text string |
|---|
<reponame>Edelweiss35/deep-machine-learning
"""
"""
import numpy as np
import scipy as sp
import pylab as py
from .neuralNetwork import NNC
from .stackedAutoEncoder import SAEC
__all__ = ['NNC','SAEC'
]
|
<filename>S2SRL/retriever_pretrain.py
import os
import json
import torch
import random
from datetime import datetime
from statistics import mean
from libbots import adabound, data, model, metalearner, retriever_module
MAX_TOKENS = 40
MAX_MAP = 1000000
DIC_PATH = '../data/auto_QA_data/share.question'
SAVES_DIR = '../da... |
import copy
import cmath
import h5py
import math
import numpy
import scipy.linalg
import sys
import time
from pauxy.walkers.multi_ghf import MultiGHFWalker
from pauxy.walkers.single_det import SingleDetWalker
from pauxy.walkers.multi_det import MultiDetWalker
from pauxy.walkers.multi_coherent import MultiCoherentWalker... |
<reponame>j-erler/sz_tools<filename>sz_tools/ilc.py
import numpy as np
import healpy as hp
import datetime
from astropy.io import fits
from astropy.io import ascii
from scipy import ndimage
import sz_tools as sz
import os.path
datapath = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
fwhm2sigma = 1/... |
from abc import ABC, abstractmethod
import numpy as np
import scipy.stats as stats
from beartype import beartype
from UQpy.utilities.ValidationTypes import RandomStateType
class Criterion(ABC):
@beartype
def __init__(self):
self.a = 0
self.b = 0
self.samples = np.zeros(shape=(0, 0))
... |
import os
import sys
import json
import open3d as o3d
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from tqdm import tqdm
from sklearn.neighbors import NearestNeighbors
COLORMAP = 'jet'
def read_config():
with open('config.json', 'r') as file:
return j... |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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... |
import numpy as np
from time import time
from scipy.spatial.distance import pdist, cdist
from copy import deepcopy
import random
"""
Scripts to compute LJ energy and force
"""
def LJ(pos):
"""
Calculate the total energy
"""
distance = pdist(pos)
r6 = np.power(distance, 6)
r12 = np.multiply(r6... |
<gh_stars>1-10
from timeit import default_timer as timer
import numpy as np
import scipy.sparse as sp
def cosine_similarity(input, alpha=0.5, asym=True, h=0., dtype=np.float32):
"""
Calculate the cosine similarity
Parameters
-------------
input : sparse matrix
input matrix (columns repre... |
#===============================================================================
# --- Massive imports
import ROOT
import ostap.fixes.fixes
from ostap.core.core import cpp, Ostap
from ostap.core.core import pwd, cwd, ROOTCWD
from ostap.core.core import rootID, funcID, funID, fID, histoID, hID, dsID
from ostap.core.core... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# =============================================================================
# Created By : <NAME>
# Date Last Modified: Aug 1 2021
# =============================================================================
import numpy as np
import matplotlib.pyplot ... |
<filename>core/ifs.py
from tqdm import tqdm
from os.path import join as make_path
from scipy.ndimage import gaussian_filter
import numpy as np
from matplotlib import pyplot as plt
from .base import BaseProcessor
class ProcessorIFS(BaseProcessor):
"""Integrated frequency spectrum"""
def __init__(self, experi... |
import numpy as np
from torch.utils.data import Dataset, DataLoader, ConcatDataset
from torch.utils.data.dataset import random_split
import torch
from scipy.stats import spearmanr
from resmem import ResMem, transformer
from matplotlib import pyplot as plt
import seaborn as sns
from torchvision import transforms
import ... |
<reponame>dkuegler/i3PosNet
# Copyright 2019 <NAME>, Technical University of Darmstadt, Darmstadt
#
# 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/... |
<filename>calculus/fractions.py
from fractions import Fraction
from timeit import timeit
def python_fraction():
return Fraction(22, 7)
timeit("python_fraction()", setup="from __main__ import python_fraction", number=1000)
# 0.002394800000004693
def frac_operator():
return 22 / 7
timeit("frac_operator()"... |
<reponame>felidsche/BigDataBench_V5.0_BigData_ComponentBenchmark<filename>Hadoop/SIFT/hadoop-SIFT/hipi-SIFT/util/showCovarianceOutput.py
#!/usr/bin/python
import argparse, sys
import numpy as np
from matplotlib import pyplot as plt
import scipy.sparse.linalg as LA
# Parse command line
parser = argparse.ArgumentParser... |
################################################################################
##### Module with numerically robust implementation of the hyper-exponentially-
##### modified Gaussian probability density function
##### Author: <NAME>
##### Import packages
import numpy as np
import lmfit as fit
from numpy import exp
f... |
<reponame>zhenlingcn/deep-symbolic-regression<gh_stars>0
"""Plot distributions and expectations of rewards for risk-seeking vs standard policy gradient."""
