text string |
|---|
#!/usr/bin/env python
# ------------------------------------------------------------------------
# Copyright 2018, <NAME>
# Statisticalhitfinder is distributed under the terms of the Simplified BSD License.
# -------------------------------------------------------------------------
import os
os.system("source /home/a... |
from functools import lru_cache
from typing import Tuple
import numpy as np
from scipy.optimize import minimize
from scipy.optimize import OptimizeResult
from scipy.optimize import root
from scipy.special import gammainc as gammaf
from scipy.stats import beta as beta_dist
from scipy.stats import gamma as gamma_dist
fr... |
""" Cast Copy Tranpose is used in numpy LinearAlgebra.py to convert
C ordered arrays to Fortran order arrays before calling Fortran
functions. A couple of C implementations are provided here that
show modest speed improvements. One is an "inplace" transpose that
does an in memory transpose of an array... |
<gh_stars>0
#!/usr/bin/env python
"""Train a simple deep CNN on the CIFAR10 small images dataset.
GPU run command with Theano backend (with TensorFlow, the GPU is automatically used):
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatx=float32 python cifar10_cnn.py
It gets down to 0.65 test logloss in 25 epochs, and dow... |
# --------------------------------------------------------
# Seg-FCN for Dragon
# Copyright (c) 2017 SeetaTech
# Source Code by <NAME>
# Re-Written by <NAME>
# --------------------------------------------------------
import dragon.vm.caffe as caffe
import dragon.core.workspace as ws
import numpy as np
from PIL import... |
<filename>watertap3/watertap3/utils/ml_regression.py
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
__all__ = ['make_df_for_ml',
'make_simple_poly',
'get_linear_regr... |
<reponame>charles-stan/learn_python_Stanier<filename>E13b_ode_single_ver_b.py
"""
Example E13b_ode_single_ver_b.py
Solving a single ODE for a level tank
<NAME>
<EMAIL>
Oct 2, 2019
version b.
derivative function and driver in same file
defining problem parameters as a dictionary
in the driver and passing ... |
import datetime
import pytz
import statistics
from custom_user.models import AbstractEmailUser
from django.conf import settings
from django.contrib.auth.models import Group, Permission
from django.core.cache import cache
from django.db import models
from django.db.models import signals
from django.utils.translation im... |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 17 11:54:36 2021
@author: Robert https://github.com/rdzudzar
"""
# Package imports
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import cmasher as cmr
import numpy as np
#from scipy import stats
import scipy.stats
import math
from bokeh.plott... |
# Heaviside treatment functions
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from crosspy.XCF import fxcorr
import os
import cv2 as cv
from numba import double, jit, njit, vectorize
from numba import int32, float32, uint8, float64, int64, boolean
from scipy.signal import fftconvolve
from... |
<gh_stars>0
# Copyright (c) 2017 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... |
## auxiliary.py
# auxiliary functions defined for convenience to help in internal tasks
# imports
from scipy.optimize import fsolve
from random import uniform, gauss
import numpy as np
import sys
import os
# local imports
from .cosmology import H, dL
# get N randomly generated events from a given distribution, usi... |
<reponame>cmaclell/humanranker
import sys
from math import exp
from math import log
from math import sqrt
import argparse
import numpy as np
#from scipy.optimize import minimize
from scipy.optimize import basinhopping
from scipy.optimize import check_grad
#from scipy.optimize import approx_fprime
# Regularization Para... |
#!/usr/bin/python
"""
Sets of class to provide tools for manipulating Wavelet Transform
HISTORY:
2010.06.17:
- first shot to create the main class
- Load the shared object for the convolution ("Atrous" algorithm). See the source in IDL/wavelet.
