text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
from unittest.mock import patch
from numpy.testing import assert_equal
from glue.core import Data, DataCollection, HubListener, ComponentID
from glue.core import message as msg
from glue.core.component_link import ComponentLink
from glue.core.parse import ParsedCommand, ParsedComponentLink
from ..component_arithmeti... | {"hexsha": "01ddc766349c0e2cecec84f1f26aa56b4f84d62b", "size": 6288, "ext": "py", "lang": "Python", "max_stars_repo_path": "glue/dialogs/component_arithmetic/qt/tests/test_component_arithmetic.py", "max_stars_repo_name": "HPLegion/glue", "max_stars_repo_head_hexsha": "1843787ccb4de852dfe103ff58473da13faccf5f", "max_sta... |
%
% Copyright 2014, NICTA
%
% This software may be distributed and modified according to the terms of
% the BSD 2-Clause license. Note that NO WARRANTY is provided.
% See "LICENSE_BSD2.txt" for details.
%
% @TAG(NICTA_BSD)
%
\section{Message Sequences}
This section will provide a series of example message sequences f... | {"hexsha": "24b4ccdd354cca195f3ebbf71a0b88ac934e95cd", "size": 20013, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "design/protocol.tex", "max_stars_repo_name": "celskeggs/refos", "max_stars_repo_head_hexsha": "f8f7cca23114c970754511c1a1cfba1ec439440f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
import numpy as np
def calc_angle(u: np.ndarray, v: np.ndarray):
return np.arccos(np.dot(u / np.linalg.norm(u), v / np.linalg.norm(v))) * 57.2958
def check_right_angle(angle: float, epsilon: float):
return abs(angle - 90) < epsilon
def side_lengths_are_too_different(u: np.array, v: np.array, t: np.array, ... | {"hexsha": "b8a2f8911a915d0d5ff77c4a14076eade3d0ad1d", "size": 2153, "ext": "py", "lang": "Python", "max_stars_repo_path": "calculation.py", "max_stars_repo_name": "OsamaAlhalabi/Sudoku-Solver-Real-Time", "max_stars_repo_head_hexsha": "746fc610ff34037e549c8d172a228b875346279e", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
.. module:: examples.IGESTest
:platform: Agnostic
:synopsis: Test IGES system
.. requires numpy, os (startfile)
.. Created on Tue Apr 2 18:33:27 2013
.. codeauthor:: Rod Persky <rodney.persky {removethis} AT gmail _DOT_ com>
.. Licensed under the Academic Fre... | {"hexsha": "330e0ae8eac4651294d1209af36b94a3cb2e30dd", "size": 12468, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/examples/demos/IGESTest.py", "max_stars_repo_name": "Rod-Persky/pyIGES", "max_stars_repo_head_hexsha": "07d70e53456c2f30d2867fcab5b237d7b27acfeb", "max_stars_repo_licenses": ["AFL-3.0"], "ma... |
import numpy as np
def select_mating_pool(pop, fitness, num_parents):
# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
parents = np.empty((num_parents, pop.shape[1]))
parents_fitness = np.empty(num_parents)
for parent_num in... | {"hexsha": "c66eb9740c223ccd3b9b8e81549c65ca7f69b44e", "size": 1792, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnn/donas_utils/ga.py", "max_stars_repo_name": "eric8607242/darts", "max_stars_repo_head_hexsha": "34c79a0956039f56a6a87bfb7f4b1ae2af615bea", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
SUBROUTINE MATDUM (IA,IPRC,NPL,NOUT)
C
C THIS ROUTINE IS CALLED ONLY BY MATPRN TO PRINT UP TO 5 MATRICES
C
C IF IPRC = 0, MATRICES ARE PRINTED IN THEIR ORIG. PRECISION FORMAT
C IF IPRC = 1, MATRICES ARE PRINTED IN SINGLE PRECISION E FORMAT
C IF IPRC = 2, MATRICES ARE PRINTED IN DOUBLE PRECIS... | {"hexsha": "75f0747cd0013a5b473377d4b9f33327cd2b29bd", "size": 10948, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/matdum.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count":... |
[STATEMENT]
lemma weight_while_conv_iter:
"weight_spmf (while s) = (SUP n. measure (measure_spmf (iter n s)) {s. \<not> guard s})"
(is "?lhs = ?rhs")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. weight_spmf (local.while s) = (\<Squnion>n. Sigma_Algebra.measure (measure_spmf (iter n s)) {s. \<not> guard s})
[PR... | {"llama_tokens": 15314, "file": "Probabilistic_While_While_SPMF", "length": 93} |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 20 15:12:49 2016
@author: uzivatel
"""
import numpy as np
from copy import deepcopy
from ...General.UnitsManager import PositionUnitsManaged,EnergyUnitsManaged,energy_units
from ...General.types import UnitsManagedArray,UnitsManaged
from ..positioningTools i... | {"hexsha": "9383d05959f6b5607c17fc55ce711d3b23394afb", "size": 13730, "ext": "py", "lang": "Python", "max_stars_repo_path": "QChemTool/QuantumChem/Classes/general.py", "max_stars_repo_name": "slamavl/QChemTool", "max_stars_repo_head_hexsha": "b6b17adf6cfa8ac1db47acba93aab1ee49c1be47", "max_stars_repo_licenses": ["MIT"]... |
# This file is part of IntegerSequences.
# Copyright Peter Luschny. License is MIT.
(@__DIR__) ∉ LOAD_PATH && push!(LOAD_PATH, (@__DIR__))
module SeqTests
using Test, OEISUtils, SeqUtils
export SeqTest, is_oeis_installed
const ShowTest = false
# Directory of oeis local data.
srcdir = realpath(joinpath(dirname(@__F... | {"hexsha": "1bb0868f3c936b193e3015464b2e2def100193b8", "size": 2799, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SeqTests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/IntegerSequences.jl-b4b868b0-69a7-11e9-2db0-173b4e8e576c", "max_stars_repo_head_hexsha": "5718a938e44b1f14c7115f2940f96fa344b72ce8", ... |
import argparse
import math
import os
import pickle
import random
import sys
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch import nn
from torch.optim import lr_scheduler
from torch.utils import data
import torchvision.transforms as transforms
import transforms as extended_transforms
... | {"hexsha": "17552ea18bdede94f558d6d873969b0018347863", "size": 28041, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_multi.py", "max_stars_repo_name": "wenliangdai/sunets-reproduce", "max_stars_repo_head_hexsha": "d92efa80e8314aea153d498cce3c9c6e30c252bd", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 25 14:34:55 2020
@author: micha
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from MD_Analysis import Angle_Calc
from Transformations import Transformations
#Choose desired pdb
pdb="pdbs/WT_295K_200ns_50ps_0_run.pdb"
#Extract ... | {"hexsha": "fef4d978c5638145ca6adee6cea882c228626a8d", "size": 2568, "ext": "py", "lang": "Python", "max_stars_repo_path": "DMD/WaveletDMD.py", "max_stars_repo_name": "yusovm/GEMSEC", "max_stars_repo_head_hexsha": "d9abd43d27e05607e7b1ea8c99fcc736abd204fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
using Test
# Test that all models run
outdir = joinpath(@__DIR__, "../output")
samplesize = 10
rm(outdir, recursive=true) # NB !!cleans out the 'output' folder!!