import os
import sys
import matplotlib
from matplotlib import pyplot as plt
from scipy.stats import gaussian_kde, sem
import numpy as np
import pandas as pd
from ... |
# -*- coding: utf-8 -*-
'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2019, 2020 <NAME> <<EMAIL>>
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 wi... |
<reponame>dieterich-lab/riboseq-utils
import logging
import os
import pandas as pd
import riboutils.ribo_filenames as filenames
logger = logging.getLogger(__name__)
class _return_key_dict(dict):
def __missing__(self,key):
return key
###
# The following labels are used to group similar ORF types.
###... |
<reponame>jerrynlp/AutoSum
import Syntax as sx
import argparse
import numpy as np
from scipy import spatial
class Phrase:
"""Information of a phrase"""
def __init__(self, word, word_before, word_after, postag_before, postag_after, chapter_id, sentence_id, negation):
self.negation = negation
self... |
from __future__ import division
import os
import numpy as np
import cv2
from scipy.misc import imresize
from dataloaders.helpers import *
from torch.utils.data import Dataset
class TrainLoader(Dataset):
def __init__(self, train=True,
inputRes=None,
db_root_dir=None,
... |
'''
load lsun dataset as numpy array
usage:
import lsun
(test_x, test_y) = load_lsun_test()
'''
import tarfile
from PIL import Image
from scipy.ndimage import filters
import os
import tensorflow as tf
import numpy as np
import io
TRAIN_X_ARR_PATH = '/home/cwx17/new_data/constant/train.npy'
TEST_X_ARR_PATH... |
#! /usr/bin/env python
import numpy as np
import scipy
"""
Simple utilities for managing snapshots and
creating training, testing data
"""
def prepare_data(data, soln_names, **options):
"""
Utility to extract snapshots and time
arrays from raw data, by ignoring
initial spin-up times, skipping over
... |
<reponame>xwjBupt/BraTS-DMFNet
import os
import time
import logging
import torch
import imageio
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import nibabel as nib
import scipy.misc
cudnn.benchmark = True
path = os.path.dirname(__file__)
# dice socre is equa... |
from scipy.spatial import cKDTree
import numpy as np
weightedflux = lambda flux, gw, nearest: np.sum(flux[nearest]*gw,axis=-1)
def gaussian_weights( X, w=None, neighbors=100, feature_scale=1000):
'''
<NAME>: Gaussian weights of nearest neighbors
'''
if isinstance(w, type(None)): w = np.ones(X.shap... |
<filename>more-examples/cantor-bouquet.py
# Author: alexn11 (<EMAIL>)
# Created: 2019-05-18
# Copyright (C) 2019, 2020 <NAME>
# License: MIT License
import sys
import math
import cmath
import mathsvg
smallest_interval = 0.0003
density = 0.5
max_length = 1.
