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{
"filename": "fitcache.py",
"repo_name": "Fermipy/fermipy",
"repo_path": "fermipy_extracted/fermipy-master/fermipy/fitcache.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import pyLikelihood as pyLike
from fermipy import utils
from fermipy import gtutils
def get_fitcache_pars(fitcache):
pars = pyLike.FloatVector()
cov = pyLike.FloatVector()
pyLike.Vector_Hep_to_Stl(fitcache.currentPars(), pars)
pyLike.Matrix_Hep_to_Stl(fitcache.currentCov(), cov)
pars = np.array(pars)
cov = np.array(cov)
npar = len(pars)
cov = cov.reshape((npar, npar))
err = np.sqrt(np.diag(cov))
return pars, err, cov
class FitCache(object):
def __init__(self, like, params, tol, max_iter, init_lambda, use_reduced):
self._like = like
self._init_fitcache(params, tol, max_iter, init_lambda, use_reduced)
@property
def fitcache(self):
return self._fitcache
@property
def params(self):
return self._cache_params
def _init_fitcache(self, params, tol, max_iter, init_lambda, use_reduced):
free_params = [p for p in params if p['free'] is True]
free_norm_params = [p for p in free_params if p['is_norm'] is True]
for p in free_norm_params:
bounds = self._like[p['idx']].getBounds()
self._like[p['idx']].setBounds(*utils.update_bounds(1.0, bounds))
self._like[p['idx']] = 1.0
self._like.syncSrcParams()
self._fs_wrapper = pyLike.FitScanModelWrapper_Summed(
self._like.logLike)
self._fitcache = pyLike.FitScanCache(self._fs_wrapper,
str('fitscan_testsource'),
tol, max_iter, init_lambda,
use_reduced, False, False)
for p in free_norm_params:
self._like[p['idx']] = p['value']
self._like[p['idx']].setBounds(p['min'], p['max'])
self._like.syncSrcParams()
self._all_params = gtutils.get_params_dict(self._like)
self._params = params
self._cache_params = free_norm_params
self._cache_param_idxs = [p['idx'] for p in self._cache_params]
npar = len(self.params)
self._prior_vals = np.ones(npar)
self._prior_errs = np.ones(npar)
self._prior_cov = np.ones((npar, npar))
self._has_prior = np.array([False] * npar)
def get_pars(self):
return get_fitcache_pars(self.fitcache)
def check_params(self, params):
if len(params) != len(self._params):
return False
free_params = [p for p in params if p['free'] is True]
free_norm_params = [p for p in free_params if p['is_norm'] is True]
cache_src_names = np.array(self.fitcache.templateSourceNames())
for i, p in enumerate(free_norm_params):
if p['idx'] not in self._cache_param_idxs:
return False
# Check if any fixed parameters changed
for i, p in enumerate(self._params):
if p['free']:
continue
if p['src_name'] in cache_src_names:
continue
if not np.isclose(p['value'], params[i]['value']):
return False
if not np.isclose(p['scale'], params[i]['scale']):
return False
return True
def update_source(self, name):
src_names = self.fitcache.templateSourceNames()
if not name in src_names:
return
self.fitcache.updateTemplateForSource(str(name))
def refactor(self):
npar = len(self.params)
self._free_pars = [True] * npar
par_scales = np.ones(npar)
ref_vals = np.array(self.fitcache.refValues())
cache_src_names = np.array(self.fitcache.templateSourceNames())
update_sources = []
all_params = gtutils.get_params_dict(self._like)
for src_name in cache_src_names:
pars0 = all_params[src_name]
pars1 = self._all_params[src_name]
for i, (p0, p1) in enumerate(zip(pars0, pars1)):
if p0['is_norm']:
continue
if (np.isclose(p0['value'], p1['value']) and
np.isclose(p0['scale'], p1['scale'])):
continue
update_sources += [src_name]
for i, p in enumerate(self.params):
self._free_pars[i] = self._like[p['idx']].isFree()
par_scales[i] = self._like[p['idx']].getValue() / ref_vals[i]
for src_name in update_sources:
norm_val = self._like.normPar(src_name).getValue()
par_name = self._like.normPar(src_name).getName()
bounds = self._like.normPar(src_name).getBounds()
idx = self._like.par_index(src_name, par_name)
self._like[idx].setBounds(*utils.update_bounds(1.0, bounds))
self._like[idx] = 1.0
self.fitcache.updateTemplateForSource(str(src_name))
self._like[idx] = norm_val
self._like[idx].setBounds(*bounds)
self._all_params = all_params
self.fitcache.refactorModel(self._free_pars, par_scales, False)
# Set priors
self._set_priors_from_like()
if np.any(self._has_prior):
self._build_priors()
def update(self, params, tol, max_iter, init_lambda, use_reduced):
try:
self.fitcache.update()
self.fitcache.setInitLambda(init_lambda)
self.fitcache.setTolerance(tol)
self.fitcache.setMaxIter(max_iter)
except Exception:
print('ERROR')
if not self.check_params(params):
self._init_fitcache(params, tol, max_iter,
init_lambda, use_reduced)
self.refactor()
def _set_priors_from_like(self):
prior_vals, prior_errs, has_prior = gtutils.get_priors(self._like)
if not np.any(has_prior):
self._has_prior.fill(False)
return
for i, p in enumerate(self.params):
self._prior_vals[i] = prior_vals[p['idx']]
self._prior_errs[i] = prior_errs[p['idx']]
self._has_prior[i] = True # has_prior[p['idx']]
if not has_prior[p['idx']]:
self._prior_errs[i] = 1E3
self._prior_cov = np.diag(np.array(self._prior_errs)**2)
def set_priors(self, vals, err):
self._prior_vals = np.array(vals, ndmin=1)
self._prior_errs = err
self._prior_cov = np.diag(np.array(err)**2)
self._has_prior = np.array([True] * len(vals))
def _build_priors(self):
free_pars = np.array(self._free_pars)
ref_vals = np.array(self.fitcache.refValues())
pars = self._prior_vals / ref_vals
cov = np.ravel(self._prior_cov / np.outer(ref_vals, ref_vals))
pars = pars[free_pars]
cov = cov[np.ravel(np.outer(free_pars, free_pars))]
has_prior = self._has_prior[free_pars]
self.fitcache.buildPriorsFromExternal(pars, cov, has_prior.tolist())
def fit(self, verbose=0):
try:
return self.fitcache.fitCurrent(3, verbose)
except Exception:
return self.fitcache.fitCurrent(bool(np.any(self._has_prior)),
False, verbose)
|
FermipyREPO_NAMEfermipyPATH_START.@fermipy_extracted@fermipy-master@fermipy@fitcache.py@.PATH_END.py
|
{
"filename": "layers.py",
"repo_name": "jakeret/tf_unet",
"repo_path": "tf_unet_extracted/tf_unet-master/tf_unet/layers.py",
"type": "Python"
}
|
# tf_unet 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 3 of the License, or
# (at your option) any later version.
#
# tf_unet 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 tf_unet. If not, see <http://www.gnu.org/licenses/>.
'''
Created on Aug 19, 2016
author: jakeret
'''
from __future__ import print_function, division, absolute_import, unicode_literals
import tensorflow as tf
def weight_variable(shape, stddev=0.1, name="weight"):
initial = tf.truncated_normal(shape, stddev=stddev)
return tf.Variable(initial, name=name)
def weight_variable_devonc(shape, stddev=0.1, name="weight_devonc"):
return tf.Variable(tf.truncated_normal(shape, stddev=stddev), name=name)
def bias_variable(shape, name="bias"):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial, name=name)
def conv2d(x, W, b, keep_prob_):
with tf.name_scope("conv2d"):
conv_2d = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
conv_2d_b = tf.nn.bias_add(conv_2d, b)
return tf.nn.dropout(conv_2d_b, keep_prob_)
def deconv2d(x, W,stride):
with tf.name_scope("deconv2d"):
x_shape = tf.shape(x)
output_shape = tf.stack([x_shape[0], x_shape[1]*2, x_shape[2]*2, x_shape[3]//2])
return tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding='VALID', name="conv2d_transpose")
def max_pool(x,n):
return tf.nn.max_pool(x, ksize=[1, n, n, 1], strides=[1, n, n, 1], padding='VALID')
def crop_and_concat(x1,x2):
with tf.name_scope("crop_and_concat"):
x1_shape = tf.shape(x1)
x2_shape = tf.shape(x2)
# offsets for the top left corner of the crop
offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, (x1_shape[2] - x2_shape[2]) // 2, 0]
size = [-1, x2_shape[1], x2_shape[2], -1]
x1_crop = tf.slice(x1, offsets, size)
return tf.concat([x1_crop, x2], 3)
def pixel_wise_softmax(output_map):
with tf.name_scope("pixel_wise_softmax"):
max_axis = tf.reduce_max(output_map, axis=3, keepdims=True)
exponential_map = tf.exp(output_map - max_axis)
normalize = tf.reduce_sum(exponential_map, axis=3, keepdims=True)
return exponential_map / normalize
def cross_entropy(y_,output_map):
return -tf.reduce_mean(y_*tf.log(tf.clip_by_value(output_map,1e-10,1.0)), name="cross_entropy")
|
jakeretREPO_NAMEtf_unetPATH_START.@tf_unet_extracted@tf_unet-master@tf_unet@layers.py@.PATH_END.py
|
{
"filename": "params.py",
"repo_name": "dullemond/radmc3d-2.0",
"repo_path": "radmc3d-2.0_extracted/radmc3d-2.0-master/python/radmc3dPy/radmc3dPy/params.py",
"type": "Python"
}
|
"""This module contains classes for handling of parameters of the model setup and for file I/O
"""
from __future__ import absolute_import
from __future__ import print_function
import traceback
import importlib
try:
import numpy as np
except ImportError:
np = None
print(' Numpy cannot be imported ')
print(' To use the python module of RADMC-3D you need to install Numpy')
print(traceback.format_exc())
from . natconst import *
class radmc3dPar(object):
"""Parameter class for a RADMC-3D model.
Attributes
----------
ppar : dictionary
Contains parameter values with parameter names as keys
pdesc : dictionary
Contains parameter description (comments in the parameter file) with parameter names as keys
pblock : dictionary
Contains the block names in the parameter file and parameter names as values
pvalstr: dictionary
Contains parameter values as strings with parameter names as keys
"""
def __init__(self):
self.ppar = {}
self.pdesc = {}
self.pblock = {}
self.pvalstr = {}
def readPar(self, fname=''):
"""Reads a parameter file.
The parameters in the files should follow python syntax
Parameters
----------
fname : str, optional
File name to be read (if omitted problem_params.inp is used)
Returns
-------
Returns a dictionary with the parameter names as keys
"""
if fname == '':
fname = 'problem_params.inp'
with open(fname, 'r') as rfile:
cchar = '#'
lbchar = ""
# Add pi to the local variables
pi = np.pi
# --------------------------------------------------------------------------------------------------------
# First read every line that is not commented (i.e. does not begin with a comment character)
# --------------------------------------------------------------------------------------------------------
dumlist = []
dumline = '-'
dumline = rfile.readline()
while dumline != '':
# First check if the line is commented out, in which case ignore it
comment = False
if dumline[0] == cchar:
if dumline.find('Block') < 0:
comment = True
# OK, the line is not commented out, now check if it contains a '=' sign (if not ignore the line)
if not comment:
# Check if we have an empty line in which case also ignore it
if dumline.strip() != '':
dumlist.append(dumline)
# Read the next line
dumline = rfile.readline()
# -------------------------------------------------------------------------------------------------------------
# After every line in the file was read try to decode the lines to
# [variable name] = [variable value] # [comment]
# also try to catch if an expression has been broken into multiple lines
# -------------------------------------------------------------------------------------------------------------
varlist = []
iline = 0
while iline < len(dumlist):
# First check if the line contains an '=' sign if not we have a problem
# expression broken into multiple lines are should already be dealt with
ind = dumlist[iline].find('=')
if ind <= 0:
if dumlist[iline].find('Block') <= 0:
raise SyntaxError(' Invalid expression in line ' + ("%d" % iline))
else:
if dumlist[iline].find(':') <= 0:
raise SyntaxError('Invalid block identified. \n'
+ ' The syntax of the block name field is : \n'
+ ' # Block : BlockName ')
else:
blockname = dumlist[iline].split(':')[1].strip()
else:
# The line contains a '=' sign and a variable name, so let's check if the
# value expression is broken into multiple lines
vlist = dumlist[iline].split('=')
lbind = vlist[1].find('\\')
cind = vlist[1].find('#')
# The line is full not broken
if lbind == -1:
# Check if there is a comment field
if cind >= 0:
vlist = [vlist[0], vlist[1][:cind], vlist[1][cind + 1:], blockname]
else:
vlist = [vlist[0], vlist[1][:cind], ' ', blockname]
varlist.append(vlist)
# The value expression is broken into multiple lines; take all lines and join the pieces
else:
# Check if there is any comment in the line
inBrokenLine = False
if cind >= 0:
# Part of the line is commented, now check if the line break is before or after the
# comment character
if lbind > cind:
# The line break is in the comment field so there is no real line break
vlist = [vlist[0], vlist[1][:cind], vlist[1][cind + 1:], blockname]
else:
# The line break is before the comment character
inBrokenLine = True
expr = vlist[1][:lbind]
com = vlist[1][cind + 1:]
else:
inBrokenLine = True
expr = vlist[1][:lbind]
com = ' '
if inBrokenLine:
# Now gather all other pieces of this line
iline2 = 0
while inBrokenLine:
iline2 = iline2 + 1
dummy = dumlist[iline + iline2]
# Search for comments
cind2 = dummy.find('#')
# Search for another line break
lbind2 = dummy.find('\\')
# TODO:
# At the moment I neglect the possiblity that the second line in a broken long line begins
# with a linebreak or commented out
# There is comment
if cind2 > 0:
# There is line break
if lbind2 > 0:
# The line break is commented out
if lbind2 > cind:
expr = expr + dummy[:cind2].strip()
com = com + dummy[cind2 + 1:]
inBrokenLine = False
else:
# The line break is not commented out
expr = expr + dummy[:lbind2].strip()
com = com + dummy[cind2 + 1:]
else:
# There is no line break
expr = expr + dummy[:cind2].strip()
com = com + dummy[cind2 + 1:]
inBrokenLine = False
# There is no comment
else:
# There is a line break
if lbind2 > 0:
expr = expr + dummy[:lbind2].strip()
com = com + dummy[cind2 + 1:]
# There is no line break
else:
expr = expr + dummy[:cind2].strip()
com = com + ' '
inBrokenLine = False
iline = iline + iline2
vlist = [vlist[0], expr, com, blockname]
varlist.append(vlist)
iline = iline + 1
# -------------------------------------------------------------------------------------------------------
# Now evaluate the expressions in the value field and make the final dictionary
# -------------------------------------------------------------------------------------------------------
self.ppar = {}
glob = globals()
glob['pi'] = np.pi
loc = locals()
loc['pi'] = np.pi
for i in range(len(varlist)):
try:
val = eval(varlist[i][1], glob)
glob[varlist[i][0].strip()] = val
except:
try:
val = eval(varlist[i][1], loc)
loc[varlist[i][0].strip()] = val
except:
raise SyntaxError('Unknown expression "' + varlist[i][1] + '"')
self.ppar[varlist[i][0].strip()] = val
self.pvalstr[varlist[i][0].strip()] = varlist[i][1].strip()
self.pdesc[varlist[i][0].strip()] = varlist[i][2].strip()
self.pblock[varlist[i][0].strip()] = varlist[i][3].strip()
return
# --------------------------------------------------------------------------------------------------
def setPar(self, parlist=None):
"""Sets a parameter in the radmc3DPar class.
If the paramter is already defined its value will be modified
Parameters
----------
parlist : list
If the parameter is already defined parlist should be a two element
list 1) parameter name, 2) parameter expression/value as a string
If the parameter is not yet defined parlist should be a four element
list 1) parameter name, 2) parameter expression/value as a string
3) Parameter description (= comment field in the parameter file)
"""
if parlist is None:
raise ValueError('Unknown parlist. \n No parameters to set.')
else:
parname = parlist[0].strip()
#
# Check whether or not the parameter is already defined
#
new_par = False
if len(parlist) == 2:
if parname not in self.ppar:
raise ValueError(' parlist has too few elements.\n'
+ ' The argument of radmc3dPar.setPar() should be a four element list if a new \n'
+ ' parameter is defined 1) parameter name, 2) parameter expression/value as a '
+ ' string\n'
+ ' 3) Parameter description (= comment field in the parameter file) \n'
+ ' 4) The name of the block in which the parameter must be placed in the \n'
+ ' problem_params.inp file')
else:
new_par = True
# Add pi to the local variables
pi = np.pi
#
# Add the parameter to the dictionaries /change its value
#
glob = globals()
glob['pi'] = np.pi
loc = locals()
loc['pi'] = np.pi
try:
self.ppar[parname] = eval(parlist[1].strip(), glob)
glob[parname] = self.ppar[parname]
except:
try:
self.ppar[parname] = eval(parlist[1].strip(), loc)
loc[parname] = self.ppar[parname]
except:
raise SyntaxError('Unknown expression ' + parlist[1].strip())
self.pvalstr[parname] = parlist[1].strip()
if new_par:
if parname not in self.pdesc:
self.pdesc[parname] = parlist[2].strip()
if len(parlist) == 4:
if parname not in self.pblock:
self.pblock[parname] = parlist[3].strip()
def loadDefaults(self, model='', ppar=None, reset=True):
"""Sets all parameters to default values.
Parameters
----------
model : str
Model name whose paraemters should also be loaded
ppar : dictionary
Contains parameter values as string and parameter names as keys
Default values will be re-set to the values in this dictionary
reset : bool
If True the all class attributes will be re-initialized before
the default values would be loaded. I.e. it will remove all entries
from the dictionary that does not conain default values either in this
function or in the optional ppar keyword argument
"""
if reset:
self.ppar = {}
self.pvalstr = {}
self.pdesc = {}
self.pblock = {}
#
# Radiation sources
#
self.setPar(['incl_disc_stellarsrc', 'True', '# Switches on (True) or off (False) discrete stellar sources)',
'Radiation sources'])
self.setPar(['mstar', '[1.0*ms]', '# Mass of the star(s)', 'Radiation sources'])
self.setPar(['rstar', '[2.0*rs]', '# Radius of the star(s)', 'Radiation sources'])
self.setPar(['tstar', '[4000.0]', '# Effective temperature of the star(s) [K]', 'Radiation sources'])
self.setPar(
['pstar', '[0.0, 0.0, 0.0]', '# Position of the star(s) (cartesian coordinates)', 'Radiation sources'])
self.setPar(['staremis_type', '["blackbody"]', '# Stellar emission type ("blackbody", "kurucz", "nextgen")',
'Radiation sources'])
self.setPar(
['incl_cont_stellarsrc', 'False', '# Switches on (True) or off (False) continuous stellar sources )',
'Radiation sources'])
#
# Grid parameters
#
self.setPar(['crd_sys', "'sph'", ' Coordinate system used (car/sph)', 'Grid parameters'])
self.setPar(
['grid_style', '0', ' 0 - Regular grid, 1 - Octree AMR, 10 - Layered/nested grid (not yet supported)',
'Grid parameters'])
self.setPar(
['nx', '50', ' Number of grid points in the first dimension (to switch off this dimension set it to 0)',
'Grid parameters'])
self.setPar(
['ny', '30', ' Number of grid points in the second dimension (to switch off this dimension set it to 0)',
'Grid parameters'])
self.setPar(
['nz', '36', ' Number of grid points in the third dimension (to switch off this dimension set it to 0)',
'Grid parameters'])
self.setPar(['xbound', '[1.0*au, 100.*au]', ' Boundaries for the x grid', 'Grid parameters'])
self.setPar(['ybound', '[0.0, pi]', ' Boundaries for the y grid', 'Grid parameters'])
self.setPar(['zbound', '[0.0, 2.0*pi]', ' Boundraries for the z grid', 'Grid parameters'])
self.setPar(['xres_nlev', '3', 'Number of refinement levels (spherical coordinates only', 'Grid parameters'])
self.setPar(['xres_nspan', '3', 'Number of the original grid cells to refine (spherical coordinates only)',
'Grid parameters'])
self.setPar(
['xres_nstep', '3', 'Number of grid cells to create in a refinement level (spherical coordinates only)',
'Grid parameters'])
self.setPar(['wbound', '[0.1, 7.0, 25., 1e4]', ' Boundraries for the wavelength grid', 'Grid parameters'])
self.setPar(['nw', '[19, 50, 30]', ' Number of points in the wavelength grid', 'Grid parameters'])
self.setPar(['levelMaxLimit', '5', ' Highest refinement level in octree AMR', 'Grid parameters'])
#
# Dust opacity
#
self.setPar(['lnk_fname',
"['/disk2/juhasz/Data/JPDOC/astrosil/astrosil_WD2001_new.lnk']",
' ', 'Dust opacity'])
self.setPar(['gdens', '[3.6, 1.8]', ' Bulk density of the materials in g/cm^3', 'Dust opacity'])
self.setPar(['gsmin', '0.1', ' Minimum grain size', 'Dust opacity'])
self.setPar(['gsmax', '10.0', ' Maximum grain size', 'Dust opacity'])
self.setPar(['ngs', '1', ' Number of grain sizes', 'Dust opacity'])
self.setPar(['gsdist_powex', '-3.5', ' Grain size distribution power exponent', 'Dust opacity'])
self.setPar(['nscatang', '180', ' Number of scattering angles (only for scattering_mode_max=5)',
'Dust opacity'])
self.setPar(['logawidth', '0', ' If >0 the opacity will be averaged over a small sample around the specified '
'grain size, with logawidth being the variance of the Gaussian distribution. ',
'Dust opacity'])
self.setPar(['wfact', '3.0', ' Grid width of na sampling points in units of logawidth.', 'Dust opacity'])
self.setPar(['na', '20', ' Number of size sampling points (if logawidth set, default=20', 'Dust opacity'])
self.setPar(['chopforwardt', '0.0', ' If >0 this gives the angle (in degrees from forward) within which the '
'scattering phase function should be kept constant', 'Dust opacity'])
self.setPar(['errtol', '0.01', ' Tolerance of the relative difference between kscat and the integral over the '
'zscat Z11 element over angle.', 'Dust opacity'])
self.setPar(['verbose', 'False', ' If set to True, the code will give some feedback so that one knows what it '
'is doing if it becomes slow.', 'Dust opacity'])
self.setPar(['extrapolate', 'True', ' If True extrapolates for wavelengths outside the ones covered by the '
'optical constants', 'Dust opacity'])
self.setPar(['mixabun', '[0.75, 0.25]', ' Mass fractions of the dust componetns to be mixed', 'Dust opacity'])
self.setPar(['dustkappa_ext', "['silicate']", ' ', 'Dust opacity'])
#
# Gas line RT
#
self.setPar(
['gasspec_mol_name', "['co']", ' Name of the gas species - the extension of the molecule_EXT.inp file',
'Gas line RT'])
self.setPar(['gasspec_mol_abun', '[1e-4]', ' Abundance of the molecule', 'Gas line RT'])
self.setPar(['gasspec_mol_dbase_type', "['leiden']", ' leiden or linelist', 'Gas line RT'])
self.setPar(
['gasspec_colpart_name', "['h2']", ' Name of the gas species - the extension of the molecule_EXT.inp file',
'Gas line RT'])
self.setPar(['gasspec_colpart_abun', '[1e0]', ' Abundance of the molecule', 'Gas line RT'])
self.setPar(['gasspec_vturb', '0.1e5', ' Microturbulence', 'Gas line RT'])
#
# Code parameters
#
self.setPar(['nphot', 'int(1e5)', ' Nr of photons for the thermal Monte Carlo', 'Code parameters'])
self.setPar(['nphot_scat', 'int(3e5)', ' Nr of photons for the scattering Monte Carlo (for images)',
'Code parameters'])
self.setPar(['nphot_spec', 'int(1e5)', ' Nr of photons for the scattering Monte Carlo (for spectra)',
'Code parameters'])
self.setPar(
['scattering_mode_max', '1', ' 0 - no scattering, 1 - isotropic scattering, 2 - anizotropic scattering',
'Code parameters'])
self.setPar(['lines_mode', '-1', ' Line raytracing mode', 'Code parameters'])
self.setPar(['istar_sphere', '0',
' 1 - take into account the finite size of the star, 0 - take the star to be point-like',
'Code parameters'])
self.setPar(['itempdecoup', '1', ' Enable for different dust components to have different temperatures',
'Code parameters'])
self.setPar(['tgas_eq_tdust', '1', ' Take the dust temperature to identical to the gas temperature',
'Code parameters'])
self.setPar(
['rto_style', '1', ' Format of outpuf files (1-ascii, 2-unformatted f77, 3-binary', 'Code parameters'])
self.setPar(
['modified_random_walk', '0', ' Switched on (1) and off (0) modified random walk', 'Code parameters'])
#
# Model parameters
#
if model != '':
try:
mdl = importlib.import_module(model)
except:
try:
mdl = importlib.import_module('radmc3dPy.models.' + model)
except:
print(model + '.py could not be imported. \n '
+ 'The model files should either be in the current working directory \n '
+ 'or in the radmc3d python module directory')
print(traceback.format_exc())
modpar = mdl.getDefaultParams()
for i in range(len(modpar)):
dum = modpar[i]
if len(dum) == 3:
dum.append('Model ' + model)
self.setPar(dum)
def printPar(self):
"""Prints the parameters of the current model.
"""
#
# First get the unique block names
#
blocknames = ['Radiation sources', 'Grid parameters', 'Dust opacity', 'Gas line RT', 'Code parameters']
for key in self.pblock.keys():
dum = self.pblock[key]
if not blocknames.__contains__(dum):
blocknames.append(dum)
#
# Get the parameter block names and distionary keys
#
par_keys = list(self.pblock.keys())
par_block = list(self.pblock.values())
#
# Print the parameters by blocks
#
for iblock in blocknames:
print(
'%s' % '# ------------------------------------------------------------------------------------------'
+ '-------------------------------')
txt = '# Block: ' + iblock
print('%s' % txt)
print(
'%s' % '# ------------------------------------------------------------------------------------------'
+ '-------------------------------')
keys = []
for i in range(len(par_block)):
if par_block[i] == iblock:
keys.append(par_keys[i])
keys.sort()
for key in keys:
print(key.ljust(25) + ' = ' + self.pvalstr[key].strip() + ' # ' + self.pdesc[key].strip())
# --------------------------------------------------------------------------------------------------
def writeParfile(self, fname=''):
"""Writes a parameter file.
Parameters
----------
fname : str, optional
File name to be read (if omitted problem_params.inp is used)
"""
if fname == '':
fname = 'problem_params.inp'
print('Writing ' + fname)
#
# First get the uniq block names
#
blocknames = ['Radiation sources', 'Grid parameters', 'Dust opacity', 'Gas line RT', 'Code parameters']
for key in self.pblock:
dum = self.pblock[key]
if not blocknames.__contains__(dum):
blocknames.append(dum)
with open(fname, 'w') as wfile:
#
# Write header
#
wfile.write(
'%s\n' % ('#########################################################################################'
+ '##################################'))
wfile.write('%s\n' % '# RADMC-3D PARAMETER SETUP')
wfile.write('%s\n' % '# Created by the python module of RADMC-3D')
wfile.write(
'%s\n' % ('#########################################################################################'
+ '##################################'))
#
# Get the parameter block names and distionary keys
#
par_keys = list(self.pblock.keys())
par_block = list(self.pblock.values())
#
# Write the parameterfile
#
for iblock in blocknames:
wfile.write(
'%s\n' % ('# -----------------------------------------------------------------------------------'
+ '--------------------------------------'))
txt = '# Block: ' + iblock
wfile.write('%s\n' % txt)
wfile.write(
'%s\n' % ('# -----------------------------------------------------------------------------------'
+ '--------------------------------------'))
keys = []
for i in range(len(par_block)):
if par_block[i] == iblock:
keys.append(par_keys[i])
keys.sort()
for key in keys:
wfile.write(key.ljust(25) + ' = ' + self.pvalstr[key].strip() + ' # '
+ self.pdesc[key].strip() + '\n')
|
dullemondREPO_NAMEradmc3d-2.0PATH_START.@radmc3d-2.0_extracted@radmc3d-2.0-master@python@radmc3dPy@radmc3dPy@params.py@.PATH_END.py
|
{
"filename": "matrix_square_root_op_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/kernel_tests/linalg/matrix_square_root_op_test.py",
"type": "Python"
}
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for tensorflow.ops.math_ops.matrix_square_root."""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import test_util
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import stateless_random_ops
from tensorflow.python.platform import test
class SquareRootOpTest(test.TestCase):
def _verifySquareRoot(self, matrix, np_type):
matrix = matrix.astype(np_type)
# Verify that matmul(sqrtm(A), sqrtm(A)) = A
sqrt = gen_linalg_ops.matrix_square_root(matrix)
square = test_util.matmul_without_tf32(sqrt, sqrt)
self.assertShapeEqual(matrix, square)
self.assertAllClose(matrix, square, rtol=1e-4, atol=1e-3)
def _verifySquareRootReal(self, x):
for np_type in [np.float32, np.float64]:
self._verifySquareRoot(x, np_type)
def _verifySquareRootComplex(self, x):
for np_type in [np.complex64, np.complex128]:
self._verifySquareRoot(x, np_type)
def _makeBatch(self, matrix1, matrix2):
matrix_batch = np.concatenate(
[np.expand_dims(matrix1, 0),
np.expand_dims(matrix2, 0)])
matrix_batch = np.tile(matrix_batch, [2, 3, 1, 1])
return matrix_batch
def _testMatrices(self, matrix1, matrix2):
# Real
self._verifySquareRootReal(matrix1)
self._verifySquareRootReal(matrix2)
self._verifySquareRootReal(self._makeBatch(matrix1, matrix2))
matrix1 = matrix1.astype(np.complex64)
matrix2 = matrix2.astype(np.complex64)
matrix1 += 1j * matrix1
matrix2 += 1j * matrix2
self._verifySquareRootComplex(matrix1)
self._verifySquareRootComplex(matrix2)
self._verifySquareRootComplex(self._makeBatch(matrix1, matrix2))
@test_util.run_without_tensor_float_32
def testSymmetricPositiveDefinite(self):
matrix1 = np.array([[2., 1.], [1., 2.]])
matrix2 = np.array([[3., -1.], [-1., 3.]])
self._testMatrices(matrix1, matrix2)
@test_util.run_without_tensor_float_32
def testAsymmetric(self):
matrix1 = np.array([[0., 4.], [-1., 5.]])
matrix2 = np.array([[33., 24.], [48., 57.]])
self._testMatrices(matrix1, matrix2)
@test_util.run_without_tensor_float_32
def testIdentityMatrix(self):
# 2x2
identity = np.array([[1., 0], [0, 1.]])
self._verifySquareRootReal(identity)
# 3x3
identity = np.array([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
self._verifySquareRootReal(identity)
@test_util.run_without_tensor_float_32
def testEmpty(self):
self._verifySquareRootReal(np.empty([0, 2, 2]))
self._verifySquareRootReal(np.empty([2, 0, 0]))
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32
def testWrongDimensions(self):
# The input to the square root should be at least a 2-dimensional tensor.
tensor = constant_op.constant([1., 2.])
with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
gen_linalg_ops.matrix_square_root(tensor)
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32
def testNotSquare(self):
with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)):
tensor = constant_op.constant([[1., 0., -1.], [-1., 1., 0.]])
self.evaluate(gen_linalg_ops.matrix_square_root(tensor))
@test_util.run_in_graph_and_eager_modes(use_gpu=True)
@test_util.run_without_tensor_float_32
def testConcurrentExecutesWithoutError(self):
matrix_shape = [5, 5]
seed = [42, 24]
matrix1 = stateless_random_ops.stateless_random_normal(
shape=matrix_shape, seed=seed)
matrix2 = stateless_random_ops.stateless_random_normal(
shape=matrix_shape, seed=seed)
self.assertAllEqual(matrix1, matrix2)
square1 = math_ops.matmul(matrix1, matrix1)
square2 = math_ops.matmul(matrix2, matrix2)
sqrt1 = gen_linalg_ops.matrix_square_root(square1)
sqrt2 = gen_linalg_ops.matrix_square_root(square2)
all_ops = [sqrt1, sqrt2]
sqrt = self.evaluate(all_ops)
self.assertAllClose(sqrt[0], sqrt[1])
if __name__ == "__main__":
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@kernel_tests@linalg@matrix_square_root_op_test.py@.PATH_END.py
|
{
"filename": "concatlogs.py",
"repo_name": "itseez/opencv",
"repo_path": "opencv_extracted/opencv-master/modules/ts/misc/concatlogs.py",
"type": "Python"
}
|
#!/usr/bin/env python
""" Combines multiple uniform HTML documents with tables into a single one.
HTML header from the first document will be used in the output document. Largest
`<tbody>...</tbody>` part from each document will be joined together.
"""
from optparse import OptionParser
import glob, sys, os, re
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-o", "--output", dest="output", help="output file name", metavar="FILENAME", default=None)
(options, args) = parser.parse_args()
if not options.output:
sys.stderr.write("Error: output file name is not provided")
exit(-1)
files = []
for arg in args:
if ("*" in arg) or ("?" in arg):
files.extend([os.path.abspath(f) for f in glob.glob(arg)])
else:
files.append(os.path.abspath(arg))
html = None
for f in sorted(files):
try:
fobj = open(f)
if not fobj:
continue
text = fobj.read()
if not html:
html = text
continue
idx1 = text.find("<tbody>") + len("<tbody>")
idx2 = html.rfind("</tbody>")
html = html[:idx2] + re.sub(r"[ \t\n\r]+", " ", text[idx1:])
except:
pass
if html:
idx1 = text.find("<title>") + len("<title>")
idx2 = html.find("</title>")
html = html[:idx1] + "OpenCV performance testing report" + html[idx2:]
open(options.output, "w").write(html)
else:
sys.stderr.write("Error: no input data")
exit(-1)
|
itseezREPO_NAMEopencvPATH_START.@opencv_extracted@opencv-master@modules@ts@misc@concatlogs.py@.PATH_END.py
|
{
"filename": "hook-regions.py",
"repo_name": "spacetelescope/jdaviz",
"repo_path": "jdaviz_extracted/jdaviz-main/standalone/hooks/hook-regions.py",
"type": "Python"
}
|
from PyInstaller.utils.hooks import collect_submodules, collect_data_files
hiddenimports = collect_submodules("regions")
# for CITATION.rst
datas = collect_data_files('regions')
|
spacetelescopeREPO_NAMEjdavizPATH_START.@jdaviz_extracted@jdaviz-main@standalone@hooks@hook-regions.py@.PATH_END.py
|
{
"filename": "Tutorial-GiRaFFE_NRPy-Basic_Equations.ipynb",
"repo_name": "zachetienne/nrpytutorial",
"repo_path": "nrpytutorial_extracted/nrpytutorial-master/in_progress-GiRaFFE_NRPy/Tutorial-GiRaFFE_NRPy-Basic_Equations.ipynb",
"type": "Jupyter Notebook"
}
|
# `GiRaFFE_NRPy`: Basic Equations
## Authors: Patrick Nelson &
In this tutorial, we introduce the basic GRFFE equations that we wish to solve.
Throughout this section and beyond, we will work in geometrized units such that $G=c=1$. As is standard in NRPy+,
* Greek indices refer to four-dimensional quantities where the zeroth component indicates temporal (time) component,
* and Latin indices refer to three-dimensional quantities. This is somewhat counterintuitive since Python always indexes its lists starting from 0. As a result, the zeroth component of three-dimensional quantities will necessarily indicate the first *spatial* direction.
As a corollary, any expressions involving mixed Greek and Latin indices will need to offset one set of indices by one: A Latin index in a four-vector will be incremented and a Greek index in a three-vector will be decremented. For further detail and examples, see [this notebook](../Tutorial-GRFFE_Equations-Cartesian.ipynb).
To describe spacetime, we will use a standard 3+1 decomposition in which the line element is given as
$$
ds^2 = -\alpha^2 dt^2 + \gamma_{ij} \left( dx^i + \beta^i dt \right) \left( dx^j + \beta^j dt \right).
$$
where $\alpha$ is the lapse function, $\beta^i$ is the shift vector, $\gamma_{ij}$ is the spatial three metric. Given two adjacent spatial hypersurfaces with coordinate times $t_0$ and $t_0+dt$, $\alpha dt$ is the proper time interval between the two hypersurfaces, $\beta^i$ represents the magnitude of the spatial coordinate shift between them, and $\gamma_{ij}$ is the spatial three metric within a hypersurface.
We will use the pure electromagnetic stress energy tensor,
$$
T^{\mu\nu}_{\rm EM} = b^2 u^{\mu} u^{\nu} + \frac{1}{2} b^2 g^{\mu\nu} - b^\mu b^\nu,
$$
where
\begin{align}
\sqrt{4\pi} b^0 = B^0_{\rm (u)} &= \frac{u_j B^j}{\alpha} \\
\sqrt{4\pi} b^i = B^i_{\rm (u)} &= \frac{B^i + (u_j B^j) u^i}{\alpha u^0},\\
\end{align}
and $u^\mu$ is the four-velocity. We will not couple this stress-energy tensor to the Einstein Tensor because the plasma we consider is extremely diffuse.
**Derivation from $F^{\mu\nu}$, including the constraints of GRFFE**
In place of the magnetic field $B^i$, we choose to evolve the vector potential $A_i$. By doing so, we guarantee that the magnetic field is divergenceless to round-off level. We also evolve the Poynting flux $\tilde{S}_i$, which in GRFFE, represents the momentum flux in the plasma, and $\psi^6 \Phi$, where $\psi^6=\sqrt{\gamma}$ and $\gamma$ is the determinant of the three metric.
We thus arrive at the following set of equations to evolve:
\begin{align}
\partial_t \tilde{S}_i + \partial_j \left( \alpha \sqrt{\gamma} T^j_{{\rm EM} i} \right) &= \frac{1}{2} \alpha \sqrt{\gamma} T^{\mu \nu}_{\rm EM} \partial_i g_{\mu \nu} \\
\partial_t [\sqrt{\gamma} \Phi] &= -\partial_j (\alpha\sqrt{\gamma}A^j - \beta^j [\sqrt{\gamma} \Phi]) - \xi \alpha [\sqrt{\gamma} \Phi] \\
\partial_t A_i &= \epsilon_{ijk} v^j B^k - \partial_i (\alpha \Phi - \beta^j A_j) \\
\end{align}
Here, the four velocity $u^\mu$ is the velocity of the plasma as measured by a normal observer. There are several possible choices of three-velocity that can be made; the ones which we will use here are the drift velocity $v^i$ and the Valencia three-velocity $\bar{v}^i$. While we have chosen to use the Valencia three-velocity in this version of `GiRaFFE`, we have also frequently made use of a relationship expressing this in terms of the drift velocity, which was used in the original `GiRaFFE`. The usefulness of this relationship to drift velocity extends beyond merely translating the original code. As discussed in [Paschalidis, et al.](https://arxiv.org/pdf/1310.3274.pdf), Sec. III.A (just above Eq. 45, with a proof in Appendix A), there is a one-parameter family of velocity definitions that fulfill the GRFFE conditions. The drift velocity sets this parameter to 0, which minimizes the Lorentz factor and *guarantees* that the four-velocity and magnetic fields are orthogonal to each other. This simplifies the form of $b^\mu$ and quantities that depend on it.
This must be taken into account in developing unit tests, because NRPy+'s GRFFE module defaults to using a definition of $b^\mu$ that does not assume that this criterion is met, while the original `GiRaFFE` code assumes this in its C2P and P2C solvers. So, if we do not guarantee that our test data fulfills this criterion, these two different routines will produce different results. We will now go through the derivation of the equation used by `GiRaFFE` from first principles to show where this extra term appears.
<font color='yellow'><b>Terrence says: What extra term?</b></font>
This is the equation used by `GiRaFFE`, pulled from Eqs. 47 and 85 of [this paper](https://arxiv.org/abs/1310.3274):
$$\tilde{S}_i = \gamma_{ij} \frac{\bar{v}^j \sqrt{\gamma}B^2}{4 \pi},$$
or $$\tilde{S}_i = \gamma_{ij} \frac{(v^j + \beta^j) \sqrt{\gamma}B^2}{4 \pi \alpha},$$
where $\bar{v}^j$ is the Valencia 3-velocity and $v^j$ is the drift velocity.
In IllinoisGRMHD (IGM), the expression used is $\tilde{S}_i = \alpha \sqrt{\gamma} T^0_{{\rm EM}i},$
where
\begin{align}
T^{\mu\nu}_{\rm EM} &= b^2 u^{\mu} u^{\nu} + \frac{1}{2} b^2 g^{\mu\nu} - b^\mu b^\nu \\
b^0 &= \frac{u_j B^j}{\sqrt{4\pi} \alpha} \\
b^i &= \frac{B^i + (u_j B^j) u^i}{\sqrt{4\pi} \alpha u^0} \\
u^i &= u^0 v^i.
\end{align}
**The above has been pulled from the C2P unit test. This is probably a better permanent home for this discussion.**
<font color='yellow'><b>Terrence says: Can we remove the above?</b></font>
We also use the Valencia three-velocity $\bar{v}^i$ and the magnetic field $B^i$ as primitives, which can be defined in terms of the conservative variables:
\begin{align}
B^i &= \epsilon^{ijk} \partial_j A_k \\
\bar{v}^i &= 4 \pi \frac{\gamma^{ij} {\tilde S}_j}{\sqrt{\gamma} B^2} \\
\end{align}
```python
import os,sys
nrpy_dir_path = os.path.join("..")
if nrpy_dir_path not in sys.path:
sys.path.append(nrpy_dir_path)
import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface
cmd.output_Jupyter_notebook_to_LaTeXed_PDF("GiRaFFE_NRPy_Tutorial")
```
|
zachetienneREPO_NAMEnrpytutorialPATH_START.@nrpytutorial_extracted@nrpytutorial-master@in_progress-GiRaFFE_NRPy@Tutorial-GiRaFFE_NRPy-Basic_Equations.ipynb@.PATH_END.py
|
{
"filename": "conftest.py",
"repo_name": "rmjarvis/TreeCorr",
"repo_path": "TreeCorr_extracted/TreeCorr-main/tests/conftest.py",
"type": "Python"
}
|
def pytest_collectstart(collector):
if hasattr(collector, 'skip_compare'):
collector.skip_compare += 'stderr',
|
rmjarvisREPO_NAMETreeCorrPATH_START.@TreeCorr_extracted@TreeCorr-main@tests@conftest.py@.PATH_END.py
|
{
"filename": "_widthsrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/choropleth/marker/line/_widthsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WidthsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="widthsrc", parent_name="choropleth.marker.line", **kwargs
):
super(WidthsrcValidator, 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@choropleth@marker@line@_widthsrc.py@.PATH_END.py
|
{
"filename": "kepler-22.ipynb",
"repo_name": "timothydmorton/isochrones",
"repo_path": "isochrones_extracted/isochrones-master/notebooks/kepler-22.ipynb",
"type": "Jupyter Notebook"
}
|
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from isochrones.dartmouth import Dartmouth_Isochrone
dar = Dartmouth_Isochrone()
from isochrones.starmodel import StarModel
```
Here, I create a stellar model object with observations of $T_{eff}$, $\log g$, and $[Fe/H]$ corresponding to properties in the Huber stellar properties catalog paper, which agree closely with the Borucki (2011) values, if not exactly.
```
mod = StarModel(dar, Teff=(5642,50), logg=(4.44,0.06), feh=(-0.27,0.08))
```
I then run an MCMC chain using the Dartmouth isochrone grids, which can predict $T_{eff}$ and $\log g$ (and $[Fe/H]$) as a function of mass, age, and $[Fe/H]$, where the likelihood surface is determined by the observations above. This gives chains of mass, age, and $[Fe/H]$ that can be used to read off distributions of other stellar properties, such as radius.
```
mod.fit_mcmc()
```
We can look at the result of these chains, which support the idea of an older, smaller star than the literature values (plotted with red vertical lines) seem to indicate. This discrepancy is pretty severe, and I'm not sure how to explain it.
```
mod.plot_samples('radius')
plt.axvline(0.979, color='r', ls='--')
mod.plot_samples('mass')
plt.axvline(0.970, color='r', ls='--')
mod.plot_samples('age')
```
Just for a sanity check, let's look directly at the Dartmouth isochrone grid (the MASTERDF dataframe), query within the literature range of mass and logg, and show the resulting effective temperatures:
```
from isochrones.dartmouth import MASTERDF
subdf = MASTERDF.query('feh > -0.31 and feh < -0.19 and M > 0.95 and M < 1 and logg > 4.42 and logg < 4.46')
plt.hist(np.array(10**subdf['logTeff']));
```
Nowhere do we see anything close to 5640 K. What is going on here?
```
mod.save_hdf('kep22.h5')
```
```
mod2 = StarModel.load_hdf('kep22.h5')
```
```
mod2.plot_samples('radius')
```
```
```
|
timothydmortonREPO_NAMEisochronesPATH_START.@isochrones_extracted@isochrones-master@notebooks@kepler-22.ipynb@.PATH_END.py
|
{
"filename": "extension.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/setuptools/py3/setuptools/extension.py",
"type": "Python"
}
|
import re
import functools
import distutils.core
import distutils.errors
import distutils.extension
from typing import TYPE_CHECKING
from .monkey import get_unpatched
def _have_cython():
"""
Return True if Cython can be imported.
"""
cython_impl = 'Cython.Distutils.build_ext'
try:
# from (cython_impl) import build_ext
__import__(cython_impl, fromlist=['build_ext']).build_ext
except Exception:
return False
return True
# for compatibility
have_pyrex = _have_cython
if TYPE_CHECKING:
# Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
_Extension = distutils.core.Extension
else:
_Extension = get_unpatched(distutils.core.Extension)
class Extension(_Extension):
"""
Describes a single extension module.
This means that all source files will be compiled into a single binary file
``<module path>.<suffix>`` (with ``<module path>`` derived from ``name`` and
``<suffix>`` defined by one of the values in
``importlib.machinery.EXTENSION_SUFFIXES``).
In the case ``.pyx`` files are passed as ``sources and`` ``Cython`` is **not**
installed in the build environment, ``setuptools`` may also try to look for the
equivalent ``.cpp`` or ``.c`` files.
:arg str name:
the full name of the extension, including any packages -- ie.
*not* a filename or pathname, but Python dotted name
:arg list[str] sources:
list of source filenames, relative to the distribution root
(where the setup script lives), in Unix form (slash-separated)
for portability. Source files may be C, C++, SWIG (.i),
platform-specific resource files, or whatever else is recognized
by the "build_ext" command as source for a Python extension.
:keyword list[str] include_dirs:
list of directories to search for C/C++ header files (in Unix
form for portability)
:keyword list[tuple[str, str|None]] define_macros:
list of macros to define; each macro is defined using a 2-tuple:
the first item corresponding to the name of the macro and the second
item either a string with its value or None to
define it without a particular value (equivalent of "#define
FOO" in source or -DFOO on Unix C compiler command line)
:keyword list[str] undef_macros:
list of macros to undefine explicitly
:keyword list[str] library_dirs:
list of directories to search for C/C++ libraries at link time
:keyword list[str] libraries:
list of library names (not filenames or paths) to link against
:keyword list[str] runtime_library_dirs:
list of directories to search for C/C++ libraries at run time
(for shared extensions, this is when the extension is loaded).
Setting this will cause an exception during build on Windows
platforms.
:keyword list[str] extra_objects:
list of extra files to link with (eg. object files not implied
by 'sources', static library that must be explicitly specified,
binary resource files, etc.)
:keyword list[str] extra_compile_args:
any extra platform- and compiler-specific information to use
when compiling the source files in 'sources'. For platforms and
compilers where "command line" makes sense, this is typically a
list of command-line arguments, but for other platforms it could
be anything.
:keyword list[str] extra_link_args:
any extra platform- and compiler-specific information to use
when linking object files together to create the extension (or
to create a new static Python interpreter). Similar
interpretation as for 'extra_compile_args'.
:keyword list[str] export_symbols:
list of symbols to be exported from a shared extension. Not
used on all platforms, and not generally necessary for Python
extensions, which typically export exactly one symbol: "init" +
extension_name.
:keyword list[str] swig_opts:
any extra options to pass to SWIG if a source file has the .i
extension.
:keyword list[str] depends:
list of files that the extension depends on
:keyword str language:
extension language (i.e. "c", "c++", "objc"). Will be detected
from the source extensions if not provided.
:keyword bool optional:
specifies that a build failure in the extension should not abort the
build process, but simply not install the failing extension.
:keyword bool py_limited_api:
opt-in flag for the usage of :doc:`Python's limited API <python:c-api/stable>`.
:raises setuptools.errors.PlatformError: if 'runtime_library_dirs' is
specified on Windows. (since v63)
"""
def __init__(self, name, sources, *args, **kw):
# The *args is needed for compatibility as calls may use positional
# arguments. py_limited_api may be set only via keyword.
self.py_limited_api = kw.pop("py_limited_api", False)
super().__init__(name, sources, *args, **kw)
def _convert_pyx_sources_to_lang(self):
"""
Replace sources with .pyx extensions to sources with the target
language extension. This mechanism allows language authors to supply
pre-converted sources but to prefer the .pyx sources.
"""
if _have_cython():
# the build has Cython, so allow it to compile the .pyx files
return
lang = self.language or ''
target_ext = '.cpp' if lang.lower() == 'c++' else '.c'
sub = functools.partial(re.sub, '.pyx$', target_ext)
self.sources = list(map(sub, self.sources))
class Library(Extension):
"""Just like a regular Extension, but built as a library instead"""
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@setuptools@py3@setuptools@extension.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "ggalloni/cobaya",
"repo_path": "cobaya_extracted/cobaya-master/cobaya/theories/cosmo/__init__.py",
"type": "Python"
}
|
from .boltzmannbase import BoltzmannBase, PowerSpectrumInterpolator
|
ggalloniREPO_NAMEcobayaPATH_START.@cobaya_extracted@cobaya-master@cobaya@theories@cosmo@__init__.py@.PATH_END.py
|
{
"filename": "thHeat.py",
"repo_name": "magic-sph/magic",
"repo_path": "magic_extracted/magic-master/python/magic/thHeat.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
from magic import scanDir, MagicSetup, Movie, chebgrid, rderavg, AvgField
import os, pickle
try:
from scipy.integrate import simps
except:
from scipy.integrate import simpson as simps
json_model = {'phys_params': [],
'time_series': { 'heat': ['topnuss', 'botnuss']},
'spectra': {},
'radial_profiles': {}}
class ThetaHeat(MagicSetup):
r"""
This class allows to conduct some analysis of the latitudinal
variation of the heat transfer. It relies on the movie files
:ref:`ATmov.TAG <secMovieFile>` and :ref:`AHF_mov.TAG <secMovieFile>`.
As it's a bit time-consuming, the calculations are stored in a
python.pickle file to quicken future usage of the data.
Since this function is supposed to use time-averaged quantities, the usual
procedure is first to define the initial averaging time using
:py:class:`AvgField <magic.AvgField>`: (this needs to be done only once)
>>> a = AvgField(tstart=2.58)
Once the ``tInitAvg`` file exists, the latitudinal heat transfer analysis
can be done using:
>>> # For chunk-averages over 10^\degree in the polar and equatorial regions.
>>> th = ThetaHeat(angle=10)
>>> # Formatted output
>>> print(th)
"""
def __init__(self, iplot=False, angle=10, pickleName='thHeat.pickle',
quiet=False):
"""
:param iplot: a boolean to toggle the plots on/off
:type iplot: bool
:param angle: the integration angle in degrees
:type angle: float
:pickleName: calculations a
:param quiet: a boolean to switch on/off verbose outputs
:type quiet: bool
"""
angle = angle * np.pi / 180
if os.path.exists('tInitAvg'):
f = open('tInitAvg', 'r')
tstart = float(f.readline())
f.close()
logFiles = scanDir('log.*')
tags = []
for lg in logFiles:
nml = MagicSetup(quiet=True, nml=lg)
if nml.start_time > tstart:
if os.path.exists('ATmov.{}'.format(nml.tag)):
tags.append(nml.tag)
if len(tags) == 0:
tags = [nml.tag]
if not quiet:
print('Only 1 tag: {}'.format(tags))
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
a = AvgField(model=json_model, write=False)
self.nuss = 0.5 * (a.topnuss_av+a.botnuss_av)
else:
logFiles = scanDir('log.*')
if len(logFiles) > 0:
MagicSetup.__init__(self, quiet=True, nml=logFiles[-1])
if not os.path.exists(pickleName):
# reading ATmov
k = 0
for tag in tags:
f = 'ATmov.{}'.format(tag)
if os.path.exists(f):
if k == 0:
m = Movie(file=f, iplot=False)
print(f)
else:
m += Movie(file=f, iplot=False)
print(f)
k += 1
# reading AHF_mov
kk = 0
for tag in tags:
f = 'AHF_mov.{}'.format(tag)
if os.path.exists(f):
if kk == 0:
m1 = Movie(file=f, iplot=False)
print(f)
else:
m1 += Movie(file=f, iplot=False)
print(f)
kk += 1
self.tempmean = m.data[0, ...].mean(axis=0)
self.tempstd = m.data[0, ...].std(axis=0)
self.colat = m.theta
if kk > 0: # i.e. at least one AHF_mov file has been found
self.fluxmean = m1.data[0, ...].mean(axis=0)
self.fluxstd = m1.data[0, ...].std(axis=0)
else:
self.fluxmean = rderavg(self.tempmean, m.radius, exclude=False)
self.fluxstd = rderavg(self.tempstd, m.radius, exclude=False)
# Pickle saving
try:
with open(pickleName, 'wb') as f:
pickle.dump([self.colat, self.tempmean, self.tempstd,
self.fluxmean, self.fluxstd], f)
except PermissionError:
print('No write access in the current directory')
else:
with open(pickleName, 'rb') as f:
dat = pickle.load(f)
if len(dat) == 5:
self.colat, self.tempmean, self.tempstd, \
self.fluxmean, self.fluxstd = dat
else:
self.colat, self.tempmean, self.fluxmean = dat
self.fluxstd = np.zeros_like(self.fluxmean)
self.tempstd = np.zeros_like(self.fluxmean)
self.ri = self.radratio/(1.-self.radratio)
self.ro = 1./(1.-self.radratio)
self.ntheta, self.nr = self.tempmean.shape
if not hasattr(self, 'radial_scheme') or \
(self.radial_scheme=='CHEB' and self.l_newmap==False):
# Redefine to get double precision
self.radius = chebgrid(self.nr-1, self.ro, self.ri)
else:
self.radius = m.radius
th2D = np.zeros((self.ntheta, self.nr), dtype=self.radius.dtype)
#self.colat = np.linspace(0., np.pi, self.ntheta)
for i in range(self.ntheta):
th2D[i, :] = self.colat[i]
self.temprmmean = 0.5*simps(self.tempmean*np.sin(th2D), x=th2D, axis=0)
self.temprmstd = 0.5*simps(self.tempstd*np.sin(th2D), x=th2D, axis=0)
sinTh = np.sin(self.colat)
# Conducting temperature profile (Boussinesq only!)
if self.ktops == 1 and self.kbots == 1:
self.tcond = self.ri*self.ro/self.radius-self.ri+self.temprmmean[0]
self.fcond = -self.ri*self.ro/self.radius**2
elif self.ktops == 1 and self.kbots != 1:
qbot = -1.
ttop = self.temprmmean[0]
self.fcond = -self.ri**2 / self.radius**2
self.tcond = self.ri**2/self.radius - self.ri**2/self.ro + ttop
else:
if os.path.exists('pscond.dat'):
dat = np.loadtxt('pscond.dat')
self.tcond = dat[:, 1]
self.fcond = rderavg(self.tcond, self.radius)
self.nusstopmean = self.fluxmean[:, 0] / self.fcond[0]
self.nussbotmean = self.fluxmean[:, -1] / self.fcond[-1]
self.nusstopstd = self.fluxstd[:, 0] / self.fcond[0]
self.nussbotstd = self.fluxstd[:, -1] / self.fcond[-1]
# Close to the equator
mask2D = (th2D>=np.pi/2.-angle/2.)*(th2D<=np.pi/2+angle/2.)
mask = (self.colat>=np.pi/2.-angle/2.)*(self.colat<=np.pi/2+angle/2.)
fac = 1./simps(sinTh[mask], x=self.colat[mask])
self.nussBotEq = fac*simps(self.nussbotmean[mask]*sinTh[mask], x=self.colat[mask])
self.nussTopEq = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
sinC = sinTh.copy()
sinC[~mask] = 0.
fac = 1./simps(sinC, x=self.colat)
tempC = self.tempmean.copy()
tempC[~mask2D] = 0.
self.tempEqmean = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
tempC = self.tempstd.copy()
tempC[~mask2D] = 0.
self.tempEqstd = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
dtempEq = rderavg(self.tempEqmean, self.radius)
self.betaEq = dtempEq[len(dtempEq)//2]
# 45\deg inclination
mask2D = (th2D>=np.pi/4.-angle/2.)*(th2D<=np.pi/4+angle/2.)
mask = (self.colat>=np.pi/4.-angle/2.)*(self.colat<=np.pi/4+angle/2.)
fac = 1./simps(np.sin(self.colat[mask]), x=self.colat[mask])
nussBot45NH = fac*simps(self.nussbotmean[mask]*sinTh[mask], x=self.colat[mask])
nussTop45NH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
sinC = sinTh.copy()
sinC[~mask] = 0.
fac = 1./simps(sinC, x=self.colat)
tempC = self.tempmean.copy()
tempC[~mask2D] = 0.
temp45NH = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
mask2D = (th2D>=3.*np.pi/4.-angle/2.)*(th2D<=3.*np.pi/4+angle/2.)
mask = (self.colat>=3.*np.pi/4.-angle/2.)*(self.colat<=3.*np.pi/4+angle/2.)
fac = 1./simps(np.sin(self.colat[mask]), x=self.colat[mask])
nussBot45SH = fac*simps(self.nussbotmean[mask]*sinTh[mask], x=self.colat[mask])
nussTop45SH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
sinC = sinTh.copy()
sinC[~mask] = 0.
fac = 1./simps(sinC, x=self.colat)
tempC = self.tempmean.copy()
tempC[~mask2D] = 0.
temp45SH = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
self.nussTop45 = 0.5*(nussTop45NH+nussTop45SH)
self.nussBot45 = 0.5*(nussBot45NH+nussBot45SH)
self.temp45 = 0.5*(temp45NH+temp45SH)
dtemp45 = rderavg(self.temp45, self.radius)
self.beta45 = dtemp45[len(dtemp45)//2]
# Polar regions
mask2D = (th2D<=angle/2.)
mask = (self.colat<=angle/2.)
fac = 1./simps(np.sin(self.colat[mask]), x=self.colat[mask])
nussBotPoNH = fac*simps(self.nussbotmean[mask]*sinTh[mask], x=self.colat[mask])
nussTopPoNH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
sinC = sinTh.copy()
sinC[~mask] = 0.
fac = 1./simps(sinC, x=self.colat)
tempC = self.tempmean.copy()
tempC[~mask2D] = 0.
tempPolNHmean = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
tempC = self.tempstd.copy()
tempC[~mask2D] = 0.
tempPolNHstd = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
mask2D = (th2D>=np.pi-angle/2.)
mask = (self.colat>=np.pi-angle/2.)
fac = 1./simps(np.sin(self.colat[mask]), self.colat[mask])
nussBotPoSH = fac*simps(self.nussbotmean[mask]*sinTh[mask], x=self.colat[mask])
nussTopPoSH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
sinC = sinTh.copy()
sinC[~mask] = 0.
fac = 1./simps(sinC, x=self.colat)
tempC = self.tempmean.copy()
tempC[~mask2D] = 0.
tempPolSHmean = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
tempC = self.tempstd.copy()
tempC[~mask2D] = 0.
tempPolSHstd = fac*simps(tempC*np.sin(th2D), x=th2D, axis=0)
self.nussBotPo = 0.5*(nussBotPoNH+nussBotPoSH)
self.nussTopPo = 0.5*(nussTopPoNH+nussTopPoSH)
self.tempPolmean = 0.5*(tempPolNHmean+tempPolSHmean)
self.tempPolstd= 0.5*(tempPolNHstd+tempPolSHstd)
dtempPol = rderavg(self.tempPolmean, self.radius)
self.betaPol = dtempPol[len(dtempPol)//2]
# Inside and outside TC
angleTC = np.arcsin(self.ri/self.ro)
mask2D = (th2D<=angleTC)
mask = (self.colat<=angleTC)
fac = 1./simps(np.sin(self.colat[mask]), x=self.colat[mask])
nussITC_NH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
mask2D = (th2D>=np.pi-angleTC)
mask = (self.colat>=np.pi-angleTC)
fac = 1./simps(np.sin(self.colat[mask]), self.colat[mask])
nussITC_SH = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
self.nussITC = 0.5*(nussITC_NH+nussITC_SH)
mask2D = (th2D>=angleTC)*(th2D<=np.pi-angleTC)
mask = (self.colat>=angleTC)*(self.colat<=np.pi-angleTC)
fac = 1./simps(sinTh[mask], self.colat[mask])
self.nussOTC = fac*simps(self.nusstopmean[mask]*sinTh[mask], x=self.colat[mask])
if iplot:
self.plot()
if not quiet:
print(self)
def __str__(self):
"""
Formatted outputs
>>> th = ThetaHeat()
>>> print(th)
"""
if self.ek == -1:
ek = 0. # to avoid the -1 for the non-rotating cases
else:
ek = self.ek
if self.mode == 0:
st ='{:9.3e}{:9.2e}{:9.2e}{:9.2e}{:5.2f}'.format(self.ra, ek,
self.pr, self.prmag, self.strat)
else:
st = '{:.3e}{:12.5e}{:5.2f}{:6.2f}{:6.2f}'.format(self.ra, ek,
self.strat, self.pr, self.radratio)
st += '{:12.5e}'.format(self.nuss)
st += '{:12.5e}{:12.5e}{:12.5e}'.format(self.nussBotEq, self.nussBot45,
self.nussBotPo)
st += '{:12.5e}{:12.5e}{:12.5e}'.format(self.nussTopEq, self.nussTop45,
self.nussTopPo)
st += ' {:12.5e} {:12.5e} {:12.5e}'.format(self.betaEq, self.beta45,
self.betaPol)
return st
def plot(self):
"""
Plotting function
"""
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(self.radius, self.tcond-self.tcond[0], 'k--', label='Cond. temp.')
ax.fill_between(self.radius,
self.temprmmean-self.temprmstd-self.temprmmean[0],
self.temprmmean+self.temprmstd-self.temprmmean[0],
alpha=0.2)
ax.plot(self.radius, self.temprmmean-self.temprmmean[0], ls='-', c='C0',
lw=2, label='Mean temp.')
ax.fill_between(self.radius,
self.tempEqmean-self.tempEqstd-self.temprmmean[0],
self.tempEqmean+self.tempEqstd-self.temprmmean[0],
alpha=0.2)
ax.plot(self.radius, self.tempEqmean-self.temprmmean[0],
ls='-.', c='C1', lw=2, label='Temp. equat')
ax.fill_between(self.radius,
self.tempPolmean-self.tempPolstd-self.temprmmean[0],
self.tempPolmean+self.tempPolstd-self.temprmmean[0],
alpha=0.2)
ax.plot(self.radius, self.tempPolmean-self.temprmmean[0],
ls='--', c='C2', lw=2, label='Temp. Pol')
ax.set_xlim(self.ri, self.ro)
ax.set_ylim(0., 1.)
ax.set_ylabel('T')
ax.set_xlabel('r')
ax.legend(loc='upper right', frameon=False)
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.fill_between(self.colat*180./np.pi, self.nusstopmean-self.nusstopstd,
self.nusstopmean+self.nusstopstd, alpha=0.2)
ax1.plot(self.colat*180./np.pi, self.nusstopmean, ls='-', color='C0',
lw=2, label='Top Nu')
ax1.fill_between(self.colat*180./np.pi, self.nussbotmean-self.nussbotstd,
self.nussbotmean+self.nussbotstd, alpha=0.2)
ax1.plot(self.colat*180./np.pi, self.nussbotmean, ls='--', c='C1',
lw=2, label='Bot Nu')
ax1.set_xlim(0., 180.)
ax1.set_ylabel('Nu')
ax1.set_xlabel('Theta')
ax1.legend(loc='upper right', frameon=False)
ax1.axvline(180./np.pi*np.arcsin(self.ri/self.ro), color='k', linestyle='--')
ax1.axvline(180-180./np.pi*np.arcsin(self.ri/self.ro), color='k', linestyle='--')
if __name__ == '__main__':
t = ThetaHeat(iplot=True)
plt.show()
|
magic-sphREPO_NAMEmagicPATH_START.@magic_extracted@magic-master@python@magic@thHeat.py@.PATH_END.py
|
{
"filename": "_legendrank.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/histogram2dcontour/_legendrank.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LegendrankValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="legendrank", parent_name="histogram2dcontour", **kwargs
):
super(LegendrankValidator, 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@histogram2dcontour@_legendrank.py@.PATH_END.py
|
{
"filename": "like_bmon.py",
"repo_name": "ledatelescope/bifrost",
"repo_path": "bifrost_extracted/bifrost-master/tools/like_bmon.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# Copyright (c) 2017-2023, The Bifrost Authors. All rights reserved.
# Copyright (c) 2017-2023, The University of New Mexico. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of The Bifrost Authors nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import sys
import glob
import time
import curses
import socket
import argparse
import traceback
from datetime import datetime
from io import StringIO
os.environ['VMA_TRACELEVEL'] = '0'
from bifrost.proclog import PROCLOG_DIR, load_by_pid
from bifrost import telemetry
telemetry.track_script()
BIFROST_STATS_BASE_DIR = PROCLOG_DIR
def get_transmit_receive():
"""
Read in the /dev/bifrost ProcLog data and return block-level information
about udp* blocks.
"""
## Find all running processes
pidDirs = glob.glob(os.path.join(BIFROST_STATS_BASE_DIR, '*'))
pidDirs.sort()
## Load the data
blockList = {}
for pidDir in pidDirs:
pid = int(os.path.basename(pidDir), 10)
contents = load_by_pid(pid)
for block in contents.keys():
if block[:3] != 'udp':
continue
t = time.time()
try:
log = contents[block]['stats']
good = log['ngood_bytes']
missing = log['nmissing_bytes']
invalid = log['ninvalid_bytes']
late = log['nlate_bytes']
nvalid = log['nvalid']
except KeyError:
good, missing, invalid, late, nvalid = 0, 0, 0, 0, 0
blockList[f"{pid}-{block}"] = {'pid': pid, 'name':block,
'time':t,
'good': good, 'missing': missing,
'invalid': invalid, 'late': late,
'nvalid': nvalid}
return blockList
def get_command_line(pid):
"""
Given a PID, use the /proc interface to get the full command line for
the process. Return an empty string if the PID doesn't have an entry in
/proc.
"""
cmd = ''
try:
with open(f"/proc/{pid}/cmdline", 'r') as fh:
cmd = fh.read()
cmd = cmd.replace('\0', ' ')
except IOError:
pass
return cmd
def get_statistics(blockList, prevList):
"""
Given a list of running blocks and a previous version of that, compute
basic statistics for the UDP blocks.
"""
# Loop over the blocks to find udp_capture and udp_transmit blocks
output = {'updated': datetime.now()}
for block in blockList:
if block.find('udp_capture') != -1 \
or block.find('udp_sniffer') != -1 \
or block.find('udp_verbs_capture') != -1:
## udp_capture is RX
good = True
type = 'rx'
curr = blockList[block]
try:
prev = prevList[block]
except KeyError:
prev = curr
elif block.find('udp_transmit') != -1:
## udp_transmit is TX
good = True
type = 'tx'
curr = blockList[block]
try:
prev = prevList[block]
except KeyError:
prev = curr
else:
## Other is not relevant
good = False
## Skip over irrelevant blocks
if not good:
continue
## PID
pid = curr['pid']
## Computed statistics - rates
try:
drate = (curr['good' ] - prev['good' ]) / (curr['time'] - prev['time'])
prate = (curr['nvalid'] - prev['nvalid']) / (curr['time'] - prev['time'])
except ZeroDivisionError:
drate = 0.0
prate = 0.0
## Computed statistics - packet loss - global
try:
gloss = 100.0*curr['missing']/(curr['good'] + curr['missing'])
except ZeroDivisionError:
gloss = 0.0
## Computed statistics - packet loss - current
try:
closs = 100.0*(curr['missing']-prev['missing'])/(curr['missing']-prev['missing']+curr['good']-prev['good'])
except ZeroDivisionError:
closs = 0.0
## Save
### Setup
try:
output[pid][type]
except KeyError:
output[pid] = {}
output[pid]['rx' ] = {'good':0, 'missing':0, 'invalid':0, 'late':0, 'drate':0.0, 'prate':0.0, 'gloss':0.0, 'closs':0.0}
output[pid]['tx' ] = {'good':0, 'missing':0, 'invalid':0, 'late':0, 'drate':0.0, 'prate':0.0, 'gloss':0.0, 'closs':0.0}
output[pid]['cmd'] = get_command_line(pid)
### Actual data
output[pid][type]['good' ] += curr['good' ]
output[pid][type]['missing'] += curr['missing']
output[pid][type]['invalid'] += curr['invalid']
output[pid][type]['late' ] += curr['late' ]
output[pid][type]['drate' ] += max([0.0, drate])
output[pid][type]['prate' ] += max([0.0, prate])
output[pid][type]['gloss' ] = max([output[pid][type]['gloss' ], min([gloss, 100.0])])
output[pid][type]['closs' ] = max([output[pid][type]['closs' ], min([closs, 100.0])])
# Done
return output
def _set_units(value):
"""
Convert a value in bytes so a human-readable format with units.
"""
if value > 1024.0**3:
value = value / 1024.0**3
unit = 'GB'
elif value > 1024.0**2:
value = value / 1024.0**2
unit = 'MB'
elif value > 1024.0**1:
value = value / 1024.0*1
unit = 'kB'
else:
unit = ' B'
return value, unit
def _add_line(screen, y, x, string, *args):
"""
Helper function for curses to add a line, clear the line to the end of
the screen, and update the line number counter.
"""
screen.addstr(y, x, string, *args)
screen.clrtoeol()
return y + 1
_REDRAW_INTERVAL_SEC = 0.2
def main(args):
hostname = socket.gethostname()
blockList = get_transmit_receive()
order = sorted([blockList[key]['pid'] for key in blockList])
order = set(order)
nPID = len(order)
scr = curses.initscr()
curses.noecho()
curses.cbreak()
scr.keypad(1)
scr.nodelay(1)
size = scr.getmaxyx()
std = curses.A_NORMAL
rev = curses.A_REVERSE
poll_interval = 1.0
tLastPoll = 0.0
try:
sel = 0
off = 0
while True:
t = time.time()
## Interact with the user
c = scr.getch()
curses.flushinp()
if c == ord('q'):
break
elif c == curses.KEY_UP:
sel -= 1
elif c == curses.KEY_DOWN:
sel += 1
elif c == curses.KEY_LEFT:
off -= 8
elif c == curses.KEY_RIGHT:
off += 8
## Find the current selected process and see if it has changed
newSel = min([nPID-1, max([0, sel])])
if newSel != sel:
tLastPoll = 0.0
sel = newSel
## Do we need to poll the system again?
if t-tLastPoll > poll_interval:
## Save what we had before
prevList = blockList
## Find all running processes
pidDirs = glob.glob(os.path.join(BIFROST_STATS_BASE_DIR, '*'))
pidDirs.sort()
## Load the data
blockList = get_transmit_receive()
## Sort
order = sorted([blockList[key]['pid'] for key in blockList])
order = list(set(order))
nPID = len(order)
## Stats
stats = get_statistics(blockList, prevList)
## Mark
tLastPoll = time.time()
## Clear
act = None
## For sel to be valid - this takes care of any changes between when
## we get what to select and when we polled the bifrost logs
sel = min([nPID-1, sel])
## Deal with more pipelines than there is screen space by skipping
## over some at the beginning of the list
to_skip = 0
if sel > size[0] - 13:
to_skip = sel - size[0] + 13
## Display
k = 0
### General - selected
try:
output = ' PID: %i on %s' % (order[sel], hostname)
except IndexError:
output = ' PID: n/a on %s' % (hostname,)
output += ' '*(size[1]-len(output)-len(os.path.basename(__file__))-1)
output += os.path.basename(__file__)+' '
output += '\n'
k = _add_line(scr, k, 0, output, std)
### General - header
k = _add_line(scr, k, 0, ' ', std)
output = '%7s %9s %6s %9s %6s' % ('PID', 'RX Rate', 'RX #/s', 'TX Rate', 'TX #/s')
output += ' '*(size[1]-len(output))
output += '\n'
k = _add_line(scr, k, 0, output, rev)
### Data
for i,o in enumerate(order):
if i < to_skip:
continue
curr = stats[o]
if o == order[sel]:
act = curr
drateR, prateR = curr['rx']['drate'], curr['rx']['prate']
drateR, drateuR = _set_units(drateR)
drateT, prateT = curr['tx']['drate'], curr['tx']['prate']
drateT, drateuT = _set_units(drateT)
output = '%7i %7.2f%2s %6i %7.2f%2s %6i\n' % (o, drateR, drateuR, prateR, drateT, drateuT, prateT)
try:
if o == order[sel]:
sty = std|curses.A_BOLD
else:
sty = std
except IndexError:
sty = std
k = _add_line(scr, k, 0, output, sty)
if k > size[0]-10:
break
while k < size[0]-9:
output = ' '
k = _add_line(scr, k, 0, output, std)
### Details of selected
output = 'Details - %8s %19s %19s' % (stats['updated'].strftime("%H:%M:%S"), 'RX', 'TX')
output += ' '*(size[1]-len(output))
output += '\n'
k = _add_line(scr, k, 0, output, rev)
if act is not None:
off = min([max([0, len(act['cmd'])-size[1]+23]), max([0, off])])
output = 'Good: %18iB %18iB\n' % (act['rx']['good' ], act['tx']['good' ])
k = _add_line(scr, k, 0, output, std)
output = 'Missing: %18iB %18iB\n' % (act['rx']['missing'], act['tx']['missing'])
k = _add_line(scr, k, 0, output, std)
output = 'Invalid: %18iB %18iB\n' % (act['rx']['invalid'], act['tx']['invalid'])
k = _add_line(scr, k, 0, output, std)
output = 'Late: %18iB %18iB\n' % (act['rx']['late' ], act['tx']['late' ])
k = _add_line(scr, k, 0, output, std)
output = 'Global Missing: %18.2f%% %18.2f%%\n' % (act['rx']['gloss' ], act['tx']['gloss' ])
k = _add_line(scr, k, 0, output, std)
output = 'Current Missing: %18.2f%% %18.2f%%\n' % (act['rx']['closs' ], act['tx']['closs' ])
k = _add_line(scr, k, 0, output, std)
output = 'Command: %s' % act['cmd'][off:]
k = _add_line(scr, k, 0, output[:size[1]], std)
### Clear to the bottom
scr.clrtobot()
### Refresh
scr.refresh()
## Sleep
time.sleep(_REDRAW_INTERVAL_SEC)
except KeyboardInterrupt:
pass
except Exception as error:
exc_type, exc_value, exc_traceback = sys.exc_info()
fileObject = StringIO()
traceback.print_tb(exc_traceback, file=fileObject)
tbString = fileObject.getvalue()
fileObject.close()
scr.keypad(0)
curses.echo()
curses.nocbreak()
curses.endwin()
try:
print("%s: failed with %s at line %i" % (os.path.basename(__file__), str(error), exc_traceback.tb_lineno))
for line in tbString.split('\n'):
print(line)
except NameError:
pass
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Monitor the packets capture/transmit status of Bifrost pipelines',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
args = parser.parse_args()
main(args)
|
ledatelescopeREPO_NAMEbifrostPATH_START.@bifrost_extracted@bifrost-master@tools@like_bmon.py@.PATH_END.py
|
{
"filename": "test_detrender.py",
"repo_name": "konkolyseismolab/autoeap",
"repo_path": "autoeap_extracted/autoeap-master/tests/test_detrender.py",
"type": "Python"
}
|
import numpy as np
from autoeap.detrender import detrend_wrt_PDM
from numpy.testing import assert_array_almost_equal
import os
PACKAGEDIR = os.path.abspath(os.path.dirname(__file__))
def test_detrender():
time,flux,flux_err = np.genfromtxt(os.path.join(PACKAGEDIR,"EPIC220198696_c8_autoEAP.lc"),skip_header=1,unpack=True)
corr_flux = detrend_wrt_PDM(time,flux,flux_err)
corr_flux_orig = np.genfromtxt(os.path.join(PACKAGEDIR,"EPIC220198696_c8_autoEAP.lc_corr"))
assert_array_almost_equal(corr_flux_orig,corr_flux)
|
konkolyseismolabREPO_NAMEautoeapPATH_START.@autoeap_extracted@autoeap-master@tests@test_detrender.py@.PATH_END.py
|
{
"filename": "_threshold.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/indicator/gauge/_threshold.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Threshold(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "indicator.gauge"
_path_str = "indicator.gauge.threshold"
_valid_props = {"line", "thickness", "value"}
# line
# ----
@property
def line(self):
"""
The 'line' property is an instance of Line
that may be specified as:
- An instance of :class:`plotly.graph_objs.indicator.gauge.threshold.Line`
- A dict of string/value properties that will be passed
to the Line constructor
Supported dict properties:
color
Sets the color of the threshold line.
width
Sets the width (in px) of the threshold line.
Returns
-------
plotly.graph_objs.indicator.gauge.threshold.Line
"""
return self["line"]
@line.setter
def line(self, val):
self["line"] = val
# thickness
# ---------
@property
def thickness(self):
"""
Sets the thickness of the threshold line as a fraction of the
thickness of the gauge.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float
"""
return self["thickness"]
@thickness.setter
def thickness(self, val):
self["thickness"] = val
# value
# -----
@property
def value(self):
"""
Sets a treshold value drawn as a line.
The 'value' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["value"]
@value.setter
def value(self, val):
self["value"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
line
:class:`plotly.graph_objects.indicator.gauge.threshold.
Line` instance or dict with compatible properties
thickness
Sets the thickness of the threshold line as a fraction
of the thickness of the gauge.
value
Sets a treshold value drawn as a line.
"""
def __init__(self, arg=None, line=None, thickness=None, value=None, **kwargs):
"""
Construct a new Threshold object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.indicator.gauge.Threshold`
line
:class:`plotly.graph_objects.indicator.gauge.threshold.
Line` instance or dict with compatible properties
thickness
Sets the thickness of the threshold line as a fraction
of the thickness of the gauge.
value
Sets a treshold value drawn as a line.
