repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/plot.py | import numpy as np
import matplotlib.pyplot as plt
from V_plot import *
from u_plot import *
from plot_trajectory import *
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 15
A = torch.load('./data/hyper_b/data.pt')[:,9:14,... | 2,394 | 26.848837 | 106 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/plot_loss.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import pylustrator
pylustrator.start()
import seaborn as sns
sns.set_theme(style="whitegrid")
L1 = torch.load('./data/harmonic/loss_icnn.pt')[2:] # delete large first tow numbers
L2 = torch.load('./data/harmonic/loss_quad.pt')
L3 = torch.load('./data/har... | 3,889 | 45.86747 | 181 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/AS.py | import torch
import torch.nn.functional as F
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_hidden,n_hidd... | 2,766 | 26.39604 | 143 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/generate.py | import numpy as np
import math
import torch
import numpy as np
import timeit
from AS import *
from Control_Nonlinear_Icnn import *
start = timeit.default_timer()
# Harmonic linear oscillator
model = Net(D_in,H1,D_out)
# Generate trajectory with nonlinaer AS control
def algo2(z,X,N,dt):
model = Net(D_in,H1,D_out... | 2,835 | 33.585366 | 113 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/ES_ICNN.py | import torch
import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
from Control_Nonlinear_Icnn import *
# Drift function
def harmonic(x):
y = []
beta = 0.5
for i in range(0,len(x)):
f = [x[i,1],-x[i,0]-2*beta*x[i,1]]
y.append(f)
y = to... | 2,972 | 26.527778 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/ES_Quadratic.py | import torch
import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hid... | 3,376 | 26.016 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/plot.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
matplotlib.rcParams['text.usetex'] = True
import sys
sys.path.append('./data/harmonic')
'''
Data is dictionary {'X'，'Y'，'Z'，'W'}，corresponds to 20 sample trajectories unde... | 11,933 | 39.317568 | 114 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/table1.py | import numpy as np
import torch
data = torch.load('./data/harmonic/data_long.pt')
# Calculate the data in table1
def L2_norm(st,a):
Y = data[st][torch.tensor([0,1,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19]),:,:]
Y = Y.detach().numpy()
X = np.linalg.norm(Y,axis=2)
Z = np.mean(X,0)
index = np.where(Z... | 513 | 24.7 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/Control_Nonlinear_Icnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ICNN(nn.Module):
def __init__(self, input_shape, layer_sizes, activation_fn):
super(ICNN, self).__init__()
self._input_shape = input_shape
self._layer_sizes = layer_sizes
self._activation_fn = activation_fn
... | 3,754 | 34.424528 | 122 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/functions.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
import matplotlib.pyplot as plt
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6, 0.2], # olive
# [0.5333333333333333, 0.13333333333333333, 0.3333333333333333], # wine
[255/255, 165/255, 0],
... | 2,097 | 32.301587 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/run.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
from functions import *
def f(x,u=0):
a, b, c = 1, 1, 1
U2 = np.array([0.5, 0.74645887, 1.05370735, 0.38154169, 1.68833014, 0.83746371])
x1, x2, x3, x4, ... | 4,155 | 29.335766 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/NODE.py | # import sys
# sys.path.append('./neural_sde/NODE')
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--method', type=str, choices=['dopri5', 'adams'], default='dopri5')
parser.add_argument('... | 4,670 | 31.213793 | 118 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/NSC_train.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=1000)
parser.add_argument('--num', type=float, default=6)
parser.add_argument('--lr', type=float, default=0.05)
args = parser.parse_a... | 4,004 | 31.04 | 153 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/AS.py | import torch
import torch.nn.functional as F
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_h... | 1,716 | 21.012821 | 72 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/functions.py | import numpy as np
import math
import torch
import timeit
from scipy import integrate
import matplotlib.pyplot as plt
start = timeit.default_timer()
np.random.seed(1)
class Net(torch.nn.Module):
def __init__(self, n_input, n_hidden, n_output):
super(Net, self).__init__()
torch.manual_seed(2)
... | 6,281 | 26.432314 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/plot_appendix.py | import numpy as np
import matplotlib.pyplot as plt
import torch
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdot', lw=1.5)
