text string |
|---|
<reponame>cajal/inception_loop2019<filename>staticnet_analyses/utils.py<gh_stars>1-10
is_cuda = lambda m: next(m.parameters()).is_cuda
from datajoint.expression import QueryExpression
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
from contextlib import contextmanager
import hashlib
from sc... |
<reponame>CCMMMA/deep-learning-weather-pattern-recognition<filename>lib/clustering/nec/negentropy_clustering.py
import numpy as np
from scipy.io import loadmat
from clustering.nec.layers import som, som_cluters, SOM
from clustering.nec.clustering import agglomerative
from absl import app
from absl import flags
import m... |
# Import Modulues
#==================================
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
from matplotlib import cm
from collections import OrderedDict
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from... |
# -*- coding: utf-8 -*-
# @Time : 2019-01-15 10:17
# @Author : finupgroup
# @FileName: VariableCluster.py
# @Software: PyCharm
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import PCA
from scipy import stats
import numpy as np
import pandas as pd
import random
def _choose_cluster(a... |
<reponame>hvdthong/DeepJTT_MSR<gh_stars>10-100
from clean_commit import loading_variable
import matplotlib.pyplot as plt
from statistics import mean, stdev
def statistic_msg(data):
data = [len(d.split()) for d in data]
plt.hist(data)
plt.title('Message')
plt.xlabel("Length")
plt.ylabel("Frequency"... |
<reponame>precisely/ldpred
#!/usr/bin/env python
"""
Implements LDpred, an approximate Gibbs sampler that calculate posterior means of effects, conditional on LD information.
The method requires the user to have generated a coordinated dataset using coord_genotypes.py
Usage:
ldpred --coord=COORD_DATA_FILE --ld_radiu... |
<reponame>HCGB-IGTP/BacterialTyper<gh_stars>1-10
#!/usr/bin/env python3
#################################################################
## <NAME> ##
## Copyright (C) 2019-2020 <NAME> Lab, IGTP, Spain ##
################################################################... |
#-------------------------------------------------------------------------------
# Name: modul_xyz
# Purpose:
#
# Author: s6anloew
#
# Created: 03.09.2014
# Copyright: (c) s6anloew 2014
# Licence: <your licence>
#----------------------------------------------------------------------------... |
import sympy as sp
import numpy as np
from typing import Any, Dict, Iterator, List, Optional, Tuple, TYPE_CHECKING, Union
from pyomo.environ import (
ConcreteModel, Constraint, Set, Var, Param,
)
from sympy import Matrix as Mat
from . import utils, visual
from .system import System3D
from .links import Link3D
if T... |
import numpy as np
from scipy.ndimage.morphology import binary_dilation
from vision_utils.boxutils import *
def block_masks(image,masks,dilation_factor=None,fill_color=0):
if dilation_factor is not None:
masks = binary_dilation(masks,np.ones((dilation_factor,dilation_factor)))
m = np.repeat(np.expand_dims(masks,2... |
from load_data import Data
import numpy as np
import time
import torch
from collections import defaultdict
import argparse
import scipy.sparse as sp
from collections import Counter
import itertools
from scipy import sparse
torch.manual_seed(1337)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if... |
from numpy import pi
import numpy as np
import math
#from sympy import Matrix
import pylab
#import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#from scipy.interpolate import Rbf
import pickle
from scipy.sparse import csr_matrix
from scipy.sparse import lil_matrix
from scipy.sparse.linalg import sps... |
<gh_stars>100-1000
"""
Convert a Matlab matrix file (like those found at sparse.tamu.edu) to scipy sparse .npz file.
"""
import numpy as np
import scipy.io
from scipy.sparse import save_npz
def matlab2npy(mlfile):
d = scipy.io.loadmat(mlfile)
for i in [1, 2, 0]:
a = d["Problem"][0][0][i]
try... |
<filename>nipy/neurospin/spatial_models/structural_bfls.py<gh_stars>1-10
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
The main routine of this module aims at performing the
extraction of ROIs from multisubject dataset using the localization.
