text string |
|---|
<reponame>zhoujinhai/MeshCNN
import os
import numpy as np
from scipy.spatial import KDTree
import glob
def create_tree(array):
"""
根据点集建立kd_tree
"""
tree = KDTree(array)
return tree
def get_gum_line_pts(gum_line_path):
"""
读取牙龈线文件，pts格式
"""
f = open(gum_line_path)
pts = []
... |
from scipy.optimize import curve_fit
import numpy as np
def uptake_func(po2, a, b, c):
return a - ((b * po2) / (c + po2))
def fit():
uptake = [10.91, 10.9, 7.7, 5.6, 1.0, 0.628, 0.5]
po2 = [0.0, 0.0076, 0.76, 3.8, 38, 60, 152]
#po2 = [0.0, 0.76, 3.8, 38, 152]
pars, cov = curve_fit(f=uptake_func... |
###############################################################################
# AdiabaticContractionWrapperPotential.py: Wrapper to adiabatically
# contract a DM halo in response
# to the growth of a baryonic
# ... |
<filename>fourier_accountant/experimental/binomial_mechanism.py
"""
Experimental implementation for computing tight DP guarantees for the binomial mechanism.
The method is described in the manuscript
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT .
"""
import numpy as np
import scipy
i... |
import numpy as np
import cv2
import os
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from sklearn import linear_model
from scipy import stats
from collections import deque
from camera_cal_test import camera_cal
import gradients_colors_thresholding_test as grad_color_thres
from perspective_... |
import types
from screws.freeze.main import FrozenOnly
from scipy import sparse as spspa
from root.config.main import *
from tools.linear_algebra.gathering.regular.chain_matrix.main import Chain_Gathering_Matrix
from tools.linear_algebra.data_structures.global_matrix.main import GlobalVector
from tools.linear_algeb... |
from __future__ import division
import os
import torch
import torch.nn as nn
import scipy.io as sio
import torchvision.utils as vutils
import numpy as np
def getInput(args, data):
input_list = [data['img']]
# print("getinput input_list:", len(input_list)) getinput input_list: 1
input_list.append(data['mas... |
from scipy.stats import spearmanr
import numpy as np
import pandas as pd
from sklearn.metrics import make_scorer
from scipy.stats import skew, kurtosis
def spearman(y_true, y_pred):
""" Calculate Spearman correlation """
corr = spearmanr(y_true, y_pred, axis=0)[0]
return 0 if np.isnan(corr) else corr
de... |
import numpy as np
import os
import cv2
import glob
from scipy.spatial.transform import Rotation as R
from torchvision import transforms as T
from tqdm import tqdm
from torch.utils.data import Dataset
from rlpyt.utils.collections import namedarraytuple
from rlpyt.utils.buffer import buffer_from_example
OfflineSamples ... |
<reponame>zsmn/numerical_methods<gh_stars>1-10
import math
import sympy as sym
import matplotlib.pyplot as graphic
from sympy.parsing.sympy_parser import parse_expr
pts_y = [] # resultados
pts_t = [] # passos
y, t = sym.symbols('y t')
consts_bashforth = [
[1.0],
[3.0/2.0,-1.0/2.0],
[23.0/12.0,-4.0/3.0,5.0/12.0]... |
import os
import io
import numpy as np
import torch
from scipy.ndimage.filters import gaussian_filter
from scipy.interpolate import RectBivariateSpline
from scipy.spatial.distance import cdist
from skimage.transform import resize as resize_image
from skimage import measure
from skimage.color import rgb2gray
from tqdm i... |
from __future__ import print_function
from scipy.io import arff
from prepare_data import prepare_compas,prepare_IBM_adult, prepare_law
import functools
import numpy as np
import pandas as pd
import sys
from fair_logloss import DP_fair_logloss_classifier, EOPP_fair_logloss_classifier, EODD_fair_logloss_classifier
def... |
"""Compare times required for turtle to draw lines at different orientations."""
from time import perf_counter
import statistics
import turtle
turtle.setup(1200, 600)
screen = turtle.Screen()
ANGLES = (0, 3.695220532) # In degrees.
