text string |
|---|
import numpy as np
import prody as pr
from prody.measure.transform import calcRMSD
from scipy.spatial.distance import cdist
import itertools
from sklearn.neighbors import NearestNeighbors
from .vdmer import pair_wise_geometry_matrix
class Search_filter:
def __init__(self, filter_abple = False, filter_phipsi = True... |
from __future__ import print_function
from IPython.core.debugger import set_trace
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import config as cf
import numpy as np
import torchvision
import torchvision.transforms as transforms
#imp... |
# AUTOGENERATED! DO NOT EDIT! File to edit: 04_carion2020end.ipynb (unless otherwise specified).
__all__ = ['coco_vocab', 'bb_pad', 'ParentSplitter', 'box_cxcywh_to_xyxy', 'box_xyxy_to_cxcywh', 'TensorBBoxWH',
'TensorBBoxTL', 'ToWH', 'ToXYXY', 'ToTL', 'box_area', 'all_op', 'generalized_box_iou', 'DETRLoss',... |
<filename>tools/sparse_dense_size_comparison.py
# Compare memory usage of a dense and a sparse adjancency matrix.
#
# Requires numpy. Install it with `pip3 install --user numpy`
# Authors: <NAME>, <NAME>
import numpy as np
from scipy.sparse import csr_matrix
import sys
def load_matrix(file):
matrix = np.loadtxt... |
import logging
import scipy.optimize
class MotionOptimizer(object):
def __init__(self, motion, evaluator):
self.logger = logging.getLogger(__name__)
self.motion = motion
self.evaluator = evaluator
def obj(self, x):
self.counter += 1
self.motion.set_params(x)
co... |
"""Input/output functions."""
import astropy.io.fits as fits
from astropy.table import Table
import numpy as np
import astropy.units as u
from astropy.coordinates import (
EarthLocation,
AltAz,
Angle,
ICRS,
GCRS,
SkyCoord,
get_sun,
)
import os
from astropy.time import Time
import warnings
fr... |
<reponame>Smear-Lab/Olfactory_Search
#Misc
import os, time, argparse
import h5py, json
import glob, fnmatch,pdb
from tqdm import tqdm
import multiprocessing
#Base
import numpy as np
import pandas as pd
import scipy.stats as st
from sklearn.model_selection import StratifiedKFold
#Plotting
import matplotlib
matplotlib.us... |
<gh_stars>0
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.animation as animation
import matplotlib.colors as mcolors
from scipy.interpolate import interp1d
from scipy.integrate import solve_ivp
import os
import re
import numpy as np
import h5py
import sys
from os.path import dir... |
#!/usr/bin/env python
"""
Artificial Intelligence for Humans
Volume 3: Deep Learning and Neural Networks
Python Version
http://www.aifh.org
http://www.jeffheaton.com
Code repository:
https://github.com/jeffheaton/aifh
Copyright 2015 by <NAME>
Licensed under the Apache License, Versio... |
<filename>task3.py<gh_stars>0
import os
import random
from itertools import cycle
import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy import interp
from skimage import exposure
from skimage.feature import hog
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.preprocess... |
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import SGD
import torch as t
from scipy import constants
import numpy as np
import pandas as pd
from pyhdx.models import Protein
class DeltaGFit(nn.Module):
def __init__(self, deltaG):
super(DeltaGFit, self).__init__()
self.del... |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------------
from collections import deque
import cv2
import numpy as np
im... |
import numpy
import torch
import scipy
import scipy.sparse as sp
import logging
from six.moves import xrange
from collections import OrderedDict
import sys
import pdb
from sklearn import metrics
import torch.nn.functional as F
from torch.autograd import Variable
def compute_metrics(predictions, targets):
pred=predic... |
<reponame>ChristianDjurhuus/RAA
from src.models.train_DRRAA_module import DRRAA
from src.models.train_LSM_module import LSM
from src.models.train_BDRRAA_module import BDRRAA
import torch
import matplotlib.pyplot as plt
import numpy as np
import json
import scipy.stats as st
import matplotlib as mpl
def sparse_experime... |
<filename>experiment-2/02_gm_correlations_across_masks.py
"""Experiment 2, Analysis Group 2.
Comparing measures of global signal.
