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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scipy | scipy-main/tools/wheels/check_license.py | #!/usr/bin/env python
"""
check_license.py [MODULE]
Check the presence of a LICENSE.txt in the installed module directory,
and that it appears to contain text prevalent for a SciPy binary
distribution.
"""
import os
import sys
import io
import re
import argparse
def check_text(text):
ok = "Copyright (c)" in tex... | 1,227 | 20.54386 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/test_functions.py | import time
import numpy as np
from numpy import sin, cos, pi, exp, sqrt, abs
from scipy.optimize import rosen
class SimpleQuadratic:
def fun(self, x):
return np.dot(x, x)
def der(self, x):
return 2. * x
def hess(self, x):
return 2. * np.eye(x.size)
class AsymmetricQuadratic:... | 8,342 | 25.235849 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize_lap.py | from concurrent.futures import ThreadPoolExecutor, wait
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy.optimize import linear_sum_assignment
from scipy.spatial.distance import cdist
def random_uniform(shape):
return np.random.uniform(-20, 20, shape)
def ra... | 1,956 | 27.362319 | 76 | py |
scipy | scipy-main/benchmarks/benchmarks/cython_special.py | import re
import numpy as np
from scipy import special
from .common import with_attributes, safe_import
with safe_import():
from scipy.special import cython_special
FUNC_ARGS = {
'airy_d': (1,),
'airy_D': (1,),
'beta_dd': (0.25, 0.75),
'erf_d': (1,),
'erf_D': (1+1j,),
'exprel_d': (1e-6,)... | 1,956 | 26.56338 | 76 | py |
scipy | scipy-main/benchmarks/benchmarks/lsq_problems.py | """Benchmark problems for nonlinear least squares."""
import inspect
import sys
import numpy as np
from numpy.polynomial.chebyshev import Chebyshev
from scipy.integrate import odeint
class LSQBenchmarkProblem:
"""Template class for nonlinear least squares benchmark problems.
The optimized variable is n-dime... | 17,304 | 34.461066 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/stats_sampling.py | import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy import stats
with safe_import():
from scipy.stats import sampling
with safe_import():
from scipy import special
# Beta distribution with a = 2, b = 3
class contdist1:
def __init__(self):
self.mode = 1... | 8,743 | 27.115756 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/fft_basic.py | """ Test functions for fftpack.basic module
"""
from numpy import arange, asarray, zeros, dot, exp, pi, double, cdouble
from numpy.random import rand
import numpy as np
from concurrent import futures
import os
import scipy.fftpack
import numpy.fft
from .common import Benchmark, safe_import
with safe_import() as exc:
... | 10,009 | 27.197183 | 85 | py |
scipy | scipy-main/benchmarks/benchmarks/special.py | import numpy as np
from .common import Benchmark, with_attributes, safe_import
with safe_import():
from scipy.special import ai_zeros, bi_zeros, erf, expn
with safe_import():
# wasn't always in scipy.special, so import separately
from scipy.special import comb
with safe_import():
from scipy.special im... | 1,576 | 22.191176 | 67 | py |
scipy | scipy-main/benchmarks/benchmarks/cluster.py | import numpy as np
from numpy.testing import suppress_warnings
from .common import Benchmark, safe_import
with safe_import():
from scipy.cluster.hierarchy import linkage
from scipy.cluster.vq import kmeans, kmeans2, vq
class HierarchyLinkage(Benchmark):
params = ['single', 'complete', 'average', 'weight... | 1,784 | 25.641791 | 76 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_csgraph.py | """benchmarks for the scipy.sparse.csgraph module"""
import numpy as np
import scipy.sparse
from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse.csgraph import laplacian
class Laplacian(Benchmark):
params = [
[30, 300, 900],
['dense', 'coo', 'csc', 'csr', 'dia'],
... | 867 | 27 | 78 | py |
scipy | scipy-main/benchmarks/benchmarks/linalg.py | import math
import numpy.linalg as nl
import numpy as np
from numpy.testing import assert_
from numpy.random import rand
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg as sl
def random(size):
return rand(*size)
class Bench(Benchmark):
params = [
[20, 100, ... | 7,306 | 28.345382 | 93 | py |
scipy | scipy-main/benchmarks/benchmarks/ndimage_interpolation.py | import numpy as np
from .common import Benchmark
try:
from scipy.ndimage import (geometric_transform, affine_transform, rotate,
zoom, shift, map_coordinates)
except ImportError:
pass
def shift_func_2d(c):
return (c[0] - 0.5, c[1] - 0.5)
def shift_func_3d(c):
return (... | 2,227 | 31.289855 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/stats.py | import warnings
import numpy as np
from .common import Benchmark, safe_import, is_xslow
with safe_import():
import scipy.stats as stats
with safe_import():
from scipy.stats._distr_params import distcont, distdiscrete
try: # builtin lib
from itertools import compress
except ImportError:
pass
class ... | 26,280 | 34.371467 | 98 | py |
scipy | scipy-main/benchmarks/benchmarks/peak_finding.py | """Benchmarks for peak finding related functions."""
from .common import Benchmark, safe_import
with safe_import():
from scipy.signal import find_peaks, peak_prominences, peak_widths
from scipy.datasets import electrocardiogram
class FindPeaks(Benchmark):
"""Benchmark `scipy.signal.find_peaks`.
