text string |
|---|
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency
from .mismatch import (
get_reference_base,
calculate_mismatch_odds_ratio,
MISMATCH_COUNTS_COLUMNS,
)
RESULT_COLUMNS = [
'chrom', 'pos', 'strand', 'mismatch_odds_ratio',
'homog_G_kd', 'homog_G_kd_pval',
'homog_... |
import numpy as np
from chainer import Variable
from sdtw.dataset import load_ucr
from sdtw.chainer_func import SoftDTWLoss
from scipy.optimize import check_grad
def _func(z, X):
shape = (-1, X.shape[1])
Z = z.reshape(*shape)
return SoftDTWLoss(gamma=0.1)(Z, X).data
def _grad(z, X):
shape = (-1, X.... |
#Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
import pandas as pd
from scipy.signal import correlate
from pickle import load, dump
## Newman deconvolution
def natural_estimate(data,w):
sol = [0 for i in range(len(w))]
l = len(data)
for n,i in enume... |
<gh_stars>1-10
# PYTHON 3.9.1 Test
"""
<NAME>
<EMAIL>
Proceed Formally.
"""
"""
VNA Visualizer is an api developed to help visualize the Slifer Laboratory's VNA data. It is meant to be ran
with the gui, but can be called directly from LABView for real-time fitting; since this is python, proceed
formally for this too... |
<gh_stars>1-10
import statistics
import classifiers
import statistical_main
import numpy as np
from scipy import stats
import operator
def classifiers_chooser(features_path):
iterations = 11
x, y = classifiers.load_features(features_path)
dt = []
knn = []
nb = []
svm = []
rf = []
mlp... |
# -*- coding: utf-8 -*-
#
# * Copyright (c) 2009-2017. Authors: see NOTICE file.
# *
# * 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... |
"""
Here the MIL classifier performance will be tested on the input similarity matrices
There are 3 CV possibilities:
- leave-one-patient-out CV (also with random labels)
- leave-one-chromosome-out CV (also possible with feature elimination)
- leave-bags-out-CV
"""
import sys
import numpy as np
fr... |
<reponame>Krissmedt/imprunko
import numpy as np
import math
from pylab import *
import os, sys
from scipy.stats import gaussian_kde
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from scipy.special import kn
#set seed to get reproducible errors & results
np.random.seed(1)
#update system path as we ... |
from unittest import TestCase
import logging
import numpy as np
import numpy.linalg as la
from scipy.linalg import cho_factor, cho_solve
from ssmtoybox.bq.bqmtran import GaussianProcessTransform
from ssmtoybox.mtran import MonteCarloTransform, UnscentedTransform
from ssmtoybox.ssmod import ReentryVehicle2DTransition
... |
<reponame>Hyeji-Kim/ENC<gh_stars>10-100
import numpy as np
import copy
import json
import os
import os.path as osp
import sys
import time
import itertools
import google.protobuf as pb
import random
from argparse import ArgumentParser
from pprint import pprint
import subprocess
from scipy import interpolate
from scipy... |
<reponame>ronikobrosly/causal-curve<filename>causal_curve/gps_classifier.py
"""
Defines the Generalized Prospensity Score (GPS) classifier model class
"""
from pprint import pprint
import numpy as np
from scipy.special import logit
from causal_curve.gps_core import GPS_Core
class GPS_Classifier(GPS_Core):
"""
... |
# -*- coding: utf-8 -*-
import IPython
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
import scipy.stats as ss
import seaborn as sns
from sklearn.metrics import auc, roc_curve
import statsmodels.graphics.gofplots as smg
class Plot:
_... |
import numpy as np
import scipy.linalg as sl
import peakfit.shapes as ps
def calculate_shape_heights(params, cluster):
shapes = []
for index in range(len(cluster.peaks)):
pre = f"p{index}_"
shapes.append(
ps.pvoigt2d(
cluster.x,
cluster.y,
... |
"""Wav source generator
It contains sine tone, white noise and increasing tone generator.
