text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
shovel_latencies <- function(dir1, dir2) {
d1tod2 <- shovel_latency(dir1, dir2)
d2tod1 <- shovel_latency(dir2, dir1)
df <- data.frame(quantile(d1tod2$latency, c(0, .1, .5, .95, .99, 1)),
quantile(d2tod1$latency, c(0, .1, .5, .95, .99, 1)))
names(df) <- c(paste(dir1, "to", dir2, sep=" "),
... | {"hexsha": "4de85547b988db24081b059e528a053f6b62c9bd", "size": 839, "ext": "r", "lang": "R", "max_stars_repo_path": "priv/shovel.r", "max_stars_repo_name": "russelldb/rabl", "max_stars_repo_head_hexsha": "9aa140ef5ec09959393adba5a98321b6df8323e1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13, "max_s... |
"""
replica_fidelity(df::DataFrame; p_field = :hproj, skip = 0)
Compute the fidelity of the average coefficient vector and the projector defined in
`p_field` from the result of replica [`lomc!()`](@ref) passed as argument `df`,
using replicas `_1` and `_2`.
Calls [`ratio_of_means()`](@ref) to perform a blocking an... | {"hexsha": "9a35c381078e7c49d40cf7184ac038096600eea0", "size": 1369, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/StatsTools/fidelity.jl", "max_stars_repo_name": "joachimbrand/Rimu.jl", "max_stars_repo_head_hexsha": "ee5237794c82e7dc83a9562768cf37c3979c7f55", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import Statistics.LinearRegression
import Statistics.Sample
import qualified Data.Vector.Unboxed as U
import Control.Monad.Random
import Control.Monad
import Control.Applicative
import System.Random.MWC
import System.Random.MWC.Distributions
import qualified Data.Packed.Vector as V
import Graphics.Rendering.Plot
main ... | {"hexsha": "46352d50aba46457bad827276912f89ddd472832", "size": 4214, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "tests/linreg.hs", "max_stars_repo_name": "alpmestan/statistics-linreg", "max_stars_repo_head_hexsha": "14c2f10088d1914b0303c191ec0d7243c8eb83ee", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma invariantQCharacterizationAfterApplyBackjump_1:
assumes
"InvariantConsistent (getM state)"
"InvariantUniq (getM state)"
"InvariantWatchListsContainOnlyClausesFromF (getWatchList state) (getF state)" and
"InvariantWatchListsUniq (getWatchList state)" and
"InvariantWatchListsCharacterization (... | {"llama_tokens": 68503, "file": "SATSolverVerification_ConflictAnalysis", "length": 65} |
#=
Code related with input output (IO) of .nc files directly to/from ClimArrays
utilizing the NCDatasets.jl package and a buttload of convenience code.
An initial version of parts of this code was taken from:
https://github.com/rafaqz/GeoData.jl
=#
using NCDatasets: NCDatasets, NCDataset
export NCDatasets, NCDataset
ex... | {"hexsha": "0587b5a6931e37e0cfe627f5903cdfacbd2e1481", "size": 1789, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/io/netcdf.jl", "max_stars_repo_name": "Balinus/ClimateTypes.jl", "max_stars_repo_head_hexsha": "35b5e8f85638b7f1d3127b7a446de38afba2c6b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import Bio.SeqUtils.ProtParam
import os
import numpy as np
SET_NAME = 'MMP-cluster'
IF_ONLY_HEAVY = False
CNT_DB = 2
CNT_TARGET = 1
REFERENCE_PATH_TESTCASE = './testCase/MMP-cluster/reference-PDB/'
TARGETING_PATH_TESTCASE = './testCase/MMP-cluster/targeting-MMP/'
TARGET_DESIRE_SIZE = 166 #44 #MMP-cluster
# Chothia... | {"hexsha": "39353bc3e62c84bff9ded7c8804063acfe682985", "size": 23004, "ext": "py", "lang": "Python", "max_stars_repo_path": "ASAP/FeatureExtraction.py", "max_stars_repo_name": "HassounLab/ASAP", "max_stars_repo_head_hexsha": "fc02471cd352da1a7783ea48a5caf7874fe4910a", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma minus_eq: "x - y = abs_nat (rep_nat x - rep_nat y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x - y = abs_nat (rep_nat x - rep_nat y)
[PROOF STEP]
by (metis abs_minus rep_inverse) | {"llama_tokens": 92, "file": "Polynomials_Term_Order", "length": 1} |
import cv2
import pandas as pd
from face_alignment_1 import face_alignment
from face_base import find_face
from face_base import license_detection_Rough
from face_base import license_detection_Detailed
from smooth_sharpen import smooth
from smooth_sharpen import sharpen
from face_base import divide_image
from face_base... | {"hexsha": "37f4419008e2d47b043dbe9ead82398c78862287", "size": 4357, "ext": "py", "lang": "Python", "max_stars_repo_path": "performance_new.py", "max_stars_repo_name": "zacQin/ID_card_identification_2", "max_stars_repo_head_hexsha": "b359e8b0d26352ce359b280c36f2ffa837aa9611", "max_stars_repo_licenses": ["Apache-2.0"], ... |
c
c Program runs the subroutine iri_sm to obtain IRI13 densities
c along an L-shell.
c
c dlg June 3, 2009 fixed issue with trying to calculate bridge for locations
c below the F2 peak along the selected L-shell
c dlg June 11, 2009 added switchon feature to field aligned bridge funct... | {"hexsha": "e1af3dc2fde1b36994efefcf94e54ded7dc68d2c", "size": 5940, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "PyGCPM/__data/libgcpm/gcpm/iri_ps_bridge.for", "max_stars_repo_name": "mattkjames7/PyGCPM", "max_stars_repo_head_hexsha": "90d1c29b82b7b286f570eb49f7bf7618ddc4717b", "max_stars_repo_licenses": [... |
\cleardoublepage%
\phantomsection\addcontentsline{toc}{chapter}{Introduction}%
\chapter*{Introduction}
As evidenced by Figure~\ref{fig:donald} and a number of films including \emph{Eternal Sunshine of the Spotless Mind} (2004) and \emph{The Discovery} (2017), the idea of directly connecting our brains to machines has ... | {"hexsha": "f3ec389636dca74cfae5a3e3817c289ea5155e84", "size": 39695, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "introduction.tex", "max_stars_repo_name": "lrkrol/dissertation", "max_stars_repo_head_hexsha": "548167344fada64384f95d23be67a48ee08f7449", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count... |
import os
import numpy as np
import torch
from torch import nn
import gin
from sparse_causal_model_learner_rl.trainable.fcnet import build_activation
@gin.configurable
class AbstractCombinedModel(nn.Module):
def __init__(self, n_models, input_shape, output_shape):
super(AbstractCombinedModel, s... | {"hexsha": "2e70128a1770db0d3268059518577a4ebfc5794a", "size": 7584, "ext": "py", "lang": "Python", "max_stars_repo_path": "sparse_causal_model_learner_rl/trainable/combined.py", "max_stars_repo_name": "sergeivolodin/causality-disentanglement-rl", "max_stars_repo_head_hexsha": "5a41b4a2e3d85fa7e9c8450215fdc6cf954df867"... |
"""Answer to Exercise 1.4
Author: Yuhuang Hu
Email : yuhuang.hu@ini.uzh.ch
"""
from __future__ import print_function
import numpy as np
import keras.backend as K
# define list of placeholders for variables
N = 3
theta = [K.placeholder(shape=(), dtype=np.float32) for i in range(N+1)]
x = K.placeholder(shape=(), dtype... | {"hexsha": "4737dda146e89a2050d6e4e270dc51e895062989", "size": 813, "ext": "py", "lang": "Python", "max_stars_repo_path": "session_01/ex-1-4.py", "max_stars_repo_name": "PnS2018/exercise-solutions", "max_stars_repo_head_hexsha": "156c07a4cf92f3b6b8af1ac7608a957eba5deba6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import tensorflow as tf
import numpy as np
from PIL import Image
import imageio
import cv2
import glob
from skvideo.io import FFmpegWriter as VideoWriter
image_shape = (160, 576)
filename = 'um_000004.png'
image_file = './data/data_road/testing/image_2/' + filename
def get_input_image(path):
image = Image.open(pa... | {"hexsha": "f192c9b245ac0d7dcfb2808dd7cd4bdc198c5e1a", "size": 3134, "ext": "py", "lang": "Python", "max_stars_repo_path": "run.py", "max_stars_repo_name": "daltonrenaldo/CarND-Semantic-Segmentation", "max_stars_repo_head_hexsha": "720d89a125449f74697e6da4ed59e5934959f306", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
Case 27:
This case study a three bus system with 1 machine (One d- One q-: 4th order model), a VSM of 19 states and an infinite source.
