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{
"filename": "function_utils.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/util/function_utils.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.
# ==============================================================================
"""Utility to retrieve function args."""
import functools
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect
def _is_bound_method(fn):
_, fn = tf_decorator.unwrap(fn)
return tf_inspect.ismethod(fn) and (fn.__self__ is not None)
def _is_callable_object(obj):
return hasattr(obj, '__call__') and tf_inspect.ismethod(obj.__call__)
def fn_args(fn):
"""Get argument names for function-like object.
Args:
fn: Function, or function-like object (e.g., result of `functools.partial`).
Returns:
`tuple` of string argument names.
Raises:
ValueError: if partial function has positionally bound arguments
"""
if isinstance(fn, functools.partial):
args = fn_args(fn.func)
args = [a for a in args[len(fn.args):] if a not in (fn.keywords or [])]
else:
if _is_callable_object(fn):
fn = fn.__call__
args = tf_inspect.getfullargspec(fn).args
if _is_bound_method(fn) and args:
# If it's a bound method, it may or may not have a self/cls first
# argument; for example, self could be captured in *args.
# If it does have a positional argument, it is self/cls.
args.pop(0)
return tuple(args)
def has_kwargs(fn):
"""Returns whether the passed callable has **kwargs in its signature.
Args:
fn: Function, or function-like object (e.g., result of `functools.partial`).
Returns:
`bool`: if `fn` has **kwargs in its signature.
Raises:
`TypeError`: If fn is not a Function, or function-like object.
"""
if isinstance(fn, functools.partial):
fn = fn.func
elif _is_callable_object(fn):
fn = fn.__call__
elif not callable(fn):
raise TypeError(
'Argument `fn` should be a callable. '
f'Received: fn={fn} (of type {type(fn)})')
return tf_inspect.getfullargspec(fn).varkw is not None
def get_func_name(func):
"""Returns name of passed callable."""
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func):
return func.__name__
elif tf_inspect.ismethod(func):
return '%s.%s' % (
func.__self__.__class__.__name__,
func.__func__.__name__,
)
else: # Probably a class instance with __call__
return str(type(func))
else:
raise ValueError(
'Argument `func` must be a callable. '
f'Received func={func} (of type {type(func)})')
def get_func_code(func):
"""Returns func_code of passed callable, or None if not available."""
_, func = tf_decorator.unwrap(func)
if callable(func):
if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
return func.__code__
# Since the object is not a function or method, but is a callable, we will
# try to access the __call__method as a function. This works with callable
# classes but fails with functool.partial objects despite their __call__
# attribute.
try:
return func.__call__.__code__
except AttributeError:
return None
else:
raise ValueError(
'Argument `func` must be a callable. '
f'Received func={func} (of type {type(func)})')
_rewriter_config_optimizer_disabled = None
def get_disabled_rewriter_config():
global _rewriter_config_optimizer_disabled
if _rewriter_config_optimizer_disabled is None:
config = config_pb2.ConfigProto()
rewriter_config = config.graph_options.rewrite_options
rewriter_config.disable_meta_optimizer = True
_rewriter_config_optimizer_disabled = config.SerializeToString()
return _rewriter_config_optimizer_disabled
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@util@function_utils.py@.PATH_END.py
|
{
"filename": "_reversescale.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatter/marker/line/_reversescale.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ReversescaleValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self, plotly_name="reversescale", parent_name="scatter.marker.line", **kwargs
):
super(ReversescaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatter@marker@line@_reversescale.py@.PATH_END.py
|
{
"filename": "_enabled.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/coloraxis/colorbar/tickformatstop/_enabled.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class EnabledValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self,
plotly_name="enabled",
parent_name="layout.coloraxis.colorbar.tickformatstop",
**kwargs,
):
super(EnabledValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@coloraxis@colorbar@tickformatstop@_enabled.py@.PATH_END.py
|
{
"filename": "huggingface_hub.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/llms/huggingface_hub.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import HuggingFaceHub
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"HuggingFaceHub": "langchain_community.llms"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"HuggingFaceHub",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@llms@huggingface_hub.py@.PATH_END.py
|
{
"filename": "imports.py",
"repo_name": "CMB-S4/spt3g_software",
"repo_path": "spt3g_software_extracted/spt3g_software-master/gcp/tests/imports.py",
"type": "Python"
}
|
import spt3g._libgcp
for k in dir(spt3g._libgcp):
print(k, getattr(spt3g._libgcp, k))
from spt3g._libgcp import ACUStatus
import spt3g.gcp
from spt3g import gcp
from spt3g.gcp import ACUStatus
|
CMB-S4REPO_NAMEspt3g_softwarePATH_START.@spt3g_software_extracted@spt3g_software-master@gcp@tests@imports.py@.PATH_END.py
|
{
"filename": "acorns-adi.py",
"repo_name": "t-brandt/acorns-adi",
"repo_path": "acorns-adi_extracted/acorns-adi-master/acorns-adi.py",
"type": "Python"
}
|
#!/usr/bin/env python
#
# Original filename: flat_coadd.py
#
# Author: Tim Brandt
# Email: tbrandt@astro.princeton.edu
# Date: 7 June 2011
#
# Summary: Reduce HiCIAO ADI Data
#
import optparse, sys, re, os
import pyfits as pyf
import numpy as np
from scipy import signal
from adiparam import *
import centroid
import transform
import parallel
import combine
import loci
import pca
import utils
import pickle
import addsource
import locitools
import photometry
def main():
"""
Main program for ADI data reduction, configured with a call to
adiparam.GetConfig(), which brings up a GUI to set parameters.
The pipeline is currently designed for SEEDS data taken without
an occulting mask.
You must have scipy, numpy, pyephem, multiprocessing, and matplotlib
installed to use this pipeline.
"""
parser = optparse.OptionParser(usage=__doc__)
parser.add_option("-p", "--prefix", dest="prefix", default="HICA",
help="Specify raw file name prefix (default=%default)")
opts, args = parser.parse_args()
exec_path = os.path.dirname(os.path.realpath(__file__))
filesetup, adipar, locipar = GetConfig(prefix=opts.prefix)
nframes = len(filesetup.framelist)
ngroup = 1 + int((nframes - 1) / locipar.max_n)
flat = pyf.open(filesetup.flat)
if filesetup.pixmask is not None:
hotpix = pyf.open(filesetup.pixmask)
else:
hotpix = None
dimy, dimx = pyf.open(filesetup.framelist[0])[-1].data.shape
mem, ncpus, storeall = utils.config(nframes, dimy * dimx)
if filesetup.scale_phot:
x, y = np.meshgrid(np.arange(7) - 3, np.arange(7) - 3)
window = (x**2 + y**2 < 2.51**2) * 1.0
window /= np.sum(window)
ref_phot, ref_psf = photometry.calc_phot(filesetup, adipar, flat,
hotpix, mem, window)
else:
ref_psf = None
ref_phot = None
################################################################
# WCS coordinates are not reliable in HiCIAO data with the image
# rotator off. Compute parallactic angle. Otherwise, trust the
# WCS coordinates.
################################################################
if 'HICA' in filesetup.framelist[0]:
pa = np.asarray([transform.get_pa(frame) * -1 * np.pi / 180
for frame in filesetup.framelist])
else:
pa = np.ones(len(filesetup.framelist))
for i in range(len(filesetup.framelist)):
cd2_1 = pyf.open(filesetup.framelist[i])[0].header['cd2_1']
cd2_2 = pyf.open(filesetup.framelist[i])[0].header['cd2_2']
pa[i] = -np.arctan2(cd2_1, cd2_2)
fullframe = re.sub("-C.*fits", ".fits", filesetup.framelist[0])
try:
objname = pyf.open(fullframe)[0].header['OBJECT']
except:
objname = "Unknown_Object"
objname = re.sub(' ', '_', objname)
np.savetxt(filesetup.output_dir + '/' + objname + '_palist.dat', pa)
dr_rms = None
####################################################################
# Default save/resume points: destriping, recentering, final files
# Configuration gives the option to skip the destriping step (only
# performing a flat-field), the dewarping, and the centering.
####################################################################
if np.all(utils.check_files(filesetup, ext="_r")):
print "\nResuming reduction from recentered files."
if ngroup == 1:
flux = utils.read_files(filesetup, ext="_r")
else:
flux = utils.read_files(filesetup, ext="_r")
else:
if storeall and np.all(utils.check_files(filesetup, ext="_ds")):
flux = utils.read_files(filesetup, ext="_ds")
elif not np.all(utils.check_files(filesetup, ext="_ds")):
flux = parallel._destripe(filesetup, flat, hotpix, mem, adipar,
write_files=True, storeall=storeall,
full_destripe=adipar.full_destripe,
do_horiz=adipar.full_destripe)
else:
flux = None
if adipar.dewarp:
flux = parallel._dewarp(filesetup, mem, flux=flux, storeall=storeall)
if adipar.do_centroid:
centers, dr_rms = centroid.fit_centroids(filesetup, flux, pa,
storeall=storeall,
objname=objname,
method=adipar.center,
psf_dir=exec_path+'/psfref', ref_psf=ref_psf)
#centers = np.ndarray((nframes, 2))
#centers[:, 0] = 1026 - 128
#centers[:, 1] = 949 + 60
#dr_rms = 30
np.savetxt(filesetup.output_dir + '/' + objname +
'_centers.dat', centers)
####################################################################
# Recenter the data onto a square array of the largest dimension
# such that the entire array has data
####################################################################
mindim = min(dimy - centers[:, 0].max(), centers[:, 0].min(),
dimx - centers[:, 1].max(), centers[:, 1].min())
mindim = int(mindim) * 2 - 1
flux = parallel._rotate_recenter(filesetup, flux, storeall=storeall,
centers=centers, newdimen=mindim,
write_files=True)
nframes = len(filesetup.framelist)
####################################################################
# Perform scaled PCA on the flux array; alternatively, read in an
# array of principal components. Neither is currently used.
####################################################################
if False:
pcapath = '/scr/wakusei1/users/tbrandt'
flux, pca_arr = pca.pca(flux, ncomp=20, nread=2, dosub=True,
pcadir=pcapath + '/psfref')
for i in range(nframes):
out = pyf.HDUList(pyf.PrimaryHDU(flux[i].astype(np.float32),
pyf.open(filesetup.framelist[i])[0].header))
rootfile = re.sub('.*/', '', filesetup.framelist[i])
out.writeto(filesetup.reduce_dir + '/' + re.sub('.fits', '_r.fits', rootfile), clobber=True)
if dr_rms is None:
dr_rms = 20
elif False:
pca_dir = '.'
npca = 40
pca_arr = np.zeros((npca, flux.shape[1], flux.shape[2]), np.float32)
for i in range(npca):
tmp = pyf.open(pca_dir + '/pcacomp_' + str(i) + '.fits')[0].data
dy, dx = [tmp.shape[0] // 2, tmp.shape[1] // 2]
pca_arr[i, yc - dy:yc + dy + 1, xc - dx:xc + dx + 1] = tmp
else:
pca_arr = None
####################################################################
# Find the n closest matches to each frame. Not currently used.
####################################################################
if False:
corr = pca.allcorr(range(int(locipar.rmax)), flux, n=80)
ngroup = 1
else:
corr = None
####################################################################
# Subtract a radial profile from each frame. Not currently used.
####################################################################
if False:
flux = parallel._radialsub(filesetup, flux, mode='median',
center=None, rmax=None, smoothwidth=0)
####################################################################
# Run LOCI if that ADI reduction method is chosen
####################################################################
partial_sub = None
full_pa = pa.copy()
full_framelist = [frame for frame in filesetup.framelist]
for igroup in range(ngroup):
if ngroup > 1:
filesetup.framelist = full_framelist[igroup::ngroup]
if np.all(utils.check_files(filesetup, ext="_r")):
flux = utils.read_files(filesetup, ext="_r")
else:
print "Unable to read recentered files for LOCI."
sys.exit()
pa = full_pa[igroup::ngroup]
x = np.arange(flux.shape[1]) - flux.shape[1] // 2
x, y = np.meshgrid(x, x)
r = np.sqrt(x**2 + y**2)
if adipar.adi == 'LOCI':
################################################################
# Set the maximum radius at which to perform LOCI
################################################################
deltar = np.sqrt(np.pi * locipar.fwhm**2 / 4 * locipar.npsf)
rmax = int(flux.shape[1] // 2 - deltar - 50)
locipar.rmax = min(locipar.rmax, rmax)
if dr_rms is None:
nf, dy, dx = flux.shape
fluxmed = np.median(flux, axis=0)[dy // 2 - 100:dy // 2 + 101,
dx // 2 - 100:dx // 2 + 101]
sat = fluxmed > 0.7 * fluxmed.max()
r2 = r[dy//2 - 100:dy//2 + 101, dx//2 - 100:dx//2 + 101]**2
dr_rms = np.sqrt(np.sum(r2 * sat) / np.sum(sat))
################################################################
# This is regular LOCI
################################################################
if locipar.feedback == 0:
partial_sub = loci.loci(flux, pa, locipar, mem, mode='LOCI',
pca_arr=None, r_ex=dr_rms, corr=corr,
method='matrix', do_partial_sub=True,
sub_dir=exec_path)
################################################################
# The next block runs LOCI once, de-rotates, takes the median,
# and re-rotates to each frame's position angle. It then runs
# LOCI again to over-correct the result. Not recommended for
# SEEDS data with AO188.
################################################################
else:
fluxref = np.ndarray(flux.shape, np.float32)
fluxref[:] = flux
loci.loci(fluxref, pca_arr, pa, locipar, mem, mode='LOCI',
r_ex=dr_rms, pca_arr=pca_arr,
corr=corr, method='matrix', do_partial_sub=False)
for i in range(flux.shape[0]):
np.putmask(fluxref[i], r > locipar.rmax - 1, 0)
np.putmask(fluxref[i], r < dr_rms + 1, 0)
locipar.rmax -= 100
fluxref = parallel._rotate_recenter(filesetup, fluxref, theta=pa)
for i in range(flux.shape[0]):
np.putmask(fluxref[i], r > locipar.rmax - 1, 0)
np.putmask(fluxref[i], r < dr_rms + 1, 0)
locipar.rmax -= 100
fluxmed = np.median(fluxref, axis=0)
for i in range(flux.shape[0]):
fluxref[i] = fluxmed * locipar.feedback
fluxref = parallel._rotate_recenter(filesetup, fluxref, theta=-pa)
loci.loci(flux, pa, locipar, mem, mode='refine', fluxref=fluxref,
pca_arr=pca_arr, rmin=dr_rms, r_ex=dr_rms)
################################################################
# Mask saturated areas (< dr_rms), do median subtraction at radii
# beyond the limit of the LOCI reduction
################################################################
fluxmed = np.median(flux, axis=0)
for i in range(flux.shape[0]):
np.putmask(flux[i], r < dr_rms + 2, 0)
np.putmask(flux[i], r > locipar.rmax - 1, flux[i] - fluxmed)
####################################################################
# Alternative to LOCI: median PSF subtraction
####################################################################
elif adipar.adi == 'median':
medpsf = np.median(flux, axis=0)
for i in range(flux.shape[0]):
flux[i] -= medpsf
else:
print "Error: ADI reduction method " + adipar.adi + " not recognized."
#sys.exit(1)
####################################################################
# Derotate, combine flux array using mean/median hybrid (see
# Brandt+ 2012), measure standard deviation at each radius
####################################################################
if igroup == 0:
newhead = utils.makeheader(flux[0], pyf.open(fullframe)[0].header,
full_framelist, adipar, locipar)
flux = parallel._rotate_recenter(filesetup, flux, theta=pa)
fluxtmp, noise = combine.meanmed(flux)
fluxbest = fluxtmp / ngroup
if partial_sub is not None:
partial_sub_tot = partial_sub / ngroup
else:
flux = parallel._rotate_recenter(filesetup, flux, theta=pa)
fluxtmp, noise = combine.meanmed(flux)
fluxbest += fluxtmp / ngroup
if partial_sub is not None:
partial_sub_tot += partial_sub / ngroup
filesetup.framelist = full_framelist
if partial_sub is not None:
partial_sub = partial_sub_tot
####################################################################
# Rescale all arrays to 2001x2001 so that the center is pixel number
# (1000, 1000) indexed from 0. Use NaN to pad arrays.
####################################################################
fluxbest = utils.arr_resize(fluxbest)
if partial_sub is not None:
partial_sub = utils.arr_resize(partial_sub, newdim=fluxbest.shape[0]).astype(np.float32)
fluxbest /= partial_sub
out = pyf.HDUList(pyf.PrimaryHDU(partial_sub))
out.writeto('partial_sub2.fits', clobber=True)
x, y = np.meshgrid(np.arange(7) - 3, np.arange(7) - 3)
window = (x**2 + y**2 < 2.51**2) * 1.0
window /= np.sum(window)
fluxbest = signal.convolve2d(fluxbest, window, mode='same')
noise = combine.radprof(fluxbest, mode='std', smoothwidth=2, sigrej=4.5)[0]
r = utils.arr_resize(r)
if dr_rms is not None:
np.putmask(fluxbest, r < dr_rms + 3, np.nan)
np.putmask(fluxbest, r > locipar.rmax - 2, np.nan)
fluxsnr = (fluxbest / noise).astype(np.float32)
####################################################################
# 5-sigma sensitivity maps--just multiply by the scaled aperture
# photometry of the central star
####################################################################
if partial_sub is not None:
sensitivity = noise * 5 / partial_sub
####################################################################
# Photometry of the central star
####################################################################
if filesetup.scale_phot:
#ref_phot = photometry.calc_phot(filesetup, adipar, flat,
# hotpix, mem, window)[0]
sensitivity /= ref_phot
fluxbest /= ref_phot
noise /= ref_phot
sig_sens = combine.radprof(sensitivity, mode='std', smoothwidth=0)[0]
outfile = open(filesetup.output_dir + '/' + objname +
'_5sigma_sensitivity.dat', 'w')
for i in range(sig_sens.shape[0] // 2, sig_sens.shape[0]):
iy = sig_sens.shape[0] // 2
if np.isfinite(sensitivity[iy, i]):
outfile.write('%8d %12.5e %12.5e %12e\n' %
(i - iy, sensitivity[iy, i], sig_sens[iy, i],
partial_sub[iy, i]))
outfile.close()
else:
np.savetxt(filesetup.output_dir + '/' + objname + '_noiseprofile.dat',
noise[noise.shape[0] // 2, noise.shape[1] // 2:].T)
####################################################################
# Write the output fits files.
####################################################################
snr = pyf.HDUList(pyf.PrimaryHDU(fluxsnr.astype(np.float32), newhead))
final = pyf.HDUList(pyf.PrimaryHDU(fluxbest.astype(np.float32), newhead))
if partial_sub is not None:
contrast = pyf.HDUList(pyf.PrimaryHDU(sensitivity.astype(np.float32), newhead))
name_base = filesetup.output_dir + '/' + objname
snr.writeto(name_base + '_snr.fits', clobber=True)
final.writeto(name_base + '_final.fits', clobber=True)
if partial_sub is not None:
contrast.writeto(name_base + '_5sigma_sensitivity.fits', clobber=True)
#############################################################
# end
#############################################################
if __name__ == '__main__':
main()
|
t-brandtREPO_NAMEacorns-adiPATH_START.@acorns-adi_extracted@acorns-adi-master@acorns-adi.py@.PATH_END.py
|
{
"filename": "test_box.py",
"repo_name": "philbull/FastBox",
"repo_path": "FastBox_extracted/FastBox-main/fastbox/tests/test_box.py",
"type": "Python"
}
|
#!/usr/bin/env python
import pytest
import numpy as np
from fastbox.box import CosmoBox, default_cosmo
def test_gaussian_box():
"""Generate Gaussian density field in box."""
# Realise Gaussian box
np.random.seed(11)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=False)
box.realise_density()
# Check that density field is valid
assert box.delta_x.shape == (16, 16, 16)
assert box.delta_x.dtype == np.float64
assert np.all(~np.isnan(box.delta_x))
# Realise density field with same random seed and realise_now=True, and
# manually setting the redshift and a single box_scale
np.random.seed(11)
box2 = CosmoBox(cosmo=default_cosmo, box_scale=1e2, nsamp=16,
redshift=0., realise_now=True)
assert np.allclose(box.delta_x, box2.delta_x)
# Check that pixel resolution etc. is correct
assert box.Lx == box.Ly == box.Lz == 1e2
assert box.x.size == box.y.size == box.z.size == 16
assert np.isclose(np.max(box.x) - np.min(box.x), 1e2)
# Check that cuboidal boxes work
box3 = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 2e2, 1e3), nsamp=16,
redshift=1., realise_now=True)
assert box3.delta_x.shape == (16, 16, 16)
assert box3.delta_x.dtype == np.float64
assert np.all(~np.isnan(box3.delta_x))
def test_lognormal_box():
"""Generate log-normal density field in box."""
# Realise Gaussian box
np.random.seed(11)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=True)
# Apply log-normal transform
delta_log = box.lognormal(box.delta_x)
# Check that log-normal density field is valid
assert delta_log.shape == (16, 16, 16)
# assert delta_log.dtype == np.float64
assert np.all(~np.isnan(delta_log))
assert np.all(delta_log >= -1.) # delta_log >= -1
def test_box_redshift_space_density():
"""Check that a redshift-space density field can be generated."""
# Realise Gaussian box and velocity field
np.random.seed(11)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=False)
box.realise_density()
box.realise_velocity()
# Get redshift-space density field
vel_z = np.fft.ifftn(box.velocity_k[2]).real
delta_s = box.redshift_space_density(delta_x=box.delta_x, velocity_z=vel_z,
sigma_nl=200., method='linear')
# Check that redshift-space density field is valid
assert delta_s.shape == (16, 16, 16)
# assert delta_s.dtype == np.float64
assert np.all(~np.isnan(delta_s))
def test_box_transfer_function():
"""Check that a transfer function can be applied to the density field."""
# Realise Gaussian box
np.random.seed(11)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=True)
# Gaussian box with beam smoothing and foreground cut
transfer_fn = lambda k_perp, k_par: \
(1. - np.exp(-0.5 * (k_par/0.001)**2.)) \
* np.exp(-0.5 * (k_perp/0.1)**2.)
delta_smoothed = box.apply_transfer_fn(box.delta_k, transfer_fn=transfer_fn)
# Check that smoothed density field is valid
assert delta_smoothed.shape == (16, 16, 16)
# assert delta_smoothed.dtype == np.float64
assert np.all(~np.isnan(delta_smoothed))
def test_box_power_spectrum():
"""Check that the theoretical and box power spectra can be calculated."""
# Realise Gaussian box
np.random.seed(14)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e3, 1e3, 1e3), nsamp=64,
realise_now=False)
box.realise_density()
# Calculate binned power spectrum and theoretical power spectrum
re_k, re_pk, re_stddev = box.binned_power_spectrum()
th_k, th_pk = box.theoretical_power_spectrum()
# Check that sigma(R) and sigma_8 can be calculated
sigR = box.sigmaR(R=8.) # R in units of Mpc/h
sig8 = box.sigma8()
assert np.isclose(sigR, sig8)
# Run built-in test to print a report on sampling accuracy
box.test_sampling_error()
# Check that sigma_8 calculated from box is close to input cosmo sigma_8
# (this depends on box size/resolution)
assert np.abs(sig8 - box.cosmo['sigma8']) < 0.09 # 0.09 is empirical
def test_box_coordinates():
"""Check that pixel and frequency coordinates are returned."""
# Realise Gaussian box
np.random.seed(22)
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e3, 1e3, 1e3), nsamp=16,
realise_now=True, redshift=0.8)
# Check pixel array
ang_x, ang_y = box.pixel_array()
ang_x2, ang_y2 = box.pixel_array(redshift=0.82)
# ^Higher z, so further away, so smaller angle
# Check for valid output
assert np.all(~np.isnan(ang_x))
assert np.all(~np.isnan(ang_y))
assert np.all(~np.isnan(ang_x2))
assert np.all(~np.isnan(ang_y2))
# Square box => equal pixel sizes
assert np.isclose(ang_x[1] - ang_x[0], ang_y[1] - ang_y[0])
# Check that higher redshift pixels are smaller
assert ang_x[1] - ang_x[0] > ang_x2[1] - ang_x2[0]
assert ang_y[1] - ang_y[0] > ang_y2[1] - ang_y2[0]
# Check that frequency array goes in descending order (highest z coord =>
# lowest frequency)
assert np.all(np.diff(box.freq_array()) < 0.) # negative differences
assert np.all(np.diff(box.freq_array(redshift=2.)) < 0.) # negative differences
def test_box_errors():
"""Check that correct errors are raised for invalid input."""
# Invalid cosmology object passed in
with pytest.raises(TypeError):
box = CosmoBox(cosmo=[0.7, 0.3], box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=False)
def test_box_builtin_tests():
"""Run the built-in tests in the CosmoBox object."""
box = CosmoBox(cosmo=default_cosmo, box_scale=(1e2, 1e2, 1e2), nsamp=16,
realise_now=True)
# Test Parseval's theorem (integrals of power in real and Fourier space are
# equal)
s1, s2 = box.test_parseval()
assert np.isclose(s1, s2)
|
philbullREPO_NAMEFastBoxPATH_START.@FastBox_extracted@FastBox-main@fastbox@tests@test_box.py@.PATH_END.py
|
{
"filename": "replace.py",
"repo_name": "nasa/kepler-pipeline",
"repo_path": "kepler-pipeline_extracted/kepler-pipeline-master/source-code/java/pi/python-src/replace.py",
"type": "Python"
}
|
#!/usr/bin/python
#
# Copyright 2017 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All Rights Reserved.
#
# This file is available under the terms of the NASA Open Source Agreement
# (NOSA). You should have received a copy of this agreement with the
# Kepler source code; see the file NASA-OPEN-SOURCE-AGREEMENT.doc.
#
# No Warranty: THE SUBJECT SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY
# WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY,
# INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SUBJECT SOFTWARE
# WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM
# INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL BE ERROR
# FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM
# TO THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER,
# CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT
# OF ANY RESULTS, RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY
# OTHER APPLICATIONS RESULTING FROM USE OF THE SUBJECT SOFTWARE.
# FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND LIABILITIES
# REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE ORIGINAL SOFTWARE,
# AND DISTRIBUTES IT "AS IS."
#
# Waiver and Indemnity: RECIPIENT AGREES TO WAIVE ANY AND ALL CLAIMS
# AGAINST THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND
# SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT. IF RECIPIENT'S USE OF
# THE SUBJECT SOFTWARE RESULTS IN ANY LIABILITIES, DEMANDS, DAMAGES,
# EXPENSES OR LOSSES ARISING FROM SUCH USE, INCLUDING ANY DAMAGES FROM
# PRODUCTS BASED ON, OR RESULTING FROM, RECIPIENT'S USE OF THE SUBJECT
# SOFTWARE, RECIPIENT SHALL INDEMNIFY AND HOLD HARMLESS THE UNITED
# STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL AS ANY
# PRIOR RECIPIENT, TO THE EXTENT PERMITTED BY LAW. RECIPIENT'S SOLE
# REMEDY FOR ANY SUCH MATTER SHALL BE THE IMMEDIATE, UNILATERAL
# TERMINATION OF THIS AGREEMENT.
#
import os, sys
usage = "usage: %s search_text replace_text file" % os.path.basename(sys.argv[0])
if len(sys.argv) < 4:
print usage
else:
stext = sys.argv[1]
rtext = sys.argv[2]
infile = sys.argv[3]
outfile = infile + '.tmp'
print 'Replacing all occurences of', stext, 'with', rtext, 'in file', infile
input = open(infile)
output = open(outfile, 'w')
for s in input:
newText = s.replace(stext, rtext)
if newText is not s:
print s, '->', newText
output.write(newText)
input.close()
output.close()
os.rename(infile,infile+'.old')
os.rename(outfile,infile)
|
nasaREPO_NAMEkepler-pipelinePATH_START.@kepler-pipeline_extracted@kepler-pipeline-master@source-code@java@pi@python-src@replace.py@.PATH_END.py
|
{
"filename": "test_stackexchange.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/integration_tests/utilities/test_stackexchange.py",
"type": "Python"
}
|
"""Integration test for Stack Exchange."""
from langchain_community.utilities import StackExchangeAPIWrapper
def test_call() -> None:
"""Test that call runs."""
stackexchange = StackExchangeAPIWrapper() # type: ignore[call-arg]
output = stackexchange.run("zsh: command not found: python")
assert output != "hello"
def test_failure() -> None:
"""Test that call that doesn't run."""
stackexchange = StackExchangeAPIWrapper() # type: ignore[call-arg]
output = stackexchange.run("sjefbsmnf")
assert output == "No relevant results found for 'sjefbsmnf' on Stack Overflow"
def test_success() -> None:
"""Test that call that doesn't run."""
stackexchange = StackExchangeAPIWrapper() # type: ignore[call-arg]
output = stackexchange.run("zsh: command not found: python")
assert "zsh: command not found: python" in output
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@integration_tests@utilities@test_stackexchange.py@.PATH_END.py
|
{
"filename": "_size.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/violin/legendgrouptitle/font/_size.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="size", parent_name="violin.legendgrouptitle.font", **kwargs
):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@violin@legendgrouptitle@font@_size.py@.PATH_END.py
|
{
"filename": "_marker.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/treemap/_marker.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class MarkerValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="marker", parent_name="treemap", **kwargs):
super(MarkerValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Marker"),
data_docs=kwargs.pop(
"data_docs",
"""
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.colorscale`. Has an
effect only if colorsis set to a numerical
array. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette
will be chosen according to whether numbers in
the `color` array are all positive, all
negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
colors) or the bounds set in `marker.cmin` and
`marker.cmax` Has an effect only if colorsis
set to a numerical array. Defaults to `false`
when `marker.cmin` and `marker.cmax` are set by
the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if colorsis set to a numerical
array. Value should have the same units as
colors and if set, `marker.cmin` must be set as
well.
cmid
Sets the mid-point of the color domain by
scaling `marker.cmin` and/or `marker.cmax` to
be equidistant to this point. Has an effect
only if colorsis set to a numerical array.
Value should have the same units as colors. Has
no effect when `marker.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if colorsis set to a numerical
array. Value should have the same units as
colors and if set, `marker.cmax` must be set as
well.
coloraxis
Sets a reference to a shared color axis.
References to these shared color axes are
"coloraxis", "coloraxis2", "coloraxis3", etc.
Settings for these shared color axes are set in
the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple
color scales can be linked to the same color
axis.
colorbar
:class:`plotly.graph_objects.treemap.marker.Col
orBar` instance or dict with compatible
properties
colors
Sets the color of each sector of this trace. If
not specified, the default trace color set is
used to pick the sector colors.
colorscale
Sets the colorscale. Has an effect only if
colorsis set to a numerical array. The
colorscale must be an array containing arrays
mapping a normalized value to an rgb, rgba,
hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and
highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in
color space, use`marker.cmin` and
`marker.cmax`. Alternatively, `colorscale` may
be a palette name string of the following list:
Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Bl
ues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E
arth,Electric,Viridis,Cividis.
colorssrc
Sets the source reference on Chart Studio Cloud
for colors .
depthfade
Determines if the sector colors are faded
towards the background from the leaves up to
the headers. This option is unavailable when a
`colorscale` is present, defaults to false when
`marker.colors` is set, but otherwise defaults
to true. When set to "reversed", the fading
direction is inverted, that is the top elements
within hierarchy are drawn with fully saturated
colors while the leaves are faded towards the
background color.
line
:class:`plotly.graph_objects.treemap.marker.Lin
e` instance or dict with compatible properties
pad
:class:`plotly.graph_objects.treemap.marker.Pad
` instance or dict with compatible properties
reversescale
Reverses the color mapping if true. Has an
effect only if colorsis set to a numerical
array. If true, `marker.cmin` will correspond
to the last color in the array and
`marker.cmax` will correspond to the first
color.
showscale
Determines whether or not a colorbar is
displayed for this trace. Has an effect only if
colorsis set to a numerical array.
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@treemap@_marker.py@.PATH_END.py
|
{
"filename": "sr_mhd_linwave.py",
"repo_name": "PrincetonUniversity/athena",
"repo_path": "athena_extracted/athena-master/tst/regression/scripts/tests/sr/sr_mhd_linwave.py",
"type": "Python"
}
|
"""
Regression test based on SR MHD linear wave convergence problem.
Runs a linear wave convergence test in 3D and checks L1 errors as saved by the
executable in linearwave-errors.dat.
"""
# Modules
import logging
import numpy as np
import scripts.utils.athena as athena
import sys
sys.path.insert(0, '../../vis/python')
import athena_read # noqa
athena_read.check_nan_flag = True
logger = logging.getLogger('athena' + __name__[7:]) # set logger name based on module
# Prepare Athena++
def prepare(**kwargs):
logger.debug('Running test ' + __name__)
athena.configure('sb',
prob='gr_linear_wave',
coord='cartesian',
flux='hlld', **kwargs)
athena.make()
# Run Athena++
def run(**kwargs):
# Parameters
rho = 1.0
pgas = 0.5
vx = 0.1
vy = 0.15
vz = 0.05
bx = 1.0
by = 2.0/3.0
bz = 1.0/3.0
gamma_adi = 4.0/3.0
# Go through all waves at low and high resolutions
for wave_flag in range(7):
wavespeed = calculate_wavespeed(rho, pgas, vx, vy, vz, bx, by, bz, gamma_adi,
wave_flag)
time = 1.0/abs(wavespeed)
for res in (16, 32):
arguments = ['time/ncycle_out=100',
'time/tlim='+repr(time),
'time/cfl_number=0.3',
'output1/dt=-1',
'mesh/nx1='+repr(res),
'mesh/nx2='+repr(res/2),
'mesh/nx3='+repr(res/2),
'meshblock/nx1='+repr(res/2),
'meshblock/nx2='+repr(res/2),
'meshblock/nx3='+repr(res/2),
'hydro/gamma='+repr(gamma_adi),
'problem/wave_flag='+repr(wave_flag),
'problem/compute_error=true',
'problem/rho='+repr(rho),
'problem/pgas='+repr(pgas),
'problem/vx='+repr(vx),
'problem/vy='+repr(vy),
'problem/vz='+repr(vz),
'problem/Bx='+repr(bx),
'problem/By='+repr(by),
'problem/Bz='+repr(bz)]
athena.run('mhd_sr/athinput.linear_wave', arguments)
# Analyze outputs
def analyze():
# Expected wave properties
names = ('leftgoing fast', 'leftgoing Alfven', 'leftgoing slow', 'entropy',
'rightgoing slow', 'rightgoing Alfven', 'rightgoing fast')
high_res_errors = (4.0e-8, 3.0e-8, 3.0e-8, 2.0e-8, 4.0e-8, 3.0e-8, 3.0e-8)
error_ratio = 0.4
# Read data from error file
filename = 'bin/linearwave-errors.dat'
data = athena_read.error_dat(filename)
# Check errors
status = True
for wave_flag in range(7):
if data[2*wave_flag+1][4] > high_res_errors[wave_flag]:
logger.warning('{0} wave error too large ({1} vs. {2})'.format(
names[wave_flag], data[2*wave_flag+1][4], high_res_errors[wave_flag]))
status = False
if data[2*wave_flag+1][4]/data[2*wave_flag][4] > error_ratio:
logger.warning('{0} wave error not converging ({1} to {2})'.format(
names[wave_flag], data[2*wave_flag][4], data[2*wave_flag+1][4]))
status = False
return status
# Lab-frame wavespeed calculator
def calculate_wavespeed(rho, pgas, vx, vy, vz, bx, by, bz, gamma_adi, wave_flag):
# Handle simple entropy case
if wave_flag == 3:
return vx
# Calculate 4-vectors
v_sq = vx**2 + vy**2 + vz**2
u = np.empty(4)
u[0] = 1.0 / (1.0 - v_sq)**0.5
u[1] = u[0]*vx
u[2] = u[0]*vy
u[3] = u[0]*vz
b = np.empty(4)
b[0] = bx*u[1] + by*u[2] + bz*u[3]
b[1] = 1.0/u[0] * (bx + b[0]*u[1])
b[2] = 1.0/u[0] * (by + b[0]*u[2])
b[3] = 1.0/u[0] * (bz + b[0]*u[3])
# Calculate useful scalars
gamma_adi_red = gamma_adi / (gamma_adi-1.0)
b_sq = -b[0]**2 + sum(b[1:]**2)
wgas = rho + gamma_adi_red * pgas
wtot = wgas + b_sq
cs_sq = gamma_adi * pgas / wgas
# Calculate Alfven speeds
lambda_ap = (b[1] + wtot**0.5 * u[1]) / (b[0] + wtot**0.5 * u[0])
lambda_am = (b[1] - wtot**0.5 * u[1]) / (b[0] - wtot**0.5 * u[0])
if wave_flag == 1:
return min(lambda_ap, lambda_am)
if wave_flag == 5:
return max(lambda_ap, lambda_am)
# Calculate magnetosonic speeds
factor_a = wgas * (1.0/cs_sq - 1.0)
factor_b = -(wgas + b_sq/cs_sq)
a4 = factor_a * u[0]**4 - factor_b * u[0]**2 - b[0]**2
a3 = (-factor_a * 4.0 * u[0]**4 * vx
+ factor_b * 2.0 * u[0]**2 * vx + 2.0 * b[0] * b[1])
a2 = (factor_a * 6.0 * u[0]**4 * vx**2
+ factor_b * u[0]**2 * (1.0-vx**2) + b[0]**2 - b[1]**2)
a1 = (-factor_a * 4.0 * u[0]**4 * vx**3
- factor_b * 2.0 * u[0]**2 * vx - 2.0 * b[0] * b[1])
a0 = factor_a * u[0]**4 * vx**4 + factor_b * u[0]**2 * vx**2 + b[1]**2
roots = sorted(np.roots([a4, a3, a2, a1, a0]))
if wave_flag == 0:
return roots[0]
if wave_flag == 2:
return roots[1]
if wave_flag == 4:
return roots[2]
if wave_flag == 6:
return roots[3]
|
PrincetonUniversityREPO_NAMEathenaPATH_START.@athena_extracted@athena-master@tst@regression@scripts@tests@sr@sr_mhd_linwave.py@.PATH_END.py
|
{
"filename": "fastjsonschema_exceptions.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/setuptools/py3/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py",
"type": "Python"
}
|
import re
SPLIT_RE = re.compile(r'[\.\[\]]+')
class JsonSchemaException(ValueError):
"""
Base exception of ``fastjsonschema`` library.
"""
class JsonSchemaValueException(JsonSchemaException):
"""
Exception raised by validation function. Available properties:
* ``message`` containing human-readable information what is wrong (e.g. ``data.property[index] must be smaller than or equal to 42``),
* invalid ``value`` (e.g. ``60``),
* ``name`` of a path in the data structure (e.g. ``data.property[index]``),
* ``path`` as an array in the data structure (e.g. ``['data', 'property', 'index']``),
* the whole ``definition`` which the ``value`` has to fulfil (e.g. ``{'type': 'number', 'maximum': 42}``),
* ``rule`` which the ``value`` is breaking (e.g. ``maximum``)
* and ``rule_definition`` (e.g. ``42``).
.. versionchanged:: 2.14.0
Added all extra properties.
"""
def __init__(self, message, value=None, name=None, definition=None, rule=None):
super().__init__(message)
self.message = message
self.value = value
self.name = name
self.definition = definition
self.rule = rule
@property
def path(self):
return [item for item in SPLIT_RE.split(self.name) if item != '']
@property
def rule_definition(self):
if not self.rule or not self.definition:
return None
return self.definition.get(self.rule)
class JsonSchemaDefinitionException(JsonSchemaException):
"""
Exception raised by generator of validation function.
"""
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@setuptools@py3@setuptools@config@_validate_pyproject@fastjsonschema_exceptions.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/cli/langchain_cli/integration_template/tests/__init__.py",
"type": "Python"
}
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@cli@langchain_cli@integration_template@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "eis_fit_cube_example.py",
"repo_name": "USNavalResearchLaboratory/eispac",
"repo_path": "eispac_extracted/eispac-main/examples/eis_fit_cube_example.py",
"type": "Python"
}
|
import matplotlib.pyplot as plt
import astropy.units as u
import eispac
if __name__ == '__main__':
# input data and template files
data_filepath = '../data/eis_20190404_131513.data.h5'
template_filepath = '../templates/eis_template_dir/fe_12_195_119.2c.template.h5'
# read fit template
tmplt = eispac.EISFitTemplate.read_template(template_filepath)
# Read spectral window into an EISCube
data_cube = eispac.read_cube(data_filepath, tmplt.central_wave)
# Select a cutout of the raster (note the order of array & plotting indices!)
# Note: we want the full wavelength axis, so we set the range far outside the bounds of EIS
cutout_extent = [48, 165, 254, 378] # units of [arcsec]
# w_coords = data_cube.axis_world_coords('em.wl')
lower_left = (cutout_extent[2]*u.arcsec, cutout_extent[0]*u.arcsec, 0*u.angstrom)
upper_right = (cutout_extent[3]*u.arcsec, cutout_extent[1]*u.arcsec, 1000*u.angstrom)
raster_cutout = data_cube.crop_by_coords(lower_left, upper_corner=upper_right)
# Fit the data, then save it to disk and test loading it back in
fit_res = eispac.fit_spectra(raster_cutout, tmplt, ncpu='max')
save_filepaths = eispac.save_fit(fit_res, save_dir='cwd')
load_fit = eispac.read_fit(save_filepaths[0])
# Extract array of total data and fit intensites
sum_data_inten = raster_cutout.sum_spectra().data
fit_wave_cube, fit_inten_cube = fit_res.get_fit_profile(component=[0,1])
sum_fit_inten = fit_inten_cube.sum(axis=2)
# Extract example fit profiles at a higher spectral resolution than the data
ex_coords = [43, 28] # [Y,X] array coords in units of [pixels]
fit_x, fit_y = fit_res.get_fit_profile(coords=ex_coords, num_wavelengths=100)
c0_fit_x, c0_fit_y = fit_res.get_fit_profile(component=0, coords=ex_coords,
num_wavelengths=100)
c1_fit_x, c1_fit_y = fit_res.get_fit_profile(component=1, coords=ex_coords,
num_wavelengths=100)
c2_fit_x, c2_fit_y = fit_res.get_fit_profile(component=2, coords=ex_coords,
num_wavelengths=100)
sub_data = raster_cutout.data[ex_coords[0], ex_coords[1], :]
sub_wave = raster_cutout.wavelength[ex_coords[0], ex_coords[1], :]
sub_err = raster_cutout.uncertainty.array[ex_coords[0], ex_coords[1], :]
# Make a multi-panel figure with the cutout and example
fig = plt.figure()
plot_grid = fig.add_gridspec(nrows=2, ncols=2, hspace=0.5, wspace=0.3)
data_subplt = fig.add_subplot(plot_grid[0,0])
data_subplt.imshow(sum_data_inten, origin='lower', extent=cutout_extent, cmap='gray')
data_subplt.set_title('Data Cutout')
data_subplt.set_xlabel('Solar-X [arcsec]')
data_subplt.set_ylabel('Solar-Y [arcsec]')
fit_subplt = fig.add_subplot(plot_grid[0,1])
fit_subplt.imshow(sum_fit_inten, origin='lower', extent=cutout_extent, cmap='gray')
fit_subplt.set_title('Total Fit Intensity')
fit_subplt.set_xlabel('Solar-X [arcsec]')
fit_subplt.set_ylabel('Solar-Y [arcsec]')
profile_subplt = fig.add_subplot(plot_grid[1,:])
profile_subplt.errorbar(sub_wave, sub_data, yerr=sub_err,
ls='', marker='o', color='k')
profile_subplt.plot(fit_x, fit_y, color='b', label='Combined profile')
profile_subplt.plot(c0_fit_x, c0_fit_y, color='r', label='Gaussian 1')
profile_subplt.plot(c1_fit_x, c1_fit_y, color='r', ls='--', label='Gaussian 2')
profile_subplt.plot(c2_fit_x, c2_fit_y, color='g', label='Background')
profile_subplt.set_title(f'Cutout indices iy = {ex_coords[0]}, ix = {ex_coords[1]}')
profile_subplt.set_xlabel('Wavelength [$\AA$]')
profile_subplt.set_ylabel('Intensity ['+raster_cutout.unit.to_string()+']')
profile_subplt.legend(loc='upper left')
plt.show()
|
USNavalResearchLaboratoryREPO_NAMEeispacPATH_START.@eispac_extracted@eispac-main@examples@eis_fit_cube_example.py@.PATH_END.py
|
{
"filename": "_color_data.py",
"repo_name": "matplotlib/matplotlib",
"repo_path": "matplotlib_extracted/matplotlib-main/lib/matplotlib/_color_data.py",
"type": "Python"
}
|
BASE_COLORS = {
'b': (0, 0, 1), # blue
'g': (0, 0.5, 0), # green
'r': (1, 0, 0), # red
'c': (0, 0.75, 0.75), # cyan
'm': (0.75, 0, 0.75), # magenta
'y': (0.75, 0.75, 0), # yellow
'k': (0, 0, 0), # black
'w': (1, 1, 1), # white
}
# These colors are from Tableau
TABLEAU_COLORS = {
'tab:blue': '#1f77b4',
'tab:orange': '#ff7f0e',
'tab:green': '#2ca02c',
'tab:red': '#d62728',
'tab:purple': '#9467bd',
'tab:brown': '#8c564b',
'tab:pink': '#e377c2',
'tab:gray': '#7f7f7f',
'tab:olive': '#bcbd22',
'tab:cyan': '#17becf',
}
# This mapping of color names -> hex values is taken from
# a survey run by Randall Munroe see:
# https://blog.xkcd.com/2010/05/03/color-survey-results/
# for more details. The results are hosted at
# https://xkcd.com/color/rgb/
# and also available as a text file at
# https://xkcd.com/color/rgb.txt
#
# License: https://creativecommons.org/publicdomain/zero/1.0/
XKCD_COLORS = {
'cloudy blue': '#acc2d9',
'dark pastel green': '#56ae57',
'dust': '#b2996e',
'electric lime': '#a8ff04',
'fresh green': '#69d84f',
'light eggplant': '#894585',
'nasty green': '#70b23f',
'really light blue': '#d4ffff',
'tea': '#65ab7c',
'warm purple': '#952e8f',
'yellowish tan': '#fcfc81',
'cement': '#a5a391',
'dark grass green': '#388004',
'dusty teal': '#4c9085',
'grey teal': '#5e9b8a',
'macaroni and cheese': '#efb435',
'pinkish tan': '#d99b82',
'spruce': '#0a5f38',
'strong blue': '#0c06f7',
'toxic green': '#61de2a',
'windows blue': '#3778bf',
'blue blue': '#2242c7',
'blue with a hint of purple': '#533cc6',
'booger': '#9bb53c',
'bright sea green': '#05ffa6',
'dark green blue': '#1f6357',
'deep turquoise': '#017374',
'green teal': '#0cb577',
'strong pink': '#ff0789',
'bland': '#afa88b',
'deep aqua': '#08787f',
'lavender pink': '#dd85d7',
'light moss green': '#a6c875',
'light seafoam green': '#a7ffb5',
'olive yellow': '#c2b709',
'pig pink': '#e78ea5',
'deep lilac': '#966ebd',
'desert': '#ccad60',
'dusty lavender': '#ac86a8',
'purpley grey': '#947e94',
'purply': '#983fb2',
'candy pink': '#ff63e9',
'light pastel green': '#b2fba5',
'boring green': '#63b365',
'kiwi green': '#8ee53f',
'light grey green': '#b7e1a1',
'orange pink': '#ff6f52',
'tea green': '#bdf8a3',
'very light brown': '#d3b683',
'egg shell': '#fffcc4',
'eggplant purple': '#430541',
'powder pink': '#ffb2d0',
'reddish grey': '#997570',
'baby shit brown': '#ad900d',
'liliac': '#c48efd',
'stormy blue': '#507b9c',
'ugly brown': '#7d7103',
'custard': '#fffd78',
'darkish pink': '#da467d',
'deep brown': '#410200',
'greenish beige': '#c9d179',
'manilla': '#fffa86',
'off blue': '#5684ae',
'battleship grey': '#6b7c85',
'browny green': '#6f6c0a',
'bruise': '#7e4071',
'kelley green': '#009337',
'sickly yellow': '#d0e429',
'sunny yellow': '#fff917',
'azul': '#1d5dec',
'darkgreen': '#054907',
'green/yellow': '#b5ce08',
'lichen': '#8fb67b',
'light light green': '#c8ffb0',
'pale gold': '#fdde6c',
'sun yellow': '#ffdf22',
'tan green': '#a9be70',
'burple': '#6832e3',
'butterscotch': '#fdb147',
'toupe': '#c7ac7d',
'dark cream': '#fff39a',
'indian red': '#850e04',
'light lavendar': '#efc0fe',
'poison green': '#40fd14',
'baby puke green': '#b6c406',
'bright yellow green': '#9dff00',
'charcoal grey': '#3c4142',
'squash': '#f2ab15',
'cinnamon': '#ac4f06',
'light pea green': '#c4fe82',
'radioactive green': '#2cfa1f',
'raw sienna': '#9a6200',
'baby purple': '#ca9bf7',
'cocoa': '#875f42',
'light royal blue': '#3a2efe',
'orangeish': '#fd8d49',
'rust brown': '#8b3103',
'sand brown': '#cba560',
'swamp': '#698339',
'tealish green': '#0cdc73',
'burnt siena': '#b75203',
'camo': '#7f8f4e',
'dusk blue': '#26538d',
'fern': '#63a950',
'old rose': '#c87f89',
'pale light green': '#b1fc99',
'peachy pink': '#ff9a8a',
'rosy pink': '#f6688e',
'light bluish green': '#76fda8',
'light bright green': '#53fe5c',
'light neon green': '#4efd54',
'light seafoam': '#a0febf',
'tiffany blue': '#7bf2da',
'washed out green': '#bcf5a6',
'browny orange': '#ca6b02',
'nice blue': '#107ab0',
'sapphire': '#2138ab',
'greyish teal': '#719f91',
'orangey yellow': '#fdb915',
'parchment': '#fefcaf',
'straw': '#fcf679',
'very dark brown': '#1d0200',
'terracota': '#cb6843',
'ugly blue': '#31668a',
'clear blue': '#247afd',
'creme': '#ffffb6',
'foam green': '#90fda9',
'grey/green': '#86a17d',
'light gold': '#fddc5c',
'seafoam blue': '#78d1b6',
'topaz': '#13bbaf',
'violet pink': '#fb5ffc',
'wintergreen': '#20f986',
'yellow tan': '#ffe36e',
'dark fuchsia': '#9d0759',
'indigo blue': '#3a18b1',
'light yellowish green': '#c2ff89',
'pale magenta': '#d767ad',
'rich purple': '#720058',
'sunflower yellow': '#ffda03',
'green/blue': '#01c08d',
'leather': '#ac7434',
'racing green': '#014600',
'vivid purple': '#9900fa',
'dark royal blue': '#02066f',
'hazel': '#8e7618',
'muted pink': '#d1768f',
'booger green': '#96b403',
'canary': '#fdff63',
'cool grey': '#95a3a6',
'dark taupe': '#7f684e',
'darkish purple': '#751973',
'true green': '#089404',
'coral pink': '#ff6163',
'dark sage': '#598556',
'dark slate blue': '#214761',
'flat blue': '#3c73a8',
'mushroom': '#ba9e88',
'rich blue': '#021bf9',
'dirty purple': '#734a65',
'greenblue': '#23c48b',
'icky green': '#8fae22',
'light khaki': '#e6f2a2',
'warm blue': '#4b57db',
'dark hot pink': '#d90166',
'deep sea blue': '#015482',
'carmine': '#9d0216',
'dark yellow green': '#728f02',
'pale peach': '#ffe5ad',
'plum purple': '#4e0550',
'golden rod': '#f9bc08',
'neon red': '#ff073a',
'old pink': '#c77986',
'very pale blue': '#d6fffe',
'blood orange': '#fe4b03',
'grapefruit': '#fd5956',
'sand yellow': '#fce166',
'clay brown': '#b2713d',
'dark blue grey': '#1f3b4d',
'flat green': '#699d4c',
'light green blue': '#56fca2',
'warm pink': '#fb5581',
'dodger blue': '#3e82fc',
'gross green': '#a0bf16',
'ice': '#d6fffa',
'metallic blue': '#4f738e',
'pale salmon': '#ffb19a',
'sap green': '#5c8b15',
'algae': '#54ac68',
'bluey grey': '#89a0b0',
'greeny grey': '#7ea07a',
'highlighter green': '#1bfc06',
'light light blue': '#cafffb',
'light mint': '#b6ffbb',
'raw umber': '#a75e09',
'vivid blue': '#152eff',
'deep lavender': '#8d5eb7',
'dull teal': '#5f9e8f',
'light greenish blue': '#63f7b4',
'mud green': '#606602',
'pinky': '#fc86aa',
'red wine': '#8c0034',
'shit green': '#758000',
'tan brown': '#ab7e4c',
'darkblue': '#030764',
'rosa': '#fe86a4',
'lipstick': '#d5174e',
'pale mauve': '#fed0fc',
'claret': '#680018',
'dandelion': '#fedf08',
'orangered': '#fe420f',
'poop green': '#6f7c00',
'ruby': '#ca0147',
'dark': '#1b2431',
'greenish turquoise': '#00fbb0',
'pastel red': '#db5856',
'piss yellow': '#ddd618',
'bright cyan': '#41fdfe',
'dark coral': '#cf524e',
'algae green': '#21c36f',
'darkish red': '#a90308',
'reddy brown': '#6e1005',
'blush pink': '#fe828c',
'camouflage green': '#4b6113',
'lawn green': '#4da409',
'putty': '#beae8a',
'vibrant blue': '#0339f8',
'dark sand': '#a88f59',
'purple/blue': '#5d21d0',
'saffron': '#feb209',
'twilight': '#4e518b',
'warm brown': '#964e02',
'bluegrey': '#85a3b2',
'bubble gum pink': '#ff69af',
'duck egg blue': '#c3fbf4',
'greenish cyan': '#2afeb7',
'petrol': '#005f6a',
'royal': '#0c1793',
'butter': '#ffff81',
'dusty orange': '#f0833a',
'off yellow': '#f1f33f',
'pale olive green': '#b1d27b',
'orangish': '#fc824a',
'leaf': '#71aa34',
'light blue grey': '#b7c9e2',
'dried blood': '#4b0101',
'lightish purple': '#a552e6',
'rusty red': '#af2f0d',
'lavender blue': '#8b88f8',
'light grass green': '#9af764',
'light mint green': '#a6fbb2',
'sunflower': '#ffc512',
'velvet': '#750851',
'brick orange': '#c14a09',
'lightish red': '#fe2f4a',
'pure blue': '#0203e2',
'twilight blue': '#0a437a',
'violet red': '#a50055',
'yellowy brown': '#ae8b0c',
'carnation': '#fd798f',
'muddy yellow': '#bfac05',
'dark seafoam green': '#3eaf76',
'deep rose': '#c74767',
'dusty red': '#b9484e',
'grey/blue': '#647d8e',
'lemon lime': '#bffe28',
'purple/pink': '#d725de',
'brown yellow': '#b29705',
'purple brown': '#673a3f',
'wisteria': '#a87dc2',
'banana yellow': '#fafe4b',
'lipstick red': '#c0022f',
'water blue': '#0e87cc',
'brown grey': '#8d8468',
'vibrant purple': '#ad03de',
'baby green': '#8cff9e',
'barf green': '#94ac02',
'eggshell blue': '#c4fff7',
'sandy yellow': '#fdee73',
'cool green': '#33b864',
'pale': '#fff9d0',
'blue/grey': '#758da3',
'hot magenta': '#f504c9',
'greyblue': '#77a1b5',
'purpley': '#8756e4',
'baby shit green': '#889717',
'brownish pink': '#c27e79',
'dark aquamarine': '#017371',
'diarrhea': '#9f8303',
'light mustard': '#f7d560',
'pale sky blue': '#bdf6fe',
'turtle green': '#75b84f',
'bright olive': '#9cbb04',
'dark grey blue': '#29465b',
'greeny brown': '#696006',
'lemon green': '#adf802',
'light periwinkle': '#c1c6fc',
'seaweed green': '#35ad6b',
'sunshine yellow': '#fffd37',
'ugly purple': '#a442a0',
'medium pink': '#f36196',
'puke brown': '#947706',
'very light pink': '#fff4f2',
'viridian': '#1e9167',
'bile': '#b5c306',
'faded yellow': '#feff7f',
'very pale green': '#cffdbc',
'vibrant green': '#0add08',
'bright lime': '#87fd05',
'spearmint': '#1ef876',
'light aquamarine': '#7bfdc7',
'light sage': '#bcecac',
'yellowgreen': '#bbf90f',
'baby poo': '#ab9004',
'dark seafoam': '#1fb57a',
'deep teal': '#00555a',
'heather': '#a484ac',
'rust orange': '#c45508',
'dirty blue': '#3f829d',
'fern green': '#548d44',
'bright lilac': '#c95efb',
'weird green': '#3ae57f',
'peacock blue': '#016795',
'avocado green': '#87a922',
'faded orange': '#f0944d',
'grape purple': '#5d1451',
'hot green': '#25ff29',
'lime yellow': '#d0fe1d',
'mango': '#ffa62b',
'shamrock': '#01b44c',
'bubblegum': '#ff6cb5',
'purplish brown': '#6b4247',
'vomit yellow': '#c7c10c',
'pale cyan': '#b7fffa',
'key lime': '#aeff6e',
'tomato red': '#ec2d01',
'lightgreen': '#76ff7b',
'merlot': '#730039',
'night blue': '#040348',
'purpleish pink': '#df4ec8',
'apple': '#6ecb3c',
'baby poop green': '#8f9805',
'green apple': '#5edc1f',
'heliotrope': '#d94ff5',
'yellow/green': '#c8fd3d',
'almost black': '#070d0d',
'cool blue': '#4984b8',
'leafy green': '#51b73b',
'mustard brown': '#ac7e04',
'dusk': '#4e5481',
'dull brown': '#876e4b',
'frog green': '#58bc08',
'vivid green': '#2fef10',
'bright light green': '#2dfe54',
'fluro green': '#0aff02',
'kiwi': '#9cef43',
'seaweed': '#18d17b',
'navy green': '#35530a',
'ultramarine blue': '#1805db',
'iris': '#6258c4',
'pastel orange': '#ff964f',
'yellowish orange': '#ffab0f',
'perrywinkle': '#8f8ce7',
'tealish': '#24bca8',
'dark plum': '#3f012c',
'pear': '#cbf85f',
'pinkish orange': '#ff724c',
'midnight purple': '#280137',
'light urple': '#b36ff6',
'dark mint': '#48c072',
'greenish tan': '#bccb7a',
'light burgundy': '#a8415b',
'turquoise blue': '#06b1c4',
'ugly pink': '#cd7584',
'sandy': '#f1da7a',
'electric pink': '#ff0490',
'muted purple': '#805b87',
'mid green': '#50a747',
'greyish': '#a8a495',
'neon yellow': '#cfff04',
'banana': '#ffff7e',
'carnation pink': '#ff7fa7',
'tomato': '#ef4026',
'sea': '#3c9992',
'muddy brown': '#886806',
'turquoise green': '#04f489',
'buff': '#fef69e',
'fawn': '#cfaf7b',
'muted blue': '#3b719f',
'pale rose': '#fdc1c5',
'dark mint green': '#20c073',
'amethyst': '#9b5fc0',
'blue/green': '#0f9b8e',
'chestnut': '#742802',
'sick green': '#9db92c',
'pea': '#a4bf20',
'rusty orange': '#cd5909',
'stone': '#ada587',
'rose red': '#be013c',
'pale aqua': '#b8ffeb',
'deep orange': '#dc4d01',
'earth': '#a2653e',
'mossy green': '#638b27',
'grassy green': '#419c03',
'pale lime green': '#b1ff65',
'light grey blue': '#9dbcd4',
'pale grey': '#fdfdfe',
'asparagus': '#77ab56',
'blueberry': '#464196',
'purple red': '#990147',
'pale lime': '#befd73',
'greenish teal': '#32bf84',
'caramel': '#af6f09',
'deep magenta': '#a0025c',
'light peach': '#ffd8b1',
'milk chocolate': '#7f4e1e',
'ocher': '#bf9b0c',
'off green': '#6ba353',
'purply pink': '#f075e6',
'lightblue': '#7bc8f6',
'dusky blue': '#475f94',
'golden': '#f5bf03',
'light beige': '#fffeb6',
'butter yellow': '#fffd74',
'dusky purple': '#895b7b',
'french blue': '#436bad',
'ugly yellow': '#d0c101',
'greeny yellow': '#c6f808',
'orangish red': '#f43605',
'shamrock green': '#02c14d',
'orangish brown': '#b25f03',
'tree green': '#2a7e19',
'deep violet': '#490648',
'gunmetal': '#536267',
'blue/purple': '#5a06ef',
'cherry': '#cf0234',
'sandy brown': '#c4a661',
'warm grey': '#978a84',
'dark indigo': '#1f0954',
'midnight': '#03012d',
'bluey green': '#2bb179',
'grey pink': '#c3909b',
'soft purple': '#a66fb5',
'blood': '#770001',
'brown red': '#922b05',
'medium grey': '#7d7f7c',
'berry': '#990f4b',
'poo': '#8f7303',
'purpley pink': '#c83cb9',
'light salmon': '#fea993',
'snot': '#acbb0d',
'easter purple': '#c071fe',
'light yellow green': '#ccfd7f',
'dark navy blue': '#00022e',
'drab': '#828344',
'light rose': '#ffc5cb',
'rouge': '#ab1239',
'purplish red': '#b0054b',
'slime green': '#99cc04',
'baby poop': '#937c00',
'irish green': '#019529',
'pink/purple': '#ef1de7',
'dark navy': '#000435',
'greeny blue': '#42b395',
'light plum': '#9d5783',
'pinkish grey': '#c8aca9',
'dirty orange': '#c87606',
'rust red': '#aa2704',
'pale lilac': '#e4cbff',
'orangey red': '#fa4224',
'primary blue': '#0804f9',
'kermit green': '#5cb200',
'brownish purple': '#76424e',
'murky green': '#6c7a0e',
'wheat': '#fbdd7e',
'very dark purple': '#2a0134',
'bottle green': '#044a05',
'watermelon': '#fd4659',
'deep sky blue': '#0d75f8',
'fire engine red': '#fe0002',
'yellow ochre': '#cb9d06',
'pumpkin orange': '#fb7d07',
'pale olive': '#b9cc81',
'light lilac': '#edc8ff',
'lightish green': '#61e160',
'carolina blue': '#8ab8fe',
'mulberry': '#920a4e',
'shocking pink': '#fe02a2',
'auburn': '#9a3001',
'bright lime green': '#65fe08',
'celadon': '#befdb7',
'pinkish brown': '#b17261',
'poo brown': '#885f01',
'bright sky blue': '#02ccfe',
'celery': '#c1fd95',
'dirt brown': '#836539',
'strawberry': '#fb2943',
'dark lime': '#84b701',
'copper': '#b66325',
'medium brown': '#7f5112',
'muted green': '#5fa052',
"robin's egg": '#6dedfd',
'bright aqua': '#0bf9ea',
'bright lavender': '#c760ff',
'ivory': '#ffffcb',
'very light purple': '#f6cefc',
'light navy': '#155084',
'pink red': '#f5054f',
'olive brown': '#645403',
'poop brown': '#7a5901',
'mustard green': '#a8b504',
'ocean green': '#3d9973',
'very dark blue': '#000133',
'dusty green': '#76a973',
'light navy blue': '#2e5a88',
'minty green': '#0bf77d',
'adobe': '#bd6c48',
'barney': '#ac1db8',
'jade green': '#2baf6a',
'bright light blue': '#26f7fd',
'light lime': '#aefd6c',
'dark khaki': '#9b8f55',
'orange yellow': '#ffad01',
'ocre': '#c69c04',
'maize': '#f4d054',
'faded pink': '#de9dac',
'british racing green': '#05480d',
'sandstone': '#c9ae74',
'mud brown': '#60460f',
'light sea green': '#98f6b0',
'robin egg blue': '#8af1fe',
'aqua marine': '#2ee8bb',
'dark sea green': '#11875d',
'soft pink': '#fdb0c0',
'orangey brown': '#b16002',
'cherry red': '#f7022a',
'burnt yellow': '#d5ab09',
'brownish grey': '#86775f',
'camel': '#c69f59',
'purplish grey': '#7a687f',
'marine': '#042e60',
'greyish pink': '#c88d94',
'pale turquoise': '#a5fbd5',
'pastel yellow': '#fffe71',
'bluey purple': '#6241c7',
'canary yellow': '#fffe40',
'faded red': '#d3494e',
'sepia': '#985e2b',
'coffee': '#a6814c',
'bright magenta': '#ff08e8',
'mocha': '#9d7651',
'ecru': '#feffca',
'purpleish': '#98568d',
'cranberry': '#9e003a',
'darkish green': '#287c37',
'brown orange': '#b96902',
'dusky rose': '#ba6873',
'melon': '#ff7855',
'sickly green': '#94b21c',
'silver': '#c5c9c7',
'purply blue': '#661aee',
'purpleish blue': '#6140ef',
'hospital green': '#9be5aa',
'shit brown': '#7b5804',
'mid blue': '#276ab3',
'amber': '#feb308',
'easter green': '#8cfd7e',
'soft blue': '#6488ea',
'cerulean blue': '#056eee',
'golden brown': '#b27a01',
'bright turquoise': '#0ffef9',
'red pink': '#fa2a55',
'red purple': '#820747',
'greyish brown': '#7a6a4f',
'vermillion': '#f4320c',
'russet': '#a13905',
'steel grey': '#6f828a',
'lighter purple': '#a55af4',
'bright violet': '#ad0afd',
'prussian blue': '#004577',
'slate green': '#658d6d',
'dirty pink': '#ca7b80',
'dark blue green': '#005249',
'pine': '#2b5d34',
'yellowy green': '#bff128',
'dark gold': '#b59410',
'bluish': '#2976bb',
'darkish blue': '#014182',
'dull red': '#bb3f3f',
'pinky red': '#fc2647',
'bronze': '#a87900',
'pale teal': '#82cbb2',
'military green': '#667c3e',
'barbie pink': '#fe46a5',
'bubblegum pink': '#fe83cc',
'pea soup green': '#94a617',
'dark mustard': '#a88905',
'shit': '#7f5f00',
'medium purple': '#9e43a2',
'very dark green': '#062e03',
'dirt': '#8a6e45',
'dusky pink': '#cc7a8b',
'red violet': '#9e0168',
'lemon yellow': '#fdff38',
'pistachio': '#c0fa8b',
'dull yellow': '#eedc5b',
'dark lime green': '#7ebd01',
'denim blue': '#3b5b92',
'teal blue': '#01889f',
'lightish blue': '#3d7afd',
'purpley blue': '#5f34e7',
'light indigo': '#6d5acf',
'swamp green': '#748500',
'brown green': '#706c11',
'dark maroon': '#3c0008',
'hot purple': '#cb00f5',
'dark forest green': '#002d04',
'faded blue': '#658cbb',
'drab green': '#749551',
'light lime green': '#b9ff66',
'snot green': '#9dc100',
'yellowish': '#faee66',
'light blue green': '#7efbb3',
'bordeaux': '#7b002c',
'light mauve': '#c292a1',
'ocean': '#017b92',
'marigold': '#fcc006',
'muddy green': '#657432',
'dull orange': '#d8863b',
'steel': '#738595',
'electric purple': '#aa23ff',
'fluorescent green': '#08ff08',
'yellowish brown': '#9b7a01',
'blush': '#f29e8e',
'soft green': '#6fc276',
'bright orange': '#ff5b00',
'lemon': '#fdff52',
'purple grey': '#866f85',
'acid green': '#8ffe09',
'pale lavender': '#eecffe',
'violet blue': '#510ac9',
'light forest green': '#4f9153',
'burnt red': '#9f2305',
'khaki green': '#728639',
'cerise': '#de0c62',
'faded purple': '#916e99',
'apricot': '#ffb16d',
'dark olive green': '#3c4d03',
'grey brown': '#7f7053',
'green grey': '#77926f',
'true blue': '#010fcc',
'pale violet': '#ceaefa',
'periwinkle blue': '#8f99fb',
'light sky blue': '#c6fcff',
'blurple': '#5539cc',
'green brown': '#544e03',
'bluegreen': '#017a79',
'bright teal': '#01f9c6',
'brownish yellow': '#c9b003',
'pea soup': '#929901',
'forest': '#0b5509',
'barney purple': '#a00498',
'ultramarine': '#2000b1',
'purplish': '#94568c',
'puke yellow': '#c2be0e',
'bluish grey': '#748b97',
'dark periwinkle': '#665fd1',
'dark lilac': '#9c6da5',
'reddish': '#c44240',
'light maroon': '#a24857',
'dusty purple': '#825f87',
'terra cotta': '#c9643b',
'avocado': '#90b134',
'marine blue': '#01386a',
'teal green': '#25a36f',
'slate grey': '#59656d',
'lighter green': '#75fd63',
'electric green': '#21fc0d',
'dusty blue': '#5a86ad',
'golden yellow': '#fec615',
'bright yellow': '#fffd01',
'light lavender': '#dfc5fe',
'umber': '#b26400',
'poop': '#7f5e00',
'dark peach': '#de7e5d',
'jungle green': '#048243',
'eggshell': '#ffffd4',
'denim': '#3b638c',
'yellow brown': '#b79400',
'dull purple': '#84597e',
'chocolate brown': '#411900',
'wine red': '#7b0323',
'neon blue': '#04d9ff',
'dirty green': '#667e2c',
'light tan': '#fbeeac',
'ice blue': '#d7fffe',
'cadet blue': '#4e7496',
'dark mauve': '#874c62',
'very light blue': '#d5ffff',
'grey purple': '#826d8c',
'pastel pink': '#ffbacd',
'very light green': '#d1ffbd',
'dark sky blue': '#448ee4',
'evergreen': '#05472a',
'dull pink': '#d5869d',
'aubergine': '#3d0734',
'mahogany': '#4a0100',
'reddish orange': '#f8481c',
'deep green': '#02590f',
'vomit green': '#89a203',
'purple pink': '#e03fd8',
'dusty pink': '#d58a94',
'faded green': '#7bb274',
'camo green': '#526525',
'pinky purple': '#c94cbe',
'pink purple': '#db4bda',
'brownish red': '#9e3623',
'dark rose': '#b5485d',
'mud': '#735c12',
'brownish': '#9c6d57',
'emerald green': '#028f1e',
'pale brown': '#b1916e',
'dull blue': '#49759c',
'burnt umber': '#a0450e',
'medium green': '#39ad48',
'clay': '#b66a50',
'light aqua': '#8cffdb',
'light olive green': '#a4be5c',
'brownish orange': '#cb7723',
'dark aqua': '#05696b',
'purplish pink': '#ce5dae',
'dark salmon': '#c85a53',
'greenish grey': '#96ae8d',
'jade': '#1fa774',
'ugly green': '#7a9703',
'dark beige': '#ac9362',
'emerald': '#01a049',
'pale red': '#d9544d',
'light magenta': '#fa5ff7',
'sky': '#82cafc',
'light cyan': '#acfffc',
'yellow orange': '#fcb001',
'reddish purple': '#910951',
'reddish pink': '#fe2c54',
'orchid': '#c875c4',
'dirty yellow': '#cdc50a',
'orange red': '#fd411e',
'deep red': '#9a0200',
'orange brown': '#be6400',
'cobalt blue': '#030aa7',
'neon pink': '#fe019a',
'rose pink': '#f7879a',
'greyish purple': '#887191',
'raspberry': '#b00149',
'aqua green': '#12e193',
'salmon pink': '#fe7b7c',
'tangerine': '#ff9408',
'brownish green': '#6a6e09',
'red brown': '#8b2e16',
'greenish brown': '#696112',
'pumpkin': '#e17701',
'pine green': '#0a481e',
'charcoal': '#343837',
'baby pink': '#ffb7ce',
'cornflower': '#6a79f7',
'blue violet': '#5d06e9',
'chocolate': '#3d1c02',
'greyish green': '#82a67d',
'scarlet': '#be0119',
'green yellow': '#c9ff27',
'dark olive': '#373e02',
'sienna': '#a9561e',
'pastel purple': '#caa0ff',
'terracotta': '#ca6641',
'aqua blue': '#02d8e9',
'sage green': '#88b378',
'blood red': '#980002',
'deep pink': '#cb0162',
'grass': '#5cac2d',
'moss': '#769958',
'pastel blue': '#a2bffe',
'bluish green': '#10a674',
'green blue': '#06b48b',
'dark tan': '#af884a',
'greenish blue': '#0b8b87',
'pale orange': '#ffa756',
'vomit': '#a2a415',
'forrest green': '#154406',
'dark lavender': '#856798',
'dark violet': '#34013f',
'purple blue': '#632de9',
'dark cyan': '#0a888a',
'olive drab': '#6f7632',
'pinkish': '#d46a7e',
'cobalt': '#1e488f',
'neon purple': '#bc13fe',
'light turquoise': '#7ef4cc',
'apple green': '#76cd26',
'dull green': '#74a662',
'wine': '#80013f',
'powder blue': '#b1d1fc',
'off white': '#ffffe4',
'electric blue': '#0652ff',
'dark turquoise': '#045c5a',
'blue purple': '#5729ce',
'azure': '#069af3',
'bright red': '#ff000d',
'pinkish red': '#f10c45',
'cornflower blue': '#5170d7',
'light olive': '#acbf69',
'grape': '#6c3461',
'greyish blue': '#5e819d',
'purplish blue': '#601ef9',
'yellowish green': '#b0dd16',
'greenish yellow': '#cdfd02',
'medium blue': '#2c6fbb',
'dusty rose': '#c0737a',
'light violet': '#d6b4fc',
'midnight blue': '#020035',
'bluish purple': '#703be7',
'red orange': '#fd3c06',
'dark magenta': '#960056',
'greenish': '#40a368',
'ocean blue': '#03719c',
'coral': '#fc5a50',
'cream': '#ffffc2',
'reddish brown': '#7f2b0a',
'burnt sienna': '#b04e0f',
'brick': '#a03623',
'sage': '#87ae73',
'grey green': '#789b73',
'white': '#ffffff',
"robin's egg blue": '#98eff9',
'moss green': '#658b38',
'steel blue': '#5a7d9a',
'eggplant': '#380835',
'light yellow': '#fffe7a',
'leaf green': '#5ca904',
'light grey': '#d8dcd6',
'puke': '#a5a502',
'pinkish purple': '#d648d7',
'sea blue': '#047495',
'pale purple': '#b790d4',
'slate blue': '#5b7c99',
'blue grey': '#607c8e',
'hunter green': '#0b4008',
'fuchsia': '#ed0dd9',
'crimson': '#8c000f',
'pale yellow': '#ffff84',
'ochre': '#bf9005',
'mustard yellow': '#d2bd0a',
'light red': '#ff474c',
'cerulean': '#0485d1',
'pale pink': '#ffcfdc',
'deep blue': '#040273',
'rust': '#a83c09',
'light teal': '#90e4c1',
'slate': '#516572',
'goldenrod': '#fac205',
'dark yellow': '#d5b60a',
'dark grey': '#363737',
'army green': '#4b5d16',
'grey blue': '#6b8ba4',
'seafoam': '#80f9ad',
'puce': '#a57e52',
'spring green': '#a9f971',
'dark orange': '#c65102',
'sand': '#e2ca76',
'pastel green': '#b0ff9d',
'mint': '#9ffeb0',
'light orange': '#fdaa48',
'bright pink': '#fe01b1',
'chartreuse': '#c1f80a',
'deep purple': '#36013f',
'dark brown': '#341c02',
'taupe': '#b9a281',
'pea green': '#8eab12',
'puke green': '#9aae07',
'kelly green': '#02ab2e',
'seafoam green': '#7af9ab',
'blue green': '#137e6d',
'khaki': '#aaa662',
'burgundy': '#610023',
'dark teal': '#014d4e',
'brick red': '#8f1402',
'royal purple': '#4b006e',
'plum': '#580f41',
'mint green': '#8fff9f',
'gold': '#dbb40c',
'baby blue': '#a2cffe',
'yellow green': '#c0fb2d',
'bright purple': '#be03fd',
'dark red': '#840000',
'pale blue': '#d0fefe',
'grass green': '#3f9b0b',
'navy': '#01153e',
'aquamarine': '#04d8b2',
'burnt orange': '#c04e01',
'neon green': '#0cff0c',
'bright blue': '#0165fc',
'rose': '#cf6275',
'light pink': '#ffd1df',
'mustard': '#ceb301',
'indigo': '#380282',
'lime': '#aaff32',
'sea green': '#53fca1',
'periwinkle': '#8e82fe',
'dark pink': '#cb416b',
'olive green': '#677a04',
'peach': '#ffb07c',
'pale green': '#c7fdb5',
'light brown': '#ad8150',
'hot pink': '#ff028d',
'black': '#000000',
'lilac': '#cea2fd',
'navy blue': '#001146',
'royal blue': '#0504aa',
'beige': '#e6daa6',
'salmon': '#ff796c',
'olive': '#6e750e',
'maroon': '#650021',
'bright green': '#01ff07',
'dark purple': '#35063e',
'mauve': '#ae7181',
'forest green': '#06470c',
'aqua': '#13eac9',
'cyan': '#00ffff',
'tan': '#d1b26f',
'dark blue': '#00035b',
'lavender': '#c79fef',
'turquoise': '#06c2ac',
'dark green': '#033500',
'violet': '#9a0eea',
'light purple': '#bf77f6',
'lime green': '#89fe05',
'grey': '#929591',
'sky blue': '#75bbfd',
'yellow': '#ffff14',
'magenta': '#c20078',
'light green': '#96f97b',
'orange': '#f97306',
'teal': '#029386',
'light blue': '#95d0fc',
'red': '#e50000',
'brown': '#653700',
'pink': '#ff81c0',
'blue': '#0343df',
'green': '#15b01a',
'purple': '#7e1e9c'}
# Normalize name to "xkcd:<name>" to avoid name collisions.
XKCD_COLORS = {'xkcd:' + name: value for name, value in XKCD_COLORS.items()}
# https://drafts.csswg.org/css-color-4/#named-colors
CSS4_COLORS = {
'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkgrey': '#A9A9A9',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkslategrey': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dimgrey': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'grey': '#808080',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgray': '#D3D3D3',
'lightgreen': '#90EE90',
'lightgrey': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightslategrey': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'rebeccapurple': '#663399',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#F4A460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'slategrey': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}
|
matplotlibREPO_NAMEmatplotlibPATH_START.@matplotlib_extracted@matplotlib-main@lib@matplotlib@_color_data.py@.PATH_END.py
|
{
"filename": "gen_qa_identity_models.py",
"repo_name": "triton-inference-server/server",
"repo_path": "server_extracted/server-main/qa/common/gen_qa_identity_models.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# Copyright 2019-2024, NVIDIA CORPORATION & AFFILIATES. 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 NVIDIA CORPORATION 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 argparse
import os
from builtins import range
import gen_ensemble_model_utils as emu
import numpy as np
from gen_common import (
np_to_model_dtype,
np_to_onnx_dtype,
np_to_tf_dtype,
np_to_trt_dtype,
)
FLAGS = None
np_dtype_string = np.dtype(object)
from typing import List, Tuple
def create_tf_modelfile(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_tf_model(dtype, dtype, dtype, shape, shape, shape):
return
tf_dtype = np_to_tf_dtype(dtype)
# Create the model that copies inputs to corresponding outputs.
tf.compat.v1.reset_default_graph()
for io_num in range(io_cnt):
input_name = "INPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
if max_batch == 0:
tin = tf.compat.v1.placeholder(
tf_dtype, tu.shape_to_tf_shape(shape), input_name
)
else:
tin = tf.compat.v1.placeholder(
tf_dtype,
[
None,
]
+ tu.shape_to_tf_shape(shape),
input_name,
)
toutput = tf.identity(tin, name=output_name)
# Use model name based on io_cnt and non-batching variant
if create_savedmodel:
model_name = tu.get_zero_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", io_cnt, dtype
)
else:
model_name = tu.get_zero_model_name(
"graphdef_nobatch" if max_batch == 0 else "graphdef", io_cnt, dtype
)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
if create_savedmodel:
with tf.compat.v1.Session() as sess:
input_dict = {}
output_dict = {}
for io_num in range(io_cnt):
input_name = "INPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
input_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
input_name + ":0"
)
output_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
output_name + ":0"
)
input_dict[input_name] = input_tensor
output_dict[output_name] = output_tensor
tf.compat.v1.saved_model.simple_save(
sess,
model_version_dir + "/model.savedmodel",
inputs=input_dict,
outputs=output_dict,
)
else:
with tf.compat.v1.Session() as sess:
graph_io.write_graph(
sess.graph.as_graph_def(),
model_version_dir,
"model.graphdef",
as_text=False,
)
def create_tf_modelconfig(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_tf_model(dtype, dtype, dtype, shape, shape, shape):
return
shape_str = tu.shape_to_dims_str(shape)
# Use a different model name for the non-batching variant
if create_savedmodel:
model_name = tu.get_zero_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", io_cnt, dtype
)
else:
model_name = tu.get_zero_model_name(
"graphdef_nobatch" if max_batch == 0 else "graphdef", io_cnt, dtype
)
config_dir = os.path.join(models_dir, model_name)
config = """
name: "{}"
platform: "{}"
max_batch_size: {}
""".format(
model_name,
"tensorflow_savedmodel" if create_savedmodel else "tensorflow_graphdef",
max_batch,
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "INPUT{}"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT{}"
data_type: {}
dims: [ {} ]
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(dtype),
shape_str,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_ensemble_modelfile(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_ensemble_model(
"zero", dtype, dtype, dtype, shape, shape, shape
):
return
emu.create_identity_ensemble_modelfile(
"zero",
models_dir,
model_version,
max_batch,
dtype,
[shape] * io_cnt,
[shape] * io_cnt,
)
def create_ensemble_modelconfig(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_ensemble_model(
"zero", dtype, dtype, dtype, shape, shape, shape
):
return
emu.create_identity_ensemble_modelconfig(
"zero",
models_dir,
model_version,
max_batch,
dtype,
[shape] * io_cnt,
[shape] * io_cnt,
[shape] * io_cnt,
[shape] * io_cnt,
)
def create_onnx_modelfile(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
return
onnx_dtype = np_to_onnx_dtype(dtype)
# Create the model
model_name = tu.get_zero_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", io_cnt, dtype
)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
batch_dim = [] if max_batch == 0 else [None]
onnx_nodes = []
onnx_inputs = []
onnx_outputs = []
idx = 0
for io_num in range(io_cnt):
# Repeat so that the variable dimension name is different
in_shape, idx = tu.shape_to_onnx_shape(shape, idx)
out_shape, idx = tu.shape_to_onnx_shape(shape, idx)
in_name = "INPUT{}".format(io_num)
out_name = "OUTPUT{}".format(io_num)
onnx_inputs.append(
onnx.helper.make_tensor_value_info(
in_name, onnx_dtype, batch_dim + in_shape
)
)
onnx_outputs.append(
onnx.helper.make_tensor_value_info(
out_name, onnx_dtype, batch_dim + out_shape
)
)
onnx_nodes.append(onnx.helper.make_node("Identity", [in_name], [out_name]))
graph_proto = onnx.helper.make_graph(
onnx_nodes, model_name, onnx_inputs, onnx_outputs
)
if FLAGS.onnx_opset > 0:
model_opset = onnx.helper.make_operatorsetid("", FLAGS.onnx_opset)
model_def = onnx.helper.make_model(
graph_proto, producer_name="triton", opset_imports=[model_opset]
)
else:
model_def = onnx.helper.make_model(graph_proto, producer_name="triton")
os.makedirs(model_version_dir, exist_ok=True)
onnx.save(model_def, model_version_dir + "/model.onnx")
def create_onnx_modelconfig(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
return
# Use a different model name for the non-batching variant
model_name = tu.get_zero_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", io_cnt, dtype
)
config_dir = os.path.join(models_dir, model_name)
config = emu.create_general_modelconfig(
model_name,
"onnxruntime_onnx",
max_batch,
emu.repeat(dtype, io_cnt),
emu.repeat(shape, io_cnt),
emu.repeat(shape, io_cnt),
emu.repeat(dtype, io_cnt),
emu.repeat(shape, io_cnt),
emu.repeat(shape, io_cnt),
emu.repeat(None, io_cnt),
force_tensor_number_suffix=True,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_libtorch_modelfile(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_libtorch_model(
dtype, dtype, dtype, shape, shape, shape, max_batch
):
return
model_name = tu.get_zero_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch", io_cnt, dtype
)
# Create the model
if io_cnt == 1:
if dtype == np_dtype_string:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(self, input0: List[str]) -> List[str]:
return input0
else:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(self, input0):
return input0
elif io_cnt == 2:
if dtype == np_dtype_string:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(
self, input0: List[str], input1: List[str]
) -> Tuple[List[str], List[str]]:
return input0, input1
else:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(self, input0, input1):
return input0, input1
elif io_cnt == 3:
if dtype == np_dtype_string:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(
self, input0: List[str], input1: List[str], input2: List[str]
) -> Tuple[List[str], List[str], List[str]]:
return input0, input1, input2
else:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(self, input0, input1, input2):
return input0, input1, input2
elif io_cnt == 4:
if dtype == np_dtype_string:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(
self,
input0: List[str],
input1: List[str],
input2: List[str],
input3: List[str],
) -> Tuple[List[str], List[str], List[str], List[str]]:
return input0, input1, input2, input3
else:
class IdentityNet(nn.Module):
def __init__(self):
super(IdentityNet, self).__init__()
def forward(self, input0, input1, input2, input3):
return input0, input1, input2, input3
identityModel = IdentityNet()
traced = torch.jit.script(identityModel)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
traced.save(model_version_dir + "/model.pt")
def create_libtorch_modelconfig(
create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
):
if not tu.validate_for_libtorch_model(
dtype, dtype, dtype, shape, shape, shape, max_batch
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
# Use a different model name for the non-batching variant
model_name = tu.get_zero_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch", io_cnt, dtype
)
shape_str = tu.shape_to_dims_str(shape)
config_dir = os.path.join(models_dir, model_name)
config = """
name: "{}"
platform: "pytorch_libtorch"
max_batch_size: {}
version_policy: {}
""".format(
model_name, max_batch, version_policy_str
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "INPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(dtype),
shape_str,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_libtorch_linalg_modelfile(create_savedmodel, models_dir, model_version):
model_name = "libtorch_float32_linalg"
# To test the linalg library, this script uses two inverse matrix operations
# to return the original input.
class IdentityNet(nn.Module):
def __init__(self, ref_pts):
super(IdentityNet, self).__init__()
ref_pts = torch.as_tensor(ref_pts)
self.register_buffer("ref_pts", ref_pts)
def forward(self, src: torch.Tensor):
X = torch.linalg.tensorsolve(self.ref_pts, src)
Y = torch.tensordot(self.ref_pts, X, dims=X.ndim)
return Y
ref_pts = torch.eye(2 * 3 * 4).reshape(2 * 3, 4, 2, 3, 4)
identityModel = IdentityNet(ref_pts)
traced = torch.jit.script(identityModel)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
traced.save(model_version_dir + "/model.pt")
def create_libtorch_linalg_modelconfig(create_savedmodel, models_dir, model_version):
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
model_name = "libtorch_float32_linalg"
dtype = np.float32
io_cnt = 1
max_batch = 0
shape = [6, 4]
shape_str = tu.shape_to_dims_str(shape)
config_dir = os.path.join(models_dir, model_name)
config = """
name: "{}"
platform: "pytorch_libtorch"
max_batch_size: {}
version_policy: {}
""".format(
model_name, max_batch, version_policy_str
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "INPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(dtype),
shape_str,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_openvino_modelfile(
models_dir, model_version, io_cnt, max_batch, dtype, shape
):
batch_dim = (
[]
if max_batch == 0
else [
max_batch,
]
)
if not tu.validate_for_openvino_model(
dtype, dtype, dtype, batch_dim + shape, batch_dim + shape, batch_dim + shape
):
return
# Create the model
model_name = tu.get_zero_model_name(
"openvino_nobatch" if max_batch == 0 else "openvino", io_cnt, dtype
)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
openvino_inputs = []
openvino_outputs = []
for io_num in range(io_cnt):
in_name = "INPUT{}".format(io_num)
out_name = "OUTPUT{}".format(io_num)
openvino_inputs.append(
ov.opset1.parameter(shape=batch_dim + shape, dtype=dtype, name=in_name)
)
openvino_outputs.append(
ov.opset1.result(openvino_inputs[io_num], name=out_name)
)
model = ov.Model(openvino_outputs, openvino_inputs, model_name)
os.makedirs(model_version_dir, exist_ok=True)
ov.serialize(
model, model_version_dir + "/model.xml", model_version_dir + "/model.bin"
)
def create_openvino_modelconfig(
models_dir, model_version, io_cnt, max_batch, dtype, shape
):
batch_dim = (
[]
if max_batch == 0
else [
max_batch,
]
)
if not tu.validate_for_openvino_model(
dtype, dtype, dtype, batch_dim + shape, batch_dim + shape, batch_dim + shape
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
# Use a different model name for the non-batching variant
model_name = tu.get_zero_model_name(
"openvino_nobatch" if max_batch == 0 else "openvino", io_cnt, dtype
)
shape_str = tu.shape_to_dims_str(shape)
config_dir = os.path.join(models_dir, model_name)
config = """
name: "{}"
backend: "openvino"
max_batch_size: {}
version_policy: {}
""".format(
model_name, max_batch, version_policy_str
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "INPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT__{}"
data_type: {}
dims: [ {} ]
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(dtype),
shape_str,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_plan_modelfile(
create_savedmodel,
models_dir,
model_version,
io_cnt,
max_batch,
dtype,
shape,
profile_max_size,
):
if not tu.validate_for_trt_model(dtype, dtype, dtype, shape, shape, shape):
return
# generate models with different configuration to ensure test coverage
if dtype != np.float32:
create_plan_dynamic_rf_modelfile(
models_dir, model_version, io_cnt, max_batch, dtype, shape, profile_max_size
)
else:
create_plan_dynamic_modelfile(
models_dir, model_version, io_cnt, max_batch, dtype, shape, profile_max_size
)
def create_plan_dynamic_rf_modelfile(
models_dir, model_version, io_cnt, max_batch, dtype, shape, profile_max_size
):
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
shape_with_batchsize = [i for i in shape]
else:
shape_with_batchsize = [-1] + [i for i in shape]
trt_dtype = np_to_trt_dtype(dtype)
trt_memory_format = trt.TensorFormat.LINEAR
for io_num in range(io_cnt):
in_node = network.add_input(
"INPUT{}".format(io_num), trt_dtype, shape_with_batchsize
)
in_node.allowed_formats = 1 << int(trt_memory_format)
out_node = network.add_identity(in_node)
out_node.get_output(0).name = "OUTPUT{}".format(io_num)
out_node.get_output(0).dtype = trt_dtype
network.mark_output(out_node.get_output(0))
out_node.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if trt_dtype == trt.int8:
in_node.dynamic_range = (-128.0, 127.0)
out_node.get_output(0).dynamic_range = (-128.0, 127.0)
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in shape:
if i == -1:
# Generating a very generous optimization profile
min_shape = min_shape + [1]
opt_shape = opt_shape + [8]
max_shape = max_shape + [profile_max_size]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
for io_num in range(io_cnt):
profile.set_shape("INPUT{}".format(io_num), min_shape, opt_shape, max_shape)
flags = 1 << int(trt.BuilderFlag.DIRECT_IO)
flags |= 1 << int(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS)
flags |= 1 << int(trt.BuilderFlag.REJECT_EMPTY_ALGORITHMS)
datatype_set = set([trt_dtype])
for dt in datatype_set:
if dt == trt.int8:
flags |= 1 << int(trt.BuilderFlag.INT8)
elif dt == trt.float16:
flags |= 1 << int(trt.BuilderFlag.FP16)
config = builder.create_builder_config()
config.flags = flags
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
config.add_optimization_profile(profile)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_zero_model_name(
"plan_nobatch" if max_batch == 0 else "plan", io_cnt, dtype
)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_shape_tensor_modelfile(
models_dir,
model_version,
io_cnt,
max_batch,
dtype,
shape,
profile_max_size,
shape_tensor_input_dtype,
):
# Note that resize layer does not support int tensors.
# The model takes two inputs (INPUT and DUMMY_INPUT)
# and produce two outputs.
# OUTPUT : The shape of resized output 'DUMMY_OUTPUT'.
# DUMMY_OUTPUT : Obtained after resizing 'DUMMY_INPUT'
# to shape specified in 'INPUT'.
# Note that values of OUTPUT tensor must be identical
# to INPUT values
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
shape_with_batchsize = len(shape)
dummy_shape = [-1] * shape_with_batchsize
else:
shape_with_batchsize = len(shape) + 1
dummy_shape = [-1] * shape_with_batchsize
trt_dtype = np_to_trt_dtype(dtype)
trt_shape_dtype = np_to_trt_dtype(shape_tensor_input_dtype)
trt_memory_format = trt.TensorFormat.LINEAR
for io_num in range(io_cnt):
in_node = network.add_input(
"INPUT{}".format(io_num), trt_shape_dtype, [shape_with_batchsize]
)
in_node.allowed_formats = 1 << int(trt_memory_format)
dummy_in_node = network.add_input(
"DUMMY_INPUT{}".format(io_num), trt_dtype, dummy_shape
)
dummy_in_node.allowed_formats = 1 << int(trt_memory_format)
resize_layer = network.add_resize(dummy_in_node)
resize_layer.set_input(1, in_node)
out_node = network.add_shape(resize_layer.get_output(0))
dummy_out_node = resize_layer.get_output(0)
out_node.get_output(0).name = "OUTPUT{}".format(io_num)
dummy_out_node.name = "DUMMY_OUTPUT{}".format(io_num)
dummy_out_node.dtype = trt_dtype
network.mark_output(dummy_out_node)
dummy_out_node.allowed_formats = 1 << int(trt_memory_format)
out_node.get_output(0).dtype = trt.int64
network.mark_output_for_shapes(out_node.get_output(0))
out_node.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if trt_dtype == trt.int8:
in_node.dynamic_range = (-128.0, 127.0)
out_node.get_output(0).dynamic_range = (-128.0, 127.0)
config = builder.create_builder_config()
min_prefix = []
opt_prefix = []
max_prefix = []
if max_batch != 0:
min_prefix = [1]
opt_prefix = [max(1, max_batch)]
max_prefix = [max(1, max_batch)]
min_shape = min_prefix + [1] * len(shape)
opt_shape = opt_prefix + [8] * len(shape)
max_shape = max_prefix + [profile_max_size] * len(shape)
profile = builder.create_optimization_profile()
for io_num in range(io_cnt):
profile.set_shape_input(
"INPUT{}".format(io_num), min_shape, opt_shape, max_shape
)
profile.set_shape(
"DUMMY_INPUT{}".format(io_num), min_shape, opt_shape, max_shape
)
config.add_optimization_profile(profile)
flags = 1 << int(trt.BuilderFlag.DIRECT_IO)
flags |= 1 << int(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS)
flags |= 1 << int(trt.BuilderFlag.REJECT_EMPTY_ALGORITHMS)
datatype_set = set([trt_dtype])
for dt in datatype_set:
if dt == trt.int8:
flags |= 1 << int(trt.BuilderFlag.INT8)
elif dt == trt.float16:
flags |= 1 << int(trt.BuilderFlag.FP16)
config.flags = flags
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_zero_model_name(
"plan_nobatch" if max_batch == 0 else "plan", io_cnt, dtype
)
model_name = model_name + "_" + np.dtype(shape_tensor_input_dtype).name
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_dynamic_modelfile(
models_dir, model_version, io_cnt, max_batch, dtype, shape, profile_max_size
):
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
shape_with_batchsize = [i for i in shape]
else:
shape_with_batchsize = [-1] + [i for i in shape]
trt_dtype = np_to_trt_dtype(dtype)
for io_num in range(io_cnt):
in_node = network.add_input(
"INPUT{}".format(io_num), trt_dtype, shape_with_batchsize
)
out_node = network.add_identity(in_node)
out_node.get_output(0).name = "OUTPUT{}".format(io_num)
network.mark_output(out_node.get_output(0))
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in shape:
if i == -1:
# Generating a very generous optimization profile
min_shape = min_shape + [1]
opt_shape = opt_shape + [8]
max_shape = max_shape + [profile_max_size]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
for io_num in range(io_cnt):
profile.set_shape("INPUT{}".format(io_num), min_shape, opt_shape, max_shape)
config = builder.create_builder_config()
config.add_optimization_profile(profile)
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
if FLAGS.tensorrt_compat:
config.set_flag(trt.BuilderFlag.VERSION_COMPATIBLE)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name_base = "plan"
if max_batch == 0:
model_name_base += "_nobatch"
if FLAGS.tensorrt_compat:
model_name_base += "_compatible"
model_name = tu.get_zero_model_name(model_name_base, io_cnt, dtype)
model_version_dir = os.path.join(models_dir, model_name, str(model_version))
os.makedirs(model_version_dir, exist_ok=True)
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_modelconfig(
create_savedmodel,
models_dir,
model_version,
io_cnt,
max_batch,
dtype,
shape,
shape_tensor_input_dtype=None,
):
if not tu.validate_for_trt_model(dtype, dtype, dtype, shape, shape, shape):
return
shape_str = tu.shape_to_dims_str(shape)
model_name_base = "plan"
if max_batch == 0:
model_name_base += "_nobatch"
if FLAGS.tensorrt_compat:
model_name_base += "_compatible"
model_name = tu.get_zero_model_name(model_name_base, io_cnt, dtype)
if shape_tensor_input_dtype:
model_name = model_name + "_" + np.dtype(shape_tensor_input_dtype).name
config_dir = os.path.join(models_dir, model_name)
if FLAGS.tensorrt_shape_io:
shape_tensor_dim = len(shape)
config = """
name: "{}"
platform: "tensorrt_plan"
max_batch_size: {}
""".format(
model_name, max_batch
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "DUMMY_INPUT{}"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT{}"
data_type: {}
dims: [ {} ]
is_shape_tensor: true
}}
]
output [
{{
name: "DUMMY_OUTPUT{}"
data_type: {}
dims: [ {} ]
}},
{{
name: "OUTPUT{}"
data_type: TYPE_INT64
dims: [ {} ]
is_shape_tensor: true
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(shape_tensor_input_dtype),
shape_tensor_dim,
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
shape_tensor_dim,
)
else:
config = """
name: "{}"
platform: "tensorrt_plan"
max_batch_size: {}
""".format(
model_name, max_batch
)
for io_num in range(io_cnt):
config += """
input [
{{
name: "INPUT{}"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT{}"
data_type: {}
dims: [ {} ]
}}
]
""".format(
io_num,
np_to_model_dtype(dtype),
shape_str,
io_num,
np_to_model_dtype(dtype),
shape_str,
)
os.makedirs(config_dir, exist_ok=True)
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_shape_tensor_models(
models_dir, dtype, shape, shape_tensor_input_dtype, io_cnt=1, no_batch=True
):
model_version = 1
create_plan_modelconfig(
True,
models_dir,
model_version,
io_cnt,
8,
dtype,
shape,
shape_tensor_input_dtype,
)
create_plan_shape_tensor_modelfile(
models_dir, model_version, io_cnt, 8, dtype, shape, 32, shape_tensor_input_dtype
)
if no_batch:
create_plan_modelconfig(
True,
models_dir,
model_version,
io_cnt,
0,
dtype,
shape,
shape_tensor_input_dtype,
)
create_plan_shape_tensor_modelfile(
models_dir,
model_version,
io_cnt,
0,
dtype,
shape,
32,
shape_tensor_input_dtype,
)
def create_models(models_dir, dtype, shape, io_cnt=1, no_batch=True):
model_version = 1
if FLAGS.graphdef:
create_tf_modelconfig(False, models_dir, model_version, io_cnt, 8, dtype, shape)
create_tf_modelfile(False, models_dir, model_version, io_cnt, 8, dtype, shape)
if no_batch:
create_tf_modelconfig(
False, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_tf_modelfile(
False, models_dir, model_version, io_cnt, 0, dtype, shape
)
if FLAGS.savedmodel:
create_tf_modelconfig(True, models_dir, model_version, io_cnt, 8, dtype, shape)
create_tf_modelfile(True, models_dir, model_version, io_cnt, 8, dtype, shape)
if no_batch:
create_tf_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_tf_modelfile(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
if FLAGS.onnx:
create_onnx_modelconfig(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
create_onnx_modelfile(True, models_dir, model_version, io_cnt, 8, dtype, shape)
if no_batch:
create_onnx_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_onnx_modelfile(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
if FLAGS.openvino:
create_openvino_modelconfig(models_dir, model_version, io_cnt, 8, dtype, shape)
create_openvino_modelfile(models_dir, model_version, io_cnt, 8, dtype, shape)
if no_batch:
create_openvino_modelconfig(
models_dir, model_version, io_cnt, 0, dtype, shape
)
create_openvino_modelfile(
models_dir, model_version, io_cnt, 0, dtype, shape
)
if FLAGS.libtorch:
create_libtorch_modelconfig(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
create_libtorch_modelfile(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
if no_batch:
create_libtorch_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_libtorch_modelfile(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
if FLAGS.tensorrt or FLAGS.tensorrt_compat:
create_plan_modelconfig(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
create_plan_modelfile(
True, models_dir, model_version, io_cnt, 8, dtype, shape, 32
)
if no_batch:
create_plan_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_plan_modelfile(
True, models_dir, model_version, io_cnt, 0, dtype, shape, 32
)
if FLAGS.tensorrt_big:
create_plan_modelconfig(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
create_plan_modelfile(
True, models_dir, model_version, io_cnt, 8, dtype, shape, 16 * 1024 * 1024
)
if no_batch:
create_plan_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_plan_modelfile(
True,
models_dir,
model_version,
io_cnt,
0,
dtype,
shape,
16 * 1024 * 1024,
)
if FLAGS.ensemble:
emu.create_nop_modelconfig(models_dir, shape, dtype)
create_ensemble_modelconfig(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
create_ensemble_modelfile(
True, models_dir, model_version, io_cnt, 8, dtype, shape
)
if no_batch:
create_ensemble_modelconfig(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
create_ensemble_modelfile(
True, models_dir, model_version, io_cnt, 0, dtype, shape
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--models_dir", type=str, required=True, help="Top-level model directory"
)
parser.add_argument(
"--graphdef",
required=False,
action="store_true",
help="Generate GraphDef models",
)
parser.add_argument(
"--savedmodel",
required=False,
action="store_true",
help="Generate SavedModel models",
)
parser.add_argument(
"--onnx",
required=False,
action="store_true",
help="Generate Onnx Runtime Onnx models",
)
parser.add_argument(
"--onnx_opset",
type=int,
required=False,
default=0,
help="Opset used for Onnx models. Default is to use ONNXRT default",
)
parser.add_argument(
"--libtorch",
required=False,
action="store_true",
help="Generate Pytorch LibTorch models",
)
parser.add_argument(
"--openvino",
required=False,
action="store_true",
help="Generate OpenVino models",
)
parser.add_argument(
"--tensorrt",
required=False,
action="store_true",
help="Generate TensorRT PLAN models",
)
parser.add_argument(
"--tensorrt-big",
required=False,
action="store_true",
help="Generate TensorRT PLAN models w/ opt profile with large max",
)
parser.add_argument(
"--tensorrt-compat",
required=False,
action="store_true",
help="Generate TensorRT version-compatible models",
)
parser.add_argument(
"--tensorrt-shape-io",
required=False,
action="store_true",
help="Generate TensorRT PLAN models w/ shape tensor i/o",
)
parser.add_argument(
"--ensemble",
required=False,
action="store_true",
help="Generate ensemble models",
)
FLAGS, unparsed = parser.parse_known_args()
if FLAGS.graphdef or FLAGS.savedmodel:
import tensorflow as tf
from tensorflow.python.framework import graph_io
tf.compat.v1.disable_eager_execution()
if FLAGS.onnx:
import onnx
if FLAGS.libtorch:
import torch
from torch import nn
if (
FLAGS.tensorrt
or FLAGS.tensorrt_big
or FLAGS.tensorrt_compat
or FLAGS.tensorrt_shape_io
):
import tensorrt as trt
if FLAGS.openvino:
import openvino.runtime as ov
import test_util as tu
# Create models with variable-sized input and output. For big
# and version-compatible TensorRT models, only create the one
# needed for testing.
if FLAGS.tensorrt_big:
create_models(FLAGS.models_dir, np.float32, [-1], io_cnt=1)
elif FLAGS.tensorrt_compat:
create_models(FLAGS.models_dir, np.float32, [-1], io_cnt=1, no_batch=False)
elif FLAGS.tensorrt_shape_io:
create_shape_tensor_models(
FLAGS.models_dir, np.float32, [-1, -1], np.int32, io_cnt=1
)
create_shape_tensor_models(
FLAGS.models_dir, np.float32, [-1, -1], np.int64, io_cnt=1
)
else:
create_models(FLAGS.models_dir, bool, [-1], io_cnt=1)
create_models(FLAGS.models_dir, np.float32, [-1], io_cnt=1)
create_models(FLAGS.models_dir, np.float32, [-1], io_cnt=3)
create_models(FLAGS.models_dir, np.float16, [-1, -1], io_cnt=1)
create_models(FLAGS.models_dir, np.float16, [-1, -1], io_cnt=3)
create_models(FLAGS.models_dir, np_dtype_string, [-1], io_cnt=1)
create_models(FLAGS.models_dir, np_dtype_string, [-1, -1], io_cnt=3)
# Create libtorch linalg model
if FLAGS.libtorch:
model_version = 1
create_libtorch_linalg_modelconfig(True, FLAGS.models_dir, model_version)
create_libtorch_linalg_modelfile(True, FLAGS.models_dir, model_version)
|
triton-inference-serverREPO_NAMEserverPATH_START.@server_extracted@server-main@qa@common@gen_qa_identity_models.py@.PATH_END.py
|
{
"filename": "_ticklabeloverflow.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/isosurface/colorbar/_ticklabeloverflow.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicklabeloverflowValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="ticklabeloverflow",
parent_name="isosurface.colorbar",
**kwargs,
):
super(TicklabeloverflowValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
values=kwargs.pop("values", ["allow", "hide past div", "hide past domain"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@isosurface@colorbar@_ticklabeloverflow.py@.PATH_END.py
|
{
"filename": "move.py",
"repo_name": "3fon3fonov/exostriker",
"repo_path": "exostriker_extracted/exostriker-main/exostriker/lib/emcee_ES/moves/move.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
import numpy as np
__all__ = ["Move"]
class Move(object):
def tune(self, state, accepted):
pass
def update(self, old_state, new_state, accepted, subset=None):
"""Update a given subset of the ensemble with an accepted proposal
Args:
coords: The original ensemble coordinates.
log_probs: The original log probabilities of the walkers.
blobs: The original blobs.
new_coords: The proposed coordinates.
new_log_probs: The proposed log probabilities.
new_blobs: The proposed blobs.
accepted: A vector of booleans indicating which walkers were
accepted.
subset (Optional): A boolean mask indicating which walkers were
included in the subset. This can be used, for example, when
updating only the primary ensemble in a :class:`RedBlueMove`.
