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# -*- coding: utf-8 -*-
"""
Created on Fri Feb 9 12:48:42 2017
@author: cdw2be
"""
import warnings
warnings.simplefilter('ignore', UserWarning)
import tkinter as tk
from tkinter import filedialog
from tkinter import ttk
from tkinter import font
from tkinter import messagebox
import mrimodel
import confocalmodel
import... | {"hexsha": "36cb04b0feed3f75ae6f5645af200b7c8a891a3c", "size": 32470, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/modelGUI.py", "max_stars_repo_name": "cardiacbiomechanicsgroup/lvdatamap", "max_stars_repo_head_hexsha": "d9020f9baaf9a77f4c9b9138758663361369d48e", "max_stars_repo_licenses": ["MIT"], "ma... |
subroutine dlocate(xx,n,is,ie,x,j)
C%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
C %
C Copyright (C) 1996, The Board of Trustees of the Leland Stanford %
C Junior University. All rights reserved. ... | {"hexsha": "e9f87db5d5735e3616015a23f697797c12e4a15f", "size": 2144, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "visim/visim_src/gslib/dlocate.f", "max_stars_repo_name": "fcecinati/QUICS_UOB", "max_stars_repo_head_hexsha": "88cc8534a304520f5b25f84f4516712befaf13b3", "max_stars_repo_licenses": ["BSD-2-Clause"... |
import numpy as np
from .._model import Generalmodel
# import spartan2.ioutil as ioutil
from spartan.util.ioutil import saveDictListData, loadDictListData
class IAT(Generalmodel):
aggiat = {} # key:user; value:iat list
user_iatpair = {} # key:user; value: (iat1, iat2) list
iatpair_user = {} # key:(iat1... | {"hexsha": "fe7549963d564245b76a68bb4b6c19a713cfc33a", "size": 5023, "ext": "py", "lang": "Python", "max_stars_repo_path": "spartan/model/iat/iat.py", "max_stars_repo_name": "sunxiaobing1999/spartan2", "max_stars_repo_head_hexsha": "95e80fce52c7c9274e7424fb4d9c6511b128b4c4", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# Copyright (c) 2018 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 app... | {"hexsha": "bd548009b3ada9512e4b5f7d7b61b67b0717a39b", "size": 5628, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py", "max_stars_repo_name": "lijiancheng0614/Paddle", "max_stars_repo_head_hexsha": "f980b29e6259b8e51f4ee04260e3a84233f337df", "max_s... |
library(sf)
library(watershed)
library(raster)
library(data.table)
dem = raster("data/dem.tif")
stream = stack("output/neretva.grd")
corine = st_read("data/neretva_lc.gpkg")
geo = st_read("data/neretva_geology.gpkg")
geo = st_transform(geo, st_crs(corine))
Tp = pixel_topology(stream)
neretva_rn = vectorise_stream(str... | {"hexsha": "52e1b8e6bb289403a4dd5823016a71975e39a5c0", "size": 1310, "ext": "r", "lang": "R", "max_stars_repo_path": "r/stream_vector.r", "max_stars_repo_name": "flee-group/neretva_rn", "max_stars_repo_head_hexsha": "b960fb157ced5533bc39bbfbe7b516cbbbfe50a6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
import numpy as np
import scipy.interpolate as interp
import astropy.units as u
from . import detector
from . import binary
def Get_SNR_Matrix(
source, instrument, var_x, sample_rate_x, var_y, sample_rate_y, **kwargs
):
"""Calculates SNR Matrix
Parameters
----------
source: object
Insta... | {"hexsha": "065616b19651efd757b1637dffa5afe796668b4b", "size": 19859, "ext": "py", "lang": "Python", "max_stars_repo_path": "gwent/snr.py", "max_stars_repo_name": "ark0015/GWDetectorDesignToolkit", "max_stars_repo_head_hexsha": "6ee2f7a633c973ea10b450257b1ad4dbd0323738", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import random
import numpy as np
import pytest
from pandas import DataFrame
from tests.utils import assert_dataframes_equals
from weaverbird.backends.pandas_executor.steps.statistics import execute_statistics
from weaverbird.pipeline.steps import StatisticsStep
@pytest.fixture
def sample_df():
return DataFrame(... | {"hexsha": "2620532ac8e6c5ea45a98778f3ae3ccd5ad7b26c", "size": 2132, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/tests/steps/test_statistics.py", "max_stars_repo_name": "JeremyJacquemont/weaverbird", "max_stars_repo_head_hexsha": "e04ab6f9c8381986ab71078e5199ece7a875e743", "max_stars_repo_licenses": [... |
%
% IEEE Transactions on Microwave Theory and Techniques example
% Tibault Reveyrand - http://www.microwave.fr
%
% http://www.microwave.fr/LaTeX.html
% ---------------------------------------
% ================================================
% Please HIGHLIGHT the new inputs such like this :
% Text :
% \hl{comment... | {"hexsha": "9aa35656a29b486f8624e20f33808ee570095950", "size": 26131, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "MTT_reveyrand.tex", "max_stars_repo_name": "BaiLiping/Paper4", "max_stars_repo_head_hexsha": "27044c80ecae9e8c03582237f08a8cd840ef8b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#ifndef IRODS_RING_BUFFER_HPP
#define IRODS_RING_BUFFER_HPP
#include <boost/circular_buffer.hpp>
#include "lock_and_wait_strategy.hpp"
#include <iterator>
namespace irods {
namespace experimental {
// ring buffer with protection for overwrites
template <typename T>
class circular_buffer {
public... | {"hexsha": "a525eca1cab483c2a120b9046e5d19477bd5fdb7", "size": 3602, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "s3/s3_transport/include/circular_buffer.hpp", "max_stars_repo_name": "alanking/irods_resource_plugin_s3", "max_stars_repo_head_hexsha": "492839f885f432d30fa904ac9d5f89369d248ece", "max_stars_repo_li... |
#!/usr/bin/env python
"""
##############################################
Testing Package Reliability Growth Data Module
##############################################
"""
# -*- coding: utf-8 -*-
#
# rtk.testing.growth.Growth.py is part of The RTK Project
#
# All rights reserved.
# Copyright 2007 - 2017 Andrew Ro... | {"hexsha": "bdf422b82f8a0fd9c6a705e227b84bc07a8c1d30", "size": 48132, "ext": "py", "lang": "Python", "max_stars_repo_path": "rtk/testing/growth/Growth.py", "max_stars_repo_name": "rakhimov/rtk", "max_stars_repo_head_hexsha": "adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
[STATEMENT]
lemma vector_inf_closed:
"vector x \<Longrightarrow> vector y \<Longrightarrow> vector (x \<sqinter> y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>vector x; vector y\<rbrakk> \<Longrightarrow> vector (x \<sqinter> y)
[PROOF STEP]
by (simp add: vector_inf_comp) | {"llama_tokens": 105, "file": "Stone_Relation_Algebras_Relation_Algebras", "length": 1} |
// Boost.Polygon library voronoi_structures_test.cpp file
// Copyright Andrii Sydorchuk 2010-2012.
// 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://www.boost.org for updates, d... | {"hexsha": "1693c317ee3774a2ff3839f739f7ee94073b7ba5", "size": 3962, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/polygon/test/voronoi_structures_test.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max... |
import numpy as np
class RealignMatrix(object):
@staticmethod
def get_M_aligned_to_x(M, x):
x = x/np.linalg.norm(x)
z = np.cross(x, M[1][:3])
z = z/np.linalg.norm(z)
y = np.cross(z, x)
y = y/np.linalg.norm(y)
return np.array([x.tolist()+[0],y.tolist()+[0],z.... | {"hexsha": "e75028e8d2c286d79274c768d99e4ab79e4ee14b", "size": 2576, "ext": "py", "lang": "Python", "max_stars_repo_path": "new_challenge_old_research/realign_matrix.py", "max_stars_repo_name": "JeromeEippers/python_rnd_collection", "max_stars_repo_head_hexsha": "8383a9759197cfd4c560792f0f06ba981bb1f933", "max_stars_re... |
import copy
from datetime import datetime
import pandas as pd
import numpy as np
import warnings
from .events import UnplugEvent
from .interface import Interface, InvalidScheduleError
class Simulator:
""" Central class of the acnsim package.