2010.06.18:
- Load con... |
#!/usr/bin/env python
from __future__ import print_function
import sys
import math
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
from scipy.stats import norm
from scipy.stats import spearmanr
from evaluation_metrics import AUC, average_AUC
def rmse(pred_array, ref_array):
"""
Calcula... |
<reponame>KRITGYA2001/Data-Science-with-Python
#!/usr/bin/env python
# coding: utf-8
# Machine Learning :- is a form of AI that teaches computers to think in a similar way to how humans do.
# Learning and improving upon past experiences.
# It works by exploring data and identifying patterns, and involves minimal human... |
<filename>laika/dgps.py
# Import dependencies
import os
import numpy as np
from datetime import datetime
from scipy.spatial import cKDTree
from .gps_time import GPSTime
from .constants import SECS_IN_YEAR
from . import raw_gnss as raw
from .rinex_file import RINEXFile
from .downloader import download_cors_coords
from .... |
import numpy as np
from sklearn.model_selection import train_test_split, KFold
from scipy.io import loadmat
n_node = 10 # num of nodes in hidden layer
n_layer = 2 # num of hidden layers
lam = 1 # regularization parameter, lambda
w_range = [-1, 1] # range of random weights
b_range = [0, 1] # range of random bia... |
"""
"""
'''
import logging
import numpy as np
import scipy as sp
import scipy.optimize # noqa
import zcode.astro as zastro
import zcode.math as zmath
from zcode.constants import SPLC, DAY, MSOL, PC
SIGMA_TO_FWHM = 2*np.sqrt(2*np.log(2.0))
import bhem
class MBH:
def __init__(self, mass, fedd, dist):
... |
<reponame>bachow/kaggle-amazon-contest
'''
__author__ = '<NAME>'
__date__ = '2013-07-29'
4 degree and rare event feature engineering, fit to logistic regression model
'''
from numpy import array, hstack
from sklearn import linear_model
from scipy import sparse, stats
from kaggle import *
import numpy as ... |
<gh_stars>0
import numpy
import os
import pandas
import json
from src.lib.util import ElectronicTools, Constants
from src.lib.periodic_table import PeriodicTable
from scipy.integrate import trapz, quad
from scipy.interpolate import interp1d
class BohrWeisskopfBase:
"""
Relative Bohr Weisskopf correction usin... |
import numpy as np
from scipy.sparse import csr_matrix
def load_matrix_data(filename):
""" Load data.
Args:
filename: A string. The path to the data file.
Returns:
A tuple, (X, y). X is a compressed sparse row matrix of floats with
shape [num_examples, num_features]. y is a dense... |
import numpy as np
import pandas as pd
from scipy.stats import norm, rankdata
from scipy import spatial
class GeostatsDataFrame(object):
"""A class to load an transform a table of xy + feature values into
a compliant dataframe for variogram calculations"""
coord_cols = {'x':'x', 'y':'y'}
random_se... |
<gh_stars>0
import os
import math
import torch
import random
import scipy as sp
import scipy.stats
import numpy as np
import torch.nn as nn
from PIL import Image
import torchvision.utils as vutils
from alisuretool.Tools import Tools
from tensorboardX import SummaryWriter
from torch.optim.lr_scheduler import StepLR
impo... |
<reponame>twopis/twopis
# -*- coding: utf-8 -*-
# Code for creating many graphs
import numpy as np
import json
import copy
from scipy.stats import beta, linregress
import matplotlib.patches as mpatches
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d impor... |
# -*- coding: utf-8 -*-
#!/usr/bin/env python
#
# Copyright 2013-2016 BigML
#
# 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 requi... |
import numpy as np
import cv2
import warnings
warnings.filterwarnings('ignore')
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
import os
import scipy
import imageio
from scipy.ndimage import gaussian_filter1d, gaussian_filter
from sklearn import linear_model
from sklearn.model_selection impo... |
<reponame>jatinchowdhury18/AudioDSPy<filename>tests/test_farina.py
from unittest import TestCase
import numpy as np
import scipy.signal as signal
import audio_dspy as adsp
_fs_ = 44100
class TestFarina(TestCase):
def setUp(self):
g = adsp.delay_feedback_gain_for_t60(1, _fs_, 0.5)
self.N = int(0.... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import os
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
import ... |
import matplotlib.pyplot as plt
import random as ran
import numpy as np
import os
from scipy import stats
result=0
choice=0
rate=0
resultado=False
exit=False
min = 0
max = 37
cantidadTiradas = 100
ruleta = []
def CrearRuleta():
ruleta.extend(range(min,max))
print("La ruleta es la siguiente:", ruleta)
def... |
# base umap embedding with tensorflow
# Author: <NAME>
import tensorflow as tf
import numpy as np
from tfumap.base import UMAP_tensorflow
from tqdm.autonotebook import tqdm
import os
import pandas as pd
import tempfile
import pickle
from pathlib2 import Path
import codecs
from numba import TypingError
tf.get_logger()... |
<reponame>CaptainEven/MOTEvaluate
"""
2D MOT2016 Evaluation Toolkit
An python reimplementation of toolkit in
2DMOT16(https://motchallenge.net/data/MOT16/)
This file executes the evaluation.
