flatten(-2))))
```
## SVI
We train our guide with a standard stochastic variational inference (SVI) pipeline. We use 16 particles to reduce the variance of the ELBO and clip the norm of the gradients to make training more stable.
```python
pyro.clear_param_store()
svi = SVI(model, guide, optim=ClippedAdam({"lr": 1e-3, "clip_norm": 10.0}), loss=Trace_ELBO(num_particles=16, vectorize_particles=True))
for step in range(4096 + 1):
elbo = svi.step(x_star)
if step % 256 == 0:
print(f'({step})', elbo)
```
(0) 209195.08367919922
(256) -25.225540161132812
(512) -99.09033203125
(768) -102.66302490234375
(1024) -138.8058319091797
(1280) -92.15625
(1536) -136.78167724609375
(1792) -87.76119995117188
(2048) -116.21714782714844
(2304) -162.0266571044922
(2560) -91.13175964355469
(2816) -164.86270141601562
(3072) -98.17607116699219
(3328) -102.58432006835938
(3584) -151.61912536621094
(3840) -77.94436645507812
(4096) -121.82719421386719
## Posterior predictive
```python
z = flow(x_star[:, 0].flatten()).sample((4096,))
x = likelihood(z).sample()
fig = corner(x.numpy())
overplot_points(fig, x_star[:, 0].numpy())
```
```python
z = flow(x_star[:, 1].flatten()).sample((4096,))
x = likelihood(z).sample()
fig = corner(x.numpy())
overplot_points(fig, x_star[:, 1].numpy())
```
|
pyro-pplREPO_NAMEpyroPATH_START.@pyro_extracted@pyro-master@tutorial@source@svi_flow_guide.ipynb@.PATH_END.py
|
{
"filename": "basic.py",
"repo_name": "dfm/george",
"repo_path": "george_extracted/george-main/src/george/solvers/basic.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
from __future__ import division, print_function
__all__ = ["BasicSolver"]
import numpy as np
from scipy.linalg import cholesky, cho_solve
class BasicSolver(object):
"""
This is the most basic solver built using :func:`scipy.linalg.cholesky`.
kernel (george.kernels.Kernel): A subclass of :class:`Kernel` specifying
the kernel function.
"""
def __init__(self, kernel):
self.kernel = kernel
self._computed = False
self._log_det = None
@property
def computed(self):
"""
A flag indicating whether or not the covariance matrix was computed
and factorized (using the :func:`compute` method).
"""
return self._computed
@computed.setter
def computed(self, v):
self._computed = v
@property
def log_determinant(self):
"""
The log-determinant of the covariance matrix. This will only be
non-``None`` after calling the :func:`compute` method.
"""
return self._log_det
@log_determinant.setter
def log_determinant(self, v):
self._log_det = v
def compute(self, x, yerr):
"""
Compute and factorize the covariance matrix.
Args:
x (ndarray[nsamples, ndim]): The independent coordinates of the
data points.
yerr (ndarray[nsamples] or float): The Gaussian uncertainties on
the data points at coordinates ``x``. These values will be
added in quadrature to the diagonal of the covariance matrix.
"""
# Compute the kernel matrix.
K = self.kernel.get_value(x)
K[np.diag_indices_from(K)] += yerr ** 2
# Factor the matrix and compute the log-determinant.
self._factor = (cholesky(K, overwrite_a=True, lower=False), False)
self.log_determinant = 2 * np.sum(np.log(np.diag(self._factor[0])))
self.computed = True
def apply_inverse(self, y, in_place=False):
r"""
Apply the inverse of the covariance matrix to the input by solving
.. math::
K\,x = y
Args:
y (ndarray[nsamples] or ndadrray[nsamples, nrhs]): The vector or
matrix :math:`y`.
in_place (Optional[bool]): Should the data in ``y`` be overwritten
with the result :math:`x`? (default: ``False``)
"""
return cho_solve(self._factor, y, overwrite_b=in_place)
def dot_solve(self, y):
r"""
Compute the inner product of a vector with the inverse of the
covariance matrix applied to itself:
.. math::
y\,K^{-1}\,y
Args:
y (ndarray[nsamples]): The vector :math:`y`.
"""
return np.dot(y.T, cho_solve(self._factor, y))
def apply_sqrt(self, r):
"""
Apply the Cholesky square root of the covariance matrix to the input
vector or matrix.
Args:
r (ndarray[nsamples] or ndarray[nsamples, nrhs]: The input vector
or matrix.
"""
return np.dot(r, self._factor[0])
def get_inverse(self):
"""
Get the dense inverse covariance matrix. This is used for computing
gradients, but it is not recommended in general.
"""
return self.apply_inverse(np.eye(len(self._factor[0])), in_place=True)
|
dfmREPO_NAMEgeorgePATH_START.@george_extracted@george-main@src@george@solvers@basic.py@.PATH_END.py
|
{
"filename": "_tickformatstops.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/polar/radialaxis/_tickformatstops.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickformatstopsValidator(_plotly_utils.basevalidators.CompoundArrayValidator):
def __init__(
self,
plotly_name="tickformatstops",
parent_name="layout.polar.radialaxis",
**kwargs,
):
super(TickformatstopsValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Tickformatstop"),
data_docs=kwargs.pop(
"data_docs",
"""
dtickrange
range [*min*, *max*], where "min", "max" -
dtick values which describe some zoom level, it
is possible to omit "min" or "max" value by
passing "null"
enabled
Determines whether or not this stop is used. If
`false`, this stop is ignored even within its
`dtickrange`.
name
When used in a template, named items are
created in the output figure in addition to any
items the figure already has in this array. You
can modify these items in the output figure by
making your own item with `templateitemname`
matching this `name` alongside your
modifications (including `visible: false` or
`enabled: false` to hide it). Has no effect
outside of a template.
templateitemname
Used to refer to a named item in this array in
the template. Named items from the template
will be created even without a matching item in
the input figure, but you can modify one by
making an item with `templateitemname` matching
its `name`, alongside your modifications
(including `visible: false` or `enabled: false`
to hide it). If there is no template or no
matching item, this item will be hidden unless
you explicitly show it with `visible: true`.
value
string - dtickformat for described zoom level,
the same as "tickformat"
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@polar@radialaxis@_tickformatstops.py@.PATH_END.py
|
{
"filename": "metadata_routing.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/scikit-learn/py3/sklearn/utils/metadata_routing.py",
"type": "Python"
}
|
"""
The :mod:`sklearn.utils.metadata_routing` module includes utilities to route
metadata within scikit-learn estimators.
"""
# This module is not a separate sub-folder since that would result in a circular
# import issue.
#
# Author: Adrin Jalali <adrin.jalali@gmail.com>
# License: BSD 3 clause
from ._metadata_requests import WARN, UNUSED, UNCHANGED # noqa
from ._metadata_requests import get_routing_for_object # noqa
from ._metadata_requests import MetadataRouter # noqa
from ._metadata_requests import MetadataRequest # noqa
from ._metadata_requests import MethodMapping # noqa
from ._metadata_requests import process_routing # noqa
from ._metadata_requests import _MetadataRequester # noqa
from ._metadata_requests import _routing_enabled # noqa
from ._metadata_requests import _raise_for_params # noqa
from ._metadata_requests import _RoutingNotSupportedMixin # noqa
from ._metadata_requests import _raise_for_unsupported_routing # noqa
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@scikit-learn@py3@sklearn@utils@metadata_routing.py@.PATH_END.py
|
{
"filename": "polars_dataframe.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/document_loaders/polars_dataframe.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import PolarsDataFrameLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"PolarsDataFrameLoader": "langchain_community.document_loaders"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"PolarsDataFrameLoader",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@document_loaders@polars_dataframe.py@.PATH_END.py
|
{
"filename": "io_tools.py",
"repo_name": "mikecokina/elisa",
"repo_path": "elisa_extracted/elisa-master/src/elisa/analytics/binary_fit/io_tools.py",
"type": "Python"
}
|
import numpy as np
from . mixins import MCMCMixin
from . shared import AbstractFit
from .. params import parameters
from .. params.parameters import ParameterMeta, BinaryInitialParameters
def filter_chain(mcmc_fit_cls, **boundaries):
"""
Filtering mcmc chain down to given parameter intervals. This function is usable in case of bimodal distribution of
the MCMC chain.
:param mcmc_fit_cls: MCMC fitting cls instance e.g.: (LCFitMCMC, RVFitMCMC)
:param boundaries: Dict; dictionary of boundaries in flat format (using @)
e.g. {`primary@te_ff`: (5000, 6000), other parameters ...}
:return: numpy.array; filtered flat chain
"""
for key, boundary in boundaries.items():
if not isinstance(boundary, (tuple, list, np.ndarray)):
raise TypeError(f'`{key}` boundary is not tuple or list.')
if len(boundary) != 2:
raise TypeError(f'`{key}` has incorrect length of {len(boundary)}.')
if key not in mcmc_fit_cls.variable_labels:
raise NameError(f'{key} is not valid model parameter.')
column_idx = mcmc_fit_cls.variable_labels.index(key)
column = mcmc_fit_cls.flat_chain[:, column_idx]
condition_mask = np.logical_and(
column > parameters.normalize_value(boundary[0], *mcmc_fit_cls.normalization[key]),
column < parameters.normalize_value(boundary[1], *mcmc_fit_cls.normalization[key])
)
if np.sum(condition_mask) == 0:
raise ValueError(f'Boundaries for {key} yielded an empty array.')
setattr(mcmc_fit_cls, 'flat_chain', mcmc_fit_cls.flat_chain[condition_mask, :])
fitted_params = {key: mcmc_fit_cls.flat_result[key] for key in mcmc_fit_cls.variable_labels}
update_solution(mcmc_fit_cls, fitted_params, percentiles=None)
return mcmc_fit_cls.flat_chain
def load_chain(mcmc_fit_cls, fit_id, discard=0, percentiles=None):
"""
Function loads MCMC chain along with auxiliary data from json file created after each MCMC run.
:param percentiles: List; percentile intervals used to generate confidence intervals, provided in form:
[percentile for lower bound of confidence interval, percentile of the centre,
percentile for the upper bound of confidence interval]
:param mcmc_fit_cls: MCMC fitting cls instance e.g.: (LCFitMCMC, RVFitMCMC)
:param discard: int; Discard the first discard steps in the chain as burn-in. (default: 0)
:param fit_id: str; chain identificator or filename containing the chain
:return: Tuple[numpy.ndarray, list, dict]; flattened mcmc chain, labels of variables in `flat_chain` columns,
{var_name: (min_boundary, max_boundary), ...} dictionary of
boundaries defined by user for each variable needed
to reconstruct real values from normalized `flat_chain` array
"""
data = MCMCMixin.load_flat_chain(fit_id=fit_id)
mcmc_fit_cls.flat_chain = np.array(data['flat_chain'])[discard:, :]
mcmc_fit_cls.variable_labels = data['fitable_parameters']
mcmc_fit_cls.normalization = data['normalization']
update_solution(mcmc_fit_cls, data['fitable'], percentiles)
return mcmc_fit_cls.flat_chain, mcmc_fit_cls.variable_labels, mcmc_fit_cls.normalization
def update_solution(mcmc_fit_cls, fitted_params, percentiles):
"""
Updating solutions based on the distribution of to MCMC chain.
:param mcmc_fit_cls: MCMC fitting cls instance e.g.: (LCFitMCMC, RVFitMCMC)
:param fitted_params: Dict; only variable part of flat_result
:param percentiles: List; percentiles used for evaluation of confidence intervals
:return: Tuple;
"""
fitable = {key: ParameterMeta(**val) for key, val in fitted_params.items()}
# reproducing results from chain
flat_result_update = MCMCMixin.resolve_mcmc_result(mcmc_fit_cls.flat_chain, fitable,
mcmc_fit_cls.normalization, percentiles=percentiles)
if mcmc_fit_cls.result is not None:
mcmc_fit_cls.flat_result.update(flat_result_update)
# evaluating constraints
fit_params = parameters.serialize_result(mcmc_fit_cls.flat_result)
constrained = BinaryInitialParameters(**fit_params).get_constrained()
mcmc_fit_cls.flat_result = AbstractFit.eval_constrained_results(mcmc_fit_cls.flat_result, constrained)
mcmc_fit_cls.result = parameters.serialize_result(mcmc_fit_cls.flat_result)
else:
msg = 'Load fit parameters before loading the chain. For eg. UPDATE THIS.'
raise ValueError(msg)
def write_ln(write_fn, designation, value, bot, top, unit, status, line_sep, precision=8):
val = round(value, precision) if type(value) is not str else value
return write_fn(f"{designation:<35} "
f"{val:>20}"
f"{bot:>20}"
f"{top:>20}"
f"{unit:>20} "
f"{status:<50}{line_sep}")
def write_param_ln(fit_params, param_id, designation, write_fn, line_sep, precision=8):
"""
Auxiliary function to the fit_summary functions, produces one
line in output for given parameter that is present in `fit_params`.
:param precision: int;
:param fit_params: Dict;
:param param_id: str; name os the parameter in `fit_params`
:param designation: str; displayed name of the parameter
:param write_fn: function used to write into console or to the file
:param line_sep: str; symbols to finish the line
:return:
"""
if 'confidence_interval' in fit_params[param_id]:
bot = fit_params[param_id]['value'] - fit_params[param_id]['confidence_interval']['min']
top = fit_params[param_id]['confidence_interval']['max'] - fit_params[param_id]['value']
aux = np.abs([bot, top])
aux[aux == 0] = 1e6
sig_figures = -int(np.log10(np.min(aux))//1) + 1
bot = round(bot, sig_figures)
top = round(top, sig_figures)
else:
bot, top = '-', '-',
sig_figures = precision
status = 'Not recognized'
if 'fixed' in fit_params[param_id]:
status = 'Fixed' if fit_params[param_id]['fixed'] else 'Variable'
elif 'constraint' in fit_params[param_id].keys():
status = fit_params[param_id]['constraint']
elif param_id in ['r_squared']:
status = 'Derived'
unit = str(fit_params[param_id]['unit']) if 'unit' in fit_params[param_id].keys() else '-'
args = write_fn, designation, round(fit_params[param_id]['value'], sig_figures), bot, top, unit, status, line_sep
return write_ln(*args)
def write_propagated_ln(values, fit_params, param_id, designation, write_fn, line_sep, unit):
"""
Auxiliary function to the fit_summary functions, produces one
line in output for given parameter that is present in `fit_params`.
:param values:
:param fit_params: Dict;
:param param_id: str; name os the parameter in `fit_params`
:param designation: str; displayed name of the parameter
:param write_fn: function used to write into console or to the file
:param line_sep: str; symbols to finish the line
:return:
"""
# if parameter does not exists in given fitting mode, the line in summary is omitted
if np.isnan(values).any():
return
aux = np.abs([values[1], values[2]])
aux[aux <= 1e-15] = 1e-15
sig_figures = -int(np.log10(np.min(aux))//1) + 1
values = np.round(values, sig_figures)
if param_id not in fit_params.keys():
status = 'Derived'
elif 'fixed' in fit_params[param_id]:
status = 'Fixed' if fit_params[param_id]['fixed'] else 'Variable'
elif 'constraint' in fit_params[param_id].keys():
status = fit_params[param_id]['constraint']
elif param_id in ['r_squared']:
status = 'Derived'
else:
status = 'Unknown'
return write_ln(write_fn, designation, values[0], values[1], values[2], unit, status, line_sep)
|
mikecokinaREPO_NAMEelisaPATH_START.@elisa_extracted@elisa-master@src@elisa@analytics@binary_fit@io_tools.py@.PATH_END.py
|
{
"filename": "los_dg19.py",
"repo_name": "swagnercarena/paltas",
"repo_path": "paltas_extracted/paltas-main/paltas/Substructure/los_dg19.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
Define the class to draw line of sight substructure for a lens according to
https://arxiv.org/pdf/1909.02573.pdf
This module contains the functions needed to turn the parameters of the
los halo distribution into masses, concentrations, and positions.
"""
from .los_base import LOSBase
import numba
import numpy as np
from colossus.lss import peaks, bias
from ..Utils import power_law, cosmology_utils
from . import nfw_functions
import lenstronomy.Util.util as util
from lenstronomy.LensModel.Profiles.nfw import NFW
from scipy.signal import fftconvolve
class LOSDG19(LOSBase):
"""Class for rendering the line of sight structure according to DG19.
Args:
los_parameters (dict): A dictionary containing the type of
los distribution and the value for each of its parameters.
main_deflector_parameters (dict): A dictionary containing the type of
main deflector and the value for each of its parameters.
source_parameters (dict): A dictionary containing the type of the
source and the value for each of its parameters.
cosmology_parameters (str,dict, or colossus.cosmology.Cosmology):
Either a name of colossus cosmology, a dict with 'cosmology name':
name of colossus cosmology, an instance of colussus cosmology, or a
dict with H0 and Om0 ( other parameters will be set to defaults).
Notes:
Required Parameters
- delta_los - mass function normalization
- m_min - minimum rendered mass in units of M_sun
- m_max - maximum rendered mass in units of M_sun
- z_min - minimum redshift at which to render los halos
- dz - redshift bin width
- cone_angle - cone opening angle for rendering los halos
- c_0 - concentration normalization
- conc_zeta - concentration redshift power law slope
- conc_beta - concentration peak height power law slope
- conc_m_ref - concentration peak height pivot mass
- dex_scatter - scatter in concentration in units of dex
- alpha_dz_factor- deflection angle correction redshift bin width
"""
# Define the parameters we expect to find for the DG_19 model
required_parameters = ('m_min','m_max','z_min','dz','cone_angle',
'r_max','r_min','c_0','conc_zeta','conc_beta','conc_m_ref',
'dex_scatter','delta_los','alpha_dz_factor')
def __init__(self,los_parameters,main_deflector_parameters,
source_parameters,cosmology_parameters):
# Initialize the super class
super().__init__(los_parameters,main_deflector_parameters,
source_parameters,cosmology_parameters)
@staticmethod
@numba.njit
def nu_f_nu(nu):
"""Calculates nu f(nu) for the Sheth Tormen 2001 model.
Args:
nu (np.array): An array of nu values to at which to
calculate nu_f_nu
Returns:
(np.array): The value of nu f(nu)
"""
# Parameters fit to simulations (A is fixed so that the integral
# for all nu returns 1).
A = 0.32218
q = 0.3
a = 0.707
# Calculate the fundamental unit of our equation
nu_2 = a*np.square(nu)
# Finally calculate and return nu f(nu)
nu_f_nu = (2*A*(1+nu_2**(-q))*np.sqrt(nu_2/(2*np.pi))*np.exp(-nu_2/2))
return nu_f_nu
def dn_dm(self,m,z):
"""Returns the mass function at a given mass and redshift.
Args:
m (np.array): An array of the mass values at which to calculate
the mass function in units of M_sun.
z (np.array): Either one redshift at which to calculate the
mass function or one redshift for each mass input.
Returns:
(np.array): The values of the mass function at each mass in units
of physical number density in units of 1/(M_sun*kpc^3).
Notes:
It is slower to pass in multiple z values that are identical.
If only a few redshift bins are being considered, run this
function on each bin with a float redshift.
"""
# First we need to do some unit conversaion to the units used by the
# nu_f_nu function. Note that these are all comoving values we're
# using because colossus expects them, but nu is dimensionless.
h = self.cosmo.h
delta_c = peaks.collapseOverdensity(z=z,corrections=True)
r = peaks.lagrangianR(m*h)
sigma = self.cosmo.sigma(r,z)
nu = delta_c/sigma
# Calcualte our mass function from its parts
nfn_eval = self.nu_f_nu(nu)
d_ln_sigma_d_ln_r = self.cosmo.sigma(r,z,derivative=True)
# Density is returned in units of M_sun*h^2/kpc^3
rho_m = self.cosmo.rho_m(z)*h**2
return -1/3*nfn_eval*rho_m/m**2*d_ln_sigma_d_ln_r
def power_law_dn_dm(self,z,m_min,m_max,n_dm=100):
"""Returns the best fit power law parameters for the physical number
density at a given redshift and mass range.
Args:
z (float): The redshift at which to calculate the power law
parameters.
m_min (float): The lower bound of the mass M_sun
m_max (float): The upper bound of the mass M_sun
n_dm (int): The number of dm samples to consider in the fit.
Returns:
(tuple): The power law slope and the norm for the power law in
units of 1/M_sun/kpc**3
"""
m = np.logspace(np.log10(m_min),np.log10(m_max),n_dm)
lm = np.log(m)
dn_dm = self.dn_dm(m,z)
ldn_dm = np.log(dn_dm)
# The MLE estimate from the slope assuming Gaussian noise on the
# log quantity
slope_estimate = 1/n_dm * np.sum(ldn_dm) * np.sum(lm) - np.sum(
ldn_dm*lm)
slope_estimate /= -np.sum(lm*lm) + 1/n_dm*np.sum(lm)**2
# The MLE estimate on the norm
norm_estimate = np.exp(1/n_dm*np.sum(ldn_dm-slope_estimate*lm))
return slope_estimate, norm_estimate
def two_halo_boost(self,z,z_lens,dz,lens_m200,r_max,r_min,n_quads=100):
"""Calculates the boost from the two halo term of the host halo at
the given redshift.
Args:
z (float): The redshift to calculate the boost at.
z_len (float): The redshift of the main deflector
dz (float): The thickness of the redshift slice to consider
lens_m200 (float): The mass of the host lens in units of
M_sun with mass definition 200c.
r_max (float): The maximum radius to calculate the correlation
function out to.
r_min (float): The minimum radius to consider the correlation
funciton to (to avoid overlap with substructure draws).
n_quads (int): The number of points to use in averaging the
two halo term of the redshift slice.
Returns
(float): The boost at the given redshift.
"""
# Get a range of z values we will average over.
z_range = np.linspace(z,z+dz,n_quads)
# Only consider the two point function in the regime where it is
# large and we are outside the virial radius of the host halo.
z_min = z_range[np.argmin(np.abs(z_range-z_lens))]
r_cmv_min = np.abs(self.cosmo.comovingDistance(z_min,z_lens))
if r_cmv_min >= r_max*self.cosmo.h:
return 1
r_cmv = np.abs(self.cosmo.comovingDistance(z_range,z_lens))
# Don't try to calculate inside the virial radius.
r_cmv = r_cmv[r_cmv>r_min*self.cosmo.h]
# Get the two halo term in the slice
xi_halo = self.cosmo.correlationFunction(r_cmv,z_lens)
xi_halo *= bias.haloBias(lens_m200*self.cosmo.h,z_lens,mdef='200c',
model='tinker10')
return 1+np.mean(xi_halo)
def cone_angle_to_radius(self,z,z_lens,z_source,cone_angle,
angle_buffer=0.8):
"""Returns the radius in kpc at the given redshift for the given cone
angle.
Args:
z (float): The redshift at which to calculate the radius
z_lens (float): The redshift of the main deflector
z_source (float): The redshift of the source
dz (float): The thickness of the redshift slice to consider
cone_angle (float): The opening angle of the cone for the los
realization in units of arcseconds.
angle_buffer (float): A buffer for how small the second cone
gets as it approaches the source. Should be between 0 and
1 with 1 meaning that the second cone converges to a point
at the source.
Retruns:
(float): The radius in units of physical kpc.
"""
# Get the conversion between the angular opening and kpc
kpc_per_arcsecond = cosmology_utils.kpc_per_arcsecond(z,self.cosmo)
r_los = kpc_per_arcsecond*cone_angle*0.5
# If we're past the lens, shrink the light cone proportional to the
# distance to the source.
if z > z_lens:
# Formula picked to match double cone in DG19
scal_factor = angle_buffer
scal_factor *= self.cosmo.comovingDistance(z_source)
scal_factor /= self.cosmo.comovingDistance(z_lens,z_source)
scal_factor *= self.cosmo.comovingDistance(z_lens,z)
scal_factor /= self.cosmo.comovingDistance(z)
r_los *= 1 - scal_factor
return r_los
def volume_element(self,z,z_lens,z_source,dz,cone_angle,angle_buffer=0.8):
"""Returns the physical volume element at the given redshift
Args:
z (float): The redshift at which to calculate the volume element
z_lens (float): The redshift of the main deflector
z_source (float): The redshift of the source
dz (float): The thickness of the redshift slice to consider
cone_angle (float): The opening angle of the cone for the los
realization in units of arcseconds.
Retruns:
(float): The physical volume element in kpc**3
Notes:
This depends on parameters like the cone angle, the redshift of
the source, and the redshift of the lens.
"""
r_los = self.cone_angle_to_radius(z+dz/2,z_lens,z_source,cone_angle,
angle_buffer=angle_buffer)
# Get the thickness of our cone slice in physical units of kpc
dz_in_kpc = self.cosmo.comovingDistance(z,z+dz)/self.cosmo.h
dz_in_kpc /= (1+z)
# Mpc to kpc
dz_in_kpc *= 1000
return dz_in_kpc * np.pi * r_los**2
def draw_nfw_masses(self,z):
"""Draws from the Sheth Tormen mass function with an additional
correction for two point correlation with main lens.
Args:
z (float): The redshift at which to draw the masses
Returns:
(np.array): An array with the drawn masses in units of M_sun.
"""
# Pull the parameters we need from the input dictionaries
# Units of M_sun
lens_m200 = self.main_deflector_parameters['M200']
z_lens = self.main_deflector_parameters['z_lens']
z_source = self.source_parameters['z_source']
dz = self.los_parameters['dz']
# Units of arcsecond
cone_angle = self.los_parameters['cone_angle']
# Units of Mpc
r_max = self.los_parameters['r_max']
# Units of Mpc
r_min = self.los_parameters['r_min']
# Units of M_sun
m_min = self.los_parameters['m_min']
# Units of M_sun
m_max = self.los_parameters['m_max']
delta_los = max(0, self.los_parameters['delta_los'])
# Get the parameters of the power law fit to the Sheth Tormen mass
# function
pl_slope, pl_norm = self.power_law_dn_dm(z+dz/2,m_min,m_max)
# Scale the norm by the total volume and the two point correlation.
dV = self.volume_element(z,z_lens,z_source,dz,cone_angle)
halo_boost = self.two_halo_boost(z,z_lens,dz,lens_m200,r_max,r_min)
pl_norm *= dV * halo_boost * delta_los
# Draw from our power law and return the masses.
masses = power_law.power_law_draw(m_min,m_max,pl_slope,pl_norm)
return masses
def sample_los_pos(self,z,n_los):
"""Draws the positions for the line of sight substructure at the given
redshift.
Args:
z (float): The redshift to place the los halos at.
n_los (int): The number of los halos to draw position for
Returns:
(np.array): A n_los x 2 array giving the x,y position of the
line of sight structure in units of kpc.
"""
# Pull the parameters we need from the input dictionaries
# Units of M_sun
z_lens = self.main_deflector_parameters['z_lens']
z_source = self.source_parameters['z_source']
dz = self.los_parameters['dz']
# Units of arcsecond
cone_angle = self.los_parameters['cone_angle']
r_los = self.cone_angle_to_radius(z+dz/2,z_lens,z_source,cone_angle)
# Draw the radii of the los halos.
r_draws = r_los*np.sqrt(np.random.rand(n_los))
theta = 2*np.pi*np.random.rand(n_los)
# Create an array for the coordinates
cart_pos = np.zeros((n_los,2))
cart_pos[:,0] = r_draws * np.cos(theta)
cart_pos[:,1] = r_draws * np.sin(theta)
return cart_pos
def mass_concentration(self,z,m_200,scatter_mult=1.0):
"""Returns the concentration of halos at a certain mass given the
parameterization of DG_19.
Args:
z (float): The redshift of the nfw halos
m_200 (np.array): array of M_200 of the nfw halo units of M_sun
scatter_mult (float): an additional scaling to the scatter. Likely
only useful for los rendering to force scatter to 0.
Returns:
(np.array): The concentration for each halo.
"""
# Get the concentration parameters
c_0 = self.los_parameters['c_0']
zeta = self.los_parameters['conc_zeta']
beta = self.los_parameters['conc_beta']
m_ref = self.los_parameters['conc_m_ref']
dex_scatter = self.los_parameters['dex_scatter']*scatter_mult
# The peak calculation is done by colossus. The cosmology must have
# already been set. Note these functions expect M_sun/h units (which
# you get by multiplying by h
# https://www.astro.ljmu.ac.uk/~ikb/research/h-units.html)
h = self.cosmo.h
peak_heights = peaks.peakHeight(m_200*h,z)
peak_height_ref = peaks.peakHeight(m_ref*h,0)
# Now get the concentrations and add scatter
concentrations = c_0*(1+z)**(zeta)*(peak_heights/peak_height_ref)**(
-beta)
if isinstance(concentrations,np.ndarray):
conc_scatter = np.random.randn(len(concentrations))*dex_scatter
elif isinstance(concentrations,float):
conc_scatter = np.random.randn()*dex_scatter
concentrations = 10**(np.log10(concentrations)+conc_scatter)
return concentrations
def convert_to_lenstronomy(self,z,z_masses,z_cart_pos):
"""Converts the subhalo masses and position to truncated NFW profiles
for lenstronomy
Args:
z (float): The redshift for each of the halos
z_masses (np.array): The masses of each of the halos that
were drawn
z_cart_pos (np.array): A n_los x 2D array of the position of the
halos that were drawn
Returns:
([string,...],[dict,...]): A tuple containing the list of models
and the list of kwargs for the truncated NFWs.
"""
z_source = self.source_parameters['z_source']
# First, draw a concentration for all our LOS structure from our mass
# concentration relation
concentration = self.mass_concentration(z,z_masses)
# Now convert our mass and concentration into the lenstronomy
# parameters
z_r_200 = nfw_functions.r_200_from_m(z_masses,z,self.cosmo)
z_r_scale = z_r_200/concentration
z_rho_nfw = nfw_functions.rho_nfw_from_m_c(z_masses,concentration,
self.cosmo,r_scale=z_r_scale)
# Convert to lenstronomy units
z_r_scale_ang, alpha_Rs = nfw_functions.convert_to_lenstronomy_NFW(
z_r_scale,z,z_rho_nfw,z_source,self.cosmo)
kpc_per_arcsecond = cosmology_utils.kpc_per_arcsecond(z,self.cosmo)
cart_pos_ang = z_cart_pos / np.expand_dims(kpc_per_arcsecond,-1)
# Populate the parameters for each lens
model_list = []
kwargs_list = []
for i in range(len(z_masses)):
model_list.append('NFW')
kwargs_list.append({'alpha_Rs':alpha_Rs[i], 'Rs':z_r_scale_ang[i],
'center_x':cart_pos_ang[i,0],'center_y':cart_pos_ang[i,1]})
return (model_list,kwargs_list)
def draw_los(self):
"""Draws masses, concentrations,and positions for the los substructure
of a main lens halo.
Returns:
(tuple): A tuple of three lists: the first is the profile type for
each los halo returned, the second is the lenstronomy kwargs
for that halo, and the third is a list of redshift values for
each profile.
Notes:
The returned lens model list includes terms to correct for
the average deflection angle introduced from the los halos.
"""
# Distribute line of sight substructure according to
# https://arxiv.org/pdf/1909.02573.pdf. This also includes a
# correction for the average deflection angle introduced by
# the addition of the substructure.
los_model_list = []
los_kwargs_list = []
los_z_list = []
# Pull the paramters we need
z_min = self.los_parameters['z_min']
z_source = self.source_parameters['z_source']
dz = self.los_parameters['dz']
# Add halos from the starting reshift to the source redshift.
# Note most of the calculations are done at z + dz/2, so you
# want to stop at z_source-dz.
z_range = np.arange(z_min,z_source-dz,dz)
# Round the z_range to improve caching hits.
z_range = list(np.round(z_range,2))
# Iterate through each z and add the halos.
for z in z_range:
# Draw the masses and positions at this redshift from our
# model
z_masses = self.draw_nfw_masses(z)
# Don't add anything to the model if no masses were drawn
if z_masses.size == 0:
continue
z_cart_pos = self.sample_los_pos(z,len(z_masses))
# Convert the mass and positions to lenstronomy models
# and kwargs and append to our lists.
model_list, kwargs_list = self.convert_to_lenstronomy(
z,z_masses,z_cart_pos)
los_model_list += model_list
los_kwargs_list += kwargs_list
# TODO - correction for average line of sight
los_z_list += [z+dz/2]*len(model_list)
return (los_model_list, los_kwargs_list, los_z_list)
def calculate_average_alpha(self,num_pix):
""" Calculates the average deflection maps from the los at each
redshift specified by the los parameters and returns corresponding
lenstronomy objects.
Args:
num_pix (int): The number of pixels to sample for our
interpolation maps.
Returns:
(tuple): A tuple of two lists: the first is the interpolation
profile type for each redshift slice and the second is the
lenstronomy kwargs for that profile.
Notes:
The average los deflection angles of the lenstronomy objects will
be the negative of the average (since we want to subtract the
average effect not add it). Pixel scale will be set such that
at each redshift a box of 5*r_los is captured.
"""
# Pull the parameters we need.
z_min = self.los_parameters['z_min']
z_source = self.source_parameters['z_source']
z_lens = self.main_deflector_parameters['z_lens']
dz = self.los_parameters['dz']
dz *= self.los_parameters['alpha_dz_factor']
delta_los = max(0, self.los_parameters['delta_los'])
cone_angle = self.los_parameters['cone_angle']
m_min = self.los_parameters['m_min']
# Units of M_sun
m_max = self.los_parameters['m_max']
z_range = np.arange(z_min,z_source-dz,dz)
# Round the z_range to improve caching hits. Add the dz/2 shift that
# gets output by draw_los.
z_range = list(np.round(z_range,2)+dz/2)
# The lists where we'll store the lenstornomy variables
interp_model_list = []
interp_kwargs_list = []
interp_z_list = []
for zi, z in enumerate(z_range):
# First we make the grid on which we'll conduct the interpolation.
r_los = self.cone_angle_to_radius(z+dz/2,z_lens,z_source,
cone_angle)
kpc_per_arcsecond = cosmology_utils.kpc_per_arcsecond(z,self.cosmo)
r_los = r_los/kpc_per_arcsecond
# Set the pixel scale to capture a box of dimension 5*r_los.
pixel_scale = 5*r_los/num_pix
x_grid, y_grid = util.make_grid(numPix=num_pix,
deltapix=pixel_scale)
x_axes, y_axes = util.get_axes(x_grid, y_grid)
# Then we create the 2d rendering disk we want to convolve our
# NFW with
disk_bool = np.zeros(x_grid.shape)
disk_bool[np.sqrt(x_grid**2+y_grid**2)<r_los] = 1
disk_bool = util.array2image(disk_bool)
# Next we calculate the NFW parameters for our NFW of average
# mass.
pl_slope, pl_norm = self.power_law_dn_dm(z+dz/2,m_min,m_max)
m_average = (power_law.power_law_integrate(m_min,m_max,pl_slope+1)/
power_law.power_law_integrate(m_min,m_max,pl_slope))
c_average = self.mass_concentration(z,m_average,scatter_mult=0.0)
# Convert our parameters to the lenstronomy definition
r_200_avg = nfw_functions.r_200_from_m(m_average,z,self.cosmo)
r_scale_avg = r_200_avg/c_average
rho_nfw_avg = nfw_functions.rho_nfw_from_m_c(m_average,c_average,
self.cosmo,r_scale=r_scale_avg)
r_scale_ang_avg, alpha_Rs_avg = (
nfw_functions.convert_to_lenstronomy_NFW(r_scale_avg,z,
rho_nfw_avg,z_source,self.cosmo))
# Get our deflection angle and potential for this NFW profile
ax,ay = NFW().derivatives(x_grid,y_grid,r_scale_ang_avg,
alpha_Rs_avg)
a = NFW().function(x_grid,y_grid,r_scale_ang_avg,alpha_Rs_avg)
ax = util.array2image(ax)
ay = util.array2image(ay)
a = util.array2image(a)
# Convolve our deflection angles and potential with the disk
ax_conv = fftconvolve(ax,disk_bool,mode='same')
ay_conv = fftconvolve(ay,disk_bool,mode='same')
a_conv = fftconvolve(a,disk_bool,mode='same')
# Finally, we just need to calculate how many of our
# m_average NFW we expect per pixel. So far our convolution
# is the deflection angle assuming 1 NFW / pixel.
# First we calculate the number of m_avg nfw per kpc^3
m_total = power_law.power_law_integrate(m_min,m_max,pl_slope+1)
m_total *= pl_norm * delta_los
n_total = m_total/m_average
# Now we multiply by the length of our redshift slice to get per
# kpc^2 and then multiply by kpc_per_arcsecond to get per
# arcsecond^2
dz_in_kpc = self.cosmo.comovingDistance(z-dz/2,z+dz/2)/self.cosmo.h
dz_in_kpc /= (1+z-dz/2)
# Mpc to kpc
dz_in_kpc *= 1000
n_total *= dz_in_kpc
n_total *= kpc_per_arcsecond**2
# Final convert from per arcsecond^2 to per pixel
n_total *= pixel_scale**2
ax_conv *= n_total
ay_conv *= n_total
a_conv *= n_total
# And now we can return the parameters of our interpol
# profile
interp_model_list.append('INTERPOL')
interp_kwargs_list.append({'grid_interp_x':x_axes,
'grid_interp_y':y_axes, 'f_x':-ax_conv,
'f_y':-ay_conv,'f_':-a_conv})
interp_z_list.append(z)
return (interp_model_list,interp_kwargs_list,interp_z_list)
|
swagnercarenaREPO_NAMEpaltasPATH_START.@paltas_extracted@paltas-main@paltas@Substructure@los_dg19.py@.PATH_END.py
|
{
"filename": "input_pipeline.py",
"repo_name": "google/flax",
"repo_path": "flax_extracted/flax-main/examples/lm1b_nnx/input_pipeline.py",
"type": "Python"
}
|
# Copyright 2024 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Input pipeline for a LM1B dataset."""
import os
import tensorflow as tf
import tensorflow_datasets as tfds
import tokenizer
from clu import deterministic_data
from configs import default
AUTOTUNE = tf.data.experimental.AUTOTUNE
Features = dict[str, tf.Tensor]
class NormalizeFeatureNamesOp:
"""Normalizes feature names to 'inputs' and 'targets'."""
def __init__(self, ds_info: tfds.core.DatasetInfo):
self.ds_info = ds_info
def __call__(self, features: Features) -> Features:
features['inputs'] = features.pop('text')
# Unnecessary step used for uniformizing with examples/wmt.
features['targets'] = features['inputs']
return features
def get_raw_dataset(
dataset_builder: tfds.core.DatasetBuilder, split: str
) -> tf.data.Dataset:
"""Loads a raw text dataset and normalizes feature keys.
Args:
dataset_builder: TFDS dataset builder that can build `split`.
split: Split to use. This must be the full split. We shard the split across
multiple hosts and currently don't support sharding subsplits.
Returns:
Dataset with source and target language features mapped to 'inputs' and
'targets'.
"""
num_examples = dataset_builder.info.splits[split].num_examples
per_host_split = deterministic_data.get_read_instruction_for_host(
split, num_examples, drop_remainder=False
)
ds = dataset_builder.as_dataset(split=per_host_split, shuffle_files=False)
ds = ds.map(
NormalizeFeatureNamesOp(dataset_builder.info), num_parallel_calls=AUTOTUNE
)
return ds
def pack_dataset(
dataset: tf.data.Dataset,
key2length: int | dict[str, int],
keys: list[str] | None = None,
) -> tf.data.Dataset:
"""Creates a 'packed' version of a dataset on-the-fly.
Adapted from the mesh-tf implementation.
This is meant to replace the irritation of having to create a separate
"packed" version of a dataset to train efficiently on TPU.
Each example in the output dataset represents several examples in the
input dataset.
For each key in the input dataset, two additional keys are created:
<key>_segmentation: an int32 tensor identifying the parts
representing the original example.
<key>_position: an int32 tensor identifying the position within the original
example.
Example:
Two input examples get combined to form an output example.
The input examples are:
{"inputs": [8, 7, 1, 0], "targets":[4, 1, 0]}
{"inputs": [2, 3, 4, 1], "targets":[5, 6, 1]}
The output example is:
{
"inputs": [8, 7, 1, 2, 3, 4, 1, 0, 0, 0]
"inputs_segmentation": [1, 1, 1, 2, 2, 2, 2, 0, 0, 0]
"inputs_position": [0, 1, 2, 0, 1, 2, 3, 0, 0, 0]
"targets": [4, 1, 5, 6, 1, 0, 0, 0, 0, 0]
"targets_segmentation": [1, 1, 2, 2, 2, 0, 0, 0, 0, 0]
"targets_position": [0, 1, 0, 1, 2, 0, 0, 0, 0, 0]
}
0 represents padding in both the inputs and the outputs.
Sequences in the incoming examples are truncated to length "length", and the
sequences in the output examples all have fixed (padded) length "length".
Args:
dataset: a tf.data.Dataset
key2length: an integer, or a dict from feature-key to integer
keys: a list of strings (e.g. ["inputs", "targets"])
Returns:
a tf.data.Dataset
"""
shapes = tf.nest.map_structure(lambda spec: spec.shape, dataset.element_spec)
if keys is None:
keys = list(shapes.keys())
for k in keys:
if k not in shapes:
raise ValueError(
'Key %s not found in dataset. Available keys are %s'
% (k, shapes.keys())
)
if not shapes[k].is_compatible_with(tf.TensorShape([None])): # type: ignore[wrong-arg-types]
raise ValueError('Tensors to be packed must be one-dimensional.')
# make sure that the length dictionary contains all keys as well as the
# keys suffixed by "_segmentation" and "_position"
if isinstance(key2length, int):
key2length = {k: key2length for k in keys}
for k in keys:
for suffix in ['_segmentation', '_position']:
key2length[k + suffix] = key2length[k]
# trim to length
dataset = dataset.map(
lambda x: {k: x[k][: key2length[k]] for k in keys},
num_parallel_calls=AUTOTUNE,
)
# Setting batch_size=length ensures that the concatenated sequences (if they
# have length >=1) are sufficient to fill at least one packed example.
batch_size = max(key2length.values())
dataset = dataset.padded_batch(
batch_size, padded_shapes={k: [-1] for k in keys}
)
dataset = _pack_with_tf_ops(dataset, keys, key2length)
# Set the Tensor shapes correctly since they get lost in the process.
def my_fn(x):
return {k: tf.reshape(v, [key2length[k]]) for k, v in x.items()}
return dataset.map(my_fn, num_parallel_calls=AUTOTUNE)
def _pack_with_tf_ops(
dataset: tf.data.Dataset, keys: list[str], key2length: dict[str, int]
) -> tf.data.Dataset:
"""Helper-function for packing a dataset which has already been batched.
Helper for pack_dataset() Uses tf.while_loop.
Args:
dataset: a dataset containing padded batches of examples.
keys: a list of strings
key2length: a dict from feature-key to integer
Returns:
a dataset.
"""
empty_example = {}
for k in keys:
empty_example[k] = tf.zeros([0], dtype=tf.int32)
empty_example[k + '_position'] = tf.zeros([0], dtype=tf.int32)
keys_etc = empty_example.keys()
def write_packed_example(partial, outputs):
new_partial = empty_example.copy()
new_outputs = {}
for k in keys_etc:
new_outputs[k] = outputs[k].write(
outputs[k].size(),
tf.pad(partial[k], [[0, key2length[k] - tf.size(partial[k])]]),
)
return new_partial, new_outputs
def map_fn(x):
"""Internal function to flat_map over.
Consumes a batch of input examples and produces a variable number of output
examples.
Args:
x: a single example
Returns:
a tf.data.Dataset
"""
partial = empty_example.copy()
i = tf.zeros([], dtype=tf.int32)
dynamic_batch_size = tf.shape(x[keys[0]])[0]
outputs = {}
for k in keys:
outputs[k] = tf.TensorArray(
tf.int32, size=0, dynamic_size=True, element_shape=[key2length[k]]
)
outputs[k + '_position'] = tf.TensorArray(
tf.int32, size=0, dynamic_size=True, element_shape=[key2length[k]]
)
def body_fn(i, partial, outputs):
"""Body function for while_loop.
Args:
i: integer scalar
partial: dictionary of Tensor (partially-constructed example)
outputs: dictionary of TensorArray
Returns:
A triple containing the new values of the inputs.
