text string |
|---|
# -*- coding: utf-8 -*-
"""
A script for generating initial conditions for an axisymmetric
protoplanetary disc, assuming an isothermal equation of state and
steady-state.
Please note, as long as the previous steps have been completed, you
can start at any step
Look carefully at the settings saved in settings_file (by... |
<filename>server.py
import io
import socket
import struct
from PIL import Image
from datetime import datetime
import scipy.ndimage as ndi
def run_server():
# Start a socket listening for connections on 0.0.0.0:8000 (0.0.0.0 means
# all interfaces)
server_socket = socket.socket()
server_socket.bind(('0... |
import numpy as np
import pydrake.solvers.mathematicalprogram as mp
from scipy.sparse import csc_matrix
from scipy.sparse.linalg import lsqr
"""
QP:
min. 1 / 2 * z.dot(Q).dot(z) + b.dot(z)
s.t. G.dot(z) <= e
"""
class QpDerivativesKktPinv:
def __init__(self):
self.n_z = 0 # number of decision variables.... |
from scipy.stats import pearsonr
def calculate_corr(seq_i, seq_j):
if len(seq_i)>=len(seq_j):
longer_signal=seq_i
shorter_signal=seq_j
else:
longer_signal=seq_j
shorter_signal=seq_i
LD=len(longer_signal)
LK=len(shorter_signal)
corr=[]
for a in ran... |
#coding:utf-8
from typing import Callable, Iterable, Iterator, Tuple, Union, Generic, TypeVar
from numpy import ndarray
from scipy.sparse.base import spmatrix
T = TypeVar("T")
# Alias for return types
R = TypeVar("R", contravariant=True)
newaxis = None
NoneType = type(None)
class SparseMatrix(Generic[T], spmatrix):
... |
import torch
from torch import nn
from torch import optim
from model.model_preprocessing import Preprocess
from model.model_encoder import pulsar_encoder
from model.model_tcn_multi import TemporalConvNet_multi
from model.model_output import OutputLayer
from model.model_multiclass import MultiClass
try:
from model.m... |
'''
This script creates json files which can be used to render QQ plots.
'''
# TODO: share the list `variants` with Manhattan and top_{hits,loci}.
# TODO: make gc_lambda for maf strata, and show them if they're >1.1?
# TODO: copy changes from <https://github.com/statgen/encore/blob/master/plot-epacts-output/make_qq_j... |
<gh_stars>10-100
import os
import pickle
import numpy as np
import tmap as tm
from annoy import AnnoyIndex
from faerun import Faerun
from scipy.spatial.distance import cosine as cosine_distance
import matplotlib.pyplot as plt
CFG_TMAP = tm.LayoutConfiguration()
CFG_TMAP.k = 50
CFG_TMAP.kc = 50
CFG_TMAP.sl_scaling_min ... |
r"""@package motsfinder.axisym.trackmots.optimize
Functions to perform various MOTS optimizations during tracking.
The main optimization performed here is the numerical search for close to
optimal bipolar coordinate parameters.
