text string |
|---|
<filename>fgivenx/mass.py
""" Utilities for computing the probability mass function. """
import scipy.stats
import scipy.interpolate
import numpy
from fgivenx.parallel import parallel_apply
from fgivenx.io import CacheException, Cache
def PMF(samples, y):
""" Compute the probability mass function.
The se... |
<gh_stars>1-10
import sys
import numpy as np
from scipy.stats import norm
import torch
import torch.nn.functional as F
from fairseq.models.transformer import *
from collections import OrderedDict
from pdb import set_trace as bp
from tqdm import tqdm
import pickle
f = open('combined_text.txt')
corpus = []
for l in f:
... |
<gh_stars>0
import numpy as np
from os import listdir
import skimage.transform
import torch
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torch.autograd ... |
<gh_stars>0
import sys
try:
import math
import sympy as sym
from sympy import sin,cos,tan,N
except:
print("Packages not installed. Please install 'sympy' package")
sys.exit()
print("Box Evolutionary method")
x = sym.Symbol('x')
y = sym.Symbol('y')
def substitute(pt):
return (f.s... |
<gh_stars>0
import numpy as np
import cvxpy as cp
import scipy.linalg
import scipy.optimize
import matplotlib.pyplot as plt
import pickle
#%%%
ENV = 'power_system_2'
names = ['RCI Network','Policy Network']
folders = ['rpn','pn']
#colors = []#'b'#'r'
plt.figure(1,figsize=(12,4),dpi=500)
plt.rcParams["font.family"] =... |
<reponame>movingpictures83/SpearmanTiesMulti
import sys
#import numpy
import PyPluMA
import math
import scipy.stats
class SpearmanTiesMultiPlugin:
def input(self, filename):
self.myfile = filename
def run(self):
filestuff = open(self.myfile, 'r')
file1 = open(PyPluMA.prefix() + "/" + filestuff... |
# Load library dependencies
import numpy as np
import numpy.linalg
import scipy as sp
import scipy.optimize
import scipy.stats
import pandas as pd
# Import additional functions called by eplm()
from .print_coef import print_coef
from .ols import ols
from .logit import logit
from .poisson import poisson
from .iv impo... |
<gh_stars>1-10
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import numpy as np
from scipy.special import digamma, polygamma
from decimal import Decimal
def E_step_Plain(alpha, BETA, doc,Phi0, gamma0, max_iter=100, tol=1e-3):
"""
Latent Dirichlet Allocation: E-step.
Do to a specific document.
------------... |
<reponame>dfm/SurPyval
import autograd.numpy as np
from autograd import jacobian, hessian, elementwise_grad
from scipy.optimize import minimize
import surpyval
import inspect
import warnings
from ..parametric.fitters import bounds_convert, fix_idx_and_function
from .regression import Regression
class ParameterSubstit... |
<filename>src/frontend/mod/session.py
import re
import sympy
import tkinter as tk
from scipy import optimize as opt
from tkinter import *
from tkinter.scrolledtext import *
from backend.lib.libc2e2 import Model
from frontend.mod.constants import *
class Property():
def __init__(self):
# Property values
... |
<filename>src/models/predict_model.py<gh_stars>1-10
import pandas as pd
from src.data.Dataset import save_all_3d_vols_new
from src.data.Postprocess import undo_generator_steps
def pred_fold(config, debug=True):
# make sure all neccessary params in config are set
# if not set them with default values
from... |
#!/usr/bin/env python3
import wellmap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress
def load_cq(path):
return (pd
.read_csv(path)
.rename(columns={'Cq': 'row'})
.melt(
id_vars=['row'],
... |
#!/usr/bin/env python
#
# Created by: <NAME>, April 2002
#
from __future__ import division, print_function, absolute_import
__usage__ = """
Build linalg:
python setup.py build
Run tests if scipy is installed:
python -c 'import scipy;scipy.linalg.test()'
"""
import math
import numpy as np
from numpy.testing impo... |
<gh_stars>0
from __future__ import print_function
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from scipy import ndimage as nd
from skimage import data
from skimage.util import img_as_float
from skimage.filter import gabor_kernel
from skimage.feature import hog
from skimage import data, colo... |
<reponame>EdYuTo/Captcha-training-AI
import numpy as np
import scipy.signal
import cv2
from imutils import paths
import time
def image_to_feature_vector(image, size=(32, 32)):
return cv2.resize(image, size).flatten()
def feature_vector_to_image(image, size=(32, 32, 3)):
return img.reshape(size)
sharpen_kerne... |
<reponame>tschoi6712/pythonDataAnalysis2nd
"""
Pandas(1)
"""