NUM_RUNS = 20
SPEED = 0
for angle in ANGLES:
times = []
for _ in range(NUM_R... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import util
import util_car
from util import activation_summaries
import tensorflow.contrib.slim as slim
from tensorflow.python.ops import init_ops
import math, importlib, sys
import num... |
<reponame>victortocantins/emg3d<filename>tests/test_solver.py<gh_stars>1-10
import pytest
import numpy as np
import scipy.linalg as sl
import scipy.interpolate as si
from os.path import join, dirname
from numpy.testing import assert_allclose
import emg3d
from emg3d import solver
from . import alternatives, helpers
#... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 4 10:34:22 2019
A class based formulation of other analyses.
It is structured as:
Dataset
_| |_
| |
Analysis Forecast
... |
#!/usr/bin/env python
from __future__ import division
from __future__ import print_function
from builtins import str
from builtins import range
from past.utils import old_div
import sys
import scipy
import copy
import os
from pmagpy import pmag
def main(command_line=True, **kwargs):
"""
NAME
generic_ma... |
#!/usr/bin/env python
"""
@File : linear_system.py
@Time : 2021/11/13
@Desc : Class definition for the linear system
"""
# ==============================================================================
# Standard Python modules
# ==========================================================================... |
<reponame>anupgp/astron
import numpy as np
np.random.seed(875431)
import pandas as pd
from scipy import signal
import os
import astron_common_functions as astronfuns
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.font_manager as font_manager
print("matplotlibrc loc: ",matplotlib.matplotlib_fna... |
from Segmentation.utilities import *
import json
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from scipy.misc import imread
from scipy import ndimage, signal
from skimage import morphology, feature, exposure
import neurofinder
import cv2 as cv
import ast
from PIL import Image
... |
<gh_stars>0
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2017, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------... |
<filename>evaluate_Codalab.py
import warnings
warnings.filterwarnings("ignore")
import sys
import os
import os.path
import numpy as np
import random
import csv
from glob import glob
#set random seed
random.seed(0)
print("V4V evaluation script v1.0.0\n")
msg = 'Please create a `results.txt` file and zip it into `su... |
<gh_stars>1-10
#Standard python libraries
import numpy as np
import os
import itertools
from scipy.sparse import csr_matrix, kron, identity
from .eigen_generator import EigenGenerator
class CalculateCartesianDipoleOperatorLowMemory(EigenGenerator):
"""This class calculates the dipole operator in the eigenbasis of... |
<reponame>lindenmp/NormativeNeuroDev_CrossSec<filename>code/clean_node_metrics.py
#!/usr/bin/env python
# coding: utf-8
# # Preamble
# In[1]:
import os, sys, glob
import pandas as pd
import numpy as np
import scipy as sp
from scipy import stats
import scipy.io as sio
import statsmodels.api as sm
import seaborn as s... |
<reponame>sumau/tick<gh_stars>100-1000
# License: BSD 3 clause
import warnings
import numpy as np
from numpy.linalg import norm
from scipy.optimize import check_grad, fmin_bfgs
import unittest
class TestGLM(unittest.TestCase):
def __init__(self, *args, dtype="float64", **kwargs):
unittest.TestCase.__ini... |
import numpy
import json
import cv2
import numpy as np
import os
import scipy.misc as misc
# Add Ignore to vessel
#############################################################################################
def show(Im):
cv2.imshow("show",Im.astype(np.uint8))
cv2.waitKey()
cv2.destroyAllWindows(... |
<reponame>daverblair/CrypticPhenotypeAnalysisScripts<gh_stars>0
import pandas as pd
import numpy as np
from scipy.stats.mstats import mquantiles
from scipy.stats import spearmanr,chi2,beta
import matplotlib.pyplot as plt
from matplotlib import cm
import seaborn as sns
sns.set(context='talk',color_codes=True,style='tic... |
# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""I/O test cases."""
