text string |
|---|
# Masses of compact remnant from CO core masses
__author__ = "<NAME> (<EMAIL>)"
# for fit
import numpy as np
import scipy
from scipy.optimize import curve_fit
# for plot
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
def linear(x, a, b):
return a * x + b
def f... |
#!/usr/bin/env python
import numpy as np
from math import sin, cos, radians
from functools import reduce
from scipy.linalg import inv
# call the class with Monocular(intrinsic, height, pitch, yaw, roll, sensor_location)
class Monocular:
def __init__(self, intrinsic, height, pitch, yaw, roll, sensor_location):
... |
<filename>model/reader/ucf_reader.py
import random
import numpy as np
from scipy import misc # for imread
from utils.find_border import find_border
import h5py
import math
import os
from scipy.cluster.vq import kmeans,kmeans2,vq
def filter_trajs_kmeans(trajs, num_centroids):
num_trajs = trajs.shape[0]
len_tr... |
<reponame>chigur/pose<gh_stars>0
import os
import re
import sys
import cv2
import math
import time
import scipy
import argparse
import matplotlib
from torch import np
import pylab as plt
from joblib import Parallel, delayed
import util
import torch
import torch as T
import torch.nn as nn
import torch.nn.functional as F... |
<filename>fury/primitive.py
"""Module dedicated for basic primitive."""
from os.path import join as pjoin
from distutils.version import LooseVersion
import numpy as np
from fury.data import DATA_DIR
from fury.transform import cart2sphere
from fury.utils import fix_winding_order
from scipy.spatial import ConvexHull, tra... |
<reponame>Chrisebell24/Copulas<filename>copulas/univariate/gaussian.py<gh_stars>0
import logging
import numpy as np
import pandas as pd
from scipy.stats import norm
from copulas.univariate.base import Univariate
LOGGER = logging.getLogger(__name__)
class GaussianUnivariate(Univariate):
"""Gaussian univariate m... |
"""Main Filter class."""
import enum
from dataclasses import dataclass, field
from typing import Iterable, NamedTuple
import numpy as np
import xarray as xr
from scipy import interpolate
from .gpu_compat import get_array_module
from .kernels import ALL_KERNELS, BaseLaplacian, GridType
FilterShape = enum.Enum("Fil... |
<filename>py/desispec/scatteredlight.py
'''
Try to model and remove the scattered light
'''
import time
import numpy as np
import scipy.interpolate
import astropy.io.fits as pyfits
from scipy.signal import fftconvolve
from scipy.interpolate import interp1d
from desispec.image import Image
from desiutil.log import get_l... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.io import loadmat
data = loadmat('ex3data1.mat')
print(data)
# load data
X = data['X']
y = data['y']
print(X.shape,y.shape)
# one-hot encoding
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
y_oneh... |
<filename>MHD/FEniCS/StabNS/NSprecondSetup.py
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
import numpy
from dolfin import compile_extension_module, tic, toc, DirichletBC, Expression, TestFunctions, TrialFunctions, Function
from scipy.sparse import coo_matrix, spdiags
import time
d... |
<filename>credit_detection.py
#coding:utf-8
# 信用卡数据异常检测
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
from sklearn.model_selection import train_test_split
LABELS=['Normal','Fraud']
# 加载数据
df=pd.read_csv('data/creditcard.csv')
print(... |
<reponame>pvthinker/argopy
import numpy as np
import pandas as pd
import tools
import tiles
import interp
import stats
import computational as cpt
import matplotlib.pyplot as plt
import gsw
from scipy import interpolate
from scipy import integrate
import os
time_flag = 'annual' #'DJF' # 'annual'
typestat = 'zmean'
s... |
<reponame>rjweiss/rosetta
import os
import unittest
from StringIO import StringIO
from scipy import sparse
from rosetta import TokenizerBasic
from rosetta.text.streamers import TextFileStreamer, TextIterStreamer
from rosetta.text.streamers import MySQLStreamer, MongoStreamer
from rosetta.common import DocIDError, T... |
<gh_stars>1-10
from os import system
import numpy as np
import scipy.optimize as op
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import ListedColormap
from scipy.stats import norm
from scipy.stats import multivariate_normal
#############################################... |
<filename>sympy/physics/pring.py
from __future__ import print_function, division
from sympy import sqrt, exp, S, pi, I
from sympy.physics.quantum.constants import hbar
def wavefunction(n, x):
"""
Returns the wavefunction for particle on ring.
