text string |
|---|
import numpy as np
from scipy import stats
from pysofia import svm_train, svm_predict, learner_type, loop_type, eta_type
def test_1():
np.random.seed(0)
X = np.random.randn(200, 5)
query_id = np.ones(len(X))
w = np.random.randn(5)
y = np.dot(X, w)
coef = svm_train(X, y, query_id, 1., X.shape[0... |
<filename>notebook/scipy_sparse_method.py
import numpy as np
from scipy.sparse import csr_matrix, lil_matrix
l = [[0, 1, 2],
[3, 0, 4],
[0, 0, 0]]
csr = csr_matrix(l)
lil = lil_matrix(l)
print(csr.sum())
# 10
print(csr.mean())
# 1.111111111111111
print(csr.max())
# 4
print(csr.min())
# 0
# print(lil.ma... |
<reponame>raun1/Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-<filename>src/comp_net_raw.py<gh_stars>10-100
# coding: utf-8
# In[2]:
import keras
import scipy as sp
import scipy.misc, scipy.ndimage.interpolation
from medpy import metric
import numpy as np
import os
from keras import losses... |
###############################################################################################################################
# This script implements a simplification of the evolutionary process proposed by Real et al.: https://arxiv.org/abs/1802.01548v7#
#############################################################... |
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.regularizers import l2
from keras.optimizers import SGD ,Adagrad
from scipy.io import loadmat, savemat
from keras.models import model_from_json
import theano.tensor as T
import theano
import csv
import configparser
import... |
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
import statsmodels.api as sm
def separate_frequency_linear(HISm_mean, REA=0):
hig_list = []
low_list = []
org_list = []
if REA == 0:
hig_list = []
low_list = []
org_list = []
for i in range(HISm_... |
<gh_stars>0
import os
import csv
from math import sqrt, pi, sin, cos, tan, atan
from cmath import phase
from calculation import TIntensity, TStokesVector, TStokesNaturalVector
from gradient import Gradient
class TTask12:
def __init__(self, Idx, Alfa, Beta):
self.Idx = Idx
self.Alfa = Alfa
... |
<reponame>dellani/TractSeg<filename>tractseg/libs/plot_utils.py<gh_stars>0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
import math
import numpy as np
import nibabel as nib
import torch
from nibabel import trackvis
from dipy.tra... |
<reponame>bjodah/pyneqsys
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# PYTHON_ARGCOMPLETE_OK
# Pass --help flag for help on command-line interface
import sympy as sp
import numpy as np
from pyneqsys.symbolic import SymbolicSys
def solve(guess_a, guess_b, power, solver='scipy'):
""" Constructs a pyneqsys.sy... |
<reponame>AmirooR/caffe_video_segmentation
import caffe
import numpy as np
from matplotlib.pyplot import imshow, show, figure
from skimage import io
from skimage.transform import resize
from scipy.sparse import csr_matrix
path = 'test_pywarping_layer.prototxt'
img_paths = ['input_0.jpg', 'input_1.jpg']
im_shape = (100... |
<filename>galpy/orbit/integrateLinearOrbit.py
import ctypes
import ctypes.util
from numpy.ctypeslib import ndpointer
import numpy
from scipy import integrate
from .. import potential
from ..util.multi import parallel_map
from .integratePlanarOrbit import _parse_integrator, _parse_tol
from .integrateFullOrbit import _pa... |
from sympy import solve, sin, cos, pprint
from sympy.abc import x, y
from sympy.plotting import plot
import numpy as np
sol = solve(x**2+2*x+5, x)
pprint(sol)
plot(sin(x))
|
<reponame>oublalkhalid/Time-series-anomaly<filename>anom_detect.py<gh_stars>0
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
class anom_detect():
"""Anomaly detection for time series data
The method can be used to computed a movi... |
<filename>inception.py
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.stats import entropy
from data_sampler import SequentialSampler, BatchSampler
from utl import aduc
k3same = dict(kernel_size=3, stride=1, padding=1)
k5same = dict(kernel_size=5, stride=1, padding=2)... |
<filename>tests/evaluation/detection/test_eval.py
# <Copyright 2022, Argo AI, LLC. Released under the MIT license.>
"""Detection evaluation unit tests.
Only the last two unit tests here use map ROI information.