"""
can_append = True
one_example = {}
for k in keys:
val = tf.cast(x[k][i], tf.int32)
val = val[: tf.reduce_sum(tf.cast(tf.not_equal(val, 0), tf.int32))]
one_example[k] = val
for k in keys:
can_append = tf.logical_and(
can_append,
tf.less_equal(
tf.size(partial[k]) + tf.size(one_example[k]), key2length[k]
),
)
def false_fn():
return write_packed_example(partial, outputs)
def true_fn():
return partial, outputs
partial, outputs = tf.cond(can_append, true_fn, false_fn)
new_partial = {}
for k in keys:
new_seq = one_example[k][: key2length[k]]
new_seq_len = tf.size(new_seq)
new_partial[k] = tf.concat([partial[k], new_seq], 0)
new_partial[k + '_position'] = tf.concat(
[partial[k + '_position'], tf.range(new_seq_len)], 0
)
partial = new_partial
return i + 1, partial, outputs
# For loop over all examples in the batch.
i, partial, outputs = tf.while_loop(
cond=lambda *_: True,
body=body_fn,
loop_vars=(i, partial, outputs),
shape_invariants=(
tf.TensorShape([]),
{k: tf.TensorShape([None]) for k in keys_etc}, # type: ignore[wrong-arg-types]
{k: tf.TensorShape(None) for k in keys_etc}, # type: ignore[wrong-arg-types]
),
maximum_iterations=dynamic_batch_size,
)
_, outputs = write_packed_example(partial, outputs)
packed = {k: outputs[k].stack() for k in keys_etc}
for k in keys:
packed[k + '_segmentation'] = tf.cumsum(
tf.cast(tf.equal(packed[k + '_position'], 0), tf.int32), axis=1
) * tf.cast(tf.not_equal(packed[k], 0), tf.int32)
return packed
dataset = dataset.map(map_fn, num_parallel_calls=AUTOTUNE)
return dataset.unbatch()
# -----------------------------------------------------------------------------
# Main dataset prep routines.
# -----------------------------------------------------------------------------
def preprocess_data(
dataset,
shuffle: bool,
num_epochs: int | None = 1,
pack_examples: bool = True,
shuffle_buffer_size: int = 1024,
max_length: int = 512,
batch_size: int = 256,
drop_remainder: bool = True,
prefetch_size: int = AUTOTUNE,
):
"""Shuffle and batch/pack the given dataset."""
def length_filter(max_len):
def filter_fn(x):
source, target = x['inputs'], x['targets']
l = tf.maximum(tf.shape(source)[0], tf.shape(target)[0])
return tf.less(l, max_len + 1)
return filter_fn
if max_length > 0:
dataset = dataset.filter(length_filter(max_length))
if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size)
dataset = dataset.repeat(num_epochs)
if pack_examples:
dataset = pack_dataset(dataset, max_length)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
else: # simple (static-shape) padded batching
dataset = dataset.padded_batch(
batch_size,
padded_shapes={'inputs': max_length, 'targets': max_length},
padding_values={'inputs': 0, 'targets': 0},
drop_remainder=drop_remainder,
)
if prefetch_size:
dataset = dataset.prefetch(prefetch_size)
return dataset
def get_datasets(
config: default.Config,
*,
n_devices: int,
vocab_path: str | None = None,
):
"""Load and return dataset of batched examples for use during training."""
if vocab_path is None:
vocab_path = os.path.expanduser('~/lm1b_sentencepiece_model')
train_ds_builder = tfds.builder(config.dataset_name)
train_data = get_raw_dataset(train_ds_builder, 'train')
if config.eval_dataset_name:
eval_ds_builder = tfds.builder(config.eval_dataset_name)
else:
eval_ds_builder = train_ds_builder
eval_data = get_raw_dataset(eval_ds_builder, config.eval_split)
# Tokenize data.
sp_tokenizer = tokenizer.load_or_train_tokenizer(
train_data,
vocab_path=vocab_path,
vocab_size=config.vocab_size,
max_corpus_chars=config.max_corpus_chars,
)
train_data = train_data.map(
tokenizer.TokenizeOp(sp_tokenizer), num_parallel_calls=AUTOTUNE
)
eval_data = eval_data.map(
tokenizer.TokenizeOp(sp_tokenizer), num_parallel_calls=AUTOTUNE
)
batch_size = config.per_device_batch_size * n_devices
if config.eval_per_device_batch_size > 0:
eval_batch_size = config.eval_per_device_batch_size * n_devices
else:
eval_batch_size = batch_size
train_ds = preprocess_data(
train_data,
shuffle=True,
num_epochs=None,
pack_examples=True,
batch_size=batch_size,
max_length=config.max_target_length,
)
eval_ds = preprocess_data(
eval_data,
shuffle=False,
pack_examples=False,
batch_size=eval_batch_size,
max_length=config.max_eval_target_length,
)
predict_ds = preprocess_data(
eval_data,
shuffle=False,
pack_examples=False,
batch_size=eval_batch_size,
max_length=config.max_predict_length,
drop_remainder=False,
)
return train_ds, eval_ds, predict_ds, sp_tokenizer
|
googleREPO_NAMEflaxPATH_START.@flax_extracted@flax-main@examples@lm1b_nnx@input_pipeline.py@.PATH_END.py
|
{
"filename": "test_fail.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/simplejson/py2/simplejson/tests/test_fail.py",
"type": "Python"
}
|
import sys
from unittest import TestCase
import simplejson as json
# 2007-10-05
JSONDOCS = [
# http://json.org/JSON_checker/test/fail1.json
'"A JSON payload should be an object or array, not a string."',
# http://json.org/JSON_checker/test/fail2.json
'["Unclosed array"',
# http://json.org/JSON_checker/test/fail3.json
'{unquoted_key: "keys must be quoted"}',
# http://json.org/JSON_checker/test/fail4.json
'["extra comma",]',
# http://json.org/JSON_checker/test/fail5.json
'["double extra comma",,]',
# http://json.org/JSON_checker/test/fail6.json
'[ , "<-- missing value"]',
# http://json.org/JSON_checker/test/fail7.json
'["Comma after the close"],',
# http://json.org/JSON_checker/test/fail8.json
'["Extra close"]]',
# http://json.org/JSON_checker/test/fail9.json
'{"Extra comma": true,}',
# http://json.org/JSON_checker/test/fail10.json
'{"Extra value after close": true} "misplaced quoted value"',
# http://json.org/JSON_checker/test/fail11.json
'{"Illegal expression": 1 + 2}',
# http://json.org/JSON_checker/test/fail12.json
'{"Illegal invocation": alert()}',
# http://json.org/JSON_checker/test/fail13.json
'{"Numbers cannot have leading zeroes": 013}',
# http://json.org/JSON_checker/test/fail14.json
'{"Numbers cannot be hex": 0x14}',
# http://json.org/JSON_checker/test/fail15.json
'["Illegal backslash escape: \\x15"]',
# http://json.org/JSON_checker/test/fail16.json
'[\\naked]',
# http://json.org/JSON_checker/test/fail17.json
'["Illegal backslash escape: \\017"]',
# http://json.org/JSON_checker/test/fail18.json
'[[[[[[[[[[[[[[[[[[[["Too deep"]]]]]]]]]]]]]]]]]]]]',
# http://json.org/JSON_checker/test/fail19.json
'{"Missing colon" null}',
# http://json.org/JSON_checker/test/fail20.json
'{"Double colon":: null}',
# http://json.org/JSON_checker/test/fail21.json
'{"Comma instead of colon", null}',
# http://json.org/JSON_checker/test/fail22.json
'["Colon instead of comma": false]',
# http://json.org/JSON_checker/test/fail23.json
'["Bad value", truth]',
# http://json.org/JSON_checker/test/fail24.json
"['single quote']",
# http://json.org/JSON_checker/test/fail25.json
'["\ttab\tcharacter\tin\tstring\t"]',
# http://json.org/JSON_checker/test/fail26.json
'["tab\\ character\\ in\\ string\\ "]',
# http://json.org/JSON_checker/test/fail27.json
'["line\nbreak"]',
# http://json.org/JSON_checker/test/fail28.json
'["line\\\nbreak"]',
# http://json.org/JSON_checker/test/fail29.json
'[0e]',
# http://json.org/JSON_checker/test/fail30.json
'[0e+]',
# http://json.org/JSON_checker/test/fail31.json
'[0e+-1]',
# http://json.org/JSON_checker/test/fail32.json
'{"Comma instead if closing brace": true,',
# http://json.org/JSON_checker/test/fail33.json
'["mismatch"}',
# http://code.google.com/p/simplejson/issues/detail?id=3
u'["A\u001FZ control characters in string"]',
# misc based on coverage
'{',
'{]',
'{"foo": "bar"]',
'{"foo": "bar"',
'nul',
'nulx',
'-',
'-x',
'-e',
'-e0',
'-Infinite',
'-Inf',
'Infinit',
'Infinite',
'NaM',
'NuN',
'falsy',
'fal',
'trug',
'tru',
'1e',
'1ex',
'1e-',
'1e-x',
]
SKIPS = {
1: "why not have a string payload?",
18: "spec doesn't specify any nesting limitations",
}
class TestFail(TestCase):
def test_failures(self):
for idx, doc in enumerate(JSONDOCS):
idx = idx + 1
if idx in SKIPS:
json.loads(doc)
continue
try:
json.loads(doc)
except json.JSONDecodeError:
pass
else:
self.fail("Expected failure for fail%d.json: %r" % (idx, doc))
def test_array_decoder_issue46(self):
# http://code.google.com/p/simplejson/issues/detail?id=46
for doc in [u'[,]', '[,]']:
try:
json.loads(doc)
except json.JSONDecodeError:
e = sys.exc_info()[1]
self.assertEqual(e.pos, 1)
self.assertEqual(e.lineno, 1)
self.assertEqual(e.colno, 2)
except Exception:
e = sys.exc_info()[1]
self.fail("Unexpected exception raised %r %s" % (e, e))
else:
self.fail("Unexpected success parsing '[,]'")
def test_truncated_input(self):
test_cases = [
('', 'Expecting value', 0),
('[', "Expecting value or ']'", 1),
('[42', "Expecting ',' delimiter", 3),
('[42,', 'Expecting value', 4),
('["', 'Unterminated string starting at', 1),
('["spam', 'Unterminated string starting at', 1),
('["spam"', "Expecting ',' delimiter", 7),
('["spam",', 'Expecting value', 8),
('{', "Expecting property name enclosed in double quotes or '}'", 1),
('{"', 'Unterminated string starting at', 1),
('{"spam', 'Unterminated string starting at', 1),
('{"spam"', "Expecting ':' delimiter", 7),
('{"spam":', 'Expecting value', 8),
('{"spam":42', "Expecting ',' delimiter", 10),
('{"spam":42,', 'Expecting property name enclosed in double quotes',
11),
('"', 'Unterminated string starting at', 0),
('"spam', 'Unterminated string starting at', 0),
('[,', "Expecting value", 1),
('--', 'Expecting value', 0),
('"\x18d', "Invalid control character %r", 1),
]
for data, msg, idx in test_cases:
try:
json.loads(data)
except json.JSONDecodeError:
e = sys.exc_info()[1]
self.assertEqual(
e.msg[:len(msg)],
msg,
"%r doesn't start with %r for %r" % (e.msg, msg, data))
self.assertEqual(
e.pos, idx,
"pos %r != %r for %r" % (e.pos, idx, data))
except Exception:
e = sys.exc_info()[1]
self.fail("Unexpected exception raised %r %s" % (e, e))
else:
self.fail("Unexpected success parsing '%r'" % (data,))
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@simplejson@py2@simplejson@tests@test_fail.py@.PATH_END.py
|
{
"filename": "test_search_tool.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/partners/exa/tests/integration_tests/test_search_tool.py",
"type": "Python"
}
|
from langchain_exa import (
ExaSearchResults, # type: ignore[import-not-found, import-not-found]
)
def test_search_tool() -> None:
tool = ExaSearchResults()
res = tool.invoke({"query": "best time to visit japan", "num_results": 5})
print(res) # noqa: T201
assert not isinstance(res, str) # str means error for this tool\
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@partners@exa@tests@integration_tests@test_search_tool.py@.PATH_END.py
|
{
"filename": "test_widget_upload.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/ipywidgets/py3/ipywidgets/widgets/tests/test_widget_upload.py",
"type": "Python"
}
|
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import datetime as dt
from unittest import TestCase
from unittest.mock import MagicMock
from traitlets import TraitError
from ipywidgets import FileUpload
FILE_UPLOAD_FRONTEND_CONTENT = {
'name': 'file-name.txt',
'type': 'text/plain',
'size': 20760,
'last_modified': 1578578296434,
'content': memoryview(b'file content'),
}
class TestFileUpload(TestCase):
def test_construction(self):
uploader = FileUpload()
# Default
assert uploader.accept == ''
assert not uploader.multiple
assert not uploader.disabled
def test_construction_with_params(self):
uploader = FileUpload(
accept='.txt', multiple=True, disabled=True)
assert uploader.accept == '.txt'
assert uploader.multiple
assert uploader.disabled
def test_empty_initial_value(self):
uploader = FileUpload()
assert uploader.value == ()
def test_receive_single_file(self):
uploader = FileUpload()
message = {'value': [FILE_UPLOAD_FRONTEND_CONTENT]}
uploader.set_state(message)
assert len(uploader.value) == 1
(uploaded_file,) = uploader.value
assert uploaded_file.name == 'file-name.txt'
assert uploaded_file.type == 'text/plain'
assert uploaded_file.size == 20760
assert uploaded_file.content.tobytes() == b'file content'
assert (
uploaded_file.last_modified ==
dt.datetime(2020, 1, 9, 13, 58, 16, 434000, tzinfo=dt.timezone.utc)
)
def test_receive_multiple_files(self):
uploader = FileUpload(multiple=True)
message = {
'value': [
FILE_UPLOAD_FRONTEND_CONTENT,
{**FILE_UPLOAD_FRONTEND_CONTENT, **{'name': 'other-file-name.txt'}}
]
}
uploader.set_state(message)
assert len(uploader.value) == 2
assert uploader.value[0].name == 'file-name.txt'
assert uploader.value[1].name == 'other-file-name.txt'
def test_serialization_deserialization_integrity(self):
# The value traitlet needs to remain unchanged following
# a serialization / deserialization roundtrip, otherwise
# the kernel dispatches it back to the frontend following
# a state change, because it doesn't recognize that the
# property_lock entry is the same as the new value.
from ipykernel.comm import Comm
uploader = FileUpload()
mock_comm = MagicMock(spec=Comm)
mock_comm.send = MagicMock()
mock_comm.kernel = 'does not matter'
uploader.comm = mock_comm
message = {'value': [FILE_UPLOAD_FRONTEND_CONTENT]}
uploader.set_state(message)
# Check that no message is sent back to the frontend
# as a result of setting the state.
mock_comm.send.assert_not_called()
def test_resetting_value(self):
# Simulate an upload, then resetting the value from the
# kernel.
uploader = FileUpload()
message = {'value': [FILE_UPLOAD_FRONTEND_CONTENT]}
uploader.set_state(message)
uploader.value = [] # reset value to an empty file list
assert uploader.get_state(key='value') == {'value': []}
def test_setting_non_empty_value(self):
# Simulate user setting a value for the upload from the kernel.
uploader = FileUpload()
content = memoryview(b'some content')
uploader.value = [{
'name': 'some-name.txt',
'type': 'text/plain',
'size': 561,
'last_modified': dt.datetime(2020, 1, 9, 13, 58, 16, 434000, tzinfo=dt.timezone.utc),
'content': content
}]
state = uploader.get_state(key='value')
assert len(state['value']) == 1
[entry] = state['value']
assert entry['name'] == 'some-name.txt'
assert entry['type'] == 'text/plain'
assert entry['size'] == 561
assert entry['last_modified'] == 1578578296434
assert entry['content'] == content
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@ipywidgets@py3@ipywidgets@widgets@tests@test_widget_upload.py@.PATH_END.py
|
{
"filename": "performance_counters.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/utilities/performance_counters.py",
"type": "Python"
}
|
import atexit
import time
from bisect import insort
from collections import defaultdict
from datetime import datetime as dt
from functools import wraps
from yt.config import ytcfg
from yt.funcs import mylog
class PerformanceCounters:
_shared_state = {} # type: ignore
def __new__(cls, *args, **kwargs):
self = object.__new__(cls, *args, **kwargs)
self.__dict__ = cls._shared_state
return self
def __init__(self):
self.counters = defaultdict(lambda: 0.0)
self.counting = defaultdict(lambda: False)
self.starttime = defaultdict(lambda: 0)
self.endtime = defaultdict(lambda: 0)
self._on = ytcfg.get("yt", "time_functions")
self.exit()
def __call__(self, name):
if not self._on:
return
if self.counting[name]:
self.counters[name] = time.time() - self.counters[name]
self.counting[name] = False
self.endtime[name] = dt.now()
else:
self.counters[name] = time.time()
self.counting[name] = True
self.starttime[name] = dt.now()
def call_func(self, func):
if not self._on:
return func
@wraps(func)
def func_wrapper(*args, **kwargs):
self(func.__name__)
func(*args, **kwargs)
self(func.__name__)
return func_wrapper
def print_stats(self):
mylog.info("Current counter status:\n")
times = []
for i in self.counters:
insort(times, [self.starttime[i], i, 1]) # 1 for 'on'
if not self.counting[i]:
insort(times, [self.endtime[i], i, 0]) # 0 for 'off'
shifts = {}
order = []
endtimes = {}
shift = 0
multi = 5
for i in times:
# a starting entry
if i[2] == 1:
shifts[i[1]] = shift
order.append(i[1])
shift += 1
if i[2] == 0:
shift -= 1
endtimes[i[1]] = self.counters[i[1]]
line = ""
for i in order:
if self.counting[i]:
line = "%s%s%i : %s : still running\n" % (
line,
" " * shifts[i] * multi,
shifts[i],
i,
)
else:
line = "%s%s%i : %s : %0.3e\n" % (
line,
" " * shifts[i] * multi,
shifts[i],
i,
self.counters[i],
)
mylog.info("\n%s", line)
def exit(self):
if self._on:
atexit.register(self.print_stats)
yt_counters = PerformanceCounters()
time_function = yt_counters.call_func
class ProfilingController:
def __init__(self):
self.profilers = {}
def profile_function(self, function_name):
def wrapper(func):
try:
import cProfile
except ImportError:
return func
my_prof = cProfile.Profile()
self.profilers[function_name] = my_prof
@wraps(func)
def run_in_profiler(*args, **kwargs):
my_prof.enable()
func(*args, **kwargs)
my_prof.disable()
return run_in_profiler
return wrapper
def write_out(self, filename_prefix):
if ytcfg.get("yt", "internals", "parallel"):
pfn = "%s_%03i_%03i" % (
filename_prefix,
ytcfg.get("yt", "internals", "global_parallel_rank"),
ytcfg.get("yt", "internals", "global_parallel_size"),
)
else:
pfn = f"{filename_prefix}"
for n, p in sorted(self.profilers.items()):
fn = f"{pfn}_{n}.cprof"
mylog.info("Dumping %s into %s", n, fn)
p.dump_stats(fn)
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@utilities@performance_counters.py@.PATH_END.py
|
{
"filename": "cli.md",
"repo_name": "dokkum/maskfill",
"repo_path": "maskfill_extracted/maskfill-main/docs/cli.md",
"type": "Markdown"
}
|
### CLI Usage
When you install `maskfill` it will create a `maskfill` executable callable from the shell. You can print the usage via
```
maskfill -h
```
which will return something like this:
```bash
usage: maskfill [-h] [-e EXTENSION] [-v] [-s SIZE] [-o OPERATOR] [-n] [-w] input mask output
positional arguments:
input input image
mask mask image, with values 0 = good, 1 = bad
output output image
options:
-h, --help show this help message and exit
-e EXTENSION, --extension EXTENSION
fits extension of data
-v, --verbose print actions
-s SIZE, --size SIZE scale of median filter (default = 3)
-o OPERATOR, --operator OPERATOR
replace pixels with mean or median (default = median)
-n, --nosmooth omit boxcar smoothing at the end (default = False)
-w, --writesteps write result after each iteration, as _iter_#.fits
```
The simplest call is something like
```
maskfill im.fits mask.fits out.fits
```
or
```
maskfill im mask out #file extension assumed to be .fits
```
in which you provide the input image, mask image, and name of the output file (if the `.fits` is omitted, `maskfill` will add it, though if your files have alternate extensions like `.fit` you should specify the full name). There are also several optional arguments and flags.
- `-e X` or `--extension X`: if the image and mask are not in the 0th fits extension, specify it here
- `-s X` or `--size X`: if you want a larger window kernel than the minimum 3x3, specify it here (faster, but less accurate results)
- `-o median` or `--operator median`: either 'median' or 'mean', defines how masked pixels are filled in based on their neighbors
- `-n` or `--nosmooth`: disable a final-step boxcar smoothing of the filled in mask pixels
- `-w` or `--writesteps`: write `_iter_N` fits files after each iteration of the algorithm (default is False)
- `-v` or `--verbose`: verbose output (shows the progress of iterations and number of remaining masked pixels).
The output is saved in the fits file with the provided output name. By default, after infilling, a smoothing step (using the same window, but a `mean` filter) is used to reduce sharp edges introduced by the iterative infilling. When enabled, the output fits file will contain the smoothed output image in the 0th extension, and the unsmoothed version post infilling in the 1st extension. If `nosmooth` is flagged, the 0th extension will contain the unsmoothed output. Information about which type of output is in which extension is added to the header.
|
dokkumREPO_NAMEmaskfillPATH_START.@maskfill_extracted@maskfill-main@docs@cli.md@.PATH_END.py
|
{
"filename": "precompute_response_sh.py",
"repo_name": "HydraRadio/Hydra",
"repo_path": "Hydra_extracted/Hydra-main/scripts/precompute_response_sh.py",
"type": "Python"
}
|
#!/usr/bin/env python
import numpy as np
from mpi4py import MPI
from pyuvdata import UVData
import healpy as hp
import argparse, os, sys, time
import pyuvsim
sys.path.insert(0,'/home/phil/hera/Hydra/')
#sys.path.insert(0,'/cosma/home/dp270/dc-bull2/software/Hydra/')
import hydra
# Set up argparser
description = "Precompute visibility response of an array to each spherical harmonic mode."
parser = argparse.ArgumentParser(description=description)
parser.add_argument("--template", type=str, action="store",
required=True, dest="template",
help="Path to template UVData file.")
parser.add_argument("--lmax", type=int, action="store", default=4,
required=False, dest="lmax",
help="Set the random seed.")
parser.add_argument("--nside", type=int, action="store", default=32,
required=False, dest="nside",
help="Set the healpix resolution (nside).")
parser.add_argument("--outdir", type=str, action="store",
required=True, dest="outdir",
help="Path to output directory.")
args = parser.parse_args()
# Configure mpi
comm = MPI.COMM_WORLD
myid = comm.Get_rank()
nworkers = comm.Get_size()
# Set-up variables
lmax = args.lmax
nside = args.nside
outdir = args.outdir
template = args.template
# Check that output directory exists
if myid == 0:
if not os.path.exists(outdir):
os.makedirs(outdir)
print("\nOutput directory:", outdir)
comm.Barrier()
if myid == 0:
print("(Workers finished testing outdir.)")
# Load template UVData object
if myid == 0:
print("Template file:", template)
uvd = UVData()
uvd.read_uvh5(template, read_data=False)
if myid == 0:
print(" Read uvh5 file metadata.")
comm.Barrier()
if myid == 0:
print("(Workers finished loading metadata.)")
# Get freqs, lsts, ants etc.
freqs = np.unique(uvd.freq_array)
lsts = np.unique(uvd.lst_array)
antpos, antnums = uvd.get_ENU_antpos(center=False, pick_data_ants=True)
ants = {}
for i in range(len(antnums)):
ants[antnums[i]] = antpos[i]
# Get number of modes
_ell, _m = hp.Alm().getlm(lmax=lmax)
Nmodes = _ell.size
# Print basic info
if myid == 0:
print("lmax: %d" % lmax)
print("modes: %d" % Nmodes)
print("nside: %d" % nside)
print("Frequencies: %5.1f -- %5.1f MHz (%d channels)" \
% (freqs.min()/1e6, freqs.max()/1e6, freqs.size))
print("LSTs: %5.4f -- %5.4f rad (%d times)" \
% (lsts.min(), lsts.max(), lsts.size))
print("(Identical Gaussian beams)")
print("-"*50)
# Split idxs into ordered blocks per worker
idxs = np.arange(freqs.size)
blocks = np.array_split(idxs, nworkers)
max_block_size = np.max([b.size for b in blocks])
# Simple Gaussian beams for now
beams = [pyuvsim.AnalyticBeam('gaussian', diameter=14.)
for i in range(len(antnums))]
# Output metadata
if myid == 0:
metafile = os.path.join(outdir, "response_sh_metadata")
print("Output file:", "response_sh_metadata")
with open(metafile, 'w') as f:
f.write("template: %s\n" % template)
f.write("lmax: %d\n" % lmax)
f.write("modes: %d\n" % Nmodes)
f.write("nside: %d\n" % nside)
f.write("freqs: %s\n" % freqs)
f.write("lsts: %s\n" % lsts)
f.write("blocks: %s\n" % blocks)
f.write("antnums: %s\n" % antnums)
f.write("antpos: %s\n" % antpos)
# Run calculation on each worker
# (NFREQS, NTIMES, NANTS, NANTS, NMODES) if polarized=False
#v = np.zeros((max_block_size, lsts.size, len(ants), len(ants), Nmodes))
comm.Barrier()
if myid == 0:
print("(Workers starting simulation.)")
# Loop over blocks, one block per worker
# Run simulation for each block of frequencies
tstart = time.time()
ell, m, vis = hydra.vis_simulator.simulate_vis_per_alm(
lmax=lmax,
nside=nside,
ants=ants,
freqs=freqs[blocks[myid]],
lsts=lsts,
beams=beams,
polarized=False,
precision=2,
latitude=np.deg2rad(-30.7215),
use_feed="x",
multiprocess=False,
amplitude=1.
)
# vis shape (NAXES, NFEED, NFREQS, NTIMES, NANTS, NANTS, NMODES)
# (NFREQS, NTIMES, NANTS, NANTS, NMODES) if pol False
print("(Worker %03d) Run took %5.1f min" % (myid, (time.time() - tstart)/60.))
comm.Barrier()
if myid == 0:
print("(Workers finished simulation.)")
# Save operator to .npy file for each chunk
outfile = os.path.join(outdir, "response_sh_%04d" % myid)
np.save(outfile, vis)
print("Output file:", "response_sh_%04d" % myid)
# Output ell, m values
if myid == 0:
out_lm = os.path.join(outdir, "response_sh_ellm")
print("Output file:", "response_sh_ellm")
np.save(out_lm, np.column_stack((ell, m)))
comm.Barrier()
sys.exit(0)
"""
# Allocate receive buffer on root worker and gather values
# (NOTE: I think we need the blocks on each worker to be the same size to avoid
# weird overlaps happening when we do Gather)
allv = None
if myid == 0:
allv = np.zeros([nworkers,
max_block_size,
v.shape[-1]],
dtype=float)
comm.Gather(v, allv, root=0)
if myid != 0:
del v # free some memory
# Concatenate into a single array on root worker with the right shape
if myid == 0:
allv_flat = np.concatenate([allv[i,:len(blocks[i])] for i in range(len(blocks))])
del allv # save some memory again
print(allv_flat)
"""
|
HydraRadioREPO_NAMEHydraPATH_START.@Hydra_extracted@Hydra-main@scripts@precompute_response_sh.py@.PATH_END.py
|
{
"filename": "test_asfreq.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/frame/methods/test_asfreq.py",
"type": "Python"
}
|
from datetime import datetime
import numpy as np
import pytest
from pandas._libs.tslibs.offsets import MonthEnd
from pandas import (
DataFrame,
DatetimeIndex,
PeriodIndex,
Series,
date_range,
period_range,
to_datetime,
)
import pandas._testing as tm
from pandas.tseries import offsets
class TestAsFreq:
def test_asfreq2(self, frame_or_series):
ts = frame_or_series(
[0.0, 1.0, 2.0],
index=DatetimeIndex(
[
datetime(2009, 10, 30),
datetime(2009, 11, 30),
datetime(2009, 12, 31),
],
dtype="M8[ns]",
freq="BME",
),
)
daily_ts = ts.asfreq("B")
monthly_ts = daily_ts.asfreq("BME")
tm.assert_equal(monthly_ts, ts)
daily_ts = ts.asfreq("B", method="pad")
monthly_ts = daily_ts.asfreq("BME")
tm.assert_equal(monthly_ts, ts)
daily_ts = ts.asfreq(offsets.BDay())
monthly_ts = daily_ts.asfreq(offsets.BMonthEnd())
tm.assert_equal(monthly_ts, ts)
result = ts[:0].asfreq("ME")
assert len(result) == 0
assert result is not ts
if frame_or_series is Series:
daily_ts = ts.asfreq("D", fill_value=-1)
result = daily_ts.value_counts().sort_index()
expected = Series(
[60, 1, 1, 1], index=[-1.0, 2.0, 1.0, 0.0], name="count"
).sort_index()
tm.assert_series_equal(result, expected)
def test_asfreq_datetimeindex_empty(self, frame_or_series):
# GH#14320
index = DatetimeIndex(["2016-09-29 11:00"])
expected = frame_or_series(index=index, dtype=object).asfreq("h")
result = frame_or_series([3], index=index.copy()).asfreq("h")
tm.assert_index_equal(expected.index, result.index)
@pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
def test_tz_aware_asfreq_smoke(self, tz, frame_or_series):
dr = date_range("2011-12-01", "2012-07-20", freq="D", tz=tz)
obj = frame_or_series(
np.random.default_rng(2).standard_normal(len(dr)), index=dr
)
# it works!
obj.asfreq("min")
def test_asfreq_normalize(self, frame_or_series):
rng = date_range("1/1/2000 09:30", periods=20)
norm = date_range("1/1/2000", periods=20)
vals = np.random.default_rng(2).standard_normal((20, 3))
obj = DataFrame(vals, index=rng)
expected = DataFrame(vals, index=norm)
if frame_or_series is Series:
obj = obj[0]
expected = expected[0]
result = obj.asfreq("D", normalize=True)
tm.assert_equal(result, expected)
def test_asfreq_keep_index_name(self, frame_or_series):
# GH#9854
index_name = "bar"
index = date_range("20130101", periods=20, name=index_name)
obj = DataFrame(list(range(20)), columns=["foo"], index=index)
obj = tm.get_obj(obj, frame_or_series)
assert index_name == obj.index.name
assert index_name == obj.asfreq("10D").index.name
def test_asfreq_ts(self, frame_or_series):
index = period_range(freq="Y", start="1/1/2001", end="12/31/2010")
obj = DataFrame(
np.random.default_rng(2).standard_normal((len(index), 3)), index=index
)
obj = tm.get_obj(obj, frame_or_series)
result = obj.asfreq("D", how="end")
exp_index = index.asfreq("D", how="end")
assert len(result) == len(obj)
tm.assert_index_equal(result.index, exp_index)
result = obj.asfreq("D", how="start")
exp_index = index.asfreq("D", how="start")
assert len(result) == len(obj)
tm.assert_index_equal(result.index, exp_index)
def test_asfreq_resample_set_correct_freq(self, frame_or_series):
# GH#5613
# we test if .asfreq() and .resample() set the correct value for .freq
dti = to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"])
obj = DataFrame({"col": [1, 2, 3]}, index=dti)
obj = tm.get_obj(obj, frame_or_series)
# testing the settings before calling .asfreq() and .resample()
assert obj.index.freq is None
assert obj.index.inferred_freq == "D"
# does .asfreq() set .freq correctly?
assert obj.asfreq("D").index.freq == "D"
# does .resample() set .freq correctly?
assert obj.resample("D").asfreq().index.freq == "D"
def test_asfreq_empty(self, datetime_frame):
# test does not blow up on length-0 DataFrame
zero_length = datetime_frame.reindex([])
result = zero_length.asfreq("BME")
assert result is not zero_length
def test_asfreq(self, datetime_frame):
offset_monthly = datetime_frame.asfreq(offsets.BMonthEnd())
rule_monthly = datetime_frame.asfreq("BME")
tm.assert_frame_equal(offset_monthly, rule_monthly)
rule_monthly.asfreq("B", method="pad")
# TODO: actually check that this worked.
# don't forget!
rule_monthly.asfreq("B", method="pad")
def test_asfreq_datetimeindex(self):
df = DataFrame(
{"A": [1, 2, 3]},
index=[datetime(2011, 11, 1), datetime(2011, 11, 2), datetime(2011, 11, 3)],
)
df = df.asfreq("B")
assert isinstance(df.index, DatetimeIndex)
ts = df["A"].asfreq("B")
assert isinstance(ts.index, DatetimeIndex)
def test_asfreq_fillvalue(self):
# test for fill value during upsampling, related to issue 3715
# setup
rng = date_range("1/1/2016", periods=10, freq="2s")
# Explicit cast to 'float' to avoid implicit cast when setting None
ts = Series(np.arange(len(rng)), index=rng, dtype="float")
df = DataFrame({"one": ts})
# insert pre-existing missing value
df.loc["2016-01-01 00:00:08", "one"] = None
actual_df = df.asfreq(freq="1s", fill_value=9.0)
expected_df = df.asfreq(freq="1s").fillna(9.0)
expected_df.loc["2016-01-01 00:00:08", "one"] = None
tm.assert_frame_equal(expected_df, actual_df)
expected_series = ts.asfreq(freq="1s").fillna(9.0)
actual_series = ts.asfreq(freq="1s", fill_value=9.0)
tm.assert_series_equal(expected_series, actual_series)
def test_asfreq_with_date_object_index(self, frame_or_series):
rng = date_range("1/1/2000", periods=20)
ts = frame_or_series(np.random.default_rng(2).standard_normal(20), index=rng)
ts2 = ts.copy()
ts2.index = [x.date() for x in ts2.index]
result = ts2.asfreq("4h", method="ffill")
expected = ts.asfreq("4h", method="ffill")
tm.assert_equal(result, expected)
def test_asfreq_with_unsorted_index(self, frame_or_series):
# GH#39805
# Test that rows are not dropped when the datetime index is out of order
index = to_datetime(["2021-01-04", "2021-01-02", "2021-01-03", "2021-01-01"])
result = frame_or_series(range(4), index=index)
expected = result.reindex(sorted(index))
expected.index = expected.index._with_freq("infer")
result = result.asfreq("D")
tm.assert_equal(result, expected)
def test_asfreq_after_normalize(self, unit):
# https://github.com/pandas-dev/pandas/issues/50727
result = DatetimeIndex(
date_range("2000", periods=2).as_unit(unit).normalize(), freq="D"
)
expected = DatetimeIndex(["2000-01-01", "2000-01-02"], freq="D").as_unit(unit)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"freq, freq_half",
[
("2ME", "ME"),
(MonthEnd(2), MonthEnd(1)),
],
)
def test_asfreq_2ME(self, freq, freq_half):
index = date_range("1/1/2000", periods=6, freq=freq_half)
df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0], index=index)})
expected = df.asfreq(freq=freq)
index = date_range("1/1/2000", periods=3, freq=freq)
result = DataFrame({"s": Series([0.0, 2.0, 4.0], index=index)})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"freq, freq_depr",
[
("2ME", "2M"),
("2ME", "2m"),
("2QE", "2Q"),
("2QE-SEP", "2Q-SEP"),
("1BQE", "1BQ"),
("2BQE-SEP", "2BQ-SEP"),
("2BQE-SEP", "2bq-sep"),
("1YE", "1y"),
("2YE-MAR", "2Y-MAR"),
],
)
def test_asfreq_frequency_M_Q_Y_raises(self, freq, freq_depr):
msg = f"Invalid frequency: {freq_depr}"
index = date_range("1/1/2000", periods=4, freq=f"{freq[1:]}")
df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)})
with pytest.raises(ValueError, match=msg):
df.asfreq(freq=freq_depr)
@pytest.mark.parametrize(
"freq, error_msg",
[
(
"2MS",
"Invalid frequency: 2MS",
),
(
offsets.MonthBegin(),
r"\<MonthBegin\> is not supported as period frequency",
),
(
offsets.DateOffset(months=2),
r"\<DateOffset: months=2\> is not supported as period frequency",
),
],
)
def test_asfreq_unsupported_freq(self, freq, error_msg):
# https://github.com/pandas-dev/pandas/issues/56718
index = PeriodIndex(["2020-01-01", "2021-01-01"], freq="M")
df = DataFrame({"a": Series([0, 1], index=index)})
with pytest.raises(ValueError, match=error_msg):
df.asfreq(freq=freq)
@pytest.mark.parametrize(
"freq, freq_depr",
[
("2YE", "2A"),
("2BYE-MAR", "2BA-MAR"),
],
)
def test_asfreq_frequency_A_BA_raises(self, freq, freq_depr):
msg = f"Invalid frequency: {freq_depr}"
index = date_range("1/1/2000", periods=4, freq=freq)
df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)})
with pytest.raises(ValueError, match=msg):
df.asfreq(freq=freq_depr)
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@frame@methods@test_asfreq.py@.PATH_END.py
|
{
"filename": "mpi_pool.py",
"repo_name": "nye17/javelin",
"repo_path": "javelin_extracted/javelin-master/javelin/emcee_internal/mpi_pool.py",
"type": "Python"
}
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, print_function, absolute_import,
unicode_literals)
from six.moves import range
__all__ = ["MPIPool"]
# On some systems mpi4py is available but broken
# we avoid crashes by importing it only when
# an MPI Pool is explicitly created.
#Still make it a global to avoid messing up other things.
MPI = None
class _close_pool_message(object):
def __repr__(self):
return "<Close pool message>"
class _function_wrapper(object):
def __init__(self, function):
self.function = function
def _error_function(task):
raise RuntimeError("Pool was sent tasks before being told what "
"function to apply.")
class MPIPool(object):
"""
A pool that distributes tasks over a set of MPI processes. MPI is an
API for distributed memory parallelism. This pool will let you run
emcee without shared memory, letting you use much larger machines
with emcee.
The pool only support the :func:`map` method at the moment because
this is the only functionality that emcee needs. That being said,
this pool is fairly general and it could be used for other purposes.
Contributed by `Joe Zuntz <https://github.com/joezuntz>`_.
:param comm: (optional)
The ``mpi4py`` communicator.
:param debug: (optional)
If ``True``, print out a lot of status updates at each step.
:param loadbalance: (optional)
if ``True`` and ntask > Ncpus, tries to loadbalance by sending
out one task to each cpu first and then sending out the rest
as the cpus get done.
"""
def __init__(self, comm=None, debug=False, loadbalance=False):
global MPI
try:
import mpi4py.MPI
MPI = mpi4py.MPI
except ImportError:
#re-raise with a more user-friendly error
raise ImportError("Please install mpi4py")
self.comm = MPI.COMM_WORLD if comm is None else comm
self.rank = self.comm.Get_rank()
self.size = self.comm.Get_size() - 1
self.debug = debug
self.function = _error_function
self.loadbalance = loadbalance
if self.size == 0:
raise ValueError("Tried to create an MPI pool, but there "
"was only one MPI process available. "
"Need at least two.")
def is_master(self):
"""
Is the current process the master?
"""
return self.rank == 0
def wait(self):
"""
If this isn't the master process, wait for instructions.
"""
if self.is_master():
raise RuntimeError("Master node told to await jobs.")
status = MPI.Status()
while True:
# Event loop.
# Sit here and await instructions.
if self.debug:
print("Worker {0} waiting for task.".format(self.rank))
# Blocking receive to wait for instructions.
task = self.comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
if self.debug:
print("Worker {0} got task {1} with tag {2}."
.format(self.rank, task, status.tag))
# Check if message is special sentinel signaling end.
# If so, stop.
if isinstance(task, _close_pool_message):
if self.debug:
print("Worker {0} told to quit.".format(self.rank))
break
# Check if message is special type containing new function
# to be applied
if isinstance(task, _function_wrapper):
self.function = task.function
if self.debug:
print("Worker {0} replaced its task function: {1}."
.format(self.rank, self.function))
continue
# If not a special message, just run the known function on
# the input and return it asynchronously.
result = self.function(task)
if self.debug:
print("Worker {0} sending answer {1} with tag {2}."
.format(self.rank, result, status.tag))
self.comm.isend(result, dest=0, tag=status.tag)
def map(self, function, tasks):
"""
Like the built-in :func:`map` function, apply a function to all
of the values in a list and return the list of results.
:param function:
The function to apply to the list.
:param tasks:
The list of elements.
"""
ntask = len(tasks)
# If not the master just wait for instructions.
if not self.is_master():
self.wait()
return
if function is not self.function:
if self.debug:
print("Master replacing pool function with {0}."
.format(function))
self.function = function
F = _function_wrapper(function)
# Tell all the workers what function to use.
requests = []
for i in range(self.size):
r = self.comm.isend(F, dest=i + 1)
requests.append(r)
# Wait until all of the workers have responded. See:
# https://gist.github.com/4176241
MPI.Request.waitall(requests)
if (not self.loadbalance) or (ntask <= self.size):
# Do not perform load-balancing - the default load-balancing
# scheme emcee uses.
# Send all the tasks off and wait for them to be received.
# Again, see the bug in the above gist.
requests = []
for i, task in enumerate(tasks):
worker = i % self.size + 1
if self.debug:
print("Sent task {0} to worker {1} with tag {2}."
.format(task, worker, i))
r = self.comm.isend(task, dest=worker, tag=i)
requests.append(r)
MPI.Request.waitall(requests)
# Now wait for the answers.
results = []
for i in range(ntask):
worker = i % self.size + 1
if self.debug:
print("Master waiting for worker {0} with tag {1}"
.format(worker, i))
result = self.comm.recv(source=worker, tag=i)
results.append(result)
return results
else:
# Perform load-balancing. The order of the results are likely to
# be different from the previous case.
for i, task in enumerate(tasks[0:self.size]):
worker = i+1
if self.debug:
print("Sent task {0} to worker {1} with tag {2}."
.format(task, worker, i))
# Send out the tasks asynchronously.
self.comm.isend(task, dest=worker, tag=i)
ntasks_dispatched = self.size
results = [None]*ntask
for itask in range(ntask):
status = MPI.Status()
# Receive input from workers.
result = self.comm.recv(source=MPI.ANY_SOURCE,
tag=MPI.ANY_TAG, status=status)
worker = status.source
i = status.tag
results[i] = result
if self.debug:
print("Master received from worker {0} with tag {1}"
.format(worker, i))
# Now send the next task to this idle worker (if there are any
# left).
if ntasks_dispatched < ntask:
task = tasks[ntasks_dispatched]
i = ntasks_dispatched
if self.debug:
print("Sent task {0} to worker {1} with tag {2}."
.format(task, worker, i))
# Send out the tasks asynchronously.
self.comm.isend(task, dest=worker, tag=i)
ntasks_dispatched += 1
return results
def bcast(self, *args, **kwargs):
"""
Equivalent to mpi4py :func:`bcast` collective operation.
"""
return self.comm.bcast(*args, **kwargs)
def close(self):
"""
Just send a message off to all the pool members which contains
the special :class:`_close_pool_message` sentinel.
"""
if self.is_master():
for i in range(self.size):
self.comm.isend(_close_pool_message(), dest=i + 1)
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
|
nye17REPO_NAMEjavelinPATH_START.@javelin_extracted@javelin-master@javelin@emcee_internal@mpi_pool.py@.PATH_END.py
|
{
"filename": "_logsumexp.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/scipy/py2/scipy/special/_logsumexp.py",
"type": "Python"
}
|
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib._util import _asarray_validated
__all__ = ["logsumexp", "softmax"]
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
"""Compute the log of the sum of exponentials of input elements.
Parameters
----------
a : array_like
Input array.
axis : None or int or tuple of ints, optional
Axis or axes over which the sum is taken. By default `axis` is None,
and all elements are summed.
.. versionadded:: 0.11.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the
result as dimensions with size one. With this option, the result
will broadcast correctly against the original array.
.. versionadded:: 0.15.0
b : array-like, optional
Scaling factor for exp(`a`) must be of the same shape as `a` or
broadcastable to `a`. These values may be negative in order to
implement subtraction.
.. versionadded:: 0.12.0
return_sign : bool, optional
If this is set to True, the result will be a pair containing sign
information; if False, results that are negative will be returned
as NaN. Default is False (no sign information).
.. versionadded:: 0.16.0
Returns
-------
res : ndarray
The result, ``np.log(np.sum(np.exp(a)))`` calculated in a numerically
more stable way. If `b` is given then ``np.log(np.sum(b*np.exp(a)))``
is returned.
sgn : ndarray
If return_sign is True, this will be an array of floating-point
numbers matching res and +1, 0, or -1 depending on the sign
of the result. If False, only one result is returned.
See Also
--------
numpy.logaddexp, numpy.logaddexp2
Notes
-----
Numpy has a logaddexp function which is very similar to `logsumexp`, but
only handles two arguments. `logaddexp.reduce` is similar to this
function, but may be less stable.
Examples
--------
>>> from scipy.special import logsumexp
>>> a = np.arange(10)
>>> np.log(np.sum(np.exp(a)))
9.4586297444267107
>>> logsumexp(a)
9.4586297444267107
With weights
>>> a = np.arange(10)
>>> b = np.arange(10, 0, -1)
>>> logsumexp(a, b=b)
9.9170178533034665
>>> np.log(np.sum(b*np.exp(a)))
9.9170178533034647
Returning a sign flag
>>> logsumexp([1,2],b=[1,-1],return_sign=True)
(1.5413248546129181, -1.0)
Notice that `logsumexp` does not directly support masked arrays. To use it
on a masked array, convert the mask into zero weights:
>>> a = np.ma.array([np.log(2), 2, np.log(3)],
... mask=[False, True, False])
>>> b = (~a.mask).astype(int)
>>> logsumexp(a.data, b=b), np.log(5)
1.6094379124341005, 1.6094379124341005
"""
a = _asarray_validated(a, check_finite=False)
if b is not None:
a, b = np.broadcast_arrays(a, b)
if np.any(b == 0):
a = a + 0. # promote to at least float
a[b == 0] = -np.inf
a_max = np.amax(a, axis=axis, keepdims=True)
if a_max.ndim > 0:
a_max[~np.isfinite(a_max)] = 0
elif not np.isfinite(a_max):
a_max = 0
if b is not None:
b = np.asarray(b)
tmp = b * np.exp(a - a_max)
else:
tmp = np.exp(a - a_max)
# suppress warnings about log of zero
with np.errstate(divide='ignore'):
s = np.sum(tmp, axis=axis, keepdims=keepdims)
if return_sign:
sgn = np.sign(s)
s *= sgn # /= makes more sense but we need zero -> zero
out = np.log(s)
if not keepdims:
a_max = np.squeeze(a_max, axis=axis)
out += a_max
if return_sign:
return out, sgn
else:
return out
def softmax(x, axis=None):
r"""
Softmax function
The softmax function transforms each element of a collection by
computing the exponential of each element divided by the sum of the
exponentials of all the elements. That is, if `x` is a one-dimensional
numpy array::
softmax(x) = np.exp(x)/sum(np.exp(x))
Parameters
----------
x : array_like
Input array.
axis : int or tuple of ints, optional
Axis to compute values along. Default is None and softmax will be
computed over the entire array `x`.
Returns
-------
s : ndarray
An array the same shape as `x`. The result will sum to 1 along the
specified axis.
Notes
-----
The formula for the softmax function :math:`\sigma(x)` for a vector
:math:`x = \{x_0, x_1, ..., x_{n-1}\}` is
.. math:: \sigma(x)_j = \frac{e^{x_j}}{\sum_k e^{x_k}}
The `softmax` function is the gradient of `logsumexp`.
.. versionadded:: 1.2.0
Examples
--------
>>> from scipy.special import softmax
>>> np.set_printoptions(precision=5)
>>> x = np.array([[1, 0.5, 0.2, 3],
... [1, -1, 7, 3],
... [2, 12, 13, 3]])
...
Compute the softmax transformation over the entire array.
>>> m = softmax(x)
>>> m
array([[ 4.48309e-06, 2.71913e-06, 2.01438e-06, 3.31258e-05],
[ 4.48309e-06, 6.06720e-07, 1.80861e-03, 3.31258e-05],
[ 1.21863e-05, 2.68421e-01, 7.29644e-01, 3.31258e-05]])
>>> m.sum()
1.0000000000000002
Compute the softmax transformation along the first axis (i.e. the columns).
>>> m = softmax(x, axis=0)
>>> m
array([[ 2.11942e-01, 1.01300e-05, 2.75394e-06, 3.33333e-01],
[ 2.11942e-01, 2.26030e-06, 2.47262e-03, 3.33333e-01],
[ 5.76117e-01, 9.99988e-01, 9.97525e-01, 3.33333e-01]])
>>> m.sum(axis=0)
array([ 1., 1., 1., 1.])
Compute the softmax transformation along the second axis (i.e. the rows).
>>> m = softmax(x, axis=1)
>>> m
array([[ 1.05877e-01, 6.42177e-02, 4.75736e-02, 7.82332e-01],
[ 2.42746e-03, 3.28521e-04, 9.79307e-01, 1.79366e-02],
[ 1.22094e-05, 2.68929e-01, 7.31025e-01, 3.31885e-05]])
>>> m.sum(axis=1)
array([ 1., 1., 1.])