Not... | 1,523 | 26.214286 | 75 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize_milp.py | import os
import numpy as np
from numpy.testing import assert_allclose
from .common import Benchmark, safe_import
with safe_import():
from scipy.optimize import milp
with safe_import():
from scipy.optimize.tests.test_linprog import magic_square
# MIPLIB 2017 benchmarks included with permission of the auth... | 2,427 | 30.947368 | 78 | py |
scipy | scipy-main/benchmarks/benchmarks/linalg_solve_toeplitz.py | """Benchmark the solve_toeplitz solver (Levinson recursion)
"""
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg
class SolveToeplitz(Benchmark):
params = (
('float64', 'complex128'),
(100, 300, 1000),
('toeplitz', 'generic')
)
... | 1,102 | 25.261905 | 65 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse.py | """
Simple benchmarks for the sparse module
"""
import warnings
import time
import timeit
import pickle
import numpy
import numpy as np
from numpy import ones, array, asarray, empty
from .common import Benchmark, safe_import
with safe_import():
from scipy import sparse
from scipy.sparse import (coo_matrix, d... | 14,747 | 29.471074 | 96 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_linalg_onenormest.py | """Compare the speed of exact one-norm calculation vs. its estimation.
"""
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.sparse
import scipy.special # import cycle workaround for some versions
import scipy.sparse.linalg
class BenchmarkOneNormEst(Benchmar... | 1,883 | 32.052632 | 115 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_linalg_solve.py | """
Check the speed of the conjugate gradient solver.
"""
import numpy as np
from numpy.testing import assert_equal
from .common import Benchmark, safe_import
with safe_import():
from scipy import linalg, sparse
from scipy.sparse.linalg import cg, minres, gmres, tfqmr, spsolve
with safe_import():
from sci... | 2,078 | 27.094595 | 85 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_csgraph_maxflow.py | import numpy as np
import scipy.sparse
from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse.csgraph import maximum_flow
class MaximumFlow(Benchmark):
params = [[200, 500, 1500], [0.1, 0.3, 0.5]]
param_names = ['n', 'density']
def setup(self, n, density):
# Create... | 714 | 30.086957 | 77 | py |
scipy | scipy-main/benchmarks/benchmarks/signal.py | from itertools import product
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.signal as signal
class Resample(Benchmark):
# Some slow (prime), some fast (in radix)
param_names = ['N', 'num']
params = [[977, 9973, 2 ** 14, 2 ** 16]] * 2
def setup(s... | 6,839 | 26.46988 | 77 | py |
scipy | scipy-main/benchmarks/benchmarks/cluster_hierarchy_disjoint_set.py | import numpy as np
try:
from scipy.cluster.hierarchy import DisjointSet
except ImportError:
pass
from .common import Benchmark
class Bench(Benchmark):
params = [[100, 1000, 10000]]
param_names = ['n']
def setup(self, n):
# Create random edges
rng = np.random.RandomState(seed=0)
... | 1,653 | 26.566667 | 55 | py |
scipy | scipy-main/benchmarks/benchmarks/signal_filtering.py | import numpy as np
import timeit
from concurrent.futures import ThreadPoolExecutor, wait
from .common import Benchmark, safe_import
with safe_import():
from scipy.signal import (lfilter, firwin, decimate, butter, sosfilt,
medfilt2d)
class Decimate(Benchmark):
param_names = ['q'... | 3,044 | 28 | 82 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_linalg_lobpcg.py | from functools import partial
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy import array, r_, ones, arange, sort, diag, cos, rand, pi
from scipy.linalg import eigh, orth, cho_factor, cho_solve
import scipy.sparse
from scipy.sparse.linalg import lobpcg
... | 3,646 | 31.5625 | 93 | py |
scipy | scipy-main/benchmarks/benchmarks/integrate.py | import numpy as np
from .common import Benchmark, safe_import
from scipy.integrate import quad
with safe_import():
import ctypes
import scipy.integrate._test_multivariate as clib_test
from scipy._lib import _ccallback_c
with safe_import() as exc:
from scipy import LowLevelCallable
from_cython = L... | 3,261 | 27.365217 | 90 | py |
scipy | scipy-main/benchmarks/benchmarks/linalg_sqrtm.py | """ Benchmark linalg.sqrtm for various blocksizes.