# minor gr... | 2,306 | 30.60274 | 102 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/functions.py | import numpy as np
from scipy import integrate
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6, 0.2], # olive
# [0.5333333333333333, 0.13333333333333333, 0.3333333... | 2,307 | 31.507042 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/run.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
from functions import *
from cvxopt import solvers,matrix
def f(x,u=0):
u,v = x
G = 9.81 # gravity
L = 0.5 # length of the pole
m = 0.15 # ball m... | 4,956 | 29.598765 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/NSC_train.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=1000)
parser.add_argument('--num', type=float, default=2)
parser.add_argument('--lr', type=float, default=0.05)
args = parser.parse_a... | 3,379 | 28.137931 | 153 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/comparison/lqr.py | import numpy as np
from cvxopt import solvers,matrix
import matplotlib.pyplot as plt
import torch
def harmonic(n,dt):
x0 = np.array([2.0,2.0])
X = np.zeros([n,2])
X[0,:]=x0
z = np.random.normal(0, 1, n)
for i in range(n-1):
x1,x2 = X[i,:]
X[i+1,0] = x1 + (x2-4.45*x1-0.09*x2)*dt
... | 662 | 21.1 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/comparison/riccati.py | import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
#由题目定义矩阵
A = np.matrix([[0, 1.0], [-1.0, -1.0]])
AT = np.matrix([[0,-1.0], [1, -1.0]])
B = np.matrix([[1.0,0.0], [0.0,1.0]])
BT = np.matrix([[1.0,0.0], [0.0,1.0]])
F = np.matrix([[1, 0], [0, 2]])
Q = np.matrix([[20.0, 0.0], [0.0, 20.0]])
R =... | 1,075 | 22.911111 | 62 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/comparison/run.py | import numpy as np
from cvxopt import solvers,matrix
import matplotlib.pyplot as plt
import torch
import seaborn as sns
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet,self).__init__()
torch.manual_seed(2)
self.layer1=torch.nn.Linear(n_inp... | 10,101 | 40.572016 | 116 | py |
gsdmm | gsdmm-master/setup.py | from setuptools import setup
VERSION=0.1
INSTALL_REQUIRES = [
'numpy'
]
setup(
name='gsdmm',
packages=['gsdmm'],
version=0.1,
url='https://www.github.com/rwalk/gsdmm',
author='Ryan Walker',
author_email='[email protected]',
description='GSDMM: Short text clustering ',
license='MIT... | 363 | 18.157895 | 48 | py |
gsdmm | gsdmm-master/gsdmm/mgp.py | from numpy.random import multinomial
from numpy import log, exp
from numpy import argmax
import json
class MovieGroupProcess:
def __init__(self, K=8, alpha=0.1, beta=0.1, n_iters=30):
'''
A MovieGroupProcess is a conceptual model introduced by Yin and Wang 2014 to
describe their Gibbs sampl... | 7,818 | 37.141463 | 115 | py |
gsdmm | gsdmm-master/gsdmm/__init__.py | from .mgp import MovieGroupProcess | 34 | 34 | 34 | py |
gsdmm | gsdmm-master/test/__init__.py | 0 | 0 | 0 | py | |
gsdmm | gsdmm-master/test/test_gsdmm.py | from unittest import TestCase
from gsdmm.mgp import MovieGroupProcess
import numpy
class TestGSDMM(TestCase):
'''This class tests the Panel data structures needed to support the RSK model'''
def setUp(self):
numpy.random.seed(47)
def tearDown(self):
numpy.random.seed(None)
def comput... | 2,638 | 24.621359 | 94 | py |
MixLacune | MixLacune-main/process-lacunes.py | # -*- coding: utf-8 -*-
import os
import torch
import torchvision
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import SimpleITK as sitk
import glob
import torch.nn as nn
import nibabel as nib
import shutil
device = torch.device('cuda' if torch.cud... | 21,262 | 36.173077 | 155 | py |
MixLacune | MixLacune-main/process.py |
import SimpleITK
import numpy as np
from evalutils import SegmentationAlgorithm
from evalutils.validators import UniqueImagesValidator
# added imports
#import tensorflow as tf
from typing import Tuple, List
from pathlib import Path
import re
import subprocess
from evalutils.io import (ImageLoader, SimpleITKLoader)... | 5,806 | 40.776978 | 121 | py |
slfrank | slfrank-master/tests/test_linop.py | import unittest
import numpy as np
import numpy.testing as npt
from linop import DiagSum
if __name__ == '__main__':
unittest.main()
class TestLinop(unittest.TestCase):
def test_DiagSum(self):
X = np.array([[1, 2], [3, 4]])
A = DiagSum(2)
npt.assert_allclose(A(X), [3, 5, 2])
| 311 | 18.5 | 44 | py |
slfrank | slfrank-master/slfrank/design.py | import numpy as np
import cvxpy as cp
import sigpy as sp
import sigpy.mri.rf as rf
import scipy.sparse
from . import linop, prox, transform
def design_rf(n=64, tb=4, ptype='ex', d1=0.01, d2=0.01, phase='linear',
oversamp=15, lamda=None, solver='PDHG',
max_iter=None, sigma=None, verbose=Tru... | 13,848 | 35.253927 | 152 | py |
slfrank | slfrank-master/slfrank/linop.py | import sigpy as sp
import numpy as np
import numba as nb
class DiagSum(sp.linop.Linop):
"""A Linop that sums along the diagonals of a matrix.