"""
# compute in log space for numerical stability
return np.exp(x - logsumexp(x, axis=axis, keepdims=True))
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@scipy@py2@scipy@special@_logsumexp.py@.PATH_END.py
|
{
"filename": "triples_integrate_tides.py",
"repo_name": "djmunoz/kozaipy",
"repo_path": "kozaipy_extracted/kozaipy-master/kozaipy/triples_integrate_tides.py",
"type": "Python"
}
|
import numpy as np
import scipy.integrate as integ
import kozaipy.triples as triples
#import bsint
def threebody_ode_vf_tides(t,y,\
m0,m1,m2,
R0, R1,
rg_0, rg_1,
k2_0, k2_1,
tv0, tv1,
octupole,
extra_forces_conservative,
extra_forces_dissipative,
solve_for_spin_vector):
# time-dependent variables
#####################################################################
#time-dependent variables ###########################################
# get the active variables
jj = 0
# for the inner binary
if (triples.triple_data['inner_orbit']):
einx = y[jj+0]
einy = y[jj+1]
einz = y[jj+2]
hinx = y[jj+3]
hiny = y[jj+4]
hinz = y[jj+5]
jj+=6
# for the outer orbit
if (triples.triple_data['outer_orbit']):
eoutx = y[jj+0]
eouty = y[jj+1]
eoutz = y[jj+2]
houtx = y[jj+3]
houty = y[jj+4]
houtz = y[jj+5]
jj+=6
# for the spin (specific) angular momenta
if (triples.triple_data['spin0']):
if not (triples.triple_data['spinorbit_align0']):
Omega0x = y[jj+0]
Omega0y = y[jj+1]
Omega0z = y[jj+2]
jj+=3
else:
Omega0 = y[jj+0]
jj+=1
if (triples.triple_data['spin1']):
if not (triples.triple_data['spinorbit_align1']):
Omega1x = y[jj+0]
Omega1y = y[jj+1]
Omega1z = y[jj+2]
jj+=3
else:
Omega1 = y[jj+0]
jj+=1
####################################################
# Some quantities defined for convenience
mu = m0 * m1 / (m0 + m1)
ein_squared = einx**2 + einy**2 + einz**2
ein = np.sqrt(ein_squared)
one_minus_einsq = 1 - ein_squared
one_minus_einsq_sqrt = np.sqrt(one_minus_einsq)
one_minus_einsq_squared = one_minus_einsq * one_minus_einsq
one_minus_einsq_fifth = one_minus_einsq_squared * one_minus_einsq_squared * one_minus_einsq
hin = np.sqrt(hinx**2 + hiny**2 + hinz**2)
eout_squared = eoutx**2 + eouty**2 + eoutz**2
hout = np.sqrt(houtx**2 + houty**2 + houtz**2)
eout = np.sqrt(eout_squared)
one_minus_eoutsq = 1 - eout_squared
one_minus_eoutsq_sqrt = np.sqrt(one_minus_eoutsq)
one_minus_eoutsq_squared = one_minus_eoutsq * one_minus_eoutsq
Gm_in = triples.constants.G * (m0 + m1)
Gm_out = triples.constants.G * (m0 + m1 + m2)
ain = hin * hin / (1 - ein * ein) / Gm_in
aout = hout * hout / (1 - eout * eout) / Gm_out
norbit_in = np.sqrt(Gm_in/ain/ain/ain)
norbit_out = np.sqrt(Gm_out/aout/aout/aout)
L_in = np.sqrt(Gm_in * ain)
L_out = np.sqrt(Gm_out * aout)
# unit vectors
uinx, uiny, uinz = einx/ein, einy/ein, einz/ein
ninx, niny, ninz = hinx/hin, hiny/hin, hinz/hin
vinx = (niny * uinz - ninz * uiny)
viny = (ninz * uinx - ninx * uinz)
vinz = (ninx * uiny - niny * uinx)
noutx, nouty, noutz = houtx/hout, houty/hout, houtz/hout
uoutx, uouty, uoutz = eoutx/eout, eouty/eout, eoutz/eout
nindotnout = noutx * ninx + nouty * niny + noutz * ninz
uindotnout = noutx * uinx + nouty * uiny + noutz * uinz
uindotuout = uoutx * uinx + uouty * uiny + uoutz * uinz
nindotuout = uoutx * ninx + uouty * niny + uoutz * ninz
nincrossuin_x = niny * uinz - ninz * uiny
nincrossuin_y = ninz * uinx - ninx * uinz
nincrossuin_z = ninx * uiny - niny * uinx
nincrossuout_x = niny * uoutz - ninz * uouty
nincrossuout_y = ninz * uoutx - ninx * uoutz
nincrossuout_z = ninx * uouty - niny * uoutx
uincrossuout_x = uiny * uoutz - uinz * uouty
uincrossuout_y = uinz * uoutx - uinx * uoutz
uincrossuout_z = uinx * uouty - uiny * uoutx
uoutcrossnout_x = uouty * noutz - uoutz * nouty
uoutcrossnout_y = uoutz * noutx - uoutx * noutz
uoutcrossnout_z = uoutx * nouty - uouty * noutx
nincrossnout_x = niny * noutz - ninz * nouty
nincrossnout_y = ninz * noutx - ninx * noutz
nincrossnout_z = ninx * nouty - niny * noutx
uincrossnout_x = uiny * noutz - uinz * nouty
uincrossnout_y = uinz * noutx - uinx * noutz
uincrossnout_z = uinx * nouty - uiny * noutx
ein_fourth = ein_squared * ein_squared
ein_sixth = ein_fourth * ein_squared
f2 = 1 + 7.5 * ein_squared + 5.625 * ein_fourth + 0.3125 * ein_sixth
f3 = 1 + 3.75 * ein_squared + 1.875 * ein_fourth + 0.078125 * ein_sixth
f4 = 1 + 1.5 * ein_squared + 0.125 * ein_fourth
f5 = 1 + 3.0 * ein_squared + 0.375 * ein_fourth
if (ein < 1e-10):
ein = 0
ein_squared = 0
one_minus_einsq = 1
one_minus_einsq_sqrt = 1
one_minus_einsq_squared = 1
one_minus_einsq_fifth = 1
ein_fourth = 0
ein_sixth = 0
f2, f3, f4, f5 = 1, 1, 1, 1
if (triples.triple_data['spin0']):
I0 = rg_0 * m0 * R0 * R0
if not (triples.triple_data['spinorbit_align0']):
Omega0_u = (Omega0x * uinx + Omega0y * uiny + Omega0z * uinz)
Omega0_n = (Omega0x * ninx + Omega0y * niny + Omega0z * ninz)
Omega0_v = (Omega0x * vinx + Omega0y * viny + Omega0z * vinz)
Omega0 = np.sqrt(Omega0_u**2 + Omega0_v**2 + Omega0_n**2)
else:
Omega0_u = 0
Omega0_v = 0
Omega0_n = Omega0
elif (triples.triple_data['pseudosynch0']):
Omega0_u = 0
Omega0_n = f2/f5/one_minus_einsq/one_minus_einsq_sqrt * norbit_in
Omega0_v = 0
Omega0 = Omega0_n
else:
Omega0_u, Omega0_v, Omega0_n, Omega0 = 0, 0, 0, 0
if (triples.triple_data['spin1']):
I1 = rg_1 * m1 * R1 * R1
if not (triples.triple_data['spinorbit_align1']):
Omega1_u = (Omega1x * uinx + Omega1y * uiny + Omega1z * uinz)
Omega1_n = (Omega1x * ninx + Omega1y * niny + Omega1z * ninz)
Omega1_v = (Omega1x * vinx + Omega1y * viny + Omega1z * vinz)
Omega1 = np.sqrt(Omega1_u**2 + Omega1_v**2 + Omega1_n**2)
else:
Omega1_u = 0
Omega1_v = 0
Omega1_n = Omega1
elif (triples.triple_data['pseudosynch1']):
Omega1_u = 0
Omega1_n = f2/f5/one_minus_einsq/one_minus_einsq_sqrt * norbit_in
Omega1_v = 0
Omega1 = Omega1_n
else:
Omega1_u, Omega1_v, Omega1_n, Omega1 = 0, 0, 0, 0
if (extra_forces_conservative):
size_ratio0 = R0/ain
size_ratio0_fifth = size_ratio0 * size_ratio0 * size_ratio0 * size_ratio0 * size_ratio0
size_ratio0_eighth = size_ratio0 * size_ratio0 * size_ratio0 * size_ratio0_fifth
size_ratio1 = R1/ain
size_ratio1_fifth = size_ratio1 * size_ratio1 * size_ratio1 * size_ratio1 * size_ratio1
size_ratio1_eighth = size_ratio1 * size_ratio1 * size_ratio1 * size_ratio1_fifth
V0 = 0
W0 = 0
X0 = -1.0/norbit_in * m1 * k2_0 * size_ratio0_fifth / mu * Omega0_n * Omega0_u / one_minus_einsq_squared
Y0 = -1.0/norbit_in * m1 * k2_0 * size_ratio0_fifth / mu * Omega0_n * Omega0_v / one_minus_einsq_squared
Z0 = 1.0/norbit_in * m1 * k2_0 * size_ratio0_fifth /mu * (0.5 * (2 * Omega0_n**2 - Omega0_u**2 - Omega0_v**2) / one_minus_einsq_squared \
+ 15 * triples.constants.G * m1 / ain**3 * f4 / one_minus_einsq_fifth)
V1 = 0
W1 = 0
X1 = -1.0/norbit_in * m0 * k2_1 * size_ratio1_fifth / mu * Omega1_n * Omega1_u / one_minus_einsq_squared
Y1 = -1.0/norbit_in * m0 * k2_1 * size_ratio1_fifth / mu * Omega1_n * Omega1_v / one_minus_einsq_squared
Z1 = 1.0/norbit_in * m0 * k2_1 * size_ratio1_fifth /mu * (0.5*(2 * Omega1_n**2 - Omega1_u**2 - Omega1_v**2) / one_minus_einsq_squared \
+ 15 * triples.constants.G * m0 / ain**3 * f4 / one_minus_einsq_fifth)
ZGR = 3 * triples.constants.G * (m0 + m1) * norbit_in / ain / triples.constants.CLIGHT / triples.constants.CLIGHT / one_minus_einsq
if (extra_forces_dissipative):
if (tv0 is not None):
timelag0 = 1.5 / tv0 * R0 * R0 * R0 / triples.constants.G / m0 * (1 + 2 * k2_0)**2/ k2_0
if (tv1 is not None):
timelag1 = 1.5 / tv1 * R1 * R1 * R1 / triples.constants.G / m1 * (1 + 2 * k2_1)**2/ k2_1
#tf0 = tv0/9 / size_ratio0_eighth * m0**2 / ((m0 + m1)*m1) / (1 + 2 * k2_0)**2
#tf1 = tv1/9 / size_ratio1_eighth * m1**2 / ((m0 + m1)*m0) / (1 + 2 * k2_1)**2
#timelag0 = 1.5 / tv0 * R0 * R0 * R0 / triples.constants.G / m0 * (1 + 2 * k2_0)**2/ k2_0
#timelag1 = 1.5 / tv1 * R1 * R1 * R1 / triples.constants.G / m1 * (1 + 2 * k2_1)**2/ k2_1
#print(timelag0,tv0,timelag1,tv1)
tf0 = m0 /m1 / size_ratio0_fifth / norbit_in /norbit_in / timelag0 /6 / k2_0
tf1 = m1 /m0 / size_ratio1_fifth / norbit_in /norbit_in / timelag1 /6 / k2_1
#if (t > 3.01e9 *365.25): print t,tf0,tf1,size_ratio0,size_ratio1
Q0 = 4.0/3 * k2_0 / (1 + 2 * k2_0)**2 * triples.constants.G * m0/ R0**3 * tv0 / norbit_in
Q1 = 4.0/3 * k2_1 / (1 + 2 * k2_1)**2 * triples.constants.G * m1/ R1**3 * tv1 / norbit_in
V0 += 9.0 / tf0 * (f3 / one_minus_einsq_fifth/one_minus_einsq/one_minus_einsq_sqrt - \
11.0/18 * Omega0_n / norbit_in * f4 / one_minus_einsq_fifth)
W0 += 1.0 / tf0 * (f2 / one_minus_einsq_fifth/one_minus_einsq/one_minus_einsq_sqrt - \
Omega0_n / norbit_in * f5 / one_minus_einsq_fifth)
X0 += -1.0/norbit_in * Omega0_v /2/tf0 * (1 + 4.5 * ein_squared + 0.625 * ein_fourth) / one_minus_einsq_fifth
Y0 += 1.0/norbit_in * Omega0_u /2/tf0 * (1 + 1.5 * ein_squared + 0.125 * ein_fourth) / one_minus_einsq_fifth
V1 += 9.0 / tf1 * (f3 / one_minus_einsq_fifth/one_minus_einsq/one_minus_einsq_sqrt - \
11.0/18 * Omega1_n /norbit_in * f4 / one_minus_einsq_fifth)
W1 += 1.0 / tf1 * (f2 / one_minus_einsq_fifth/one_minus_einsq/one_minus_einsq_sqrt - \
Omega1_n / norbit_in * f5 /one_minus_einsq_fifth)
X1 += -1.0/norbit_in * Omega1_v /2/tf1 * (1 + 4.5 * ein_squared + 0.625 * ein_fourth) / one_minus_einsq_fifth
Y1 += 1.0/norbit_in * Omega1_u /2/tf1 * (1 + 1.5 * ein_squared + 0.125 * ein_fourth) / one_minus_einsq_fifth
else:
V0, W0, X0, Y0, Z0 = 0, 0, 0, 0, 0
V1, W1, X1, Y1, Z1 = 0, 0, 0, 0, 0
ZGR = 0
##########################################################################
# System of differential equations
# Equations of motion at the quadrupole level:
epsilon_in = 0.5 * (m0 * m1/(m0 + m1)/(m0 + m1)) * ain * ain / aout / aout / one_minus_eoutsq_sqrt / one_minus_eoutsq
epsilon_out = m2 / (m0 + m1) * ain * ain * ain / aout / aout / aout / one_minus_eoutsq / one_minus_eoutsq_sqrt
# For the inner orbit
coeff_ein = 0.75 * epsilon_out * ein * one_minus_einsq_sqrt
deinx_dt = norbit_in * coeff_ein * (nindotnout * uincrossnout_x + 2 * nincrossuin_x - 5 * uindotnout * nincrossnout_x)
deiny_dt = norbit_in * coeff_ein * (nindotnout * uincrossnout_y + 2 * nincrossuin_y - 5 * uindotnout * nincrossnout_y)
deinz_dt = norbit_in * coeff_ein * (nindotnout * uincrossnout_z + 2 * nincrossuin_z - 5 * uindotnout * nincrossnout_z)
coeff_hin = L_in * 0.75 * epsilon_out
dhinx_dt = norbit_in * coeff_hin * (one_minus_einsq * nindotnout * nincrossnout_x - 5 * ein_squared * uindotnout * uincrossnout_x)
dhiny_dt = norbit_in * coeff_hin * (one_minus_einsq * nindotnout * nincrossnout_y - 5 * ein_squared * uindotnout * uincrossnout_y)
dhinz_dt = norbit_in * coeff_hin * (one_minus_einsq * nindotnout * nincrossnout_z - 5 * ein_squared * uindotnout * uincrossnout_z)
# For the outer orbit
coeff_eout = 1.5 * epsilon_in * eout / one_minus_eoutsq_sqrt
deoutx_dt = norbit_out * coeff_eout * (-one_minus_einsq * nindotnout * nincrossuout_x\
+ 5 * ein_squared * uindotnout * uincrossuout_x \
+ 0.5 * ((1 - 6 * ein_squared) + \
25 * ein_squared * uindotnout * uindotnout \
- 5 * one_minus_einsq * nindotnout * nindotnout) * uoutcrossnout_x)
deouty_dt = norbit_out * coeff_eout * (-one_minus_einsq * nindotnout * nincrossuout_y\
+ 5 * ein_squared * uindotnout * uincrossuout_y \
+ 0.5 * ((1 - 6 * ein_squared) + \
25 * ein_squared * uindotnout * uindotnout \
- 5 * one_minus_einsq * nindotnout * nindotnout) * uoutcrossnout_y)
deoutz_dt = norbit_out * coeff_eout * (-one_minus_einsq * nindotnout * nincrossuout_z\
+ 5 * ein_squared * uindotnout * uincrossuout_z \
+ 0.5 * ((1 - 6 * ein_squared) + \
25 * ein_squared * uindotnout * uindotnout \
- 5 * one_minus_einsq * nindotnout * nindotnout) * uoutcrossnout_z)
coeff_hout = L_out * 1.5 * epsilon_in
dhoutx_dt = norbit_out * coeff_hout * (-one_minus_einsq * nindotnout * nincrossnout_x + 5 * ein_squared * uindotnout * uincrossnout_x)
dhouty_dt = norbit_out * coeff_hout * (-one_minus_einsq * nindotnout * nincrossnout_y + 5 * ein_squared * uindotnout * uincrossnout_y)
dhoutz_dt = norbit_out * coeff_hout * (-one_minus_einsq * nindotnout * nincrossnout_z + 5 * ein_squared * uindotnout * uincrossnout_z)
if (solve_for_spin_vector):
if (triples.triple_data['spin0']):
dSpin0x_dt = 0
dSpin0y_dt = 0
dSpin0z_dt = 0
if (triples.triple_data['spin1']):
dSpin1x_dt = 0
dSpin1y_dt = 0
dSpin1z_dt = 0
else:
if (triples.triple_data['spin0']):
dOmega0x_dt = 0
dOmega0y_dt = 0
dOmega0z_dt = 0
if (triples.triple_data['spin1']):
dOmega1x_dt = 0
dOmega1y_dt = 0
dOmega1z_dt = 0
if (octupole):
epsilon_oct = (m0 - m1)/(m0 + m1) * (ain/aout)
# For the inner orbit
coeff_ein_oct = -1.171875 * epsilon_out * epsilon_oct * eout / one_minus_eoutsq * one_minus_einsq_sqrt
deinx_dt += norbit_in * coeff_ein_oct * ((2 * ein_squared * uindotnout * nindotnout) * uincrossuout_x +\
0.2 * (8 * ein_squared - 1 - \
35 * ein_squared * uindotnout * uindotnout + \
5 * one_minus_einsq * nindotnout * nindotnout) * nincrossuout_x + \
2 * ein_squared * (uindotuout * nindotnout + \
uindotnout * nindotuout) * uincrossnout_x + \
2 * (one_minus_einsq * nindotnout * nindotuout - \
7 * ein_squared * uindotnout * uindotuout) * nincrossnout_x +\
3.2 * ein_squared * uindotuout * nincrossuin_x)
deiny_dt += norbit_in * coeff_ein_oct * ((2 * ein_squared * uindotnout * nindotnout) * uincrossuout_y +\
0.2 * (8 * ein_squared - 1 - \
35 * ein_squared * uindotnout * uindotnout + \
5 * one_minus_einsq * nindotnout * nindotnout) * nincrossuout_y + \
2 * ein_squared * (uindotuout * nindotnout + \
uindotnout * nindotuout) * uincrossnout_y + \
2 * (one_minus_einsq * nindotnout * nindotuout - \
7 * ein_squared * uindotnout * uindotuout) * nincrossnout_y +\
3.2 * ein_squared * uindotuout * nincrossuin_y)
deinz_dt += norbit_in * coeff_ein_oct * ((2 * ein_squared * uindotnout * nindotnout) * uincrossuout_z +\
0.2 * (8 * ein_squared - 1 - \
35 * ein_squared * uindotnout * uindotnout + \
5 * one_minus_einsq * nindotnout * nindotnout) * nincrossuout_z + \
2 * ein_squared * (uindotuout * nindotnout + \
uindotnout * nindotuout) * uincrossnout_z + \
2 * (one_minus_einsq * nindotnout * nindotuout - \
7 * ein_squared * uindotnout * uindotuout) * nincrossnout_z +\
3.2 * ein_squared * uindotuout * nincrossuin_z)
coeff_hin_oct = -L_in * 1.171875 * epsilon_out * epsilon_oct * eout / one_minus_eoutsq * ein
dhinx_dt += norbit_in * coeff_hin_oct * (2 * one_minus_einsq * (uindotuout * nindotnout + \
uindotnout * nindotuout) * nincrossnout_x +\
2 * (one_minus_einsq * nindotuout * nindotnout - \
7 * ein_squared * uindotuout * uindotnout) * uincrossnout_x +\
2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_x +\
0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout +\
5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_x)
dhiny_dt += norbit_in * coeff_hin_oct * (2 * one_minus_einsq * (uindotuout * nindotnout + \
uindotnout * nindotuout) * nincrossnout_y +\
2 * (one_minus_einsq * nindotuout * nindotnout - \
7 * ein_squared * uindotuout * uindotnout) * uincrossnout_y +\
2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_y +\
0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout +\
5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_y)
dhinz_dt += norbit_in * coeff_hin_oct * (2 * one_minus_einsq * (uindotuout * nindotnout + \
uindotnout * nindotuout) * nincrossnout_z +\
2 * (one_minus_einsq * nindotuout * nindotnout - \
7 * ein_squared * uindotuout * uindotnout) * uincrossnout_z +\
2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_z +\
0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout +\
5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_z)
# For the outer orbit
coeff_eout_oct = -2.34375 * epsilon_in * epsilon_oct * eout / one_minus_eoutsq / one_minus_eoutsq_sqrt * ein
deoutx_dt += norbit_out * coeff_eout_oct * (-2 * eout * one_minus_einsq * (uindotnout * nindotuout +\
nindotnout * uindotuout) * nincrossuout_x\
-2 * one_minus_eoutsq/eout * one_minus_einsq * uindotnout * nindotnout * nincrossnout_x\
-2 * eout * (one_minus_einsq * nindotuout * nindotnout -\
7 * ein_squared * uindotuout * uindotnout) * uincrossuout_x \
-one_minus_eoutsq/eout * 0.2 *(8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout\
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossnout_x\
- eout * (0.4 *(1 - 8 * ein_squared) * uindotuout +\
14 * one_minus_einsq * uindotnout * nindotuout * nindotnout +\
1.4 * uindotuout * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout+\
5 * one_minus_einsq * nindotnout * nindotnout)) * uoutcrossnout_x)
deouty_dt += norbit_out * coeff_eout_oct * (-2 * eout * one_minus_einsq * (uindotnout * nindotuout +\
nindotnout * uindotuout) * nincrossuout_y\
-2 * one_minus_eoutsq/eout * one_minus_einsq * uindotnout * nindotnout * nincrossnout_y\
-2 * eout * (one_minus_einsq * nindotuout * nindotnout -\
7 * ein_squared * uindotuout * uindotnout) * uincrossuout_y \
-one_minus_eoutsq/eout * 0.2 *(8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout\
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossnout_y\
- eout * (0.4 *(1 - 8 * ein_squared) * uindotuout +\
14 * one_minus_einsq * uindotnout * nindotuout * nindotnout +\
1.4 * uindotuout * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout+\
5 * one_minus_einsq * nindotnout * nindotnout)) * uoutcrossnout_y)
deoutz_dt += norbit_out * coeff_eout_oct * (-2 * eout * one_minus_einsq * (uindotnout * nindotuout +\
nindotnout * uindotuout) * nincrossuout_z\
-2 * one_minus_eoutsq/eout * one_minus_einsq * uindotnout * nindotnout * nincrossnout_z\
-2 * eout * (one_minus_einsq * nindotuout * nindotnout -\
7 * ein_squared * uindotuout * uindotnout) * uincrossuout_z \
-one_minus_eoutsq/eout * 0.2 *(8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout\
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossnout_z\
- eout * (0.4 *(1 - 8 * ein_squared) * uindotuout +\
14 * one_minus_einsq * uindotnout * nindotuout * nindotnout +\
1.4 * uindotuout * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout+\
5 * one_minus_einsq * nindotnout * nindotnout)) * uoutcrossnout_z)
coeff_hout_oct = -L_out * 2.34375 * epsilon_in * epsilon_oct * eout / one_minus_eoutsq * ein
dhoutx_dt += norbit_out * coeff_hout_oct * (-2 * one_minus_einsq * (uindotnout * nindotuout + uindotuout * nindotnout) * nincrossnout_x\
-2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_x\
-2 * (one_minus_einsq * nindotuout * nindotnout \
- 7 * ein_squared * uindotuout * uindotnout) * uincrossnout_x\
-0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout \
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_x)
dhouty_dt += norbit_out * coeff_hout_oct * (-2 * one_minus_einsq * (uindotnout * nindotuout + uindotuout * nindotnout) * nincrossnout_y\
-2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_y\
-2 * (one_minus_einsq * nindotuout * nindotnout \
- 7 * ein_squared * uindotuout * uindotnout) * uincrossnout_y\
-0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout \
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_y)
dhoutz_dt += norbit_out * coeff_hout_oct * (-2 * one_minus_einsq * (uindotnout * nindotuout + uindotuout * nindotnout) * nincrossnout_z\
-2 * one_minus_einsq * uindotnout * nindotnout * nincrossuout_z\
-2 * (one_minus_einsq * nindotuout * nindotnout \
- 7 * ein_squared * uindotuout * uindotnout) * uincrossnout_z\
-0.2 * (8 * ein_squared - 1 - 35 * ein_squared * uindotnout * uindotnout \
+ 5 * one_minus_einsq * nindotnout * nindotnout) * uincrossuout_z)
if (extra_forces_conservative):
deinx_dt += ein * ((Z0 + Z1 + ZGR) * vinx - (Y0 + Y1) * ninx - (V0 + V1) * uinx)
deiny_dt += ein * ((Z0 + Z1 + ZGR) * viny - (Y0 + Y1) * niny - (V0 + V1) * uiny)
deinz_dt += ein * ((Z0 + Z1 + ZGR) * vinz - (Y0 + Y1) * ninz - (V0 + V1) * uinz)
dhinx_dt += hin * ((Y0 + Y1) * uinx - (X0 + X1) * vinx - (W0 + W1) * ninx)
dhiny_dt += hin * ((Y0 + Y1) * uiny - (X0 + X1) * viny - (W0 + W1) * niny)
dhinz_dt += hin * ((Y0 + Y1) * uinz - (X0 + X1) * vinz - (W0 + W1) * ninz)
if (triples.triple_data['spin0']):
if not (triples.triple_data['spinorbit_align0']):
if (solve_for_spin_vector):
dSpin0x_dt += mu * hin * (-Y0 * uinx + X0 * vinx + W0 * ninx)
dSpin0y_dt += mu * hin * (-Y0 * uiny + X0 * viny + W0 * niny)
dSpin0z_dt += mu * hin * (-Y0 * uinz + X0 * vinz + W0 * ninz)
else:
dOmega0x_dt += mu * hin / I0 * (-Y0 * uinx + X0 * vinx + W0 * ninx)
dOmega0y_dt += mu * hin / I0 * (-Y0 * uiny + X0 * viny + W0 * niny)
dOmega0z_dt += mu * hin / I0 * (-Y0 * uinz + X0 * vinz + W0 * ninz)
else:
dOmega0_dt = mu * hin / I0 * W0
if (triples.triple_data['spin1']):
if not (triples.triple_data['spinorbit_align1']):
if (solve_for_spin_vector):
dSpin1x_dt += mu * hin * (-Y1 * uinx + X1 * vinx + W1 * ninx)
dSpin1y_dt += mu * hin * (-Y1 * uiny + X1 * viny + W1 * niny)
dSpin1z_dt += mu * hin * (-Y1 * uinz + X1 * vinz + W1 * ninz)
else:
dOmega1x_dt += mu * hin / I1 * (-Y1 * uinx + X1 * vinx + W1 * ninx)
dOmega1y_dt += mu * hin / I1 * (-Y1 * uiny + X1 * viny + W1 * niny)
dOmega1z_dt += mu * hin / I1 * (-Y1 * uinz + X1 * vinz + W1 * ninz)
else:
dOmega1_dt = mu * hin / I1 * W1
########################################################################
# vector differential equations
diffeq_list = []
# for the inner binary
if (triples.triple_data['inner_orbit']):
diffeq_list += [deinx_dt,
deiny_dt,
deinz_dt,
dhinx_dt,
dhiny_dt,
dhinz_dt]
# for the outer orbit
if (triples.triple_data['outer_orbit']):
diffeq_list += [deoutx_dt,
deouty_dt,
deoutz_dt,
dhoutx_dt,
dhouty_dt,
dhoutz_dt]
# for the spin (specific) angular momenta
if (triples.triple_data['spin0']):
if (not triples.triple_data['pseudosynch0']):
if not (triples.triple_data['spinorbit_align0']):
diffeq_list += [dOmega0x_dt,
dOmega0y_dt,
dOmega0z_dt]
else:
diffeq_list += [dOmega0_dt]
if (triples.triple_data['spin1']):
if not (triples.triple_data['pseudosynch1']):
if not (triples.triple_data['spinorbit_align1']):
diffeq_list += [dOmega1x_dt,
dOmega1y_dt,
dOmega1z_dt]
else:
diffeq_list += [dOmega1_dt]
return diffeq_list
def threebody_ode_vf_tides_modified(y,t,\
m0,m1,m2,
R0, R1,
rg_0, rg_1,
k2_0, k2_1,
tv0, tv1,
octupole,
extra_forces_conservative,
extra_forces_dissipative,
solve_for_spin_vector):
return threebody_ode_vf_tides(t,y,\
m0,m1,m2,
R0, R1,
rg_0, rg_1,
k2_0, k2_1,
tv0, tv1,
octupole,
extra_forces_conservative,
extra_forces_dissipative,
solve_for_spin_vector)
|
djmunozREPO_NAMEkozaipyPATH_START.@kozaipy_extracted@kozaipy-master@kozaipy@triples_integrate_tides.py@.PATH_END.py
|
{
"filename": "_hoverformat.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/polar/radialaxis/_hoverformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class HoverformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="hoverformat", parent_name="layout.polar.radialaxis", **kwargs
):
super(HoverformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@polar@radialaxis@_hoverformat.py@.PATH_END.py
|
{
"filename": "test_derivative_util.py",
"repo_name": "lenstronomy/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/test/test_Util/test_derivative_util.py",
"type": "Python"
}
|
__author__ = "sibirrer"
import lenstronomy.Util.derivative_util as calc_util
import pytest
from lenstronomy.Util import util
from lenstronomy.Util import param_util
import numpy.testing as npt
import numpy as np
class TestCalcUtil(object):
"""Tests the Gaussian methods."""
def setup_method(self):
pass
def test_d_r_dx(self):
x = 1
y = 0
out = calc_util.d_r_dx(x, y)
assert out == 1
x, y = util.make_grid(numPix=10, deltapix=0.1)
dx = 0.000001
out = calc_util.d_r_dx(x, y)
r, phi = param_util.cart2polar(x, y)
r_dx, phi_dx = param_util.cart2polar(x + dx, y)
dr_dx = (r_dx - r) / dx
npt.assert_almost_equal(dr_dx, out, decimal=5)
def test_d_r_dy(self):
x = 1
y = 0
out = calc_util.d_r_dy(x, y)
assert out == 0
x, y = util.make_grid(numPix=10, deltapix=0.1)
dy = 0.000001
out = calc_util.d_r_dy(x, y)
r, phi = param_util.cart2polar(x, y)
r_dy, phi_dy = param_util.cart2polar(x, y + dy)
dr_dy = (r_dy - r) / dy
npt.assert_almost_equal(dr_dy, out, decimal=5)
def test_d_x_diffr_dx(self):
x = 1
y = 0
out = calc_util.d_x_diffr_dx(x, y)
assert out == 0
x = 0
y = 1
out = calc_util.d_x_diffr_dx(x, y)
assert out == 1
def test_d_y_diffr_dx(self):
x = 1
y = 0
out = calc_util.d_y_diffr_dx(x, y)
assert out == 0
x = 0
y = 1
out = calc_util.d_y_diffr_dx(x, y)
assert out == 0
def test_d_y_diffr_dy(self):
x = 1
y = 0
out = calc_util.d_y_diffr_dy(x, y)
assert out == 1
x = 0
y = 1
out = calc_util.d_y_diffr_dy(x, y)
assert out == 0
def test_d_x_diffr_dy(self):
x = 1
y = 0
out = calc_util.d_x_diffr_dy(x, y)
assert out == 0
x = 0
y = 1
out = calc_util.d_x_diffr_dy(x, y)
assert out == 0
def test_d_phi_dx(self):
x, y = np.array([1.0, 0.0, -1.0]), np.array([1.0, 1.0, -1.0])
dx, dy = 0.0001, 0.0001
r, phi = param_util.cart2polar(x, y, center_x=0, center_y=0)
d_phi_dx = calc_util.d_phi_dx(x, y)
d_phi_dy = calc_util.d_phi_dy(x, y)
r_dx, phi_dx = param_util.cart2polar(x + dx, y, center_x=0, center_y=0)
r_dy, phi_dy = param_util.cart2polar(x, y + dy, center_x=0, center_y=0)
d_phi_dx_num = (phi_dx - phi) / dx
d_phi_dy_num = (phi_dy - phi) / dy
npt.assert_almost_equal(d_phi_dx, d_phi_dx_num, decimal=4)
npt.assert_almost_equal(d_phi_dy, d_phi_dy_num, decimal=4)
def test_d_phi_dxx(self):
x, y = util.make_grid(numPix=10, deltapix=0.1)
delta = 0.00001
d_phi_dx = calc_util.d_phi_dx(x, y)
d_phi_dx_delta = calc_util.d_phi_dx(x + delta, y)
d_phi_dy = calc_util.d_phi_dy(x, y)
d_phi_dxx = calc_util.d_phi_dxx(x, y)
d_phi_dxx_num = (d_phi_dx_delta - d_phi_dx) / delta
npt.assert_almost_equal(d_phi_dxx_num, d_phi_dxx, decimal=1)
d_phi_dy_delta = calc_util.d_phi_dy(x, y + delta)
d_phi_dyy = calc_util.d_phi_dyy(x, y)
d_phi_dyy_num = (d_phi_dy_delta - d_phi_dy) / delta
npt.assert_almost_equal(d_phi_dyy_num, d_phi_dyy, decimal=1)
d_phi_dx_delta_y = calc_util.d_phi_dx(x, y + delta)
d_phi_dxy = calc_util.d_phi_dxy(x, y)
d_phi_dxy_num = (d_phi_dx_delta_y - d_phi_dx) / delta
npt.assert_almost_equal(d_phi_dxy_num, d_phi_dxy, decimal=1)
def test_d_r_dxx(self):
x, y = util.make_grid(numPix=10, deltapix=0.1)
delta = 0.00001
d_r_dx = calc_util.d_r_dx(x, y)
d_r_dx_delta = calc_util.d_r_dx(x + delta, y)
d_r_dy = calc_util.d_r_dy(x, y)
d_r_dxx = calc_util.d_r_dxx(x, y)
d_r_dxx_num = (d_r_dx_delta - d_r_dx) / delta
npt.assert_almost_equal(d_r_dxx_num, d_r_dxx, decimal=1)
d_r_dy_delta = calc_util.d_r_dy(x, y + delta)
d_r_dyy = calc_util.d_r_dyy(x, y)
d_r_dyy_num = (d_r_dy_delta - d_r_dy) / delta
npt.assert_almost_equal(d_r_dyy_num, d_r_dyy, decimal=1)
d_r_dx_delta_y = calc_util.d_r_dx(x, y + delta)
d_r_dxy = calc_util.d_r_dxy(x, y)
d_r_dxy_num = (d_r_dx_delta_y - d_r_dx) / delta
npt.assert_almost_equal(d_r_dxy_num, d_r_dxy, decimal=1)
if __name__ == "__main__":
pytest.main()
|
lenstronomyREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@test@test_Util@test_derivative_util.py@.PATH_END.py
|
{
"filename": "generate_PCA_files.py",
"repo_name": "federicomarulli/CosmoBolognaLib",
"repo_path": "CosmoBolognaLib_extracted/CosmoBolognaLib-master/External/CLASS/external/distortions/generate_PCA_files.py",
"type": "Python"
}
|
#!/usr/bin/env python
import numpy as np
import sys
import scipy.interpolate as sciint
from numpy.linalg import norm as vector_norm
from numpy.linalg import eigh as eigen_vals_vecs
import os
import matplotlib.pyplot as plt
# Read inputs
if(len(sys.argv)==14):
sd_detector_name = sys.argv[1]
sd_detector_nu_min = eval(sys.argv[2])
sd_detector_nu_max = eval(sys.argv[3])
sd_detector_nu_delta = eval(sys.argv[4])
sd_detector_bin_number = eval(sys.argv[5])
sd_z_min = eval(sys.argv[6])
sd_z_max = eval(sys.argv[7])
sd_z_size = eval(sys.argv[8])
sd_detector_delta_Ic = eval(sys.argv[9])
sd_PCA_size = eval(sys.argv[10])
z_th = eval(sys.argv[11])
DI_units = eval(sys.argv[12]) # = 2.70062634e-18
x_to_nu = eval(sys.argv[13]) # = 56.7798
has_noisefile = False
elif(len(sys.argv)==11):
sd_detector_name = sys.argv[1]
sd_external_path = sys.argv[2]
sd_noisefile_name = sys.argv[3]
sd_z_min = eval(sys.argv[4])
sd_z_max = eval(sys.argv[5])
sd_z_size = eval(sys.argv[6])
sd_PCA_size = eval(sys.argv[7])
z_th = eval(sys.argv[8])
DI_units = eval(sys.argv[9]) # = 2.70062634e-18
x_to_nu = eval(sys.argv[10]) # = 56.7798
has_noisefile = True
else:
raise Exception("generate_PCA_files.py received invalid input arguments")
def PCA_string_to_array(line,delimiter=" "):
line = line.replace("\n","")
if delimiter is not "\t":
line = line.replace("\t","")
return np.array([float(x) for x in line.split(delimiter) if (x is not "" and x is not " ")])
def read_noisefile(filename):
with open(filename) as det_noise:
header = True
while(header):
line = det_noise.readline()
if(line.startswith("#")):
continue
header=False
Nrows,Ncols = PCA_string_to_array(line)
#Skip first line containing Nx,Ncols
line = det_noise.readline()
cols = []
while(line):
cols.append(PCA_string_to_array(line))
line = det_noise.readline()
cols = np.array(cols).T
assert(int(Ncols)==len(cols))
assert(int(Nrows)==len(cols[0]))
return len(cols[0]),cols[0]/x_to_nu,cols[1]*1e-26
dir_path = os.path.dirname(os.path.realpath(__file__))
# Read external file Greens_data.dat
readfile = "Greens_data.dat"
with open(os.path.join(dir_path,readfile)) as f:
# Read the header first
header = True
while(header):
line = f.readline()
if(line.startswith("#")):
continue
# The first line of the header without the "#" is still part of the header
header=False
# Read the first line specifying z
Greens_z = PCA_string_to_array(f.readline())
Greens_Nz = len(Greens_z)
Greens_lnz = np.log(Greens_z+1.)
# Read T_ini,T_last and rho
Greens_T_ini = PCA_string_to_array(f.readline())
Greens_T_last = PCA_string_to_array(f.readline())
Greens_drho = PCA_string_to_array(f.readline())
# Calculate the difference in Temperature
Greens_dT = (Greens_T_last-Greens_T_ini)/Greens_T_ini
# Read the rest of the file
done = False
Greens_data_full = []
while(not done):
line = f.readline()
if(not line):
done = True
else:
Greens_data_full.append(PCA_string_to_array(line))
Greens_data_full = np.array(Greens_data_full).T
# Seperate the rest of the data into x, Green(z,x) and the blackbody
Greens_x = Greens_data_full[0]
Greens_Nx = len(Greens_x)
Greens_G_th = Greens_data_full[1:Greens_Nz+1]
Greens_blackbody = Greens_data_full[Greens_Nz+1]
# Spline Greens function for interpolation
Greens_G_th_Spline = [None for index_x_old in range(Greens_Nx)]
for index_x_old in range(Greens_Nx):
Greens_G_th_Spline[index_x_old] = sciint.CubicSpline(Greens_lnz,Greens_G_th[:,index_x_old])
# Spline Greens dT for interpolation
Greens_T_ini_Spline = sciint.CubicSpline(Greens_lnz,Greens_T_ini)
Greens_T_last_Spline = sciint.CubicSpline(Greens_lnz,Greens_T_last)
Greens_dT_Spline = sciint.CubicSpline(Greens_lnz,Greens_dT)
Greens_drho_Spline = sciint.CubicSpline(Greens_lnz,Greens_drho)
# Define new z and x arrays
Nz_arr = sd_z_size
z_arr = np.logspace(np.log10(sd_z_min),np.log10(sd_z_max),Nz_arr)
lnz_arr = np.log(z_arr+1.)
if has_noisefile:
Nx_arr,x_arr,deltaIc_arr = read_noisefile(os.path.join(sd_external_path,sd_noisefile_name))
else:
Nx_arr = sd_detector_bin_number+1
x_arr = np.linspace(sd_detector_nu_min/x_to_nu,sd_detector_nu_max/x_to_nu,Nx_arr)
# Define visibility function
#bb_vis = np.exp(-(z_arr/2.021e6)**2.5)
bb_vis = np.exp(-(z_arr/z_th)**2.5)
# The Gth file of Chluba subtracts away some part of the G_T distortion into a shift from T_ini to T_last
# Here we calculate backwards, and obtain the shift of f_g due to the internal dT
df_g = Greens_dT_Spline(lnz_arr)/Greens_drho_Spline(lnz_arr)
# Initialize spectral shapes
G_th = np.zeros((Nx_arr,Nz_arr))
DI_T_shift = np.zeros((Nx_arr,Nz_arr))
Gdist = np.zeros(Nx_arr)
Ydist = np.zeros(Nx_arr)
Mdist = np.zeros(Nx_arr)
# Interpolate Green's function
index_x_old = 0
for index_x_new,x in enumerate(x_arr):
# Define spectral shapes
Gdist[index_x_new] = (x**4*np.exp(x)/(np.exp(x)-1)**2)*DI_units*1.0e18
Ydist[index_x_new] = Gdist[index_x_new]*(x/np.tanh(x/2.)-4.)
Mdist[index_x_new] = Gdist[index_x_new]*(1./2.19229-1./x)
x_s = Greens_T_ini_Spline(lnz_arr)/Greens_T_last_Spline(lnz_arr)*x
x_z = x*lnz_arr/lnz_arr
DI_T_shift[index_x_new,:] = DI_units*1.0e26*x_z**3.*(np.exp(-x_s)/(1.-np.exp(-x_s))-np.exp(-x_z)/(1.-np.exp(-x_z)))/Greens_drho_Spline(lnz_arr)
try:
# Find position in xarray
while(x>Greens_x[index_x_old]):
index_x_old += 1
# Linear interpolation in x
frac = (x-Greens_x[index_x_old])/(Greens_x[index_x_old+1]-Greens_x[index_x_old])
# Cubic interpolation for all values of z
lowx_vals = Greens_G_th_Spline[index_x_old](lnz_arr)
highx_vals = Greens_G_th_Spline[index_x_old+1](lnz_arr)
G_th[index_x_new,:] = (lowx_vals*(1.-frac)+highx_vals*frac)
G_th[index_x_new,:] *= bb_vis*1.e-8
G_th[index_x_new,:] += DI_T_shift[index_x_new,:]*1.e-8
#G_th[index_x_new,:] += Gdist[index_x_new]*df_g
except:
raise ValueError("{} is not in the file range [{},{}] for file '{}'".format(x,Greens_x[0],Greens_x[-1],readfile))
# Begin orthonormlization
# Y distortion
e_Y = Ydist/vector_norm(Ydist)
M_Y = np.dot(e_Y,Mdist)
G_Y = np.dot(e_Y,Gdist)
# Mu distortion
Mperp = Mdist-M_Y*e_Y
e_M = Mperp/vector_norm(Mperp)
G_M = np.dot(e_M,Gdist)
# G distortion
Gperp = Gdist-G_Y*e_Y-G_M*e_M
e_G = Gperp/vector_norm(Gperp)
f_g = np.zeros(Nz_arr)
f_mu = np.zeros(Nz_arr)
f_y = np.zeros(Nz_arr)
# Now, factorize G into orthonormal subspace
for index_z in range(Nz_arr):
# Compute non-normalized components
f_g[index_z] = (np.dot(G_th[:,index_z],e_G))/vector_norm(Gperp)
f_mu[index_z] = (np.dot(G_th[:,index_z],e_M)-G_M*f_g[index_z])/vector_norm(Mperp)
f_y[index_z] = (np.dot(G_th[:,index_z],e_Y)-M_Y*f_mu[index_z]-G_Y*f_g[index_z])/vector_norm(Ydist)
# Now we can re-normalize our functions and add the shift
J_g = 4.*f_g
J_mu = f_mu/1.401
J_y = 4.*f_y
# Calculate non-normalized residual
Residual = np.zeros((Nx_arr,Nz_arr))
for index_x in range(Nx_arr):
for index_z in range(Nz_arr):
Residual[index_x,index_z] = G_th[index_x,index_z]-Gdist[index_x]*f_g[index_z]-Ydist[index_x]*f_y[index_z]-Mdist[index_x]*f_mu[index_z]
# Calculate Fisher matrix
Fisher = np.zeros((Nz_arr,Nz_arr))
delta_ln_z = np.log(z_arr[1])-np.log(z_arr[0])
for index_za in range(Nz_arr):
for index_zb in range(Nz_arr):
if has_noisefile:
Fisher[index_za,index_zb] = np.sum(Residual[:,index_za]*Residual[:,index_zb]*pow(delta_ln_z/deltaIc_arr[:]*1.e8,2.))
else:
Fisher[index_za,index_zb] = np.sum(Residual[:,index_za]*Residual[:,index_zb]*pow(delta_ln_z/sd_detector_delta_Ic*1.e8,2.))
# Solve eigenvalue problem
eigvals,eigvecs = eigen_vals_vecs(Fisher)
eigvals = eigvals[::-1]
eigvecs = eigvecs[:,::-1]
E_vecs = np.real(eigvecs[:,:sd_PCA_size]).T
S_vecs = np.zeros((sd_PCA_size,Nx_arr))
for index_pca in range(sd_PCA_size):
for index_x in range(Nx_arr):
S_vecs[index_pca][index_x] = np.dot(E_vecs[index_pca],Residual[index_x,:]*delta_ln_z)
# Create output files
form = "%.6e" #Output formatting
# Write file for branching ratio (Evec)
with open(os.path.join(dir_path,sd_detector_name+"_branching_ratios.dat"),"w") as brfile:
brfile.write("# In the file there is: z, J_T, J_y, J_mu, E_i (i=1-{})\n".format(sd_PCA_size))
brfile.write("# The first line contains the number of lines and the number of columns.\n".format(sd_PCA_size))
brfile.write("{} {}\n".format(Nz_arr,sd_PCA_size))
for index_z in range(Nz_arr):
brfile.write((form+" ") % z_arr[index_z])
brfile.write((form+" ") % f_g[index_z])
brfile.write((form+" ") % f_y[index_z])
brfile.write((form ) % f_mu[index_z])
for index_pca in range(sd_PCA_size):
brfile.write((" "+form) % E_vecs[index_pca][index_z])
brfile.write("\n")
# Write file for distortion shapes (Svec)
with open(os.path.join(dir_path,sd_detector_name+"_distortions_shapes.dat"),"w") as dsfile:
dsfile.write("# In the file there is: nu, G_T, Y_SZ, M_mu, S_i (i=1-{})\n".format(sd_PCA_size))
dsfile.write("# The first line contains the number of lines and the number of columns.\n".format(sd_PCA_size))
dsfile.write("{} {}\n".format(Nx_arr,sd_PCA_size))
for index_x in range(Nx_arr):
dsfile.write((form+" ") % (x_arr[index_x]*x_to_nu))
dsfile.write((form+" ") % Gdist[index_x])
dsfile.write((form+" ") % Ydist[index_x])
dsfile.write((form ) % Mdist[index_x])
for index_pca in range(sd_PCA_size):
dsfile.write((" "+form) % S_vecs[index_pca][index_x])
dsfile.write("\n")
# Update list of detectors
# Open and read already present list
with open(os.path.join(dir_path,"detectors_list.dat"),"a") as detector_file:
if has_noisefile:
detector_file.write('%s %s\n' % (sd_detector_name, sd_noisefile_name))
else:
detector_file.write('%s %.6e %.6e %.6e %i %.6e\n' % (sd_detector_name, sd_detector_nu_min, sd_detector_nu_max, sd_detector_nu_delta, sd_detector_bin_number, sd_detector_delta_Ic))
|
federicomarulliREPO_NAMECosmoBolognaLibPATH_START.@CosmoBolognaLib_extracted@CosmoBolognaLib-master@External@CLASS@external@distortions@generate_PCA_files.py@.PATH_END.py
|
{
"filename": "vlsvvariables.py",
"repo_name": "fmihpc/analysator",
"repo_path": "analysator_extracted/analysator-master/pyVlsv/vlsvvariables.py",
"type": "Python"
}
|
#
# This file is part of Analysator.
# Copyright 2013-2016 Finnish Meteorological Institute
# Copyright 2017-2018 University of Helsinki
#
# For details of usage, see the COPYING file and read the "Rules of the Road"
# at http://www.physics.helsinki.fi/vlasiator/
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
activepopulation='proton' # default
speciesdict ={
'avgs': 'p',
'proton': 'p',
'helium': 'He',
'oxygen': 'O',
'electron': 'e',
}
speciesamu ={
'avgs': 1,
'proton': 1,
'helium': 4,
'oxygen': 16,
# 'electron': 5.4461702e-4, # true electron mass
'electron': 5.4461702e-3, # electron mass x10 used in Tempo
}
speciescharge ={
'avgs': 1,
'proton': 1,
'helium': 2,
'oxygen': 1,
'electron': -1,
}
speciesprecipitationenergybins ={ # values updated when opening the vlsvreader object
'avgs': -1,
'proton': -1,
'helium': -1,
'oxygen': -1,
'electron': -1,
}
# Define some units for intrinsic values
unitsdict = {
'rhom': 'kg/m3',
'rhoq': 'C/m3',
'rho': '1/m3',
'rhobackstream': '1/m3',
'rhononbackstream': '1/m3',
'rho_v': '1/m2s',
'rhovbackstream': '1/m2s',
'rhovnonbackstream': '1/m2s',
'v': 'm/s',
'vbackstream': 'm/s',
'nNonbackstream': 'm/s',
'b': 'T',
'b_vol': 'T',
'background_b': 'T',
'perturbed_b': 'T',
'bgb': 'T',
'perb': 'T',
'perb_vol': 'T',
'e': 'V/m',
'e_vol': 'V/m',
'exhall_000_100': 'V/m',
'exhall_001_101': 'V/m',
'exhall_010_110': 'V/m',
'exhall_011_111': 'V/m',
'eyhall_000_010': 'V/m',
'eyhall_001_011': 'V/m',
'eyhall_100_110': 'V/m',
'eyhall_101_111': 'V/m',
'ezhall_000_001': 'V/m',
'ezhall_010_011': 'V/m',
'ezhall_100_101': 'V/m',
'ezhall_110_111': 'V/m',
'pressure': 'Pa',
'pressure_dt2': 'Pa',
'pressure_r': 'Pa',
'pressure_v': 'Pa',
'ptensordiagonal': 'Pa',
'ptensoroffdiagonal': 'Pa',
'ptensorbackstreamdiagonal': 'Pa',
'ptensorbackstreamoffdiagonal': 'Pa',
'ptensornonbackstreamdiagonal': 'Pa',
'ptensornonbackstreamoffdiagonal': 'Pa',
'max_v_dt': 's',
'max_r_dt': 's',
'max_fields_dt': 's',
'minvalue': 's3/m6',
'effectivesparsitythreshold': 's3/m6',
'rho_loss_adjust': '1/m3',
'energydensity': 'eV/cm3',
'precipitationdiffflux': '1/(cm2 sr s eV)',
'precipitationintegralenergyflux': 'keV/(cm2 sr s)',
'precipitationmeanenergy': 'keV'
}
# Define some LaTeX markup names for intrinsic values
latexdict = {
'rhom': r'$\rho_m$',
'rhoq': r'$\rho_q$',
'rho': r'$n_\mathrm{p}$',
'rhobackstream': r'$n_\mathrm{p,st}$',
'rhononbackstream': r'$n_\mathrm{p,th}$',
'rho_v': r'$\Gamma_\mathrm{p}$',
'rhovbackstream': r'$\Gamma_\mathrm{p,st}$',
'rhovnonbackstream': r'$\Gamma_\mathrm{p,th}$',
'v': r'$V$',
'vbackstream': r'$V_\mathrm{p,st}$',
'vnonbackstream': r'$V_\mathrm{p,th}$',
'b': r'$B$',
'b_vol': r'$B_\mathrm{vol}$',
'background_b': r'$B_\mathrm{bg}$',
'perturbed_b': r'$B_\mathrm{pert}$',
'bgb': r'$B_\mathrm{bg}$',
'perb': r'B_\mathrm{pert}$',
'perb_vol': r'B_\mathrm{vol,pert}$',
'e': r'$E$',
'e_vol': r'$E_\mathrm{vol}$',
'exhall_000_100': r'$E_\mathrm{Hall,000,100}$',
'exhall_001_101': r'$E_\mathrm{Hall,001,101}$',
'exhall_010_110': r'$E_\mathrm{Hall,010,110}$',
'exhall_011_111': r'$E_\mathrm{Hall,011,111}$',
'eyhall_000_010': r'$E_\mathrm{Hall,000,010}$',
'eyhall_001_011': r'$E_\mathrm{Hall,001,011}$',
'eyhall_100_110': r'$E_\mathrm{Hall,100,110}$',
'eyhall_101_111': r'$E_\mathrm{Hall,101,111}$',
'ezhall_000_001': r'$E_\mathrm{Hall,000,001}$',
'ezhall_010_011': r'$E_\mathrm{Hall,010,011}$',
'ezhall_100_101': r'$E_\mathrm{Hall,100,101}$',
'ezhall_110_111': r'$E_\mathrm{Hall,110,111}$',
'pressure': r'$P$',
'pressure_dt2': r'$P_{\mathrm{d}t/2}}$',
'pressure_r': r'$P_r$',
'pressure_v': r'$P_v$',
'ptensordiagonal': r'$\mathcal{P}_\mathrm{diag}$',
'ptensoroffdiagonal': r'$\mathcal{P}_\mathrm{off-diag}$',
'ptensorbackstreamdiagonal': r'$\mathcal{P}_\mathrm{st,diag}$',
'ptensorbackstreamoffdiagonal': r'$\mathcal{P}_\mathrm{st,off-diag}$',
'ptensornonbackstreamdiagonal': r'$\mathcal{P}_\mathrm{th,diag}$',
'ptensornonbackstreamoffdiagonal': r'$\mathcal{P}_\mathrm{th,off-diag}$',
'max_v_dt': r'$\Delta t_{\mathrm{max},v}$',
'max_r_dt': r'$\Delta t_{\mathrm{max},r}$',
'max_fields_dt': r'$\Delta t_\mathrm{max,FS}$',
'minvalue': r'$f_\mathrm{Min}$',
'effectivesparsitythreshold': r'$f_\mathrm{Min}$',
'rho_loss_adjust': r'$\Delta_\mathrm{loss} n_\mathrm{p}$',
}
# Define some LaTeX markup names for intrinsic values
latexdictmultipop = {
'rho': r'$n_\mathrm{REPLACEPOP}$',
'rhobackstream': r'$n_\mathrm{REPLACEPOP,st}$',
'rhononbackstream': r'$n_\mathrm{REPLACEPOP,th}$',
'rho_v': r'$\Gamma_\mathrm{REPLACEPOP}$',
'rhovbackstream': r'$\Gamma_\mathrm{REPLACEPOP,st}$',
'rhovnonbackstream': r'$\Gamma_\mathrm{REPLACEPOP,th}$',
'v': r'$V_\mathrm{REPLACEPOP}$',
'vbackstream': r'$V_\mathrm{REPLACEPOP,st}$',
'vnonbackstream': r'$V_\mathrm{REPLACEPOP,th}$',
'pressure': r'$P_\mathrm{REPLACEPOP}$',
'pressure_dt2': r'$P_{\mathrm{REPLACEPOP},\mathrm{d}t/2}}$',
'pressure_r': r'$P_{\mathrm{REPLACEPOP},r}$',
'pressure_v': r'$P_{\mathrm{REPLACEPOP},v}$',
'ptensordiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,diag}$',
'ptensoroffdiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,off-diag}$',
'ptensorbackstreamdiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,st,diag}$',
'ptensorbackstreamoffdiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,st,off-diag}$',
'ptensornonbackstreamdiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,th,diag}$',
'ptensornonbackstreamoffdiagonal': r'$\mathcal{P}_\mathrm{REPLACEPOP,th,off-diag}$',
'minvalue': r'$f_\mathrm{REPLACEPOP,Min}}$',
'effectivesparsitythreshold': r'$f_\mathrm{REPLACEPOP,Min}$',
'energydensity': r'$U_\mathrm{REPLACEPOP}$',
'precipitationdiffflux': r'$\mathcal{F}_{\mathrm{prec},\mathrm{REPLACEPOP}}$',
'precipitationintegralenergyflux': r'$\int \mathcal{F}_{\mathrm{prec},\mathrm{REPLACEPOP}}$',
'precipitationmeanenergy': r'$<E_{\mathrm{prec},\mathrm{REPLACEPOP}}>$'
}
# Define some LaTeX markup units for intrinsic values
latexunitsdict = {
'rhom': r'$\mathrm{kg}\,\mathrm{m}^{-3}$',
'rhoq': r'$\mathrm{C}\,\mathrm{m}^{-3}$',
'rho': r'$\mathrm{m}^{-3}$',
'rhobackstream': r'$\mathrm{m}^{-3}$',
'rhononbackstream': r'$\mathrm{m}^{-3}$',
'rho_v': r'$\mathrm{m}^{-2}$s',
'rhovbackstream': r'$\mathrm{m}^{-2}$s',
'rhovnonbackstream': r'$\mathrm{m}^{-2}$s',
'v': r'$\mathrm{m}\,\mathrm{s}^{-1}$',
'vbackstream': r'$\mathrm{m}\,\mathrm{s}^{-1}$',
'vnonbackstream': r'$\mathrm{m}\,\mathrm{s}^{-1}$',
'b': r'T',
'b_vol': r'T',
'background_b': r'T',
'perturbed_b': r'T',
'bgb': r'T',
'perb': r'T',
'perb_vol': r'T',
'e': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'e_vol': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'exhall_000_100': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'exhall_001_101': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'exhall_010_110': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'exhall_011_111': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'eyhall_000_010': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'eyhall_001_011': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'eyhall_100_110': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'eyhall_101_111': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'ezhall_000_001': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'ezhall_010_011': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'ezhall_100_101': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'ezhall_110_111': r'$\mathrm{V}\,\mathrm{m}^{-1}$',
'pressure': r'Pa',
'pressure_dt2': r'Pa',
'pressure_r': r'Pa',
'pressure_v': r'Pa',
'ptensordiagonal': r'Pa',
'ptensoroffdiagonal': r'Pa',
'ptensorbackstreamdiagonal': r'Pa',
'ptensorbackstreamoffdiagonal': r'Pa',
'ptensornonbackstreamdiagonal': r'Pa',
'ptensornonbackstreamoffdiagonal': r'Pa',
'max_v_dt': r's',
'max_r_dt': r's',
'max_fields_dt': r's',
'minvalue': r'$\mathrm{m}^{-6}\,\mathrm{s}^{3}$',
'effectivesparsitythreshold': r'$\mathrm{m}^{-6}\,\mathrm{s}^{3}$',
'rho_loss_adjust': r'$\mathrm{m}^{-3}$',
'energydensity': r'$\mathrm{eV}\,\mathrm{cm}^{-3}$',
'precipitationdiffflux': r'$\mathrm{cm}^{-2} \,\mathrm{sr}^{-1}\,\mathrm{s}^{-1}\,\mathrm{eV}^{-1}$',
'precipitationintegralenergyflux': r'$\mathrm{keV} \, \mathrm{cm}^{-2} \, \mathrm{sr}^{-1} \, \mathrm{s}^{-1}$',
'precipitationmeanenergy': r'keV'
}
|
fmihpcREPO_NAMEanalysatorPATH_START.@analysator_extracted@analysator-master@pyVlsv@vlsvvariables.py@.PATH_END.py
|
{
"filename": "constants.py",
"repo_name": "PabloVD/HaloGraphNet",
"repo_path": "HaloGraphNet_extracted/HaloGraphNet-master/Source/constants.py",
"type": "Python"
}
|
#----------------------------------------------------------------------
# List of constants and some common functions
# Author: Pablo Villanueva Domingo
# Last update: 10/11/21
#----------------------------------------------------------------------
import numpy as np
import torch
import os
import random
# Random seeds
torch.manual_seed(12345)
np.random.seed(12345)
random.seed(12345)
#--- PARAMETERS AND CONSTANTS ---#
# Reduced Hubble constant
hred = 0.7
# Root path for simulations
simpathroot = "/projects/QUIJOTE/CAMELS/Sims/"
# Box size in comoving kpc/h
boxsize = 25.e3
# Validation and test size
valid_size, test_size = 0.15, 0.15
# Batch size
batch_size = 128
# 1 if train for performing symbolic regression later, 0 otherwise
sym_reg = 0
# 1 if use L1 regularization with messages. Needed for symbolic regression
use_l1 = 0
# Weight of the message L1 regularization in the total loss respect to the standard loss (used for symbolic regression)
l1_reg = 0.01
#--- FUNCTIONS ---#
# Name of the model and hyperparameters
def namemodel(params):
use_model, learning_rate, weight_decay, n_layers, k_nn, n_epochs, training, simsuite, simset, n_sims = params
return simsuite+"_"+simset+"_model_"+use_model+"_lr_{:.2e}_weightdecay_{:.2e}_layers_{:d}_knn_{:.2e}_epochs_{:d}".format(learning_rate, weight_decay, n_layers, k_nn, n_epochs)
# Change to the other CAMELS simulation suite
def changesuite(suite):
if suite=="IllustrisTNG":
newsuite = "SIMBA"
elif suite=="SIMBA":
newsuite = "IllustrisTNG"
return newsuite
# Choose color depending on the CAMELS simulation suite
def colorsuite(suite):
if suite=="IllustrisTNG": return "purple"
elif suite=="SIMBA": return "deepskyblue"
|
PabloVDREPO_NAMEHaloGraphNetPATH_START.@HaloGraphNet_extracted@HaloGraphNet-master@Source@constants.py@.PATH_END.py
|
{
"filename": "axes_grid.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/mpl_toolkits/axisartist/axes_grid.py",
"type": "Python"
}
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import mpl_toolkits.axes_grid1.axes_grid as axes_grid_orig
from .axes_divider import LocatableAxes
class CbarAxes(axes_grid_orig.CbarAxesBase, LocatableAxes):
def __init__(self, *kl, **kwargs):
orientation=kwargs.pop("orientation", None)
if orientation is None:
raise ValueError("orientation must be specified")
self.orientation = orientation
self._default_label_on = False
self.locator = None
super(LocatableAxes, self).__init__(*kl, **kwargs)
def cla(self):
super(LocatableAxes, self).cla()
self._config_axes()
class Grid(axes_grid_orig.Grid):
_defaultLocatableAxesClass = LocatableAxes
class ImageGrid(axes_grid_orig.ImageGrid):
_defaultLocatableAxesClass = LocatableAxes
_defaultCbarAxesClass = CbarAxes
AxesGrid = ImageGrid
|
waynebhayesREPO_NAMESpArcFiRePATH_START.@SpArcFiRe_extracted@SpArcFiRe-master@scripts@SpArcFiRe-pyvenv@lib@python2.7@site-packages@mpl_toolkits@axisartist@axes_grid.py@.PATH_END.py
|
{
"filename": "_cmin.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/splom/marker/line/_cmin.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CminValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="cmin", parent_name="splom.marker.line", **kwargs):
super(CminValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {"cauto": False}),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@splom@marker@line@_cmin.py@.PATH_END.py
|
{
"filename": "test_Sersic.py",
"repo_name": "LSSTDESC/chroma",
"repo_path": "chroma_extracted/chroma-master/tests/deprecated/test_Sersic.py",
"type": "Python"
}
|
import _mypath
import numpy as np
def test_Sersic():
from chroma.Sersic import Sersic
'''Creating a test case for the various ways of initializing Sersics'''
y0 = 0.1
x0 = 0.3
n = 1.5
peak = 1.0
# a, b, phi
phi = 0.1
a = 0.5
b = 0.2
s1 = Sersic(y0, x0, n, peak=peak, a=a, b=b, phi=phi)
print 'It is an error if following 9 or so values do not all match'
print s1(0.2, 2.1)
# C11, C12, C22
C11 = (np.cos(phi)**2.0 / a**2.0 + np.sin(phi)**2.0 / b**2.0)
C22 = (np.sin(phi)**2.0 / a**2.0 + np.cos(phi)**2.0 / b**2.0)
C12 = 0.5 * (1.0/a**2.0 - 1.0/b**2.0) * np.sin(2.0 * phi)
s2 = Sersic(y0, x0, n, peak=peak, C11=C11, C12=C12, C22=C22)
print s2(0.2, 2.1)
# r_e, b_over_a, phi
r_e = np.sqrt(a * b)
b_over_a = b / a
s3 = Sersic(y0, x0, n, peak=peak, r_e=r_e, b_over_a=b_over_a, phi=phi)
print s3(0.2, 2.1)
# r_e, emag, phi
emag = (a**2.0 - b**2.0) / (a**2.0 + b**2.0)
s4 = Sersic(y0, x0, n, peak=peak, r_e=r_e, emag=emag, phi=phi)
print s4(0.2, 2.1)
# r_e, gmag, phi
gmag = (a - b) / (a + b)
s5 = Sersic(y0, x0, n, peak=peak, r_e=r_e, gmag=gmag, phi=phi)
print s5(0.2, 2.1)
# r_e, e1, e2
e1 = emag * np.cos(2.0 * phi)
e2 = emag * np.sin(2.0 * phi)
s6 = Sersic(y0, x0, n, peak=peak, r_e=r_e, e1=e1, e2=e2)
print s6(0.2, 2.1)
# r_e, g1, g2
g1 = gmag * np.cos(2.0 * phi)
g2 = gmag * np.sin(2.0 * phi)
s7 = Sersic(y0, x0, n, peak=peak, r_e=r_e, g1=g1, g2=g2)
print s7(0.2, 2.1)
# FWHM instead of r_e
FWHM = 2 * r_e * (np.log(2.0) / Sersic.compute_kappa(n))**n
s8 = Sersic(y0, x0, n, peak=peak, r_e=r_e, b_over_a=b_over_a, phi=phi)
print s8(0.2, 2.1)
# flux instead of peak
flux=Sersic.compute_flux(n, r_e, peak)
s9 = Sersic(y0, x0, n, flux=flux, a=a, b=b, phi=phi)
print s9(0.2, 2.1)
# make sure have access to r_e
print 'It is an error if following 9 or so values do not all match'
print s1.r_e
print s2.r_e
print s3.r_e
print s4.r_e
print s5.r_e
print s6.r_e
print s7.r_e
print s8.r_e
print s9.r_e
# make sure have access to a
print 'It is an error if following 9 or so values do not all match'
print s1.a
print s2.a
print s3.a
print s4.a
print s5.a
print s6.a
print s7.a
print s8.a
print s9.a
# make sure have access to b
print 'It is an error if following 9 or so values do not all match'
print s1.b
print s2.b
print s3.b
print s4.b
print s5.b
print s6.b
print s7.b
print s8.b
print s9.b
# make sure have access to phi
print 'It is an error if following 9 or so values do not all match'
print s1.phi
print s2.phi
print s3.phi
print s4.phi
print s5.phi
print s6.phi
print s7.phi
print s8.phi
print s9.phi
# make sure have access to C11
print 'It is an error if following 9 or so values do not all match'
print s1.C11
print s2.C11
print s3.C11
print s4.C11
print s5.C11
print s6.C11
print s7.C11
print s8.C11
print s9.C11
# make sure have access to C22
print 'It is an error if following 9 or so values do not all match'
print s1.C22
print s2.C22
print s3.C22
print s4.C22
print s5.C22
print s6.C22
print s7.C22
print s8.C22
print s9.C22
# make sure have access to C12
print 'It is an error if following 9 or so values do not all match'
print s1.C12
print s2.C12
print s3.C12
print s4.C12
print s5.C12
print s6.C12
print s7.C12
print s8.C12
print s9.C12
if __name__ == '__main__':
test_Sersic()
|
LSSTDESCREPO_NAMEchromaPATH_START.@chroma_extracted@chroma-master@tests@deprecated@test_Sersic.py@.PATH_END.py
|
{
"filename": "_aspectmode.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/scene/_aspectmode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class AspectmodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="aspectmode", parent_name="layout.scene", **kwargs):
super(AspectmodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
implied_edits=kwargs.pop("implied_edits", {}),
values=kwargs.pop("values", ["auto", "cube", "data", "manual"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@scene@_aspectmode.py@.PATH_END.py
|
{
"filename": "data_processing.py",
"repo_name": "ejhigson/nestcheck",
"repo_path": "nestcheck_extracted/nestcheck-master/nestcheck/data_processing.py",
"type": "Python"
}
|
#!/usr/bin/env python
r"""Module containing functions for loading and processing output files
produced by nested sampling software.