The test changes botht he voltage magnitude and phase angle of the source bus.
"""
##################################################
############### LOAD DATA ###################... | {"hexsha": "a4346c53f5bb5b84afb74cfac8653c613f634c68", "size": 4862, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_case27_source_bus_voltage_change.jl", "max_stars_repo_name": "tavovalmo/PowerSimulationsDynamics.jl", "max_stars_repo_head_hexsha": "61ba0433ab89caa37f9cf2caedaa2bcc2566591c", "max_stars_... |
[STATEMENT]
lemma dlts_rel_eq[unfolded vimage2p_def]:
"BNF_Def.vimage2p un_DLTS un_DLTS (rel_fun (=) (rel_option (=))) = (=)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. BNF_Def.vimage2p un_DLTS un_DLTS (rel_map (=)) = (=)
[PROOF STEP]
by (auto simp add: vimage2p_def pmf.rel_eq option.rel_eq fun.rel_eq fun_eq_i... | {"llama_tokens": 161, "file": "Probabilistic_System_Zoo_Probabilistic_Hierarchy", "length": 1} |
import os
import re
import shutil
import subprocess
from subprocess import CalledProcessError
from cStringIO import StringIO
import nibabel as nb
import numpy as np
from django.core.exceptions import ValidationError
from django.forms import ModelForm
from django.forms.models import (
ModelMultipleChoiceField
)
# ... | {"hexsha": "4af7e47020393d0cace1572802a7ed6a315bed7d", "size": 45749, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurovault/apps/statmaps/forms.py", "max_stars_repo_name": "aphroditepv/NeuroVault", "max_stars_repo_head_hexsha": "14dbe45c24897f250938716c8d6a015a3b06df93", "max_stars_repo_licenses": ["MIT"], ... |
import torch
import numpy as np
import time
import torchvision
model = torch.hub.load('pytorch/vision:v0.6.0', 'squeezenet1_1', pretrained=True)
model.eval()
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "cat.png")
from PIL import Image
from torchvision import transforms
input_i... | {"hexsha": "b97423bd557e8b4487fba99aeb1c69c0ac77e920", "size": 1563, "ext": "py", "lang": "Python", "max_stars_repo_path": "squeezenet-v1.1-pytorch.py", "max_stars_repo_name": "tom-gall/torch-bench", "max_stars_repo_head_hexsha": "8d2a938c21637d4305d88a7b2c42d860672d5be2", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
// Copyright 2016 Yahoo Inc.
// Licensed under the terms of the Apache 2.0 license.
// Please see LICENSE file in the project root for terms.
#ifndef CAFFE_DISTRI_SOCKET_HPP_
#define CAFFE_DISTRI_SOCKET_HPP_
#include <stdio.h>
#include <map>
#include <string>
#include <vector>
#include <boost/thread.hpp>
#include <boo... | {"hexsha": "89b03e5d38ca134fa1bea49b5bfa3d76a6a2bae1", "size": 4473, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "caffe-distri/include/util/socket.hpp", "max_stars_repo_name": "jenniew/IntelCaffeOnSpark_mirror", "max_stars_repo_head_hexsha": "7b79ff25d5eed5f472ea7b1572f9c7fa9dcdc46c", "max_stars_repo_licenses":... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from a2c_ppo_acktr.distributions import Bernoulli, Categorical, DiagGaussian, MultiCategoricalDistribution, \
RobotARCategoricalDistribution
from a2c_ppo_acktr.utils import init
import gym
from models.blocks import RMCBlock
cl... | {"hexsha": "dd26b2c3fc1a10d6c606502d393e9e764e0095ff", "size": 11889, "ext": "py", "lang": "Python", "max_stars_repo_path": "a2c_ppo_acktr/model.py", "max_stars_repo_name": "ava6969/AR-Project", "max_stars_repo_head_hexsha": "d34369178d41ac79d73710a2b6681dbde3910e9d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
function bpsksys(bin,f)
disp('========================================');
disp(' HAM DIEU CHE DICH 2 PHA: BPSK');
disp(' VI DU:bpsksys([0 1 0 1 1 0 1 1 0],3)');
disp('Written by Nguyen Hoang Minh DHCNTPHCM. he..he..');
disp('========================================');
bin=[0 1 0 1 1 0 1 1 1 0];f=3;k=1000;
t=0:2*pi/(... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/30770-digital-analog-modulation/SignalModula... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import unittest
from unittest import TestCase
from itertools import chain
import numpy as np
from numpy.lib import NumpyVersion
import sys
sys.path.append('../')
from fpq.vector import *
import fpq.fp
class TestVector(TestCase):
def test_is_valid_format(self):
... | {"hexsha": "b6380a98f9598dd7ea19c4ccb85b2996a18a5da7", "size": 20869, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_vector.py", "max_stars_repo_name": "Hasenpfote/fpq", "max_stars_repo_head_hexsha": "3154ed1b1d5eca08255e8359b5027439af43691c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
'''
Finding the best fit linear slope for a dataset example
'''
from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
# test data
xs = np.array([1,2,3,4,5,6], dtype=np.float64)
ys = np.array([5,4,6,5,6,7], dtype=np.float64)
# gene... | {"hexsha": "c00bc499d6e4dc7e449723463e969d6e60c6f6d5", "size": 854, "ext": "py", "lang": "Python", "max_stars_repo_path": "Regression/Linear Regression/sklearn/best-fit-line.py", "max_stars_repo_name": "adam-bhaiji/machine-learning", "max_stars_repo_head_hexsha": "4ea97d6f802791077b8a19ccc2678cff8edcb630", "max_stars_r... |
import networkx as nx
import matplotlib.pyplot as plt
class Top_Sort:
# A recursive function used by topologicalSort
def __topologicalSortUtil(self, v, visited, stack, G):
# Mark the current node as visited.
visited[v] = True
# Recur for all the vertices adjacent to... | {"hexsha": "8c8f3fd5798c79a9cbda0e540bc92ad515412067", "size": 2828, "ext": "py", "lang": "Python", "max_stars_repo_path": "graph_algo_vis/topological_sort.py", "max_stars_repo_name": "Akarsh654/Graph-Algorithms-Package", "max_stars_repo_head_hexsha": "ceb417ca5e79ca6a26d709aea47ceddb1dbffc4c", "max_stars_repo_licenses... |
# BSD Licensed, Copyright (c) 2006-2008 MetaCarta, Inc.
from TileCache.Layer import MetaLayer
import osgeo.gdal as gdal
import osgeo.gdal_array as gdalarray
import numpy
import PIL
class GDAL(MetaLayer):
"""
The GDAL Layer allows you to set up any GDAL datasource in TileCache.
