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# 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 you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "12160cf6f18e79594c1fcc23d820b86a020cf557", "size": 1667, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/examples/mnist/mnist-data.jl", "max_stars_repo_name": "Vikas-kum/incubator-mxnet", "max_stars_repo_head_hexsha": "ba02bf2fe2da423caa59ddb3fd5e433b90b730bf", "max_stars_repo_licenses": ["Apach... |
from random import *
import math
import argparse
import json
from PIL import Image, ImageDraw, ImageOps
from filters import *
from strokesort import *
import perlin
from util import *
no_cv = False
export_path = "output/out.svg"
draw_contours = True
draw_hatch = True
show_bitmap = False
resolution = 1024
hatch_size ... | {"hexsha": "7c4d92eaf61ced5382be41c570882d7b0c8e82d9", "size": 9115, "ext": "py", "lang": "Python", "max_stars_repo_path": "linedraw.py", "max_stars_repo_name": "evildmp/linedraw", "max_stars_repo_head_hexsha": "fd5d5c602714a746b7b7a86248b536a1d2e92329", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_s... |
\section{\module{fl} ---
FORMS library interface for GUI applications}
\declaremodule{builtin}{fl}
\platform{IRIX}
\modulesynopsis{FORMS library interface for GUI applications.}
This module provides an interface to the FORMS Library\index{FORMS
Library} by Mark Overmars\index{Overmars, Mark}. The source ... | {"hexsha": "26133fde840b44102ce1d9729762ab3dd3049f02", "size": 15558, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libfl.tex", "max_stars_repo_name": "marcosptf/cpython-2.0.1", "max_stars_repo_head_hexsha": "73c739a764e8b1dc84640e73b880bc66e1916bca", "max_stars_repo_licenses": ["PSF-2.0"], "max_stars_co... |
# coding=utf-8
# Copyright 2020 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... | {"hexsha": "e18f5b71634d54735f79f8b2ad778acfebc719d2", "size": 15327, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/benchmarks/TFT/libs/hyperparam_opt.py", "max_stars_repo_name": "lpd6375/qlib", "max_stars_repo_head_hexsha": "3a911bc09ba5136cd7c61c2c8dcca8a63339e738", "max_stars_repo_licenses": ["MIT"... |
"""
Integration test taking in csv of local attributions
and producing csv of global attributions
"""
import glob
import os
import numpy as np
import pytest
from gam import gam
def test_read_csv():
g = gam.GAM(attributions_path="tests/test_attributes.csv")
g._read_local()
assert hasattr(g, "attributi... | {"hexsha": "83c01de3b979b0fdef2413c4e6065a18f1be8f95", "size": 4054, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_gam.py", "max_stars_repo_name": "timwong101/project-gam", "max_stars_repo_head_hexsha": "6a0b87418091772517e2f3b2339e8998c43ffc54", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# -*- coding: utf-8 -*-
import gzip
from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import sklearn.preprocessing
def extract_params(statefile):
"""Extract the alpha and beta values from the statefile.
Args:
statefile (str): Path to statefile... | {"hexsha": "eab5b21924a9b4873244c7701e1c5a31cfd9bab5", "size": 2928, "ext": "py", "lang": "Python", "max_stars_repo_path": "GoH/verify_model.py", "max_stars_repo_name": "jerielizabeth/GoH", "max_stars_repo_head_hexsha": "7c16eac86d76525170330924348cecccce3aa5cf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
from io import BytesIO
from django.shortcuts import render
from django.http import HttpResponse
import librosa
import soundfile as sf
from .models import File
from devices.models import DeviceContext
from projects.models import Project
import scipy.io.wavfile as sa
# Create your views here.
def list_files(request,... | {"hexsha": "191381c288ae90ddeef840a9b7b2778653e707d2", "size": 1131, "ext": "py", "lang": "Python", "max_stars_repo_path": "files/views.py", "max_stars_repo_name": "plaf2000/webspec", "max_stars_repo_head_hexsha": "487ccccff088ddbda0e5e475aaad167a01f4aab2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
from .propagator import Propagator
from ..optics import Wavefront, make_agnostic_optical_element
from ..field import Field
@make_agnostic_optical_element()
class FraunhoferPropagator(Propagator):
'''A monochromatic perfect lens propagator.
This implements the propagation of a wavefront through a... | {"hexsha": "e44356e27de64727928de326d4f1629606ffaae7", "size": 3440, "ext": "py", "lang": "Python", "max_stars_repo_path": "hcipy/propagation/fraunhofer.py", "max_stars_repo_name": "rahulbhadani/hcipy", "max_stars_repo_head_hexsha": "b52726cb9502b5225ddff9d7b1ff417f2350cda8", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/python3
# Name: Chenying Wang
# Email: chenying.wang@usc.edu
# USC ID: ****-****-**
# Date: Friday, March 20, 2020
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import sys
COLOR = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']
def main(input_csv, output_file):
fea... | {"hexsha": "376989e14bdefdf7351ce3073de9614a97fb572c", "size": 1017, "ext": "py", "lang": "Python", "max_stars_repo_path": "ee569/hw4/plot/plot_3d.py", "max_stars_repo_name": "chenying-wang/usc-ee-coursework-public", "max_stars_repo_head_hexsha": "5bc94c2350bcebf1036fb058fe7dc4f7e31e1de1", "max_stars_repo_licenses": ["... |
import sys
sys.path.append('..')
from neml import elasticity, interpolate
from neml.math import tensors, rotations
import unittest
from common import *
import numpy as np
import numpy.linalg as la
class CommonElasticity(object):
"""
Tests that could apply to any elastic model.
"""
def test_C2S(self):
... | {"hexsha": "fee23822f0fc0a2b43e892beba29e1f991ae2b36", "size": 3766, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_elasticity.py", "max_stars_repo_name": "ajey091/neml", "max_stars_repo_head_hexsha": "23dd2cdb83057fdd17a37fa19f4592c54f821dbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6,... |
from functools import partial
import mmcv
import numpy as np
from six.moves import map, zip
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
num_imgs = tensor.size(0)
mean = np.array(mean, dtype=np.float32)
std = np.array(std, dtype=np.float32)
imgs = []
for img_id in range(nu... | {"hexsha": "1921ec5beff20a368a32a2d7c953f59a696b9fa5", "size": 2496, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmdet/core/utils/misc.py", "max_stars_repo_name": "youshyee/Greatape-Detection", "max_stars_repo_head_hexsha": "333b63d8f76538659bcd2bc6022128830a7a435b", "max_stars_repo_licenses": ["Apache-2.0"]... |
import requests
import os
from datetime import datetime
import json
from bs4 import BeautifulSoup as bs
import time
import random
import numpy
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKCYAN = '\033[96m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
... | {"hexsha": "b1dc4b81ce50670ee0ed435fd489e7dbe2144e1d", "size": 72400, "ext": "py", "lang": "Python", "max_stars_repo_path": "myigbot.py", "max_stars_repo_name": "vishaljoshi789/MyIGBot", "max_stars_repo_head_hexsha": "1fa3920e478464098bd6ece7d7828bfd2b62d3eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
"""
SCRIPT FOR TRAINING 2DCNN MODELS
Run with two arguments - arg1=region, arg2=model type
"""
import os, sys
import torch
import numpy as np
import time
from CNN import *
from Training import *
from Data_maker_loader import *
from random import randint, uniform, choice
if sys.argv[2] == "2D":
from CNN import *
el... | {"hexsha": "6748bf14deb0144e8563fb8b269a59fbaf75a6ad", "size": 11082, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/2DCNN_training.py", "max_stars_repo_name": "PatBall1/DeepForestcast", "max_stars_repo_head_hexsha": "f9444490d71b89aa7823e830cf7fbe6752c74d9a", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright - Transporation, Bots, and Disability Lab - Carnegie Mellon University
# Released under MIT License
"""
Common 2D Rotation Operations
"""
from .basic import *
import numpy as np
__all__ = [
'clip_radian_rotation', 'find_rotation', "theta_to_clock",
'find_theta_distance', 'deg_to_theta'
]
def cl... | {"hexsha": "7efaa8c48988e6f13d1347c70f74011b121cbe71", "size": 3147, "ext": "py", "lang": "Python", "max_stars_repo_path": "alloy/math/rotation_2D.py", "max_stars_repo_name": "CMU-TBD/alloy", "max_stars_repo_head_hexsha": "cf66738e044613fb274bd1b159864a7600e15cb5", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""Spectral Temporal SIMilarity"""
from dataclasses import dataclass
import numpy as np
from vibromaf.signal.spectrum import compute_spectral_support
from vibromaf.signal.transform import PerceptualSpectrumBuilder, preprocess_input_signal
def st_sim(distorted: np.array, reference: np.array, eta: float = 2 / 3) -> ... | {"hexsha": "9d9edd0718569e4ebf9521c1e9f94bdf60d6125b", "size": 2454, "ext": "py", "lang": "Python", "max_stars_repo_path": "vibromaf/metrics/stsim.py", "max_stars_repo_name": "hofbi/vibromaf", "max_stars_repo_head_hexsha": "7678042d18fa3b4ab006283bdbd1b1cc6d84e822", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import cPickle as pck
import numpy as np
from make_input.qe_input import makeQEInput_new
from make_input.qe_run import run_qe_hpc
from tqdm import tqdm
from make_input.SSSP_acc_PBE_info import wfccutoffs,rhocutoffs
calculation_type = '"vc-relax"'
sites_z = [14]
kpt = [2,2,2]
Nkpt = 3000
# rhocutoff ,wfccutoff = No... | {"hexsha": "3493f980b5b818aaf1375334f42591178ae75274", "size": 2479, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_relax_Si_new.py", "max_stars_repo_name": "felixmusil/run_qe", "max_stars_repo_head_hexsha": "10001c2779788122e59b299d088ef83821e24a38", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
// This file is part of MLDB. Copyright 2015 mldb.ai inc. All rights reserved.