Mean cortical signal of MEDN correlated with signal of all gray matter
- Distribution of Pearson correlation coefficients
- Page 2, right column, first paragraph
Mean cortical signal ... |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 21 13:00:28 2021
@author: OTPS
"""
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from PIL import Image
img1 = Image.open(r"fit to merge.png", mode='r')
img2 = Image.open(r"base to merge.png")
img1.paste(img2, ... |
from __future__ import division, print_function
print("""
Numerical homogenisation based on exact integration, which is described in
<NAME>, Improved guaranteed computable bounds on homogenized properties
of periodic media by FourierGalerkin method with exact integration,
Int. J. Numer. Methods Eng., 2016.
This is a ... |
# coding: utf-8
# This script makes a 3D plot of the Southern Ocean topography.
#
# The data comes from some geophysiscists at Columbia. The product is "MGDS: Global Multi-Resolution Topography". These folks took all multibeam swath data that they can get their hands on and filled gaps with Smith and Sandwell. See ht... |
from scipy.spatial.distance import cdist, euclidean
def geometric_median(X, eps=1e-5):
"""Computes the geometric median of the columns of X, up to a tolerance epsilon.
The geometric median is the vector that minimizes the mean Euclidean norm to
each column of X.
"""
y = np.mean(X, 0)
while Tru... |
<reponame>hplgit/fem-book<filename>doc/.src/book/src/approx1D.py
"""
Approximation of functions by linear combination of basis functions in
function spaces and the least squares method or the collocation method
for determining the coefficients.
"""
from __future__ import print_function
import sympy as sym
import nump... |
<reponame>gellens/Master_thesis_JAQ_code<gh_stars>0
# import matplotlib
# import statsmodels as sm
# import scipy.stats as st
# import pandas as pd
# import warnings
import json
import os
from scipy.stats import gamma
from scipy.stats import lognorm
from scipy.stats import pareto
from scipy.stats import norm
import nu... |
<filename>codes/sensitivity_analysis_withRealParameters.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from fipy import *
from numpy import *
import scipy.sparse as sp
import scipy.sparse.linalg as la
import parameterFunctions.immuneResponse as delt
import parameterFunctions.sigmaF as sigmaF
import inspect
from coll... |
<gh_stars>0
# -*- coding: utf-8 -*-
import warnings
warnings.filterwarnings('ignore')
import pickle
import yaml
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix, hstack as sparse_hstack, vstack as sparse_vstack
from sklearn.linear_model import LogisticRegression
from s... |
<gh_stars>0
#-*- coding:utf-8 -*-
from PIL import Image
import numpy as np
from scipy.io import loadmat
from scipy.io import savemat
def sigmoid(z):
g=1/(1+np.exp(-z))
return g
img=Image.open('test.png')
img=img.convert('L')
grey=img.getdata()
X=np.asarray(grey)
X=np.mat(X.ravel())
theta=loadmat('theta')
theta1=the... |
import numpy as np
import pandas as pd
import os
from joblib import dump
from sklearn.model_selection import train_test_split, RandomizedSearchCV, GridSearchCV
from sklearn.metrics import classification_report, recall_score, precision_recall_fscore_support
from sklearn.ensemble import GradientBoostingClassifier
from ... |
import argparse
import matplotlib
import scipy.stats
matplotlib.use("Agg")
import sys
import matplotlib.pyplot as plt
import numpy as np
import os
sys.path.insert(0, '/root/jcw78/process_pcap_traces/')
import graph_utils
graph_utils.latexify(space_below_graph=0.4)
def tensorflow(folder, name_map):
# In tensorflow... |
<reponame>Kurokesu/SCF4-SDK<filename>src/gui_L087 (for C1_PRO_X18 camera) PARFOCAL_DEMO/sweep.py
import cv2
import os
import serial
import sys
import scf4_tools
import time
import threading
import camera
import numpy as np
from scipy.interpolate import interp1d
from tqdm import tqdm
CHB_MOVE = 7
CHA_MOVE = 6
CHB... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 18 18:15:50 2016
@name: Mixed MultiNomial Logit
@author: <NAME>
@summary: Contains functions necessary for estimating mixed multinomial logit
models (with the help of the "base_multinomial_cm.py" file).