usage:
python evaluate.py
--bm Whether to evaluate multiple files(benchmarks)
--seqmap [filename] ... |
#!/usr/bin/env python3
import numpy as np
from scipy.integrate import quad
from astropy import constants as const
from astropy import units as u
from astropy.cosmology import LambdaCDM
cosmo = LambdaCDM(H0=70 * u.km / u.Mpc / u.s, Om0=0.3, Ode0=0.7) # define cosmology
def linear_to_angular_dist(distance, photo_z):... |
<filename>simpleplanets_kepler_known_test.py
from simpleabc import simple_abc
import simple_model
import numpy as np
import pickle
from scipy import stats
import time
steps = 10
eps = 1
min_part = 100
stars = pickle.load(file('stars.pkl'))
model = simple_model.MyModel(stars)
#obs = np.recfromcsv('04012015_trimmed.... |
<filename>book_figures/chapter8/fig_regression_mu_z.py<gh_stars>1-10
"""
Cosmology Regression Example
----------------------------
Figure 8.2
Various regression fits to the distance modulus vs. redshift relation for a
simulated set of 100 supernovas, selected from a distribution
:math:`p(z) \propto (z/z_0)^2 \exp[(z/z... |
"""
THIS CODE IS UNDER THE BSD 2-Clause LICENSE. YOU CAN FIND THE COMPLETE
FILE AT THE SOURCE DIRECTORY.
Copyright (C) 2017 <NAME> - All rights reserved
@author : <EMAIL>
Publication:
A Novel Unsupervised Analysis of E... |
<reponame>mjdroz/StatisticsCalculator
from Calculator.division import division
from Calculator.square_root import squareRoot
from StatisticsCalc.mean import mean
from StatisticsCalc.standard_deviation import standard_deviation
from scipy import stats
def confidenceIntervalTop (data, confidence_level):
try:
... |
<gh_stars>10-100
import numpy as np
import string
from scipy.optimize import minimize
from src.Models.models import ParseModelOutput
from src.utils.train_utils import chamfer
from src.utils.train_utils import validity
class Optimize:
"""
Post processing visually guided search using Powell optimizer.
"""
... |
from torch.optim import lr_scheduler
import torch.utils.data as dataset
from torch.utils.data import DataLoader
from modelZoo.gumbel_module import *
from dataset.SytheticData import *
from modelZoo.BinaryCoding import *
from utils import *
import scipy.io
random.seed(0)
gpu_id = 1
import pdb
Epoch = 100
N = 15*2
LR = ... |
<filename>09-Registration/code/Utils.py
from ast import Num
from os import stat
import pandas as pd
import pandas
import open3d as o3d
import open3d
import numpy as np
import numpy
import copy
from scipy.spatial.transform import Rotation
class pointcloud:
def __init__(self):
pass
# 从文件中读取点云
@s... |
<gh_stars>1-10
#!/usr/bin/env python3
'''
Generate sample inputs: 'input_cmap.png' and 'input_image.png'.