/* svd_utils_test.cc
Jeremy Barnes, 18 November 2012
Copyright (c) 2012 mldb.ai inc. All rights reserved.
Test for SVD utilities
*/
#define BOOST_TEST_MAIN
#define BOOST_TEST_DYN_LINK
#include <boost/test/unit_test.hpp>
#includ... | {"hexsha": "c0d3f684004a42bfb8377d98229a9eb82c1d3ec1", "size": 1932, "ext": "cc", "lang": "C++", "max_stars_repo_path": "testing/svd_utils_test.cc", "max_stars_repo_name": "kstepanmpmg/mldb", "max_stars_repo_head_hexsha": "f78791cd34d01796705c0f173a14359ec1b2e021", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%% function [XPP,YPP]=cast2(t,XP,YP)
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%% Enveloppe convexe d'une courbe de Bézier
%% Construction des points de contrôle
%% Deuxième partie t dans [0.5,... | {"hexsha": "0d56cd30501eabacf43b9872bdb63691cd3a2b81", "size": 1476, "ext": "py", "lang": "Python", "max_stars_repo_path": "cast2.py", "max_stars_repo_name": "JosueGauthier/Surface-de-Bezier-Python", "max_stars_repo_head_hexsha": "e37847296cbbe36f0c72c9cefc1861870ce883db", "max_stars_repo_licenses": ["MIT"], "max_stars... |
module Mod_sld_ExternalForces
use typre
use Mod_sld_BaseElmope
implicit none
private
public SetPointersExternalForces
integer(ip), allocatable :: kfl_IsSet
contains
!----------------------------------------------------------------------------
!Setting Pointers
subroutine SetPoint... | {"hexsha": "5c388a06ab2f3c81ba63e1c7775e23684a6988ce", "size": 1251, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/modules/solids/models/Mod_sld_ExternalForces.f90", "max_stars_repo_name": "ciaid-colombia/InsFEM", "max_stars_repo_head_hexsha": "be7eb35baa75c31e3b175e95286549ccd84f8d40", "max_stars_re... |
import numpy as np
import struct
UINT8 = "B"
UINT16 = "H"
UINT32 = "I"
class SABFormat(object):
def __init__(self):
super(SABFormat, self).__init__()
self.RadialHeaderSize = 128
self.InfSize = 28
def RadialHeader(self):
return (
('reserve0', '14s'),
('... | {"hexsha": "3243ac81ecece94ab06868ba5d06fff4c1bbf13e", "size": 2183, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycwr/io/BaseDataProtocol/SABProtocol.py", "max_stars_repo_name": "zhaopingsun/pycwr", "max_stars_repo_head_hexsha": "7459371588e6d0d6d0737e249afa3921fe073151", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env Rscript
# run with Rscript plot-gradients.r -i taxontable -w 'Bacteroides,Prevotella' -o outdir
# REQUIRED GLOBAL VARIABLES: PLEASE EDIT
#source(paste(Sys.getenv('MWAS_DIR'),'/lib/gradients.r',sep=''))
#source(paste(Sys.getenv('MWAS_DIR'),'/lib/util.r',sep=''))
require('RColorBrewer')
require('optparse... | {"hexsha": "6f404a801934d1a9c86dc3d39370fcdf82ba2192", "size": 5758, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/util/plot-gradients.r", "max_stars_repo_name": "hhuang2018/mwas", "max_stars_repo_head_hexsha": "26518c937be831026c6015f8fdfb764c1d3a58be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
from glob import glob
import numpy as np
import pandas as pd
DATASET_NAME = "adhd_sin"
DATASET_PATH = f"../datasets/{DATASET_NAME}"
REFN_DATA_PATH = f"{DATASET_PATH}" # reference data
REFN_FXTN_PATH = f"{REFN_DATA_PATH}_fxtn" # fixations from reference data
SYNT_DATA_PATH = f"{DATASET_PATH}_synt" # synt... | {"hexsha": "38c3648958de49fd9f967580cef4d5d7cb546d80", "size": 3393, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/eyetracking/dataset_compare.py", "max_stars_repo_name": "nirdslab/streaminghub", "max_stars_repo_head_hexsha": "a0d9f5f8be0ee6f090bd2b48b9f596695497c2bf", "max_stars_repo_licenses": ["MIT... |
#
# TODO: should ITensorMap be a special version of
# an ITensorNetwork with input and output indices specified?
#
# T is however the nodes are indexed
# TODO: how to deal with 2D, multiple networks, etc.?
# struct IndexSetNetwork{T}
# # Use Vector{SortedVector{Pair{T, IndexSet}}}
# data::Vector{Vector{Pair{T, In... | {"hexsha": "543d53d7f9701d1480d256ecfe62627e870ebe2c", "size": 7226, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/itensormap.jl", "max_stars_repo_name": "LHerviou/ITensorInfiniteMPS.jl", "max_stars_repo_head_hexsha": "1489817f7960903e84331b324d810631074d0f35", "max_stars_repo_licenses": ["MIT"], "max_stars... |
delivery_util(reward::Float64, ie::InteractionEvent) = reward
"""
Look up the CDF of the Epanechnikov distribution for the arrival time.
