text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
from typing import Dict, List
import os
import copy
import json
import numpy as np
import gym
from tqdm import tqdm
from DDQN import Agent
if __name__ == '__main__':
# Init. Environment
env = gym.make('CartPole-v1')
env.reset()
# Init. Datapath
data_path = os.path.abspath('Expert/data')
# ... | {"hexsha": "d7e04659e9f1f7f0cd5ee075130bfaf308b40667", "size": 1548, "ext": "py", "lang": "Python", "max_stars_repo_path": "Expert/test.py", "max_stars_repo_name": "KanishkNavale/Generative-Adversarial-Imitation-Learning", "max_stars_repo_head_hexsha": "0226cc0be67f7b0d3c8867e887d8f5bd64d110a2", "max_stars_repo_license... |
#include <ThermalAnalysis/LinearCombination.hpp>
#include <boost/program_options.hpp>
#include <boost/filesystem.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/optional.hpp>
#include <boost/optional/optional_io.hpp>
#include <yaml-cpp/yaml.h>
#define UNITCONVERT_NO_BACKWARD_COMPATIBLE_NAMESPACE
#include <Un... | {"hexsha": "1799c63e35b387845535acb5ee0607fedd3c65d8", "size": 9496, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "applications/tempBuilder.cpp", "max_stars_repo_name": "CD3/ThermalAnalysis", "max_stars_repo_head_hexsha": "4e268d697a8b7140a2e01d48d49b995c7b58a248", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\section{Toolchain}
MARK II is not only SoC, there is also several tools that will help you to
write software for MARK II. They are written in Python and are placed in tools
directory. Tools are primary intended for using under Linux with python 2.7.
Typical work flow is on image \ref{fig:toolchain_workflow}, and tool... | {"hexsha": "d496a92b4cbbf110215a5fdd8a9e64772b15d4f7", "size": 1116, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/refman/tex/toolchain/toolchain.tex", "max_stars_repo_name": "VladisM/MARK_II-SoC", "max_stars_repo_head_hexsha": "58a441675729d4036b503c2a4743fd181daaf5af", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import os
import re
from scipy import misc
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
import platform
def tiling_flat(input_directory='prediction_inter'):
root = ''
imgs = np.array([])
... | {"hexsha": "7795413fbbcb49248a8d6ab93aca5de80ee00f3f", "size": 13798, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tiling.py", "max_stars_repo_name": "peterchencyc/deep-baking", "max_stars_repo_head_hexsha": "653183676baf32598b5df2814d22bccc03138241", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
#!/usr/bin/env python
# coding: utf-8
from __future__ import division
import sys
import argparse
import numpy as np
import tables as tb
import cv2
from pyaam.muct import MuctDataset
from pyaam.draw import draw_polygons, draw_texture, draw_face
from pyaam.utils import get_vertices, get_aabb, normalize, get_mask
from ... | {"hexsha": "1620685670e1231378d2915c2a49342b2b59e7d9", "size": 3881, "ext": "py", "lang": "Python", "max_stars_repo_path": "do_perts.py", "max_stars_repo_name": "zangkaiqiang/pyaam", "max_stars_repo_head_hexsha": "3c59026df17fb0b4588797026d5a2fe64d05fca9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_... |
import os
import numpy as np
# list all fort.1## files in current directory
onlyfiles = [f for f in os.listdir('.') if os.path.isfile(os.path.join('.', f))]
temp = []
for f in onlyfiles:
if 'fort' in f:
temp.append(f)
onlyfiles = temp
size1 = os.stat((onlyfiles[-1])).st_size
size2 = os.stat((onlyfiles[-2])... | {"hexsha": "09eccae9b3c0933ce572f87871f5931e29094006", "size": 1918, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/getPolarStat1.py", "max_stars_repo_name": "varennes/particletrack", "max_stars_repo_head_hexsha": "64fa0bf456a9995c6115f4488ed3868a20a0205c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/-
Copyright (c) 2021 Microsoft Corporation. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Gabriel Ebner
-/
prelude
import Init.NotationExtra
namespace Nat
private theorem log2_terminates : ∀ n, n ≥ 2 → n / 2 < n
| 2, _ => by decide
| 3, _ => by decide
| n+4, ... | {"author": "Kha", "repo": "lean4-nightly", "sha": "b4c92de57090e6c47b29d3575df53d86fce52752", "save_path": "github-repos/lean/Kha-lean4-nightly", "path": "github-repos/lean/Kha-lean4-nightly/lean4-nightly-b4c92de57090e6c47b29d3575df53d86fce52752/stage0/src/Init/Data/Nat/Log2.lean"} |
import os
import time
import pandas as pd
import numpy as np
import tsam.timeseriesaggregation as tsam
def test_durationRepresentation():
raw = pd.read_csv(
os.path.join(os.path.dirname(__file__), "..", "examples", "testdata.csv"),
index_col=0,
)
starttime = time.time()
aggregatio... | {"hexsha": "530a19971346bc8747b13f494a2c44b16643664c", "size": 2456, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_durationRepresentation.py", "max_stars_repo_name": "FZJ-IEK3-VSA/tsam", "max_stars_repo_head_hexsha": "a896367dbb2c8725a63c3ff6424ca37b40396528", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 2 17:58:35 2020
@author: german
"""
from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
get_ipython().run_line_magic('tensorflow_version', '2.x')
except Exception:... | {"hexsha": "a3a61a64cfd9b368edb42693527de6dfeb416123", "size": 2219, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/variationalnn_floquet/NN_model.py", "max_stars_repo_name": "gsinuco/VariationalNN_Floquet", "max_stars_repo_head_hexsha": "46605e53c29801b9aaedfe9a61fe886239bc2112", "max_stars_repo_licenses":... |
import numpy as np
class DTree(object):
"""This is basic implementation of decision tree.
As of now it only works on data that has descrete feature values.
If any feature of input data has continuos value then this will not work.
TODO: Add support for continuous values as well.
"""
def __init__(self):
pass
... | {"hexsha": "8de9bd5b4932dc3524c4d89fd9f697ccc9edfae3", "size": 3541, "ext": "py", "lang": "Python", "max_stars_repo_path": "DTree.py", "max_stars_repo_name": "prasanna08/MachineLearning", "max_stars_repo_head_hexsha": "5ccd17db85946630730ee382b7cc258d4fa866e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, ... |
[STATEMENT]
lemma divisor_set_mult:
"divisor_set (m*n) = {i*j| i j. (i \<in> divisor_set m)\<and>(j \<in> divisor_set n)}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. divisor_set (m * n) = {i * j |i j. i \<in> divisor_set m \<and> j \<in> divisor_set n}
[PROOF STEP]
using divisor_set divisor_def
[PROOF STATE]
... | {"llama_tokens": 297, "file": "Amicable_Numbers_Amicable_Numbers", "length": 2} |
[STATEMENT]
lemma tsp__ncop2:
assumes "A B C TSP P Q"
shows "\<not> Coplanar A B C Q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> Coplanar A B C Q
[PROOF STEP]
using TSP_def assms
[PROOF STATE]
proof (prove)
using this:
?A ?B ?CTSP ?P ?Q \<equiv> \<not> Coplanar ?A ?B ?C ?P \<and> \<not> Coplanar ?A ?B... | {"llama_tokens": 220, "file": "IsaGeoCoq_Tarski_Neutral", "length": 2} |
%% MULTIGRID OF FOR THE STOKES EQNS IN 2D
%
% This example is to show the convergence of multigrid methods for various
% finite element approximation of the Stokes equation on the unit square:
%
% -div(mu*grad u) + grad p = f in \Omega,
% - div u = 0 in \Omega,
% ... | {"author": "lyc102", "repo": "ifem", "sha": "29f31c812001ca8d93dad08e67208ca60e8716d4", "save_path": "github-repos/MATLAB/lyc102-ifem", "path": "github-repos/MATLAB/lyc102-ifem/ifem-29f31c812001ca8d93dad08e67208ca60e8716d4/example/solver/Stokesasmgrate.m"} |
struct SilvisModel{T}<:AbstractLESModel
c::T
cp::T
Δ²::T
tau::SymTrTenField{T,3,2,false}
reduction::Vector{T}
end
function SilvisModel(c::T,cp::T,Δ::Real,dim::NTuple{3,Integer}) where {T<:Real}
data = SymTrTenField{T}(dim,(LX,LY,LZ))
#fill!(data,0)
reduction = zeros(THR ? Threads.nthrea... | {"hexsha": "0021915f61a62d261d99b7f4bc9870e21fa7d520", "size": 1003, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LESmodels/silvismodel.jl", "max_stars_repo_name": "favba/FluidFlowSimulation.jl", "max_stars_repo_head_hexsha": "0fec70546e87ac05992c1de31bd3913a4f35aff7", "max_stars_repo_licenses": ["MIT"], "... |
struct MatrixLangevin{N,T,n,k} <: ParameterizedMeasure{N}
par::NamedTuple{N,T}
manifold::Stiefel{n,k,ℝ}
end
function MatrixLangevin(n, k; kwargs...)