"""
if subset is None:
subset = np.ones(len(old_state.coords), dtype=bool)
m1 = subset & accepted
m2 = accepted[subset]
old_state.coords[m1] = new_state.coords[m2]
old_state.log_prob[m1] = new_state.log_prob[m2]
if new_state.blobs is not None:
if old_state.blobs is None:
raise ValueError(
"If you start sampling with a given log_prob, "
"you also need to provide the current list of "
"blobs at that position."
)
old_state.blobs[m1] = new_state.blobs[m2]
return old_state
|
3fon3fonovREPO_NAMEexostrikerPATH_START.@exostriker_extracted@exostriker-main@exostriker@lib@emcee_ES@moves@move.py@.PATH_END.py
|
{
"filename": "property_importer.py",
"repo_name": "pynbody/tangos",
"repo_path": "tangos_extracted/tangos-master/tangos/tools/property_importer.py",
"type": "Python"
}
|
import numbers
import numpy as np
from .. import core, parallel_tasks
from ..log import logger
from ..util import proxy_object, timestep_object_cache
from . import GenericTangosTool
class PropertyImporter(GenericTangosTool):
tool_name = 'import-properties'
tool_description = 'Import properties that were calculated by the halo finder'
@classmethod
def add_parser_arguments(self, parser):
parser.add_argument('--sims', '--for', action='store', nargs='*',
metavar='simulation_name',
help='Specify a simulation (or multiple simulations) to run on')
parser.add_argument('--type', action='store', type=str, dest='typetag', default='halo',
help="Specify the object type to run on by tag name (e.g. 'halo' or 'group')")
parser.add_argument('properties', action='store', nargs='*',
help="The names of the halo-finder pre-calculated properties to import; if not specified, all available properties are imported.")
parser.add_argument('--backwards', action='store_true',
help='Process low-z timesteps first')
def process_options(self, options):
self.options = options
def _create_property(self, name, object, value):
"""Create a single database property corresponding to the given value
See _create_properties for more information."""
if isinstance(value, proxy_object.ProxyObjectBase):
value = value.relative_to_timestep_cache(self._object_cache).resolve(self._session)
if value is not None:
return core.halo_data.HaloLink(object, value, name)
elif isinstance(value, numbers.Number):
return core.halo_data.HaloProperty(object, name, value)
elif isinstance(value, np.ndarray):
if np.issubdtype(value.dtype, np.number):
return core.halo_data.HaloProperty(object, name, value)
else:
logger.warning("Ignoring stat file entry key='%s' value='%s' as the value is not a number or an array of numbers",
name.text, value)
elif value is not None:
logger.warning("Ignoring stat file entry key='%s' value='%s' as the value is not a number or an array of numbers",
name.text, value)
return None
def _create_properties(self, name, object, values):
"""Create database property or properties corresponding to the given values.
The values can be proxy objects, to indicate a link should be created
:arg name: the name ORM object
:arg object: the object with which the property should be associated
:arg values: the value, or a list of values
:returns: a list of objects to be added to the database (always a list, even if there is only one value)
"""
if isinstance(values, list):
objects = [self._create_property(name, object, v) for v in values]
else:
objects = [self._create_property(name, object, values)]
return filter(lambda x: x is not None, objects)
def _import_properties_for_timestep(self, ts, property_names, object_typetag):
"""Import the named properties for a specific timestep
:arg ts: the database timestep
:arg property_names: list of names to import, or empty list to import all available names
:arg object_typetag: the type tag of the objects for which properties will be imported
:type ts: core.timestep.TimeStep
"""
logger.info("Processing %s", ts)
if len(property_names)==0:
property_names = self.handler.available_object_property_names_for_timestep(ts.extension, object_typetag)
self._object_cache = timestep_object_cache.TimestepObjectCache(ts)
self._session = core.Session.object_session(ts)
property_db_names = [core.dictionary.get_or_create_dictionary_item(self._session, name) for name in
property_names]
rows_to_store = []
for values in self.handler.iterate_object_properties_for_timestep(ts.extension, object_typetag, property_names):
if len(values)!=2+len(property_db_names):
raise RuntimeError(f"Incorrect length of row returned from iterate_object_properties_for_timestep. Check implementation of {type(self.handler)}.")
db_object = self._object_cache.resolve_from_finder_offset(values[0], object_typetag)
if db_object is not None:
for db_name, value in zip(property_db_names, values[2:]):
rows_to_store+=self._create_properties(db_name, db_object, value)
logger.info("Add %d properties", len(rows_to_store))
with parallel_tasks.ExclusiveLock("add_properties"):
self._session.add_all(rows_to_store)
self._session.commit()
def run_calculation_loop(self):
base_sim = core.sim_query_from_name_list(self.options.sims)
names = self.options.properties
object_typetag = self.options.typetag
for x in base_sim:
timesteps = core.get_default_session().query(core.timestep.TimeStep).filter_by(
simulation_id=x.id, available=True).order_by(core.timestep.TimeStep.redshift.desc()).all()
if self.options.backwards:
timesteps = timesteps[::-1]
self.handler = x.get_output_handler()
for ts in parallel_tasks.distributed(timesteps):
self._import_properties_for_timestep(ts, names, object_typetag)
|
pynbodyREPO_NAMEtangosPATH_START.@tangos_extracted@tangos-master@tangos@tools@property_importer.py@.PATH_END.py
|
{
"filename": "conf.py",
"repo_name": "pyro-ppl/pyro",
"repo_path": "pyro_extracted/pyro-master/docs/source/conf.py",
"type": "Python"
}
|
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import os
import sys
import sphinx_rtd_theme
# import pkg_resources
# -*- coding: utf-8 -*-
#
# Pyro documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 15 17:16:14 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
sys.path.insert(0, os.path.abspath("../.."))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.intersphinx", #
"sphinx.ext.todo", #
"sphinx.ext.mathjax", #
"sphinx.ext.ifconfig", #
"sphinx.ext.viewcode", #
"sphinx.ext.githubpages", #
"sphinx.ext.graphviz", #
"sphinx.ext.autodoc",
"sphinx.ext.doctest",
'sphinx.ext.napoleon',
]
# Disable documentation inheritance so as to avoid inheriting
# docstrings in a different format, e.g. when the parent class
# is a PyTorch class.
autodoc_inherit_docstrings = False
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = ".rst"
# The master toctree document.
master_doc = "index"
# General information about the project.
project = u"Pyro"
copyright = u"2017-2018, Uber Technologies, Inc"
author = u"Uber AI Labs"
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
version = ""
if "READTHEDOCS" not in os.environ:
# if developing locally, use pyro.__version__ as version
from pyro import __version__ # noqaE402
version = __version__
# release version
release = version
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = "en"
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
# do not prepend module name to functions
add_module_names = False
# -- Options for HTML output ----------------------------------------------
# logo
html_logo = "_static/img/pyro_logo_wide.png"
# logo
html_favicon = "_static/img/favicon/favicon.ico"
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
"navigation_depth": 3,
"logo_only": True,
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
html_style = "css/pyro.css"
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = "Pyrodoc"
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, "Pyro.tex", u"Pyro Documentation", u"Uber AI Labs", "manual"),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, "pyro", u"Pyro Documentation", [author], 1)]
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
"Pyro",
u"Pyro Documentation",
author,
"Pyro",
"Deep Universal Probabilistic Programming.",
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {
"python": ("https://docs.python.org/3/", None),
"torch": ("https://pytorch.org/docs/master/", None),
"funsor": ("http://funsor.pyro.ai/en/stable/", None),
"opt_einsum": ("https://optimized-einsum.readthedocs.io/en/stable/", None),
"scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
"Bio": ("https://biopython.org/docs/latest/api/", None),
"horovod": ("https://horovod.readthedocs.io/en/stable/", None),
"graphviz": ("https://graphviz.readthedocs.io/en/stable/", None),
}
# document class constructors (__init__ methods):
""" comment out this functionality for now;
def skip(app, what, name, obj, skip, options):
if name == "__init__":
return False
return skip
"""
def setup(app):
app.add_css_file("css/pyro.css")
# app.connect("autodoc-skip-member", skip)
# @jpchen's hack to get rtd builder to install latest pytorch
# See similar line in the install section of .travis.yml
if "READTHEDOCS" in os.environ:
os.system("pip install numpy")
os.system(
"pip install torch==2.0+cpu torchvision==0.15.0+cpu "
"-f https://download.pytorch.org/whl/torch_stable.html"
)
|
pyro-pplREPO_NAMEpyroPATH_START.@pyro_extracted@pyro-master@docs@source@conf.py@.PATH_END.py
|
{
"filename": "_color.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/sankey/node/line/_color.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(self, plotly_name="color", parent_name="sankey.node.line", **kwargs):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@sankey@node@line@_color.py@.PATH_END.py
|
{
"filename": "sunf90.py",
"repo_name": "rat-pac/rat-pac",
"repo_path": "rat-pac_extracted/rat-pac-master/python/SCons/Tool/sunf90.py",
"type": "Python"
}
|
"""SCons.Tool.sunf90
Tool-specific initialization for sunf90, the Sun Studio F90 compiler.
There normally shouldn't be any need to import this module directly.
It will usually be imported through the generic SCons.Tool.Tool()
selection method.
"""
#
# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 The SCons Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
# KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
__revision__ = "src/engine/SCons/Tool/sunf90.py 4043 2009/02/23 09:06:45 scons"
import SCons.Util
from FortranCommon import add_all_to_env
compilers = ['sunf90', 'f90']
def generate(env):
"""Add Builders and construction variables for sun f90 compiler to an
Environment."""
add_all_to_env(env)
fcomp = env.Detect(compilers) or 'f90'
env['FORTRAN'] = fcomp
env['F90'] = fcomp
env['SHFORTRAN'] = '$FORTRAN'
env['SHF90'] = '$F90'
env['SHFORTRANFLAGS'] = SCons.Util.CLVar('$FORTRANFLAGS -KPIC')
env['SHF90FLAGS'] = SCons.Util.CLVar('$F90FLAGS -KPIC')
def exists(env):
return env.Detect(compilers)
# Local Variables:
# tab-width:4
# indent-tabs-mode:nil
# End:
# vim: set expandtab tabstop=4 shiftwidth=4:
|
rat-pacREPO_NAMErat-pacPATH_START.@rat-pac_extracted@rat-pac-master@python@SCons@Tool@sunf90.py@.PATH_END.py
|
{
"filename": "plotters.py",
"repo_name": "pyspeckit/pyspeckit",
"repo_path": "pyspeckit_extracted/pyspeckit-master/pyspeckit/spectrum/plotters.py",
"type": "Python"
}
|
"""
=======
Plotter
=======
.. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com>
"""
from __future__ import print_function
import matplotlib
import matplotlib.figure
import numpy as np
import astropy.units as u
import copy
import inspect
from astropy import log
# this mess is to handle a nested hell of different versions of matplotlib
# (>=1.3 has BoundMethodProxy somewhere, >=3 gets rid of it) and python
# (python >=3.4 has WeakMethod, earlier versions don't)
try:
from matplotlib.cbook import BoundMethodProxy
except ImportError:
try:
from matplotlib.cbook import _BoundMethodProxy as BoundMethodProxy
except ImportError:
try:
from matplotlib.cbook import WeakMethod
except ImportError:
try:
from weakref import WeakMethod
except ImportError:
try:
from weakrefmethod import WeakMethod
except ImportError:
raise ImportError("Could not import WeakMethod from "
"anywhere. Try installing the "
"weakrefmethod package or use a more "
"recent version of python or matplotlib")
class BoundMethodProxy(WeakMethod):
@property
def func(self):
return self()
from . import widgets
from ..specwarnings import warn
interactive_help_message = """
Interactive key commands for plotter. An additional help message may appear if
you have initiated the fitter.
'?' - bring up this message
'f' - initiate the /f/itter
'b' - initiate the /b/aseliner
'B' - initiate the /b/aseliner (reset the selection too)
'r' - re-attach matplotlib keys
'R' - redraw the plot cleanly
'i' : individual components / show each fitted component
"""
xlabel_table = {'speed': 'Velocity'}
class Plotter(object):
"""
Class to plot a spectrum
"""
def __init__(self, Spectrum, autorefresh=True, title="", xlabel=None,
silent=True, plotscale=1.0, **kwargs):
import matplotlib.pyplot
self._pyplot = matplotlib.pyplot
self.figure = None
self.axis = None
self.Spectrum = Spectrum
# plot parameters
self.offset = 0.0 # vertical offset
self.autorefresh = autorefresh
self.xlabel = xlabel
self.title = title
self.errorplot = None
self.plotkwargs = kwargs
self._xlim = [None,None]
self._ylim = [None,None]
self.debug = False
self.keyclick = None
self.silent = silent
self.plotscale = plotscale
self._xclick1 = None
self._xclick2 = None
self.automake_fitter_tool = False
self._active_gui = None
@property
def _xunit(self):
return self.Spectrum.xarr.unit
def _get_prop(xy, minmax):
def getprop(self):
if xy == 'x':
if minmax == 'min':
if self._xlim[0] is not None and self._xunit:
try:
self._xlim[0]._unit = self._xunit
except AttributeError:
self._xlim[0] = u.Quantity(self._xlim[0], self._xunit)
return self._xlim[0]
elif minmax == 'max':
if self._xlim[1] is not None and self._xunit:
try:
self._xlim[1]._unit = self._xunit
except AttributeError:
self._xlim[1] = u.Quantity(self._xlim[1], self._xunit)
return self._xlim[1]
elif xy == 'y':
if minmax == 'min':
return self._ylim[0]
elif minmax == 'max':
return self._ylim[1]
return getprop
def _set_prop(xy, minmax):
def setprop(self, value):
if self.debug:
frm = inspect.stack()
print(frm[1],"Setting %s%s to %s" % (xy,minmax,value))
if xy == 'x':
if minmax == 'min':
self._xlim[0] = value
elif minmax == 'max':
self._xlim[1] = value
elif xy == 'y':
if minmax == 'min':
self._ylim[0] = value
elif minmax == 'max':
self._ylim[1] = value
return setprop
xmin = property(fget=_get_prop('x','min'),fset=_set_prop('x','min'))
xmax = property(fget=_get_prop('x','max'),fset=_set_prop('x','max'))
ymin = property(fget=_get_prop('y','min'),fset=_set_prop('y','min'))
ymax = property(fget=_get_prop('y','max'),fset=_set_prop('y','max'))
def _disconnect_matplotlib_keys(self):
"""
Disconnected the matplotlib key-press callbacks
"""
if self.figure is not None:
cbs = self.figure.canvas.callbacks.callbacks
# this may cause problems since the dict of key press events is a
# dict, i.e. not ordered, and we want to pop the first one...
mpl_keypress_handler = self.figure.canvas.manager.key_press_handler_id
try:
self._mpl_key_callbacks = {mpl_keypress_handler:
cbs['key_press_event'].pop(mpl_keypress_handler)}
except KeyError:
bmp = BoundMethodProxy(self.figure.canvas.manager.key_press)
self._mpl_key_callbacks = {mpl_keypress_handler:
bmp}
def _reconnect_matplotlib_keys(self):
"""
Reconnect the previously disconnected matplotlib keys
"""
if self.figure is not None and hasattr(self,'_mpl_key_callbacks'):
self.figure.canvas.callbacks.callbacks['key_press_event'].update(self._mpl_key_callbacks)
elif self.figure is not None:
mpl_keypress_handler = self.figure.canvas.manager.key_press_handler_id
try:
bmp = BoundMethodProxy(self.figure.canvas.manager.key_press)
self.figure.canvas.callbacks.callbacks['key_press_event'].update({mpl_keypress_handler:
bmp})
except AttributeError as ex:
print(f"Error {ex} was raised when trying to connect the key_press handler. "
"Please file an issue on github. You may try a different matplotlib backend "
"as a temporary workaround")
def __call__(self, figure=None, axis=None, clear=True, autorefresh=None,
plotscale=1.0, override_plotkwargs=False, **kwargs):
"""
Plot a spectrum
Keywords:
figure - either a matplotlib figure instance or a figure number
to pass into pyplot.figure.
axis - Alternative to figure, can pass an axis instance and use
it as the plotting canvas
clear - Clear the axis before plotting?
"""
# figure out where to put the plot
if isinstance(figure,matplotlib.figure.Figure):
self.figure = figure
self.axis = self.figure.gca()
elif type(figure) is int:
self.figure = self._pyplot.figure(figure)
self.axis = self.figure.gca()
elif self.figure is None:
if isinstance(axis,matplotlib.axes.Axes):
self.axis = axis
self.figure = axis.figure
else:
self.figure = self._pyplot.figure()
if hasattr(self.figure, 'number') and not self._pyplot.fignum_exists(self.figure.number):
self.figure = self._pyplot.figure(self.figure.number)
# always re-connect the interactive keys to avoid frustration...
self._mpl_reconnect()
if axis is not None:
#self._mpl_disconnect()
self.axis = axis
self.figure = axis.figure
#self._mpl_connect()
elif len(self.figure.axes) > 0 and self.axis is None:
self.axis = self.figure.axes[0] # default to first axis
elif self.axis is None:
self.axis = self.figure.gca()
# A check to deal with issue #117: if you close the figure, the axis
# still exists, but it cannot be reattached to a figure
if (hasattr(self.axis.get_figure(), 'number') and
not (self.axis.get_figure() is self._pyplot.figure(self.axis.get_figure().number))):
self.axis = self.figure.gca()
if self.axis is not None and self.axis not in self.figure.axes:
# if you've cleared the axis, but the figure is still open, you
# need a new axis
self.figure.add_axes(self.axis)
if clear and self.axis is not None:
self.axis.clear()
# Need to empty the stored model plots
if hasattr(self.Spectrum, 'fitter'):
self.Spectrum.fitter.clear()
if autorefresh is not None:
self.autorefresh = autorefresh
self.plotscale = plotscale
if self.plotkwargs and not override_plotkwargs:
self.plotkwargs.update(kwargs)
else:
self.plotkwargs = kwargs
self.plot(**kwargs)
def _mpl_connect(self):
if self.keyclick is None:
self.keyclick = self.figure.canvas.mpl_connect('key_press_event',self.parse_keys)
def _mpl_disconnect(self):
self.figure.canvas.mpl_disconnect(self.keyclick)
self.keyclick = None
def disconnect(self):
"""
Disconnect the matplotlib interactivity of this pyspeckit plotter.
"""
self._mpl_disconnect()
def connect(self):
"""
Connect to the matplotlib key-parsing interactivity
"""
self._mpl_connect()
def _mpl_reconnect(self):
self._mpl_disconnect()
self._mpl_connect()
# disable fullscreen & grid
self._pyplot.rcParams['keymap.fullscreen'] = 'ctrl+f'
self._pyplot.rcParams['keymap.grid'] = 'ctrl+g'
def plot(self, offset=0.0, xoffset=0.0, color='k', drawstyle='steps-mid',
linewidth=0.5, errstyle=None, erralpha=0.2, errcolor=None,
silent=None, reset=True, refresh=True, use_window_limits=None,
useOffset=False, **kwargs):
"""
Plot the spectrum!
Tries to automatically find a reasonable plotting range if one is not
set.
Parameters
----------
offset : float
vertical offset to add to the spectrum before plotting. Useful if
you want to overlay multiple spectra on a single plot
xoffset: float
An x-axis shift. I don't know why you'd want this...
color : str
default to plotting spectrum in black
drawstyle : 'steps-mid' or str
'steps-mid' for histogram-style plotting. See matplotlib's plot
for more information
linewidth : float
Line width in pixels. Narrow lines are helpful when histo-plotting
errstyle : 'fill', 'bars', or None
can be "fill", which draws partially transparent boxes around the
data to show the error region, or "bars" which draws standard
errorbars. ``None`` will display no errorbars
useOffset : bool
Use offset-style X/Y coordinates (e.g., 1 + 1.483e10)? Defaults to
False because these are usually quite annoying.
xmin/xmax/ymin/ymax : float
override defaults for plot range. Once set, these parameters are
sticky (i.e., replotting will use the same ranges). Passed to
`reset_limits`
reset_[xy]limits : bool
Reset the limits to "sensible defaults". Passed to `reset_limits`
ypeakscale : float
Scale up the Y maximum value. Useful to keep the annotations away
from the data. Passed to `reset_limits`
reset : bool
Reset the x/y axis limits? If set, `reset_limits` will be called.
"""
if self.axis is None:
raise Exception("You must call the Plotter class to initiate the canvas before plotting.")
self.offset = offset
# there is a bug where this only seems to update the second time it is called
self.label(**kwargs)
self.label(**kwargs)
for arg in ['title','xlabel','ylabel']:
if arg in kwargs:
kwargs.pop(arg)
reset_kwargs = {}
for arg in ['xmin', 'xmax', 'ymin', 'ymax', 'reset_xlimits',
'reset_ylimits', 'ypeakscale']:
if arg in kwargs:
reset_kwargs[arg] = kwargs.pop(arg)
if (use_window_limits is None and any(k in reset_kwargs for k in
('xmin','xmax','reset_xlimits'))):
use_window_limits = False
if use_window_limits:
self._stash_window_limits()
# for filled errorbars, order matters.
inds = np.argsort(self.Spectrum.xarr)
if errstyle is not None:
if errcolor is None:
errcolor = color
if errstyle == 'fill':
self.errorplot = [self.axis.fill_between(steppify(self.Spectrum.xarr.value[inds]+xoffset, isX=True),
steppify((self.Spectrum.data*self.plotscale+self.offset-self.Spectrum.error*self.plotscale)[inds]),
steppify((self.Spectrum.data*self.plotscale+self.offset+self.Spectrum.error*self.plotscale)[inds]),
facecolor=errcolor, edgecolor=errcolor, alpha=erralpha, **kwargs)]
elif errstyle == 'bars':
self.errorplot = self.axis.errorbar(self.Spectrum.xarr[inds].value+xoffset,
self.Spectrum.data[inds]*self.plotscale+self.offset,
yerr=self.Spectrum.error[inds]*self.plotscale,
ecolor=errcolor, fmt='none',
**kwargs)
self._spectrumplot = self.axis.plot(self.Spectrum.xarr.value[inds]+xoffset,
self.Spectrum.data[inds]*self.plotscale+self.offset,
color=color,
drawstyle=drawstyle,
linewidth=linewidth, **kwargs)
self.axis.ticklabel_format(useOffset=useOffset)
if use_window_limits:
self._reset_to_stashed_limits()
if silent is not None:
self.silent = silent
if reset:
self.reset_limits(use_window_limits=use_window_limits, **reset_kwargs)
if self.autorefresh and refresh:
self.refresh()
# Maybe it's OK to call 'plot' when there is an active gui tool
# (e.g., baseline or specfit)?
#if self._active_gui:
# self._active_gui = None
# warn("An active GUI was found while initializing the "
# "plot. This is somewhat dangerous and may result "
# "in broken interactivity.")
def _stash_window_limits(self):
self._window_limits = self.axis.get_xlim(),self.axis.get_ylim()
if self.debug:
print("Stashed window limits: ",self._window_limits)
def _reset_to_stashed_limits(self):
self.axis.set_xlim(*self._window_limits[0])
self.axis.set_ylim(*self._window_limits[1])
self.xmin,self.xmax = self._window_limits[0]
self.ymin,self.ymax = self._window_limits[1]
if self.debug:
print("Recovered window limits: ",self._window_limits)
def reset_limits(self, xmin=None, xmax=None, ymin=None, ymax=None,
reset_xlimits=True, reset_ylimits=True, ypeakscale=1.2,
silent=None, use_window_limits=False, **kwargs):
"""
Automatically or manually reset the plot limits
"""
# if not use_window_limits: use_window_limits = False
if self.debug:
frame = inspect.currentframe()
args, _, _, values = inspect.getargvalues(frame)
print(zip(args,values))
if use_window_limits:
# this means DO NOT reset!
# it simply sets self.[xy][min/max] = current value
self.set_limits_from_visible_window()
else:
if silent is not None:
self.silent = silent
# if self.xmin and self.xmax:
if (reset_xlimits or self.Spectrum.xarr.min().value < self.xmin or self.Spectrum.xarr.max().value > self.xmax):
if not self.silent:
warn("Resetting X-axis min/max because the plot is out of bounds.")
self.xmin = None
self.xmax = None
if xmin is not None:
self.xmin = u.Quantity(xmin, self._xunit)
elif self.xmin is None:
self.xmin = u.Quantity(self.Spectrum.xarr.min().value, self._xunit)
if xmax is not None:
self.xmax = u.Quantity(xmax, self._xunit)
elif self.xmax is None:
self.xmax = u.Quantity(self.Spectrum.xarr.max().value, self._xunit)
xpixmin = np.argmin(np.abs(self.Spectrum.xarr.value-self.xmin.value))
xpixmax = np.argmin(np.abs(self.Spectrum.xarr.value-self.xmax.value))
if xpixmin>xpixmax:
xpixmin,xpixmax = xpixmax,xpixmin
elif xpixmin == xpixmax:
if reset_xlimits:
raise Exception("Infinite recursion error. Maybe there are no valid data?")
if not self.silent:
warn("ERROR: the X axis limits specified were invalid. Resetting.")
self.reset_limits(reset_xlimits=True, ymin=ymin, ymax=ymax,
reset_ylimits=reset_ylimits,
ypeakscale=ypeakscale, **kwargs)
return
if self.ymin is not None and self.ymax is not None:
# this is utter nonsense....
if (np.nanmax(self.Spectrum.data) < self.ymin or np.nanmin(self.Spectrum.data) > self.ymax
or reset_ylimits):
if not self.silent and not reset_ylimits:
warn("Resetting Y-axis min/max because the plot is out of bounds.")
self.ymin = None
self.ymax = None
if ymin is not None:
self.ymin = ymin
elif self.ymin is None:
yminval = np.nanmin(self.Spectrum.data[xpixmin:xpixmax])
# Increase the range fractionally. This means dividing a positive #, multiplying a negative #
if yminval < 0:
self.ymin = float(yminval)*float(ypeakscale)
else:
self.ymin = float(yminval)/float(ypeakscale)
if ymax is not None:
self.ymax = ymax
elif self.ymax is None:
ymaxval = (np.nanmax(self.Spectrum.data[xpixmin:xpixmax])-self.ymin)
if ymaxval > 0:
self.ymax = float(ymaxval) * float(ypeakscale) + self.ymin
else:
self.ymax = float(ymaxval) / float(ypeakscale) + self.ymin
self.ymin += self.offset
self.ymax += self.offset
self.axis.set_xlim(self.xmin.value if hasattr(self.xmin, 'value') else self.xmin,
self.xmax.value if hasattr(self.xmax, 'value') else self.xmax)
self.axis.set_ylim(self.ymin, self.ymax)
def label(self, title=None, xlabel=None, ylabel=None, verbose_label=False,
**kwargs):
"""
Label the plot, with an attempt to parse standard units into nice latex labels
Parameters
----------
title : str
xlabel : str
ylabel : str
verbose_label: bool
"""
if title is not None:
self.title = title
elif hasattr(self.Spectrum,'specname'):
self.title = self.Spectrum.specname
if self.title != "":
self.axis.set_title(self.title)
if xlabel is not None:
log.debug("setting xlabel={0}".format(xlabel))
self.xlabel = xlabel
elif self._xunit:
try:
self.xlabel = xlabel_table[str(self._xunit.physical_type).lower()]
except KeyError:
self.xlabel = str(self._xunit.physical_type)
# WAS: self.xlabel += " ("+u.Unit(self._xunit).to_string()+")"
self.xlabel += " ({0})".format(self._xunit.to_string())
log.debug("xunit is {1}. set xlabel={0}".format(self.xlabel,
self._xunit))
if verbose_label:
self.xlabel = "%s %s" % (str(self.Spectrum.xarr.velocity_convention),
self.xlabel)
else:
log.warn("Plotter: xlabel was not set")
if self.xlabel is not None:
self.axis.set_xlabel(self.xlabel)
if ylabel is not None:
self.axis.set_ylabel(ylabel)
elif self.Spectrum.unit in ['Ta*','Tastar']:
self.axis.set_ylabel("$T_A^*$ (K)")
elif self.Spectrum.unit in ['K']:
self.axis.set_ylabel("Brightness Temperature $T$ (K)")
elif self.Spectrum.unit == 'mJy':
self.axis.set_ylabel("$S_\\nu$ (mJy)")
elif self.Spectrum.unit == 'Jy':
self.axis.set_ylabel("$S_\\nu$ (Jy)")
else:
if isinstance(self.Spectrum.unit, str) and "$" in self.Spectrum.unit:
# assume LaTeX already
self.axis.set_ylabel(self.Spectrum.unit)
elif isinstance(self.Spectrum.unit, str):
self.axis.set_ylabel(self.Spectrum.unit)
else:
label_units = self.Spectrum.unit.to_string(format='latex')
if 'mathring{A}' in label_units:
label_units = label_units.replace('\\mathring{A}', 'A')
if '\\overset' in label_units:
label_units = label_units.replace('\\overset', '^')
self.axis.set_ylabel(label_units)
@property
def ylabel(self):
return self.axis.get_ylabel()
def refresh(self):
if self.axis is not None:
self.axis.figure.canvas.draw()
def savefig(self,fname,bbox_inches='tight',**kwargs):
"""
simple wrapper of maplotlib's savefig.
"""
self.axis.figure.savefig(fname,bbox_inches=bbox_inches,**kwargs)
def parse_keys(self,event):
"""
Parse key commands entered from the keyboard
"""
if hasattr(event,'key'):
if event.key == '?':
print(interactive_help_message)
elif event.key == 'f':
print("\n\nFitter initiated from the interactive plotter.")
# extra optional text:
# Matplotlib shortcut keys ('g','l','p',etc.) are disabled. Re-enable with 'r'"
if self._active_gui == self.Spectrum.specfit and self._active_gui._check_connections(verbose=False):
print("Fitter is already active. Use 'q' to quit the fitter.")
elif self._active_gui == self.Spectrum.specfit and not self._active_gui._check_connections(verbose=False):
# forcibly clear connections
self._active_gui.clear_all_connections()
# the 'clear_all_connections' code *explicitly* makes the
# following line correct, except in the case that there is
# no canvas...
assert self._active_gui is None
self.activate_interactive_fitter()
else:
self.activate_interactive_fitter()
assert self._active_gui == self.Spectrum.specfit
assert self._active_gui._check_connections(verbose=False)
if not hasattr(self,'FitterTool') and self.automake_fitter_tool:
self.FitterTool = widgets.FitterTools(self.Spectrum.specfit, self.figure)
elif hasattr(self,'FitterTool') and self.FitterTool.toolfig.number not in self._pyplot.get_fignums():
self.FitterTool = widgets.FitterTools(self.Spectrum.specfit, self.figure)
elif event.key is not None and event.key.lower() == 'b':
if event.key == 'b':
print("\n\nBaseline initiated from the interactive plotter")
elif event.key == 'B':
print("\n\nBaseline initiated from the interactive plotter (with reset)")
print("Matplotlib shortcut keys ('g','l','p',etc.) are disabled. Re-enable with 'r'")
self.activate_interactive_baseline_fitter(reset_selection=(event.key=='B'))
if not hasattr(self,'FitterTool') and self.automake_fitter_tool:
self.FitterTool = widgets.FitterTools(self.Spectrum.specfit, self.figure)
elif hasattr(self,'FitterTool') and self.FitterTool.toolfig.number not in self._pyplot.get_fignums():
self.FitterTool = widgets.FitterTools(self.Spectrum.specfit, self.figure)
elif event.key == 'r':
# print("\n\nReconnected matplotlib shortcut keys.")
self._reconnect_matplotlib_keys()
elif event.key == 'R':
self()
elif event.key == 'i':
self.Spectrum.specfit.plot_fit(show_components=True)
def get_two_clicks(self,event):
if self._xclick1 is None:
self._xclick1 = event.xdata
elif self._xclick2 is None:
self._xclick2 = event.xdata
def set_limits_from_visible_window(self, debug=False):
""" Hopefully self-descriptive: set the x and y limits from the
currently visible window (use this if you use the pan/zoom tools or
manually change the limits) """
if debug:
print("Changing x limits from {},{} to {},{}".format(self.xmin,self.xmax,self.axis.get_xlim()[0],self.axis.get_xlim()[1]))
print("Changing y limits from {},{} to {},{}".format(self.ymin,self.ymax,self.axis.get_ylim()[0],self.axis.get_ylim()[1]))
self.xmin, self.xmax = self.axis.get_xlim()
self.ymin, self.ymax = self.axis.get_ylim()
if debug:
print("New x limits {},{} == {},{}".format(self.xmin,self.xmax,self.axis.get_xlim()[0],self.axis.get_xlim()[1]))
print("New y limits {},{} == {},{}".format(self.ymin,self.ymax,self.axis.get_ylim()[0],self.axis.get_ylim()[1]))
def copy(self, parent=None):
"""
Create a copy of the plotter with blank (uninitialized) axis & figure
[ parent ]
A spectroscopic axis instance that is the parent of the specfit
instance. This needs to be specified at some point, but defaults
to None to prevent overwriting a previous plot.
"""
newplotter = copy.copy(self)
newplotter.Spectrum = parent
newplotter.axis = None
newplotter.figure = None
return newplotter
def line_ids(self, line_names, line_xvals, xval_units=None, auto_yloc=True,
velocity_offset=None, velocity_convention='radio',
auto_yloc_fraction=0.9, **kwargs):
"""
Add line ID labels to a plot using lineid_plot
http://oneau.wordpress.com/2011/10/01/line-id-plot/
https://github.com/phn/lineid_plot
http://packages.python.org/lineid_plot/
Parameters
----------
line_names : list
A list of strings to label the specified x-axis values
line_xvals : list
List of x-axis values (e.g., wavelengths) at which to label the lines.
Can be a list of quantities.
xval_units : string
The unit of the line_xvals if they are not given as quantities
velocity_offset : quantity
A velocity offset to apply to the inputs if they are in frequency
or wavelength units
velocity_convention : 'radio' or 'optical' or 'doppler'
Used if the velocity offset is given
auto_yloc : bool
If set, overrides box_loc and arrow_tip (the vertical position of
the lineid labels) in kwargs to be `auto_yloc_fraction` of the plot
range
auto_yloc_fraction: float in range [0,1]
The fraction of the plot (vertically) at which to place labels
Examples
--------
>>> import numpy as np
>>> import pyspeckit
>>> sp = pyspeckit.Spectrum(
xarr=pyspeckit.units.SpectroscopicAxis(np.linspace(-50,50,101),
unit='km/s', refX=6562.8, refX_unit='angstrom'),
data=np.random.randn(101), error=np.ones(101))
>>> sp.plotter()
>>> sp.plotter.line_ids(['H$\\alpha$'],[6562.8],xval_units='angstrom')
"""
import lineid_plot
if velocity_offset is not None:
assert velocity_offset.unit.is_equivalent(u.km/u.s)
doppler = getattr(u, 'doppler_{0}'.format(velocity_convention))
if self.Spectrum.xarr.refX is not None:
equivalency = doppler(self.Spectrum.xarr.refX)
else:
equivalency = doppler(self.Spectrum.xarr.as_unit(u.GHz)[0])
xvals = []
linenames_toplot = []
for xv,ln in zip(line_xvals, line_names):
if hasattr(xv, 'unit'):
pass
else:
xv = u.Quantity(xv, xval_units)
xv = xv.to(u.km/u.s,
equivalencies=equivalency)
if velocity_offset is not None:
xv = xv + velocity_offset
xv = xv.to(self.Spectrum.xarr.unit, equivalencies=equivalency)
if self.Spectrum.xarr.in_range(xv):
xvals.append(xv.value)
linenames_toplot.append(ln)
if len(xvals) != len(line_xvals):
log.warn("Skipped {0} out-of-bounds lines when plotting line IDs."
.format(len(line_xvals)-len(xvals)))
if auto_yloc:
yr = self.axis.get_ylim()
kwargs['box_loc'] = (yr[1]-yr[0])*auto_yloc_fraction + yr[0]
kwargs['arrow_tip'] = (yr[1]-yr[0])*(auto_yloc_fraction*0.9) + yr[0]
lineid_plot.plot_line_ids(self.Spectrum.xarr,
self.Spectrum.data,
xvals,
linenames_toplot,
ax=self.axis,
**kwargs)
def line_ids_from_measurements(self, auto_yloc=True,
auto_yloc_fraction=0.9, **kwargs):
"""
Add line ID labels to a plot using lineid_plot
http://oneau.wordpress.com/2011/10/01/line-id-plot/
https://github.com/phn/lineid_plot
http://packages.python.org/lineid_plot/
Parameters
----------
auto_yloc : bool
If set, overrides box_loc and arrow_tip (the vertical position of
the lineid labels) in kwargs to be `auto_yloc_fraction` of the plot
range
auto_yloc_fraction: float in range [0,1]
The fraction of the plot (vertically) at which to place labels
Examples
--------
>>> import numpy as np
>>> import pyspeckit
>>> sp = pyspeckit.Spectrum(
xarr=pyspeckit.units.SpectroscopicAxis(np.linspace(-50,50,101),
units='km/s', refX=6562.8, refX_unit='angstroms'),
data=np.random.randn(101), error=np.ones(101))
>>> sp.plotter()
>>> sp.specfit(multifit=None, fittype='gaussian', guesses=[1,0,1]) # fitting noise....
>>> sp.measure()
>>> sp.plotter.line_ids_from_measurements()
"""
import lineid_plot
if hasattr(self.Spectrum,'measurements'):
measurements = self.Spectrum.measurements
if auto_yloc:
yr = self.axis.get_ylim()
kwargs['box_loc'] = (yr[1]-yr[0])*auto_yloc_fraction + yr[0]
kwargs['arrow_tip'] = (yr[1]-yr[0])*(auto_yloc_fraction*0.9) + yr[0]
lineid_plot.plot_line_ids(self.Spectrum.xarr, self.Spectrum.data,
[v['pos'] for v in
measurements.lines.values()],
measurements.lines.keys(), ax=self.axis,
**kwargs)
else:
warn("Cannot add line IDs from measurements unless measurements have been made!")
def activate_interactive_fitter(self):
"""
Attempt to activate the interactive fitter
"""
if self._active_gui is not None:
# This should not be reachable. Clearing connections is the
# "right" behavior if this becomes reachable, but I'd rather raise
# an exception because I don't want to get here ever
self._active_gui.clear_all_connections()
raise ValueError("GUI was active when 'f' key pressed")
self._activate_interactive(self.Spectrum.specfit, interactive=True)
def activate_interactive_baseline_fitter(self, **kwargs):
"""
Attempt to activate the interactive baseline fitter
"""
if self._active_gui is not None:
# This should not be reachable. Clearing connections is the
# "right" behavior if this becomes reachable, but I'd rather raise
# an exception because I don't want to get here ever
gui_was = self._active_gui
self._active_gui.clear_all_connections()
raise ValueError("GUI {0} was active when 'b' key pressed"
.format(gui_was))
self._activate_interactive(self.Spectrum.baseline, interactive=True,
**kwargs)
def _activate_interactive(self, object_to_activate, **kwargs):
self._disconnect_matplotlib_keys()
self._active_gui = object_to_activate
# activating the gui calls clear_all_connections, which disconnects the
# gui
try:
self._active_gui(**kwargs)
self._active_gui = object_to_activate
assert self._active_gui is not None
except Exception as ex:
self._active_gui = None
raise ex
def parse_units(labelstring):
import re
labelstring = re.sub("um","$\\mu$m",labelstring)
labelstring = re.sub("-1","$^{-1}$",labelstring)
labelstring = re.sub("-2","$^{-2}$",labelstring)
labelstring = re.sub("-3","$^{-3}$",labelstring)
labelstring = re.sub("ergss","ergs s",labelstring)
return labelstring
def parse_norm(norm):
"""
Expected format: norm = 10E15
"""
try:
base, exp = norm.split('E')
except ValueError:
base, exp = norm.split('e')
if float(base) == 1.0:
norm = '10'
else:
norm = base
norm += '^{%s}' % exp
return norm
def steppify(arr,isX=False):
"""
*support function*
Converts an array to double-length for step plotting
"""
if isX:
interval = abs(arr[1:]-arr[:-1]) / 2.0
newarr = np.array(list(zip(arr[:-1]-interval,arr[:-1]+interval))).ravel()
newarr = np.concatenate([newarr,2*[newarr[-1]+interval[-1]]])
else:
newarr = np.array(list(zip(arr,arr))).ravel()
return newarr
|
pyspeckitREPO_NAMEpyspeckitPATH_START.@pyspeckit_extracted@pyspeckit-master@pyspeckit@spectrum@plotters.py@.PATH_END.py
|
{
"filename": "_size.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/densitymapbox/hoverlabel/font/_size.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="size", parent_name="densitymapbox.hoverlabel.font", **kwargs
):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@densitymapbox@hoverlabel@font@_size.py@.PATH_END.py
|
{
"filename": "dbapi20.py",
"repo_name": "mhammond/pywin32",
"repo_path": "pywin32_extracted/pywin32-main/adodbapi/test/dbapi20.py",
"type": "Python"
}
|
#!/usr/bin/env python
"""Python DB API 2.0 driver compliance unit test suite.
This software is Public Domain and may be used without restrictions.
"Now we have booze and barflies entering the discussion, plus rumours of
DBAs on drugs... and I won't tell you what flashes through my mind each
time I read the subject line with 'Anal Compliance' in it. All around
this is turning out to be a thoroughly unwholesome unit test."
-- Ian Bicking
"""
__version__ = "$Revision: 1.15.0 $"[11:-2]
__author__ = "Stuart Bishop <stuart@stuartbishop.net>"
import sys
import time
import unittest
# set this to "True" to follow API 2.0 to the letter
TEST_FOR_NON_IDEMPOTENT_CLOSE = False
# Revision 1.15 2019/11/22 00:50:00 kf7xm
# Make Turn off IDEMPOTENT_CLOSE a proper skipTest
# Revision 1.14 2013/05/20 11:02:05 kf7xm
# Add a literal string to the format insertion test to catch trivial re-format algorithms
# Revision 1.13 2013/05/08 14:31:50 kf7xm
# Quick switch to Turn off IDEMPOTENT_CLOSE test. Also: Silence teardown failure
# Revision 1.12 2009/02/06 03:35:11 kf7xm
# Tested okay with Python 3.0, includes last minute patches from Mark H.
#
# Revision 1.1.1.1.2.1 2008/09/20 19:54:59 rupole
# Include latest changes from main branch
# Updates for py3k
#
# Revision 1.11 2005/01/02 02:41:01 zenzen
# Update author email address
#
# Revision 1.10 2003/10/09 03:14:14 zenzen
# Add test for DB API 2.0 optional extension, where database exceptions
# are exposed as attributes on the Connection object.
#
# Revision 1.9 2003/08/13 01:16:36 zenzen
# Minor tweak from Stefan Fleiter
#
# Revision 1.8 2003/04/10 00:13:25 zenzen
# Changes, as per suggestions by M.-A. Lemburg
# - Add a table prefix, to ensure namespace collisions can always be avoided
#
# Revision 1.7 2003/02/26 23:33:37 zenzen
# Break out DDL into helper functions, as per request by David Rushby
#
# Revision 1.6 2003/02/21 03:04:33 zenzen
# Stuff from Henrik Ekelund:
# added test_None
# added test_nextset & hooks
#
# Revision 1.5 2003/02/17 22:08:43 zenzen
# Implement suggestions and code from Henrik Eklund - test that cursor.arraysize
# defaults to 1 & generic cursor.callproc test added
#
# Revision 1.4 2003/02/15 00:16:33 zenzen
# Changes, as per suggestions and bug reports by M.-A. Lemburg,
# Matthew T. Kromer, Federico Di Gregorio and Daniel Dittmar
# - Class renamed
# - Now a subclass of TestCase, to avoid requiring the driver stub
# to use multiple inheritance
# - Reversed the polarity of buggy test in test_description
# - Test exception hierarchy correctly
# - self.populate is now self._populate(), so if a driver stub
# overrides self.ddl1 this change propogates
# - VARCHAR columns now have a width, which will hopefully make the
# DDL even more portible (this will be reversed if it causes more problems)
# - cursor.rowcount being checked after various execute and fetchXXX methods
# - Check for fetchall and fetchmany returning empty lists after results
# are exhausted (already checking for empty lists if select retrieved
# nothing
# - Fix bugs in test_setoutputsize_basic and test_setinputsizes
#
class DatabaseAPI20Test(unittest.TestCase):
"""Test a database self.driver for DB API 2.0 compatibility.
This implementation tests Gadfly, but the TestCase
is structured so that other self.drivers can subclass this
test case to ensure compiliance with the DB-API. It is
expected that this TestCase may be expanded in the future
if ambiguities or edge conditions are discovered.
The 'Optional Extensions' are not yet being tested.
self.drivers should subclass this test, overriding setUp, tearDown,
self.driver, connect_args and connect_kw_args. Class specification
should be as follows:
import dbapi20
class mytest(dbapi20.DatabaseAPI20Test):
[...]
Don't 'import DatabaseAPI20Test from dbapi20', or you will
confuse the unit tester - just 'import dbapi20'.
"""
# The self.driver module. This should be the module where the 'connect'
# method is to be found
driver = None
connect_args = () # List of arguments to pass to connect
connect_kw_args = {} # Keyword arguments for connect
table_prefix = "dbapi20test_" # If you need to specify a prefix for tables
ddl1 = "create table %sbooze (name varchar(20))" % table_prefix
ddl2 = "create table %sbarflys (name varchar(20), drink varchar(30))" % table_prefix
xddl1 = "drop table %sbooze" % table_prefix
xddl2 = "drop table %sbarflys" % table_prefix
lowerfunc = "lower" # Name of stored procedure to convert string->lowercase
# Some drivers may need to override these helpers, for example adding
# a 'commit' after the execute.
def executeDDL1(self, cursor):
cursor.execute(self.ddl1)
def executeDDL2(self, cursor):
cursor.execute(self.ddl2)
def setUp(self):
"""self.drivers should override this method to perform required setup
if any is necessary, such as creating the database.
"""
pass
def tearDown(self):
"""self.drivers should override this method to perform required cleanup
if any is necessary, such as deleting the test database.
The default drops the tables that may be created.
"""
try:
con = self._connect()
try:
cur = con.cursor()
for ddl in (self.xddl1, self.xddl2):
try:
cur.execute(ddl)
con.commit()
except self.driver.Error:
# Assume table didn't exist. Other tests will check if
# execute is busted.
pass
finally:
con.close()
except Exception:
pass
def _connect(self):
try:
r = self.driver.connect(*self.connect_args, **self.connect_kw_args)
except AttributeError:
self.fail("No connect method found in self.driver module")
return r
def test_connect(self):
con = self._connect()
con.close()
def test_apilevel(self):
try:
# Must exist
apilevel = self.driver.apilevel
# Must equal 2.0
self.assertEqual(apilevel, "2.0")
except AttributeError:
self.fail("Driver doesn't define apilevel")
def test_threadsafety(self):
try:
# Must exist
threadsafety = self.driver.threadsafety
# Must be a valid value
self.assertTrue(threadsafety in (0, 1, 2, 3))
except AttributeError:
self.fail("Driver doesn't define threadsafety")
def test_paramstyle(self):
try:
# Must exist
paramstyle = self.driver.paramstyle
# Must be a valid value
self.assertTrue(
paramstyle in ("qmark", "numeric", "named", "format", "pyformat")
)
except AttributeError:
self.fail("Driver doesn't define paramstyle")
def test_Exceptions(self):
# Make sure required exceptions exist, and are in the
# defined hierarchy.
if sys.version[0] == "3": # under Python 3 StardardError no longer exists
self.assertTrue(issubclass(self.driver.Warning, Exception))
self.assertTrue(issubclass(self.driver.Error, Exception))
else:
self.failUnless(issubclass(self.driver.Warning, Exception))
self.failUnless(issubclass(self.driver.Error, Exception))
self.assertTrue(issubclass(self.driver.InterfaceError, self.driver.Error))
self.assertTrue(issubclass(self.driver.DatabaseError, self.driver.Error))
self.assertTrue(issubclass(self.driver.OperationalError, self.driver.Error))
self.assertTrue(issubclass(self.driver.IntegrityError, self.driver.Error))
self.assertTrue(issubclass(self.driver.InternalError, self.driver.Error))
self.assertTrue(issubclass(self.driver.ProgrammingError, self.driver.Error))
self.assertTrue(issubclass(self.driver.NotSupportedError, self.driver.Error))
def test_ExceptionsAsConnectionAttributes(self):
# OPTIONAL EXTENSION
# Test for the optional DB API 2.0 extension, where the exceptions
# are exposed as attributes on the Connection object
# I figure this optional extension will be implemented by any
# driver author who is using this test suite, so it is enabled
# by default.
con = self._connect()
drv = self.driver
self.assertTrue(con.Warning is drv.Warning)
self.assertTrue(con.Error is drv.Error)
self.assertTrue(con.InterfaceError is drv.InterfaceError)
self.assertTrue(con.DatabaseError is drv.DatabaseError)
self.assertTrue(con.OperationalError is drv.OperationalError)
self.assertTrue(con.IntegrityError is drv.IntegrityError)
self.assertTrue(con.InternalError is drv.InternalError)
self.assertTrue(con.ProgrammingError is drv.ProgrammingError)
self.assertTrue(con.NotSupportedError is drv.NotSupportedError)
def test_commit(self):
con = self._connect()
try:
# Commit must work, even if it doesn't do anything
con.commit()
finally:
con.close()
def test_rollback(self):
con = self._connect()
# If rollback is defined, it should either work or throw
# the documented exception
if hasattr(con, "rollback"):
try:
con.rollback()
except self.driver.NotSupportedError:
pass
def test_cursor(self):
con = self._connect()
try:
cur = con.cursor()
finally:
con.close()
def test_cursor_isolation(self):
con = self._connect()
try:
# Make sure cursors created from the same connection have
# the documented transaction isolation level
cur1 = con.cursor()
cur2 = con.cursor()
self.executeDDL1(cur1)
cur1.execute(
"insert into %sbooze values ('Victoria Bitter')" % (self.table_prefix)
)
cur2.execute("select name from %sbooze" % self.table_prefix)
booze = cur2.fetchall()
self.assertEqual(len(booze), 1)
self.assertEqual(len(booze[0]), 1)
self.assertEqual(booze[0][0], "Victoria Bitter")
finally:
con.close()
def test_description(self):
con = self._connect()
try:
cur = con.cursor()
self.executeDDL1(cur)
self.assertEqual(
cur.description,
None,
"cursor.description should be none after executing a "
"statement that can return no rows (such as DDL)",
)
cur.execute("select name from %sbooze" % self.table_prefix)
self.assertEqual(
len(cur.description), 1, "cursor.description describes too many columns"
)
self.assertEqual(
len(cur.description[0]),
7,
"cursor.description[x] tuples must have 7 elements",
)
self.assertEqual(
cur.description[0][0].lower(),
"name",
"cursor.description[x][0] must return column name",
)
self.assertEqual(
cur.description[0][1],
self.driver.STRING,
"cursor.description[x][1] must return column type. Got %r"
% cur.description[0][1],
)
# Make sure self.description gets reset
self.executeDDL2(cur)
self.assertEqual(
cur.description,
None,
"cursor.description not being set to None when executing "
"no-result statements (eg. DDL)",
)
finally:
con.close()
def test_rowcount(self):
con = self._connect()
try:
cur = con.cursor()
self.executeDDL1(cur)
self.assertTrue(
cur.rowcount in (-1, 0), # Bug #543885
"cursor.rowcount should be -1 or 0 after executing no-result "
"statements",
)
cur.execute(
"insert into %sbooze values ('Victoria Bitter')" % (self.table_prefix)
)
self.assertTrue(
cur.rowcount in (-1, 1),
"cursor.rowcount should == number or rows inserted, or "
"set to -1 after executing an insert statement",
)
cur.execute("select name from %sbooze" % self.table_prefix)
self.assertTrue(
cur.rowcount in (-1, 1),
"cursor.rowcount should == number of rows returned, or "
"set to -1 after executing a select statement",
)
self.executeDDL2(cur)
self.assertEqual(
cur.rowcount,
-1,
"cursor.rowcount not being reset to -1 after executing "
"no-result statements",
)
finally:
con.close()
lower_func = "lower"
def test_callproc(self):
con = self._connect()
try:
cur = con.cursor()
if self.lower_func and hasattr(cur, "callproc"):
r = cur.callproc(self.lower_func, ("FOO",))
self.assertEqual(len(r), 1)
self.assertEqual(r[0], "FOO")
r = cur.fetchall()
self.assertEqual(len(r), 1, "callproc produced no result set")
self.assertEqual(len(r[0]), 1, "callproc produced invalid result set")
self.assertEqual(r[0][0], "foo", "callproc produced invalid results")
finally:
con.close()
def test_close(self):
con = self._connect()
try:
cur = con.cursor()
finally:
con.close()
# cursor.execute should raise an Error if called after connection
# closed
self.assertRaises(self.driver.Error, self.executeDDL1, cur)
# connection.commit should raise an Error if called after connection'
# closed.'
self.assertRaises(self.driver.Error, con.commit)
# connection.close should raise an Error if called more than once
#!!! reasonable persons differ about the usefulness of this test and this feature !!!
if TEST_FOR_NON_IDEMPOTENT_CLOSE:
self.assertRaises(self.driver.Error, con.close)
else:
self.skipTest(
"Non-idempotent close is considered a bad thing by some people."
)
def test_execute(self):
con = self._connect()
try:
cur = con.cursor()
self._paraminsert(cur)
finally:
con.close()
def _paraminsert(self, cur):
self.executeDDL2(cur)
cur.execute(
"insert into %sbarflys values ('Victoria Bitter', 'thi%%s :may ca%%(u)se? troub:1e')"
% (self.table_prefix)
)
self.assertTrue(cur.rowcount in (-1, 1))
if self.driver.paramstyle == "qmark":
cur.execute(
"insert into %sbarflys values (?, 'thi%%s :may ca%%(u)se? troub:1e')"
% self.table_prefix,
("Cooper's",),
)
elif self.driver.paramstyle == "numeric":
cur.execute(
"insert into %sbarflys values (:1, 'thi%%s :may ca%%(u)se? troub:1e')"
% self.table_prefix,
("Cooper's",),
)
elif self.driver.paramstyle == "named":
cur.execute(
"insert into %sbarflys values (:beer, 'thi%%s :may ca%%(u)se? troub:1e')"
% self.table_prefix,
{"beer": "Cooper's"},
)
elif self.driver.paramstyle == "format":
cur.execute(
"insert into %sbarflys values (%%s, 'thi%%s :may ca%%(u)se? troub:1e')"
% self.table_prefix,
("Cooper's",),
)
elif self.driver.paramstyle == "pyformat":
cur.execute(
"insert into %sbarflys values (%%(beer)s, 'thi%%s :may ca%%(u)se? troub:1e')"
% self.table_prefix,
{"beer": "Cooper's"},
)
else:
self.fail("Invalid paramstyle")
self.assertTrue(cur.rowcount in (-1, 1))
cur.execute("select name, drink from %sbarflys" % self.table_prefix)
res = cur.fetchall()
self.assertEqual(len(res), 2, "cursor.fetchall returned too few rows")
beers = [res[0][0], res[1][0]]
beers.sort()
self.assertEqual(
beers[0],
"Cooper's",
"cursor.fetchall retrieved incorrect data, or data inserted incorrectly",
)
self.assertEqual(
beers[1],
"Victoria Bitter",
"cursor.fetchall retrieved incorrect data, or data inserted incorrectly",
)
trouble = "thi%s :may ca%(u)se? troub:1e"
self.assertEqual(
res[0][1],
trouble,
"cursor.fetchall retrieved incorrect data, or data inserted "
f"incorrectly. Got={res[0][1]!r}, Expected={trouble!r}",
)
self.assertEqual(
res[1][1],
trouble,
"cursor.fetchall retrieved incorrect data, or data inserted "
f"incorrectly. Got={res[1][1]!r}, Expected={trouble!r}",
)
def test_executemany(self):
con = self._connect()
try:
cur = con.cursor()
self.executeDDL1(cur)
largs = [("Cooper's",), ("Boag's",)]
margs = [{"beer": "Cooper's"}, {"beer": "Boag's"}]
if self.driver.paramstyle == "qmark":
cur.executemany(
"insert into %sbooze values (?)" % self.table_prefix, largs
)
elif self.driver.paramstyle == "numeric":
cur.executemany(
"insert into %sbooze values (:1)" % self.table_prefix, largs
)
elif self.driver.paramstyle == "named":
cur.executemany(
"insert into %sbooze values (:beer)" % self.table_prefix, margs
)
elif self.driver.paramstyle == "format":
cur.executemany(
"insert into %sbooze values (%%s)" % self.table_prefix, largs
)
elif self.driver.paramstyle == "pyformat":
cur.executemany(
"insert into %sbooze values (%%(beer)s)" % (self.table_prefix),
margs,
)
else:
self.fail("Unknown paramstyle")
self.assertTrue(
cur.rowcount in (-1, 2),
"insert using cursor.executemany set cursor.rowcount to "
"incorrect value %r" % cur.rowcount,
)
cur.execute("select name from %sbooze" % self.table_prefix)
res = cur.fetchall()
self.assertEqual(
len(res), 2, "cursor.fetchall retrieved incorrect number of rows"
)
beers = [res[0][0], res[1][0]]
beers.sort()
self.assertEqual(
beers[0], "Boag's", 'incorrect data "%s" retrieved' % beers[0]
)
self.assertEqual(beers[1], "Cooper's", "incorrect data retrieved")
finally:
con.close()
def test_fetchone(self):
con = self._connect()
try:
cur = con.cursor()
# cursor.fetchone should raise an Error if called before
# executing a select-type query
self.assertRaises(self.driver.Error, cur.fetchone)
# cursor.fetchone should raise an Error if called after
# executing a query that cannnot return rows
self.executeDDL1(cur)
self.assertRaises(self.driver.Error, cur.fetchone)
cur.execute("select name from %sbooze" % self.table_prefix)
self.assertEqual(
cur.fetchone(),
None,
"cursor.fetchone should return None if a query retrieves no rows",
)
self.assertTrue(cur.rowcount in (-1, 0))
# cursor.fetchone should raise an Error if called after
# executing a query that cannnot return rows
cur.execute(
"insert into %sbooze values ('Victoria Bitter')" % (self.table_prefix)
)
self.assertRaises(self.driver.Error, cur.fetchone)
cur.execute("select name from %sbooze" % self.table_prefix)
r = cur.fetchone()
self.assertEqual(
len(r), 1, "cursor.fetchone should have retrieved a single row"
)
self.assertEqual(
r[0], "Victoria Bitter", "cursor.fetchone retrieved incorrect data"
)
self.assertEqual(
cur.fetchone(),
None,
"cursor.fetchone should return None if no more rows available",
)
self.assertTrue(cur.rowcount in (-1, 1))
finally:
con.close()
samples = [
"Carlton Cold",
"Carlton Draft",
"Mountain Goat",
"Redback",
"Victoria Bitter",
"XXXX",
]
def _populate(self):
"""Return a list of sql commands to setup the DB for the fetch
tests.
"""
populate = [
"insert into %sbooze values ('%s')" % (self.table_prefix, s)
for s in self.samples
]
return populate
def test_fetchmany(self):
con = self._connect()
try:
cur = con.cursor()
# cursor.fetchmany should raise an Error if called without
# issuing a query
self.assertRaises(self.driver.Error, cur.fetchmany, 4)
self.executeDDL1(cur)
for sql in self._populate():
cur.execute(sql)
cur.execute("select name from %sbooze" % self.table_prefix)
r = cur.fetchmany()
self.assertEqual(
len(r),
1,
"cursor.fetchmany retrieved incorrect number of rows, "
"default of arraysize is one.",
)
cur.arraysize = 10
r = cur.fetchmany(3) # Should get 3 rows
self.assertEqual(
len(r), 3, "cursor.fetchmany retrieved incorrect number of rows"
)
r = cur.fetchmany(4) # Should get 2 more
self.assertEqual(
len(r), 2, "cursor.fetchmany retrieved incorrect number of rows"
)
r = cur.fetchmany(4) # Should be an empty sequence
self.assertEqual(
len(r),
0,
"cursor.fetchmany should return an empty sequence after "
"results are exhausted",
)
self.assertTrue(cur.rowcount in (-1, 6))
# Same as above, using cursor.arraysize
cur.arraysize = 4
cur.execute("select name from %sbooze" % self.table_prefix)
r = cur.fetchmany() # Should get 4 rows
self.assertEqual(
len(r), 4, "cursor.arraysize not being honoured by fetchmany"
)
r = cur.fetchmany() # Should get 2 more
self.assertEqual(len(r), 2)
r = cur.fetchmany() # Should be an empty sequence
self.assertEqual(len(r), 0)
self.assertTrue(cur.rowcount in (-1, 6))
cur.arraysize = 6
cur.execute("select name from %sbooze" % self.table_prefix)
rows = cur.fetchmany() # Should get all rows
self.assertTrue(cur.rowcount in (-1, 6))
self.assertEqual(len(rows), 6)
self.assertEqual(len(rows), 6)
rows = [r[0] for r in rows]
rows.sort()
# Make sure we get the right data back out
for i in range(0, 6):
self.assertEqual(
rows[i],
self.samples[i],
"incorrect data retrieved by cursor.fetchmany",
)
rows = cur.fetchmany() # Should return an empty list
self.assertEqual(
len(rows),
0,
"cursor.fetchmany should return an empty sequence if "
"called after the whole result set has been fetched",
)
self.assertTrue(cur.rowcount in (-1, 6))
self.executeDDL2(cur)
cur.execute("select name from %sbarflys" % self.table_prefix)
r = cur.fetchmany() # Should get empty sequence
self.assertEqual(
len(r),
0,
"cursor.fetchmany should return an empty sequence if "
"query retrieved no rows",
)
self.assertTrue(cur.rowcount in (-1, 0))
finally:
con.close()
def test_fetchall(self):
con = self._connect()
try:
cur = con.cursor()
# cursor.fetchall should raise an Error if called
# without executing a query that may return rows (such
# as a select)
self.assertRaises(self.driver.Error, cur.fetchall)
self.executeDDL1(cur)
for sql in self._populate():
cur.execute(sql)
# cursor.fetchall should raise an Error if called
# after executing a a statement that cannot return rows
self.assertRaises(self.driver.Error, cur.fetchall)
cur.execute("select name from %sbooze" % self.table_prefix)
rows = cur.fetchall()
self.assertTrue(cur.rowcount in (-1, len(self.samples)))
self.assertEqual(
len(rows),
len(self.samples),
"cursor.fetchall did not retrieve all rows",
)
rows = [r[0] for r in rows]
rows.sort()
for i in range(0, len(self.samples)):
self.assertEqual(
rows[i], self.samples[i], "cursor.fetchall retrieved incorrect rows"
)
rows = cur.fetchall()
self.assertEqual(
len(rows),
0,
"cursor.fetchall should return an empty list if called "
"after the whole result set has been fetched",
)
self.assertTrue(cur.rowcount in (-1, len(self.samples)))
self.executeDDL2(cur)
cur.execute("select name from %sbarflys" % self.table_prefix)
rows = cur.fetchall()
self.assertTrue(cur.rowcount in (-1, 0))
self.assertEqual(
len(rows),
0,
"cursor.fetchall should return an empty list if "
"a select query returns no rows",
)
finally:
con.close()
def test_mixedfetch(self):
con = self._connect()
try:
cur = con.cursor()
self.executeDDL1(cur)
for sql in self._populate():
cur.execute(sql)
cur.execute("select name from %sbooze" % self.table_prefix)
rows1 = cur.fetchone()
rows23 = cur.fetchmany(2)
rows4 = cur.fetchone()
rows56 = cur.fetchall()
self.assertTrue(cur.rowcount in (-1, 6))
self.assertEqual(
len(rows23), 2, "fetchmany returned incorrect number of rows"
)
self.assertEqual(
len(rows56), 2, "fetchall returned incorrect number of rows"
)
rows = [rows1[0]]
rows.extend([rows23[0][0], rows23[1][0]])
rows.append(rows4[0])
rows.extend([rows56[0][0], rows56[1][0]])
rows.sort()
for i in range(0, len(self.samples)):
self.assertEqual(
rows[i], self.samples[i], "incorrect data retrieved or inserted"
)
finally:
con.close()
def help_nextset_setUp(self, cur):
"""Should create a procedure called deleteme
that returns two result sets, first the
number of rows in booze then "name from booze"
"""
raise NotImplementedError("Helper not implemented")
# sql="""
# create procedure deleteme as
# begin
# select count(*) from booze
# select name from booze
# end
# """
# cur.execute(sql)
def help_nextset_tearDown(self, cur):
"If cleaning up is needed after nextSetTest"
raise NotImplementedError("Helper not implemented")
# cur.execute("drop procedure deleteme")
def test_nextset(self):
raise NotImplementedError("Drivers need to override this test")
def test_arraysize(self):
# Not much here - rest of the tests for this are in test_fetchmany
con = self._connect()
try:
cur = con.cursor()
self.assertTrue(
hasattr(cur, "arraysize"), "cursor.arraysize must be defined"
)
finally:
con.close()
def test_setinputsizes(self):
con = self._connect()
try:
cur = con.cursor()
cur.setinputsizes((25,))
self._paraminsert(cur) # Make sure cursor still works
finally:
con.close()
def test_setoutputsize_basic(self):
# Basic test is to make sure setoutputsize doesn't blow up
con = self._connect()
try:
cur = con.cursor()
cur.setoutputsize(1000)
cur.setoutputsize(2000, 0)
self._paraminsert(cur) # Make sure the cursor still works
finally:
con.close()
def test_setoutputsize(self):
# Real test for setoutputsize is driver dependant
raise NotImplementedError("Driver needed to override this test")
def test_None(self):
con = self._connect()
try:
cur = con.cursor()
self.executeDDL1(cur)
cur.execute("insert into %sbooze values (NULL)" % self.table_prefix)
cur.execute("select name from %sbooze" % self.table_prefix)
r = cur.fetchall()
self.assertEqual(len(r), 1)
self.assertEqual(len(r[0]), 1)
self.assertEqual(r[0][0], None, "NULL value not returned as None")
finally:
con.close()
def test_Date(self):
d1 = self.driver.Date(2002, 12, 25)
d2 = self.driver.DateFromTicks(time.mktime((2002, 12, 25, 0, 0, 0, 0, 0, 0)))
# Can we assume this? API doesn't specify, but it seems implied
# self.assertEqual(str(d1),str(d2))
def test_Time(self):
t1 = self.driver.Time(13, 45, 30)
t2 = self.driver.TimeFromTicks(time.mktime((2001, 1, 1, 13, 45, 30, 0, 0, 0)))
# Can we assume this? API doesn't specify, but it seems implied
# self.assertEqual(str(t1),str(t2))
def test_Timestamp(self):
t1 = self.driver.Timestamp(2002, 12, 25, 13, 45, 30)
t2 = self.driver.TimestampFromTicks(
time.mktime((2002, 12, 25, 13, 45, 30, 0, 0, 0))
)
# Can we assume this? API doesn't specify, but it seems implied
# self.assertEqual(str(t1),str(t2))
def test_Binary(self):
b = self.driver.Binary(b"Something")
b = self.driver.Binary(b"")
def test_STRING(self):
self.assertTrue(hasattr(self.driver, "STRING"), "module.STRING must be defined")
def test_BINARY(self):
self.assertTrue(
hasattr(self.driver, "BINARY"), "module.BINARY must be defined."
)
def test_NUMBER(self):
self.assertTrue(
hasattr(self.driver, "NUMBER"), "module.NUMBER must be defined."
)
def test_DATETIME(self):
self.assertTrue(
hasattr(self.driver, "DATETIME"), "module.DATETIME must be defined."
)
def test_ROWID(self):
self.assertTrue(hasattr(self.driver, "ROWID"), "module.ROWID must be defined.")
|
mhammondREPO_NAMEpywin32PATH_START.@pywin32_extracted@pywin32-main@adodbapi@test@dbapi20.py@.PATH_END.py
|
{
"filename": "_stream.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/table/_stream.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class StreamValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="stream", parent_name="table", **kwargs):
super(StreamValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Stream"),
data_docs=kwargs.pop(
"data_docs",
"""
maxpoints
Sets the maximum number of points to keep on
the plots from an incoming stream. If
`maxpoints` is set to 50, only the newest 50
points will be displayed on the plot.
token
The stream id number links a data trace on a
plot with a stream. See https://chart-
studio.plotly.com/settings for more details.
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@table@_stream.py@.PATH_END.py
|
{
"filename": "_len.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/bar/marker/colorbar/_len.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LenValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="len", parent_name="bar.marker.colorbar", **kwargs):
super(LenValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@bar@marker@colorbar@_len.py@.PATH_END.py
|
{
"filename": "test_simulations_gs_param_hist.py",
"repo_name": "mirochaj/ares",
"repo_path": "ares_extracted/ares-main/tests/test_simulations_gs_param_hist.py",
"type": "Python"
}
|
"""
test_21cm_parameterized.py
Author: Jordan Mirocha
Affiliation: University of Colorado at Boulder
Created on: Wed Aug 6 08:54:15 MDT 2014
Description: 21-cm signal in absence of astrophysical sources.
"""
import ares
import numpy as np
def test():
# Create instance of Hydrogen class
hydr = ares.physics.Hydrogen(approx_thermal_history='piecewise')
# Analytic approximation to thermal history
Tk = lambda z: hydr.cosm.Tgas(z)
# Spin temperature (arguments: z, Tk, Ja, xHII, ne)
Ts = lambda z: hydr.SpinTemperature(z, Tk(z), 0.0, 0.0, 0.0)
# Brightness temperature (arguments: z, Ts, xavg optional)
dTb = lambda z: hydr.get_21cm_dTb(z, Ts(z))
# Define redshift interval of interest
z = np.linspace(10, 1e3, 500)
# Get CosmoRec recombination history
CR = hydr.cosm._ics.get_inits_rec()
# Assume neutral medium for simplicity
Ts_CR = hydr.SpinTemperature(CR['z'], CR['Tk'], 0.0, 0.0, 0.0)
dTb_CR = hydr.get_21cm_dTb(CR['z'], Ts_CR)
if __name__ == '__main__':
test()
|
mirochajREPO_NAMEaresPATH_START.@ares_extracted@ares-main@tests@test_simulations_gs_param_hist.py@.PATH_END.py
|
{
"filename": "example11_parse_measurements_log.py",
"repo_name": "Vital-Fernandez/lime",
"repo_path": "lime_extracted/lime-master/examples/tutorials/various/example11_parse_measurements_log.py",
"type": "Python"
}
|
import numpy as np
from astropy.io import fits
import lime
def import_osiris_fits(file_address, ext=0):
# Open fits file
with fits.open(file_address) as hdul:
data, hdr = hdul[ext].data, hdul[ext].header
w_min, dw, n_pix = hdr['CRVAL1'], hdr['CD1_1'], hdr['NAXIS1']
w_max = w_min + dw * n_pix
wavelength = np.linspace(w_min, w_max, n_pix, endpoint=False)
return wavelength, data, hdr
# State the data files
obsFitsFile = './sample_data/gp121903_osiris.fits'
lineMaskFile = './sample_data/osiris_bands.txt'
cfgFile = './sample_data/config_file.cfg'
# Load spectrum
wave, flux, header = import_osiris_fits(obsFitsFile)
# Load mask
mask = lime.load_frame(lineMaskFile)
# Load configuration
obs_cfg = lime.load_cfg(cfgFile)
fit_cfg = obs_cfg['gp121903_line_fitting']
# Declare line measuring object
z_obj = obs_cfg['sample_data']['z_array'][2]
norm_flux = obs_cfg['sample_data']['norm_flux']
gp_spec = lime.Spectrum(wave, flux, redshift=z_obj, norm_flux=norm_flux)
# Find lines
peaks_table, matched_masks_DF = gp_spec.match_line_mask(mask, obs_cfg['sample_data']['noiseRegion_array'])
# Measure the emission lines
for i, lineLabel in enumerate(matched_masks_DF.index.values):
wave_regions = matched_masks_DF.loc[lineLabel, 'w1':'w6'].values
gp_spec.fit_from_wavelengths(lineLabel, wave_regions, user_cfg=fit_cfg)
# Save the results
lime.save_frame('./sample_data/example_3.txt', gp_spec.frame)
# Add new parameters to the log
parameters = ['eqw_gaussian',
'eqw_gaussian_err']
formulation = ['profile_flux/cont',
'(profile_flux/cont) * sqrt((profile_flux_err/profile_flux)**2 + (std_cont/cont)**2)']
lime.log_parameters_calculation('./sample_data/example_3.txt', parameters, formulation)
|
Vital-FernandezREPO_NAMElimePATH_START.@lime_extracted@lime-master@examples@tutorials@various@example11_parse_measurements_log.py@.PATH_END.py
|
{
"filename": "_pi_pi_etap.py",
"repo_name": "LoganAMorrison/Hazma",
"repo_path": "Hazma_extracted/Hazma-master/hazma/form_factors/vector/_pi_pi_etap.py",
"type": "Python"
}
|
"""
Module implementing the pi-pi-eta' form factor.
"""
from dataclasses import InitVar, dataclass, field
from typing import overload
import numpy as np
from hazma.phase_space import PhaseSpaceDistribution1D
from hazma.utils import ComplexArray, ComplexOrComplexArray, RealArray, RealOrRealArray
from ._three_body import Couplings, VectorFormFactorPPP2
from ._utils import FPI_GEV, METAP_GEV, MPI_GEV
METAP = METAP_GEV * 1e3
MPI = MPI_GEV * 1e3
@dataclass
class VectorFormFactorPiPiEtaPrimeFitData:
"""Storage class for the fit parameters of the pi-pi-eta' vector
form-factor.
Attributes
----------
masses: RealArray
VMD resonance masses.
widths: RealArray
VMD resonance widths.
amps: RealArray
VMD resonance amplitudes.
phases: RealArray
VMD resonance phases.
"""
masses: RealArray = field(
default_factory=lambda: np.array([0.77549, 1.54, 1.76, 2.11])
)
widths: RealArray = field(
default_factory=lambda: np.array([0.1494, 0.356, 0.113, 0.176])
)
amps: RealArray = field(default_factory=lambda: np.array([1.0, 0.0, 0.0, 0.02]))
phases: RealArray = field(
default_factory=lambda: np.array([0, np.pi, np.pi, np.pi])
)
@dataclass
class VectorFormFactorPiPiEtaPrime(VectorFormFactorPPP2):
r"""Class for computing the pi-pi-eta' vector form-factor.
Attributes
----------
fsp_masses: (float,float,float)
Masses of the final state particles.
fit_data: VectorFormFactorPiPiEtaPrimeFitData
Fitted parameters for the pion-pion-eta vector form-factor.
Methods
-------
form_factor
Compute the un-integrated form-factor.
integrated_form_factor
Compute the form-factor integrated over phase-space.
width
Compute the decay width of a vector into pi-pi-eta'.
cross_section
Compute the dark matter annihilation cross section into pi-pi-eta'.
"""
fsp_masses: tuple[float, float, float] = field(
init=False, default=(METAP, MPI, MPI)
)
_fsp_masses: tuple[float, float, float] = field(
init=False, default=(METAP_GEV, MPI_GEV, MPI_GEV)
)
fit_data: VectorFormFactorPiPiEtaPrimeFitData = field(init=False)
masses: InitVar[RealArray] = field(default=np.array([0.77549, 1.54, 1.76, 2.11]))
widths: InitVar[RealArray] = field(default=np.array([0.1494, 0.356, 0.113, 0.176]))
amps: InitVar[RealArray] = field(default=np.array([1.0, 0.0, 0.0, 0.02]))
phases: InitVar[RealArray] = field(default=np.array([0.0, np.pi, np.pi, np.pi]))
def __post_init__(
self,
masses: RealArray,
widths: RealArray,
amps: RealArray,
phases: RealArray,
):
self.fit_data = VectorFormFactorPiPiEtaPrimeFitData(
masses=masses,
widths=widths,
amps=amps,
phases=phases,
)
def __bw0(self, s):
m0 = self.fit_data.masses[0]
w0 = self.fit_data.widths[0]
w = (
w0
* m0**2
/ s
* ((s - 4.0 * MPI_GEV**2) / (m0**2 - 4.0 * MPI_GEV**2)) ** 1.5
)
return m0**2 / (m0**2 - s - 1j * np.sqrt(s) * w)
def __bw(self, s):
w = self.fit_data.widths * s / self.fit_data.masses**2
bw = self.fit_data.masses**2 / (
self.fit_data.masses**2 - s - 1j * np.sqrt(s) * w
)
bw[0] = self.__bw0(s)
return bw
def _form_factor(self, q, s, couplings: Couplings):
"""
Compute the form factor for a vector decaying into two charged pions and
an eta-prime.
"""
pre = np.sqrt(2.0) / (4.0 * np.sqrt(3.0) * np.pi**2 * FPI_GEV**3)
ci1 = couplings[0] - couplings[1]
amps = self.fit_data.amps * np.exp(1j * self.fit_data.phases)
amps /= np.sum(amps)
return pre * ci1 * self.__bw0(s) * np.sum(amps * self.__bw(q**2))
@overload
def form_factor( # pylint: disable=arguments-differ
self, q: float, s: float, couplings: Couplings
) -> complex: ...
@overload
def form_factor( # pylint: disable=arguments-differ
self, q: float, s: RealArray, couplings: Couplings
) -> ComplexArray: ...
def form_factor( # pylint: disable=arguments-differ
self, q: float, s: RealOrRealArray, couplings: Couplings
) -> ComplexOrComplexArray:
r"""Compute the form factor for a vector decaying into two pions and an
eta'.
Parameters
----------
q:
Center-of-mass energy in MeV.
s: float
Squared invariant mass of the pions s = (p2+p3)^2.
t: float
Squared invariant mass of the eta' and last pion t=(p1+p3)^2.
gvuu, gvdd: float
Coupling of vector to up-quarks and down-quarks.
"""
qq = q * 1e-3
ss = s * 1e-6
ff = self._form_factor(qq, ss, couplings) * 1e-9
return ff
@overload
def integrated_form_factor( # pylint: disable=arguments-differ
self, q: float, couplings: Couplings
) -> float: ...
@overload
def integrated_form_factor( # pylint: disable=arguments-differ
self, q: RealArray, couplings: Couplings
) -> RealArray: ...
def integrated_form_factor( # pylint: disable=arguments-differ
self, q: float | RealArray, couplings: Couplings
) -> float | RealArray:
"""
Compute the form factor for a vector decaying into two charged pions and
an eta' integrated over the three-body phase-space.
Parameters
----------
q: float or array-like
Center-of-mass energy in MeV.
gvuu, gvdd: float
Vector coupling to up-quarks and down-quarks.
"""
return self._integrated_form_factor(q=q, couplings=couplings)
@overload
def width( # pylint: disable=arguments-differ
self, mv: float, couplings: Couplings
) -> float: ...
@overload
def width( # pylint: disable=arguments-differ
self, mv: RealArray, couplings: Couplings
) -> RealArray: ...
def width( # pylint: disable=arguments-differ
self, mv: float | RealArray, couplings: Couplings
) -> float | RealArray:
r"""Compute the partial decay width of a massive vector into an eta' and
two pions.
Parameters
----------
mv: float
Mass of the vector.
gvuu, gvdd: float
Coupling of vector to up-quarks and down-quarks.
Returns
-------
width: float
Decay width of vector into an eta' and two pions.
"""
return self._width(mv=mv, couplings=couplings)
@overload
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: float,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
) -> float: ...
@overload
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealArray,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
) -> RealArray: ...
def cross_section( # pylint: disable=arguments-differ,too-many-arguments
self,
q: RealOrRealArray,
mx: float,
mv: float,
gvxx: float,
wv: float,
couplings: Couplings,
) -> RealOrRealArray:
r"""Compute the cross section for dark matter annihilating into an eta'
and two pions.