"""
function delivery_success_prob(std_scale::Float64, ref_time::Float64, ie::InteractionEvent)
travel_time = ie.timestamps[SUCCESS] - ie.timestamps[FINISH]
# TODO: Need s... | {"hexsha": "499b3dffbe82bda6cf47d40094d52b3025a38fcb", "size": 5853, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/domains/routing/routing_scoba.jl", "max_stars_repo_name": "sisl/SCoBA.jl", "max_stars_repo_head_hexsha": "e66633dcf044decfb63b2d3cb1b5b059f79cd6ea", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import torch
import numpy as np
from utils.block_diag_matrix import block_diag_irregular
from scipy.spatial import distance_matrix
def compute_adjs(args, seq_start_end):
adj_out = []
for _, (start, end) in enumerate(seq_start_end):
mat = []
for t in range(0, args.obs_len + args.pred_len):
... | {"hexsha": "d1e0677fa7f582efc4167bff7afb79397a695c20", "size": 3227, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/adj_matrix.py", "max_stars_repo_name": "alessiabertugli/AC-VRNN", "max_stars_repo_head_hexsha": "3a204bd23a7b90c3939efc6468fa6477c31a733f", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
from cvxpy import *
import matplotlib.pyplot as pyplot
import heapq
import time
settings.USE_CVXCANON = True
ANSWERS = []
TIME = 0
np.random.seed(0)
m=100
k=40 # max # permuted measurements
n=20
A=10 * np.random.randn(m,n)
x_true=np.random.randn(n,1) # true x value
y_true = A.dot(x_true) + np.random... | {"hexsha": "8a2d74b42dbe7952449f4c43d3d5f95068a571e0", "size": 1724, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvxpy/cvxcore/tests/python/364A_scripts/lsq_permute.py", "max_stars_repo_name": "jasondark/cvxpy", "max_stars_repo_head_hexsha": "56aaa01b0e9d98ae5a91a923708129a7b37a6f18", "max_stars_repo_license... |
import matplotlib
import numpy as np
from PIL import Image
from scipy.signal import convolve2d
matplotlib.use('agg')
if __name__ == '__main__':
file = 'images/histeq.png'
img = Image.open(file)
img = img.convert('L')
# im = np.array(img, dtype=np.float64)
# im = im[:, 15:615]
#
# kernel ... | {"hexsha": "173fe00beb5e2583680fcbfb5f409cf24f90e070", "size": 475, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "Patrick22414/cw-computer-vision", "max_stars_repo_head_hexsha": "899ed5ee6346ebd1b2b52ea2b9f618d90596a458", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
#
# Tests for the Processed Variable class
#
import pybamm
import casadi
import numpy as np
import unittest
import tests
class TestProcessedSymbolicVariable(unittest.TestCase):
def test_processed_variable_0D(self):
# without inputs
y = pybamm.StateVector(slice(0, 1))
var = 2 * y
v... | {"hexsha": "1346de88a295cdea615aeb6e2a78597652d66ab4", "size": 11566, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/test_solvers/test_processed_symbolic_variable.py", "max_stars_repo_name": "DrSOKane/PyBaMM", "max_stars_repo_head_hexsha": "903b4a05ef5a4f91633e990d4aec12c53df723a2", "max_stars_repo_l... |
Lutheran Episcopal Christian Fellowship (LECF) is a campus student Religious and Spiritual Organizations organization of Christians and seekers of the Lutheran, Episcopal (Anglican), and other traditions as well as seekers. LECF meets at The Belfry.
LECF is a progressive Christian group, in the liberal Christian tr... | {"hexsha": "ddba611017071a5004258b2d43c146f6ddf5a15f", "size": 1887, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Lutheran_Episcopal_Christian_Fellowship.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses"... |
// Copyright Carl Philipp Reh 2009 - 2016.
// 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)
#include <fcppt/algorithm/fold.hpp>
#include <fcppt/preprocessor/disable_gcc_warning.hpp>
#include... | {"hexsha": "cb00738e8a849f4c2af3868b77d1ee02f40e8870", "size": 1183, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/algorithm/fold.cpp", "max_stars_repo_name": "vinzenz/fcppt", "max_stars_repo_head_hexsha": "3f8cc5babdee178a9bbd06ca3ce7ad405d19aa6a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count":... |
import argparse
import numpy as np
import json
import torch
from torchvision import datasets, transforms, models
from utility import process_image
from model import load_checkpoint
parser = argparse.ArgumentParser(description='Predict the top K most likely flower classes based on image path and saved checkpoint')
#... | {"hexsha": "cb74915b4b17b4b56c49503c598701f95fbc9473", "size": 3797, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "cynthia3r/flower_image_classifier", "max_stars_repo_head_hexsha": "bac89f88410642d164030fd60436597ba5270f20", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import cv2
import sys
import random
import math
import re
import time
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import skimage
import glob
ROOT_DIR = os.getcwd()
sys.path.append(ROOT_DIR)
from Mask_RCNN.mrcnn import util... | {"hexsha": "f75314bb4952672c082c83819e9de3a44c8ad901", "size": 2097, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference_2.py", "max_stars_repo_name": "mosvlad/tumor_mask_rcnn", "max_stars_repo_head_hexsha": "16d6b20431553e6e1cf1594686a1f503171d5f8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import pickle
import numpy as np
import matplotlib.pylab as plt
import powerlaw as pl
def plot_avalanches(aval_times, aval_sizes):
"""Plot avalanche events distrubutions
Includes plots and power-law fits for duration, size, and average size
"""
# figure main parameters
FIG_SIZE = (6, 5)
... | {"hexsha": "832a767ed9908d3aa3e7738ce6c9e25bdd6f400f", "size": 3505, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_avalanches.py", "max_stars_repo_name": "delpapa/sandpilemodel", "max_stars_repo_head_hexsha": "6d176ff2e711f33668a33ea1947d71a69393871e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from os import name
from numpy import load
from .bed import load_from_bed, recode_zarr
import pandas
import numpy
from .load import BinaryICDLoader
import zarr
import pytest
@pytest.mark.skip(reason="Requires gwas results")
def test_load_from_bed():
bfile_prefix = '/media/data1/ag3r/ukb/runs/all/split/train'
... | {"hexsha": "0058f176d1a2b9559e85109c66008974a7dfccb3", "size": 2746, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ukb_loader/bed_test.py", "max_stars_repo_name": "alex-medvedev-msc/ukb_loader", "max_stars_repo_head_hexsha": "4940f3859eb1cc167bd768def97ce8d55c14892b", "max_stars_repo_licenses": ["MIT"], "m... |
module da_minimisation
!---------------------------------------------------------------------------
! Purpose: Collection of routines associated with minimisation.
!---------------------------------------------------------------------------
use module_configure, only : grid_config_rec_type
use module_... | {"hexsha": "248cc5906eff9b29ef72d112095479d1f1483b87", "size": 11855, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "var/da/da_minimisation/da_minimisation.f90", "max_stars_repo_name": "wasserblum/wrf-teb", "max_stars_repo_head_hexsha": "38f741a996868634d9b1bc6bc055f640a1b38751", "max_stars_repo_licenses": ["... |
#load python included modules
import tkinter as tk
from tkinter import filedialog
#load additional python modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
root = tk.Tk()
root.withdraw()
#parameters to load data
x_axis_name = "genotype"
y_axis_name = ... | {"hexsha": "f51ec755111a2b98c01218d6780949ec784022b3", "size": 1279, "ext": "py", "lang": "Python", "max_stars_repo_path": "barplot FISH results.py", "max_stars_repo_name": "BioJoe/automated-FISH-analyses", "max_stars_repo_head_hexsha": "c2859fe9ee8fc122e3651537ead3c5ccb7e270a1", "max_stars_repo_licenses": ["MIT"], "ma... |
x = [ 1 2 3 4 5 6];
y = [ 2 6 8 7 8 5];
barh(x,y);
title('\bfExample of a Horizontal Bar Plot');
xlabel('\bf\ity');
ylabel('\bf\itx');
axis([0 10 0 7]);
| {"author": "101Hub", "repo": "Matlab101", "sha": "07273f68f1147a110443aeb121fa10962234f298", "save_path": "github-repos/MATLAB/101Hub-Matlab101", "path": "github-repos/MATLAB/101Hub-Matlab101/Matlab101-07273f68f1147a110443aeb121fa10962234f298/assets/\u300aMatlab\u7f16\u7a0b\u300b\u6e90\u7801/chap6/barh_plot.m"} |
using Revise, Test, ForwardDiff, Parameters, Setfield, Plots, LinearAlgebra
using BifurcationKit, Test
const BK = BifurcationKit
norminf(x) = norm(x, Inf)
####################################################################################################
function COm(u, p)
@unpack q1,q2,q3,q4,q5,q6,k = p
x, y, s = ... | {"hexsha": "a741aa133e516468d9daca681cafb90ff68d55e0", "size": 3868, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/COModel.jl", "max_stars_repo_name": "free-Gift-card/BifurcationKit.jl", "max_stars_repo_head_hexsha": "07938db6909fa00b10736f916750d19f92b87e22", "max_stars_repo_licenses": ["MIT"], "max_s... |
subroutine decay
!! ~ ~ ~ PURPOSE ~ ~ ~
!! this subroutine calculates degradation of pesticide in the soil and on
!! the plants
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ... | {"hexsha": "ea09a07fe7fbc8051f48635a676801bf39ae4729", "size": 3773, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "swat_cli/rev670_source/decay.f", "max_stars_repo_name": "GISWAT/erosion-sediment", "max_stars_repo_head_hexsha": "6ab469eba99cba8e5c365cd4d18cba2e8781ccf6", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma env_restr_esing[simp]:
"x\<in> S \<Longrightarrow> esing x\<cdot>v f|` S = esing x\<cdot>v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> S \<Longrightarrow> esing x\<cdot>v f|` S = esing x\<cdot>v
[PROOF STEP]
by (auto intro: env_restr_useless dest: subsetD[OF edom_esing_subset]) | {"llama_tokens": 133, "file": "Launchbury_Env", "length": 1} |
"""
Defines abstract samplers.