The Simulator class is the central place where everything about a ... | {"hexsha": "86c23b987269dddc53aede242793c42c6b0fae6d", "size": 9036, "ext": "py", "lang": "Python", "max_stars_repo_path": "acnportal/acnsim/simulator.py", "max_stars_repo_name": "irasus-technologies/acnportal", "max_stars_repo_head_hexsha": "f6ac7b9ddb28ab48177c51a676f1619e88ea91e0", "max_stars_repo_licenses": ["BSD-3... |
"""Adopted from https://github.com/DylanWusee/pointconv_pytorch/blob/master/model/pointconv.py"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from time import time
import numpy as np
def timeit(tag, t):
print("{}: {}s".format(tag, time() - t))
return time()
def square_distance(src, ds... | {"hexsha": "13ca4e6c4202ca7697c095ec6d3209646ed79c6c", "size": 15423, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/model/pointconv.py", "max_stars_repo_name": "code-roamer/IF-Defense", "max_stars_repo_head_hexsha": "4e2462b66fa1eac90cfbf61fa0dc635d223fdf2f", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import scipy.spatial.distance as d
import matplotlib.pyplot as plt
# Helper Functions
def qsort(a, i):
return sorted(a, key = lambda arr: arr[i])
def search(a, pos, value_start, value_end):
'''
Search for a value within ordered lists.
Never used directly -> helper of helper.
'''
if len(a... | {"hexsha": "4a9a2e4228ad6580d642558e7729f7daaf1ad65c", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "helpers.py", "max_stars_repo_name": "DiogoRibeiro7/Physics", "max_stars_repo_head_hexsha": "f10e2df956055f498643490744131c34dbaccdc4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import numpy as np
import os
import torch
from torch import nn
from blocks import LinearBlock, Conv2dBlock, ResBlocks, ActFirstResBlock
from vgg_tro_channel3_modi import vgg19_bn
from recognizer.models.encoder_vgg import Encoder as rec_encoder
from recognizer.models.decoder import Decoder as rec_decoder
from recognizer... | {"hexsha": "49a1d76149f594456a840c5a413e56508704e691", "size": 12529, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules_tro.py", "max_stars_repo_name": "omni-us/ContentDistillation_HTR", "max_stars_repo_head_hexsha": "be5af3cbc3a49dc5febf9b57480257faa42c7272", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
# pylint: skip-file
"""reV SAM unit test module
"""
import os
from pkg_resources import get_distribution
from packaging import version
import pytest
import numpy as np
import pandas as pd
import warnings
from reV.SAM.defaults import (DefaultPvWattsv5, DefaultPvWattsv7,
... | {"hexsha": "db41dc25391f56460fe23151773fc1d8fed87f59", "size": 7014, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_sam.py", "max_stars_repo_name": "pjstanle/reV", "max_stars_repo_head_hexsha": "c22c620749747022a65d2a98a99beef804849ee6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
import numpy as np
import torch
from torchvision.transforms import ToPILImage, ToTensor
from eda.image.transforms.compose import Compose
from eda.image.transforms.transform import EdaTransform
from eda.image.utils import default_loader
class Mixup(EdaTransform):
def __init__(
self,
name=None,
... | {"hexsha": "4836f8080aa377a49620063134bca937fd4cd8aa", "size": 2730, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_augmentation/eda/image/transforms/mixup.py", "max_stars_repo_name": "simran-arora/emmental-tutorials", "max_stars_repo_head_hexsha": "249a82a57be58e960408a45e2e0daa72980d210a", "max_stars_rep... |
/* Copyright 2016-2017 Joaquin M Lopez Munoz.
* 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://www.boost.org/libs/poly_collection for library home page.
*/
#ifndef BOOST_POLY_COLLECTION_DETAIL_P... | {"hexsha": "2334347419c0976ee5501360129ffea94fbd8136", "size": 34226, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost/boost/poly_collection/detail/poly_collection.hpp", "max_stars_repo_name": "alexhenrie/poedit", "max_stars_repo_head_hexsha": "b9b31a111d9e8a84cf1e698aff2c922a79bdd859", "max_stars_repo_l... |
#! /usr/bin/env python
from __future__ import print_function
import os
import sys
import io
import csv
from optparse import OptionParser
import numpy as np
import tensorflow as tf
from flask import Flask, jsonify, render_template, request
from tensorflow.contrib import learn
import data_helpers
from flask_restplus im... | {"hexsha": "dd2c42bd62fa740efb83b6473f85d0a758055b9e", "size": 10595, "ext": "py", "lang": "Python", "max_stars_repo_path": "crees_server.py", "max_stars_repo_name": "evhart/comrades-crees", "max_stars_repo_head_hexsha": "c06260bde5ae664ddd199bcf368a2da9e246da6e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import torch
import cole as cl
import numpy as np
import argparse
import os
cl.set_data_path("./data")
device = "cuda"
_BASE_PATH = ".."
def calc_full_grad_norm(loaders, model):
opt = torch.optim.SGD(model.parameters(), lr=0.01)
n_samples = 0
opt.zero_grad()
for loader in loaders:
for (x, y) ... | {"hexsha": "5175715db0e115f14150ecbaaaa0f49078d26ceb", "size": 3232, "ext": "py", "lang": "Python", "max_stars_repo_path": "grad_norm_exp/grad_norms.py", "max_stars_repo_name": "Mattdl/RehearsalRevealed", "max_stars_repo_head_hexsha": "f9cd2548f6c6d3ff119b40fecdb0df6fcd1525f6", "max_stars_repo_licenses": ["MIT"], "max_... |
using Test
# write your own tests here
@test 1 == 1
using DataFrames
using ExoplanetsSysSim
function run_constructor_tests()
ExoplanetsSysSim.SimulationParameters.test_sim_param_constructors()
sim_param = ExoplanetsSysSim.setup_sim_param_demo()
ExoplanetsSysSim.test_orbit_constructors()
ExoplanetsSysSim.test... | {"hexsha": "8e9a0737fdf1bceb243b3f6c210efbebd710241e", "size": 1100, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "aditya-sengupta/ExoplanetsSysSim.jl", "max_stars_repo_head_hexsha": "df552110db61453cbb8584657ba79f92f741909c", "max_stars_repo_licenses": ["MIT"], "max_s... |
from gym_kuka_mujoco.utils.kinematics import inverseKin
from gym_kuka_mujoco.utils.quaternion import identity_quat
from gym_kuka_mujoco.envs.assets import kuka_asset_dir
import os
import mujoco_py
import numpy as np
# Get the model path
model_filename = 'full_pushing_experiment_no_gravity.xml'
model_path = os.path.joi... | {"hexsha": "7f870004e34b99315ebece9fb93165d549eee206", "size": 1368, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/test_mujoco/inverse_kinematics_block_push.py", "max_stars_repo_name": "leonmkim/gym-kuka-mujoco", "max_stars_repo_head_hexsha": "ed45ae74d10e69f4e51439de2d1d0c0811623b6b", "max_stars_repo... |