The rest apply no filtering to objects that have their corners located outside of the ROI.
"""
import math... |
# ---------------------------------------------------------------------------------------------------------------------
# Aufgabe 15: Branching DQN with soft copy of weights (weighted update)
# 19.02.2022, <NAME>
#
# Implementation changes:
# - New class StateDictHelper for the state_dict calculations
# - New methode B... |
# MIT License
#
# Copyright (c) 2022 Quandela
#
# 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, pub... |
<reponame>LavaRNG/lava
import numpy as np
from scipy import special as sp
from scipy import misc as ms
from math import pi as PI
from math import isnan
# estimates
NP = np.array([10**i for i in range(1,7)]) # 10:1M
NR = np.array([10**i for i in range(1,7)]) # 10:1M
# hyperparameters
A = np.array([10**i for i in range... |
<reponame>gumpy-hybridBCI/gumpy-Realtime<filename>src/ssvep/preprocess.py
import mne
import os
import scipy.io as sio
import numpy as np
import matplotlib.pyplot as plt
from mne.time_frequency import psd_welch
mne.set_log_level("ERROR")
recording_dir = os.path.join(os.path.dirname(__file__), "..", "REC")
stimulation... |
<gh_stars>0
#!/usr/bin/env python
""" Plot backed by a pandas DataFrame. """
# Major library imports
from numpy import linspace
from pandas import DataFrame
from scipy.special import jn
# Enthought library imports
from enable.api import Component, ComponentEditor
from traits.api import HasTraits, Instance
from traits... |
<gh_stars>10-100
import plotly.graph_objs as go
from plotly.offline import plot
import numpy as np
from scipy.spatial.transform.rotation import Rotation
from scipy.spatial.ckdtree import cKDTree
def plot_box(pd, pos, quat, size):
d = -size
p = size
X = np.array([[d[0], d[0], p[0], p[0], d[0], d[0], p[0], ... |
import numpy as np
from scipy.sparse.csgraph import shortest_path, dijkstra, floyd_warshall, bellman_ford, johnson
from scipy.sparse import csr_matrix
n = 100
c = n * 2
np.random.seed(1)
d = np.random.randint(0, n, c)
i = np.random.randint(0, n, c)
j = np.random.randint(0, n, c)
csr = csr_matrix((d, (i, j)), shape=(n... |
# -*- coding:Utf-8 -*-
import numpy as np
import scipy as sp
import time
import pdb
import os
import sys
import pickle as pk
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection # scenario CDF mode 3D
from matplotlib.colors import colorConverter # scenario CDF mode 3D
from pylayers.l... |
from os.path import dirname, abspath, join
import numpy as np
from matplotlib import patches
from matplotlib import pyplot as plt
from scipy.interpolate import interp1d
import pdb
from sofacontrol.utils import load_data
path = dirname(abspath(__file__))
#############################################
# Problem 1, Fig... |
import numpy as np
import pytest
from sklearn.utils.testing import assert_array_equal
from scipy import sparse
from anndata.tests.helpers import gen_adata, subset_func, asarray
@pytest.fixture(
params=[np.array, sparse.csr_matrix, sparse.csc_matrix],
ids=["np_array", "scipy_csr", "scipy_csc"],
)
def matrix_t... |
<gh_stars>1-10
import artm
import operator
import functools
import numpy as np
import pandas as pd
from collections import Counter, OrderedDict
from scipy.optimize import curve_fit
from .base_score import BaseScore
# change log style
lc = artm.messages.ConfigureLoggingArgs()
lc.minloglevel = 3
lib = artm.wrapper.Lib... |
import numpy as np
import scipy.interpolate
from scipy.interpolate import make_interp_spline, BSpline, CubicSpline
from scipy.spatial.transform import Rotation as Rot
import torch
import os
import json
def shiftRaceline(raceline: np.ndarray, reference_vec: np.ndarray, distance: float, s = None):
diffs = raceline[1... |
<reponame>fdeloche/fmaskedCAP-model
import torch
import copy
import numpy as np
import matplotlib.pyplot as pl
from scipy.optimize import curve_fit
from functools import partial
class PowerLawLatencies:
'''
Links frequencies and latencies (power law model).
