text string |
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#!/usr/bin/env python3
#python
import time
from os import mkdir, listdir
from os.path import isdir, isfile
from itertools import chain
#from pickle import load
#external
import numpy as np
np.set_printoptions(precision=10, threshold=np.inf)
from scipy.optimize import least_squares
from matplotlib import pyplot as plt... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.2.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # s_bo... |
import gensim
import smart_open
from scipy import spatial
import numpy as np
from paragraph_similarity.common.paragraph import Paragraph
from paragraph_similarity.common.result import Result
from paragraph_similarity.common.similarity_model import SimilarityModel
class Doc2VecSimilarityModel(SimilarityModel):
d... |
<gh_stars>1-10
import pandas as pd
from scipy import stats
from .. import distributions
from . import utilities
# Fit the pareto distribution to Levy-Stable data
DESIRED_ALPHA = stats.uniform.rvs(1, 2, 1)[0]
BETA = 1.0 # forces rvs to be strictly positive
STABLE_RVS = stats.levy_stable.rvs(DESIRED_ALPHA - 1, BETA, s... |
<gh_stars>0
# =======IF=======
from statistics import mean # média importada
n1 = float(input('Digite sua primeira nota: '))
n2 = float(input('Digite sua segunda nota: '))
n3 = float(input('Digite sua terceira nota: '))
m = mean([n1, n2, n3])
print('Sua média é: {:.2f}' .format(m))
if m >= 7: # se a média for maio... |
<reponame>decabyte/vehicle_core
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Software License Agreement (BSD License)
#
# Copyright (c) 2014, Ocean Systems Laboratory, Heriot-Watt University, UK.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are pe... |
import gym
#from gym import ...
#! /usr/bin/env python
import copy
from copy import deepcopy
import rospy
import threading
import quaternion
import numpy as np
from geometry_msgs.msg import Point
from visualization_msgs.msg import *
#from franka_interface import ArmInterface
#from panda_robot import PandaArm
import ma... |
<reponame>mcmahon-lab/error_mitigation_vqe
import numpy as np
from copy import deepcopy
from scipy.optimize import minimize
import csv
import os
import re
import os.path
from os import path
from math import isnan
import time
from library import *
from datetime import datetime
# if you use a gpu, change this to set wh... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2021 <NAME> <<EMAIL>>
#
# Distributed under terms of the MIT license.
"""
This file is for automatic modified bases signal extraction. The overall
idea is to first find the correct current level for polyA tail with kde,
... |
<reponame>bradkav/imripy
import numpy as np
from scipy.integrate import quad
hubble_const = 2.3e-10 # in 1/pc
Omega_0_m = 0.3111
Omega_0_L = 0.6889
def HubbleLaw(d_lum):
"""
The simple Hubble Law relating the luminosity distance to the redshift
Parameters:
d_lum : float or array_like
... |
# Naive Bayes and Hyperparameter Optimization
*<NAME>, May 5th, 2021*
# Importing our libraries
import pandas as pd
import altair as alt
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.neighbors import KNeighborsClassifier, KNei... |
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import sys
import scipy
unit_M = 1
unit_D = 1
unit_E = 1
unit_t = 1
e_charge = 1
initialized = False
def __init__():
"""
Initialize module
"""
pass
def init(unit_M_, unit_D_, unit_E_):
"""
Initialize units
... |
<reponame>neonnnnn/pyrfm<filename>pyrfm/random_feature/tests/test_scrk.py
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_allclose_dense_sparse
from pyrfm import anova, SignedCirculantRandomKernel
import pytest
# generate data
rng = np.random.RandomState(0)
X = rng.ran... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Neural Network Verification Model Translation Tool (NNVMT)
@author:
<NAME>(<EMAIL>)
<NAME> (<EMAIL>)
"""
from __future__ import division, print_function, unicode_literals
import numpy as np
import os
from src.NeuralNetParser import NeuralNetParser
import scipy.... |
# -*- coding: utf-8 -*-
from math import prod
from statistics import mean
class Calc:
def add(self, *s):
return sum(s)
def subtract(self, a, b):
return a - b
def multiply(self, *s):
if 0 in s:
raise ValueError
return prod(s)