Returns
-------
Threshold
"""
super(Threshold, self).__init__("threshold")
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.indicator.gauge.Threshold
constructor must be a dict or
an instance of :class:`plotly.graph_objs.indicator.gauge.Threshold`"""
)
# 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("line", None)
_v = line if line is not None else _v
if _v is not None:
self["line"] = _v
_v = arg.pop("thickness", None)
_v = thickness if thickness is not None else _v
if _v is not None:
self["thickness"] = _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@indicator@gauge@_threshold.py@.PATH_END.py
|
{
"filename": "_family.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/treemap/legendgrouptitle/font/_family.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FamilyValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self,
plotly_name="family",
parent_name="treemap.legendgrouptitle.font",
**kwargs,
):
super(FamilyValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@treemap@legendgrouptitle@font@_family.py@.PATH_END.py
|
{
"filename": "test_pca_common.py",
"repo_name": "jakevdp/wpca",
"repo_path": "wpca_extracted/wpca-master/wpca/tests/test_pca_common.py",
"type": "Python"
}
|
from wpca.tests.tools import assert_columns_allclose_upto_sign
from wpca import PCA, WPCA, EMPCA
from sklearn.decomposition import PCA as SKPCA
from sklearn.utils.estimator_checks import check_estimator
import numpy as np
from numpy.testing import assert_allclose, assert_raises
ESTIMATORS = [PCA, WPCA, EMPCA]
KWDS = {PCA: {}, WPCA: {}, EMPCA: {'random_state': 0}}
N_COMPONENTS = range(1, 6)
SHAPES = [(10, 5), (6, 10)]
rand = np.random.RandomState(42)
DATA = {shape: rand.randn(*shape) for shape in SHAPES}
def test_estimator_checks():
for Estimator in ESTIMATORS:
yield check_estimator, Estimator
def test_components_None():
def check_components(Estimator, shape):
X = DATA[shape]
pca = Estimator(**KWDS[Estimator]).fit(X)
skpca = SKPCA().fit(X)
assert_columns_allclose_upto_sign(pca.components_.T,
skpca.components_.T)
for Estimator in ESTIMATORS:
for shape in SHAPES:
if shape[0] > shape[1]:
yield check_components, Estimator, shape
def test_components_vs_sklearn():
def check_components(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator]).fit(X)
skpca = SKPCA(n_components).fit(X)
assert_columns_allclose_upto_sign(pca.components_.T,
skpca.components_.T)
for Estimator in ESTIMATORS:
for shape in SHAPES:
for n_components in N_COMPONENTS:
yield check_components, Estimator, n_components, shape
def test_explained_variance_vs_sklearn():
def check_explained_variance(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator]).fit(X)
skpca = SKPCA(n_components).fit(X)
assert_allclose(pca.explained_variance_,
skpca.explained_variance_)
assert_allclose(pca.explained_variance_ratio_,
skpca.explained_variance_ratio_)
for Estimator in ESTIMATORS:
for shape in SHAPES:
for n_components in N_COMPONENTS:
yield check_explained_variance, Estimator, n_components, shape
def test_transform_vs_sklearn():
def check_transform(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator])
skpca = SKPCA(n_components)
Y = pca.fit_transform(X)
Ysk = skpca.fit_transform(X)
assert_columns_allclose_upto_sign(Y, Ysk)
for Estimator in ESTIMATORS:
for shape in SHAPES:
for n_components in N_COMPONENTS:
yield check_transform, Estimator, n_components, shape
def test_transform_vs_fit_transform():
def check_transform(Estimator, n_components, shape):
X = DATA[shape]
Y1 = Estimator(n_components, **KWDS[Estimator]).fit(X).transform(X)
Y2 = Estimator(n_components, **KWDS[Estimator]).fit_transform(X)
assert_columns_allclose_upto_sign(Y1, Y2)
for Estimator in ESTIMATORS:
for shape in SHAPES:
for n_components in N_COMPONENTS:
yield check_transform, Estimator, n_components, shape
def test_pca_reconstruct():
def check_reconstruct(Estimator, shape):
X = DATA[shape]
pca = Estimator(n_components=min(shape), **KWDS[Estimator])
assert_allclose(X, pca.fit(X).reconstruct(X))
assert_allclose(X, pca.fit_reconstruct(X))
for Estimator in ESTIMATORS:
for shape in SHAPES:
yield check_reconstruct, Estimator, shape
def test_bad_inputs():
rand = np.random.RandomState(42)
X = rand.rand(10, 3)
bad = (X > 0.8)
def check_bad_inputs(Estimator, bad_val):
X[bad] = bad_val
pca = Estimator()
assert_raises(ValueError, pca.fit, X)
for Estimator in ESTIMATORS:
for bad_val in [np.inf, np.nan]:
yield check_bad_inputs, Estimator, bad_val
|
jakevdpREPO_NAMEwpcaPATH_START.@wpca_extracted@wpca-master@wpca@tests@test_pca_common.py@.PATH_END.py
|
{
"filename": "Intermediate-1--RheologicalParameterExploration_Functional.ipynb",
"repo_name": "jrenaud90/TidalPy",
"repo_path": "TidalPy_extracted/TidalPy-main/Demos/Intermediate-1--RheologicalParameterExploration_Functional.ipynb",
"type": "Jupyter Notebook"
}
|
# Rheological Parameter Exploration
Using TidalPy's high-level functional programming, we will see how different rheological parameters affect tidal dissipation
```python
import numpy as np
import matplotlib.pyplot as plt
# plt.style.use('dark_background')
from matplotlib.gridspec import GridSpec
from cmcrameri import cm as cmc
import ipywidgets as widgets
from ipywidgets import interact, IntSlider, FloatSlider
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf')
from TidalPy.constants import mass_solar, radius_solar, luminosity_solar, mass_earth, radius_earth, au, G
from TidalPy.toolbox import quick_tidal_dissipation
from TidalPy.utilities.conversions import days2rads
from TidalPy.utilities.numpy_helper.array_other import neg_array_for_log_plot
from TidalPy.stellar import equilibrium_insolation_williams
plt.rcParams['figure.figsize'] = [9.5 * .75, 6 * .75]
plt.rcParams.update({'font.size': 14})
%matplotlib notebook
```
C:\Users\joepr\AppData\Local\Temp\ipykernel_4236\4264696300.py:9: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`
set_matplotlib_formats('pdf')
### Planetary Properties
We will use planetary properties approximately based on the exoplanet: TRAPPIST 1e
```python
# TRAPPIST-1 (the star) Parameters [Stassun+2019, Gillon+2017]
star_lumonsity = 10**(-3.28) * luminosity_solar
star_radius = 0.1148270 * radius_solar
star_mass = 0.09077820 * mass_solar
# TRAPPIST-1e Parameters [Agol+2021, Grimm+2018]
planet_radius = 0.920 * radius_earth
planet_mass = 0.692 * mass_earth
planet_semi_major_axis = 0.02925 * au
planet_orbital_period = 6.101013
planet_orbital_frequency = days2rads(planet_orbital_period)
planet_eccentricity = 0.00510
planet_obliquity = np.radians(90. - 89.793)
# # Sun
# star_lumonsity = luminosity_solar
# star_radius = radius_solar
# star_mass = mass_solar
# # Mercury Parameters
# planet_radius = 0.3829 * radius_earth
# planet_mass = 0.0553 * mass_earth
# planet_semi_major_axis = 0.38709893 * au
# planet_orbital_period = 87.96926
# planet_orbital_frequency = days2rads(planet_orbital_period)
# planet_eccentricity = 0.20563069
# planet_obliquity = np.radians(0.0329) # [Genova+2019]
planet_moi = (2. / 5.) * planet_mass * planet_radius**2
# We will assume that only 50% of the world's volume is participating in tidal dissipation
tidal_scale = 0.5
# Calculate other properties
planet_density = planet_mass / ((4. / 3.) * np.pi * planet_radius**3)
planet_gravity = G * planet_mass / (planet_radius**2)
# Conversions
sec2_2_yr2 = (3.154e7)**2
```
## Viscosity Domain
Here we calculate tidal heating versus insolation temperature in over a domain of mantle viscosity
```python
# Setup domain
viscosity_domain = np.logspace(14., 24., 100)
shear_domain = 50.e9 * np.ones_like(viscosity_domain)
# Setup figure
fig_sync, axes_sync = plt.subplots(ncols=2, nrows=1, figsize=(9, 5))
fig_sync.tight_layout(pad=4.0)
ax_sync_heating = axes_sync[0]
ax_sync_torque = axes_sync[1]
ax_sync_heating.set(xlabel='Mantle Viscosity [Pa s]', ylabel='Tidal Heating / Insolation', yscale='log',
xscale='log', ylim=(1e-3, 1e3))
ax_sync_torque.set(xlabel='Mantle Viscosity [Pa s]', ylabel='Spin Rate Derivative [rad yr$^{-2}$]\nDashed = Negative',
yscale='log', xscale='log')
# Plot lines
ax_sync_heating.axhline(y=1e0, color='k', ls=':')
rheo_lines_heating = [ax_sync_heating.plot(viscosity_domain, viscosity_domain, 'k', label='Maxwell')[0],
ax_sync_heating.plot(viscosity_domain, viscosity_domain, 'b', label='Andrade')[0],
ax_sync_heating.plot(viscosity_domain, viscosity_domain, 'm', label='Sundberg')[0]]
rheo_lines_torque = [ax_sync_torque.plot(viscosity_domain, viscosity_domain, 'k', label='Maxwell')[0],
ax_sync_torque.plot(viscosity_domain, viscosity_domain, 'b', label='Andrade')[0],
ax_sync_torque.plot(viscosity_domain, viscosity_domain, 'm', label='Sundberg-Cooper')[0]]
rheo_lines_torque_neg = [ax_sync_torque.plot(viscosity_domain, viscosity_domain, '--k', label='Maxwell')[0],
ax_sync_torque.plot(viscosity_domain, viscosity_domain, '--b', label='Andrade')[0],
ax_sync_torque.plot(viscosity_domain, viscosity_domain, '--m', label='Sundberg')[0]]
plt.show()
def sync_rotation(voigt_compliance_offset=.2,
voigt_viscosity_offset=.02,
alpha=.333,
zeta_power=0.,
critical_period=100.,
albedo=0.3,
eccentricity_pow=np.log10(planet_eccentricity),
obliquity_deg=0.,
force_spin_sync=True,
spin_orbit_ratio=1.,
eccentricity_truncation_lvl=2,
max_tidal_order_l=2):
eccentricity = 10.**eccentricity_pow
zeta = 10.**(zeta_power)
obliquity = np.radians(obliquity_deg)
critical_freq = 2. * np.pi / (86400. * critical_period)
dissipation_data = dict()
rheology_data = {
'maxwell': (rheo_lines_heating[0], rheo_lines_torque[0], rheo_lines_torque_neg[0],
tuple()),
'andrade': (rheo_lines_heating[1], rheo_lines_torque[1], rheo_lines_torque_neg[1],
(alpha, zeta)),
'sundberg': (rheo_lines_heating[2], rheo_lines_torque[2], rheo_lines_torque_neg[2],
(voigt_compliance_offset, voigt_viscosity_offset, alpha, zeta)),
}
if force_spin_sync:
spin_period = None
else:
# The ratio is in the denominator since it is a frequency ratio.
spin_period = planet_orbital_period / spin_orbit_ratio
# Calculate insolation based on distance and eccentricity
insolation = \
equilibrium_insolation_williams(star_lumonsity, planet_semi_major_axis, albedo, planet_radius, eccentricity)
for rheo_name, (heating_line, torque_line, torque_neg_line, rheo_input) in rheology_data.items():
# Perform main tidal calculation
dissipation_data[rheo_name] = \
quick_tidal_dissipation(star_mass, planet_radius, planet_mass, planet_gravity, planet_density, planet_moi,
viscosity=viscosity_domain, shear_modulus=shear_domain, rheology=rheo_name,
complex_compliance_inputs=rheo_input, eccentricity=eccentricity, obliquity=obliquity,
orbital_period=planet_orbital_period, spin_period=spin_period,
max_tidal_order_l=max_tidal_order_l,
eccentricity_truncation_lvl=eccentricity_truncation_lvl)
spin_derivative = dissipation_data[rheo_name]['dUdO'] * (star_mass / planet_moi)
heating_line.set_ydata(dissipation_data[rheo_name]['tidal_heating'] / insolation)
# Convert spin_derivative from rad s-2 to hour per year
spin_derivative = sec2_2_yr2 * spin_derivative
spin_derivative_pos = np.copy(spin_derivative)
spin_derivative_pos[spin_derivative_pos<=0.] = np.nan
spin_derivative_neg = np.copy(spin_derivative)
spin_derivative_neg[spin_derivative_neg>0.] = np.nan
torque_line.set_ydata(np.abs(spin_derivative_pos))
torque_neg_line.set_ydata(np.abs(spin_derivative_neg))
ax_sync_heating.legend(loc='upper right', fontsize=12)
ax_sync_heating.relim()
ax_sync_heating.autoscale_view()
ax_sync_heating.set_title('$e = ' + f'{eccentricity:0.3f}' +'$')
ax_sync_torque.relim()
ax_sync_torque.autoscale_view()
ax_sync_torque.set_title('Spin / n = ' + f'${spin_orbit_ratio:0.1f}$')
fig_sync.canvas.draw_idle()
run_interactive_sync = interact(
sync_rotation,
voigt_compliance_offset=FloatSlider(value=0.2, min=0.01, max=1., step=0.05, description='$\\delta{}J$'),
voigt_viscosity_offset=FloatSlider(value=0.02, min=0.01, max=0.1, step=0.01, description='$\\delta{}\\eta$'),
alpha=FloatSlider(value=0.33, min=0.05, max=0.8, step=0.02, description='$\\alpha_{\\text{And}}$'),
zeta_power=FloatSlider(value=0., min=-5., max=5., step=0.5, description='$\\zeta_{\\text{And}}^{X}$'),
critical_period=FloatSlider(value=100., min=30., max=150., step=10, description='$P_{crit}$'),
albedo=FloatSlider(value=0.3, min=0.1, max=0.9, step=0.1, description='Albedo'),
eccentricity_pow=FloatSlider(value=-0.522879, min=-4, max=-0.09, step=0.05, description='$e^{X}$'),
obliquity_deg=FloatSlider(value=0, min=0., max=90., step=1., description='Obliquity'),
force_spin_sync=False,
spin_orbit_ratio=FloatSlider(value=1., min=0.5, max=9., step=.1, description='$\\dot{\\theta} / n$'),
eccentricity_truncation_lvl=IntSlider(value=8, min=2, max=20, step=2, description='$e$ Truncation'),
max_tidal_order_l=IntSlider(value=2, min=2, max=3, step=1, description='Max Order $l$')
)
```
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" width="900">
interactive(children=(FloatSlider(value=0.2, description='$\\delta{}J$', max=1.0, min=0.01, step=0.05), FloatS…
## Frequency Domain
```python
# Setup domain
period_domain = np.linspace(0., 150., 100)
freqeuncy_domain = days2rads(period_domain)
# Setup figure
fig_freq, axes_freq = plt.subplots(ncols=2, nrows=1, figsize=(9, 5))
# fig_freq.tight_layout()
plt.subplots_adjust(wspace=0.75)
ax_freq_love_real = axes_freq[0]
ax_freq_love_imag = ax_freq_love_real.twinx()
ax_freq_torque = axes_freq[1]
ax_freq_love_real.set(xlabel='Orbital Period [days]', ylabel='Re[$k_{2}$]', yscale='linear',
xscale='linear', ylim=(0.5, 1.5))
ax_freq_love_imag.set(xlabel='Orbital Period [days]', ylabel='-Im[$k_{2}$] (dotted)', yscale='log',
xscale='linear', ylim=(1e-3, 0))
ax_freq_torque.set(xlabel='Orbital Period [days]', ylabel='Spin Rate Derivative [rad yr$^{-2}$]\nDashed = Negative',
yscale='log', xscale='linear')
# Plot lines
rheo_lines_love_real = [ax_freq_love_real.plot(period_domain, freqeuncy_domain, 'k', label='Maxwell')[0],
ax_freq_love_real.plot(period_domain, freqeuncy_domain, 'b', label='Andrade')[0],
ax_freq_love_real.plot(period_domain, freqeuncy_domain, 'm', label='Sundberg')[0],
ax_freq_love_real.plot(period_domain, freqeuncy_domain, '--b', label='Andrade ($\\omega$)')[0]]
rheo_lines_love_imag = [ax_freq_love_imag.plot(period_domain, freqeuncy_domain, 'k', label='Maxwell', ls=':')[0],
ax_freq_love_imag.plot(period_domain, freqeuncy_domain, 'b', label='Andrade', ls=':')[0],
ax_freq_love_imag.plot(period_domain, freqeuncy_domain, 'm', label='Sundberg', ls=':')[0],
ax_freq_love_imag.plot(period_domain, freqeuncy_domain, '--b', label='Andrade ($\\omega$)', ls=':')[0]]
rheo_lines_torque = [ax_freq_torque.plot(period_domain, freqeuncy_domain, 'k', label='Maxwell')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, 'b', label='Andrade')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, 'm', label='Sundberg-Cooper')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, '--b', label='Andrade ($\\omega$)')[0]]
rheo_lines_torque_neg = [ax_freq_torque.plot(period_domain, freqeuncy_domain, '--k', label='Maxwell')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, '--b', label='Andrade')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, '--m', label='Sundberg')[0],
ax_freq_torque.plot(period_domain, freqeuncy_domain, ':b', label='Andrade ($\\omega$)')[0]]
plt.show()
def love_number_calc(voigt_compliance_offset=.2,
voigt_viscosity_offset=.02,
alpha=.333,
zeta_power=0.,
critical_period=100.,
viscosity_power=18.,
shear_power=10.,
eccentricity_pow=np.log10(planet_eccentricity),
obliquity_deg=0.,
force_spin_sync=True,
spin_period=20.,
eccentricity_truncation_lvl=2,
max_tidal_order_l=2):
eccentricity = 10.**eccentricity_pow
zeta = 10.**(zeta_power)
obliquity = np.radians(obliquity_deg)
dissipation_data = dict()
critical_freq = 2. * np.pi / (86400. * critical_period)
rheology_data = {
'maxwell': (rheo_lines_love_real[0], rheo_lines_love_imag[0], rheo_lines_torque[0], rheo_lines_torque_neg[0],
tuple()),
'andrade': (rheo_lines_love_real[1], rheo_lines_love_imag[1], rheo_lines_torque[1], rheo_lines_torque_neg[1],
(alpha, zeta)),
'sundberg': (rheo_lines_love_real[2], rheo_lines_love_imag[2], rheo_lines_torque[2], rheo_lines_torque_neg[2],
(voigt_compliance_offset, voigt_viscosity_offset, alpha, zeta)),
'andrade_freq': (rheo_lines_love_real[3], rheo_lines_love_imag[3], rheo_lines_torque[3], rheo_lines_torque_neg[3],
(alpha, zeta, critical_freq))
}
if force_spin_sync:
spin_period = None
else:
# The ratio is in the denominator since it is a frequency ratio.
spin_period = spin_period
for rheo_name, (love_real_line, love_imag_line, torque_line, torque_neg_line, rheo_input) in rheology_data.items():
# Perform main tidal calculation
dissipation_data[rheo_name] = \
quick_tidal_dissipation(star_mass, planet_radius, planet_mass, planet_gravity, planet_density, planet_moi,
viscosity=10**viscosity_power, shear_modulus=10**shear_power, rheology=rheo_name,
complex_compliance_inputs=rheo_input, eccentricity=eccentricity, obliquity=obliquity,
orbital_period=period_domain, spin_period=spin_period,
max_tidal_order_l=max_tidal_order_l,
eccentricity_truncation_lvl=eccentricity_truncation_lvl)
love_real_line.set_ydata(np.real(dissipation_data[rheo_name]['love_number_by_orderl'][2]))
love_imag_line.set_ydata(-np.imag(dissipation_data[rheo_name]['love_number_by_orderl'][2]))
spin_derivative = dissipation_data[rheo_name]['dUdO'] * (star_mass / planet_moi)
# Convert spin_derivative from rad s-2 to hour per year
spin_derivative = sec2_2_yr2 * spin_derivative
spin_derivative_pos = np.copy(spin_derivative)
spin_derivative_pos[spin_derivative_pos<=0.] = np.nan
spin_derivative_neg = np.copy(spin_derivative)
spin_derivative_neg[spin_derivative_neg>0.] = np.nan
torque_line.set_ydata(np.abs(spin_derivative_pos))
torque_neg_line.set_ydata(np.abs(spin_derivative_neg))
if spin_period is not None:
ax_freq_love_real.axvline(x=spin_period, ls=':', c='green')
ax_freq_love_real.axvline(x=89., ls=':', c='k')
ax_freq_love_real.legend(loc='lower right', fontsize=12)
ax_freq_love_real.relim()
ax_freq_love_real.autoscale_view()
ax_freq_love_real.set_title('$e = ' + f'{eccentricity:0.3f}' +'$')
ax_freq_love_imag.relim()
ax_freq_love_imag.autoscale_view()
ax_freq_love_imag.set_title('$e = ' + f'{eccentricity:0.3f}' +'$')
ax_freq_torque.relim()
ax_freq_torque.autoscale_view()
fig_freq.canvas.draw_idle()
run_interactive_love = interact(
love_number_calc,
voigt_compliance_offset=FloatSlider(value=0.2, min=0.01, max=1., step=0.05, description='$\\delta{}J$'),
voigt_viscosity_offset=FloatSlider(value=0.02, min=0.01, max=0.1, step=0.01, description='$\\delta{}\\eta$'),
alpha=FloatSlider(value=0.33, min=0.05, max=0.8, step=0.02, description='$\\alpha_{\\text{And}}$'),
zeta_power=FloatSlider(value=0., min=-5., max=5., step=0.5, description='$\\zeta_{\\text{And}}^{X}$'),
critical_period=FloatSlider(value=100., min=30., max=150., step=10, description='$P_{crit}$'),
viscosity_power=FloatSlider(value=17., min=14, max=28, step=1.0, description='$\\eta^{X}$'),
shear_power=FloatSlider(value=10., min=7., max=11., step=0.5, description='$\\mu^{X}$'),
eccentricity_pow=FloatSlider(value=np.log10(0.2), min=np.log10(0.001), max=np.log10(0.8), step=0.05,
description='$e^{X}$'),
obliquity_deg=FloatSlider(value=0, min=0., max=90., step=1., description='Obliquity'),
force_spin_sync=True,
spin_period=FloatSlider(value=59., min=1.0, max=200., step=2., description='$P_{s}$'),
eccentricity_truncation_lvl=IntSlider(value=8, min=2, max=20, step=2, description='$e$ Truncation'),
max_tidal_order_l=IntSlider(value=2, min=2, max=3, step=1, description='Max Order $l$')
)
```
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" width="900">
C:\Users\joepr\AppData\Local\Temp\ipykernel_4236\184398865.py:15: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.
ax_freq_love_imag.set(xlabel='Orbital Period [days]', ylabel='-Im[$k_{2}$] (dotted)', yscale='log',
C:\Users\joepr\AppData\Local\Temp\ipykernel_4236\184398865.py:28: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string "--b" (-> linestyle='--'). The keyword argument will take precedence.
ax_freq_love_imag.plot(period_domain, freqeuncy_domain, '--b', label='Andrade ($\\omega$)', ls=':')[0]]
interactive(children=(FloatSlider(value=0.2, description='$\\delta{}J$', max=1.0, min=0.01, step=0.05), FloatS…
## Resonance Trapping
Here we look at how material properties change how a planet can become trapped at a higher-order spin-orbit resonance
```python
# Setup scales
scale_info_list = [
((86400.)**2 * (360. / (2. * np.pi)), '[deg day$^{-2}$]'),
((2.628e+6)**2 * (360. / (2. * np.pi)), '[deg month$^{-2}$]'),
((3.154e7/2)**2 * (360. / (2. * np.pi)), '[deg semi-yr$^{-2}$]'),
((3.154e7)**2 * (360. / (2. * np.pi)), '[deg yr$^{-2}$]'),
((3.154e7 * 10)**2 * (360. / (2. * np.pi)),'[deg dayr$^{-2}$]'),
((3.154e7 * 100)**2 * (360. / (2. * np.pi)), '[deg hyr$^{-2}$]'),
((3.154e7 * 1000)**2 * (360. / (2. * np.pi)), '[deg kyr$^{-2}$]')
]
cpoints = np.linspace(-3, 3, 45)
zticks = [-3, -2, -1, 0, 1, 2, 3]
zlabels = ['$-10^{3}$', '$-10^{2}$', '$-10^{1}$',
'$\\;\\;\\;10^{0}$', '$\\;\\;\\;10^{1}$', '$\\;\\;\\;10^{2}$', '$\\;\\;\\;10^{3}$']
# Setup domain
x = eccentricity_domain = np.logspace(-2., 0., 80)
y = spin_ratio_domain = np.linspace(0.5, 6., 70)
eccen_mtx, spin_ratio_mtx = np.meshgrid(eccentricity_domain, spin_ratio_domain)
shape = eccen_mtx.shape
eccen_mtx = eccen_mtx.flatten()
spin_ratio_mtx = spin_ratio_mtx.flatten()
spin_frequency = spin_ratio_mtx * planet_orbital_frequency
# Setup figure
fig_trap = plt.figure()
gs = GridSpec(1, 2, figure=fig_trap, wspace=None, hspace=None, width_ratios=[.98, .02])
ax_trap = fig_trap.add_subplot(gs[0, 0])
ax_cb = fig_trap.add_subplot(gs[0, 1])
fig_sync.tight_layout()
ax_cb.set(ylabel='Spin Rate Derivative [rad s-2]')
plt.show()
def sync_rotation(cbar_scale_set=2,
voigt_compliance_offset=.2,
voigt_viscosity_offset=.02,
alpha=.333,
zeta_power=0.,
viscosity_power=22.,
shear_power=10.69897,
obliquity_deg=0.,
eccentricity_truncation_lvl=2,
max_tidal_order_l=2):
viscosity = 10.**viscosity_power
shear = 10.**shear_power
zeta = 10.**zeta_power
obliquity = np.radians(obliquity_deg)
rheo_input = (voigt_compliance_offset, voigt_viscosity_offset, alpha, zeta)
if max_tidal_order_l > 2 and eccentricity_truncation_lvl == 22:
raise NotImplemented
dissipation_data = \
quick_tidal_dissipation(star_mass, planet_radius, planet_mass, planet_gravity, planet_density, planet_moi,
viscosity=viscosity, shear_modulus=shear, rheology='sundberg',
complex_compliance_inputs=rheo_input, eccentricity=eccen_mtx, obliquity=obliquity,
orbital_frequency=planet_orbital_frequency, spin_frequency=spin_frequency,
max_tidal_order_l=max_tidal_order_l,
eccentricity_truncation_lvl=eccentricity_truncation_lvl)
spin_derivative = dissipation_data['dUdO'] * (star_mass / planet_moi)
scale, unit_label = scale_info_list[cbar_scale_set]
spin_derivative *= scale
dspin_dt_targ_pos, dspin_dt_targ_neg = neg_array_for_log_plot(spin_derivative)
# Make data Symmetric Log (for negative logscale plotting)
logpos = np.log10(dspin_dt_targ_pos)
logpos[logpos < 0.] = 0.
negative_index = ~np.isnan(dspin_dt_targ_neg)
logneg = np.log10(dspin_dt_targ_neg[negative_index])
logneg[logneg < 0.] = 0.
dspin_dt_targ_combo = logpos
dspin_dt_targ_combo[negative_index] = -logneg
dspin_dt_targ_combo = dspin_dt_targ_combo.reshape(shape)
ax_trap.clear()
cb_data = ax_trap.contourf(x, y, dspin_dt_targ_combo, cpoints, cmap=cmc.vik)
ax_cb.clear()
cb = plt.colorbar(cb_data, cax=ax_cb, ticks=zticks)
ax_trap.set(xlabel='Eccentricity', ylabel='Spin Rate / Orbital Motion', yscale='linear',
xscale='log')
cb.set_label('Spin Rate Derivative ' + unit_label)
cb.ax.set_yticklabels(zlabels)
fig_trap.canvas.draw_idle()
plt.tight_layout()
run_interactive_sync = interact(
sync_rotation,
cbar_scale_set = IntSlider(value=3, min=0, max=6, step=1, description='Cbar Scale'),
voigt_compliance_offset=FloatSlider(value=0.2, min=0.01, max=1., step=0.05, description='$\\delta{}J$'),
voigt_viscosity_offset=FloatSlider(value=0.02, min=0.01, max=0.1, step=0.01, description='$\\delta{}\\eta$'),
alpha=FloatSlider(value=0.333, min=0.05, max=0.8, step=0.02, description='$\\alpha_{\\text{And}}$'),
zeta_power=FloatSlider(value=0., min=-5., max=5., step=0.5, description='$\\zeta_{\\text{And}}^{X}$'),
viscosity_power=FloatSlider(value=22., min=14, max=28, step=1.0, description='$\\eta^{X}$'),
shear_power=FloatSlider(value=10.69897, min=7., max=11., step=0.5, description='$\\mu^{X}$'),
obliquity_deg=FloatSlider(value=0, min=0., max=90., step=1., description='Obliquity'),
eccentricity_truncation_lvl=IntSlider(value=4, min=2, max=20, step=2, description='$e$ Truncation'),
max_tidal_order_l=IntSlider(value=2, min=2, max=3, step=1, description='Max $l$')
)
```
<IPython.core.display.Javascript object>
<img 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" width="713">
C:\Users\joepr\AppData\Local\Temp\ipykernel_4236\4271813673.py:31: UserWarning: The figure layout has changed to tight
fig_sync.tight_layout()
interactive(children=(IntSlider(value=3, description='Cbar Scale', max=6), FloatSlider(value=0.2, description=…
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jrenaud90REPO_NAMETidalPyPATH_START.@TidalPy_extracted@TidalPy-main@Demos@Intermediate-1--RheologicalParameterExploration_Functional.ipynb@.PATH_END.py
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{
"filename": "MCMC2.py",
"repo_name": "FRBs/zdm",
"repo_path": "zdm_extracted/zdm-main/zdm/MCMC2.py",
"type": "Python"
}
|
"""
File: MCMC2.py
Author: Jordan Hoffmann
Date: 06/12/23
Purpose:
Contains functions used for MCMC runs of the zdm code. MCMC_wrap2.py is the
main function which does the calling and this holds functions which do the
MCMC analysis.
This function re-initialises the grids on every run while MCMC.py updates the grid.
Typically this run is more efficient than MCMC.py.
"""
import numpy as np
import zdm.iteration as it
from pkg_resources import resource_filename
import emcee
import scipy.stats as st
import time
from zdm import loading
from zdm import parameters
from astropy.cosmology import Planck18
import multiprocessing as mp
from zdm import misc_functions as mf
from zdm import repeat_grid
#==============================================================================
def calc_log_posterior(param_vals, state, params, surveys_sep, grid_params, Pn=True, log_halo=False):
"""
Calculates the log posterior for a given set of parameters. Assumes uniform
priors between the minimum and maximum values provided in 'params'.
Inputs:
param_vals (np.array) = Array of the parameter values for this step
state (params.state) = State object to modify
params (dictionary) = Parameter names, min and max values
surveys_sep (list) = surveys_sep[0] : list of non-repeater surveys
surveys_sep[1] : list of repeater surveys
grid_params (dictionary) = nz, ndm, dmmax
Pn (bool) = Include Pn or not
log_halo (bool) = Use a log uniform prior on DMhalo
Outputs:
llsum (double) = Total log likelihood for param_vals which is equivalent
to log posterior (un-normalised) due to uniform priors
"""
# t0 = time.time()
# Can use likelihoods instead of posteriors because we only use uniform priors which just changes normalisation of posterior
# given every value is in the correct range. If any value is not in the correct range, log posterior is -inf
in_priors = True
param_dict = {}
for i, (key,val) in enumerate(params.items()):
if param_vals[i] < val['min'] or param_vals[i] > val['max']:
in_priors = False
break
param_dict[key] = param_vals[i]
if in_priors is False:
llsum = -np.inf
else:
# minimise_const_only does the grid updating so we don't need to do it explicitly beforehand
# In an MCMC analysis the parameter spaces are sampled throughout and hence with so many parameters
# it is easy to reach impossible regions of the parameter space. This results in math errors
# (log(0), log(negative), sqrt(negative), divide 0 etc.) and hence we assume that these math errors
# correspond to an impossible region of the parameter space and so set ll = -inf
try:
# Set state
state.update_params(param_dict)
surveys = surveys_sep[0] + surveys_sep[1]
# Recreate grids every time, but not surveys, so must update survey params
for i,s in enumerate(surveys):
if 'DMhalo' in param_dict:
if log_halo:
DMhalo = 10**param_dict['DMhalo']
else:
DMhalo = param_dict['DMhalo']
s.init_DMEG(DMhalo)
s.get_efficiency_from_wlist(s.DMlist,s.wlist,s.wplist,model=s.meta['WBIAS'])
# Initialise grids
grids = []
if len(surveys_sep[0]) != 0:
zDMgrid, zvals,dmvals = mf.get_zdm_grid(
state, new=True, plot=False, method='analytic',
nz=grid_params['nz'], ndm=grid_params['ndm'], dmmax=grid_params['dmmax'],
datdir=resource_filename('zdm', 'GridData'))
# generates zdm grid
grids += mf.initialise_grids(surveys_sep[0], zDMgrid, zvals, dmvals, state, wdist=True, repeaters=False)
if len(surveys_sep[1]) != 0:
zDMgrid, zvals,dmvals = mf.get_zdm_grid(
state, new=True, plot=False, method='analytic',
nz=grid_params['nz'], ndm=grid_params['ndm'], dmmax=grid_params['dmmax'],
datdir=resource_filename('zdm', 'GridData'))
# generates zdm grid
grids += mf.initialise_grids(surveys_sep[1], zDMgrid, zvals, dmvals, state, wdist=True, repeaters=True)
# Minimse the constant accross all surveys
newC, llC = it.minimise_const_only(None, grids, surveys, update=True)
if Pn:
for g in grids:
g.state.FRBdemo.lC = newC
if isinstance(g, repeat_grid.repeat_Grid):
g.calc_constant()
# calculate all the likelihoods
llsum = 0
for s, grid in zip(surveys, grids):
llsum += it.get_log_likelihood(grid,s,Pn=Pn)
except ValueError as e:
print("ValueError, setting likelihood to -inf: " + str(e))
llsum = -np.inf
if np.isnan(llsum):
print("llsum was NaN. Setting to -infinity", param_dict)
llsum = -np.inf
# print("Posterior calc time: " + str(time.time()-t0) + " seconds", flush=True)
return llsum
#==============================================================================
def mcmc_runner(logpf, outfile, state, params, surveys, grid_params, nwalkers=10, nsteps=100, nthreads=1, Pn=True, log_halo=False):
"""
Handles the MCMC running.
Inputs:
logpf (function) = Log posterior function handle
outfile (string) = Name of the output file (excluding .h5 extension)
state (params.state) = State object to modify
params (dictionary) = Parameter names, min and max values
surveys (list) = surveys_sep[0] : list of non-repeater surveys
surveys_sep[1] : list of repeater surveys
grid_params (dictionary) = nz, ndm, dmmax
nwalkers (int) = Number of walkers
nsteps (int) = Number of steps
nthreads (int) = Number of threads (currently not implemented - uses default)
Pn (bool) = Include Pn or not
log_halo (bool) = Use a log uniform prior on DMhalo
Outputs:
posterior_sample (emcee.EnsembleSampler) = Final sample
outfile.h5 (HDF5 file) = HDF5 file containing the sampler
"""
ndim = len(params)
starting_guesses = []
# Produce starting guesses for each parameter
for key,val in params.items():
starting_guesses.append(st.uniform(loc=val['min'], scale=val['max']-val['min']).rvs(size=[nwalkers]))
print(key + " priors: " + str(val['min']) + "," + str(val['max']))
starting_guesses = np.array(starting_guesses).T
backend = emcee.backends.HDFBackend(outfile+'.h5')
backend.reset(nwalkers, ndim)
start = time.time()
with mp.Pool() as pool:
sampler = emcee.EnsembleSampler(nwalkers, ndim, logpf, args=[state, params, surveys, grid_params, Pn, log_halo], backend=backend, pool=pool)
sampler.run_mcmc(starting_guesses, nsteps, progress=True)
end = time.time()
print("Total time taken: " + str(end - start))
posterior_sample = sampler.get_chain()
return posterior_sample
#==============================================================================
|
FRBsREPO_NAMEzdmPATH_START.@zdm_extracted@zdm-main@zdm@MCMC2.py@.PATH_END.py
|
{
"filename": "demo_axes_grid.py",
"repo_name": "matplotlib/matplotlib",
"repo_path": "matplotlib_extracted/matplotlib-main/galleries/examples/axes_grid1/demo_axes_grid.py",
"type": "Python"
}
|
"""
==============
Demo Axes Grid
==============
Grid of 2x2 images with a single colorbar or with one colorbar per Axes.
"""
import matplotlib.pyplot as plt
from matplotlib import cbook
from mpl_toolkits.axes_grid1 import ImageGrid
fig = plt.figure(figsize=(10.5, 2.5))
Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") # 15x15 array
extent = (-3, 4, -4, 3)
# A grid of 2x2 images with 0.05 inch pad between images and only the
# lower-left Axes is labeled.
grid = ImageGrid(
fig, 141, # similar to fig.add_subplot(141).
nrows_ncols=(2, 2), axes_pad=0.05, label_mode="1")
for ax in grid:
ax.imshow(Z, extent=extent)
# This only affects Axes in first column and second row as share_all=False.
grid.axes_llc.set(xticks=[-2, 0, 2], yticks=[-2, 0, 2])
# A grid of 2x2 images with a single colorbar.
grid = ImageGrid(
fig, 142, # similar to fig.add_subplot(142).
nrows_ncols=(2, 2), axes_pad=0.0, label_mode="L", share_all=True,
cbar_location="top", cbar_mode="single")
for ax in grid:
im = ax.imshow(Z, extent=extent)
grid.cbar_axes[0].colorbar(im)
for cax in grid.cbar_axes:
cax.tick_params(labeltop=False)
# This affects all Axes as share_all = True.
grid.axes_llc.set(xticks=[-2, 0, 2], yticks=[-2, 0, 2])
# A grid of 2x2 images. Each image has its own colorbar.
grid = ImageGrid(
fig, 143, # similar to fig.add_subplot(143).
nrows_ncols=(2, 2), axes_pad=0.1, label_mode="1", share_all=True,
cbar_location="top", cbar_mode="each", cbar_size="7%", cbar_pad="2%")
for ax, cax in zip(grid, grid.cbar_axes):
im = ax.imshow(Z, extent=extent)
cax.colorbar(im)
cax.tick_params(labeltop=False)
# This affects all Axes as share_all = True.
grid.axes_llc.set(xticks=[-2, 0, 2], yticks=[-2, 0, 2])
# A grid of 2x2 images. Each image has its own colorbar.
grid = ImageGrid(
fig, 144, # similar to fig.add_subplot(144).
nrows_ncols=(2, 2), axes_pad=(0.45, 0.15), label_mode="1", share_all=True,
cbar_location="right", cbar_mode="each", cbar_size="7%", cbar_pad="2%")
# Use a different colorbar range every time
limits = ((0, 1), (-2, 2), (-1.7, 1.4), (-1.5, 1))
for ax, cax, vlim in zip(grid, grid.cbar_axes, limits):
im = ax.imshow(Z, extent=extent, vmin=vlim[0], vmax=vlim[1])
cb = cax.colorbar(im)
cb.set_ticks((vlim[0], vlim[1]))
# This affects all Axes as share_all = True.
grid.axes_llc.set(xticks=[-2, 0, 2], yticks=[-2, 0, 2])
plt.show()
|
matplotlibREPO_NAMEmatplotlibPATH_START.@matplotlib_extracted@matplotlib-main@galleries@examples@axes_grid1@demo_axes_grid.py@.PATH_END.py
|
{
"filename": "ecliptic.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/coordinates/builtin_frames/ecliptic.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from astropy import units as u
from astropy.coordinates import representation as r
from astropy.coordinates.attributes import QuantityAttribute, TimeAttribute
from astropy.coordinates.baseframe import BaseCoordinateFrame, base_doc
from astropy.utils.decorators import format_doc
from .utils import DEFAULT_OBSTIME, EQUINOX_J2000
__all__ = [
"BarycentricMeanEcliptic",
"BarycentricTrueEcliptic",
"BaseEclipticFrame",
"CustomBarycentricEcliptic",
"GeocentricMeanEcliptic",
"GeocentricTrueEcliptic",
"HeliocentricEclipticIAU76",
"HeliocentricMeanEcliptic",
"HeliocentricTrueEcliptic",
]
doc_components_ecl = """
lon : `~astropy.coordinates.Angle`, optional, keyword-only
The ecliptic longitude for this object (``lat`` must also be given and
``representation`` must be None).
lat : `~astropy.coordinates.Angle`, optional, keyword-only
The ecliptic latitude for this object (``lon`` must also be given and
``representation`` must be None).
distance : `~astropy.units.Quantity` ['length'], optional, keyword-only
The distance for this object from the {0}.
(``representation`` must be None).
pm_lon_coslat : `~astropy.units.Quantity` ['angualar speed'], optional, keyword-only
The proper motion in the ecliptic longitude (including the ``cos(lat)``
factor) for this object (``pm_lat`` must also be given).
pm_lat : `~astropy.units.Quantity` ['angular speed'], optional, keyword-only
The proper motion in the ecliptic latitude for this object
(``pm_lon_coslat`` must also be given).
radial_velocity : `~astropy.units.Quantity` ['speed'], optional, keyword-only
The radial velocity of this object.
"""
@format_doc(
base_doc, components=doc_components_ecl.format("specified location"), footer=""
)
class BaseEclipticFrame(BaseCoordinateFrame):
"""
A base class for frames that have names and conventions like that of
ecliptic frames.
.. warning::
In the current version of astropy, the ecliptic frames do not yet have
stringent accuracy tests. We recommend you test to "known-good" cases
to ensure this frames are what you are looking for. (and then ideally
you would contribute these tests to Astropy!)
"""
default_representation = r.SphericalRepresentation
default_differential = r.SphericalCosLatDifferential
doc_footer_geo = """
Other parameters
----------------
equinox : `~astropy.time.Time`, optional
The date to assume for this frame. Determines the location of the
x-axis and the location of the Earth (necessary for transformation to
non-geocentric systems). Defaults to the 'J2000' equinox.
obstime : `~astropy.time.Time`, optional
The time at which the observation is taken. Used for determining the
position of the Earth. Defaults to J2000.
"""
@format_doc(
base_doc, components=doc_components_ecl.format("geocenter"), footer=doc_footer_geo
)
class GeocentricMeanEcliptic(BaseEclipticFrame):
"""
Geocentric mean ecliptic coordinates. These origin of the coordinates are the
geocenter (Earth), with the x axis pointing to the *mean* (not true) equinox
at the time specified by the ``equinox`` attribute, and the xy-plane in the
plane of the ecliptic for that date.
Be aware that the definition of "geocentric" here means that this frame
*includes* light deflection from the sun, aberration, etc when transforming
to/from e.g. ICRS.
The frame attributes are listed under **Other Parameters**.
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation)"
)
@format_doc(
base_doc, components=doc_components_ecl.format("geocenter"), footer=doc_footer_geo
)
class GeocentricTrueEcliptic(BaseEclipticFrame):
"""
Geocentric true ecliptic coordinates. These origin of the coordinates are the
geocenter (Earth), with the x axis pointing to the *true* (not mean) equinox
at the time specified by the ``equinox`` attribute, and the xy-plane in the
plane of the ecliptic for that date.
Be aware that the definition of "geocentric" here means that this frame
*includes* light deflection from the sun, aberration, etc when transforming
to/from e.g. ICRS.
The frame attributes are listed under **Other Parameters**.
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation)"
)
doc_footer_bary = """
Other parameters
----------------
equinox : `~astropy.time.Time`, optional
The date to assume for this frame. Determines the location of the
x-axis and the location of the Earth and Sun.
Defaults to the 'J2000' equinox.
"""
@format_doc(
base_doc, components=doc_components_ecl.format("barycenter"), footer=doc_footer_bary
)
class BarycentricMeanEcliptic(BaseEclipticFrame):
"""
Barycentric mean ecliptic coordinates. These origin of the coordinates are the
barycenter of the solar system, with the x axis pointing in the direction of
the *mean* (not true) equinox as at the time specified by the ``equinox``
attribute (as seen from Earth), and the xy-plane in the plane of the
ecliptic for that date.
The frame attributes are listed under **Other Parameters**.
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
@format_doc(
base_doc, components=doc_components_ecl.format("barycenter"), footer=doc_footer_bary
)
class BarycentricTrueEcliptic(BaseEclipticFrame):
"""
Barycentric true ecliptic coordinates. These origin of the coordinates are the
barycenter of the solar system, with the x axis pointing in the direction of
the *true* (not mean) equinox as at the time specified by the ``equinox``
attribute (as seen from Earth), and the xy-plane in the plane of the
ecliptic for that date.
The frame attributes are listed under **Other Parameters**.
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
doc_footer_helio = """
Other parameters
----------------
equinox : `~astropy.time.Time`, optional
The date to assume for this frame. Determines the location of the
x-axis and the location of the Earth and Sun.
Defaults to the 'J2000' equinox.
obstime : `~astropy.time.Time`, optional
The time at which the observation is taken. Used for determining the
position of the Sun. Defaults to J2000.
"""
@format_doc(
base_doc,
components=doc_components_ecl.format("sun's center"),
footer=doc_footer_helio,
)
class HeliocentricMeanEcliptic(BaseEclipticFrame):
"""
Heliocentric mean ecliptic coordinates. These origin of the coordinates are the
center of the sun, with the x axis pointing in the direction of
the *mean* (not true) equinox as at the time specified by the ``equinox``
attribute (as seen from Earth), and the xy-plane in the plane of the
ecliptic for that date.
The frame attributes are listed under **Other Parameters**.
{params}
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation)"
)
@format_doc(
base_doc,
components=doc_components_ecl.format("sun's center"),
footer=doc_footer_helio,
)
class HeliocentricTrueEcliptic(BaseEclipticFrame):
"""
Heliocentric true ecliptic coordinates. These origin of the coordinates are the
center of the sun, with the x axis pointing in the direction of
the *true* (not mean) equinox as at the time specified by the ``equinox``
attribute (as seen from Earth), and the xy-plane in the plane of the
ecliptic for that date.
The frame attributes are listed under **Other Parameters**.
{params}
"""
equinox = TimeAttribute(default=EQUINOX_J2000, doc="The equinox time")
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation)"
)
@format_doc(base_doc, components=doc_components_ecl.format("sun's center"), footer="")
class HeliocentricEclipticIAU76(BaseEclipticFrame):
"""
Heliocentric mean (IAU 1976) ecliptic coordinates. These origin of the coordinates are the
center of the sun, with the x axis pointing in the direction of
the *mean* (not true) equinox of J2000, and the xy-plane in the plane of the
ecliptic of J2000 (according to the IAU 1976/1980 obliquity model).
It has, therefore, a fixed equinox and an older obliquity value
than the rest of the frames.
The frame attributes are listed under **Other Parameters**.
{params}
"""
obstime = TimeAttribute(
default=DEFAULT_OBSTIME, doc="The reference time (e.g., time of observation)"
)
@format_doc(base_doc, components=doc_components_ecl.format("barycenter"), footer="")
class CustomBarycentricEcliptic(BaseEclipticFrame):
"""
Barycentric ecliptic coordinates with custom obliquity.
These origin of the coordinates are the
barycenter of the solar system, with the x axis pointing in the direction of
the *mean* (not true) equinox of J2000, and the xy-plane in the plane of the
ecliptic tilted a custom obliquity angle.
The frame attributes are listed under **Other Parameters**.
"""
obliquity = QuantityAttribute(
default=84381.448 * u.arcsec, unit=u.arcsec, doc="The obliquity of the ecliptic"
)
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@coordinates@builtin_frames@ecliptic.py@.PATH_END.py
|
{
"filename": "svm_quality_analysis.py",
"repo_name": "spacetelescope/drizzlepac",
"repo_path": "drizzlepac_extracted/drizzlepac-main/drizzlepac/haputils/svm_quality_analysis.py",
"type": "Python"
}
|
"""Code that evaluates the quality of the SVM products generated by the drizzlepac package.
The JSON files generated here can be converted directly into a Pandas DataFrame
using the syntax:
>>> import json
>>> import pandas as pd
>>> with open("<rootname>_astrometry_resids.json") as jfile:
>>> resids = json.load(jfile)
>>> pdtab = pd.DataFrame(resids)
These DataFrames can then be concatenated using:
>>> allpd = pdtab.concat([pdtab2, pdtab3])
where 'pdtab2' and 'pdtab3' are DataFrames generated from other datasets. For
more information on how to merge DataFrames, see
https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html
Visualization of these Pandas DataFrames with Bokeh can follow the example
from:
https://programminghistorian.org/en/lessons/visualizing-with-bokeh
"""
# Standard library imports
import argparse
import collections
from datetime import datetime
import glob
import json
import math
import os
import pickle
import sys
import time
# Related third party imports
from astropy.coordinates import SkyCoord
from astropy.io import ascii, fits
from astropy.stats import sigma_clipped_stats
from astropy.table import Table, Column
from bokeh.layouts import row, column
from bokeh.plotting import figure, output_file, save
from bokeh.models import ColumnDataSource, Label
from bokeh.models.tools import HoverTool
from itertools import chain
import numpy as np
from photutils.detection import DAOStarFinder
from scipy import ndimage
from scipy.spatial import KDTree
# Local application imports
from drizzlepac import util, wcs_functions
from drizzlepac.haputils import hla_flag_filter
from drizzlepac.haputils import catalog_utils
from drizzlepac.haputils import astrometric_utils as au
from drizzlepac.haputils import cell_utils
import drizzlepac.haputils.comparison_utils as cu
import drizzlepac.haputils.diagnostic_utils as du
try:
import drizzlepac.devutils.comparison_tools.compare_sourcelists as csl
except ImportError:
print("Unable to import compare_sourcelists! HAP vs. HLA sourcelist comparisons will be skipped.")
from drizzlepac.haputils import read_hla_catalog
from stsci.tools import logutil
from stwcs import wcsutil
from stwcs.wcsutil import HSTWCS
from drizzlepac.haputils import quality_analysis as qa
from drizzlepac.haputils.pandas_utils import PandasDFReader
__taskname__ = 'svm_quality_analysis'
MSG_DATEFMT = '%Y%j%H%M%S'
SPLUNK_MSG_FORMAT = '%(asctime)s %(levelname)s src=%(name)s- %(message)s'
log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout,
format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
# ----------------------------------------------------------------------------------------------------------------------
def characterize_gaia_distribution(hap_obj, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Statistically describe distribution of GAIA sources in footprint.
Computes and writes the file to a json file:
- Number of GAIA sources
- X centroid location
- Y centroid location
- X offset of centroid from image center
- Y offset of centroid from image center
- X standard deviation
- Y standard deviation
- minimum closest neighbor distance
- maximum closest neighbor distance
- mean closest neighbor distance
- standard deviation of closest neighbor distances
Parameters
----------
hap_obj : drizzlepac.haputils.Product.FilterProduct
hap product object to process
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
Nothing
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: characterize_gaia_distribution. *****\n')
# get table of GAIA sources in footprint
gaia_table = generate_gaia_catalog(hap_obj, columns_to_remove=['mag', 'objID', 'GaiaID'])
# return without creating a .json if no GAIA sources were found
if not gaia_table:
log.error("*** No GAIA sources were found. Characterization of GAIA distribution cannot be computed. "
"No json file will be produced.***")
return
# if log_level is either 'DEBUG' or 'NOTSET', write out GAIA sources to DS9 region file
if log_level <= logutil.logging.DEBUG:
reg_file = "{}_gaia_sources.reg".format(hap_obj.drizzle_filename[:-9])
cname = gaia_table.colnames[0]
orig_cname = cname
gaia_table.rename_column(cname, "#{}".format(cname)) # Make reg file DS9-compatible
gaia_table.write(reg_file, format='ascii.csv')
log.debug("Wrote GAIA source RA and Dec positions to DS9 region file '{}'".format(reg_file))
# Reset the column name back
cname = gaia_table.colnames[0]
gaia_table.rename_column(cname, orig_cname)
# convert RA, Dec to image X, Y
outwcs = HSTWCS(hap_obj.drizzle_filename + "[1]")
x, y = outwcs.all_world2pix(gaia_table['RA'], gaia_table['DEC'], 1)
# compute stats for the distribution
centroid = [np.mean(x), np.mean(y)]
centroid_offset = []
for idx in range(0, 2):
centroid_offset.append(outwcs.wcs.crpix[idx] - centroid[idx])
std_dev = [np.std(x), np.std(y)]
# Find straight-line distance to the closest neighbor for each GAIA source
xys = np.array([x, y])
xys = xys.reshape(len(x), 2)
tree = KDTree(xys)
neighborhood = tree.query(xys, 2)
min_seps = np.empty([0])
for sep_pair in neighborhood[0]:
min_seps = np.append(min_seps, sep_pair[1])
# add statistics to out_dict
out_dict = collections.OrderedDict()
out_dict["Number of GAIA sources"] = len(gaia_table)
axis_list = ["X", "Y"]
title_list = ["centroid", "offset", "standard deviation"]
for item_value, item_title in zip([centroid, centroid_offset, std_dev], title_list):
for axis_item in enumerate(axis_list):
log.info("{} {} ({}): {}".format(axis_item[1], item_title, "pixels",
item_value[axis_item[0]]))
out_dict["{} {}".format(axis_item[1], item_title)] = item_value[axis_item[0]]
min_sep_stats = [min_seps.min(), min_seps.max(), min_seps.mean(), min_seps.std()]
min_sep_title_list = ["minimum neighbor distance",
"maximum neighbor distance",
"mean neighbor distance",
"standard deviation of neighbor distances"]
for item_value, item_title in zip(min_sep_stats, min_sep_title_list):
log.info("{} ({}): {}".format(item_title, "pixels", item_value))
out_dict[item_title] = item_value
# write catalog to HapDiagnostic-formatted .json file.
diag_obj = du.HapDiagnostic(log_level=log_level)
diag_obj.instantiate_from_hap_obj(hap_obj,
data_source="{}.characterize_gaia_distribution".format(__taskname__),
description="A statistical characterization of the distribution of "
"GAIA sources in image footprint",
timestamp=json_timestamp, time_since_epoch=json_time_since_epoch)
diag_obj.add_data_item(out_dict, "distribution characterization statistics",
descriptions={"Number of GAIA sources": "Number of GAIA sources in image footprint",
"X centroid": "X centroid", "Y centroid": "Y centroid",
"X offset": "X offset of centroid from image center",
"Y offset": "Y offset of centroid from image center",
"X standard deviation": "X standard deviation of mean offset",
"Y standard deviation": "Y standard deviation of mean offset",
"minimum neighbor distance": "distance of closest neighbor",
"maximum neighbor distance": "distance of furthest neighbor",
"mean neighbor distance": "mean distance of neighbors",
"standard deviation of neighbor distances": "standard deviation of mean distance"},
units={"Number of GAIA sources": "unitless",
"X centroid": "pixels", "Y centroid": "pixels",
"X offset": "pixels",
"Y offset": "pixels",
"X standard deviation": "pixels",
"Y standard deviation": "pixels",
"minimum neighbor distance": "pixels",
"maximum neighbor distance": "pixels",
"mean neighbor distance": "pixels",
"standard deviation of neighbor distances": "pixels"})
diag_obj.write_json_file(hap_obj.drizzle_filename[:-9] + "_svm_gaia_distribution_characterization.json",
clobber=True)
# ----------------------------------------------------------------------------------------------------------------------
def compare_num_sources(drizzle_list, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Determine the number of viable sources actually listed in SVM output catalogs.
Parameters
----------
drizzle_list: list of strings
Drizzle files for the Total products which were mined to generate the output catalogs.
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
.. note:: This routine can be run either as a direct call from the hapsequencer.py routine,
or it can invoked by a simple Python driver (or from within a Python session) by providing
the names of the previously computed files as lists. The files must exist in the working directory.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: compare_num_sources. *****\n')
pnt_suffix = '_point-cat.ecsv'
seg_suffix = '_segment-cat.ecsv'
# Get a list of the catalog files (*.ecsv) in the current working directory
catalog_files = []
basepath = os.getcwd()
for entry in os.listdir(basepath):
if os.path.isfile(os.path.join(basepath, entry)):
if entry.endswith("ecsv"):
catalog_files.append(entry)
# Generate a separate JSON file for each detector
# Drizzle filename example: hst_11665_06_wfc3_ir_total_ib4606_drz.fits
# The filename is all lower-case by design.
for drizzle_file in drizzle_list:
if not os.path.exists(drizzle_file):
log.warning("[compare_num_sources] Input {} not found. Skipping comparison.".format(drizzle_file))
continue
tokens = drizzle_file.split('_')
detector = tokens[4]
sources_dict = {'detector': detector, 'point': 0, 'segment': 0}
# Construct the output JSON filename
json_filename = drizzle_file[:-9] + '_svm_num_sources.json'
# Construct catalog names for catalogs that should have been produced
prefix = '_'.join(tokens[0:-1])
cat_names = [prefix + pnt_suffix, prefix + seg_suffix]
# If the catalog were actually produced, get the number of sources.
# A catalog may not be produced because it was not requested, or there
# was an error. However, for the purposes of this program, it is OK
# that no catalog was produced.
for catalog in cat_names:
if catalog in catalog_files:
# if the catalog exists, open it and find the number of sources string
num_sources = -1
cat_type = ""
file = open(catalog, 'r')
for line in file:
sline = line.strip()
# All the comments are grouped at the start of the file. When
# the first non-comment line is found, there is no need to look further.
if not sline.startswith('#'):
log.info("Number of sources not reported in Catalog: {}.".format(catalog))
break
# When the matching comment line is found, get the value.
if sline.find('Number of sources') != -1:
num_sources = sline.split(' ')[-1][0:-1]
log.info("Catalog: {} Number of sources: {}.".format(catalog, num_sources))
break
cat_type = 'point' if catalog.find("point") != -1 else 'segment'
sources_dict[cat_type] = int(num_sources)
# Set up the diagnostic object and write out the results
diagnostic_obj = du.HapDiagnostic()
diagnostic_obj.instantiate_from_fitsfile(drizzle_file,
data_source="{}.compare_num_sources".format(__taskname__),
description="Number of sources in Point and Segment "
"catalogs",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
diagnostic_obj.add_data_item(sources_dict, 'number_of_sources',
descriptions={'detector': 'Detector in use',
'point': 'Number of detected sources in Point catalog',
'segment': 'Number of detected sources in Segmented catalog'},
units={'detector': 'unitless', 'point': 'unitless', 'segment': 'unitless'})
diagnostic_obj.write_json_file(json_filename)
log.info("Generated quality statistics (number of sources) as {}.".format(json_filename))
# Clean up
del diagnostic_obj
# ------------------------------------------------------------------------------------------------------------
def compare_ra_dec_crossmatches(hap_obj, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Compare the equatorial coordinates of cross-matches sources between the Point and Segment catalogs.
The results .json file contains the following information:
- image header information
- cross-match details (input catalog lengths, number of cross-matched sources, coordinate system)
- catalog containing RA and dec values of cross-matched point catalog sources
- catalog containing RA and dec values of cross-matched segment catalog sources
- catalog containing MagAp1 and MagAp2 values of cross-matched point catalog sources
- catalog containing MagAp1 and MagAp2 values of cross-matched segment catalog sources
- Statistics describing the on-sky separation of the cross-matched point and segment catalogs
(non-clipped and sigma-clipped mean, median and standard deviation values)
- Statistics describing the mean, std, and median for the differences of magnitudes of the
cross-matched point and segment catalogs
Parameters
----------
hap_obj : drizzlepac.haputils.Product.FilterProduct
hap filter product object to process
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
--------
nothing.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: compare_ra_dec_crossmatches. *****\n')
sl_names = [hap_obj.point_cat_filename, hap_obj.segment_cat_filename]
img_names = [hap_obj.drizzle_filename, hap_obj.drizzle_filename]
good_flag_sum = 255 # all bits good
for name in sl_names:
if name.rstrip == '' or not os.path.exists(name):
log.warning("[compare_ra_dec_crossmatches] Catalog {} Missing! No comparison can be made.".format(name))
return
diag_obj = du.HapDiagnostic(log_level=log_level)
diag_obj.instantiate_from_hap_obj(hap_obj,
data_source="{}.compare_ra_dec_crossmatches".format(__taskname__),
description="matched point and segment catalog RA and Dec values",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
json_results_dict = collections.OrderedDict()
# add reference and comparison catalog filenames as header elements
json_results_dict["point catalog filename"] = sl_names[0]
json_results_dict["segment catalog filename"] = sl_names[1]
# 1: Read in sourcelists files into astropy table or 2-d array so that individual columns from each
# sourcelist can be easily accessed later in the code.
point_data, seg_data = cu.slFiles2dataTables(sl_names)
log.info("Valid point data columns: {}".format(list(point_data.keys())))
log.info("Valid segment data columns: {}".format(list(seg_data.keys())))
log.info("\n")
log.info("Data columns to be compared:")
columns_to_compare = list(set(point_data.keys()).intersection(set(seg_data.keys())))
for listItem in sorted(columns_to_compare):
log.info(listItem)
log.info("\n")
# 2: Run starmatch_hist to get list of matched sources common to both input sourcelists
sl_lengths = [len(point_data['RA']), len(seg_data['RA'])]
json_results_dict['point catalog length'] = sl_lengths[0]
json_results_dict['segment catalog length'] = sl_lengths[1]
# Guard against empty catalogs being compared
if min(sl_lengths) == 0:
log.warning("*** No matching sources were found. Comparisons cannot be computed. "
"No json file will be produced.***")
return
matching_lines_ref, matching_lines_img = cu.getMatchedLists(sl_names, img_names, sl_lengths,
log_level=log_level)
json_results_dict['number of cross-matches'] = len(matching_lines_ref)
# Report number and percentage of the total number of detected ref and comp sources that were matched
log.info("Cross-matching results")
log.info(
"Point sourcelist: {} of {} total sources cross-matched ({}%)".format(len(matching_lines_ref),
sl_lengths[0],
100.0 *
(float(len(matching_lines_ref))
/ float(sl_lengths[0]))))
log.info(
"Segment sourcelist: {} of {} total sources cross-matched ({}%)".format(len(matching_lines_img),
sl_lengths[1],
100.0 *
(float(
len(matching_lines_img))
/ float(sl_lengths[1]))))
# return without creating a .json if no cross-matches are found
if len(matching_lines_ref) == 0 or len(matching_lines_img) == 0:
log.warning("*** No matching sources were found. Comparisons cannot be computed. "
"No json file will be produced.***")
return
# 2: Create masks to remove missing values or values not considered "good" according to user-specified
# good bit values
# 2a: create mask that identifies lines any value from any column is missing
missing_mask = cu.mask_missing_values(point_data, seg_data, matching_lines_ref, matching_lines_img,
columns_to_compare)
# 2b: create mask based on flag values
matched_values = cu.extractMatchedLines("FLAGS", point_data, seg_data, matching_lines_ref,
matching_lines_img)
bitmask = cu.make_flag_mask(matched_values, good_flag_sum, missing_mask)
matched_values_ra = cu.extractMatchedLines("RA", point_data, seg_data, matching_lines_ref,
matching_lines_img, bitmask=bitmask)
matched_values_dec = cu.extractMatchedLines("DEC", point_data, seg_data, matching_lines_ref,
matching_lines_img, bitmask=bitmask)
matched_values_magap1 = cu.extractMatchedLines("MAGNITUDE1", point_data, seg_data, matching_lines_ref,
matching_lines_img, bitmask=bitmask)
matched_values_magap2 = cu.extractMatchedLines("MAGNITUDE2", point_data, seg_data, matching_lines_ref,
matching_lines_img, bitmask=bitmask)
if matched_values_ra.shape[1] > 0 and matched_values_ra.shape[1] == matched_values_dec.shape[1]:
# get coordinate system type from fits headers
point_frame = fits.getval(img_names[0], "radesys", ext=('sci', 1)).lower()
seg_frame = fits.getval(img_names[1], "radesys", ext=('sci', 1)).lower()
# Add 'ref_frame' and 'comp_frame" values to header so that will SkyCoord() execute OK
json_results_dict["point frame"] = point_frame
json_results_dict["segment frame"] = seg_frame
# convert reference and comparison RA/Dec values into SkyCoord objects
matched_values_point = SkyCoord(matched_values_ra[0, :], matched_values_dec[0, :], frame=point_frame,
unit="deg")
matched_values_seg = SkyCoord(matched_values_ra[1, :], matched_values_dec[1, :], frame=seg_frame,
unit="deg")
# convert to ICRS coord system
if point_frame != "icrs":
matched_values_point = matched_values_point.icrs
if seg_frame != "icrs":
matched_values_seg = matched_values_seg.icrs
# compute on-sky separations in arcseconds
sep = matched_values_seg.separation(matched_values_point).arcsec
# Compute and store statistics on separations
sep_stat_dict = collections.OrderedDict()
sep_stat_dict["Non-clipped min"] = np.min(sep)
sep_stat_dict["Non-clipped max"] = np.max(sep)
sep_stat_dict["Non-clipped mean"] = np.mean(sep)
sep_stat_dict["Non-clipped median"] = np.median(sep)
sep_stat_dict["Non-clipped standard deviation"] = np.std(sep)
sigma = 3
maxiters = 3
clipped_stats = sigma_clipped_stats(sep, sigma=sigma, maxiters=maxiters)
sep_stat_dict["{}x{} sigma-clipped mean".format(maxiters, sigma)] = clipped_stats[0]
sep_stat_dict["{}x{} sigma-clipped median".format(maxiters, sigma)] = clipped_stats[1]
sep_stat_dict["{}x{} sigma-clipped standard deviation".format(maxiters, sigma)] = clipped_stats[2]
#
# Compute statistics on the photometry differences
#
# Compute the differences (Point - Segment)
delta_phot_magap1 = np.subtract(matched_values_magap1[0], matched_values_magap1[1])
delta_phot_magap2 = np.subtract(matched_values_magap2[0], matched_values_magap2[1])
# Compute some basic statistics: mean difference and standard deviation, and median difference
phot_stat_dict = collections.OrderedDict()
phot_stat_dict["mean_dmagap1"] = np.mean(delta_phot_magap1)
phot_stat_dict["std_dmagap1"] = np.std(delta_phot_magap1)
phot_stat_dict["median_dmagap1"] = np.median(delta_phot_magap1)
phot_stat_dict["mean_dmagap2"] = np.mean(delta_phot_magap2)
phot_stat_dict["std_dmagap2"] = np.std(delta_phot_magap2)
phot_stat_dict["median_dmagap2"] = np.median(delta_phot_magap2)
# Create output catalogs for json file
out_cat_point = Table([matched_values_ra[0], matched_values_dec[0], matched_values_magap1[0],
matched_values_magap2[0]], names=("Right ascension", "Declination",
"MagAp1", "MagAp2"))
out_cat_seg = Table([matched_values_ra[1], matched_values_dec[1], sep, matched_values_magap1[1],
matched_values_magap2[1]], names=("Right ascension", "Declination",
"Separation",
"MagAp1", "MagAp2"))
for table_item in [out_cat_point, out_cat_seg]:
for col_name in ["Right ascension", "Declination"]:
table_item[col_name].unit = "degrees" # Add correct units
for col_name in ["MagAp1", "MagAp2"]:
table_item[col_name].unit = "ABMag" # Add correct units
out_cat_seg['Separation'].unit = "arcseconds"
# add various data items to diag_obj
diag_obj.add_data_item(json_results_dict, "Cross-match details",
descriptions={"point catalog filename": "ECSV point catalog filename",
"segment catalog filename": "ECSV segment catalog filename",
"point catalog length": "Number of entries in point catalog",
"segment catalog length": "Number of entries in segment catalog",
"number of cross-matches": "Number of cross-matches between point and segment catalogs",
"point frame": "Coordinate reference frame",
"segment frame": "Coordinate reference frame"},
units={"point catalog filename": "unitless",
"segment catalog filename": "unitless",
"point catalog length": "unitless",
"segment catalog length": "unitless",
"number of cross-matches": "unitless",
"point frame": "unitless",
"segment frame": "unitless"})
diag_obj.add_data_item(out_cat_point, "Cross-matched point catalog",
descriptions={"Right ascension": "ICRS Right ascension",
"Declination": "ICRS Declination",
"MagAp1": "Magnitude Aperture 1",
"MagAp2": "Magnitude Aperture 2"},
units={"Right ascension": "degrees", "Declination": "degrees",
"MagAp1": "ABMag", "MagAp2": "ABMag"})
diag_obj.add_data_item(out_cat_seg, "Cross-matched segment catalog",
descriptions={"Right ascension": "ICRS Right ascension",
"Declination": "ICRS Declination",
"Separation": "Segment minus Point on-sky coordinate separation",
"MagAp1": "Magnitude Aperture 1",
"MagAp2": "Magnitude Aperture 2"},
units={"Right ascension": "degrees", "Declination": "degrees",
"Separation": "arcseconds",
"MagAp1": "ABMag", "MagAp2": "ABMag"})
diag_obj.add_data_item(sep_stat_dict, "Segment - point on-sky separation statistics",
descriptions={"Non-clipped min": "Non-clipped min difference",
"Non-clipped max": "Non-clipped max difference",
"Non-clipped mean": "Non-clipped mean difference",
"Non-clipped median": "Non-clipped median difference",
"Non-clipped standard deviation": "Non-clipped standard deviation of differences",
"3x3 sigma-clipped mean": "3x3 sigma-clipped mean difference",
"3x3 sigma-clipped median": "3x3 sigma-clipped median difference",
"3x3 sigma-clipped standard deviation": "3x3 sigma-clipped standard deviation of differences"},
units={"Non-clipped min": "arcseconds", "Non-clipped max": "arcseconds",
"Non-clipped mean": "arcseconds", "Non-clipped median": "arcseconds",
"Non-clipped standard deviation": "arcseconds",
"3x3 sigma-clipped mean": "arcseconds", "3x3 sigma-clipped median": "arcseconds",
"3x3 sigma-clipped standard deviation": "arcseconds"})
diag_obj.add_data_item(phot_stat_dict, "Delta_Photometry",
descriptions={'mean_dmagap1': 'dMagAp1_Mean_Differences(Point-Segment)',
'std_dmagap1': 'dMagAp1_StdDev_of_Mean_Differences',
'median_dmagap1': 'dMagAp1_Median_Differences(Point-Segment)',
'mean_dmagap2': 'dMagAp2_Mean_Differences(Point-Segment)',
'std_dmagap2': 'dMagAp2_StdDev_of_Mean_Differences',
'median_dmagap2': 'dMagAp2_Median_Differences(Point-Segment)'},
units={'mean_dmagap1': 'ABMag',
'std_dmagap1': 'ABMag',
'median_dmagap1': 'ABMag',
'mean_dmagap2': 'ABMag',
'std_dmagap2': 'ABMag',
'median_dmagap2': 'ABMag'})
# write everything out to the json file
json_filename = hap_obj.drizzle_filename[:-9]+"_svm_point_segment_crossmatch.json"
diag_obj.write_json_file(json_filename, clobber=True)
else:
log.warning("Point vs. segment catalog cross match test could not be performed.")
# ------------------------------------------------------------------------------------------------------------
def compare_interfilter_crossmatches(total_obj_list, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Compare X, Y positions of cross matched sources from different filter-level products
Parameters
----------
total_obj_list : list
list of drizzlepac.haputils.Product.TotalProduct objects to process
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
Nothing
"""
# Initiate logging!
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: compare_interfilter_crossmatches. *****\n')
# Check to make sure there's at last 2 filter-level products. If not, return.
num_filter_prods = 0
for total_obj in total_obj_list:
num_filter_prods += len(total_obj.fdp_list)
if num_filter_prods == 1:
log.error("At least 2 filter-level products are necessary to perform analysis of inter-filter "
"cross matched sources. Only 1 filter-level product was found. ")
return
elif num_filter_prods == 0:
log.error("At least 2 filter-level products are necessary to perform analysis of inter-filter "
"cross matched sources. No filter-level products were found. ")
return
else:
log.info("Found {} filter-level products for use in analysis of inter-filter cross matched source"
" positions".format(num_filter_prods))
filtobj_dict = {}
xmatch_ref_imgname = None
max_sources = 0
ctr = 1
for total_obj in total_obj_list:
for filt_obj in total_obj.fdp_list:
log.info("{} {}: {} {}".format(">"*20, ctr, filt_obj.drizzle_filename, "<"*20))
filtobj_dict[filt_obj.drizzle_filename] = find_hap_point_sources(filt_obj, log_level=log_level)
n_sources = len(filtobj_dict[filt_obj.drizzle_filename]['sources'])
log.info("Identified {} sources in {}".format(n_sources, filt_obj.drizzle_filename))
if n_sources > max_sources:
max_sources = n_sources
xmatch_ref_imgname = filt_obj.drizzle_filename
xmatch_ref_catname = filtobj_dict[filt_obj.drizzle_filename]['cat_name']
ctr += 1
log.info("")
log.info("Crossmatch reference image {} contains {} sources.".format(xmatch_ref_imgname, max_sources))
# Perform coord transform and write temp cat files for cross match
temp_cat_file_list = []
log.info("")
for imgname in filtobj_dict.keys():
filtobj_dict[imgname] = transform_coords(filtobj_dict[imgname],
xmatch_ref_imgname,
log_level=log_level)
filtobj_dict[imgname]["sources"].write(filtobj_dict[imgname]['cat_name'], format="ascii.ecsv")
log.info("Wrote temporary source catalog {}".format(filtobj_dict[imgname]['cat_name']))
# write out ds9 region files if log level is 'debug'
if log_level == logutil.logging.DEBUG:
out_reg_stuff = {"xyorig": ['xcentroid', 'ycentroid'],
"rd": ['ra', 'dec'],
"xyref": ['xcentroid_ref', 'ycentroid_ref']}
for reg_type in out_reg_stuff.keys():
reg_table = filtobj_dict[imgname]["sources"].copy()
reg_table.keep_columns(out_reg_stuff[reg_type])
reg_table.rename_column(out_reg_stuff[reg_type][0], "#"+out_reg_stuff[reg_type][0])
reg_filename = filtobj_dict[imgname]['cat_name'].replace(".ecsv",
"_{}_all.reg".format(reg_type))
reg_table.write(reg_filename, format='ascii.csv')
log.debug("wrote region file {}".format(reg_filename))
del reg_filename
# Perform cross-match based on X, Y coords
for imgname in filtobj_dict.keys():
if imgname != xmatch_ref_imgname:
try:
log.info(" ")
xmatch_comp_imgname = imgname
xmatch_comp_catname = filtobj_dict[imgname]['cat_name']
filtername_ref = filtobj_dict[xmatch_ref_imgname]['filt_obj'].filters
filtername_comp = filtobj_dict[imgname]['filt_obj'].filters
# Perform crossmatching; get lists of crossmatched sources in the reference and comparison
log.info("{} Crossmatching {} -> {} {}".format(">" * 20, xmatch_comp_catname,
xmatch_ref_catname, "<" * 20))
sl_names = [xmatch_ref_catname, xmatch_comp_catname]
img_names = [xmatch_ref_imgname, xmatch_comp_imgname]
sl_lengths = [max_sources, len(filtobj_dict[xmatch_comp_imgname]["sources"])]
matching_lines_ref, matching_lines_comp = cu.getMatchedLists(sl_names, img_names, sl_lengths,
log_level=log_level)
# Report number and percentage of the total number of detected ref and comp sources that were
# matched
xmresults = []
xmresults.append("Reference sourcelist: {} of {} total reference sources ({}%) cross-matched.".format(len(matching_lines_ref),
sl_lengths[0],
100.0 * (float(len(matching_lines_ref)) / float(sl_lengths[0]))))
xmresults.append("Comparison sourcelist: {} of {} total comparison sources ({}%) cross-matched.".format(len(matching_lines_comp),
sl_lengths[1],
100.0 * (float(len(matching_lines_comp)) / float(sl_lengths[1]))))
# display crossmatch results
padding = math.ceil((max([len(xmresults[0]), len(xmresults[1])]) - 18) / 2)
log.info("{}Crossmatch results".format(" "*padding))
for item in xmresults:
log.info(item)
log.info("")
if len(matching_lines_ref) > 0:
# instantiate diagnostic object to store test results for eventual .json file output
diag_obj = du.HapDiagnostic(log_level=log_level)
diag_obj.instantiate_from_hap_obj(filtobj_dict[xmatch_comp_imgname]['filt_obj'],
data_source="{}.compare_interfilter_crossmatches".format(__taskname__),
description="Interfilter cross-matched comparison and reference catalog X and Y values",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
json_results_dict = collections.OrderedDict()
json_results_dict["reference image name"] = xmatch_ref_imgname
json_results_dict["comparison image name"] = xmatch_comp_imgname
json_results_dict['reference catalog length'] = sl_lengths[0]
json_results_dict['comparison catalog length'] = sl_lengths[1]
json_results_dict['number of cross-matches'] = len(matching_lines_ref)
json_results_dict['percent of all identified reference sources crossmatched'] = 100.0 * (
float(len(matching_lines_ref)) / float(sl_lengths[0]))
json_results_dict['percent of all identified comparison sources crossmatched'] = 100.0 * (
float(len(matching_lines_comp)) / float(sl_lengths[1]))
json_results_dict['reference image platescale'] = filtobj_dict[xmatch_ref_imgname]['filt_obj'].meta_wcs.pscale
# store cross-match details
diag_obj.add_data_item(json_results_dict, "Interfilter cross-match details",
descriptions={
"reference image name": "Crossmatch reference image name",
"comparison image name": "Crossmatch comparison image name",
"reference catalog length": "Number of entries in point catalog",
"comparison catalog length": "Number of entries in segment catalog",
"number of cross-matches": "Number of cross-matches between point and segment catalogs",
"percent of all identified reference sources crossmatched": "percent of all identified reference sources crossmatched",
"percent of all identified comparison sources crossmatched": "percent of all identified comparison sources crossmatched",
"reference image platescale": "Platescale of the crossmatch reference image"},
units={"reference image name": "unitless",
"comparison image name": "unitless",
"reference catalog length": "unitless",
"comparison catalog length": "unitless",
"number of cross-matches": "unitless",
"percent of all identified reference sources crossmatched": "unitless",
"percent of all identified comparison sources crossmatched": "unitless",
"reference image platescale": "arcseconds/pixel"})
# Generate tables containing just "xcentroid_ref" and "ycentroid_ref" columns with only
# the cross-matched reference sources
matched_ref_coords = filtobj_dict[xmatch_ref_imgname]["sources"].copy()
matched_ref_coords.keep_columns(['xcentroid_ref', 'ycentroid_ref'])
matched_ref_coords = matched_ref_coords[matching_lines_ref]
# store reference matched sources catalog
diag_obj.add_data_item(matched_ref_coords, "Interfilter cross-matched reference catalog",
descriptions={"xcentroid_ref": "xcentroid_ref",
"ycentroid_ref": "ycentroid_ref"},
units={"xcentroid_ref": "pixels", "ycentroid_ref": "pixels"})
# write out ds9 region files if log level is 'debug'
if log_level == logutil.logging.DEBUG:
reg_filename = "{}_{}_ref_matches.reg".format(filtername_comp, filtername_ref)
matched_ref_coords.write(reg_filename, format='ascii.csv')
log.debug("wrote region file {}".format(reg_filename))
del reg_filename
# Generate tables containing just "xcentroid_ref" and "ycentroid_ref" columns with only
# the cross-matched comparison sources
matched_comp_coords = filtobj_dict[imgname]["sources"].copy()
matched_comp_coords.keep_columns(['xcentroid_ref', 'ycentroid_ref'])
matched_comp_coords = matched_comp_coords[matching_lines_comp]
# store comparison matched sources catalog
diag_obj.add_data_item(matched_comp_coords,
"Interfilter cross-matched comparison catalog",
descriptions={"xcentroid_ref": "xcentroid_ref",
"ycentroid_ref": "ycentroid_ref"},
units={"xcentroid_ref": "pixels", "ycentroid_ref": "pixels"})
# write out ds9 region file if log level is 'debug'
if log_level == logutil.logging.DEBUG:
reg_filename = "{}_{}_comp_matches.reg".format(filtername_comp, filtername_ref)
matched_comp_coords.write(reg_filename, format='ascii.csv')
log.debug("wrote region file {}".format(reg_filename))
del reg_filename
# compute statistics
xy_separations_table = Table()
for colname in ["xcentroid_ref", "ycentroid_ref"]:
sep = matched_comp_coords[colname] - matched_ref_coords[colname]
# Add column of comp-ref differences to table
xy_separations_table['delta_{}'.format(colname)] = sep
# Compute and store statistics on separations
sep_stat_dict = collections.OrderedDict()
sep_stat_dict["Non-clipped min"] = np.min(sep)
sep_stat_dict["Non-clipped max"] = np.max(sep)
sep_stat_dict["Non-clipped mean"] = np.mean(sep)
sep_stat_dict["Non-clipped median"] = np.median(sep)
sep_stat_dict["Non-clipped standard deviation"] = np.std(sep)
sigma = 3
maxiters = 3
clipped_stats = sigma_clipped_stats(sep, sigma=sigma, maxiters=maxiters)
sep_stat_dict["{}x{} sigma-clipped mean".format(maxiters, sigma)] = clipped_stats[0]
sep_stat_dict["{}x{} sigma-clipped median".format(maxiters, sigma)] = clipped_stats[1]
sep_stat_dict["{}x{} sigma-clipped standard deviation".format(maxiters, sigma)] = clipped_stats[2]
sep_stat_dict["Reference image platescale"] = filtobj_dict[xmatch_ref_imgname][
'filt_obj'].meta_wcs.pscale
# Store statistics as new data section
diag_obj.add_data_item(sep_stat_dict, "Interfilter cross-matched {} comparison - reference separation statistics".format(colname),
descriptions={"Non-clipped min": "Non-clipped min difference",
"Non-clipped max": "Non-clipped max difference",
"Non-clipped mean": "Non-clipped mean difference",
"Non-clipped median": "Non-clipped median difference",
"Non-clipped standard deviation": "Non-clipped standard deviation of differences",
"3x3 sigma-clipped mean": "3x3 sigma-clipped mean difference",
"3x3 sigma-clipped median": "3x3 sigma-clipped median difference",
"3x3 sigma-clipped standard deviation": "3x3 sigma-clipped standard deviation of differences",
"Reference image platescale": "Platescale of the crossmatch reference image"},
units={"Non-clipped min": "pixels",
"Non-clipped max": "pixels",
"Non-clipped mean": "pixels",
"Non-clipped median": "pixels",
"Non-clipped standard deviation": "pixels",
"3x3 sigma-clipped mean": "pixels",
"3x3 sigma-clipped median": "pixels",
"3x3 sigma-clipped standard deviation": "pixels",
"Reference image platescale": "arcseconds/pixel"})
# display stats
padding = math.ceil((74 - 61) / 2)
log.info("{}Interfilter cross-matched {} comparison - reference separation statistics".format(" "*padding, colname))
max_str_length = 0
for stat_key in sep_stat_dict.keys():
if len(stat_key) > max_str_length:
max_str_length = len(stat_key)
for stat_key in sep_stat_dict.keys():
padding = " " * (max_str_length - len(stat_key))
log.info("{}{}: {} {}".format(padding, stat_key, sep_stat_dict[stat_key],
diag_obj.out_dict['data']["Interfilter cross-matched {} comparison - reference separation statistics".format(colname)]['units'][stat_key]))
log.info("")
# store separations table
diag_obj.add_data_item(xy_separations_table,
"Interfilter cross-matched comparison - reference separations",
descriptions={"delta_x": "delta_x",
"delta_y": "delta_y"},
units={"delta_x": "pixels", "delta_y": "pixels"})
# write everything out to the json file
json_filename = filtobj_dict[xmatch_comp_imgname]['filt_obj'].drizzle_filename[
:-9] + "_svm_interfilter_crossmatch.json"
diag_obj.write_json_file(json_filename, clobber=True)
else:
filt_names = "{} - {}".format(filtername_comp, filtername_ref)
log.warning("{} interfilter cross match test could not be performed.".format(filt_names))
except Exception:
log.warning("HAP Point sourcelist interfilter comparison (compare_interfilter_crossmatches) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
finally:
if os.path.exists(filtobj_dict[imgname]['cat_name']):
log.info("removing temporary catalog file {}".format(filtobj_dict[imgname]['cat_name'])) # Housekeeping. Delete each temp *_point-cat-fxm.ecsv file after use.
os.remove(filtobj_dict[imgname]['cat_name'])
# Housekeeping. Delete the reference *_point-cat-fxm.ecsv file created for cross-matching, and the
# filtobj_dict dictionary
log.info("")
if os.path.exists(xmatch_ref_catname):
log.info("removing temporary reference catalog file {}".format(xmatch_ref_catname))
os.remove(xmatch_ref_catname)
del filtobj_dict
# ------------------------------------------------------------------------------------------------------------
def transform_coords(filtobj_subdict, xmatch_ref_imgname, log_level=logutil.logging.NOTSET):
"""Transform comparison image frame of reference x, y coords to RA and dec, then back to x, y coords in
the cross-match reference image's frame of reference
Parameters
----------
filtobj_subdict : dict
dictionary containing a drizzlepac.haputils.Product.FilterProduct and corresponding source catalog
generated earlier in the run by **find_hap_point_sources**.
xmatch_ref_imgname : str
name of the reference image whose WCS info will be used to convert RA and dec values into x, y coords
in a common frame
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
filtobj_dict : dict
the input **filtobj_dict** dictionary with the 'sources' table updated to include freshly computed ra,
dec, x_centroid_ref, and y_centroid_ref columns
"""
# Initiate logging!
log.setLevel(log_level)
# 1: stack up xcentroid and ycentorid columns from sources table
xy_centroid_values = np.stack((filtobj_subdict['sources']['xcentroid'],
filtobj_subdict['sources']['ycentroid']), axis=1)
# 2: perform coordinate transforms.
origin = 0
fits_exten = "[1]"
imgname = filtobj_subdict['filt_obj'].drizzle_filename
# 2a: perform xcentroid, ycentroid -> ra, dec transform
ra_dec_values = hla_flag_filter.xytord(xy_centroid_values, imgname, fits_exten, origin=origin)
# 2b: perform ra, dec -> x_centroid_ref, y_centroid_ref transform
xy_centroid_ref_values = hla_flag_filter.rdtoxy(ra_dec_values, xmatch_ref_imgname, fits_exten,
origin=origin)
# 3: add new columns to filtobj_subdict['sources'] table
title_data_dict = collections.OrderedDict()
title_data_dict["ra"] = ra_dec_values[:, 0]
title_data_dict["dec"] = ra_dec_values[:, 1]
title_data_dict["xcentroid_ref"] = xy_centroid_ref_values[:, 0]
title_data_dict["ycentroid_ref"] = xy_centroid_ref_values[:, 1]
col_ctr = 3
for col_name in title_data_dict.keys():
col_to_add = Column(name=col_name, data=title_data_dict[col_name], dtype=np.float64)
filtobj_subdict['sources'].add_column(col_to_add, index=col_ctr)
col_ctr += 1
return filtobj_subdict
def find_gaia_sources(hap_obj, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Creates a catalog of all GAIA sources in the footprint of a specified HAP final product image, and
stores the GAIA object catalog as a hap diagnostic json file. The catalog contains RA, Dec and magnitude
of each identified source. The catalog is sorted in descending order by brightness.
Parameters
----------
hap_obj : drizzlepac.haputils.Product.TotalProduct, drizzlepac.haputils.Product.FilterProduct, or
drizzlepac.haputils.Product.ExposureProduct, depending on input.
hap product object to process
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
Nothing.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: find_gaia_sources. *****\n')
gaia_table = generate_gaia_catalog(hap_obj, columns_to_remove=['objID', 'GaiaID'])
if not gaia_table:
log.error("*** No GAIA sources were found. Finding of GAIA sources cannot be computed. "
"No json file will be produced.***")
return
# write catalog to HapDiagnostic-formatted .json file.
diag_obj = du.HapDiagnostic(log_level=log_level)
diag_obj.instantiate_from_hap_obj(hap_obj,
data_source="{}.find_gaia_sources".format(__taskname__),
description="A table of GAIA sources in image footprint",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
diag_obj.add_data_item(gaia_table, "GAIA sources", descriptions={"RA": "Right Ascension", "DEC": "Declination", "mag": "AB Magnitude"}, units={"RA": "degrees", "DEC": "degrees", "mag": "unitless"}) # write catalog of identified GAIA sources
diag_obj.add_data_item({"Number of GAIA sources": len(gaia_table)}, "Number of GAIA sources", descriptions={"Number of GAIA sources": 'Number of GAIA sources in image footprint'}, units={"Number of GAIA sources": "unitless"}) # write the number of GAIA sources
diag_obj.write_json_file(hap_obj.drizzle_filename[:-9]+"_svm_gaia_sources.json", clobber=True)
# Clean up
del diag_obj
del gaia_table
# ----------------------------------------------------------------------------------------------------------------------
def find_hap_point_sources(filt_obj, log_level=logutil.logging.NOTSET):
"""Identifies point sources in HAP imagery products and returns a dictionary containg **filt_obj** and
a catalog of identified sources.
Parameters
----------
filt_obj : drizzlepac.haputils.Product.FilterProduct
HAP filter product to process
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
A three-element dictionary containing **filt_obj**, the source catalog and the temporary catalog filename
keyed 'filt_obj', 'sources' and 'cat_name', respectively.