"""
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg
class Sqrtm(Benchmark):
params = [
['float64', 'complex128'],
[64, 256],
[32, 64, 256]
]
param_names = ['dtype', 'n', ... | 776 | 21.852941 | 67 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize.py | import os
import time
import inspect
import json
import traceback
from collections import defaultdict
import numpy as np
from . import test_functions as funcs
from . import go_benchmark_functions as gbf
from .common import Benchmark, is_xslow, safe_import
from .lsq_problems import extract_lsq_problems
with safe_impo... | 21,806 | 34.172581 | 102 | py |
scipy | scipy-main/benchmarks/benchmarks/io_matlab.py | from .common import set_mem_rlimit, run_monitored, get_mem_info
import os
import tempfile
from io import BytesIO
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy.io import savemat, loadmat
class MemUsage(Benchmark):
param_names = ['size', 'compressed']
timeou... | 3,221 | 26.775862 | 83 | py |
scipy | scipy-main/benchmarks/benchmarks/common.py | """
Airspeed Velocity benchmark utilities
"""
import sys
import os
import re
import time
import textwrap
import subprocess
import itertools
import random
class Benchmark:
"""
Base class with sensible options
"""
pass
def is_xslow():
try:
return int(os.environ.get('SCIPY_XSLOW', '0'))
... | 5,643 | 23.754386 | 76 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_linalg_svds.py | import os
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse.linalg import svds
class BenchSVDS(Benchmark):
# Benchmark SVD using the MatrixMarket test matrices recommended by the
# author of PROPACK at http://sun.stanford.edu/~rmunk/PROPACK/
params = ... | 1,053 | 33 | 75 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize_qap.py | import numpy as np
from .common import Benchmark, safe_import
import os
with safe_import():
from scipy.optimize import quadratic_assignment
# XXX this should probably have an is_xslow with selected tests.
# Even with this, it takes ~30 seconds to collect the ones to run
# (even if they will all be skipped in the... | 2,852 | 45.016129 | 78 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_csgraph_dijkstra.py | """benchmarks for the scipy.sparse.csgraph module"""
import numpy as np
import scipy.sparse
from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse.csgraph import dijkstra
class Dijkstra(Benchmark):
params = [
[30, 300, 900],
[True, False],
['random', 'star']... | 1,452 | 32.790698 | 76 | py |
scipy | scipy-main/benchmarks/benchmarks/__init__.py | import numpy as np
import random
np.random.seed(1234)
random.seed(1234)
| 73 | 11.333333 | 20 | py |
scipy | scipy-main/benchmarks/benchmarks/spatial.py | import numpy as np
from .common import Benchmark, LimitedParamBenchmark, safe_import
with safe_import():
from scipy.spatial import cKDTree, KDTree
with safe_import():
from scipy.spatial import distance
with safe_import():
from scipy.spatial import ConvexHull, Voronoi
with safe_import():
from scipy.spa... | 16,940 | 34.29375 | 108 | py |
scipy | scipy-main/benchmarks/benchmarks/linalg_logm.py | """ Benchmark linalg.logm for various blocksizes.