TODO LIST
---------
- Pink noise
- Brown noise
- improved White noise
"""
import wave
import math
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as signal
from scipy.io import wa... |
<reponame>hdiddle13/Sat2Graph
# from http.server import BaseHTTPRequestHandler
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
import logging
import json
import os
import os.path
import scipy.ndimage
import math
import cv2
import numpy as np
import tensorflow as tf
from time import time
from subp... |
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import random
import time
import sys
from scipy.spatial.distance import cosine
from utils.accum_trainer import AccumTrainer
from utils.ops import sample_action
from scene_loader import THORDiscreteEnvironment as Environment
from dagger_policy_generator... |
import pandas as pd
import scipy.io as sio
from pathlib import Path
import feather
import deepdish as dd
from sklearn.model_selection import train_test_split
def read_dataframe(path):
"""
Read the DataFrame.
Parameters
----------
path : str
Path to DataFrame.
Returns
-------
... |
<gh_stars>1-10
from collections import OrderedDict
from contextlib import contextmanager
import sympy
from ..core.abstract_algebra import Expression
__all__ = [
"no_instance_caching",
"temporary_instance_cache",
"temporary_rules",
"symbols",
]
@contextmanager
def no_instance_caching():
"""Temp... |
<reponame>wararaki/irspack
from abc import ABC, abstractmethod
from typing import Any, List
from scipy import sparse as sps
from scipy.sparse.csr import csr_matrix
class BaseEncoder(ABC):
names: List[Any]
def __init___(self, *args: Any, **kwargs: Any) -> None:
pass
@abstractmethod
def trans... |
from typing import List
from scipy.spatial.distance import cdist
from scipy.stats import stats
from scipy.stats.stats import DescribeResult
def create_feature_names(single_value_features: List[str], distribution_features: List[str]):
"""
:param single_value_features: names of features modeles by a single val... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
__author__ = 'ar'
import pandas as pd
import os
import skimage.morphology as skmorph
import skimage.io as skio
import skimage.transform as sktf
# from skimage.measure import label, regionprops
import skimage.measure as skmes
import numpy as np
import matplotlib.pyplot as plt
i... |
<reponame>yburansac/MachineLearning
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import tabulate
import pickle
import scipy.stats
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import Gri... |
# %% [markdown]
# ##
import os
import warnings
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import poisson
from sklearn.exceptions import ConvergenceWarning
from sklearn.manifold import MDS,... |
"""Transfer matrix example.
Solve for both an isotropic and anisotropic multilayer.
"""
from builtins import range
import scipy
import pylab
import EMpy
n = scipy.array([1., 2., 2.3, 4.3, 3.])
d = scipy.array([scipy.inf, 1e-6, 2.3e-6, 0.1e-6, scipy.inf])
iso_layers = EMpy.utils.Multilayer()
aniso_layers = EMpy.uti... |
<gh_stars>1-10
"""
"""
import numpy as np
from scipy import signal
def moving_average(a, n=6):
"""
Filter moving average
@param a: array
@type a: numpy.array
@return: array with filter
@rtype: numpy.array
"""
ret = np.cumsum(a, dtype=float)
return (ret[n - 1:] - ret[:1 - n]) / n... |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Numba Extensions
####################
The `numba`_ package provides a mechanism for compiling Python code. With
appropriate options, compilation can provide massive speedups to s... |
<reponame>lorycontixd/LiteBIRD-inclination-analysis
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
from __future__ import print_function
import math
import models
import importlib
from tabulate import tabulate
from dataclasses import dataclass
import json
import time
import os,sys
from pathlib import Path
from shutil... |
<filename>autograd/scipy/stats/norm.py
"""Gradients of the normal distribution."""
