text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import cv2
import time
import os
import math
import random
from musthe import *
from synthesizer import Player, Synthesizer, Waveform
import skimage.measure
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def root():
return render_template('index.html')
... | {"hexsha": "cc98458d22b5748448ea7533846981821e21a5ce", "size": 4526, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "abhishekbabu/musique", "max_stars_repo_head_hexsha": "e94ecb19ab0210797718750d4d1f0eb2759d768a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
"""Provides Elite."""
from typing import NamedTuple, Tuple, Union
import numpy as np
class Elite(NamedTuple):
"""Represents a single elite in an archive.
Note that since this class is a namedtuple, its fields may be accessed
either by name or by integer indices.
"""
#: Parameters of the elite's... | {"hexsha": "f137250354b4520ee5aaa3e8daa78a828faa5a33", "size": 639, "ext": "py", "lang": "Python", "max_stars_repo_path": "ribs/archives/_elite.py", "max_stars_repo_name": "icaros-usc/pyribs", "max_stars_repo_head_hexsha": "ef289a930e7a8a51286cf657f7e4b29551277350", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import iris # NOQA
import netCDF4 # NOQA
import numpy # NOQA
import glob # NOQA
import collections # NOQA
import datetime # NOQA
import matplotlib # NOQA
import matplotlib.pyplot as plt # NOQA
import cf_units # NOQA
from .utility import * # NOQA
from .dataset import * # NOQA
from .extract_timeseries import ... | {"hexsha": "2ba92c54db9e8dce0126b1eafcdce93548c9e351", "size": 845, "ext": "py", "lang": "Python", "max_stars_repo_path": "galene/__init__.py", "max_stars_repo_name": "tkarna/galene", "max_stars_repo_head_hexsha": "a05463d3d0c9191c51893df4593d9ce0252d25fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
def np_mul_2_3():
return np.multiply(2, 3)
def np_add_2_3():
return np.add(2, 3)
def py_mul_2_3():
return 2 * 3
def py_add_2_3():
return 2 + 3
def entry_py_mul_2_3():
return py_mul_2_3()
def entry_py_add_2_3():
return py_add_2_3()
| {"hexsha": "2b75393f0ac390b794fc5474fbaa85709c05010f", "size": 286, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/funcs.py", "max_stars_repo_name": "NaleRaphael/bytejection", "max_stars_repo_head_hexsha": "67a9ca2bc4533cee4742969925f9d678aed4c3b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#---------------train the CNN or the ensemble-----------------
#importing required libraries and modules
import os
import sys
import cv2
import numpy as np
import tflearn
from preprocess import Preprocess
from data_split import Load
from conv_net import CNN
from ensemble import Ensemble
def load_nu... | {"hexsha": "e187e7460a9b13e80ebca1d140f982efb3ef1787", "size": 2487, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train.py", "max_stars_repo_name": "chatdip98/Acoustic-Scene-Classification", "max_stars_repo_head_hexsha": "cf410f14a1efb3e3dd2bbc240c24882969be98a9", "max_stars_repo_licenses": ["MIT"], "max_... |
# Copyright 2019 The ASReview 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 applicabl... | {"hexsha": "904ec940ef4b67ac57b10c7960a443de25633aec", "size": 18596, "ext": "py", "lang": "Python", "max_stars_repo_path": "asreview/review/base.py", "max_stars_repo_name": "Sybren-UU/asreview", "max_stars_repo_head_hexsha": "a5ec3ca0ff6b1e1b60ad9b34d1d0f664f8cbedc2", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# ### Loading some packages
using GeoPhyInv
using SparseArrays
using StatsBase
using LinearAlgebra
using Random
using LinearAlgebra
using Test
using ForwardDiff
using Calculus
#src # include("core.jl")
#src # include("expt.jl")
# ### Solve for ``ψ`` in a `PoissonExpt`
# This module represents an explicit, direct spar... | {"hexsha": "66c21e1317c886128bbcd191cf908294d948f915", "size": 1995, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Poisson/forw.jl", "max_stars_repo_name": "ayushinav/GeoPhyInv.jl", "max_stars_repo_head_hexsha": "b0ce642161cb5300e2e7a5bd737b58fe37ddbfeb", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
//
// Simulator.cpp
// proteintools
//
// Created by Salik Syed on 10/5/17.
// Copyright © 2017 N/A. All rights reserved.
//
#include "Simulator.hpp"
#include "PDBGeometry.hpp"
#include "Residue.hpp"
#include <iostream>
#include <Eigen/Geometry>
using namespace std;
#define TORSION_EPSILON 0.00001f
Simulator::Si... | {"hexsha": "bbcfaca21a2c2261bccdcd8e1dcab7286204a350", "size": 6177, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "proteintools/proteintools/Simulator.cpp", "max_stars_repo_name": "saliksyed/protein-tools-cpp", "max_stars_repo_head_hexsha": "0101d1e0da125bcb36e70291290d25387999e197", "max_stars_repo_licenses": [... |
from pandas import read_csv
import numpy as np
from matplotlib import pyplot
from scipy.optimize import curve_fit
url='https://raw.githubusercontent.com/jbrownlee/Datasets/master/longley.csv'
dataframe= read_csv(url,header=None)
data=dataframe.values
xdata,ydata=data[:,4],data[:,-1]
def objective(x, a, b, c, ... | {"hexsha": "70faeaa7b815d0485634437cb9dcb2e12574b563", "size": 2204, "ext": "py", "lang": "Python", "max_stars_repo_path": "Regression Curve Fitting.py", "max_stars_repo_name": "HarduinLearnsCoding/Pattern-Recognition", "max_stars_repo_head_hexsha": "d2275a851fb3aaa71936fb45c23be74d641625a1", "max_stars_repo_licenses":... |
from PIL import Image
import numpy as np
from robustness.datasets import ImageNet
from robustness.model_utils import make_and_restore_model
import torch
import matplotlib.pyplot as plt
ds = ImageNet('/tmp')
model, _ = make_and_restore_model(arch='resnet50', dataset=ds,
resume_path='/home/siddhant/Downl... | {"hexsha": "c73299d0bc791d7e6f9fb4d3ac414cff506287f5", "size": 1568, "ext": "py", "lang": "Python", "max_stars_repo_path": "gen_targeted_adv_samples.py", "max_stars_repo_name": "agarwalsiddhant10/blackbox-smoothing", "max_stars_repo_head_hexsha": "cf18a9dc45f807494955d0cf19a3d1dd4315b54f", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma trans_le_add1_hmset: "i \<le> j \<Longrightarrow> i \<le> j + m" for i j m :: hmultiset
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. i \<le> j \<Longrightarrow> i \<le> j + m
[PROOF STEP]
by (simp add: add_increasing2) | {"llama_tokens": 102, "file": "Nested_Multisets_Ordinals_Hereditary_Multiset", "length": 1} |
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_probability as tfp
import datetime
import os, sys
from argparse import ArgumentParser
# Debug module
# from tensorflow.python import debug as tf_debug
import numpy as np
import warnings
from ke... | {"hexsha": "53782e53026d2f474f816208c2cfa70442a11422", "size": 19440, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/TMC.py", "max_stars_repo_name": "LinuNils/TMC_reproduced", "max_stars_repo_head_hexsha": "91c5c877d1adc89626bb80e59233f72228a6d4f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import tensorflow as tf