'''
from PIL import Image
import matplotlib.cm
import numpy as np
import scipy.misc
def save_image_png(image, filename):
image = (image * 255).astype(np.uint8)
image = Image.fromarray(image)
image.sa... |
from math import floor, ceil
from matplotlib.axes import Axes
from mpl_format.axes import AxesFormatter
from mpl_format.axes.axis_utils import new_axes
from mpl_format.compound_types import Color
from numpy import linspace
from pandas import DataFrame, Series
from scipy.stats import rv_continuous
from typing import It... |
<gh_stars>0
#! /usr/bin/env python3
##############################################
# #
# Ferdinand 0.40, <NAME>, LLNL #
# #
# gnd,endf,fresco,azure,hyrma #
# #
#######... |
<reponame>omarmaddouri/GCNCC_cross_validated<filename>scripts/BRC_microarray/Netherlands/utils.py
from __future__ import print_function
import sys
from os.path import dirname, abspath
sys.path.append(dirname(dirname(abspath(__file__))))
import scipy.sparse as sp
import numpy as np
from scipy.sparse.linalg import eigs... |
import pandas as pd
import numpy as np
import bisect
import scipy.stats as stats
from matplotlib import pyplot as plt
N_BINS = 30
BIN_SIZE = np.array([5, 0.1])
FIGSIZE = (10, 5)
errors = pd.DataFrame(columns=['Peso', 'Altura', 'Genero'])
def head(n=5):
return df.head(n)
def get_bin(a, x):
ind = bise... |
#!/usr/bin/env python
from argparse import ArgumentParser
from datetime import datetime
import numpy as np
import scipy.linalg as linalg
import tables
if __name__ == '__main__':
arg_parser = ArgumentParser(description='compute and check SVD')
arg_parser.add_argument('file', help='HDF5 file containingn matrix... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import cv2
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.opt... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os.path
import glob
import numpy as np
from scipy.spatial.distance import pdist
input_file = 'dataset_procrustes.txt'
output_file = 'distances.txt'
output_list = []
with open(input_file, 'r') as fd_in, open(output_file, 'w') as fd_out:
for i, line in enumerate... |
from django.shortcuts import render, redirect
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.decorators import login_required
from pymongo import MongoClient
import pymongo
import statistics
from operator import itemgetter
def signin(request):
if request.method == 'POST':
... |
<gh_stars>0
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas
import scipy.stats
import seaborn
import os
from Bio.PDB import Superimposer, PDBParser
from ModelFeatures import extract_vectors_model_feature, ModelFeatures
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spat... |
<reponame>Tung-I/RoboticArmSimulator
# Code base from pybullet examples https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/bullet/ kuka_diverse_object_gym_env.py
import random
import os
from gym import spaces
import time
import json
import pybullet as p
import numpy as np
import ... |
<gh_stars>0
# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... |
import numpy as np
import json
from sirius import DFT_ground_state_find
from sirius.ot.minimize import minimize, inner
from sirius.ot import Energy, ApplyHamiltonian, ConstrainedGradient
from sirius.baarman import stiefel_project_tangent, stiefel_decompose_tangent, stiefel_transport_operators
from sirius.baarman import... |
<reponame>antonhibl/q2-composition
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# -------------... |
<filename>PegsOnDisksUpright/python/rl_environment_pegs_on_disks.py
'''Reinforcement learning (RL) environment for the upright pegs on disks domain.'''
# python
import os
import fnmatch
from copy import copy
from time import sleep, time
# scipy
from scipy.io import loadmat
from matplotlib import pyplot
from scipy.spat... |
import time
import matplotlib.pyplot as plt
from labvision import camera, images
import numpy as np
from scipy import ndimage
from labequipment import shaker
data_save = "/media/data/Data/Orderphobic/TwoIntruders/Logging/301120_liquid_ramps_x.txt"
cam_num = camera.guess_camera_number()
cam = camera.Camera(cam_num... |
import statistics
class SimulationStatistics:
def __init__(self, simulationResultList):
self.simulationResultList = simulationResultList
def PrintSimulationStatistics(self):
minMoves = min(self.simulationResultList, key=lambda x: x.moves)
maxMoves = max(self.simulationResultList... |
import numpy as np
from scipy.spatial.distance import mahalanobis
from sklearn.cluster import KMeans
from kneed import KneeLocator
from matplotlib import pyplot as plt
from typing import *
euclidean_distance = lambda a,b: np.linalg.norm(a-b, axis=1)
class OneCluster():
"""
A fake KMeans of just one cluster.