from __future__ import absolute_import
import scipy.stats
import autograd.numpy as anp
from autograd.core import primitive
from autograd.numpy.numpy_grads import unbroadcast
pdf = primitive(scipy.stats.norm.pdf)
cdf = primitive(scipy.... |
<filename>scripts/foregrounds.py
import numpy as np
import pylab as pl
import pickle
import logging
import matplotlib
if __name__=='__main__':
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from astropy.io import fits
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib as mpl
import ma... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
from backpack import backpack
from backpack.extensions import BatchGrad
from lib.BaseAlg import BaseAlg, get_device, Network,Network_NL, obs_data_a... |
<reponame>int-brain-lab/sorting_analysis
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import gridspec
import numpy as np
import pandas as pd
import os
import shutil
import cv2
import glob2
import parmap
from matplotlib_venn import venn3, venn3_circles
from numba impor... |
<reponame>euq/klis-tasks<filename>nlp/baseline.py
import re
import os
import csv
import codecs
import pickle
import argparse
import sklearn
import scipy
# import pandas as pd
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
parser = argparse.ArgumentParser()
parser.add_argument('--remove-stopw... |
<filename>3dplotSample.py
#!/usr/bin/python
# coding: UTF-8
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# import numpy as np
# dataFile = open('sample.txt','r')
# lines = dataFile.readlines()
# x_params = []
# y_params = []
# z_params = []
# model_x1 = []
# model_x2 = []
# model_x3 =... |
import csv
import json
import numpy as np
from scipy.stats import entropy
from active_cnn import data
from active_cnn import model
from active_cnn import preprocessing
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def activeCnn(pool_csv,training_set_csv,n_classes,cat_list,candidate_classes,batch_size,random_sa... |
from picamera.array import PiRGBArray
from picamera import PiCamera
import cv2
import numpy as np
import time
import random
from fractions import Fraction
from PIL import Image
from math import cos
from sympy import Point, Polygon, pi
#cap = cv2.VideoCapture(0)
camera = PiCamera()
camera.resolution = (426, 240)
ca... |
<gh_stars>100-1000
import torch.nn as nn
from dgl.graph import DGLGraph
from dgl import backend as F
from scipy import sparse
import numpy as np
def pairwise_squared_distance(x):
'''
x : (n_samples, n_points, dims)
return : (n_samples, n_points, n_points)
'''
x2s = (x * x).sum(-1, keepdim=True)
... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 15 17:09:21 2017
@author: apbar
"""
import copy
import numpy as np
import scipy.stats as stats
def makeRanks(*args):
"""
Converts tuple of arrays of values into ranks.
Parameters
----------
sample1, sample2, ... : tuple_array_like
... |
<gh_stars>1-10
# <NAME> (<EMAIL>, <EMAIL>)
# Last edit: 03242019
import sys
print('Python v' + sys.version)
import pandas as pd
import numpy as np
from numba import jit
import numba
import functools
import os, math, re, warnings, gc, pickle, gzip, io, time
import matplotlib as mpl
import matplotlib.pyplot as plt
from i... |
<filename>smerfs/condition.py<gh_stars>1-10
"""
Build the coefficients for conditional sampling, e.g. g_m(z1) | g_m(z_1)
"""
from __future__ import print_function, division, unicode_literals, absolute_import
from numpy import float64, arange, pi, cos, sin, empty, transpose, isfinite, \
dot, expand_dims
from time im... |
<gh_stars>0
# Source:
## Autor: <NAME> and <NAME>
## Extra: https://arxiv.org/pdf/1505.04597.pdf
import numpy as np
from scipy import ndimage
from skimage.morphology import convex_hull_image, thin
from scipy.spatial.distance import cdist
def getunetweightmap( merged_mask, masks, w0=10, sigma=5, ):
# WxHxN t... |
'''
Estimate filter response using white noise
Use PSD to identify system
XiaoCY 2021-02-09
'''
#%%
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig
import springlib as sp
fs = 1000. # sampling frequency (Hz)
Wp = 30./(fs/2) # passband corner frequency (norma... |
"""
Laja quadrature.
After paper by Narayan and Jakeman.
"""
import numpy
from scipy.optimize import fminbound
import chaospy.quad
def quad_leja(order, dist):
"""
Generate Leja quadrature node.