#!/usr/bin/env python
# Two environment variables influence this script.
#
# GEOS_LIBRARY_PATH: a path to a GEOS C shared library.
#
# GEOS_CONFIG: the path to a geos-config program that points to GEOS version,
# headers, and libraries.
#
# NB: within this setup scripts, software versions are evaluated according
# to ... | {"hexsha": "00167c53318d39910fd24d98e38d7238d98c77e4", "size": 4029, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "mindw/shapely", "max_stars_repo_head_hexsha": "2f552833cef80ec3fc4990e8df10cc153d41d5be", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "... |
from torch.utils.data.dataset import Dataset
import os
import cv2
from PIL import Image
import numpy as np
from sklearn.preprocessing import LabelEncoder # CrossEntropyLoss expects class indices
class Mit67Dataset(Dataset):
def __init__(self, path, transform, enc=None):
self.X = []
self.y = []
... | {"hexsha": "7008d4155bf8a124e10001957343ae8dec649010", "size": 1312, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cnn/dataset.py", "max_stars_repo_name": "jordiae/DeepLearning-MAI", "max_stars_repo_head_hexsha": "e12b6975d8de6cbe89f812bf691a7f7e95213552", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
% -*- root: developer-guide.tex -*-
\section{Random Clifford sampling procedure}
This section provides documentation for the routine found in \texttt{src/cliffords/swap-representation.lisp}. The n-qubit Clifford group grows rapidly with the number of qubits, in particular as $\prod^n_{i=1} 2(4^i - 1)4^i$. In addition... | {"hexsha": "60114909f3d7bbffbd3d742cf6cf572dd2dcc427", "size": 5004, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/canonical-representation.tex", "max_stars_repo_name": "stylewarning/quilc", "max_stars_repo_head_hexsha": "86b017109d185a7d03a98cc223aee1e02b32d584", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from ctypes import *
from numpy.random import normal
import time
import numpy as np
from dolfin import *
from mesh_generation import sphere_mesh
from utils import solve_problem
from Problem import Problem
path_to_c = './fast_spher_harms.so'
sph = CDLL(path_to_c)
sph.sin_term.restype = c_float
sph.cos_term.restype ... | {"hexsha": "318e66905dc7fdf33430acb143024f997301f61b", "size": 1624, "ext": "py", "lang": "Python", "max_stars_repo_path": "field_sfem.py", "max_stars_repo_name": "erik-grennberg-jansson/matern_sfem", "max_stars_repo_head_hexsha": "1e9468084abf41cc0ae85f1b4b1254904ed2d72f", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# Parameters controlling how a plot appears
const title_font_desc = "'PT Sans','Helvetica Neue','Helvetica',sans-serif"
const label_font_desc = "'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif"
# Choose highlight color by darkening the fill color
function default_discrete_highlight_color(fill_color::ColorV... | {"hexsha": "490dc41cabe49ba9c9b69ac7edf7dca17977e1a3", "size": 5785, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/theme.jl", "max_stars_repo_name": "mbauman/Gadfly.jl", "max_stars_repo_head_hexsha": "04b1b7deb29d7f40fef2e2d78e3e0ac6adbdab3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
"""Create Bosch competition datasets with leak"""
## Bosch Production Line Performance - Kaggle
## 1) Download train and test data from Slack public URLs
## 2) Unzip .zip files
## 3) Combine train and test data
## 4) Create leak features for train and test data based on row ids and row order
## 5) Import the dat... | {"hexsha": "8b8f30eb7ad1ad1fcc4e3e609ab5312a95fe90ea", "size": 3197, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/kaggle_bosch.py", "max_stars_repo_name": "james94/driverlessai-recipes", "max_stars_repo_head_hexsha": "87c35460db59ffda8dc18ad82cb3a9b8291410e4", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from namsa import SupercellBuilder, MSAGPU
from utils import *
import numpy as np
from time import time
import sys, os, re
import h5py
from mpi4py import MPI
from itertools import chain
import tensorflow as tf
import lmdb
comm = MPI.COMM_WORLD
comm_size = comm.Get_size()
comm_rank = comm.Get_rank()
def simulate(fil... | {"hexsha": "ee290adbdddba8ab657bbbc3c16c8cd2ff7bb20b", "size": 9007, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/summit_scripts/sim_batch_debug.py", "max_stars_repo_name": "nlaanait/namsa", "max_stars_repo_head_hexsha": "55f82ecf1c82601fcb81815d5e60705506c01e1e", "max_stars_repo_licenses": ["MIT"], "... |
'''
Created on Mar 25, 2018
@author: ywz
'''
import numpy, random, os
import tensorflow as tf
from replay_memory import ReplayMemory
from optimizer import Optimizer
from q_network import QNetwork
class DQN:
def __init__(self, config, game, directory, callback=None, summary_writer=None):
sel... | {"hexsha": "eb040524dd701753ecb85a8cb27b48e79ca1c843", "size": 7186, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter03/q_learning.py", "max_stars_repo_name": "jvstinian/Python-Reinforcement-Learning-Projects", "max_stars_repo_head_hexsha": "6c97c68351fc4af426cb5c3583d75aebfabac8aa", "max_stars_repo_licen... |
using Images, MXNet
### LOADING THE MODEL
const MODEL_NAME = "weights/mobilenet-v2/mobilenet_v2"
const MODEL_CLASS_NAMES = "weights/mobilenet-v2/synset.txt"
nnet = mx.load_checkpoint(MODEL_NAME, 0, mx.FeedForward; context = mx.gpu());
synset = readlines(MODEL_CLASS_NAMES);
### SEARCH FOR A LAYER OF INTERESTS
layers ... | {"hexsha": "4ceba957e133dced80b820e4fbc36af2fcb317c6", "size": 1104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter09/4_loading_mobilenetv2.jl", "max_stars_repo_name": "tjburch/Hands-On-Computer-Vision-with-Julia", "max_stars_repo_head_hexsha": "bf1008087e9c5427ee37e6ef33bac07979cf8854", "max_stars_repo_... |
# This file is a part of ValueShapes.jl, licensed under the MIT License (MIT).
"""
ReshapedDist <: Distribution
An multivariate distribution reshaped using a given
[`AbstractValueShape`](@ref).
Constructors:
```julia
ReshapedDist(dist::MultivariateDistribution, shape::AbstractValueShape)
```
In addition, ... | {"hexsha": "8f25d998c9291595ff2df71f9a6bf5f4b30e59da", "size": 3850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/reshaped_dist.jl", "max_stars_repo_name": "sthayashi/ValueShapes.jl", "max_stars_repo_head_hexsha": "f87a92d261a0889e8efef0641bd49c626bf7c02c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module JuliaCommunityStatistics
using GitHub
using ProgressMeter
using Dates
using DataFrames
import GitHub: name
export jlrepo, auth
const auth = authenticate(ENV["GH_AUTH"])
const jlrepo = repo("JuliaLang/julia"; auth=auth)
export get_all_prs
function get_all_prs(;state="all")
prs = PullRequest[]
@showpro... | {"hexsha": "6c0bf29329396d00ce946e3bb3536cd4c9139fe1", "size": 2972, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/JuliaCommunityStatistics.jl", "max_stars_repo_name": "rick2047/JuliaCommunityStatistics.jl", "max_stars_repo_head_hexsha": "3d42d1a9aab5af7d044fd4795223eb0f93bb0ac2", "max_stars_repo_licenses":... |
# python correlation_cm_sh.py NUM_GROUPS colormap
# python correlation_cm_sh.py 50 hot
import setproctitle
setproctitle.setproctitle("covid-19-vac@chenlin")
import sys
import os
import datetime
import pandas as pd
import numpy as np
import constants
import functions
import pdb
from sklearn.preproc... | {"hexsha": "7ca58f8334fe79fc3c289504604b963d62a0089d", "size": 16944, "ext": "py", "lang": "Python", "max_stars_repo_path": "correlation_cm_sh.py", "max_stars_repo_name": "LinChen-65/utility-equity-covid-vac", "max_stars_repo_head_hexsha": "9194ee0e019b3160254401b84d369900a527da7e", "max_stars_repo_licenses": ["MIT"], ... |
################################################################################
#
# Package : AlphaPy
# Module : market_variables
# Created : July 11, 2013
#
# Copyright 2017 ScottFree Analytics LLC
# Mark Conway & Robert D. Scott II
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may ... | {"hexsha": "77ee8ea4204f8fd84f574ea93382fb86cda78990", "size": 44191, "ext": "py", "lang": "Python", "max_stars_repo_path": "alphapy/market_variables.py", "max_stars_repo_name": "MichaelFriedberg/AlphaPy", "max_stars_repo_head_hexsha": "a5d33d1d021bbcec533286af91e30e6a61f4f85d", "max_stars_repo_licenses": ["Apache-2.0"... |
"""
Copyright (c) 2018-2021 Intel Corporation