Areas not covered by the im... | {"hexsha": "59c21bc90fa1ad0f3fe8af04876449cb5a6ef39d", "size": 5594, "ext": "py", "lang": "Python", "max_stars_repo_path": "public/cgi/tilecache/TileCache/Layers/GDAL.py", "max_stars_repo_name": "l34marr/mapwarper", "max_stars_repo_head_hexsha": "0bc844c6a9f002157d0c67d2f792b986be0f9133", "max_stars_repo_licenses": ["M... |
#!/usr/local/bin/python3
# use age for lineaer regression
# accuracy 0.7890
# kaggle score 0.7655 (same as female alone)
import sys # pylint: disable=unused-import
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
import warnings
warn... | {"hexsha": "8e295f83c178ec1659214a83d64cba60a85d1f8f", "size": 1440, "ext": "py", "lang": "Python", "max_stars_repo_path": "src-examples/003-age.py", "max_stars_repo_name": "peterorum/kaggle-titanic", "max_stars_repo_head_hexsha": "ae2d5f6fb62ecfee0c2c9f473c3c9d6e7ded836f", "max_stars_repo_licenses": ["MIT"], "max_star... |
#this file contains a common tracking code for both elevator and rover
#It checks variable from file config.npy to figure out its own type
import time
from datetime import datetime
import subprocess
import numpy as np
from numpy import linalg
from numpy.linalg import inv
import math
import cmath
import linalgfunc
im... | {"hexsha": "57e362dfec7baf2a2199c9a511f6bc1b1693aba6", "size": 8811, "ext": "py", "lang": "Python", "max_stars_repo_path": "ExperimentCode/STAC_3D_Air_ExSeeking.py", "max_stars_repo_name": "pratapbhanusolanki/tmech2021", "max_stars_repo_head_hexsha": "8b3d23f3c384482da2f3143b7abd33ac6b65d911", "max_stars_repo_licenses"... |
import pytest
def test_x_minus_xt():
import jax
import jax.numpy as jnp
import sake
key = jax.random.PRNGKey(2666)
x = jax.random.normal(key=key, shape=(5, 3))
x_minus_xt = sake.functional.get_x_minus_xt(x)
assert x_minus_xt.shape == (5, 5, 3)
def test_x_minus_xt_norm():
import jax
... | {"hexsha": "a23192e13d6b38aa0240402b81bcc56cd7cba53f", "size": 863, "ext": "py", "lang": "Python", "max_stars_repo_path": "sake/tests/test_functional.py", "max_stars_repo_name": "yuanqing-wang/sake", "max_stars_repo_head_hexsha": "9968aeb51ced20e47646762d15416d38b59f8102", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#-*- encoding: utf-8 -*-
import argparse
from tkinter.constants import TRUE
import numpy as np
import tkinter as tk
from tkinter.ttk import Label
from multiprocessing import Process, Queue
import time
class App(object):
def __init__(self, queue):
self.q = queue
self.root = tk.Tk()
self.w... | {"hexsha": "5c7564a235a93b0b9da82ce27267878bfe7f5c6b", "size": 2021, "ext": "py", "lang": "Python", "max_stars_repo_path": "zynq/tmp.py", "max_stars_repo_name": "Roxbili/kws-demo", "max_stars_repo_head_hexsha": "7e0674f1407572fc8f148293b23fa20a5164bc5e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 16:45:40 2020
@author: ogurcan
"""
import networkx as nx
import h5py as h5
import matplotlib.pylab as plt
import numpy as np
#nwflname='run-GOY/nwfile.pkl'
#nwflname='run-WS04-static/nwfile.pkl'
nwflname='run-NW04-static/nwfile.pkl'
gr=nx.read_g... | {"hexsha": "695b48de342ba24b51eddfa1a0191132c77977d7", "size": 1095, "ext": "py", "lang": "Python", "max_stars_repo_path": "draw_bipartite_nw.py", "max_stars_repo_name": "gurcani/dycon", "max_stars_repo_head_hexsha": "64313471a9222682dce12f8623eb5d0563a8bb5c", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_count":... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Niccolò Bonacchi
# @Date: 2018-02-20 14:46:10
# matplotlib.use('Qt5Agg')
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
def make_fig(sph):
plt.ion()
f = plt.figure() # figsize=(19.2, 10.8), dpi=100)
ax_bars = plt.sub... | {"hexsha": "2d9a67f1574b1c6efa64e702f1b1155de9dee2f3", "size": 8074, "ext": "py", "lang": "Python", "max_stars_repo_path": "tasks/_iblrig_tasks_trainingChoiceWorld/online_plots.py", "max_stars_repo_name": "int-brain-lab/iblr", "max_stars_repo_head_hexsha": "18569278fc2d8cd3266adb2a5f660a43f8f2582e", "max_stars_repo_lic... |
""" This file implements the GA algorithm and acts as main(). """
# standard library
import multiprocessing as mp
import subprocess as sp
import logging
import glob
import shutil
import os
import time
import sys
from traceback import print_exc
from json import dumps, dump
from copy import deepcopy, copy
# external lib... | {"hexsha": "6ab847203d3bc7ba87dafbd477b6e149a4f38561", "size": 55285, "ext": "py", "lang": "Python", "max_stars_repo_path": "ilustrado/ilustrado.py", "max_stars_repo_name": "ml-evs/ilustrado", "max_stars_repo_head_hexsha": "3121ecaff9cb517f3946b2283bf50dce499caad9", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python3
import numpy
import psycopg2
import dummy
from psycopg2.extensions import register_adapter
from psycopg2.extras import Json
# Start a postgres database via Docker
# docker run -ti --rm --name word_psql -e POSTGRES_PASSWORD=mikolov -p 5433:5432 postgres:10.5
def adapt_numpy_ndarray(numpy_nda... | {"hexsha": "569d149543b48f87f8cef3ac17874c6a46e198a6", "size": 1170, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/psql_example.py", "max_stars_repo_name": "krokodilj/word_embedding_storage", "max_stars_repo_head_hexsha": "206c14cee1af0768b6e187167333dcccf0095e9d", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma lt_tail_max:
assumes "tail p \<noteq> 0" and "v \<in> keys p" and "v \<prec>\<^sub>t lt p"
shows "v \<preceq>\<^sub>t lt (tail p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. v \<preceq>\<^sub>t lt (tail p)
[PROOF STEP]
proof (rule lt_max_keys, simp add: keys_tail assms(2))
[PROOF STATE]
pro... | {"llama_tokens": 334, "file": "Polynomials_MPoly_Type_Class_Ordered", "length": 5} |
import numpy as np
import os
import bilby.core.prior
from bilby.core.prior import PriorDict
import redback.model_library
from redback.utils import logger
def get_priors(model, times=None, y=None, yerr=None, dt=None, **kwargs):
prompt_prior_functions = dict(gaussian=get_gaussian_priors, skew_gaussian=get_skew_ga... | {"hexsha": "7b4dad189e93fa8a84816256f8531367d1945685", "size": 4050, "ext": "py", "lang": "Python", "max_stars_repo_path": "redback/priors.py", "max_stars_repo_name": "nikhil-sarin/redback", "max_stars_repo_head_hexsha": "b0023b770a3c0a25a18c4f6ff1a07339be7f83fe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Riduzione della dimensionalità
Fino ad ora abbiamo visto come le feature siano importanti per poter definire un algoritmo in grado di eseguire il proprio compito imparando dai dati, ora il problema è che ci potremmo trovare in condizioni in cui sfortunatamente abbiamo troppe feature e troppi pochi dati(troppe colonn... | {"hexsha": "3a8523ed7f36fcf78498c3c7f1700c26c8f2ad5b", "size": 67801, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "3.machine learning/7-PCA_LDA_NMF.ipynb", "max_stars_repo_name": "matinator/Starting-Finance-Club-Torino", "max_stars_repo_head_hexsha": "8abda7caa769f2dc237c4ff520ef1b40038a5f65", "m... |
# -*- coding: utf-8 -*-
## @package inversetoon.batch.generate_isophote_scene
#
# Isophote scene generator.
# @author tody
# @date 2015/07/31
import numpy as np
from inversetoon.batch.batch import normalDataSetBatch
from inversetoon.core.silhouette import silhoutteCurve
from inversetoon.io.image import... | {"hexsha": "ea9395b920c5fda52d509d7db5510bae8728f14f", "size": 1774, "ext": "py", "lang": "Python", "max_stars_repo_path": "inversetoon/batch/generate_isophote_scene.py", "max_stars_repo_name": "tody411/InverseToon", "max_stars_repo_head_hexsha": "bc5b922cae9bbf99ed1f020c93b1577c4747ff92", "max_stars_repo_licenses": ["... |
# import standard plotting and animation
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.ticker import FormatStrFormatter
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
from IPython.display import clear_output
# import standard librar... | {"hexsha": "37db2cafcfd8adcbccde2258facb71389010d387", "size": 2605, "ext": "py", "lang": "Python", "max_stars_repo_path": "posts/dynamic_systems_unlimited_memory/library/exponential_average_animator.py", "max_stars_repo_name": "jermwatt/blog", "max_stars_repo_head_hexsha": "3dd0d464d7a17c1c7a6508f714edc938dc3c03e9", "... |
#
# Base solver class
#
import pybamm
import numpy as np
from scipy import optimize
from scipy.sparse import issparse
class DaeSolver(pybamm.BaseSolver):
"""Solve a discretised model.