"""
# Initiate logging!
log.setLevel(log_level)
# Need to create footprint mask of number_of_images_per_pixel
footprint = cell_utils.SkyFootprint(filt_obj.meta_wcs)
exp_list = [e.full_filename for e in filt_obj.edp_list]
footprint.build(exp_list)
# Initialize image object
img_obj = catalog_utils.CatalogImage(filt_obj.drizzle_filename, footprint.total_mask, log_level)
img_obj.compute_background(filt_obj.configobj_pars.get_pars("catalog generation")['bkg_box_size'],
filt_obj.configobj_pars.get_pars("catalog generation")['bkg_filter_size'])
img_obj.build_kernel(filt_obj.configobj_pars.get_pars("catalog generation")['bkg_box_size'],
filt_obj.configobj_pars.get_pars("catalog generation")['bkg_filter_size'],
filt_obj.configobj_pars.get_pars("catalog generation")['dao']['TWEAK_FWHMPSF'])
# Perform background subtraction
image = img_obj.data.copy()
image -= img_obj.bkg_background_ra
# create mask to reject any sources located less than 10 pixels from a image/chip edge
wht_image = img_obj.data.copy()
binary_inverted_wht = np.where(wht_image == 0, 1, 0)
exclusion_mask = ndimage.binary_dilation(binary_inverted_wht, iterations=10)
# Identify sources
nsigma = filt_obj.configobj_pars.get_pars("alignment")["generate_source_catalogs"]["nsigma"]
log.info("DAOStarFinder(fwhm={}, threshold={}*{})".format(img_obj.kernel_fwhm,
nsigma, img_obj.bkg_rms_median))
daofind = DAOStarFinder(fwhm=img_obj.kernel_fwhm, threshold=nsigma * img_obj.bkg_rms_median)
sources = Table(daofind(image, mask=exclusion_mask))
cat_name = filt_obj.product_basename + "_point-cat-fxm.ecsv"
return {"filt_obj": filt_obj, "sources": sources, "cat_name": cat_name}
# ----------------------------------------------------------------------------------------------------------------------
def generate_gaia_catalog(hap_obj, columns_to_remove=None):
"""Uses astrometric_utils.create_astrometric_catalog() to create a catalog of all GAIA sources in the
image footprint. This catalog contains right ascension, declination, and magnitude values, and is sorted
in descending order by brightness.
Parameters
----------
hap_obj : drizzlepac.haputils.Product.TotalProduct, drizzlepac.haputils.Product.FilterProduct, or
drizzlepac.haputils.Product.ExposureProduct, depending on input.
hap product object to process
columns_to_remove : list
list of columns to remove from the table of GAIA sources returned by this function
Returns
-------
gaia_table : astropy table
table containing right ascension, declination, and magnitude of all GAIA sources identified in the
image footprint, sorted in descending order by brightness.
"""
# Gather list of input flc/flt images
img_list = []
log.debug("GAIA catalog will be created using the following input images:")
# Create a list of the input flc.fits/flt.fits that were drizzled to create the final HAP product being
# processed here. edp_item.info and hap_obj.info are both structured as follows:
# <proposal id>_<visit #>_<instrument>_<detector>_<input filename>_<filter>_<drizzled product
# image filetype>
# Example: '10265_01_acs_wfc_j92c01b9q_flc.fits_f606w_drc'
# what is being extracted here is just the input filename, which in this case is 'j92c01b9q_flc.fits'.
if hasattr(hap_obj, "edp_list"): # for total and filter product objects
if not os.path.exists(hap_obj.drizzle_filename) or len(hap_obj.edp_list) == 0:
# No valid products to evaluate
log.warning('[generate_gaia_catalog] {} was not created. Skipping.'.format(hap_obj.drizzle_filename))
return None
for edp_item in hap_obj.edp_list:
parse_info = edp_item.info.split("_")
imgname = "{}_{}".format(parse_info[4], parse_info[5])
log.debug(imgname)
img_list.append(imgname)
else: # For single-exposure product objects
parse_info = hap_obj.info.split("_")
imgname = "{}_{}".format(parse_info[4], parse_info[5])
log.debug(imgname)
img_list.append(imgname)
# generate catalog of GAIA sources
gaia_table = au.create_astrometric_catalog(img_list, gaia_only=True, use_footprint=True)
if len(gaia_table) > 0:
# trim off specified columns, but
# only if the specified columns already exist in the table
#
if columns_to_remove:
existing_cols = [col for col in columns_to_remove if col in gaia_table.colnames]
gaia_table.remove_columns(existing_cols)
# remove sources outside image footprint
outwcs = wcsutil.HSTWCS(hap_obj.drizzle_filename, ext=1)
x, y = outwcs.all_world2pix(gaia_table['RA'], gaia_table['DEC'], 1)
imghdu = fits.open(hap_obj.drizzle_filename)
in_img_data = imghdu['WHT'].data.copy()
in_img_data = np.where(in_img_data == 0, np.nan, in_img_data)
mask = au.within_footprint(in_img_data, outwcs, x, y)
gaia_table = gaia_table[mask]
# Report results to log
if len(gaia_table) == 0:
log.warning("No GAIA sources were found!")
elif len(gaia_table) == 1:
log.info("1 GAIA source was found.")
else:
log.info("{} GAIA sources were found.".format(len(gaia_table)))
return gaia_table
# ----------------------------------------------------------------------------------------------------------------------
def compare_photometry(drizzle_list, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Compare photometry measurements for sources cross matched between the Point and Segment catalogs.
DEPRECATED
Report the magnitudes, as well as the mean difference, standard deviation of the mean, and median
differences between the Point and Segment catalogs.
Parameters
----------
drizzle_list: list of strings
Drizzle files for the Filter products which were mined to generate the output catalogs.
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
.. note:: This routine can be run either as a direct call from the hapsequencer.py routine,
or it can invoked by a simple Python driver (or from within a Python session) by providing
the names of the previously computed files as lists. The files must exist in the working directory.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: compare_photometry. *****\n')
pnt_suffix = '_point-cat.ecsv'
seg_suffix = '_segment-cat.ecsv'
good_flag_sum = 255
phot_column_names = ["MagAp1", "MagAp2"]
error_column_names = ["MagErrAp1", "MagErrAp2"]
# Generate a separate JSON file for each detector and filter product
# Drizzle filename example: hst_11665_06_wfc3_ir_f110w_ib4606_drz.fits.
# The "product" in this context is a filter name.
# The filename is all lower-case by design.
for drizzle_file in drizzle_list:
if not os.path.exists(drizzle_file):
log.warning("[compare_photometry] Input {} not found. Skipping comparison.".format(drizzle_file))
return # So calling routine can continue to next test
tokens = drizzle_file.split('_')
detector = tokens[4]
filter_name = tokens[5]
ipppss = tokens[6]
# Set up the diagnostic object
diagnostic_obj = du.HapDiagnostic()
diagnostic_obj.instantiate_from_fitsfile(drizzle_file,
data_source="{}.compare_photometry".format(__taskname__),
description="Photometry differences in Point and "
"Segment catalogs",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
summary_dict = {'detector': detector, 'filter_name': filter_name}
# Construct the output JSON filename
json_filename = '_'.join([ipppss, detector, 'svm', filter_name, 'photometry.json'])
# Construct catalog names for catalogs that should have been produced
# For any drizzled product, only two catalogs can be produced at most (point and segment).
prefix = '_'.join(tokens[0:-1])
cat_names = [prefix + pnt_suffix, prefix + seg_suffix]
# Check that both catalogs exist
for catalog in cat_names:
does_exist = os.path.isfile(catalog)
if not does_exist:
log.warning("Catalog {} does not exist. Both the Point and Segment catalogs must exist "
"for comparison.".format(catalog))
log.warning("Program skipping comparison of catalogs associated "
"with {}.\n".format(drizzle_file))
return # So calling routine can continue to next test
# If the catalogs were actually produced, then get the data.
tab_point_measurements = ascii.read(cat_names[0])
tab_seg_measurements = ascii.read(cat_names[1])
# Unfortunately the Point and Segment catalogs use different names for the X and Y values
# Point: ([X|Y]-Center) Segment: ([X|Y]-Centroid. Reset the coordinate columns to be only X or Y.
tab_point_measurements.rename_column('X-Center', 'X')
tab_point_measurements.rename_column('Y-Center', 'Y')
tab_seg_measurements.rename_column('X-Centroid', 'X')
tab_seg_measurements.rename_column('Y-Centroid', 'Y')
cat_lengths = [len(tab_point_measurements), len(tab_seg_measurements)]
# Determine the column names common to both catalogs as a list
common_columns = list(set(tab_point_measurements.colnames).intersection(
set(tab_seg_measurements.colnames)))
# Use the utilities in devutils to match the sources in the two lists - get
# the indices of the matches.
matches_point_to_seg, matches_seg_to_point = cu.getMatchedLists(cat_names,
[drizzle_file,
drizzle_file],
cat_lengths,
log_level=log_level)
# Move on to the next comparison without creating a .json if no cross-matches are found
if len(matches_point_to_seg) == 0 or len(matches_seg_to_point) == 0:
log.warning("Catalog {} and Catalog {} had no matching sources.".format(cat_names[0],
cat_names[1]))
log.warning("Program skipping comparison of catalog indices associated "
"with {}. No JSON file will be produced.\n".format(drizzle_file))
continue
# There are nan values present in the catalogs - create a mask which identifies these rows
# which are missing valid data
missing_values_mask = cu.mask_missing_values(tab_point_measurements, tab_seg_measurements,
matches_point_to_seg, matches_seg_to_point,
common_columns)
# Extract the Flag column from the two catalogs and get an ndarray (2, length)
flag_matching = cu.extractMatchedLines('Flags', tab_point_measurements, tab_seg_measurements,
matches_point_to_seg, matches_seg_to_point)
# Generate a mask to accommodate the missing, as well as the "flagged" entries
flag_values_mask = cu.make_flag_mask(flag_matching, good_flag_sum, missing_values_mask)
# Extract the columns of interest from the two catalogs for each desired measurement
# and get an ndarray (2, length)
# array([[21.512, ..., 2.944], [21.6 , ..., 22.98]],
# [[21.872, ..., 2.844], [21.2 , ..., 22.8]])
for index, phot_column_name in enumerate(phot_column_names):
matching_phot_rows = cu.extractMatchedLines(phot_column_name, tab_point_measurements,
tab_seg_measurements, matches_point_to_seg,
matches_seg_to_point, bitmask=flag_values_mask)
# Compute the differences (Point - Segment)
delta_phot = np.subtract(matching_phot_rows[0], matching_phot_rows[1])
# Compute some basic statistics: mean difference and standard deviation, median difference,
median_delta_phot = np.median(delta_phot)
mean_delta_phot = np.mean(delta_phot)
std_delta_phot = np.std(delta_phot)
# NEED A BETTER WAY TO ASSOCIATE THE ERRORS WITH THE MEASUREMENTS
# Compute the corresponding error of the differences
matching_error_rows = cu.extractMatchedLines(error_column_names[index],
tab_point_measurements, tab_seg_measurements,
matches_point_to_seg, matches_seg_to_point,
bitmask=flag_values_mask)
# Compute the error of the delta value (square root of the sum of the squares)
result_error = np.sqrt(np.add(np.square(matching_error_rows[0]),
np.square(matching_error_rows[1])))
stat_key = 'Delta_' + phot_column_name
stat_dict = {stat_key: {'Mean': mean_delta_phot, 'StdDev': std_delta_phot,
'Median': median_delta_phot}}
# Write out the results
diagnostic_obj.add_data_item(stat_dict,
'Statistics_' + phot_column_name,
descriptions={stat_key + '.Mean': phot_column_name + '_Mean_Differences(Point-Segment)',
stat_key + '.StdDev': phot_column_name + '_StdDev_of_Mean_Differences',
stat_key + '.Median': phot_column_name + '_Median_Differences(Point-Segment)'},
units={stat_key + '.Mean': 'ABMag',
stat_key + '.StdDev': 'ABMag',
stat_key + '.Median': 'ABMag'})
diagnostic_obj.write_json_file(json_filename)
log.info("Generated photometry comparison for Point - Segment matches "
"sources {}.".format(json_filename))
# Clean up
del diagnostic_obj
# ----------------------------------------------------------------------------------------------------------------------
def report_wcs(total_product_list, json_timestamp=None, json_time_since_epoch=None,
log_level=logutil.logging.NOTSET):
"""Report the WCS information for each exposure of a total data product.
Parameters
----------
total_product_list: list of HAP TotalProduct objects, one object per instrument detector
(drizzlepac.haputils.Product.TotalProduct)
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and
written to the .log file. Default value is 'NOTSET'.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: report_wcs. *****\n')
aposteriori_list = ['FIT', 'SVM', 'MVM']
# Generate a separate JSON file for each ExposureProduct object
for total_product in total_product_list:
detector = total_product.detector
# Loop over all the individual exposures in the list which comprise the total product
for edp_object in total_product.edp_list:
# Set up the diagnostic object
diagnostic_obj = du.HapDiagnostic()
diagnostic_obj.instantiate_from_hap_obj(edp_object,
data_source="{}.report_wcs".format(__taskname__),
description="WCS information",
timestamp=json_timestamp,
time_since_epoch=json_time_since_epoch)
# Construct the output JSON filename
json_filename = '_'.join([edp_object.product_basename, 'svm_wcs.json'])
# For exposures with multiple science extensions (multiple chips),
# generate a combined WCS
num_sci_ext, extname = util.count_sci_extensions(edp_object.full_filename)
extname_list = []
for x in range(num_sci_ext):
extname_list.append((extname, x+1))
metawcs = wcs_functions.make_mosaic_wcs(edp_object.full_filename)
# Get information from the active WCS
active_wcs_dict = {'primary_wcsname': metawcs.wcs.name,
'crpix1': metawcs.wcs.crpix[0],
'crpix2': metawcs.wcs.crpix[1],
'crval1': metawcs.wcs.crval[0],
'crval2': metawcs.wcs.crval[1],
'scale': metawcs.pscale,
'orientation': metawcs.orientat,
'exposure': edp_object.exposure_name}
diagnostic_obj.add_data_item(active_wcs_dict, 'PrimaryWCS',
descriptions={'primary_wcsname': 'Active WCS',
'crpix1': 'X coord of reference pixel',
'crpix2': 'Y coord of reference pixel',
'crval1': 'RA of reference pixel',
'crval2': 'Dec of reference pixel',
'scale': 'Plate scale',
'orientation': 'Position angle of Image Y axis (East of North)',
'exposure': 'Exposure name'},
units={'primary_wcsname': 'unitless',
'crpix1': 'pixels',
'crpix2': 'pixels',
'crval1': 'degrees',
'crval2': 'degrees',
'scale': 'arcseconds/pixel',
'orientation': 'degrees',
'exposure': 'unitless'})
# Determine the possible alternate WCS solutions in the header
dict_of_wcskeys_names = wcsutil.altwcs.wcsnames(edp_object.full_filename, ext=1)
# Ignore the OPUS ("O") WCS, as well as the duplicate of the active WCS
dict_of_wcskeys_names.pop('O')
reverse_dict = {}
for key, value in dict_of_wcskeys_names.items():
reverse_dict.setdefault(value, set()).add(key)
keys_with_dups = set(chain.from_iterable(values for key, values in reverse_dict.items() if len(values) > 1))
# Make a list of the keys which contain duplicate values...
if keys_with_dups:
list_keys = list(keys_with_dups)
# ...ignore the primary key as it is important, and...
#MDD
#list_keys.remove(' ')
# ...remove the duplicates.
for popkey in list_keys:
dict_of_wcskeys_names.pop(popkey)
# The remaining dictionary items all need to share the same IDC base
# solution as the "active" solution in order to make a consistent comparison -
# remove any outliers.
loc = metawcs.wcs.name.find("_")
root_idc = metawcs.wcs.name[0:loc]
bad_match_key = []
for key, value in dict_of_wcskeys_names.items():
if root_idc in value:
continue
else:
bad_match_key.append(key)
for bad_key in bad_match_key:
dict_of_wcskeys_names.pop(bad_key)
log.info("Remaining WCS keys and names based on same reference root {}".format(dict_of_wcskeys_names))
# If there is anything left to compare, then do it.
# Want to gather the WCS for the default (IDC_rootname), apriori (any name without
# FIT, SVM, or MVM), and aposteriori (any name containing FIT, SVM, or MVM) solutions.
if len(dict_of_wcskeys_names) > 1:
# Activate an alternate WCS in order to gather its information.
# First copy the original primary WCS to an alternate (in case there was
# not already a duplicate). Use key 'Z'. *** FIX MDD Should check for Z in use.
wcsutil.altwcs.archive_wcs(edp_object.full_filename, ext=extname_list, wcskey='Z')
icnt = 0
# Restore an alternate to be the primary WCS
# Examples of WCS name formats:
# Default WCS name: IDC_xxxxxxxxx
# A priori WCS name: IDC_xxxxxxxxx-HSC30
# A posteriori WCS name: IDC_xxxxxxxxx-FIT_REL_GAIAedr3
for key, value in dict_of_wcskeys_names.items():
if key != ' ':
alt_key = key
alt_wcs_name = value.strip()
if '-' not in alt_wcs_name and alt_wcs_name.startswith('IDC_'):
alt_name = 'AlternateWCS_default'
delta_name = 'DeltaWCS_default'
elif '-' in alt_wcs_name and 'FIT' not in alt_wcs_name:
alt_name = 'AlternateWCS_apriori'
delta_name = 'DeltaWCS_apriori'
elif list(filter(alt_wcs_name.find, aposteriori_list)) != []:
alt_name = 'AlternateWCS_aposteriori'
delta_name = 'DeltaWCS_aposteriori'
else:
log.info('WCS name {} is unknown. Moving on to next WCS solution.\n'.format(alt_wcs_name))
continue
wcsutil.altwcs.restoreWCS(edp_object.full_filename, ext=extname_list, wcskey=key)
# Create a metawcs for this alternate WCS
alt_metawcs = wcs_functions.make_mosaic_wcs(edp_object.full_filename)
# Get information from the alternate/active WCS
alt_wcs_dict = {'alt_wcsname': alt_wcs_name,
'crpix1': alt_metawcs.wcs.crpix[0],
'crpix2': alt_metawcs.wcs.crpix[1],
'crval1': alt_metawcs.wcs.crval[0],
'crval2': alt_metawcs.wcs.crval[1],
'scale': alt_metawcs.pscale,
'orientation': alt_metawcs.orientat,
'exposure': edp_object.exposure_name}
diagnostic_obj.add_data_item(alt_wcs_dict, alt_name,
descriptions={'alt_wcsname': 'Alternate WCS',
'crpix1': 'X coord of reference pixel',
'crpix2': 'Y coord of reference pixel',
'crval1': 'RA of reference pixel',
'crval2': 'Dec of reference pixel',
'scale': 'Plate scale',
'orientation': 'Position angle of Image Y axis (East of North)',
'exposure': 'Exposure name'},
units={'alt_wcsname': 'unitless',
'crpix1': 'pixels',
'crpix2': 'pixels',
'crval1': 'degrees',
'crval2': 'degrees',
'scale': 'arcseconds/pixel',
'orientation': 'degrees',
'exposure': 'unitless'})
delta_wcs_name = metawcs.wcs.name + '_minus_' + alt_wcs_name
diff_wcs_dict = {'delta_wcsname': delta_wcs_name,
'd_crpix1': active_wcs_dict['crpix1'] - alt_wcs_dict['crpix1'],
'd_crpix2': active_wcs_dict['crpix2'] - alt_wcs_dict['crpix2'],
'd_crval1': active_wcs_dict['crval1'] - alt_wcs_dict['crval1'],
'd_crval2': active_wcs_dict['crval2'] - alt_wcs_dict['crval2'],
'd_scale': active_wcs_dict['scale'] - alt_wcs_dict['scale'],
'd_orientation': active_wcs_dict['orientation'] - alt_wcs_dict['orientation'],
'exposure': edp_object.exposure_name}
diagnostic_obj.add_data_item(diff_wcs_dict, delta_name,
descriptions={'delta_wcsname': 'Active-Alternate WCS',
'd_crpix1': 'delta_X of reference pixel',
'd_crpix2': 'delta_Y of reference pixel',
'd_crval1': 'delta_RA of reference pixel',
'd_crval2': 'delta_Dec of reference pixel',
'd_scale': 'delta_Plate scale',
'd_orientation': 'delta_Position angle of Image Y axis (East of North)',
'exposure': 'Exposure name'},
units={'delta_wcsname': 'unitless',
'd_crpix1': 'pixels',
'd_crpix2': 'pixels',
'd_crval1': 'degrees',
'd_crval2': 'degrees',
'd_scale': 'arcseconds/pixel',
'd_orientation': 'degrees',
'exposure': 'unitless'})
# Delete the original alternate WCS...
wcsutil.altwcs.deleteWCS(edp_object.full_filename, ext=extname_list, wcskey=alt_key)
# ... and archive the current primary with its original key
wcsutil.altwcs.archive_wcs(edp_object.full_filename, ext=extname_list, wcskey=alt_key)
icnt += 1
else:
continue
# When comparisons are done between the original primary WCS and all
# the alternates, restore the original primary WCS with key Z.
wcsutil.altwcs.restoreWCS(edp_object.full_filename, ext=extname_list, wcskey='Z')
# Delete the extra copy of the primary
wcsutil.altwcs.deleteWCS(edp_object.full_filename, ext=extname_list, wcskey='Z')
else:
log.info("This dataset only has the Primary and OPUS WCS values")
diagnostic_obj.write_json_file(json_filename)
# Clean up
del diagnostic_obj
# This routine does not return any values
# ----------------------------------------------------------------------------------------------------------------------
def run_hla_sourcelist_comparison(total_list, diagnostic_mode=False, json_timestamp=None,
json_time_since_epoch=None, log_level=logutil.logging.NOTSET):
""" This subroutine automates execution of drizzlepac/devutils/comparison_tools/compare_sourcelists.py to
compare HAP-generated filter catalogs with their HLA classic counterparts.
NOTE: In order for this subroutine to run, the following environment variables need to be set:
- HLA_CLASSIC_BASEPATH
- HLA_BUILD_VER
Alternatively, if the HLA classic path is unavailable, The comparison can be run using locally stored HLA
classic files. The relevant HLA classic imagery and sourcelist files must be placed in a subdirectory of
the current working directory called 'hla_classic'.
Parameters
----------
total_list: list
List of TotalProduct objects, one object per instrument/detector combination is
a visit. The TotalProduct objects are comprised of FilterProduct and ExposureProduct
objects.
diagnostic_mode : Boolean, optional.
create intermediate diagnostic files? Default value is False.
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
RETURNS
-------
Nothing.
"""
log.setLevel(log_level)
log.info('\n\n***** Begin Quality Analysis Test: run_hla_sourcelist_comparison. *****\n')
# get HLA classic path details from envroment variables
hla_classic_basepath = os.getenv('HLA_CLASSIC_BASEPATH')
hla_build_ver = os.getenv("HLA_BUILD_VER")
for tot_obj in total_list:
combo_comp_pdf_list = []
if hla_classic_basepath and hla_build_ver and os.path.exists(hla_classic_basepath):
hla_cassic_basepath = os.path.join(hla_classic_basepath, tot_obj.instrument, hla_build_ver)
hla_classic_path = os.path.join(hla_cassic_basepath,
tot_obj.prop_id,
tot_obj.prop_id + "_" + tot_obj.obset_id) # Generate path to HLA classic products
elif os.path.exists(os.path.join(os.getcwd(), "hla_classic")): # For local testing
hla_classic_basepath = os.path.join(os.getcwd(), "hla_classic")
hla_classic_path = hla_classic_basepath
else:
return # bail out if HLA classic path can't be found.
for filt_obj in tot_obj.fdp_list:
hap_imgname = filt_obj.drizzle_filename
hla_imgname = glob.glob("{}/{}{}_dr*.fits".format(hla_classic_path,
filt_obj.basename,
filt_obj.filters))[0]
if not os.path.exists(hap_imgname) or not os.path.exists(hla_imgname): # Skip filter if one or both of the images can't be found
continue
for hap_sourcelist_name in [filt_obj.point_cat_filename, filt_obj.segment_cat_filename]:
if hap_sourcelist_name.endswith("point-cat.ecsv"):
hla_classic_cat_type = "dao"
plotfile_prefix = filt_obj.product_basename + "_point"
else:
hla_classic_cat_type = "sex"
plotfile_prefix = filt_obj.product_basename + "_segment"
if hla_classic_basepath and hla_build_ver and os.path.exists(hla_classic_basepath):
hla_sourcelist_name = "{}/logs/{}{}_{}phot.txt".format(hla_classic_path,
filt_obj.basename,
filt_obj.filters,
hla_classic_cat_type)
else:
hla_sourcelist_name = "{}/{}{}_{}phot.txt".format(hla_classic_path, filt_obj.basename,
filt_obj.filters, hla_classic_cat_type)
if not os.path.exists(hap_sourcelist_name) or not os.path.exists(hla_sourcelist_name): # Skip catalog type if one or both of the catalogs can't be found
continue
# convert HLA Classic RA and Dec values to HAP reference frame so the RA and Dec comparisons are correct
updated_hla_sourcelist_name = correct_hla_classic_ra_dec(hla_sourcelist_name,
hap_imgname,
hla_classic_cat_type,
log_level)
log.info("HAP image: {}".format(os.path.basename(hap_imgname)))
log.info("HLA Classic image: {}".format(os.path.basename(hla_imgname)))
log.info("HAP catalog: {}".format(os.path.basename(hap_sourcelist_name)))
log.info("HLA Classic catalog: {}".format(os.path.basename(updated_hla_sourcelist_name)))
# once all file exist checks are passed, execute sourcelist comparison
return_status = csl.comparesourcelists(slNames=[updated_hla_sourcelist_name,
hap_sourcelist_name],
imgNames=[hla_imgname, hap_imgname],
good_flag_sum=255,
plotGen="file",
plotfile_prefix=plotfile_prefix,
output_json_filename=hap_sourcelist_name.replace(".ecsv",
"_svm_compare_sourcelists.json"),
verbose=True,
log_level=log_level,
json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch,
debugMode=diagnostic_mode)
combo_comp_pdf_filename = "{}_comparison_plots.pdf".format(plotfile_prefix)
if os.path.exists(combo_comp_pdf_filename):
combo_comp_pdf_list.append(combo_comp_pdf_filename)
if len(combo_comp_pdf_list) > 0: # combine all plots generated by compare_sourcelists.py for this total object into a single pdf file
total_combo_comp_pdf_filename = "{}_svm_comparison_plots.pdf".format(tot_obj.drizzle_filename[:-9].replace("_total", ""))
cu.pdf_merger(total_combo_comp_pdf_filename, combo_comp_pdf_list)
log.info("Sourcelist comparison plots saved to file {}.".format(total_combo_comp_pdf_filename))
# ----------------------------------------------------------------------------------------------------------------------
def correct_hla_classic_ra_dec(orig_hla_classic_sl_name, hap_imgname, cattype, log_level):
"""
This subroutine runs Rick White's read_hla_catalog script to convert the RA and Dec values from a HLA Classic
sourcelist into same reference frame used by the HAP sourcelists. Additionally, the new RA and Dec values
are then transformed to produce X and Y coordinates that are in the HAP image frame of reference. A new
version of the input file with the converted X and Y and RA and Dec values is written to the current
working directory named <INPUT SOURCELIST NAME>_corrected.txt.
Parameters
----------
orig_hla_classic_sl_name : string
name of the HLA Classic sourcelist whose RA and Dec values will be converted.
hap_imgname : string
name of HAP image. The WCS info from this image will be used to transform the updated RA and DEC
values to new X and Y values in the this image's frame of reference
cattype : string
HLA Classic catalog type. Either 'sex' (source extractor) or 'dao' (DAOphot).
log_level : int
The desired level of verboseness in the log statements displayed on the screen and written to the .log file.
Returns
-------
mod_sl_name : string
Name of the new version of the input file with the converted RA and Dec values
"""
try:
mod_sl_name = os.path.basename(orig_hla_classic_sl_name)
# Execute read_hla_catalog.read_hla_catalog() to convert RA and Dec values
dataset = mod_sl_name.replace("_{}phot.txt".format(cattype), "")
modcat = read_hla_catalog.read_hla_catalog(dataset, cattype=cattype, applyomega=True, multiwave=False,
verbose=True, trim=False, log_level=log_level)
# sort catalog with updated RA, DEC values so that ordering is the same as the uncorrected table and everything maps correclty.
if cattype == "dao":
sortcoltitle = "ID"
x_coltitle = "X-Center"
y_coltitle = "Y-Center"
if cattype == "sex":
sortcoltitle = "NUMBER"
x_coltitle = "X-IMAGE"
y_coltitle = "Y-IMAGE"
modcat.sort(sortcoltitle)
# Identify RA and Dec column names in the new catalog table object
for ra_col_title in ["ra", "RA", "ALPHA_J2000", "alpha_j2000"]:
if ra_col_title in modcat.colnames:
true_ra_col_title = ra_col_title
log.debug("RA Col_name: {}".format(true_ra_col_title))
break
for dec_col_title in ["dec", "DEC", "Dec", "DELTA_J2000", "delta_j2000"]:
if dec_col_title in modcat.colnames:
true_dec_col_title = dec_col_title
log.debug("DEC Col_name: {}".format(true_dec_col_title))
break
# transform new RA and Dec values into X and Y values in the HAP reference frame
ra_dec_values = np.stack((modcat[true_ra_col_title], modcat[true_dec_col_title]), axis=1)
new_xy_values = hla_flag_filter.rdtoxy(ra_dec_values, hap_imgname, '[sci,1]')
# get HLA Classic sourcelist data, replace existing RA and Dec column data with the converted RA and Dec column data
cat = Table.read(orig_hla_classic_sl_name, format='ascii')
cat['RA'] = modcat[true_ra_col_title]
cat['DEC'] = modcat[true_dec_col_title]
# update existing X and Y values with new X and Y values transformed from new RA and Dec values.
cat[x_coltitle] = new_xy_values[:, 0]
cat[y_coltitle] = new_xy_values[:, 1]
# Write updated version of HLA Classic catalog to current working directory
mod_sl_name = mod_sl_name.replace(".txt", "_corrected.txt")
log.info("Corrected version of HLA Classic file {} with new X, Y and RA, Dec values written to {}.".format(os.path.basename(orig_hla_classic_sl_name), mod_sl_name))
cat.write(mod_sl_name, format="ascii.csv")
return mod_sl_name
except Exception:
log.warning("There was a problem converting the RA and Dec values. Using original uncorrected HLA Classic sourcelist instead.")
log.warning("Comparisons may be of questionable quality.")
return orig_hla_classic_sl_name
# ----------------------------------------------------------------------------------------------------------------------
def run_quality_analysis(total_obj_list, run_compare_num_sources=True, run_find_gaia_sources=True,
run_compare_hla_sourcelists=True, run_compare_ra_dec_crossmatches=True,
run_characterize_gaia_distribution=True, run_compare_photometry=True,
run_compare_interfilter_crossmatches=True, run_report_wcs=True,
log_level=logutil.logging.NOTSET):
"""Run the quality analysis functions
Parameters
----------
total_obj_list : list
List of one or more HAP drizzlepac.haputils.Product.TotalProduct object(s) to process
run_compare_num_sources : bool, optional
Run 'compare_num_sources' test? Default value is True.
run_find_gaia_sources : bool, optional
Run 'find_gaia_sources' test? Default value is True.
run_compare_hla_sourcelists : bool, optional
Run 'run_compare_sourcelists' test? Default value is True.
run_compare_ra_dec_crossmatches : bool, optional
Run 'compare_ra_dec_crossmatches' test? Default value is True.
run_characterize_gaia_distribution : bool, optional
Run 'characterize_gaia_distribution' test? Default value is True.
run_compare_photometry : bool, optional
Run 'compare_photometry' test? Default value is True.
run_compare_interfilter_crossmatches : bool, optional
Run 'compare_filter_cross_match' test? Default value is True.
run_report_wcs : bool, optional
Run 'report_wcs' test? Devault value is True.
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
-------
Nothing.
"""
log.setLevel(log_level)
# generate a timestamp values that will be used to make creation time, creation date and epoch values
# common to each json file
json_timestamp = datetime.now().strftime("%m/%d/%YT%H:%M:%S")
json_time_since_epoch = time.time()
# Determine number of sources in Point and Segment catalogs
if run_compare_num_sources:
try:
total_drizzle_list = []
for total_obj in total_obj_list:
total_drizzle_list.append(total_obj.drizzle_filename)
compare_num_sources(total_drizzle_list, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
except Exception:
log.warning("HAP Point vs. HAP Segment sourcelist length comparison (compare_num_sources) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Identify the number of GAIA sources in final product footprints
if run_find_gaia_sources:
try:
for total_obj in total_obj_list:
find_gaia_sources(total_obj, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
for filter_obj in total_obj.fdp_list:
find_gaia_sources(filter_obj, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
for exp_obj in filter_obj.edp_list:
find_gaia_sources(exp_obj, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
except Exception:
log.warning("GAIA sources count (find_gaia_sources) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Compare HAP sourcelists to their HLA Classic counterparts
if run_compare_hla_sourcelists:
try:
if log_level == logutil.logging.DEBUG:
diag_mode = True
else:
diag_mode = False
run_hla_sourcelist_comparison(total_obj_list,
diagnostic_mode=diag_mode,
json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch,
log_level=log_level)
except Exception:
log.warning("HAP vs. HLA sourcelist comparison (compare_sourcelists) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Get point/segment cross-match RA/Dec statistics
if run_compare_ra_dec_crossmatches:
try:
for total_obj in total_obj_list:
for filter_obj in total_obj.fdp_list:
compare_ra_dec_crossmatches(filter_obj, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
except Exception:
log.warning("HAP Point vs. HAP Segment sourcelist RA/Dec comparison (compare_ra_dec_crossmatches) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Statistically characterize GAIA distribution
if run_characterize_gaia_distribution:
try:
for total_obj in total_obj_list:
for filter_obj in total_obj.fdp_list:
characterize_gaia_distribution(filter_obj, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch,
log_level=log_level)
except Exception:
log.warning("GAIA source distribution characterization (characterize_gaia_distribution) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Photometry of cross-matched sources in Point and Segment catalogs for Filter products
if run_compare_photometry:
try:
tot_len = len(total_obj_list)
filter_drizzle_list = []
temp_list = []
for tot in total_obj_list:
temp_list = [x.drizzle_filename for x in tot.fdp_list]
filter_drizzle_list.extend(temp_list)
compare_photometry(filter_drizzle_list, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
except Exception:
log.warning("HAP Point vs. HAP Segment sourcelist photometry comparison (compare_photometry) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Compare inter-filter cross matched HAP sources
if run_compare_interfilter_crossmatches:
try:
compare_interfilter_crossmatches(total_obj_list, json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch, log_level=log_level)
except Exception:
log.warning("HAP Point sourcelist interfilter comparison (compare_interfilter_crossmatches) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# Report WCS info
if run_report_wcs:
try:
report_wcs(total_obj_list, json_timestamp=json_timestamp, json_time_since_epoch=json_time_since_epoch,
log_level=log_level)
except Exception:
log.warning("WCS reporting (report_wcs) encountered a problem.")
log.exception("message")
log.warning("Continuing to next test...")
# ============================================================================================================
if __name__ == "__main__":
# process command-line inputs with argparse
parser = argparse.ArgumentParser(description='Perform quality assessments of the SVM products generated '
'by the drizzlepac package. NOTE: if no QA switches (-cgd, '
'-cns, -cp, -cxm, or -fgs) are specified, ALL QA steps will '
'be executed.')
parser.add_argument('input_filename', help='_total_list.pickle file that holds vital information about '
'the processing run')
parser.add_argument('-cgd', '--run_characterize_gaia_distribution', required=False, action='store_true',
help="Statistically describe distribution of GAIA sources in footprint.")
parser.add_argument('-cns', '--run_compare_num_sources', required=False, action='store_true',
help='Determine the number of viable sources actually listed in SVM output catalogs.')
parser.add_argument('-cp', '--run_compare_photometry', required=False, action='store_true',
help="Compare photometry measurements for sources cross matched between the Point "
"and Segment catalogs.")
parser.add_argument('-cxm', '--run_compare_ra_dec_crossmatches', required=False, action='store_true',
help="Compare RA/Dec position measurements for sources cross matched between the "
"Point and Segment catalogs.")
parser.add_argument('-fgs', '--run_find_gaia_sources', required=False, action='store_true',
help="Determine the number of GAIA sources in the footprint of a specified HAP final "
"product image")
parser.add_argument('-fxm', '--run_compare_interfilter_crossmatches', required=False, action='store_true',
help="Compare positions of cross matched sources from different filter-level "
"products")
parser.add_argument('-hla', '--run_compare_hla_sourcelists', required=False, action='store_true',
help="Compare HAP sourcelists to their HLA classic counterparts")
parser.add_argument('-wcs', '--run_report_wcs', required=False, action='store_true',
help="Report the WCS information for each exposure of a total data product")
parser.add_argument('-l', '--log_level', required=False, default='info',
choices=['critical', 'error', 'warning', 'info', 'debug'],
help='The desired level of verboseness in the log statements displayed on the screen '
'and written to the .log file. The level of verboseness from left to right, and '
'includes all log statements with a log_level left of the specified level. '
'Specifying "critical" will only record/display "critical" log statements, and '
'specifying "error" will record/display both "error" and "critical" log '
'statements, and so on.')
user_args = parser.parse_args()
# set up logging
log_dict = {"critical": logutil.logging.CRITICAL,
"error": logutil.logging.ERROR,
"warning": logutil.logging.WARNING,
"info": logutil.logging.INFO,
"debug": logutil.logging.DEBUG}
log_level = log_dict[user_args.log_level]
log.setLevel(log_level)
# verify that input file exists
if not os.path.exists(user_args.input_filename):
err_msg = "File {} doesn't exist.".format(user_args.input_filename)
log.critical(err_msg)
raise Exception(err_msg)
# check that at least one QA switch is turned on
all_qa_steps_off = True
max_step_str_length = 0
for kv_pair in user_args._get_kwargs():
if kv_pair[0] not in ['input_filename', 'run_all', 'log_level']:
if len(kv_pair[0])-4 > max_step_str_length:
max_step_str_length = len(kv_pair[0])-4
if kv_pair[1]:
all_qa_steps_off = False
# if no QA steps are explicitly turned on in the command-line call, run ALL the QA steps
if all_qa_steps_off:
log.info("No specific QA switches were turned on. All QA steps will be executed.")
user_args.run_characterize_gaia_distribution = True
user_args.run_compare_num_sources = True
user_args.run_compare_photometry = True
user_args.run_compare_hla_sourcelists = True
user_args.run_compare_ra_dec_crossmatches = True
user_args.run_compare_interfilter_crossmatches = True
user_args.run_find_gaia_sources = True
user_args.run_report_wcs = True
# display status summary indicating which QA steps are turned on and which steps are turned off
toplinestring = "-"*(int(max_step_str_length/2)-6)
log.info("{}QA step run status{}".format(toplinestring, toplinestring))
for kv_pair in user_args._get_kwargs():
if kv_pair[0] not in ['input_filename', 'run_all', 'log_level']:
if kv_pair[1]:
run_status = "ON"
else:
run_status = "off"
log.info("{}{} {}".format(kv_pair[0][4:], " "*(max_step_str_length-(len(kv_pair[0])-4)),
run_status))
log.info("-"*(max_step_str_length+6))
# execute specified tests
filehandler = open(user_args.input_filename, 'rb')
total_obj_list = pickle.load(filehandler)
run_quality_analysis(total_obj_list,
run_compare_num_sources=user_args.run_compare_num_sources,
run_find_gaia_sources=user_args.run_find_gaia_sources,
run_compare_ra_dec_crossmatches=user_args.run_compare_ra_dec_crossmatches,
run_characterize_gaia_distribution=user_args.run_characterize_gaia_distribution,
run_compare_hla_sourcelists=user_args.run_compare_hla_sourcelists,
run_compare_photometry=user_args.run_compare_photometry,
run_compare_interfilter_crossmatches=user_args.run_compare_interfilter_crossmatches,
run_report_wcs=user_args.run_report_wcs,
log_level=log_level)
# -----------------------------------------------------------------------------
#
# Generate plots for these results
#
# -----------------------------------------------------------------------------
HOVER_COLUMNS = ['gen_info.instrument',
'gen_info.detector',
'gen_info.filter',
'gen_info.imgname',
'header.DATE-OBS',
'header.RA_TARG',
'header.DEC_TARG',
'header.GYROMODE',
'header.EXPTIME']
FIGURE_TOOLS = 'pan,wheel_zoom,box_zoom,zoom_in,zoom_out,xbox_select,reset,save'
def build_svm_plots(data_source, output_basename=''):
"""Create all the plots for the results generated by these comparisons
Parameters
----------
data_source : str
Filename for master data file which contains all the results. This will
typically be an HSF5 file generated by the JSON harvester.
"""
if output_basename == '':
output_basename = "svm_qa"
else:
output_basename = "{}_svm_qa".format(output_basename)
# Generate plots for point-segment catalog cross-match comparisons
xmatch_col_names = HOVER_COLUMNS + ['Cross-match_details.number_of_cross-matches',
'Cross-match_details.point_catalog_filename',
'Cross-match_details.point_catalog_length',
'Cross-match_details.point_frame',
'Cross-match_details.segment_catalog_filename',
'Cross-match_details.segment_catalog_length',
'Cross-match_details.segment_frame',
'Cross-matched_point_catalog.Declination',
'Cross-matched_point_catalog.Right ascension',
'Cross-matched_segment_catalog.Declination',
'Cross-matched_segment_catalog.Right ascension',
'Segment_-_point_on-sky_separation_statistics.3x3_sigma-clipped_mean',
'Segment_-_point_on-sky_separation_statistics.3x3_sigma-clipped_median',
'Segment_-_point_on-sky_separation_statistics.3x3_sigma-clipped_standard_deviation',
'Segment_-_point_on-sky_separation_statistics.Non-clipped_max',
'Segment_-_point_on-sky_separation_statistics.Non-clipped_mean',
'Segment_-_point_on-sky_separation_statistics.Non-clipped_median',
'Segment_-_point_on-sky_separation_statistics.Non-clipped_min',
'Segment_-_point_on-sky_separation_statistics.Non-clipped_standard_deviation']
xmatch_cols = get_pandas_data(data_source, xmatch_col_names)
xmatch_plots_name = build_crossmatch_plots(xmatch_cols, xmatch_col_names, output_basename=output_basename)
# -----------------------------------------------------------------------------
# Functions for generating each data plot
#
def build_crossmatch_plots(xmatchCDS, data_cols, output_basename='svm_qa'):
"""
Generate the cross-match statistics plots for the comparison between the
point catalog and the segment catalog.
Parameters
----------
xmatchCDS : Pandas ColumnDataSource
This object contains all the columns relevant to the cross-match plots.
data_cols : list
The list of column names for the columns read in to the ``xmatchCDS`` object.
output_basename : str
String to use as the start of the filename for the output plot pages.
Returns
-------
output : str
Name of HTML file where the plot was saved.
"""
output_basename = "{}_crossmatch_comparison".format(output_basename)
if not output_basename.endswith('.html'):
output = output_basename + '.html'
else:
output = output_basename
# Set the output file immediately as advised by Bokeh.
output_file(output)
num_hover_cols = len(HOVER_COLUMNS)
colormap = [qa.DETECTOR_LEGEND[x] for x in xmatchCDS.data[data_cols[1]]]
xmatchCDS.data['colormap'] = colormap
inst_det = ["{}/{}".format(i, d) for (i, d) in zip(xmatchCDS.data[data_cols[0]],
xmatchCDS.data[data_cols[1]])]
xmatchCDS.data[qa.INSTRUMENT_COLUMN] = inst_det
plot_list = []
hist0, edges0 = np.histogram(xmatchCDS.data[data_cols[num_hover_cols]], bins=50)
title0 = 'Number of Point-to-Segment Cross-matched sources'
p0 = [plot_histogram(title0, hist0, edges0, y_start=0, fill_color='navy',
background_fill_color='#fafafa', xlabel='Number of Cross-matched sources',
ylabel='Number of products')]
plot_list += p0
hist1, edges1 = np.histogram(xmatchCDS.data[data_cols[num_hover_cols + 11]], bins=50)
title1 = 'Mean Separation (Sigma-clipped) of Point-to-Segment Cross-matched sources'
p1 = [plot_histogram(title1, hist1, edges1, y_start=0,
fill_color='navy', background_fill_color='#fafafa',
xlabel='Mean Separation of Cross-matched sources (arcseconds)',
ylabel='Number of products')]
plot_list += p1
hist2, edges2 = np.histogram(xmatchCDS.data[data_cols[num_hover_cols + 12]], bins=50)
title2 = 'Median Separation (Sigma-clipped) of Point-to-Segment Cross-matched sources'
p2 = [plot_histogram(title2, hist2, edges2, y_start=0,
fill_color='navy', background_fill_color='#fafafa',
xlabel='Median Separation of Cross-matched sources (arcseconds)',
ylabel='Number of products')]
plot_list += p2
hist3, edges3 = np.histogram(xmatchCDS.data[data_cols[num_hover_cols + 13]], bins=50)
title3 = 'Standard-deviation (sigma-clipped) of Separation of Point-to-Segment Cross-matched sources'
p3 = [plot_histogram(title3, hist3, edges3, y_start=0,
fill_color='navy', background_fill_color='#fafafa',
xlabel='STD(Separation) of Cross-matched sources (arcseconds)',
ylabel='Number of products')]
plot_list += p3
# Save the plot to an HTML file
save(column(plot_list))
return output
# -----------------------------------------------------------------------------
# Utility functions for plotting
#
# MDD Deprecated - only used by build_crossmatch_plots in this module which
# have not been migrated. The get_pandas_data in svm_quality_graphics.py
# should be used on migrated code. NOTE: The returned value from the
# migrated version of get_pandas_data is a DataFrame
# and not a ColumnDataSource. I suggest you do not convert the DataFrame
# into a ColumnDataSource until you are ready to plot as this is really
# what the ColumnDataSource is there to support (IMHO).
def get_pandas_data(data_source, data_columns):
"""Load the harvested data, stored in a CSV file, into local arrays.
Parameters
==========
data_source : str
Name of the file containing the Pandas Dataframe created by the harvester.
data_columns : list
List of column names which should be extracted from the master file
``data_source`` for use in a plot.
Returns
=======
phot_data : Pandas ColumnDataSource
Dataframe which is a subset of the input Pandas dataframe written out as
a CSV file. The subset dataframe consists of only the requested columns
and rows where all of the requested columns did not contain NaNs.
"""
# Instantiate a Pandas Dataframe Reader (lazy instantiation)
# df_handle = PandasDFReader_CSV("svm_qa_dataframe.csv")
df_handle = PandasDFReader(data_source, log_level=logutil.logging.NOTSET)
# In this particular case, the names of the desired columns do not
# have to be further manipulated, for example, to add dataset specific
# names.
#
# Get the relevant column data, eliminating all rows which have NaNs
# in any of the relevant columns.
if data_source.endswith(".h5"):
data_cols = df_handle.get_columns_HDF5(data_columns)
else:
data_cols = df_handle.get_columns_CSV(data_columns)
# Setup the source of the data to be plotted so the axis variables can be
# referenced by column name in the Pandas dataframe
dataDF = ColumnDataSource(data_cols)
num_of_datasets = len(dataDF.data['index'])
print('Number of datasets: {}'.format(num_of_datasets))
return dataDF
# MDD Deprecated - only used by build_crossmatch_plots which have not been migrated.
# This is only still here if you want to run the graphics in this routine to see
# what the code does. This routine should NOT be migrated.
def plot_histogram(title, hist, edges, y_start=0,
fill_color='navy', background_fill_color='#fafafa',
xlabel='', ylabel=''):
p = figure(title=title, tools=FIGURE_TOOLS,
background_fill_color=background_fill_color)
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color=fill_color, line_color="white", alpha=0.5)
p.y_range.start = y_start
p.legend.location = "center_right"
p.xaxis.axis_label = xlabel
p.yaxis.axis_label = ylabel
p.grid.grid_line_color = "white"
return p
|
spacetelescopeREPO_NAMEdrizzlepacPATH_START.@drizzlepac_extracted@drizzlepac-main@drizzlepac@haputils@svm_quality_analysis.py@.PATH_END.py
|
{
"filename": "test_python_parser_only.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/io/parser/test_python_parser_only.py",
"type": "Python"
}
|
"""
Tests that apply specifically to the Python parser. Unless specifically
stated as a Python-specific issue, the goal is to eventually move as many of
these tests out of this module as soon as the C parser can accept further
arguments when parsing.
"""
from __future__ import annotations
import csv
from io import (
BytesIO,
StringIO,
TextIOWrapper,
)
from typing import TYPE_CHECKING
import numpy as np
import pytest
from pandas.errors import (
ParserError,
ParserWarning,
)
from pandas import (
DataFrame,
Index,
MultiIndex,
)
import pandas._testing as tm
if TYPE_CHECKING:
from collections.abc import Iterator
def test_default_separator(python_parser_only):
# see gh-17333
#
# csv.Sniffer in Python treats "o" as separator.
data = "aob\n1o2\n3o4"
parser = python_parser_only
expected = DataFrame({"a": [1, 3], "b": [2, 4]})
result = parser.read_csv(StringIO(data), sep=None)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("skipfooter", ["foo", 1.5, True])
def test_invalid_skipfooter_non_int(python_parser_only, skipfooter):
# see gh-15925 (comment)
data = "a\n1\n2"
parser = python_parser_only
msg = "skipfooter must be an integer"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=skipfooter)
def test_invalid_skipfooter_negative(python_parser_only):
# see gh-15925 (comment)
data = "a\n1\n2"
parser = python_parser_only
msg = "skipfooter cannot be negative"
with pytest.raises(ValueError, match=msg):
parser.read_csv(StringIO(data), skipfooter=-1)
@pytest.mark.parametrize("kwargs", [{"sep": None}, {"delimiter": "|"}])
def test_sniff_delimiter(python_parser_only, kwargs):
data = """index|A|B|C
foo|1|2|3
bar|4|5|6
baz|7|8|9
"""
parser = python_parser_only
result = parser.read_csv(StringIO(data), index_col=0, **kwargs)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
columns=["A", "B", "C"],
index=Index(["foo", "bar", "baz"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_sniff_delimiter_comment(python_parser_only):
data = """# comment line
index|A|B|C
# comment line
foo|1|2|3 # ignore | this
bar|4|5|6
baz|7|8|9
"""
parser = python_parser_only
result = parser.read_csv(StringIO(data), index_col=0, sep=None, comment="#")
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
columns=["A", "B", "C"],
index=Index(["foo", "bar", "baz"], name="index"),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("encoding", [None, "utf-8"])
def test_sniff_delimiter_encoding(python_parser_only, encoding):
parser = python_parser_only
data = """ignore this
ignore this too
index|A|B|C
foo|1|2|3
bar|4|5|6
baz|7|8|9
"""
if encoding is not None:
data = data.encode(encoding)
data = BytesIO(data)
data = TextIOWrapper(data, encoding=encoding)
else:
data = StringIO(data)
result = parser.read_csv(data, index_col=0, sep=None, skiprows=2, encoding=encoding)
expected = DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
columns=["A", "B", "C"],
index=Index(["foo", "bar", "baz"], name="index"),
)
tm.assert_frame_equal(result, expected)
def test_single_line(python_parser_only):
# see gh-6607: sniff separator
parser = python_parser_only
result = parser.read_csv(StringIO("1,2"), names=["a", "b"], header=None, sep=None)
expected = DataFrame({"a": [1], "b": [2]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("kwargs", [{"skipfooter": 2}, {"nrows": 3}])
def test_skipfooter(python_parser_only, kwargs):
# see gh-6607
data = """A,B,C
1,2,3
4,5,6
7,8,9
want to skip this
also also skip this
"""
parser = python_parser_only
result = parser.read_csv(StringIO(data), **kwargs)
expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["A", "B", "C"])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"compression,klass", [("gzip", "GzipFile"), ("bz2", "BZ2File")]
)
def test_decompression_regex_sep(python_parser_only, csv1, compression, klass):
# see gh-6607
parser = python_parser_only
with open(csv1, "rb") as f:
data = f.read()
data = data.replace(b",", b"::")
expected = parser.read_csv(csv1)
module = pytest.importorskip(compression)
klass = getattr(module, klass)
with tm.ensure_clean() as path:
with klass(path, mode="wb") as tmp:
tmp.write(data)
result = parser.read_csv(path, sep="::", compression=compression)
tm.assert_frame_equal(result, expected)
def test_read_csv_buglet_4x_multi_index(python_parser_only):
# see gh-6607
data = """ A B C D E
one two three four
a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640
a q 20 4 0.4473 1.4152 0.2834 1.00661 0.1744
x q 30 3 -0.6662 -0.5243 -0.3580 0.89145 2.5838"""
parser = python_parser_only
expected = DataFrame(
[
[-0.5109, -2.3358, -0.4645, 0.05076, 0.3640],
[0.4473, 1.4152, 0.2834, 1.00661, 0.1744],
[-0.6662, -0.5243, -0.3580, 0.89145, 2.5838],
],
columns=["A", "B", "C", "D", "E"],
index=MultiIndex.from_tuples(
[("a", "b", 10.0032, 5), ("a", "q", 20, 4), ("x", "q", 30, 3)],
names=["one", "two", "three", "four"],
),
)
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
def test_read_csv_buglet_4x_multi_index2(python_parser_only):
# see gh-6893
data = " A B C\na b c\n1 3 7 0 3 6\n3 1 4 1 5 9"
parser = python_parser_only
expected = DataFrame.from_records(
[(1, 3, 7, 0, 3, 6), (3, 1, 4, 1, 5, 9)],
columns=list("abcABC"),
index=list("abc"),
)
result = parser.read_csv(StringIO(data), sep=r"\s+")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("add_footer", [True, False])
def test_skipfooter_with_decimal(python_parser_only, add_footer):
# see gh-6971
data = "1#2\n3#4"
parser = python_parser_only
expected = DataFrame({"a": [1.2, 3.4]})
if add_footer:
# The stray footer line should not mess with the
# casting of the first two lines if we skip it.
kwargs = {"skipfooter": 1}
data += "\nFooter"
else:
kwargs = {}
result = parser.read_csv(StringIO(data), names=["a"], decimal="#", **kwargs)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sep", ["::", "#####", "!!!", "123", "#1!c5", "%!c!d", "@@#4:2", "_!pd#_"]
)
@pytest.mark.parametrize(
"encoding", ["utf-16", "utf-16-be", "utf-16-le", "utf-32", "cp037"]
)
def test_encoding_non_utf8_multichar_sep(python_parser_only, sep, encoding):
# see gh-3404
expected = DataFrame({"a": [1], "b": [2]})
parser = python_parser_only
data = "1" + sep + "2"
encoded_data = data.encode(encoding)
result = parser.read_csv(
BytesIO(encoded_data), sep=sep, names=["a", "b"], encoding=encoding
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("quoting", [csv.QUOTE_MINIMAL, csv.QUOTE_NONE])
def test_multi_char_sep_quotes(python_parser_only, quoting):
# see gh-13374
kwargs = {"sep": ",,"}
parser = python_parser_only
data = 'a,,b\n1,,a\n2,,"2,,b"'
if quoting == csv.QUOTE_NONE:
msg = "Expected 2 fields in line 3, saw 3"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), quoting=quoting, **kwargs)
else:
msg = "ignored when a multi-char delimiter is used"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), quoting=quoting, **kwargs)
def test_none_delimiter(python_parser_only):
# see gh-13374 and gh-17465
parser = python_parser_only
data = "a,b,c\n0,1,2\n3,4,5,6\n7,8,9"
expected = DataFrame({"a": [0, 7], "b": [1, 8], "c": [2, 9]})
# We expect the third line in the data to be
# skipped because it is malformed, but we do
# not expect any errors to occur.
with tm.assert_produces_warning(
ParserWarning, match="Skipping line 3", check_stacklevel=False
):
result = parser.read_csv(
StringIO(data), header=0, sep=None, on_bad_lines="warn"
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("data", ['a\n1\n"b"a', 'a,b,c\ncat,foo,bar\ndog,foo,"baz'])
@pytest.mark.parametrize("skipfooter", [0, 1])
def test_skipfooter_bad_row(python_parser_only, data, skipfooter):
# see gh-13879 and gh-15910
parser = python_parser_only
if skipfooter:
msg = "parsing errors in the skipped footer rows"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), skipfooter=skipfooter)
else:
msg = "unexpected end of data|expected after"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), skipfooter=skipfooter)
def test_malformed_skipfooter(python_parser_only):
parser = python_parser_only
data = """ignore
A,B,C
1,2,3 # comment
1,2,3,4,5
2,3,4
footer
"""
msg = "Expected 3 fields in line 4, saw 5"
with pytest.raises(ParserError, match=msg):
parser.read_csv(StringIO(data), header=1, comment="#", skipfooter=1)
def test_python_engine_file_no_next(python_parser_only):
parser = python_parser_only
class NoNextBuffer:
def __init__(self, csv_data) -> None:
self.data = csv_data
def __iter__(self) -> Iterator:
return self.data.__iter__()
def read(self):
return self.data
def readline(self):
return self.data
parser.read_csv(NoNextBuffer("a\n1"))
@pytest.mark.parametrize("bad_line_func", [lambda x: ["2", "3"], lambda x: x[:2]])
def test_on_bad_lines_callable(python_parser_only, bad_line_func):
# GH 5686
parser = python_parser_only
data = """a,b
1,2
2,3,4,5,6
3,4
"""
bad_sio = StringIO(data)
result = parser.read_csv(bad_sio, on_bad_lines=bad_line_func)
expected = DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
tm.assert_frame_equal(result, expected)
def test_on_bad_lines_callable_write_to_external_list(python_parser_only):
# GH 5686
parser = python_parser_only
data = """a,b
1,2
2,3,4,5,6
3,4
"""
bad_sio = StringIO(data)
lst = []
def bad_line_func(bad_line: list[str]) -> list[str]:
lst.append(bad_line)
return ["2", "3"]
result = parser.read_csv(bad_sio, on_bad_lines=bad_line_func)
expected = DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
tm.assert_frame_equal(result, expected)
assert lst == [["2", "3", "4", "5", "6"]]
@pytest.mark.parametrize("bad_line_func", [lambda x: ["foo", "bar"], lambda x: x[:2]])
@pytest.mark.parametrize("sep", [",", "111"])
def test_on_bad_lines_callable_iterator_true(python_parser_only, bad_line_func, sep):
# GH 5686
# iterator=True has a separate code path than iterator=False
parser = python_parser_only
data = f"""
0{sep}1
hi{sep}there
foo{sep}bar{sep}baz
good{sep}bye
"""
bad_sio = StringIO(data)
result_iter = parser.read_csv(
bad_sio, on_bad_lines=bad_line_func, chunksize=1, iterator=True, sep=sep
)
expecteds = [
{"0": "hi", "1": "there"},
{"0": "foo", "1": "bar"},
{"0": "good", "1": "bye"},
]
for i, (result, expected) in enumerate(zip(result_iter, expecteds)):
expected = DataFrame(expected, index=range(i, i + 1))
tm.assert_frame_equal(result, expected)
def test_on_bad_lines_callable_dont_swallow_errors(python_parser_only):
# GH 5686
parser = python_parser_only
data = """a,b
1,2
2,3,4,5,6
3,4
"""
bad_sio = StringIO(data)
msg = "This function is buggy."
def bad_line_func(bad_line):
raise ValueError(msg)
with pytest.raises(ValueError, match=msg):
parser.read_csv(bad_sio, on_bad_lines=bad_line_func)
def test_on_bad_lines_callable_not_expected_length(python_parser_only):
# GH 5686
parser = python_parser_only
data = """a,b
1,2
2,3,4,5,6
3,4
"""
bad_sio = StringIO(data)
result = parser.read_csv_check_warnings(
ParserWarning, "Length of header or names", bad_sio, on_bad_lines=lambda x: x
)
expected = DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
tm.assert_frame_equal(result, expected)
def test_on_bad_lines_callable_returns_none(python_parser_only):
# GH 5686
parser = python_parser_only
data = """a,b
1,2
2,3,4,5,6
3,4
"""
bad_sio = StringIO(data)
result = parser.read_csv(bad_sio, on_bad_lines=lambda x: None)
expected = DataFrame({"a": [1, 3], "b": [2, 4]})
tm.assert_frame_equal(result, expected)
def test_on_bad_lines_index_col_inferred(python_parser_only):
# GH 5686
parser = python_parser_only
data = """a,b
1,2,3
4,5,6
"""
bad_sio = StringIO(data)
result = parser.read_csv(bad_sio, on_bad_lines=lambda x: ["99", "99"])
expected = DataFrame({"a": [2, 5], "b": [3, 6]}, index=[1, 4])
tm.assert_frame_equal(result, expected)
def test_index_col_false_and_header_none(python_parser_only):
# GH#46955
parser = python_parser_only
data = """
0.5,0.03
0.1,0.2,0.3,2
"""
result = parser.read_csv_check_warnings(
ParserWarning,
"Length of header",
StringIO(data),
sep=",",
header=None,
index_col=False,
)
expected = DataFrame({0: [0.5, 0.1], 1: [0.03, 0.2]})
tm.assert_frame_equal(result, expected)
def test_header_int_do_not_infer_multiindex_names_on_different_line(python_parser_only):
# GH#46569
parser = python_parser_only
data = StringIO("a\na,b\nc,d,e\nf,g,h")
result = parser.read_csv_check_warnings(
ParserWarning, "Length of header", data, engine="python", index_col=False
)
expected = DataFrame({"a": ["a", "c", "f"]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"dtype", [{"a": object}, {"a": str, "b": np.int64, "c": np.int64}]
)
def test_no_thousand_convert_with_dot_for_non_numeric_cols(python_parser_only, dtype):
# GH#50270
parser = python_parser_only
data = """\
a;b;c
0000.7995;16.000;0
3.03.001.00514;0;4.000
4923.600.041;23.000;131"""
result = parser.read_csv(
StringIO(data),
sep=";",
dtype=dtype,
thousands=".",
)
expected = DataFrame(
{
"a": ["0000.7995", "3.03.001.00514", "4923.600.041"],
"b": [16000, 0, 23000],
"c": [0, 4000, 131],
}
)
if dtype["a"] == object:
expected["a"] = expected["a"].astype(object)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"dtype,expected",
[
(
{"a": str, "b": np.float64, "c": np.int64},
{
"b": [16000.1, 0, 23000],
"c": [0, 4001, 131],
},
),
(
str,
{
"b": ["16,000.1", "0", "23,000"],
"c": ["0", "4,001", "131"],
},
),
],
)
def test_no_thousand_convert_for_non_numeric_cols(python_parser_only, dtype, expected):
# GH#50270
parser = python_parser_only
data = """a;b;c
0000,7995;16,000.1;0
3,03,001,00514;0;4,001
4923,600,041;23,000;131
"""
result = parser.read_csv(
StringIO(data),
sep=";",
dtype=dtype,
thousands=",",
)
expected = DataFrame(expected)
expected.insert(0, "a", ["0000,7995", "3,03,001,00514", "4923,600,041"])
tm.assert_frame_equal(result, expected)
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@io@parser@test_python_parser_only.py@.PATH_END.py
|
{
"filename": "slice2d_2di.py",
"repo_name": "spedas/pyspedas",
"repo_path": "pyspedas_extracted/pyspedas-master/pyspedas/particles/spd_slice2d/slice2d_2di.py",
"type": "Python"
}
|
import numpy as np
import scipy.interpolate
import scipy.spatial
def slice2d_2di(datapoints, xyz, resolution, thetarange=None, zdirrange=None):
"""
Produces slice by interpolating projected data. Based on spd_slice2d_2di in IDL
"""
# cut by theta value
if thetarange is not None:
thetarange = [np.nanmin(thetarange), np.nanmax(thetarange)]
r = np.sqrt(xyz[:, 0]**2 + xyz[:, 1]**2 + xyz[:, 2]**2)
eachangle = np.arcsin(xyz[:, 2]/r)*180.0/np.pi
index = np.argwhere((eachangle <= thetarange[1]) & (eachangle >= thetarange[0])).flatten()
if len(index) != 0:
if len(index) != len(r):
xyz = xyz[index, :]
datapoints = datapoints[index]
# cut by z-axis value
if zdirrange is not None:
zdirrange = [np.nanmin(zdirrange), np.nanmax(zdirrange)]
index = np.argwhere((xyz[:, 2] >= zdirrange[0]) & (xyz[:, 2] <= zdirrange[1])).flatten()
if len(index) != 0:
if len(index) != len(datapoints):
xyz = xyz[index, :]
datapoints = datapoints[index]
x = xyz[:, 0]
y = xyz[:, 1]
# average duplicates along the way
# note: the following algorithm was translated directly
# from IDL, and apparently came from thm_esa_slice2d
# originally
# uni2 = np.unique(x, return_index=True)[1]+1
uni2 = np.unique(x, return_index=True)[1]
uni1 = np.insert(uni2[0:len(uni2)-1] + 1, 0, 0)
kk = 0
for i in range(0, len(uni2)):
yi = y[uni1[i]:uni2[i]+1]
xi = x[uni1[i]:uni2[i]+1]
datapointsi = datapoints[uni1[i]:uni2[i]+1]
xi = xi[np.argsort(yi)]
datapointsi = datapointsi[np.argsort(yi)]
yi = yi[np.argsort(yi)]
index2 = np.unique(yi, return_index=True)[1]
if len(index2) == 1:
index1 = 0
else:
index1 = np.insert(index2[0:len(index2)-1]+1, 0, 0)
for j in range(0, len(index2)):
if isinstance(index1, int):
y[kk] = yi[index1]
x[kk] = xi[index1]
if index1 == index2[j]:
datapoints[kk] = datapointsi[index1]
else:
datapoints[kk] = np.nanmean(datapointsi[index1:index2[j]+1])
else:
y[kk] = yi[index1[j]]
x[kk] = xi[index1[j]]
if index1[j] == index2[j]:
datapoints[kk] = datapointsi[index1[j]]
else:
datapoints[kk] = np.nanmean(datapointsi[index1[j]:index2[j]+1])
kk = kk + 1
y = y[0:kk]
x = x[0:kk]
datapoints = datapoints[0:kk]
xmax = np.nanmax(np.abs(np.array([y, x])))
xrange = [-1*xmax, xmax]
xgrid = np.linspace(xrange[0], xrange[1], num=resolution)
ygrid = np.linspace(xrange[0], xrange[1], num=resolution)
out = np.zeros((resolution, resolution))
grid_interpolator = griddata_tri(datapoints, x, y)
for xi, xp in enumerate(xgrid):
for yi, yp in enumerate(ygrid):
out[xi, yi] = grid_interpolator((xp, yp))
return {'data': out, 'xgrid': xgrid, 'ygrid': ygrid}
def griddata_tri(data, x, y):
cart_temp = np.array([x, y])
points = np.stack(cart_temp).T
delaunay = scipy.spatial.Delaunay(points)
return scipy.interpolate.LinearNDInterpolator(delaunay, data)
|
spedasREPO_NAMEpyspedasPATH_START.@pyspedas_extracted@pyspedas-master@pyspedas@particles@spd_slice2d@slice2d_2di.py@.PATH_END.py
|
{
"filename": "_textfont.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/scatterpolargl/_textfont.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Textfont(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "scatterpolargl"
_path_str = "scatterpolargl.textfont"
_valid_props = {
"color",
"colorsrc",
"family",
"familysrc",
"size",
"sizesrc",
"style",
"stylesrc",
"variant",
"variantsrc",
"weight",
"weightsrc",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# colorsrc
# --------
@property
def colorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `color`.
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["colorsrc"]
@colorsrc.setter
def colorsrc(self, val):
self["colorsrc"] = val
# family
# ------
@property
def family(self):
"""
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".
The 'family' property is a string and must be specified as:
- A non-empty string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# familysrc
# ---------
@property
def familysrc(self):
"""
Sets the source reference on Chart Studio Cloud for `family`.
The 'familysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["familysrc"]
@familysrc.setter
def familysrc(self, val):
self["familysrc"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# sizesrc
# -------
@property
def sizesrc(self):
"""
Sets the source reference on Chart Studio Cloud for `size`.
The 'sizesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["sizesrc"]
@sizesrc.setter
def sizesrc(self, val):
self["sizesrc"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# stylesrc
# --------
@property
def stylesrc(self):
"""
Sets the source reference on Chart Studio Cloud for `style`.
The 'stylesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["stylesrc"]
@stylesrc.setter
def stylesrc(self, val):
self["stylesrc"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# variantsrc
# ----------
@property
def variantsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `variant`.
The 'variantsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["variantsrc"]
@variantsrc.setter
def variantsrc(self, val):
self["variantsrc"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'bold']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# weightsrc
# ---------
@property
def weightsrc(self):
"""
Sets the source reference on Chart Studio Cloud for `weight`.
The 'weightsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["weightsrc"]
@weightsrc.setter
def weightsrc(self, val):
self["weightsrc"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
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`.
style
Sets whether a font should be styled with a normal or
italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud for
`style`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud for
`variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud for
`weight`.
"""
def __init__(
self,
arg=None,
color=None,
colorsrc=None,
family=None,
familysrc=None,
size=None,
sizesrc=None,
style=None,
stylesrc=None,
variant=None,
variantsrc=None,
weight=None,
weightsrc=None,
**kwargs,
):
"""
Construct a new Textfont object
Sets the text font.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.scatterpolargl.Textfont`
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`.
style
Sets whether a font should be styled with a normal or
italic face from its family.
stylesrc
Sets the source reference on Chart Studio Cloud for
`style`.
variant
Sets the variant of the font.
variantsrc
Sets the source reference on Chart Studio Cloud for
`variant`.
weight
Sets the weight (or boldness) of the font.
weightsrc
Sets the source reference on Chart Studio Cloud for
`weight`.
Returns
-------
Textfont
"""
super(Textfont, self).__init__("textfont")
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.scatterpolargl.Textfont
constructor must be a dict or
an instance of :class:`plotly.graph_objs.scatterpolargl.Textfont`"""
)
# 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("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("colorsrc", None)
_v = colorsrc if colorsrc is not None else _v
if _v is not None:
self["colorsrc"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("familysrc", None)
_v = familysrc if familysrc is not None else _v
if _v is not None:
self["familysrc"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("sizesrc", None)
_v = sizesrc if sizesrc is not None else _v
if _v is not None:
self["sizesrc"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("stylesrc", None)
_v = stylesrc if stylesrc is not None else _v
if _v is not None:
self["stylesrc"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("variantsrc", None)
_v = variantsrc if variantsrc is not None else _v
if _v is not None:
self["variantsrc"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
_v = arg.pop("weightsrc", None)
_v = weightsrc if weightsrc is not None else _v
if _v is not None:
self["weightsrc"] = _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@scatterpolargl@_textfont.py@.PATH_END.py
|
{
"filename": "_autorangeoptions.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/polar/radialaxis/_autorangeoptions.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class AutorangeoptionsValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self,
plotly_name="autorangeoptions",
parent_name="layout.polar.radialaxis",
**kwargs,
):
super(AutorangeoptionsValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Autorangeoptions"),
data_docs=kwargs.pop(
"data_docs",
"""
clipmax
Clip autorange maximum if it goes beyond this
value. Has no effect when
`autorangeoptions.maxallowed` is provided.
clipmin
Clip autorange minimum if it goes beyond this
value. Has no effect when
`autorangeoptions.minallowed` is provided.
include
Ensure this value is included in autorange.
includesrc
Sets the source reference on Chart Studio Cloud
for `include`.
maxallowed
Use this value exactly as autorange maximum.
minallowed
Use this value exactly as autorange minimum.
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@polar@radialaxis@_autorangeoptions.py@.PATH_END.py
|
{
"filename": "_bordercolorsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/ohlc/hoverlabel/_bordercolorsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BordercolorsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="bordercolorsrc", parent_name="ohlc.hoverlabel", **kwargs
):
super(BordercolorsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@ohlc@hoverlabel@_bordercolorsrc.py@.PATH_END.py
|
{
"filename": "bench.py",
"repo_name": "mikecokina/elisa",
"repo_path": "elisa_extracted/elisa-master/scripts/benchmark/mp/bench.py",
"type": "Python"
}
|
import json
import os.path as op
import tempfile
import numpy as np
import os
from time import time
from elisa.binary_system import system
from elisa.conf import settings
from elisa.observer import observer
from elisa.logger import getLogger
from matplotlib import pyplot as plt
from importlib import reload
from elisa.utils import is_empty
logger = getLogger('benchmark')
STYPE_TO_FILENAME = {
"detached.ecc.sync": "detached.ecc.sync.json",
"detached.circ.sync": "detached.circ.sync.json",
"detached.circ.async": "detached.circ.async.json"
}
DATA = op.join(op.abspath(op.dirname(__file__)), "data")
def get_params(stype):
filename = STYPE_TO_FILENAME[stype]
fpath = op.join(DATA, filename)
with open(fpath, "r") as f:
return json.loads(f.read())
def store_result(filename, data):
tempdir = tempfile.gettempdir()
fpath = op.join(tempdir, filename)
with open(fpath, "w") as f:
f.write(json.dumps(data, indent=4))
return fpath
class BenchMark(object):
def __init__(self, stype, n_steps=5, n_each=5, n_from=10, n_to=200, multiprocess=True):
self.stype = stype
self.n_steps = n_steps
self.n_from = n_from
self.n_to = n_to
self.n_each = n_each
self.mp_result = {"cores": int(os.cpu_count()), "n_phases": [], "elapsed_time": []}
self.sc_result = {"cores": 1, "n_phases": [], "elapsed_time": []}
settings.POINTS_ON_ECC_ORBIT = -1
settings.MAX_RELATIVE_D_R_POINT = 0.0
self._multiprocess = True
setattr(self, "multiprocess", multiprocess)
@property
def multiprocess(self):
return self._multiprocess
@multiprocess.setter
def multiprocess(self, value):
self._multiprocess = value
settings.NUMBER_OF_PROCESSES = int(os.cpu_count()) if self.multiprocess else 1
reload(system)
reload(observer)
def eval(self, store=False):
logger.info("starting benchmark evaluation")
data = get_params(stype=self.stype)
for n_run in range(self.n_from, self.n_to, self.n_each):
if n_run % 5 == 0:
logger.info(f'evaluating run for {n_run} phases')
inter_run = []
phases = np.linspace(-0.6, 0.6, n_run)
# initialize objects
binary = system.BinarySystem.from_json(data)
o = observer.Observer(passband='Generic.Bessell.V', system=binary)
for _ in range(0, self.n_steps):
start_time = time()
o.observe.lc(phases=phases)
inter_run.append(time() - start_time)
if self.multiprocess:
self.mp_result["n_phases"].append(n_run)
self.mp_result["elapsed_time"].append(np.mean(inter_run))
else:
self.sc_result["n_phases"].append(n_run)
self.sc_result["elapsed_time"].append(np.mean(inter_run))
logger.info("benchmark evaluation finished")
if store:
mp_in_name = {True: "multiprocess", False: "singleprocess"}
filename = f'{mp_in_name[self.multiprocess]}.{self.stype}.json'
result = self.mp_result if self.multiprocess else self.sc_result
stored_in = store_result(filename, result)
logger.info(f"result stored in {stored_in}")
def plot(self):
plt.figure(figsize=(8, 6))
if not is_empty(self.mp_result["n_phases"]):
plt.plot(self.mp_result["n_phases"], np.round(self.mp_result["elapsed_time"], 2), label=f"multiprocessing")
if not is_empty(self.sc_result["n_phases"]):
plt.plot(self.sc_result["n_phases"], np.round(self.sc_result["elapsed_time"], 2), label=f"singlecore")
plt.legend()
plt.xlabel('n_phases [-]')
plt.ylabel('elapsed_time [s]')
plt.show()
def main():
bm = BenchMark('detached.circ.sync', n_steps=10, n_from=10, n_to=400)
bm.eval(store=True)
bm.multiprocess = False
bm.eval(store=True)
if __name__ == "__main__":
main()
|
mikecokinaREPO_NAMEelisaPATH_START.@elisa_extracted@elisa-master@scripts@benchmark@mp@bench.py@.PATH_END.py
|
{
"filename": "object_helpers.py",
"repo_name": "danielkoll/PyRADS",
"repo_path": "PyRADS_extracted/PyRADS-master/pyrads/object_helpers.py",
"type": "Python"
}
|
from __future__ import division, print_function, absolute_import
from copy import copy # to copy lists of objects...