def divide(self, a,... |
# Program 02e: Numerical and truncated series solutions.
# See Figure 2.6.
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import numpy as np
def ODE2(X, t):
x = X[0]
y = X[1]
dxdt = y
dydt = x - t ** 2 * y
return [dxdt, dydt]
X0 = [1, 0]
t = np.linspace(0, 10, 1000)
sol = ode... |
<filename>_build/jupyter_execute/notebooks/math/03 Major Distribution CDFs and PDFs.py
#!/usr/bin/env python
# coding: utf-8
# # Major Distribution CDFs and PDFs
#
# **<NAME>, PhD**
#
# This demo is based on the original Matlab demo accompanying the <a href="https://mitpress.mit.edu/books/applied-computational-econ... |
import argparse
import csv
import numpy
import logging
from pathlib import Path
from scipy.optimize import linear_sum_assignment
logger = logging.getLogger(__name__)
def assign2groups(file_path: Path, bad_assignment_cost=255):
if not file_path.exists():
raise FileNotFoundError(file_path)
with file... |
<filename>Fancy_aggregations/supervised_MPA.py
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 30 13:30:43 2020
@author: javi-
"""
import numpy as np
import sklearn.linear_model
from . import penalties as pn
from . import binary_parser as bp
# ========================================================================... |
'''
# This is an 80 character line #
Read in:
-file name
-bin size
Output (for each timestep):
-histogram of local density (number density, area fraction)
-need to process this into text file that gives just the one-two-three?
'''
import sys
# Run locall... |
<reponame>Goodpaster/QSoME<filename>qsome/custom_diis.py
#Implement EDIIS+DIIS and ADIIS+DIIS
#By <NAME>
import scipy
import numpy as np
from pyscf import lib, scf
DEBUG = False
class EDIIS(scf.diis.EDIIS):
def update(self, s, d, f, elec_e):
if self._head >= self.space:
self._head = 0
... |
<gh_stars>1-10
"""
CCT 建模优化代码
COSY 扩展代码
作者：赵润晓
日期：2021年6月3日
"""
import multiprocessing # 多线程计算
import time # 统计计算时长
from typing import Callable, Dict, Generic, Iterable, List, NoReturn, Optional, Tuple, TypeVar, Union
import matplotlib.pyplot as plt
import math
import random # 随机数
import sys
import os # 查看CPU核心数
... |
<reponame>eliottkalfon/evolution_opt
#!/usr/bin/env python
# coding: utf-8
'''
This module is a Python implementation of a genetic algorithm with a regularized evolution process.
It was inspired by the following paper:
Saltori, Cristiano, et al. "Regularized Evolutionary Algorithm for Dynamic Neural Topology Sear... |
#!env python3
# AUTHOR INFORMATION ##########################################################
# file : bernstein_bijector.py
# brief : [Description]
#
# author : <NAME>
# created : 2020-09-11 14:14:24
# changed : 2020-12-07 16:29:11
# DESCRIPTION #################################################################
#... |
<filename>evaluate_model.py
import argparse
import logging
import os
import numpy as np
import scipy.io as sio
import tensorflow as tf
import utils
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', help='gpu device ID', default='0')
parser.add_argument('-m', '--model_dir', help='model directory',... |
<reponame>NeTatsu/video-diff<filename>Python/Clustering.py
"""
The most efficient would be to use OpenCV's
cv::flann::hierarchicalClustering .
But we do NOT have Python bindings to it.