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 wri... | {"hexsha": "717fcb5dc43fc2bf09a980ab2f11b0455f9df995", "size": 26324, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/accuracy_checker/accuracy_checker/adapters/yolo.py", "max_stars_repo_name": "PinDanil/open_model_zoo", "max_stars_repo_head_hexsha": "8538b2769d65d7ca24dd36db0340a9c143583812", "max_stars_r... |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 11 05:43:40 2019
@author: Roopak Ingole
"""
import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
from moviepy.editor import VideoFileClip
import os
import collections
import math
debug = 0
# HYPERPAR... | {"hexsha": "2c0e5e701f172e125664fc6c25d8c6e16af8e91a", "size": 33817, "ext": "py", "lang": "Python", "max_stars_repo_path": "advanced_lane_line.py", "max_stars_repo_name": "roopakingole/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "e11c1a5e5dc3511c5013701e48125215c7877f7d", "max_stars_repo_licenses": ["MIT... |
import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm
if __name__ == '__main__':
x = np.array([[1, 2], [2, 3], [3, 3], [2, 1], [3, 2]])
y = np.array([1, 1, 1, -1, -1])
clf = svm.SVC(kernel='linear', C=10)
clf.fit(x, y)
print('w1: ' + str(clf.coef_[0][0]))
print('w2: ' + st... | {"hexsha": "58740a935b8e1446ea9f17d94a9ecbc255ff9f43", "size": 1128, "ext": "py", "lang": "Python", "max_stars_repo_path": "BUPT/Machine-Learning-I/Assignment2/slm_7_2.py", "max_stars_repo_name": "dachr8/Exercise", "max_stars_repo_head_hexsha": "2e567f9edcf0d06ca4ed99cb65a0264546a36d63", "max_stars_repo_licenses": ["MI... |
import argparse
import json
import pandas as pd
import numpy as np
import plotly.express as px
def main():
parser = argparse.ArgumentParser(description="MSQL CMD")
parser.add_argument('input_extracted_json', help='input_extracted_json')
parser.add_argument('output_summary_html', help='output_summary_html')... | {"hexsha": "47486de453120f4aaa6ca600adeb899b55f79f07", "size": 7026, "ext": "py", "lang": "Python", "max_stars_repo_path": "workflow/bin/summarize_extracted.py", "max_stars_repo_name": "sarvarkaxxorov/MassQueryLanguage", "max_stars_repo_head_hexsha": "b7618ba7fb5343c252c5691dc574f4193fb8e83e", "max_stars_repo_licenses"... |
import numpy as np
from PIL import Image
def invert_image(image):
all_pixels = np.array(
[
[
[*image.getpixel((width_counter, height_counter)), 255]
for width_counter in range(image.width)
]
for height_counter in range(image.height)
... | {"hexsha": "af687f49124c6569135dca3f1f56074fe8e78e91", "size": 454, "ext": "py", "lang": "Python", "max_stars_repo_path": "{{cookiecutter.package_name}}/{{cookiecutter.package_name}}/processing.py", "max_stars_repo_name": "spirousschuh/cookiecutter-git-workshop-documentation", "max_stars_repo_head_hexsha": "4fb62eda345... |
# # Exact Optimization with Rational Arithmetic
# This example can be found in section 4.3 [in the paper](https://arxiv.org/pdf/2104.06675.pdf).
# The package allows for exact optimization with rational arithmetic. For this, it suffices to set up the LMO
# to be rational and choose an appropriate step-size rule as det... | {"hexsha": "3d0bd154fffac2ea395de6876ae6bde6e8eca3c6", "size": 2630, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/docs_4_rational_opt.jl", "max_stars_repo_name": "gdalle/FrankWolfe.jl-2", "max_stars_repo_head_hexsha": "c3b3903c4808e24aa9e0f655aa2f8de0f2c1571c", "max_stars_repo_licenses": ["MIT"], "max... |
_with_vdw(a::PDBAtom, resname_a::String) = (resname_a, a.atom) in keys(vanderwaalsradius)
_with_cov(a::PDBAtom, resname_a::String) = a.element in keys(covalentradius)
ishydrophobic(a::PDBAtom, resname_a::String) = (resname_a, a.atom) in _hydrophobic
"""
Returns true if the atom, e.g. `("HIS","CG")`, is an aromatic a... | {"hexsha": "912133cb8f45a8457a2f7702df0740b5b95bf309", "size": 9456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PDB/Interaction.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/MIToS.jl-51bafb47-8a16-5ded-8b04-24ef4eede0b5", "max_stars_repo_head_hexsha": "6237d5a885e43f49b74e0a9e56120012711b2d... |
[STATEMENT]
lemma permutep_id [simp]: "permutep id mon = mon"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. permutep id mon = mon
[PROOF STEP]
by transfer auto | {"llama_tokens": 64, "file": "Symmetric_Polynomials_Symmetric_Polynomials", "length": 1} |
"""Tests to run with a running daemon."""
import subprocess
import sys
import operator
import time
import numpy as np
from aiida import orm
from aiida.engine.daemon.client import get_daemon_client
from aiida.engine import launch
from aiida.common import exceptions
from aiida_optimize.engines import Bisection
from aii... | {"hexsha": "dc2d823aef6e5654eb01d1351261062d660fb5a4", "size": 3344, "ext": "py", "lang": "Python", "max_stars_repo_path": ".github/system_tests/test_daemon.py", "max_stars_repo_name": "greschd/aiida_optimize", "max_stars_repo_head_hexsha": "4c7bc76e4ad7e40f6105e60f34b7a20e1ab3a122", "max_stars_repo_licenses": ["Apache... |
/-
Copyright (c) 2017 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl, Mario Carneiro, Patrick Massot
-/
import topology.maps
import order.filter.pi
import data.fin.tuple
/-!
# Constructions of new topological spaces from old ones
This f... | {"author": "nick-kuhn", "repo": "leantools", "sha": "567a98c031fffe3f270b7b8dea48389bc70d7abb", "save_path": "github-repos/lean/nick-kuhn-leantools", "path": "github-repos/lean/nick-kuhn-leantools/leantools-567a98c031fffe3f270b7b8dea48389bc70d7abb/src/topology/constructions.lean"} |
import os
import pandas as pd
import numpy as np
import math
import pickle
os.chdir('C:/Users/VADDADISAIRAHUL/Downloads/indian_movies_data_final/')
successful_list = ['All Time Blockbuster','Blockbuster','Hit','Super Hit','Semi Hit','Above Average','Average']
unsuccessful_list = ['Flop','Below Average','Disa... | {"hexsha": "2ef1e9673db1cb1f8f196577fbd049041cc2e825", "size": 6012, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/movie_data_preprocessing.py", "max_stars_repo_name": "VaddadiSaiRahul/Early-Movie-Success-Prediction", "max_stars_repo_head_hexsha": "49062a62b9527e4f9ff5aa3b904563d670701d07", "max_stars_re... |
# -*- coding: utf-8 -*-
"""RHESSI TimeSeries subclass definitions."""
from collections import OrderedDict
import datetime
import matplotlib.dates
import matplotlib.pyplot as plt
from pandas import DataFrame
from sunpy.timeseries.timeseriesbase import GenericTimeSeries
from sunpy.util.metadata import MetaDict
from sunp... | {"hexsha": "c470e62bf678eaa0eff0d18be7a2c675237403c7", "size": 5464, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunpy/timeseries/sources/rhessi.py", "max_stars_repo_name": "drewleonard42/sunpy", "max_stars_repo_head_hexsha": "79ca90a032213d82d42a3657a693b20b99b22464", "max_stars_repo_licenses": ["BSD-2-Clau... |
#!/usr/bin/python3
#-*- coding: UTF-8 -*-
import struct
import os
import sys
import numpy as np
#import matplotlib.pyplot as plt
import PIL.Image
if len(sys.argv) == 3:
print("ubyteFileName:", sys.argv[1])
print("savePath:", sys.argv[2])
print("")
else:
print("USED: ", sys.argv[0], " ubyteFileName saveP... | {"hexsha": "e37d054bb189e1f4a5982306ac79e3006e04b2aa", "size": 995, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mnist/cov_image_to_bmp.py", "max_stars_repo_name": "ZhengPengqiao/studyCaffe", "max_stars_repo_head_hexsha": "dda514fdb5903ef53944dd7a355dc8aadcd3a78a", "max_stars_repo_licenses": ["Intel"... |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 17 19:52:06 2017
@author: Gowtham
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv("HR_comma_sep.csv")
X = dataset.iloc[:,[0,1,2,3,4,5,7,8,9] ].values
y = dataset.iloc[:, 6].values
from sklearn.preprocess... | {"hexsha": "b0c7457046127ff43a934ac685ba429f9ba05e35", "size": 1895, "ext": "py", "lang": "Python", "max_stars_repo_path": "hr.py", "max_stars_repo_name": "Gowtham1729/Human-Resources-Prediction", "max_stars_repo_head_hexsha": "5deb28bbae65da88b5a7ad864a0c6992ebd50247", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
'''
File name: autoencoder_train_CNN_vs_MLP.py
Author: Lloyd Windrim
Date created: August 2019
Python package: deephyp
Description: An example script for training an MLP (or dense) autoencoder and a convolutional autoencoder on the
Pavia Uni hyperspectral dataset.