import os
from subprocess import check_call
from io import StringIO
import filecmp
import shutil
import numpy as np
import pytest
from h5py import File as H5File
import nibabel a... |
<filename>src/ghhops-server-py/AAG/guidedprojectionbase.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from timeit import default_timer as timer
import numpy as np
from scipy import sparse
try:
fro... |
<gh_stars>1-10
"""
*Calculates average 500mb height per 100 years in CESM control*
"""
import numpy as np
from netCDF4 import Dataset
from scipy.stats import nanmean
years = '13000101-13991231' #arbitrary period selected
### Import Tmax
def aveH5(years):
"""
Calculates average 500mb height for a 100 year peri... |
"""
File: encoders.py
Author: Team ohia.ai
Description: Generalized encoder classes with a consistent Sklearn-like API
"""
import time
import numpy as np
import pandas as pd
from statsmodels.distributions.empirical_distribution import ECDF
from sklearn.linear_model import Ridge
from scipy.stats import rankdata
def ru... |
<reponame>Chenguang-Zhu/relancer<filename>relancer-exp/original_notebooks/wenruliu_adult-income-dataset/income-prediction-using-multi-model-and-eda.py
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python ... |
import numpy as np
from scipy.optimize import minimize
from scipy.io import loadmat
from math import sqrt,exp
import pickle
import sys
from time import time
def initializeWeights(n_in, n_out):
"""
# initializeWeights return the random weights for Neural Network given the
# number of node in ... |
<filename>ASE/ASE_local.py<gh_stars>0
#!/usr/bin/env python
################################################################################
# ASE.py
#
# looks for allele specific expression in various ways, can be used on local laptop computer
# writes orthology file based on YGAP annotations
# will use bowtie2 mapped... |
#!/usr/bin/env python
import numpy as np
from scipy.special import expit as sigmoid
# predictions from a random forest
input_file = 'adult/y_and_p.csv'
print "loading data..."
y_and_p = np.loadtxt( input_file, delimiter = ',' )
y = y_and_p[:,0]
p = y_and_p[:,1]
# y need to be 0/1
y[y == -1] = 0
|
import numpy as np
import scipy.stats
def return_constant_ind(mat):
std = np.std(mat, axis=0)
return std == 0
def inv_norm_by_col(mat):
res_mat, good_ind = _prep_mat(mat)
res_mat[:, good_ind] = np.apply_along_axis(inv_norm_vec, 0, mat[:, good_ind])
return res_mat
def inv_norm_vec(vec, offset ... |
from torch.utils.data import Dataset
import pickle as pkl
import numpy as np
from os import walk
from h5py import File
import scipy.io as sio
from utils import data_utils
from matplotlib import pyplot as plt
import torch
class Datasets(Dataset):
def __init__(self, opt, actions=None, split=0):
path_to_da... |
<reponame>jah1994/TheThresher
# imports
import numpy as np
import torch
from astropy.io import fits
from skimage.feature import register_translation
from scipy.ndimage import shift
# Convert np.ndarrays to torch.Tensors whith dims: NHWC
def convert_to_tensor(image):
if type(image) is np.ndarray:
image = image.... |
<filename>kymatio/phaseharmonics2d/tests/test_rec_dirac2d_gpu.py
# TEST ON GPU
#import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
import torch
from torch.autograd import Variable, grad
from time import time
#---- create image without/with marks----#
size=32
# ---... |
import numpy
import pickle
import random
import os
import config
from numpy import array, sqrt, square
from numpy.linalg import norm
from os import listdir
from os.path import join
from PIL import Image
from scipy.ndimage.filters import sobel
from sklearn.svm import SVR, SVC
from sklearn.linear_model import LogisticRe... |
# MIT License
#
# Copyright (c) 2019 TU Delft Embedded and Networked Systems Group/
# Sustainable Systems Laboratory.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, in... |
<reponame>mattkjames7/PyMess<filename>PyMess/Pos/PlotOrbit.py<gh_stars>0
import numpy as np
import matplotlib.pyplot as plt
from .GetPosition import GetPosition
import DateTimeTools as TT
from scipy.interpolate import InterpolatedUnivariateSpline
def PlotOrbit(Date,ut,Range=[-3,3],Center=[0.0,0.0,0.0],PlanetAlpha=0.5... |
"""
Modified pyramids routines from skimage for anisotropic 3D volumes.
