text stringlengths 0 27.1M | meta dict |
|---|---|
from sympy import symbols, Mul, sin, Integral, oo, Eq, Sum, sqrt, exp, pi, Dummy
from sympy.core.expr import unchanged
from sympy.stats import (Normal, Poisson, variance, Covariance, Variance,
Probability, Expectation, Moment, CentralMoment)
from sympy.stats.rv import probability, expectation
... | {
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function fill_wavelets!(iss::Int64, wavelets::Array{Array{Float64,2},2}, srcwav::Array{SrcWav}, sflags::Vector{Int64})
npw = size(wavelets,1)
nt = size(wavelets,2)
δt = step(srcwav[1][1].grid)
for ipw=1:npw
ns, snfield = size(wavelets[ipw,1]) # ns may vary with ipw
for ifield=1:snfield, is=1:ns
snt = lengt... | {
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# -*- coding: utf-8 -*-
import sys
import subprocess
import time
from tempfile import NamedTemporaryFile
import toml
import asteval
import tqdm
import os
from collections import deque
import numpy as np
from collections import namedtuple
import itertools
import h5py
import Geant4 as g4
from Geant4.hepunit import *
cla... | {
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# # Arbitrary Precision
#
# COSMO allows you to solve problems with arbitrary floating-point precision, e.g. by using `BigFloat` problem data. To do this, the desired floating point type has to be consistent across the model `COSMO.Model{<: AbstractFloat}`, the input data and the (optional) settings object `COSMO.Setti... | {
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import sys
sys.path.insert(0,'./')
from argparse import ArgumentParser
import pytorch_lightning as pl
import json
from typing import Iterator, List, Dict, Optional
import torch
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
# for dataset reader
from allennlp.data.data_loaders import M... | {
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! Copyright (c) 2019, ARM Ltd. All rights reserved.
!
! 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 o... | {
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from __future__ import print_function
# Example of a *very* simple variabiilty metric
# krughoff@uw.edu, ebellm, ljones
import numpy as np
from scipy.signal import lombscargle
from rubin_sim.maf.metrics import BaseMetric
__all__ = ['PeriodDeviationMetric']
def find_period_LS(times, mags, minperiod=2., maxperiod=35... | {
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import torch
import math
import numpy as np
from .random_fields import GaussianRF
from timeit import default_timer
import argparse
class KolmogorovFlow2d(object):
def __init__(self, w0, Re, n):
# Grid size
self.s = w0.size()[-1]
assert self.s == w0.size()[-2], "Grid must be uniform in... | {
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 16 11:02:22 2020
@author: sopmathieu
This file contains different functions to compute and analyze the autocorrelation
of time-series that may contain missing values.
These functions compute the autocorrelation and the autocovariance of a series
at a desired lag. They ... | {
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import numpy as np
def pad_images_similar(img1, img2):
list_of_images = [img1, img2]
padded_list = []
a = 0
b = 0
for image in list_of_images:
x, y = image.shape
if x > a:
a = x
if y > b:
b = y
for image in list_of_images:
nuc_a, nuc_b ... | {
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"... |
# make_experiments.py
# Author: Noam Buckman
# Description: Generate Simulation Settings
# This Notebook generates and saves multiple experiment savings for
# analyzing the effects of different parameters.
# The settings can be varied by % human (non-communicating) vehicles and SVO type.
# Output: Multiple Pickle (.p... | {
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"""
External indices
"""
import numpy as np
from sklearn.metrics.cluster.supervised import check_clusterings
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.metrics import accuracy_score
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
def _conting... | {
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# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Tests for telescope objects and functions.