"""
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg
class Logm(Benchmark):
params = [
['float64', 'complex128'],
[64, 256],
['gen', 'her', 'pos']
]
param_names = ['dtype', ... | 785 | 20.833333 | 49 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize_linprog.py | import os
import numpy as np
from numpy.testing import suppress_warnings
from .common import Benchmark, is_xslow, safe_import
with safe_import():
from scipy.optimize import linprog, OptimizeWarning
with safe_import():
from scipy.optimize.tests.test_linprog import lpgen_2d, magic_square
with safe_import():
... | 8,116 | 34.291304 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/fftpack_pseudo_diffs.py | """ Benchmark functions for fftpack.pseudo_diffs module
"""
from numpy import arange, sin, cos, pi, exp, tanh, sign
from .common import Benchmark, safe_import
with safe_import():
from scipy.fftpack import diff, fft, ifft, tilbert, hilbert, shift, fftfreq
def direct_diff(x, k=1, period=None):
fx = fft(x)
... | 2,237 | 21.836735 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/optimize_zeros.py | from math import sqrt, exp, cos, sin
import numpy as np
from .common import Benchmark, safe_import
# Import testing parameters
with safe_import():
from scipy.optimize._tstutils import methods, mstrings, functions, fstrings
from scipy.optimize import newton # newton predates benchmarks
class Zeros(Benchmark):
... | 3,776 | 31.282051 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_csgraph_matching.py | import numpy as np
import scipy.sparse
from scipy.spatial.distance import cdist
from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse.csgraph import maximum_bipartite_matching,\
min_weight_full_bipartite_matching
class MaximumBipartiteMatching(Benchmark):
params = [[5000,... | 3,047 | 34.44186 | 95 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_linalg_expm.py | """benchmarks for the scipy.sparse.linalg._expm_multiply module"""
import math
import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg
from scipy.sparse.linalg import expm as sp_expm
from scipy.sparse.linalg import expm_multiply
def random_sparse_csr(m, n, n... | 2,246 | 29.364865 | 74 | py |
scipy | scipy-main/benchmarks/benchmarks/sparse_matrix_power.py | from .common import Benchmark, safe_import
with safe_import():
from scipy.sparse import random
class BenchMatrixPower(Benchmark):
params = [
[0, 1, 2, 3, 8, 9],
[1000],
[1e-6, 1e-3],
]
param_names = ['x', 'N', 'density']
def setup(self, x: int, N: int, density: float):
... | 465 | 22.3 | 64 | py |
scipy | scipy-main/benchmarks/benchmarks/blas_lapack.py | import numpy as np
from .common import Benchmark, safe_import
with safe_import():
import scipy.linalg.blas as bla
class GetBlasLapackFuncs(Benchmark):
"""
Test the speed of grabbing the correct BLAS/LAPACK routine flavor.
In particular, upon receiving strange dtype arrays the results shouldn't
d... | 1,015 | 29.787879 | 87 | py |
scipy | scipy-main/benchmarks/benchmarks/interpolate.py | import numpy as np
from .common import run_monitored, set_mem_rlimit, Benchmark, safe_import
with safe_import():
from scipy.stats import spearmanr
with safe_import():
import scipy.interpolate as interpolate
class Leaks(Benchmark):
unit = "relative increase with repeats"
def track_leaks(self):
... | 14,152 | 31.092971 | 121 | py |
scipy | scipy-main/benchmarks/benchmarks/linprog_benchmark_files/__init__.py | # -*- coding: utf-8 -*-
"""
==============================================================================
`` -- Problems for testing linear programming routines
==============================================================================
This module provides a comprehensive set of problems for benchmarking linear ... | 671 | 31 | 78 | py |
scipy | scipy-main/benchmarks/benchmarks/tests/test_go_benchmark_functions.py | """
Unit tests for the global optimization benchmark functions
"""
import numpy as np
from .. import go_benchmark_functions as gbf
import inspect
class TestGoBenchmarkFunctions:
def setup_method(self):
bench_members = inspect.getmembers(gbf, inspect.isclass)
self.benchmark_functions = {it[0]:it[1... | 2,650 | 33.428571 | 77 | py |
scipy | scipy-main/benchmarks/benchmarks/tests/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_C.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import (abs, asarray, cos, exp, floor, pi, sign, sin, sqrt, sum,
size, tril, isnan, atleast_2d, repeat)
from numpy.testing import assert_almost_equal
from .go_benchmark import Benchmark
class CarromTable(Benchmark):
r"""
CarromTable obj... | 18,458 | 30.880829 | 108 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_M.py | # -*- coding: utf-8 -*-
from numpy import (abs, asarray, cos, exp, log, arange, pi, prod, sin, sqrt,
sum, tan)
from .go_benchmark import Benchmark, safe_import
with safe_import():
from scipy.special import factorial
class Matyas(Benchmark):
r"""
Matyas objective function.
This cl... | 20,910 | 28.043056 | 82 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_B.py | # -*- coding: utf-8 -*-
from numpy import abs, cos, exp, log, arange, pi, sin, sqrt, sum
from .go_benchmark import Benchmark
class BartelsConn(Benchmark):
r"""
Bartels-Conn objective function.
The BartelsConn [1]_ global optimization problem is a multimodal
minimization problem defined as follows:
... | 21,664 | 27.506579 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_Y.py | # -*- coding: utf-8 -*-
from numpy import abs, sum, cos, pi
from .go_benchmark import Benchmark
class YaoLiu04(Benchmark):
r"""
Yao-Liu 4 objective function.
This class defines the Yao-Liu function 4 [1]_ global optimization problem. This
is a multimodal minimization problem defined as follows:
... | 2,881 | 29.989247 | 84 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_H.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import abs, arctan2, asarray, cos, exp, arange, pi, sin, sqrt, sum
from .go_benchmark import Benchmark
class Hansen(Benchmark):
r"""
Hansen objective function.