Version 1 only works for MNL kernel... |
<filename>panopticon/wme.py
"""
wme.py
====================================
wme
"""
# second version
import numpy as np
from tqdm import tqdm
import pandas as pd
from scipy import stats
from itertools import islice
from scipy.sparse import coo_matrix, save_npz
from panopticon.utilities import get_valid_gene_info
def... |
<gh_stars>0
#!/usr/bin/env python
#Examples of irreductible polynomes 16 degree
#x^16 + x^9 + x^8 + x^7 + x^6 + x^4 + x^3 + x^2 + 1
#x^16 + x^12 + x^3 + x^1 + 1
#x^16 + x^12 + x^7 + x^2 + 1
from sympy.polys.domains import ZZ
from sympy.polys.galoistools import gf_gcdex, gf_strip
def gf_inv(a): # irriducible pol... |
import math
import numpy as np
from scipy import interpolate
class Polyline(list):
@staticmethod
def _2Dcheck(value):
if len(value) != 2:
raise ValueError("Value must be 2-D.")
def __init__(self):
super().__init__()
def __setitem__(self, key, value):
... |
<filename>finite_element_networks/lightning/data/common.py<gh_stars>1-10
from dataclasses import dataclass
from typing import Callable, Optional
import numpy as np
from scipy.spatial import Delaunay
from ...data import TimeEncoder
from ...domain import (
BoundaryAnglePredicate,
CellPredicate,
Domain,
... |
# DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.
#
# This material is based upon work supported by the Assistant Secretary of Defense for Research and
# Engineering under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions,
# findings, conclusions or recommendat... |
<reponame>IdanAzuri/tensorflow-generative-model-collections
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import scipy.misc
import glob
import scipy
import utils
import tensorflow as tf
""" param """
epoch = 50
batch_size = 64
lr = 0.0002
z_dim = 100
n... |
<reponame>yirencaifu/pyWindMongoDB<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 27 08:11:48 2014
@author: space_000
"""
from scipy.io import loadmat
from WindPy import w
import pymongo as mg
from wsiTools import findDate
from mgWsi import upiter
d=loadmat('D:\FieldSHSZ')
Field=d['Field'].tolist()
stride... |
'''
If you find this useful, please give a thumbs up!
Thanks!
- Claire & Alhan
https://github.com/alhankeser/kaggle-petfinder
'''
# External libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# from sklearn.linear_model import LogisticRegression
from sklearn.ensemb... |
#!/usr/bin/env python3
#
# Copyright (C) 2017 <NAME>
import argparse
import csv
import os
import sys
import tempfile
import time
import signal
import statistics
import psutil
from plumbum import colors
from plumbum import local
from plumbum.cmd import grep
from plumbum.commands.processes import ProcessExecutionError
f... |
<reponame>IanFla/Importance-Sampling
import numpy as np
import scipy.stats as st
from niscv_v2.basics.exp import Exp
from niscv_v2.basics import utils
import multiprocessing
import os
from functools import partial
from datetime import datetime as dt
import pickle
def experiment(dim, fun, size_est, sn, show, size_kn, ... |
<reponame>biasvariancelabs/aitlas<filename>aitlas/datasets/sat6.py
import csv
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.io
import numpy as np
import random
from ..base import BaseDataset
from .schemas import MatDatasetSchema
"""
The format of the mat dataset is:
train_x 28... |
from pathlib import Path
import tempfile
from unittest.mock import MagicMock
import pytest
import numpy as np
import pandas as pd
from scipy import sparse
import nibabel
import nilearn
from nilearn.datasets import _testing
from nilearn.datasets._testing import request_mocker # noqa: F401
def make_fake_img():
r... |
<gh_stars>1-10
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import seaborn as sns
from sklearn.model_selection import train_test_split
import math
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.metrics import mean_squared_error, mean_absolute_error
import traceback
import stati... |
# define a class for networks
class Network(object):
'''
Networks have two states: the data state where they are stored as: matrix and
nodes and a viz state where they are stored as: viz.links, viz.row_nodes, viz.
col_nodes.