## 1. pandas DataFrames - 관계형 데이터베이스의 일종인 2차원 자료구조
import numpy as np
import scipy as sp
import pandas as pd
# Load the data file into data frame
from pandas.io.parsers import read_csv
df = read_csv("ch3-1.WHO_first9cols.csv") # 데이터 파일을 데이... |
# Copyright 2017 The dm_control Authors.
#
# 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 i... |
<reponame>martinlarsalbert/rolldecay-estimators<gh_stars>1-10
import pandas as pd
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
from rolldecayestimators.estimator import RollDecay
from rolldecayestimators.direct_estimator import DirectEstimator
from rolldecayestimators.simplified_ikeda import c... |
# coding: utf-8
# Copyright (c) 2020 The MITRE Corporation.
"""
Estimator module to train/fit/estimate individual models with fixed hyperparameters.
Estimators are used by trainers to manage training with specific datasets; in addition,
the estimator API supports inference/encoding with fitted models.
"""
import loggi... |
<reponame>horseriver/csgm<filename>src/celebA_estimators.py
"""Estimators for compressed sensing"""
# pylint: disable = C0301, C0103, C0111, R0914
import copy
import heapq
import tensorflow as tf
import numpy as np
import utils
import scipy.fftpack as fftpack
import pywt
import celebA_model_def
from celebA_utils impo... |
from numpy import *
from matplotlib.pyplot import *
from scipy.signal import *
from scipy.interpolate import splrep, splev
from scipy.integrate import odeint
f = 1
f2 = 1
t = linspace(0, 10, 10000)
per = linspace(0, 100, 100000)
x = zeros(len(t))
xmin = zeros(len(t))
xmax = zeros(len(t))
k = 4 # number of mod
newsi ... |
import tensorflow.contrib.slim as slim
import scipy.misc
import tensorflow as tf
from tqdm import tqdm
import numpy as np
import shutil
import utils
import os
"""
An implementation of the neural network used for
super-resolution of images as described in:
`Enhanced Deep Residual Networks for Single Image Super-Resolu... |
<reponame>agnsal/SalutariDiscreteLogProblemSolver
'''
Copyright 2019 <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/LICENSE-2.0
Unless required by applicabl... |
import argparse
import yaml
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy.stats
from tqdm import tqdm
from torch.utils.data import DataLoader
import random
import datasets
import models
import utils
import utils.few_shot as fs
from datasets.samplers import Categorie... |
<reponame>rscalzo/sami
"""
This file contains some functions used for recovering the dither pattern of a set of SAMI observations.
These revolve around finding the centroid for each source in each IFU and then computing the best cordinate
transformation to bring each RSS frame on the coordinate system of a reference ... |
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import erf
def hinton(matrix, max_weight=None, ax=None):
"""Draw Hinton diagram for visualizing a weight matrix."""
ax = ax if ax is not None else plt.gca()
if not max_weight:
max_weight = 2 ** np.ceil(... |
<reponame>Joao-Tiago-Almeida/NLG-model
import os
import glob
import pandas
import re
import numpy as np
from sklearn import model_selection
from sklearn import linear_model
from sklearn import svm
from sklearn.metrics import explained_variance_score
import matplotlib.pyplot as plt
import seaborn as sns
import json
from... |
<reponame>jcrist/pydy<gh_stars>100-1000
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Exercise 4.1 from Kane 1985"""
from sympy.physics.mechanics import dot, MechanicsStrPrinter
from sympy.physics.mechanics import ReferenceFrame, Point
from sympy import symbols, sin, cos
from sympy.simplify.simplify import trigsimp... |
<reponame>kevin5naug/homework
#Reference:
#1. https://github.com/mabirck/CS294-DeepRL/blob/master/lectures/class-5/REINFORCE.py
#2. https://github.com/JamesChuanggg/pytorch-REINFORCE/blob/master/reinforce_continuous.py
#3. https://github.com/pytorch/examples/blob/master/reinforcement_learning/actor_critic.py
# With th... |
<reponame>pick2510/MesoscaleTools<filename>MesoscaleTools/numerical_utils.py
import xarray
import numpy as np
import scipy
def destagger_dimension(data, stagger_dim, name):
var_shape = data.shape
num_dims = data.ndim
stagger_dim_size = var_shape[stagger_dim]
full_slice = slice(None)
slice1 = slice... |
<filename>pysilsub/devicen.py<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
silentsub.device
================
A generic device class for multiprimary light stimulators.