n is the quantum number, x is the angle,
here n can be pos... |
import numpy as np
import scipy.spatial.distance
# listes [[x, y], [x, y], [x, y]...]
def precision_recall(dets, gts, tolerance=3):
dists = scipy.spatial.distance.cdist(dets, gts)
idx = np.argsort(dists.flatten())
ys = (idx / gts.shape[0]).astype(int)
xs = (idx % gts.shape[0])
affecte... |
<reponame>bcarr092/CSignal
from csignal_tests import *
import re
import sys
import time
import os
import cmath
import math
import unittest
import random
import struct
import string
import wave
dataFile = None
class TestsEqualizer( unittest.TestCase ):
def tearDown( self ):
self.assertEquals (
csignal_de... |
<gh_stars>1-10
#!/usr/bin/env python
"""Generate a mask that covers the tissue.
"""
import sys
import argparse
import os
import numpy as np
import pickle
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.morphology import binary_fill_holes
from scipy.ndimage import distance_transform_edt
import... |
<filename>results/process_code.py
import argparse
import sys
import re
import itertools
import matplotlib.pyplot as plt
from collections import defaultdict
import numpy as np
import scipy.special
import tqdm
line_re = re.compile(r'([STHP])-([0-9]+)\t(.*)')
def read_file(fname):
with open(fname, 'r') as f:
for l... |
<gh_stars>0
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
import sys
import random
def parse_index_file(filename):
"""Parse index file."""
index = []
for line in open(filename):
index.append(int(line.strip... |
<reponame>diegovalsesia/piunet<filename>Code/piunet/main.py
import os
import time
import argparse
import numpy as np
import scipy.io as sio
import h5py
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from config import Config
from losses import l1_registered_... |
<gh_stars>10-100
import scipy.io
import numpy as np
import pickle
import torch
mat = scipy.io.loadmat('data_symlinks/hico_clean/anno.mat')
#mat_det = scipy.io.loadmat('anno_bbox.mat')
#mat_det['bbox_test'][0][1000][2][0][2][0]
#0-imge name- label(2) - 0- label index(0~N) - [labelname(0~600), subj _box, obj_box]
... |
import matplotlib.pyplot as plt
from scipy import stats
x = [5,7,8,7,2,17,2,9,4,11,12,9,6]
y = [99,86,87,88,111,86,103,87,94,78,77,85,86]
slope, intercept, r, p, std_err = stats.linregress(x, y)
print("slope : {} , intercept : {} , std_err : {}".format(slope,intercept,std_err))
def myfunc(x):
return slope * x + in... |
<gh_stars>1-10
import tensorflow as tf
import numpy as np
import skimage.io
import itertools
import os
import bz2
import argparse
import scipy
import skimage.transform
import time
import matplotlib.pyplot as plt
plt.switch_backend('agg')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
CONTENT_LAYERS =... |
#!/usr/bin/env python
##!/home/users/sblair/anaconda2/bin/python
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 26 14:23:52 2017
@author: stu
"""
import sys
sys.path.insert(1,'.')
import pyPartition as pp
#from pymetis import part_graph #<-- requires that the PrgEnv-intel module be selected
import numpy as np
import... |
from typing import Tuple
from sympy import symbols, nsimplify, integrate
from sympy.core.mul import Mul
from rcdesign.is456 import ecy, ecu
# from rcdesign.stressblock import StressBlock
class LSMStressBlock:
def __init__(self, label: str = "IS 456 LSM", ecy: float = ecy, ecu: float = ecu):
self.label =... |
from scipy import stats
def ks_test_max_per_channel(img, mask, focus_region):
"""Compute a 2-sample Kolmogorov-Smirnov statistic on each channel of image
returning the max value across channels. Ignore the background regions
img - array of shape (x, y, z) with z being the channels.
mask - bool array... |
# PyVision License
#
# Copyright (c) 2006-2008 <NAME>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list o... |
<reponame>LBJ-Wade/NX01<filename>NX01_master.py
#!/usr/bin/env python
"""
Created by stevertaylor
Copyright (c) 2014 <NAME>
Code contributions by <NAME> (piccard) and <NAME> (PAL/PAL2).