"""
import numpy as np
import csb.core
from abc import ABCMeta, abstractmethod, abstractproperty
class DimensionError(TypeError):
pass
class AbstractSampler(object):
"""
Abstract interface for sampling algorithms.
"""
__metaclass__ = ABCMeta
@abstrac... | {"hexsha": "f821101d585f307b54d18e22e6e847a9025edd45", "size": 3221, "ext": "py", "lang": "Python", "max_stars_repo_path": "csb/statistics/samplers/__init__.py", "max_stars_repo_name": "ujjwalsh/CSB", "max_stars_repo_head_hexsha": "cbe04f35e1ecace7fa01cabce669d99714b9dd38", "max_stars_repo_licenses": ["MIT"], "max_star... |
using HybridSystems
include(joinpath(dirname(dirname(pathof(HybridSystems))), "examples", "cruise_control.jl"));
if true
D = 1.0
U = 1.0
v_shift = 2.0
vmin = -1.0
vmax = 2.0
v = (1.0,)
m = 1.0
m0 = 1.0
h = 0.5
kd = 1/2
ks = 1/2
Δv = 5.0
else
function constant(scaling... | {"hexsha": "6fbf1341b6a7b04ead3fb1a929e58f1a8746e305", "size": 2912, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/cruise_control.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SwitchOnSafety.jl-ceb7f16a-07bf-5f4a-9354-b68f01b1610f", "max_stars_repo_head_hexsha": "e9fefe2cb8f45f27ed9ea95d3... |
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
class SVMClassifier:
def __init__(self, visualization=True):
self.visualization = visualization
self.colors = {0: 'r', 1: 'b'}
if self.visualization:
self.fig = plt.figure()
self.ax = s... | {"hexsha": "9c482a5a41b5d8e61aa36633a7a1129e696a4750", "size": 3089, "ext": "py", "lang": "Python", "max_stars_repo_path": "manual_svm_test.py", "max_stars_repo_name": "dkirel/ManualSVM", "max_stars_repo_head_hexsha": "0c8aaf5631f272d537126a8cd6237eaa937413cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma eq_fin_le_fininf_transp[intro, trans]:
assumes "w\<^sub>1 =\<^sub>F w\<^sub>2" "w\<^sub>2 \<preceq>\<^sub>F\<^sub>I w\<^sub>3"
shows "w\<^sub>1 \<preceq>\<^sub>F\<^sub>I w\<^sub>3"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. w\<^sub>1 \<preceq>\<^sub>F\<^sub>I w\<^sub>3
[PROOF STEP]
... | {"llama_tokens": 253, "file": "Partial_Order_Reduction_Traces", "length": 2} |
\documentclass[a4paper,12 pt]{article}
\usepackage{graphicx}
\usepackage{caption}
\usepackage{refstyle}
\usepackage{wrapfig}
\usepackage{subcaption}
\usepackage{geometry}
\geometry{
a4paper,
total={210mm,297mm},
left=25mm,
right=25mm,
top=25mm,
bottom=25mm,
}
\title {Project Report \\ Sensor Module Interfaci... | {"hexsha": "8f637edd40afee97fa12c3325073d7cfeae7dae0", "size": 9443, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "5.IMU/Gyroscope/documentation&tutorial/Gyro_manual/Gyroscope.tex", "max_stars_repo_name": "eyantra/Sensor-Module-Interfacing", "max_stars_repo_head_hexsha": "a5d7d3dc259fb7ddf369c513d20f011a099c927f... |
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from math import cos, sin
from IPython import embed
robot_radius = 3.35/2.0;
def draw_circle(ax, pos_ang, radius, color, label=None):
pos = pos_ang[:2]
circ = plt.Circle(pos, radius, color=color, label=label)
ax.add_artist(circ);
... | {"hexsha": "9e7fadd0e49a451522752e0970ce0eacce77e39b", "size": 3845, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/visualize_circle_world.py", "max_stars_repo_name": "LAIRLAB/qr_trees", "max_stars_repo_head_hexsha": "66eb7310daa1d9978158198a508d02bf2128a377", "max_stars_repo_licenses": ["BSD-3-Claus... |
# Copyright (c) 2019 PaddlePaddle 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 applicable law ... | {"hexsha": "b7e1eb1c7bf0fe0a2024ed8ef29270687fae5a3a", "size": 2763, "ext": "py", "lang": "Python", "max_stars_repo_path": "fluid/PaddleCV/yolov3/config.py", "max_stars_repo_name": "kuke/models", "max_stars_repo_head_hexsha": "b610d1654fa1d728fe2171fb02ee47497942fe24", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
[STATEMENT]
lemma comp_left_increasing_sup:
"x * y \<le> (x \<squnion> z) * y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x * y \<le> (x \<squnion> z) * y
[PROOF STEP]
by (simp add: comp_left_isotone) | {"llama_tokens": 100, "file": "Stone_Relation_Algebras_Relation_Algebras", "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 25 19:57:35 2020
Read and write event data.
Conversion of event data into frames (images, 2D):
- histograms of events
- thresholded (1 f-stop)
- brightness increment images
- time surfaces: exponential decay or average time
With polarity on the same representation or spl... | {"hexsha": "50273ac00b62fd070d98f4ba37953d2f0fec08cb", "size": 15488, "ext": "py", "lang": "Python", "max_stars_repo_path": "ex2_events_visualization.py", "max_stars_repo_name": "tub-rip/events_viz", "max_stars_repo_head_hexsha": "dfc6fd27688c70f11e98b349111e35a5aad9a718", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import tensorflow as tf