@author: jtm
"""
from typing import List, Union, Optional, Tuple, Any
from scipy.interpolate import interp1d
from scipy.optimize... |
<reponame>opunsoars/pitchly
import glob
import numpy as np
import pandas as pd
import scipy.signal as signal
from .params import prm
match_dir = "/media/opunsoars/My Book/playground/friends_of_tracking/friends_of_tracking/\
datahub/metrica_sports/sample-data/data/Sample_Game_1"
def modify_cols(tracking_df):
... |
<gh_stars>0
from fractions import Fraction
def reshufle(r, terminals):
offset = 0
for i in terminals:
v = r[i]
del r[i]
r.insert(offset, v)
offset +=1
return r
def order_matrix(M):
# get row indices for terminal and non-terminal states
terminals = []
transients ... |
<filename>Textbook/Chapter 8/regressionOld.py
#econFreedomRegression.py
import pandas as pd
import copy
import numpy as np
from scipy.stats import t
import matplotlib.pyplot as plt
from stats import Stats
import sys
class Regression:
def __init__(self):
self.stats = Stats()
self.all_regressions = {... |
<gh_stars>0
import numpy as np
import math
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
from scipy import stats
from aria_component import aria_component
def plot_QoI_pdfs(components, pce_coeffs):
# Calculate pce dimension (total number of exogenous ports in network)... |
#%%
from pathlib import Path
from matplotlib import pyplot as plt
import scipy.signal as signal
import numpy as np
import pandas as pd
import nptdms
from nptdms import TdmsFile
from pros_noisefiltering.gen_functions import spect, plot_spect_comb2
from pros_noisefiltering.Graph_data_container import Graph_data_contai... |
<gh_stars>0
import numbers
from typing import Collection, Any, Mapping
import numpy
from ..workbench.em_framework import parameters as workbench_param
from scipy import stats
from scipy.stats._distn_infrastructure import rv_frozen
from ..util import distributions, DistributionTypeError, DistributionFreezeError
from ... |
import numpy as np
import os
import argparse
import glob
import imageio
from scipy.sparse import load_npz, save_npz
from sklearn.mixture import GaussianMixture
def threshold_noise(Y,K=6):
nonzero_thresh = np.percentile(Y,1,axis=1)*1.1 + 1
T = np.zeros(Y.shape[0])
for j in range(Y.shape[0]):
Models = []
for i in... |
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from scipy import stats
import os
import importlib
import DeepNetPI_V2
import DataGen_V2
import utils
from sklearn.metrics import r2_score
import os
import random
import itertools
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_CPP_MIN_... |
import os
import itertools
import numpy as np
import tensorflow as tf
import scipy.io
#import pandas as pd
#import seaborn as sns
import keras
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense, Dropout, Activation, BatchNormalization,Flatten,Conv2D
from keras.layers.... |
<filename>model_and_simulate/chaos/chaos_simulation.py
"""Module with chaos simulation class."""
from typing import Optional, Callable
import numpy as np
from numpy.random import default_rng
from scipy.integrate import solve_ivp
# SEED = 1234
rng = default_rng() # keyword seed=SEED
class ChaosSimulation:
"""Cl... |
# Copyright (c) 2012-2014 The GPy authors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
# Multioutput likelihood structure is similar to the
# corresponding structure in GPstuff. If building complex
# multioutput models on top of this class and need a reference,
# check GPstuff project.... |
<reponame>DSchreyer/crisprquant<filename>bin/AnalyzeControl.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 29 15:10:27 2016
@author: philipp
"""
# Estimate mean counts and variance from control samples
# =======================================================================
# Imports
from __f... |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 14:41:37 2016
Suggested anity checks:
