text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
#include "shift/rc/action_mesh_import_ply.hpp"
#include "shift/rc/optimizer_mesh/filter.hpp"
#include "shift/rc/resource_compiler_impl.hpp"
#include <shift/resource_db/mesh.hpp>
#include <shift/log/log.hpp>
#include <shift/math/vector.hpp>
#include <boost/endian/conversion.hpp>
#include <filesystem>
#include <fstream>
... | {"hexsha": "02a69fbf9a7030caee5e8ce1a8ae6af6eac3ef1f", "size": 26045, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "shift/rc/private/shift/rc/action_mesh_import_ply.cpp", "max_stars_repo_name": "cspanier/shift", "max_stars_repo_head_hexsha": "5b3b9be310155fbc57d165d06259b723a5728828", "max_stars_repo_licenses": ... |
# Copyright 2021 PaddleFSL Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | {"hexsha": "a43229cdc6de66d26780e62e592dce0fa73caafb", "size": 9365, "ext": "py", "lang": "Python", "max_stars_repo_path": "PaddleFSL/paddlefsl/model_zoo/maml_mol.py", "max_stars_repo_name": "Chaoqun-Guo/FSL-Mate", "max_stars_repo_head_hexsha": "06d725a5aa6e49a36fed9718d4872f86dfe14323", "max_stars_repo_licenses": ["MI... |
function peak_similarity(connectivity, pmap, tspan, init_cond)
model = create_model(connectivity)
@nonamespace u₀map = [model.a[1] => init_cond[1], model.a[2] => init_cond[2],
model.a[3] => init_cond[3]]
prob = ODEProblem(model, u₀map, tspan, pmap)
sol = solve(prob, lsoda())
pks, ~ = c... | {"hexsha": "0e25896b5a331cc0a141bd11158b7aad3cde6598", "size": 2116, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_periodogram_peak.jl", "max_stars_repo_name": "ftavella/TunabilityOscillations.jl", "max_stars_repo_head_hexsha": "14879d11c93fe0b67561bd39ffe340329125752e", "max_stars_repo_licenses": ["M... |
#!/usr/bin/env python
""" Numbers recognition """
"""____________________"""
""" TRAIN MODEL
"""
###### Set global Theano config #######
import os
t_flags = "mode=FAST_RUN,device=cpu,floatX=float32, optimizer='fast_run', allow_gc=False"
print("Theano Flags: " + t_flags)
os.environ["THEANO_FLAGS"] = t_flags
#... | {"hexsha": "6262fe399e3d53d97e924b1af91face586ebfc0c", "size": 3224, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/numbers_recognition/train_model.py", "max_stars_repo_name": "joergfranke/recnet", "max_stars_repo_head_hexsha": "bfb8a359207258d4c2f71fe4a1304764f6f355cb", "max_stars_repo_licenses": ["MI... |
import numpy as np
from libs.normalise_angle import normalise_angle
class StanleyController:
def __init__(self, control_gain=2.5, softening_gain=1.0, yaw_rate_gain=0.0, steering_damp_gain=0.0, max_steer=np.deg2rad(24), wheelbase=0.0, path_x=None, path_y=None, path_yaw=None):
"""
Stanley ... | {"hexsha": "32229d61bc5e5cc92445b60cffd0458bb378815f", "size": 4815, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/stanley_controller.py", "max_stars_repo_name": "britig/S2RL-Policies", "max_stars_repo_head_hexsha": "b9c74b7f5efec225920c09f7e8e82d8555d61bd9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
% SP_TO_VTK: Export to VTK format for plotting (store data in
% binary base64 encoded format).
%
% sp_to_vtk (u, space, geometry, npts, filename,
% fieldname, [option], [precision])
% sp_to_vtk (u, space, geometry, pts, filename,
% fieldname, [option], [precision])
%
% INPUT:
% ... | {"author": "rafavzqz", "repo": "geopdes", "sha": "3bfa57b1a38bd4da3148536c9f67cce81afce701", "save_path": "github-repos/MATLAB/rafavzqz-geopdes", "path": "github-repos/MATLAB/rafavzqz-geopdes/geopdes-3bfa57b1a38bd4da3148536c9f67cce81afce701/geopdes/inst/obsolete/sp_to_vtk_raw.m"} |
function IterativeCallback(time_choice, user_affect!,tType = Float64;
initial_affect = false,
initialize = (cb,u,t,integrator) -> u_modified!(integrator, initial_affect),
kwargs...)
# Value of `t` at which `f` should be called next:
... | {"hexsha": "4bbc28331d7603f6c19f89f162eeb6a87816f6ca", "size": 4166, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/iterative_and_periodic.jl", "max_stars_repo_name": "colebrookson/DiffEqCallbacks.jl", "max_stars_repo_head_hexsha": "525241a807987f1ba714a71513adc06c4e8c4f55", "max_stars_repo_licenses": ["MIT"... |
[STATEMENT]
lemma exp_golomb_bit_count_exact:
"bit_count (N\<^sub>e n) = 2 * \<lfloor>log 2 (n+1)\<rfloor> + 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bit_count (N\<^sub>e n) = ereal (real_of_int (2 * \<lfloor>log 2 (real (n + 1))\<rfloor> + 1))
[PROOF STEP]
by (simp add:N\<^sub>e_def elias_gamma_bit_count... | {"llama_tokens": 157, "file": "Prefix_Free_Code_Combinators_Prefix_Free_Code_Combinators", "length": 1} |
\subsection{Density matrix}
| {"hexsha": "f91ba45a0aaf8f4b8bbe655d0b61546ee0afe4a8", "size": 31, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/physics/QM/06-04-density.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_licenses": ["M... |
#
# Copyright (c) 2020-present, Andrei Yaskovets
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
from fplib.minutae import MnType
def _get_core(minutae: np.array):
return [point for point in minutae if point[2] ==... | {"hexsha": "28270cfd709626ae2afa04886c27f5380cdb3f0b", "size": 4748, "ext": "py", "lang": "Python", "max_stars_repo_path": "fplib/feature.py", "max_stars_repo_name": "marcohatran/pyfnprint", "max_stars_repo_head_hexsha": "be4318e0eaaf2132370a864a5448800105ad9e71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import pomegranate as pm
# from semisup.frameworks.CPLELearning import CPLELearningModel
from pomegranate import BayesClassifier, NaiveBayes
from sklearn.semi_supervised import LabelPropagation, LabelSpreading
from scipy.sparse import csgraph
from .PseudoLabeler import PseudoLabeler
# from semisup... | {"hexsha": "cd0ecc81684b560c85d0ce2238b4879e428305ab", "size": 10201, "ext": "py", "lang": "Python", "max_stars_repo_path": "seqlearner/SemiSupervisedLearner.py", "max_stars_repo_name": "EliHei/SeqLearn", "max_stars_repo_head_hexsha": "bfcc6f1a48c3eb2e5002f72a02390360fa51498e", "max_stars_repo_licenses": ["MIT"], "max_... |
from typing import Tuple
import cv2
import numpy as np
from .image_filter import image_filter
from .kernels import sharpening_kernel
from ..utils.constants import DEFAULT_KERNEL_SIZE
from ..utils.types import RangedNumber
def convert_to_hsv(image: np.ndarray) -> np.ndarray:
"""
Converts an image to hsv - Ma... | {"hexsha": "dba4a0a0e2daa030c0854387b92944b77c64e38c", "size": 10194, "ext": "py", "lang": "Python", "max_stars_repo_path": "ovl/image_filters/image_filters.py", "max_stars_repo_name": "SerpentBit/ovl", "max_stars_repo_head_hexsha": "e11baa551f4e8e9682518e595de2f3b81aae8848", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import pandas as pd
import numpy as np
'''
@ More methods of outlier treatment
- Isolation Forest
- KNN
- Average KNN
- Angle Based Outlier Detection
- Clustering Based local outlier factor
- Feature Bagging
'''
class OutlierTreatment():
def __init__ (self, method='iqr', tol = 1.5):
"""
This... | {"hexsha": "f38d87a62f33e4c7aed252fbd2534c4f98eb3987", "size": 3596, "ext": "py", "lang": "Python", "max_stars_repo_path": "fast_ml/outlier_treatment.py", "max_stars_repo_name": "samarth-agrawal-86/fast_ml", "max_stars_repo_head_hexsha": "8943d6e72522ab423d1ea537b38be765e32ee478", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.callbacks import TensorBoard
from keras.... | {"hexsha": "55b95b8bd382cfa74b38f58ea8a410188d82ec1a", "size": 5206, "ext": "py", "lang": "Python", "max_stars_repo_path": "NLP/Keras-SentimentAnalysis-Conv1D.py", "max_stars_repo_name": "sunnyshah2894/Tensorflow", "max_stars_repo_head_hexsha": "715f9f53bc9c7ddfe448e7543314d491008cfd51", "max_stars_repo_licenses": ["MI... |
import sys
# from scipy.special import comb
end = int(sys.argv[1])
comb_cache = [1]
def comb(n, k):
result = comb_cache[k - 1] * n // k
if k == len(comb_cache):
comb_cache.append(result)
else:
comb_cache[k] = result
return result
fub = [1]
for n in range(1, end + 1):
fub.appe... | {"hexsha": "e9fe18ea92243c5dd48229eb9f19cb9316ebb2a8", "size": 390, "ext": "py", "lang": "Python", "max_stars_repo_path": "fubini.py", "max_stars_repo_name": "Geo5/hackathon-fubini", "max_stars_repo_head_hexsha": "86bec8755a888992f372eaec5d20dba0d39d0b41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
/*
* Copyright (c) 2016-2018 Nicholas Corgan (n.corgan@gmail.com)
*
* Distributed under the MIT License (MIT) (See accompanying file LICENSE.txt
* or copy at http://opensource.org/licenses/MIT)
*/
#include "../utils/misc.hpp"
#include "database_common.hpp"
#include "id_to_index.hpp"
#include "id_to_string.hpp"
#... | {"hexsha": "8f374b6b4196071246401b5eb89346f001499c66", "size": 8434, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/database/id_to_index.cpp", "max_stars_repo_name": "ncorgan/libpkmn", "max_stars_repo_head_hexsha": "c683bf8b85b03eef74a132b5cfdce9be0969d523", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module ALaCarte
import Fix
%access public export
||| Position of a sub-type in a super-type composed of signatures `fs`.