par = NamedTuple(kwargs)
return MatrixLangevin(par, Stiefel(n, k))
end
const MatrixVonMisesFisher = MatrixLangevin
Manifolds.base_manifold(d::MatrixLange... | {"hexsha": "b24783fc6a0640c82247551b383d6e8cf95fce56", "size": 870, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stiefel/matrixlangevin.jl", "max_stars_repo_name": "TigerZhao007/ManifoldMeasures.jl", "max_stars_repo_head_hexsha": "797f0bbe70bd553987fb4375b332ac0e907cc14e", "max_stars_repo_licenses": ["MIT"... |
#GET MNIST DATA
import numpy as np
from sklearn.datasets import fetch_openml
#DOWN-SAMPLE DATASET
NKEEP=5000;
#GET DATA
mnist = fetch_openml('mnist_784')
x = np.array(mnist.data).astype(int);
y = np.array(mnist.target).astype(int);
#RANDOMLY KEEP NKEEP SAMPLES
INDX=np.random.choice(len(y),NKEEP, replace=False)... | {"hexsha": "deec08aa042f9210174d08f9636a4a0b034fab6e", "size": 439, "ext": "py", "lang": "Python", "max_stars_repo_path": "ANLY-501-INTRO/LAB11/MNIST/DATA/GET-MNIST-DATA.py", "max_stars_repo_name": "rexarski/ggtown-ds", "max_stars_repo_head_hexsha": "00bbb26e28b4431cf4aeff68ea0b3b9220af0b1f", "max_stars_repo_licenses":... |
[STATEMENT]
lemma dropWhile_replicate[simp]:
"dropWhile P (replicate n x) = (if P x then [] else replicate n x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. dropWhile P (replicate n x) = (if P x then [] else replicate n x)
[PROOF STEP]
using dropWhile_eq_self_iff
[PROOF STATE]
proof (prove)
using this:
(dropWhi... | {"llama_tokens": 202, "file": null, "length": 2} |
import .nonempty_list
open util.data.nonempty_list
namespace util.data.bin_tree'
universes u v
inductive bin_tree' (α : Type u)
| leaf : α → bin_tree'
| branch : bin_tree' → bin_tree' → bin_tree'
namespace bin_tree'
variables {α : Type u} {β : Type v}
def map (f : α → β) : bin_tree' α → bin_tree' β
| (leaf x) :=... | {"author": "semorrison", "repo": "lean-monoidal-categories", "sha": "81f43e1e0d623a96695aa8938951d7422d6d7ba6", "save_path": "github-repos/lean/semorrison-lean-monoidal-categories", "path": "github-repos/lean/semorrison-lean-monoidal-categories/lean-monoidal-categories-81f43e1e0d623a96695aa8938951d7422d6d7ba6/src/monoi... |
from __future__ import division
import math
import mbuild as mb
import numpy as np
from scipy.spatial import distance
def _fast_sphere_pattern(n, radius):
"""Faster version of mBuild's SpherePattern. """
phi = (1 + np.sqrt(5)) / 2
long_incr = 2*np.pi / phi
dz = 2.0 / float(n)
bands = np.arange(n)... | {"hexsha": "2a96575307361e26909d25124db8f17fa1b6dda9", "size": 2723, "ext": "py", "lang": "Python", "max_stars_repo_path": "cgnp_patchy/lib/nanoparticles/Nanoparticle.py", "max_stars_repo_name": "cjspindel/cgnp_patchy", "max_stars_repo_head_hexsha": "12d401c90795ecddb9c4ea0433dc26c4d31d80b6", "max_stars_repo_licenses":... |
import json
from pathlib import Path
import h5py
import numpy as np
import torchvision.transforms as transforms
import torch
import torch.utils.data as data
from tqdm import tqdm
from scipy import interpolate
from bootstrap.datasets.dataset import Dataset
from bootstrap.lib.logger import Logger
d... | {"hexsha": "39c0f0ba0b4b57b7e988304a0556a6b68a61581e", "size": 8192, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/bdd.py", "max_stars_repo_name": "valeoai/BEEF", "max_stars_repo_head_hexsha": "f1c5f3708ba91f6402dd05814b76dca1d9012942", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4, ... |
import numpy as np
def roundLikeNCI(np_float64):
outval = np.float64(np_float64) * np.float64(1000.0)
if outval - outval.astype(np.int) >= np.float(0.5):
outval = outval.astype(np.int) + 1
else:
outval = outval.astype(np.int)
return np.float(outval) / np.float(1000)
| {"hexsha": "93c3695d33da5d0850064dbe0d10e4456b81bdc3", "size": 301, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "VisExcell/riskmodels", "max_stars_repo_head_hexsha": "012bbfd563482ba09585cd042b1f9465253ab1f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 9 19:21:52 2019
@author: YQ
"""
import tensorflow as tf
class Generator(tf.keras.Model):
def __init__(self):
super(Generator, self).__init__()
self.d1 = tf.keras.layers.Dense(1024, use_bias=False)
self.a1 = tf.keras.layers.ReLU()
... | {"hexsha": "fe1ea9debc20622c3565dd237997996c9ab6d65d", "size": 3962, "ext": "py", "lang": "Python", "max_stars_repo_path": "gan.py", "max_stars_repo_name": "k-eato/InfoGAN_Tensorflow2.0", "max_stars_repo_head_hexsha": "a3fa4ae6b83f786047abd267c04875eb8fb5b94a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, ... |
import os, sys
import argparse
import numpy as np
import pandas as pd
from datetime import datetime
from multiprocessing import Pool
if __package__ is None:
sys.path.append( os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) ) )
from experiment_handler.pupil_data_reader import get_fixation_events, get_e... | {"hexsha": "9a2895a5b27b8f97f7219960374bde5f025fa942", "size": 9898, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_calculations/eye_feature_extractor.py", "max_stars_repo_name": "phev8/dataset_tools", "max_stars_repo_head_hexsha": "a9ba158f7a7144819d934f9bc6e7a280f27db7d4", "max_stars_repo_licenses": [... |
#!/usr/bin/env python
"""
Created on May 17th, 2018 by Chen Yang
This script defines Poisson-Geometric distribution and Weibull-Geometric distribution
"""
import numpy as np
from math import ceil
from scipy.stats import rv_discrete, poisson, geom
# Scipy geometric starts with x = 1
class poisgeom_gen(rv_discrete):... | {"hexsha": "4d895da282e4b05cb72d2be4eb7724ebf3a19fb4", "size": 1811, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/NanoSim/src/mixed_model.py", "max_stars_repo_name": "G-Thomson/DAJIN", "max_stars_repo_head_hexsha": "e702f465c015da33fabcfc43213f346acd7e0415", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import numpy as np
import galsim
import piff
import ngmix
from ngmix.fitting import LMSimple
from ngmix.admom import Admom
from scipy.interpolate import CloughTocher2DInterpolator
class DES_Piff(object):
"""A wrapper for Piff to use with Galsim.
