text string |
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<reponame>tansey/deep-dose-response<filename>python/step6_factorize_features.py<gh_stars>1-10
'''
Creates a binary matrix of biomarker features and runs a binary matrix
factorization routine on it. Code for factorizing the matrix is courtesy
of <NAME>. We use k=50 latent factors and run the model for 30 minutes
which a... |
<gh_stars>10-100
import matplotlib.pyplot as plt
import numpy as np
from scipy.linalg import expm, inv, eig
from sklearn.metrics import accuracy_score, plot_confusion_matrix
from sklearn.neural_network import MLPClassifier
from bayesian_decision_tree.classification import PerpendicularClassificationTree
def get_cova... |
import numpy as np
import scipy.stats
def L2_norm(x):
return np.sqrt(np.sum(np.square(x), axis=-1))
class Circle(object):
def __init__(self, ndim, r=1000, origin=0.0):
if np.isscalar(origin):
self.origin = origin * np.ones(ndim)
else:
self.origin = np.array(origin)
... |
#
# Packt Publishing
# Hands-on Tensorflow Lite for Intelligent Mobile Apps
# @author: <NAME>
#
# Section 5: Gesture recognition
# Video 5-3: Parameter study and data augmentation
#
from PIL import Image
import numpy as np
import scipy.misc
import os
def hotvector(vector,classes):
''' This function will transform a ... |
import pygad as pg
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import utils
import glob
from multiprocessing import Pool
filename = __file__
def plot(args):
halo = args[0]
definition = args[1]
print args
path = '/ptmp/mpa/naab/REFINED/%s/SF_X/4x-2phase/out/snap_%s_4x_???... |
<filename>arlobot_bringup/src/nodes/olddrivenode.py<gh_stars>0
#!/usr/bin/env python
"""
----------------------------------------------------------------------------------------------------
File: olddrivenode.py
Description: Provides implementation of the DriveNode responsible for:
* driving the moto... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import joblib
import yaml
from datetime import datetime
import os
import shutil
import seaborn as sns
import pickle
from pickle import dump
from scipy.signal import find_peaks
from sklearn import metrics
from sklearn.model_selection import cross_va... |
<reponame>johnbanq/modl
import scipy.sparse as sp
from numpy.testing import assert_equal
from modl.utils.recsys.cross_validation import ShuffleSplit
def test_shuffle_split():
X = [[3, 0, 0, 1],
[2, 0, 5, 0],
[0, 4, 3, 0],
[0, 0, 2, 0]]
X = sp.coo_matrix(X)
cv = ShuffleSplit(n_... |
<reponame>atsoukevin93/tumorgrowth<filename>codes/power_dichotomy_algorithm.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from fipy import *
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as la
from matplotlib import pyplot as plt
import parameterFunctions.immuneResponse as delt
import para... |
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import multivariate_normal
from mpl_toolkits.mplot3d import proj3d
from matplotlib.patches import FancyArrowPatch
from matplotlib import cm
from matplotlib import rc
__author__ = 'ernesto'
# if use latex or mathtext
rc('text', usetex=True)
rc('mathte... |
<gh_stars>0
import numpy as np
import time
import torch
import torch.nn as nn
import torch.autograd
import h5py
import torch.optim as optim
import scipy.io
from torch.autograd import Variable
import torch.optim as optim
from enum import Enum
from HeatEquation.Dataset.Baseline import HeatEquationDataset
from Schrodinge... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 20 14:34:15 2021
@author: tobrien
Script for re-analysing Ida's data based upon my script -- refer to this script
for nice comments on what each line of the script is doing!
Script that loops through different strains and does:
-First step of K... |
##
"""
The script implement three steps for refining the segmentation results based
on the fitted SSM.