"""
import logging
from math import ceil
import numpy as np
import scipy.ndimage as ndi
from utoolbox.container import AbstractAlgorithm, ImplTypes, interface
__all__ = [
'GaussianPyramid'
]
logger = logging.getLogger(__name__)
def _smooth... |
<filename>my_explainers/my_explainers/my_local_explainer.py
import pandas as pd
import numpy as np
import plotly.offline as py
import plotly.graph_objects as go
import scipy as sp
import copy
import shap
from statistics import mode
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import L... |
<reponame>jorgehatccrma/pygrfnn
"""
Rhythm processing model
"""
from __future__ import division
from time import time
import sys
sys.path.append('../') # needed to run the examples from within the package folder
import numpy as np
from scipy.signal import hilbert
from scipy.io import loadmat
from pygrfnn.network... |
<reponame>Scott-Rubey/AudioSampler<filename>test_Map.py<gh_stars>0
from unittest import TestCase
from KeyboardMap import KeyboardMap
import pyrubberband as prb
import numpy as np
from scipy.io.wavfile import write, read
class TestMap(TestCase):
def setUp(self):
self.keymap = KeyboardMap(69, None, 44100)
c... |
<gh_stars>10-100
from __future__ import print_function
from __future__ import absolute_import
from tests.test_base import *
from qgate.script import *
from qgate.simulator.pyruntime import adjoint
from qgate.model import gate_type as gtype
from qgate.model.expand import expand_exp, expand_pmeasure, expand_pprob
from q... |
import numpy
import pylab
from scipy.stats import poisson
Li = 1e03
Lr = 1e02
c = 0.5
Dm = 20
fold_changes = numpy.array([2,3,4,5,6,7,8,9,10])
Navg = c*Dm*Li/Lr*1.0
Nmins = Navg/fold_changes
print Navg
print Nmins
perrs = poisson.cdf(Nmins, Navg)
pylab.semilogy(fold_changes, perrs,'b.-')
print poisson.cdf(10,100... |
<filename>src/dpsrvf/match_utils.py<gh_stars>0
from dpsrvf import dpmatch
import numpy as np
from scipy.interpolate import interp1d
def group_action_by_gamma(q, gamma):
'''
Computes composition of q and gamma and normalizes by gradient
Inputs:
-q: An (n,T) matrix representing a Square-Root Velocity Fun... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Train the model"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import torch
import numpy as np
import os
import scipy
import matplotlib.image as mpimg
import torch.nn ... |
<reponame>alewis/jax_vumps<filename>testing/old_contractions.py
"""
A docstring
"""
import numpy as np
import scipy as sp
import numpy.linalg as npla
import scipy.linalg as spla
from functools import reduce
from bhtools.tebd.scon import scon
import bhtools.tebd.utils as utils
#from scipy.linalg import solve
"""
Con... |
<filename>notebooks/setup.py
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.1.7
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # Setu... |
#!/usr/bin/python -u
import argparse
import numpy as np
import scipy.spatial
import data
parser = argparse.ArgumentParser(description='Evaluate embeddings somehow...')
parser.add_argument('--data', '-d', required=True, help='Training data directory')
parser.add_argument('--file', '-f', required=True, help='Embeddin... |
<gh_stars>0
"""
測試自己的資料集，並存成檢測結果圖
"""
import torch, os, cv2
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
import torch
import scipy.special, tqdm
import numpy as np
import torchvision.transforms as transforms
from data.dataset import Lane... |
<reponame>mdnls/tramp
import numpy as np
from .base_prior import Prior
from ..utils.misc import vonmises_v_to_b, complex2array, array2complex
from ..utils.integration import vonmises_measure
import scipy.special as sp
class VonMisesPrior(Prior):
def __init__(self, size, b):
'''
Isotropic Von Mises... |
<filename>validation_tests/analytical_exact/subcritical_depth_expansion/analytical_depth_expansion.py
"""
Supercritical flow over a bump.