"""
import numpy as np
from scipy.interpolate import lagrange
from ..curve import Bipola... |
<reponame>IDKiro/dnd-submit-pytorch<gh_stars>1-10
import numpy as np
import os
import h5py
import scipy.io
import math
import skimage
import cv2
import random
import time
import matplotlib.pyplot as plt
from scipy.ndimage.filters import convolve
import pyximport; pyximport.install()
from .tone_mapping_cython import CR... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from mm.models import MeshModel
from mm.utils.opengl import Render
from mm.optimize.camera import estimateCamMat, splitCamMat
import mm.optimize.image as opt
from mm.utils.mesh import calcNormals, generateFace, generateTexture, barycentricReconstruction
from mm.utils.trans... |
# -*- coding: utf-8 -*-
from scipy.stats import rv_continuous
from scipy.stats import rv_discrete
from scipy.stats import _continuous_distns as crv_helper
from scipy.stats import _discrete_distns as drv_helper
import scipy.special as special
import numpy as np
import matplotlib.pyplot as plt
def plothistogram(values... |
<reponame>JPLMLIA/OWLS-Autonomy<filename>src/helm_dhm/simulator/utils.py
"""
Utility functions for HELM simulator
"""
import json
import logging
import os.path as op
import numpy as np
from scipy import stats
VALID_CONFIG_DISTS = ['truncnorm']
VALID_CONFIG_SHAPES = ['gaussian', 'airy_disk']
def create_dist_objs(**k... |
import torch.nn.functional as F
import numpy as np
from options.train_options import TrainOptions
from utils.timer import Timer
import os
from data import CreateSrcDataLoader
from data import CreateTrgDataLoader
from model import CreateModel
#import tensorboardX
import torch.backends.cudnn as cudnn
import torch
from to... |
<gh_stars>10-100
"""
The algorithms used here can be found in:
<NAME> and <NAME>,
"The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF"",
2013,
https://arxiv.org/abs/1304.2865
"""
import numpy as np
from scipy import stats
from scipy.special import expit as sigmoid
from .pav_ro... |
import logging
from typing import Any, Optional, Union, List, Collection, Dict, Tuple, Callable
import itertools
from copy import deepcopy
import textwrap
# these imports need to be here so that ask_for_eval works
import numpy as np
import scipy as sp
from simulacra import units as u
logger = logging.getLogger(__nam... |
<reponame>kkkkv/tgnms
#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import dataclasses
import json
import logging
from statistics import mean
from typing import Dict, Optional
from terragraph_thrift.Controller.ttypes import IperfTransportProtocol
@dataclasses.dataclass
class ParsedI... |
<filename>fair_ssl/methods/lfvae.py
import tensorflow as tf
import numpy as np
from tqdm import tqdm
from tf_utils import logger
import pickle as pkl
import os
# from .vfae import VariationalFairClassifier, VariationalFairAutoEncoder
from .vae import VariationalAutoEncoder
from utils import demographic_parity, equalize... |
#!/usr/bin/env python3
"""Extract cities from OSM.
It required the xml extracts:
for F in central-america-latest.osm.pbf australia-oceania-latest.osm.pbf south-america-latest.osm.pbf africa-latest.osm.pbf asia-latest.osm.pbf north-america-latest.osm.pbf europe-latest.osm.pbf; do osmosis --read-pbf ${F} --tf accept-no... |
<filename>fruits/elderberries/benchmarks2018/figures.py
from collections import Counter
from itertools import groupby
FONTSIZE = 15
import matplotlib
matplotlib.use('Agg')
matplotlib.rc('font', size=FONTSIZE)
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
from numba import njit
import ... |
<reponame>jlehrer1/comparative-organoids
import random
import pathlib
import os
import argparse
import ast
from itertools import product
import numpy as np
from scipy.stats import loguniform
def run_search(
N: int,
class_label: str,
) -> None:
"""
Runs hyperparameter search by scaling i GPU jobs... |
<gh_stars>0
""" Import needed modules """
"-----------------------------------------------------------------------------"
import numpy as np
import cantera as ct
from math import exp, pi
from scipy.optimize import fsolve
from Shared_Funcs.pem_pak import *
from scipy.sparse import coo_matrix
from Shared_Funcs.pe... |
<reponame>clazaro/Kratos
import KratosMultiphysics as KM
import KratosMultiphysics.ShallowWaterApplication as SW
import KratosMultiphysics.KratosUnittest as KratosUnittest