"""
import os
from astropy.coordinates import EarthLocation
import numpy as np
import pytest
import pyuvdata
from pyuvdata.data import DAT... | {
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module Ch05.VarArg
import Data.Vect
--------------------------------------------------------------------------------
-- Auxillary stuff for defining functions with a variable # of args
--------------------------------------------------------------------------------
||| Type of vectors of fixed length of elements of ... | {
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from __future__ import division
from numpy import *
from interpolation.cartesian import mlinspace
d = 2 # number of dimension
Ng = 1000 # number of points on the grid
K = int(Ng**(1/d)) # nb of points in each dimension
N = 10000 # nb of points to evaluate
a = array([0.0]*d, dtype=fl... | {
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#this is a python script that calls a c++ excutable
import time
import numpy as np
import subprocess
import os
def generate_embedding(norb,nimp,u,afm):
'''
Generate the 1-body embedding Hamiltonian.
'''
tmpdir = "/home/sunchong/work/finiteTMPS/tests/call/dump/"
impsite = np.arange(nimp)+1
np.s... | {
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"""
Toolbox for statistical methods. For all functions, this toolbox assumes that
the first dimension is the temporal sampling dimension.
Author: Andre Perkins
"""
import numpy as np
import numexpr as ne
import dask.array as da
import logging
from scipy.linalg import svd
from scipy.ndimage import convolve1d
from skl... | {
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# Optimality conditions
Now we will move to studying constrained optimizaton problems i.e., the full problem
$$
\begin{align} \
\min \quad &f(x)\\
\text{s.t.} \quad & g_j(x) \geq 0\text{ for all }j=1,\ldots,J\\
& h_k(x) = 0\text{ for all }k=1,\ldots,K\\
&x\in \mathbb R^n.
\end{align}
$$
In order to identify which poi... | {
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"""
This module allows detect spots in fluorescence microscopy images. The algorithms
are translations of those published in Aguet et al. Dev. Cell 2013. Refer to that publication
and corresponding code for detailed information.
"""
# Author: Guillaume Witz, Biozentrum Basel, 2019
# License: MIT License
import numpy... | {
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import sys
import numpy as np
import cv2
filename = 'space_shuttle.jpg'
if len(sys.argv) > 1:
filename = sys.argv[1]
img = cv2.imread(filename)
if img is None:
print('Image load failed!')
exit()
# Load network
net = cv2.dnn.readNet('bvlc_googlenet.caffemodel', 'deploy.prototxt')
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from __future__ import annotations
from typing import Tuple, NoReturn
from ...base import BaseEstimator
import numpy as np
from itertools import product
from IMLearn.metrics.loss_functions import misclassification_error
class DecisionStump(BaseEstimator):
"""
A decision stump classifier for {-1,1} labels acco... | {
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import re
import numpy as np
def write_point_cloud(ply_filename, points):
formatted_points = []
for point in points:
formatted_points.append("%f %f %f %d %d %d 0\n" % (point[0], point[1], point[2], point[3], point[4], point[5]))
out_file = open(ply_filename, "w")
out_file.write('''ply
fo... | {
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#!/usr/bin/env python
import numpy as np
import rospy
from minau.srv import ArmControl, SetHeadingVelocity, SetHeadingDepth
from geometry_msgs.msg import Vector3
from minau.msg import ControlStatus
from sensor_msgs.msg import Joy
# def vel(heading,speed):
# heading_rad = heading*np.pi/180
# return Vector3(spee... | {
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import tensorflow as tf
from tensorflow import distributions
import numpy as np
def evaluate_sample(model, data_sample, session):
"""
Samples parameter realizations from the variational posterior distributions and
performs inference
Args:
model: the model to evaluate
data_iterat... | {
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# -*- coding: utf-8 -*-
"""
Created on Tue May 17 15:50:25 2016
@author: hossam
"""
from pathlib import Path
import optimizers.PSO as pso
import optimizers.MVO as mvo
import optimizers.GWO as gwo
import optimizers.MFO as mfo
import optimizers.CS as cs
import optimizers.BAT as bat
import optimizers.WOA as woa
import op... | {
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function [texture structure] = structure_texture_decomposition_rof(im, theta, nIters, alp)
%
% Decompose the input IMAGE into structure and texture parts using the
% Rudin-Osher-Fatemi method. The final output is a linear combination
% of the decomposed texture and the structure parts.