allowed_object_types = [ "disconnected-straight-brush... |
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils import data
from data_loader import DATA_LOADER as dataloader
import final_classifier as classifier
import models
import random
import torch.autograd as autograd
from torch.autograd import Variable
import classifier
import classifier2
impor... |
<filename>src/models/simple_variability.py
"""
Calculates basic statistics on preprocessed heartbeat data such as variance, entropy, and cross entropy
"""
import os
import numpy as np
from scipy.stats import entropy
# from skimage.filters.rank import entropy
from matplotlib import pyplot as plt
from src.utils.dsp_util... |
<reponame>dev-rinchin/RePlay
from typing import Any, List, Optional, Set, Union
import numpy as np
import pyspark.sql.types as st
from pyspark.ml.linalg import DenseVector, Vectors, VectorUDT
from pyspark.sql import Column, DataFrame, Window, functions as sf
from scipy.sparse import csr_matrix
from replay.constants ... |
<gh_stars>0
import bisect
from collections import deque
from copy import deepcopy
from fractions import Fraction
from functools import reduce
import heapq as hq
import io
from itertools import combinations, permutations
import math
from math import factorial
import re
import sys
sys.setrecursionlimit(10000)
#from numb... |
<gh_stars>10-100
# vim: set fileencoding=<utf-8> :
# Copyright 2018-2020 <NAME> and <NAME>
'''Sketchlib functions for database construction'''
# universal
import os
import sys
import subprocess
# additional
import collections
import pickle
import time
from tempfile import mkstemp
from multiprocessing import Pool, Loc... |
import os
import cv2
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import copy
from datasets import W300LP, VW300, AFLW2000, LS3DW
import models
from models.fan_model import FAN
from utils.evaluation import get_preds
CHECKPOINT_PATH = "./checkpoint_4Module/fan3d_wo_norm_att/model_best.... |
# Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from fairmotion.utils import constants, utils
from fairmotion.ops import conversions, math as math_ops
from scipy.spatial.transform import Rotation
def Q_op(Q, op, xyzw_in=True):
"""
Perform operations on quaternion. The oper... |
<filename>benchmarks/benchmarks/optimize_linprog.py
"""
Benchmarks for Linear Programming
"""
from __future__ import division, print_function, absolute_import
# Import testing parameters
try:
from scipy.optimize import linprog
from scipy.linalg import toeplitz
from scipy.optimize.tests.test_linprog import ... |
from __future__ import absolute_import, division, print_function
import time as tim
from functools import partial
from multiprocessing import Pool
import numpy as np
from scipy.integrate import quad
from scipy.special import erf
def finite_line_source(
time, alpha, borehole1, borehole2, reaSource=True, imgS... |
<reponame>jameslz/sistr_cmd
import zlib
from collections import defaultdict
import numpy as np
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import fcluster, linkage
NT_TO_INT = {'A':1,'C':2,'G':3,'T':4,'N':5}
INT_TO_NT = {1:'A',2:'C',3:'G',4:'T',5:'N'}
def group_alleles_by_size(... |
<filename>src/thex/apps/utils/data_utils.py
import statistics
from pathlib import Path
import pandas as pd
import math
def roundUp(x, WINDOWSIZE):
return int(math.ceil(x / WINDOWSIZE)) * WINDOWSIZE
def check_input_columns(cols):
expected_cols = {
'Chromosome': str,
'Window': int,
'New... |
<filename>quadcopter/ipopt/quadcopter.py
import sys
sys.path.append(r"/home/andrea/casadi-py27-np1.9.1-v2.4.2")
from casadi import *
from numpy import *
from scipy.linalg import *
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
from math import atan2, asin
import pdb
N = 5 # Control dis... |
<filename>demo_model_SPAD.py
"""
This code shows the multilayer perceptron model and the implementation of the
algorithm for "Spatial images from temporal data".
paper link: https://www.osapublishing.org/optica/abstract.cfm?uri=optica-7-8-900
Authors: <NAME>, <NAME>, <NAME>, <NAME>, <NAME>,
<NAME>, <NAME>,... |
import numpy as np
from scipy.signal import convolve2d
from scipy import ndimage as ndi
from skimage._shared.testing import fetch
import skimage
from skimage.data import camera
from skimage import restoration
from skimage.restoration import uft
test_img = skimage.img_as_float(camera())
def test_wiener():
psf = ... |
<gh_stars>0
import os
import unittest
import numpy as np
import tempfile
from mytardisdatacert import previewimage
from scipy.ndimage import imread
class PreviewImageFilterTests(unittest.TestCase):
"""Tests for PreviewImage Filter"""
def setUp(self):
self.multi_image_path = "./mytardisdatacert/tests/... |
<reponame>nbara/python-meegk
"""Audio and signal processing tools."""