The goal is to start in a data-state and produce a viz-state of the network
that... |
<reponame>theunissenlab/sounsig<gh_stars>10-100
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
from sklearn.ensemble impor... |
<filename>structsolve/arc_length_riks.py
import numpy as np
from numpy import dot
from scipy.sparse import csr_matrix, vstack as spvstack, hstack as sphstack
from .static import solve
from .logger import msg, warn
def _solver_arc_length_riks(an, silent=False):
r"""Arc-Length solver using the Riks method
"""... |
<reponame>RawPikachu/valor
from sql import ValorSQL
from util import guild_name_from_tag
import matplotlib.pyplot as plt
import matplotlib.dates as md
from scipy.interpolate import make_interp_spline
from matplotlib.ticker import MaxNLocator
import numpy as np
from datetime import datetime
import time
def plot_process... |
#!/usr/bin/env
# -*- coding: utf-8 -*-
# Copyright (C) <NAME> - All Rights Reserved
# Unauthorized copying of this file, via any medium is strictly prohibited
# Proprietary and confidential
# Written by <NAME> <<EMAIL>>, January 2017
import os
import scipy.io as sio
import utils.datasets as utils
# ----------------... |
<gh_stars>0
from scipy.stats import chi2
import numpy as np
from matplotlib import pyplot as plt
from scipy import optimize
import pickle
objects = []
with open("priceZZZ", "rb") as openfile:
while True:
try:
objects.append(pickle.load(openfile))
except EOFError:
... |
<gh_stars>0
from nltk.corpus import reuters
import sys
import numpy as np
from scipy import optimize
# Loading data here
train_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])
test_documents, test_categories = zip(*[(reuters.raw(i), ... |
<reponame>WattSocialBot/ijcnlp2017-customer-feedback<filename>src/classifier.py
__author__ = "bplank"
import argparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score, classifica... |
<filename>tests/tests.py
#!/usr/bin python
# -*- coding: utf-8 -*-
from __future__ import print_function
from unittest import (TestCase, skip, skipIf)
from uvmod.stats import LnLike, LS_estimates, LnPrior, LnPost, hdi_of_mcmc
from uvmod.models import Model_1d, Model_2d_isotropic, Model_2d_anisotropic
# TODO: Use ``np.... |
<reponame>chelseajohn/dlplatform
from DLplatform.aggregating import Aggregator
from DLplatform.parameters import Parameters
from typing import List
import numpy as np
from scipy.spatial.distance import cdist, euclidean
class GeometricMedian(Aggregator):
'''
Provides a method to calculate an averaged model fro... |
<gh_stars>1-10
import logging
import numpy as np
import pandas as pd
from sklearn.neighbors.kde import KernelDensity
from scipy.optimize import minimize
from src.utils import cov2corr
class MarcenkoPastur:
def __init__(self, points=1000):
"""
Marcenko-Pastur
:param points:
:type... |
<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from scipy.signal import savgol_filter
import sys
def Interpolate(time, mask, y):
yy = np.array(y)
t_ = np.delete(time, mask)
y_ = np.delete(y, mask, axis = 0)
if len(yy.shape) == 1:
yy[mask] = np.interp(time[mask], t... |
<filename>myhabitatagent.py
import argparse
import habitat
import random
import numpy as np
import scipy
import os
import cv2
import time
from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower
from habitat.utils.visualizations import maps
from gibsonagents.expert import Expert
from gibsonagents.pathp... |
<reponame>santutu/league-director
import copy
import statistics
from operator import attrgetter
from PySide2.QtCore import Signal, Qt, QEvent
from PySide2.QtGui import QPen, QMouseEvent
from PySide2.QtWidgets import QGraphicsView, QGraphicsScene, QAbstractScrollArea, QApplication, QGraphicsItem
from leaguedirector.li... |
import argparse
import numpy as np
import os
import sys
import matplotlib
matplotlib.use('Agg')
import json
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits.axes_grid.inset_locator
import helper as hf
import plot_helper as phf
import seaborn as sns
import scipy.stats as stat
fr... |
from scipy import stats
import pandas as pd
import numpy as np
path_mutlivariate_feat_imps = '/n/groups/patel/samuel/EWAS/feature_importances_paper/'
Environmental = ['Clusters_Alcohol', 'Clusters_Diet', 'Clusters_Education', 'Clusters_ElectronicDevices',
'Clusters_Employment', 'Clusters_FamilyHistory'... |
#!/usr/bin/python3
import functools
import multiprocessing
import random
import unittest
import numpy as np
import scipy.special
import helper.basis
import helper.grid
import tests.misc
class Test45SpatAdaptiveUP(tests.misc.CustomTestCase):
@staticmethod
def createDataHermiteHierarchization(p):
n, d, b = 4,... |
<reponame>tusharkh/PyGEM-Clone
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import netCDF4 as nc
from scipy.stats import linregress
import cartopy.crs as ccrs
import cartopy as car
#========== IMPORT INPUT AND FUNCTIONS FROM MODULES ============================================================... |
<filename>Code/branches/Pre-Prospectus/python/SourceFiles/Geometry.py
__id__ = "$Id: Geometry.py 51 2007-04-25 20:43:07Z jlconlin $"
__author__ = "$Author: jlconlin $"
__version__ = " $Revision: 51 $"
__date__ = "$Date: 2007-04-25 14:43:07 -0600 (Wed, 25 Apr 2007) $"
import scipy
import Errors
class Ge... |
<gh_stars>1-10
"""Transformations to be used on tremor accelerometry data (e.g.: FFT)."""