Parameters
----------
q: float
Center-of-mass energy.
mx: float
Mass of the dark matter in MeV.
mv: float
Mass of the vector mediator in MeV.
gvxx: float
Coupling of vector to dark matter.
wv: float
Width of the vector in MeV.
gvuu, gvdd: float
Coupling of vector to up-quarks and down-quarks.
Returns
-------
cs: float or array-like
Annihilation cross section into an eta' and two pions.
"""
return self._cross_section(
q=q, mx=mx, mv=mv, gvxx=gvxx, wv=wv, couplings=couplings
)
def energy_distributions( # pylint: disable=arguments-differ
self,
q: float,
nbins: int,
*,
couplings: Couplings,
) -> list[PhaseSpaceDistribution1D]:
r"""Compute the energy distributions of the final state pions and eta'.
Parameters
----------
q: float
Center-of-mass energy.
nbins: float
Number of bins used to generate distribution.
gvuu, gvdd: float
Coupling of vector to up- and down-quarks.
Returns
-------
dists: List[PhaseSpaceDistribution1D]
List of the energy distributions.
"""
return self._energy_distributions(q=q, nbins=nbins, couplings=couplings)
def invariant_mass_distributions( # pylint: disable=arguments-differ
self, q: float, nbins: int, *, couplings: Couplings
) -> dict[tuple[int, int], PhaseSpaceDistribution1D]:
r"""Compute the invariant-mass distributions of the all pairs of the
final-state particles.
Parameters
----------
q: float
Center-of-mass energy.
nbins: float
Number of bins used to generate distribution.
gvuu, gvdd: float
Coupling of vector to up- and down-quarks.
Returns
-------
dists: Dict[(int,int), PhaseSpaceDistribution1D]
Dictionary of the invariant-mass distributions. Keys specify the
pair of particles the distribution represents.
"""
return self._invariant_mass_distributions(q=q, nbins=nbins, couplings=couplings)
|
LoganAMorrisonREPO_NAMEHazmaPATH_START.@Hazma_extracted@Hazma-master@hazma@form_factors@vector@_pi_pi_etap.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "sjforeman/bskit",
"repo_path": "bskit_extracted/bskit-master/README.md",
"type": "Markdown"
}
|
# bskit
`bskit` is a Python package for measuring density bispectra from snapshots of cosmological N-body or hydrodynamical simulations. It can measure auto or cross bispectra in a user-specified set of triangle bins (that is, triplets of 3-vector wavenumbers (k_1,k_2,k_3) such that k_1, k_2, and k_3 fall into separately specified vector magnitude bins and k_1+k_2+k_3=0). Several common sets of bins are also implemented:
- all triangle bins with min(|k_1|,|k_2|,|k_3|) > k_min and max(|k_1|,|k_2|,|k_3|) < k_max for specified k_min and k_max;
- equilateral triangles between specified k_min and k_max;
- isosceles triangles defined by m*|k_1| ~ |k_2| ~ |k_3| for specified multiplier m;
- squeezed isosceles triangles with |k_1| fixed and |k_2| ~ |k_3|.
`bskit` is built upon the [nbodykit](github.com/bccp/nbodykit) simulation analysis package, and users should familiarize themselves with the central `nbodykit` concepts in the [documentation](https://nbodykit.readthedocs.io/en/latest/) before getting started with `bskit`.
This package uses the FFT-based bispectrum measurement algorithm presented e.g. in Sec. 2 of
> Tomlinson et al., *Efficient parallel algorithm for estimating higher-order polyspectra*, [Astrophys. J., 158, 116 (2019)](10.3847/1538-3881/ab3223), arXiv:[1904.11055](https://arxiv.org/abs/1904.11055)
(see below for further references), and was first used for the bispectrum measurements in the following paper:
> Foreman, Coulton, Villaescusa-Navarro, and Barreira, *Baryonic effects on the matter bispectrum*, 2019, arXiv:[1910.03597](https://arxiv.org/abs/1910.03597)
## Installation
`bskit` requires Python 3; otherwise, its main dependency is on `nbodykit`. After following the `nbodykit` [installation instructions](https://nbodykit.readthedocs.io/en/latest/getting-started/install.html) (preferably via `conda`), all `bskit` dependencies will also be installed. At this point, simply clone this repository and ensure that the root `bskit` directory is in your `PYTHONPATH`, e.g. via
``
export PYTHONPATH=/path/to/bskit:$PYTHONPATH
``
## Usage
Usage instructions and a guide to the included examples can be found [here](https://github.com/sjforeman/bskit/blob/master/usage.md).
## References
If `bskit` is used in original research, please cite the associated paper:
> Foreman, Coulton, Villaescusa-Navarro, and Barreira, *Baryonic effects on the matter bispectrum*, 2019, arXiv:[1910.03597](https://arxiv.org/abs/1910.03597)
In addition, please cite the `nbodykit` paper,
> Hand et al., *nbodykit: an open-source, massively parallel toolkit for large-scale structure*, [Astron. J., 156, 160 (2018)](https://dx.doi.org/10.3847/1538-3881/aadae0), arXiv:[1712.05834](https://arxiv.org/abs/1712.05834)
and the following standard references for the FFT-based bispectrum estimator that the package implements:
> Scoccimarro, *The Bispectrum: From Theory to Observations*, [Astrophys. J., 544, 597 (2000)](https://dx.doi.org/10.1086/317248), arXiv:[astro-ph/0004086](https://arxiv.org/abs/astro-ph/0004086)
> Sefusatti et al., *Accurate estimators of correlation functions in Fourier space*, [Mon. Not. Roy. Astron. Soc., 460, 3624 (2016)](https://dx.doi.org/10.1093/mnras/stw1229), arXiv:[1512.07295](https://arxiv.org/abs/1512.07295)
## Questions?
If you have any questions or would like to contribute to this code, please open an issue or email Simon directly.
|
sjforemanREPO_NAMEbskitPATH_START.@bskit_extracted@bskit-master@README.md@.PATH_END.py
|
{
"filename": "make_lightcones_for_fisher.py",
"repo_name": "charlottenosam/21cmfish",
"repo_path": "21cmfish_extracted/21cmfish-master/scripts/make_lightcones_for_fisher.py",
"type": "Python"
}
|
import py21cmfast as p21c
import os
import glob
import numpy as np
import time
from joblib import Parallel, delayed
import argparse
import configparser
import multiprocessing
import py21cmfish as p21fish
import logging
logger = logging.getLogger("21cmFAST")
logger.setLevel(logging.INFO)
print(f"21cmFAST version is {p21c.__version__}")
# ==============================================================================
# python make_lightcones_for_fisher.py ../21cmFAST_config_files/Park19.config --dry_run
# TODO =====
# Took ---- Finished making lightcones, took 15.86 hours ---- for ETHOS.
# Took 11 mins to make PS
#
#
# python scripts/make_lightcones_for_fisher.py 21cmFAST_config_files/ETHOS.config --num_cores 2 --h_PEAK 0 --random_seed $r
# ==============================================================================
# ==============================================================================
#
# Script to create set of 21cmFAST simulations for Fisher matrix analysis.
# Loads a configuration file of default parameters, and parameters to vary
#
# ==============================================================================
# ==============================================================================
#
# Import config files
config = configparser.ConfigParser(delimiters=':')
config.optionxform = str
# Managing arguments with argparse (see http://docs.python.org/howto/argparse.html)
parser = argparse.ArgumentParser()
# ---- required arguments ---- :
parser.add_argument("config_file", type=str, help="Path to config file")
# ---- optional arguments ----
parser.add_argument("--h_PEAK", type=float, help="h_PEAK for ETHOS model, only used if USE_ETHOS = True [default = vary]")
parser.add_argument("--N_THREADS", type=int, help="Number of threads for 21cmFAST [default = 1, clogs memory if you use too many]")
parser.add_argument("--num_cores", type=int, help="Number of cores to run on [default = n_cpu - 1]")
parser.add_argument("--q_scale", type=float, help="Percentage step for the parameters [default = 3%]")
parser.add_argument("--random_seed", type=int, help="Random seed [default = 12345]")
# ---- flags ------
parser.add_argument("--save_Tb", action='store_true', help="Save BrightnessTemp boxes [default = False]")
parser.add_argument("--fix_astro_params", action='store_true', help="Fix astro params (only vary k_peak, h_peak for ETHOS runs) [default = False]")
parser.add_argument("--test_linear", action='store_true', help="Test linearity of PS derivatives by creating lightcones on a wider grid of parameters [default = False]")
parser.add_argument("--clobber", action='store_true', help="make new lightcones [default = False]")
parser.add_argument("--dry_run", action='store_true', help="Just print the parameters, don't run anything [default = False]")
args = parser.parse_args()
# ==============================================================================
# Run Parameters
num_cores = multiprocessing.cpu_count() - 1
if args.num_cores:
num_cores = args.num_cores
logger.info(f'Running on {num_cores} cores')
N_THREADS = 1
if args.N_THREADS:
N_THREADS = args.N_THREADS
logger.info(f'Running on {N_THREADS} threads')
q_scale = 3
if args.q_scale:
q_scale = args.q_scale
logger.info(f'Calculating derivatives at {q_scale} percent from fiducial')
if args.h_PEAK is not None:
h_PEAK = args.h_PEAK
fix_h_PEAK = True
h_peaks = [h_PEAK]
logger.info(f'Running with fixed h_peak = {h_PEAK}')
else:
fix_h_PEAK = False
h_PEAK = 0. # default
h_peaks = np.arange(0., 1.1, 0.1)
logger.info(f'Running with varied h_peak [if USE_ETHOS = True]')
logger.info(f'Will make lightcones for h_peak={h_peaks}')
save_Tb = False
if args.save_Tb:
save_Tb = True
logger.info(f'Saving BrightnessTemp coeval boxes')
vary_array = np.array([-1,1])
if args.test_linear:
vary_array = np.arange(-10,11)
vary_array = np.delete(vary_array,np.where(vary_array==0))
logger.info(f'Testing linearity of derivatives on a larger grid +/-{q_scale*np.max(vary_array)}% of fiducial')
fix_astro_params = False
if args.fix_astro_params:
fix_astro_params = True
logger.info(f'Fixing astro params')
clobber = False
if args.clobber:
clobber = True
logger.info(f'Clobber = True - making new lightcones')
random_seed = 12345
if args.random_seed:
random_seed = args.random_seed
logger.info(f'Using random_seed = {random_seed}')
# ==============================================================================
# Get config
config_file = args.config_file
config.read(config_file)
logger.info(f'Running with {config.get("run","name")}...')
# ==============================================================================
output_dir = config.get('run','output_dir')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
logger.info(f'Loading from cache at {output_dir}')
p21c.config['direc'] = output_dir
# --------------------------------------
lightcone_quantities = ("brightness_temp", 'density')
global_quantities = ("brightness_temp", 'density', 'xH_box')
# ==================================
# parameters
# Fidicual parameters
user_params = dict(config.items('user_params'))
user_params = {key:p21fish.read_config_params(user_params[key]) for key in user_params}
user_params["N_THREADS"] = N_THREADS
flag_options = dict(config.items('flag_options'))
flag_options = {key:p21fish.read_config_params(flag_options[key]) for key in flag_options}
astro_params_fid = dict(config.items('astro_params'))
astro_params_fid = {key:float(astro_params_fid[key]) for key in astro_params_fid}
if fix_astro_params:
astro_params_vary = []
else:
astro_params_vary = config.get('vary','astro_params_vary').split('\n')
astro_params_vary = list(filter(None, astro_params_vary))
# ==================================
min_redshift = float(config.get('redshifts','min'))
max_redshift = float(config.get('redshifts','max'))
HII_DIM = user_params["HII_DIM"]
BOX_LEN = user_params["BOX_LEN"]
logger.info(f'Making lightcone from z={min_redshift}-{max_redshift}')
logger.info(f'Box HII_DIM={HII_DIM}, BOX_LEN={BOX_LEN}')
# Clean up types
if save_Tb:
clear_kind = ['IonizedBox','TsBox']
else:
clear_kind = ['IonizedBox','TsBox','BrightnessTemp', 'PerturbedField']
# ==================================
# Make dictionary of sets of parameters for each run
astro_params_run_all = {}
# Set up parameters for fisher runs
if flag_options['USE_ETHOS'] is True:
dict_prefix = f'h_PEAK_{h_PEAK:.1f}_'
else:
dict_prefix = ''
astro_params_run_all[f'{dict_prefix}fid'] = astro_params_fid
for param in astro_params_vary:
p_fid = astro_params_fid[param]
# Make smaller for L_X
if param == 'L_X':
q = 0.001*vary_array
else:
q = q_scale/100*vary_array
if p_fid == 0.:
p = q
else:
p = p_fid - q*p_fid
astro_params_run = astro_params_fid.copy()
for i,pp in enumerate(p):
astro_params_run[param] = pp
if param == 'L_X': # change L_X and L_X_MINI at the same time
astro_params_run['L_X_MINI'] = pp
astro_params_run_all[f'{dict_prefix}{param}_{q[i]}'] = astro_params_run.copy()
# TODO nicer for not ETHOS runs
if flag_options['USE_ETHOS'] is True:
# Vary k_peak and h_peak
# inv_k_peak = np.array([0.01, 0.03])
# inv_k_peak = np.array([1e-4, 0.001, 0.002, 0.003])
# inv_k_peak = np.array([1e-8, 1e-6, 1e-4])
# inv_k_peak = np.array([1e-8, 1e-6, 0.002, 0.003])
# inv_k_peak = np.array([1e-5, 5e-5, 1e-4, 5e-4, 1e-3])
inv_k_peak = np.array([1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 5e-5, 5e-4, 1e-3]) # test for convergence
# inv_k_peak = np.array([1e-5, 5e-5, 5e-4]) # this was default for h_peak = 0?
for h_peak in h_peaks:
for inv_k in inv_k_peak:
log_k_peak = np.log10(1/inv_k)
astro_params_run = astro_params_fid.copy()
astro_params_run['log10_k_PEAK'] = log_k_peak
astro_params_run['h_PEAK'] = h_peak
astro_params_run_all[f'h_PEAK_{h_peak:.1f}_inv_k_PEAK_{inv_k}'] = astro_params_run.copy()
logger.info(f'Going to make {len(astro_params_run_all)} lightcones')
if 'ALPHA_ESC_-0.03' in astro_params_run_all:
assert astro_params_run_all['ALPHA_ESC_-0.03']['ALPHA_ESC'] != astro_params_run_all['ALPHA_ESC_0.03']['ALPHA_ESC'],\
'Parameters havent changed between fisher runs!!!'
if 'ALPHA_STAR_MINI_-0.03' in astro_params_run_all:
assert astro_params_run_all['ALPHA_STAR_MINI_-0.03']['ALPHA_STAR_MINI'] != astro_params_run_all['ALPHA_STAR_-0.03']['ALPHA_STAR'],\
'ALPHA_STAR and ALPHA_STAR_MINI messed up!!!'
if args.dry_run:
for key in astro_params_run_all:
print(key,':')
logger.info(f'',astro_params_run_all[key])
else:
# ==================================
# Initial Conditions
logger.info(f'Making initial conditions')
initial_conditions = p21c.initial_conditions(user_params=user_params,
random_seed=random_seed,
direc=output_dir)
# Find ICs and perturbed fields
PerturbedField_files = glob.glob(f'{output_dir}PerturbedField*')
IC_files = glob.glob(f'{output_dir}InitialConditions*')
logger.info(f'Loaded or made initial conditions')
# Will not write more boxes
# p21c.config['write'] = False
# ==================================
# Run each filter
def make_lightcone(astro_params_key):
"""
Make lightcone for a given set of astroparams
"""
# Save output for each parameter to a new directory
# if save_Tb:
output_dir_lc = f'{output_dir}_{astro_params_key}'
if not os.path.exists(output_dir_lc):
os.makedirs(output_dir_lc)
# put PerturbedFields in output_dir_lc
if len(PerturbedField_files) > 0:
for PF in PerturbedField_files:
PF_file = PF.split('/')[-1]
linked_file = f'{output_dir_lc}/{PF_file}'
if not os.path.exists(linked_file):
os.symlink(PF, linked_file)
for IC in IC_files:
IC_file = IC.split('/')[-1]
linked_file = f'{output_dir_lc}/{IC_file}'
if not os.path.exists(linked_file):
os.symlink(IC, linked_file)
direc = output_dir_lc
# else:
# direc = None
# Lightcone filename
suffix = f'HIIDIM={HII_DIM}_BOXLEN={BOX_LEN}_fisher_{astro_params_key}'
lightcone_filename = f'LightCone_z{min_redshift:.1f}_{suffix}_r{random_seed}.h5'
logger.info(f'Will save lightcone to {lightcone_filename}')
t1 = time.time()
if not os.path.exists(f'{output_dir}{lightcone_filename}'):
lightcone = p21c.run_lightcone(
redshift = min_redshift,
max_redshift = max_redshift,
lightcone_quantities=lightcone_quantities,
global_quantities=global_quantities,
init_box = initial_conditions,
user_params = user_params,
flag_options = flag_options,
astro_params = astro_params_run_all[astro_params_key],
random_seed = random_seed,
direc=direc,
write=save_Tb
)
# save in main dir
lightcone_save = lightcone.save(fname=lightcone_filename, direc=output_dir, clobber=True)
logger.info(f'Saved lightcone to {lightcone_save}')
else:
logger.info(f'{lightcone_filename} already exists, skipping...')
# Clean up
for kind in clear_kind:
logger.info(f'Clearing cache')
p21c.cache_tools.clear_cache(direc=output_dir_lc, kind=kind)
os.system(f"rm -rf {output_dir_lc}")
t2 = time.time()
logger.info(f'Done with {astro_params_key}, took {(t2-t1)/3600:.2f} hours')
return
t1 = time.time()
if num_cores == 1:
print(astro_params_run_all.keys())
for key in astro_params_run_all.keys():
logger.info(f'Saved making lightcone for {key}')
make_lightcone(key)
else:
Parallel(n_jobs=num_cores)(delayed(make_lightcone)(key) for key in astro_params_run_all.keys())
t2 = time.time()
logger.info(f'---- Finished making lightcones, took {(t2-t1)/3600:.2f} hours')
|
charlottenosamREPO_NAME21cmfishPATH_START.@21cmfish_extracted@21cmfish-master@scripts@make_lightcones_for_fisher.py@.PATH_END.py
|
{
"filename": "_decomp_qr.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/scipy/py3/scipy/linalg/_decomp_qr.py",
"type": "Python"
}
|
"""QR decomposition functions."""
import numpy
# Local imports
from .lapack import get_lapack_funcs
from ._misc import _datacopied
__all__ = ['qr', 'qr_multiply', 'rq']
def safecall(f, name, *args, **kwargs):
"""Call a LAPACK routine, determining lwork automatically and handling
error return values"""
lwork = kwargs.get("lwork", None)
if lwork in (None, -1):
kwargs['lwork'] = -1
ret = f(*args, **kwargs)
kwargs['lwork'] = ret[-2][0].real.astype(numpy.int_)
ret = f(*args, **kwargs)
if ret[-1] < 0:
raise ValueError("illegal value in %dth argument of internal %s"
% (-ret[-1], name))
return ret[:-2]
def qr(a, overwrite_a=False, lwork=None, mode='full', pivoting=False,
check_finite=True):
"""
Compute QR decomposition of a matrix.
Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal
and R upper triangular.
Parameters
----------
a : (M, N) array_like
Matrix to be decomposed
overwrite_a : bool, optional
Whether data in `a` is overwritten (may improve performance if
`overwrite_a` is set to True by reusing the existing input data
structure rather than creating a new one.)
lwork : int, optional
Work array size, lwork >= a.shape[1]. If None or -1, an optimal size
is computed.
mode : {'full', 'r', 'economic', 'raw'}, optional
Determines what information is to be returned: either both Q and R
('full', default), only R ('r') or both Q and R but computed in
economy-size ('economic', see Notes). The final option 'raw'
(added in SciPy 0.11) makes the function return two matrices
(Q, TAU) in the internal format used by LAPACK.
pivoting : bool, optional
Whether or not factorization should include pivoting for rank-revealing
qr decomposition. If pivoting, compute the decomposition
``A P = Q R`` as above, but where P is chosen such that the diagonal
of R is non-increasing.
check_finite : bool, optional
Whether to check that the input matrix contains only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Returns
-------
Q : float or complex ndarray
Of shape (M, M), or (M, K) for ``mode='economic'``. Not returned
if ``mode='r'``.
R : float or complex ndarray
Of shape (M, N), or (K, N) for ``mode='economic'``. ``K = min(M, N)``.
P : int ndarray
Of shape (N,) for ``pivoting=True``. Not returned if
``pivoting=False``.
Raises
------
LinAlgError
Raised if decomposition fails
Notes
-----
This is an interface to the LAPACK routines dgeqrf, zgeqrf,
dorgqr, zungqr, dgeqp3, and zgeqp3.
If ``mode=economic``, the shapes of Q and R are (M, K) and (K, N) instead
of (M,M) and (M,N), with ``K=min(M,N)``.
Examples
--------
>>> import numpy as np
>>> from scipy import linalg
>>> rng = np.random.default_rng()
>>> a = rng.standard_normal((9, 6))
>>> q, r = linalg.qr(a)
>>> np.allclose(a, np.dot(q, r))
True
>>> q.shape, r.shape
((9, 9), (9, 6))
>>> r2 = linalg.qr(a, mode='r')
>>> np.allclose(r, r2)
True
>>> q3, r3 = linalg.qr(a, mode='economic')
>>> q3.shape, r3.shape
((9, 6), (6, 6))
>>> q4, r4, p4 = linalg.qr(a, pivoting=True)
>>> d = np.abs(np.diag(r4))
>>> np.all(d[1:] <= d[:-1])
True
>>> np.allclose(a[:, p4], np.dot(q4, r4))
True
>>> q4.shape, r4.shape, p4.shape
((9, 9), (9, 6), (6,))
>>> q5, r5, p5 = linalg.qr(a, mode='economic', pivoting=True)
>>> q5.shape, r5.shape, p5.shape
((9, 6), (6, 6), (6,))
"""
# 'qr' was the old default, equivalent to 'full'. Neither 'full' nor
# 'qr' are used below.
# 'raw' is used internally by qr_multiply
if mode not in ['full', 'qr', 'r', 'economic', 'raw']:
raise ValueError("Mode argument should be one of ['full', 'r',"
"'economic', 'raw']")
if check_finite:
a1 = numpy.asarray_chkfinite(a)
else:
a1 = numpy.asarray(a)
if len(a1.shape) != 2:
raise ValueError("expected a 2-D array")
M, N = a1.shape
overwrite_a = overwrite_a or (_datacopied(a1, a))
if pivoting:
geqp3, = get_lapack_funcs(('geqp3',), (a1,))
qr, jpvt, tau = safecall(geqp3, "geqp3", a1, overwrite_a=overwrite_a)
jpvt -= 1 # geqp3 returns a 1-based index array, so subtract 1
else:
geqrf, = get_lapack_funcs(('geqrf',), (a1,))
qr, tau = safecall(geqrf, "geqrf", a1, lwork=lwork,
overwrite_a=overwrite_a)
if mode not in ['economic', 'raw'] or M < N:
R = numpy.triu(qr)
else:
R = numpy.triu(qr[:N, :])
if pivoting:
Rj = R, jpvt
else:
Rj = R,
if mode == 'r':
return Rj
elif mode == 'raw':
return ((qr, tau),) + Rj
gor_un_gqr, = get_lapack_funcs(('orgqr',), (qr,))
if M < N:
Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qr[:, :M], tau,
lwork=lwork, overwrite_a=1)
elif mode == 'economic':
Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qr, tau, lwork=lwork,
overwrite_a=1)
else:
t = qr.dtype.char
qqr = numpy.empty((M, M), dtype=t)
qqr[:, :N] = qr
Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qqr, tau, lwork=lwork,
overwrite_a=1)
return (Q,) + Rj
def qr_multiply(a, c, mode='right', pivoting=False, conjugate=False,
overwrite_a=False, overwrite_c=False):
"""
Calculate the QR decomposition and multiply Q with a matrix.
Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal
and R upper triangular. Multiply Q with a vector or a matrix c.
Parameters
----------
a : (M, N), array_like
Input array
c : array_like
Input array to be multiplied by ``q``.
mode : {'left', 'right'}, optional
``Q @ c`` is returned if mode is 'left', ``c @ Q`` is returned if
mode is 'right'.
The shape of c must be appropriate for the matrix multiplications,
if mode is 'left', ``min(a.shape) == c.shape[0]``,
if mode is 'right', ``a.shape[0] == c.shape[1]``.
pivoting : bool, optional
Whether or not factorization should include pivoting for rank-revealing
qr decomposition, see the documentation of qr.
conjugate : bool, optional
Whether Q should be complex-conjugated. This might be faster
than explicit conjugation.
overwrite_a : bool, optional
Whether data in a is overwritten (may improve performance)
overwrite_c : bool, optional
Whether data in c is overwritten (may improve performance).
If this is used, c must be big enough to keep the result,
i.e. ``c.shape[0]`` = ``a.shape[0]`` if mode is 'left'.
Returns
-------
CQ : ndarray
The product of ``Q`` and ``c``.
R : (K, N), ndarray
R array of the resulting QR factorization where ``K = min(M, N)``.
P : (N,) ndarray
Integer pivot array. Only returned when ``pivoting=True``.
Raises
------
LinAlgError
Raised if QR decomposition fails.
Notes
-----
This is an interface to the LAPACK routines ``?GEQRF``, ``?ORMQR``,
``?UNMQR``, and ``?GEQP3``.
.. versionadded:: 0.11.0
Examples
--------
>>> import numpy as np
>>> from scipy.linalg import qr_multiply, qr
>>> A = np.array([[1, 3, 3], [2, 3, 2], [2, 3, 3], [1, 3, 2]])
>>> qc, r1, piv1 = qr_multiply(A, 2*np.eye(4), pivoting=1)
>>> qc
array([[-1., 1., -1.],
[-1., -1., 1.],
[-1., -1., -1.],
[-1., 1., 1.]])
>>> r1
array([[-6., -3., -5. ],
[ 0., -1., -1.11022302e-16],
[ 0., 0., -1. ]])
>>> piv1
array([1, 0, 2], dtype=int32)
>>> q2, r2, piv2 = qr(A, mode='economic', pivoting=1)
>>> np.allclose(2*q2 - qc, np.zeros((4, 3)))
True
"""
if mode not in ['left', 'right']:
raise ValueError("Mode argument can only be 'left' or 'right' but "
"not '{}'".format(mode))
c = numpy.asarray_chkfinite(c)
if c.ndim < 2:
onedim = True
c = numpy.atleast_2d(c)
if mode == "left":
c = c.T
else:
onedim = False
a = numpy.atleast_2d(numpy.asarray(a)) # chkfinite done in qr
M, N = a.shape
if mode == 'left':
if c.shape[0] != min(M, N + overwrite_c*(M-N)):
raise ValueError('Array shapes are not compatible for Q @ c'
' operation: {} vs {}'.format(a.shape, c.shape))
else:
if M != c.shape[1]:
raise ValueError('Array shapes are not compatible for c @ Q'
' operation: {} vs {}'.format(c.shape, a.shape))
raw = qr(a, overwrite_a, None, "raw", pivoting)
Q, tau = raw[0]
gor_un_mqr, = get_lapack_funcs(('ormqr',), (Q,))
if gor_un_mqr.typecode in ('s', 'd'):
trans = "T"
else:
trans = "C"
Q = Q[:, :min(M, N)]
if M > N and mode == "left" and not overwrite_c:
if conjugate:
cc = numpy.zeros((c.shape[1], M), dtype=c.dtype, order="F")
cc[:, :N] = c.T
else:
cc = numpy.zeros((M, c.shape[1]), dtype=c.dtype, order="F")
cc[:N, :] = c
trans = "N"
if conjugate:
lr = "R"
else:
lr = "L"
overwrite_c = True
elif c.flags["C_CONTIGUOUS"] and trans == "T" or conjugate:
cc = c.T
if mode == "left":
lr = "R"
else:
lr = "L"
else:
trans = "N"
cc = c
if mode == "left":
lr = "L"
else:
lr = "R"
cQ, = safecall(gor_un_mqr, "gormqr/gunmqr", lr, trans, Q, tau, cc,
overwrite_c=overwrite_c)
if trans != "N":
cQ = cQ.T
if mode == "right":
cQ = cQ[:, :min(M, N)]
if onedim:
cQ = cQ.ravel()
return (cQ,) + raw[1:]
def rq(a, overwrite_a=False, lwork=None, mode='full', check_finite=True):
"""
Compute RQ decomposition of a matrix.
Calculate the decomposition ``A = R Q`` where Q is unitary/orthogonal
and R upper triangular.
Parameters
----------
a : (M, N) array_like
Matrix to be decomposed
overwrite_a : bool, optional
Whether data in a is overwritten (may improve performance)
lwork : int, optional
Work array size, lwork >= a.shape[1]. If None or -1, an optimal size
is computed.
mode : {'full', 'r', 'economic'}, optional
Determines what information is to be returned: either both Q and R
('full', default), only R ('r') or both Q and R but computed in
economy-size ('economic', see Notes).
check_finite : bool, optional
Whether to check that the input matrix contains only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Returns
-------
R : float or complex ndarray
Of shape (M, N) or (M, K) for ``mode='economic'``. ``K = min(M, N)``.
Q : float or complex ndarray
Of shape (N, N) or (K, N) for ``mode='economic'``. Not returned
if ``mode='r'``.
Raises
------
LinAlgError
If decomposition fails.
Notes
-----
This is an interface to the LAPACK routines sgerqf, dgerqf, cgerqf, zgerqf,
sorgrq, dorgrq, cungrq and zungrq.
If ``mode=economic``, the shapes of Q and R are (K, N) and (M, K) instead
of (N,N) and (M,N), with ``K=min(M,N)``.
Examples
--------
>>> import numpy as np
>>> from scipy import linalg
>>> rng = np.random.default_rng()
>>> a = rng.standard_normal((6, 9))
>>> r, q = linalg.rq(a)
>>> np.allclose(a, r @ q)
True
>>> r.shape, q.shape
((6, 9), (9, 9))
>>> r2 = linalg.rq(a, mode='r')
>>> np.allclose(r, r2)
True
>>> r3, q3 = linalg.rq(a, mode='economic')
>>> r3.shape, q3.shape
((6, 6), (6, 9))
"""
if mode not in ['full', 'r', 'economic']:
raise ValueError(
"Mode argument should be one of ['full', 'r', 'economic']")
if check_finite:
a1 = numpy.asarray_chkfinite(a)
else:
a1 = numpy.asarray(a)
if len(a1.shape) != 2:
raise ValueError('expected matrix')
M, N = a1.shape
overwrite_a = overwrite_a or (_datacopied(a1, a))
gerqf, = get_lapack_funcs(('gerqf',), (a1,))
rq, tau = safecall(gerqf, 'gerqf', a1, lwork=lwork,
overwrite_a=overwrite_a)
if not mode == 'economic' or N < M:
R = numpy.triu(rq, N-M)
else:
R = numpy.triu(rq[-M:, -M:])
if mode == 'r':
return R
gor_un_grq, = get_lapack_funcs(('orgrq',), (rq,))
if N < M:
Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq[-N:], tau, lwork=lwork,
overwrite_a=1)
elif mode == 'economic':
Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq, tau, lwork=lwork,
overwrite_a=1)
else:
rq1 = numpy.empty((N, N), dtype=rq.dtype)
rq1[-M:] = rq
Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq1, tau, lwork=lwork,
overwrite_a=1)
return R, Q
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@scipy@py3@scipy@linalg@_decomp_qr.py@.PATH_END.py
|
{
"filename": "pallas_call_registration.py",
"repo_name": "google/jax",
"repo_path": "jax_extracted/jax-main/jax/_src/pallas/mosaic/pallas_call_registration.py",
"type": "Python"
}
|
# Copyright 2023 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.
"""Contains registrations for pallas_call on TPU."""
from __future__ import annotations
import os
import tempfile
from typing import Any
import jax
from jax import dtypes
from jax._src import config
from jax._src import core as jax_core
from jax._src import sharding_impls
from jax._src import tpu_custom_call
from jax._src.interpreters import mlir
from jax._src.lib.mlir import ir
from jax._src.pallas import core
from jax._src.pallas.mosaic import core as tpu_core
from jax._src.pallas.mosaic import lowering
from jax._src.pallas.mosaic import verification
from jax.experimental import mosaic
from jax.experimental.mosaic.dialects import tpu
def _maybe_cast_to_int(x: jax.Array | jax_core.AbstractValue):
"""Casts boolean values to integers.
We perform this cast because Mosaic does not directly support bool values
for Memrefs. Instead, we load bools as integers and cast them to bools
after loading from a memref inside of the kernel.
"""
assert isinstance(
x, (jax.Array, jax_core.ShapedArray, jax_core.DShapedArray)
), type(x)
if isinstance(x, jax.Array):
if dtypes.issubdtype(x.dtype, jax.numpy.bool_):
return x.astype(lowering.BOOL_MEMREF_TYPE)
return x
else:
if dtypes.issubdtype(x.dtype, jax.numpy.bool_):
return jax_core.ShapedArray(x.shape, lowering.BOOL_MEMREF_TYPE)
return x
_DUMP_PROMELA_TO = config.string_flag(
"jax_pallas_dump_promela_to",
default=os.getenv("JAX_PALLAS_DUMP_PROMELA_TO", ""),
help=(
"If set, dumps a Promela model of the kernel to the specified"
" directory. The model can verify that the kernel is free of data"
" races, deadlocks, etc."
),
)
def _get_memory_space_from_aval(
out_aval: jax_core.AbstractValue,
) -> tpu_custom_call.MemorySpace | None:
if not isinstance(out_aval, jax_core.ShapedArray):
raise ValueError('Memory spaces not defined for non-ShapedArrays')
if not isinstance(out_aval, core.ShapedArrayWithMemorySpace):
# If we are passed a regular old ShapedArray, we don't constrain the
# memory space
return None
# If we are passed an aval with an explicit memory space tag, we use it
# to constrain the memory space.
match out_aval.memory_space:
case None:
return None
case tpu_core.TPUMemorySpace.ANY:
return None
case tpu_core.TPUMemorySpace.VMEM:
return tpu_custom_call.MemorySpace.VMEM
case tpu_core.TPUMemorySpace.SEMAPHORE:
return tpu_custom_call.MemorySpace.SEMAPHORE_MEM
return None
def _get_memory_spaces_from_avals(
out_avals: tuple[jax_core.AbstractValue, ...],
) -> tuple[tpu_custom_call.MemorySpace | None, ...] | None:
output_memory_spaces = None
if any(
isinstance(out_aval, core.ShapedArrayWithMemorySpace)
for out_aval in out_avals
):
output_memory_spaces = tuple(map(_get_memory_space_from_aval, out_avals))
return output_memory_spaces
def pallas_call_tpu_lowering_rule(
ctx: mlir.LoweringRuleContext,
*in_nodes,
jaxpr: jax_core.Jaxpr,
name_and_src_info: core.NameAndSrcInfo,
grid_mapping: core.GridMapping,
input_output_aliases: tuple[tuple[int, int], ...],
debug: bool,
interpret: bool,
compiler_params: dict[str, Any],
cost_estimate: core.CostEstimate | None,
out_avals: tuple[jax_core.AbstractValue, ...],
):
"""Lowers a pallas_call to a Mosaic TPU custom call."""
del interpret
if debug:
print(f"\nThe kernel jaxpr for pallas_call {name_and_src_info}:")
print(jaxpr)
if "mosaic" in compiler_params:
mosaic_params = compiler_params["mosaic"]
else:
mosaic_params = {}
mesh = None
axis_context = ctx.module_context.axis_context
if axis_context is not None:
if isinstance(axis_context, sharding_impls.SPMDAxisContext):
mesh = axis_context.mesh
mlir_ctx = mlir.JaxIrContext()
mlir_ctx.append_dialect_registry(mlir.upstream_dialects)
mlir_ctx.load_all_available_dialects()
tpu.register_dialect(mlir_ctx)
def lower_module(for_verification: bool):
if for_verification or tpu_core.runtime_assert_enabled():
mlir_ctx.allow_unregistered_dialects = True
with mlir_ctx, ir.Location.unknown(mlir_ctx):
dimension_semantics = mosaic_params.get("dimension_semantics", None)
return lowering.lower_jaxpr_to_module(
ctx, mlir_ctx, grid_mapping, jaxpr,
dimension_semantics=dimension_semantics, mesh=mesh,
for_verification=for_verification,
name_and_src_info=name_and_src_info)
mosaic_module, extra_args = lower_module(for_verification=False)
if debug:
print(f"\nThe Mosaic module for pallas_call {name_and_src_info}:")
print(mosaic_module)
num_extra_args = len(extra_args)
num_dyn_bounds = grid_mapping.num_dynamic_grid_bounds
input_output_aliases = tuple(
(a[0] + num_dyn_bounds + num_extra_args, a[1])
for a in input_output_aliases
)
if promela_dump_path := _DUMP_PROMELA_TO.value:
num_devices = 1 if mesh is None else mesh.devices.size
num_cores = (
jax.devices()[0].num_cores
if mesh is None
else mesh.devices[0].num_cores
)
verification_module, _ = lower_module(for_verification=True)
model = verification.export_promela_model(
verification_module, num_devices, num_cores
)
if promela_dump_path == "stdout":
print(f"The Promela model for pallas_call {name_and_src_info}:")
print(model)
else:
if promela_dump_path == "sponge":
promela_dump_path = os.getenv("TEST_UNDECLARED_OUTPUTS_DIR", "")
if not promela_dump_path:
raise ValueError(
"TEST_UNDECLARED_OUTPUTS_DIR must be set when"
" --jax_pallas_dump_promela_to=sponge"
)
dump_ctx = tempfile.NamedTemporaryFile(
mode="w",
prefix=name_and_src_info.name + "-",
suffix=".pml",
dir=promela_dump_path, delete=False,
)
with dump_ctx as f:
f.write(model)
# Replace in_avals to physical avals.
# This step is required for mapping logical types to physical types.
# (e.g. PRNG key -> uint32[2])
physical_avals = [jax_core.physical_aval(aval) for aval in ctx.avals_in]
ctx = ctx.replace(avals_in=physical_avals)
# Booleans are loaded into the kernel as integers.
def _maybe_cast_inputs(*args):
args = [_maybe_cast_to_int(x) for x in args]
return args
kernel_in_avals = [_maybe_cast_to_int(x) for x in ctx.avals_in]
kernel_out_avals = [_maybe_cast_to_int(x) for x in out_avals]
cast_ctx = ctx.replace(avals_out=kernel_in_avals)
in_nodes = mlir.lower_fun(_maybe_cast_inputs)(cast_ctx, *in_nodes)
# Dynamic grid bounds have to go at the front.
dynamic_grid_args, args = in_nodes[:num_dyn_bounds], in_nodes[num_dyn_bounds:]
kernel_ctx = ctx.replace(avals_in=kernel_in_avals, avals_out=kernel_out_avals)
output_memory_spaces = _get_memory_spaces_from_avals(out_avals)
if cost_estimate is not None:
mosaic_cost_estimate = tpu_custom_call.CostEstimate(
flops=cost_estimate.flops,
bytes_accessed=cost_estimate.bytes_accessed,
transcendentals=cost_estimate.transcendentals,
)
else:
mosaic_cost_estimate = None
out_nodes = mosaic.lower_module_to_custom_call(
kernel_ctx,
*dynamic_grid_args,
*extra_args,
*args,
module=mosaic_module,
out_type=kernel_out_avals,
backend="tpu",
kernel_name=name_and_src_info.name,
cost_estimate=mosaic_cost_estimate,
vmem_limit_bytes=mosaic_params.get("vmem_limit_bytes"),
flags=mosaic_params.get("flags"),
allow_input_fusion=mosaic_params.get("allow_input_fusion"),
input_output_aliases=input_output_aliases,
serialization_format=mosaic_params.get("serialization_format", 1),
device_type=mosaic_params.get("device_type"),
internal_scratch_in_bytes=mosaic_params.get("internal_scratch_in_bytes"),
collective_id=mosaic_params.get("collective_id", None),
output_memory_spaces=output_memory_spaces,
)
_maybe_cast_to_bool = lambda x, aval: x.astype(
jax.numpy.bool_) if aval.dtype == jax.numpy.bool_ else x
def _maybe_cast_outputs(*args):
args = [_maybe_cast_to_bool(x, aval) for x, aval in zip(args, out_avals)]
return args
cast_ctx = ctx.replace(avals_in=kernel_out_avals)
return mlir.lower_fun(_maybe_cast_outputs)(cast_ctx, *out_nodes)
|
googleREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@jax@_src@pallas@mosaic@pallas_call_registration.py@.PATH_END.py
|
{
"filename": "helpers.py",
"repo_name": "jabesq-org/pyatmo",
"repo_path": "pyatmo_extracted/pyatmo-master/src/pyatmo/helpers.py",
"type": "Python"
}
|
"""Collection of helper functions."""
from __future__ import annotations
import logging
from typing import Any, cast
from pyatmo.const import RawData
from pyatmo.exceptions import NoDevice
LOG: logging.Logger = logging.getLogger(__name__)
def fix_id(raw_data: RawData) -> dict[str, Any]:
"""Fix known errors in station ids like superfluous spaces."""
if not raw_data:
return raw_data
for station in raw_data:
if not isinstance(station, dict):
continue
if station.get("_id") is None:
continue
station["_id"] = cast(dict, station)["_id"].replace(" ", "")
for module in station.get("modules", {}):
module["_id"] = module["_id"].replace(" ", "")
return raw_data
def extract_raw_data(resp: Any, tag: str) -> dict[str, Any]:
"""Extract raw data from server response."""
raw_data = {}
if tag == "body":
return {"public": resp["body"], "errors": []}
if resp is None or "body" not in resp or tag not in resp["body"]:
LOG.debug("Server response (tag: %s): %s", tag, resp)
raise NoDevice("No device found, errors in response")
if tag == "homes":
return {
tag: fix_id(resp["body"].get(tag)),
"errors": resp["body"].get("errors", []),
}
if not (raw_data := fix_id(resp["body"].get(tag))):
LOG.debug("Server response (tag: %s): %s", tag, resp)
raise NoDevice("No device data available")
return {tag: raw_data, "errors": resp["body"].get("errors", [])}
|
jabesq-orgREPO_NAMEpyatmoPATH_START.@pyatmo_extracted@pyatmo-master@src@pyatmo@helpers.py@.PATH_END.py
|
{
"filename": "collecting_1000th.ipynb",
"repo_name": "jan-rybizki/Galaxia_wrap",
"repo_path": "Galaxia_wrap_extracted/Galaxia_wrap-master/notebook/notebook_sweep/collecting_1000th.ipynb",
"type": "Jupyter Notebook"
}
|
```python
%pylab inline
from astropy.io import fits
from numpy.lib.recfunctions import stack_arrays
```
Populating the interactive namespace from numpy and matplotlib
/home/rybizki/anaconda3/lib/python3.6/site-packages/astropy/extern/bundled/six.py:60: ResourceWarning: unclosed file <_io.TextIOWrapper name='/home/rybizki/anaconda3/lib/python3.6/site-packages/astropy/extern/bundled/six.py' mode='r' encoding='utf-8'>
class X(object):
```python
mc = fits.getdata("../output/GDR3mock_extra/MCs_0/nbody.fits")
mc.dtype
print(len(mc))
mc = mc[np.random.choice(np.arange(len(mc)),int(len(mc)/100),replace=False)]
print(len(mc))
```
/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__
return f(*args, **kwds)
/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__
return f(*args, **kwds)
2728627
27286
```python
cl = fits.getdata("../output/GDR3mock_extra/Clusters_1/nbody.fits")
cl.dtype
print(len(cl))
cl = cl[np.random.choice(np.arange(len(cl)),int(len(cl)/1000),replace=False)]
print(len(cl))
```
441697
441
```python
mw = fits.getdata("../output/GDR3mock/0/GDR3mock207.fits")
mw.dtype
print(len(mw))
```
1546654
```python
x = stack_arrays((mc,cl,mw),usemask=False,asrecarray=True)
```
```python
x = np.sort(x,order = 'source_id')
```
```python
edges = round(len(x)/400)
edges_array = x[::edges].source_id
edges_array[0] = 0
edges_array = np.hstack((edges_array,[8931576916874782720]))
print(edges_array)
```
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3242442335564333056 3328470908460335104 3351027045906776064
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4061834065991434240 4062363893157068800 4062492261139611648
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4103357569290993664 4104180175787261952 4105068890420150272
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4116166123840339968 4117045630063345664 4118075357062496256
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4125097937829101568 4132304137037545472 4138499163505557504
4144234456674205696 4145768687711813632 4148283751840874496
4151153339750416384 4154588729471664128 4156720235841323008
4160109549153419264 4164476053424701440 4173161748607533056
4183065084198649856 4194740488936357888 4201030485721219072
4203246001651187712 4204504083471532032 4207748673565622272
4214137351519076352 4236123701424160768 4249140888304877568
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4261775445139128320 4263664062518263808 4265313123801497600
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4280098668977061888 4285079628449579008 4288820063928057856
4292441202394923008 4294774812745662464 4297876672486572032
4302944081060823040 4307653873478139904 4311003638731374592
4313171051027628032 4315005620538310656 4317185780297498624
4319582887444742144 4322968180667449344 4346606340634836992
4372445757240770560 4414351272111505408 4471222102308945920
4479473318600114176 4490903429165613056 4505091733368864768
4508939336871313408 4513839894556049408 4517168391131234304
4523225497609437184 4537650746568474624 4587791466092298240
4651041147358019584 4651929999429861376 4654742378375020544
4657309325709017088 4658131141931302912 4659220586155737088
4685905458483953664 4752009334496428032 5048627575618797568
5219424715445108736 5233961496114888704 5237655511586832384
5241283796879278080 5247578260430127104 5253317539328425984
5255512336336158720 5259769920236814336 5297659812484481024
5305849353045278720 5309762411849318400 5313139321295863808
5323313445985058816 5331000028695625728 5335160855573037056
5337642556396142592 5340266987572428800 5343656403963740160
5350462621457842176 5354722576180445184 5359458413279444992
5372887470343979008 5404963145103966208 5410571788276989952
5424220988185247744 5442675225705578496 5508257452170149888
5521906755157622784 5529933739796201472 5540320791784062976
5546108208675815424 5583112135069663232 5595949689238192128
5601613136193912832 5614335276001263616 5621941697442217984
5640128856716214272 5671690544149954560 5700966140751118336
5716945240158371840 5766837504414056448 5792882426615169024
5802883584381419520 5818014616726274048 5824544513204420608
5827317722047840256 5831197726783569920 5833472822499868672
5834633528821678080 5835820554703077376 5836584268607782912
5844699317255798784 5848652851831635968 5850924511574097920
5852524266632773632 5854022419945095168 5857492066325495808
5858927272597127168 5860855678553292800 5862852907065409536
5865046604561514496 5869000963771072512 5871370892365266944
5874081463405641728 5875585801470869504 5876931225746145280
5879421001107767296 5881995988620541952 5884080800105758720
5887131841793622016 5888770698234560512 5890363031589748736
5893058312546549760 5895628043019354112 5900250699140169728
5904354901168750592 5913874541561511936 5919725420890030080
5924441569858945024 5926972130230009856 5929102227850395648
5930678309049335808 5931684087310843904 5932349738522247168
5933174578401509376 5934095969145585664 5935796020280557568
5937111070547116032 5938525764054941696 5940292851039469568
5942406318546485248 5944115818609508352 5947882333129408512
5950513670613106688 5952165721193578496 5953570931413614592
5956003807048499200 5957924550783008768 5959238638976892928
5960370448758734848 5961583038285479936 5963056933622513664
5965315330505965568 5968019063958405120 5969643214431322112
5971116078976204800 5972674464909885440 5976388340310605824
5978043002231193600 5979719860542767104 5980600672435830784
5983573786237075456 5986047549960617984 5988772071054770176
5991739515499184128 5995044510013325312 6000602713090424832
6009501438651138048 6018305022096310272 6022181968815325184
6026558643569164288 6028494643027509248 6029656311421992960
6032022529164443648 6035830893845676032 6046287902260854784
6054119551786811392 6057337650522619904 6060242697682157568
6064027697741299712 6070987949942505472 6076648098363342848
6090704048854401024 6105062502482051072 6131616773444730880
6189459537561387008 6225770977788166144 6260198885877088256
6362808609516027904 6442940742070435840 6618300339755941888
6652038613536079872 6679890273859796992 6706241753646628864
6716109767426703360 6722991267107569664 6726135492405886976
6728692372336541696 6733950168221089792 6736718979017998336
6755124150831939584 6762744900283793408 6780826540801261568
6866936202679812096 8931576916874782720]
```python
np.save('edges.npy', edges_array)
```
|
jan-rybizkiREPO_NAMEGalaxia_wrapPATH_START.@Galaxia_wrap_extracted@Galaxia_wrap-master@notebook@notebook_sweep@collecting_1000th.ipynb@.PATH_END.py
|
{
"filename": "astroserver.py",
"repo_name": "Fermipy/fermipy",
"repo_path": "fermipy_extracted/fermipy-master/fermipy/scripts/astroserver.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function
import subprocess
from subprocess import call
import os
import glob
import argparse
from os.path import join, basename, dirname, splitext
import yaml
import numpy as np
#from dsphs.utils.utc2met import utc2met
from fermipy.utils import mkdir
from fermipy.batch import bsub
class astroserver(object):
""" Wrapper around the glast astroserver.
Pass in command-line args as kwargs changing
'_' to '-'. Checks kwargs f"""
def __init__(self):
self.exe = "/u/gl/glast/astroserver/prod/astro"
p = subprocess.Popen((self.exe + " --help").split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout, stderr = p.communicate()
self.opts = stdout
def __call__(self, arg, **kwargs):
command = self.exe
for key, val in kwargs.items():
kwarg = key.replace('_', '-')
if kwarg not in self.opts:
raise Exception("%s\n%s" % (self.exe, self.opts))
command += " -%s %s" % (kwarg, val)
command += " %s" % arg
return command
# Julian Year (365.25 days) in seconds
YEAR = 31557600
def main():
usage = "Usage: %(prog)s [options] input"
description = "python script"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument("-d", "--dryrun", action='store_true')
parser.add_argument("-s", "--sleep", default='1m',
help="Pause between")
parser.add_argument("--ls1", action='store_true', default=False,
help='Fetch LS1 files.')
parser.add_argument("--emin", default=100)
parser.add_argument("--emax", default=1e6)
parser.add_argument("--tmin", default=239557414, type=int,
help="Min time; default is start of first LAT run")
parser.add_argument("--tmax", default=None, type=int,
help="Default is current time.")
parser.add_argument("--evtclass", default="Source",
help="Event class")
parser.add_argument("--evtsample", default="P7.6_P130_BASE",
choices=['P7.6_P130_BASE', 'P6_public_v3',
'P7_P202_BASE', 'P7_P203_BASE', 'P8_P301_BASE',
'P8_P302_BASE', 'P8_P302_ALL'],
help="Event sample")
parser.add_argument("--chunk", default=int(YEAR // 12), type=int,
help="Time chunk for download. Default is ~1 month.")
args = parser.parse_args()
basedir = os.environ['PWD']
codedir = join(basedir, dirname(os.path.relpath(__file__)))
logdir = join(basedir, "log")
if not args.dryrun:
logdir = mkdir(logdir)
astro = astroserver()
chunk = args.chunk
# Might want to think more about how tmin and tmax are set
first = args.tmin
if args.tmax is None:
args.tmax = int(utc2met())
emin, emax = args.emin, args.emax
evtclass = args.evtclass
evtsample = args.evtsample
sample = '_'.join(evtsample.split('_')[:-1])
events = evtclass.upper()
# Break data into chunks
epsilon = 1e-6
times = np.arange(args.tmin, args.tmax + epsilon, chunk).astype(int)
# Get new full ft2 file.
# Assumption is that it is a longer time period...
ft2 = join(basedir, "%s_%s_%s_%s_ft2.fits" %
(sample, events, min(times), max(times)))
jobname = 'ft2'
if os.path.exists(ft2):
# exact ft2 already exists; skip
print("%s exists; skipping.\n" % ft2)
else:
# Remove old ft2 file and replace with link
if not args.dryrun:
# for f in glob.glob(join(basedir, "*ft2.fits")):
# os.remove(f)
# os.symlink(ft2, f)
for f in glob.glob(join(basedir, "*ft2_fix_checksums.sh")):
os.remove(f)
logfile = join(logdir, basename(ft2).replace('fits', 'log'))
command = astro('storeft2',
output_ft2_30s=ft2,
_event_sample=evtsample,
minTimestamp=min(times),
maxTimestamp=max(times),
excludeMaxTimestamp='',
quiet='',
brief='',
)
print(command)
bsub(jobname, command, logfile, sleep=args.sleep, submit=not args.dryrun,
W=1000, R='rhel60')
# Download ft1, ft2 files
ft1dir = mkdir(join(basedir, 'ft1'))
ft1_lst, ft1_cmnds, ft1_logs = [], [], []
ls1dir = mkdir(join(basedir, 'ls1'))
ls1_lst, ls1_cmnds, ls1_logs = [], [], []
ft2dir = mkdir(join(basedir, 'ft2'))
ft2_lst, ft2_cmnds, ft2_logs = [], [], []
for tmin, tmax in zip(times[:-1], times[1:]):
# If ft1 file exists, skip it...
ft1 = join(ft1dir, "%s_%s_%s_%s_ft1.fits" %
(sample, events, tmin, tmax))
if os.path.exists(ft1):
print("%s exists; skipping.\n" % ft1)
else:
ft1_kw = dict(_output_ft1=ft1,
_event_sample=evtsample,
minTimestamp=tmin,
maxTimestamp=tmax,
minEnergy=emin,
maxEnergy=emax,
_event_class_name=evtclass,
excludeMaxTimestamp='',
quiet='',
brief='')
ft1_cmnd = astro("store", **ft1_kw)
ft1_logs.append(join(logdir, basename(ft1).replace('fits', 'log')))
ft1_cmnds.append(ft1_cmnd)
ft1_lst.append(ft1)
# If ls1 file exists, skip it...
ls1 = join(ls1dir, "%s_%s_%s_%s_ls1.fits" %
(sample, events, tmin, tmax))
if not args.ls1:
print("%s; skipping.\n" % ls1)
elif os.path.exists(ls1):
print("%s exists; skipping.\n" % ls1)
else:
ls1_kw = dict(_output_ls1=ls1,
_event_sample=evtsample,
_output_ls1_max_bytes_per_file=0,
minTimestamp=tmin,
maxTimestamp=tmax,
minEnergy=emin,
maxEnergy=emax,
_event_class_name=evtclass,
excludeMaxTimestamp='',
quiet='',
brief='')
ls1_cmnd = astro("store", **ls1_kw)
ls1_logs.append(join(logdir, basename(ls1).replace('fits', 'log')))
ls1_cmnds.append(ls1_cmnd)
ls1_lst.append(ls1)
# If ft2 file exists, skip it...
ft2 = join(ft2dir, "%s_%s_%s_%s_ft2.fits" %
(sample, events, tmin, tmax))
if os.path.exists(ft2):
print("%s exists; skipping.\n" % ft2)
else:
ft2_cmnd = astro('storeft2',
output_ft2_30s=ft2,
_event_sample=evtsample,
minTimestamp=tmin,
maxTimestamp=tmax,
excludeMaxTimestamp='',
quiet='',
brief='',
)
ft2_logs.append(join(logdir, basename(ft2).replace('fits', 'log')))
ft2_cmnds.append(ft2_cmnd)
ft2_lst.append(ft2)
resources = 'bullet,hequ,kiso'
bsub('ft1', ft1_cmnds, ft1_logs, sleep=args.sleep, submit=not args.dryrun,
W=1000, R=resources)
bsub('ls1', ls1_cmnds, ls1_logs, sleep=args.sleep, submit=not args.dryrun,
W=1000, R=resources)
bsub('ft2', ft2_cmnds, ft2_logs, sleep=args.sleep, submit=not args.dryrun,
W=1000, R=resources)
# Create list of ft1 files
ft1_lstfile = join(basedir, "%s_%s_%s_%s_ft1.lst" %
(sample, events, min(times), max(times)))
ls1_lstfile = join(basedir, "%s_%s_%s_%s_ls1.lst" %
(sample, events, min(times), max(times)))
ft2_lstfile = join(basedir, "%s_%s_%s_%s_ft2.lst" %
(sample, events, min(times), max(times)))
if not args.dryrun:
for f in glob.glob(join(basedir, "*.lst")):
os.remove(f)
print("Creating ft1 file list: %s" % ft1_lstfile)
np.savetxt(ft1_lstfile, ft1_lst, fmt='%s')
print("Creating ls1 file list: %s" % ls1_lstfile)
np.savetxt(ls1_lstfile, ls1_lst, fmt='%s')
print("Creating ft2 file list: %s" % ft2_lstfile)
np.savetxt(ft2_lstfile, ft2_lst, fmt='%s')
if __name__ == "__main__":
main()
|
FermipyREPO_NAMEfermipyPATH_START.@fermipy_extracted@fermipy-master@fermipy@scripts@astroserver.py@.PATH_END.py
|
{
"filename": "test_simpleRun.ipynb",
"repo_name": "anchal-009/SAVED21cm",
"repo_path": "SAVED21cm_extracted/SAVED21cm-master/tests_new/test_simpleRun.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import sys; sys.path.insert(1, "./../")
from src.runpipe import Pipeline
from settings import Settings
%matplotlib inline
```
```python
set = Settings()
set.ANT = ["dipole", "logspiral", "sinuous"]
set.LST = 2
set.PATH21TS = "../data/TS21/lfcal_training_set_8-2020.hdf5"
set.PATHFGTS = "../data/TSFG/Nreg_1/"
set.printSettings()
```
------------------ Settings for the pipeline ------------------
Frequencies: Between (50 - 200) MHz with step size of 1 MHz.
21 TS: ../data/TS21/lfcal_training_set_8-2020.hdf5
FG TS: ../data/TSFG/Nreg_1/
Number of time bins: 2
Start time of each time bin: 00:00:00 03:00:00
Each value is this list is integrated for 6 bins
Antenna design: ['dipole', 'logspiral', 'sinuous']
Integration time for each time bin: 12.0 h
Total number of foreground modes: 80
Total number of 21cm modes: 80
Information Criterion: DIC
Filename to store the info criteria: ./DIC_search.txt
Visualization: True
Save figures: False
```python
pipeline = Pipeline(nu=set.NU, nLST=set.LST, ant=set.ANT, path21TS=set.PATH21TS, pathFgTS=set.PATHFGTS,
obsDate="2019-10-01", obsDateTime=set.timeList, intBins=set.intBins, numReg=1, fgModel="gsm",
dT=set.dT, modesFg=set.MODES_FG, modes21=set.MODES_21, quantity=set.QUANTITY, file=set.FNAME,
visual=set.VISUALS, index21=467)
```
```python
_ = pipeline.runPipeline()
```
-------------------- Running the pipeline ---------------------
Modelling set: Reading 21 modelling set...Done!
Modelling set: Reading FG modelling set...Done!
Basis: Performing SVD of 21 modelling set...Done!
Basis: Performing SVD of FG modelling set...Done!
Info Criterion: Calculating info criterion over grid...Done!
Info Critetion: Searching for minima over the grid...Done!
Info Criterion: IC is minimzed for 4 FG and 14 signal modes.
Extractor: Extracting the 21cm signal...Done!
Extractor: RMS uncertainty = 3.43 mK
Extractor: Signal Bias Statistic = 1.00
Extractor: Normalized Deviance = 0.95
```python
```
|
anchal-009REPO_NAMESAVED21cmPATH_START.@SAVED21cm_extracted@SAVED21cm-master@tests_new@test_simpleRun.ipynb@.PATH_END.py
|
{
"filename": "_color.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/treemap/pathbar/textfont/_color.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="color", parent_name="treemap.pathbar.textfont", **kwargs
):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@treemap@pathbar@textfont@_color.py@.PATH_END.py
|
{
"filename": "TO_DO.md",
"repo_name": "rpoleski/MulensModel",
"repo_path": "MulensModel_extracted/MulensModel-master/examples/example_16/TO_DO.md",
"type": "Markdown"
}
|
### To be discussed:
- periodic variables
- log10 variables etc.
- for plots: t_0, \Delta t_0, or t_0 - 2456780 ???
### Documentation:
- Delta t_0
- binary source
- x_caustic_in etc.
- ob08092-o4_prior.yaml
- posterior files
- print model
- example with add_2450000: False
- yaml output in README.md
# NOW - plot trajectory:
- test if works in ulens_model_plot.py
- add satellite trajectory
- add legend
- mark lens positions (no built-in function)
- note in README.md
## List of task to be done:
( **boldface** - do this before sending e-mail around)
- **some documentation - see above**
- Mroz+20 - finish
- MN: add option to plot best model from each mode
- MN: add more parameters to _parse_fitting_parameters_MultiNest(): n_clustering_params, max_iter, resume [previous run], const_efficiency_mode, wrapped_params [list of 0 or 1 (1 for wrap arround)], mode_tolerance, log_zero, seed [random no. generator seed], verbose [need update on sampling progress?]; FOR MORE INFO SEE: https://github.com/JohannesBuchner/PyMultiNest/blob/master/pymultinest/run.py AND https://github.com/farhanferoz/MultiNest/blob/master/MultiNest_v3.12/nested.F90 AND https://github.com/JohannesBuchner/MultiNest/blob/master/README
- example with add_2450000: False
- script and MM versions should be printed
- EMCEE: we should have settings in one dict - similarly to self._kwargs_MultiNest
- EMCEE backend - https://emcee.readthedocs.io/en/stable/user/backends/#emcee.backends.HDFBackend
- add one more fitting method? scipy.optimize, ultranest, https://lmfit.github.io/lmfit-py/, sfit by Jen, ???
- add check if 't_0' is covered by data and give warning if not
- print fixed parameters at begin or "no fixed parameters", so that full model can be extracted without the input file
- LD coeffs - there should be check which bands there compare to the ones in datasets
- random seed - first just print it early on (if used in calculations); then allow setting it for exact reproduction of results
- all_parameters in _get_parameters_ordered() and _check_fixed_parameters() - combine in a single one
- note that parameters are re-ordered (maybe in future add option for specifying order)
- datasets - guessing 245/246; plotting as well
- no_negative_blending_flux - only first dataset, or all datasets? Maybe add one more option
- allow plotting multiple models
- allow plotting many random models from posterior
- add beta distribution to allowed distributions (search for "gauss")
- for plot script add printing chi2 and fluxes
- EMCEE: some of the starting values are calculated based on equation given in yaml file, eg. "s: equation 100 / t_E" and then substitute each value of t_E and then use: "exec('out = 100 / 20.12345')" and use variable 'out'; This requires import from math of log, log10, arcsin etc.; make sure "s" in x_caustic_in is not replaced etc.;
- if Cassan08 paramaterization is used then make sure times are >2450000.
- add automatic "obvious" checks on parameters: t_E>0, rho>0, s>0, 1>q>0 - even if they are not provided, then model should be rejected and warning given
- if magnification calculations break then give warning, reject the model, and continue
- binary source models - print fluxes of both sources separately
- warnings if plots will overwrite existing files
- check if output files (including plots) exists at the begin - similar to _warn_about_existing_output_files_MultiNest()
- plot title
- make plots tighter, i.e., reduce white space
- EMCEE: add ln_prior values to blob? At some point we will want to save that information in output files
- settings['input_file_root'] = input_file_root - in final function and use it for default output files names
- EMCEE: posterior output: 1) add log(prior), 2) add chi2 or equivalent, 3) add option to add fluxes
- EMCEE: print number of models calculated
- MN: for multimode version add option to print statistics of all modes combined
- MN: separate corner plot for each mode (requires same shift to be used in _shift_t_0_in_samples())
- periodic variables - suggest it for alpha, x_caustic_X
- check if data files exist
- allow log10() of parameter
- Event.get_chi2() - add fit_blending=False option (actually this is different in MM v2)
- allow turning off flux printing
- warnings on time plotting and data limits - checks for add/subtract 245/246
- if code fails during fitting, then it should still print the best model found so far - add try/except in _run_fit()
- example how to run fits on a grid of (s,q)
- allow periodic (either based on number of steps, or execution time) print of best model etc.
- print every n-th model
- for parallax models check if t_0_par is fixed and give warning, if not
- fits with 0 blending flux for some datasets
- when plotting best model, plot ~100 points based on t_E etc. + all visible epochs in data so that anomalies are not missed etc.
- add option to adjust Y scale to plot model fully
- in _parse_fit_constraints_prior() add a check if the priors are defined for fit parameters
- flux constraints for binary source models (note that for plotting it is now set to first dataset)
- triangle and trace plots - add option to plot burn-in as well
- methods - if only single string is provided, then this is a default method
- move _get_weighted_percentile() to a separate file with utils because it doesnt depend on self; maybe there are other similar functions
- allow LD parameters to be fitted
- for trace and triangle plots, the setting `shift t_0` is common - it should be checked if it`s not set twice to different values
|
rpoleskiREPO_NAMEMulensModelPATH_START.@MulensModel_extracted@MulensModel-master@examples@example_16@TO_DO.md@.PATH_END.py
|
{
"filename": "test_ini.py",
"repo_name": "timothydmorton/isochrones",
"repo_path": "isochrones_extracted/isochrones-master/isochrones/tests/test_ini.py",
"type": "Python"
}
|
import os
import numpy as np
from isochrones.mist import MIST_Isochrone
from isochrones import StarModel, BinaryStarModel, TripleStarModel
from isochrones.starmodel import BasicStarModel
FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__)))
MIST = MIST_Isochrone()
def test_ini():
_check_ini(MIST)
#################
def _check_ini(ic):
single_dirs = ["star1"]
binary_dirs = ["star2"]
triple_dirs = ["star3", "star4"]
for d in single_dirs:
SingleCheck().check(ic, os.path.join(FOLDER, d))
for d in binary_dirs:
BinaryCheck().check(ic, os.path.join(FOLDER, d))
BinaryCheck_Unassoc().check(ic, os.path.join(FOLDER, d))
for d in triple_dirs:
TripleCheck().check(ic, os.path.join(FOLDER, d))
TripleCheck_Unassoc1().check(ic, os.path.join(FOLDER, d))
TripleCheck_Unassoc2().check(ic, os.path.join(FOLDER, d))
# _ini1(ic)
# _ini2(ic)
# _ini3(ic)
# _ini3_2(ic)
# _ini4(ic)
class IniCheck(object):
index = 0
def get_mod(self, ic, folder):
return StarModel.from_ini(ic, folder=folder, index=self.index)
def check_asserts(self, mod):
eep_pars = mod.convert_pars_to_eep(self.pars)
print((self.pars, eep_pars))
assert self.n_params == len(eep_pars)
assert mod.n_params == self.n_params
assert mod.obs.systems == self.systems
assert mod.obs.Nstars == self.Nstars
assert np.isfinite(mod.lnlike(eep_pars))
def check_p0(self, mod):
p0 = mod.emcee_p0(10)
nbad = 0
for i, p in enumerate(p0):
if not np.isfinite(mod.lnpost(p)):
print(p)
nbad += 1
assert nbad == 0
def check(self, ic, folder):
print("checking {}".format(folder))
mod = self.get_mod(ic, folder)
mod.print_ascii()
self.check_asserts(mod)
self.check_p0(mod)
# if hasattr(self, 'get_mod_special'):
# mod = self.get_mod_special(ic, folder)
# self.check_asserts(mod)
class SingleCheck(IniCheck):
pars = [1.0, 9.4, 0.0, 100, 0.2]
n_params = 5
systems = [0]
Nstars = {0: 1}
class BinaryCheck(IniCheck):
pars = [1.0, 0.5, 9.4, 0.0, 100, 0.2]
n_params = 6
systems = [0]
Nstars = {0: 2}
def get_mod_special(self, ic, folder):
return BinaryStarModel(ic, folder=folder)
class BinaryCheck_Unassoc(IniCheck):
pars = [1.0, 9.4, 0.0, 100, 0.2, 0.8, 9.7, 0.1, 300, 0.3]
n_params = 10
index = [0, 1]
systems = [0, 1]
Nstars = {0: 1, 1: 1}
class TripleCheck(IniCheck):
pars = [1.0, 0.8, 0.5, 9.4, 0.0, 100, 0.2]
n_params = 7
systems = [0]
Nstars = {0: 3}
def get_mod_special(self, ic, folder):
return TripleStarModel(ic, folder=folder)
class TripleCheck_Unassoc1(IniCheck):
pars = [1.0, 0.8, 9.4, 0.0, 100, 0.2, 1.0, 9.7, 0.0, 200, 0.5]
n_params = 11
index = [0, 0, 1]
systems = [0, 1]
Nstars = {0: 2, 1: 1}
class TripleCheck_Unassoc2(IniCheck):
pars = [1.0, 9.4, 0.0, 100, 0.2, 1.0, 0.8, 9.7, 0.0, 200, 0.5]
n_params = 11
index = [0, 1, 1]
systems = [0, 1]
Nstars = {0: 1, 1: 2}
|
timothydmortonREPO_NAMEisochronesPATH_START.@isochrones_extracted@isochrones-master@isochrones@tests@test_ini.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "telegraphic/fits2hdf",
"repo_path": "fits2hdf_extracted/fits2hdf-master/fits2hdf/__init__.py",
"type": "Python"
}
|
from __future__ import absolute_import
from . import io
from . import idi
from . import pyhdfits
from . import pyhdfits as pf
|
telegraphicREPO_NAMEfits2hdfPATH_START.@fits2hdf_extracted@fits2hdf-master@fits2hdf@__init__.py@.PATH_END.py
|
{
"filename": "SED_prior_model_v2.ipynb",
"repo_name": "H-E-L-P/XID_plus",
"repo_path": "XID_plus_extracted/XID_plus-master/docs/build/html/notebooks/examples/SED_prior_model_v2.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import os
import numpy as np
from astropy.io import ascii
from scipy.interpolate import interp1d
import xidplus
temps=os.listdir('/Users/pdh21/astrodata/SEDs/Berta2013/templates_berta_norm_LIR/')
```
```python
temps
```
['Blue_SF_glx.norm_LIR',
'BroadFIR_SF_glx.norm_LIR',
'Cold_glx.norm_LIR',
'Elliptical.norm_LIR',
'Ly_break.norm_LIR',
'MIR_powlaw_SF_glx.norm_LIR',
'MIRex_SF_glx.norm_LIR',
'Mod_SF_glx.norm_LIR',
'Obs_SF_glx.norm_LIR',
'PAH_DF_glx.norm_LIR',
'Red_SF_glx_1.norm_LIR',
'Red_SF_glx_2.norm_LIR',
'Secular_glx.norm_LIR',
'SF_glx_1.norm_LIR',
'SF_glx_2.norm_LIR',
'SF_Type1_AGN_1.norm_LIR',
'SF_Type1_AGN_2.norm_LIR',
'SF_Type1_AGN_3.norm_LIR',
'SF_Type1_AGN_4.norm_LIR',
'SF_Type2_AGN_1.norm_LIR',
'SF_Type2_AGN_2.norm_LIR',
'SF_Type2_AGN_3.norm_LIR',
'Si_break.norm_LIR',
'Spiral.norm_LIR',
'Torus.norm_LIR',
'Type1_AGN_1.norm_LIR',
'Type2_AGN_1.norm_LIR',
'Type2_AGN_2.norm_LIR',
'Warm_SF_glx.norm_LIR',
'WeakPAH_SF_glx_1.norm_LIR',
'WeakPAH_SF_glx_2.norm_LIR',
'Young_SF_glx.norm_LIR']
Generate Redshift Grid and convert to denominator for flux conversion (e.g. $4 \pi D_l^2)$
```python
red=np.arange(0,8,0.001)
red[0]=0.000001
from astropy.cosmology import Planck13
import astropy.units as u
div=(4.0*np.pi * np.square(Planck13.luminosity_distance(red).cgs))
div=div.value
```
```python
len(red)
```
8000
Get appropriate filters
```python
from xidplus import filters
filter=filters.FilterFile(file=xidplus.__path__[0]+'/../test_files/filters.res')
```
```python
filter.names()
```
1 Koo-Kron U+ filter (Koo's thesis) - 0001
2 Koo-Kron J+ filter (Koo's thesis) - 0002
3 Koo-Kron F+ filter (Koo's thesis) - 0003
4 Koo-Kron N+ filter (Koo's thesis) - 0004
5 Koo-Kron R band (=127+RG610, data from Koo, Durham) - 0005
6 Couch and Newell (80) BJ (photographic) filter - 0006
7 Couch and Newell (80) RF (photographic) filter - 0007
8 Koo-Kron U+ filter (Bruzual's thesis) - 0008
9 Koo-Kron J+ filter (Bruzual's thesis) - 0009
10 Koo-Kron F+ filter (Bruzual's thesis) - 0010
11 Koo-Kron N+ filter (Bruzual's thesis) - 0011
12 Buser's U filter - 0012
13 Buser's B2 filter - 0013
14 Buser's B3 filter - 0014
15 Buser's V filter - 0015
16 Matthews and Sandage U filter - 0016
17 Matthews and Sandage B filter - 0017
18 Matthews and Sandage V filter - 0018
19 Sandage and Smith B filter - 0019
20 Sandage and Smith V filter - 0020
21 Sandage and Smith R filter - 0021
22 ST-UV14 filter - 0022
23 ST-UV17 filter - 0023
24 ST-UV22 filter - 0024
25 ST-UV27 filter - 0025
26 OAO-UV1 filter - 0026
27 OAO-UV2 filter - 0027
28 OAO-UV3 filter - 0028
29 OAO-UV4 filter - 0029
30 OAO-UV5 filter - 0030
31 OAO-UV6 filter - 0031
32 Johnson's R filter - 0032
33 Johnson's I filter - 0033
34 Johnson's J filter - 0034
35 Johnson's K filter - 0035
36 Johnson's L filter - 0036
37 Butcher's r filter - 0037
38 Butcher's i filter - 0038
39 Butcher-Oemler R filter (10/75 1978, data from Koo, Durham) - 0039
40 Butcher-Oemler R filter ( 5/76 1978, data from Koo, Durham) - 0040
41 Bessell u filter - 0041
42 Bessell g filter - 0042
43 Bessell r filter - 0043
44 UKIRT H FILTER (Leiden, 1983) - 0044
45 R. S. Ellis U(PE) filter - 0045
46 R. S. Ellis J filter - 0046
47 R. S. Ellis R filter - 0047
48 R. S. Ellis N filter - 0048
49 C. MacKay and P. Hall KG3 filter (Cambridge) - 0049
50 C. MacKay and P. Hall I filter (Cambridge) - 0050
51 Gunn g filter + four-shooter Ti CCD + Palomar 200" atmospher - 0051
52 Gunn r filter + four-shooter Ti CCD + Palomar 200" atmospher - 0052
53 Gunn i filter + four-shooter Ti CCD + Palomar 200" atmospher - 0053
54 Gunn z filter + four-shooter Ti CCD + Palomar 200" atmospher - 0054
55 IR J filter + Palomar 200 IR detectors + atmosphere - 0055
56 IR H filter + Palomar 200 IR detectors + atmosphere - 0056
57 IR K filter + Palomar 200 IR detectors + atmosphere - 0057
58 NOAO CTIO 4m ISPI J#186 - 0058
59 NOAO CTIO 4m ISPI H#187 - 0059
60 NOAO CTIO 4m ISPI K'#188 - 0060
61 A. Tyson J filter - 0061
62 A. Tyson R filter - 0062
63 A. Tyson I filter - 0063
64 ANS 1550 Wide Filter (J. Koorneef) - 0064
65 ANS 1800 Filter (J. Koorneef) - 0065
66 ANS 2200 Filter (J. Koorneef) - 0066
67 ANS 2500 Filter (J. Koorneef) - 0067
68 ANS 3300 Filter (J. Koorneef) - 0068
69 Approximate U band for Lilly and Cowie - 0069
70 Approximate I band for Lilly and Cowie - 0070
71 IRAS 12 micron, Neugebauer etal 1984,ApJL,278,L1 - 0071
72 IRAS 25 micron, Neugebauer etal 1984,ApJL,278,L1 - 0072
73 IRAS 60 micron, Neugebauer etal 1984,ApJL,278,L1 - 0073
74 IRAS 100 micron, Neugebauer etal 1984,ApJL,278,L1 - 0074
75 H filter Bessell and Brett PASP 100, 1134, 1988 - 0075
76 J filter Bessell and Brett PASP 100, 1134, 1988 - 0076
77 K filter Bessell and Brett PASP 100, 1134, 1988 - 0077
78 L (3.5 microns) filter Bessell and Brett PASP 100, 1134, 1988 - 0078
79 L' (3.8 microns) filter Bessell and Brett PASP 100, 1134, 1988 - 0079
80 M filter Bessell and Brett PASP 100, 1134, 1988 - 0080
81 IRAM MAMBO-1 1.2 mm, 37 channel (winter 99/00 -today) - 0081
82 IRAM MAMBO-2 1.2 mm,117 channel - 0082
83 g Gunn (original) - 0083
84 r Gunn (original) - 0084
85 i Gunn (original) - 0085
86 z (original) - 0086
87 z + RCA - 0087
88 CCD RCA ESO (JPP reference) - 0088
89 CCD RCA CAHA (Manual d'utilisateurs) - 0089
90 B CAHA (original manuel) - 0090
91 B Bessell - 0091
92 V Bessell - 0092
93 R Bessell - 0093
94 I Bessell - 0094
95 K Prime CFHT Redeye - 0095
96 CCD RCA2 CFHT (Manuel utilisateurs) - 0096
97 Bj TYSON (orig. filter AT, private com.) - 0097
98 CCD TEK#25 (ESO, Manuel Utilisateurs) - 0098
99 CCD LORAL#34 (ESO, Manuel Utilisateurs) - 0099
100 CCD SAIC#1 (CFH, Manuel Utilisateurs) - 0100
101 CCD Lick2 CFHT (CFH, Manuel Utilisateurs) - 0101
102 ESO NTT SUSI B Bessell#639 - 0102
103 ESO NTT SUSI V Bessell#641 - 0103
104 ESO NTT SUSI R Bessell#642 - 0104
105 ESO NTT EMMI V#606 - 0105
106 B#4402 CFHT - 0106
107 R#4609 CFHT - 0107
108 B #1412 CFHT FOCAM - 0108
109 B #1414 CFHT B Tyson selon JB - 0109
110 V #1504 CFHT - 0110
111 V #1510 CFHT FOCAM - 0111
112 R #1611 CFHT - 0112
113 I #1808 CFHT FOCAM - 0113
114 I #1809 CFHT FOCAM - 0114
115 Thomson THX 31156 CCD#17 ESO - 0115
116 Thomson THX 31156 CCD#18 ESO - 0116
117 R#585 Bessell ESO - 0117
118 K #6 UKIRT - 0118
119 Passe-tout - 0119
120 F555W + WFPC2 normalized - 0120
121 F814W + WFPC2 normalized - 0121
122 F300W + WFPC2 normalized - 0122
123 F450W + WFPC2 normalized - 0123
124 F606W + WFPC2 normalized - 0124
125 F702W + WFPC2 normalized - 0125
126 F675W + WFPC2 normalized - 0126
127 F336W + WFPC2 normalized - 0127
128 ESO NTT 3.6m SOFI Js - 0128
129 ESO NTT 3.6m SOFI J - 0129
130 ESO NTT 3.6m SOFI H - 0130
131 ESO NTT 3.6m SOFI Ks - 0131
132 KPNO IRIM 2.12 Filter - 0132
133 KPNO IRIM 2.14 Filter - 0133
134 KPNO IRIM 2.16 Filter - 0134
135 KPNO IRIM H Filter - 0135
136 KPNO IRIM J Filter - 0136
137 KPNO IRIM K Filter - 0137
138 KPNO IRIM K' Filter - 0138
139 VLT Test Camera Detector's Quantum Efficiency - 0139
140 B-band filter of the VLT Test Camera - 0140
141 V-band filter of the VLT Test Camera - 0141
142 R-band filter of the VLT Test Camera - 0142
143 I-band filter of the VLT Test Camera - 0143
144 SUSI2's CCDs Quantum Efficiency - 0144
145 SUSI Bessell U #801 - 0145
146 SUSI Bessell B #811 - 0146
147 SUSI Bessell V #812 - 0147
148 SUSI Bessell R #813 - 0148
149 SUSI Bessell I #814 - 0149
150 FORS Standard U (including instrument + CCD) - 0150
151 FORS Standard B (including instrument + CCD) - 0151
152 FORS Standard V (including instrument + CCD) - 0152
153 FORS Cousins R (including instrument + CCD) - 0153
154 FORS Cousins I (including instrument + CCD) - 0154
155 FORS Gunn G (including instrument + CCD) - 0155
156 ESO 2.2m WFI U#841 + CCD#57 + wfi_2p2_optics (U/38 AKA U38) - 0156
157 ESO 2.2m WFI B#842 + CCD#57 (old B/99, for new see B/123) - 0157
158 ESO 2.2m WFI V#843 + CCD#57 + wfi_2p2_optics (V/89) - 0158
159 ESO 2.2m WFI Rc#844 + CCD#57 + wfi_2p2_optics (Rc/162) - 0159
160 ESO 2.2m WFI Ic#845 + CCD#57 + wfi_2p2_optics (Ic/lwp) - 0160
161 ESO 2.2m WFI Z#846 + CCD#57 + wfi_2p2_optics (Z+/61) - 0161
162 ESO 2.2m WFI U#877 + CCD57 + wfi_2p2_optics (U/50 AKA U35) - 0162
163 ESO 2.2m WFI B#878 + CCD#57 + wfi_2p2_optic (latest B filter B/123) - 0163
164 SDSS u (http://www.sdss.org/dr7/instruments/imager/index.html) - 0164
165 SDSS g (http://www.sdss.org/dr7/instruments/imager/index.html) - 0165
166 SDSS r (http://www.sdss.org/dr7/instruments/imager/index.html) - 0166
167 SDSS i (http://www.sdss.org/dr7/instruments/imager/index.html) - 0167
168 SDSS z (http://www.sdss.org/dr7/instruments/imager/index.html) - 0168
169 ESO VST OmegaCAM u - 0169
170 ESO VST OmegaCAM g - 0170
171 ESO VST OmegaCAM r - 0171
172 ESO VST OmegaCAM i - 0172
173 ESO VST OmegaCAM z - 0173
174 CFHT CFH12k B (Mould) - 0174
175 CFHT CFH12k V (Mould) - 0175
176 CFHT CFH12k R (Mould) - 0176
177 CFHT CFH12k I (Mould) - 0177
178 CFHT CFH12k Z (Prime) - 0178
179 JCMT SCUBA 450 micron - 0179
180 JCMT SCUBA 850 micron - 0180
181 AzTEC 1.1 mm - 0181
182 Infamous 2.2m UH8K B filter + loral3 + MK atmosphere - 0182
183 2.2m UH8K V filter + loral 3 + atmosphere - 0183
184 2.2m UH8K I filter + MK atmosphere - 0184
185 KPNO B, from AAT Users Manual - 0185
186 H+K filter - 0186
187 Wyin filter U (filter + CCD reponse) - 0187
188 Wyin filter B (filter + CCD reponse) - 0188
189 ESO VLT ISAAC J (ESO web pages) - 0189
190 ESO VLT ISAAC H (ESO web pages) - 0190
191 ESO VLT ISAAC Ks (ESO web pages) - 0191
192 ESO VLT ISAAC L (ESO web pages) - 0192
193 ESO VLT ISAAC M (ESO web pages) - 0193
194 Palomar 200" WIRC J - 0194
195 Palomar 200" WIRC K - 0195
196 Calar Alto 3.5m Omega2000 J - 0196
197 Calar Alto 3.5m OmegaPrime K - 0197
198 Spitzer IRAC CH1 (3.6 micron) - 0198
199 Spitzer IRAC CH2 (4.5 microns) - 0199
200 Spitzer IRAC CH3 (5.8 microns) - 0200
201 Spitzer IRAC CH4 (8.0 microns) - 0201
202 Spitzer MIPS CH1 (24 microns) - 0202
203 Subaru SuprimeCam U - 0203
204 Subaru SuprimeCam B - 0204
205 Subaru SuprimeCam V - 0205
206 Subaru SuprimeCam r - 0206
207 Subaru SuprimeCam i - 0207
208 Subaru SuprimeCam z - 0208
209 UH 2.2m QUIRC H+K (AKA HK') - 0209
210 CFHT MEgaCam i2 AKA y (new,after October 2007 - http://cadcwww.dao.nrc.ca/megapipe/docs/filters.html) - 0210
211 NOAO KPNO 4m FLAMINGOS J (J-2000toJuly2003) - 0211
212 NOAO KPNO 4m FLAMINGOS H (H-2000toJuly2003) - 0212
213 NOAO KPNO 4m FLAMINGOS Ks (Ks-2000toJuly2003) - 0213
214 Spitzer MIPS CH2 (70 micron) - 0214
215 Spitzer MIPS CH3 (160 micron) - 0215
216 SPIRE 250 micron - 0216
217 SPIRE 350 micron - 0217
218 SPIRE 500 micron - 0218
219 2MASS J - 0219
220 2MASS H - 0220
221 2MASS Ks - 0221
222 UKIRT WFCAM (UKIDSS) J - 0222
223 UKIRT WFCAM (UKIDSS) H - 0223
224 UKIRT WFCAM (UKIDSS) K - 0224
225 INT WFC u - 0225
226 INT WFC g - 0226
227 INT WFC r - 0227
228 INT WFC i - 0228
229 INT WFC z - 0229
230 NOAO KPNO 4m MOSAIC1 U band (k1001) - 0230
231 NOAO KPNO 4m MOSAIC1 g band (SDSS k1017) - 0231
232 NOAO KPNO 4m MOSAIC1 r band (SDSS k1018) - 0232
233 NOAO KPNO 4m MOSAIC1 i band (SDSS k1019) - 0233
234 NOAO KPNO 4m MOSAIC1 z band (SDSS k1020) - 0234
235 NOAO CTIO 4m MOSAIC2 u band (SDSS c6022) - 0235
236 NOAO CTIO 4m MOSAIC2 g band (SDSS c6017) - 0236
237 NOAO CTIO 4m MOSAIC2 r band (SDSS c6018) - 0237
238 NOAO CTIO 4m MOSAIC2 i band (SDSS c6019) - 0238
239 NOAO CTIO 4m MOSAIC2 z band (SDSS c6022) - 0239
240 NOAO CTIO 4m MOSAIC2 U band (c6001) - 0240
241 CFHT MEgaCam u* http://www2.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/extra/filters.html - 0241
242 CFHT MegaCam g http://www2.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/extra/filters.html - 0242
243 CFHT MegaCam r http://www2.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/extra/filters.html - 0243
244 CFHT MegaCam i AKA i1 (old, before Octorber 2007 for new see CFHT MegaCam i2 AKA y; http://cadcwww.dao.nrc.ca/megapipe/docs/filters.html) - 0244
245 CFHT MegaCam z http://www2.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/extra/filters.html - 0245
246 AKARI N60 http://www.ir.isas.jaxa.jp/AKARI/Observation/RSRF/FIS_FAD/index.html - 0246
247 AKARI WIDE-S http://www.ir.isas.jaxa.jp/AKARI/Observation/RSRF/FIS_FAD/index.html - 0247
248 AKARI WIDE-L http://www.ir.isas.jaxa.jp/AKARI/Observation/RSRF/FIS_FAD/index.html - 0248
249 AKARI N160 http://www.ir.isas.jaxa.jp/AKARI/Observation/RSRF/FIS_FAD/index.html - 0249
250 PACS 70 Instrument Simulator as of Herschel Launch - 0250
251 PACS 100 Instrument Simulator as of Herschel Launch - 0251
252 PACS 160 Instrument Simulator as of Herschel Launch - 0252
253 NOAO KPNO 4m FLAMINGOS J from 2003 to present: http://flamingos.astro.ufl.edu/Filter_Info/index.html (FLAMINGOS.BARR.J.MAN240B.WarmFilter.txt) - 0253
254 NOAO KPNO 4m FLAMINGOS H from 2003 to present: http://flamingos.astro.ufl.edu/Filter_Info/index.html (FLAMINGOS.BARR.H.MAN109A.WarmFilter.txt) - 0254
255 NOAO KPNO 4m FLAMINGOS Ks from 2003 to present: http://flamingos.astro.ufl.edu/Filter_Info/index.html (FLAMINGOS.BARR.Ks.MAN306A.WarmFilter.txt) - 0255
256 NOAO KPNO 4m FLAMINGOS K from 2003 to present: http://flamingos.astro.ufl.edu/Filter_Info/index.html (FLAMINGOS.K-band.2000toPresentDay.NOAO-OCLI-Filter.txt) - 0256
257 Subaru SuprimeCam Rc - 0257
258 Subaru SuprimeCam Ic - 0258
259 Subaru SuprimeCam g - 0259
260 Spitzer IRS 16 micron (bluePUtrans) - 0260
261 Spitzer IRS 22 micron (redPUtrans) - 0261
262 ESO VLT VIMOS U (transmission is average of 4 quadrants) - 0262
263 ESO VLT VIMOS B (transmission is average of 4 quadrants) - 0263
264 ESO VLT VIMOS V (transmission is average of 4 quadrants) - 0264
265 ESO VLT VIMOS R (transmission is average of 4 quadrants) - 0265
266 ESO VLT VIMOS I (transmission is average of 4 quadrants) - 0266
267 ESO VLT VIMOS z (transmission is average of 4 quadrants) - 0267
268 NOAO KPNO 4m MOSAIC1 R - 0268
269 UKIRT WFCAM (UKIDSS) Z - 0269
270 UKIRT WFCAM (UKIDSS) Y - 0270
271 CFHT WIRCam J (cfh8101) - 0271
272 CFHT WIRCam H (cfh8201) - 0272
273 CFHT WIRCam Ks (cfh8302) - 0273
274 NOAO KPNO 4m MOSAIC1 Bw - 0274
275 NOAO KPNO 4m MOSAIC1 B - 0275
276 NOAO KPNO 4m MOSAIC1 V - 0276
277 NOAO KPNO 4m MOSAIC1 I - 0277
278 NOAO CTIO 4m MOSAIC2 B - 0278
279 NOAO CTIO 4m MOSAIC2 V - 0279
280 NOAO CTIO 4m MOSAIC2 R - 0280
281 NOAO CTIO 4m MOSAIC2 I - 0281
282 TIFKAM/ONIS J - 0282
283 TIFKAM/ONIS H - 0283
284 TIFKAM/ONIS K - 0284
285 90prime SDSS-u - 0285
286 90prime SDSS-z - 0286
287 90prime U - 0287
288 90prime B - 0288
289 90prime V - 0289
290 90prime R - 0290
291 90prime I - 0291
292 90prime_Washington_M - 0292
293 NEWFIRM J - 0293
294 NEWFIRM H - 0294
295 NEWFIRM Ks - 0295
296 GALEX NUV - 0296
297 GALEX FUV - 0297
298 MMT Megacam u - 0298
299 MMT Megacam g - 0299
300 MMT Megacam r - 0300
301 MMT Megacam i - 0301
302 MMT Megacam z - 0302
303 Subaru MOIRCS Y - 0303
304 Subaru MOIRCS J - 0304
305 Subaru MOIRCS H - 0305
306 Subaru MOIRCS Ks - 0306
307 Subaru MOIRCS K - 0307
308 APEX SABOCA 350 micron - 0308
309 APEX LABOCA 850 micron - 0309
310 HST NIC3 F110W (J) - 0310
311 HST NIC3 F160W (H) - 0311
312 HST NIC3 F222M (K) - 0312
313 HST ACS/WFC F435W (B) - 0313
314 HST ACS/WFC F606W (V) - 0314
315 HST ACS/WFC F814W (I) - 0315
316 HST ACS/WFC F475W (g) - 0316
317 HST ACS/WFC F625W (r) - 0317
318 HST ACS/WFC F775W (i) - 0318
319 HST ACS/WFC F850LP (z) - 0319
320 Subaru SuprimeCam IA427 - 0320
321 Subaru SuprimeCam IA445 - 0321
322 Subaru SuprimeCam IA464 - 0322
323 Subaru SuprimeCam IA484 - 0323
324 Subaru SuprimeCam IA505 - 0324
325 Subaru SuprimeCam IA527 - 0325
326 Subaru SuprimeCam IA550 - 0326
327 Subaru SuprimeCam IA574 - 0327
328 Subaru SuprimeCam IA598 - 0328
329 Subaru SuprimeCam IA624 - 0329
330 Subaru SuprimeCam IA651 - 0330
331 Subaru SuprimeCam IA679 - 0331
332 Subaru SuprimeCam IA709 - 0332
333 Subaru SuprimeCam IA738 - 0333
334 Subaru SuprimeCam IA767 - 0334
335 Subaru SuprimeCam IA797 - 0335
336 Subaru SuprimeCam IA827 - 0336
337 Subaru SuprimeCam IA856 - 0337
338 Subaru SuprimeCam IA907 - 0338
339 Subaru SuprimeCam NA656 - 0339
340 Subaru SuprimeCam NB711 - 0340
341 Subaru SuprimeCam NB816 - 0341
342 Subaru SuprimeCam NB921 - 0342
343 LBT-LBC blue Uspec - 0343
344 LBT-LBC blue U - 0344
345 LBT-LBC blue B - 0345
346 LBT-LBC blue V - 0346
347 LBT-LBC blue g (#1) - 0347
348 LBT-LBC blue r (#1) - 0348
349 LBT-LBC red V - 0349
350 LBT-LBC red R - 0350
351 LBT-LBC red I - 0351
352 LBT-LBC red r - 0352
353 LBT-LBC red i - 0353
354 LBT-LBC red z - 0354
355 LBT-LBC red F972N20 - 0355
356 LBT-LBC red Y - 0356
357 ISO CAM LW2 (6.7/7 micron) - 0357
358 ISO CAM LW10 (12 micron) - 0358
359 ISO CAM LW3 (14.3/15 micron) -0359
360 ISO PHT C100-DETECTOR C90-FILTER (90/5 micron) - 0360
361 ISO PHT C200-DETECTOR C160-FILTER (170/5 micron) - 0361
362 VISTA VIRCAM Z - 0362
363 VISTA VIRCAM Y - 0363
364 VISTA VIRCAM J - 0364
365 VISTA VIRCAM H - 0365
366 VISTA VIRCAM Ks - 0366
367 HST WFC3 F125W [J band]- 0367
368 HST WFC3 F160W [H band]- 0368
369 AKARI IRC N2 - 0369
370 AKARI IRC N3 - 0370
371 AKARI IRC N4 - 0371
372 AKARI IRC S7 - 0372
373 AKARI IRC S9W - 0373
374 AKARI IRC S11 - 0374
375 AKARI IRC L15 - 0375
376 AKARI IRC L18W - 0376
377 AKARI IRC L24 - 0377
378 WISE 1 (3.4 mum) - 0378
379 WISE 2 (4.6 mum) - 0379
380 WISE 3 (12 mum) - 0380
381 WISE 4 (22 mum) - 0381
382 Pan-STARRS1 gp1 - 0382
383 Pan-STARRS1 rp1 - 0383
384 Pan-STARRS1 ip1 - 0384
385 Pan-STARRS1 zp1 - 0385
386 Pan-STARRS1 yp1 - 0386
387 Pan-STARRS1 wp1 - 0387
388
```python
SPIRE_250=filter.filters[215]
SPIRE_350=filter.filters[216]
SPIRE_500=filter.filters[217]
MIPS_24=filter.filters[201]
PACS_100=filter.filters[250]
PACS_160=filter.filters[251]
bands=[SPIRE_250,SPIRE_350,SPIRE_500,MIPS_24,PACS_100,PACS_160]
eff_lam=[250.0,350.0,500.0,24.0, 100.0,160.0]
```
```python
for b in bands:
print(b.name)
```
SPIRE 250 micron - 0216
SPIRE 350 micron - 0217
SPIRE 500 micron - 0218
Spitzer MIPS CH1 (24 microns) - 0202
PACS 100 Instrument Simulator as of Herschel Launch - 0251
PACS 160 Instrument Simulator as of Herschel Launch - 0252
```python
import pandas as pd
template=ascii.read('/Users/pdh21/astrodata/SEDs/Berta2013/templates_berta_norm_LIR/'+temps[0])
df=pd.DataFrame(template['col1'].data/1E4,columns=['wave'])
print(template['col1'].data/1E4)
SEDs=np.empty((len(temps),len(bands),red.size))
for i in range(0,len(temps)):
template=ascii.read('/Users/pdh21/astrodata/SEDs/Berta2013/templates_berta_norm_LIR/'+temps[i])
df[temps[i]]=1E30*3.826E33*template['col2']*((template['col1']/1E4)**2)/3E14
flux=template['col2']*((template['col1']/1E4)**2)/3E14
wave=template['col1']/1E4
ind=(wave > 8) & (wave < 1E3)
print(np.trapz(template['col2'][ind],x=wave[ind]*1E4))
print(np.trapz(flux[ind][::-1],x=3E14/(wave[ind][::-1])))
for z in range(0,red.size):
sed=interp1d((red[z]+1.0)*wave, flux)
for b in range(0,len(bands)):
SEDs[i,b,z]=1E30*3.826E33*(1.0+red[z])*filters.fnu_filt(sed(bands[b].wavelength/1E4),3E8/(bands[b].wavelength/1E10),bands[b].transmission,3E8/(eff_lam[b]*1E-6),sed(eff_lam[b]))/div[z]
```
[ 9.09999900e-03 9.40000000e-03 9.59999900e-03 ..., 1.92899989e+03
1.93899920e+03 1.94899898e+03]
0.999999999975
9.99948820898e-05
0.99999999803
9.99972496544e-05
1.00000000022
9.99986398139e-05
0.999999998638
9.99981291963e-05
0.99999999959
9.99980481754e-05
0.999999998941
9.9994351254e-05
0.999999999473
9.99945356128e-05
0.999999998487
9.99985287929e-05
0.999999999224
9.99924626717e-05
1.00000000252
9.99942862546e-05
0.999999999244
9.99934616998e-05
0.999999998734
9.99978906062e-05
0.999999998627
9.99975982287e-05
1.00000000001
9.99978318847e-05
1.00000000032
9.99982075976e-05
0.999999999528
9.99931710553e-05
0.999999998064
9.99973969093e-05
0.999999998802
9.99981096303e-05
0.999999998357
9.99934745402e-05
1.0000000015
9.99946455636e-05
1.00000000065
9.99976450848e-05
0.999999999482
9.9997162806e-05
0.999999997908
9.99972721906e-05
1.00000000223
9.99944923657e-05
1.00000000041
9.99917794923e-05
1.00000000008
9.99882557381e-05
1.00000000182
9.99945487078e-05
0.99999999873
9.99930391064e-05
1.00000000335
9.99925550047e-05
1.00000000127
9.99968344864e-05
1.00000000007
9.99978574898e-05
0.999999999908
9.99987803337e-05
## Read in Michael's templates
Individual infrared templates are given in
http://astro.imperial.ac.uk/public/mrr/swirephotzcat/templates/cirrus.dat
http://astro.imperial.ac.uk/public/mrr/swirephotzcat/templates/M82.dat
http://astro.imperial.ac.uk/public/mrr/swirephotzcat/templates/A220.dat
as pairs of numbers log10 lambda(mu), log vu S(nu),
and in
http://astro.imperial.ac.uk/public/mrr/swirephotzcat/templates/dusttor.dat
as pairs of numbers log10 lambda(mu), vu S(nu) [first two columns only].