mkdir(outdir)
include("../analysis/allscc/runmodel.jl")
@test sum([length(files) for (root, dirs, files) in walkdir(outdir)]) == 60
rm(outdir, recursive=tru... | {"hexsha": "f290001cf8eff9e6861e82b0417bcaf8965dad86", "size": 1390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_extensions.jl", "max_stars_repo_name": "lrennels/MimiPAGE2020.jl", "max_stars_repo_head_hexsha": "3537b1b136279f4216d8e89355df7d4b3da0a316", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from runner.action.set.variable import Variable
class Discrete(Variable):
"""Discrete variable
low <= variable < high with step (high - low)/num or 1 (default)
Args:
low (float): low boundary
high (float): high boundary
num (int): number of... | {"hexsha": "f47eb8b277b133db1005eccda6e1a28cc76c5720", "size": 911, "ext": "py", "lang": "Python", "max_stars_repo_path": "runner/action/set/discrete.py", "max_stars_repo_name": "PC-Trip/runner", "max_stars_repo_head_hexsha": "e13291e25a2bc2962523a1de2d13725609497cb5", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import unittest
import torch
from torch.autograd import Variable
from global_variables.global_variables import use_cuda
... | {"hexsha": "b7ff9bd56d56c9784eda669381a0c2631de358aa", "size": 5634, "ext": "py", "lang": "Python", "max_stars_repo_path": "top_down_bottom_up/unittests.py", "max_stars_repo_name": "lirhea/pythia", "max_stars_repo_head_hexsha": "6bcf9f64f9f42a45e17dd6681fea6ff16819b817", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# matplotlib backtest for missing $DISPLAY
import matplotlib
matplotlib.use('Agg')
# scientific computing library
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
# visualization tools
import matplotlib.pyplot as plt
import se... | {"hexsha": "84952f11c7e06381e065131c5b65f8941fd98359", "size": 6053, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/svm_ovr.py", "max_stars_repo_name": "filangel/Eigenfaces", "max_stars_repo_head_hexsha": "55ddb705611ee351cc856d5a927a4dc82acaff03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from h5parm.utils import make_example_datapack, make_soltab
import numpy as np
import pytest
def test_datapack():
datapack = make_example_datapack(4,5,6,["X"],clobber=True)
phase,axes = datapack.phase
datapack.phase = phase+1.
phasep1, axes = datapack.phase
assert np.all(np.isclose(phasep1, phase+1... | {"hexsha": "768b3bda211e2291f3d26286d1feeba5f9065a0a", "size": 1064, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_datapack.py", "max_stars_repo_name": "Joshuaalbert/h5parm", "max_stars_repo_head_hexsha": "b1f2cc5fc661793494aca9c50e1374aa9809943a", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
def pbubble_vazquez(Rsb, sg2, api, temp2):
import numpy as np
# c1, c2, c3 coefficient from Vazquez-Beggs
if api <=30:
c1 = 0.0362
c2 = 1.0937
c3 = 25.7240
if api > 30:
c1 = 0.0178
c2 = 1.187
c3 = 23.9310
P_bubble_vaz = (Rsb / (c1 * sg2 * np.exp((c3 * api)/(temp2 + 459.67)... | {"hexsha": "da82e64a9357a209c68a104c781489e083256f59", "size": 383, "ext": "py", "lang": "Python", "max_stars_repo_path": "Unit 2 Review of Rock and Fluid Properties/functions/pbubble.py", "max_stars_repo_name": "datasolver/reservoir-engineering", "max_stars_repo_head_hexsha": "82393cff19e700fe3c7ac5c1a32ba877a4e40750"... |
#include <iostream>
#include <memory>
#include <utility>
#include <boost/asio.hpp>
#include <string>
#include "my_async.hpp"
//#define USE_TCP_SSL
#ifdef USE_TCP_SSL
#include "server_certificate.hpp"
#endif
#define ADDRESS "0.0.0.0"
#define PORT 8089
template<bool UseSSL>
class Simple_Echo : public My_Async::TC... | {"hexsha": "74cde06e3a26dae815459215b35650cc10fd0e55", "size": 1827, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/tcp_server_simple_echo_main.cpp", "max_stars_repo_name": "rnascunha/my_async", "max_stars_repo_head_hexsha": "5fbe3a46e87a2d74fc07d16252a3b3cf488b4684", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
from metod_alg import objective_functions as mt_obj
from metod_alg import metod_analysis as mt_ays
def test_1():
"""Numerical example for mt_ays.quantities()."""
g = mt_obj.several_quad_gradient
matrix_test = np.zeros((2, 2, 2))
store_x0 = np.zeros((2, 2))
x = np.array([0.1, 0.... | {"hexsha": "e5bc5c90fa3b519c5b68ab4a015098b4c2c2d14b", "size": 1142, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_quantities.py", "max_stars_repo_name": "Megscammell/METOD-Algorithm", "max_stars_repo_head_hexsha": "7518145ec100599bddc880f5f52d28f9a3959108", "max_stars_repo_licenses": ["MIT"], "max_... |
//============================================================================
// Name : Request
// Author : Avi
// Revision : $Revision: #21 $
//
// Copyright 2009- ECMWF.
// This software is licensed under the terms of the Apache Licence version 2.0
// which can be obtained at http://www.apache.org/l... | {"hexsha": "a3933aa1dcb6f2447a5c2160031896b25eb5ecf6", "size": 13875, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ACore/test/TestLog.cpp", "max_stars_repo_name": "mpartio/ecflow", "max_stars_repo_head_hexsha": "ea4b89399d1e7b897ff48c59b1e885e6d53cc8d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import pandas as pd
import numpy as np
# Examples to skip due to e.g them missing, loading issues
LOCATIONS_TO_SKIP = [537]
CANNOT_OPEN = ['99136aa6-21bc-11ea-a13a-137349068a90.jpg',
'87022118-21bc-11ea-a13a-137349068a90.jpg',
'8f17b296-21bc-11ea-a13a-137349068a90.jpg',
'... | {"hexsha": "d181a7fd5b926c9be3f32810d5571f0aa4fc527e", "size": 7061, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset_preprocessing/iwildcam/create_split.py", "max_stars_repo_name": "amirfeder/wilds_calibration", "max_stars_repo_head_hexsha": "ace5f9d02208d9e1090c9a7d8ab07add4d9c7c08", "max_stars_repo_lic... |
% \section{201703-1}
% \input{problem/10/201703-1-p.tex} | {"hexsha": "fe708040eb75b07db17b0f1a82053233ecefbd88", "size": 57, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "problem/10/201703-1.tex", "max_stars_repo_name": "lxlonlyn/CSP-Project", "max_stars_repo_head_hexsha": "9d432ec2255b170f2bb1e0879e42c93f80a1b21c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import tkinter as tk
import numpy as np
from display_an import ROIdisplay, MainDisplay
class ROIdraw():
def init_var(self):
self._roidrawline = tk.IntVar()
self._roidrawcircle = tk.IntVar()
self._roidrawrect = tk.IntVar()
self._roidrawline.set(0)
self._roidrawcircle.set(0)
self._roidrawrect.set(0)
... | {"hexsha": "3fd78a486435d8dc5cfb09a92d66d5061ac65eac", "size": 8814, "ext": "py", "lang": "Python", "max_stars_repo_path": "fias/menuroi_an.py", "max_stars_repo_name": "cristina-mt/fias", "max_stars_repo_head_hexsha": "ce264754997e14a403a9a1d3c5c6c0af646d4463", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
using MUMPS
if VERSION >= v"0.5.0-dev+7720"
using Base.Test
else
using BaseTestNext
const Test = BaseTestNext
end
include("getDivGrad.jl");
A = getDivGrad(32,32,16);
n = size(A,1);
# REAL: test for single rhs
println("Test for real SPD matrix: one sparse random rhs");
rhs = sprandn(n,1,0.05);