from KratosMultiphysics.process_factory import KratosProcessFactory
import os
try:
import numpy
missing_numpy = False
except ImportError:
... |
<gh_stars>0
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as op
# Hypothesis
def Hypothesis(X,theta):
a=np.dot(X,theta)
sigmoid=1/(1+np.exp(-a))
return sigmoid
# cost function
def costFunction(theta,X,Y):
hypothesis=Hypothesis(X,theta... |
<filename>post_scripts/animate_saved_data.py
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 6 08:46:59 2020
@author: Tom
"""
import pybamm
import openpnm as op
import matplotlib.pyplot as plt
import ecm
import os
from scipy import io
import numpy as np
plt.style.use('default')
plt.close("all")
pybamm.set_logging... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
import sys; sys.path.insert(1,'../')
from firefly import *
from numpy.random import randint, normal
import networkx as nx
import numpy as np
import csv
from tqdm import trange, tqdm
from scipy import stats, io
import collections
import math
from kuramoto import *
import pandas as ... |
<gh_stars>1-10
# --- built in ---
import os
import sys
import time
import json
import logging
import unittest
# --- 3rd party ---
import numpy as np
import tensorflow as tf
from scipy import stats as sp_stats
from parameterized import parameterized
# --- my module ---
from unstable_baselines.lib import prob
from tes... |
import argparse
from typing import Text
import struct
import os
import numpy as np
import onnx
import caffe2.python.onnx.backend as backend
import magic
from scipy.io.wavfile import read
import imageio
__author__ = "<NAME> (University of Tuebingen, Chair for Embedded Systems)"
supported_file_types = ['audio/x-wav',... |
<reponame>Koeng101/colony_picker<filename>colony_picker/utils.py<gh_stars>1-10
from typing import Any, Optional
import textwrap
from scipy.spatial.transform import Rotation
import random
import cv2
import numpy as np
def print_describe(name: str, item: Any):
"""Print an item with a description, where the item is ... |
<gh_stars>1-10
import numpy as np
import scipy.optimize
from .c_fastpixelint import pixelintegrate
from .integrate import radintpix, azimintpix, radint_nsector
from .. import misc
from ..misc.basicfit import findpeak_asymmetric
from ..misc.errorvalue import ErrorValue
def findbeam_gravity(data, mask):
"""Find be... |
<reponame>thomasarmstrong/sstcam-simulation<filename>sstcam_simulation/camera/tests/test_pulse.py
from sstcam_simulation.camera.pulse import ReferencePulse, GenericPulse
from sstcam_simulation.camera.constants import CONTINUOUS_READOUT_SAMPLE_WIDTH
import numpy as np
from scipy.stats import norm
import pytest
classes... |
### electoral crime and performance paper
# judicial decisions script
# this script wrangles the judicial decisions downloaded from the tse
# website. we use the textual info in the sentences to determine the
# (class) allegations against individual candidates running for office
# author: <NAME>
# by <EMAIL>
# impo... |
<gh_stars>0
##Exploring 2D GMM's (Multi-dimensional)
##not perfectly implemented - clusters don't seem to separate nicely
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
from scipy.stats import norm
from scipy.stats import multivariate_normal
import argparse
import pandas as pd
import ra... |
import datetime
import pickle
#import bz2
from statistics import mean
def predict(cat, weeks):
if cat == 0:
# data = bz2.BZ2File("ads_app\data\model_baju.pbz2", 'rb')
# model = pickle.load(data)
model = pickle.load(open("ads_app\data\model_baju.sav","rb"))
elif cat == 1:
model = p... |
<filename>statest/quantile/some_distributions.py
from scipy.stats import norm, lognorm, expon, lomax, weibull_min, fisk
import numpy as np
def rvs_fn1(n):
return norm.rvs(10, 1, size=n)
def rvs_fn2(n):
return lognorm.rvs(1, 0, size=n)
def rvs_fn3(n):
return np.random.exponential(size=n)
def rvs_fn4(... |
<filename>InputPipeline/ContourAwareDataset.py
import os.path
from InputPipeline.base_dataset import BaseDataset, get_params, get_transform, get_downscale_transform
from InputPipeline.image_folder import make_dataset
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import torch
impor... |
# -*- coding: utf-8 -*-
"""
Evaluate intra-class correlation coefficients (ICC) for each radiomics feature by comparing extractions from
each set of segmentations (e.g. normal, eroded, dilated segmentations).
