text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma OT_14_correct: "OT_14.correctness M C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. OT_14.correctness M C
[PROOF STEP]
unfolding OT_14.correctness_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. protocol_14_OT M C = funct_OT_14 M C
[PROOF STEP]
using correctness_OT_14
[PROOF STATE]
proof (p... | {"llama_tokens": 221, "file": "Multi_Party_Computation_OT14", "length": 3} |
#Script to plot Rydberg radial wave functions
#23/07/2017
using Plots, JLD, LaTeXStrings
pyplot()
include("functions.jl")
PyPlot.close("all")
#Input information
atom = "87Rb"
nn = 50
ll = 0
jj = 0.5
#Calculate wave function
normY_sol, rr = numerovfunc(atom,nn,ll,jj)
#Rescale for plotting
plotscale = sqrt(rr)
probam... | {"hexsha": "be90041f3c263260609d90173d7359c9d6ed07e0", "size": 1099, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia/PlotWaveFunction.jl", "max_stars_repo_name": "CSChisholm/Rydberg", "max_stars_repo_head_hexsha": "13bd7ff296533c9cc9fc9cb8ffc3f7c6752a80bb", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
!
! Copyright 2013 Guy Munhoven
!
! This file is part of SolveSAPHE.
! SolveSAPHE is free software: you can redistribute it and/or modify
! it under the terms of the GNU Lesser General Public License as published by
! the Free Software Foundation, either version 3 of the License, or
! (at your option... | {"hexsha": "973b3d6473113a4ca8fd4a6e09c29404e54c7e9a", "size": 66828, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/waq/packages/waq_kernel/src/waq_process/solvesaphe/mod_chemconst.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha":... |
import math
import torch
import paddle
import pgl
import numpy as np
import paddle.fluid as F
import paddle.fluid.layers as L
import copy
from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
from ogb.nodeproppred import Evaluator
from utils import to_undirected, add_self_loop, linear_warmup_deca... | {"hexsha": "d9780e3d8f24f9d974b89cc575523a3ab0508530", "size": 13340, "ext": "py", "lang": "Python", "max_stars_repo_path": "ogb_examples/nodeproppred/unimp/main_product.py", "max_stars_repo_name": "zbmain/PGL", "max_stars_repo_head_hexsha": "dbded6a1543248b0a33c05eb476ddc513401a774", "max_stars_repo_licenses": ["Apach... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
# Refer... | {"hexsha": "7fe1ae7abe46b72c354dbb82ef29a55fff380c64", "size": 6973, "ext": "py", "lang": "Python", "max_stars_repo_path": "caffe2/python/operator_test/bbox_transform_test.py", "max_stars_repo_name": "shigengtian/caffe2", "max_stars_repo_head_hexsha": "e19489d6acd17fea8ca98cd8e4b5b680e23a93c5", "max_stars_repo_licenses... |
from __future__ import print_function
import torch
import torch.nn as nn
import pickle
import data_prep as prep
from torchvision import transforms, utils
import torch.nn.parallel
import numpy as np
from torch.utils.data import DataLoader
from generator import Generator
from discriminator import Discriminator
from torch... | {"hexsha": "59e5e6442bd8d698391ccdb58cb20bdb91cedd46", "size": 3357, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference/infer.py", "max_stars_repo_name": "biringaChi/PIF", "max_stars_repo_head_hexsha": "5eca2a7bab8b6acf24db24c37dfaf9f41e66d88f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
using Lindenmayer, Luxor, Colors, ColorSchemes
crystal = LSystem(Dict(
"F" => "9F[F-]+*",
),
"F")
plant = LSystem(Dict(
"A" => "UBB8D", # initialize
"X" => "*[-F*X*]+F*X"),
"AX")
global x = 0
function f(t::Turtle)
pos = Point(t.xpos, t.ypos)
if x == 0
# we'll just do this at th... | {"hexsha": "42388b3046699e21eec6cd73e5a5959928e6b2fa", "size": 957, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/logo.jl", "max_stars_repo_name": "cormullion/Lindenmayer", "max_stars_repo_head_hexsha": "977f22c8386ae50a7fe4992d3b6a7656acfc3ed5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31, "... |
import numpy as np
from tensorflow.contrib.graph_editor import Transformer
def crop(image, bbox, x, y, length):
x, y, bbox = x.astype(np.int), y.astype(np.int), bbox.astype(np.int)
x_min, y_min, x_max, y_max = bbox
w, h = x_max - x_min, y_max - y_min
# Crop image to bbox
image = image[y_min:y_min... | {"hexsha": "b5dfb121615cfb2b05b1f38e73962de4166a2cad", "size": 3032, "ext": "py", "lang": "Python", "max_stars_repo_path": "my_cv/utils/numpy_handle_image.py", "max_stars_repo_name": "strawsyz/straw", "max_stars_repo_head_hexsha": "db313c78c2e3c0355cd10c70ac25a15bb5632d41", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <exception>
#include <ctime>
#include <boost/program_options.hpp>
#include <boost/random.hpp>
#include "scene.h"
#include "../../src/parameters/ParamParser_getopt.hpp"
#include "../../src/pointsets/Pointset.hpp"
#include "../../src/io/fileIO.hpp"
dou... | {"hexsha": "d7f84fe66a1ea6ce4ca0c31e977061d45411855d", "size": 3218, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "externals/bnot/main.cpp", "max_stars_repo_name": "FrancoisGaits/utk", "max_stars_repo_head_hexsha": "8c408dd79635f98c46ed075c098f15e23972aad0", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], "... |