This wrapper uses ngmix to fit smooth models to the... | {"hexsha": "d20da3483078be3ca69cf51633708b70f1fe235e", "size": 6582, "ext": "py", "lang": "Python", "max_stars_repo_path": "matts_misc/des_piff.py", "max_stars_repo_name": "beckermr/misc", "max_stars_repo_head_hexsha": "da8fed310a0c99d7a5a10a1bfa74aac4db676475", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
struct OneHotTensor{N, T} <: Block
classes::AbstractVector{T}
end
function checkblock(block::OneHotTensor{N}, a::AbstractArray{T, M}) where {M, N, T}
return N + 1 == M && last(size(a)) == length(block.classes)
end
mockblock(block::OneHotTensor{0}) = encode(
OneHot(), Validation(), Label(block.classes), ... | {"hexsha": "16414c8136366ba01f0922eb50e3ef508c7a894d", "size": 3079, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/encodings/onehot.jl", "max_stars_repo_name": "inferential/FastAI.jl", "max_stars_repo_head_hexsha": "3a017af061a1125231fe7d7a4ec98da0a255d781", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
function [set_ar, varxh, klifit] = ARShat_misd(ng,xg,L,lag_max,set_ar_start);
%ARSHAT_MISD AR models of increasing order from measurements with missing data
% [set_ar, varxh, klifit] = ARShat_misd(ng,xg,[Lmin Lmax],lag_max) estimates
% autoregressive models from measurements containing missing data for
% orders ... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/3680-automatic-spectral-analysis/AutomaticSp... |
abstract type AbelianExt end
mutable struct KummerExt <: AbelianExt
zeta::nf_elem
n::Int
gen::Vector{FacElem{nf_elem, AnticNumberField}}
AutG::GrpAbFinGen
frob_cache::Dict{NfOrdIdl, GrpAbFinGenElem}
frob_gens::Tuple{Vector{NfOrdIdl}, Vector{GrpAbFinGenElem}}
gen_mod_nth_power::Vector{FacElem{nf_elem, An... | {"hexsha": "b274fbce85b51c0335468e00a2c6d62972dd93bf", "size": 8211, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RCF/kummer_extensions.jl", "max_stars_repo_name": "edgarcosta/Hecke.jl", "max_stars_repo_head_hexsha": "3ba4c63908eaa256150a055491a6387a45b081ec", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
// Copyright (c) 2001-2011 Hartmut Kaiser
// Copyright (c) 2001-2011 Joel de Guzman
//
// 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)
#if !defined(BOOST_SPIRIT_KARMA_DEBUG_HANDLER_APR_21_2010_0148PM)
#define B... | {"hexsha": "ccf5dfc007321007d661428ff6e42544dfad7df3", "size": 3962, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/boost_1_72_0/boost/spirit/home/karma/nonterminal/debug_handler.hpp", "max_stars_repo_name": "henrywarhurst/matrix", "max_stars_repo_head_hexsha": "317a2a7c35c1c7e3730986668ad2270dc19809ef", "ma... |
import numpy as np
Z = np.zeros((10,8))
for i in range(10):
da = np.loadtxt('reweighted_hist_%d.dat'%(i))
for j in range(13):
if da[j,0] < 8:
Z[i,int(da[j,0])] = np.exp(-da[j,1])
print(Z[0,:])
for i in range(10):
Z[i,:] /= (Z[i,0]+Z[i,2])
#for j in range(8):
# Z[i,j] /= (Z[i... | {"hexsha": "b78251cc8c2e16c9bd2f2a62052308ca89d86eaa", "size": 715, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/tobs200/make_avg_thing.py", "max_stars_repo_name": "addschile/qtps", "max_stars_repo_head_hexsha": "3220af82d409526463dc4fe9e4ea869d655c0bd8", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
r"""
Vector Bundle Fiber Elements
The class :class:`VectorBundleFiberElement` implements vectors in the fiber of
a vector bundle.
AUTHORS:
- Michael Jung (2019): initial version
"""
#******************************************************************************
# Copyright (C) 2019 Michael Jung <micjung at u... | {"hexsha": "048275a8495093686b9cf116d3b5a9c4609ec2c3", "size": 3885, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/manifolds/vector_bundle_fiber_element.py", "max_stars_repo_name": "UCD4IDS/sage", "max_stars_repo_head_hexsha": "43474c96d533fd396fe29fe0782d44dc7f5164f7", "max_stars_repo_licenses": ["BS... |
[STATEMENT]
lemma step_list_current [simp]: "invar small \<Longrightarrow> list_current (step small) = list_current small"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invar small \<Longrightarrow> Small.list_current (step small) = Small.list_current small
[PROOF STEP]
by(induction small rule: step_state.induct)(a... | {"llama_tokens": 111, "file": "Real_Time_Deque_Small_Proof", "length": 1} |
\section{Objectives}
\label{sec:objective}
In this work, our objective will be to compare this new reinforcement learning framework for active network management to a more classical framework based on mathematical optimization.
We will compare the two methods on the three following points :
\begin{itemize}
\item Assum... | {"hexsha": "5e0ea04713a9c37adb7d896432d3a0741b56934b", "size": 444, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/objective.tex", "max_stars_repo_name": "qlete/ANManagement", "max_stars_repo_head_hexsha": "04a6436fdd6c90bcb51c24e3e4ebdc003e450230", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
Inductive Nat :=
|O : Nat
|S : Nat -> Nat.
Check Nat_ind.
Fixpoint plus(n:Nat)(m:Nat) : Nat :=
match n with
|O => m
|S v => S(plus v m)
end.
Lemma O_plus_n_is_n:
forall n, plus O n=n.
Proof.
(* tactics *)
intros n.
simpl.
reflexivity.
Qed.
Lemma n_plus_0_is_n:
forall n, plus n O = n.
Proof.
... | {"author": "IonitaCatalin", "repo": "programming-language-principle", "sha": "e6a5b4f5284f28127707dc1b8838bad29f215c69", "save_path": "github-repos/coq/IonitaCatalin-programming-language-principle", "path": "github-repos/coq/IonitaCatalin-programming-language-principle/programming-language-principle-e6a5b4f5284f2812770... |
/*=============================================================================
Copyright (c) 2001-2011 Joel de Guzman
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
=========================================... | {"hexsha": "80c06eb37a1a82f2d7cda34a19cc5f05952d7920", "size": 8861, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/lib/boost/spirit/home/qi/auxiliary/lazy.hpp", "max_stars_repo_name": "nlchao/ofxBoost", "max_stars_repo_head_hexsha": "a7024684a5ad541eb9055030d0cd32045eac4024", "max_stars_repo_licenses": ["BSL... |
(* week-06_miscellany.v *)
(* FPP 2020 - YSC3236 2020-2021, Sem1 *)
(* Olivier Danvy <danvy@yale-nus.edu.sg> *)
(* Version of 20 Sep 2020 *)
(* was: *)
(* Version of 19 Sep 2020 *)
(* ********** *)
Require Import Arith Bool.
(* ********** *)
Lemma about_decomposing_a_pair_using_the_injection_tactic :
forall i j :... | {"author": "soedirgo", "repo": "fpp", "sha": "5a43df151c5c8bc3f49d449ffd6f3eac67a16eab", "save_path": "github-repos/coq/soedirgo-fpp", "path": "github-repos/coq/soedirgo-fpp/fpp-5a43df151c5c8bc3f49d449ffd6f3eac67a16eab/w06/week-06_miscellany.v"} |
import argparse
import numpy
import yaml
import h5sparse
import scipy.sparse
def merge_hdf5s(hdf5_paths, output_path):
arrays_to_merge = []
for hdf5_path in hdf5_paths:
arrays_to_merge.append(h5sparse.File(hdf5_path)["data"].value)
merged_array = scipy.sparse.hstack(arrays_to_merge, format="coo")... | {"hexsha": "0665970214fab32c51f088a3d3db84b231c7efa6", "size": 1908, "ext": "py", "lang": "Python", "max_stars_repo_path": "tasks/merge/merge_sparse_hdf5/merge_sparse_hdf5.py", "max_stars_repo_name": "RGLab/table-testing", "max_stars_repo_head_hexsha": "51c1bd5049c46a8970d4693287bad6e4bdb54ea3", "max_stars_repo_license... |
import imutils
from scipy.spatial import distance as dist
from collections import OrderedDict
import matplotlib.pyplot as plt
class ShapeDetector:
def __init__(self):
pass
def detect(self, c):
shape = "unidentified"
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.... | {"hexsha": "f9f29db66a60188d0755be57973daae27c73ac1d", "size": 1841, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW10/2.py", "max_stars_repo_name": "oghahroodi/IUST-Computer-Vision", "max_stars_repo_head_hexsha": "54f50324d5a385d412334e1d985d12270e4b5770", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <chrono>
#include <random>
#include <boost/program_options.hpp>
#include "myDist.hpp"
#include "matGen_lapack.hpp"
namespace po = boost::program_options;
int main(int argc, char* argv[]){
po::options_description desc("Artificial Matrices MT: Options");
desc.add_options()
("help,h","show the help"... | {"hexsha": "ed029eb1d9ebb40f3ff2f7a10d7ebb4659c38df8", "size": 3898, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/driver_lapack.cpp", "max_stars_repo_name": "SMG2S/DEMAGIS", "max_stars_repo_head_hexsha": "9332fb687129d15024d49eb0a7027b552c1c91c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Copyright (c) 2016, 2017, 2018, 2019 Chris Cummins.