from __future__ import annotations
from typing import Iterable
import numpy as np
import pandas as pd
from scipy.signal import periodogram
def fft_spectra(
input_dataframe: pd.DataFrame,
columns: Iterable[str] | None =... |
<gh_stars>10-100
""" Analyze MCMC output - chain length, etc. """
# Built-in libraries
from collections import OrderedDict
import datetime
import glob
import os
import pickle
# External libraries
import cartopy
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.pyplot import MaxNLocator
from matp... |
import argparse
from distutils.util import strtobool
import json
import os
import pickle
import tensorflow as tf
import numpy as np
from softlearning.policies.utils import get_policy_from_variant
from softlearning.samplers import rollouts
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argume... |
<gh_stars>0
import pandas as pd
import pandas_profiling
from path import Path
import numpy as np
from scipy.stats import chi2_contingency
from collections import Counter
root = Path('/home/roit/datasets/kaggle/2016b')
dump_path = root/'dump'
ge_info = root/'gene_info'
exitnpy = False
if exitnpy==False:
genes_di... |
<filename>Python/data/preprocess.py
import numpy as np
import scipy.ndimage.measurements as scipy_measurements
import miapy.data.transformation as miapy_tfm
class ClipNegativeTransform(miapy_tfm.Transform):
def __init__(self, entries=('images',)) -> None:
super().__init__()
self.entries = entries... |
"""
Some elements of the finite difference routines were adapted from HP Langtangen's wonderful book on the FD method for python:
https://hplgit.github.io/fdm-book/doc/pub/book/html/._fdm-book-solarized001.html
"""
import numpy as np
from scipy.integrate import simps
class Wave1D:
"""
A utility class for sim... |
# -*- coding: utf-8 -*-
"""
Written by <NAME>
Email: danaukes<at>gmail.com
Please see LICENSE for full license.
"""
import pynamics
from pynamics.tree_node import TreeNode
from pynamics.vector import Vector
from pynamics.rotation import Rotation, RotationalVelocity
from pynamics.name_generator import NameGenerator
fr... |
import numpy as np
import scipy.stats as st
import statsmodels as sm
from scipy import optimize
y = np.random.randint(2, size=(100,1))
x = np.random.normal(0,1,(100,2))
res_correct = sm.discrete.discrete_model.Logit(y,x).fit()
res_correct.params
def Logit(b,y,x):
# y = np.random.randint(2, size=(100,1))
# x... |
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
class MatchPredictor:
""" Class to calculates the probabilities for different scores (outcomes) of two teams.