Example:
>>> abscisas, weights = quad_leja(3, chaospy.Normal(0, 1))
>>> print(numpy.around(ab... |
#libraries to load
import alpha,math,Resonance,cmath
import numpy,scipy,FK
# own fit values and parametrization, taken from 1911.11147
mOmega_p_ = 1.43
mOmega_pp_ = 1.67
gOmega_p_ = 0.215
gOmega_pp_ = 0.113
mOmega_ = 0.78265
mEta_ = 0.547862
a_Omega_p_ = 0.0862
a_Omega_pp_ = 0.0648
phi_Omega_p_ = 0
phi_Omega_pp_... |
<filename>ps_fig2.py<gh_stars>10-100
#make Figure 3
import json
import bisect
import pylab as pb
import scipy.stats as stats
import numpy as np
import pickle
import matplotlib.pyplot as plt # Matplotlib's pyplot: MATLAB-like syntax
print "loading data"
data = json.load(open('data_by_cookie.json'))
# ba... |
<filename>Analytics/resources/request_for_data.py
"""
API for retrieving data from backend
Note: If no parameters are passed then by default all the themes are returned
@params
Parameters can be passed with url using get requests
theme: accepts an integer id and return subthemes for that theme
subtheme: accepts an i... |
<filename>toybox/toys/core.py
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import multivariate_normal
from skimage import filters
from toybox.symmetry.operators import propagate
from toybox.symmetry.parsers import parse_hermann_mauguin
from toybox.tools import check_points, check_point, equi... |
import functools, hashlib
import numpy as np
from scipy.stats import norm as ndist
import regreg.api as rr
from selection.tests.instance import gaussian_instance
from selection.learning.learners import mixture_learner
from selection.learning.utils import naive_partial_model_inference, pivot_plot
from selection.lear... |
<gh_stars>1-10
#!/usr/bin/env python
#nknguyen at soe ucsc edu
#Tue Jul 17 09:09:24 PDT 2012
#Immuno-seq pipeline
'''
Overlap stats
'''
import os, sys, re, time, gzip, random
import random as rand
import cPickle as pickle
from optparse import OptionParser
from jobTree.scriptTree.target import Target
from jobTree.s... |
<gh_stars>0
import scipy.sparse as sp
import scipy.sparse.linalg as la
import numpy as np
import math as m
import time
import sys
import os
import pandas as pd
import statsmodels.formula.api as sm
resultDir = os.environ.get('RESULTS')
if resultDir == None :
print ("WARNING! $RESULTS not set! Attempt to write resu... |
from artificial_idiot.search.search import Search
from artificial_idiot.game.state import State
from artificial_idiot.machine_learning.network import Network
from artificial_idiot.game.node import (
Node, RLNode, InitialRLNode, WinningRLNode
)
import numpy as np
from random import random
from artificial_idiot.util.... |
"""
unfolds merges dataframes into a big feature matrix
All the features are labeled with a date and two keys for identification
Better explainations and schemas can be found on the repo wiki
"""
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from multiprocessing.pool import Pool
from skl... |
import math, sys, os, time
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import bayes_mvs as bayesest
sys.path.insert(0, '../../PyEcoLib')
from simulator import Simulator
mean_size = 3
doubling_time = 18
tmax = 180
sample_time = 2
div_steps = 10
ncells = 1000
gr = np.l... |
<gh_stars>100-1000
################################################################################
# Copyright (C) 2015 <NAME>
#
# This file is licensed under the MIT License.
################################################################################
"""
Stochastic variational inference on mixture of Gaussians... |
<reponame>audreyseo/tisane<gh_stars>1-10
import numpy as np
import plotly.graph_objects as go
import tweedie
from scipy.special import logit
from scipy import stats
import pandas as pd
def simulate_data_dist(
family: str, dataMean: float = None, dataStdDev: float = None, dataSize: int = None
):
size = dataSiz... |
<reponame>c-hydro/hyde<gh_stars>0
"""
Library Features:
Name: lib_data_io_netcdf
Author(s): <NAME> (<EMAIL>)
Date: '20180521'
Version: '2.0.7'
"""
#################################################################################
# Library
import logging
import numpy as np
from src.common.d... |
import anndata
from scipy.sparse import csr_matrix
from .utils import init_r0_pulse
from typing import Optional, Union
from ..dynamo_logger import (
main_exception,
main_warning,
LoggerManager,
)
from ..utils import copy_adata
def get_pulse_r0(
adata: anndata.AnnData,
genes: Union[list, str] = "u... |
from __future__ import division
import warnings
import gc
import numpy as np
import random, string
from subprocess import Popen, PIPE
import ConfigParser
import math
import cPickle as pickle
from scipy.sparse.linalg import *
from scipy.sparse import csr_matrix, coo_matrix
import myenums
configParser = ConfigParser.... |
"""
Goodness-of-fit tests based on the empirical distribution function.
Interface is similar to `scipy.stats.kstest()`, but you may choose or provide
a statistic calculation routine and a matching distribution.