import numpy as np
import scipy.signal as ss
from scipy.linalg import lstsq, solve, toeplitz
from scipy.signal import lfilter
from .covariances import convmtx
def modulation_index(phase, amp, n_bins=18):
u"""Compute the Modulation Index (MI) b... |
<reponame>aparecidovieira/keras_segmentation<gh_stars>1-10
import numpy as np
import cv2, sys
import itertools
#from pilutil import *
from scipy.misc import imread
# sys.path.append('..')
from models.common import lanenet_wavelet
from keras.utils import to_categorical
#from scipy.misc import imread
#from matplotlib i... |
import tensorflow as tf
import scipy.sparse
import numpy as np
import os, time, collections, shutil, sys
ROOT_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
sys.path.append(ROOT_PATH)
import math
class base_model(object):
def __init__(self):
self.regularizers = []
self... |
#!/usr/bin/python
import sys
import time
import threading
import numpy
import string
import copy
from scipy.optimize import curve_fit
from math import sqrt,exp,log,pi,acos,atan,cos,asin
def g_CIMP(x):
x=x[0,:]
g=numpy.zeros(len(x))
#g=2.691*(1-0.2288964/x)*1/((1+0.16*x)*(1+1.35*numpy.exp(-x/0.2)))
if (max(x)<... |
import datetime
import numpy as np
import matplotlib.pyplot as plt
from numpy.lib.function_base import append
import sympy as sp
from multiprocessing import Pool
import os
import cppsolver as cs
from tqdm import tqdm
from ..filter import Magnet_UKF, Magnet_KF
from ..solver import Solver, Solver_jac
class Simu_Data:
... |
import json
import numpy as np
import wfdb
from scipy.signal import find_peaks
from sklearn.preprocessing import scale
from torch.utils.data import DataLoader, Dataset
class EcgDataset1D(Dataset):
def __init__(self, ann_path, mapping_path):
super().__init__()
self.data = json.load(open(ann_path))... |
"""
Makes several plots of the data for better analysis.
"""
import matplotlib.pyplot as plt
import scipy.stats as st
from scipy.stats import norm
from numpy import linspace
def plot_distribution(style, name, bins, data):
"""
Fits the distribution [name] to [data] and plots a histogram with [bins]
and a l... |
import sys
from mongoengine import *
import requests
import xml.etree.ElementTree as ET
from datetime import datetime, timedelta
import pytz
from ..Logs.service_logs import bike_log
from ..Config.config_handler import read_config
from ..Parkings_API.parkings_collections_db import ParkingsAvailability, ParkingAvailabili... |
import numpy as np
import pickle
from dataclasses import dataclass,field
from tasks.base_task import BaseTask
from dataclass.configs import BaseDataClass
from dataclass.choices import TUPLETRIPPLE_CHOICES
from tasks import register_task
from helper.utils import compress_datatype
from sklearn.base import Transfo... |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 6 18:14:52 2021
@author: Me
"""
import numpy as np
import matplotlib.pyplot as plt
from photutils.datasets import make_noise_image
from perlin_numpy import generate_perlin_noise_2d
import numba
from scipy.stats import multivariate_normal
from scipy.optimize i... |
<gh_stars>1-10
import copy
from math import ceil
from typing import List
from matplotlib import pyplot as plt
import numpy as np
import scipy.stats
from baselines.ga.multi_pop_ga.multi_population_ga_pcg import MultiPopGAPCG, SingleElementFitnessFunction, SingleElementGAIndividual
from games.game import Game
from games... |
import sys
import traceback
import random
import sympy
import numpy
import scipy
genList = []
class Generator:
def __init__(self, title, id, generalProb, generalSol, func):
self.title = title
self.id = id
self.generalProb = generalProb
self.generalSol = generalSol
self.fu... |
from app import db, login
from flask_login import UserMixin, LoginManager
from werkzeug.security import generate_password_hash, check_password_hash
from flask_login import current_user
from statistics import stdev
@login.user_loader
def load_user(id):
return User.query.get(int(id))
enrolments = db.Table('enrolme... |
""" Module for synthetic and real datasets with available ground truth feature importance explanations. Also contains
methods and classes for decisionRule data manipulation.