x = solveMU... | {"hexsha": "5896557c47c27caa344df85663c4652b9ac7e570", "size": 1679, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tests/testSparseRHS.jl", "max_stars_repo_name": "JuliaPackageMirrors/MUMPS.jl", "max_stars_repo_head_hexsha": "af520c77f6c2e4f388cebe48478511b0e9d33ae5", "max_stars_repo_licenses": ["MIT"], "max_st... |
import gym
import numpy as np
class NormalizedWrapper(gym.Wrapper):
"""
:param env: (gym.Env) Gym environment that will be wrapped
"""
def __init__(self, env):
# Call the parent constructor, so we can access self.env later
super(NormalizedWrapper, self).__init__(env)
a... | {"hexsha": "d34e8847b01162e4fde95d9b0cc65bbc7ff0637f", "size": 2923, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/deepvss/rlAdventure2DDPG/Utils/Normalization.py", "max_stars_repo_name": "tsb4/dayTradingEnv", "max_stars_repo_head_hexsha": "16d1970a41c8933970152f1f41e504340d48cb08", "max_stars_repo_lice... |
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
class preprocess_datasets():
def __init__(self, dataset_name):
self.dataset_name = dataset_name
def preprocess_dataset(self):
'''
Preprocess and
Attibutes
... | {"hexsha": "fcf8dd4a777bc31c63cebb19608c9b1171a72232", "size": 7264, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python scripts/Preprocess_data.py", "max_stars_repo_name": "LisaKouts/Fuzzy-rough-Uncertainty-Bias", "max_stars_repo_head_hexsha": "068b7a0bb7e9d4f1a1cfaccf923364bcc358bbde", "max_stars_repo_licen... |
import io
import codecs
import textwrap
import numpy as np
from PIL import Image
img = np.array([32, 32, 32], dtype=np.uint8).reshape(1,1,3)
img = np.broadcast_to(img, (128, 128, 3))
print(img.shape)
img = Image.fromarray(img)
f = io.BytesIO()
img.save(f, format='png')
img = f.getvalue()
img = codecs.encode(img, 'bas... | {"hexsha": "d205b148dbfd97da7963b4b256b0114241f31823", "size": 404, "ext": "py", "lang": "Python", "max_stars_repo_path": "gen_img.py", "max_stars_repo_name": "sasdf/player", "max_stars_repo_head_hexsha": "35bdbc51d13a65fe3f0bc565a683d00dcb3c140d", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max... |
import os
import pathlib
import numpy as np
import keras_tuner as kt
from sklearn.preprocessing import OneHotEncoder
from sklearn.svm import SVC, LinearSVC
from sklearn import metrics
from sklearn import model_selection
from sklearn import pipeline
from sklearn.metrics import accuracy_score
# Load the data
PATH = pat... | {"hexsha": "a25343c23c8f56ebe06e288dc6d7b42e062c2697", "size": 2607, "ext": "py", "lang": "Python", "max_stars_repo_path": "8_3_SVM.py", "max_stars_repo_name": "webclinic017/time-series-pipeline", "max_stars_repo_head_hexsha": "5ac418b91e395a48cba397f95d25d221adfff9bd", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
//
// Boost.Process
// Regression tests for the status class.
//
// Copyright (c) 2006 Julio M. Merino Vidal.
//
// 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 <boost/filesystem/operations.hpp>
#in... | {"hexsha": "b771728af137d043897ea3acf14c86d2ae60232f", "size": 833, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "external/boost/process/libs/process/test/status_test.cpp", "max_stars_repo_name": "saga-project/saga-cpp", "max_stars_repo_head_hexsha": "7376c0de0529e7d7b80cf08b94ec484c2e56d38e", "max_stars_repo_li... |
import numpy as np
from model_service.tfserving_model_service import TfServingBaseService
import pandas as pd
import tensorflow as tf
import tensorflow.keras.layers as layers
import os
class TradFC(tf.keras.Model):
def __init__(self):
super(TradFC, self).__init__()
self.bn0 = layers.BatchNormali... | {"hexsha": "952bc3cf7bd56ff3a0bdb5ba8134fddb80c8f8ee", "size": 6714, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/customize_service.py", "max_stars_repo_name": "Lmy0217/WirelessChannelAI", "max_stars_repo_head_hexsha": "b01f29c6240897e9e495b54994fd94a8a7b941ac", "max_stars_repo_licenses": ["MIT"], "max_... |
** Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
** See https://llvm.org/LICENSE.txt for license information.
** SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
* Test integer DO loops (with CONTINUE as last statement).
program p
parameter (N = 26)
integer r... | {"hexsha": "69904b14187f57ef754fffad9457c374047a6f95", "size": 2787, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "test/f90_correct/src/hb00.f", "max_stars_repo_name": "abrahamtovarmob/flang", "max_stars_repo_head_hexsha": "bcd84b29df046b6d6574f0bfa34ea5059092615a", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from PIL import Image
import math
from numpy import ndarray
import os
class image_splitter_merger():
def __init__(self,img_split_size, buffer= 50):
self.out_w = img_split_size[0]
self.out_h = img_split_size[1]
self.buf = int(buffer)
self.buf_hf = math.ceil(buffer/2.0)
self.... | {"hexsha": "7338f6da9d5328377bb8586a3dc3e1d08870a7c4", "size": 3031, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/utils/image_splitter_merger.py", "max_stars_repo_name": "Grusinator/BirdClassification", "max_stars_repo_head_hexsha": "c78ca3dbf70c2509c79ca4641102a2d725084d2a", "max_stars_repo_licenses": ["... |
import pq_induction_principles
import minimal_sub_pq_gen_group
universe u
section pq_like_eq_equalizer
variables {G : Type u} [group G] --[inhabited Q]
lemma prod_in_free_gen_list (gen : set G) (x : list (free_gen_group_sub_pq gen)) (hx : of ((x.map of).prod) = (x.map (of ∘ of)).prod) : (x.map coe).prod ∈ (free_g... | {"author": "torstein-vik", "repo": "power-quandle-lean", "sha": "452437602c4be2e6c5ad5f5224b068baabfdf9e1", "save_path": "github-repos/lean/torstein-vik-power-quandle-lean", "path": "github-repos/lean/torstein-vik-power-quandle-lean/power-quandle-lean-452437602c4be2e6c5ad5f5224b068baabfdf9e1/src/pq_like_eq_equalizer.le... |
\ util.f - memory mapping utility functions
\ ------------------------------------------------------------------------
.( util.f )
\ ------------------------------------------------------------------------
\ getters and setters. only defined the most commonly used ones
: h.mapa@ ( heap --- mem-map ) h.mapa @ ;
... | {"hexsha": "8ad16151b098b2453d25b3371e1df4433e4dc32e", "size": 8201, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/ext/memman/util.f", "max_stars_repo_name": "mark4th/t4", "max_stars_repo_head_hexsha": "31964d4cbee56284fa96c3e52089a3a9944d7975", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "m... |
import os
from constants import *
from the_logger import nlp_logger
from padder import pad
from book import Book
import pickle
import numpy as np
# Load the books
pym = Book("Arthur Gordon Pym")
tom = Book("Tom Sawyer")
eureka = Book("Eureka")
huck = Book("Huckleberry Finn")
pym.from_file(PYM_FILE)
tom.from_file(TO... | {"hexsha": "1e87910226616d3fa7c8ac22c9ed3299c7eed97d", "size": 2681, "ext": "py", "lang": "Python", "max_stars_repo_path": "validation_multi.py", "max_stars_repo_name": "angelnew/biblioeater", "max_stars_repo_head_hexsha": "b4da9235f845c9256f1b8d35e8a93df8b07cfb5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
u"""Implementation of HyperLogLog
This implements the HyperLogLog algorithm for cardinality estimation, found
in
Philippe Flajolet, Éric Fusy, Olivier Gandouet and Frédéric Meunier.