<NAME>, ANU 2014
"""
from numpy import zeros
from scipy.optimize import fsolve
from anuga import g
qA = 1.0 # This is the imposed momentum
hx = 1.0 # This is the w... |
<filename>data/base.py<gh_stars>100-1000
# -----------------------------------------------------------------------------
# Code adapted from https://github.com/akanazawa/cmr/blob/master/data/base.py
#
# MIT License
#
# Copyright (c) 2018 akanazawa
#
# Permission is hereby granted, free of charge, to any person obtai... |
<reponame>andobrescu/Multi_task_plant_phenotyping<gh_stars>1-10
import os
import traceback
import scipy.misc as misc
import skimage.transform as skt
import matplotlib.pyplot as plt
import numpy as np
import glob
import pandas as pd
import random
from keras.utils import to_categorical
from PIL import Image, ImageOps
d... |
from sympy import symbols
from pdf import createpdf
from latex import createlatex
if __name__ == "__main__":
X1, X2, X3 = symbols('X1 X2 X3')
nr = 18
eq1 = 2/3*X1+2/3*X2
eq2 = -X1+X2
eq3 = X3
createlatex(eq1, eq2, eq3, filename='variant{0}_latex'.format(nr))
|
import numpy as np
import scipy.optimize as opt
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
class Ps10:
def __init__(self,t=200, sigmaz=2,rhoz=0.5,xz=0, h=10, l=(0,1,2)):
self.t = t
self.sigmaz = sigmaz
self.rhoz = rhoz
self.xz = xz
self.randstate = np... |
#+/usr/bin/env python3
from __future__ import print_function
import argparse
import logging
import pandas
from scipy.stats import ttest_ind
from singleqc import configure_logging
logger = logging.getLogger('gene_spike_ratio')
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
... |
import numpy as np
from scipy.io import wavfile
from Crypto.Cipher import AES
from Crypto.PublicKey import RSA
from Crypto.Signature import pkcs1_15
from Crypto.Hash import SHA256
from Crypto.Util.Padding import pad, unpad
from Crypto.Util.strxor import strxor
import random
import struct
from reedsolo import RSCodec
im... |
<reponame>jawadsh123/DeepDreamPy
import tensorflow as tf
import numpy as np
from PIL import Image
from scipy.ndimage.filters import gaussian_filter
import math
import random
import inception5h
model = inception5h.Inception5h()
# calcuating gradient equation
LAYER_INDEX = 7
layer_tensor = model.layer_t... |
<reponame>prabeshpaudel/trans-voice-app<gh_stars>0
import sys
import os
import numpy as np
# import librosa
# import librosa.display
import matplotlib.pyplot as plot
import crepe
from scipy.io import wavfile
path = os.getcwd()
filename = sys.argv[1]
##print(os.path.join(path,filename))
fileLocation = o... |
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
from typing import Any
from typing import Optional
from functools import partial
from .c import ema
from .c import rolling_min
from .c import rolling_max
from .c import rolling_sum
from .c import naive_ema
from .c import naive_rolling_min
... |
import numpy as np
import cv2
import os
import time
import random
import matplotlib.image as mpimg
import scipy.misc
import datetime
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import pickle
import scipy.io as sio
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection imp... |
<reponame>allegro/klejbenchmark-baselines
import typing as t
import numpy as np
import pandas as pd
from scipy.stats import spearmanr
from sklearn.metrics import accuracy_score, f1_score
from klejbenchmark_baselines.config import Config
from klejbenchmark_baselines.metrics import weighted_mean_absolute_error
class ... |
"""
Diagnostic module
"""
from typing import Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mrtool import MRBRT, LinearCovModel, LogCovModel, MRData
from mrtool.core.other_sampling import (extract_simple_lme_hessian,
extract_simple_lme_specs)
fr... |
""" Utilities for performing data validation and analysis """