"""
from __future__ import division
import os, math, optparse, time, cProfile
import json, sys, glob
import cPickle as pickle
from... |
<gh_stars>0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
... |
#
# Copyright 2019 The FATE Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
import numpy as np
from scipy.interpolate import UnivariateSpline
def light_efficiency(electric_current, electric_voltage, radiant_power, light_type):
spl_IV = UnivariateSpline(electric_current, electric_voltage)
spl_IP = UnivariateSpline(electric_current, radiant_power)
I = np.linspace(np.amin(electric_curren... |
<reponame>markdewing/qmc_kernels<gh_stars>0
# Compare generalized eigenvalue problem from QMCPACK
# Comes from using linear method.
import numpy as np
import scipy.linalg
import h5py
f = h5py.File("linear_matrices.h5","r")
# Load matrices
ovlp = np.array(f['overlap'])
ham = np.array(f['Hamiltonian'])
# Get shifts... |
<filename>camera_calibration_ws/monodepth-FPN/MonoDepth-FPN-PyTorch/dataset/nyuv2_dataset.py
import torch.utils.data as data
import numpy as np
from PIL import Image
from path import Path
from constants import *
from torchvision.transforms import Resize, Compose, ToPILImage, ToTensor, RandomHorizontalFlip, CenterCrop, ... |
<filename>modules/evaluator/FID/fid_score.py<gh_stars>0
#!/usr/bin/env python3
"""Calculates the Frechet IS Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while ... |
from pytorch_pretrained_bert import BertTokenizer, BertConfig, BertModel
from pytorch_pretrained_bert.modeling import BertPreTrainedModel, BertPreTrainingHeads
import torch
import pandas as pd
import numpy as np
from scipy.spatial.distance import cosine
import time
tokenizer = BertTokenizer.from_pretrained('bert-base-... |
<reponame>bryan-flywire/openem
__copyright__ = "Copyright (C) 2018 CVision AI."
__license__ = "GPLv3"
# This file is part of OpenEM, released under GPLv3.
# OpenEM is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundatio... |
import numpy
from scipy.linalg import eigh, cholesky
from scipy.stats import norm, johnsonsu
from distribution import MSSKDistribution
class CorrelatedNonNormalRandomVariates(object):
def __init__(self,moments,correlations,num_samples, method='cholesky'):
#List of 4 moments array.
#NXN correl... |
<gh_stars>0
import pickle
import numpy as np
from scipy.stats import f as F
from scipy import stats
from matplotlib import pyplot as plt
# READ DATA
ang = [0, 15, 30, 45, 60, 75]
stokes_param = 'U'
L = []
for a in ang:
S = stokes_param + 'pol_' + str(a) + 'deg.pickle'
filename = '/'.join(['data', S])
wi... |
import numpy as np
from scipy import ndimage as nd
from .pyudwt import Denoise2D1DHardMRS
b3spline = np.array([1.,4.,6.,4.,1.]) / 16.
# Try to use MUCH faster median implementation
# from bottleck, else fallback to numpy.median
try:
from bottleneck import median as the_median
except ImportError:
the_median =... |
<gh_stars>1-10
import math
from utils.MathUtils import *
from utils.MathConstants import *
import pandas as pd
from statistics import median
import numpy as np
class Scheduler():
def __init__(self):
self.env = None
def setEnvironment(self, env):
self.env = env
def selection(self):
... |
import sys
import numpy as np
from timeit import default_timer as timer
from scipy.sparse import block_diag, coo_matrix
from common.estimator import EstimatorModel
from common.regression import max_affine_predict
from optim.quadprog import qp_solve, convert_matrix_to_qp_solver_format, QP_BACKEND__DEFAULT
class PCNL... |
<reponame>david-zwicker/sensing-normalized-results
#!/usr/bin/env python2
from __future__ import division
import sys, os
sys.path.append(os.path.join(os.getcwd(), '../src'))
import multiprocessing as mp
import itertools
import numpy as np
from scipy import special, optimize, stats
import matplotlib.pyplot as plt
im... |
# -*- coding: utf-8 -*-
import random
from multiprocessing import Pool
import numpy as np
from scipy.optimize import minimize
from tqdm import tqdm
from . import funcs
# Default settings.
MAX_SEARCHES = 25
SOLVER = 'Nelder-Mead'
class LPPLS:
"""Class for Log-Periodic Power Law Singularity Model.