%
% According to Wedel etal "An ... | {
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import pandas as pd
import statsmodels.api as sm
from patsy import dmatrices
from statsmodels.stats.outliers_influence import variance_inflation_factor
def vif(df, y, x, merge_coef = False):
import scorecardpy as sc
df = sc.germancredit()
y = 'creditability'
x = ['age_in_years', 'credit_amount', 'present_resid... | {
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import numpy as np
# Calculating the time needed for task i when allocated in DC j
def dijstra(G, src, des):
'''Find the shortest path lenght from src to des
Args:
G: A graph representing the network
src: The source node
des: The destination node
Returns:
The length ... | {
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import sys
import subprocess
from SALib.test_functions import Ishigami
import numpy as np
import re
salib_cli = "./src/SALib/scripts/salib.py"
ishigami_fp = "./src/SALib/test_functions/params/Ishigami.txt"
if sys.version_info[0] == 2:
subprocess.run = subprocess.call
def test_delta():
cmd = "python {cli} sa... | {
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# import libraries here
import numpy as np
import cv2
def count_blood_cells(image_path):
"""
Procedura prima putanju do fotografije i vraca broj crvenih krvnih zrnaca, belih krvnih zrnaca i
informaciju da li pacijent ima leukemiju ili ne, na osnovu odnosa broja krvnih zrnaca
Ova procedura se poziva a... | {
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... | {
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import traces.PyTrace as PT
import numpy as np
import traces.conicsolve as con
import pdb,sys
from traces.axro.WSverify import traceChaseParam
import matplotlib.pyplot as plt
import time
import utilities.plotting as uplt
#Need to determine resolution and effective area
#as a function of shell radius
#Loop through all ... | {
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"""
Plot test files of the forcings
Notes
-----
Reference : Kay et al. [2014]
Author : Zachary Labe
Date : 1 February 2018
"""
### Import modules
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import nclcmaps as ncm
import datetime... | {
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import os
import csv
import sys
import re
from surprise import Dataset
from surprise import Reader
from collections import defaultdict
import numpy as np
class Load_Data:
# These two dictionaries will be used to fetch id from movieName and vice versa.
movieID_to_name = {}
name_to_movieID = {}
# de... | {
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/* Copyright (C) 2014 InfiniDB, Inc.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; version 2 of
the License.
This program is distributed in the hope that it will be useful,
but W... | {
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\chapter{{\tt ILUMtx}: Incomplete $LU$ Matrix Object}
\label{chapter:ILUMtx}
\par
The {\tt ILUMtx} object represents and approximate (incomplete)
$(L+I)D(I+U)$, $(U^T+I)D(I+U)$ or $(U^H+I)D(I+U)$ factorization.
It is a very simple object, rows and columns of $L$ and $U$ are
stored as single vectors.
All computations ... | {
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from __future__ import absolute_import, division, print_function, unicode_literals
import glob
import os
import numpy as np
import argparse
import json
import torch
from scipy.io.wavfile import write
from env import AttrDict
from meldataset import MAX_WAV_VALUE
from models import Generator
from time import time
h = N... | {
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'''This module contains all functions needed for
the fully handmade neural network, as well as the
model class itself'''
#import pandas as pd
import numpy as np
import seaborn as sns
#from progressbar.bar import ProgressBar
## Functions
def compute_activation (X, activation_type):
'''Defining activation function... | {
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[STATEMENT]
lemma irreflexiveDecisionLess:
shows "(x, x) \<notin> decisionLess"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x, x) \<notin> decisionLess
[PROOF STEP]
unfolding decisionLess_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x, x) \<notin> {(l1, l2). isDecision l1 \<and> \<not> isDecision l2}... | {
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"""Locator functions to interact with geographic data"""
import numpy as np
import pandas as pd
import flood_tool.geo as geo
__all__ = ['Tool']
def clean_postcodes(postcodes):
"""
Takes list or array of postcodes, and returns it in a cleaned numpy array
"""
postcode_df = pd.DataFrame({'Postcode':post... | {
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import numpy as np
from operator import itemgetter
from scipy.linalg import cholesky, cho_solve
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.base import clone
from sklearn.utils import check_random_state
from sklearn.utils.optimize import _check_optimize_result
from .kernels import RBF, Wh... | {
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(* Title: HOL/Algebra/Lattice.thy
Author: Clemens Ballarin, started 7 November 2003
Copyright: Clemens Ballarin
Most congruence rules by Stephan Hohe.
