text string |
|---|
import os
import sys
import pickle
import numpy as np
import pandas as pd
import scipy.sparse as sp
from pathlib import Path
reaction_num = int(sys.argv[1])
with open('data/candidates_single.txt') as f:
candidates_smis = [s.rstrip() for s in f.readlines()]
n_candidates = len(candidates_smis)
candidates_smis = np.... |
"""pytest fixtures for bac_advanced_ml.solvers unit tests.
.. codeauthor:: <NAME> <<EMAIL>>
"""
import numpy as np
import pytest
import scipy.linalg as linalg
from sklearn.datasets import make_spd_matrix
@pytest.fixture(scope="session")
def convex_quad_min(global_seed):
"""Returns objective, gradient, Hessian, ... |
<reponame>sahilg1998/robotics-toolbox-python<filename>roboticstoolbox/mobile/vehicle.py
"""
Python Vehicle
@Author: <NAME>
TODO: Comments + Sphynx Docs Structured Text
TODO: Bug-fix, testing
Not ready for use yet.
"""
from abc import ABC, abstractmethod
from numpy import disp
from scipy import integrate
from scipy imp... |
<gh_stars>0
import json
import logging
import numpy as np
from PIL import Image
from scipy.spatial.transform import Rotation
class DatasetLoader:
def load(self, path, args):
parts_path = path / args.models_dir
part_ids = self.load_part_ids(parts_path)
parts = dict((part_id, self.load_par... |
<filename>ASGama CTF/[CRYPTO] RSA/solver.py
from binascii import *
from Crypto.Util.number import *
from sympy import *
n = 15719648961151124406259408275130518526692619002644355904409352031187
e = 65537
c = 13328445333056206565801100615758037997272587858304987765122445734130
p = 3852454912858673504993326758109153
q = ... |
import numpy as np
from numpy.lib.npyio import save
import vtk
from vtk.util.numpy_support import vtk_to_numpy
from vtk.util.numpy_support import numpy_to_vtk
import matplotlib.tri as mtri
import os
import sys
from scipy.interpolate import griddata
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.fi... |
from fractions import gcd
|
import os
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from scipy.io import loadmat
from utilities.data_processing import DataProcessing
from keras.models import load_model
from pipeline.models import DenoisingAutoencoder
def trainNNchr(inputM, targetM, params, dp_ob):
print('Training autoen... |
<filename>tests/frbpoppy_frbcat.py
"""Plot the DM distribution obtained with frbpoppy against frbcat results."""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredText
from scipy.stats import ks_2samp
from frbpoppy import Survey, Frbcat, pprint
from quick import get_cosmic_po... |
from __future__ import print_function, absolute_import
from sklearn.base import BaseEstimator, ClassifierMixin
from scipy import stats
import numpy as np
import itertools
from scipy.stats import sem
class VotingEnsemble(BaseEstimator, ClassifierMixin):
def __init__(self, models, voter='majority', use_proba... |
<reponame>FlantasticDan/mocapBoston
"""Tools for detecting and identifying markers in images."""
from statistics import mode
import os
import sys
import cv2
import numpy as np
from shapely.geometry.point import Point
from shapely.geometry.polygon import Polygon
from shapely.geometry import LineString
# File Manageme... |
<reponame>ZhangjieLyu/PHBS_AppliedEconometric_SS2020
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 17 14:31:10 2020
@author: Robert(factor computation), Mumu(data filter)
"""
#%% load library
# built-in library
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import collections
impor... |
from py_db import db
from decimal import Decimal
import NSBL_helpers as helper
from datetime import datetime
from time import time
import numpy as np
import argparse
import math
from scipy.stats import norm as NormDist, binom as BinomDist
# script that produces in-playoffs probability charts
db = db('NSBL')
def p... |
<gh_stars>0
r"""
Wind stress from WRF atmospheric model
wind stress is defined as
.. math:
tau_w = C_D \rho_{air} \|U_{10}\| U_{10}
where :math:`C_D` is the drag coefficient, :math:`\rho_{air}` is the density of
air, and :math:`U_{10}` is wind speed 10 m above the sea surface.