All of the datasets must be instanced first. Then, when sliced, they all return the observations, labels and ground
truth explanations, respectiv... |
<gh_stars>10-100
#!/usr/bin/env python
""" Simulate Hamiltonian of 2-node circuits with arbitrary capacitances and junctions """
import numpy as np
import numpy.linalg
import scipy as sp
import csv
import os
def solver_2node(Carr, Larr, Jarr, phiExt=0, qExt=[0,0], n=40, normalized=True):
"""
Calculates flux or... |
from websocket import create_connection
import io, sys, json, base64
from json import dumps
from PIL import Image
import cv2
import numpy as np
import numpy as np
from pyquaternion import Quaternion as qu
from scipy.spatial.transform import Rotation as R
import pandas as pd
from tqdm import tqdm
import os
import pickle... |
<filename>aerosandbox/geometry/airfoil.py
from aerosandbox.geometry.common import *
from aerosandbox.tools.airfoil_fitter.airfoil_fitter import AirfoilFitter
from scipy.interpolate import interp1d
class Airfoil:
def __init__(self,
name=None, # Examples: 'naca0012', 'ag10', 's1223', or anything y... |
import math
from scipy.special import erf
from numpy import poly1d
from numpy import pi, sin, linspace
from numpy import exp, cos
# scipy erfc does not support complex numbers but erf does
def erfc(z):
if z == complex(0,0):
return 1.0
else:
return 1.0-erf(z)
def analytic_solutio... |
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <rawcell>
# #!/usr/bin/env python
# <codecell>
from __future__ import division
from __future__ import with_statement
import numpy as np
from pylab import ion
import matplotlib as mpl
from matplotlib.path import Path
from matplotlib import pyplot as plt
from matp... |
import os
from functools import reduce
from collections import deque
import numpy as np
import scipy as sp
from numpy import linalg as LA
from scipy.spatial import distance_matrix
from Transformations import rotation_matrix, superimposition_matrix
from SWCExtractor import Vertex
from Obj3D import Point3D, Sphere, Co... |
# (c) 2017 <NAME>
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
import nanopores
from find_binding_probability import (binding_prob,
binding_prob_from_data, invert_monotone)
# load data
P2 = np.linspace(0, 1, 100)
P2a = P2[P2 > 0.... |
import unittest
import pandas as pd
import numpy as np
from numpy.testing import assert_array_almost_equal
from pandas.testing import assert_frame_equal
from nancorrmp.nancorrmp import NaNCorrMp
from scipy.stats import pearsonr
class TestNaNCorrMp(unittest.TestCase):
X = pd.DataFrame({'a': [1, 5, 7, 9, 4], 'b': [... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy import optimize
import pandas as pd
from scipy.stats import binom, poisson
def cdf(x, func, max, step, *fargs):
"""calculate cdf for function. extra arguments (after x) for func in should be given in fargs
func is the abitra... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import interpolate
df = pd.read_csv('data/turbine_structure_UAE.csv')
radius = df['r'].to_numpy()
rho = df['rho'].to_numpy()
EI_edge = df['ES'].to_numpy()
EI_flap = df['FS'].to_numpy()
plt.plot(radius,rho)
N_total = 32#int(input("Ple... |
from approx1D import least_squares_numerical
import sympy as sym
from numpy import tanh, sin, pi, linspace
import matplotlib.pyplot as plt
import time, os
x = linspace(0, 2*pi, 1001)
#x = linspace(0, 2*pi, 3)
s = 20
s = 2000
def f(x):
return tanh(s*(x-pi))
# Need psi(x) with a parameter i: use a class
"""
s= 20... |
<reponame>DentonW/Ps-H-Scattering<gh_stars>1-10
#!/usr/bin/python
#TODO: Add checks for whether files are good
#TODO: Make relative difference function
import sys, scipy, pylab
import numpy as np
from math import *
import matplotlib.pyplot as plt
from xml.dom.minidom import parse, parseString
from xml.dom import min... |
from typing import Optional
import numpy as np
from scipy.spatial.distance import cdist
from src.data.data_class import TrainDataSet, TestDataSet
class KernelIVModel:
def __init__(self, X_train: np.ndarray, alpha: np.ndarray, sigma: float):
"""
Parameters
----------
X_train: np.... |
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
from SlidingWindowVideoTDA.VideoTools import *
from Alignment.AllTechniques import *
from Alignment.AlignmentTools import *
from Alignment.Alignments import *
from Alignment.DTWGPU import *
from Skeleton import *
from Weizmann import *
from Paper... |
<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: © 2021 Massachusetts Institute of Technology.
