text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
This Python script calculates the Street-Network Disconnectedness index (SNDi).
Journal article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223078
Authors: Christopher Barrington-Leigh and Adam Millard-Ball
Date: November 26, 2019
Adapted for momepy by: Andres Morfin Veytia
Date: Septembe... | {"hexsha": "ef297266410c55f78bfa054308026b20aee0da26", "size": 14428, "ext": "py", "lang": "Python", "max_stars_repo_path": "momepy/sndi.py", "max_stars_repo_name": "amorfinv/momepy", "max_stars_repo_head_hexsha": "6741471bdaf2bfa0d94f3ed4724dd464129db32b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
import os
import logging
import datetime
import gc
import joblib
import tqdm
import tempfile
import math
import numpy as np
import torch
import torchvision
import skimage
import skimage.io
import skimage.transform
import skimage.measure
import skimage.morphology
import justdeepit.utils
from justdeepit.models.abstract i... | {"hexsha": "4a72a2903dadfbcaab7f46a6cb00ebcc7716cff6", "size": 36378, "ext": "py", "lang": "Python", "max_stars_repo_path": "justdeepit/models/utils/u2net.py", "max_stars_repo_name": "biunit/agrolens", "max_stars_repo_head_hexsha": "4ffe4ec47c0eb1149b81b745dc8ba3a6e7caedab", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#ifndef __IRODS_FIRST_CLASS_OBJECT_HPP__
#define __IRODS_FIRST_CLASS_OBJECT_HPP__
// =-=-=-=-=-=-=-
#include "irods_log.hpp"
#include "irods_resource_types.hpp"
#include "irods_network_types.hpp"
// =-=-=-=-=-=-=-
// irods includes
#include "rcConnect.h"
// =-=-=-=-=-=-=-
// boost includs
#include <boost/shared_ptr.... | {"hexsha": "db6180541f338e9365a9642db2ae302eabde29d5", "size": 1253, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "iRODS/lib/core/include/irods_first_class_object.hpp", "max_stars_repo_name": "iychoi/cyverse-irods", "max_stars_repo_head_hexsha": "0070b8677a82e763f1d940ae6537b1c8839a628a", "max_stars_repo_license... |
function y = poly_env( p, x )
%POLY_ENV Evaluate the convex or concave envelope of a polynomial.
% POLY_ENV( P, X ) uses a semidefinite program to compute the value of the
% convex or concave envelope of the polynomial represented by the vector
% P. The format of the vector P is identical to that required by POL... | {"author": "yu-jiang", "repo": "radpbook", "sha": "88b9fa7d0a541099cdd1ac29383c89e087d1d895", "save_path": "github-repos/MATLAB/yu-jiang-radpbook", "path": "github-repos/MATLAB/yu-jiang-radpbook/radpbook-88b9fa7d0a541099cdd1ac29383c89e087d1d895/tools/cvx-w64/cvx/functions/poly_env.m"} |
#ifndef ASLAM_BACKEND_DV_MATRIX_HPP
#define ASLAM_BACKEND_DV_MATRIX_HPP
#include <aslam/backend/JacobianContainer.hpp>
#include <boost/shared_ptr.hpp>
#include <set>
namespace aslam {
namespace backend {
/**
* \class MatrixExpressionNode
* \brief The superclass of all classes representing transformations.
*/
cla... | {"hexsha": "8ed6f24348d75a445d9863475208be613b54e29a", "size": 1418, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "aslam_optimizer/aslam_backend_expressions/include/aslam/backend/MatrixExpressionNode.hpp", "max_stars_repo_name": "PushyamiKaveti/kalibr", "max_stars_repo_head_hexsha": "d8bdfc59ee666ef854012becc935... |
import sys
import glob
import os
import numpy as np
import multiprocessing as mp
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy.interpolate import make_interp_spline,BSpline
#from scipy.misc import imresize
#from scipy.misc import imsave
from PIL import Image
from imageio import imwrite
d... | {"hexsha": "2fb45e4df130b34cbe072e763fc896bcbd9a1e42", "size": 3210, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_scripts/ex_wga_norm_thresh.py", "max_stars_repo_name": "SpeerLab/STORM-UI-", "max_stars_repo_head_hexsha": "232752dfa11e0fd2f4fda9fe0db36ac3ceb953d3", "max_stars_repo_licenses": ["MIT"], ... |
# test_stats.py collects trial avg progress reversal/recovery stats
import cv2
import os
import re
import argparse
import numpy as np
from scipy import stats
from utils import get_prediction_vis
from logger import Logger
if __name__ == '__main__':
# parse arguments
parser = argparse.ArgumentParser()
parse... | {"hexsha": "23ddc0dbee4f403497cb9489bf90e938a7c8a36d", "size": 10752, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_stats.py", "max_stars_repo_name": "Pandinosaurus/good_robot", "max_stars_repo_head_hexsha": "61217d65f040d536e54804150ce8abcf97343410", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
"""
PolyaGammaPSWSampler(b::Int, z::Real)
PSW sampler ([1]) for a Polya-Gamma distribution with parameters `b` and `z`,
and Laplace transform
```math
\\mathcal{L}(t) = \\cosh^b(z) \\cosh^{-b}(\\sqrt{2t + z^2})
```
References
* [1] <https://doi.org/10.1080/01621459.2013.829001>
"""
struct PolyaGamm... | {"hexsha": "6e2e56fa355097f685a392e427d42d143e70e845", "size": 2657, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/polyagammapswsampler.jl", "max_stars_repo_name": "igutierrezm/PolyaGammaSamplers.jl", "max_stars_repo_head_hexsha": "f176bd8e98dd78c70e755c39f48ba7318f74503f", "max_stars_repo_licenses": ["MIT"... |
/**************************************************************
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to y... | {"hexsha": "5a37d35318bfd9cf518d387472f613f03ef504bd", "size": 5611, "ext": "hxx", "lang": "C++", "max_stars_repo_path": "main/slideshow/source/engine/shapes/viewappletshape.hxx", "max_stars_repo_name": "Grosskopf/openoffice", "max_stars_repo_head_hexsha": "93df6e8a695d5e3eac16f3ad5e9ade1b963ab8d7", "max_stars_repo_lic... |
module Milann
using Flux
import Flux.Tracker: data, @grad, track
using Statistics
# This implemens a MIL version where the bag instances are stored in a continuous tensor and bags are delimited by ranges.
export RangeMIL, segmax, segmean, segmax_naive, segmean_naive, segmaxmean
struct RangeMIL
premodel
ag... | {"hexsha": "dd39ad1d78f26013d882695695ac7d455ba4cbb0", "size": 5288, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Milann.jl", "max_stars_repo_name": "dhonza/Milann.jl", "max_stars_repo_head_hexsha": "2edb44a14e03f2d09cb567988e52648f6a35cbb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
c Subroutine to remove duplicate data from claremorris(auto)
c AJ_Kettle, Dec18/2017
SUBROUTINE clean_daydata(l_mlent,s_filename,s_filename_test,
+ l_datalines_pre,s_vec_stnnum_pre,
+ s_vec_date_pre,s_vec_time_pre,
+ f_vec_rain_mm_pre,f_vec_maxdy_c_pre,f_vec_mindy_c_pre,
... | {"hexsha": "ace05386ea64ca0e26a4cf8f8253cdd1e7109438", "size": 4250, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Github_meteireann_daily_monthly/Subroutine/clean_daydata.f", "max_stars_repo_name": "ajkettle/glamod-nuim", "max_stars_repo_head_hexsha": "beb37a3c2acab3f334918dda3e366c7882cc0960", "max_stars_rep... |
@def Ax_min begin #(U)
# minimum longitudinal acceleration for given speed
Ax_min = Array(Float64,(length(U),1))
for i in eachindex(U)
Ax_min[i,1] = AXC[5]*U[i]^3 + AXC[6]*U[i]^2 + AXC[7]*U[i] + AXC[8]
end
Ax_min
end
| {"hexsha": "5165a408325a1aca9d7e746904431f1c8a576f29", "size": 225, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Three_DOF/Ax_min.jl", "max_stars_repo_name": "ChrisRackauckas/VehicleModels.jl", "max_stars_repo_head_hexsha": "1d55b456bd1693f143f6e4153a4b2b61bc978228", "max_stars_repo_licenses": ["MIT"], "ma... |
import time
import rospy
import rospkg
import os
import sys
import numpy as np
import tensorflow as tf
from styx_msgs.msg import TrafficLight
from io import StringIO
MINIMUM_CONFIDENCE = 0.4
class TLClassifier(object):
def __init__(self, simulator):
# current_path = os.path.dirname(os.path.realpath(__fi... | {"hexsha": "478e55204ccd9674d3919014d3981adbbd847c1c", "size": 3953, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/tl_detector/light_classification/tl_classifier.py", "max_stars_repo_name": "dalacan/Project-Capstone", "max_stars_repo_head_hexsha": "714751b2644b945d86acdd0a9980435e2806f1d9", "max_stars_... |
"""
Ajay Kc
013213328
EE381
Project 4 Part 1
The problem plots a probability distribution function of S, where S is a Random Variable of sum
of the widths of n books. The value of n is 1,5,10,and 15. For each value of n, the experimental
PDF and normal distribution function is calculated.