import numpy as np
import math
# ======================================================h===================== #
# TensorFlow implementation of Text Boxes encoding / decoding.
# =========================================================================== #
def tf_text_bboxes_encode_layer(bb... | {"hexsha": "c06bf8cc15b265093a3f079ec72c0ab25d70e93a", "size": 10065, "ext": "py", "lang": "Python", "max_stars_repo_path": "openvision/ocr/textbox/nets/textbox_common.py", "max_stars_repo_name": "liuzz1983/open_vision", "max_stars_repo_head_hexsha": "f346e2f789944ea590c1d263e72a6e93490bb3a0", "max_stars_repo_licenses"... |
from argparse import ArgumentParser
import os
import cv2
import numpy as np
import torch
import pytorch_lightning as pl
from pytorch_lightning import Trainer, loggers
from torchsummary import summary
import torch.nn.functional as F
from autoencoder import Autoencoder
from harbour_datamodule import list_frames_in_dir
... | {"hexsha": "34e0177e697ea98cee1ad015cce46cdc1dbaf92c", "size": 4761, "ext": "py", "lang": "Python", "max_stars_repo_path": "embed.py", "max_stars_repo_name": "markpp/thermal_autoencoder", "max_stars_repo_head_hexsha": "a122128f973f89aa61f641449d5cbe2dd222b40f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as np
from pyscf.lib.linalg_helper import eig
from pyscf.lib.numpy_helper import einsum
from scipy import linalg as la
import matplotlib.pyplot as plt
def createMPO(hamType,hamParams):
############################################
# Determine MPO
Sp = np.array([[0,1],[0,0]])
Sm = np.array(... | {"hexsha": "6189fc7aa4b7060f197a8c8e5819cced3f2ba735", "size": 8981, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/iMPS_2site.py", "max_stars_repo_name": "philliphelms/iMPS", "max_stars_repo_head_hexsha": "ce7e097bf67ad68a01cf8180c3f7577660c38ee8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File : Communication.py
# @Time : 2021/11/5 11:20 上午
# @Author : Mingxue Cai
# @Email : im_caimingxue@163.com
# @github : https://github.com/caimingxue/magnetic-robot-simulation
# @notice ：
from math import *
import numpy as np
import struct
from TCP import TCPClient
fr... | {"hexsha": "2c5f0a784facc7dcab1f7bc98a0ce04bb541d5ba", "size": 2146, "ext": "py", "lang": "Python", "max_stars_repo_path": "color_tracker/utils/communication.py", "max_stars_repo_name": "caimingxue/color_tracker", "max_stars_repo_head_hexsha": "11e00daf540a46022dc16a2f79ce4a787dce4f9b", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma iso5_sharp [simp]: "(((x \<sqinter> nc) \<cdot> 1\<^sub>\<pi>) \<parallel> nc) \<cdot> 1\<^sub>\<pi> = (x \<sqinter> nc) \<cdot> 1\<^sub>\<pi>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x \<sqinter> nc) \<cdot> 1\<^sub>\<pi> \<parallel> nc \<cdot> 1\<^sub>\<pi> = (x \<sqinter> nc) \<cdot> 1\<... | {"llama_tokens": 172, "file": "Multirelations_C_Algebras", "length": 1} |
function gather_check_dims(X::AbstractArray{Tx,Nx},
Y::AbstractArray{Ty,Ny},
idx::AbstractArray{Tidx,Nidx}) where
{Tx,Ty,Tidx<:IntOrIntTuple,Nx,Ny,Nidx}
M = NNlib.typelength(Tidx)
dims = gather_check_dims(Nx, Ny, M, Nidx)
size... | {"hexsha": "dcfd29b03249e8ef8f88cea846440fd9303cf2ab", "size": 2166, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/gather.jl", "max_stars_repo_name": "yuehhua/NNlibCUDA.jl", "max_stars_repo_head_hexsha": "96a334633ef3a3707c85fc1754c2c7eb8849db4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import numpy
from IPython.display import HTML
import ipywidgets
from matplotlib import animation, pyplot
def create_init_fig(wrapped_signal, freq_arr, xcm_arr):
""" creates initial figure needed for animation, but it doesn't display it.
"""
fig, ax = pyplot.subplots(figsize=(10.0, 5.0))
pyplot.tig... | {"hexsha": "d236ac4ca7e988a60f80eae023eb738eb0ea38a3", "size": 3141, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/almost_fourier_helper.py", "max_stars_repo_name": "engineersCode/EngComp5_surfourier", "max_stars_repo_head_hexsha": "bdf2eb7330e555106f17e64b693f0fbdd04b7710", "max_stars_repo_licenses": ... |
from timeit import timeit
from typing import Type
import numpy as np
import tensorflow as tf
from tensorflow.python.framework.errors_impl import FailedPreconditionError
import sandblox as sx
import sandblox.util.tf_util as U
from sandblox.core.io import bind_resolved
from sandblox.test.core.foo import FooLogic
clas... | {"hexsha": "3b31f04834674c2a14f8b2add0c020b80aac7419", "size": 7279, "ext": "py", "lang": "Python", "max_stars_repo_path": "sandblox/test/core/base.py", "max_stars_repo_name": "reubenjohn/sandblox", "max_stars_repo_head_hexsha": "0b7917eb866ddbc4749a098884046d4ebb441985", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import pickle
import numpy as np
from .band_interface import *
from .s1_interface import BigEarthNet_S1_Patch
from .s2_interface import BigEarthNet_S2_Patch
# FUTURE: Write a base class that gives the
# common skeleton to inherit from
class BigEarthNet_S1_S2_Patch:
def __init__(
self,
bandVH: np... | {"hexsha": "7dfa3dc577aacbc5c738e29f9d2143abf7f40c30", "size": 3400, "ext": "py", "lang": "Python", "max_stars_repo_path": "bigearthnet_patch_interface/merged_interface.py", "max_stars_repo_name": "kai-tub/bigearthnet_patch_interface", "max_stars_repo_head_hexsha": "395f40f486c471f383a74667d1ae2006ee13e328", "max_stars... |
[STATEMENT]
lemma index_of_r_to_l_lm: "nat_to_pr index_of_r_to_l (c_pair x (c_pair y z)) = c_pair (c_pair x y) z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nat_to_pr index_of_r_to_l (c_pair x (c_pair y z)) = c_pair (c_pair x y) z
[PROOF STEP]
apply(unfold index_of_r_to_l_def)
[PROOF STATE]
proof (prove)
goal (1... | {"llama_tokens": 1086, "file": "Recursion-Theory-I_RecEnSet", "length": 8} |
mutable struct LossFunction{Δ, FT, ML, F, T, L, P}
first_targets :: FT
max_simulation_length :: ML
field_weights :: F # scenario weights
time_series :: T
profile :: L
end
allsame(x) = all(y -> y ≈ first(x), x)
t_interval(data) = data.t[2:end] .- data.t[1:end-1]
funct... | {"hexsha": "145ade70ed40aefb915fb59395478b3426b25f56", "size": 7280, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LossFunctions/loss_function.jl", "max_stars_repo_name": "adelinehillier/OceanTurbulenceParameterEstimation.jl", "max_stars_repo_head_hexsha": "c0253976746d43c6667414be3801343818f6b2bf", "max_st... |
import argparse
import numpy as np
import timm
import torch
from onnx.optimizer import optimize
from timm.models import load_checkpoint
from models.t2t_vit import *
try:
import onnx
import onnxruntime as rt
except ImportError as e:
raise ImportError(f'Please install onnx and onnxruntime first. {e}')
de... | {"hexsha": "bfd1b9bd9fc333e95e6649f20b1e7214a780b0dc", "size": 2741, "ext": "py", "lang": "Python", "max_stars_repo_path": "export.py", "max_stars_repo_name": "druzhkov-paul/T2T-ViT", "max_stars_repo_head_hexsha": "819c3ddc4cb6f464d4a9866d8713c7ace42ebf6c", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "max_stars_... |
#include <iostream>
#include <iomanip>
#include <stdexcept>
#include <math.h>
#include <set>
#include <boost/multiprecision/gmp.hpp>
#include <boost/multiprecision/number.hpp>
using namespace std;
using namespace boost::multiprecision;
int target = 100;
int main(int argc, char** argv) {
set<mpz_int> visited;
fo... | {"hexsha": "d0530bd585bcffa537a5673ef00590e556f78ac6", "size": 622, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "29.cpp", "max_stars_repo_name": "DouglasSherk/project-euler", "max_stars_repo_head_hexsha": "f3b188b199ff31671c6d7683b15675be7484c5b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
'''This module contains the S-model Environment.'''
import numpy as np
# from random import uniform as u
class SEnvironment(object):
'''The S-model Learning Environment.'''
def __init__(self, p_vector, precision=1):
'''Create a probability vector from the probability of
success vector.'''