Args:
n (int): width of matrix.
"""
def __init__(self, n):
self.n = n
super().__init__((2 * n - 1, ), (n, n))
def _apply(self... | 1,294 | 18.923077 | 68 | py |
slfrank | slfrank-master/slfrank/transform.py | import numpy as np
def hard_pulse(a, b, b1):
"""Apply hard pulse rotation to input magnetization.
Args:
theta (complex float): complex B1 value in radian.
Returns:
array: magnetization array after hard pulse rotation,
in representation consistent with input.
"""
c = ... | 3,163 | 23.71875 | 70 | py |
slfrank | slfrank-master/slfrank/plot.py | import numpy as np
import matplotlib.pyplot as plt
import sigpy.mri.rf as rf
from . import transform
from . import design
def plot_slr_pulses(pulse_slr, pulse_slfrank,
m=1000, ptype='ex', phase='linear',
omega_range=[-np.pi, np.pi],
tb=4, d1=0.01, d2=0.01,
... | 6,895 | 40.293413 | 94 | py |
slfrank | slfrank-master/slfrank/__init__.py | from .design import *
from .linop import *
from .prox import *
from .transform import *
from .plot import *
| 108 | 17.166667 | 24 | py |
slfrank | slfrank-master/slfrank/prox.py | import sigpy as sp
class Objective(sp.prox.Prox):
def __init__(self, shape, lamda):
self.lamda = lamda
super().__init__(shape)
def _prox(self, alpha, input):
xp = sp.get_array_module(input)
n = (len(input) - 1) // 2
output = input.copy()
output[1, 0] += alpha
... | 468 | 22.45 | 46 | py |
stistools | stistools-master/stistools/r_util.py | import os
import os.path
import copy
NOT_APPLICABLE = 'n/a'
def expandFileName(filename):
"""Expand environment variable in a file name.
If the input file name begins with either a Unix-style or IRAF-style
environment variable (e.g. $lref/name_dqi.fits or lref$name_dqi.fits
respectively), this routi... | 2,505 | 27.804598 | 78 | py |
stistools | stistools-master/stistools/basic2d.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
from stsci.tools import parseinput, teal
__doc__ = """
Perform basic 2-D calibration of STIS data.
Examples
--------
In Python without TEAL:
>>> import stistools
>>> stistools.basic2d.basic2d("o66p01020_raw.fits", verbose=Tru... | 12,134 | 30.437824 | 76 | py |
stistools | stistools-master/stistools/calstis.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
from stsci.tools import parseinput, teal
__doc__ = """
Calibrate STIS data.
The input raw files should be in the default directory. This is not
always necessary, but it will always work. For spectroscopic data, if
a path is sp... | 7,287 | 27.46875 | 76 | py |
stistools | stistools-master/stistools/tastis.py | #! /usr/bin/env python
from math import modf, sqrt
import os
import argparse
from astropy.io import fits
import numpy as np
__doc__ = """
Analyze STIS target acquisition images. :func:`tastis` will print general
information about each input target acquisition image, and will analyze both
types of STIS target acquisi... | 37,270 | 39.119483 | 90 | py |
stistools | stistools-master/stistools/inttag.py | #! /usr/bin/env python
import numpy as np
from astropy.io import fits
import astropy.stats
from astropy import units as u
from astropy.time import Time
from datetime import datetime as dt
__doc__ = """
The task :func:`inttag` converts an events table of TIME-TAG mode STIS data into a raw, time-integrated ACCUM
image.... | 16,741 | 39.148681 | 117 | py |
stistools | stistools-master/stistools/stisnoise.py | #!/usr/bin/env python
import math
from astropy.io import fits
import numpy
import numpy.fft as fft
from scipy import ndimage
from scipy import signal
__version__ = '5.6 (2016-Mar-02)'
def _median(arg):
return numpy.sort(arg)[arg.shape[0]//2]
def medianfilter(time_series, width):
tlen = time_series.shape[... | 12,097 | 36.80625 | 79 | py |
stistools | stistools-master/stistools/ctestis.py | from astropy.io import fits
import numpy as np
__doc__ = """
The purpose of this ctestis task is to correct signal levels of point-like
sources in photometry tables measured from STIS CCD images for charge loss
due to imperfect Charge Transfer Efficiency (CTE). The algorithm used to
correct for CTE-induced signal loss... | 10,161 | 35.163701 | 82 | py |
stistools | stistools-master/stistools/observation.py | from astropy.io import fits
def initObservation(input, instrument, sci_num):
"""Construct an Observation object for the current mode.