*)
theory Lattice
imports Congruence
begin
section \<open>Orders and Lattices\<close>
subsection \<open>Partial Orders\<close>
record 'a gorder =... | {
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# import numpy as np
# import gym
# import torch
# from typing import Dict, Any
# from copy import deepcopy
# from malib.algorithm.common.model import MLPCritic
# from malib.algorithm.common.policy import Policy
#
#
# class QMIX(Policy):
# def __init__(
# self,
# registered_name: str,
# obse... | {
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/***************************************************************************
* Software License Agreement (BSD License) *
* Copyright (C) 2012 by Markus Bader <markus.bader@tuwien.ac.at> *
* *
* Redistr... | {
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# -*- coding: utf-8 -*-
"""FFT functions.
This module contains FFT functions that support centered operation.
"""
import numpy as np
from sigpy import config, util
if config.cupy_enabled:
import cupy as cp
def fft(input, oshape=None, axes=None, center=True, norm='ortho'):
"""FFT function that supports cent... | {
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import pygame
import numpy as np
import random
pygame.init()
clock = pygame.time.Clock()
display = pygame.display.set_mode((1280, 720))
screen = pygame.display.get_surface()
width,height = screen.get_width(), screen.get_height()
pygame.display.set_caption('water cellular automata')
rows = int(height / 8)
... | {
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[STATEMENT]
lemma irreducible\<^sub>dD:
assumes "irreducible\<^sub>d p"
shows "degree p > 0" "\<And>q r. degree q < degree p \<Longrightarrow> degree r < degree p \<Longrightarrow> p \<noteq> q * r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < degree p &&& (\<And>q r. \<lbrakk>degree q < degree p; degree r... | {
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#pragma once
#include <boost/core/noncopyable.hpp>
#include <cstdint>
#include <functional>
#include <memory>
#include <optional>
#include <string>
#include <utility>
namespace koinos::mq {
enum class retry_policy
{
none,
exponential_backoff
};
enum class error_code : int64_t
{
success,
failure,
tim... | {
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import ipywidgets as widgets
from ipywidgets import interact
import matplotlib.pyplot as plt
import numpy as np
def browse_track_multi(img, liste_a, segmentation):
nt, ny, nx = img.shape
def plot_track(i, save=False, name_img = "file.png"):
fig, axes = plt.subplots(1,1, figsize=(8, 8))
axes.ims... | {
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from typing import Dict, List, Tuple, Union
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import Dataset
from .prepare_data import process_labels, process_tokens
class NERDataset(Dataset):
"""
PyTorch Dataset for NER data format.
Dataset might be prepr... | {
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import os
import h5py
import json
import pickle
import random
import numpy as np
from pathlib import Path
from sklearn.cluster import MiniBatchKMeans
# root path
ROOT_PATH = Path(os.path.dirname(__file__)).parent
# set seed value
SEED = 1234
random.seed(SEED)
np.random.seed(SEED)
# read data
hdf5_data = h5py.File(os... | {
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/*==============================================================================
Copyright (c) 2016 Matt Calabrese
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
===================================================... | {
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cd("..")