See if you have the time:
http://opencvpython.blogspot.ro/2013/01/k-means-clustering-3-working-with-opencv.html
Other idea... |
<reponame>dfm/turnstile
# -*- coding: utf-8 -*-
from __future__ import division, print_function
__all__ = ["FP"]
import numpy as np
from scipy.linalg import cho_factor, cho_solve
try:
from astropy.io import fits
from astropy.wcs import WCS
except ImportError:
fits = None
from ..pipeline import Pipeline... |
import numpy as np
import pandas as pd
from scipy.stats import binom_test
data = pd.read_csv(r'C:\Users\william\OneDrive\Desktop\Second Paper\Code\qualitative_results.csv')
total = len(data)
total_pm = len(data[data.Chosen == 'PixelCNN'])
print()
print('Overall PixelMiner percent:', str(total_pm/total))
chosen_ln ... |
import math
import pybullet as p
import numpy as np
import copy
import sys
sys.path.append("../")
#sys.path.append("/HPS/Shimada/work/rbdl37/rbdl/build/python")
import rbdl
from scipy.spatial.transform import Rotation as Rot
from scipy.spatial.transform import Slerp
class KinematicUtil():
def motio... |
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import numpy as np
my_dataset = pd.read_excel('USGS_BCR2G.xls', sheet_name='Sheet1')
fig, ax = plt.subplots()
ax.hist(my_dataset.La, bins='auto', density=True, edgecolor='#000000', color='#c7ddf4', label="USGS BCR2G")
ax.set_xlabel("La [p... |
<gh_stars>0
from scipy.spatial.distance import cityblock
from sklearn.metrics import roc_curve
import pandas
import numpy as np
np.set_printoptions(suppress = True)
class ManhattanVerifier:
def __init__(self, subjects):
self.user_scores = []
self.imposter_scores = []
self.mean_vector = [... |
import numpy as np
from numpy import genfromtxt
import random
from scipy import signal
import datetime
import os
from scipy.ndimage.interpolation import zoom
import tensorflow as tf
import tensorflow.contrib.eager as tfe
class spectral_autoencoder(tf.keras.Model):
def __init__(self, model_features):
super(... |
<reponame>BastiHz/epicycles<gh_stars>1-10
import math
import cmath
import pygame
import pygame.gfxdraw
from src import constants
from src import transform
class Epicycles:
def __init__(self, points, n, fade, reverse,
surface_center, debug):
self.angular_velocity = constants.DEFAULT_ANGU... |
<filename>Max2SAT_pysat/rc2_runtime_histogram.py
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import binned_statistic
def average_data(data):
num_repeats = len(data[:, 0])
num_x_vals = len(data[0, :])
y_av = np.zeros(num_x_vals)
y_std_error = np.zeros(num_x_vals)
for x in r... |
<reponame>StalinMazaEpn/ejercicios_python<filename>imagenes/edicion_imagen.py<gh_stars>0
#ALGORITMOS FUNDAMENTALES
#AUTOR: <NAME>
#VERSION 2.3
#TRATAMIENTO DE IMAGENES EN PYTHON - edicion_imagen.py
#2016-Ene-09
#DOCENTE: Ing. <NAME>
#MODULO DE LIBRERIAS
from scipy import misc
from scipy import *
import numpy... |
<gh_stars>1-10
import numpy as np
import scipy.signal as signal
import scipy.interpolate as ip
from typing import List, Tuple
def sgolay(order : int, framelen : int) -> Tuple:
"""
Parameters
----------
order : int
The order of the polynomial used to fit the samples. polyorder must be ... |
import os
import tqdm
import soundfile as sf
import pandas as pd
import numpy as np
from scipy import stats
from utils.path_utils import project_root
from utils.get_librispeech_paths import get_librispeech_paths
from frequency_feats import freq_feats
from utils.fund_estiamtion.yin import compute_yin
def extract_feat... |
<filename>level_17/level_17.py
#!/usr/bin/python
from scipy.misc import comb
from itertools import combinations
TARGET = 150
MIN_BATCH = 2
bottles = []
with open('in.txt', 'r') as f:
for line in f:
bottles.append(int(line))
""" test case """
#bottles = [20, 15, 10, 5, 5]
#TARGET = 25
if(sum(bottles) == TARGET):
... |
<filename>common/z_table.py
# Import all libraries for this portion of the blog post
from scipy.integrate import quad
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def print_normal_distribution():
# print standard normal distribution
x = np.linspace(-4, 4, num=100)
constant = 1.0... |
<filename>supg/datasource/csv_source.py
from collections import defaultdict
import pandas as pd
import numpy as np
import scipy.special
import feather
from supg import datasource
def load_jackson_source(probs_fname, csv_fname, obj_name):
# y_true
df_csv = pd.read_csv(csv_fname)
df_csv = df_csv[df_csv['o... |
import numpy as np
from scipy.stats import chi2
import sophus as sp
import time
from collections import namedtuple
from utils import *
from feature import Feature