Step 1: Close holes in regions dictated by the SSM
step 2: Remove unconnected components outside the span of the SSM
Step 3: Remove voxels that are further than the 95% of contour distances
Processed segmentation ma... |
<gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import matplotlib.gridspec as gridspec
import pandas as pd
import warnings
np.random.seed(1)
warnings.filterwarnings("ignore")
sns.set(palette="colorblind")
sys.path.insert(0, "../reports/code_blocks/")
import reflecto... |
<filename>structureimpute/explore/set_one_validate_null_to_another_validate.py<gh_stars>1-10
from __future__ import print_function
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="ticks")
sns.set_context("poster")
plt.rcParams["font.family"] = "Helvetica"
imp... |
# Copyright (c) 2016, <NAME>
# Licensed under the BSD 3-clause license (see LICENSE)
import numpy as np
import scipy.linalg as la
from .test_matrix_base import MatrixTestBase
from .kronecker import Kronecker
from .numpy_matrix import NumpyMatrix
from .matrix import Matrix
from .sum_matrix import SumMatrix
from .toepl... |
<filename>src/object_detection/Object_detection_image.py
import os
import uuid
import cv2
import numpy as np
import tensorflow as tf
import sys
import scipy
import scipy.misc
# This is needed since the notebook is stored in the object_detection folder.
from api_results.clasification_result import ClassificationResult
... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 10 12:18:45 2022
@author: maout
"""
import time
import torch
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from typing import Union
# GPU + autodiff library
from torch.autogra... |
"""Conversion functions for weather radar and rainfall data."""
from numpy import isfinite, log, ubyte
from scipy.ndimage import gaussian_filter
from skimage.exposure import equalize_hist, rescale_intensity
def dBZ_to_ubyte(I, dBZ_min=-10.0, dBZ_max=50.0, filter_stddev=3.0):
"""Convert a dBZ field into a 8-bit imag... |
<filename>create_market.py
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
from scipy.stats import chi2
import numpy as np
import random, pickle
from scipy.optimize import fsolve
from initial_market import StartMarket
from matplotlib import pyplot as plt
class init_agents(StartMarket):
def createAgents... |
<filename>seeds/hmmer.py
import os
import numpy as np
import pandas as pd
from scipy import sparse
from seeds import Seed
from Utils import ColourClass, Utilities
from Utils.HmmerTbloutParser import HmmerTbloutFile
class HMMerSeed(Seed):
def __init__(self, hmmer, proteins, terms, go, blacklist, goa, protein_fo... |
import scipy.special
import numpy
from pylab import plot,show
def gauss(n):
x,w = scipy.special.orthogonal.p_roots(n)
x=(x+1)/2.0
w=.5*w
return x,w
def tensorquad(x,wx,y,wy):
"""Combine two quadrules in the x and y direction into a 2D tensor rule"""
nx=len(x)
ny=len(y)
m=numpy.m... |
<reponame>AlbertoJimenezDiaz/ctplanet
'''
Functions for calculating the shape of hydrostatic density interfaces and their
gravitational potential in a planet with a non-hydrostatic lithosphere.
'''
import numpy as np
import scipy.linalg.lapack as lapack
import pyshtools as pysh
# ==== HydrostaticShapeLith ====
def ... |
import numpy as np
import seren3
# the_mass_bins=[7., 8., 9., 10.]
def plot(path, iout, pickle_path, the_mass_bins=[7., 8., 9., 10,], lab='', ax=None, **kwargs):
import pickle
from seren3.analysis.plots import fit_scatter
from seren3.utils import flatten_nested_array
import matplotlib.pylab as plt
... |
<gh_stars>1-10
import os
import os.path
import numpy as np
import pandas as pd
from PIL import Image
from scipy import ndimage, spatial
from scipy.stats import skew
import skimage.feature
np.set_printoptions(threshold=np.inf)
def skewness_cells(image):
indices = np.where(image)
y_coord = np.asarray(indices[0]... |
<gh_stars>1-10
from math import e, factorial,log, gamma, sqrt, floor, exp
from matplotlib import pyplot as pt
from numpy.random import geometric, poisson, exponential
from scipy.stats import ks_2samp
from scipy.stats import norm#,poisson
from numpy import linspace
import re
def computeEvents(V, ttx, trx, tn):
Eb =... |
<filename>anisotropic/utils.py<gh_stars>1-10
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.prepro import *
# from config import config, log_config
#
# img_path = config.TRAIN.img_path
import scipy
import numpy as np
import skimage
def Subpixel_mod(X, scale=2):
I = X.outputs
bsize, a, b, c ... |
class Solver(object):
def get(self, Otrain, Ftrain, xmin, xmax):
raise NotImplementedError()
class Fmin(Solver):
def get(self, Otrain, Ftrain, xmin, xmax):
y = np.linspace(xmin, xmax, self._nbins)
I = np.where((y >= np.min(Otrain)) & (y <= np.max(Otrain)))[0]
assert(len(I) > 0)
... |
# -*- encoding: utf-8 -*-
"""
tests.help.test_helping module
"""
import pytest
import datetime
import pysodium
import fractions
from dataclasses import dataclass, asdict
from keri.help.helping import isign, sceil
from keri.help.helping import mdict