"""
import numpy as np
impo... | {"hexsha": "d1727b5dfd5045ee6a511416c3e7987301d7c0f4", "size": 2018, "ext": "py", "lang": "Python", "max_stars_repo_path": "CentralLimitTheorem/ExponentiallyDistributedRV.py", "max_stars_repo_name": "ajaykc7/PythonProjects", "max_stars_repo_head_hexsha": "01c5be78ffb0af9bda56e7b892a559a9453c1492", "max_stars_repo_licen... |
import numpy as np
import pytest
import autogalaxy as ag
grid = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [2.0, 4.0]])
class TestEllipticalGaussian:
def test__deflections_correct_values(self):
gaussian = ag.mp.EllipticalGaussian(
centre=(0.0, 0.0),
elliptical_comp... | {"hexsha": "1bd7e4b589944d17cda55876e1afa4c04e40d962", "size": 59281, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_autogalaxy/unit/profiles/mass_profiles/test_stellar_mass_profiles.py", "max_stars_repo_name": "jonathanfrawley/PyAutoGalaxy_copy", "max_stars_repo_head_hexsha": "1cedbfdcf65020538128163f7d8a... |
from IPython.display import HTML
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from IPython.display import YouTubeVideo
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram
from matplotlib.colors import ListedCo... | {"hexsha": "0fa3bb2ef7e4beb73e5859c379ea294100b44979", "size": 12023, "ext": "py", "lang": "Python", "max_stars_repo_path": "HPnex/classification.py", "max_stars_repo_name": "PanditPranav/PREDICT_network_analysis", "max_stars_repo_head_hexsha": "8e7d24b3e549c818c99fb132ac7e190d6561df6f", "max_stars_repo_licenses": ["MI... |
import numpy as np
from standard import config, DataManager, AbstractExchange
class SimulationExchange(AbstractExchange.AbstractExchange):
def __init__(self, wallets: dict):
if 'USD' not in wallets:
wallets['USD'] = 0
self.dm = DataManager.DataManager(start_date=config.start_date_tes... | {"hexsha": "7eb5da4029116e0d0c495b7122482cfde4158253", "size": 3361, "ext": "py", "lang": "Python", "max_stars_repo_path": "standard/SimulationExchange.py", "max_stars_repo_name": "vt-technologies-us/cryptobeet", "max_stars_repo_head_hexsha": "db1ac3c72774baea908f1c1fcdd3d3219175a812", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from com_cheese_api.cmm.utl.file import FileReader
from com_cheese_api.ext.db import url, db, openSession, engine
from konlpy.tag import Okt
from collections import Counter
from wordclou... | {"hexsha": "b5e329d8880faded2bdfccbbe428bab789c3e61e", "size": 9458, "ext": "py", "lang": "Python", "max_stars_repo_path": "proj/cheese-emp-ai/com_cheese_api/cop/ord/order/model/order_dfo.py", "max_stars_repo_name": "youjeong2/EMP", "max_stars_repo_head_hexsha": "1afdf2789b09e55e4f6a2241a2cc2d0d1b607d9e", "max_stars_re... |
"""
Cauchy(μ, σ)
The *Cauchy distribution* with location `μ` and scale `σ` has probability density function
```math
f(x; \\mu, \\sigma) = \\frac{1}{\\pi \\sigma \\left(1 + \\left(\\frac{x - \\mu}{\\sigma} \\right)^2 \\right)}
```
```julia
Cauchy() # Standard Cauchy distribution, i.e. Cauchy(0, 1)
Cauchy(... | {"hexsha": "34a06952ee5566d35d4a85889da5829b39d2d9fe", "size": 2821, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/univariate/continuous/cauchy.jl", "max_stars_repo_name": "ludkinm/Distributions.jl", "max_stars_repo_head_hexsha": "7ec5b512139e8fb0a46b7a283d42d95a7b540bd0", "max_stars_repo_licenses": ["MIT"]... |
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, mean_absolute_error, mean_squared_error
from sklearn.util... | {"hexsha": "58e3b0090b68e6f507f2944dce0c64c7b229636e", "size": 10148, "ext": "py", "lang": "Python", "max_stars_repo_path": "draw_graphs.py", "max_stars_repo_name": "ba-san/DL-CV-tools", "max_stars_repo_head_hexsha": "ae5895a189fde39abe342ec19eaca2f388504b48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
# Network representation learning with Line algorithm
# Author: Sebastian Haslebacher 2021-12-22
import networkx as nx # https://networkx.org/documentation/stable/tutorial.html
import numpy as np
import random
import argparse
import pickle
class Sampler:
"""
Maintains data-structure for negative sampling.
... | {"hexsha": "9f3782e517f1dea43774318f5bb70c4ddf3ecdf0", "size": 3828, "ext": "py", "lang": "Python", "max_stars_repo_path": "Line-1/main.py", "max_stars_repo_name": "Manu-Fraile/Network-Representation-Learning", "max_stars_repo_head_hexsha": "d84414c144cc6146d406e606ed5be8120d4244a9", "max_stars_repo_licenses": ["MIT"],... |
import collections
import sys
import numpy as np
from typing import Any, Callable, List, Union, Sequence, Optional
from torch.utils.data import Subset
from monai.data import CacheDataset
from generator_coords import CoordsGenerator
class BrainCacheDataset(CacheDataset):
"""General purpose dataset class with sev... | {"hexsha": "6a6e5abc05db4fe84b9270346de3a9a52687efd5", "size": 4750, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/brain_dataset_cache.py", "max_stars_repo_name": "ssktotoro/neuro", "max_stars_repo_head_hexsha": "2d6d4cd060ee368d67c3437cac2b64eeb7d7180c", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
!! Copyright (C) Stichting Deltares, 2012-2016.
!!
!! This program is free software: you can redistribute it and/or modify
!! it under the terms of the GNU General Public License version 3,
!! as published by the Free Software Foundation.
!!