- For same A coefficients, check if chords are equal
- As A increases, the chord for cruise should shrink
- For A coefficients, check if cos(beta)=0
- For same A coefficients, check for calculate_psi_goal if psi_bas... |
#!/usr/bin/env python
from mpl_toolkits.basemap import Basemap, cm
# requires netcdf4-python (netcdf4-python.googlecode.com)
from netCDF4 import Dataset as NetCDFFile
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime as dt
from datetime import timedelta as td
import sys
from scipy import... |
<gh_stars>10-100
# Copyright (c) 2017-present, Facebook, Inc.
#
# 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 applica... |
<reponame>imdeepmind/AgeGenderNetwork
## IMPORTING THE DEPENDENCIES
import pandas as pd
import numpy as np
from scipy.io import loadmat
from utils import compare_date, clean_full_date
## LOADING THE .MAT FILE
wiki_mat = './dataset/unprocessed/wiki_crop/wiki.mat'
wiki_data = loadmat(wiki_mat)
wiki = wiki_data['wiki']
... |
<filename>neodroidagent/utilities/signal/numpy_discounting.py<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import Iterable, Union
import numpy
__author__ = "<NAME>"
__doc__ = r"""
Created on 09/10/2019
"""
from scipy.signal import lfilter
__all__ = ["discount_signa... |
<filename>scratch.py
# <NAME>
# Sun May 30 10:01:36 EDT 2021
import sympy as sp
# simple integrals
from sympy.abc import *
x = sp.Symbol('x')
f = sp.integrate(x ** 2 + x + 1, x)
# solving
from sympy.solvers import solve
sp.solve = solve
from z3 import *
# solve assumes arg1 = 0
## This solver is also naked of unit... |
"""
ana.py
======
Contain mathematical functions to make results analysis
(compute angular resolution, effective surface, true_energy resolution... )
"""
import numpy as np
from scipy.stats import binned_statistic, norm
from astropy.io.ascii import read
import astropy.units as u
from sklearn import metrics
from ..io im... |
# !/usr/bin/env python3
# coding:utf-8
import sys
import os
import git
from sys import argv
from socket import *
import socket as Socket
from uuid import uuid4
from fractions import gcd
import random
HOST = "0.0.0.0"
NOMAL_PORT = 33844
VALID_PORT = 33845
# For git
URL = '<EMAIL>:ertlnagoya/Update_Test.git'
DIRECTOR... |
<filename>ml4tc/utils/imputation.py<gh_stars>1-10
"""Imputation of missing data."""
import numpy
from scipy.interpolate import interp1d
from gewittergefahr.gg_utils import error_checking
from ml4tc.utils import general_utils
from ml4tc.utils import example_utils
from ml4tc.utils import normalization
LARGE_NEGATIVE_NU... |
from keras import backend as K
import tensorflow as tf
from scipy import ndimage as ndi
import keras
def weighted_binary_crossentropy(y_true, y_pred):
weight = y_true[:, :, :, -1]
y_true = y_true[:, :, :, 0]
y_pred = K.constant(y_pred) if not K.is_tensor(y_pred) else y_pred
y_true = K.cast(y_true, y_... |
# coding: utf-8
from sympy.core import Basic
from sympy.core.containers import Tuple
from sympy.core import Expr
from sympy.parsing.sympy_parser import parse_expr
from sympde.topology.basic import Boundary, Union
from sympde.topology.space import VectorFunctionSpace, ScalarFunctionSpace
from sympde.topology.space im... |
<reponame>PHParis/ConProKnow<filename>conproknow/gold_standard/gold_standard.py
from json import load
from typing import List, Tuple, Set, Dict
from statistics import mean
from math import floor
from conproknow.identity.context import ContextWGoldStand, Context
from conproknow.algo.lattice_builder import get_propagatio... |
<filename>img/cal_stat.py
import numpy as np
from numpy import ma
import math
import scipy as sp
import params as pm
#--
def rmse(assimilations,truths,add=1.0e-20):
rms = []
assimilations = np.array(assimilations)
truths = np.array(truths)
# assimilations is 2d arry
for i in range(0,len(truths)):
... |
<filename>src/edbscan/main.py
"""
EDBSCAN: Enforced Density-Based Spatial Clustering of Applications with Noise.