Parameters
----------
rtol : float, optional
The relative tolerance for the solver (default is 1e-6).
ato... | {"hexsha": "ba14d26d2cbf5f62e0a15317efedce6c4a381e69", "size": 12151, "ext": "py", "lang": "Python", "max_stars_repo_path": "pybamm/solvers/dae_solver.py", "max_stars_repo_name": "htorodriguez/PyBaMM", "max_stars_repo_head_hexsha": "91e051e8ce287824b41f238ae39f3208606228ff", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
// Copyright 2014 BVLC and contributors.
#include <algorithm>
#include <vector>
#include <cmath>
#include "google/protobuf/descriptor.h"
#include "google/protobuf/descriptor.h"
#include "caffe/layer.hpp"
#include "caffe/util/rng.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/layers/flow_augmentation_l... | {"hexsha": "51b628a82efb1f6c3966844c539fc04d5284773f", "size": 2942, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/caffe/layers/flow_augmentation_layer.cpp", "max_stars_repo_name": "AyaLotfy/flownet2", "max_stars_repo_head_hexsha": "e3e3dd043d9a65bc8727429938a0d88539f906fd", "max_stars_repo_licenses": ["FSFA... |
from typing import Type
import torch
from torch import nn
import numpy as np
from nes import NES, Policy, default_config
from nes.config import default_config, Config
config = Config(default_config)
class Ackley(Policy):
def __init__(self):
super().__init__()
self.params = nn.Parameter(torch.r... | {"hexsha": "3029cfbf1f3d3dbd3184923d7a64433b295b34ec", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ackley.py", "max_stars_repo_name": "goktug97/nes-torch", "max_stars_repo_head_hexsha": "016f2618d2f5019718c62359eebb9fd939647607", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
import numpy, logging
from sys import exit
from Classes.DotData import DotData
from Operations.Shari_Operations.localize.xpopMerge import xpopMerge
from Operations.Shari_Operations.localize.Scenario import GetSelectionScenarios, GetScenarios
from Operations.MiscUtil import MakeAlphaNum, Dict, Sfx, progress, AddFileSfx
... | {"hexsha": "0466cff04c72a86c7320f9131d3a336c0541bc81", "size": 12885, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/Operations/Shari_Operations/localize/mergeSims.py", "max_stars_repo_name": "broadinstitute/cms", "max_stars_repo_head_hexsha": "4743ffd3feac08f02be7719c82b3371cb94a4d6b", "max_stars_repo_lice... |
import warnings
from typing import Union
import numpy as np
from scipy.special import betaln
from scipy.special import psi, polygamma
from autoconf import cached_property
from ..messages.abstract import AbstractMessage
def grad_betaln(ab):
psiab = psi(ab.sum(axis=1, keepdims=True))
return psi(ab) - psiab
... | {"hexsha": "1b31637d78b6fd5c2ef87ba5c8d333e14156e153", "size": 4524, "ext": "py", "lang": "Python", "max_stars_repo_path": "autofit/messages/beta.py", "max_stars_repo_name": "caoxiaoyue/PyAutoFit", "max_stars_repo_head_hexsha": "819cd2acc8d4069497a161c3bb6048128e44d828", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy
from crystal_util import bragg_calc2
import scipy.constants as codata
def crystal_shadow(filename, str, phot_in):
'''
#+
# Singapore Synchrotron Light Source (SSLS)
# :Author: X.J. Yu, slsyxj@nus.edu.sg
# :Name: crystal_shadow
# :Purpose: create a shadow data file for a any cryst... | {"hexsha": "9b4ce491a6202ecb1215b6d4e9a7b015df4deac9", "size": 7210, "ext": "py", "lang": "Python", "max_stars_repo_path": "yb66/create_shadowfile.py", "max_stars_repo_name": "91902078/yb66", "max_stars_repo_head_hexsha": "ece7f637ac8bacb1ba51a6f1f6f1f2e9cdb91bd9", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_cou... |
/*=============================================================================
Copyright (c) 2002 2004 2006 Joel de Guzman
Copyright (c) 2004 Eric Niebler
http://spirit.sourceforge.net/
Use, modification and distribution is subject to the Boost Software
License, Version 1.0. (See accompanying file... | {"hexsha": "6d9368f3dce4cad94c39807b3a8969c8ee205966", "size": 47914, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/quickbook/src/main_grammar.cpp", "max_stars_repo_name": "cpp-pm/boost", "max_stars_repo_head_hexsha": "38c6c8c07f2fcc42d573b10807fef27ec14930f8", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
"""
Name: Pham Tuan Anh
Class: K63-K2
MSSV: 18020116
You should understand the code you write.
"""
import numpy as np
import cv2
import sys
def q_0(input_file, output_file, delay=1):
"""
:param input_file:
:param output_file:
:param delay:
:return:
"""
img = cv2.imread(input_file, cv2.I... | {"hexsha": "8366a93a3aa0cab3464b3dd04bc0f9ffbf67d56e", "size": 2478, "ext": "py", "lang": "Python", "max_stars_repo_path": "week02/week02.py", "max_stars_repo_name": "ptanh2k/int3404", "max_stars_repo_head_hexsha": "ad39ce61b768ef7b55936561c13bfa1c3adf0e92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
program bspline_tests
use utils
use bspline
use finite_elements
use ogpf
implicit none
!---------------
! Variables
!---------------
integer :: i,j,total_tests, passed_tests
real(wp), allocatable :: y(:),x(:)
real(wp) :: result,true
!---------------
! Logic
!---------------
total_tests = 1
passed_tests = 0
write(*,*... | {"hexsha": "5f3aa56bc74f068580f03c2b7c0125d2ce0f15b8", "size": 6488, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "tests/bspline_tests.f95", "max_stars_repo_name": "AndreTGMello/numerical-analysis-course", "max_stars_repo_head_hexsha": "e2b4b6e7c74e8db9f4f637e7bab5b73ef119a23f", "max_stars_repo_licenses": ["... |
"""Generates server for backend API"""
from flask import Flask, request
import json
import numpy as np
from skimage.transform import resize
from . import predict as pred
from . import transform_data as td
from . import config as cf
flask_app = Flask(__name__)
host = cf.HOST
port = cf.BACKEND_PORT
# load model
lea... | {"hexsha": "4ed2f0338406cf9225b2f58e1286a41932193780", "size": 2261, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/backend_api.py", "max_stars_repo_name": "lwang94/sem_size_analysis", "max_stars_repo_head_hexsha": "803251cdcab3d8304a365df9ac5879fcd9346270", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
//=========================================================================
// Copyright (c) Kitware, Inc.
// All rights reserved.
// See LICENSE.txt for details.