... | {"hexsha": "6c972d994fc47546fd056a01b8f4f5cca73346ca", "size": 798, "ext": "py", "lang": "Python", "max_stars_repo_path": "distest/senvironment.py", "max_stars_repo_name": "0xSteve/detection_learning", "max_stars_repo_head_hexsha": "e767d740ffbb2df4570d8522a29062eca01b14ee", "max_stars_repo_licenses": ["Apache-2.0"], "... |
## from Markdown.jl
import Base: display, show
graph_types = AbstractString["Plot", "FramedPlot"]
function tohtml(io::IO, m::MIME"text/html", x)
show(io, m, x)
end
function tohtml(io::IO, m::MIME"text/latex", x)
show(io, m, x)
end
function tohtml(io::IO, m::MIME"text/plain", x)
show(io, m, x)
end
function t... | {"hexsha": "7994f97166f0f58b5782f93fdde313d7328fa243", "size": 4937, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/markdown-additions.jl", "max_stars_repo_name": "jverzani/WeavePynb.jl", "max_stars_repo_head_hexsha": "5c2834f717fb3583d1fc245ed767b4d17aae6b39", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import unittest
from ..weights import W, WSP
from .. import util
from ..util import WSP2W, lat2W
from ..contiguity import Rook
from ...io.fileio import FileIO as psopen
from ... import examples
from ..distance import KNN
import numpy as np
NPTA3E = np.testing.assert_array_almost_equal
class TestW(unittest.TestCase):... | {"hexsha": "fd1832a57e71073fa84f4ec8754dbeae38205ff9", "size": 20248, "ext": "py", "lang": "Python", "max_stars_repo_path": "libpysal/weights/tests/test_weights.py", "max_stars_repo_name": "Kanahiro/dbf-df-translator", "max_stars_repo_head_hexsha": "6603ca1ac306203bf8c95e6545685c509324a438", "max_stars_repo_licenses": ... |
#
# created by Severin Dicks (IBSM, Freiburg)
#
#
import cupy as cp
import cudf
import cugraph
import anndata
import time
import numpy as np
import pandas as pd
import scipy
import math
from scipy import sparse
import seaborn as sns
import matplotlib.pyplot as plt
from cuml.manifold import TSNE
from cuml.cluster im... | {"hexsha": "1018f18126bbc162ff17e2b9a0f516acf53962e8", "size": 17904, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/scanpy_gpu_funcs.py", "max_stars_repo_name": "metzgerpatrick/rapids_singlecell", "max_stars_repo_head_hexsha": "319dabba5e6b15eb24e8ebc1b95e0d309b96bcc4", "max_stars_repo_licenses": ["MIT"],... |
from difflib import SequenceMatcher
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
import matplotlib as mpl
import argparse, math, random, gzip, pickle, types
from collections import defaultdict
import os
import adjustText
# Change following routines for other environments:
L_init = ... | {"hexsha": "2f12c8527cd1f000d26491e6863834fc796a0997", "size": 11394, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analysis/p_unbound_analysis.py", "max_stars_repo_name": "Utenaq/GenoFold", "max_stars_repo_head_hexsha": "31b626ebf3d08b16b1cdf6544c36bbea75147719", "max_stars_repo_licenses": ["MIT"], "max_stars... |
constant f : Nat → Nat
@[simp] axiom fEq (x : Nat) (h : x ≠ 0) : f x = x
example (x : Nat) (h : x ≠ 0) : f x = x + 0 := by
simp (discharger := trace_state; exact (fun h' => h') h)
example (x y : Nat) (h1 : x ≠ 0) (h2 : y ≠ 0) (h3 : x = y) : f x = f y + 0 := by
simp (discharger := trace_state; assumption)
assump... | {"author": "Kha", "repo": "lean4-nightly", "sha": "b4c92de57090e6c47b29d3575df53d86fce52752", "save_path": "github-repos/lean/Kha-lean4-nightly", "path": "github-repos/lean/Kha-lean4-nightly/lean4-nightly-b4c92de57090e6c47b29d3575df53d86fce52752/tests/lean/simpDisch.lean"} |
#!/usr/bin/python
import numpy as np
import scipy
import sys
import random
import time
from math import pi ,sqrt, cos, sin
random.seed(time.time())
M = int(float(sys.argv[1]))
nrepeat = int(sys.argv[2])
nMol = nrepeat*nrepeat*nrepeat
nAtoms = nMol
d = [0.0,3.11,4.0,4.48,4.93,5.31,5.65]
d0 = d[M]
pdb = open("initia... | {"hexsha": "aafc332e1f9e182025aa2ada7ef25926b65f6cf1", "size": 1059, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/study.cases/LAMMPS/setup/model/writeInitEqu.py", "max_stars_repo_name": "JonathanLehner/korali", "max_stars_repo_head_hexsha": "90f97d8e2fed2311f988f39cfe014f23ba7dd6cf", "max_stars_repo_... |
import gc
import os
import tqdm
import cv2
import torch
import numpy as np
import pandas as pd
import segmentation_models_pytorch as smp
import pickle
from torch.utils.data import DataLoader
from clouds.models import Pretrained
from clouds.io import CloudDataset, ClassificationCloudDataset
from clouds.inference impor... | {"hexsha": "aa21a34a5d13a06991008bbb08603684b9ec9a3b", "size": 3619, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/create_clf_pseudo.py", "max_stars_repo_name": "jchen42703/understanding-clouds-kaggle", "max_stars_repo_head_hexsha": "6972deb25cdf363ae0d9a9ad26d538280613fc94", "max_stars_repo_licenses":... |
import gym
import time
from gym.envs.registration import register
import argparse
import numpy as np
parser = argparse.ArgumentParser(description=None)
parser.add_argument('-e', '--env', default='collect', type=str)
args = parser.parse_args()
def main():
if args.env == 'soccer':
register(
id... | {"hexsha": "8ace97a5197de018f39bc1388718b202a99bb586", "size": 1094, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_env.py", "max_stars_repo_name": "euodiadodd1/RL_berry_poisoning_game", "max_stars_repo_head_hexsha": "46ce3e0d14651c82b56430308f992810dd5a4e05", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
from flask import Flask, request, send_file
import sys
import torch
import numpy as np
from scipy.io import wavfile
import io
from nemo.collections.tts.models import TalkNetSpectModel
from nemo.collections.tts.models import TalkNetPitchModel
from nemo.collections.tts.models import TalkNetDursModel
import json... | {"hexsha": "d94eb8ed04c6c1322aff411ab8ef1c55d822c9dc", "size": 3468, "ext": "py", "lang": "Python", "max_stars_repo_path": "mycroft_talknet.py", "max_stars_repo_name": "abb128/ControllableTalkNet", "max_stars_repo_head_hexsha": "6c806c5d6cd0cb9fe7725fc16ce85e59cc55dbfd", "max_stars_repo_licenses": ["CC0-1.0"], "max_sta... |
#include <cmath>
#include <boost/numeric/conversion/cast.hpp>
#include <boost/math/special_functions/factorials.hpp>
#include "kernel.h"
const std::map<std::string, double> calculator::WExpression::global_constants
{
{"pi", boost::math::constants::pi<double>()},
{"e", boost::math::constants::e<double>()}
};
d... | {"hexsha": "4cc3361c6f50c20e006e5f120887451fc2e8df35", "size": 11774, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kernel.cpp", "max_stars_repo_name": "vega1986/wcalc_expression_parser", "max_stars_repo_head_hexsha": "e9645a5fa8086c4108ce4dc1f3ad7da3cead6480", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
act.qc.radiometer_tests
------------------------------
Tests specific to radiometers
"""
from scipy.fftpack import rfft, rfftfreq
import numpy as np
import xarray as xr
import pandas as pd
import datetime
import dask
import warnings
from act.utils.datetime_utils import determine_time_delta
from act.utils.geo_uti... | {"hexsha": "1833aa72d84a385027cfef5fa5a51c13f6924206", "size": 8681, "ext": "py", "lang": "Python", "max_stars_repo_path": "act/qc/radiometer_tests.py", "max_stars_repo_name": "rcjackson/ACT", "max_stars_repo_head_hexsha": "c57fb55094b142bbbef63e7069d4024049996139", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