Not for clinical use.
SPDX-FileCopyrightText: 2021 Medical Physics Unit, McGill University, Montreal, CA... |
<filename>scripts/thickness_load_h5.py
import os
import h5py
import matplotlib.pyplot as plt
from pathlib import Path
from time import time, strftime
from glob import glob
import pandas as pd
import numpy as np
import scipy.ndimage as ndi
import argparse
from rabbitccs.data.utilities import load, save, print_orthogon... |
#!/usr/bin/env python
"""
radarData.py
This file holds the RadarData class that hold the radar data and processes it.
@author: <NAME>
"""
import ipdb
import scipy as sp
# My modules
from isrutilities.physConstants import v_C_0, v_Boltz
from SimISR import Path
from SimISR.IonoContainer import IonoContainer
from SimISR.... |
# Extra evaluation functions for additional ML tasks
import pandas as pd
import numpy as np
import pickle
import os
from pathlib import Path
from cvss import CVSS2
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, precision_recall_curve, auc, matthews_corrcoef
from xgboost import XGBC... |
import medis.speckle_nulling.sn_preprocessing as pre
import cv2
import os
import matplotlib.pyplot as plt
import astropy.io.fits as pf
import ipdb
import medis.speckle_nulling.sn_math as snm
import numpy as np
from configobj import ConfigObj
import medis.speckle_nulling.sn_filehandling as flh
import medis.speckle_nulli... |
#!/usr/bin/env ipython
import pandas as pd
from scipy import signal
def filter_signal(data, nSamples = 1000, frameSize = 40e-9):
'''Butterworth filter of signal.'''
fs = float(nSamples)/frameSize # sample rate (1000 samples in 40ns)
nyq = fs*0.5 # Nyquist frequency
high = 900e6/nyq # high frequency cut... |
<reponame>webbjj/clustertools
""" Generic recipes for making key calculations
"""
__author__ = "<NAME>"
__all__ = [
'nbinmaker',
"binmaker",
'roaming_nbinmaker',
"roaming_binmaker",
"power_law_distribution_function",
"dx_function",
"tapered_dx_function",
"x_hist",
"mean_prof",
"... |
<reponame>Betterton-Lab/CyLaKS<filename>gradOpt_lattice_coop.py
#!/usr/bin/python
import os
import numpy as np
import scipy
import logging
import subprocess
import matlab.engine
from scipy.optimize import least_squares
from subprocess import call
from subprocess import Popen
from math import sqrt
MATLAB = matlab.engine... |
<filename>doc/dustfm.py
#!/bin/python
'''
script for generating plots for the paper
'''
import os
import sys
import h5py
import numpy as np
import corner as DFM
from astrologs.astrologs import Astrologs
# -- galpopfm --
from galpopfm.catalogs import Catalog
from galpopfm import dustfm as dustFM
from galpopfm i... |
<reponame>ronaldosvieira/reinforced-greediness<filename>agent.py
import json
import pathlib
import sys
import time
import numpy as np
from scipy.special import softmax
from sortedcontainers import SortedSet
from src.engine import State, Action, ActionType
base_path = str(pathlib.Path(__file__).parent.absolute())
PA... |
<gh_stars>0
"""
cnn_certify_ibp.py
Certifies networks with IBP
Copyright (C) 2021, <NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
"""
import numpy as np
from numb... |
<filename>rsatoolbox/util/pooling.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 23:58:28 2020
@author: heiko
"""
import numpy as np
import scipy.sparse
from scipy.stats import rankdata
from rsatoolbox.rdm import RDMs
from rsatoolbox.util.matrix import get_v
def pool_rdm(rdms, method='c... |
<gh_stars>1-10
from nipype.interfaces.ants import RegistrationSynQuick, ApplyTransforms
import nibabel as nib
from scipy import ndimage
import numpy as np
import os
import shutil
import argparse
import glob
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--case1", required=True)
... |
<filename>colorpy/colorpy-0.1.0/thinfilm.py
'''
thinfilm.py - Thin film interference colors.