Background: threads
-------------------
``nestcheck``'s error estimates and diagnostics rely on the decomposition
of a nested sampling run into multiple runs, each with a single live point.
We refer to these constituent single live point runs as *threads*.
See "Sampling Errors In Nested Sampling Parameter Estimation" (Higson et
al. 2018) for a detailed discussion, including an algorithm for dividing
nested sampling runs into their constituent threads.
Nested sampling run format
--------------------------
``nestcheck`` stores nested sampling runs in a standard format as python
dictionaries. For a run with :math:`n_\mathrm{samp}` samples, the keys are:
logl: 1d numpy array
Loglikelihood values (floats) for each sample.
Shape is (:math:`n_\mathrm{samp}`,).
thread_labels: 1d numpy array
Integer label for each point representing which thread each point
belongs to.
Shape is (:math:`n_\mathrm{samp}`,).
For some thread label k, the thread's start (birth)
log-likelihood and end log-likelihood are given by
thread_min_max[k, :].
thread_min_max: 2d numpy array
Shape is (:math:`n_\mathrm{threads}`, 2).
Each row with index k contains the logl from within which the first
point in the thread with label k was sampled (the "birth contour") and
the logl of the final point in the thread.
The birth contour is -inf if the thread began by sampling from the
whole prior.
theta: 2d numpy array
Parameter values for samples - each row represents a sample.
Shape is (:math:`n_\mathrm{samp}`, d) where d is number of dimensions.
nlive_array: 1d numpy array
Number of live points present between the previous point and
this point.
output: dict (optional)
Dict containing extra information about the run.
Samples are arranged in ascending order of logl.
Processing nested sampling software output
------------------------------------------
To process output files for a nested sampling run into the format described
above, the following information is required:
* Samples' loglikelihood values;
* Samples' parameter values;
* Information allowing decomposition into threads and identifying each thread's
birth contour (starting logl).
The first two items are self-explanatory, but the latter is more challenging
as it can take different formats and may not be provided by all nested sampling
software packages.
Sufficient information for thread decomposition and calculating the number of
live points (including for dynamic nested sampling) is provided by a list of
the loglikelihoods from within which each point was sampled (the points'
birth contours). This is output by ``PolyChord`` >= v1.13 and ``MultiNest``
>= v3.11, and is used in the output processing for these packages via the
``birth_inds_given_contours`` and ``threads_given_birth_inds`` functions.
Also sufficient is a list of the indexes of the point which was removed
at the step when each point was sampled ("birth indexes"), as this can be
mapped to the birth contours and vice versa.
``process_dynesty_run`` does not require the ``birth_inds_given_contours`` and
``threads_given_birth_inds`` functions as ``dynesty`` results objects
already include thread labels via their ``samples_id`` property. If the
``dynesty`` run is dynamic, the ``batch_bounds`` property is need to determine
the threads' starting birth contours.
Adding a new processing function for another nested sampling package
--------------------------------------------------------------------
You can add new functions to process output from other nested sampling
software, provided the output files include the required information for
decomposition into threads. Depending on how this information is provided you
may be able to adapt ``process_polychord_run`` or ``process_dynesty_run``.
If thread decomposition information if provided in a different format, you
will have to write your own helper functions to process the output into the
``nestcheck`` dictionary format described above.
"""
import os
import re
import warnings
import copy
import numpy as np
import nestcheck.io_utils
import nestcheck.ns_run_utils
import nestcheck.parallel_utils
@nestcheck.io_utils.save_load_result
def batch_process_data(file_roots, **kwargs):
"""Process output from many nested sampling runs in parallel with optional
error handling and caching.
The result can be cached using the 'save_name', 'save' and 'load' kwargs
(by default this is not done). See save_load_result docstring for more
details.
Remaining kwargs passed to parallel_utils.parallel_apply (see its
docstring for more details).
Parameters
----------
file_roots: list of strs
file_roots for the runs to load.
base_dir: str, optional
path to directory containing files.
process_func: function, optional
function to use to process the data.
func_kwargs: dict, optional
additional keyword arguments for process_func.
errors_to_handle: error or tuple of errors, optional
which errors to catch when they occur in processing rather than
raising.
save_name: str or None, optional
See nestcheck.io_utils.save_load_result.
save: bool, optional
See nestcheck.io_utils.save_load_result.
load: bool, optional
See nestcheck.io_utils.save_load_result.
overwrite_existing: bool, optional
See nestcheck.io_utils.save_load_result.
Returns
-------
list of ns_run dicts
List of nested sampling runs in dict format (see the module
docstring for more details).
"""
base_dir = kwargs.pop('base_dir', 'chains')
process_func = kwargs.pop('process_func', process_polychord_run)
func_kwargs = kwargs.pop('func_kwargs', {})
func_kwargs['errors_to_handle'] = kwargs.pop('errors_to_handle', ())
data = nestcheck.parallel_utils.parallel_apply(
process_error_helper, file_roots, func_args=(base_dir, process_func),
func_kwargs=func_kwargs, **kwargs)
# Sort processed runs into the same order as file_roots (as parallel_apply
# does not preserve order)
data = sorted(data,
key=lambda x: file_roots.index(x['output']['file_root']))
# Extract error information and print
errors = {}
for i, run in enumerate(data):
if 'error' in run:
try:
errors[run['error']].append(i)
except KeyError:
errors[run['error']] = [i]
for error_name, index_list in errors.items():
message = (error_name + ' processing ' + str(len(index_list)) + ' / '
+ str(len(file_roots)) + ' files')
if len(index_list) != len(file_roots):
message += ('. Roots with errors have (zero based) indexes: '
+ str(index_list))
print(message)
# Return runs which did not have errors
return [run for run in data if 'error' not in run]
def process_error_helper(root, base_dir, process_func, errors_to_handle=(),
**func_kwargs):
"""Wrapper which applies process_func and handles some common errors so one
bad run does not spoil the whole batch.
Useful errors to handle include:
OSError: if you are not sure if all the files exist
AssertionError: if some of the many assertions fail for known reasons;
for example is there are occasional problems decomposing runs into threads
due to limited numerical precision in logls.
Parameters
----------
root: str
File root.
base_dir: str
Directory containing file.
process_func: func
Function for processing file.
errors_to_handle: error type or tuple of error types
Errors to catch without throwing an exception.
func_kwargs: dict
Kwargs to pass to process_func.
Returns
-------
run: dict
Nested sampling run dict (see the module docstring for more
details) or, if an error occured, a dict containing its type
and the file root.
"""
try:
return process_func(root, base_dir, **func_kwargs)
except errors_to_handle as err:
run = {'error': type(err).__name__,
'output': {'file_root': root}}
return run
def process_polychord_run(file_root, base_dir, process_stats_file=True,
**kwargs):
"""Loads data from a PolyChord run into the nestcheck dictionary format for
analysis.
N.B. producing required output file containing information about the
iso-likelihood contours within which points were sampled (where they were
"born") requies PolyChord version v1.13 or later and the setting
write_dead=True.
Parameters
----------
file_root: str
Root for run output file names (PolyChord file_root setting).
base_dir: str
Directory containing data (PolyChord base_dir setting).
process_stats_file: bool, optional
Should PolyChord's <root>.stats file be processed? Set to False if you
don't have the <root>.stats file (such as if PolyChord was run with
write_stats=False).
kwargs: dict, optional
Options passed to ns_run_utils.check_ns_run.
Returns
-------
ns_run: dict
Nested sampling run dict (see the module docstring for more details).
"""
# N.B. PolyChord dead points files also contains remaining live points at
# termination
samples = np.loadtxt(os.path.join(base_dir, file_root) + '_dead-birth.txt')
ns_run = process_samples_array(samples, **kwargs)
ns_run['output'] = {'base_dir': base_dir, 'file_root': file_root}
if process_stats_file:
try:
ns_run['output'] = process_polychord_stats(file_root, base_dir)
except (OSError, IOError, ValueError, IndexError, NameError,
TypeError) as err:
warnings.warn(
('process_polychord_stats raised {} processing {}.stats file. '
' I am proceeding without the .stats file.').format(
type(err).__name__, os.path.join(base_dir, file_root)),
UserWarning)
return ns_run
def process_multinest_run(file_root, base_dir, **kwargs):
"""Loads data from a MultiNest run into the nestcheck dictionary format for
analysis.
N.B. producing required output file containing information about the
iso-likelihood contours within which points were sampled (where they were
"born") requies MultiNest version 3.11 or later.
Parameters
----------
file_root: str
Root name for output files. When running MultiNest, this is determined
by the nest_root parameter.
base_dir: str
Directory containing output files. When running MultiNest, this is
determined by the nest_root parameter.
kwargs: dict, optional
Passed to ns_run_utils.check_ns_run (via process_samples_array)
Returns
-------
ns_run: dict
Nested sampling run dict (see the module docstring for more details).
"""
# Load dead and live points
dead = np.loadtxt(os.path.join(base_dir, file_root) + 'dead-birth.txt')
live = np.loadtxt(os.path.join(base_dir, file_root)
+ 'phys_live-birth.txt')
# Remove unnecessary final columns
dead = dead[:, :-2]
live = live[:, :-1]
assert dead[:, -2].max() < live[:, -2].min(), (
'final live points should have greater logls than any dead point!',
dead, live)
ns_run = process_samples_array(np.vstack((dead, live)), **kwargs)
assert np.all(ns_run['thread_min_max'][:, 0] == -np.inf), (
'As MultiNest does not currently perform dynamic nested sampling, all '
'threads should start by sampling the whole prior.')
ns_run['output'] = {}
ns_run['output']['file_root'] = file_root
ns_run['output']['base_dir'] = base_dir
return ns_run
def process_dynesty_run(results):
"""Transforms results from a dynesty run into the nestcheck dictionary
format for analysis. This function has been tested with dynesty v9.2.0.
Note that the nestcheck point weights and evidence will not be exactly
the same as the dynesty ones as nestcheck calculates logX volumes more
precisely (using the trapezium rule).
This function does not require the birth_inds_given_contours and
threads_given_birth_inds functions as dynesty results objects
already include thread labels via their samples_id property. If the
dynesty run is dynamic, the batch_bounds property is need to determine
the threads' starting birth contours.
Parameters
----------
results: dynesty results object
N.B. the remaining live points at termination must be included in the
results (dynesty samplers' run_nested method does this if
add_live_points=True - its default value).
Returns
-------
ns_run: dict
Nested sampling run dict (see the module docstring for more details).
"""
samples = np.zeros((results.samples.shape[0],
results.samples.shape[1] + 3))
samples[:, 0] = results.logl
samples[:, 1] = results.samples_id
samples[:, 3:] = results.samples
unique_th, first_inds = np.unique(results.samples_id, return_index=True)
assert np.array_equal(unique_th, np.asarray(range(unique_th.shape[0])))
thread_min_max = np.full((unique_th.shape[0], 2), np.nan)
is_dynamic_dynesty = False
try:
# Try processing standard nested sampling results
assert unique_th.shape[0] == results.nlive
assert np.array_equal(
np.unique(results.samples_id[-results.nlive:]),
np.asarray(range(results.nlive))), (
'perhaps the final live points are not included?')
thread_min_max[:, 0] = -np.inf
except AttributeError:
# If results has no nlive attribute, it must be dynamic nested sampling
assert unique_th.shape[0] == sum(results.batch_nlive)
# if the object has a samples_n attribute, it is from dynesty
if hasattr(results, 'samples_n'):
is_dynamic_dynesty = True
#numpy diff goes out[i] = samples[i+1] - samples[i], so it records the
#samples added/removed at samples[i]
diff_nlive = np.diff(results.samples_n)
#results.samples_n tells us how many live samples there are at a given iteration,
#so use the diff of this to assign the samples[change_in_nlive_at_sample (col 2)]
#value. We know we want the last n_live to end with 1
samples[:-1,2] = diff_nlive
for th_lab, ind in zip(unique_th, first_inds):
thread_min_max[th_lab, 0] = (
results.batch_bounds[results.samples_batch[ind], 0])
for th_lab in unique_th:
final_ind = np.where(results.samples_id == th_lab)[0][-1]
thread_min_max[th_lab, 1] = results.logl[final_ind]
if not is_dynamic_dynesty:
samples[final_ind, 2] = -1
assert np.all(~np.isnan(thread_min_max))
run = nestcheck.ns_run_utils.dict_given_run_array(samples, thread_min_max)
nestcheck.ns_run_utils.check_ns_run(run)
return run
def process_polychord_stats(file_root, base_dir):
"""Reads a PolyChord <root>.stats output file and returns the information
contained in a dictionary.
Parameters
----------
file_root: str
Root for run output file names (PolyChord file_root setting).
base_dir: str
Directory containing data (PolyChord base_dir setting).
Returns
-------
output: dict
See PolyChord documentation for more details.
"""
filename = os.path.join(base_dir, file_root) + '.stats'
output = {'base_dir': base_dir,
'file_root': file_root}
with open(filename, 'r') as stats_file:
lines = stats_file.readlines()
output['logZ'] = float(lines[8].split()[2])
output['logZerr'] = float(lines[8].split()[4])
# Cluster logZs and errors
output['logZs'] = []
output['logZerrs'] = []
for line in lines[14:]:
if line[:5] != 'log(Z':
break
output['logZs'].append(float(
re.findall(r'=(.*)', line)[0].split()[0]))
output['logZerrs'].append(float(
re.findall(r'=(.*)', line)[0].split()[2]))
# Other output info
nclust = len(output['logZs'])
output['ncluster'] = nclust
output['nposterior'] = int(lines[20 + nclust].split()[1])
output['nequals'] = int(lines[21 + nclust].split()[1])
output['ndead'] = int(lines[22 + nclust].split()[1])
output['nlive'] = int(lines[23 + nclust].split()[1])
try:
output['nlike'] = [int(x) for x in lines[24 + nclust].split()[1:]]
if len(output['nlike']) == 1:
output['nlike'] = output['nlike'][0]
except ValueError:
# if nlike has too many digits, PolyChord just writes ***** to .stats
# file. This causes a ValueError
output['nlike'] = np.nan
line = lines[25 + nclust].split()
i = line.index('(')
# If there are multiple parameter speeds then multiple values are written
# for avnlike and avnlikeslice
output['avnlike'] = [float(x) for x in line[1:i]]
# If only one value, keep as float
if len(output['avnlike']) == 1:
output['avnlike'] = output['avnlike'][0]
output['avnlikeslice'] = [float(x) for x in line[i+1:-3]]
# If only one value, keep as float
if len(output['avnlikeslice']) == 1:
output['avnlikeslice'] = output['avnlikeslice'][0]
# Means and stds of dimensions (not produced by PolyChord<=1.13)
if len(lines) > 29 + nclust:
output['param_means'] = []
output['param_mean_errs'] = []
for line in lines[29 + nclust:]:
if '------------------' in line:
# A line of dashes is used to show the start of the derived
# parameters in the .stats file for later versions of
# PolyChord
continue
output['param_means'].append(float(line.split()[1]))
output['param_mean_errs'].append(float(line.split()[3]))
return output
def process_samples_array(samples, **kwargs):
"""Convert an array of nested sampling dead and live points of the type
produced by PolyChord and MultiNest into a nestcheck nested sampling run
dictionary.
Parameters
----------
samples: 2d numpy array
Array of dead points and any remaining live points at termination.
Has #parameters + 2 columns:
param_1, param_2, ... , logl, birth_logl
kwargs: dict, optional
Options passed to birth_inds_given_contours
Returns
-------
ns_run: dict
Nested sampling run dict (see the module docstring for more
details). Only contains information in samples (not additional
optional output key).
"""
samples = samples[np.argsort(samples[:, -2])]
ns_run = {}
ns_run['logl'] = samples[:, -2]
ns_run['theta'] = samples[:, :-2]
birth_contours = samples[:, -1]
# birth_contours, ns_run['theta'] = check_logls_unique(
# samples[:, -2], samples[:, -1], samples[:, :-2])
birth_inds = birth_inds_given_contours(
birth_contours, ns_run['logl'], **kwargs)
ns_run['thread_labels'] = threads_given_birth_inds(birth_inds)
unique_threads = np.unique(ns_run['thread_labels'])
assert np.array_equal(unique_threads,
np.asarray(range(unique_threads.shape[0])))
# Work out nlive_array and thread_min_max logls from thread labels and
# birth contours
thread_min_max = np.zeros((unique_threads.shape[0], 2))
# NB delta_nlive indexes are offset from points' indexes by 1 as we need an
# element to represent the initial sampling of live points before any dead
# points are created.
# I.E. birth on step 1 corresponds to replacing dead point zero
delta_nlive = np.zeros(samples.shape[0] + 1)
for label in unique_threads:
thread_inds = np.where(ns_run['thread_labels'] == label)[0]
# Max is final logl in thread
thread_min_max[label, 1] = ns_run['logl'][thread_inds[-1]]
thread_start_birth_ind = birth_inds[thread_inds[0]]
# delta nlive indexes are +1 from logl indexes to allow for initial
# nlive (before first dead point)
delta_nlive[thread_inds[-1] + 1] -= 1
if thread_start_birth_ind == birth_inds[0]:
# thread minimum is -inf as it starts by sampling from whole prior
thread_min_max[label, 0] = -np.inf
delta_nlive[0] += 1
else:
assert thread_start_birth_ind >= 0
thread_min_max[label, 0] = ns_run['logl'][thread_start_birth_ind]
delta_nlive[thread_start_birth_ind + 1] += 1
ns_run['thread_min_max'] = thread_min_max
ns_run['nlive_array'] = np.cumsum(delta_nlive)[:-1]
return ns_run
def birth_inds_given_contours(birth_logl_arr, logl_arr, **kwargs):
"""Maps the iso-likelihood contours on which points were born to the
index of the dead point on this contour.
MultiNest and PolyChord use different values to identify the inital live
points which were sampled from the whole prior (PolyChord uses -1e+30
and MultiNest -0.179769313486231571E+309). However in each case the first
dead point must have been sampled from the whole prior, so for either
package we can use
init_birth = birth_logl_arr[0]
If there are many points with the same logl_arr and dup_assert is False,
these points are randomly assigned an order (to ensure results are
consistent, random seeding is used).
Parameters
----------
logl_arr: 1d numpy array
logl values of each point.
birth_logl_arr: 1d numpy array
Birth contours - i.e. logl values of the iso-likelihood contour from
within each point was sampled (on which it was born).
dup_assert: bool, optional
See ns_run_utils.check_ns_run_logls docstring.
dup_warn: bool, optional
See ns_run_utils.check_ns_run_logls docstring.
Returns
-------
birth_inds: 1d numpy array of ints
Step at which each element of logl_arr was sampled. Points sampled from
the whole prior are assigned value -1.
"""
dup_assert = kwargs.pop('dup_assert', False)
dup_warn = kwargs.pop('dup_warn', False)
if kwargs:
raise TypeError('Unexpected **kwargs: {0}'.format(kwargs))
assert logl_arr.ndim == 1, logl_arr.ndim
assert birth_logl_arr.ndim == 1, birth_logl_arr.ndim
# Check for duplicate logl values (if specified by dup_assert or dup_warn)
nestcheck.ns_run_utils.check_ns_run_logls(
{'logl': logl_arr}, dup_assert=dup_assert, dup_warn=dup_warn)
# Random seed so results are consistent if there are duplicate logls
state = np.random.get_state() # Save random state before seeding
np.random.seed(0)
# Calculate birth inds
init_birth = birth_logl_arr[0]
assert np.all(birth_logl_arr <= logl_arr), (
logl_arr[birth_logl_arr > logl_arr])
birth_inds = np.full(birth_logl_arr.shape, np.nan)
birth_inds[birth_logl_arr == init_birth] = -1
for i, birth_logl in enumerate(birth_logl_arr):
if not np.isnan(birth_inds[i]):
# birth ind has already been assigned
continue
dup_deaths = np.where(logl_arr == birth_logl)[0]
if dup_deaths.shape == (1,):
# death index is unique
birth_inds[i] = dup_deaths[0]
continue
# The remainder of this loop deals with the case that multiple points
# have the same logl value (=birth_logl). This can occur due to limited
# precision, or for likelihoods with contant regions. In this case we
# randomly assign the duplicates birth steps in a manner
# that provides a valid division into nested sampling runs
dup_births = np.where(birth_logl_arr == birth_logl)[0]
assert dup_deaths.shape[0] > 1, dup_deaths
if np.all(birth_logl_arr[dup_deaths] != birth_logl):
# If no points both are born and die on this contour, we can just
# randomly assign an order
np.random.shuffle(dup_deaths)
inds_to_use = dup_deaths
else:
# If some points are both born and die on the contour, we need to
# take care that the assigned birth inds do not result in some
# points dying before they are born
try:
inds_to_use = sample_less_than_condition(
dup_deaths, dup_births)
except ValueError:
raise ValueError((
'There is no way to allocate indexes dup_deaths={} such '
'that each is less than dup_births={}.').format(
dup_deaths, dup_births))
try:
# Add our selected inds_to_use values to the birth_inds array
# Note that dup_deaths (and hence inds to use) may have more
# members than dup_births, because one of the duplicates may be
# the final point in a thread. We therefore include only the first
# dup_births.shape[0] elements
birth_inds[dup_births] = inds_to_use[:dup_births.shape[0]]
except ValueError:
warnings.warn((
'for logl={}, the number of points born (indexes='
'{}) is bigger than the number of points dying '
'(indexes={}). This indicates a problem with your '
'nested sampling software - it may be caused by '
'a bug in PolyChord which was fixed in PolyChord '
'v1.14, so try upgrading. I will try to give an '
'approximate allocation of threads but this may '
'fail.').format(
birth_logl, dup_births, inds_to_use), UserWarning)
extra_inds = np.random.choice(
inds_to_use, size=dup_births.shape[0] - inds_to_use.shape[0])
inds_to_use = np.concatenate((inds_to_use, extra_inds))
np.random.shuffle(inds_to_use)
birth_inds[dup_births] = inds_to_use[:dup_births.shape[0]]
assert np.all(~np.isnan(birth_inds)), np.isnan(birth_inds).sum()
np.random.set_state(state) # Reset random state
return birth_inds.astype(int)
def sample_less_than_condition(choices_in, condition):
"""Creates a random sample from choices without replacement, subject to the
condition that each element of the output is greater than the corresponding
element of the condition array.
condition should be in ascending order.
"""
output = np.zeros(min(condition.shape[0], choices_in.shape[0]))
choices = copy.deepcopy(choices_in)
for i, _ in enumerate(output):
# randomly select one of the choices which meets condition
avail_inds = np.where(choices < condition[i])[0]
selected_ind = np.random.choice(avail_inds)
output[i] = choices[selected_ind]
# remove the chosen value
choices = np.delete(choices, selected_ind)
return output
def threads_given_birth_inds(birth_inds):
"""Divides a nested sampling run into threads, using info on the indexes
at which points were sampled. See "Sampling errors in nested sampling
parameter estimation" (Higson et al. 2018) for more information.
Parameters
----------
birth_inds: 1d numpy array
Indexes of the iso-likelihood contours from within which each point was
sampled ("born").
Returns
-------
thread_labels: 1d numpy array of ints
labels of the thread each point belongs to.
"""
unique, counts = np.unique(birth_inds, return_counts=True)
# First get a list of all the indexes on which threads start and their
# counts. This is every point initially sampled from the prior, plus any
# indexes where more than one point is sampled.
thread_start_inds = np.concatenate((
unique[:1], unique[1:][counts[1:] > 1]))
thread_start_counts = np.concatenate((
counts[:1], counts[1:][counts[1:] > 1] - 1))
thread_labels = np.full(birth_inds.shape, np.nan)
thread_num = 0
for nmulti, multi in enumerate(thread_start_inds):
for i, start_ind in enumerate(np.where(birth_inds == multi)[0]):
# unless nmulti=0 the first point born on the contour (i=0) is
# already assigned to a thread
if i != 0 or nmulti == 0:
# check point has not already been assigned
assert np.isnan(thread_labels[start_ind])
thread_labels[start_ind] = thread_num
# find the point which replaced it
next_ind = np.where(birth_inds == start_ind)[0]
while next_ind.shape != (0,):
# check point has not already been assigned
assert np.isnan(thread_labels[next_ind[0]])
thread_labels[next_ind[0]] = thread_num
# find the point which replaced it
next_ind = np.where(birth_inds == next_ind[0])[0]
thread_num += 1
if not np.all(~np.isnan(thread_labels)):
warnings.warn((
'{} points (out of a total of {}) were not given a thread label! '
'This is likely due to small numerical errors in your nested '
'sampling software while running the calculation or writing the '
'input files. '
'I will try to give an approximate answer by randomly assigning '
'these points to threads.'
'\nIndexes without labels are {}'
'\nIndexes on which threads start are {} with {} threads '
'starting on each.').format(
(np.isnan(thread_labels)).sum(), birth_inds.shape[0],
np.where(np.isnan(thread_labels))[0],
thread_start_inds, thread_start_counts))
inds = np.where(np.isnan(thread_labels))[0]
state = np.random.get_state() # Save random state before seeding
np.random.seed(0) # make thread decomposition is reproducible
for ind in inds:
# Get the set of threads with members both before and after ind to
# ensure we don't change nlive_array by extending a thread
labels_to_choose = np.intersect1d( # N.B. this removes nans too
thread_labels[:ind], thread_labels[ind + 1:])
if labels_to_choose.shape[0] == 0:
# In edge case that there is no intersection, just randomly
# select from non-nan thread labels
labels_to_choose = np.unique(
thread_labels[~np.isnan(thread_labels)])
thread_labels[ind] = np.random.choice(labels_to_choose)
np.random.set_state(state) # Reset random state
assert np.all(~np.isnan(thread_labels)), (
'{} points still do not have thread labels'.format(
(np.isnan(thread_labels)).sum()))
assert np.array_equal(thread_labels, thread_labels.astype(int)), (
'Thread labels should all be ints!')
thread_labels = thread_labels.astype(int)
# Check unique thread labels are a sequence from 0 to nthreads-1
assert np.array_equal(
np.unique(thread_labels),
np.asarray(range(sum(thread_start_counts)))), (
str(np.unique(thread_labels)) + ' is not equal to range('
+ str(sum(thread_start_counts)) + ')')
return thread_labels
|
ejhigsonREPO_NAMEnestcheckPATH_START.@nestcheck_extracted@nestcheck-master@nestcheck@data_processing.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/ternary/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._uirevision import UirevisionValidator
from ._sum import SumValidator
from ._domain import DomainValidator
from ._caxis import CaxisValidator
from ._bgcolor import BgcolorValidator
from ._baxis import BaxisValidator
from ._aaxis import AaxisValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._uirevision.UirevisionValidator",
"._sum.SumValidator",
"._domain.DomainValidator",
"._caxis.CaxisValidator",
"._bgcolor.BgcolorValidator",
"._baxis.BaxisValidator",
"._aaxis.AaxisValidator",
],
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@ternary@__init__.py@.PATH_END.py
|
{
"filename": "astCalc.py",
"repo_name": "mattyowl/astLib",
"repo_path": "astLib_extracted/astLib-main/astLib/astCalc.py",
"type": "Python"
}
|
"""Module for performing common calculations.
(c) 2007-2011 Matt Hilton
(c) 2013-2014 Matt Hilton & Steven Boada
The focus in this module is at present on calculations of distances in a given
cosmology. The parameters for the cosmological model are set using the
variables OMEGA_M0, OMEGA_L0, OMEGA_R0, H0 in the module namespace.
"""
OMEGA_M0 = 0.3
"""The matter density parameter at z=0."""
OMEGA_L0 = 0.7
"""The dark energy density (in the form of a cosmological constant) at z=0."""
OMEGA_R0 = 8.24E-5
"""The radiation density at z=0 (note this is only used currently in calculation of L{Ez})."""
H0 = 70.0
"""The Hubble parameter (in km/s/Mpc) at z=0."""
C_LIGHT = 3.0e5
"""The speed of light in km/s."""
import math
try:
from scipy import integrate
except ImportError:
print("WARNING: astCalc failed to import scipy modules - some functions will not work")
#------------------------------------------------------------------------------
def dl(z):
"""Calculates the luminosity distance in Mpc at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: luminosity distance in Mpc
"""
DM = dm(z)
DL = (1.0+z)*DM
return DL
#------------------------------------------------------------------------------
def da(z):
"""Calculates the angular diameter distance in Mpc at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: angular diameter distance in Mpc
"""
DM = dm(z)
DA = DM/(1.0+z)
return DA
#------------------------------------------------------------------------------
def dm(z):
"""Calculates the transverse comoving distance (proper motion distance) in
Mpc at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: transverse comoving distance (proper motion distance) in Mpc
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
# Integration limits
xMax = 1.0
xMin = 1.0 / (1.0 + z)
# Function to be integrated
yn = lambda x: (1.0/math.sqrt(OMEGA_M0*x + OMEGA_L0*math.pow(x, 4) +
OMEGA_K*math.pow(x, 2)))
integralValue, integralError = integrate.quad(yn, xMin, xMax)
if OMEGA_K > 0.0:
DM = (C_LIGHT/H0 * math.pow(abs(OMEGA_K), -0.5) *
math.sinh(math.sqrt(abs(OMEGA_K)) * integralValue))
elif OMEGA_K == 0.0:
DM = C_LIGHT/H0 * integralValue
elif OMEGA_K < 0.0:
DM = (C_LIGHT/H0 * math.pow(abs(OMEGA_K), -0.5) *
math.sin(math.sqrt(abs(OMEGA_K)) * integralValue))
return DM
#------------------------------------------------------------------------------
def dc(z):
"""Calculates the line of sight comoving distance in Mpc at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: transverse comoving distance (proper motion distance) in Mpc
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
# Integration limits
xMax = 1.0
xMin = 1.0 / (1.0 + z)
# Function to be integrated
yn = lambda x: (1.0/math.sqrt(OMEGA_M0*x + OMEGA_L0*math.pow(x, 4) +
OMEGA_K*math.pow(x, 2)))
integralValue, integralError = integrate.quad(yn, xMin, xMax)
DC= C_LIGHT/H0*integralValue
return DC
#------------------------------------------------------------------------------
def dVcdz(z):
"""Calculates the line of sight comoving volume element per steradian dV/dz
at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: comoving volume element per steradian
"""
dH = C_LIGHT/H0
dVcdz=(dH*(math.pow(da(z),2))*(math.pow(1+z,2))/Ez(z))
return dVcdz
#------------------------------------------------------------------------------
def dl2z(distanceMpc):
"""Calculates the redshift z corresponding to the luminosity distance given
in Mpc.
@type distanceMpc: float
@param distanceMpc: distance in Mpc
@rtype: float
@return: redshift
"""
dTarget = distanceMpc
toleranceMpc = 0.1
zMin = 0.0
zMax = 10.0
diff = dl(zMax) - dTarget
while diff < 0:
zMax = zMax + 5.0
diff = dl(zMax) - dTarget
zTrial = zMin + (zMax-zMin)/2.0
dTrial = dl(zTrial)
diff = dTrial - dTarget
while abs(diff) > toleranceMpc:
if diff > 0:
zMax = zMax - (zMax-zMin)/2.0
else:
zMin = zMin + (zMax-zMin)/2.0
zTrial = zMin + (zMax-zMin)/2.0
dTrial = dl(zTrial)
diff = dTrial - dTarget
return zTrial
#------------------------------------------------------------------------------
def dc2z(distanceMpc):
"""Calculates the redshift z corresponding to the comoving distance given
in Mpc.
@type distanceMpc: float
@param distanceMpc: distance in Mpc
@rtype: float
@return: redshift
"""
dTarget = distanceMpc
toleranceMpc = 0.1
zMin = 0.0
zMax = 10.0
diff = dc(zMax) - dTarget
while diff < 0:
zMax = zMax + 5.0
diff = dc(zMax) - dTarget
zTrial = zMin + (zMax-zMin)/2.0
dTrial = dc(zTrial)
diff = dTrial - dTarget
while abs(diff) > toleranceMpc:
if diff > 0:
zMax = zMax - (zMax-zMin)/2.0
else:
zMin = zMin + (zMax-zMin)/2.0
zTrial = zMin + (zMax-zMin)/2.0
dTrial = dc(zTrial)
diff = dTrial - dTarget
return zTrial
#------------------------------------------------------------------------------
def t0():
"""Calculates the age of the universe in Gyr at z=0 for the current set of
cosmological parameters.
@rtype: float
@return: age of the universe in Gyr at z=0
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
# Integration limits
xMax = 1.0
xMin = 0
# Function to be integrated
yn = lambda x: (x/math.sqrt(OMEGA_M0*x + OMEGA_L0*math.pow(x, 4) +
OMEGA_K*math.pow(x, 2)))
integralValue, integralError = integrate.quad(yn, xMin, xMax)
T0 = (1.0/H0*integralValue*3.08e19)/3.16e7/1e9
return T0
#------------------------------------------------------------------------------
def tl(z):
""" Calculates the lookback time in Gyr to redshift z for the current set
of cosmological parameters.
@type z: float
@param z: redshift
@rtype: float
@return: lookback time in Gyr to redshift z
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
# Integration limits
xMax = 1.0
xMin = 1./(1.+z)
# Function to be integrated
yn = lambda x: (x/math.sqrt(OMEGA_M0*x + OMEGA_L0*math.pow(x, 4) +
OMEGA_K*math.pow(x, 2)))
integralValue, integralError = integrate.quad(yn, xMin, xMax)
T0 = (1.0/H0*integralValue*3.08e19)/3.16e7/1e9
return T0
#------------------------------------------------------------------------------
def tz(z):
"""Calculates the age of the universe at redshift z for the current set of
cosmological parameters.
@type z: float
@param z: redshift
@rtype: float
@return: age of the universe in Gyr at redshift z
"""
TZ = t0() - tl(z)
return TZ
#------------------------------------------------------------------------------
def tl2z(tlGyr):
"""Calculates the redshift z corresponding to lookback time tlGyr given in
Gyr.
@type tlGyr: float
@param tlGyr: lookback time in Gyr
@rtype: float
@return: redshift
@note: Raises ValueError if tlGyr is not positive.
"""
if tlGyr < 0.:
raise ValueError('Lookback time must be positive')
tTarget = tlGyr
toleranceGyr = 0.001
zMin = 0.0
zMax = 10.0
diff = tl(zMax) - tTarget
while diff < 0:
zMax = zMax + 5.0
diff = tl(zMax) - tTarget
zTrial = zMin + (zMax-zMin)/2.0
tTrial = tl(zTrial)
diff = tTrial - tTarget
while abs(diff) > toleranceGyr:
if diff > 0:
zMax = zMax - (zMax-zMin)/2.0
else:
zMin = zMin + (zMax-zMin)/2.0
zTrial = zMin + (zMax-zMin)/2.0
tTrial = tl(zTrial)
diff = tTrial - tTarget
return zTrial
#------------------------------------------------------------------------------
def tz2z(tzGyr):
"""Calculates the redshift z corresponding to age of the universe tzGyr
given in Gyr.
@type tzGyr: float
@param tzGyr: age of the universe in Gyr
@rtype: float
@return: redshift
@note: Raises ValueError if Universe age not positive
"""
if tzGyr <= 0:
raise ValueError('Universe age must be positive.')
tl = t0() - tzGyr
z = tl2z(tl)
return z
#------------------------------------------------------------------------------
def absMag(appMag, distMpc):
"""Calculates the absolute magnitude of an object at given luminosity
distance in Mpc.
@type appMag: float
@param appMag: apparent magnitude of object
@type distMpc: float
@param distMpc: distance to object in Mpc
@rtype: float
@return: absolute magnitude of object
"""
absMag = appMag - (5.0*math.log10(distMpc*1.0e5))
return absMag
#------------------------------------------------------------------------------
def Ez(z):
"""Calculates the value of E(z), which describes evolution of the Hubble
parameter with redshift, at redshift z for the current set of cosmological
parameters. See, e.g., Bryan & Norman 1998 (ApJ, 495, 80).
@type z: float
@param z: redshift
@rtype: float
@return: value of E(z) at redshift z
"""
Ez = math.sqrt(Ez2(z))
return Ez
#------------------------------------------------------------------------------
def Ez2(z):
"""Calculates the value of E(z)^2, which describes evolution of the Hubble
parameter with redshift, at redshift z for the current set of cosmological
parameters. See, e.g., Bryan & Norman 1998 (ApJ, 495, 80).
@type z: float
@param z: redshift
@rtype: float
@return: value of E(z)^2 at redshift z
"""
# This form of E(z) is more reliable at high redshift. It is basically the
# same for all redshifts below 10. But above that, the radiation term
# begins to dominate. From Peebles 1993.
Ez2 = (OMEGA_R0 * math.pow(1.0+z, 4) +
OMEGA_M0* math.pow(1.0+z, 3) +
(1.0- OMEGA_M0- OMEGA_L0) *
math.pow(1.0+z, 2) + OMEGA_L0)
return Ez2
#------------------------------------------------------------------------------
def OmegaMz(z):
"""Calculates the matter density of the universe at redshift z. See, e.g.,
Bryan & Norman 1998 (ApJ, 495, 80).
@type z: float
@param z: redshift
@rtype: float
@return: matter density of universe at redshift z
"""
ez2 = Ez2(z)
Omega_Mz = (OMEGA_M0*math.pow(1.0+z, 3))/ez2
return Omega_Mz
#------------------------------------------------------------------------------
def OmegaLz(z):
""" Calculates the dark energy density of the universe at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: dark energy density of universe at redshift z
"""
ez2 = Ez2(z)
return OMEGA_L0/ez2
#------------------------------------------------------------------------------
def OmegaRz(z):
""" Calculates the radiation density of the universe at redshift z.
@type z: float
@param z: redshift
@rtype: float
@return: radiation density of universe at redshift z
"""
ez2 = Ez2(z)
return OMEGA_R0*math.pow(1+z, 4)/ez2
#------------------------------------------------------------------------------
def DeltaVz(z):
"""Calculates the density contrast of a virialised region S{Delta}V(z),
assuming a S{Lambda}CDM-type flat cosmology. See, e.g., Bryan & Norman
1998 (ApJ, 495, 80).
@type z: float
@param z: redshift
@rtype: float
@return: density contrast of a virialised region at redshift z
@note: If OMEGA_M0+OMEGA_L0 is not equal to 1, this routine exits and
prints an error
message to the console.
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
if OMEGA_K == 0.0:
Omega_Mz = OmegaMz(z)
deltaVz = (18.0*math.pow(math.pi, 2)+82.0*(Omega_Mz-1.0)-39.0 *
math.pow(Omega_Mz-1, 2))
return deltaVz
else:
raise Exception("cosmology is NOT flat.")
#------------------------------------------------------------------------------
def RVirialXRayCluster(kT, z, betaT):
"""Calculates the virial radius (in Mpc) of a galaxy cluster at redshift z
with X-ray temperature kT, assuming self-similar evolution and a flat
cosmology. See Arnaud et al. 2002 (A&A, 389, 1) and Bryan & Norman 1998
(ApJ, 495, 80). A flat S{Lambda}CDM-type flat cosmology is assumed.
@type kT: float
@param kT: cluster X-ray temperature in keV
@type z: float
@param z: redshift
@type betaT: float
@param betaT: the normalisation of the virial relation, for which Evrard et
al. 1996 (ApJ,469, 494) find a value of 1.05
@rtype: float
@return: virial radius of cluster in Mpc
@note: If OMEGA_M0+OMEGA_L0 is not equal to 1, this routine exits and
prints an error message to the console.
"""
OMEGA_K = 1.0 - OMEGA_M0 - OMEGA_L0
if OMEGA_K == 0.0:
Omega_Mz = OmegaMz(z)
deltaVz = (18.0 * math.pow(math.pi, 2) + 82.0 * (Omega_Mz-1.0)- 39.0 *
math.pow(Omega_Mz-1, 2))
deltaz = (deltaVz*OMEGA_M0)/(18.0*math.pow(math.pi, 2)*Omega_Mz)
# The equation quoted in Arnaud, Aghanim & Neumann is for h50, so need
# to scale it
h50 = H0/50.0
Rv = (3.80*math.sqrt(betaT)*math.pow(deltaz, -0.5) *
math.pow(1.0+z, (-3.0/2.0)) * math.sqrt(kT/10.0)*(1.0/h50))
return Rv
else:
raise Exception("cosmology is NOT flat.")
#------------------------------------------------------------------------------
|
mattyowlREPO_NAMEastLibPATH_START.@astLib_extracted@astLib-main@astLib@astCalc.py@.PATH_END.py
|
{
"filename": "test_condor.py",
"repo_name": "sampsyo/clusterfutures",
"repo_path": "clusterfutures_extracted/clusterfutures-master/tests/test_condor.py",
"type": "Python"
}
|
from testpath import MockCommand
import cfut
from .utils import run_all_outstanding_work
def square(n):
return n * n
def test_submit():
executor = cfut.CondorExecutor(debug=True, keep_logs=True)
try:
with MockCommand.fixed_output('condor_submit', stdout='Proc 0.0') as csub:
fut = executor.submit(square, 2)
csub.assert_called()
assert not fut.done()
run_all_outstanding_work()
assert fut.result(timeout=3) == 4
finally:
executor.shutdown(wait=False)
CONDOR_JOB_COUNT = """
from pathlib import Path
counter_file = Path(__file__).parent / 'condor_job_id'
if counter_file.is_file():
count = int(counter_file.read_text().strip()) + 1
else:
count = 0
counter_file.write_text(str(count))
print("Proc {}.0".format(count))
"""
def test_map():
executor = cfut.CondorExecutor(debug=True, keep_logs=True)
try:
with MockCommand('condor_submit', python=CONDOR_JOB_COUNT) as csub:
result_iter = executor.map(square, range(4), timeout=5)
csub.assert_called()
run_all_outstanding_work()
assert list(result_iter) == [0, 1, 4, 9]
finally:
executor.shutdown(wait=False)
|
sampsyoREPO_NAMEclusterfuturesPATH_START.@clusterfutures_extracted@clusterfutures-master@tests@test_condor.py@.PATH_END.py
|
{
"filename": "convert.py",
"repo_name": "apertif/apercal",
"repo_path": "apercal_extracted/apercal-master/apercal/modules/convert.py",
"type": "Python"
}
|
import glob
import logging
import numpy as np
import pandas as pd
from os import path
import os
from apercal.modules.base import BaseModule
from apercal.subs import setinit as subs_setinit
from apercal.subs import managefiles as subs_managefiles
from apercal.subs.param import get_param_def
from apercal.subs import param as subs_param
from apercal.subs import msutils as subs_msutils
from apercal.libs import lib
from apercal.exceptions import ApercalException
logger = logging.getLogger(__name__)
exportuvfits_cmd = 'exportuvfits(vis="{vis}", fitsfile="{fits}",datacolumn="{datacolumn}", ' \
'combinespw=True, padwithflags=True, multisource=True, writestation=True)'
def mspath_to_fitspath(prefix, ms, ext='UVFITS'):
return path.join(prefix, ms.split('/')[-1].rstrip('MS') + ext)
class convert(BaseModule):
"""
Class to convert data from MS-format into UVFITS, and from UVFITS into MIRIAD format. Resulting datasets will
have the endings .MS, .UVFITS, and .mir.