#
# clgen is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# clgen is distribute... | {"hexsha": "66cb54524a2204fbcfaa56e55d9b2a9800b73869", "size": 8598, "ext": "py", "lang": "Python", "max_stars_repo_path": "deeplearning/clgen/models/keras_backend_test.py", "max_stars_repo_name": "Zacharias030/ProGraML", "max_stars_repo_head_hexsha": "cd99d2c5362acd0b24ee224492bb3e8c4d4736fb", "max_stars_repo_licenses... |
# Copyright (c) 2020 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": "e6e962e0b97dd1569d5a79f24a4fa4e44b0662e4", "size": 9203, "ext": "py", "lang": "Python", "max_stars_repo_path": "pahelix/utils/splitters.py", "max_stars_repo_name": "WorldEditors/PaddleHelix", "max_stars_repo_head_hexsha": "7dbe947417538d7478fbab4438905b30c1d709c3", "max_stars_repo_licenses": ["Apache-2.0"],... |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "2a19fb8e1e4ff257a331f111ca165328c29f1b79", "size": 11177, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/dcase2020_desed_fuss_baseline/make_mixing_list.py", "max_stars_repo_name": "marciopuga/sound-separation", "max_stars_repo_head_hexsha": "0b23ae22123b041b9538295f32a92151cb77bff9", "max_sta... |
(*
* Copyright 2022, Proofcraft Pty Ltd
* Copyright 2020, Data61, CSIRO (ABN 41 687 119 230)
*
* SPDX-License-Identifier: GPL-2.0-only
*)
(*
AARCH64-specific VSpace invariants
*)
theory ArchVSpace_AI
imports VSpacePre_AI
begin
context Arch begin global_naming AARCH64
sublocale
set_vcpu: non_vspace_non_cap_no... | {"author": "seL4", "repo": "l4v", "sha": "9ba34e269008732d4f89fb7a7e32337ffdd09ff9", "save_path": "github-repos/isabelle/seL4-l4v", "path": "github-repos/isabelle/seL4-l4v/l4v-9ba34e269008732d4f89fb7a7e32337ffdd09ff9/proof/invariant-abstract/AARCH64/ArchVSpace_AI.thy"} |
"""
MIT License
Copyright (c) 2020 Licht Takeuchi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish... | {"hexsha": "d048ac7e0591a128cc435624aa97eb45da370855", "size": 2331, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolov4/util/image_util.py", "max_stars_repo_name": "Licht-T/tf-yolov4", "max_stars_repo_head_hexsha": "355ac532228031d9a0928e962271244f49a898d5", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
\chapter{Results}
\label{ch:results}
\begin{itemize}
\item Expound \#6 (\emph{How will you execute your idea?}) and \#7 (\emph{What is the empirical evidence that your idea works?}).
\item Make a complete description of your experimental setup (\eg dataset, train and test/validation configurations, hardware co... | {"hexsha": "345802aa9ad5d47b65d33422fc90ca2e696c8d84", "size": 1684, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/5-results.tex", "max_stars_repo_name": "baudm/ngse-manuscript", "max_stars_repo_head_hexsha": "df0c02de230f0dfab1489ca10c5b4d824d5999a4", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import argparse
import ast
import logging
import os
import sys; sys.path.append(os.path.join(sys.path[0], '..'))
import time
import model_zoo
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
from dataset import imagenet_data_dali
from mmcv impo... | {"hexsha": "953f55ff7e99c7dcb022c03b8e2edc5272144f89", "size": 7267, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_apis/train_dist.py", "max_stars_repo_name": "JaminFong/dali-pytorch", "max_stars_repo_head_hexsha": "7bd5d2380d210a32d24c7309da69c8d2c5db8759", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#-------------------------
#3d Renderer
#Daniel Miron
#7/5/2013
#
#-------------------------
import h5py
import numpy as np
import sys
import pickle
from OpenGL.GLUT import *
from OpenGL.GLU import *
from OpenGL.GL import *
import arcball as arc
import matplotlib.pyplot as plt
import cv2
class Viewer:
def __ini... | {"hexsha": "93ef6b70a77f2b1a2477f36642f50cabf19a199c", "size": 11629, "ext": "py", "lang": "Python", "max_stars_repo_path": "Renderer/blocking.py", "max_stars_repo_name": "Rhoana/rhoana", "max_stars_repo_head_hexsha": "b4027a57451d175ea02c2c7ef472cf9c4e1a0efc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26,... |
import numpy as np
from typing import List, Union, Tuple, Sequence, Optional
from numbers import Number
from numpy.lib.arraysetops import isin
__all__ = [
'cat', 'chunk', 'gather', 'index_select', 'masked_select', 'movedim', 'swapdims', 'narrow',
'nonzero', 'scatter', 'scatter_add', 'scatter_', 'scatter_add_... | {"hexsha": "d59c85c331ba40a8fd8515dfda68f9752cdcf484", "size": 13059, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpytorch/ops.py", "max_stars_repo_name": "ashawkey/numpytorch", "max_stars_repo_head_hexsha": "6b715d6f53eb515091fc1fa22af01bed4c7fb802", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under... | {"hexsha": "b1f964ba60a0ec5d8ebc6b17c186f733ecef254d", "size": 4407, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/main/python/tests/test_matrix_rand.py", "max_stars_repo_name": "escher-m/systemds", "max_stars_repo_head_hexsha": "6dea896dc0db29c07bfcd24b73a7d37f91b59620", "max_stars_repo_licenses": ["Apach... |
function [w0, phi] = adw_fan_new(sg, ig, wi)
%function [w0, phi] = adw_fan_new(sg, ig, wi)
%
% Compute the angular-dependent weighting for fan-beam geometry.
% w0(\Phi) = w(x0, y0, \Phi)
% For fully corrected penalty:
% w(s',\beta') * J(s') | \phi'=\Phi + ...