This class defines the Hansen [1]_ global optimization problem. This is a
multimodal m... | 11,278 | 28.99734 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_Q.py | # -*- coding: utf-8 -*-
from numpy import abs, sum, arange, sqrt
from .go_benchmark import Benchmark
class Qing(Benchmark):
r"""
Qing objective function.
This class defines the Qing [1]_ global optimization problem. This is a
multimodal minimization problem defined as follows:
.. math::
... | 3,859 | 29.634921 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_L.py | # -*- coding: utf-8 -*-
from numpy import sum, cos, exp, pi, arange, sin
from .go_benchmark import Benchmark
class Langermann(Benchmark):
r"""
Langermann objective function.
This class defines the Langermann [1]_ global optimization problem. This
is a multimodal minimization problem defined as follo... | 9,861 | 28.975684 | 184 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_I.py | # -*- coding: utf-8 -*-
from numpy import sin, sum
from .go_benchmark import Benchmark
class Infinity(Benchmark):
r"""
Infinity objective function.
This class defines the Infinity [1]_ global optimization problem. This
is a multimodal minimization problem defined as follows:
.. math::
... | 1,115 | 25.571429 | 77 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_T.py | # -*- coding: utf-8 -*-
from numpy import abs, asarray, cos, exp, arange, pi, sin, sum, atleast_2d
from .go_benchmark import Benchmark
class TestTubeHolder(Benchmark):
r"""
TestTubeHolder objective function.
This class defines the TestTubeHolder [1]_ global optimization problem. This
is a multimodal... | 12,710 | 31.592308 | 82 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_G.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import abs, sin, cos, exp, floor, log, arange, prod, sqrt, sum
from .go_benchmark import Benchmark
class Gear(Benchmark):
r"""
Gear objective function.
This class defines the Gear [1]_ global optimization problem. This
is a multimodal minimizati... | 6,489 | 28.103139 | 81 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_W.py | # -*- coding: utf-8 -*-
from numpy import atleast_2d, arange, sum, cos, exp, pi
from .go_benchmark import Benchmark
class Watson(Benchmark):
r"""
Watson objective function.
This class defines the Watson [1]_ global optimization problem. This is a
unimodal minimization problem defined as follows:
... | 9,789 | 29.216049 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_univariate.py | # -*- coding: utf-8 -*-
from numpy import cos, exp, log, pi, sin, sqrt
from .go_benchmark import Benchmark, safe_import
with safe_import():
try:
from scipy.special import factorial # new
except ImportError:
from scipy.misc import factorial # old
#-------------------------------------------... | 16,917 | 22.175342 | 104 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_R.py | # -*- coding: utf-8 -*-
from numpy import abs, sum, sin, cos, asarray, arange, pi, exp, log, sqrt
from scipy.optimize import rosen
from .go_benchmark import Benchmark
class Rana(Benchmark):
r"""
Rana objective function.
This class defines the Rana [1]_ global optimization problem. This is a
multimod... | 12,436 | 29.408313 | 85 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_S.py | # -*- coding: utf-8 -*-
from numpy import (abs, asarray, cos, floor, arange, pi, prod, roll, sin,
sqrt, sum, repeat, atleast_2d, tril)
from numpy.random import uniform
from .go_benchmark import Benchmark
class Salomon(Benchmark):
r"""
Salomon objective function.
This class defines the... | 40,893 | 28.915143 | 83 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_N.py | # -*- coding: utf-8 -*-
from numpy import cos, sqrt, sin, abs
from .go_benchmark import Benchmark
class NeedleEye(Benchmark):
r"""
NeedleEye objective function.
This class defines the Needle-Eye [1]_ global optimization problem. This is a
a multimodal minimization problem defined as follows:
..... | 4,127 | 26.52 | 84 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_X.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import abs, sum, sin, cos, pi, exp, arange, prod, sqrt
from .go_benchmark import Benchmark
class XinSheYang01(Benchmark):
r"""
Xin-She Yang 1 objective function.
This class defines the Xin-She Yang 1 [1]_ global optimization problem.
This is a mu... | 7,769 | 31.107438 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_E.py | # -*- coding: utf-8 -*-
from numpy import abs, asarray, cos, exp, arange, pi, sin, sqrt, sum
from .go_benchmark import Benchmark
class Easom(Benchmark):
r"""
Easom objective function.
This class defines the Easom [1]_ global optimization problem. This is a
a multimodal minimization problem defined a... | 9,797 | 31.12459 | 97 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_V.py | # -*- coding: utf-8 -*-
from numpy import sum, cos, sin, log
from .go_benchmark import Benchmark
class VenterSobiezcczanskiSobieski(Benchmark):
r"""
Venter Sobiezcczanski-Sobieski objective function.