run(`bash build.sh`)
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import pandas as pd
import numpy as np
from datetime import datetime
df = pd.read_csv('3WLA.txt',sep='\t')
epoch = datetime(1980, 1, 1)
df["Cardinality TW"] = df["Week-End"].apply(lambda x: (datetime.strptime(x, "%d-%b-%Y")-epoch))
df["Activity CL"] = df["Activity"].apply(lambda x: x.lower().strip().replac... | {
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# coding: utf-8
# This notebook will make abundance matched catalogs for Jeremy and Zhongxu. I'm gonna send this notebook along to them as well in case there's something not quite right that they want to adjust. The catalogs requested were defined as follows:
# - 2 catalogs, M_peak and V_max @ M_peak (which is how, I... | {
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import unittest
from opentamp.core.internal_repr import parameter
from opentamp.core.util_classes import robot_predicates, pr2_predicates, matrix
from opentamp.core.util_classes.openrave_body import OpenRAVEBody
from errors_exceptions import PredicateException, ParamValidationException
from opentamp.core.util_classes.p... | {
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PROGRAM exemple
USE parlib
USE lib
IMPLICIT NONE
REAL:: more
more = addition(a,b)
WRITE(*,*) "The result: ", more
END PROGRAM
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dir <- "~/workspace/dyn-urg/files/results/time-series-dynamism-experiment/"
nonhomogTS <- read.table(paste(dir,"non-homog-poisson-dynamism.csv",sep=""), quote="\"",as.is=T,colClasses=list("numeric"))
homogTS <- read.table(paste(dir,"homog-poisson-dynamism.csv",sep=""), quote="\"",as.is=T,colClasses=list("numeric"))
n... | {
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import numpy as np
class solver:
def __init__(self):
self.grid= np.ones((9,9,9),dtype=np.uint8)
self.Org=None
self.isSolved=False
self.Error=False
def setField(self,x,y,value):
if value>0:
self.grid[x,y,:]=0
self.grid[x,y,value-1]=1
e... | {
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# -*- coding: utf-8 -*-
"""
Generating image window by weighted sampling map from input image
This can also be considered as a `weighted random cropping` layer of the
input image
"""
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from niftynet.engine.image_... | {
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include("Include.jl")
# extra:
using Flux
using Flux: @epochs
using BSON: @save
# load training set -
full_training_data_frame = load_fibrinolysis_training_data()
# filter out the data -
experimental_data_table = filter(:visitid => x -> (x == 2 || x == 3 || x == 1), full_training_data_frame)
# initialize storage fo... | {
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from copy import deepcopy
import numpy as np
import torch
from ..data import FlatTransition
from . import OffPolicyAlgorithm
from .goal_sampling_strategy import GoalSamplingStrategy
class HAC(OffPolicyAlgorithm):
"""Hierarchical Actor-Critic."""
supported_goal_sampling_strategies = {"future", "episode", "fi... | {
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using RegexTools
using Test
@testset "RegexTools.jl" begin
@testset "hex_escape" begin
for char in Char.(0:126)
rawstr = string(char)
regstr = RegexTools.hex_escape(rawstr)
reg = Regex(regstr)
m = match(reg, rawstr)
@test !isnoth... | {
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"""Reads a multi-tile CBF image, discovering its detector geometry automatically"""
import sys
import numpy
import pycbf
from scitbx.array_family import flex
from dxtbx.format.FormatCBF import FormatCBF
from dxtbx.format.FormatCBFFull import FormatCBFFull
from dxtbx.format.FormatStill import FormatStill
from dxtbx... | {
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"""
Programmer:
Date of Development:
This code has been developed according to the procedures mentioned in the following research article:
" "
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
from Py_FS.wrapper.nat... | {
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*dk,ltripedge
function ltripedge(i,iedg,itet,itetoff,itettyp,iparent,jtet,
& jtetoff,mbndry,nef_cmo,icr1,icontab)
C
C
C #####################################################################
C
C PURPOSE -
C
C This function is true iff the given edge is determined
C to be a triple edge.