Thi... |
<filename>sgimc/qa_objective/__init__.py
"""Sparse-dense operations for IMC."""
import numpy as np
from sklearn.metrics import mean_squared_error, accuracy_score
from .base import QuadraticApproximation
from scipy.special import expit
class QAObjectiveL2Loss(QuadraticApproximation):
"""Quadratic Approximation f... |
<filename>stable_projects/predict_phenotypes/He2019_KRDNN/replication/CBIG_KRDNN_proc_data.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by <NAME> and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import numpy as np
import scipy.io as sio
from cbig.H... |
<reponame>SepandKashani/sdr
import numpy as np
import scipy.stats as ss
"""
Statistic routines.
"""
def error_rate(x: np.ndarray, y: np.ndarray) -> float:
"""
Compute the error rate between two arrays.
Parameters
----------
x: np.ndarray
y: np.ndarray
Returns
-------
r: float
... |
import pandas as pd
from bin.pipeline import __creer_tableau_, __definir_les_donnees_
import sqlite3
from sqlite3 import connect
# télécherger la banque de données
df = pd.read_excel('AmelieBoucher_Plan_Psy4016_30032022_Student-mat.xlsx')
data_sql = df[['sex', 'address', 'Fedu', 'Medu', 'famrel', 'G1', 'G2', 'G3']]
... |
<reponame>hildenost/uintahtools
from functools import partial
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import matplotlib.colors as colors
import numpy as np
from scipy.interpolate import griddata
import pandas as pd
import seaborn as sns
sns.set_style("white")
from uintahtools.udaframe import UdaF... |
<reponame>VChristiaens/VIP2.7<filename>vip/stats/distances.py
#! /usr/bin/env python
"""
Distance between images.
"""
from __future__ import division
__author__ = '<NAME> @ ULg'
__all__ = ['cube_distance',
'cube_distance_to_frame']
import numpy as np
import scipy.stats
from matplotlib import pyplot as pl... |
<filename>feature_extraction.py
#!/usr/bin/env python
# import sys
# import os
#
# # Using https://stackoverflow.com/questions/51520/how-to-get-an-absolute-file-path-in-python
# utils_path = os.path.abspath("utils")
#
# # Using https://askubuntu.com/questions/470982/how-to-add-a-python-module-to-syspath/471168
# sys.p... |
import calendar
import numpy as np
import pandas as pd
import re
import scipy.interpolate as interp
import urllib
import warnings
from datetime import datetime as dt,timedelta
from ..plot import Plot
from .tools import *
from ..tracks.tools import *
try:
import cartopy.feature as cfeature
from cartopy import ... |
<reponame>Mattzor/exkalman
"""
Copyright (c) 2017, <NAME>
Copyright (c) 2017, <NAME>
Copyright (c) 2017, <NAME>
Copyright (c) 2017, <NAME>
Copyright (c) 2017, <NAME>
Copyright (c) 2017, <NAME>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided t... |
from sklearn.cluster import MeanShift
from scipy.optimize import minimize
from ase import Atom
import numpy as np
from random import random
def min_dist(pos, host):
"""minimum distance from position to a host atom :param pos: vector x,y,z
position :param host: host atoms object :return: float, minimum distance... |
import torch.utils.data as data
from PIL import Image
import cv2
import os
import os.path
import torch
import numpy as np
import torchvision.transforms as transforms
import argparse
import time
import random
from lib.transformations import quaternion_from_euler, euler_matrix, random_quaternion, quaternion_matrix
import... |
<filename>viterbi.py
import codecs
import numpy
from scipy.io import loadmat
te = loadmat('matlab/te.mat')
t = te['t']
e = te['e']
print(e)
chars = []
dicts = {}
with codecs.open('pku_dic/pku_dict.utf8', 'r', encoding='utf8') as f:
lines = f.readlines()
for line in lines:
for w in line:
c... |
<reponame>gavinmischler/spikeFRInder
"""
Functions to reproduce figure 3 from the paper. The data can be downloaded
from http://crcns.org/data-sets/methods/cai-1. We took the data
and initially converted it to .txt files which are read in by this script.