import numpy as np
import tools.processing as pre
text = pre.get_text("data/ref_text2.txt")
sentences = text.replace("\n", ";")
vocab = pre.Vocabulary(sentences)
embedding_dimension = 3
word2index_map = {}
index = 0
# for sent in sentences:
# for word in sent.lower().split():
# i... | {"hexsha": "db18f198284f33e2c3074e0d5882f8f80cc87292", "size": 1664, "ext": "py", "lang": "Python", "max_stars_repo_path": "deprecated/loader.py", "max_stars_repo_name": "frankzl/deep-rap", "max_stars_repo_head_hexsha": "f992081b136e02d6ee5f976f0343f7e3220a1f39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
[STATEMENT]
lemma fls_inverse_X_power:
"inverse ((fls_X::'a::division_ring fls) ^ n) = fls_X_inv ^ n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. inverse (fls_X ^ n) = fls_X_inv ^ n
[PROOF STEP]
by (simp add: fls_inverse_X_power') | {"llama_tokens": 116, "file": null, "length": 1} |
import argparse
import glob
import os
import subprocess
import chainer
import cupy as cp
import neural_renderer
import numpy as np
import scipy.misc
import tqdm
import deep_dream_3d
def make_gif(working_directory, filename):
# generate gif (need ImageMagick)
options = '-delay 8 -loop 0 -layers optimize'
... | {"hexsha": "10c870559c4f0d665968fdb18bf7982cdef0bc1f", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/run.py", "max_stars_repo_name": "hiroharu-kato/deep_dream_3d", "max_stars_repo_head_hexsha": "8f9f4ab0897bb7c453a09bf11652c4dbe80cb714", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# ============================================================================
# 付録 C ガス給湯機及びガス給湯温水暖房機の給湯部
# ============================================================================
import numpy as np
# ============================================================================
# C.2 消費電力量
# ===================... | {"hexsha": "3020625b755379f264993fc543c46f5040f8078e", "size": 16361, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyhees/section7_1_c.py", "max_stars_repo_name": "jjj-design/pyhees", "max_stars_repo_head_hexsha": "d63e7cd84abfc2f509bc1cd1256598a10aac1825", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\section{201912-5}
\input{problem/18/201912-5-p.tex} | {"hexsha": "99edc64deed820314392093eef828945f6e6fe8b", "size": 53, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "problem/18/201912-5.tex", "max_stars_repo_name": "xqy2003/CSP-Project", "max_stars_repo_head_hexsha": "26ef348463c1f948c7c7fb565edf900f7c041560", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import random
import gym
import numpy as np
from preprocessing import process_frame
class GameWrapper:
"""Wrapper for the environment provided by Gym"""
def __init__(self, env_name, no_op_steps=10, history_length=4):
self.env = gym.make(env_name)
self.no_op_steps = no_op_steps
self.... | {"hexsha": "27f78a521738015b6361451b5f82f4035079ef92", "size": 3098, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gamewrapper.py", "max_stars_repo_name": "alexcosta13/explainable-breakout", "max_stars_repo_head_hexsha": "483448b04747cd4bc8609be2e4141176e3b05b94", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma euler_witness_exists_nat:
assumes "odd n" "\<not>prime n" "2 < n"
shows "\<exists>a. euler_witness (int a) n \<and> coprime a n \<and> 0 < a \<and> a < n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>a. euler_witness (int a) n \<and> coprime a n \<and> 0 < a \<and> a < n
[PROOF STEP]... | {"llama_tokens": 421, "file": "Probabilistic_Prime_Tests_Euler_Witness", "length": 3} |
from functools import partial
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from jax import nn
from rljax.network.base import MLP
from rljax.network.conv import DQNBody, SLACDecoder, SLACEncoder
class CumProbNetwork(hk.Module):
"""
Fraction Proposal Network for FQF.
"""
d... | {"hexsha": "ac1a533c289080abea50b571d38d8f368b0f1547", "size": 7286, "ext": "py", "lang": "Python", "max_stars_repo_path": "rljax/network/misc.py", "max_stars_repo_name": "julio-cmdr/rljax", "max_stars_repo_head_hexsha": "cbca4638deb6d4e960e71a862573129ba4c5ea79", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 18 20:00:16 2019
@author: kristl
"""
"""
# Example for SOPLS
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import SOPLS
Y_df = pd.read_table('./data/D.txt', index_col=0)
Y = Y_df.v... | {"hexsha": "b58920dbada38e2cd214c9f01589c61f82391ffa", "size": 12029, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/legacy/resources/SOPLS.py", "max_stars_repo_name": "NMBU-Data-Science/multi-hoggorm", "max_stars_repo_head_hexsha": "a1de0044073c84845031b79a6a183748a0178ce8", "max_stars_repo_licenses": ["MI... |
# Raytracer.jl
# Raytracing for the generation of photorealistic images in Julia
# Copyright (c) 2021 Samuele Colombo, Paolo Galli
# Unit test file for world.jl
@testset "World" begin
@testset "RayIntersection" begin
world = World()
sphere1 = Sphere(transformation=translation(VEC_X * 2f0))
... | {"hexsha": "070b2ed02ab46b3e38fb273c83464b90524bf1a2", "size": 1521, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_world.jl", "max_stars_repo_name": "Paolo97Gll/Raytracer.jl", "max_stars_repo_head_hexsha": "14abd7ae89e0adfc9b2b79d0ad516be40215dad3", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
(* Title: HOL/Algebra/Product_Groups.thy
Author: LC Paulson (ported from HOL Light)
*)
section \<open>Product and Sum Groups\<close>
theory Product_Groups
imports Elementary_Groups "HOL-Library.Equipollence"
begin
subsection \<open>Product of a Family of Groups\<close>
definition product_group::... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Algebra/Product_Groups.thy"} |
(**
CoLoR, a Coq library on rewriting and termination.
See the COPYRIGHTS and LICENSE files.
- Frederic Blanqui, 2008-02-22, 2009-10-20 (rpo)
convert CoLoR terms into Coccinelle terms
*)
Set Implicit Arguments.
From CoLoR Require Import LogicUtil ATerm VecUtil.
From CoLoR Require VecUtil more_list APosition AConte... | {"author": "fblanqui", "repo": "color", "sha": "f2ef98f7d13c5d71dd2a614ed2e6721703a34532", "save_path": "github-repos/coq/fblanqui-color", "path": "github-repos/coq/fblanqui-color/color-f2ef98f7d13c5d71dd2a614ed2e6721703a34532/Conversion/Coccinelle.v"} |
"""
Adapted from http://www.astrobetter.com/visualization-fun-with-python-2d-histogram-with-1d-histograms-on-axes/
Thanks Jess K!
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter, MaxNLocator
plt.ion()
def centroid(data, x, y):
"""
Determine centroid of 2D da... | {"hexsha": "fb1da93f0acaee09b9a8209ff72322cf7d77ca0a", "size": 7727, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotRoutine.py", "max_stars_repo_name": "tmccrack/fttWIYN", "max_stars_repo_head_hexsha": "1586e97a62b3c2bd6b460015a6bc045d8f88e0ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
'''
@author: luislortega
'''