C
C... | {
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from copper.cop.cop_node import CopNode
import pyopencl as cl
import numpy
from PIL import Image
class COP2_Comp_Add(CopNode):
'''
This filter adds foreground over background using OpenCL
'''
type_name = "add"
category = "comps"
def __init__(self, engine, parent):
super(CLC_Comp_Add, self).__init__(engine, ... | {
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subroutine readopac(fname, f, fx, fy, fxy)
c
c***********************************************************************
c read opacity tables
c***********************************************************************
c
include'titan.imp'
include'titan.par'
include'titan.com'
c
logical lxst... | {
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#!/usr/bin/env python
import rospy
from geometry_msgs.msg import Twist, Quaternion #, Point, Pose, TwistWithCovariance, Vector3
import tf
import numpy as np
def main():
rospy.init_node("sphero_simple_openloop")
pub_twist = rospy.Publisher("/cmd_vel", Twist, queue_size=1)
T = Twist()
r = rospy.Rate(1... | {
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from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import json
import torch
import numpy as np
import random
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import sys
import time
import argparse
from src.models.m... | {
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"""
@author: Vincent Bonnet
@description : Constraint data structure
"""
import numpy as np
import core.jit.item_utils as item_utils
class Constraint:
def __init__(self, num_nodes : int):
# Constraint Property
self.stiffness = np.float64(0.0)
self.damping = np.float64(0.0)
# Node... | {
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import os
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + '/../..')
from andi_funcs import TrackGeneratorSegmentation, import_tracks, import_labels, package_tracks
from models import segmentation_model_2d
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import Mode... | {
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[STATEMENT]
lemma lex_two_SN_order_pair:
assumes o1: "SN_order_pair s1 ns1" and o2: "SN_order_pair s2 ns2"
shows "SN_order_pair (lex_two s1 ns1 s2) (lex_two s1 ns1 ns2)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. SN_order_pair (lex_two s1 ns1 s2) (lex_two s1 ns1 ns2)
[PROOF STEP]
proof -
[PROOF STATE]
proof ... | {
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[STATEMENT]
lemma Substable_intro_diamond[Substable_intros]:
assumes "Substable cond \<psi>"
shows "Substable cond (\<lambda> \<phi> . \<^bold>\<diamond>(\<psi> \<phi>))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Substable cond (\<lambda>\<phi>. \<^bold>\<diamond>\<psi> \<phi>)
[PROOF STEP]
unfolding co... | {
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import matplotlib
import numpy as np
matplotlib.use('Agg')
import shap
def test_multiply():
""" Basic LSTM example from keras
"""
try:
import keras
import numpy as np
import tensorflow as tf
from keras.datasets import imdb
from keras.models import Sequential
... | {
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from .enums import CitySize, AreaKind
import numpy as np
class OkumuraHata:
def __init__(
self,
frequency,
transmitter_height,
receiver_height,
city_size,
area_kind,
):
self.frequency = frequency
self.transmitter_height = transmitter_height
self.receiver_height = ... | {
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///////////////////////////////////////////
// http://progsch.net/wordpress/?p=81
#include <boost/thread.hpp>
#include <deque>
class ThreadPool;
// our worker thread objects
class Worker {
public:
Worker(ThreadPool &s) : pool(s) { }
void operator()();
private:
ThreadPool &pool;
};
// the actual thread pool
class... | {
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'''
This module contains functions for retrieving Kartverket map data for specified areas.
Notes
-----
The retrieval of map data uses `owslib`. Read more in the `owslib image
tutorial
<https://geopython.github.io/OWSLib/index.html?highlight=webmapservice>`_.