# ==================================================================================
# Retrieve object(s) from obj list that match obj.attribute == value
# or function(obj.attribute,value).
# Either return object or list.
# Usages:
# sublist = get_objects_from_list(masterlist,"name","Dry_50")
# sublist = get_objects_from_list(masterlist,"omega",5,comparefn=largerthan)
# (note: comparefn(x,y) must return a boolean!)
def get_objects_from_list(obj_list,attribute,value,comparefn=None):
if comparefn is None:
x = [obj for obj in obj_list if getattr(obj,attribute)==value]
else:
x = [obj for obj in obj_list if comparefn(getattr(obj,attribute),value)]
if len(x)==1:
x = x[0]
return x
|
danielkollREPO_NAMEPyRADSPATH_START.@PyRADS_extracted@PyRADS-master@pyrads@object_helpers.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattercarpet/line/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._width import WidthValidator
from ._smoothing import SmoothingValidator
from ._shape import ShapeValidator
from ._dash import DashValidator
from ._color import ColorValidator
from ._backoffsrc import BackoffsrcValidator
from ._backoff import BackoffValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._width.WidthValidator",
"._smoothing.SmoothingValidator",
"._shape.ShapeValidator",
"._dash.DashValidator",
"._color.ColorValidator",
"._backoffsrc.BackoffsrcValidator",
"._backoff.BackoffValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattercarpet@line@__init__.py@.PATH_END.py
|
{
"filename": "test_scaling.py",
"repo_name": "pymc-devs/pymc",
"repo_path": "pymc_extracted/pymc-main/tests/tuning/test_scaling.py",
"type": "Python"
}
|
# Copyright 2024 The PyMC Developers
#
# 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.
import warnings
import numpy as np
from pymc.tuning import scaling
from tests import models
def test_adjust_precision():
a = np.array([-10, -0.01, 0, 10, 1e300, -np.inf, np.inf])
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "divide by zero encountered in log", RuntimeWarning)
a1 = scaling.adjust_precision(a)
assert all((a1 > 0) & (a1 < 1e200))
def test_guess_scaling():
start, model, _ = models.non_normal(n=5)
a1 = scaling.guess_scaling(start, model=model)
assert all((a1 > 0) & (a1 < 1e200))
|
pymc-devsREPO_NAMEpymcPATH_START.@pymc_extracted@pymc-main@tests@tuning@test_scaling.py@.PATH_END.py
|
{
"filename": "uves.py",
"repo_name": "AWehrhahn/PyReduce",
"repo_path": "PyReduce_extracted/PyReduce-master/pyreduce/instruments/uves.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
Handles instrument specific info for the UVES spectrograph
Mostly reading data from the header
"""
import logging
import os.path
import numpy as np
from astropy.io import fits
from dateutil import parser
from .common import Instrument, getter, observation_date_to_night
logger = logging.getLogger(__name__)
class UVES(Instrument):
def add_header_info(self, header, mode, **kwargs):
"""read data from header and add it as REDUCE keyword back to the header"""
# "Normal" stuff is handled by the general version, specific changes to values happen here
# alternatively you can implement all of it here, whatever works
header = super().add_header_info(header, mode)
header["e_ra"] /= 15
if header["e_jd"] is not None:
header["e_jd"] += header["e_exptime"] / (7200 * 24) + 0.5
return header
def get_wavecal_filename(self, header, mode, **kwargs):
"""Get the filename of the wavelength calibration config file"""
info = self.load_info()
specifier = int(header[info["wavecal_specifier"]])
cwd = os.path.dirname(__file__)
fname = "{instrument}_{mode}_{specifier}nm_2D.npz".format(
instrument="uves", mode=mode.lower(), specifier=specifier
)
fname = os.path.join(cwd, "..", "wavecal", fname)
return fname
def get_mask_filename(self, mode, **kwargs):
i = self.name.lower()
m = mode.lower()
fname = f"mask_{i}_{m}.fits.gz"
cwd = os.path.dirname(__file__)
fname = os.path.join(cwd, "..", "masks", fname)
return fname
|
AWehrhahnREPO_NAMEPyReducePATH_START.@PyReduce_extracted@PyReduce-master@pyreduce@instruments@uves.py@.PATH_END.py
|
{
"filename": "test_fit_imaging_plotters.py",
"repo_name": "Jammy2211/PyAutoLens",
"repo_path": "PyAutoLens_extracted/PyAutoLens-main/test_autolens/imaging/plot/test_fit_imaging_plotters.py",
"type": "Python"
}
|
from os import path
import pytest
import autolens.plot as aplt
directory = path.dirname(path.realpath(__file__))
@pytest.fixture(name="plot_path")
def make_fit_imaging_plotter_setup():
return path.join(
"{}".format(path.dirname(path.realpath(__file__))), "files", "plots", "fit"
)
def test__fit_quantities_are_output(
fit_imaging_x2_plane_7x7, include_2d_all, plot_path, plot_patch
):
fit_plotter = aplt.FitImagingPlotter(
fit=fit_imaging_x2_plane_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(path=plot_path, format="png")),
)
fit_plotter.figures_2d(
data=True,
noise_map=True,
signal_to_noise_map=True,
model_image=True,
residual_map=True,
normalized_residual_map=True,
chi_squared_map=True,
)
assert path.join(plot_path, "data.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") in plot_patch.paths
assert path.join(plot_path, "signal_to_noise_map.png") in plot_patch.paths
assert path.join(plot_path, "model_image.png") in plot_patch.paths
assert path.join(plot_path, "residual_map.png") in plot_patch.paths
assert path.join(plot_path, "normalized_residual_map.png") in plot_patch.paths
assert path.join(plot_path, "chi_squared_map.png") in plot_patch.paths
plot_patch.paths = []
fit_plotter.figures_2d(
data=True,
noise_map=False,
signal_to_noise_map=False,
model_image=True,
chi_squared_map=True,
)
assert path.join(plot_path, "data.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") not in plot_patch.paths
assert path.join(plot_path, "signal_to_noise_map.png") not in plot_patch.paths
assert path.join(plot_path, "model_image.png") in plot_patch.paths
assert path.join(plot_path, "residual_map.png") not in plot_patch.paths
assert path.join(plot_path, "normalized_residual_map.png") not in plot_patch.paths
assert path.join(plot_path, "chi_squared_map.png") in plot_patch.paths
def test__figures_of_plane(
fit_imaging_x2_plane_7x7, include_2d_all, plot_path, plot_patch
):
fit_plotter = aplt.FitImagingPlotter(
fit=fit_imaging_x2_plane_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(path=plot_path, format="png")),
)
fit_plotter.figures_2d_of_planes(
subtracted_image=True, model_image=True, plane_image=True
)
assert path.join(plot_path, "source_subtracted_image.png") in plot_patch.paths
assert path.join(plot_path, "lens_subtracted_image.png") in plot_patch.paths
assert path.join(plot_path, "lens_model_image.png") in plot_patch.paths
assert path.join(plot_path, "source_model_image.png") in plot_patch.paths
assert path.join(plot_path, "plane_image_of_plane_0.png") in plot_patch.paths
assert path.join(plot_path, "plane_image_of_plane_1.png") in plot_patch.paths
plot_patch.paths = []
fit_plotter.figures_2d_of_planes(
subtracted_image=True, model_image=True, plane_index=0, plane_image=True
)
assert path.join(plot_path, "source_subtracted_image.png") in plot_patch.paths
assert (
path.join(plot_path, "lens_subtracted_image.png") not in plot_patch.paths
)
assert path.join(plot_path, "lens_model_image.png") in plot_patch.paths
assert path.join(plot_path, "source_model_image.png") not in plot_patch.paths
assert path.join(plot_path, "plane_image_of_plane_0.png") in plot_patch.paths
assert path.join(plot_path, "plane_image_of_plane_1.png") not in plot_patch.paths
def test_subplot_fit_is_output(
fit_imaging_x2_plane_7x7, include_2d_all, plot_path, plot_patch
):
fit_plotter = aplt.FitImagingPlotter(
fit=fit_imaging_x2_plane_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(plot_path, format="png")),
)
fit_plotter.subplot_fit()
assert path.join(plot_path, "subplot_fit.png") in plot_patch.paths
fit_plotter.subplot_fit_log10()
assert path.join(plot_path, "subplot_fit_log10.png") in plot_patch.paths
def test__subplot_of_planes(
fit_imaging_x2_plane_7x7, include_2d_all, plot_path, plot_patch
):
fit_plotter = aplt.FitImagingPlotter(
fit=fit_imaging_x2_plane_7x7,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(plot_path, format="png")),
)
fit_plotter.subplot_of_planes()
assert path.join(plot_path, "subplot_of_plane_0.png") in plot_patch.paths
assert path.join(plot_path, "subplot_of_plane_1.png") in plot_patch.paths
plot_patch.paths = []
fit_plotter.subplot_of_planes(plane_index=0)
assert path.join(plot_path, "subplot_of_plane_0.png") in plot_patch.paths
assert path.join(plot_path, "subplot_of_plane_1.png") not in plot_patch.paths
|
Jammy2211REPO_NAMEPyAutoLensPATH_START.@PyAutoLens_extracted@PyAutoLens-main@test_autolens@imaging@plot@test_fit_imaging_plotters.py@.PATH_END.py
|
{
"filename": "stratsi_pert.py",
"repo_name": "minkailin/stratsi",
"repo_path": "stratsi_extracted/stratsi-master/caseA_eta0.1/stratsi_pert.py",
"type": "Python"
}
|
"""
stratified linear analysis of the streaming instability
"""
from stratsi_params import *
from eigenproblem import Eigenproblem
'''
output control
'''
nz_out = 1024
out_scale= nz_out/nz_waves
'''
read in background vertical profiles of vgx, vgy, vdx, vdy
'''
horiz_eqm = h5py.File('./eqm_horiz.h5', 'r')
zaxis_horiz= horiz_eqm['z'][:]
vgx = horiz_eqm['vgx'][:]
vgy = horiz_eqm['vgy'][:]
vdx = horiz_eqm['vdx'][:]
vdy = horiz_eqm['vdy'][:]
horiz_eqm.close()
'''
setup domain and calculate derivatives of vertical profiles as needed
'''
z_basis = de.Chebyshev('z', nz_waves, interval=(zmin,zmax))
domain_EVP = de.Domain([z_basis], comm=MPI.COMM_SELF)
'''
the linear problem
W = delta_rhog/rhog = delta_ln_rhog, Q=delta_eps/eps = delta_ln_eps, U = velocities
W_p = W_primed (dW/dz)...etc
'''
if (viscosity_pert == True) and (diffusion == True):#full problem: include viscosity and particle diffusion
waves = de.EVP(domain_EVP, ['W','Ugx','Ugx_p','Ugy','Ugy_p','Ugz','Q','Q_p','Udx','Udy','Udz'], eigenvalue='sigma',tolerance=tol)
if (viscosity_pert == True) and (diffusion == False):#include viscosity but no particle diffusion
waves = de.EVP(domain_EVP, ['W','Ugx','Ugx_p','Ugy','Ugy_p','Ugz','Q','Udx','Udy','Udz'], eigenvalue='sigma',tolerance=tol)
if (viscosity_pert == False) and (diffusion == True):#ignore gas viscosity but include particle diffusion
waves = de.EVP(domain_EVP, ['W','Ugx','Ugy','Ugz','Q','Q_p','Udx','Udy','Udz'], eigenvalue='sigma',tolerance=tol)
if (viscosity_pert == False) and (diffusion == False):#ignore gas viscosity and ignore diffusion
waves = de.EVP(domain_EVP, ['W','Ugx','Ugy','Ugz','Q','Udx','Udy','Udz'], eigenvalue='sigma',tolerance=tol)
'''
constant parameters
'''
waves.parameters['delta'] = delta
waves.parameters['alpha'] = alpha
waves.parameters['eta_hat'] = eta_hat
waves.parameters['inv_stokes'] = 1.0/stokes
waves.parameters['kx'] = kx
'''
non-constant coefficients I: epsilon, rhod, rhog, vdz
'''
z = domain_EVP.grid(0)
dln_rhog0 = domain_EVP.new_field()
d2ln_rhog0 = domain_EVP.new_field()
dln_rhod0 = domain_EVP.new_field()
epsilon0 = domain_EVP.new_field()
depsilon0 = domain_EVP.new_field()
d2epsilon0 = domain_EVP.new_field()
dln_epsilon0= domain_EVP.new_field()
vdz0 = domain_EVP.new_field()
dvdz0 = domain_EVP.new_field()
dln_rhog0['g'] = dln_rhog(z)
d2ln_rhog0['g'] = d2ln_rhog(z)
dln_rhod0['g'] = dln_rhod(z)
epsilon0['g'] = epsilon(z)
depsilon0['g'] = depsilon(z)
d2epsilon0['g'] = d2epsilon(z)
dln_epsilon0['g'] = dln_epsilon(z)
vdz0['g'] = vdz(z)
dvdz0['g'] = dvdz(z)
waves.parameters['dln_rhog0'] = dln_rhog0
waves.parameters['d2ln_rhog0'] = d2ln_rhog0
waves.parameters['dln_rhod0'] = dln_rhod0
waves.parameters['epsilon0'] = epsilon0
waves.parameters['depsilon0'] = depsilon0
waves.parameters['d2epsilon0'] = d2epsilon0
waves.parameters['dln_epsilon0'] = dln_epsilon0
waves.parameters['vdz0'] = vdz0
waves.parameters['dvdz0'] = dvdz0
'''
non-constant coefficients II: vgx, vgy, vdx, vdy
'''
vgx0 = domain_EVP.new_field()
dvgx0 = domain_EVP.new_field()
d2vgx0= domain_EVP.new_field()
vgy0 = domain_EVP.new_field()
dvgy0 = domain_EVP.new_field()
d2vgy0= domain_EVP.new_field()
vdx0 = domain_EVP.new_field()
dvdx0 = domain_EVP.new_field()
vdy0 = domain_EVP.new_field()
dvdy0 = domain_EVP.new_field()
scale = nz_vert/nz_waves
vgx0.set_scales(scale)
dvgx0.set_scales(scale)
d2vgx0.set_scales(scale)
vgy0.set_scales(scale)
dvgy0.set_scales(scale)
d2vgy0.set_scales(scale)
vdx0.set_scales(scale)
dvdx0.set_scales(scale)
vdy0.set_scales(scale)
dvdy0.set_scales(scale)
vgx0['g'] = vgx
vgx0.differentiate('z', out=dvgx0)
dvgx0.differentiate('z',out=d2vgx0)
vgy0['g'] = vgy
vgy0.differentiate('z', out=dvgy0)
dvgy0.differentiate('z',out=d2vgy0)
vdx0['g'] = vdx
vdx0.differentiate('z', out=dvdx0)
vdy0['g'] = vdy
vdy0.differentiate('z', out=dvdy0)
vgx0.set_scales(1, keep_data=True)
dvgx0.set_scales(1, keep_data=True)
d2vgx0.set_scales(1, keep_data=True)
vgy0.set_scales(1, keep_data=True)
dvgy0.set_scales(1, keep_data=True)
d2vgy0.set_scales(1, keep_data=True)
vdx0.set_scales(1, keep_data=True)
dvdx0.set_scales(1, keep_data=True)
vdy0.set_scales(1, keep_data=True)
dvdy0.set_scales(1, keep_data=True)
waves.parameters['vdx0'] = vdx0
waves.parameters['dvdx0'] = dvdx0
waves.parameters['vdy0'] = vdy0
waves.parameters['dvdy0'] = dvdy0
waves.parameters['vgx0'] = vgx0
waves.parameters['dvgx0'] = dvgx0
waves.parameters['d2vgx0'] = d2vgx0
waves.parameters['vgy0'] = vgy0
waves.parameters['dvgy0'] = dvgy0
waves.parameters['d2vgy0'] = d2vgy0
# W is (delta_rhog)/rhog, Q is (delta_epsilon)/epsilon
waves.substitutions['ikx'] = "1j*kx"
waves.substitutions['delta_ln_rhod'] = "Q + W"
if diffusion == True:
waves.substitutions['dQ'] = "Q_p"
waves.substitutions['delta_eps_p_over_eps'] = "dln_epsilon0*Q + Q_p"
waves.substitutions['delta_eps_pp_over_eps'] = "d2epsilon0*Q/epsilon0 + 2*dln_epsilon0*Q_p + dz(Q_p)"
if diffusion == False:
waves.substitutions['dQ'] = "dz(Q)"
waves.substitutions['delta_ln_rhod_p'] = "dQ + dz(W)"
waves.substitutions['delta_ln_taus'] = "0"
#dust continuity equation
waves.substitutions['dust_mass_LHS']="sigma*delta_ln_rhod + ikx*(Udx + vdx0*delta_ln_rhod) + dln_rhod0*(Udz + vdz0*delta_ln_rhod) + vdz0*delta_ln_rhod_p + dvdz0*delta_ln_rhod + dz(Udz)"
if diffusion == True:
waves.substitutions['dust_mass_RHS']="delta*(dln_epsilon0*(dln_rhog0*W + dz(W)) + d2epsilon0*W/epsilon0 - kx*kx*Q + dln_rhog0*delta_eps_p_over_eps + delta_eps_pp_over_eps)"
if diffusion == False:
waves.substitutions['dust_mass_RHS']="0"
#dust x-mom equation
waves.substitutions['dust_xmom_LHS']="sigma*Udx + dvdx0*Udz + ikx*vdx0*Udx + vdz0*dz(Udx)"
waves.substitutions['dust_xmom_RHS']="2*Udy + inv_stokes*delta_ln_taus*(vdx0 - vgx0) - inv_stokes*(Udx - Ugx)"
#dust y-mom equation
waves.substitutions['dust_ymom_LHS']="sigma*Udy + dvdy0*Udz + ikx*vdx0*Udy + vdz0*dz(Udy)"
waves.substitutions['dust_ymom_RHS']="-0.5*Udx + inv_stokes*delta_ln_taus*(vdy0 - vgy0) - inv_stokes*(Udy - Ugy)"
#dust z-mom
waves.substitutions['dust_zmom_LHS']="sigma*Udz + dvdz0*Udz + ikx*vdx0*Udz + vdz0*dz(Udz)"
waves.substitutions['dust_zmom_RHS']="inv_stokes*delta_ln_taus*vdz0 - inv_stokes*(Udz - Ugz)"
#gas continuity equation
waves.substitutions['gas_mass_LHS']="sigma*W + ikx*(Ugx + vgx0*W) + dln_rhog0*Ugz + dz(Ugz)"
#linearized viscous forces on gas
#could also use eqm eqns to replace first bracket, so that we don't need to take numerical derivs of vgx..etc
if viscosity_pert == True:
waves.substitutions['delta_vgz_pp']="-( sigma*dz(W) + ikx*(Ugx_p + dvgx0*W + vgx0*dz(W)) + d2ln_rhog0*Ugz + dln_rhog0*dz(Ugz) )" #take deriv of gas mass eq to get d2(delta_vgz)/dz2
waves.substitutions['delta_Fvisc_x'] = "alpha*(ikx*dz(Ugz)/3 - 4*kx*kx*Ugx/3 + dln_rhog0*(ikx*Ugz + Ugx_p) + dz(Ugx_p)) - alpha*(dln_rhog0*dvgx0 + d2vgx0)*W"
waves.substitutions['delta_Fvisc_y'] = "alpha*(dln_rhog0*Ugy_p - kx*kx*Ugy + dz(Ugy_p)) - alpha*(dln_rhog0*dvgy0 + d2vgy0)*W"
waves.substitutions['delta_Fvisc_z'] = "alpha*(ikx*Ugx_p/3 - kx*kx*Ugz + dln_rhog0*(4*dz(Ugz)/3 - 2*ikx*Ugx/3) + 4*delta_vgz_pp/3)"
if viscosity_pert == False:
waves.substitutions['delta_Fvisc_x'] = "0"
waves.substitutions['delta_Fvisc_y'] = "0"
waves.substitutions['delta_Fvisc_z'] = "0"
#linearized back-reaction force on gas
if backreaction == True:
waves.substitutions['delta_backreaction_x']="inv_stokes*epsilon0*( (vgx0 - vdx0)*(delta_ln_taus - Q) - (Ugx - Udx) )"
waves.substitutions['delta_backreaction_y']="inv_stokes*epsilon0*( (vgy0 - vdy0)*(delta_ln_taus - Q) - (Ugy - Udy) )"
waves.substitutions['delta_backreaction_z']="inv_stokes*epsilon0*( ( - vdz0)*(delta_ln_taus - Q) - (Ugz - Udz) )"
if backreaction == False:
waves.substitutions['delta_backreaction_x']="0"
waves.substitutions['delta_backreaction_y']="0"
waves.substitutions['delta_backreaction_z']="0"
#gas equations
waves.add_equation("gas_mass_LHS = 0 ")
waves.add_equation("sigma*Ugx + dvgx0*Ugz + ikx*vgx0*Ugx - 2*Ugy + ikx*W - delta_backreaction_x - delta_Fvisc_x = 0")
waves.add_equation("sigma*Ugy + dvgy0*Ugz + ikx*vgx0*Ugy + 0.5*Ugx - delta_backreaction_y - delta_Fvisc_y = 0")
waves.add_equation("sigma*Ugz + ikx*vgx0*Ugz + dz(W) - delta_backreaction_z - delta_Fvisc_z = 0")
#dust equations
waves.add_equation("dust_mass_LHS - dust_mass_RHS = 0 ")
waves.add_equation("dust_xmom_LHS - dust_xmom_RHS = 0 ")
waves.add_equation("dust_ymom_LHS - dust_ymom_RHS = 0 ")
waves.add_equation("dust_zmom_LHS - dust_zmom_RHS = 0")
#equations for first derivs of perts, i.e. dz(Q) = Q_p ...etc
#in our formulation of second derivs of [epsilon, delta_vgx, delta_vgy] appear for full problem with viscosity
if diffusion == True:
waves.add_equation("dz(Q) - Q_p = 0")
if viscosity_pert == True:
waves.add_equation("dz(Ugx) - Ugx_p = 0")
waves.add_equation("dz(Ugy) - Ugy_p = 0")
'''
boundary conditions (reflection)
'''
#mid-plane symmetry conditions on center-of-mass velocities, as in one-fluid case
waves.add_bc('left(dz(W))=0')
waves.add_bc('left(dQ)=0')
waves.add_bc('left(dz(Ugx - epsilon0*vgx0*Q/(1+epsilon0) + epsilon0*Udx + epsilon0*vdx0*Q/(1+epsilon0)))=0')
waves.add_bc('left(dz(Ugy - epsilon0*vgy0*Q/(1+epsilon0) + epsilon0*Udy + epsilon0*vdy0*Q/(1+epsilon0)))=0')
waves.add_bc('left(Ugz + epsilon0*Udz)=0')
waves.add_bc('right(dz(W))=0')
if diffusion == True:
waves.add_bc('right(Q)=0')
if viscosity_pert == True:
waves.add_bc('left(Ugx_p)=0')
waves.add_bc('left(Ugy_p)=0')
waves.add_bc('right(Ugx_p)=0')
waves.add_bc('right(Ugy_p)=0')
'''
eigenvalue problem, sweep through kx space
for each kx, filter modes and keep most unstable one
'''
EP_list = [Eigenproblem(waves), Eigenproblem(waves, sparse=True)]
kx_space = np.logspace(np.log10(kx_min),np.log10(kx_max), num=nkx)
eigenfreq = []
eigenfunc = {'W':[], 'Q':[], 'Ugx':[], 'Ugy':[], 'Ugz':[], 'Udx':[], 'Udy':[], 'Udz':[]}
for i, kx in enumerate(kx_space):
if ((i == 0) and (first_solve_dense == True)) or all_solve_dense == True:
EP = EP_list[0]
else:
EP = EP_list[1]
EP.EVP.namespace['kx'].value = kx
EP.EVP.parameters['kx'] = kx
if all_solve_dense == True:
EP.solve()
else:
if i == 0:
if first_solve_dense == True:
EP.solve()
else:
trial = eigen_trial
EP.solve(N=Neig, target = trial)
else:
#trial = eigenfreq[i-1]
EP.solve(N=Neig, target = trial)
EP.reject_spurious()
abs_sig = np.abs(EP.evalues_good)
sig_acceptable = (abs_sig < sig_filter) & (EP.evalues_good.real > 0.0)
sigma = EP.evalues_good[sig_acceptable]
sigma_index= EP.evalues_good_index[sig_acceptable]
eigenfunc['W'].append([])
eigenfunc['Q'].append([])
eigenfunc['Ugx'].append([])
eigenfunc['Ugy'].append([])
eigenfunc['Ugz'].append([])
eigenfunc['Udx'].append([])
eigenfunc['Udy'].append([])
eigenfunc['Udz'].append([])
if sigma.size > 0:
eigenfreq.append(sigma)
for n, mode in enumerate(sigma_index):
EP.solver.set_state(mode)
W = EP.solver.state['W']
Q = EP.solver.state['Q']
Ugx = EP.solver.state['Ugx']
Ugy = EP.solver.state['Ugy']
Ugz = EP.solver.state['Ugz']
Udx = EP.solver.state['Udx']
Udy = EP.solver.state['Udy']
Udz = EP.solver.state['Udz']
W.set_scales(scales=out_scale)
Q.set_scales(scales=out_scale)
Ugx.set_scales(scales=out_scale)
Ugy.set_scales(scales=out_scale)
Ugz.set_scales(scales=out_scale)
Udx.set_scales(scales=out_scale)
Udy.set_scales(scales=out_scale)
Udz.set_scales(scales=out_scale)
eigenfunc['W'][i].append(np.copy(W['g']))
eigenfunc['Q'][i].append(np.copy(Q['g']))
eigenfunc['Ugx'][i].append(np.copy(Ugx['g']))
eigenfunc['Ugy'][i].append(np.copy(Ugy['g']))
eigenfunc['Ugz'][i].append(np.copy(Ugz['g']))
eigenfunc['Udx'][i].append(np.copy(Udx['g']))
eigenfunc['Udy'][i].append(np.copy(Udy['g']))
eigenfunc['Udz'][i].append(np.copy(Udz['g']))
else:
eigenfreq.append(np.array([np.nan]))
eigenfunc['W'][i].append(np.zeros(nz_out))
eigenfunc['Q'][i].append(np.zeros(nz_out))
eigenfunc['Ugx'][i].append(np.zeros(nz_out))
eigenfunc['Ugy'][i].append(np.zeros(nz_out))
eigenfunc['Ugz'][i].append(np.zeros(nz_out))
eigenfunc['Udx'][i].append(np.zeros(nz_out))
eigenfunc['Udy'][i].append(np.zeros(nz_out))
eigenfunc['Udz'][i].append(np.zeros(nz_out))
growth = eigenfreq[i].real
freq = eigenfreq[i].imag
g1 = np.argmax(growth)
trial = eigenfreq[i][g1]
'''
print results to screen (most unstable mode)
'''
for i, kx in enumerate(kx_space):
growth = eigenfreq[i].real
g1 = np.argmax(growth)
print("i, kx, growth, freq = {0:3d} {1:1.2e} {2:9.6f} {3:9.6f}".format(i, kx, eigenfreq[i][g1].real, -eigenfreq[i][g1].imag))
'''
data output
'''
with h5py.File('stratsi_modes.h5','w') as outfile:
z_out = domain_EVP.grid(0, scales=out_scale)
scale_group = outfile.create_group('scales')
scale_group.create_dataset('kx_space',data=kx_space)
scale_group.create_dataset('z', data=z_out)
scale_group.create_dataset('zmax', data=zmax)
tasks_group = outfile.create_group('tasks')
for i, freq in enumerate(eigenfreq):
data_group = tasks_group.create_group('k_{:03d}'.format(i))
data_group.create_dataset('freq',data=freq)
data_group.create_dataset('eig_W',data=eigenfunc['W'][i])
data_group.create_dataset('eig_Q',data=eigenfunc['Q'][i])
data_group.create_dataset('eig_Ugx',data=eigenfunc['Ugx'][i])
data_group.create_dataset('eig_Ugy',data=eigenfunc['Ugy'][i])
data_group.create_dataset('eig_Ugz',data=eigenfunc['Ugz'][i])
data_group.create_dataset('eig_Udx',data=eigenfunc['Udx'][i])
data_group.create_dataset('eig_Udy',data=eigenfunc['Udy'][i])
data_group.create_dataset('eig_Udz',data=eigenfunc['Udz'][i])
outfile.close()
|
minkailinREPO_NAMEstratsiPATH_START.@stratsi_extracted@stratsi-master@caseA_eta0.1@stratsi_pert.py@.PATH_END.py
|
{
"filename": "polygon.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/utilities/polygon.py",
"type": "Python"
}
|
"""
Util that calls several of Polygon's stock market REST APIs.
Docs: https://polygon.io/docs/stocks/getting-started
"""
import json
from typing import Any, Dict, Optional
import requests
from langchain_core.utils import get_from_dict_or_env
from pydantic import BaseModel, model_validator
POLYGON_BASE_URL = "https://api.polygon.io/"
class PolygonAPIWrapper(BaseModel):
"""Wrapper for Polygon API."""
polygon_api_key: Optional[str] = None
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate that api key in environment."""
polygon_api_key = get_from_dict_or_env(
values, "polygon_api_key", "POLYGON_API_KEY"
)
values["polygon_api_key"] = polygon_api_key
return values
def get_financials(self, ticker: str) -> Optional[dict]:
"""
Get fundamental financial data, which is found in balance sheets,
income statements, and cash flow statements for a given ticker.
/vX/reference/financials
"""
url = (
f"{POLYGON_BASE_URL}vX/reference/financials?"
f"ticker={ticker}&"
f"apiKey={self.polygon_api_key}"
)
response = requests.get(url)
data = response.json()
status = data.get("status", None)
if status not in ("OK", "STOCKBUSINESS", "STOCKSBUSINESS"):
raise ValueError(f"API Error: {data}")
return data.get("results", None)
def get_last_quote(self, ticker: str) -> Optional[dict]:
"""
Get the most recent National Best Bid and Offer (Quote) for a ticker.
/v2/last/nbbo/{ticker}
"""
url = f"{POLYGON_BASE_URL}v2/last/nbbo/{ticker}?apiKey={self.polygon_api_key}"
response = requests.get(url)
data = response.json()
status = data.get("status", None)
if status not in ("OK", "STOCKBUSINESS", "STOCKSBUSINESS"):
raise ValueError(f"API Error: {data}")
return data.get("results", None)
def get_ticker_news(self, ticker: str) -> Optional[dict]:
"""
Get the most recent news articles relating to a stock ticker symbol,
including a summary of the article and a link to the original source.
/v2/reference/news
"""
url = (
f"{POLYGON_BASE_URL}v2/reference/news?"
f"ticker={ticker}&"
f"apiKey={self.polygon_api_key}"
)
response = requests.get(url)
data = response.json()
status = data.get("status", None)
if status not in ("OK", "STOCKBUSINESS", "STOCKSBUSINESS"):
raise ValueError(f"API Error: {data}")
return data.get("results", None)
def get_aggregates(self, ticker: str, **kwargs: Any) -> Optional[dict]:
"""
Get aggregate bars for a stock over a given date range
in custom time window sizes.
/v2/aggs/ticker/{ticker}/range/{multiplier}/{timespan}/{from_date}/{to_date}
"""
timespan = kwargs.get("timespan", "day")
multiplier = kwargs.get("timespan_multiplier", 1)
from_date = kwargs.get("from_date", None)
to_date = kwargs.get("to_date", None)
adjusted = kwargs.get("adjusted", True)
sort = kwargs.get("sort", "asc")
url = (
f"{POLYGON_BASE_URL}v2/aggs"
f"/ticker/{ticker}"
f"/range/{multiplier}"
f"/{timespan}"
f"/{from_date}"
f"/{to_date}"
f"?apiKey={self.polygon_api_key}"
f"&adjusted={adjusted}"
f"&sort={sort}"
)
response = requests.get(url)
data = response.json()
status = data.get("status", None)
if status not in ("OK", "STOCKBUSINESS", "STOCKSBUSINESS"):
raise ValueError(f"API Error: {data}")
return data.get("results", None)
def run(self, mode: str, ticker: str, **kwargs: Any) -> str:
if mode == "get_financials":
return json.dumps(self.get_financials(ticker))
elif mode == "get_last_quote":
return json.dumps(self.get_last_quote(ticker))
elif mode == "get_ticker_news":
return json.dumps(self.get_ticker_news(ticker))
elif mode == "get_aggregates":
return json.dumps(self.get_aggregates(ticker, **kwargs))
else:
raise ValueError(f"Invalid mode {mode} for Polygon API.")
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@utilities@polygon.py@.PATH_END.py
|
{
"filename": "remote_execution_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/eager/remote_execution_test.py",
"type": "Python"
}
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for remote eager execution."""
from absl.testing import parameterized
import numpy as np
from tensorflow.core.protobuf import cluster_pb2
from tensorflow.core.protobuf import tensorflow_server_pb2
from tensorflow.python import pywrap_tfe
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import remote
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import script_ops
from tensorflow.python.platform import test
from tensorflow.python.training import server_lib
JOB_NAME = "remote_device"
ALT_JOB_NAME = "alt_remote_device"
def get_server_def(job_name, local_server_port, remote_server_addresses,
task_index):
"""Returns a server def with a single job + multiple tasks."""
cluster_def = cluster_pb2.ClusterDef()
job_def = cluster_def.job.add()
job_def.name = job_name
job_def.tasks[0] = "localhost:%d" % local_server_port
for i, remote_server_address in enumerate(remote_server_addresses, start=1):
job_def.tasks[i] = remote_server_address
server_def = tensorflow_server_pb2.ServerDef(
cluster=cluster_def,
job_name=job_name,
task_index=task_index,
protocol="grpc")
return server_def
class RemoteExecutionTest(test.TestCase, parameterized.TestCase):
def __init__(self, methodName="runTest"): # pylint: disable=invalid-name
super(RemoteExecutionTest, self).__init__(methodName)
self._cached_server1 = server_lib.Server.create_local_server()
self._cached_server2 = server_lib.Server.create_local_server()
self._cached_server1_target = self._cached_server1.target[len("grpc://"):]
self._cached_server2_target = self._cached_server2.target[len("grpc://"):]
def setUp(self):
super(RemoteExecutionTest, self).setUp()
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
context.set_server_def(
server_def=get_server_def(
JOB_NAME,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def tearDown(self):
super(RemoteExecutionTest, self).tearDown()
# Clear the current device scope and reset the context to avoid polluting
# other test cases.
ops.device(None).__enter__()
context._reset_context()
@test_util.run_in_async_and_sync_mode
@test_util.run_gpu_only
def testGpuToRemoteCopy(self):
"""Tests that the remote copy happens satisfactorily."""
x1 = array_ops.ones([2, 2]).gpu()
with ops.device("/job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x2 = x1._copy() # pylint: disable=protected-access
np.testing.assert_array_equal(x1.numpy(), x2.numpy())
@test_util.run_in_async_and_sync_mode
@test_util.run_gpu_only
def testGpuToRemoteOp(self):
with ops.device("gpu:0"):
x = array_ops.ones([2, 2])
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
y = math_ops.matmul(x, x)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
@test_util.run_in_async_and_sync_mode
def testDefunMatmul(self):
"""Basic remote eager execution with defun."""
mm_defun = def_function.function(math_ops.matmul)
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x1 = array_ops.ones([2, 2])
with ops.device("job:%s/replica:0/task:2/device:CPU:0" % JOB_NAME):
x2 = array_ops.ones([2, 2])
y = mm_defun(x1, x2)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
@test_util.run_in_async_and_sync_mode
def testSimpleMatmul(self):
"""Basic remote eager execution."""
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x1 = array_ops.ones([2, 2])
with ops.device("job:%s/replica:0/task:2/device:CPU:0" % JOB_NAME):
x2 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x2)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
def testEagerPyFuncPlacement(self):
if not ops.executing_eagerly_outside_functions():
return
def f(x):
return math_ops.square(x)
with ops.device("/job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
const_op = constant_op.constant(3.0, dtype=dtypes.float32)
# PyFuncOp should be placed on the localhost's address space.
py_func_op = script_ops.eager_py_func(
func=f, inp=[const_op], Tout=dtypes.float32)
self.assertEqual(py_func_op.device,
"/job:%s/replica:0/task:0/device:CPU:0" % JOB_NAME)
self.assertEqual(self.evaluate(py_func_op), 9.0)
@test_util.run_in_async_and_sync_mode
def testSimpleWeightRead(self):
"""Basic remote eager weight read."""
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
w = resource_variable_ops.ResourceVariable([[2.0]])
loss = w * w
np.testing.assert_array_equal([[4.0]], loss.numpy())
@test_util.run_in_async_and_sync_mode
def testTapeWeightRead(self):
"""Remote eager weight read in a tape."""
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
w = resource_variable_ops.ResourceVariable([[3.0]])
with backprop.GradientTape() as tape:
loss = w * w
grad = tape.gradient(loss, w)
np.testing.assert_array_equal([[9.0]], loss.numpy())
np.testing.assert_array_equal([[6.0]], grad.numpy())
@test_util.run_in_async_and_sync_mode
def testServerDefChanged(self):
"""Update server def, and run ops on new cluster."""
context.set_server_def(
server_def=get_server_def(
ALT_JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % ALT_JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
# Set the server def back to JOB_NAME
context.set_server_def(
server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
@test_util.run_in_async_and_sync_mode
def testConnectToRemoteServer(self):
"""Basic server connection."""
context._reset_context()
remote.connect_to_remote_host(self._cached_server1_target)
with ops.device("job:worker/replica:0/task:0/device:CPU:0"):
x1 = array_ops.ones([2, 2])
x2 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x2)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
@test_util.run_in_async_and_sync_mode
def testContextDeviceUpdated(self):
"""Tests that the context device is correctly updated."""
with ops.device("cpu:0"):
x1 = array_ops.ones([2, 2])
x2 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x2)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
# `y` is placed on the local CPU as expected.
self.assertEqual(y.device,
"/job:%s/replica:0/task:0/device:CPU:0" % JOB_NAME)
if __name__ == "__main__":
ops.enable_eager_execution()
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@eager@remote_execution_test.py@.PATH_END.py
|
{
"filename": "index_route.py",
"repo_name": "SKA-INAF/caesar-rest",
"repo_path": "caesar-rest_extracted/caesar-rest-master/caesar_rest/index_route.py",
"type": "Python"
}
|
##############################
# MODULE IMPORTS
##############################
from flask import current_app, Blueprint, render_template, request, redirect
from caesar_rest import oidc
from caesar_rest.decorators import custom_require_login
# Get logger
#import logging
#logger = logging.getLogger(__name__)
from caesar_rest import logger
##############################
# CREATE BLUEPRINT
##############################
index_bp = Blueprint('index', __name__)
@index_bp.route('/')
def index():
#return render_template('index.html')
aai_enabled= current_app.config['USE_AAI']
has_oidc= (oidc is not None)
logger.debug("use_aai? %d has_oidc? %d" % (aai_enabled, has_oidc))
if aai_enabled and has_oidc:
if oidc.user_loggedin:
return render_template('index.html', username=oidc.user_getfield('email'))
#return 'Welcome {email}'.format(email=oidc.user_getfield('email'))
else:
return 'Not logged in, <a href="/login">Log in</a> '
else:
return render_template('index.html')
@index_bp.route('/login')
#@oidc.require_login
@custom_require_login
def login():
aai_enabled= current_app.config['USE_AAI']
has_oidc= (oidc is not None)
logger.debug("use_aai? %d has_oidc? %d" % (aai_enabled, has_oidc))
if aai_enabled and has_oidc:
user_info = oidc.user_getinfo(['preferred_username', 'email', 'sub', 'name'])
# return value is a dictionary with keys the above keywords
return redirect("/", code=302) #'Welcome {user_info}'.format(user_info=user_info['name'])
else:
return render_template('index.html')
@index_bp.route('/logout')
def logout():
"""Performs local logout by removing the session cookie."""
aai_enabled= current_app.config['USE_AAI']
has_oidc= (oidc is not None)
logger.debug("use_aai? %d has_oidc? %d" % (aai_enabled, has_oidc))
if aai_enabled and has_oidc:
oidc.logout()
return 'Hi, you have been logged out! <a href="/">Return</a>'
else:
return 'Bye bye'
|
SKA-INAFREPO_NAMEcaesar-restPATH_START.@caesar-rest_extracted@caesar-rest-master@caesar_rest@index_route.py@.PATH_END.py
|
{
"filename": "plot_fastcc_lfi.py",
"repo_name": "mpeel/fastcc",
"repo_path": "fastcc_extracted/fastcc-master/plot_fastcc_lfi.py",
"type": "Python"
}
|
# Quick script to plot the LFI colour correction curve
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
font = {'family' : 'normal',
'size' : 12}
matplotlib.rc('font', **font)
P30 = [1.00513, 0.00301399, -0.00300699, 28.4]
P44 = [0.994769, 0.00596703, -0.00173626, 44.1]
P70 = [0.989711, 0.0106943, -0.00328671, 70.4]
alphas = np.arange(-3.0,4.0,0.1)
plt.plot(alphas,P30[0]+P30[1]*alphas+P30[2]*alphas*alphas,label='28.4 GHz')
plt.plot(alphas,P44[0]+P44[1]*alphas+P44[2]*alphas*alphas,label='44.1 GHz')
plt.plot(alphas,P70[0]+P70[1]*alphas+P70[2]*alphas*alphas,label='70.4 GHz')
l = plt.legend(prop={'size':12})
l.set_zorder(20)
plt.xlabel('Spectral index',)
plt.ylabel('Colour correction')
plt.savefig('plots_2022_07_26/planck_lfi_cc.pdf')
plt.clf()
plt.close()
|
mpeelREPO_NAMEfastccPATH_START.@fastcc_extracted@fastcc-master@plot_fastcc_lfi.py@.PATH_END.py
|
{
"filename": "_uirevision.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/ternary/_uirevision.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class UirevisionValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(
self, plotly_name="uirevision", parent_name="layout.ternary", **kwargs
):
super(UirevisionValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@ternary@_uirevision.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattergeo/hoverlabel/font/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="textcase", parent_name="scattergeo.hoverlabel.font", **kwargs
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattergeo@hoverlabel@font@_textcase.py@.PATH_END.py
|
{
"filename": "cauchy.py",
"repo_name": "google/jax",
"repo_path": "jax_extracted/jax-main/jax/_src/scipy/stats/cauchy.py",
"type": "Python"
}
|
# Copyright 2018 The JAX 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
#
# https://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.
import numpy as np
from jax import lax
from jax._src.lax.lax import _const as _lax_const
from jax._src.numpy.util import promote_args_inexact
from jax.numpy import arctan
from jax._src.typing import Array, ArrayLike
def logpdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy log probability distribution function.
JAX implementation of :obj:`scipy.stats.cauchy` ``logpdf``.
The Cauchy probability distribution function is
.. math::
f(x) = \frac{1}{\pi(1 + x^2)}
Args:
x: arraylike, value at which to evaluate the PDF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of logpdf values
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
x, loc, scale = promote_args_inexact("cauchy.logpdf", x, loc, scale)
pi = _lax_const(x, np.pi)
scaled_x = lax.div(lax.sub(x, loc), scale)
normalize_term = lax.log(lax.mul(pi, scale))
return lax.neg(lax.add(normalize_term, lax.log1p(lax.mul(scaled_x, scaled_x))))
def pdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy probability distribution function.
JAX implementation of :obj:`scipy.stats.cauchy` ``pdf``.
The Cauchy probability distribution function is
.. math::
f(x) = \frac{1}{\pi(1 + x^2)}
Args:
x: arraylike, value at which to evaluate the PDF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of pdf values
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
return lax.exp(logpdf(x, loc, scale))
def cdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy cumulative distribution function.
JAX implementation of :obj:`scipy.stats.cauchy` ``cdf``.
The cdf is defined as
.. math::
f_{cdf} = \int_{-\infty}^x f_{pdf}(y) \mathrm{d}y
where here :math:`f_{pdf}` is the Cauchy probability distribution function,
:func:`jax.scipy.stats.cauchy.pdf`.
Args:
x: arraylike, value at which to evaluate the CDF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of cdf values.
See Also:
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
x, loc, scale = promote_args_inexact("cauchy.cdf", x, loc, scale)
pi = _lax_const(x, np.pi)
scaled_x = lax.div(lax.sub(x, loc), scale)
return lax.add(_lax_const(x, 0.5), lax.mul(lax.div(_lax_const(x, 1.), pi), arctan(scaled_x)))
def logcdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy log cumulative distribution function.
JAX implementation of :obj:`scipy.stats.cauchy` ``logcdf``
The cdf is defined as
.. math::
f_{cdf} = \int_{-\infty}^x f_{pdf}(y) \mathrm{d}y
where here :math:`f_{pdf}` is the Cauchy probability distribution function,
:func:`jax.scipy.stats.cauchy.pdf`.
Args:
x: arraylike, value at which to evaluate the CDF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of logcdf values.
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
return lax.log(cdf(x, loc, scale))
def sf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy distribution log survival function.
JAX implementation of :obj:`scipy.stats.cauchy` ``sf``.
The survival function is defined as
.. math::
f_{sf}(x) = 1 - f_{cdf}(x)
where :math:`f_{cdf}(x)` is the cumulative distribution function,
:func:`jax.scipy.stats.cauchy.cdf`.
Args:
x: arraylike, value at which to evaluate the SF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of sf values
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
x, loc, scale = promote_args_inexact("cauchy.sf", x, loc, scale)
return cdf(-x, -loc, scale)
def logsf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy distribution log survival function.
JAX implementation of :obj:`scipy.stats.cauchy` ``logsf``
The survival function is defined as
.. math::
f_{sf}(x) = 1 - f_{cdf}(x)
where :math:`f_{cdf}(x)` is the cumulative distribution function,
:func:`jax.scipy.stats.cauchy.cdf`.
Args:
x: arraylike, value at which to evaluate the SF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of logsf values.
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.isf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
x, loc, scale = promote_args_inexact("cauchy.logsf", x, loc, scale)
return logcdf(-x, -loc, scale)
def isf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy distribution inverse survival function.
JAX implementation of :obj:`scipy.stats.cauchy` ``isf``.
Returns the inverse of the survival function,
:func:`jax.scipy.stats.cauchy.sf`.
Args:
q: arraylike, value at which to evaluate the ISF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of isf values.
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.ppf`
"""
q, loc, scale = promote_args_inexact("cauchy.isf", q, loc, scale)
pi = _lax_const(q, np.pi)
half_pi = _lax_const(q, np.pi / 2)
unscaled = lax.tan(lax.sub(half_pi, lax.mul(pi, q)))
return lax.add(lax.mul(unscaled, scale), loc)
def ppf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
r"""Cauchy distribution percent point function.
JAX implementation of :obj:`scipy.stats.cauchy` ``ppf``.
The percent point function is defined as the inverse of the
cumulative distribution function, :func:`jax.scipy.stats.cauchy.cdf`.
Args:
q: arraylike, value at which to evaluate the PPF
loc: arraylike, distribution offset parameter
scale: arraylike, distribution scale parameter
Returns:
array of ppf values.
See Also:
- :func:`jax.scipy.stats.cauchy.cdf`
- :func:`jax.scipy.stats.cauchy.pdf`
- :func:`jax.scipy.stats.cauchy.sf`
- :func:`jax.scipy.stats.cauchy.logcdf`
- :func:`jax.scipy.stats.cauchy.logpdf`
- :func:`jax.scipy.stats.cauchy.logsf`
- :func:`jax.scipy.stats.cauchy.isf`
"""
q, loc, scale = promote_args_inexact("cauchy.ppf", q, loc, scale)
pi = _lax_const(q, np.pi)
half_pi = _lax_const(q, np.pi / 2)
unscaled = lax.tan(lax.sub(lax.mul(pi, q), half_pi))
return lax.add(lax.mul(unscaled, scale), loc)
|
googleREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@jax@_src@scipy@stats@cauchy.py@.PATH_END.py
|
{
"filename": "_showticksuffix.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/smith/realaxis/_showticksuffix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowticksuffixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="showticksuffix",
parent_name="layout.smith.realaxis",
**kwargs,
):
super(ShowticksuffixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@smith@realaxis@_showticksuffix.py@.PATH_END.py
|
{
"filename": "rename_flux_txt.py",
"repo_name": "HeloiseS/FUSS",
"repo_path": "FUSS_extracted/FUSS-master/iraf_scripts/rename_flux_txt.py",
"type": "Python"
}
|
import os
f1 = open('list_dat_flux', 'r')
f2 = open('list_err_flux', 'r')
for line in f1:
name = line[:-23]
ap = line [-11:-10]
new_name = name+"_ap"+ap+".txt"
os.system("mv "+line[:-1]+" "+new_name)
#print new_name
for line in f2:
name = line[:-23]
ap = line [-11:-10]
new_name = name+"_ap"+ap+"_err.txt"
#print new_name
os.system("mv "+line[:-1]+" "+new_name)
|
HeloiseSREPO_NAMEFUSSPATH_START.@FUSS_extracted@FUSS-master@iraf_scripts@rename_flux_txt.py@.PATH_END.py
|
{
"filename": "rawfiles.py",
"repo_name": "plazar/TOASTER",
"repo_path": "TOASTER_extracted/TOASTER-master/rawfiles.py",
"type": "Python"
}
|
#!/usr/bin/env python
"""
rawfiles.py
A multi-purpose program to interact with rawfiles.
Patrick Lazarus, Dec 8, 2012
"""
import utils
import database
import errors
toolkit = ['get_rawfile_id', \
'load_rawfile', \
'replace_rawfile', \
'diagnose_rawfile', \
'overlapping_rawfile', \
]
def main():
args.func(args)
if __name__ == '__main__':
parser = utils.DefaultArguments(prog='rawfiles.py', \
description='A multi-purpose program to interact ' \
'with rawfiles.')
subparsers = parser.add_subparsers(help='Available functionality. ' \
'To get more detailed help for each function ' \
'provide the "-h/--help" argument following the ' \
'function.')
mod = __import__('toolkit.rawfiles', globals(), locals(), toolkit)
for tool_name in toolkit:
tool = getattr(mod, tool_name)
toolparser = subparsers.add_parser(tool.SHORTNAME,
help=tool.DESCRIPTION)
toolparser.set_defaults(func=tool.main)
tool.add_arguments(toolparser)
args = parser.parse_args()
main()
|
plazarREPO_NAMETOASTERPATH_START.@TOASTER_extracted@TOASTER-master@rawfiles.py@.PATH_END.py
|
{
"filename": "test_default.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/simplejson/py2/simplejson/tests/test_default.py",
"type": "Python"
}
|
from unittest import TestCase
import simplejson as json
class TestDefault(TestCase):
def test_default(self):
self.assertEqual(
json.dumps(type, default=repr),
json.dumps(repr(type)))
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@simplejson@py2@simplejson@tests@test_default.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/astropy/visualization/tests/__init__.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
|
waynebhayesREPO_NAMESpArcFiRePATH_START.@SpArcFiRe_extracted@SpArcFiRe-master@scripts@SpArcFiRe-pyvenv@lib@python2.7@site-packages@astropy@visualization@tests@__init__.py@.PATH_END.py
|
{
"filename": "test_Roman.py",
"repo_name": "lenstronomy/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/test/test_SimulationAPI/test_ObservationConfig/test_Roman.py",
"type": "Python"
}
|
import unittest
from lenstronomy.SimulationAPI.ObservationConfig.Roman import Roman
from lenstronomy.SimulationAPI.observation_api import Instrument, SingleBand
import lenstronomy.Util.util as util
import os
import astropy.io.fits as pyfits
class TestRoman(unittest.TestCase):
def setUp(self):
self.F062 = Roman() # default is F062
self.F087 = Roman(band="F087", survey_mode="microlensing")
self.F106 = Roman(band="F106", psf_type="GAUSSIAN")
self.F129 = Roman(band="F129", psf_type="GAUSSIAN")
self.F158 = Roman(band="F158", psf_type="GAUSSIAN")
self.F184 = Roman(band="F184", psf_type="GAUSSIAN")
self.F146 = Roman(band="F146", survey_mode="microlensing", psf_type="GAUSSIAN")
kwargs_F062 = self.F062.kwargs_single_band()
kwargs_F087 = self.F087.kwargs_single_band()
kwargs_F106 = self.F106.kwargs_single_band()
kwargs_F129 = self.F129.kwargs_single_band()
kwargs_F158 = self.F158.kwargs_single_band()
kwargs_F184 = self.F184.kwargs_single_band()
kwargs_F146 = self.F146.kwargs_single_band()
self.F062_band = SingleBand(**kwargs_F062)
self.F087_band = SingleBand(**kwargs_F087)
self.F106_band = SingleBand(**kwargs_F106)
self.F129_band = SingleBand(**kwargs_F129)
self.F158_band = SingleBand(**kwargs_F158)
self.F184_band = SingleBand(**kwargs_F184)
self.F146_band = SingleBand(**kwargs_F146)
# dictionaries mapping Roman kwargs to SingleBand kwargs
self.camera_settings = {
"read_noise": "_read_noise",
"pixel_scale": "pixel_scale",
"ccd_gain": "ccd_gain",
}
self.obs_settings = {
"exposure_time": "_exposure_time",
"sky_brightness": "_sky_brightness_",
"magnitude_zero_point": "_magnitude_zero_point",
"num_exposures": "_num_exposures",
"seeing": "_seeing",
"psf_type": "_psf_type",
}
self.instrument = Instrument(**self.F062.camera)
def test_Roman_class(self):
default = self.F062
explicit_F062 = Roman(band="F062")
self.assertEqual(explicit_F062.camera, default.camera)
self.assertEqual(explicit_F062.obs, default.obs)
with self.assertRaises(ValueError):
bad_band = Roman(band="g")
with self.assertRaises(ValueError):
bad_band_2 = Roman(band="9")
with self.assertRaises(ValueError):
bad_psf = Roman(psf_type="blah")
with self.assertRaises(ValueError):
bad_band_wide = Roman(band="F087")
with self.assertRaises(ValueError):
bad_band_microlensing = Roman(band="F062", survey_mode="microlensing")
with self.assertRaises(ValueError):
bad_survey_mode = Roman(survey_mode="blah")
def test_Roman_camera(self):
# comparing camera settings in Roman instance with those in Instrument instance
for config, setting in self.camera_settings.items():
self.assertEqual(
self.F062.camera[config],
getattr(self.instrument, setting),
msg=f"{config} did not match",
)
def test_Roman_obs(self):
# comparing obs settings in HST instance with those in SingleBand instance
for config, setting in self.obs_settings.items():
self.assertEqual(
self.F062.obs[config],
getattr(self.F062_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F087.obs[config],
getattr(self.F087_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F106.obs[config],
getattr(self.F106_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F129.obs[config],
getattr(self.F129_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F158.obs[config],
getattr(self.F158_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F184.obs[config],
getattr(self.F184_band, setting),
msg=f"{config} did not match",
)
self.assertEqual(
self.F146.obs[config],
getattr(self.F146_band, setting),
msg=f"{config} did not match",
)
def test_Roman_psf_pixel(self):
self.F062_pixel = Roman(psf_type="PIXEL")
import lenstronomy
module_path = os.path.dirname(lenstronomy.__file__)
psf_filename = os.path.join(
module_path, "SimulationAPI/ObservationConfig/PSF_models/F062.fits"
)
kernel = pyfits.getdata(psf_filename)
self.assertEqual(
self.F062_pixel.obs["kernel_point_source"].all(),
kernel.all(),
msg="PSF did not match",
)
def test_kwargs_single_band(self):
kwargs_F062 = util.merge_dicts(self.F062.camera, self.F062.obs)
self.assertEqual(self.F062.kwargs_single_band(), kwargs_F062)
if __name__ == "__main__":
unittest.main()
|
lenstronomyREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@test@test_SimulationAPI@test_ObservationConfig@test_Roman.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/lite/c/README.md",
"type": "Markdown"
}
|
# TensorFlow Lite C API
This directory contains C APIs for TensorFlow Lite. This includes C APIs
for common types, like kernels and delegates, as well as an explicit C API
for inference.
## Header summary
Each public C header contains types and methods for specific uses:
* `common.h` - Contains common C enums, types and methods used throughout
TensorFlow Lite. This includes everything from error codes, to the kernel
and delegate APIs.
* `builtin_op_data.h` - Contains op-specific data that is used for builtin
kernels. This should only be used when (re)implementing a builtin operator.
* `c_api.h` - Contains the TensorFlow Lite C API for inference. The
functionality here is largely equivalent (though a strict subset of) the
functionality provided by the C++ `Interpreter` API.
* `c_api_experimental.h` - Contains experimental C API methods for inference.
These methods are useful and usable, but aren't yet part of the stable API.
## Using the C API
See the [`c_api.h`](c_api.h) header for API usage details.
## Building the C API
A native shared library target that contains the C API for inference has been
provided. Assuming a working [bazel](https://bazel.build/versions/master/docs/install.html)
configuration, this can be built as follows:
```sh
bazel build -c opt //tensorflow/lite/c:tensorflowlite_c
```
and for Android (replace `android_arm` with `android_arm64` for 64-bit),
assuming you've
[configured your project for Android builds](../g3doc/android/lite_build.md):
```sh
bazel build -c opt --cxxopt=--std=c++11 --config=android_arm \
//tensorflow/lite/c:tensorflowlite_c
```
The generated shared library will be available in your
`bazel-bin/tensorflow/lite/c` directory. A target which packages the shared
library together with the necessary headers (`c_api.h`, `c_api_experimental.h`
and `common.h`) will be available soon, and will also be released as a prebuilt
archive (together with existing prebuilt packages for Android/iOS).
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@lite@c@README.md@.PATH_END.py
|
{
"filename": "test_npy_pkg_config.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/numpy/py3/numpy/distutils/tests/test_npy_pkg_config.py",
"type": "Python"
}
|
import os
from numpy.distutils.npy_pkg_config import read_config, parse_flags
from numpy.testing import temppath, assert_
simple = """\
[meta]
Name = foo
Description = foo lib
Version = 0.1
[default]
cflags = -I/usr/include
libs = -L/usr/lib
"""
simple_d = {'cflags': '-I/usr/include', 'libflags': '-L/usr/lib',
'version': '0.1', 'name': 'foo'}
simple_variable = """\
[meta]
Name = foo
Description = foo lib
Version = 0.1
[variables]
prefix = /foo/bar
libdir = ${prefix}/lib
includedir = ${prefix}/include
[default]
cflags = -I${includedir}
libs = -L${libdir}
"""
simple_variable_d = {'cflags': '-I/foo/bar/include', 'libflags': '-L/foo/bar/lib',
'version': '0.1', 'name': 'foo'}
class TestLibraryInfo:
def test_simple(self):
with temppath('foo.ini') as path:
with open(path, 'w') as f:
f.write(simple)
pkg = os.path.splitext(path)[0]
out = read_config(pkg)
assert_(out.cflags() == simple_d['cflags'])
assert_(out.libs() == simple_d['libflags'])
assert_(out.name == simple_d['name'])
assert_(out.version == simple_d['version'])
def test_simple_variable(self):
with temppath('foo.ini') as path:
with open(path, 'w') as f:
f.write(simple_variable)
pkg = os.path.splitext(path)[0]
out = read_config(pkg)
assert_(out.cflags() == simple_variable_d['cflags'])
assert_(out.libs() == simple_variable_d['libflags'])
assert_(out.name == simple_variable_d['name'])
assert_(out.version == simple_variable_d['version'])
out.vars['prefix'] = '/Users/david'
assert_(out.cflags() == '-I/Users/david/include')
class TestParseFlags:
def test_simple_cflags(self):
d = parse_flags("-I/usr/include")
assert_(d['include_dirs'] == ['/usr/include'])
d = parse_flags("-I/usr/include -DFOO")
assert_(d['include_dirs'] == ['/usr/include'])
assert_(d['macros'] == ['FOO'])
d = parse_flags("-I /usr/include -DFOO")
assert_(d['include_dirs'] == ['/usr/include'])
assert_(d['macros'] == ['FOO'])
def test_simple_lflags(self):
d = parse_flags("-L/usr/lib -lfoo -L/usr/lib -lbar")
assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
assert_(d['libraries'] == ['foo', 'bar'])
d = parse_flags("-L /usr/lib -lfoo -L/usr/lib -lbar")
assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
assert_(d['libraries'] == ['foo', 'bar'])
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@numpy@py3@numpy@distutils@tests@test_npy_pkg_config.py@.PATH_END.py
|
{
"filename": "tutorial-simple.ipynb",
"repo_name": "danielkoll/tidally-locked-coordinates",
"repo_path": "tidally-locked-coordinates_extracted/tidally-locked-coordinates-master/TL_coordinates/tutorial-simple.ipynb",
"type": "Jupyter Notebook"
}
|
```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
#
import get_GCM_data
import transform_to_TL_coordinates
```
### Get GCM output data.
```python
output = {'TS':'TS','CLDTOT':'CLDTOT','U':'U','V':'V'}
state = get_GCM_data.get_GCM("./", ["GCMOutput.nc"], vars_list=output)
```
### Now convert to TL coords.
### Pick Nlat/Nlon to match GCM's resolution in Earth-like coordinates, otherwise can get different global means!
```python
nlat_tl = state.lat.size # what resolution desired for interp into TL coords?
nlon_tl = state.lon.size #
lon_ss = 180. # at which lon is the substellar point?
state_TL = transform_to_TL_coordinates.transform_state(state,output,(nlat_tl,nlon_tl),lon_ss=lon_ss,do_vel=True)
```
```python
# note: in this case the time coord only has one entry.
# get rid of singleton dims with np.squeeze()
for s in [state,state_TL]:
for var in output:
x0 = getattr(s,var)
x1 = np.squeeze(x0)
setattr(s,var,x1)
```
### Check global means.
```python
print "global mean Ts in Earth coords = %.3fK" % np.average(np.average(state.TS,axis=-1) * state.weights,axis=-1)
print "global mean Ts in TL coords = %.3fK" % np.nanmean(np.nanmean(state_TL.TS,axis=-1) * state_TL.weights,axis=-1)
```
global mean Ts in Earth coords = 152.182K
global mean Ts in TL coords = 152.140K
# ----------------
### Make a plot of surface temperature in both coordinate systems
```python
cmap = 'RdBu_r'
plt.figure(figsize=[10,3.])
# -
plt.subplot(1,2,1)
CS = plt.pcolormesh(state.lon,state.lat,state.TS,cmap=cmap,vmin=200,vmax=300)
plt.colorbar(CS)
plt.xlim(0,360)
plt.ylim(-90,90)
plt.title('TS, Earth coordinates')
# -
plt.subplot(1,2,2)
CS = plt.pcolormesh(state_TL.lon,state_TL.lat,state_TL.TS,cmap=cmap,vmin=200,vmax=300)
plt.colorbar(CS)
plt.xlim(0,360)
plt.ylim(-90,90)
plt.title('TS, TL coordinates')
```
Text(0.5,1,'TS, TL coordinates')
```python
```
|
danielkollREPO_NAMEtidally-locked-coordinatesPATH_START.@tidally-locked-coordinates_extracted@tidally-locked-coordinates-master@TL_coordinates@tutorial-simple.ipynb@.PATH_END.py
|
{
"filename": "_groups.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/sankey/node/_groups.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class GroupsValidator(_plotly_utils.basevalidators.InfoArrayValidator):
def __init__(self, plotly_name="groups", parent_name="sankey.node", **kwargs):
super(GroupsValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
dimensions=kwargs.pop("dimensions", 2),
edit_type=kwargs.pop("edit_type", "calc"),
free_length=kwargs.pop("free_length", True),
implied_edits=kwargs.pop("implied_edits", {"x": [], "y": []}),
items=kwargs.pop("items", {"editType": "calc", "valType": "number"}),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@sankey@node@_groups.py@.PATH_END.py
|
{
"filename": "MyCasapy2bbs.py",
"repo_name": "saopicc/DDFacet",
"repo_path": "DDFacet_extracted/DDFacet-master/SkyModel/MyCasapy2bbs.py",
"type": "Python"
}
|
#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
from pyrap.tables import table
from pyrap.images import image
from SkyModel.Sky import ClassSM
import optparse
import numpy as np
import glob
import os
from SkyModel.Other import reformat
SaveFile="last_MyCasapy2BBS.obj"
import pickle
import scipy.ndimage
from SkyModel.Tools import ModFFTW
def read_options():
desc=""" cyril.tasse@obspm.fr"""
opt = optparse.OptionParser(usage='Usage: %prog --ms=somename.MS <options>',version='%prog version 1.0',description=desc)
group = optparse.OptionGroup(opt, "* Data-related options")
group.add_option('--ModelIm',help='Input Model image [no default]',default='')
group.add_option('--RestoredIm',default=None)
group.add_option('--ResidualIm',default=None)
#group.add_option('--Th',type="float",default=5)
group.add_option("--AutoMask",type="int",default=1)
opt.add_option_group(group)
options, arguments = opt.parse_args()
f = open(SaveFile,"wb")
pickle.dump(options,f)
#####################"
def test():
Conv=MyCasapy2BBS("/media/tasse/data/DDFacet/Test/lala2.model.fits")
Conv.GetPixCat()
Conv.ToSM()
class MyCasapy2BBS():
def __init__(self,Fits,
ImRestoredName=None,
ImResidualName=None,
Th=None,
box=(150,150),Boost=5,
ResInPix=1,AutoMask=True):
self.Fits=Fits
self.Th=Th
self.Mask=None
self.box=box
self.Boost=Boost
self.ImRestoredName=ImRestoredName
self.ImResidualName=ImResidualName
self.DoMask=False
self.ResInPix=ResInPix
self.XcYcDx=None
self.AutoMask=AutoMask
#self.XcYcDx=14000,10000,1000
#self.XcYcDx=10000,5000,1000
self.Init()
def Init(self):
self.setModelImage()
#self.setRestored()
if self.AutoMask:
self.MakeMask2()
def setModelImage(self):
print("set model image")
self.im=image(self.Fits)
im=self.im
c=im.coordinates()
dx,_=c.__dict__["_csys"]["direction0"]["cdelt"]
self.CellSizeRad=np.abs(dx)
self.PSFGaussPars=self.ResInPix*self.CellSizeRad,self.ResInPix*self.CellSizeRad,0.
Fits=self.Fits
self.Model=self.im.getdata()[0,0]
if self.XcYcDx is not None:
xc,yc,dx=self.XcYcDx
x0,x1=xc-dx,xc+dx
y0,y1=yc-dx,yc+dx
self.Model=self.Model[x0:x1,y0:y1]
#self.Plot(self.Model)
print(" done set model image")
# def setRestored(self):
# print "set restored image"
# if self.ImRestoredName is not None:
# print " read restored image"
# im=image(self.ImRestoredName)
# self.ImRestored=im.getdata()[0,0]
# elif self.ImResidualName is not None:
# print " convolve model image"
# nx,ny=self.Model.shape
# self.ImRestored=ModFFTW.ConvolveGaussian(self.Model.reshape((1,1,nx,nx)),CellSizeRad=self.CellSizeRad,GaussPars=[self.PSFGaussPars])
# self.ImRestored=self.ImRestored[0,0]
# im=image(self.ImResidualName)
# ImResidual=im.getdata()[0,0]
# xc,yc,dx=self.XcYcDx
# x0,x1=xc-dx,xc+dx
# y0,y1=yc-dx,yc+dx
# self.ImResidual=ImResidual[x0:x1,y0:y1]
# Np=1000
# Nx=self.ImResidual.shape[0]
# indx=np.int64(np.random.rand(Np)*Nx)
# indy=np.int64(np.random.rand(Np)*Nx)
# self.GlobalSTD=np.std(self.ImResidual[indx,indy])
# print " add residual image"
# self.ImRestored+=self.ImResidual
# #self.ImResidual=self.ImRestored
# del(im)
# #self.Plot(self.ImRestored)
# else:
# print " done nothing"
# print " done set restored image"
def Plot(self,data,dx=None):
import pylab
xc=data.shape[0]//2
pylab.clf()
if dx is not None:
pylab.imshow(data[xc-dx:xc+dx,xc-dx:xc+dx],interpolation="nearest",cmap="gray")
else:
pylab.imshow(data,interpolation="nearest",cmap="gray")
pylab.draw()
pylab.show()
# def ComputeNoiseMap(self):
# print "Compute noise map..."
# Boost=self.Boost
# Acopy=self.ImResidual[0::Boost,0::Boost].copy()
# SBox=(self.box[0]//Boost,self.box[1]//Boost)
# #Noise=scipy.ndimage.filters.median_filter(Acopy**2,SBox)
# #Noise-=scipy.ndimage.filters.median_filter(Acopy,SBox)**2
# # Noise=scipy.ndimage.filters.median_filter(Acopy**2,SBox)
# # #Noise-=scipy.ndimage.filters.median_filter(Acopy,SBox)**2
# # Noise=np.sqrt(np.abs(Noise))
# Noise=scipy.ndimage.filters.percentile_filter(Acopy**2, 50., size=SBox)#/3.
# #Noise-=(scipy.ndimage.filters.percentile_filter(Acopy, 50., size=SBox))**2#/3.
# Noise=np.sqrt(np.abs(Noise))
# Noise=np.abs(Noise)
# Noise[Noise==0]=self.GlobalSTD
# #Noise[:]=self.GlobalSTD
# #Noise+=scipy.ndimage.filters.percentile_filter(Acopy, 32., size=SBox)#/3.
# #ind=(np.abs(Acopy)>3.*Noise)
# #Acopy[ind]=Noise[ind]
# #Noise=np.sqrt(scipy.ndimage.filters.median_filter(np.abs(Acopy)**2,SBox))
# self.Noise=np.zeros_like(self.ImRestored)
# for i in range(Boost):
# for j in range(Boost):
# s00,s01=Noise.shape
# s10,s11=self.Noise[i::Boost,j::Boost].shape
# s0,s1=min(s00,s10),min(s10,s11)
# self.Noise[i::Boost,j::Boost][0:s0,0:s1]=Noise[:,:][0:s0,0:s1]
# print " ... done"
# ind=np.where(self.Noise==0.)
# self.Noise[ind]=1e-10
# #self.Plot(self.Noise)
# def MakeMask(self):
# if self.ImRestored==None: return
# self.ComputeNoiseMap()
# self.Mask=(self.ImRestored>(self.Th*self.Noise))
# self.DoMask=True
def MakeMask2(self):
self.DoMask=True
x,y=np.where(self.Model!=0)
RadPix=50
Nx=x.size
DMat=np.sqrt((x.reshape((Nx,1))-x.reshape((1,Nx)))**2+(y.reshape((Nx,1))-y.reshape((1,Nx)))**2)
DR=10
self.Mask=np.ones(self.Model.shape,bool)
s=self.Model[x,y]
for ipix in range(Nx):
ID=np.arange(Nx)
indClose=(DMat[ipix]<RadPix)
ID=ID[indClose]
indMask=(s[indClose]<(s[ipix]/DR))
ID=ID[indMask]
#indSel=indClose[indMask]
self.Mask[x[ID],y[ID]]=False
# self.Mask=self.Mask.T
# import pylab
# pylab.clf()
# pylab.imshow(self.Mask.T,interpolation="nearest",cmap="gray")
# pylab.draw()
# pylab.show()
def ToSM(self):
Osm=reformat.reformat(self.Fits,LastSlash=False)
SM=ClassSM.ClassSM(Osm,ReName=True,DoREG=True,SaveNp=True,FromExt=self.Cat)
SM.MakeREG()
#SM.MakeREG()
#SM.Finalise()
SM.Save()
#SM.print_sm2()
def GetPixCat(self):
im=self.im
Model=self.Model
pol,freq,decc,rac=im.toworld((0,0,0,0))
#print pol,freq,rac, decc
indx,indy=np.where(Model!=0.)
NPix=indx.size
Cat=np.zeros((NPix,),dtype=[('ra',float),('dec',float),('s',float)])
Cat=Cat.view(np.recarray)
X=[]
Y=[]
Xn=[]
Yn=[]
for ipix in range(indx.size):
x,y=indx[ipix],indy[ipix]
if self.DoMask:
if not(self.Mask[x,y]):
Xn.append(x)
Yn.append(y)
continue
s=Model[x,y]
X.append(x)
Y.append(y)
#a,b,dec,ra=im.toworld((0,0,0,0))
a,b,dec,ra=im.toworld((0,0,x,y))
#print a,b,dec,ra
#ra*=(1./60)#*np.pi/180
#dec*=(1./60)#*np.pi/180
Cat.ra[ipix]=ra
Cat.dec[ipix]=dec
Cat.s[ipix]=s
# print "======="
# #print x,y,s
# print ra,dec#,s
# if (self.Th is not None)&(self.ModelConv==None):
# Cat=Cat[np.abs(Cat.s)>(self.Th*np.max(np.abs(Cat.s)))]
Cat=Cat[Cat.s!=0]
# print "ok"
# print Xn
# print X
# import pylab
# pylab.clf()
# ax=pylab.subplot(1,2,1)
# vmin,vmax=self.ImRestored.min(),self.ImRestored.max()
# pylab.imshow(self.Mask.T,interpolation="nearest",cmap="gray")#,vmin=vmin,vmax=vmax)
# #pylab.colorbar()
# pylab.subplot(1,2,2,sharex=ax,sharey=ax)
# pylab.imshow(self.ImRestored.T,interpolation="nearest",cmap="gray",vmin=vmin,vmax=vmax)
# pylab.scatter(Xn,Yn,marker=".",color="blue")
# pylab.scatter(X,Y,marker=".",color="red")
# pylab.draw()
# pylab.show()
self.Cat=Cat
def main(options=None):
if options==None:
f = open(SaveFile,'rb')
options = pickle.load(f)
Conv=MyCasapy2BBS(options.ModelIm,
ImRestoredName=options.RestoredIm,
ImResidualName=options.ResidualIm,
#Th=options.Th,
AutoMask=options.AutoMask)
Conv.GetPixCat()
Conv.ToSM()
def driver():
read_options()
f = open(SaveFile,'rb')
options = pickle.load(f)
main(options=options)
if __name__=="__main__":
# do not place any other code here --- cannot be called as a package entrypoint otherwise, see:
# https://packaging.python.org/en/latest/specifications/entry-points/
driver()
|
saopiccREPO_NAMEDDFacetPATH_START.@DDFacet_extracted@DDFacet-master@SkyModel@MyCasapy2bbs.py@.PATH_END.py
|
{
"filename": "rnn_test.py",
"repo_name": "fchollet/keras",
"repo_path": "keras_extracted/keras-master/keras/src/layers/rnn/rnn_test.py",
"type": "Python"
}
|
import numpy as np
import pytest
from keras.src import layers
from keras.src import ops
from keras.src import testing
class OneStateRNNCell(layers.Layer):
def __init__(self, units, state_size=None, **kwargs):
super().__init__(**kwargs)
self.units = units
self.state_size = state_size if state_size else units
def build(self, input_shape):
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer="ones",
name="kernel",
)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
initializer="ones",
name="recurrent_kernel",
)
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = ops.matmul(inputs, self.kernel)
output = h + ops.matmul(prev_output, self.recurrent_kernel)
return output, [output]
class TwoStatesRNNCell(layers.Layer):
def __init__(self, units, state_size=None, **kwargs):
super().__init__(**kwargs)
self.units = units
self.state_size = state_size if state_size else [units, units]
self.output_size = units
def build(self, input_shape):
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer="ones",
name="kernel",
)
self.recurrent_kernel_1 = self.add_weight(
shape=(self.units, self.units),
initializer="ones",
name="recurrent_kernel_1",
)
self.recurrent_kernel_2 = self.add_weight(
shape=(self.units, self.units),
initializer="ones",
name="recurrent_kernel_2",
)
self.built = True
def call(self, inputs, states):
prev_1 = states[0]
prev_2 = states[0]
h = ops.matmul(inputs, self.kernel)
output_1 = h + ops.matmul(prev_1, self.recurrent_kernel_1)
output_2 = h + ops.matmul(prev_2, self.recurrent_kernel_2)
output = output_1 + output_2
return output, [output_1, output_2]
class RNNTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_basics(self):
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": OneStateRNNCell(5, state_size=5)},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": OneStateRNNCell(5, state_size=[5])},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": OneStateRNNCell(5, state_size=(5,))},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": OneStateRNNCell(5), "return_sequences": True},
input_shape=(3, 2, 4),
expected_output_shape=(3, 2, 5),
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={
"cell": OneStateRNNCell(5),
"go_backwards": True,
"unroll": True,
},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": TwoStatesRNNCell(5, state_size=[5, 5])},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=3,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": TwoStatesRNNCell(5, state_size=(5, 5))},
input_shape=(3, 2, 4),
expected_output_shape=(3, 5),
expected_num_trainable_weights=3,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
self.run_layer_test(
layers.RNN,
init_kwargs={"cell": TwoStatesRNNCell(5), "return_sequences": True},
input_shape=(3, 2, 4),
expected_output_shape=(3, 2, 5),
expected_num_trainable_weights=3,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
supports_masking=True,
)
def test_compute_output_shape_single_state(self):
sequence = np.ones((3, 4, 5))
layer = layers.RNN(OneStateRNNCell(8), return_sequences=False)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape, (3, 8))
layer = layers.RNN(OneStateRNNCell(8), return_sequences=True)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape, (3, 4, 8))
layer = layers.RNN(
OneStateRNNCell(8), return_sequences=False, return_state=True
)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape[0], (3, 8))
self.assertEqual(output_shape[1], (3, 8))
layer = layers.RNN(
OneStateRNNCell(8), return_sequences=True, return_state=True
)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape[0], (3, 4, 8))
self.assertEqual(output_shape[1], (3, 8))
def test_compute_output_shape_two_states(self):
sequence = np.ones((3, 4, 5))
layer = layers.RNN(TwoStatesRNNCell(8), return_sequences=False)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape, (3, 8))
layer = layers.RNN(TwoStatesRNNCell(8), return_sequences=True)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape, (3, 4, 8))
layer = layers.RNN(
TwoStatesRNNCell(8), return_sequences=False, return_state=True
)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape[0], (3, 8))
self.assertEqual(output_shape[1], (3, 8))
self.assertEqual(output_shape[2], (3, 8))
layer = layers.RNN(
TwoStatesRNNCell(8), return_sequences=True, return_state=True
)
output_shape = layer.compute_output_shape(sequence.shape)
self.assertEqual(output_shape[0], (3, 4, 8))
self.assertEqual(output_shape[1], (3, 8))
self.assertEqual(output_shape[2], (3, 8))
def test_dynamic_shapes(self):
sequence_shape = (None, None, 3)
layer = layers.RNN(OneStateRNNCell(8), return_sequences=False)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape, (None, 8))
layer = layers.RNN(OneStateRNNCell(8), return_sequences=True)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape, (None, None, 8))
layer = layers.RNN(
OneStateRNNCell(8), return_sequences=False, return_state=True
)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape[0], (None, 8))
self.assertEqual(output_shape[1], (None, 8))
layer = layers.RNN(
OneStateRNNCell(8), return_sequences=True, return_state=True
)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape[0], (None, None, 8))
self.assertEqual(output_shape[1], (None, 8))
layer = layers.RNN(TwoStatesRNNCell(8), return_sequences=False)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape, (None, 8))
layer = layers.RNN(TwoStatesRNNCell(8), return_sequences=True)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape, (None, None, 8))
layer = layers.RNN(
TwoStatesRNNCell(8), return_sequences=False, return_state=True
)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape[0], (None, 8))
self.assertEqual(output_shape[1], (None, 8))
self.assertEqual(output_shape[2], (None, 8))
layer = layers.RNN(
TwoStatesRNNCell(8), return_sequences=True, return_state=True
)
output_shape = layer.compute_output_shape(sequence_shape)
self.assertEqual(output_shape[0], (None, None, 8))
self.assertEqual(output_shape[1], (None, 8))
self.assertEqual(output_shape[2], (None, 8))
def test_forward_pass_single_state(self):
sequence = np.ones((1, 2, 3))
layer = layers.RNN(OneStateRNNCell(2), return_sequences=False)
output = layer(sequence)
self.assertAllClose(np.array([[9.0, 9.0]]), output)
layer = layers.RNN(OneStateRNNCell(2), return_sequences=True)
output = layer(sequence)
self.assertAllClose(np.array([[[3.0, 3.0], [9.0, 9.0]]]), output)
layer = layers.RNN(
OneStateRNNCell(2), return_sequences=False, return_state=True
)
output, state = layer(sequence)
self.assertAllClose(np.array([[9.0, 9.0]]), output)
self.assertAllClose(np.array([[9.0, 9.0]]), state)
layer = layers.RNN(
OneStateRNNCell(2), return_sequences=True, return_state=True
)
output, state = layer(sequence)
self.assertAllClose(np.array([[[3.0, 3.0], [9.0, 9.0]]]), output)
self.assertAllClose(np.array([[9.0, 9.0]]), state)
def test_forward_pass_two_states(self):
sequence = np.ones((1, 2, 3))
layer = layers.RNN(TwoStatesRNNCell(2), return_sequences=False)
output = layer(sequence)
self.assertAllClose(np.array([[18.0, 18.0]]), output)
layer = layers.RNN(TwoStatesRNNCell(2), return_sequences=True)
output = layer(sequence)
self.assertAllClose(np.array([[[6.0, 6.0], [18.0, 18.0]]]), output)
layer = layers.RNN(
TwoStatesRNNCell(2), return_sequences=False, return_state=True
)
output, state1, state2 = layer(sequence)
self.assertAllClose(np.array([[18.0, 18.0]]), output)
self.assertAllClose(np.array([[9.0, 9.0]]), state1)
self.assertAllClose(np.array([[9.0, 9.0]]), state2)
layer = layers.RNN(
TwoStatesRNNCell(2), return_sequences=True, return_state=True
)
output, state1, state2 = layer(sequence)
self.assertAllClose(np.array([[[6.0, 6.0], [18.0, 18.0]]]), output)
self.assertAllClose(np.array([[9.0, 9.0]]), state1)
self.assertAllClose(np.array([[9.0, 9.0]]), state2)
def test_passing_initial_state_single_state(self):
sequence = np.ones((2, 3, 2))
state = np.ones((2, 2))
layer = layers.RNN(OneStateRNNCell(2), return_sequences=False)
output = layer(sequence, initial_state=state)
self.assertAllClose(np.array([[22.0, 22.0], [22.0, 22.0]]), output)
layer = layers.RNN(
OneStateRNNCell(2), return_sequences=False, return_state=True
)
output, state = layer(sequence, initial_state=state)
self.assertAllClose(np.array([[22.0, 22.0], [22.0, 22.0]]), output)
self.assertAllClose(np.array([[22.0, 22.0], [22.0, 22.0]]), state)
def test_passing_initial_state_two_states(self):
sequence = np.ones((2, 3, 2))
state = [np.ones((2, 2)), np.ones((2, 2))]
layer = layers.RNN(TwoStatesRNNCell(2), return_sequences=False)
output = layer(sequence, initial_state=state)
self.assertAllClose(np.array([[44.0, 44.0], [44.0, 44.0]]), output)
layer = layers.RNN(
TwoStatesRNNCell(2), return_sequences=False, return_state=True
)
output, state_1, state_2 = layer(sequence, initial_state=state)
self.assertAllClose(np.array([[44.0, 44.0], [44.0, 44.0]]), output)
self.assertAllClose(np.array([[22.0, 22.0], [22.0, 22.0]]), state_1)
self.assertAllClose(np.array([[22.0, 22.0], [22.0, 22.0]]), state_2)
def test_statefulness_single_state(self):
sequence = np.ones((1, 2, 3))
layer = layers.RNN(OneStateRNNCell(2), stateful=True)
layer(sequence)
output = layer(sequence)
self.assertAllClose(np.array([[45.0, 45.0]]), output)
layer = layers.RNN(OneStateRNNCell(2), stateful=True, return_state=True)
layer(sequence)
output, state = layer(sequence)
self.assertAllClose(np.array([[45.0, 45.0]]), output)
self.assertAllClose(np.array([[45.0, 45.0]]), state)
def test_statefulness_two_states(self):
sequence = np.ones((1, 2, 3))
layer = layers.RNN(TwoStatesRNNCell(2), stateful=True)
layer(sequence)
output = layer(sequence)
self.assertAllClose(np.array([[90.0, 90.0]]), output)
layer = layers.RNN(
TwoStatesRNNCell(2), stateful=True, return_state=True
)
layer(sequence)
output, state_1, state_2 = layer(sequence)
self.assertAllClose(np.array([[90.0, 90.0]]), output)
self.assertAllClose(np.array([[45.0, 45.0]]), state_1)
self.assertAllClose(np.array([[45.0, 45.0]]), state_2)
def test_go_backwards(self):
sequence = np.arange(24).reshape((2, 3, 4)).astype("float32")
layer = layers.RNN(OneStateRNNCell(2), go_backwards=True)
layer(sequence)
output = layer(sequence)
self.assertAllClose(np.array([[202.0, 202.0], [538.0, 538.0]]), output)
layer = layers.RNN(OneStateRNNCell(2), stateful=True, return_state=True)
layer(sequence)
output, state = layer(sequence)
self.assertAllClose(
np.array([[954.0, 954.0], [3978.0, 3978.0]]), output
)
self.assertAllClose(np.array([[954.0, 954.0], [3978.0, 3978.0]]), state)
def test_serialization(self):
layer = layers.RNN(TwoStatesRNNCell(2), return_sequences=False)
self.run_class_serialization_test(layer)
layer = layers.RNN(OneStateRNNCell(2), return_sequences=False)
self.run_class_serialization_test(layer)
# TODO: test masking
|
fcholletREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@src@layers@rnn@rnn_test.py@.PATH_END.py
|
{
"filename": "_tickvalssrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatterternary/marker/colorbar/_tickvalssrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickvalssrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="tickvalssrc",
parent_name="scatterternary.marker.colorbar",
**kwargs,
):
super(TickvalssrcValidator, 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@scatterternary@marker@colorbar@_tickvalssrc.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "alibaba/TinyNeuralNetwork",
"repo_path": "TinyNeuralNetwork_extracted/TinyNeuralNetwork-main/setup.py",
"type": "Python"
}
|
import os
import setuptools
import subprocess
from datetime import datetime
try:
from pip._internal.req import parse_requirements
except ImportError:
from pip.req import parse_requirements
def get_sha():
try:
return subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('ascii').strip()
except (subprocess.CalledProcessError, FileNotFoundError):
return None
def get_datetime():
try:
return (
subprocess.check_output(['git', 'show', '-s', '--date=format:%Y%m%d%H%M%S', '--format=%cd'])
.decode('ascii')
.strip()
)
except (subprocess.CalledProcessError, FileNotFoundError):
now = datetime.now()
return now.strftime("%Y%m%d%H%M%S")
predefined_version = os.getenv('TINYML_BUILD_VERSION')
if predefined_version:
version = predefined_version
else:
version = '0.1.0'
sha = get_sha()
dt = get_datetime()
if sha:
version += f'.{dt}+{sha}'
else:
version += f'.{dt}'
reqs = parse_requirements('requirements.txt', session=False)
install_reqs = [str(ir.req) if hasattr(ir, 'req') else str(ir.requirement) for ir in reqs]
setuptools.setup(
name="TinyNeuralNetwork",
version=version,
author="Huanghao Ding, Jiachen Pu",
description="A collection of tools that aims for the inference performance of a AI model in AIoT scenarios.",
url="https://github.com/alibaba/TinyNeuralNetwork",
project_urls={
"Bug Tracker": "https://github.com/alibaba/TinyNeuralNetwork/issues",
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
packages=setuptools.find_packages(),
package_data={'tinynn': ['graph/configs/*.yml']},
python_requires=">=3.6",
install_requires=install_reqs,
)
|
alibabaREPO_NAMETinyNeuralNetworkPATH_START.@TinyNeuralNetwork_extracted@TinyNeuralNetwork-main@setup.py@.PATH_END.py
|
{
"filename": "test_mcs_adp.py",
"repo_name": "lwa-project/lsl",
"repo_path": "lsl_extracted/lsl-main/tests/test_mcs_adp.py",
"type": "Python"
}
|
"""
Unit test for the lsl.common.mcsADP module.
"""
import os
import unittest
from datetime import datetime
from lsl.common import mcsADP
__version__ = "0.1"
__author__ = "Jayce Dowell"
class mcs_adp_tests(unittest.TestCase):
"""A unittest.TestCase collection of unit tests for the lsl.common.mcs
module."""
def test_delay_conversion(self):
"""Test the MCS delay conversion"""
delay_in = 5.0
delay_out = mcsADP.mcsd_to_delay(mcsADP.delay_to_mcsd(delay_in))
self.assertTrue(abs(delay_in-delay_out) < 1e9/196e6/16) # Within 1/16 of a sample
def test_gain_conversion(self):
"""Test the MCS gain conversion"""
gain_in = 0.5
gain_out = mcsADP.mcsg_to_gain(mcsADP.gain_to_mcsg(0.5))
self.assertTrue(abs(gain_in-gain_out) < 1/2.**15)
def test_datetime(self):
"""Test the datetime to MJD, MPM conversion"""
dt = datetime.strptime("2012-06-15 06:34:09", "%Y-%m-%d %H:%M:%S")
mjd, mpm = mcsADP.datetime_to_mjdmpm(dt)
self.assertEqual(mjd, 56093)
self.assertEqual(mpm, 23649000)
def test_mjdmpm(self):
"""Test the MJD, MPM to datetime conversion"""
mjd, mpm = 56093, 23649000
dt = mcsADP.mjdmpm_to_datetime(mjd, mpm)
self.assertEqual(dt.strftime("%Y-%m-%d %H:%M:%S"), "2012-06-15 06:34:09")
def test_summary_limits(self):
"""Test valid summary values"""
for i in range(0, 6+1):
mcsADP.SummaryCode(i)
self.assertRaises(ValueError, mcsADP.SummaryCode, 7)
def test_summary_descriptions(self):
"""Test valid summary descriptions"""
for i in range(0, 6+1):
s = mcsADP.SummaryCode(i)
s.description
def test_sid_limits(self):
"""Test valid subsystem ID values"""
for i in range(1, 20+1):
mcsADP.SubsystemID(i)
self.assertRaises(ValueError, mcsADP.SubsystemID, 0)
self.assertRaises(ValueError, mcsADP.SubsystemID, 21)
def test_cid_limits(self):
"""Test valid command ID values"""
for i in range(0, 41+1):
mcsADP.CommandID(i)
self.assertRaises(ValueError, mcsADP.CommandID, 42)
def test_mode_limits(self):
"""Test valid observing mode values"""
for i in range(1, 9+1):
mcsADP.ObservingMode(i)
self.assertRaises(ValueError, mcsADP.ObservingMode, 0)
self.assertRaises(ValueError, mcsADP.ObservingMode, 10)
def test_pointing_correction(self):
"""Test the pointing correction function"""
az = 63.4
el = 34.2
# No rotation
theta = 0.0
phi = 0.0
psi = 0.0
azP, elP = mcsADP.apply_pointing_correction(az, el, theta, phi, psi, degrees=True)
self.assertAlmostEqual(azP, az, 1)
self.assertAlmostEqual(elP, el, 1)
# Azimuth only
theta = 0.0
phi = 0.0
psi = 1.0
azP, elP = mcsADP.apply_pointing_correction(az, el, theta, phi, psi, degrees=True)
self.assertAlmostEqual(azP, az-1.0, 1)
self.assertAlmostEqual(elP, el, 1)
# Something random
theta = 23.0
phi = 10.0
psi = 1.5
azP, elP = mcsADP.apply_pointing_correction(az, el, theta, phi, psi, degrees=True)
self.assertAlmostEqual(azP, 62.40, 1)
self.assertAlmostEqual(elP, 34.37, 1)
class mcs_adp_test_suite(unittest.TestSuite):
"""A unittest.TestSuite class which contains all of the lsl.common.mcsADP
module unit tests."""
def __init__(self):
unittest.TestSuite.__init__(self)
loader = unittest.TestLoader()
self.addTests(loader.loadTestsFromTestCase(mcs_adp_tests))
if __name__ == '__main__':
unittest.main()
|
lwa-projectREPO_NAMElslPATH_START.@lsl_extracted@lsl-main@tests@test_mcs_adp.py@.PATH_END.py
|
{
"filename": "ddp.py",
"repo_name": "desihub/LSS",
"repo_path": "LSS_extracted/LSS-main/py/LSS/DESI_ke/ddp.py",
"type": "Python"
}
|
import os
import fitsio
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from cosmo import volcom
from scipy.interpolate import interp1d
from findfile import findfile
tmr_DDP1 = [-21.8, -20.1]
tmr_DDP2 = [-20.6, -19.3]
tmr_DDP3 = [-19.6, -17.8]
root = os.environ['GOLD_DIR'] + '/ddrp_limits/'
def initialise_ddplimits(survey, Mcol='M0P0_QALL'):
assert Mcol == 'M0P0_QALL', 'Hard coded limit numbers and curves'
bpath = findfile(ftype='ddp_limit', dryrun=False, survey=survey, ddp_count= 3)
fpath = findfile(ftype='ddp_limit', dryrun=False, survey=survey, ddp_count=17)
print(f'Reading {bpath}')
print(f'Reading {fpath}')
_bright_curve = Table.read(bpath)
_faint_curve = Table.read(fpath)
# TODO/MJW/ why did this fail to catch ddp_limits not provided with SURVEYARG??
assert _bright_curve.meta['SURVEY'] == survey, f'Survey mismatch for found ddp limit files: {bfpath}'
assert _faint_curve.meta['SURVEY'] == survey, f'Survey mismatch for found ddp limit files: {fpath}'
# TODO: extend the curve limits and put bounds_error back on.
bright_curve = interp1d(_bright_curve[Mcol], _bright_curve['Z'], kind='linear', copy=True, bounds_error=False, fill_value=0.0, assume_sorted=False)
bright_curve_r = interp1d(_bright_curve['Z'], _bright_curve['M0P0_QALL'], kind='linear', copy=True, bounds_error=False, fill_value=0.0, assume_sorted=False)
faint_curve = interp1d(_faint_curve[Mcol], _faint_curve['Z'], kind='linear', copy=True, bounds_error=False, fill_value=1.0, assume_sorted=False)
faint_curve_r = interp1d(_faint_curve['Z'], _faint_curve['M0P0_QALL'], kind='linear', copy=True, bounds_error=False, fill_value=1.0, assume_sorted=False)
return bright_curve, bright_curve_r, faint_curve, faint_curve_r
def get_ddps(Area, M_0P0s, zs, survey):
result = np.zeros(len(zs) * 3, dtype=int).reshape(len(zs), 3)
resultz = np.zeros(len(zs) * 3, dtype=int).reshape(len(zs), 3)
zlims = {}
bright_curve, bright_curve_r, faint_curve, faint_curve_r = initialise_ddplimits(survey=survey)
for i, lims in enumerate([tmr_DDP1, tmr_DDP2, tmr_DDP3]):
in_ddp = (M_0P0s >= lims[0]) & (M_0P0s <= lims[1])
zmax = np.atleast_1d(faint_curve(lims[1]))[0]
zmin = np.atleast_1d(bright_curve(lims[0]))[0]
exclude = (zs > zmax) | (zs < zmin)
in_ddp = in_ddp & ~exclude
in_ddpz = ~exclude
result[in_ddp, i] = 1
resultz[in_ddpz, i] = 1
ddp_zs = zs[in_ddp]
# print(zmin, zmax, len(ddp_zs))
zmax = np.array([zmax, ddp_zs.max()]).min()
zmin = np.array([zmin, ddp_zs.min()]).max()
zlims['DDP{}_ZMIN'.format(i+1)] = zmin
zlims['DDP{}_ZMAX'.format(i+1)] = zmax
zlims['DDP{}_VZ'.format(i+1)] = volcom(zmax, Area) - volcom(zmin, Area)
zlims['DDP{}ZLIMS_NGAL'.format(i+1)] = np.count_nonzero(in_ddpz)
zlims['DDP{}_NGAL'.format(i+1)] = np.count_nonzero(in_ddp)
zlims['DDP{}_DENS'.format(i+1)] = np.count_nonzero(in_ddp) / zlims['DDP{}_VZ'.format(i+1)]
return result, resultz, zlims, faint_curve_r(zs)
if __name__ == '__main__':
initialise_ddplimits('desi', Mcol='M0P0_QALL')
print('Done.')
|
desihubREPO_NAMELSSPATH_START.@LSS_extracted@LSS-main@py@LSS@DESI_ke@ddp.py@.PATH_END.py
|
{
"filename": "test_svm_je281u.py",
"repo_name": "spacetelescope/drizzlepac",
"repo_path": "drizzlepac_extracted/drizzlepac-main/tests/hap/test_svm_je281u.py",
"type": "Python"
}
|
""" This module tests full pipeline SVM processing for an ACS WFC, full-frame, one filter dataset.