!! This program is distributed in the hope that it will be useful,
!! b... | {"hexsha": "05d4666ebd6e24fd19cf415aaf0974e54a5d8dba", "size": 3525, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/waq/packages/waq_kernel/src/waq_kernel/dlwqh6.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531... |
# make heat maps for paper
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(scales)
# read in files
OTUtables <- list.files(path='.', pattern='.csv', full.names=T)
Data1_OTU_wgs_16s <- read.csv(OTUtables[1], header=T)
Data2_OTU_wgs_microb <- read.csv(OTUtables[2], header=T)
Data3_OTU_metabar <- read.c... | {"hexsha": "2d8056f3b3793d42d0a6d1a5ae9c514182227bc6", "size": 6315, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts_to_compare_dats/heatmap_plts.r", "max_stars_repo_name": "marisalim/EverestMetagenomics2020", "max_stars_repo_head_hexsha": "18fa4355d5f89614ad6bfde402e3db18f2f4dd8c", "max_stars_repo_licenses": ... |
#!/usr/bin/env python3
import jax.numpy as np
from jax.ops import index_update
from jax.ops import index
from compas.numerical import connectivity_matrix
__all__ = ["ForceDensity", "force_equilibrium"]
class ForceDensity():
"""
A callable-object version of the force density method.
"""
def __ca... | {"hexsha": "6c409c1a6e12e4877d7d1f73f01c2ad31f6c46bd", "size": 1456, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/force_density/equilibrium.py", "max_stars_repo_name": "arpastrana/force_density", "max_stars_repo_head_hexsha": "0ceab89a8b125d9b6dbd410ba28c55df17135a89", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: utf-8 -*-
# @Author: Mariia Popova
# @Email: theo.lemaire@epfl.ch
# @Date: 2020-02-27 21:24:05
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2020-07-21 16:15:20
import numpy as np
from ..core import PointNeuron, addSonicFeatures
@addSonicFeatures
class MRGNode(PointNeuron):
''' Mamma... | {"hexsha": "2463d484db03fc58d52b119e08ddf85492e5bef0", "size": 6336, "ext": "py", "lang": "Python", "max_stars_repo_path": "PySONIC/neurons/mrg.py", "max_stars_repo_name": "scbao/pysonic", "max_stars_repo_head_hexsha": "b4ccaf49772d55f632a0995c411d1cc042d71903", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from __future__ import print_function
from orphics import maps,io,cosmology,stats
from pixell import enmap
import numpy as np
import os,sys
from soapack import interfaces as sints
from actsims import noise
froot = "/scratch/r/rbond/msyriac/data/scratch/tilec/test_lfi_v2_00_0000_deep56/"
kroot = "/scratch/r/rbond/msyri... | {"hexsha": "921a7ed82b5fc3a6a789280b3fa358024cf969f6", "size": 2405, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/debug_lfi.py", "max_stars_repo_name": "ACTCollaboration/tilec", "max_stars_repo_head_hexsha": "11ed8d027ad6ffac09b3e291a047f33e97673f14", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
#! /usr/bin/env python3
from Planet import *
import Universe
import ui
from random import randint
import numpy as np
import pygame
from pygame.locals import *
# name pos vel radius color surface_gravity
p1 = Planet("walnut", (640, 360), (2, 0), 50, (0, 0, 255), 7000)
p... | {"hexsha": "8f471e95b22439266150a180371b76a46af0c6c9", "size": 3332, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_impl/space.py", "max_stars_repo_name": "taidanh/space-simulation", "max_stars_repo_head_hexsha": "fa116c12dc5b90c8f64cad4c6f70c116cae211f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# International_Standard.py:
# Created: Mar, 2014, SUAVE Team
# Modified: Jan, 2016, M. Vegh
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
import numpy as np
from SUAVE.Attributes.Atmospheres import Atmos... | {"hexsha": "d0ee6cd5703765b7e12ed1bbf8f1582174417b33", "size": 931, "ext": "py", "lang": "Python", "max_stars_repo_path": "References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Atmospheres/Earth/International_Standard.py", "max_stars_repo_name":... |
/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2018, Bielefeld University
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the followin... | {"hexsha": "a07ebdc795996c06fe3fd93b4341e21c2429c940", "size": 7602, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "core/src/stages/simple_grasp.cpp", "max_stars_repo_name": "cpt-yoshi/moveit_task_constructor", "max_stars_repo_head_hexsha": "4aeab27ba3bed47453ffab0b454eb6eb09b54e4f", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma set_foldr_Cons:
"set (foldr (\<lambda>x xs. if P x xs then x # xs else xs) as []) \<subseteq> set as"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. set (foldr (\<lambda>x xs. if P x xs then x # xs else xs) as []) \<subseteq> set as
[PROOF STEP]
by(induct as) auto | {"llama_tokens": 115, "file": "Containers_RBT_Set2", "length": 1} |
from torchvision import datasets, transforms
from base import BaseDataLoader
import torch.utils.data as Data
import scipy.io
import torch
# MNIST数据集
class MnistDataLoader(BaseDataLoader):
"""
MNIST data loading demo using BaseDataLoader
"""
def __init__(self, data_dir, batch_size, shuffle=True, validat... | {"hexsha": "6cbb7ce9f8b562e46af01beb58565c1b5f04d243", "size": 1530, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_loader/data_loaders.py", "max_stars_repo_name": "Liut2016/ecg-ssl", "max_stars_repo_head_hexsha": "c7092f0eb079199066b6dd733280cf7308932918", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
# coding: utf-8
# <img src="imagenes/rn3.png" width="200">
# <img src="http://www.identidadbuho.uson.mx/assets/letragrama-rgb-150.jpg" width="200">
# # [Curso de Redes Neuronales](https://curso-redes-neuronales-unison.github.io/Temario/)
#
# # Una sola neurona logística
#
# [**Julio Waissman V... | {"hexsha": "9209415253b73d67124fcfb34fbf054c6afb1397", "size": 29266, "ext": "py", "lang": "Python", "max_stars_repo_path": "regresion-logistica/neurona_logistica.py", "max_stars_repo_name": "LuiGiovanni/LibretasRedesNeuronales", "max_stars_repo_head_hexsha": "31206dedea3880febd9d81179b2b3a29e21892b0", "max_stars_repo_... |
# Copyright 2017 The TensorFlow Authors. 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 applica... | {"hexsha": "e3cf55f86a00966863061d07b54850d3922213a6", "size": 2165, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/compiler/plugin/poplar/tests/64_bit_test.py", "max_stars_repo_name": "chenzhengda/tensorflow", "max_stars_repo_head_hexsha": "8debb698097670458b5f21d728bc6f734a7b5a53", "max_stars_repo_... |
#include <angles/angles.h>
#include <pluginlib/class_list_macros.h>
#include <backward_local_planner/backward_local_planner.h>
#include <visualization_msgs/MarkerArray.h>
#include <boost/intrusive_ptr.hpp>
//register this planner as a BaseLocalPlanner plugin
PLUGINLIB_EXPORT_CLASS(backward_local_planner::BackwardLocal... | {"hexsha": "631d2fa5f125184595d6b9d9876b3a78cc68eacc", "size": 15104, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "smacc_client_library/smacc_navigation/backward_local_planner/src/backward_curved_local_planner.cpp", "max_stars_repo_name": "NEU-ZJX/SMACC", "max_stars_repo_head_hexsha": "cac82a606a5456194e2ca1e40... |
""" Optimizations of the expression tree representation for better CSE
opportunities.
"""
from sympy.core import Add, Basic, Expr, Mul, S
from sympy.core.exprtools import factor_terms
from sympy.utilities.iterables import preorder_traversal
class Neg(Expr):
""" Stub to hold negated expression.