Karverket data is retrieved from the `Kartverket WMS
<http:... | {
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import numpy as np
import os
from IPSData import CollectorIPSData
import csv
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
forest = RandomForestRegressor(n_estimators = 10 , max_features = 'sqrt' , criterion = 'mse' , max_depth ... | {
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#!/usr/bin/python
import re
import numpy as np
import sys
import ChemUtils as ch
#print ch.str2composition( sys.argv[1] )
#sides = ch.parseReaction( 'Fe+O2=Fe2O3' )
#sides = ch.parseReaction( 'C12H22O11+KNO3=H2O+CO2+K2CO3+N2' )
#print sides
#print ch.reaction2string( sides )
#print ch.balanceReactionString( 'Fe+O2=... | {
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import numpy as np
from relevanceai.operations.dr.base import DimReductionBase
from typing import Optional, Dict, Any
from relevanceai.operations.cluster.constants import (
DIM_REDUCTION,
DIM_REDUCTION_DEFAULT_ARGS,
)
class PCA(DimReductionBase):
def fit(self, vectors: np.ndarray, dims: int = 3, *args, **... | {
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import numpy as np
from conv_net import conv_net
from data_utils import *
import time
class Solver(object):
"""
Solver class for train and test
methods:
- train: train conv_net
- test: test trained net
"""
def __init__(self):
# config
self.lr = 1e-4
self.weight_decay=0.0004
self.batch_si... | {
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# -*- coding: utf-8 -*-
"""
Created on Wed May 26 18:27:37 2021
@author: Laura Grandas
"""
import os
import PyPDF2
import slate3k as slate
import pandas as pd
import numpy as np
import string
# para las stopwords
import nltk
from nltk.corpus import stopwords
spanish_stopwords = stopwords.words('span... | {
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/-
Copyright (c) 2020 Microsoft Corporation. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Leonardo de Moura, Sebastian Ullrich
-/
import Lean.Util.CollectLevelParams
import Lean.Elab.DeclUtil
import Lean.Elab.DefView
import Lean.Elab.Inductive
import Lean.Elab.Struct... | {
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import argparse
import yaml
import os
import shutil
from pathlib import Path
import torch
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams["lines.linewidth"] = 0.8
from pysnn.network import SNNNetwork
from evolutionary.utils.constructors import build_netwo... | {
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"include": true,
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import numpy as np
import torch
import torch.nn as nn
import torch.functional as F
from sklearn.metrics import accuracy_score
class DMILoss:
def __call__(self, *args, **kwargs):
return self.forward(*args, **kwargs)
def forward(self, output, target):
outputs = torch.softmax(output, dim=-1)
... | {
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module complete
end
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[STATEMENT]
lemma diff_invariant_eq: "diff_invariant I f U S t\<^sub>0 G =
(\<forall>s. I s \<longrightarrow> (\<forall>X\<in>Sols f U S t\<^sub>0 s. (\<forall>t\<in>U s.(\<forall>\<tau>\<in>(down (U s) t). G (X \<tau>)) \<longrightarrow> I (X t))))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. diff_invariant I... | {
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# any functions for plots
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib import dates as mdates
from matplotlib.ticker import FormatStrFormatter
import numpy as np
import pandas as pd
from pathlib import Path
import glob, os
import datetime
def deploy... | {
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import data.finset
import data.fintype
import data.fin
import data.rat
open function
open finset
variables {α : Type*} {β : Type*}
variables [fintype α] [fintype β]
namespace fintype
theorem card_image_of_injective [decidable_eq β] {f : α → β}: injective f → finset.card ((elems α).image f) = card α :=
finset.car... | {
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#!/usr/bin/env python
"""bulge_graph.py: A graph representation of RNA secondary structure based
on its decomposition into primitive structure types: stems, hairpins,
interior loops, multiloops, etc...
for eden and graphlearn we stripped forgi down to this single file.