from flowdec import data as fd_data
from flowdec import restoration as fd_restoration
from flowdec import exec as fd_exec
from flowdec.fft_utils_tf import OPM_LOG2
from skimage import restoration as sk_restoration
from skimage.transform import resize
from sk... |
<reponame>rviviano/data-tools
# Stats utilities. Just a collection of convenience functions for basic
# descriptive stats or data cleaning.
# <NAME>
# <EMAIL>
# January 2017
from __future__ import print_function
import os, sys, subprocess, traceback
import numpy as np
import pandas as pd
import scipy.stats
from scipy... |
"""
Computational Cancer Analysis Library
Authors:
Huwate (Kwat) Yeerna (Medetgul-Ernar)
<EMAIL>
Computational Cancer Analysis Laboratory, UCSD Cancer Center
<NAME>
<EMAIL>
Computational Cancer Analysis Laboratory, UCSD Cancer Center
"""
from numpy import dot
from numpy.linalg... |
<filename>crossval/plot.py
import pandas as pd
import numpy as np
from collections import defaultdict
from sklearn.base import BaseEstimator, clone, is_classifier
from sklearn.metrics import check_scoring, roc_curve
from sklearn.model_selection import check_cv
from joblib import Parallel, delayed
from scipy import i... |
<gh_stars>10-100
# Import packages
import os
import gc
import sys
import time
import pysam
import scipy
import psutil
import random
import logging
import resource
import traceback
import numpy as np
import pandas as pd
from tqdm import tqdm
import multiprocessing
import concurrent.futures
from subprocess import call
fr... |
<filename>jetset/mcmc.py<gh_stars>10-100
__author__ = "<NAME>"
from .minimizer import _eval_res
import emcee
from itertools import cycle
import numpy as np
import scipy as sp
from scipy import stats
import corner
import dill as pickle
from multiprocessing import cpu_count, Pool
import multiprocessing as mp
import ... |
<gh_stars>0
import dotenv
# LOAD ENV
dotenv.load_dotenv(dotenv.find_dotenv(), verbose=True)
import comet_ml
import pickle
from uuid import uuid4
from typing import NamedTuple
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import os
from helper import util
from pbt.strategies import ExploitU... |
####import section####
import matplotlib; matplotlib.use('agg')
import itertools
import numpy as np
import pandas as pd
from matplotlib import *
from matplotlib import pyplot
import matplotlib.colors as colors
import os
import sys
import ntpath
import re
import glob
import scipy
import subprocess
from pylab import *
im... |
from abc import ABCMeta, abstractmethod
from itertools import product
from typing import List
import numpy as np
from sympy.utilities.iterables import multiset_permutations
from dsenum.core import get_composition, hash_in_all_configuration # type: ignore
class BaseColoringGenerator(metaclass=ABCMeta):
@abstrac... |
import torch
import numpy as np
import scipy.signal as signal
import numpy.random as random
class ToTensor(object):
def __call__(self, sample):
if len(sample) == 2:
x, y = torch.from_numpy(sample[0]), torch.from_numpy(sample[1])
x, y = x.type(torch.FloatTensor), y.type(torch.LongT... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 11 16:41:29 2019
Many of these functions were copy pasted from bctpy package:
https://github.com/aestrivex/bctpy
under GNU V3.0:
https://github.com/aestrivex/bctpy/blob/master/LICENSE
@author: sorooshafyouni
University of Oxford, 201... |
import lasagne.layers as L
import lasagne.nonlinearities as NL
import lasagne.init
import theano.tensor as TT
import theano
import lasagne
from rllab.core.lasagne_powered import LasagnePowered
from rllab.core.serializable import Serializable
from rllab.core.network import MLP
from rllab.misc import ext
from rllab.misc... |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Written by <NAME> (<EMAIL>)
# ------------------------------------------------------------------------------
import scipy
import numpy as np
f... |
# Solutions to problem 1
import scipy as sp
import numpy as sp
from matplotlib import pyplot as plt
from scipy.signal import fftconvolve
def getFrame(m):
coeffs = []
k_max = int(sp.pi*2**(m+1))
for k in xrange(k_max):
coeffs.append(-2**m*(sp.cos((k+1)*2**(-m)) - sp.cos(k*2**(-m))))
coeffs.appen... |
<reponame>Sohamsahare/open-ai-gym<filename>acrobot.py
import tensorflow as tf
import numpy as np
import random
import gym
import pickle
from statistics import median, mean
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tflearn
env =... |
<filename>code/analysis/dynamic_allocation.py<gh_stars>0
#%%
import numpy as np
import pandas as pd
import tqdm
import growth.viz
import growth.model
import scipy.integrate
import altair as alt
import altair_saver
colors, palette = growth.viz.altair_style()
alt.data_transformers.disable_max_rows()
# Set the consta... |
<gh_stars>0
# Brett (<NAME> and Hunter (Kenneth) Wapman
# October 2014
# kNN Implementation for Senior Design Project
from collections import Counter
import sets
import math
import sys
import os
from math import isinf
# Minimum normalized RSSI value detected; used as "not detected" value
MIN_DETECTED = 0
# Access P... |
<reponame>johnfmaddox/kilojoule
"""kiloJoule display module
This module provides classes for parsing python code as text and
formatting for display using \LaTeX. The primary use case is coverting
Jupyter notebook cells into MathJax output by showing a progression of
caculations from symbolic to final numeric solution ... |
<gh_stars>10-100
import itertools
import numpy as np
import pandas as pd
from scipy.stats import skew, kurtosis
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA
from metalearn.metafeatures.common_operations import profile_distribution
from metalearn.metafeatures.base import build_reso... |
import sys
import os
import math
import numpy
import pandas
from scipy.interpolate import interp1d
from .constants import *
class TwoLayerModel:
"""Defines the two-layer climate model.