In practice `C_D` depends on the w... |
<filename>examples/basics/pamap2_lstm.py<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: mnist-convnet.py
import os
import argparse
import tensorflow as tf
import numpy as np
import csv
import pandas as pd
from scipy import stats
# Just import everything into current namespace
from tensorpack impor... |
# -*- coding: utf-8 -*-
"""
Transform clusters into TableRegion/TableCells and populate them with TextLines
Created on August 2019
Copyright NAVER LABS Europe 2019
@author: <NAME>
"""
import sys, os
from optparse import OptionParser
from collections import defaultdict
from lxml import etree
import numpy as np
impo... |
<reponame>idi92/tesiAO<filename>tesi_ao/mems_command_linearization.py<gh_stars>0
import numpy as np
from scipy.interpolate import CubicSpline
from scipy.optimize import fsolve
from scipy.interpolate import interp1d
from astropy.io import fits
class MemsCommandLinearization():
def __init__(self,
... |
<filename>notebooks-text-format/dcgan_fashion_tf.py
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.3
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-i... |
import unittest
import numpy as np
from tffm import TFFMClassifier
from scipy import sparse as sp
import tensorflow as tf
class TestFM(unittest.TestCase):
def setUp(self):
# Reproducibility.
np.random.seed(0)
self.X = np.random.randn(20, 10)
self.y = np.random.binomial(1, 0.5, s... |
import numpy as np
class diagnostic(object):
"""
Take in a covariance matrix and perform some diagnostics on it.
:param C:
2D array of a covariance matrix
"""
def __init__(self, C):
C = np.array(C)
if C.ndim < 2:
raise Exception("Covariance matrix has too few d... |
import numpy as np
import scipy.sparse as ss
__all__ = [
'broadcast_to', 'broadcast_shapes', 'ufuncs_with_fixed_point_at_zero',
'intersect1d_sorted', 'union1d_sorted', 'combine_ranges', 'len_range'
]
def _broadcast_to(array, shape, subok=False):
'''copied in reduced form from numpy 1.10'''
shape = tuple(... |
<filename>kernel_regression.py
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.gaussian_process.kernels import RBF
from sklearn.linear_model import LinearRegr... |
<filename>platipy/imaging/projects/cardiac/utils.py
# Copyright 2020 University of New South Wales, University of Sydney, Ingham Institute
# 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
# ... |
<filename>pymc/sandbox/parse_winbugs.py
"""
Syntax needed:
arrays: x[n:m], x[], x[,3] just translate straight to numpy arrays
repeated structures:
for (i in a:b) {
list of statements to be repeated for increasing values of loop-variable i
}
Replace '.' with '_'
Note will allow some disallowed WinBugs syntax bec... |
import glob
from functools import partial
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import albumentations as albu
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import scipy
from hydra.utils impor... |
<reponame>christopherlovell/orientation_bias<filename>bias.py
import numpy as np
from scipy.stats import truncnorm, binned_statistic
from scipy.integrate import quad
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
class orientation_bias():
def __init__(self, lamb... |
<reponame>lambdaofgod/sparse_recovery
import numpy as np
np.seterr(all='raise')
from numpy.linalg import inv
from scipy import sparse as sp
from sklearn.linear_model import Lasso
from sparse_recovery.solvers import *
from text_embedding.documents import *
from text_embedding.features import *
# NOTE: LASSO with defau... |
import numpy as np
import scipy.sparse as sp
import torch
def raw_eeg_pick(raw):
ch_names = raw.info["ch_names"]
drop_ch_list = []
marker_list = ['T1', 'T2', 'STI', 'EMG', 'ECG', 'X', 'DC', 'Pulse', 'Wave', 'Mark', 'Sp', 'SP', 'EtCO', 'E', 'Cz']
# For some trials, Cz is problematic#
for ch_name in ... |
import time
from datetime import datetime
from os.path import join as path_join
from math import log, floor
import click
import matplotlib
import matplotlib.ticker as ticker
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['mathtext.fontset'] = 'cm'
import matplotlib.pyplot as plt
import matplotlib.p... |
<gh_stars>0
import numpy as np
import astropy.coordinates as coord
import astropy.units as un
import astropy.constants as const
import matplotlib.pyplot as plt
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
from galpy.actionAngle import actionAngleStaeckel, actionAngleAdiabatic, estimateDelta... |
""" Created on Fri Aug 24 11:47:20 2018
Author: <NAME>
This module determines total power of multijunction cells and also saves data for each bandgap sampled."""