# Gravity vector in the world frame
g = np.array([0., 0., -9.81])
class IMUState(object):
# id for next IMU state
next_id = 0
... |
"""
In this example we use the pysid library to estimate a MIMO armax model
"""
#Import Libraries
from numpy import array, convolve, concatenate, zeros
from numpy.random import rand, randn #To generate the experiment
from scipy.signal import lfilter #To generate the data
from pysid import armax #... |
import pandas as pd
import numpy as np
import zipfile
import os
import scipy as sp
import matplotlib.pyplot as plt
import plotly.express as px
import zipfile
import pathlib
#literature component
def literature_component(LC_component, max_comp_reported):
""" function to compute the literature component based on t... |
<filename>presentation/training_graphs.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay
import seaborn as sns
from matplotlib.colors import ListedColormap
def plot_points_with_noise(px, py, nx, ny):
fig = plt.figure(figsize=(4, 2), dpi=1000)
plt.tight_layout()
plt.... |
<gh_stars>0
import sympy
from ..helpers import article, pm, untangle
from ._helpers import DiskScheme
_citation = article(
authors=["<NAME>", "<NAME>"],
title="Zur numerischen Auswertung mehrdimensionaler Integrale",
journal="ZAMM",
volume="38",
number="1-2",
year="1958",
pages="1–15",
... |
<reponame>CaramelCake/compimg
"""
Image processing using kernels. Includes several ready to be used kernels
and convolution routines.
"""
import numpy as np
import compimg
from scipy import ndimage
from compimg.exceptions import (
KernelBiggerThanImageError,
KernelShapeNotOddError,
KernelNot2DArray,
)
BO... |
import dask
import datetime
import logging
import time
import numpy as np
from ml4chem.utils import convert_elapsed_time, get_chunks
from collections import OrderedDict
from scipy.linalg import cholesky
logger = logging.getLogger()
class KernelRidge(object):
"""Kernel Ridge Regression
Parameters
-------... |
<gh_stars>0
from fractions import gcd
from random import randint
def description():
return 'Find the Greated Common Divisor'
def question():
a = randint(1, 999)
b = randint(0, 999)
d = gcd(a, b)
return ('(%d,%d)' % (a, b), str(d))
|
<filename>old_scripts/hand_pose_estimation.py<gh_stars>0
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import scipy.io as sio
import os
from sklearn.manifold import Isomap
from scipy.spatial import Delaunay
from scipy.stats import special_ortho_group
# D... |
<reponame>kidrabit/Data-Visualization-Lab-RND<gh_stars>1-10
from scipy.fft import fft, ifft
x = np.array([1.0, 2.0, 1.0, -1.0, 1.5])
y = fft(x)
y
|
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from scipy.interpolate import griddata
from shapely.geometry import Polygon, Point
from stpy.borel_set import HierarchicalBorelSets, BorelSet
from stpy.point_processes.poisson_rate_estimator import PoissonRateEstimator
from stpy.kernels... |
<gh_stars>10-100
from typing import Iterable
import numpy
import pandas
import scipy.sparse as spsparse
def categorical_encode_series_to_sparse_csc_matrix(
series: Iterable, reduced_rank: bool = False
) -> spsparse.csc_matrix:
"""
Categorically encode (via dummy encoding) a `series` as a sparse matrix.
... |
import sys
import os
from scipy.interpolate import InterpolatedUnivariateSpline as interp
import numpy
import matplotlib.pyplot as plt
from astropy.io import fits as pyfits
from astropy import units, constants
from astropy import units, constants
from telfit import TelluricFitter, Modeler, DataStructures, FittingUtili... |
import time
import numpy as np
import torch
from utils import *
from params import *
from reconstruction import *
import scipy
import cv2
import skimage.measure
from itertools import compress
def lpf_detection(holo,mask,erode_size=20, dilate_size=60, threshold=10, A_min = 1, show_plot=False):
# mask for low pass f... |
<reponame>Kiminwoo/Machine-Running-Exercises
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import mglearn
# 데이터셋 만들기
# 인위적으로 만든 이진 분류 데이터셋
X, y = mglearn.datasets.make_forge()
# 산점도 그리기
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
plt.legend(["Class 0", "Class ... |
<filename>baselineUtils.py
from torch.utils.data import Dataset
import os
import pandas as pd
import numpy as np
import torch
import random
import scipy.spatial
import scipy.io
def create_dataset(
dataset_folder,
dataset_name,
val_size,
gt,
horizon,
delim="\t",
train=True,
eval=False,
... |
<filename>python/sklearn/examples/linear_model/plot_sparse_recovery.py<gh_stars>1-10
"""
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to... |
"""This module contains the alpha-s calculation."""