Description:
Reflection from a thin film, as a function of wavelength, thickness, and index of refraction of materials.
Note that film thicknesses are given in nm instead of m, as this is a more convenient unit in this case.... |
from datetime import timedelta
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, fcluster, linkage
from scipy.cluster.vq import kmeans, vq
from lagranto import trajectory
from myscripts import datadir, plotdir
styles = ['-bo', '-gx', '-rx', '-cx', '-mx', '-yx',
... |
import numpy as np
import matplotlib.pyplot as pl
from scipy.io import netcdf
def readResults(rootFile):
# Read inverted profiles
ff = netcdf.netcdf_file(rootFile+'.inversion', 'r')
synthProf = ff.variables['map'][:]
ff.close()
# Read inverted parameters
ff = netcdf.netcdf_file(rootFile+'.parameters', 'r')
par... |
<reponame>BigOto2/BlenderRenderDNS<filename>Render2018/lib/noncart_to_cart_test.py
import numpy as np
import scipy.interpolate
#Temporary
import mcubes
import cgns_load_data
def conv_noncart_to_cart(points, values, xrange, yrange, zrange):
"""
:param points: Data point locations (non-cartesian system)
:pa... |
import numpy as np
import pandas as pd
from scipy import interpolate
import json
from datetime import datetime, timedelta
import requests
import io
print("RUNNING!")
print(datetime.now())
def myconverter(o):
if isinstance(o, datetime):
return o.__str__()
url = "https://coronavirus.data.gov.uk/downlo... |
<reponame>andreamatt/DM-Count
from typing import List
from scipy.io import loadmat
from PIL import Image
import numpy as np
import os
from joblib import Parallel, delayed
import shutil
from preprocess.util import ImageInfo, cal_new_size, printStats, random_phase
def generate_data(img_path, min_size, max_size):
im... |
<reponame>rokoatcoffee/py-pi<filename>pi.py
#!/usr/bin/python3.7
import argparse
from random import random
from statistics import mean, stdev
def pi() -> float:
'''
pi function uses a million points to approximate the value of π.
This is done by summing a circle's area. As points are randomly
scattered inside... |
#
# Copyright © 2020 United States Government as represented by the Administrator
# of the National Aeronautics and Space Administration. No copyright is claimed
# in the United States under Title 17, U.S. Code. All Other Rights Reserved.
#
# SPDX-License-Identifier: NASA-1.3
#
"""Sky backgrounds."""
from importlib imp... |
<reponame>RockWenJJ/ierg5350-assignment-2021
"""
This file implements the some helper functions.
Note that many codes in this file is not written by us. The credits go to the
original writers.
-----
*2021-2022 1st term, IERG 5350: Reinforcement Learning. Department of Information Engineering,
The Chinese University ... |
# -*- coding: utf-8 -*-
"""Header here."""
import numpy as np
import scipy.stats as sps
import sys
import os
import copy
#the following imports are sloppy until a module is actually built...