'''
import scipy.io
import ... | {"hexsha": "b9712bb38a16c7f012f4674bf9ef284bfca55306", "size": 3647, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/autoencoder_train_CNN_vs_MLP.py", "max_stars_repo_name": "forkbabu/hyperspectral-autoencoders", "max_stars_repo_head_hexsha": "0b2cb987ca9a3aa8a27a5fe0241ca6f76c56a8ab", "max_stars_repo_l... |
#!/usr/bin/env python3
"""
Calculates the correlation for N pair, up to N_max, using the parity criteria.
2016.11.22 Alessandro Cere
"""
import glob
import numpy as np
import subprocess
from math import pi
from uncertainties import unumpy
sink_file = 'par_chsh.dat'
sink_file_err = 'par_chsh_err.dat'
N_max = 20
... | {"hexsha": "ec1291f5d241f4c3d0faca6da2449400c159aa5a", "size": 2841, "ext": "py", "lang": "Python", "max_stars_repo_path": "parity.py", "max_stars_repo_name": "acere/Bell-manypairs", "max_stars_repo_head_hexsha": "8708c6914003fe284ba77af4359ad6fe83a0564f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
(* SPDX-License-Identifier: GPL-2.0 *)
Require Import Coqlib.
Require Import AST.
Require Import Integers.
Require Import Values.
Require Import Cop.
Require Import Clight.
Require Import CDataTypes.
Require Import Ctypes.
Require Import Ident.
Local Open Scope Z_scope.
Definition _Rd : ident := 999%positive.
Defini... | {"author": "VeriGu", "repo": "VRM-proof", "sha": "9e3c9751f31713a133a0a7e98f3d4c9600ca7bde", "save_path": "github-repos/coq/VeriGu-VRM-proof", "path": "github-repos/coq/VeriGu-VRM-proof/VRM-proof-9e3c9751f31713a133a0a7e98f3d4c9600ca7bde/sekvm/PageIndex/Code.v"} |
import seaborn as sns
import sys
import csv
from statistics import stdev
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
pd.set_option('display.max_rows', None)
files = [
{'file': 'b000', 'bonus': '0.00'},
{'file': 'b001', 'bonus': '0.01'},
{'file':... | {"hexsha": "247ee1fe132049b1764410cca95a901c0e3cfade", "size": 3431, "ext": "py", "lang": "Python", "max_stars_repo_path": "article/figure-2a-2c/plot-05-25-1.py", "max_stars_repo_name": "guilherme-araujo/gsop-dist", "max_stars_repo_head_hexsha": "15da82ffa8add74cc61b95d3544ec3aaa0e71a32", "max_stars_repo_licenses": ["M... |
/*
* AddingNoise.cpp
*
* Created on: May 22, 2015
* Author: dbazazian
*/
// #define STANDARD_DEVIATION_NEIGHBORS
#define GAUSSIAN_NOISE
#ifdef STANDARD_DEVIATION_NEIGHBORS
#include <iostream>
#include <stdio.h> /* printf, NULL */
#include <stdlib.h> /* srand, rand */
#include "time.h"
#include <... | {"hexsha": "7ba2f2bfbcb771d86bff35d23224498daa41583b", "size": 9585, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "AddingNoise.cpp", "max_stars_repo_name": "n1ckfg/Edge_Extraction", "max_stars_repo_head_hexsha": "2bbe215350faf02334652af54eac4f4666872d4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10... |
#!/usr/bin/env python
#
# File: vis_hostage.py
#
# Created: Monday, August 1 2016 by rejuvyesh <mail@rejuvyesh.com>
#
from __future__ import absolute_import, print_function
import argparse
import json
import pprint
from gym import spaces
import h5py
import numpy as np
import tensorflow as tf
import rltools.algos
im... | {"hexsha": "4fa1d46fbb3403a433f7e5089c31f38a24696e73", "size": 3705, "ext": "py", "lang": "Python", "max_stars_repo_path": "vis/rltools/vis_hostage.py", "max_stars_repo_name": "SurvivorT/SRTP", "max_stars_repo_head_hexsha": "1ddc0c4ec31d61daf9f4292c533722e61818eb51", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from pyamg.testing import *
import numpy
import scipy
from scipy.sparse import spdiags, csr_matrix, bsr_matrix, eye
from scipy import arange, ones, zeros, array, allclose, zeros_like, \
tril, diag, triu, rand, asmatrix, mat
from scipy.linalg import solve
from pyamg.gallery import poisson, sprand, elasticit... | {"hexsha": "29dc65050d6672664132d8fe3d1d1790960f4bfd", "size": 58860, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyamg/relaxation/tests/test_relaxation.py", "max_stars_repo_name": "pombreda/pyamg", "max_stars_repo_head_hexsha": "ecd464de4d16e16bc905d84df181025ddf3c1958", "max_stars_repo_licenses": ["BSD-3-C... |
#include <idmlib/tdt/temporal_kpe.h>
#include <boost/algorithm/string/split.hpp>
#include <boost/program_options.hpp>
#include <boost/filesystem.hpp>
#include <boost/date_time/gregorian/gregorian.hpp>
#include <sf1common/ScdParser.h>
#include <idmlib/similarity/term_similarity.h>
#include "../TestResources.h"
using nam... | {"hexsha": "272c153e09f73329e3a60313ffdaa09a18877a98", "size": 8239, "ext": "cc", "lang": "C++", "max_stars_repo_path": "test/tdt/tdt_scd_tool.cc", "max_stars_repo_name": "izenecloud/idmlib", "max_stars_repo_head_hexsha": "ec6afd44490170a70ef980afa6d21fba8c77ed9d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import cv2
import base as bs
import numpy as np
def sobel_filter(img, K_size=3, sigma=1.3):
if len(img.shape) == 3:
H, W, C = img.shape
else:
img = np.expand_dims(img, axis=-1)
H, W, C = img.shape
##padding
pad = K_size // 2
out_v = np.zeros((H + pad * 2, W + pad * 2, ... | {"hexsha": "ca074265094ce38327ecdfc044fc031a8e308127", "size": 1889, "ext": "py", "lang": "Python", "max_stars_repo_path": "MyAns/q16.py", "max_stars_repo_name": "mtbkb/Gasyori100knock", "max_stars_repo_head_hexsha": "03b9c85dde2c467403185521620ee9823f1d52e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma fcomp_fconst_on_fid_on[simp]: "fconst_on A c \<circ>\<^sub>\<bullet> fid_on A = fconst_on A c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fconst_on A c \<circ>\<^sub>\<bullet> fid_on A = fconst_on A c
[PROOF STEP]
by auto | {"llama_tokens": 106, "file": "CZH_Foundations_czh_sets_CZH_Sets_FBRelations", "length": 1} |
import numpy as np
p = [[1, 0], [0, 1]]
q = [[1, 2], [3, 4]]
print("original matrix:")
print(p)
print(q)
result = np.outer(p, q)
print("Outer product of the said two vectors:")
print(result) | {"hexsha": "9aa3b46aa790b9694d95e85036034e4d49a1ed12", "size": 198, "ext": "py", "lang": "Python", "max_stars_repo_path": "outer.py", "max_stars_repo_name": "Abhi-thecoder/Inner-Outer-Product-of-Vectors", "max_stars_repo_head_hexsha": "d28f25d7a76d5e2bb4ad067c7673420ffe089169", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python