from keri.help.helping import extractValues
from keri.help.helping ... |
__author__ = 'mricha56'
__version__ = '4.0'
# Interface for accessing the PASCAL in Detail dataset. detail is a Python API
# that assists in loading, parsing, and visualizing the annotations of PASCAL
# in Detail. Please visit https://sites.google.com/view/pasd/home for more
# information about the PASCAL in Detail cha... |
"""
Constant and units conversion functions for for petroleum engineering calculations
"""
import numpy as np
import scipy.constants as const
g = const.g # gravity
pi = const.pi
pressure_sc_bar = 1 # pressure standard condition
temperature_sc_C = 15 # temperature standard condition
const_at = 98066.5 # techni... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import argparse
import re
import shlex
import time
from contextlib import suppress
from functools import partial
from pathlib import Path
from statistics import mean, median
from threading import Event, Thread
from typing import Any, Dict, List, Optional, Set, Hashable, Un... |
#!/usr/bin/env python
#
# Copyright (C) 2019
# <NAME>
# Centre of Excellence Cognitive Interaction Technology (CITEC)
# Bielefeld University
#
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of so... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import make_interp_spline, BSpline
from matplotlib.pyplot import MultipleLocator
#%matplotlib inline
df=pd.read_csv('pred_r.csv')
df2=pd.read_csv('real_r.csv')
#print(df)
x=list(range(len(df)))
datelist=list()
datelist2=list... |
import sys
import os
import pickle
import tarfile
import numpy as np
import urllib
import zipfile
import fnmatch
import shutil
import gzip
import cPickle as cPkl
import pickle as pkl
def whiten(X_train, X_valid):
offset = np.mean(X_train, 0)
scale = np.std(X_train, 0).clip(min=1)
X_train = (X_train - offs... |
<filename>rssympim/examples/performance_benchmarks/performance.py
# # Performance Testing for SymPIM-rz - script generated from IPython notebook
#
# This will follow the BeamLoad approach, varying the number of modes in the longitudinal and radial direction as well as the number of macro-particles, to produce a few pl... |
__author__ = 'DafniAntotsiou'
'''
This script calculates dataset DTW scor.
It uses the fastdtw python package @https://pypi.org/project/fastdtw/
'''
from cat_dauggi.functions import read_npz
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean
import argparse
from baselines.common.misc_util import... |
<filename>proclivity.py
from __future__ import division #For decimal division.
import numpy as np #For use in numerical computation.
from matplotlib import pylab as plt #Plotting.
import argparse #For commandline input
import scipy.io #For loading sparse matrices.
import sys
import time #Check time of computation.
impo... |
# -*- coding: utf-8 -*-
from __future__ import print_function
import weakref
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from acq4.Manager import getManager
from acq4.modules.TaskRunner.analysisModules.AnalysisModule import AnalysisModule
from acq4.util import Qt
from acq4.util.debug import ... |
<filename>util/download.py
from statistics import mode
import requests
import base64
headers_with_book118 = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
"Accept-Encoding": "gzip, deflate, br",
"Accept-Language":... |
<reponame>virati/cortical_signatures
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 23 16:13:54 2018
@author: virati
This scipt is focused on characterizing the ONTarget response
Includes some DTI support modeling which should be split out
"""
from DBSpace.control import proc_dEEG
import DBSpa... |
<gh_stars>0
# coding: utf-8
# カルマンフィルタ p107から
from robot import *
from noise_robot import *
from scipy.stats import multivariate_normal
from matplotlib.patches import Ellipse
# カルマンフィルタの実装
class KalmanFilter:
# envmap : 地図
# init_pose : 初期姿勢
# motion_noise_stds : 動きに加えるノイズの標準偏差
def __init__(self, ... |
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('mystyle')
import scipy.interpolate as interpol
nu,eV,Sy,Br,IC,pp,ColdDisk,Refl,Tot,ICin = np.loadtxt('lumThermal.dat',unpack=True)
NT_logeV,NT_logSyp,NT_logIC,NT_logpIC,NT_logpp,NT_logpg,NT_logNotAbs, NT_logAbs = np.loadtxt('lum... |
<filename>src/extrapolated_lowess/extrapolated_lowess.py
import logging
import numpy as np
from scipy import linalg
LOGGER = logging.getLogger(__name__)
def extrapolated_lowess(x_data, y_data, alpha=1, y_std=None):
r"""Performs a locally-weighted regression (LOWESS) and extrapolation for
missing dependent-... |
<reponame>shishitao/boffi_dynamics<filename>dati_2014/08/ex2.py<gh_stars>0
from scipy import *
from scipy.linalg import eigh
story_stiffness = range(23,11,-1)
story_stiffness.append(0)
K = matrix(zeros((13,13)))
M = matrix(zeros((12,12)))
for i in range(12):
M[i,i] = 1.0
K[i,i] = story_stiffness[i] + story_s... |
<filename>Probability Statistics Beginner/Linear regression-15.py
## 2. Drawing lines ##
import matplotlib.pyplot as plt
import numpy as np
x = [0, 1, 2, 3, 4, 5]