from balldroptestfuncs import balldropmodel_linear,\
balldropmodel_grav, balldroptrue
SCRIPT_DIR = os.path.dirname(os.path.r... |
<filename>energyPATHWAYS/util.py
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 08 10:12:52 2015
@author: <NAME> & <NAME>
Contains unclassified global functions
"""
from energyPATHWAYS.error import ColumnNotFound
import pandas as pd
import os
import numpy as np
from collections import defaultdict, MutableSet
import... |
<filename>rnnhearer/visualization.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import interp
from keras.callbacks import History
from sklearn.metrics import auc, confusion_matrix, roc_curve
from typing import Any, Dict, List, Union
from .data_manipulation im... |
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# License: BSD (3-clause)
import numpy as np
from scipy import linalg
from .fiff.constants import FIFF
from .fiff.open import fiff_open
from .fiff.tree import dir_tree_find
from .fiff.tag import find_tag
#
# These fiff definitions are not needed elsewhere
#... |
<filename>backend/app/face_searcher.py<gh_stars>1-10
import pickle
import json
import numpy as np
from scipy.spatial import distance
class face_searcher:
def __init__(self):
with open("embeddings.pickle", "rb") as handle:
self.d = pickle.load(handle)
self.list_items = []
for it... |
<gh_stars>100-1000
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from scipy.spatial import distance
def finding_text_similarity_using_cosine_distance(s1, s2):
# sentences to list
allsentences = [s1, s2]
# Using Count Vectorizer
count_vectorizer = CountVectorize... |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""Script for data pre-processing."""
import tensorflow as tf
import numpy as np
import cv2
from PIL import Image
import os
from scipy.io import loadmat,savemat
from renderer import face_decoder
from training.networks_recon import R_Net
from pr... |
"""
Classes for implementing prior probabilities in the context of Bayesian
modelling and inference.
"""
# std libs
import numbers
import functools as ftl
# third-party libs
import numpy as np
from scipy import stats
from scipy.stats._distn_infrastructure import rv_frozen
# local libs
from obstools.modelling.utils i... |
<filename>old/loaders/transform.py
import abc
import librosa
import numpy as np
import os
import random
import scipy.io.wavfile as sio
import subprocess
import tempfile
import types
from loaders.dataset import manager
# replacement for os.tempnam
def tempnam():
f = tempfile.NamedTemporaryFile(delete = False, dir ... |
import math
import numpy as np
import json
from scipy.spatial import distance
import copy
import os
def term_freq (document):
dict_tf = dict()
total_word = 0
for word in document:
if word.strip() != "":
total_word += 1
if word not in dict_tf:
dict_tf[wor... |
<gh_stars>0
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats
import matplotlib.gridspec as gridspec
# mpl.use('TkAgg')
# create base data
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(32000,200000,3650),
np.random.norma... |
import numpy as np
from scipy.sparse import csr_matrix, identity
def div1D(k, m, dx):
""" Computes a m+2 by m+1 one-dimensional mimetic divergence operator
Arguments:
k (int): Order of accuracy
m (int): Number of cells
dx (float): Grid step size
Returns:
:obj:`ndarray` co... |
import numpy as np
from SafePDP import SafePDP
from SafePDP import PDP
from JinEnv import JinEnv
from casadi import *
import scipy.io as sio
import matplotlib.pyplot as plt
import time
import random
# --------------------------- load environment ----------------------------------------
env = JinEnv.Rocket()
Jx, Jy, J... |
<filename>model.py
import csv
from scipy import ndimage
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import cv2
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Cropping2D
from keras.layers i... |
import _benchmark
import io
import sys
import os.path as path
import math
import subprocess
import numpy as np
import pandas as pd
import scipy.stats
from table import PairTable, SummaryTable
thisdir = path.dirname(path.realpath(__file__))
tabledir = path.join(thisdir, '..', 'latex', 'report', 'tables')
figuredir =... |
<reponame>Jvanrhijn/tsp-local-search
import numpy as np
import scipy.linalg as linalg
from util.sinkhorn_knopp import SinkhornKnopp
np.random.seed(4554365)
def generate_stochastic_matrix(n, iter_max=10):
mat = np.random.rand(n, n)
return mat/np.sum(mat, axis=0)[:, None]
def sort_by_statistic_average(ma... |
<filename>ianalysis.py
"""This is used to analyze the predictions when adding interventions. only return the gamma & eta score. only aggregrate by [subj, subj_intv]
"""
import sys
import argparse
import json
import scipy
from scipy import stats
import numpy as np
import itertools
import collections
import math
# sys.p... |
<gh_stars>1-10
import os
import os.path as osp
import sys
import numpy as np
import random
import collections
import torch
import torchvision
from scipy.ndimage import zoom
import cv2
from torch.utils import data
from cues_reader import CuesReader
class VOCDataSet(data.Dataset):
def __init__(self, ... |
<filename>fracdiff/examples.py
"""
From <NAME>. 5
In practice, I suggest you experiment with the following transformation of your
features: First, compute a cumulative sum of the time series. This guarantees that
some order of differentiation is needed. Second, compute the FFD(d) series for var-
ious d ∈ [0, 1]. Third... |
<gh_stars>1-10
"""
Data quality routines
Data quality functions
======================
- :py:meth:`good_channels`
Issues
======
Auxiliary functions are still lacking documentation.