"""
__slots_... | {"hexsha": "a74c5b1eefb546108a76fe5d5e2ae544dabba244", "size": 1295, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/simplify/cse_opts.py", "max_stars_repo_name": "goodok/sympy", "max_stars_repo_head_hexsha": "de84ed2139125a755ea7b6ba91d945d9fbbe5ed9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import argparse
import json
import pickle as pkl
from os.path import basename
import numpy as np
def parse_argument():
parser = argparse.ArgumentParser("Convert json gt to roidb")
parser.add_argument("--json", type=str, required=True)
args = parser.parse_args()
return args.json
def json_to_roidb(js... | {"hexsha": "f38f3d629456089d29d286078b62d2a6ab466732", "size": 751, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/json_to_roidb.py", "max_stars_repo_name": "winnerineast/simpledet", "max_stars_repo_head_hexsha": "cb25cea2bf0df9cc7de008c853cf4bcb4f09415d", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
\PassOptionsToPackage{dvipsnames,svgnames*,x11names*}{xcolor}
%
\documentclass[
]{article}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\ifnum 0\ifxetex 1\fi\ifluatex 1... | {"hexsha": "3a9b4bd470155d6386c813f68554079c33a59493", "size": 6539, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "test/diff.tex", "max_stars_repo_name": "davidar/pandiff", "max_stars_repo_head_hexsha": "d1d468b2c4d81c622ff431ef718b1bf0daaa03db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 163, "max_s... |
/**
* .file test/exces/entity.cpp
* .brief Test case for entity type and related functionality.
*
* .author Matus Chochlik
*
* Copyright 2011-2013 Matus Chochlik. 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... | {"hexsha": "2abfef00f33fc3f848d64d3f95a468652c061579", "size": 1166, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/exces/entity.cpp", "max_stars_repo_name": "matus-chochlik/exces", "max_stars_repo_head_hexsha": "50b57ce4c9f6c41ab2eacfae054529cbbe6164c0", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_co... |
using ConvDiffMIPDECO
using Test
using jInv.Mesh
domain = [0. 3. 0 1. 0 2.]
n = 3*[7 9 12] .- 1
M = getRegularMesh(domain,n)
Mass, Mass_const, SM = getFEMMatrices3D(M)
e = ones(prod(M.n.+1))
@test abs(prod((domain[2:2:end]-domain[1:2:end])) - dot(e,Mass*e))/dot(e,Mass*e) < 1e-2
f = getFEMsource3D(M)
v = Ma... | {"hexsha": "6949f734b49d4a4ed83dc127318925d5c1541ca6", "size": 339, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testFEM3D.jl", "max_stars_repo_name": "JuliaInv/ConvDiffMIPDECO", "max_stars_repo_head_hexsha": "95a3aead09d34811c4c2ca14279df93a0b40ecdb", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
program demo_system_perm
use M_system, only : system_perm, system_stat
use,intrinsic :: iso_fortran_env, only : int64
implicit none
character(len=4096) :: string
integer(kind=int64) :: values(13)
integer :: ierr
characte... | {"hexsha": "ad26fd329e14bceef0461797b1040fe8af0af05b", "size": 946, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "example/demo_system_perm.f90", "max_stars_repo_name": "urbanjost/M_system", "max_stars_repo_head_hexsha": "5d419badbe993538ddca866e951d19d5c73d1fff", "max_stars_repo_licenses": ["Unlicense"], "ma... |
from time import time
from scipy.special import comb as scipy_choose
import math
import numpy as np
class timer:
def __init__(self, name="timer"):
self.name = name
def __enter__(self):
print("timing: %s"%self.name)
self.starttime = time()
def __exit__(self, type, value, traceback)... | {"hexsha": "18f2899a2661db583cff7e1eda988de7297f6fd6", "size": 565, "ext": "py", "lang": "Python", "max_stars_repo_path": "choose38.py", "max_stars_repo_name": "hugoleeney/python_benchmarks", "max_stars_repo_head_hexsha": "941077974df24d2288a5b3ca6003fc67b50a7b27", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding:utf-8 -*-
"""
"""
import numpy as np
from hypernets.utils import logging
logger = logging.get_logger(__name__)
#
# _STRATEGY_THRESHOLD = 'threshold'
# _STRATEGY_QUANTILE = 'quantile'
# _STRATEGY_NUMBER = 'number'
# _STRATEGY_DEFAULT = _STRATEGY_THRESHOLD
#
# _DEFAULT_THRESHOLD = 0.8
# _DEFAULT_QUANTI... | {"hexsha": "a9f69eeb1669ebc82173ee2c8d008b0465f4dd86", "size": 7854, "ext": "py", "lang": "Python", "max_stars_repo_path": "hypernets/tabular/pseudo_labeling.py", "max_stars_repo_name": "DataCanvasIO/Hypernets", "max_stars_repo_head_hexsha": "4dd8b9846a3e205f03a23a770169cd1d14a98f03", "max_stars_repo_licenses": ["Apach... |
/*=============================================================================
Copyright (c) 2007 Tobias Schwinger
Use modification and distribution are subject to 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).
========... | {"hexsha": "b18f41095eec594c02e1bc3ee72b6caf9f9a78a7", "size": 4050, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/libs/functional/forward/test/forward_adapter.cpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "m... |
"""
Creates an augmented version of the Labeled Faces in the Wild dataset.
Run with:
python generate_dataset.py --path="/foo/bar/lfw"
"""
from __future__ import print_function, division
import os
import random
import re
import numpy as np
from scipy import misc
from ImageAugmenter import create_aug_matrices
from sk... | {"hexsha": "cb53a4325ab2810412ba71eec8398d9ff9fb7071", "size": 7921, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/generate_dataset.py", "max_stars_repo_name": "Gufra-Ycyr/face-generator2", "max_stars_repo_head_hexsha": "c721c152a4fa922a3cd802f55831e38f5d503709", "max_stars_repo_licenses": ["MIT"], "ma... |
import time
from dataclasses import dataclass
from pathlib import Path
import logging
import math
import numpy as np
import networkx as nx
import igraph as ig
from typing import List, Dict, Tuple, Union, ClassVar
from os.path import join
import matplotlib.pyplot as plt
import pickle
import seaborn as sns
from collect... | {"hexsha": "0862390ccdbd91ccc8b3db2cfeeca9f0ce5bcfe4", "size": 9719, "ext": "py", "lang": "Python", "max_stars_repo_path": "VRG/src/subgraph_enumeration.py", "max_stars_repo_name": "satyakisikdar/Attributed-VRG", "max_stars_repo_head_hexsha": "502375d6a62eb84563c2fb6786e2c257edc32e0c", "max_stars_repo_licenses": ["MIT"... |
# pylint: disable=missing-function-docstring, missing-module-docstring/
import numpy as np
from pyccel.decorators import inline
pi = 3.14159
@inline
def get_powers(s : int):
return s, s*s, s*s*s
@inline
def power_4(s : int):
tmp = s*s
return tmp*tmp
@inline
def f(s : int):
return power_4(s) / 2
@in... | {"hexsha": "e983886ddf06ea5192b71b99fabaecc0b4b1fccb", "size": 822, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pyccel/scripts/decorators_inline.py", "max_stars_repo_name": "nandiniraja348/pyccel", "max_stars_repo_head_hexsha": "d857efcb8ff327f72473daeb86903c7e9ef93a36", "max_stars_repo_licenses": ["MI... |
#include <boost/qvm/mat_traits_defaults.hpp>
| {"hexsha": "5c2c16ce5328a38a5fe34c129c492200bccd614d", "size": 45, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_qvm_mat_traits_defaults.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1... |
import warnings
import numpy as np
import pytest
import theano
import theano.tensor as tt
from theano import config, scalar
from theano.gof import Apply, Op, Type, utils
from theano.tensor.basic import _allclose
@pytest.fixture(scope="module", autouse=True)
def set_theano_flags():
with theano.change_flags(compu... | {"hexsha": "92bc235c9224727b47b6e257c81acd54e0d23a26", "size": 10761, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/gof/test_compute_test_value.py", "max_stars_repo_name": "MarcoGorelli/Theano-PyMC", "max_stars_repo_head_hexsha": "55ef0e6b7380e062bdd09fcd7f820643f8521fb0", "max_stars_repo_licenses": ["BS... |
#! /usr/bin/env python
"""
Spatial adjust and test precision
"""
import numpy as np
from sklearn import linear_model
from sklearn import cross_validation
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_squared_error
import argparse, sys, csv, os, time
def getArgs():