data Sig : (fs : List (Type -> Type)) -> (a : Type) -> Type where
||| The sub-type `f` is located at the head of the list of composed types `fs`.
Here : f a -> Sig (f :: fs) a
||| The... | {"hexsha": "568d709f74b0aec4944871b9a4d6cdf852ab80b7", "size": 2091, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "src/ALaCarte.idr", "max_stars_repo_name": "BakerSmithA/alacarte-idris", "max_stars_repo_head_hexsha": "885a1a9ca3bdc1ff3ef64339cbbd740fdcec15ca", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
\documentclass[
twoside,
11pt, a4paper,
footinclude=true,
headinclude=true,
cleardoublepage=empty
]{scrreprt}
\usepackage{lipsum}
\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{graphicx}
\usepackage{caption}
\usepackage[x11names]{xcolor}
\u... | {"hexsha": "b40bf265af3c0fbc62641e5ead6f73813c9738c8", "size": 3777, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sci-paper/paper.tex", "max_stars_repo_name": "Alisa-lisa/templates", "max_stars_repo_head_hexsha": "68ebab79aeb2e7ae40a58a43bbf0d5397861d54b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
===================
Bar demo with units
===================
A plot using a variety of centimetre and inch conversions. This example shows
how default unit introspection works (ax1), how various keywords can be used to
set the x and y units to override the defaults (ax2, ax3, ax4) and how one can
set the xlimits us... | {"hexsha": "d956c1760bd342cd34a706aef6bf04c73fa0c029", "size": 1052, "ext": "py", "lang": "Python", "max_stars_repo_path": "matplotlib_examples/examples_src/units/bar_demo2.py", "max_stars_repo_name": "xzlmark/webspider", "max_stars_repo_head_hexsha": "133c620c65aa45abea1718b0dada09618c2115bf", "max_stars_repo_licenses... |
from klampt import WorldModel,RobotModel,RobotModelLink,Geometry3D
from klampt.math import vectorops,so3,se3
from klampt.model import ik
from klampt import vis
from klampt.io import numpy_convert
import math
import numpy as np
from klampt.model.create import primitives
#I seem to have reached an impasse; that importi... | {"hexsha": "8ea6c6a7a92a56d1d17bbdb8b86d3dac5dfbf15a", "size": 4442, "ext": "py", "lang": "Python", "max_stars_repo_path": "piano.py", "max_stars_repo_name": "nikwalia/piano-man", "max_stars_repo_head_hexsha": "b2f3c508aa53bae341664231e4f04866f7c0ad34", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
"""
The MIT License (MIT)
Copyright (c) 2014 Tolga Birdal, Eldar Insafutdinov
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, ... | {"hexsha": "aff008168d0714ce48a2a8a63adf5c5f5d932213", "size": 2622, "ext": "py", "lang": "Python", "max_stars_repo_path": "2Dpm/util/quaternion_average.py", "max_stars_repo_name": "Sirish07/2D_projection_matching", "max_stars_repo_head_hexsha": "11c8ea81e3cbf5ecd3daba602cde0b7a9efcc15d", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma flip_self [simp]: "(a \<leftrightarrow> a) = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a \<leftrightarrow> a) = 0
[PROOF STEP]
unfolding flip_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (atom a \<rightleftharpoons> atom a) = 0
[PROOF STEP]
by (rule swap_self) | {"llama_tokens": 133, "file": "Nominal2_Nominal2_Base", "length": 2} |
# functions for io
# not done yet
function show(io::IO, ::MIME"text/plain", group::PTGroup)
print(io,"group_id = $(group.groupid)\n")
print(io,"n = $(group.n)\n")
print(io,"sires = $(group.sires)\n")
print(io,"dams = $(group.dams)\n")
if isempty(group.generation)
print(io,"max generation = empty l... | {"hexsha": "405e5cd7286d4da50f59aaa04bd4265847028461", "size": 3005, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/io.jl", "max_stars_repo_name": "masuday/ProgenyTestingTools.jl", "max_stars_repo_head_hexsha": "f09d457c839cb191e1c9340c35a101ba8d56784d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from .extension import _wspd, _wdir
from .destag import destagger
from .util import extract_vars, either
from .decorators import convert_units
from .metadecorators import set_wind_metadata
@convert_units("wind", "m s-1")
def _calc... | {"hexsha": "40f6fecb284c74fba2f640e32b0c67f159a459e7", "size": 21115, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wrf/g_wind.py", "max_stars_repo_name": "khallock/wrf-python", "max_stars_repo_head_hexsha": "9c5825c101722e7eddece2ca13cc8e9d9f96a21e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
"""
parameter(sma, ecc)
Get the parameter (semi-latus rectum) of the orbit.
"""
parameter(sma, ecc) = sma * (1 - ecc * ecc)
"""
periapsis_radius(sma, ecc)
Get the smallest radius along the orbit.
"""
periapsis_radius(sma, ecc) = sma * (1 - ecc)
"""
apoapsis_radius(sma, ecc)
Get the largest radius along... | {"hexsha": "a8c5fccc35b5bd308d469cd5099425139f703f35", "size": 2807, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/twobody/twobody.jl", "max_stars_repo_name": "rjpower4/Pat.jl", "max_stars_repo_head_hexsha": "79893b6f76b24db89ae6f06524a759c8210c8515", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
# Copyright (c) 2019. TsumiNa. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
from collections import OrderedDict
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from shutil import rmtree
import torch
import ... | {"hexsha": "d557d5bc0d722bed603cb5d175f4add23cdf0088", "size": 11561, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/models/test_extension.py", "max_stars_repo_name": "aya-miyazaki/XenonPy", "max_stars_repo_head_hexsha": "90971cc362402715ba15c63f5d75070f9680fd78", "max_stars_repo_licenses": ["BSD-3-Clause... |
#!/usr/bin/env python
from __future__ import print_function
import dace
import mkl
import numpy as np
import os
import scipy.linalg as linalg
import csv
from numpy.fft import fft
#####################################
N = dace.symbol('N')
@dace.program(dace.complex128[N], dace.complex128[N])
def DFT(X, Y):
# Ge... | {"hexsha": "6f32dcaccdb83b5d8a2fadd9bf88a47262aca6bb", "size": 1562, "ext": "py", "lang": "Python", "max_stars_repo_path": "dace-dft.py", "max_stars_repo_name": "Gabbeo/dace-fft", "max_stars_repo_head_hexsha": "0a3ac4fb37dfb230c16ebccb10ba5e34dbd42fb2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1,... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.montgomery64_2e129m25_3limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition opp :
{ opp : feBW_small -> feBW_small
| forall a, phiM_small (opp a) = F.opp (phiM_small a) }.