forgi: https://github.com/pkerpedj... | {
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
import cv2
import numpy as np
from maskrcnn_benchmark.config import cfg
from demo.predictor import ICDARDemo, RRPNDemo
from maskrcnn_benchmark.utils.visualize import vis_image, write_result_ICDAR_RRPN2polys, zip_dir
from maskrcnn_benchmark.data.datasets.irra_int... | {
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#!/usr/bin/env python
# coding: utf-8
# # Fifth exercice: Non-Cartesian radial under-sampling
#
# In this notebook, you can play with the design parameters to regenerate different radial in-out patterns (so, we draw radial spokes over a rotating angle of $\pi$). You can play with the number of shots by changing the u... | {
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import os
import pickle
from typing import Dict
import colorlog
import tensorflow as tf
import numpy as np
__PATH__ = os.path.abspath(os.path.dirname(__file__))
_PARLAI_PAD = '__null__'
_PARLAI_GO = '__start__'
_PARLAI_EOS = '__end__'
_PARLAI_UNK = '__unk__'
_PARLAI_START_VOCAB = [_PARLAI_PAD, _PARLAI_GO, _PARLAI_EO... | {
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from __future__ import absolute_import, division, print_function, unicode_literals
import time
import numpy as np
from noise import snoise3
from ..mode import Mode
class Noise(Mode):
class State(object):
RED = "RED"
GREEN = "GRN"
BLUE = "BLUE"
GREYSCALE = "GREY"
FULL_COL... | {
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import numpy as np
import subprocess
import os
import glob
#NUM_TRIES = 5
#NUM_EPOCHS = 200
NUM_TRIES = 1
NUM_EPOCHS = 20
#DATASETS=["UWaveGestureLibraryAll","FacesUCR","ECG5000"]
#DATASETS=["Datasets/CDOT/Time_Series_For_Clustering_El_Paso_with_Weather_data.csv"]
DATASETS=["CDOT"]
exmpl_range = np.arange(4, 29, step=8... | {
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# https://github.com/nirupamaprv/Analyze-AB-test-Results/blob/master/Analyze%20A:B%20Test%20Results-Quiz-Answers.txt
# hypothesis is less related to p-value
import pandas as pd
import numpy as np
import random
import matplotlib
matplotlib.use('TkAgg')
#We are setting the seed to assure that we get the same answers ... | {
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#script to create moisture capital map for testing (for % chg)
#requires climate data from cru_ts4.zip (via https://crudata.uea.ac.uk/cru/data/hrg/)
rm(list = ls())
library(raster)
library(tidyverse)
#set scenario parms
initial_yr <- 2018
final_yr <- 2035
perc_chg <- -20 #this is the % difference over the entire... | {
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#!/usr/bin/env python3
import argparse
from collections import defaultdict
from typing import Dict, List, Union, Any
import xml.etree.ElementTree as ET
from xml.dom import minidom
from xml.sax.saxutils import unescape
import networkx as nx
import yaml
import sys
import io
def bandwidth_conversion(x):
# shadow... | {
"alphanum_fraction": 0.6611102375,
"author": null,
"avg_line_length": 40.5031847134,
"converted": null,
"ext": "py",
"file": null,
"hexsha": "cfbe16d1c41d73418199b655eced9d4b0c60d5c0",
"include": true,
"lang": "Python",
"length": null,
"llama_tokens": null,
"mathlib_filename": null,
"max_for... |
include("faber.jl")
include("types.jl")
"""
acc_rej(n::Int64, S::System, X::AbstractModel, f1::Regular, u, v)
For Regular sampling_scheme: always accept
"""
function acc_rej(n::Int64, S::System, X::AbstractModel, f1::Regular, u::Float64, v::Float64, t::Float64)
return true
end
"""
acc_rej(n::Int64, S::Sy... | {
"alphanum_fraction": 0.6051915048,
"author": null,
"avg_line_length": 33.3878787879,
"converted": null,
"ext": "jl",
"file": null,
"hexsha": "47e4ffb39c2be1340fcf4210ed7ffbf3fc208010",
"include": null,
"lang": "Julia",
"length": null,
"llama_tokens": null,
"mathlib_filename": null,
"max_fork... |
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