import cv2 as cv
import numpy as np
import os
from time import time
from windowcapture import WindowCapture
from vision import Vision
import pyautogui
# Change the working directory to the folder this script is in.
# Doing this because I'll be putting the files from each video in their own... | {"hexsha": "660c81f42d00b9de8aa39b18c6219a952091e4d7", "size": 1815, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "luislortega/AimPro", "max_stars_repo_head_hexsha": "abc436a79eff42acfcd00b4a4d2d07c5a6fef4e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
/**
* @project zapdos
* @file include/utils/SharedTable.hpp
* @author S Roychowdhury < sroycode at gmail dot com >
* @version 1.0.0
*
* @section LICENSE
*
* Copyright (c) 2018-2020 S Roychowdhury
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of
* this software and associat... | {"hexsha": "77d8f7372a359a5975d7d8edb969fb1a1d5554cb", "size": 4601, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/utils/SharedTable.hpp", "max_stars_repo_name": "sroycode/zapdos", "max_stars_repo_head_hexsha": "8818ef109e072dcbe990914d9a2a6d70ef190d3e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import matplotlib.pyplot as plt
import json
import os.path
class APSTrainingScore:
def __init__(self):
self.filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'training_score.json')
with open(self.filename) as json_data:
self.score_dict = json.load(... | {"hexsha": "a6351638300dda20e738a0bf2a992aefd3ee7389", "size": 2322, "ext": "py", "lang": "Python", "max_stars_repo_path": "aps/config/training_score.py", "max_stars_repo_name": "kmunve/APS", "max_stars_repo_head_hexsha": "4c2f254ede83a3a311cbedc90c76db9ee367a000", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from pathlib import Path
import numpy as np
from scipy.optimize import minimize_scalar
def align(input):
arr = np.fromstring(input, sep=",")
def min_fun(x):
return np.sum(np.abs(arr - x))
return min_fun
def align2(input):
arr = np.fromstring(input, sep=",")
def min_fun(x):
re... | {"hexsha": "e8724590cc48cd644513b09b5c90e898e230deed", "size": 1210, "ext": "py", "lang": "Python", "max_stars_repo_path": "day7/src.py", "max_stars_repo_name": "shimst3r/aoc2021", "max_stars_repo_head_hexsha": "980a3d87e7748ac7f5b53c13288c5fb814993640", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
import time
import numpy as np
from .player import Player
class PlayerFinder:
TIMEOUT_TIME = 25
JOINING_TIME = 3
def __init__(self, joining_stage=True):
self.players = {}
self.joining = {}
self.joining_stage = joining_stage
def update(self, bounding_boxes, ids):
'''Re... | {"hexsha": "5d436b8a37a365e5f03c5abca87cb5ac24b81740", "size": 1972, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/player_finder.py", "max_stars_repo_name": "thomsen85/LegoPokerDealer", "max_stars_repo_head_hexsha": "89fbf0123d1f4463493801349ad8b5ab06705a83", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#! /usr/bin/env python3
# Copyright 2019 Kyle Steckler
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify, me... | {"hexsha": "7f8c66171bdfe2f4180056753e0fb58902a385af", "size": 4887, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdss_query.py", "max_stars_repo_name": "kylesteckler/galaxy-classification", "max_stars_repo_head_hexsha": "7d9311301a6d2885a2488d6510fde0f402fdaffc", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. 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/LI... | {"hexsha": "4f08d155de92e03e4b6d197deab5440f60a8e630", "size": 23454, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/vl_tasks/run_vl.py", "max_stars_repo_name": "Alab-NII/eval_vl_glue", "max_stars_repo_head_hexsha": "74e7691828f394554370158f852fe04af9be0d79", "max_stars_repo_licenses": ["Apache-2.0"]... |
using Pkg
Pkg.activate(".")
verbose = true
if verbose println("# Loading RvSpecML") end
using RvSpectML
if verbose println("# Loading other packages") end
using DataFrames, Query, Statistics
using Dates
# USER: The default paths that specify where datafiles can be entered here or overridden in example... | {"hexsha": "466b755461cbfe5995636d98fc9b03b262c9f957", "size": 16917, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples_old/old/expres_pipeline_1_broken.jl", "max_stars_repo_name": "alexander-wise/RvSpectML.jl", "max_stars_repo_head_hexsha": "8fd030f4a8b6478193ed36be7a3174cd2ea7b5aa", "max_stars_repo_licen... |
The Davis International Folk Dancers have fun doing dancing dances from Bulgaria, Israel, Armenia, Romania, Russia, Greece, Turkey, and other countries. Take this quiz to find out if you might like international folk dancing:
Do you want to learn some fancy moves for your feet?
Are you interested in hearing s... | {"hexsha": "85f9ed4f8263877ac1b5add3376cda7c1ea0ba9c", "size": 2020, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Davis_International_Folk_Dancers.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT... |
########### Importing Libraries ##############
from preprocessing import Functions
import numpy as np
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import ConvLSTM2D,Conv2DTranspose, LayerNormalization, BatchNormalization, TimeDist... | {"hexsha": "2e5398adf872e5810cfbc968132dc8794afd73ed", "size": 3307, "ext": "py", "lang": "Python", "max_stars_repo_path": "Train/model.py", "max_stars_repo_name": "NIK-99/IRIS", "max_stars_repo_head_hexsha": "262ea244ed883266505d6be07e5e6ac77cbe2fae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
# coding: utf-8
# In[1]:
# import matplotlib
# matplotlib.use('Agg')
# get_ipython().magic(u'matplotlib inline')
# import matplotlib.pyplot as plt
# plt.rcParams['image.cmap'] = 'gray'
from glob import glob
import SimpleITK as sitk
SMALL_SIZE = 14
MEDIUM_SIZE = 16
BIGGER_SIZE = 18
# plt.rc('font', size=SMALL_SIZE)... | {"hexsha": "76f68cd99834ecd8279cc34214e510eee4c96dc8", "size": 33893, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/AnatomyNet.py", "max_stars_repo_name": "wentaozhu/AnatomyNet-for-anatomical-segmentation", "max_stars_repo_head_hexsha": "296227838c1c68baef5836ba5a9d31ea311f35a3", "max_stars_repo_licenses":... |
\subsection{Definitions}
\begin{itemize}
\item $\lambda$ : intrinsic coordinate, including masses and spins.
\item $\theta$ : extrinsic coordinate, including $d,RA,DEC,\iota,\psi_L,t,\phi_{\rm orb}$
\item $p_s(\theta)$: (joint) sampling prior in extrinsic dimensions
\item $p(\theta)$ : prior on extrinsic parameters
... | {"hexsha": "3d90c7fa023e5737cc2fa56a943ee761ecbf1d8c", "size": 2781, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "MonteCarloMarginalizeCode/Notes/paper/notation_etc.tex", "max_stars_repo_name": "spfanning/research-projects-RIT", "max_stars_repo_head_hexsha": "34afc69ccb502825c81285733dac8ff993f79503", "max_star... |
using ApproxFun, Base.Test
c = rand(1000)
x=rand(10000)
f=Fun(c,Chebyshev)
y=f(x)
y=f(x)
@time y=f(x)
println("Clenshaw large coeffs, many points: Time should be ~0.024")
# 0.012482274 with unsafe_view
# 0.024306262 with inbounds
y=f(.1)
y=f(.1)
y=f(.1)
@time y=f(.1);
println("Clenshaw large coeffs, 1 point: Time... | {"hexsha": "8058a4b599b8e4d9a3808d46c76a8f850b1aca12", "size": 765, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/SpeedTest.jl", "max_stars_repo_name": "JuliaPackageMirrors/ApproxFun.jl", "max_stars_repo_head_hexsha": "f73e9d168b0d139efa2953b1bad7fac808db2d8d", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
import os
import random
import bpy
# set current path
abspath = os.path.abspath(__file__)
dname = os.path.dirname(os.path.dirname(abspath))
os.chdir(dname)
scn = bpy.context.scene
FPS = scn.render.fps
# set output folder and get highest index
data_folder = '../../output/Cartpole/'
data_folder +=... | {"hexsha": "b943c746e5f677d5e8a28d1a1a0cbd3908c0b7e7", "size": 1729, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/Cartpole/import_cartpole.py", "max_stars_repo_name": "boyali/SCpp", "max_stars_repo_head_hexsha": "3bc49a169e7edfb0144575dfa55807df40eea58d", "max_stars_repo_licenses": ["MIT"], "max_st... |
from distutils.core import setup
from distutils.extension import Extension
import distutils.sysconfig
import numpy
import tempfile
import os
import subprocess
import shutil
def check_for_openmp():
"""Check whether the default compiler supports OpenMP.