log(f)= log(A) + alpha log ( |t-t0|)
|t-t0| (mode:'bo... |
import os
import time
import numpy as np
from scipy import sparse as spsp
import dgl
import backend as F
import unittest, pytest
from dgl.graph_index import create_graph_index
from numpy.testing import assert_array_equal
def create_random_graph(n):
arr = (spsp.random(n, n, density=0.001, format='coo') != 0).astyp... |
import numpy as np
import torch
from scipy.stats import truncnorm, truncexpon
from torch import nn
from torch.nn.functional import interpolate
from paderbox.transform.module_fbank import hz2mel, mel2hz
from einops import rearrange
from padertorch.utils import to_list
from typing import Tuple, List
import torch.nn.func... |
"""
Utility functions to fit and apply coordinates transformation from FVC to FP
"""
import json
from pkg_resources import resource_filename
import numpy as np
from scipy.interpolate import interp1d
from desimeter.transform.zhaoburge import getZhaoBurgeXY, transform, fitZhaoBurge
from desimeter.trig import average_an... |
# Copyright 2020 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... |
"""
Implements 'propagation', whereby terms from the full ConceptNet graph are
assigned vectors from the embeddings produced by retrofitting against the
reduced graph.
"""
import numpy as np
import pandas as pd
from scipy.sparse import diags
from conceptnet5.builders.reduce_assoc import ConceptNetAssociationGraph
fro... |
<filename>utils/utils_test.py
import numpy as np
import scipy.sparse as sp
import torch
import time
import random
from utils.tool import read_data, write_dic, dictionary, normalize, sparse_mx_to_torch_sparse_tensor
def encoding_test(run = 10, train_dataset = "fb237_v1", test_dataset = "fb237_v1_ind"):
"... |
<reponame>Abdumaleek/infinity-mirror
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Train on CPU (hide GPU) due to memory constraints
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import tensorflow as tf
import numpy as np
import scipy.sparse as sp
from collections import namedtuple
from src.gae.gae.optimizer imp... |
import numpy as np
from scipy.spatial import distance
def cayley_menger_analysis(vertices, d = 2):
"""
Determines volume and circumradius for a tetrahedron given the vertices
https://westy31.home.xs4all.nl/Circumsphere/ncircumsphere.htm#Coxeter
"""
if d == 3:
cm_matrix = np.array([[0, 1, 1,... |
import pickle, glob, sys, csv, warnings
import numpy as np
from feature_extraction_utils import _load_file, _save_file, _get_node_info
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import accuracy_score, confusion_matrix, auc, roc_curve
from sklearn.naive_bayes import GaussianNB
from sklear... |
import csv
import numpy as np
import cv2
from scipy import ndimage
#this fumction reads the CSV file and return left,center and right images. Also the steering measurments.
def read_csv():
lines = list()
with open("./data/driving_log.csv") as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.a... |
import numpy as np
from scipy.stats import binom
from statsmodels.stats.multitest import multipletests
from .common import *
def predict_expression(transcripts, init_site_range, p):
""" Probability that at least the number of observed transcripts in the initiation site
is due to random chance assuming th... |
<reponame>princeton-computational-imaging/NLOSFeatureEmbeddings
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
################################################################
class lct_fk_fast(nn.Module):
def __init__(self, spatial=256, crop=512, \
... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import cholesky, cho_solve
import seaborn as sns
sns.set_style('darkgrid')
class GP:
def __init__(self, x_train: np.ndarray, y_train: np.ndarray, noise_var: float = 1., lscale: float = 1.,
k_var: float = 1., prior_mean: float =... |
<gh_stars>1-10
""" Define the class for adaptive loss scaling. """
from timeit import default_timer as timer
import numpy as np
from scipy.special import erfinv
class AdaLoss(object):
""" Implementation of the adaptive loss scaling method. """
def __init__(
self,
func_params=None,
s... |
<reponame>MasaKat0/D3RE
import numpy as np
import six
from scipy import optimize
from sklearn import metrics
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
def train(x_train, t_train, x_test, t_test, epoch, model, optimizer,... |