parser =... | {"hexsha": "bcb692bb9aa8114502c54435e5e15d9a4c3ac5d0", "size": 6314, "ext": "py", "lang": "Python", "max_stars_repo_path": "afproj.py", "max_stars_repo_name": "africker/afproj-1", "max_stars_repo_head_hexsha": "91568a12708f7a3d2eaddf112060d957c2a3e5e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
"""{ATAM} Active Transport Modelling Functions"""
# Dependencies
import os
import pandas as pd
import numpy as np
import csv
import networkx as nx
# Model class
class Model:
"Model class"
# Init
def __init__(self, run_name):
# Name
self.run_name = run_name
# Input fil... | {"hexsha": "6839e24cf1f47ce04a292b09073c8e1118be17ec", "size": 14291, "ext": "py", "lang": "Python", "max_stars_repo_path": "brisbane/02_scripts/model_functions.py", "max_stars_repo_name": "atam-analytics/atam", "max_stars_repo_head_hexsha": "1d1502c43a20f13147af178098b8a3dd740a72f2", "max_stars_repo_licenses": ["MIT"]... |
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.preprocessing import image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection... | {"hexsha": "8e004e1d6ca10dde2ba1b1a67ff51d5e9e9f69c5", "size": 1931, "ext": "py", "lang": "Python", "max_stars_repo_path": "interview/task2/model_building.py", "max_stars_repo_name": "st186/Task_for_ComputerVision", "max_stars_repo_head_hexsha": "c48b5667acec60f28e504432863a96336d4d6679", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma INF_limit_inter:
assumes hyp: "\<exists>\<^sub>\<infinity> n. w n \<in> S"
and fin: "finite (S \<inter> range w)"
shows "\<exists>a. a \<in> limit w \<inter> S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>a. a \<in> limit w \<inter> S
[PROOF STEP]
proof (rule ccontr)
[PROOF ST... | {"llama_tokens": 1425, "file": null, "length": 16} |
require(httr)
data = '{"keywords":"php","page":1,"searchMode":1}'
res <- httr::POST(url = 'http://us.jooble.org/api/xxxxxxxxxxxxxxxx', body = data)
| {"hexsha": "991ab6270e18720cb13355b3a792b842740208f1", "size": 150, "ext": "r", "lang": "R", "max_stars_repo_path": "fixtures/r/post_xpost.r", "max_stars_repo_name": "kado0413/curlconverter", "max_stars_repo_head_hexsha": "a532bd415853f350198d891e14c59babed16902e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
[STATEMENT]
lemma Lambert_W'_asymp_equiv'_at_left_0 [asymp_equiv_intros]:
"Lambert_W' \<sim>[at_left 0] (\<lambda>x. ln (-x))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Lambert_W' \<sim>[at_left 0] (\<lambda>x. ln (- x))
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Lambert_W' \<sim>[... | {"llama_tokens": 995, "file": "Lambert_W_Lambert_W", "length": 10} |
// Copyright (c) 2019 fortiss GmbH, Julian Bernhard, Klemens Esterle, Patrick Hart, Tobias Kessler
//
// This work is licensed under the terms of the MIT license.
// For a copy, see <https://opensource.org/licenses/MIT>.
#ifndef MODULES_WORLD_WORLD_HPP_
#define MODULES_WORLD_WORLD_HPP_
#include <unordered_map>
#incl... | {"hexsha": "922b43e7c6d150cad5b8905390a3ca2a15f83c4d", "size": 4291, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/world/world.hpp", "max_stars_repo_name": "cirrostratus1/bark", "max_stars_repo_head_hexsha": "6629a9bbc455d0fd708e09bb8e162425e62c4165", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
from PIL import Image
import requests
import torch
import torch.... | {"hexsha": "a0e39293a8b90ecb9d13649b5b8df9293adef007", "size": 4887, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mnist/torchscript_mnist_e2e.py", "max_stars_repo_name": "JasonMaojinsong/torch-mlir", "max_stars_repo_head_hexsha": "d50d3a5b1d1fa34f73e5107bf72d2d19430a08a2", "max_stars_repo_licenses": ... |
using HybridSystems
using Polyhedra
using SwitchOnSafety
using Test
include("solvers.jl")
include("jsr.jl")
include("invariant.jl")
include("../examples/run_examples.jl")
| {"hexsha": "5fbf9a416a3d511b68352f596ec6bf515a16ce53", "size": 174, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "blegat/SwitchOnSafety.jl", "max_stars_repo_head_hexsha": "c5461c0d9fbdf63ed0fb284962808b963d218070", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
import os
import sys
# ensure `tests` directory path is on top of Python's module search
filedir = os.path.dirname(__file__)
sys.path.insert(0, filedir)
while filedir in sys.path[1:]:
sys.path.pop(sys.path.index(filedir)) # avoid duplication
import pytest
import numpy as np
from copy impo... | {"hexsha": "1ca282af95292079f389f3a3586f710829ce4dae", "size": 3216, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/introspection_test.py", "max_stars_repo_name": "Mario-Kart-Felix/deeptrain", "max_stars_repo_head_hexsha": "45e066e9aa97c16780682d62250516c7d64d9897", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from generate_all_def import read_one_hot_feature_list
import pickle
import numpy as np
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
import sklearn.model_selection as ms # import cross_val_scores
import csv
from sklearn.metrics import confu... | {"hexsha": "01e1e4b235f7748044b5f421d292ba5710faf604", "size": 4437, "ext": "py", "lang": "Python", "max_stars_repo_path": "attack-adgraph-pipeline/script/classifier.py", "max_stars_repo_name": "seclab-ucr/A4", "max_stars_repo_head_hexsha": "1cb3fec31d9e5e9eb2fb448e28b11ce3641b72cc", "max_stars_repo_licenses": ["MIT"],... |
import feedparser
import pprint
import requests
import pandas as pd
import numpy as np
def loadFiles( codes ):
"""Devuelve una lista de dataframes para solo codigo"""
#codes = ['Est_Mercat_Immobiliari_Lloguer_Mitja_Mensual']
parameters = {'rows': '1000'}
url = 'http://opendata-ajuntament.barcelona.ca... | {"hexsha": "35a8d8d538185caf0b8596014301a7c41345de6c", "size": 1217, "ext": "py", "lang": "Python", "max_stars_repo_path": "loadFiles.py", "max_stars_repo_name": "djangosee/housing", "max_stars_repo_head_hexsha": "7ab172127967b8440bdc841d087ae84141b8e683", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
using Test
include("reduce.jl")
if capability(device()) < v"3.0"
@warn("this example requires a newer GPU")
exit(0)
end
len = 10^7
input = ones(Int32, len)
output = similar(input)
# CPU
cpu_val = reduce(+, input)
# CUDAnative
let
gpu_input = CuTestArray(input)
gpu_output = CuTestArray(output)
g... | {"hexsha": "1961cbcb65b5e690c0d027d61c0b4f61b2657651", "size": 426, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/reduce/verify.jl", "max_stars_repo_name": "chengchingwen/CUDAnative.jl", "max_stars_repo_head_hexsha": "d4861f5244c7c8172bf0d05d026aee41cb470e51", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma true_clss_mset_set[simp]: "finite CC \<Longrightarrow> I \<Turnstile>m mset_set CC \<longleftrightarrow> I \<Turnstile>s CC"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite CC \<Longrightarrow> (I \<Turnstile>m mset_set CC) = (I \<Turnstile>s CC)
[PROOF STEP]
unfolding true_clss_def true_cls_... | {"llama_tokens": 204, "file": "Ordered_Resolution_Prover_Herbrand_Interpretation", "length": 2} |
from abc import ABC, abstractmethod
import numpy as np
class Ship(ABC):
def __init__(self):
self.x = np.zeros(2)
self.controls = {
'N': self.north,
'S': self.south,
'E': self.east,
'W': self.west,
'L': self.left,
'R': self.rig... | {"hexsha": "524eafbf32fac47bae76a52df159d65f85322eb5", "size": 2844, "ext": "py", "lang": "Python", "max_stars_repo_path": "2020/12/solution.py", "max_stars_repo_name": "dglmoore/advent-of-code", "max_stars_repo_head_hexsha": "ca6e39a842a84ad5271891535c9323e057261d44", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
(*
(C) Copyright 2010, COQTAIL team
Project Info: http://sourceforge.net/projects/coqtail/
This library is free software; you can redistribute it and/or modify it
under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation; either version 2.1 of the License, or
(at your optio... | {"author": "coqtail", "repo": "coqtail", "sha": "96799cc6901b9cdb1c5f47add0f9cd6584fdd389", "save_path": "github-repos/coq/coqtail-coqtail", "path": "github-repos/coq/coqtail-coqtail/coqtail-96799cc6901b9cdb1c5f47add0f9cd6584fdd389/rls/rls1/Complex/Croot_n.v"} |
#== # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
#
# Auxiliary functions to pretty print tables.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ==#
export @ptconfclean, @ptconf, @pt
# Global configuration object.
const _pt_conf = PrettyTa... | {"hexsha": "53328e7fe2ce7f84dbbabc623886edd76e66e974", "size": 3913, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/helpers.jl", "max_stars_repo_name": "charleskawczynski/PrettyTables.jl", "max_stars_repo_head_hexsha": "0ab64207f0689e35ad2e3e807ca1d2dc1357c7b8", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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 applicab... | {"hexsha": "230bfece16b20efe55718a5e3cdd2624cce4dd4c", "size": 17282, "ext": "py", "lang": "Python", "max_stars_repo_path": "pse/dm_control/agents/drq_sac_agent.py", "max_stars_repo_name": "pedersor/google-research", "max_stars_repo_head_hexsha": "6fa751dd261b3f6d918fd2cd35efef5d8bf3eea6", "max_stars_repo_licenses": ["... |
push!(LOAD_PATH,"../src/")
using Documenter, FourierAnalysis
makedocs(
sitename="FourierAnalysis",
authors="Marco Congedo, CNRS, France",
modules=[FourierAnalysis],
pages = [
"index.md",
"Main Module" => "MainModule.md",
"Tapering Window" => "tapers.md",
"frequency domain" => Any[
... | {"hexsha": "29233fb3e496a137ff7c300abebe8ac23c7f5812", "size": 1540, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "laborg/FourierAnalysis.jl", "max_stars_repo_head_hexsha": "7ef12fa0dc8254fdcd501d41a1d93e9d5317ab71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
\name{circos.genomicInitialize}
\alias{circos.genomicInitialize}
\title{
Initialize circular plot with any genomic data
}
\description{
Initialize circular plot with any genomic data
}
\usage{
circos.genomicInitialize(data, sector.names = NULL, major.by = NULL,
plotType = c("axis", "labels"), tickLabelsStartFromZer... | {"hexsha": "7be93587c17343a615d3cf7f689766844ba44e2c", "size": 2793, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/circos.genomicInitialize.rd", "max_stars_repo_name": "calpan/circlize", "max_stars_repo_head_hexsha": "33f8f23663768367188e50e93d3f9b2b57edd0e7", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module time_advance
public :: init_time_advance, finish_time_advance
public :: advance_stella
public :: time_gke, time_parallel_nl
public :: checksum
private
interface get_dgdy
module procedure get_dgdy_2d
module procedure get_dgdy_3d
module procedure get_dgdy_4d
end interface... | {"hexsha": "8b68d9a08cb1c8e2a347dac9a721ab7b87bac6f3", "size": 124243, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "time_advance.f90", "max_stars_repo_name": "HanneThienpondt/stella-1", "max_stars_repo_head_hexsha": "38282d09b39c1cbd1833c1ab57d602a4015ad5df", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from IAlgorithm import IAlgorithm
import numpy as np
__author__ = 'simon'
class Pad(IAlgorithm):
''' Pads the input array using numpy.pad '''
def __init__(self, target_width = None, target_height = None):
self.target_width = target_width
self.target_height = target_height
def _co... | {"hexsha": "7b2696c2217a1cd657cbd8b8e382a91f099f7364", "size": 1406, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pad.py", "max_stars_repo_name": "cvjena/classification_framework_py", "max_stars_repo_head_hexsha": "4d7ef08cad118c8e871b1053e747cc3ad454c009", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from __future__ import division
import os
import random
import geopandas
import networkx as nx
import pandas as pd
import shapely
from cea.technologies.network_layout.substations_location import \
calc_substation_location as substation_location
__author__ = "Sebastian Troitzsch"
__copyright__ = "Copyright 2019,... | {"hexsha": "e685bd57e86874815fb1d428620997aa9b6965d8", "size": 9267, "ext": "py", "lang": "Python", "max_stars_repo_path": "legacy/flexibility_model/mpc_district/planning_and_operation_preprocess_network.py", "max_stars_repo_name": "AlexJew/CityEnergyAnalyst", "max_stars_repo_head_hexsha": "6eb372c79e5100a2d0abce78561a... |
[STATEMENT]
lemma exists_a_w:
assumes "symmetric g"
and "forest f"
and "f \<le> --g"
and "regular f"
and "(\<exists>w . minimum_spanning_forest w g \<and> f \<le> w \<squnion> w\<^sup>T)"
and "vector j"
and "regular j"
and "forest h"
and "forest_modulo_equivalence (forest_components h)... | {"llama_tokens": 286295, "file": "Relational_Minimum_Spanning_Trees_Boruvka", "length": 788} |
import pandas as pd
import numpy as np
import os
import streamlit as st
import streamlit.components.v1 as components
_RELEASE = False
if not _RELEASE:
_attribution_heatmap_table = components.declare_component(
"attribution_heatmap_table", url="http://localhost:3001",
)
else:
parent_dir = os.path.... | {"hexsha": "d257175c7fe5a9e52efec72ebe3e9ddd244a20ae", "size": 1383, "ext": "py", "lang": "Python", "max_stars_repo_path": "attribution-heatmap-table/AttributionHeatmapTable/__init__.py", "max_stars_repo_name": "youozhan/component-template", "max_stars_repo_head_hexsha": "e50d272c4b3419934ed9299c8d3ef36ecfa4b138", "max... |
# Autogenerated wrapper script for Conduit_jll for x86_64-w64-mingw32-cxx11
export libconduit, libconduit_blueprint, libconduit_relay
JLLWrappers.@generate_wrapper_header("Conduit")
JLLWrappers.@declare_library_product(libconduit, "libconduit.dll")
JLLWrappers.@declare_library_product(libconduit_blueprint, "libconduit... | {"hexsha": "c63adf7d304107148f1e611ed78b051b53918989", "size": 962, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/x86_64-w64-mingw32-cxx11.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Conduit_jll.jl", "max_stars_repo_head_hexsha": "a3e4cdf579ae4de463a521fe31c45af4635be643", "max_stars_repo_lice... |
import torch
from network import PPOACNet
from components import get_ppo_ac_cfg_defaults, Task, Storage, tensor, to_np, random_sample
import numpy as np
hyper_parameter = get_ppo_ac_cfg_defaults().HYPER_PARAMETER.clone()
train_parameter = get_ppo_ac_cfg_defaults().TRAIN_PARAMETER.clone()
class PPOACAgent(torch.nn.Mo... | {"hexsha": "f1d7baf1ed284b7b3b628dae40d955bbd7aa3dd3", "size": 4486, "ext": "py", "lang": "Python", "max_stars_repo_path": "agent/PPO_AC_Agent.py", "max_stars_repo_name": "auroua/Udacity_Continuous_Control", "max_stars_repo_head_hexsha": "fcc21f8682b745f55c2900616eac632f430ab828", "max_stars_repo_licenses": ["MIT"], "m... |
import unittest
import numpy as np
from iv_jett import iv_init
class TestBetas(unittest.TestCase):
def test_square_instruments(self):
z = np.random.rand(255, 5)
x = np.random.rand(255, 5)
y = np.random.rand(255, 5)
betas = np.linalg.inv(np.transpose(z) @ x) @ np.transpose(z) @ y