```python
from astropy.table import Table
```
```python
cirrus=Table.read('/Users/pdh21/astrodata/SEDs/MRR/cirrus.dat', format='ascii')
dusttor=Table.read('/Users/pdh21/astrodata/SEDs/MRR/dusttor.dat', format='ascii')
M82=Table.read('/Users/pdh21/astrodata/SEDs/MRR/M82.dat', format='ascii')
A220=Table.read('/Users/pdh21/astrodata/SEDs/MRR/A220.dat', format='ascii')
dusttor['col2']=np.log(dusttor['col2'])
cirrus.add_row([0.1,-15])
M82.add_row([0.1,-10])
```
```python
import pylab as plt
%matplotlib inline
plt.plot(cirrus['col1'],cirrus['col2'])
plt.plot(dusttor['col1'],dusttor['col2'])
plt.plot(M82['col1'],M82['col2'])
plt.plot(A220['col1'],A220['col2'])
```
[<matplotlib.lines.Line2D at 0x113c575c0>]
```python
import pandas as pd
df_comb=pd.DataFrame(np.power(10.0,cirrus['col1'].data),columns=['wave'])
MRR_temps=[cirrus, A220,M82,dusttor]
SEDs_comb=np.empty((len(MRR_temps),len(bands),red.size))
for i in range(0,len(MRR_temps)):
flux=np.power(10.0,MRR_temps[i]['col2'])/(3.0E14/np.power(10.0,MRR_temps[i]['col1']))
wave=np.power(10.0,MRR_temps[i]['col1'])
ind=(wave > 8) & (wave < 1E3)
flux=1E-4*flux/np.trapz(flux[ind],x=3E14/wave[ind])
print(np.trapz(flux[ind],x=3E14/wave[ind]))
sed=interp1d(wave, 1E30*3.826E33*flux)
df_comb[str(i)]=sed(df_comb['wave'])
for z in range(0,red.size):
sed=interp1d((red[z]+1.0)*wave, flux)
for b in range(0,len(bands)):
try:
SEDs_comb[i,b,z]=1E30*3.826E33*(1.0+red[z])*filters.fnu_filt(sed(bands[b].wavelength/1E4),3E8/(bands[b].wavelength/1E10),bands[b].transmission,3E8/(eff_lam[b]*1E-6),sed(eff_lam[b]))/div[z]
except ValueError:
print(red[z],bands[b].name)
```
0.0001
0.0001
0.0001
0.0001
```python
import pylab as plt
%matplotlib inline
plt.semilogy(red,SEDs_comb[0,0,:]*np.power(10.0,9))
plt.semilogy(red,SEDs_comb[0,1,:]*np.power(10.0,9),c='g')
plt.semilogy(red,SEDs_comb[0,2,:]*np.power(10.0,9),c='r')
plt.semilogy(red,SEDs_comb[0,3,:]*np.power(10.0,9),c='m')
```
[<matplotlib.lines.Line2D at 0x116954c88>]
```python
div_test=(4.0*np.pi * np.square(Planck13.luminosity_distance(0.001).cgs))
div_test=div_test.value
```
```python
plt.loglog(df_comb['wave'],np.power(10.0,8)*(1.0+0.001)*df_comb['0']/div_test)
plt.loglog(df_comb['wave'],np.power(10.0,8)*(1.0+0.001)*df_comb['1']/div_test)
plt.loglog(df_comb['wave'],np.power(10.0,8)*(1.0+0.001)*df_comb['2']/div_test)
plt.loglog(df_comb['wave'],np.power(10.0,8)*(1.0+0.001)*df_comb['3']/div_test)
plt.loglog(df['wave'],np.power(10.0,8)*(1.0+0.001)*df[temps[0]]/div_test)
```
[<matplotlib.lines.Line2D at 0x115d0f518>]
```python
df_comb
```
<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>wave</th>
<th>0</th>
<th>1</th>
<th>2</th>
<th>3</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1450.106836</td>
<td>1.468242e+44</td>
<td>5.529822e+43</td>
<td>1.761320e+43</td>
<td>1.372804e+34</td>
</tr>
<tr>
<th>1</th>
<td>1200.107086</td>
<td>2.976032e+44</td>
<td>1.129513e+44</td>
<td>3.620497e+43</td>
<td>6.056712e+34</td>
</tr>
<tr>
<th>2</th>
<td>1000.000000</td>
<td>5.700954e+44</td>
<td>2.175560e+44</td>
<td>7.035876e+43</td>
<td>1.740812e+35</td>
</tr>
<tr>
<th>3</th>
<td>831.955314</td>
<td>1.122917e+45</td>
<td>4.327839e+44</td>
<td>1.413508e+44</td>
<td>9.295101e+35</td>
</tr>
<tr>
<th>4</th>
<td>691.990289</td>
<td>2.110608e+45</td>
<td>8.205426e+44</td>
<td>2.716678e+44</td>
<td>4.729118e+36</td>
</tr>
<tr>
<th>5</th>
<td>575.042575</td>
<td>3.977567e+45</td>
<td>1.570801e+45</td>
<td>5.281633e+44</td>
<td>2.223205e+37</td>
</tr>
<tr>
<th>6</th>
<td>478.960832</td>
<td>7.157437e+45</td>
<td>2.874161e+45</td>
<td>9.859575e+44</td>
<td>8.889300e+37</td>
</tr>
<tr>
<th>7</th>
<td>398.015514</td>
<td>1.260310e+46</td>
<td>5.177155e+45</td>
<td>1.819945e+45</td>
<td>2.591120e+38</td>
</tr>
<tr>
<th>8</th>
<td>330.978665</td>
<td>2.140314e+46</td>
<td>9.078020e+45</td>
<td>3.287216e+45</td>
<td>1.298570e+39</td>
</tr>
<tr>
<th>9</th>
<td>274.979299</td>
<td>3.443718e+46</td>
<td>1.523601e+46</td>
<td>5.723190e+45</td>
<td>6.115512e+39</td>
</tr>
<tr>
<th>10</th>
<td>228.981291</td>
<td>5.189644e+46</td>
<td>2.423936e+46</td>
<td>9.523535e+45</td>
<td>2.586673e+40</td>
</tr>
<tr>
<th>11</th>
<td>190.989724</td>
<td>7.179886e+46</td>
<td>3.599077e+46</td>
<td>1.490041e+46</td>
<td>9.061064e+40</td>
</tr>
<tr>
<th>12</th>
<td>158.019251</td>
<td>9.071259e+46</td>
<td>5.039543e+46</td>
<td>2.231558e+46</td>
<td>2.655488e+41</td>
</tr>
<tr>
<th>13</th>
<td>132.010963</td>
<td>1.033469e+47</td>
<td>6.582504e+46</td>
<td>3.145489e+46</td>
<td>1.085482e+42</td>
</tr>
<tr>
<th>14</th>
<td>109.999320</td>
<td>1.004009e+47</td>
<td>7.635388e+46</td>
<td>4.027896e+46</td>
<td>4.111664e+42</td>
</tr>
<tr>
<th>15</th>
<td>100.000000</td>
<td>9.212077e+46</td>
<td>7.798142e+46</td>
<td>4.372081e+46</td>
<td>5.894249e+42</td>
</tr>
<tr>
<th>16</th>
<td>91.201084</td>
<td>8.160703e+46</td>
<td>7.814859e+46</td>
<td>4.681642e+46</td>
<td>1.390562e+43</td>
</tr>
<tr>
<th>17</th>
<td>75.892699</td>
<td>5.546354e+46</td>
<td>7.147286e+46</td>
<td>5.000900e+46</td>
<td>3.903423e+43</td>
</tr>
<tr>
<th>18</th>
<td>63.095734</td>
<td>3.092200e+46</td>
<td>5.663712e+46</td>
<td>4.795437e+46</td>
<td>9.113723e+43</td>
</tr>
<tr>
<th>19</th>
<td>60.006735</td>
<td>2.568547e+46</td>
<td>5.185148e+46</td>
<td>4.655949e+46</td>
<td>1.382843e+44</td>
</tr>
<tr>
<th>20</th>
<td>52.504920</td>
<td>1.526819e+46</td>
<td>3.898083e+46</td>
<td>4.136454e+46</td>
<td>2.527836e+44</td>
</tr>
<tr>
<th>21</th>
<td>43.701868</td>
<td>7.986703e+45</td>
<td>2.368379e+46</td>
<td>3.218016e+46</td>
<td>1.298396e+45</td>
</tr>
<tr>
<th>22</th>
<td>36.298610</td>
<td>5.091052e+45</td>
<td>1.307709e+46</td>
<td>2.272209e+46</td>
<td>4.673905e+45</td>
</tr>
<tr>
<th>23</th>
<td>30.200213</td>
<td>3.652057e+45</td>
<td>6.988133e+45</td>
<td>1.496333e+46</td>
<td>1.460741e+46</td>
</tr>
<tr>
<th>24</th>
<td>24.998273</td>
<td>2.686814e+45</td>
<td>3.522501e+45</td>
<td>9.378820e+45</td>
<td>2.154686e+46</td>
</tr>
<tr>
<th>25</th>
<td>20.897773</td>
<td>2.086592e+45</td>
<td>1.704673e+45</td>
<td>5.880418e+45</td>
<td>2.108957e+46</td>
</tr>
<tr>
<th>26</th>
<td>17.398027</td>
<td>1.750079e+45</td>
<td>9.682804e+44</td>
<td>3.783452e+45</td>
<td>1.734996e+46</td>
</tr>
<tr>
<th>27</th>
<td>14.501068</td>
<td>1.604270e+45</td>
<td>1.106678e+45</td>
<td>2.995768e+45</td>
<td>1.317103e+46</td>
</tr>
<tr>
<th>28</th>
<td>12.001071</td>
<td>1.540734e+45</td>
<td>3.505509e+44</td>
<td>1.854890e+45</td>
<td>1.419808e+46</td>
</tr>
<tr>
<th>29</th>
<td>11.601122</td>
<td>2.439819e+45</td>
<td>2.792294e+44</td>
<td>1.881561e+45</td>
<td>1.393493e+46</td>
</tr>
<tr>
<th>30</th>
<td>11.301081</td>
<td>1.103818e+46</td>
<td>4.621374e+44</td>
<td>3.928242e+45</td>
<td>1.373751e+46</td>
</tr>
<tr>
<th>31</th>
<td>10.999932</td>
<td>2.434656e+45</td>
<td>1.525040e+44</td>
<td>1.465204e+45</td>
<td>1.326228e+46</td>
</tr>
<tr>
<th>32</th>
<td>10.899084</td>
<td>1.475019e+45</td>
<td>1.186728e+44</td>
<td>1.199246e+45</td>
<td>1.306905e+46</td>
</tr>
<tr>
<th>33</th>
<td>9.709570</td>
<td>1.370243e+45</td>
<td>3.578171e+43</td>
<td>6.619089e+44</td>
<td>1.117062e+46</td>
</tr>
<tr>
<th>34</th>
<td>9.399397</td>
<td>1.329161e+45</td>
<td>3.351901e+43</td>
<td>6.185548e+44</td>
<td>1.098289e+46</td>
</tr>
<tr>
<th>35</th>
<td>8.990834</td>
<td>1.333821e+45</td>
<td>4.999783e+43</td>
<td>6.962548e+44</td>
<td>1.073561e+46</td>
</tr>
<tr>
<th>36</th>
<td>8.590135</td>
<td>1.896783e+45</td>
<td>1.071029e+44</td>
<td>9.306968e+44</td>
<td>1.004993e+46</td>
</tr>
<tr>
<th>37</th>
<td>8.390735</td>
<td>1.223088e+45</td>
<td>1.342552e+44</td>
<td>8.826290e+44</td>
<td>9.655134e+45</td>
</tr>
<tr>
<th>38</th>
<td>8.199738</td>
<td>1.177952e+45</td>
<td>1.897366e+44</td>
<td>9.410186e+44</td>
<td>9.276975e+45</td>
</tr>
<tr>
<th>39</th>
<td>7.689534</td>
<td>3.816344e+45</td>
<td>7.737687e+44</td>
<td>2.560874e+45</td>
<td>8.105200e+45</td>
</tr>
<tr>
<th>40</th>
<td>7.000032</td>
<td>1.065829e+45</td>
<td>4.258250e+44</td>
<td>9.635587e+44</td>
<td>6.300909e+45</td>
</tr>
<tr>
<th>41</th>
<td>6.609978</td>
<td>6.471539e+44</td>
<td>3.015790e+44</td>
<td>6.672340e+44</td>
<td>5.280214e+45</td>
</tr>
<tr>
<th>42</th>
<td>6.369422</td>
<td>9.788532e+44</td>
<td>3.014541e+44</td>
<td>7.481319e+44</td>
<td>4.650726e+45</td>
</tr>
<tr>
<th>43</th>
<td>6.190133</td>
<td>4.646517e+45</td>
<td>6.538997e+44</td>
<td>2.133183e+45</td>
<td>4.384508e+45</td>
</tr>
<tr>
<th>44</th>
<td>6.029760</td>
<td>8.760876e+44</td>
<td>2.419475e+44</td>
<td>6.375411e+44</td>
<td>4.237337e+45</td>
</tr>
<tr>
<th>45</th>
<td>5.850595</td>
<td>4.065440e+44</td>
<td>1.763361e+44</td>
<td>4.359013e+44</td>
<td>4.072922e+45</td>
</tr>
<tr>
<th>46</th>
<td>4.799544</td>
<td>1.408016e+44</td>
<td>6.383082e+43</td>
<td>2.109882e+44</td>
<td>3.106764e+45</td>
</tr>
<tr>
<th>47</th>
<td>3.400165</td>
<td>9.718238e+42</td>
<td>1.137538e+43</td>
<td>5.553007e+43</td>
<td>1.754646e+45</td>
</tr>
<tr>
<th>48</th>
<td>3.339566</td>
<td>7.890373e+44</td>
<td>1.038773e+43</td>
<td>8.542957e+43</td>
<td>1.692606e+45</td>
</tr>
<tr>
<th>49</th>
<td>3.299894</td>
<td>7.783547e+45</td>
<td>9.741155e+42</td>
<td>3.702697e+44</td>
<td>1.651991e+45</td>
</tr>
<tr>
<th>50</th>
<td>3.269565</td>
<td>7.822896e+44</td>
<td>9.246852e+42</td>
<td>8.030234e+43</td>
<td>1.620941e+45</td>
</tr>
<tr>
<th>51</th>
<td>3.160094</td>
<td>5.448006e+42</td>
<td>7.495430e+42</td>
<td>4.481982e+43</td>
<td>1.508720e+45</td>
</tr>
<tr>
<th>52</th>
<td>3.069729</td>
<td>4.346178e+42</td>
<td>7.377868e+42</td>
<td>4.016689e+43</td>
<td>1.410144e+45</td>
</tr>
<tr>
<th>53</th>
<td>2.690296</td>
<td>1.503376e+42</td>
<td>6.884240e+42</td>
<td>2.062980e+43</td>
<td>9.962324e+44</td>
</tr>
<tr>
<th>54</th>
<td>2.511886</td>
<td>1.013882e+42</td>
<td>6.652136e+42</td>
<td>1.144348e+43</td>
<td>8.016116e+44</td>
</tr>
<tr>
<th>55</th>
<td>1.258925</td>
<td>4.165696e+40</td>
<td>4.008308e+41</td>
<td>1.440649e+39</td>
<td>5.149757e+42</td>
</tr>
</tbody>
</table>
</div>
```python
np.save('SED_comb', SEDs_comb)
```
```python
df_comb.to_pickle('SEDS_IR_comb_full.pkl')
```
```python
ls
```
SEDS_Herschel_full.pkl
SEDS_IR_comb_full.pkl
SEDS_IR_full.pkl
SEDS_full.pkl
SED_Herschel.npy
SED_IR.npy
SED_IR_sig.npy
SED_SPIRE_PACS100.npy
SED_comb.npy
SED_prior_model.ipynb
SED_prior_model_v2.ipynb
XID+SPIRE.pkl
XID+example_run_script.ipynb
XID+example_run_script_SED.ipynb
XID+posterior_analysis_validation.ipynb
foo.html
log10_SED_IR_sig.npy
test.fits
test.pkl
```python
from bokeh.io import output_notebook, show
from bokeh.layouts import gridplot, column
from bokeh.plotting import figure
from bokeh.io import push_notebook
output_notebook()
from bokeh.models import HoverTool, Range1d
from bokeh.models import ColumnDataSource, DataSource
from bokeh.models import CustomJS, ColumnDataSource, Slider
```
<div class="bk-root">
<a href="http://bokeh.pydata.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
<span id="fc787028-0eba-40c5-8440-ab44739a7eee">Loading BokehJS ...</span>
</div>
```python
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()
plot_options = dict(width=250, plot_height=250)
LIR=12
# create a new plot
source = ColumnDataSource(
data=dict(
x=SEDs[:,0,200]*10.0**LIR,
y=SEDs[:,1,200]*10.0**LIR,
z=SEDs[:,2,200]*10.0**LIR,
width=(SEDs[:,0,200]*10.0**LIR)/5.0,
height=(SEDs[:,1,200]*10.0**LIR)/5.0,
depth=(SEDs[:,2,200]*10.0**LIR)/5.0,
desc=temps,
)
)
hover1 = HoverTool(
tooltips=[
("SED", "@desc"),
]
)
hover2 = HoverTool(
tooltips=[
("SED", "@desc"),
]
)
hover3 = HoverTool(
tooltips=[
("SED", "@desc"),
]
)
s1 = figure(**plot_options,tools=[hover1, 'pan', 'wheel_zoom'])
s1.circle('x', 'y', size=10, source=source,color="navy", alpha=0.0)
s1.ellipse('x', 'y', height='height',width='width', source=source,color="navy", alpha=0.2)
s1.yaxis.axis_label = r'350'
# create a new plot and share both ranges
s2 = figure(x_range=s1.x_range, **plot_options,tools=[hover2, 'pan', 'wheel_zoom'])
s2.circle('x', 'z', size=10, source=source,color="navy", alpha=0.0)
s2.ellipse('x', 'z',height='depth',width='width' , source=source,color="navy", alpha=0.2)
s2.yaxis.axis_label = r'500'
s2.xaxis.axis_label = r'250'
# create a new plot and share only one range
s3 = figure(x_range=s1.y_range,y_range=s2.y_range, **plot_options,tools=[hover3, 'pan', 'wheel_zoom'])
s3.circle('y', 'z', size=10, source=source,color="navy", alpha=0.0)
s3.ellipse('y', 'z',height='depth',width='height', source=source,color="navy", alpha=0.2)
s3.xaxis.axis_label = r'350'
p = gridplot([[s1,],[s2, s3]])
def update(LIR=12,z=red[200]):
ind=np.long(z*100)
print(ind)
source.data['x']=SEDs[:,0,ind]*10.0**LIR
source.data['y']=SEDs[:,1,ind]*10.0**LIR
source.data['z']=SEDs[:,2,ind]*10.0**LIR
source.data['width']=np.full(SEDs.shape[0],np.std(SEDs[:,0,ind]*10.0**LIR))
source.data['depth']=np.full(SEDs.shape[0],np.std(SEDs[:,1,ind]*10.0**LIR))
source.data['height']=np.full(SEDs.shape[0],np.std(SEDs[:,2,ind]*10.0**LIR))
push_notebook()
show(p, notebook_handle=True)
interact(update,LIR=(8,14,0.01),z=(red[0],red[-1],0.01))
```
<div class="bk-root">
<a href="http://bokeh.pydata.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
<span id="68d58652-53e2-48c0-934c-bae8ca860349">Loading BokehJS ...</span>
</div>
<div class="bk-root">
<div class="bk-plotdiv" id="0823d6c9-6577-49ef-976f-a959a4a82186"></div>
</div>
<script type="text/javascript">
(function(global) {
function now() {
return new Date();
}
var force = false;
if (typeof (window._bokeh_onload_callbacks) === "undefined" || force === true) {
window._bokeh_onload_callbacks = [];
window._bokeh_is_loading = undefined;
}
if (typeof (window._bokeh_timeout) === "undefined" || force === true) {
window._bokeh_timeout = Date.now() + 0;
window._bokeh_failed_load = false;
}
var NB_LOAD_WARNING = {'data': {'text/html':
"<div style='background-color: #fdd'>\n"+
"<p>\n"+
"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \n"+
"may be due to a slow or bad network connection. Possible fixes:\n"+
"</p>\n"+
"<ul>\n"+
"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\n"+
"<li>use INLINE resources instead, as so:</li>\n"+
"</ul>\n"+
"<code>\n"+
"from bokeh.resources import INLINE\n"+
"output_notebook(resources=INLINE)\n"+
"</code>\n"+
"</div>"}};
function display_loaded() {
if (window.Bokeh !== undefined) {
var el = document.getElementById("0823d6c9-6577-49ef-976f-a959a4a82186");
el.textContent = "BokehJS " + Bokeh.version + " successfully loaded.";
} else if (Date.now() < window._bokeh_timeout) {
setTimeout(display_loaded, 100)
}
}if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {
comm_manager = Jupyter.notebook.kernel.comm_manager
comm_manager.register_target("87154ed0-e30c-4803-9790-c352897e1969", function () {});
}
function run_callbacks() {
try {
window._bokeh_onload_callbacks.forEach(function(callback) { callback() });
}
finally {
delete window._bokeh_onload_callbacks
}
console.info("Bokeh: all callbacks have finished");
}
function load_libs(js_urls, callback) {
window._bokeh_onload_callbacks.push(callback);
if (window._bokeh_is_loading > 0) {
console.log("Bokeh: BokehJS is being loaded, scheduling callback at", now());
return null;
}
if (js_urls == null || js_urls.length === 0) {
run_callbacks();
return null;
}
console.log("Bokeh: BokehJS not loaded, scheduling load and callback at", now());
window._bokeh_is_loading = js_urls.length;
for (var i = 0; i < js_urls.length; i++) {
var url = js_urls[i];
var s = document.createElement('script');
s.src = url;
s.async = false;
s.onreadystatechange = s.onload = function() {
window._bokeh_is_loading--;
if (window._bokeh_is_loading === 0) {
console.log("Bokeh: all BokehJS libraries loaded");
run_callbacks()
}
};
s.onerror = function() {
console.warn("failed to load library " + url);
};
console.log("Bokeh: injecting script tag for BokehJS library: ", url);
document.getElementsByTagName("head")[0].appendChild(s);
}
};var element = document.getElementById("0823d6c9-6577-49ef-976f-a959a4a82186");
if (element == null) {
console.log("Bokeh: ERROR: autoload.js configured with elementid '0823d6c9-6577-49ef-976f-a959a4a82186' but no matching script tag was found. ")
return false;
}
var js_urls = [];
var inline_js = [
function(Bokeh) {
(function() {
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var render_items = [{"docid":"90a18792-3a2a-4257-8879-201a0a1838be","elementid":"0823d6c9-6577-49ef-976f-a959a4a82186","modelid":"19984f90-d412-45bc-a8fe-13f55b5d4b67","notebook_comms_target":"87154ed0-e30c-4803-9790-c352897e1969"}];
Bokeh.embed.embed_items(docs_json, render_items);
};
if (document.readyState != "loading") fn();
else document.addEventListener("DOMContentLoaded", fn);
})();
},
function(Bokeh) {
}
];
function run_inline_js() {
if ((window.Bokeh !== undefined) || (force === true)) {
for (var i = 0; i < inline_js.length; i++) {
inline_js[i](window.Bokeh);
}if (force === true) {
display_loaded();
}} else if (Date.now() < window._bokeh_timeout) {
setTimeout(run_inline_js, 100);
} else if (!window._bokeh_failed_load) {
console.log("Bokeh: BokehJS failed to load within specified timeout.");
window._bokeh_failed_load = true;
} else if (force !== true) {
var cell = $(document.getElementById("0823d6c9-6577-49ef-976f-a959a4a82186")).parents('.cell').data().cell;
cell.output_area.append_execute_result(NB_LOAD_WARNING)
}
}
if (window._bokeh_is_loading === 0) {
console.log("Bokeh: BokehJS loaded, going straight to plotting");
run_inline_js();
} else {
load_libs(js_urls, function() {
console.log("Bokeh: BokehJS plotting callback run at", now());
run_inline_js();
});
}
}(this));
</script>
<function __main__.update>
```python
samps=np.empty((6,2000))
for i in range(0,2000):
samps[:,i]=np.sum(SEDs_comb[:,:,np.random.randint(len(red))].T*10.0**np.random.uniform(low=np.array([5,5,5,5]),high=np.array([12,12,12,12]))
,axis=1)
```
```python
df=pd.DataFrame(samps.T,columns=['250','350','500','24', '100', '160'])
import seaborn as sns
import pylab as plt
%matplotlib inline
g=sns.PairGrid(df)
g.map_diag(sns.kdeplot)
g.map_lower(sns.kdeplot,n_levels=20, shade=True,shade_lowest=False)
g.map_upper(plt.scatter, alpha=0.1)
for i in range(0,6):
for j in range(0,6):
g.axes[i,j].set_ylim(-10,30)
g.axes[i,j].set_xlim(-10,30)
```
```python
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()
plot_options = dict(width=250, plot_height=250)
LIR=np.array([12,12,12,12])
# create a new plot
source = ColumnDataSource(
data=dict(
s250=SEDs_comb[:,0,200]*10.0**LIR,
s350=SEDs_comb[:,1,200]*10.0**LIR,
s500=SEDs_comb[:,2,200]*10.0**LIR,
s24=SEDs_comb[:,3,200]*10.0**LIR,
s100=SEDs_comb[:,4,200]*10.0**LIR,
s160=SEDs_comb[:,5,200]*10.0**LIR,
)
)
s0_0 = figure(**plot_options,tools=[ 'pan', 'wheel_zoom'])
s0_0.circle('s100', 's160', size=10, source=source,color="navy", alpha=0.2)
s0_0.yaxis.axis_label = r'160'
# create a new plot and share both ranges
s0_1 = figure(x_range=s0_0.x_range, **plot_options,tools=[ 'pan', 'wheel_zoom'])
s0_1.circle('s100', 's250', size=10, source=source,color="navy", alpha=0.2)
s0_1.yaxis.axis_label = r'250'
s0_2 = figure(x_range=s0_0.x_range, **plot_options,tools=[ 'pan', 'wheel_zoom'])
s0_2.circle('s100', 's350', size=10, source=source,color="navy", alpha=0.0)
s0_2.yaxis.axis_label = r'350'
s0_3 = figure(x_range=s0_0.x_range, **plot_options,tools=['pan', 'wheel_zoom'])
s0_3.circle('s100', 's500', size=10, source=source,color="navy", alpha=0.2)
s0_3.yaxis.axis_label = r'500'
s0_3.xaxis.axis_label = r'100'
s1_1 = figure(x_range=s0_0.y_range,y_range=s0_1.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s1_1.circle('s160', 's250', size=10, source=source,color="navy", alpha=0.2)
s1_1.yaxis.axis_label = r'250'
s1_2 = figure(x_range=s0_0.y_range,y_range=s0_2.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s1_2.circle('s160', 's350', size=10, source=source,color="navy", alpha=0.0)
s1_2.yaxis.axis_label = r'350'
s1_3 = figure(x_range=s0_0.y_range,y_range=s0_3.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s1_3.circle('s160', 's500', size=10, source=source,color="navy", alpha=0.0)
s1_3.yaxis.axis_label = r'500'
s1_3.xaxis.axis_label = r'160'
s2_2 = figure(x_range=s0_1.y_range,y_range=s0_2.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s2_2.circle('s250', 's350', size=10, source=source,color="navy", alpha=0.0)
s2_2.yaxis.axis_label = r'350'
s2_3 = figure(x_range=s0_1.y_range,y_range=s0_3.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s2_3.circle('s250', 's500', size=10, source=source,color="navy", alpha=0.0)
s2_3.yaxis.axis_label = r'500'
s2_3.xaxis.axis_label = r'250'
s3_3 = figure(x_range=s0_2.y_range,y_range=s0_3.y_range, **plot_options,tools=['pan', 'wheel_zoom'])
s3_3.circle('s350', 's500', size=10, source=source,color="navy", alpha=0.0)
s3_3.yaxis.axis_label = r'500'
s3_3.xaxis.axis_label = r'350'
p = gridplot([[s0_0,],[s0_1,s1_1,],[s0_2,s1_2,s2_2,],[s0_3,s1_3,s2_3,s3_3]])
def update(LIR_1=12,LIR_2=12,LIR_3=12,LIR_4=12,z=red[200]):
LIR=np.array([LIR_1,LIR_2,LIR_3,LIR_4])
ind=np.long(z*100)
print(ind)
source.data['s250']=SEDs_comb[:,0,ind]*10.0**LIR
source.data['s350']=SEDs_comb[:,1,ind]*10.0**LIR
source.data['s500']=SEDs_comb[:,2,ind]*10.0**LIR
source.data['s100']=SEDs_comb[:,3,ind]*10.0**LIR
source.data['s160']=SEDs_comb[:,4,ind]*10.0**LIR
push_notebook()
show(p, notebook_handle=True)
interact(update,LIR_1=(8,14,0.01),LIR_2=(8,14,0.01),LIR_3=(8,14,0.01),LIR_4=(8,14,0.01),z=(red[0],red[-1],0.01))
```
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<script type="text/javascript">
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var NB_LOAD_WARNING = {'data': {'text/html':
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comm_manager = Jupyter.notebook.kernel.comm_manager
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}
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}
var js_urls = [];
var inline_js = [
function(Bokeh) {
(function() {
var fn = function() {
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var render_items = [{"docid":"d37f38e3-4fa3-4ba8-aa19-142af4ef8a48","elementid":"c5e3c6ff-0c28-4d7d-95c8-1292587a694e","modelid":"6ee4b9e8-7227-4869-96cb-b2670c0c72cc","notebook_comms_target":"e25712c2-76fe-4aed-84ff-b37767249a07"}];
Bokeh.embed.embed_items(docs_json, render_items);
};
if (document.readyState != "loading") fn();
else document.addEventListener("DOMContentLoaded", fn);
})();
},
function(Bokeh) {
}
];
function run_inline_js() {
if ((window.Bokeh !== undefined) || (force === true)) {
for (var i = 0; i < inline_js.length; i++) {
inline_js[i](window.Bokeh);
}if (force === true) {
display_loaded();
}} else if (Date.now() < window._bokeh_timeout) {
setTimeout(run_inline_js, 100);
} else if (!window._bokeh_failed_load) {
console.log("Bokeh: BokehJS failed to load within specified timeout.");
window._bokeh_failed_load = true;
} else if (force !== true) {
var cell = $(document.getElementById("c5e3c6ff-0c28-4d7d-95c8-1292587a694e")).parents('.cell').data().cell;
cell.output_area.append_execute_result(NB_LOAD_WARNING)
}
}
if (window._bokeh_is_loading === 0) {
console.log("Bokeh: BokehJS loaded, going straight to plotting");
run_inline_js();
} else {
load_libs(js_urls, function() {
console.log("Bokeh: BokehJS plotting callback run at", now());
run_inline_js();
});
}
}(this));
</script>
<function __main__.update>
```python
####log 10 version
from ipywidgets import interact
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
from bokeh.plotting import figure
output_notebook()
plot_options = dict(width=250, plot_height=250)
LIR=12
# create a new plot
source = ColumnDataSource(
data=dict(
s250=np.log10(SEDs[:,0,200]*10.0**LIR),
s350=np.log10(SEDs[:,1,200]*10.0**LIR),
s500=np.log10(SEDs[:,2,200]*10.0**LIR),
s100=np.log10(SEDs[:,3,200]*10.0**LIR),
s160=np.log10(SEDs[:,4,200]*10.0**LIR),
s250_sig=np.full(SEDs.shape[0],sig[0,200]),
s350_sig=np.full(SEDs.shape[0],sig[1,200]),
s500_sig=np.full(SEDs.shape[0],sig[2,200]),
s100_sig=np.full(SEDs.shape[0],sig[3,200]),
s160_sig=np.full(SEDs.shape[0],sig[4,200]),
desc=temps,
)
)
hover=[]
for i in range(0,10):
hover.append(HoverTool(
tooltips=[
("SED", "@desc"),
]
))
s0_0 = figure(**plot_options,tools=[hover[0], 'pan', 'wheel_zoom'])
s0_0.circle('s100', 's160', size=10, source=source,color="navy", alpha=0.0)
s0_0.ellipse('s100', 's160', height='s160_sig',width='s100_sig', source=source,color="navy", alpha=0.2)
s0_0.yaxis.axis_label = r'160'
# create a new plot and share both ranges
s0_1 = figure(x_range=s0_0.x_range, **plot_options,tools=[hover[1], 'pan', 'wheel_zoom'])
s0_1.circle('s100', 's250', size=10, source=source,color="navy", alpha=0.0)
s0_1.ellipse('s100', 's250',height='s250_sig',width='s100_sig' , source=source,color="navy", alpha=0.2)
s0_1.yaxis.axis_label = r'250'
s0_2 = figure(x_range=s0_0.x_range, **plot_options,tools=[hover[2], 'pan', 'wheel_zoom'])
s0_2.circle('s100', 's350', size=10, source=source,color="navy", alpha=0.0)
s0_2.ellipse('s100', 's350',height='s350_sig',width='s100_sig' , source=source,color="navy", alpha=0.2)
s0_2.yaxis.axis_label = r'350'
s0_3 = figure(x_range=s0_0.x_range, **plot_options,tools=[hover[3], 'pan', 'wheel_zoom'])
s0_3.circle('s100', 's500', size=10, source=source,color="navy", alpha=0.0)
s0_3.ellipse('s100', 's500',height='s500_sig',width='s100_sig' , source=source,color="navy", alpha=0.2)
s0_3.yaxis.axis_label = r'500'
s0_3.xaxis.axis_label = r'100'
s1_1 = figure(x_range=s0_0.y_range,y_range=s0_1.y_range, **plot_options,tools=[hover[4], 'pan', 'wheel_zoom'])
s1_1.circle('s160', 's250', size=10, source=source,color="navy", alpha=0.0)
s1_1.ellipse('s160', 's250',height='s250_sig',width='s160_sig' , source=source,color="navy", alpha=0.2)
s1_1.yaxis.axis_label = r'250'
s1_2 = figure(x_range=s0_0.y_range,y_range=s0_2.y_range, **plot_options,tools=[hover[5], 'pan', 'wheel_zoom'])
s1_2.circle('s160', 's350', size=10, source=source,color="navy", alpha=0.0)
s1_2.ellipse('s160', 's350',height='s350_sig',width='s160_sig' , source=source,color="navy", alpha=0.2)
s1_2.yaxis.axis_label = r'350'
s1_3 = figure(x_range=s0_0.y_range,y_range=s0_3.y_range, **plot_options,tools=[hover[6], 'pan', 'wheel_zoom'])
s1_3.circle('s160', 's500', size=10, source=source,color="navy", alpha=0.0)
s1_3.ellipse('s160', 's500',height='s500_sig',width='s160_sig' , source=source,color="navy", alpha=0.2)
s1_3.yaxis.axis_label = r'500'
s1_3.xaxis.axis_label = r'160'
s2_2 = figure(x_range=s0_1.y_range,y_range=s0_2.y_range, **plot_options,tools=[hover[7], 'pan', 'wheel_zoom'])
s2_2.circle('s250', 's350', size=10, source=source,color="navy", alpha=0.0)
s2_2.ellipse('s250', 's350',height='s350_sig',width='s250_sig' , source=source,color="navy", alpha=0.2)
s2_2.yaxis.axis_label = r'350'
s2_3 = figure(x_range=s0_1.y_range,y_range=s0_3.y_range, **plot_options,tools=[hover[8], 'pan', 'wheel_zoom'])
s2_3.circle('s250', 's500', size=10, source=source,color="navy", alpha=0.0)
s2_3.ellipse('s250', 's500',height='s500_sig',width='s250_sig' , source=source,color="navy", alpha=0.2)
s2_3.yaxis.axis_label = r'500'
s2_3.xaxis.axis_label = r'250'
s3_3 = figure(x_range=s0_2.y_range,y_range=s0_3.y_range, **plot_options,tools=[hover[9], 'pan', 'wheel_zoom'])
s3_3.circle('s350', 's500', size=10, source=source,color="navy", alpha=0.0)
s3_3.ellipse('s350', 's500',height='s500_sig',width='s350_sig' , source=source,color="navy", alpha=0.2)
s3_3.yaxis.axis_label = r'500'
s3_3.xaxis.axis_label = r'350'
p = gridplot([[s0_0,],[s0_1,s1_1,],[s0_2,s1_2,s2_2,],[s0_3,s1_3,s2_3,s3_3]])
def update(LIR=12,z=red[200]):
ind=np.long(z*100)
print(ind)
source.data['s250']=np.log10(SEDs[:,0,ind]*10.0**LIR)
source.data['s350']=np.log10(SEDs[:,1,ind]*10.0**LIR)
source.data['s500']=np.log10(SEDs[:,2,ind]*10.0**LIR)
source.data['s100']=np.log10(SEDs[:,3,ind]*10.0**LIR)
source.data['s160']=np.log10(SEDs[:,4,ind]*10.0**LIR)
source.data['s250_sig']=np.full(SEDs.shape[0],sig[0,ind])#+LIR
source.data['s350_sig']=np.full(SEDs.shape[0],sig[1,ind])#+LIR
source.data['s500_sig']=np.full(SEDs.shape[0],sig[2,ind])#+LIR
source.data['s100_sig']=np.full(SEDs.shape[0],sig[3,ind])#+LIR
source.data['s160_sig']=np.full(SEDs.shape[0],sig[4,ind])#+LIR
push_notebook()
show(p, notebook_handle=True)
interact(update,LIR=(8,14,0.01),z=(red[0],red[-1],0.01))
```
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<function __main__.update>
```python
np.full(SEDs.shape[0],sig[0,200])
```
array([ 0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231, 0.02756231, 0.02756231, 0.02756231,
0.02756231, 0.02756231])
```python
for t in range(0,SEDs.shape[0]):
cov=np.zeros((SEDs.shape[1],SEDs.shape[1]))
for i in range(0,SEDs.shape[1]):
cov[i,i]=0.3*SEDs[t,i,200]*10.0**LIR
if t ==0:
normal=np.random.multivariate_normal(SEDs[t,:,200]*10.0**LIR,cov, 100)
else:
normal=np.vstack((normal,np.random.multivariate_normal(SEDs[t,:,200]*10.0**LIR,cov, 100)))
```
```python
for t in range(0,SEDs.shape[0]):
cov=np.zeros((SEDs.shape[1],SEDs.shape[1]))
for i in range(0,SEDs.shape[1]):
cov[i,i]=0.3*np.std(np.log10(SEDs[:,i,200]*10.0**LIR))
if t ==0:
log_normal=np.random.multivariate_normal(np.log10(SEDs[t,:,200]*10.0**LIR),cov, 100)
else:
log_normal=np.vstack((log_normal,np.random.multivariate_normal(np.log10(SEDs[t,:,200]*10.0**LIR),cov, 100)))
```
```python
LIR
```
12
```python
normal.shape
```
(3200, 6)
```python
df=pd.DataFrame(normal,columns=['250','350','500','24', '100', '160'])
```
```python
import seaborn as sns
import pylab as plt
%matplotlib inline
g=sns.PairGrid(df)
g.map_diag(sns.kdeplot)
g.map_lower(sns.kdeplot,n_levels=20, shade=True,shade_lowest=False)
g.map_upper(plt.scatter, alpha=0.1)
g.data=pd.DataFrame(np.power(10.0,log_normal),columns=['250','350','500','24', '100', '160'])
g.map_diag(sns.kdeplot)
g.map_lower(sns.kdeplot,n_levels=20, shade=True,shade_lowest=False, cmap="Reds", alpha=0.3)
g.map_upper(plt.scatter, alpha=0.1, color='r')
```
<seaborn.axisgrid.PairGrid at 0x1416d2828>
```python
g=sns.PairGrid(pd.DataFrame(log_normal,columns=['250','350','500','24', '100', '160']))
g.map_diag(sns.kdeplot)
g.map_lower(sns.kdeplot,n_levels=20, shade=True,shade_lowest=False)
g.map_upper(plt.scatter, alpha=0.1)
```
<seaborn.axisgrid.PairGrid at 0x152999fd0>
```python
from sklearn.neighbors import NearestNeighbors
import numpy as np
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(X)
distances, indices = nbrs.kneighbors(X)
indices
```
array([[0, 1, 2],
[1, 0, 2],
[2, 1, 0],
[3, 4, 5],
[4, 3, 5],
[5, 4, 3]])
```python
SEDs.shape
```
(32, 6, 800)
```python
sig=np.empty((SEDs.shape[0],SEDs.shape[2]))
for i in range(0,SEDs.shape[2]):
nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(SEDs[:,:,i])
distances, indices = nbrs.kneighbors(SEDs[:,:,i])
sig[:,i]=distances[:,1]
```
```python
for i in range(0,SEDs.shape[2]):
sig[:,i]=
```
```python
LIR=8
sig[0,:]*np.power(10.0,10)
```
array([ 2.84348739e+10, 2.89852761e+02, 7.39912471e+01,
3.28076832e+01, 1.84215686e+01, 1.18328268e+01,
8.27184419e+00, 6.08707386e+00, 4.64899418e+00,
3.66615825e+00, 2.96778394e+00, 2.45399866e+00,
2.05449490e+00, 1.74368629e+00, 1.50220233e+00,
1.31623894e+00, 1.16295300e+00, 1.03081403e+00,
9.17509676e-01, 8.22262017e-01, 7.40674759e-01,
6.70752248e-01, 6.11260293e-01, 5.61158752e-01,
5.15448571e-01, 4.74220188e-01, 4.37594705e-01,
4.05656978e-01, 3.77708809e-01, 3.52824123e-01,
3.30828331e-01, 3.10742355e-01, 2.92361705e-01,
2.74746025e-01, 2.58322317e-01, 2.43058547e-01,
2.29758060e-01, 2.17732475e-01, 2.06540620e-01,
1.95669097e-01, 1.85435595e-01, 1.76616959e-01,
1.68405149e-01, 1.60811937e-01, 1.53362109e-01,
1.46320176e-01, 1.39890665e-01, 1.34135236e-01,
1.29036046e-01, 1.24362649e-01, 1.19743997e-01,
1.15061009e-01, 1.10486805e-01, 1.06270075e-01,
1.02373815e-01, 9.87416706e-02, 9.52888937e-02,
9.20433354e-02, 8.90041903e-02, 8.61122189e-02,
8.32837622e-02, 8.05738546e-02, 7.79885019e-02,
7.56974344e-02, 7.35582020e-02, 7.15360532e-02,
6.95923739e-02, 6.75860093e-02, 6.56830879e-02,
6.38573457e-02, 6.21128026e-02, 6.04294689e-02,
5.88192829e-02, 5.72369981e-02, 5.56733061e-02,
5.40824107e-02, 5.25701684e-02, 5.11019703e-02,
4.97307376e-02, 4.85555518e-02, 4.74010274e-02,
4.63600110e-02, 4.53813169e-02, 4.42815893e-02,
4.33002962e-02, 4.23199145e-02, 4.13333434e-02,
4.03725861e-02, 3.93910538e-02, 3.84494975e-02,
3.75666708e-02, 3.67284608e-02, 3.59681593e-02,
3.52488580e-02, 3.45362778e-02, 3.38418499e-02,
3.31514376e-02, 3.24784700e-02, 3.18159433e-02,
3.11313414e-02, 3.04672219e-02, 2.98517291e-02,
2.92494461e-02, 2.86952512e-02, 2.81774603e-02,
2.76668325e-02, 2.71762558e-02, 2.67157887e-02,
2.63030891e-02, 2.59132331e-02, 2.55286794e-02,
2.51409740e-02, 2.47303080e-02, 2.42381445e-02,
2.37153960e-02, 2.31796301e-02, 2.26762602e-02,
2.22043280e-02, 2.17710051e-02, 2.13649315e-02,
2.10009285e-02, 2.06568416e-02, 2.03217726e-02,
1.99767915e-02, 1.96368164e-02, 1.92951047e-02,
1.89720988e-02, 1.86612372e-02, 1.83890822e-02,
1.81259013e-02, 1.78776352e-02, 1.76334181e-02,
1.73949021e-02, 1.71463594e-02, 1.69012627e-02,
1.66576158e-02, 1.64188126e-02, 1.61842791e-02,
1.59489852e-02, 1.56980586e-02, 1.54545953e-02,
1.52158014e-02, 1.49875115e-02, 1.47659847e-02,
1.45365759e-02, 1.43109822e-02, 1.40944704e-02,
1.38717765e-02, 1.36540940e-02, 1.34449362e-02,
1.32779927e-02, 1.31148913e-02, 1.29523625e-02,
1.27898962e-02, 1.26311938e-02, 1.24627634e-02,
1.22608300e-02, 1.20654310e-02, 1.18810451e-02,
1.17129809e-02, 1.15499596e-02, 1.13897721e-02,
1.12317980e-02, 1.10782688e-02, 1.09221933e-02,
1.07591137e-02, 1.06006364e-02, 1.04496559e-02,
1.03128900e-02, 1.01802156e-02, 1.00560976e-02,
9.94332036e-03, 9.83485926e-03, 9.73172900e-03,
9.63502153e-03, 9.54018174e-03, 9.44410218e-03,
9.33802156e-03, 9.23263334e-03, 9.12469266e-03,
9.01593047e-03, 8.90464084e-03, 8.79427248e-03,
8.68799693e-03, 8.58194076e-03, 8.47944633e-03,
8.37967076e-03, 8.27689317e-03, 8.17732411e-03,
8.07844041e-03, 7.97876216e-03, 7.88128095e-03,
7.78619394e-03, 7.69347206e-03, 7.60598626e-03,
7.52152897e-03, 7.44254768e-03, 7.37567565e-03,
7.31313452e-03, 7.25809696e-03, 7.19852737e-03,
7.13932965e-03, 7.08532043e-03, 7.03420807e-03,
6.97448755e-03, 6.91234247e-03, 6.84947879e-03,
6.79634685e-03, 6.74711098e-03, 6.69857739e-03,
6.63980852e-03, 6.57616594e-03, 6.51177074e-03,
6.44271869e-03, 6.36856032e-03, 6.28367067e-03,
6.19438637e-03, 6.10915872e-03, 6.02650228e-03,
5.94201725e-03, 5.85229304e-03, 5.76515452e-03,
5.68714028e-03, 5.61731331e-03, 5.54787459e-03,
5.48371832e-03, 5.42442438e-03, 5.36953129e-03,
5.31643750e-03, 5.26578241e-03, 5.21726715e-03,
5.17089592e-03, 5.12034012e-03, 5.06249100e-03,
5.00640082e-03, 4.95181079e-03, 4.89764375e-03,
4.84244390e-03, 4.78853098e-03, 4.73691679e-03,
4.68970304e-03, 4.64310529e-03, 4.59742329e-03,
4.55291045e-03, 4.50995132e-03, 4.46848623e-03,
4.42771038e-03, 4.38646339e-03, 4.33722154e-03,
4.28450582e-03, 4.23276443e-03, 4.18215166e-03,
4.13187005e-03, 4.08196305e-03, 4.03337769e-03,
3.98600599e-03, 3.94533936e-03, 3.91442805e-03,
3.88442266e-03, 3.85511437e-03, 3.82694174e-03,
3.80031071e-03, 3.77420346e-03, 3.74851017e-03,
3.72538786e-03, 3.68907864e-03, 3.65358671e-03,
3.61904767e-03, 3.58542379e-03, 3.55277221e-03,
3.52128822e-03, 3.49083780e-03, 3.45726083e-03,
3.41999460e-03, 3.38379399e-03, 3.34879131e-03,
3.31596048e-03, 3.28481381e-03, 3.25438891e-03,
3.22482982e-03, 3.19655032e-03, 3.17059335e-03,
3.14826687e-03, 3.12209266e-03, 3.09175298e-03,
3.05869374e-03, 3.02525582e-03, 2.99193718e-03,
2.96011240e-03, 2.93040902e-03, 2.90197846e-03,
2.87442284e-03, 2.84832096e-03, 2.82329373e-03,
2.79921201e-03, 2.77678993e-03, 2.75579979e-03,
2.73549925e-03, 2.71581244e-03, 2.69366031e-03,
2.67132634e-03, 2.64943031e-03, 2.62801732e-03,
2.60729525e-03, 2.58589879e-03, 2.56296040e-03,
2.54095493e-03, 2.51884950e-03, 2.49711108e-03,
2.47552777e-03, 2.45474711e-03, 2.43383134e-03,
2.41329624e-03, 2.39339135e-03, 2.37400287e-03,
2.35499734e-03, 2.33665815e-03, 2.31833164e-03,
2.29901723e-03, 2.27700384e-03, 2.25509672e-03,
2.23399478e-03, 2.21315845e-03, 2.19297875e-03,
2.17432822e-03, 2.15573291e-03, 2.13812546e-03,
2.12055195e-03, 2.10322311e-03, 2.08628329e-03,
2.07013745e-03, 2.05398182e-03, 2.03821435e-03,
2.02125840e-03, 2.00224477e-03, 1.98339694e-03,
1.96483655e-03, 1.94670260e-03, 1.93021758e-03,
1.91480489e-03, 1.89959748e-03, 1.88472121e-03,
1.86992932e-03, 1.85709911e-03, 1.84720443e-03,
1.83776738e-03, 1.82810102e-03, 1.81815867e-03,
1.80609738e-03, 1.79286525e-03, 1.78024596e-03,
1.76763912e-03, 1.75537199e-03, 1.74329976e-03,
1.72938793e-03, 1.71548217e-03, 1.70183732e-03,
1.68869884e-03, 1.67905021e-03, 1.67128035e-03,
1.66383588e-03, 1.65656130e-03, 1.64939910e-03,
1.64254032e-03, 1.63454656e-03, 1.62487198e-03,
1.61581016e-03, 1.60665918e-03, 1.59630616e-03,
1.58326643e-03, 1.56980181e-03, 1.55640258e-03,
1.54338332e-03, 1.53051839e-03, 1.51780572e-03,
1.50719851e-03, 1.49714886e-03, 1.48766200e-03,
1.47834876e-03, 1.46990865e-03, 1.46267679e-03,
1.45537688e-03, 1.44819938e-03, 1.44120655e-03,
1.43447735e-03, 1.42794540e-03, 1.42001292e-03,
1.41145423e-03, 1.40310912e-03, 1.39478952e-03,
1.38675236e-03, 1.37898134e-03, 1.37177338e-03,
1.36463144e-03, 1.35760564e-03, 1.35069474e-03,
1.34374919e-03, 1.33704175e-03, 1.33044360e-03,
1.32376966e-03, 1.31719169e-03, 1.31070934e-03,
1.30312430e-03, 1.29521474e-03, 1.28718756e-03,
1.27938777e-03, 1.27198132e-03, 1.26462276e-03,
1.25748181e-03, 1.25218401e-03, 1.24777767e-03,
1.24339477e-03, 1.23937206e-03, 1.23569608e-03,
1.23298055e-03, 1.23033503e-03, 1.22745598e-03,
1.22490801e-03, 1.22226075e-03, 1.21977730e-03,
1.21713124e-03, 1.21100823e-03, 1.20475219e-03,
1.19849176e-03, 1.19232916e-03, 1.18587711e-03,
1.17896929e-03, 1.17203096e-03, 1.16516590e-03,
1.15848318e-03, 1.15172305e-03, 1.14525549e-03,
1.13875970e-03, 1.13390904e-03, 1.12945224e-03,
1.12506394e-03, 1.12071716e-03, 1.11655913e-03,
1.11232325e-03, 1.10847355e-03, 1.10481980e-03,
1.10135746e-03, 1.09775878e-03, 1.09436428e-03,
1.09115231e-03, 1.08778948e-03, 1.08426946e-03,
1.08093028e-03, 1.07763300e-03, 1.07441637e-03,
1.07126578e-03, 1.06809874e-03, 1.06439935e-03,
1.06073422e-03, 1.05718412e-03, 1.05368505e-03,
1.05023629e-03, 1.04683720e-03, 1.04247232e-03,
1.03786370e-03, 1.03331352e-03, 1.02882108e-03,
1.02438584e-03, 1.02053421e-03, 1.01713748e-03,
1.01419111e-03, 1.01122780e-03, 1.00840365e-03,
1.00560522e-03, 1.00285609e-03, 1.00021336e-03,
9.97618804e-04, 9.95001386e-04, 9.92454155e-04,
9.90069412e-04, 9.87626402e-04, 9.85204302e-04,
9.82593522e-04, 9.79425692e-04, 9.76318329e-04,
9.73224498e-04, 9.70182865e-04, 9.67131283e-04,
9.64164873e-04, 9.61156420e-04, 9.58163086e-04,
9.55207728e-04, 9.52292146e-04, 9.49367597e-04,
9.46337416e-04, 9.42971814e-04, 9.39613319e-04,
9.36275738e-04, 9.32977299e-04, 9.29728148e-04,
9.26530391e-04, 9.23393980e-04, 9.20318980e-04,
9.17324581e-04, 9.14398213e-04, 9.11525615e-04,
9.08704608e-04, 9.05888729e-04, 9.03352772e-04,
9.01123821e-04, 8.99632960e-04, 8.98176182e-04,
8.96735697e-04, 8.95328797e-04, 8.93954513e-04,
8.92632867e-04, 8.91375335e-04, 8.90211893e-04,
8.89090485e-04, 8.88014770e-04, 8.86953143e-04,
8.85955860e-04, 8.84979302e-04, 8.83962516e-04,
8.81077609e-04, 8.78096406e-04, 8.75094426e-04,
8.72066541e-04, 8.69088241e-04, 8.66238147e-04,
8.63411253e-04, 8.59837750e-04, 8.56014876e-04,
8.52164405e-04, 8.48324409e-04, 8.44592305e-04,
8.40826194e-04, 8.36854504e-04, 8.30307286e-04,
8.24100803e-04, 8.18155072e-04, 8.12400548e-04,
8.06764096e-04, 8.01329873e-04, 7.95661994e-04,
7.89973552e-04, 7.84151650e-04, 7.78415865e-04,
7.72325873e-04, 7.66474122e-04, 7.60436820e-04,
7.54893553e-04, 7.49576008e-04, 7.43183417e-04,
7.36911037e-04, 7.30825939e-04, 7.24760225e-04,
7.18681554e-04, 7.12966715e-04, 7.06996681e-04,
7.01306066e-04, 6.95545927e-04, 6.89405308e-04,
6.83411921e-04, 6.77259605e-04, 6.71402397e-04,
6.65683320e-04, 6.59709217e-04, 6.52840192e-04,
6.46112636e-04, 6.39548815e-04, 6.32932204e-04,
6.26403851e-04, 6.19859905e-04, 6.13482837e-04,
6.07204854e-04, 6.00974808e-04, 5.94606642e-04,
5.88346596e-04, 5.82252824e-04, 5.76269700e-04,
5.70276655e-04, 5.64369126e-04, 5.59466986e-04,
5.55715906e-04, 5.52011447e-04, 5.48394195e-04,
5.44814821e-04, 5.41376613e-04, 5.37838296e-04,
5.34205526e-04, 5.30518925e-04, 5.26249879e-04,
5.21946708e-04, 5.17707699e-04, 5.13524061e-04,
5.09496342e-04, 5.05425067e-04, 5.01381311e-04,
4.96703893e-04, 4.91999888e-04, 4.87090769e-04,
4.82223409e-04, 4.77614318e-04, 4.72758237e-04,
4.67861388e-04, 4.62919684e-04, 4.58395264e-04,
4.54090202e-04, 4.49786192e-04, 4.45543583e-04,
4.41381276e-04, 4.37434063e-04, 4.33299772e-04,
4.29135788e-04, 4.24813249e-04, 4.20150928e-04,
4.15722623e-04, 4.11012035e-04, 4.06615482e-04,
4.02303047e-04, 3.98212978e-04, 3.93983549e-04,
3.89897361e-04, 3.85876642e-04, 3.81861802e-04,
3.78111986e-04, 3.74386731e-04, 3.70750282e-04,
3.67201424e-04, 3.63886171e-04, 3.60533095e-04,
3.57665518e-04, 3.55090536e-04, 3.52368987e-04,
3.49481618e-04, 3.46585631e-04, 3.43569228e-04,
3.40704464e-04, 3.37978317e-04, 3.35195373e-04,
3.32435226e-04, 3.29712311e-04, 3.27036982e-04,
3.24459851e-04, 3.21964735e-04, 3.19532140e-04,
3.17062098e-04, 3.14622235e-04, 3.11825913e-04,
3.09134398e-04, 3.06504321e-04, 3.03963652e-04,
3.01540879e-04, 2.99197599e-04, 2.96821014e-04,
2.94594504e-04, 2.92501715e-04, 2.90447918e-04,
2.88243781e-04, 2.86397812e-04, 2.84373386e-04,
2.82587812e-04, 2.80778218e-04, 2.79041411e-04,
2.77495493e-04, 2.76053069e-04, 2.74596247e-04,
2.73416833e-04, 2.72492974e-04, 2.71768698e-04,
2.71362703e-04, 2.70874347e-04, 2.70804473e-04,
2.70617823e-04, 2.69937212e-04, 2.69735701e-04,
2.69207759e-04, 2.69239738e-04, 2.68518165e-04,
2.68373731e-04, 2.67964392e-04, 2.67631287e-04,
2.67424842e-04, 2.67223347e-04, 2.67093639e-04,
2.67353515e-04, 2.67281749e-04, 2.67287790e-04,
2.67626530e-04, 2.67296850e-04, 2.66694752e-04,
2.66310610e-04, 2.65344188e-04, 2.64401998e-04,
2.63041122e-04, 2.61987142e-04, 2.60425478e-04,
2.58766346e-04, 2.56538400e-04, 2.54464878e-04,
2.52218225e-04, 2.49997980e-04, 2.47574426e-04,
2.45216120e-04, 2.43647718e-04, 2.41587444e-04,
2.39752741e-04, 2.38014439e-04, 2.36481734e-04,
2.34562209e-04, 2.33715156e-04, 2.32477531e-04,
2.31953291e-04, 2.30864583e-04, 2.30302902e-04,
2.29350369e-04, 2.28588398e-04, 2.27970538e-04,
2.27007555e-04, 2.26287065e-04, 2.25432980e-04,
2.24719243e-04, 2.24257463e-04, 2.23676591e-04,
2.22935225e-04, 2.22446431e-04, 2.22126996e-04,
2.21696588e-04, 2.21227732e-04, 2.20540910e-04,
2.19990068e-04, 2.19125327e-04, 2.18538291e-04,
2.17781685e-04, 2.17134442e-04, 2.16676256e-04,
2.16255119e-04, 2.15723637e-04, 2.15110858e-04,
2.14585292e-04, 2.13890323e-04, 2.13497974e-04,
2.12885426e-04, 2.12291830e-04, 2.11943303e-04,
2.11361802e-04, 2.10759898e-04, 2.10222764e-04,
2.09647415e-04, 2.09111501e-04, 2.08723810e-04,
2.08559327e-04, 2.08324237e-04, 2.08262820e-04,
2.08167032e-04, 2.08561737e-04, 2.08690346e-04,
2.09174216e-04, 2.09687739e-04, 2.10188694e-04,
2.11270962e-04, 2.12074443e-04, 2.13729523e-04,
2.15643622e-04, 2.17312720e-04, 2.19654409e-04,
2.22535499e-04, 2.24862568e-04, 2.28510526e-04,
2.32225146e-04, 2.36449162e-04, 2.40884433e-04,
2.45547090e-04, 2.49783795e-04, 2.54242031e-04,
2.58748176e-04, 2.62921691e-04, 2.67162663e-04,
2.71492166e-04, 2.75915605e-04, 2.78738422e-04,
2.79428816e-04, 2.78862765e-04, 2.78831115e-04,
2.78397779e-04, 2.78247815e-04, 2.78412109e-04,
2.77930368e-04, 2.77400206e-04, 2.77338078e-04,
2.76925517e-04, 2.76958186e-04, 2.76489190e-04,
2.76119540e-04, 2.72192675e-04, 2.66340431e-04,
2.60170280e-04, 2.54452273e-04, 2.49218154e-04,
2.42186846e-04, 2.36606679e-04, 2.31823526e-04,
2.26369552e-04, 2.22013355e-04])
```python
sig=np.empty((SEDs.shape[1],SEDs.shape[2]))
for i in range(0,SEDs.shape[2]):
sig[:,i]=0.3*np.std(np.log10(SEDs[:,:,i]*10.0**LIR),axis=0)
```
```python
np.save('log10_SED_IR_sig', sig)
```
```python
np.trapz(df['Blue_SF_glx.norm_LIR'][(df['wave']>8) & (df['wave']<1000)][::-1],x=3.0E8/(df['wave'][(df['wave']>8) & (df['wave']<1000)][::-1]*1E-6))*1E-26/1E4
```
3.82580418875477e+29
```python
df['wave']
```
0 0.009100
1 0.009400
2 0.009600
3 0.009800
4 0.010000
5 0.010200
6 0.010400
7 0.010600
8 0.010800
9 0.011000
10 0.011400
11 0.011800
12 0.012100
13 0.012500
14 0.012700
15 0.012800
16 0.013100
17 0.013200
18 0.013400
19 0.013700
20 0.014000
21 0.014300
22 0.014700
23 0.015100
24 0.015500
25 0.015900
26 0.016200
27 0.016600
28 0.017000
29 0.017300
...
10975 1658.999475
10976 1669.000456
10977 1679.000889
10978 1689.000047
10979 1699.000924
10980 1708.999095
10981 1718.999480
10982 1728.999534
10983 1739.000479
10984 1748.999742
10985 1759.000491
10986 1769.000155
10987 1779.000020
10988 1788.999448
10989 1798.999774
10990 1809.000387
10991 1819.000670
10992 1829.000006
10993 1838.999793
10994 1848.999437
10995 1859.000386
10996 1869.000021
10997 1878.999805
10998 1888.999164
10999 1898.999610
11000 1909.000599
11001 1918.999474
11002 1928.999890
11003 1938.999196
11004 1948.998977
Name: wave, Length: 11005, dtype: float64
```python
template=ascii.read('/Users/pdh21/astrodata/SEDs/Berta2013/templates_berta_norm_LIR/'+temps[0])
```
```python
np.trapz(template['col2'][(template['col1']>8E3) & (template['col1']<1E6)],x=template['col1'][(template['col1']>8E3) & (template['col1']<1E6)])
```
0.99065289555174796
```python
template['col1']
```
<Column name='col1' dtype='float64' length=11005>
<table>
<tr><td>90.99999</td></tr>
<tr><td>94.0</td></tr>
<tr><td>95.99999</td></tr>
<tr><td>98.0</td></tr>
<tr><td>100.0</td></tr>
<tr><td>102.00001</td></tr>
<tr><td>104.0</td></tr>
<tr><td>105.99997</td></tr>
<tr><td>107.99998</td></tr>
<tr><td>109.99997</td></tr>
<tr><td>113.99998</td></tr>
<tr><td>118.0</td></tr>
<tr><td>...</td></tr>
<tr><td>18389997.9277</td></tr>
<tr><td>18489994.3743</td></tr>
<tr><td>18590003.8614</td></tr>
<tr><td>18690000.2133</td></tr>
<tr><td>18789998.0454</td></tr>
<tr><td>18889991.6412</td></tr>
<tr><td>18989996.1042</td></tr>
<tr><td>19090005.9874</td></tr>
<tr><td>19189994.7448</td></tr>
<tr><td>19289998.9015</td></tr>
<tr><td>19389991.964</td></tr>
<tr><td>19489989.7706</td></tr>
</table>
```python
print(np.trapz(template['col2'][(template['col1']<8E3)],x=template['col1'][(template['col1']<8E3)]))
print(np.trapz(template['col2'][(template['col1']>8E3) & (template['col1']<1E6)],x=template['col1'][(template['col1']>8E3) & (template['col1']<1E6)]))
print(np.trapz(template['col2'][(template['col1']<1E6)],x=template['col1'][(template['col1']<1E6)]))
```
0.210849971767
0.990652895552
1.20152082665
```python
plt.loglog(df['wave'],df['Blue_SF_glx.norm_LIR'])
```
[<matplotlib.lines.Line2D at 0x120493cc0>]
```python
print(np.trapz(df['Blue_SF_glx.norm_LIR'][(df['wave']>8) & (df['wave']<1000)][::-1]
,x=3.0E8/(df['wave'][(df['wave']>8) & (df['wave']<1000)][::-1]*1E-6))*1E-26/1E4)
print(np.trapz(df['Blue_SF_glx.norm_LIR'][(df['wave']<8)][::-1]
,x=3.0E8/(df['wave'][(df['wave']<8)][::-1]*1E-6))*1E-26/1E4)
```
3.82580418875e+29
1.7493619078e+29
```python
```
2.1954022988505746
## Test stan script
```python
code="""
functions {
int intFloor(int leftStart, int rightStart, real iReal)
{
// This is absurd. Use bisection algorithm to find int floor.
int left;
int right;
left <- leftStart;
right <- rightStart;
while((left + 1) < right) {
int mid;
// print("left, right, mid, i, ", left, ", ", right, ", ", mid, ", ", iReal);
mid <- left + (right - left) / 2;
if(iReal < mid) {
right <- mid;
}
else {
left <- mid;
}
}
return left;
}
// Interpolate arr using a non-integral index i
// Note: 1 <= i <= length(arr)
real interpolateLinear(real[] arr, real i)
{
int iLeft;
real valLeft;
int iRight;
real valRight;
// print("interpolating ", i);
// Get i, value at left. If exact time match, then return value.
iLeft <- intFloor(1, size(arr), i);
valLeft <- arr[iLeft];
if(iLeft == i) {
return valLeft;
}
// Get i, value at right.
iRight <- iLeft + 1;
valRight <- arr[iRight];
// Linearly interpolate between values at left and right.
print(valLeft + (valRight - valLeft) * (i - iLeft));
return valLeft + (valRight - valLeft) * (i - iLeft);
}
}
data {
int<lower=0> nsrc;//number of sources
// ----SED templates----
int nTemp;
int nz;
int nband;
real SEDs[nTemp,nband,nz];
vector[nband] flux[nsrc];//vector of source src_fes
vector[nband] flux_sig[nsrc];//vector of source src_fes
}
parameters {
vector<lower=5, upper=14>[nTemp] Nbb[nsrc];
real<lower=0.001,upper=8> z[nsrc];
}
transformed parameters{
vector[nband] src_f[nsrc];//vector of source src_fes
for (i in 1:nsrc){
vector[nTemp] f_tmp[nband];
for (b in 1:nband){
for (t in 1:nTemp){
f_tmp[b,t]=log10(Nbb[i,t]+interpolateLinear(SEDs[t,b], z[i]*1000.0));
}
src_f[i,b]=sum(f_tmp[b]);
}
}
}
model {
for (s in 1:nsrc){
flux[s] ~ normal(src_f[s],flux_sig[s]);
}
}
"""
```
```python
import pystan
```
```python
flux=np.sum(SEDs_comb[:,:,3000].T*10.0**np.array([12,11,11,9]),axis=1)+np.random.normal(0,[1,1,1,0.02,0.5,0.5])
```
```python
flux
```
array([ 2.80180271, 3.14830712, 4.99304504, 0.00677361, 0.72103822,
0.41795028])
```python
data={
'nsrc':1,
'nTemp':SEDs_comb.shape[0],
'nz':SEDs_comb.shape[2],
'nband':6,
'SEDs':SEDs_comb,
'flux':flux[np.newaxis],
'flux_sig':np.array([[1,1,1,0.2,0.5,0.5]])}
```
```python
sm=pystan.StanModel(model_code=code)
```
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_aa797e4b6080b4b9bbdf06f49392a718 NOW.
```python
fit_IR_combSED=sm.sampling(data=data,verbose=True, iter=1000,chains=1,seed=194838)
```
```python
fit_IR_combSED
```
Inference for Stan model: anon_model_aa797e4b6080b4b9bbdf06f49392a718.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
Nbb[0,0] 9.34 0.11 2.37 5.25 7.22 9.47 11.69 12.83 452 1.0
Nbb[0,1] 9.96 0.12 2.44 5.32 7.85 10.63 12.24 12.84 391 1.0
Nbb[0,2] 10.85 0.15 2.51 5.4 8.99 12.36 12.79 13.05 288 1.0
Nbb[0,3] 8.74 0.09 2.15 5.18 6.87 8.72 10.64 12.19 594 1.0
z[0] 5.49 0.09 1.55 2.48 4.31 5.58 6.79 7.9 276 1.0
src_f[0,0] 1.76 0.02 0.58 0.7 1.37 1.75 2.12 2.99 999 1.0
src_f[0,1] 3.35 0.02 0.75 1.82 2.84 3.39 3.87 4.8 964 1.0
src_f[0,2] 4.8 0.03 0.97 2.98 4.11 4.82 5.45 6.7 1215 1.0
src_f[0,3] 0.02 1.6e-3 0.03 8.7e-4 3.4e-3 6.7e-3 0.02 0.12 443 1.0
src_f[0,4] 0.25 6.3e-3 0.17 0.06 0.14 0.23 0.31 0.71 746 1.0
src_f[0,5] 0.59 8.3e-3 0.26 0.18 0.41 0.57 0.75 1.19 958 1.0
lp__ -0.79 0.1 1.93 -5.51 -1.83 -0.41 0.66 1.7 351 1.0
Samples were drawn using NUTS at Tue Dec 19 15:59:45 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
```python
SEDs_com
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-54-6bca12ddae3b> in <module>()
----> 1 SEDs_com
NameError: name 'SEDs_com' is not defined
```python
import xidplus.stan_fit.stan_utility as stan_utility
stan_utility.check_treedepth(fit_IR_combSED)
stan_utility.check_energy(fit_IR_combSED)
stan_utility.check_div(fit_IR_combSED)
```
0 of 2000 iterations saturated the maximum tree depth of 10 (0.0%)
3.0 of 2000 iterations ended with a divergence (0.15%)
Try running with larger adapt_delta to remove the divergences
```python
samples=fit_IR_combSED.extract()
```
```python
nondiv_params, div_params = stan_utility.partition_div(fit_IR_combSED)
```
WARNING:root:`dtypes` ignored when `permuted` is False.
```python
plt.plot(nondiv_params['Nbb'][:,1],nondiv_params['Nbb'][:,0], 'ro')
plt.plot(div_params['Nbb'][:,1],div_params['Nbb'][:,0], 'go')
```
[<matplotlib.lines.Line2D at 0x125030860>]
```python
plt.plot(nondiv_params['Nbb'][:,1],nondiv_params['Nbb'][:,3], 'ro')
plt.plot(div_params['Nbb'][:,1],div_params['Nbb'][:,3], 'go')
```
[<matplotlib.lines.Line2D at 0x1283b40b8>]
```python
plt.plot(nondiv_params['Nbb'][:,2],nondiv_params['Nbb'][:,3], 'ro')
plt.plot(div_params['Nbb'][:,2],div_params['Nbb'][:,3], 'go')
```
[<matplotlib.lines.Line2D at 0x127a3c860>]
```python
plt.plot(nondiv_params['z'][:],nondiv_params['Nbb'][:,0], 'ro')
plt.plot(div_params['z'][:],div_params['Nbb'][:,1], 'go')
```
[<matplotlib.lines.Line2D at 0x12819bdd8>]
```python
plt.figure(figsize=(10,10))
plt.plot(nondiv_params['Nbb'][:,0],nondiv_params['z'], 'ro')
plt.plot(div_params['Nbb'][:,0],div_params['z'], 'go')
plt.plot(x,y,'bo', alpha=0.2)
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-62-905f240c51b5> in <module>()
2 plt.plot(nondiv_params['Nbb'][:,0],nondiv_params['z'], 'ro')
3 plt.plot(div_params['Nbb'][:,0],div_params['z'], 'go')
----> 4 plt.plot(x,y,'bo', alpha=0.2)
NameError: name 'x' is not defined
```python
div_params['Nbb'].shape
```
(108, 4)
```python
red[2000]
```
2.0
```python
SEDs_comb.shape
```
(4, 6, 8000)
```python
x,y=np.meshgrid(np.arange(11.8,12.5, 0.1), red)
```
```python
plt.plot(samples['z'])
```
[<matplotlib.lines.Line2D at 0x12818e9e8>]
```python
plt.plot(samples['Nbb'][:,0,0])
```
[<matplotlib.lines.Line2D at 0x1279541d0>]
```python
div_z=[]
for z in div_params['z']:
div_z.append(np.abs(red-z).min())
nondiv_z=[]
for z in nondiv_params['z']:
nondiv_z.append(np.abs(red-z).min())
```
```python
plt.hist(div_z,color='b', alpha=0.5);
plt.hist(nondiv_z,color='r', alpha=0.5);
```
```python
import seaborn as sns
sns.set_style("white")
b=0
plt.figure(figsize=(6,6))
s1=0
from astropy.cosmology import Planck13
violin_parts=plt.violinplot(samples['src_f'][:,s1,0:3],[250,350,500], points=60, widths=100,
showmeans=True, showextrema=True, showmedians=True,bw_method=0.5)
# Make all the violin statistics marks red:
for partname in ('cbars','cmins','cmaxes','cmeans','cmedians'):
vp = violin_parts[partname]
vp.set_edgecolor('purple')
vp.set_linewidth(1)
for pc in violin_parts['bodies']:
pc.set_facecolor('purple')
violin_parts=plt.violinplot(samples['src_f'][:,s1,0:3],[250,350,500], points=60, widths=100,
showmeans=True, showextrema=True, showmedians=True,bw_method=0.5)
# Make all the violin statistics marks red:
for partname in ('cbars','cmins','cmaxes','cmeans','cmedians'):
vp = violin_parts[partname]
vp.set_edgecolor('green')
vp.set_linewidth(1)
for pc in violin_parts['bodies']:
pc.set_facecolor('green')
violin_parts=plt.violinplot(samples['src_f'][:,s1,3:6],[24,100,160], points=60, widths=20,showmeans=True, showextrema=True, showmedians=True,bw_method=0.5)
# Make all the violin statistics marks red:
for partname in ('cbars','cmins','cmaxes','cmeans','cmedians'):
vp = violin_parts[partname]
vp.set_edgecolor('green')
vp.set_linewidth(1)
for pc in violin_parts['bodies']:
pc.set_facecolor('green')
for s in np.arange(0,1000,1):
z= samples['z'][s]
div=(4.0*np.pi * np.square(Planck13.luminosity_distance(z).cgs))
div=div.value
tot_sed=np.power(10.0,samples['Nbb'][s,s1,0])*(1.0+z)*df_comb[str(b)]/div
for b in range(1,4):
tot_sed+=np.power(10.0,samples['Nbb'][s,s1,b])*(1.0+z)*df_comb[str(b)]/div
plt.loglog((z+1.0)*df_comb['wave'],tot_sed, 'b', alpha=0.1)
plt.ylim(10E-7,10E2)
plt.xlim(5,5E3)
#plt.plot([3.6,4.5,5.7,7.9],[2.91E-3,2.38E-3,2.12E-3,9.6E-3], 'ro')
plt.xlabel('Wavelength (microns)')
plt.ylabel('Flux (mJy)')
```
<matplotlib.text.Text at 0x1265bcd30>
```python
b=5
plt.hist(samples['src_f'][:,0,b], alpha=0.5,normed=True);
plt.hist(nondiv_params['src_f'][:,b], color='red', alpha=0.5,normed=True);
plt.hist(div_params['src_f'][:,b], color='green', alpha=0.5,normed=True);
```
```python
samples['z'].shape
```
(1000,)
## why/where am I getting divergent transitions?
Its not obvious any parameters are causing the problem.
They do not occur at any particular likelihood value
Is it the grid causing issue?
Is it the uniform prior?
```python
plt.plot(samples['lp__'],samples['Nbb'][:,0,3], 'o',alpha=0.2)
plt.plot(samples['lp__'][diverg],samples['Nbb'][diverg,0,3], 'ro',alpha=0.2)
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-67-34144de92271> in <module>()
1 plt.plot(samples['lp__'],samples['Nbb'][:,0,3], 'o',alpha=0.2)
----> 2 plt.plot(samples['lp__'][diverg],samples['Nbb'][diverg,0,3], 'ro',alpha=0.2)
NameError: name 'diverg' is not defined
```python
diverg=fit_IR_combSED.get_sampler_params()[0]['divergent__']==1.0
```
```python
samples['Nbb'].shape
```
(1000, 1, 4)
```python
np.arange(0,1000)[diverg]
```
array([ 1, 2, 3, 4, 62, 91, 100, 101, 123, 129, 151, 188, 192,
224, 251, 425, 451])
```python
samples['z'][424:427]
```
array([ 7.31326683, 2.97283728, 4.12264925])
## GP alternative
```python
GPcode="""functions {
vector gp_pred_rng(real[] x2,
vector y1, real[] x1,
real alpha, real rho, real sigma, real delta) {
int N1 = rows(y1);
int N2 = size(x2);
vector[N2] f2;
{
matrix[N1, N1] K = cov_exp_quad(x1, alpha, rho)
+ diag_matrix(rep_vector(square(sigma), N1));
matrix[N1, N1] L_K = cholesky_decompose(K);
vector[N1] L_K_div_y1 = mdivide_left_tri_low(L_K, y1);
vector[N1] K_div_y1 = mdivide_right_tri_low(L_K_div_y1', L_K)';
matrix[N1, N2] k_x1_x2 = cov_exp_quad(x1, x2, alpha, rho);
vector[N2] f2_mu = (k_x1_x2' * K_div_y1);
matrix[N1, N2] v_pred = mdivide_left_tri_low(L_K, k_x1_x2);
matrix[N2, N2] cov_f2 = cov_exp_quad(x2, alpha, rho) - v_pred' * v_pred
+ diag_matrix(rep_vector(delta, N2));
f2 = multi_normal_rng(f2_mu, cov_f2);
}
return f2;
}
}
data {
int<lower=1> N;
real x[N];
vector[N] y;
int<lower=1> N_predict;
real x_predict[N_predict];
real<lower=0> rho;
real<lower=0> alpha;
real<lower=0> sigma;
}
transformed data {
matrix[N, N] cov = cov_exp_quad(x, alpha, rho)
+ diag_matrix(rep_vector(1e-10, N));
matrix[N, N] L_cov = cholesky_decompose(cov);
}
parameters {}
model {}
generated quantities {
vector[N_predict] f_predict = gp_pred_rng(x_predict, y, x, alpha, rho, sigma, 1e-10);
vector[N_predict] y_predict;
for (n in 1:N_predict)
y_predict[n] = normal_rng(f_predict[n], sigma);
}
"""
```
```python
sm=pystan.StanModel(model_code=GPcode)
```
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_66d2b322c018ce81f1c399f4e91c226b NOW.
```python
data={
'N':SEDs_comb.shape[2],
'x':red,
'y':SEDs_comb[0,0,:],
'N_predict':1,
'x_predict':[3.2],
'rho':0.1,
'alpha':1,
'sigma':0.1
}
```
```python
SEDs_comb.shape
```
(4, 6, 8000)
```python
GP=sm.sampling(data=data,verbose=True, iter=1000,chains=1,seed=194838,algorithm="Fixed_param")
```
Thinking and what to do in 2018:
* First try log10 space for the SED_comb run. This may help divergence transitions
* Try interpolation via GPs
```python
```
|
H-E-L-PREPO_NAMEXID_plusPATH_START.@XID_plus_extracted@XID_plus-master@docs@build@html@notebooks@examples@SED_prior_model_v2.ipynb@.PATH_END.py
|
{
"filename": "masterdark.py",
"repo_name": "juanep97/iop4",
"repo_path": "iop4_extracted/iop4-main/iop4admin/modeladmins/masterdark.py",
"type": "Python"
}
|
from django.contrib import admin
from django.utils.html import format_html
from django.urls import reverse
from django.utils.safestring import mark_safe
from iop4api.filters import *
from iop4api.models import *
from .fitfile import AdminFitFile, action_mark_ignore, action_unmark_ignore
import logging
logger = logging.getLogger(__name__)
class AdminMasterDark(AdminFitFile):
model = MasterDark
list_display = ['id', 'telescope', 'night', 'instrument', 'imgsize', 'exptime', 'get_masterbias', 'get_built_from', 'options', 'status']
list_filter = (
RawFitIdFilter,
RawFitTelescopeFilter,
RawFitNightFilter,
RawFitInstrumentFilter,
RawFitFlagFilter,
"imgsize",
)
actions = [action_mark_ignore, action_unmark_ignore]
@admin.display(description='Options')
def options(self, obj):
url_details = reverse('iop4admin:iop4api_masterdark_details', args=[obj.id])
url_viewer= reverse('iop4admin:iop4api_masterdark_details', args=[obj.id])
return format_html(rf'<a href="{url_details}">details</a> / <a href="{url_viewer}">advanced viewer</a>')
@admin.display(description='Telescope')
def telescope(self, obj):
return obj.epoch.telescope
@admin.display(description='Night')
def night(self, obj):
return obj.epoch.night
@admin.display(description='MasterBias')
def get_masterbias(self, obj):
self.allow_tags = True
if obj.masterbias is None:
return "-"
url = reverse('iop4admin:%s_%s_changelist' % (MasterBias._meta.app_label, MasterBias._meta.model_name)) + f"?id={obj.masterbias.id}"
return mark_safe(rf'<a href="{url}">{obj.masterbias.id}</a>')
|
juanep97REPO_NAMEiop4PATH_START.@iop4_extracted@iop4-main@iop4admin@modeladmins@masterdark.py@.PATH_END.py
|
{
"filename": "_testutils.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/scipy/special/_testutils.py",
"type": "Python"
}
|
from __future__ import division, print_function, absolute_import
import os
from distutils.version import LooseVersion
import functools
import numpy as np
from numpy.testing import assert_
import pytest
import scipy.special as sc
__all__ = ['with_special_errors', 'assert_tol_equal', 'assert_func_equal',
'FuncData']
#------------------------------------------------------------------------------
# Check if a module is present to be used in tests
#------------------------------------------------------------------------------
class MissingModule(object):
def __init__(self, name):
self.name = name
def check_version(module, min_ver):
if type(module) == MissingModule:
return pytest.mark.skip(reason="{} is not installed".format(module.name))
return pytest.mark.skipif(LooseVersion(module.__version__) < LooseVersion(min_ver),
reason="{} version >= {} required".format(module.__name__, min_ver))
#------------------------------------------------------------------------------
# Enable convergence and loss of precision warnings -- turn off one by one
#------------------------------------------------------------------------------
def with_special_errors(func):
"""
Enable special function errors (such as underflow, overflow,
loss of precision, etc.)
"""
@functools.wraps(func)
def wrapper(*a, **kw):
with sc.errstate(all='raise'):
res = func(*a, **kw)
return res
return wrapper
#------------------------------------------------------------------------------
# Comparing function values at many data points at once, with helpful
#------------------------------------------------------------------------------
def assert_tol_equal(a, b, rtol=1e-7, atol=0, err_msg='', verbose=True):
"""Assert that `a` and `b` are equal to tolerance ``atol + rtol*abs(b)``"""
def compare(x, y):
return np.allclose(x, y, rtol=rtol, atol=atol)
a, b = np.asanyarray(a), np.asanyarray(b)
header = 'Not equal to tolerance rtol=%g, atol=%g' % (rtol, atol)
np.testing.utils.assert_array_compare(compare, a, b, err_msg=str(err_msg),
verbose=verbose, header=header)
#------------------------------------------------------------------------------
# Comparing function values at many data points at once, with helpful
# error reports
#------------------------------------------------------------------------------
def assert_func_equal(func, results, points, rtol=None, atol=None,
param_filter=None, knownfailure=None,
vectorized=True, dtype=None, nan_ok=False,
ignore_inf_sign=False, distinguish_nan_and_inf=True):
if hasattr(points, 'next'):
# it's a generator
points = list(points)
points = np.asarray(points)
if points.ndim == 1:
points = points[:,None]
nparams = points.shape[1]
if hasattr(results, '__name__'):
# function
data = points
result_columns = None
result_func = results
else:
# dataset
data = np.c_[points, results]
result_columns = list(range(nparams, data.shape[1]))
result_func = None
fdata = FuncData(func, data, list(range(nparams)),
result_columns=result_columns, result_func=result_func,
rtol=rtol, atol=atol, param_filter=param_filter,
knownfailure=knownfailure, nan_ok=nan_ok, vectorized=vectorized,
ignore_inf_sign=ignore_inf_sign,
distinguish_nan_and_inf=distinguish_nan_and_inf)
fdata.check()
class FuncData(object):
"""
Data set for checking a special function.
Parameters
----------
func : function
Function to test
filename : str
Input file name
param_columns : int or tuple of ints
Columns indices in which the parameters to `func` lie.
Can be imaginary integers to indicate that the parameter
should be cast to complex.
result_columns : int or tuple of ints, optional
Column indices for expected results from `func`.
result_func : callable, optional
Function to call to obtain results.
rtol : float, optional
Required relative tolerance. Default is 5*eps.
atol : float, optional
Required absolute tolerance. Default is 5*tiny.
param_filter : function, or tuple of functions/Nones, optional
Filter functions to exclude some parameter ranges.
If omitted, no filtering is done.
knownfailure : str, optional
Known failure error message to raise when the test is run.
If omitted, no exception is raised.
nan_ok : bool, optional
If nan is always an accepted result.
vectorized : bool, optional
Whether all functions passed in are vectorized.
ignore_inf_sign : bool, optional
Whether to ignore signs of infinities.
(Doesn't matter for complex-valued functions.)
distinguish_nan_and_inf : bool, optional
If True, treat numbers which contain nans or infs as as
equal. Sets ignore_inf_sign to be True.