# Import libraries.
import numpy as np
from scipy.optimize import minimize_scalar
import single_cell_power, spectral, sunlight
def save_r... |
import numpy
import radiotelescope
import powerbox
from scipy import interpolate
import sys
sys.path.append('../../../redundant_calibration/code/SCAR')
from single_dipole_PS_impact import main as old_code
from Single_Dipole_PS_Impact_OO import main as new_code
from radiotelescope import RadioTelescope
from skymode... |
import os
import logging
import numpy as np
import torch.utils.data
import scipy.ndimage.interpolation as interp
import skimage.transform
import warnings
from utils import yuv
class DownSample:
def __init__(self, down_resolution):
self.down_resolution = down_resolution
def __call__(self, Y, U, V):
... |
import time
import cv2
import numpy as np
import json
import tensorflow as tf
from scipy.spatial import distance as dist
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from .model.yolo import Yolo
with open("config.json", "r") as file:
config = json.load(file)
class YoloSocialDi... |
import argparse
from io import BytesIO as _BytesIO
from pathlib import Path
import numpy as _np
import pandas as _pd
from datetime import datetime
from scipy.interpolate import InterpolatedUnivariateSpline
from gn_lib.gn_io.common import path2bytes
import wget
def gpsweekD(yr,doy):
"""
Convert year, day-of... |
<reponame>wdpozzo/bbh_cosmology<gh_stars>0
#!/usr/bin/env python
import unittest
import numpy as np
import cpnest.model
import sys
import os
from optparse import OptionParser
import itertools as it
import cosmology as cs
import readdata
from scipy.misc import logsumexp
class CosmologicalModel(cpnest.model.Model):
... |
"""
_ _
_ __ ___ (_) ___ | | multimedia &
| '_ ` _ \ | |/ __|| | information
| | | | | || |\__ \| | security
|_| |_| |_||_||___/|_| lab
__________________________________________________
|__________________________________________________|
misl.ece.d... |
import matplotlib.pyplot as plt
import numpy as np
from scipy import special
from random import randint
import sys
try:
f1 = str(sys.argv[1])
trace_size = int(sys.argv[2])
zipf_a = float(sys.argv[3])
fw1 = open(f1, 'w')
except IndexError:
print("Error: no Filename")
sy... |
<reponame>SchiffFlieger/semantic-segmentation-master-thesis
import os
import cv2
import numpy as np
import scipy.spatial as sp
from scripts.common.constants import LABEL_RGB_VALUES
def validate_image(image):
segments = []
for col in LABEL_RGB_VALUES:
segments += [image == col]
return np.all(np.... |
import argparse
from argparse import RawTextHelpFormatter
import os
import sympy as sp
import numpy as np
from numpy import linalg as npla
import matplotlib.pyplot as plt
import pylab as pl
from scipy.integrate import odeint
from _plane_sys_args_aux import add_xyt_params, add_plot_params
from _homo_plane_sys_analyzer_... |
import re
import cv2
import numpy as np
import os
import matplotlib.pyplot as plt
from scipy.special import comb, perm
def calculate_2():
r_3s = []
delta = 0.01
for ploy in datas:
n = 3
c_x, c_y = ploy[0]
r_3_sum = 0
remain = 0 # 每段线剩下的长度
dots = []
for i in... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os, sys
import random
import numpy as np
from scipy.stats import entropy
import gym
from gym import spaces
################## Global Params ####################################
BOIDS = 10
SPEED_LIMIT = 100
MAX_BOUNDARY = 1000
BOUNDARY_FORCE = 50
DT = 0.07
MODE = "... |
<filename>apps/core/templatetags/docutils_extensions/directives.py
from __future__ import division
from __future__ import unicode_literals
import codecs
import hashlib
import json
import os
import posixpath
import random
import re
import shutil
import yaml
from subprocess import Popen, PIPE
from PIL import Image
fro... |
<filename>venv/lib/python3.9/site-packages/vmo/analysis/segmentation.py<gh_stars>1-10
import librosa
import scipy
import scipy.linalg as linalg
import scipy.signal as sig
import numpy as np