"""
module_name = 'CONVERT'
convert_fluxcal = True # Convert the flux calibrator dataset
convert_polcal = True # Convert the polarised calibrator dataset
convert_target = True # Convert the target beam dataset
convert_removeuvfits = True # Remove the UVFITS files
convert_removems = True # Remove measurement sets
def __init__(self, file_=None, **kwargs):
self.default = lib.load_config(self, file_)
subs_setinit.setinitdirs(self)
def get_crosscalsubdir_path(self, beam=None):
if not beam:
beam = self.beam
if self.subdirification:
return path.join(self.basedir, beam, self.crosscalsubdir)
else:
return os.getcwd()
def go(self):
"""
Executes the whole conversion from MS format to MIRIAD format of the flux calibrator, polarisation calibrator
and target dataset in the following order:
ms2uvfits
uvfits2miriad
"""
logger.info('Beam ' + self.beam + ': FILE CONVERSION started')
self.ms2miriad()
logger.info('Beam ' + self.beam + ': FILE CONVERSION done')
def ms2miriad(self):
"""
Converts the data from MS to MIRIAD format via UVFITS using drivecasa. Does it for the flux calibrator,
polarisation calibrator, and target field independently.
"""
subs_setinit.setinitdirs(self)
ccalbeam = 'ccal_B' + str(self.beam).zfill(2)
cbeam = 'convert_B' + str(self.beam).zfill(2)
# Read the parameters from crosscal
# and check before doing anything
# Status of the solution transfer for the target, flux calibrator and polarisation calibrator
ccal_targetbeams_transfer = get_param_def(
self, ccalbeam + '_targetbeams_transfer', False)
ccal_calibration_calibrator_finished = get_param_def(
self, ccalbeam + '_calibration_calibrator_finished', False)
if not ccal_calibration_calibrator_finished:
error = "Beam {}: Will not convert files to miriad format because cross-calibration failed.".format(str(self.beam).zfill(2))
logger.error(error)
raise ApercalException(error)
elif not ccal_targetbeams_transfer:
error = "Beam {}: Will not convert files to miriad format because cross-calibration solutions were not successfully applied to target.".format(str(self.beam).zfill(2))
logger.error(error)
raise ApercalException(error)
# Create the parameters for the parameter file for converting from MS to UVFITS format
# Flux calibrator MS dataset available?
convertfluxcalmsavailable = get_param_def(self, cbeam + '_fluxcal_MSavailable', False)
# Polarised calibrator MS dataset available?
convertpolcalmsavailable = get_param_def(self, cbeam + '_polcal_MSavailable', False)
# Target beam MS dataset available?
converttargetbeamsmsavailable = get_param_def(self, cbeam + '_targetbeams_MSavailable', False)
# Flux calibrator MS dataset converted to UVFITS?
convertfluxcalms2uvfits = get_param_def(self, cbeam + '_fluxcal_MS2UVFITS', False)
# Polarised calibrator MS dataset converted to UVFITS?
convertpolcalms2uvfits = get_param_def(self, cbeam + '_polcal_MS2UVFITS', False)
# Target beam MS dataset converted to UVFITS?
converttargetbeamsms2uvfits = get_param_def(self, cbeam + '_targetbeams_MS2UVFITS', False)
# Flux calibrator UVFITS dataset available?
convertfluxcaluvfitsavailable = get_param_def(self, cbeam + '_fluxcal_UVFITSavailable', False)
# Polarised calibrator UVFITS dataset available?
convertpolcaluvfitsavailable = get_param_def(self, cbeam + '_polcal_UVFITSavailable', False)
# Target beam UVFITS dataset available?
converttargetbeamsuvfitsavailable = get_param_def(self, cbeam + '_targetbeams_UVFITSavailable', False)
# Flux calibrator UVFITS dataset converted to MIRIAD?
convertfluxcaluvfits2miriad = get_param_def(self, cbeam + '_fluxcal_UVFITS2MIRIAD', False)
# Polarised calibrator UVFITS dataset converted to MIRIAD?
convertpolcaluvfits2miriad = get_param_def(self, cbeam + '_polcal_UVFITS2MIRIAD', False)
# Target beam UVFITS dataset converted to MIRIAD?
converttargetbeamsuvfits2miriad = get_param_def(self, cbeam + '_targetbeams_UVFITS2MIRIAD', False)
# Check which datasets are available in MS format #
if self.fluxcal != '':
convertfluxcalmsavailable = path.isdir(self.get_fluxcal_path())
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset not specified. Cannot convert flux calibrator!')
if self.polcal != '':
convertpolcalmsavailable = path.isdir(self.get_polcal_path())
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not specified. Cannot convert polarised calibrator!')
if self.target != '':
converttargetbeamsmsavailable = path.isdir(self.get_target_path())
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not specified. Cannot convert target beams!')
# Save the derived parameters for the availability to the parameter file
subs_param.add_param(self, cbeam + '_fluxcal_MSavailable', convertfluxcalmsavailable)
subs_param.add_param(self, cbeam + '_polcal_MSavailable', convertpolcalmsavailable)
subs_param.add_param(self, cbeam + '_targetbeams_MSavailable', converttargetbeamsmsavailable)
# Convert the flux calibrator
if self.convert_fluxcal:
if self.fluxcal != '':
if not convertfluxcaluvfits2miriad:
if convertfluxcalmsavailable:
logger.debug('Beam ' + self.beam + ': Converting flux calibrator dataset from MS to UVFITS format.')
subs_managefiles.director(self, 'mk', self.get_crosscalsubdir_path(),
verbose=False)
fluxcal_ms = self.get_fluxcal_path()
# convert only if corrected data column exists
if subs_msutils.has_correcteddata(fluxcal_ms):
datacolumn = "corrected"
fluxcal_fits = mspath_to_fitspath(self.get_crosscalsubdir_path(), fluxcal_ms)
fc_convert = exportuvfits_cmd.format(vis=self.get_fluxcal_path(), fits=fluxcal_fits, datacolumn=datacolumn)
lib.run_casa([fc_convert], timeout=3600)
if path.isfile(fluxcal_fits):
convertfluxcalms2uvfits = True
logger.info('Beam ' + self.beam + ': Converted flux calibrator dataset from MS to UVFITS format!')
else:
convertfluxcalms2uvfits = False
logger.warning('Beam ' + self.beam + ': Could not convert flux calibrator dataset {} '
'from MS to UVFITS format!'.format(fluxcal_fits))
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator does not have a corrected_data column! Not '
'converting flux calibrator dataset!')
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset {} not available!'.format(self.get_fluxcal_path()))
else:
logger.info('Beam ' + self.beam + ': Flux calibrator dataset was already converted from MS to UVFITS format')
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset not specified. Cannot convert flux calibrator!')
else:
logger.warning('Beam ' + self.beam + ': Not converting flux calibrator dataset!')
# Convert the polarised calibrator
if self.convert_polcal:
if self.polcal != '':
if not convertpolcaluvfits2miriad:
if convertpolcalmsavailable:
logger.debug('Beam ' + self.beam + ': Converting polarised calibrator dataset from MS to UVFITS format.')
subs_managefiles.director(self, 'mk', self.get_crosscalsubdir_path(), verbose=False)
polcal_ms = self.get_polcal_path()
# convert only if corrected data column exists
if subs_msutils.has_correcteddata(polcal_ms):
datacolumn = "corrected"
polcal_fits = mspath_to_fitspath(self.get_crosscalsubdir_path(), polcal_ms)
pc_convert = exportuvfits_cmd.format(vis=polcal_ms, fits=polcal_fits, datacolumn=datacolumn)
lib.run_casa([pc_convert], timeout=3600)
if path.isfile(polcal_fits):
convertpolcalms2uvfits = True
logger.info('Beam ' + self.beam + ': Converted polarised calibrator dataset from MS to UVFITS format!')
else:
convertpolcalms2uvfits = False
logger.warning('Beam ' + self.beam + ': Could not convert polarised calibrator dataset from MS to UVFITS format!')
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator does not have a corrected_data column! Not '
'converting polarised calibrator dataset!')
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not available!')
else:
logger.info('Beam ' + self.beam + ': Polarised calibrator dataset was already converted from MS to UVFITS format')
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not specified. Cannot convert polarised calibrator!')
else:
logger.warning('Beam ' + self.beam + ': Not converting polarised calibrator dataset!')
# Convert the target beams
if self.convert_target:
if self.target != '':
logger.info('Beam ' + self.beam + ': Converting target beam dataset from MS to UVFITS format.')
if not converttargetbeamsuvfits2miriad:
if converttargetbeamsmsavailable:
subs_managefiles.director(self, 'mk', self.get_crosscalsubdir_path(), verbose=False)
target_ms = self.get_target_path()
target_fits = mspath_to_fitspath(self.get_crosscalsubdir_path(), target_ms)
# only convert if corrected data column exists
if subs_msutils.has_correcteddata(target_ms):
datacolumn = "corrected"
tg_convert = exportuvfits_cmd.format(vis=target_ms, fits=target_fits, datacolumn=datacolumn)
lib.run_casa([tg_convert], timeout=10000)
if path.isfile(target_fits):
converttargetbeamsms2uvfits = True
logger.debug('Beam ' + self.beam + ': Converted dataset of target beam from MS to UVFITS format!')
else:
converttargetbeamsms2uvfits = False
logger.warning('Beam ' + self.beam + ': Could not convert dataset for target beam from MS to UVFITS format!')
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset does not have a corrected_data column! Not '
'converting target beam dataset!')
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not available!')
else:
logger.info('Beam ' + self.beam + ': Target beam dataset was already '
'converted from MS to UVFITS format')
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not specified. Cannot convert target beam dataset!')
else:
logger.warning('Beam ' + self.beam + ': Not converting target beam dataset!')
# Save the derived parameters for the MS to UVFITS conversion to the parameter file
subs_param.add_param(self, cbeam + '_fluxcal_MS2UVFITS', convertfluxcalms2uvfits)
subs_param.add_param(self, cbeam + '_polcal_MS2UVFITS', convertpolcalms2uvfits)
subs_param.add_param(self, cbeam + '_targetbeams_MS2UVFITS', converttargetbeamsms2uvfits)
# Check which datasets are available in UVFITS format #
if self.fluxcal != '':
crosscal_fluxcal = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.fluxcal)
convertfluxcaluvfitsavailable = path.isfile(crosscal_fluxcal)
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset not specified. Cannot convert flux calibrator!')
if self.polcal != '':
crosscal_polcal = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.polcal)
convertpolcaluvfitsavailable = path.isfile(crosscal_polcal)
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not specified. Cannot convert polarised calibrator!')
if self.target != '':
crosscal_target = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.target)
converttargetbeamsuvfitsavailable = path.isfile(crosscal_target)
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not specified. Cannot convert target beam!')
# Save the derived parameters for the availability to the parameter file
subs_param.add_param(self, cbeam + '_fluxcal_UVFITSavailable', convertfluxcaluvfitsavailable)
subs_param.add_param(self, cbeam + '_polcal_UVFITSavailable', convertpolcaluvfitsavailable)
subs_param.add_param(self, cbeam + '_targetbeams_UVFITSavailable', converttargetbeamsuvfitsavailable)
# Convert the available UVFITS-datasets to MIRIAD format #
# Convert the flux calibrator
if self.convert_fluxcal:
if self.fluxcal != '':
if not convertfluxcaluvfits2miriad:
if convertfluxcaluvfitsavailable:
logger.debug('Beam ' + self.beam + ': Converting flux calibrator dataset from UVFITS to MIRIAD format.')
subs_managefiles.director(self, 'ch', self.get_crosscalsubdir_path(), verbose=False)
fits = lib.miriad('fits')
fits.op = 'uvin'
fits.in_ = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.fluxcal)
fits.out = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.fluxcal, ext='mir')
fits.go()
if path.isdir(fits.out):
convertfluxcaluvfits2miriad = True
logger.info('Beam ' + self.beam + ': Converted flux calibrator dataset from UVFITS to MIRIAD format!')
else:
convertfluxcaluvfits2miriad = False
logger.warning('Beam ' + self.beam + ': Could not convert flux calibrator dataset {} from UVFITS to '
'MIRIAD format!'.format(fits.out))
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset not available!')
else:
logger.info('Beam ' + self.beam + ': Flux calibrator dataset was already converted from UVFITS to MIRIAD format')
else:
logger.warning('Beam ' + self.beam + ': Flux calibrator dataset not specified. Cannot convert flux calibrator!')
else:
logger.warning('Beam ' + self.beam + ': Not converting flux calibrator dataset!')
# Convert the polarised calibrator
if self.convert_polcal:
if self.polcal != '':
if not convertpolcaluvfits2miriad:
if convertpolcaluvfitsavailable:
logger.debug('Beam ' + self.beam + ': Converting polarised calibrator dataset from UVFITS to MIRIAD format.')
subs_managefiles.director(self, 'ch', self.get_crosscalsubdir_path(), verbose=False)
fits = lib.miriad('fits')
fits.op = 'uvin'
fits.in_ = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.polcal)
fits.out = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.polcal, ext='mir')
fits.go()
if path.isdir(fits.out):
convertpolcaluvfits2miriad = True
logger.info('Beam ' + self.beam + ': Converted polarised calibrator dataset from UVFITS to MIRIAD format!')
else:
convertpolcaluvfits2miriad = False
logger.warning(
'Beam ' + self.beam + ': Could not convert polarised calibrator dataset from UVFITS to MIRIAD format!')
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not available!')
else:
logger.info('Beam ' + self.beam + ': Polarised calibrator dataset was already converted from UVFITS to MIRIAD format')
else:
logger.warning('Beam ' + self.beam + ': Polarised calibrator dataset not specified. Cannot convert polarised calibrator!')
else:
logger.warning('Beam ' + self.beam + ': Not converting polarised calibrator dataset!')
# Convert the target beams
if self.convert_target:
if self.target != '':
logger.info('Beam ' + self.beam + ': Converting target beam dataset from UVFITS to MIRIAD format.')
if not converttargetbeamsuvfits2miriad:
if converttargetbeamsuvfitsavailable:
subs_managefiles.director(self, 'ch', self.get_crosscalsubdir_path(), verbose=False)
fits = lib.miriad('fits')
fits.op = 'uvin'
fits.in_ = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.target)
fits.out = mspath_to_fitspath(self.get_crosscalsubdir_path(), self.target, ext='mir')
fits.go()
if path.isdir(fits.out):
converttargetbeamsuvfits2miriad = True
logger.debug('Beam ' + self.beam + ': Converted target beam dataset from '
'UVFITS to MIRIAD format!')
else:
converttargetbeamsuvfits2miriad = False
logger.warning('Beam ' + self.beam + ': Could not convert target beam dataset '
'{} from UVFITS to MIRIAD format!'.format(fits.out))
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not available!')
else:
logger.info('Beam ' + self.beam + ': Target beam dataset was already converted '
'from MS to UVFITS format')
else:
logger.warning('Beam ' + self.beam + ': Target beam dataset not specified. Cannot convert target beam datasets!')
else:
logger.warning('Beam ' + self.beam + ': Not converting target beam dataset!')
# Save the derived parameters for the MS to UVFITS conversion to the parameter file
subs_param.add_param(self, cbeam + '_fluxcal_UVFITS2MIRIAD', convertfluxcaluvfits2miriad)
subs_param.add_param(self, cbeam + '_polcal_UVFITS2MIRIAD', convertpolcaluvfits2miriad)
subs_param.add_param(self, cbeam + '_targetbeams_UVFITS2MIRIAD', converttargetbeamsuvfits2miriad)
if self.convert_averagems and self.subdirification:
logger.info('Beam ' + self.beam +
': Averaging down target measurement set')
average_cmd = 'mstransform(vis="{vis}", outputvis="{outputvis}", chanaverage=True, chanbin=64)'
vis = self.get_target_path()
outputvis = vis.replace(".MS", "_avg.MS")
lib.run_casa([average_cmd.format(vis=vis, outputvis=outputvis)], timeout=10000)
# Remove measurement sets if wanted
if self.convert_removems and self.subdirification:
logger.info('Beam ' + self.beam + ': Removing measurement sets')
vis = self.get_target_path()
if path.exists(vis):
subs_managefiles.director(self, 'rm', vis)
# Remove the UVFITS files if wanted
if self.convert_removeuvfits and self.subdirification:
logger.info('Beam ' + self.beam + ': Removing all UVFITS files')
if self.fluxcal != '' and path.exists(mspath_to_fitspath(self.get_crosscalsubdir_path(), self.fluxcal)) and convertfluxcalms2uvfits:
subs_managefiles.director(self, 'rm', mspath_to_fitspath(self.get_crosscalsubdir_path(), self.fluxcal))
logger.info('Beam ' + self.beam + ': Removed fluxcal UVFITS files')
else:
logger.warning('Beam ' + self.beam +
': No fluxcal UVFITS file available for removing')
if self.polcal != '' and path.exists(mspath_to_fitspath(self.get_crosscalsubdir_path(), self.polcal)) and convertpolcalms2uvfits:
subs_managefiles.director(self, 'rm', mspath_to_fitspath(self.get_crosscalsubdir_path(), self.polcal))
logger.info('Beam ' + self.beam +
': Removed polcal UVFITS files')
else:
logger.warning('Beam ' + self.beam +
': No polcal UVFITS file available for removing')
if self.target != '' and path.exists(mspath_to_fitspath(self.get_crosscalsubdir_path(), self.target)) and convertfluxcalms2uvfits:
subs_managefiles.director(self, 'rm', mspath_to_fitspath(self.get_crosscalsubdir_path(), self.target))
logger.info('Beam ' + self.beam +
': Removed target UVFITS files')
else:
logger.warning('Beam ' + self.beam +
': No target UVFITS file available for removing')
def summary(self):
"""
Creates a general summary of the parameters in the parameter file generated during CONVERT. No detailed summary
is available for CONVERT.
returns (DataFrame): A python pandas dataframe object, which can be looked at with the style function in the
notebook
"""
# Load the parameters from the parameter file
FMSA = subs_param.get_param(self, 'convert_fluxcal_MSavailable')
PMSA = subs_param.get_param(self, 'convert_polcal_MSavailable')
TMSA = subs_param.get_param(self, 'convert_targetbeams_MSavailable')
FMS2UV = subs_param.get_param(self, 'convert_fluxcal_MS2UVFITS')
PMS2UV = subs_param.get_param(self, 'convert_polcal_MS2UVFITS')
TMS2UV = subs_param.get_param(self, 'convert_targetbeams_MS2UVFITS')
FUV2mir = subs_param.get_param(self, 'convert_fluxcal_UVFITS2MIRIAD')
PUV2mir = subs_param.get_param(self, 'convert_polcal_UVFITS2MIRIAD')
TUV2mir = subs_param.get_param(self, 'convert_targetbeams_UVFITS2MIRIAD')
# Create the data frame
beam_range = range(self.NBEAMS)
dataset_beams = [self.target[:-3] + ' Beam ' + str(b).zfill(2) for b in beam_range]
dataset_indices = ['Flux calibrator (' + self.fluxcal[:-3] + ')',
'Polarised calibrator (' + self.polcal[:-3] + ')'] + dataset_beams
all_MA = np.full(39, False)
all_MA[0] = FMSA
all_MA[1] = PMSA
all_MA[2:] = TMSA
all_M2U = np.full(39, False)
all_M2U[0] = FMS2UV
all_M2U[1] = PMS2UV
all_M2U[2:] = TMS2UV
all_U2mir = np.full(39, False)
all_U2mir[0] = FUV2mir
all_U2mir[1] = PUV2mir
all_U2mir[2:] = TUV2mir
df_msav = pd.DataFrame(np.ndarray.flatten(all_MA), index=dataset_indices, columns=['Available?'])
df_ms2uv = pd.DataFrame(np.ndarray.flatten(all_M2U), index=dataset_indices, columns=['MS -> UVFITS'])
df_uv2mir = pd.DataFrame(np.ndarray.flatten(all_U2mir), index=dataset_indices, columns=['UVFITS -> MIRIAD'])
df = pd.concat([df_msav, df_ms2uv, df_uv2mir], axis=1)
return df
def reset(self):
"""
Function to reset the current step and remove all generated data. Be careful! Deletes all data generated in
this step!
"""
subs_setinit.setinitdirs(self)
cbeam = 'convert_B' + str(self.beam).zfill(2)
logger.warning('Beam ' + self.beam + ': Deleting all converted data.')
path = self.get_crosscalsubdir_path()
if os.path.isdir(path):
subs_managefiles.director(self, 'rm', path + '/*')
logger.warning('Beam ' + self.beam + ': Deleting all parameter file entries for CONVERT module')
subs_param.del_param(self, cbeam + '_fluxcal_MSavailable')
subs_param.del_param(self, cbeam + '_polcal_MSavailable')
subs_param.del_param(self, cbeam + '_targetbeams_MSavailable')
subs_param.del_param(self, cbeam + '_fluxcal_MS2UVFITS')
subs_param.del_param(self, cbeam + '_polcal_MS2UVFITS')
subs_param.del_param(self, cbeam + '_targetbeams_MS2UVFITS')
subs_param.del_param(self, cbeam + '_fluxcal_UVFITSavailable')
subs_param.del_param(self, cbeam + '_polcal_UVFITSavailable')
subs_param.del_param(self, cbeam + '_targetbeams_UVFITSavailable')
subs_param.del_param(self, cbeam + '_fluxcal_UVFITS2MIRIAD')
subs_param.del_param(self, cbeam + '_polcal_UVFITS2MIRIAD')
subs_param.del_param(self, cbeam + '_targetbeams_UVFITS2MIRIAD')
def reset_all(self):
"""
Function to reset the current step and remove all generated data for all beams. Be careful! Deletes all data generated in
this step!
"""
subs_setinit.setinitdirs(self)
for b in range(self.NBEAMS):
cbeam = 'convert_B' + str(b).zfill(2)
logger.warning('Beam ' + str(b).zfill(2) + ': Deleting all converted data.')
path = self.get_crosscalsubdir_path(str(b).zfill(2))
if os.path.isdir(path):
subs_managefiles.director(self, 'rm', path + '/*')
logger.warning('Beam ' + str(b).zfill(2) + ': Deleting all parameter file entries for CONVERT module')
subs_param.del_param(self, cbeam + '_fluxcal_MSavailable')
subs_param.del_param(self, cbeam + '_polcal_MSavailable')
subs_param.del_param(self, cbeam + '_targetbeams_MSavailable')
subs_param.del_param(self, cbeam + '_fluxcal_MS2UVFITS')
subs_param.del_param(self, cbeam + '_polcal_MS2UVFITS')
subs_param.del_param(self, cbeam + '_targetbeams_MS2UVFITS')
subs_param.del_param(self, cbeam + '_fluxcal_UVFITSavailable')
subs_param.del_param(self, cbeam + '_polcal_UVFITSavailable')
subs_param.del_param(self, cbeam + '_targetbeams_UVFITSavailable')
subs_param.del_param(self, cbeam + '_fluxcal_UVFITS2MIRIAD')
subs_param.del_param(self, cbeam + '_polcal_UVFITS2MIRIAD')
subs_param.del_param(self, cbeam + '_targetbeams_UVFITS2MIRIAD')
|
apertifREPO_NAMEapercalPATH_START.@apercal_extracted@apercal-master@apercal@modules@convert.py@.PATH_END.py
|
{
"filename": "planet_growth.py",
"repo_name": "miosta/drift_composition",
"repo_path": "drift_composition_extracted/drift_composition-main/examples/drift_composition/planet_growth.py",
"type": "Python"
}
|
import numpy as np
from drift_composition.constants import k_boltzmann, m_hydrogen, G_Msun, Rau, Msun, yr, Mearth
def seed_mass(hr, flaring, gas_slope, dist):
dist = dist*Rau
vk = np.sqrt(mass_star*G_Msun/dist)
pres_grad = 2*(flaring-1)+gas_slope
eta = - 0.5* hr**2 * pres_grad
m_min = (eta*vk)**3/G_Msun/vk*dist/np.sqrt(3)
return m_min
def loc_disc (g_val, Rg, dist):
cid = np.argmin(np.abs(Rg-dist))
loc_val = g_val[cid]
#d_val = (g_val[cid+1]-g_val[cid-1])/(Rg[cid+1]-Rg[cid-1])
#loc_val = g_val[cid] + d_val*(dist-Rg[cid])
return loc_val
class Planet:
def __init__(self, mass, mc, mg, f_comp, dist=10.0):
self.mass = mass
self.mc = mc
self.mg = mg
self.dist = dist
self.f_comp = f_comp
class PlanetEnv:
def __init__(self, grid, alpha, mu, mass_star):
self.alpha = alpha
self.mu = mu
self.mass_star = mass_star
self.grid = grid
def temp(self, T, dist):
return loc_disc(T, self.grid.Rc, dist)
def sig_gas(self, disc, dist):
return loc_disc(disc.Sigma_gas, self.grid.Rc, dist)
def sig_dust(self, disc, dist):
return loc_disc(disc.Sigma_dust, self.grid.Rc, dist)
def mols(self, disc):
molc = disc.Molecules
return molc
def sig_mol(self, disc, dist):
molc = disc.Molecules
sigma_mol = disc.Sigma_mol
sig_mol_d = {}
sig_mol_g = {}
for mol in molc:
s_mol_d = loc_disc(sigma_mol[mol.name][:,1], self.grid.Rc, dist)
sig_mol_d[mol.name] = s_mol_d
s_mol_g = loc_disc(sigma_mol[mol.name][:,0], self.grid.Rc, dist)
sig_mol_g[mol.name] = s_mol_g
return sig_mol_g, sig_mol_d
def Stokes(self, disc, dist):
st = disc.Stokes
if np.isscalar(st):
stokes = st
else:
stokes = loc_disc(st, self.grid.Re, dist)
return stokes
def vk(self, dist):
return np.sqrt(self.mass_star*G_Msun/dist)
def hr(self, T, dist):
temp = loc_disc(T, self.grid.Rc, dist)
cs = np.sqrt(k_boltzmann/self.mu/m_hydrogen*temp)
return cs/(np.sqrt(self.mass_star*G_Msun/dist))
#def hr (temperature,dist, star_mass, mu):
# vk = np.sqrt(mass_star*G_Msun/dist)
# cs = np.sqrt(k_boltzmann/mu/m_hydrogen*temperature)
# hr = 0.05#cs/vk
# return hr
def pebble_accretion(planet, p_env, disc, T):
mass_p = planet.mass
dist = planet.dist
hr = p_env.hr(T,dist)
stokes = p_env.Stokes(disc,dist)
mass_star = p_env.mass_star
alpha = p_env.alpha
pebble_density = p_env.sig_dust(disc,dist)
r_hill = dist*(mass_p/mass_star/3.)**(1./3.)
v_hill = r_hill * np.sqrt(mass_star*G_Msun / dist**3)
h_peb = hr * dist * np.sqrt(alpha / stokes)
dm_2d = 2.0 * (stokes / 0.1)**(2. / 3.) * r_hill * v_hill * pebble_density
dm_3d = dm_2d * (r_hill * np.pi**0.5 / 2**1.5 / h_peb *(stokes/0.1)**(1./3.))
crit_h = np.pi* (stokes/0.1)**(1./3.) * r_hill /2/np.sqrt(2*np.pi)
if h_peb > crit_h: dm_peb = dm_2d
else: dm_peb = dm_3d
return dm_peb/Msun*yr
def gas_accretion(planet, p_env, disc, T, f=0.2, kap=0.05, rho_c=5.):
mass_p = planet.mass
mc = planet.mc
mg = planet.mg
dist = planet.dist
gas_density = p_env.sig_gas(disc,dist)
temperature = p_env.temp(T,dist)
hr = p_env.hr(T,dist)
r_hill = dist*(mass_p/p_env.mass_star/3.)**(1./3.)
omg_k = np.sqrt(p_env.mass_star*G_Msun/dist**3)
if mc > mg:
dm_gas = (0.00175/f/f/ kap * (rho_c/5.5)**(-1./6.) * np.sqrt(81/temperature)
*(mc/(Mearth/Msun))**(11./3.) * (0.1*Mearth/Msun / mg) * Mearth/1e6)/Msun
else:
dm_low = 0.83 * omg_k * gas_density * (hr*dist)**2 * (r_hill/hr/dist)**(4.5) /Msun*yr
dm_high = 0.14 * omg_k * gas_density * (hr*dist)**2 /Msun*yr
dm_gas = np.min((dm_low,dm_high))
return dm_gas
def mass_growth(planet, p_env, disc, T, dt):
dist = planet.dist
mol_comp = planet.f_comp
#print(planet.mass, 20 * (p_env.hr(T, dist)/0.05)**3. * Mearth/Msun, p_env.hr(T, dist))
if planet.mass > 20 * (p_env.hr(T, dist)/0.05)**3. * Mearth/Msun:
dm_peb = 0
else:
dm_peb = pebble_accretion(planet, p_env, disc, T)
dm_gas = gas_accretion(planet, p_env, disc, T)
mc = planet.mc + dm_peb*dt
mg = planet.mg + dm_gas*dt
#print(dm_peb,dm_gas)
sg = p_env.sig_gas(disc,dist)
sd = p_env.sig_dust(disc,dist)
molg, mold = p_env.sig_mol(disc,dist)
mol_names = list(molg.keys())
for mol in mol_names:
dm_mol_g = dm_gas*(molg[mol]/sg)
dm_mol_d = dm_peb*(mold[mol]/sd)
mol_comp[mol][0] = mol_comp[mol][0] + dm_mol_g*dt
mol_comp[mol][1] = mol_comp[mol][1] + dm_mol_d*dt
#mass = planet.mass+dm*dt
new_planet = Planet(mc+mg, mc, mg, mol_comp, planet.dist)
return new_planet
|
miostaREPO_NAMEdrift_compositionPATH_START.@drift_composition_extracted@drift_composition-main@examples@drift_composition@planet_growth.py@.PATH_END.py
|
{
"filename": "_color.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/legend/title/font/_color.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="color", parent_name="layout.legend.title.font", **kwargs
):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "legend"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@legend@title@font@_color.py@.PATH_END.py
|
{
"filename": "_text.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/contour/_text.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="text", parent_name="contour", **kwargs):
super(TextValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "data"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@contour@_text.py@.PATH_END.py
|
{
"filename": "bokeh_plot_rank.py",
"repo_name": "arviz-devs/arviz",
"repo_path": "arviz_extracted/arviz-main/examples/bokeh/bokeh_plot_rank.py",
"type": "Python"
}
|
"""
Rank plot
=========
"""
import arviz as az
data = az.load_arviz_data("centered_eight")
ax = az.plot_rank(data, var_names=("tau", "mu"), backend="bokeh")
|
arviz-devsREPO_NAMEarvizPATH_START.@arviz_extracted@arviz-main@examples@bokeh@bokeh_plot_rank.py@.PATH_END.py
|
{
"filename": "ChainContext.py",
"repo_name": "cosmo-ethz/CosmoHammer",
"repo_path": "CosmoHammer_extracted/CosmoHammer-master/cosmoHammer/ChainContext.py",
"type": "Python"
}
|
PARENT_KEY = "key_parent"
PARAMS_KEY = "key_params"
DATA_KEY = "key_data"
class ChainContext(object):
"""
Context holding a dict to store data and information durring the computation of the likelihood
"""
def __init__(self, parent, params):
"""
Constructor of the context
"""
self._data = dict()
self.add(PARENT_KEY, parent)
self.add(PARAMS_KEY, params)
self.add(DATA_KEY, dict())
def add(self, key, value):
"""
Adds the value to the context using the key
:param key: string
key to use
:param value: object
the value to store
"""
self._data[key] = value
def remove(self, key):
"""
Removes the value from the context
:param key: string
key to remove from the context
"""
assert key != None
del(self._data[key])
def contains(self, key):
"""
Checks if the key is in the context
:param key: string
key to check
:return: True if the key is in the context
"""
return key in self._data
def get(self, key, default=None):
"""
Returns the value stored in the context at the key or the default value in the
context doesn't contain the key
:param key: string
key to use
:param default: string
the default value to use if the key is not available
"""
if(self.contains(key)):
return self._data[key]
return default
def getParams(self):
"""
Returns the currently processed parameters
:return: The param of this context
"""
return self.get(PARAMS_KEY)
def getParent(self):
"""
Returns the parent
:return: The parent chain of this context
"""
return self.get(PARENT_KEY)
def getData(self):
"""
Returns the data
:return: The data of this context
"""
return self.get(DATA_KEY)
|
cosmo-ethzREPO_NAMECosmoHammerPATH_START.@CosmoHammer_extracted@CosmoHammer-master@cosmoHammer@ChainContext.py@.PATH_END.py
|
{
"filename": "_array.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/bar/error_x/_array.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ArrayValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="array", parent_name="bar.error_x", **kwargs):
super(ArrayValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "data"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@bar@error_x@_array.py@.PATH_END.py
|
{
"filename": "inform_all.py",
"repo_name": "LSSTDESC/rail_pipelines",
"repo_path": "rail_pipelines_extracted/rail_pipelines-main/src/rail/pipelines/estimation/inform_all.py",
"type": "Python"
}
|
#!/usr/bin/env python
# coding: utf-8
import ceci
from rail.core.stage import RailStage, RailPipeline
from rail.utils.algo_library import PZ_ALGORITHMS
input_file = 'rubin_dm_dc2_example.pq'
class InformPipeline(RailPipeline):
default_input_dict={'input':'dummy.in'}
def __init__(self, algorithms: dict | None=None):
RailPipeline.__init__(self)
DS = RailStage.data_store
DS.__class__.allow_overwrite = True
if algorithms is None:
algorithms = PZ_ALGORITHMS
for key, val in algorithms.items():
the_class = ceci.PipelineStage.get_stage(val['Inform'], val['Module'])
the_informer = the_class.make_and_connect(
name=f'inform_{key}',
hdf5_groupname='',
)
self.add_stage(the_informer)
|
LSSTDESCREPO_NAMErail_pipelinesPATH_START.@rail_pipelines_extracted@rail_pipelines-main@src@rail@pipelines@estimation@inform_all.py@.PATH_END.py
|
{
"filename": "XRDCalibrationFrame.py",
"repo_name": "xraypy/xraylarch",
"repo_path": "xraylarch_extracted/xraylarch-master/larch/wxxrd/XRDCalibrationFrame.py",
"type": "Python"
}
|
#!/usr/bin/env pythonw
'''
popup for 2D XRD calibration
'''
import os
import numpy as np
import wx
from wxmplot.imagepanel import ImagePanel
from larch.io import tifffile
from larch.xrd import lambda_from_E, E_from_lambda
from larch.utils import get_cwd
from .ImageControlsFrame import ImageToolboxFrame
HAS_pyFAI = False
try:
import pyFAI
import pyFAI.calibrant
#from pyFAI.calibration import Calibration
HAS_pyFAI = True
except ImportError:
pass
###################################
class CalibrationPopup(wx.Frame):
def __init__(self,parent):
self.frame = wx.Frame.__init__(self, parent, title='Calibration',size=(900,700))
self.parent = parent
self.statusbar = self.CreateStatusBar(2,wx.CAPTION )
self.default_cal = 0
self.default_det = 0
self.img_fname = ''
try:
self.raw_img = parent.plt_img ## raw_img or flp_img or plt_img mkak 2016.10.28
self.img_fname = 'Image from diFFit2D viewer.'
except:
self.raw_img = np.zeros((1024,1024))
self.Init()
self.Show()
# wx.Window.GetEffectiveMinSize
# wx.GetBestSize(self)
self.setDefaults()
def Init(self):
self.panel = wx.Panel(self)
self.DirectionsSizer()
self.MainSizer()
# self.OKsizer()
self.framebox = wx.BoxSizer(wx.VERTICAL)
self.framebox.Add(self.dirbox, flag=wx.ALL|wx.EXPAND, border=10)
self.framebox.Add(self.mainbox, flag=wx.ALL|wx.EXPAND, border=10)
# self.framebox.Add(self.okbox, flag=wx.ALL|wx.ALIGN_RIGHT, border=10)
###########################
## Pack all together in self.panel
self.panel.SetSizer(self.framebox)
###########################
## Set default information
self.stepno = 0
self.checkRANGE()
self.showDirection()
def setDefaults(self):
## Sets some typical defaults specific to GSE 13-ID procedure
self.entr_pix.SetValue('400') ## binned pixels (2x200um)
self.entr_EorL.SetValue('19.0') ## 19.0 keV
self.entr_dist.SetValue('0.5') ## 0.5 m
self.ch_det.SetSelection(self.default_det) ## Perkin detector
self.ch_cal.SetSelection(self.default_cal) ## CeO2
self.entr_calimg.SetValue(self.img_fname)
self.entr_cntrx.SetValue(str(int(self.raw_img.shape[0]/2))) ## x-position of beam
self.entr_cntry.SetValue(str(int(self.raw_img.shape[1]/2))) ## y-position of beam
self.onDorPSel()
def DirectionsSizer(self):
###########################
## Directions
dirbx = wx.StaticBox(self.panel,label='DIRECTIONS', size=(100, 50))
self.dirbox = wx.StaticBoxSizer(dirbx,wx.VERTICAL)
hbox_direct = wx.BoxSizer(wx.HORIZONTAL)
self.followdir = wx.StaticText(self.panel,label='')
#hbox_direct.Add(self.txt_shp, flag=wx.RIGHT, border=8)
hbox_direct.Add(self.followdir, flag=wx.ALL|wx.EXPAND, border=8)
self.dirbox.Add(hbox_direct, flag=wx.ALL|wx.EXPAND, border=10)
hbox_next = wx.BoxSizer(wx.HORIZONTAL)
self.btn_prev = wx.Button(self.panel,label='PREVIOUS')
self.btn_next = wx.Button(self.panel,label='NEXT')
self.btn_prev.Bind(wx.EVT_BUTTON,self.onPREVIOUS)
self.btn_next.Bind(wx.EVT_BUTTON,self.onNEXT)
hbox_next.Add(self.btn_prev, flag=wx.ALL, border=8)
hbox_next.Add((-1, 100))
hbox_next.Add(self.btn_next, flag=wx.ALIGN_RIGHT|wx.ALL, border=8)
self.dirbox.Add(hbox_next, flag=wx.ALL|wx.EXPAND, border=10)
def MainSizer(self):
self.mainbox = wx.BoxSizer(wx.VERTICAL)
###########################
## -----> Main Panel
self.hmain = wx.BoxSizer(wx.HORIZONTAL)
self.ImageSizer()
self.ParameterSizer()
self.hmain.Add(self.imagebox,proportion=1,flag=wx.ALL|wx.EXPAND, border=10)
self.hmain.Add(self.parbox, flag=wx.ALL, border=10)
self.mainbox.Add(self.hmain, flag=wx.ALL|wx.EXPAND, border=10)
def ParameterSizer(self):
'''
This is where the parameters will be.
'''
#self.parbox = wx.BoxSizer(wx.VERTICAL)
prbx = wx.StaticBox(self.panel,label='PARAMETERS', size=(50, 100))
self.parbox = wx.StaticBoxSizer(prbx,wx.VERTICAL)
###########################
## Establish lists from pyFAI
clbrnts = [] #['None']
self.dets = [] #['None']
for key,value in pyFAI.detectors.ALL_DETECTORS.items():
self.dets.append(key)
if key == 'perkin':
self.default_det = len(self.dets)-1
for key,value in pyFAI.calibrant.ALL_CALIBRANTS.items():
clbrnts.append(key)
if key == 'CeO2':
self.default_cal = len(clbrnts)-1
#####
## Calibration Image selection
hbox_cal1 = wx.BoxSizer(wx.HORIZONTAL)
ttl_calimg = wx.StaticText(self.panel, label='Calibration Image:' )
self.entr_calimg = wx.TextCtrl(self.panel, size=(210, -1))
# btn_calimg = wx.Button(self.panel, label='Browse...')
# btn_calimg.Bind(wx.EVT_BUTTON, self.loadIMAGE)
hbox_cal1.Add(ttl_calimg, flag=wx.RIGHT, border=8)
hbox_cal1.Add(self.entr_calimg, flag=wx.RIGHT|wx.EXPAND, border=8)
# hbox_cal1.Add(btn_calimg, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal1, flag=wx.BOTTOM|wx.TOP, border=8)
btn_calimg = wx.Button(self.panel, label='Browse...')
btn_calimg.Bind(wx.EVT_BUTTON, self.loadIMAGE)
self.parbox.Add(btn_calimg, flag=wx.BOTTOM|wx.ALIGN_RIGHT, border=8)
#####
## Calibrant selection
hbox_cal2 = wx.BoxSizer(wx.HORIZONTAL)
ttl_cal = wx.StaticText(self.panel, label='Calibrant:')
self.ch_cal = wx.Choice(self.panel,choices=clbrnts)
self.ch_cal.Bind(wx.EVT_CHOICE, self.onCalSel)
hbox_cal2.Add(ttl_cal, flag=wx.RIGHT, border=8)
hbox_cal2.Add(self.ch_cal, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal2, flag=wx.BOTTOM, border=30)
#####
## Set-up specific parameters
hbox_cal3 = wx.BoxSizer(wx.HORIZONTAL)
txt_exp = wx.StaticText(self.panel, label='SET-UP PARAMETERS')
btn_pni = wx.Button(self.panel, label='Load file')
btn_pni.Bind(wx.EVT_BUTTON, self.openPONI)
hbox_cal3.Add(txt_exp, flag=wx.RIGHT, border=8)
hbox_cal3.Add(btn_pni, flag=wx.LEFT, border=60)
self.parbox.Add(hbox_cal3, flag=wx.BOTTOM, border=8)
#####
## Detector selection
hbox_cal4 = wx.BoxSizer(wx.HORIZONTAL)
self.ch_DorP = wx.Choice(self.panel,choices=['Detector name','Pixel size (um)'])
self.ch_det = wx.Choice(self.panel, choices=self.dets)
self.entr_pix = wx.TextCtrl(self.panel, size=(110, -1))
self.ch_det.Bind(wx.EVT_CHOICE, self.onDetSel)
self.ch_DorP.Bind(wx.EVT_CHOICE, self.onDorPSel)
hbox_cal4.Add(self.ch_DorP, flag=wx.RIGHT, border=8)
hbox_cal4.Add(self.ch_det, flag=wx.RIGHT, border=8)
hbox_cal4.Add(self.entr_pix, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal4, flag=wx.BOTTOM, border=8)
#####
## Energy or Wavelength
hbox_cal5 = wx.BoxSizer(wx.HORIZONTAL)
self.ch_EorL = wx.Choice(self.panel,choices=['Energy (keV)','Wavelength (A)'])
self.entr_EorL = wx.TextCtrl(self.panel, size=(110, -1))
self.ch_EorL.Bind(wx.EVT_CHOICE, self.onEorLSel)
hbox_cal5.Add(self.ch_EorL, flag=wx.RIGHT, border=8)
hbox_cal5.Add(self.entr_EorL, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal5, flag=wx.BOTTOM, border=8)
## Distance
hbox_cal6 = wx.BoxSizer(wx.HORIZONTAL)
ttl_dist = wx.StaticText(self.panel, label='Detector distance (m):')
self.entr_dist = wx.TextCtrl(self.panel, size=(110, -1))
hbox_cal6.Add(ttl_dist, flag=wx.RIGHT, border=8)
hbox_cal6.Add(self.entr_dist, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal6, flag=wx.BOTTOM, border=8)
## Beam center x
hbox_cal7 = wx.BoxSizer(wx.HORIZONTAL)
ttl_cntrx = wx.StaticText(self.panel, label='Beam center, x (pixels):')
self.entr_cntrx = wx.TextCtrl(self.panel, size=(110, -1))
hbox_cal7.Add(ttl_cntrx, flag=wx.RIGHT, border=8)
hbox_cal7.Add(self.entr_cntrx, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal7, flag=wx.BOTTOM, border=8)
## Beam center y
hbox_cal8 = wx.BoxSizer(wx.HORIZONTAL)
ttl_cntry = wx.StaticText(self.panel, label='Beam center, y (pixels):')
self.entr_cntry = wx.TextCtrl(self.panel, size=(110, -1))
hbox_cal8.Add(ttl_cntry, flag=wx.RIGHT, border=8)
hbox_cal8.Add(self.entr_cntry, flag=wx.RIGHT, border=8)
self.parbox.Add(hbox_cal8, flag=wx.BOTTOM, border=8)
def onCalSel(self,event=None):
print('Selected calibrant: %s' % self.ch_cal.GetString(self.ch_cal.GetSelection()))
def onDetSel(self,event=None):
print('Selected detector: %s' % self.ch_det.GetString(self.ch_det.GetSelection()))
def onEorLSel(self,event=None):
if self.ch_EorL.GetSelection() == 1:
energy = float(self.entr_EorL.GetValue()) ## units keV
wavelength = lambda_from_E(energy) ## units: A
self.entr_EorL.SetValue(str(wavelength))
else:
wavelength = float(self.entr_EorL.GetValue())*1e-10 ## units: m
energy = E_from_lambda(wavelength) ## units: keV
self.entr_EorL.SetValue(str(energy))
def onDorPSel(self,event=None):
if self.ch_DorP.GetSelection() == 0:
self.entr_pix.Hide()
self.ch_det.Show()
else:
self.ch_det.Hide()
self.entr_pix.Show()
self.panel.GetSizer().Layout()
self.panel.GetParent().Layout()
def loadIMAGE(self,event=None):
wildcards = 'XRD image (*.edf,*.tif,*.tiff)|*.tif;*.tiff;*.edf|All files (*.*)|*.*'
if os.path.exists(self.entr_calimg.GetValue()):
dfltDIR = self.entr_calimg.GetValue()
else:
dfltDIR = get_cwd()
dlg = wx.FileDialog(self, message='Choose XRD calibration file',
defaultDir=dfltDIR,
wildcard=wildcards, style=wx.FD_OPEN)
path, read = None, False
if dlg.ShowModal() == wx.ID_OK:
read = True
path = dlg.GetPath().replace('\\', '/')
dlg.Destroy()
if read:
try:
# self.raw_img = plt.imread(path)
self.raw_img = tifffile.imread(path)
#self.raw_img = fabio.open(path).data
except:
print('Image not properly opened.')
pass
self.plot2Dimg.display(self.raw_img)
self.plot2Dimg.redraw()
self.AutoContrast()
self.entr_calimg.Clear()
self.entr_calimg.SetValue(path) #os.path.split(path)[-1]
def ImageSizer(self):
'''
Image Panel
'''
self.imagebox = wx.BoxSizer(wx.VERTICAL)
self.plot2Dimage()
self.btn_image = wx.Button(self.panel,label='IMAGE TOOLS')
self.btn_image.Bind(wx.EVT_BUTTON,self.onImageTools)
self.imagebox.Add(self.plot2Dimg,proportion=1,flag=wx.ALL|wx.EXPAND, border=10)
self.imagebox.Add(self.btn_image, flag=wx.ALL, border=10)
# def OKsizer(self):
# ###########################
# ## OK - CANCEL
# self.okbox = wx.BoxSizer(wx.HORIZONTAL)
#
# okBtn = wx.Button(self.panel, wx.ID_OK )
# canBtn = wx.Button(self.panel, wx.ID_CANCEL )
#
# self.okbox.Add(canBtn, flag=wx.RIGHT, border=5)
# self.okbox.Add(okBtn, flag=wx.RIGHT, border=5)
def write_message(self, s, panel=0):
"""write a message to the Status Bar"""
self.SetStatusText(s, panel)
def onImageTools(self,event=None):
self.toolbox = ImageToolboxFrame(self.plot2Dimg,self.raw_img)
def plot2Dimage(self):
self.plot2Dimg = ImagePanel(self.panel,size=(300, 300))
self.plot2Dimg.messenger = self.write_message
self.plot2Dimg.display(self.raw_img)
self.AutoContrast()
self.plot2Dimg.redraw()
def AutoContrast(self):
self.minINT = int(np.min(self.raw_img))
self.maxINT = int(np.max(self.raw_img)/15) # /15 scales image to viewable
if self.maxINT == self.minINT:
self.minINT = self.minINT-50
self.maxINT = self.minINT+100
self.minCURRENT = self.minINT
self.maxCURRENT = self.maxINT
if self.maxCURRENT > self.maxINT:
self.maxCURRENT = self.maxINT
self.plot2Dimg.conf.auto_intensity = False
self.plot2Dimg.conf.int_lo[0] = self.minCURRENT
self.plot2Dimg.conf.int_hi[0] = self.maxCURRENT
# self.plot2Dimg.conf.int_lo['int'] = self.minCURRENT
# self.plot2Dimg.conf.int_hi['int'] = self.maxCURRENT
## vertical flip default
self.plot2Dimg.conf.flip_ud = True
self.plot2Dimg.conf.flip_lr = False
self.plot2Dimg.redraw()
def checkRANGE(self):
if self.stepno <= 0:
self.stepno = 0
self.btn_prev.Disable()
else:
self.btn_prev.Enable()
if self.stepno >= 8:
self.stepno = 8
self.btn_next.Disable()
else:
self.btn_next.Enable()
def onNEXT(self,event=None):
self.stepno = self.stepno + 1
self.checkRANGE()
self.showDirection()
def onPREVIOUS(self,event=None):
self.stepno = self.stepno - 1
self.checkRANGE()
self.showDirection()
def showDirection(self):
dirsteps = ['Enter parameters into the fields below.',
'Select point(s) on the first ring.',
'Select point(s) on the second ring.',
'Select point(s) on the third ring.',
'Select point(s) on the fourth ring.',
'Select point(s) on the fifth ring.',
'Select point(s) on the sixth ring.',
'Check preliminary calibration. Continue for final refinement.',
'Refinement complete.' ]
self.followdir.SetLabel(dirsteps[self.stepno])
def openPONI(self,event=None):
wildcards = 'pyFAI calibration file (*.poni)|*.poni|All files (*.*)|*.*'
dlg = wx.FileDialog(self, message='Choose pyFAI calibration file',
defaultDir=get_cwd(),
wildcard=wildcards, style=wx.FD_OPEN)
path, read = None, False
if dlg.ShowModal() == wx.ID_OK:
read = True
path = dlg.GetPath().replace('\\', '/')
dlg.Destroy()
if read:
try:
print
self.ai = pyFAI.load(path)
print('Loading calibration file: %s' % path)
except:
print('Not recognized as a pyFAI calibration file: %s' % path)
return
## Sets viewer to values in .poni file
self.entr_dist.SetValue('%0.4f' % self.ai._dist)
self.entr_pix.SetValue('%0.1f' % float(self.ai.detector.pixel1*1000000.))