Parameters
----------
input: str
The name of an input file.
instrument: str
Value of keyword INSTRUME, should be "COS" or "STIS"
Returns
... | 2,082 | 24.096386 | 71 | py |
stistools | stistools-master/stistools/gettable.py | import math
import numpy as np
from astropy.io import fits
STRING_WILDCARD = "ANY"
INT_WILDCARD = -1
def getTable(table, filter, sortcol=None,
exactly_one=False, at_least_one=False):
"""Return row(s) of a table that match the filter.
Rows that match every item in the filter (a dictionary of
... | 5,092 | 30.245399 | 76 | py |
stistools | stistools-master/stistools/wavecal.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
import numpy.random as rn # used by mkRandomName
from astropy.io import fits
from stsci.tools import parseinput, teal
"""
Perform wavelength calibration of STIS data.
Examples
--------
In Python without TEAL:
>>... | 17,785 | 30.817531 | 79 | py |
stistools | stistools-master/stistools/doppinfo.py | #! /usr/bin/env python
import sys
import math
import numpy as np
from astropy.io import fits
from . import observation
from . import orbit
__doc__ = """
This class computes Doppler shift information for each imset of a dataset.
Keywords will be read from the science file and from the support file. The
Doppler shift... | 20,721 | 34.422222 | 87 | py |
stistools | stistools-master/stistools/x1d.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
from stsci.tools import parseinput, teal
__doc__ = """
Extract 1-D spectrum.
Examples
--------
In Python without TEAL:
>>> import stistools
>>> stistools.x1d.x1d("o66p01020_flt.fits", output="test_x1d.fits",
... ... | 13,036 | 28.697039 | 88 | py |
stistools | stistools-master/stistools/ocrreject.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
from stsci.tools import parseinput, teal
__doc__ = """
Add STIS exposures, rejecting cosmic rays.
Examples
--------
In Python without TEAL:
>>> import stistools
>>> stistools.ocrreject.ocrreject("o3tt02020_flt.fits",
... ... | 11,774 | 30.483957 | 77 | py |
stistools | stistools-master/stistools/__init__.py | from __future__ import absolute_import
from .version import *
from . import calstis
from . import basic2d
from . import ocrreject
from . import wavecal
from . import x1d
from . import x2d
from . import mktrace
from . import sshift
from . import stisnoise
from . import wx2d
from . import inttag
from . import doppinfo
f... | 583 | 23.333333 | 69 | py |
stistools | stistools-master/stistools/radialvel.py | import numpy as N
DEG_RAD = N.pi / 180. # degrees to radians
ARCSEC_RAD = N.pi / (180.*3600.) # arcseconds to radians
REFDATE = 51544.5 # MJD for 2000 Jan 1, 12h UT
KM_AU = 1.4959787e8 # kilometers per astronomical unit
SEC_DAY = 86400. # seconds per d... | 7,261 | 30.034188 | 80 | py |
stistools | stistools-master/stistools/x2d.py | #! /usr/bin/env python
import os
import sys
import getopt
import glob
import subprocess
from stsci.tools import parseinput, teal
__doc__ = """
Rectify 2-D STIS spectral data.
Examples
--------
In Python without TEAL:
>>> import stistools
>>> stistools.x2d.x2d("o66p01020_flt.fits", output="test_x2d.fits",
... ... | 9,046 | 28.469055 | 81 | py |
stistools | stistools-master/stistools/wx2d.py | #! /usr/bin/env python
import sys
import os
import os.path
import math
import numpy as N
from scipy import signal as convolve
from astropy.io import fits
from . import gettable
from . import wavelen
from . import r_util
__version__ = "1.3 (2016 Feb 24)"
def wx2d(input, output, wavelengths=None, helcorr="",
... | 33,439 | 31.720157 | 88 | py |
stistools | stistools-master/stistools/mktrace.py | #!/usr/bin/env python
import numpy as np
from astropy.io import fits
import os.path
from scipy import signal
from scipy import ndimage as ni
from stsci.tools import gfit, linefit
from stsci.tools import fileutil as fu
__doc__ = """
Refine a STIS trace table.