Author: <NAME> <<EMAIL>>
"""
from __future__ import annotations
import warnings
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
from numpy.typing import N... |
<reponame>binggu56/lime<gh_stars>1-10
import numpy as np
import sympy as sym
#import math
from DiffFreqs import factorial
from scipy.special import genlaguerre
# from mpmath import laguerre
def laguerre(a, n):
"""
Generates the nth laguerre polynomial with superscript a
using the Rodrigues formula.... |
import time
import numpy as np
def jacobi(A, b, max_iter_time=5000, min_iter_time=100, tolerance=1e-10):
"""
Jacobi iteration for solving the linear equations with below form:
A * u = b
---------------
:param A: (N, N) array_like
Matrix A (n*n size). Square input data.
:param b: ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : Apr-18-20 19:49
# @Author : <NAME> (<EMAIL>)
# @Link : https://github.com/KellyHwong/kaggle_digit_recognizer
# @Link : https://www.kaggle.com/c/digit-recognizer
import os
import json
import pandas as pd
import numpy as np
import matplotlib.p... |
## WAVE EQ
""" this script solves the 1D-wave equations, i.e.
du/dt = -d/dx(1/2*u^2 + g*h - nu*du/dx) + F
dh/dt = -d/dx(u*h)."""
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
import time as tictoc
from scipy.signal import tukey
## physical parameters
Lx = 1. ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 16 17:25:18 2018
@author: akankshabindal
"""
# Modified version of dart_env.py to support 2 skeletons
import os
from gym import error, spaces
from gym.utils import seeding
import numpy as np
from os import path
import gym
import six
from collect... |
# Copyright (c) 2015-2018 by the parties listed in the AUTHORS file.
# All rights reserved. Use of this source code is governed by
# a BSD-style license that can be found in the LICENSE file.
import copy
import numpy as np
import scipy.signal
from toast._libtoast import filter_polynomial as polyfilter
from .signal... |
# -*- coding: utf-8 -*-
####################################################
# 作者: 刘朝阳
# 时间: 2020.05.01
# 功能: 计算EAR和MAR
# 使用说明: 自动调用, 无序操作.
####################################################
import numpy as np
from scipy.spatial import distance as dist
class aspect_ratio_estimation:
def __init... |
<filename>kuka_arm/scripts/IK_server.py
#!/usr/bin/env python
# Copyright (C) 2017 Udacity Inc.
#
# This file is part of Robotic Arm: Pick and Place project for Udacity
# Robotics nano-degree program
#
# All Rights Reserved.
# Author: <NAME>
# import modules
import rospy
import tf
from kuka_arm.srv import *
from tra... |
<reponame>ssykira/optic-nerve
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 06 22:10:57 2015
@author: <NAME>.
"""
import os
import glob
from math import sqrt
import numpy as np
import scipy as sp
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.cm as cm
class ImLibException(Exception):
p... |
<reponame>singh96aman/pyawake
'''
Title : Indexing Python Module for pyAwake
Author : <NAME>
Last Edited : 08 August, 2019
'''
import os,sys
import json
import ast
import time
import threading
import shutil
from datetime import datetime
import pytz
import h5py
import csv
import sys
import ... |
<reponame>francisanokye/CRISP-Probabilistic-Model-for-COVID-19-
import argparse
import sys
sys.path.append('..')