LPPLS is a m... |
import sys
import numpy as np
from scipy.interpolate import interp1d
from scipy.stats import norm,t
#sys.path.append('/Users/jmilli/Dropbox/lib_py/image_utilities') # add path to our file
#from image_tools import *
# 2017-11-24 JMi: adapted the case of an odd image size
# 2015-02-10 JMi: changed the definition of radi... |
<filename>kaldi_io/LDA_LPLDA.py<gh_stars>10-100
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
from scipy import linalg
from sklearn.utils.multiclass import unique_labels
from sklearn.utils import check_array, check_X_y
from sklearn.utils.validation import check_is_fitted
import LDA
i... |
#Created by JetBrains PyCharm
#Project Name: SoundAnalyzer with RaspberryPi
#Author: <NAME>
#University: Cergy-Pontoise
#E-mail : <EMAIL>
import numpy
from scipy.signal import bilinear
def A_weighting(fs):
"""Design of an A-weighting filter.
b, a = A_weighting(fs) designs a digital A-weighting filter for
... |
<gh_stars>0
import gym
from gym import spaces
from gym.utils import seeding
import autograd.numpy as np
from scipy.stats import beta
class LQRv1(gym.Env):
def __init__(self):
self.dm_state = 2
self.dm_act = 1
self.dt = 0.01
self.x0 = np.array([0., 0.])
self.g = np.array... |
<gh_stars>0
# -*- coding:uft-8 -*-
from os import path
from netCDF4 import Dataset, num2date
from scipy.io import loadmat
from yaml import full_load
from RBR.ctd import convert2nc as conv2nc_rbr
from RDI.util import gen_time
from util import detect_brand
def ctd_ref_data(adcp_path, time_offset, adcp_hgt):
ext =... |
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import numpy
try:
import scipy.sparse as sp
import scipy.sparse
except ImportError:
pass # The variable enable_sparse will be used to disable the test file.
import theano
from theano import sparse, conf... |
<filename>Selenium/QQ/utils/ocr4qqcaptcha.py
import glob
import numpy as np
from scipy import misc
from keras.layers import Input, Convolution2D, MaxPooling2D, Flatten, Activation, Dense
from keras.models import Model
from keras.utils.np_utils import to_categorical
imgs = glob.glob('sample/*.jpg')
img_size = misc.imr... |
import numpy
import sympy
from fractions import Fraction
def derive(h, height, width):
rows = []
for i in range(height):
row = []
for j in range(width):
row.append(1 if h & (1 << (i * width + j)) else 0)
rows.append(row)
columns = []
for j in range(width):
column = []
fo... |
<reponame>rabernat/scikit-downscale
import numpy as np
from scipy.spatial import cKDTree
from sklearn.base import RegressorMixin
from sklearn.linear_model import LinearRegression
from sklearn.linear_model.base import LinearModel
from sklearn.utils.validation import check_is_fitted
from .utils import ensure_samples_fea... |
<gh_stars>0
import numpy as np
import pylab
from scipy.optimize import line_search
def steepest_descent(grad_fun,params,num_iters, *varargs):
## Learning Rates
#eta = 0.1
eta = 2
#eta = 3
## Momentum
alpha=0.7
momentum=True
d = np.ones(params.shape)
d = d / np.linalg.norm(d)
... |
<filename>core/utils/segmentation_metrics.py
import numpy as np
import scipy
import sklearn.metrics
import skimage
from skimage.segmentation.boundaries import find_boundaries
from sklearn.cluster import KMeans
import torch
from torchvision import transforms
import torch.nn.functional as F
import pdb
def object_id_has... |
<gh_stars>1-10
import numpy as np
import scipy
import scipy.signal
# Class used for updating plots in callbacks.
class DecimatingDisplay(object):
def __init__(self, data, t, dt, title_func, lines, lc, markers, histf):
# assume lines and data have the same order
# and first two data elements are x,... |
<gh_stars>1-10
import pandas as pd
import struct
import numpy as np
from more_itertools import run_length
from bitstring import BitArray
from scipy import signal
def bin2df(full_path):
"""
Reads geneactiv .bin files into a pandas dataframe.