This routine is adapted from yt, thanks to Nathan
Gol... | {"hexsha": "7172044bbc1cb710b72e659e8e0ef9ec91f3c905", "size": 5074, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "xiaohanzai/fake_spectra", "max_stars_repo_head_hexsha": "170b42ac7732eb4f299617a1049cd3eabecfa3a7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# -*- coding: UTF-8 -*-
#!/usr/bin/python3
"""
CLDC task classifier
"""
#************************************************************
# Imported Libraries
#************************************************************
import numpy as np
import torch
import torch.nn as nn
from sklearn.manifold import TSNE
from matplot... | {"hexsha": "20b206f598e32213201e719bc1660dd69cac2738", "size": 1702, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_model/mlp/cldc_classifier.py", "max_stars_repo_name": "onlyrico/mling_sdgms", "max_stars_repo_head_hexsha": "ef6015d1a815a317f16fa1e42cbb048e4fe443f7", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import sys
_default_backend = 'numpy'
if 'GEOMSTATS_BACKEND' in os.environ:
_backend = os.environ['GEOMSTATS_BACKEND']
else:
_backend = _default_backend
_BACKEND = _backend
from .common import * # NOQA
if _BACKEND == 'numpy':
sys.stderr.write('Using numpy backend\n')
from .numpy import *... | {"hexsha": "7cfeaff03a77145b1b82da722d4bc66a96363fda", "size": 816, "ext": "py", "lang": "Python", "max_stars_repo_path": "geomstats/backend/__init__.py", "max_stars_repo_name": "oesteban/geomstats", "max_stars_repo_head_hexsha": "e0b53777cc27cf446d55eeac1533f4c3bc0ae681", "max_stars_repo_licenses": ["MIT"], "max_stars... |
using DiffEqFlux, Flux, OrdinaryDiffEq, Test, Optim, DiffEqSensitivity
x = Float32[0.8; 0.8]
tspan = (0.0f0,10.0f0)
ann = Chain(Dense(2,10,tanh), Dense(10,1))
p = Float32[-2.0,1.1]
p2,re = Flux.destructure(ann)
_p = [p;p2]
θ = [x;_p]
function dudt2_(u,p,t)
x, y = u
[(re(p[3:end])(u)[1]),p[1]*y +... | {"hexsha": "27c4083ef72c312d6438f691badfee94cbabef13", "size": 2026, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/partial_neural.jl", "max_stars_repo_name": "jonniedie/DiffEqFlux.jl", "max_stars_repo_head_hexsha": "0b2b4db87c1658e2008c770ffd0bd39427837fc2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright 2022 Google LLC.
#
# 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 agreed to in writing, ... | {"hexsha": "949ece336d09a95c27aa868b22096ab8bd13be6b", "size": 19217, "ext": "py", "lang": "Python", "max_stars_repo_path": "rax/_src/losses_test.py", "max_stars_repo_name": "google/rax", "max_stars_repo_head_hexsha": "d6370d574246db9fb0566317f7cac8cd331526d7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
// Copyright 2012 John Maddock. Distributed under the Boost
// Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt
#ifndef BOOST_MP_CPP_INT_CHECKED_HPP
#define BOOST_MP_CPP_INT_CHECKED_HPP
#include <climits>
#include <limits>
#include <type_tra... | {"hexsha": "5d621403d0e218a17403a689f50ac25c9ae47e57", "size": 5242, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/multiprecision/cpp_int/checked.hpp", "max_stars_repo_name": "mariospr/multiprecision", "max_stars_repo_head_hexsha": "4720edda9e3058ba68be8ae6c29342536b9ce142", "max_stars_repo_license... |
//---------------------------------------------------------------------------//
// Copyright (c) 2013-2014 Kyle Lutz <kyle.r.lutz@gmail.com>
//
// 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
//
// See http:/... | {"hexsha": "ca55356f27750d8999e40c0986b2c8a480520ae6", "size": 3767, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ios/Pods/boost-for-react-native/boost/compute/interop/opengl/opengl_renderbuffer.hpp", "max_stars_repo_name": "rudylee/expo", "max_stars_repo_head_hexsha": "b3e65a7a5b205f14a3eb6cd6fa8d13c8d663b1cc"... |
"""Unit tests for relentless.simulate.lammps."""
import tempfile
import unittest
try:
import lammps
except ImportError:
pass
import numpy
import relentless
from ..potential.test_pair import LinPot
@unittest.skipIf(not relentless.simulate.lammps._lammps_found, "LAMMPS not installed")
class test_LAMMPS(unittes... | {"hexsha": "792597d242fd90461c1bc4295d02e31d0c3b2d66", "size": 9907, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/simulate/test_lammps.py", "max_stars_repo_name": "mphowardlab/relentless", "max_stars_repo_head_hexsha": "e89b0d461106273569d08f1cf268dad1f223ce8d", "max_stars_repo_licenses": ["BSD-3-Clause... |
////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2011 Bryce Lelbach
//
// 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)
////////////////////////////////////////////... | {"hexsha": "5a23fdfd7c48d56bdb2c6dac277c6a377601fc75", "size": 537, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cmake/tests/gcc_version.cpp", "max_stars_repo_name": "akemp/hpx", "max_stars_repo_head_hexsha": "1ddf7282e322c30d82f2be044071aed14807ebe1", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": ... |
INTEGER SIZE
PARAMETER(SIZE=50000)
| {"hexsha": "e5b100f88ace86e7c985b7484f0211d3c1ccd5d2", "size": 48, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/SAC/kernels/ALPHABLENDING/ALPHABLENDING_INC.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_sta... |
import numpy as np
import itertools
import gpuscheduler
import argparse
import os
import uuid
import hashlib
import glob
import math
from itertools import product
from torch.optim.lr_scheduler import OneCycleLR
from os.path import join
parser = argparse.ArgumentParser(description='Compute script.')
parser.add_argumen... | {"hexsha": "2eacddda826d89c40658fa9c1125609cd4bf058e", "size": 12601, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/cc_small/base_grid.py", "max_stars_repo_name": "TimDettmers/sched", "max_stars_repo_head_hexsha": "e16735f2c2eb6a51f5cf29ead534041574034e2e", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""Make images using CASA."""
import numpy
import math
import shutil
import os
import time
from os.path import join
import json
import utilities
def fov_to_cellsize(fov, im_size):
"""Obatin cellsize from fov and image size."""