from crisp import Distribution, PopulationInfectionStatus
from scipy.stats import nbinom
from itertools import count
from matplotlib.pyplot import *
from tqdm import tqdm
my_parser = argparse.ArgumentPar... |
#!/usr/bin/python
import pandas as pd
import numpy as np
import scipy
import math
import os
import matplotlib.pyplot as plt
import seaborn as sns
import requests
import nltk
import gensim
import csv
from sklearn.metrics.pairwise import cosine_similarity
from collections import Counter
import math
from nltk import word_... |
<filename>applications/popart/deep_voice/audio_utils.py<gh_stars>100-1000
# Copyright (c) 2020 Graphcore Ltd. All rights reserved.
from scipy import signal
import numpy as np
REF_DB = 20.0
MIN_DB = -100.0
def preemphasis(x, coef=0.97):
""" Pre-emphasis - apply filter to boost frequency range susceptible... |
<gh_stars>0
from __future__ import division
import numpy as np
from scipy.sparse import spdiags
from scipy.sparse.linalg import spsolve, cg
import matplotlib.pyplot as plt
def fd_order2_ode(func,a1,a2,a3,a=0.,b=1.,alpha=1.,beta=3.,N=5):
# A Simple Finite Difference Scheme to solve BVP's of the form
# a1(x)u''(x) + ... |
<reponame>AndresGarciaEscalante/RoboND-Kinematics-Project
#!/usr/bin/python
# Author: <NAME>
# Testing the forward kinematics of Kuka KR210
import numpy as np
from sympy import symbols, cos, sin, pi, pprint
from sympy.matrices import Matrix
# Your FK code here
# Create symbols
q1, q2, q3, q4, q5, q6, q7 = symbols('q1... |
<reponame>manrodan/gitcourse
#!/usr/bin/env python
# coding: utf-8
#forecasting, getting bounding boxes, labels and probabilities
def forecast(meter):
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
from azure.cognitiveservices.vision.customvision.training impor... |
<filename>mapspec/spectrum.py
import scipy as sp
from scipy.interpolate import interp1d
from scipy.integrate import simps
from scipy.signal import get_window
from scipy import linalg,optimize
from numpy.polynomial.hermite import Hermite as H
from astropy.io import ascii,fits
from astropy.table import Table,Column
imp... |
import numpy as np
from scipy.spatial import distance
speed = 3
def getAvgHeight(data):
try:
return np.mean(data[0<data[:,0],0])
except ValueError:
return 0
def getMaxHeight(data):
try:
return np.max(data[0<data[:,0],0])
except ValueError:
return 0
def getAvgWidth(data... |
"""
kkpy.io
========================
Functions to read and write files
.. currentmodule:: io
.. autosummary::
kkpy.io.read_aws
kkpy.io.read_2dvd_rho
kkpy.io.read_mxpol_rhi_with_hc
kkpy.io.read_dem
"""
import numpy as np
import pandas as pd
import datetime
import glob
import os
import sys
def read_a... |
import itertools
import math
import os
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
from mne import baseline
from scipy.ndimage import convolve1d
from scipy import stats
from sklearn.feature_selection import mutual_info_classif
from sklearn.metrics import accuracy_score, precision_r... |
<reponame>naveenjafer/language
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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/LICE... |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "<NAME>" at 11:59, 17/03/2020 %
# ... |
# IDONE uses a piece-wise linear surrogate model for optimization of
# expensive cost functions with integer variables.
#
# IDONE_minimize(obj, x0, lb, ub, max_evals) solves the minimization problem
#
# min f(x)
# st. lb<=x<=ub, x is integer
#
# where obj is the objective function, x0 the initial guess,
# lb and ub are... |
<filename>src/utilities.py
"""
This file contains all the utilities used in other scripts.
"""
import os
import sys
import subprocess
import numpy as np
import cv2
from natsort import natsorted
import scipy.io as scio
from tqdm import tqdm
import datetime
import inspect
import matplotlib.pyplot as plt
from scipy.ndimag... |
<reponame>mcnowinski/various-and-sundry
# this program requires the 32 bit version of Python!!
import os
import glob
import math
import subprocess
import re
import sys
import string
from decimal import Decimal
from astropy.io import fits
from astropy.wcs import WCS
import matplotlib.pyplot as plt
import numpy as np
im... |
import glob
import xarray as xr
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import matplotlib.path as mpath
import cartopy.crs as ccrs
import cartopy.feature
import matplotlib.gridspec as gridspec
import xesmf as xe
import cartopy.feature as feat
folder = ['/projects/NS9252K/n... |
<filename>cycle_slip.py
import logging
import os
import time
import sys
import re
import datetime
import dateparser
import georinex as gr
import numpy as np
import downloads as dw
import parser as pr
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from pandas.plotting import register_matplot... |
<gh_stars>1-10
from __future__ import division
import math, sys, copy
import scipy
nelo_divided_by_nt = 800 / math.log(10) # 347.43558552260146
def MLE(pdf, s):
"""
This function computes the maximum likelood estimate for
a discrete distribution with expectation value s,
given an observed (i.e. empirical) distr... |
# -*- coding: utf-8 -*-
''' Data Handler Module
This module contains a class for managing a data processing pipeline
'''
from time import time
from datetime import timedelta
import numpy as np
import pandas as pd
from scipy.stats import mode, skew
from scipy.interpolate import interp1d
from sklearn.cluster import DBS... |
import numpy as np
import scipy.sparse
import torch
from torch.utils.data import Dataset
def vectorize_texts(tokenized_texts, word2id, word2freq, mode='tfidf', scale=True):
assert mode in {'tfidf', 'idf', 'tf', 'bin'}
# считаем количество употреблений каждого слова в каждом документе
result = scipy.spars... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
This module contains all the classes used in order to analyze trace
of experiments.