% w(s',\beta') * J(s') | \phi'=\Phi-pi
% See fessler chapte... | {"author": "JeffFessler", "repo": "mirt", "sha": "b7f36cc46916821e8bc8502301b1554ebc7efe1d", "save_path": "github-repos/MATLAB/JeffFessler-mirt", "path": "github-repos/MATLAB/JeffFessler-mirt/mirt-b7f36cc46916821e8bc8502301b1554ebc7efe1d/penalty/adw_fan_new.m"} |
module module_fr_fire_util
integer,save:: &
fire_print_msg=1, &
fire_print_file=1, &
fuel_left_method=1, &
fuel_left_irl=2, &
fuel_left_jrl=2, &
boundary_guard=-1, &
fire_grows_only=1, &
fire_upwinding=3, &
fire_upwind_split=0, &
fire_test_steps=0, &
fire_topo_from_atm=1, &
fire_advection=0
real, sav... | {"hexsha": "47b24da515fd5a6f21dae1727477d10acab5186f", "size": 30142, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "WRF-CHEM/phys/module_fr_fire_util.f90", "max_stars_repo_name": "ksetigui/paper_gmd-2020-50", "max_stars_repo_head_hexsha": "1c4bf2b0946bc31cfb443686c8aa1e33755d5fd2", "max_stars_repo_licenses":... |
"""This module transfers the files from Green's textbook to data frames."""
import numpy as np
import pandas as pd
def transfer_data():
"""Transfer data from .txt-file to pickled pandas.DataFrame."""
df = pd.read_csv(
"TableF5-2.txt",
sep=r"\s+",
engine="python",
usecols=["Year... | {"hexsha": "4327bc719285212aa4b6d46eeef779c7e72235b4", "size": 725, "ext": "py", "lang": "Python", "max_stars_repo_path": "labs/optimization/material/transfer_data.py", "max_stars_repo_name": "MImmesberger/ose-course-scientific-computing", "max_stars_repo_head_hexsha": "d0c28f44dbcab19db3dcc1d2e452fdc001c7e9b4", "max_s... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and 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 json
import math
import os
from typing import List, Optional, Tuple, Union
import numpy as... | {"hexsha": "7175791ee375fcae14cd077d3dd0cb1f56b9c721", "size": 8727, "ext": "py", "lang": "Python", "max_stars_repo_path": "augly/image/utils/utils.py", "max_stars_repo_name": "lyakaap/AugLy", "max_stars_repo_head_hexsha": "e287b4e5abc994ac0b52723c908b65ac0f4219c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Analyze EPSCs or IPSCs
Or EPSPs and IPSPs...
This module provides the following analyses:
1. Amplitudes from a train
2. Paired pulse facilitation for pulse pairs, and the first pair in a train.
3. Current-voltage relationship in voltage clamp measured over a time window
The results of the analysis are stored in ... | {"hexsha": "ff55df030dc8e68c513fd1b8dd00b63a254c4ddb", "size": 55900, "ext": "py", "lang": "Python", "max_stars_repo_path": "ephys/ephysanalysis/PSCAnalyzer.py", "max_stars_repo_name": "pbmanis/ephys", "max_stars_repo_head_hexsha": "1360adfbb800b1fed7f982c638906aa0d41a017c", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import json
import random
from random import shuffle
from pathlib import Path
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
from torch_scatter import scatter
from torch_geometric.data import Data, InMemoryDataset
from feature import one_of_k_encoding, toxcast_tasks
from utils impo... | {"hexsha": "a4838bfca3acedf463fa0237fddb2e16448aa40f", "size": 14299, "ext": "py", "lang": "Python", "max_stars_repo_path": "src_1gp/dataset.py", "max_stars_repo_name": "yvquanli/GLAM", "max_stars_repo_head_hexsha": "82d91265a517e3b4813197fe2764b36c751657c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "... |
"""
This module provides concrete implementations of `Number` that represent
1st, 2nd and general order tensors.
## Why
The main feature of this module is that the provided types do not extend from `AbstractArray`, but from `Number`!
This allows one to work with them as if they were scalar values in broadcasted oper... | {"hexsha": "3cfa04bdb04064017bf97a222c5b724d06ad176c", "size": 2598, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TensorValues/TensorValues.jl", "max_stars_repo_name": "Omega-xyZac/Gridap.jl", "max_stars_repo_head_hexsha": "c9f0ca39a0c84646ea9e4b57fd39d3a6db59c044", "max_stars_repo_licenses": ["MIT"], "max... |
from collections import defaultdict
from typing import Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
from nuplan.common.actor_state.ego_state import EgoState
from nuplan.common.actor_state.state_representation import Point2D, StateSE2, StateVector2D, TimePoint
from nuplan.common.actor_stat... | {"hexsha": "ab0c1ec86362682900f293ee104617127374e00f", "size": 14854, "ext": "py", "lang": "Python", "max_stars_repo_path": "nuplan/planning/scenario_builder/test/mock_abstract_scenario.py", "max_stars_repo_name": "motional/nuplan-devkit", "max_stars_repo_head_hexsha": "e39029e788b17f47f2fcadb774098ef8fbdd0d67", "max_s... |
[STATEMENT]
lemma ndet_cond_distr: "(P \<sqinter> (Q \<triangleleft> b \<triangleright> R)) = ((P \<sqinter> Q) \<triangleleft> b \<triangleright> (P \<sqinter> R))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (P \<or> (Q \<triangleleft> b \<triangleright> R)) = (P \<or> Q \<triangleleft> b \<triangleright> P \... | {"llama_tokens": 158, "file": "Circus_Relations", "length": 1} |
""" Utilities used for unit tests """
import numpy as np
def return_na_check(data):
"""Helper function for tests to check if the data returned is a
numpy array and that the imputed data has no NaN's.
Parameters
----------
data: numpy.ndarray
Data to impute.
Returns
-------... | {"hexsha": "989e66bdacf2fadb98439053434b8df6f46cc7b9", "size": 415, "ext": "py", "lang": "Python", "max_stars_repo_path": "impyute/ops/testing.py", "max_stars_repo_name": "eltonlaw/impy", "max_stars_repo_head_hexsha": "b76a6b4bd3da36515d5f1fa87f35d0c3f4209c83", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 316... |
#!/usr/bin/env python
"""
Created on Nov 1, 2015
@author: Alan L. Hutchison, alanlhutchison@uchicago.edu, Aaron R. Dinner Group, University of Chicago
This script is a bootstrapped expansion of the eJTK method described in
Hutchison AL, Maienschein-Cline M, and Chiang AH et al. Improved statistical methods enable gre... | {"hexsha": "27c7d0bb7c63c139e80916f6ff67ff281c11458c", "size": 19889, "ext": "py", "lang": "Python", "max_stars_repo_path": "BooteJTK-CalcP.py", "max_stars_repo_name": "vishalbelsare/BooteJTK", "max_stars_repo_head_hexsha": "3cc4b2530a3c615d06f132ce05e637dbedfa90d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""If enough time, compare our implementation
with https://github.com/CitrineInformatics/lolo.
"""
from typing import Any, Sequence
import numpy as np
from numpy.typing import NDArray
from sklearn.ensemble import RandomForestRegressor as RFR
Array = NDArray[np.float64]
class RandomForestRegressor(RFR):
"""Adap... | {"hexsha": "378e47568c68402e67d22efe1e1e206a61cd6ef6", "size": 7438, "ext": "py", "lang": "Python", "max_stars_repo_path": "thermo/rf.py", "max_stars_repo_name": "janosh/thermo", "max_stars_repo_head_hexsha": "0202a47ec8abacfd49b065ddd13ad060b0b9a1a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_star... |
//////////////////////////////////////////////////////////////////////////////
//
// (C) Copyright Ion Gaztanaga 2005-2012. 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/libs/interpr... | {"hexsha": "882256e934435165ec0e08c77a4f54a3cc8bbb95", "size": 17418, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "thirdparty/boost/include/boost/interprocess/interprocess_fwd.hpp", "max_stars_repo_name": "jason-fox/Fast-RTPS", "max_stars_repo_head_hexsha": "af466cfe63a8319cc9d37514267de8952627a9a4", "max_stars... |
# -*- coding: utf-8 -*-
"""
@date: 2020/3/2 上午8:07
@file: car_detector.py
@author: zj
@description: 车辆类别检测器
"""
import time
import copy
import cv2
import numpy as np
import torch
import torch.nn as nn
from torchvision.models import alexnet
import torchvision.transforms as transforms
import selectivesearch
import uti... | {"hexsha": "3e91a8a10f1e433493e19a29713eaed5a8fd564a", "size": 4961, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/car_detector.py", "max_stars_repo_name": "kyeonminsu/RCNN", "max_stars_repo_head_hexsha": "6f4e2ce072aa17b92932a8a88a0a6c1a535b3c38", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
program n
use percolation, critical_probability => pc
use utilities
implicit none
integer :: num_Ls, num_xs, i, j, fileunit, num_samples
integer, dimension(:), allocatable :: Ls
real(kind=dp), dimension(:), allocatable :: xs, invPI, Lpow
real(kind=dp) :: nu, tolerance, const, slope, pc
... | {"hexsha": "e7c526f4e7edfaa7a4d1ce54a33a75ec8ce5a81a", "size": 1733, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/n.f90", "max_stars_repo_name": "anjohan/fys4460-3", "max_stars_repo_head_hexsha": "8dd9dabbfd55ab82f9fcb9bc0b90ce6188d9a99c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import set_keep
import numpy as np
import resnet_model
import argparse
parser = argparse.ArgumentParser(description='Define parameters.')