r_max = numpy.sin(numpy.array(fov, numpy.double) / 2. * (nu... | {"hexsha": "ec7477cc0d9869610773f4208890019104b218d4", "size": 4080, "ext": "py", "lang": "Python", "max_stars_repo_path": "time_smearing_tests/image.py", "max_stars_repo_name": "OxfordSKA/oskar_reference_simulations", "max_stars_repo_head_hexsha": "b2108f6dc963720391782e2ec843cfea5e441ace", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma scast_distrib:
fixes M :: "'a::len word \<Rightarrow> 'a::len word \<Rightarrow> 'a::len word"
fixes M' :: "'b::len word \<Rightarrow> 'b::len word \<Rightarrow> 'b::len word"
fixes L :: "int \<Rightarrow> int \<Rightarrow> int"
assumes lift_M: "\<And>x y. uint (M x y) = L (uint x) (uint y) m... | {"llama_tokens": 980, "file": "Word_Lib_Word_Lemmas", "length": 7} |
from my_widgets import LabelSlider
from process import Image, FitFunctions, FitBroadening
from process_monitor import Monitor
from PyQt5 import QtCore, QtWidgets, QtGui, QtChart
from sys import getsizeof
from sklearn.mixture import BayesianGaussianMixture
from sklearn.mixture._gaussian_mixture import _estimate_gaussian... | {"hexsha": "3cfce1e76b89bb6fb5e60b283d47fed03540167f", "size": 65944, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/gmm.py", "max_stars_repo_name": "yux1991/PyRHEED", "max_stars_repo_head_hexsha": "b39ad03651c92e3649069919ae48b1e5158cd3dd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max... |
#include "scoreboard.h"
#include <boost/optional/optional_io.hpp>
#include <sstream>
using namespace hangman;
scoreboard::scoreboard(std::shared_ptr<hangman::word> word,
std::shared_ptr<hangman::player> player)
: word_(word), player_(player) {
word->state_changed.connect(
boost::bin... | {"hexsha": "037ccf7712dd57dd083130418571ed1ccfd884ba", "size": 1174, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/scoreboard.cpp", "max_stars_repo_name": "a-ostrovsky/hangman_kata", "max_stars_repo_head_hexsha": "3eaa179148f5584b33e768d3b06d2cb24e5766e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import scipy
import statsmodels.api as sm
from scipy import stats
from statsmodels.formula.api import ols
from utils.save_data import write_csv
import pandas as pd
def compare_variances(data, factor, outcome):
# Compare the variances
summary = []
for outcome in outcome:
grouped = data ... | {"hexsha": "35e9a9a259fe0e8a4846f8efc6395b8b7c523394", "size": 1711, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference/F.py", "max_stars_repo_name": "TimS70/WebET_Analysis", "max_stars_repo_head_hexsha": "32fc2e1b70c2dad5637ee1614a6a651bc8d458b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
r"""
Ordination methods (:mod:`skbio.maths.stats.ordination`)
========================================================
.. currentmodule:: skbio.maths.stats.ordination
This module contains several ordination methods, including Principal
Coordinate Analysis, Correspondence Analysis, Redundancy Analysis and
Canonical Co... | {"hexsha": "e5faf974e982ef9a9302a12810fb37517ca16e4b", "size": 1061, "ext": "py", "lang": "Python", "max_stars_repo_path": "skbio/maths/stats/ordination/__init__.py", "max_stars_repo_name": "Jorge-C/bipy", "max_stars_repo_head_hexsha": "1097cefafc6f9bbb9d96f25b569892a3fe3f3600", "max_stars_repo_licenses": ["BSD-3-Claus... |
This notebook supplements the manuscript "Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing" by Robert Marsland III and Pankaj Mehta.
In this work, we show that the cumulative fatalities $N(t)$ for every region with more than 500 deaths as of April 15, 2020 is we... | {"hexsha": "e036582f22da9ac7d7dca284f2745facc27d1fbc", "size": 643273, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "COVID-19 predictor.ipynb", "max_stars_repo_name": "Emergent-Behaviors-in-Biology/covid19", "max_stars_repo_head_hexsha": "ad2cd64f92ccd2d36db7cb5f78f0593719122fe5", "max_stars_repo_... |
import pytest
import numpy as np
from pyrho.core.pgrid import PGrid
A, B = 1, 2
NX, NY = 3, 2
@pytest.fixture
def pgrid_example():
def f(x, y):
return np.sin(NX * x * 2 * np.pi) + np.cos(NY * y * 2 * np.pi)
xx = np.linspace(0, A, 20, endpoint=False)
yy = np.linspace(0, B, 40, endpoint=False)
... | {"hexsha": "c007a6936087b860a01e8b3867b816c413fe9ad9", "size": 417, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyrho/core/tests/test_pgrid.py", "max_stars_repo_name": "mattmcdermott/pyrho", "max_stars_repo_head_hexsha": "7ab3bd893a8b310b8be61f33a1105b090a46cd32", "max_stars_repo_licenses": ["BSD-3-Claus... |
------------------------------------------------------------------------
-- Safe modules that use --erased-cubical and --guardedness
------------------------------------------------------------------------
{-# OPTIONS --safe --erased-cubical --guardedness #-}
module README.Safe.Cubical.Erased.Guardedness where
-- M-... | {"hexsha": "37a3a3842669512c07c0cb86108057bf1223e940", "size": 471, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "README/Safe/Cubical/Erased/Guardedness.agda", "max_stars_repo_name": "nad/equality", "max_stars_repo_head_hexsha": "402b20615cfe9ca944662380d7b2d69b0f175200", "max_stars_repo_licenses": ["MIT"], "m... |
\section{Constraint Satisfaction Problems}
Outline:
\begin{itemize}
\item A special subset of search problems
\item State is defined by variables Xi with values from a domain D (sometimes D depends on i)
\item Goal test is a set of constraints specifying allowable combinations of values for subsets of vari... | {"hexsha": "6703f9bdcd8b5c83e5708e7e051aaea3e3fa2f23", "size": 4829, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notes/csp.tex", "max_stars_repo_name": "Calcifer777/columbia-ai", "max_stars_repo_head_hexsha": "aaa7173bca6f2bc9edfe6fe55b5a1a37ab310066", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
"""
Module with utility functions for BLTandPantelides.
* Author: Hilding Elmqvist, Mogram AB
* Date: July-August 2016
* License: MIT
"""
module BLTandPantelidesUtilities
#using ..BLTandPantelides
using ..ModiaLogging
export buildExtendedSystem, addDependencies, buildFullIncidence
export invertDer, invertAssign
... | {"hexsha": "38090903fe0c1fcf31005673ab2466c1ed3501c4", "size": 9301, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/symbolic/BLTandPantelides/BLTandPantelidesUtilities.jl", "max_stars_repo_name": "traversaro/Modia.jl", "max_stars_repo_head_hexsha": "581e6062983020ad6cd6cb366b0ea838fc5f03c2", "max_stars_repo_... |
function test_ode_test ( )
%*****************************************************************************80
%
%% TEST_ODE_TEST tests the TEST_ODE library.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 23 February 2013
%
% Author:
%
% John Burkardt
%
timestamp (... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_ode/test_ode_test.m"} |
[STATEMENT]
lemma cube:
shows "(v \\ t) \\ (u \\ t) = (v \\ u) \\ (t \\ u)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (v \ t) \ (u \ t) = (v \ u) \ (t \ u)
[PROOF STEP]
using cube_ax
[PROOF STATE]
proof (prove)
using this:
(?v \ ?t) \ (?u \ ?t) \<noteq> null \<Longrightarrow> (?v \ ?t) \ (?u \ ?t) = (?v \ ?... | {"llama_tokens": 207, "file": "ResiduatedTransitionSystem_ResiduatedTransitionSystem", "length": 2} |
/*
* This file is part of Poedit (https://poedit.net)
*
* Copyright (C) 2013-2020 Vaclav Slavik
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including wi... | {"hexsha": "ba7ea1268365e5e0c851c9e3f4b168bc9252e3a9", "size": 27164, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tm/transmem.cpp", "max_stars_repo_name": "Fat-Zer/poedit", "max_stars_repo_head_hexsha": "a6fc147c6e1d342d4bfe28c3956a4dc1df0eea0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
[STATEMENT]
lemma card_le_PiE_subindex:
assumes "A \<subseteq> A'" "Pi\<^sub>E A' B \<noteq> {}"
shows "PiE A B \<lesssim> PiE A' B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Pi\<^sub>E A B \<lesssim> Pi\<^sub>E A' B
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Pi\<^sub>E A B \<les... | {"llama_tokens": 2047, "file": null, "length": 24} |
#include "ManagerUtils/ArgHelper/interface/Parsermgr.hpp"
#include <boost/exception/diagnostic_information.hpp>
#include <boost/lexical_cast.hpp>
#include <boost/multiprecision/cpp_dec_float.hpp>
#include <iostream>
namespace opt = boost::program_options;
using namespace std;
using Double = boost::multiprecision::cpp_... | {"hexsha": "ca25678fa95453de45a630d09c32bfef46a7b7f1", "size": 3416, "ext": "cc", "lang": "C++", "max_stars_repo_path": "ArgHelper/src/Parsermgr.cc", "max_stars_repo_name": "sam7k9621/ManagerUtils", "max_stars_repo_head_hexsha": "7b9317df002b3df6f23ae9e559d35bb1fc15b6f6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python3
""" Base class for Faceswap :mod:`~plugins.convert.mask` Plugins """
import logging
import numpy as np
from plugins.convert._config import Config
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
def _get_config(plugin_name, configfile=None):
""" Return the :attr:`lib... | {"hexsha": "0008dd35e262543034066f8b9de8a8aebeded499", "size": 4512, "ext": "py", "lang": "Python", "max_stars_repo_path": "faceswap/plugins/convert/mask/_base.py", "max_stars_repo_name": "huangjunxiong11/FaceMap", "max_stars_repo_head_hexsha": "f320ce517edcaa4d9963ad0571e686cbe07fdfbb", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#
# written by Shotaro Fujimoto
# 2016-10-21
from triangular import LatticeTriangular as LT
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import matplotlib.animation as animation
import networkx as nx
# import pygraphviz
import numpy as np
class InsideStri... | {"hexsha": "032990b677da93221e02d4aaa0223bb01f837bd5", "size": 17009, "ext": "py", "lang": "Python", "max_stars_repo_path": "triangular_lattice/growing_string_inside.py", "max_stars_repo_name": "ssh0/growing-string", "max_stars_repo_head_hexsha": "2e43916e91157dfb4253775149b35ec9d81ef14d", "max_stars_repo_licenses": ["... |
import os
from os.path import exists, join
import json
from utils import count_data
import argparse
import numpy as np
try:
DATA_DIR = os.environ['DATA']
except KeyError:
print('please use environment variable to specify data directories')
def main(args):
data_path = join(DATA_DIR, args.split)
n_da... | {"hexsha": "8ac51ca6a6ac7e2ca869a2836edf733a5b87db3b", "size": 1731, "ext": "py", "lang": "Python", "max_stars_repo_path": "compute_doc_len_stat.py", "max_stars_repo_name": "kenchan0226/AbsThenExtPublic", "max_stars_repo_head_hexsha": "567811d6c76fe51c2c368eeaca1761eb322db2a2", "max_stars_repo_licenses": ["MIT"], "max_... |
\documentclass[11pt]{article}
\usepackage[left=1in,right=1in,top=1in,bottom=1in]{geometry}
\usepackage{syntax}
\usepackage{multicol}
\usepackage{hyperref}
\usepackage{comment}
\newcommand{\sizet}{size\textunderscore{}t}
\title{MERCATOR Reference Manual, v0.9.8}
\begin{document}
\maketitle
\noindent
Copyright (C) 2... | {"hexsha": "1c5ddfebea9973b4b2b551f4400258d5a4c9b0ea", "size": 54069, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/mercator-manual.tex", "max_stars_repo_name": "jdbuhler/mercator", "max_stars_repo_head_hexsha": "f61f2185bc9cdb96838ddd9700464cebed21b5d2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import math
import numpy as np
import pandas as pd
def series_rolling(x, window, stride):
if not isinstance(window, (int, float)) or not isinstance(stride, (int, float)):
window_size = math.floor(window / x.index.freq)
stride_size = math.floor(stride / x.index.freq)
end_index = x.shape[0] - w... | {"hexsha": "7bcbf65718e04311679ad8abe4081f61c5ae53f3", "size": 3779, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyqc/utils.py", "max_stars_repo_name": "wangsen992/pyqc", "max_stars_repo_head_hexsha": "7909426111bd069f295cc477c65f343aa5a7e437", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
basic operations II
"""
import numpy as np
arr = np.array([[1, 5, 6],
[4, 7, 2],
[3, 1, 9]])
# maximum element of array
print("Largest element is:", 0)
# minimum element of array
print("Smallest element is:", 0)
# maximum element per row
print("Row-wise maximum elements:",
... | {"hexsha": "3e424e504cb2f0ed9e02da4e0c7c8ec0a8abca3a", "size": 775, "ext": "py", "lang": "Python", "max_stars_repo_path": "pset_pandas1_basics/ndarrs/p5.py", "max_stars_repo_name": "mottaquikarim/pydev-psets", "max_stars_repo_head_hexsha": "9749e0d216ee0a5c586d0d3013ef481cc21dee27", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: utf-8 -*-
import os
import platform
import numpy as np
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
RAW_PATH = os.path.expanduser("~") + "/data/quora/"
FEAT_PATH = os.path.expanduser("~") + "/data/quora/features/"
SUB_PATH = os.path.expanduser("~") + "/data/quora/submission/"
... | {"hexsha": "adc17fda7222d39d25f346fd663aad52d14c9dc3", "size": 1190, "ext": "py", "lang": "Python", "max_stars_repo_path": "quora/solution/config.py", "max_stars_repo_name": "zonemercy/Kaggle", "max_stars_repo_head_hexsha": "35ecb08272b6491f5e6756c97c7dec9c46a13a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
####Inference Engine
from openvino.inference_engine import IENetwork, IEPlugin
import os
import time
import cv2
import argparse
import numpy as np
import tkinter as tk
root= tk.Tk()
canvas1 = tk.Canvas(root, width = 300, height = 300)
canvas1.pack()
target_names = {1: 'Bilateral cerebellar hemispheres', 2: 'Bilatera... | {"hexsha": "4587e136d452f52b1eda158e9e3c008d15bc83c4", "size": 3428, "ext": "py", "lang": "Python", "max_stars_repo_path": "mri_infer.py", "max_stars_repo_name": "yogya-ch/Acute_infarct", "max_stars_repo_head_hexsha": "c90220da1f8da8264f9461910ccb6ce5aa43ea70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma cong_trans_a: "R \<in> congruences \<Longrightarrow> R a b \<Longrightarrow> R b c \<Longrightarrow> R a c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>R \<in> congruences; R a b; R b c\<rbrakk> \<Longrightarrow> R a c