<filename>vispol/test.py
import vispol
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('C:/python scripts/ciecam02 plot')
import Read_Meredith as rm
import scipy.io as sio
import colorspacious as clr
from scipy.stats import entropy
from scipy.signal import convolve2d
from scipy.signal impo... |
from numpy import *
import theano
import theano.tensor as T
import theano.typed_list as tl
import theano.sparse as sparse
from scipy.misc import logsumexp
from scipy.optimize import fmin_ncg
import scipy.sparse as sp
import time
random.seed(1)
K = 5 #nClasses
N = 150 #nSamples
D = 3 #nFeatures
#single precision for... |
<gh_stars>0
import numpy as np
import numpy.random as rand
import scipy as sp
import matplotlib.pyplot as plt
def gen_training(func, x, var=1):
y = func(x)
y += rand.normal(0, var, y.shape)
return y
def const(x):
return lambda y: x
def lms(funcs, x, y, alpha):
(m,) = x.shape
(n,) = funcs.... |
<filename>python/algo/ff9.py
#!/usr/env/python
import time
import numpy as np
# import matplotlib.pyplot as plt
from scipy import signal as sig
from numba import jit
# from ..datasets import synthetic as synth
# from ..datasets import read_msrc as msrc
from ..utils import arrays as ar
from ..utils import sliding_win... |
import tensorflow as tf
import numpy as np
import pandas as pd
from scipy import optimize, stats
from collections import OrderedDict
import argparse
# likelihood function for MK test
class SimpleMK(object):
def __init__(self,
neutral_div,
neutral_poly,
foreground... |
<gh_stars>0
import numpy,copy
class SO():
def __init__(self):#This class can only be inherited from
pass
def drawPSFandSB(self,band):
dat=self.stochasticobservingdata[band]
k=numpy.random.randint(len(dat[:,0]))
return dat[k,0],dat[k,1]
def CalculateETSB(self,sbs,ban... |
from collections import OrderedDict
import numpy as np
import os
from hazel.atmosphere import General_atmosphere
from hazel.util import i0_allen
from hazel.codes import sir_code
from hazel.io import Generic_SIR_file
import scipy.interpolate as interp
from hazel.exceptions import NumericalErrorSIR
from hazel.transforms ... |
# --------------
import pandas as pd
import scipy.stats as stats
import math
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#Sample_Size
sample_size=2000
#Z_Critical Score
z_critical = stats.norm.ppf(q = 0.95)
# path [File location variable]
#Code starts here
data = ... |
#!/usr/bin/python2.7
from fractions import gcd
N = 1000000
phi = [0, 1]
for n in range(2, N+1):
phi.append(0)
for n in range(2, N+1):
if phi[n] == 0:
phi[n] = n-1
k = 1
p = n
while (p**k) <= N:
pk = p**k
phi[pk] = (pk/p) * (p-1)
m = 2
... |
<filename>model_large_dataset.py
import random
import string
import os
import pandas as pd
import numpy as np
import scipy
import joblib
from scipy.stats import uniform
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import f1_score, classification_report, confusion_matrix
from sklearn.pipe... |
<filename>tests/test_variant_effect.py
import copy
import os
import sys
import warnings
import cyvcf2
import numpy as np
import pandas as pd
import pybedtools as pb
import pytest
from scipy.special import logit
import config
import kipoi
import kipoi_veff
import kipoi_veff as ve
import kipoi_veff.snv_predict as sp
im... |
"""
Test that batch and run-order correction behaves sensibly with a combination of synthetic and model datasets.
"""
import scipy
import pandas
import numpy
import seaborn as sns
import sys
import unittest
import os
sys.path.append("..")
import nPYc
from generateTestDataset import generateTestDataset
class test_ro... |
<reponame>SophieHerbst/mne-bids
"""Utility functions to copy raw data files.
When writing BIDS datasets, we often move and/or rename raw data files. several
original data formats have properties that restrict such operations. That is,
moving/renaming raw data files naively might lead to broken files, for example
due t... |
"""Makes flattened views of volumetric data on the cortical surface.
"""
from six import string_types
from functools import reduce
import os
import glob
import numpy as np
import string
from .. import utils
from .. import dataset
from ..database import db
from ..options import config
def make_flatmap_image(braindata... |
<gh_stars>10-100
"""Cross-tabulation module
The module implements the cross-tabulation analysis.