import sklearn.cluster as sklhc
import scipy.cluster.hierarchy as scihc
from collections import OrderedDict
from .analysis import ... |
<reponame>WangXinyan940/powerfit<filename>scripts/rot_search.py
from __future__ import division
from argparse import ArgumentParser, FileType
from time import time
import os
import numpy as np
from scipy.ndimage import laplace
from powerfit import Volume, Structure, quat_to_rotmat, proportional_orientations, determin... |
<filename>old_modules/recognize.py
#------------------------------------------------------------
# SEGMENT, RECOGNIZE and COUNT fingers from a video sequence
#------------------------------------------------------------
# organize imports
import cv2
import imutils
import numpy as np
from sklearn.metrics import... |
<reponame>ml-research/MoRT_NMI
from numpy.random import randn
import numpy as np
from scipy.stats import spearmanr, pearsonr
fname = "./data/parsed_yes_no_BERTBias.csv"
sentences_ = list()
actions = list()
with open(fname, "r") as f:
for i, line in enumerate(f.readlines()):
if i == 0:
contin... |
import numpy as np
import pandas as pd
from numpy import ndarray
from numpy.polynomial.polynomial import polyfit, polyval
from pandas import DataFrame
from scipy.interpolate import UnivariateSpline as USpline
from scipy.optimize import curve_fit
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from... |
#!/usr/bin/env python
from __future__ import division
import subprocess as sp
import os
import io
import sys
import re
from copy import deepcopy
import psycopg2
import psycopg2.extras
import subprocess
from operator import itemgetter
from collections import OrderedDict, Counter
import cv2
from fractions import Fractio... |
from thermostat.stats import combine_output_dataframes
from thermostat.stats import compute_summary_statistics
from thermostat.stats import summary_statistics_to_csv
from .fixtures.thermostats import thermostat_emg_aux_constant_on_outlier
from thermostat.multiple import multiple_thermostat_calculate_epa_field_savings_m... |
#Standard python libraries
import numpy as np
import os
import itertools
from scipy.sparse import csr_matrix, kron, identity
from .eigen_generator import EigenGenerator
from .eigenstates import LadderOperators
class CalculateCartesianDipoleOperatorLowMemory(EigenGenerator):
"""This class calculates the dipole ope... |
<gh_stars>1-10
import sys
import os
import torch
import pdb
import pickle
import argparse
import configparser
import matplotlib.pyplot as plt
from scipy.io import loadmat
sys.path.append("../src")
import plot.svGPFA.plotUtilsPlotly
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("pEstNu... |
<reponame>NalediMadlopha/google-python-exercises
"""
Convolution (using FFT, NTT, FWHT), Subset Convolution,
Covering Product, Intersecting Product
"""
from __future__ import print_function, division
from sympy.core import S
from sympy.core.compatibility import range, as_int
from sympy.core.function import expand_mul
... |
<reponame>substandard-hacks/drone-drink-delivery-system
import bluepy
import binascii
import time
from statistics import stdev, mean
from bluepy.btle import DefaultDelegate, BluepyHelper, BTLEException, ScanEntry
class Scanner(BluepyHelper):
def __init__(self, iface=0):
BluepyHelper.__init__(self)
... |
<reponame>nandasanchit17/mgc-django<filename>mysvm/feature.py<gh_stars>100-1000
# feature.py
# Author: <NAME>
# Date: Sun Apr 28 2017
# Modified on : Tue May 2 16:50:36 IST 2017
import numpy as np
import scipy.io.wavfile
from python_speech_features import mfcc
import glob
import collections
from pydub import AudioSeg... |
<filename>scripts/classical/small_verify/plot_tomove.py
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
from small_verify_fncs import simulate, get_coverage_estimates
import sys
sys.path.insert(0,'../../../undetected_extinctions') # so I can import the undetected extinctions package
fro... |
#!/usr/bin/env python
"""Calculate regionprops of segments.