This class defines the Venter Sobiezcczanski-Sobieski [1]_ global optimization
problem. This is a mu... | 2,709 | 30.511628 | 82 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/__init__.py | # -*- coding: utf-8 -*-
"""
==============================================================================
`go_benchmark_functions` -- Problems for testing global optimization routines
==============================================================================
This module provides a comprehensive set of problems f... | 2,646 | 35.260274 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_U.py | # -*- coding: utf-8 -*-
from numpy import abs, sin, cos, pi, sqrt
from .go_benchmark import Benchmark
class Ursem01(Benchmark):
r"""
Ursem 1 objective function.
This class defines the Ursem 1 [1]_ global optimization problem. This is a
unimodal minimization problem defined as follows:
.. math::... | 5,167 | 29.946108 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_D.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import abs, cos, exp, arange, pi, sin, sqrt, sum, zeros, tanh
from numpy.testing import assert_almost_equal
from .go_benchmark import Benchmark
class Damavandi(Benchmark):
r"""
Damavandi objective function.
This class defines the Damavandi [1]_ global... | 17,905 | 30.414035 | 95 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_Z.py | # -*- coding: utf-8 -*-
from numpy import abs, sum, sign, arange
from .go_benchmark import Benchmark
class Zacharov(Benchmark):
r"""
Zacharov objective function.
This class defines the Zacharov [1]_ global optimization problem. This
is a multimodal minimization problem defined as follows:
.. ma... | 6,737 | 28.682819 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_P.py | # -*- coding: utf-8 -*-
from numpy import (abs, sum, sin, cos, sqrt, log, prod, where, pi, exp, arange,
floor, log10, atleast_2d, zeros)
from .go_benchmark import Benchmark
class Parsopoulos(Benchmark):
r"""
Parsopoulos objective function.
This class defines the Parsopoulos [1]_ globa... | 20,990 | 27.873453 | 84 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_A.py | # -*- coding: utf-8 -*-
from numpy import abs, cos, exp, pi, prod, sin, sqrt, sum
from .go_benchmark import Benchmark
class Ackley01(Benchmark):
r"""
Ackley01 objective function.
The Ackley01 [1]_ global optimization problem is a multimodal minimization
problem defined as follows:
.. math::
... | 7,993 | 27.55 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_J.py | # -*- coding: utf-8 -*-
from numpy import sum, asarray, arange, exp
from .go_benchmark import Benchmark
class JennrichSampson(Benchmark):
r"""
Jennrich-Sampson objective function.
This class defines the Jennrich-Sampson [1]_ global optimization problem. This
is a multimodal minimization problem defin... | 3,581 | 32.166667 | 82 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_O.py | # -*- coding: utf-8 -*-
from numpy import sum, cos, exp, pi, asarray
from .go_benchmark import Benchmark
class OddSquare(Benchmark):
r"""
Odd Square objective function.
This class defines the Odd Square [1]_ global optimization problem. This
is a multimodal minimization problem defined as follows:
... | 1,919 | 29.47619 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_benchmark.py | # -*- coding: utf-8 -*-
import numpy as np
from numpy import abs, asarray
from ..common import safe_import
with safe_import():
from scipy.special import factorial
class Benchmark:
"""
Defines a global optimization benchmark problem.
This abstract class defines the basic structure of a global
o... | 5,925 | 27.490385 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_F.py | # -*- coding: utf-8 -*-
from .go_benchmark import Benchmark
class FreudensteinRoth(Benchmark):
r"""
FreudensteinRoth objective function.
This class defines the Freudenstein & Roth [1]_ global optimization problem.
This is a multimodal minimization problem defined as follows:
.. math::
... | 1,329 | 28.555556 | 80 | py |
scipy | scipy-main/benchmarks/benchmarks/go_benchmark_functions/go_funcs_K.py | # -*- coding: utf-8 -*-
from numpy import asarray, atleast_2d, arange, sin, sqrt, prod, sum, round
from .go_benchmark import Benchmark
class Katsuura(Benchmark):
r"""
Katsuura objective function.