"""
from __future__ import annotations
import itertools
from typing import Any, Optional, Union
from patsy import dmatrix
import numpy as np
import pandas as pd
from scipy.stats import chi2, f
from samplics.estima... |
<reponame>indiradutta/PULSE<gh_stars>0
import torch
import torchvision
import numpy as np
import sys
import os
import glob
import dlib
import gdown
import json
import scipy
import scipy.ndimage
import PIL
import PIL.Image
from pathlib import Path
__PREFIX__ = os.path.dirname(os.path.realpath(__file__))
class Prepr... |
<filename>bdgym/envs/utils.py<gh_stars>0
"""General utility functions
Credit to: https://github.com/eleurent/highway-env
"""
from typing import Union, Tuple, List
import numpy as np
from scipy.stats import truncnorm
Interval = Union[
np.ndarray,
Tuple[float, float],
List[float]
]
def lmap(v: float, x: ... |
# Copyright (c) 2020 NVIDIA Corporation. All rights reserved.
# This work is licensed under the NVIDIA Source Code License - Non-commercial. Full
# text can be found in LICENSE.md
import cv2
import math
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image as PILImage
from scipy.ndimage.filters impo... |
<gh_stars>0
import os
from itertools import combinations
from typing import Tuple, Optional, Union, Callable, Dict, Iterable
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.colors as colors
import matplotlib.cm as cm
from matplotlib.backend... |
"""
Provides class Hiarrchy for the analysis of multiple segementations orgainized
in a hierarchy (each segmentation is a subset of the next one).
# Author: <NAME> (Max Planck Institute for Biochemistry)
# $Id$
"""
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import ... |
''' These give the derivations for Euler angles to rotation matrix and
Euler angles to quaternion. We use the rotation matrix derivation only
in the tests. The quaternion derivation is in the tests, and,
in more compact form, in the ``euler2quat`` code.
The rotation matrices operate on column vectors, thus, if ``R``... |
<filename>astro345_fall2015/kepler_cleanedup.py
import math
import numpy
import scipy
import pylab
import scipy.optimize
#function definitions.
#the 0.2 is the t-Tau moved to the other side so we can solve for x when y is 0.
def f(x):
y = x - 0.2 * numpy.sin(x) - 0.8
return y
def f_prime(x):
y = ... |
<gh_stars>10-100
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
import itertools
import logging
import re
from collections import OrderedDict
from typing import List
import mxnet as mx
import scipy as sp
from mxnet import nd, gluon
from tqdm import tqdm
from data.AugmentedAST import Augment... |
"""Common algebra of "quantum" objects.
Quantum objects have an associated Hilbert space, and they support (at least
partially) summation, products, multiplication with a scalar, and adjoints.
The algebra defined in this module is the superset of the Hilbert space algebra
of states (augmented by the tensor product), ... |
#!/home/renato/anaconda2/bin/python
import numpy as np
import matplotlib.pyplot as plt
import os, sys
from scipy.interpolate import interp2d
from pylab import *
import pandas as pd
print "--------------------------------------------------------------------------"
print "------------------------ Start GroIMP 1.5 --... |
<filename>regression/module_NN_ens.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import tensorflow as tf
import datetime
from scipy.special import erf
import importlib
import utils
importlib.reload(utils)
from utils import *
class NN():
def __init__(self,
activation_fn, x_di... |
"""
Helpers to prepare input data for models
stack, shuffle, normalize
__author__: <NAME>
"""
import os
import warnings
from typing import List, Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.interpolate
from scipy import stats
from scipy.stats import norm
from skl... |
<reponame>BrancoLab/LocomotionControl
from loguru import logger
import numpy as np
from scipy.signal import medfilt
import pandas as pd
from fcutils.maths.geometry import (
calc_distance_between_points_in_a_vector_2d as get_speed_from_xy,
)
from fcutils.maths.geometry import (
calc_angle_between_points_of_vect... |
<reponame>jma712/DIRECT<filename>src/main_disent.py<gh_stars>1-10
'''
Disentangled multiple cause effect learning
2020-07-08
'''
import time
import numpy as np
import torch
from torch import optim
from torch import nn
from torch.nn import functional as F
from torchvision.utils import save_image
from torch.utils.data i... |
# -MPdSH
'''_____Standard imports_____'''
import numpy as np
import scipy.fftpack as fp
import scipy
'''_____Project imports_____'''
from src.toolbox.filters import butter_highpass_filter
#from src.toolbox.calibration_processing import linearize_spectra, compensate_dispersion
from src.toolbox.maths import spectra2ali... |
"""
MissX Imputer for Missing Data
Modified code of missforest (https://github.com/stekhoven/missForest)
- The imputer was modified so that...