Parameters
----------
full_path : str
Full path ... |
<reponame>smeschke/juggling
import cv2, math
import numpy as np
import pandas as pd
import scipy
from scipy import signal
# Read data and video
path = 'ss5_id_321'
df = pd.read_csv('/home/stephen/Desktop/'+path+'.csv')
cap = cv2.VideoCapture('/home/stephen/Desktop/'+path+'.MP4')
# Create video out file
w,h = 480,848
v... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.font_manager as fm
mpl.rcParams['font.family'] = 'CMU Serif'
mpl.rcParams["mathtext.fontset"] = "stix"
mpl.rcParams["font.serif"] = [mpl.rcParams['font.family']] + mpl.rcParams["font.serif"]
mpl.rcParams['axes.labelsize'] = 20.
m... |
from typing import Optional
import numpy as np # type: ignore
from scipy.stats import chi2 # type: ignore
from survival_evaluation.types import NumericArrayLike
from survival_evaluation.utility import (
KaplanMeier,
KaplanMeierArea,
to_array,
validate_size,
)
# pylint: disable=too-many-arguments
d... |
'''
'''
import os
import sys
import h5py
import numpy as np
from scipy.stats import chi2
np.seterr(divide='ignore', invalid='ignore')
# -- abcpmc --
import abcpmc
from abcpmc import mpi_util
# -- galpopfm --
from . import dustfm as dustFM
from . import measure_obs as measureObs
dat_dir = os.environ['GALPOPFM_... |
"""SOMClustering class.
Copyright (c) 2019-2021 <NAME>.
All rights reserved.
"""
import itertools
from typing import List, Optional, Sequence, Tuple
import numpy as np
import scipy.spatial.distance as dist
from joblib import Parallel, delayed, effective_n_jobs
from sklearn.decomposition import PCA
from sklearn.prep... |
from typing import Generator, TypeVar, Generic, Tuple, List, Iterator, Union
import itertools
import time
import random
import numpy as np
from scipy.special import softmax
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
V = TypeVar('V')
class GenCacher(Generic[V]):
... |
<filename>model/metric.py
"""Module for computing performance metrics
"""
import math
import numbers
from pathlib import Path
import numpy as np
import torch
import scipy.stats
from sklearn.metrics import average_precision_score
def t2v_metrics(sims, query_masks=None):
"""Compute retrieval metrics from a simili... |
"""
Particle Filter helper functions
"""
import configparser
import json
import math
import os
from collections import defaultdict
from io import BytesIO
from itertools import permutations
from itertools import product
from pathlib import Path
import imageio
import matplotlib.pyplot as plt
import numpy as np
import pa... |
"""Module for remapping complex data for display."""
from inspect import getmembers, isfunction
import sys
import numpy as np
from scipy.stats import scoreatpercentile as prctile
__classification__ = "UNCLASSIFIED"
__author__ = "<NAME>"
def get_remap_list():
"""
Create list of remap functions accessible fr... |
<gh_stars>10-100
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
import numpy as np
import logging
class HungarianMatcher(nn.Module):... |
import numpy as np
import scipy as sp
import scipy.linalg as spl
from collections import defaultdict
from sklearn.linear_model import LinearRegression
# Reference Marker Configuration (in m)
_THICKNESS = 0.002
_RADIUS = 0.006522 + _THICKNESS # Radius of tube
_CABLE_DRIVEN_RADIUS = 0.015
def rotate_point_around_point... |
<gh_stars>1-10
"""
Some functions to calculate frequentist p-values (and CLs) for the "on-off"
problem, that is, a counting experiment in an "on" region with background
expectation, signal expectation and an uncertainty on the background
expectation, constrained by a count in an "off" region.