"HyperLogLog: the analysis of a near-optimal cardinality estimation
algorithm". 2007 Confere... | {"hexsha": "0ec1de9d916a6a6b619ded5684d3418ac181e82c", "size": 2514, "ext": "py", "lang": "Python", "max_stars_repo_path": "dask/dataframe/hyperloglog.py", "max_stars_repo_name": "ZEFR-INC/dask", "max_stars_repo_head_hexsha": "18a026836b2ad6151a25c4177a98d5b7111a72d5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
[STATEMENT]
lemma related_exp_fun:
"related_fun cs n e \<longleftrightarrow> related_exp (Sabs cs) (Ast.Fun (as_string n) e) \<and> n |\<notin>| ids (Sabs cs) \<and> n |\<notin>| all_consts"
(is "?lhs \<longleftrightarrow> ?rhs")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. related_fun cs n e = (related_exp (S... | {"llama_tokens": 1401, "file": "CakeML_Codegen_Backend_CakeML_Backend", "length": 11} |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 23 22:36:14 2019
INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO PÁRA - IFPA ANANINDEUA
@author:
Prof. Dr. Denis C. L. Costa
Discentes:
Heictor Alves de Oliveira Costa
Lucas Pompeu Neves
Grupo... | {"hexsha": "927afeb6e35507e9fa5a9839d0923ba96ffff72b", "size": 1989, "ext": "py", "lang": "Python", "max_stars_repo_path": "derivadas_parciais.py", "max_stars_repo_name": "lucaspompeun/metodos-matematicos-aplicados-nas-engenharias-via-sistemas-computacionais", "max_stars_repo_head_hexsha": "008d397f76a935af1aba530cc013... |
! Listado de commons utilizados, que podrían pasar a un módulo:
! COMMON /CRIT/TC(nco),PC(nco),DCeos(nco),omg(nco)
! COMMON /COMPONENTS/ ac(nco),b(nco),delta1(nco),rk(nco),Kij_or_K0,NTDEP
! COMMON /MODEL/ NMODEL
! COMMON /rule/ncomb
! COMMON /bcross/bij(nco,nco)
! COMMON /Tdep/ Kinf,Tstar (escalares por ahora... | {"hexsha": "257358b1a140d52aeeecd7d991c0e282ba0377e8", "size": 28923, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "PTFlashSur.f90", "max_stars_repo_name": "pysg/pyther", "max_stars_repo_head_hexsha": "6a47fc41533cc50bc64134e42ddd3ed8d54d75c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_s... |
#!/usr/bin/env python2
import numpy as np
import pandas as pd
from mdtraj import Trajectory
from mdtraj import Topology
from mdtraj import element
from mdtraj.geometry import distance
from top_manip import typed_elementwise_rep
mapping_options = {}
def mode_rows(a):
"""Efficiently returns the most common row of a... | {"hexsha": "bec4a9f7a391eb3e8a90f7c3c5f2500e26bff8a3", "size": 27843, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cgmap.py", "max_stars_repo_name": "fieryd/cgmap", "max_stars_repo_head_hexsha": "8b09cf1392d22be4f735d3259ee9d624f3e45210", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
from .network import *
from .devices import *
from cvxpy import Parameter
| {"hexsha": "e5ea9b190ef2de992313719b6f846b5147a39989", "size": 75, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvxpower/__init__.py", "max_stars_repo_name": "allenlawrence94/cvxpower", "max_stars_repo_head_hexsha": "2f76bb117c918d66c62f9a6a4ac84bd4ff582486", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# -*- coding: utf-8 -*-
import numpy as np
import math
__all__ = ["haversine",
"manhattan",
"euclidean",
"minkowski",
"cosine_similarity",
"hamming",
"mahalanobis"]
def _validate_vector(vector, dtype=None):
vector = np.asarray(vector, dtype=dtype).squeeze()
vec... | {"hexsha": "1f821067897c0a393fbf8cdc93c89f5718a021d8", "size": 3262, "ext": "py", "lang": "Python", "max_stars_repo_path": "interspace/interspace.py", "max_stars_repo_name": "rehanguha/interspace", "max_stars_repo_head_hexsha": "cdd81728814c462364aea5c4b2f0ff4180b167c8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import math
from nodes import Node_group
from PyQt5 import QtGui
from core.node_edge import *
from core import clipboard
from core.node import *
import logging
import numpy as np
from core.minimap import MiniMap
from core.historyStack import SceneHistory
MODE_NOOP = 1
MODE_EDGE_DRAG = 2
MODE_EDGE_CUT = 3
class Sce... | {"hexsha": "7618b08ebdc7f215982c4ef4b632e4882fd8a9c5", "size": 16791, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/scene.py", "max_stars_repo_name": "rkavenaghi/MECOM", "max_stars_repo_head_hexsha": "294b280529f3498a145adb191cd9eb0a82dbdf0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# -*- coding: utf-8 -*-
"""
The :mod:`parsimony.utils.utils` module includes common functions and
constants.
Please add anything useful or that you need throughout the whole package to
this module.
Created on Thu Feb 8 09:22:00 2013
Copyright (c) 2013-2014, CEA/DSV/I2BM/Neurospin. All rights reserved.
@author: Tom... | {"hexsha": "28c7ea9516996e33a9873d317970e61da4a0e8b1", "size": 19035, "ext": "py", "lang": "Python", "max_stars_repo_path": "parsimony/utils/utils.py", "max_stars_repo_name": "nguigs/pylearn-parsimony", "max_stars_repo_head_hexsha": "f712d2828823d6d55a2470ce060bcaeda2d0589a", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
'''
This module defines a TimeSeries object that will be used in all other components. It provides all the preprocessing
methods for time series data as specified in the Project 1 handout.
Authors: Yifeng Cui and Jacob Rammer
Group name: The Classy Coders
Most recent modification: 2/9/21
'''
import pandas as pd
import... | {"hexsha": "62d91761ca4bcf3d32c9501ac860f664f56eaa93", "size": 12344, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "bgunnar5/TimeSeries", "max_stars_repo_head_hexsha": "25cdf9f3d5f7cb66b0064f7a29de35df7c668c6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import math
NSAMPLE = 1000
x_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T
r_data = np.float32(np.random.normal(size=(NSAMPLE, 1)))
y_data = np.float32(np.sin(0.75*x_data)*7.0+x_data*0.5+r_data*1.0)
plt.figure(figsize=(8, 8... | {"hexsha": "84cdc39c692e478232144d29f57e20fcbcc09192", "size": 7591, "ext": "py", "lang": "Python", "max_stars_repo_path": "ncfm/mdn.py", "max_stars_repo_name": "kirk86/kaggle", "max_stars_repo_head_hexsha": "8178ed790bc1f8fcd2cd7a01560e5f25f01c07cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
[STATEMENT]
lemma has_fields_decl_above:
assumes "P \<turnstile> C has_fields FDTs" "((F,D),Tm) \<in> set FDTs"
shows "P \<turnstile> C \<preceq>\<^sup>* D"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P \<turnstile> C \<preceq>\<^sup>* D
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
P \<tur... | {"llama_tokens": 233, "file": "JinjaThreads_Common_TypeRel", "length": 2} |
import numpy as np
import band
import re
import math
CASTEP_MATCH = re.compile(r"^[ ]*(?P<index>\d+)[ ]+(?P<x>\S+)[ ]+(?P<y>\S+)[ ]+(?P<z>\S+)[ ]+(?P<fl_x>\S+)(?P<evals>.+)$",re.MULTILINE)
FCC_SPECIAL_POINT_DICT = {
'G': [0,0,0],
'X': [0.0,0.5,0.5],
'W': [0.25,0.75,0.5],
'K': [0.375,0.75,0.375],
'... | {"hexsha": "60b733dbeb34b2a6558f7da06c5ac6ca648d6ce6", "size": 1696, "ext": "py", "lang": "Python", "max_stars_repo_path": "band/castep_ext.py", "max_stars_repo_name": "Walter-Feng/myModule", "max_stars_repo_head_hexsha": "f8cf065d52153ef3d386d10be1771e80cf5af4e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import matplotlib.pyplot as plt
import lib
from pathlib import Path
import pandas
def main():
# ShockleyResistanceDiodeModel.derive_equations()
# exit()
path = 'example_data.csv'
if not Path('example_data.csv').exists():
with open(path, 'w') as file:
file.write('Vd [V];Id [mA]\n')
prin... | {"hexsha": "45892c15b9b2207f0e510c732fa4673994ac7935", "size": 1210, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "dvorst/fit_diode_to_data", "max_stars_repo_head_hexsha": "fb95437a323e1e6aa4399b3b072ff84c092f02da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import os
import random
import logging
import numpy as np
from tensorboardX import SummaryWriter
import torch
import torch.optim as optim
import torch.nn.functional as F
import argparse
from libbots import data, model, utils
from model_test import run_test_mutual
device = torch.device("cuda" if torch.cuda.is_availa... | {"hexsha": "577cd426de86d41249ba0170bc2024429f6d8ac1", "size": 8890, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_rl_MMI.py", "max_stars_repo_name": "eyalbd2/LanguageModel-UsingRL", "max_stars_repo_head_hexsha": "b3467430be102ba23b1ab7e7a9515496c140f524", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
"""Some functions to plot data, fit models etc."""