- Custom predictors can be used
- Predictions are done in parallel (for faster calculation)
- Delete codes for classification
"""
import warnings
i... |
<reponame>KorlaMarch/tuplex<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# ## Flights core-exp plot
# In[1]:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import json
import seaborn as sns
import datetime
from matplotlib.patches import Patch
import matplot... |
<gh_stars>0
from scipy import signal
import numpy as np
class NpCircularArray:
def __init__(self, n, length):
self.arr = np.zeros([length, n])
def append(self, arr):
self.arr[0:-1, :] = self.arr[1:, :]
self.arr[-1, :] = arr
def set_all(self, arr):
self.arr = np.tile(arr, ... |
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
import scipy.io as sio
from scipy import sparse
import time
from CSMSSMTools import *
def getDiffusionMap(SSM, Kappa, t = -1, includeDiag = True, thresh = 5e-4, NEigs = 51):
"""
:par... |
"""
NOTE: below is some legacy code that is not compatible with current BayesFast
we will revise it later
"""
import numpy as np
from scipy.special import expit
import multiprocessing as mp
import warnings
import time
from ..samplers.pymc3.nuts import NUTS
from ..utils.warnings import SamplingProgess
from ..uti... |
import numpy as np
from panqec.codes import StabilizerCode
from panqec.decoders import BaseDecoder
from panqec.error_models import BaseErrorModel
from typing import Dict
import panqec.bsparse as bsparse
from panqec.bpauli import bcommute
from scipy.sparse import csr_matrix
PAULI_I = 0
PAULI_X = 1
PAULI_Y = 2
PAULI_Z ... |
import pandas as pd
import seaborn as sns
import numpy as np
import plotly.express as px
import json
import os
import matplotlib.pyplot as plt
import nltk
import statistics as sts
# %matplotlib inline
from urllib.request import urlopen
from nltk.corpus import stopwords
from nltk.stem import RSLPStemmer
from sklearn... |
import numba as nb
import numpy as np
import warnings
from scipy import optimize
from .utils import (
preprocess_trajs,
get_nfeatures,
trajs_matmul,
symeig,
solve_stationary,
compute_ic,
compute_c0,
batch_compute_ic,
batch_compute_c0,
is_cutlag,
)
# ----------------------------... |
r"""
Solve Klein-Gordon equation on [-2pi, 2pi]**3 with periodic bcs
u_tt = div(grad(u)) - u + u*|u|**2 (1)
Discretize in time by defining f = u_t and use mixed formulation
f_t = div(grad(u)) - u + u*|u|**2 (1)
u_t = f (2)
with both u(x, y, z, t=0) and f... |
'''
Define Bernstein Polynomials
i and p index the BPs
t is the time variable
h is the size of the timestep
'''
import scipy.special
import math
def BP(t: float, i: int, p: int, h: float):
if i < 1 or i > p:
return 0
elif t >= 0 and t <= h:
norm_coef = scipy.special.binom(p-1,i-1)
t = t/h
return norm_... |
<gh_stars>0
import sys
if sys.version_info < (3,):
range = xrange
import numpy as np
import pandas as pd
import scipy.stats as ss
from .. import families as fam
from .. import output as op
from .. import tests as tst
from .. import tsm as tsm
from .. import data_check as dc
from .garch_recursions import garch_re... |
<gh_stars>1-10
__author__ = 'dash'
import os
import numpy as np
from PIL import Image
import random
from bucketdata import BucketData
from scipy import signal
class DataGen(object):
GO = 1
EOS = 2
def __init__(self,
data_root, annotation_fn,
evaluate=False,
... |
<reponame>Rapid-Design-of-Systems-Laboratory/beluga-legacy<filename>examples/Air Traffic Noise Minimization/AircraftNoiseCtrl_test.py
#Generates a dictionary of possible control solutions for the noise minimization
#problem. The output is meant to be passed directly into ctrl_sol on line 289
#of NecessaryConditions.py.... |
from imblearn.metrics import classification_report_imbalanced as imbal_class_report
from scipy.stats import randint as sp_randint
from sklearn import metrics
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, VotingClassifier, BaggingClassifier, \
AdaBoostClassifier, ExtraTreesClassifi... |