... |
<reponame>aiporre/uBAM
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Python 2.7
@author: <NAME>
<EMAIL>
Last Update: 23.8.2018
Use Generative Model for posture extrapolation
"""
from datetime import datetime
import os, sys, numpy as np, argparse
from time import time
from tqdm import tqdm, trange
import matplotl... |
<filename>emcwrap/plots.py<gh_stars>0
#!/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.lines import Line2D
from .stats import calc_min_interval as hpd
def fast_kde(x, bw=4.5):
"""
A fft-based Gaussian kernel density estimate (... |
import logging
import numpy as np
from scipy import stats
from minos import genotyper
class GenotypeConfidenceSimulator:
def __init__(
self,
mean_depth,
depth_variance,
error_rate,
allele_length=1,
iterations=10000,
call_hets=False,
):
self.mean... |
from sklearn.cross_validation import KFold
from sklearn.cross_validation import train_test_split
from sklearn.metrics import mean_squared_error
from math import sqrt
import numpy as np
import pandas as pd
import scipy as sci
### Plotting function ###
from matplotlib import pyplot as plt
from sklearn.metrics import r... |
<filename>optimization/rgb/run_RGB_opt.py
import numpy as np
import scipy.io as scio
import cv2
import tensorflow as tf
import tensorflow.contrib.opt as tf_opt
import os
from absl import app, flags
import sys
sys.path.append("../..")
from RGB_load import RGB_load
from utils.render_img import render_img_in_different_p... |
<filename>EPBoost/EPBoost_Train.py
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 18 14:00:08 2019
@author: Wangzihang
"""
# system modules
import os
import time
import sys
import pandas as pd
# numpy
import numpy,random,math
# classifier
from sklearn.model_selection import StratifiedKFold, cross_val... |
<filename>Numpy Testing/NumpyFFTGraphing.py
import numpy as np
from scipy.fftpack import fft , fft2
import matplotlib.pyplot as plt
import time
import Adafruit_ADS1x15
GAIN = 1
ADS1115 = 0x00
adc = Adafruit_ADS1x15.ADS1015()
N = input("Input # of points(samples)/sec: ")
T = 1.0/N
x=np.linspace(0, 2*np.pi*N*T, N... |
import numpy as np
import h5py
from scipy.stats import multivariate_normal
x, y = np.mgrid[3:5:100j, 3:5:100j]
xy = np.column_stack([x.flat, y.flat])
mu = np.array([4.0, 4.0])
sigma = np.array([0.2, 0.3])
covariance = np.diag(sigma ** 2)
z = multivariate_normal.pdf(xy, mean=mu, cov=covariance)
z = z.reshape(x.shape)
... |
r"""
Push Forward Based Inference
============================
This tutorial describes push forward based inference (PFI) [BJWSISC2018]_.
PFI solves the inverse problem of inferring parameters :math:`\rv` of a deterministic model :math:`f(\rv)` from stochastic observational data on quantities of interest. The solutio... |
<filename>bacteria_archaea/marine/cell_num/marine_prokaryote_cell_number.py
# coding: utf-8
# In[1]:
# Load dependencies
import pandas as pd
import numpy as np
from scipy.stats import gmean
pd.options.display.float_format = '{:,.1e}'.format
import sys
sys.path.insert(0, '../../../statistics_helper')
from CI_helper ... |
<reponame>AppTestBot/AppTestBot
from PIL import Image, ImageChops
from skimage import io
from skimage.measure import compare_ssim as ssim
import cv2 ... |
<filename>lib/visual_envs.py
import numpy as np
import random
import itertools
import scipy.ndimage
import scipy.misc
import matplotlib.pyplot as plt
from numpy.random import rand
from scipy.ndimage import gaussian_filter
class gameOb():
def __init__(self,coordinates,size,color,reward,name):
self.x = coor... |
<reponame>smonsays/presynaptic-stochasticity
"""
Copyright (c) <NAME>
All rights reserved.