This script can be run either from scratch, or from a stored fi... |
<filename>hqli.py
# High Quality Linear Interpolation algorithm implementation
# Reference: <NAME>., <NAME>, <NAME>.
# http://research.microsoft.com/pubs/102068/demosaicing_icassp04.pdf
#
#
import numpy as np
import scipy.signal as signal
# Four kernels to be convolved with CFA array
def _G_at_BR(cfa):
... |
import numpy as np
from scipy.stats import binom, binom_test
from PIL import Image, ImageDraw, ImageFont, ImageMath
import os
basedir = '.\\Images\\RotatingCube\\'
if os.name == 'posix':
basedir = 'Images/RotatingCube/'
base_y = 400
base_x = 100
n = 5
scale = 300
def draw_binom():
im = Image.new("RGB", (512,... |
<reponame>jd-13/ElectroMap
"""
"""
import numpy as np
import scipy
def activationmapoff(pixelSize,
framerate,
images,
mask,
velalgo,
before,
tfilt,
usespline,
... |
"""
This source includs two types of classes or functions.
1. Evaluation methods:
This source includs some evaluation metrics, such as r-square, CI, mse ...
or even confidence intervals and ECE, which are uncertainty measures.
2. Some plotting functions.
"""
from lifelines.utils import concordance_index
... |
import pandas as pd
import numpy as np
import math
import scipy.stats as stats
def _chk_asarray(a, axis):
if axis is None:
a = np.ravel(a)
outaxis = 0
else:
a = np.asarray(a)
outaxis = axis
if a.ndim == 0:
a = np.atleast_1d(a)
return a, outaxis
def _square_o... |
<gh_stars>1-10
# coding: utf-8
from __future__ import absolute_import, print_function
""" An abstract model class for stellar spectra """
__author__ = "<NAME> <<EMAIL>>"
import logging
import os
import yaml
import numpy as np
from functools import partial
from scipy import stats
__all__ = ["Model"]
logger = loggi... |
# Copyright 2017 University of Maryland.
#
# This file is part of Sesame. It is subject to the license terms in the file
# LICENSE.rst found in the top-level directory of this distribution.
import numpy as np
from PyQt5.QtCore import *
from PyQt5 import QtCore
import logging
import sesame
from ..solvers import Solver... |
from sympy.core.evalf import INF
from omniqubo.models.sympyopt.sympyopt import SympyOpt
class TestIntVar:
def test_eq(self):
sympyopt1 = SympyOpt()
sympyopt2 = SympyOpt()
sympyopt3 = SympyOpt()
sympyopt4 = SympyOpt()
y1 = sympyopt1.variables[sympyopt1.int_var(name="y").na... |
from random import randint
from math import ceil
import numpy as np
import scipy.io.wavfile
import scipy.signal
from app.hparams import hparams
def prompt_yesno(q_):
while True:
action = input(q_ + ' [Y]es [n]o : ')
if action == 'Y':
return True
elif action == 'n':
... |
import numpy as np
from numpy.ma import isin
from scipy.ndimage.interpolation import affine_transform
import torch
import matplotlib.pyplot as plt
import os
from pathlib import Path
import glob
from torchvision.transforms import RandomAffine
import torchvision
import time
import random
import pickle
from PIL import Ima... |
# from scipy import stats
import numpy as np
import pickle
import pandas as pd
import traceback
from sklearn.linear_model import LogisticRegression
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn import preprocessing
# from scipy... |
"""
* Copyright 2019 EPAM Systems
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,... |
from .multimodal.scicar.cell_lines import rna_cells_url
from .multimodal.scicar.cell_lines import rna_genes_url
from .utils import loader
import anndata
import numpy as np
import pandas as pd
import scipy.sparse
@loader
def load_sample_data(test=True):
"""Create a simple dataset to use for testing in multimodal ... |
# -*- coding: utf-8 -*-
"""
# Rule Extraction for Unsupervised Outlier Detection
Example of usage of a library that wrapping an unsupervised outlier detection algorithm (OneClassSVM) of scikit-learn
it can infer rules that are comprehensible for human beings, so the'll be able to easily understand why an specific data... |
<gh_stars>1000+
from argparse import Namespace
from importlib import import_module
import io
import re
import sys
from typing import Any, BinaryIO, Dict, List, Tuple, TYPE_CHECKING
import numpy
import yaml
from labours.objects import DevDay
if TYPE_CHECKING:
from scipy.sparse.csr import csr_matrix
class Reader... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Created on Fri May 10 13:30:43 2019
@author: Darin
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import scipy.sparse as sparse
import Material
import Update
cl... |
#!/usr/bin/env python
"""
Calculates R0 by country.