self.ch_DorP.SetSelection(1)
self.entr_EorL.SetValue('%0.4f' % float(self.ai._wavelength*1.e10))
self.ch_EorL.SetSelection(1)
self.onDorPSel()
cenx = float(self.ai._poni1)/float(self.ai.detector.pixel1)
ceny = float(self.ai._poni2)/float(self.ai.detector.pixel2)
self.entr_cntrx.SetValue('%0.3f' % cenx)
self.entr_cntry.SetValue('%0.3f' % ceny)
class CalXRD(wx.Dialog):
""""""
#----------------------------------------------------------------------
def __init__(self):
if HAS_pyFAI:
## Constructor
dialog = wx.Dialog.__init__(self, None, title='XRD Calibration',size=(460, 440))
## remember: size=(width,height)
self.panel = wx.Panel(self)
self.InitUI()
self.Centre()
self.Show()
## Sets some typical defaults specific to GSE 13-ID procedure
self.pixel.SetValue('400') ## binned pixels (2x200um)
self.EorL.SetValue('19.0') ## 19.0 keV
self.Distance.SetValue('0.5') ## 0.5 m
self.detslct.SetSelection(22) ## Perkin detector
self.calslct.SetSelection(20) ## CeO2
if self.slctDorP.GetSelection() == 0:
self.sizer.Hide(self.pixel)
## Do not need flags if defaults are set
#self.FlagCalibrant = False
#self.FlagDetector = False
self.FlagCalibrant = True
self.FlagDetector = True
else:
print('pyFAI must be available for calibration.')
return
def InitUI(self):
## Establish lists from pyFAI
clbrnts = [] #['None']
self.dets = [] #['None']
for key,value in pyFAI.detectors.ALL_DETECTORS.items():
self.dets.append(key)
for key,value in pyFAI.calibrant.ALL_CALIBRANTS.items():
clbrnts.append(key)
self.CaliPath = None
## Calibration Image selection
caliImg = wx.StaticText(self.panel, label='Calibration Image:' )
self.calFil = wx.TextCtrl(self.panel, size=(190, -1))
fileBtn1 = wx.Button(self.panel, label='Browse...' )
## Calibrant selection
self.calslct = wx.Choice(self.panel,choices=clbrnts)
CalLbl = wx.StaticText(self.panel, label='Calibrant:' ,style=LEFT)
## Detector selection
self.slctDorP = wx.Choice(self.panel,choices=['Detector','Pixel size (um)'])
self.detslct = wx.Choice(self.panel, choices=self.dets)
self.pixel = wx.TextCtrl(self.panel, size=(140, -1))
## Energy or Wavelength
self.slctEorL = wx.Choice(self.panel,choices=['Energy (keV)','Wavelength (A)'])
self.EorL = wx.TextCtrl(self.panel, size=(140, -1))
## Refine label
RefLbl = wx.StaticText(self.panel, label='To be refined...' ,style=LEFT)
## Distance
self.Distance = wx.TextCtrl(self.panel, size=(140, -1))
DstLbl = wx.StaticText(self.panel, label='Distance (m):' ,style=LEFT)
hlpBtn = wx.Button(self.panel, wx.ID_HELP )
okBtn = wx.Button(self.panel, wx.ID_OK )
canBtn = wx.Button(self.panel, wx.ID_CANCEL )
self.Bind(wx.EVT_BUTTON, self.onBROWSE1, fileBtn1 )
self.calslct.Bind(wx.EVT_CHOICE, self.onCalSel)
self.detslct.Bind(wx.EVT_CHOICE, self.onDetSel)
self.slctDorP.Bind(wx.EVT_CHOICE, self.onDorPSel)
self.slctEorL.Bind(wx.EVT_CHOICE, self.onEorLSel)
self.sizer = wx.GridBagSizer(3, 3)
self.sizer.Add(caliImg, pos = ( 1,1) )
self.sizer.Add(self.calFil, pos = ( 1,2), span = (1,2) )
self.sizer.Add(fileBtn1, pos = ( 1,4) )
self.sizer.Add(CalLbl, pos = ( 3,1) )
self.sizer.Add(self.calslct, pos = ( 3,2), span = (1,2) )
self.sizer.Add(self.slctDorP, pos = ( 4,1) )
self.sizer.Add(self.detslct, pos = ( 4,2), span = (1,4) )
self.sizer.Add(self.pixel, pos = ( 5,2), span = (1,2) )
self.sizer.Add(self.slctEorL, pos = ( 6,1) )
self.sizer.Add(self.EorL, pos = ( 6,2), span = (1,2) )
self.sizer.Add(RefLbl, pos = ( 8,1) )
self.sizer.Add(DstLbl, pos = ( 9,1) )
self.sizer.Add(self.Distance, pos = ( 9,2), span = (1,2) )
self.sizer.Add(hlpBtn, pos = (11,1) )
self.sizer.Add(canBtn, pos = (11,2) )
self.sizer.Add(okBtn, pos = (11,3) )
self.FindWindowById(wx.ID_OK).Disable()
self.panel.SetSizer(self.sizer)
def onCalSel(self,event=None):
#if self.calslct.GetSelection() == 0:
# self.FlagCalibrant = False
#else:
# self.FlagCalibrant = True
self.checkOK()
def onDetSel(self,event=None):
#if self.detslct.GetSelection() == 0:
# self.FlagDetector = False
#else:
# self.FlagDetector = True
self.checkOK()
def onCheckOK(self,event=None):
self.checkOK()
def checkOK(self):
if self.FlagCalibrant and self.CaliPath is not None:
if self.slctDorP.GetSelection() == 1:
self.FindWindowById(wx.ID_OK).Enable()
else:
if self.FlagDetector:
self.FindWindowById(wx.ID_OK).Enable()
else:
self.FindWindowById(wx.ID_OK).Disable()
else:
self.FindWindowById(wx.ID_OK).Disable()
def onEorLSel(self,event=None):
if self.slctEorL.GetSelection() == 1:
energy = float(self.EorL.GetValue()) ## units keV
wavelength = lambda_from_E(energy) ## units: A
self.EorL.SetValue(str(wavelength))
else:
wavelength = float(self.EorL.GetValue()) ## units: A
energy = E_from_lambda(wavelength) ## units: keV
self.EorL.SetValue(str(energy))
self.checkOK()
def onDorPSel(self,event=None):
if self.slctDorP.GetSelection() == 0:
self.sizer.Hide(self.pixel)
self.sizer.Show(self.detslct)
else:
self.sizer.Hide(self.detslct)
self.sizer.Show(self.pixel)
self.checkOK()
def onBROWSE1(self,event=None):
wildcards = 'XRD image (*.edf,*.tif,*.tiff)|*.tif;*.tiff;*.edf|All files (*.*)|*.*'
if os.path.exists(self.calFil.GetValue()):
dfltDIR = self.calFil.GetValue()
else:
dfltDIR = get_cwd()
dlg = wx.FileDialog(self, message='Choose XRD calibration file',
defaultDir=dfltDIR,
wildcard=wildcards, style=wx.FD_OPEN)
path, read = None, False
if dlg.ShowModal() == wx.ID_OK:
read = True
path = dlg.GetPath().replace('\\', '/')
dlg.Destroy()
if read:
self.calFil.Clear()
self.calFil.SetValue(os.path.split(path)[-1])
self.CaliPath = path
self.checkOK()
# # # # # # # WAS IN mapviewer.py ; needs to be corrected or removed
#
# # # # def onCalXRD(self, evt=None):
# # # # """
# # # # Perform calibration with pyFAI
# # # # mkak 2016.09.16
# # # # """
# # # #
# # # # ### can this pop up pyFAI or Dioptas GUI instead of creating own?
# # # #
# # # # myDlg = CalXRD()
# # # #
# # # # path, read = None, False
# # # # if myDlg.ShowModal() == wx.ID_OK:
# # # # read = True
# # # #
# # # # myDlg.Destroy()
# # # #
# # # # if read:
# # # #
# # # # usr_calimg = myDlg.CaliPath
# # # #
# # # # if myDlg.slctEorL.GetSelection() == 1:
# # # # usr_lambda = float(myDlg.EorL.GetValue())*1e-10 ## units: m
# # # # usr_E = E_from_lambda(usr_lambda,lambda_units='m') ## units: keV
# # # # else:
# # # # usr_E = float(myDlg.EorL.GetValue()) ## units keV
# # # # usr_lambda = lambda_from_E(usr_E,lambda_units='m') ## units: m
# # # #
# # # # if myDlg.slctDorP.GetSelection() == 1:
# # # # usr_pixel = float(myDlg.pixel.GetValue())*1e-6
# # # # else:
# # # # usr_det = myDlg.detslct.GetString(myDlg.detslct.GetSelection())
# # # # usr_clbrnt = myDlg.calslct.GetString(myDlg.calslct.GetSelection())
# # # # usr_dist = float(myDlg.Distance.GetValue())
# # # #
# # # # verbose = True #False
# # # # if verbose:
# # # # print('\n=== Calibration input ===')
# # # # print('XRD image: %s' % usr_calimg)
# # # # print('Calibrant: %s' % usr_clbrnt)
# # # # if myDlg.slctDorP.GetSelection() == 1:
# # # # print('Pixel size: %0.1f um' % (usr_pixel*1e6))
# # # # else:
# # # # print('Detector: %s' % usr_det)
# # # # print('Incident energy: %0.2f keV (%0.4f A)' % (usr_E,usr_lambda*1e10))
# # # # print('Starting distance: %0.3f m' % usr_dist)
# # # # print('=========================\n')
# # # #
# # # # ## Adapted from pyFAI-calib
# # # # ## note: -l:units mm; -dist:units m
# # # # ## mkak 2016.09.19
# # # #
# # # # if myDlg.slctDorP.GetSelection() == 1:
# # # # pform1 = 'pyFAI-calib -c %s -p %s -e %0.1f -dist %0.3f %s'
# # # # command1 = pform1 % (usr_clbrnt,usr_pixel,usr_E,usr_dist,usr_calimg)
# # # # else:
# # # # pform1 = 'pyFAI-calib -c %s -D %s -e %0.1f -dist %0.3f %s'
# # # # command1 = pform1 % (usr_clbrnt,usr_det,usr_E,usr_dist,usr_calimg)
# # # # pform2 = 'pyFAI-recalib -i %s -c %s %s'
# # # # command2 = pform2 % (usr_calimg.split('.')[0]+'.poni',usr_clbrnt,usr_calimg)
# # # #
# # # # if verbose:
# # # # print('\nNot functioning within code yet... but you could execute:')
# # # # print('\t $ %s' % command1)
# # # # print('\t $ %s\n\n' % command2)
# class diFFit_XRDcal(wx.App):
# def __init__(self):
# wx.App.__init__(self)
#
# def run(self):
# self.MainLoop()
#
# def createApp(self):
# frame = CalibrationPopup()
# frame.Show()
# self.SetTopWindow(frame)
#
# def OnInit(self):
# self.createApp()
# return True
#
# class DebugViewer(diFFit_XRDcal):
# def __init__(self, **kws):
# diFFit_XRDcal.__init__(self, **kws)
#
# def OnInit(self):
# #self.Init()
# self.createApp()
# #self.ShowInspectionTool()
# return True
#
# if __name__ == '__main__':
# diFFit_XRDcal().run()
|
xraypyREPO_NAMExraylarchPATH_START.@xraylarch_extracted@xraylarch-master@larch@wxxrd@XRDCalibrationFrame.py@.PATH_END.py
|
{
"filename": "network.py",
"repo_name": "fabiorigamonti/bang",
"repo_path": "bang_extracted/bang-main/build/lib.linux-x86_64-3.8/src/BANG/network.py",
"type": "Python"
}
|
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import data as d
import matplotlib.pyplot as plt
from collections import OrderedDict
'''
This is the Residual in Residual neural network structure!
'''
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
# I'LL PUT THEM IN DIFFERENT CLASSES SUCH THAT IF WE WANT TO ADD SIMILAR LAYERS IS EASIER
# out1 = 52
# out2 = 12
class features_extraction(nn.Module):
def __init__(self,img_fil,out1,kernel_size=5,bias=True):
super(features_extraction,self).__init__()
self.feat_ext = nn.Conv2d(img_fil,
out1,
kernel_size,
stride=1,
padding=2,
bias=bias)
self.p_RELU = nn.PReLU() # as leaky Relu but slope is learnable
#self.p_RELU = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self,x):
return self.p_RELU(self.feat_ext(x))
class schrinking(nn.Module):
def __init__(self,out1,out2,kernel_size=1,bias=True):
super(schrinking,self).__init__()
self.shrink = nn.Conv2d(out1,
out2,
kernel_size,
stride=1,
padding=0,
bias=bias)
self.p_RELU = nn.PReLU()
#self.p_RELU = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self,x):
return self.p_RELU(self.shrink(x))
class mapping(nn.Module):
def __init__(self,out2,n_layers,kernel_size=3,bias=True):
super(mapping,self).__init__()
mapping_block = OrderedDict()
for i in range(n_layers):
mapping_block[str(i)] = nn.Conv2d(out2,
out2,
kernel_size,
stride=1,
padding=1,
bias=bias)
mapping_block[str(i)] = nn.PReLU()
#mapping_block[str(i)] = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.mapping_block = nn.Sequential(mapping_block)
def forward(self,x):
return self.mapping_block(x)
class expanding(nn.Module):
def __init__(self,out2,out1,kernel_size=1,bias=True):
super(expanding,self).__init__()
self.exp_layer = nn.Conv2d(out2,
out1,
kernel_size,
stride=1,
padding=0,
bias=bias)
self.p_RELU = nn.PReLU()
#self.p_RELU = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self,x):
return self.p_RELU(self.exp_layer(x))
class transp_conv(nn.Module):
def __init__(self,out1,img_filter,kernel_size=9,scaling=4,bias=True):
super(transp_conv,self).__init__()
self.deconv_layer = nn.ConvTranspose2d(out1,
img_filter,
kernel_size,
stride=2,
padding=4,
output_padding=1,
bias=bias)
def forward(self,x):
return self.deconv_layer(x)
class FSRCNN_net(nn.Module):
def __init__(self,img_filter,out1,out2,bias=True):
super(FSRCNN_net,self).__init__()
self.step1 = features_extraction(img_filter,out1)
self.step2 = schrinking(out1,out2)
self.step3 = mapping(out2,10)
self.step4 = expanding(out2,out1)
self.step5 = transp_conv(out1,2*out2)
self.step6 = transp_conv(2*out2,img_filter)
def forward(self,x):
return self.step6(self.step5(self.step4(self.step3(self.step2(self.step1(x))))))
def initialize_weights(model):
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight.data)
#elif isinstance(m, nn.ConvTranspose2d):
# nn.init.kaiming_normal_(m.weight.data)
if __name__=='__main__':
device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size,img_filter,out1,out2 = 32,1,30,10
model = FSRCNN_net(img_filter, # filter imager grey or RGB
out1, # filter for feat extraction
out2) # reduced filter for mapping
fake_img = torch.randn((batch_size,img_filter,20,20))
HR_img = model(fake_img)
|
fabiorigamontiREPO_NAMEbangPATH_START.@bang_extracted@bang-main@build@lib.linux-x86_64-3.8@src@BANG@network.py@.PATH_END.py
|
{
"filename": "_ticktext.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/yaxis/_ticktext.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicktextValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="ticktext", parent_name="layout.yaxis", **kwargs):
super(TicktextValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "ticks"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@yaxis@_ticktext.py@.PATH_END.py
|
{
"filename": "gravsphere_initialise_LeoI.py",
"repo_name": "justinread/gravsphere",
"repo_path": "gravsphere_extracted/gravsphere-master/gravsphere_initialise_LeoI.py",
"type": "Python"
}
|
import numpy as np
from constants import *
from functions import *
#This file contains all the code options and choices for
#running a given model. Throughout, -1 means auto-calculate.
#Data files and output base filename:
whichgal = 'LeoI'
infile = output_base+whichgal+'/'+whichgal
outdirbase = output_base+whichgal+'/'
#Plot ranges and sample points [-1 means auto-calculate]:
rplot_inner = 1e-2
rplot_outer = 5.0
rplot_pnts = 50
y_sigLOSmax = 15
ymin_Sigstar = 1e-4
ymax_Sigstar = 100
yMlow = 1e4
yMhigh = 1e10
yrholow = 1e5
yrhohigh = 1e10
alp3sig = 0.0
sigmlow = 1e-3
sigmhigh = 5.0
#Code options:
propermotion = 'no'
virialshape = 'yes'
#Overplot true solution (for mock data). If
#yes, then the true solutions should be passed
#in: ranal,betatrue(ranal),betatruestar(ranal),
#truemass(ranal),trueden(ranal),truedlnrhodlnr(ranal).
overtrue = 'no'
#Radial grid range for Jeans calculation:
rmin = -1.0
rmax = -1.0
#Galaxy properties. Assume here that the baryonic mass
#has the same radial profile as the tracer stars. If this
#is not the case, you should set Mstar_rad and Mstar_prof
#here. The variables barrad_min, barrad_max and bar_pnts
#set the radial range and sampling of the baryonic mass model.
Mstar = 5.5e6
Mstar_err = Mstar * 0.25
baryonmass_follows_tracer = 'yes'
barrad_min = 0.0
barrad_max = 10.0
bar_pnts = 250
###########################################################
#Priors
#For surface density fit tracertol = [0,1] sets the spread
#around the best-fit value from the binulator.
tracertol = 0.1
#Cosmology priors on the coreNFWtides model. mWDM(keV) is
#the mass of a thermal relic; <0 means CDM; sig_c200 is
#the scatter of c200 in log10 space. If the cosmo_cprior
#is set, then we include a Gaussian spread in M200-c200 in
#the likelihood. Without this, M200-c200 enters only if
#used to set the priors, below.
cosmo_cprior = 'yes'
sig_c200 = 0.1
mWDM = -1
if (mWDM > 0):
cosmo_cfunc = lambda M200,h : \
cosmo_cfunc_WDM(M200,h,OmegaM,rhocrit,mWDM)
#Velocity anisotropy priors:
betr0min = -2
betr0max = 0.0
betnmin = 1.0
betnmax = 3.0
bet0min = -0.01
bet0max = 0.01
betinfmin = -0.1
betinfmax = 1.0
#CoreNFWtides priors:
logM200low = 7.5
logM200high = 11.5
#clow = cosmo_cfunc(10.0**logM200high,h)
#logclow = np.log10(clow)-sig_c200
#clow = 10.0**logclow
#chigh = cosmo_cfunc(10.0**logM200low,h)*1.4
#logchigh = np.log10(chigh)+sig_c200*2.0
#chigh = 10.0**logchigh
clow = 1.0
chigh = 50.0
rclow = 1e-2
rchigh = 10.0
logrclow = np.log10(rclow)
logrchigh = np.log10(rchigh)
nlow = 0.0
nhigh = 1.0
rtlow = 1.0
rthigh = 20.0
logrtlow = np.log10(rtlow)
logrthigh = np.log10(rthigh)
dellow = 3.01
delhigh = 5.0
if (cosmo_cprior == 'yes'):
clow = 1.0
chigh = 100.0
#Priors on central dark mass [set logMcenlow/high very negative
#to switch this off. Mcen is the mass in Msun; acen is the
#scale length in kpc, usually assumed smaller than Rhalf
#to avoid degeneracies with the stellar mass]:
logMcenlow = -4
logMcenhigh = -3
acenlow = 1e-5
acenhigh = 1e-2
#Priors on rotation [Arot defined as:
#vphimean^2 / (2 sigr^2) = Arot(r/Rhalf) which yields linear
#rotation with radius. (Arot = 0.5 means an equal balance of
#rotation and pressure support at Rhalf.)]:
Arotlow = 0.0
Arothigh = 1.0e-12
#Priors on distance [True distance follows as:
#dgal_kpc * drange s.t. we usually want drangelow < 1.0 and
#drangehigh > 1.0]:
dgal_kpc = 254.0
drangelow = 0.99999
drangehigh = 1.00001
###########################################################
#Post processing options:
#For calculating D+J-factors:
calc_Jfac = 'no'
alpha_Jfac_deg = 0.5
calc_Dfac = 'no'
alpha_Dfac_deg = 0.5
|
justinreadREPO_NAMEgravspherePATH_START.@gravsphere_extracted@gravsphere-master@gravsphere_initialise_LeoI.py@.PATH_END.py
|
{
"filename": "fx_root_jh.py",
"repo_name": "kevin218/POET",
"repo_path": "POET_extracted/POET-master/code/lib/fx_root_jh.py",
"type": "Python"
}
|
from numarray import *
#
# Copyright (c) 1994-2005, Research Systems, Inc. All rights reserved.
# Unauthorized reproduction prohibited.
# Modifications by Joseph Harrington are in the public domain.
#+
# NAME:
# FX_ROOT_JH
#
# PURPOSE:
# This function computes real and complex roots (zeros) of
# a univariate nonlinear function. This version improves on
# that in the IDL release by offering _EXTRA, STATUS, and a
# sanity check on TOL.
#
# CATEGORY:
# Nonlinear Equations/Root Finding
#
# CALLING SEQUENCE:
# Result = FX_ROOT(X, Func)
#
# INPUTS:
# X : A 3-element initial guess vector of type real or complex.
# Real initial guesses may result in real or complex roots.
# Complex initial guesses will result in complex roots.
#
# Func: A scalar string specifying the name of a user-supplied IDL
# function that defines the univariate nonlinear function.
# This function must accept the vector argument X.
#
# KEYWORD PARAMETERS:
# DOUBLE: If set to a non-zero value, computations are done in
# double precision arithmetic.
#
# ITMAX: Set this keyword to specify the maximum number of iterations
# The default is 100.
#
# STOP: Set this keyword to specify the stopping criterion used to
# judge the accuracy of a computed root, r(k).
# STOP = 0 implements an absolute error criterion between two
# successively-computed roots, |r(k) - r(k+1)|.
# STOP = 1 implements a functional error criterion at the
# current root, |Func(r(k))|. The default is 0.
#
# TOL: Set this keyword to specify the stopping error tolerance.
# If the STOP keyword is set to 0, the algorithm stops when
# |x(k) - x(k+1)| < TOL.
# If the STOP keyword is set to 1, the algorithm stops when
# |Func(x(k))| < TOL. The default is 1.0e-4.
# Tol is limited to machine precision. If set below
# precision, it will be reset to precision IN THE
# CALLER.
#
# STATUS: (returned) Set to 0 if the algorithm did not
# converge, 1 if it did IN THE CALLER.
#
# _EXTRA: Structure containing parameters to pass to FUNC.
#
# EXAMPLE:
# Define an IDL function named FUNC.
# function FUNC, x
# return, exp(sin(x)^2 + cos(x)^2 - 1) - 1
# end
#
# Define a real 3-element initial guess vector.
# x = [0.0, -!pi/2, !pi]
#
# Compute a root of the function using double-precision arithmetic.
# root = FX_ROOT(x, 'FUNC', /double)
#
# Check the accuracy of the computed root.
# print, exp(sin(root)^2 + cos(root)^2 - 1) - 1
#
# Define a complex 3-element initial guess vector.
# x = [complex(-!pi/3, 0), complex(0, !pi), complex(0, -!pi/6)]
#
# Compute a root of the function.
# root = FX_ROOT(x, 'FUNC')
#
# Check the accuracy of the computed root.
# print, exp(sin(root)^2 + cos(root)^2 - 1) - 1
#
# PROCEDURE:
# FX_ROOT implements an optimal Muller's method using complex
# arithmetic only when necessary.
#
# SIDE EFFECTS:
# Sets STATUS and may set TOL IN THE CALLER.
#
# REFERENCE:
# Numerical Recipes, The Art of Scientific Computing (Second Edition)
# Cambridge University Press
# ISBN 0-521-43108-5
#
# MODIFICATION HISTORY:
# Written by: GGS, RSI, March 1994
# Modified: GGS, RSI, September 1994
# Added support for double-precision complex inputs.
# 2005-02-07 jh Added _extra.
# 2005-02-12 jh Added status, tol protection. Fixed indentation.
#-
def fx_root_jh(xi, func, double=None, itmax=None, stop=None, tol=None, status=None, extra=None):
#on_error, 2 ;Return to caller if error occurs.
e = extra
status = 0
x = xi + 0.0 #Create an internal floating-point variable, x.
sx = size(x)
if sx[1] != 3:
message('x must be a 3-element initial guess vector.')
#Initialize keyword parameters.
"""
if (double is not None) != 0:
if bitwise_or(sx[2] == 4, sx[2] == 5):
x = x + 0.0e0
else:
x = dcomplex(x)
"""
tn = size(x, tnam=True)
if (itmax is not None) == 0:
itmax = 100
if (stop is not None) == 0:
stop = 0
if (tol is not None) == 0:
tol = 1.0e-4
# protect against division by zero from too small a tol
if bitwise_or(tn == 'DOUBLE', tn == 'DCOMPLEX'):
tol = maximum(tol, (machar(d=True)).eps)
else:
tol = maximum(tol, (machar()).eps)
#Initialize stopping criterion and iteration count.
cond = 0
it = 0
#Begin to iteratively compute a root of the nonlinear function.
while (it < itmax and cond != 1):
q = (x[2] - x[1]) / (x[1] - x[0])
pls = (1 + q)
f = call_function(func, x, extra=e)
a = q * f[2] - q * pls * f[1] + q ** 2 * f[0]
b = (2 * q + 1) * f[2] - pls ** 2 * f[1] + q ** 2 * f[0]
c = pls * f[2]
disc = b ** 2 - 4 * a * c
roc = size(disc) #Real or complex discriminant?
if bitwise_and(roc[1] != 6, roc[1] != 9): #Proceed toward real root.
if disc < 0: #Switch to complex root.
#Single-precision complex.
if bitwise_and((double is not None) == 0, sx[2] != 9):
r0 = b + complex(0, sqrt(abs(disc)))
r1 = b - complex(0, sqrt(abs(disc)))
else: #Double-precision complex.
r0 = b + dcomplex(0, sqrt(abs(disc)))
r1 = b - dcomplex(0, sqrt(abs(disc)))
if abs(r0) > abs(r1):
div = r0
else:
div = r1
else: # real root
rr0 = b + sqrt(disc)
rr1 = b - sqrt(disc)
div = ((abs(rr0) >= abs(rr1)) and [rr0] or [rr1])[0]
else: #Proceed toward complex root.
c0 = b + sqrt(disc)
c1 = b - sqrt(disc)
if abs(c0) > abs(c1):
div = c0
else:
div = c1
root = x[2] - (x[2] - x[1]) * (2 * c / div)
#Absolute error tolerance.
if bitwise_and(stop == 0, abs(root - x[2]) <= tol):
cond = 1
else:
evalfunc = call_function(func, root, extra=e)
#Functional error tolerance.
if bitwise_and(stop != 0, abs(evalfunc) <= tol):
cond = 1
else:
if evalfunc == 0:
cond = 1
x = concatenate([x[1], x[2], root])
it = it + 1
if bitwise_and(it >= itmax, cond == 0):
print('Algorithm failed to converge within given parameters.')
else:
status = 1
return root
|
kevin218REPO_NAMEPOETPATH_START.@POET_extracted@POET-master@code@lib@fx_root_jh.py@.PATH_END.py
|
{
"filename": "_std.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/py/py2/py/_std.py",
"type": "Python"
}
|
import sys
import warnings
class PyStdIsDeprecatedWarning(DeprecationWarning):
pass
class Std(object):
""" makes top-level python modules available as an attribute,
importing them on first access.
"""
def __init__(self):
self.__dict__ = sys.modules
def __getattr__(self, name):
warnings.warn("py.std is deprecated, please import %s directly" % name,
category=PyStdIsDeprecatedWarning,
stacklevel=2)
try:
m = __import__(name)
except ImportError:
raise AttributeError("py.std: could not import %s" % name)
return m
std = Std()
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@py@py2@py@_std.py@.PATH_END.py
|
{
"filename": "_size.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolar/hoverlabel/font/_size.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="size", parent_name="scatterpolar.hoverlabel.font", **kwargs
):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolar@hoverlabel@font@_size.py@.PATH_END.py
|
{
"filename": "_dataset.py",
"repo_name": "pytorch/vision",
"repo_path": "vision_extracted/vision-main/torchvision/prototype/datasets/utils/_dataset.py",
"type": "Python"
}
|
import abc
import importlib
import pathlib
from typing import Any, Collection, Dict, Iterator, List, Optional, Sequence, Union
from torchdata.datapipes.iter import IterDataPipe
from torchvision.datasets.utils import verify_str_arg
from ._resource import OnlineResource
class Dataset(IterDataPipe[Dict[str, Any]], abc.ABC):
@staticmethod
def _verify_str_arg(
value: str,
arg: Optional[str] = None,
valid_values: Optional[Collection[str]] = None,
*,
custom_msg: Optional[str] = None,
) -> str:
return verify_str_arg(value, arg, valid_values, custom_msg=custom_msg)
def __init__(
self, root: Union[str, pathlib.Path], *, skip_integrity_check: bool = False, dependencies: Collection[str] = ()
) -> None:
for dependency in dependencies:
try:
importlib.import_module(dependency)
except ModuleNotFoundError:
raise ModuleNotFoundError(
f"{type(self).__name__}() depends on the third-party package '{dependency}'. "
f"Please install it, for example with `pip install {dependency}`."
) from None
self._root = pathlib.Path(root).expanduser().resolve()
resources = [
resource.load(self._root, skip_integrity_check=skip_integrity_check) for resource in self._resources()
]
self._dp = self._datapipe(resources)
def __iter__(self) -> Iterator[Dict[str, Any]]:
yield from self._dp
@abc.abstractmethod
def _resources(self) -> List[OnlineResource]:
pass
@abc.abstractmethod
def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
pass
@abc.abstractmethod
def __len__(self) -> int:
pass
def _generate_categories(self) -> Sequence[Union[str, Sequence[str]]]:
raise NotImplementedError
|
pytorchREPO_NAMEvisionPATH_START.@vision_extracted@vision-main@torchvision@prototype@datasets@utils@_dataset.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/bar/error_y/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._width import WidthValidator
from ._visible import VisibleValidator
from ._valueminus import ValueminusValidator
from ._value import ValueValidator
from ._type import TypeValidator
from ._tracerefminus import TracerefminusValidator
from ._traceref import TracerefValidator
from ._thickness import ThicknessValidator
from ._symmetric import SymmetricValidator
from ._color import ColorValidator
from ._arraysrc import ArraysrcValidator
from ._arrayminussrc import ArrayminussrcValidator
from ._arrayminus import ArrayminusValidator
from ._array import ArrayValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._width.WidthValidator",
"._visible.VisibleValidator",
"._valueminus.ValueminusValidator",
"._value.ValueValidator",
"._type.TypeValidator",
"._tracerefminus.TracerefminusValidator",
"._traceref.TracerefValidator",
"._thickness.ThicknessValidator",
"._symmetric.SymmetricValidator",
"._color.ColorValidator",
"._arraysrc.ArraysrcValidator",
"._arrayminussrc.ArrayminussrcValidator",
"._arrayminus.ArrayminusValidator",
"._array.ArrayValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@bar@error_y@__init__.py@.PATH_END.py
|
{
"filename": "c_router.py",
"repo_name": "mikecokina/elisa",
"repo_path": "elisa_extracted/elisa-master/src/elisa/single_system/curves/c_router.py",
"type": "Python"
}
|
import numpy as np
from ... logger import getLogger
from ... import const
from .. container import SinglePositionContainer
from . import utils as crv_utils, c_managed
from ... observer.mp_manager import manage_observations
logger = getLogger('single_system.curves.curves')
def resolve_curve_method(system, fn_array):
"""
Resolves which curve calculating method to use based on the properties of the SingleSystem.
:param system: elisa.single_system.SingleSystem;
:param fn_array: Tuple; list of curve calculating functions in specific order
(system with pulsations, system without pulsations)
:return: curve calculating method chosen from `fn_array`
"""
if system.star.has_pulsations():
logger.debug('Calculating light curve for star system with pulsation')
return fn_array[1]
else:
logger.debug('Calculating light curve for a non pulsating single star system')
return fn_array[0]
# raise NotImplementedError("System type not implemented or invalid.")
def prep_initial_system(single, **kwargs):
"""
Prepares base single system from which curves will be calculated in case of single system without pulsations.
:param single: elisa.single_system.system.SingleSystem;
:return: elisa.single_system.container.SystemContainer;
"""
from_this = dict(single_system=single, position=const.Position(0, np.nan, 0.0, np.nan, 0.0))
initial_system = SinglePositionContainer.from_single_system(**from_this)
do_pulsations = kwargs.get('build_pulsations', True)
initial_system.build(do_pulsations)
return initial_system
def produce_curves_wo_pulsations(single, initial_system, phases, curve_fn, crv_labels, **kwargs):
"""
General function for creation of single system light curve without pulsations.
:param single: elisa.single_system.system.SingleSystem;
:param initial_system: elisa.single_system.container.SystemContainer
:param phases: numpy.array;
:param curve_fn: callable; function to calculate given type of the curve
:param crv_labels: List; labels of the calculated curves (passbands, components,...)
:param kwargs: Dict;
* ** passband ** * - Dict[str, elisa.observer.PassbandContainer]
* ** left_bandwidth ** * - float
* ** right_bandwidth ** * - float
* ** position_method** * - function definition; to evaluate orbital positions
* ** phases ** * - numpy.array
:return: Dict; calculated curves
"""
crv_utils.prep_surface_params(initial_system, return_values=False, write_to_containers=True, **kwargs)
fn_args = (single, initial_system, crv_labels, curve_fn)
return manage_observations(fn=c_managed.produce_curves_wo_pulsations_mp, fn_args=fn_args, position=phases, **kwargs)
def produce_curves_with_pulsations(single, initial_system, phases, curve_fn, crv_labels, **kwargs):
"""
General function for creation of single system light curve with pulsations.
:param single: elisa.single_system.system.SingleSystem;
:param initial_system: elisa.single_system.container.SystemContainer;
:param phases: numpy.array;
:param curve_fn: callable; function to calculate given type of the curve
:param crv_labels: List; labels of the calculated curves (passbands, components,...)
:param kwargs: Dict;
* ** passband ** * - Dict[str, elisa.observer.PassbandContainer]
* ** left_bandwidth ** * - float
* ** right_bandwidth ** * - float
* ** position_method** * - function definition; to evaluate orbital positions
* ** phases ** * - numpy.array
:return: Dict; calculated curves
"""
fn_args = (single, initial_system, crv_labels, curve_fn)
return manage_observations(fn=c_managed.produce_curves_with_pulsations_mp, fn_args=fn_args, position=phases, **kwargs)
|
mikecokinaREPO_NAMEelisaPATH_START.@elisa_extracted@elisa-master@src@elisa@single_system@curves@c_router.py@.PATH_END.py
|
{
"filename": "test_html2text_transformer.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/unit_tests/document_transformers/test_html2text_transformer.py",
"type": "Python"
}
|
"""Unit tests for html2text document transformer."""
import pytest
from langchain_core.documents import Document
from langchain_community.document_transformers import Html2TextTransformer
@pytest.mark.requires("html2text")
def test_transform_empty_html() -> None:
html2text_transformer = Html2TextTransformer()
empty_html = "<html></html>"
documents = [Document(page_content=empty_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == "\n\n"
@pytest.mark.requires("html2text")
def test_extract_paragraphs() -> None:
html2text_transformer = Html2TextTransformer()
paragraphs_html = (
"<html><h1>Header</h1><p>First paragraph.</p>"
"<p>Second paragraph.</p><h1>Ignore at end</h1></html>"
)
documents = [Document(page_content=paragraphs_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"# Header\n\n"
"First paragraph.\n\n"
"Second paragraph.\n\n"
"# Ignore at end\n\n"
)
@pytest.mark.requires("html2text")
def test_extract_html() -> None:
html2text_transformer = Html2TextTransformer()
paragraphs_html = (
"<html>Begin of html tag"
"<h1>Header</h1>"
"<p>First paragraph.</p>"
"Middle of html tag"
"<p>Second paragraph.</p>"
"End of html tag"
"</html>"
)
documents = [Document(page_content=paragraphs_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"Begin of html tag\n\n"
"# Header\n\n"
"First paragraph.\n\n"
"Middle of html tag\n\n"
"Second paragraph.\n\n"
"End of html tag\n\n"
)
@pytest.mark.requires("html2text")
def test_remove_style() -> None:
html2text_transformer = Html2TextTransformer()
with_style_html = (
"<html><style>my_funky_style</style><p>First paragraph.</p></html>"
)
documents = [Document(page_content=with_style_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == "First paragraph.\n\n"
@pytest.mark.requires("html2text")
def test_ignore_links() -> None:
html2text_transformer = Html2TextTransformer(ignore_links=False)
multiple_tags_html = (
"<h1>First heading.</h1>"
"<p>First paragraph with an <a href='http://example.com'>example</a></p>"
)
documents = [Document(page_content=multiple_tags_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"# First heading.\n\n"
"First paragraph with an [example](http://example.com)\n\n"
)
html2text_transformer = Html2TextTransformer(ignore_links=True)
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"# First heading.\n\n" "First paragraph with an example\n\n"
)
@pytest.mark.requires("html2text")
def test_ignore_images() -> None:
html2text_transformer = Html2TextTransformer(ignore_images=False)
multiple_tags_html = (
"<h1>First heading.</h1>"
"<p>First paragraph with an "
"<img src='example.jpg' alt='Example image' width='500' height='600'></p>"
)
documents = [Document(page_content=multiple_tags_html)]
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"# First heading.\n\n"
"First paragraph with an \n\n"
)
html2text_transformer = Html2TextTransformer(ignore_images=True)
docs_transformed = html2text_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"# First heading.\n\n" "First paragraph with an\n\n"
)
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@unit_tests@document_transformers@test_html2text_transformer.py@.PATH_END.py
|
{
"filename": "HGP_2018_ds633_cov.py",
"repo_name": "CaymanUnterborn/ExoPlex",
"repo_path": "ExoPlex_extracted/ExoPlex-master/ExoPlex/burnman/minerals/HGP_2018_ds633_cov.py",
"type": "Python"
}
|
# This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2021 by the BurnMan team, released under the GNU
# GPL v2 or later.
"""
HGP_2018 (ds-633) zero-point energy covariance matrix
Derived from Holland, Green and Powell (2018) and references therein.
Dataset version 6.33.
The values in this document are all in S.I. units,
unlike those in the original tc-ds633.txt.
File autogenerated using HGP633data_to_burnman.py.
"""
from numpy import array
cov = {'covariance_matrix': array([[ 2.863000e+05, 1.550000e+04, 3.550000e+04, -3.530000e+04,
1.249000e+05, 1.245100e+06, 6.726000e+05, 2.723000e+05,
1.550000e+04, 2.695000e+05, 1.540000e+04, 1.517000e+05,
2.700000e+04, -5.080000e+04, 0.000000e+00, 1.517000e+05,
-6.400000e+03, 1.509000e+05, 2.900000e+03, 6.062000e+05,
0.000000e+00, 4.032000e+05, 2.100000e+03, 2.750000e+04,
-7.260000e+04, -2.690000e+04, 4.820000e+04, 4.515000e+05,
-2.470000e+04, 2.288000e+05, 4.329000e+05, -3.870000e+04,
-1.720000e+05, -3.080000e+04, -3.080000e+04, -3.110000e+04,
-3.070000e+04, -5.340000e+04, -3.090000e+04, 2.191000e+05,
-3.173000e+05, -2.818000e+05, 1.013000e+05, -3.330000e+04,
-2.390000e+04, 8.930000e+04, -9.900000e+04, -7.090000e+04,
-7.090000e+04, -4.780000e+04, -2.480000e+04, -3.930000e+04,
-3.830000e+04, 1.550000e+04, -1.187000e+05, -2.700000e+03,
-8.530000e+04, 1.172000e+05, -4.280000e+04, -9.910000e+04,
2.595000e+05, 2.594000e+05, -6.700000e+03, 9.000000e+03,
8.745000e+05, 2.033000e+05, 3.190000e+05, 1.143800e+06,
-1.760000e+04, 1.520000e+04, 1.730000e+04, -1.310000e+04,
9.800000e+03, 3.037000e+05, 3.037000e+05, 3.037000e+05,
3.037000e+05, 3.560000e+04, 3.560000e+04, 9.920000e+04,
1.459000e+05, 1.100000e+04, -7.300000e+03, 0.000000e+00,
1.550000e+04, 1.700000e+04, -5.800000e+04, 8.000000e+02,
2.430000e+04, 2.430000e+04, -7.500000e+03, -7.500000e+03,
-7.500000e+03, 7.662000e+05, 9.530000e+04, 3.566000e+05,
5.020000e+05, 4.594000e+05, 5.780000e+04, 3.933000e+05,
1.039000e+05, 1.081600e+06, 1.432000e+05, 1.081700e+06,
1.445000e+05, 6.739000e+05, 3.594000e+05, 1.553000e+05,
-1.760000e+05, 1.204000e+05, -1.380000e+04, 1.990000e+05,
-1.227000e+05, 7.960000e+04, -5.410000e+04, -3.850000e+04,
-8.910000e+04, 3.229000e+05, -7.860000e+04, -5.290000e+04,
1.205000e+05, 4.155000e+05, 4.729000e+05, 6.695000e+05,
4.649000e+05, 8.740000e+05, -1.500000e+03, 4.330000e+04,
2.208000e+05, -4.760000e+04, 2.910000e+04, 4.740000e+05,
7.020000e+04, 2.718000e+05, 2.920000e+04, 4.060000e+05,
7.340000e+04, 4.549000e+05, -1.110000e+04, -4.550000e+04,
-2.230000e+04, 4.404000e+05, 4.404000e+05, 3.310000e+04,
4.420000e+04, 7.152000e+05, 7.045000e+06, 1.230000e+04,
1.230000e+04, -7.160000e+04, -7.160000e+04, -3.820000e+04,
-7.170000e+04, -1.326000e+05, -7.160000e+04, 2.010000e+04,
2.010000e+04, 2.010000e+04, 2.010000e+04, 3.200000e+04,
-2.410000e+04, -2.400000e+04, -2.400000e+04, 4.640000e+04,
2.489000e+05, 0.000000e+00, -2.280000e+04, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, -1.443000e+05, -1.089000e+05, -9.030000e+04,
-1.425000e+05, 1.800000e+03, 1.600000e+03, 6.170000e+04,
6.160000e+04, -7.900000e+03, -2.300000e+04, -1.030000e+04,
9.720000e+04, -1.680000e+04, -1.800000e+03, -5.000000e+03,
-5.080000e+04, 1.517000e+05, -4.500000e+03, -2.300000e+03,
0.000000e+00, 0.000000e+00, -3.300000e+03, 1.116000e+05,
-1.260000e+04, -5.200000e+03, -5.100000e+03, -5.100000e+03,
0.000000e+00, 0.000000e+00, 8.000000e+04, -5.340000e+04,
-7.000000e+03, 4.150000e+04, 2.087000e+05, -1.160000e+04,
1.286000e+05, 1.000000e+05, -2.560000e+04, -2.300000e+03,
-2.810000e+04, -2.810000e+04, 1.240000e+05, -3.200000e+03,
0.000000e+00, 9.490000e+04, -3.150000e+04, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, -1.000000e+02, -3.000000e+02, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 9.720000e+04,
-2.300000e+04, -6.700000e+03, -2.300000e+03, -4.500000e+03,
2.010000e+04, -1.000000e+02, 2.863000e+05, 1.550000e+04,
-7.500000e+03, 3.037000e+05, 1.459000e+05, -3.070000e+04,
-3.820000e+04, -7.160000e+04, 1.230000e+04, -2.400000e+04,
-9.030000e+04, -1.030000e+04, -5.100000e+03, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
2.010000e+04, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00],
[ 1.550000e+04, 3.933000e+05, 3.280000e+04, -3.000000e+02,
7.600000e+03, 4.930000e+04, 1.830000e+04, 1.730000e+04,
3.932000e+05, 1.770000e+04, 3.922000e+05, 3.290000e+04,
2.198000e+05, -6.960000e+04, 0.000000e+00, 3.290000e+04,
2.440000e+04, 3.310000e+04, 2.540000e+04, 1.312000e+05,
0.000000e+00, 2.730000e+04, 5.558000e+05, 5.710000e+04,
-6.000000e+02, 3.622000e+05, 9.306000e+05, 9.690000e+04,
7.300000e+04, 1.894000e+05, 2.915000e+05, 5.621000e+05,
-3.180000e+04, -1.900000e+04, -1.900000e+04, -1.840000e+04,
-1.910000e+04, -6.180000e+04, -1.900000e+04, -3.200000e+05,
3.843000e+05, -2.802000e+05, -3.690000e+04, 1.386000e+05,
-2.690000e+04, 7.500000e+03, -2.490000e+04, -1.290000e+04,
-1.290000e+04, 1.706000e+05, 3.528000e+05, 1.860000e+04,
1.300000e+03, -4.700000e+03, 1.711000e+05, 1.088700e+06,
-7.140000e+04, 3.170000e+04, 2.390000e+04, -2.490000e+04,
7.740000e+04, 7.740000e+04, 4.360000e+05, 9.730000e+04,
-7.400000e+03, 7.740000e+04, 2.150000e+04, 9.200000e+03,
-5.700000e+03, 7.500000e+04, 3.840000e+04, -3.910000e+04,
2.410000e+04, 6.470000e+04, 6.470000e+04, 6.470000e+04,
6.470000e+04, 4.440000e+05, 4.440000e+05, -3.740000e+04,
5.580000e+04, 2.451000e+05, -4.900000e+03, 0.000000e+00,
1.766000e+05, 2.990000e+04, -4.660000e+04, 4.650000e+04,
3.710000e+04, 3.710000e+04, 2.420000e+04, 2.420000e+04,
2.430000e+04, 2.588000e+05, 1.206900e+06, 5.170000e+04,
3.300000e+03, 1.372000e+05, 7.058000e+05, -1.840000e+04,
1.030400e+06, 2.767000e+05, 1.604200e+06, 2.767000e+05,
1.604800e+06, 7.280000e+04, -2.489000e+05, -3.509000e+05,
5.007000e+05, 1.310000e+04, 1.889000e+05, 3.524100e+06,
-5.980000e+04, 4.220000e+04, 2.184000e+05, -2.440000e+04,
-6.860000e+04, -4.480000e+04, 5.181000e+05, -1.500000e+04,
-1.469000e+05, -5.300000e+03, 1.463000e+05, -7.400000e+03,
-1.098000e+05, 9.490000e+04, 8.715000e+05, 4.220000e+04,
-2.450000e+04, 3.270000e+05, 1.318000e+05, 1.471000e+05,
6.736000e+05, 4.520000e+04, 1.335000e+05, -1.060000e+04,
7.168000e+05, 2.015000e+05, 3.010000e+04, 2.500000e+04,
2.086000e+05, 4.610000e+04, 4.610000e+04, 6.152000e+05,
1.105400e+06, 2.264000e+05, 8.648000e+05, 4.520000e+04,
4.530000e+04, 9.900000e+03, 1.000000e+04, -1.000000e+02,
9.900000e+03, -1.180000e+04, 1.000000e+04, 5.080000e+04,
5.080000e+04, 5.080000e+04, 5.070000e+04, 4.860000e+04,
-5.210000e+04, -5.200000e+04, -5.200000e+04, -8.200000e+04,
2.050000e+04, 0.000000e+00, -3.100000e+04, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, -3.123000e+05, -8.980000e+04, -3.990000e+04,
-2.450000e+04, 1.018000e+05, 1.023000e+05, 2.544000e+05,
2.544000e+05, -5.500000e+03, -2.390000e+04, -2.670000e+04,
-1.240000e+04, 1.730000e+05, 1.178000e+05, -2.800000e+03,
-6.960000e+04, 3.280000e+04, 2.927000e+05, 3.000000e+02,
0.000000e+00, -2.200000e+03, -3.050000e+04, -4.200000e+04,
1.285000e+05, -1.150000e+04, -1.160000e+04, -1.160000e+04,
0.000000e+00, 0.000000e+00, -8.720000e+04, 8.860000e+04,
4.508000e+05, 8.460000e+04, -5.430000e+04, 1.189000e+05,
-1.750000e+04, -1.270000e+04, -3.550000e+04, 1.460000e+05,
-2.440000e+04, -2.440000e+04, -1.550000e+04, 1.562000e+05,
-3.100000e+03, -3.990000e+04, 1.319000e+05, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 3.000000e+02, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, -1.240000e+04,
-2.390000e+04, -9.100000e+03, 3.000000e+02, 2.927000e+05,
5.080000e+04, 0.000000e+00, 1.550000e+04, 3.933000e+05,
2.420000e+04, 6.470000e+04, 5.580000e+04, -1.910000e+04,
-1.000000e+02, 1.000000e+04, 4.530000e+04, -5.200000e+04,
-3.990000e+04, -2.670000e+04, -1.160000e+04, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
5.080000e+04, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00],
[ 3.550000e+04, 3.280000e+04, 1.022500e+06, 3.900000e+03,
1.950000e+04, 1.262000e+05, 5.520000e+04, 3.020000e+04,
3.280000e+04, 2.910000e+04, 3.270000e+04, 3.840000e+04,
4.110000e+04, -5.530000e+04, 0.000000e+00, 3.840000e+04,
2.350000e+04, 3.810000e+04, 4.700000e+03, 1.534000e+05,
0.000000e+00, 5.980000e+04, 5.810000e+04, 1.492900e+06,
1.270000e+04, 5.950000e+04, 1.034000e+05, 1.152000e+05,
6.840000e+04, 1.805000e+05, 2.748000e+05, 1.791000e+05,
7.690000e+04, -1.290000e+04, -1.280000e+04, -1.290000e+04,
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'wu',
'mang',
'cor',
'mcor',
'hem',
'esk',
'bix',
'NiO',
'pnt',
'geik',
'ilm',
'bdy',
'bdyT',
'bdyC',
'ten',
'cup',
'sp',
'herc',
'mt',
'mft',
'qnd',
'usp',
'picr',
'br',
'dsp',
'gth',
'cc',
'arag',
'mag',
'sid',
'rhc',
'dol',
'ank',
'syv',
'hlt',
'pyr',
'trot',
'tro',
'lot',
'trov',
'any',
'iron',
'Ni',
'Cu',
'gph',
'diam',
'S',
'H2O',
'CO2',
'CO',
'CH4',
'O2',
'H2',
'S2',
'H2S',
'syvL',
'hltL',
'perL',
'limL',
'corL',
'eskL',
'hemL',
'qL',
'h2oL',
'foL',
'faL',
'woL',
'enL',
'diL',
'silL',
'anL',
'kspL',
'abL',
'neL',
'lcL',
'ruL',
'bdyL',
'H+',
'Cl-',
'OH-',
'Na+',
'K+',
'Ca++',
'Mg++',
'Fe++',
'Al+++',
'CO3--',
'AlOH3',
'AlOH4-',
'KOH',
'HCl',
'KCl',
'NaCl',
'CaCl2',
'CaCl+',
'MgCl2',
'MgCl+',
'FeCl2',
'aqSi',
'HS-',
'HSO3-',
'SO42-',
'HSO4-']}
|
CaymanUnterbornREPO_NAMEExoPlexPATH_START.@ExoPlex_extracted@ExoPlex-master@ExoPlex@burnman@minerals@HGP_2018_ds633_cov.py@.PATH_END.py
|
{
"filename": "_shape.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/pie/marker/pattern/_shape.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShapeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="shape", parent_name="pie.marker.pattern", **kwargs):
super(ShapeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "style"),
values=kwargs.pop("values", ["", "/", "\\", "x", "-", "|", "+", "."]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@pie@marker@pattern@_shape.py@.PATH_END.py
|
{
"filename": "horizontal_incidence.py",
"repo_name": "neeravkaushal/RIDs-in-WCDs",
"repo_path": "RIDs-in-WCDs_extracted/RIDs-in-WCDs-master/horizontal_incidence.py",
"type": "Python"
}
|
#!/usr/bin/env python
#coding: utf-8
#PYTHON Code: Simulation for horizontal incidence of muon
#Author: Neerav Kaushal
#Import Libraries
import matplotlib.pyplot as plt
from numpy import sin, cos, sqrt, pi, linspace, arange, deg2rad, rad2deg, array, zeros
from numpy import arcsin, arccos,sort, argsort, argwhere, argmin, argmax, interp, concatenate
from scipy.spatial import distance
import warnings
from numpy import linalg as LA
warnings.simplefilter('ignore')
#Initialize Parameters
n = 1.33 #------------------------------------ Refractive index of medium
c = 299792458/n #----------------------------- Speed of light in medium
R = 7.3/2 #----------------------------------- Radius of tank
v = n * c #----------------------------------- Particle Speed
times = linspace(1e-11,1e-7,200000)
c1 = (0, 0, 0) #------------------------------ Central PMT number 1
c2 = (1.85*cos(2*pi/3) , 1.85*sin(2*pi/3), 0) # Non-Radial PMT number 2
c3 = (1.85*cos(4*pi/3) , 1.85*sin(4*pi/3), 0) # Non-Radial PMT number 3
c4 = (1.85*cos(0 ) , 1.85*sin(0 ), 0) # Radial PMT number 4
xA,xB,h= 3.6,-1.5, 0.5
A = array( [xA , sqrt(R**2-xA**2), h] ) #--- Entry Point of muon
B = array( [xB , -sqrt(R**2-xB**2), h] ) #--- Exit point of muon
AB = B-A
nAB = LA.norm(AB) #-----------------------------Muon path length
den = c*c - v*v
print('A : ', A)
print('B : ', B)
#Bird's View of tank
plt.figure(figsize=(10,8))
angs = linspace(0,6.28,1000)
xs,ys = R*cos(angs), R*sin(angs)
plt.plot(xs,ys,lw=3)
plt.scatter(A[0],A[1],c='r',s=500)
plt.scatter(B[0],B[1],c='b',s=500)
plt.scatter(c1[0],c1[1],c='k',s=200)
plt.scatter(c2[0],c2[1],c='k',s=200)
plt.scatter(c3[0],c3[1],c='k',s=200)
plt.scatter(c4[0],c4[1],c='k',s=200)
plt.axhline(0)
plt.axvline(0)
plt.axis('scaled')
plt.arrow(A[0],A[1],B[0]-3.1,B[1]-0.3,head_width=0.4,head_length=0.4,fc='k',ec='k',lw=2)
plt.text(A[0]-0.5,A[1]+0.25, "A", fontsize=26)
plt.text(B[0]-0.2,B[1]+0.35, "B", fontsize=26)
plt.text(c1[0]+0.2,c1[1]+0.25, "$D_1$", fontsize=24)
plt.text(c2[0]-0.8,c2[1], "$D_2$", fontsize=24)
plt.text(c3[0]-0.8,c3[1], "$D_3$", fontsize=24)
plt.text(c4[0]+0.1,c4[1]+0.25, "$D_4$", fontsize=24)
plt.xlim(-R-0.2,R+0.2)
plt.ylim(-R-0.2,R+0.2)
plt.xticks(arange(-4.0,4.1,1))
plt.tick_params(axis='both', direction='in', labelsize=18)
plt.tick_params(labeltop=True, labelright=True, labelbottom=True)
plt.tick_params(labelleft=True, bottom=True, top=True, left=True, right=True)
plt.grid(True)
plt.show()
#Necessary functions
#Calculate brightness at the muon entry point
def entry_brightness(L,c,v,alpha,den):
tt = L/c
aterm = (c*c*tt*v-L*v*v*cos(alpha))
bterm = (v*v*( -L*L*v*v + c*c*L*L + c*c*tt*tt*v*v - 2*c*c*L*tt*v*cos(alpha) + L*L*v*v*cos(alpha)**2))
xp = (aterm + sqrt(bterm)) / den
cterm = (c*c*v)
dterm = (c*c*v*v*v*(tt*v-L*cos(alpha)))
vp = (cterm + (dterm/sqrt(bterm))) / den
kp = sqrt( L*L + xp*xp - 2*L*xp*cos(alpha) )
betap = alpha
vtp = vp*sin(betap)
omegap = vtp / kp
bp = abs(omegap/(kp**2))
return bp
def sec(x):
return 1/cos(x)
def tan(x):
return sin(x)/cos(x)
#Different plotting scenarios
def plus_t_vs_x (a, b, color, label):
plt.plot(a, b, c=color, ls='-' , lw=2.5, label=label)
def minus_t_vs_x(a, b, color, label):
plt.plot(a, b, c=color, ls='--', lw=2.5, label=label)
def both_t_vs_x (a1, b1, a2, b2, color, label):
plt.plot(a1, b1, c=color, ls='-' , lw=2.5, label=label)
plt.plot(a2, b2, c=color, ls='--', lw=2.5)
plt.xlabel(r'time since muon entry (in ns)',fontsize=18)
plt.ylabel(r'image distance $x_{pm}$ from entry point (in meters)', fontsize=18)
plt.axhline(xc, c='k', ls=':')
def plus_t_vs_b (a, b, color, label):
plt.plot(a, b, c=color, ls='-', lw=2.5, label=label)
def minus_t_vs_b(a, b, color, label):
plt.plot(a, b, c=color, ls='--', lw=2.5, label=label)
def both_t_vs_b (a1, b1, a2, b2, color, label):
plt.plot(a1, b1, c=color, ls='-' , lw=2.5, label=label)
plt.plot(a2, b2, c=color, ls='--', lw=2.5)
plt.axhline(1,c='k',ls=':')
plt.xlabel(r'time since muon entry (in ns)',fontsize=18)
plt.ylabel(r'relative brightness ($b/b_{entry}$)', fontsize=18)
plt.yscale('log')
plt.ylim(1e-2,1e+4)
def plus_t_vs_ang(a, b, color, label):
plt.plot(a, b, c=color, ls='-', lw=2.5, label=label)
def minus_t_vs_ang(a, b, color, label):
plt.plot(a, b, c=color, ls='--', lw=2.5, label=label)
def both_t_vs_ang(a1, b1, a2, b2, color, label):
plt.plot(a1, b1, c=color, ls='-' , lw=2.5, label=label)
plt.plot(a2, b2, c=color, ls='--', lw=2.5)
plt.axhline(phic, c='k', ls=':')
plt.xlabel(r'time since muon entry (in ns)',fontsize=18)
plt.ylabel(r'angular locations $\phi_{pm}\;(in\;degrees)$',fontsize=18)
def plus_b_vs_ang(a, b, color, label):
plt.plot(a, b, c=color, ls='-', lw=2.5, label=label)
def minus_b_vs_ang(a, b, color, label):
plt.plot(a, b, c=color, ls='--', lw=2.5, label=label)
def both_b_vs_ang(a1, b1, a2, b2, color, label):
plt.plot(a1, b1, c=color, ls='-' , lw=2.5, label=label)
plt.plot(a2, b2, c=color, ls='--', lw=2.5)
plt.axvline(phic, c='k', ls=':')
plt.axhline(1,c='k',ls=':')
plt.xlabel(r'angular locations $\phi_{pm}\;(in\;degrees)$',fontsize=18)
plt.ylabel(r'relative brightness ($b/b_{entry}$)', fontsize=18)
plt.yscale('log')
plt.ylim(1e-2,1e+4)
#Computations and plotting
plt.figure()
plotme = 't vs b' #---------Plot type selection
#plotme = 't vs x'
#plotme = 't vs ang'
#plotme = 'b vs ang'
detector_coordinates = [c1,c2,c3,c4]
colors = ['k','r','b','g']
detectors = [1,2,3,4]
labels = ['Central Detector', "Detector 2",'Detector 3','Detector 4']
for D, color, detector, mylabel in zip(detector_coordinates, colors, detectors, labels):
print("==============================================")
print("Detector ", detector)
AD = D - A
BD = D - B
L = LA.norm(AD)
alpha = arccos((sum(AD*AB))/(L*nAB)) #-Angle between detector and muon track through entry point A
xc = L*cos(alpha) - (c*L*sin(alpha))/sqrt(-den) #-Critical height
print('XC: ',round(xc,3), " m")
T,XP,XM,BP,BM,PHIP,PHIM = [],[],[],[],[],[],[]
for iii,t in enumerate(times):
x = v*t #------------Distance traveled by muon in time t
ratio = x/nAB
X = array([ (1-ratio)*A[0] + ratio*B[0] , (1-ratio)*A[1] + ratio*B[1] , h ])
AX,DX = X - A, X-D
k = sqrt(L*L+x*x-2*L*x*cos(alpha)) #--Distance between detector and muon at time t
t1,t2 = t, k/c
tt = t1 + t2 #-----------------Total time taken by detector to see the muon
aterm = (c*c*tt*v-L*v*v*cos(alpha))
bterm = (v*v*( -L*L*v*v + c*c*L*L + c*c*tt*tt*v*v - 2*c*c*L*tt*v*cos(alpha) + L*L*v*v*cos(alpha)**2))
xp = (aterm + sqrt(bterm)) / den #------Distance of first cherenkov image from entry point A
xm = (aterm - sqrt(bterm)) / den #------------------second----------------------------------
cterm = (c*c*v)
dterm = (c*c*v*v*v*(tt*v-L*cos(alpha)))
vp = (cterm + (dterm/sqrt(bterm))) / den #----Velocity of first cherenkov image
vm = (cterm - (dterm/sqrt(bterm))) / den #----------------second---------------
kp = sqrt( L*L + xp*xp - 2*L*xp*cos(alpha) ) #---Distance of first cherenkov image from detector
km = sqrt( L*L + xm*xm - 2*L*xm*cos(alpha) ) #---------------second-----------------------------
betap = pi - arccos( (xp*xp + kp*kp - L*L) / (2*xp*kp) )
betam = pi - arccos( (xm*xm + km*km - L*L) / (2*xm*km) )
vtp = vp*sin(betap) #------Transverse velocity of first cherenkov image
vtm = vm *sin(betam ) #-----------------------------second---------------
omegap = vtp / kp
omegam = vtm / km
bp,bm = abs(omegap/(kp**2)), abs(omegam/(km **2)) #------Brightness of first and second cherenkov images
phip = arccos( (L*L + kp*kp - xp*xp ) / (2*L*kp) ) #----Angular location of images as seen by detector
phim = arccos( (L*L + km*km - xm*xm ) / (2*L*km) ) #----------------------------do--------------------
XP.append(xp); XM.append(xm); BP.append(bp); BM.append(bm)
PHIP.append(rad2deg(phip)); PHIM.append(rad2deg(phim)); T.append(tt)
XP, XM, BP, BM, T, PHIP, PHIM = array(XP), array(XM), array(BP), array(BM), array(T), array(PHIP), array(PHIM)
TT = T * 1e+9 #------Convert time in nanoseconds
conp = [(XP>=0) & (XP<=nAB)] #---Consider only the images inside the tank
conm = [(XM>=0) & (XM<=nAB)] #----------------do-------------------------
pluslen = len (XP[conp])
minuslen = len (XM[conm])
kc = sqrt( L*L + xc*xc - 2*L*xc*cos(alpha) ) #---Distance between detector & first RID location
phic = rad2deg(arccos( (L*L + kc*kc - xc*xc ) / (2*L*kc) )) #--Angular location of first RID
if detector==1:
Bnorm = entry_brightness(L,c,v,alpha,den) #---Use this to normalize brightness wrt the entry point
#---as seen by the central detector
if pluslen == 0:
if minuslen == 0:
print("Images outside tank. Skipping...")