import pdb
import os
import matplotlib.pyplot as plt
import matplotlib.dates
import numpy as np
from osgeo import gdal
from pheno_utils import *
def quadratic_model ( p, agdd ):
"""A quadratic phenology model. Takes in a lenght 3 vector wit... | {"hexsha": "2a7639318bc861f038b92cbebc0a271dbb5f14bf", "size": 11384, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/python/phenology.py", "max_stars_repo_name": "jgomezdans/GEOG0133", "max_stars_repo_head_hexsha": "a3aaab62d7c5c78ee12e57b46ee783ba664de567", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 9 11:39:06 2022
@author: SimenLab
"""
import pathlib
import numpy as np
import pandas as pd
import utilities
import qualitative
# data_path = r"./Data/data_save/1_8dfp_ntc3_512_1.npz"
def load_data(path):
load_path = pathlib.Path(path)
data = np.load(load_path... | {"hexsha": "79b7e15960d057e9962d9cbb8ba43ef6bbdd237a", "size": 6337, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantitative_beta.py", "max_stars_repo_name": "BadenLab/2Panalysis", "max_stars_repo_head_hexsha": "8b23b3eaa1700f4fb5729eb7e89bfd55e7b4811a", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
subroutine insertKnot(u, r, t, k, coef, nctl, ndim, t_new, coef_new, ileft)
!***DESCRIPTION
!
! Written by Gaetan Kenway
!
! Abstract insertKnot inserts a knot u into the curve, r times
! Adapted from "The NURBS Book" Algorithm 5.1
! Description of Arguments
! Input
! u ... | {"hexsha": "662fee55a4eaa9a1cd2db88146f948cba93d2750", "size": 2911, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/insertKnot.f90", "max_stars_repo_name": "eirikurj/pyspline", "max_stars_repo_head_hexsha": "91dd6d432deac3b1647508bf4fca985e704da77c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 8 17:56:19 2018
@author: kristianeschenburg
"""
import numpy as np
def mutual_information(counts):
"""
Compute the mutual information of a matrix.
Parameters:
- - - - -
counts: numpy array of size N x M, where ce... | {"hexsha": "4471acf4c19794c6cc35454dae5148db4df24409", "size": 1976, "ext": "py", "lang": "Python", "max_stars_repo_path": "statsni/information.py", "max_stars_repo_name": "kristianeschenburg/statsni", "max_stars_repo_head_hexsha": "184ca9fb8058c359af1ac9524af22a93c22b7f75", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
(* Author: John Harrison
Author: Robert Himmelmann, TU Muenchen (Translation from HOL light)
Huge cleanup by LCP
*)
section \<open>Henstock-Kurzweil Gauge Integration in Many Dimensions\<close>
theory Henstock_Kurzweil_Integration
imports
Lebesgue_Measure Tagged_Division
begin
lemma no... | {"author": "m-fleury", "repo": "isabelle-emacs", "sha": "756c662195e138a1941d22d4dd7ff759cbf6b6b9", "save_path": "github-repos/isabelle/m-fleury-isabelle-emacs", "path": "github-repos/isabelle/m-fleury-isabelle-emacs/isabelle-emacs-756c662195e138a1941d22d4dd7ff759cbf6b6b9/src/HOL/Analysis/Henstock_Kurzweil_Integration.... |
from __future__ import division
from builtins import str
import numpy as np
import pandas as pd
from ..base.transform_primitive_base import (
TransformPrimitive,
make_trans_primitive
)
from featuretools.variable_types import (
Boolean,
Datetime,
DatetimeTimeIndex,
Id,
LatLong,
Numeri... | {"hexsha": "70d046397053d6facc21106282ffd5e7d5a5d480", "size": 10011, "ext": "py", "lang": "Python", "max_stars_repo_path": "featuretools/primitives/standard/transform_primitive.py", "max_stars_repo_name": "alexandermiller702/featuretools", "max_stars_repo_head_hexsha": "70a6f34e80c679872a4f11076cd959b14b1319c6", "max_... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import random
import habitat
import numba
import numpy as np
import torch
from habitat import get_config
fro... | {"hexsha": "f30992a62104c43313d0b534d62006d4f1f39835", "size": 2427, "ext": "py", "lang": "Python", "max_stars_repo_path": "challenge_2020/challenge2021_corruptions.py", "max_stars_repo_name": "m43/PointNav-VO", "max_stars_repo_head_hexsha": "8e1a950dc929effdc3e1bff683547fece7ad2a23", "max_stars_repo_licenses": ["Apach... |
"""
============
Thresholding
============
Thresholding is used to create a binary image from a grayscale image [1]_.
.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
.. seealso::
A more comprehensive presentation on
:ref:`sphx_glr_auto_examples_applications_plot_thresholding.py`
"""... | {"hexsha": "6853b1dba60f257e6e4b49cd569c3f7468224b35", "size": 4620, "ext": "py", "lang": "Python", "max_stars_repo_path": "BanDiTS/single_band_thresholding.py", "max_stars_repo_name": "jziemer1996/BanDiTS", "max_stars_repo_head_hexsha": "f6a2b2fda4363f83de18059366381c4fbe7f1e5b", "max_stars_repo_licenses": ["WTFPL"], ... |
"""
Class for the analysis of and to provide statistics about a single message.
**Intra-Message Analysis**
:author: Stephan Kleber
"""
import numpy
import pandas
from bitarray import bitarray
from typing import Dict, List, Tuple, Union, Type
from scipy.ndimage.filters import gaussian_filter1d
# The analyzer impleme... | {"hexsha": "1083a405edf07d5a34c5962683da607b50f7f75c", "size": 47780, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/inference/analyzers.py", "max_stars_repo_name": "tatref/nemesys", "max_stars_repo_head_hexsha": "5043986dd6084ba2d9b7719aa1911462eed3f1b2", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using LinearAlgebra
using TensorOperations
using Test
using puMPS
using puMPS.MPS
include("mps.jl") | {"hexsha": "2ddee0d262a2bf712bd12bb6a155db499764beca", "size": 101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "FuTen/puMPS", "max_stars_repo_head_hexsha": "103cb1a03a863c40240f0c6a192ec2cd7cc954e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_star... |
# python3
# Copyright 2018 DeepMind Technologies Limited. 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 re... | {"hexsha": "1cb1b8aae27553ca8809307592a39600be57f796", "size": 11757, "ext": "py", "lang": "Python", "max_stars_repo_path": "acme/agents/jax/sac/learning.py", "max_stars_repo_name": "Tsaousis/acme", "max_stars_repo_head_hexsha": "14278693bcc5fef0839ac60792d452d3d80acfd7", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma llength_lmirror_aux: "llength (lmirror_aux acc xs) = 2 * llength xs + llength acc"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. llength (lmirror_aux acc xs) = 2 * llength xs + llength acc
[PROOF STEP]
apply(coinduction arbitrary: acc xs rule: enat_coinduct)
[PROOF STATE]
proof (prove)
goal (1 sub... | {"llama_tokens": 899, "file": "Coinductive_Examples_LMirror", "length": 5} |