# SPDX-FileCopyrightText: © 2021 <NAME> <<EMAIL>>
# NOTICE: authors should document their contributions in concisely in NOTICE
# with details inline in ... |
# simulate bright sources
from pathlib import Path
import logging
import warnings
import click
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import astropy.units as u
from astropy.coordinates import SkyCoord
from gammapy.cube import (
MapDataset,
MapDatasetEventSampler,
Ma... |
<reponame>mfalkiewicz/functional_gradients
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import scipy.optimize as opt
from scipy.special import erf
from .due import due, Doi
__all__ = []
# Use duecredit (duecredit.org) to provide a citation to relevant work t... |
<gh_stars>10-100
"""
Testing class for the demo.
"""
from absl import flags
import os
import os.path as osp
import numpy as np
import torch
import torchvision
from torch.autograd import Variable
import scipy.misc
import pdb
import copy
import scipy.io as sio
from ..nnutils import test_utils
from ..nnutils import net_b... |
<reponame>MattiasBeming/LiU-AI-Project-Active-Learning-for-Music
# FMA: A Dataset For Music Analysis
# <NAME>, <NAME>, <NAME>,
# <NAME>, EPFL LTS2.
# All features are extracted
# using [librosa](https://github.com/librosa/librosa).
# Note:
# This file was edited to work for emo-music in our project.
# All credit for ... |
<reponame>taconite/PTF
"""
Code to fit SMPL (pose, shape) to IPNet predictions using pytorch, kaolin.
"""
import os
os.environ['PYOPENGL_PLATFORM'] = 'osmesa'
import torch
import trimesh
import argparse
import numpy as np
import pickle as pkl
from kaolin.rep import TriangleMesh as tm
from kaolin.metrics.mesh import lap... |
import scipy.stats as stats
from scipy.special import erf
from functools import partial
import numpy as np
import sys
import os
########################################################################################################################
# Define the probability distribution of the random parameters... |
<reponame>pPatrickK/crazyswarm<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 2 07:32:22 2019
@author: diewa
"""
import CF_functions as cff
from scipy.io import savemat
import os
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Converting log data to Matlab fi... |
"""Beam lifetime calculation."""
import os as _os
import importlib as _implib
from copy import deepcopy as _dcopy
import numpy as _np
from mathphys.functions import get_namedtuple as _get_namedtuple
from mathphys import constants as _cst, units as _u, \
beam_optics as _beam
from . import optics as _optics
if _i... |
# -*- coding: utf-8 -*-
import numpy
import scipy.linalg
import sklearn.cross_decomposition
import sklearn.metrics
class LinearCCA(object):
def __init__(self, n_components):
self._n_components = n_components
self._wx = None
self._wy = None
def fit(self, X, Y):
""" fit the mode... |
import math
import operator
import diffrax
import equinox as eqx
import jax
import jax.numpy as jnp
import jax.random as jrandom
import pytest
import scipy.stats
from helpers import all_ode_solvers, random_pytree, shaped_allclose, treedefs
@pytest.mark.parametrize(
"solver_ctr",
(
diffrax.Euler,
... |
import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error
from sklearn.metrics import fbeta_score, make_scorer
import re
from sklearn.preprocessing im... |
<filename>src/fesolvers.py
'''
finite element solvers for the displacement from stiffness matrix and force
'''
import numpy as np
# https://docs.scipy.org/doc/scipy-0.18.1/reference/sparse.html
from scipy.sparse import coo_matrix, lil_matrix, csc_matrix, csr_matrix
from scipy.sparse.linalg import spsolve
class FESolv... |
# -*- coding: utf-8 -*-
"""
This module contains all classes and functions dedicated to the processing and
analysis of a decay data.