Attributes:
default (dict): Default parameters for running the two-layer model.
Overwritten using **kw... |
<filename>ABC_stat_select/assess_sv_estimation.py
#!/usr/bin/env python
"""Procedures to assess the performance of stochastic volatility estimators"""
import numpy as np
import selection as select
import simulate_data as sim
import summary_stats as sum_stat
import estimators as estim
from scipy import stats
from skle... |
<reponame>serafim-costa/LDP_Protocols
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created by tianhao.wang at 9/18/18
"""
import abc
import math
import numpy as np
from scipy.stats import norm
class FO(object):
__metaclass__ = abc.ABCMeta
def __init__(self, args):
self.args = args
... |
from .base_distribution import distribution
import numpy as np
from mpmath import mp
from scipy.optimize import minimize
class powerlaw(distribution):
'''
Discrete power law distributions, given by
P(x) ~ x^(-alpha)
'''
def __init__(self):
super(powerlaw, self).__init__()
self.nam... |
<reponame>garethnisbet/T-BOTS
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
plt.ion()
dutycycle = 128./256
v1 = np.loadtxt('WaveData698.csv')[200:2000,:]
measuredI = 0.025
stalledI = 0.25
Vemf = 3.63
Hz = 31399.
Hz2 = Hz/2
dutycycle = 127./256
Vemf = Vemf*dutycycle
veff=v1[:,1]... |
from __future__ import division
import posixpath
from collections import defaultdict
import numpy as np
from satmeta.s2 import meta as s2meta
from satmeta.s2 import utils as s2utils
from satmeta import utils
from satmeta import converters
ANGLES_TAGS = {
'Viewing_Incidence': (
'Viewing_Incidence_... |
<filename>mcmc.py
import numpy as np
import scipy as sc
import math
# ---------------------------------------------------------
# Class for MCMC
# Creates an MCMC object and define methods for sampling
# ---------------------------------------------------------
class MarkovChainMonteCarlo:
... |
<reponame>yuanchen-zhu/wiggle-localization
#!/usr/bin/python
import wx, numpy, OpenGL.GLU, OpenGL.GL
from wx.glcanvas import GLCanvas
from numpy import *
from scipy.linalg.basic import *
from scipy.linalg.decomp import rq
from OpenGL.GLU import *
from OpenGL.GL import *
from arcball import ArcBall
import pickle
import... |
<reponame>ciiram/PyPol_II
# This file contains a number of useful function definitions for implementing the
# delay estimation using a Gaussian process framework without convolution
#
# <NAME>, 2014
# D<NAME>i University of Technology.
# Nyeri-Kenya
import pylab as pb
import numpy as np
import scipy as sp
from scip... |
<filename>wc_model_gen/eukaryote/initialize_model.py
""" Initialize the construction of wc_lang-encoded models from wc_kb-encoded knowledge base.
:Author: <NAME> <<EMAIL>>
:Date: 2019-01-09
:Copyright: 2019, Karr Lab
:License: MIT
"""
from wc_utils.util.chem import EmpiricalFormula, OpenBabelUtils
from wc_utils.util.... |
<reponame>AdamStone/hypergeometric
""" Fit literature N4 data vs. bivariate Wallenius model
Considers three possible outcomes for modifier partitioning:
Trigonal B -> tetrahedral B (N4 = fraction tetrahedral B)
Trigonal Al -> tetrahedral Al (L4 = fraction tetrahedral Al)
NBO conversion on Si tetrahedron, Q... |
<gh_stars>10-100
# import necessary libraries
import numpy as np
import matplotlib.pyplot as pl
# for the animation
import matplotlib.animation as animation
from matplotlib.colors import Normalize
from scipy.sparse import spdiags
import matplotlib as mpl
def get_laplacian(N):
"""Construct a sparse matrix that a... |
<gh_stars>10-100
from math import sqrt, pi
import numpy as np
from scipy.special import hermite
from scipy.integrate import dblquad
from ..utils import InvalidMatrix
def disentangled_gaussian_wavefcn():
""" Return the function of normalized disentangled Gaussian systems.
:return: function of two variables... |
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