# Going by our formula, every y value at a position is the same as the x-value in the same position.
# We could write y = x, but let's write them all out t... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 23 14:33:44 2015
@author: Eric
"""
from os import chdir
chdir("..")
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from pca.pca import PCA
import SAILnet
imfile = "patches.mat"
imname = "patches"
nullpca = PCA()
normalpca = PCA(... |
<gh_stars>1-10
import constants
import math
import statistics
def normalize_audio_file(x, set_dB=110):
Pref = constants.Pref # load reference sound pressure
amp_value = Pref * 10 ** (set_dB / 20) # calculate amp value with reference from db
# root_mean_square = math.sqrt(statistics.mean(x ** 2.)) # RMS... |
<filename>recommender/train.py
import json
import os
from argparse import ArgumentParser
from collections import OrderedDict
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
from scipy.optimize import linear_sum_... |
<reponame>apirzadeh1365/omic_project<filename>main/dashboard/pages/spo2/oxygensat.py
"""
This module contains the page that shows the oxygen saturation (SpO2) levels.
<NAME>:
- Created spo2 plot
<NAME>:
- Created general structure
- Implemented spo2 plot
- Refactored whole spo2 plot in several functions
"""
import... |
<filename>create_Multi.py
import numpy as np
import scipy.io as sio
def read_gct(fn):
with open(fn, 'rb') as f:
for i, line in enumerate(f):
if i == 1:
row = line.split('\t')
p = int(row[0])
n = int(row[1])
break
X = np.zeros([... |
# -*- coding: utf-8 -*-
"""
@author: <NAME> & <NAME>
"""
#Import Statements
from scipy.constants import physical_constants, Boltzmann, R, femto, pico, nano
from math import pi, pow, sqrt, cos, radians, exp, floor
import codons
import pandas
import json, sys, os
def is_number(self,c):
numbers = ".0123456789"
i... |
import six
import torch
import ubelt as ub
if six.PY2:
from fractions import gcd
else:
from math import gcd
def rectify_nonlinearity(key=ub.NoParam, dim=2):
"""
Allows dictionary based specification of a nonlinearity
Example:
>>> rectify_nonlinearity('relu')
ReLU(...)
>>> ... |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------------------------
"""
Helpers to process schemas.
"""
... |
#!/usr/bin/env python3
"""
Main script to create spectrogram images
Original Data Information
--------------------------
High Pass Filter: 0
Low Pass Filter: 104
Useful Information
BDF file detected
Setting channel info structure...
Creating raw.info structure...
<Info | 7 no... |
<gh_stars>0
import numpy as np
from numpy.lib.arraysetops import isin
import torch
from scipy.optimize import linear_sum_assignment
from torch.nn.modules.activation import Sigmoid, Softmax
from toolbox.utils import get_device, greedy_qap, perm_matrix
import torch.nn.functional as F
from sklearn.cluster import KMeans
im... |
# -*- coding: utf-8 -*-
"""
Validation of Laplacian using spherical harmonics
====================================================================
Study the eigenvalue spectrum of the discretize laplace-beltrami operator
on a spherical mesh. Compare the spectrum to analytical solution.