"""
def __init__(self, func, data, param_columns, result_columns=None,
result_func=None, rtol=None, atol=None, param_filter=None,
knownfailure=None, dataname=None, nan_ok=False, vectorized=True,
ignore_inf_sign=False, distinguish_nan_and_inf=True):
self.func = func
self.data = data
self.dataname = dataname
if not hasattr(param_columns, '__len__'):
param_columns = (param_columns,)
self.param_columns = tuple(param_columns)
if result_columns is not None:
if not hasattr(result_columns, '__len__'):
result_columns = (result_columns,)
self.result_columns = tuple(result_columns)
if result_func is not None:
raise ValueError("Only result_func or result_columns should be provided")
elif result_func is not None:
self.result_columns = None
else:
raise ValueError("Either result_func or result_columns should be provided")
self.result_func = result_func
self.rtol = rtol
self.atol = atol
if not hasattr(param_filter, '__len__'):
param_filter = (param_filter,)
self.param_filter = param_filter
self.knownfailure = knownfailure
self.nan_ok = nan_ok
self.vectorized = vectorized
self.ignore_inf_sign = ignore_inf_sign
self.distinguish_nan_and_inf = distinguish_nan_and_inf
if not self.distinguish_nan_and_inf:
self.ignore_inf_sign = True
def get_tolerances(self, dtype):
if not np.issubdtype(dtype, np.inexact):
dtype = np.dtype(float)
info = np.finfo(dtype)
rtol, atol = self.rtol, self.atol
if rtol is None:
rtol = 5*info.eps
if atol is None:
atol = 5*info.tiny
return rtol, atol
def check(self, data=None, dtype=None):
"""Check the special function against the data."""
if self.knownfailure:
pytest.xfail(reason=self.knownfailure)
if data is None:
data = self.data
if dtype is None:
dtype = data.dtype
else:
data = data.astype(dtype)
rtol, atol = self.get_tolerances(dtype)
# Apply given filter functions
if self.param_filter:
param_mask = np.ones((data.shape[0],), np.bool_)
for j, filter in zip(self.param_columns, self.param_filter):
if filter:
param_mask &= list(filter(data[:,j]))
data = data[param_mask]
# Pick parameters from the correct columns
params = []
for j in self.param_columns:
if np.iscomplexobj(j):
j = int(j.imag)
params.append(data[:,j].astype(complex))
else:
params.append(data[:,j])
# Helper for evaluating results
def eval_func_at_params(func, skip_mask=None):
if self.vectorized:
got = func(*params)
else:
got = []
for j in range(len(params[0])):
if skip_mask is not None and skip_mask[j]:
got.append(np.nan)
continue
got.append(func(*tuple([params[i][j] for i in range(len(params))])))
got = np.asarray(got)
if not isinstance(got, tuple):
got = (got,)
return got
# Evaluate function to be tested
got = eval_func_at_params(self.func)
# Grab the correct results
if self.result_columns is not None:
# Correct results passed in with the data
wanted = tuple([data[:,icol] for icol in self.result_columns])
else:
# Function producing correct results passed in
skip_mask = None
if self.nan_ok and len(got) == 1:
# Don't spend time evaluating what doesn't need to be evaluated
skip_mask = np.isnan(got[0])
wanted = eval_func_at_params(self.result_func, skip_mask=skip_mask)
# Check the validity of each output returned
assert_(len(got) == len(wanted))
for output_num, (x, y) in enumerate(zip(got, wanted)):
if np.issubdtype(x.dtype, np.complexfloating) or self.ignore_inf_sign:
pinf_x = np.isinf(x)
pinf_y = np.isinf(y)
minf_x = np.isinf(x)
minf_y = np.isinf(y)
else:
pinf_x = np.isposinf(x)
pinf_y = np.isposinf(y)
minf_x = np.isneginf(x)
minf_y = np.isneginf(y)
nan_x = np.isnan(x)
nan_y = np.isnan(y)
olderr = np.seterr(all='ignore')
try:
abs_y = np.absolute(y)
abs_y[~np.isfinite(abs_y)] = 0
diff = np.absolute(x - y)
diff[~np.isfinite(diff)] = 0
rdiff = diff / np.absolute(y)
rdiff[~np.isfinite(rdiff)] = 0
finally:
np.seterr(**olderr)
tol_mask = (diff <= atol + rtol*abs_y)
pinf_mask = (pinf_x == pinf_y)
minf_mask = (minf_x == minf_y)
nan_mask = (nan_x == nan_y)
bad_j = ~(tol_mask & pinf_mask & minf_mask & nan_mask)
point_count = bad_j.size
if self.nan_ok:
bad_j &= ~nan_x
bad_j &= ~nan_y
point_count -= (nan_x | nan_y).sum()
if not self.distinguish_nan_and_inf and not self.nan_ok:
# If nan's are okay we've already covered all these cases
inf_x = np.isinf(x)
inf_y = np.isinf(y)
both_nonfinite = (inf_x & nan_y) | (nan_x & inf_y)
bad_j &= ~both_nonfinite
point_count -= both_nonfinite.sum()
if np.any(bad_j):
# Some bad results: inform what, where, and how bad
msg = [""]
msg.append("Max |adiff|: %g" % diff.max())
msg.append("Max |rdiff|: %g" % rdiff.max())
msg.append("Bad results (%d out of %d) for the following points (in output %d):"
% (np.sum(bad_j), point_count, output_num,))
for j in np.where(bad_j)[0]:
j = int(j)
fmt = lambda x: "%30s" % np.array2string(x[j], precision=18)
a = " ".join(map(fmt, params))
b = " ".join(map(fmt, got))
c = " ".join(map(fmt, wanted))
d = fmt(rdiff)
msg.append("%s => %s != %s (rdiff %s)" % (a, b, c, d))
assert_(False, "\n".join(msg))
def __repr__(self):
"""Pretty-printing, esp. for Nose output"""
if np.any(list(map(np.iscomplexobj, self.param_columns))):
is_complex = " (complex)"
else:
is_complex = ""
if self.dataname:
return "<Data for %s%s: %s>" % (self.func.__name__, is_complex,
os.path.basename(self.dataname))
else:
return "<Data for %s%s>" % (self.func.__name__, is_complex)
|
waynebhayesREPO_NAMESpArcFiRePATH_START.@SpArcFiRe_extracted@SpArcFiRe-master@scripts@SpArcFiRe-pyvenv@lib@python2.7@site-packages@scipy@special@_testutils.py@.PATH_END.py
|
{
"filename": "recipes_ARC_LS_SPECT.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/geminidr/niri/recipes/sq/recipes_ARC_LS_SPECT.py",
"type": "Python"
}
|
"""
MS: this is just an MVP, copying the corresponding GNIRS recipe.
Expect Olesja will improve this after she finds some bandwidth.
Recipes available to data with tags ['NIRI', 'SPECT', 'LS'],
excluding data with tags ['FLAT', 'DARK', 'BIAS'].
These are NIRI longslit arc-lamp or sky-line calibrations.
Default is "makeProcessedArc".
"""
recipe_tags = {'NIRI', 'SPECT', 'LS', 'ARC'}
def makeProcessedArc(p):
"""
Process NIRI longslist arc and calculate wavelength and distortion
solutions. No stacking, arcs are processed individually if more than
one is given.
Inputs are:
* raw arc
* processed flat
"""
p.prepare()
p.addDQ()
p.ADUToElectrons()
p.addVAR(poisson_noise=True, read_noise=True)
p.nonlinearityCorrect()
p.flatCorrect()
p.makeIRAFCompatible()
p.determineWavelengthSolution()
p.determineDistortion()
p.storeProcessedArc()
p.writeOutputs()
_default = makeProcessedArc
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@geminidr@niri@recipes@sq@recipes_ARC_LS_SPECT.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "mikecokina/elisa",
"repo_path": "elisa_extracted/elisa-master/src/elisa/observer/__init__.py",
"type": "Python"
}
|
mikecokinaREPO_NAMEelisaPATH_START.@elisa_extracted@elisa-master@src@elisa@observer@__init__.py@.PATH_END.py
|
|
{
"filename": "ifttt.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/tools/ifttt.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import IFTTTWebhook
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"IFTTTWebhook": "langchain_community.tools"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"IFTTTWebhook",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@tools@ifttt.py@.PATH_END.py
|
{
"filename": "_hoverinfo.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/bar/_hoverinfo.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class HoverinfoValidator(_plotly_utils.basevalidators.FlaglistValidator):
def __init__(self, plotly_name="hoverinfo", parent_name="bar", **kwargs):
super(HoverinfoValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
extras=kwargs.pop("extras", ["all", "none", "skip"]),
flags=kwargs.pop("flags", ["x", "y", "z", "text", "name"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@bar@_hoverinfo.py@.PATH_END.py
|
{
"filename": "11143_rval_050015.py",
"repo_name": "shreeyesh-biswal/Rvalue_3D",
"repo_path": "Rvalue_3D_extracted/Rvalue_3D-main/Codes/Zero/AR_11143/11143_rval_050015.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 26 20:36:28 2022
@author: shreeyeshbiswal
"""
import os
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
AR = "11143"
core_dir = "/home/shreeyeshbiswal/IDLWorkspace/Dataset_PF/"
base_dir = "/home/shreeyeshbiswal/IDLWorkspace/Dataset_PF/AR_" + AR
dir_list = sorted(os.listdir(base_dir))
n = len(dir_list)
m = 10 # values per file
d = '15'
th = '50'
rval_matrix = np.zeros(shape=(n,m))
index = np.arange(0,n)
height = np.arange(0,m)*0.36
P4 = 'Log of R-value (Mx); AR ' + AR
colorbarticks = [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]
cbar_min = 15
cbar_max = 23
for i in range(0,n):
Time_tag = dir_list[i]
Time = Time_tag[0:19]
Hour = Time[11:13]
print(Time)
dir = "/home/shreeyeshbiswal/IDLWorkspace/Dataset_PF/AR_" + AR + "/" + Time_tag
os.chdir(dir)
# the if-else statement takes care of missing data
if len(os.listdir(dir)) != 0:
rval = np.loadtxt("PF_ext_rvals_050015_" + Time + ".dat")
rval = rval + 15.1172 # LOG FACTOR FOR 1.3141 x 10^15
print(rval)
print(np.shape(rval))
rval_matrix[i,:] = rval
print(Hour)
else:
rval_matrix[i,:] = np.nan
print("Empty directory")
os.chdir(core_dir)
x = np.arange(0,n)
figure(figsize=(10,10), dpi=100000)
figure, axs = plt.subplots(10)
figure.set_figheight(15)
figure.set_figwidth(9)
cm = plt.cm.get_cmap('afmhot')
mpl.rc('xtick', labelsize=13)
# Plot
sc = axs[0].scatter(x, rval_matrix[:,9], c = rval_matrix[:,9], vmin=cbar_min, vmax=cbar_max, s=10, cmap=cm)
for i in range(0,m):
axs[i].scatter(x, rval_matrix[:,9-i], c = rval_matrix[:,9-i], vmin=cbar_min, vmax=cbar_max, s=10, cmap=cm)
for i in range(0,m):
axs[i].set_ylim([cbar_min, cbar_max])
plt.setp(plt.gcf().get_axes(), xticks=[], yticks=[]);
axs[9].tick_params(axis='x', labelsize=16)
axs[9].set_xticks(np.arange(0,n,24))
# Hide the ylims of individual boxes
for i in range(0,m):
axs[i].set_yticks([])
# Show heights in the altitude
heightfont = 16
for i in range(0,m):
max_alt = (m-1)*0.36
altitude = max_alt-(i*0.36)
alt_str = "{:.2f}".format(altitude)
axs[i].set_ylabel(alt_str + ' ', fontsize = heightfont, rotation = 0)
# Orient the text
st = dir_list[0]
start_time = st[0:4] + '/' + st[5:7] + '/' + st[8:10] + '/' + st[11:13] + ':' + st[14:16]
axs[0].text(12, (cbar_max + (0.35*(cbar_max - cbar_min))), P4, fontsize=23)
axs[5].text(-36, cbar_min + 0.5*(cbar_max - cbar_min), 'Height (Mm)', rotation = 90, fontsize=18)
axs[9].text(-15, (cbar_min - (0.65*(cbar_max - cbar_min))), 'Time after ' + start_time + ' (hrs)' + '; ($B_{th}$, $D_{sep}$) = ' + '(' + th + ',' + d + ')', rotation = 0, fontsize=18)
figure.subplots_adjust(right=0.8)
cbar_ax = figure.add_axes([0.85, 0.15, 0.05, 0.7])
cbar_ax.tick_params(labelsize=16)
figure.colorbar(sc, cax=cbar_ax, ticks=range(cbar_min,cbar_max+1,1))
plt.subplots_adjust(wspace=0.5, hspace=0)
plt.show()
mpl.rcParams.update(mpl.rcParamsDefault)
|
shreeyesh-biswalREPO_NAMERvalue_3DPATH_START.@Rvalue_3D_extracted@Rvalue_3D-main@Codes@Zero@AR_11143@11143_rval_050015.py@.PATH_END.py
|
{
"filename": "generate_observation.py",
"repo_name": "Smithsonian/ngehtsim",
"repo_path": "ngehtsim_extracted/ngehtsim-main/examples/example_data_generation/generate_observation.py",
"type": "Python"
}
|
#######################################################
# imports
import ngehtsim.obs.obs_generator as og
import ngehtsim.obs.obs_plotter as op
import ngehtsim.metrics as cm
#######################################################
# generate an observation
# input settings file
yamlfile = './settings.yaml'
# initialize the observation generator
obsgen = og.obs_generator(settings_file=yamlfile)
# generate the observation
obs = obsgen.make_obs()
# save it as a uvfits file
obs.save_uvfits('./example_datafile.uvfits')
#######################################################
# make some plots of the data
op.plot_uv(obs, filename='./example_plot_uv.png')
op.plot_amp(obs, filename='./example_plot_amp.png')
op.plot_phase(obs, filename='./example_plot_phase.png')
op.plot_snr(obs, filename='./example_plot_snr.png')
#######################################################
# compute various metrics
# compute FF metric
ff = cm.calc_ff(obs, fov=200.0)
print('FF metric value is: ', ff)
# compute BFF metric for each Stokes parameter
for stokes in ['I', 'Q', 'U', 'V']:
bff = cm.calc_bff(obs, fov=200.0, stokes=stokes)
print('Stokes ' + stokes + ' BFF metric value is: ', bff)
# compute LCG metric
lcg = cm.calc_lcg(obs)
print('LCG metric value is: ', lcg)
# compute PSS metric
pss = cm.calc_pss(obs)
print('PSS metric value (in Jy) is: ', pss)
# compute angular resolution metric with different weightings
for weighting in ['natural', 'uniform', 'robust']:
ar = cm.calc_ar(obs, artype='mean', weighting=weighting)
print('Average beam size (in uas) with ' + weighting + ' weighting is: ', ar)
#######################################################
# plot a metric versus time for the observation
op.plot_snapshot(obs, obsgen, 'FF', fov=200.0, filename='./example_plot_FF.png')
|
SmithsonianREPO_NAMEngehtsimPATH_START.@ngehtsim_extracted@ngehtsim-main@examples@example_data_generation@generate_observation.py@.PATH_END.py
|
{
"filename": "test_avg.py",
"repo_name": "NannyML/nannyml",
"repo_path": "nannyml_extracted/nannyml-main/tests/stats/test_avg.py",
"type": "Python"
}
|
# Author: Niels Nuyttens <niels@nannyml.com>
# Author: Nikolaos Perrakis <nikos@nannyml.com>
#
# License: Apache Software License 2.0
"""Tests for Drift package."""
import pytest
import pandas as pd
from typing import Tuple
from nannyml.datasets import load_synthetic_car_loan_dataset
from nannyml.stats import SummaryStatsAvgCalculator
@pytest.fixture
def binary_classification_data() -> Tuple[pd.DataFrame, pd.DataFrame]: # noqa: D103
reference, monitored, _ = load_synthetic_car_loan_dataset()
return reference.head(15_000), monitored.tail(5_000)
@pytest.fixture
def calculator_results(binary_classification_data):
reference, monitored = binary_classification_data
column_names = ['car_value', 'debt_to_income_ratio', 'driver_tenure']
calc = SummaryStatsAvgCalculator(
column_names=column_names,
chunk_size=5_000
).fit(reference)
results = calc.calculate(data=monitored)
return results
def test_stats_avg_calculator_with_default_params_should_not_fail( # noqa: D103
binary_classification_data
):
reference, monitored = binary_classification_data
try:
calc = SummaryStatsAvgCalculator(
column_names=['car_value', 'debt_to_income_ratio', 'driver_tenure'],
).fit(reference)
_ = calc.calculate(data=monitored)
except Exception:
pytest.fail()
@pytest.mark.parametrize(
'column, expected_dir',
[
('value', {
'car_value': [29660.4932, 29617.694, 29577.5972, 48706.3372],
'debt_to_income_ratio': [0.5851, 0.5827, 0.5863, 0.585],
'driver_tenure': [4.6161, 4.6169, 4.5716, 4.6028],
}),
('sampling_error', {
'car_value': [287.7624, 287.7624, 287.7624, 287.7624],
'debt_to_income_ratio': [0.0022, 0.0022, 0.0022, 0.0022],
'driver_tenure': [0.0325, 0.0325, 0.0325, 0.0325],
}),
('upper_confidence_boundary', {
'car_value': [30523.7803, 30480.9811, 30440.8843, 49569.6243],
'debt_to_income_ratio': [0.5917, 0.5893, 0.593, 0.5916],
'driver_tenure': [4.7136, 4.7144, 4.6691, 4.7003],
}),
('lower_confidence_boundary', {
'car_value': [28797.2061, 28754.4069, 28714.3101, 47843.0501],
'debt_to_income_ratio': [0.5785, 0.5761, 0.5797, 0.5784],
'driver_tenure': [4.5187, 4.5195, 4.4741, 4.5053],
}),
('upper_threshold', {
'car_value': [29720.1392, 29720.1392, 29720.1392, 29720.1392],
'debt_to_income_ratio': [0.5892, 0.5892, 0.5892, 0.5892],
'driver_tenure': [4.6651, 4.6651, 4.6651, 4.6651],
}),
('lower_threshold', {
'car_value': [29517.0504, 29517.0504, 29517.0504, 29517.0504],
'debt_to_income_ratio': [0.5802, 0.5802, 0.5802, 0.5802],
'driver_tenure': [4.538, 4.538, 4.538, 4.538],
}),
('alert', {
'car_value': [False, False, False, True],
'debt_to_income_ratio': [False, False, False, False],
'driver_tenure': [False, False, False, False],
}),
],
)
def test_stats_avg_calculator_results(calculator_results, column, expected_dir): # noqa: D103
column_names = ['car_value', 'debt_to_income_ratio', 'driver_tenure']
eval_cols = [(col, column) for col in column_names]
exp_cols = pd.MultiIndex.from_tuples(eval_cols)
expected = pd.DataFrame(expected_dir)
expected.columns = exp_cols
pd.testing.assert_frame_equal(calculator_results.to_df()[eval_cols].round(4), expected)
def test_stats_avg_calculator_returns_distinct_but_consistent_results_when_reused( # noqa: D103
binary_classification_data
):
reference, monitored = binary_classification_data
column_names = ['car_value', 'debt_to_income_ratio', 'driver_tenure']
calc = SummaryStatsAvgCalculator(
column_names=column_names,
chunk_size=5_000
).fit(reference)
results1 = calc.calculate(data=monitored)
results2 = calc.calculate(data=monitored)
assert results1 is not results2
pd.testing.assert_frame_equal(results1.to_df(), results2.to_df())
def test_stats_avg_calculator_returns_distinct_but_consistent_results_when_data_reused( # noqa: D103
binary_classification_data
):
reference, monitored = binary_classification_data
reference2 = reference.copy(deep=True)
monitored2 = monitored.copy(deep=True)
column_names = ['car_value', 'debt_to_income_ratio', 'driver_tenure']
calc = SummaryStatsAvgCalculator(
column_names=column_names,
chunk_size=5_000
).fit(reference2)
results = calc.calculate(data=monitored2) # noqa: F841
pd.testing.assert_frame_equal(monitored, monitored2)
pd.testing.assert_frame_equal(reference, reference2)
|
NannyMLREPO_NAMEnannymlPATH_START.@nannyml_extracted@nannyml-main@tests@stats@test_avg.py@.PATH_END.py
|
{
"filename": "deterministics.ipynb",
"repo_name": "statsmodels/statsmodels",
"repo_path": "statsmodels_extracted/statsmodels-main/examples/notebooks/deterministics.ipynb",
"type": "Jupyter Notebook"
}
|
# Deterministic Terms in Time Series Models
```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rc("figure", figsize=(16, 9))
plt.rc("font", size=16)
```
## Basic Use
Basic configurations can be directly constructed through `DeterministicProcess`. These can include a constant, a time trend of any order, and either a seasonal or a Fourier component.
The process requires an index, which is the index of the full-sample (or in-sample).
First, we initialize a deterministic process with a constant, a linear time trend, and a 5-period seasonal term. The `in_sample` method returns the full set of values that match the index.
```python
from statsmodels.tsa.deterministic import DeterministicProcess
index = pd.RangeIndex(0, 100)
det_proc = DeterministicProcess(index, constant=True, order=1, seasonal=True, period=5)
det_proc.in_sample()
```
The `out_of_sample` returns the next `steps` values after the end of the in-sample.
```python
det_proc.out_of_sample(15)
```
`range(start, stop)` can also be used to produce the deterministic terms over any range including in- and out-of-sample.
### Notes
* When the index is a pandas `DatetimeIndex` or a `PeriodIndex`, then `start` and `stop` can be date-like (strings, e.g., "2020-06-01", or Timestamp) or integers.
* `stop` is always included in the range. While this is not very Pythonic, it is needed since both statsmodels and Pandas include `stop` when working with date-like slices.
```python
det_proc.range(190, 210)
```
## Using a Date-like Index
Next, we show the same steps using a `PeriodIndex`.
```python
index = pd.period_range("2020-03-01", freq="M", periods=60)
det_proc = DeterministicProcess(index, constant=True, fourier=2)
det_proc.in_sample().head(12)
```
```python
det_proc.out_of_sample(12)
```
`range` accepts date-like arguments, which are usually given as strings.
```python
det_proc.range("2025-01", "2026-01")
```
This is equivalent to using the integer values 58 and 70.
```python
det_proc.range(58, 70)
```
## Advanced Construction
Deterministic processes with features not supported directly through the constructor can be created using `additional_terms` which accepts a list of `DetermisticTerm`. Here we create a deterministic process with two seasonal components: day-of-week with a 5 day period and an annual captured through a Fourier component with a period of 365.25 days.
```python
from statsmodels.tsa.deterministic import Fourier, Seasonality, TimeTrend
index = pd.period_range("2020-03-01", freq="D", periods=2 * 365)
tt = TimeTrend(constant=True)
four = Fourier(period=365.25, order=2)
seas = Seasonality(period=7)
det_proc = DeterministicProcess(index, additional_terms=[tt, seas, four])
det_proc.in_sample().head(28)
```
## Custom Deterministic Terms
The `DetermisticTerm` Abstract Base Class is designed to be subclassed to help users write custom deterministic terms. We next show two examples. The first is a broken time trend that allows a break after a fixed number of periods. The second is a "trick" deterministic term that allows exogenous data, which is not really a deterministic process, to be treated as if was deterministic. This lets use simplify gathering the terms needed for forecasting.
These are intended to demonstrate the construction of custom terms. They can definitely be improved in terms of input validation.
```python
from statsmodels.tsa.deterministic import DeterministicTerm
class BrokenTimeTrend(DeterministicTerm):
def __init__(self, break_period: int):
self._break_period = break_period
def __str__(self):
return "Broken Time Trend"
def _eq_attr(self):
return (self._break_period,)
def in_sample(self, index: pd.Index):
nobs = index.shape[0]
terms = np.zeros((nobs, 2))
terms[self._break_period :, 0] = 1
terms[self._break_period :, 1] = np.arange(self._break_period + 1, nobs + 1)
return pd.DataFrame(terms, columns=["const_break", "trend_break"], index=index)
def out_of_sample(
self, steps: int, index: pd.Index, forecast_index: pd.Index = None
):
# Always call extend index first
fcast_index = self._extend_index(index, steps, forecast_index)
nobs = index.shape[0]
terms = np.zeros((steps, 2))
# Assume break period is in-sample
terms[:, 0] = 1
terms[:, 1] = np.arange(nobs + 1, nobs + steps + 1)
return pd.DataFrame(
terms, columns=["const_break", "trend_break"], index=fcast_index
)
```
```python
btt = BrokenTimeTrend(60)
tt = TimeTrend(constant=True, order=1)
index = pd.RangeIndex(100)
det_proc = DeterministicProcess(index, additional_terms=[tt, btt])
det_proc.range(55, 65)
```
Next, we write a simple "wrapper" for some actual exogenous data that simplifies constructing out-of-sample exogenous arrays for forecasting.
```python
class ExogenousProcess(DeterministicTerm):
def __init__(self, data):
self._data = data
def __str__(self):
return "Custom Exog Process"
def _eq_attr(self):
return (id(self._data),)
def in_sample(self, index: pd.Index):
return self._data.loc[index]
def out_of_sample(
self, steps: int, index: pd.Index, forecast_index: pd.Index = None
):
forecast_index = self._extend_index(index, steps, forecast_index)
return self._data.loc[forecast_index]
```
```python
import numpy as np
gen = np.random.default_rng(98765432101234567890)
exog = pd.DataFrame(gen.integers(100, size=(300, 2)), columns=["exog1", "exog2"])
exog.head()
```
```python
ep = ExogenousProcess(exog)
tt = TimeTrend(constant=True, order=1)
# The in-sample index
idx = exog.index[:200]
det_proc = DeterministicProcess(idx, additional_terms=[tt, ep])
```
```python
det_proc.in_sample().head()
```
```python
det_proc.out_of_sample(10)
```
## Model Support
The only model that directly supports `DeterministicProcess` is `AutoReg`. A custom term can be set using the `deterministic` keyword argument.
**Note**: Using a custom term requires that `trend="n"` and `seasonal=False` so that all deterministic components must come from the custom deterministic term.
### Simulate Some Data
Here we simulate some data that has an weekly seasonality captured by a Fourier series.
```python
gen = np.random.default_rng(98765432101234567890)
idx = pd.RangeIndex(200)
det_proc = DeterministicProcess(idx, constant=True, period=52, fourier=2)
det_terms = det_proc.in_sample().to_numpy()
params = np.array([1.0, 3, -1, 4, -2])
exog = det_terms @ params
y = np.empty(200)
y[0] = det_terms[0] @ params + gen.standard_normal()
for i in range(1, 200):
y[i] = 0.9 * y[i - 1] + det_terms[i] @ params + gen.standard_normal()
y = pd.Series(y, index=idx)
ax = y.plot()
```
The model is then fit using the `deterministic` keyword argument. `seasonal` defaults to False but `trend` defaults to `"c"` so this needs to be changed.
```python
from statsmodels.tsa.api import AutoReg
mod = AutoReg(y, 1, trend="n", deterministic=det_proc)
res = mod.fit()
print(res.summary())
```
We can use the `plot_predict` to show the predicted values and their prediction interval. The out-of-sample deterministic values are automatically produced by the deterministic process passed to `AutoReg`.
```python
fig = res.plot_predict(200, 200 + 2 * 52, True)
```
```python
auto_reg_forecast = res.predict(200, 211)
auto_reg_forecast
```
## Using with other models
Other models do not support `DeterministicProcess` directly. We can instead manually pass any deterministic terms as `exog` to model that support exogenous values.
Note that `SARIMAX` with exogenous variables is OLS with SARIMA errors so that the model is
$$
\begin{align*}
\nu_t & = y_t - x_t \beta \\
(1-\phi(L))\nu_t & = (1+\theta(L))\epsilon_t.
\end{align*}
$$
The parameters on deterministic terms are not directly comparable to `AutoReg` which evolves according to the equation
$$
(1-\phi(L)) y_t = x_t \beta + \epsilon_t.
$$
When $x_t$ contains only deterministic terms, these two representation are equivalent (assuming $\theta(L)=0$ so that there is no MA).
```python
from statsmodels.tsa.api import SARIMAX
det_proc = DeterministicProcess(idx, period=52, fourier=2)
det_terms = det_proc.in_sample()
mod = SARIMAX(y, order=(1, 0, 0), trend="c", exog=det_terms)
res = mod.fit(disp=False)
print(res.summary())
```
The forecasts are similar but differ since the parameters of the `SARIMAX` are estimated using MLE while `AutoReg` uses OLS.
```python
sarimax_forecast = res.forecast(12, exog=det_proc.out_of_sample(12))
df = pd.concat([auto_reg_forecast, sarimax_forecast], axis=1)
df.columns = columns = ["AutoReg", "SARIMAX"]
df
```
|
statsmodelsREPO_NAMEstatsmodelsPATH_START.@statsmodels_extracted@statsmodels-main@examples@notebooks@deterministics.ipynb@.PATH_END.py
|
{
"filename": "_showtickprefix.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/choroplethmapbox/colorbar/_showtickprefix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowtickprefixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="showtickprefix",
parent_name="choroplethmapbox.colorbar",
**kwargs
):
super(ShowtickprefixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
role=kwargs.pop("role", "style"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@choroplethmapbox@colorbar@_showtickprefix.py@.PATH_END.py
|
{
"filename": "sim_chime_repeaters.py",
"repo_name": "TRASAL/frbpoppy",
"repo_path": "frbpoppy_extracted/frbpoppy-master/tests/dm_snr/sim_chime_repeaters.py",
"type": "Python"
}
|
"""Plot DM/SNR distributions of repeater populations observed with CHIME."""
import numpy as np
import matplotlib.pyplot as plt
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, plot
from frbpoppy import split_pop, pprint
from tests.convenience import hist, plot_aa_style, rel_path
DAYS = 4
INTERACTIVE_PLOT = False
PLOTTING_LIMIT_N_SRCS = 0
SNR = True
r = CosmicPopulation.simple(n_srcs=int(1e4), n_days=DAYS, repeaters=True)
r.set_dist(z_max=2)
r.set_lum(model='powerlaw', low=1e35, high=1e45, power=-1.7,
per_source='different')
r.set_time(model='poisson', rate=3)
r.set_dm_igm(model='ioka', slope=1000, std=0)
r.set_dm(mw=False, igm=True, host=False)
r.set_w('constant', value=1)
r.generate()
# Set up survey
survey = Survey('chime-frb', n_days=DAYS)
survey.set_beam(model='chime-frb')
survey.snr_limit = 1e-13
surv_pop = SurveyPopulation(r, survey)
pprint(f'{r.n_bursts()}:{surv_pop.n_bursts()}')
pprint(f'{surv_pop.n_sources()} sources detected')
if r.n_bursts() < PLOTTING_LIMIT_N_SRCS:
pprint('Not sufficient FRB sources for plotting')
exit()
# Split population into seamingly one-off and repeater populations
mask = ((~np.isnan(surv_pop.frbs.time)).sum(1) > 1)
pop_rep, pop_one = split_pop(surv_pop, mask)
pop_rep.name += ' (> 1 burst)'
pop_one.name += ' (1 burst)'
if INTERACTIVE_PLOT:
plot(r, pop_rep, pop_one, frbcat=False, mute=False)
# Plot dm distribution
if SNR:
plot_aa_style(cols=2)
f, (ax1, ax2) = plt.subplots(1, 2)
else:
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
pops = (r, pop_rep, pop_one)
for i, pop in enumerate(pops):
# Distinguish populations
if pop.name.endswith('(1 burst)'):
label = '1 burst'
linestyle = 'solid'
elif pop.name.endswith('(> 1 burst)'):
label = '$>$1 burst'
linestyle = 'dashed'
else:
label = 'cosmic'
linestyle = 'dashdot'
pprint(f'Number of bursts in {label}: {pop.n_bursts()}')
# Do stuff with data
dm = pop.frbs.dm
x, y = hist(dm)
# Plot DM distributions
ax1.step(x, y, where='mid', linestyle=linestyle, label=label,
color=colors[i])
# Plot fluence distributions
snr = pop.frbs.snr
if snr is None:
continue
if not SNR:
continue
try:
ax2.step(*hist(snr, bin_type='log'), where='mid', linestyle=linestyle,
color=colors[i])
except ValueError:
pprint('Zero sources available to plot')
continue
ax1.set_xlabel(r'DM ($\textrm{pc}\ \textrm{cm}^{-3}$)')
ax1.set_ylabel('Fraction')
if SNR:
ax2.set_xlabel(r'SNR')
ax2.set_xscale('log')
ax2.set_yscale('log')
ax2.yaxis.tick_right()
plt.figlegend(loc='upper center', ncol=len(pops), framealpha=1)
else:
plt.figlegend(loc='upper center', ncol=3, framealpha=1, prop={'size': 8},
bbox_to_anchor=(0.5, 1.07), bbox_transform=ax1.transAxes)
plt.tight_layout()
plt.savefig(rel_path('plots/sim_dm_snr_chime.pdf'))
plt.clf()
|
TRASALREPO_NAMEfrbpoppyPATH_START.@frbpoppy_extracted@frbpoppy-master@tests@dm_snr@sim_chime_repeaters.py@.PATH_END.py
|
{
"filename": "_tickmode.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattermapbox/marker/colorbar/_tickmode.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickmodeValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="tickmode",
parent_name="scattermapbox.marker.colorbar",
**kwargs,
):
super(TickmodeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
implied_edits=kwargs.pop("implied_edits", {}),
values=kwargs.pop("values", ["auto", "linear", "array"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattermapbox@marker@colorbar@_tickmode.py@.PATH_END.py
|
{
"filename": "tod.py",
"repo_name": "hpc4cmb/toast",
"repo_path": "toast_extracted/toast-main/src/toast/tod/tod.py",
"type": "Python"
}
|
# Copyright (c) 2015-2020 by the parties listed in the AUTHORS file.
# All rights reserved. Use of this source code is governed by
# a BSD-style license that can be found in the LICENSE file.
from ..mpi import MPI
import numpy as np
from ..dist import distribute_samples
from ..cache import Cache
from .. import qarray as qa
from ..timing import function_timer
from .interval import Interval
class TOD(object):
"""
Base class for an object that provides detector pointing and
timestreams for a single observation.
This class provides high-level functions that are common to all derived
classes. It also defines the internal methods that should be overridden
by all derived classes. These internal methods throw an exception if they
are called. A TOD base class should never be directly instantiated.
Args:
mpicomm (mpi4py.MPI.Comm): the MPI communicator over which the
data is distributed, or None.
detectors (list): The list of detector names.
samples (int): The total number of samples.
detindx (dict): the detector indices for use in simulations. Default is
{ x[0] : x[1] for x in zip(detectors, range(len(detectors))) }.
detranks (int): The dimension of the process grid in the detector
direction. If not None, the MPI communicator size must be evenly divisible
by this number.
detbreaks (list): Optional list of hard breaks in the detector
distribution.
sampsizes (list): Optional list of sample chunk sizes which
cannot be split.
sampbreaks (list): Optional list of hard breaks in the sample
distribution.
meta (dict): Optional dictionary of metadata properties.
"""
def __init__(
self,
mpicomm,
detectors,
samples,
detindx=None,
detranks=1,
detbreaks=None,
sampsizes=None,
sampbreaks=None,
meta=None,
):
self._mpicomm = mpicomm
self._detranks = detranks
self._sampranks = 1
self._rank_det = 0
self._rank_samp = 0
self._comm_row = None
self._comm_col = None
rank = 0
if mpicomm is None:
if detranks != 1:
raise RuntimeError("MPI is disabled, so detranks must equal 1")
else:
rank = mpicomm.rank
if mpicomm.size % detranks != 0:
raise RuntimeError(
"The number of detranks ({}) does not divide evenly into the "
"communicator size ({})".format(detranks, mpicomm.size)
)
self._sampranks = mpicomm.size // detranks
self._rank_det = mpicomm.rank // self._sampranks
self._rank_samp = mpicomm.rank % self._sampranks
# Split the main communicator into process row and column
# communicators, since this is useful for gathering data in some
# operations.
if self._sampranks == 1:
self._comm_row = MPI.COMM_SELF
else:
self._comm_row = self._mpicomm.Split(self._rank_det, self._rank_samp)
if self._detranks == 1:
self._comm_col = MPI.COMM_SELF
else:
self._comm_col = self._mpicomm.Split(self._rank_samp, self._rank_det)
self._dets = detectors
self._nsamp = samples
self._sizes = sampsizes
self.meta = meta
if meta is None:
self.meta = {}
if detindx is not None:
for d in self._dets:
if d not in detindx:
raise RuntimeError("detindx must have a value for every detector")
self._detindx = detindx
else:
self._detindx = {x[0]: x[1] for x in zip(detectors, range(len(detectors)))}
# if sizes is specified, it must be consistent with
# the total number of samples.
if self._sizes is not None:
test = np.sum(self._sizes)
if samples != test:
raise RuntimeError(
"Sum of sampsizes ({}) does not equal total samples ({})"
"".format(test, samples)
)
(self._dist_dets, self._dist_samples, self._dist_sizes) = distribute_samples(
self._mpicomm,
self._dets,
self._nsamp,
detranks=self._detranks,
detbreaks=detbreaks,
sampsizes=sampsizes,
sampbreaks=sampbreaks,
)
if self._sizes is None:
# in this case, the chunks just come from the uniform distribution.
self._sizes = [self._dist_samples[x][1] for x in range(self._sampranks)]
if rank == 0:
# check that all processes have some data, otherwise print warning
for d in range(self._detranks):
if len(self._dist_dets[d]) == 0:
print(
"WARNING: detector rank {} has no detectors"
" assigned.".format(d)
)
for r in range(self._sampranks):
if self._dist_samples[r][1] <= 0:
print(
"WARNING: sample rank {} has no data assigned "
"in TOD.".format(r)
)
self.cache = Cache()
TIMESTAMP_NAME = "timestamps"
"""Default cache name for timestamps."""
COMMON_FLAG_NAME = "common_flags"
"""Default cache name for common flags."""
VELOCITY_NAME = "velocity"
"""Default cache name for velocity."""
POSITION_NAME = "position"
"""Default cache name for position."""
SIGNAL_NAME = "signal"
"""Default cache name for signal."""
FLAG_NAME = "flags"
"""Default cache name for flags."""
POINTING_NAME = "quat"
"""Default cache name for pointing quaternions."""
HWP_ANGLE_NAME = "hwp_angle"
"""Default cache name for HWP angle."""
def __repr__(self):
clsname = self.__class__.__name__
csize = self.cache.report(silent=True)
lsamp = self._dist_samples[self._rank_samp]
ldet = self._dist_dets[self._rank_det]
ldetstr = [" {}".format(x) for x in ldet]
lines = [
" {} total detectors and {} total samples".format(
len(self._dets), self._nsamp
),
" Using MPI communicator {}".format(self._mpicomm),
" In grid dimensions {} sample ranks x {} detranks".format(
self._sampranks, self._detranks
),
" Process at ({}, {}) in grid has data for:".format(
self._rank_samp, self._rank_det
),
" Samples {} - {} (inclusive)".format(lsamp[0], lsamp[0] + lsamp[1] - 1),
" Detectors:",
]
lines.extend(ldetstr)
lines.append(" Cache contains {} bytes".format(csize))
return "<{}\n{}\n>".format(clsname, "\n".join(lines))
@property
def detectors(self):
"""
(list): The total list of detectors.
"""
return self._dets
def detoffset(self):
"""
Return dictionary of detector quaternions.
This returns a dictionary with the detector names as the keys and the
values are 4-element numpy arrays containing the quaternion offset
from the boresight.
Args:
None
Returns (dict):
the dictionary of quaternions.
"""
raise NotImplementedError("Fell through to TOD base class method")
return None
@property
def detindx(self):
"""
(dict): The detector indices.
"""
return self._detindx
@property
def local_dets(self):
"""
(list): The detectors assigned to this process.
"""
return self._dist_dets[self._rank_det]
@property
def total_chunks(self):
"""
(list): the full list of sample chunk sizes that were used in the
data distribution.
"""
return self._sizes
@property
def dist_chunks(self):
"""
(list): this is a list of 2-tuples, one for each column of the process
grid. Each element of the list is the same as the information returned
by the "local_chunks" member for a given process column.
"""
return self._dist_sizes
@property
def local_chunks(self):
"""
(2-tuple): the first element of the tuple is the index of the
first chunk assigned to this process (i.e. the index in the list
given by the "total_chunks" member). The second element of the
tuple is the number of chunks assigned to this process.
"""
return self._dist_sizes[self._rank_samp]
def local_times(self, name=None, **kwargs):
"""Timestamps covering locally stored data.
Args:
name (str): Optional cache key to use.
Returns:
A cache reference to a timestamp vector. If 'name' is None
a default name 'timestamps' is used and the vector may be
constructed and cached using the 'read_times' method.
If 'name' is given, then the times must already be cached.
"""
if name is None:
cachename = self.TIMESTAMP_NAME
if not self.cache.exists(cachename):
times = self.read_times(**kwargs)
self.cache.put(cachename, times)
else:
cachename = name
return self.cache.reference(cachename)
def local_signal(self, det, name=None, **kwargs):
"""Locally stored signal.
Args:
det (str): Name of the detector.
name (str): Optional cache key to use.
Returns:
A cache reference to a signal vector. If 'name' is None
a default name 'signal' is used and the vector may be
constructed and cached using the 'read' method.
If 'name' is given, then the signal must already be cached.
"""
if name is None:
cachename = "{}_{}".format(self.SIGNAL_NAME, det)
if not self.cache.exists(cachename):
signal = self.read(detector=det, **kwargs)
self.cache.put(cachename, signal)
else:
cachename = "{}_{}".format(name, det)
return self.cache.reference(cachename)
def local_pointing(self, det, name=None, **kwargs):
"""Locally stored pointing.
Args:
det (str): Name of the detector.
name (str): Optional cache key to use.
Returns:
A cache reference to a pointing array. If 'name' is None
a default name 'quat' is used and the array may be
constructed and cached using the 'read_pntg' method.
If 'name' is given, then the pointing must already be cached.
"""
if name is None:
cachename = "{}_{}".format(self.POINTING_NAME, det)
if not self.cache.exists(cachename):
quats = self.read_pntg(detector=det, **kwargs)
self.cache.put(cachename, quats)
else:
cachename = "{}_{}".format(name, det)
return self.cache.reference(cachename)
def local_position(self, name=None, **kwargs):
"""Locally stored position.
Args:
name (str): Optional cache key to use.
Returns:
A cache reference to a position array. If 'name' is None
a default name 'position' is used and the array may be
constructed and cached using the 'read_position' method.
If 'name' is given, then the position must already be cached.
"""
if name is None:
cachename = self.POSITION_NAME
if not self.cache.exists(cachename):
pos = self.read_position(**kwargs)
self.cache.put(cachename, pos)
else:
cachename = name
return self.cache.reference(cachename)
def local_velocity(self, name=None, **kwargs):
"""Locally stored velocity.
Args:
name (str): Optional cache key to use.
Returns:
A cache reference to a velocity array. If 'name' is None
a default name 'velocity' is used and the array may be
constructed and cached using the 'read_velocity' method.
If 'name' is given, then the velocity must already be cached.
"""
if name is None:
cachename = self.VELOCITY_NAME
if not self.cache.exists(cachename):
vel = self.read_velocity(**kwargs)
self.cache.put(cachename, vel)
else:
cachename = name
return self.cache.reference(cachename)
def local_flags(self, det, name=None, **kwargs):
"""Locally stored flags.
Args:
det (str): Name of the detector.
name (str): Optional cache key to use.
Returns:
A cache reference to a flag vector. If 'name' is None
a default name 'flags' is used and the vector may be
constructed and cached using the 'read_flags' method.
If 'name' is given, then the flags must already be cached.
"""
if name is None:
cachename = "{}_{}".format(self.FLAG_NAME, det)
if not self.cache.exists(cachename):
flags = self.read_flags(detector=det, **kwargs)
self.cache.put(cachename, flags)
else:
cachename = "{}_{}".format(name, det)
return self.cache.reference(cachename)
def local_common_flags(self, name=None, **kwargs):
"""Locally stored common flags.
Args:
name (str): Optional cache key to use.
Returns:
A cache reference to a common flag vector. If 'name' is None
a default name 'common_flags' is used and the vector may be
constructed and cached using the 'read_common_flags' method.
If 'name' is given, then the flags must already be cached.
"""
if name is None:
cachename = self.COMMON_FLAG_NAME
if not self.cache.exists(cachename):
common_flags = self.read_common_flags(**kwargs)
self.cache.put(cachename, common_flags)
else:
cachename = name
return self.cache.reference(cachename)
def local_hwp_angle(self, name=None, **kwargs):
"""Locally stored half-wave plate angle.
Args:
name (str): Optional cache key to use.
Returns:
A cache reference to a hwp angle vector. If 'name' is None
a default name 'hwp_angle' is used and the vector may be
constructed and cached using the 'read_hwp_angle' method.
If 'name' is given, then the angles must already be cached.
"""
if name is None:
cachename = self.HWP_ANGLE_NAME
if not self.cache.exists(cachename):
hwp_angle = self.read_hwp_angle(**kwargs)
if hwp_angle is None:
return None
self.cache.put(cachename, hwp_angle)
else:
cachename = name
return self.cache.reference(cachename)
def local_intervals(self, intervals):
"""Translate observation-wide intervals into local sample indices."""
if intervals is None:
intervals = [
Interval(start=0, stop=0, first=0, last=self.total_samples - 1)
]
offset, nsamp = self.local_samples
local_intervals = []
times = self.local_times()
if len(times) != nsamp:
raise RuntimeError(
"Length of cached timestamps does not match local samples. "
"Cannot produce local intervals."
)
for ival in intervals:
previous_last = None
if ival.last >= offset and ival.first < offset + nsamp:
local_first = max(0, ival.first - offset)
if previous_last is not None and previous_last >= local_first:
raise RuntimeError("Provided intervals overlap")
local_last = min(nsamp - 1, ival.last - offset)
previous_last = local_last
local_start = times[local_first]
local_stop = times[local_last]
local_intervals.append(
Interval(
start=local_start,
stop=local_stop,
first=local_first,
last=local_last,
)
)
return local_intervals
@property
def total_samples(self):
"""
(int): the total number of samples in this TOD.
"""
return self._nsamp
@property
def dist_samples(self):
"""
(list): This is a list of 2-tuples, with one element per column
of the process grid. Each tuple is the same information
returned by the "local_samples" member for the corresponding
process grid column rank.
"""
return self._dist_samples
@property
def local_samples(self):
"""
(2-tuple): The first element of the tuple is the first global
sample assigned to this process. The second element of
the tuple is the number of samples assigned to this
process.
"""
return self._dist_samples[self._rank_samp]
@property
def mpicomm(self):
"""
(mpi4py.MPI.Comm): the communicator assigned to this TOD.
"""
return self._mpicomm
@property
def grid_size(self):
"""
(tuple): the dimensions of the process grid in (detector, sample)
directions.
"""
return (self._detranks, self._sampranks)
@property
def grid_ranks(self):
"""
(tuple): the ranks of this process in the (detector, sample)
directions.
"""
return (self._rank_det, self._rank_samp)
@property
def grid_comm_row(self):
"""
(mpi4py.MPI.Comm): a communicator across all detectors in the same
row of the process grid (or None).
"""
return self._comm_row
@property
def grid_comm_col(self):
"""
(mpi4py.MPI.Comm): a communicator across all detectors in the same
column of the process grid (or None).
"""
return self._comm_col
def _get(self, detector, start, n):
raise NotImplementedError("Fell through to TOD._get base class method")
return None
def _put(self, detector, start, data):
raise NotImplementedError("Fell through to TOD._put base class method")
return
def _get_boresight(self, start, n):
raise NotImplementedError(
"Fell through to TOD._get_boresight base class method"
)
return None
def _put_boresight(self, start, data):
raise NotImplementedError(
"Fell through to TOD._put_boresight base class method"
)
return
def _get_boresight_azel(self, start, n):
raise NotImplementedError(
"Fell through to TOD._get_boresight_azel base class method"
)
return None
def _put_boresight_azel(self, start, data):
raise NotImplementedError(
"Fell through to TOD._put_boresight_azel base class method"
)
return
def _get_pntg(self, detector, start, n):
raise NotImplementedError("Fell through to TOD._get_pntg base class method")
return None
def _put_pntg(self, detector, start, data):
raise NotImplementedError("Fell through to TOD._put_pntg base class method")
return
def _get_flags(self, detector, start, n):
raise NotImplementedError("Fell through to TOD._get_flags base class method")
return None
def _put_flags(self, detector, start, flags):
raise NotImplementedError("Fell through to TOD._put_flags base class method")
return
def _get_common_flags(self, start, n):
raise NotImplementedError(
"Fell through to TOD._get_common_flags base class method"
)
return None
def _put_common_flags(self, start, flags):
raise NotImplementedError(
"Fell through to TOD._put_common_flags base class method"
)
return None
def _get_hwp_angle(self, start, n):
raise NotImplementedError(
"Fell through to TOD._get_hwp_angle base class method"
)
return None
def _put_hwp_angle(self, start, flags):
raise NotImplementedError(
"Fell through to TOD._put_hwp_angle base class method"
)
return None
def _get_times(self, start, n):
raise NotImplementedError("Fell through to TOD._get_times base class method")
return None
def _put_times(self, start, stamps):
raise NotImplementedError("Fell through to TOD._put_times base class method")
return None
def _get_position(self, start, n):
raise NotImplementedError("Fell through to TOD._get_position base class method")
return None
def _put_position(self, start, pos):
raise NotImplementedError("Fell through to TOD._put_position base class method")
return
def _get_velocity(self, start, n):
raise NotImplementedError("Fell through to TOD._get_velocity base class method")
return None
def _put_velocity(self, start, vel):
raise NotImplementedError("Fell through to TOD._put_velocity base class method")
return
# Read and write the common timestamps
@function_timer
def read_times(self, local_start=0, n=0, **kwargs):
"""Read timestamps.
This reads the common set of timestamps that apply to all detectors
in the TOD.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
(array): a numpy array containing the timestamps.
"""
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read times- process has no assigned local samples"
)
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_times(local_start, n, **kwargs)
@function_timer
def write_times(self, local_start=0, stamps=None, **kwargs):
"""Write timestamps.
This writes the common set of timestamps that apply to all detectors
in the TOD.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
stamps (array): the array of timestamps to write.
"""
if stamps is None:
raise ValueError("you must specify the vector of time stamps")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write times- process has no assigned local samples"
)
if (local_start < 0) or (local_start + stamps.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + stamps.shape[0] - 1)
)
self._put_times(local_start, stamps, **kwargs)
return
# Read and write telescope boresight pointing
@function_timer
def read_boresight(self, local_start=0, n=0, **kwargs):
"""Read boresight quaternion pointing.
This returns the pointing of the boresight in quaternions.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
A 2D array of shape (n, 4)
"""
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError("cannot read boresight- process has no local samples")
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_boresight(local_start, n, **kwargs)
@function_timer
def write_boresight(self, local_start=0, data=None, **kwargs):
"""Write boresight quaternion pointing.
This writes the quaternion pointing for the boresight.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
data (array): 2D array of quaternions with shape[1] == 4.
"""
if len(data.shape) != 2:
raise ValueError("data should be a 2D array")
if data.shape[1] != 4:
raise ValueError("data should have second dimension of size 4")
if self.local_samples[1] <= 0:
raise RuntimeError("cannot write boresight- process has no local samples")
if (local_start < 0) or (local_start + data.shape[0] > self.local_samples[1]):
raise ValueError("local sample range is invalid")
self._put_boresight(local_start, data, **kwargs)
return
@function_timer
def read_boresight_azel(self, local_start=0, n=0, **kwargs):
"""Read boresight Azimuth / Elevation quaternion pointing.
This returns the pointing of the boresight in the horizontal coordinate
system, if it exists.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
A 2D array of shape (n, 4)
Raises:
NotImplementedError: if the telescope is not on the Earth.
"""
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError("cannot read boresight- process has no local samples")
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_boresight_azel(local_start, n, **kwargs)
@function_timer
def write_boresight_azel(self, local_start=0, data=None, **kwargs):
"""Write boresight Azimuth / Elevation quaternion pointing.
This writes the quaternion pointing for the boresight in the horizontal
coordinate system, if it exists.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
data (array): 2D array of quaternions with shape[1] == 4.
Raises:
RuntimeError or AttributeError : if the telescope is not on
the Earth.
"""
if len(data.shape) != 2:
raise ValueError("data should be a 2D array")
if data.shape[1] != 4:
raise ValueError("data should have second dimension of size 4")
if self.local_samples[1] <= 0:
raise RuntimeError("cannot write boresight- process has no local samples")
if (local_start < 0) or (local_start + data.shape[0] > self.local_samples[1]):
raise ValueError("local sample range is invalid")
self._put_boresight_azel(local_start, data, **kwargs)
return
# Read and write detector data
@function_timer
def read(self, detector=None, local_start=0, n=0, **kwargs):
"""Read detector data.
This returns the timestream data for a single detector.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
An array containing the data.
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError("cannot read- process has no assigned local samples")
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get(detector, local_start, n, **kwargs)
@function_timer
def write(self, detector=None, local_start=0, data=None, **kwargs):
"""Write detector data.
This writes the detector data.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
data (array): the data array.
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if data is None:
raise ValueError("data array must be specified")
if self.local_samples[1] <= 0:
raise RuntimeError("cannot write- process has no assigned local samples")
if (local_start < 0) or (local_start + data.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + data.shape[0] - 1)
)
self._put(detector, local_start, data, **kwargs)
return
# Read and write detector quaternion pointing
@function_timer
def read_pntg(self, detector=None, local_start=0, n=0, **kwargs):
"""Read detector quaternion pointing.
This returns the pointing for a single detector in quaternions.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
A 2D array of shape (n, 4)
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read pntg- process has no assigned local samples"
)
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_pntg(detector, local_start, n, **kwargs)
@function_timer
def write_pntg(self, detector=None, local_start=0, data=None, **kwargs):
"""Write detector quaternion pointing.
This writes the quaternion pointing for a single detector.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
data (array): 2D array of quaternions with shape[1] == 4.
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if data is None:
raise ValueError("data must be specified")
if len(data.shape) != 2:
raise ValueError("data should be a 2D array")
if data.shape[1] != 4:
raise ValueError("data should have second dimension of size 4")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write pntg- process has no assigned local samples"
)
if (local_start < 0) or (local_start + data.shape[0] > self.local_samples[1]):
raise ValueError("local sample range is invalid")
self._put_pntg(detector, local_start, data, **kwargs)
return
# Read and write detector flags
@function_timer
def read_flags(self, detector=None, local_start=0, n=0, **kwargs):
"""Read detector flags.
This returns the detector-specific flags.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
An array containing the detector flags.
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read flags- process has no assigned local samples"
)
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_flags(detector, local_start, n, **kwargs)
@function_timer
def read_common_flags(self, local_start=0, n=0, **kwargs):
"""Read common flags.
This reads the common set of flags that should be applied to all
detectors.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
(array): a numpy array containing the flags.
"""
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read common flags- process has no assigned local samples"
)
if n == 0:
n = self.local_samples[1] - local_start
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_common_flags(local_start, n, **kwargs)
@function_timer
def write_common_flags(self, local_start=0, flags=None, **kwargs):
"""Write common flags.
This writes the common set of flags that should be applied to all
detectors.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
flags (array): array containing the flags to write.
"""
if flags is None:
raise ValueError("flags must be specified")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write common flags- process has no assigned local samples"
)
if (local_start < 0) or (local_start + flags.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + flags.shape[0] - 1)
)
self._put_common_flags(local_start, flags, **kwargs)
return
@function_timer
def read_hwp_angle(self, local_start=0, n=0, **kwargs):
"""Read half-wave plate angle
This reads the common HWP angle that should be applied to all
detectors.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
(array): a numpy array containing the angles or None if the
angle is not defined.
"""
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read HWP angle- process has no assigned local samples"
)
if n == 0:
n = self.local_samples[1] - local_start
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
try:
hwpangle = self._get_hwp_angle(local_start, n, **kwargs)
except:
hwpangle = None
return hwpangle
@function_timer
def write_hwp_angle(self, local_start=0, hwpangle=None, **kwargs):
"""Write half-wave plate angle
This writes the common HWP angle that should be applied to all
detectors.
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
flags (array): array containing the flags to write.
"""
if hwpangle is None:
raise ValueError("hwpangle must be specified")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write HWP angle- process has no assigned local samples"
)
if (local_start < 0) or (local_start + flags.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + flags.shape[0] - 1)
)
self._put_hwp_angle(local_start, flags, **kwargs)
return
@function_timer
def write_flags(self, detector=None, local_start=0, flags=None, **kwargs):
"""Write detector flags.
This writes the detector-specific flags.
Args:
detector (str): the name of the detector.
local_start (int): the sample offset relative to the first locally
assigned sample.
flags (array): the detector flags.
"""
if detector is None:
raise ValueError("you must specify the detector")
if detector not in self.local_dets:
raise ValueError("detector {} not found".format(detector))
if flags is None:
raise ValueError("flags must be specified")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write flags- process has no assigned local samples"
)
if (local_start < 0) or (local_start + flags.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + flags.shape[0] - 1)
)
self._put_flags(detector, local_start, flags, **kwargs)
return
# Read and write telescope position
@function_timer
def read_position(self, local_start=0, n=0, **kwargs):
"""Read telescope position.
This reads the telescope position in solar system barycenter
coordinates (in Kilometers).
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
(array): a 2D numpy array containing the x,y,z coordinates at each
sample.
"""
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read position- process has no assigned local samples"
)
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_position(local_start, n, **kwargs)
@function_timer
def write_position(self, local_start=0, pos=None, **kwargs):
"""Write telescope position.
This writes the telescope position in solar system barycenter
coordinates (in Kilometers).
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
pos (array): the 2D array of x,y,z coordinates at each sample.
"""
if pos is None:
raise ValueError("you must specify the array of coordinates")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write position- process has no assigned local samples"
)
if (local_start < 0) or (local_start + pos.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + pos.shape[0] - 1)
)
self._put_position(local_start, pos, **kwargs)
return
# Read and write telescope velocity
@function_timer
def read_velocity(self, local_start=0, n=0, **kwargs):
"""Read telescope velocity.
This reads the telescope velocity in solar system barycenter
coordinates (in Kilometers/s).
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
n (int): the number of samples to read. If zero, read to end.
Returns:
(array): a 2D numpy array containing the x,y,z velocity components
at each sample.
"""
if n == 0:
n = self.local_samples[1] - local_start
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot read position- process has no assigned local samples"
)
if (local_start < 0) or (local_start + n > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + n - 1)
)
return self._get_velocity(local_start, n, **kwargs)
@function_timer
def write_velocity(self, local_start=0, vel=None, **kwargs):
"""Write telescope velocity.
This writes the telescope velocity in solar system barycenter
coordinates (in Kilometers/s).
Args:
local_start (int): the sample offset relative to the first locally
assigned sample.
vel (array): the 2D array of x,y,z velocity components at each
sample.
"""
if vel is None:
raise ValueError("you must specify the array of velocities.")
if self.local_samples[1] <= 0:
raise RuntimeError(
"cannot write times- process has no assigned local samples"
)
if (local_start < 0) or (local_start + vel.shape[0] > self.local_samples[1]):
raise ValueError(
"local sample range {} - {} is invalid"
"".format(local_start, local_start + vel.shape[0] - 1)
)
self._put_velocity(local_start, vel, **kwargs)
return
class TODCache(TOD):
"""TOD class that uses a memory cache for storage.
This class simply uses a manually managed Cache object to store time
ordered data. You must "write" the data before you can "read" it.
Args:
mpicomm (mpi4py.MPI.Comm): the MPI communicator over which the
data is distributed (or None).
detectors (list): The list of detector names.
samples (int): The total number of samples.
detindx (dict): the detector indices for use in simulations. Default is
{ x[0] : x[1] for x in zip(detectors, range(len(detectors))) }.
detquats (dict): Dictionary of detector quaternions.
detranks (int): The dimension of the process grid in the detector
direction. The MPI communicator size must be evenly divisible
by this number.
detbreaks (list): Optional list of hard breaks in the detector
distribution.
sampsizes (list): Optional list of sample chunk sizes which
cannot be split.
sampbreaks (list): Optional list of hard breaks in the sample
distribution.
"""
def __init__(
self,
mpicomm,
detectors,
samples,
detindx=None,
detquats=None,
detranks=1,
detbreaks=None,
sampsizes=None,
sampbreaks=None,
):
super().__init__(
mpicomm,
detectors,
samples,
detindx=detindx,
detranks=detranks,
detbreaks=detbreaks,
sampsizes=sampsizes,
sampbreaks=sampbreaks,
)
self._detquats = detquats
self._pref_detdata = self.SIGNAL_NAME + "_" # "toast_tod_detdata_"
self._pref_detflags = self.FLAG_NAME + "_" # "toast_tod_detflags_"
self._pref_detpntg = "toast_tod_detpntg_"
self._bore = "toast_boresight"
self._bore_azel = "toast_boresight_azel"
self._common = self.COMMON_FLAG_NAME # "toast_tod_common_flags"
self._stamps = self.TIMESTAMP_NAME # "toast_tod_stamps"
self._pos = "toast_tod_pos"
self._vel = "toast_tod_vel"
def detoffset(self):
if self._detquats is None:
raise NotImplementedError("TODCache does not contain detector quaternions.")
return None
else:
return self._detquats
# This class just uses a Cache object to store things.
def _get(self, detector, start, n):
if detector not in self.local_dets:
raise ValueError(
"detector {} not assigned to local process".format(detector)
)
cachedata = "{}{}".format(self._pref_detdata, detector)
if not self.cache.exists(cachedata):
raise ValueError("detector {} data not yet written".format(detector))
dataref = self.cache.reference(cachedata)[start : start + n]
return dataref
def _put(self, detector, start, data):
if detector not in self.local_dets:
raise ValueError(
"detector {} not assigned to local process".format(detector)
)
cachedata = "{}{}".format(self._pref_detdata, detector)
if not self.cache.exists(cachedata):
self.cache.create(cachedata, np.float64, (self.local_samples[1],))
n = data.shape[0]
refdata = self.cache.reference(cachedata)[start : start + n]
refdata[:] = data
return
def _get_boresight(self, start, n):
if not self.cache.exists(self._bore):
raise ValueError("boresight not yet written")
ref = self.cache.reference(self._bore)[start : start + n, :]
return ref
def _put_boresight(self, start, data):
if not self.cache.exists(self._bore):
self.cache.create(self._bore, np.float64, (self.local_samples[1], 4))
ref = self.cache.reference(self._bore)
ref[start : (start + data.shape[0]), :] = data
return
def _get_boresight_azel(self, start, n):
if not self.cache.exists(self._bore_azel):
raise ValueError("boresight not yet written")
ref = self.cache.reference(self._bore_azel)[start : start + n, :]
return ref
def _put_boresight_azel(self, start, data):
if not self.cache.exists(self._bore_azel):
self.cache.create(self._bore_azel, np.float64, (self.local_samples[1], 4))
ref = self.cache.reference(self._bore_azel)
ref[start : (start + data.shape[0]), :] = data
return
def _get_pntg(self, detector, start, n):
cachepntg = "{}{}".format(self._pref_detpntg, detector)
if not self.cache.exists(cachepntg):
# No detector-specific pointing written. See if we have
# boresight pointing and detector quaternions.
if self.cache.exists(self._bore) and (self._detquats is not None):
return qa.mult(
self.cache.reference(self._bore)[start : start + n, :],
self._detquats[detector],
)
else:
raise ValueError(
"detector {}: pointing data not yet written, and boresight"
" and detector quaternions do not exist.".format(detector)
)
else:
return self.cache.reference(cachepntg)[start : start + n, :]
def _put_pntg(self, detector, start, data):
if detector not in self.local_dets:
raise ValueError(
"detector {} not assigned to local process".format(detector)
)
cachepntg = "{}{}".format(self._pref_detpntg, detector)
if not self.cache.exists(cachepntg):
self.cache.create(cachepntg, np.float64, (self.local_samples[1], 4))
pntgref = self.cache.reference(cachepntg)[start : (start + data.shape[0]), :]
pntgref[:] = data
return
def _get_flags(self, detector, start, n):
if detector not in self.local_dets:
raise ValueError(
"detector {} not assigned to local process".format(detector)
)
cacheflags = "{}{}".format(self._pref_detflags, detector)
if not self.cache.exists(cacheflags):
raise ValueError("detector {} flags not yet written".format(detector))
flagsref = self.cache.reference(cacheflags)[start : start + n]
return flagsref
def _put_flags(self, detector, start, flags):
if detector not in self.local_dets:
raise ValueError(
"detector {} not assigned to local process".format(detector)
)
cacheflags = "{}{}".format(self._pref_detflags, detector)
if not self.cache.exists(cacheflags):
self.cache.create(cacheflags, np.uint8, (self.local_samples[1],))
n = flags.shape[0]
refflags = self.cache.reference(cacheflags)[start : start + n]
refflags[:] = flags
return
def _get_common_flags(self, start, n):
if not self.cache.exists(self._common):
raise ValueError("common flags not yet written")
comref = self.cache.reference(self._common)[start : start + n]
return comref
def _put_common_flags(self, start, flags):
if not self.cache.exists(self._common):
self.cache.create(self._common, np.uint8, (self.local_samples[1],))
n = flags.shape[0]
comref = self.cache.reference(self._common)[start : start + n]
comref[:] = flags
return
def _get_times(self, start, n):
if not self.cache.exists(self._stamps):
raise ValueError("timestamps not yet written")
ref = self.cache.reference(self._stamps)[start : start + n]
return ref
def _put_times(self, start, stamps):
if not self.cache.exists(self._stamps):
self.cache.create(self._stamps, np.float64, (self.local_samples[1],))
n = stamps.shape[0]
ref = self.cache.reference(self._stamps)[start : start + n]
ref[:] = stamps
return
def _get_position(self, start, n):
if not self.cache.exists(self._pos):
raise ValueError("telescope position not yet written")
ref = self.cache.reference(self._pos)[start : start + n]
return ref
def _put_position(self, start, pos):
if not self.cache.exists(self._pos):
self.cache.create(self._pos, np.float64, (self.local_samples[1], 3))
n = pos.shape[0]
ref = self.cache.reference(self._pos)[start : start + n, :]
ref[:, :] = pos
return
def _get_velocity(self, start, n):
if not self.cache.exists(self._vel):
raise ValueError("telescope velocity not yet written")
ref = self.cache.reference(self._vel)[start : start + n]
return ref
def _put_velocity(self, start, vel):
if not self.cache.exists(self._vel):
self.cache.create(self._vel, np.float64, (self.local_samples[1], 3))
n = vel.shape[0]
ref = self.cache.reference(self._vel)[start : start + n, :]
ref[:, :] = vel
return
|
hpc4cmbREPO_NAMEtoastPATH_START.@toast_extracted@toast-main@src@toast@tod@tod.py@.PATH_END.py
|
{
"filename": "bhlight.py",
"repo_name": "AFD-Illinois/ebhlight",
"repo_path": "ebhlight_extracted/ebhlight-master/script/bhlight.py",
"type": "Python"
}
|
################################################################################
# #
# BASE MODULE FOR PYTHON SCRIPTING #
# #
################################################################################
import os
import sys; sys.dont_write_bytecode = True
PATHS = {}
PATHS['BASE'] = os.path.join(os.path.abspath(__file__).rsplit('/', 2)[0], '')
PATHS['CORE'] = os.path.join(PATHS['BASE'], 'core')
PATHS['SCRIPT'] = os.path.join(PATHS['BASE'], 'script')
PATHS['ANALYSIS'] = os.path.join(PATHS['BASE'], 'script', 'analysis')
PATHS['MACHINE'] = os.path.join(PATHS['BASE'], 'script', 'machine')
PATHS['PROB'] = os.getcwd()
sys.path.insert(0, PATHS['SCRIPT'])
sys.path.insert(0, PATHS['ANALYSIS'])
sys.path.insert(0, PATHS['MACHINE'])
import units
cgs = units.get_cgs()
import util
import config
import hdf5_to_dict as io
def build(PROBLEM):
config.build(PROBLEM, PATHS)
print(os.getcwd().split('/')[-1])
|
AFD-IllinoisREPO_NAMEebhlightPATH_START.@ebhlight_extracted@ebhlight-master@script@bhlight.py@.PATH_END.py
|
{
"filename": "loss-function-short-desc.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/docs/en/_includes/work_src/reusage/loss-function-short-desc.md",
"type": "Markdown"
}
|
The [metric](../../../concepts/loss-functions.md) to use in training. The specified value also determines the machine learning problem to solve. Some metrics support optional parameters (see the [Objectives and metrics](../../../concepts/loss-functions.md) section for details on each metric).
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@docs@en@_includes@work_src@reusage@loss-function-short-desc.md@.PATH_END.py
|
{
"filename": "test_traps.py",
"repo_name": "wfirst-cgi/emccd_detect",
"repo_path": "emccd_detect_extracted/emccd_detect-master/arcticpy_folder/build/lib/test_arcticpy/test_traps.py",
"type": "Python"
}
|
import numpy as np
import pytest
from scipy import integrate
import matplotlib.pyplot as plt
from copy import deepcopy
import arcticpy as ac
# Example traps (timescale such that 50% (25%) of charges released each step)
traps_1_spec = [
ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5)),
]
traps_2_spec = [
ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5)),
ac.TrapInstantCapture(density=5, release_timescale=-1 / np.log(0.75)),
]
# Example watermarks
trap_manager_1_col = ac.TrapManagerInstantCapture(
traps=traps_1_spec, n_columns=1, max_n_transfers=6
)
trap_manager_2_col = ac.TrapManagerInstantCapture(
traps=traps_2_spec, n_columns=2, max_n_transfers=3
)
trap_manager_3_col = ac.TrapManagerInstantCapture(
traps=traps_2_spec, n_columns=3, max_n_transfers=6
)
unset = trap_manager_1_col.unset
watermarks_1_col = np.array(
[
# Total volumes
[
[0],
[0.5],
[0.2],
[0.1],
[unset],
[unset],
],
# Individual volumes
[
[0],
[0.3],
[0.1],
[0.1],
[unset],
[unset],
],
# Fill fractions
[
[0],
[0.6],
[0.8],
[1],
[unset],
[unset],
],
]
)
watermarks_2_col = np.array(
[
# Total volumes, each column
[
[0.8, 0],
[0.4, 0.5],
[unset, unset],
],
# Individual volumes, each column
[
[0.4, 0],
[0.4, 0.5],
[unset, unset],
],
# Fill fractions of first trap species, each column
[
[0.5, 0],
[1, 1],
[unset, unset],
],
# Fill fractions of second trap species, each column
[
[0.75, 0],
[1, 1],
[unset, unset],
],
]
)
watermarks_3_col = np.array(
[
# Total volumes, each column
[
[0, 0.1, 0.4],
[0.5, 0.7, 0.3],
[0.2, 0, 0.2],
[0.1, 0.2, 0.1],
[unset, unset, unset],
[unset, unset, unset],
],
# Individual volumes, each column
[
[0, 0.1, 0.1],
[0.3, 0.5, 0.1],
[0.1, 0, 0.1],
[0.1, 0.1, 0.1],
[unset, unset, unset],
[unset, unset, unset],
],
# Fill fractions of first trap species, each column
[
[0, 1, 0.4],
[0.6, 0.6, 0.6],
[0.8, 0, 0.8],
[1, 1, 1],
[unset, unset, unset],
[unset, unset, unset],
],
# Fill fractions of second trap species, each column
[
[0, 1, 0.7],
[0.8, 0.8, 0.8],
[0.9, 0, 0.9],
[1, 1, 1],
[unset, unset, unset],
[unset, unset, unset],
],
]
)
unset_watermark_index_1_col = trap_manager_1_col.unset_watermark_index_from_watermarks(
watermarks_1_col
)
unset_watermark_index_2_col = trap_manager_2_col.unset_watermark_index_from_watermarks(
watermarks_2_col
)
unset_watermark_index_3_col = trap_manager_3_col.unset_watermark_index_from_watermarks(
watermarks_3_col
)
class TestTraps:
def test__electrons_released_from_electrons_and_dwell_time(self):
trap = ac.Trap(release_timescale=1.0)
assert trap.electrons_released_from_electrons_and_dwell_time(
electrons=1.0
) == pytest.approx(0.6321, 1e-4)
assert trap.electrons_released_from_electrons_and_dwell_time(
electrons=2.0
) == pytest.approx(2.0 * 0.6321, 1e-4)
trap = ac.Trap(release_timescale=2.0)
assert trap.electrons_released_from_electrons_and_dwell_time(
electrons=1.0
) == pytest.approx(0.39346, 1e-4)
assert trap.electrons_released_from_electrons_and_dwell_time(
electrons=2.0
) == pytest.approx(2.0 * 0.39346, 1e-4)
class TestInitialWatermarks:
def test__initial_watermark_array__shape_from_numbers_of_traps_columns_and_transfers(
self,
):
for n_traps, n_columns, max_n_transfers in zip(
[1, 2, 3, 4],
[4, 2, 1, 7],
[3, 4, 5, 1],
):
traps = [ac.Trap()] * n_traps
trap_manager = ac.TrapManager(
traps=traps, n_columns=n_columns, max_n_transfers=max_n_transfers
)
assert trap_manager.watermarks == pytest.approx(
np.ones((2 + n_traps, max_n_transfers * 2 + 1, n_columns)) * unset
)
class TestTrapManagerUtilities:
def test__n_traps_per_pixel(self):
assert trap_manager_3_col.n_traps_per_pixel == pytest.approx([10, 5])
def test__unset_watermark_index_from_watermarks(self):
unset_watermark_index = (
trap_manager_3_col.unset_watermark_index_from_watermarks(watermarks_3_col)
)
assert unset_watermark_index == 4
def test__empty_all_traps(self):
trap_manager_3_col.watermarks = deepcopy(watermarks_3_col)
trap_manager_3_col.empty_all_traps()
assert trap_manager_3_col.watermarks == pytest.approx(
np.ones_like(watermarks_3_col) * unset
)
def test__n_trapped_electrons_from_watermarks(self):
# Empty watermarks
trap_manager_3_col.empty_all_traps()
assert trap_manager_3_col.n_trapped_electrons_from_watermarks(
watermarks=trap_manager_3_col.watermarks
) == pytest.approx([0, 0, 0])
# Example watermarks, 2 columns
assert trap_manager_2_col.n_trapped_electrons_from_watermarks(
watermarks=watermarks_2_col
) == pytest.approx(
[
# First column, (individual volumes * fill fractions) * density
(0.4 * 0.5 + 0.4 * 1) * traps_2_spec[0].density
+ (0.4 * 0.75 + 0.4 * 1) * traps_2_spec[1].density,
# Second column, (individual volumes * fill fractions) * density
(0 + 0.5 * 1) * traps_2_spec[0].density
+ (0 + 0.5 * 1) * traps_2_spec[1].density,
]
)
# Example watermarks, 3 columns
assert trap_manager_3_col.n_trapped_electrons_from_watermarks(
watermarks=watermarks_3_col
) == pytest.approx(
# First trap species, (individual volumes * fill fractions) * density
np.sum(
watermarks_3_col[1, :unset_watermark_index_3_col]
* watermarks_3_col[2, :unset_watermark_index_3_col],
axis=0,
)
* traps_2_spec[0].density
# Second trap species, (individual volumes * fill fractions) * density
+ np.sum(
watermarks_3_col[1, :unset_watermark_index_3_col]
* watermarks_3_col[3, :unset_watermark_index_3_col],
axis=0,
)
* traps_2_spec[1].density
)
class TestElectronsReleasedAndCapturedInstantCapture:
def test__empty_release(self):
trap_manager_1_col.empty_all_traps()
n_electrons_released = trap_manager_1_col.n_electrons_released()
assert n_electrons_released == pytest.approx(0)
assert np.all(trap_manager_1_col.watermarks == unset)
trap_manager_3_col.empty_all_traps()
n_electrons_released = trap_manager_1_col.n_electrons_released()
assert n_electrons_released == pytest.approx(0)
assert np.all(trap_manager_3_col.watermarks == unset)
def test__single_trap_release__single_column(self):
trap_manager_1_col.watermarks = deepcopy(watermarks_1_col)
n_trapped_electrons_initial = (
trap_manager_1_col.n_trapped_electrons_from_watermarks(
watermarks=trap_manager_1_col.watermarks
)
)
n_electrons_released = trap_manager_1_col.n_electrons_released()
# Half released
assert n_electrons_released == n_trapped_electrons_initial / 2
watermarks = deepcopy(watermarks_1_col)
watermarks[2, :unset_watermark_index_1_col] /= 2
assert trap_manager_1_col.watermarks == pytest.approx(watermarks)
def test__multiple_traps_release__single_column(self):
trap_manager_2_col.watermarks = deepcopy(watermarks_2_col)
n_trapped_electrons_initial = (
trap_manager_2_col.n_trapped_electrons_from_watermarks(
watermarks=trap_manager_2_col.watermarks
)
)
n_electrons_released = trap_manager_2_col.n_electrons_released()
# Half released from first species, 25% released from second species
assert n_electrons_released == pytest.approx(
[
0.5 * (0.4 * 0.5 + 0.4 * 1) * traps_2_spec[0].density
+ 0.25 * (0.4 * 0.75 + 0.4 * 1) * traps_2_spec[1].density,
0.5 * (0 + 0.5 * 1) * traps_2_spec[0].density
+ 0.25 * (0 + 0.5 * 1) * traps_2_spec[1].density,
]
)
assert trap_manager_2_col.watermarks == pytest.approx(
np.array(
[
watermarks_2_col[0],
watermarks_2_col[1],
[
[0.25, 0],
[0.5, 0.5],
[unset, unset],
],
[
[0.75 * 0.75, 0],
[0.75, 0.75],
[unset, unset],
],
]
)
)
def test__single_trap_release__change_time(self):
# Compared with test__single_trap_release__single_column: 1/3 the dwell
# time with 1/3 the lifetime --> same result
traps = [
ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5) / 3)
]
trap_manager = ac.TrapManagerInstantCapture(
traps=traps, n_columns=1, max_n_transfers=6
)
trap_manager.watermarks = deepcopy(watermarks_1_col)
n_trapped_electrons_initial = trap_manager.n_trapped_electrons_from_watermarks(
watermarks=trap_manager.watermarks
)
n_electrons_released = trap_manager.n_electrons_released(dwell_time=1 / 3)
assert n_electrons_released == pytest.approx(n_trapped_electrons_initial / 2)
watermarks = deepcopy(watermarks_1_col)
watermarks[2, :unset_watermark_index_1_col] /= 2
assert trap_manager.watermarks == pytest.approx(watermarks)
def test__first_capture(self):
ccd = ac.CCD(well_fill_power=0.5, full_well_depth=10000, well_notch_depth=1e-7)
n_free_electrons = [2500] # --> cloud fractional volume = 0.5
trap_manager = ac.TrapManagerInstantCapture(
traps=traps_1_spec, n_columns=2, max_n_transfers=6
)
n_electrons_captured = trap_manager.n_electrons_captured(
n_free_electrons=n_free_electrons,
ccd_filling_function=ccd.well_filling_function(),
)
assert n_electrons_captured == pytest.approx(5)
assert trap_manager.watermarks[0, 0] == pytest.approx([0.5, 0.5])
assert trap_manager.watermarks[1, 0] == pytest.approx([0.5, 0.5])
assert trap_manager.watermarks[2, 0] == pytest.approx([1, 1])
assert (trap_manager.watermarks[:, 1:] == unset).all
def test__full_release_and_capture__multiple_traps__multiple_columns(self):
ccd = ac.CCD(well_fill_power=1, full_well_depth=1000, well_notch_depth=1e-7)
trap_manager_3_col.watermarks = deepcopy(watermarks_3_col)
# Release
n_electrons_released = trap_manager_3_col.n_electrons_released()
watermarks = deepcopy(watermarks_3_col)
watermarks[2, :unset_watermark_index_3_col] *= 0.5
watermarks[3, :unset_watermark_index_3_col] *= 0.75
assert trap_manager_3_col.watermarks == pytest.approx(watermarks)
assert n_electrons_released == pytest.approx([2.3375, 3.25, 1.825])
# Capture
n_free_electrons = [300, 150, 0] # --> volumes = [0.3, 0.15, 0]
n_electrons_captured = trap_manager_3_col.n_electrons_captured(
n_free_electrons=n_free_electrons,
ccd_filling_function=ccd.well_filling_function(),
)
assert trap_manager_3_col.watermarks == pytest.approx(
np.array(
[
# Total volumes, each column
[
[0, 0.1, 0.4],
[0.5, 0.7, 0.3],
[0.2, 0, 0.2],
[0.1, 0.2, 0.1],
[0.3, 0.15, 0],
[unset, unset, unset],
],
# Indv. volumes, each column
[
[0, 0.1, 0.1],
[0.2, 0.5, 0.1],
[0.1, 0, 0.1],
[0.1, 0.05, 0.1],
[0.1, 0.05, 0],
[unset, unset, unset],
],
# Fill fractions, first trap species, each column
[
[1, 1, 0.2],
[0.3, 0.3, 0.3],
[1, 1, 0.4],
[1, 0.5, 0.5],
[1, 1, 1],
[unset, unset, unset],
],
# Fill fractions, second trap species, each column
[
[1, 1, 0.525],
[0.6, 0.6, 0.6],
[1, 1, 0.675],
[1, 0.75, 0.75],
[1, 1, 1],
[unset, unset, unset],
],
]
)
)
assert n_electrons_captured == pytest.approx([2.2875, 0.9375, 0])
# Check combined release and capture function gives same results
watermarks_out = deepcopy(trap_manager_3_col.watermarks)
trap_manager_3_col.watermarks = deepcopy(watermarks_3_col)
n_electrons_released_and_captured = (
trap_manager_3_col.n_electrons_released_and_captured(
n_free_electrons=np.array(n_free_electrons)
- np.array(n_electrons_released),
ccd_filling_function=ccd.well_filling_function(),
)
)
assert n_electrons_released - n_electrons_captured == pytest.approx(
n_electrons_released_and_captured
)
assert trap_manager_3_col.watermarks == pytest.approx(watermarks_out)
def test__capture_same_cloud_volume_as_existing_watermark(self):
ccd = ac.CCD(well_fill_power=1, full_well_depth=1000, well_notch_depth=0)
n_free_electrons = [200] # --> cloud fractional volume = 0.2
trap_manager_1_col.watermarks = np.array(
[
[
[0.3],
[0.2],
[0.1],
[unset],
],
[
[0.1],
[0.1],
[0.1],
[unset],
],
[
[0.125],
[0.25],
[0.5],
[unset],
],
]
)
n_electrons_captured = trap_manager_1_col.n_electrons_captured(
n_free_electrons=n_free_electrons,
ccd_filling_function=ccd.well_filling_function(),
)
# Normal total volume and fill fraction, but zero individual volume
assert trap_manager_1_col.watermarks == pytest.approx(
np.array(
[
[
[0.3],
[0.2],
[0.1],
[0.2],
],
[
[0.1],
[0.1],
[0.1],
[0],
],
[
[0.125],
[1],
[1],
[1],
],
]
)
)
assert (
n_electrons_captured == (0.5 * 0.1 + 0.75 * 0.1) * traps_1_spec[0].density
)
def test__not_enough_capture__first_capture(self):
ccd = ac.CCD(well_fill_power=0.5, full_well_depth=10000, well_notch_depth=1e-7)
n_free_electrons = [
2.5e-3 # --> cloud fractional volume = 4.9999e-4, enough = 0.50001
]
trap_manager = ac.TrapManagerInstantCapture(
traps=traps_1_spec, n_columns=2, max_n_transfers=6
)
n_electrons_captured = trap_manager.n_electrons_captured(
n_free_electrons=n_free_electrons,
ccd_filling_function=ccd.well_filling_function(),
)
assert n_electrons_captured == pytest.approx(2.5e-3)
assert trap_manager.watermarks[0, 0] == pytest.approx([4.9999e-4, 4.9999e-4])
assert trap_manager.watermarks[1, 0] == pytest.approx([4.9999e-4, 4.9999e-4])
assert trap_manager.watermarks[2, 0] == pytest.approx([0.50001, 0.50001])
assert (trap_manager.watermarks[:, 1:] == unset).all
def test__not_enough_capture__multiple_traps_capture(self):
ccd = ac.CCD(well_fill_power=0.1, full_well_depth=1000, well_notch_depth=1e-7)
n_free_electrons = [3, 3]
# -->
volume = 0.55938668
enough_1 = 0.74182903
enough_2 = 0.74704911
trap_manager_2_col.watermarks = np.array(
[
[
[0.8, 0],
[0.4, 0.5],
[unset, unset],
],
[
[0.4, 0],
[0.4, 0.5],
[unset, unset],
],
[
[0.25, 0],
[0.5, 0.5],
[unset, unset],
],
[
[0.5625, 0],
[0.75, 0.75],
[unset, unset],
],
]
)
n_electrons_captured = trap_manager_2_col.n_electrons_captured(
n_free_electrons=n_free_electrons,
ccd_filling_function=ccd.well_filling_function(),
)
assert trap_manager_2_col.watermarks == pytest.approx(
np.array(
[
[
[0.8, 0],
[0.4, 0.5],
[volume, volume],
],
[
[0.8 - volume, 0],
[0.4, 0.5],
[volume - 0.4, volume - 0.5],
],
[
[0.25, enough_2],
[0.5 + 0.5 * enough_1, 0.5 + 0.5 * enough_2],
[enough_1, enough_2],
],
[
[0.5625, enough_2],
[0.75 + 0.25 * enough_1, 0.75 + 0.25 * enough_2],
[enough_1, enough_2],
],
]
)
)
assert n_electrons_captured == pytest.approx(
[
enough_1
* (
0.4
* (0.5 * traps_2_spec[0].density + 0.25 * traps_2_spec[1].density)
+ (volume - 0.4)
* (traps_2_spec[0].density + traps_2_spec[1].density)
)
- (volume - 0.4)
* (0.25 * traps_2_spec[0].density + 0.5625 * traps_2_spec[1].density),
enough_2
* (
0.5
* (0.5 * traps_2_spec[0].density + 0.25 * traps_2_spec[1].density)
+ (volume - 0.5)
* (traps_2_spec[0].density + traps_2_spec[1].density)
),
]
)
class TestAllTrapManager:
def test__single_or_multiple_trap_managers__add_cti_similar_result(self):
image = np.zeros((6, 2))
image[1, 1] = 1000
print("image\n", image)
# Single trap manager
traps = traps_2_spec
ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
image_single = ac.add_cti(image=image, parallel_traps=traps, parallel_ccd=ccd)
# Multiple trap managers
traps = [[traps_2_spec[0]], [traps_2_spec[1]]]
# print("\n\n # # multi")
image_multi = ac.add_cti(image=image, parallel_traps=traps, parallel_ccd=ccd)
# Slightly different result because the single trap manager has both
# traps release followed by both capture, but the multi has one release
# then capture, followed by the other release then capture.
assert image_single == pytest.approx(image_multi, rel=1e-3)
def test__n_trapped_electrons_currently(self):
trap_managers = ac.AllTrapManager(
traps=[traps_2_spec, traps_1_spec],
n_columns=3,
max_n_transfers=6,
ccd=ac.CCD(),
)
trap_managers[0][0].watermarks = deepcopy(watermarks_3_col)
trap_managers[0][1].watermarks = watermarks_3_col[:3] # Ignore 2nd species
n_trapped_electrons_1 = trap_manager_3_col.n_trapped_electrons_from_watermarks(
watermarks=watermarks_3_col
)
n_trapped_electrons_2 = ac.TrapManagerInstantCapture(
traps=traps_1_spec, n_columns=3, max_n_transfers=6
).n_trapped_electrons_from_watermarks(watermarks=watermarks_3_col[:3])
assert trap_managers.n_trapped_electrons_currently == pytest.approx(
n_trapped_electrons_1 + n_trapped_electrons_2
)
def test__save_and_restore(self):
trap_managers = ac.AllTrapManager(
traps=traps_2_spec, n_columns=3, max_n_transfers=6, ccd=ac.CCD()
)
trap_managers[0][0].watermarks = deepcopy(watermarks_3_col)
trap_managers.save()
trap_managers[0][0].empty_all_traps()
# Confirm emptied
assert (trap_managers[0][0].watermarks == unset).all()
trap_managers.restore()
# Confirm restored
assert trap_managers[0][0].watermarks == pytest.approx(watermarks_3_col)
class TestMiscTrapParameters:
def test__delta_ellipticity_of_trap(self):
trap = ac.Trap(density=0.5, release_timescale=2.0)
assert trap.delta_ellipticity == pytest.approx(0.047378295117617694, 1.0e-5)
def test__delta_ellipticity_of_arctic_params(self):
parallel_1_trap = ac.Trap(density=0.1, release_timescale=4.0)
parallel_2_trap = ac.Trap(density=0.1, release_timescale=4.0)
serial_1_trap = ac.Trap(density=0.2, release_timescale=2.0)
serial_2_trap = ac.Trap(density=0.7, release_timescale=7.0)
trap_manager = ac.TrapManager(
traps=[parallel_1_trap], n_columns=1, max_n_transfers=1
)
assert trap_manager.delta_ellipticity == parallel_1_trap.delta_ellipticity
trap_manager = ac.TrapManager(
traps=[parallel_1_trap, parallel_2_trap], n_columns=1, max_n_transfers=1
)
assert (
trap_manager.delta_ellipticity
== parallel_1_trap.delta_ellipticity + parallel_2_trap.delta_ellipticity
)
trap_manager = ac.TrapManager(
traps=[serial_1_trap], n_columns=1, max_n_transfers=1
)
assert trap_manager.delta_ellipticity == serial_1_trap.delta_ellipticity
trap_manager = ac.TrapManager(
traps=[serial_1_trap, serial_2_trap], n_columns=1, max_n_transfers=1
)
assert (
trap_manager.delta_ellipticity
== serial_1_trap.delta_ellipticity + serial_2_trap.delta_ellipticity
)
trap_manager = ac.TrapManager(
traps=[parallel_1_trap, parallel_2_trap, serial_1_trap, serial_2_trap],
n_columns=1,
max_n_transfers=1,
)
assert trap_manager.delta_ellipticity == pytest.approx(
parallel_1_trap.delta_ellipticity
+ parallel_2_trap.delta_ellipticity
+ serial_1_trap.delta_ellipticity
+ serial_2_trap.delta_ellipticity,
1.0e-6,
)
def test_1_trap__density_01__1000_column_pixels__1_row_pixel_so_100_traps__poisson_density_near_01(
self,
):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(0.1,),
)
),
shape=(1000, 1),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [0.098]
def test__1_trap__density_1__1000_column_pixels_so_1000_traps__1_row_pixel__poisson_value_is_near_1(
self,
):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(1.0,),
)
),
shape=(1000, 1),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [0.992]
def test__1_trap__density_1___2_row_pixels__poisson_value_is_near_1(self):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(1.0,),
)
),
shape=(1000, 2),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [0.992, 0.962]
def test__2_trap__1_row_pixel__poisson_for_each_trap_drawn(self):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(1.0, 2.0),
)
),
shape=(1000, 1),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [0.992, 1.946]
def test__2_trap__2_row_pixel__poisson_for_each_trap_drawn(self):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(1.0, 2.0),
)
),
shape=(1000, 2),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [
0.992,
1.946,
0.968,
1.987,
]
def test__same_as_above_but_3_trap_and_new_values(self):
parallel_vary = ac.Trap.poisson_trap(
trap=list(
map(
lambda density: ac.Trap(density=density, release_timescale=1.0),
(1.0, 2.0, 0.1),
)
),
shape=(1000, 3),
seed=1,
)
assert [trap.density for trap in parallel_vary] == [
0.992,
1.946,
0.09,
0.991,
1.99,
0.098,
0.961,
1.975,
0.113,
]
#
# class TestTrapManagerTrackTime:
# def test__fill_fraction_from_time_elapsed(self):
#
# trap = ac.TrapInstantCapture(density=10, release_timescale=2)
#
# fill = trap.fill_fraction_from_time_elapsed(1)
# assert fill == np.exp(-0.5)
#
# time_elapsed = trap.time_elapsed_from_fill_fraction(0.5)
# assert time_elapsed == -2 * np.log(0.5)
#
# assert fill == trap.fill_fraction_from_time_elapsed(
# trap.time_elapsed_from_fill_fraction(fill)
# )
# assert time_elapsed == trap.time_elapsed_from_fill_fraction(
# trap.fill_fraction_from_time_elapsed(time_elapsed)
# )
#
# def test__watermarks_converted_to_fill_fractions_from_elapsed_times(self):
#
# trap = ac.TrapInstantCapture(density=10, release_timescale=2)
# trap_manager = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
# watermarks_fill = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# watermarks_time = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
#
# assert watermarks_fill == pytest.approx(
# trap_manager.watermarks_converted_to_fill_fractions_from_elapsed_times(
# watermarks_time
# )
# )
# assert watermarks_time == pytest.approx(
# trap_manager.watermarks_converted_to_elapsed_times_from_fill_fractions(
# watermarks_fill
# )
# )
# assert watermarks_fill == pytest.approx(
# trap_manager.watermarks_converted_to_fill_fractions_from_elapsed_times(
# trap_manager.watermarks_converted_to_elapsed_times_from_fill_fractions(
# watermarks_fill
# )
# )
# )
#
# def test__n_trapped_electrons_from_watermarks_using_time(self):
#
# trap = ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5))
# trap_manager_fill = ac.TrapManagerInstantCapture(
# traps=[trap], n_columns=2, max_n_transfers=6
# )
# trap_manager_time = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
#
# trap_manager_fill.watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# trap_manager_time.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# n_electrons_fill = trap_manager_fill.n_trapped_electrons_from_watermarks(
# watermarks=trap_manager_fill.watermarks
# )
# n_electrons_time = trap_manager_time.n_trapped_electrons_from_watermarks(
# watermarks=trap_manager_time.watermarks
# )
#
# assert n_electrons_fill == n_electrons_time
#
# def test__electrons_released_and_captured_using_time(self):
#
# n_free_electrons = 5e4 # cloud fractional volume ~= 0.656
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
# # ccd = ac.CCDPhase(ccd)
#
# trap = ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5))
# trap_manager_fill = ac.TrapManagerInstantCapture(
# traps=[trap], n_columns=2, max_n_transfers=6
# )
# trap_manager_time = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
#
# trap_manager_fill.watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# trap_manager_time.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
#
# net_electrons_fill = trap_manager_fill.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
# net_electrons_time = trap_manager_time.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# assert net_electrons_fill == net_electrons_time
# assert trap_manager_fill.watermarks == pytest.approx(
# trap_manager_time.watermarks_converted_to_fill_fractions_from_elapsed_times(
# trap_manager_time.watermarks
# )
# )
#
# def test__electrons_released_and_captured_using_time_multiple_traps(self):
# n_free_electrons = 1e3
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
# # ccd = ac.CCDPhase(ccd)
#
# trap_1 = ac.TrapInstantCapture(density=10, release_timescale=-1 / np.log(0.5))
# trap_2 = ac.TrapInstantCapture(density=10, release_timescale=-2 / np.log(0.5))
# trap_manager_fill = ac.TrapManagerInstantCapture(
# traps=[trap_1, trap_2], n_columns=2, max_n_transfers=6
# )
# trap_manager_time = ac.TrapManagerTrackTime(
# traps=[trap_1, trap_2], n_columns=2, max_n_transfers=6
# )
#
# trap_manager_fill.watermarks = np.array(
# [
# [0.5, 0.8, 0.6,],
# [0.2, 0.4, 0.2,],
# [0.1, 0.3, 0.1,],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# )
# trap_manager_time.watermarks = np.array(
# [
# [
# 0.5,
# trap_1.time_elapsed_from_fill_fraction(0.8),
# trap_2.time_elapsed_from_fill_fraction(0.6),
# ],
# [
# 0.2,
# trap_1.time_elapsed_from_fill_fraction(0.4),
# trap_2.time_elapsed_from_fill_fraction(0.2),
# ],
# [
# 0.1,
# trap_1.time_elapsed_from_fill_fraction(0.3),
# trap_2.time_elapsed_from_fill_fraction(0.1),
# ],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# )
#
# net_electrons_fill = trap_manager_fill.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
# net_electrons_time = trap_manager_time.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# assert net_electrons_fill == pytest.approx(net_electrons_time)
# assert trap_manager_fill.watermarks == pytest.approx(
# trap_manager_time.watermarks_converted_to_fill_fractions_from_elapsed_times(
# trap_manager_time.watermarks
# )
# )
#
#
# class TestTrapLifetimeContinuum:
# def test__distribution_of_traps_with_lifetime(self):
#
# release_timescale_mu = -1 / np.log(0.5)
# release_timescale_sigma = 0.5
#
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
#
# # Check that the integral from zero to infinity is one
# assert integrate.quad(
# trap.distribution_of_traps_with_lifetime,
# 0,
# np.inf,
# args=(trap.release_timescale_mu, trap.release_timescale_sigma),
# )[0] == pytest.approx(1)
#
# def test__fill_fraction_from_time_elapsed_narrow_continuum(self):
#
# # Check that narrow continuum gives similar results to single release_timescale
# # Simple trap
# trap = ac.TrapInstantCapture(density=10, release_timescale=1)
# fill_single = trap.fill_fraction_from_time_elapsed(1)
#
# # Narrow continuum
# release_timescale_mu = 1
# release_timescale_sigma = 0.1
#
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
# fill_continuum = trap.fill_fraction_from_time_elapsed(1)
#
# assert fill_continuum == pytest.approx(fill_single, rel=0.01)
#
# def test__time_elapsed_from_fill_fraction_narrow_continuum(self):
#
# # Check that narrow continuum gives similar results to single release_timescale
# # Simple trap
# trap = ac.TrapInstantCapture(density=10, release_timescale=1)
# time_single = trap.time_elapsed_from_fill_fraction(0.5)
#
# # Narrow continuum
# release_timescale_mu = 1
# release_timescale_sigma = 0.1
#
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
# time_continuum = trap.time_elapsed_from_fill_fraction(0.5)
#
# assert time_continuum == pytest.approx(time_single, rel=0.01)
#
# def test__fill_fraction_from_time_elapsed_continuum(self):
#
# release_timescale_mu = 1
# release_timescale_sigma = 0.5
#
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
#
# fill = trap.fill_fraction_from_time_elapsed(2)
# time_elapsed = trap.time_elapsed_from_fill_fraction(0.4)
#
# assert fill == pytest.approx(
# trap.fill_fraction_from_time_elapsed(
# trap.time_elapsed_from_fill_fraction(fill)
# )
# )
# assert time_elapsed == pytest.approx(
# trap.time_elapsed_from_fill_fraction(
# trap.fill_fraction_from_time_elapsed(time_elapsed)
# )
# )
#
# def test__n_trapped_electrons_from_watermarks(self):
#
# # Single trap
# trap = ac.TrapInstantCapture(density=10, release_timescale=1)
# trap_manager_single = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
# trap_manager_single.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# n_electrons_single = trap_manager_single.n_trapped_electrons_from_watermarks(
# trap_manager_single.watermarks
# )
#
# # Continua
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# for sigma in [0.1, 1, 2]:
# median = 1
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=median,
# release_timescale_sigma=sigma,
# )
# trap_manager_continuum = ac.TrapManagerTrackTime(
# traps=[trap], n_columns=2, max_n_transfers=6
# )
# trap_manager_continuum.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# n_electrons_continuum = trap_manager_continuum.n_trapped_electrons_from_watermarks(
# trap_manager_continuum.watermarks
# )
#
# assert n_electrons_continuum == pytest.approx(n_electrons_single)
#
# def test__electrons_released_and_captured_continuum(self):
#
# # Check that narrow continuum gives similar results to single traps
# # and that a wider continuum gives somewhat similar results
#
# n_free_electrons = 5e4 # cloud fractional volume ~= 0.656
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
# # ccd = ac.CCDPhase(ccd)
#
# # Single trap
# trap = ac.TrapInstantCapture(density=10, release_timescale=1)
# trap_manager_single = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
# trap_manager_single.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# net_electrons_single = trap_manager_single.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# # Narrow continuum
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# release_timescale_mu = 1
# release_timescale_sigma = 0.01
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
# trap_manager_narrow = ac.TrapManagerTrackTime(traps=[trap], n_columns=2, max_n_transfers=6)
# trap_manager_narrow.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# net_electrons_narrow = trap_manager_narrow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# # Continuum
# release_timescale_mu = 1
# release_timescale_sigma = 1
# trap = ac.TrapLifetimeContinuumAbstract(
# density=10,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
# trap_manager_continuum = ac.TrapManagerTrackTime(
# traps=[trap], n_columns=2, max_n_transfers=6
# )
# trap_manager_continuum.watermarks = np.array(
# [
# [0.5, trap.time_elapsed_from_fill_fraction(0.8)],
# [0.2, trap.time_elapsed_from_fill_fraction(0.4)],
# [0.1, trap.time_elapsed_from_fill_fraction(0.2)],
# [0, 0],
# [0, 0],
# [0, 0],
# ]
# )
# net_electrons_continuum = trap_manager_continuum.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# assert net_electrons_narrow == pytest.approx(net_electrons_single, rel=1e-4)
# assert net_electrons_continuum == pytest.approx(net_electrons_single, rel=2)
#
# assert trap_manager_narrow.watermarks == pytest.approx(
# trap_manager_single.watermarks, rel=1e-4
# )
# assert trap_manager_continuum.watermarks == pytest.approx(
# trap_manager_single.watermarks, rel=0.5
# )
#
# def test__TrapLogNormalLifetimeContinuum(self):
#
# release_timescale_mu = -1 / np.log(0.5)
# release_timescale_sigma = 0.5
#
# trap = ac.TrapLogNormalLifetimeContinuum(
# density=10,
# release_timescale_mu=release_timescale_mu,
# release_timescale_sigma=release_timescale_sigma,
# )
#
# # Check that the integral from zero to infinity is one
# assert integrate.quad(
# trap.distribution_of_traps_with_lifetime,
# 0,
# np.inf,
# args=(trap.release_timescale_mu, trap.release_timescale_sigma),
# )[0] == pytest.approx(1)
#
# # Check the automatic distribution function is set correctly
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# assert trap.distribution_of_traps_with_lifetime(
# 1.2345, release_timescale_mu, release_timescale_sigma
# ) == trap_distribution(1.2345, release_timescale_mu, release_timescale_sigma)
#
# def test__electrons_released_and_captured_compare_continuum_with_distributions_of_single_traps(
# self,
# ):
#
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
#
# density = 10
# release_timescale = 5
# sigma = 1
# linear_min_lifetime = 1e-3
# linear_max_lifetime = 1000
# linear_sample = 10000
# min_log_lifetime = -3
# max_log_lifetime = 5
# log_sample = 1000
# t_elapsed = 1
# dwell_time = 1
# n_free_electrons = 1e4
#
# # Log-normal distribution
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# # Split into two
# def trap_distribution_a(release_timescale, median, sigma):
# return (
# 2
# * np.heaviside(release_timescale - 2 * median, 0)
# * trap_distribution(release_timescale, median, sigma)
# )
#
# def trap_distribution_b(release_timescale, median, sigma):
# return (
# 2
# * np.heaviside(2 * median - release_timescale, 0)
# * trap_distribution(release_timescale, median, sigma)
# )
#
# # Continuum traps
# trap_continuum = ac.TrapLifetimeContinuumAbstract(
# density=density,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale,
# release_timescale_sigma=sigma,
# )
# trap_manager_continuum = ac.TrapManagerTrackTime(
# traps=[trap_continuum], n_columns=2, max_n_transfers=6
# )
# trap_manager_continuum.watermarks = np.array(
# [[0.5, t_elapsed], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],]
# )
# net_electrons_continuum = trap_manager_continuum.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# # Separated continuum traps
# trap_continuum_split_a = ac.TrapLifetimeContinuumAbstract(
# density=density / 2,
# distribution_of_traps_with_lifetime=trap_distribution_a,
# release_timescale_mu=release_timescale,
# release_timescale_sigma=sigma,
# )
# trap_continuum_split_b = ac.TrapLifetimeContinuumAbstract(
# density=density / 2,
# distribution_of_traps_with_lifetime=trap_distribution_b,
# release_timescale_mu=release_timescale,
# release_timescale_sigma=sigma,
# )
# trap_manager_continuum_split = ac.TrapManagerTrackTime(
# traps=[trap_continuum_split_a, trap_continuum_split_b], n_columns=2, max_n_transfers=6
# )
# trap_manager_continuum_split.watermarks = np.array(
# [[0.5, t_elapsed, t_elapsed], [0] * 3, [0] * 3, [0] * 3, [0] * 3, [0] * 3,]
# )
# net_electrons_continuum_split = trap_manager_continuum_split.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# # Equivalent distributions of single traps, linearly spaced
# lifetimes_linear = np.linspace(
# linear_min_lifetime, linear_max_lifetime, linear_sample
# )
# densities_linear = trap_distribution(lifetimes_linear, release_timescale, sigma)
# densities_linear *= density / densities_linear.sum()
# traps_linear = [
# ac.TrapInstantCapture(density=density, release_timescale=release_timescale)
# for density, release_timescale in zip(densities_linear, lifetimes_linear)
# ]
# trap_manager_linear = ac.TrapManagerTrackTime(
# traps=traps_linear, n_columns=2, max_n_transfers=6
# )
# trap_manager_linear.watermarks = np.array(
# [
# np.append([0.5], [t_elapsed] * linear_sample),
# [0] * (linear_sample + 1),
# [0] * (linear_sample + 1),
# [0] * (linear_sample + 1),
# [0] * (linear_sample + 1),
# [0] * (linear_sample + 1),
# ]
# )
# net_electrons_linear = trap_manager_linear.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# # Equivalent distributions of single traps, logarithmically spaced