Proof.
Set Ltac P... | {"author": "anonymous-code-submission-01", "repo": "sp2019-54-code", "sha": "8867f5bed0821415ec99f593b1d61f715ed4f789", "save_path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code", "path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code/sp2019-54-code-8867f5bed0821415ec99f593b1d61f715ed4f7... |
import Data.Vect
-- Page 50
-- Exercise 2
{- reversei : List Char -> List Char
reversei [] = []
reversei (x :: xs) = reversei xs ++ [x]
reverse : String -> String
reverse x = pack $ reversei $ unpack x -}
-- Exercise 2
palindrome2 : String -> Bool
palindrome2 x = x == reverse x
-- Exercise 3
palindrome3 : String -... | {"hexsha": "d10306bfc0d9b7b4efe2093062deb23b921cb6e9", "size": 1842, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "B_type_driven_development/Week_6/week6_1.idr", "max_stars_repo_name": "JnxF/advanced-software-analysis", "max_stars_repo_head_hexsha": "3ad336918f4aa6272d6d2feebf4e02ee264e8e0b", "max_stars_repo_l... |
#!/usr/bin/env python
""" lambdata - a Data Science Helper
"""
import numpy as np
import pandas as pd
import random
VERSION = 0
ONES = np.ones(100)
ONES_DF = pd.DataFrame(ONES)
# Checking a dataframe for nulls
def check_nulls(df):
print(df.isnull().sum())
# Making more rows of randomized column data
def more_... | {"hexsha": "b8eb064cbd8c38ee7c2c592bfc61c7f52df60ed4", "size": 524, "ext": "py", "lang": "Python", "max_stars_repo_path": "lambdata_joshdsolis/__init__.py", "max_stars_repo_name": "joshdsolis/lambdata", "max_stars_repo_head_hexsha": "c42a4c0753afc2fb67ad081e471a82cb2c2b0d18", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
program SERIES
implicit none
real x,sum
integer i,n
write(*,*)'give the value of x and n'
read(*,*)x,n
i=1
sum=1.0
10 sum=sum+x**i
if (i.lt.n)then
i=i+1
go to 10
endif
write(*,*)"the sum is=",sum
pause
stop
end
| {"hexsha": "23758ca7fed23a44b669f776ca1150de780dac48", "size": 310, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "SUM(X^N).f", "max_stars_repo_name": "Bithika9/Fortran", "max_stars_repo_head_hexsha": "f1cbf7780383e0e53db5480f11edeb2387805fb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
#!/usr/bin/env python
"""
Copyright 2019 Jesus Villalba (Johns Hopkins University)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import sys
import os
from jsonargparse import (
ArgumentParser,
ActionConfigFile,
ActionParser,
namespace_to_dict,
)
import time
import logging
import nump... | {"hexsha": "0e2f017306e13012b314e9c460a0b75a19306ca4", "size": 15508, "ext": "py", "lang": "Python", "max_stars_repo_path": "hyperion/bin/torch-extract-xvectors-slidwin.py", "max_stars_repo_name": "hyperion-ml/hyperion", "max_stars_repo_head_hexsha": "c4c9eee0acab1ba572843373245da12d00dfffaa", "max_stars_repo_licenses"... |
\documentclass[12pt]{article}
\include{preamble}
\title{Math 390.4 / 650.3 Spring 2018 \\ Midterm Examination Two}
\author{Professor Adam Kapelner}
\date{Monday, April 16, 2018}
\begin{document}
\maketitle
\noindent Full Name \line(1,0){410}
\thispagestyle{empty}
\section*{Code of Academic Integrity}
\footnotes... | {"hexsha": "d6531ee28fb99af6c7541e057fd80bd45428fcbb", "size": 17309, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "exams/midterm2/midterm2.tex", "max_stars_repo_name": "alphanota/QC_Math_390.4_Spring_2018", "max_stars_repo_head_hexsha": "798c265510d074d83ac1e6f7b9b685e629b2742d", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma moebius_pt_moebius_translation_inf [simp]:
shows "moebius_pt (moebius_translation v) \<infinity>\<^sub>h = \<infinity>\<^sub>h"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. moebius_pt (moebius_translation v) \<infinity>\<^sub>h = \<infinity>\<^sub>h
[PROOF STEP]
unfolding moebius_translation_de... | {"llama_tokens": 202, "file": "Complex_Geometry_Moebius", "length": 2} |
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 16 22:47:22 2020
@author: kamakshi_behl
"""
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
def datahandler(filename):
data=pd.DataFrame(pd.read_csv(filename))
print("Data to be operated is ")
print(data)
... | {"hexsha": "e168ccbe79abdc1fc519c37b2d7bacf533be1a41", "size": 891, "ext": "py", "lang": "Python", "max_stars_repo_path": "datahandler.py", "max_stars_repo_name": "kamakshibehl/missing_values_kamakshi", "max_stars_repo_head_hexsha": "2ba37620a7ecfc2b2a63dbe0245038c2fcd2ebb6", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""Categorical LSTM Policy.
A policy represented by a Categorical distribution
which is parameterized by a Long short-term memory (LSTM).
"""
# pylint: disable=wrong-import-order
import akro
import numpy as np
import tensorflow as tf
from garage.tf.models import CategoricalLSTMModel
from garage.tf.policies... | {"hexsha": "6a709f94e62f4e63eb237e4668d4e40b6633751f", "size": 13484, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/garage/tf/policies/categorical_lstm_policy.py", "max_stars_repo_name": "fangqyi/garage", "max_stars_repo_head_hexsha": "ddafba385ef005f46f913ab352f9638760e5b412", "max_stars_repo_licenses": [... |
# Copyright 2020, Battelle Energy Alliance, LLC
# ALL RIGHTS RESERVED
import numpy as np
def run(self,Input):
t_shutdown = 10 # days
repl_cost = 4.68 # M$
risk_free_rate = 0.03
hard_savings = 0.