See Eur.Phys.J.C71, `arXi... |
#Continuum Plotting
#<NAME>
#21/03/16
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as interp
import pyfits as pf
import glob
from ipdb import set_trace as st
def find_nearest(array,value,forcefloor=0):
idx=(np.abs(array-value)).argmin()
if forcefloor==1:
if array[idx] >... |
from ocetrac.track import (
_morphological_operations,
_apply_mask,
_label_either,
_filter_area,
_wrap,
track,
)
import pytest
import xarray as xr
import numpy as np
import scipy.ndimage
from skimage.measure import regionprops
from skimage.measure import label as label_np
import dask.array as ... |
<reponame>dloney/proteus
"""Tools for working with water waves.
The primary objective of this module is to provide solutions (exact and
approximate) for the free surface deformation and subsurface velocity
components of water waves. These can be used as boundary conditions, wave
generation sources, and validation sol... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 5 12:42:47 2021
@author: mavroudo
"""
import pandas as pd
import numpy as np
from statistics import mean
from autorank import autorank, create_report, plot_stats
method_name = "Distance-based"
methods = ['Top-ζ','LOF','Probabilistic','Distance-B... |
<filename>me_biomass/load_model.py
import pickle
from cobrame.io.json import load_json_me_model
import cobrame
from sympy import Basic
from os.path import dirname, abspath
currency_met_to_synthesis_rxn = {'coa': 'DPCOAK',
'thf': 'DHFS',
# use this reac... |
<reponame>giovp/SingleCellOpenProblems
from ....tools.decorators import method
from ....tools.utils import check_version
import numpy as np
@method(
method_name="NMF-reg",
paper_name="Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution", # noqa: E501
paper_ur... |
<filename>attgcn_preprocessor/utils/test_utils.py
import math
import numpy as np
from scipy.special import softmax
import torch.nn.functional as F
import torch.nn as nn
from attgcn_preprocessor.utils.plot_utils import plot_predictions_cluster
from attgcn_preprocessor.config import *
from sklearn.utils.class_weight imp... |
#!/usr/bin/env python
import rospy
import numpy as np
from scipy import signal
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
# Applies a filter to images received on a specified topic and publishes the filtered image
class Filter:
# Initialize the filter
def __init__(self, fi... |
from typing import Optional, Dict, List, Tuple, AbstractSet
from sympy import Poly, prod, factorial
from sympy.abc import x
from ccc.polynomialtracker import PolynomialTracker
class Sequence(PolynomialTracker):
"""
Track sequences that meet specific constraints.
"""
def __init__(
self,
... |
from __future__ import print_function
import os
import sys
import scipy
import scipy
import logging
import scipy.io
import threading
import subprocess
import numpy as np
import pandas as pd
from VGG import VGG
import seaborn as sns
from skimage import io
from io import BytesIO
import tensorflow as tf
from scipy import ... |
<filename>kernel_matrix_benchmarks/algorithms/ckdtree.py
from __future__ import absolute_import
from scipy.spatial import cKDTree
from kernel_matrix_benchmarks.algorithms.base import BaseANN
class CKDTree(BaseANN):
"""KD-Tree implementation, based on SciPy."""