"""
# Import libraries
import argparse
from sys import exit
from os.path import exists, isfile
from os import mkdir, listdir, remove
import pandas as pd
import numpy as np
from scipy.linalg import eig
from json import loads, dumps
def main():
parser = getArgum... |
import sys
sys.path.append("/ubc_primitives/primitives/regCCFS/src")
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
plt.style.use('seaborn-white')
from predict_from_CCF import predictFromCCF
from utils.commonUtils import islogical
from utils.ccfUtils import mat_unique
... |
<filename>OldCrap/curve_fit.py
import math
import numpy as np
from scipy.optimize import minimize
points = []
with open('1d.csv') as f:
for line in f:
tokens = line.strip().split(',')
if len(tokens) == 2:
points.append([float(t) for t in tokens])
points = [p for p in points if abs(p[0... |
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
r_val = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768] # Rank, x
n_val = [96499, 26057, 10501, 4183, 2861, 913, 509, 298, 158, 85, 31, 19, 8, 3] # Occurances, y
log_r_val = [np.log(r) for r in r_val] # ... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
A script to build the tf-idf document matrices for retrieval.
Adapted from <NAME>'s work at github.com/faceb... |
import numpy
import argparse
from scipy import stats
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
import pysam
CHROMS = ['chr%i' % i for i in xrange(1,23)] + ['chrX']
READ_LEN = 20
MARGIN = 5
CHROM_LENS = {"chr1": 249250621, "chr2": 243199373, "chr3": 198022430, "chr4": 191154276, "chr5": 1809... |
import ipdb as pb
import pandas as pd
import numpy as np
from scipy.stats import beta
CUML_KEY = "prop_exploring_ppd_cuml"
SNAPSHOT_KKEY = "exploring_ppd_at_this_n"
def plot_phi(df, num_sims, n, c, ax, es = 0):
"""
get prop in cond 1 for when exploring
"""
# pb.set_trace()
step_sizes = [int(np.ce... |
import kmbio.PDB
import numpy as np
import pytest
import torch
from kmtools import structure_tools
from scipy import sparse
from pagnn.utils import (
array_to_seq,
get_distances,
permute_adjacency,
permute_adjacency_dense,
permute_sequence,
permute_structure,
seq_to_array,
)
@pytest.mark.... |
<reponame>facebookresearch/decodable_information_bottleneck
"""
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import logging
import math
import random
from itertools import zip_longest
impor... |
import sys
import numpy as np
import cv2
from scipy.spatial.transform import Rotation
import imutils
import itertools
from torchvision import transforms
from rrc_example_package import rearrange_dice_env
import trifinger_simulation.tasks.rearrange_dice as task
from trifinger_object_tracking.py_lightblue_segmenter imp... |
<filename>dreem_learning_open/preprocessings/epoch_features_processing.py
import json
import numpy as np
from scipy.signal import stft
from scipy.stats import entropy
def index_window(signal, signal_properties, increment_duration=30, padding_duration=None):
signal_frequency = signal_properties['fs']
index_wi... |
<gh_stars>10-100
# These need conda (via stackvana). Not pip-installable
import lsst.afw.cameraGeom as cameraGeom
from lsst.obs.lsst import LsstCamMapper
# This is not on conda yet, but is pip installable.
# We'll need to get Matt to add this to conda-forge probably.
import batoid
import numpy as np
import erfa # ... |
import numpy
import scipy
import math
# Explicit simulation
# \dot x = Ax +a + B \sigma
# \sigma \in Sgn (Cx+D)
def computeOneStepExplicit(x,ti,tf,A,B,C,D,a):
info=0
xi=numpy.array(x)
h=tf-ti
y=numpy.dot(C,xi)+D
print("y=", y)
sigma = numpy.array(y)
print(numpy.size(x))
for i in ran... |
<gh_stars>10-100
import glob
import numpy as np
from string import digits
from scipy.interpolate import pchip, Akima1DInterpolator
from openmdao.main.api import Component, Assembly
from openmdao.lib.datatypes.api import VarTree, Float, Array, Bool, Str, List, Int
from fusedwind.turbine.geometry_vt import BladeSurfac... |
<filename>labs/04_conv_nets_2/compute_representations.py
from keras.applications.resnet50 import ResNet50
from keras.models import Model
from keras.applications.imagenet_utils import preprocess_input
import h5py
import numpy as np
from scipy.misc import imread, imresize
from lxml import etree
import os
annotations = [... |
'''Various useful routines maybe not appropriate elsewhere'''
import numpy
import os
import scipy.sparse
import sys
import subprocess
import types
import time
from functools import reduce
import socket
def get_git_revision_hash():
""" Return git revision.