self.sws_npv_a = Input['sws_p_failure'] * t_shutdown + repl_cost + hard_savings
self.sws_npv_b = self.sws_npv_a... | {"hexsha": "6ff86c4dbb6d08336c131a7f8d7dde4c9fa77cc8", "size": 346, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/MilestoneTestsSeptFY19/use_case_II/MC/service_water_system.py", "max_stars_repo_name": "dgarrett622/LOGOS", "max_stars_repo_head_hexsha": "7234b8b5e80bc79526b4cbced7efd5ae482f7c44", "max_star... |
import numpy as np
import pandas as pd
from int16hash import int16hash, search_hash
from imgfeature import ImSim
from time import time
def fp2des(fp):
kp_num = int(len(fp) / (64))
ut8arr = np.array([int(fp[i:i+2], 16) for i in range(0, len(fp), 2)], dtype=np.uint8)
return ut8arr.reshape(kp_num, 32)
class ... | {"hexsha": "380e71b57fac163096308d4b270a20eaf544ea6d", "size": 1230, "ext": "py", "lang": "Python", "max_stars_repo_path": "pimquery.py", "max_stars_repo_name": "TubatuBD/pimquery", "max_stars_repo_head_hexsha": "714f14000d207289c69e3a39fe4703d87bbe2d47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import numpy as np
import pandas as pd
import streamlit as st
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report
from sklearn.datasets import load_diabetes, load_boston
st.title("The EDA App")
st.write("Upload dataset in CSV format and the app will show its Exploratory ... | {"hexsha": "59688158d12147d515cd8a9fddbae87258b7e04e", "size": 1771, "ext": "py", "lang": "Python", "max_stars_repo_path": "eda.py", "max_stars_repo_name": "Gaurav3099/The-EDA-App", "max_stars_repo_head_hexsha": "b7c55c7495e8a7a5c90c1f0da9b7ff1fafd4cbbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
from numpy import zeros, pi
from skimage.morphology import watershed, disk, rectangle, dilation
from skimage.filters import sobel, sobel_h
from skimage.filters.rank import median
from .utils import norm, image_cart_to_polar, image_polar_to_cart
def watershed_edge(image, dilationSize=0, radial=True, filterSize=0):
... | {"hexsha": "4fd3717a23d1b00d50aa2cf4ed50cedbfe6e4e57", "size": 2476, "ext": "py", "lang": "Python", "max_stars_repo_path": "single_cell_detect/single_cell_detect.py", "max_stars_repo_name": "sofroniewn/SingleCellDetect", "max_stars_repo_head_hexsha": "46e662866966ca15d04ab9c872e469c99342a838", "max_stars_repo_licenses"... |
%
% File: chap02.tex
% Author: Derrick Choe, Paul Le Tran
% Description: Regressions Diagnostics and Results.
%
\let\textcircled=\pgftextcircled
\chapter{Regressions Diagnostics \& Results}
\label{chap:3}
%=========================================================
\section{Industry Concentration \& Profitability}
\lab... | {"hexsha": "fcd53e450ee6af06732c4b12841efc006df43759", "size": 20635, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/chapters/chapter02/chap02.tex", "max_stars_repo_name": "PaulTran47/ECON190", "max_stars_repo_head_hexsha": "d5f2b7e32a26662252722be69fe246487fc0f3fb", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma zcf_monom_sub':
assumes "p \<in> carrier P"
assumes "a \<in> carrier R"
shows "zcf ((monom P a (Suc n)) of p) = a \<otimes> zcf p [^] (Suc n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. zcf (Cring_Poly.compose R (monom P a (Suc n)) p) = a \<otimes> zcf p [^] Suc n
[PROOF STEP]
using zcf_... | {"llama_tokens": 648, "file": "Padic_Ints_Cring_Poly", "length": 2} |
# BSD 3-Clause License; see https://github.com/scikit-hep/uproot4/blob/main/LICENSE
from __future__ import absolute_import
import sys
import json
import numpy
import pytest
import skhep_testdata
import uproot
def test_leaf_interpretation():
with uproot.open(
skhep_testdata.data_path("uproot-sample-6.2... | {"hexsha": "3f8739d031977df7894a555c31cc4bcbe062137d", "size": 36441, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_0018-array-fetching-interface.py", "max_stars_repo_name": "ryuwd/uproot4", "max_stars_repo_head_hexsha": "20d8575e941c32559c7b5e62b0ed5f92bc4927d0", "max_stars_repo_licenses": ["BSD-3-... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.multiprocessing import Pool
from functools import partial
from .proj_utils import network as net_utils
from .proj_utils.model_utils import *
from .proj_utils.torch_utils import to_device
class MakeLayers(nn... | {"hexsha": "8385444b7a902a452c5907b4d6c9a0dc87d17107", "size": 10059, "ext": "py", "lang": "Python", "max_stars_repo_path": "srdense/cyclenet.py", "max_stars_repo_name": "ypxie/SuperRes", "max_stars_repo_head_hexsha": "1dded37fc24d99ca32cef88e8ccc3f2f0a3738c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, ... |
import matplotlib.pyplot as plt
import numpy as np
class Pokedex:
@staticmethod
def show_list(data):
if data != []:
for i in data:
for j in i:
print(j.capitalize())
else:
print("No se han encontrado tipos con ese nombre")
@static... | {"hexsha": "ea41389e8389d45dbf21533d95cb01e79cc05a3a", "size": 1755, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/pokedex.py", "max_stars_repo_name": "DaletWolff/Crud_pokedex", "max_stars_repo_head_hexsha": "6edc7df0aa94e22111c371b65b825d225d415b5d", "max_stars_repo_licenses": ["Unlicense"], "max_star... |
[STATEMENT]
lemma traces_alt:
shows "traces A = {tr . \<exists> e . is_exec_of A e
\<and> tr = trace (ioa.asig A) e}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. local.traces A = {tr. \<exists>e. is_exec_of A e \<and> tr = trace (ioa.asig A) e}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgo... | {"llama_tokens": 2870, "file": "Abortable_Linearizable_Modules_IOA", "length": 31} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 6 16:55:52 2017
@author: top40ub
"""
#import numpy as np
import numpy as np
from random import shuffle
import multiprocessing
"""
The python file containing all needed self writen functions to deciced the
actions a growth cone can do in one gr... | {"hexsha": "b780140832baeaf875c9e848cf0ca4189c1de70a", "size": 25472, "ext": "py", "lang": "Python", "max_stars_repo_path": "Math_and_Simulation/Step_Maker.py", "max_stars_repo_name": "CIA-CCTB/pythrahyper_net", "max_stars_repo_head_hexsha": "7fb30fdf8add7386a1022f16e933e4179c08c627", "max_stars_repo_licenses": ["MIT"]... |
import pytest
import networkx as nx
from networkx.testing import assert_edges_equal
def test_union_all_attributes():
g = nx.Graph()
g.add_node(0, x=4)
g.add_node(1, x=5)
g.add_edge(0, 1, size=5)
g.graph['name'] = 'g'
h = g.copy()
h.graph['name'] = 'h'
h.graph['attr'] = 'attr'
h.no... | {"hexsha": "d4d6c6eec9904a2224e82cd327babb63efd2d76b", "size": 5866, "ext": "py", "lang": "Python", "max_stars_repo_path": "networkx/algorithms/operators/tests/test_all.py", "max_stars_repo_name": "jmmcd/networkx", "max_stars_repo_head_hexsha": "207ff7d1e9bfaff013ac77c8d6bb79619892c994", "max_stars_repo_licenses": ["BS... |
// MIT License
//
// Copyright (c) 2020 Lennart Braun
//
// 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, modif... | {"hexsha": "6130a26badd619af655cc204d2192b1b7bfa3bcb", "size": 6214, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/test_communication_layer.cpp", "max_stars_repo_name": "Udbhavbisarya23/MOTION2NX", "max_stars_repo_head_hexsha": "eb26f639d8c1729cebfa85dd3bf41b770cebe92b", "max_stars_repo_licenses": ["MIT... |
import copy
from collections import namedtuple
import random
from sklearn.neighbors import KernelDensity
import numpy as np
import pickle
import time
# from dppy.finite_dpps import FiniteDPP
# Taken from
# https://github.com/pytorch/tutorials/blob/master/Reinforcement%20(Q-)Learning%20with%20PyTorch.ipynb
tuplenames =... | {"hexsha": "9944a3f47891743cb1b1c5985ebd2d183820128a", "size": 31514, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/replay_memory.py", "max_stars_repo_name": "FanmingL/ESCP", "max_stars_repo_head_hexsha": "518f13f8b002d142f670f52d9ef34778e2c2d59f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
from PIL import ImageGrab
import cv2
import time
start_time = time.time()
x = 1
counter = 0
while(True):
printscreen_pil = ImageGrab.grab()
printscreen_numpy = np.array(printscreen_pil,dtype='uint8')\
.reshape((printscreen_pil.size[1],printscreen_pil.size[0],3))
# cv2.imshow('w... | {"hexsha": "0610984959cca185394efcc5503c8a8e225827dd", "size": 603, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_Implimentations/PIL.py", "max_stars_repo_name": "Atharva-Gundawar/Screen-recorder", "max_stars_repo_head_hexsha": "87a245df54bd988947b4486440cc754e016ee4e5", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma nested_prop_atoms_subfrmlsn:
"nested_prop_atoms \<phi> \<subseteq> subfrmlsn \<phi>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nested_prop_atoms \<phi> \<subseteq> subfrmlsn \<phi>
[PROOF STEP]
by (induction \<phi>) auto | {"llama_tokens": 100, "file": "LTL_Equivalence_Relations", "length": 1} |
# --------------
#Importing header files
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data= pd.read_csv(path)
loan_status=data['Loan_Status'].value_counts()
plt.bar(loan_status.index, loan_status)
plt.show()
#Code starts here
# --------------
#Code starts here
property_and_loan=... | {"hexsha": "be3d4fb9e791f08edefd5cb1c280493e3a242d56", "size": 1649, "ext": "py", "lang": "Python", "max_stars_repo_path": "Shivarj-Jadhav/code.py", "max_stars_repo_name": "Shivraj-Jadhav/greyatom-python-for-data-science", "max_stars_repo_head_hexsha": "645518e0265246afebefa382085bfb1711be720e", "max_stars_repo_license... |
import argparse
import csv
import logging
import os
import random
import subprocess
from typing import Iterable, List
import numpy as np
import ray
from ray.experimental.raysort import constants
from ray.experimental.raysort import logging_utils
from ray.experimental.raysort import sortlib
from ray.experimental.rayso... | {"hexsha": "1cc8d0df1c5af60cdfa3f08b0b732aeee8409454", "size": 10155, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ray/experimental/raysort/main.py", "max_stars_repo_name": "77loopin/ray", "max_stars_repo_head_hexsha": "9322f6aab53f4ca5baf5a3573e1ffde12feae519", "max_stars_repo_licenses": ["Apache-2.0"... |
function [N,E] = rentian_scaling_2d(A,XY,n,tol)
% RENTIAN_SCALING_2D Rentian scaling for networks embedded in two dimensions.