# lifetimes_log = np.logspace(min_log_lifetime, max_log_lifetime, log_sample)
# lifetimes_fractional_widths = np.append(
# np.append(
# np.exp(0.5 * (np.log(lifetimes_log[1]) + np.log(lifetimes_log[0]))),
# np.exp(0.5 * (np.log(lifetimes_log[2:]) + np.log(lifetimes_log[:-2]))),
# ),
# np.exp(0.5 * (np.log(lifetimes_log[-1]) + np.log(lifetimes_log[-2]))),
# )
# densities_log = trap_distribution(lifetimes_log, release_timescale, sigma)
# densities_log *= lifetimes_fractional_widths
# densities_log *= density / densities_log.sum()
# traps_log = [
# ac.TrapInstantCapture(density=density, release_timescale=release_timescale)
# for density, release_timescale in zip(densities_log, lifetimes_log)
# ]
# trap_manager_log = ac.TrapManagerTrackTime(traps=traps_log, n_columns=2, max_n_transfers=6)
# trap_manager_log.watermarks = np.array(
# [
# np.append([0.5], [t_elapsed] * log_sample),
# [0] * (log_sample + 1),
# [0] * (log_sample + 1),
# [0] * (log_sample + 1),
# [0] * (log_sample + 1),
# [0] * (log_sample + 1),
# ]
# )
# net_electrons_log = trap_manager_log.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
# net_electrons_log_2 = trap_manager_log.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# )
#
# assert net_electrons_continuum == pytest.approx(net_electrons_continuum_split)
# assert net_electrons_continuum == pytest.approx(net_electrons_linear, rel=0.001)
# assert net_electrons_continuum == pytest.approx(net_electrons_log, rel=0.001)
#
# def test__trails_from_continuum_traps_compare_with_distributions_of_single_traps(
# self,
# ):
#
# # This test is VERY slow!
#
# size = 10
# pixels = np.arange(size)
# image_orig = np.zeros((size, 1))
# image_orig[1, 0] = 1e4
#
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7)
#
# density = 10
# release_timescale = 5
# sigma = 1
# min_log_lifetime = -3
# max_log_lifetime = 5
# log_sample = 100
#
# # Log-normal distribution
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2) / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# # Continuum traps
# trap_continuum = ac.TrapLifetimeContinuumAbstract(
# density=density,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale,
# release_timescale_sigma=sigma,
# )
# image_continuum = ac.add_cti(
# image=image_orig, parallel_traps=[trap_continuum], parallel_ccd=ccd,
# )
#
# # Equivalent distributions of single traps, logarithmically spaced
# lifetimes_log = np.logspace(min_log_lifetime, max_log_lifetime, log_sample)
# lifetimes_fractional_widths = np.append(
# np.append(
# np.exp(0.5 * (np.log(lifetimes_log[1]) + np.log(lifetimes_log[0]))),
# np.exp(0.5 * (np.log(lifetimes_log[2:]) + np.log(lifetimes_log[:-2]))),
# ),
# np.exp(0.5 * (np.log(lifetimes_log[-1]) + np.log(lifetimes_log[-2]))),
# )
# densities_log = trap_distribution(lifetimes_log, release_timescale, sigma)
# densities_log *= lifetimes_fractional_widths
# densities_log *= density / densities_log.sum()
# traps_log = [
# ac.TrapInstantCapture(density=density, release_timescale=release_timescale)
# for density, release_timescale in zip(densities_log, lifetimes_log)
# ]
# image_log = ac.add_cti(
# image=image_orig, parallel_traps=traps_log, parallel_ccd=ccd,
# )
#
# assert image_continuum == pytest.approx(image_log)
#
# def test__plot_trails_from_continuum_traps_different_distributions(self,):
#
# # Plotting test -- manually set True to make the plot
# do_plot = False
# # do_plot = True
#
# if do_plot:
# size = 20
# pixels = np.arange(size)
# image_orig = np.zeros((size, 1))
# image_orig[1, 0] = 1e4
#
# ccd = ac.CCD(
# well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=1e-7
# )
#
# density = 1000
# release_timescale = 3
#
# # Log-normal distribution
# def trap_distribution(release_timescale, median, sigma):
# return np.exp(
# -((np.log(release_timescale) - np.log(median)) ** 2)
# / (2 * sigma ** 2)
# ) / (release_timescale * sigma * np.sqrt(2 * np.pi))
#
# plt.figure()
#
# # Single trap
# trap_single = ac.TrapInstantCapture(
# density=density, release_timescale=release_timescale
# )
# image_single = ac.add_cti(
# image=image_orig, parallel_traps=[trap_single], parallel_ccd=ccd,
# )
# plt.scatter(pixels, image_single[:, 0], c="k", marker=".", label="Single")
#
# # Pure exponential for comparison
# exp_trail = np.exp(-pixels[2:] / release_timescale)
# exp_trail *= image_single[2, 0] / exp_trail[0]
# plt.plot(pixels[2:], exp_trail, c="k", alpha=0.3)
#
# # Different sigma scales
# for sigma in [0.1, 0.5, 1, 2]:
# trap_continuum = ac.TrapLifetimeContinuumAbstract(
# density=density,
# distribution_of_traps_with_lifetime=trap_distribution,
# release_timescale_mu=release_timescale,
# release_timescale_sigma=sigma,
# )
# image_continuum = ac.add_cti(
# image=image_orig, parallel_traps=[trap_continuum], parallel_ccd=ccd,
# )
# plt.plot(
# pixels, image_continuum[:, 0], label=r"$\sigma = %.1f$" % sigma
# )
#
# plt.legend()
# plt.yscale("log")
# plt.xlabel("Pixel")
# plt.ylabel("Counts")
#
# plt.show()
#
#
# class TestElectronsReleasedAndCapturedIncludingSlowTraps:
#
# ccd = ac.CCD(well_fill_power=0.8, full_well_depth=8.47e4, well_notch_depth=0)
#
# density = 10
# release_timescale = 1
#
# # Old-style traps
# traps_instant = [
# ac.TrapInstantCapture(density=density, release_timescale=release_timescale)
# ]
# trap_manager_instant = ac.TrapManagerInstantCapture(
# traps=traps_instant, n_columns=2, max_n_transfers=6
# )
#
# # Fast capture
# traps_fast = [
# ac.Trap(
# density=density, release_timescale=release_timescale, capture_timescale=0
# )
# ]
# trap_manager_fast = ac.TrapManager(traps=traps_fast, n_columns=2, max_n_transfers=3)
#
# # Slow capture
# traps_slow = [
# ac.Trap(
# density=density, release_timescale=release_timescale, capture_timescale=0.1
# )
# ]
# trap_manager_slow = ac.TrapManager(traps=traps_slow, n_columns=2, max_n_transfers=3)
#
# def test__collapse_redundant_watermarks(self):
#
# # None full
# watermarks = self.trap_manager_fast.collapse_redundant_watermarks(
# np.array([[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]])
# )
# assert watermarks == pytest.approx(
# np.array([[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]])
# )
#
# # None overwritten
# watermarks = self.trap_manager_fast.collapse_redundant_watermarks(
# np.array([[0.5, 1], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]])
# )
# assert watermarks == pytest.approx(
# np.array([[0.5, 1], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]])
# )
#
# # Some overwritten
# watermarks = self.trap_manager_fast.collapse_redundant_watermarks(
# np.array([[0.5, 1], [0.2, 1], [0.1, 0.2], [0, 0], [0, 0], [0, 0]])
# )
# assert watermarks == pytest.approx(
# np.array([[0.7, 1], [0.1, 0.2], [0, 0], [0, 0], [0, 0], [0, 0]])
# )
#
# # All overwritten
# watermarks = self.trap_manager_fast.collapse_redundant_watermarks(
# np.array([[0.5, 1], [0.2, 1], [0.1, 1], [0, 0], [0, 0], [0, 0]])
# )
# assert watermarks == pytest.approx(
# np.array([[0.8, 1], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]])
# )
#
# # Some overwritten, with copy
# (
# watermarks,
# watermarks_copy,
# ) = self.trap_manager_fast.collapse_redundant_watermarks(
# watermarks=np.array(
# [[0.5, 1], [0.2, 1], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# ),
# watermarks_copy=np.array(
# [[0.5, 0.5], [0.2, 0.5], [0.1, 0.1], [0, 0], [0, 0], [0, 0]]
# ),
# )
# assert watermarks == pytest.approx(
# np.array([[0.7, 1], [0.1, 0.2], [0, 0], [0, 0], [0, 0], [0, 0]])
# )
# assert watermarks_copy == pytest.approx(
# np.array([[0.7, 0.5], [0.1, 0.1], [0, 0], [0, 0], [0, 0], [0, 0]])
# )
#
# # Multiple trap species, some overwritten, with copy
# (
# watermarks,
# watermarks_copy,
# ) = self.trap_manager_fast.collapse_redundant_watermarks(
# watermarks=np.array(
# [
# [0.4, 1, 1],
# [0.2, 1, 1],
# [0.1, 0.2, 0.3],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# ),
# watermarks_copy=np.array(
# [
# [0.4, 0.5, 0.8],
# [0.2, 0.5, 0.4],
# [0.1, 0.1, 0.2],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# ),
# )
# assert watermarks == pytest.approx(
# np.array(
# [
# [0.6, 1, 1],
# [0.1, 0.2, 0.3],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# )
# )
# assert watermarks_copy == pytest.approx(
# np.array(
# [
# [0.6, 0.5, 2 / 3],
# [0.1, 0.1, 0.2],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# )
# )
#
# # Multiple trap species, not all full
# watermarks = self.trap_manager_fast.collapse_redundant_watermarks(
# watermarks=np.array(
# [
# [0.4, 1, 1],
# [0.3, 1, 1],
# [0.2, 1, 0.9],
# [0.1, 0.2, 0.3],
# [0, 0, 0],
# [0, 0, 0],
# ]
# ),
# )
# assert watermarks == pytest.approx(
# np.array(
# [
# [0.7, 1, 1],
# [0.2, 1, 0.9],
# [0.1, 0.2, 0.3],
# [0, 0, 0],
# [0, 0, 0],
# [0, 0, 0],
# ]
# )
# )
#
# def test__first_slow_capture(self):
#
# n_free_electrons = 5e4 # cloud fractional volume ~= 0.656
#
# net_electrons_instant = self.trap_manager_instant.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_fast = self.trap_manager_fast.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_slow = self.trap_manager_slow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
#
# # Fast traps reproduce old-style behaviour
# assert self.trap_manager_fast.watermarks == pytest.approx(
# self.trap_manager_instant.watermarks
# )
# assert net_electrons_fast == net_electrons_instant
#
# # Slow traps capture fewer electrons but same watermark volumes
# assert self.trap_manager_slow.watermarks[:, 0] == pytest.approx(
# self.trap_manager_instant.watermarks[:, 0]
# )
# assert net_electrons_instant < net_electrons_slow
#
# def test__new_lowest_watermark_slow_capture(self):
#
# n_free_electrons = 5e3 # cloud fractional volume ~= 0.104
#
# watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# self.trap_manager_instant.watermarks = deepcopy(watermarks)
# self.trap_manager_fast.watermarks = deepcopy(watermarks)
# self.trap_manager_slow.watermarks = deepcopy(watermarks)
#
# net_electrons_instant = self.trap_manager_instant.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_fast = self.trap_manager_fast.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_slow = self.trap_manager_slow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
#
# # Fast traps reproduce old-style behaviour
# assert self.trap_manager_fast.watermarks == pytest.approx(
# self.trap_manager_instant.watermarks, rel=1e-3
# )
# assert net_electrons_fast == pytest.approx(net_electrons_instant, rel=1e-3)
#
# # Slow traps capture less than fast
# assert net_electrons_fast < net_electrons_slow
#
# # Lowest watermark volumes add up to previous volume, fill fractions
# # increased below the cloud, decreased above it
# assert self.trap_manager_slow.watermarks[:3, 0].sum() == watermarks[0, 0]
# assert (self.trap_manager_slow.watermarks[:1, 1] > watermarks[0, 1]).all()
# assert self.trap_manager_slow.watermarks[2, 1] < watermarks[0, 1]
#
# # Upper watermark volumes unchanged, fill fractions decreased
# assert self.trap_manager_slow.watermarks[3:, 0] == pytest.approx(
# watermarks[1:-2, 0]
# )
# assert (self.trap_manager_slow.watermarks[3:, 1] <= watermarks[1:-2, 1]).all()
#
# def test__new_middle_watermark_slow_capture(self):
#
# n_free_electrons = 5e4 # cloud fractional volume ~= 0.656
#
# watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# self.trap_manager_instant.watermarks = deepcopy(watermarks)
# self.trap_manager_fast.watermarks = deepcopy(watermarks)
# self.trap_manager_slow.watermarks = deepcopy(watermarks)
#
# net_electrons_instant = self.trap_manager_instant.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_fast = self.trap_manager_fast.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_slow = self.trap_manager_slow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
#
# assert self.trap_manager_fast.watermarks == pytest.approx(
# self.trap_manager_instant.watermarks, rel=1e-3
# )
# assert net_electrons_fast == pytest.approx(net_electrons_instant, rel=1e-3)
#
# # Slow traps capture less than fast
# assert net_electrons_fast < net_electrons_slow
#
# # Lowest watermark volume unchanged, fill fractions increased
# assert self.trap_manager_slow.watermarks[0, 0] == watermarks[0, 0]
# assert self.trap_manager_slow.watermarks[0, 1] > watermarks[0, 1]
#
# # mu watermark volumes add up to previous volume, fill fractions
# # increased below the cloud, decreased above it
# assert self.trap_manager_slow.watermarks[1:4, 0].sum() == watermarks[1, 0]
# assert (self.trap_manager_slow.watermarks[1:3, 1] > watermarks[1, 1]).all()
# assert self.trap_manager_slow.watermarks[3, 1] < watermarks[1, 1]
#
# # Upper watermark volumes unchanged, fill fractions decreased
# assert self.trap_manager_slow.watermarks[4:, 0] == pytest.approx(
# watermarks[2:-2, 0]
# )
# assert (self.trap_manager_slow.watermarks[4:, 1] <= watermarks[2:-2, 1]).all()
#
# def test__new_highest_watermark_slow_capture(self):
#
# n_free_electrons = 7e4 # cloud fractional volume ~= 0.859
#
# watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# self.trap_manager_instant.watermarks = deepcopy(watermarks)
# self.trap_manager_fast.watermarks = deepcopy(watermarks)
# self.trap_manager_slow.watermarks = deepcopy(watermarks)
#
# net_electrons_instant = self.trap_manager_instant.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_fast = self.trap_manager_fast.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_slow = self.trap_manager_slow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=self.ccd.well_filling_function(),
# dwell_time=1,
# )
#
# # Fast traps reproduce old-style behaviour
# assert self.trap_manager_fast.watermarks == pytest.approx(
# self.trap_manager_instant.watermarks, rel=1e-3
# )
# assert net_electrons_fast == pytest.approx(net_electrons_instant, rel=1e-3)
#
# # Slow traps capture less than fast
# assert net_electrons_fast < net_electrons_slow
#
# # Lower watermark volumes unchanged, fill fractions increased
# assert (self.trap_manager_slow.watermarks[:3, 0] == watermarks[:3, 0]).all()
# assert (self.trap_manager_slow.watermarks[:3, 1] > watermarks[:3, 1]).all()
#
# # New upper watermark volume added, fill fraction increased
# assert self.trap_manager_slow.watermarks[3, 0] > watermarks[3, 0]
# assert self.trap_manager_slow.watermarks[3, 1] > watermarks[3, 1]
#
# def test__no_available_electrons_slow_capture(self):
#
# ccd = ac.CCD(well_fill_power=0.5, full_well_depth=10000, well_notch_depth=1e-7)
# n_free_electrons = 0
#
# watermarks = np.array(
# [[0.5, 0.8], [0.2, 0.4], [0.1, 0.2], [0, 0], [0, 0], [0, 0]]
# )
# self.trap_manager_instant.watermarks = deepcopy(watermarks)
# self.trap_manager_fast.watermarks = deepcopy(watermarks)
# self.trap_manager_slow.watermarks = deepcopy(watermarks)
#
# net_electrons_instant = self.trap_manager_instant.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_fast = self.trap_manager_fast.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# dwell_time=1,
# )
# net_electrons_slow = self.trap_manager_slow.n_electrons_released_and_captured(
# n_free_electrons=n_free_electrons,
# ccd_filling_function=ccd.well_filling_function(),
# dwell_time=1,
# )
#
# # Fast traps reproduce old-style behaviour
# assert self.trap_manager_fast.watermarks == pytest.approx(
# self.trap_manager_instant.watermarks, rel=1e-3
# )
# assert net_electrons_fast == pytest.approx(net_electrons_instant, rel=1e-3)
#
# # Slow traps capture less than fast
# assert net_electrons_fast < net_electrons_slow
#
# # Lowest watermark volumes add up to previous volume, fill fractions
# # increased in the new lowest level, decreased above it
# assert self.trap_manager_slow.watermarks[:2, 0].sum() == watermarks[0, 0]
# assert self.trap_manager_slow.watermarks[0, 1] > watermarks[0, 1]
# assert self.trap_manager_slow.watermarks[1, 1] < watermarks[0, 1]
#
# # Upper watermark volumes unchanged, fill fractions decreased
# assert self.trap_manager_slow.watermarks[2:, 0] == pytest.approx(
# watermarks[1:-1, 0]
# )
# assert (self.trap_manager_slow.watermarks[2:, 1] <= watermarks[1:-1, 1]).all()
#
# # Fast traps reproduce old-style behaviour
# assert net_electrons_fast == pytest.approx(net_electrons_instant)
# # Slow traps re-capture less so net release slightly more
# assert net_electrons_fast < net_electrons_slow
#
# def test__updated_watermarks_from_capture_not_enough(self):
#
# # Initial watermarks with updated volumes to match current watermarks
# watermarks_initial = np.array(
# [[0.5, 0.8], [0.1, 0.4], [0.1, 0.4], [0.1, 0.2], [0, 0], [0, 0]]
# )
# # Initial number of trapped electrons
# trapped_electrons_initial = self.trap_manager_slow.n_trapped_electrons_from_watermarks(
# watermarks=watermarks_initial
# )
#
# watermarks = np.array(
# [[0.5, 0.9], [0.1, 0.8], [0.1, 0.4], [0.1, 0.2], [0, 0], [0, 0]]
# )
# self.trap_manager_slow.watermarks = watermarks
# # Expected number of trapped electrons
# trapped_electrons_attempted = (
# self.trap_manager_slow.n_trapped_electrons_from_watermarks(
# watermarks=watermarks
# )
# - trapped_electrons_initial
# )
#
# # But only half the required number of electrons available
# n_free_electrons = 0.5 * trapped_electrons_attempted
# enough = n_free_electrons / trapped_electrons_attempted
#
# watermarks_not_enough = self.trap_manager_slow.updated_watermarks_from_capture_not_enough(
# self.trap_manager_slow.watermarks, watermarks_initial, enough
# )
#
# # Filled half-way to their old-style-capture fill fractions
# assert watermarks_not_enough == pytest.approx(
# np.array([[0.5, 0.85], [0.1, 0.6], [0.1, 0.4], [0.1, 0.2], [0, 0], [0, 0]])
# )
#
# # Resulting number of trapped electrons
# self.trap_manager_slow.watermarks = watermarks_not_enough
# trapped_electrons_final = (
# self.trap_manager_slow.n_trapped_electrons_from_watermarks(
# watermarks=watermarks_not_enough
# )
# - trapped_electrons_initial
# )
#
# # Only capture the available electrons
# assert trapped_electrons_final == pytest.approx(n_free_electrons)
#
|
wfirst-cgiREPO_NAMEemccd_detectPATH_START.@emccd_detect_extracted@emccd_detect-master@arcticpy_folder@build@lib@test_arcticpy@test_traps.py@.PATH_END.py
|
{
"filename": "intro.md",
"repo_name": "exo-cesm/CESM2.1.3",
"repo_path": "CESM2.1.3_extracted/CESM2.1.3-main/intro.md",
"type": "Markdown"
}
|
# Welcome to your Jupyter Book
This is a small sample book to give you a feel for how book content is
structured.
It shows off a few of the major file types, as well as some sample content.
It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information.
Check out the content pages bundled with this sample book to see more.
```{tableofcontents}
```
|
exo-cesmREPO_NAMECESM2.1.3PATH_START.@CESM2.1.3_extracted@CESM2.1.3-main@intro.md@.PATH_END.py
|
{
"filename": "base.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/scipy/integrate/_ivp/base.py",
"type": "Python"
}
|
from __future__ import division, print_function, absolute_import
import numpy as np
def check_arguments(fun, y0, support_complex):
"""Helper function for checking arguments common to all solvers."""
y0 = np.asarray(y0)
if np.issubdtype(y0.dtype, np.complexfloating):
if not support_complex:
raise ValueError("`y0` is complex, but the chosen solver does "
"not support integration in a complex domain.")
dtype = complex
else:
dtype = float
y0 = y0.astype(dtype, copy=False)
if y0.ndim != 1:
raise ValueError("`y0` must be 1-dimensional.")
def fun_wrapped(t, y):
return np.asarray(fun(t, y), dtype=dtype)
return fun_wrapped, y0
class OdeSolver(object):
"""Base class for ODE solvers.
In order to implement a new solver you need to follow the guidelines:
1. A constructor must accept parameters presented in the base class
(listed below) along with any other parameters specific to a solver.
2. A constructor must accept arbitrary extraneous arguments
``**extraneous``, but warn that these arguments are irrelevant
using `common.warn_extraneous` function. Do not pass these
arguments to the base class.
3. A solver must implement a private method `_step_impl(self)` which
propagates a solver one step further. It must return tuple
``(success, message)``, where ``success`` is a boolean indicating
whether a step was successful, and ``message`` is a string
containing description of a failure if a step failed or None
otherwise.
4. A solver must implement a private method `_dense_output_impl(self)`
which returns a `DenseOutput` object covering the last successful
step.
5. A solver must have attributes listed below in Attributes section.
Note that `t_old` and `step_size` are updated automatically.
6. Use `fun(self, t, y)` method for the system rhs evaluation, this
way the number of function evaluations (`nfev`) will be tracked
automatically.
7. For convenience a base class provides `fun_single(self, t, y)` and
`fun_vectorized(self, t, y)` for evaluating the rhs in
non-vectorized and vectorized fashions respectively (regardless of
how `fun` from the constructor is implemented). These calls don't
increment `nfev`.
8. If a solver uses a Jacobian matrix and LU decompositions, it should
track the number of Jacobian evaluations (`njev`) and the number of
LU decompositions (`nlu`).
9. By convention the function evaluations used to compute a finite
difference approximation of the Jacobian should not be counted in
`nfev`, thus use `fun_single(self, t, y)` or
`fun_vectorized(self, t, y)` when computing a finite difference
approximation of the Jacobian.
Parameters
----------
fun : callable
Right-hand side of the system. The calling signature is ``fun(t, y)``.
Here ``t`` is a scalar and there are two options for ndarray ``y``.
It can either have shape (n,), then ``fun`` must return array_like with
shape (n,). Or alternatively it can have shape (n, n_points), then
``fun`` must return array_like with shape (n, n_points) (each column
corresponds to a single column in ``y``). The choice between the two
options is determined by `vectorized` argument (see below).
t0 : float
Initial time.
y0 : array_like, shape (n,)
Initial state.
t_bound : float
Boundary time --- the integration won't continue beyond it. It also
determines the direction of the integration.
vectorized : bool
Whether `fun` is implemented in a vectorized fashion.
support_complex : bool, optional
Whether integration in a complex domain should be supported.
Generally determined by a derived solver class capabilities.
Default is False.
Attributes
----------
n : int
Number of equations.
status : string
Current status of the solver: 'running', 'finished' or 'failed'.
t_bound : float
Boundary time.
direction : float
Integration direction: +1 or -1.
t : float
Current time.
y : ndarray
Current state.
t_old : float
Previous time. None if no steps were made yet.
step_size : float
Size of the last successful step. None if no steps were made yet.
nfev : int
Number of the system's rhs evaluations.
njev : int
Number of the Jacobian evaluations.
nlu : int
Number of LU decompositions.
"""
TOO_SMALL_STEP = "Required step size is less than spacing between numbers."
def __init__(self, fun, t0, y0, t_bound, vectorized,
support_complex=False):
self.t_old = None
self.t = t0
self._fun, self.y = check_arguments(fun, y0, support_complex)
self.t_bound = t_bound
self.vectorized = vectorized
if vectorized:
def fun_single(t, y):
return self._fun(t, y[:, None]).ravel()
fun_vectorized = self._fun
else:
fun_single = self._fun
def fun_vectorized(t, y):
f = np.empty_like(y)
for i, yi in enumerate(y.T):
f[:, i] = self._fun(t, yi)
return f
def fun(t, y):
self.nfev += 1
return self.fun_single(t, y)
self.fun = fun
self.fun_single = fun_single
self.fun_vectorized = fun_vectorized
self.direction = np.sign(t_bound - t0) if t_bound != t0 else 1
self.n = self.y.size
self.status = 'running'
self.nfev = 0
self.njev = 0
self.nlu = 0
@property
def step_size(self):
if self.t_old is None:
return None
else:
return np.abs(self.t - self.t_old)
def step(self):
"""Perform one integration step.
Returns
-------
message : string or None
Report from the solver. Typically a reason for a failure if
`self.status` is 'failed' after the step was taken or None
otherwise.
"""
if self.status != 'running':
raise RuntimeError("Attempt to step on a failed or finished "
"solver.")
if self.n == 0 or self.t == self.t_bound:
# Handle corner cases of empty solver or no integration.
self.t_old = self.t
self.t = self.t_bound
message = None
self.status = 'finished'
else:
t = self.t
success, message = self._step_impl()
if not success:
self.status = 'failed'
else:
self.t_old = t
if self.direction * (self.t - self.t_bound) >= 0:
self.status = 'finished'
return message
def dense_output(self):
"""Compute a local interpolant over the last successful step.
Returns
-------
sol : `DenseOutput`
Local interpolant over the last successful step.
"""
if self.t_old is None:
raise RuntimeError("Dense output is available after a successful "
"step was made.")
if self.n == 0 or self.t == self.t_old:
# Handle corner cases of empty solver and no integration.
return ConstantDenseOutput(self.t_old, self.t, self.y)
else:
return self._dense_output_impl()
def _step_impl(self):
raise NotImplementedError
def _dense_output_impl(self):
raise NotImplementedError
class DenseOutput(object):
"""Base class for local interpolant over step made by an ODE solver.
It interpolates between `t_min` and `t_max` (see Attributes below).
Evaluation outside this interval is not forbidden, but the accuracy is not
guaranteed.
Attributes
----------
t_min, t_max : float
Time range of the interpolation.
"""
def __init__(self, t_old, t):
self.t_old = t_old
self.t = t
self.t_min = min(t, t_old)
self.t_max = max(t, t_old)
def __call__(self, t):
"""Evaluate the interpolant.
Parameters
----------
t : float or array_like with shape (n_points,)
Points to evaluate the solution at.
Returns
-------
y : ndarray, shape (n,) or (n, n_points)
Computed values. Shape depends on whether `t` was a scalar or a
1-d array.
"""
t = np.asarray(t)
if t.ndim > 1:
raise ValueError("`t` must be float or 1-d array.")
return self._call_impl(t)
def _call_impl(self, t):
raise NotImplementedError
class ConstantDenseOutput(DenseOutput):
"""Constant value interpolator.
This class used for degenerate integration cases: equal integration limits
or a system with 0 equations.
"""
def __init__(self, t_old, t, value):
super(ConstantDenseOutput, self).__init__(t_old, t)
self.value = value
def _call_impl(self, t):
if t.ndim == 0:
return self.value
else:
ret = np.empty((self.value.shape[0], t.shape[0]))
ret[:] = self.value[:, None]
return ret
|
waynebhayesREPO_NAMESpArcFiRePATH_START.@SpArcFiRe_extracted@SpArcFiRe-master@scripts@SpArcFiRe-pyvenv@lib@python2.7@site-packages@scipy@integrate@_ivp@base.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "lockepatton/sonipy",
"repo_path": "sonipy_extracted/sonipy-master/tests/__init__.py",
"type": "Python"
}
|
lockepattonREPO_NAMEsonipyPATH_START.@sonipy_extracted@sonipy-master@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "channel_info.py",
"repo_name": "nu-radio/NuRadioMC",
"repo_path": "NuRadioMC_extracted/NuRadioMC-master/NuRadioReco/detector/detector_browser/channel_info.py",
"type": "Python"
}
|
from NuRadioReco.detector.detector_browser.app import app
from dash import html
from dash.dependencies import Input, Output
import NuRadioReco.detector.detector_browser.detector_provider
layout = html.Div([
html.Div([
html.Div('Channel Info', className='panel panel-heading'),
html.Div([
html.Div('', id='channel-info-table')
], className='panel panel-body')
], className='panel panel-default')
])
@app.callback(
Output('channel-info-table', 'children'),
[Input('selected-station', 'children'),
Input('selected-channel', 'children')]
)
def update_channel_info_table(station_id, channel_id):
"""
Controls the content of the channel properties table
Parameters:
---------------------
station_id: int
ID of the station whose properties are displayed
channel_id: int
ID of the channel whose properties are displayed
"""
detector_provider = NuRadioReco.detector.detector_browser.detector_provider.DetectorProvider()
detector = detector_provider.get_detector()
if station_id is None or channel_id is None:
return ''
if detector is None:
return ''
if channel_id not in detector.get_channel_ids(station_id):
print('channel not in station', channel_id)
return ''
channel_info = detector.get_channel(station_id, channel_id)
table_rows = [
html.Div(
'Station {}, Channel {}'.format(station_id, channel_id),
className='custom-table-header'
)
]
for key, value in channel_info.items():
table_rows.append(html.Div([
html.Div(key, className='custom-table-title'),
html.Div(value, className='custom-table-cell')
], className='custom-table-row'))
return table_rows
|
nu-radioREPO_NAMENuRadioMCPATH_START.@NuRadioMC_extracted@NuRadioMC-master@NuRadioReco@detector@detector_browser@channel_info.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/indexes/datetimes/__init__.py",
"type": "Python"
}
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@indexes@datetimes@__init__.py@.PATH_END.py
|
|
{
"filename": "_colorsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattermapbox/hoverlabel/font/_colorsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="colorsrc",
parent_name="scattermapbox.hoverlabel.font",
**kwargs
):
super(ColorsrcValidator, 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@scattermapbox@hoverlabel@font@_colorsrc.py@.PATH_END.py
|
{
"filename": "parsescan.py",
"repo_name": "igmhub/baofit",
"repo_path": "baofit_extracted/baofit-master/data/parsescan.py",
"type": "Python"
}
|
#!/usr/bin/env python
# usage example: ./parsescan.py BOSSDR11QSOLyaF_scan.dat BOSSDR11QSOLyaF.scan 10 11
import sys
# expected command-line args are <infile> <outfile> <index1> <index2>
infile,outfile,index1,index2 = sys.argv[1:]
# check that the parameter indices are integers
try:
index1,index2 = map(int,(index1,index2))
except ValueError, e:
print 'indices should be integer'
sys.exit(-1)
try:
# open the files
with open(infile,'r') as fin, open(outfile,'w') as fout:
# read the header...
# npar = total number of floating+fixed parameters
# ndump = number of r values used to dump ell=0,2,4 multipoles for each fit
# nfit = number of different fits performed (1 or 2)
npar,ndump,nfit = map(int,fin.readline().split())
# best-fit errors on each parameter from each fit
errors = map(float,fin.readline().split())
if len(errors) != npar*nfit:
print 'unexpected length of header line 2'
sys.exit(-1)
# parameter values (and optional multipole dumps) from best fit and each scan point
bestfit = map(float,fin.readline().split())
if len(bestfit) != 1+npar+3*ndump:
print 'unexpected length',len(bestfit),'of header line 3'
sys.exit(-1)
print 'best fit is at (%.3f,%.3f)' % (bestfit[index1],bestfit[index2])
min1 = max1 = bestfit[index1]
min2 = max2 = bestfit[index2]
bestchisq = bestfit[npar]
lineno = 3
for line in fin.readlines():
lineno += 1
scanfit = map(float,line.split())
if len(scanfit) != 1+npar+3*ndump:
print 'unexpected length',len(scanfit),'of line',lineno
sys.exit(-1)
print >>fout, scanfit[index1],scanfit[index2],scanfit[npar]-bestchisq
min1 = min(min1,scanfit[index1])
max1 = max(max1,scanfit[index1])
min2 = min(min2,scanfit[index2])
max2 = max(max2,scanfit[index2])
print 'parsed %d scan points covering [%.3f,%.3f] x [%.3f,%.3f]' % (lineno-3,min1,max1,min2,max2)
except IOError,e:
print str(e)
sys.exit(-1)
|
igmhubREPO_NAMEbaofitPATH_START.@baofit_extracted@baofit-master@data@parsescan.py@.PATH_END.py
|
{
"filename": "2022_06_20_123921_7296741dff68_add_protected_column_for_block_types.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/src/prefect/server/database/_migrations/versions/postgresql/2022_06_20_123921_7296741dff68_add_protected_column_for_block_types.py",
"type": "Python"
}
|
"""Add protected column for block types
Revision ID: 7296741dff68
Revises: d335ad57d5ba
Create Date: 2022-06-20 12:39:21.112876
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = "7296741dff68"
down_revision = "29ad9bef6147"
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("block_type", schema=None) as batch_op:
batch_op.add_column(
sa.Column("is_protected", sa.Boolean(), server_default="0", nullable=False)
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("block_type", schema=None) as batch_op:
batch_op.drop_column("is_protected")
# ### end Alembic commands ###
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@src@prefect@server@database@_migrations@versions@postgresql@2022_06_20_123921_7296741dff68_add_protected_column_for_block_types.py@.PATH_END.py
|
{
"filename": "atmosphere.py",
"repo_name": "LSSTDESC/Spectractor",
"repo_path": "Spectractor_extracted/Spectractor-master/spectractor/simulation/atmosphere.py",
"type": "Python"
}
|
import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from scipy.interpolate import interp1d, RegularGridInterpolator
from spectractor.config import set_logger
import spectractor.parameters as parameters
import spectractor.simulation.libradtran as libradtran
from spectractor.simulation.throughput import plot_transmission_simple
class Atmosphere:
def __init__(self, airmass, pressure, temperature, lambda_min=250, lambda_max=1200, altitude=parameters.OBS_ALTITUDE):
"""Class to evaluate an atmospheric transmission using Libradtran.
Parameters
----------
airmass: float
Airmass of the source object.
pressure: float
Pressure of the atmosphere at observatory altitude in hPa.
temperature: float
Temperature of the atmosphere at observatory altitude in Celsius degrees.
lambda_min: float
Minimum wavelength for simulation in nm (default: 250).
lambda_max: float
Maximum wavelength for simulation in nm (default: 1200).
altitude: float
Observatory altitude in km (default: parameters.OBS_ALTITUDE).
Examples
--------
>>> a = Atmosphere(airmass=1.2, pressure=800, temperature=5)
>>> print(a.airmass)
1.2
>>> print(a.pressure)
800
>>> print(a.temperature)
5
>>> print(a.transmission(500))
1.0
"""
self.my_logger = set_logger(self.__class__.__name__)
self.airmass = airmass
self.pressure = pressure
self.temperature = temperature
self.altitude = altitude
self.pwv = None
self.ozone = None
self.aerosols = None
self.transmission = lambda x: np.ones_like(x).astype(float)
self.lambda_min = lambda_min
self.lambda_max = lambda_max
self.title = ""
self.label = ""
self.emulator = None
self.angstrom_exponent_default = 1.2
if parameters.SPECTRACTOR_ATMOSPHERE_SIM.lower() == "getobsatmo":
import getObsAtmo
if not getObsAtmo.is_obssite(parameters.OBS_NAME):
raise ValueError(f"getObsAtmo does not have observatory site {parameters.OBS_NAME}.")
self.emulator = getObsAtmo.ObsAtmo(obs_str=parameters.OBS_NAME, pressure=self.pressure)
self.emulator.lambda0 = 500.
self.angstrom_exponent_default = 1.2
self.lambda_min = self.emulator.WLMIN
self.lambda_max = self.emulator.WLMAX
elif parameters.SPECTRACTOR_ATMOSPHERE_SIM.lower() == "none":
raise ValueError(f"Can not compute atmospheric transmission with {parameters.SPECTRACTOR_ATMOSPHERE_SIM=}. "
f"Check your configuration.")
elif parameters.SPECTRACTOR_ATMOSPHERE_SIM.lower() == "libradtran":
self.emulator = None
else:
raise ValueError(f"Unknown value for {parameters.SPECTRACTOR_ATMOSPHERE_SIM=}.")
def set_title(self):
"""Make a title string for the simulation.
"""
self.title = f'Atmospheric transmission with z={self.airmass:4.2f}, P={self.pressure:4.2f} hPa, ' \
rf'T={self.temperature:4.2f}$\degree$C'
def set_label(self):
"""Make a label string for the simulation.
"""
self.label = f'PWV={self.pwv:4.2f}mm, OZ={self.ozone:4.2f}DB, VAOD={self.aerosols:4.2f} '
def set_lambda_range(self, lambdas):
"""Reset the Atmosphere wavelength range for optimized computations.
Parameters
----------
lambdas: array_like
Wavelength array in nm.
Examples
--------
>>> a = Atmosphere(airmass=1.2, pressure=800, temperature=5, lambda_min=350, lambda_max=1000)
>>> a.lambda_min
350
>>> a.lambda_max
1000
>>> a.set_lambda_range(np.arange(400, 810, 10))
>>> a.lambda_min
400
>>> a.lambda_max
800
"""
self.lambda_min = int(np.min(lambdas))
self.lambda_max = int(np.ceil(np.max(lambdas)))
def simulate(self, aerosols, ozone, pwv, angstrom_exponent=None):
"""Simulate the atmosphere transparency with Libradtran given atmospheric composition.
Values outside the Libradtran simulation range are set to zero.
Parameters
----------
aerosols: float
VAOD Vertical Aerosols Optical Depth.
ozone: float
Ozone quantity in Dobson.
pwv: float
Precipitable Water Vapor quantity in mm.
angstrom_exponent: float, optional
Angstrom exponent for aerosols.
If None, the Atmosphere.angstrom_exponent_default value is used (default: None).
Returns
-------
transmission: callable
The transmission function of wavelengths in nm.
Examples
--------
>>> parameters.SPECTRACTOR_ATMOSPHERE_SIM = "getobsatmo"
>>> a = Atmosphere(airmass=1.2, pressure=800, temperature=5, lambda_min=350, lambda_max=1000)
CTIO site name validated as CTIO observatory
>>> transmission = a.simulate(aerosols=0.05, ozone=400, pwv=5, angstrom_exponent=None)
>>> a.ozone
400
>>> a.pwv
5
>>> a.aerosols
0.05
>>> transmission([350, 550, 600, 800, 950])
array([0.49958183, 0.82905252, 0.83742397, 0.93720044, 0.71533991])
>>> a.plot_transmission()
>>> transmission_ang_exp = a.simulate(aerosols=0.05, ozone=400, pwv=5, angstrom_exponent=2)
>>> transmission_ang_exp([350, 550, 600, 800, 950])
array([0.48462457, 0.83231609, 0.84292117, 0.94728051, 0.72336351])
>>> a.plot_transmission()
Test concordance of atmospheric simualtors without emulator
>>> parameters.SPECTRACTOR_ATMOSPHERE_SIM = "libradtran"
>>> transmission_ang_exp2 = a.simulate(aerosols=0.05, ozone=400, pwv=5, angstrom_exponent=2)
>>> transmission_ang_exp2([350, 550, 600, 800, 950])
array([0.4846117, 0.8323524, 0.8426985, 0.9465884, 0.71872 ])
.. doctest::
:hide:
>>> assert transmission is not None
>>> assert transmission_ang_exp is not None
>>> assert a.transmission(500) > 0
>>> assert a.transmission(1000) > 0
.. plot::
from spectractor.simulation.atmosphere import Atmosphere
a = Atmosphere(airmass=1.2, pressure=800, temperature=5, lambda_min=300, lambda_max=1000)
transmission = a.simulate(ozone=400, pwv=5, aerosols=0.05)
a.plot_transmission()
"""
self.pwv = pwv
self.ozone = ozone
self.aerosols = aerosols
self.set_title()
self.set_label()
self.my_logger.debug(f'\n\t{self.title}\n\t\t{self.label}')
if angstrom_exponent is not None and angstrom_exponent < 0:
raise ValueError(f"If not None, angstrom_exponnent must be positive. Got {angstrom_exponent=}.")
if parameters.SPECTRACTOR_ATMOSPHERE_SIM.lower() == "getobsatmo":
if angstrom_exponent is None:
angstrom_exponent = 1.2 # value that makes getObsAtmo and Libradtran class close
wl = parameters.LAMBDAS
atm = self.emulator.GetAllTransparencies(wl, am=self.airmass, pwv=pwv, oz=ozone,
tau=aerosols, beta=angstrom_exponent, flagAerosols=True)
elif parameters.SPECTRACTOR_ATMOSPHERE_SIM.lower() == "libradtran":
lib = libradtran.Libradtran()
wl, atm = lib.simulate(self.airmass, aerosols, ozone, pwv, self.pressure, angstrom_exponent=angstrom_exponent,
lambda_min=self.lambda_min, lambda_max=self.lambda_max, altitude=self.altitude)
else:
raise ValueError(f"Unknown value for {parameters.SPECTRACTOR_ATMOSPHERE_SIM=}.")
self.transmission = interp1d(wl, atm, kind='linear', bounds_error=False, fill_value=(0, 0))
return self.transmission
def plot_transmission(self, lambdas=parameters.LAMBDAS):
"""Plot the atmospheric transmission computed with Libradtran.
Parameters
----------
lambdas: array_like, optional
Array of wavelengths in nm (default: parameters.LAMBDAS).
Examples
--------
>>> a = Atmosphere(airmass=1.2, pressure=800, temperature=5)
>>> transmission = a.simulate(ozone=400, pwv=5, aerosols=0.05)
>>> a.plot_transmission()
"""
plot_transmission_simple(plt.gca(), lambdas, self.transmission(lambdas),
title=self.title, label=self.label)
if parameters.DISPLAY: # pragma: no cover
plt.show()
else:
plt.close('all')
class AtmosphereGrid(Atmosphere):
def __init__(self, image_filename="", spectrum_filename="", atmgrid_filename="",
airmass=1., pressure=800., temperature=10.,
pwv_grid=[0, 10, 10], ozone_grid=[100, 700, 7], aerosol_grid=[0, 0.1, 10],
lambdas=parameters.LAMBDAS, altitude=parameters.OBS_ALTITUDE):
"""Class to load and interpolate grids of atmospheric transmission computed with Libradtran.
Parameters
----------
image_filename: str, optional
The original image fits file name from which the grid was computed or has to be computed (default: "").
spectrum_filename: str, optional
The file name of the spectrum fits file name from which the grid was computed or has to be computed (default: "").
atmgrid_filename: str, optional
The file name of the atmospheric grid if it exists (default: "").
airmass: float, optional
Airmass of the source object (default: 1). Overwritten if spectrum_filename is given.
pressure: float, optional
Pressure of the atmosphere at observatory altitude in hPa (default: 800). Overwritten if spectrum_filename is given.
temperature: float, optional
Temperature of the atmosphere at observatory altitude in Celsius degrees (default: 10). Overwritten if spectrum_filename is given.
pwv_grid: list, optional
List of 3 numbers for the PWV quantity: min, max, number of simulations (default: [0, 10, 10]).
ozone_grid: list, optional
List of 3 numbers for the ozone quantity: min, max, number of simulations (default: [100, 700, 7]).
aerosol_grid: list, optional
List of 3 numbers for the aerosol quantity: min, max, number of simulations (default: [0, 0.1, 10]).
lambdas: array_like, optional
Array of wavelengths (default: parameters.LAMBDAS).
altitude: float
Observatory altitude in km (default: parameters.OBS_ALTITUDE).
Examples
--------
>>> a = AtmosphereGrid(atmgrid_filename='./tests/data/reduc_20170530_134_atmsim.fits')
>>> a.image_filename.split('/')[-1]
'reduc_20170530_134_spectrum.fits'
"""
Atmosphere.__init__(self, airmass, pressure, temperature,
lambda_min=np.min(lambdas), lambda_max=np.max(lambdas), altitude=altitude)
self.my_logger = set_logger(self.__class__.__name__)
self.image_filename = image_filename
if spectrum_filename != "":
self.image_filename = spectrum_filename
self.filename = atmgrid_filename
# Definition of data format for the atmospheric grid
self.index_atm_count = 0 # row 0 : count number
self.index_atm_aer = 1 # row 1 : aerosol value
self.index_atm_pwv = 2 # row 2 : pwv value
self.index_atm_oz = 3 # row 3 : ozone value
self.index_atm_data = 4 # row 4 : data start
# specify parameters for the atmospheric grid
self.lambdas = lambdas
self.model = None
self.atmgrid = None
self.NB_ATM_HEADER = self.index_atm_data + 1
self.NB_ATM_DATA = len(self.lambdas) - 1
self.NB_ATM_POINTS = 0
self.AER_Points = np.array([])
self.OZ_Points = np.array([])
self.PWV_Points = np.array([])
# set the initial grid
self.set_grid(pwv_grid=pwv_grid, ozone_grid=ozone_grid, aerosol_grid=aerosol_grid, lambdas=self.lambdas)
self.header = fits.Header()
if atmgrid_filename != "":
self.load_file(atmgrid_filename)
if spectrum_filename != "":
hdr = fits.getheader(spectrum_filename)
self.pressure = hdr["OUTPRESS"]
self.temperature = hdr["OUTTEMP"]
self.airmass = hdr["AIRMASS"]
def set_grid(self, pwv_grid=[0, 10, 10], ozone_grid=[100, 700, 7], aerosol_grid=[0, 0.1, 10],
lambdas=parameters.LAMBDAS):
"""Set the size of the simulation grid self.atmgrid before compute it.
The first column of self.atmgrid will contain the wavelengths set by lambdas argument,
the other columns the future simulations.
Parameters
----------
pwv_grid: list
List of 3 numbers for the PWV quantity: min, max, number of simulations (default: [0, 10, 10]).
ozone_grid: list
List of 3 numbers for the ozone quantity: min, max, number of simulations (default: [100, 700, 7]).
aerosol_grid: list
List of 3 numbers for the aerosol quantity: min, max, number of simulations (default: [0, 0.1, 10]).
lambdas: array_like, optional
Array of wavelengths (default: parameters.LAMBDAS).
"""
self.lambdas = lambdas
# aerosols
NB_AER_POINTS = int(aerosol_grid[2])
AER_MIN = float(aerosol_grid[0])
AER_MAX = float(aerosol_grid[1])
# ozone
NB_OZ_POINTS = int(ozone_grid[2])
OZ_MIN = float(ozone_grid[0])
OZ_MAX = float(ozone_grid[1])
# pwv
NB_PWV_POINTS = int(pwv_grid[2])
PWV_MIN = float(pwv_grid[0])
PWV_MAX = float(pwv_grid[1])
# definition of the grid
self.AER_Points = np.linspace(AER_MIN, AER_MAX, NB_AER_POINTS)
self.OZ_Points = np.linspace(OZ_MIN, OZ_MAX, NB_OZ_POINTS)
self.PWV_Points = np.linspace(PWV_MIN, PWV_MAX, NB_PWV_POINTS)
# total number of points
self.NB_ATM_POINTS = NB_AER_POINTS * NB_OZ_POINTS * NB_PWV_POINTS
# create the numpy array that will contain the atmospheric grid
self.atmgrid = np.zeros((self.NB_ATM_POINTS + 1, self.NB_ATM_HEADER + self.NB_ATM_DATA))
self.atmgrid[0, self.index_atm_data:] = self.lambdas
def compute(self):
"""Compute atmospheric transmissions and fill self.atmgrid.
The wavelengths used for the computation are the ones set by self.lambdas.
Returns
-------
atmospheric_grid: array_like
The atmospheric grid self.atmgrid.
Examples
--------
>>> a = AtmosphereGrid(image_filename='tests/data/reduc_20170605_028.fits',
... pwv_grid=[5, 5, 1], ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
>>> atmospheric_grid = a.compute()
>>> atmospheric_grid # doctest: +ELLIPSIS
array([[0.000000e+00, ...
...])
>>> a.save_file(a.image_filename.replace('.fits', '_atmsim.fits'))
>>> a.plot_transmission()
.. plot::
from spectractor.simulation.atmosphere import AtmosphereGrid
a = AtmosphereGrid(image_filename='tests/data/reduc_20170605_028.fits', pwv_grid=[5, 5, 1],
ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
atmospheric_grid = a.compute()
a.plot_transmission()
.. doctest::
:hide:
>>> assert os.path.isfile(a.image_filename.replace('.fits', '_atmsim.fits'))
>>> assert np.all(np.isclose(a.atmgrid[0, a.index_atm_data:], parameters.LAMBDAS))
>>> assert not np.any(np.isclose(a.atmgrid[1, a.index_atm_data:],
... np.zeros_like(parameters.LAMBDAS), rtol=1e-6))
>>> assert a.atmgrid.shape == (3, a.index_atm_data+len(parameters.LAMBDAS))
"""
# first determine the length
self.my_logger.debug(f'\n\tAtmosphere simulations for z={self.airmass:4.2f}, P={self.pressure:4.2f}hPa, '
rf'T={self.temperature:4.2f}$\degree$C, for data-file={self.image_filename} ')
count = 0
for aer in self.AER_Points:
for pwv in self.PWV_Points:
for oz in self.OZ_Points:
count += 1
# fills headers info in the numpy array
self.atmgrid[count, self.index_atm_count] = count
self.atmgrid[count, self.index_atm_aer] = aer
self.atmgrid[count, self.index_atm_pwv] = pwv
self.atmgrid[count, self.index_atm_oz] = oz
transmission = super(AtmosphereGrid, self).simulate(aerosols=aer, ozone=oz, pwv=pwv)
transm = transmission(self.lambdas)
self.atmgrid[count, self.index_atm_data:] = transm # each of atmospheric spectrum
return self.atmgrid
def plot_transmission(self, lambdas=parameters.LAMBDAS):
"""Plot the atmospheric transmission contained in the grid.
Parameters
----------
lambdas: array_like, optional
Array of wavelengths in nm (default: parameters.LAMBDAS).
Examples
--------
>>> a = AtmosphereGrid(atmgrid_filename='tests/data/reduc_20170530_134_atmsim.fits')
>>> a.plot_transmission()
.. plot::
from spectractor.simulation.atmosphere import AtmosphereGrid
a = AtmosphereGrid(image_filename='tests/data/reduc_20170605_028.fits', pwv_grid=[5, 5, 1],
ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
atmospheric_grid = a.compute()
a.plot_transmission()
"""
plt.figure()
counts = self.atmgrid[1:, self.index_atm_count]
for count in counts:
label = f'PWV={self.atmgrid[int(count), self.index_atm_pwv]} ' \
f'OZ={self.atmgrid[int(count), self.index_atm_oz]} ' \
f'VAOD={self.atmgrid[int(count), self.index_atm_aer]}'
plot_transmission_simple(plt.gca(), lambdas, np.interp(lambdas, self.lambdas, self.atmgrid[int(count), self.index_atm_data:]),
title="Atmospheric grid", label=label)
if parameters.DISPLAY: # pragma: no cover
plt.show()
else:
plt.close('all')
def plot_transmission_image(self):
"""Plot the atmospheric transmission contained in the grid using imshow.
Examples
--------
>>> a = AtmosphereGrid(atmgrid_filename='tests/data/reduc_20170530_134_atmsim.fits')
>>> a.plot_transmission_image()
.. plot::
from spectractor.simulation.atmosphere import AtmosphereGrid
a = AtmosphereGrid(image_filename='tests/data/reduc_20170530_134_atmsim.fits', pwv_grid=[5, 5, 1],
ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
atmospheric_grid = a.compute()
a.plot_transmission_image()
"""
plt.figure()
img = plt.imshow(self.atmgrid[1:, self.index_atm_data:], origin='lower', cmap='jet', aspect="auto")
plt.grid(True)
plt.xlabel(r"$\lambda$ [nm]")
plt.ylabel("Simulation number")
plt.title("Atmospheric variations")
cbar = plt.colorbar(img)
cbar.set_label('Atmospheric transmission')
if parameters.DISPLAY:
plt.show()
else:
plt.close('all')
def save_file(self, filename=""):
"""Save the atmospheric grid in a fits file.
Parameters
----------
filename: str
The output file name.
Examples
--------
>>> a = AtmosphereGrid(image_filename='tests/data/reduc_20170605_028.fits',
... pwv_grid=[5, 5, 1], ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
>>> atmospheric_grid = a.compute()
>>> a.save_file(a.image_filename.replace('.fits', '_atmsim.fits'))
.. doctest::
:hide:
>>> assert os.path.isfile('tests/data/reduc_20170605_028_atmsim.fits')
"""
hdr = fits.Header()
if filename != "":
self.filename = filename
if self.filename == "":
self.my_logger.error('\n\tNo file name is given...')
else:
hdr['ATMSIM'] = parameters.SPECTRACTOR_ATMOSPHERE_SIM
hdr['DATAFILE'] = self.image_filename
hdr['SIMUFILE'] = os.path.basename(self.filename)
hdr['AIRMASS'] = self.airmass
hdr['PRESSURE'] = self.pressure
hdr['TEMPERAT'] = self.temperature
hdr['NBATMPTS'] = self.NB_ATM_POINTS
hdr['NBAERPTS'] = self.AER_Points.size
hdr['AERMIN'] = self.AER_Points.min()
hdr['AERMAX'] = self.AER_Points.max()
hdr['NBPWVPTS'] = self.PWV_Points.size
hdr['PWVMIN'] = self.PWV_Points.min()
hdr['PWVMAX'] = self.PWV_Points.max()
hdr['NBOZPTS'] = self.OZ_Points.size
hdr['OZMIN'] = self.OZ_Points.min()
hdr['OZMAX'] = self.OZ_Points.max()
hdr['NBWLBIN'] = self.lambdas.size
hdr['WLMIN'] = np.min(self.lambdas)
hdr['WLMAX'] = np.max(self.lambdas)
hdr['IDX_CNT'] = self.index_atm_count
hdr['IDX_AER'] = self.index_atm_aer
hdr['IDX_PWV'] = self.index_atm_pwv
hdr['IDX_OZ'] = self.index_atm_oz
hdr['IDX_DATA'] = self.index_atm_data
hdu = fits.PrimaryHDU(self.atmgrid, header=hdr)
hdu.writeto(self.filename, overwrite=True)
self.my_logger.info(f'\n\tAtmosphere.save atm-file={self.filename}')
def load_file(self, filename):
"""Load the atmospheric grid from a fits file and interpolate across the points
using RegularGridInterpolator. Automatically called from __init__.
Parameters
----------
filename: str
The input file name.
Examples
--------
>>> a = AtmosphereGrid(image_filename='tests/data/reduc_20170605_028.fits',
... pwv_grid=[5, 5, 1], ozone_grid=[400, 400, 1], aerosol_grid=[0.0, 0.1, 2])
>>> atmospheric_grid = a.compute()
>>> a.save_file(a.image_filename.replace('.fits', '_atmsim.fits'))
>>> assert os.path.isfile('tests/data/reduc_20170605_028_atmsim.fits')
>>> a.load_file(a.image_filename.replace('.fits', '_atmsim.fits'))
>>> a.AER_Points
array([0. , 0.1])
>>> a.PWV_Points
array([5.])
>>> a.OZ_Points
array([400.])
"""
if filename != "":
self.filename = filename
if self.filename == "":
self.my_logger.error('\n\tNo file name is given...')
else:
with fits.open(self.filename) as hdu:
hdr = hdu[0].header
self.header = hdr
self.image_filename = hdr['DATAFILE']
self.airmass = hdr['AIRMASS']
self.pressure = hdr['PRESSURE']
self.temperature = hdr['TEMPERAT']
self.NB_ATM_POINTS = hdr['NBATMPTS']
NB_AER_POINTS = hdr['NBAERPTS']
AER_MIN = hdr['AERMIN']
AER_MAX = hdr['AERMAX']
NB_PWV_POINTS = hdr['NBPWVPTS']
PWV_MIN = hdr['PWVMIN']
PWV_MAX = hdr['PWVMAX']
NB_OZ_POINTS = hdr['NBOZPTS']
OZ_MIN = hdr['OZMIN']
OZ_MAX = hdr['OZMAX']
self.AER_Points = np.linspace(AER_MIN, AER_MAX, NB_AER_POINTS)
self.OZ_Points = np.linspace(OZ_MIN, OZ_MAX, NB_OZ_POINTS)
self.PWV_Points = np.linspace(PWV_MIN, PWV_MAX, NB_PWV_POINTS)
NBWLBINS = hdr['NBWLBIN']
self.index_atm_count = hdr['IDX_CNT']
self.index_atm_aer = hdr['IDX_AER']
self.index_atm_pwv = hdr['IDX_PWV']
self.index_atm_oz = hdr['IDX_OZ']
self.index_atm_data = hdr['IDX_DATA']
self.atmgrid = np.zeros((self.NB_ATM_POINTS + 1, self.NB_ATM_HEADER + NBWLBINS - 1))
self.atmgrid[:, :] = hdu[0].data[:, :]
self.my_logger.debug(f'\n\tAtmosphere.load_image atm-file={self.filename}')
# interpolate the grid
self.lambdas = self.atmgrid[0, self.index_atm_data:]
self.model = RegularGridInterpolator((self.lambdas, self.OZ_Points, self.PWV_Points, self.AER_Points), (
self.atmgrid[1:, self.index_atm_data:].reshape(NB_AER_POINTS, NB_PWV_POINTS,
NB_OZ_POINTS,
len(self.lambdas))).T, bounds_error=False, fill_value=0)
def simulate(self, ozone, pwv, aerosols, angstrom_exponent=None):
"""Interpolate from the atmospheric grid to get the atmospheric transmission.
First ozone, second pwv, last aerosols, to respect order of loops when generating the grid
Parameters
----------
ozone: float
Ozone quantity in Dobson.
pwv: float
Precipitable Water Vapor quantity in mm.
aerosols: float
VAOD Vertical Aerosols Optical Depth.
angstrom_exponent: float, optional
Angstrom exponent for aerosols.
If None, the Atmosphere.angstrom_exponent_default value is used (default: None).
Examples
--------
.. plot::
:include-source:
>>> from spectractor.simulation.atmosphere import AtmosphereGrid, plot_transmission_simple
>>> from spectractor import parameters
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> a = AtmosphereGrid(atmgrid_filename='tests/data/reduc_20170530_134_atmsim.fits')
>>> lambdas = np.arange(200, 1200)
>>> fig = plt.figure()
>>> for pwv in np.arange(5):
... transmission = a.simulate(ozone=400, pwv=pwv, aerosols=0.05)
... plot_transmission_simple(plt.gca(), lambdas, transmission(lambdas),
... title=a.title, label=a.label)
>>> if parameters.DISPLAY: plt.show()
"""
if angstrom_exponent is not None and angstrom_exponent < 0:
raise ValueError(f"Angstrom exponent not implemented in AtmosphericGrid() yet. "
f"Please provide angstrom_exponent=None. Got {angstrom_exponent=} instead.")
self.pwv = pwv
self.ozone = ozone
self.aerosols = aerosols
self.set_title()
self.set_label()
ones = np.ones_like(self.lambdas)
points = np.array([self.lambdas, ozone * ones, pwv * ones, aerosols * ones]).T
atm = self.model(points)
self.transmission = interp1d(self.lambdas, atm, kind='linear', bounds_error=False, fill_value=(0, 0))
return self.transmission
if __name__ == "__main__":
import doctest
doctest.testmod()
|
LSSTDESCREPO_NAMESpectractorPATH_START.@Spectractor_extracted@Spectractor-master@spectractor@simulation@atmosphere.py@.PATH_END.py
|
{
"filename": "callStack.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/gempy/library/config/callStack.py",
"type": "Python"
}
|
#
# LSST Data Management System
# Copyright 2017 AURA/LSST.
#
# This product includes software developed by the
# LSST Project (http://www.lsst.org/).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the LSST License Statement and
# the GNU General Public License along with this program. If not,
# see <https://www.lsstcorp.org/LegalNotices/>.
#
__all__ = ['getCallerFrame', 'getStackFrame', 'StackFrame', 'getCallStack']
from builtins import object
import inspect
import linecache
def getCallerFrame(relative=0):
"""Retrieve the frame for the caller
By "caller", we mean our user's caller.
Parameters
----------
relative : `int`, non-negative
Number of frames above the caller to retrieve.
Returns
-------
frame : `__builtin__.Frame`
Frame for the caller.
"""
frame = inspect.currentframe().f_back.f_back # Our caller's caller
for ii in range(relative):
frame = frame.f_back
return frame
def getStackFrame(relative=0):
"""Retrieve the stack frame for the caller
By "caller", we mean our user's caller.
Parameters
----------
relative : `int`, non-negative
Number of frames above the caller to retrieve.
Returns
-------
frame : `StackFrame`
Stack frame for the caller.
"""
frame = getCallerFrame(relative + 1)
return StackFrame.fromFrame(frame)
class StackFrame(object):
"""A single element of the stack trace
This differs slightly from the standard system mechanisms for
getting a stack trace by the fact that it does not look up the
source code until it is absolutely necessary, reducing the I/O.
Parameters
----------
filename : `str`
Name of file containing the code being executed.
lineno : `int`
Line number of file being executed.
function : `str`
Function name being executed.
content : `str` or `None`
The actual content being executed. If not provided, it will be
loaded from the file.
"""
_STRIP = "/DRAGONS/" # String to strip from the filename
def __init__(self, filename, lineno, function, content=None):
loc = filename.rfind(self._STRIP)
if loc > 0:
filename = filename[loc + len(self._STRIP):]
self.filename = filename
self.lineno = lineno
self.function = function
self._content = content
@property
def content(self):
"""
Getter for content being executed. Load from file on demand.
"""
if self._content is None:
self._content = linecache.getline(self.filename, self.lineno).strip()
return self._content
@classmethod
def fromFrame(cls, frame):
"""
Construct from a Frame object
inspect.currentframe() provides a Frame object. This is
a convenience constructor to interpret that Frame object.
Parameters
----------
frame : `Frame`
Frame object to interpret.
Returns
-------
output : `StackFrame`
Constructed object.
"""
filename = frame.f_code.co_filename
lineno = frame.f_lineno
function = frame.f_code.co_name
return cls(filename, lineno, function)
def __repr__(self):
return "%s(%s, %s, %s)" % (self.__class__.__name__, self.filename, self.lineno, self.function)
def format(self, full=False):
"""Format for printing
Parameters
----------
full : `bool`
Print full details, including content being executed?
Returns
-------
result : `str`
Formatted string.
"""
result = " File %s:%s (%s)" % (self.filename, self.lineno, self.function)
if full:
result += "\n %s" % (self.content,)
return result
def getCallStack(skip=0):
"""
Retrieve the call stack for the caller
By "caller", we mean our user's caller - we don't include ourselves
or our caller.
The result is ordered with the most recent frame last.
Parameters
----------
skip : `int`, non-negative
Number of stack frames above caller to skip.
Returns
-------
output : `list` of `StackFrame`
The call stack.
"""
frame = getCallerFrame(skip + 1)
stack = []
while frame:
stack.append(StackFrame.fromFrame(frame))
frame = frame.f_back
return list(reversed(stack))
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@gempy@library@config@callStack.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/treemap/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._visible import VisibleValidator
from ._valuessrc import ValuessrcValidator
from ._values import ValuesValidator
from ._uirevision import UirevisionValidator
from ._uid import UidValidator
from ._tiling import TilingValidator
from ._texttemplatesrc import TexttemplatesrcValidator
from ._texttemplate import TexttemplateValidator
from ._textsrc import TextsrcValidator
from ._textposition import TextpositionValidator
from ._textinfo import TextinfoValidator
from ._textfont import TextfontValidator
from ._text import TextValidator
from ._stream import StreamValidator
from ._sort import SortValidator
from ._root import RootValidator
from ._pathbar import PathbarValidator
from ._parentssrc import ParentssrcValidator
from ._parents import ParentsValidator
from ._outsidetextfont import OutsidetextfontValidator
from ._opacity import OpacityValidator
from ._name import NameValidator
from ._metasrc import MetasrcValidator
from ._meta import MetaValidator
from ._maxdepth import MaxdepthValidator
from ._marker import MarkerValidator
from ._level import LevelValidator
from ._legendwidth import LegendwidthValidator
from ._legendrank import LegendrankValidator
from ._legendgrouptitle import LegendgrouptitleValidator
from ._legend import LegendValidator
from ._labelssrc import LabelssrcValidator
from ._labels import LabelsValidator
from ._insidetextfont import InsidetextfontValidator
from ._idssrc import IdssrcValidator
from ._ids import IdsValidator
from ._hovertextsrc import HovertextsrcValidator
from ._hovertext import HovertextValidator
from ._hovertemplatesrc import HovertemplatesrcValidator
from ._hovertemplate import HovertemplateValidator
from ._hoverlabel import HoverlabelValidator
from ._hoverinfosrc import HoverinfosrcValidator
from ._hoverinfo import HoverinfoValidator
from ._domain import DomainValidator
from ._customdatasrc import CustomdatasrcValidator
from ._customdata import CustomdataValidator
from ._count import CountValidator
from ._branchvalues import BranchvaluesValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._visible.VisibleValidator",
"._valuessrc.ValuessrcValidator",
"._values.ValuesValidator",
"._uirevision.UirevisionValidator",
"._uid.UidValidator",
"._tiling.TilingValidator",
"._texttemplatesrc.TexttemplatesrcValidator",
"._texttemplate.TexttemplateValidator",
"._textsrc.TextsrcValidator",
"._textposition.TextpositionValidator",
"._textinfo.TextinfoValidator",
"._textfont.TextfontValidator",
"._text.TextValidator",
"._stream.StreamValidator",
"._sort.SortValidator",
"._root.RootValidator",
"._pathbar.PathbarValidator",
"._parentssrc.ParentssrcValidator",
"._parents.ParentsValidator",
"._outsidetextfont.OutsidetextfontValidator",
"._opacity.OpacityValidator",
"._name.NameValidator",
"._metasrc.MetasrcValidator",
"._meta.MetaValidator",
"._maxdepth.MaxdepthValidator",
"._marker.MarkerValidator",
"._level.LevelValidator",
"._legendwidth.LegendwidthValidator",
"._legendrank.LegendrankValidator",
"._legendgrouptitle.LegendgrouptitleValidator",
"._legend.LegendValidator",
"._labelssrc.LabelssrcValidator",
"._labels.LabelsValidator",
"._insidetextfont.InsidetextfontValidator",
"._idssrc.IdssrcValidator",
"._ids.IdsValidator",
"._hovertextsrc.HovertextsrcValidator",
"._hovertext.HovertextValidator",
"._hovertemplatesrc.HovertemplatesrcValidator",
"._hovertemplate.HovertemplateValidator",
"._hoverlabel.HoverlabelValidator",
"._hoverinfosrc.HoverinfosrcValidator",
"._hoverinfo.HoverinfoValidator",
"._domain.DomainValidator",
"._customdatasrc.CustomdatasrcValidator",
"._customdata.CustomdataValidator",
"._count.CountValidator",
"._branchvalues.BranchvaluesValidator",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@treemap@__init__.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/box/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._line import Line
from ._marker import Marker
from ._selected import Selected
from ._stream import Stream
from ._unselected import Unselected
from . import hoverlabel
from . import legendgrouptitle
from . import marker
from . import selected
from . import unselected
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[".hoverlabel", ".legendgrouptitle", ".marker", ".selected", ".unselected"],
[
"._hoverlabel.Hoverlabel",
"._legendgrouptitle.Legendgrouptitle",
"._line.Line",
"._marker.Marker",
"._selected.Selected",
"._stream.Stream",
"._unselected.Unselected",
],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@box@__init__.py@.PATH_END.py
|
{
"filename": "household.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/tests/test-projects/tasks/household.py",
"type": "Python"
}
|
from prefect import task
@task
def do_the_dishes():
return "The dishes are :sparkles: clean!"
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@tests@test-projects@tasks@household.py@.PATH_END.py
|
{
"filename": "contributing.md",
"repo_name": "google/flax",
"repo_path": "flax_extracted/flax-main/docs/contributing.md",
"type": "Markdown"
}
|
# How to contribute
Everyone can contribute to Flax, and the Flax development team values everyone's contributions!
You can contribute in many more ways than just writing code. Answering questions
on the [Flax GitHub Discussions page](https://github.com/google/flax/discussions), helping
each other, and improving Flax documentation are extremely valuable to the Flax
ecosystem.
We also appreciate if you spread the word, for instance by starring the [Flax GitHub repository](https://github.com/google/flax),
or referencing Flax in blog posts of projects that used it.
This project follows
[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
## Ways to contribute
We welcome pull requests (PRs), in particular for those issues
[marked as PR-ready](https://github.com/google/flax/issues?q=is%3Aopen+is%3Aissue+label%3A%22Status%3A+pull+requests+welcome%22).
For other proposals, you should first open a GitHub Issue or a GitHub Discussion to
start a conversation about your planned contribution.
## Contributing code using pull requests
The Flax development team performs all development using [Git](https://git-scm.com/). To contribute,
you should have basic knowledge of [Git](https://git-scm.com/) and [GitHub](https://docs.github.com).
(You can learn how to set up Git by following Git's official
[Getting Started - First-Time Git Setup](https://git-scm.com/book/en/v2/Getting-Started-First-Time-Git-Setup)
and GitHub's [Set Up Git](https://docs.github.com/en/get-started/quickstart/set-up-git) guides.)
To contribute code to Flax on GitHub, follow these steps:
### To create a pull request from a fork
1. Using GitHub's web UI, fork the Flax repository by clicking the 'Fork' button on the
[`github.com/google/flax` repository page](http://www.github.com/google/flax). This creates a
fork (a copy) of the Flax repository in your own GitHub.
Reference: [Creating a pull request from a fork](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork).
2. Install [Python >=3.7](https://www.python.org/downloads/).
3. (Optional) Create a virtual environment or a Docker container. See
[`dev/README.md`](https://github.com/google/flax/blob/main/dev/README.md)
for details on how to set up a Docker Container. To set up a virtual environment,
run the following:
```bash
python3 -m virtualenv env
. env/bin/activate
```
This ensures all your dependencies are installed in this environment.
4. Clone your local forked Flax repo with `git clone`. Then, install the required packages
with [PyPi](https://pip.pypa.io/en/stable/cli/pip_install/). This enables you to immediately
test the code after modifying it:
```bash
git clone https://github.com/YOUR_USERNAME/flax
cd flax
pip install -e ".[all,testing,docs]"
```
You can also use [uv](https://docs.astral.sh/uv/) to setup
the development environment:
```bash
uv sync --all-extras
```
5. Set up pre-commit hooks, this will run some automated checks during each `git` commit and
possibly update some files that require changes.
```bash
pip install pre-commit
pre-commit install
```
6. Add the Google Flax repo (not your fork) as an upstream remote, so you can use it to sync your
changes.
```bash
git remote add upstream http://www.github.com/google/flax
```
7. Create a branch, such as `my_development_branch`, you will develop from:
```bash
git checkout -b my_development_branch
```
8. Implement your changes using your favorite editor (we recommend
[Visual Studio Code](https://code.visualstudio.com/)).
Make sure the tests pass by running the following command from the top of
the repository:
```bash
./tests/run_all_tests.sh
```
9. Once you finish making changes, don't forget to create commits
([learn how to write a commit message](https://chris.beams.io/posts/git-commit/)):
```bash
git add file1.py file2.py ...
# or use `git add .` to add all changed files
git commit -m "Your commit message"
```
Then sync your code with the main repository:
```bash
git fetch upstream
git rebase upstream/main
```
10. Finally, push your commit on your `my_development_branch`, and create a remote
branch in your fork that you can use to create a pull request from:
```bash
git push --set-upstream origin my_development_branch
```
After running the command, you should get a GitHub link in your (VS Code) terminal output for creating a pull request.
If you don't receive a link after `git push`, use the [GitHub web UI](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request?tool=webui) to create a pull request.
11. Make sure your pull request passes the
[Flax PR checklist](https://github.com/google/flax/blob/main/.github/pull_request_template.md#checklist).
If so, create a pull request from the Flax repository and send it for review.
Consult [GitHub Help](https://help.github.com/articles/about-pull-requests/)
for more information on using pull requests.
You can learn more in GitHub's [Creating a pull request from a fork
](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). documentation.
### Adding or updating dependencies
To add or update dependencies, you must use `uv` after
updating the `pyproject.toml` file to ensure that the `uv.lock` file is up-to-date.
```bash
uv sync --all-extras
```
Alternatively use can use `uv add` to add or update the dependencies automatically, for example:
```bash
uv add 'some-package>=1.2.3'
```
### Updating Jupyter Notebooks
We use [jupytext](https://jupytext.readthedocs.io/) to maintain two synced copies of docs
in `docs/notebooks`: one in the Jupyter Notebook (`.ipynb`) format, and one in Markdown (`.md`).
The former can be opened and executed directly in [Google Colab](https://colab.research.google.com/).
Markdown makes it easier to track changes/diffs within version control and, for example, GitHub
web UI, since `.ipynb` files are based on JSON.
#### Editing Jupyter Notebooks (`.ipynb`)
For making large changes that substantially modify code and outputs, it's recommended to edit
the notebooks in [Jupyter](https://jupyter.org/install) or in [Colab](https://colab.research.google.com/).
If you choose to work in Colab, go to **File** and click **Upload notebook**, then pick your file.
After loading it into Colab and editing it, make sure you run the cells, and that there aren't any errors.
Click on **Runtime**, then select **Run all**. After you finish, click **File** > **Download** > **Download ipynb**.
You may also want to test that the file executes properly by using `sphinx-build`, as explained above.
After you make changes in your Jupyter Notebook, follow the steps _Syncing notebooks_ below.
#### Editing Markdown files (`.md`)
For making smaller changes to the text content of the notebooks, it is easiest to edit the
`.md` versions using a text editor.
After you make changes in your Markdown file, follow the steps _Syncing notebooks_ below.
#### Syncing notebooks
After editing either the `.ipynb` or `.md` versions of the docs, sync the two versions
using [jupytext](https://jupytext.readthedocs.io/) by running `jupytext --sync` on the updated
notebooks.
First, make sure you have jupytext installed. The jupytext version should match
the one specified in [.pre-commit-config.yaml](https://github.com/google/flax/blob/main/.pre-commit-config.yaml)
(currently, it is v1.13.8).
```bash
pip install jupytext==1.13.8
```
Then, after you have made your changes in the Jupyter Notebook, sync the contents with its Markdown-equivalent
file by running the following command:
```bash
jupytext --sync path/to/the/file.ipynb
```
Similarly, to sync your Markdown file with its Jupyter Notebook version, run:
```bash
jupytext --sync path/to/the/file.md
```
Note that if you receive an error, and it is the first time you worked in a Jupyter Notebook, you may need
to (re)create a synced copy of the document (which is explained in detail in _Creating new notebooks_ section below):
```bash
jupytext --set-formats ipynb,md:myst path/to/the/notebook.ipynb
```
Once you're finished with syncing the `.md` and `.ipynb` files, you can check that they are properly synced using the
[pre-commit](https://pre-commit.com/) framework to perform the same checks used
in the Flax GitHub CI:
```bash
git add docs -u # pre-commit runs on files in git staging.
pre-commit run jupytext
```
#### Creating new notebooks
If you are adding a new Jupyter Notebook to the documentation, you can use `jupytext --set-formats`.
It can set up both the Jupyter Notebook (`.ipynb`) and Markdown (`.md`) versions of the file:
```bash
jupytext --set-formats ipynb,md:myst path/to/the/notebook.ipynb
```
This works by adding a `"jupytext"` metadata field to the notebook file which specifies the
desired formats. The `jupytext --sync` command can then recognize them when invoked.
After you make changes in your file(s), follow the steps from the _Syncing notebooks_
section above to keep the contents of both Markdown and Jupyter Notebook files in sync.
#### Notebooks within the Sphinx build
Some of the notebooks are built automatically as part of the pre-submit checks and
as part of the [Read the Docs](https://flax.readthedocs.io/en/latest) build.
The build will fail if cells raise errors. If the errors are intentional, you can either catch them,
or tag the cell with `raises-exceptions` metadata ([example PR](https://github.com/jax-ml/jax/pull/2402/files)).
You have to add this metadata by hand in the `.ipynb` file. It will be preserved when somebody else
re-saves the notebook.
We exclude some notebooks from the build because, for example, they contain long computations.
See `exclude_patterns` in [`conf.py`](https://github.com/google/flax/blob/main/docs/conf.py).
### Updating the pull request contents
Every pull request should ideally be limited to just one commit, so if you have multiple commits please squash them.
Assuming you now have only one commit in your pull request, and want to add changes requested during review:
1. Make the changes locally in your editor.
2. Run `git commit -a --amend`. This updates the commit contents and allows you to edit the commit message.
3. At this point, `git push` alone will result in an error. Instead, use `git push --force`.
4. Check that it's done: The changes to your commit should be immediately reflected in the Github web UI.
## Troubleshooting
### Too many commits in a pull request
If your PR has too many commits associated with it (for example, more than five),
you need to squash them. Otherwise, the Flax docs build process may fail with an
error message. This is because of the following reasons:
* There are more than five commits in your pull request; and
* The Flax source sync process fails when the commit tree is too large.
To squash your commits, you can rebase your branch to `main` and create a new
commit containing all your changes, run the following command:
```bash
git rebase main && git reset --soft main && git commit
```
This will apply all your changes to the main branch. Note that if you had to
resolve any conflicts while working on your change (for instance, you did a
`pull upstream main` which led to conflict), then you will have to resolve these
conflicts again.
After you have successfully rebased your branch, you should push your changes.
And because you changed the commit history, you may have to use `git push --force`.
## Contributor License Agreement
Contributions to this project must be accompanied by a Contributor License
Agreement. You (or your employer) retain the copyright to your contribution;
this simply gives us permission to use and redistribute your contributions as
part of the project. Head over to <https://cla.developers.google.com/> to see
your current agreements on file or to sign a new one.
You generally only need to submit a CLA once, so if you've already submitted one
(even if it was for a different project), you probably don't need to do it
again.
|
googleREPO_NAMEflaxPATH_START.@flax_extracted@flax-main@docs@contributing.md@.PATH_END.py
|
{
"filename": "parsec.py",
"repo_name": "danxhuber/evolstate",
"repo_path": "evolstate_extracted/evolstate-master/parsec.py",
"type": "Python"
}
|
# code to generate TAMS and base of the RGB files from Parsec models
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import ascii
import glob
# models from https://people.sissa.it/~sbressan/CAF09_V1.2S_M36_LT/
# solar metallicity
files=glob.glob('Z0.017Y0.279/*')
# [M/H]~0.3
#files=glob.glob('Z0.03Y0.302/*')
plt.ion()
plt.clf()
f = open('tams_parsec.txt','w')
f2 = open('rgb_parsec.txt','w')
for i in range(0,50):
data=ascii.read(files[i])
print(i,files[i])
#rad=np.log10(10**data['LOG_R']/6.96e10)
rad=np.log10(np.sqrt(10**data['LOG_L'] * (10**data['LOG_TE']/5777.)**(-4.)))
age=(data['AGE'])*1e-9
um=np.where(data['PHASE'] > 4.)[0]
plt.plot(data['LOG_TE'][um],rad[um],'.',color='black',ms=0.2)
um=np.where((data['PHASE'] == 7.) & (age < 20.))[0]
if (len(um) != 0):
plt.plot(data['LOG_TE'][um],rad[um],'o',color='red')
f.write('%12.5f %12.5f \n' % (10**data['LOG_TE'][um[0]],10**rad[um[0]]))
um=np.where((data['PHASE'] == 8.) & (age < 20.))[0]
if (len(um) != 0):
plt.plot(data['LOG_TE'][um],rad[um],'o',color='green')
f2.write('%12.5f %12.5f \n' % (10**data['LOG_TE'][um[0]],10**rad[um[0]]))
#plt.draw()
#input(':')
f.close()
f2.close()
|
danxhuberREPO_NAMEevolstatePATH_START.@evolstate_extracted@evolstate-master@parsec.py@.PATH_END.py
|
{
"filename": "splittst.py",
"repo_name": "mhammond/pywin32",
"repo_path": "pywin32_extracted/pywin32-main/Pythonwin/pywin/Demos/splittst.py",
"type": "Python"
}
|
import commctrl
import fontdemo
import win32ui
from pywin.mfc import docview, window
# derive from CMDIChild. This does much work for us.
class SplitterFrame(window.MDIChildWnd):
def __init__(self):
# call base CreateFrame
self.images = None
window.MDIChildWnd.__init__(self)
def OnCreateClient(self, cp, context):
splitter = win32ui.CreateSplitter()
doc = context.doc
frame_rect = self.GetWindowRect()
size = ((frame_rect[2] - frame_rect[0]), (frame_rect[3] - frame_rect[1]) // 2)
sub_size = (size[0] // 2, size[1])
splitter.CreateStatic(self, 2, 1)
self.v1 = win32ui.CreateEditView(doc)
self.v2 = fontdemo.FontView(doc)
# CListControl view
self.v3 = win32ui.CreateListView(doc)
sub_splitter = win32ui.CreateSplitter()
# pass "splitter" so each view knows how to get to the others
sub_splitter.CreateStatic(splitter, 1, 2)
sub_splitter.CreateView(self.v1, 0, 0, (sub_size))
sub_splitter.CreateView(self.v2, 0, 1, (0, 0)) # size ignored.
splitter.SetRowInfo(0, size[1], 0)
splitter.CreateView(self.v3, 1, 0, (0, 0)) # size ignored.