Three concrete tests are provided:
* `ks_test()` -- Kolmogorov-Smirnov supremum statistic; almost the same ... |
from pdb import set_trace as T
import scipy.stats as stats
from matplotlib import pyplot as plt
import numpy as np
import os
import vec_noise
from imageio import imread, imsave
from tqdm import tqdm
from neural_mmo.forge.blade.lib import material
def mkdir(path):
try:
os.mkdir(path)
except:
pass
... |
"""Calculate the errors on the fitted quantities."""
import logging
from pathlib import Path
import numpy as np
import scipy.stats
import astropy.units as u
from astropy.table import Table
from .utils import load_yaml
from .fit_models import Log10Parabola
from .conf import config
log = logging.getLogger(__name__)
de... |
<reponame>AlfJohnFred/NCI-Thesis-emotion
import os
import pandas as pd
import numpy as np
import librosa
import scipy
import csv
def get_field_names():
fieldnames = ['filename']
for i in range(0, 40):
fieldnames.append('mfcc_feature_' + str(i))
for i in range(0, 12):
fieldnames.append('chr... |
<gh_stars>1-10
#!/usr/bin/evn python3
from pylab2 import *
import os,sys
import scipy as sp
from scipy import (optimize,interpolate,io,signal)
from scipy.fftpack import fft
from pyproj import Proj, transform
import importlib as imp
from mpi4py import MPI
from netCDF4 import Dataset
from io import StringIO
im... |
import scipy.sparse as sparse
import numpy as np
import copy
class ParamBag(object):
"""
Container object for groups of related parameters.
Key functionality
* run-time dimensionality verification, ensure matrices have correct size
* easy access to the parameters for one component
* remove/del... |
from abc import abstractclassmethod
from collections import defaultdict
import warnings
import numpy as np
from scipy.special import logsumexp
from sklearn.utils import check_X_y
from sklearn.preprocessing import LabelBinarizer
class Naive_Bayes(object):
"""Naive Bayes classifier
The input and the target is ... |
<gh_stars>1-10
# (C)Copyright <NAME> 2019, all rights reserved
# This file is under the Apache 2.0 license, for more information, visit the LICENSE.txt file
from scipy.spatial.distance import euclidean
import collections
import numpy as np
class main:
# Source code for the K-Best-Average Algorithm(In development)
... |
"""
Utility functions for working with dataframes
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from sklearn.model_selection import train_test_split
print("Testing")
def tvt_split(X):
"""
A function that splits a dataframe into train, valdation, and t... |
<gh_stars>0
#! /usr/bin/env python
## Soil-Plant-Atmospheric Continuum calculation emphasizing on surface energy balance
## Developed initially by Ardiansyah (UNSOED), http://ardiansyah.net
##ardiansyah@AL-FATIH-II:~/Desktop/oosheet-1.0$ oocalc OoPython-ETo.ods -accept="socket,host=localhost,port=2002;urp;StarOffice.S... |
# -*- coding: utf-8 -*-
import numpy as np
import scipy as sp
def Qx(w):
return np.array([[ 1, 0, 0],
[ 0, np.cos(w), np.sin(w)],
[ 0, -np.sin(w), np.cos(w)]])
def Qz(w):
return np.array([[ np.cos(w), np.sin(w), 0],... |
<reponame>mhashim6/neuro
import numpy as np
import scipy.special
import neuro_mnist as mnist
class NeuralNetwork:
def __init__(self, inputNodes, outputNodes, hiddenNodes, learningRate):
self.inodes = inputNodes
self.onodes = outputNodes
self.hnodes = hiddenNodes
self.lr = learningR... |
import sys, os
import numpy as np
import scipy
import itertools
import time
from math import factorial
import copy as cp
import sys
import tools
import pickle
from fermicluster import *
from pyscf_helper import *
import pyscf
ttt = time.time()
pyscf.lib.num_threads(1) #with degenerate states and multiple processors t... |
<gh_stars>100-1000
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import sys
sys.path.insert(0,'third_party')
sys.path.insert(0,'./')
import numpy as np
import trimesh
import torch
import cv2
import pdb
from scipy.spatial.transform import Rotation as R
from utils.io import mkdir_p
import argp... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -------------------------------------------------------------------
# Filename: cross_correlation.py
# Author: <NAME>, <NAME>, <NAME>
# Email: <EMAIL>
#
# Copyright (C) 2008-2016 <NAME>, <NAME>, <NAME>
# ---------------------------------------------------------------... |
<reponame>Jeansding/Malaya<filename>malaya/summarize.py<gh_stars>1-10
import sys
import warnings
if not sys.warnoptions:
warnings.simplefilter('ignore')
import numpy as np
import re
import random
from scipy.linalg import svd
from operator import itemgetter
from sklearn.feature_extraction.text import TfidfVectoriz... |
from scipy.integrate import solve_ivp
class Izhikevich:
""" Creates a Izhikevich Model """
def __init__(self, a=0.02, b=0.2, c=-65, d=8):
""" Initializes the model.