parser.add_argument('--n_epoch', type=int, default=10)
parser.add_argument('--n_batch', type=int, default=6... | {"hexsha": "511994564ec16f11cb41fe3baf33f2622bc3afe5", "size": 5854, "ext": "py", "lang": "Python", "max_stars_repo_path": "support/resnet-tensorflow-master/main.py", "max_stars_repo_name": "sjkim04/AlphaGOZero-python-tensorflow", "max_stars_repo_head_hexsha": "32434d55466480ed2d3d042be654e62cf70d7cce", "max_stars_repo... |
import numpy as np
__all__ = ['BufferDescription']
class BufferDescription:
def __init__(self, buffer_index, buffer_dtype, attributes):
"""
* buffer_index is a buffer object index (as returned by glGenBuffers).
* buffer_dtype is the dtype of the numpy array that will be loaded
... | {"hexsha": "34ad0e735f60c22aea98e2877ae120a41f814f73", "size": 692, "ext": "py", "lang": "Python", "max_stars_repo_path": "glx/shader_program/buffer_description.py", "max_stars_repo_name": "NeilGirdhar/glx", "max_stars_repo_head_hexsha": "643abc73e05f94ea56a00deb927a3978f01184f2", "max_stars_repo_licenses": ["MIT"], "m... |
from __future__ import division
from ImportTrackMateData import *
import TrackClassGlobals as TCG
import TrackFilterFunctions as TFF
from Serializers import *
from General import *
import sys, traceback, datetime, os.path, math
import numpy as np
import pylab as P
from copy import deepcopy
import matplotlib.c... | {"hexsha": "0bad3a8d6837d8da4d5af0118a22343451b9780e", "size": 23813, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/TrackClass.py", "max_stars_repo_name": "AndrewHanSolo/CMP", "max_stars_repo_head_hexsha": "a2271ca8e6c1ac1fd4fb783dc6c44662f8c29482", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
/*
* mutgos_server_main.cpp
*/
#include <unistd.h>
#include <string>
#include <iostream>
#include <boost/program_options.hpp>
#include "logging/log_Logger.h"
#include "text/text_StringConversion.h"
#include "utilities/mutgos_config.h"
#include "osinterface/osinterface_Signals.h"
#include "utilities/memory_Thread... | {"hexsha": "e445c5bfd0698a6085889ad437461c7c7353423e", "size": 9847, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/exe/mutgos_server/mutgos_server_main.cpp", "max_stars_repo_name": "mutgos/mutgos_server", "max_stars_repo_head_hexsha": "115270d07db22d320e0b51095e9219f0a0e15ddb", "max_stars_repo_licenses": ["M... |
### Abstract parameter NStates
abstract type ParameterNState{S<:ValueSupport, F<:VariateForm} <: VariableNState{F} end
const MarkovChain = ParameterNState
DiscreteParameterNState{F<:VariateForm} = ParameterNState{Discrete, F}
ContinuousParameterNState{F<:VariateForm} = ParameterNState{Continuous, F}
UnivariateParam... | {"hexsha": "c0e5699bd5a2a474c91e05a85152475a31e0f0ba", "size": 853, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nstates/ParameterNStates/ParameterNStates.jl", "max_stars_repo_name": "teresy/Klara.jl", "max_stars_repo_head_hexsha": "ffa4f6d06e38b233dccc92f749e26d28d083f994", "max_stars_repo_licenses": ["MI... |
function LineAxis(parent::Scene; kwargs...)
attrs = merge!(Attributes(kwargs), default_attributes(LineAxis))
decorations = Dict{Symbol, Any}()
@extract attrs (endpoints, limits, flipped, ticksize, tickwidth,
tickcolor, tickalign, ticks, ticklabelalign, ticklabelrotation, ticksvisible,
tic... | {"hexsha": "7daa1f1a7835bb9b35782d8bd62731d338cdc2bd", "size": 10759, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lineaxis.jl", "max_stars_repo_name": "Datseris/MakieLayout.jl", "max_stars_repo_head_hexsha": "156a1da51b8c8eea8f5799bac80e0d463c9eb4a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import time
class RandomSearch(object):
def __init__(self, file=None):
if file is not None:
with open(file) as fp:
for i, line in enumerate(fp):
if i == 0:
self.n = int(line[2:])
self.cost_m... | {"hexsha": "38ae81c7b7a2cf48b52ebdaf40d501d8d0820241", "size": 2404, "ext": "py", "lang": "Python", "max_stars_repo_path": "RandomSearch.py", "max_stars_repo_name": "Netherwulf/QAP_Genetic_Algorithm", "max_stars_repo_head_hexsha": "292d2456f978e8613ba9b6ef74653cf7b936407d", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
clustering_utils.py: utilitary functions for the clustering.py module.
"""
import numpy as np
from enum import IntEnum
from .utils import find_in_sequence
class Link(IntEnum):
"""
Represents state of coreferring links.
Must be negative integers to not interfere with the clustering process.
"""
... | {"hexsha": "1577d38738d1df21a60bdf096aa6ca9a02c9b4ef", "size": 2391, "ext": "py", "lang": "Python", "max_stars_repo_path": "coref/clustering_utils.py", "max_stars_repo_name": "AndreFCruz/coref-web-platform", "max_stars_repo_head_hexsha": "845fd5461aad19a0f221077dbfbfd1d01766f0d6", "max_stars_repo_licenses": ["MIT"], "m... |
import scipy.optimize
# import numpy as np
import autograd.numpy as np # Thinly-wrapped numpy
from autograd import grad
import tensorflow as tf
from baselines import logger
import baselines.common.tf_util as U
class EtaOmegaOptimizer(object):
"""
Finds eta and omega Lagrange multipliers.
"""
def __... | {"hexsha": "0f3d08dfbf8539b6f33a0e34688bf95b55cfb612", "size": 9854, "ext": "py", "lang": "Python", "max_stars_repo_path": "Extra Algos/z_copos_mpi_ori/eta_omega_dual.py", "max_stars_repo_name": "ShuoZ9379/Integration_SIL_and_MBL", "max_stars_repo_head_hexsha": "d7df6501a665d65eb791f7fd9b8e85fd660e6320", "max_stars_rep... |
import numpy as np
import plotly.graph_objects as go
def trace_bodies(system, origin, t):
body_positions = system.all_positions_relative_to(origin, t)
x, y, z = zip(*[position for _, position in body_positions.items()])
return go.Scatter3d(x=x, y=y, z=z, mode="markers")
def trace_orbit_trajectory(trajec... | {"hexsha": "1914b39fd7656ae54ce4459d60be1e9d2d6f5084", "size": 989, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/engine/plot.py", "max_stars_repo_name": "RomainEndelin/keplerian_orbits", "max_stars_repo_head_hexsha": "3380e5d9a1006e73580cf3a86cb10845196c405d", "max_stars_repo_licenses": ["MIT"], "max_s... |
#load CSV
import csv
import numpy as np
from pandas import read_csv
from pandas import set_option
# load using csv
# filename = 'pima-indians-diabetes.data.csv'
# raw_data = open(filename, 'r')
# reader = csv.reader(raw_data, delimiter = ',', quoting = csv.QUOTE_NONE)
# x = list(reader)
# data = np.array(x).astype('f... | {"hexsha": "4b725ffd2312393aba68d6bf00298512142c7087", "size": 805, "ext": "py", "lang": "Python", "max_stars_repo_path": "archive/Model/others/load_csv.py", "max_stars_repo_name": "KrisCheng/Hitchhiker-Guide-to-Machine-Learning", "max_stars_repo_head_hexsha": "676edabc8690727b22189536b28de3e2dad0f08c", "max_stars_repo... |
Require Import VST.progs.conclib.