# ==============================================================================
# Copyright 2020-present NAVER Corp.
#
# 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.o... | {"hexsha": "9fda4d24e724d77e49553bc556938a3cbc4879e1", "size": 5555, "ext": "py", "lang": "Python", "max_stars_repo_path": "kd/extract_embeddings.py", "max_stars_repo_name": "Mithilesh1609/assembled-cnn", "max_stars_repo_head_hexsha": "e0227eecbf3a7fd4fe99a954068c85ffbed94c53", "max_stars_repo_licenses": ["Apache-2.0"]... |
[STATEMENT]
lemma comp_by_index_inj: "comp_by_index x1 y1 = comp_by_index x2 y2 \<Longrightarrow> x1=x2 \<and> y1=y2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. comp_by_index x1 y1 = comp_by_index x2 y2 \<Longrightarrow> x1 = x2 \<and> y1 = y2
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1... | {"llama_tokens": 866, "file": "Recursion-Theory-I_PRecFun2", "length": 9} |
"""Unit tests for the jicbioimage.segment package."""
import unittest
import numpy as np
class GenericUnitTests(unittest.TestCase):
def test_version_is_string(self):
import jicbioimage.segment
self.assertTrue(isinstance(jicbioimage.segment.__version__, str))
class ConnectedComponentsTests(uni... | {"hexsha": "d6d0888903a88a5ac18f698df25d6ddeb77649eb", "size": 6335, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/segment_unit_tests.py", "max_stars_repo_name": "JIC-CSB/jicbioimage.segment", "max_stars_repo_head_hexsha": "289e5ab834913326a097e57bea458ea0737efb0c", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
class Layer(object):
def __init__(self, name, trainable=False):
self.name = name
self.trainable = trainable
self._saved_tensor = None
def forward(self, input):
pass
def backward(self, grad_output):
pass
def update(self, config):
pas... | {"hexsha": "433eff8c301adbd4e707158a8461049f51a3a955", "size": 2629, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW1/codes/layers.py", "max_stars_repo_name": "liuyuezhangadam/artificial-neural-network", "max_stars_repo_head_hexsha": "36fcd9625633eeb8eb0a924274dd5a8564ffe6e0", "max_stars_repo_licenses": ["MIT... |
#include <fstream>
#include <assert.h>
#include <thread>
#include <memory>
#include <boost/algorithm/string.hpp>
#include <boost/lexical_cast.hpp>
#include <boost/optional.hpp>
#include <stdlib.h>
#include "TdSpiUser.h"
//global variables
TThostFtdcBrokerIDType brokerID ;
TThostFtdcInvestorIDType investorID;
TTho... | {"hexsha": "1f324adab9c30362bb20f7fe38e09087c59829c8", "size": 9493, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "main/main_trade.cpp", "max_stars_repo_name": "DavidLongbottom/apolo", "max_stars_repo_head_hexsha": "1e980ff551cbff803b591ebbc90b1b6bd09bfeaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import matplotlib.pyplot as plt
import numpy as np
import itertools
from sklearn import tree
import pydotplus
DATA_PATH = '../definitive_data_folder'
PLOT_PATH = DATA_PATH + '/plots'
try: os.mkdir(PLOT_PATH)
except: pass
try: os.mkdir(PLOT_PATH + '/trees')
except: pass
try: os.mkdir(PLOT_PATH + '/feature_im... | {"hexsha": "630b104a13647ff7c9095744c3fce553bb7e680c", "size": 2854, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_extraction/src/support_functions.py", "max_stars_repo_name": "HPAI-BSC/chromoplexy_analysis", "max_stars_repo_head_hexsha": "07170ab878ade82ebd2d75a43cbead84c8c10d73", "max_stars_repo_lice... |
##########################################################################
#
# MRC FGU Computational Genomics Group
#
# $Id$
#
# Copyright (C) 2009 Andreas Heger
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by ... | {"hexsha": "a3104be4b7770bb965452dc725cc94002b9eabb9", "size": 5126, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/optic/transcripts2links.py", "max_stars_repo_name": "CGATOxford/Optic", "max_stars_repo_head_hexsha": "2df92e953b5139ff4e5c383cb4383e6367cd47f1", "max_stars_repo_licenses": ["MIT"], "max_s... |
function colsum = r8col_sum ( m, n, a )
%*****************************************************************************80
%
%% R8COL_SUM sums the columns of an R8COL.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 24 April 2005
%
% Author:
%
% John Burkardt
%
% Pa... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/r8lib/r8col_sum.m"} |
import os, sys
import math, time
import contextlib
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst
import numpy as np
import torch, torchvision
frame_format, pixel_bytes, model_precision = 'RGBA', 4, 'fp32'
model_dtype = torch.float16 if model_precision == 'fp16' else torch.float32
device = to... | {"hexsha": "4306de7756b96042db84e433372c06894a2ad03a", "size": 4749, "ext": "py", "lang": "Python", "max_stars_repo_path": "tuning_baseline.py", "max_stars_repo_name": "CraigCarey/pytorch-video-pipeline", "max_stars_repo_head_hexsha": "64ad8d403dc6bffeaf1bf2054c211685a0d10a23", "max_stars_repo_licenses": ["MIT"], "max_... |
import networkx as nx
import matplotlib.pyplot as plt
from networkx.readwrite import json_graph
import json
import random
G = nx.Graph()
G.add_node(1)
G.add_nodes_from([2, 3])
#H = nx.path_graph(10)
#G.add_nodes_from(H)
G.add_edge(1, 2)
e = (2, 3)
G.add_edge(*e)
G.add_edges_from([(1, 2, {'value':0.83}), (1, 3, {'valu... | {"hexsha": "d8a348bca78f3c00a9e17712346f42c85a1a3796", "size": 1018, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayes.py", "max_stars_repo_name": "stajilov/bayesera", "max_stars_repo_head_hexsha": "270cd1b76712e7518c8d164a71ebc48016bd6f6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
# %%
"""We wanna get familiar with array manipulation(File 17-21)."""
import numpy as np
# %%
"""
np.insert won't change the original data.
It will return a copy of the data.