- A trace is generated from the science file and a trace
... | 13,184 | 30.694712 | 101 | py |
stistools | stistools-master/stistools/add_stis_s_region.py | #!/usr/bin/env python
import glob
import math
import os
import sys
import argparse
from astropy.io import fits
from astropy.wcs import WCS
import numpy as np
import logging
import pysiaf
__doc__ = """
This script will calculate an S_REGION string for STIS data and assign it
to the S_REGION keyword in the science d... | 18,923 | 37.938272 | 112 | py |
stistools | stistools-master/stistools/wavelen.py |
import numpy as N
from . import evaldisp
from . import gettable
from . import radialvel
from . import r_util
DEG_RAD = N.pi / 180. # degrees to radians
SPEED_OF_LIGHT = 299792.458 # km / s
def compute_wavelengths(shape, phdr, hdr, helcorr):
"""Compute a 2-D array of wavelengths, o... | 7,760 | 34.438356 | 78 | py |
stistools | stistools-master/stistools/sshift.py | #!/usr/bin/env python
"""
A Python module for aligning the spectra in different flat-fielded
images of an IMSET. These files can then be combined with
along-the-slit dithering to reject hot pixels and cosmic rays. The
POSTARG2 keyword is used to determine the number of rows to be
shifted.
"""
from astropy.io import ... | 9,359 | 34.589354 | 93 | py |
stistools | stistools-master/stistools/evaldisp.py |
def newton(x, coeff, cenwave, niter=4):
"""Return the wavelength corresponding to pixel x.
The dispersion solution is evaluated iteratively, and the slope
(dispersion) for Newton's method is determined numerically, using a
difference in wavelength of one Angstrom. Note that the evalDisp
in this ... | 2,679 | 27.510638 | 72 | py |
stistools | stistools-master/stistools/orbit.py | import math
from astropy.io import fits
TWOPI = (math.pi * 2.0)
SEC_PER_DAY = 86400.0
class HSTOrbit(object):
"""Orbital parameters.
The public methods are getOrbitper and getPos.
"""
def __init__(self, spt):
"""Orbital parameters.
Parameters
----------
spt: str
... | 5,144 | 29.443787 | 71 | py |
stistools | stistools-master/stistools/defringe/_fit1d.py | #
# This module reproduces much of the behaviour of IRAF 1-d fitting routines
import math
import numpy as np
from scipy.interpolate import LSQUnivariateSpline
def fit1d(x, y, weights=None,
naverage=1,
function="spline3",
order=3,
low_reject=3.0,
high_reject=3.0,
... | 10,262 | 30.194529 | 104 | py |
stistools | stistools-master/stistools/defringe/defringe.py | #! /usr/bin/env python
import argparse
from astropy.io import fits
import datetime
import os
import textwrap
import re
import numpy as np
from ..r_util import expandFileName
# 4 bad detector pixel or beyond aperture
# 8 data masked by occulting bar
# 512 bad pixel in reference file
sdqflags = 4 + 8 + 512 ... | 8,739 | 39.841121 | 109 | py |
stistools | stistools-master/stistools/defringe/normspflat.py | #! /usr/bin/env python
import os
import numpy as np
import warnings
from astropy.io import fits
from ..r_util import expandFileName
from ..calstis import calstis
from ._fit1d import fit1d
# Keyword choices for calstis reduction:
PERFORM = {
'ALL': ['DQICORR', 'BLEVCORR', 'BIASCORR', 'DARKCORR', 'FLATCORR', 'C... | 16,395 | 45.05618 | 117 | py |
stistools | stistools-master/stistools/defringe/_findloc.py | import numpy as np
from astropy.modeling import models, fitting
# from lines 280 through 344 of mkfringeflat.cl
def find_loc(input, low_frac=0.2, high_frac=0.8, low_line_frac=0.4):
"""Find the cross-dispersion location of the target spectrum.
Parameters
----------
input: ndarray
The input scie... | 2,537 | 31.961039 | 78 | py |
stistools | stistools-master/stistools/defringe/mkfringeflat.py | #! /usr/bin/env python
from astropy.io import fits
from astropy.nddata.blocks import block_reduce
import numpy as np
import math
import os
from scipy.ndimage import shift
from ._findloc import find_loc
from ._response import response
__version__ = 0.1
def mkfringeflat(inspec, inflat, outflat, do_shift=True, beg_shif... | 19,503 | 38.885481 | 124 | py |
stistools | stistools-master/stistools/defringe/__init__.py | from .prepspec import prepspec
from .normspflat import normspflat
from .mkfringeflat import mkfringeflat
from .defringe import defringe
__doc__ = """
.. HST/STIS CCD Defringing Tools
-----------------------------
- `prepspec` — Calibrate STIS CCD G750L or G750M spectrum before defringing
- `normspflat` — N... | 1,073 | 38.777778 | 123 | py |
stistools | stistools-master/stistools/defringe/prepspec.py | #! /usr/bin/env python
import os
import shutil
import stat
import re
from astropy.io import fits
from tempfile import mkdtemp
import warnings
from ..r_util import expandFileName
from ..calstis import calstis
def prepspec(inspec, outroot='./', darkfile=None, pixelflat=None, initguess=None):
"""Calibrate STIS CCD... | 8,610 | 43.61658 | 121 | py |
stistools | stistools-master/stistools/defringe/_response.py | import math
import numpy as np
from astropy.io import fits
from ._fit1d import fit1d
def response(calibration_array, normalization_array,
threshold=None,
function="spline3",
sample='*',
naverage=2,
order=10,
low_reject=3.0,
hig... | 4,070 | 36.694444 | 90 | py |
stistools | stistools-master/tests/resources.py | """HSTCAL regression test helpers."""