#
# ----------------------------------------------------------------------
#
# Brad T. Aagaard, U.S. Geological Survey
# Charles A. Williams, GNS Science
# Matthew G. Knepley, University of Chicago
#
# This code was developed as part of the Computational Infrastructure
# for Geodynamics (http://ge... | {"hexsha": "aadb48bda250d0860d99054d2ca79f3e6cbac622", "size": 4207, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylith/tests/Fault.py", "max_stars_repo_name": "joegeisz/pylith", "max_stars_repo_head_hexsha": "f74060b7b19d7e90abf8597bbe9250c96593c0ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import pickle
import pandas as pd
import chardet
import re
import numpy as np
from nltk.stem.cistem import Cistem
from statistics import mean
stemmer = Cistem()
with open('../preprocessing/wordfreq.pkl', 'rb') as f:
dereko = pickle.load(f)
INPUT = "../data/input.xlsx"
#with open(IMPORT_FILE, 'rb') as f:
# encod... | {"hexsha": "225934b1ec4a4dbd24acf50a23b570ea00844b20", "size": 1224, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/model.py", "max_stars_repo_name": "moritzlschuler/learngermanwithsongs", "max_stars_repo_head_hexsha": "a7f0e470e8688de42a4c437cc3dc93ea0f1108a1", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Autogenerated wrapper script for YASM_jll for x86_64-w64-mingw32
export vsyasm, yasm, ytasm
using NASM_jll
JLLWrappers.@generate_wrapper_header("YASM")
JLLWrappers.@declare_executable_product(vsyasm)
JLLWrappers.@declare_executable_product(yasm)
JLLWrappers.@declare_executable_product(ytasm)
function __init__()
... | {"hexsha": "257866152f55f90e159d19bcbbaf409ad4ef4914", "size": 692, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/x86_64-w64-mingw32.jl", "max_stars_repo_name": "JuliaBinaryWrappers/YASM_jll.jl", "max_stars_repo_head_hexsha": "cba1e0ac48aa3406aa50efbd7e5b8f946bc0e42f", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 6 21:53:21 2017
@author: Gerardo A. Rivera Tello
"""
import numpy as np
import matplotlib.pyplot as plt
#%%
def plot_data(data,cbar=0,save_img=0):
plot,axs = plt.subplots()
raw_data = axs.imshow(data,interpolation="gaussian",cmap='jet')
... | {"hexsha": "99badb6dbea5f582505cfcb570306a411d39b743", "size": 1078, "ext": "py", "lang": "Python", "max_stars_repo_path": "NETCDF scripts/Time Steps of Binary Data File/read_bin.py", "max_stars_repo_name": "DangoMelon0701/PyRemote-Sensing", "max_stars_repo_head_hexsha": "fa12545b89c937baf5f1be39a4b2f4eebf714a9a", "max... |
[STATEMENT]
lemma map_le_on_disj_left:
"\<lbrakk> h' \<subseteq>\<^sub>m h ; h\<^sub>0 \<bottom> h\<^sub>1 ; h' = h\<^sub>0 ++ h\<^sub>1 \<rbrakk> \<Longrightarrow> h\<^sub>0 \<subseteq>\<^sub>m h"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>h' \<subseteq>\<^sub>m h; h\<^sub>0 \<bottom> h\<^sub>1; h' =... | {"llama_tokens": 328, "file": "Separation_Algebra_Map_Extra", "length": 2} |
#include <iostream>
#include "DebugPlotVisualization.hpp"
#include <QLabel>
#include "qcustomplot.h"
#include <deque>
#include <Eigen/Core>
#include <QAction>
#include <mutex>
using namespace vizkit3d;
struct DebugPlotVisualization::Data {
std::deque<Eigen::Vector2d> data;
std::mutex dataMutex;
QDockWidge... | {"hexsha": "9bb876ca8725d85ed8ed074460a69dc8aa3f0d57", "size": 4873, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "viz/DebugPlotVisualization.cpp", "max_stars_repo_name": "pierrewillenbrockdfki/gui-vizkit3d_debug_drawings", "max_stars_repo_head_hexsha": "553b0bac93ef1f410e4b9842e8d9aa7391e5ddab", "max_stars_repo... |
"""
Packing module
==============
:synopsis: Prepares packed spheres for tessellation.
.. moduleauthor:: Pavel Ferkl <pavel.ferkl@gmail.com>
.. moduleauthor:: Mohammad Marvi-Mashhadi <mohammad.marvi@imdea.org>
"""
from __future__ import division, print_function
import struct
import os
import time
import ra... | {"hexsha": "2680220ffc3ae1b3d6c4012806b556af44d6a387", "size": 9560, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/foamgen/packing.py", "max_stars_repo_name": "japaf/foamgen", "max_stars_repo_head_hexsha": "6f456796e79de344eefb21a1ad121fd869f9fd9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import numpy as np
import pandas as pd
#Read in data
def read_files(input_data, holdout_data):
"""Both options can be either df or csv files and are parsed here.
Input:
input_data: string, name of table in database
holdout_data: The holdout data as string filename, df
Return:... | {"hexsha": "f4e28aaf19652a529c63ce56332fe9d75eb72793", "size": 9498, "ext": "py", "lang": "Python", "max_stars_repo_path": "dame_flame/flame_db/checker.py", "max_stars_repo_name": "ALEXLANGLANG/DAME-FLAME-Python-Package", "max_stars_repo_head_hexsha": "5fdcaa71cb3708418326348876b1070fc540c65e", "max_stars_repo_licenses... |
# coding=utf8
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | {"hexsha": "2decab061cf948379822a09b7eeafa95921dd29b", "size": 35028, "ext": "py", "lang": "Python", "max_stars_repo_path": "recognition/densenet/code/main.py", "max_stars_repo_name": "HLIG/HUAWEI_OCR2019", "max_stars_repo_head_hexsha": "1070d6291072e0223c2624f686766d0f3065e9c6", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma tendsto_dist [tendsto_intros]:
fixes l m :: "'a::metric_space"
assumes f: "(f \<longlongrightarrow> l) F"
and g: "(g \<longlongrightarrow> m) F"
shows "((\<lambda>x. dist (f x) (g x)) \<longlongrightarrow> dist l m) F"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((\<lambda>x. dist (f x... | {"llama_tokens": 1635, "file": null, "length": 17} |
# coding: utf-8
""" Generate a grid of initial conditions for freqmap'ing """
from __future__ import division, print_function
__author__ = "adrn <adrn@astro.columbia.edu>"
# Standard library
import os
import sys
# Third-party
from astropy import log as logger
import gary.potential as gp
import matplotlib.pyplot as... | {"hexsha": "46f5a8330c4251aec53d7975ed1bb8233830a71b", "size": 5946, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/make_grid.py", "max_stars_repo_name": "adrn/StreamMorphology", "max_stars_repo_head_hexsha": "99a2da560b58e6e47259d1cd2f0cc9ba1641424d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using FEM