"""
help(np.insert)
# %%
a = np.arange(1, 11)
a
# %%
np.insert(a, 1, values=50)
# %%
np.insert(a, 10, values=50)
# %%
a
# %%
"... | {"hexsha": "0463b1d1dfa952bbfedd1147080f7410aa5ff9f4", "size": 1523, "ext": "py", "lang": "Python", "max_stars_repo_path": "NumPy/21_InsertAndDeleteFunction.py", "max_stars_repo_name": "ErfanRasti/PythonCodes", "max_stars_repo_head_hexsha": "5e4569b760b60c9303d5cc68650a2448c9065b6d", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma nodes_empty[simp]: "nodes empty = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nodes FiniteGraph.empty = {}
[PROOF STEP]
unfolding empty_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nodes \<lparr>nodes = {}, edges = {}\<rparr> = {}
[PROOF STEP]
by simp | {"llama_tokens": 120, "file": "Network_Security_Policy_Verification_Lib_FiniteGraph", "length": 2} |
"""
Simulate a quadrotor following a 3D trajectory
Author: Daniel Ingram (daniel-s-ingram)
"""
from math import cos, sin
import numpy as np
from Quadrotor import Quadrotor
from TrajectoryGenerator import TrajectoryGenerator
from mpl_toolkits.mplot3d import Axes3D
show_animation = True
# Simulation parameters
g = 9.... | {"hexsha": "e00b3fff48a18cef1471ab77bbeda2bfa9b87129", "size": 5568, "ext": "py", "lang": "Python", "max_stars_repo_path": "AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py", "max_stars_repo_name": "pruidzeko/PythonRobotics", "max_stars_repo_head_hexsha": "5ff9b70d737121c2947d844ecfb1fa07... |
import unittest
import math
import pyomo.environ as pe
import coramin
import numpy as np
from coramin.relaxations.segments import compute_k_segment_points
class TestUnivariateExp(unittest.TestCase):
@classmethod
def setUpClass(cls):
model = pe.ConcreteModel()
cls.model = model
model.y ... | {"hexsha": "e41cfeb17767b0e79b409e8ff8f6f0b3522229c7", "size": 11932, "ext": "py", "lang": "Python", "max_stars_repo_path": "coramin/relaxations/tests/test_univariate_relaxations.py", "max_stars_repo_name": "dilr/Coramin", "max_stars_repo_head_hexsha": "22187e5f9e1631867c29f981ff6dc035341bd23d", "max_stars_repo_license... |
from __future__ import print_function
from orphics import maps,io,cosmology,stats,mpi
from pixell import enmap
import numpy as np
import os,sys
from soapack import interfaces as sints
from tilec import utils as tutils,covtools,pipeline
import symlens
from enlib import bench
"""
compares binned theory to input CMB, I t... | {"hexsha": "0ee10de6629325fe19f156e78a36e32af220d2d4", "size": 1560, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/lensed_cl_verify.py", "max_stars_repo_name": "ACTCollaboration/tilec", "max_stars_repo_head_hexsha": "11ed8d027ad6ffac09b3e291a047f33e97673f14", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
from egonetwork import EgoNetwork
from evaluation import Evaluation
import numpy as np
NUM_CIRCLE = 11
ALPHA = 0.5
LAMBDA = 1
'''
This is CESNA implemented all in matrix multiplication.
However, we realize that the f matrix is dependent on each cell's update.
Please refer to cesnanew.py for our final implementation.
... | {"hexsha": "b3844099917f54f4b0af26359d843aa88180ce98", "size": 5146, "ext": "py", "lang": "Python", "max_stars_repo_path": "cesnaplus.py", "max_stars_repo_name": "spicyShoyo/hsfinalpoj", "max_stars_repo_head_hexsha": "f843f5ee33914950a2911042f19f5cbdf0df3b41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#!/usr/bin/env python
# Author: Tony Zheng
# ME 131 Lab 7
import rospy
import time
import roslib
import sys
import cv2
import numpy as np
# state estimation node
class camera_node():
def __init__(self):
self.vid = cv2.VideoCapture("/dev/video6")
self.vid.set(12,5) #contrast
s... | {"hexsha": "d16c615f557cb1cc7a354cd3a72b167cb2d68604", "size": 2198, "ext": "py", "lang": "Python", "max_stars_repo_path": "workspace/src/labs/src/lab7/camera.py", "max_stars_repo_name": "RaulQT/como", "max_stars_repo_head_hexsha": "555342bf56621f34a91024d95aab1cf44806e6a3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2020
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
#... | {"hexsha": "8bc7b38a32e8a781fcb9749f3faac640ab7f8bc9", "size": 23073, "ext": "py", "lang": "Python", "max_stars_repo_path": "pynet/models/sononet.py", "max_stars_repo_name": "CorentinAmbroise/pynet", "max_stars_repo_head_hexsha": "c353e5f80e75f785a460422ab7b39fa8f776991a", "max_stars_repo_licenses": ["CECILL-B"], "max_... |
## @package utils
# Module caffe2.contrib.perf_contbld.utils
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import getpass
import time
from collections import defaultdict
import numpy as np
from caffe2.proto import p... | {"hexsha": "41cc43dedacb65c72949e55e3c9a252b894c2412", "size": 3891, "ext": "py", "lang": "Python", "max_stars_repo_path": "caffe2/contrib/perf_contbld/utils.py", "max_stars_repo_name": "kyper999/caffe2-2", "max_stars_repo_head_hexsha": "7c10b470de8d19261f10e958846d7efcfd751a09", "max_stars_repo_licenses": ["MIT"], "ma... |
\name{[.HeatmapList}
\alias{[.HeatmapList}
\alias{Extract.HeatmapList}
\title{
Subset a HeatmapList object
}
\description{
Subset a HeatmapList object
}
\usage{
\method{[}{HeatmapList}(x, i, j)
}
\arguments{
\item{x}{A \code{\link{HeatmapList-class}} object}
\item{i}{row indices}
\item{j}{column indices}
}
\det... | {"hexsha": "77f3387891e6a5675678c78ac53a27bc20bfc95c", "size": 1178, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/Extract.HeatmapList.rd", "max_stars_repo_name": "zhongmicai/complexHeatmap", "max_stars_repo_head_hexsha": "02ad1d0a5097d21f748c4bab5f97d1505cdd8642", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
import pickle
import neat
import gym
import numpy as np
import convlstm
import matplotlib.pyplot as plt
filepath = "/Users/farhanqureshi/Downloads/Bitcoin.csv"
starting_cash = 1000
att = convlstm.stat(filepath,starting_cash)
gprof = []
act_price = []
unit = []
# load the winner
with open('winner', 'rb') as f:... | {"hexsha": "6dfb1208672422b404a8ba11f95e068dd4986a18", "size": 1245, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "FarhanQ7/asdfsd", "max_stars_repo_head_hexsha": "e4bbdf5d3b44714915c1cc06ce05ee453f0248ed", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, ... |
import DirectDepWithoutCompatEntry
import ScientificTypes
import Test
Test.@testset "DirectDepWithoutCompatEntry.jl" begin
Test.@test DirectDepWithoutCompatEntry.f(1) == 2
end
| {"hexsha": "a190db125bee76d73dfe09f4c70c522722c83ec1", "size": 181, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_packages/force_latest_compatible_version/DirectDepWithoutCompatEntry/test/runtests.jl", "max_stars_repo_name": "bjarthur/Pkg.jl", "max_stars_repo_head_hexsha": "7f526047ab71e7a5787e151764f... |
import numpy as np
import pandas as pd
from scipy.spatial import distance
def find_cluster(data_frame, data_frame_cluster):
cluster = {}
for i, center in enumerate(data_frame_cluster.values):
cluster[i] = []
for j, point in enumerate(data_frame.values):
euclDist = float('inf')
eucl... | {"hexsha": "3e23c47395d9788f489d6d5361f1a881032a3dbb", "size": 2134, "ext": "py", "lang": "Python", "max_stars_repo_path": "Kmeans.py", "max_stars_repo_name": "jenildesai25/k_means_implementation", "max_stars_repo_head_hexsha": "3d1520f211e1c26ef75741c50be13707cb8df1ed", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# coding=utf-8
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team 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... | {"hexsha": "4c1e00aada03108d62796e08ecd5b07837b73432", "size": 5418, "ext": "py", "lang": "Python", "max_stars_repo_path": "language/bert/tf_SUT.py", "max_stars_repo_name": "photoszzt/inference", "max_stars_repo_head_hexsha": "5cc9406ee686da3a217a7f946569d7b3b968921e", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import pickle
import numpy as np
from numpy import dot
from numpy.linalg import norm
from sklearn.cluster import KMeans
embeddings = np.load("data/embeddings/sentence_embeddings.npy")
sentences = pickle.load(open("data/embeddings/sentences.pkl", "rb"))
clustering = KMeans(n_clusters=10, random_state=42).fit(embeddin... | {"hexsha": "26d9a234c60843671a645b16c6be5d5a3761b44e", "size": 1523, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/textprocessing/cluster_sentences.py", "max_stars_repo_name": "crow-intelligence/text-visualization", "max_stars_repo_head_hexsha": "e62dff053cdbab7c4c6d38f7dc8733faed3a6341", "max_stars_repo_l... |
import glob
import os
import numpy as np
import torch
from nn_common_modules import losses as additional_losses
from torch.optim import lr_scheduler
import utils.common_utils as common_utils
from utils.log_utils import LogWriter
CHECKPOINT_DIR = 'checkpoints'
CHECKPOINT_EXTENSION = 'pth.tar'
class Solver(object):
... | {"hexsha": "9a97561c5375a2adc403d328f97fdd3768a0657f", "size": 8661, "ext": "py", "lang": "Python", "max_stars_repo_path": "solver.py", "max_stars_repo_name": "BorisMansencal/quickNAT_pytorch", "max_stars_repo_head_hexsha": "1853afbe409f2fec6db298c70a3dd0ae088091f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
!> Submodule file parallel_errors_mpi_petsc.smod
!> @build mpi petsc
!