from six.moves import urllib
import getpass
import os
import sys
import math
from io import StringIO
import shutil
import datetime
from os.path import splitext
from difflib import unified_diff
import pytest
import requests
from astropy.io import fits
from astropy.io.fits import FI... | 12,883 | 33.449198 | 79 | py |
stistools | stistools-master/tests/test_tastis.py | import pytest
from stistools.tastis import tastis
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestDoppinfo(BaseSTIS):
input_loc = 'tastis'
ref_loc = 'tastis'
def test_header_update1(self, capsys):
"""
oc7w11viq # ACQ/PEAK-UP, RETURN-TO-BRIG... | 30,805 | 61.869388 | 116 | py |
stistools | stistools-master/tests/conftest.py | """Project default for pytest"""
import os
import pytest
import re
import crds
from astropy.tests.helper import enable_deprecations_as_exceptions
# Uncomment the following line to treat all DeprecationWarnings as exceptions
enable_deprecations_as_exceptions()
def pytest_addoption(parser):
# Add option to run sl... | 2,737 | 27.821053 | 108 | py |
stistools | stistools-master/tests/test_wx2d.py | import pytest
from stistools.wx2d import wx2d
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestWx2d(BaseSTIS):
input_loc = 'wx2d'
ref_loc = 'wx2d'
def test_wx2d_t1(self):
"""
Test for wx2d using rows parameter
"""
# Prepar... | 1,251 | 24.04 | 76 | py |
stistools | stistools-master/tests/test_calstis.py | from stistools.calstis import calstis
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestCalstis(BaseSTIS):
input_loc = 'calstis'
ref_loc = 'calstis/ref'
def test_ccd_imaging(self):
"""
This test is for calstis on CCD imaging data
"""
... | 4,115 | 30.419847 | 70 | py |
stistools | stistools-master/tests/test_basic2d.py | from stistools.basic2d import basic2d
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestBasic2d(BaseSTIS):
input_loc = 'basic2d'
ref_loc = 'basic2d/ref'
def test_basic2d_lev1a(self):
"""
BASIC2D - stsdas/hst_calib/stis/basic2d: level 1a
... | 5,491 | 40.606061 | 79 | py |
stistools | stistools-master/tests/test_doppinfo.py | from stistools.doppinfo import Doppinfo
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestDoppinfo(BaseSTIS):
input_loc = 'doppinfo'
ref_loc = 'doppinfo'
def test_doppinfo_basic(self, capsys):
"""
Test stis.doppnfo.Doppinfo with defaults, no u... | 6,932 | 51.522727 | 118 | py |
stistools | stistools-master/tests/test_ctestis.py | import numpy as np
from stistools.ctestis import ctestis
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestCtestis(BaseSTIS):
"""
We need to add more tests for this
"""
input_loc = 'ctestis'
ref_loc = 'ctestis'
def test_single_value_ctestis(self, ... | 4,270 | 38.546296 | 79 | py |
stistools | stistools-master/tests/test_mktrace.py | from stistools.mktrace import mktrace
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestMktrace(BaseSTIS):
input_loc = 'mktrace'
ref_loc = 'mktrace'
atol = 1e-14
def test_mktrace_t1(self, capsys):
"""
This tests a basic usage of stis.mktr... | 2,525 | 35.085714 | 96 | py |
stistools | stistools-master/tests/test_stisnoise.py | from stistools.stisnoise import stisnoise
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestStisnoise(BaseSTIS):
input_loc = 'stisnoise'
ref_loc = 'stisnoise'
# Really should add some output and return array checks for these tests
def test_stisnoise_t1(s... | 1,364 | 25.764706 | 75 | py |
stistools | stistools-master/tests/test_ocrreject.py | from stistools.ocrreject import ocrreject
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestOcrreject(BaseSTIS):
input_loc = 'ocrreject'
ref_loc = 'ocrreject/ref'
input_list = ["o58i01q7q_flt.fits", "o58i01q8q_flt.fits",
"o58i01q9q_flt.fits"... | 2,283 | 35.253968 | 78 | py |