MIT License
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 without restriction, including without limit... |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 13:23:05 2020
@author: kvstr
"""
import numpy as np
import scipy.sparse as sparse
from scipy.sparse import linalg
from scipy.linalg import solve_banded
from scipy.interpolate import griddata
import time
from numba import njit
from numba import prange
import matplotlib.... |
import numpy as np
from scipy.signal import find_peaks, peak_widths, peak_prominences
from scipy.signal import savgol_filter
import matplotlib.pyplot as plt
from scipy.signal import find_peaks, peak_widths, peak_prominences
import pandas as pd
def dist_sarco(img, meta, lines, directory, plot=False, save = False):
... |
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import math, sys
import statistics
import scipy
from sklearn import decomposition as sd
from sklearn import preprocessing
from sklearn.cluster import KMeans
from sklearn.preprocessing... |
<filename>src/pl_max.py
"""Programação linear com Python (Maximização).
max z = 5*x1 + 7*x2
sujeito a:
x1 <= 16
2*x1 + 3*x2 <= 19
x1 + x2 <= 8
x1, x2 >= 0
"""
import numpy as np
from scipy.optimize import linprog
# Defina a matriz de restrições de desigualdade
# OBS: as restrições ... |
import numpy as np
from astropy.table import Table
from scipy.stats import norm
import astropy.units as u
from ..binning import calculate_bin_indices
ONE_SIGMA_QUANTILE = norm.cdf(1) - norm.cdf(-1)
def angular_resolution(
events, energy_bins, energy_type="true",
):
"""
Calculate the angular resolution.... |
""" The variables submodule.
This module contains symbolic representations of all ARTS workspace variables.
The variables are loaded dynamically when the module is imported, which ensures that they
up to date with the current ARTS build.
TODO: The group names list is redudant w.rt. group_ids.keys(). Should be remove... |
__module_name__ = "_read_h5.py"
__author__ = ", ".join(["<NAME>"])
__email__ = ", ".join(["<EMAIL>",])
# package imports #
# --------------- #
from anndata import AnnData
import h5py
import licorice
import numpy as np
import pandas as pd
from scipy import sparse
def _check_hdf5_file_keys(key_list):
"""I gu... |
#Usage
#Input:
# provide command line argument of 'filename', the ASCII formatted stats from
# the Ken Massey's stats page (http://www.masseyratings.com/data.php)
#
#Output:
# txt file with list of teams sorted by Massey's LSR ranking method based on point differential
# http://www.masseyratings.com/theory/masse... |
#!/usr/bin/env python
# compare_image_dirs.py
# Copyright (c) 2013-2016 <NAME>
# See LICENSE for details
# pylint: disable=C0111
# Standard library imports
from __future__ import print_function
import argparse
import glob
import os
import sys
# PyPI imports
import numpy
import scipy
import scipy.misc
# Putil imports
i... |
#!/usr/bin/env python
import numpy as np
import os,sys
import argparse
import matplotlib
matplotlib.use('Agg')
import subprocess
from io import StringIO
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import itertools
import pandas as pd
from collections import defaultdict
import scipy
from scipy.interp... |
<filename>project_3/src/fid.py<gh_stars>0
import torch
from scipy import linalg
from torchmetrics import Metric
from torchvision import transforms
import numpy as np
def get_activations_step(model, images):
with torch.no_grad():
pred = model(images)[0]
pred = pred.squeeze(3).squeeze(2).cpu().numpy()... |
<filename>PopPUNK/bgmm.py
# vim: set fileencoding=<utf-8> :
# Copyright 2018-2020 <NAME> and <NAME>
'''BGMM using sklearn'''
# universal
import os
import sys
# additional
import operator
import numpy as np
from scipy import linalg
try: # SciPy >= 0.19
from scipy.special import logsumexp as sp_logsumexp
except I... |
<reponame>atb-data/neoantigen-landscape-msi
import os
import math
from pathlib import Path
import excel_processing
from unique_count import read_data
from scipy import stats
from matplotlib import pyplot as plt
def get_positive(path):
with open(path) as f:
result = []
for line in f:
name = line.strip(... |
import gym
import numpy as np
import random
import matplotlib.pyplot as plt
import math
import tensorflow as tf
import tensorflow_quantum as tfq
from collections import deque
import cirq
import sympy
#tf.compat.v1.disable_eager_execution()
class A2C_agent(object):
def __init__(self, action_size, state_size):
... |
<filename>autoflow/feature_engineer/generate/autofeat/autofeat.py
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: MIT
from __future__ import unicode_literals, division, print_function, absolute_import
from builtins import range
from copy import copy
from typing import List, Optional
import numpy as np
... |
<filename>classifier/jeff_munge.py
import os
import pandas as pd
import sqlalchemy
import ujson as json
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import SelectFromModel
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import cross_val_score
from sklear... |
<reponame>njcuk9999/jwst-mtl
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# General imports.