%
% [N,E] = rentian_scaling_2d(A,XY,n,tol)
%
% Physical Rentian scaling (or more simply Rentian scaling) is a property
% of systems that are cost-efficiently embedded into physical space. It ... | {"author": "canlab", "repo": "CanlabCore", "sha": "af242e120f0480c4feaeea90471c015a14f1f60e", "save_path": "github-repos/MATLAB/canlab-CanlabCore", "path": "github-repos/MATLAB/canlab-CanlabCore/CanlabCore-af242e120f0480c4feaeea90471c015a14f1f60e/CanlabCore/External/2019_03_03_BCT/rentian_scaling_2d.m"} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import os
from collections import defaultdict
import numpy as np
from scipy.spatial import distance
from tqdm import tqdm
np.set_printoptions(threshold=np.inf, suppress=True)
def main(args):
num_batches = args.num_batches
bert_data = defaultdic... | {"hexsha": "d9fea9fcac86b19eee3335d40fb2b75ae5608ce8", "size": 4205, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/compute_rep_variance.py", "max_stars_repo_name": "Woffee/deformer", "max_stars_repo_head_hexsha": "8f5330f3e85599a9c57965a16c3e737f9146fcc7", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma k_mod_eq: "(\<forall>p::nat. prime p \<and> [p = 3] (mod 4) \<longrightarrow> P p) = (\<forall>k. prime (4*k+3) \<longrightarrow> P (4*k+3))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<forall>p. prime p \<and> [p = 3] (mod 4) \<longrightarrow> P p) = (\<forall>k. prime (4 * k + 3) \<longrigh... | {"llama_tokens": 3927, "file": "SumSquares_TwoSquares", "length": 32} |
function get_grouped_by(organisms::Vector{DetailedOrganism}, properties::PropertyType...)
return _group_by(organisms, get_group_function(properties))
end
get_group_function(properties) = x -> [get(x, p) for p in properties]
function _group_by(list, group_function)
groups = Dict()
for item in list
... | {"hexsha": "99a01722a5126e3995c31da6079ead4d54e7f3ac", "size": 517, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/group_by.jl", "max_stars_repo_name": "tochsner/ALifeBenchmark", "max_stars_repo_head_hexsha": "e67491e48f45a883d28a742eb7f2c8fbb22167ef", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
''' Simulation of Duffing oscillator which is harmonic motion with a
sinusoidal driving force and damping. It exhibits chaotic behaviour for some
combinations of driving and damping parameters. This example demonstrates the
use of mathtext on ``Div``, ``Paragraph`` and ``Slider`` objects, as well as
axis labels, and al... | {"hexsha": "6aad3214aa0889d9f019430d4913c9eb66a9999a", "size": 4411, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/app/duffing_oscillator.py", "max_stars_repo_name": "g-parki/bokeh", "max_stars_repo_head_hexsha": "664ead5306bba64609e734d4105c8aa8cfb76d81", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import warnings
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import assert_frame_equal
from woodwork.logical_types import (
Boolean,
Categorical,
Double,
Integer,
NaturalLanguage,
)
from rayml.pipelines.components import PerColumnImputer
from rayml... | {"hexsha": "86fafadf14924d29f8576e225d26aaf5a711b203", "size": 12301, "ext": "py", "lang": "Python", "max_stars_repo_path": "rayml/tests/component_tests/test_per_column_imputer.py", "max_stars_repo_name": "gcode-ai/rayml", "max_stars_repo_head_hexsha": "92c4f3c6041f465fee27a6c03bd7959c4ef21124", "max_stars_repo_license... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
############################################
# GUI cropper image using PIL and Tkinter #
############################################
# To use, go python pythonGUI.py 'filename'
from Tkinter import *
from PIL... | {"hexsha": "26ef2dd9499db7a9e3e43aed1fe0957759f3ec60", "size": 3814, "ext": "py", "lang": "Python", "max_stars_repo_path": "gui.py", "max_stars_repo_name": "cuongdtnguyen/poster-reader", "max_stars_repo_head_hexsha": "78f5693d86ac47c3d6329cf0ad4348fc6b73ec8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# MIT License
# Copyright (c) 2020 Christa Cuchiero, Wahid Khosrawi, Josef Teichmann
# 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 r... | {"hexsha": "4d6ecaef08ba17e990185613fb50af05c8e3b4bf", "size": 4981, "ext": "py", "lang": "Python", "max_stars_repo_path": "runfile_localVol.py", "max_stars_repo_name": "buwu-DWJ/neural_locVol", "max_stars_repo_head_hexsha": "a3703fa83edb4694f8c1596676869b2533ade7ad", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# This file is a part of Julia. License is MIT: https://julialang.org/license
if !isempty(ARGS)
ARGS[1] == "0" && exit(0)
end
# Prevent this from being put into the Main namespace
let
M = Module()
@eval M begin
if !isdefined(Base, :uv_eventloop)
Base.reinit_stdio()
end
Base.include(@__MODULE__, joinpath(Sys.B... | {"hexsha": "0b9cc449a85a0f019bf2ca5a2ff1f34815379021", "size": 6390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "contrib/generate_precompile.jl", "max_stars_repo_name": "syntapy/julia", "max_stars_repo_head_hexsha": "4fc446f1790fe04e227ff96ab75a01d130e2d930", "max_stars_repo_licenses": ["Zlib"], "max_stars_co... |
import global_var
import numpy as np
import matplotlib.pyplot as plt
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
def g(t):
return np.sin(np.pi*t)
def perform_graph(time_perform, trans_perform, dis_perform):
generation = global_var.n_generations
num = global_var.task_num
x_ = [["r_time", "b_ti... | {"hexsha": "06c23c0509f74ec0140f77a6183ed5c334dbc4c9", "size": 939, "ext": "py", "lang": "Python", "max_stars_repo_path": "transporter/Simulation/perform_graph.py", "max_stars_repo_name": "cscUOU/Shipyard-process-optimization", "max_stars_repo_head_hexsha": "cefddd2e953ab6b685771d3c388ae46c7d06bdf3", "max_stars_repo_li... |
import time
import numpy
import zmq
from zmq import devices
def heart(name=None, heart_server_add=None):
dev = devices.ThreadDevice(zmq.FORWARDER, zmq.SUB, zmq.DEALER)
dev.setsockopt_in(zmq.SUBSCRIBE, b"")
dev.connect_in('tcp://{0}:15555'.format(heart_server_add))
dev.connect_out('tcp://{0}:15556'.fo... | {"hexsha": "a984625a6131b77e89871a0db5d28dd070f7878e", "size": 673, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/pub_sub_tcp/heart.py", "max_stars_repo_name": "hugoren/schedule_sanic_zmq_server", "max_stars_repo_head_hexsha": "a8e32f57d35acebf69257c47c22abc16c15c372d", "max_stars_repo_licenses": ["MIT... |