stistools | stistools-master/tests/test_defringe.py | from stistools.defringe import normspflat, prepspec, mkfringeflat, defringe
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestDefringe(BaseSTIS):
input_loc = 'defringe'
def test_normspflat_g750l(self):
"""Compare normspflat output for a g750l spectrum"""... | 4,310 | 29.359155 | 75 | py |
stistools | stistools-master/tests/__init__.py | 0 | 0 | 0 | py | |
stistools | stistools-master/tests/test_inttag.py | from stistools.inttag import inttag
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestInttag(BaseSTIS):
input_loc = 'inttag'
def test_accum_lores(self):
"""Compare accum image output for a single lowres imset"""
self.get_data("input", "oddv01050_t... | 3,152 | 36.535714 | 102 | py |
stistools | stistools-master/tests/Test_x1d.py | from stistools.x1d import x1d
from .resources import BaseSTIS
import pytest
@pytest.mark.bigdata
@pytest.mark.slow
class TestX1d(BaseSTIS):
input_loc = 'x1d'
ref_loc = 'x1d/ref'
def test_x1d(self):
"""
Basic x1d test, mostly using default parameters
"""
# Prepare input f... | 606 | 21.481481 | 73 | py |
stistools | stistools-master/tests/helpers/utils.py | import os
import re
import requests
from astropy.io import fits
__all__ = ['cmp_fitshdr', 'cmp_gen_hdrkeywords',
'word_precision_check', 'abspath',
'download', 'check_url']
RE_URL = re.compile('\w+://\S+')
default_compare = dict(
ignore_keywords=['DATE', 'CAL_VER', 'CAL_VCS', 'CRDS_VER', 'C... | 5,942 | 25.650224 | 79 | py |
stistools | stistools-master/tests/helpers/__init__.py | from .io import *
from .utils import *
| 39 | 12.333333 | 20 | py |
stistools | stistools-master/tests/helpers/io.py | import copy
import json
import os
import shutil
from .utils import check_url, download
UPLOAD_SCHEMA = {"files": [
{"pattern": "",
"target": "",
"props": None,
"recursive": "false",
"flat": "true",
... | 3,882 | 31.630252 | 104 | py |
stistools | stistools-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# stistools documentation build configuration file, created by
# Warren Hack on Mon Oct 1 2012.
#
# 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... | 6,974 | 31.746479 | 95 | py |
sst-macro | sst-macro-master/sstmac/sst_core/sstmacro.py | # Load module function in Python is changed
# to look for a libmacro.so in LD_LIBRARY_PATH
import sst
import sst.macro
smallLatency = "1ps"
def getParam(params, paramName, paramNS=None):
if not paramName in params:
import sys
if paramNs:
sys.stderr.write("Missing parameter '%s' in namespace '%s'\n" % ... | 10,780 | 31.570997 | 95 | py |
sst-macro | sst-macro-master/sstmac/skeletons/offered_load/traffic.py |
def getVals(model):
fileName = "%s.out" % model
import re
text = open(fileName).read()
regexp = re.compile("throughput=\s+(\d+[.]\d+)")
matches = regexp.findall(text)
return map(float, matches)
def absError(lvals, rvals):
length = len(lvals)
err = 0
for i in range(length):
r = lvals[i]
l = r... | 1,195 | 19.271186 | 52 | py |
sst-macro | sst-macro-master/examples/snappr.py | import sst
sst.setProgramOption("timebase", "100as")
import sst.macro
from sst.macro import Interconnect
swParams = {
"name" : "snappr",
"router" : {
"seed" : "42",
"name" : "dragonfly_minimal",
},
"link" : {
"bandwidth" : "1.0GB/s",
"latency" : "100ns",
"credits" : "8KB",
},
"logp" : {... | 1,348 | 15.8625 | 41 | py |
sst-macro | sst-macro-master/examples/macrels.py | import sst
sst.setProgramOption("timebase", "100as")
import sst.macro
from sst.macro import Interconnect
swParams = {
"name" : "logp",
"out_in_latency" : "2us",
}
appParams = {
"allocation" : "first_available",
"indexing" : "block",
"name" : "mpi_ping_all",
"launch_cmd" : "aprun -n 80 -N 2",
}
memParams ... | 894 | 14.431034 | 41 | py |
sst-macro | sst-macro-master/examples/pisces.py | import sst