"""
import datetime
import glob
import os
import pytest
from drizzlepac.haputils import astroquery_utils as aqutils
from drizzlepac import runsinglehap
from astropy.io import fits, ascii
from pathlib import Path
"""
test_svm_je281u.py
This test file can be executed in the following manner:
$ pytest -s --basetemp=/internal/hladata/yourUniqueDirectoryHere test_svm_je281u.py >& je281u.log &
$ tail -f je281u.log
* Note: When running this test, the `--basetemp` directory should be set to a unique
existing directory to avoid deleting previous test output.
* The POLLER_FILE exists in the tests/hap directory.
* If running manually with `--basetemp`, the je281u.log file will still be written to the
originating directory.
"""
WCS_SUB_NAME = "FIT_SVM_GAIA"
POLLER_FILE = "acs_e28_1u_input.out"
@pytest.fixture(scope="module")
def read_csv_for_filenames():
# Read the CSV poller file residing in the tests directory to extract the individual visit FLT/FLC filenames
path = os.path.join(os.path.dirname(__file__), POLLER_FILE)
table = ascii.read(path, format="no_header")
filename_column = table.colnames[0]
filenames = list(table[filename_column])
print("\nread_csv_for_filenames. Filesnames from poller: {}".format(filenames))
return filenames
@pytest.fixture(scope="module")
def gather_data_for_processing(read_csv_for_filenames, tmp_path_factory):
# Create working directory specified for the test
curdir = tmp_path_factory.mktemp(os.path.basename(__file__))
os.chdir(curdir)
# Establish FLC/FLT lists and obtain the requested data
flc_flag = ""
flt_flag = ""
# In order to obtain individual FLC or FLT images from MAST (if the files are not reside on disk) which
# may be part of an ASN, use only IPPPSS with a wildcard. The unwanted images have to be removed
# after-the-fact.
for fn in read_csv_for_filenames:
if fn.lower().endswith("flc.fits") and flc_flag == "":
flc_flag = fn[0:6] + "*"
elif fn.lower().endswith("flt.fits") and flt_flag == "":
flt_flag = fn[0:6] + "*"
# If both flags have been set, then break out the loop early. It may be
# that all files have to be checked which means the for loop continues
# until its natural completion.
if flc_flag and flt_flag:
break
# Get test data through astroquery - only retrieve the pipeline processed FLC and/or FLT files
# (e.g., j*_flc.fits) as necessary. The logic here and the above for loop is an attempt to
# avoid downloading too many images which are not needed for processing.
flcfiles = []
fltfiles = []
if flc_flag:
flcfiles = aqutils.retrieve_observation(flc_flag, suffix=["FLC"], product_type="pipeline")
if flt_flag:
fltfiles = aqutils.retrieve_observation(flt_flag, suffix=["FLT"], product_type="pipeline")
flcfiles.extend(fltfiles)
# Keep only the files which exist in BOTH lists for processing
files_to_process= set(read_csv_for_filenames).intersection(set(flcfiles))
# Identify unwanted files from the download list and remove from disk
files_to_remove = set(read_csv_for_filenames).symmetric_difference(set(flcfiles))
try:
for ftr in files_to_remove:
os.remove(ftr)
except Exception as x_cept:
print("")
print("Exception encountered: {}.".format(x_cept))
print("The file {} could not be deleted from disk. ".format(ftr))
print("Remove files which are not used for processing from disk manually.")
print("\ngather_data_for_processing. Gathered data: {}".format(files_to_process))
return files_to_process
@pytest.fixture(scope="module")
def gather_output_data(construct_manifest_filename):
# Determine the filenames of all the output files from the manifest
files = []
with open(construct_manifest_filename, 'r') as fout:
for line in fout.readlines():
files.append(line.rstrip("\n"))
print("\ngather_output_data. Output data files: {}".format(files))
return files
@pytest.fixture(scope="module")
def construct_manifest_filename(read_csv_for_filenames):
# Construct the output manifest filename from input file keywords
inst = fits.getval(read_csv_for_filenames[0], "INSTRUME", ext=0).lower()
root = fits.getval(read_csv_for_filenames[0], "ROOTNAME", ext=0).lower()
tokens_tuple = (inst, root[1:4], root[4:6], "manifest.txt")
manifest_filename = "_".join(tokens_tuple)
print("\nconstruct_manifest_filename. Manifest filename: {}".format(manifest_filename))
return manifest_filename
@pytest.fixture(scope="module", autouse=True)
def svm_setup(gather_data_for_processing):
# Act: Process the input data by executing runsinglehap - time consuming activity
current_dt = datetime.datetime.now()
print(str(current_dt))
print("\nsvm_setup fixture")
# Read the "poller file" and download the input files, as necessary
input_names = gather_data_for_processing
# Run the SVM processing
path = os.path.join(os.path.dirname(__file__), POLLER_FILE)
try:
status = runsinglehap.perform(path)
# Catch anything that happens and report it. This is meant to catch unexpected errors and
# generate sufficient output exception information so algorithmic problems can be addressed.
except Exception as except_details:
print(except_details)
pytest.fail("\nsvm_setup. Exception Visit: {}\n", path)
current_dt = datetime.datetime.now()
print(str(current_dt))
# TESTS
def test_svm_manifest_name(construct_manifest_filename):
# Construct the manifest filename from the header of an input file in the list and check it exists.
path = Path(construct_manifest_filename)
print("\ntest_svm_manifest. Filename: {}".format(path))
# Ensure the manifest file uses the proper naming convention
assert(path.is_file())
def test_svm_wcs(gather_output_data):
# Check the output primary WCSNAME includes FIT_SVM_GAIA as part of the string value
tdp_files = [files for files in gather_output_data if files.lower().find("total") > -1 and files.lower().endswith(".fits")]
for tdp in tdp_files:
wcsname = fits.getval(tdp, "WCSNAME", ext=1).upper()
print("\ntest_svm_wcs. WCSNAME: {} Output file: {}".format(wcsname, tdp))
assert WCS_SUB_NAME in wcsname, f"WCSNAME is not as expected for file {tdp}."
def test_svm_cat_sources(gather_output_data):
# Check the output catalogs should contain > 0 measured sources
cat_files = [files for files in gather_output_data if files.lower().endswith("-cat.ecsv")]
for cat in cat_files:
table_length = len(ascii.read(cat, format="ecsv"))
print("\ntest_svm_cat_sources. Number of sources in catalog {} is {}.".format(cat, table_length))
assert table_length > 0, f"Catalog file {cat} is unexpectedly empty"
|
spacetelescopeREPO_NAMEdrizzlepacPATH_START.@drizzlepac_extracted@drizzlepac-main@tests@hap@test_svm_je281u.py@.PATH_END.py
|
{
"filename": "prop_dftidefs.py",
"repo_name": "ajeldorado/falco-python",
"repo_path": "falco-python_extracted/falco-python-master/falco/proper/prop_dftidefs.py",
"type": "Python"
}
|
# Copyright 2016, 2017 California Institute of Technology
# Users must agree to abide by the restrictions listed in the
# file "LegalStuff.txt" in the PROPER library directory.
#
# PROPER developed at Jet Propulsion Laboratory/California Inst. Technology
# Original IDL version by John Krist
# Python translation by Navtej Saini, with Luis Marchen and Nikta Amiri
#
# Modified by J. Krist - 19 April 2019 - added DFTI_THREAD_LIMIT
import ctypes as _ctypes
import falco.proper as proper
# enum DFTI_CONFIG_PARAM from mkl_dfti.h
DFTI_FORWARD_DOMAIN = _ctypes.c_int(0) # Domain for forward transform, no default
DFTI_DIMENSION = _ctypes.c_int(1) # Dimension, no default
DFTI_LENGTHS = _ctypes.c_int(2) # length(s) of transform, no default
DFTI_PRECISION = _ctypes.c_int(3) # Precision of computation, no default
DFTI_FORWARD_SCALE = _ctypes.c_int(4) # Scale factor for forward transform, default = 1.0
DFTI_BACKWARD_SCALE = _ctypes.c_int(5) # Scale factor for backward transform, default = 1.0
DFTI_FORWARD_SIGN = _ctypes.c_int(6) # Default for forward transform = DFTI_NEGATIVE
DFTI_NUMBER_OF_TRANSFORMS = _ctypes.c_int(7) # Number of data sets to be transformed, default = 1
DFTI_COMPLEX_STORAGE = _ctypes.c_int(8) # Representation for complex domain, default = DFTI_COMPLEX_COMPLEX
DFTI_REAL_STORAGE = _ctypes.c_int(9) # Rep. for real domain, default = DFTI_REAL_REAL
DFTI_CONJUGATE_EVEN_STORAGE = _ctypes.c_int(10) # Rep. for conjugate even domain, default = DFTI_COMPLEX_REAL
DFTI_PLACEMENT = _ctypes.c_int(11) # Placement of result, default = DFTI_INPLACE
DFTI_INPUT_STRIDES = _ctypes.c_int(12) # Stride information of input data, default = tigthly
DFTI_OUTPUT_STRIDES = _ctypes.c_int(13) # Stride information of output data, default = tigthly
DFTI_INPUT_DISTANCE = _ctypes.c_int(14) # Distance information of input data, default = 0
DFTI_OUTPUT_DISTANCE = _ctypes.c_int(15) # Distance information of output data, default = 0
DFTI_INITIALIZATION_EFFORT = _ctypes.c_int(16) # Effort spent in initialization, default = DFTI_MEDIUM
DFTI_WORKSPACE = _ctypes.c_int(17) # Use of workspace during computation, default = DFTI_ALLOW
DFTI_ORDERING = _ctypes.c_int(18) # Possible out of order computation, default = DFTI_ORDERED
DFTI_TRANSPOSE = _ctypes.c_int(19) # Possible transposition of result, default = DFTI_NONE
DFTI_DESCRIPTOR_NAME = _ctypes.c_int(20) # name of descriptor, default = string of zero length
DFTI_PACKED_FORMAT = _ctypes.c_int(21) # packed format for real transform, default = DFTI_CCS_FORMAT
# below 4 parameters for get_value functions only
DFTI_COMMIT_STATUS = _ctypes.c_int(22) # Whether descriptor has been commited
DFTI_VERSION = _ctypes.c_int(23) # DFTI implementation version number
DFTI_FORWARD_ORDERING = _ctypes.c_int(24) # The ordering of forward transform
DFTI_BACKWARD_ORDERING = _ctypes.c_int(25) # The ordering of backward transform
# below for set_value and get_value functions
DFTI_NUMBER_OF_USER_THREADS = _ctypes.c_int(26) # number of user's threads) default = 1
DFTI_THREAD_LIMIT = _ctypes.c_int(27) # number of user's threads) default = 1
# DFTI options values
DFTI_COMMITTED = _ctypes.c_int(30) # status - commit
DFTI_UNCOMMITTED = _ctypes.c_int(31) # status - uncommit
DFTI_COMPLEX = _ctypes.c_int(32) # General domain
DFTI_REAL = _ctypes.c_int(33) # Real domain
DFTI_CONJUGATE_EVEN = _ctypes.c_int(34) # Conjugate even domain
DFTI_SINGLE = _ctypes.c_int(35) # Single precision
DFTI_DOUBLE = _ctypes.c_int(36) # Double precision
DFTI_NEGATIVE = _ctypes.c_int(37) # -i, for setting definition of transform
DFTI_POSITIVE = _ctypes.c_int(38) # +i, for setting definition of transform
DFTI_COMPLEX_COMPLEX = _ctypes.c_int(39) # Representation method for domain
DFTI_COMPLEX_REAL = _ctypes.c_int(40) # Representation method for domain
DFTI_REAL_COMPLEX = _ctypes.c_int(41) # Representation method for domain
DFTI_REAL_REAL = _ctypes.c_int(42) # Representation method for domain
DFTI_INPLACE = _ctypes.c_int(43) # Result overwrites input
DFTI_NOT_INPLACE = _ctypes.c_int(44) # Result placed differently than input
DFTI_LOW = _ctypes.c_int(45) # A low setting
DFTI_MEDIUM = _ctypes.c_int(46) # A medium setting
DFTI_HIGH = _ctypes.c_int(47) # A high setting
DFTI_ORDERED = _ctypes.c_int(48) # Data on forward and backward domain ordered
DFTI_BACKWARD_SCRAMBLED = _ctypes.c_int(49) # Data on forward ordered and backward domain scrambled
DFTI_FORWARD_SCRAMBLED = _ctypes.c_int(50) # Data on forward scrambled and backward domain ordered
DFTI_ALLOW = _ctypes.c_int(51) # Allow certain request or usage
DFTI_AVOID = _ctypes.c_int(52) # Avoid certain request or usage
DFTI_NONE = _ctypes.c_int(53) # none certain request or usage
DFTI_CCS_FORMAT = _ctypes.c_int(54) # ccs format for real DFT
DFTI_PACK_FORMAT = _ctypes.c_int(55) # pack format for real DFT
DFTI_PERM_FORMAT = _ctypes.c_int(56) # perm format for real DFT
DFTI_CCE_FORMAT = _ctypes.c_int(57) # cce format for real DFT
# and not scrambled:
# error values:
DFTI_NO_ERROR = _ctypes.c_int(0)
DFTI_MEMORY_ERROR = _ctypes.c_int(1)
DFTI_INVALID_CONFIGURATION = _ctypes.c_int(2)
DFTI_INCONSISTENT_CONFIGURATION = _ctypes.c_int(3)
DFTI_MULTITHREADED_ERROR = _ctypes.c_int(4)
DFTI_BAD_DESCRIPTOR = _ctypes.c_int(5)
DFTI_UNIMPLEMENTED = _ctypes.c_int(6)
DFTI_MKL_INTERNAL_ERROR = _ctypes.c_int(7)
DFTI_NUMBER_OF_THREADS_ERROR = _ctypes.c_int(8)
DFTI_1D_LENGTH_EXCEEDS_INT32 = _ctypes.c_int(9)
def DftiErrorMessage(e):
""" Intel MKL DFT error messages.
Parameters
----------
e : int
DFT error code
Returns
-------
None
"""
if e == DFTI_NO_ERROR.value:
pass
elif e == DFTI_MEMORY_ERROR.value:
print("DFTI Error : Memory error")
elif e == DFTI_INVALID_CONFIGURATION.value:
print("DFTI Error : Invalid configuration")
elif e == DFTI_INCONSISTENT_CONFIGURATION.value:
print("DFTI Error : Inconsistent configuration")
elif e == DFTI_MULTITHREADED_ERROR.value:
print("DFTI Error : Multithreaded error")
elif e == DFTI_BAD_DESCRIPTOR.value:
print("DFTI Error : Bad descriptor")
elif e == DFTI_UNIMPLEMENTED.value:
print("DFTI Error : Unimplemented")
elif e == DFTI_MKL_INTERNAL_ERROR.value:
print("DFTI Error : MKL internal error")
elif e == DFTI_NUMBER_OF_THREADS_ERROR.value:
print("DFTI Error : Number of threads error")
elif e == DFTI_1D_LENGTH_EXCEEDS_INT32.value:
print("DFTI Error : 1D length exceeds int32")
else:
print("Unknown error code")
|
ajeldoradoREPO_NAMEfalco-pythonPATH_START.@falco-python_extracted@falco-python-master@falco@proper@prop_dftidefs.py@.PATH_END.py
|
{
"filename": "PcBsB.py",
"repo_name": "nickhand/pyRSD",
"repo_path": "pyRSD_extracted/pyRSD-master/pyRSD/rsd/power/gal/Pcs/PcBsB.py",
"type": "Python"
}
|
from .. import TwoHaloTerm, OneHaloTerm, DampedGalaxyPowerTerm
class PcBsB_2h(TwoHaloTerm):
"""
The 2-halo term for `PcBsB`
"""
name = 'PcBsB_2h'
def __init__(self, model):
super(PcBsB_2h, self).__init__(model, 'b1_cB', 'b1_sB')
class PcBsB_1h(OneHaloTerm):
"""
The 1-halo term for `PcBsB`
"""
name = 'NcBs'
class PcBsB(DampedGalaxyPowerTerm):
"""
The cross power spectrum between centrals with satellites
in the same halo (`cenB`) and >1 satellites (`satB`)
"""
name = "PcBsB"
def __init__(self, model):
super(PcBsB, self).__init__(model, PcBsB_2h, PcBsB_1h, sigma1='sigma_c', sigma2='sigma_sB')
@property
def coefficient(self):
return self.model.fcB * self.model.fsB
|
nickhandREPO_NAMEpyRSDPATH_START.@pyRSD_extracted@pyRSD-master@pyRSD@rsd@power@gal@Pcs@PcBsB.py@.PATH_END.py
|
{
"filename": "prior_dict.py",
"repo_name": "bek0s/gbkfit",
"repo_path": "gbkfit_extracted/gbkfit-master/src/gbkfit/fitting/prior/prior_dict.py",
"type": "Python"
}
|
import collections.abc
import copy
import numpy as np
__all__ = [
'PriorDict'
]
class PriorDict(collections.abc.Mapping):
def __init__(self, priors):
self._priors = copy.deepcopy(priors)
def __getitem__(self, key):
return self._priors.__getitem__(key)
def __iter__(self):
return self._priors.__iter__()
def __len__(self):
return self._priors.__len__()
def __repr__(self):
return self._priors.__repr__()
def __str__(self):
return self._priors.__str__()
def sample(self, keys, size=None):
return [self[k].sample(size) for k in keys]
def rescale(self, sample):
return [self[k].rescale(v) for k, v in sample.items()]
def prob(self, sample):
return np.product([self[k].prob(v) for k, v in sample.items()])
def log_prob(self, sample):
return np.sum([self[k].log_prob(v) for k, v in sample.items()])
|
bek0sREPO_NAMEgbkfitPATH_START.@gbkfit_extracted@gbkfit-master@src@gbkfit@fitting@prior@prior_dict.py@.PATH_END.py
|
{
"filename": "_title.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/heatmapgl/colorbar/_title.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Title(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "heatmapgl.colorbar"
_path_str = "heatmapgl.colorbar.title"
_valid_props = {"font", "side", "text"}
# font
# ----
@property
def font(self):
"""
Sets this color bar's title font. Note that the title's font
used to be set by the now deprecated `titlefont` attribute.
The 'font' property is an instance of Font
that may be specified as:
- An instance of :class:`plotly.graph_objs.heatmapgl.colorbar.title.Font`
- A dict of string/value properties that will be passed
to the Font constructor
Supported dict properties:
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".
size
Returns
-------
plotly.graph_objs.heatmapgl.colorbar.title.Font
"""
return self["font"]
@font.setter
def font(self, val):
self["font"] = val
# side
# ----
@property
def side(self):
"""
Determines the location of color bar's title with respect to
the color bar. Note that the title's location used to be set by
the now deprecated `titleside` attribute.
The 'side' property is an enumeration that may be specified as:
- One of the following enumeration values:
['right', 'top', 'bottom']
Returns
-------
Any
"""
return self["side"]
@side.setter
def side(self, val):
self["side"] = val
# text
# ----
@property
def text(self):
"""
Sets the title of the color bar. Note that before the existence
of `title.text`, the title's contents used to be defined as the
`title` attribute itself. This behavior has been deprecated.
The 'text' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["text"]
@text.setter
def text(self, val):
self["text"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
font
Sets this color bar's title font. Note that the title's
font used to be set by the now deprecated `titlefont`
attribute.
side
Determines the location of color bar's title with
respect to the color bar. Note that the title's
location used to be set by the now deprecated
`titleside` attribute.
text
Sets the title of the color bar. Note that before the
existence of `title.text`, the title's contents used to
be defined as the `title` attribute itself. This
behavior has been deprecated.
"""
def __init__(self, arg=None, font=None, side=None, text=None, **kwargs):
"""
Construct a new Title object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.heatmapgl.colorbar.Title`
font
Sets this color bar's title font. Note that the title's
font used to be set by the now deprecated `titlefont`
attribute.
side
Determines the location of color bar's title with
respect to the color bar. Note that the title's
location used to be set by the now deprecated
`titleside` attribute.
text
Sets the title of the color bar. Note that before the
existence of `title.text`, the title's contents used to
be defined as the `title` attribute itself. This
behavior has been deprecated.
Returns
-------
Title
"""
super(Title, self).__init__("title")
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.heatmapgl.colorbar.Title
constructor must be a dict or
an instance of :class:`plotly.graph_objs.heatmapgl.colorbar.Title`"""
)
# 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("font", None)
_v = font if font is not None else _v
if _v is not None:
self["font"] = _v
_v = arg.pop("side", None)
_v = side if side is not None else _v
if _v is not None:
self["side"] = _v
_v = arg.pop("text", None)
_v = text if text is not None else _v
if _v is not None:
self["text"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@heatmapgl@colorbar@_title.py@.PATH_END.py
|
{
"filename": "test_app.py",
"repo_name": "spacetelescope/jdaviz",
"repo_path": "jdaviz_extracted/jdaviz-main/jdaviz/tests/test_app.py",
"type": "Python"
}
|
import pytest
import numpy as np
from astropy import units as u
from astropy.wcs import WCS
from specutils import Spectrum1D
from jdaviz import Application, Specviz
from jdaviz.configs.default.plugins.gaussian_smooth.gaussian_smooth import GaussianSmooth
from jdaviz.utils import flux_conversion
# This applies to all viz but testing with Imviz should be enough.
def test_viewer_calling_app(imviz_helper):
viewer = imviz_helper.default_viewer._obj
assert viewer.session.jdaviz_app is imviz_helper.app
def test_get_tray_item_from_name():
app = Application(configuration='default')
plg = app.get_tray_item_from_name('g-gaussian-smooth')
assert isinstance(plg, GaussianSmooth)
with pytest.raises(KeyError, match='not found in app'):
app.get_tray_item_from_name('imviz-compass')
def test_nonstandard_specviz_viewer_name(spectrum1d):
config = {'settings': {'configuration': 'nonstandard',
'data': {'parser': 'specviz-spectrum1d-parser'},
'visible': {'menu_bar': False,
'toolbar': True,
'tray': True,
'tab_headers': False},
'context': {'notebook': {'max_height': '750px'}}},
'toolbar': ['g-data-tools', 'g-subset-tools'],
'tray': ['g-metadata-viewer',
'g-plot-options',
'g-subset-tools',
'g-gaussian-smooth',
'g-model-fitting',
'g-unit-conversion',
'g-line-list',
'specviz-line-analysis',
'export'],
'viewer_area': [{'container': 'col',
'children': [{'container': 'row',
'viewers': [{'name': 'H',
'plot': 'specviz-profile-viewer',
'reference': 'h'},
{'name': 'K',
'plot': 'specviz-profile-viewer',
'reference': 'k'}]}]}]}
class Customviz(Specviz):
_default_configuration = config
_default_spectrum_viewer_reference_name = 'h'
viz = Customviz()
assert viz.app.get_viewer_reference_names() == ['h', 'k']
viz.load_data(spectrum1d, data_label='example label')
with pytest.raises(ValueError):
viz.get_data("non-existent label")
def test_duplicate_data_labels(specviz_helper, spectrum1d):
specviz_helper.load_data(spectrum1d, data_label="test")
specviz_helper.load_data(spectrum1d, data_label="test")
dc = specviz_helper.app.data_collection
assert dc[0].label == "test"
assert dc[1].label == "test (1)"
specviz_helper.load_data(spectrum1d, data_label="test_1")
specviz_helper.load_data(spectrum1d, data_label="test")
assert dc[2].label == "test_1"
assert dc[3].label == "test (2)"
def test_duplicate_data_labels_with_brackets(specviz_helper, spectrum1d):
specviz_helper.load_data(spectrum1d, data_label="test[test]")
specviz_helper.load_data(spectrum1d, data_label="test[test]")
dc = specviz_helper.app.data_collection
assert len(dc) == 2
assert dc[0].label == "test[test]"
assert dc[1].label == "test[test] (1)"
def test_return_data_label_is_none(specviz_helper):
data_label = specviz_helper.app.return_data_label(None)
assert data_label == "Unknown"
def test_return_data_label_is_image(specviz_helper):
data_label = specviz_helper.app.return_data_label("data/path/test.jpg")
assert data_label == "test[jpg]"
def test_hdulist_with_filename(cubeviz_helper, image_cube_hdu_obj):
image_cube_hdu_obj.file_name = "test"
data_label = cubeviz_helper.app.return_data_label(image_cube_hdu_obj)
assert data_label == "test[HDU object]"
def test_file_path_not_image(imviz_helper, tmp_path):
path = tmp_path / "myimage.fits"
path.touch()
data_label = imviz_helper.app.return_data_label(str(path))
assert data_label == "myimage"
def test_unique_name_variations(specviz_helper, spectrum1d):
data_label = specviz_helper.app.return_unique_name(None)
assert data_label == "Unknown"
specviz_helper.load_data(spectrum1d, data_label="test[flux]")
data_label = specviz_helper.app.return_data_label("test[flux]", ext="flux")
assert data_label == "test[flux][flux]"
data_label = specviz_helper.app.return_data_label("test", ext="flux")
assert data_label == "test[flux] (1)"
def test_substring_in_label(specviz_helper, spectrum1d):
specviz_helper.load_data(spectrum1d, data_label="M31")
specviz_helper.load_data(spectrum1d, data_label="M32")
data_label = specviz_helper.app.return_data_label("M")
assert data_label == "M"
@pytest.mark.parametrize('data_label', ('111111', 'aaaaa', '///(#$@)',
'two spaces repeating',
'word42word42word two spaces'))
def test_edge_cases(specviz_helper, spectrum1d, data_label):
dc = specviz_helper.app.data_collection
specviz_helper.load_data(spectrum1d, data_label=data_label)
specviz_helper.load_data(spectrum1d, data_label=data_label)
assert dc[1].label == f"{data_label} (1)"
specviz_helper.load_data(spectrum1d, data_label=data_label)
assert dc[2].label == f"{data_label} (2)"
def test_case_that_used_to_break_return_label(specviz_helper, spectrum1d):
specviz_helper.load_data(spectrum1d, data_label="this used to break (1)")
specviz_helper.load_data(spectrum1d, data_label="this used to break")
dc = specviz_helper.app.data_collection
assert dc[0].label == "this used to break (1)"
assert dc[1].label == "this used to break (2)"
def test_viewer_renaming_specviz(specviz_helper):
viewer_names = [
'spectrum-viewer',
'second-viewer-name',
'third-viewer-name'
]
for i in range(len(viewer_names) - 1):
specviz_helper.app._update_viewer_reference_name(
old_reference=viewer_names[i],
new_reference=viewer_names[i + 1]
)
assert specviz_helper.app.get_viewer(viewer_names[i+1]) is not None
def test_viewer_renaming_imviz(imviz_helper):
with pytest.raises(ValueError, match="'imviz-0' cannot be changed"):
imviz_helper.app._update_viewer_reference_name(
old_reference='imviz-0',
new_reference='this-is-forbidden'
)
with pytest.raises(ValueError, match="does not exist"):
imviz_helper.app._update_viewer_reference_name(
old_reference='non-existent',
new_reference='this-is-forbidden'
)
def test_data_associations(imviz_helper):
shape = (10, 10)
data_parent = np.ones(shape, dtype=float)
data_child = np.zeros(shape, dtype=int)
imviz_helper.load_data(data_parent, data_label='parent_data')
imviz_helper.load_data(data_child, data_label='child_data', parent='parent_data')
assert imviz_helper.app._get_assoc_data_children('parent_data') == ['child_data']
assert imviz_helper.app._get_assoc_data_parent('child_data') == 'parent_data'
with pytest.raises(NotImplementedError):
# we don't (yet) allow children of children:
imviz_helper.load_data(data_child, data_label='grandchild_data', parent='child_data')
with pytest.raises(ValueError):
# ensure the parent actually exists:
imviz_helper.load_data(data_child, data_label='child_data', parent='absent parent')
def test_to_unit(cubeviz_helper):
# custom cube to have Surface Brightness units
wcs_dict = {"CTYPE1": "WAVE-LOG", "CTYPE2": "DEC--TAN", "CTYPE3": "RA---TAN",
"CRVAL1": 4.622e-7, "CRVAL2": 27, "CRVAL3": 205,
"CDELT1": 8e-11, "CDELT2": 0.0001, "CDELT3": -0.0001,
"CRPIX1": 0, "CRPIX2": 0, "CRPIX3": 0, "PIXAR_SR": 8e-11}
w = WCS(wcs_dict)
flux = np.zeros((30, 20, 3001), dtype=np.float32)
flux[5:15, 1:11, :] = 1
cube = Spectrum1D(flux=flux * (u.MJy / u.sr), wcs=w, meta=wcs_dict)
cubeviz_helper.load_data(cube, data_label="test")
# this can be removed once spectra pass through spectral extraction
extract_plg = cubeviz_helper.plugins['Spectral Extraction']
extract_plg.aperture = extract_plg.aperture.choices[-1]
extract_plg.aperture_method.selected = 'Exact'
extract_plg.wavelength_dependent = True
extract_plg.function = 'Sum'
# set so pixel scale factor != 1
extract_plg.reference_spectral_value = 0.000001
extract_plg.extract()
data = cubeviz_helper.app.data_collection[-1].data
values = [1]
original_units = u.MJy / u.sr
target_units = u.MJy
value = flux_conversion(values, original_units,
target_units, data.get_object(cls=Spectrum1D))
# will be a uniform array since not wavelength dependent
# so test first value in array
assert np.allclose(value[0], 8e-11)
# Change from Fnu to Flam (with values shape matching spectral axis)
values = np.ones(3001)
original_units = u.MJy
target_units = u.erg / u.cm**2 / u.s / u.AA
new_values = flux_conversion(values, original_units,
target_units, data.get_object(cls=Spectrum1D))
assert np.allclose(new_values,
(values * original_units)
.to_value(target_units,
equivalencies=u.spectral_density(cube.spectral_axis)))
# Change from Fnu to Flam (with a shape (2,) array of values indicating we
# are probably converting the limits)
values = [1, 2]
original_units = u.MJy
target_units = u.erg / u.cm**2 / u.s / u.AA
new_values = flux_conversion(values, original_units,
target_units, data.get_object(cls=Spectrum1D))
# In this case we do a regular spectral density conversion, but using the
# first value in the spectral axis for the equivalency
assert np.allclose(new_values,
([1, 2] * original_units)
.to_value(target_units,
equivalencies=u.spectral_density(cube.spectral_axis[0])))
def test_all_plugins_have_description(cubeviz_helper, specviz_helper,
mosviz_helper, imviz_helper,
rampviz_helper, specviz2d_helper):
"""
Test that all plugins for all configs have a plugin_description
attribute, which controls what is displayed under the plugin title in the
tray. Doesn't test what they are, just that they are not empty.
"""
config_helpers = [cubeviz_helper, specviz_helper, mosviz_helper,
imviz_helper, rampviz_helper, specviz2d_helper]
for config_helper in config_helpers:
for item in config_helper.plugins:
assert config_helper.plugins[item]._obj.plugin_description != ''
|
spacetelescopeREPO_NAMEjdavizPATH_START.@jdaviz_extracted@jdaviz-main@jdaviz@tests@test_app.py@.PATH_END.py
|
{
"filename": "msi.py",
"repo_name": "hmuellergoe/mrbeam",
"repo_path": "mrbeam_extracted/mrbeam-main/mr_beam/imagingbase/imagingbase/operators/msi.py",
"type": "Python"
}
|
import numpy as np
from regpy.operators import Operator
from MSI.MSDecomposition import WaveletTransform2D as msiWaveletTransform2D
from MSI.MSDecomposition import DoG2D as msiDoG2D
from MSI.MSMDDecomposition import DoG2D as msmdDoG2D
from MSI.MSDictionaries import WTDictionary as msiWTDictionary
from MSI.MSDictionaries import DOGDictionary as msiDOGDictionary
from MSI.MSMDDictionaries import DOGDictionary as msmdDOGDictionary
from MSI.MSDictionaries import RectDictionary as msiRectDictionary
from MSI.MSDictionaries import BesselDictionary as msiBesselDictionary
from MSI.MSMDDictionaries import BesselDictionary as msmdBesselDictionary
from lightwise.nputils import convolve
from joblib import Parallel, delayed
class WaveletTransform(Operator):
def __init__(self, domain, wavelet_fct='b1', wt_dec = 'uiwt', min_scale=1, max_scale=4, **args):
assert len(domain.shape) == 2
self.wt_trafo = msiWaveletTransform2D(wavelet_fct=wavelet_fct, wt_dec = wt_dec,
min_scale=min_scale, max_scale=max_scale)
self.shape = domain.shape
self.nr_scales = int(max_scale-min_scale)
self.toret = np.zeros((self.nr_scales, self.shape[0], self.shape[1]))
codomain = domain**self.nr_scales
super().__init__(domain, codomain, linear=True)
def _eval(self, x):
list_scales = self.wt_trafo.decompose( x )
self._extract(list_scales)
return self.toret.flatten()
def _adjoint(self, y):
scales = y.reshape(self.nr_scales, self.shape[0], self.shape[1])
for i in range(self.nr_scales):
self.toret[i] = self.wt_trafo.compute_scale(scales[i], i)
return np.sum(self.toret, axis=0)
def _extract(self, list_scales):
for i in range(self.nr_scales):
self.toret[i] = list_scales[i][0]
class DOGTransform(Operator):
def __init__(self, domain, widths, angle=0, ellipticities=1, md=False, **args):
assert len(domain.shape) == 2
if md:
self.dog_trafo = msmdDoG2D(widths, angle=angle, ellipticities=ellipticities)
else:
self.dog_trafo = msiDoG2D(widths, angle=angle, ellipticities=ellipticities)
self.shape = domain.shape
self.nr_scales = len(widths)
self.toret = np.zeros((self.nr_scales, self.shape[0], self.shape[1]))
codomain = domain**self.nr_scales
super().__init__(domain, codomain, linear=True)
def _eval(self, x):
list_scales = self.dog_trafo.decompose( x )
self._extract(list_scales)
self.toret[-1] = x - np.sum(self.toret[:-1, :], axis=0)
return self.toret.flatten()
def _adjoint(self, y):
scales = y.reshape(self.nr_scales, self.shape[0], self.shape[1])
last_scale = scales[-1]
scales = scales[:-1]-last_scale
for i in range(self.nr_scales-1):
self.toret[i] = self.dog_trafo.compute_scale(scales[i], i)
return np.sum(self.toret, axis=0)+last_scale
def _extract(self, list_scales):
for i in range(self.nr_scales-1):
self.toret[i] = list_scales[i][0]
class DWTDictionary(Operator):
def __init__(self, domain, codomain, wavelet_fct='b1', wt_dec = 'uiwt', min_scale=1, max_scale=4, shape=None, **args):
self.wtdict = msiWTDictionary(wavelet_fct=wavelet_fct, wt_dec=wt_dec, min_scale=min_scale, max_scale=max_scale)
self.wavelets = self.wtdict.compute_wavelets()
self.length = self.wtdict.length+1
# for i in range(self.length):
# self.wavelets[i] /= np.max(self.wavelets[i])
super().__init__(domain, codomain, linear=True)
if shape == None:
shape = codomain.shape
self.shape = shape
self.to_pad = (self.codomain.shape[0]-self.shape[0])//2
def _eval(self, x):
array = x.reshape((self.length, self.shape[0], self.shape[1]))
toret = np.zeros(self.codomain.shape)
for i in range(self.length):
to_convolve = np.pad(array[i], self.to_pad)
toret += convolve(to_convolve, self.wavelets[i], mode='same')
return toret
def _adjoint(self, y):
toret = np.zeros((self.length, self.codomain.shape[0], self.codomain.shape[1]))
for i in range(self.length):
toret[i, :, :] = convolve(y, self.wavelets[i], mode='same')
return toret[ : , self.to_pad:self.to_pad+self.shape[0] , self.to_pad:self.to_pad+self.shape[1] ].flatten()
class DOGDictionary(Operator):
def __init__(self, domain, codomain, widths, shape, ellipticities = 1, angle = 0, md=False, num_cores=1, smoothing_scale=None, **args):
self.num_cores = num_cores
if md:
self.dogdict = msmdDOGDictionary(widths, shape, ellipticities=ellipticities, angle=angle, **args)
else:
self.dogdict = msiDOGDictionary(widths, shape, ellipticities=ellipticities, angle=angle, **args)
self.dogs = self.dogdict.compute_dogs()
if smoothing_scale != None:
self.set_smoothing_scale(smoothing_scale)
self.length = self.dogdict.length
self.shape = shape
self.weights = np.ones(self.length)
super().__init__(domain, codomain, linear=True)
def _eval(self, x):
array = x.reshape((self.length, self.shape[0], self.shape[1]))
toret = np.zeros(self.codomain.shape)
res = Parallel(n_jobs=self.num_cores)(delayed(convolve)(array[i], self.dogs[i], mode='same') for i in range(self.length))
for i in range(self.length):
toret += self.weights[i] * res[i]
return toret
def _adjoint(self, y):
toret = np.zeros((self.length, self.shape[0], self.shape[1]))
res = Parallel(n_jobs=self.num_cores)(delayed(convolve)(y, self.dogs[i], mode='same') for i in range(self.length))
for i in range(self.length):
toret[i, :, :] = self.weights[i] * res[i]
return toret.flatten()
def set_weights(self, weights):
self.weights = weights
def set_smoothing_scale(self, width):
self.dogs = self.dogdict.set_smoothing_scale(self.dogs.copy(), width)
class RectDictionary(Operator):
def __init__(self, domain, widths, fourier, shape, **args):
self.fourier = fourier
self.rectdict = msiRectDictionary(widths, fourier.codomain, **args)
self.rects = self.rectdict.compute_rects()
self.length = self.rectdict.length
self.shape = shape
self.weights = np.ones(self.length)
super().__init__(domain, fourier.codomain, linear=True)
def _eval(self, x):
array = x.reshape((self.length, self.shape[0], self.shape[1]))
toret = np.zeros(self.codomain.shape, dtype=complex)
for i in range(self.length):
toret += self.weights[i] * self.fourier(array[i]) * self.rects[i]
return toret
def _adjoint(self, y):
toret = np.zeros((self.length, self.shape[0], self.shape[1]))
for i in range(self.length):
toret[i, :, :] = self.weights[i] * self.fourier.adjoint( self.rects[i] * y )
return toret.flatten()
def set_weights(self, weights):
self.weights = weights
def set_smoothing_scale(self, width):
self.rects = self.rectdict.set_smoothing_scale(self.rects.copy(), width)
class BesselDictionary(Operator):
def __init__(self, domain, codomain, widths, shape, md=False, num_cores=1, smoothing_scale=None, **args):
self.num_cores = num_cores
if md:
self.besseldict = msmdBesselDictionary(widths, shape, **args)
else:
self.besseldict = msiBesselDictionary(widths, shape, **args)
self.wavelets = self.besseldict.compute_wavelets()
if smoothing_scale != None:
self.set_smoothing_scale(smoothing_scale)
self.length = self.besseldict.length
self.shape = shape
self.weights = np.ones(self.length)
super().__init__(domain, codomain, linear=True)
def _eval(self, x):
array = x.reshape((self.length, self.shape[0], self.shape[1]))
toret = np.zeros(self.codomain.shape)
res = Parallel(n_jobs=self.num_cores)(delayed(convolve)(array[i], self.wavelets[i], mode='same') for i in range(self.length))
for i in range(self.length):
toret += self.weights[i] * res[i]
return toret
def _adjoint(self, y):
toret = np.zeros((self.length, self.shape[0], self.shape[1]))
res = Parallel(n_jobs=self.num_cores)(delayed(convolve)(y, self.wavelets[i], mode='same') for i in range(self.length))
for i in range(self.length):
toret[i, :, :] = self.weights[i] * res[i]
return toret.flatten()
def set_weights(self, weights):
self.weights = weights
def set_smoothing_scale(self, width):
self.wavelets = self.besseldict.set_smoothing_scale(self.wavelets.copy(), width)
|
hmuellergoeREPO_NAMEmrbeamPATH_START.@mrbeam_extracted@mrbeam-main@mr_beam@imagingbase@imagingbase@operators@msi.py@.PATH_END.py
|
{
"filename": "los_distributions.py",
"repo_name": "sibirrer/hierArc",
"repo_path": "hierArc_extracted/hierArc-main/hierarc/Sampling/Distributions/los_distributions.py",
"type": "Python"
}
|
import numpy as np
from hierarc.Util.distribution_util import PDFSampling
from scipy.stats import genextreme
class LOSDistribution(object):
"""Line of sight distribution drawing."""
def __init__(
self,
global_los_distribution=False,
los_distributions=None,
individual_distribution=None,
kwargs_individual=None,
):
"""
:param global_los_distribution: if integer, will draw from the global kappa distribution specified in that
integer. If False, will instead draw from the distribution specified in kappa_pdf.
:type global_los_distribution: bool or int
:param los_distributions: list of all line of sight distributions parameterized
:type los_distributions: list of str or None
:param individual_distribution: name of the individual distribution ["GEV" and "PDF"]
:type individual_distribution: str or None
:param kwargs_individual: dictionary of the parameters of the individual distribution
If individual_distribution is "PDF":
"pdf_array": array of probability density function of the external convergence distribution
binned according to kappa_bin_edges
"bin_edges": array of length (len(kappa_pdf)+1), bin edges of the kappa PDF
If individual_distribution is "GEV":
"xi", "mean", "sigma"
:type kwargs_individual: dict or None
"""
self._global_los_distribution = global_los_distribution
if (
isinstance(self._global_los_distribution, int)
and self._global_los_distribution is not False
):
self._draw_kappa_global = True
self._los_distribution = los_distributions[global_los_distribution]
else:
self._draw_kappa_global = False
if not self._draw_kappa_global and individual_distribution is not None:
if individual_distribution == "PDF":
self._kappa_dist = PDFSampling(**kwargs_individual)
elif individual_distribution == "GEV":
self._kappa_dist = GEV(**kwargs_individual)
else:
raise ValueError(
"individual_distribution %s not supported. Chose among 'GEV' and 'PDF'"
)
self._draw_kappa_individual = True
else:
self._draw_kappa_individual = False
def draw_los(self, kwargs_los, size=1):
"""Draw from the distribution of line of sight convergence.
:param kwargs_los: line of sight parameters
:type kwargs_los: list of dict
:param size: how many samples to be drawn
:type size: int>0
:return: external convergence draw
"""
if self._draw_kappa_individual is True:
kappa_ext_draw = self._kappa_dist.draw(n=size)
elif self._draw_kappa_global:
kwargs_los_i = kwargs_los[self._global_los_distribution]
if self._los_distribution == "GAUSSIAN":
los_mean = kwargs_los_i["mean"]
los_sigma = kwargs_los_i["sigma"]
kappa_ext_draw = np.random.normal(los_mean, los_sigma, size=size)
elif self._los_distribution == "GEV":
mean = kwargs_los_i["mean"]
sigma = kwargs_los_i["sigma"]
xi = kwargs_los_i["xi"]
kappa_ext_draw = genextreme.rvs(c=xi, loc=mean, scale=sigma, size=size)
else:
raise ValueError(
"line of sight distribution %s not valid." % self._los_distribution
)
else:
kappa_ext_draw = 0
return kappa_ext_draw
def draw_bool(self, kwargs_los):
"""Whether single-valued or extended distribution (need to draw from)
:param kwargs_los: list of keyword arguments for line of sight distributions
:return: boolean, True with samples need to be drawn, else False
"""
if self._draw_kappa_individual is True:
return True
elif self._draw_kappa_global is True:
if kwargs_los[self._global_los_distribution]["sigma"] != 0:
return True
return False
class GEV(object):
"""Draw from General Extreme Value distribution."""
def __init__(self, xi, mean, sigma):
"""
:param xi: Xi value of GEV
:param mean: mean of GEV
:param sigma: sigma of GEV
"""
self._xi = xi
self._mean = mean
self._sigma = sigma
def draw(self, n=1):
"""Draws from the PDF of the GEV distribution.
:param n: number of draws from distribution
:type n: int
:return: draws according to the PDF of the distribution
"""
kappa_ext_draw = genextreme.rvs(
c=self._xi, loc=self._mean, scale=self._sigma, size=n
)
return kappa_ext_draw
|
sibirrerREPO_NAMEhierArcPATH_START.@hierArc_extracted@hierArc-main@hierarc@Sampling@Distributions@los_distributions.py@.PATH_END.py
|
{
"filename": "line_plot.py",
"repo_name": "yt-project/yt",
"repo_path": "yt_extracted/yt-main/yt/visualization/line_plot.py",
"type": "Python"
}
|
from collections import defaultdict
import numpy as np
from matplotlib.colors import LogNorm, Normalize, SymLogNorm
from yt.funcs import is_sequence, mylog
from yt.units.unit_object import Unit # type: ignore
from yt.units.yt_array import YTArray
from yt.visualization.plot_container import (
BaseLinePlot,
PlotDictionary,
invalidate_plot,
)
class LineBuffer:
r"""
LineBuffer(ds, start_point, end_point, npoints, label = None)
This takes a data source and implements a protocol for generating a
'pixelized', fixed-resolution line buffer. In other words, LineBuffer
takes a starting point, ending point, and number of sampling points and
can subsequently generate YTArrays of field values along the sample points.
Parameters
----------
ds : :class:`yt.data_objects.static_output.Dataset`
This is the dataset object holding the data that can be sampled by the
LineBuffer
start_point : n-element list, tuple, ndarray, or YTArray
Contains the coordinates of the first point for constructing the LineBuffer.
Must contain n elements where n is the dimensionality of the dataset.
end_point : n-element list, tuple, ndarray, or YTArray
Contains the coordinates of the first point for constructing the LineBuffer.
Must contain n elements where n is the dimensionality of the dataset.
npoints : int
How many points to sample between start_point and end_point
Examples
--------
>>> lb = yt.LineBuffer(ds, (0.25, 0, 0), (0.25, 1, 0), 100)
>>> lb["all", "u"].max()
0.11562424257143075 dimensionless
"""
def __init__(self, ds, start_point, end_point, npoints, label=None):
self.ds = ds
self.start_point = _validate_point(start_point, ds, start=True)
self.end_point = _validate_point(end_point, ds)
self.npoints = npoints
self.label = label
self.data = {}
def keys(self):
return self.data.keys()
def __setitem__(self, item, val):
self.data[item] = val
def __getitem__(self, item):
if item in self.data:
return self.data[item]
mylog.info("Making a line buffer with %d points of %s", self.npoints, item)
self.points, self.data[item] = self.ds.coordinates.pixelize_line(
item, self.start_point, self.end_point, self.npoints
)
return self.data[item]
def __delitem__(self, item):
del self.data[item]
class LinePlotDictionary(PlotDictionary):
def __init__(self, data_source):
super().__init__(data_source)
self.known_dimensions = {}
def _sanitize_dimensions(self, item):
field = self.data_source._determine_fields(item)[0]
finfo = self.data_source.ds.field_info[field]
dimensions = Unit(
finfo.units, registry=self.data_source.ds.unit_registry
).dimensions
if dimensions not in self.known_dimensions:
self.known_dimensions[dimensions] = item
return self.known_dimensions[dimensions]
def __getitem__(self, item):
ret_item = self._sanitize_dimensions(item)
return super().__getitem__(ret_item)
def __setitem__(self, item, value):
ret_item = self._sanitize_dimensions(item)
super().__setitem__(ret_item, value)
def __contains__(self, item):
ret_item = self._sanitize_dimensions(item)
return super().__contains__(ret_item)
class LinePlot(BaseLinePlot):
r"""
A class for constructing line plots
Parameters
----------
ds : :class:`yt.data_objects.static_output.Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
fields : string / tuple, or list of strings / tuples
The name(s) of the field(s) to be plotted.
start_point : n-element list, tuple, ndarray, or YTArray
Contains the coordinates of the first point for constructing the line.
Must contain n elements where n is the dimensionality of the dataset.
end_point : n-element list, tuple, ndarray, or YTArray
Contains the coordinates of the first point for constructing the line.
Must contain n elements where n is the dimensionality of the dataset.
npoints : int
How many points to sample between start_point and end_point for
constructing the line plot
figure_size : int or two-element iterable of ints
Size in inches of the image.
Default: 5 (5x5)
fontsize : int
Font size for all text in the plot.
Default: 14
field_labels : dictionary
Keys should be the field names. Values should be latex-formattable
strings used in the LinePlot legend
Default: None
Example
-------
>>> import yt
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> plot = yt.LinePlot(ds, "density", [0, 0, 0], [1, 1, 1], 512)
>>> plot.add_legend("density")
>>> plot.set_x_unit("cm")
>>> plot.set_unit("density", "kg/cm**3")
>>> plot.save()
"""
_plot_dict_type = LinePlotDictionary
_plot_type = "line_plot"
_default_figure_size = (5.0, 5.0)
_default_font_size = 14.0
def __init__(
self,
ds,
fields,
start_point,
end_point,
npoints,
figure_size=None,
fontsize: float | None = None,
field_labels=None,
):
"""
Sets up figure and axes
"""
line = LineBuffer(ds, start_point, end_point, npoints, label=None)
self.lines = [line]
self._initialize_instance(self, ds, fields, figure_size, fontsize, field_labels)
self._setup_plots()
@classmethod
def _initialize_instance(
cls, obj, ds, fields, figure_size, fontsize, field_labels=None
):
obj._x_unit = None
obj._titles = {}
data_source = ds.all_data()
obj.fields = data_source._determine_fields(fields)
obj.include_legend = defaultdict(bool)
super(LinePlot, obj).__init__(
data_source, figure_size=figure_size, fontsize=fontsize
)
if field_labels is None:
obj.field_labels = {}
else:
obj.field_labels = field_labels
for f in obj.fields:
if f not in obj.field_labels:
obj.field_labels[f] = f[1]
def _get_axrect(self):
fontscale = self._font_properties._size / self.__class__._default_font_size
top_buff_size = 0.35 * fontscale
x_axis_size = 1.35 * fontscale
y_axis_size = 0.7 * fontscale
right_buff_size = 0.2 * fontscale
if is_sequence(self.figure_size):
figure_size = self.figure_size
else:
figure_size = (self.figure_size, self.figure_size)
xbins = np.array([x_axis_size, figure_size[0], right_buff_size])
ybins = np.array([y_axis_size, figure_size[1], top_buff_size])
x_frac_widths = xbins / xbins.sum()
y_frac_widths = ybins / ybins.sum()
return (
x_frac_widths[0],
y_frac_widths[0],
x_frac_widths[1],
y_frac_widths[1],
)
@classmethod
def from_lines(
cls, ds, fields, lines, figure_size=None, font_size=None, field_labels=None
):
"""
A class method for constructing a line plot from multiple sampling lines
Parameters
----------
ds : :class:`yt.data_objects.static_output.Dataset`
This is the dataset object corresponding to the
simulation output to be plotted.
fields : field name or list of field names
The name(s) of the field(s) to be plotted.
lines : list of :class:`yt.visualization.line_plot.LineBuffer` instances
The lines from which to sample data
figure_size : int or two-element iterable of ints
Size in inches of the image.
Default: 5 (5x5)
font_size : int
Font size for all text in the plot.
Default: 14
field_labels : dictionary
Keys should be the field names. Values should be latex-formattable
strings used in the LinePlot legend
Default: None
Example
--------
>>> ds = yt.load(
... "SecondOrderTris/RZ_p_no_parts_do_nothing_bcs_cone_out.e", step=-1
... )
>>> fields = [field for field in ds.field_list if field[0] == "all"]
>>> lines = [
... yt.LineBuffer(ds, [0.25, 0, 0], [0.25, 1, 0], 100, label="x = 0.25"),
... yt.LineBuffer(ds, [0.5, 0, 0], [0.5, 1, 0], 100, label="x = 0.5"),
... ]
>>> lines.append()
>>> plot = yt.LinePlot.from_lines(ds, fields, lines)
>>> plot.save()
"""
obj = cls.__new__(cls)
obj.lines = lines
cls._initialize_instance(obj, ds, fields, figure_size, font_size, field_labels)
obj._setup_plots()
return obj
def _setup_plots(self):
if self._plot_valid:
return
for plot in self.plots.values():
plot.axes.cla()
for line in self.lines:
dimensions_counter = defaultdict(int)
for field in self.fields:
finfo = self.ds.field_info[field]
dimensions = Unit(
finfo.units, registry=self.ds.unit_registry
).dimensions
dimensions_counter[dimensions] += 1
for field in self.fields:
# get plot instance
plot = self._get_plot_instance(field)
# calculate x and y
x, y = self.ds.coordinates.pixelize_line(
field, line.start_point, line.end_point, line.npoints
)
# scale x and y to proper units
if self._x_unit is None:
unit_x = x.units
else:
unit_x = self._x_unit
unit_y = plot.norm_handler.display_units
x.convert_to_units(unit_x)
y.convert_to_units(unit_y)
# determine legend label
str_seq = []
str_seq.append(line.label)
str_seq.append(self.field_labels[field])
delim = "; "
legend_label = delim.join(filter(None, str_seq))
# apply plot to matplotlib axes
plot.axes.plot(x, y, label=legend_label)
# apply log transforms if requested
norm = plot.norm_handler.get_norm(data=y)
y_norm_type = type(norm)
if y_norm_type is Normalize:
plot.axes.set_yscale("linear")
elif y_norm_type is LogNorm:
plot.axes.set_yscale("log")
elif y_norm_type is SymLogNorm:
plot.axes.set_yscale("symlog")
else:
raise NotImplementedError(
f"LinePlot doesn't support y norm with type {type(norm)}"
)
# set font properties
plot._set_font_properties(self._font_properties, None)
# set x and y axis labels
axes_unit_labels = self._get_axes_unit_labels(unit_x, unit_y)
if self._xlabel is not None:
x_label = self._xlabel
else:
x_label = r"$\rm{Path\ Length" + axes_unit_labels[0] + "}$"
if self._ylabel is not None:
y_label = self._ylabel
else:
finfo = self.ds.field_info[field]
dimensions = Unit(
finfo.units, registry=self.ds.unit_registry
).dimensions
if dimensions_counter[dimensions] > 1:
y_label = (
r"$\rm{Multiple\ Fields}$"
+ r"$\rm{"
+ axes_unit_labels[1]
+ "}$"
)
else:
y_label = (
finfo.get_latex_display_name()
+ r"$\rm{"
+ axes_unit_labels[1]
+ "}$"
)
plot.axes.set_xlabel(x_label)
plot.axes.set_ylabel(y_label)
# apply title
if field in self._titles:
plot.axes.set_title(self._titles[field])
# apply legend
dim_field = self.plots._sanitize_dimensions(field)
if self.include_legend[dim_field]:
plot.axes.legend()
self._plot_valid = True
@invalidate_plot
def annotate_legend(self, field):
"""
Adds a legend to the `LinePlot` instance. The `_sanitize_dimensions`
call ensures that a legend label will be added for every field of
a multi-field plot
"""
dim_field = self.plots._sanitize_dimensions(field)
self.include_legend[dim_field] = True
@invalidate_plot
def set_x_unit(self, unit_name):
"""Set the unit to use along the x-axis
Parameters
----------
unit_name: str
The name of the unit to use for the x-axis unit
"""
self._x_unit = unit_name
@invalidate_plot
def set_unit(self, field, new_unit):
"""Set the unit used to plot the field
Parameters
----------
field: str or field tuple
The name of the field to set the units for
new_unit: string or Unit object
"""
field = self.data_source._determine_fields(field)[0]
pnh = self.plots[field].norm_handler
pnh.display_units = new_unit
@invalidate_plot
def annotate_title(self, field, title):
"""Set the unit used to plot the field
Parameters
----------
field: str or field tuple
The name of the field to set the units for
title: str
The title to use for the plot
"""
self._titles[self.data_source._determine_fields(field)[0]] = title
def _validate_point(point, ds, start=False):
if not is_sequence(point):
raise RuntimeError("Input point must be array-like")
if not isinstance(point, YTArray):
point = ds.arr(point, "code_length", dtype=np.float64)
if len(point.shape) != 1:
raise RuntimeError("Input point must be a 1D array")
if point.shape[0] < ds.dimensionality:
raise RuntimeError("Input point must have an element for each dimension")
# need to pad to 3D elements to avoid issues later
if point.shape[0] < 3:
if start:
val = 0
else:
val = 1
point = np.append(point.d, [val] * (3 - ds.dimensionality)) * point.uq
return point
|
yt-projectREPO_NAMEytPATH_START.@yt_extracted@yt-main@yt@visualization@line_plot.py@.PATH_END.py
|
{
"filename": "provabgs_internal.ipynb",
"repo_name": "changhoonhahn/provabgs",
"repo_path": "provabgs_extracted/provabgs-main/nb/rug/provabgs_internal.ipynb",
"type": "Jupyter Notebook"
}
|
# run entire `provabgs` pipeline on fake observations generated using the pipeline
This will serve as an internal test that the model and inference pipelines are working!
```python
import os
import numpy as np
# --- plotting ---
import corner as DFM
import matplotlib as mpl
import matplotlib.pyplot as plt
#if 'NERSC_HOST' not in os.environ.keys():
# mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['axes.xmargin'] = 1
mpl.rcParams['xtick.labelsize'] = 'x-large'
mpl.rcParams['xtick.major.size'] = 5
mpl.rcParams['xtick.major.width'] = 1.5
mpl.rcParams['ytick.labelsize'] = 'x-large'
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['ytick.major.width'] = 1.5
mpl.rcParams['legend.frameon'] = False
```
```python
import gqp_mc.util as UT
```
```python
from provabgs import infer as Infer
from provabgs import models as Models
from provabgs import corrprior as Corrprior
```
/global/u1/c/chahah/projects/provabgs/src/provabgs/models.py:25: UserWarning: import error with fsps; only use emulators
warnings.warn('import error with fsps; only use emulators')
```python
prior = Infer.load_priors([
Infer.UniformPrior(9., 12., label='sed'),
Infer.FlatDirichletPrior(4, label='sed'), # flat dirichilet priors
Infer.UniformPrior(0., 1., label='sed'), # burst fraction
Infer.UniformPrior(0., 13.27, label='sed'), # tburst
Infer.UniformPrior(6.9e-5, 7.3e-3, label='sed'),# uniform priors on ZH coeff
Infer.UniformPrior(6.9e-5, 7.3e-3, label='sed'),# uniform priors on ZH coeff
Infer.UniformPrior(0., 3., label='sed'), # uniform priors on dust1
Infer.UniformPrior(0., 3., label='sed'), # uniform priors on dust2
Infer.UniformPrior(-2.2, 0.4, label='sed') # uniform priors on dust_index
])
```
# read mock spectra
```python
wave_obs = np.load('/global/cscratch1/sd/chahah/gqp_mc/mini_mocha/provabgs_mocks/provabgs_mock.wave.npy')
theta_obs = np.load('/global/cscratch1/sd/chahah/gqp_mc/mini_mocha/provabgs_mocks/provabgs_mock.theta.npy')
flux_obs = np.load('/global/cscratch1/sd/chahah/gqp_mc/mini_mocha/provabgs_mocks/provabgs_mock.flux.npy')
z_obs = 0.2
```
```python
unt_theta_obs = prior.untransform(theta_obs)
```
```python
m_nmf = Models.NMF(burst=True, emulator=True)
```
```python
fig = plt.figure(figsize=(10,5))
sub = fig.add_subplot(111)
for f in flux_obs[:10]: sub.plot(wave_obs, f)
sub.set_xlim(3e3, 1e4)
```
(3000.0, 10000.0)
# load in MCMC chains run on cori
```python
desi_mcmc = Infer.desiMCMC(model=m_nmf, prior=prior)
```
```python
chain_dir = os.path.join(UT.dat_dir(), 'mini_mocha', 'provabgs_mocks')
f_chain = lambda i: os.path.join(chain_dir, 'provabgs_mock.%i.chain.npy' % i)
```
```python
chains = []
for i in range(theta_obs.shape[0]):
chains.append(np.load(f_chain(i)))
```
```python
for i, chain in enumerate(chains[:2]):
flat_chain = desi_mcmc._flatten_chain(chain[500:,:,:])
unt_chain = prior.untransform(flat_chain)
args, kwargs = desi_mcmc._lnPost_args_kwargs(
wave_obs=wave_obs,
flux_obs=flux_obs[i],
flux_ivar_obs=np.ones(len(wave_obs)),
zred=z_obs,
vdisp=0
)
tts, lnPs = [], []
for tt in unt_chain[::100]:
tts.append(tt)
lnPs.append(desi_mcmc.lnPost(tt, *args, **kwargs))
unt_theta_bf = tts[np.argmax(lnPs)]
unt_theta_median = np.median(unt_chain, axis=0)
print(unt_theta_obs[i])
print(desi_mcmc.lnPost(unt_theta_obs[i], *args, **kwargs))
print(unt_theta_bf)
print(np.max(lnPs))
unt_theta_bf = tts[np.argmax(lnPs)]
fig = plt.figure(figsize=(10, 10))
for ii in range(theta_obs.shape[1]-1):
sub = fig.add_subplot(theta_obs.shape[1]-1, 1, ii+1)
for j in range(30):
unt_chain_i = desi_mcmc.prior.untransform(chain[:,j,:])
sub.plot(unt_chain_i[:,ii], c='k', lw=0.5)
sub.axhline(unt_theta_obs[i][ii], color='C0')
sub.axhline(unt_theta_bf[ii], color='C1')
sub.set_xlim(0,2500)
fig = DFM.corner(unt_chain, truths=unt_theta_obs[i])
axes = np.array(fig.axes).reshape((unt_theta_obs.shape[1], unt_theta_obs.shape[1]))
# Loop over the histograms
for yi in range(unt_theta_obs.shape[1]):
for xi in range(yi):
ax = axes[yi, xi]
ax.axvline(unt_theta_bf[xi], color="r")
ax.axhline(unt_theta_bf[yi], color="r")
ax.plot(unt_theta_bf[xi], unt_theta_bf[yi], "sr")
_, flux_model_bf = m_nmf.sed(desi_mcmc.prior.transform(unt_theta_bf), z_obs, wavelength=wave_obs)
fig = plt.figure(figsize=(10,5))
sub = fig.add_subplot(111)
sub.plot(wave_obs, flux_obs[i], c='k')
sub.plot(wave_obs, flux_model_bf, ls=':')
sub.set_xlim(3e3, 1e4)
```
/global/u1/c/chahah/projects/provabgs/src/provabgs/models.py:849: RuntimeWarning: overflow encountered in exp
layers.append((betas_[i] + (1.-betas_[i])*1./(1.+np.exp(-alphas_[i]*act[-1])))*act[-1])
/global/u1/c/chahah/projects/provabgs/src/provabgs/models.py:908: RuntimeWarning: overflow encountered in exp
layers.append((betas_[i] + (1.-betas_[i])*1./(1.+np.exp(-alphas_[i]*act[-1])))*act[-1])
/global/u1/c/chahah/projects/provabgs/src/provabgs/models.py:849: RuntimeWarning: overflow encountered in exp
layers.append((betas_[i] + (1.-betas_[i])*1./(1.+np.exp(-alphas_[i]*act[-1])))*act[-1])
/global/u1/c/chahah/projects/provabgs/src/provabgs/models.py:908: RuntimeWarning: overflow encountered in exp
layers.append((betas_[i] + (1.-betas_[i])*1./(1.+np.exp(-alphas_[i]*act[-1])))*act[-1])
[1.01390127e+01 3.91571329e-01 9.02925829e-01 5.26697840e-01
3.40199486e-01 6.88412113e+00 3.29431210e-03 1.93145680e-03
8.05072701e-01 1.18940177e+00 2.30787999e-01]
-4.73326172782329e-30
[ 9.93041254e+00 6.87279259e-01 7.48231324e-01 3.98169587e-01
1.22256995e-01 6.62481547e+00 3.64596274e-03 3.92064895e-03
8.90622671e-02 1.11176948e+00 -5.60270231e-01]
-0.09389060666338597
[ 1.01411885e+01 1.91344665e-01 1.74655028e-01 9.15617735e-01
8.00960900e-01 7.92339993e+00 4.32473759e-03 8.30121109e-04
2.09031622e+00 1.03557692e+00 -1.20725626e+00]
0.0
[ 1.00918023e+01 2.81918822e-01 2.22160981e-01 2.45111232e-01
8.45490163e-01 7.25616164e+00 1.35705690e-03 2.91902908e-03
1.19631747e+00 8.88098464e-01 -1.51448419e+00]
-0.015541733367142157
```python
flat_chains = []
avgSFRs_1gyr_true, avgSFRs_1gyr_inf = [], []
Z_MW_true, Z_MW_inf = [], []
for i, chain in enumerate(chains):
flat_chain = desi_mcmc._flatten_chain(chain[500:,:,:])
flat_chains.append(flat_chain)
avgSFRs_1gyr_true.append(m_nmf.avgSFR(theta_obs[i], z_obs, dt=1.0))
avgSFRs_1gyr_inf.append(m_nmf.avgSFR(flat_chain, z_obs, dt=1.0))
Z_MW_true.append(m_nmf.Z_MW(theta_obs[i], m_nmf.cosmo.age(z_obs).value))
Z_MW_inf.append(m_nmf.Z_MW(flat_chain, m_nmf.cosmo.age(z_obs).value))
flat_chains = np.array(flat_chains)
avgSFRs_1gyr_true = np.array(avgSFRs_1gyr_true)
avgSFRs_1gyr_inf = np.array(avgSFRs_1gyr_inf)
Z_MW_true = np.array(Z_MW_true)
Z_MW_inf = np.array(Z_MW_inf)
```
```python
for i in range(5):#len(flat_chains)):
theta_deriv = np.array([flat_chains[i,:,0], np.log10(avgSFRs_1gyr_inf[i])-flat_chains[i,:,0], Z_MW_inf[i]])
print(np.log10(avgSFRs_1gyr_true[i])-theta_obs[i][0])
DFM.corner(theta_deriv.T,
truths=np.array([theta_obs[i][0], np.log10(avgSFRs_1gyr_true[i])-theta_obs[i][0], Z_MW_true[i]]),
range=[(9, 12), (-13, -9.), (0., 0.05)])
```
[-10.56248557]
/global/homes/c/chahah/.conda/envs/gqp/lib/python3.7/site-packages/ipykernel_launcher.py:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
"""
[-12.74029697]
[-12.36572104]
[-10.85738931]
WARNING:root:Too few points to create valid contours
[-9.2925216]
```python
fig = plt.figure(figsize=(20,10))
sub = fig.add_subplot(231)
logm_quantiles = np.array([DFM.quantile(flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),0], logm_quantiles[:,1],
yerr=[logm_quantiles[:,1]-logm_quantiles[:,0], logm_quantiles[:,2]-logm_quantiles[:,1]], fmt='.C0')
sub.plot([9., 12.], [9., 12], c='k', ls='--')
sub.set_xlim(9., 12.)
sub.set_ylim(9., 12.)
sub = fig.add_subplot(232)
logssfr_true = np.log10(avgSFRs_1gyr_true.flatten())-theta_obs[:len(flat_chains),0]
logssfr_quantiles = np.array([DFM.quantile(np.log10(avgSFRs_1gyr_inf[i,::10]) - flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(logssfr_true, logssfr_quantiles[:,1],
yerr=[logssfr_quantiles[:,1]-logssfr_quantiles[:,0], logssfr_quantiles[:,2]-logssfr_quantiles[:,1]], fmt='.C0')
sub.plot([-9., -15.], [-9., -15], c='k', ls='--')
sub.set_xlim(-13, -9.)
sub.set_ylim(-13, -9.)
sub = fig.add_subplot(233)
zmw_quantiles = np.array([DFM.quantile(Z_MW_inf[i][::10], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(Z_MW_true, zmw_quantiles[:,1], yerr=[zmw_quantiles[:,1] - zmw_quantiles[:,0], zmw_quantiles[:,2] - zmw_quantiles[:,1]], fmt='.C0')
sub.plot([0., 1e-1], [0., 1e-1], c='k', ls='--')
sub.set_xlim(0., 4e-2)
sub.set_ylim(0., 4e-2)
sub = fig.add_subplot(234)
dust1_quantiles = np.array([DFM.quantile(flat_chains[i,:,-3], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-3], dust1_quantiles[:,1],
yerr=[dust1_quantiles[:,1] - dust1_quantiles[:,0], dust1_quantiles[:,2] - dust1_quantiles[:,1]], fmt='.C0')
sub.plot([prior.range[0][-3], prior.range[1][-3]], [prior.range[0][-3], prior.range[1][-3]], c='k', ls='--')
sub.set_xlim(prior.range[0][-3], prior.range[1][-3])
sub.set_ylim(prior.range[0][-3], prior.range[1][-3])
sub = fig.add_subplot(235)
dust2_quantiles = np.array([DFM.quantile(flat_chains[i,:,-2], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-2], dust2_quantiles[:,1],
yerr=[dust2_quantiles[:,1] - dust2_quantiles[:,0], dust2_quantiles[:,2] - dust2_quantiles[:,1]], fmt='.C0')
sub.plot([prior.range[0][-2], prior.range[1][-2]], [prior.range[0][-2], prior.range[1][-2]], c='k', ls='--')
sub.set_xlim(prior.range[0][-2], prior.range[1][-2])
sub.set_ylim(prior.range[0][-2], prior.range[1][-2])
sub = fig.add_subplot(236)
dust_index_quantiles = np.array([DFM.quantile(flat_chains[i,:,-1], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-1], dust_index_quantiles[:,1],
yerr=[dust_index_quantiles[:,1] - dust_index_quantiles[:,0], dust_index_quantiles[:,2] - dust_index_quantiles[:,1]], fmt='.C0')
sub.plot([prior.range[0][-1], prior.range[1][-1]], [prior.range[0][-1], prior.range[1][-1]], c='k', ls='--')
sub.set_xlim(prior.range[0][-1], prior.range[1][-1])
sub.set_ylim(prior.range[0][-1], prior.range[1][-1])
```
(-2.2, 0.4)
# `Corrprior.CorrectPrior` object to impose uniform prior on derived properties
```python
CP_kde = Corrprior.CorrectPrior(
m_nmf,
prior,
zred=z_obs,
props=['logavgssfr_1gyr', 'z_mw'],
Nprior=100000,
range=[(-13., -9), (2e-3, 0.035)],
method='kde',
bandwidth=0.05,
debug=True
)
```
... calculating log avg sSFR_1Gyr
... calculating mass-weighted Z
... fitting prior(derived prop)
... fitting pdf using kde
```python
fig = CP_kde.validate()
```
```python
f_wimp = lambda i: os.path.join(chain_dir, 'provabgs_mock.%i.cp_wimp.npy' % i)
```
```python
ws_imp = []
for i, flat_chain in enumerate(flat_chains):
if os.path.isfile(f_wimp(i)):
w_imp = np.load(f_wimp(i))
else:
w_imp = CP_kde.get_importance_weights(
flat_chain[::10,:],
outlier=0.01,
debug=True)
np.save(f_wimp(i), w_imp)
ws_imp.append(w_imp)
```
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```python
for i in range(5):#len(flat_chains)):
theta_deriv = np.array([flat_chains[i,:,0], np.log10(avgSFRs_1gyr_inf[i])-flat_chains[i,:,0], Z_MW_inf[i]])
fig = DFM.corner(theta_deriv[:,::10].T,
truths=np.array([theta_obs[i][0], np.log10(avgSFRs_1gyr_true[i])-theta_obs[i][0], Z_MW_true[i]]),
range=[(9, 12), (-13., -9), (0., 0.05)],
hist_kwargs={'density': True},
hist2d_kwargs={'smooth': True, 'plot_density': False, 'plot_datapoint': False})
_ = DFM.corner(theta_deriv[:,::10].T,
weights=ws_imp[i],
truths=np.array([theta_obs[i][0], np.log10(avgSFRs_1gyr_true[i])-theta_obs[i][0], Z_MW_true[i]]),
range=[(9, 12), (-13., -9), (0., 0.05)], color='C1',
hist_kwargs={'density': True},
hist2d_kwargs={'smooth': True, 'plot_density': False, 'plot_datapoint': False},
fig=fig)
```
/global/homes/c/chahah/.conda/envs/gqp/lib/python3.7/site-packages/ipykernel_launcher.py:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
"""
/global/homes/c/chahah/.conda/envs/gqp/lib/python3.7/site-packages/ipykernel_launcher.py:11: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
# This is added back by InteractiveShellApp.init_path()
WARNING:root:Too few points to create valid contours
WARNING:root:Too few points to create valid contours
```python
fig = plt.figure(figsize=(20,10))
sub = fig.add_subplot(231)
logm_quantiles = np.array([DFM.quantile(flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),0], logm_quantiles[:,1],
yerr=[logm_quantiles[:,1]-logm_quantiles[:,0], logm_quantiles[:,2]-logm_quantiles[:,1]], fmt='.C0')
logm_quantiles = np.array([DFM.quantile(flat_chains[i,::10,0], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),0], logm_quantiles[:,1],
yerr=[logm_quantiles[:,1]-logm_quantiles[:,0], logm_quantiles[:,2]-logm_quantiles[:,1]], fmt='.C1')
sub.plot([9., 12.], [9., 12], c='k', ls='--')
sub.set_xlim(9., 12.)
sub.set_ylim(9., 12.)
sub = fig.add_subplot(232)
logssfr_true = np.log10(avgSFRs_1gyr_true.flatten())-theta_obs[:len(flat_chains),0]
logssfr_quantiles = np.array([DFM.quantile(np.log10(avgSFRs_1gyr_inf[i,::10]) - flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(logssfr_true, logssfr_quantiles[:,1],
yerr=[logssfr_quantiles[:,1]-logssfr_quantiles[:,0], logssfr_quantiles[:,2]-logssfr_quantiles[:,1]], fmt='.C0')
logssfr_quantiles = np.array([DFM.quantile(np.log10(avgSFRs_1gyr_inf[i,::10]) - flat_chains[i,::10,0], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(logssfr_true, logssfr_quantiles[:,1],
yerr=[logssfr_quantiles[:,1]-logssfr_quantiles[:,0], logssfr_quantiles[:,2]-logssfr_quantiles[:,1]], fmt='.C1')
sub.plot([-9., -15.], [-9., -15], c='k', ls='--')
sub.set_xlim(-13, -9.)
sub.set_ylim(-13, -9.)
sub = fig.add_subplot(233)
zmw_quantiles = np.array([DFM.quantile(Z_MW_inf[i][::10], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(Z_MW_true, zmw_quantiles[:,1], yerr=[zmw_quantiles[:,1] - zmw_quantiles[:,0], zmw_quantiles[:,2] - zmw_quantiles[:,1]], fmt='.C0')
zmw_quantiles = np.array([DFM.quantile(Z_MW_inf[i][::10], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(Z_MW_true, zmw_quantiles[:,1], yerr=[zmw_quantiles[:,1] - zmw_quantiles[:,0], zmw_quantiles[:,2] - zmw_quantiles[:,1]], fmt='.C1')
sub.plot([0., 1e-1], [0., 1e-1], c='k', ls='--')
sub.set_xlim(5e-3, 3e-2)
sub.set_ylim(5e-3, 3e-2)
sub = fig.add_subplot(234)
dust1_quantiles = np.array([DFM.quantile(flat_chains[i,:,-3], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-3], dust1_quantiles[:,1],
yerr=[dust1_quantiles[:,1] - dust1_quantiles[:,0], dust1_quantiles[:,2] - dust1_quantiles[:,1]], fmt='.C0')
dust1_quantiles = np.array([DFM.quantile(flat_chains[i,::10,-3], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-3], dust1_quantiles[:,1],
yerr=[dust1_quantiles[:,1] - dust1_quantiles[:,0], dust1_quantiles[:,2] - dust1_quantiles[:,1]], fmt='.C1')
sub.plot([prior.range[0][-3], prior.range[1][-3]], [prior.range[0][-3], prior.range[1][-3]], c='k', ls='--')
sub.set_xlim(prior.range[0][-3], prior.range[1][-3])
sub.set_ylim(prior.range[0][-3], prior.range[1][-3])
sub = fig.add_subplot(235)
dust2_quantiles = np.array([DFM.quantile(flat_chains[i,:,-2], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-2], dust2_quantiles[:,1],
yerr=[dust2_quantiles[:,1] - dust2_quantiles[:,0], dust2_quantiles[:,2] - dust2_quantiles[:,1]], fmt='.C0')
dust2_quantiles = np.array([DFM.quantile(flat_chains[i,::10,-2], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-2], dust2_quantiles[:,1],
yerr=[dust2_quantiles[:,1] - dust2_quantiles[:,0], dust2_quantiles[:,2] - dust2_quantiles[:,1]], fmt='.C1')
sub.plot([prior.range[0][-2], prior.range[1][-2]], [prior.range[0][-2], prior.range[1][-2]], c='k', ls='--')
sub.set_xlim(prior.range[0][-2], prior.range[1][-2])
sub.set_ylim(prior.range[0][-2], prior.range[1][-2])
sub = fig.add_subplot(236)
dust_index_quantiles = np.array([DFM.quantile(flat_chains[i,:,-1], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-1], dust_index_quantiles[:,1],
yerr=[dust_index_quantiles[:,1] - dust_index_quantiles[:,0], dust_index_quantiles[:,2] - dust_index_quantiles[:,1]], fmt='.C0')
dust_index_quantiles = np.array([DFM.quantile(flat_chains[i,::10,-1], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),-1], dust_index_quantiles[:,1],
yerr=[dust_index_quantiles[:,1] - dust_index_quantiles[:,0], dust_index_quantiles[:,2] - dust_index_quantiles[:,1]], fmt='.C1')
sub.plot([prior.range[0][-1], prior.range[1][-1]], [prior.range[0][-1], prior.range[1][-1]], c='k', ls='--')
sub.set_xlim(prior.range[0][-1], prior.range[1][-1])
sub.set_ylim(prior.range[0][-1], prior.range[1][-1])
```
(-2.2, 0.4)
```python
fig = plt.figure(figsize=(20,5))
sub = fig.add_subplot(131)
logm_quantiles = np.array([DFM.quantile(flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),0], logm_quantiles[:,1] - theta_obs[:len(flat_chains),0],
yerr=[logm_quantiles[:,1]-logm_quantiles[:,0], logm_quantiles[:,2]-logm_quantiles[:,1]], fmt='.C0')
logm_quantiles = np.array([DFM.quantile(flat_chains[i,::10,0], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(theta_obs[:len(flat_chains),0], logm_quantiles[:,1] - theta_obs[:len(flat_chains),0],
yerr=[logm_quantiles[:,1]-logm_quantiles[:,0], logm_quantiles[:,2]-logm_quantiles[:,1]], fmt='.C1')
sub.plot([9., 12.], [0, 0], c='k', ls='--')
sub.set_xlim(9., 12.)
#sub.set_ylim(9., 12.)
sub = fig.add_subplot(132)
logssfr_true = np.log10(avgSFRs_1gyr_true.flatten())-theta_obs[:len(flat_chains),0]
logssfr_quantiles = np.array([DFM.quantile(np.log10(avgSFRs_1gyr_inf[i,::10]) - flat_chains[i,::10,0], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(logssfr_true, logssfr_quantiles[:,1] - logssfr_true,
yerr=[logssfr_quantiles[:,1]-logssfr_quantiles[:,0], logssfr_quantiles[:,2]-logssfr_quantiles[:,1]], fmt='.C0')
logssfr_quantiles = np.array([DFM.quantile(np.log10(avgSFRs_1gyr_inf[i,::10]) - flat_chains[i,::10,0], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(logssfr_true, logssfr_quantiles[:,1] - logssfr_true,
yerr=[logssfr_quantiles[:,1]-logssfr_quantiles[:,0], logssfr_quantiles[:,2]-logssfr_quantiles[:,1]], fmt='.C1')
sub.plot([-9., -15.], [0, 0], c='k', ls='--')
sub.set_xlim(-13, -9.)
#sub.set_ylim(-13, -9.)
sub = fig.add_subplot(133)
zmw_quantiles = np.array([DFM.quantile(Z_MW_inf[i], [0.16, 0.5, 0.84]) for i in range(flat_chains.shape[0])])
sub.errorbar(Z_MW_true, zmw_quantiles[:,1].flatten() - Z_MW_true.flatten(),
yerr=[zmw_quantiles[:,1] - zmw_quantiles[:,0], zmw_quantiles[:,2] - zmw_quantiles[:,1]], fmt='.C0')
zmw_quantiles = np.array([DFM.quantile(Z_MW_inf[i][::10], [0.16, 0.5, 0.84], weights=ws_imp[i]) for i in range(flat_chains.shape[0])])
sub.errorbar(Z_MW_true, zmw_quantiles[:,1].flatten() - Z_MW_true.flatten(),
yerr=[zmw_quantiles[:,1] - zmw_quantiles[:,0], zmw_quantiles[:,2] - zmw_quantiles[:,1]], fmt='.C1')
sub.plot([0., 1e-1], [0., 0.], c='k', ls='--')
sub.set_xlim(0., 4e-2)
#sub.set_ylim(0., 4e-2)
```
(0.0, 0.04)
SSFR (especially low SSFR) is definitely less biased. Mass-weighted Z, however, is still quite biased. I think this has to do with the narrow range $Z\in[5\times 10^{-3}, 0.03]$ I imposed on it.
```python
f_cpchain = lambda i: os.path.join(chain_dir, 'provabgs_mock.%i.cp.chain.npy' % i)
```
```python
cp_chains = []
for i in range(theta_obs.shape[0]):
cp_chains.append(np.load(f_cpchain(i)))
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-31-b6f2ea958da9> in <module>
1 cp_chains = []
2 for i in range(theta_obs.shape[0]):
----> 3 cp_chains.append(np.load(f_cpchain(i)))
~/.conda/envs/gqp/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)
415 own_fid = False
416 else:
--> 417 fid = stack.enter_context(open(os_fspath(file), "rb"))
418 own_fid = True
419
FileNotFoundError: [Errno 2] No such file or directory: '/global/cscratch1/sd/chahah/gqp_mc/mini_mocha/provabgs_mocks/provabgs_mock.7.cp.chain.npy'
```python
```
|
changhoonhahnREPO_NAMEprovabgsPATH_START.@provabgs_extracted@provabgs-main@nb@rug@provabgs_internal.ipynb@.PATH_END.py
|
{
"filename": "check_spect.py",
"repo_name": "GeminiDRSoftware/GHOSTDR",
"repo_path": "GHOSTDR_extracted/GHOSTDR-master/utils/check_spect.py",
"type": "Python"
}
|
#!/usr/bin/env python3
from __future__ import division, print_function
import sys
import astropy.io.fits as pf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
class Plotter:
def __init__(self, fname1, fname2):
""" A class to handle the plotting and animation of two spectra.