# -*- coding: utf-8 -*-
# Copyright (c) 2017 Interstellar Technologies Inc. All Rights Reserved.
# Authors : Takahiro Inagawa, Kazuki Sakaki
#
# Lisence : MIT Lisence
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), ... | {"hexsha": "b6b2f947575f7889ab5ad568e14b66007adf6afb", "size": 53441, "ext": "py", "lang": "Python", "max_stars_repo_path": "OpenGoddard/optimize.py", "max_stars_repo_name": "likping/OpenGoddard", "max_stars_repo_head_hexsha": "0906ee85038de85d7683e19532df62fcd53a9e28", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma set1_FGcontra_bound:
fixes x :: "(_, 'co1, 'co2, 'co3, 'co4, 'co5,
'contra1, 'contra2, 'contra3, 'contra4, 'contra5, 'f1, 'f2) FGcontra"
shows "card_of (set1_FGcontra x) <o (bd_FGcontra :: ('co1, 'co2, 'co3, 'co4, 'co5,
'contra1, 'contra2, 'contra3, 'contra4, 'contra5, 'f1, 'f2) FGcontrabd... | {"llama_tokens": 347, "file": "BNF_CC_Composition", "length": 3} |
# utils.py
# Ben Cook (bcook@cfa.harvard.edu)
import numpy as np
from scipy.misc import logsumexp
from astropy.io import fits
import os, sys
# A module to create various utility functions
def make_pcmd(mags):
pcmd = np.copy(mags)
n_filters = pcmd.shape[0]
for i in range(1, n_filters):
pcmd[i] = m... | {"hexsha": "3ff77e9e7b1226eadc5d240194b7aa73c6cbfb80", "size": 5699, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcmdpy/utils/utils.py", "max_stars_repo_name": "johnnygreco/pcmdpy", "max_stars_repo_head_hexsha": "fe38db999f4445c98bde168867274654b2be4dbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
\documentclass[a4paper,12pt,titlepage]{scrartcl}
\usepackage[utf8]{inputenc}
\usepackage{hyperref}
\hypersetup{
colorlinks=true,
linkcolor=black,
filecolor=magenta,
urlcolor=blue,
}
\usepackage{graphicx}
\graphicspath{ {./images/} }
\usepackage{fancyhdr}
\usepackage{lastpage}
\usepackage{list... | {"hexsha": "f5af53793407fab072e4de3e4a03a2696d5691d2", "size": 8968, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/UserGuide/main.tex", "max_stars_repo_name": "SimLej18/KiloGuide", "max_stars_repo_head_hexsha": "5100df4da103f9d29113231ad87024a83c239faf", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_s... |
"""
KMATools
Package for parsing various files produced by KMA. Tested on KMA 1.3.22.
"""
module KMATools
using BioSymbols: DNA
imap(f) = x -> Iterators.map(f, x)
ifilter(f) = x -> Iterators.filter(f, x)
const SPA_HEADER = join(
[
"#Template",
"Num",
"Score",
"Expected",
... | {"hexsha": "87550d9a620c3846f5a3641f3ad26d60a81df6d4", "size": 6619, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/KMATools.jl", "max_stars_repo_name": "jakobnissen/KMATools.jl", "max_stars_repo_head_hexsha": "ec29bb5282c80dcab46b2d8fa399b788e33ed4df", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# # from models.MyGANet4 import GANet
# #
# # model = GANet()
# # for name, module in model.named_children():
# # print(name)
#
# import torch
# import torch.nn as nn
#
# a = torch.randn(2, 3, 2, 2) # 右图
# b = torch.ones(2, 1, 2, 2) # disp
# print(a)
#
# def warp(x, disp):
# """
# warp an image/tensor (... | {"hexsha": "0358d05b3ae93118c34878e2a379824528aaabea", "size": 1803, "ext": "py", "lang": "Python", "max_stars_repo_path": "view.py", "max_stars_repo_name": "hx-Tang/GANet", "max_stars_repo_head_hexsha": "8935c9d3d82189fa6f940c2a877534a398a041e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 21 14:51:28 2018
@author: yujika
"""
import pickle
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#%matplotlib qt
import util
def corners_unwarp(img, nx, ny, mtx, dist):
img_und = cv2.undistort(img, mtx, d... | {"hexsha": "fee2d7dc35559927031b95c10c362674814f0f0b", "size": 5568, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_work/camera_calibration.py", "max_stars_repo_name": "yujika/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "d8f671c60930353f7cd3e7b1c12f7d81fe50ab6f", "max_stars_repo_licenses": ["MI... |
""" This module determines Auger, radiative, surface, and trap-assited recombination.
Used primarily by find_current function in single_cell_power.
Uses spectral.py to get radiative recombination and carriers.py for carrier concentration.
Created 12/18/2019 by Nicholas Irvin"""
import numpy as np
import math
... | {"hexsha": "5412d79f5e1997a4d7b90194978fcf8bc371e2c6", "size": 20311, "ext": "py", "lang": "Python", "max_stars_repo_path": "recombination.py", "max_stars_repo_name": "npirvin/Radiative-Transport-PV", "max_stars_repo_head_hexsha": "d5236bfae5789fd2be5bb0190e962a83c24b3ae7", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description: Using a two-layer network to predict the ozone layer thickness
from data above Palmerston North in New Zealand between 1996 and 2004.
"""
from pylab import *
import numpy as np #numerical package for scientific computing
import mlpcn
#ozone layer thickn... | {"hexsha": "2d15eec0d4762acbb7b30763403602bb1c11cbde", "size": 2953, "ext": "py", "lang": "Python", "max_stars_repo_path": "ffnn/time_series_problem.py", "max_stars_repo_name": "RaoulMa/NeuralNets", "max_stars_repo_head_hexsha": "f49072ac88686f753f9b5815d6cc5e71d536c3d2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import math
fig, ax = plt.subplots()
# pos1 = ax.get_position()
# pos2 = [pos1.x0 + 0.1, pos1.y0 + 0.1, pos1.width, pos1.height]
# ax.set_position(pos2)
class Block:
def __init__(self,x1,y1,x2,y2,vex=0, vey=0):
self.fr ... | {"hexsha": "5d64e6e179111f826d6af6aac68286c280dd0ec8", "size": 4290, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tests/TestPipelinePlanner/TestCurveFollow.py", "max_stars_repo_name": "TheFactory22/RBotFirmware", "max_stars_repo_head_hexsha": "cb7ea74869189f015578cb31ddc0516c72f1ea00", "max_stars_repo_license... |
#include <boost/numeric/ublas/matrix_proxy.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/vector_proxy.hpp>
#include <cmath>
#include <cstring>
#include <memory>
#include <vector>
#include <limits>
#include <algorithm>
#include <utility>
#include ... | {"hexsha": "888fb6da2f3f9d92199e55594465861cb872c42b", "size": 22163, "ext": "cc", "lang": "C++", "max_stars_repo_path": "models/statespace/fast_c/transient_combined.cc", "max_stars_repo_name": "davmre/sigvisa", "max_stars_repo_head_hexsha": "91a1f163b8f3a258dfb78d88a07f2a11da41bd04", "max_stars_repo_licenses": ["BSD-3... |