... | {"hexsha": "a7810057af82505f1043c8d0837a2e7f6753ff31", "size": 836, "ext": "py", "lang": "Python", "max_stars_repo_path": "Projects/project_2_packages/iv_jett/test/test_estimates.py", "max_stars_repo_name": "jettpettus/modularizationandtesting", "max_stars_repo_head_hexsha": "16f5e463196f6ab086c9f5b7ecd953f67336bcae", ... |
from .header import Header
import astropy.io.fits as pyfits
import numpy
from scipy import ndimage
from scipy import __version__ as scipyversion
from scipy.interpolate import interp1d
class Data(Header):
"""A class which contains the processes for handling spectral data which
is common for handling 1D, 2D and... | {"hexsha": "73250090063febec0f3327f923b46621dc7390a5", "size": 24797, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyParadise/data.py", "max_stars_repo_name": "brandherd/PyParadise", "max_stars_repo_head_hexsha": "1c65bf634e17931f165fd88b9938f604b9371e2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import os
from tqdm import tqdm
import pickle as p
import nltk
from collections import defaultdict
import random
import numpy as np
class Batch:
'''
0. should have extra utterances for padding, hence dimension-0 of utterances and labels
are different. Specifically, utterances.d_0 = labels.d_0 + 2 * (window... | {"hexsha": "e39b2bf45127b0e9c7e05f7eaf8f346fe691548a", "size": 14987, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/speakerChangeTagger/textData.py", "max_stars_repo_name": "stoplime/OpenAudioAI", "max_stars_repo_head_hexsha": "4ed6fb78b976fd7c4d2b4661849562b28003a2ed", "max_stars_repo_licenses": ["MIT"],... |
import os
import cv2
import sys
import tensorflow as tf
import numpy as np
from styx_msgs.msg import TrafficLight
TRAFFIC_LIGHT_CLASS = 10
TRAFFIC_LIGHT_MIN_SCORE = 0.80
class TLClassifier(object):
def __init__(self):
script_dir = os.getcwd()
research_dir = os.path.join(script_dir, "research")
... | {"hexsha": "95885cb333224f9f31c5012a429102479c9f7583", "size": 7416, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/tl_detector/light_classification/tl_classifier.py", "max_stars_repo_name": "tekisetsu/udacity_capstone", "max_stars_repo_head_hexsha": "b86dc5914c69fdd9ed16eb074c01729d717a5e9e", "max_star... |
[STATEMENT]
lemma Amicable_pair_example_smallest_odd_odd: "12285 Amic 14595"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 12285 Amic 14595
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. 12285 Amic 14595
[PROOF STEP]
have A: "set(divisors_nat (12285)) = {1, 3, 5, 7, 9, 13, 15, 21, 27, 35, 39,... | {"llama_tokens": 4358, "file": "Amicable_Numbers_Amicable_Numbers", "length": 32} |
# module Bukdu
import Base: pipeline
"""
pipeline(block::Function, routers...)
"""
function pipeline(block::Function, pipes...)
for pipe::Symbol in pipes
pipelines = get(Routing.routing_pipelines, pipe, [])
push!(pipelines, block)
Routing.routing_pipelines[pipe] = pipelines
end
end... | {"hexsha": "b89b750c30cbdcc9d5c39beedb211b25f63f5cdf", "size": 337, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pipelines.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Bukdu.jl-3e78a19a-cc83-51d8-955b-515f39fd7955", "max_stars_repo_head_hexsha": "3c2f188e2050873f612c1e356ef5fcfd1de7aacb", "max_stars_... |
# -*- coding: utf-8 -*-
"""
========================
Gaussian KDE and Extents
========================
Smooth marginalised distributions with a Gaussian KDE, and pick custom extents.
Note that invoking the KDE on large data sets will significantly increase rendering time when
you have a large number of points. You c... | {"hexsha": "da592d66719ad8f319c1f19cdb455571ff2d153f", "size": 1039, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/customisations/plot_kde_extents.py", "max_stars_repo_name": "Jiaming1999/ChainConsumer", "max_stars_repo_head_hexsha": "5606696525d91f11d8093085934fa352b98ce97c", "max_stars_repo_licenses... |
library(tidyverse)
library(scales)
library(caTools)
cvdata.us <- readRDS('data/cvdata.us.RDS')
# Create a nestedn hierarchy of node structures:
#
# {
# name: "name",
# children: [
# node,
# node,
# ...
# ]
# }
#
# {
# name: "name", value: "value"
# }
#
#
# Division => State => County =... | {"hexsha": "f34f53f7d9adf42a0550ddfab45e33f1db694dce", "size": 3473, "ext": "r", "lang": "R", "max_stars_repo_path": "tree.r", "max_stars_repo_name": "bkayser/CV19", "max_stars_repo_head_hexsha": "8de30e205e5615be504dc894f4aa1ec4ec6999bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_sta... |
#pragma once
#include <gsl\gsl>
#include <winrt\Windows.Foundation.h>
#include <d3d11.h>
#include "DrawableGameComponent.h"
#include "MatrixHelper.h"
#include "DirectionalLight.h"
namespace Library
{
class Texture2D;
class ProxyModel;
}
namespace Rendering
{
class NormalMappingMaterial;
class NormalMappingDemo ... | {"hexsha": "52f288eb006991c9f185f89e96c9e3a2d246179b", "size": 1815, "ext": "h", "lang": "C", "max_stars_repo_path": "source/6.1_Normal_Mapping/NormalMappingDemo.h", "max_stars_repo_name": "ssshammi/real-time-3d-rendering-with-directx-and-hlsl", "max_stars_repo_head_hexsha": "05a05c5c26784dafa9a89747276f385252951f2f", ... |
"""
Handle vibrational data info
"""
import os
import numpy
import autofile
import autorun
import automol.geom
import projrot_io
from phydat import phycon
from mechlib.amech_io import printer as ioprinter
from mechlib.amech_io._path import job_path
from mechroutines.pf.models import typ
from mechroutines.pf.models i... | {"hexsha": "711f2cde72c0a8ad2e2cdd0124f82195b42cfc16", "size": 13518, "ext": "py", "lang": "Python", "max_stars_repo_path": "mechroutines/pf/models/_vib.py", "max_stars_repo_name": "sjklipp/mechdriver", "max_stars_repo_head_hexsha": "17c3d9bc82116954b331955e87a60e9adc5e1de9", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import pandas as pd
from rdflib import URIRef, BNode, Literal, Graph
from rdflib.namespace import RDF, RDFS, FOAF, XSD
from rdflib import Namespace
import numpy as np
import math
import sys
import argparse
import json
import requests
collection = requests.get("https://nakamura196.github.io/piranesi/print/iiif/top.json... | {"hexsha": "d370b63bc7e8a3e80e4a58169d485627a43efd6c", "size": 1033, "ext": "py", "lang": "Python", "max_stars_repo_path": "batch/iizuka/check.py", "max_stars_repo_name": "utda/piranesi", "max_stars_repo_head_hexsha": "28c41334fdc7df44bd1b892542eb0423edd13013", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
#This file is used to process images
from whitebox import WhiteboxTools
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import math
import cv2
from shapely.geometry import Point,LineString, MultiPoint
from shapely.ops import split
#Cut out lakes from stream shapefile
def clip_lakes(s_file,w... | {"hexsha": "f98a224f62e4cfe97a911eff327add22e9063b85", "size": 6962, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess.py", "max_stars_repo_name": "codykupf/river-cnn", "max_stars_repo_head_hexsha": "d1c297d78cebd57520f05f954283f37c3f6762e2", "max_stars_repo_licenses": ["ECL-2.0"], "max_stars_count": nu... |