!> @details Implementation (using MPI and PETSc) of parallel
!! error handling
submodule (parallel) parallel_errors_mpi_petsc
use mpi
use parallel_types_mpi, only: parallel_environment_mpi
implicit none
contains
!> Error handling fo... | {"hexsha": "528a1d075215eecece3a1024b91b13ad18ec7c51", "size": 1749, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/parallel/petsc/parallel_errors_mpi_petsc.f90", "max_stars_repo_name": "asimovpp/asimov-ccs", "max_stars_repo_head_hexsha": "f8624d418fb847d104f8e753a21326e7026277fd", "max_stars_repo_license... |
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import os
df = pd.read_csv("data/legend.csv") #ที่อยู่ของlegend.csv
option = []
for file in os.listdir("notblur/"): #ที่อยู่ของรูปภาพที่ไม่ใช้
option.append(file)
print(len(option))
test_df = df[df["image"].isin(option)]
test_d... | {"hexsha": "a298d725403caccd0c3ac29cf90e7c07dd9424ce", "size": 1567, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "CN240Depression/CN240", "max_stars_repo_head_hexsha": "b0c8a1638d7bd99521f6d02dae7af95865080fe8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Various general tools used by the code.
"""
import numpy as np
import astropy.units as u
import astropy.constants as c
from astropy.stats import poisson_conf_interval
from scipy.signal import correlate
from scipy.optimize import curve_fit, minimize
from scipy.interpol... | {"hexsha": "22c9909221e0eb075453a54d8c70b11ff38d9a1d", "size": 9604, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunburn/tools.py", "max_stars_repo_name": "ladsantos/blastoise", "max_stars_repo_head_hexsha": "30458f41c5432c97f71a54a73a89fa1193e28b38", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
subroutine geometry_create_lg(imsgin,xmsgin,cmsgin,msgtype,
* nwds,ierror)
C
C
C#######################################################################
C
C PURPOSE -
C
C Create a new geometry entry
c geometry/create/geom_name
c
C
C INPUT ARGUMENTS -
C
C imsgin() - Integer ... | {"hexsha": "5b20010d111f2e495e645666617dd0d5aea4a63d", "size": 3931, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/geometry_create_lg.f", "max_stars_repo_name": "millerta/LaGriT-1", "max_stars_repo_head_hexsha": "511ef22f3b7e839c7e0484604cd7f6a2278ae6b9", "max_stars_repo_licenses": ["CNRI-Python"], "max_st... |
\chapter{Memory Allocators}
\epigraph{Memory memory everywhere but not a allocation to be made}{A really fragmented heap}
\section{Introduction}
Memory allocation is very important!
Allocating and de-allocating heap memory is one of the most common operations in any application.
The heap at the system level is conti... | {"hexsha": "2da81267a4f3b810b4461390146b95b2998d743f", "size": 33340, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "malloc/malloc.tex", "max_stars_repo_name": "BenjaminPollak/coursebook", "max_stars_repo_head_hexsha": "4646102b5f4c3d283885ba1b221da71a5e509eeb", "max_stars_repo_licenses": ["CC-BY-3.0", "CC-BY-4.0... |
# -*- coding: utf-8 -*-
"""
Produces fake instrument data for testing.
"""
from __future__ import print_function
from __future__ import absolute_import
import os
import numpy as np
import pandas as pds
import xarray as xr
import pysat
from pysat.instruments.methods import testing as test
platform = 'pysat'
name = 't... | {"hexsha": "b739c46ec365cd0690ae8782d75af0d140f3a847", "size": 6642, "ext": "py", "lang": "Python", "max_stars_repo_path": "pysat/instruments/pysat_testing2d_xarray.py", "max_stars_repo_name": "jklenzing/pysat", "max_stars_repo_head_hexsha": "e84002e57f8a808d35562f51ce957361d99c4070", "max_stars_repo_licenses": ["BSD-3... |
import multiprocessing
import sys
import time
import pandas as pd
import numpy as np
import public
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from typing import Collection
from typing import List
from itertools import combinations
from fastcore.imports import in_noteb... | {"hexsha": "af1685126e6f04c53fdf2fe520d0636b740564c8", "size": 5157, "ext": "py", "lang": "Python", "max_stars_repo_path": "mvtk/supervisor/utils.py", "max_stars_repo_name": "FINRAOS/model-validation-toolkit", "max_stars_repo_head_hexsha": "57bf478712649e08635b2a6952c5b60ecf189ca5", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import matplotlib.pyplot as plt
import time as tm
plt.close()
def logistic_func(r,x):
return r*x*(1-x)
def xnext(r,xo,N):
no = 0
X = np.zeros(r.size*N)
A = np.zeros(r.size*N)
#Iterate for each value of r in the array of total_of_r (7000) values.
for r_value in r:
xinit = xo
#For each... | {"hexsha": "0b9dfc2b248aaf11267ef82ad0b1f512af7115f2", "size": 1783, "ext": "py", "lang": "Python", "max_stars_repo_path": "Strogatz/Logistic_Bifurcation.py", "max_stars_repo_name": "yuchiaol/Non-linear-dynamics-Strogatz", "max_stars_repo_head_hexsha": "79e1171d4a88193d3da67c15f41212a96e6854dd", "max_stars_repo_license... |
(******************************************************************************)
(* Project: Isabelle/UTP Toolkit *)
(* File: Partial_Fun.thy *)
(* Authors: Simon Foster and Frank Zeyda ... | {"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/UTP/toolkit/Partial_Fun.thy"} |
import copy
import os
import random
from collections import namedtuple, deque, Iterable
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from src.model import Dueling_DQN
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
class DQNAgent:... | {"hexsha": "bc5434d6ecffa918db799addfdf5af1d511a0095", "size": 7191, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/agent.py", "max_stars_repo_name": "misterdev/flatland-marl", "max_stars_repo_head_hexsha": "347cddf5cec1e4184426ea4227a00206aed3bcaf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9,... |
! { dg-do run }
!
! Check PDT type extension and simple OOP.
!
module vars
integer :: d_dim = 4
integer :: mat_dim = 256
integer, parameter :: ftype = kind(0.0d0)
end module
use vars
implicit none
integer :: i
type :: mytype (a,b)
integer, kind :: a = kind(0.0e0)
integer, LEN :: b = 4
integer... | {"hexsha": "e364eeae6dfc0ab3ec765088c9a51ace72e421d5", "size": 2046, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/pdt_3.f03", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_licenses"... |
Describe Users/hellacooldude here.
20100130 14:04:57 nbsp Thanks for adding such an informative comment about The Willows, and welcome to the wiki! Users/NickSchmalenberger
| {"hexsha": "a41802a8624a031b42179614f82e5b418e2ea523", "size": 174, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/hellacooldude.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
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