[PROOF STEP]
apply (simp add: congruences_def)
[PROOF STATE]
proof (... | {"llama_tokens": 611, "file": "PseudoHoops_PseudoHoopFilters", "length": 3} |
import whatsup.plan as plan
from whatsup.imports import *
import numpy as np
from exopop.Confirmed import Confirmed
p = plan.Plan(semester='2016A', start='2016-04-16', finish='2016-05-24', maxairmass=2.5, maxsun=-6.0)
p.known = Confirmed()
distance = 100.0
transmission = p.known.standard[np.array([p.known.find('GJ11... | {"hexsha": "aa8e3f25241efb9d0486069f5635529e7635b0af", "size": 562, "ext": "py", "lang": "Python", "max_stars_repo_path": "testscripts/gj1132.py", "max_stars_repo_name": "zkbt/whatsup", "max_stars_repo_head_hexsha": "0a9a878b47a42973f5f9ffdda051960c1cb560b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
#!/usr/bin/env python3
from argparse import ArgumentParser
import os
import pickle
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_validate
from scipy.stats import norm
import tqdm
from chg.db import database
from chg.defaults import CHG_PROJ_RANKER
from... | {"hexsha": "303017180f0115e8fa869d8989f10af9b34b3c82", "size": 9674, "ext": "py", "lang": "Python", "max_stars_repo_path": "chg/ranker/model_based_ranking.py", "max_stars_repo_name": "josepablocam/changestructor", "max_stars_repo_head_hexsha": "21712cb11951564b255287cbdc4a3a5b73c70ffd", "max_stars_repo_licenses": ["MIT... |
SUBROUTINE STASEA (STATEB,LIMIT,ITEMA)
C SEARCH FOR A COMPATIBLE STATE
C VERSION WITHOUT 'NUCS' DATA STRUCTURE
C GF 30.07.1980
C
INCLUDE 'PARS.f'
INCLUDE 'ITES.f'
INCLUDE 'PRES.f'
INCLUDE 'STAS.f'
INCLUDE 'SYMS.f'
INTEGER*2 I1,I2
= ,GOT ! RESULT ... | {"hexsha": "ac28688c1bb2d465f2fadbc164eb07c2a2c6858b", "size": 2032, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "legacy/bofor_2005/STASEA.f", "max_stars_repo_name": "gfis/jextra", "max_stars_repo_head_hexsha": "bdad8fd33fdf633cf2ff4c1879e1f61935c3d636", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"], "... |
#!/usr/bin/env python
# Standard library
from typing import Tuple
# 3rd party packages
import numpy as np
# Local source
from parametrization_clean.domain.crossover.strategy import ICrossoverStrategy
from parametrization_clean.domain.individual import Individual
from parametrization_clean.domain.root_individual impo... | {"hexsha": "7ed5982ace081183ca1104ac2f6c6a70c27d15e6", "size": 1082, "ext": "py", "lang": "Python", "max_stars_repo_path": "parametrization_clean/domain/crossover/uniform.py", "max_stars_repo_name": "cdaksha/parametrization_clean", "max_stars_repo_head_hexsha": "702243d87c2045cf8155f3c18134665871f3b170", "max_stars_rep... |
'''
-----------------------------------------------------------------------
Additional Documentation
Made by Zachary A Brader, Kieran Coito, Pedro Goncalves Mokarzel
while attending University of Washington Bothell
Made in 03/09/2020
Based on instruction in CSS 458,
taught by professor Johnny Li... | {"hexsha": "f61f8526ee68b3e1a0a31286cb521ed404d8935f", "size": 73767, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_utils.py", "max_stars_repo_name": "pgmoka/checkout-simulator", "max_stars_repo_head_hexsha": "bce7e68ba47b9309f19514a9199d43bdbbbc4ffc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#pragma once
#include <bunsan/utility/resolver.hpp>
#include <bunsan/utility/utility.hpp>
#include <boost/property_tree/ptree.hpp>
#include <boost/serialization/access.hpp>
#include <boost/serialization/nvp.hpp>
#include <functional>
#include <string>
namespace bunsan::utility {
namespace detail {
template <typena... | {"hexsha": "91fd03697366bbaacc8532fa1e2b6180879e5056", "size": 1852, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "bunsan/utility/include/bunsan/utility/factory_options.hpp", "max_stars_repo_name": "bacsorg/bacs", "max_stars_repo_head_hexsha": "2b52feb9efc805655cdf7829cf77ee028d567969", "max_stars_repo_licenses"... |
import unittest
import os
import random
import glob
import numpy as np
from pymatgen import SETTINGS
from pymatgen.io.vasp.outputs import Vasprun
from pymatgen.analysis.surface_analysis import SurfaceEnergyAnalyzer
from pymatgen.util.testing import PymatgenTest
__author__ = "Richard Tran"
__copyright__ = "Copyright ... | {"hexsha": "dc3aadc47b068416ca51608d5cb1fb0ebf56f50d", "size": 3105, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/analysis/tests/test_surface_analysis.py", "max_stars_repo_name": "ltalirz/pymatgen", "max_stars_repo_head_hexsha": "894cdb2ec7b9bd74f0ac3cdad40d144203ccdcf6", "max_stars_repo_licenses": [... |
#----------------------------------------------------------------
# NAME || AM || e-mail
# Georgios Vardakas || 432 || geoo1995@gmail.com
# Dimitra Triantali || 431 || dimitra.triantali@gmail.com
#----------------------------------------------------------------
# Course: Optimization
# Project 1
# W... | {"hexsha": "df1ff426c72111c190face3c452663eb0b8bc25f", "size": 19072, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimization.py", "max_stars_repo_name": "giorgosVardakas/Gradient-Based-Optimization-Methods", "max_stars_repo_head_hexsha": "0166780cb5a2906feb8b27ba299439e16257621f", "max_stars_repo_licenses"... |
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_datasets as tfds
import time
from source.Selective_walk import SelectiveWalk
from source.Evolution_ import Evolution
from source.sample import Sample
import logging
import jax.numpy as np
logg... | {"hexsha": "84798212095b69a0231dc432d94dfd458ced0beb", "size": 3339, "ext": "py", "lang": "Python", "max_stars_repo_path": "runoverfit.py", "max_stars_repo_name": "NMVRodrigues/TFNE", "max_stars_repo_head_hexsha": "44538844f115ee11bbc58d4b7ba33526f17e2264", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
# coding: utf-8
from datetime import date
import numpy as np
import apertools.sario as sario
import apertools.utils as utils
import apertools.subset as subset
from apertools.constants import PHASE_TO_CM
from apertools.deramp import remove_ramp
MENTONE_EQ_DATE = date(2020, 3, 26)
# TODO: Make a cli version...
# TODO: ... | {"hexsha": "a630ca450adaebd889a99d6bcc3405fdb82f05a9", "size": 7151, "ext": "py", "lang": "Python", "max_stars_repo_path": "apertools/coseismic_stack.py", "max_stars_repo_name": "scottstanie/apertools", "max_stars_repo_head_hexsha": "f959d03038e77444204c1ff224ddd8357db3fc04", "max_stars_repo_licenses": ["MIT"], "max_st... |
\documentclass[main.tex]{subfiles}
\begin{document}
\marginpar{Tuesday\\ 2020-8-18, \\ compiled \\ \today}
Let us go into some more details regarding how the radiation field looks.