//
// This software is distributed WITHOUT ANY WARRANTY; without even
// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
// PURPOSE... | {"hexsha": "4f3791f4fba2390855c87d6be9ee13266e60e6f9", "size": 5949, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "smtk/session/vtk/operators/Write.cxx", "max_stars_repo_name": "jcfr/SMTK", "max_stars_repo_head_hexsha": "0069ea37f8f71a440b8f10a157b84a56ca004551", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"]... |
import numpy as np
import torch
from torch.utils.data import Dataset
from torchsparse import SparseTensor
from torchsparse.utils import sparse_quantize
import lidar_det.utils.jrdb_transforms as jt
import lidar_det.utils.utils_box3d as ub3d
from .utils import collate_sparse_tensors, boxes_to_target
# from .utils imp... | {"hexsha": "aefd0432893f1bb8cf05501e2190a9ed1549c3e0", "size": 14667, "ext": "py", "lang": "Python", "max_stars_repo_path": "lidar_det/dataset/dataset_det3d.py", "max_stars_repo_name": "VisualComputingInstitute/Person_MinkUNet", "max_stars_repo_head_hexsha": "fa39764245a022740c0a3d8c85026532fff93e74", "max_stars_repo_l... |
#Ref: Sreenivas Sarwar Anik
"""
1st approach: Perform CLAHE
# Equalize light by performing CLAHE on the Luminance channel
# The equalize part alreay covered as aprt of previous tutorials about CLAHE
# This kind of works but you can still see shading after the correction.
2nd approach:
Apply rolling ball background s... | {"hexsha": "a61464a33dbd30b8d213af5309136d0e728b54b5", "size": 2098, "ext": "py", "lang": "Python", "max_stars_repo_path": "117_shading_correction_using_rolling_ball.py", "max_stars_repo_name": "Data-Laboratory/WorkExamples", "max_stars_repo_head_hexsha": "27e58207e664da7813673e6792c0c30c0a5bf74c", "max_stars_repo_lice... |
# copyright (C) 2013 Atsushi Togo
# All rights reserved.
#
# This file is part of phonopy.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notic... | {"hexsha": "1bf12919db14c3e8a1d34e4870e7d62281257c31", "size": 11237, "ext": "py", "lang": "Python", "max_stars_repo_path": "phonopy/phonon/tetrahedron_mesh.py", "max_stars_repo_name": "ttadano/phonopy", "max_stars_repo_head_hexsha": "8c03955b2636b22b86e9324f5afcfa36396fa988", "max_stars_repo_licenses": ["BSD-3-Clause"... |
\section{Modal pomsets}
In order to perform a sharper analysis of dependency, we present an alternate
semantics using modal pomsets defined below. Modal pomsets make a formal
distinction between strong order and weak order.
\begin{definition}
A \emph{modal (memory model) pomset} is a tuple
$(\Event, {\sle}, {\gtN}... | {"hexsha": "82cb4609778b8971fbcad66f82cde7d5f54642d1", "size": 25732, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "drf-proof.tex", "max_stars_repo_name": "chicago-relaxed-memory/memory-model", "max_stars_repo_head_hexsha": "fd606fdb6a04685d9bb0bee61a5641e4623b10be", "max_stars_repo_licenses": ["CC-BY-4.0"], "ma... |
from pathlib import Path
from torchvision import transforms as trans
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import numpy as np
import cv2
import bcolz
import pickle
import mxnet as mx
from tqdm import tqdm
def load_bin(path, rootdir, transform, image_size=[112, 112]):
if not rootd... | {"hexsha": "befddee8c70f73b644bf6265f151977b92700580", "size": 2274, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/preprocess.py", "max_stars_repo_name": "leon2milan/faceRecognition", "max_stars_repo_head_hexsha": "c69271c9f808a63fa7dbb856e7726a59a4817515", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# inc_data_dfg.r
myC1a<-rgb(251,212,150,maxColorValue=255)
myC2a<-rgb(237,153,118,maxColorValue=255)
myC3a<-rgb(179,213,148,maxColorValue=255)
myC4a<-rgb(112,200,230,maxColorValue=255)
myC1b<-rgb(243,178,40,maxColorValue=255)
myC2b<-rgb(220,62,42,maxColorValue=255)
myC3b<-rgb(109,182,68,maxColorValue=255)
myC4b<-rgb(0... | {"hexsha": "91f539f31b1351f54fcb246d510a658ed1c58d80", "size": 761, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/inc_data_dfg.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys
import numpy as np
reff=sys.argv[1]
hybf=sys.argv[2]
def read_f(fname):
res={}
with open(fname, 'r') as fin:
for line in fin:
uttid, ali = line.split()[0], line.split()[1:]
res[uttid]=np.array([ int(x) for x in ali])
return res
ref=read_f(reff)
hyb=read_f(hybf)... | {"hexsha": "045d1bc35eeef9dc4a12797fa681a20ecab35391", "size": 651, "ext": "py", "lang": "Python", "max_stars_repo_path": "egs/codeswitching/asr/local_jqg01/data/phone/acc.py", "max_stars_repo_name": "luyizhou4/espnet", "max_stars_repo_head_hexsha": "a408b9372df3f57ef33b8a378a8d9abc7f872cf5", "max_stars_repo_licenses":... |
#!/usr/bin/env python
# coding: utf-8
#
# Neng Lu
# nengl@student.unimelb.edu.au
# ANU & Unimelb
# Canberra, Australia
#
# Version: 1.0
# First version 14 May, 2020
# Last modified 22 May, 2020
import numpy as np
import math
from osgeo import gdal
from osgeo import osr
def testimport():
print("It works!")
#---... | {"hexsha": "2a0c20d9d8981d579851969e26d9464e203e0ae5", "size": 5689, "ext": "py", "lang": "Python", "max_stars_repo_path": "qixiang/functions.py", "max_stars_repo_name": "NengLu/UWG_QiXiang", "max_stars_repo_head_hexsha": "b2a5782c7794cf2505c696075f437097b03b7662", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import pandas as pd
import numpy as np
import umap
import sklearn.cluster as cluster
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
import spacy
import unicodedata
import matplotlib.pyplot as plt
import logging
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
logging.getL... | {"hexsha": "2589dbf56797c92d3681ef3ca913920f5c6a86a7", "size": 13358, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/semanticClustering/experimentUtil.py", "max_stars_repo_name": "mikiec84/SemanticModels.jl", "max_stars_repo_head_hexsha": "f81baf0789cc547375f300429d0fd49c866d5339", "max_stars_repo_lice... |
######
#
# 2-dimensional stuff.
#
function regulargrid2d(box, res)
xmin, ymin, xmax, ymax = box
rx, ry = res
dx = (xmax-xmin)/(rx-1)
dy = (ymax-ymin)/(ry-1)
vs_cnt = rx*ry # vertices count
es_cnt = (rx-1)*ry + rx*(ry-1)+ (rx-1)*(rx-1) # Horizontal + vertical + diagonal
fs_cnt = 2*(rx-1)*(r... | {"hexsha": "fe1872dfa6338fc417bd459aebb035654079647d", "size": 5394, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grids.jl", "max_stars_repo_name": "valerocar/LevelSets.jl", "max_stars_repo_head_hexsha": "9cfc4c0bb2cb80a0d96dca81431daf50aa1f703c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma less_eq_multiset_empty_left[simp]:
shows "{#} \<le> M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {#} \<le> M
[PROOF STEP]
by (simp add: subset_eq_imp_le_multiset) | {"llama_tokens": 88, "file": null, "length": 1} |
[STATEMENT]
lemma orthogonal_complement_orthogonal_complement_closure_cspan:
\<open>orthogonal_complement (orthogonal_complement S) = closure (cspan S)\<close> for S :: \<open>'a::chilbert_space set\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. orthogonal_complement (orthogonal_complement S) = closure (cs... | {"llama_tokens": 842, "file": "Complex_Bounded_Operators_Complex_Inner_Product", "length": 10} |
import os
import sys
from functools import partial
import numpy as np
import pytest
import scipy
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from scipy.stats import norm
from respy import RespyCls
from respy.fortran.interface ... | {"hexsha": "6607969d6fc0a3b92bf5d95498a56a0d038ebe2d", "size": 35693, "ext": "py", "lang": "Python", "max_stars_repo_path": "respy/tests/test_f2py.py", "max_stars_repo_name": "tobiasraabe/respy_for_ma", "max_stars_repo_head_hexsha": "405f40851b176705fe924220fba606263d47f3d6", "max_stars_repo_licenses": ["MIT"], "max_st... |
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!! SUBROUTINES FOR BOUNDARY CONDITIONS !!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
subroutine apply_BC()
use constants
implicit none
integer :: ... | {"hexsha": "b192a1a98622e6ccbcc8d6dd4902404bf3265198", "size": 13210, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "ibc.f90", "max_stars_repo_name": "cbellei/mION", "max_stars_repo_head_hexsha": "378d1f5ea8c419fdf063947ff54643a580b82174", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