(* Axiomatization of view shifts, PCMs, and ghost state *)
Class PCM (A : Type) :=
{ join : A -> A -> A -> Prop;
join_comm : forall a b c (Hjoin : join a b c), join b a c;
join_assoc : forall a b c d e (Hjoin1 : join a b c) (Hjoin2 : join c d e),
exists c',... | {"author": "ildyria", "repo": "coq-verif-tweetnacl", "sha": "8181ab4406cefd03ab0bd53d4063eb1644a2673d", "save_path": "github-repos/coq/ildyria-coq-verif-tweetnacl", "path": "github-repos/coq/ildyria-coq-verif-tweetnacl/coq-verif-tweetnacl-8181ab4406cefd03ab0bd53d4063eb1644a2673d/packages/coq-vst/coq-vst.2.0/progs/ghost... |
import pytest
from ffai.core.game import *
from unittest.mock import *
import numpy as np
@patch("ffai.core.game.Game")
def test_armour_with_mighty_blow(mock_game):
# patch the mock game proc stack
stack = Stack()
mock_game.state.stack = stack
with patch("ffai.core.util.Stack", new_callable=Propert... | {"hexsha": "a758a571c48af1b788cade9ef8e0330f1f193e92", "size": 2777, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/game/test_armor.py", "max_stars_repo_name": "gsverhoeven/ffai", "max_stars_repo_head_hexsha": "673ff00e1aac905381cdfb1228ccfcfccda97d1f", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import curses
import numpy as np
scr = curses.initscr()
curses.halfdelay(5)
curses.noecho()
while True:
char = scr.getch()
scr.clear()
if char != curses.ERR:
scr.addstr(0, 0, chr(char))
print(char)
print(type(char))
# If char is anumber key
if char > 47 and char < ... | {"hexsha": "2be42d817281ce53d91a833593eaa958cfaa707b", "size": 814, "ext": "py", "lang": "Python", "max_stars_repo_path": "SLAM/Label.py", "max_stars_repo_name": "Radar3699/RANGER", "max_stars_repo_head_hexsha": "a9daee555f77abf8aa12c7aee7059c054cc4a443", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
@testset "Print/show" begin
# Test whether we can reconstruct the objects from their show values
"Simulates a user printing an object in the REPL, copying and pasting the result and pressing enter"
function show_and_eval(object)
io = IOBuffer()
show(io, object)
io |> take! |> String... | {"hexsha": "1da0f16717eded712443cece0425b60fa989b859", "size": 797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/print_tests.jl", "max_stars_repo_name": "andyDoucette/MultipleScattering.jl", "max_stars_repo_head_hexsha": "5f076c1049dddaa7c1c7d73ab8f18b4090eab350", "max_stars_repo_licenses": ["MIT"], "max_... |
# EXCLUDE FROM TESTING
using VectorEngine
function do_sigsegv(addr::Int64)
@veshow(unsafe_load(convert(Ptr{Int64}, addr)))
return
end
vesig = VectorEngine.vefunction(do_sigsegv, Tuple{Int64})
vesig(0)
synchronize()
| {"hexsha": "59c421e3d1c76839c95d96eb649418f22aa0956e", "size": 225, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/ve_sigsegv.jl", "max_stars_repo_name": "sx-aurora-dev/VectorEngine.jl", "max_stars_repo_head_hexsha": "57ca5a26653aafe17eb6b5d7036e1698846c202a", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
# coding: utf-8
import argparse
import os
import glob
import itertools
from pathlib import Path
from typing import Dict, List, Tuple
from collections import defaultdict
import json
import time
import logging
import random
import pandas as pd
import numpy as np
import re
import torch
from torch... | {"hexsha": "d6ed75022e3108bbd8f364b863f6dbf6c1c7df7e", "size": 27861, "ext": "py", "lang": "Python", "max_stars_repo_path": "Finetune PLMs/finetuneT5.py", "max_stars_repo_name": "anonymous-scholar/ICDM2021-TCube", "max_stars_repo_head_hexsha": "be10c73a455c98fccc03e7d28003c8a3adcfc324", "max_stars_repo_licenses": ["MIT... |
from multiprocessing import JoinableQueue, Process, Value
import argparse
import os
import torch
import matplotlib.pyplot as plt
from game.Player import RandomPlayer
from ai.RLPlayer import RLPlayer
from game.Game import Game, game_process
import numpy as np
from tqdm import tqdm
def parse_args():
parser = argpa... | {"hexsha": "b6091755def5d46c6ff56da11dcaf7a5b1d6da4d", "size": 6160, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl_training.py", "max_stars_repo_name": "Unn20/achtung_die_kurve", "max_stars_repo_head_hexsha": "e2dbb1752c070cfc398e415d5a427384c0230f3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import requests
import json
import pandas as pd
import math
from datetime import timedelta, datetime
import numpy as np
class Oiko():
def __init__(self, api_key):
self.api_key = api_key
def is_leap_year(year):
return (year % 4 == 0 and year % 100 != 0) or year % 400 == 0
def solar_angle... | {"hexsha": "35b74dd9730788b5fc3d3bad0dcd835cf0783caf", "size": 13160, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/oiko/oiko.py", "max_stars_repo_name": "oikoweather/oiko", "max_stars_repo_head_hexsha": "c17bcb774c88244562cc81b07a6b1aae8f98cebd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
(* To be imported qualified. *)
Require
MathClasses.categories.varieties MathClasses.theory.rings.
Require Import
Coq.setoid_ring.Ring
MathClasses.interfaces.abstract_algebra MathClasses.interfaces.universal_algebra MathClasses.theory.ua_homomorphisms MathClasses.misc.workaround_tactics.
Inductive op := plus | m... | {"author": "coq-community", "repo": "math-classes", "sha": "c11eb05a1e58a7293ef9a9a046ca02a9fd5b44bc", "save_path": "github-repos/coq/coq-community-math-classes", "path": "github-repos/coq/coq-community-math-classes/math-classes-c11eb05a1e58a7293ef9a9a046ca02a9fd5b44bc/varieties/rings.v"} |
!****h* ROBODoc/H5P (_F90)
!
! NAME
! H5P_PROVISIONAL
!
! PURPOSE
!
! This file contains Fortran 90 interfaces for H5P functions. It contains
! the same functions as H5Pff_F03.f90 but excludes the Fortran 2003 functions
! and the interface listings. This file will be compiled instead of H5Pff_F03.f90
! if Fortran ... | {"hexsha": "ebdd185d0dae79ab36910a3af14b1d4983b11b9c", "size": 30868, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "submodules/hdf5/fortran/src/H5Pff_F90.f90", "max_stars_repo_name": "pbasting/cactus", "max_stars_repo_head_hexsha": "833d8ca015deecdfa5d0aca01211632cdaca9e58", "max_stars_repo_licenses": ["MIT-... |
import numpy as np
class History(object):
def __init__(self, history_length=1):
self.history_length = history_length
self._empty = True
self.history = None
def add(self, obs):
if len(obs.shape) > 1:
obs = np.transpose(obs, (2, 0, 1))
if self.history is Non... | {"hexsha": "2ec6a7df1a995523ad586d1417b380f3f041ffc2", "size": 755, "ext": "py", "lang": "Python", "max_stars_repo_path": "lwrl/utils/history.py", "max_stars_repo_name": "sealday/lwrl", "max_stars_repo_head_hexsha": "52bcd67751e605c38db4afa609c58938c7034e8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
"""
Extinction Curves
"""
import numpy as np
from scipy import interpolate, interp
from astropy import units
import dust_extinction.parameter_averages as dustext_par
import dust_extinction.averages as dustext_avg
from dust_extinction.helpers import _test_valid_x_range
from beast.config import __ROOT__
__all__ = [
... | {"hexsha": "37f3a701eb4210799a620b0f42ae243d8389af9f", "size": 21049, "ext": "py", "lang": "Python", "max_stars_repo_path": "beast/physicsmodel/dust/extinction.py", "max_stars_repo_name": "galaxyumi/beast", "max_stars_repo_head_hexsha": "f5ce89d73c88ce481b04fc31a8c099c9c19041fb", "max_stars_repo_licenses": ["BSD-3-Clau... |
#! /usr/bin/env python2
import numpy as np
import cv2
import ipdb
from quick_robust.quick_robust import quick_robust
import time
class RobustQuadraticSolver:
def __init__(self,
flow_bases_u, # U basis (n_bases x (width*height))
flow_bases_v, # V basis (n_bases x (widght*height... | {"hexsha": "a1beef4dacf13d2ec8850e7a9d46c7938c78cd9f", "size": 7854, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcaflow/solver/RobustQuadraticSolver.py", "max_stars_repo_name": "Zekhire/pcaflow", "max_stars_repo_head_hexsha": "75f7b8b1df1f1b2b244eb6e0377abaf8b1a5d278", "max_stars_repo_licenses": ["RSA-MD"],... |
// Copyright John Maddock 2011.