else:
print('One image moving towards exit B.')
TT,XM,BM,PHIM = TT[conm], XM[conm], BM[conm]/Bnorm, PHIM[conm]
if plotme=='t vs b' :minus_t_vs_b (a=TT, b=BM, color=color, label=mylabel)
elif plotme=='t vs x' :minus_t_vs_x (a=TT, b=XM, color=color, label=mylabel)
elif plotme=='t vs ang':minus_t_vs_ang(a=TT, b=PHIM, color=color, label=mylabel)
elif plotme=='b vs ang':minus_b_vs_ang(a=PHIM, b=BM, color=color, label=mylabel)
elif pluslen != 0:
if minuslen == 0:
print('One image moving towards entry A.')
TT,XP,BP,PHIP = TT[conp], XP[conp], BP[conp]/Bnorm, PHIP[conp]
if plotme=='t vs b' :plus_t_vs_b (a=TT, b=BP, color=color, label=mylabel)
elif plotme=='t vs x' :plus_t_vs_x (a=TT, b=XP, color=color, label=mylabel)
elif plotme=='t vs ang':plus_t_vs_ang(a=TT, b=PHIP, color=color, label=mylabel)
elif plotme=='b vs ang':plus_b_vs_ang(a=PHIP, b=BP, color=color, label=mylabel)
else:
print('Both images moving')
TTm, XM, BM, PHIM = TT[conm], XM[conm], BM[conm]/Bnorm, PHIM[conm]
TTp, XP, BP, PHIP = TT[conp], XP[conp], BP[conp]/Bnorm, PHIP[conp]
if plotme=='t vs b' :both_t_vs_b (a1=TTp, b1=BP, a2=TTm, b2=BM, color=color, label=mylabel)
elif plotme=='t vs x' :both_t_vs_x (a1=TTp, b1=XP, a2=TTm, b2=XM, color=color, label=mylabel)
elif plotme=='t vs ang':both_t_vs_ang(a1=TTp, b1=PHIP, a2=TTm, b2=PHIM, color=color, label=mylabel)
elif plotme=='b vs ang':both_b_vs_ang(a1=PHIP, b1=BP, a2=PHIM, b2=BM , color=color, label=mylabel)
plt.tick_params(axis='both', direction='in', labelsize=18)
plt.legend(prop={'size': 14})
plt.show()
|
neeravkaushalREPO_NAMERIDs-in-WCDsPATH_START.@RIDs-in-WCDs_extracted@RIDs-in-WCDs-master@horizontal_incidence.py@.PATH_END.py
|
{
"filename": "human.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/callbacks/human.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.human import (
AsyncHumanApprovalCallbackHandler,
HumanApprovalCallbackHandler,
HumanRejectedException,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"HumanRejectedException": "langchain_community.callbacks.human",
"HumanApprovalCallbackHandler": "langchain_community.callbacks.human",
"AsyncHumanApprovalCallbackHandler": "langchain_community.callbacks.human",
}
_import_attribute = create_importer(__file__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"HumanRejectedException",
"HumanApprovalCallbackHandler",
"AsyncHumanApprovalCallbackHandler",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@callbacks@human.py@.PATH_END.py
|
{
"filename": "_ticks.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattergl/marker/colorbar/_ticks.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicksValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="ticks", parent_name="scattergl.marker.colorbar", **kwargs
):
super(TicksValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
values=kwargs.pop("values", ["outside", "inside", ""]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattergl@marker@colorbar@_ticks.py@.PATH_END.py
|
{
"filename": "test_event.py",
"repo_name": "ebachelet/pyLIMA",
"repo_path": "pyLIMA_extracted/pyLIMA-master/pyLIMA/tests/test_event.py",
"type": "Python"
}
|
import numpy as np
from pyLIMA import telescopes, event
def test_event():
lightcurve = np.array([[2456789, 12.8, 0.01], [2458888, 12, 0.25]])
telo = telescopes.Telescope(name='fake', camera_filter='I',
lightcurve=lightcurve,
lightcurve_names=['time', 'mag', 'err_mag'],
lightcurve_units=['JD', 'mag', 'mag'])
telo2 = telescopes.Telescope(name='fake2', camera_filter='I',
lightcurve=lightcurve,
lightcurve_names=['time', 'mag', 'err_mag'],
lightcurve_units=['JD', 'mag', 'mag'])
ev = event.Event(ra=20, dec=-20)
ev.telescopes.append(telo)
ev.telescopes.append(telo2)
telo.initialize_positions()
telo2.initialize_positions()
ev.find_survey("fake2")
assert ev.ra == 20
assert ev.dec == -20
assert ev.survey == "fake2"
assert ev.telescopes[0] == telo2
ev.compute_parallax_all_telescopes(['Full', 2456780])
assert np.allclose(telo.deltas_positions['photometry'],
np.array([[-6.68645090e-03, -6.11752451e+00],
[-4.33482159e-03, -3.26412162e+01]]))
assert np.allclose(telo2.deltas_positions['photometry'],
np.array([[-6.68645090e-03, -6.11752451e+00],
[-4.33482159e-03, -3.26412162e+01]]))
assert ev.total_number_of_data_points() == 4
assert np.allclose(ev.North, [0.3213938, 0.11697778, 0.93969262])
assert np.allclose(ev.East, [-0.34202014, 0.93969262, 0.])
|
ebacheletREPO_NAMEpyLIMAPATH_START.@pyLIMA_extracted@pyLIMA-master@pyLIMA@tests@test_event.py@.PATH_END.py
|
{
"filename": "_arrayminussrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/histogram/error_y/_arrayminussrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ArrayminussrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="arrayminussrc", parent_name="histogram.error_y", **kwargs
):
super(ArrayminussrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@histogram@error_y@_arrayminussrc.py@.PATH_END.py
|
{
"filename": "hub_mixin.py",
"repo_name": "qubvel/segmentation_models.pytorch",
"repo_path": "segmentation_models.pytorch_extracted/segmentation_models.pytorch-main/segmentation_models_pytorch/base/hub_mixin.py",
"type": "Python"
}
|
import json
from pathlib import Path
from typing import Optional, Union
from functools import wraps
from huggingface_hub import (
PyTorchModelHubMixin,
ModelCard,
ModelCardData,
hf_hub_download,
)
MODEL_CARD = """
---
{{ card_data }}
---
# {{ model_name }} Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {{ model_parameters }}
```
## Model metrics
{{ metrics | default("[More Information Needed]", true) }}
## Dataset
Dataset name: {{ dataset | default("[More Information Needed]", true) }}
## More Information
- Library: {{ repo_url | default("[More Information Needed]", true) }}
- Docs: {{ docs_url | default("[More Information Needed]", true) }}
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
"""
def _format_parameters(parameters: dict):
params = {k: v for k, v in parameters.items() if not k.startswith("_")}
params = [
f'"{k}": {v}' if not isinstance(v, str) else f'"{k}": "{v}"'
for k, v in params.items()
]
params = ",\n".join([f" {param}" for param in params])
params = "{\n" + f"{params}" + "\n}"
return params
class SMPHubMixin(PyTorchModelHubMixin):
def generate_model_card(self, *args, **kwargs) -> ModelCard:
model_parameters_json = _format_parameters(self.config)
metrics = kwargs.get("metrics", None)
dataset = kwargs.get("dataset", None)
if metrics is not None:
metrics = json.dumps(metrics, indent=4)
metrics = f"```json\n{metrics}\n```"
tags = self._hub_mixin_info.model_card_data.get("tags", []) or []
tags.extend(["segmentation-models-pytorch", "semantic-segmentation", "pytorch"])
model_card_data = ModelCardData(
languages=["python"],
library_name="segmentation-models-pytorch",
license="mit",
tags=tags,
pipeline_tag="image-segmentation",
)
model_card = ModelCard.from_template(
card_data=model_card_data,
template_str=MODEL_CARD,
repo_url="https://github.com/qubvel/segmentation_models.pytorch",
docs_url="https://smp.readthedocs.io/en/latest/",
model_parameters=model_parameters_json,
model_name=self.__class__.__name__,
metrics=metrics,
dataset=dataset,
)
return model_card
@wraps(PyTorchModelHubMixin.save_pretrained)
def save_pretrained(
self, save_directory: Union[str, Path], *args, **kwargs
) -> Optional[str]:
model_card_kwargs = kwargs.pop("model_card_kwargs", {})
if "dataset" in kwargs:
model_card_kwargs["dataset"] = kwargs.pop("dataset")
if "metrics" in kwargs:
model_card_kwargs["metrics"] = kwargs.pop("metrics")
kwargs["model_card_kwargs"] = model_card_kwargs
# set additional attribute to be able to deserialize the model
self.config["_model_class"] = self.__class__.__name__
try:
# call the original save_pretrained
result = super().save_pretrained(save_directory, *args, **kwargs)
finally:
self.config.pop("_model_class", None)
return result
@property
def config(self) -> dict:
return self._hub_mixin_config
@wraps(PyTorchModelHubMixin.from_pretrained)
def from_pretrained(pretrained_model_name_or_path: str, *args, **kwargs):
config_path = Path(pretrained_model_name_or_path) / "config.json"
if not config_path.exists():
config_path = hf_hub_download(
pretrained_model_name_or_path,
filename="config.json",
revision=kwargs.get("revision", None),
)
with open(config_path, "r") as f:
config = json.load(f)
model_class_name = config.pop("_model_class")
import segmentation_models_pytorch as smp
model_class = getattr(smp, model_class_name)
return model_class.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
|
qubvelREPO_NAMEsegmentation_models.pytorchPATH_START.@segmentation_models.pytorch_extracted@segmentation_models.pytorch-main@segmentation_models_pytorch@base@hub_mixin.py@.PATH_END.py
|
{
"filename": "surveys.py",
"repo_name": "latrop/DECA",
"repo_path": "DECA_extracted/DECA-master/prep_modules/surveys.py",
"type": "Python"
}
|
#!/usr/bin/python
# -*- coding: cp1251 -*-
import sys
import math
import numpy as np
from scipy import stats
import scipy as sp
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.patches as patches
import matplotlib.path as path
from matplotlib.ticker import NullFormatter
from numpy import *
from pylab import *
import os
import shutil
import subprocess
import random
#******** Functions for calculating m0,mags and errors ********#
# _dr7 - for dr<=7
# _dr8 - for dr>=8
def m0_dr7(exptime,aa,kk,airmass,pix2sec):
# Calculated for arcsec^2!
return 2.5*log10(exptime) - (aa+kk*airmass) #5.*log10(pix2sec)
def m0_dr8(pix2sec):
# Calculated for arcsec^2!
return 22.5 #+ 5.*log10(pix2sec)
def m0_dr8_dn(k):
# Calculated for arcsec^2!
return 22.5 - 2.5*log10(k)
def mag_dr7(DN,exptime,aa,kk,airmass,pix2sec):
# Calculated for arcsec^2!
return -2.5*log10(DN) + 5.*log10(pix2sec) + 2.5*log10(exptime) - (aa+kk*airmass)
def DN_err_dr7(DN,skyErr,GAIN,darkVariance,pix2sec):
# Calculated for arcsec^2!
# DN = DN(sky) + DN(object)
return sqrt( DN/((pix2sec**2.)*GAIN) + 1./(pix2sec**2.) *(darkVariance + skyErr) )
def mag_err_dr7(DN,skyErr,GAIN,darkVariance):
# Calculated for arcsec^2!
return 2.5/ln(10.) * DN_err_dr7(DN,skyErr,GAIN,darkVariance,pix2sec)/DN
def mag_dr8(DN,pix2sec):
return 22.5 - 2.5*log10(DN) + 5.*log10(pix2sec)
def DN_err_dr8(DN, GAIN,darkVariance,pix2sec):
# Calculated for arcsec^2!
# DN = DN(sky) + DN(object)
return sqrt( DN/((pix2sec**2.)*GAIN) + 1./(pix2sec**2.)*darkVariance )
def mag_err_dr8(DN,GAIN,darkVariance,pix2sec):
# Calculated for arcsec^2!
return 2.5/ln(10.) * DN_err_dr8(DN, GAIN,darkVariance,pix2sec)/DN
# For UKIDSS
def header_extr(gal_image):
hdulist = pyfits.open(gal_image)#, do_not_scale_image_data=True, mode='update')
prihdr = hdulist[1].header
nx = prihdr['NAXIS1']
ny = prihdr['NAXIS2']
pix2sec = prihdr['PIXLSIZE']
GAIN = prihdr['GAIN']
read_out_noise = prihdr['READNOIS']
sky_level = prihdr['SKYLEVEL']
sky_noise = prihdr['SKYNOISE']
fwhm = prihdr['SEEING'] * pix2sec # now in arcsec
m0 = prihdr['MAGZPT']
A = prihdr['EXTINCT']
prihdr0 = hdulist[0].header
exptime = prihdr0['EXP_TIME']
NCOMBINE = prihdr0['NEXP']
#del prihdr['CTYPE1']
#del prihdr['CTYPE2']
#hdulist.flush()
return nx,ny,pix2sec,GAIN,read_out_noise,sky_level,fwhm,m0,A,exptime,NCOMBINE
|
latropREPO_NAMEDECAPATH_START.@DECA_extracted@DECA-master@prep_modules@surveys.py@.PATH_END.py
|
{
"filename": "quickstart.ipynb",
"repo_name": "ExObsSim/ExoRad2-public",
"repo_path": "ExoRad2-public_extracted/ExoRad2-public-master/examples/quickstart.ipynb",
"type": "Jupyter Notebook"
}
|
# Exorad 2.0
This Notebook will show you how to use exorad library to build your own pipeline.
Before we start, let's silent the exorad logger.
```python
import warnings
warnings.filterwarnings("ignore")
from exorad.log import disableLogging
disableLogging()
```
## Preparing the instrument
### Load the instrument descrition
The first step is to load the instrument description.
We use here the payload described in `examples/payload_example.xml`.
We call the `LoadOptions` task that parses the xml file into a Python dictionary.
```python
from exorad.tasks import LoadOptions
payload_file = 'payload_example.xml'
loadOptions = LoadOptions()
payload = loadOptions(filename=payload_file)
```
## build the channels
Once we have the payload description we can build the channels using the `BuildChannels` taks, this will iterate over the channel and build each of the instruments listed in the payload config. To give it a closer look, let's do it step by step.
Inside `example_payload.xml` are described two channels: "Phot" that is a photometer and "Spec" that is a spectrometer. We want to build them and store them into a dictionary
```python
channels = {}
from exorad.tasks import BuildInstrument
buildInstrument = BuildInstrument()
channels['Phot'] = buildInstrument(type="photometer",
name = "Phot",
description=payload['channel']['Phot'],
payload=payload,
write=False, output=None)
channels['Spec'] = buildInstrument(type="spectrometer",
name = "Spec",
description=payload['channel']['Spec'],
payload=payload,
write=False, output=None)
```
## Plot instrument photo-conversion efficiency
Thanks to exorad plotter you can easily plot the channels photon-conversion efficiency. To do so, we need to merge the channel output table to a cumulative table.
```python
from exorad.tasks import MergeChannelsOutput
mergeChannelsOutput = MergeChannelsOutput()
table = mergeChannelsOutput(channels=channels)
from exorad.utils.plotter import Plotter
plotter = Plotter(channels=channels, input_table=table)
efficiency_fig = plotter.plot_efficiency()
```
## Acess the payload data
Assume you want to edit one of the payload parameters, for example you quant to move the Quatum Efficiency for the photometer from 0.55 to 0.65.
Then you will need to build the channels again and produce an updated efficiency figure.
```python
payload['channel']['Phot']['detector']['qe']['value'] = 0.65
from exorad.tasks import BuildChannels
buildChannels = BuildChannels()
channels = buildChannels(payload=payload, write=False, output=None)
table = mergeChannelsOutput(channels=channels)
plotter = Plotter(channels=channels, input_table=table)
efficiency_fig = plotter.plot_efficiency()
```
## Explore the telescope self emission
Even withot a target, we still have signal in our telescope coming from self emission. This can be expored with exorad.
We can make a plot of the signals using the previous plotter. We have to manually set the lower limit for y-axes because exorad assumes 1e-3 ct/s as lower limit, but for the instrument we built the self emission is far lower because of the low temperature assumed (~60K for optics).
The self emission is stored in the channel output table in a column named `instrument_signal`. Information on the signal produced by each optical element can be retrieved in the channel dictionary under `['built_instr']['optical_path']['signal_table']`
```python
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax = plotter.plot_signal(ax, ylim=1e-32, scale='log', channel_edges=False)
```
## Observing a target list
### Load a Target list
To observe a list of targets we first need to define them. Exorad can load target list from file, as the one you can find in `examples/test_target.csv`, or directly from Python. Because the first case is covered by the documentation, let's focus here on the latter. To describe a target in python, you need to use Astropy QTable and to follow the same notation used in the file and described in the documentation. Here we produce an example considering a single target called "test" that has mass 1 solar masses, effective temperature 5000 K, radius 1 solar radius and 10 pc away from us. Obviously, you can add more element to the list if you have more than one target.
```python
from astropy.table import QTable, Column
import astropy.units as u
names = Column(['test'], name='star name')
masses = Column([1]*u.M_sun, name='star M')
temperatures = Column([5000]*u.K, name='star Teff')
radii = Column([1] * u.R_sun, name='star R')
distances = Column([10] * u.pc, name='star D')
magK = Column([0]* u.Unit(""), name='star magK')
raw_targetlist = QTable([names, masses,temperatures, radii, distances, magK])
from exorad.tasks import LoadTargetList
loadTargetList = LoadTargetList()
targets = loadTargetList(target_list=raw_targetlist)
# "targets" is now a list of Target classes.
# To read the content of the loaded element we need to convert the Target class into a dictionary
print(targets.target[0].to_dict())
```
{'star': {'M': {'value': 1.0, 'unit': 'solMass'}, 'Teff': {'value': 5000.0, 'unit': 'K'}, 'R': {'value': 1.0, 'unit': 'solRad'}, 'D': {'value': 10.0, 'unit': 'pc'}, 'magK': {'value': 0.0, 'unit': ''}}, 'id': 0}
### Foregrounds
Before you can observe a target you first need to prepare the table to fill. For that you need to call `PrepareTarget`. This populates the target attribute `table` that contains the merged channel tables and will be populated with the successive steps.
Then we can think about the foregrounds. These are defined in the payload configuration file. In out case we have indicated a zodiacal foreground and a custom one described by a csv file. These are listed now in `payload['common']['foreground']`. The Task `EstimateForegrounds` builds both of them in one shot, but for the sake of learning, let's produce them one per time with their specific classes. The task mentioned requires the target as input and returns it as output because adds foreground information to the class.
Remember that the order is important when you list your contributions in your payload configuration file, because foregrounds can have both emission and transmission. In this optic, an element locate before another, has its light passed through the second one and so its total signal contribution is reduced.
```python
from exorad.tasks import PrepareTarget, EstimateForeground, EstimateZodi
target = targets.target[0]
wl_min, wl_max = payload['common']['wl_min']['value'], payload['common']['wl_max']['value']
prepareTarget = PrepareTarget()
target = prepareTarget(target=target, channels=channels)
estimateZodi = EstimateZodi()
target = estimateZodi(zodi=payload['common']['foreground']['zodiacal'],
target=target,
wl_range=(wl_min, wl_max))
estimateForeground = EstimateForeground()
target = estimateForeground(foreground=payload['common']['foreground']['skyFilter'],
target=target,
wl_range=(wl_min, wl_max))
# We plot now the foreground radiances
fig_zodi, ax = target.foreground['zodi'].plot()
fig_zodi.suptitle('zodi')
fig_sky, ax = target.foreground['skyFilter'].plot()
fig_sky.suptitle('skyFilter')
```
Text(0.5, 0.98, 'skyFilter')
Once the contributions has been estimated, we can propagate them. Here we propagate and plot the foregrounds signal. The `PropagateForegroundLight` task also populates the target table with the computed foreground signal.
```python
from exorad.tasks import PropagateForegroundLight
propagateForegroundLight = PropagateForegroundLight()
target = propagateForegroundLight(channels=channels, target=target)
plotter = Plotter(channels=channels, input_table=target.table)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax = plotter.plot_signal(ax, scale='log', channel_edges=False)
# We show here the information content of the target table, that now contains also the foreground signal
print(target.table.keys())
```
['chName', 'Wavelength', 'Bandwidth', 'LeftBinEdge', 'RightBinEdge', 'QE', 'WindowSize', 'TR', 'instrument_signal', 'instrument_MaxSignal_inPixel', 'skyFilter_signal', 'skyFilter_MaxSignal_inPixel', 'zodi_signal', 'zodi_MaxSignal_inPixel']
### Target source
We can now load the light source we are gonna use for the target. As described in the documentation, we can use a black body, or a phoenix star or a custom sed described in a csv file. Here we use a black body, as indicated in the payload configuration file `<sourceSpectrum> planck </sourceSpectrum>`, and now in the dic `payload['common']['sourceSpectrum']`.
```python
from exorad.tasks import LoadSource
loadSource = LoadSource()
target, sed = loadSource(target=target,
source=payload['common']['sourceSpectrum'],
wl_range=(wl_min, wl_max))
fig_source, ax=sed.plot()
fig_source.suptitle(target.name)
```
Text(0.5, 0.98, 'test')
We can now propagate the source light. The light signal information will be added also to the target table
```python
from exorad.tasks import PropagateTargetLight
propagateTargetLight = PropagateTargetLight()
target = propagateTargetLight(channels=channels, target=target)
plotter = Plotter(channels=channels, input_table=target.table)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax = plotter.plot_signal(ax, scale='log', channel_edges=False)
# We show here the information content of the target table, that now contains also the source signal
print(target.table.keys())
```
['chName', 'Wavelength', 'Bandwidth', 'LeftBinEdge', 'RightBinEdge', 'QE', 'WindowSize', 'TR', 'instrument_signal', 'instrument_MaxSignal_inPixel', 'skyFilter_signal', 'skyFilter_MaxSignal_inPixel', 'zodi_signal', 'zodi_MaxSignal_inPixel', 'foreground_transmission', 'starFlux', 'starSignal', 'star_signal_inAperture', 'star_MaxSignal_inPixel']
## Estimate the noise
The noise estimation is the last step. Exorad computes the photon noise from every signal considered so far, but aldo dark current noise and read noise from the detector. It also takes into account for custom noise source that can be added at channel level or at common level in the payload description.
Finally, all these information will be adedd to the target table, that is now our final product.
```python
from exorad.tasks import EstimateNoise
estimateNoise = EstimateNoise()
target = estimateNoise(target=target, channels=channels)
plotter = Plotter(channels=channels, input_table=target.table)
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax = plotter.plot_noise(ax, scale='log', channel_edges=False)
# We show here the information content of the target table, that now contains also all the noise contributions
print(target.table.keys())
```
['chName', 'Wavelength', 'Bandwidth', 'LeftBinEdge', 'RightBinEdge', 'QE', 'WindowSize', 'TR', 'instrument_signal', 'instrument_MaxSignal_inPixel', 'skyFilter_signal', 'skyFilter_MaxSignal_inPixel', 'zodi_signal', 'zodi_MaxSignal_inPixel', 'foreground_transmission', 'starFlux', 'starSignal', 'star_signal_inAperture', 'star_MaxSignal_inPixel', 'MaxSignal_inPixel', 'saturation_time', 'frameTime', 'instrument_signal_noise', 'skyFilter_signal_noise', 'zodi_signal_noise', 'star_signal_inAperture_noise', 'darkcurrent_noise', 'read_noise', 'total_noise', 'gain_noise', 'gain 2_noise', 'gain 3_noise']
|
ExObsSimREPO_NAMEExoRad2-publicPATH_START.@ExoRad2-public_extracted@ExoRad2-public-master@examples@quickstart.ipynb@.PATH_END.py
|
{
"filename": "_pie.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/layout/template/data/_pie.py",
"type": "Python"
}
|
from plotly.graph_objs import Pie
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@layout@template@data@_pie.py@.PATH_END.py
|
{
"filename": "_insidetextfont.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/treemap/_insidetextfont.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class InsidetextfontValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="insidetextfont", parent_name="treemap", **kwargs):
super(InsidetextfontValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Insidetextfont"),
data_docs=kwargs.pop(
"data_docs",
"""
color
colorsrc
Sets the source reference on Chart Studio Cloud
for color .
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for family .
size
sizesrc
Sets the source reference on Chart Studio Cloud
for size .
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@treemap@_insidetextfont.py@.PATH_END.py
|
{
"filename": "observers.py",
"repo_name": "gammapy/gammapy",
"repo_path": "gammapy_extracted/gammapy-main/gammapy/utils/observers.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Location of gamma-ray observatories."""
import astropy.units as u
from astropy.coordinates import EarthLocation
__all__ = ["observatory_locations"]
observatory_locations = {}
"""Gamma-ray observatory locations (dict).
This is a dict with observatory names as keys
and values of type `~astropy.coordinates.EarthLocation`.
Not that with ``EarthLocation`` the orientation of angles is as follows:
- longitude is east for positive values and west for negative values
- latitude is north for positive values and south for negative values
Available observatories (alphabetical order):
- ``cta_south`` and ``cta_north`` for CTA, see
`Website <https://www.cta-observatory.org/>`__ and
`Wikipedia <https://en.wikipedia.org/wiki/Cherenkov_Telescope_Array>`__
- ``hawc`` for HAWC, see
`Website <https://www.hawc-observatory.org/>`__ and
`Wikipedia <https://en.wikipedia.org/wiki/High_Altitude_Water_Cherenkov_Experiment>`__
- ``hegra`` for HEGRA, see `Wikipedia <https://en.wikipedia.org/wiki/HEGRA>`__
- ``hess`` for HESS, see
`Website <https://www.mpi-hd.mpg.de/hfm/HESS/>`__ and
`Wikipedia <https://en.wikipedia.org/wiki/HESS>`__
- ``magic`` for MAGIC, see
`Website <https://wwwmagic.mpp.mpg.de/>`__ and
`Wikipedia <https://en.wikipedia.org/wiki/MAGIC_(telescope)>`__
- ``milagro`` for MILAGRO, see
`Wikipedia <https://en.wikipedia.org/wiki/Milagro_(experiment)>`__)
- ``veritas`` for VERITAS, see
`Website <https://veritas.sao.arizona.edu/>`__ and
`Wikipedia <https://en.wikipedia.org/wiki/VERITAS>`__
- ``whipple`` for WHIPPLE, see
`Wikipedia <https://en.wikipedia.org/wiki/Fred_Lawrence_Whipple_Observatory>`__
Examples
--------
>>> from gammapy.data import observatory_locations
>>> observatory_locations['hess']
>>> list(observatory_locations.keys())
"""
# Values from https://www.cta-observatory.org/about/array-locations/chile/
# Latitude: 24d41m0.34s South, Longitude: 70d18m58.84s West, Height: not given
# Email from Gernot Maier on Sep 8, 2017, stating what they use in the CTA MC group:
# lon=-70.31634499364885d, lat=-24.68342915473787d, height=2150m
observatory_locations["cta_south"] = EarthLocation(
lon="-70d18m58.84s", lat="-24d41m0.34s", height="2150m"
)
# Values from https://www.cta-observatory.org/about/array-locations/la-palma/
# Latitude: 28d45m43.7904s North, Longitude: 17d53m31.218s West, Height: 2200 m
# Email from Gernot Maier on Sep 8, 2017, stating what they use in the CTA MC group for MST-1:
# lon=-17.891571d, lat=28.762158d, height=2147m
observatory_locations["cta_north"] = EarthLocation(
lon="-17d53m31.218s", lat="28d45m43.7904s", height="2147m"
)
# HAWC location taken from https://arxiv.org/pdf/1108.6034v2.pdf
observatory_locations["hawc"] = EarthLocation(
lon="-97d18m34s", lat="18d59m48s", height="4100m"
)
# https://en.wikipedia.org/wiki/HEGRA
observatory_locations["hegra"] = EarthLocation(
lon="28d45m42s", lat="17d53m27s", height="2200m"
)
# Precision position of HESS from the HESS software (slightly different from Wikipedia)
observatory_locations["hess"] = EarthLocation(
lon="16d30m00.8s", lat="-23d16m18.4s", height="1835m"
)
observatory_locations["magic"] = EarthLocation(
lon="-17d53m24s", lat="28d45m43s", height="2200m"
)
observatory_locations["milagro"] = EarthLocation(
lon="-106.67625d", lat="35.87835d", height="2530m"
)
observatory_locations["veritas"] = EarthLocation(
lon="-110d57m07.77s", lat="31d40m30.21s", height="1268m"
)
# WHIPPLE coordinates taken from the Observatory Wikipedia page:
# https://en.wikipedia.org/wiki/Fred_Lawrence_Whipple_Observatory
observatory_locations["whipple"] = EarthLocation(
lon="-110d52m42s", lat="31d40m52s", height="2606m"
)
# communication with ASTRI Project Manager
observatory_locations["astri"] = EarthLocation(
lon="-16d30m20.99s", lat="28d18m00.0s", height="2370m"
)
# coordinates from fact-tools (based on google earth)
observatory_locations["fact"] = EarthLocation(
lat=28.761647 * u.deg,
lon=-17.891116 * u.deg,
height=2200 * u.m,
)
|
gammapyREPO_NAMEgammapyPATH_START.@gammapy_extracted@gammapy-main@gammapy@utils@observers.py@.PATH_END.py
|
{
"filename": "_colorbar.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/marker/_colorbar.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorbarValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="colorbar", parent_name="scattersmith.marker", **kwargs
):
super(ColorbarValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "ColorBar"),
data_docs=kwargs.pop(
"data_docs",
"""
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
labelalias
Replacement text for specific tick or hover
labels. For example using {US: 'USA', CA:
'Canada'} changes US to USA and CA to Canada.
The labels we would have shown must match the
keys exactly, after adding any tickprefix or
ticksuffix. For negative numbers the minus sign
symbol used (U+2212) is wider than the regular
ascii dash. That means you need to use −1
instead of -1. labelalias can be used with any
axis type, and both keys (if needed) and values
(if desired) can include html-like tags or
MathJax.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this
number. This only has an effect when
`tickformat` is "SI" or "B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
orientation
Sets the orientation of the colorbar.
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see: h
ttps://github.com/d3/d3-format/tree/v1.4.5#d3-
format. And for dates see:
https://github.com/d3/d3-time-
format/tree/v2.2.3#locale_format. We add two
items to d3's date formatter: "%h" for half of
the year as a decimal number as well as "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
"09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.scatter
smith.marker.colorbar.Tickformatstop` instances
or dicts with compatible properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.scattersmith.marker.colorbar.tickformatstopde
faults), sets the default property values to
use for elements of
scattersmith.marker.colorbar.tickformatstops
ticklabeloverflow
Determines how we handle tick labels that would
overflow either the graph div or the domain of
the axis. The default value for inside tick
labels is *hide past domain*. In other cases
the default is *hide past div*.
ticklabelposition
Determines where tick labels are drawn relative
to the ticks. Left and right options are used
when `orientation` is "h", top and bottom when
`orientation` is "v".
ticklabelstep
Sets the spacing between tick labels as
compared to the spacing between ticks. A value
of 1 (default) means each tick gets a label. A
value of 2 means shows every 2nd label. A
larger value n means only every nth tick is
labeled. `tick0` determines which labels are
shown. Not implemented for axes with `type`
"log" or "multicategory", or when `tickmode` is
"array".
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud
for `ticktext`.
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud
for `tickvals`.
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.scattersmith.marke
r.colorbar.Title` instance or dict with
compatible properties
x
Sets the x position with respect to `xref` of
the color bar (in plot fraction). When `xref`
is "paper", defaults to 1.02 when `orientation`
is "v" and 0.5 when `orientation` is "h". When
`xref` is "container", defaults to 1 when
`orientation` is "v" and 0.5 when `orientation`
is "h". Must be between 0 and 1 if `xref` is
"container" and between "-2" and 3 if `xref` is
"paper".
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar. Defaults to "left" when `orientation` is
"v" and "center" when `orientation` is "h".
xpad
Sets the amount of padding (in px) along the x
direction.
xref
Sets the container `x` refers to. "container"
spans the entire `width` of the plot. "paper"
refers to the width of the plotting area only.
y
Sets the y position with respect to `yref` of
the color bar (in plot fraction). When `yref`
is "paper", defaults to 0.5 when `orientation`
is "v" and 1.02 when `orientation` is "h". When
`yref` is "container", defaults to 0.5 when
`orientation` is "v" and 1 when `orientation`
is "h". Must be between 0 and 1 if `yref` is
"container" and between "-2" and 3 if `yref` is
"paper".
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
Defaults to "middle" when `orientation` is "v"
and "bottom" when `orientation` is "h".
ypad
Sets the amount of padding (in px) along the y
direction.
yref
Sets the container `y` refers to. "container"
spans the entire `height` of the plot. "paper"
refers to the height of the plotting area only.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@marker@_colorbar.py@.PATH_END.py
|
{
"filename": "_namelengthsrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/choroplethmap/hoverlabel/_namelengthsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class NamelengthsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="namelengthsrc",
parent_name="choroplethmap.hoverlabel",
**kwargs,
):
super(NamelengthsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@choroplethmap@hoverlabel@_namelengthsrc.py@.PATH_END.py
|
{
"filename": "geom.py",
"repo_name": "gammapy/gammapy",
"repo_path": "gammapy_extracted/gammapy-main/gammapy/maps/hpx/geom.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Utilities for dealing with HEALPix projections and mappings."""
import copy
import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.units import Quantity
from gammapy.utils.array import is_power2
from ..axes import MapAxes
from ..coord import MapCoord, skycoord_to_lonlat
from ..geom import Geom, pix_tuple_to_idx
from ..utils import INVALID_INDEX, coordsys_to_frame, frame_to_coordsys
from .io import HPX_FITS_CONVENTIONS, HpxConv
from .utils import (
coords_to_vec,
get_nside_from_pix_size,
get_pix_size_from_nside,
get_subpixels,
get_superpixels,
match_hpx_pix,
nside_to_order,
parse_hpxregion,
ravel_hpx_index,
unravel_hpx_index,
)
# Not sure if we should expose this in the docs or not:
# HPX_FITS_CONVENTIONS, HpxConv
__all__ = ["HpxGeom"]
class HpxGeom(Geom):
"""Geometry class for HEALPix maps.
This class performs mapping between partial-sky indices (pixel
number within a HEALPix region) and all-sky indices (pixel number
within an all-sky HEALPix map). Multi-band HEALPix geometries use
a global indexing scheme that assigns a unique pixel number based
on the all-sky index and band index. In the single-band case the
global index is the same as the HEALPix index.
By default, the constructor will return an all-sky map.
Partial-sky maps can be defined with the ``region`` argument.
Parameters
----------
nside : `~numpy.ndarray`
HEALPix NSIDE parameter, the total number of pixels is
12*nside*nside. For multi-dimensional maps one can pass
either a single ``nside`` value or a vector of ``nside`` values
defining the pixel size for each image plane. If ``nside`` is not
a scalar then its dimensionality should match that of the
non-spatial axes. If nest is True, ``nside`` must be a power of 2,
less than 2**30.
nest : bool
Indexing scheme. If True, "NESTED" scheme. If False, "RING" scheme.
frame : {"icrs", "galactic"}
Coordinate system. Default is "icrs".
region : str or tuple
Spatial geometry for partial-sky maps. If None, the map will
encompass the whole sky. String input will be parsed
according to HPX_REG header keyword conventions. Tuple
input can be used to define an explicit list of pixels
encompassed by the geometry.
axes : list
Axes for non-spatial dimensions.
"""
is_hpx = True
is_region = False
def __init__(self, nside, nest=True, frame="icrs", region=None, axes=None):
from healpy.pixelfunc import check_nside
check_nside(nside, nest=nest)
self._nside = np.array(nside, ndmin=1)
self._axes = MapAxes.from_default(axes, n_spatial_axes=1)
if self.nside.size > 1 and self.nside.shape != self.shape_axes:
raise ValueError(
"Wrong dimensionality for nside. nside must "
"be a scalar or have a dimensionality consistent "
"with the axes argument."
)
self._frame = frame
self._nest = nest
self._ipix = None
self._region = region
self._create_lookup(region)
self._npix = self._npix * np.ones(self.shape_axes, dtype=int)
def _create_lookup(self, region):
"""Create local-to-global pixel lookup table."""
if isinstance(region, str):
ipix = [
self.get_index_list(nside, self._nest, region)
for nside in self._nside.flat
]
self._ipix = [
ravel_hpx_index((p, i * np.ones_like(p)), np.ravel(self.npix_max))
for i, p in enumerate(ipix)
]
self._region = region
self._indxschm = "EXPLICIT"
self._npix = np.array([len(t) for t in self._ipix])
if self.nside.ndim > 1:
self._npix = self._npix.reshape(self.nside.shape)
self._ipix = np.concatenate(self._ipix)
elif isinstance(region, tuple):
region = [np.asarray(t) for t in region]
m = np.any(np.stack([t >= 0 for t in region]), axis=0)
region = [t[m] for t in region]
self._ipix = ravel_hpx_index(region, self.npix_max)
self._ipix = np.unique(self._ipix)
region = unravel_hpx_index(self._ipix, self.npix_max)
self._region = "explicit"
self._indxschm = "EXPLICIT"
if len(region) == 1:
self._npix = np.array([len(region[0])])
else:
self._npix = np.zeros(self.shape_axes, dtype=int)
idx = np.ravel_multi_index(region[1:], self.shape_axes)
cnt = np.unique(idx, return_counts=True)
self._npix.flat[cnt[0]] = cnt[1]
elif region is None:
self._region = None
self._indxschm = "IMPLICIT"
self._npix = self.npix_max
else:
raise ValueError(f"Invalid region string: {region!r}")
def local_to_global(self, idx_local):
"""Compute a global index (all-sky) from a local (partial-sky) index.
Parameters
----------
idx_local : tuple
A tuple of pixel indices with local HEALPix pixel indices.
Returns
-------
idx_global : tuple
A tuple of pixel index vectors with global HEALPix pixel indices.
"""
if self._ipix is None:
return idx_local
if self.nside.size > 1:
idx = ravel_hpx_index(idx_local, self._npix)
else:
idx_tmp = tuple(
[idx_local[0]] + [np.zeros(t.shape, dtype=int) for t in idx_local[1:]]
)
idx = ravel_hpx_index(idx_tmp, self._npix)
idx_global = unravel_hpx_index(self._ipix[idx], self.npix_max)
return idx_global[:1] + tuple(idx_local[1:])
def global_to_local(self, idx_global, ravel=False):
"""Compute global (all-sky) index from a local (partial-sky) index.
Parameters
----------
idx_global : tuple
A tuple of pixel indices with global HEALPix pixel indices.
ravel : bool, optional
Return a raveled index. Default is False.
Returns
-------
idx_local : tuple
A tuple of pixel indices with local HEALPix pixel indices.
"""
if (
isinstance(idx_global, int)
or (isinstance(idx_global, tuple) and isinstance(idx_global[0], int))
or isinstance(idx_global, np.ndarray)
):
idx_global = unravel_hpx_index(np.array(idx_global, ndmin=1), self.npix_max)
if self.nside.size == 1:
idx = np.array(idx_global[0], ndmin=1)
else:
idx = ravel_hpx_index(idx_global, self.npix_max)
if self._ipix is not None:
retval = np.full(idx.size, -1, "i")
m = np.isin(idx.flat, self._ipix)
retval[m] = np.searchsorted(self._ipix, idx.flat[m])
retval = retval.reshape(idx.shape)
else:
retval = idx
if self.nside.size == 1:
idx_local = tuple([retval] + list(idx_global[1:]))
else:
idx_local = unravel_hpx_index(retval, self._npix)
m = np.any(np.stack([t == INVALID_INDEX.int for t in idx_local]), axis=0)
for i, t in enumerate(idx_local):
idx_local[i][m] = INVALID_INDEX.int
if not ravel:
return idx_local
else:
return ravel_hpx_index(idx_local, self.npix)
def cutout(self, position, width, **kwargs):
"""Create a cutout around a given position.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`
Diameter of the circular cutout region.