Attributes
----------
l1 : float
Projected score for team 1 (expectation value for Poisson distribution)
... |
<gh_stars>10-100
from flask import Flask, render_template, request, send_file
from flask_pymongo import PyMongo
import json
import sg_core_api as sgapi
import os
import pathlib
import numpy as np
from bson.json_util import dumps
from bson.objectid import ObjectId
from datetime import datetime
from scipy.interpolate imp... |
# <NAME>
import os
import cv2
import platform
import numpy as np
from predict import predict
from scipy.misc import imresize
from multiprocessing import Process
from keras.models import model_from_json
img_size = 64
channel_size = 1
def main():
# Getting model:
model_file = open('Data/Model/model.json', 'r')
... |
<reponame>TUM-E21-ThinFilms/direfl
#!/usr/bin/env python
# This program is public domain
#
# Phase inversion author: <NAME>
# Translated from Mathematica by <NAME>
#
# Phase reconstruction author: <NAME>
# Converted from Fortran by <NAME>
#
# Reflectivity calculation author: <NAME>
#
# The National Institute of Standa... |
<gh_stars>0
import cv2
import numpy as np
import tensorflow as tf
import time
import statistics
import h5py
vid_file = '/home/vijayaganesh/Videos/Google Chrome Dinosaur Game [Bird Update] BEST SCORE OF THE WORLD (No hack).mp4'
data_file = 'training_data.txt'
roi_x = 320
roi_y = 120
roi_w = 459
roi_h = 112
font = cv2.... |
import statistics
from datetime import date
import psycopg2
from psycopg2 import sql
class Log:
def __init__(self, score, gameday):
#gather player data
self.name = score.get('name')
self.team = (score.get('team')).name
self.date = gameday
self.mins = round(((score.get('seco... |
"""Model wrapper class for performing GradCam visualization with a ShowAndTellModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from im2txt import show_and_tell_model
from im2txt.inference_utils import inference_wrapper_base
import numpy as np
im... |
<reponame>shaifulcse/codemetrics-with-context-replication
"""
"""
import re
import os
import matplotlib.pyplot as plt
import re
import numpy as np
import math
from scipy.stats.stats import pearsonr
from scipy.stats.stats import kendalltau
import scipy
from matplotlib.patches import Rectangle
from scipy import stats
i... |
<filename>image processing/4/1/1.py
from skimage.io import imread, imsave
from numpy import ones
from scipy.signal import convolve2d
import warnings
warnings.filterwarnings("ignore")
img = imread('img.png')
img = convolve2d(img, ones((5, 5), dtype=int), mode='valid') // 25
imsave('out_img.png', img) |
<reponame>Wang-ZhengYi/ED_Chapter5_code
#!\usr\bin\python3
# -*- coding: utf-8 -*-
'''
Created on Oct. 2019
ED_Chapter4
@author: ZYW @ BNU
'''
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from scipy import interpolate
from mpl_toolkits.mplot3d import Axes3D
import os
from matplotlib ... |
'''
Optimal hyperparameters for CM + Laplacian kernel
Ea: alpha 1e-11, gamma 1e-4
polarizability: alpha 1e-3, gamma 1e-4
HOMO-LUMO gap: alpha 1e-2, gamma 1e-4
Dipole moment: alpha 1e-1, gamma 1e-3
Optimal hyperparameters for BoB + Laplacian kernel
Ea: alpha 1e-11, gamma 1e... |
<gh_stars>0
from django.db import models
from django.utils import timezone
from django.contrib.auth.models import User
from django.db.models import Q
from django.core.exceptions import ObjectDoesNotExist
from django.http import Http404
from users.models import Profile
from django.contrib.auth.models import User
from st... |
<filename>src/wavecalLib.py
#!/usr/bin/env python
from __future__ import print_function, division, unicode_literals
import numpy as np
import copy
import scipy.optimize
from skimage import filters
from skimage import morphology
from scipy import interpolate
from astropy.stats import biweight_location, mad_std
from co... |
<reponame>jrekoske/reduced-order-shaking
import os
import pickle
import logging
import numpy as np
import pandas as pd
from scipy.stats import qmc
from romshake.sample import voronoi
from romshake.core.reduced_order_model import ReducedOrderModel
FNAME = 'rom_builder.pkl'
class NumericalRomBuilder():
def __init... |
"""
test evfuncs module
"""
import os
from glob import glob
import unittest
import numpy as np
from scipy.io import loadmat
import evfuncs
class TestEvfuncs(unittest.TestCase):
def setUp(self):
self.test_data_dir = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
'.', '... |
# -*- coding: utf-8 -*-
"""
Functions and classes for manipulating 10X Visium spatial transcriptomic (ST) and
histological imaging data
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import scanpy as sc
sc.set_figure_params(dpi=1... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 8 10:01:34 2019
@author: ecramer
"""
import numpy as np
from scipy import interpolate
from skimage.feature import peak_local_max
class Contourer():
""" TODO: Full writeup of class documentation here.