"""
import numpy as np
import ch_util.ephemeris as ch_eph
from ch_util import andata
from ch_util import tools
from ch_util import ... |
"""
==============================================================
Curve fitting: temperature as a function of month of the year
==============================================================
We have the min and max temperatures in Alaska for each months of the
year. We would like to find a function to describe this y... |
<gh_stars>1-10
import numpy as np
from scipy.optimize import minimize, rosen
class WF:
def __init__(self):
pass
def f(self, opt, pos):
return rosen(opt)
wf = WF()
opt = [1.3, 0.7, 0.8, 1.9, 1.2]
pos = np.random.rand(10, 3)
res = minimize(wf.f, opt, args=(pos,), method='Nelder-Mead', tol=1E... |
<filename>Implementations/gama.py
# Como executar:
# $ python gama.py img_1.tif saida <gama>
# <gama> é um número float, maior do que 0.
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
from skimage import exposure
def loadImg(arg):
return misc.imread(arg)
# Lê a imagem a part... |
<gh_stars>0
"""
Implements a Bot to play connect4.
"""
import random
from copy import deepcopy
from statistics import mean
from . import c4
class Quatro:
"""Represents a Bot that can play connect4"""
def __init__(self, difficulty):
"""Initializes the Bot
Arguments:
- board: handle t... |
import glob
import matplotlib.pylab as plt
import numpy as np
import math
import sys
import timeit
import cv2
import scipy.spatial.distance
import json
import get_maps
import preprocessing
import filtering
import descriptor
import template
import minutiae_AEC
import show
import enhancement_AEC
import os
from skimage im... |
<reponame>enijkamp/CoopNets-1<filename>util/images.py<gh_stars>0
import math
import numpy as np
import scipy.misc
def cell2img(cell_image, image_size=100, margin_syn=2):
num_cols = cell_image.shape[1] // image_size
num_rows = cell_image.shape[0] // image_size
images = np.zeros((num_cols * num_rows, image... |
<gh_stars>0
import torch
import torch.nn.functional as F
import scipy.optimize
import seaborn as sn
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
def per_sample_hungarian_loss(sample_np):
row_idx, col_idx = scipy.optimize.linear_sum_assignment(sample_np)
return row_idx, col_idx
def hungar... |
<gh_stars>0
import pandas as pd
import numpy as np
import scipy.stats as ss
import scipy
import pandas as pd
import numpy as np
import math
def compute_drawdown(return_series: pd.Series):
"""
Takes a time series of asset returns
Returns a DataFrame with columns for wealth index, peaks, and drawdons
... |
"""Implementations of the IPFP algorithm to solve for equilibrium and do comparative statics
in several variants of the `Choo and Siow 2006 <https://www.jstor.org/stable/10.1086/498585?seq=1>`_ model:
* homoskedastic with singles (as in Choo and Siow 2006)