Let us suppose that the angle between the acceleration \(\dot{\vec{u}}\) and the observation unit vector \(\vec{n}\) is \(\Theta \): the... | {"hexsha": "918d7ca6d865b945fe5ca53f79d95ed75c805c1e", "size": 7655, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ap_second_semester/radiative_processes/mar26.tex", "max_stars_repo_name": "jacopok/notes", "max_stars_repo_head_hexsha": "805ebe1be49bbd14c6b46b24055f9fc7d1cd2586", "max_stars_repo_licenses": ["Apac... |
import warnings
def fxn():
warnings.warn("deprecated", DeprecationWarning)
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import pickle
import cv2
import os
import json
import sys
import lmdb
from collections import defaultdict
import random
from utils import *
from datetime impo... | {"hexsha": "321866cff7ef442633e7554e4bfdd6f32d72ce87", "size": 9573, "ext": "py", "lang": "Python", "max_stars_repo_path": "Feature_Extraction.py", "max_stars_repo_name": "GAIA-DARPA-AIDA/grounding-merging", "max_stars_repo_head_hexsha": "600760326c2322e8dec36f862b02c3a30abbb8ee", "max_stars_repo_licenses": ["Apache-2.... |
__precompile__()
module Wavelets
include("util.jl")
include("wt.jl")
include("transforms.jl")
include("threshold.jl")
include("plot.jl")
using Reexport
@reexport using .Util, .WT, .Transforms, .Threshold, .Plot
end
| {"hexsha": "2f1517808d0b3ee446e72f5b4f41fcc36bfa279e", "size": 221, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Wavelets.jl", "max_stars_repo_name": "JuliaPackageMirrors/Wavelets.jl", "max_stars_repo_head_hexsha": "39f076014406712adfb8b797d55a22ad53c3814b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
// The MIT License (MIT)
//
// Copyright (c) 2015 Jonathan McCluskey and William Harding
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitat... | {"hexsha": "b1d99ad851362d89b620f8e2a7f95acd451b79a7", "size": 2290, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/libcrypto/test/check_Utilities.cpp", "max_stars_repo_name": "ToadRedCarp/koolkash-digital-cash-protocol", "max_stars_repo_head_hexsha": "ad8b1ed8fdb79658c7d74934db53463d02c5cb42", "max_stars_rep... |
import unittest
import numpy as np
import six
import chainer
from chainer import testing
from chainer_tests.dataset_tests.tabular_tests import dummy_dataset
# filter out invalid combinations of params
def _filter_params(params):
for param in params:
if param['out_mode'] is None and \
isinstan... | {"hexsha": "96193ed1eccc313e7ec4c5347c46ccd345463e81", "size": 5484, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/chainer_tests/dataset_tests/tabular_tests/test_transform.py", "max_stars_repo_name": "zjzh/chainer", "max_stars_repo_head_hexsha": "e9da1423255c58c37be9733f51b158aa9b39dc93", "max_stars_repo... |
[STATEMENT]
lemma dim_poly_greater_ex_coeff: "dim_poly x > d \<Longrightarrow> \<exists>i\<ge>d. coeff x i \<noteq> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. d < dim_poly x \<Longrightarrow> \<exists>i\<ge>d. Abstract_Linear_Poly.coeff x i \<noteq> 0
[PROOF STEP]
by (simp split: if_splits) (meson Max_in coef... | {"llama_tokens": 148, "file": "Linear_Programming_Matrix_LinPoly", "length": 1} |
\chapter{Practical recommendations}
\label{practical-recommendations}
% A section which is missing in something called a "Cookbook" would be
% $ practical recommendations on how to input Unicode characters. There are
% various character selection tools, shortcuts on the keyboard, the
% shapecatcher website references ... | {"hexsha": "60e614050fc6933fb2cc4bd2d205f566392bbec0", "size": 10490, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "book/chapters/practical_recommendations.tex", "max_stars_repo_name": "unicode-cookbook/unicode", "max_stars_repo_head_hexsha": "f6172408352709b24122acf0aeff0ad7e8a8f6ab", "max_stars_repo_licenses":... |
(* Title: Lightweight Java, the definition
Authors: Rok Strnisa <rok@strnisa.com>, 2006
Matthew Parkinson <matt@matthewp.com>, 2006
Maintainer:
Note: This file should _not_ be modified directly. Please see the
accompanying README file.
*)
(* generated by ... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/LightweightJava/Lightweight_Java_Definition.thy"} |
import matplotlib
matplotlib.use('Pdf')
import matplotlib.pyplot as plt
import numpy as np
import os.path as osp
import rllab.misc.logger as logger
import rllab_maml.plotter as plotter
import tensorflow as tf
import time
from rllab_maml.algos.base import RLAlgorithm
from sandbox_maml.rocky.tf.policies.base import Pol... | {"hexsha": "6855c05c30582090154cfb0dcaa5a047d8cf4660", "size": 12256, "ext": "py", "lang": "Python", "max_stars_repo_path": "sandbox/ours/algos/MAML/batch_maml_polopt.py", "max_stars_repo_name": "jackwilkinson255/mbmpo_master", "max_stars_repo_head_hexsha": "e9e0eaf542c7895764dcb0bfee28752818124ff2", "max_stars_repo_li... |
import Base.-, Base.+
# @inline -(a::WrappingInt32, b::WrappingInt32) = a.val - b.val
@inline +(a::WrappingInt32, b::UInt32) = WrappingInt32(a.val + b)
@inline -(a::WrappingInt32, b::UInt32) = a + -b
@inline +(a::WrappingInt32, b::Integer) = a + UInt32(b)
@inline -(a::WrappingInt32, b::Integer) = a + -UInt32(b)
functi... | {"hexsha": "ec5805900829495357c0b58318eda7cfa07cc689", "size": 915, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrapping_integers.jl", "max_stars_repo_name": "AquaIndigo/JLSponge", "max_stars_repo_head_hexsha": "06b6204c40c89194762ae4c15f0526cefb689f0e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using SnoopCompile
using Test
uncompiled(x) = x + 1
if VERSION >= v"1.2.0-DEV.573"
include_string(Main, """
@testset "snoopi" begin
timing_data = @snoopi uncompiled(2)
@test any(td->td[2].def.name == :uncompiled, timing_data)
# Ensure older methods can be tested
a = rand(Float16... | {"hexsha": "4d747fbe818a302889826fe0d75c91c5bd064d9d", "size": 1993, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SnoopCompile.jl-aa65fe97-06da-5843-b5b1-d5d13cad87d2", "max_stars_repo_head_hexsha": "3a8460017f1e6a538b6a860c2dcfd29f1ae75670", "ma... |
# -*- coding: utf-8 -*-
# Authors: Federico Raimondo <federaimondo@gmail.com>
# simplified BSD-3 license
import os.path as op
from numpy.testing import assert_array_equal
from scipy import io as sio
from mne.io import read_raw_eximia
from mne.io.tests.test_raw import _test_raw_reader
from mne.datasets.testi... | {"hexsha": "7d9acecca2c3002b6c50e9aa68a8022c999e9f7a", "size": 1675, "ext": "py", "lang": "Python", "max_stars_repo_path": "mne/io/eximia/tests/test_eximia.py", "max_stars_repo_name": "0reza/mne-python", "max_stars_repo_head_hexsha": "da02a256423404a81929d6de278bc63d3192a280", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import FinanceLib as Fl
import FinanceLib.FixedIncomes.MoneyMarkets as MM
import FinanceLib.FixedIncomes as FI
@testset "FinanceLib.FixedIncomes " begin
@testset "MoneyMarkets" begin
@test MM.tBillR(150,98_000,100_000) ≈ 0.048
@test MM.tBillD(0.048, 150, 100_000) == 2_... | {"hexsha": "4295d0406b70fdd3762c4d7376838384fa6674a2", "size": 728, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FixedIncomes/runtests.jl", "max_stars_repo_name": "n-kishaloy/FinanceLib.jl", "max_stars_repo_head_hexsha": "0c8ca56a7e366e8d411bba59f38bca6f0c9ac9d9", "max_stars_repo_licenses": ["MIT"], "max_s... |
import os
import numpy as np
from sklearn import manifold
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
import matplotlib.pyplot as plt
from keras.layers import Input
from core.util import print_accuracy,LearningHandler
from core import Conv
import scipy.io as scio
import tensorflow as tf
import scipy.io as sio
from sklearn... | {"hexsha": "e9c8cf417da937a175c7c56484fc11d17d08107f", "size": 3423, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/applications/ADDC.py", "max_stars_repo_name": "xdxuyang/Adversarial-Learning-for-Robust-Deep-Clustering", "max_stars_repo_head_hexsha": "90b88e0d83ad2225dbe8534cd21d63982dd8b34e", "max_stars_r... |
"""Test the old numpy pickler, compatibility version."""
import random
# numpy_pickle is not a drop-in replacement of pickle, as it takes
# filenames instead of open files as arguments.
from joblib import numpy_pickle_compat
def test_z_file(tmpdir):
# Test saving and loading data with Zfiles.
filename = tmp... | {"hexsha": "5e8319212b606dbebf27bfed32c109c00294929e", "size": 624, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/lib/python3.8/site-packages/joblib/test/test_numpy_pickle_compat.py", "max_stars_repo_name": "avrumnoor/NewsSummarizer", "max_stars_repo_head_hexsha": "a963497ef9bc62d2148aa28e624ea32955992f57... |
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