import numpy
from scipy import stats
from pygaps import logger
from pygaps.characterisation.area_bet import area_BET
from pygaps.characterisation.area_lang import area_langmuir
from pygaps.core.adsorbate import Adsorbate
from pygaps.core.baseisotherm import BaseIsot... |
import scipy.io as sio
import math
import statistics
matfile = sio.loadmat('changed_param_2.mat')
old_mat = sio.loadmat('learned_all_param_2.mat')
#for i in range(15):
#print matfile['p'][[11 ,67 ,225,336,357,444,635,679],0],old_mat['p'][[11 ,67 ,225,336,357,444,635,679],0]
count = 0
mea_ceil = []
mea_floor = []
mea_... |
#
# SPDX-License-Identifier: Apache-2.0
#
# Copyright 2020 <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 app... |
# -*- coding: utf-8 -*-
"""
*Module* ``project.generator``
This module provides some classes to generate and handle
different types of data during running the application. This can be used
outside the application as it works independently.
"""
from statistics import stdev
from collections import Counter
from random ... |
<reponame>ameya30/IMaX_pole_data_scripts
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 15 11:31:16 2017
@author: prabhu
"""
from matplotlib import pyplot as plt
import matplotlib as mpl
import cmocean
import numpy as np
from astropy.io import fits
from matplotlib import pyplot as plt
from sc... |
<reponame>aimalz/justice<filename>test/datasets/sharded_plasticc_test.py
# -*- coding: utf-8 -*-
"""Sharded plasticc dataset test."""
import collections
import itertools
import pytest
import scipy.stats
from justice.datasets import sharded_plasticc
def test_id_split_distribution():
for distribution in [(0.1, 0.... |
<reponame>IIRM/EnergyMetering<filename>main.py<gh_stars>0
import itertools
import pathlib
import pandas as pd
import numpy as np
import statistics as stat
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from date_and_time import getFullDateInfo
from date_and_time import get_time_shift_forward_dat... |
import numpy as np
import time
import argparse
import os
from scipy.optimize import least_squares
import math
import tensorflow as tf
import PNS
import itk
import glob
import json
print("Tensorflow version:", tf.__version__)
parser = argparse.ArgumentParser(description='Run PNS on encoded images that live on spher... |
import os
from data import common
import numpy as np
import scipy.misc as misc
import torch
import torch.utils.data as data
class LRHRDataset(data.Dataset):
def name(self):
return 'LRHRDataset'
def __init__(self, opt):
super(LRHRDataset, self).__init__()
# self.args = args
s... |
from numpy.lib.shape_base import expand_dims
import pandas as pd
import numpy as np
import cvxpy as cp
import numpy as np
from scipy import sparse
import time
from emm.solvers import *
import emm
from sklearn.preprocessing import StandardScaler
class marginal:
def __init__(self, feature, fun, loss, standardize=Fa... |
<gh_stars>1-10
from get_data import *
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from PIL import Image
# Main function to build a heatmap
def build_map(positions, status,
resolution=0.00025, oob=0.005,
min_free=10, min_busy=10,
max_dist=0.001):
... |
import jax.numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression, Ridge
from scipy.interpolate import UnivariateSpline
class ConstantLearner(object):
def fit(self, x, y):
self.const = 1
def predict(self, x):
return self.const
tree_lear... |
'''
Copyright 2022 Airbus SAS
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
dis... |
<reponame>aashishyadavally/storyteller<gh_stars>1-10
"""Contains important utilities which assist query procesing pipeline.