"""
import sys
import argparse
# conda install cython
# conda install pytest
# conda install pandas
# pip install ~/workspace/scikit-image/ # scikit-image==0.16.dev0
import os
import re
import glob
import pickle
import numpy as np
import pandas as pd
fro... |
<filename>examples/FFTHvsFEM/FFTH_GaNi.py
import numpy as np
import scipy.sparse.linalg as sp
import itertools
from functions import get_matinc, material_coef_at_grid_points, enlarge, square_weights
# PARAMETERS
dim = 2 # dimension (works for 2D and 3D)
N = 5*np.ones(dim, dtype=np.int) # number of grid points
ph... |
import scipy
import os
import cv2
import numpy as np
from map import HeatMap
from sklearn.metrics import jaccard_similarity_score
from timer import Timer
from gc_executor import GC_executor
def generate_objectness_map(heatMapObj, image, hr_method='interpolation', use_gradcam=True):
"""
Generates the objectnes... |
# External modules
import numpy as np
from scipy.special import digamma
# Own modules
from expfam.misc import log_mvar_beta
#
# Parameter mappings
#
def map_from_eta_to_alpha(eta):
"""Map parameters from eta-space to alpha-space."""
return eta
def map_from_alpha_to_eta(alpha):
"""Map parameters from ... |
<filename>Jueves/libro.py
'''
Clase Libro
'''
import statistics
class Libro:
titulo = ""
autor = ""
precio = 0
def __init__(self,titulo,autor,precio):
self.titulo = titulo
self.autor = autor
self.precio = precio
libros = []
archivo = open("libros.csv", "r")
for renglon in arc... |
import datetime
import os
from scipy.io import netcdf
BG_FIRST = 0
BG_LAST = 251
LIGHT_SPEED = 3E8
def add_general_header_info(measurement, CHANNEL_IDs):
last_profile = measurement['data'][len(measurement['data']) - 1]
last_profile['stop'] = last_profile['start'] + \
int(last_profile['header']['sho... |
<reponame>eddy-geek/interpret
# Copyright (c) 2019 Microsoft Corporation
# Distributed under the MIT software license
# TODO: Test EBMUtils
from math import ceil, isnan
from .internal import Native, Booster, InteractionDetector
# from scipy.special import expit
from sklearn.utils.extmath import softmax
from sklearn.... |
<reponame>richford/hbn-pod2-qc
#!/opt/conda/bin/python
import argparse
import dask.dataframe as dd
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import os.path as op
import pandas as pd
import pingouin as pg
import re
import s3fs
import seaborn as sns
import shap
from glob import glob
from ... |
from .. import util
from ..probabilities import pulsars, mass
from ..core.data import Observations, Model
import h5py
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
import matplotlib.colors as mpl_clr
import astropy.visualization as astroviz
__all__ = ['ModelVisualizer', 'CIModelVisuali... |
""" This is where input will be taken to form suggestions of most relevant charities """
from heapq import nsmallest
from scipy import spatial
from .models import Organization
import basilica
import json
import decimal
BASILICA = basilica.Connection('684f0990-8710-309c-2f92-4d2b27b3b8ad')
class DecimalEncoder(json.J... |
import numpy as np
import keras.backend as K
from scipy.ndimage.interpolation import map_coordinates
from deform_conv.deform_conv import (
tf_map_coordinates,
sp_batch_map_coordinates, tf_batch_map_coordinates,
sp_batch_map_offsets, tf_batch_map_offsets
)
def test_tf_map_coordinates():
np.random.seed... |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 27 17:24:31 2012
@author: eba
"""
from numpy import *
from scipy import *
from matplotlib.pyplot import *
from MHFPython import *
from plotGaussians import *
mean = Matrix31()
assign(mean,[0,0,0])
widths = Matrix31()
assign(widths,[20,20,1])
result = GH3list()
maxVars... |
<reponame>drunkcoding/model-inference<filename>tests/confidence/local_known_inference.py
import copy
import functools
import gc
from hfutils.constants import TASK_TO_LABELS
from seaborn.distributions import histplot
import torch
import logging
import numpy as np
from transformers.data.data_collator import (
DataCol... |
<filename>src/models/architectures/tools/interp_utils.py
import torch
import torch.nn as nn
import numpy as np
from scipy.interpolate import interp1d
# from lib.utils.geometry import rotation_matrix_to_angle_axis, rot6d_to_rotmat
# from lib.models.smpl import SMPL, SMPL_MODEL_DIR, H36M_TO_J14, SMPL_MEAN_PARAMS
from .in... |
"""
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
Code for point-cloud-based motion estimation
Input: {x, y, z} localization estimate, spike times, amplitudes, geometry
Output: point-cloud-based motion estimation
"""
import numpy as np
import matplotlib.pyplot as plt
f... |
#! /usr/bin/env python
"""
Generate eyediagram and save it to eye.png
"""