using Test
@testset "FEM.jl" begin
# Write your tests here.
end
| {"hexsha": "b33de4686e1f2a22202ebb2f082346bfaa4091e9", "size": 79, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "Andre-Fontenelle/FEM.jl", "max_stars_repo_head_hexsha": "5189e57310cb8c791914b4ec0e3c2d33ff037750", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- encoding: utf-8 -*-
import numpy as np
import warnings
from sklearn.metrics.classification import type_of_target
from sklearn.base import BaseEstimator
import sklearn.utils
import scipy.sparse
import autosklearn.automl
from autosklearn.metrics import f1_macro, accuracy, r2
from autosklearn.constants import *
f... | {"hexsha": "37c6d12eb75d1ee75c2731a0f52a6eb9f18429e1", "size": 24093, "ext": "py", "lang": "Python", "max_stars_repo_path": "autosklearn/estimators.py", "max_stars_repo_name": "jimgoo/auto-sklearn", "max_stars_repo_head_hexsha": "a263efb49f7b7f597963bc1e787105ea7615ea75", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
#pragma once
#include "common.hpp"
#include "sessions.hpp"
#include <boost/asio/io_context.hpp>
#include <boost/asio/ip/address.hpp>
#include <boost/beast/http/message.hpp>
#include <boost/beast/http/string_body.hpp>
#include <boost/beast/websocket.hpp>
#include <boost/url/url_view.hpp>
#include <string>
#include <s... | {"hexsha": "44ed2e30f5269141f5a873019ec5ff23fe7c5b6c", "size": 1728, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "http/http_request.hpp", "max_stars_repo_name": "pachu-nc/bmcweb", "max_stars_repo_head_hexsha": "aab0d90061b1b3ebd15f0976e188d59facb0a956", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
# coding: utf-8
# # Exercício - Theo - Marcus Leandro
#
# ### Objetivo
# - Resolver exercícios mencionados no link https://stonepgto.slack.com/archives/CHH394R4Z/p1555332079003900
#
#
# ### Resumo comando das questões
#
# 11. Reajuste de salário baseado em condição e apresentação descritiva da relação de nova e a... | {"hexsha": "74e32619f8e4f670da17e8fed4862597e0c959d4", "size": 5639, "ext": "py", "lang": "Python", "max_stars_repo_path": "exercicios_abril_19/Exercicio Python - Theo - Marcus Leandro 19 abril.py", "max_stars_repo_name": "theocarvalho/aula_python_marcus_leandro", "max_stars_repo_head_hexsha": "119158680e97ae8dc8bbb1a2... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import numpy as np
import ase.io
from ase import Atoms, Atom
def write_xyz(*args,**kwargs):
"""positions in cartesian (AA) and forces in eV/AA"""
# symbols and positions are required
if 'symbols' in kwargs.keys():
symbols = kwargs['symbol... | {"hexsha": "80e5bb8d4790b2857423af39ba327109b1721689", "size": 3130, "ext": "py", "lang": "Python", "max_stars_repo_path": "common/coreXYZ.py", "max_stars_repo_name": "hsulab/DailyScripts", "max_stars_repo_head_hexsha": "26b03cfb721fd66f39c86df50d2ec5866e651d6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import scipy.ndimage as ndi
import skimage.measure
class General(object):
def __init__(self, filePath_pointCloud_csv, raster_shape):
self.set_pointCloud(
filePath_pointCloud_csv, raster_shape
);
def se... | {"hexsha": "dc3e0861ea764b24832b93564c59df4e9247ec9b", "size": 4172, "ext": "py", "lang": "Python", "max_stars_repo_path": "point_analysis/labelling.py", "max_stars_repo_name": "ComteDeLooz/protect", "max_stars_repo_head_hexsha": "d8b8b404315c6ba90cd56c1b394ce24c2118f8ee", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
\section{Results}
\label{sec:Results}
In this section we describe the results of our methodologies on the observational and treatment data. We investigate the relations between features and symptoms of the real data provided in the files\footnote{See GitHub} and determine answers to our questions from the data.
\subs... | {"hexsha": "f1233b6839f8d5d16979fb31362fbfe49b7b33df", "size": 17817, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "project1/report/content/results.tex", "max_stars_repo_name": "fabiorodp/IN_STK5000_Adaptive_methods_for_data_based_decision_making", "max_stars_repo_head_hexsha": "f8c049ceed6e3123e8676bcd9b29afaba... |
import os
import cv2
import torch
import numpy as np
import mxnet as mx
import torch.nn.functional as F
import torchvision.transforms as T
# torch.manual_seed(1234)
def get_person_id_category(record):
starting_piece_of_record = record.read_idx(0)
header_in_starting_piece_of_record, _ = mx.recordio.unpack(start... | {"hexsha": "a8df952e224f329b2e713537e567b7b55c4fb3bf", "size": 5582, "ext": "py", "lang": "Python", "max_stars_repo_path": "facedataset.py", "max_stars_repo_name": "zhangruihan1/robust-face-recognition", "max_stars_repo_head_hexsha": "a7a03c7d260768fed2dfbe5a3af8dd65d6839ca5", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
import sys
import argparse
from astropy.io import fits
header_dict={'PROPID':'50A', 'PROPOSER':'20A', 'OBJECT':'100A', 'RA':'12A', 'DEC':'12A', 'EPOCH':'E', 'EQUINOX':'E', 'DATE-OBS':'10A', 'UTC-OBS':'12A', 'TIME-OBS':'12A', 'EXPTIME':'D', 'OBSMODE':'20A', 'DETMODE':'20A', 'CCDTYPE':'8A', 'NCCD... | {"hexsha": "0be80480aa8fe4b6fc058c9354bae77f4cfaef35", "size": 6729, "ext": "py", "lang": "Python", "max_stars_repo_path": "plugins/fitsheadercheck.py", "max_stars_repo_name": "Richard-Tarbell/pysalt", "max_stars_repo_head_hexsha": "2815d5533c7e60b7042f2bc3cf46cecdd38fc609", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
#
# Copyright (c) 2017, UT-BATTELLE, LLC
# All rights reserved.
#
# This software is released under the BSD license detailed
# in the LICENSE file in the top level a-prime directory
#
#python script to plot wind stress vectors and magnitude over the oceans using
#CF variables TAUX and TAUY
import matplotlib as mpl
#ch... | {"hexsha": "bea1dae4c9b1c89164e717e58f9e898fd6b79371", "size": 11195, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/plot_climo_vector.py", "max_stars_repo_name": "E3SM-Project/a-prime", "max_stars_repo_head_hexsha": "a8c084ab6f727904a2b38d8a93b9c83e2f978e3f", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
using BaseBenchmarks
using BenchmarkTools
using Compat
using Compat.Test
if VERSION >= v"0.7.0-DEV.2954"
using Distributed
end
addprocs(1)
BaseBenchmarks.loadall!()
@test begin
run(BaseBenchmarks.SUITE, verbose = true, samples = 1,
evals = 2, gctrial = false, gcsample = false);
true
end
| {"hexsha": "714c7dae2da15ce2efe1645df59c71fecfa6c9fc", "size": 312, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "mschauer/BaseBenchmarks.jl", "max_stars_repo_head_hexsha": "08baef1618ebf33f53a905bb131a51c3e53e1eb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module utils_mod
contains
subroutine get_file_name(file_name)
use, intrinsic :: iso_fortran_env, only : error_unit
implicit none
character(len=*), intent(out) :: file_name
character(len=1024) :: argv
if (command_argument_count() < 1) then
write (unit=error_unit,... | {"hexsha": "88f89b871bcd2dbcf9c749875beafd76477a6927", "size": 1530, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "OpenMP/MultiLevel/LinearAlgebra/src/utils_mod.f90", "max_stars_repo_name": "Gjacquenot/training-material", "max_stars_repo_head_hexsha": "16b29962bf5683f97a1072d961dd9f31e7468b8d", "max_stars_re... |
import numpy as np
from ..space import Box, Discrete
class BoxWrapper(Box):
DEFAULT_INF_CEILING = 100
def __init__(self, gym_box, discretization_shape=None, inf_ceiling=None):
self.inf_ceiling = BoxWrapper.DEFAULT_INF_CEILING if inf_ceiling is None else inf_ceiling
self.gym_space = gym_box
... | {"hexsha": "b308068498e909d185b62d25f8365f8568ce930c", "size": 1509, "ext": "py", "lang": "Python", "max_stars_repo_path": "edge/gym_wrappers/space_wrapper.py", "max_stars_repo_name": "Data-Science-in-Mechanical-Engineering/edge", "max_stars_repo_head_hexsha": "586eaba2f0957e75940f4f19fa774603f57eae89", "max_stars_repo... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# Import the TensorFlow and output the verion
get_ipython().system('pip install tensorflow==1.14.0')
import tensorflow as tf
print("\n\nTensorFlow version:", tf.__version__)
# In[2]:
n_inputs = 28 * 28
n_hidden1 = 300
n_hidden2 = 100
n_outputs = 10
# In[3]:
tf.... | {"hexsha": "7b9328e9ce0f8b99dfc03c50f9f3441069c3d3c4", "size": 72074, "ext": "py", "lang": "Python", "max_stars_repo_path": "DNN on MNIST /mnist_classifcation.py", "max_stars_repo_name": "DhruvAwasthi/ModelsCollection", "max_stars_repo_head_hexsha": "80ab3ada2d5cb23cce7a3db23be1ec1dc14d8733", "max_stars_repo_licenses":... |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "880f3dcc4d3c2cb88ae5fec8e7c4b249e47e0482", "size": 31720, "ext": "py", "lang": "Python", "max_stars_repo_path": "auto_yolo/models/air.py", "max_stars_repo_name": "cvoelcker/auto_yolo", "max_stars_repo_head_hexsha": "9137ca48a0413d347b1cb97947079d2cea1d25a2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using Test
using CVChannel
"""
This script verifies that the channels formed from axisymmetric states are multiplicative
for qubits and qutrits.