"""
import sys
import json
import heapq
import subprocess
from pathlib import Path
import numpy as np
import scipy
from scipy.spatial.distance import cosine, euclidean
import spacy
import dotp... |
<gh_stars>0
from astropy.io import fits
import numpy as np
import os, fnmatch
from scipy import interpolate
from scipy import ndimage
def writefits(obj, varname, snap=None, instrument = 'MURaM',
name='ar098192', origin='HGCR ', z_tau51m = None):
if varname[:2] == 'lg':
varnamefits='lg('+var... |
<reponame>rollends/red-prism<gh_stars>1-10
#!env python3
#
import argparse
from functools import partial
from itertools import chain
import json
import logging
import matplotlib
import numpy as np
import pickle
import redis
import scipy ... |
<reponame>ExaScience/smurff
#!/usr/bin/env python
import unittest
import numpy as np
import scipy.sparse as sp
import smurff
def matrix_with_explicit_zeros():
matrix_rows = np.array([0, 0, 1, 1, 2, 2])
matrix_cols = np.array([0, 1, 0, 1, 0, 1])
matrix_vals = np.array([0, 1, 0, 1, 0, 1], dtype=np.float6... |
import collections
from nltk.tokenize import RegexpTokenizer
import nltk
import pandas as pd
import subprocess
import os
import re
from scipy.stats import truncnorm
from typing import List, Dict, Set, Union, Tuple, OrderedDict
nltk.download('stopwords')
from nltk.corpus import stopwords
import pickle
def substitute_n... |
from pyamg.testing import *
import numpy
from numpy import ones, eye, zeros, bincount, empty, asarray, array
from numpy.random import seed
from scipy import rand
from scipy.sparse import csr_matrix, csc_matrix, coo_matrix
from pyamg.gallery import poisson, load_example
from pyamg.graph import *
from pyamg import amg_... |
#!/usr/bin/env python
###############################################################################
# binning.py - A binning algorithm spinning off of the methodology of
# Lorikeet
###############################################################################
# ... |
<reponame>alexrobomind/diagmap
import scipy.interpolate
import scipy.spatial
import multiprocessing.pool
import networkx as nx
import numpy as np
from tqdm.auto import tqdm, trange
def _calculate_distances(points, ax, single_section=False):
"""Actual function implementation"""
n_surfs = points.shape[1]
n... |
<gh_stars>10-100
import numpy as np
from scipy.optimize import fminbound
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ''
... |
<reponame>likun-stat/scalemixture_spline<filename>scratch.py
import os
os.chdir("/Users/LikunZhang/Desktop/PyCode/")
import scalemixture_py.integrate as utils
import scalemixture_py.priors as priors
import scalemixture_py.generic_samplers as sampler
import numpy as np
import cProfile
import matplotlib.pyplot as plt
f... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed May 10 10:52:22 2017
@author: jakobg
"""
from __future__ import division, print_function
import glob
import os
import numpy as np
import clusterbuster.iout.misc as iom
from scipy.stats import logistic
def AddFilesTo... |
"""
3D Animation
~~~~~~~~~~~~
Example of creating a 3D animation of the Cox et al. (2013) torsional
oscillations.
"""
import numpy as np
import scipy.interpolate
import matplotlib.pyplot as plt
import sys
sys.path.append('../') #So taco_vis.py is visible to import
from taco_vis import FLOW
#########################... |
# -*- coding: utf-8 -*-
import os
import moojoos as mj
import numpy as np
from pylab import *
from PIL import Image
from scipy.ndimage import filters
# target files for comparison
files = [ 'rena_sharp.jpg', 'rena_gaussian_10.jpg' ]
cd = os.path.dirname(os.path.abspath(__file__))
# gray-scaled images
... |
# ---------------------------------------------------
# code credits: https://github.com/CQFIO/PhotographicImageSynthesis
# ---------------------------------------------------
import numpy as np
import scipy
from config import *
import tensorflow as tf
def read_image(file_name, resize=True, fliplr=False):
image... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
pysilsub.problem
================
Help solving silent substitution problems with linear algebra and optimisation.