import skimage.morphology as skm
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from skimage.color import rgb2gray
def dispersionratio(image, alpha_th=5):
#image = misc.imread("../data/cellI4.tif")
[h,l]=image.shape
sortim = np.sort(np.reshape(i... |
<reponame>LMNS3d/sharpy
"""
@modified <NAME>
"""
import ctypes as ct
import numpy as np
import scipy as sc
import os
import itertools
import warnings
import sharpy.structure.utils.xbeamlib as xbeamlib
from sharpy.utils.solver_interface import solver, BaseSolver
import sharpy.utils.settings as settings
import sharpy.... |
import numpy as np
import scipy.stats as ss
def const_prior(t, p: float = 0.25):
"""
Constant prior for every datapoint
Arguments:
p - probability of event
"""
return np.log(p)
def geom_prior(t, p: float = 0.25):
"""
geometric prior for every datapoint
Refer to https://docs.s... |
import tensorflow as tf
import numpy as np
import argparse
import socket
import importlib
import time
import os
import scipy.misc
import sys
import h5py
import math
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.j... |
<reponame>FoxFortino/adfox<gh_stars>10-100
import os
from scipy.signal import medfilt
from astrodash.preprocessing import ReadSpectrumFile, ProcessingTools, PreProcessSpectrum
from astrodash.array_tools import zero_non_overlap_part, normalise_spectrum
class CombineSnAndHost(object):
def __init__(self, snInfo, gal... |
<gh_stars>0
import numpy as np
import scipy as sp
import scipy.linalg as linalg
def my_LDA(X, Y):
"""
Train a LDA classifier from the training set
X: training data
Y: class labels of training data
"""
classLabels = np.unique(Y) # different class labels on the dataset
classNum = len(classL... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
### Require Anaconda3
### ============================
### 3D FSC Software Package
### Analysis Section
### Written by <NAME> and <NAME>
### Downloaded from https://github.com/nysbc/Anisotropy
###
### See Paper:
### Addressing preferred specimen orientation in single-part... |
import os
import time
import scipy
import numpy as np
def mel_scale(freq):
return 1127.0 * np.log(1.0 + float(freq)/700)
def inv_mel_scale(mel_freq):
return 700 * (np.exp(float(mel_freq)/1127) - 1)
class MelBank(object):
def __init__(self,
low_freq=20,
high_freq=8000, ... |
from typing import Optional, Tuple, List, Callable
import logging
import numpy as np
from progressbar import progressbar
from scipy.interpolate import interp1d
from dat_analysis.core_util import data_row_name_append, get_data_index
from dat_analysis import useful_functions as U
logger = logging.getLogger(__name__)
... |
"""DRO and DORO Training Algorithms
Reference:
[1] Hashimoto et al., Fairness Without Demographics in Repeated
Loss Minimization, ICML 2018.
"""
import math
import scipy.optimize as sopt
import torch
import torch.nn
from torch import optim
from torch.nn.modules.module import Module
from torch.utils.data import... |
<reponame>jknox13/iterative_hierarchical_clustering<filename>flithic/clustering.py
# Authors: <NAME> <EMAIL>
# License:
# TODO: FIX lowest level of dendrogram (linkage property)
# TODO: option to run clustering past tolerance will make this more like
# scipy.cluster.hierarchy.
# TODO: incorporate heapq - priorit... |
import os
import glob
import tensorflow as tf
import tensorflow_datasets as tfds
from scipy.interpolate import interp1d
from astropy.table import Table
from astropy.io import fits
import numpy as np
import pandas as pd # To extract the SnapNumLastMajorMerger values from TNG100_SDSS_MajorMergers.csv
_DESCRIPTION = """
... |
<reponame>PabloAlvarado/ssgp
from gpflow.kernels import Matern52, Matern32, Matern12
from gpitch.kernels import Matern32sm, MercerCosMix
from gpitch.methods import find_ideal_f0, init_cparam
import numpy as np
import gpflow
from scipy import signal
def init_iv(x, num_sources, nivps_a, nivps_c, fs):
"""
Initia... |
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