The first is the extracted spectrum produced by the pipeline,
and the second is the check spectrum produced by the simulator.
"""
self.fname1 = fname1
self.fname2 = fname2
# Open the science frame
scihdu = pf.open(self.fname1)
scidata = scihdu[1].data
# Gumby check for hires
ishigh = False
if 'high' in self.fname1:
ishigh = True
# Open the check frame
chkhdu = pf.open(self.fname2)
chkdata = chkhdu[0].data
self.fig = plt.figure()
# Accumulate the data
# This code evolved via a lot of experiments with different
# styles of plots and could probably be made a lot tidier but
# I'm not spending time on it now.
verts1 = []
verts2 = []
xs = np.arange(len(scidata[0,:,0]))
self.zs = np.arange(scidata.shape[0])
for i in self.zs:
chkorder = chkdata[i]
sciorder = scidata[i,:,0]
# Each order of the check spectrum is scaled so that
# its median matches the median of the extracted spectrum.
# This is because the check spectrum is in arbitrary units.
chkmedian = np.median(chkorder)
if abs(chkmedian) > 0.01:
scale = np.median(sciorder) / np.median(chkorder)
else:
scale = 1
verts1.append(list(zip(xs, sciorder)))
verts2.append(list(zip(xs, scale * chkorder)))
self.v1 = np.asarray(verts1)
self.v2 = np.asarray(verts2)
# Work out the ranges for the axes
x0 = self.v1[:,:,0].min()
x1 = self.v1[:,:,0].max()
# Scale the y axis so we screen out the most extreme points
y0, y1 = np.percentile(scidata[:,:,0], (2,98))
# Construct the two (empty) plots
plt.subplot(2, 1, 1)
plt.title('%s' % self.fname1)
self.l1, = plt.plot([], [])
plt.xlim(x0, x1)
plt.ylim(y0, y1)
plt.grid()
plt.subplot(2, 1, 2)
plt.title('%s' % self.fname2)
self.l2, = plt.plot([], [])
plt.xlim(x0, x1)
plt.ylim(y0, y1)
plt.grid()
# Don't waste space, use a tight layout
self.fig.tight_layout()
def update(self, num):
""" The function that allows us to animate the orders (i.e. extensions).
It also lets us plot a single specific extension if we want to.
"""
plt.subplot(2, 1, 1)
plt.title('%s %d' % (self.fname1, num))
self.l1.set_data(self.v1[num,:,0], self.v1[num,:,1])
plt.subplot(2, 1, 2)
plt.title('%s %d' % (self.fname2, num))
self.l2.set_data(self.v2[num,:,0], self.v2[num,:,1])
return self.l1, self.l2
def onClick(self, event):
""" Pause/Resume the animation, based on a click in the plot.
"""
if self.anim_running:
self.animator.event_source.stop()
self.anim_running = False
else:
self.animator.event_source.start()
self.anim_running = True
def start_animation(self):
""" Start the animation running.
"""
# Setting blit=True for the animation just makes a mess on my screen
self.animator = animation.FuncAnimation(self.fig, self.update, self.zs, fargs=None, interval=500)
# The user can pause the animation by clicking on the plot
self.anim_running = True
self.fig.canvas.mpl_connect('button_press_event', self.onClick)
plt.show()
if __name__ == "__main__":
if len(sys.argv) == 3:
# Animate all extensions
plotter = Plotter(sys.argv[1], sys.argv[2])
plotter.start_animation()
elif len(sys.argv) == 4:
# Just show the given extension
plotter = Plotter(sys.argv[1], sys.argv[2])
plotter.update(int(sys.argv[3]))
plt.show()
else:
print("Usage: %s extracted.fits check.fits [ext_number]" % sys.argv[0])
|
GeminiDRSoftwareREPO_NAMEGHOSTDRPATH_START.@GHOSTDR_extracted@GHOSTDR-master@utils@check_spect.py@.PATH_END.py
|
{
"filename": "_bgcolor.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatter3d/hoverlabel/_bgcolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BgcolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="bgcolor", parent_name="scatter3d.hoverlabel", **kwargs
):
super(BgcolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatter3d@hoverlabel@_bgcolor.py@.PATH_END.py
|
{
"filename": "jupyter.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/jupyter-core/py2/jupyter.py",
"type": "Python"
}
|
"""Launch the root jupyter command"""
if __name__ == '__main__':
from jupyter_core.command import main
main()
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@jupyter-core@py2@jupyter.py@.PATH_END.py
|
{
"filename": "plot_photometry.py",
"repo_name": "skypyproject/skypy",
"repo_path": "skypy_extracted/skypy-main/examples/galaxies/plot_photometry.py",
"type": "Python"
}
|
"""
Optical Photometry
==================
This example demonstrates how to model galaxy photometric magnitudes using the
kcorrect spectral energy distribution templates as implemented in SkyPy.
"""
# %%
# kcorrect Spectral Templates
# ---------------------------
#
# In SkyPy, the rest-frame spectral energy distributions (SEDs) of galaxies can
# be modelled as a linear combination of the five kcorrect basis templates
# [1]_. One possible model for the coefficients is a redshift-dependent
# Dirichlet distribution [2]_ which can be sampled from using the
# :func:`dirichlet_coefficients <skypy.galaxies.spectrum.dirichlet_coefficients>`
# function. The coefficients are then taken by the :meth:`kcorrect.absolute_magnitudes
# <skypy.utils.photometry.SpectrumTemplates.absolute_magnitudes>` and
# :meth:`kcorrect.apparent_magnitudes
# <skypy.utils.photometry.SpectrumTemplates.apparent_magnitudes>`
# methods to calculate the relevant photometric quantities using the
# :doc:`speclite <speclite:overview>` package. Note that since the kcorrect
# templates are defined per unit stellar mass, the total stellar mass of each
# galaxy must either be given or calculated from its absolute magnitude in
# another band using :meth:`kcorrect.stellar_mass
# <skypy.galaxies.spectrum.KCorrectTemplates.stellar_mass>`.
# An example simulation for the SDSS u- and r-band apparent magnitudes of "red"
# and "blue" galaxy populations is given by the following config file:
#
# .. literalinclude:: ../../../examples/galaxies/sdss_photometry.yml
# :language: YAML
# :caption: examples/galaxies/sdss_photometry.yml
#
# The config file can be downloaded
# :download:`here <../../../examples/galaxies/sdss_photometry.yml>`
# and the simulation can be run either from the command line and saved to FITS
# files:
#
# .. code-block:: bash
#
# $ skypy examples/galaxies/sdss_photometry.yml sdss_photometry.fits
#
# or in a python script using the :class:`Pipeline <skypy.pipeline.Pipeline>`
# class as demonstrated in the `SDSS Photometry`_ section below. For more
# details on writing config files see the :doc:`Pipeline Documentation </pipeline/index>`.
#
# SDSS Photometry
# ---------------
#
# Here we compare the apparent magnitude distributions of our simulated
# galaxies with data from a :math:`10 \, \mathrm{deg^2}` region of the Sloan
# Digital Sky Survey [3]_. The binned SDSS magnitude distributions were
# generated from a query of the DR7 data release and can be downloaded
# :download:`here <../../../examples/galaxies/sdss_dered_10deg2.ecsv>`.
from astropy.table import Table, vstack
from matplotlib import pyplot as plt
import numpy as np
from skypy.pipeline import Pipeline
# Execute SkyPy galaxy photometry simulation pipeline
pipeline = Pipeline.read("sdss_photometry.yml")
pipeline.execute()
skypy_galaxies = vstack([pipeline['blue_galaxies'], pipeline['red_galaxies']])
# SDSS magnitude distributions for a 10 degree^2 region
sdss_data = Table.read("sdss_dered_10deg2.ecsv", format='ascii.ecsv')
# Plot magnitude distributions for SkyPy simulation and SDSS data
bins = np.linspace(14.95, 25.05, 102)
plt.hist(skypy_galaxies['mag_r'], bins=bins, alpha=0.5, color='r', label='SkyPy-r')
plt.hist(skypy_galaxies['mag_u'], bins=bins, alpha=0.5, color='b', label='SkyPy-u')
plt.plot(sdss_data['magnitude'], sdss_data['dered_r'], color='r', label='SDSS-r')
plt.plot(sdss_data['magnitude'], sdss_data['dered_u'], color='b', label='SDSS-u')
plt.xlim(16, 24)
plt.yscale('log')
plt.xlabel(r'$\mathrm{Apparent\,Magnitude}$')
plt.ylabel(r'$\mathrm{N} \, [\mathrm{deg}^{-2} \, \mathrm{mag}^{-1}]$')
plt.legend()
plt.show()
# %%
# References
# ----------
#
# .. [1] M. R. Blanton and S. Roweis, 2007, AJ, 125, 2348
# .. [2] J. Herbel, T. Kacprzak, A. Amara, A. Refregier, C.Bruderer and
# A. Nicola 2017, JCAP, 1708, 035
# .. [3] K. N. Abazajian et al. 2009, ApJS, 182, 543
#
|
skypyprojectREPO_NAMEskypyPATH_START.@skypy_extracted@skypy-main@examples@galaxies@plot_photometry.py@.PATH_END.py
|
{
"filename": "test_csharp.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/unit_tests/document_loaders/parsers/language/test_csharp.py",
"type": "Python"
}
|
import unittest
import pytest
from langchain_community.document_loaders.parsers.language.csharp import CSharpSegmenter
@pytest.mark.requires("tree_sitter", "tree_sitter_languages")
class TestCSharpSegmenter(unittest.TestCase):
def setUp(self) -> None:
self.example_code = """namespace World
{
}
class Hello
{
static void Main(string []args)
{
System.Console.WriteLine("Hello, world.");
}
}
interface Human
{
void breathe();
}
enum Tens
{
Ten = 10,
Twenty = 20
}
struct T
{
}
record Person(string FirstName, string LastName, string Id)
{
internal string Id { get; init; } = Id;
}"""
self.expected_simplified_code = """// Code for: namespace World
// Code for: class Hello
// Code for: interface Human
// Code for: enum Tens
// Code for: struct T
// Code for: record Person(string FirstName, string LastName, string Id)"""
self.expected_extracted_code = [
"namespace World\n{\n}",
"class Hello\n{\n static void Main(string []args)\n {\n "
'System.Console.WriteLine("Hello, world.");\n }\n}',
"interface Human\n{\n void breathe();\n}",
"enum Tens\n{\n Ten = 10,\n Twenty = 20\n}",
"struct T\n{\n}",
"record Person(string FirstName, string LastName, string Id)\n{\n "
"internal string Id { get; init; } = Id;\n}",
]
def test_is_valid(self) -> None:
self.assertTrue(CSharpSegmenter("int a;").is_valid())
self.assertFalse(CSharpSegmenter("a b c 1 2 3").is_valid())
def test_extract_functions_classes(self) -> None:
segmenter = CSharpSegmenter(self.example_code)
extracted_code = segmenter.extract_functions_classes()
self.assertEqual(extracted_code, self.expected_extracted_code)
def test_simplify_code(self) -> None:
segmenter = CSharpSegmenter(self.example_code)
simplified_code = segmenter.simplify_code()
self.assertEqual(simplified_code, self.expected_simplified_code)
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@unit_tests@document_loaders@parsers@language@test_csharp.py@.PATH_END.py
|
{
"filename": "Multiple Responses.ipynb",
"repo_name": "lee-group-cmu/RFCDE",
"repo_path": "RFCDE_extracted/RFCDE-master/python/vignettes/Multiple Responses.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import rfcde
import numpy as np
import matplotlib.pyplot as plt
```
/usr/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
from pandas.core import datetools
RFCDE extends to multiple response variables to estimate joint conditional densities. Consider data from the following model
\begin{eqnarray}
X &\sim& U(0, 1) \\
Z_{1} &\sim& U(0, X) \\
Z_{2} &\sim& U(X, Z_{1})
\end{eqnarray}
This exhibits dependence not only between the responses and the covariates, but also between the responses themselves. Thus merely estimating the univariate conditional densities would lose the information about the dependence between $Z_{1}$ and $Z_{2}$.
```python
np.random.seed(42)
def generate_data(n):
x = np.random.uniform(0, 1, (n, 1))
z1 = np.random.uniform(0, x[:,0], n)
z2 = np.random.uniform(z1, x[:,0], n)
return x, np.array([z1, z2]).T
n_train = 10000
x_train, z_train = generate_data(n_train)
x_test = np.array([0.3, 0.6, 0.9])
```
## Training
Training is the same as for the univariate case. A tensor basis is used for the density estimates at each node; you can specify the number of basis functions for each dimension or just specify a single number which is applied to every dimension. Note that this scales like $n^d$ for $n$ basis functions in $d$ dimensions.
```python
n_trees = 100
mtry = 1
node_size = 20
n_basis = [15, 15] # or 15
forest = rfcde.RFCDE(n_trees=n_trees, mtry=mtry, node_size=node_size, n_basis=n_basis)
forest.train(x_train, z_train)
```
## Prediction
Prediction requires a multivariate grid of points at which to evaluate the density. Bandwidths can be specified by a covariance matrix, a float (which scales the identity covariance) or a string (see documentation for details).
```python
bandwidth = 0.05
n_grid = 30
z1, z2 = np.meshgrid(np.linspace(0, 1, n_grid),
np.linspace(0, 1, n_grid))
z_grid = np.array([z1.flatten(), z2.flatten()]).T
x_test = np.array([0.3])
density = forest.predict(x_test, z_grid, bandwidth)
```
```python
plt.contourf(np.linspace(0, 1, n_grid),
np.linspace(0, 1, n_grid),
density.reshape((n_grid, n_grid)))
plt.plot([0, x_test, 0, 0], [0, x_test, x_test, 0], color='white')
plt.show()
```
|
lee-group-cmuREPO_NAMERFCDEPATH_START.@RFCDE_extracted@RFCDE-master@python@vignettes@Multiple Responses.ipynb@.PATH_END.py
|
{
"filename": "_rotation.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/pie/_rotation.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class RotationValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="rotation", parent_name="pie", **kwargs):
super(RotationValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
max=kwargs.pop("max", 360),
min=kwargs.pop("min", -360),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@pie@_rotation.py@.PATH_END.py
|
{
"filename": "_borderwidth.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/icicle/marker/colorbar/_borderwidth.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BorderwidthValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="borderwidth", parent_name="icicle.marker.colorbar", **kwargs
):
super(BorderwidthValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@icicle@marker@colorbar@_borderwidth.py@.PATH_END.py
|
{
"filename": "colormaps.py",
"repo_name": "jiffyclub/palettable",
"repo_path": "palettable_extracted/palettable-master/palettable/cmocean/colormaps.py",
"type": "Python"
}
|
"""
Color maps from Kristen Thyng's cmocean package
Learn more at https://github.com/matplotlib/cmocean and view the cmocean license at
https://github.com/matplotlib/cmocean/blob/master/LICENSE.txt.
"""
_ALGAE = [
[215, 249, 208],
[214, 248, 206],
[212, 247, 205],
[211, 246, 203],
[210, 245, 202],
[209, 244, 200],
[207, 244, 199],
[206, 243, 197],
[205, 242, 196],
[204, 241, 195],
[202, 240, 193],
[201, 239, 192],
[200, 238, 190],
[199, 237, 189],
[197, 236, 187],
[196, 235, 186],
[195, 235, 185],
[194, 234, 183],
[192, 233, 182],
[191, 232, 180],
[190, 231, 179],
[189, 230, 177],
[187, 229, 176],
[186, 228, 175],
[185, 228, 173],
[184, 227, 172],
[182, 226, 171],
[181, 225, 169],
[180, 224, 168],
[178, 223, 166],
[177, 222, 165],
[176, 222, 164],
[175, 221, 162],
[173, 220, 161],
[172, 219, 160],
[171, 218, 158],
[170, 218, 157],
[168, 217, 156],
[167, 216, 154],
[166, 215, 153],
[164, 214, 152],
[163, 213, 150],
[162, 213, 149],
[160, 212, 148],
[159, 211, 146],
[158, 210, 145],
[157, 209, 144],
[155, 209, 143],
[154, 208, 141],
[153, 207, 140],
[151, 206, 139],
[150, 205, 138],
[149, 205, 136],
[147, 204, 135],
[146, 203, 134],
[145, 202, 133],
[143, 202, 131],
[142, 201, 130],
[140, 200, 129],
[139, 199, 128],
[138, 199, 126],
[136, 198, 125],
[135, 197, 124],
[133, 196, 123],
[132, 196, 122],
[131, 195, 121],
[129, 194, 119],
[128, 193, 118],
[126, 193, 117],
[125, 192, 116],
[123, 191, 115],
[122, 190, 114],
[120, 190, 113],
[119, 189, 111],
[117, 188, 110],
[116, 187, 109],
[114, 187, 108],
[113, 186, 107],
[111, 185, 106],
[110, 185, 105],
[108, 184, 104],
[107, 183, 103],
[105, 182, 102],
[103, 182, 101],
[102, 181, 100],
[100, 180, 99],
[98, 180, 98],
[97, 179, 97],
[95, 178, 96],
[93, 178, 95],
[91, 177, 94],
[90, 176, 93],
[88, 175, 93],
[86, 175, 92],
[84, 174, 91],
[82, 173, 90],
[80, 173, 89],
[78, 172, 89],
[76, 171, 88],
[74, 171, 87],
[72, 170, 87],
[70, 169, 86],
[68, 168, 85],
[66, 168, 85],
[63, 167, 84],
[61, 166, 84],
[59, 166, 84],
[57, 165, 83],
[55, 164, 83],
[52, 163, 83],
[50, 163, 82],
[48, 162, 82],
[46, 161, 82],
[44, 160, 82],
[42, 160, 82],
[40, 159, 81],
[38, 158, 81],
[36, 157, 81],
[34, 156, 81],
[32, 156, 81],
[30, 155, 81],
[28, 154, 81],
[27, 153, 81],
[25, 152, 81],
[24, 151, 80],
[22, 150, 80],
[21, 150, 80],
[19, 149, 80],
[18, 148, 80],
[16, 147, 80],
[15, 146, 80],
[14, 145, 80],
[13, 144, 79],
[12, 143, 79],
[11, 143, 79],
[10, 142, 79],
[9, 141, 79],
[9, 140, 79],
[8, 139, 78],
[8, 138, 78],
[7, 137, 78],
[7, 136, 78],
[7, 135, 77],
[7, 134, 77],
[7, 134, 77],
[7, 133, 77],
[7, 132, 77],
[7, 131, 76],
[7, 130, 76],
[8, 129, 76],
[8, 128, 75],
[8, 127, 75],
[9, 126, 75],
[9, 125, 75],
[10, 124, 74],
[10, 124, 74],
[11, 123, 74],
[11, 122, 73],
[12, 121, 73],
[12, 120, 73],
[13, 119, 72],
[13, 118, 72],
[14, 117, 72],
[14, 116, 71],
[15, 115, 71],
[15, 115, 71],
[16, 114, 70],
[16, 113, 70],
[17, 112, 69],
[17, 111, 69],
[18, 110, 69],
[18, 109, 68],
[18, 108, 68],
[19, 107, 67],
[19, 106, 67],
[20, 106, 67],
[20, 105, 66],
[20, 104, 66],
[21, 103, 65],
[21, 102, 65],
[21, 101, 64],
[22, 100, 64],
[22, 99, 64],
[22, 98, 63],
[23, 98, 63],
[23, 97, 62],
[23, 96, 62],
[23, 95, 61],
[24, 94, 61],
[24, 93, 60],
[24, 92, 60],
[24, 91, 59],
[24, 91, 59],
[25, 90, 58],
[25, 89, 58],
[25, 88, 57],
[25, 87, 57],
[25, 86, 56],
[25, 85, 56],
[25, 84, 55],
[25, 84, 55],
[26, 83, 54],
[26, 82, 53],
[26, 81, 53],
[26, 80, 52],
[26, 79, 52],
[26, 78, 51],
[26, 77, 51],
[26, 77, 50],
[26, 76, 50],
[26, 75, 49],
[26, 74, 48],
[26, 73, 48],
[26, 72, 47],
[26, 71, 47],
[26, 71, 46],
[26, 70, 46],
[26, 69, 45],
[26, 68, 44],
[26, 67, 44],
[25, 66, 43],
[25, 65, 43],
[25, 64, 42],
[25, 64, 41],
[25, 63, 41],
[25, 62, 40],
[25, 61, 39],
[25, 60, 39],
[24, 59, 38],
[24, 59, 38],
[24, 58, 37],
[24, 57, 36],
[24, 56, 36],
[24, 55, 35],
[23, 54, 34],
[23, 53, 34],
[23, 53, 33],
[23, 52, 32],
[23, 51, 32],
[22, 50, 31],
[22, 49, 30],
[22, 48, 30],
[22, 47, 29],
[21, 47, 28],
[21, 46, 28],
[21, 45, 27],
[20, 44, 26],
[20, 43, 26],
[20, 42, 25],
[20, 41, 24],
[19, 41, 24],
[19, 40, 23],
[19, 39, 22],
[18, 38, 22],
[18, 37, 21],
[18, 36, 20],
]
_AMP = [
[241, 237, 236],
[241, 236, 235],
[240, 235, 233],
[239, 233, 232],
[239, 232, 231],
[238, 231, 229],
[238, 230, 228],
[237, 229, 227],
[237, 227, 225],
[236, 226, 224],
[236, 225, 222],
[235, 224, 221],
[235, 223, 220],
[234, 221, 218],
[234, 220, 217],
[233, 219, 215],
[233, 218, 214],
[233, 217, 212],
[232, 216, 211],
[232, 214, 210],
[231, 213, 208],
[231, 212, 207],
[230, 211, 205],
[230, 210, 204],
[230, 209, 202],
[229, 207, 201],
[229, 206, 200],
[228, 205, 198],
[228, 204, 197],
[228, 203, 195],
[227, 201, 194],
[227, 200, 192],
[226, 199, 191],
[226, 198, 189],
[226, 197, 188],
[225, 196, 187],
[225, 195, 185],
[225, 193, 184],
[224, 192, 182],
[224, 191, 181],
[223, 190, 179],
[223, 189, 178],
[223, 188, 176],
[222, 186, 175],
[222, 185, 174],
[222, 184, 172],
[221, 183, 171],
[221, 182, 169],
[221, 181, 168],
[220, 180, 166],
[220, 178, 165],
[220, 177, 163],
[219, 176, 162],
[219, 175, 161],
[219, 174, 159],
[218, 173, 158],
[218, 172, 156],
[217, 170, 155],
[217, 169, 153],
[217, 168, 152],
[216, 167, 150],
[216, 166, 149],
[216, 165, 148],
[215, 164, 146],
[215, 162, 145],
[215, 161, 143],
[214, 160, 142],
[214, 159, 140],
[214, 158, 139],
[213, 157, 137],
[213, 156, 136],
[213, 154, 135],
[212, 153, 133],
[212, 152, 132],
[212, 151, 130],
[211, 150, 129],
[211, 149, 127],
[211, 148, 126],
[210, 146, 125],
[210, 145, 123],
[210, 144, 122],
[209, 143, 120],
[209, 142, 119],
[209, 141, 118],
[208, 140, 116],
[208, 139, 115],
[208, 137, 113],
[207, 136, 112],
[207, 135, 111],
[207, 134, 109],
[206, 133, 108],
[206, 132, 106],
[205, 131, 105],
[205, 129, 104],
[205, 128, 102],
[204, 127, 101],
[204, 126, 100],
[204, 125, 98],
[203, 124, 97],
[203, 122, 96],
[203, 121, 94],
[202, 120, 93],
[202, 119, 91],
[201, 118, 90],
[201, 117, 89],
[201, 116, 87],
[200, 114, 86],
[200, 113, 85],
[200, 112, 84],
[199, 111, 82],
[199, 110, 81],
[198, 109, 80],
[198, 107, 78],
[198, 106, 77],
[197, 105, 76],
[197, 104, 74],
[197, 103, 73],
[196, 101, 72],
[196, 100, 71],
[195, 99, 70],
[195, 98, 68],
[195, 97, 67],
[194, 95, 66],
[194, 94, 65],
[193, 93, 63],
[193, 92, 62],
[192, 91, 61],
[192, 89, 60],
[192, 88, 59],
[191, 87, 58],
[191, 86, 57],
[190, 84, 56],
[190, 83, 54],
[189, 82, 53],
[189, 81, 52],
[189, 79, 51],
[188, 78, 50],
[188, 77, 49],
[187, 76, 48],
[187, 74, 48],
[186, 73, 47],
[186, 72, 46],
[185, 70, 45],
[185, 69, 44],
[184, 68, 43],
[184, 66, 43],
[183, 65, 42],
[183, 64, 41],
[182, 63, 41],
[181, 61, 40],
[181, 60, 39],
[180, 59, 39],
[180, 57, 38],
[179, 56, 38],
[178, 55, 38],
[178, 53, 37],
[177, 52, 37],
[176, 51, 37],
[176, 49, 37],
[175, 48, 36],
[174, 47, 36],
[174, 45, 36],
[173, 44, 36],
[172, 43, 36],
[171, 42, 36],
[170, 40, 36],
[170, 39, 36],
[169, 38, 36],
[168, 37, 36],
[167, 36, 36],
[166, 34, 37],
[165, 33, 37],
[164, 32, 37],
[163, 31, 37],
[162, 30, 37],
[161, 29, 37],
[160, 28, 38],
[159, 27, 38],
[158, 26, 38],
[157, 25, 38],
[156, 24, 39],
[155, 23, 39],
[154, 22, 39],
[153, 21, 39],
[152, 21, 39],
[151, 20, 40],
[149, 19, 40],
[148, 19, 40],
[147, 18, 40],
[146, 17, 40],
[145, 17, 41],
[144, 16, 41],
[142, 16, 41],
[141, 16, 41],
[140, 15, 41],
[139, 15, 41],
[137, 15, 41],
[136, 15, 41],
[135, 14, 41],
[133, 14, 41],
[132, 14, 41],
[131, 14, 41],
[129, 14, 41],
[128, 14, 41],
[127, 14, 41],
[125, 14, 41],
[124, 14, 41],
[123, 14, 41],
[121, 14, 41],
[120, 14, 40],
[119, 14, 40],
[117, 14, 40],
[116, 14, 40],
[115, 14, 39],
[113, 14, 39],
[112, 14, 39],
[111, 14, 38],
[109, 14, 38],
[108, 15, 38],
[107, 15, 37],
[105, 15, 37],
[104, 15, 37],
[103, 15, 36],
[101, 15, 36],
[100, 14, 35],
[99, 14, 35],
[97, 14, 34],
[96, 14, 34],
[95, 14, 33],
[93, 14, 33],
[92, 14, 33],
[91, 14, 32],
[90, 14, 31],
[88, 14, 31],
[87, 14, 30],
[86, 14, 30],
[84, 13, 29],
[83, 13, 29],
[82, 13, 28],
[81, 13, 28],
[79, 13, 27],
[78, 13, 26],
[77, 12, 26],
[75, 12, 25],
[74, 12, 25],
[73, 12, 24],
[72, 11, 23],
[70, 11, 23],
[69, 11, 22],
[68, 11, 22],
[67, 10, 21],
[65, 10, 20],
[64, 10, 20],
[63, 10, 19],
[61, 9, 18],
[60, 9, 18],
]
_BALANCE = [
[24, 28, 67],
[25, 30, 70],
[26, 31, 73],
[27, 33, 76],
[28, 34, 79],
[29, 35, 82],
[30, 37, 85],
[31, 38, 88],
[32, 39, 91],
[33, 41, 95],
[33, 42, 98],
[34, 43, 101],
[35, 45, 105],
[36, 46, 108],
[37, 47, 111],
[37, 48, 115],
[38, 50, 118],
[39, 51, 122],
[39, 52, 125],
[40, 54, 129],
[40, 55, 132],
[41, 56, 136],
[41, 58, 140],
[41, 59, 143],
[41, 60, 147],
[41, 62, 151],
[41, 63, 154],
[41, 64, 158],
[41, 66, 162],
[40, 67, 165],
[39, 69, 169],
[38, 71, 172],
[37, 72, 176],
[35, 74, 179],
[33, 76, 182],
[31, 78, 184],
[28, 80, 186],
[25, 82, 188],
[22, 85, 189],
[19, 87, 190],
[16, 89, 190],
[13, 91, 190],
[12, 94, 190],
[10, 96, 190],
[10, 98, 190],
[10, 100, 190],
[11, 102, 189],
[13, 104, 189],
[15, 106, 189],
[17, 108, 188],
[19, 110, 188],
[22, 112, 188],
[25, 114, 187],
[27, 116, 187],
[30, 118, 187],
[33, 120, 187],
[35, 122, 186],
[38, 123, 186],
[41, 125, 186],
[43, 127, 186],
[46, 129, 186],
[48, 131, 186],
[51, 132, 186],
[54, 134, 186],
[56, 136, 186],
[59, 137, 186],
[62, 139, 186],
[64, 141, 186],
[67, 143, 186],
[70, 144, 186],
[72, 146, 186],
[75, 148, 186],
[78, 149, 186],
[81, 151, 186],
[83, 153, 186],
[86, 154, 187],
[89, 156, 187],
[92, 157, 187],
[95, 159, 187],
[98, 160, 187],
[101, 162, 188],
[104, 164, 188],
[107, 165, 188],
[110, 167, 189],
[113, 168, 189],
[117, 170, 190],
[120, 171, 190],
[123, 172, 191],
[126, 174, 191],
[129, 175, 192],
[133, 177, 192],
[136, 178, 193],
[139, 180, 194],
[142, 181, 195],
[145, 183, 195],
[148, 184, 196],
[152, 186, 197],
[155, 187, 198],
[158, 188, 199],
[161, 190, 200],
[164, 191, 201],
[167, 193, 202],
[170, 194, 203],
[173, 196, 204],
[176, 197, 205],
[179, 199, 206],
[182, 201, 207],
[185, 202, 208],
[188, 204, 210],
[191, 205, 211],
[193, 207, 212],
[196, 208, 213],
[199, 210, 215],
[202, 212, 216],
[205, 213, 217],
[208, 215, 218],
[211, 217, 220],
[213, 218, 221],
[216, 220, 222],
[219, 222, 224],
[222, 224, 225],
[225, 225, 227],
[227, 227, 228],
[230, 229, 230],
[233, 231, 231],
[235, 233, 233],
[238, 234, 234],
[241, 236, 236],
[241, 236, 235],
[240, 234, 233],
[239, 232, 230],
[238, 229, 227],
[237, 227, 224],
[236, 224, 222],
[235, 222, 219],
[234, 220, 216],
[233, 217, 213],
[232, 215, 210],
[231, 213, 207],
[230, 210, 205],
[229, 208, 202],
[229, 206, 199],
[228, 203, 196],
[227, 201, 193],
[226, 199, 190],
[225, 196, 187],
[225, 194, 184],
[224, 192, 181],
[223, 189, 178],
[223, 187, 176],
[222, 185, 173],
[221, 182, 170],
[220, 180, 167],
[220, 178, 164],
[219, 175, 161],
[218, 173, 158],
[218, 171, 155],
[217, 169, 152],
[216, 166, 150],
[216, 164, 147],
[215, 162, 144],
[214, 159, 141],
[214, 157, 138],
[213, 155, 135],
[212, 153, 132],
[211, 150, 129],
[211, 148, 127],
[210, 146, 124],
[209, 143, 121],
[209, 141, 118],
[208, 139, 115],
[207, 137, 112],
[207, 134, 110],
[206, 132, 107],
[205, 130, 104],
[205, 127, 101],
[204, 125, 99],
[203, 123, 96],
[202, 121, 93],
[202, 118, 91],
[201, 116, 88],
[200, 114, 85],
[199, 111, 83],
[199, 109, 80],
[198, 107, 77],
[197, 104, 75],
[196, 102, 72],
[195, 99, 70],
[195, 97, 67],
[194, 95, 65],
[193, 92, 63],
[192, 90, 60],
[191, 87, 58],
[190, 85, 56],
[190, 82, 54],
[189, 80, 52],
[188, 77, 50],
[187, 75, 48],
[186, 72, 46],
[185, 69, 44],
[184, 67, 43],
[183, 64, 41],
[182, 61, 40],
[180, 59, 39],
[179, 56, 38],
[178, 53, 37],
[177, 51, 37],
[175, 48, 36],
[174, 46, 36],
[172, 43, 36],
[171, 41, 36],
[169, 38, 36],
[167, 36, 36],
[165, 33, 37],
[163, 31, 37],
[161, 29, 37],
[159, 27, 38],
[157, 25, 38],
[155, 23, 39],
[153, 22, 39],
[151, 20, 40],
[148, 19, 40],
[146, 18, 40],
[144, 16, 41],
[141, 16, 41],
[139, 15, 41],
[136, 15, 41],
[134, 14, 41],
[131, 14, 41],
[128, 14, 41],
[126, 14, 41],
[123, 14, 41],
[120, 14, 40],
[118, 14, 40],
[115, 14, 39],
[112, 14, 39],
[109, 14, 38],
[107, 15, 37],
[104, 15, 37],
[101, 15, 36],
[99, 14, 35],
[96, 14, 34],
[94, 14, 33],
[91, 14, 32],
[88, 14, 31],
[86, 14, 30],
[83, 13, 29],
[81, 13, 28],
[78, 13, 27],
[75, 12, 25],
[73, 12, 24],
[70, 11, 23],
[68, 11, 22],
[65, 10, 20],
[63, 10, 19],
[60, 9, 18],
]
_CURL = [
[21, 29, 68],
[21, 30, 68],
[21, 31, 69],
[22, 32, 69],
[22, 33, 70],
[22, 34, 70],
[22, 35, 71],
[23, 36, 71],
[23, 37, 72],
[23, 38, 72],
[23, 39, 73],
[23, 40, 74],
[24, 41, 74],
[24, 42, 75],
[24, 43, 75],
[24, 44, 76],
[24, 45, 76],
[25, 46, 77],
[25, 47, 77],
[25, 48, 78],
[25, 49, 79],
[25, 50, 79],
[26, 51, 80],
[26, 52, 80],
[26, 53, 81],
[26, 54, 81],
[26, 54, 82],
[26, 55, 83],
[27, 56, 83],
[27, 57, 84],
[27, 58, 84],
[27, 59, 85],
[27, 60, 86],
[27, 61, 86],
[27, 62, 87],
[27, 63, 87],
[28, 64, 88],
[28, 65, 88],
[28, 66, 89],
[28, 66, 90],
[28, 67, 90],
[28, 68, 91],
[28, 69, 91],
[28, 70, 92],
[28, 71, 93],
[28, 72, 93],
[28, 73, 94],
[28, 74, 94],
[28, 75, 95],
[28, 76, 95],
[28, 76, 96],
[28, 77, 97],
[28, 78, 97],
[28, 79, 98],
[28, 80, 98],
[28, 81, 99],
[28, 82, 99],
[28, 83, 100],
[28, 84, 101],
[28, 85, 101],
[27, 86, 102],
[27, 87, 102],
[27, 88, 103],
[27, 88, 103],
[27, 89, 104],
[27, 90, 104],
[26, 91, 105],
[26, 92, 106],
[26, 93, 106],
[26, 94, 107],
[26, 95, 107],
[25, 96, 108],
[25, 97, 108],
[25, 98, 109],
[25, 99, 109],
[24, 100, 110],
[24, 101, 110],
[24, 101, 111],
[23, 102, 111],
[23, 103, 112],
[23, 104, 112],
[22, 105, 113],
[22, 106, 113],
[22, 107, 114],
[21, 108, 114],
[21, 109, 115],
[20, 110, 115],
[20, 111, 115],
[20, 112, 116],
[19, 113, 116],
[19, 114, 117],
[19, 115, 117],
[18, 116, 118],
[18, 117, 118],
[18, 118, 118],
[17, 118, 119],
[17, 119, 119],
[17, 120, 120],
[17, 121, 120],
[17, 122, 120],
[17, 123, 121],
[17, 124, 121],
[17, 125, 121],
[17, 126, 122],
[17, 127, 122],
[17, 128, 122],
[18, 129, 123],
[18, 130, 123],
[19, 131, 123],
[19, 132, 123],
[20, 132, 124],
[21, 133, 124],
[22, 134, 124],
[22, 135, 124],
[23, 136, 125],
[25, 137, 125],
[26, 138, 125],
[27, 139, 125],
[28, 140, 126],
[30, 141, 126],
[31, 141, 126],
[33, 142, 126],
[34, 143, 126],
[36, 144, 127],
[37, 145, 127],
[39, 146, 127],
[41, 147, 127],
[42, 147, 127],
[44, 148, 127],
[46, 149, 128],
[48, 150, 128],
[50, 151, 128],
[52, 152, 128],
[54, 152, 128],
[56, 153, 129],
[58, 154, 129],
[59, 155, 129],
[61, 156, 129],
[63, 156, 129],
[65, 157, 130],
[67, 158, 130],
[69, 159, 130],
[71, 159, 130],
[73, 160, 131],
[75, 161, 131],
[78, 161, 131],
[80, 162, 132],
[82, 163, 132],
[84, 164, 132],
[86, 164, 133],
[87, 165, 133],
[89, 166, 133],
[91, 166, 134],
[93, 167, 134],
[95, 168, 135],
[97, 169, 135],
[99, 169, 136],
[101, 170, 136],
[103, 171, 137],
[105, 171, 137],
[107, 172, 138],
[109, 173, 138],
[111, 173, 139],
[113, 174, 139],
[114, 175, 140],
[116, 175, 141],
[118, 176, 141],
[120, 177, 142],
[122, 177, 142],
[124, 178, 143],
[125, 179, 144],
[127, 179, 144],
[129, 180, 145],
[131, 181, 146],
[133, 181, 147],
[134, 182, 147],
[136, 183, 148],
[138, 183, 149],
[139, 184, 150],
[141, 185, 151],
[143, 186, 151],
[145, 186, 152],
[146, 187, 153],
[148, 188, 154],
[150, 188, 155],
[151, 189, 156],
[153, 190, 157],
[155, 190, 158],
[156, 191, 159],
[158, 192, 159],
[160, 192, 160],
[161, 193, 161],
[163, 194, 162],
[164, 195, 163],
[166, 195, 164],
[168, 196, 165],
[169, 197, 166],
[171, 197, 168],
[172, 198, 169],
[174, 199, 170],
[176, 200, 171],
[177, 200, 172],
[179, 201, 173],
[180, 202, 174],
[182, 203, 175],
[183, 203, 176],
[185, 204, 178],
[186, 205, 179],
[188, 206, 180],
[189, 206, 181],
[191, 207, 182],
[192, 208, 183],
[194, 209, 185],
[195, 209, 186],
[197, 210, 187],
[198, 211, 188],
[200, 212, 190],
[201, 212, 191],
[203, 213, 192],
[204, 214, 193],
[206, 215, 195],
[207, 216, 196],
[209, 216, 197],
[210, 217, 199],
[211, 218, 200],
[213, 219, 201],
[214, 220, 203],
[216, 221, 204],
[217, 221, 205],
[219, 222, 207],
[220, 223, 208],
[221, 224, 209],
[223, 225, 211],
[224, 226, 212],
[226, 226, 214],
[227, 227, 215],
[228, 228, 216],
[230, 229, 218],
[231, 230, 219],
[233, 231, 221],
[234, 232, 222],
[235, 233, 223],
[237, 234, 225],
[238, 234, 226],
[240, 235, 228],
[241, 236, 229],
[242, 237, 231],
[244, 238, 232],
[245, 239, 234],
[247, 240, 235],
[248, 241, 237],
[249, 242, 238],
[251, 243, 240],
[252, 244, 241],
[253, 245, 243],
[255, 246, 244],
[254, 246, 245],
[253, 245, 243],
[252, 244, 241],
[252, 242, 240],
[251, 241, 238],
[250, 240, 236],
[250, 239, 235],
[249, 237, 233],
[249, 236, 231],
[248, 235, 230],
[248, 234, 228],
[247, 232, 226],
[246, 231, 225],
[246, 230, 223],
[245, 229, 221],
[245, 228, 220],
[244, 226, 218],
[244, 225, 216],
[243, 224, 214],
[243, 223, 213],
[242, 221, 211],
[242, 220, 209],
[242, 219, 208],
[241, 218, 206],
[241, 216, 204],
[240, 215, 203],
[240, 214, 201],
[239, 213, 200],
[239, 211, 198],
[238, 210, 196],
[238, 209, 195],
[238, 208, 193],
[237, 207, 191],
[237, 205, 190],
[236, 204, 188],
[236, 203, 187],
[236, 202, 185],
[235, 200, 183],
[235, 199, 182],
[235, 198, 180],
[234, 197, 179],
[234, 195, 177],
[233, 194, 175],
[233, 193, 174],
[233, 192, 172],
[232, 190, 171],
[232, 189, 169],
[232, 188, 168],
[231, 187, 166],
[231, 186, 165],
[231, 184, 163],
[230, 183, 162],
[230, 182, 160],
[230, 181, 159],
[229, 179, 157],
[229, 178, 156],
[229, 177, 154],
[228, 176, 153],
[228, 174, 152],
[228, 173, 150],
[227, 172, 149],
[227, 171, 147],
[227, 169, 146],
[226, 168, 145],
[226, 167, 143],
[226, 166, 142],
[225, 164, 141],
[225, 163, 139],
[225, 162, 138],
[224, 161, 137],
[224, 159, 136],
[224, 158, 134],
[224, 157, 133],
[223, 155, 132],
[223, 154, 131],
[223, 153, 130],
[222, 152, 128],
[222, 150, 127],
[222, 149, 126],
[221, 148, 125],
[221, 147, 124],
[220, 145, 123],
[220, 144, 122],
[220, 143, 121],
[219, 142, 120],
[219, 140, 119],
[219, 139, 118],
[218, 138, 117],
[218, 137, 116],
[217, 135, 115],
[217, 134, 114],
[217, 133, 114],
[216, 132, 113],
[216, 130, 112],
[215, 129, 111],
[215, 128, 110],
[214, 127, 110],
[214, 125, 109],
[214, 124, 108],
[213, 123, 108],
[213, 122, 107],
[212, 120, 106],
[212, 119, 106],
[211, 118, 105],
[211, 117, 105],
[210, 116, 104],
[210, 114, 104],
[209, 113, 103],
[208, 112, 103],
[208, 111, 102],
[207, 110, 102],
[207, 108, 101],
[206, 107, 101],
[205, 106, 101],
[205, 105, 100],
[204, 104, 100],
[204, 103, 100],
[203, 101, 99],
[202, 100, 99],
[202, 99, 99],
[201, 98, 99],
[200, 97, 98],
[200, 96, 98],
[199, 95, 98],
[198, 93, 98],
[197, 92, 98],
[197, 91, 97],
[196, 90, 97],
[195, 89, 97],
[194, 88, 97],
[194, 87, 97],
[193, 86, 97],
[192, 85, 97],
[191, 84, 96],
[191, 83, 96],
[190, 82, 96],
[189, 81, 96],
[188, 79, 96],
[187, 78, 96],
[186, 77, 96],
[186, 76, 96],
[185, 75, 96],
[184, 74, 96],
[183, 73, 96],
[182, 72, 96],
[181, 71, 96],
[180, 70, 96],
[180, 69, 96],
[179, 68, 96],
[178, 67, 96],
[177, 66, 96],
[176, 65, 96],
[175, 65, 96],
[174, 64, 96],
[173, 63, 96],
[172, 62, 96],
[171, 61, 96],
[170, 60, 96],
[169, 59, 96],
[168, 58, 96],
[167, 57, 96],
[166, 56, 96],
[165, 55, 96],
[164, 54, 96],
[163, 54, 96],
[162, 53, 96],
[161, 52, 96],
[160, 51, 96],
[159, 50, 96],
[158, 49, 96],
[157, 48, 96],
[156, 47, 96],
[155, 47, 96],
[154, 46, 96],
[153, 45, 97],
[152, 44, 97],
[151, 43, 97],
[150, 43, 97],
[149, 42, 97],
[147, 41, 97],
[146, 40, 97],
[145, 39, 96],
[144, 39, 96],
[143, 38, 96],
[142, 37, 96],
[141, 37, 96],
[140, 36, 96],
[138, 35, 96],
[137, 34, 96],
[136, 34, 96],
[135, 33, 96],
[134, 32, 96],
[133, 32, 96],
[131, 31, 96],
[130, 31, 96],
[129, 30, 95],
[128, 29, 95],
[127, 29, 95],
[125, 28, 95],
[124, 28, 95],
[123, 27, 94],
[122, 27, 94],
[120, 26, 94],
[119, 26, 94],
[118, 25, 93],
[117, 25, 93],
[115, 25, 93],
[114, 24, 92],
[113, 24, 92],
[112, 24, 92],
[110, 23, 91],
[109, 23, 91],
[108, 23, 90],
[106, 22, 90],
[105, 22, 89],
[104, 22, 89],
[102, 22, 88],
[101, 21, 88],
[100, 21, 87],
[98, 21, 87],
[97, 21, 86],
[96, 20, 85],
[94, 20, 85],
[93, 20, 84],
[92, 20, 83],
[90, 20, 82],
[89, 20, 82],
[88, 19, 81],
[87, 19, 80],
[85, 19, 79],
[84, 19, 78],
[83, 19, 78],
[81, 19, 77],
[80, 18, 76],
[79, 18, 75],
[77, 18, 74],
[76, 18, 73],
[75, 18, 72],
[73, 18, 71],
[72, 17, 70],
[71, 17, 69],
[70, 17, 68],
[68, 17, 67],
[67, 16, 66],
[66, 16, 65],
[64, 16, 64],
[63, 16, 63],
[62, 15, 62],
[61, 15, 61],
[59, 15, 60],
[58, 15, 59],
[57, 14, 58],
[56, 14, 57],
[54, 14, 55],
[53, 13, 54],
[52, 13, 53],
]
_DEEP = [
[253, 254, 204],
[251, 253, 203],
[249, 252, 202],
[247, 251, 200],
[245, 250, 199],
[243, 250, 198],
[241, 249, 197],
[239, 248, 196],
[237, 247, 195],
[235, 247, 193],
[233, 246, 192],
[231, 245, 191],
[229, 244, 190],
[227, 244, 189],
[225, 243, 188],
[223, 242, 187],
[221, 242, 186],
[219, 241, 185],
[217, 240, 184],
[215, 239, 183],
[212, 239, 182],
[210, 238, 181],
[208, 237, 180],
[206, 236, 179],
[204, 236, 179],
[202, 235, 178],
[200, 234, 177],
[198, 234, 176],
[196, 233, 175],
[193, 232, 175],
[191, 231, 174],
[189, 231, 173],
[187, 230, 172],
[185, 229, 172],
[183, 229, 171],
[181, 228, 170],
[178, 227, 170],
[176, 226, 169],
[174, 226, 169],
[172, 225, 168],
[170, 224, 168],
[167, 224, 167],
[165, 223, 167],
[163, 222, 166],
[161, 221, 166],
[159, 221, 165],
[156, 220, 165],
[154, 219, 165],
[152, 218, 164],
[150, 218, 164],
[148, 217, 164],
[146, 216, 164],
[144, 215, 164],
[141, 215, 163],
[139, 214, 163],
[137, 213, 163],
[135, 212, 163],
[133, 211, 163],
[131, 211, 163],
[129, 210, 163],
[127, 209, 163],
[125, 208, 163],
[124, 207, 163],
[122, 206, 163],
[120, 206, 163],
[118, 205, 163],
[117, 204, 163],
[115, 203, 163],
[113, 202, 163],
[112, 201, 163],
[110, 200, 163],
[109, 199, 163],
[107, 198, 163],
[106, 197, 164],
[105, 196, 164],
[103, 195, 164],
[102, 194, 164],
[101, 194, 164],
[100, 193, 164],
[99, 192, 164],
[98, 191, 164],
[97, 190, 164],
[96, 189, 164],
[95, 188, 164],
[94, 187, 164],
[93, 186, 164],
[92, 185, 164],
[91, 184, 164],
[90, 183, 164],
[90, 182, 164],
[89, 180, 164],
[88, 179, 164],
[88, 178, 164],
[87, 177, 164],
[86, 176, 164],
[86, 175, 164],
[85, 174, 163],
[85, 173, 163],
[84, 172, 163],
[83, 171, 163],
[83, 170, 163],
[82, 169, 163],
[82, 168, 163],
[81, 167, 163],
[81, 166, 162],
[81, 165, 162],
[80, 164, 162],
[80, 163, 162],
[79, 162, 162],
[79, 161, 162],
[79, 160, 162],
[78, 159, 161],
[78, 158, 161],
[77, 157, 161],
[77, 156, 161],
[77, 155, 161],
[76, 154, 160],
[76, 153, 160],
[75, 152, 160],
[75, 151, 160],
[75, 150, 160],
[74, 149, 159],
[74, 148, 159],
[74, 147, 159],
[73, 146, 159],
[73, 145, 158],
[73, 144, 158],
[72, 143, 158],
[72, 142, 158],
[72, 141, 157],
[71, 140, 157],
[71, 139, 157],
[71, 138, 157],
[70, 137, 157],
[70, 136, 156],
[70, 135, 156],
[69, 134, 156],
[69, 133, 156],
[69, 132, 155],
[68, 131, 155],
[68, 130, 155],
[68, 129, 155],
[68, 128, 155],
[67, 127, 154],
[67, 126, 154],
[67, 125, 154],
[66, 124, 154],
[66, 123, 153],
[66, 122, 153],
[66, 121, 153],
[65, 120, 153],
[65, 119, 153],
[65, 118, 152],
[64, 117, 152],
[64, 116, 152],
[64, 115, 152],
[64, 114, 152],
[64, 113, 151],
[63, 112, 151],
[63, 111, 151],
[63, 110, 151],
[63, 109, 151],
[63, 108, 150],
[62, 107, 150],
[62, 106, 150],
[62, 105, 150],
[62, 104, 150],
[62, 103, 149],
[62, 102, 149],
[62, 101, 149],
[62, 100, 149],
[62, 99, 148],
[62, 98, 148],
[62, 97, 148],
[62, 96, 148],
[62, 95, 147],
[62, 94, 147],
[62, 92, 147],
[62, 91, 147],
[62, 90, 146],
[62, 89, 146],
[62, 88, 146],
[62, 87, 145],
[62, 86, 145],
[63, 85, 144],
[63, 84, 144],
[63, 83, 143],
[63, 82, 143],
[63, 80, 142],
[64, 79, 141],
[64, 78, 141],
[64, 77, 140],
[64, 76, 139],
[65, 75, 138],
[65, 74, 137],
[65, 73, 136],
[65, 72, 135],
[65, 71, 133],
[65, 70, 132],
[65, 69, 131],
[65, 68, 129],
[66, 67, 128],
[65, 66, 126],
[65, 65, 125],
[65, 64, 123],
[65, 64, 122],
[65, 63, 120],
[65, 62, 118],
[65, 61, 117],
[64, 60, 115],
[64, 60, 113],
[64, 59, 112],
[64, 58, 110],
[63, 57, 108],
[63, 56, 107],
[63, 56, 105],
[62, 55, 103],
[62, 54, 102],
[61, 53, 100],
[61, 53, 98],
[61, 52, 97],
[60, 51, 95],
[60, 50, 93],
[59, 50, 92],
[59, 49, 90],
[58, 48, 88],
[58, 48, 87],
[57, 47, 85],
[57, 46, 84],
[56, 45, 82],
[56, 45, 81],
[55, 44, 79],
[54, 43, 77],
[54, 42, 76],
[53, 42, 74],
[53, 41, 73],
[52, 40, 71],
[52, 40, 70],
[51, 39, 68],
[50, 38, 67],
[50, 37, 65],
[49, 37, 64],
[48, 36, 62],
[48, 35, 61],
[47, 34, 59],
[47, 34, 58],
[46, 33, 57],
[45, 32, 55],
[45, 31, 54],
[44, 31, 52],
[43, 30, 51],
[43, 29, 50],
[42, 28, 48],
[41, 28, 47],
[40, 27, 45],
[40, 26, 44],
]
_DELTA = [
[17, 32, 64],
[18, 32, 65],
[18, 33, 67],
[19, 34, 68],
[20, 34, 70],
[20, 35, 71],
[21, 36, 73],
[22, 36, 75],
[22, 37, 76],
[23, 38, 78],
[23, 38, 79],
[24, 39, 81],
[25, 40, 83],
[25, 40, 84],
[26, 41, 86],
[27, 42, 88],
[27, 42, 89],
[28, 43, 91],
[28, 44, 93],
[29, 44, 95],
[30, 45, 96],
[30, 46, 98],
[31, 46, 100],
[31, 47, 102],
[32, 48, 103],
[32, 48, 105],
[33, 49, 107],
[33, 49, 109],
[34, 50, 111],
[34, 51, 112],
[35, 51, 114],
[35, 52, 116],
[36, 53, 118],
[36, 53, 120],
[37, 54, 122],
[37, 55, 124],
[37, 55, 126],
[38, 56, 128],
[38, 56, 130],
[38, 57, 132],
[38, 58, 134],
[39, 58, 135],
[39, 59, 137],
[39, 60, 139],
[39, 61, 141],
[38, 61, 143],
[38, 62, 145],
[38, 63, 146],
[37, 64, 148],
[37, 65, 149],
[36, 66, 150],
[35, 67, 151],
[35, 68, 152],
[34, 69, 153],
[33, 70, 154],
[33, 72, 154],
[32, 73, 155],
[31, 74, 155],
[31, 75, 155],
[30, 76, 156],
[30, 77, 156],
[30, 78, 156],
[29, 79, 156],
[29, 81, 156],
[28, 82, 157],
[28, 83, 157],
[28, 84, 157],
[28, 85, 157],
[27, 86, 157],
[27, 87, 158],
[27, 88, 158],
[27, 89, 158],
[27, 90, 158],
[27, 91, 158],
[27, 92, 158],
[27, 93, 158],
[27, 94, 159],
[27, 95, 159],
[27, 96, 159],
[27, 97, 159],
[27, 98, 159],
[27, 99, 159],
[27, 100, 159],
[28, 101, 160],
[28, 102, 160],
[28, 103, 160],
[28, 104, 160],
[29, 105, 160],
[29, 106, 160],
[29, 107, 161],
[30, 108, 161],
[30, 109, 161],
[30, 110, 161],
[31, 111, 161],
[31, 112, 162],
[31, 113, 162],
[32, 114, 162],
[32, 115, 162],
[33, 116, 162],
[33, 117, 163],
[34, 118, 163],
[34, 119, 163],
[35, 120, 163],
[35, 121, 163],
[36, 122, 164],
[36, 123, 164],
[37, 124, 164],
[37, 125, 164],
[38, 126, 165],
[39, 127, 165],
[39, 128, 165],
[40, 128, 165],
[40, 129, 165],
[41, 130, 166],
[42, 131, 166],
[42, 132, 166],
[43, 133, 166],
[43, 134, 167],
[44, 135, 167],
[45, 136, 167],
[45, 137, 167],
[46, 138, 168],
[47, 139, 168],
[48, 140, 168],
[48, 141, 168],
[49, 142, 169],
[50, 143, 169],
[50, 144, 169],
[51, 145, 169],
[52, 146, 170],
[53, 146, 170],
[54, 147, 170],
[54, 148, 170],
[55, 149, 171],
[56, 150, 171],
[57, 151, 171],
[58, 152, 171],
[59, 153, 172],
[60, 154, 172],
[61, 155, 172],
[62, 156, 172],
[63, 157, 172],
[64, 158, 173],
[65, 159, 173],
[66, 160, 173],
[67, 160, 173],
[68, 161, 174],
[70, 162, 174],
[71, 163, 174],
[72, 164, 174],
[73, 165, 174],
[75, 166, 175],
[76, 167, 175],
[78, 168, 175],
[79, 169, 175],
[81, 169, 175],
[82, 170, 176],
[84, 171, 176],
[86, 172, 176],
[87, 173, 176],
[89, 174, 176],
[91, 174, 177],
[93, 175, 177],
[94, 176, 177],
[96, 177, 177],
[98, 178, 178],
[100, 178, 178],
[102, 179, 178],
[104, 180, 178],
[106, 181, 179],
[108, 181, 179],
[110, 182, 179],
[112, 183, 180],
[114, 184, 180],
[116, 184, 181],
[118, 185, 181],
[120, 186, 181],
[122, 186, 182],
[124, 187, 182],
[126, 188, 183],
[128, 189, 183],
[130, 189, 184],
[132, 190, 184],
[134, 191, 185],
[136, 191, 185],
[138, 192, 186],
[139, 193, 186],
[141, 194, 187],
[143, 194, 187],
[145, 195, 188],
[147, 196, 189],
[149, 196, 189],
[151, 197, 190],
[152, 198, 190],
[154, 199, 191],
[156, 199, 192],
[158, 200, 192],
[160, 201, 193],
[161, 202, 194],
[163, 202, 194],
[165, 203, 195],
[167, 204, 196],
[168, 205, 196],
[170, 205, 197],
[172, 206, 198],
[173, 207, 198],
[175, 208, 199],
[177, 208, 200],
[179, 209, 200],
[180, 210, 201],
[182, 211, 202],
[184, 212, 202],
[185, 212, 203],
[187, 213, 204],
[189, 214, 205],
[190, 215, 205],
[192, 216, 206],
[193, 216, 207],
[195, 217, 208],
[197, 218, 208],
[198, 219, 209],
[200, 220, 210],
[202, 221, 211],
[203, 221, 211],
[205, 222, 212],
[206, 223, 213],
[208, 224, 213],
[209, 225, 214],
[211, 226, 215],
[213, 227, 216],
[214, 227, 216],
[216, 228, 217],
[217, 229, 218],
[219, 230, 218],
[220, 231, 219],
[222, 232, 220],
[223, 233, 220],
[225, 234, 221],
[227, 235, 222],
[228, 236, 222],
[230, 237, 223],
[231, 238, 224],
[233, 238, 224],
[234, 239, 225],
[236, 240, 225],
[238, 241, 226],
[239, 242, 226],
[241, 243, 227],
[242, 244, 227],
[244, 245, 228],
[246, 246, 228],
[247, 247, 229],
[249, 248, 229],
[251, 249, 229],
[252, 250, 230],
[254, 251, 230],
[255, 253, 205],
[254, 252, 203],
[254, 250, 201],
[253, 249, 199],
[252, 248, 197],
[252, 247, 194],
[251, 246, 192],
[250, 244, 190],
[249, 243, 188],
[249, 242, 186],
[248, 241, 184],
[247, 240, 182],
[247, 238, 180],
[246, 237, 177],
[246, 236, 175],
[245, 235, 173],
[244, 234, 171],
[243, 233, 169],
[243, 231, 167],
[242, 230, 165],
[241, 229, 162],
[241, 228, 160],
[240, 227, 158],
[239, 226, 156],
[239, 225, 154],
[238, 223, 152],
[237, 222, 150],
[237, 221, 147],
[236, 220, 145],
[235, 219, 143],
[234, 218, 141],
[234, 217, 139],
[233, 216, 137],
[232, 215, 134],
[231, 214, 132],
[231, 213, 130],
[230, 212, 128],
[229, 211, 126],
[228, 210, 123],
[227, 208, 121],
[227, 207, 119],
[226, 206, 117],
[225, 205, 115],
[224, 205, 113],
[223, 204, 110],
[222, 203, 108],
[221, 202, 106],
[220, 201, 104],
[219, 200, 102],
[218, 199, 100],
[217, 198, 97],
[216, 197, 95],
[215, 196, 93],
[214, 195, 91],
[213, 194, 89],
[212, 193, 87],
[211, 193, 85],
[210, 192, 83],
[209, 191, 81],
[208, 190, 79],
[206, 189, 76],
[205, 189, 74],
[204, 188, 72],
[203, 187, 70],
[201, 186, 69],
[200, 185, 67],
[199, 185, 65],
[197, 184, 63],
[196, 183, 61],
[195, 183, 59],
[193, 182, 57],
[192, 181, 55],
[190, 180, 54],
[189, 180, 52],
[187, 179, 50],
[186, 178, 48],
[184, 178, 47],
[183, 177, 45],
[181, 176, 43],
[180, 176, 42],
[178, 175, 40],
[177, 174, 39],
[175, 174, 37],
[173, 173, 35],
[172, 173, 34],
[170, 172, 32],
[169, 171, 31],
[167, 171, 30],
[165, 170, 28],
[164, 169, 27],
[162, 169, 25],
[160, 168, 24],
[159, 168, 23],
[157, 167, 21],
[155, 166, 20],
[154, 166, 19],
[152, 165, 18],
[150, 165, 16],
[149, 164, 15],
[147, 163, 14],
[145, 163, 13],
[143, 162, 12],
[142, 162, 11],
[140, 161, 10],
[138, 160, 9],
[136, 160, 8],
[135, 159, 8],
[133, 159, 7],
[131, 158, 7],
[129, 157, 6],
[128, 157, 6],
[126, 156, 6],
[124, 156, 6],
[122, 155, 6],
[121, 154, 6],
[119, 154, 6],
[117, 153, 6],
[115, 153, 6],
[113, 152, 6],
[112, 151, 7],
[110, 151, 7],
[108, 150, 7],
[106, 149, 8],
[104, 149, 9],
[102, 148, 9],
[101, 148, 10],
[99, 147, 11],
[97, 146, 11],
[95, 146, 12],
[93, 145, 13],
[92, 144, 14],
[90, 144, 15],
[88, 143, 15],
[86, 142, 16],
[84, 142, 17],
[82, 141, 18],
[81, 140, 18],
[79, 140, 19],
[77, 139, 20],
[75, 138, 21],
[73, 138, 22],
[72, 137, 22],
[70, 136, 23],
[68, 136, 24],
[66, 135, 25],
[64, 134, 25],
[63, 133, 26],
[61, 133, 27],
[59, 132, 28],
[57, 131, 28],
[56, 131, 29],
[54, 130, 30],
[52, 129, 30],
[50, 128, 31],
[49, 127, 32],
[47, 127, 32],
[45, 126, 33],
[44, 125, 33],
[42, 124, 34],
[40, 124, 35],
[39, 123, 35],
[37, 122, 36],
[36, 121, 36],
[34, 120, 37],
[33, 120, 37],
[31, 119, 38],
[30, 118, 38],
[28, 117, 39],
[27, 116, 39],
[26, 115, 39],
[24, 115, 40],
[23, 114, 40],
[22, 113, 41],
[21, 112, 41],
[19, 111, 41],
[18, 110, 42],
[17, 109, 42],
[16, 108, 42],
[15, 108, 43],
[15, 107, 43],
[14, 106, 43],
[13, 105, 43],
[13, 104, 43],
[12, 103, 44],
[12, 102, 44],
[11, 101, 44],
[11, 100, 44],
[11, 99, 44],
[11, 99, 44],
[11, 98, 45],
[11, 97, 45],
[11, 96, 45],
[11, 95, 45],
[11, 94, 45],
[12, 93, 45],
[12, 92, 45],
[12, 91, 45],
[13, 90, 45],
[13, 89, 45],
[14, 88, 45],
[14, 87, 45],
[15, 86, 44],
[15, 85, 44],
[16, 84, 44],
[16, 84, 44],
[16, 83, 44],
[17, 82, 44],
[17, 81, 44],
[18, 80, 43],
[18, 79, 43],
[19, 78, 43],
[19, 77, 43],
[20, 76, 42],
[20, 75, 42],
[20, 74, 42],
[21, 73, 42],
[21, 72, 41],
[22, 71, 41],
[22, 70, 41],
[22, 69, 40],
[23, 68, 40],
[23, 67, 39],
[23, 66, 39],
[23, 65, 39],
[24, 64, 38],
[24, 63, 38],
[24, 63, 37],
[24, 62, 37],
[25, 61, 36],
[25, 60, 36],
[25, 59, 35],
[25, 58, 35],
[25, 57, 34],
[25, 56, 34],
[25, 55, 33],
[25, 54, 33],
[25, 53, 32],
[25, 52, 31],
[25, 51, 31],
[25, 50, 30],
[25, 49, 30],
[25, 48, 29],
[25, 47, 28],
[25, 46, 28],
[25, 45, 27],
[25, 44, 26],
[25, 44, 25],
[25, 43, 25],
[25, 42, 24],
[24, 41, 23],
[24, 40, 23],
[24, 39, 22],
[24, 38, 21],
[24, 37, 20],
[23, 36, 19],
[23, 35, 19],
]
_DENSE = [
[230, 241, 241],
[228, 240, 240],
[227, 239, 239],
[225, 238, 239],
[223, 237, 238],
[221, 237, 237],
[220, 236, 237],
[218, 235, 236],
[216, 234, 236],
[215, 233, 235],
[213, 233, 235],
[211, 232, 234],
[209, 231, 234],
[208, 230, 233],
[206, 229, 233],
[204, 228, 232],
[203, 228, 232],
[201, 227, 232],
[199, 226, 231],
[198, 225, 231],
[196, 224, 230],
[194, 223, 230],
[193, 223, 230],
[191, 222, 230],
[190, 221, 229],
[188, 220, 229],
[186, 219, 229],
[185, 218, 228],
[183, 218, 228],
[182, 217, 228],
[180, 216, 228],
[178, 215, 228],
[177, 214, 227],
[175, 213, 227],
[174, 212, 227],
[172, 212, 227],
[171, 211, 227],
[169, 210, 227],
[168, 209, 227],
[166, 208, 227],
[165, 207, 226],
[163, 206, 226],
[162, 206, 226],
[160, 205, 226],
[159, 204, 226],
[158, 203, 226],
[156, 202, 226],
[155, 201, 226],
[154, 200, 226],
[152, 199, 226],
[151, 198, 226],
[150, 197, 226],
[148, 197, 226],
[147, 196, 226],
[146, 195, 226],
[144, 194, 226],
[143, 193, 226],
[142, 192, 226],
[141, 191, 226],
[140, 190, 226],
[138, 189, 227],
[137, 188, 227],
[136, 187, 227],
[135, 186, 227],
[134, 185, 227],
[133, 184, 227],
[132, 183, 227],
[131, 182, 227],
[130, 181, 227],
[129, 180, 227],
[128, 179, 227],
[127, 178, 227],
[127, 177, 228],
[126, 176, 228],
[125, 175, 228],
[124, 174, 228],
[123, 173, 228],
[123, 172, 228],
[122, 171, 228],
[121, 170, 228],
[121, 169, 228],
[120, 168, 228],
[120, 167, 228],
[119, 166, 228],
[119, 165, 228],
[118, 164, 229],
[118, 163, 229],
[117, 161, 229],
[117, 160, 229],
[117, 159, 229],
[117, 158, 229],
[116, 157, 229],
[116, 156, 228],
[116, 155, 228],
[116, 154, 228],
[116, 152, 228],
[115, 151, 228],
[115, 150, 228],
[115, 149, 228],
[115, 148, 228],
[115, 147, 227],
[115, 145, 227],
[115, 144, 227],
[115, 143, 227],
[115, 142, 226],
[116, 141, 226],
[116, 139, 226],
[116, 138, 226],
[116, 137, 225],
[116, 136, 225],
[116, 135, 224],
[116, 133, 224],
[117, 132, 223],
[117, 131, 223],
[117, 130, 222],
[117, 129, 222],
[117, 127, 221],
[117, 126, 221],
[118, 125, 220],
[118, 124, 220],
[118, 123, 219],
[118, 121, 218],
[118, 120, 218],
[119, 119, 217],
[119, 118, 216],
[119, 117, 215],
[119, 115, 215],
[119, 114, 214],
[120, 113, 213],
[120, 112, 212],
[120, 111, 211],
[120, 110, 210],
[120, 108, 210],
[120, 107, 209],
[120, 106, 208],
[121, 105, 207],
[121, 104, 206],
[121, 102, 205],
[121, 101, 204],
[121, 100, 203],
[121, 99, 202],
[121, 98, 201],
[121, 97, 200],
[121, 96, 199],
[121, 94, 197],
[121, 93, 196],
[121, 92, 195],
[121, 91, 194],
[121, 90, 193],
[121, 89, 192],
[121, 88, 191],
[121, 87, 189],
[121, 86, 188],
[121, 84, 187],
[121, 83, 186],
[121, 82, 184],
[121, 81, 183],
[121, 80, 182],
[121, 79, 181],
[120, 78, 179],
[120, 77, 178],
[120, 76, 177],
[120, 75, 175],
[120, 74, 174],
[120, 73, 173],
[119, 72, 171],
[119, 71, 170],
[119, 70, 169],
[119, 69, 167],
[119, 67, 166],
[118, 66, 165],
[118, 65, 163],
[118, 64, 162],
[118, 63, 160],
[117, 62, 159],
[117, 61, 157],
[117, 60, 156],
[116, 59, 155],
[116, 59, 153],
[116, 58, 152],
[115, 57, 150],
[115, 56, 149],
[115, 55, 147],
[114, 54, 146],
[114, 53, 144],
[114, 52, 143],
[113, 51, 141],
[113, 50, 140],
[112, 49, 138],
[112, 48, 136],
[111, 47, 135],
[111, 46, 133],
[110, 45, 132],
[110, 45, 130],
[109, 44, 129],
[109, 43, 127],
[108, 42, 126],
[108, 41, 124],
[107, 40, 122],
[107, 40, 121],
[106, 39, 119],
[106, 38, 117],
[105, 37, 116],
[104, 36, 114],
[104, 36, 113],
[103, 35, 111],
[103, 34, 109],
[102, 33, 108],
[101, 33, 106],
[101, 32, 104],
[100, 31, 103],
[99, 31, 101],
[98, 30, 99],
[98, 29, 98],
[97, 29, 96],
[96, 28, 94],
[95, 27, 93],
[95, 27, 91],
[94, 26, 89],
[93, 26, 88],
[92, 25, 86],
[91, 25, 84],
[90, 24, 83],
[90, 24, 81],
[89, 23, 80],
[88, 23, 78],
[87, 22, 76],
[86, 22, 75],
[85, 22, 73],
[84, 21, 72],
[83, 21, 70],
[82, 21, 68],
[81, 20, 67],
[80, 20, 65],
[79, 20, 64],
[78, 19, 62],
[77, 19, 61],
[75, 19, 59],
[74, 19, 58],
[73, 18, 56],
[72, 18, 55],
[71, 18, 54],
[70, 18, 52],
[69, 17, 51],
[68, 17, 50],
[66, 17, 48],
[65, 17, 47],
[64, 16, 46],
[63, 16, 45],
[62, 16, 43],
[60, 16, 42],
[59, 15, 41],
[58, 15, 40],
[57, 15, 39],
[56, 15, 37],
[54, 14, 36],
]
_GRAY = [
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[1, 1, 1],
[1, 1, 1],
[2, 2, 2],
[2, 2, 2],
[3, 3, 3],
[3, 3, 3],
[4, 4, 4],
[5, 5, 5],
[5, 5, 5],
[6, 6, 6],
[7, 7, 7],
[8, 8, 8],
[9, 9, 9],
[10, 10, 10],
[11, 11, 11],
[12, 12, 12],
[13, 13, 13],
[15, 14, 14],
[16, 15, 15],
[17, 16, 16],
[18, 17, 17],
[19, 18, 18],
[20, 19, 19],
[21, 20, 20],
[22, 21, 21],
[23, 22, 22],
[24, 23, 23],
[25, 24, 24],
[26, 25, 25],
[27, 26, 26],
[28, 27, 27],
[29, 28, 28],
[29, 29, 29],
[30, 30, 30],
[31, 31, 31],
[32, 32, 32],
[33, 33, 33],
[34, 34, 34],
[35, 35, 35],
[36, 36, 36],
[37, 37, 36],
[38, 38, 37],
[39, 38, 38],
[40, 39, 39],
[41, 40, 40],
[42, 41, 41],
[43, 42, 42],
[43, 43, 43],
[44, 44, 44],
[45, 45, 45],
[46, 46, 46],
[47, 47, 46],
[48, 48, 47],
[49, 48, 48],
[50, 49, 49],
[51, 50, 50],
[52, 51, 51],
[53, 52, 52],
[53, 53, 53],
[54, 54, 54],
[55, 55, 55],
[56, 56, 55],
[57, 57, 56],
[58, 57, 57],
[59, 58, 58],
[60, 59, 59],
[61, 60, 60],
[62, 61, 61],
[62, 62, 62],
[63, 63, 63],
[64, 64, 64],
[65, 65, 64],
[66, 66, 65],
[67, 66, 66],
[68, 67, 67],
[69, 68, 68],
[70, 69, 69],
[71, 70, 70],
[71, 71, 71],
[72, 72, 72],
[73, 73, 72],
[74, 74, 73],
[75, 75, 74],
[76, 75, 75],
[77, 76, 76],
[78, 77, 77],
[79, 78, 78],
[80, 79, 79],
[80, 80, 80],
[81, 81, 80],
[82, 82, 81],
[83, 83, 82],
[84, 84, 83],
[85, 84, 84],
[86, 85, 85],
[87, 86, 86],
[88, 87, 87],
[89, 88, 88],
[90, 89, 89],
[90, 90, 90],
[91, 91, 90],
[92, 92, 91],
[93, 93, 92],
[94, 94, 93],
[95, 94, 94],
[96, 95, 95],
[97, 96, 96],
[98, 97, 97],
[99, 98, 98],
[100, 99, 99],
[101, 100, 100],
[101, 101, 100],
[102, 102, 101],
[103, 103, 102],
[104, 104, 103],
[105, 105, 104],
[106, 106, 105],
[107, 107, 106],
[108, 107, 107],
[109, 108, 108],
[110, 109, 109],
[111, 110, 110],
[112, 111, 111],
[113, 112, 112],
[114, 113, 113],
[115, 114, 113],
[115, 115, 114],
[116, 116, 115],
[117, 117, 116],
[118, 118, 117],
[119, 119, 118],
[120, 120, 119],
[121, 121, 120],
[122, 122, 121],
[123, 123, 122],
[124, 124, 123],
[125, 125, 124],
[126, 125, 125],
[127, 126, 126],
[128, 127, 127],
[129, 128, 128],
[130, 129, 129],
[131, 130, 130],
[132, 131, 131],
[133, 132, 132],
[134, 133, 133],
[135, 134, 134],
[136, 135, 135],
[137, 136, 136],
[138, 137, 137],
[139, 138, 138],
[140, 139, 139],
[141, 140, 140],
[142, 141, 141],
[143, 142, 142],
[144, 143, 143],
[145, 144, 144],
[146, 145, 145],
[147, 146, 146],
[148, 147, 147],
[149, 148, 148],
[150, 149, 149],
[151, 150, 150],
[152, 151, 151],
[153, 152, 152],
[154, 153, 153],
[155, 154, 154],
[156, 155, 155],
[157, 157, 156],
[158, 158, 157],
[159, 159, 158],
[160, 160, 159],
[161, 161, 160],
[162, 162, 161],
[163, 163, 162],
[164, 164, 163],
[165, 165, 164],
[166, 166, 165],
[167, 167, 166],
[168, 168, 167],
[170, 169, 168],
[171, 170, 169],
[172, 171, 170],
[173, 172, 172],
[174, 173, 173],
[175, 175, 174],
[176, 176, 175],
[177, 177, 176],
[178, 178, 177],
[179, 179, 178],
[180, 180, 179],
[182, 181, 180],
[183, 182, 181],
[184, 183, 182],
[185, 185, 184],
[186, 186, 185],
[187, 187, 186],
[188, 188, 187],
[189, 189, 188],
[190, 190, 189],
[192, 191, 190],
[193, 192, 191],
[194, 194, 193],
[195, 195, 194],
[196, 196, 195],
[197, 197, 196],
[198, 198, 197],
[200, 199, 198],
[201, 200, 199],
[202, 202, 201],
[203, 203, 202],
[204, 204, 203],
[205, 205, 204],
[207, 206, 205],
[208, 208, 206],
[209, 209, 208],
[210, 210, 209],
[211, 211, 210],
[213, 212, 211],
[214, 214, 212],
[215, 215, 214],
[216, 216, 215],
[217, 217, 216],
[219, 218, 217],
[220, 220, 219],
[221, 221, 220],
[222, 222, 221],
[224, 223, 222],
[225, 225, 223],
[226, 226, 225],
[227, 227, 226],
[229, 228, 227],
[230, 230, 228],
[231, 231, 230],
[232, 232, 231],
[234, 234, 232],
[235, 235, 234],
[236, 236, 235],
[238, 237, 236],
[239, 239, 237],
[240, 240, 239],
[242, 241, 240],
[243, 243, 241],
[244, 244, 243],
[245, 245, 244],
[247, 247, 245],
[248, 248, 247],
[249, 249, 248],
[251, 251, 249],
[252, 252, 251],
[254, 253, 252],
[255, 255, 253],
]
_HALINE = [
[42, 24, 108],
[42, 25, 110],
[42, 25, 113],
[43, 25, 115],
[43, 25, 117],
[44, 26, 120],
[44, 26, 122],
[45, 26, 125],
[45, 26, 127],
[45, 27, 130],
[46, 27, 132],
[46, 27, 135],
[46, 28, 137],
[46, 28, 140],
[46, 28, 142],
[46, 29, 145],
[46, 29, 147],
[46, 30, 149],
[46, 30, 152],
[46, 31, 154],
[45, 32, 156],
[45, 33, 157],
[44, 34, 159],
[43, 36, 160],
[42, 37, 161],
[41, 39, 162],
[40, 41, 163],
[38, 43, 163],
[37, 45, 163],
[36, 46, 163],
[34, 48, 163],
[33, 50, 162],
[32, 52, 162],
[30, 53, 161],
[29, 55, 161],
[28, 57, 160],
[27, 58, 160],
[25, 60, 159],
[24, 61, 158],
[23, 63, 158],
[22, 64, 157],
[21, 65, 156],
[20, 67, 156],
[19, 68, 155],
[18, 69, 155],
[17, 71, 154],
[16, 72, 153],
[15, 73, 153],
[15, 74, 152],
[14, 76, 151],
[13, 77, 151],
[13, 78, 150],
[13, 79, 150],
[12, 80, 149],
[12, 81, 149],
[12, 82, 148],
[12, 83, 148],
[12, 84, 147],
[13, 85, 147],
[13, 86, 146],
[13, 87, 146],
[14, 88, 145],
[14, 89, 145],
[15, 90, 145],
[15, 91, 144],
[16, 92, 144],
[17, 93, 143],
[17, 94, 143],
[18, 95, 143],
[19, 96, 142],
[20, 97, 142],
[20, 98, 142],
[21, 99, 142],
[22, 99, 141],
[23, 100, 141],
[24, 101, 141],
[24, 102, 140],
[25, 103, 140],
[26, 104, 140],
[27, 105, 140],
[28, 106, 140],
[29, 107, 139],
[29, 107, 139],
[30, 108, 139],
[31, 109, 139],
[32, 110, 139],
[33, 111, 139],
[34, 112, 138],
[34, 113, 138],
[35, 113, 138],
[36, 114, 138],
[37, 115, 138],
[38, 116, 138],
[38, 117, 138],
[39, 118, 138],
[40, 118, 137],
[41, 119, 137],
[41, 120, 137],
[42, 121, 137],
[43, 122, 137],
[43, 123, 137],
[44, 124, 137],
[45, 124, 137],
[45, 125, 137],
[46, 126, 137],
[47, 127, 137],
[47, 128, 137],
[48, 129, 137],
[49, 130, 137],
[49, 130, 136],
[50, 131, 136],
[51, 132, 136],
[51, 133, 136],
[52, 134, 136],
[52, 135, 136],
[53, 136, 136],
[53, 137, 136],
[54, 137, 136],
[55, 138, 136],
[55, 139, 136],
[56, 140, 136],
[56, 141, 136],
[57, 142, 136],
[57, 143, 136],
[58, 144, 135],
[58, 144, 135],
[59, 145, 135],
[59, 146, 135],
[60, 147, 135],
[60, 148, 135],
[61, 149, 135],
[61, 150, 135],
[62, 151, 135],
[62, 152, 134],
[63, 153, 134],
[63, 153, 134],
[64, 154, 134],
[65, 155, 134],
[65, 156, 133],
[66, 157, 133],
[66, 158, 133],
[67, 159, 133],
[67, 160, 132],
[68, 161, 132],
[68, 162, 132],
[69, 163, 132],
[70, 164, 131],
[70, 164, 131],
[71, 165, 131],
[72, 166, 130],
[72, 167, 130],
[73, 168, 130],
[74, 169, 129],
[74, 170, 129],
[75, 171, 129],
[76, 172, 128],
[76, 173, 128],
[77, 174, 127],
[78, 174, 127],
[79, 175, 126],
[80, 176, 126],
[81, 177, 125],
[81, 178, 125],
[82, 179, 124],
[83, 180, 124],
[84, 181, 123],
[85, 182, 123],
[86, 183, 122],
[87, 184, 121],
[88, 184, 121],
[90, 185, 120],
[91, 186, 119],
[92, 187, 119],
[93, 188, 118],
[94, 189, 117],
[95, 190, 117],
[97, 191, 116],
[98, 191, 115],
[99, 192, 114],
[101, 193, 114],
[102, 194, 113],
[104, 195, 112],
[105, 196, 111],
[107, 196, 110],
[108, 197, 110],
[110, 198, 109],
[112, 199, 108],
[113, 200, 107],
[115, 200, 106],
[117, 201, 105],
[119, 202, 104],
[120, 203, 104],
[122, 203, 103],
[124, 204, 102],
[126, 205, 101],
[128, 206, 100],
[130, 206, 99],
[132, 207, 98],
[134, 208, 98],
[137, 208, 97],
[139, 209, 96],
[141, 210, 95],
[143, 210, 95],
[146, 211, 94],
[148, 211, 93],
[151, 212, 93],
[153, 212, 93],
[155, 213, 92],
[158, 214, 92],
[160, 214, 92],
[163, 215, 92],
[165, 215, 92],
[168, 216, 92],
[170, 216, 92],
[173, 216, 92],
[175, 217, 93],
[177, 217, 93],
[180, 218, 94],
[182, 218, 95],
[184, 219, 96],
[187, 219, 97],
[189, 220, 98],
[191, 220, 99],
[193, 221, 100],
[196, 221, 101],
[198, 222, 102],
[200, 222, 103],
[202, 223, 105],
[204, 223, 106],
[206, 224, 108],
[208, 224, 109],
[210, 225, 111],
[212, 225, 112],
[214, 226, 114],
[216, 226, 115],
[218, 227, 117],
[220, 227, 119],
[222, 228, 121],
[224, 229, 122],
[225, 229, 124],
[227, 230, 126],
[229, 230, 128],
[231, 231, 129],
[233, 231, 131],
[235, 232, 133],
[236, 233, 135],
[238, 233, 137],
[240, 234, 138],
[242, 234, 140],
[243, 235, 142],
[245, 236, 144],
[247, 236, 146],
[248, 237, 148],
[250, 238, 150],
[252, 238, 152],
[253, 239, 154],
]
_ICE = [
[4, 6, 19],
[5, 6, 20],
[5, 7, 21],
[6, 8, 23],
[7, 9, 24],
[8, 10, 26],
[9, 11, 27],
[10, 12, 29],
[11, 13, 30],
[12, 13, 31],
[13, 14, 33],
[14, 15, 34],
[15, 16, 36],
[16, 17, 37],
[17, 18, 39],
[18, 19, 40],
[19, 19, 42],
[20, 20, 43],
[21, 21, 44],
[22, 22, 46],
[23, 23, 47],
[23, 24, 49],
[24, 24, 50],
[25, 25, 52],
[26, 26, 53],
[27, 27, 55],
[28, 28, 56],
[29, 28, 58],
[30, 29, 59],
[31, 30, 61],
[31, 31, 62],
[32, 31, 64],
[33, 32, 65],
[34, 33, 67],
[35, 34, 68],
[36, 34, 70],
[37, 35, 71],
[37, 36, 73],
[38, 37, 74],
[39, 37, 76],
[40, 38, 78],
[41, 39, 79],
[41, 40, 81],
[42, 40, 82],
[43, 41, 84],
[44, 42, 85],
[44, 43, 87],
[45, 43, 89],
[46, 44, 90],
[47, 45, 92],
[47, 46, 94],
[48, 47, 95],
[49, 47, 97],
[49, 48, 98],
[50, 49, 100],
[51, 50, 102],
[51, 50, 103],
[52, 51, 105],
[53, 52, 107],
[53, 53, 108],
[54, 53, 110],
[54, 54, 112],
[55, 55, 113],
[56, 56, 115],
[56, 57, 117],
[57, 57, 118],
[57, 58, 120],
[58, 59, 122],
[58, 60, 123],
[58, 61, 125],
[59, 62, 127],
[59, 62, 128],
[60, 63, 130],
[60, 64, 132],
[60, 65, 133],
[61, 66, 135],
[61, 67, 137],
[61, 68, 138],
[62, 69, 140],
[62, 70, 141],
[62, 71, 143],
[62, 72, 144],
[62, 73, 146],
[62, 73, 147],
[63, 74, 149],
[63, 75, 150],
[63, 76, 151],
[63, 78, 153],
[63, 79, 154],
[63, 80, 155],
[63, 81, 157],
[63, 82, 158],
[63, 83, 159],
[63, 84, 160],
[63, 85, 161],
[63, 86, 162],
[63, 87, 163],
[63, 88, 164],
[63, 89, 165],
[62, 90, 166],
[62, 92, 167],
[62, 93, 168],
[62, 94, 169],
[62, 95, 170],
[62, 96, 171],
[62, 97, 171],
[62, 98, 172],
[62, 99, 173],
[62, 101, 173],
[62, 102, 174],
[62, 103, 175],
[62, 104, 175],
[62, 105, 176],
[62, 106, 176],
[63, 107, 177],
[63, 108, 178],
[63, 110, 178],
[63, 111, 179],
[63, 112, 179],
[63, 113, 180],
[64, 114, 180],
[64, 115, 180],
[64, 116, 181],
[64, 117, 181],
[65, 118, 182],
[65, 120, 182],
[66, 121, 183],
[66, 122, 183],
[66, 123, 183],
[67, 124, 184],
[67, 125, 184],
[68, 126, 185],
[68, 127, 185],
[69, 128, 185],
[69, 129, 186],
[70, 130, 186],
[70, 132, 187],
[71, 133, 187],
[71, 134, 187],
[72, 135, 188],
[73, 136, 188],
[73, 137, 188],
[74, 138, 189],
[75, 139, 189],
[75, 140, 189],
[76, 141, 190],
[77, 142, 190],
[78, 143, 191],
[78, 144, 191],
[79, 145, 191],
[80, 146, 192],
[81, 148, 192],
[81, 149, 192],
[82, 150, 193],
[83, 151, 193],
[84, 152, 194],
[85, 153, 194],
[85, 154, 194],
[86, 155, 195],
[87, 156, 195],
[88, 157, 195],
[89, 158, 196],
[90, 159, 196],
[91, 160, 197],
[92, 161, 197],
[93, 162, 197],
[94, 163, 198],
[95, 164, 198],
[95, 166, 199],
[96, 167, 199],
[97, 168, 199],
[98, 169, 200],
[99, 170, 200],
[100, 171, 201],
[101, 172, 201],
[103, 173, 201],
[104, 174, 202],
[105, 175, 202],
[106, 176, 203],
[107, 177, 203],
[108, 178, 203],
[109, 179, 204],
[110, 180, 204],
[111, 181, 205],
[113, 182, 205],
[114, 184, 206],
[115, 185, 206],
[116, 186, 206],
[117, 187, 207],
[119, 188, 207],
[120, 189, 208],
[121, 190, 208],
[123, 191, 208],
[124, 192, 209],
[125, 193, 209],
[127, 194, 210],
[128, 195, 210],
[130, 196, 211],
[131, 197, 211],
[133, 198, 211],
[134, 199, 212],
[136, 200, 212],
[137, 201, 213],
[139, 202, 213],
[140, 203, 214],
[142, 204, 214],
[144, 205, 215],
[146, 206, 215],
[147, 207, 216],
[149, 208, 216],
[151, 209, 217],
[153, 210, 217],
[154, 211, 218],
[156, 212, 218],
[158, 213, 219],
[160, 214, 220],
[162, 214, 220],
[164, 215, 221],
[166, 216, 222],
[168, 217, 222],
[169, 218, 223],
[171, 219, 224],
[173, 220, 224],
[175, 221, 225],
[177, 222, 226],
[179, 223, 227],
[181, 224, 227],
[183, 225, 228],
[185, 226, 229],
[186, 227, 230],
[188, 228, 231],
[190, 229, 231],
[192, 230, 232],
[194, 230, 233],
[196, 231, 234],
[198, 232, 235],
[200, 233, 236],
[201, 234, 237],
[203, 235, 238],
[205, 236, 239],
[207, 237, 239],
[209, 238, 240],
[211, 239, 241],
[213, 240, 242],
[214, 241, 243],
[216, 242, 244],
[218, 243, 245],
[220, 244, 246],
[222, 245, 247],
[224, 246, 248],
[225, 247, 249],
[227, 249, 250],
[229, 250, 251],
[231, 251, 251],
[232, 252, 252],
[234, 253, 253],
]
_MATTER = [
[254, 237, 176],
[253, 236, 175],
[253, 234, 173],
[253, 233, 172],
[253, 232, 171],
[253, 230, 169],
[253, 229, 168],
[253, 227, 167],
[253, 226, 165],
[252, 225, 164],
[252, 223, 163],
[252, 222, 161],
[252, 220, 160],
[252, 219, 159],
[252, 218, 157],
[252, 216, 156],
[251, 215, 155],
[251, 213, 154],
[251, 212, 152],
[251, 211, 151],
[251, 209, 150],
[251, 208, 148],
[251, 207, 147],
[250, 205, 146],
[250, 204, 145],
[250, 202, 144],
[250, 201, 142],
[250, 200, 141],
[250, 198, 140],
[250, 197, 139],
[249, 195, 138],
[249, 194, 136],
[249, 193, 135],
[249, 191, 134],
[249, 190, 133],
[249, 189, 132],
[248, 187, 131],
[248, 186, 130],
[248, 184, 128],
[248, 183, 127],
[248, 182, 126],
[247, 180, 125],
[247, 179, 124],
[247, 178, 123],
[247, 176, 122],
[247, 175, 121],
[246, 174, 120],
[246, 172, 119],
[246, 171, 118],
[246, 169, 117],
[245, 168, 116],
[245, 167, 115],
[245, 165, 114],
[245, 164, 113],
[245, 163, 112],
[244, 161, 111],
[244, 160, 110],
[244, 159, 109],
[244, 157, 108],
[243, 156, 107],
[243, 154, 106],
[243, 153, 105],
[242, 152, 104],
[242, 150, 104],
[242, 149, 103],
[242, 148, 102],
[241, 146, 101],
[241, 145, 100],
[241, 143, 99],
[240, 142, 99],
[240, 141, 98],
[240, 139, 97],
[239, 138, 96],
[239, 137, 96],
[239, 135, 95],
[238, 134, 94],
[238, 133, 94],
[238, 131, 93],
[237, 130, 92],
[237, 129, 92],
[237, 127, 91],
[236, 126, 90],
[236, 124, 90],
[236, 123, 89],
[235, 122, 89],
[235, 120, 88],
[234, 119, 88],
[234, 118, 87],
[233, 116, 87],
[233, 115, 86],
[233, 114, 86],
[232, 112, 86],
[232, 111, 85],
[231, 110, 85],
[231, 108, 85],
[230, 107, 84],
[230, 106, 84],