Returns
-------
cutout : `~gammapy.maps.WcsNDMap`
Cutout map.
"""
if not self.is_regular:
raise ValueError("Can only do a cutout from a regular map.")
width = u.Quantity(width, "deg").value
return self.create(
nside=self.nside,
nest=self.nest,
width=width,
skydir=position,
frame=self.frame,
axes=self.axes,
)
def coord_to_pix(self, coords):
import healpy as hp
coords = MapCoord.create(
coords, frame=self.frame, axis_names=self.axes.names
).broadcasted
theta, phi = coords.theta, coords.phi
if self.axes:
idxs = self.axes.coord_to_idx(coords, clip=True)
bins = self.axes.coord_to_pix(coords)
# FIXME: Figure out how to handle coordinates out of
# bounds of non-spatial dimensions
if self.nside.size > 1:
nside = self.nside[tuple(idxs)]
else:
nside = self.nside
m = ~np.isfinite(theta)
theta[m] = 0.0
phi[m] = 0.0
pix = hp.ang2pix(nside, theta, phi, nest=self.nest)
pix = tuple([pix]) + bins
if np.any(m):
for p in pix:
p[m] = INVALID_INDEX.int
else:
pix = (hp.ang2pix(self.nside, theta, phi, nest=self.nest),)
return pix
def pix_to_coord(self, pix):
import healpy as hp
if self.axes:
bins = []
vals = []
for i, ax in enumerate(self.axes):
bins += [pix[1 + i]]
vals += [ax.pix_to_coord(pix[1 + i])]
idxs = pix_tuple_to_idx(bins)
if self.nside.size > 1:
nside = self.nside[idxs]
else:
nside = self.nside
ipix = np.round(pix[0]).astype(int)
m = ipix == INVALID_INDEX.int
ipix[m] = 0
theta, phi = hp.pix2ang(nside, ipix, nest=self.nest)
coords = [np.degrees(phi), np.degrees(np.pi / 2.0 - theta)]
coords = tuple(coords + vals)
if np.any(m):
for c in coords:
c[m] = INVALID_INDEX.float
else:
ipix = np.round(pix[0]).astype(int)
theta, phi = hp.pix2ang(self.nside, ipix, nest=self.nest)
coords = (np.degrees(phi), np.degrees(np.pi / 2.0 - theta))
return coords
def pix_to_idx(self, pix, clip=False):
# FIXME: Look for better method to clip HPX indices
idx = pix_tuple_to_idx(pix)
idx_local = self.global_to_local(idx)
for i, _ in enumerate(idx):
if clip:
if i > 0:
np.clip(idx[i], 0, self.axes[i - 1].nbin - 1, out=idx[i])
else:
np.clip(idx[i], 0, None, out=idx[i])
else:
if i > 0:
mask = (idx[i] < 0) | (idx[i] >= self.axes[i - 1].nbin)
np.putmask(idx[i], mask, -1)
else:
mask = (idx_local[i] < 0) | (idx[i] < 0)
np.putmask(idx[i], mask, -1)
return tuple(idx)
@property
def axes(self):
"""List of non-spatial axes."""
return self._axes
@property
def axes_names(self):
"""All axes names."""
return ["skycoord"] + self.axes.names
@property
def shape_axes(self):
"""Shape of non-spatial axes."""
return self.axes.shape
@property
def data_shape(self):
"""Shape of the `~numpy.ndarray` matching this geometry."""
npix_shape = tuple([np.max(self.npix)])
return (npix_shape + self.axes.shape)[::-1]
@property
def data_shape_axes(self):
"""Shape of data of the non-spatial axes and unit spatial axes."""
return self.axes.shape[::-1] + (1,)
@property
def ndim(self):
"""Number of dimensions as an integer."""
return len(self._axes) + 2
@property
def ordering(self):
"""HEALPix ordering ('NESTED' or 'RING')."""
return "NESTED" if self.nest else "RING"
@property
def nside(self):
"""NSIDE in each band."""
return self._nside
@property
def order(self):
"""The order in each band (``NSIDE = 2 ** ORDER``).
Set to -1 for bands with NSIDE that is not a power of 2.
"""
return nside_to_order(self.nside)
@property
def nest(self):
"""Whether HEALPix order is nested as a boolean."""
return self._nest
@property
def npix(self):
"""Number of pixels in each band.
For partial-sky geometries this can
be less than the number of pixels for the band NSIDE.
"""
return self._npix
@property
def npix_max(self):
"""Maximum number of pixels."""
maxpix = 12 * self.nside**2
return maxpix * np.ones(self.shape_axes, dtype=int)
@property
def frame(self):
return self._frame
@property
def projection(self):
"""Map projection."""
return "HPX"
@property
def region(self):
"""Region string."""
return self._region
@property
def is_allsky(self):
"""Flag for all-sky maps."""
return self._region is None
@property
def is_regular(self):
"""Flag identifying whether this geometry is regular in non-spatial dimensions.
False for multi-resolution or irregular geometries.
If True, all image planes have the same pixel geometry.
"""
if self.nside.size > 1 or self.region == "explicit":
return False
else:
return True
@property
def center_coord(self):
"""Map coordinates of the center of the geometry as a tuple."""
lon, lat, frame = skycoord_to_lonlat(self.center_skydir)
return tuple([lon, lat]) + self.axes.center_coord
@property
def center_pix(self):
"""Pixel coordinates of the center of the geometry as a tuple."""
return self.coord_to_pix(self.center_coord)
@property
def center_skydir(self):
"""Sky coordinate of the center of the geometry.
Returns
-------
center : `~astropy.coordinates.SkyCoord`
Center position.
"""
import healpy as hp
if self.is_allsky:
lon, lat = 0.0, 0.0
elif self.region == "explicit":
idx = unravel_hpx_index(self._ipix, self.npix_max)
nside = self._get_nside(idx)
vec = hp.pix2vec(nside, idx[0], nest=self.nest)
lon, lat = hp.vec2ang(np.mean(vec, axis=1), lonlat=True)
else:
tokens = parse_hpxregion(self.region)
if tokens[0] in ["DISK", "DISK_INC"]:
lon, lat = float(tokens[1]), float(tokens[2])
elif tokens[0] == "HPX_PIXEL":
nside_pix = int(tokens[2])
ipix_pix = int(tokens[3])
if tokens[1] == "NESTED":
nest_pix = True
elif tokens[1] == "RING":
nest_pix = False
else:
raise ValueError(f"Invalid ordering scheme: {tokens[1]!r}")
theta, phi = hp.pix2ang(nside_pix, ipix_pix, nest_pix)
lon, lat = np.degrees(phi), np.degrees((np.pi / 2) - theta)
return SkyCoord(lon, lat, frame=self.frame, unit="deg")
@property
def pixel_scales(self):
self.angle_ = """Pixel scale.
Returns
-------
angle: `~astropy.coordinates.Angle`
"""
return get_pix_size_from_nside(self.nside) * u.deg
def interp_weights(self, coords, idxs=None):
"""Get interpolation weights for given coordinates.
Parameters
----------
coords : `MapCoord` or dict
Input coordinates.
idxs : `~numpy.ndarray`, optional
Indices for non-spatial axes.
Default is None.
Returns
-------
weights : `~numpy.ndarray`
Interpolation weights.
"""
import healpy as hp
coords = MapCoord.create(coords, frame=self.frame).broadcasted
if idxs is None:
idxs = self.coord_to_idx(coords, clip=True)[1:]
theta, phi = coords.theta, coords.phi
m = ~np.isfinite(theta)
theta[m] = 0
phi[m] = 0
if not self.is_regular:
nside = self.nside[tuple(idxs)]
else:
nside = self.nside
pix, wts = hp.get_interp_weights(nside, theta, phi, nest=self.nest)
wts[:, m] = 0
pix[:, m] = INVALID_INDEX.int
if not self.is_regular:
pix_local = [self.global_to_local([pix] + list(idxs))[0]]
else:
pix_local = [self.global_to_local(pix, ravel=True)]
# If a pixel lies outside of the geometry set its index to the center pixel
m = pix_local[0] == INVALID_INDEX.int
if m.any():
coords_ctr = [coords.lon, coords.lat]
coords_ctr += [ax.pix_to_coord(t) for ax, t in zip(self.axes, idxs)]
idx_ctr = self.coord_to_idx(coords_ctr)
idx_ctr = self.global_to_local(idx_ctr)
pix_local[0][m] = (idx_ctr[0] * np.ones(pix.shape, dtype=int))[m]
pix_local += [np.broadcast_to(t, pix_local[0].shape) for t in idxs]
return pix_local, wts
@property
def ipix(self):
"""HEALPix pixel and band indices for every pixel in the map."""
return self.get_idx()
def is_aligned(self, other):
"""Check if HEALPix geoms and extra axes are aligned.
Parameters
----------
other : `HpxGeom`
Other geometry.
Returns
-------
aligned : bool
Whether geometries are aligned.
"""
for axis, otheraxis in zip(self.axes, other.axes):
if axis != otheraxis:
return False
if not self.nside == other.nside:
return False
elif not self.frame == other.frame:
return False
elif not self.nest == other.nest:
return False
else:
return True
def to_nside(self, nside):
"""Upgrade or downgrade the resolution to a given NSIDE.
Parameters
----------
nside : int
HEALPix NSIDE parameter.
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
"""
if not self.is_regular:
raise ValueError("Upgrade and degrade only implemented for standard maps")
axes = copy.deepcopy(self.axes)
return self.__class__(
nside=nside, nest=self.nest, frame=self.frame, region=self.region, axes=axes
)
def to_binsz(self, binsz):
"""Change pixel size of the geometry.
Parameters
----------
binsz : float or `~astropy.units.Quantity`
New pixel size. A float is assumed to be in degree.
Returns
-------
geom : `WcsGeom`
Geometry with new pixel size.
"""
binsz = u.Quantity(binsz, "deg").value
if self.is_allsky:
return self.create(
binsz=binsz,
frame=self.frame,
axes=copy.deepcopy(self.axes),
)
else:
return self.create(
skydir=self.center_skydir,
binsz=binsz,
width=self.width.to_value("deg"),
frame=self.frame,
axes=copy.deepcopy(self.axes),
)
def separation(self, center):
"""Compute sky separation with respect to a given center.
Parameters
----------
center : `~astropy.coordinates.SkyCoord`
Center position.
Returns
-------
separation : `~astropy.coordinates.Angle`
Separation angle array (1D).
"""
coord = self.to_image().get_coord()
return center.separation(coord.skycoord)
def to_swapped(self):
"""Geometry copy with swapped ORDERING (NEST->RING or vice versa).
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
"""
axes = copy.deepcopy(self.axes)
return self.__class__(
self.nside,
not self.nest,
frame=self.frame,
region=self.region,
axes=axes,
)
def to_image(self):
return self.__class__(
np.max(self.nside), self.nest, frame=self.frame, region=self.region
)
def to_cube(self, axes):
axes = copy.deepcopy(self.axes) + axes
return self.__class__(
np.max(self.nside),
self.nest,
frame=self.frame,
region=self.region,
axes=axes,
)
def _get_neighbors(self, idx):
import healpy as hp
nside = self._get_nside(idx)
idx_nb = (hp.get_all_neighbours(nside, idx[0], nest=self.nest),)
idx_nb += tuple([t[None, ...] * np.ones_like(idx_nb[0]) for t in idx[1:]])
return idx_nb
def _pad_spatial(self, pad_width):
if self.is_allsky:
raise ValueError("Cannot pad an all-sky map.")
idx = self.get_idx(flat=True)
idx_r = ravel_hpx_index(idx, self.npix_max)
# TODO: Pre-filter indices to find those close to the edge
idx_nb = self._get_neighbors(idx)
idx_nb = ravel_hpx_index(idx_nb, self.npix_max)
for _ in range(pad_width):
mask_edge = np.isin(idx_nb, idx_r, invert=True)
idx_edge = idx_nb[mask_edge]
idx_edge = np.unique(idx_edge)
idx_r = np.sort(np.concatenate((idx_r, idx_edge)))
idx_nb = unravel_hpx_index(idx_edge, self.npix_max)
idx_nb = self._get_neighbors(idx_nb)
idx_nb = ravel_hpx_index(idx_nb, self.npix_max)
idx = unravel_hpx_index(idx_r, self.npix_max)
return self.__class__(
self.nside.copy(),
self.nest,
frame=self.frame,
region=idx,
axes=copy.deepcopy(self.axes),
)
def crop(self, crop_width):
if self.is_allsky:
raise ValueError("Cannot crop an all-sky map.")
idx = self.get_idx(flat=True)
idx_r = ravel_hpx_index(idx, self.npix_max)
# TODO: Pre-filter indices to find those close to the edge
idx_nb = self._get_neighbors(idx)
idx_nb = ravel_hpx_index(idx_nb, self.npix_max)
for _ in range(crop_width):
# Mask of pixels that have at least one neighbor not
# contained in the geometry
mask_edge = np.any(np.isin(idx_nb, idx_r, invert=True), axis=0)
idx_r = idx_r[~mask_edge]
idx_nb = idx_nb[:, ~mask_edge]
idx = unravel_hpx_index(idx_r, self.npix_max)
return self.__class__(
self.nside.copy(),
self.nest,
frame=self.frame,
region=idx,
axes=copy.deepcopy(self.axes),
)
def upsample(self, factor):
if not is_power2(factor):
raise ValueError("Upsample factor must be a power of 2.")
if self.is_allsky:
return self.__class__(
self.nside * factor,
self.nest,
frame=self.frame,
region=self.region,
axes=copy.deepcopy(self.axes),
)
idx = list(self.get_idx(flat=True))
nside = self._get_nside(idx)
idx_new = get_subpixels(idx[0], nside, nside * factor, nest=self.nest)
for i in range(1, len(idx)):
idx[i] = idx[i][..., None] * np.ones(idx_new.shape, dtype=int)
idx[0] = idx_new
return self.__class__(
self.nside * factor,
self.nest,
frame=self.frame,
region=tuple(idx),
axes=copy.deepcopy(self.axes),
)
def downsample(self, factor, axis_name=None):
if not is_power2(factor):
raise ValueError("Downsample factor must be a power of 2.")
if axis_name is not None:
raise ValueError("Currently the only valid axis name is None.")
if self.is_allsky:
return self.__class__(
self.nside // factor,
self.nest,
frame=self.frame,
region=self.region,
axes=copy.deepcopy(self.axes),
)
idx = list(self.get_idx(flat=True))
nside = self._get_nside(idx)
idx_new = get_superpixels(idx[0], nside, nside // factor, nest=self.nest)
idx[0] = idx_new
return self.__class__(
self.nside // factor,
self.nest,
frame=self.frame,
region=tuple(idx),
axes=copy.deepcopy(self.axes),
)
@classmethod
def create(
cls,
nside=None,
binsz=None,
nest=True,
frame="icrs",
region=None,
axes=None,
skydir=None,
width=None,
):
"""Create an HpxGeom object.
Parameters
----------
nside : int or `~numpy.ndarray`, optional
HEALPix NSIDE parameter. This parameter sets the size of
the spatial pixels in the map. If nest is True, ``nside`` must be a
power of 2, less than 2**30.
Default is None.
binsz : float or `~numpy.ndarray`, optional
Approximate pixel size in degrees. An ``nside`` will be
chosen that corresponds to a pixel size closest to this
value. This option is superseded by ``nside``.
Default is None.
nest : bool, optional
Indexing scheme. If True, "NESTED" scheme. If False, "RING" scheme.
Default is True.
frame : {"icrs", "galactic"}
Coordinate system, either Galactic ("galactic") or Equatorial ("icrs").
Default is "icrs".
region : str, optional
HEALPix region string. Allows for partial-sky maps. Default is None.
axes : list, optional
List of axes for non-spatial dimensions. Default is None.
skydir : tuple or `~astropy.coordinates.SkyCoord`, optional
Sky position of map center. Can be either a SkyCoord
object or a tuple of longitude and latitude in deg in the
coordinate system of the map. Default is None.
width : float, optional
Diameter of the map in degrees. If set the map will
encompass all pixels within a circular region centered on
``skydir``. Default is None.
Returns
-------
geom : `~HpxGeom`
A HEALPix geometry object.
Examples
--------
>>> from gammapy.maps import HpxGeom, MapAxis
>>> axis = MapAxis.from_bounds(0,1,2)
>>> geom = HpxGeom.create(nside=16) # doctest: +SKIP
>>> geom = HpxGeom.create(binsz=0.1, width=10.0) # doctest: +SKIP
>>> geom = HpxGeom.create(nside=64, width=10.0, axes=[axis]) # doctest: +SKIP
>>> geom = HpxGeom.create(nside=[32,64], width=10.0, axes=[axis]) # doctest: +SKIP
"""
if nside is None and binsz is None:
raise ValueError("Either nside or binsz must be defined.")
if nside is None and binsz is not None:
nside = get_nside_from_pix_size(binsz)
if skydir is None:
lon, lat = (0.0, 0.0)
elif isinstance(skydir, tuple):
lon, lat = skydir
elif isinstance(skydir, SkyCoord):
lon, lat, frame = skycoord_to_lonlat(skydir, frame=frame)
else:
raise ValueError(f"Invalid type for skydir: {type(skydir)!r}")
if region is None and width is not None:
region = f"DISK({lon},{lat},{width/2})"
return cls(nside, nest=nest, frame=frame, region=region, axes=axes)
@classmethod
def from_header(cls, header, hdu_bands=None, format=None):
"""Create an HPX object from a FITS header.
Parameters
----------
header : `~astropy.io.fits.Header`
The FITS header.
hdu_bands : `~astropy.io.fits.BinTableHDU`, optional
The BANDS table HDU. Default is None.
format : str, optional
FITS convention. Default is None.
If None the format is guessed. The following
formats are supported:
- "gadf"
- "fgst-ccube"
- "fgst-ltcube"
- "fgst-bexpcube"
- "fgst-srcmap"
- "fgst-template"
- "fgst-srcmap-sparse"
- "galprop"
- "galprop2"
- "healpy"
Returns
-------
hpx : `~HpxGeom`
HEALPix geometry.
"""
if format is None:
format = HpxConv.identify_hpx_format(header)
conv = HPX_FITS_CONVENTIONS[format]
axes = MapAxes.from_table_hdu(hdu_bands, format=format)
if header["PIXTYPE"] != "HEALPIX":
raise ValueError(
f"Invalid header PIXTYPE: {header['PIXTYPE']} (must be HEALPIX)"
)
if header["ORDERING"] == "RING":
nest = False
elif header["ORDERING"] == "NESTED":
nest = True
else:
raise ValueError(
f"Invalid header ORDERING: {header['ORDERING']} (must be RING or NESTED)"
)
if hdu_bands is not None and "NSIDE" in hdu_bands.columns.names:
nside = hdu_bands.data.field("NSIDE").reshape(axes.shape).astype(int)
elif "NSIDE" in header:
nside = header["NSIDE"]
elif "ORDER" in header:
nside = 2 ** header["ORDER"]
else:
raise ValueError("Failed to extract NSIDE or ORDER.")
try:
frame = coordsys_to_frame(header[conv.frame])
except KeyError:
frame = header.get("COORDSYS", "icrs")
try:
region = header["HPX_REG"]
except KeyError:
try:
region = header["HPXREGION"]
except KeyError:
region = None
return cls(nside, nest, frame=frame, region=region, axes=axes)
@classmethod
def from_hdu(cls, hdu, hdu_bands=None):
"""Create an HPX object from a BinTable HDU.
Parameters
----------
hdu : `~astropy.io.fits.BinTableHDU`
The FITS HDU.
hdu_bands : `~astropy.io.fits.BinTableHDU`, optional
The BANDS table HDU. Default is None.
Returns
-------
hpx : `~HpxGeom`
HEALPix geometry.
"""
# FIXME: Need correct handling of IMPLICIT and EXPLICIT maps
# if HPX region is not defined then geometry is defined by
# the set of all pixels in the table
if "HPX_REG" not in hdu.header:
pix = (hdu.data.field("PIX"), hdu.data.field("CHANNEL"))
else:
pix = None
return cls.from_header(hdu.header, hdu_bands=hdu_bands, pix=pix)
def to_header(self, format="gadf", **kwargs):
"""Build and return FITS header for this HEALPix map."""
header = fits.Header()
format = kwargs.get("format", HPX_FITS_CONVENTIONS[format])
# FIXME: For some sparse maps we may want to allow EXPLICIT
# with an empty region string
indxschm = kwargs.get("indxschm", None)
if indxschm is None:
if self._region is None:
indxschm = "IMPLICIT"
elif self.is_regular == 1:
indxschm = "EXPLICIT"
else:
indxschm = "LOCAL"
if "FGST" in format.convname.upper():
header["TELESCOP"] = "GLAST"
header["INSTRUME"] = "LAT"
header[format.frame] = frame_to_coordsys(self.frame)
header["PIXTYPE"] = "HEALPIX"
header["ORDERING"] = self.ordering
header["INDXSCHM"] = indxschm
header["ORDER"] = np.max(self.order)
header["NSIDE"] = np.max(self.nside)
header["FIRSTPIX"] = 0
header["LASTPIX"] = np.max(self.npix_max) - 1
header["HPX_CONV"] = format.convname.upper()
if self.frame == "icrs":
header["EQUINOX"] = (2000.0, "Equinox of RA & DEC specifications")
if self.region:
header["HPX_REG"] = self._region
return header
def _make_bands_cols(self):
cols = []
if self.nside.size > 1:
cols += [fits.Column("NSIDE", "I", array=np.ravel(self.nside))]
return cols
@staticmethod
def get_index_list(nside, nest, region):
"""Get list of pixels indices for all the pixels in a region.
Parameters
----------
nside : int
HEALPix NSIDE parameter.
nest : bool
Indexing scheme. If True, "NESTED" scheme. If False, "RING" scheme.
region : str
HEALPix region string.
Returns
-------
ilist : `~numpy.ndarray`
List of pixel indices.
"""
import healpy as hp
# TODO: this should return something more friendly than a tuple
# e.g. a namedtuple or a dict
tokens = parse_hpxregion(region)
reg_type = tokens[0]
if reg_type == "DISK":
lon, lat = float(tokens[1]), float(tokens[2])
radius = np.radians(float(tokens[3]))
vec = coords_to_vec(lon, lat)[0]
ilist = hp.query_disc(nside, vec, radius, inclusive=False, nest=nest)
elif reg_type == "DISK_INC":
lon, lat = float(tokens[1]), float(tokens[2])
radius = np.radians(float(tokens[3]))
vec = coords_to_vec(lon, lat)[0]
fact = int(tokens[4])
ilist = hp.query_disc(
nside, vec, radius, inclusive=True, nest=nest, fact=fact
)
elif reg_type == "HPX_PIXEL":
nside_pix = int(tokens[2])
if tokens[1] == "NESTED":
ipix_ring = hp.nest2ring(nside_pix, int(tokens[3]))
elif tokens[1] == "RING":
ipix_ring = int(tokens[3])
else:
raise ValueError(f"Invalid ordering scheme: {tokens[1]!r}")
ilist = match_hpx_pix(nside, nest, nside_pix, ipix_ring)
else:
raise ValueError(f"Invalid region type: {reg_type!r}")
return ilist
@property
def width(self):
"""Width of the map."""
# TODO: simplify
import healpy as hp
if self.is_allsky:
width = 180.0
elif self.region == "explicit":
idx = unravel_hpx_index(self._ipix, self.npix_max)
nside = self._get_nside(idx)
ang = hp.pix2ang(nside, idx[0], nest=self.nest, lonlat=True)
dirs = SkyCoord(ang[0], ang[1], unit="deg", frame=self.frame)
width = np.max(dirs.separation(self.center_skydir))
else:
tokens = parse_hpxregion(self.region)
if tokens[0] in {"DISK", "DISK_INC"}:
width = float(tokens[3])
elif tokens[0] == "HPX_PIXEL":
pix_size = get_pix_size_from_nside(int(tokens[2]))
width = 2.0 * pix_size
return u.Quantity(width, "deg")
def _get_nside(self, idx):
if self.nside.size > 1:
return self.nside[tuple(idx[1:])]
else:
return self.nside
def to_wcs_geom(self, proj="AIT", oversample=2, width_pix=None):
"""Make a WCS projection appropriate for this HEALPix pixelization.
Parameters
----------
proj : str, optional
Projection type of WCS geometry.
Default is "AIT".
oversample : float, optional
Oversampling factor for WCS map. This will be the
approximate ratio of the width of a HEALPix pixel to a WCS
pixel. If this parameter is None then the width will be
set from ``width_pix``.
Default is 2.
width_pix : int, optional
Width of the WCS geometry in pixels. The pixel size will
be set to the number of pixels satisfying ``oversample``
or ``width_pix`` whichever is smaller. If this parameter
is None then the width will be set from ``oversample``.
Default is None.
Returns
-------
wcs : `~gammapy.maps.WcsGeom`
WCS geometry.
"""
from gammapy.maps import WcsGeom
pix_size = get_pix_size_from_nside(self.nside)
binsz = np.min(pix_size) / oversample
width = 2.0 * self.width.to_value("deg") + np.max(pix_size)
if width_pix is not None and int(width / binsz) > width_pix:
binsz = width / width_pix
if width > 90.0:
width = min(360.0, width), min(180.0, width)
axes = copy.deepcopy(self.axes)
return WcsGeom.create(
width=width,
binsz=binsz,
frame=self.frame,
axes=axes,
skydir=self.center_skydir,
proj=proj,
)
def to_wcs_tiles(self, nside_tiles=4, margin="0 deg"):
"""Create WCS tiles geometries from HPX geometry with given nside.
The HEALPix geom is divide into superpixels defined by ``nside_tiles``,
which are then represented by a WCS geometry using a tangential
projection. The number of WCS tiles is given by the number of pixels
for the given ``nside_tiles``.
Parameters
----------
nside_tiles : int, optional
HEALPix NSIDE parameter for super pixel tiles.
Default is 4.
margin : `~astropy.units.Quantity`, optional
Width margin of the WCS tile.
Default is "0 deg".
Returns
-------
wcs_tiles : list
List of WCS tile geometries.
"""
import healpy as hp
from gammapy.maps import WcsGeom
margin = u.Quantity(margin)
if nside_tiles >= self.nside:
raise ValueError(f"nside_tiles must be < {self.nside}")
if not self.is_allsky:
raise ValueError("to_wcs_tiles() is only supported for all sky geoms")
binsz = np.degrees(hp.nside2resol(self.nside)) * u.deg
hpx = self.to_image().to_nside(nside=nside_tiles)
wcs_tiles = []
for pix in range(int(hpx.npix[0])):
skydir = hpx.pix_to_coord([pix])
vtx = hp.boundaries(nside=hpx.nside.item(), pix=pix, nest=hpx.nest, step=1)
lon, lat = hp.vec2ang(vtx.T, lonlat=True)
boundaries = SkyCoord(lon * u.deg, lat * u.deg, frame=hpx.frame)
# Compute maximum separation between all pairs of boundaries and take it
# as width
width = boundaries.separation(boundaries[:, np.newaxis]).max()
wcs_tile_geom = WcsGeom.create(
skydir=(float(skydir[0].item()), float(skydir[1].item())),
width=width + margin,
binsz=binsz,
frame=hpx.frame,
proj="TAN",
axes=self.axes,
)
wcs_tiles.append(wcs_tile_geom)
return wcs_tiles
def get_idx(
self,
idx=None,
local=False,
flat=False,
sparse=False,
mode="center",
axis_name=None,
):
# TODO: simplify this!!!
if idx is not None and np.any(np.array(idx) >= np.array(self.shape_axes)):
raise ValueError(f"Image index out of range: {idx!r}")
# Regular all- and partial-sky maps
if self.is_regular:
pix = [np.arange(np.max(self._npix))]
if idx is None:
for ax in self.axes:
if mode == "edges" and ax.name == axis_name:
pix += [np.arange(-0.5, ax.nbin, dtype=float)]
else:
pix += [np.arange(ax.nbin, dtype=int)]
else:
pix += [t for t in idx]
pix = np.meshgrid(*pix[::-1], indexing="ij", sparse=sparse)[::-1]
pix = self.local_to_global(pix)
# Non-regular all-sky
elif self.is_allsky and not self.is_regular:
shape = (np.max(self.npix),)
if idx is None:
shape = shape + self.shape_axes
else:
shape = shape + (1,) * len(self.axes)
pix = [np.full(shape, -1, dtype=int) for i in range(1 + len(self.axes))]
for idx_img in np.ndindex(self.shape_axes):
if idx is not None and idx_img != idx:
continue
npix = self._npix[idx_img]
if idx is None:
s_img = (slice(0, npix),) + idx_img
else:
s_img = (slice(0, npix),) + (0,) * len(self.axes)
pix[0][s_img] = np.arange(self._npix[idx_img])
for j in range(len(self.axes)):
pix[j + 1][s_img] = idx_img[j]
pix = [p.T for p in pix]
# Explicit pixel indices
else:
if idx is not None:
npix_sum = np.concatenate(([0], np.cumsum(self._npix)))
idx_ravel = np.ravel_multi_index(idx, self.shape_axes)
s = slice(npix_sum[idx_ravel], npix_sum[idx_ravel + 1])
else:
s = slice(None)
pix_flat = unravel_hpx_index(self._ipix[s], self.npix_max)
shape = (np.max(self.npix),)
if idx is None:
shape = shape + self.shape_axes
else:
shape = shape + (1,) * len(self.axes)
pix = [np.full(shape, -1, dtype=int) for _ in range(1 + len(self.axes))]
for idx_img in np.ndindex(self.shape_axes):
if idx is not None and idx_img != idx:
continue
npix = int(self._npix[idx_img].item())
if idx is None:
s_img = (slice(0, npix),) + idx_img
else:
s_img = (slice(0, npix),) + (0,) * len(self.axes)
if self.axes:
m = np.all(
np.stack([pix_flat[i + 1] == t for i, t in enumerate(idx_img)]),
axis=0,
)
pix[0][s_img] = pix_flat[0][m]
else:
pix[0][s_img] = pix_flat[0]
for j in range(len(self.axes)):
pix[j + 1][s_img] = idx_img[j]
pix = [p.T for p in pix]
if local:
pix = self.global_to_local(pix)
if flat:
pix = tuple([p[p != INVALID_INDEX.int] for p in pix])
return pix
def region_mask(self, regions):
"""Create a mask from a given list of regions.
The mask is filled such that a pixel inside the region is filled with
"True". To invert the mask, e.g. to create a mask with exclusion regions
the tilde (~) operator can be used (see example below).
Parameters
----------
regions : str, `~regions.Region` or list of `~regions.Region`
Region or list of regions (pixel or sky regions accepted).
A region can be defined as a string ind DS9 format as well.
See http://ds9.si.edu/doc/ref/region.html for details.
Returns
-------
mask_map : `~gammapy.maps.WcsNDMap` of boolean type
Boolean region mask.
"""
from gammapy.maps import Map, RegionGeom
if not self.is_regular:
raise ValueError("Multi-resolution maps not supported yet")
# TODO: use spatial coordinates only...
geom = RegionGeom.from_regions(regions)
coords = self.get_coord()
mask = geom.contains(coords)
return Map.from_geom(self, data=mask)
def get_coord(
self, idx=None, flat=False, sparse=False, mode="center", axis_name=None
):
if mode == "edges" and axis_name is None:
raise ValueError("Mode 'edges' requires axis name to be defined")
pix = self.get_idx(
idx=idx, flat=flat, sparse=sparse, mode=mode, axis_name=axis_name
)
data = self.pix_to_coord(pix)
coords = MapCoord.create(
data=data, frame=self.frame, axis_names=self.axes.names
)
return coords
def contains(self, coords):
idx = self.coord_to_idx(coords)
return np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0)
def solid_angle(self):
"""Solid angle array as a `~astropy.units.Quantity` in ``sr``.
The array has the same dimensionality as ``map.nside``
as all pixels have the same solid angle.
"""
import healpy as hp
return Quantity(hp.nside2pixarea(self.nside), "sr")
def __str__(self):
lon, lat = self.center_skydir.data.lon.deg, self.center_skydir.data.lat.deg
return (
f"{self.__class__.__name__}\n\n"
f"\taxes : {self.axes_names}\n"
f"\tshape : {self.data_shape[::-1]}\n"
f"\tndim : {self.ndim}\n"
f"\tnside : {self.nside[0]}\n"
f"\tnested : {self.nest}\n"
f"\tframe : {self.frame}\n"
f"\tprojection : {self.projection}\n"
f"\tcenter : {lon:.1f} deg, {lat:.1f} deg\n"
)
def is_allclose(self, other, rtol_axes=1e-6, atol_axes=1e-6):
"""Compare two data IRFs for equivalency.
Parameters
----------
other : `HpxGeom`
Geometry to compare against.
rtol_axes : float, optional
Relative tolerance for axes comparison.
Default is 1e-6.
atol_axes : float, optional
Relative tolerance for axes comparison.
Default is 1e-6.
Returns
-------
is_allclose : bool
Whether the geometry is all close.
"""
if not isinstance(other, self.__class__):
return TypeError(f"Cannot compare {type(self)} and {type(other)}")
if self.is_allsky and not other.is_allsky:
return False
if self.data_shape != other.data_shape:
return False
axes_eq = self.axes.is_allclose(other.axes, rtol=rtol_axes, atol=atol_axes)
hpx_eq = (
self.nside == other.nside
and self.frame == other.frame
and self.order == other.order
and self.nest == other.nest
)
return axes_eq and hpx_eq
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
return self.is_allclose(other=other)
def __ne__(self, other):
return not self.__eq__(other)
|
gammapyREPO_NAMEgammapyPATH_START.@gammapy_extracted@gammapy-main@gammapy@maps@hpx@geom.py@.PATH_END.py
|
{
"filename": "test.py",
"repo_name": "xwzhang98/SREmulator",
"repo_path": "SREmulator_extracted/SREmulator-main/map2map/map2map/test.py",
"type": "Python"
}
|
import os
import sys
import warnings
from pprint import pprint
import numpy as np
import torch
from torch.utils.data import DataLoader
from .data import FieldDataset
from .data import norms
from . import models
from .models import narrow_cast
from .utils import import_attr, load_model_state_dict
from .models import narrow_like
from .data import norms
def test(args):
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
warnings.warn("Not parallelized but given more than 1 GPUs")
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = torch.device("cuda", 0)
torch.backends.cudnn.benchmark = True
else: # CPU multithreading
device = torch.device("cpu")
if args.num_threads is None:
args.num_threads = int(os.environ["SLURM_CPUS_ON_NODE"])
torch.set_num_threads(args.num_threads)
print("pytorch {}".format(torch.__version__))
pprint(vars(args))
sys.stdout.flush()
test_dataset = FieldDataset(
in_patterns=args.test_in_patterns,
tgt_patterns=args.test_tgt_patterns,
style_pattern=args.test_style_pattern,
noise_patterns=args.test_noise_patterns,
noise_style_pattern=args.test_noise_style_pattern,
in_norms=args.in_norms,
tgt_norms=args.tgt_norms,
callback_at=args.callback_at,
augment=False,
aug_shift=None,
aug_add=None,
aug_mul=None,
crop=args.crop,
crop_start=args.crop_start,
crop_stop=args.crop_stop,
crop_step=args.crop_step,
in_pad=args.in_pad,
tgt_pad=args.tgt_pad,
scale_factor=args.scale_factor,
**args.misc_kwargs,
)
test_loader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.loader_workers,
pin_memory=True,
)
in_chan = test_dataset.in_chan
out_chan = test_dataset.tgt_chan
style_size = test_dataset.style_size
model = import_attr(args.model, models, callback_at=args.callback_at)
model = model(
2 * sum(in_chan),
sum(out_chan),
style_size=style_size,
scale_factor=args.scale_factor,
**args.misc_kwargs,
)
model.to(device)
generator = G(6, 6, 1, 8)
state = torch.load(
"/hildafs/home/xzhangn/xzhangn/emulator_sr/5-training/test_pretrained/GAN_state/state_710.pt",
map_location=device,
)
G_state = state["model"]
generator.load_state_dict(G_state)
del state
del G_state
generator.to(device)
generator.eval()
state = torch.load(args.load_state, map_location=device)
load_model_state_dict(model, state["model"], strict=args.load_state_strict)
print(
"model state at epoch {} loaded from {}".format(state["epoch"], args.load_state)
)
del state
model.eval()
with torch.no_grad():
for i, data in enumerate(test_loader):
input, target, style = data["input"], data["target"], data["style"]
noise = data["noise"]
input = input.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
style = style.to(device, non_blocking=True)
noise = noise.to(device, non_blocking=True)
with torch.no_grad():
sr_out = generator(input, style)
sr_out = narrow_like(sr_out, noise)
output = model(sr_out, style, noise)
if i < 5:
print("##### sample :", i)
print("input shape :", input.shape)
print("output shape :", output.shape)
print("target shape :", target.shape)
print("style shape :", style.shape)
norms.cosmology.dis(output[:, :3], a=style.item(), undo=True)
norms.cosmology.vel(target[:, 3:], a=style.item(), undo=True)
# test_dataset.assemble('_in', in_chan, input,
# data['input_relpath'])
test_dataset.assemble("_out", out_chan, output, data["target_relpath"])
# test_dataset.assemble('_tgt', out_chan, target,
# data['target_relpath'])
|
xwzhang98REPO_NAMESREmulatorPATH_START.@SREmulator_extracted@SREmulator-main@map2map@map2map@test.py@.PATH_END.py
|
{
"filename": "Plot-Thermo-Slopes-checkpoint.ipynb",
"repo_name": "AMReX-Astro/MAESTROeX",
"repo_path": "MAESTROeX_extracted/MAESTROeX-main/Exec/science/urca/analysis/.ipynb_checkpoints/Plot-Thermo-Slopes-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
AMReX-AstroREPO_NAMEMAESTROeXPATH_START.@MAESTROeX_extracted@MAESTROeX-main@Exec@science@urca@analysis@.ipynb_checkpoints@Plot-Thermo-Slopes-checkpoint.ipynb@.PATH_END.py
|
|
{
"filename": "amcsd.py",
"repo_name": "xraypy/xraylarch",
"repo_path": "xraylarch_extracted/xraylarch-master/larch/xrd/amcsd.py",
"type": "Python"
}
|
#!/usr/bin/env python
"""
replaced by larixite code
"""
from larixite.amcsd import (get_amcsd, get_cif, find_cifs,
parse_cif_file, CifStructure)
|
xraypyREPO_NAMExraylarchPATH_START.@xraylarch_extracted@xraylarch-master@larch@xrd@amcsd.py@.PATH_END.py
|
{
"filename": "test_diagnostics.py",
"repo_name": "probabilists/lampe",
"repo_path": "lampe_extracted/lampe-master/tests/test_diagnostics.py",
"type": "Python"
}
|
r"""Tests for the lampe.diagnostics module."""
import torch
from lampe.diagnostics import *
from zuko.distributions import Independent, Normal, Truncated
def test_expected_coverage_mc():
posterior = lambda x: Independent(Normal(0, 1 + x**2), 1)
x = torch.randn(1024, 2)
theta = posterior(x).sample()
pairs = list(zip(theta, x))
# Exact
estimator = posterior
levels, coverages = expected_coverage_mc(estimator, pairs, n=1024)
assert torch.allclose(levels, coverages, atol=1e-1)
# Conservative
estimator = lambda x: Independent(Normal(0, 2 + x**2), 1)
levels, coverages = expected_coverage_mc(estimator, pairs, n=1024)
assert (coverages > levels).float().mean() > 0.9
# Overconfident
estimator = lambda x: Independent(Normal(0, 0.5 + x**2), 1)
levels, coverages = expected_coverage_mc(estimator, pairs, n=1024)
assert (coverages < levels).float().mean() > 0.9
def test_expected_coverage_ni():
posterior = lambda x: Independent(Truncated(Normal(0, 1 + x**2), -3, 3), 1)
x = torch.randn(1024, 2)
theta = posterior(x).sample()
pairs = list(zip(theta, x))
domain = (-3 * torch.ones(2), 3 * torch.ones(2))
# Exact
estimator = lambda theta, x: posterior(x).log_prob(theta)
levels, coverages = expected_coverage_ni(estimator, pairs, domain, bins=128)
assert torch.allclose(levels, coverages, atol=1e-1)
# Conservative
estimator = lambda theta, x: Truncated(Normal(0, 2 + x**2), -3, 3).log_prob(theta).sum(-1)
levels, coverages = expected_coverage_ni(estimator, pairs, domain, bins=128)
assert (coverages > levels).float().mean() > 0.9
# Overconfident
estimator = lambda theta, x: Truncated(Normal(0, 0.5 + x**2), -3, 3).log_prob(theta).sum(-1)
levels, coverages = expected_coverage_ni(estimator, pairs, domain, bins=128)
assert (coverages < levels).float().mean() > 0.9
|
probabilistsREPO_NAMElampePATH_START.@lampe_extracted@lampe-master@tests@test_diagnostics.py@.PATH_END.py
|
{
"filename": "index.md",
"repo_name": "ChandraCXC/iris",
"repo_path": "iris_extracted/iris-master/test-components/src/site/markdown/index.md",
"type": "Markdown"
}
|
# test-components
This page contains developer information for the `test-components` module.
Please see the [Project Documentation][proj-info] for more details.
Otherwise, return to the main [Iris user documentation][user-docs].
[proj-info]: ./project-info.html
[user-docs]: ../index.html
|
ChandraCXCREPO_NAMEirisPATH_START.@iris_extracted@iris-master@test-components@src@site@markdown@index.md@.PATH_END.py
|
{
"filename": "test_constant.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/constants/tests/test_constant.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import pytest
from astropy.constants import Constant
from astropy.units import Quantity as Q
def test_c():
from astropy.constants import c
# c is an exactly defined constant, so it shouldn't be changing
assert c.value == 2.99792458e8 # default is S.I.
assert c.si.value == 2.99792458e8
assert c.cgs.value == 2.99792458e10
# make sure it has the necessary attributes and they're not blank
assert c.uncertainty == 0 # c is a *defined* quantity
assert c.name
assert c.reference
assert c.unit
def test_h():
from astropy.constants import h
# check that the value is fairly close to what it should be (not exactly
# checking because this might get updated in the future)
assert abs(h.value - 6.626e-34) < 1e-38
assert abs(h.si.value - 6.626e-34) < 1e-38
assert abs(h.cgs.value - 6.626e-27) < 1e-31
# make sure it has the necessary attributes and they're not blank
assert h.uncertainty == 0 # CODATA 2018 set h to exact value
assert h.name
assert h.reference
assert h.unit
def test_e():
"""Tests for #572 demonstrating how EM constants should behave."""
from astropy.constants import e
# A test quantity
E = Q(100, "V/m")
# Without specifying a system e should not combine with other quantities
pytest.raises(TypeError, lambda: e * E)
# Try it again (as regression test on a minor issue mentioned in #745 where
# repeated attempts to use e in an expression resulted in UnboundLocalError
# instead of TypeError)
pytest.raises(TypeError, lambda: e * E)
# e.cgs is too ambiguous and should not work at all
pytest.raises(TypeError, lambda: e.cgs * E)
assert isinstance(e.si, Q)
assert isinstance(e.gauss, Q)
assert isinstance(e.esu, Q)
assert e.si * E == Q(100, "eV/m")
assert e.gauss * E == Q(e.gauss.value * E.value, "Fr V/m")
assert e.esu * E == Q(e.esu.value * E.value, "Fr V/m")
def test_g0():
"""Tests for #1263 demonstrating how g0 constant should behave."""
from astropy.constants import g0
# g0 is an exactly defined constant, so it shouldn't be changing
assert g0.value == 9.80665 # default is S.I.
assert g0.si.value == 9.80665
assert g0.cgs.value == 9.80665e2
# make sure it has the necessary attributes and they're not blank
assert g0.uncertainty == 0 # g0 is a *defined* quantity
assert g0.name
assert g0.reference
assert g0.unit
# Check that its unit have the correct physical type
assert g0.unit.physical_type == "acceleration"
def test_b_wien():
"""b_wien should give the correct peak wavelength for
given blackbody temperature. The Sun is used in this test.
"""
from astropy import units as u
from astropy.constants import b_wien
t = 5778 * u.K
w = (b_wien / t).to(u.nm)
assert round(w.value) == 502
def test_unit():
from astropy import constants as const
from astropy import units as u
for val in vars(const).values():
if isinstance(val, Constant):
# Getting the unit forces the unit parser to run. Confirm
# that none of the constants defined in astropy have
# invalid unit.
assert not isinstance(val.unit, u.UnrecognizedUnit)
def test_copy():
from astropy import constants as const
cc = copy.deepcopy(const.c)
assert cc == const.c
cc = copy.copy(const.c)
assert cc == const.c
def test_view():
"""Check that Constant and Quantity views can be taken (#3537, #3538)."""
from astropy.constants import c
c2 = c.view(Constant)
assert c2 == c
assert c2.value == c.value
# make sure it has the necessary attributes and they're not blank
assert c2.uncertainty == 0 # c is a *defined* quantity
assert c2.name == c.name
assert c2.reference == c.reference
assert c2.unit == c.unit
q1 = c.view(Q)
assert q1 == c
assert q1.value == c.value
assert type(q1) is Q
assert not hasattr(q1, "reference")
q2 = Q(c)
assert q2 == c
assert q2.value == c.value
assert type(q2) is Q
assert not hasattr(q2, "reference")
c3 = Q(c, subok=True)
assert c3 == c
assert c3.value == c.value
# make sure it has the necessary attributes and they're not blank
assert c3.uncertainty == 0 # c is a *defined* quantity
assert c3.name == c.name
assert c3.reference == c.reference
assert c3.unit == c.unit
c4 = Q(c, subok=True, copy=False)
assert c4 is c
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@constants@tests@test_constant.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/src/prefect/events/schemas/__init__.py",
"type": "Python"
}
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@src@prefect@events@schemas@__init__.py@.PATH_END.py
|
|
{
"filename": "example_optimize_pv.py",
"repo_name": "geodynamics/burnman",
"repo_path": "burnman_extracted/burnman-main/examples/example_optimize_pv.py",
"type": "Python"
}
|
# This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU
# GPL v2 or later.
"""
example_optimize_pv
-------------------
Vary the amount perovskite vs. ferropericlase and compute the error in the
seismic data against PREM. For more extensive comments on this setup, see tutorial/step_2.py
*Uses:*
* :doc:`mineral_database`
* :class:`burnman.Composite`
* :class:`burnman.seismic.PREM`
* :func:`burnman.geotherm.brown_shankland`
* :func:`burnman.Material.evaluate`
* :func:`burnman.utils.math.compare_l2`
*Demonstrates:*
* compare errors between models
* loops over models
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import burnman
from burnman import minerals
if __name__ == "__main__":
# Define reference model and depth to evaluate
seismic_model = burnman.seismic.PREM()
number_of_points = 20
depths = np.linspace(700e3, 2800e3, number_of_points)
(
seis_p,
seis_rho,
seis_vp,
seis_vs,
seis_vphi,
seis_K,
seis_G,
) = seismic_model.evaluate(
["pressure", "density", "v_p", "v_s", "v_phi", "K", "G"], depths
)
# Define geotherm
temperature = burnman.geotherm.brown_shankland(depths)
# Define solid solutions
perovskite = minerals.SLB_2011.mg_fe_perovskite()
# Set molar_fraction of fe_perovskite and al_perovskite:
perovskite.set_composition([0.94, 0.06, 0.0])
ferropericlase = minerals.SLB_2011.ferropericlase()
# Set molar_fraction of MgO and FeO:
ferropericlase.set_composition([0.8, 0.2])
def material_error(amount_perovskite):
# Define composite using the values
rock = burnman.Composite(
[perovskite, ferropericlase], [amount_perovskite, 1.0 - amount_perovskite]
)
# Compute velocities
mat_rho, mat_vp, mat_vs, mat_vphi, mat_K, mat_G = rock.evaluate(
["density", "v_p", "v_s", "v_phi", "K_S", "G"], seis_p, temperature
)
print("Calculations are done for:")
rock.debug_print()
# Calculate errors
[vs_err, vphi_err, rho_err, K_err, G_err] = burnman.utils.math.compare_l2(
depths,
[mat_vs, mat_vphi, mat_rho, mat_K, mat_G],
[seis_vs, seis_vphi, seis_rho, seis_K, seis_G],
)
# Normalize errors
vs_err = vs_err / np.mean(seis_vs) ** 2.0
vphi_err = vphi_err / np.mean(seis_vphi) ** 2.0
rho_err = rho_err / np.mean(seis_rho) ** 2.0
K_err = K_err / np.mean(seis_K) ** 2.0
G_err = G_err / np.mean(seis_G) ** 2.0
return vs_err, vphi_err, rho_err, K_err, G_err
# Run through fractions of perovskite
xx = np.linspace(0.0, 1.0, 40)
errs = np.array([material_error(x) for x in xx])
# Plot results
yy_vs = errs[:, 0]
yy_vphi = errs[:, 1]
yy_rho = errs[:, 2]
yy_K = errs[:, 3]
yy_G = errs[:, 4]
plt.plot(xx, yy_vs, "r-x", label=("vs error"))
plt.plot(xx, yy_vphi, "b-x", label=("vphi error"))
plt.plot(xx, yy_rho, "m-x", label=("rho error"))
plt.plot(xx, yy_K, "g-x", label=("K error"))
plt.plot(xx, yy_G, "y-x", label=("G error"))
plt.yscale("log")
plt.xlabel("% Perovskite")
plt.ylabel("Error")
plt.legend()
plt.show()
|
geodynamicsREPO_NAMEburnmanPATH_START.@burnman_extracted@burnman-main@examples@example_optimize_pv.py@.PATH_END.py
|
{
"filename": "main.py",
"repo_name": "cdslaborg/paramonte",
"repo_path": "paramonte_extracted/paramonte-main/example/fortran/pm_mathGammaNR/getGammaIncLowNR/main.py",
"type": "Python"
}
|
#!/usr/bin/env python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import glob
import sys
fontsize = 17
kind = "RK"
label = [ r"shape: $\kappa = 1.0$"
, r"shape: $\kappa = 2.5$"
, r"shape: $\kappa = 5.0$"
]
pattern = "*." + kind + ".txt"
fileList = glob.glob(pattern)
if len(fileList) == 1:
df = pd.read_csv(fileList[0], delimiter = " ")
fig = plt.figure(figsize = 1.25 * np.array([6.4, 4.8]), dpi = 200)
ax = plt.subplot()
for i in range(1,len(df.values[0,:]+1)):
plt.plot( df.values[:, 0]
, df.values[:,i]
, linewidth = 2
)
plt.xticks(fontsize = fontsize - 2)
plt.yticks(fontsize = fontsize - 2)
ax.set_xlabel("x", fontsize = fontsize)
ax.set_ylabel("Regularized Lower\nIncomplete Gamma Function", fontsize = fontsize)
plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
ax.tick_params(axis = "y", which = "minor")
ax.tick_params(axis = "x", which = "minor")
ax.legend ( label
, fontsize = fontsize
#, loc = "center left"
#, bbox_to_anchor = (1, 0.5)
)
plt.savefig(fileList[0].replace(".txt",".png"))
else:
sys.exit("Ambiguous file list exists.")
|
cdslaborgREPO_NAMEparamontePATH_START.@paramonte_extracted@paramonte-main@example@fortran@pm_mathGammaNR@getGammaIncLowNR@main.py@.PATH_END.py
|
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