# -*- coding: utf-8 -*-
#!/usr/bin/python
"""
The model module
================
"""
def f(x):
return x*x
import numpy as np
from pandas import get_dummies
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=DeprecationWarning)
#import importlib
from sklearn.discriminant_ana... | {"hexsha": "570f8783935730aa50af833009ad91401ad65d5b", "size": 32644, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_ddspls/model.py", "max_stars_repo_name": "hlorenzo/py_ddspls", "max_stars_repo_head_hexsha": "95c84c2e18018bd1c8b196f627306c56e029a9f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
@test splitstrip("\ttest\ttest2\n", "\t") == ["test", "test2"]
| {"hexsha": "066f383e491bf387f68bfe567e5f4b246bd56bca", "size": 64, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/text.jl", "max_stars_repo_name": "zhmz90/BasePlus.jl", "max_stars_repo_head_hexsha": "22f2d8b15f56c8db61a2d63b424314f6426abb9c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import cv2
import numpy as np
import transforms3d as tfs
from rospy import logerr, logwarn, loginfo
from easy_handeye.handeye_calibration import HandeyeCalibration
class HandeyeCalibrationBackendOpenCV(object):
MIN_SAMPLES = 2 # TODO: correct? this is what is stated in the paper, but sounds strange
"""Mini... | {"hexsha": "201544d91cd7687a1db597583b7e2087907ca4cc", "size": 3711, "ext": "py", "lang": "Python", "max_stars_repo_path": "ws_icra2022/src/easy_handeye/easy_handeye/src/easy_handeye/handeye_calibration_backend_opencv.py", "max_stars_repo_name": "yanseim/Vision-Based-Control", "max_stars_repo_head_hexsha": "4a92103d997... |
#pragma once
#include "../error.hpp"
#include "../log.hpp"
#include "function.hpp"
#include <boost/algorithm/string.hpp>
#include <boost/asio.hpp>
#include <cctype>
#include <curl/curl.h>
#include <map>
#include <string>
namespace curlio::detail {
struct Insensitive_less {
bool operator()(const std::string& lhs, c... | {"hexsha": "96cf30683dbc82b8cd9767341035e7a9c4ef36c7", "size": 4404, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "curlio/detail/header_collector.hpp", "max_stars_repo_name": "terrakuh/curlio", "max_stars_repo_head_hexsha": "ecb9872095800b644de7398b0543c942749804ea", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
'''
Created on Jul 11, 2014
@author: Victoria Lynn Ward
vlw27@cornell.edu
Cornell University
python script for a 4 objective 3d scatter plot
Modified by Julianne Quinn
June 16, 2015
'''
from matplotlib import pyplot as plt
#from matplotlib.backends import backend_agg as agg #raster backend
from mpl_toolkit... | {"hexsha": "6876a571b62682440bf95554e0bafd43e183c483", "size": 5484, "ext": "py", "lang": "Python", "max_stars_repo_path": "FigureGeneration/makeFigure4.py", "max_stars_repo_name": "federatedcloud/Lake_Problem_DPS", "max_stars_repo_head_hexsha": "07600c49ed543165ccdc642c1097b3bed87c28f0", "max_stars_repo_licenses": ["B... |
# -*- coding: utf-8 -*-
"""
Function for parameter estimation.
"""
import numpy as np
import pandas as pd
import xarray as xr
def param_est_xr(self, ds, freq='D', z_msl=None, lat=None, lon=None, TZ_lon=None,
z_u=2, K_rs=0.16, a_s=0.25, b_s=0.5, alb=0.23, dt_index_name='date'):
"""
Function t... | {"hexsha": "21567b2c12386f72ddcbf2d3180ac5341ae29d73", "size": 15338, "ext": "py", "lang": "Python", "max_stars_repo_path": "eto/param_est_xr.py", "max_stars_repo_name": "nelerey/ETo", "max_stars_repo_head_hexsha": "239bd67e65d40c6967a34aac9b50ecf2db4871a1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
[STATEMENT]
lemma "\<turnstile>\<^sub>2 {\<lambda>s. enat (2 + 3*n) + emb (s ''x'' = int n)} ''y'' ::= N 0;; wsum {\<lambda>s. 0 }"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<turnstile>\<^sub>2 {\<lambda>s. enat (2 + 3 * n) + \<up> (s ''x'' = int n)} ''y'' ::= N 0;; wsum {\<lambda>s. 0}
[PROOF STEP]
proof -
[P... | {"llama_tokens": 2925, "file": "Hoare_Time_Quant_Examples", "length": 28} |
#!/bin/env python
"""Train a VAE MNIST generator.
Usage:
* Train a model:
`python mnist_vae.py train`
* Generate samples from a trained model:
`python mnist_vae.py sample`
* Generate latent space interpolations from a trained model:
`python mnist_vae.py interpolate`
"""
import argparse
import os
import numpy as... | {"hexsha": "2ee8669e80e0326c886e68b27c9136433fcada42", "size": 22850, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/generative_models/mnist_vae.py", "max_stars_repo_name": "13952522076/diffvg", "max_stars_repo_head_hexsha": "2c5af9ecf470b1c7071e821583e5ba09cb2c4622", "max_stars_repo_licenses": ["Apache-2.... |
{-# LANGUAGE TypeSynonymInstances #-}
{-# LANGUAGE FlexibleInstances #-}
module Marvin.Test.TestUtils (
isAround
, nestedFromList
, trainMatrix
, equals
, (+-)
) where
import Marvin.Test.Metric
import Marvin.API as Marvin
import Test.QuickCheck hiding (vector)
import qualified Test.QuickCheck as QC (vector)... | {"hexsha": "9e3f4d05b1cd7b042caaacefbd6494a5cf919961", "size": 4617, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "test-suite/Marvin/Test/TestUtils.hs", "max_stars_repo_name": "gaborhermann/marvin", "max_stars_repo_head_hexsha": "5c616709f0645d4b1f13caa20820a39ee31774de", "max_stars_repo_licenses": ["Apache-2... |
using VoronoiCells
using GeometryBasics
using Test
@testset "Plotting" begin
@testset "Edges for plotting" begin
points = [Point2(0.25, 0.25), Point2(0.75, 0.25), Point2(0.5, 0.75)]
rect = Rectangle(Point2(0, 0), Point2(1, 1))
tess = voronoicells(points, rect)
p = VoronoiCells.cor... | {"hexsha": "1d7b6f4f1c2d96d1e3486ab77b343e61f1b8cdcb", "size": 926, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Plot.jl", "max_stars_repo_name": "philbit/VoronoiCells.jl", "max_stars_repo_head_hexsha": "19ac0d1dcc222fbf10c9f27c1afc83dd3c47e8c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 25, ... |
# Copyright (c) 2020, CNRS
# Authors: Pierre Fernbach <pfernbac@laas.fr>
import unittest
import subprocess
import time
from mlp import LocoPlanner, Config
from utils import check_motion