Steps:
1. generate the contours for each factor... |
<reponame>nuttamas/PycQED_py3
import numpy
from scipy import *
def lorentzian(x_data, y_data):
p=4*[0]
y_min = min(y_data)
index_y_min = y_data.tolist().index(y_min)
x_min = x_data[index_y_min]
y_max = max(y_data)
index_y_max = y_data.tolist().index(y_max)
y_mean = y_data.mean()
HM = (y... |
<gh_stars>1-10
from fractions import Fraction
x, d = input().split(' ')
d = int(d)
k = len(x) - x.index('.') - d - 1
a, b = x[0:-d].replace('.', ''), 10 ** k
ab = Fraction(int(a), b)
rd = Fraction(int(x[-d:]), (10 ** d - 1) * b)
result = ab + rd
print(str(result.numerator) + '/' + str(result.denominator))
|
import ruamel.yaml as yaml
import numpy as np
import matplotlib.pyplot as plt
import MatplotlibSettings
from scipy.interpolate import make_interp_spline, BSpline
# Loada data
data = np.loadtxt("FOvsAsy2.dat")
f, (ax1, ax2) = plt.subplots(2, 1, sharex = "all", gridspec_kw = dict(width_ratios = [1], height_ratios = [4,... |
<reponame>SANDEEPREDDY56712/OELP_6thSem
import pandas as pd
from sklearn.decomposition import PCA
import DataPreprocessing as dp
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from scipy.stats import pearsonr
##########... |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
bank=pd.read_csv(path)
categorical_var=bank.select_dtypes(include='object')
print(categorical_var)
numerical_var=bank.select_dtypes(include='number')
print(numerical_var)
# code starts here
# code ends here
#... |
import numpy as np
import math
from statistics import median
from scipy.stats import skew
import weightedstats as ws
from statsmodels.stats.stattools import medcouple
class Med_couple:
def __init__(self,data):
self.data = np.sort(data,axis = None)[::-1] # sorted decreasing
self.med = np.medi... |
import QUANTAXIS as QA
from numpy import *
from scipy.signal import savgol_filter
import numpy as np
import matplotlib.pyplot as plt
from QUANTAXIS.QAIndicator.talib_numpy import *
import mpl_finance as mpf
import matplotlib.dates as mdates
def smooth_demo():
data2 = QA.QA_fetch_crypto_asset_day_adv(['huobi'],
... |
"""Random number generators for random augmentation parametrization"""
from typing import Optional, Tuple
import numpy as np
import scipy.stats
class RandomSampler:
"""Samples random variables from a ``scipy.stats`` distribution."""
def __init__(
self,
rv: scipy.stats.rv_continuous,
... |
import numpy as np
import scipy as sp
from scipy import stats as sps
import scipy.optimize as op
import qp
class composite(object):
def __init__(self, components, vb=True):
"""
A probability distribution that is a linear combination of scipy.stats.rv_continuous objects
Parameters
... |
from dolfin import *
from numpy import *
import scipy as Sci
import scipy.linalg
from math import pi,sin,cos,sqrt
import scipy.sparse as sps
import scipy.io as save
import scipy
import pdb
parameters['linear_algebra_backend'] = 'uBLAS'
j = 1
n = 2
n =2
# print n
mesh = UnitSquareMesh(n,n)
# mesh = Mesh('untitled.xml... |
"""
This module finds diffusion paths through a structure based on a given potential field.
If you use PathFinder algorithm for your research, please consider citing the following work:
<NAME>, <NAME>, <NAME>, <NAME>, <NAME>,
The Journal of Chemical Physics 145 (7), 074112
"""
from __future__ import division
... |
<reponame>LasLitz/ma-doc-embeddings<filename>experiments/book_comparison.py
import os
from collections import defaultdict
import random
from typing import Dict, List
import pandas as pd
from scipy.stats import stats
from lib2vec.corpus_structure import Corpus
from experiments.predicting_high_rated_books import mcnema... |
<filename>src/dbspro/cli/correctfastq.py
"""
Correct FASTQ/FASTA with the corrected sequences from starcode clustering
"""
from collections import defaultdict
import logging
import os
import statistics
from pathlib import Path
from typing import Iterator, Tuple, List, Set, Dict
import dnaio
from tqdm import tqdm
from ... |
<reponame>huangysh/ASCA_Cluster
# -*- coding: utf-8 -*-
# **********************************************************************************************************************
# MIT License
# Copyright (c) 2020 School of Environmental Science and Engineering, Shanghai Jiao Tong University
# Permission is hereby gra... |
import os
import numpy as np
import pandas as pd
import xarray as xr
import pickle as pkl
from datetime import datetime
from scipy import ndimage as ndi
import SimpleITK as sitk
import skimage as skim
from skimage import feature, morphology
import glob
class RegHearts:
'''Class that generates liver masks for MRE ... |
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