@author: jtm, ms
"""
# Here are the cases that we want to have in the silent substitution module:
# Single-direction modulations
# Max. contrast within... |
##############################################################################
# imports
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
##############################################################################
############################################################################... |
<gh_stars>1-10
from __future__ import print_function
import unittest
import numpy as np
import scipy.sparse as sp
from SimPEG import (
Mesh, DataMisfit, Maps, Utils, Regularization, InvProblem, Optimization,
Directives, Inversion
)
from SimPEG.EM.Static import DC
np.random.seed(82)
class DataMisfitTest(un... |
import pandas as pd
import numpy as np
import swifter
from collections import Counter
import statistics
from statistics import mode
import pickle
import time
import math
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, roc_curve
from keras.callbacks import ReduceLROnPlateau, TensorBoard, Early... |
# coding: utf-8
## Load, Visualize MCMC Results
# 5]:
## get_ipython().magic(u'matplotlib inline')
import pyfits
import numpy as np
import matplotlib
matplotlib.rcParams['font.size'] = 15
from matplotlib import pyplot as plt
import sys
sys.path.append('../')
import photPack2
from astropy.time import Time
import emc... |
<reponame>cmcuervol/Estefania<filename>Dias_Desp_Nub.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import pearsonr
from scipy import stats
# from mpl_toolkits.axes_grid1 import host_subplot
# import mpl_toolkits.axisarti... |
<reponame>BrancoLab/BehaviourAnalysis
# %%
import sys
sys.path.append('C:\\Users\\Federico\\Documents\\GitHub\\BehaviourAnalysis')
from Utilities.imports import *
import statsmodels.api as sm
from pandas.plotting import scatter_matrix
from scipy.optimize import curve_fit
from scipy import signal
from sklearn.model_sel... |
<filename>micemag/fbutils/fbfit.py
import pickle
import sys
import os
import scipy as sp
import numpy as np
import iminuit as minuit
import fbutils as _fb
import micemag.utils as _paths
class FBfitClass:
def __init__(self, field, coil, magnet, zmax=1.8, rmax=0.15, n=2, l=20, m=10, \
verbose=Tru... |
"""
Mask R-CNN
Display and Visualization Functions.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by <NAME>
"""
import math
import pickle
import random
import itertools
import colorsys
import numpy as np
import IPython.display
from scipy import interpolate
import... |
import os
import glob
import pickle
import socket
import pandas as pd
from scipy.stats import spearmanr
import tensorflow as tf
from tensorflow.keras.models import load_model
print(f'Tensorflow version: {tf.__version__}')
# Custom imports
from werdich_cfr.models.Modeltrainer_Inc2 import VideoTrainer
from werdich_cfr.... |
<reponame>KeshavAdityaRP/coronaryHeartDiseasePredictor
# from sklearn.datasets import load_digits
# from sklearn.manifold import MDS
# X, _ = load_digits(return_X_y=True)
# print (X.shape)
# embedding = MDS(n_components=2)
# X_transformed = embedding.fit_transform(X[:10])
# print (X_transformed)
from scipy.spatial im... |
<gh_stars>0
import time
import numpy as np
from numpy.random import RandomState, SeedSequence, MT19937
import scipy.sparse as sp
from scipy.linalg import norm
from scipy.sparse.linalg import splu
import scipy.io
from PCG import PCG
from BasicPreconditioner import *
from string import Template
from KrylovUtils import *
... |
<gh_stars>1-10
#============================================================
# File solver.py
#
# QMINOS as generic LP solver
# - including DQQ of Ma et al.
#
# <NAME>, SBRG, UCSD
#
# 27 Apr 2016: first version
# 05 May 2016: ported from polytope.py from cobrame
# 10 May 2016: standalone version
# 11 Oct 2018: por... |
<filename>feature-generation/calculate_edgeDensity.py
'''
Edge Density calculation
Detect edges using Canny Edge Detection
@author: <NAME>/kkgadiraju
Source: http://docs.opencv.org/3.1.0/da/d22/tutorial_py_canny.html
'''
import cv2
import struct
import random
import gdal, ogr, osr
from gdalconst import *
import numpy ... |
<reponame>puyamirkarimi/quantum-walks
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import binom
from scipy import linalg
import math
from matplotlib.ticker import MultipleLocator
N = 60 # number of random steps
timesteps = 40
P = 2*N+1 # number of positions
gamma = 0.5 # hoppin... |
<filename>src/InstPyr/Control/SysId.py
# class SysID:
# def __init__(self):
# pass
#
# @classmethod
#
import numpy as np
from scipy import signal as sig
from scipy import optimize as opt
import pandas as pd
import matplotlib.pyplot as plt
from gekko import GEKKO
import math
MODELPATH='C:\\Users\\soods\... |
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