Args:
a (float): Time recovery of recovery variable u.
b (float): Sensitivity of recovery variable u.
... |
import collections
import numpy as np
import uncertainties
import pint
from uncertainties import ufloat
from uncertainties import ufloat_fromstr
from pint import UnitRegistry
import string
#import latex
from uncertainties.unumpy import (nominal_values as noms, std_devs as stds)
import uncertainties.unumpy as unp
import... |
<gh_stars>10-100
import numpy as np
from scipy.stats import spearmanr
from typing import *
def RC_score(real_score : List[float], estimate_score : List[float]) -> float:
'''
Rank correlation mesures the rank relation coeffient between groudtruth and estimate values.
The higher the value, the more ... |
<gh_stars>0
#Dump each file to a flattened array-feature vector is all files interleaved
import glob
import os
import numpy as np
from scipy.io import netcdf as ncd
def extract_data(filename, var_name):
fp = ncd.netcdf_file(os.path.join('../data',filename))
d = fp.variables[var_name][:,:,:]
fp.close()
return d
... |
import numpy as np
from scipy import interpolate, stats
import time
from . import mdfmodels
import os
datapath = os.path.join(os.path.dirname(__file__),"data")
default_feh = np.arange(-5,1,0.005)
def load_data(name, minlogpdf=-999):
assert name in ["leaky_box","pre_enriched_box","extra_gas"]
inputs = np.lo... |
<filename>Assignments/HW6-RegressionProblems2-Yan Gu/SVMs/svmSolver(1).py
import sympy as sp
import numpy as np
class SVM:
def __init__(self):
self.m = 4
def fit(self, X, y):
alpha = [1] * (self.m - 1)
alpha_prime = 0
print("Initial ALPHA: ", alpha)
beta ... |
<reponame>broadinstitute/lincs-profiling-comparison
import sys
import pathlib
import argparse
import numpy as np
import pandas as pd
from scipy.stats import describe
from cmapPy.math import fast_corr
from pycytominer.cyto_utils import infer_cp_features
from util import diffuse_wells, load_args
# Define command argum... |
import os
import pdb
import argparse
import numpy as np
import tensorflow as tf
from scipy.stats import pearsonr
from tensorflow.keras import backend as K
from model.model_fn import build_compile_model
parser = argparse.ArgumentParser(
description='gamma-net predicts log10 gamma power for a given image... |
import os
import numpy as np
from sklearn.svm import SVR, SVC
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.cross_decomposition import CCA
from scipy.linalg import null_space
from scipy import stats
from tqdm import tqdm
from sklearn.metrics.pairwise import cosine_similarity
from ... |
<filename>asv/embedd.py
import argparse
import numpy as np
import glob
import torch
import os
import sys
import pathlib
from kaldi_io import read_mat_scp, open_or_fd, write_vec_flt
import model as model_
import scipy.io as sio
from utils.utils import *
def get_freer_gpu():
os.system('nvidia-smi -q -d Memory |grep -A... |
# -*- coding: utf-8 -*-
"""
Created on Thu May 7 09:12:56 2015
@author: anderson
"""
# importing modules
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig
from . import adjust_spines
def plot_single_hfo(hfo, envelope = False, xlim =[-1,1], cutoff = None, v... |
import numpy as np
import pandas
import random
import re
import sys
from scipy.stats import pearsonr, spearmanr
# ausiliary functions
def buildSeriesByCategory(df, categories):
res = []
for cat in categories:
occ = df.loc[df["category"] == cat].shape[0]
res.append(occ)
res_series = pandas... |
<reponame>dpopadic/arpmRes<filename>scripts/sources/s_execution_opt_satisfaction_quantile.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
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# kernel... |
"""Joe copula."""