"""
import numpy as np... |
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
from sklearn import linear_model
import seaborn
data = pd.read_csv('dataset.csv')
data = data.fillna(value=data.mean())
(data['BsmtFinType2'].fillna(value='VARIOUS',inplace=True))
(data['BsmtFinType1'].fillna(value='VARIOUS',inplace=T... |
<filename>src/clophfit/old/fit_titration.py<gh_stars>0
#!/usr/bin/env python
import os
import argparse
import numpy as np
import pandas as pd
from collections import namedtuple
from scipy import optimize
import matplotlib.pyplot as plt
import seaborn
def main():
"""Fit pH and cl titrations where data are spectr... |
#!/bin/python3
import os
#
# Complete the storyOfATree function below.
#
def storyOfATree(n, edges, k, guesses):
#
# Write your code here.
#
import fractions
# Compute neighbors of nodes.
neighbors = [[] for _ in range(n)]
for [u, v] in edges:
neighbors[u - 1].append(v - 1)
... |
"""Restricted Boltzmann Machine with softmax visible units.
Based on sklearn's BernoulliRBM class.
"""
# Authors: <NAME> <<EMAIL>>
# <NAME>
# <NAME>
# <NAME>
# License: BSD 3 clause
import time
import re
import numpy as np
import scipy.sparse as sp
from sklearn.base import BaseEstimator
f... |
"""Create constant and point scatterer models."""
import numpy as np
import scipy.special
import scipy.integrate
from scipy.ndimage.interpolation import shift
from smii.modeling.propagators.propagators import (Scalar1D, Scalar2D)
from smii.modeling.wavelets.wavelets import ricker
from smii.modeling.forward_model import... |
#!/usr/bin/python3
import logging
import numpy as np
import pandas as pd
import gzip
import itertools
from scipy.io import mmread,mmwrite
from scipy.sparse import coo_matrix
from os.path import join as pjoin
from os import linesep
np.random.seed(12345)
#Number of NTC gRNAs to keep
ng_negselect=15
#Number of TSS targe... |
<gh_stars>1-10
'''
This code reads in a batch of very high resolution spectra and degrades them
to a lower resolution, assuming a Gaussian line-spread function. The main use
case is that we produce synthetic spectra (from an updated version of the Kurucz
line list by default) at R~300,000 and need to convolve them the... |
<gh_stars>1-10
# 质量波动和应力波动模型
import os,yaml,sys
import numpy as np
import scipy.constants as C
# 定义全局变量
pi = C.pi
h = C.Planck
hbar = h/(2*pi)
R = C.R
k = C.k
prompt = ">>>"
def periodic_table():
CurrentPath=os.getcwd()
YamlFile=os.path.join(CurrentPath,"periodic-table.yaml")
with open(YamlFile,"r") as... |
#!/usr/bin/env python
import h5py
import os
import scipy as sp
import pdb
import utilities.hdf5 as hdf5
import fnmatch
import sys
if __name__ == "__main__":
tempvarfiles = sys.argv[1]
fn_output = sys.argv[2]
files = []
for rts,dirs,fs in os.walk(tempvarfiles):
fs = fnmatch.filter(fs, '*.hdf5'... |
<gh_stars>0
#Imports
from scipy.integrate import dblquad
#Constants
LowerLimit_x = 0.0
UpperLimit_x = 10.0
LowerLimit_y = 0.0
UpperLimit_y = 10.0
#Defining Function
def function(x,y):
return (x**2)+(y**2)
# Turning the constants into a function. (Makes the code work).
def Lfy(x):
return LowerLimit_y
def Ufy(x):
... |
from __future__ import print_function
import numpy as np
from scipy.special import gamma, gammaincc
from scipy.interpolate import splev, splrep
from scipy.optimize import brentq
class target_LF:
'''
Class containing methods for calculating the target luminosity
function of the MXXL mock catalogue
'''
... |
import numpy as np
from scipy import special
from zenquant.ctastrategy import (
CtaTemplate,
StopOrder,
TickData,
BarData,
TradeData,
OrderData,
BarGenerator,
)
from zenquant.trader.constant import (
Status,
Direction,
Offset,
Exchange
)
import lightgbm as lgb
from tzlocal i... |
<filename>examples/camera_example.py<gh_stars>1-10
import matplotlib.pyplot as plt
import matplotlib
import visgeom as vg
import numpy as np
from scipy.spatial.transform import Rotation
# Use Qt 5 backend in visualisation.
matplotlib.use('qt5agg')
# Create axis.
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.se... |
<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Wed May 15 17:15:19 2019
@author: philipp
"""
# =======================================================================
# Sort genes by adjusted robust rank aggregation (Li et al., Genome Biology 2014)
# ======================================... |
#!/usr/bin/env python
# coding: utf-8
# - Edge weight is inferred by GNNExplainer and node importance is given by five Ebay annotators. Not every annotator has annotated each node.