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pylab as plt
import matplotlib
from scipy.signal import lsim, zpk2tf
font = { 'size' : 19}
matplotlib.rc('font', **font)
def plot(index):
ts = 1e-12 # time res... |
<reponame>DushyantChauhan/ACL-2020-MUStARD-Extension
import numpy as np, json
import pickle, sys, argparse
import keras
from keras.models import Model
from keras import backend as K
from keras import initializers
from keras.optimizers import RMSprop
from keras.utils import to_categorical
from keras.callbacks import Ear... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as optimize
def sigmoid(z):
a = 1 + np.exp(-z)
return 1 / a
def cost_function(theta, X, y):
m = len(X)
X = np.concatenate((np.ones((m, 1)), X), axis=1)
# Calculate Cost J
h_theta = sigmoid(np.dot(X, theta[np.newaxis].T)... |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.mlab as ml
filename = 'highMT_l_40000_cb85a9498bae460cbdb61a1a8df1a462'
dat = np.loadtxt('data/'+filename+'.txt' , delimiter=',', skiprows=1, \
usecols = (1,3,4,7), unpack=False... |
import numpy as np
import math
import itertools
from tqdm import trange
from elasticsearch.helpers import bulk
from elasticsearch import Elasticsearch
from sentence_transformers import SentenceTransformer
from scipy.spatial.distance import cdist
from index_setup import verify_and_configure_index
class DialogEval(obje... |
<filename>submissions/available/NNSlicer/NNSlicer/logics/save_logics.py
import csv
import random
from functools import partial
from typing import Callable, Optional
from pdb import set_trace as st
import os
import random
import pandas as pd
from typing import Any, Callable, Dict, Iterable, List, Tuple, Union
import nu... |
"""
Posterior predictive check. Examine the veracity of the winning model by
simulating data sampled from the winning model and see if the simulated data
'look like' the actual data.
"""
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
# Specify known va... |
<gh_stars>1-10
import sys
import os.path
from part1 import State, parse_initial_state, parse_rules, simulate, sum_state
from typing import List, Tuple
from statistics import mean, variance
Sample = Tuple[int, int]
def hash_state(state: State) -> str:
"""Create a string representing the pattern of plants
**st... |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 28 11:44:27 2016
@author: yungkuo
"""
import numpy as np
from lmfit.models import PolynomialModel
import scipy.ndimage as ndi
import pandas as pd
import ROI
import matplotlib.pyplot as plt
from IPython.display import display, FileLink
def plot_QCSE_report(movie, pt, sca... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""Game data transformers."""
from itertools import chain
import numpy as np
import pandas as pd
from pytility import arg_to_iter, clear_list, parse_int
from scipy.sparse import csr_matrix
from sklearn.compose import make_column_transformer
from sklearn.feature_extraction.text... |
import os
import numpy as np
from ddpg import DDPG
from gym_duckietown.simulator import Simulator
import torch
from statistics import median
import matplotlib.pyplot as plt
env = Simulator(seed=123, map_name="zigzag_dists", max_steps=5000001, domain_rand=True, camera_width=640,
camera_height=480, accep... |
<gh_stars>0
# File produced automatically by PNCodeGen.ipynb
from scipy.integrate import solve_ivp
import numpy as np
from numpy import dot, cross, log, sqrt, pi
from numpy import euler_gamma as EulerGamma
from numba import jit, njit, float64, boolean
from numba.typed import List
from scipy.interpolate import Interpola... |
<reponame>SimonFH/DM559
import numpy as np
from sympy import *
import sys
import math as math
from fractions import Fraction as f
np.set_printoptions(precision=3,suppress=True)
#def printm(a):
# """Prints the array as strings
# :a: numpy array
# :returns: prints the array
# """
# def p(x):
# retu... |
<reponame>mdnunez/electroencephalopy
# Copyright (C) 2016 <NAME>
#
# License: BSD (3-clause)
# # Record of Revisions
#
# Date Programmers Descriptions of Change
# ==== ================ ======================
# 03/26/16 <NAME> ... |
<filename>RandomForestClassifier/main.py
import argparse
from scipy.optimize import differential_evolution
from sklearn.ensemble import RandomForestClassifier
from imblearn.metrics import geometric_mean_score
import numpy as np
import pickle
with open('../X_train.pickle', 'rb') as f:
X_train = pickle.load(f)
w... |
from scipy import ndimage
import numpy as np
from nephelae.array import ScaledArray
from .FactoryBorder import FactoryBorder
from .MacroscopicFunctions import compute_cross_section_border, threshold_array
class BorderIncertitude(FactoryBorder):
def __init__(self, name, valueMap, stdMap):