import os
from mlp.utils.cs_tools import addPhaseFromConfig, setFinalState
from pinocchio import SE3
from numpy import array
import mu... | {"hexsha": "6316145e290f46beb7fb89d8d086af52ba6edc78", "size": 2377, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/test_talos_stairs_manual_limb_rrt.py", "max_stars_repo_name": "daeunSong/multicontact-locomotion-planning", "max_stars_repo_head_hexsha": "0aeabe6a7a8d49e54d6996a6126740cc90aa0050", "... |
#!/usr/bin/env python
import os
import sys
import warnings
import datetime
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
from tshcal.filters.pylive import live_plot_xy
from tshcal.common.accel_packet import guess_packet, sql_connect
from tshcal.common.time_utils import unix_to_human_time
wa... | {"hexsha": "d29338e6cc6b65ca55889591612858fc3aa0ce43", "size": 12218, "ext": "py", "lang": "Python", "max_stars_repo_path": "filters/lowpass_main.py", "max_stars_repo_name": "kenhro/tshcal", "max_stars_repo_head_hexsha": "62c800e6cc26d5f617650585b5a7506deba700f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from scipy import linalg, sparse, stats
def skew(x):
"""Create skew-symmetric 3x3 matrix of 3x1 vector x"""
return np.array([
[0, -x[2, 0], x[1, 0]],
[x[2, 0], 0, -x[0, 0]],
[-x[1, 0], x[0, 0], 0],
])
def normS(x):
"""Spherically normalize n 3d vectors in for... | {"hexsha": "aeb170fc18e3b69f8e904bdb42b3e8a887b32d69", "size": 11576, "ext": "py", "lang": "Python", "max_stars_repo_path": "bacs/bacs.py", "max_stars_repo_name": "zauberzeug/bacs", "max_stars_repo_head_hexsha": "88577ce15363848531366c5f0dcf8bc6155d911f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_s... |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .jl
# format_name: light
# format_version: '1.3'
# jupytext_version: 0.8.6
# kernelspec:
# display_name: Julia 1.0.3
# language: julia
# name: julia-1.0
# ---
using DifferentialEquations
using LinearAlgebra
usi... | {"hexsha": "ef897d4178c24db21856782d1273ddd8256ba8c7", "size": 4567, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/typing/files/SIRSDynamicsLargePopulationsJulia.jl", "max_stars_repo_name": "mikiec84/SemanticModels.jl", "max_stars_repo_head_hexsha": "f81baf0789cc547375f300429d0fd49c866d5339", "max_star... |
import argparse
import collections
import datetime
import os
import shutil
import time
import dataset
import mlconfig
import toolbox
import torch
import util
import madrys
import numpy as np
from evaluator import Evaluator
from tqdm import tqdm
from trainer import Trainer
mlconfig.register(madrys.MadrysLoss)
# General... | {"hexsha": "0207e60541d5ba1a0d1c5ca7d0d2f67260aec7b3", "size": 24083, "ext": "py", "lang": "Python", "max_stars_repo_path": "perturbation.py", "max_stars_repo_name": "liuyixin-louis/unlearnable-example", "max_stars_repo_head_hexsha": "3c914cd25257f0390a6a166edb8b46ccaf9b0a6b", "max_stars_repo_licenses": ["MIT"], "max_s... |
const coords = [
(1,1),
(1,2),(2,1),
(3,1),(2,2),(1,3),
(1,4),(2,3),(3,2),(4,1),
(5,1),(4,2),(3,3),(2,4),(1,5),
(1,6),(2,5),(3,4),(4,3),(5,2),(6,1),
(7,1),(6,2),(5,3),(4,4),(3,5),(2,6),(1,7),
(1,8),(2,7),(3,6),(4,5),(5,4),(6,3),(7,2),(8,1),
(8,2),(7,3),(6,4),(5,5),(4,6),(3,7),(2,8),
(3,8),(4,7),(5,6),(6,5),(7,4),(8,3),... | {"hexsha": "3ebcac2b759b56083ae16c0c367e3ec1687e8fd1", "size": 3456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/entropycoding.jl", "max_stars_repo_name": "maccam912/JPEGs.jl", "max_stars_repo_head_hexsha": "751c07ccd3ee863f63c65737bd7b3aba638cb3e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from neural_compressor.adaptor.tf_utils.graph_rewriter.generic.grappler_pass import GrapplerOptimizer
from neural_compressor.adaptor.tf_utils.util import disable_random
class TestGrapplerPass(unittest.TestCase):
@disable_random()
def test_g... | {"hexsha": "5a19bcddeedd6d082387cc6bd47162f98fda71be", "size": 1810, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_tensorflow_grappler_pass.py", "max_stars_repo_name": "kevinintel/neural-compressor", "max_stars_repo_head_hexsha": "b57645566aeff8d3c18dc49d2739a583c072f940", "max_stars_repo_licenses": ... |
import os, sys; sys.path.insert(0, os.path.abspath("."))
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import rsmf
import pandas as pd
from scenarios.three_satellites.common_functions import sat_dist_curved, elevation_curved, eta_atm, eta_dif
from scenarios.three_satellites.common_params i... | {"hexsha": "19315ffd40c7f5b7f5c8d746d8c10d077a4601b8", "size": 10971, "ext": "py", "lang": "Python", "max_stars_repo_path": "scenarios/three_satellites/plot_rsmf_fourlink.py", "max_stars_repo_name": "jwallnoefer/multisat_qrepeater_sim_archive", "max_stars_repo_head_hexsha": "69b4c242fb760cf195871f38b3172d4dfd26c01a", "... |
@testset "Parallel processing" begin
W = addprocs(2)
@everywhere using GigaSOM
som = initGigaSOM(pbmc8_data, 10, 10, seed = 1234)
@testset "Check SOM dimensions" begin
@test size(som.codes) == (100, 10)
@test som.xdim == 10
@test som.ydim == 10
@test som.numCodes == 1... | {"hexsha": "0fc3153edb941a2ad1f060e7045a90198feda81b", "size": 2050, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testParallel.jl", "max_stars_repo_name": "oHunewald/GigaSOM.jl", "max_stars_repo_head_hexsha": "dd00899e514bea125306a1926452222eca007a10", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
"""
This module contains class DataSets.
"""
# pylint: disable=bad-continuation
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
from sklearn.model_selection import train_test_split
from utils import sample_uniform, to_tensor
class FunctionDataSet(Dataset):
... | {"hexsha": "b761e9864b5dc31aadfa4f7e35ade9c44849fd99", "size": 7006, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets.py", "max_stars_repo_name": "narroyo1/sffnn", "max_stars_repo_head_hexsha": "a3d7d8dd7eec76c0dca3aa57e18844b30b75b3b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_star... |
#!/usr/bin/env python3
import json
import datetime
import os
import numpy as np
import torch
from torchsummary import summary
import wandb
class Experiment:
"""
A class to be inherited from different experiments for easy logging and saving models.