[229, 104, 84],
[229, 103, 84],
[228, 102, 83],
[227, 100, 83],
[227, 99, 83],
[226, 98, 83],
[226, 96, 83],
[225, 95, 83],
[224, 94, 83],
[224, 93, 83],
[223, 91, 83],
[223, 90, 83],
[222, 89, 83],
[221, 88, 83],
[220, 86, 83],
[220, 85, 83],
[219, 84, 83],
[218, 83, 83],
[217, 81, 83],
[217, 80, 83],
[216, 79, 84],
[215, 78, 84],
[214, 77, 84],
[213, 76, 84],
[213, 75, 84],
[212, 74, 85],
[211, 72, 85],
[210, 71, 85],
[209, 70, 86],
[208, 69, 86],
[207, 68, 86],
[206, 67, 86],
[205, 66, 87],
[204, 65, 87],
[203, 64, 87],
[202, 63, 88],
[201, 62, 88],
[200, 61, 88],
[199, 61, 89],
[198, 60, 89],
[197, 59, 89],
[196, 58, 90],
[195, 57, 90],
[194, 56, 90],
[193, 55, 91],
[192, 54, 91],
[191, 54, 91],
[190, 53, 92],
[189, 52, 92],
[187, 51, 92],
[186, 50, 93],
[185, 50, 93],
[184, 49, 93],
[183, 48, 94],
[182, 47, 94],
[181, 47, 94],
[179, 46, 95],
[178, 45, 95],
[177, 45, 95],
[176, 44, 95],
[175, 43, 96],
[174, 43, 96],
[172, 42, 96],
[171, 41, 96],
[170, 41, 97],
[169, 40, 97],
[167, 40, 97],
[166, 39, 97],
[165, 38, 98],
[164, 38, 98],
[163, 37, 98],
[161, 37, 98],
[160, 36, 98],
[159, 36, 98],
[158, 35, 99],
[156, 35, 99],
[155, 34, 99],
[154, 34, 99],
[153, 34, 99],
[151, 33, 99],
[150, 33, 99],
[149, 32, 99],
[147, 32, 99],
[146, 31, 99],
[145, 31, 99],
[144, 31, 99],
[142, 30, 99],
[141, 30, 99],
[140, 30, 99],
[138, 29, 99],
[137, 29, 99],
[136, 29, 99],
[134, 29, 99],
[133, 28, 99],
[132, 28, 99],
[130, 28, 99],
[129, 28, 99],
[128, 27, 98],
[126, 27, 98],
[125, 27, 98],
[124, 27, 98],
[122, 27, 98],
[121, 26, 97],
[120, 26, 97],
[118, 26, 97],
[117, 26, 97],
[116, 26, 96],
[114, 26, 96],
[113, 25, 96],
[112, 25, 95],
[110, 25, 95],
[109, 25, 94],
[107, 25, 94],
[106, 25, 94],
[105, 25, 93],
[103, 25, 93],
[102, 24, 92],
[101, 24, 92],
[99, 24, 91],
[98, 24, 91],
[97, 24, 90],
[95, 24, 90],
[94, 24, 89],
[93, 23, 88],
[91, 23, 88],
[90, 23, 87],
[89, 23, 87],
[87, 23, 86],
[86, 23, 85],
[85, 23, 85],
[83, 22, 84],
[82, 22, 83],
[81, 22, 83],
[79, 22, 82],
[78, 22, 81],
[77, 21, 81],
[75, 21, 80],
[74, 21, 79],
[73, 21, 78],
[71, 21, 78],
[70, 20, 77],
[69, 20, 76],
[68, 20, 75],
[66, 20, 75],
[65, 19, 74],
[64, 19, 73],
[62, 19, 72],
[61, 19, 71],
[60, 18, 71],
[59, 18, 70],
[57, 18, 69],
[56, 17, 68],
[55, 17, 67],
[54, 17, 66],
[52, 17, 65],
[51, 16, 65],
[50, 16, 64],
[48, 15, 63],
[47, 15, 62],
]
_OXY = [
[64, 5, 5],
[65, 5, 5],
[67, 6, 6],
[68, 6, 6],
[71, 6, 7],
[72, 6, 7],
[73, 6, 7],
[75, 6, 8],
[77, 7, 8],
[79, 7, 9],
[80, 7, 9],
[81, 7, 9],
[84, 7, 10],
[85, 7, 11],
[87, 7, 11],
[88, 7, 11],
[91, 7, 12],
[92, 7, 12],
[93, 7, 12],
[95, 7, 13],
[98, 7, 13],
[99, 7, 14],
[100, 7, 14],
[102, 7, 14],
[104, 7, 14],
[106, 6, 15],
[107, 6, 15],
[109, 6, 15],
[111, 6, 15],
[113, 6, 15],
[114, 6, 15],
[115, 5, 15],
[118, 5, 15],
[120, 5, 15],
[121, 5, 15],
[122, 5, 15],
[125, 5, 14],
[126, 5, 14],
[127, 6, 13],
[129, 6, 13],
[131, 8, 12],
[132, 9, 12],
[133, 10, 11],
[134, 12, 11],
[136, 14, 10],
[137, 16, 10],
[138, 17, 9],
[139, 19, 9],
[141, 21, 8],
[142, 23, 8],
[143, 24, 8],
[80, 79, 79],
[80, 80, 80],
[81, 81, 80],
[82, 81, 81],
[83, 83, 83],
[84, 84, 83],
[85, 84, 84],
[86, 85, 85],
[87, 87, 86],
[88, 87, 87],
[89, 88, 88],
[89, 89, 88],
[91, 90, 90],
[92, 91, 91],
[92, 92, 91],
[93, 93, 92],
[95, 94, 94],
[95, 95, 94],
[96, 96, 95],
[97, 96, 96],
[98, 98, 97],
[99, 99, 98],
[100, 99, 99],
[101, 100, 100],
[102, 102, 101],
[103, 102, 102],
[104, 103, 103],
[104, 104, 103],
[106, 105, 105],
[107, 106, 106],
[107, 107, 106],
[108, 108, 107],
[110, 109, 109],
[111, 110, 110],
[111, 111, 110],
[112, 112, 111],
[114, 113, 113],
[114, 114, 113],
[115, 115, 114],
[116, 115, 115],
[118, 117, 116],
[118, 118, 117],
[119, 119, 118],
[120, 119, 119],
[121, 121, 120],
[122, 122, 121],
[123, 123, 122],
[124, 123, 123],
[125, 125, 124],
[126, 126, 125],
[127, 127, 126],
[129, 128, 127],
[129, 129, 128],
[130, 130, 129],
[131, 131, 130],
[133, 132, 131],
[133, 133, 132],
[134, 134, 133],
[135, 135, 134],
[137, 136, 135],
[137, 137, 136],
[138, 138, 137],
[139, 139, 138],
[141, 140, 140],
[141, 141, 140],
[142, 142, 141],
[143, 143, 142],
[145, 144, 144],
[146, 145, 144],
[146, 146, 145],
[147, 147, 146],
[149, 149, 148],
[150, 149, 149],
[151, 150, 149],
[151, 151, 150],
[153, 153, 152],
[154, 154, 153],
[155, 154, 154],
[156, 155, 154],
[157, 157, 156],
[158, 158, 157],
[159, 159, 158],
[160, 160, 159],
[162, 161, 160],
[163, 162, 161],
[163, 163, 162],
[164, 164, 163],
[166, 166, 165],
[167, 166, 166],
[168, 167, 167],
[169, 168, 167],
[170, 170, 169],
[171, 171, 170],
[172, 172, 171],
[173, 173, 172],
[175, 174, 174],
[176, 175, 174],
[177, 176, 175],
[177, 177, 176],
[179, 179, 178],
[180, 180, 179],
[181, 181, 180],
[183, 183, 182],
[184, 183, 183],
[185, 184, 183],
[186, 185, 184],
[187, 187, 186],
[188, 188, 187],
[189, 189, 188],
[190, 190, 189],
[192, 192, 191],
[193, 193, 192],
[194, 194, 193],
[195, 195, 194],
[197, 197, 195],
[198, 197, 196],
[199, 198, 197],
[200, 199, 198],
[202, 201, 200],
[203, 202, 201],
[204, 203, 202],
[204, 204, 203],
[206, 206, 205],
[207, 207, 206],
[208, 208, 207],
[209, 209, 208],
[211, 211, 210],
[212, 212, 211],
[213, 213, 212],
[214, 214, 213],
[216, 216, 215],
[217, 217, 216],
[218, 218, 217],
[219, 219, 218],
[221, 221, 220],
[222, 222, 221],
[223, 223, 222],
[224, 224, 223],
[226, 226, 225],
[227, 227, 226],
[228, 228, 227],
[230, 229, 228],
[232, 231, 230],
[233, 232, 231],
[234, 233, 232],
[235, 235, 233],
[237, 237, 235],
[238, 238, 236],
[239, 239, 238],
[240, 240, 239],
[242, 242, 241],
[243, 243, 242],
[244, 244, 243],
[248, 254, 105],
[246, 253, 103],
[245, 252, 100],
[244, 252, 98],
[241, 250, 93],
[240, 249, 90],
[239, 248, 87],
[238, 247, 84],
[236, 245, 78],
[235, 243, 75],
[235, 242, 72],
[234, 241, 69],
[234, 238, 64],
[234, 237, 62],
[234, 235, 61],
[234, 234, 59],
[234, 231, 56],
[233, 230, 55],
[233, 228, 54],
[233, 227, 52],
[233, 224, 50],
[232, 223, 49],
[232, 221, 48],
[232, 220, 48],
[232, 217, 46],
[231, 216, 45],
[231, 215, 44],
[231, 213, 43],
[230, 211, 42],
[230, 209, 41],
[230, 208, 40],
[229, 207, 40],
[229, 204, 38],
[229, 203, 38],
[228, 201, 37],
[228, 200, 37],
[227, 198, 35],
[227, 196, 35],
[227, 195, 34],
[226, 194, 33],
[226, 191, 32],
[225, 190, 32],
[225, 189, 31],
[224, 187, 31],
[224, 185, 30],
[223, 184, 29],
[223, 182, 29],
[223, 181, 28],
[222, 179, 27],
[221, 177, 26],
[221, 176, 26],
[221, 175, 25],
]
_PHASE = [
[168, 120, 13],
[169, 119, 15],
[171, 118, 17],
[172, 117, 19],
[174, 116, 20],
[175, 115, 22],
[177, 114, 24],
[178, 113, 25],
[179, 112, 27],
[181, 111, 29],
[182, 110, 30],
[183, 109, 32],
[185, 108, 34],
[186, 107, 35],
[187, 106, 37],
[189, 105, 38],
[190, 104, 40],
[191, 103, 42],
[192, 102, 43],
[193, 101, 45],
[194, 100, 46],
[196, 98, 48],
[197, 97, 50],
[198, 96, 51],
[199, 95, 53],
[200, 94, 55],
[201, 93, 56],
[202, 92, 58],
[203, 90, 60],
[204, 89, 62],
[205, 88, 63],
[206, 87, 65],
[207, 86, 67],
[208, 84, 69],
[208, 83, 71],
[209, 82, 73],
[210, 81, 75],
[211, 79, 77],
[212, 78, 79],
[213, 77, 81],
[213, 75, 83],
[214, 74, 85],
[215, 73, 87],
[216, 71, 90],
[216, 70, 92],
[217, 69, 94],
[217, 67, 97],
[218, 66, 99],
[219, 64, 102],
[219, 63, 104],
[220, 61, 107],
[220, 60, 109],
[221, 58, 112],
[221, 57, 115],
[221, 56, 118],
[222, 54, 120],
[222, 53, 123],
[222, 51, 126],
[222, 50, 129],
[223, 49, 132],
[223, 47, 135],
[223, 46, 138],
[223, 45, 141],
[223, 43, 144],
[223, 42, 147],
[222, 41, 151],
[222, 40, 154],
[222, 40, 157],
[222, 39, 160],
[221, 38, 163],
[221, 38, 166],
[220, 37, 169],
[220, 37, 173],
[219, 37, 176],
[218, 37, 179],
[218, 38, 182],
[217, 38, 185],
[216, 39, 188],
[215, 39, 190],
[214, 40, 193],
[213, 41, 196],
[212, 42, 199],
[211, 43, 201],
[210, 45, 204],
[209, 46, 206],
[208, 47, 208],
[207, 49, 211],
[205, 50, 213],
[204, 52, 215],
[203, 53, 217],
[201, 55, 219],
[200, 57, 221],
[198, 58, 223],
[197, 60, 225],
[195, 62, 226],
[194, 63, 228],
[192, 65, 229],
[190, 67, 231],
[189, 69, 232],
[187, 70, 233],
[185, 72, 235],
[184, 74, 236],
[182, 75, 237],
[180, 77, 238],
[178, 79, 239],
[176, 80, 239],
[174, 82, 240],
[172, 84, 241],
[170, 85, 241],
[169, 87, 242],
[167, 89, 243],
[164, 90, 243],
[162, 92, 243],
[160, 93, 244],
[158, 95, 244],
[156, 96, 244],
[154, 98, 244],
[152, 99, 244],
[149, 101, 244],
[147, 102, 244],
[145, 104, 244],
[143, 105, 244],
[140, 107, 243],
[138, 108, 243],
[135, 109, 243],
[133, 111, 242],
[130, 112, 241],
[128, 113, 241],
[125, 115, 240],
[123, 116, 239],
[120, 117, 239],
[118, 119, 238],
[115, 120, 237],
[112, 121, 236],
[110, 122, 235],
[107, 123, 233],
[104, 124, 232],
[102, 126, 231],
[99, 127, 230],
[96, 128, 228],
[93, 129, 227],
[90, 130, 225],
[88, 131, 223],
[85, 132, 222],
[82, 133, 220],
[79, 134, 218],
[77, 135, 216],
[74, 135, 215],
[71, 136, 213],
[69, 137, 211],
[66, 138, 209],
[64, 138, 207],
[61, 139, 205],
[59, 140, 203],
[56, 140, 201],
[54, 141, 199],
[52, 142, 196],
[50, 142, 194],
[48, 143, 192],
[46, 143, 190],
[44, 144, 188],
[42, 144, 186],
[40, 145, 184],
[39, 145, 182],
[37, 145, 180],
[36, 146, 178],
[35, 146, 176],
[33, 146, 174],
[32, 147, 172],
[31, 147, 170],
[30, 147, 168],
[29, 148, 166],
[28, 148, 164],
[27, 148, 162],
[26, 148, 160],
[25, 149, 158],
[25, 149, 156],
[24, 149, 154],
[23, 149, 152],
[22, 150, 150],
[21, 150, 148],
[20, 150, 146],
[20, 150, 144],
[19, 151, 142],
[18, 151, 140],
[17, 151, 138],
[16, 151, 136],
[15, 151, 134],
[14, 152, 132],
[13, 152, 130],
[13, 152, 128],
[12, 152, 126],
[12, 152, 124],
[11, 153, 121],
[11, 153, 119],
[11, 153, 117],
[12, 153, 115],
[13, 153, 112],
[14, 153, 110],
[15, 154, 107],
[17, 154, 105],
[19, 154, 102],
[21, 154, 99],
[23, 154, 97],
[25, 154, 94],
[28, 154, 91],
[31, 154, 88],
[33, 154, 85],
[36, 154, 82],
[39, 154, 79],
[43, 154, 76],
[46, 154, 73],
[49, 153, 70],
[53, 153, 67],
[56, 153, 64],
[60, 153, 60],
[64, 152, 57],
[67, 152, 54],
[71, 151, 50],
[75, 151, 47],
[79, 150, 44],
[83, 150, 41],
[86, 149, 38],
[90, 148, 35],
[94, 148, 32],
[97, 147, 30],
[101, 146, 27],
[104, 145, 25],
[107, 144, 23],
[111, 144, 22],
[114, 143, 20],
[116, 142, 19],
[119, 141, 18],
[122, 140, 17],
[125, 139, 16],
[127, 139, 15],
[130, 138, 15],
[132, 137, 14],
[134, 136, 14],
[136, 135, 14],
[139, 134, 13],
[141, 133, 13],
[143, 132, 13],
[145, 131, 13],
[147, 131, 13],
[149, 130, 13],
[151, 129, 13],
[153, 128, 13],
[155, 127, 13],
[157, 126, 13],
[159, 125, 13],
[161, 124, 13],
[162, 123, 13],
[164, 122, 13],
[166, 121, 13],
[168, 120, 13],
]
_SOLAR = [
[51, 20, 24],
[53, 20, 24],
[54, 21, 25],
[55, 21, 25],
[56, 21, 26],
[58, 22, 26],
[59, 22, 27],
[60, 23, 27],
[61, 23, 28],
[62, 24, 28],
[64, 24, 29],
[65, 24, 29],
[66, 25, 29],
[67, 25, 30],
[69, 26, 30],
[70, 26, 31],
[71, 26, 31],
[72, 27, 31],
[74, 27, 32],
[75, 27, 32],
[76, 28, 32],
[77, 28, 33],
[79, 28, 33],
[80, 29, 33],
[81, 29, 34],
[82, 30, 34],
[84, 30, 34],
[85, 30, 34],
[86, 31, 35],
[87, 31, 35],
[89, 31, 35],
[90, 32, 35],
[91, 32, 35],
[92, 32, 36],
[94, 33, 36],
[95, 33, 36],
[96, 33, 36],
[97, 34, 36],
[99, 34, 36],
[100, 34, 36],
[101, 35, 36],
[102, 35, 37],
[104, 35, 37],
[105, 36, 37],
[106, 36, 37],
[107, 36, 37],
[109, 37, 37],
[110, 37, 37],
[111, 37, 37],
[112, 38, 37],
[114, 38, 37],
[115, 39, 36],
[116, 39, 36],
[117, 39, 36],
[119, 40, 36],
[120, 40, 36],
[121, 41, 36],
[122, 41, 36],
[123, 42, 36],
[124, 42, 35],
[126, 43, 35],
[127, 43, 35],
[128, 44, 35],
[129, 44, 34],
[130, 45, 34],
[131, 45, 34],
[132, 46, 34],
[133, 46, 33],
[135, 47, 33],
[136, 48, 33],
[137, 48, 33],
[138, 49, 32],
[139, 49, 32],
[140, 50, 32],
[141, 51, 31],
[142, 52, 31],
[143, 52, 31],
[144, 53, 30],
[145, 54, 30],
[146, 54, 30],
[147, 55, 29],
[147, 56, 29],
[148, 57, 29],
[149, 58, 29],
[150, 58, 28],
[151, 59, 28],
[152, 60, 28],
[153, 61, 27],
[154, 62, 27],
[154, 63, 27],
[155, 64, 26],
[156, 64, 26],
[157, 65, 26],
[158, 66, 26],
[159, 67, 25],
[159, 68, 25],
[160, 69, 25],
[161, 70, 25],
[162, 71, 24],
[163, 72, 24],
[163, 73, 24],
[164, 74, 24],
[165, 74, 23],
[166, 75, 23],
[166, 76, 23],
[167, 77, 23],
[168, 78, 22],
[168, 79, 22],
[169, 80, 22],
[170, 81, 22],
[171, 82, 22],
[171, 83, 21],
[172, 84, 21],
[173, 85, 21],
[173, 86, 21],
[174, 87, 21],
[175, 88, 20],
[175, 89, 20],
[176, 90, 20],
[177, 91, 20],
[177, 92, 20],
[178, 93, 20],
[178, 94, 20],
[179, 95, 20],
[180, 96, 19],
[180, 97, 19],
[181, 98, 19],
[182, 99, 19],
[182, 100, 19],
[183, 101, 19],
[183, 102, 19],
[184, 104, 19],
[184, 105, 19],
[185, 106, 19],
[186, 107, 19],
[186, 108, 19],
[187, 109, 19],
[187, 110, 19],
[188, 111, 19],
[188, 112, 19],
[189, 113, 19],
[190, 114, 19],
[190, 115, 19],
[191, 116, 19],
[191, 117, 19],
[192, 118, 19],
[192, 119, 20],
[193, 121, 20],
[193, 122, 20],
[194, 123, 20],
[194, 124, 20],
[195, 125, 20],
[195, 126, 21],
[196, 127, 21],
[196, 128, 21],
[197, 129, 21],
[197, 130, 21],
[198, 132, 22],
[198, 133, 22],
[199, 134, 22],
[199, 135, 22],
[199, 136, 23],
[200, 137, 23],
[200, 138, 23],
[201, 139, 24],
[201, 140, 24],
[202, 142, 24],
[202, 143, 25],
[203, 144, 25],
[203, 145, 25],
[203, 146, 26],
[204, 147, 26],
[204, 148, 26],
[205, 150, 27],
[205, 151, 27],
[206, 152, 28],
[206, 153, 28],
[206, 154, 28],
[207, 155, 29],
[207, 156, 29],
[208, 158, 30],
[208, 159, 30],
[208, 160, 31],
[209, 161, 31],
[209, 162, 32],
[209, 164, 32],
[210, 165, 32],
[210, 166, 33],
[211, 167, 33],
[211, 168, 34],
[211, 169, 34],
[212, 171, 35],
[212, 172, 36],
[212, 173, 36],
[213, 174, 37],
[213, 175, 37],
[213, 177, 38],
[214, 178, 38],
[214, 179, 39],
[214, 180, 39],
[215, 181, 40],
[215, 183, 40],
[215, 184, 41],
[215, 185, 42],
[216, 186, 42],
[216, 188, 43],
[216, 189, 43],
[217, 190, 44],
[217, 191, 44],
[217, 193, 45],
[217, 194, 46],
[218, 195, 46],
[218, 196, 47],
[218, 198, 47],
[218, 199, 48],
[219, 200, 49],
[219, 201, 49],
[219, 203, 50],
[219, 204, 50],
[220, 205, 51],
[220, 206, 52],
[220, 208, 52],
[220, 209, 53],
[221, 210, 54],
[221, 212, 54],
[221, 213, 55],
[221, 214, 55],
[221, 215, 56],
[222, 217, 57],
[222, 218, 57],
[222, 219, 58],
[222, 221, 59],
[222, 222, 59],
[222, 223, 60],
[223, 225, 61],
[223, 226, 61],
[223, 227, 62],
[223, 229, 63],
[223, 230, 63],
[223, 231, 64],
[223, 233, 65],
[224, 234, 65],
[224, 235, 66],
[224, 237, 67],
[224, 238, 67],
[224, 240, 68],
[224, 241, 69],
[224, 242, 69],
[224, 244, 70],
[224, 245, 71],
[224, 246, 71],
[224, 248, 72],
[224, 249, 73],
[224, 251, 73],
[225, 252, 74],
[225, 253, 75],
]
_SPEED = [
[255, 253, 205],
[254, 252, 203],
[254, 250, 201],
[253, 249, 199],
[252, 248, 197],
[252, 247, 194],
[251, 246, 192],
[250, 244, 190],
[249, 243, 188],
[249, 242, 186],
[248, 241, 184],
[247, 240, 182],
[247, 238, 180],
[246, 237, 177],
[246, 236, 175],
[245, 235, 173],
[244, 234, 171],
[243, 233, 169],
[243, 231, 167],
[242, 230, 165],
[241, 229, 162],
[241, 228, 160],
[240, 227, 158],
[239, 226, 156],
[239, 225, 154],
[238, 223, 152],
[237, 222, 150],
[237, 221, 147],
[236, 220, 145],
[235, 219, 143],
[234, 218, 141],
[234, 217, 139],
[233, 216, 137],
[232, 215, 134],
[231, 214, 132],
[231, 213, 130],
[230, 212, 128],
[229, 211, 126],
[228, 210, 123],
[227, 208, 121],
[227, 207, 119],
[226, 206, 117],
[225, 205, 115],
[224, 205, 113],
[223, 204, 110],
[222, 203, 108],
[221, 202, 106],
[220, 201, 104],
[219, 200, 102],
[218, 199, 100],
[217, 198, 97],
[216, 197, 95],
[215, 196, 93],
[214, 195, 91],
[213, 194, 89],
[212, 193, 87],
[211, 193, 85],
[210, 192, 83],
[209, 191, 81],
[208, 190, 79],
[206, 189, 76],
[205, 189, 74],
[204, 188, 72],
[203, 187, 70],
[201, 186, 69],
[200, 185, 67],
[199, 185, 65],
[197, 184, 63],
[196, 183, 61],
[195, 183, 59],
[193, 182, 57],
[192, 181, 55],
[190, 180, 54],
[189, 180, 52],
[187, 179, 50],
[186, 178, 48],
[184, 178, 47],
[183, 177, 45],
[181, 176, 43],
[180, 176, 42],
[178, 175, 40],
[177, 174, 39],
[175, 174, 37],
[173, 173, 35],
[172, 173, 34],
[170, 172, 32],
[169, 171, 31],
[167, 171, 30],
[165, 170, 28],
[164, 169, 27],
[162, 169, 25],
[160, 168, 24],
[159, 168, 23],
[157, 167, 21],
[155, 166, 20],
[154, 166, 19],
[152, 165, 18],
[150, 165, 16],
[149, 164, 15],
[147, 163, 14],
[145, 163, 13],
[143, 162, 12],
[142, 162, 11],
[140, 161, 10],
[138, 160, 9],
[136, 160, 8],
[135, 159, 8],
[133, 159, 7],
[131, 158, 7],
[129, 157, 6],
[128, 157, 6],
[126, 156, 6],
[124, 156, 6],
[122, 155, 6],
[121, 154, 6],
[119, 154, 6],
[117, 153, 6],
[115, 153, 6],
[113, 152, 6],
[112, 151, 7],
[110, 151, 7],
[108, 150, 7],
[106, 149, 8],
[104, 149, 9],
[102, 148, 9],
[101, 148, 10],
[99, 147, 11],
[97, 146, 11],
[95, 146, 12],
[93, 145, 13],
[92, 144, 14],
[90, 144, 15],
[88, 143, 15],
[86, 142, 16],
[84, 142, 17],
[82, 141, 18],
[81, 140, 18],
[79, 140, 19],
[77, 139, 20],
[75, 138, 21],
[73, 138, 22],
[72, 137, 22],
[70, 136, 23],
[68, 136, 24],
[66, 135, 25],
[64, 134, 25],
[63, 133, 26],
[61, 133, 27],
[59, 132, 28],
[57, 131, 28],
[56, 131, 29],
[54, 130, 30],
[52, 129, 30],
[50, 128, 31],
[49, 127, 32],
[47, 127, 32],
[45, 126, 33],
[44, 125, 33],
[42, 124, 34],
[40, 124, 35],
[39, 123, 35],
[37, 122, 36],
[36, 121, 36],
[34, 120, 37],
[33, 120, 37],
[31, 119, 38],
[30, 118, 38],
[28, 117, 39],
[27, 116, 39],
[26, 115, 39],
[24, 115, 40],
[23, 114, 40],
[22, 113, 41],
[21, 112, 41],
[19, 111, 41],
[18, 110, 42],
[17, 109, 42],
[16, 108, 42],
[15, 108, 43],
[15, 107, 43],
[14, 106, 43],
[13, 105, 43],
[13, 104, 43],
[12, 103, 44],
[12, 102, 44],
[11, 101, 44],
[11, 100, 44],
[11, 99, 44],
[11, 99, 44],
[11, 98, 45],
[11, 97, 45],
[11, 96, 45],
[11, 95, 45],
[11, 94, 45],
[12, 93, 45],
[12, 92, 45],
[12, 91, 45],
[13, 90, 45],
[13, 89, 45],
[14, 88, 45],
[14, 87, 45],
[15, 86, 44],
[15, 85, 44],
[16, 84, 44],
[16, 84, 44],
[16, 83, 44],
[17, 82, 44],
[17, 81, 44],
[18, 80, 43],
[18, 79, 43],
[19, 78, 43],
[19, 77, 43],
[20, 76, 42],
[20, 75, 42],
[20, 74, 42],
[21, 73, 42],
[21, 72, 41],
[22, 71, 41],
[22, 70, 41],
[22, 69, 40],
[23, 68, 40],
[23, 67, 39],
[23, 66, 39],
[23, 65, 39],
[24, 64, 38],
[24, 63, 38],
[24, 63, 37],
[24, 62, 37],
[25, 61, 36],
[25, 60, 36],
[25, 59, 35],
[25, 58, 35],
[25, 57, 34],
[25, 56, 34],
[25, 55, 33],
[25, 54, 33],
[25, 53, 32],
[25, 52, 31],
[25, 51, 31],
[25, 50, 30],
[25, 49, 30],
[25, 48, 29],
[25, 47, 28],
[25, 46, 28],
[25, 45, 27],
[25, 44, 26],
[25, 44, 25],
[25, 43, 25],
[25, 42, 24],
[24, 41, 23],
[24, 40, 23],
[24, 39, 22],
[24, 38, 21],
[24, 37, 20],
[23, 36, 19],
[23, 35, 19],
]
_TEMPO = [
[255, 246, 244],
[253, 245, 243],
[252, 244, 241],
[251, 243, 240],
[249, 242, 238],
[248, 241, 237],
[247, 240, 235],
[245, 239, 234],
[244, 238, 232],
[242, 237, 231],
[241, 236, 229],
[240, 235, 228],
[238, 234, 226],
[237, 234, 225],
[235, 233, 223],
[234, 232, 222],
[233, 231, 221],
[231, 230, 219],
[230, 229, 218],
[228, 228, 216],
[227, 227, 215],
[226, 226, 214],
[224, 226, 212],
[223, 225, 211],
[221, 224, 209],
[220, 223, 208],
[219, 222, 207],
[217, 221, 205],
[216, 221, 204],
[214, 220, 203],
[213, 219, 201],
[211, 218, 200],
[210, 217, 199],
[209, 216, 197],
[207, 216, 196],
[206, 215, 195],
[204, 214, 193],
[203, 213, 192],
[201, 212, 191],
[200, 212, 190],
[198, 211, 188],
[197, 210, 187],
[195, 209, 186],
[194, 209, 185],
[192, 208, 183],
[191, 207, 182],
[189, 206, 181],
[188, 206, 180],
[186, 205, 179],
[185, 204, 178],
[183, 203, 176],
[182, 203, 175],
[180, 202, 174],
[179, 201, 173],
[177, 200, 172],
[176, 200, 171],
[174, 199, 170],
[172, 198, 169],
[171, 197, 168],
[169, 197, 166],
[168, 196, 165],
[166, 195, 164],
[164, 195, 163],
[163, 194, 162],
[161, 193, 161],
[160, 192, 160],
[158, 192, 159],
[156, 191, 159],
[155, 190, 158],
[153, 190, 157],
[151, 189, 156],
[150, 188, 155],
[148, 188, 154],
[146, 187, 153],
[145, 186, 152],
[143, 186, 151],
[141, 185, 151],
[139, 184, 150],
[138, 183, 149],
[136, 183, 148],
[134, 182, 147],
[133, 181, 147],
[131, 181, 146],
[129, 180, 145],
[127, 179, 144],
[125, 179, 144],
[124, 178, 143],
[122, 177, 142],
[120, 177, 142],
[118, 176, 141],
[116, 175, 141],
[114, 175, 140],
[113, 174, 139],
[111, 173, 139],
[109, 173, 138],
[107, 172, 138],
[105, 171, 137],
[103, 171, 137],
[101, 170, 136],
[99, 169, 136],
[97, 169, 135],
[95, 168, 135],
[93, 167, 134],
[91, 166, 134],
[89, 166, 133],
[87, 165, 133],
[86, 164, 133],
[84, 164, 132],
[82, 163, 132],
[80, 162, 132],
[78, 161, 131],
[75, 161, 131],
[73, 160, 131],
[71, 159, 130],
[69, 159, 130],
[67, 158, 130],
[65, 157, 130],
[63, 156, 129],
[61, 156, 129],
[59, 155, 129],
[58, 154, 129],
[56, 153, 129],
[54, 152, 128],
[52, 152, 128],
[50, 151, 128],
[48, 150, 128],
[46, 149, 128],
[44, 148, 127],
[42, 147, 127],
[41, 147, 127],
[39, 146, 127],
[37, 145, 127],
[36, 144, 127],
[34, 143, 126],
[33, 142, 126],
[31, 141, 126],
[30, 141, 126],
[28, 140, 126],
[27, 139, 125],
[26, 138, 125],
[25, 137, 125],
[23, 136, 125],
[22, 135, 124],
[22, 134, 124],
[21, 133, 124],
[20, 132, 124],
[19, 132, 123],
[19, 131, 123],
[18, 130, 123],
[18, 129, 123],
[17, 128, 122],
[17, 127, 122],
[17, 126, 122],
[17, 125, 121],
[17, 124, 121],
[17, 123, 121],
[17, 122, 120],
[17, 121, 120],
[17, 120, 120],
[17, 119, 119],
[17, 118, 119],
[18, 118, 118],
[18, 117, 118],
[18, 116, 118],
[19, 115, 117],
[19, 114, 117],
[19, 113, 116],
[20, 112, 116],
[20, 111, 115],
[20, 110, 115],
[21, 109, 115],
[21, 108, 114],
[22, 107, 114],
[22, 106, 113],
[22, 105, 113],
[23, 104, 112],
[23, 103, 112],
[23, 102, 111],
[24, 101, 111],
[24, 101, 110],
[24, 100, 110],
[25, 99, 109],
[25, 98, 109],
[25, 97, 108],
[25, 96, 108],
[26, 95, 107],
[26, 94, 107],
[26, 93, 106],
[26, 92, 106],
[26, 91, 105],
[27, 90, 104],
[27, 89, 104],
[27, 88, 103],
[27, 88, 103],
[27, 87, 102],
[27, 86, 102],
[28, 85, 101],
[28, 84, 101],
[28, 83, 100],
[28, 82, 99],
[28, 81, 99],
[28, 80, 98],
[28, 79, 98],
[28, 78, 97],
[28, 77, 97],
[28, 76, 96],
[28, 76, 95],
[28, 75, 95],
[28, 74, 94],
[28, 73, 94],
[28, 72, 93],
[28, 71, 93],
[28, 70, 92],
[28, 69, 91],
[28, 68, 91],
[28, 67, 90],
[28, 66, 90],
[28, 66, 89],
[28, 65, 88],
[28, 64, 88],
[27, 63, 87],
[27, 62, 87],
[27, 61, 86],
[27, 60, 86],
[27, 59, 85],
[27, 58, 84],
[27, 57, 84],
[27, 56, 83],
[26, 55, 83],
[26, 54, 82],
[26, 54, 81],
[26, 53, 81],
[26, 52, 80],
[26, 51, 80],
[25, 50, 79],
[25, 49, 79],
[25, 48, 78],
[25, 47, 77],
[25, 46, 77],
[24, 45, 76],
[24, 44, 76],
[24, 43, 75],
[24, 42, 75],
[24, 41, 74],
[23, 40, 74],
[23, 39, 73],
[23, 38, 72],
[23, 37, 72],
[23, 36, 71],
[22, 35, 71],
[22, 34, 70],
[22, 33, 70],
[22, 32, 69],
[21, 31, 69],
[21, 30, 68],
[21, 29, 68],
]
_THERMAL = [
[4, 35, 51],
[4, 36, 53],
[4, 37, 55],
[4, 37, 57],
[5, 38, 59],
[5, 39, 61],
[5, 39, 63],
[5, 40, 65],
[5, 41, 67],
[6, 41, 69],
[6, 42, 71],
[6, 43, 73],
[7, 43, 75],
[7, 44, 77],
[7, 44, 80],
[8, 45, 82],
[8, 46, 84],
[9, 46, 86],
[9, 47, 89],
[10, 47, 91],
[11, 48, 93],
[12, 48, 96],
[12, 48, 98],
[13, 49, 101],
[14, 49, 103],
[15, 50, 106],
[16, 50, 108],
[18, 50, 111],
[19, 51, 114],
[20, 51, 116],
[22, 51, 119],
[23, 51, 122],
[25, 51, 124],
[26, 52, 127],
[28, 52, 130],
[30, 52, 132],
[31, 52, 135],
[33, 52, 138],
[35, 52, 140],
[37, 52, 143],
[39, 52, 145],
[42, 51, 147],
[44, 51, 149],
[46, 51, 151],
[48, 51, 153],
[51, 51, 155],
[53, 51, 156],
[55, 51, 157],
[57, 51, 158],
[60, 51, 159],
[62, 52, 159],
[64, 52, 159],
[66, 52, 160],
[68, 53, 160],
[70, 53, 160],
[71, 54, 160],
[73, 54, 159],
[75, 55, 159],
[77, 55, 159],
[78, 56, 158],
[80, 57, 158],
[82, 57, 157],
[83, 58, 157],
[85, 59, 157],
[86, 59, 156],
[88, 60, 156],
[89, 61, 155],
[91, 61, 155],
[92, 62, 154],
[94, 63, 154],
[95, 63, 153],
[96, 64, 153],
[98, 65, 152],
[99, 65, 152],
[101, 66, 151],
[102, 67, 151],
[103, 67, 150],
[105, 68, 150],
[106, 69, 149],
[108, 69, 149],
[109, 70, 148],
[110, 71, 148],
[112, 71, 148],
[113, 72, 147],
[114, 72, 147],
[116, 73, 146],
[117, 74, 146],
[118, 74, 146],
[120, 75, 145],
[121, 75, 145],
[122, 76, 145],
[124, 77, 144],
[125, 77, 144],
[126, 78, 144],
[128, 78, 143],
[129, 79, 143],
[131, 80, 143],
[132, 80, 142],
[133, 81, 142],
[135, 81, 142],
[136, 82, 141],
[137, 82, 141],
[139, 83, 141],
[140, 83, 140],
[142, 84, 140],
[143, 84, 140],
[144, 85, 139],
[146, 85, 139],
[147, 86, 139],
[149, 86, 138],
[150, 87, 138],
[151, 87, 138],
[153, 88, 137],
[154, 88, 137],
[156, 89, 137],
[157, 89, 136],
[159, 90, 136],
[160, 90, 135],
[162, 91, 135],
[163, 91, 134],
[165, 92, 134],
[166, 92, 134],
[168, 93, 133],
[169, 93, 132],
[171, 93, 132],
[172, 94, 131],
[174, 94, 131],
[175, 95, 130],
[177, 95, 130],
[178, 96, 129],
[180, 96, 128],
[181, 97, 128],
[183, 97, 127],
[184, 98, 126],
[186, 98, 126],
[187, 98, 125],
[189, 99, 124],
[190, 99, 123],
[192, 100, 123],
[193, 100, 122],
[195, 101, 121],
[196, 101, 120],
[198, 102, 119],
[199, 102, 118],
[201, 103, 117],
[202, 103, 116],
[204, 104, 115],
[205, 104, 114],
[206, 105, 113],
[208, 105, 112],
[209, 106, 111],
[211, 106, 110],
[212, 107, 109],
[214, 108, 108],
[215, 108, 107],
[216, 109, 106],
[218, 110, 105],
[219, 110, 104],
[220, 111, 102],
[222, 112, 101],
[223, 112, 100],
[224, 113, 99],
[225, 114, 98],
[227, 114, 96],
[228, 115, 95],
[229, 116, 94],
[230, 117, 93],
[231, 118, 91],
[232, 119, 90],
[234, 120, 89],
[235, 121, 88],
[236, 121, 86],
[237, 122, 85],
[238, 123, 84],
[238, 125, 83],
[239, 126, 82],
[240, 127, 80],
[241, 128, 79],
[242, 129, 78],
[243, 130, 77],
[243, 131, 76],
[244, 133, 75],
[245, 134, 74],
[245, 135, 73],
[246, 136, 72],
[246, 138, 71],
[247, 139, 70],
[247, 140, 69],
[248, 142, 68],
[248, 143, 67],
[249, 145, 67],
[249, 146, 66],
[249, 147, 65],
[250, 149, 65],
[250, 150, 64],
[250, 152, 63],
[251, 153, 63],
[251, 155, 62],
[251, 156, 62],
[251, 158, 62],
[251, 159, 61],
[251, 161, 61],
[252, 163, 61],
[252, 164, 61],
[252, 166, 60],
[252, 167, 60],
[252, 169, 60],
[252, 170, 60],
[252, 172, 60],
[252, 174, 60],
[252, 175, 60],
[252, 177, 60],
[251, 178, 61],
[251, 180, 61],
[251, 182, 61],
[251, 183, 61],
[251, 185, 62],
[251, 187, 62],
[251, 188, 62],
[250, 190, 63],
[250, 191, 63],
[250, 193, 64],
[250, 195, 64],
[249, 196, 65],
[249, 198, 65],
[249, 200, 66],
[248, 201, 67],
[248, 203, 67],
[248, 205, 68],
[247, 206, 69],
[247, 208, 69],
[247, 210, 70],
[246, 211, 71],
[246, 213, 71],
[245, 215, 72],
[245, 216, 73],
[244, 218, 74],
[244, 220, 75],
[243, 221, 75],
[243, 223, 76],
[242, 225, 77],
[242, 226, 78],
[241, 228, 79],
[241, 230, 80],
[240, 232, 81],
[239, 233, 81],
[239, 235, 82],
[238, 237, 83],
[237, 238, 84],
[237, 240, 85],
[236, 242, 86],
[235, 244, 87],
[234, 245, 88],
[234, 247, 89],
[233, 249, 90],
[232, 250, 91],
]
_TURBID = [
[233, 246, 171],
[232, 245, 170],
[232, 243, 168],
[231, 242, 167],
[230, 241, 165],
[230, 240, 164],
[229, 239, 162],
[229, 238, 161],
[228, 236, 159],
[228, 235, 158],
[227, 234, 156],
[227, 233, 155],
[226, 232, 154],
[226, 231, 152],
[225, 229, 151],
[224, 228, 149],
[224, 227, 148],
[223, 226, 146],
[223, 225, 145],
[222, 224, 143],
[222, 223, 142],
[221, 221, 141],
[221, 220, 139],
[220, 219, 138],
[220, 218, 136],
[219, 217, 135],
[219, 216, 134],
[218, 215, 132],
[218, 213, 131],
[217, 212, 130],
[217, 211, 128],
[217, 210, 127],
[216, 209, 126],
[216, 208, 124],
[215, 207, 123],
[215, 206, 122],
[214, 204, 120],
[214, 203, 119],
[213, 202, 118],
[213, 201, 116],
[212, 200, 115],
[212, 199, 114],
[211, 198, 113],
[211, 197, 111],
[211, 195, 110],
[210, 194, 109],
[210, 193, 108],
[209, 192, 107],
[209, 191, 105],
[208, 190, 104],
[208, 189, 103],
[207, 188, 102],
[207, 187, 101],
[207, 186, 100],
[206, 184, 98],
[206, 183, 97],
[205, 182, 96],
[205, 181, 95],
[204, 180, 94],
[204, 179, 93],
[203, 178, 92],
[203, 177, 91],
[202, 176, 90],
[202, 175, 89],
[202, 174, 88],
[201, 172, 87],
[201, 171, 86],
[200, 170, 85],
[200, 169, 84],
[199, 168, 83],
[199, 167, 82],
[198, 166, 81],
[198, 165, 81],
[197, 164, 80],
[197, 163, 79],
[196, 162, 78],
[196, 161, 77],
[195, 160, 77],
[195, 159, 76],
[194, 158, 75],
[194, 156, 74],
[193, 155, 74],
[193, 154, 73],
[192, 153, 72],
[192, 152, 72],
[191, 151, 71],
[191, 150, 71],
[190, 149, 70],
[189, 148, 69],
[189, 147, 69],
[188, 146, 68],
[188, 145, 68],
[187, 144, 67],
[187, 143, 67],
[186, 142, 66],
[185, 141, 66],
[185, 140, 66],
[184, 139, 65],
[183, 138, 65],
[183, 137, 64],
[182, 137, 64],
[182, 136, 64],
[181, 135, 64],
[180, 134, 63],
[179, 133, 63],
[179, 132, 63],
[178, 131, 62],
[177, 130, 62],
[177, 129, 62],
[176, 128, 62],
[175, 127, 61],
[175, 126, 61],
[174, 125, 61],
[173, 125, 61],
[172, 124, 61],
[172, 123, 61],
[171, 122, 60],
[170, 121, 60],
[169, 120, 60],
[168, 119, 60],
[168, 119, 60],
[167, 118, 60],
[166, 117, 60],
[165, 116, 60],
[164, 115, 59],
[164, 114, 59],
[163, 114, 59],
[162, 113, 59],
[161, 112, 59],
[160, 111, 59],
[159, 110, 59],
[158, 110, 59],
[158, 109, 59],
[157, 108, 59],
[156, 107, 59],
[155, 107, 59],
[154, 106, 59],
[153, 105, 59],
[152, 104, 59],
[151, 104, 58],
[150, 103, 58],
[150, 102, 58],
[149, 101, 58],
[148, 101, 58],
[147, 100, 58],
[146, 99, 58],
[145, 98, 58],
[144, 98, 58],
[143, 97, 58],
[142, 96, 58],
[141, 96, 58],
[140, 95, 58],
[139, 94, 58],
[138, 94, 58],
[137, 93, 58],
[136, 92, 58],
[135, 92, 57],
[134, 91, 57],
[133, 90, 57],
[132, 90, 57],
[131, 89, 57],
[130, 88, 57],
[129, 88, 57],
[128, 87, 57],
[127, 86, 57],
[126, 86, 57],
[125, 85, 56],
[124, 84, 56],
[123, 84, 56],
[122, 83, 56],
[121, 83, 56],
[120, 82, 56],
[119, 81, 56],
[118, 81, 56],
[117, 80, 55],
[116, 79, 55],
[115, 79, 55],
[114, 78, 55],
[113, 78, 55],
[112, 77, 55],
[111, 76, 54],
[110, 76, 54],
[109, 75, 54],
[108, 75, 54],
[107, 74, 53],
[106, 73, 53],
[105, 73, 53],
[103, 72, 53],
[102, 72, 53],
[101, 71, 52],
[100, 70, 52],
[99, 70, 52],
[98, 69, 52],
[97, 69, 51],
[96, 68, 51],
[95, 67, 51],
[94, 67, 51],
[93, 66, 50],
[92, 66, 50],
[91, 65, 50],
[90, 65, 49],
[89, 64, 49],
[88, 63, 49],
[87, 63, 48],
[86, 62, 48],
[85, 62, 48],
[84, 61, 48],
[83, 60, 47],
[82, 60, 47],
[81, 59, 47],
[80, 59, 46],
[79, 58, 46],
[78, 57, 46],
[77, 57, 45],
[75, 56, 45],
[74, 56, 44],
[73, 55, 44],
[72, 54, 44],
[71, 54, 43],
[70, 53, 43],
[69, 53, 43],
[68, 52, 42],
[67, 51, 42],
[66, 51, 41],
[65, 50, 41],
[64, 50, 41],
[63, 49, 40],
[62, 48, 40],
[61, 48, 39],
[60, 47, 39],
[59, 47, 39],
[58, 46, 38],
[57, 45, 38],
[56, 45, 37],
[55, 44, 37],
[54, 43, 36],
[53, 43, 36],
[52, 42, 36],
[51, 42, 35],
[50, 41, 35],
[49, 40, 34],
[48, 40, 34],
[47, 39, 33],
[46, 38, 33],
[45, 38, 32],
[44, 37, 32],
[43, 36, 31],
[42, 36, 31],
[41, 35, 31],
[40, 35, 30],
[39, 34, 30],
[38, 33, 29],
[37, 33, 29],
[36, 32, 28],
[35, 31, 28],
[34, 31, 27],
]
|
jiffyclubREPO_NAMEpalettablePATH_START.@palettable_extracted@palettable-master@palettable@cmocean@colormaps.py@.PATH_END.py
|
{
"filename": "_arrayminus.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattergl/error_x/_arrayminus.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ArrayminusValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(
self, plotly_name="arrayminus", parent_name="scattergl.error_x", **kwargs
):
super(ArrayminusValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattergl@error_x@_arrayminus.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/tools/python/__init__.py",
"type": "Python"
}
|
from typing import Any
def __getattr__(name: str = "") -> Any:
raise AttributeError(
"This tool has been moved to langchain experiment. "
"This tool has access to a python REPL. "
"For best practices make sure to sandbox this tool. "
"Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
"To keep using this code as is, install langchain experimental and "
"update relevant imports replacing 'langchain' with 'langchain_experimental'"
)
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@tools@python@__init__.py@.PATH_END.py
|
{
"filename": "csv_logger.py",
"repo_name": "keras-team/keras",
"repo_path": "keras_extracted/keras-master/keras/src/callbacks/csv_logger.py",
"type": "Python"
}
|
import collections
import csv
import numpy as np
from keras.src.api_export import keras_export
from keras.src.callbacks.callback import Callback
from keras.src.utils import file_utils
@keras_export("keras.callbacks.CSVLogger")
class CSVLogger(Callback):
"""Callback that streams epoch results to a CSV file.
Supports all values that can be represented as a string,
including 1D iterables such as `np.ndarray`.
Args:
filename: Filename of the CSV file, e.g. `'run/log.csv'`.
separator: String used to separate elements in the CSV file.
append: Boolean. True: append if file exists (useful for continuing
training). False: overwrite existing file.
Example:
```python
csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])
```
"""
def __init__(self, filename, separator=",", append=False):
super().__init__()
self.sep = separator
self.filename = file_utils.path_to_string(filename)
self.append = append
self.writer = None
self.keys = None
self.append_header = True
def on_train_begin(self, logs=None):
if self.append:
if file_utils.exists(self.filename):
with file_utils.File(self.filename, "r") as f:
self.append_header = not bool(len(f.readline()))
mode = "a"
else:
mode = "w"
self.csv_file = file_utils.File(self.filename, mode)
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
def handle_value(k):
is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
if isinstance(k, str):
return k
elif (
isinstance(k, collections.abc.Iterable)
and not is_zero_dim_ndarray
):
return f"\"[{', '.join(map(str, k))}]\""
else:
return k
if self.keys is None:
self.keys = sorted(logs.keys())
# When validation_freq > 1, `val_` keys are not in first epoch logs
# Add the `val_` keys so that its part of the fieldnames of writer.
val_keys_found = False
for key in self.keys:
if key.startswith("val_"):
val_keys_found = True
break
if not val_keys_found:
self.keys.extend(["val_" + k for k in self.keys])
if not self.writer:
class CustomDialect(csv.excel):
delimiter = self.sep
fieldnames = ["epoch"] + self.keys
self.writer = csv.DictWriter(
self.csv_file, fieldnames=fieldnames, dialect=CustomDialect
)
if self.append_header:
self.writer.writeheader()
row_dict = collections.OrderedDict({"epoch": epoch})
row_dict.update(
(key, handle_value(logs.get(key, "NA"))) for key in self.keys
)
self.writer.writerow(row_dict)
self.csv_file.flush()
def on_train_end(self, logs=None):
self.csv_file.close()
self.writer = None
|
keras-teamREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@src@callbacks@csv_logger.py@.PATH_END.py
|
{
"filename": "PreFittedModify.py",
"repo_name": "yaolun/Her_line_fit",
"repo_path": "Her_line_fit_extracted/Her_line_fit-master/PreFittedModify.py",
"type": "Python"
}
|
def PreFittingModify(indir, outdir, obs):
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
from astropy.io import ascii
import matplotlib.pyplot as plt
import numpy as np
import os
# modify outdir
# outdir = outdir+obs[0]+'/data/'
if not os.path.isdir(outdir):
os.makedirs(outdir)
if not os.path.isfile(indir+obs[3]+'_spire_sect.txt'):
print(obs[0]+' is not found.')
return None
# read in the spectrum
spire_spec = ascii.read(indir+obs[3]+'_spire_sect.txt', data_start=4)
# convert it to the usual format
spire_wl = np.hstack((spire_spec['wave_segm1_0'][spire_spec['wave_segm1_0'] >= 310].data,
spire_spec['wave_segm2_0'][(spire_spec['wave_segm2_0'] < 310) & (spire_spec['wave_segm2_0'] > 195)].data))
spire_flux = np.hstack((spire_spec['flux_segm1_0'][spire_spec['wave_segm1_0'] >= 310].data,
spire_spec['flux_segm2_0'][(spire_spec['wave_segm2_0'] < 310) & (spire_spec['wave_segm2_0'] > 195)].data))
sorter = np.argsort(spire_wl)
spire_wl = spire_wl[sorter].data
spire_flux = spire_flux[sorter].data
# Write to file
foo = open(outdir+obs[0]+'_spire_corrected.txt','w')
foo.write('%s \t %s \n' % ('Wavelength(um)', 'Flux_Density(Jy)'))
for i in range(len(spire_wl)):
foo.write('%f \t %f \n' % (spire_wl[i], spire_flux[i]))
foo.close()
# read in the photometry
# spire_phot = ascii.read(outdir+obs[0]+'phot_sect.txt', data_start=4)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.plot(spire_wl, spire_flux)
# ax.errorbar(spire_phot['wavelength(um)'], spire_phot['flux(Jy)'], yerr=spire_phot['uncertainty(Jy)'],
# fmt='s', color='m', linestyle='None')
ax.set_xlabel(r'$\rm{Wavelength\,[\mu m]}$',fontsize=20)
ax.set_ylabel(r'$\rm{Flux\,Density\,[Jy]}$',fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
fig.savefig(outdir+obs[0]+'_spire_corrected.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
def SPIRE1d_fit(indir, objname, global_dir, wl_shift=0):
import os
from astropy.io import ascii
if not os.path.isfile(indir+'data/'+objname+'_spire_corrected.txt'):
print(objname+' is not found.')
return None
# read RA/Dec
radec_slw = ascii.read(indir+'/data/cube/'+objname+'_radec_slw.txt')
import pidly
idl = pidly.IDL('/opt/local/exelis/idl83/bin/idl')
idl('.r /home/bettyjo/yaolun/programs/line_fitting/gauss.pro')
idl('.r /home/bettyjo/yaolun/programs/line_fitting/extract_spire.pro')
idl.pro('extract_spire', indir=indir+'data/', filename=objname+'_spire_corrected',
outdir=indir+'advanced_products/', plotdir=indir+'advanced_products/plots/', noiselevel=3,
ra=radec_slw['RA(deg)'][radec_slw['Pixel'] == 'SLWC3'], dec=radec_slw['Dec(deg)'][radec_slw['Pixel'] == 'SLWC3'],
global_noise=20, localbaseline=10, continuum=1, flat=1, object=objname, double_gauss=1, fx=1, current_pix=1,
print_all=global_dir+'_lines', wl_shift=wl_shift)
def SPIRE1D_run(obsid=None, indir=None, outdir=None, global_dir=None, wl_shift=0):
if obsid == None:
# observation info
obsid = [['AB_Aur','1342217842','1342217843','0'],\
['AS205','1342215737','1342215738','0'],\
['B1-a','1342216182','1342216183','1342249475'],\
['B1-c','1342216213','1342216214','1342249476'],\
['B335','1342208889','1342208888','1342253652'],\
['BHR71','1342212230','1342212231','1342248249'],\
['Ced110','0','0','1342248246'],\
['DG_Tau','1342225730','1342225731','0'],\
['EC82','1342192975','1342219435','0'],\
['Elias29','1342228519','1342228520','0'],\
['FUOri','1342250907','1342250908','1342230412'],\
['GSS30-IRS1','1342215678','1342215679','1342251286'],\
['HD100453','1342211695','1342211696','0'],\
['HD100546','1342188037','1342188038','0'],\
['HD104237','1342207819','1342207820','0'],\
['HD135344B-1','1342213921','1342213922','0'],\
['HD139614','1342215683','1342215684','0'],\
['HD141569','1342213913','0','0'],\
['HD142527','1342216174','1342216175','0'],\
['HD142666','1342213916','0','0'],\
['HD144432','1342213919','0','0'],\
['HD144668','1342215641','1342215642','0'],\
['HD150193','1342227068','0','0'],\
['HD163296','1342217819','1342217820','0'],\
['HD169142','1342206987','1342206988','0'],\
['HD179218','1342208884','1342208885','0'],\
['HD203024','1342206975','0','0'],\
['HD245906','1342228528','0','0'],\
['HD35187','1342217846','0','0'],\
['HD36112','1342228247','1342228248','0'],\
['HD38120','1342226212','1342226213','0'],\
['HD50138','1342206991','1342206992','0'],\
['HD97048','1342199412','1342199413','0'],\
['HD98922','1342210385','0','0'],\
['HH46','0','0','1342245084'],\
['HH100','0','0','1342252897'],\
['HT_Lup','1342213920','0','0'],\
['IRAM04191','1342216654','1342216655','0'],\
['IRAS03245','1342214677','1342214676','1342249053'],\
['IRAS03301','1342215668','1342216181','1342249477'],\
['DKCha','1342188039','1342188040','1342254037'],\
['IRAS15398','0','0','1342250515'],\
['IRS46','1342228474','1342228475','1342251289'],\
['IRS48','1342227069','1342227070','0'],\
['IRS63','1342228473','1342228472','0'],\
['L1014','1342208911','1342208912','1342245857'],\
['L1157','1342208909','1342208908','1342247625'],\
['L1448-MM','1342213683','1342214675','0'],\
['L1455-IRS3','1342204122','1342204123','1342249474'],\
['L1489','1342216216','1342216215','0'],\
['L1527','1342192981','1342192982','0'],\
['L1551-IRS5','1342192805','1342229711','1342249470'],\
['L483','0','0','1342253649'],\
['L723-MM','0','0','1342245094'],\
['RCrA-IRS5A','1342207806','1342207805','1342253646'],\
['RCrA-IRS7B','1342207807','1342207808','1342242620'],\
['RCrA-IRS7C','1342206990','1342206989','1342242621'],\
['RNO90','1342228206','0','0'],\
['RNO91','0','0','1342251285'],\
['RU_Lup','1342215682','0','0'],\
['RY_Lup','1342216171','0','0'],\
['S_Cra','1342207809','1342207810','0'],\
['SR21','1342227209','1342227210','0'],\
['Serpens-SMM3','1342193216','1342193214','0'],\
['Serpens-SMM4','1342193217','1342193215','0'],\
['TMC1','1342225803','1342225804','1342250512'],\
['TMC1A','1342192987','1342192988','1342250510'],\
['TMR1','1342192985','1342192986','1342250509'],\
['V1057_Cyg','1342235853','1342235852','1342221695'],\
['V1331_Cyg','1342233446','1342233445','1342221694'],\
['V1515_Cyg','1342235691','1342235690','1342221685'],\
['V1735_Cyg','1342235849','1342235848','1342219560'],\
['VLA1623','1342213918','1342213917','1342251287'],\
['WL12','1342228187','1342228188','1342251290']]
if indir == None:
indir = '/home/bettyjo/yaolun/CDF_SPIRE_reduction/'
if outdir == None:
outdir = '/home/bettyjo/yaolun/CDF_archive/'
if global_dir == None:
global_dir = outdir
for obs in obsid:
if obs[3] == '0':
continue
# exclude HH100
if obs[0] == 'HH100':
continue
PreFittingModify(outdir+obs[0]+'/spire/data/', outdir+obs[0]+'/spire/data/', obs)
SPIRE1d_fit(outdir+obs[0]+'/spire/', obs[0], global_dir, wl_shift=wl_shift)
|
yaolunREPO_NAMEHer_line_fitPATH_START.@Her_line_fit_extracted@Her_line_fit-master@PreFittedModify.py@.PATH_END.py
|
{
"filename": "yolo-world.md",
"repo_name": "ultralytics/ultralytics",
"repo_path": "ultralytics_extracted/ultralytics-main/docs/en/models/yolo-world.md",
"type": "Markdown"
}
|
---
comments: true
description: Explore the YOLO-World Model for efficient, real-time open-vocabulary object detection using Ultralytics YOLOv8 advancements. Achieve top performance with minimal computation.
keywords: YOLO-World, Ultralytics, open-vocabulary detection, YOLOv8, real-time object detection, machine learning, computer vision, AI, deep learning, model training
---
# YOLO-World Model
The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://www.ultralytics.com/) [YOLOv8](yolov8.md)-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/cfTKj96TjSE"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> YOLO World training workflow on custom dataset
</p>
## Overview
YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome [Transformer](https://www.ultralytics.com/glossary/transformer) models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-[language modeling](https://www.ultralytics.com/glossary/language-modeling) and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.
## Key Features
1. **Real-time Solution:** Harnessing the computational speed of CNNs, YOLO-World delivers a swift open-vocabulary detection solution, catering to industries in need of immediate results.
2. **Efficiency and Performance:** YOLO-World slashes computational and resource requirements without sacrificing performance, offering a robust alternative to models like SAM but at a fraction of the computational cost, enabling real-time applications.
3. **Inference with Offline Vocabulary:** YOLO-World introduces a "prompt-then-detect" strategy, employing an offline vocabulary to enhance efficiency further. This approach enables the use of custom prompts computed apriori, including captions or categories, to be encoded and stored as offline vocabulary embeddings, streamlining the detection process.
4. **Powered by YOLOv8:** Built upon [Ultralytics YOLOv8](yolov8.md), YOLO-World leverages the latest advancements in real-time object detection to facilitate open-vocabulary detection with unparalleled accuracy and speed.
5. **Benchmark Excellence:** YOLO-World outperforms existing open-vocabulary detectors, including MDETR and GLIP series, in terms of speed and efficiency on standard benchmarks, showcasing YOLOv8's superior capability on a single NVIDIA V100 GPU.
6. **Versatile Applications:** YOLO-World's innovative approach unlocks new possibilities for a multitude of vision tasks, delivering speed improvements by orders of magnitude over existing methods.
## Available Models, Supported Tasks, and Operating Modes
This section details the models available with their specific pre-trained weights, the tasks they support, and their compatibility with various operating modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), denoted by ✅ for supported modes and ❌ for unsupported modes.
!!! note
All the YOLOv8-World weights have been directly migrated from the official [YOLO-World](https://github.com/AILab-CVC/YOLO-World) repository, highlighting their excellent contributions.
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
| --------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
## Zero-shot Transfer on COCO Dataset
| Model Type | mAP | mAP50 | mAP75 |
| --------------- | ---- | ----- | ----- |
| yolov8s-world | 37.4 | 52.0 | 40.6 |
| yolov8s-worldv2 | 37.7 | 52.2 | 41.0 |
| yolov8m-world | 42.0 | 57.0 | 45.6 |
| yolov8m-worldv2 | 43.0 | 58.4 | 46.8 |
| yolov8l-world | 45.7 | 61.3 | 49.8 |
| yolov8l-worldv2 | 45.8 | 61.3 | 49.8 |
| yolov8x-world | 47.0 | 63.0 | 51.2 |
| yolov8x-worldv2 | 47.1 | 62.8 | 51.4 |
## Usage Examples
The YOLO-World models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development.
### Train Usage
!!! tip
We strongly recommend to use `yolov8-worldv2` model for custom training, because it supports deterministic training and also easy to export other formats i.e onnx/tensorrt.
[Object detection](https://www.ultralytics.com/glossary/object-detection) is straightforward with the `train` method, as illustrated below:
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:
```python
from ultralytics import YOLOWorld
# Load a pretrained YOLOv8s-worldv2 model
model = YOLOWorld("yolov8s-worldv2.pt")
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```
=== "CLI"
```bash
# Load a pretrained YOLOv8s-worldv2 model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640
```
### Predict Usage
Object detection is straightforward with the `predict` method, as illustrated below:
!!! example
=== "Python"
```python
from ultralytics import YOLOWorld
# Initialize a YOLO-World model
model = YOLOWorld("yolov8s-world.pt") # or select yolov8m/l-world.pt for different sizes
# Execute inference with the YOLOv8s-world model on the specified image
results = model.predict("path/to/image.jpg")
# Show results
results[0].show()
```
=== "CLI"
```bash
# Perform object detection using a YOLO-World model
yolo predict model=yolov8s-world.pt source=path/to/image.jpg imgsz=640
```
This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
### Val Usage
Model validation on a dataset is streamlined as follows:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Create a YOLO-World model
model = YOLO("yolov8s-world.pt") # or select yolov8m/l-world.pt for different sizes
# Conduct model validation on the COCO8 example dataset
metrics = model.val(data="coco8.yaml")
```
=== "CLI"
```bash
# Validate a YOLO-World model on the COCO8 dataset with a specified image size
yolo val model=yolov8s-world.pt data=coco8.yaml imgsz=640
```
### Track Usage
Object tracking with YOLO-World model on a video/images is streamlined as follows:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Create a YOLO-World model
model = YOLO("yolov8s-world.pt") # or select yolov8m/l-world.pt for different sizes
# Track with a YOLO-World model on a video
results = model.track(source="path/to/video.mp4")
```
=== "CLI"
```bash
# Track with a YOLO-World model on the video with a specified image size
yolo track model=yolov8s-world.pt imgsz=640 source="path/to/video/file.mp4"
```
!!! note
The YOLO-World models provided by Ultralytics come pre-configured with [COCO dataset](../datasets/detect/coco.md) categories as part of their offline vocabulary, enhancing efficiency for immediate application. This integration allows the YOLOv8-World models to directly recognize and predict the 80 standard categories defined in the COCO dataset without requiring additional setup or customization.
### Set prompts
The YOLO-World framework allows for the dynamic specification of classes through custom prompts, empowering users to tailor the model to their specific needs **without retraining**. This feature is particularly useful for adapting the model to new domains or specific tasks that were not originally part of the [training data](https://www.ultralytics.com/glossary/training-data). By setting custom prompts, users can essentially guide the model's focus towards objects of interest, enhancing the relevance and accuracy of the detection results.
For instance, if your application only requires detecting 'person' and 'bus' objects, you can specify these classes directly:
!!! example
=== "Custom Inference Prompts"
```python
from ultralytics import YOLO
# Initialize a YOLO-World model
model = YOLO("yolov8s-world.pt") # or choose yolov8m/l-world.pt
# Define custom classes
model.set_classes(["person", "bus"])
# Execute prediction for specified categories on an image
results = model.predict("path/to/image.jpg")
# Show results
results[0].show()
```
You can also save a model after setting custom classes. By doing this you create a version of the YOLO-World model that is specialized for your specific use case. This process embeds your custom class definitions directly into the model file, making the model ready to use with your specified classes without further adjustments. Follow these steps to save and load your custom YOLOv8 model:
!!! example
=== "Persisting Models with Custom Vocabulary"
First load a YOLO-World model, set custom classes for it and save it:
```python
from ultralytics import YOLO
# Initialize a YOLO-World model
model = YOLO("yolov8s-world.pt") # or select yolov8m/l-world.pt
# Define custom classes
model.set_classes(["person", "bus"])
# Save the model with the defined offline vocabulary
model.save("custom_yolov8s.pt")
```
After saving, the custom_yolov8s.pt model behaves like any other pre-trained YOLOv8 model but with a key difference: it is now optimized to detect only the classes you have defined. This customization can significantly improve detection performance and efficiency for your specific application scenarios.
```python
from ultralytics import YOLO
# Load your custom model
model = YOLO("custom_yolov8s.pt")
# Run inference to detect your custom classes
results = model.predict("path/to/image.jpg")
# Show results
results[0].show()
```
### Benefits of Saving with Custom Vocabulary
- **Efficiency**: Streamlines the detection process by focusing on relevant objects, reducing computational overhead and speeding up inference.
- **Flexibility**: Allows for easy adaptation of the model to new or niche detection tasks without the need for extensive retraining or data collection.
- **Simplicity**: Simplifies deployment by eliminating the need to repeatedly specify custom classes at runtime, making the model directly usable with its embedded vocabulary.
- **Performance**: Enhances detection [accuracy](https://www.ultralytics.com/glossary/accuracy) for specified classes by focusing the model's attention and resources on recognizing the defined objects.
This approach provides a powerful means of customizing state-of-the-art object detection models for specific tasks, making advanced AI more accessible and applicable to a broader range of practical applications.
## Reproduce official results from scratch(Experimental)
### Prepare datasets
- Train data
| Dataset | Type | Samples | Boxes | Annotation Files |
| ----------------------------------------------------------------- | --------- | ------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| [Objects365v1](https://opendatalab.com/OpenDataLab/Objects365_v1) | Detection | 609k | 9621k | [objects365_train.json](https://opendatalab.com/OpenDataLab/Objects365_v1) |
| [GQA](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip) | Grounding | 621k | 3681k | [final_mixed_train_no_coco.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_mixed_train_no_coco.json) |
| [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/) | Grounding | 149k | 641k | [final_flickr_separateGT_train.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_train.json) |
- Val data
| Dataset | Type | Annotation Files |
| ------------------------------------------------------------------------------------------------------- | --------- | ------------------------------------------------------------------------------------------------------ |
| [LVIS minival](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) | Detection | [minival.txt](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/lvis.yaml) |
### Launch training from scratch
!!! note
`WorldTrainerFromScratch` is highly customized to allow training yolo-world models on both detection datasets and grounding datasets simultaneously. More details please checkout [ultralytics.model.yolo.world.train_world.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/world/train_world.py).
!!! example
=== "Python"
```python
from ultralytics import YOLOWorld
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
data = dict(
train=dict(
yolo_data=["Objects365.yaml"],
grounding_data=[
dict(
img_path="../datasets/flickr30k/images",
json_file="../datasets/flickr30k/final_flickr_separateGT_train.json",
),
dict(
img_path="../datasets/GQA/images",
json_file="../datasets/GQA/final_mixed_train_no_coco.json",
),
],
),
val=dict(yolo_data=["lvis.yaml"]),
)
model = YOLOWorld("yolov8s-worldv2.yaml")
model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch)
```
## Citations and Acknowledgements
We extend our gratitude to the [Tencent AILab Computer Vision Center](https://www.tencent.com/) for their pioneering work in real-time open-vocabulary object detection with YOLO-World:
!!! quote ""
=== "BibTeX"
```bibtex
@article{cheng2024yolow,
title={YOLO-World: Real-Time Open-Vocabulary Object Detection},
author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},
journal={arXiv preprint arXiv:2401.17270},
year={2024}
}
```
For further reading, the original YOLO-World paper is available on [arXiv](https://arxiv.org/pdf/2401.17270v2.pdf). The project's source code and additional resources can be accessed via their [GitHub repository](https://github.com/AILab-CVC/YOLO-World). We appreciate their commitment to advancing the field and sharing their valuable insights with the community.
## FAQ
### What is the YOLO-World model and how does it work?
The YOLO-World model is an advanced, real-time object detection approach based on the [Ultralytics YOLOv8](yolov8.md) framework. It excels in Open-Vocabulary Detection tasks by identifying objects within an image based on descriptive texts. Using vision-language modeling and pre-training on large datasets, YOLO-World achieves high efficiency and performance with significantly reduced computational demands, making it ideal for real-time applications across various industries.
### How does YOLO-World handle inference with custom prompts?
YOLO-World supports a "prompt-then-detect" strategy, which utilizes an offline vocabulary to enhance efficiency. Custom prompts like captions or specific object categories are pre-encoded and stored as offline vocabulary [embeddings](https://www.ultralytics.com/glossary/embeddings). This approach streamlines the detection process without the need for retraining. You can dynamically set these prompts within the model to tailor it to specific detection tasks, as shown below:
```python
from ultralytics import YOLOWorld
# Initialize a YOLO-World model
model = YOLOWorld("yolov8s-world.pt")
# Define custom classes
model.set_classes(["person", "bus"])
# Execute prediction on an image
results = model.predict("path/to/image.jpg")
# Show results
results[0].show()
```
### Why should I choose YOLO-World over traditional Open-Vocabulary detection models?
YOLO-World provides several advantages over traditional Open-Vocabulary detection models:
- **Real-Time Performance:** It leverages the computational speed of CNNs to offer quick, efficient detection.
- **Efficiency and Low Resource Requirement:** YOLO-World maintains high performance while significantly reducing computational and resource demands.
- **Customizable Prompts:** The model supports dynamic prompt setting, allowing users to specify custom detection classes without retraining.
- **Benchmark Excellence:** It outperforms other open-vocabulary detectors like MDETR and GLIP in both speed and efficiency on standard benchmarks.
### How do I train a YOLO-World model on my dataset?
Training a YOLO-World model on your dataset is straightforward through the provided Python API or CLI commands. Here's how to start training using Python:
```python
from ultralytics import YOLOWorld
# Load a pretrained YOLOv8s-worldv2 model
model = YOLOWorld("yolov8s-worldv2.pt")
# Train the model on the COCO8 dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
Or using CLI:
```bash
yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640
```
### What are the available pre-trained YOLO-World models and their supported tasks?
Ultralytics offers multiple pre-trained YOLO-World models supporting various tasks and operating modes:
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
| --------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8s-world | [yolov8s-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8s-worldv2 | [yolov8s-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8m-world | [yolov8m-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8m-worldv2 | [yolov8m-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8l-world | [yolov8l-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8l-worldv2 | [yolov8l-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
| YOLOv8x-world | [yolov8x-world.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-world.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ❌ |
| YOLOv8x-worldv2 | [yolov8x-worldv2.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-worldv2.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
### How do I reproduce the official results of YOLO-World from scratch?
To reproduce the official results from scratch, you need to prepare the datasets and launch the training using the provided code. The training procedure involves creating a data dictionary and running the `train` method with a custom trainer:
```python
from ultralytics import YOLOWorld
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
data = {
"train": {
"yolo_data": ["Objects365.yaml"],
"grounding_data": [
{
"img_path": "../datasets/flickr30k/images",
"json_file": "../datasets/flickr30k/final_flickr_separateGT_train.json",
},
{
"img_path": "../datasets/GQA/images",
"json_file": "../datasets/GQA/final_mixed_train_no_coco.json",
},
],
},
"val": {"yolo_data": ["lvis.yaml"]},
}
model = YOLOWorld("yolov8s-worldv2.yaml")
model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch)
```
|
ultralyticsREPO_NAMEultralyticsPATH_START.@ultralytics_extracted@ultralytics-main@docs@en@models@yolo-world.md@.PATH_END.py
|
{
"filename": "example_gs_multipop.ipynb",
"repo_name": "mirochaj/ares",
"repo_path": "ares_extracted/ares-main/docs/examples/example_gs_multipop.ipynb",
"type": "Jupyter Notebook"
}
|
# Models with Multiple Source Populations
*ARES* can handle an arbitrary number of source populations. To
access this functionality, create a dictionary representing each source
population of interest. Below, we'll create a population representative of PopII stars and another representative of PopIII stars.
Before we start, it is important to note that in *ARES*, source populations are identified by their spectra over some contiguous interval in photon energy. This can be somewhat counterintuitive. For example, though UV emission from stars and X-ray emission from their compact remnants, e.g., X-ray binary systems, are both natural byproducts of star formation, we treat them as separate source populations in *ARES* even though the emission from each type of source is related to the same rate of star formation. However, because stars and XRBs have very different spectra, whose normalizations are parameterized differently, it is more convenient in the code to keep them separate. Because of this, what you might think of as a single source population (stars and their remnants) actually constitutes *two* source populations in *ARES*.
Let's start with a PopII source population, and a few standard imports:
```python
%pylab inline
import ares
import numpy as np
import matplotlib.pyplot as pl
```
Populating the interactive namespace from numpy and matplotlib
```python
pars = \
{
'problem_type': 100, # Blank slate global 21-cm signal
# Setup star formation
'pop_Tmin{0}': 1e4, # atomic cooling halos
'pop_fstar{0}': 1e-1, # 10% star formation efficiency
# Setup UV emission
'pop_sed_model{0}': True,
'pop_sed{0}': 'bb', # PopII stars -> 10^4 K blackbodies
'pop_temperature{0}': 1e4,
'pop_rad_yield{0}': 1e42,
'pop_fesc{0}': 0.2,
'pop_Emin{0}': 10.19,
'pop_Emax{0}': 24.6,
'pop_EminNorm{0}': 13.6,
'pop_EmaxNorm{0}': 24.6,
'pop_lya_src{0}': True,
'pop_ion_src_cgm{0}': True,
'pop_heat_src_igm{0}': False,
# Setup X-ray emission
'pop_sed{1}': 'pl',
'pop_alpha{1}': -1.5,
'pop_rad_yield{1}': 2.6e38,
'pop_Emin{1}': 2e2,
'pop_Emax{1}': 3e4,
'pop_EminNorm{1}': 5e2,
'pop_EmaxNorm{1}': 8e3,
'pop_lya_src{1}': False,
'pop_ion_src_cgm{1}': False,
'pop_heat_src_igm{1}': True,
'pop_sfr_model{1}': 'link:sfrd:0',
}
```
**NOTE:** See [problem_types](../problem_types.html) for more information about why we chose ``problem_type=100`` here.
We might as well go ahead and run this to establish a baseline:
```python
sim = ares.simulations.Global21cm(**pars)
sim.run()
ax, zax = sim.GlobalSignature(color='k')
```
# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.
############################################################################
## ARES Simulation: Overview ##
############################################################################
## ---------------------------------------------------------------------- ##
## Source Populations ##
## ---------------------------------------------------------------------- ##
## sfrd sed radio O/IR Lya LW LyC Xray RTE ##
## pop #0 : fcoll yes - - x x x - ##
## pop #1 : sfrd->0 yes - - - - - x ##
## ---------------------------------------------------------------------- ##
## Physics ##
## ---------------------------------------------------------------------- ##
## cgm_initial_temperature : [10000.0] ##
## clumping_factor : 1 ##
## secondary_ionization : 1 ##
## approx_Salpha : 1 ##
## include_He : False ##
## feedback_LW : False ##
############################################################################
# Loaded $ARES/input/hmf/hmf_ST_planck_TTTEEE_lowl_lowE_best_logM_1400_4-18_z_1201_0-60.hdf5.
gs-21cm: 100% |#############################################| Time: 0:00:04
Now, let's add a PopIII-like source population. We'll assume that PopIII sources are brighter on average (in both the UV and X-ray) but live in lower mass halos. We could just copy-pase the dictionary above, change the population ID numbers and, for example, the UV and X-ray ``pop_rad_yield`` parameters. Or, we could use some built-in tricks to speed this up.
First, let's take the PopII parameter set and make a ``ParameterBundle`` object:
```python
popII = ares.util.ParameterBundle(**pars)
```
This let's us easily extract parameters according to their ID number, and assign new ones
```python
popIII_uv = popII.pars_by_pop(0, True)
popIII_uv.num = 2
popIII_xr = popII.pars_by_pop(1, True)
popIII_xr.num = 3
```
The second argument tells *ARES* to remove the parameter ID numbers.
Now, we can simply reset the ID numbers and update a few important parameters:
```python
popIII_uv['pop_Tmin{2}'] = 300
popIII_uv['pop_Tmax{2}'] = 1e4
popIII_uv['pop_temperature{2}'] = 1e5
popIII_uv['pop_fstar{2}'] = 1e-4
popIII_xr['pop_sfr_model{3}'] = 'link:sfrd:2'
popIII_xr['pop_rad_yield{3}'] = 2.6e39
```
Now, let's make the final parameter dictionary and run it:
```python
pars.update(popIII_uv)
pars.update(popIII_xr)
sim2 = ares.simulations.Global21cm(**pars)
sim2.run()
ax, zax = sim.GlobalSignature(color='k')
ax, zax = sim2.GlobalSignature(color='b', ax=ax)
```
# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.