# - Seed is the txn to explain.
# - id is the community id.
import os
import pickle
import math
from tqdm.auto import tqdm
import random... |
import numpy as np
import scipy
from active_semi_clustering.exceptions import EmptyClustersException
from active_semi_clustering.farthest_first_traversal import weighted_farthest_first_traversal
from .constraints import preprocess_constraints
# np.seterr('raise')
class MPCKMeansMF:
"""
MPCK-Means that learn... |
import numpy as np
import numpy.polynomial.chebyshev as C
import time
from scipy.interpolate import BarycentricInterpolator as bi
from barycentric import Barycentric
if __name__ == '__main__':
ni = 500
ne = 20000
# Interpolation points
xi = C.chebpts1(ni)
# Evalutation points
xe = np.linspac... |
<gh_stars>10-100
import datetime
import scipy as sp
from pymote import *
from pymote.conf import global_settings
from pymote import propagation
from toplogies import Topology
from pymote.utils import plotter
from pymote.utils.filing import get_path, date2str,\
DATA_DIR, TOPOLOGY_DIR, CHART_DIR, DATETIME_DIR
imp... |
<gh_stars>1-10
import pytest
import itertools
import numpy as np
from scipy import sparse
from sklearn.datasets import make_classification, make_regression
from gsroptim.sgl_tools import generate_data
from gsroptim.logreg import logreg_path
from gsroptim.lasso import lasso_path
from gsroptim.multi_task_lasso import m... |
import numpy as np
import scipy
from tsa.science import numpy_ext as npx
# def binned_timeseries_1d(times, values, time_units_per_bin=1, time_unit='D', statistic='mean'):
# '''
# '''
# first, last = npx.bounds(times)
# bins = npx.datespace(first, last, time_units_per_bin, time_unit).astype('datetime64... |
#!/usr/bin/env python
""" convolve.py -- Convolve sourceimage to a lower resolution image. Outputs the lower resolution image as a fits file.
Usage: convolve [-h] [-v] [-o SAVELOC] [--overwrite] (pixel | arcsec) <fitsfile> <init_res> <final_rez>
Arguments:
fitsfile (string)
Path to image to be convolved.
... |
<reponame>ABaldrati/SupeRAuGAN
import datetime
import gc
from argparse import ArgumentParser
from math import log10
from pathlib import Path
from statistics import mean
import numpy as np
import pytorch_ssim
import torch
import torchvision.utils as utils
from lpips import lpips
from torch import optim
from torch.nn im... |
<reponame>vb690/machine_learning_exercises<gh_stars>0
import os
from tqdm import tqdm
import numpy as np
from scipy.interpolate import griddata
from sklearn.preprocessing import KBinsDiscretizer
import imageio
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib
def save_3D_a... |
<filename>velodyn/velocity_divergence.py<gh_stars>10-100
"""Compute divergence maps from RNA velocity fields"""
import numpy as np
import anndata
from sklearn.neighbors import NearestNeighbors
from scipy.stats import norm as normal
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
# modified f... |
# coding: utf-8
# In[1]:
#get_ipython().magic(u'matplotlib inline')
# In[2]:
import os
import numpy as np
np.set_printoptions(precision=3, linewidth=250)
import scipy as sp
from scipy import signal, io
import pandas as pd
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import matplotlib.... |
<gh_stars>0
import numpy as np
def sparse_diags(A):
# x = dia_matrix(A)
max_diag_size = A.diagonal(0).shape[0]
d = []
data = []
for diag in range(-A.shape[0], A.shape[1]):
diag_value = A.diagonal(diag)
if np.any(diag_value):
d.append(diag)
if diag < 0:
... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 29 19:18:23 2016
@author: <NAME>
"""
# =============================================================================
# Standard Python modules
# =============================================================================
import os, sys, time
from scipy.optimize import ... |
<filename>Tutorial-2.py<gh_stars>0
"""For a given dataset: (1,1.2), (2,1.9), (3,3.2)
Find the line which fits the data using maximum likelihood function. Plot the line with the given dataset and post it here in the Google class room. Also create your github account and post the link of the code along with the plot so t... |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import pandas... |
from __future__ import print_function, division
from ctypes import POINTER, c_int64, c_float, c_char_p, create_string_buffer
from pyscf.nao.m_libnao import libnao
# interfacing with fortran subroutines
libnao.siesta_hsx_size.argtypes = (c_char_p, POINTER(c_int64), POINTER(c_int64))
libnao.siesta_hsx_read.argtypes = (... |
"""
playing around with fbprophet
"""
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fbprophet import Prophet
from scipy.stats import boxcox
from scipy.special import inv_boxcox
from rich import print
columns = ["created_at", "id"]
tic = dt.datetime.now()
_df = pd.re... |
<reponame>Anysomeday/SpecPatConv3D-Network<gh_stars>10-100
import numpy as np
from random import shuffle
import scipy.io as io
import argparse
from helper import *
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='Indian_pines', help='default:Indian_pines, options: Salinas, KSC, Botsw... |