"""
println("Verifying qubit multiplicativity of axisymmetric channels")
@testset "qubit multiplicativity of axisymmetric channels" begin
y_step = 0.1
x_step = 0.1
... | {"hexsha": "e655ce4258e9bba235be64eee894e960ec433f55", "size": 2169, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "script/verify/multiplicativity-of-qutrit-axisymmetric-channels.jl", "max_stars_repo_name": "ChitambarLab/CVChannel.jl", "max_stars_repo_head_hexsha": "479fa1e70d19b5434137f9017d99830796802d87", "ma... |
\documentclass[11pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{amsthm} %numéroter les questions
\usepackage[english]{babel}
\usepackage{datetime}
\usepackage{xspace} % typographie IN
\usepackage{hyperref}% hyperliens
\usepackage[all]{hypcap} %lien pointe en haut des figures
\usep... | {"hexsha": "261f5fd5907db3430b964c83fc066b0d59b66278", "size": 10382, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "projet/partie 3/elech310_projet_partie3_eng.tex", "max_stars_repo_name": "qgontie/ELECH310", "max_stars_repo_head_hexsha": "4ff2797a618ddde2ed564c6f504956c4334b7b70", "max_stars_repo_licenses": ["M... |
from __future__ import division
import unittest
import numpy as np
from numpy import testing as np_testing
from pax.plugins.peak_processing.BasicProperties import integrate_until_fraction, put_w_in_center_of_field
class TestPeakProperties(unittest.TestCase):
def test_integrate_until_fraction(self):
# Te... | {"hexsha": "0ddae8c995ed97349e47411d71055dd1d6b8ab29", "size": 2427, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_peak_properties.py", "max_stars_repo_name": "jacr20/pax", "max_stars_repo_head_hexsha": "d64d0ae4e4ec3e9bb3e61065ed92e9ea23328940", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
from __future__ import print_function
import sys
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import torch.nn.init as init
import argparse
import numpy as np
from torch.autograd import Variable
import torch.util... | {"hexsha": "8a50152d9066f04d9eb55f4d01b226f5bc9caf96", "size": 17744, "ext": "py", "lang": "Python", "max_stars_repo_path": "refinedet_train_test.py", "max_stars_repo_name": "AndOneDay/PytorchSSD", "max_stars_repo_head_hexsha": "a9f2cde8d149e14cab3feb0084b5be3c1e6c97c6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pandas as pd
import numpy as np
import nltk
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
import re
class KaggleWord2VecUtility(object):
@staticmethod
def reviewto_wordlist(review,remove_stopwords=False):
review_text=BeautifulSoup(review,"lxml").get_text()
review_text=re.sub("[^a-zA-Z]"... | {"hexsha": "9440e93a6b07975faab0e919347dcfb6da56fd54", "size": 840, "ext": "py", "lang": "Python", "max_stars_repo_path": "Input_data/KaggleWord2VecUtility.py", "max_stars_repo_name": "mohsincl/ML-Projects", "max_stars_repo_head_hexsha": "5ef14257f2fdd3ae438557b8ddcdbf316bd1dc2e", "max_stars_repo_licenses": ["MIT"], "m... |
// Copyright (c) 2015, Daniel Pfeifer <daniel@pfeifer-mail.de>
//
// Permission to use, copy, modify, and/or distribute this software for any
// purpose with or without fee is hereby granted, provided that the above
// copyright notice and this permission notice appear in all copies.
//
// THE SOFTWARE IS PROVIDED "AS ... | {"hexsha": "6ad15169a9175292dc8d45aab821b47f0f16bdb5", "size": 8307, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/byom/dynamic_view.hpp", "max_stars_repo_name": "purpleKarrot/BYOM", "max_stars_repo_head_hexsha": "1de5f53a1185b37676d76399bd67ff9e08ad828a", "max_stars_repo_licenses": ["0BSD"], "max_stars_... |
[STATEMENT]
lemma all_larger_zero_in_csset: "\<forall>x. x \<in> consumption_set"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x. x \<in> consumption_set
[PROOF STEP]
using cons_set_props pre_arrow_debreu_consumption_set_def
[PROOF STATE]
proof (prove)
using this:
pre_arrow_debreu_consumption_set consumpt... | {"llama_tokens": 209, "file": "First_Welfare_Theorem_Microeconomics_Private_Ownership_Economy", "length": 2} |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
import os
from madminer.analysis import DataAnalyzer
from madminer.utils.various import math_commands, weighted_quantile, sanitize_array, mdot
from madminer.utils.various import less_logging
from madmi... | {"hexsha": "9c166932b57e72f1f7c40344681851ea7d740f07", "size": 66171, "ext": "py", "lang": "Python", "max_stars_repo_path": "madminer/fisherinformation/information.py", "max_stars_repo_name": "siyuchen95/madminer", "max_stars_repo_head_hexsha": "dfcbd7ee26c47dd294610c195fafce15f74c10eb", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/python
import unittest
import numpy as np
import tensorflow as tf
from kblocks.ops.interp import linear_interp
class TestInterp(tf.test.TestCase):
def test_intercepts3d(self):
grid = np.array([[0, 1, 2], [10, 11, 12], [20, 21, 22]], dtype=np.float32)
grid = np.stack([grid, grid + 10... | {"hexsha": "2e0cd48e75de151c4e65589c2e47758282a7b6c9", "size": 2100, "ext": "py", "lang": "Python", "max_stars_repo_path": "kblocks/ops/interp_test.py", "max_stars_repo_name": "SamuelMarks/kblocks", "max_stars_repo_head_hexsha": "461705c6e89d3ae1c2d3ee90e27c580e683062a9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import iris
import os
import copy
import xarray as xr
import numpy as np
import umdates_utils as um
## FILES -> IRIS
def file_to_cube(filename, filepath, constraints={}, verbose=True):
# Load a cube from a file
cube = iris.load_cube(os.path.join(filepath, filename), iris.AttributeConstraint(**constraints))
... | {"hexsha": "0a7e9b86dd3b8fd208a07fd9d11b83cee1d2062e", "size": 6142, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/crd_utils.py", "max_stars_repo_name": "informatics-lab/pangolin__kevin_scratch", "max_stars_repo_head_hexsha": "98bbaa5205433a16dad44600128aeee029e73122", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/python3
import time
import pyaudio
import audioop
import pigpio
import numpy as np
import math
import threading
from flask import Flask
from util import fft, get_rgb_vol, get_rgb_freq_vol, colors, transform_brightness
# Raspberry PI GPIO pins
R = 17
G = 22
B = 24
# Microphone settings
fs = 32000
sample_f... | {"hexsha": "cd3feeed17867b0cf65a06ac49653a4f3a59e791", "size": 3332, "ext": "py", "lang": "Python", "max_stars_repo_path": "led_server/server.py", "max_stars_repo_name": "jalgroy/raspberrypi-led-server", "max_stars_repo_head_hexsha": "8cf518a284e79d7142da838e08dd42e1f060ff72", "max_stars_repo_licenses": ["MIT"], "max_s... |
section \<open>Proof Helpers\<close>
text\<open>In this section we define and prove lemmas that help to show that all identified critical
conditions hold for concurrent operations. Many of the following parts are derivations from the
definitions and lemmas of Gomes et al.\<close>
theory
"IMAP-proof-helpers"
imp... | {"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/IMAP-CRDT/IMAP-proof-helpers.thy"} |
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