Adapted from:
http://stackoverflow.com/ques... |
import os
import torch
import random
import scipy.io
import typing as t
import numpy as np
import torchio as tio
from glob import glob
from functools import partial
from torch.utils.data import DataLoader
def get_scan_shape(filename: str):
extension = filename[:-3]
if extension == 'mat':
data = scipy.io.loadm... |
<filename>utils/util.py
import json
import torch
import soundfile
import librosa
import numpy as np
import pandas as pd
from pathlib import Path
from itertools import repeat
from collections import OrderedDict
from scipy.signal import butter, lfilter
def ensure_dir(dirname):
dirname = Path(dirname)
if not dir... |
import os
import cv2
import scipy.io as sio
from shutil import copyfile,rmtree
from xml.etree.ElementTree import Element, SubElement, tostring
from xml.dom.minidom import parseString
gen_path = "VOC_hand_dataset"
if os.path.exists(gen_path):
rmtree(gen_path)
gen_annot_path = os.path.join(gen_path,"VOC2007","Annota... |
<filename>Neuropixels_multi_comparison/run_sorters.py<gh_stars>1-10
import spikeinterface.extractors as se
import spikeinterface.toolkit as st
import spikeinterface.sorters as ss
import numpy as np
import scipy
from pathlib import Path
import os
p = Path('.')
results_folder = p / 'results'
working_folder = p / 'workin... |
#!/usr/bin/env python
import copy, os, h5py, argparse
import numpy as np
import scipy as sp
import matplotlib
matplotlib.use('Agg')
from pylab import rcParams
import matplotlib.pyplot as plt
rcParams.update({'figure.autolayout': True,
'font.size': 12})
folder = '/home/jesse/plots/ds/data'
data = ... |
<reponame>KshitijAggarwal/FRB
""" Codes for Scattering by ISM
Based on formalism derived by Macquart & Koay 2013, ApJ, 776, 125
Modified by Prochaska & Neeleman 2017
"""
from __future__ import print_function, absolute_import, division, unicode_literals
import numpy as np
from scipy.special import gamma
from astr... |
import json
import pandas
import dicttoxml
import numpy as np
import datetime
from scipy import interpolate
import raman_configs
def to_list_all(data):
# recursively check for arrays in dict and convert them to lists
if type(data) == np.ndarray:
return data.tolist()
if type(data) == list:
... |
# -*- coding: utf-8 -*-
#
#
# TODO: - add tabulation for Bilaplacian
"""This module contains different functions to create and treate the GLT symbols."""
from sympy import Symbol
from sympy import Function
from sympy import bspline_basis
from sympy import sympify
from sympy import lambdify
from sympy import cos
from ... |
<reponame>PYSFE/SFEPraPy
# __author__ = "RVC"
# __email__= "<EMAIL>"
# __date__= "2017-11-12"
from copy import deepcopy
from scipy.stats import uniform
from scipy.stats import gumbel_r
from scipy.stats import norm
from scipy.stats import lognorm
from scipy.stats import t
import numpy as np
## inverse CDF function #... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright 2020-2022 <NAME>. All Rights Reserved.
See Licence file for details.