using PastaQ
using ITensors
using Printf
# 1. Prepation of a thermal state
#
# In this example, we show how to prepare the finite-temperature state
# of a many-body system:
#
# ρ̂(β) = exp(-β Ĥ)
#
# where Ĥ is the Hamiltonian and β is the inverse temperature.
# We specificallty consider the one-dimensional Ising m... | {"hexsha": "7422babd0596338ae3f20a9e8334e63dc399f890", "size": 3475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/5_finitetemperature.jl", "max_stars_repo_name": "GTorlai/PastaQ.jl", "max_stars_repo_head_hexsha": "fc5ae805681df2de8d3fe105b600156997140c18", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
% mycorr2 modified version of the 2D correlation
% for the use with im2col and col2im
% see GETPOINT
%
%
% $Id: mycorr2.m,v 2.0 2003/06/19 12:06:52 svoboda Exp $
% Note: It written in order to gain speed. The clarity of the code suffers accordingly
function R = mycorr2(X,G,Gn,Gn2)
% Gn = G-mean(G);
... | {"author": "strawlab", "repo": "MultiCamSelfCal", "sha": "0a26c88c63d8513eab76553033a9a6fb15ba6575", "save_path": "github-repos/MATLAB/strawlab-MultiCamSelfCal", "path": "github-repos/MATLAB/strawlab-MultiCamSelfCal/MultiCamSelfCal-0a26c88c63d8513eab76553033a9a6fb15ba6575/MultiCamSelfCal/FindingPoints/mycorr2.m"} |
from pyscipopt import Sepa, Conshdlr, SCIP_RESULT, SCIP_STAGE
from time import time
import networkx as nx
import numpy as np
from utils.scip_models import maxcut_mccormic_model, MccormickCycleSeparator
from utils.misc import get_separator_cuts_applied
from utils.data import get_gnn_data
import os
import torch
import p... | {"hexsha": "edef21bea54b66f4f52385a9864670493fdf9d4b", "size": 9723, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/samplers.py", "max_stars_repo_name": "avrech/learning2cut", "max_stars_repo_head_hexsha": "c0febe84db5097413823821510a4ae3c996dec93", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
"""
Models for the joint probability distribution.
"""
from abc import ABC, abstractmethod
import numpy as np
import scipy.integrate as integrate
from virocon.distributions import ConditionalDistribution
from virocon.intervals import NumberOfIntervalsSlicer
__all__ = ["GlobalHierarchicalModel"]
class Multivariat... | {"hexsha": "890b56743d134853b9f30ed0048b7801d2c5471c", "size": 19902, "ext": "py", "lang": "Python", "max_stars_repo_path": "virocon/jointmodels.py", "max_stars_repo_name": "ahaselsteiner/viroconcom", "max_stars_repo_head_hexsha": "69b903dabde1de73c85b5648f66523b9c151996e", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""Least squares fitting.
Implements a penalised least-squares fit.
putting point data onto the mesh.
The penalty term (or smoothing term) is controlled by the smoothing
parameter alpha.
With a value of alpha=0, the fit function will attempt
to interpolate as closely as possible in the least-squares... | {"hexsha": "55e1a374ba0859ac219f7d7b84a558a37cfae057", "size": 22547, "ext": "py", "lang": "Python", "max_stars_repo_path": "anuga/fit_interpolate/fit.py", "max_stars_repo_name": "samcom12/anuga_core", "max_stars_repo_head_hexsha": "f4378114dbf02d666fe6423de45798add5c42806", "max_stars_repo_licenses": ["Python-2.0", "O... |
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 28 22:43:10 2016
@author: kevin
"""
#%%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly
import plotly.plotly as py
from plotly.graph_objs import *
plotly.tools.set_credentials_file(username='kevyin', api_key='n3c33j5hac')
from ggplot ... | {"hexsha": "2354f83250ffb1469da963b2fce7fc5a9916b98c", "size": 1870, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples-python27/analyse_example.py", "max_stars_repo_name": "kevyin/sitta", "max_stars_repo_head_hexsha": "e2504dc6dddb57742deb22d6ce881925b59070c8", "max_stars_repo_licenses": ["MIT"], "max_sta... |
%% loftLinQuad2hex
% Below is a demonstration of the features of the |loftLinQuad2hex| function
%%
clear; close all; clc;
%% Syntax
% |[varargout]=loftLinQuad2hex(Fq,Vq,Vq2,numSteps);|
%% Description
% UNDOCUMENTED
%% Examples
%
%%
%
% <<gibbVerySmall.gif>>
%
% _*GIBBON*_
% <www.gibboncode.org>
%
% _Kevin Ma... | {"author": "gibbonCode", "repo": "GIBBON", "sha": "8178520664a6148db939eaea87e75b3cba4f2b4f", "save_path": "github-repos/MATLAB/gibbonCode-GIBBON", "path": "github-repos/MATLAB/gibbonCode-GIBBON/GIBBON-8178520664a6148db939eaea87e75b3cba4f2b4f/docs/HELP_loftLinQuad2hex.m"} |
import pdb
import sys
from functools import reduce
import numpy as np
from prompt_toolkit import prompt
from tabulate import tabulate
from ..metadata_interface import *
from ..common import *
class ReplUi(object):
def __init__(self, all_tagsets, pgid=None):
self._init_brick(all_tagsets)
self.pg... | {"hexsha": "912532dd7c028573ea6415185fa702001034d53d", "size": 9845, "ext": "py", "lang": "Python", "max_stars_repo_path": "plastering/uis/cmdline_ui.py", "max_stars_repo_name": "MingzheWu418/plastering", "max_stars_repo_head_hexsha": "322531e934c3acf2ecc8f520b37a6d255b9959c2", "max_stars_repo_licenses": ["MIT"], "max_... |
################################
# EvoMan FrameWork - V1.0 2016 #
# Author: Karine Miras #
# karine.smiras@gmail.com #
################################
import sys
import numpy
import random
import Base
from Base.SpriteConstants import *
from Base.SpriteDefinition import *
from sensors import Sensors
til... | {"hexsha": "a84eab0590d1492a10d5111b6cfabee07fdf57e3", "size": 11183, "ext": "py", "lang": "Python", "max_stars_repo_path": "evoman/enemy4.py", "max_stars_repo_name": "ChristophHoenes/EWoMan2", "max_stars_repo_head_hexsha": "c3a117ba1b217d8b4a8f678a5cb4fda471134bfa", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import random
from typing import Tuple
import discord
import numpy
from discord.ext import commands
from .base_cog import BaseCog
from ..utils.converters import BoolConverter
from ..utils.exceptions import CommandError
class PUBGCog(BaseCog):
"""PUBG commands"""
EMOJI = "<:pubghelm:56552... | {"hexsha": "e9a7fb41cbd993bf2ee336b2a08c05aa8dc9bb82", "size": 6678, "ext": "py", "lang": "Python", "max_stars_repo_path": "vjemmie/cogs/pubg_cog.py", "max_stars_repo_name": "PederHA/vjemmie", "max_stars_repo_head_hexsha": "e3742380d3ea06de90f8227a0934569f8fd02b5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/*
Copyright 2013 Adobe
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
*/
/*************************************************************************************************/
#include <adobe/config.hpp>
#inc... | {"hexsha": "16bf19b666b45c335620c1929ee363acf726825a", "size": 12506, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/unit_tests/copy_on_write/cow_test.cpp", "max_stars_repo_name": "ilelann/adobe_source_libraries", "max_stars_repo_head_hexsha": "82224d13335398dfebfc77addabab28c4296ecba", "max_stars_repo_licen... |
(F::FqFiniteField)(coeffs::Array{T,1}) where {T<:Union{Integer,fmpz}} = begin
g = gen(F)
x = zero(F)
for (i, c) in enumerate(coeffs)
x += c * g^(i-1)
end
x
end
(F::FqNmodFiniteField)(coeffs::Array{T,1}) where {T<:Union{Integer,fmpz}} = begin
g = gen(F)
x = zero(F)
for (i, c) in ... | {"hexsha": "4d5f260871c97890f932d0f3d8d7d78401b4d7a7", "size": 3966, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Math/Algebra/field.jl", "max_stars_repo_name": "Samayel/Brainstorm.jl", "max_stars_repo_head_hexsha": "9d83bb0a104973e498ba4ca84b0a27ede6c053ac", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
(*
File: HOL/Computational_Algebra/Squarefree.thy
Author: Manuel Eberl <manuel@pruvisto.org>
Squarefreeness and decomposition of ring elements into square part and squarefree part
*)
section \<open>Squarefreeness\<close>
theory Squarefree
imports Primes
begin
(* TODO: Generalise to n-th powers *)
def... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/Computational_Algebra/Squarefree.thy"} |
# adapted from https://github.com/yangheng95/LC-ABSA/blob/c945a94e0f86116c5578245aa9ad36c46c7b9c4a/models/lc_apc/lcf_bert.py
# according to
import copy
from argparse import Namespace
from typing import Dict
import numpy as np
import torch
import torch.nn as nn
from transformers.modeling_bert import BertPooler, BertSel... | {"hexsha": "f79d7e2ced217adb6732defe255e2c5276aae52f", "size": 8304, "ext": "py", "lang": "Python", "max_stars_repo_path": "NewsSentiment/models/singletarget/lcf2.py", "max_stars_repo_name": "fhamborg/NewsMTSC", "max_stars_repo_head_hexsha": "5a8f88d7fbb921090e984cc378b02d75524c1025", "max_stars_repo_licenses": ["MIT"]... |
c
c-----------------------------------------------------------------------
c subroutine: r8tx
c radix 8 iteration subroutine
c-----------------------------------------------------------------------
c
subroutine r8tx(nxtlt, nthpo, lengt, cr0, cr1, cr2, cr3, cr4,
* cr5, cr6, cr7, ci0, ci1, ci2, ci3, ci4, c... | {"hexsha": "9cc4f591faa587671eb683fe461fa74c1ca8f09d", "size": 3366, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iraf.v2161/math/ieee/chap1/r8tx.f", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], "max_s... |
/**************************************************************
*
* 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 y... | {"hexsha": "ff38aabf8fb292176a2e15ecf3ff226223fd1537", "size": 17970, "ext": "hxx", "lang": "C++", "max_stars_repo_path": "main/unotools/inc/unotools/localedatawrapper.hxx", "max_stars_repo_name": "Grosskopf/openoffice", "max_stars_repo_head_hexsha": "93df6e8a695d5e3eac16f3ad5e9ade1b963ab8d7", "max_stars_repo_licenses"... |
include(joinpath("gammaReg", "chosenVariables_inverse_test.jl"))
include(joinpath("gammaReg", "chosenVariables_log_test.jl"))
include(joinpath("gammaReg", "research_inverse_test.jl"))
include(joinpath("gammaReg", "research_inverse_test.jl"))
| {"hexsha": "34f1547617077eabe04ba581b89be1a6617c35c6", "size": 243, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dataAnalysis/Duration/gammaReg_test.jl", "max_stars_repo_name": "AlexLsn/CSO", "max_stars_repo_head_hexsha": "b1e2eb949003d9a2ea865581da554ea4ca7c8cf7", "max_stars_repo_licenses": ["MIT"], "max... |
"""
sciwebvis.material
------------------
:copyright: 2015, Juan David Adarve. See AUTHORS for more details
:license: 3-clause BSD, see LICENSE for more details
"""
import numpy as np
from jinja2 import Environment, PackageLoader
from .JSRenderable import JSRenderable
from .color import Color
# from... | {"hexsha": "d310ea6709e311c84e9d23986e03e95b5ceb3595", "size": 5959, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/sciwebvis/material.py", "max_stars_repo_name": "jadarve/sciwebvis", "max_stars_repo_head_hexsha": "887268b310067809a7a7495952f76b1e70aeed64", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import numpy as np
from collections import Counter
from sklearn.preprocessing import StandardScaler
def min_max_normalize(X):
"""Min-Max normalization function
X = (X - Xmin)/(Xmax - Xmin)"""
samples, features = X.shape
for i in range(features):
xmin = X[:, i].min()
xmax = X[:, i].max... | {"hexsha": "1f0fdbe4391269c67fb3e0acaf22c4268dd4f5ff", "size": 2004, "ext": "py", "lang": "Python", "max_stars_repo_path": "Assignment-2/utils.py", "max_stars_repo_name": "PranjalGupta2199/py-classifier", "max_stars_repo_head_hexsha": "e74be0349d755201b7265f125ef81fceec174f64", "max_stars_repo_licenses": ["MIT"], "max_... |
import tvm
from tvm import topi
import numpy as np
import torch
import torchvision
from torch.autograd import Variable
from torchvision import transforms
from tvm.tensor_graph.nn.layers import Layer
from tvm.tensor_graph.nn.functional import dense, gemm
from tvm.tensor_graph.core import compute, GraphTensor, GraphOp, G... | {"hexsha": "34f0818143f0849a44af93e6bf5eb355e969015c", "size": 4585, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tvm/tensor_graph/testing/models/SCRNN.py", "max_stars_repo_name": "QinHan-Erin/AMOS", "max_stars_repo_head_hexsha": "634bf48edf4015e4a69a8c32d49b96bce2b5f16f", "max_stars_repo_licenses": ["... |
#pragma once
#include <polyfem/ProblemWithSolution.hpp>
#include <Eigen/Dense>
#include <vector>
#include <string>
namespace polyfem
{
class State;
class KernelProblem : public ProblemWithSolution
{
public:
KernelProblem(const std::string &name);
VectorNd eval_fun(const VectorNd &pt, const double t) const... | {"hexsha": "d9dfe8e1c10e6d0cc2f73e5b366248c1da21c672", "size": 997, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/problem/KernelProblem.hpp", "max_stars_repo_name": "danielepanozzo/polyfem", "max_stars_repo_head_hexsha": "34a7719c2a3874b7ecc865c28d8b3d9bbdf7d0ba", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import re
import inspect
import time
import pandas as pd
import numpy as np
import ipywidgets as ipw
import traitlets as tra
from multiprocessing import Process
from datetime import datetime
from IPython import display
from collections.abc import Iterator
try:
from utils import frontend as utils
from processin... | {"hexsha": "96cc1d9b841b3d3afd6650a6955a42b1f1b936a6", "size": 19165, "ext": "py", "lang": "Python", "max_stars_repo_path": "interface.py", "max_stars_repo_name": "nuki111/env_explore", "max_stars_repo_head_hexsha": "b5dfa05fbcfb0126e246e4ef4eb5a392a8615cf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
[STATEMENT]
lemma funas_ctxt_of_gctxt_conv [simp]:
"funas_ctxt (ctxt_of_gctxt C) = funas_gctxt C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. funas_ctxt (ctxt_of_gctxt C) = funas_gctxt C
[PROOF STEP]
by (induct C) (auto simp flip: funas_gterm_gterm_of_term) | {"llama_tokens": 132, "file": "Regular_Tree_Relations_Util_Ground_Ctxt", "length": 1} |
import os
import json
from pdf2words import document
import numpy as np
import re
from operator import itemgetter
from collections import OrderedDict
class name_scoring:
def __init__(self):
self.top_words = []
self.clusters = []
self.score = []
self.flg = 0
self.size = []
... | {"hexsha": "76f32185fa21baf1417552938d4d82dcabbb2772", "size": 14011, "ext": "py", "lang": "Python", "max_stars_repo_path": "flask/pdf2words/name_score.py", "max_stars_repo_name": "ishan-modi/docker", "max_stars_repo_head_hexsha": "768ccc3450043d41d1de21ebef28aef6ce4d6149", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import sys
import re
import importlib
import torch
import numpy as np
import random
# @NOTE: https://stackoverflow.com/a/1176023/2425365
first_cap_re = re.compile('(.)([A-Z][a-z]+)')
all_cap_re = re.compile('([a-z])([A-Z])')
def to_camel_case(name: str):
cap_sub = first_cap_re.sub(r'\1_\2', name)
retu... | {"hexsha": "8fe83935cfaf217f9386df3c82c3d4380d1ef93b", "size": 1225, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchrl/utils/misc.py", "max_stars_repo_name": "srikarym/torchrl", "max_stars_repo_head_hexsha": "fee98e78ac1657a2c9a4063dd8d63ba207a121e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#!/usr/bin/env python
# coding=utf-8
import torch.nn.functional as F
from scipy.spatial.distance import cdist
from utils.utils_pytorch import *
import matplotlib.pyplot as plt
from utils.general import plot_cm
import sys
sys.path.append("..")
from datasets.utils_dataset import merge_images_labels
def test_ac(tg_model,... | {"hexsha": "283b22edd98537c21a1d18544d4b1a22ce2c20a2", "size": 49089, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/compute_accuracy.py", "max_stars_repo_name": "xmengxin/MFGR", "max_stars_repo_head_hexsha": "ba807d0f52c0eb00d330eaa9bcef56c1343d2588", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
r"""
Module defining Pyclaw geometry objects.