"""
import logging
import os # used in docstrings
import pytest # used in docstrings
import tempfile # used in docstrings
import yaml # used in docstrings
import h5py
import copy
from... |
import glob
import os
from typing import List, Tuple
import cv2
import h5py
import numpy as np
import scipy.io as sio
from tqdm import tqdm
from mmhuman3d.core.conventions.keypoints_mapping import convert_kps
from mmhuman3d.data.data_structures.human_data import HumanData
from .base_converter import BaseModeConverter... |
<gh_stars>0
from __future__ import division
import pickle as pkl
import obonet
import json
import numpy as np
import re
import string
import random
from gensim import models, corpora, matutils
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from scipy... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import dct
def hann_window(N):
"""
Create the Hann window 0.5*(1-cos(2pi*n/N))
"""
return 0.5*(1 - np.cos(2*np.pi*np.arange(N)/N))
def specgram(x, win_length, hop_length, win_fn = hann_window):
"""
Compute the non-redundant ... |
<reponame>Bermuhz/DataMiningCompetitionFirstPrize<gh_stars>100-1000
from sklearn.linear_model import LogisticRegression
from commons import variables
from commons import tools
from scipy.stats import mode
def learn(x, y, test_x):
# set sample weight
weight_list = []
for j in range(len(y)):
if y[... |
"""
Unsupervised MoE Variational AutoEncoder (VAE)
==============================================
Credit: <NAME>
Based on:
- https://towardsdatascience.com/mixture-of-variational-autoencoders-
a-fusion-between-moe-and-vae-22c0901a6675
The Variational Autoencoder (VAE) is a neural networks that try to learn the
s... |
# Calculates enriched and natural isotopic molecular masses in g/mol
# Front matter
##############
import re
import time
import pandas as pd
import numpy as np
from scipy import constants
start_time = time.time()
# Define list of compositions to calculate molecular mass of
##########################################... |
#!/usr/bin/env python
# encoding: utf-8
"""Convert Segmentation to Mask Image."""
import os
import argparse
import collections
import numpy as np
from astropy.io import fits
# Scipy
import scipy.ndimage as ndimage
def run(segFile, sigma=6.0, mskThr=0.01, objs=None, removeCen=True):
"""Convert segmentation map ... |
import numpy as np
from scipy.spatial import distance
# read the data using scipy
points = np.loadtxt('input.txt', delimiter=', ')
# build a grid of the appropriate size - note the + 1 to ensure all points
# are within the grid
xmin, ymin = points.min(axis=0) - 1
xmax, ymax = points.max(axis=0) + 2
# and use mesgrid... |
<reponame>mcd4874/NeurIPS_competition<gh_stars>10-100
import numpy as np
# from intra_alignment import CORAL_map, GFK_map, PCA_map
# from label_prop import label_prop
import numpy as np
import pulp
def label_prop(C, nt, Dct, lp="linear"):
# Inputs:
# C : Number of share classes between src and tar
... |
from argparse import ArgumentParser
import json
import scipy.io as sio
import sys
import os
import pandas as pd
import numpy as np
def parse_options():
parser = ArgumentParser()
#parser.add_argument("-a", "--all", required=False, default=False,
# action="store_true",
# ... |
<reponame>juliadeneva/NICERsoft<gh_stars>0
#!/usr/bin/env python
from __future__ import print_function, division
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.path as mplPath
import os.path as path
import argparse
import astropy.units as u
from astropy.time import Time, TimeDelta
from astropy imp... |
<filename>theano/sandbox/linalg/tests/test_kron.py
from nose.plugins.skip import SkipTest
import numpy
from theano import tensor, function
from theano.tests import unittest_tools as utt
from theano.sandbox.linalg.kron import Kron, kron
try:
import scipy.linalg
imported_scipy = True
except ImportError:
imp... |
from PIL import Image
from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt
from scipy import ndimage
import numpy as np
import math
import glob
def show_single(img, cr, f=lambda x:x):
""" Plot an image with the crop information as lines """
if isinstance(img, str):
im = Image.open(img)... |
# -*- coding: utf-8 -*-
# _predictSNR.py
# Module providing predictSNR
# Copyright 2013 <NAME>
# This file is part of python-deltasigma.
#
# python-deltasigma is a 1:1 Python replacement of Richard Schreier's
# MATLAB delta sigma toolbox (aka "delsigma"), upon which it is heavily based.
# The delta sigma toolbox is (c... |
"""
Model for radial basis function (RBF) interpolation.
"""
# Author: <NAME> <<EMAIL>>
# License: BSD 3 clause
import numpy as np
import pickle
import scipy.interpolate
import os
class RBF:
def __init__(self):
pass
def set_data(self, features, targets, D, denom_sq):
self.features = featu... |
<filename>src/python/zquantum/core/wip/circuits/_gates.py
"""Data structures for ZQuantum gates."""
import math
from dataclasses import dataclass, replace
from functools import singledispatch
from numbers import Number
from typing import Callable, Dict, Tuple, Union, Iterable
import numpy as np
import sympy
from typin... |
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