For instance we analyze PCAP by defaults but it would be completely acceptable
to have some log analyzer code here.
"""
from collections import defaultdict
from csv import DictWriter
from ma... |
<reponame>dagisky/Genetic-Algorithm-Hyperparmeter-Search
import random
import numpy as np
from functools import total_ordering
import bisect
import statistics
from inst_func_eval import InstFunEvaluator
#%%
@total_ordering
class EvaluatedIndiv :
def __init__(self, indiv, fitness, gen_num) :
self.indiv =... |
<gh_stars>0
#
# Functions to interface with the matlab code
#
import os
import numpy as np
import pandas as pd
import scipy.io
from typing import List
import birdspy as bs
class MatlabInterface:
"""Class to interface python methods with matlab
"""
@staticmethod
def ground_truth_conversion(save_loc:... |
<gh_stars>0
import numpy as np
import matplotlib as mpl
import scipy.linalg as lg
def figsize(scale):
fig_width_pt = 246.0
inches_per_pt = 1.0/72.27
golden_mean = (np.sqrt(5.0)-1.0)/2.
fig_width = fig_width_pt * inches_per_pt * scale
fig_height = fig_width * golden_mean / scale * 0.4
fig_size =... |
<reponame>correlatedmaterialslaboratory/dmftmodel
"""
7/16/2015
@mu_c calculation with fixed U at very low temp like b=400/800
@then nom=200/400
"""
import sys
import os
from scipy import *
from scipy import integrate
from scipy import interpolate
from pylab import *
from ftns_Fourier import Create_om_mesh,Spline_Rea... |
<reponame>sn6uv/sympy<gh_stars>1-10
import os,sys,sympy
from sympy.galgebra.GA import MV, ZERO, ONE, HALF
from sympy import collect, symbols
def F(x, n, nbar):
"""
Conformal Mapping Function
"""
Fx = HALF*((x*x)*n+2*x-nbar)
return(Fx)
if __name__ == '__main__':
|
<gh_stars>1-10
#!/usr/bin/env python3
import argparse
import os
import scipy.misc
import tensorflow as tf
from network import FACE_COMPLETION
# add arguments for training the network
parser = argparse.ArgumentParser()
# Obligatory parameter to be specified while running training script
parser.add_argument('--datase... |
import math
import numbers
import numpy as np
from skimage.filters import gaussian
from skimage import img_as_float
from scipy import ndimage
import torch
from torch import nn
from torch.nn import functional as F
import kornia
from loguru import logger
from kornia.filters import filter3d
def make_gaussian_kernel(ker... |
import argparse
import sys
import random
import torch
import torchvision
from torchvision import transforms
from PIL import Image
import os
import scipy.io as sio
# import pandas as pd
from tqdm import tqdm
from data.util import is_image_file, read_img, augment, augment4rep
from models.test_libs import ... |
<reponame>damslab/reproducibility
import sys
import time
import numpy as np
import scipy as sp
from scipy.sparse import csr_matrix
import pandas as pd
import math
import warnings
import os
# Force to CPU (default is GPU)
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import tensorflow as tf
from tensorflow.keras.layers.exper... |
<reponame>sbernasek/flyqma<filename>flyqma/annotation/mixtures/bivariate.py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from scipy.stats import norm, multivariate_normal
from .univariate import UnivariateMixture
from .visualization import BivariateVisualization
class B... |
import warnings
warnings.filterwarnings("ignore")
import nltk
from nltk.tokenize import word_tokenize,sent_tokenize
from nltk.translate.bleu_score import corpus_bleu,sentence_bleu
from tqdm import tqdm
from collections import defaultdict
import collections
import math
from statistics import mean, median,variance,stdev
... |
<gh_stars>1-10
import multiprocessing
import os
from collections import Callable
from platform import system
from time import time
from socket import gethostname
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec, rcParams
from matplotlib.ticker import MaxNLocator
from matplotlib.figure ... |
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