"""
import argparse
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import sys
sys.path.append('../')
from PDE_solver import SIR_PDEroutine
from Likelihood im... |
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
import librosa
from numpy import genfromtxt
from keras.models import load_model
#removal function
def denoise(data,pred):
noise, sr2 = librosa.load(pred)
reduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noise, verbose=True)
print(... |
#! /usr/bin/env python
#
# Copyright 2018 California Institute of Technology
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... |
import os
from fltk import Fl
from skate_cma.skate_env2 import SkateDartEnv
from PyCommon.modules.GUI import hpSimpleViewer as hsv
from PyCommon.modules.Renderer import ysRenderer as yr
import numpy as np
import pickle
import math
from scipy.spatial.transform import Rotation
import pydart2 as pydart
from PyCommon.mod... |
"""
kcca.py
====================================
Python module for kernel canonical correlation analysis (kCCA)
Code modified from UC Berkeley, Gallant lab
(https://github.com/gallantlab/pyrcca)
Copyright 2016, UC Berkeley, Gallant lab.
"""
from .base import BaseEmbed
from ..utils.utils import check_Xs
import numpy ... |
<reponame>alexquach/responsible-ai-widgets
# Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
import importlib
from packaging import version
import numpy as np
from sklearn.metrics import confusion_matrix
from scipy import stats
from fairlearn.metrics._extra_metrics import (
_root_mean_square... |
<filename>size_constrained_clustering/sklearn_import/metrics/pairwise.py
import itertools
import warnings
from functools import partial
import numpy as np
from scipy.sparse import issparse, csr_matrix
from scipy.spatial import distance
from joblib import cpu_count, delayed, Parallel
from sklearn_import.metrics.pairwi... |
<filename>nlt/util/geom.py
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... |
<gh_stars>0
import numpy as np
import pickle
import pdb
import os
import matplotlib.pyplot as plt
from generator import ImageDataGenerator
from model import buildModel_U_net
from keras import backend as K
from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler
from scipy import misc
import scipy.n... |
<reponame>lizhun-2002/handwritten-OTP-authentication-system<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 24 09:23:54 2017
@author: LZ
"""
import os
import os.path
import numpy as np
from PIL import Image
from keras.models import load_model
from keras import backend as K
from keras.backend import clear_... |
<filename>python/ransac_1d.py
import numpy as np
import scipy # use np if scipy unavailable
import scipy.linalg # use np if scipy unavailable
## Copyright (c) 2004-2007, <NAME>. All rights reserved.
## Copyright (c) 2017, <NAME>, <NAME>. All rights reserved.
## Redistribution and use in source and binary f... |
#
# Author: <NAME>
# and <NAME> <<EMAIL>)
# Lincense: Academic Free License (AFL) v3.0
#
import numpy as np
from math import pi
from mpi4py import MPI
try:
from scipy import comb
except ImportError:
from scipy.special import comb
import prosper.em as em
import prosper.utils.parallel as parallel
... |
import time, os, csv, random
import cma
import numpy as np
import pandas as pd
from scipy.linalg import cholesky
from numpy.linalg import LinAlgError
from numpy.random import standard_normal
from sklearn.decomposition import PCA
from copy import deepcopy
import matplotlib.pyplot as plt
from nevergrad.functions import... |
"""
Script used to plot Fig.3 and the subfigures in Fig.9 of [arXiv:2012.01459]
"""
import pickle
import os
import numpy as np
from qutip import Bloch as Bloch
from scipy.integrate import cumtrapz, simps
from qc_floquet import *
from numpy.polynomial.polynomial import Polynomial
from scipy.optimize import curve_fi... |
<gh_stars>10-100
""" This takes a vocabulary file and a frameIndexLU file. and then marks the frames as contexts for the words listed under the same frame. so for example there should be an entry for (renounced, Abandonment)
0 2031 Abandonment renounced.v
0 2031 Abandonment forsaken.n
grep -n renounced $VOCAB_500K_FILE... |
import numpy as np
import math
from scipy.signal import savgol_filter
import pdb
class NoTelescope(object):
def __init__(self, horizon):
self.horizon = horizon
self.i0 = 0
self.idxs = [self.horizon - 1]
def check_calibrate(self):
return False
def sample_idx(self):
... |
<gh_stars>0
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from numpy import linalg as LA
import scipy.linalg as scla
def load_data():
'''
this function reads the data from the data folder
(directory is same as the location of this script)
'''
cfb = np.array(pd.read_csv('CF... |
#imports
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import pickle
import random
from scipy import sparse
import itertools
from scipy.io import savemat, loadmat
import re
import os
import pandas as pd
from nltk.stem import WordNetLemmatizer
import nltk
import argparse
fr... |
from scipy import ndimage
from core.utils import *
import csv
import numpy as np
import pandas as pd
import pickle
import os
import json
visual_noun = ['man','people','woman','street','table','person','group','field','tennis','train','room','plate','dog','cat'
,'baseball','water','bathroom','sign','kitchen','fo... |
import numpy as np
from scipy import ndimage
from scipy.ndimage import morphology
from heuristics.conditions import Condition
class PlayerInBiggestRegionCondition(Condition):
""" Checks if the controlled player is in the biggest contiguous region of all players."""