super().__init__... |
################################################################################
#
# Model.py (c) <NAME>
# Insight Data Science Fellowship Program
# <EMAIL>
#
# Make and update model fit continuously based on new input data.
# Make projection based on current best price and compare with orig... |
"""
A class for calculating statistical validation:
in other words, surrogate analysis followed by a
one-sample t-test
"""
import numpy as np
from scipy.special import comb
from scipy import stats
import random
class StatisticalValidation():
def __init__(self):
self.random_p... |
<reponame>Madmalicius/PiR-repo
'''
Any questions ask Mikkel
'''
import os
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry.polygon import LinearRing
from scipy.spatial.distance import cdist
import math
from utm import utmconv
import pandas as pd
#FIELD OF VIEW OF CAMERA IN METERS
FIELD_OF_VIEW ... |
<reponame>MatthiasWunsch/python-geospatial-analysis-cookbook
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from shapely.geometry import LineString
from shapely.geometry import MultiLineString
from scipy.spatial import Voronoi
import numpy as np
class Centerline(object):
def __init__(self, inputGEOM, dist=0.5):
... |
<gh_stars>1-10
from __future__ import division
from scipy.stats import norm, expon
from scipy.linalg import det, inv
from allnorm import allnorm
from pandas import read_excel
from copulae import FrankCopula
import numpy as np
from math import pi
def allfrank(x, y, thetaInit=1.4):
sample = len(x)
# Convert t... |
# -*- coding: utf-8 -*-
"""AIproject.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1HwKwwDluRiWBNcTE5-f3wH_uCmB-bkB8
"""
#importing needed Libraries
import pandas as pd
import numpy as nmpy
from matplotlib import pyplot as plt
from sklearn.mo... |
import json
import numpy as np
import os
from scipy import spatial
import sys
import psycopg2
from psycopg2.extras import execute_batch
class Database:
def __init__(self, host, database, user, password):
self.host, self.database = host, database
self.user, self.password = user, password
try... |
# Reads an image from disk and scales and crops to match a target resolution and aspect ratio.
import os
from scipy import misc
# Specifies which is the largest size on any side of the picture. (Caters for portrait and landscape)
FIXED_MAX_DIMENSION = 500.0
# For each allocated class, save pictures in their containin... |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import sampler_new
import h5py
import matplotlib
matplotlib.use('Agg')
from matplotlib import rc
rc('font',**{'family':'serif','serif':'Computer Modern Roman','size':12})
rc('text', usetex=True)
from matplotlib import cm
import numpy... |
# -*- coding: utf-8 -*-
"""
@authors:
<NAME>
(<EMAIL>, <EMAIL>)
<NAME>
(<EMAIL>)
@version: beta 2.0
--Quick guide--
1. General info
2. MakeMyGate requirements
3. Installing python and modules
a. Anaconda distribution
b. Miniconda
c. Package manager
1. MakeMyGate is a program dedicated to vis... |
import numpy as np
import matplotlib.pyplot as plt
from scipy import integrate
import itertools, operator, random, math
from scipy.sparse.linalg import spsolve_triangular
from sklearn import linear_model
import pandas as pd
def random_sampling(data, porpotion):
sampled_data = np.empty(data.shape)
sampled_data[... |
# License: BSD 3 clause
from datetime import datetime
from typing import Tuple, List
import numpy as np
import pyspark.sql.functions as sf
from pandas.core.series import Series
from pyspark.sql import DataFrame
from scipy.sparse import csr_matrix
from scalpel.core.cohort import Cohort
from scalpel.drivers.base impor... |
<filename>yodotube/videoreader/models.py
from django.db import models
import requests
import json, statistics, ipfshttpclient, os
from django import forms
# Create your models here.
class UploadModel(models.Model):
def uploadVideo(video, title):
fd = os.path.basename(str(title));
with open(fd, 'wb... |
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