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import warnings
import six
from six.moves import range
from six.moves import zip
deprec_message = "'edges' has been deprecated; please use 'nodes' instead."
# =============... | {"hexsha": "d2c12e020c3cbffbe52ef5a2a6737b90293c766c", "size": 29397, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyclaw/geometry.py", "max_stars_repo_name": "BrisaDavis/pyclaw", "max_stars_repo_head_hexsha": "439e5b3f8f1d3892578368c17c4ad584fda706c2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#ifndef BOOST_MPL_AUX_MSVC_ETI_BASE_HPP_INCLUDED
#define BOOST_MPL_AUX_MSVC_ETI_BASE_HPP_INCLUDED
// Copyright Aleksey Gurtovoy 2001-2004
//
// 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://ww... | {"hexsha": "4fbf226090e4518546a97c8a6309253c44196e1c", "size": 1756, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lshkit/trunk/3rd-party/boost/boost/mpl/aux_/msvc_eti_base.hpp", "max_stars_repo_name": "wzj1695224/BinClone", "max_stars_repo_head_hexsha": "3b6dedb9a1f08be6dbcdce8f3278351ef5530ed8", "max_stars_rep... |
# 导入包
import zipfile
import paddle
import paddle.fluid as fluid
import matplotlib.pyplot as plt
import matplotlib.image as mping
from PIL import Image
import json
import numpy as np
import cv2
import sys
import time
import h5py
# import scipy.io as io
from matplotlib import pyplot as plt
from scipy.ndimage.filters impo... | {"hexsha": "9965c3ec361436e9af0e16d7f8b897c3185be918", "size": 21130, "ext": "py", "lang": "Python", "max_stars_repo_path": "crowd_density_detection.py", "max_stars_repo_name": "ArseneLupinhb/crowd_density_detection", "max_stars_repo_head_hexsha": "a4f4e955319926a57dcfc0b446f6d40b0449df30", "max_stars_repo_licenses": [... |
from collections import defaultdict
import numpy as np
from datetime import datetime
from graph import Graph
from graph import FloatVec
from graph import LongVec
from graph import LongPair
from graph import PairVec
from graph import constrainedGreedyAdditiveEdgeContraction
import progressbar
import math
def constant_l... | {"hexsha": "c314ff96234ee596398413560ee7b440f5f60bcc", "size": 4941, "ext": "py", "lang": "Python", "max_stars_repo_path": "deploy_python/openem/tracking/graph_utils.py", "max_stars_repo_name": "openem-team/openem", "max_stars_repo_head_hexsha": "45222c9c77084eacab278da25a8734ae7d43f677", "max_stars_repo_licenses": ["M... |
import numpy as np
import keras
from keras import backend as K
from keras.layers.core import Dense
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Model
from keras... | {"hexsha": "8867d713464dc5a67cd98650434be2f7e58d5600", "size": 781, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/model.py", "max_stars_repo_name": "codergab/Vusion", "max_stars_repo_head_hexsha": "44ed404112943531e645db5e4cb03fe44d08a2ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
using LazyArrays, ArrayLayouts, LinearAlgebra, FillArrays
import LazyArrays: materialize!, MemoryLayout, triangulardata, LazyLayout, UnknownLayout, LazyMatrix
# used to test general matrix backends
struct MyMatrix{T} <: LazyMatrix{T}
A::Matrix{T}
end
MyMatrix{T}(::UndefInitializer, n::Int, m::Int) where T = MyMat... | {"hexsha": "577332913ad0e72ab8c772e55a3ecced602470c3", "size": 12905, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/lazymultests.jl", "max_stars_repo_name": "johnbcoughlin/LazyArrays.jl", "max_stars_repo_head_hexsha": "6fcc9b900aa147222b259037fd48d4698ad1ad54", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
The functions in this module calculate different graph-level properties.
The first function is a wrapper that
subsamples networks from a list of null models to output a dataframe of set sizes.
"""
__author__ = 'Lisa Rottjers'
__email__ = 'lisa.rottjers@kuleuven.be'
__status__ = 'Development'
__license__ = 'Apache... | {"hexsha": "da0638de422e26f8cf322702447473851bc7b08e", "size": 7733, "ext": "py", "lang": "Python", "max_stars_repo_path": "anuran/graphvals.py", "max_stars_repo_name": "ramellose/anuran", "max_stars_repo_head_hexsha": "8541f9cedbca00981257564fb8562d46fa5f5cab", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
// Copyright 2017 The Ray 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 i... | {"hexsha": "5bfa82965a83e80a21f8aacf43d8ba2ff28fa889", "size": 3885, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/ray/core_worker/transport/thread_pool_manager.cc", "max_stars_repo_name": "daobook/ray", "max_stars_repo_head_hexsha": "af9f1ef4dc160e0671206556b387f8017f3c3930", "max_stars_repo_licenses": ["Apa... |
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
C code and some fundamental functions
'''
from pyscf.lib import parameters
param = parameters
from pyscf.lib import numpy_helper
from pyscf.lib import linalg_helper
from pyscf.lib import logger
from pyscf.lib.misc import *
from pyscf.lib.numpy_helper import *
from p... | {"hexsha": "162173341e35fbc0c6ce22b666ae3dd0f368b269", "size": 449, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/__init__.py", "max_stars_repo_name": "gmwang18/pyscf", "max_stars_repo_head_hexsha": "fcd6877751661c8a9743c1c872a4a2b65f6dd7ac", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": ... |
import numpy as np
import torch
import torch.nn as nn
from mbmf import utils
class HybridAgent(nn.Module):
""" Take an ensemble of SAC agents and an MPC planner """
def __init__(self,
sac_agents,
planner,
ensemble_model,
buffer,
... | {"hexsha": "bfa53c0f20474ec4d4fd045c5796a033922752fc", "size": 5487, "ext": "py", "lang": "Python", "max_stars_repo_path": "mbmf/control/hybrid.py", "max_stars_repo_name": "BerenMillidge/state-augmentation", "max_stars_repo_head_hexsha": "b3834b3a99be9854ecf83b6d83e9fbb2184f4861", "max_stars_repo_licenses": ["MIT"], "m... |
"""
FMA Helpers
"""
from typing import List, Optional, Tuple, Union
import numpy as np
import pandas as pd
_LIST_GENRE_COLUMNS: Tuple[str, ...] = ("track_genres", "track_genres_all")
def join_columns(df: pd.DataFrame) -> pd.DataFrame:
df.columns = ["_".join(i) for i in df.columns]
return df
def sort... | {"hexsha": "fec2cd0ae9f2b6198c2cb3ef0f41999539a84a3a", "size": 1706, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/fma/data/_helpers.py", "max_stars_repo_name": "TariqAHassan/wav2rec", "max_stars_repo_head_hexsha": "8d3f33291f246d80a4935cf7aa2cc75f110d9c15", "max_stars_repo_licenses": ["MIT"], "max... |
(* This code is copyrighted by its authors; it is distributed under *)
(* the terms of the LGPL license (see LICENSE and description files) *)
(****************************************************************************)
(* *)
(* ... | {"author": "coq-contribs", "repo": "fairisle", "sha": "e36087a6b7e52ef3c6dcfdeba1298e8fde1260a0", "save_path": "github-repos/coq/coq-contribs-fairisle", "path": "github-repos/coq/coq-contribs-fairisle/fairisle-e36087a6b7e52ef3c6dcfdeba1298e8fde1260a0/Libraries/Lib_Arithmetic/Lib_Plus.v"} |
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