# Setup items in the imagelist
self.images = win32ui.CreateImageList(32, 32, 1, 5, 5)
self.images.Add(win32ui.GetApp().LoadIcon(win32ui.IDR_MAINFRAME))
self.images.Add(win32ui.GetApp().LoadIcon(win32ui.IDR_PYTHONCONTYPE))
self.images.Add(win32ui.GetApp().LoadIcon(win32ui.IDR_TEXTTYPE))
self.v3.SetImageList(self.images, commctrl.LVSIL_NORMAL)
self.v3.InsertItem(0, "Icon 1", 0)
self.v3.InsertItem(0, "Icon 2", 1)
self.v3.InsertItem(0, "Icon 3", 2)
# self.v3.Arrange(commctrl.LVA_DEFAULT) Hmmm - win95 aligns left always???
return 1
def OnDestroy(self, msg):
window.MDIChildWnd.OnDestroy(self, msg)
if self.images:
self.images.DeleteImageList()
self.images = None
def InitialUpdateFrame(self, doc, makeVisible):
self.v1.ReplaceSel("Hello from Edit Window 1")
self.v1.SetModifiedFlag(0)
class SampleTemplate(docview.DocTemplate):
def __init__(self):
docview.DocTemplate.__init__(
self, win32ui.IDR_PYTHONTYPE, None, SplitterFrame, None
)
def InitialUpdateFrame(self, frame, doc, makeVisible):
# print("frame is ", frame, frame._obj_)
# print("doc is ", doc, doc._obj_)
self._obj_.InitialUpdateFrame(frame, doc, makeVisible) # call default handler.
frame.InitialUpdateFrame(doc, makeVisible)
def demo():
template = SampleTemplate()
doc = template.OpenDocumentFile(None)
doc.SetTitle("Splitter Demo")
if __name__ == "__main__":
import demoutils
if demoutils.NeedGoodGUI():
demo()
|
mhammondREPO_NAMEpywin32PATH_START.@pywin32_extracted@pywin32-main@Pythonwin@pywin@Demos@splittst.py@.PATH_END.py
|
{
"filename": "DivFrhoResolutionStudy.py",
"repo_name": "mmicromegas/ransX",
"repo_path": "ransX_extracted/ransX-master/EQUATIONS/FOR_RESOLUTION_STUDY/DivFrhoResolutionStudy.py",
"type": "Python"
}
|
import numpy as np
from scipy import integrate
import matplotlib.pyplot as plt
from UTILS.Calculus import Calculus
from UTILS.SetAxisLimit import SetAxisLimit
from UTILS.Tools import Tools
from UTILS.Errors import Errors
import sys
# Theoretical background https://arxiv.org/abs/1401.5176
# Mocak, Meakin, Viallet, Arnett, 2014, Compressible Hydrodynamic Mean-Field #
# Equations in Spherical Geometry and their Application to Turbulent Stellar #
# Convection Data #
class DivFrhoResolutionStudy(Calculus, SetAxisLimit, Tools, Errors, object):
def __init__(self, filename, ig, intc, data_prefix):
super(DivFrhoResolutionStudy, self).__init__(ig)
# load data to list of structured arrays
eht = []
for ffile in filename:
eht.append(self.customLoad(ffile))
# declare data lists
xzn0, nx, ny, nz, tavg = [], [], [], [], []
divfrho = []
for i in range(len(filename)):
# load grid
xzn0.append(np.asarray(eht[i].item().get('xzn0')))
nx.append(np.asarray(eht[i].item().get('nx')))
ny.append(np.asarray(eht[i].item().get('ny')))
nz.append(np.asarray(eht[i].item().get('nz')))
tavg.append(np.asarray(eht[i].item().get('tavg')))
# pick specific Reynolds-averaged mean fields according to:
# https://github.com/mmicromegas/ransX/blob/master/DOCS/ransXimplementationGuide.pdf
divfrho.append(self.Div((np.asarray(eht[i].item().get('ddux')[intc])-
np.asarray(eht[i].item().get('dd')[intc])*np.asarray(eht[i].item().get('ux')[intc])),np.asarray(eht[i].item().get('xzn0'))))
# share data globally
self.data_prefix = data_prefix
self.xzn0 = xzn0
self.nx = nx
self.ny = ny
self.nz = nz
self.divfrho = divfrho
self.ig = ig
self.tavg = tavg
def plot_divfrho(self, LAXIS, xbl, xbr, ybu, ybd, ilg):
"""Plot div TurbulentMass flux in the model"""
if (LAXIS != 2):
print("ERROR(DivFrhoResolutionStudy.py): Only LAXIS=2 is supported.")
sys.exit()
# load x GRID
grd = self.xzn0
# load DATA to plot
plt1 = self.divfrho
nx = self.nx
ny = self.ny
nz = self.nz
# find maximum resolution data
grd_maxres = self.maxresdata(grd)
plt1_maxres = self.maxresdata(plt1)
plt_interp = []
for i in range(len(grd)):
plt_interp.append(np.interp(grd_maxres, grd[i], plt1[i]))
# create FIGURE
plt.figure(figsize=(7, 6))
# format AXIS, make sure it is exponential
plt.gca().yaxis.get_major_formatter().set_powerlimits((0, 0))
plt10_tmp = plt1[0]
plt11_tmp = plt1[0]
plt1_foraxislimit = []
plt1max = np.max(plt1[0])
for plt1i in plt1:
if (np.max(plt1i) > plt1max):
plt1_foraxislimit = plt1i
# set plot boundaries
to_plot = [plt1_foraxislimit]
self.set_plt_axis(LAXIS, xbl, xbr, ybu, ybd, to_plot)
# plot DATA
plt.title(r"-Div $\overline{\rho' u'_x}$ ")
for i in range(len(grd)):
plt.plot(grd[i], -1.*plt1[i], label=str(self.nx[i]) + ' x ' + str(self.ny[i]) + ' x ' + str(self.nz[i])+ ' '+'(tavg = ' + str(np.int(self.tavg[i])) +' s)')
#bbndry = grd[0][289]
#tbndry = grd[0][316]
#plt.text(tbndry,0.8e2,r"$\sim$0.23 Hp")
# convective boundary
#plt.axvline(bbndry, linestyle='--', linewidth=0.7, color='k')
#plt.axvline(tbndry, linestyle='--', linewidth=0.7, color='k')
# define and show x/y LABELS
if self.ig == 1:
setxlabel = r"x (cm)"
setylabel = r"$-\nabla_x \overline{\rho' u'_x}}$"
plt.xlabel(setxlabel)
plt.ylabel(setylabel)
elif self.ig == 2:
setxlabel = r"r (cm)"
setylabel = r"$-\nabla_r \overline{\rho' u'_r}}$"
plt.xlabel(setxlabel)
plt.ylabel(setylabel)
plt.axhline(y=0., linestyle='--',color='k')
# show LEGEND
plt.legend(loc=ilg, prop={'size': 14})
# display PLOT
plt.show(block=False)
# save PLOT
plt.savefig('RESULTS/' + self.data_prefix + 'mean_divfrho.png')
plt.savefig('RESULTS/' + self.data_prefix + 'mean_divfrho.eps')
# find data with maximum resolution
def maxresdata(self, data):
tmp = 0
for idata in data:
if idata.shape[0] > tmp:
data_maxres = idata
else:
tmp = idata.shape[0]
return data_maxres
|
mmicromegasREPO_NAMEransXPATH_START.@ransX_extracted@ransX-master@EQUATIONS@FOR_RESOLUTION_STUDY@DivFrhoResolutionStudy.py@.PATH_END.py
|
{
"filename": "test_fm_fakedisk.py",
"repo_name": "vortex-exoplanet/VIP",
"repo_path": "VIP_extracted/VIP-master/tests/pre_3_10/test_fm_fakedisk.py",
"type": "Python"
}
|
"""
Tests for fm/fakecomp.py
"""
import sys
sys.path.append(".../tests")
from tests.helpers import aarc
import sys
sys.path.append(".../tests")
from tests.helpers import fixture
import sys
sys.path.append(".../tests")
from tests.helpers import np
from vip_hci.fm import cube_inject_fakedisk
from vip_hci.fm import cube_inject_trace
# ===== utility functions
@fixture(scope="module", params=["3D"])
def dataset(request):
"""
Create 3D and 4D datasets for use with ``test_cube_inject_companions``.
"""
if request.param == "3D":
cube = np.zeros((3, 25, 25))
psf = np.ones((1, 1))
angles = np.array([0, 90, 180])
return cube, psf, angles
def test_cube_inject_fakedisk(dataset):
"""
Verify position of injected disk image with 1 value, for 3D and 4D cases.
"""
def _expected():
"""
Expected positions.
"""
return [(15, 12), (12, 15), (9, 12)]
psf = np.zeros((25, 25))
psf[15, 12] = 1
_, _, angles = dataset
cube = cube_inject_fakedisk(psf, angle_list=angles)
# find coords
coords = []
for i in range(cube.shape[0]):
max_idx = np.argmax(cube[i])
coords.append(np.unravel_index(max_idx, cube[0].shape))
yx_expected = _expected()
aarc(coords, yx_expected)
def test_cube_inject_trace(dataset):
"""
Verify position of injected disk image with 1 value, for 3D and 4D cases.
"""
def _expected(ang):
"""
Expected positions.
"""
if ang == 0:
return [(7, 12), (12, 8), (15, 12)]
elif ang == 90:
return [(12, 7), (12, 15), (16, 12)]
elif ang == 180:
return [(9, 12), (12, 16), (17, 12)]
cube, psf, angles = dataset
rads = [3, 4, 5]
thetas = [90, 180, 270]
cube = cube_inject_trace(
cube,
psf,
angles,
flevel=1,
rad_dists=rads,
theta=thetas,
plsc=0.01225,
n_branches=1,
imlib="vip-fft",
interpolation="lanczos4",
verbose=True,
)
for i in range(cube.shape[0]):
# find coords of trace in each image of the cube
coords = []
nspi = len(rads)
frame_tmp = cube[i].copy()
for s in range(nspi):
max_idx = np.argmax(frame_tmp)
coords_tmp = np.unravel_index(max_idx, frame_tmp.shape)
coords.append(coords_tmp)
frame_tmp[coords_tmp] = 0
idx_order = np.argsort(np.sum(coords, axis=1))
coords_sort = [coords[i] for i in idx_order]
yx_expected = _expected(angles[i])
aarc(coords_sort, yx_expected)
|
vortex-exoplanetREPO_NAMEVIPPATH_START.@VIP_extracted@VIP-master@tests@pre_3_10@test_fm_fakedisk.py@.PATH_END.py
|
{
"filename": "optical_flow.py",
"repo_name": "itseez/opencv",
"repo_path": "opencv_extracted/opencv-master/samples/dnn/optical_flow.py",
"type": "Python"
}
|
#!/usr/bin/env python
'''
This sample using FlowNet v2 and RAFT model to calculate optical flow.
FlowNet v2 Original Paper: https://arxiv.org/abs/1612.01925.
FlowNet v2 Repo: https://github.com/lmb-freiburg/flownet2.
Download the converted .caffemodel model from https://drive.google.com/open?id=16qvE9VNmU39NttpZwZs81Ga8VYQJDaWZ
and .prototxt from https://drive.google.com/file/d/1RyNIUsan1ZOh2hpYIH36A-jofAvJlT6a/view?usp=sharing.
Otherwise download original model from https://lmb.informatik.uni-freiburg.de/resources/binaries/flownet2/flownet2-models.tar.gz,
convert .h5 model to .caffemodel and modify original .prototxt using .prototxt from link above.
RAFT Original Paper: https://arxiv.org/pdf/2003.12039.pdf
RAFT Repo: https://github.com/princeton-vl/RAFT
Download the .onnx model from here https://github.com/opencv/opencv_zoo/raw/281d232cd99cd920853106d853c440edd35eb442/models/optical_flow_estimation_raft/optical_flow_estimation_raft_2023aug.onnx.
'''
import argparse
import os.path
import numpy as np
import cv2 as cv
class OpticalFlow(object):
def __init__(self, model, height, width, proto=""):
if proto:
self.net = cv.dnn.readNetFromCaffe(proto, model)
else:
self.net = cv.dnn.readNet(model)
self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
self.height = height
self.width = width
def compute_flow(self, first_img, second_img):
inp0 = cv.dnn.blobFromImage(first_img, size=(self.width, self.height))
inp1 = cv.dnn.blobFromImage(second_img, size=(self.width, self.height))
self.net.setInputsNames(["img0", "img1"])
self.net.setInput(inp0, "img0")
self.net.setInput(inp1, "img1")
flow = self.net.forward()
output = self.motion_to_color(flow)
return output
def motion_to_color(self, flow):
arr = np.arange(0, 255, dtype=np.uint8)
colormap = cv.applyColorMap(arr, cv.COLORMAP_HSV)
colormap = colormap.squeeze(1)
flow = flow.squeeze(0)
fx, fy = flow[0, ...], flow[1, ...]
rad = np.sqrt(fx**2 + fy**2)
maxrad = rad.max() if rad.max() != 0 else 1
ncols = arr.size
rad = rad[..., np.newaxis] / maxrad
a = np.arctan2(-fy / maxrad, -fx / maxrad) / np.pi
fk = (a + 1) / 2.0 * (ncols - 1)
k0 = fk.astype(np.int32)
k1 = (k0 + 1) % ncols
f = fk[..., np.newaxis] - k0[..., np.newaxis]
col0 = colormap[k0] / 255.0
col1 = colormap[k1] / 255.0
col = (1 - f) * col0 + f * col1
col = np.where(rad <= 1, 1 - rad * (1 - col), col * 0.75)
output = (255.0 * col).astype(np.uint8)
return output
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Use this script to calculate optical flow',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-input', '-i', required=True, help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--height', default=320, type=int, help='Input height')
parser.add_argument('--width', default=448, type=int, help='Input width')
parser.add_argument('--proto', '-p', default='', help='Path to prototxt.')
parser.add_argument('--model', '-m', required=True, help='Path to model.')
args, _ = parser.parse_known_args()
if not os.path.isfile(args.model):
raise OSError("Model does not exist")
if args.proto and not os.path.isfile(args.proto):
raise OSError("Prototxt does not exist")
winName = 'Calculation optical flow in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
cap = cv.VideoCapture(args.input if args.input else 0)
hasFrame, first_frame = cap.read()
if args.proto:
divisor = 64.
var = {}
var['ADAPTED_WIDTH'] = int(np.ceil(args.width/divisor) * divisor)
var['ADAPTED_HEIGHT'] = int(np.ceil(args.height/divisor) * divisor)
var['SCALE_WIDTH'] = args.width / float(var['ADAPTED_WIDTH'])
var['SCALE_HEIGHT'] = args.height / float(var['ADAPTED_HEIGHT'])
config = ''
proto = open(args.proto).readlines()
for line in proto:
for key, value in var.items():
tag = "$%s$" % key
line = line.replace(tag, str(value))
config += line
caffemodel = open(args.model, 'rb').read()
opt_flow = OpticalFlow(caffemodel, var['ADAPTED_HEIGHT'], var['ADAPTED_WIDTH'], bytearray(config.encode()))
else:
opt_flow = OpticalFlow(args.model, 360, 480)
while cv.waitKey(1) < 0:
hasFrame, second_frame = cap.read()
if not hasFrame:
break
flow = opt_flow.compute_flow(first_frame, second_frame)
first_frame = second_frame
cv.imshow(winName, flow)
|
itseezREPO_NAMEopencvPATH_START.@opencv_extracted@opencv-master@samples@dnn@optical_flow.py@.PATH_END.py
|
{
"filename": "InstrumentData.py",
"repo_name": "agreenbaum/ImPlaneIA",
"repo_path": "ImPlaneIA_extracted/ImPlaneIA-master/nrm_analysis/InstrumentData.py",
"type": "Python"
}
|
#! /usr/bin/env python
"""
InstrumentData Class -- defines data format, wavelength info, mask geometry
Instruments/masks supported:
GPI NRM
NIRISS AMI
VISIR SAM
** we like acronyms **
"""
# Standard Imports
from __future__ import print_function
import numpy as np
from astropy.io import fits
import os, sys, time
# Module imports
# mask geometries, GPI, NIRISS, VISIR supported...
from .misctools.mask_definitions import NRM_mask_definitions
from .misctools import utils
um = 1.0e-6
# utility routines for InstrumentData classes
def show_cvsupport_threshold(instr):
""" Show threshold for where 'splodge' data in CV space contains signal """
print("cvsupport_threshold is: ", instr.cvsupport_threshold)
print(instr.cvsupport_threshold)
def set_cvsupport_threshold(instr, k, v):
""" Set threshold for where 'splodge' data in CV space contains signal
Parameters
----------
instr: InstrumentData instance
thresh: Threshold for the absolute value of the FT(interferogram).
Normalize abs(CV = FT(a)) for unity peak, and define the support
of "good" CV when this is above threshold
"""
instr.cvsupport_threshold[k] = v
print("New cvsupport_threshold is: ", instr.cvsupport_threshold)
class GPI:
def __init__(self, reffile, **kwargs):
"""
Initialize GPI class
ARGUMENTS:
reffile - one or a list of reference fits files
gpi-pipeline reduced containing useful header info
optionally: 'gpifilterpath'
- Point to a directory which contains GPI filter files
code will read in the relevant file and pick out a
sample of wavelengths and transmissions that span the
list, to be used later to generate the model.
"""
# only one NRM on GPI:
self.arrname = "gpi_g10s40"
self.pscale_mas = 14.1667 #14.27 looks like a better match March 2019
self.pscale_rad = utils.mas2rad(self.pscale_mas)
self.mask = NRM_mask_definitions(maskname=self.arrname)
self.mask.ctrs = np.array(self.mask.ctrs)
# Hard code -1.5 deg rotation in data (April 2016)
# (can be moved to NRM_mask_definitions later)
self.mask.ctrs = utils.rotate2dccw(self.mask.ctrs, (-3.7)*np.pi/180.)
# Add in hole/baseline properties ?
self.holeshape="circ"
affine2d = kwargs.get( 'affine2d', None)
if affine2d is None:
self.affine2d = utils.Affine2d(mx=1.0,my=1.0,
sx=0.0,sy=0.0,
xo=0.0,yo=0.0, name="Ideal")
else:
self.affine2d = affine2d
# Get info from reference file
self.hdr0 = []
self.hdr1 = []
self.refdata = []
if type(reffile)==str:
reffile = [reffile,]
for fn in reffile:
reffits = fits.open(fn)
self.hdr0.append(reffits[0].header)
self.hdr1.append(reffits[1].header)
self.refdata.append(reffits[1].data)
reffits.close()
# instrument settings
self.mode = self.hdr0[0]["DISPERSR"]
self.obsmode = self.hdr0[0]["OBSMODE"]
self.band = self.obsmode[-1] # K1 is two letters
self.ref_imgs_dir = "refimgs_"+self.band+"/"
# finding centroid from phase slope only considered cv_phase data
# when cv_abs data exceeds this cvsupport_threshold.
# Absolute value of cv data normalized to unity maximum
# for the threshold application.
# Data reduction gurus: tweak the threshold value with experience...
self.cvsupport_threshold = {"Y":0.02, "J":0.02, "H":0.02, "1":0.02, "2":0.02}
self.threshold = self.cvsupport_threshold[self.band]
# Special mode for collapsed data
if self.hdr1[0]["NAXIS3"]==1:
# This is just a way handle data that is manually collapsed.
# Not a standard data format for GPI.
print("No NAXIS3 keyword. This is probably collapsed data.")
print("Going to fake some stuff now")
self.mode = "WOLLASTON_FAKEOUT"
# wavelength info: spect mode or pol more
if "PRISM" in self.mode:
# GPI's spectral mode
self.nwav = self.hdr1[0]["NAXIS3"]
self.wls = np.linspace(self.hdr1[0]["CRVAL3"], \
self.hdr1[0]["CRVAL3"]+\
self.hdr1[0]['CD3_3']*self.nwav,\
self.nwav)*um
self.eff_band = um*np.ones(self.nwav)*(self.wls[-1] - \
self.wls[0])/self.nwav
elif "WOLLASTON" in self.mode:
# GPI's pol mode. Will define this for the DIFFERENTIAL VISIBILITIES
# diff vis: two channels 0/45 and 22/67
self.nwav = 2
# Define the bands in case we use a tophat filter
band_ctrs = {"Y":(1.14+0.95)*um/2., "J":(1.35+1.12)*um/2., \
"H":(1.80+1.50)*um/2., "1":(2.19+1.9)*um/2., \
"2":(2.4+2.13)*um/2.0}
band_wdth = {"Y":(1.14-0.95)*um, "J":(1.35-1.12)*um, "H":(1.80-1.50)*um, \
"1":(2.19-1.9)*um, "2":(2.4-2.13)*um}
wghts = np.ones(15)
wavls = np.linspace(band_ctrs[self.band]-band_wdth[self.band]/2.0, \
band_ctrs[self.band]+band_wdth[self.band]/2.0, num=15)
if 'gpifilterpath' in kwargs:
print("Using GPI filter file ", end='')
if self.band=="Y":
filterfile = kwargs["gpifilterpath"]+"GPI-filter-Y.fits"
print(kwargs["gpifilterpath"]+"GPI-filter-Y.fits")
cutoff=0.7
if self.band=="J":
filterfile = kwargs["gpifilterpath"]+"GPI-filter-J.fits"
print(kwargs["gpifilterpath"]+"GPI-filter-J.fits")
cutoff=0.7
if self.band=="H":
filterfile = kwargs["gpifilterpath"]+"GPI-filter-H.fits"
print(kwargs["gpifilterpath"]+"GPI-filter-H.fits")
cutoff=0.7
if self.band=="1":
filterfile = kwargs["gpifilterpath"]+"GPI-filter-K1.fits"
print(kwargs["gpifilterpath"]+"GPI-filter-K1.fits")
cutoff=0.94
if self.band=="2":
filterfile = kwargs["gpifilterpath"]+"GPI-filter-K2.fits"
print(kwargs["gpifilterpath"]+"GPI-filter-K2.fits")
cutoff=0.94
# Read in gpi filter file
fitsfilter = fits.open(filterfile)[1].data
wavls = []
wghts = []
# Sample the filter file so the filter is only 50 elements long
skip = len(fitsfilter[0][0]) / 50
for ii in range(len(fitsfilter[0][0])/skip):
if fitsfilter[0][1][skip*ii]>cutoff:
wavls.append(fitsfilter[0][0][skip*ii]*1.0e-6)
wghts.append(fitsfilter[0][1][skip*ii])
lam_c = band_ctrs[self.band]
lam_w = band_wdth[self.band]
transmission = np.array([[wghts[f], wavls[f]] for f in range(len(wghts))])
if "FAKEOUT" in self.mode:
self.nwav=1
self.wls = [transmission, ]
self.eff_band = np.array([lam_w, ])
else:
self.wls = [transmission, transmission]
self.eff_band = np.array([lam_w, lam_w])
else:
sys.exit("Check your reference file header. "+\
"Keywork DISPERSR='{0}' not understood".format(self.mode))
# For OIFits structure
self.wavextension = (self.wls, self.eff_band)
if "FAKEOUT" in self.mode:
self.wavextension = ([lam_c,], [lam_w,])
# Observation info
self.telname= "GEMINI"
self.ra, self.dec = self.hdr0[0]["RA"], self.hdr0[0]["DEC"]
try:
self.date = self.hdr0[0]["DATE"]
except:
self.date = self.hdr0[0]["DATE-OBS"]
self.month = self.date[-5:-3]
self.day = self.date[-2:]
self.year = self.date[:4]
#self.parang = self.hdr0["PAR_ANG"]
# AVPARANG added Aug 2 2016
self.parangs = []
self.itime = []
self.crpa = []
for ii in range(len(reffile)):
self.parangs.append(self.hdr1[ii]["AVPARANG"])
self.itime.append(self.hdr1[ii]["ITIME"])
if "CRPA" in self.hdr0[ii]:
self.crpa.append(self.hdr0[ii]["CRPA"])
self.avparang = np.mean(self.parangs)
if len(self.crpa)>0:
self.avcassang = np.mean(self.crpa)
else:
self.avcassang = 0.0
self.parang_range = abs(self.parangs[-1] - self.parangs[0])
self.totalinttime = np.sum(self.itime)
try:
self.pa = self.hdr0[0]["PA"]
except:
self.pa = 0.0
try:
self.objname = self.hdr0[0]["OBJECT"]
except:
self.objname = "Unknown"
# Look for additional keyword arguments ?
def read_data(self, fn):
fitsfile = fits.open(fn)
sci=fitsfile[1].data
hdr=fitsfile[1].header
fitsfile.close()
#fitshdr = fitsfile[0].header
if 'distorcorr' in fn:
self.sub_dir_str = fn[-32:-21]
else:
self.sub_dir_str = fn[-21:-10]
return sci, hdr
class VISIR:
def __init__(self, objname="obj", band="11.3", src = "A0V",
affine2d=None):
"""
Initialize VISIR class
ARGUMENTS:
objname - string w/name of object observed
src - if pysynphot is installed, can provide a guess at the stellar spectrum
"""
self.band = band
self.objname = objname
self.arrname = "visir_sam"
self.pscale_mas = 45
self.pscale_rad = utils.mas2rad(self.pscale_mas)
self.mask = NRM_mask_definitions(maskname=self.arrname)
self.mask.ctrs = np.array(self.mask.ctrs)
#self.mask.ctrs[:,1]*=-1
self.holeshape="hex"
#self.mask.ctrs = utils.rotate2dccw(self.mask.ctrs, 7.5*np.pi/180.)
if affine2d is None:
self.affine2d = utils.Affine2d(mx=1.0,my=1.0,
sx=0.0,sy=0.0,
xo=0.0,yo=0.0, name="Ideal")
else:
self.affine2d = affine2d
# tophat filter
# this can be swapped with an actual filter file
if self.band=="11.3":
self.lam_c = 11.3*1e-6 # 11.3 microns
self.lam_w = 0.6/11.3 # 0.6 micron bandpass
elif self.band=="10.5":
self.lam_c = 10.6*1e-6 # 11.3 microns
self.lam_w = 0.1/10.5 # 0.6 micron bandpass
else:
raise ValueError("options for band are '11.3' or '10.5' \n{0} not supported".format(band))
self.filt = utils.tophatfilter(self.lam_c, self.lam_w, npoints=10)
try:
self.wls = [utils.combine_transmission(self.filt, src), ]
except:
self.wls = [self.filt, ]
#self.wavextension = (self.lam_c, self.lam_w)
#self.wavextension = (self.lam_c*np.ones(self.nexp), \
# self.lam_w*np.ones(self.nexp))
self.wavextension = ([self.lam_c,], [self.lam_w,])
self.nwav=1
# finding centroid from phase slope only considered cv_phase data when cv_abs data exceeds this.
# absolute value of cv data normalized to unity maximum for the threshold application.
self.cvsupport_threshold = {"10.5":0.02, "11.3":0.02} # Gurus: tweak with use...
self.threshold = self.cvsupport_threshold[self.band]
self.ref_imgs_dir = "refimgs/"
#############################
# Observation info - I don't know yet how JWST data headers will be structured
self.telname= "VLT"
try:
self.ra, self.dec = self.hdr0["RA"], self.hdr0["DEC"]
except:
self.ra, self.dec = 00, 00
try:
self.date = self.hdr0["DATE"]
self.month = self.date[-5:-3]
self.day = self.date[-2:]
self.year = self.date[:4]
except:
lt = time.localtime()
self.date = "{0}{1:02d}{2:02d}".format(lt[0],lt[1],lt[2])
self.month = lt[1]
self.day = lt[2]
self.year = lt[0]
try:
self.parang = self.hdr0["PAR_ANG"]
except:
self.parang = 00
try:
self.pa = self.hdr0["PA"]
except:
self.pa = 00
try:
self.itime = self.hdr1["ITIME"]
except:
self.itime = 00
#############################
def read_data(self, fn):
# for datacube of exposures, need to read as 3D (nexp, npix, npix)
fitsfile = fits.open(fn)
scidata=fitsfile[0].data
hdr=fitsfile[0].header
#self.sub_dir_str = self.filt+"_"+objname
self.sub_dir_str = '/' + fn.split('/')[-1].replace('.fits', '')
#self.nexp = scidata.shape[0]
# rewrite wavextension to be same length as nexp
if len(scidata.shape)==3:
self.nwav=scidata.shape[0]
[self.wls.append(self.wls[0]) for f in range(self.nwav-1)]
return scidata, hdr
elif len(scidata.shape)==2:
return np.array([scidata,]), hdr
else:
sys.exit("invalid data dimensions for NIRISS. Should have dimensionality of 2 or 3.")
return scidata, hdr
class NIRISS:
def __init__(self, filt,
objname="obj",
src="A0V",
out_dir='',
chooseholes=None,
affine2d=None,
**kwargs):
"""
Initialize NIRISS class
ARGUMENTS:
kwargs:
UTR
Or just look at the file structure
Either user has webbpsf and filter file can be read, or this will use a tophat and give a warning
"""
if chooseholes:
print(" **** InstrumentData.NIRISS: ", chooseholes)
# define bandpass either by tophat or webbpsf filt file
#self.wls = np.array([self.bandpass,])
self.filt = filt
self.objname = objname
#############################
lam_c = {"F277W":2.77e-6, "F380M": 3.8e-6, "F430M": 4.3e-6, "F480M": 4.8e-6}
lam_w = {"F277W":0.2, "F380M": 0.1, "F430M": 0.05, "F480M": 0.08}
lam_bin = {"F277W": 50, "F380M": 20, "F430M":20, "F480M":30}
#############################
# only one NRM on JWST:
self.arrname = "jwst_g7s6c"
self.pscale_mas = 65
self.pscale_rad = utils.mas2rad(self.pscale_mas)
self.mask = NRM_mask_definitions(maskname=self.arrname, chooseholes=chooseholes )
self.mask.ctrs = np.array(self.mask.ctrs)
# Hard code any rotations?
# (can be moved to NRM_mask_definitions later)
# Add in hole/baseline properties ?
self.holeshape="hex"
# save affine deformation of pupil object or create a no-deformation object.
# We apply this when sampling the PSF, not to the pupil geometry.
# This will set a default Ideal or a measured rotation, for example,
# and include pixel scale changes due to pupil distortion.
# Separating detector tilt pixel scale effects from pupil distortion effects is
# yet to be determined... see comments in Affine class definition.
# AS AZG 2018 08 15 Ann Arbor
if affine2d is None:
self.affine2d = utils.Affine2d(mx=1.0,my=1.0,
sx=0.0,sy=0.0,
xo=0.0,yo=0.0, name="Ideal")
else:
self.affine2d = affine2d
# finding centroid from phase slope only considered cv_phase data
# when cv_abs data exceeds this cvsupport_threshold.
# Absolute value of cv data normalized to unity maximum
# for the threshold application.
# Data reduction gurus: tweak the threshold value with experience...
# Gurus: tweak cvsupport with use...
self.cvsupport_threshold = {"F277W":0.02, "F380M": 0.02, "F430M": 0.02, "F480M": 0.02}
show_cvsupport_threshold(self)
self.threshold = self.cvsupport_threshold[filt]
self.ref_imgs_dir = os.path.join(out_dir,"refimgs_"+self.filt+"/")
# Wavelength info for NIRISS bands F277W, F380M, F430M, or F480M
try:
# If user has webbpsf installed, this will work
#self.throughput = utils.get_webbpsf_filter(self.filt+"_throughput.fits", \
# trim = (lam_c[self.filt], lam_w[self.filt]))
self.throughput = utils.trim_webbpsf_filter(self.filt, specbin=lam_bin[self.filt])
except:
self.throughput = utils.tophatfilter(lam_c[self.filt], lam_w[self.filt], npoints=11)
try:
self.wls = [utils.combine_transmission(self.throughput, src), ]
except:
self.wls = [self.throughput, ]
self.wavextension = ([lam_c[self.filt],], [lam_w[self.filt],])
self.nwav=1
#############################
# Observation info - I don't know yet how JWST data headers will be structured
self.telname= "JWST"
try:
self.ra, self.dec = self.hdr0["RA"], self.hdr0["DEC"]
except:
self.ra, self.dec = 00, 00
try:
self.date = self.hdr0["DATE"]
self.month = self.date[-5:-3]
self.day = self.date[-2:]
self.year = self.date[:4]
except:
lt = time.localtime()
self.date = "{0}{1:02d}{2:02d}".format(lt[0],lt[1],lt[2])
self.month = lt[1]
self.day = lt[2]
self.year = lt[0]
try:
self.parang = self.hdr0["PAR_ANG"]
except:
self.parang = 00
try:
self.pa = self.hdr0["PA"]
except:
self.pa = 00
try:
self.itime = self.hdr1["ITIME"]
except:
self.itime = 00
#############################
def read_data(self, fn, mode="slice"):
# mode options are slice or UTR
# for single slice data, need to read as 3D (1, npix, npix)
# for utr data, need to read as 3D (ngroup, npix, npix)
fitsfile = fits.open(fn)
scidata=fitsfile[0].data
hdr=fitsfile[0].header
#self.sub_dir_str = self.filt+""
self.sub_dir_str = '/' + fn.split('/')[-1].replace('.fits', '')
if len(scidata.shape)==3:
self.nwav=scidata.shape[0]
[self.wls.append(self.wls[0]) for f in range(self.nwav-1)]
return scidata, hdr
elif len(scidata.shape)==2:
return np.array([scidata,]), hdr
else:
sys.exit("invalid data dimensions for NIRISS. Should have dimensionality of 2 or 3.")
def _generate_filter_files():
"""Either from WEBBPSF, or tophat, etc. A set of filter files will also be provided"""
return None
class NIRC2:
def __init__(self, reffile, **kwargs):
"""
Initialize NIRC2 class
ARGUMENTS:
objname - string w/name of object observed
src - if pysynphot is installed, can provide a guess at the stellar spectrum
IFU simulation option set IFU = True
"""
if "IFU" in kwargs:
if kwargs["IFU"]==True:
self.mode = "PRISM"
else:
self.mode = "BROADBAND"
else:
self.mode = "BROADBAND"
if "src" in kwargs:
src = kwargs["src"]
else:
pass
affine2d = kwargs.get( 'affine2d', None )
if affine2d is None:
self.affine2d = utils.Affine2d(mx=1.0,my=1.0,
sx=0.0,sy=0.0,
xo=0.0,yo=0.0, name="Ideal")
else:
self.affine2d = affine2d
self.arrname = "NIRC2_9NRM"
self.pscale_mas = 9.952 # mas
self.pscale_rad = utils.mas2rad(self.pscale_mas)
self.mask = NRM_mask_definitions(maskname=self.arrname)
self.mask.ctrs = np.array(self.mask.ctrs)
# Hard code -1.5 deg rotation in data (April 2016)
# (can be moved to NRM_mask_definitions later)
self.mask.ctrs = utils.rotate2dccw(self.mask.ctrs, 1.0*np.pi/180.0)#, np.pi/10.)
# Add in hole/baseline properties ?
self.holeshape="circ"
self.threshold = 0.02
# Get info from reference file
self.hdr = []
#self.refdata = []
if type(reffile)==str:
reffile = [reffile,]
for fn in reffile:
reffits = fits.open(fn)
self.hdr.append(reffits[0].header)
reffits.close()
# instrument settings
self.band = self.hdr[0]["FWINAME"]
self.objname = self.hdr[0]["OBJECT"]
# tophat filter
# this can be swapped with an actual filter file
#band_ctrs = {"Kp":1.633*um/2.0,"Lp":3.1*um}
band_ctrs = {"J":1.248*um, "H":1.633*um, "CH4_short":1.5923*um,"Kp":2.2*um,"Lp":3.1*um}
band_wdth = {"J":0.163*um, "H":0.296*um, "CH4_short":(0.1257)*um,"Kp":(0.3)*um, "Lp":(4.126 - 3.426)*um}
lam_c = band_ctrs[self.band]
lam_w = band_wdth[self.band]
# wavelength info: spect mode or pol more
if "PRISM" in self.mode:
# GPI's spectral mode
self.nwav = 36 #self.hdr1[0]["NAXIS3"]
self.wls = np.linspace(lam_c - lam_w/2.0, lam_c+lam_w/2.0, num=36)*1e-6
self.eff_band = um*np.ones(self.nwav)*(self.wls[-1] - self.wls[0])/self.nwav
# For OIFits structure
self.wavextension = (self.wls, self.eff_band)
elif "BROADBAND" in self.mode:
# Copied from GPI's pol mode.
self.nwav=1
# Define the bands in case we use a tophat filter
wghts = np.ones(11)
wavls = np.linspace(lam_c-lam_w/2.0, \
lam_c+lam_w/2.0, num=len(wghts))
transmission = np.array([[wghts[f], wavls[f]] for f in range(len(wghts))])
self.wls = [transmission, ]
#self.wls = [np.sum(wghts*wavls) /float(len(wavls)), ]
self.eff_band = np.array([lam_w, ])
# For OIFits structure
self.wavextension = ([lam_c,], [lam_w,])
try:
self.wls = [utils.combine_transmission(transmission, src), ]
except:
self.wls = [transmission, ]
#self.wavextension = (lam_c, lam_w)
self.nwav=1
# finding centroid from phase slope only considered cv_phase data when cv_abs data exceeds this.
# absolute value of cv data normalized to unity maximum for the threshold application.
self.cvsupport_threshold = {"threshold":0.02} # Gurus: tweak with use...
self.ref_imgs_dir = "refimgs/"
#############################
# Observation info - I don't know yet how JWST data headers will be structured
self.telname= "Keck"
try:
self.ra, self.dec = self.hdr[0]["RA"], self.hdr[0]["DEC"]
except:
self.ra, self.dec = 00, 00
try:
self.date = self.hdr[0]["DATE"]
self.month = self.date[8:10]
self.day = self.date[5:7]
self.year = self.date[:4]
except:
lt = time.localtime()
self.date = "{0}{1:02d}{2:02d}".format(lt[0],lt[1],lt[2])
self.month = lt[1]
self.day = lt[2]
self.year = lt[0]
self.parangs = []
self.itime = []
self.crpa = []
self.rotposns = []
self.instangs = []
self.derotangs = []
for ii in range(len(reffile)):
self.parangs.append(self.hdr[ii]["PARANG"])
self.rotposns.append(self.hdr[ii]["ROTPOSN"])
self.instangs.append(self.hdr[ii]["INSTANGL"])
# From Tom Esposito:
# PARANG + ROTPOSN - INSTANGL - 0.262
self.derotangs.append(self.hdr[ii]["PARANG"]+self.hdr[ii]["ROTPOSN"] \
-self.hdr[ii]["INSTANGL"]-0.262)
self.itime.append(self.hdr[ii]["ITIME"])
if "CRPA" in self.hdr[ii]:
self.crpa.append(self.hdr[ii]["CRPA"])
self.avderotang = np.mean(self.derotangs)
self.avparang = np.mean(self.parangs)
if len(self.crpa)>0:
self.avcassang = np.mean(self.crpa)
else:
self.avcassang = 0.0
self.parang_range = abs(self.parangs[-1] - self.parangs[0])
self.totalinttime = np.sum(self.itime)
try:
self.pa = self.hdr0["PA"]
except:
self.pa = 00
#############################:w
self.parang_range = abs(self.parangs[-1] - self.parangs[0])
self.totalinttime = np.sum(self.itime)
self.ref_imgs_dir = "refimgs/"
def read_data(self, fn):
fitsfile = fits.open(fn)
sci=fitsfile[0].data
hdr=fitsfile[0].header
fitsfile.close()
#fitshdr = fitsfile[0].header
self.sub_dir_str = fn.split("/")[-1][:-5]
if len(sci.shape)==3:
if self.mode=="PRISM":
return sci, hdr
elif self.mode=="BROADBAND":
self.nwav=sci.shape[0]
[self.wls.append(self.wls[0]) for f in range(self.nwav-1)]
return sci, hdr
elif len(sci.shape)==2:
if self.mode=="BROADBAND":
return np.array([sci,]), hdr
else:
sys.exit("invalid data dimensions for NIRC2. Should have dimensionality of 2 or 3.")
return sci, hdr
|
agreenbaumREPO_NAMEImPlaneIAPATH_START.@ImPlaneIA_extracted@ImPlaneIA-master@nrm_analysis@InstrumentData.py@.PATH_END.py
|
{
"filename": "DIP.py",
"repo_name": "EjjeSynho/DIP",
"repo_path": "DIP_extracted/DIP-main/DIP.py",
"type": "Python"
}
|
#%%
# Commom modules
import torch
import numpy as np
from torch import nn
from torch.nn.functional import conv3d
class DIP(nn.Module):
def __init__(self, tel, device, norm_mode):
super().__init__()
self.oversampling = 1
self.norm_mode = norm_mode
self.img_size = tel.img_resolution
self.device = device
self.tel = tel
self.tel_pupil = torch.tensor(self.tel.pupil).to(self.device)
#flux is [photon/m2/s] per λ TODO: account for the reflectivity map
self.flux = torch.tensor( [point['flux'] for point in self.tel.src.spectrum], device=self.device )
self.λs = torch.tensor( [point['wavelength'] for point in self.tel.src.spectrum], device=self.device )
#TODO: redo flux for my sampling!
#TODO: set oversampling when undersampled
pixels_λ_D = self.tel.f/self.tel.det.pixel_size * self.λs.cpu().numpy()/self.tel.D
self.oversampling = self.oversampling + int(self.oversampling%2 != self.img_size%2)*int(self.oversampling!=1) # this is to bin images with odd number of pixels properly
pad = np.round((self.oversampling*pixels_λ_D-1)*self.tel_pupil.shape[0]/2).astype('int')
self.φ_size = self.tel_pupil.shape[0] + 2*pad
self.photons = self.flux/self.tel_pupil.sum() * self.tel.pupilReflectivity * self.tel.area * self.tel.det.sampling_time
self.padders = [torch.nn.ZeroPad2d(val.item()) for val in pad]
def _to_device_recursive(self, obj, device):
if isinstance(obj, torch.Tensor):
if obj.device != device:
if isinstance(obj, nn.Parameter):
obj.data = obj.data.to(device)
if obj.grad is not None:
obj.grad = obj.grad.to(device)
else:
obj = obj.to(device)
elif isinstance(obj, nn.Module):
obj.to(device)
elif isinstance(obj, (list, tuple)):
for item in obj:
self._to_device_recursive(item, device)
elif isinstance(obj, dict):
for item in obj.values():
self._to_device_recursive(item, device)
return obj
def to(self, device):
if isinstance(device, str):
device = torch.device(device)
if self.device == device:
return self
self.device = device
for name, attr in self.__dict__.items():
new_attr = self._to_device_recursive(attr, device)
if new_attr is not attr:
# print(f"Transferring '{name}' to device '{device}'")
setattr(self, name, new_attr)
return self
def binning(self, inp, N):
return torch.nn.functional.avg_pool2d(inp.unsqueeze(1),N,N).squeeze(1) * N**2 if N > 1 else inp
def OPD2PSF(self, photons, λ, OPD, φ_size, padder, oversampling):
amplitude = torch.sqrt(photons)*self.tel_pupil # V--- conversion of OPD [nm]->[m]
EMF = padder( amplitude * torch.exp(2j*torch.pi/λ*OPD*1e-9) )
lin = torch.linspace(0, φ_size-1, steps=φ_size, device=self.device)
xx, yy = torch.meshgrid(lin, lin, indexing='xy')
center_aligner = torch.exp(-1j*torch.pi/φ_size*(xx+yy)*(1-self.img_size%2))
PSF = torch.fft.fftshift(1./φ_size * torch.fft.fft2(EMF*center_aligner, dim=(-2,-1)), dim=(-2,-1)).abs()**2
cropper = slice(φ_size//2-(self.img_size*oversampling)//2, φ_size//2+round((self.img_size*oversampling+1e-6)/2))
return self.binning(PSF[...,cropper,cropper], oversampling)
def forward(self, OPD, obj=None):
if OPD.ndim == 2:
OPD = OPD.unsqueeze(0)
N = OPD.shape[0] # number of PSF samples in the stack
PSF = torch.zeros([N, self.img_size, self.img_size], dtype=OPD.dtype, device=self.device)
for i in range(len(self.tel.src.spectrum)):
PSF += self.OPD2PSF(self.photons[i], self.λs[i], OPD, self.φ_size[i].item(), self.padders[i], self.oversampling)
if obj is not None:
PSF_conv = conv3d(
PSF.unsqueeze(1).unsqueeze(0), obj.unsqueeze(1).unsqueeze(1),
bias=None, stride=1, padding='same', groups=N).squeeze(0).squeeze(1)
return self.normalize(PSF_conv)
else:
return self.normalize(PSF)
# Normalize a PSF batch depending on the normalization regime
def normalize(self, inp):
if self.norm_mode == 'sum':
return inp / inp.sum(dim=(1,2), keepdim=True)
elif self.norm_mode == 'max':
return inp / torch.amax(inp, dim=(1,2), keepdim=True)
else:
return inp
try:
from graphviz import Digraph
except ImportError:
pass
else:
def iter_graph(root, callback):
queue = [root]
seen = set()
while queue:
fn = queue.pop()
if fn in seen:
continue
seen.add(fn)
for next_fn, _ in fn.next_functions:
if next_fn is not None:
queue.append(next_fn)
callback(fn)
def register_hooks(var):
fn_dict = {}
def hook_c_b(fn):
def register_grad(grad_input, grad_output):
fn_dict[fn] = grad_input
fn.register_hook(register_grad)
iter_graph(var.grad_fn, hook_c_b)
def is_bad_grad(grad_output):
if grad_output is None:
return False
return grad_output.isnan().any() or (grad_output.abs() >= 1e6).any()
def make_dot():
node_attr = dict(style='filled',
shape='box',
align='left',
fontsize='12',
ranksep='0.1',
height='0.2')
dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
def size_to_str(size):
return '('+(', ').join(map(str, size))+')'
def build_graph(fn):
if hasattr(fn, 'variable'): # if GradAccumulator
u = fn.variable
node_name = 'Variable\n ' + size_to_str(u.size())
dot.node(str(id(u)), node_name, fillcolor='lightblue')
else:
def grad_ord(x):
mins = ""
maxs = ""
y = [buf for buf in x if buf is not None]
for buf in y:
min_buf = torch.abs(buf).min().cpu().numpy().item()
max_buf = torch.abs(buf).max().cpu().numpy().item()
if min_buf < 0.1 or min_buf > 99:
mins += "{:.1e}".format(min_buf) + ', '
else:
mins += str(np.round(min_buf,1)) + ', '
if max_buf < 0.1 or max_buf > 99:
maxs += "{:.1e}".format(max_buf) + ', '
else:
maxs += str(np.round(max_buf,1)) + ', '
return mins[:-2] + ' | ' + maxs[:-2]
assert fn in fn_dict, fn
fillcolor = 'white'
if any(is_bad_grad(gi) for gi in fn_dict[fn]):
fillcolor = 'red'
dot.node(str(id(fn)), str(type(fn).__name__)+'\n'+grad_ord(fn_dict[fn]), fillcolor=fillcolor)
for next_fn, _ in fn.next_functions:
if next_fn is not None:
next_id = id(getattr(next_fn, 'variable', next_fn))
dot.edge(str(next_id), str(id(fn)))
iter_graph(var.grad_fn, build_graph)
return dot
return make_dot
# Q = loss_fn(dip(OPD=GetOPD_prob(mu_A, sigma_A)), data)
# get_dot = register_hooks(Q)
# Q.backward()
# dot = get_dot()
# # #dot.save('tmp.dot') # to get .dot
# # #dot.render('tmp') # to get SVG
# dot # in Jupyter, you can just render the variable
|
EjjeSynhoREPO_NAMEDIPPATH_START.@DIP_extracted@DIP-main@DIP.py@.PATH_END.py
|
{
"filename": "create_skeleton.py",
"repo_name": "galtay/urchin",
"repo_path": "urchin_extracted/urchin-main/src/eagle/create_skeleton.py",
"type": "Python"
}
|
import os
import sys
import h5py
import h5py_wrap as h5
import numpy as np
def create_skeleton( fname0 ):
""" Given the name of any file in an Eagle snapshot, this function
creates a set of skeleton output files which contain fields for gas
particle IDs and HI number density. """
if not os.path.isfile( fname0 ):
raise IOError( fname0 + ' is not a file.' )
cmnd = 'rm -rf example_skeleton'
os.system( cmnd )
cmnd = 'mkdir example_skeleton'
os.system( cmnd )
n_files = h5.ra( fname0, '/Header', 'NumFilesPerSnapshot' )
for ifile in range( n_files ):
in_file = fname0.split('.')[0] + '.' + str(ifile) + '.hdf5'
sk_file = 'example_skeleton/out.' + str(ifile) + '.hdf5'
if os.path.exists( sk_file ):
raise IOError( 'output file already exists.' )
print 'working on file: ', in_file
print 'sk file: ', sk_file
head = h5.raa( in_file, 'Header' )
ngas = head['NumPart_ThisFile'][0]
h5_in = h5py.File( in_file, 'r' )
h5_sk = h5py.File( sk_file, 'w' )
# use copy method to copy attribute groups
#-------------------------------------------------------------
groups = ['Config', 'Constants', 'HashTable', 'Header',
'Parameters/ChemicalElements', 'RuntimePars', 'Units' ]
for grp in groups:
h5_sk.copy( h5_in[grp], h5_sk['/'] )
# copy over gas IDs
#-------------------------------------------------------------
h5_sk.create_group( 'PartType0' )
dset = 'PartType0/ParticleIDs'
h5_sk.copy( h5_in[dset], dset )
# make dummy arrays for Urchin particle data
#-------------------------------------------------------------
dum = np.ones( ngas, dtype=np.float32 ) * -1
dset_name = 'PartType0/HydrogenOneFraction'
h5_sk.create_dataset( dset_name, data=dum )
attrs = {'CGSConversionFactor': 1.0,
'h-scale-exponent': 0.0,
'aexp-scale-exponent': 0.0,
'VarDescription': 'Hydrogen neutral fraction = n_HI / (n_HI + n_HII)'}
for k,v in attrs.items():
h5_sk[dset_name].attrs[k] = v
h5_in.close()
h5_sk.close()
if __name__ == '__main__':
if len(sys.argv) != 2:
txt = 'create_skeleton takes a single file name as its only argument.'
raise SyntaxError( txt )
create_skeleton( sys.argv[1] )
|
galtayREPO_NAMEurchinPATH_START.@urchin_extracted@urchin-main@src@eagle@create_skeleton.py@.PATH_END.py
|
{
"filename": "XID+posterior_analysis_validation.ipynb",
"repo_name": "H-E-L-P/XID_plus",
"repo_path": "XID_plus_extracted/XID_plus-master/docs/build/html/notebooks/examples/XID+posterior_analysis_validation.ipynb",
"type": "Jupyter Notebook"
}
|
# XID+ Example Output Analysis
(This is based on a Jupyter notebook, available in the [XID+ package](https://github.com/H-E-L-P/XID_plus/tree/master/docs/notebooks/examples/) and can be interactively run and edited)
This notebook provides some example code for basic analysis of the XID+ outputs, including:
1. Loading up output
2. Creating Posterior replicated maps and animations
3. Creating marginalised posterior plots
4. Creating Bayesian p-value maps
Import required modules
```python
import pylab as plt
%matplotlib inline
import numpy as np
import xidplus
from xidplus import moc_routines
output_folder='./'
```
/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
data = yaml.load(f.read()) or {}
WARNING: AstropyDeprecationWarning: block_reduce was moved to the astropy.nddata.blocks module. Please update your import statement. [astropy.nddata.utils]
Load up posterior output from XID+
```python
priors,posterior=xidplus.load('test.pkl')
```
In order to compare how good our fit is, its often useful to look at original map. There is a routine within XID+ that makes the original fits map from the data stored within the prior class. Lets use that to make the SPIRE maps for the region we have fit.
Now lets use the [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) plotting package and [APLpy](http://aplpy.readthedocs.io/en/stable/) package to view those maps, plotting the sources we have fit on top of those maps.
```python
figs,fig=xidplus.plot_map(priors)
```
### Posterior replicated data
We can use each sample we have from the posterior, and use it to make a replicated map, including simulating the instrumental noise, and the estimated confusion noise. You can think of these maps as all the possible maps that are allowed by the data.
> NOTE: You will require the `FFmpeg` library installed to run the movie
```python
xidplus.replicated_map_movie(priors,posterior,50)
```
<video style="max-width:100%" controls>
<source 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type="video/mp4">
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</video>
### Joint Posterior Analysis of sources
```python
#Select source you want to plot joint distribution
s1=2
s2=18
```
Sources 2 and 18 are close together, lets look at their joint posterior probabiity distribution. The code below is an example of how you can plot the joint posterior probability density function for the $250 \mathrm{\mu m}$ flux, and an inset of the real map.
```python
import aplpy
import seaborn as sns
sns.set(color_codes=True)
import pandas as pd
sns.set_style("white")
import xidplus.posterior_maps as postmaps
labels=[r'Source 1 $250\mathrm{\mu m}$ flux (mJy)',r'Source 2 $250\mathrm{\mu m}$ flux (mJy)']
df = pd.DataFrame(posterior.samples['src_f'][:,0,[s1,s2]],columns=labels)
g = sns.PairGrid(df,size=5)
g.map_diag(sns.kdeplot,c='Red')
g.map_lower(sns.kdeplot, cmap="Reds",alpha=0.8,n_levels=10,normed=True, shade=True,shade_lowest=False)
g.set(ylim=(0,40))
g.set(xlim=(0,40))
g.axes[0,1].spines['bottom'].set_color('white')
g.axes[0,1].spines['left'].set_color('white')
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)
real_250 = aplpy.FITSFigure(postmaps.make_fits_image(priors[0],priors[0].sim)[1],figure=g.fig,subplot=(2,2,2))
real_250.show_colorscale(cmap=cmap)
real_250.show_markers(priors[0].sra, priors[0].sdec, edgecolor='black', facecolor='black',
marker='o', s=40, alpha=0.5)
real_250.recenter(priors[0].sra[s1], priors[0].sdec[s1], radius=0.01)
real_250.add_label(priors[0].sra[s1], priors[0].sdec[s1]+0.0005, 1, relative=False,size=20,color='white')
real_250.add_label(priors[0].sra[s2], priors[0].sdec[s2]-0.0010, 2, relative=False,size=20,color='white')
real_250.tick_labels.set_xformat('dd.dd')
real_250.tick_labels.set_yformat('dd.dd')
real_250.add_colorbar(axis_label_text=r'$250\mathrm{\mu m}$ flux (mJy)')
```
INFO: Auto-setting vmin to -1.522e+01 [aplpy.core]
INFO: Auto-setting vmax to 3.332e+01 [aplpy.core]
### Posterior Predictive checking and Bayesian P-value maps
When examining goodness of fits, the typical method is to look at the residuals. i.e. $\frac{data - model}{\sigma}$. Because we have distribution of $y^{rep}$, we can do this in a more probabilisitic way using posterior predictive checks. For more information on posterior predictive checks, [Gelman et al. 1996](http://www.stat.columbia.edu/~gelman/research/published/A6n41.pdf) is a good starting point.
For our case, the best way to carry out posterior predictive checks is to think about one pixel. We can look at where the real flux value for our pixel is in relation to the distribution from $y^{rep}$.
```python
from xidplus import posterior_maps as postmaps
rep_maps=postmaps.replicated_maps(priors,posterior)
import matplotlib as mpl
sns.set_style("white")
fig=plt.figure(figsize=(10,5))
# This is the colormap I'd like to use.
cm = sns.diverging_palette(220, 20, as_cmap=True)
# Get the histogramp
Y,X = np.histogram(rep_maps[0][20,:], 25, normed=1)
#C = [cm(((x-X.min())/x_span)) for x in X]
C = [cm(((((x-np.mean(rep_maps[0][20,:]))/np.std(rep_maps[0][20,:]))+6)/12.0)) for x in X]
plt.bar(X[:-1],Y,color=C,width=X[1]-X[0])
plt.xlabel('Pixel Flux mJy')
plt.ylabel('p(pixel flux)')
plt.axvline(3.9, linestyle='--')
plt.axvline(-10.1,linestyle=':')
plt.annotate('flux in map that \n model cannot explain',xy=(4, 0.01), xycoords='data',
xytext=(4, 0.1), textcoords='data',rotation='vertical',size='large')
plt.annotate('too much flux in model \n compared to map',xy=(-10, 0.01), xycoords='data',
xytext=(-10, 0.1), textcoords='data',rotation='vertical',size='large')
#ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax1 = fig.add_axes([0.82, 0.15, 0.02, 0.7])
norm = mpl.colors.Normalize(vmin=-6, vmax=6)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cm,
norm=norm,
orientation='vertical')
cb1.set_label('$\sigma$')
```
We can calculate fraction of $y^{rep}$ samples above and below real map value. This is often referred to as the Bayesian p-value and is telling us the probability of drawing the real pixel value, from our model which has been inferred on the data. This is tells us if the model is inconsistent with the data, given the uncertianties in parameters and data.
* $\sim 0.5$ means our model is consistent with the data
* 0.99 or 0.01 means model is missing something.
We can convert this to a typical '$\sigma$' level and create map versions of these Bayesian p-values:
```python
figs, fig=xidplus.plot_Bayes_pval_map(priors, posterior)
```
Red indicates the flux value in the real map is higher than our model thinks is possible. This could be indicating there is a source there that is not in our model.
Blue indicates the flux in the real map is lower than in our model. This is either indicating a very low density region or that too much flux has been assigned to one of the sources.
### Creating Catalogues
We can also create catalogues from the posterior probability density function
```python
import xidplus.catalogue as cat
```
```python
SPIRE_cat=cat.create_SPIRE_cat(posterior,priors[0],priors[1],priors[2])
SPIRE_cat.writeto('test.fits',overwrite=True)
```
### Check table
Lets read in table with Astropy table
```python
from astropy.table import Table
```
```python
catalogue=Table.read('test.fits')
```
```python
catalogue
```
<Table length=51>
<table id="table4746376360" class="table-striped table-bordered table-condensed">
<thead><tr><th>HELP_ID</th><th>RA</th><th>Dec</th><th>F_SPIRE_250</th><th>FErr_SPIRE_250_u</th><th>FErr_SPIRE_250_l</th><th>F_SPIRE_350</th><th>FErr_SPIRE_350_u</th><th>FErr_SPIRE_350_l</th><th>F_SPIRE_500</th><th>FErr_SPIRE_500_u</th><th>FErr_SPIRE_500_l</th><th>Bkg_SPIRE_250</th><th>Bkg_SPIRE_350</th><th>Bkg_SPIRE_500</th><th>Sig_conf_SPIRE_250</th><th>Sig_conf_SPIRE_350</th><th>Sig_conf_SPIRE_500</th><th>Rhat_SPIRE_250</th><th>Rhat_SPIRE_350</th><th>Rhat_SPIRE_500</th><th>n_eff_SPIRE_250</th><th>n_eff_SPIRE_500</th><th>n_eff_SPIRE_350</th><th>Pval_res_250</th><th>Pval_res_350</th><th>Pval_res_500</th></tr></thead>
<thead><tr><th></th><th>degrees</th><th>degrees</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th>mJy/Beam</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>
<thead><tr><th>str27</th><th>float64</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>
<tr><td>1891</td><td>150.74711462</td><td>2.01937229149</td><td>3.7484</td><td>7.66319</td><td>1.18164</td><td>2.14374</td><td>4.78448</td><td>0.654144</td><td>3.95658</td><td>8.78007</td><td>1.13176</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>1.00125</td><td>0.999651</td><td>0.999694</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.001</td><td>0.0</td></tr>
<tr><td>1896</td><td>150.74655644</td><td>2.01381734435</td><td>4.60005</td><td>6.3697</td><td>2.84267</td><td>3.65866</td><td>5.48689</td><td>1.85056</td><td>1.92826</td><td>4.57817</td><td>0.519253</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>1.00153</td><td>0.999281</td><td>1.00056</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.003</td><td>0.0</td></tr>
<tr><td>2640</td><td>150.742672167</td><td>2.02937330604</td><td>13.9984</td><td>18.8843</td><td>9.17463</td><td>12.8981</td><td>15.4871</td><td>9.41806</td><td>1.86951</td><td>4.1993</td><td>0.517889</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>1.00192</td><td>1.00116</td><td>1.00028</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.008</td><td>0.0</td></tr>
<tr><td>5128</td><td>150.752680282</td><td>2.03659114242</td><td>3.17935</td><td>5.00112</td><td>1.51538</td><td>1.17502</td><td>2.5795</td><td>0.316155</td><td>4.84374</td><td>8.41198</td><td>1.72842</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999281</td><td>0.999725</td><td>0.999016</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.017</td><td>0.001</td></tr>
<tr><td>5129</td><td>150.741006288</td><td>2.0332625701</td><td>4.14086</td><td>5.90487</td><td>2.33828</td><td>1.57266</td><td>3.09327</td><td>0.515144</td><td>2.12692</td><td>4.43263</td><td>0.572698</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999627</td><td>0.999396</td><td>0.999493</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.018</td><td>0.0</td></tr>
<tr><td>5130</td><td>150.747123319</td><td>2.03992641714</td><td>15.163</td><td>16.7734</td><td>13.5599</td><td>18.3508</td><td>19.8903</td><td>16.8378</td><td>14.386</td><td>17.3602</td><td>11.346</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.998979</td><td>0.998597</td><td>0.999077</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.003</td><td>0.001</td></tr>
<tr><td>5159</td><td>150.754346603</td><td>2.03381290524</td><td>15.0139</td><td>16.8077</td><td>13.0577</td><td>7.17588</td><td>9.16244</td><td>4.82038</td><td>4.09872</td><td>7.76803</td><td>1.15535</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.99918</td><td>1.00112</td><td>0.999327</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.002</td><td>0.004</td><td>0.001</td></tr>
<tr><td>6067</td><td>150.755456605</td><td>2.02992385524</td><td>2.4755</td><td>4.13946</td><td>1.0596</td><td>5.07732</td><td>7.13054</td><td>3.02158</td><td>2.407</td><td>5.26434</td><td>0.697328</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>1.00153</td><td>0.999871</td><td>0.999267</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.002</td><td>0.023</td><td>0.002</td></tr>
<tr><td>6728</td><td>150.731557942</td><td>2.03548824889</td><td>2.27546</td><td>4.03699</td><td>0.814594</td><td>1.28256</td><td>2.65632</td><td>0.377102</td><td>1.25702</td><td>2.82928</td><td>0.311752</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999194</td><td>0.999004</td><td>0.999294</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.001</td><td>0.0</td><td>0.002</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
<tr><td>54557</td><td>150.747116031</td><td>2.02270538967</td><td>4.16608</td><td>6.52053</td><td>2.01011</td><td>1.80936</td><td>3.88562</td><td>0.481275</td><td>3.85372</td><td>8.23103</td><td>1.19911</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999127</td><td>0.998905</td><td>0.999505</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.003</td><td>0.0</td></tr>
<tr><td>56696</td><td>150.721556173</td><td>2.04382481093</td><td>0.88412</td><td>2.1644</td><td>0.240798</td><td>0.949373</td><td>2.36094</td><td>0.255432</td><td>1.20004</td><td>3.06691</td><td>0.313454</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999351</td><td>0.998961</td><td>1.00171</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.014</td><td>0.002</td><td>0.008</td></tr>
<tr><td>56700</td><td>150.729337091</td><td>2.04159980217</td><td>1.48247</td><td>2.86367</td><td>0.449076</td><td>1.04582</td><td>2.22337</td><td>0.322739</td><td>1.39198</td><td>3.18743</td><td>0.412141</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999942</td><td>0.999822</td><td>0.99837</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.001</td><td>0.0</td><td>0.004</td></tr>
<tr><td>57200</td><td>150.740458126</td><td>2.05159489041</td><td>10.474</td><td>12.744</td><td>7.8894</td><td>4.53638</td><td>6.91396</td><td>2.20241</td><td>2.68414</td><td>5.65835</td><td>0.824703</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.998646</td><td>0.99865</td><td>0.999078</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.001</td><td>0.005</td></tr>
<tr><td>58792</td><td>150.739334129</td><td>2.02215285778</td><td>1.53584</td><td>2.8906</td><td>0.536787</td><td>1.04091</td><td>2.18379</td><td>0.311871</td><td>1.33951</td><td>2.95336</td><td>0.337598</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999491</td><td>1.00085</td><td>0.999512</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.002</td><td>0.0</td></tr>
<tr><td>62679</td><td>150.751015847</td><td>2.04381351831</td><td>2.44807</td><td>4.68218</td><td>0.755575</td><td>2.66707</td><td>5.36711</td><td>0.964311</td><td>3.18924</td><td>7.19739</td><td>0.878154</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999891</td><td>0.999694</td><td>1.00034</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.0</td><td>0.004</td></tr>
<tr><td>62696</td><td>150.716547293</td><td>2.02827210175</td><td>0.900113</td><td>1.87745</td><td>0.273319</td><td>1.05167</td><td>2.08977</td><td>0.323288</td><td>2.6556</td><td>4.82945</td><td>0.958473</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.998641</td><td>1.00014</td><td>0.998887</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.001</td><td>0.068</td><td>0.226</td></tr>
<tr><td>64788</td><td>150.757117895</td><td>2.01547979941</td><td>7.4053</td><td>8.99943</td><td>5.69952</td><td>6.23498</td><td>8.09541</td><td>4.30285</td><td>1.50978</td><td>3.65069</td><td>0.413473</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999124</td><td>0.99898</td><td>0.999234</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.003</td><td>0.004</td></tr>
<tr><td>64789</td><td>150.722668543</td><td>2.04549095568</td><td>0.693414</td><td>1.73135</td><td>0.171959</td><td>1.00955</td><td>2.43527</td><td>0.254088</td><td>1.50451</td><td>3.61353</td><td>0.414402</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>0.999581</td><td>1.00064</td><td>1.00024</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.319</td><td>0.001</td><td>0.002</td></tr>
<tr><td>64790</td><td>150.746006008</td><td>2.02659443544</td><td>3.66645</td><td>5.70863</td><td>1.70092</td><td>9.75185</td><td>11.836</td><td>7.74227</td><td>2.95004</td><td>5.88184</td><td>0.874799</td><td>-2.55132</td><td>-4.50904</td><td>-6.23681</td><td>1.57567</td><td>1.54154</td><td>2.35809</td><td>1.00089</td><td>1.00012</td><td>0.999632</td><td>2000.0</td><td>2000.0</td><td>2000.0</td><td>0.0</td><td>0.004</td><td>0.0</td></tr>
</table>
|
H-E-L-PREPO_NAMEXID_plusPATH_START.@XID_plus_extracted@XID_plus-master@docs@build@html@notebooks@examples@XID+posterior_analysis_validation.ipynb@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/io/clipboard/__init__.py",
"type": "Python"
}
|
"""
Pyperclip
A cross-platform clipboard module for Python,
with copy & paste functions for plain text.
By Al Sweigart al@inventwithpython.com
Licence at LICENSES/PYPERCLIP_LICENSE
Usage:
import pyperclip
pyperclip.copy('The text to be copied to the clipboard.')
spam = pyperclip.paste()
if not pyperclip.is_available():
print("Copy functionality unavailable!")
On Windows, no additional modules are needed.
On Mac, the pyobjc module is used, falling back to the pbcopy and pbpaste cli
commands. (These commands should come with OS X.).
On Linux, install xclip, xsel, or wl-clipboard (for "wayland" sessions) via
package manager.
For example, in Debian:
sudo apt-get install xclip
sudo apt-get install xsel
sudo apt-get install wl-clipboard
Otherwise on Linux, you will need the PyQt5 modules installed.
This module does not work with PyGObject yet.
Cygwin is currently not supported.
Security Note: This module runs programs with these names:
- pbcopy
- pbpaste
- xclip
- xsel
- wl-copy/wl-paste
- klipper
- qdbus
A malicious user could rename or add programs with these names, tricking
Pyperclip into running them with whatever permissions the Python process has.
"""
__version__ = "1.8.2"
import contextlib
import ctypes
from ctypes import (
c_size_t,
c_wchar,
c_wchar_p,
get_errno,
sizeof,
)
import os
import platform
from shutil import which as _executable_exists
import subprocess
import time
import warnings
from pandas.errors import (
PyperclipException,
PyperclipWindowsException,
)
from pandas.util._exceptions import find_stack_level
# `import PyQt4` sys.exit()s if DISPLAY is not in the environment.
# Thus, we need to detect the presence of $DISPLAY manually
# and not load PyQt4 if it is absent.
HAS_DISPLAY = os.getenv("DISPLAY")
EXCEPT_MSG = """
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit
https://pyperclip.readthedocs.io/en/latest/index.html#not-implemented-error
"""
ENCODING = "utf-8"
class PyperclipTimeoutException(PyperclipException):
pass
def _stringifyText(text) -> str:
acceptedTypes = (str, int, float, bool)
if not isinstance(text, acceptedTypes):
raise PyperclipException(
f"only str, int, float, and bool values "
f"can be copied to the clipboard, not {type(text).__name__}"
)
return str(text)
def init_osx_pbcopy_clipboard():
def copy_osx_pbcopy(text):
text = _stringifyText(text) # Converts non-str values to str.
with subprocess.Popen(
["pbcopy", "w"], stdin=subprocess.PIPE, close_fds=True
) as p:
p.communicate(input=text.encode(ENCODING))
def paste_osx_pbcopy():
with subprocess.Popen(
["pbpaste", "r"], stdout=subprocess.PIPE, close_fds=True
) as p:
stdout = p.communicate()[0]
return stdout.decode(ENCODING)
return copy_osx_pbcopy, paste_osx_pbcopy
def init_osx_pyobjc_clipboard():
def copy_osx_pyobjc(text):
"""Copy string argument to clipboard"""
text = _stringifyText(text) # Converts non-str values to str.
newStr = Foundation.NSString.stringWithString_(text).nsstring()
newData = newStr.dataUsingEncoding_(Foundation.NSUTF8StringEncoding)
board = AppKit.NSPasteboard.generalPasteboard()
board.declareTypes_owner_([AppKit.NSStringPboardType], None)
board.setData_forType_(newData, AppKit.NSStringPboardType)
def paste_osx_pyobjc():
"""Returns contents of clipboard"""
board = AppKit.NSPasteboard.generalPasteboard()
content = board.stringForType_(AppKit.NSStringPboardType)
return content
return copy_osx_pyobjc, paste_osx_pyobjc
def init_qt_clipboard():
global QApplication
# $DISPLAY should exist
# Try to import from qtpy, but if that fails try PyQt5 then PyQt4
try:
from qtpy.QtWidgets import QApplication
except ImportError:
try:
from PyQt5.QtWidgets import QApplication
except ImportError:
from PyQt4.QtGui import QApplication
app = QApplication.instance()
if app is None:
app = QApplication([])
def copy_qt(text):
text = _stringifyText(text) # Converts non-str values to str.
cb = app.clipboard()
cb.setText(text)
def paste_qt() -> str:
cb = app.clipboard()
return str(cb.text())
return copy_qt, paste_qt
def init_xclip_clipboard():
DEFAULT_SELECTION = "c"
PRIMARY_SELECTION = "p"
def copy_xclip(text, primary=False):
text = _stringifyText(text) # Converts non-str values to str.
selection = DEFAULT_SELECTION
if primary:
selection = PRIMARY_SELECTION
with subprocess.Popen(
["xclip", "-selection", selection], stdin=subprocess.PIPE, close_fds=True
) as p:
p.communicate(input=text.encode(ENCODING))
def paste_xclip(primary=False):
selection = DEFAULT_SELECTION
if primary:
selection = PRIMARY_SELECTION
with subprocess.Popen(
["xclip", "-selection", selection, "-o"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True,
) as p:
stdout = p.communicate()[0]
# Intentionally ignore extraneous output on stderr when clipboard is empty
return stdout.decode(ENCODING)
return copy_xclip, paste_xclip
def init_xsel_clipboard():
DEFAULT_SELECTION = "-b"
PRIMARY_SELECTION = "-p"
def copy_xsel(text, primary=False):
text = _stringifyText(text) # Converts non-str values to str.
selection_flag = DEFAULT_SELECTION
if primary:
selection_flag = PRIMARY_SELECTION
with subprocess.Popen(
["xsel", selection_flag, "-i"], stdin=subprocess.PIPE, close_fds=True
) as p:
p.communicate(input=text.encode(ENCODING))
def paste_xsel(primary=False):
selection_flag = DEFAULT_SELECTION
if primary:
selection_flag = PRIMARY_SELECTION
with subprocess.Popen(
["xsel", selection_flag, "-o"], stdout=subprocess.PIPE, close_fds=True
) as p:
stdout = p.communicate()[0]
return stdout.decode(ENCODING)
return copy_xsel, paste_xsel
def init_wl_clipboard():
PRIMARY_SELECTION = "-p"
def copy_wl(text, primary=False):
text = _stringifyText(text) # Converts non-str values to str.
args = ["wl-copy"]
if primary:
args.append(PRIMARY_SELECTION)
if not text:
args.append("--clear")
subprocess.check_call(args, close_fds=True)
else:
p = subprocess.Popen(args, stdin=subprocess.PIPE, close_fds=True)
p.communicate(input=text.encode(ENCODING))
def paste_wl(primary=False):
args = ["wl-paste", "-n"]
if primary:
args.append(PRIMARY_SELECTION)
p = subprocess.Popen(args, stdout=subprocess.PIPE, close_fds=True)
stdout, _stderr = p.communicate()
return stdout.decode(ENCODING)
return copy_wl, paste_wl
def init_klipper_clipboard():
def copy_klipper(text):
text = _stringifyText(text) # Converts non-str values to str.
with subprocess.Popen(
[
"qdbus",
"org.kde.klipper",
"/klipper",
"setClipboardContents",
text.encode(ENCODING),
],
stdin=subprocess.PIPE,
close_fds=True,
) as p:
p.communicate(input=None)
def paste_klipper():
with subprocess.Popen(
["qdbus", "org.kde.klipper", "/klipper", "getClipboardContents"],
stdout=subprocess.PIPE,
close_fds=True,
) as p:
stdout = p.communicate()[0]
# Workaround for https://bugs.kde.org/show_bug.cgi?id=342874
# TODO: https://github.com/asweigart/pyperclip/issues/43
clipboardContents = stdout.decode(ENCODING)
# even if blank, Klipper will append a newline at the end
assert len(clipboardContents) > 0
# make sure that newline is there
assert clipboardContents.endswith("\n")
if clipboardContents.endswith("\n"):
clipboardContents = clipboardContents[:-1]
return clipboardContents
return copy_klipper, paste_klipper
def init_dev_clipboard_clipboard():
def copy_dev_clipboard(text):
text = _stringifyText(text) # Converts non-str values to str.
if text == "":
warnings.warn(
"Pyperclip cannot copy a blank string to the clipboard on Cygwin. "
"This is effectively a no-op.",
stacklevel=find_stack_level(),
)
if "\r" in text:
warnings.warn(
"Pyperclip cannot handle \\r characters on Cygwin.",
stacklevel=find_stack_level(),
)
with open("/dev/clipboard", "w", encoding="utf-8") as fd:
fd.write(text)
def paste_dev_clipboard() -> str:
with open("/dev/clipboard", encoding="utf-8") as fd:
content = fd.read()
return content
return copy_dev_clipboard, paste_dev_clipboard
def init_no_clipboard():
class ClipboardUnavailable:
def __call__(self, *args, **kwargs):
raise PyperclipException(EXCEPT_MSG)
def __bool__(self) -> bool:
return False
return ClipboardUnavailable(), ClipboardUnavailable()
# Windows-related clipboard functions:
class CheckedCall:
def __init__(self, f) -> None:
super().__setattr__("f", f)
def __call__(self, *args):
ret = self.f(*args)
if not ret and get_errno():
raise PyperclipWindowsException("Error calling " + self.f.__name__)
return ret
def __setattr__(self, key, value):
setattr(self.f, key, value)
def init_windows_clipboard():
global HGLOBAL, LPVOID, DWORD, LPCSTR, INT
global HWND, HINSTANCE, HMENU, BOOL, UINT, HANDLE
from ctypes.wintypes import (
BOOL,
DWORD,
HANDLE,
HGLOBAL,
HINSTANCE,
HMENU,
HWND,
INT,
LPCSTR,
LPVOID,
UINT,
)
windll = ctypes.windll
msvcrt = ctypes.CDLL("msvcrt")
safeCreateWindowExA = CheckedCall(windll.user32.CreateWindowExA)
safeCreateWindowExA.argtypes = [
DWORD,
LPCSTR,
LPCSTR,
DWORD,
INT,
INT,
INT,
INT,
HWND,
HMENU,
HINSTANCE,
LPVOID,
]
safeCreateWindowExA.restype = HWND
safeDestroyWindow = CheckedCall(windll.user32.DestroyWindow)
safeDestroyWindow.argtypes = [HWND]
safeDestroyWindow.restype = BOOL
OpenClipboard = windll.user32.OpenClipboard
OpenClipboard.argtypes = [HWND]
OpenClipboard.restype = BOOL
safeCloseClipboard = CheckedCall(windll.user32.CloseClipboard)
safeCloseClipboard.argtypes = []
safeCloseClipboard.restype = BOOL
safeEmptyClipboard = CheckedCall(windll.user32.EmptyClipboard)
safeEmptyClipboard.argtypes = []
safeEmptyClipboard.restype = BOOL
safeGetClipboardData = CheckedCall(windll.user32.GetClipboardData)
safeGetClipboardData.argtypes = [UINT]
safeGetClipboardData.restype = HANDLE
safeSetClipboardData = CheckedCall(windll.user32.SetClipboardData)
safeSetClipboardData.argtypes = [UINT, HANDLE]
safeSetClipboardData.restype = HANDLE
safeGlobalAlloc = CheckedCall(windll.kernel32.GlobalAlloc)
safeGlobalAlloc.argtypes = [UINT, c_size_t]
safeGlobalAlloc.restype = HGLOBAL
safeGlobalLock = CheckedCall(windll.kernel32.GlobalLock)
safeGlobalLock.argtypes = [HGLOBAL]
safeGlobalLock.restype = LPVOID
safeGlobalUnlock = CheckedCall(windll.kernel32.GlobalUnlock)
safeGlobalUnlock.argtypes = [HGLOBAL]
safeGlobalUnlock.restype = BOOL
wcslen = CheckedCall(msvcrt.wcslen)
wcslen.argtypes = [c_wchar_p]
wcslen.restype = UINT
GMEM_MOVEABLE = 0x0002
CF_UNICODETEXT = 13
@contextlib.contextmanager
def window():
"""
Context that provides a valid Windows hwnd.
"""
# we really just need the hwnd, so setting "STATIC"
# as predefined lpClass is just fine.
hwnd = safeCreateWindowExA(
0, b"STATIC", None, 0, 0, 0, 0, 0, None, None, None, None
)
try:
yield hwnd
finally:
safeDestroyWindow(hwnd)
@contextlib.contextmanager
def clipboard(hwnd):
"""
Context manager that opens the clipboard and prevents
other applications from modifying the clipboard content.
"""
# We may not get the clipboard handle immediately because
# some other application is accessing it (?)
# We try for at least 500ms to get the clipboard.
t = time.time() + 0.5
success = False
while time.time() < t:
success = OpenClipboard(hwnd)
if success:
break
time.sleep(0.01)
if not success:
raise PyperclipWindowsException("Error calling OpenClipboard")
try:
yield
finally:
safeCloseClipboard()
def copy_windows(text):
# This function is heavily based on
# http://msdn.com/ms649016#_win32_Copying_Information_to_the_Clipboard
text = _stringifyText(text) # Converts non-str values to str.
with window() as hwnd:
# http://msdn.com/ms649048
# If an application calls OpenClipboard with hwnd set to NULL,
# EmptyClipboard sets the clipboard owner to NULL;
# this causes SetClipboardData to fail.
# => We need a valid hwnd to copy something.
with clipboard(hwnd):
safeEmptyClipboard()
if text:
# http://msdn.com/ms649051
# If the hMem parameter identifies a memory object,
# the object must have been allocated using the
# function with the GMEM_MOVEABLE flag.
count = wcslen(text) + 1
handle = safeGlobalAlloc(GMEM_MOVEABLE, count * sizeof(c_wchar))
locked_handle = safeGlobalLock(handle)
ctypes.memmove(
c_wchar_p(locked_handle),
c_wchar_p(text),
count * sizeof(c_wchar),
)
safeGlobalUnlock(handle)
safeSetClipboardData(CF_UNICODETEXT, handle)
def paste_windows():
with clipboard(None):
handle = safeGetClipboardData(CF_UNICODETEXT)
if not handle:
# GetClipboardData may return NULL with errno == NO_ERROR
# if the clipboard is empty.
# (Also, it may return a handle to an empty buffer,
# but technically that's not empty)
return ""
return c_wchar_p(handle).value
return copy_windows, paste_windows
def init_wsl_clipboard():
def copy_wsl(text):
text = _stringifyText(text) # Converts non-str values to str.
with subprocess.Popen(["clip.exe"], stdin=subprocess.PIPE, close_fds=True) as p:
p.communicate(input=text.encode(ENCODING))
def paste_wsl():
with subprocess.Popen(
["powershell.exe", "-command", "Get-Clipboard"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True,
) as p:
stdout = p.communicate()[0]
# WSL appends "\r\n" to the contents.
return stdout[:-2].decode(ENCODING)
return copy_wsl, paste_wsl
# Automatic detection of clipboard mechanisms
# and importing is done in determine_clipboard():
def determine_clipboard():
"""
Determine the OS/platform and set the copy() and paste() functions
accordingly.
"""
global Foundation, AppKit, qtpy, PyQt4, PyQt5
# Setup for the CYGWIN platform:
if (
"cygwin" in platform.system().lower()
): # Cygwin has a variety of values returned by platform.system(),
# such as 'CYGWIN_NT-6.1'
# FIXME(pyperclip#55): pyperclip currently does not support Cygwin,
# see https://github.com/asweigart/pyperclip/issues/55
if os.path.exists("/dev/clipboard"):
warnings.warn(
"Pyperclip's support for Cygwin is not perfect, "
"see https://github.com/asweigart/pyperclip/issues/55",
stacklevel=find_stack_level(),
)
return init_dev_clipboard_clipboard()
# Setup for the WINDOWS platform:
elif os.name == "nt" or platform.system() == "Windows":
return init_windows_clipboard()
if platform.system() == "Linux":
if _executable_exists("wslconfig.exe"):
return init_wsl_clipboard()
# Setup for the macOS platform:
if os.name == "mac" or platform.system() == "Darwin":
try:
import AppKit
import Foundation # check if pyobjc is installed
except ImportError:
return init_osx_pbcopy_clipboard()
else:
return init_osx_pyobjc_clipboard()
# Setup for the LINUX platform:
if HAS_DISPLAY:
if os.environ.get("WAYLAND_DISPLAY") and _executable_exists("wl-copy"):
return init_wl_clipboard()
if _executable_exists("xsel"):
return init_xsel_clipboard()
if _executable_exists("xclip"):
return init_xclip_clipboard()
if _executable_exists("klipper") and _executable_exists("qdbus"):
return init_klipper_clipboard()
try:
# qtpy is a small abstraction layer that lets you write applications
# using a single api call to either PyQt or PySide.
# https://pypi.python.org/project/QtPy
import qtpy # check if qtpy is installed
except ImportError:
# If qtpy isn't installed, fall back on importing PyQt4.
try:
import PyQt5 # check if PyQt5 is installed
except ImportError:
try:
import PyQt4 # check if PyQt4 is installed
except ImportError:
pass # We want to fail fast for all non-ImportError exceptions.
else:
return init_qt_clipboard()
else:
return init_qt_clipboard()
else:
return init_qt_clipboard()
return init_no_clipboard()
def set_clipboard(clipboard):
"""
Explicitly sets the clipboard mechanism. The "clipboard mechanism" is how
the copy() and paste() functions interact with the operating system to
implement the copy/paste feature. The clipboard parameter must be one of:
- pbcopy
- pyobjc (default on macOS)
- qt
- xclip
- xsel
- klipper
- windows (default on Windows)
- no (this is what is set when no clipboard mechanism can be found)
"""
global copy, paste
clipboard_types = {
"pbcopy": init_osx_pbcopy_clipboard,
"pyobjc": init_osx_pyobjc_clipboard,
"qt": init_qt_clipboard, # TODO - split this into 'qtpy', 'pyqt4', and 'pyqt5'
"xclip": init_xclip_clipboard,
"xsel": init_xsel_clipboard,
"wl-clipboard": init_wl_clipboard,
"klipper": init_klipper_clipboard,
"windows": init_windows_clipboard,
"no": init_no_clipboard,
}
if clipboard not in clipboard_types:
allowed_clipboard_types = [repr(_) for _ in clipboard_types]
raise ValueError(
f"Argument must be one of {', '.join(allowed_clipboard_types)}"
)
# Sets pyperclip's copy() and paste() functions:
copy, paste = clipboard_types[clipboard]()
def lazy_load_stub_copy(text):
"""
A stub function for copy(), which will load the real copy() function when
called so that the real copy() function is used for later calls.
This allows users to import pyperclip without having determine_clipboard()
automatically run, which will automatically select a clipboard mechanism.
This could be a problem if it selects, say, the memory-heavy PyQt4 module
but the user was just going to immediately call set_clipboard() to use a
different clipboard mechanism.
The lazy loading this stub function implements gives the user a chance to
call set_clipboard() to pick another clipboard mechanism. Or, if the user
simply calls copy() or paste() without calling set_clipboard() first,
will fall back on whatever clipboard mechanism that determine_clipboard()
automatically chooses.
"""
global copy, paste
copy, paste = determine_clipboard()
return copy(text)
def lazy_load_stub_paste():
"""
A stub function for paste(), which will load the real paste() function when
called so that the real paste() function is used for later calls.
This allows users to import pyperclip without having determine_clipboard()
automatically run, which will automatically select a clipboard mechanism.
This could be a problem if it selects, say, the memory-heavy PyQt4 module
but the user was just going to immediately call set_clipboard() to use a
different clipboard mechanism.
The lazy loading this stub function implements gives the user a chance to
call set_clipboard() to pick another clipboard mechanism. Or, if the user
simply calls copy() or paste() without calling set_clipboard() first,
will fall back on whatever clipboard mechanism that determine_clipboard()
automatically chooses.
"""
global copy, paste
copy, paste = determine_clipboard()
return paste()
def is_available() -> bool:
return copy != lazy_load_stub_copy and paste != lazy_load_stub_paste
# Initially, copy() and paste() are set to lazy loading wrappers which will
# set `copy` and `paste` to real functions the first time they're used, unless
# set_clipboard() or determine_clipboard() is called first.
copy, paste = lazy_load_stub_copy, lazy_load_stub_paste
def waitForPaste(timeout=None):
"""This function call blocks until a non-empty text string exists on the
clipboard. It returns this text.
This function raises PyperclipTimeoutException if timeout was set to
a number of seconds that has elapsed without non-empty text being put on
the clipboard."""
startTime = time.time()
while True:
clipboardText = paste()
if clipboardText != "":
return clipboardText
time.sleep(0.01)
if timeout is not None and time.time() > startTime + timeout:
raise PyperclipTimeoutException(
"waitForPaste() timed out after " + str(timeout) + " seconds."
)
def waitForNewPaste(timeout=None):
"""This function call blocks until a new text string exists on the
clipboard that is different from the text that was there when the function
was first called. It returns this text.
This function raises PyperclipTimeoutException if timeout was set to
a number of seconds that has elapsed without non-empty text being put on
the clipboard."""
startTime = time.time()
originalText = paste()
while True:
currentText = paste()
if currentText != originalText:
return currentText
time.sleep(0.01)
if timeout is not None and time.time() > startTime + timeout:
raise PyperclipTimeoutException(
"waitForNewPaste() timed out after " + str(timeout) + " seconds."
)
__all__ = [
"copy",
"paste",
"waitForPaste",
"waitForNewPaste",
"set_clipboard",
"determine_clipboard",
]
# pandas aliases
clipboard_get = paste
clipboard_set = copy
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@io@clipboard@__init__.py@.PATH_END.py
|
{
"filename": "How_to_open_the_machine_learning_H5_files_hdr2.1.ipynb",
"repo_name": "HETDEX/hetdex_api",
"repo_path": "hetdex_api_extracted/hetdex_api-master/notebooks/old_notebooks/How_to_open_the_machine_learning_H5_files_hdr2.1.ipynb",
"type": "Jupyter Notebook"
}
|
# How to Open Machine Learning Input Products
For each detectid in the line emission database (detect_hdr2.h5) we have generated 100 pixel (200 Angstrom) by 9 pixel fiber cutouts of the weighted sum of all fibers used to measured the extracton as well as cutouts of the four brightest fibers contributing to the flux. We also include the 1D spectrum (this is the same product included in the Spectra table for the detect_hdr2.h5 file) and a 30 arcsec x 30 arcsec cutout of HSC r-band imaging for the detection in available.
```python
import tables as tb
import numpy as np
from astropy.table import Table, join
import astropy.units as u
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from astropy.visualization import ZScaleInterval
from hetdex_api.config import HDRconfig
```
```python
config = HDRconfig('hdr2.1')
```
## Pytables and hetdex_api.config provide an easy interface to the ML products
```python
fileh = tb.open_file(config.detectml, 'r')
```
Here is the hierarchical structure:
```python
fileh
```
File(filename=/data/05350/ecooper/hdr2.1/detect/detect_ml_hdr2.1.h5, title='', mode='r', root_uep='/', filters=Filters(complevel=0, shuffle=False, bitshuffle=False, fletcher32=False, least_significant_digit=None))
/ (RootGroup) ''
/FiberImages (Table(1239239,)) 'Fiber Cutout Images'
description := {
"detectid": Int64Col(shape=(), dflt=0, pos=0),
"im_wave": Float32Col(shape=(100,), dflt=0.0, pos=1),
"im_sum": Float32Col(shape=(9, 100), dflt=0.0, pos=2),
"im_array": Float32Col(shape=(4, 9, 100), dflt=0.0, pos=3)}
byteorder := 'little'
chunkshape := (56,)
autoindex := True
colindexes := {
"detectid": Index(9, full, shuffle, zlib(1)).is_csi=True}
/PhotImages (Table(1239239,)) 'Photometric Images'
description := {
"detectid": Int64Col(shape=(), dflt=0, pos=0),
"im_phot": Float32Col(shape=(60, 60), dflt=0.0, pos=1),
"im_phot_hdr": StringCol(itemsize=2880, shape=(), dflt=b'', pos=2)}
byteorder := 'little'
chunkshape := (60,)
autoindex := True
colindexes := {
"detectid": Index(9, full, shuffle, zlib(1)).is_csi=True}
/Spec1D (Table(1239239,)) 'Aperture Summed Spectrum'
description := {
"detectid": Int64Col(shape=(), dflt=0, pos=0),
"spec1D": Float32Col(shape=(1036,), dflt=0.0, pos=1),
"spec1D_err": Float32Col(shape=(1036,), dflt=0.0, pos=2)}
byteorder := 'little'
chunkshape := (63,)
autoindex := True
colindexes := {
"detectid": Index(9, full, shuffle, zlib(1)).is_csi=True}
# Open the 2 Summed Fiber Image
Because we have over 1 million detections in each table and each table contains several 2D arrays, the best way to navigate the file is by detectid. We have indexed all three tables based on the detectid so it is fast to query. But we do suggest you do it one by one. Please do not make copies of every component of this file on TACC anywhere on /work. Ideally you should learn to use the h5 files, otherwise pick smaller subsets of detections to work with.
```python
detectid_obj = 2100208240
```
```python
obj_data = fileh.root.FiberImages.read_where('detectid == detectid_obj')[0]
```
```python
height=9 # in pixels
detectid = obj_data['detectid']
wave = obj_data['im_wave']
im_sum = obj_data['im_sum'] # this is the 2D summed image, 1st dim is height in fiber dims, 2nd dim is wave dim
im_array = obj_data['im_array'] # this is the 4 brightest fibers, 1st dim is fibers, 2nd dim is fiber dims, 3rd is wavelength
zscale = ZScaleInterval(contrast=0.5,krej=2.5)
vmin, vmax = zscale.get_limits(values=im_sum)
plt.figure(figsize=(12,5))
plt.imshow(im_sum,vmin=vmin, vmax=vmax,extent=[wave[0], wave[-1], -int(height/2.), int(height/2.)], origin="lower",cmap=plt.get_cmap('gray'),interpolation="none")
plt.show()
```
# Get Single Fiber cutouts for the four brightest fibers:
The 'im_array' column consists of fiber cutouts of the 4 brightest fibers
```python
# plot each fiber for 4th object in example table
height=9
detectid = obj_data['detectid']
wave = obj_data['im_wave']
im_sum = obj_data['im_sum'] # this is the 2D summed image, 1st dim is height in fiber dims, 2nd dim is wave dim
im_array = obj_data['im_array'] # this is the 4 brightest fibers, 1st dim is fibers, 2nd dim is fiber dims, 3rd is wavelength
for im_i in np.arange(0,4):
zscale = ZScaleInterval(contrast=0.5,krej=2.5)
vmin, vmax = zscale.get_limits(values=im_array[im_i])
plt.figure(figsize=(12,4))
plt.title(str(detectid))
plt.imshow(im_array[im_i],vmin=vmin, vmax=vmax,extent=[wave[0], wave[-1], -int(height/2.), int(height/2.)], origin="lower",cmap=plt.get_cmap('gray'),interpolation="none")
plt.show()
```
## Get the HSC 'r' band image if available
```python
phot_image_table = Table(fileh.root.PhotImages.read_where('detectid == detectid_obj'))
```
```python
#Loop over the images
height=9
for row in phot_image_table:
detectid = row['detectid']
im_phot = row['im_phot'] # this is the r-band image
zscale = ZScaleInterval(contrast=0.5,krej=2.5)
vmin, vmax = zscale.get_limits(values=im_phot)
plt.figure()
plt.title(str(detectid))
plt.imshow(im_phot,vmin=vmin, vmax=vmax,extent=[-15, 15, -15, 15], origin="lower",cmap=plt.get_cmap('gray'),interpolation="none")
plt.show()
```
## Get the Detection Spectrum
The 1D aperture Summed Spectrum is also contained in this file
```python
spec_table = Table(fileh.root.Spec1D.read_where('detectid == detectid_obj'))
```
```python
wave_rect = 2.0 * np.arange(1036) + 3470.0
plt.figure(figsize=(8,8))
plt.plot(wave_rect, spec_table['spec1D'][0]*10**-17 * u.erg / (u.cm ** 2 * u.s * u.AA))
plt.xlabel('wavelength (AA)')
plt.ylabel('spec 10**-17 ergs/s/cm^2/AA')
plt.title(detectid_obj)
```
Text(0.5, 1.0, '2100208240')
## PLEASE CLOSE THE H5 FILE WHEN DONE
When done with an h5 file you should close it:
```python
fileh.close()
```
```python
```
|
HETDEXREPO_NAMEhetdex_apiPATH_START.@hetdex_api_extracted@hetdex_api-master@notebooks@old_notebooks@How_to_open_the_machine_learning_H5_files_hdr2.1.ipynb@.PATH_END.py
|
{
"filename": "_currentvalue.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/slider/_currentvalue.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class CurrentvalueValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="currentvalue", parent_name="layout.slider", **kwargs
):
super(CurrentvalueValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Currentvalue"),
data_docs=kwargs.pop(
"data_docs",
"""
font
Sets the font of the current value label text.
offset
The amount of space, in pixels, between the
current value label and the slider.
prefix
When currentvalue.visible is true, this sets
the prefix of the label.
suffix
When currentvalue.visible is true, this sets
the suffix of the label.
visible
Shows the currently-selected value above the
slider.
xanchor
The alignment of the value readout relative to
the length of the slider.