import numpy
from scipy import special
from .baseclass import Archimedean, Copula
from ..baseclass import Dist
class joe(Archimedean):
"""Joe copula."""
def __init__(self, length, theta, eps=1e-6):
assert theta >= 1
self.length = length
Dist.__init__(self, th=float(t... |
## Import required modules
import matplotlib.pyplot as plt # for plotting
import matplotlib # for plotting
import numpy as np # for manipulating arrays
import os # for making/deleting directories
import bioformats # for reading image series
import javabridge # for interfacing with java (required for bioformats)
from ti... |
#!/usr/bin/python
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import scipy.spatial.distance as ssd
import scipy.stats as ss
x = np.linspace(0, 10, num=11)
x2 = np.linspace(1, 11, num=13)
y = 2*np.cos( x) + 4 + np.random.random(len(x))
y2 = 2* np.cos(x2) + 5 + np.random.r... |
import shap
import warnings
import re
warnings.filterwarnings("ignore")
import os
import argparse
import shutil
import datetime
import getpass
import uuid
import pandas as pd
import yaml
import itertools
import pickle
import subprocess
from skbio.alignment import global_pairwise_align_nucleotide
from s... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright (c) 2021 by <NAME> (https://orcid.org/0000-0003-4788-9022)
This file is licensed under CC BY-SA (Attribution-ShareAlike)
It is provided "as is", without express or implied warranty, for educational and informational purposes only.
Python class file for Doub... |
def vibrations(inp):
'''Calculate the vibrational frequencies.'''
import constants
import numpy as np
import scipy as sp
from pyscf.future import hessian
from .scf import do_scf
from .read_input import pstr
na = inp.mol.natm
n3 = na * 3
# get atom coords and sqrt of mass
q... |
<gh_stars>0
# -*- coding: utf-8 -*-
import random
from queue import PriorityQueue
import numpy as np
import scipy.misc
from .astar import astar
from .gridworld import generate_grid, MOUVEMENT
from gym_pathfinding.games.gridworld import generate_grid, is_legal, MOUVEMENT
import operator
ACTION = {mouvement: action for... |
<reponame>weightan/quaternion_polynomials
import numpy as np
from scipy.linalg import block_diag, solve
from ._classes import State, _state_or_abcd
from ._aux_linalg import haroldsvd
from ._arg_utils import _check_for_state
__all__ = ['controllability_matrix', 'observability_matrix',
'kalman_decomposition',... |
<gh_stars>1-10
import numpy as np
import scipy.io
import os
import math
import matplotlib.pyplot as plt
fs = 20
plt.rc('font', size=fs) #controls default text size
plt.rc('axes', titlesize=fs) #fontsize of the title
plt.rc('axes', labelsize=fs) #fontsize of the x and y labels
plt.rc('xtick', labelsize=fs) #... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
# -*- coding: utf-... |
<gh_stars>1-10
import cma
import nlopt
import scipy
import warnings
import numpy as np
from pyDOE2.doe_lhs import lhs
def aquisition_DIRECT(model, aq_func, cf, aq_kwargs={}):
dim = model.X.shape[1]
cf_info = CF_INFO(cf)
def f(X):
# if there's a constraint function, evaluate it
if cf_info... |
<filename>infer/swap_rpc/toy_client.py
import numpy as np
import scipy.stats
import zmq
from optparse import OptionParser
import time
from time import sleep
parser = OptionParser()
parser.add_option("--name", dest="name", default="", type="str")
(options, args) = parser.parse_args()
# client
context = zmq.Context()
s... |
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# display_name: Python 3
# language: python
# name: python3
# ---
# # Sparse Gaussian Process by Varitional Inference
# +... |
from statistics import mean
from mathplotlib import pyplot as plt
f=open('gpsdata.txt', 'r')
gps_data=f.readlines()
f.close()
#print(*gps_data)
lat_data=[]
lon_data = []
for line in gps_data:
l=line.split(',')
print(l)
if l[0]=='$GPRMC'and l[2]=='A':
lat_data.append(float(l[3] [:2]) +float(l[3] [2:])/60)
... |
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