############################################################################
## ARES Simulation: Overview ##
############################################################################
## ---------------------------------------------------------------------- ##
## Source Populations ##
## ---------------------------------------------------------------------- ##
## sfrd sed radio O/IR Lya LW LyC Xray RTE ##
## pop #0 : fcoll yes - - x x x - ##
## pop #1 : sfrd->0 yes - - - - - x ##
## pop #2 : fcoll yes - - x x x - ##
## pop #3 : sfrd->2 yes - - - - - x ##
## ---------------------------------------------------------------------- ##
## Physics ##
## ---------------------------------------------------------------------- ##
## cgm_initial_temperature : [10000.0] ##
## clumping_factor : 1 ##
## secondary_ionization : 1 ##
## approx_Salpha : 1 ##
## include_He : False ##
## feedback_LW : False ##
############################################################################
# Loaded $ARES/input/hmf/hmf_ST_planck_TTTEEE_lowl_lowE_best_logM_1400_4-18_z_1201_0-60.hdf5.
# Loaded $ARES/input/hmf/hmf_ST_planck_TTTEEE_lowl_lowE_best_logM_1400_4-18_z_1201_0-60.hdf5.
gs-21cm: 100% |#############################################| Time: 0:00:04
Note that the parameter file hangs onto the parameters of each population separately. To verify a few key changes, you could do:
```python
len(sim2.pf.pfs)
```
4
```python
for key in ['pop_Tmin', 'pop_fstar', 'pop_rad_yield']:
print(key, sim2.pf.pfs[0][key], sim2.pf.pfs[2][key])
```
pop_Tmin 10000.0 300
pop_fstar 0.1 0.0001
pop_rad_yield 1e+42 1e+42
**NOTE:** These are very simple models for PopII and PopIII stars. For more sophisticated approaches, see [More Realistic Galaxy Populations](example_pop_galaxy) and [Including Population III Stars](example_pop_popIII).
|
mirochajREPO_NAMEaresPATH_START.@ares_extracted@ares-main@docs@examples@example_gs_multipop.ipynb@.PATH_END.py
|
{
"filename": "Explorer.py",
"repo_name": "dokester/BayesicFitting",
"repo_path": "BayesicFitting_extracted/BayesicFitting-master/BayesicFitting/source/Explorer.py",
"type": "Python"
}
|
import numpy as numpy
from threading import Thread
from .Engine import Engine
from .Formatter import formatter as fmt
from . import Tools
__author__ = "Do Kester"
__year__ = 2023
__license__ = "GPL3"
__version__ = "3.2.0"
__url__ = "https://www.bayesicfitting.nl"
__status__ = "Perpetual Beta"
# *
# * This file is part of the BayesicFitting package.
# *
# * BayesicFitting is free software: you can redistribute it and/or modify
# * it under the terms of the GNU Lesser General Public License as
# * published by the Free Software Foundation, either version 3 of
# * the License, or ( at your option ) any later version.
# *
# * BayesicFitting 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 Lesser General Public License for more details.
# *
# * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
# *
# * A JAVA version of this code was part of the Herschel Common
# * Science System (HCSS), also under GPL3.
# *
# * 2003 - 2014 Do Kester, SRON (Java code)
# * 2017 - 2023 Do Kester
threadErrors = []
class Explorer( object ):
"""
Explorer is a helper class of NestedSampler, which contains and runs the
diffusion engines.
It uses Threads to parallelise the diffusion engines.
Attributes
----------
walkers : WalkerList
walkers to be explored
engines : [engine]
list of engines to be used
errdis : ErrorDistribution
to be used
rng : numpy.random.RandomState
random number generator
rate : float (1.0)
governs processing speed (vs precision)
maxtrials : int (5)
number of trials
verbose : int (0)
level of blabbering
lowLhood : float
present low likelihood level
iteration : int
counting explorer calls
Author Do Kester.
"""
TWOP32 = 2 ** 32
def __init__( self, ns, threads=False ):
"""
Construct Explorer from a NestedSampler object.
Parameters
----------
ns : NestedSampler
the calling NestedSampler. It provides the attributes.
"""
self.walkers = ns.walkers
self.engines = ns.engines
self.errdis = ns.distribution
self.rng = ns.rng
self.rate = ns.rate
self.maxtrials = ns.maxtrials
self.verbose = ns.verbose
self.threads = threads
self.usePhantoms = hasattr( ns, "usePhantoms" ) and ns.usePhantoms
self.selectEngines = self.allEngines ## default: always use all engines
for eng in self.engines :
if hasattr( eng, "slow" ) :
self.selectEngines = self.selEngines ## use selection
break
def explore( self, worst, lowLhood, iteration ):
"""
Explore the likelihood function, using threads.
Parameters
----------
worst : [int]
list of walkers to be explored/updated
lowLhood : float
level of the low likelihood
"""
self.iteration = iteration
engines = self.selectEngines( iteration )
if not self.threads :
for kw in worst :
self.exploreWalker( kw, lowLhood, engines, self.rng )
return
## We have Threads
explorerThreads = []
self.lowLhood = lowLhood
nrep = Engine.NCALLS
for kw in worst :
seed = self.rng.randint( self.TWOP32 )
exThread = ExplorerThread( "explorer_%d"%kw, kw, self, engines, seed )
exThread.start( )
explorerThreads += [exThread]
for thread in explorerThreads :
thread.join( )
for k,engine in enumerate( engines ) :
nc = 0
for i in range( nrep ) :
nc += thread.engines[k].report[i]
engine.report[i] += thread.engines[k].report[i]
engine.report[nrep] += nc
if len( threadErrors ) > 0: #check if there are any errors
for e in threadErrors:
print( e )
raise Exception( "Thread Error" )
def exploreWalker( self, kw, lowLhood, engines, rng ):
"""
Move the walker around until it is randomly distributed over the prior and
higher in logL then lowLhood
Parameters
----------
kw : int
index in walkerlist, of the walker to be explored
lowLhood : float
minimum value for the log likelihood
engine : list of Engine
to be used
rng : RandomState
random number generator
"""
if self.verbose >= 4 :
print( "-------- Start exploreWalker ---------------------------------" )
# self.checkWalkers()
walker = self.walkers[kw]
oldlogL = walker.logL
self.errdis.lowLhood = lowLhood
maxmoves = len( walker.allpars ) / self.rate
maxtrials = self.maxtrials / self.rate
moves = 0
trials = 0
while moves < maxmoves and trials < maxtrials :
for engine in rng.permutation( engines ) :
mv = engine.execute( kw, lowLhood, append=self.usePhantoms,
iteration=self.iteration )
moves += mv
update = len( self.walkers ) - 1 if self.usePhantoms else kw
if self.verbose >= 4 and mv > 0 :
wlkr = self.walkers[update]
print( "%4d %-15.15s %4d %10.3f %10.3f ==> %3d %10.3f"%
( trials, engine, update, lowLhood, oldlogL, moves,
wlkr.logL ) )
print( "IN ", fmt( walker.allpars, max=None, linelength=200 ), len( walker.allpars ) )
print( "OUT ", fmt( wlkr.allpars, max=None, linelength=200 ), len( wlkr.allpars ) )
if len( wlkr.allpars ) < len( wlkr.fitIndex ) :
raise ValueError( "Walker parameter %d fitIndex %d" %
( len( wlkr.allpars ), len( wlkr.fitIndex ) ) )
oldlogL = wlkr.logL
## check all walkers for consistency
self.logLcheck( walker )
trials += 1
if moves == 0 :
self.logLcheck( self.walkers[kw] )
if self.walkers[update].logL < lowLhood :
raise Exception( "%10.3f < %10.3f" % ( self.walkers[kw].logL, lowLhood ) )
return
def selEngines( self, iteration ) :
"""
Select engines with slowly changing parameters once per so many iterations.
Parameter
---------
iteration : int
iteration number
"""
engines = []
for eng in self.engines :
if not hasattr( eng, "slow" ) or ( iteration % eng.slow ) == 0 :
engines += [eng]
return engines
def allEngines( self, iteration ) :
"""
Always use all engines.
Parameters
----------
iteration : int
iteration number
"""
return self.engines
def checkWalkers( self ) :
for w in self.walkers :
self.logLcheck( w )
def logLcheck( self, walker ) :
"""
Sanity check when no moves are found, if the LogL is still the same as the stored logL.
Parameters
----------
walker : Walker
the one with the stored logL
Raises
------
ValueError at inconsistency.
"""
walker.check( nhyp=self.errdis.nphypar )
wlogL = self.errdis.logLikelihood( walker.problem, walker.allpars )
if walker.problem.model.npars < len( walker.allpars ) - self.errdis.nphypar :
Tools.printclass( walker )
print( "Iteration %4d %4d %10.3f %10.3f" % ( self.iteration, walker.id, walker.logL, wlogL ) )
print( fmt( walker.allpars, max=None ) )
raise ValueError( "Inconsistency in length of modelparams (%d) and allpars (%d - %d)" %
( walker.problem.model.npars, len( walker.allpars ), self.errdis.nphypar ) )
if wlogL != walker.logL :
Tools.printclass( walker )
print( "Iteration %4d %4d %10.3f %10.3f" % ( self.iteration, walker.id, walker.logL, wlogL ) )
print( fmt( walker.allpars, max=None ) )
raise ValueError( "Inconsistency between stored logL %f and calculated logL %f" %
( walker.logL, wlogL ) )
for ki in walker.fitIndex :
# if walker.fitIndex[ki] >= 0 :
if ki < 0 :
self.errdis.hyperpar[ki].prior.checkLimit( walker.allpars[ki] )
elif ki < walker.problem.model.npars :
walker.problem.model.getPrior( ki ).checkLimit( walker.allpars[ki] )
class ExplorerThread( Thread ):
"""
One thread for the Explorer. It updates one walker.
Attributes
----------
id : int
identity for thread
walkers : WalkerList
list of walkers
rng : numpy.random.RandomState
random number generator
engines : [Engine]
copy of the list of Engines of Explorer
"""
global threadErrors
def __init__( self, name, id, explorer, engines, seed ):
super( ExplorerThread, self ).__init__( name=name )
self.id = id
self.explorer = explorer
self.engines = [eng.copy() for eng in engines]
self.rng = numpy.random.RandomState( seed )
def run( self ):
try :
self.explorer.exploreWalker( self.id, self.explorer.lowLhood,
self.engines, self.rng )
except Exception as e :
threadErrors.append( [repr(e) + " occurred in walker %d" % self.id] )
raise
|
dokesterREPO_NAMEBayesicFittingPATH_START.@BayesicFitting_extracted@BayesicFitting-master@BayesicFitting@source@Explorer.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/gempy/display/__init__.py",
"type": "Python"
}
|
# gempy.display.__init__.py
# Intended to interface as much like imexam as possible
from . import connection
connect = connection.Connect
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@gempy@display@__init__.py@.PATH_END.py
|
{
"filename": "ebosscolorz.py",
"repo_name": "imcgreer/simqso",
"repo_path": "simqso_extracted/simqso-master/sdss/ebosscolorz.py",
"type": "Python"
}
|
#!/usr/bin/env python
import os,sys
from copy import deepcopy
from collections import defaultdict
import numpy as np
from scipy.stats import binned_statistic
import matplotlib.pyplot as plt
from matplotlib import ticker
from astropy.table import Table,hstack
from simqso import sqgrids as grids
import ebosscore
import ebossfit
import ebossmodels
class percfun(object):
def __init__(self,pval):
self.pval = pval
def __call__(self,x):
x = x[x<1e20]
if len(x) < 10:
return np.nan
return np.percentile(x,self.pval)
def calc_colorz(z,clrs,pvals,zedges):
zbins = zedges[:-1] + np.diff(zedges)/2
colorz = np.zeros((len(pvals),clrs.shape[-1],len(zbins)),dtype=np.float32)
for i,pval in enumerate(pvals):
clrz,_,_ = binned_statistic(z,clrs.T,percfun(pval),zedges)
colorz[i] = clrz
return colorz
def sim_colorz(simqsos,pvals,zedges,ratios=True,refBand=None,maglim=None):
b = ebosscore.BandIndexes(simqsos)
if ratios:
fluxk = 'Flux'
d = '/'
else:
fluxk = 'Mag'
d = '-'
if refBand is None:
bnames = [ d.join(bb)
for bb in zip(b.shortNames[:-1],b.shortNames[1:]) ]
else:
bnames = [ b_+d+refBand for b_ in b.shortNames if b_ != refBand ]
colorz = {}
for which in ['syn','obs']:
z = simqsos['z']
flux = simqsos[which+fluxk]
sel = np.ones(len(simqsos),dtype=bool)
if maglim is not None:
sel &= simqsos['obsMag'][:,b(refBand)] < maglim
if which=='obs':
sel &= simqsos['selected']
flux = flux[sel]
z = z[sel]
if ratios:
if refBand is None:
clrs = flux[:,:-1] / flux[:,1:]
else:
clrs = flux / flux[:,[b(refBand)]]
clrs = np.delete(clrs,b(refBand),1)
else:
clrs = -np.diff(flux,axis=1)
# needs to be masked to match observations table
clrs = np.ma.array(clrs)
colorz[which] = calc_colorz(z,clrs,pvals,zedges)
tab = Table(colorz)
return tab,bnames
def ebosscore_colorz(coreqsos,pvals,zedges,maglim=None,
refBand=None,ratios='neighboring'):
photsets = ['sdss','ukidss','wise']
features,names,refFlux = coreqsos.extract_features(featureset=photsets,
refband=refBand,
ratios=ratios)
zqso = coreqsos.specz
if maglim:
refMag = 22.5 - 2.5*np.log10(refFlux.clip(1e-5,np.inf))
ii = np.where(refMag < maglim)[0]
features = features[ii]
zqso = zqso[ii]
clrs = features.filled(1e20)
colorz = calc_colorz(zqso,clrs,pvals,zedges)
return Table(dict(ebosscore=colorz)),names
# mags
#yr = [ (-0.7,4.2), (-0.3,1.7), (-0.15,0.5), (-0.2,0.5), (-0.15,0.75) ]
fratio_yrange = {
'u/g':(-0.3,1.3), 'g/r':(0.1,1.3), 'r/i':(0.5,1.2), 'i/z':(0.5,1.3),
'W1/W2':(0.4,1.3),
}
def colorz_compare(simqsos,coreqsos,maglim=None,refBand=None):
zedges = np.linspace(0.9,4.0,32)
zbins = zedges[:-1] + np.diff(zedges)/2
pvals = [25,50,75]
ratios = 'neighboring' if refBand is None else 'byref'
colorz,simClrNames = sim_colorz(simqsos,pvals,zedges,
refBand=refBand,maglim=maglim)
refBand_ = 'i' if refBand is None else refBand
colorz2,ebossClrNames = ebosscore_colorz(coreqsos,pvals,zedges,
ratios=ratios,refBand=refBand_,
maglim=maglim)
assert np.all(np.array(simClrNames)==np.array(ebossClrNames))
colorz = hstack([colorz,colorz2])
fig = plt.figure(figsize=(9.5,7))
plt.subplots_adjust(0.055,0.05,0.99,0.99,0.23,0.15)
for j,clrName in enumerate(simClrNames):
plt.subplot(4,3,j+1)
for which in ['syn','obs','ebosscore']:
c = {'syn':'C0','obs':'C1','ebosscore':'C2'}[which]
plt.fill_between(zbins,colorz[which][0,j],colorz[which][2,j],
color=c,alpha=0.3)
plt.plot(zbins,colorz[which][1,j],c=c,ls='-')
plt.xlim(0.85,4.05)
yr = fratio_yrange.get(clrName)
if yr:
plt.ylim(*yr)
plt.ylabel(clrName,size=9)
plt.subplot(4,3,12)
plt.hist(simqsos['z'],zbins,log=True)
plt.hist(simqsos['z'][simqsos['selected']],zbins,log=True)
plt.hist(coreqsos.specz,zbins,log=True,alpha=0.5)
plt.xlim(0.85,4.05)
plt.ylabel('n(z)',size=9)
for ax in fig.get_axes():
plt.setp(ax.get_xticklabels()+ax.get_yticklabels(),fontsize=8)
ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.2))
def get_colorz_bins(mrange=(-27,-23),zrange=(0.9,4.0),nm=7,nz=500):
mbins = np.linspace(*tuple(mrange+(nm,)))
zbins = np.linspace(*tuple(zrange+(nz,)))
return mbins,zbins
def model_colorz_tracks(model,forestFile,**kwargs):
mbins,zbins = get_colorz_bins(**kwargs)
nm,nz = len(mbins),len(zbins)
M,z = np.meshgrid(mbins,zbins,indexing='ij')
M = grids.AbsMagVar(grids.FixedSampler(M.flatten()),restWave=1450)
z = grids.RedshiftVar(grids.FixedSampler(z.flatten()))
qsos = grids.QsoSimPoints([M,z],
cosmo=ebosscore.dr9cosmo,
units='luminosity')
qsos = ebosscore.runsim(model,None,forestFile,qsos,
medianforest=True,const=True,nophot=True)
synmags = np.array(qsos.data['synMag'].reshape(nm,nz,-1))
synclrs = -np.diff(synmags,axis=-1)
synfluxes = np.array(qsos.data['synFlux'].reshape(nm,nz,-1))
synfratios = synfluxes[...,:-1] / synfluxes[...,1:]
return dict(mbins=mbins,zbins=zbins,
synmags=synmags,syncolors=synclrs,
synfluxes=synfluxes,synfratios=synfratios,
qsos=qsos)
def colorz_param_trends(modelName,forestFile):
tab = Table()
#
def add_entry(tab,nm,s,cz):
for k1,k2 in [('mags','synmags'),('clrs','syncolors'),
('fluxes','synfluxes'),('fratio','synfratios')]:
k = '_'.join([nm,s,k1])
tab[k] = cz[k2]
#
model = deepcopy(ebossmodels.qso_models[modelName])
cname = model['continuum']
for j in range(len(ebossmodels.cont_models[cname][0])):
for ds in [-0.3,0.0,0.3]:
model['continuum'] = deepcopy(ebossmodels.cont_models[cname])
model['continuum'][0][j] = (model['continuum'][0][j][0]+ds,None)
print(ds,model)
cz = model_colorz_tracks(model,forestFile)
add_entry(tab,'slope%d'%j,'%+4.1f'%ds,cz)
print()
#
model = deepcopy(ebossmodels.qso_models[modelName])
emtemplate = ebossmodels.emline_models[
model['emlines']]['EmissionLineTrendFilename']
if emtemplate.endswith('v6'):
Halpha = 'HA'
else:
Halpha = 'Halpha'
if emtemplate[-2:] in ['v5','v6']:
LyB = 'LyB'
else:
LyB = 'LyB+OVI'
for l in [LyB,'LyA','CIV','MgII','Hbeta',Halpha]:
for scl in [0.5,1.0,2.0]:
model['emlines'] = {'scaleEWs':{},
'EmissionLineTrendFilename':emtemplate}
if l in ['LyA','CIV','MgII','HA']:
for c in 'bn':
model['emlines']['scaleEWs'][l+c] = scl
else:
model['emlines']['scaleEWs'][l] = scl
print(l,model)
cz = model_colorz_tracks(model,forestFile)
add_entry(tab,l,'%3.1f'%scl,cz)
print()
#
if 'dustem' in ebossmodels.qso_models[modelName]:
model = deepcopy(ebossmodels.qso_models[modelName])
dustnm = model['dustem']
comps = ebossmodels.dustem_models[dustnm]
for i,c in enumerate(comps):
for f in [0.5,1.0,2.0]:
model['dustem'] = deepcopy(ebossmodels.dustem_models[dustnm])
pars = model['dustem'][c]
model['dustem'][c] = [(pars[0][0]*f,None)] + pars[1:]
print(f,model)
cz = model_colorz_tracks(model,forestFile)
add_entry(tab,'%sfrac'%c,'%3.1f'%f,cz)
print()
for i,c in enumerate(comps):
for f in [0.7,1.0,1.3]:
model['dustem'] = deepcopy(ebossmodels.dustem_models[dustnm])
pars = model['dustem'][c]
model['dustem'][c] = [pars[0]] + [(pars[1][0]*f,None)]
print(f,model)
cz = model_colorz_tracks(model,forestFile)
add_entry(tab,'%sT'%c,'%3.1f'%f,cz)
print()
return cz['mbins'],cz['zbins'],tab
def plot_trends(modelName,trendFile,coreqsos):
mbins,zbins = get_colorz_bins()
eboss_zedges = np.linspace(0.9,4.0,32)
eboss_zbins = eboss_zedges[:-1] + np.diff(eboss_zedges)/2
pvals = [25,50,75]
obs_colorz = ebosscore_colorz(coreqsos,pvals,eboss_zedges)[0]['ebosscore']
trends = Table.read(trendFile)
pars = defaultdict(list)
for c in trends.colnames:
name,val,which = c.split('_')
if which=='clrs':
pars[name].append(val)
#nrow,ncol = 5,2
nrow,ncol = 3,2
m_i = 3
asmags = False
f = '-' if asmags else '/'
clrnames = [b1+f+b2 for b1,b2 in zip('ugri','griz')] + ['W1'+f+'W2']
pclrs = [ 'C%d'%i for i in range(1,10,2) ]
for p,vals in pars.items():
#plt.figure(figsize=(7.5,8))
plt.figure(figsize=(7.5,5.5))
plt.subplots_adjust(0.10,0.07,0.98,0.94,0.28)
plt.figtext(0.5,0.99,p,ha='center',va='top',size=15)
for pnum,j in enumerate([0,1,2,3,-1],start=1):
ax = plt.subplot(nrow,ncol,pnum)
for _i,v in enumerate(vals):
if asmags:
k = '_'.join([p,v,'clrs'])
else:
k = '_'.join([p,v,'fratio'])
ax.plot(zbins,trends[k][m_i,:,j],label=v,c=pclrs[_i])
c = 'C0'
obs_cz = obs_colorz[:,j]
if asmags:
obs_cz = -2.5*np.log10(obs_cz.clip(1e-2,np.inf))
ax.fill_between(eboss_zbins,obs_cz[0],obs_cz[2],
color=c,alpha=0.3)
ax.plot(eboss_zbins,obs_cz[1],c=c,ls='-')
ax.set_ylabel(clrnames[pnum-1])
ax.set_ylim(fratio_yrange[clrnames[pnum-1]])
plt.setp(ax.get_xticklabels()+ax.get_yticklabels(),fontsize=9)
ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.2))
if j==0: plt.legend()
plt.savefig(modelName+'_'+p+'.pdf')
plt.close()
def plot_model_trends(model='all',forestFile=None):
coreqsos = ebossfit.eBossQsos()
if model=='all':
models = list(ebossmodels.qso_models.keys())
else:
models = [model]
for modelName in models:
trendfn = modelName+'_trends.fits'
if not os.path.exists(trendfn):
m,z,trends = colorz_param_trends(modelName,forestFile)
trends.write(trendfn,overwrite=True)
else:
trends = Table.read(trendfn)
plot_trends(modelName,trendfn,coreqsos)
if __name__=='__main__':
import argparse
parser = argparse.ArgumentParser(
description='run eboss color-z simulations.')
parser.add_argument('fitsfile',nargs='*',type=str,
help='input file name(s)')
parser.add_argument('--forest',type=str,default='sdss_forest_grid',
help='file containing forest grid (default:sdss_forest_grid)')
parser.add_argument('-m','--model',type=str,default='bossdr9',
help='name of quasar model')
parser.add_argument('--trends',action="store_true",
help='show parameter color-z mean trends instead of running sim')
parser.add_argument('--tracks',action="store_true",
help='show color-z mean trends instead of running sim')
parser.add_argument('--maglim',type=float,
help='limiting magnitude')
parser.add_argument('--refband',type=str,
help='colors are relative to reference band instead of '+
'adjoining filters')
args = parser.parse_args()
if args.trends:
plot_model_trends(model=args.model,forestFile=args.forest)
elif args.tracks:
model = ebossmodels.qso_models[args.model]
cz = model_colorz_tracks(model,args.forest)
else:
coreqsos = ebossfit.eBossQsos()
for ff in args.fitsfile:
print(ff)
simqsos = Table.read(ff)
colorz_compare(simqsos,coreqsos,maglim=args.maglim,
refBand=args.refband)
plt.figtext(0.5,0.05,os.path.basename(ff).replace('.fits',''),
ha='center',size=15)
sfx = ''
if args.maglim:
sfx += '_ilt%.1f' % args.maglim
if args.refband:
sfx += '_ref-%s' % args.refband
plt.savefig(ff.replace('.fits','_colorz'+sfx+'.pdf'))
|
imcgreerREPO_NAMEsimqsoPATH_START.@simqso_extracted@simqso-master@sdss@ebosscolorz.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/pie/hoverlabel/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._namelengthsrc import NamelengthsrcValidator
from ._namelength import NamelengthValidator
from ._font import FontValidator
from ._bordercolorsrc import BordercolorsrcValidator
from ._bordercolor import BordercolorValidator
from ._bgcolorsrc import BgcolorsrcValidator
from ._bgcolor import BgcolorValidator
from ._alignsrc import AlignsrcValidator
from ._align import AlignValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._namelengthsrc.NamelengthsrcValidator",
"._namelength.NamelengthValidator",
"._font.FontValidator",
"._bordercolorsrc.BordercolorsrcValidator",
"._bordercolor.BordercolorValidator",
"._bgcolorsrc.BgcolorsrcValidator",
"._bgcolor.BgcolorValidator",
"._alignsrc.AlignsrcValidator",
"._align.AlignValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@pie@hoverlabel@__init__.py@.PATH_END.py
|
{
"filename": "test_matfuncs.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/scipy/py3/scipy/sparse/linalg/tests/test_matfuncs.py",
"type": "Python"
}
|
#
# Created by: Pearu Peterson, March 2002
#
""" Test functions for scipy.linalg._matfuncs module
"""
import math
import numpy as np
from numpy import array, eye, exp, random
from numpy.linalg import matrix_power
from numpy.testing import (
assert_allclose, assert_, assert_array_almost_equal, assert_equal,
assert_array_almost_equal_nulp, suppress_warnings)
from scipy.sparse import csc_matrix, SparseEfficiencyWarning
from scipy.sparse._construct import eye as speye
from scipy.sparse.linalg._matfuncs import (expm, _expm,
ProductOperator, MatrixPowerOperator,
_onenorm_matrix_power_nnm)
from scipy.sparse._sputils import matrix
from scipy.linalg import logm
from scipy.special import factorial, binom
import scipy.sparse
import scipy.sparse.linalg
def _burkardt_13_power(n, p):
"""
A helper function for testing matrix functions.
Parameters
----------
n : integer greater than 1
Order of the square matrix to be returned.
p : non-negative integer
Power of the matrix.
Returns
-------
out : ndarray representing a square matrix
A Forsythe matrix of order n, raised to the power p.
"""
# Input validation.
if n != int(n) or n < 2:
raise ValueError('n must be an integer greater than 1')
n = int(n)
if p != int(p) or p < 0:
raise ValueError('p must be a non-negative integer')
p = int(p)
# Construct the matrix explicitly.
a, b = divmod(p, n)
large = np.power(10.0, -n*a)
small = large * np.power(10.0, -n)
return np.diag([large]*(n-b), b) + np.diag([small]*b, b-n)
def test_onenorm_matrix_power_nnm():
np.random.seed(1234)
for n in range(1, 5):
for p in range(5):
M = np.random.random((n, n))
Mp = np.linalg.matrix_power(M, p)
observed = _onenorm_matrix_power_nnm(M, p)
expected = np.linalg.norm(Mp, 1)
assert_allclose(observed, expected)
class TestExpM:
def test_zero_ndarray(self):
a = array([[0.,0],[0,0]])
assert_array_almost_equal(expm(a),[[1,0],[0,1]])
def test_zero_sparse(self):
a = csc_matrix([[0.,0],[0,0]])
assert_array_almost_equal(expm(a).toarray(),[[1,0],[0,1]])
def test_zero_matrix(self):
a = matrix([[0.,0],[0,0]])
assert_array_almost_equal(expm(a),[[1,0],[0,1]])
def test_misc_types(self):
A = expm(np.array([[1]]))
assert_allclose(expm(((1,),)), A)
assert_allclose(expm([[1]]), A)
assert_allclose(expm(matrix([[1]])), A)
assert_allclose(expm(np.array([[1]])), A)
assert_allclose(expm(csc_matrix([[1]])).A, A)
B = expm(np.array([[1j]]))
assert_allclose(expm(((1j,),)), B)
assert_allclose(expm([[1j]]), B)
assert_allclose(expm(matrix([[1j]])), B)
assert_allclose(expm(csc_matrix([[1j]])).A, B)
def test_bidiagonal_sparse(self):
A = csc_matrix([
[1, 3, 0],
[0, 1, 5],
[0, 0, 2]], dtype=float)
e1 = math.exp(1)
e2 = math.exp(2)
expected = np.array([
[e1, 3*e1, 15*(e2 - 2*e1)],
[0, e1, 5*(e2 - e1)],
[0, 0, e2]], dtype=float)
observed = expm(A).toarray()
assert_array_almost_equal(observed, expected)
def test_padecases_dtype_float(self):
for dtype in [np.float32, np.float64]:
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
A = scale * eye(3, dtype=dtype)
observed = expm(A)
expected = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(observed, expected, nulp=100)
def test_padecases_dtype_complex(self):
for dtype in [np.complex64, np.complex128]:
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
A = scale * eye(3, dtype=dtype)
observed = expm(A)
expected = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
assert_array_almost_equal_nulp(observed, expected, nulp=100)
def test_padecases_dtype_sparse_float(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.float64
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale, dtype=dtype) * eye(3, dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csc_matrix is expensive.")
exact_onenorm = _expm(a, use_exact_onenorm=True).toarray()
inexact_onenorm = _expm(a, use_exact_onenorm=False).toarray()
assert_array_almost_equal_nulp(exact_onenorm, e, nulp=100)
assert_array_almost_equal_nulp(inexact_onenorm, e, nulp=100)
def test_padecases_dtype_sparse_complex(self):
# float32 and complex64 lead to errors in spsolve/UMFpack
dtype = np.complex128
for scale in [1e-2, 1e-1, 5e-1, 1, 10]:
a = scale * speye(3, 3, dtype=dtype, format='csc')
e = exp(scale) * eye(3, dtype=dtype)
with suppress_warnings() as sup:
sup.filter(SparseEfficiencyWarning,
"Changing the sparsity structure of a csc_matrix is expensive.")
assert_array_almost_equal_nulp(expm(a).toarray(), e, nulp=100)
def test_logm_consistency(self):
random.seed(1234)
for dtype in [np.float64, np.complex128]:
for n in range(1, 10):
for scale in [1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2]:
# make logm(A) be of a given scale
A = (eye(n) + random.rand(n, n) * scale).astype(dtype)
if np.iscomplexobj(A):
A = A + 1j * random.rand(n, n) * scale
assert_array_almost_equal(expm(logm(A)), A)
def test_integer_matrix(self):
Q = np.array([
[-3, 1, 1, 1],
[1, -3, 1, 1],
[1, 1, -3, 1],
[1, 1, 1, -3]])
assert_allclose(expm(Q), expm(1.0 * Q))
def test_integer_matrix_2(self):
# Check for integer overflows
Q = np.array([[-500, 500, 0, 0],
[0, -550, 360, 190],
[0, 630, -630, 0],
[0, 0, 0, 0]], dtype=np.int16)
assert_allclose(expm(Q), expm(1.0 * Q))
Q = csc_matrix(Q)
assert_allclose(expm(Q).A, expm(1.0 * Q).A)
def test_triangularity_perturbation(self):
# Experiment (1) of
# Awad H. Al-Mohy and Nicholas J. Higham (2012)
# Improved Inverse Scaling and Squaring Algorithms
# for the Matrix Logarithm.
A = np.array([
[3.2346e-1, 3e4, 3e4, 3e4],
[0, 3.0089e-1, 3e4, 3e4],
[0, 0, 3.221e-1, 3e4],
[0, 0, 0, 3.0744e-1]],
dtype=float)
A_logm = np.array([
[-1.12867982029050462e+00, 9.61418377142025565e+04,
-4.52485573953179264e+09, 2.92496941103871812e+14],
[0.00000000000000000e+00, -1.20101052953082288e+00,
9.63469687211303099e+04, -4.68104828911105442e+09],
[0.00000000000000000e+00, 0.00000000000000000e+00,
-1.13289322264498393e+00, 9.53249183094775653e+04],
[0.00000000000000000e+00, 0.00000000000000000e+00,
0.00000000000000000e+00, -1.17947533272554850e+00]],
dtype=float)
assert_allclose(expm(A_logm), A, rtol=1e-4)
# Perturb the upper triangular matrix by tiny amounts,
# so that it becomes technically not upper triangular.
random.seed(1234)
tiny = 1e-17
A_logm_perturbed = A_logm.copy()
A_logm_perturbed[1, 0] = tiny
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "Ill-conditioned.*")
A_expm_logm_perturbed = expm(A_logm_perturbed)
rtol = 1e-4
atol = 100 * tiny
assert_(not np.allclose(A_expm_logm_perturbed, A, rtol=rtol, atol=atol))
def test_burkardt_1(self):
# This matrix is diagonal.
# The calculation of the matrix exponential is simple.
#
# This is the first of a series of matrix exponential tests
# collected by John Burkardt from the following sources.
#
# Alan Laub,
# Review of "Linear System Theory" by Joao Hespanha,
# SIAM Review,
# Volume 52, Number 4, December 2010, pages 779--781.
#
# Cleve Moler and Charles Van Loan,
# Nineteen Dubious Ways to Compute the Exponential of a Matrix,
# Twenty-Five Years Later,
# SIAM Review,
# Volume 45, Number 1, March 2003, pages 3--49.
#
# Cleve Moler,
# Cleve's Corner: A Balancing Act for the Matrix Exponential,
# 23 July 2012.
#
# Robert Ward,
# Numerical computation of the matrix exponential
# with accuracy estimate,
# SIAM Journal on Numerical Analysis,
# Volume 14, Number 4, September 1977, pages 600--610.
exp1 = np.exp(1)
exp2 = np.exp(2)
A = np.array([
[1, 0],
[0, 2],
], dtype=float)
desired = np.array([
[exp1, 0],
[0, exp2],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_2(self):
# This matrix is symmetric.
# The calculation of the matrix exponential is straightforward.
A = np.array([
[1, 3],
[3, 2],
], dtype=float)
desired = np.array([
[39.322809708033859, 46.166301438885753],
[46.166301438885768, 54.711576854329110],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_3(self):
# This example is due to Laub.
# This matrix is ill-suited for the Taylor series approach.
# As powers of A are computed, the entries blow up too quickly.
exp1 = np.exp(1)
exp39 = np.exp(39)
A = np.array([
[0, 1],
[-39, -40],
], dtype=float)
desired = np.array([
[
39/(38*exp1) - 1/(38*exp39),
-np.expm1(-38) / (38*exp1)],
[
39*np.expm1(-38) / (38*exp1),
-1/(38*exp1) + 39/(38*exp39)],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_4(self):
# This example is due to Moler and Van Loan.
# The example will cause problems for the series summation approach,
# as well as for diagonal Pade approximations.
A = np.array([
[-49, 24],
[-64, 31],
], dtype=float)
U = np.array([[3, 1], [4, 2]], dtype=float)
V = np.array([[1, -1/2], [-2, 3/2]], dtype=float)
w = np.array([-17, -1], dtype=float)
desired = np.dot(U * np.exp(w), V)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_5(self):
# This example is due to Moler and Van Loan.
# This matrix is strictly upper triangular
# All powers of A are zero beyond some (low) limit.
# This example will cause problems for Pade approximations.
A = np.array([
[0, 6, 0, 0],
[0, 0, 6, 0],
[0, 0, 0, 6],
[0, 0, 0, 0],
], dtype=float)
desired = np.array([
[1, 6, 18, 36],
[0, 1, 6, 18],
[0, 0, 1, 6],
[0, 0, 0, 1],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_6(self):
# This example is due to Moler and Van Loan.
# This matrix does not have a complete set of eigenvectors.
# That means the eigenvector approach will fail.
exp1 = np.exp(1)
A = np.array([
[1, 1],
[0, 1],
], dtype=float)
desired = np.array([
[exp1, exp1],
[0, exp1],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_7(self):
# This example is due to Moler and Van Loan.
# This matrix is very close to example 5.
# Mathematically, it has a complete set of eigenvectors.
# Numerically, however, the calculation will be suspect.
exp1 = np.exp(1)
eps = np.spacing(1)
A = np.array([
[1 + eps, 1],
[0, 1 - eps],
], dtype=float)
desired = np.array([
[exp1, exp1],
[0, exp1],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_8(self):
# This matrix was an example in Wikipedia.
exp4 = np.exp(4)
exp16 = np.exp(16)
A = np.array([
[21, 17, 6],
[-5, -1, -6],
[4, 4, 16],
], dtype=float)
desired = np.array([
[13*exp16 - exp4, 13*exp16 - 5*exp4, 2*exp16 - 2*exp4],
[-9*exp16 + exp4, -9*exp16 + 5*exp4, -2*exp16 + 2*exp4],
[16*exp16, 16*exp16, 4*exp16],
], dtype=float) * 0.25
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_9(self):
# This matrix is due to the NAG Library.
# It is an example for function F01ECF.
A = np.array([
[1, 2, 2, 2],
[3, 1, 1, 2],
[3, 2, 1, 2],
[3, 3, 3, 1],
], dtype=float)
desired = np.array([
[740.7038, 610.8500, 542.2743, 549.1753],
[731.2510, 603.5524, 535.0884, 542.2743],
[823.7630, 679.4257, 603.5524, 610.8500],
[998.4355, 823.7630, 731.2510, 740.7038],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_10(self):
# This is Ward's example #1.
# It is defective and nonderogatory.
A = np.array([
[4, 2, 0],
[1, 4, 1],
[1, 1, 4],
], dtype=float)
assert_allclose(sorted(scipy.linalg.eigvals(A)), (3, 3, 6))
desired = np.array([
[147.8666224463699, 183.7651386463682, 71.79703239999647],
[127.7810855231823, 183.7651386463682, 91.88256932318415],
[127.7810855231824, 163.6796017231806, 111.9681062463718],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_11(self):
# This is Ward's example #2.
# It is a symmetric matrix.
A = np.array([
[29.87942128909879, 0.7815750847907159, -2.289519314033932],
[0.7815750847907159, 25.72656945571064, 8.680737820540137],
[-2.289519314033932, 8.680737820540137, 34.39400925519054],
], dtype=float)
assert_allclose(scipy.linalg.eigvalsh(A), (20, 30, 40))
desired = np.array([
[
5.496313853692378E+15,
-1.823188097200898E+16,
-3.047577080858001E+16],
[
-1.823188097200899E+16,
6.060522870222108E+16,
1.012918429302482E+17],
[
-3.047577080858001E+16,
1.012918429302482E+17,
1.692944112408493E+17],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_12(self):
# This is Ward's example #3.
# Ward's algorithm has difficulty estimating the accuracy
# of its results.
A = np.array([
[-131, 19, 18],
[-390, 56, 54],
[-387, 57, 52],
], dtype=float)
assert_allclose(sorted(scipy.linalg.eigvals(A)), (-20, -2, -1))
desired = np.array([
[-1.509644158793135, 0.3678794391096522, 0.1353352811751005],
[-5.632570799891469, 1.471517758499875, 0.4060058435250609],
[-4.934938326088363, 1.103638317328798, 0.5413411267617766],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_burkardt_13(self):
# This is Ward's example #4.
# This is a version of the Forsythe matrix.
# The eigenvector problem is badly conditioned.
# Ward's algorithm has difficulty esimating the accuracy
# of its results for this problem.
#
# Check the construction of one instance of this family of matrices.
A4_actual = _burkardt_13_power(4, 1)
A4_desired = [[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[1e-4, 0, 0, 0]]
assert_allclose(A4_actual, A4_desired)
# Check the expm for a few instances.
for n in (2, 3, 4, 10):
# Approximate expm using Taylor series.
# This works well for this matrix family
# because each matrix in the summation,
# even before dividing by the factorial,
# is entrywise positive with max entry 10**(-floor(p/n)*n).
k = max(1, int(np.ceil(16/n)))
desired = np.zeros((n, n), dtype=float)
for p in range(n*k):
Ap = _burkardt_13_power(n, p)
assert_equal(np.min(Ap), 0)
assert_allclose(np.max(Ap), np.power(10, -np.floor(p/n)*n))
desired += Ap / factorial(p)
actual = expm(_burkardt_13_power(n, 1))
assert_allclose(actual, desired)
def test_burkardt_14(self):
# This is Moler's example.
# This badly scaled matrix caused problems for MATLAB's expm().
A = np.array([
[0, 1e-8, 0],
[-(2e10 + 4e8/6.), -3, 2e10],
[200./3., 0, -200./3.],
], dtype=float)
desired = np.array([
[0.446849468283175, 1.54044157383952e-09, 0.462811453558774],
[-5743067.77947947, -0.0152830038686819, -4526542.71278401],
[0.447722977849494, 1.54270484519591e-09, 0.463480648837651],
], dtype=float)
actual = expm(A)
assert_allclose(actual, desired)
def test_pascal(self):
# Test pascal triangle.
# Nilpotent exponential, used to trigger a failure (gh-8029)
for scale in [1.0, 1e-3, 1e-6]:
for n in range(0, 80, 3):
sc = scale ** np.arange(n, -1, -1)
if np.any(sc < 1e-300):
break
A = np.diag(np.arange(1, n + 1), -1) * scale
B = expm(A)
got = B
expected = binom(np.arange(n + 1)[:,None],
np.arange(n + 1)[None,:]) * sc[None,:] / sc[:,None]
atol = 1e-13 * abs(expected).max()
assert_allclose(got, expected, atol=atol)
def test_matrix_input(self):
# Large np.matrix inputs should work, gh-5546
A = np.zeros((200, 200))
A[-1,0] = 1
B0 = expm(A)
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, "the matrix subclass.*")
sup.filter(PendingDeprecationWarning, "the matrix subclass.*")
B = expm(np.matrix(A))
assert_allclose(B, B0)
def test_exp_sinch_overflow(self):
# Check overflow in intermediate steps is fixed (gh-11839)
L = np.array([[1.0, -0.5, -0.5, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, -0.5, -0.5, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, -0.5, -0.5],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
E0 = expm(-L)
E1 = expm(-2**11 * L)
E2 = E0
for j in range(11):
E2 = E2 @ E2
assert_allclose(E1, E2)
class TestOperators:
def test_product_operator(self):
random.seed(1234)
n = 5
k = 2
nsamples = 10
for i in range(nsamples):
A = np.random.randn(n, n)
B = np.random.randn(n, n)
C = np.random.randn(n, n)
D = np.random.randn(n, k)
op = ProductOperator(A, B, C)
assert_allclose(op.matmat(D), A.dot(B).dot(C).dot(D))
assert_allclose(op.T.matmat(D), (A.dot(B).dot(C)).T.dot(D))
def test_matrix_power_operator(self):
random.seed(1234)
n = 5
k = 2
p = 3
nsamples = 10
for i in range(nsamples):
A = np.random.randn(n, n)
B = np.random.randn(n, k)
op = MatrixPowerOperator(A, p)
assert_allclose(op.matmat(B), matrix_power(A, p).dot(B))
assert_allclose(op.T.matmat(B), matrix_power(A, p).T.dot(B))
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@scipy@py3@scipy@sparse@linalg@tests@test_matfuncs.py@.PATH_END.py
|
{
"filename": "_cmax.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolargl/marker/line/_cmax.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CmaxValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="cmax", parent_name="scatterpolargl.marker.line", **kwargs
):
super(CmaxValidator, 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}),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolargl@marker@line@_cmax.py@.PATH_END.py
|
{
"filename": "00bMakeDir.py",
"repo_name": "MehrnooshTahani/MappingBLOS_MolecularClouds",
"repo_path": "MappingBLOS_MolecularClouds_extracted/MappingBLOS_MolecularClouds-master/MolecularClouds/00bMakeDir.py",
"type": "Python"
}
|
"""
This is the zeroth stage of the BLOSMapping method where the necessary directories are made
- When run, the file will ask for the name of a region of interest. It will then check to see of this region of
interest has a folder yet, if not it will make the needed folders and sub-folders.
"""
import os
import Classes.config as config
# -------- CHOOSE THE REGION OF INTEREST --------
cloudName = input("Enter the name of the region of interest: ")
cloudName = cloudName.capitalize() # Ensure only the first letter is capitalized
#cloudName = config.cloud
# -------- CHOOSE THE REGION OF INTEREST. --------
# -------- MAKE DIRECTORIES FOR THE REGION OF INTEREST --------
# Check if there is a FileOutput directory already; if not make one
# - this will house the results for all of regions of interest
os.chdir(config.dir_root)
InHome = os.listdir(config.dir_root)
if config.dir_fileOutput not in InHome:
os.mkdir(config.dir_fileOutput)
# Move into the FileOutput directory
os.chdir(config.dir_fileOutput)
# Check if there is a directory for the specified region of interest already; if not make one
InFileOutput = os.listdir()
InFileOutput = [item.lower() for item in InFileOutput]
if cloudName.lower() not in InFileOutput:
os.mkdir(cloudName)
# Move into the directory for the region of interest and make subsequent directories to house its results
os.chdir(cloudName)
os.mkdir(config.dir_plots)
os.mkdir(config.dir_densitySensitivity)
os.mkdir(config.dir_temperatureSensitivity)
print('Folder \'' + cloudName + '\' with sub-folders: \'' + config.dir_plots + '\', \'' + config.dir_densitySensitivity + '\', and \'' + config.dir_temperatureSensitivity + '\''
' created in {}'.format(os.path.join(config.dir_root, config.dir_fileOutput)))
# -------- MAKE DIRECTORIES FOR THE REGION OF INTEREST. --------
|
MehrnooshTahaniREPO_NAMEMappingBLOS_MolecularCloudsPATH_START.@MappingBLOS_MolecularClouds_extracted@MappingBLOS_MolecularClouds-master@MolecularClouds@00bMakeDir.py@.PATH_END.py
|
{
"filename": "population_model.py",
"repo_name": "tcallister/learning-p-det",
"repo_path": "learning-p-det_extracted/learning-p-det-main/code/population_model.py",
"type": "Python"
}
|
import numpyro
import numpyro.distributions as dist
import jax
import jax.numpy as jnp
from jax.scipy.special import erf,erfinv
from jax import vmap
import numpy as np
import astropy.units as u
from astropy.cosmology import Planck15,z_at_value
from interpax import interp1d
logit_std = 2.5
tmp_max = 100.
tmp_min = 2.
def truncatedNormal(samples, mu, sigma, lowCutoff, highCutoff):
"""
Jax-enabled truncated normal distribution
Parameters
----------
samples : `jax.numpy.array` or float
Locations at which to evaluate probability density
mu : float
Mean of truncated normal
sigma : float
Standard deviation of truncated normal
lowCutoff : float
Lower truncation bound
highCutoff : float
Upper truncation bound
Returns
-------
ps : jax.numpy.array or float
Probability density at the locations of `samples`
"""
a = (lowCutoff-mu)/jnp.sqrt(2*sigma**2)
b = (highCutoff-mu)/jnp.sqrt(2*sigma**2)
norm = jnp.sqrt(sigma**2*np.pi/2)*(-erf(a) + erf(b))
ps = jnp.exp(-(samples-mu)**2/(2.*sigma**2))/norm
return ps
def massModel(m1, alpha, mu_m1, sig_m1, f_peak, mMax, mMin, dmMax, dmMin):
"""
Baseline primary mass model, described as a mixture between a power law
and gaussian, with exponential tapering functions at high and low masses
Parameters
----------
m1 : array or float
Primary masses at which to evaluate probability densities
alpha : float
Power-law index
mu_m1 : float
Location of possible Gaussian peak
sig_m1 : float
Stanard deviation of possible Gaussian peak
f_peak : float
Approximate fraction of events contained within Gaussian peak (not exact due to tapering)
mMax : float
Location at which high-mass tapering begins
mMin : float
Location at which low-mass tapering begins
dmMax : float
Scale width of high-mass tapering function
dmMin : float
Scale width of low-mass tapering function
Returns
-------
p_m1s : jax.numpy.array
Unnormalized array of probability densities
"""
# Define power-law and peak
p_m1_pl = (1.+alpha)*m1**(alpha)/(tmp_max**(1.+alpha) - tmp_min**(1.+alpha))
p_m1_peak = jnp.exp(-(m1-mu_m1)**2/(2.*sig_m1**2))/jnp.sqrt(2.*np.pi*sig_m1**2)
# Compute low- and high-mass filters
low_filter = jnp.exp(-(m1-mMin)**2/(2.*dmMin**2))
low_filter = jnp.where(m1<mMin, low_filter, 1.)
high_filter = jnp.exp(-(m1-mMax)**2/(2.*dmMax**2))
high_filter = jnp.where(m1>mMax, high_filter, 1.)
# Apply filters to combined power-law and peak
return (f_peak*p_m1_peak + (1.-f_peak)*p_m1_pl)*low_filter*high_filter
def get_value_from_logit(logit_x, x_min, x_max):
"""
Function to map a variable `logit_x`, defined on `(-inf,+inf)`, to a quantity `x`
defined on the interval `(x_min,x_max)`.
Parameters
----------
logit_x : float
Quantity to inverse-logit transform
x_min : float
Lower bound of `x`
x_max : float
Upper bound of `x`
Returns
-------
x : float
The inverse logit transform of `logit_x`
dlogit_dx : float
The Jacobian between `logit_x` and `x`; divide by this quantity to convert a uniform prior on `logit_x` to a uniform prior on `x`
"""
exp_logit = jnp.exp(logit_x)
x = (exp_logit*x_max + x_min)/(1.+exp_logit)
dlogit_dx = 1./(x-x_min) + 1./(x_max-x)
return x,dlogit_dx
def baseline(sampleDict, injectionDict):
"""
Implementation of a Gaussian effective spin distribution for inference within `numpyro`
Parameters
----------
sampleDict : dict
Precomputed dictionary containing posterior samples for each event in our catalog
injectionDict : dict
Precomputed dictionary containing successfully recovered injections
Returns
-------
None
"""
#############################
# Sample our hyperparameters
#############################
logR20 = numpyro.sample("logR20", dist.Uniform(-12, 12))
alpha = numpyro.sample("alpha", dist.Normal(-2, 3))
mu_m1 = numpyro.sample("mu_m1", dist.Uniform(20, 50))
mMin = numpyro.sample("mMin", dist.Uniform(5, 15))
bq = numpyro.sample("bq", dist.Normal(0, 3))
kappa = numpyro.sample("kappa", dist.Normal(0, 5))
R20 = numpyro.deterministic("R20", 10.**logR20)
# For some hyperparameters, sample from unconstrained logit space
logit_sig_m1 = numpyro.sample("logit_sig_m1", dist.Normal(0, logit_std))
logit_log_f_peak = numpyro.sample("logit_log_f_peak", dist.Normal(0, logit_std))
logit_mMax = numpyro.sample("logit_mMax", dist.Normal(0, logit_std))
logit_log_dmMin = numpyro.sample("logit_log_dmMin", dist.Normal(0, logit_std))
logit_log_dmMax = numpyro.sample("logit_log_dmMax", dist.Normal(0, logit_std))
logit_mu_chi = numpyro.sample("logit_mu_chi", dist.Normal(0, logit_std))
logit_logsig_chi = numpyro.sample("logit_logsig_chi", dist.Normal(0, logit_std))
logit_mu_cost = numpyro.sample("logit_mu_cost", dist.Normal(0, logit_std))
logit_sig_cost = numpyro.sample("logit_sig_cost", dist.Normal(0, logit_std))
logit_f_iso = numpyro.sample("logit_f_iso", dist.Normal(0, logit_std))
# Convert to physical parameters, and get relevant jacobian
sig_m1, jac_sig_m1 = get_value_from_logit(logit_sig_m1, 2., 15.)
log_f_peak, jac_log_f_peak = get_value_from_logit(logit_log_f_peak, -6, 0.)
mMax, jac_mMax = get_value_from_logit(logit_mMax, 50., 100.)
log_dmMin, jac_log_dmMin = get_value_from_logit(logit_log_dmMin, -1, 1)
log_dmMax, jac_log_dmMax = get_value_from_logit(logit_log_dmMax, 0.5, 1.5)
mu_chi, jac_mu_chi = get_value_from_logit(logit_mu_chi, 0., 1.)
logsig_chi, jac_logsig_chi = get_value_from_logit(logit_logsig_chi, -1., 0.)
mu_cost, jac_mu_cost = get_value_from_logit(logit_mu_cost, -1., 1.)
sig_cost, jac_sig_cost = get_value_from_logit(logit_sig_cost, 0.15, 2.5)
f_iso, jac_f_iso = get_value_from_logit(logit_f_iso, 0, 1)
# Record parameters
numpyro.deterministic("sig_m1", sig_m1)
numpyro.deterministic("log_f_peak", log_f_peak)
numpyro.deterministic("mMax", mMax)
numpyro.deterministic("log_dmMin", log_dmMin)
numpyro.deterministic("log_dmMax", log_dmMax)
numpyro.deterministic("mu_chi", mu_chi)
numpyro.deterministic("logsig_chi", logsig_chi)
numpyro.deterministic("mu_cost", mu_cost)
numpyro.deterministic("sig_cost", sig_cost)
numpyro.deterministic("f_iso", f_iso)
# Convert to uniform prior by undoing normal prior in logit space and applying jacobian
numpyro.factor("p_sig_m1", logit_sig_m1**2/(2.*logit_std**2)-jnp.log(jac_sig_m1))
numpyro.factor("p_log_f_peak", logit_log_f_peak**2/(2.*logit_std**2)-jnp.log(jac_log_f_peak))
numpyro.factor("p_mMax", logit_mMax**2/(2.*logit_std**2)-jnp.log(jac_mMax))
numpyro.factor("p_log_dmMin", logit_log_dmMin**2/(2.*logit_std**2)-jnp.log(jac_log_dmMin))
numpyro.factor("p_log_dmMax", logit_log_dmMax**2/(2.*logit_std**2)-jnp.log(jac_log_dmMax))
numpyro.factor("p_mu_chi", logit_mu_chi**2/(2.*logit_std**2)-jnp.log(jac_mu_chi))
numpyro.factor("p_logsig_chi", logit_logsig_chi**2/(2.*logit_std**2)-jnp.log(jac_logsig_chi))
numpyro.factor("p_mu_cost", logit_mu_cost**2/(2.*logit_std**2)-jnp.log(jac_mu_cost))
numpyro.factor("p_sig_cost", logit_sig_cost**2/(2.*logit_std**2)-jnp.log(jac_sig_cost))
numpyro.factor("p_f_iso", logit_f_iso**2/(2.*logit_std**2)-jnp.log(jac_f_iso))
# Normalization
p_m1_norm = massModel(20., alpha, mu_m1, sig_m1, 10.**log_f_peak, mMax, mMin, 10.**log_dmMax, 10.**log_dmMin)
p_z_norm = (1.+0.2)**kappa
########################################
# Compute expected number of detections
########################################
# Read out found injections
# Note that `pop_reweight` is the inverse of the draw weights for each event
a1_det = injectionDict['a1']
a2_det = injectionDict['a2']
cost1_det = injectionDict['cost1']
cost2_det = injectionDict['cost2']
m1_det = injectionDict['m1']
m2_det = injectionDict['m2']
z_det = injectionDict['z']
dVdz_det = injectionDict['dVdz']
p_draw = injectionDict['p_draw_m1m2z']*injectionDict['p_draw_a1a2cost1cost2']
# Compute proposed population weights
p_m1_det = massModel(m1_det, alpha, mu_m1, sig_m1, 10.**log_f_peak, mMax, mMin, 10.**log_dmMax, 10.**log_dmMin)/p_m1_norm
p_m2_det = (1.+bq)*m2_det**bq/(m1_det**(1.+bq)-tmp_min**(1.+bq))
p_a1_det = truncatedNormal(a1_det, mu_chi, 10.**logsig_chi, 0, 1)
p_a2_det = truncatedNormal(a2_det, mu_chi, 10.**logsig_chi, 0, 1)
p_cost1_det = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost1_det, mu_cost, sig_cost, -1, 1)
p_cost2_det = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost2_det, mu_cost, sig_cost, -1, 1)
p_z_det = dVdz_det*(1.+z_det)**(kappa-1.)/p_z_norm
R_pop_det = R20*p_m1_det*p_m2_det*p_z_det*p_a1_det*p_a2_det*p_cost1_det*p_cost2_det
# Form ratio of proposed weights over draw weights
inj_weights = R_pop_det/(p_draw)
# As a fit diagnostic, compute effective number of injections
nEff_inj = jnp.sum(inj_weights)**2/jnp.sum(inj_weights**2)
nObs = 1.0*len(sampleDict)
numpyro.deterministic("nEff_inj_per_event", nEff_inj/nObs)
# Compute net detection efficiency and add to log-likelihood
Nexp = jnp.sum(inj_weights)/injectionDict['nTrials']
numpyro.factor("rate", -Nexp)
# This function defines the per-event log-likelihood
def logp(m1_sample, m2_sample, z_sample, dVdz_sample, a1_sample, a2_sample, cost1_sample, cost2_sample, priors):
# Compute proposed population weights
p_m1 = massModel(m1_sample, alpha, mu_m1, sig_m1, 10.**log_f_peak, mMax, mMin, 10.**log_dmMax, 10.**log_dmMin)/p_m1_norm
p_m2 = (1.+bq)*m2_sample**bq/(m1_sample**(1.+bq)-tmp_min**(1.+bq))
p_a1 = truncatedNormal(a1_sample, mu_chi, 10.**logsig_chi, 0, 1)
p_a2 = truncatedNormal(a2_sample, mu_chi, 10.**logsig_chi, 0, 1)
p_cost1 = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost1_sample, mu_cost, sig_cost, -1, 1)
p_cost2 = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost2_sample, mu_cost, sig_cost, -1, 1)
p_z = dVdz_sample*(1.+z_sample)**(kappa-1.)/p_z_norm
R_pop = R20*p_m1*p_m2*p_z*p_a1*p_a2*p_cost1*p_cost2
mc_weights = R_pop/priors
# Compute effective number of samples and return log-likelihood
n_eff = jnp.sum(mc_weights)**2/jnp.sum(mc_weights**2)
return jnp.log(jnp.mean(mc_weights)), n_eff #m_choice
# Map the log-likelihood function over each event in our catalog
log_ps, n_effs = vmap(logp)(
jnp.array([sampleDict[k]['m1'] for k in sampleDict]),
jnp.array([sampleDict[k]['m2'] for k in sampleDict]),
jnp.array([sampleDict[k]['z'] for k in sampleDict]),
jnp.array([sampleDict[k]['dVc_dz'] for k in sampleDict]),
jnp.array([sampleDict[k]['a1'] for k in sampleDict]),
jnp.array([sampleDict[k]['a2'] for k in sampleDict]),
jnp.array([sampleDict[k]['cost1'] for k in sampleDict]),
jnp.array([sampleDict[k]['cost2'] for k in sampleDict]),
jnp.array([sampleDict[k]['z_prior'] for k in sampleDict]))
# As a diagnostic, save minimum number of effective samples across all events
numpyro.deterministic('min_log_neff', jnp.min(jnp.log10(n_effs)))
# Tally log-likelihoods across our catalog
numpyro.factor("logp", jnp.sum(log_ps))
def baseline_dynamicInjections(sampleDict,injectionCDFs,Pdet):
"""
Implementation of a Gaussian effective spin distribution for inference within `numpyro`
Parameters
----------
sampleDict : `dict`
Precomputed dictionary containing posterior samples for each event in our catalog
injectionDict : `dict`
Precomputed dictionary containing successfully recovered injections
Pdet : `pdet.emulator`
Callable emulator object evaluating trained detection probabilities.
"""
#############################
# Sample our hyperparameters
#############################
logR20 = numpyro.sample("logR20", dist.Uniform(-12, 12))
alpha = numpyro.sample("alpha", dist.Normal(-2, 3))
mu_m1 = numpyro.sample("mu_m1", dist.Uniform(20, 50))
mMin = numpyro.sample("mMin", dist.Uniform(5, 15))
bq = numpyro.sample("bq", dist.Normal(0, 3))
kappa = numpyro.sample("kappa", dist.Normal(0, 5))
R20 = numpyro.deterministic("R20", 10.**logR20)
# For some hyperparameters, sample from unconstrained logit space
logit_sig_m1 = numpyro.sample("logit_sig_m1", dist.Normal(0, logit_std))
logit_log_f_peak = numpyro.sample("logit_log_f_peak", dist.Normal(0, logit_std))
logit_mMax = numpyro.sample("logit_mMax", dist.Normal(0, logit_std))
logit_log_dmMin = numpyro.sample("logit_log_dmMin", dist.Normal(0, logit_std))
logit_log_dmMax = numpyro.sample("logit_log_dmMax", dist.Normal(0, logit_std))
logit_mu_chi = numpyro.sample("logit_mu_chi", dist.Normal(0, logit_std))
logit_logsig_chi = numpyro.sample("logit_logsig_chi", dist.Normal(0, logit_std))
logit_mu_cost = numpyro.sample("logit_mu_cost", dist.Normal(0, logit_std))
logit_sig_cost = numpyro.sample("logit_sig_cost", dist.Normal(0, logit_std))
logit_f_iso = numpyro.sample("logit_f_iso", dist.Normal(0, logit_std))
# Convert to physical parameters, and get relevant jacobian
sig_m1, jac_sig_m1 = get_value_from_logit(logit_sig_m1, 2., 15.)
log_f_peak, jac_log_f_peak = get_value_from_logit(logit_log_f_peak, -6, 0.)
mMax, jac_mMax = get_value_from_logit(logit_mMax, 50., 100.)
log_dmMin, jac_log_dmMin = get_value_from_logit(logit_log_dmMin, -1, 1)
log_dmMax, jac_log_dmMax = get_value_from_logit(logit_log_dmMax, 0.5, 1.5)
mu_chi, jac_mu_chi = get_value_from_logit(logit_mu_chi, 0., 1.)
logsig_chi, jac_logsig_chi = get_value_from_logit(logit_logsig_chi, -1., 0.)
mu_cost, jac_mu_cost = get_value_from_logit(logit_mu_cost, -1., 1.)
sig_cost, jac_sig_cost = get_value_from_logit(logit_sig_cost, 0.15, 2.5)
f_iso, jac_f_iso = get_value_from_logit(logit_f_iso, 0, 1)
# Record parameters
numpyro.deterministic("sig_m1", sig_m1)
numpyro.deterministic("log_f_peak", log_f_peak)
numpyro.deterministic("mMax", mMax)
numpyro.deterministic("log_dmMin", log_dmMin)
numpyro.deterministic("log_dmMax", log_dmMax)
numpyro.deterministic("mu_chi", mu_chi)
numpyro.deterministic("logsig_chi", logsig_chi)
numpyro.deterministic("mu_cost", mu_cost)
numpyro.deterministic("sig_cost", sig_cost)
numpyro.deterministic("f_iso", f_iso)
# Convert to uniform prior by undoing normal prior in logit space and applying jacobian
numpyro.factor("p_sig_m1", logit_sig_m1**2/(2.*logit_std**2)-jnp.log(jac_sig_m1))
numpyro.factor("p_log_f_peak", logit_log_f_peak**2/(2.*logit_std**2)-jnp.log(jac_log_f_peak))
numpyro.factor("p_mMax", logit_mMax**2/(2.*logit_std**2)-jnp.log(jac_mMax))
numpyro.factor("p_log_dmMin", logit_log_dmMin**2/(2.*logit_std**2)-jnp.log(jac_log_dmMin))
numpyro.factor("p_log_dmMax", logit_log_dmMax**2/(2.*logit_std**2)-jnp.log(jac_log_dmMax))
numpyro.factor("p_mu_chi", logit_mu_chi**2/(2.*logit_std**2)-jnp.log(jac_mu_chi))
numpyro.factor("p_logsig_chi", logit_logsig_chi**2/(2.*logit_std**2)-jnp.log(jac_logsig_chi))
numpyro.factor("p_mu_cost", logit_mu_cost**2/(2.*logit_std**2)-jnp.log(jac_mu_cost))
numpyro.factor("p_sig_cost", logit_sig_cost**2/(2.*logit_std**2)-jnp.log(jac_sig_cost))
numpyro.factor("p_f_iso", logit_f_iso**2/(2.*logit_std**2)-jnp.log(jac_f_iso))
# Normalization
p_m1_norm = massModel(20., alpha, mu_m1, sig_m1, 10.**log_f_peak, mMax, mMin, 10.**log_dmMax, 10.**log_dmMin)
p_z_norm = (1.+0.2)**kappa
########################################
# Compute expected number of detections
########################################
# Read out injection CDFs
inj_m1_cdfs = injectionCDFs['inj_m1_cdfs']
inj_m2_cdfs = injectionCDFs['inj_m2_cdfs']
inj_a1_cdfs = injectionCDFs['inj_a1_cdfs']
inj_a2_cdfs = injectionCDFs['inj_a2_cdfs']
inj_cost1_cdfs = injectionCDFs['inj_cost1_cdfs']
inj_cost2_cdfs = injectionCDFs['inj_cost2_cdfs']
inj_z_cdfs = injectionCDFs['inj_z_cdfs']
# Define f(m1) over reference grid
# Use this to obtain the integral over f(m1)/f(m_ref), build CDF, and interpolate to obtain m1 values
reference_f_m1 = massModel(jnp.exp(injectionCDFs['reference_lnm1_grid']),
alpha,
mu_m1,
sig_m1,
10.**log_f_peak,
mMax,
mMin,
10.**log_dmMax,
10.**log_dmMin)
# This is the cumulative integral of f(m1)
reference_cdf_m1 = jnp.cumsum(reference_f_m1*jnp.exp(injectionCDFs['reference_lnm1_grid']))*injectionCDFs['dlnm1']
# This is the complete integral \Int f(m1) * dm1
reference_f_m1_integral = reference_cdf_m1[-1]
# This is the normalized CDF of m1.
# Use this to interpolate and draw random primary masses
reference_cdf_m1 /= reference_f_m1_integral
inj_m1 = jnp.interp(inj_m1_cdfs, reference_cdf_m1, jnp.exp(injectionCDFs['reference_lnm1_grid']))
# Secondary masses
inj_m2 = jnp.power(tmp_min**(1.+bq) + inj_m2_cdfs*(inj_m1**(1.+bq) - tmp_min**(1.+bq)), 1./(1.+bq))
# Spin magnitudes
sqrt_2 = jnp.sqrt(2.)
chi_erf_a = erf(-mu_chi/(10.**logsig_chi*sqrt_2))
chi_erf_b = erf((1-mu_chi)/(10.**logsig_chi*sqrt_2))
inj_a1 = mu_chi + 10.**logsig_chi*sqrt_2*erfinv(inj_a1_cdfs*(chi_erf_b-chi_erf_a) + chi_erf_a)
inj_a2 = mu_chi + 10.**logsig_chi*sqrt_2*erfinv(inj_a2_cdfs*(chi_erf_b-chi_erf_a) + chi_erf_a)
# Spin tilts
reference_p_cost = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(injectionCDFs['reference_cost_grid'], mu_cost, sig_cost, -1, 1)
reference_cdf_cost = jnp.cumsum(reference_p_cost)
reference_cdf_cost -= reference_cdf_cost[0]
reference_cdf_cost /= reference_cdf_cost[-1]
inj_cost1 = jnp.interp(inj_cost1_cdfs, reference_cdf_cost, injectionCDFs['reference_cost_grid'])
inj_cost2 = jnp.interp(inj_cost2_cdfs, reference_cdf_cost, injectionCDFs['reference_cost_grid'])
# Redshifts
# As described in the paper text, we will draw from a *fixed* redshift distribution,
# and subsequently reweight to target redshift distribution below
inj_kappa = -1.5
reference_f_z = injectionCDFs['reference_dVdz_grid']*(1.+injectionCDFs['reference_z_grid'])**(inj_kappa-1.)
reference_cdf_z = jnp.cumsum(reference_f_z)*injectionCDFs['dz']
reference_f_z_integral = reference_cdf_z[-1]
reference_cdf_z /= reference_f_z_integral
inj_z = jnp.interp(inj_z_cdfs, reference_cdf_z, injectionCDFs['reference_z_grid'])
# Compile dynamically drawn injections
injection_params = jnp.array([
inj_m1,
inj_m2,
inj_a1,
inj_a2,
inj_cost1,
inj_cost2,
inj_z,
injectionCDFs['cos_inclination'],
injectionCDFs['polarization'],
injectionCDFs['phi12'],
injectionCDFs['right_ascension'],
injectionCDFs['sin_declination']
])
# Evaluate Pdet
p_dets = Pdet(injection_params)
# Compute total expected detections.
# See Eqs. D6 and D7
Nexp = R20*(reference_f_m1_integral/p_m1_norm)*(reference_f_z_integral/p_z_norm)*jnp.mean(p_dets.T*(1.+inj_z)**(kappa-inj_kappa))
numpyro.factor("rate", -Nexp)
# As a fit diagnostic, compute effective number of injections
nEff_inj = jnp.sum(p_dets)**2/jnp.sum(p_dets**2)
nObs = 1.0*len(sampleDict)
numpyro.deterministic("nEff_inj_per_event", nEff_inj/nObs)
# This function defines the per-event log-likelihood
def logp(m1_sample, m2_sample, z_sample, dVdz_sample, a1_sample, a2_sample, cost1_sample, cost2_sample, priors):
# Compute proposed population weights
p_m1 = massModel(m1_sample, alpha, mu_m1, sig_m1, 10.**log_f_peak, mMax, mMin, 10.**log_dmMax, 10.**log_dmMin)/p_m1_norm
p_m2 = (1.+bq)*m2_sample**bq/(m1_sample**(1.+bq)-tmp_min**(1.+bq))
p_a1 = truncatedNormal(a1_sample, mu_chi, 10.**logsig_chi, 0, 1)
p_a2 = truncatedNormal(a2_sample, mu_chi, 10.**logsig_chi, 0, 1)
p_cost1 = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost1_sample, mu_cost, sig_cost, -1, 1)
p_cost2 = f_iso*(1./2.) + (1.-f_iso)*truncatedNormal(cost2_sample, mu_cost, sig_cost, -1, 1)
p_z = dVdz_sample*(1.+z_sample)**(kappa-1.)/p_z_norm
R_pop = R20*p_m1*p_m2*p_z*p_a1*p_a2*p_cost1*p_cost2
mc_weights = R_pop/priors
# Compute effective number of samples and return log-likelihood
n_eff = jnp.sum(mc_weights)**2/jnp.sum(mc_weights**2)
return jnp.log(jnp.mean(mc_weights)), n_eff
# Map the log-likelihood function over each event in our catalog
log_ps, n_effs = vmap(logp)(
jnp.array([sampleDict[k]['m1'] for k in sampleDict]),
jnp.array([sampleDict[k]['m2'] for k in sampleDict]),
jnp.array([sampleDict[k]['z'] for k in sampleDict]),
jnp.array([sampleDict[k]['dVc_dz'] for k in sampleDict]),
jnp.array([sampleDict[k]['a1'] for k in sampleDict]),
jnp.array([sampleDict[k]['a2'] for k in sampleDict]),
jnp.array([sampleDict[k]['cost1'] for k in sampleDict]),
jnp.array([sampleDict[k]['cost2'] for k in sampleDict]),
jnp.array([sampleDict[k]['z_prior'] for k in sampleDict]))
# As a diagnostic, save minimum number of effective samples across all events
numpyro.deterministic('min_log_neff', jnp.min(jnp.log10(n_effs)))
# Tally log-likelihoods across our catalog
numpyro.factor("logp", jnp.sum(log_ps))
|
tcallisterREPO_NAMElearning-p-detPATH_START.@learning-p-det_extracted@learning-p-det-main@code@population_model.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/bar/marker/colorbar/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._tickfont import Tickfont
from ._tickformatstop import Tickformatstop
from ._title import Title
from . import title
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[".title"],
["._tickfont.Tickfont", "._tickformatstop.Tickformatstop", "._title.Title"],
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@bar@marker@colorbar@__init__.py@.PATH_END.py
|
{
"filename": "test_utilities.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/src/integrations/prefect-aws/tests/test_utilities.py",
"type": "Python"
}
|
import pytest
from prefect_aws.utilities import (
assemble_document_for_patches,
ensure_path_exists,
hash_collection,
)
class TestHashCollection:
def test_simple_dict(self):
simple_dict = {"key1": "value1", "key2": "value2"}
assert hash_collection(simple_dict) == hash_collection(
simple_dict
), "Simple dictionary hashing failed"
def test_nested_dict(self):
nested_dict = {"key1": {"subkey1": "subvalue1"}, "key2": "value2"}
assert hash_collection(nested_dict) == hash_collection(
nested_dict
), "Nested dictionary hashing failed"
def test_complex_structure(self):
complex_structure = {
"key1": [1, 2, 3],
"key2": {"subkey1": {"subsubkey1": "value"}},
}
assert hash_collection(complex_structure) == hash_collection(
complex_structure
), "Complex structure hashing failed"
def test_unhashable_structure(self):
typically_unhashable_structure = dict(key=dict(subkey=[1, 2, 3]))
with pytest.raises(TypeError):
hash(typically_unhashable_structure)
assert hash_collection(typically_unhashable_structure) == hash_collection(
typically_unhashable_structure
), "Unhashable structure hashing failed after transformation"
class TestAssembleDocumentForPatches:
def test_initial_document(self):
patches = [
{"op": "replace", "path": "/name", "value": "Jane"},
{"op": "add", "path": "/contact/address", "value": "123 Main St"},
{"op": "remove", "path": "/age"},
]
initial_document = assemble_document_for_patches(patches)
expected_document = {"name": {}, "contact": {}, "age": {}}
assert initial_document == expected_document, "Initial document assembly failed"
class TestEnsurePathExists:
def test_existing_path(self):
doc = {"key1": {"subkey1": "value1"}}
path = ["key1", "subkey1"]
ensure_path_exists(doc, path)
assert doc == {
"key1": {"subkey1": "value1"}
}, "Existing path modification failed"
def test_new_path_object(self):
doc = {}
path = ["key1", "subkey1"]
ensure_path_exists(doc, path)
assert doc == {"key1": {"subkey1": {}}}, "New path creation for object failed"
def test_new_path_array(self):
doc = {}
path = ["key1", "0"]
ensure_path_exists(doc, path)
assert doc == {"key1": [{}]}, "New path creation for array failed"
def test_existing_path_array(self):
doc = {"key1": [{"subkey1": "value1"}]}
path = ["key1", "0", "subkey1"]
ensure_path_exists(doc, path)
assert doc == {
"key1": [{"subkey1": "value1"}]
}, "Existing path modification for array failed"
def test_existing_path_array_index_out_of_range(self):
doc = {"key1": []}
path = ["key1", "0", "subkey1"]
ensure_path_exists(doc, path)
assert doc == {
"key1": [{"subkey1": {}}]
}, "Existing path modification for array index out of range failed"
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@src@integrations@prefect-aws@tests@test_utilities.py@.PATH_END.py
|
{
"filename": "math_ops.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/framework/experimental/math_ops.py",
"type": "Python"
}
|
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Experimental impl for gen_math_ops.py using unified APIs, for testing."""
from tensorflow.python.framework.experimental import _math_ops
from tensorflow.python.framework.experimental import context_stack as context
def add(a, b, name=None):
ctx = context.get_default()
return _math_ops.add(ctx, a, b, name)
def mat_mul(a, b, name=None):
ctx = context.get_default()
return _math_ops.mat_mul(ctx, a, b, name)
def neg(a, name=None):
ctx = context.get_default()
return _math_ops.neg(ctx, a, name)
def sub(a, b, name=None):
ctx = context.get_default()
return _math_ops.sub(ctx, a, b, name)
def mul(a, b, name=None):
ctx = context.get_default()
return _math_ops.mul(ctx, a, b, name)
def log1p(a, name=None):
ctx = context.get_default()
return _math_ops.log1p(ctx, a, name)
def div_no_nan(a, b, name=None):
ctx = context.get_default()
return _math_ops.div_no_nan(ctx, a, b, name)
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@framework@experimental@math_ops.py@.PATH_END.py
|
{
"filename": "esa.py",
"repo_name": "spedas/pyspedas",
"repo_path": "pyspedas_extracted/pyspedas-master/pyspedas/projects/themis/spacecraft/particles/esa.py",
"type": "Python"
}
|
from pyspedas.projects.themis.load import load
import pytplot
def esa(trange=['2007-03-23', '2007-03-24'],
probe='c',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads Electrostatic Analyzer (ESA) data
Parameters
----------
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
Default: ['2007-03-23', '2007-03-24']
probe: str or list of str
Spacecraft probe letter(s) ('a', 'b', 'c', 'd' and/or 'e')
Default: 'c'
level: str
Data level; Valid options: 'l1', 'l2'
Default: 'l2'
suffix: str
The tplot variable names will be given this suffix.
Default: no suffix
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False; only loads data with a "VAR_TYPE" attribute of "data"
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: None; all variables are loaded
varnames: list of str
List of variable names to load
Default: Empty list, so all data variables are loaded
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables
Default: False
no_update: bool
If set, only load data from your local cache
Default: False
time_clip: bool
Time clip the variables to exactly the range specified
in the trange keyword
Default: False
Returns
-------
List of str
List of tplot variables created
Empty list if no data
Example
-------
>>> import pyspedas
>>> from pytplot import tplot
>>> esa_vars = pyspedas.projects.themis.esa(probe='d', trange=['2013-11-5', '2013-11-6'])
>>> tplot(['thd_peif_density', 'thd_peif_vthermal'])
"""
outvars = load(instrument='esa', trange=trange, level=level,
suffix=suffix, get_support_data=get_support_data,
varformat=varformat, varnames=varnames,
downloadonly=downloadonly, notplot=notplot,
probe=probe, time_clip=time_clip, no_update=no_update)
#turn off auto Y resample
efluxv = pytplot.tnames('*_en_eflux') #resampling breaks the eflux plots
if len(efluxv) > 0:
for eflv in efluxv:
pytplot.options(eflv, 'y_no_resample', 1)
return outvars
|
spedasREPO_NAMEpyspedasPATH_START.@pyspedas_extracted@pyspedas-master@pyspedas@projects@themis@spacecraft@particles@esa.py@.PATH_END.py
|
{
"filename": "qso_number_counts_metric.py",
"repo_name": "lsst/rubin_sim",
"repo_path": "rubin_sim_extracted/rubin_sim-main/rubin_sim/maf/metrics/qso_number_counts_metric.py",
"type": "Python"
}
|
__all__ = ("QSONumberCountsMetric",)
import os
import healpy as hp
import numpy as np
from rubin_scheduler.data import get_data_dir
from scipy import interpolate
from .base_metric import BaseMetric
from .exgal_m5 import ExgalM5
class QSONumberCountsMetric(BaseMetric):
"""Calculate the number of quasars expected with SNR>=5
according to the Shen et al. (2020) QLF - model A in the redshift
range zmin < z < zmax.
The 5 sigma depths are obtained using the ExgalM5 metric.
Only quasars fainter than the saturation magnitude are counted.
By default, zmin is 0.3 and zmax is the minimum between 6.7 and the
redshift at which the Lyman break matches the effective wavelength
of the band. For bands izy, zmax is 6.7. This default choice is to
match Table 10.2 for i-band quasar counts in the LSST Science book.
"""
def __init__(
self,
lsst_filter,
m5_col="fiveSigmaDepth",
units="mag",
extinction_cut=1.0,
filter_col="filter",
metric_name="QSONumberCountsMetric",
qlf_module="Shen20",
qlf_model="A",
sed_model="Richards06",
zmin=0.3,
zmax=None,
**kwargs,
):
# Declare the effective wavelengths.
self.effwavelen = {
"u": 367.0,
"g": 482.5,
"r": 622.2,
"i": 754.5,
"z": 869.1,
"y": 971.0,
}
# Dust Extinction limit.
# Regions with larger extinction and dropped from the counting.
self.extinction_cut = extinction_cut
# Save the filter information.
self.filter_col = filter_col
self.lsst_filter = lsst_filter
# Save zmin and zmax, or set zmax to the default value.
# The default zmax is the lower number between 6.7 and the
# redshift at which the Lyman break (91.2nm) hits the
# effective wavelength of the filter.
# Note that this means that for i, z and y,
# the default value for zmax is 6.7
self.zmin = zmin
if zmax is None:
zmax = np.min([6.7, self.effwavelen[self.lsst_filter] / 91.2 - 1.0])
self.zmax = zmax
# This calculation uses the ExgalM5 metric. So declare that here.
self.exgal_m5 = ExgalM5(m5_col=m5_col, units=units)
# Save the input parameters that relate to the QLF model.
self.qlf_module = qlf_module
self.qlf_model = qlf_model
self.sed_model = sed_model
# Read the long tables, which the number of quasars expected
# for a given band, qlf_module and qlf_model in a range of
# redshifts and magnitudes.
table_name = "Long_Table.LSST{0}.{1}.{2}.{3}.txt".format(
self.lsst_filter, self.qlf_module, self.qlf_model, self.sed_model
)
data_dir = os.path.join(get_data_dir(), "maf", "quasarNumberCounts")
filename = os.path.join(data_dir, table_name)
with open(filename, "r") as f:
mags = np.array([float(x) for x in f.readline().split()])
zs = np.array([float(x) for x in f.readline().split()])
mz_data = np.loadtxt(filename, skiprows=2)
# Make the long table cumulative.
c_mz_data = np.zeros((mz_data.shape[0] + 1, mz_data.shape[1] + 1))
c_mz_data[1:, 1:] = mz_data
c_mz_data = np.cumsum(c_mz_data, axis=0)
c_mz_data = np.cumsum(c_mz_data, axis=1)
# Create a 2D interpolation object for the long table.
# self.nqso_cumulative = interpolate.interp2d(zs[:-1], mags[:-1],
# #c_mz_data[:-1, :-1], kind="cubic")
self.nqso_cumulative_aux = interpolate.RectBivariateSpline(
zs[:-1], mags[:-1], c_mz_data[:-1, :-1].T, kx=3, ky=3
)
self.nqso_cumulative = lambda z_new, m_new: self.nqso_cumulative_aux(z_new, m_new).T[0]
super().__init__(
col=[m5_col, filter_col, "saturation_mag"],
metric_name=metric_name,
maps=self.exgal_m5.maps,
units=units,
**kwargs,
)
def run(self, data_slice, slice_point=None):
# exclude areas with high extinction
if slice_point["ebv"] > self.extinction_cut:
return self.badval
# For the data_slice, get the 5 sigma limiting magnitude.
d_s = data_slice[data_slice[self.filter_col] == self.lsst_filter]
mlim5 = self.exgal_m5.run(d_s, slice_point)
# Get the slicer pixel area.
nside = slice_point["nside"]
pix_area = hp.nside2pixarea(nside, degrees=True)
# tranform that limiting magnitude into an expected number of quasars.
# If there is more than one, take the faintest.
m_bright = np.max(d_s["saturation_mag"])
n11 = self.nqso_cumulative(self.zmin, m_bright)
n12 = self.nqso_cumulative(self.zmin, mlim5)
n21 = self.nqso_cumulative(self.zmax, m_bright)
n22 = self.nqso_cumulative(self.zmax, mlim5)
nqso = (n22 - n21 - n12 + n11) * pix_area
return nqso
|
lsstREPO_NAMErubin_simPATH_START.@rubin_sim_extracted@rubin_sim-main@rubin_sim@maf@metrics@qso_number_counts_metric.py@.PATH_END.py
|
{
"filename": "_tickvalssrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/parcoords/dimension/_tickvalssrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickvalssrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="tickvalssrc", parent_name="parcoords.dimension", **kwargs
):
super(TickvalssrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@parcoords@dimension@_tickvalssrc.py@.PATH_END.py
|
{
"filename": "_familysrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/treemap/hoverlabel/font/_familysrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FamilysrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="familysrc", parent_name="treemap.hoverlabel.font", **kwargs
):
super(FamilysrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@treemap@hoverlabel@font@_familysrc.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "mbursa/sim5",
"repo_path": "sim5_extracted/sim5-master/__init__.py",
"type": "Python"
}
|
from .python.sim5lib import *
from .python.sim5diskmodel import *
from .python.sim5diskspectrum import *
from .python.sim5diskraytrace import *
from .python.sim5fitstable import *
|
mbursaREPO_NAMEsim5PATH_START.@sim5_extracted@sim5-master@__init__.py@.PATH_END.py
|
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