* homoskedastic without singles
* gender-heteroskedastic: w... |
<reponame>modichirag/21cmhod<filename>code/plotting/plot_summary.py
#!/usr/bin/env python3
#
# Plots the power spectra and Fourier-space biases for the HI.
#
import numpy as np
import sys, os
import matplotlib.pyplot as plt
from scipy.interpolate import LSQUnivariateSpline as Spline
from scipy.interpolate import Interp... |
# coding: utf-8
# In[1]:
from skimage import segmentation, filters, measure
from skimage.feature import canny
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
import cv2
import numpy as np
import os
plt.set_cmap(plt.gray())
# get_ipython().magic('matplotlib inline')
# In[2]:
# fp = '../data/Z3_I... |
import tensorflow as tf
import numpy as np
import sys
import logging as log
class KernelND(object):
'''Base class for kernels in ND.
ndims : int
the number of dimensions of input
'''
def __init__(self,_hyperparams={},_hyperparams_bounds={},use_initializer=True,**kwargs):
self.use_initia... |
<filename>pydca.py
# Copyright 2020 terminus data science, LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by app... |
<filename>tests/create_reference_data.py
# coding: utf-8
# /*##########################################################################
# Copyright (C) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated document... |
import numpy as np
import matplotlib.pyplot as plt
from gym_flock.envs.spatial.utils import _get_pos_diff
from scipy.spatial import Delaunay
from pathlib import Path
def in_obstacle(obstacles, px, py):
"""
Check if query point is within any of the rectangular obstacles
:param obstacles: list of rectangula... |
<reponame>evgenykurbatov/kb21-hotjup-migration-adv<gh_stars>0
# -*- coding: utf-8 -*-
import sys
import numpy as np
from numpy import pi, sqrt, exp, sin, cos, tan, log, log10
import scipy as sp
import scipy.integrate
import ctypes
import const
from pringle import Pringle
##
## The model
##
class Model(object):
... |
<reponame>vntborgesjr/Python-Fundamentals<filename>Introduction-to-Python/03-Functions.py
# ---------------------------------------------------
# Introductio to Python - Functions
# 18 set 2020
# VNTBJR
# ---------------------------------------------------
#
# Load packages ---------------------------------------... |
<filename>BM/4.Feature_and_Filter_Analysis/4.B.Filter_analysis.py<gh_stars>1-10
#!/usr/bin/python
import glob
import sys
from scipy.stats import hmean
#>>> hmean([1, 4])
tool = sys.argv[1]
weight = float(sys.argv[2])
out = open("/data/project/MRS/5.Combined_Analysis/Filter/Fil_ana." + tool + ".txt", "w")
types = ['mx'... |
<filename>utils/rotation_utils.py
import constants
import functools
from scipy.spatial.transform.rotation import Rotation
from custom_types import *
def quat_to_rot(q):
shape = q.shape
q = q.view(-1, 4)
q_sq = 2 * q[:, :, None] * q[:, None, :]
m00 = 1 - q_sq[:, 1, 1] - q_sq[:, 2, 2]
m01 = q_sq[:, ... |
<reponame>dpopadic/ml-res
import numpy as np
# ----- heights data
x_heights = np.array([ 51.25, 53.75, 56.25, 58.75, 61.25, 63.75, 66.25,
68.75, 71.25, 73.75, 76.25, 78.75, 81.25, 83.75,
86.25, 88.75, 91.25, 93.75, 96.25, 98.75, 101.25... |
# Statistical tests on ratios
#
# <EMAIL>
import numpy as np
import numba
import copy
import scipy.stats as stats
def prodratio_eprop(A, B, sigmaA, sigmaB, sigmaAB=0, mode='ratio'):
"""
Error propagation (Taylor expansion) of product A*B or ratio A/B
Args:
A : Value A
B : Value B
sigmaA : 1 sigma uncerta... |
<gh_stars>0
import numpy as np
import pandas as pd
import matplotlib
import numpy.ma as ma
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)
import cartopy.crs as ccrs
import cartopy.util as cutil
from matplotlib.ticker import (... |
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