"""
def __init__(self, algo, dataset, params, path=... | {"hexsha": "e67542085c5a0e24056dd847f2d09e1777ae69d8", "size": 3250, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/experiment.py", "max_stars_repo_name": "laknath/exploring_meta", "max_stars_repo_head_hexsha": "103434035c92f829c847183e1b5b4b03a1b0b31d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#%%
import numpy as np
import pandas as pd
import astropy.units as u
from astropy.coordinates import SkyCoord
#from astroquery.vizier import Vizier
from otofu.obsplan import check_altitude, search_twomass
#%%
def add_vbinfo(data):
data['mg1'] = data['phot_g_mean_mag1'] + 5*np.log10(data['parallax1']) - 10
data... | {"hexsha": "bd371f74c7d9698c295d42da6791924a92c9cfde", "size": 2800, "ext": "py", "lang": "Python", "max_stars_repo_path": "gaiavb/findvb.py", "max_stars_repo_name": "kemasuda/otofu", "max_stars_repo_head_hexsha": "d873d2e7422fea421e3dec005b0d12fbd5eae0bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
import cv2
import random
import math
from functools import reduce
from ppcd.transforms import functional as func
# ----- compose -----
class Compose:
"""
根据数据增强算子对输入数据进行操作
所有操作的输入图像流形状均是 [H, W, C]，其中H为图像高，W为图像宽，C为图像通道数
Args:
transforms (list/None): 数据增强算子，默认为None
da... | {"hexsha": "30800dba41bae0f756c552bb5c6dc8c45f30ae57", "size": 22067, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppcd/transforms/transforms.py", "max_stars_repo_name": "geoyee/PdRSCD", "max_stars_repo_head_hexsha": "4a1a7256320f006c15e3e5b5b238fdfba8198853", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python
""" multipleGoals.py - Version 1.0 10-8-2020
Autor: David Barrera
Codigo modificado a partir de: https://hotblackrobotics.github.io/en/blog/2018/01/29/action-client-py/
"""
import rospy
import math
import time
import numpy
# Brings in the SimpleActionClient
import actionlib
from geometry... | {"hexsha": "2d9f1a1747676617ddb5a1fbfb02c9e017ddb4ed", "size": 3129, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/campero_common/campero_navigation/scripts/multipleGoals.py", "max_stars_repo_name": "Serru/MultiCobot-UR10-Gripper-Campero", "max_stars_repo_head_hexsha": "d442a35efe24f8361afedb5e09249b309ed7... |
import numpy as np
import xarray as xr
import os
import pytest
import tempfile
from segmentation.utils import segmentation_utils, plot_utils
def _generate_deepcell_ouput(fov_num=2):
fovs = ["fov" + str(i) for i in range(fov_num)]
models = ["pixelwise_interior", "watershed_inner", "watershed_outer",
... | {"hexsha": "7dba06c4c8c1b61836cbcea423f6622b7010f92b", "size": 11757, "ext": "py", "lang": "Python", "max_stars_repo_path": "segmentation/utils/segmentation_utils_test.py", "max_stars_repo_name": "Jaiveers21/segmentation", "max_stars_repo_head_hexsha": "1a96429ca887e3695f092ed0d967d22b271a24ed", "max_stars_repo_license... |
%% sample template file for a PhD Thesis
%% The default is with two sided setup:
\documentclass[%
% oneside % uncomment for onesided layout
]{USN-PhD}
% --- Bibliography setup ---
%%% default is the "ieee" style
\usepackage[style=ieee, sorting=none]{biblatex}
%%% If you want to use "author-year" style
%%% where `\c... | {"hexsha": "be92e730cadd9cb6f31b449422208958c8c9f18a", "size": 107405, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "OpenHPL/Resources/Documents/UsersGuide_src/UsersGuide.tex", "max_stars_repo_name": "simulatino/OpenHPL", "max_stars_repo_head_hexsha": "c725b3807a871c30d4002df10a231bfef80c8e82", "max_stars_repo_l... |
import os
import torch
from src.helper_functions.helper_functions import parse_args
from src.loss_functions.losses import AsymmetricLoss, AsymmetricLossOptimized
from src.models import create_model
import argparse
import matplotlib
import torchvision.transforms as transforms
from pgd import create_targeted_adversarial_... | {"hexsha": "894c16a6a6b275837eb732ff130b8859eaae09bf", "size": 5113, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlc-pgd-single-target.py", "max_stars_repo_name": "erwinvanthiel/ASL", "max_stars_repo_head_hexsha": "1b8846919f4bcf7bf65881faf254395cb01f8ae3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
lista = [ 1, 1+2j, True, 'aerodinamica', [1, 2, 3] ]
print(id(lista)) # El id que tenga. Algo como 1721240621384
lista.append('fluidos')
print(lista) # [1, (1+2j), True, 'aerodinamica', [1, 2, 3], 'fluidos', 'fluidos']
print(id(lista)) # El mismo id
array = np.array([ 1, 1+2j, True, 'aerod... | {"hexsha": "867e78f78d47fa1995f1da9c000ceb964bc7c48f", "size": 528, "ext": "py", "lang": "Python", "max_stars_repo_path": "sources/t10/t10ej04.py", "max_stars_repo_name": "workready/pythonbasic", "max_stars_repo_head_hexsha": "59bd82caf99244f5e711124e1f6f4dec8de22141", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import sys
import logging
from gensim.models.word2vec import LineSentence
from gensim.models.word2vec import Word2Vec
from gensim.corpora.wikicorpus import WikiCorpus
import numpy as np
import scipy.io
from nltk.corpus import stopwords
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNet... | {"hexsha": "b93c24cfb6e316ccbbaf956d33bffc7d8885b0fc", "size": 3462, "ext": "py", "lang": "Python", "max_stars_repo_path": "get_wordnet_word2vec.py", "max_stars_repo_name": "kylemin/DeViSE", "max_stars_repo_head_hexsha": "8f8a8c65a6116bfac7c5cb27541f14fb2465fdac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import argparse
import geopandas
import glob
import numpy as np
import os
import pandas as pd
from functools import reduce
import xarray as xr
from paths_bra import *
turb_path = bra_path + '/aerogeradores'
parser = argparse.ArgumentParser(description='Insert optionally GWA')
parser.add_argument('-GWA')
args = parse... | {"hexsha": "d82050e41a2777aca6d20c09680b3b1ccfeac05f", "size": 12018, "ext": "py", "lang": "Python", "max_stars_repo_path": "BRA/prepare_BRA_turbines.py", "max_stars_repo_name": "KatharinaGruber/windpower_GWA", "max_stars_repo_head_hexsha": "6d4eddc48f37cb66ac33ebab431b9a223366d4e1", "max_stars_repo_licenses": ["MIT"],... |
"""Unit tests for core.py."""
import pytest
import numpy as np
from scipy.stats import entropy
from entrogrammer import core
from entrogrammer import classifier
def test_type_error():
"""Test that an error is raised if invalid input is given."""
with pytest.raises(TypeError):
core.global_entropy('inv... | {"hexsha": "f19e22b515068581c34222e0d252798fff444758", "size": 4760, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_core.py", "max_stars_repo_name": "elbeejay/entrogrammer", "max_stars_repo_head_hexsha": "8a927b9bee29c6ac2e1248adc0c7e56d2bb3c276", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
include("utils.jl")
@testset "Symmetric Positive Definite Matrices" begin
M1 = Manifolds.SymmetricPositiveDefinite(3)
M2 = MetricManifold(Manifolds.SymmetricPositiveDefinite(3), Manifolds.LinearAffineMetric())
M3 = MetricManifold(Manifolds.SymmetricPositiveDefinite(3), Manifolds.LogCholeskyMetric())
M4 ... | {"hexsha": "847e5130d7f149fd81ba049572f42127fef2e222", "size": 3397, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/symmetric_positive_definite.jl", "max_stars_repo_name": "dehann/Manifolds.jl", "max_stars_repo_head_hexsha": "99849e7e53261641506488893ade4a0437635bb9", "max_stars_repo_licenses": ["MIT"], "ma... |
"""Helper functions for graphics with Matplotlib."""
__all__ = ['create_mpl_ax', 'create_mpl_fig']
def _import_mpl():
"""This function is not needed outside this utils module."""
try:
import matplotlib.pyplot as plt
except:
raise ImportError("Matplotlib is not found.")
return plt
... | {"hexsha": "c25792a2133fa5ddd4607370cd6a5780d9e90e34", "size": 2220, "ext": "py", "lang": "Python", "max_stars_repo_path": "statsmodels/graphics/utils.py", "max_stars_repo_name": "changhiskhan/statsmodels", "max_stars_repo_head_hexsha": "af26395e8b75b112ae7b3099532aefd8d002b8ca", "max_stars_repo_licenses": ["BSD-3-Clau... |
import numpy as np
class svm_subgrad:
def __init__(self):
self.w=None
self.loss=None
self.lamb=None
def fit(self,x,y,lamb,step_size,num_iter):
self.w=np.random.rand(x.shape[1])
self.lamb=lamb
n=0
while n<num_iter:
n+=1
margins=y*(... | {"hexsha": "f6e415891c72f3754cb1230eaeb4d0fcfcf5ed2f", "size": 1050, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/svm_subgrad.py", "max_stars_repo_name": "YichengPu/Relics", "max_stars_repo_head_hexsha": "95752a5ab62dae68bb261714709c66b260957cbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
# load PyTorch
import torch
import torch.nn as nn
from torch.utils.data import Dataset
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
# https: // github.com / Bjarten / early - stopping - pytorch
def __init__(self, patienc... | {"hexsha": "e8ed3f0ca32ca36e67ad8df607698d86f6b6b660", "size": 5041, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/pystreamfs/algorithms/cancelout.py", "max_stars_repo_name": "haugjo/pystreamfs", "max_stars_repo_head_hexsha": "ec6fa191a874f476aedfdfec20e95fa63af08077", "max_stars_repo_licenses": ["MI... |
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