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@slider@_currentvalue.py@.PATH_END.py
|
{
"filename": "oci_generative_ai.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/llms/oci_generative_ai.py",
"type": "Python"
}
|
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Dict, Iterator, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.utils import pre_init
from pydantic import BaseModel, ConfigDict, Field
from langchain_community.llms.utils import enforce_stop_tokens
CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint"
class Provider(ABC):
@property
@abstractmethod
def stop_sequence_key(self) -> str: ...
@abstractmethod
def completion_response_to_text(self, response: Any) -> str: ...
class CohereProvider(Provider):
stop_sequence_key: str = "stop_sequences"
def __init__(self) -> None:
from oci.generative_ai_inference import models
self.llm_inference_request = models.CohereLlmInferenceRequest
def completion_response_to_text(self, response: Any) -> str:
return response.data.inference_response.generated_texts[0].text
class MetaProvider(Provider):
stop_sequence_key: str = "stop"
def __init__(self) -> None:
from oci.generative_ai_inference import models
self.llm_inference_request = models.LlamaLlmInferenceRequest
def completion_response_to_text(self, response: Any) -> str:
return response.data.inference_response.choices[0].text
class OCIAuthType(Enum):
"""OCI authentication types as enumerator."""
API_KEY = 1
SECURITY_TOKEN = 2
INSTANCE_PRINCIPAL = 3
RESOURCE_PRINCIPAL = 4
class OCIGenAIBase(BaseModel, ABC):
"""Base class for OCI GenAI models"""
client: Any = Field(default=None, exclude=True) #: :meta private:
auth_type: Optional[str] = "API_KEY"
"""Authentication type, could be
API_KEY,
SECURITY_TOKEN,
INSTANCE_PRINCIPAL,
RESOURCE_PRINCIPAL
If not specified, API_KEY will be used
"""
auth_profile: Optional[str] = "DEFAULT"
"""The name of the profile in ~/.oci/config
If not specified , DEFAULT will be used
"""
model_id: Optional[str] = None
"""Id of the model to call, e.g., cohere.command"""
provider: Optional[str] = None
"""Provider name of the model. Default to None,
will try to be derived from the model_id
otherwise, requires user input
"""
model_kwargs: Optional[Dict] = None
"""Keyword arguments to pass to the model"""
service_endpoint: Optional[str] = None
"""service endpoint url"""
compartment_id: Optional[str] = None
"""OCID of compartment"""
is_stream: bool = False
"""Whether to stream back partial progress"""
model_config = ConfigDict(
extra="forbid", arbitrary_types_allowed=True, protected_namespaces=()
)
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that OCI config and python package exists in environment."""
# Skip creating new client if passed in constructor
if values["client"] is not None:
return values
try:
import oci
client_kwargs = {
"config": {},
"signer": None,
"service_endpoint": values["service_endpoint"],
"retry_strategy": oci.retry.DEFAULT_RETRY_STRATEGY,
"timeout": (10, 240), # default timeout config for OCI Gen AI service
}
if values["auth_type"] == OCIAuthType(1).name:
client_kwargs["config"] = oci.config.from_file(
profile_name=values["auth_profile"]
)
client_kwargs.pop("signer", None)
elif values["auth_type"] == OCIAuthType(2).name:
def make_security_token_signer(oci_config): # type: ignore[no-untyped-def]
pk = oci.signer.load_private_key_from_file(
oci_config.get("key_file"), None
)
with open(
oci_config.get("security_token_file"), encoding="utf-8"
) as f:
st_string = f.read()
return oci.auth.signers.SecurityTokenSigner(st_string, pk)
client_kwargs["config"] = oci.config.from_file(
profile_name=values["auth_profile"]
)
client_kwargs["signer"] = make_security_token_signer(
oci_config=client_kwargs["config"]
)
elif values["auth_type"] == OCIAuthType(3).name:
client_kwargs["signer"] = (
oci.auth.signers.InstancePrincipalsSecurityTokenSigner()
)
elif values["auth_type"] == OCIAuthType(4).name:
client_kwargs["signer"] = (
oci.auth.signers.get_resource_principals_signer()
)
else:
raise ValueError(
"Please provide valid value to auth_type, "
f"{values['auth_type']} is not valid."
)
values["client"] = oci.generative_ai_inference.GenerativeAiInferenceClient(
**client_kwargs
)
except ImportError as ex:
raise ModuleNotFoundError(
"Could not import oci python package. "
"Please make sure you have the oci package installed."
) from ex
except Exception as e:
raise ValueError(
"""Could not authenticate with OCI client.
Please check if ~/.oci/config exists.
If INSTANCE_PRINCIPAL or RESOURCE_PRINCIPAL is used,
please check the specified
auth_profile and auth_type are valid.""",
e,
) from e
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"model_kwargs": _model_kwargs},
}
def _get_provider(self, provider_map: Mapping[str, Any]) -> Any:
if self.provider is not None:
provider = self.provider
else:
if self.model_id is None:
raise ValueError(
"model_id is required to derive the provider, "
"please provide the provider explicitly or specify "
"the model_id to derive the provider."
)
provider = self.model_id.split(".")[0].lower()
if provider not in provider_map:
raise ValueError(
f"Invalid provider derived from model_id: {self.model_id} "
"Please explicitly pass in the supported provider "
"when using custom endpoint"
)
return provider_map[provider]
class OCIGenAI(LLM, OCIGenAIBase):
"""OCI large language models.
To authenticate, the OCI client uses the methods described in
https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm
The authentifcation method is passed through auth_type and should be one of:
API_KEY (default), SECURITY_TOKEN, INSTANCE_PRINCIPAL, RESOURCE_PRINCIPAL
Make sure you have the required policies (profile/roles) to
access the OCI Generative AI service.
If a specific config profile is used, you must pass
the name of the profile (from ~/.oci/config) through auth_profile.
To use, you must provide the compartment id
along with the endpoint url, and model id
as named parameters to the constructor.
Example:
.. code-block:: python
from langchain_community.llms import OCIGenAI
llm = OCIGenAI(
model_id="MY_MODEL_ID",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="MY_OCID"
)
"""
model_config = ConfigDict(
extra="forbid",
arbitrary_types_allowed=True,
)
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "oci_generative_ai_completion"
@property
def _provider_map(self) -> Mapping[str, Any]:
"""Get the provider map"""
return {
"cohere": CohereProvider(),
"meta": MetaProvider(),
}
@property
def _provider(self) -> Any:
"""Get the internal provider object"""
return self._get_provider(provider_map=self._provider_map)
def _prepare_invocation_object(
self, prompt: str, stop: Optional[List[str]], kwargs: Dict[str, Any]
) -> Dict[str, Any]:
from oci.generative_ai_inference import models
_model_kwargs = self.model_kwargs or {}
if stop is not None:
_model_kwargs[self._provider.stop_sequence_key] = stop
if self.model_id is None:
raise ValueError(
"model_id is required to call the model, "
"please provide the model_id."
)
if self.model_id.startswith(CUSTOM_ENDPOINT_PREFIX):
serving_mode = models.DedicatedServingMode(endpoint_id=self.model_id)
else:
serving_mode = models.OnDemandServingMode(model_id=self.model_id)
inference_params = {**_model_kwargs, **kwargs}
inference_params["prompt"] = prompt
inference_params["is_stream"] = self.is_stream
invocation_obj = models.GenerateTextDetails(
compartment_id=self.compartment_id,
serving_mode=serving_mode,
inference_request=self._provider.llm_inference_request(**inference_params),
)
return invocation_obj
def _process_response(self, response: Any, stop: Optional[List[str]]) -> str:
text = self._provider.completion_response_to_text(response)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to OCIGenAI generate endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = llm.invoke("Tell me a joke.")
"""
if self.is_stream:
text = ""
for chunk in self._stream(prompt, stop, run_manager, **kwargs):
text += chunk.text
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
invocation_obj = self._prepare_invocation_object(prompt, stop, kwargs)
response = self.client.generate_text(invocation_obj)
return self._process_response(response, stop)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Stream OCIGenAI LLM on given prompt.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
An iterator of GenerationChunks.
Example:
.. code-block:: python
response = llm.stream("Tell me a joke.")
"""
self.is_stream = True
invocation_obj = self._prepare_invocation_object(prompt, stop, kwargs)
response = self.client.generate_text(invocation_obj)
for event in response.data.events():
json_load = json.loads(event.data)
if "text" in json_load:
event_data_text = json_load["text"]
else:
event_data_text = ""
chunk = GenerationChunk(text=event_data_text)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@llms@oci_generative_ai.py@.PATH_END.py
|
{
"filename": "dq_init_step.py",
"repo_name": "spacetelescope/jwst",
"repo_path": "jwst_extracted/jwst-main/jwst/dq_init/dq_init_step.py",
"type": "Python"
}
|
#! /usr/bin/env python
from stdatamodels.jwst import datamodels
from ..stpipe import Step
from . import dq_initialization
__all__ = ["DQInitStep"]
class DQInitStep(Step):
"""Initialize the Data Quality extension from the
mask reference file.
The dq_init step initializes the pixeldq attribute of the
input datamodel using the MASK reference file. For some
FGS exp_types, initialize the dq attribute of the input model
instead. The dq attribute of the MASK model is bitwise OR'd
with the pixeldq (or dq) attribute of the input model.
"""
class_alias = "dq_init"
spec = """
"""
reference_file_types = ['mask']
def process(self, step_input):
"""Perform the dq_init calibration step
Parameters
----------
input : JWST datamodel
input jwst datamodel
Returns
-------
output_model : JWST datamodel
result JWST datamodel
"""
# Try to open the input as a regular RampModel
try:
input_model = datamodels.RampModel(step_input)
# Check to see if it's Guider raw data
if input_model.meta.exposure.type in dq_initialization.guider_list:
# Reopen as a GuiderRawModel
input_model.close()
input_model = datamodels.GuiderRawModel(step_input)
self.log.info("Input opened as GuiderRawModel")
except (TypeError, ValueError):
# If the initial open attempt fails,
# try to open as a GuiderRawModel
try:
input_model = datamodels.GuiderRawModel(step_input)
self.log.info("Input opened as GuiderRawModel")
except (TypeError, ValueError):
self.log.error("Unexpected or unknown input model type")
except Exception:
self.log.error("Can't open input")
raise
# Retrieve the mask reference file name
self.mask_filename = self.get_reference_file(input_model, 'mask')
self.log.info('Using MASK reference file %s', self.mask_filename)
# Check for a valid reference file
if self.mask_filename == 'N/A':
self.log.warning('No MASK reference file found')
self.log.warning('DQ initialization step will be skipped')
input_model.meta.cal_step.dq_init = 'SKIPPED'
return input_model
# Work on a copy
result = input_model.copy()
# Load the reference file
mask_model = datamodels.MaskModel(self.mask_filename)
# Apply the step
result = dq_initialization.correct_model(result, mask_model)
# Cleanup
del mask_model
del input_model
return result
|
spacetelescopeREPO_NAMEjwstPATH_START.@jwst_extracted@jwst-main@jwst@dq_init@dq_init_step.py@.PATH_END.py
|
{
"filename": "main_step2.py",
"repo_name": "shihyuntang/igrins_rv",
"repo_path": "igrins_rv_extracted/igrins_rv-master/main_step2.py",
"type": "Python"
}
|
from Engine.importmodule import *
from Engine.importmodule import read_prepdata
from Engine.set_argparse import _argparse_step2
from Engine.IO_AB import setup_templates, init_fitsread, setup_outdir
from Engine.clips import basicclip_above
from Engine.contfit import a0cont
from Engine.classes import FitObjs,InParams,_setup_bound_cut
from Engine.rebin_jv import rebin_jv
from Engine.rotint import rotint
from Engine.opt import optimizer, fmod
from Engine.outplotter import outplotter_23
from Engine.detect_peaks import detect_peaks
from Engine.crmask import cr_masker
from Engine.molmask import h2o_masker
from Engine.step2and3common_func import (setup_fitting_init_pars,
_make_dpars, trim_obs_data, trim_tel_data, check_if_template_exist,
check_user_input, setup_logger, _add_npar)
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# idx and description of optimizing parameters in pars0
# 0: The shift of the stellar template (km/s) [assigned later]
# 1: The scale factor for the stellar template
# 2: The shift of the telluric template (km/s)
# 3: The scale factor for the telluric template
# 4: vsini (km/s)
# 5: The instrumental resolution (FWHM) in pixels
# 6: Wavelength 0-pt
# 7: Wavelength linear component
# 8: Wavelength quadratic component
# 9: Wavelength cubic component
# 10: Continuum zero point
# 11: Continuum linear component
# 12: Continuum quadratic component
# 13: Instrumental resolution linear component
# 14: Instrumental resolution quadratic component
# 15: Blaze dip center location
# 16: Blaze dip full width
# 17: Blaze dip depth
# 18: Secondary blaze dip full width
# 19: Blaze dip depth
# 20: Continuum cubic component
# 21: Continuum quartic component
# 22: Continuum pentic component
# 23: Continuum hexic component
# 24: The shift of the second stellar template (km/s) [assigned later]
# 25: The scale factor for the second stellar template
# 26: Secondary vsini (km/s)
# 27: Secondary to primary flux ratio S2/S1 (km/s)
#-------------------------------------------------------------------------------
def base_dpars_dict(vsini_v1, band, order, numofpars, run_num=1, vsini_v2=-1):
"""Setup basic sets of paramaeter variable ranges
Args:
vsini_v1 (float): initial vsini value
band (str): H or K band
order (int): Current run order
run_num (int): Number of the optimize sequence that is being running
vsini_v2 (float): initial vsini value for the secondary
Returns:
dpars_org (dict): Sets of optimize parameters' variable ranges
"""
dpars_org = {}
dpars_org = _make_dpars('cont',
[10, 11, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23],
[1e7, 1, 1, 10, 30, 0.2, 50, 0.2, 1, 1, 1, 1],
numofpars, dpars_org
)
dpars_org = _make_dpars('twave',
[3, 6, 7, 8, 9],
[1, 1, 1, 1, 1],
numofpars, dpars_org
)
dpars_org = _make_dpars('ip',
[5],
[0.5],
numofpars, dpars_org
)
dpars_org = _make_dpars('s',
[0, 1],
[20, 1],
numofpars, dpars_org
)
dpars_org = _make_dpars('v',
[4],
[vsini_v1],
numofpars, dpars_org
)
dpars_org = _make_dpars('ts',
[0, 1, 3],
[20, 1, 1],
numofpars, dpars_org
)
if vsini_v2 != -1:
dpars_org = _make_dpars('s2',
[24, 25],
[20, 1],
numofpars, dpars_org
)
dpars_org = _make_dpars('v2',
[26],
[vsini_v2],
numofpars, dpars_org
)
dpars_org = _make_dpars('s1s2',
[0, 1, 24, 25],
[5, 1, 20, 1],
numofpars, dpars_org
)
# blaze fitting order setting
if band == 'H':
if order in [13]:
# fit a quadratic (2) continuum
dpars_org['cont'][20:] = 0
elif order in [6,14,21]:
# fit a cubic (3) continuum
dpars_org['cont'][21:] = 0
else:
pass
elif band == 'K':
if order in [3,5]:
# fit a cubic (3) continuum
dpars_org['cont'][21:] = 0
elif order in [4,6]:
# fit a quartic (4) continuum
dpars_org['cont'][22:] = 0
else:
pass
if run_num == 2:
dpars_org['s'][0] = 5.0 # The shift of the stellar template (km/s)
dpars_org['ts'][0] = 5.0 # The shift of the stellar template (km/s)
return dpars_org
def setup_init_rv_guess(args):
if args.guesses != '':
try:
initguesses = float(args.guesses)
initguesses_show = initguesses
except:
sys.exit('ERROR: -g ONLY TAKES A NUMBER AS INPUT!')
# Load initial RV guesses from file
if args.guessesX != '':
try:
guessdata = Table.read(
f'./Output/{args.targname}_{args.band}/'
f'Initguesser_results_{args.guessesX}.csv',
format='csv')
except:
sys.exit(
f'ERROR: "./Output/{args.targname}_{args.band}/'
f'Initguesser_results_{args.guessesX}.csv" NOT FOUND!')
initnights = np.array(guessdata['night'])
initrvs = np.array(guessdata['bestguess'])
initguesses = {}
initguesses_show = f'Initguesser_results_{args.guessesX}.csv'
for hrt in range(len(initnights)):
initguesses[str(initnights[hrt])] = float(initrvs[hrt])
if args.binary:
initrvs2 = np.array(guessdata['bestguess2'])
initguesses2 = {}
for hrt in range(len(initnights)):
initguesses2[str(initnights[hrt])] = float(initrvs2[hrt])
return initguesses, initguesses_show, initguesses2
return initguesses, initguesses_show
def mkdir_output_dic(args):
if not os.path.isdir('./Output'):
os.mkdir('./Output')
if not os.path.isdir(f'./Output/{args.targname}_{args.band}'):
os.mkdir(f'./Output/{args.targname}_{args.band}')
filesndirs = os.listdir(f'./Output/{args.targname}_{args.band}')
trk = 1; go = True
while go == True:
iniguess_dir = 'Initguesser_results_{}.csv'.format(trk)
if iniguess_dir not in filesndirs:
break
trk += 1
if not os.path.isdir(f'./Output/{args.targname}_{args.band}/figs'):
os.mkdir(f'./Output/{args.targname}_{args.band}/figs')
step2or3 = 2
temp_f_dir = f'./Output/{args.targname}_{args.band}/figs/'\
f'main_step{step2or3}_{args.band}_{trk}'
if not os.path.isdir(temp_f_dir):
os.mkdir(temp_f_dir)
outpath = f'./Output/{args.targname}_{args.band}'
return trk, outpath, step2or3, iniguess_dir
#-------------------------------------------------------------------------------
def main(args, inparam, orders, order_use, trk, step2or3, i):
"""Main function for RV fitting that will be threaded over
by multiprocessing
"""
nights = inparam.nights
night = nights[i] # current looped night
order = order_use
xbounds = inparam.xbounddict[order]
if args.debug:
print('Working on order {:02d}, night {:03d}/{:03d} '
'({}) PID:{}...'.format(int(order),
i+1,
len(inparam.nights),
night,
mp.current_process().pid) )
# Collect initial RV guesses
if type(inparam.initguesses) == dict:
initguesses = inparam.initguesses[night]
elif type(inparam.initguesses) == float:
initguesses = inparam.initguesses
else:
sys.exit(
'ERROR! EXPECTING SINGLE NUMBER OR FILE FOR INITGUESSES! QUITTING!'
)
if np.isnan(initguesses) == True:
logger.warning(
f' --> Previous run of {night} found it inadequate, skipping...'
)
return night, np.nan, np.nan, np.nan, np.nan
if args.binary:
if type(inparam.initguesses2) == dict:
initguesses2 = inparam.initguesses2[night]
elif type(inparam.initguesses2) == float:
initguesses2 = inparam.initguesses2
else:
sys.exit('ERROR! EXPECTING SINGLE NUMBER OR FILE FOR '
'INITGUESSES 2! QUITTING!')
# Collect relevant beam and filenum info
tagsnight = []; beamsnight = np.array([])
for tag in inparam.tagsB[night]:
tagsnight.append(tag)
beamsnight = np.append(beamsnight, 'B')
# Only do B exposures, and just use first B nodding
masterbeam = 'B'; beam = 'B'
try:
tag = tagsnight[0]
except IndexError:
logger.warning(f' --> No B nodding(frame) for night {night}, skipping...')
return night, np.nan, np.nan, np.nan, np.nan
if args.binary:
pars0 = setup_fitting_init_pars(
args.band, inparam.initvsini, order, inparam.initvsini2,
float(args.fluxratio)
)
else:
pars0 = setup_fitting_init_pars(args.band, inparam.initvsini, order)
A0loc = f'./Output/{args.targname}_{args.band}/A0Fits/'\
f'{night[:8]}A0_{beam}treated_{args.band}.fits'
try:
hdulist = fits.open(A0loc)
except IOError:
logger.warning(
f' --> No A0-fitted template for night {night}, skipping...'
)
return night, np.nan, np.nan, np.nan, np.nan
# Find corresponding table in fits file, given the tables do not go
# sequentially by order number due to multiprocessing in Step 1
num_orders = 0
for i in range(25):
try:
hdulist[i].columns[0].name[9:]
num_orders += 1
except:
continue
fits_layer = [ i for i in np.arange(num_orders)+1 \
if int(hdulist[i].columns[0].name[9:]) == order ][0]
tbdata = hdulist[ fits_layer ].data
flag = np.array(tbdata[f'ERRORFLAG{order}'])[0]
# Check whether Telfit hit critical error in Step 1 for the chosen order
# with this night. If so, try another order. If all hit the error, skip the night.
nexto = 0
ordertry = order
while 1 == 1:
fits_layer = [ i for i in np.arange(num_orders)+1 \
if int(hdulist[i].columns[0].name[9:]) == ordertry ][0]
tbdata = hdulist[ fits_layer ].data
flag = np.array(tbdata[f'ERRORFLAG{ordertry}'])[0]
# If Telfit hit unknown critical error in Step 1, this order can't
# be used for this night. Try another.
if flag == 1:
orderbad = ordertry
ordertry = orders[nexto]
logger.warning(f' --> TELFIT ENCOUNTERED CRITICAL ERROR IN ORDER: '
f'{orderbad} NIGHT: {night}, TRYING ORDER '
f'{ordertry} INSTEAD...')
else: # All good, continue
order = ordertry
break
nexto += 1
if nexto == len(orders):
logger.warning(f' --> TELFIT ENCOUNTERED CRITICAL ERROR IN ALL '
f'ORDERS FOR NIGHT: {night}, skipping...')
return night, np.nan, np.nan, np.nan, np.nan
# Use instrumental profile dictionary corresponding to whether IGRINS
# mounting was loose or not
if int(night[:8]) < 20180401 or int(night[:8]) > 20190531:
IPpars = inparam.ips_tightmount_pars[args.band][masterbeam][order]
else:
IPpars = inparam.ips_loosemount_pars[args.band][masterbeam][order]
watm = tbdata['WATM'+str(order)]
satm = tbdata['SATM'+str(order)]
a0contx = tbdata['X'+str(order)]
continuum = tbdata['BLAZE'+str(order)]
molnames = tbdata['MOLNAMES']
# Remove extra rows leftover from having columns of unequal length
satm = satm[(watm != 0)]
watm = watm[(watm != 0)]
# set very low points to zero so that they don't go to NaN when taken
# to an exponent by template power in fmodel_chi
satm[(satm < 1e-4)] = 0.
a0contx = a0contx[(continuum != 0)]
continuum = continuum[(continuum != 0)]
#-------------------------------------------------------------------------------
bound_cut = _setup_bound_cut(inparam.bound_cut_dic, args.band, order)
# Load target spectrum
x,wave,s,u = init_fitsread(
f'{inparam.inpath}/',
'target',
'combined'+str(masterbeam),
night,
order,
inparam.tagsB[night][0],
args.band,
bound_cut
)
# Execute S/N cut
s2n = s/u
if np.nanmedian(s2n) < float(args.SN_cut):
logger.warning(
' --> Bad S/N {:1.3f} < {} for {}{} {}... '.format(
np.nanmedian(s2n), args.SN_cut, night, beam, tag
))
pass
s_piece, u_piece, wave_piece, x_piece = trim_obs_data(
x, wave, s, u, xbounds
)
# Save data for second template cutting after optimization cycle 1 done
s_save = s_piece.copy()
x_save = x_piece.copy()
u_save = u_piece.copy()
satm_in, watm_in, wave_piece, s_piece, u_piece, x_piece = trim_tel_data(
watm, satm, wave_piece, s_piece, u_piece, x_piece
)
Rstell1 = np.median(np.diff(inparam.mwave0))
if args.binary:
Rstell2 = np.median(np.diff(inparam.mwave2))
if Rstell1 > Rstell2:
rebin2to1 = True; extra1 = 0.; extra2 = 10.
else:
rebin2to1 = False; extra1 = 10.; extra2 = 0.
mflux_in2 = inparam.mflux2[
(inparam.mwave2 > np.min(wave_piece)*1e4 - 5 - extra2) \
& (inparam.mwave2 < np.max(wave_piece)*1e4 + 5 + extra2)
]
mwave_in2 = inparam.mwave2[
(inparam.mwave2 > np.min(wave_piece)*1e4 - 5 - extra2) \
& (inparam.mwave2 < np.max(wave_piece)*1e4 + 5 + extra2)
]
Rstell = np.min([Rstell1,Rstell2])
dstep = Rstell2
nstep = int((mwave_in2[-1]-mwave_in2[0])/dstep)
mwave1 = np.linspace(mwave_in2[0],mwave_in2[-1],nstep)
mflux1 = rebin_jv(mwave_in2,mflux_in2,mwave1,False)
mwave_in2 = mwave1.copy(); mflux_in2 = mflux1.copy()
mwave_in2 = mwave_in2[1:-1]
mflux_in2 = mflux_in2[1:-1]
else:
extra1 = 0
extra2 = 0
Rstell = Rstell1
# Trim stellar template to data range +- 10 AA
mflux_in = inparam.mflux0[
(inparam.mwave0 > np.min(wave_piece)*1e4 - 5 - extra1) \
& (inparam.mwave0 < np.max(wave_piece)*1e4 + 5 + extra1)
]
mwave_in = inparam.mwave0[
(inparam.mwave0 > np.min(wave_piece)*1e4 - 5 - extra1) \
& (inparam.mwave0 < np.max(wave_piece)*1e4 + 5 + extra1)
]
Rtell = np.median(np.diff(watm_in))
if Rstell < Rtell:
sys.exit(f'Telluric template resolution ({round(Rtell,4)} AA) '
'must be finer than stellar template resolution '
'({round(Rstell,4)} AA) !')
# Rebin stellar template to uniform wavelength scale
dstep = Rstell1
nstep = int((mwave_in[-1]-mwave_in[0])/dstep)
mwave1 = np.linspace(mwave_in[0],mwave_in[-1],nstep)
mflux1 = rebin_jv(mwave_in,mflux_in,mwave1,False)
mwave_in = mwave1.copy(); mflux_in = mflux1.copy()
mwave_in = mwave_in[1:-1]
mflux_in = mflux_in[1:-1]
# Normalize continuum from A0 to flux scale of data
continuum /= np.nanmedian(continuum)
continuum *= np.nanpercentile(s_piece,99)
# --------------------------------------------------------------
par = pars0.copy()
# Get initial guess for cubic wavelength solution from reduction pipeline
f = np.polyfit(x_piece, wave_piece, 3)
q = np.poly1d(f)
initwave = q(x_piece)*1e4
# Initial RV with barycentric correction
par[0] = initguesses-inparam.bvcs[night+tag]
par[5] = IPpars[2]
par[13] = IPpars[1]
par[14] = IPpars[0]
if args.binary:
par[24] = initguesses2-inparam.bvcs[night+tag]
# setup fitting boundary
if args.binary:
dpars1 = base_dpars_dict(inparam.vsinivary, args.band,
int(order), len(pars0),
run_num=1,
vsini_v2=inparam.vsinivary2
)
dpars2 = base_dpars_dict(inparam.vsinivary, args.band,
int(order), len(pars0),
run_num=2,
vsini_v2=inparam.vsinivary2)
else:
dpars1 = base_dpars_dict(inparam.vsinivary, args.band,
int(order), len(pars0), run_num=1)
dpars2 = base_dpars_dict(inparam.vsinivary, args.band,
int(order), len(pars0), run_num=2)
continuum_in = rebin_jv(a0contx, continuum, x_piece, False)
fitobj = FitObjs(
s_piece, x_piece, u_piece, continuum_in, watm_in, satm_in,
mflux_in, mwave_in, ast.literal_eval(inparam.maskdict[order]),
masterbeam, [np.array([],dtype=int),np.array([],dtype=int)],
initwave, [])
if args.binary:
fitobj.addsecondary(mwave_in2,mflux_in2,rebin2to1)
#-------------------------------------------------------------------------------
# Initialize an array that puts hard bounds on vsini and the instrumental
# resolution to make sure they do not diverge to unphysical values
optimize = True
par_in = par.copy()
hardbounds = [par_in[4] - dpars1['v'][4], par_in[4] + dpars1['v'][4],
par_in[5] - dpars1['ip'][5], par_in[5] + dpars1['ip'][5]
]
if hardbounds[0] < 0.5:
hardbounds[0] = 0.5
if hardbounds[2] < 1:
hardbounds[2] = 1
if args.binary:
hardbounds.append(par_in[26] - dpars2['v2'][26])
hardbounds.append(par_in[26] + dpars2['v2'][26])
if hardbounds[-2] < 0.5:
hardbounds[-2] = 0.5
# Begin optimization. Fit the blaze, the wavelength solution, the telluric
# template power and RV, the stellar template power and RV, the
# zero point for the instrumental resolution, and the vsini of the star
# separately, iterating and cycling between each set of parameter fits.
cycles = 4 if args.binary else 2
optgroup1 = ['cont', 'twave', 'cont', 's',
'cont', 'twave', 's', 'cont',
'twave',
'ip', 'v',
'ip', 'v',
'twave', 's',
'twave', 's']
optgroup2 = ['cont', 'twave', 'cont', 's1s2',
'cont', 'twave', 's','s2', 'cont',
'twave',
'ip', 'v','v2',
'ip', 'v','v2',
'twave', 's','s2',
'twave', 's','s1s2']
optgroup = optgroup1.copy(); initstellpow2 = par_in[25]
par_in[25] = 0.
nk = 1
for nc, cycle in enumerate(np.arange(cycles), start=1):
if cycle == 0:
parstart = par_in.copy()
dpars = dpars1
else:
dpars = dpars2
fitobj = _add_npar(par_in, optgroup, dpars, fitobj)
for optkind in optgroup:
parfit_1 = optimizer(
parstart, dpars[optkind], hardbounds, fitobj, optimize,
binary=args.binary
)
parstart = parfit_1.copy()
if args.debug == True:
outplotter_23(
parfit_1,fitobj,'{}_{}_{}_parfit_{}{}'.format(
order,night,tag,nk,optkind),
trk, inparam, args, step2or3, order)
logger.debug(f'{order}_{tag}_{nk}_{optkind}:\n {parfit_1}')
nk += 1
# if nc == 2:
if args.binary:
optgroup = optgroup2.copy()
parstart[25] = initstellpow2
fitobj = _add_npar(par_in, optgroup, dpars, fitobj)
parfit = parfit_1.copy()
#-------------------------------------------------------------------------------
# if best fit stellar template power is very low, throw out result
if parfit[1] < 0.1:
logger.warning(f' --> Stellar template power is low for {night}! '
'Data likely being misfit! Throwing out result...')
return night, np.nan, np.nan, np.nan, np.nan
if args.binary and parfit[25] < 0.05:
logger.warning(f' --> Secondary stellar template power is low for {night}! '
'Data likely being misfit! Throwing out result...')
return night, np.nan, np.nan, np.nan, np.nan
# if best fit stellar or telluric template powers are exactly equal
# to their starting values, fit failed, throw out result
if parfit[1] == par_in[1] or parfit[3] == par_in[3] \
or (args.binary and (parfit[25] == par_in[25])):
logger.warning(f' --> Stellar or telluric template powers have not '
f'budged from starting values for {night}! Fit is '
'broken! Optimizer bounds may be unfeasible, or '
'chi-squared may be NaN? Throwing out result...')
return night, np.nan, np.nan, np.nan, np.nan
# if best fit model dips below zero at any point, we're to close to edge of
# blaze, fit may be compromised, throw out result
smod,chisq,trash,trash2 = fmod(parfit,fitobj,args.binary)
if len(smod[(smod < 0)]) > 0:
logger.warning(f' --> Best fit model dips below 0 for {night}! '
'May be too close to edge of blaze, throwing '
'out result...')
return night, np.nan, np.nan, np.nan, np.nan
#-------------------------------------------------------------------------------
if args.plotfigs == True:
parfitS1 = parfit.copy(); parfitS1[3] = 0; parfitS1[25] = 0
parfitS2 = parfit.copy(); parfitS2[3] = 0; parfitS2[1] = 0
parfitT = parfit.copy(); parfitT[1] = 0; parfitT[25] = 0
if args.binary:
outplotter_23(
parfitS1, fitobj, 'parfitS1_{}_{}_{}'.format(order,night,tag),
trk, inparam, args, step2or3, order)
outplotter_23(
parfitS2, fitobj, 'parfitS2_{}_{}_{}'.format(order,night,tag),
trk, inparam, args, step2or3, order)
else:
outplotter_23(
parfitS1, fitobj, 'parfitS_{}_{}_{}'.format(order,night,tag),
trk, inparam, args, step2or3, order)
outplotter_23(
parfitT, fitobj, 'parfitT_{}_{}_{}'.format(order,night,tag),
trk, inparam, args, step2or3,order)
outplotter_23(
parfit, fitobj, 'parfit_{}_{}_{}'.format(order,night,tag),
trk, inparam, args, step2or3, order, chi_new=chisq)
rv0 = parfit[0]
# Barycentric correction
rvsmini = rv0 + inparam.bvcs[night+tag] \
+ rv0*inparam.bvcs[night+tag]/(2.99792458e5**2)
vsinismini = parfit[4]
bestguess = np.round(rvsmini,5)
vsinimini = np.round(vsinismini,5)
if args.binary:
rv2 = parfit[24]
bestguess2 = rv2 + inparam.bvcs[night+tag] \
+ rv2*inparam.bvcs[night+tag]/(2.99792458e5**2)
vsinimini2 = parfit[26]
else:
bestguess2 = np.nan
vsinimini2 = np.nan
return night, bestguess, vsinimini, bestguess2, vsinimini2
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
if __name__ == '__main__':
args = _argparse_step2()
inpath = './Input/{}'.format(args.targname)
cdbs_loc = '~/cdbs/'
check_user_input(args, singleORdouble=1)
check_if_template_exist(args, singleORdouble=1)
initvsini = float(args.initvsini)
vsinivary = float(args.vsinivary)
if args.binary:
initvsini2 = float(args.initvsini2)
vsinivary2 = float(args.vsinivary2)
check_user_input(args, singleORdouble=2)
check_if_template_exist(args, singleORdouble=2)
#-------------------------------------------------------------------------------
# Specify initial RV guesses as a single value applied to all nights
if args.binary:
initguesses, initguesses_show, initguesses2 = setup_init_rv_guess(args)
else:
initguesses, initguesses_show = setup_init_rv_guess(args)
#------------------------------
# Read in the Prepdata under ./Input/Prpedata/
xbounddict, maskdict, tagsA, tagsB, mjds, \
bvcs, nightsFinal, orders, obs = read_prepdata(args)
if int(args.label_use) not in orders:
sys.exit(
f'Oops! -l_use INPUT "{args.label_use}" is not in "{orders}" '
'from the given WRegion list!!')
#-------------------------------------------------------------------------------
start_time = datetime.now()
print('####################################################################################\n')
print('---------------------------------------------------------------')
print(u'''
Input Parameters:
Tartget = {}
Filter = \33[37;1;41m {} band \033[0m
WaveLength file = \33[37;1;41m WaveRegions_{} \033[0m
S/N cut > \33[37;1;41m {} \033[0m
Order Use = \33[37;1;41m Order {} \033[0m
Initial vsini = \33[37;1;41m {} km/s \033[0m
vsini vary range \u00B1 \33[37;1;41m {} km/s \033[0m
RV initial guess = \33[37;1;41m {} \033[0m
Stellar template use= \33[37;1;41m {} \033[0m
syn template temp = \33[37;1;41m {} \033[0m
syn template logg = \33[37;1;41m {} \033[0m
Threads use = {}
'''.format(args.targname, args.band, args.WRegion, args.SN_cut,
args.label_use, initvsini, vsinivary, initguesses_show,
args.template, args.temperature, args.logg, args.Nthreads)
)
if not args.skip:
while True:
inpp = input("Press [Y]es to continue, [N]o to quit...\n --> ")
if 'n' in inpp.lower():
sys.exit('QUIT, PLEASE RE-ENTER YOUR PARAMETERS')
elif 'y' in inpp.lower():
break
else:
print('I cannot understand what you are saying... TRY AGAIN')
continue
if args.binary:
print(u'''
PLUS BINARY PARAMETERS:
Initial vsini #2 = \33[37;1;41m {} km/s \033[0m
vsini #2 vary range \u00B1 \33[37;1;41m {} km/s \033[0m
Stellar template #2 use = \33[37;1;41m {} \033[0m
syn template temp #2 = \33[37;1;41m {} \033[0m
syn template logg #2 = \33[37;1;41m {} \033[0m
syn template B #2 = \33[37;1;41m {} \033[0m
'''.format(initvsini2, vsinivary2, args.template2,
args.temperature2, args.logg2, args.B2)
)
if not args.skip:
while True:
inpp = input("Press [Y]es to continue, [N]o to quite...\n --> ")
if 'n' in inpp.lower():
sys.exit('QUIT, PLEASE RE-ENTER YOUR PARAMETERS')
elif 'y' in inpp.lower():
break
else:
print('I cannot understand what you are saying... TRY AGAIN')
continue
print('---------------------------------------------------------------')
print('Running Step 2 for {}...'.format(args.targname))
print('This Will Take a While..........')
#-------------------------------------------------------------------------------
trk, outpath, step2or3, iniguess_dir = mkdir_output_dic(args)
logger, stream_hander = setup_logger(args, outpath)
#-------------------------------------------------------------------------------
# Create output file to write to
logger.info(
f'Writing output to ./Output/{args.targname}_{args.band}/{iniguess_dir}'
)
filew = open(
f'./Output/{args.targname}_{args.band}/{iniguess_dir}','w')
if args.binary:
filew.write('night, bestguess, vsini, bestguess2, vsini2\n')
else:
filew.write('night, bestguess, vsini\n')
# Use subset of nights if specified
if args.nights_use != '':
nightstemp = np.array(ast.literal_eval(args.nights_use), dtype=str)
for nnn in nightstemp:
if nnn not in nightsFinal:
sys.exit(
'NIGHT {} NOT FOUND UNDER ./Input_Data/{}'.format(
nnn, args.targname
))
nightsFinal = nightstemp
print('Only processing nights: {}'.format(nightsFinal))
logger.info('Analyze with {} nights'.format(len(nightsFinal)))
intnights = np.array([int(i[:8]) for i in nightsFinal])
if len(intnights[(intnights >= 20180401) & (intnights < 20190531)]) > 0:
logger.info('''
WARNING: Some of these nights were when the IGRINS K band was defocused!
For K band RVs: IGRINS RV will take this into account and process these nights
slightly differently. When you run Step 3, RVs will be output in
two formats: one with the defocus nights separated, and the other
with all nights together.
For H band RVs: We do not expect any systematic changes in the H band as the result
of the defocus. IGRINS RV will process defocus nights the same way
as the others, but when you run Step 3, will still output the results
in two formats like it does with the K band.''')
#-------------------------------------------------------------------------------
# Retrieve stellar and telluric templates
watm,satm, mwave0, mflux0 = setup_templates(
logger, args.template, args.band, int(args.temperature),
float(args.logg), float(args.B)
)
# Save pars in class for future use
inparam = InParams(
inpath, outpath, initvsini, vsinivary, args.plotfigs, initguesses,
bvcs, tagsA, tagsB, nightsFinal, mwave0, mflux0, None, xbounddict,
maskdict
)
if args.binary:
print('\n Loading secondary stellar template... \n')
watm,satm, mwave2, mflux2 = setup_templates(
logger, args.template2, args.band, int(args.temperature2),
float(args.logg2), float(args.B2)
)
inparam.addsecondary(
initvsini2, vsinivary2, mwave2, mflux2, initguesses2
)
#-------------------------------------------------------------------------------
# if not in debug mode than enter quite mode, i.e., all message saved in log file
if not args.debug: logger.removeHandler(stream_hander)
print('\n')
# Run order by order, multiprocessing over nights within an order
func = partial(
main, args, inparam, orders, int(args.label_use), trk, step2or3
)
outs = pqdm(np.arange(len(nightsFinal)), func, n_jobs=args.Nthreads)
# for ii in np.arange(len(nightsFinal)):
# main(args, inparam, orders, int(args.label_use), trk, step2or3, ii)
# Write outputs to file
vsinis = []; finalrvs = []; vsinis2 = []; finalrvs2 = []
for n in range(len(nightsFinal)):
nightout = outs[n]
if args.binary:
filew.write(
'{}, {}, {}, {}, {}'.format(
nightout[0], nightout[1], nightout[2],
nightout[3], nightout[4])
)
else:
filew.write(
'{}, {}, {}'.format(
nightout[0], nightout[1], nightout[2])
)
filew.write('\n')
vsinis2.append(nightout[4])
finalrvs2.append(nightout[3])
vsinis.append(nightout[2])
finalrvs.append(nightout[1])
filew.close()
warning_r = log_warning_id(
f'{outpath}/{args.targname}_{args.band}.log', start_time
)
if warning_r:
print(f'''
**********************************************************************************
WARNING!! you got warning message during this run. Please check the log file under:
{outpath}/{args.targname}_{args.band}.log
**********************************************************************************
''')
if not args.debug: logger.addHandler(stream_hander)
print('--------!Initial Guess!--------')
logger.info(
'RV results: mean= {:1.4f} km/s, median= {:1.4f} km/s, std= {:1.4f} km/s'.format(
np.nanmean(finalrvs), np.nanmedian(finalrvs), np.nanstd(finalrvs) )
)
logger.info(
'vsini results: mean= {:1.4f} km/s, median= {:1.4f} km/s, std= {:1.4f} km/s'.format(
np.nanmean(vsinis), np.nanmedian(vsinis), np.nanstd(vsinis) )
)
if args.binary:
logger.info(
'RV 2 results: mean= {:1.4f} km/s, median= {:1.4f} km/s, std= {:1.4f} km/s'.format(
np.nanmean(finalrvs2), np.nanmedian(finalrvs2), np.nanstd(finalrvs2) )
)
logger.info(
'vsini 2 results: mean= {:1.4f} km/s, median= {:1.4f} km/s, std= {:1.4f} km/s'.format(
np.nanmean(vsinis2), np.nanmedian(vsinis2), np.nanstd(vsinis2) )
)
end_time = datetime.now()
logger.info(
'\nRV Initial Guess DONE... Duration: {}'.format(end_time - start_time)
)
logger.info(
f'Output saved under ./Output/{args.targname}_{args.band}/{iniguess_dir}'
)
print('---------------------------------------------------------------\n')
print('You can now try to get a better RV initial guess with by')
print('rerunning Step 2 with -gX set to the run number you just completed.')
print('OR, you can go on to the full RV analysis in Step 3.')
print('####################################################################################')
|
shihyuntangREPO_NAMEigrins_rvPATH_START.@igrins_rv_extracted@igrins_rv-master@main_step2.py@.PATH_END.py
|
{
"filename": "plot_wind.ipynb",
"repo_name": "sirocco-rt/sirocco",
"repo_path": "sirocco_extracted/sirocco-main/docs/sphinx/source/plotting/plot_wind.ipynb",
"type": "Jupyter Notebook"
}
|
# Plotting Wind Properties
As described under [Models](../output/model.rst), SIROCCO saves wind properties in binary wind_save files. This notebook explains how to read and plot wind variables for the ```cv_standard``` file found in the examples. Before running the python commands, you need to run the model from the command line. I suggest running the following commands, after you have compiled python:
mkdir cv_test
cd cv_test
cp $SIROCCO/examples/basic/cv_standard.pf .
sirocco cv_standard </code>
The model will take about 5 minutes to run on a single core. It will not converge, but will give us a model to use as an example. You should then run ```windsave2table``` on the output
windsave2table cv_standard
which will create a series of ascii files containing key variables in the wind cells. We will use these ascii files for our plots.
## Making wind plots using PySi
We will start by demonstrating how to use PySi to plot data from the wind files. In particular we will plot some key variables in the wind, followed by some ion fractions.
PySi works by setting up a ```Wind``` class which reads in and stores all the useful data and attributes from the model. This class can be used to inspect data or to plot it directly.
```python
import matplotlib.pyplot as plt
import numpy as np
import pysi
from pysi.wind import Wind
root = "cv_standard"
directory = "cv_test/"
wind = Wind(root = root, directory = directory)
```
Version: UNKNOWN
The data can be plotted easily using the ```plot_parameter``` method. This method returns Figure and Axes objects so the plot can be modified easily.
```python
fig, ax = wind.plot_parameter("t_e")
```
/Users/matthewsj/.virtualenvs/sirocco/lib/python3.10/site-packages/pysi/wind/model/plot.py:484: RuntimeWarning: divide by zero encountered in log10
parameter_points = numpy.log10(parameter_points)
```python
plot = wind.plot_parameter("t_e", "linlin")
plt.xlim(0,2e11)
_ = plt.ylim(0,2e11)
```
The ```get_windsave_descriptions``` method in the wind class comes provides a handy guide to the main columns in the .master.txt file, which can be plotted using the above methods, or accessed directly as, e.g., ```wind["ne"]```
```python
print (wind.get_windsave_descriptions())
```
x -- left-hand lower cell corner x-coordinate, cm
z -- left-hand lower cell corner z-coordinate, cm
xcen -- cell centre x-coordinate, cm
zcen -- cell centre z-coordinate, cm
i -- cell index (column)
j -- cell index (row)
inwind -- is the cell in wind (0), partially in wind (1) or out of wind (<0)
converge -- how many convergence criteria is the cell failing?
v_x -- x-velocity, cm/s
v_y -- y-velocity, cm/s
v_z -- z-velocity, cm/s
vol -- volume in cm^3
rho -- density in g/cm^3
ne -- electron density in cm^-3
t_e -- electron temperature in K
t_r -- radiation temperature in K
h1 -- H1 ion fraction
he2 -- He2 ion fraction
c4 -- C4 ion fraction
n5 -- N5 ion fraction
o6 -- O6 ion fraction
dmo_dt_x -- momentum rate, x-direction
dmo_dt_y -- momentum rate, y-direction
dmo_dt_z -- momentum rate, z-direction
ip -- U ionization parameter
xi -- xi ionization parameter
ntot -- total photons passing through cell
nrad -- total wind photons produced in cell
nioniz -- total ionizing photons passing through cell
None
We can also use the multiplot command to generate multiple plots for specified wind parameters.
This method creates subplots to visualize the specified wind parameters using either 1D or 2D
representation based on the coordinate system.
```python
fig, ax = wind.multiplot( ("ne", "t_e", "v_z", "ntot") , "loglog", nrows = 2, ncols = 2)
fig.tight_layout(pad=0.05)
```
We can also check all the possible things to plot, some of which are intuitive and some of which aren't!
```python
print (wind.things_read_in)
```
dict_keys(['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz', 'w', 'ave_freq', 'J', 'J_direct', 'J_scatt', 'lum_tot', 'heat_tot', 'heat_comp', 'heat_line', 'heat_ff', 'heat_phot', 'heat_auge', 'cool_tot', 'cool_comp', 'lum_lines', 'cool_dr', 'lum_ff', 'lum_rr', 'cool_rr', 'cool_adia', 'heat_shoc', 'ht_ln_mac', 'ht_ph_mac', 'dv_x_dx', 'dv_y_dx', 'dv_z_dx', 'dv_x_dy', 'dv_y_dy', 'dv_z_dy', 'dv_x_dz', 'dv_y_dz', 'dv_z_dz', 'div_v', 'dvds_max', 'gamma', 'dfudge', 't_e_old', 'dt_e', 'dt_e_old', 't_r_old', 'heat_tot_', 'gain', 'macro_bf_', 'H_i01_frac', 'H_i02_frac', 'He_i01_frac', 'He_i02_frac', 'He_i03_frac', 'C_i01_frac', 'C_i02_frac', 'C_i03_frac', 'C_i04_frac', 'C_i05_frac', 'C_i06_frac', 'C_i07_frac', 'N_i01_frac', 'N_i02_frac', 'N_i03_frac', 'N_i04_frac', 'N_i05_frac', 'N_i06_frac', 'N_i07_frac', 'N_i08_frac', 'O_i01_frac', 'O_i02_frac', 'O_i03_frac', 'O_i04_frac', 'O_i05_frac', 'O_i06_frac', 'O_i07_frac', 'O_i08_frac', 'O_i09_frac', 'Na_i01_frac', 'Na_i02_frac', 'Na_i03_frac', 'Na_i04_frac', 'Na_i05_frac', 'Na_i06_frac', 'Na_i07_frac', 'Na_i08_frac', 'Na_i09_frac', 'Na_i10_frac', 'Na_i11_frac', 'Na_i12_frac', 'Si_i01_frac', 'Si_i02_frac', 'Si_i03_frac', 'Si_i04_frac', 'Si_i05_frac', 'Si_i06_frac', 'Si_i07_frac', 'Si_i08_frac', 'Si_i09_frac', 'Si_i10_frac', 'Si_i11_frac', 'Si_i12_frac', 'Si_i13_frac', 'Si_i14_frac', 'Si_i15_frac', 'Ca_i01_frac', 'Ca_i02_frac', 'Ca_i03_frac', 'Ca_i04_frac', 'Ca_i05_frac', 'Ca_i06_frac', 'Ca_i07_frac', 'Ca_i08_frac', 'Ca_i09_frac', 'Ca_i10_frac', 'Ca_i11_frac', 'Ca_i12_frac', 'Ca_i13_frac', 'Ca_i14_frac', 'Ca_i15_frac', 'Ca_i16_frac', 'Ca_i17_frac', 'Ca_i18_frac', 'Ca_i19_frac', 'Ca_i20_frac', 'Ca_i21_frac', 'Fe_i01_frac', 'Fe_i02_frac', 'Fe_i03_frac', 'Fe_i04_frac', 'Fe_i05_frac', 'Fe_i06_frac', 'Fe_i07_frac', 'Fe_i08_frac', 'Fe_i09_frac', 'Fe_i10_frac', 'Fe_i11_frac', 'Fe_i12_frac', 'Fe_i13_frac', 'Fe_i14_frac', 'Fe_i15_frac', 'Fe_i16_frac', 'Fe_i17_frac', 'Fe_i18_frac', 'Fe_i19_frac', 'Fe_i20_frac', 'Fe_i21_frac', 'Fe_i22_frac', 'Fe_i23_frac', 'Fe_i24_frac', 'Fe_i25_frac', 'Fe_i26_frac', 'Fe_i27_frac', 'spec_freq', 'spec_flux', 'model_freq', 'model_flux', 'v_l', 'v_rot', 'v_r'])
## Plotting ions using PySi
PySi also allows one to plot ion fractions or densities (fractions by default), which are accessed through strings like ```Fe_i01_frac```, using astronomical notation for the ion stage. An example multiplot of the first six ionic stages of Carbon would be as follows.
```python
ions_to_plot = ["C_i{:02d}_frac".format(i+1) for i in range(6)]
fig, ax = wind.multiplot(ions_to_plot, "loglog", nrows = 3, ncols = 2, figsize=(8,10))
```
All the ions in the simulation can be viewed using the ```ions_read_in``` list.
```python
print (wind.ions_read_in)
```
['H_i01_frac', 'H_i02_frac', 'He_i01_frac', 'He_i02_frac', 'He_i03_frac', 'C_i01_frac', 'C_i02_frac', 'C_i03_frac', 'C_i04_frac', 'C_i05_frac', 'C_i06_frac', 'C_i07_frac', 'N_i01_frac', 'N_i02_frac', 'N_i03_frac', 'N_i04_frac', 'N_i05_frac', 'N_i06_frac', 'N_i07_frac', 'N_i08_frac', 'O_i01_frac', 'O_i02_frac', 'O_i03_frac', 'O_i04_frac', 'O_i05_frac', 'O_i06_frac', 'O_i07_frac', 'O_i08_frac', 'O_i09_frac', 'Na_i01_frac', 'Na_i02_frac', 'Na_i03_frac', 'Na_i04_frac', 'Na_i05_frac', 'Na_i06_frac', 'Na_i07_frac', 'Na_i08_frac', 'Na_i09_frac', 'Na_i10_frac', 'Na_i11_frac', 'Na_i12_frac', 'Si_i01_frac', 'Si_i02_frac', 'Si_i03_frac', 'Si_i04_frac', 'Si_i05_frac', 'Si_i06_frac', 'Si_i07_frac', 'Si_i08_frac', 'Si_i09_frac', 'Si_i10_frac', 'Si_i11_frac', 'Si_i12_frac', 'Si_i13_frac', 'Si_i14_frac', 'Si_i15_frac', 'Ca_i01_frac', 'Ca_i02_frac', 'Ca_i03_frac', 'Ca_i04_frac', 'Ca_i05_frac', 'Ca_i06_frac', 'Ca_i07_frac', 'Ca_i08_frac', 'Ca_i09_frac', 'Ca_i10_frac', 'Ca_i11_frac', 'Ca_i12_frac', 'Ca_i13_frac', 'Ca_i14_frac', 'Ca_i15_frac', 'Ca_i16_frac', 'Ca_i17_frac', 'Ca_i18_frac', 'Ca_i19_frac', 'Ca_i20_frac', 'Ca_i21_frac', 'Fe_i01_frac', 'Fe_i02_frac', 'Fe_i03_frac', 'Fe_i04_frac', 'Fe_i05_frac', 'Fe_i06_frac', 'Fe_i07_frac', 'Fe_i08_frac', 'Fe_i09_frac', 'Fe_i10_frac', 'Fe_i11_frac', 'Fe_i12_frac', 'Fe_i13_frac', 'Fe_i14_frac', 'Fe_i15_frac', 'Fe_i16_frac', 'Fe_i17_frac', 'Fe_i18_frac', 'Fe_i19_frac', 'Fe_i20_frac', 'Fe_i21_frac', 'Fe_i22_frac', 'Fe_i23_frac', 'Fe_i24_frac', 'Fe_i25_frac', 'Fe_i26_frac', 'Fe_i27_frac']
## More direct data access
We recommend using PySi where possible. You may, however, wish to get more direct access to the data, which can be done easily by reading in the ```cv_standard.master.txt``` file, for example using ```astropy```. In the next code block, we read in the data file and print out the columns.
```python
import matplotlib.pyplot as plt
import astropy.io.ascii as io
fname = f"{directory}{root}.master.txt"
data = io.read(fname)
print (data.colnames)
```
['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz']
```py_plot_util``` also contains some routines for reshaping and masking arrays and so on. One of the most useful for plotting is the ```wind_to_masked``` function which turns the raw 1D flattened data into a masked 2D array with the right shape which can be easily used with ```pcolormesh``` and so on. Here's an example plot of the electron density in the model.
```python
x, z, ne, inwind = util.wind_to_masked(data, value_string="ne", return_inwind=True)
plt.pcolormesh(x,z, np.log10(ne))
plt.loglog()
plt.xlim(1e9,1e12)
plt.ylim(1e8,1e12)
cbar = plt.colorbar()
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[10], line 1
----> 1 x, z, ne, inwind = util.wind_to_masked(data, value_string="ne", return_inwind=True)
2 plt.pcolormesh(x,z, np.log10(ne))
3 plt.loglog()
NameError: name 'util' is not defined
This procedure can be used to plot any of the variables in the masterfile and is a good starting point for delving into the properties of the wind if not using PySi.
Ion populations outputted from ```windsave2table``` are stored in files like ```cv_standard.C.frac.txt```, where the letter before frac denotes the element. Plots of the C III ion fraction can thus be made through commands like the following, where strings like ```i05``` index the ion for each file.
```python
carbon_ion = io.read("cv_test/cv_standard.C.frac.txt")
x, z, c3_frac, inwind = util.wind_to_masked(carbon_ion, value_string="i03", return_inwind=True)
plt.pcolormesh(x,z, np.log10(c3_frac))
plt.loglog()
plt.xlim(1e9,1e12)
plt.ylim(1e8,1e12)
cbar = plt.colorbar()
```
## Make A Basic Quick Look Wind Plot
The simplest way to make a quick look plot of the electron temperature is using the ```plot_wind.py``` routine in ```$SIROCCO/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_wind.py``` can be run from the command line using
plot_wind.py cv_standard t_e
where the second argument is the variable to plot. Alternatively, it can be run from within a python script by doing (where we are now assuming you are running this code from one directory above cv_test):
```python
import matplotlib.pyplot as plt
import numpy as np
import plot_wind
fname = "cv_test/cv_standard.master.txt"
plot_wind.doit(fname, var="t_e")
```
|
sirocco-rtREPO_NAMEsiroccoPATH_START.@sirocco_extracted@sirocco-main@docs@sphinx@source@plotting@plot_wind.ipynb@.PATH_END.py
|
{
"filename": "welcome.md",
"repo_name": "amrvac/amrvac",
"repo_path": "amrvac_extracted/amrvac-master/doc/welcome.md",
"type": "Markdown"
}
|
# Welcome page
[TOC]
# Introduction {#introduction}
This is the documentation for the 3.1 version of MPI-AMRVAC. The code is
available on [Github](https://github.com/amrvac/amrvac), and the documentation
on [amrvac.org](http://amrvac.org/). If you have questions about MPI-AMRVAC,
please send them to the mailing list: <mailto:amrvacusers@ls.kuleuven.be>, which you can also
[subscribe to](https://ls.kuleuven.be/cgi-bin/wa?SUBED1=AMRVACUSERS&A=1) and
[search](https://ls.kuleuven.be/cgi-bin/wa?A0=AMRVACUSERS).
# Quick links {#quick_links}
* @ref installation.md
* @ref getting_started.md
* @ref par.md
* @ref faq.md
* @ref demo-movies.md
* @ref contributing.md
* @ref publications.md
* @ref acknowledgments.md
* [**Recent changes**](https://github.com/amrvac/amrvac/commits/master)
# MPI-AMRVAC aims {#aims}
MPI-AMRVAC is a parallel adaptive mesh refinement framework aimed at solving
(primarily hyperbolic) partial differential equations by a number of different
numerical schemes. The emphasis is on (near) conservation laws and on
shock-dominated problems in particular. A number of *physics modules* are
included; the hydrodynamics and the magnetohydrodynamics module are most
frequently used. Users can add their own physics module or modify existing ones.
The framework supports 1D to 3D simulations, in a number of different geometries
(Cartesian, cylindrical, spherical).
MPI-AMRVAC is written in Fortran 90 and uses MPI for parallelization.
The [VACPP](vacpp.md) preprocessor is used to extend Fortran with dimensional
independent notation, but users are not required to learn the VACPP syntax.
The philosophy behind MPI-AMRVAC is to use a single versatile code with options
and switches for various problems. The advantage of such a general approach is
easier maintenance, the compatibility of different parts, and the automatic
extension of new features to existing applications. MPI-AMRVAC is not a
fool-proof black-box design. A user needs to write subroutines for initial
conditions, and for source terms or special boundary conditions when needed.
# Development {#current_develop}
MPI-AMRVAC is developed and maintained by an international team led by professor
[Rony Keppens](https://perswww.kuleuven.be/~u0016541/) from
[Centre for mathematical Plasma-Astrophysics (CmPA)](https://wis.kuleuven.be/CmPA), KU Leuven.
In November 2022, we released the current 3.0 version, with the aid of Beatrice Popescu Braileanu, Niels Claes, Chun Xia, Guo Yang, Wenzhi Ruan, Fabio Bacchini, Yuhao Zhou.
The movies generated from the demo simulations (found in the tests/demo folder) can be seen [here](demo-movies.md).
Prior, in 2016-2017, a large modernization was completed by Chun Xia and
[Jannis Teunissen](http://teunissen.net/) marked with version 2.0 with
important changes as following:
* Automatic regression tests were added
* The preprocessor is only used for the problem dimension (1D, 2D, or 3D)
* The code was modernized and re-organized into Fortran modules, and there is
now an AMRVAC library
* The focus of MPI-AMRVAC is now on non-relativistic hydro- and
magnetohydrodynamics
* Many smaller improvements to the physics modules
From 2018, more developers have joined in to contribute in various aspects.
We explicitly mention important help from Oliver Porth (main developer of MPI-AMRVAC 1.0),
and Hector Olivares, who are the developers of the sister GRMHD code [BHAC](http://bhac.science/), the Black Hole Accretion Code.
|
amrvacREPO_NAMEamrvacPATH_START.@amrvac_extracted@amrvac-master@doc@welcome.md@.PATH_END.py
|
{
"filename": "example_lognorm_censored_MAR.py",
"repo_name": "rfeldmann/leopy",
"repo_path": "leopy_extracted/leopy-master/examples/example_lognorm_censored_MAR.py",
"type": "Python"
}
|
"""Example of data with missing and censored values and lognormal distribution
This file is part of LEO-Py --
Likelihood Estimation of Observational data with Python
Copyright 2019 University of Zurich, Robert Feldmann
LEO-Py 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.
LEO-Py 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 LEO-Py. If not, see <https://www.gnu.org/licenses/>.
"""
import numpy as np
import pandas as pd
import scipy.stats
import scipy.optimize
import pytest
plot_data = False
compute_maximum_likelihood = True
np.random.seed(2)
print('Example: Partially missing, censored, and correlated data with '
'observational errors and a lognormal distribution')
dist = scipy.stats.lognorm
Ndata = 500
rho = 0.5
R = np.array([[1., rho], [rho, 1.]])
loc_true = np.array([0., 2.])
scale_true = np.array([1., 3.])
shape_true = np.array([0.5, 1.5])
print('population parameters: [{} {} {} {} {} {} {}]'.format(
*loc_true, *scale_true, *shape_true, rho))
mean_pop = scale_true * np.exp(0.5*shape_true**2) + loc_true
std_pop = (mean_pop - loc_true) * np.sqrt(np.exp(shape_true**2)-1.)
print('population statistics (mean, std, corr): [{:.3g} {:.3g} {:.3g} {:.3g} '
'{:.3g}]'.format(
*mean_pop, *std_pop, rho))
## -- create observational data
x = scipy.stats.multivariate_normal.rvs(cov=R, size=Ndata)
# print('rho(x) = {}'.format(np.corrcoef(x.T)))
rho_x_sample = np.corrcoef(x.T)[0, 1]
y = dist.ppf(scipy.stats.norm.cdf(x),
shape_true, loc=loc_true, scale=scale_true)
y_true = np.copy(y)
ey = np.zeros_like(y)
ey[:, 0] = 0.2 # 1e-6 # 0.2 # 0.1
ey[:, 1] = 0.1 # 1e-6 # 0.1
y[:, 0] += ey[:, 0] * np.random.randn(Ndata)
y[:, 1] += ey[:, 1] * np.random.randn(Ndata)
print('sample statistics full data (mean, std, corr): n={} [{:.3g} {:.3g} '
'{:.3g} {:.3g} {:.3g}]'.format(
len(y), np.nanmean(y[:, 0]), np.nanmean(y[:, 1]),
np.nanstd(y[:, 0]), np.nanstd(y[:, 1]), rho_x_sample))
# censor some data (variable 2)
ceny = np.zeros(Ndata, dtype=bool)
limy = np.zeros(Ndata)
sel = y[:, 1] < 0.4*mean_pop[1]
limy[sel] = 0.4*mean_pop[1]
ceny[sel] = True
y[sel, 1] = 0.
def logistic(x):
res = np.ones_like(x)
sel = x < 20
res[sel] = np.exp(x[sel]) / (np.exp(x[sel]) + 1.)
return res
# data missing data at random (MAR) based on values of other column
m1 = scipy.stats.bernoulli.rvs(logistic(y[:, 0]-3.)).astype(bool) # for col 1
m0 = scipy.stats.bernoulli.rvs(logistic(y[:, 1]-6.)).astype(bool) # for col 0
y[m1, 1] = np.float('NaN')
y[m0, 0] = np.float('NaN')
print('sample statistics (w/ missing as NaN): n={} [{:.3g} {:.3g} {:.3g} {:.3g} '
'N/A]'.format(
len(y), np.nanmean(y[:, 0]), np.nanmean(y[:, 1]),
np.nanstd(y[:, 0]), np.nanstd(y[:, 1])))
# complete cases + censored = don't contain NaN's
ycc = y[np.all(~np.isnan(y), axis=1)]
eycc = ey[np.all(~np.isnan(y), axis=1)]
cenycc = ceny[np.all(~np.isnan(y), axis=1)]
limycc = limy[np.all(~np.isnan(y), axis=1)]
print('sample statistics (without any NaNs): n={} [{:.3g} {:.3g} {:.3g} {:.3g}'
' {:.3g}]'.format(
len(ycc), np.nanmean(ycc[:, 0]), np.nanmean(ycc[:, 1]),
np.nanstd(ycc[:, 0]), np.nanstd(ycc[:, 1]), np.corrcoef(ycc.T)[0, 1]))
# complete cases + w/o censored
ycc2 = y[np.all(~np.isnan(y), axis=1) & (ceny == False)]
eycc2 = ey[np.all(~np.isnan(y), axis=1) & (ceny == False)]
cenycc2 = ceny[np.all(~np.isnan(y), axis=1) & (ceny == False)]
limycc2 = limy[np.all(~np.isnan(y), axis=1) & (ceny == False)]
print('sample statistics (complete cases]): n={} [{:.3g} {:.3g} {:.3g} {:.3g} '
'{:.3g}]'.format(
len(ycc2), np.nanmean(ycc2[:, 0]), np.nanmean(ycc2[:, 1]),
np.nanstd(ycc2[:, 0]), np.nanstd(ycc2[:, 1]), np.corrcoef(ycc.T)[0, 1]))
ncc = np.sum(np.all(~np.isnan(y), axis=1) & (ceny == False))
ncen = np.sum(ceny == True)
nic = np.sum(np.any(np.isnan(y), axis=1))
ncm = np.sum(np.all(np.isnan(y), axis=1))
print('{} total cases, {} complete, {} censored, {} incomplete, {} completely '
'missing'.format(Ndata, ncc, ncen, nic, ncm))
for irun in range(3):
if irun == 0:
print('--- Using all data (incl. missing) ---')
ly = y
ley = ey
lceny = ceny
llimy = limy
elif irun == 1:
print('--- Using only data without NaNs ---')
ly = ycc
ley = eycc
lceny = cenycc
llimy = limycc
else:
print('--- Using only complete cases ---')
ly = ycc2
ley = eycc2
lceny = cenycc2
llimy = limycc2
df = pd.DataFrame(np.array([ly[:, 0], ly[:, 1], ley[:, 0], ley[:, 1],
lceny, llimy]).T,
columns=['v0', 'v1', 'e_v0', 'e_v1', 'c_v1', 'l_v1'])
if plot_data:
import matplotlib.pyplot as plt
plt.ion()
plt.figure()
plt.errorbar(ly[:, 0], ly[:, 1], xerr=ley[:, 0], yerr=ley[:, 1],
fmt='.')
# show data with x-NaNs
sel = np.isnan(ly[:, 0])
if np.sum(sel) > 0:
for i, s in enumerate(range(np.sum(sel))):
if not s:
continue
x_r = 0.1*(np.random.rand()-0.5)
plt.plot(x_r, ly[i, 1], '.', color=[0.7, 0.7, 0.7],
markersize=3)
plt.annotate("", xy=(0.+0.15, 100), xytext=(0.-0.15, 100),
arrowprops=dict(arrowstyle="<->",
color=[0.7, 0.7, 0.7]))
# show data with y-NaNs
sel = np.isnan(ly[:, 1])
if np.sum(sel) > 0:
for i, s in enumerate(range(np.sum(sel))):
if not s:
continue
y_r = 30.*(np.random.rand()-0.5) + 250
plt.plot(ly[i, 0], y_r, '.', color=[0.7, 0.7, 0.7],
markersize=3)
plt.annotate("", xy=(2.5, 250+70), xytext=(2.5, 250-70),
arrowprops=dict(arrowstyle="<->",
color=[0.7, 0.7, 0.7]))
# show censored data
sel = np.isnan(ly[:, 0])
#plt.xlim([0.2, 3])
#plt.gca(clip_on=False)
plt.yscale('log')
if compute_maximum_likelihood:
import leopy
obs = leopy.Observation(df, 'test', verbosity=0)
## -- set up Likelihood and find maximum likelihood parameters
like = leopy.Likelihood(obs, p_true='lognorm', p_cond='norm',
verbosity=-1)
def f_mlnlike(x):
# print(x)
loc_true = x[0:2]
scale_true = x[2:4]
shape_true = x[4:6]
rho = x[6]
R = np.array([[1., rho], [rho, 1.]])
pp = like.p(loc_true, scale_true, shape_true=shape_true, R_true=R)
if np.sum(pp==0) > 0:
return np.inf
else:
return -np.sum(np.log(pp))
bounds = scipy.optimize.Bounds(
[-np.inf, -np.inf, 1e-3, 1e-3, 1e-3, 1e-3, 1e-3],
[np.inf, np.inf, 10., 10., 10., 10., 1-1e-3])
print('Maximizing likelihood - This may take a while...')
optres = scipy.optimize.minimize(f_mlnlike, [0., 0., 1., 1., 1., 1., 0.3],
bounds=bounds, method='SLSQP',
options={'disp': True, 'ftol': 1e-12})
print('Maximum likelihood parameters: [{:.3g} {:.3g} {:.3g} {:.3g} '
'{:.3g} {:.3g} {:.3g}]'.format(*optres.x))
loc_opt = optres.x[0:2]
scale_opt = optres.x[2:4]
shape_opt = optres.x[4:6]
rho_opt = optres.x[6]
mean_opt = scale_opt * np.exp(0.5*shape_opt**2) + loc_opt
std_opt = (mean_opt - loc_opt) * np.sqrt(np.exp(shape_opt**2)-1.)
print('Maximum likelihood statistics: [{:.3g} {:.3g} {:.3g} {:.3g} '
'{:.3g}]'.format(*mean_opt, *std_opt, rho_opt))
|
rfeldmannREPO_NAMEleopyPATH_START.@leopy_extracted@leopy-master@examples@example_lognorm_censored_MAR.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/box/selected/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._marker import Marker
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(__name__, [], ["._marker.Marker"])
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@box@selected@__init__.py@.PATH_END.py
|
{
"filename": "_align.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/indicator/title/_align.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class AlignValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(self, plotly_name="align", parent_name="indicator.title", **kwargs):
super(AlignValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
role=kwargs.pop("role", "info"),
values=kwargs.pop("values", ["left", "center", "right"]),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@indicator@title@_align.py@.PATH_END.py
|
{
"filename": "conf.py",
"repo_name": "ejhigson/perfectns",
"repo_path": "perfectns_extracted/perfectns-master/docs/conf.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'perfectns'
copyright = '2018-Present Edward Higson and contributors (MIT license).'
author = 'Edward Higson'
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
release = ''
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.coverage',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
'numpydoc',
'nbsphinx'
]
# nbspinx options
nbsphinx_execute = 'never' # use stored output of notebook so data not needed
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path .
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
# html_theme = 'alabaster'
html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself. Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'perfectnsdoc'
# -- Options for LaTeX output ------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'perfectns.tex', 'perfectns Documentation',
'Edward Higson', 'manual'),
]
# -- Options for manual page output ------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'perfectns', 'perfectns Documentation',
[author], 1)
]
# -- Options for Texinfo output ----------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'perfectns', 'perfectns Documentation',
author, 'perfectns', 'Perfect nested sampling.',
'Miscellaneous'),
]
# -- Extension configuration -------------------------------------------------
|
ejhigsonREPO_NAMEperfectnsPATH_START.@perfectns_extracted@perfectns-master@docs@conf.py@.PATH_END.py
|
{
"filename": "bspline.py",
"repo_name": "grzeimann/Panacea",
"repo_path": "Panacea_extracted/Panacea-master/bspline.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""Python/Numpy implementation of Bspline basis functions via Cox - de Boor algorithm."""
from __future__ import division, print_function, absolute_import
from functools import partial
import numpy as np
class memoize(object):
"""Cache the return value of a method.
This class is meant to be used as a decorator of methods. The return value
from a given method invocation will be cached on the instance whose method
was invoked. All arguments passed to a method decorated with memoize must
be hashable.
If a memoized method is invoked directly on its class the result will not
be cached. Instead the method will be invoked like a static method:
class Obj(object):
@memoize
def add_to(self, arg):
return self + arg
Obj.add_to(1) # not enough arguments
Obj.add_to(1, 2) # returns 3, result is not cached
Script borrowed from here:
MIT Licensed, attributed to Daniel Miller, Wed, 3 Nov 2010
http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
"""
def __init__(self, func):
self.func = func
def __get__(self, obj, objtype=None):
if obj is None:
return self.func
return partial(self, obj)
def __call__(self, *args, **kw):
obj = args[0]
try:
cache = obj.__cache
except AttributeError:
cache = obj.__cache = {}
key = (self.func, args[1:], frozenset(kw.items()))
try:
res = cache[key]
except KeyError:
res = cache[key] = self.func(*args, **kw)
return res
class Bspline():
"""Numpy implementation of Cox - de Boor algorithm in 1D."""
def __init__(self, knot_vector, order):
"""Create a Bspline object.
Parameters:
knot_vector: Python list or rank-1 Numpy array containing knot vector
entries
order: Order of interpolation, e.g. 0 -> piecewise constant between
knots, 1 -> piecewise linear between knots, etc.
Returns:
Bspline object, callable to evaluate basis functions at given
values of `x` inside the knot span.
"""
kv = np.atleast_1d(knot_vector)
if kv.ndim > 1:
raise ValueError("knot_vector must be Python list or rank-1 array, but got rank = %d" % (kv.ndim))
self.knot_vector = kv
order = int(order)
if order < 0:
raise ValueError("order must be integer >= 0, but got %d" % (order))
self.p = order
#Dummy calls to the functions for memory storage
self.__call__(0.0)
self.d(0.0)
def __basis0(self, xi):
"""Order zero basis (for internal use)."""
return np.where(np.all([self.knot_vector[:-1] <= xi,
xi < self.knot_vector[1:]],axis=0), 1.0, 0.0)
def __basis(self, xi, p, compute_derivatives=False):
"""Recursive Cox - de Boor function (for internal use).
Compute basis functions and optionally their first derivatives.
"""
if p == 0:
return self.__basis0(xi)
else:
basis_p_minus_1 = self.__basis(xi, p - 1)
first_term_numerator = xi - self.knot_vector[:-p]
first_term_denominator = self.knot_vector[p:] - self.knot_vector[:-p]
second_term_numerator = self.knot_vector[(p + 1):] - xi
second_term_denominator = (self.knot_vector[(p + 1):] -
self.knot_vector[1:-p])
#Change numerator in last recursion if derivatives are desired
if compute_derivatives and p == self.p:
first_term_numerator = p
second_term_numerator = -p
#Disable divide by zero error because we check for it
with np.errstate(divide='ignore', invalid='ignore'):
first_term = np.where(first_term_denominator != 0.0,
(first_term_numerator /
first_term_denominator), 0.0)
second_term = np.where(second_term_denominator != 0.0,
(second_term_numerator /
second_term_denominator), 0.0)
return (first_term[:-1] * basis_p_minus_1[:-1] +
second_term * basis_p_minus_1[1:])
@memoize
def __call__(self, xi):
"""Convenience function to make the object callable. Also 'memoized' for speed."""
return self.__basis(xi, self.p, compute_derivatives=False)
@memoize
def d(self, xi):
"""Convenience function to compute first derivative of basis functions. 'Memoized' for speed."""
return self.__basis(xi, self.p, compute_derivatives=True)
def plot(self):
"""Plot basis functions over full range of knots.
Convenience function. Requires matplotlib.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
from sys import stderr
print("ERROR: matplotlib.pyplot not found, matplotlib must be installed to use this function", file=stderr)
raise
x_min = np.min(self.knot_vector)
x_max = np.max(self.knot_vector)
x = np.linspace(x_min, x_max, num=1000)
N = np.array([self(i) for i in x]).T
for n in N:
plt.plot(x,n)
return plt.show()
def dplot(self):
"""Plot first derivatives of basis functions over full range of knots.
Convenience function. Requires matplotlib.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
from sys import stderr
print("ERROR: matplotlib.pyplot not found, matplotlib must be installed to use this function", file=stderr)
raise
x_min = np.min(self.knot_vector)
x_max = np.max(self.knot_vector)
x = np.linspace(x_min, x_max, num=1000)
N = np.array([self.d(i) for i in x]).T
for n in N:
plt.plot(x,n)
return plt.show()
def __diff_internal(self):
"""Differentiate a B-spline once, and return the resulting coefficients and Bspline objects.
This preserves the Bspline object nature of the data, enabling recursive implementation
of higher-order differentiation (see `diff`).
The value of the first derivative of `B` at a point `x` can be obtained as::
def diff1(B, x):
terms = B.__diff_internal()
return sum( ci*Bi(x) for ci,Bi in terms )
Returns:
tuple of tuples, where each item is (coefficient, Bspline object).
See:
`diff`: differentiation of any order >= 0
"""
assert self.p > 0, "order of Bspline must be > 0" # we already handle the other case in diff()
# https://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-derv.html
#
t = self.knot_vector
p = self.p
Bi = Bspline( t[:-1], p-1 )
Bip1 = Bspline( t[1:], p-1 )
numer1 = +p
numer2 = -p
denom1 = t[p:-1] - t[:-(p+1)]
denom2 = t[(p+1):] - t[1:-p]
with np.errstate(divide='ignore', invalid='ignore'):
ci = np.where(denom1 != 0., (numer1 / denom1), 0.)
cip1 = np.where(denom2 != 0., (numer2 / denom2), 0.)
return ( (ci,Bi), (cip1,Bip1) )
def diff(self, order=1):
"""Differentiate a B-spline `order` number of times.
Parameters:
order:
int, >= 0
Returns:
**lambda** `x`: ... that evaluates the `order`-th derivative of `B` at the point `x`.
The returned function internally uses __call__, which is 'memoized' for speed.
"""
order = int(order)
if order < 0:
raise ValueError("order must be >= 0, got %d" % (order))
if order == 0:
return self.__call__
if order > self.p: # identically zero, but force the same output format as in the general case
dummy = self.__call__(0.) # get number of basis functions and output dtype
nbasis = dummy.shape[0]
return lambda x: np.zeros( (nbasis,), dtype=dummy.dtype ) # accept but ignore input x
# At each differentiation, each term maps into two new terms.
# The number of terms in the result will be 2**order.
#
# This will cause an exponential explosion in the number of terms for high derivative orders,
# but for the first few orders (practical usage; >3 is rarely needed) the approach works.
#
terms = [ (1.,self) ]
for k in range(order):
tmp = []
for Ci,Bi in terms:
tmp.extend( (Ci*cn, Bn) for cn,Bn in Bi.__diff_internal() ) # NOTE: also propagate Ci
terms = tmp
# perform final summation at call time
return lambda x: sum( ci*Bi(x) for ci,Bi in terms )
def collmat(self, tau, deriv_order=0):
"""Compute collocation matrix.
Parameters:
tau:
Python list or rank-1 array, collocation sites
deriv_order:
int, >=0, order of derivative for which to compute the collocation matrix.
The default is 0, which means the function value itself.
Returns:
A:
if len(tau) > 1, rank-2 array such that
A[i,j] = D**deriv_order B_j(tau[i])
where
D**k = kth derivative (0 for function value itself)
if len(tau) == 1, rank-1 array such that
A[j] = D**deriv_order B_j(tau)
Example:
If the coefficients of a spline function are given in the vector c, then::
np.sum( A*c, axis=-1 )
will give a rank-1 array of function values at the sites tau[i] that were supplied
to `collmat`.
Similarly for derivatives (if the supplied `deriv_order`> 0).
"""
# get number of basis functions and output dtype
dummy = self.__call__(0.)
nbasis = dummy.shape[0]
tau = np.atleast_1d(tau)
if tau.ndim > 1:
raise ValueError("tau must be a list or a rank-1 array")
A = np.empty( (tau.shape[0], nbasis), dtype=dummy.dtype )
f = self.diff(order=deriv_order)
for i,taui in enumerate(tau):
A[i,:] = f(taui)
return np.squeeze(A)
|
grzeimannREPO_NAMEPanaceaPATH_START.@Panacea_extracted@Panacea-master@bspline.py@.PATH_END.py
|
{
"filename": "histogram_ops_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/kernel_tests/histogram_ops_test.py",
"type": "Python"
}
|
# Copyright 2015 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.histogram_ops."""
from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import test_util
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import histogram_ops
from tensorflow.python.platform import test
class BinValuesFixedWidth(test.TestCase, parameterized.TestCase):
def test_empty_input_gives_all_zero_counts(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = []
expected_bins = []
with self.cached_session():
bins = histogram_ops.histogram_fixed_width_bins(
values, value_range, nbins=5)
self.assertEqual(dtypes.int32, bins.dtype)
self.assertAllClose(expected_bins, self.evaluate(bins))
@parameterized.parameters(
np.float32, np.float64, dtypes.bfloat16.as_numpy_dtype
)
def test_1d_values_int32_output(self, dtype):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = np.array([0.0, 5.0]).astype(dtype)
values = np.array([-1.0, 0.0, 1.5, 2.0, 5.0, 15]).astype(dtype)
expected_bins = [0, 0, 1, 2, 4, 4]
with self.cached_session():
bins = histogram_ops.histogram_fixed_width_bins(
values, value_range, nbins=5)
self.assertEqual(dtypes.int32, bins.dtype)
self.assertAllClose(expected_bins, self.evaluate(bins))
def test_2d_values(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = constant_op.constant(
[[-1.0, 0.0, 1.5], [2.0, 5.0, 15]], shape=(2, 3))
expected_bins = [[0, 0, 1], [2, 4, 4]]
with self.cached_session():
bins = histogram_ops.histogram_fixed_width_bins(
values, value_range, nbins=5)
self.assertEqual(dtypes.int32, bins.dtype)
self.assertAllClose(expected_bins, self.evaluate(bins))
def test_negative_nbins(self):
value_range = [0.0, 5.0]
values = []
with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
"must > 0"):
with self.session():
bins = histogram_ops.histogram_fixed_width_bins(
values, value_range, nbins=-1)
self.evaluate(bins)
class HistogramFixedWidthTest(test.TestCase):
def setUp(self):
self.rng = np.random.RandomState(0)
def test_with_invalid_value_range(self):
values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with self.assertRaisesRegex(
(errors.InvalidArgumentError, ValueError),
"Shape must be rank 1 but is rank 0|should be a vector"):
self.evaluate(histogram_ops.histogram_fixed_width(values, 1.0))
with self.assertRaisesRegex(
(errors.InvalidArgumentError, ValueError),
"Dimension must be 2 but is 3|should be a vector of 2 elements"):
self.evaluate(
histogram_ops.histogram_fixed_width(values, [1.0, 2.0, 3.0]))
def test_with_invalid_nbins(self):
values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with self.assertRaisesRegex(
(errors.InvalidArgumentError, ValueError),
"Shape must be rank 0 but is rank 1|should be a scalar"):
self.evaluate(
histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=[1, 2]))
with self.assertRaisesRegex(
(errors.InvalidArgumentError, ValueError),
"Requires nbins > 0|should be a positive number"):
self.evaluate(
histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=-5))
def test_empty_input_gives_all_zero_counts(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = []
expected_bin_counts = [0, 0, 0, 0, 0]
hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5)
self.assertEqual(dtypes.int32, hist.dtype)
self.assertAllClose(expected_bin_counts, self.evaluate(hist))
def test_1d_values_int64_output(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
expected_bin_counts = [2, 1, 1, 0, 2]
hist = histogram_ops.histogram_fixed_width(
values, value_range, nbins=5, dtype=dtypes.int64)
self.assertEqual(dtypes.int64, hist.dtype)
self.assertAllClose(expected_bin_counts, self.evaluate(hist))
def test_1d_float64_values(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = np.float64([0.0, 5.0])
values = np.float64([-1.0, 0.0, 1.5, 2.0, 5.0, 15])
expected_bin_counts = [2, 1, 1, 0, 2]
hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5)
self.assertEqual(dtypes.int32, hist.dtype)
self.assertAllClose(expected_bin_counts, self.evaluate(hist))
def test_2d_values(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]]
expected_bin_counts = [2, 1, 1, 0, 2]
hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5)
self.assertEqual(dtypes.int32, hist.dtype)
self.assertAllClose(expected_bin_counts, self.evaluate(hist))
@test_util.run_deprecated_v1
def test_shape_inference(self):
value_range = [0.0, 5.0]
values = [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]]
expected_bin_counts = [2, 1, 1, 0, 2]
placeholder = array_ops.placeholder(dtypes.int32)
with self.session():
hist = histogram_ops.histogram_fixed_width(values, value_range, nbins=5)
self.assertAllEqual(hist.shape.as_list(), (5,))
self.assertEqual(dtypes.int32, hist.dtype)
self.assertAllClose(expected_bin_counts, self.evaluate(hist))
hist = histogram_ops.histogram_fixed_width(
values, value_range, nbins=placeholder)
self.assertEqual(hist.shape.ndims, 1)
self.assertIs(hist.shape.dims[0].value, None)
self.assertEqual(dtypes.int32, hist.dtype)
self.assertAllClose(expected_bin_counts, hist.eval({placeholder: 5}))
def test_single_bin(self):
hist = histogram_ops.histogram_fixed_width(
values=constant_op.constant([3e+38, 100], dtype=dtypes.float32),
value_range=constant_op.constant([-1e+38, 3e+38]),
nbins=1)
self.assertAllEqual(hist, [2])
def test_range_overflow(self):
hist = histogram_ops.histogram_fixed_width(
values=constant_op.constant([3e+38, 100], dtype=dtypes.float32),
value_range=constant_op.constant([-1e+38, 3e+38]),
nbins=2)
self.assertAllEqual(hist, [1, 1])
def test_large_range(self):
hist = histogram_ops.histogram_fixed_width(
values=constant_op.constant(
[-(2**31), 2**31 - 1], dtype=dtypes.int32
),
value_range=constant_op.constant(
[-(2**31), 2**31 - 1], dtype=dtypes.int32
),
nbins=2,
)
self.assertAllEqual(hist, [1, 1])
if __name__ == '__main__':
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@kernel_tests@histogram_ops_test.py@.PATH_END.py
|
{
"filename": "radialfun.py",
"repo_name": "dsavransky/EXOSIMS",
"repo_path": "EXOSIMS_extracted/EXOSIMS-master/EXOSIMS/util/radialfun.py",
"type": "Python"
}
|
"""
Utilities for radial computations on rectangular data arrays
"""
import numpy as np
from scipy.interpolate import RectBivariateSpline
from scipy import optimize
def pixel_dists(dims, center):
"""
Compute pixel distances from center of an image
Args:
dims (tuple(float,float)):
Image dimensions
center (list(float,float)):
[x,y] pixel coordinates of center
Returns:
numpy.ndarray:
Array of dimension dims with distance from center of each pixel
"""
y, x = np.indices(dims)
r = np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2)
return r
def radial_average(im, center=None, nbins=None):
"""
Compute radial average on an image
Args:
im (numpy.ndarray):
The input image. Must be 2-dimensional
center (list(float,float), optional):
[x,y] pixel coordinates of center to compute average about.
If None (default) use geometric center of input.
nbins (int, optional):
Number of bins to compute average in. If None (default) then set to
floor(N/2) where N is the maximum dimension of the input image.
Returns:
tuple:
means (numpy.ndarray):
``nbins`` element array with radial average values
bins (numpy.nadarray):
``nbins+1`` element array with bin boundaries.
bincents (numpy.ndarray):
``nbins`` elements array with bin midpoints. Equivalent to
``(bins[1:] + bins[:-1]) / 2``
"""
# gather info
dims = im.shape
assert len(dims) == 2, "Only 2D images are supported."
if center is None:
center = [dims[0] / 2.0, dims[1] / 2.0]
if nbins is None:
nbins = int(np.floor(np.max(dims) / 2))
# compute pixel distances from center
r = pixel_dists(dims, center)
# define bins and digitize distanes
bins = np.linspace(0, np.max(r), nbins + 1)
bincents = (bins[1:] + bins[:-1]) / 2
inds = np.digitize(r.ravel(), bins)
# max value will be in its own bin, so put all matching pixels in the last valid bin
inds[inds == nbins + 1] = nbins
# compute means in each bin
means = np.zeros(nbins)
imflat = im.ravel()
for j in range(1, nbins + 1):
means[j - 1] = np.nanmean(imflat[inds == j])
return means, bins, bincents
def circ_aperture(im, rho, center, return_sum=False):
"""
Extract pixels in circular aperture
Args:
im (numpy.ndarray):
The input image. Must be 2-dimensional
rho (float):
Radius of aperture (in pixels)
center (list(float,float):
[x,y] pixel coordinates of center of aperture
return_sum (bool):
Return sum. Defaults False: returns all pixels in aperture.
Returns:
numpy.ndarray:
1-dimensional array of pixel values inside aperture
"""
# compute pixel distances from center
r = pixel_dists(im.shape, center)
pix = im[r <= rho]
if return_sum:
out = np.sum(pix[np.isfinite(pix)])
else:
out = pix
return out
def com(im0, fill_val=0):
"""
Find the center-of-mass centroid of an image
Args:
im0 (numpy.ndarray):
The input image. Must be 2-dimensional
fill_val (float):
Replace any non finite values in the image with this value before
resampling. Defaults to zero.
Returns:
list:
[x,y] pixel coordinates of COM
"""
# operate on input copy and zero out bad values
im = im0.copy()
im[~np.isfinite(im)] = fill_val
y, x = np.indices(im.shape)
return [np.sum(im * inds) / np.sum(im) for inds in [x, y]]
def genwindow(dims):
"""
Create a 2D Hann window of the given dimensions
Args:
dims (tuple or list):
2-element dimensions of window (can be the .shape output of an ndarray)
Returns:
numpy.ndarray:
Window of dimensions ``dims``.
"""
window = np.ones(dims)
y, x = np.indices(dims)
for dim, inds in zip(dims, [x, y]):
window *= 0.5 - 0.5 * np.cos(2 * np.pi * inds / dim)
window = np.sqrt(window)
return window
def resample_image(im, resamp=2, fill_val=0):
"""
Create a resampled image
Args:
im (numpy.ndarray):
The input image. Must be 2-dimensional
resamp (float):
Resampling factor. Must be >=1. Defaults to 2
fill_val (float):
Replace any non finite values in the image with this value before
resampling. Defaults to zero.
Returns:
numpy.ndarray:
Resampled image.
"""
assert resamp >= 1, "resamp must be >= 1"
if resamp == 1:
return im
dims = im.shape
newdims = [(d - 1) * resamp + 1 for d in dims]
imc = im.copy()
imc[~np.isfinite(imc)] = fill_val
sp = RectBivariateSpline(np.arange(dims[0]), np.arange(dims[0]), imc)
imresamp = sp(np.arange(newdims[0]) / resamp, np.arange(newdims[1]) / resamp)
return imresamp
def gaussian(a, x0, y0, sx, sy):
"""Gaussian function
Args:
a (float):
Amplitude
x0 (float):
Center (mean) x position
y0 (float):
Center (mean) y position
sx (float):
Standard deviation in x
sy (float):
Standard deviation in y
Returns:
lambda:
Callable lambda function with input x,y returning value of Gaussian at those
coordinates
"""
return lambda x, y: a * np.exp(
-((x - x0) ** 2) / 2 / sx**2 - (y - y0) ** 2 / 2 / sy**2
)
def fitgaussian(im):
"""Fit a 2D Gaussian to data
Args:
im (numpy.ndarray):
2D data array
Returns:
tuple:
a (float):
Amplitude
x0 (float):
Center (mean) x position
y0 (float):
Center (mean) y position
sx (float):
Standard deviation in x
xy (float):
Standard deviation in y
"""
Y, X = np.indices(im.shape)
total = im.sum()
x0 = (X * im).sum() / total
y0 = (Y * im).sum() / total
col = im[:, int(x0)]
sx0 = np.sqrt(np.abs((np.arange(col.size) - x0) ** 2 * col).sum() / col.sum() / 2)
row = im[int(y0), :]
sy0 = np.sqrt(np.abs((np.arange(row.size) - y0) ** 2 * row).sum() / row.sum() / 2)
a0 = im.max()
errorfunction = lambda p: np.ravel(gaussian(*p)(X, Y) - im)
p, success = optimize.leastsq(errorfunction, (a0, x0, y0, sx0, sy0))
return p
|
dsavranskyREPO_NAMEEXOSIMSPATH_START.@EXOSIMS_extracted@EXOSIMS-master@EXOSIMS@util@radialfun.py@.PATH_END.py
|
{
"filename": "_bordercolor.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatterpolargl/marker/colorbar/_bordercolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BordercolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self,
plotly_name="bordercolor",
parent_name="scatterpolargl.marker.colorbar",
**kwargs,
):
super(BordercolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatterpolargl@marker@colorbar@_bordercolor.py@.PATH_END.py
|
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