# -*- coding: utf-8
# This script is the PyLaGriT version of LaGriT tutorial example at
# https://lanl.github.io/LaGriT/pages/tutorial/stratigraphy/index.html.
# Written by Guoyan Jiang (gyjiang@whu.edu.cn) with technical support
# from Dylan Harp (dharp@lanl.gov) and Terry Miller (tamiller@lanl.gov).
# Import ... | {"hexsha": "ee27d7525781010754fb16441f4547037966812f", "size": 15421, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyLaGriT/examples/stratigraphic_hex_mesh_tutorial.py", "max_stars_repo_name": "daniellivingston/LaGriT-CI-Test", "max_stars_repo_head_hexsha": "8c23f94150a69532be0ef8a33cd999585009530d", "max_sta... |
[STATEMENT]
lemma and_num_2097152_128: "(AND) (0b00000000001000000000000000000000::word32)
(0b00000000000000000000000010000000::word32) = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 2097152 AND 128 = 0
[PROOF STEP]
by simp | {"llama_tokens": 162, "file": "SPARCv8_SparcModel_MMU_Sparc_Properties", "length": 1} |
///=======================================================================
// Copyright 2015-2020 Clemson University
// Authors: Bradley S. Meyer
//
// 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": "4a34c1af48ac632a6a65417ab610d99aecff54f5", "size": 36220, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/rank_spanning_branchings.hpp", "max_stars_repo_name": "mbradle/rank_spanning_branchings", "max_stars_repo_head_hexsha": "86aa045beebe0e5f273f0ee1bce5e91ca4f4f079", "max_stars_repo_licenses"... |
#####
##### Tests copied from IterativeSolvers.jl
##### https://github.com/JuliaLinearAlgebra/IterativeSolvers.jl/blob/v0.9.2/src/lsmr.jl
#####
# Type used in Dampenedtest
# solve (A'A + diag(v).^2 ) x = A'b
# using LSMR in the augmented space à = [A ; diag(v)] b̃ = [b; zeros(size(A, 2)]
struct DampenedMatrix{Tv,TA<:... | {"hexsha": "d4842457ea79c45503def75e71aec19db3b62fe9", "size": 2781, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/utils/lsmr.jl", "max_stars_repo_name": "jondeuce/QSM.jl", "max_stars_repo_head_hexsha": "f960d8fe99f11d610be70051d79c2aba51f483f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Use the same scale on x and y axis
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Plot data #################
#fig, ax = plt.subplots(figsize=(5, 5))
fig, (ax1, ax2) = plt.subplots(ncols=2)
ax1.plot([0, 1])
ax2.plot(... | {"hexsha": "4040d9f4f7700f6f2225db6ea39bd8967be6636b", "size": 492, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/matplotlib/axis_equal.py", "max_stars_repo_name": "jeremiedecock/snippets", "max_stars_repo_head_hexsha": "4bd4e7f459eee610d5cf19f845299ca942ff4b64", "max_stars_repo_licenses": ["MIT"], "max... |
module RocketLowercaseOperatorTest
using Test
using Rocket
include("../test_helpers.jl")
@testset "operator: lowercase()" begin
println("Testing: operator lowercase()")
run_proxyshowcheck("Lowercase", lowercase())
run_testset([
(
source = from("Hello, world") |> lowercase(),
... | {"hexsha": "42f35e98e586368dd97387d3fb8bfbc50063dcdc", "size": 778, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/operators/test_operator_lowercase.jl", "max_stars_repo_name": "hgeorgako/Rocket.jl", "max_stars_repo_head_hexsha": "9661dad340e9a079ebd6ed57dcf9e5db31af637f", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
import argparse
import marsyas
import marsyas_util
import time
import numpy
import cv
from cv_utils import *
import math
# This program will perform real-time spectral analysis.
# TODO: Put axis indicators in the plots!
#
# The basic functionality is as follows:
# Source -> Window -> Spectra -> ... | {"hexsha": "cd1c7fa020c180191116e69a38b6b8268de2ebbc", "size": 3222, "ext": "py", "lang": "Python", "max_stars_repo_path": "marsyas-vamp/marsyas/src/marsyas_python/spectral_analysis.py", "max_stars_repo_name": "jaouahbi/VampPlugins", "max_stars_repo_head_hexsha": "27c2248d1c717417fe4d448cdfb4cb882a8a336a", "max_stars_r... |
import numpy as np
import matplotlib.pyplot as plt
import torch
from PIL import Image
from tqdm import tqdm
from pathlib import Path
from plipy.ddl_inpainting import DDLInpaintingConv
from plipy.dpdpl_inpainting import DPDPLInpaintingConv
NUM_SAMPLES = 100
def psnr(im, imref, d=1):
mse = np.mean((im - imref)*... | {"hexsha": "49ccf7ff0e01b9e865e04556a7be486707d49106", "size": 5129, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/others/inpainting_minima.py", "max_stars_repo_name": "bmalezieux/plipy", "max_stars_repo_head_hexsha": "35d17cad908219013fa43d81c4aeb6bb46ce9384", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2021 The TensorFlow Probability 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 applicable law o... | {"hexsha": "f7188051fe659ac1411c3c3c3d773672836caf24", "size": 13881, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_probability/python/distributions/student_t_process_regression_model_test.py", "max_stars_repo_name": "jakee417/probability-1", "max_stars_repo_head_hexsha": "ae7117f37ac441bc7a888167ea... |
include("train.jl")
using UnicodePlots
# get the data
# characters: [^0-9a-zA-Z&:,./()[]_-] THIS IS OLD
raw = []
open("case_names/data.txt") do f
line = 0
while !eof(f)
push!(raw, readline(f))
end # while
end # do
#println(raw[1:20])
#chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ -[]()0123456789"
chars... | {"hexsha": "1c16d1fe27172b9bf2d9e74544d951790e515aa7", "size": 1595, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "case_names/main.jl", "max_stars_repo_name": "Squalm/NeuralNetworks", "max_stars_repo_head_hexsha": "913a43e419c768e3ff29baab96c1f2871bc5e5bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python3
import cv2
import numpy as np
import depthai as dai
# Weights to use when blending depth/rgb image (should equal 1.0)
rgbWeight = 0.4
depthWeight = 0.6
def updateBlendWeights(percent_rgb):
"""
Update the rgb and depth weights used to blend depth/rgb image
@param[in] percent_rgb T... | {"hexsha": "37fb67cde274c3fafcced47adf57af4997c801d3", "size": 4309, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/StereoDepth/rgb_depth_aligned.py", "max_stars_repo_name": "MambaWong/depthai-python-1", "max_stars_repo_head_hexsha": "0d15abd77fd82b4a70e096ea5bb99237a17c9862", "max_stars_repo_licenses"... |
# Copyright (c) 2012, Nicolo Fusi
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import unittest
import numpy as np
import GPy
from ..models import BayesianGPLVM
class BGPLVMTests(unittest.TestCase):
def test_bias_kern(self):
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.r... | {"hexsha": "1192448a9772dc394bff01dee62e3333b7ef8bc3", "size": 3661, "ext": "py", "lang": "Python", "max_stars_repo_path": "GPy/testing/bgplvm_tests.py", "max_stars_repo_name": "rokroskar/GPy", "max_stars_repo_head_hexsha": "0f8dbba56d480902c86cfe8bad9e79d9eabae009", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
using OrderedBinning
using Test
@testset "non-strinct, zero tolerance" begin
boundaries = 0:3
ob = ordered_bins(boundaries; strict = false)
@test ob(-1) == 0
@test ob(0) == 1
@test ob(0.5) == 1
@test ob(3) == 3
@test ob(4) == 4
for _ in 1:100
x = rand(Bool) ? rand(0:3) : rand() ... | {"hexsha": "9960db7358fcc2b6c3a0e6f0bc10ea628b8b76f7", "size": 983, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tpapp/OrderedBinning.jl", "max_stars_repo_head_hexsha": "517821fdfa122e7952f2725e652c44e996900a06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma atom_in_atom_image [simp]: "atom j \<in> atom ` V \<longleftrightarrow> j \<in> V"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (atom j \<in> atom ` V) = (j \<in> V)
[PROOF STEP]
by auto | {"llama_tokens": 89, "file": "Incompleteness_Pseudo_Coding", "length": 1} |
type Packet
ptr::Ptr{Void}
function Packet(ptr::Ptr{Void})
p = new(ptr)
finalizer(p, destroy)
p
end
end
function Packet()
Packet(ccall((:sfPacket_create, libcsfml_network), Ptr{Void}, ()))
end
function copy(packet::Packet)
return Packet(ccall((:sfPacket_copy, libcsfml_netw... | {"hexsha": "e655d69d418a27c2926c12e8a6828a2661381550", "size": 2225, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/julia/Network/packet.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SFML.jl-d50d9232-525f-5d35-8703-6ae49672cafe", "max_stars_repo_head_hexsha": "368097db31544432de82b92c40e10080b5... |
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