This class defines the Katsuura [1]_ global optimization problem. This is a
multimodal minimization prob... | 4,627 | 29.853333 | 79 | py |
scipy | scipy-main/benchmarks/benchmarks/cutest/calfun.py | # This is a python implementation of calfun.m,
# provided at https://github.com/POptUS/BenDFO
import numpy as np
from .dfovec import dfovec
def norm(x, type=2):
if type == 1:
return np.sum(np.abs(x))
elif type == 2:
return np.sqrt(x ** 2)
else: # type==np.inf:
return max(np.abs(x)... | 1,607 | 25.8 | 61 | py |
scipy | scipy-main/benchmarks/benchmarks/cutest/dfovec.py | # This is a python implementation of dfovec.m,
# provided at https://github.com/POptUS/BenDFO
import numpy as np
def dfovec(m, n, x, nprob):
# Set lots of constants:
c13 = 1.3e1
c14 = 1.4e1
c29 = 2.9e1
c45 = 4.5e1
v = [
4.0e0,
2.0e0,
1.0e0,
5.0e-1,
2.5e-... | 10,188 | 25.955026 | 94 | py |
scipy | scipy-main/benchmarks/benchmarks/cutest/dfoxs.py | # This is a python implementation of dfoxs.m,
# provided at https://github.com/POptUS/BenDFO
import numpy as np
def dfoxs(n, nprob, factor):
x = np.zeros(n)
if nprob == 1 or nprob == 2 or nprob == 3: # Linear functions.
x = np.ones(n)
elif nprob == 4: # Rosenbrock function.
x[0] = -1.2
... | 2,637 | 26.768421 | 74 | py |
scipy | scipy-main/scipy/conftest.py | # Pytest customization
import json
import os
import warnings
import numpy as np
import numpy.array_api
import numpy.testing as npt
import pytest
from scipy._lib._fpumode import get_fpu_mode
from scipy._lib._testutils import FPUModeChangeWarning
from scipy._lib import _pep440
from scipy._lib._array_api import SCIPY_AR... | 5,991 | 33.436782 | 91 | py |
scipy | scipy-main/scipy/setup.py | def configuration(parent_package='',top_path=None):
from numpy.distutils.system_info import get_info
get_info("lapack_opt")
from numpy.distutils.misc_util import Configuration
config = Configuration('scipy',parent_package,top_path)
config.add_subpackage('_lib')
config.add_subpackage('cluster')
... | 1,182 | 32.8 | 59 | py |
scipy | scipy-main/scipy/_distributor_init.py | """ Distributor init file
Distributors: you can add custom code here to support particular distributions
of SciPy.
For example, this is a good place to put any checks for hardware requirements.
The SciPy standard source distribution will not put code in this file, so you
can safely replace this file with your own ve... | 331 | 29.181818 | 78 | py |
scipy | scipy-main/scipy/__init__.py | """
SciPy: A scientific computing package for Python
================================================
Documentation is available in the docstrings and
online at https://docs.scipy.org.
Contents
--------
SciPy imports all the functions from the NumPy namespace, and in
addition provides:
Subpackages
-----------
Using ... | 6,530 | 32.152284 | 80 | py |
scipy | scipy-main/scipy/integrate/lsoda.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
import warnings
from . import _lsoda # type: ignore
__all__ = ['lsoda'] # noqa: F822
def __dir__():
return __all__
def __getattr__(name):
if name not in __all__:
raise AttributeError(
"scipy.integrate.lsod... | 610 | 22.5 | 76 | py |
scipy | scipy-main/scipy/integrate/odepack.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.integrate` namespace for importing the functions
# included below.
import warnings
from . import _odepack_py
__all__ = ['odeint', 'ODEintWarning'] # noqa: F822
def __dir__():
return __all__
def __getattr__(name):
... | 771 | 28.692308 | 79 | py |
scipy | scipy-main/scipy/integrate/setup.py | import os
from os.path import join
from scipy._build_utils import numpy_nodepr_api
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info
from scipy._build_utils import (uses_blas64, blas_ilp64_pre_build_hoo... | 4,440 | 37.95614 | 88 | py |
scipy | scipy-main/scipy/integrate/_bvp.py | """Boundary value problem solver."""
from warnings import warn
import numpy as np
from numpy.linalg import pinv
from scipy.sparse import coo_matrix, csc_matrix
from scipy.sparse.linalg import splu
from scipy.optimize import OptimizeResult
EPS = np.finfo(float).eps
def estimate_fun_jac(fun, x, y, p, f0=None):
... | 41,067 | 34.403448 | 88 | py |
scipy | scipy-main/scipy/integrate/vode.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
import warnings
from . import _vode # type: ignore
__all__ = [ # noqa: F822
'dvode',
'zvode'
]
def __dir__():
return __all__
def __getattr__(name):
if name not in __all__:
raise AttributeError(
"sc... | 625 | 20.586207 | 76 | py |
scipy | scipy-main/scipy/integrate/_quadpack_py.py | # Author: Travis Oliphant 2001
# Author: Nathan Woods 2013 (nquad &c)
import sys
import warnings
from functools import partial
from . import _quadpack
import numpy as np
__all__ = ["quad", "dblquad", "tplquad", "nquad", "IntegrationWarning"]
error = _quadpack.error
class IntegrationWarning(UserWarning):
"""
... | 52,822 | 41.190895 | 468 | py |
scipy | scipy-main/scipy/integrate/_odepack_py.py | # Author: Travis Oliphant
__all__ = ['odeint']
import numpy as np
from . import _odepack
from copy import copy
import warnings
class ODEintWarning(Warning):
pass
_msgs = {2: "Integration successful.",
1: "Nothing was done; the integration time was 0.",
-1: "Excess work done on this call (per... | 10,769 | 40.264368 | 102 | py |
scipy | scipy-main/scipy/integrate/_quad_vec.py | import sys
import copy
import heapq
import collections
import functools
import numpy as np
from scipy._lib._util import MapWrapper, _FunctionWrapper
class LRUDict(collections.OrderedDict):
def __init__(self, max_size):
self.__max_size = max_size
def __setitem__(self, key, value):
existing_k... | 21,166 | 31.365443 | 102 | py |
scipy | scipy-main/scipy/integrate/_ode.py | # Authors: Pearu Peterson, Pauli Virtanen, John Travers
"""
First-order ODE integrators.