def __init__(self, opening_iterations=0):
... |
import math
from collections import namedtuple
import colorhash
import dask
import dask.dataframe
import numpy
import pandas
from matplotlib import pyplot
from scipy.stats import kstest, lognorm, multivariate_normal
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
class AbnormalTaskPerf... |
from __future__ import print_function, division
import numpy as np
import matplotlib.pylab as plt
import astropy.units as u
from astropy import log
from astropy.utils.console import ProgressBar
import pyspeckit
import os
# imports for the test fiteach redefinition
import time
import itertools
from astropy.extern.six i... |
<reponame>zhoufeng6288/conjugate-nonparametric-Hawkes-process<filename>conjugate_np_hawkes_new.py
import numpy as np
import copy
from scipy.stats import multinomial
from scipy.stats import multivariate_normal
from scipy.stats import gamma
from scipy.stats import expon
from scipy.stats import uniform
from scipy.special ... |
<filename>src/GraphTool.py
import numpy as np
import math
from scipy import signal
from openpyxl import Workbook
from openpyxl import load_workbook
#from openpyxl.compat import range
import openpyxl.compat
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, Navig... |
<reponame>royerloic/aydin<filename>aydin/util/denoise_nd/test/test_denoise_nd.py
# flake8: noqa
import numpy
from scipy.ndimage import gaussian_filter
from aydin.util.denoise_nd.denoise_nd import extend_nd
def test_denoise_nd():
# raw function that only supports 2D images:
def function(image, sigma):
... |
<filename>ecg-analysis-MingzheHu-Duke/test_ecg_analysis.py
import pytest
import numpy as np
def test_exception():
import numpy as np
from ecg_analysis import data_check
with pytest.raises(Exception):
data_check(np.ndarray([[1, 2], [np.nan, 3]]))
# data_processing won't be tested since it is only... |
<filename>visualizations/qerror.py
"""
Visualization of some aspects of Quantization Error
"""
import numpy as np
from visualizations.iVisualization import VisualizationInterface
from controls.controllers import QErrorController
import panel as pn
from scipy.spatial import distance_matrix, distance
class QError(Vis... |
import argparse
import os
import os.path as osp
import torch
import mmcv
from mmaction.apis import init_recognizer
from mmcv.parallel import collate, scatter
from operator import itemgetter
from mmaction.datasets.pipelines import Compose
from mmaction.datasets import build_dataloader, build_dataset
from mmcv.parallel i... |
import logging
from python_back_end.exceptions import *
import numpy as np
from python_back_end.data_cleaning.date_col_identifier import DateColIdentifier
from python_back_end.definitions import SheetTypeDefinitions
from python_back_end.program_settings import PROGRAM_PARAMETERS as pp
from python_back_end.program_set... |
<filename>data_parser.py
#---------------------------------
# NAME || AM ||
# <NAME> || 432 ||
# <NAME> || 440 ||
#---------------------------------
# Biomedical Data Analysis
# Written in Python 3.6
import scipy.io
import os
import csv
import heartpy as hp
import numpy as np
class Data_Parser:
de... |
from sigvisa.database import db
from sigvisa.database.signal_data import get_fitting_runid, read_fitting_run_iterations, NoDataException
import time
import sys
import os
import pickle
from sigvisa import Sigvisa
import numpy as np
from optparse import OptionParser
from sigvisa.models.ttime import tt_predict
from si... |
# Import all packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
# Create table using df of treatment dict
treatment = {'name': ['Daniel', 'John', 'Jane'],
'sex': ['male', 'male', 'female'],
'treat_a': ['18', 12, 24],
'treat_b': [42, 31, 27]}
treat_df = pd.Da... |
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