def __init__(self, metric, leaf_size=20):
... |
# ===============================================================================
# Copyright 2012 <NAME>
#
# 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/LI... |
import numpy as np
from numpy import linalg as LA
# Threshold
# def omp_1(A, b):
# r = b # Residual of k-1
# i = 0 # Counter
# cols = A.shape[1]
# rows = A.shape[0]
# A_reduced = np.ones((rows, 1)) # As Matrix
# A_reduced = np.delete(A_reduced, 0, 1)
# X = np.zeros((cols, 1))
# s = [... |
# construct pattern of direct beam and reflect from a model
# build a one-d model
import glob
import os
import numpy as np
import matplotlib.pyplot as plt # for showing image
#from pylab import * # for multiple figure window
from skimage import io
import re
import statsmodels.api as sm
from scipy.optimize import minimi... |
from misc import ln, softmax
import numpy as np
import scipy.special as scs
from misc import D_KL_nd_dirichlet, D_KL_dirichlet_categorical
class HierarchicalPerception(object):
def __init__(self,
generative_model_observations,
generative_model_states,
generative_... |
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by <NAME> and <NAME>
# published by PACKT Publishing
#
# It is made available under the MIT License
from __future__ import print_function
import numpy as np
from load_ml100k import get_train_test
from scipy.spatial import... |
<filename>data_munging.py
import numpy as np
import scipy.misc
import matplotlib.pyplot as plt
# import matplotlib as mpl
import os
import colorsys
import cv2
import logging
import itertools
from colorcorrect.algorithm import grey_world
from annotation import get_annotation, get_bbs
from tools_plot import dispims
fro... |
import math
import unittest
import logging
import re
import numpy as np
from imageio import imread
from scipy.ndimage.interpolation import rotate
from autocnet.examples import get_path
from autocnet.transformation import roi
from .. import ciratefi
import pytest
# Can be parameterized for more exhaustive tests
upsa... |
import cv2
import cv2.cv as cv
import math
import time
import numpy as np
import scipy.spatial.distance as spsd
def lktrack(img1, img2, ptsI, nPtsI, winsize_ncc=10, win_size_lk=4, method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
Lucas-Kanede Tracker with pyramids
**PARAMETERS**
im... |
<gh_stars>0
"""My chocobo cooking script."""
"""I left the chocobo here because I want to thank the chocobo package
""author for teaching me how to package!"""
import os
import warnings
import scipy
from sklearn.preprocessing import StandardScaler
import scipy.stats
from statsmodels.distributions.empirical_distribution... |
from decimal import Decimal
from fractions import Fraction
f = Decimal('0.1');
print(type(f));
sum = 0;
for i in range(100):
sum += f;
print(sum);
a = Fraction(1,3);
print(a); |
<filename>src/util.py
import time, random, math, numpy, os, sys, tempfile, pylab, subprocess, matplotlib, datetime, \
itertools as itl, copy, StringIO, cPickle as pickle, gc, collections, bisect, traceback
import numpy as np
import scipy.sparse
import inspect
#import networkx as nx
#graph = nx
def fail(s = ''... |
import scipy.io.wavfile as wavfile
import scipy.fft as fft
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import time
import json
import math
import getopt
import sys
import warnings
import os
import subprocess
import pathlib
he... |
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
from transformers import AutoTokenizer, MBartTokenizer
from src.envs import build_env
import torch.nn.functional as F
import datasets
import random
import pandas as pd
from datasets import Dataset
import tor... |
<filename>preoject_five_facenet/src/ForChineseCaptcha.py<gh_stars>1-10
"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
is calculated and plotted. Both t... |
"""Implementation of sample attack."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import numpy as np
import tensorflow as tf
from tensorflow.contrib.slim.nets import inception
from scipy.misc import imread
from scipy.misc import i... |
<reponame>ramanans1/planet
# Copyright 2019 The PlaNet Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
import numpy as np
import copy
from scipy.special import expit, softmax
from common.wbf_postprocess import weighted_boxes_fusion
def yolo_decode(prediction, anchors, num_classes, input_dims, scale_x_y=None, use_softmax=False):
'''Decode final layer features to bounding box parameters.'''
batch_size = np.shape... |
<gh_stars>1-10
from scipy.stats import bernoulli
def generate_bernoulli_responses_contextual(
actual_positive_rates, selected_action_ids, actual_cohort_ids):
"""
Given known actual response rates, real cohort membership, and selected
actions, simulate respons`es and return list of N responses with... |
import unittest
from SimPEG import *
from SimPEG import EM
import sys
from scipy.constants import mu_0
from SimPEG.EM.Utils.testingUtils import getFDEMProblem
testDerivs = True
testEB = True
testHJ = True
verbose = False
TOL = 1e-5
FLR = 1e-20 # "zero", so if residual below this --> pass regardless of order
CONDUCTI... |
<gh_stars>10-100
import networkx as nx
import dgl
import numpy as np
from scipy.linalg import toeplitz
import pyemd
import time
import concurrent.futures
from scipy.linalg import eigvalsh
import subprocess as sp
import os
from functools import partial
from sklearn.metrics.pairwise import pairwise_kernels
from eden.grap... |
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