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#include "pch.hpp"
#ifndef BOOST_BUILD_PCH_ENABLED
#define BOOST_MATH_OVERFLOW_ERROR_POLICY ign... | {"hexsha": "4f9690581d314dbaaac5743098166c3419215b0d", "size": 473, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/math/test/test_instances/float_test_instances_2.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da9... |
from __future__ import division
import numpy as np
def linear(F_CH4, ratio=0.15):
"""Calculates radiative forcing from oxidation of methane to H2O.
Stratospheric water vapour forcing follows a practically linear
relationship with the CH4 radiative forcing in MAGICC and AR5.
"""
F_H2O = ratio * F... | {"hexsha": "73272d4f35d2796203210f79e665059bbdcf4401", "size": 343, "ext": "py", "lang": "Python", "max_stars_repo_path": "fair/forcing/h2o_st.py", "max_stars_repo_name": "markperri/FAIR", "max_stars_repo_head_hexsha": "4aa7c6137a07585b7d56044a3e4506ca9c7de03c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# Math behind LinearExplainer with correlation feature perturbation
When we use `LinearExplainer(model, prior, feature_perturbation="correlation_dependent")` we do not use $E[f(x) \mid do(X_S = x_S)]$ to measure the impact of a set $S$ of features, but rather use $E[f(x) \mid X_S = x_s]$ under the assumption that the ... | {"hexsha": "b08d525728e387ab3ea393ea7bd2c82a80097ddb", "size": 5862, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/linear_explainer/Math behind LinearExplainer with correlation feature perturbation.ipynb", "max_stars_repo_name": "santanaangel/shap", "max_stars_repo_head_hexsha": "1c1c4a4... |
NMF_method1<-function(tg_list,data_ng,data_normalized,max_ES_cut=0.3)
{
tg_selected_R4_RR<-tg_list
data.matrix0_s<-data_ng
data_23_s<-data_normalized
Rbase_selected_R4_RR<-Compute_Rbase_SVD(data.matrix0,tg_selected_R4_RR)
stat_selected_R4_RR<-compute_IM_stat(tg_list_c=tg_selected_R4_RR)
stat_selected_R4_RR_max<-... | {"hexsha": "6819473eb7a07cd2ee112202223b08dd67646f5d", "size": 9820, "ext": "r", "lang": "R", "max_stars_repo_path": "R/NMF_functions_new.r", "max_stars_repo_name": "changwn/ICTD", "max_stars_repo_head_hexsha": "acb0d5c2c859b4c756e1ff50e6624046a2f68d36", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
% Chapter Introduction
% Section Motivation
\section{Motivation} \label{section:motivation}
% Short description of what is Linked Data, Question Answering, SPARQL. What is the relationship between these concepts? What is the current situation and what are the current demands? What is the (goal) task of this thesis? ... | {"hexsha": "dee13d7f619be3f9c658479289d7f36f4db06337", "size": 5109, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis/chapters/1-introduction.tex", "max_stars_repo_name": "xiaoyuin/tntspa", "max_stars_repo_head_hexsha": "2be4151035de7b61c02ed2df2e3c06afe78e75f1", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import pylab as pl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
def plot_steady_states(ax, states, offset=0,
color='g', label='Steady state'):
"""
Args:
* ax (Axes)
* states (pd.DataFrame): Steady States
* offset (int or float): ... | {"hexsha": "1d92dcbed0f0403cdc562dbfc648ef2708633597", "size": 6056, "ext": "py", "lang": "Python", "max_stars_repo_path": "slicedpy/plot.py", "max_stars_repo_name": "JackKelly/slicedpy", "max_stars_repo_head_hexsha": "c2fa7eb4c7b7374f8192a43d8e617b63c9e25e62", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
#!/usr/bin/env python
# This program generates a line/table with all Atom-Atom contributions to the Lennard Jones Energy between two fragments
#
# The atom order for each fragment is read from the .rtf file
# The Atom types are obtained either from the .rtf file or from the .lpun file
# The number of fragments and the ... | {"hexsha": "f88da417bf1d175645e48f043268c7471f2af609", "size": 7502, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/lj-fit/src/fit.lj/LJ_Tab_gen.py", "max_stars_repo_name": "FHedin/FittingWizard", "max_stars_repo_head_hexsha": "03ca95d52f9a4ecc1fe0466bc11d7de3e91dc6ae", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma error_free_throw [simp,intro]:
"error_free s \<Longrightarrow> error_free (abupd (throw x) s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. error_free s \<Longrightarrow> error_free (abupd (throw x) s)
[PROOF STEP]
by (cases s) (simp add: throw_def) | {"llama_tokens": 105, "file": null, "length": 1} |
################################# JSON Parsers #################################
JSON2.@format Range keywordargs begin
# lb => (default=0.0,)
# ub => (default=MAXNUMBER,)
end
JSON2.@format Flexibility keywordargs begin
# # flexibility_level => (default=0,)
# fixed_price => (default=0.0,)
# unit_pri... | {"hexsha": "8acadbd6ad004de1e586dc478812b01af9882871", "size": 5530, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parser_json.jl", "max_stars_repo_name": "ResourceMind/RVRP.jl", "max_stars_repo_head_hexsha": "5df44ca0d145e0fd5e582db179a9b9670bff7178", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
## for this one, change the order between relu and batch
import tensorflow as tf
import numpy as np
from io_Cosmo import *
import hyper_parameters_Cosmo as hp
import time
#import the Cray PE ML Plugin
import ml_comm as mc
import os
if "cori" in os.environ['HOST']:
os.environ['OMP_NUM_THREADS'] = "66"
os.environ[... | {"hexsha": "8131390640b43235e249b5e5fdd9dc222bfe8a41", "size": 18327, "ext": "py", "lang": "Python", "max_stars_repo_path": "CosmoNet_noFeed.py", "max_stars_repo_name": "NERSC/CosmoFlow", "max_stars_repo_head_hexsha": "28937fad012b8bf854916527ebfc74f60de0ac26", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_co... |
using NormalSmoothingSplines
using DoubleFloats
using Test
@testset "NormalSmoothingSplines.jl" begin
include("1D.jl")
include("2D.jl")
include("3D.jl")
include("1D_B.jl")
end
| {"hexsha": "a241dc85bd8ebd702447e981c9bf9f58677bb765", "size": 191, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "IgorKohan/NormalSmoothingSplines.jl", "max_stars_repo_head_hexsha": "828b167daba02bf637463fe00afb53ea349058fe", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
Plotting library
"""
def plot_framea_as_RGB(frames = (None,None,None)):
"""
"""
from matplotlib import pyplot as plt
from numpy import zeros
img = zeros((3000,4096,3),dtype = 'uint8')
for i in range(3):
img[:,:,i] = frames[i]
fig = plt.figure(figsize=(4, 4))
grid = plt.GridSp... | {"hexsha": "5b68f4341d049e4d71002174777ea88385ff402e", "size": 4569, "ext": "py", "lang": "Python", "max_stars_repo_path": "lcp_video/plotting.py", "max_stars_repo_name": "vstadnytskyi/lcp-video", "max_stars_repo_head_hexsha": "a65f9c8ecd370d975128af67427f3dd8141bf667", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
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