# Copyright 2019 Xanadu Quantum Technologies Inc.
# 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 applicable law or agre... |
<reponame>syats/light_topic_transitions<gh_stars>0
"""
Authors: <NAME> & <NAME> for Semantic Web Company
Cite:
<NAME>., <NAME>. "Evolution of Semantically Identified Topics"
CEUR vol 1923 (2017)
http://ceur-ws.org/Vol-1923/article-06.pdf
"""
import numpy as np
import scipy
from sci... |
<filename>TB2J/spinham/qsolver.py
#!/usr/bin/env python
import math
import numpy as np
import scipy.linalg as linalg
class QSolver(object):
def __init__(self, hamiltonian):
self.ham = hamiltonian
self.nspin = self.ham.nspin
M = linalg.norm(self.ham.spinat, axis=1)
self.M_mat=np.kron... |
<filename>207demography_2018/calc.py<gh_stars>0
#!/usr/bin/env python3
from math import *
from statistics import *
def sum_sq(data):
res = 0
for i in data:
res += i ** 2
return res
def sum_mul(data, date):
res = 0
i = 0
while i < len(data):
res += data[i] * date[i]
... |
<reponame>ctralie/DynamicsSynchronization
"""
Replicate the 1D time series reshuffling problem in the equal space paper
"""
import numpy as np
import scipy.io as sio
import scipy.linalg as slinalg
import matplotlib.pyplot as plt
from PDE2D import *
from PatchDescriptors import *
from DiffusionMaps import *
class GLSim... |
<filename>prof.py
__all__ = ['prof4']
import pyfits as pf
import numpy as np
import matplotlib.pyplot as pl
import sys
import scipy.io
class prof4(object):
def __init__(self, fileIn):
self.currentFile = fileIn
self.readData()
self.currentPos = [0,0]
# Plot with integrated maps
self.figFixedMaps = pl.figure... |
<filename>src/models/network.py<gh_stars>0
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as wn
from torch.nn.parameter import Parameter
from torch import Tensor
from src.models.utils import get_activation
from typing import List, Tuple, Dict, Union
fr... |
<reponame>sglyon/quant-econ<filename>examples/optgrowth_v0.py<gh_stars>1-10
"""
Filename: optgrowth_v0.py
Authors: <NAME> and <NAME>
A first pass at solving the optimal growth problem via value function
iteration. A more general version is provided in optgrowth.py.
"""
from __future__ import division # Omit for Pyt... |
<reponame>cgarcia-UCO/AgentSurvival
'''
Esta clase tiene agentes (clase anterior) que se mueven en el laberinto. Los agentes tienen métodos para moverse hacia adelante
y para girar a ambos lados.
HECHO Cuando un agente hace una acción, el Laberinto debería comprobar si ha agotado el número de movimientos en su turno... |
<reponame>imandrealombardo/FACT-AI<filename>Fairness_attack/run_gradient_em_attack.py<gh_stars>0
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import os
import argparse
import time
import numpy as np
import scipy.s... |
#!/usr/bin/python3
from paho.mqtt import client as mqttclient
from collections import OrderedDict
from picamera import PiCamera, Color
from telegram import Update, ChatAction
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext
import json
import time
import socket
import threadi... |
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