import numpy as np
import backend as B
import pytest
@pytest.fixture(params=[512])
def input_size(request):
return (request.param)
@pytest.fixture(params=[[128, 128, 1]])
def image_shape(request):
return request.param
@pytest.fixture(params=[1])
def min_scale(request):
return (request.param)
@pytest... | {"hexsha": "65a07b70e39263fd503b1ec63e2f916bc8be1de9", "size": 3125, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/human_pose_2d/test/backend/preprocess_test.py", "max_stars_repo_name": "ManuelMeder/paz", "max_stars_repo_head_hexsha": "6de73c0a99e0f9c7233c2797eebba2bb01631e6c", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
"""
Created on Wed May 11 13:45:00 2016
@author: kbefus
path to the cgw_model needs to be added to the path before importing
cgw_package_tools
e.g.:
import sys
kbpath = 'C:/Research/Coastalgw/Model_develop/'
sys.path.insert(1,kbpath)
"""
from __future__ import print_function... | {"hexsha": "0a4c46ca2b526cd91ef3cd2ae6e0bd9316f29778", "size": 20887, "ext": "py", "lang": "Python", "max_stars_repo_path": "cgw_model/cgw_package_tools.py", "max_stars_repo_name": "kbefus/ca_gw_slr", "max_stars_repo_head_hexsha": "7a793351be5c135ea6b39a8f59d95508fcc00c7b", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#!/usr/bin/env python
# coding: utf-8
# # PVSC Fig. 3
# In[2]:
import PV_ICE
import numpy as np
import pandas as pd
import os,sys
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 22})
plt.rcParams['figure.figsize'] = (12, 8)
# In[3]:
import os
from pathlib import Path
testfolder = str(Path().r... | {"hexsha": "3fbfdd8282749b4f023524f33d91d2e8492e408b", "size": 3887, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/tutorials/PVSC 2021 Fig. 3.py", "max_stars_repo_name": "kanderso-nrel/PV_ICE", "max_stars_repo_head_hexsha": "c37afce01c2e3c9ff22606abd950488806cdbc30", "max_stars_repo_licenses": ["BSD-3-Cla... |
F(t) = hcat(1.0)
G(t) = hcat(1.0)
Σ(t) = hcat(9.0)
Tau(t) = hcat(1.0)
μ0 = vcat(0.0)
Tau0 = hcat(100.0)
Y = [
5.22896384735402,
2.059848221254017,
11.77696919584194,
5.721925811242492,
2.647894413351219,
8.833040488128724,
10.112474414603064,
6.208953089526956,
7.483218024927158,
... | {"hexsha": "766b40b9426e2f01b2b9a57869c42ad1f04b0f44", "size": 9400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/kalman.jl", "max_stars_repo_name": "dereklhansen/SMC.jl", "max_stars_repo_head_hexsha": "1ef99d957b343f7b3ec28989fb580a2a0b920650", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# Copyright 2020 LMNT, Inc. 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 or ag... | {"hexsha": "370fe591192f39e82e7e279bef485f23f1a62b26", "size": 4293, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wavegrad/inference.py", "max_stars_repo_name": "wjqkkky/wavegrad", "max_stars_repo_head_hexsha": "3fe9959f542f90a66dfdf0cfa769504450f9ec53", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# coding=utf-8
# Copyleft 2019 project LXRT.
import argparse
import random
import numpy as np
import torch
def get_optimizer(optim):
# Bind the optimizer
if optim == 'rms':
print("Optimizer: Using RMSProp")
optimizer = torch.optim.RMSprop
elif optim == 'adam':
print("Optimizer: U... | {"hexsha": "f8b62f2cceb803ed15026447c8d4acaec58ca8a3", "size": 4654, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/finetune_param.py", "max_stars_repo_name": "jmhessel/lxmert", "max_stars_repo_head_hexsha": "4ee7b1a1ec652eb163572e3ae8189ea8b93c957e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
def _get_covered_instances(rule, fuzzy_dataset, threshold=0.001):
first_feat = list(fuzzy_dataset.keys())[0]
first_val = list(fuzzy_dataset[first_feat].keys())[0]
ds_len = len(fuzzy_dataset[first_feat][first_val])
mu = np.ones(ds_len)
for feat, val in rule.antecedent:
m... | {"hexsha": "fc3ede82f820852c47f8a6d94496ebf6960a8f31", "size": 1563, "ext": "py", "lang": "Python", "max_stars_repo_path": "teacher/metrics/rule.py", "max_stars_repo_name": "Kaysera/fuzzy-lore", "max_stars_repo_head_hexsha": "128131e0f41f480d509b63c5e75d0ce58f07bae4", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#ifndef BLACKJACK_HPP
#define BLACKJACK_HPP
#include <iostream>
#include <string>
#include <deque>
#include <vector>
#include <random>
#include <ctime>
#include <chrono>
#include "../src/relearn.hpp"
#if USING_BOOST_SERIALIZATION
#include <boost/serialization/serialization.hpp>
#include <boost/serialization/access.hpp>... | {"hexsha": "6ed5e92a812440221c51c9f55da324ed3f748563", "size": 7195, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "examples/blackjack_header.hpp", "max_stars_repo_name": "richelbilderbeek/relearn", "max_stars_repo_head_hexsha": "d31a2859d7621eb4796ea0c027765ad9f450a798", "max_stars_repo_licenses": ["Apache-2.0"]... |
import argparse
import os
import numpy as np
# DNN Libraries
from torch.utils.data import DataLoader
# Turboflow Libraries
from turboflow.dataloaders import Turb2DDataset
from turboflow.models.phyrff_hard import DivFreeRFFNet
from turboflow.utils.torch_utils import get_device
def get_path_and_prepare_folder():
... | {"hexsha": "ff32cfcd663803b1b4457cb653a0f5a0246fa037", "size": 1621, "ext": "py", "lang": "Python", "max_stars_repo_path": "recipes/turbo2D/single_images/main_nopl.py", "max_stars_repo_name": "Chutlhu/TurboSuperResultion", "max_stars_repo_head_hexsha": "6ef6db8669270681a07e028cf3fd17a747b3ba5d", "max_stars_repo_license... |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python [conda env:PROJ_irox_oer] *
# language: python
# name: conda-env-PROJ... | {"hexsha": "2760d022f732381903a25f15812c5f7b6919ea27", "size": 3323, "ext": "py", "lang": "Python", "max_stars_repo_path": "dft_workflow/job_analysis/compare_magmoms/anal_magmoms.py", "max_stars_repo_name": "raulf2012/PROJ_IrOx_OER", "max_stars_repo_head_hexsha": "56883d6f5b62e67703fe40899e2e68b3f5de143b", "max_stars_r... |
[STATEMENT]
lemma length_mtf[simp]: "size (mtf x xs) = size xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (mtf x xs) = length xs
[PROOF STEP]
by (auto simp add: mtf_def min_def) (metis index_less_size_conv leD) | {"llama_tokens": 95, "file": "List_Update_Move_to_Front", "length": 1} |
from fb import Viewer
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
from PIL import Image
N = 200
display_size = (900, 900)
cell_state = np.zeros((N, N), dtype=np.int32)
cell_state[0, N//2] = 0x80000001
mod = SourceMo... | {"hexsha": "df6dcaeea4449e5b71fd3dca174a7bee7c8dee1d", "size": 1593, "ext": "py", "lang": "Python", "max_stars_repo_path": "02_mobile_automata.py", "max_stars_repo_name": "medakk/cellular-automata", "max_stars_repo_head_hexsha": "d9f4a43cc73992e143168b0f16498707e4f001f0", "max_stars_repo_licenses": ["Unlicense"], "max_... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.