sst.setProgramOption("timebase", "100as")
import sst.macro
from sst.macro import Interconnect
swParams = {
"name" : "pisces",
"arbitrator" : "cut_through",
"mtu" : "4096",
"router" : {
"seed" : "42",
"name" : "dragonfly_minimal",
},
"link" : {
"bandwidth" : "1.0GB/s",
"latency" :... | 1,507 | 16.333333 | 41 | py |
sst-macro | sst-macro-master/examples/sculpin.py | import sst
sst.setProgramOption("timebase", "100as")
import sst.macro
from sst.macro import Interconnect
swParams = {
"name" : "sculpin",
"router" : {
"seed" : "42",
"name" : "dragonfly_minimal",
},
"link" : {
"bandwidth" : "1.0GB/s",
"latency" : "100ns",
"credits" : "4KB",
},
"logp" : ... | 1,292 | 15.576923 | 41 | py |
sst-macro | sst-macro-master/python/snappr.py | # Load module function in Python is changed
# to look for a libmacro.so in LD_LIBRARY_PATH
import sst
import sst.macro
sst.setProgramOption("timebase", "100as")
mtu = "4KB"
small_latency = "1ps"
nic_latency = "50ns"
nic_bandwidth = "1.0GB/s"
link_latency = "100ns"
link_bandwidth = "1.0GB/s"
topo_params = dict(
nam... | 4,372 | 26.161491 | 86 | py |
sst-macro | sst-macro-master/python/jobScheduler.py | from sst.merlin import *
from sst.macro import *
import sst.macro
mtu = 1204
arb = "cut_through"
buffer_size = "64KB"
topo = topoTorus()
params = sst.merlin._params
params["torus:shape"] = "2x2x2";
params["torus:width"] = "1x1x1";
params["flit_size"] = "8B"
params["link_bw"] = "10GB/s"
params["link_lat"] = "100ns"
... | 2,023 | 18.09434 | 69 | py |
sst-macro | sst-macro-master/python/arielShadowPuppet.py | import sst
import os
sst.setProgramOption("timebase", "100as")
next_core_id = 0
next_network_id = 0
next_memory_ctrl_id = 0
clock = "2660MHz"
memory_clock = "200MHz"
coherence_protocol = "MESI"
cores_per_group = 2
active_cores_per_group = 2
memory_controllers_per_group = 1
groups = 4
os.environ["OMP_NUM_THREADS"]=... | 11,126 | 37.237113 | 232 | py |
sst-macro | sst-macro-master/python/emberDefaultParams.py |
debug = 0
netConfig = {
}
networkParams = {
"packetSize" : "2048B",
"link_bw" : "4GB/s",
"link_lat" : "40ns",
"input_latency" : "50ns",
"output_latency" : "50ns",
"flitSize" : "8B",
"buffer_size" : "14KB",
}
nicParams = {
"detailedCompute.name" : "thornhill.SingleThread",
"module" :... | 2,076 | 30 | 66 | py |
sst-macro | sst-macro-master/python/emberLoadInfo.py | import sst
import copy
def calcNetMapId( nodeId, nidList ):
if nidList == 'Null':
return -1
pos = 0
a = nidList.split(',')
for b in a:
c = b.split('-')
start = int(c[0])
stop = start
if 2 == len(c):
stop = int(c[1])
if nodeId >= sta... | 10,427 | 28.047354 | 158 | py |
sst-macro | sst-macro-master/python/emberMacro.py |
import sys,getopt
import sst
from sst.merlin import *
from sst.macro import *
#debug("simple_network")
import emberLoadInfo
from emberLoadInfo import *
import random
topoParams = {
"name" : "torus",
"geometry" : "2 2 2",
}
injLat = "1us"
mtu="1KB"
bufSize = "64KB"
arb = "cut_through"
macroParams = {
"topolog... | 2,968 | 17.32716 | 84 | py |
sst-macro | sst-macro-master/python/plotSwitches.py | import sys
import os
import numpy as np
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection
import matplotlib.pyplot as plt
def genFaces(Z):
"""should be numbered like so
Z[0] = corner[:]
Z[1] = corner + yDelta
Z[2] = corner +... | 2,832 | 22.608333 | 87 | py |
sst-macro | sst-macro-master/python/merlin.py | from sst.merlin import *
from sst.macro import *
import sst.macro
mtu = 1204
arb = "cut_through"
params = sst.merlin._params
buffer_size = "64KB"
topo = topoTorus()
params["torus:shape"] = "2x2x2";
params["torus:width"] = "1x1x1";
params["flit_size"] = "8B"
params["link_bw"] = "10GB/s"
params["link_lat"] = "100ns... | 1,699 | 17.085106 | 68 | py |
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