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
... | 47,921 | 33.903132 | 90 | py |
scipy | scipy-main/scipy/integrate/dop.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
import warnings
from . import _dop # type: ignore
__all__ = [ # noqa: F822
'dopri5',
'dop853'
]
def __dir__():
return __all__
def __getattr__(name):
if name not in __all__:
raise AttributeError(
"s... | 622 | 20.482759 | 76 | py |
scipy | scipy-main/scipy/integrate/__init__.py | """
=============================================
Integration and ODEs (:mod:`scipy.integrate`)
=============================================
.. currentmodule:: scipy.integrate
Integrating functions, given function object
============================================
.. autosummary::
:toctree: generated/
quad ... | 4,074 | 36.385321 | 81 | py |
scipy | scipy-main/scipy/integrate/_quadrature.py | from __future__ import annotations
from typing import TYPE_CHECKING, Callable, Any, cast
import numpy as np
import math
import warnings
from collections import namedtuple
from scipy.special import roots_legendre
from scipy.special import gammaln, logsumexp
from scipy._lib._util import _rng_spawn
from scipy._lib.deprec... | 53,017 | 33.629654 | 113 | py |
scipy | scipy-main/scipy/integrate/quadpack.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.integrate` namespace for importing the functions
# included below.
import warnings
from . import _quadpack_py
__all__ = [ # noqa: F822
"quad",
"dblquad",
"tplquad",
"nquad",
"IntegrationWarning",
"er... | 845 | 24.636364 | 79 | py |
scipy | scipy-main/scipy/integrate/tests/test_bvp.py | import sys
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
import numpy as np
from numpy.testing import (assert_, assert_array_equal, assert_allclose,
assert_equal)
from pytest import raises as assert_raises
from scipy.sparse import coo_matrix
from sc... | 20,181 | 27.345506 | 80 | py |
scipy | scipy-main/scipy/integrate/tests/test__quad_vec.py | import pytest
import numpy as np
from numpy.testing import assert_allclose
from scipy.integrate import quad_vec
from multiprocessing.dummy import Pool
quadrature_params = pytest.mark.parametrize(
'quadrature', [None, "gk15", "gk21", "trapezoid"])
@quadrature_params
def test_quad_vec_simple(quadrature):
n... | 6,286 | 28.938095 | 90 | py |
scipy | scipy-main/scipy/integrate/tests/test_quadpack.py | import sys
import math
import numpy as np
from numpy import sqrt, cos, sin, arctan, exp, log, pi
from numpy.testing import (assert_,
assert_allclose, assert_array_less, assert_almost_equal)
import pytest
from scipy.integrate import quad, dblquad, tplquad, nquad
from scipy.special import erf, erfc
from scipy._l... | 27,983 | 40.274336 | 84 | py |
scipy | scipy-main/scipy/integrate/tests/test_quadrature.py | import pytest
import numpy as np
from numpy import cos, sin, pi
from numpy.testing import (assert_equal, assert_almost_equal, assert_allclose,
assert_, suppress_warnings)
from scipy.integrate import (quadrature, romberg, romb, newton_cotes,
cumulative_trapezoid, ... | 18,274 | 37.636364 | 82 | py |
scipy | scipy-main/scipy/integrate/tests/test_odeint_jac.py | import numpy as np
from numpy.testing import assert_equal, assert_allclose
from scipy.integrate import odeint
import scipy.integrate._test_odeint_banded as banded5x5
def rhs(y, t):
dydt = np.zeros_like(y)
banded5x5.banded5x5(t, y, dydt)
return dydt
def jac(y, t):
n = len(y)
jac = np.zeros((n, n)... | 1,816 | 23.226667 | 71 | py |
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