import numpy as np
from scipy.interpolate import interp1d, RectBivariateSpline
# Astronoomy imports.
from astropy.io import fits
# Plotting.
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
##########... |
<reponame>ellisztamas/amajus_mating
import numpy as np
import os
import pandas as pd
from scipy.stats import beta
from scipy.stats import gamma as gma
from amajusmating import mcmc
# FAPS objects and distance matrices are generated in a separate script.
exec(open('003.scripts/setup_FAPS_GPS.py').read())
# INITIALISE... |
<filename>CreateFakeData.py<gh_stars>0
import numpy as np
import scipy.sparse as sp
import matplotlib.pyplot as plt
# set initial probability distribution of semi-Markov Process
def SetInitProb(num_states):
init_prob = np.random.rand(num_states)
init_prob = init_prob/np.sum(init_prob)
return init_... |
""" making grid for plot depending on manifold and latent distribution """
import numpy as np
from scipy.special import i0
def true_density(data, manifold, latent_distribution):
if manifold == 'sphere':
theta = data[1]
phi = data[0]
if latent_distribution == 'mixture':
kappa = ... |
import numpy as np
import torch
from scipy.stats import median_absolute_deviation
from .base import Transform, DTypeMapping
from ...utils.exceptions import assert_, DTypeError
class Normalize(Transform):
"""Normalizes input to zero mean unit variance."""
def __init__(self, eps=1e-4, mean=None, std=None, ignor... |
#!/usr/bin/env python3
desc="""Requiggle basecalled FastQ files and features in BAM file.
For all reference bases we store (as BAM comments):
- normalised signal intensity mean [tag si:B,f]
- reference base probability [tag tr:B:C] retrieved from guppy (trace scaled 0-255)
- dwell time [tag dt:B:C] in signal step cap... |
# Copyright 2014-2018 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
"""
This module contains tests connected with Mercer's theorem
"""
__author__ = 'lejlot'
import numpy as np
from pykernels.basic import Linear, Polynomial, RBF
from pykernels.regular import *
from pykernels.graph.randomwalk import RandomWalk
from pykernels.graph.allgraphlets import All34Graphlets
from pykernels.graph... |
import datetime
import logging
import os
import pickle
import random
import librosa
import numpy as np
import lmdb as lmdb
import torch
from scipy.signal import savgol_filter
from scipy.stats import pearsonr
from torch.nn.utils.rnn import pad_sequence
import torch.nn.functional as F
from torch.utils.data import Datas... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 29 10:35:17 2020
@author: Tom
"""
import ecm
import configparser
import os
import numpy as np
from scipy.interpolate import NearestNDInterpolator
def load_config(path=None):
if path is None:
path = os.getcwd()
config = configparser.ConfigP... |
# 动态加载因子计算指标，可将因子性能指标分布式
import pdb, importlib, time
import numpy as np
import pandas as pd
from scipy import stats
from PyFin.api import *
from utilities.factor_se import *
from data.polymerize import DBPolymerize
from data.storage_engine import PerformanceStorageEngine, BenchmarkStorageEngine
from data.fetch_factor i... |
<filename>src/utilities/metrics_helper.py<gh_stars>10-100
# credits: https://github.com/hche11
# https://github.com/hche11/VGGSound/blob/master/utils.py
import csv
from scipy import stats
from sklearn import metrics
import numpy as np
def accuracy(output, target, topk=(1, 5)):
"""Computes the precision@k for the... |
<filename>das_decennial/programs/engine/curve.py
import numpy as np
from scipy.stats import norm as phi
from matplotlib import pyplot as plt
from scipy.stats import binom
import scipy.optimize as so
import mpmath
class BaseGeoCurve:
""" Geometric Mechanism advanced composition with rational allocations having a c... |
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