import gym
import numpy as np
from abc import abstractmethod
from fault_tolerant_flight_control_drl.agent import SAC
from fault_tolerant_flight_control_drl.tools import AltitudeTask, AttitudeTask, BodyRateTask
from fault_tolerant_flight_control_drl.tools import ReliabilityTask, DisturbanceRejectionAtt
from fault_toler... | {"hexsha": "b5023c71e12ab2bbb72a047594c6d61484d2235a", "size": 29305, "ext": "py", "lang": "Python", "max_stars_repo_path": "fault_tolerant_flight_control_drl/envs/citation/citation.py", "max_stars_repo_name": "kdally/fault-tolerant-flight-control-drl", "max_stars_repo_head_hexsha": "800a1c9319b44ab2b1d17f6e19266c2392d... |
SUBROUTINE MA_CGRM (fldnin, prsdon, rmkdon, fldnou, ier)
C************************************************************************
C* MA_CGRM *
C* *
C* This subroutine decodes the character... | {"hexsha": "2b2a2600f4726557631c3c38253f886b2f2d1b66", "size": 9810, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/bridge/ma/macgrm.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
"""Unittests for rasterio.plot"""
import numpy as np
import pytest
try:
import matplotlib as mpl
mpl.use('agg')
import matplotlib.pyplot as plt
plt.show = lambda :None
except ImportError:
plt = None
import rasterio
from rasterio.plot import (show, show_hist, get_plt,
p... | {"hexsha": "ab352a956e53032c5a7e8f3be2bf04cac0f24929", "size": 9057, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_plot.py", "max_stars_repo_name": "Juanlu001/rasterio", "max_stars_repo_head_hexsha": "21c43443288f28e9ffcc9b9183c27568a36ed21b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
import numpy as np
import pprint
import cta_fspecial
import cta_chog
class Cta_products():
def __init__(self, Fourier_coefficients, product_options, output_order=0):
self.monoms = product_options['monoms']
self.feature_order=[0,5];
self.angular_power=[self.feature_order[0],self.feature_ord... | {"hexsha": "5946bec5cd9974cccc7b49e3241e3520e1bbd765", "size": 9205, "ext": "py", "lang": "Python", "max_stars_repo_path": "cta_products.py", "max_stars_repo_name": "nyunyu122/CHOG_python", "max_stars_repo_head_hexsha": "7e929506e48f1e58d2ded9dbd9f53676ef83356e", "max_stars_repo_licenses": ["Unlicense"], "max_stars_cou... |
[STATEMENT]
lemma lran_bwd_simp: "lran a l h = (if l<h then lran a l (h-1)@[a (h-1)] else [])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lran a l h = (if l < h then lran a l (h - 1) @ [a (h - 1)] else [])
[PROOF STEP]
apply (induction a l h rule: lran.induct)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<... | {"llama_tokens": 617, "file": "IMP2_lib_IMP2_Aux_Lemmas", "length": 4} |
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon,Point
from .grids import GPS_to_grids,grids_centre
import math
import numpy as np
from .preprocess import *
def busgps_arriveinfo(data,line,stop,col = ['VehicleId','GPSDateTime','lon','lat','stopname'],
stopbuffer... | {"hexsha": "cc06eb690e9ad64e044d5ea3f2e321bc300d8b36", "size": 8592, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/transbigdata/busgps.py", "max_stars_repo_name": "martinfleis/transbigdata", "max_stars_repo_head_hexsha": "ec80fcd47bf25edef26f4838d257c3cd9b6d9e2e", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import sys
import os
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from vaccine_alloc_instance import *
import numpy as np
import random
class RandomInstanceGenerator:
def __init__(self,number_of_instances, n,c,d,q, Q_d_min, Q_d_max, Q_c_min, Q_c_max, p_availability=0.6 ):
self.number_of_instances = number_o... | {"hexsha": "6a6cfa18c5d5228d08a9fe64a9a9320213ca32a0", "size": 1438, "ext": "py", "lang": "Python", "max_stars_repo_path": "generators/random_instance_generator_1.py", "max_stars_repo_name": "severus-tux/vaccine-alloc", "max_stars_repo_head_hexsha": "5f04e93c51f637d0adad96bd35b3a2726f225701", "max_stars_repo_licenses":... |
#!/usr/bin/env python
"""
Use forced alignments to separate digit sequences into individual digits.
Author: Herman Kamper
Contact: kamperh@gmail.com
Date: 2018
Edited: Ryan Eloff
Date: June 2018
"""
from __future__ import absolute_import, division, print_function
from os import path
import argparse
import sys
impo... | {"hexsha": "2692bfd71f7d38f67e30f3e33b9a4ad67f6424c0", "size": 3846, "ext": "py", "lang": "Python", "max_stars_repo_path": "kaldi_features/tidigits/tidigits_segments_prep.py", "max_stars_repo_name": "rpeloff/multimodal_one-shot_learning", "max_stars_repo_head_hexsha": "b08b9deffea5c656f07a616f31850192e32c2aee", "max_st... |
import json
import logging
import os
import shutil
import numpy as np
import torch
from datetime import datetime, timedelta
from torch import nn, optim
from torch.nn import functional as F
from models.fc_model import FCModel
from sklearn.preprocessing import label_binarize
_RNG_SEED = None
def split(a, n):
k, m = d... | {"hexsha": "aa81355a702ec30d2c976a07a75fe872cc38bd96", "size": 15966, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "GuangyuanHao/IntraOrderPreservingCalibration", "max_stars_repo_head_hexsha": "79325ae321fc1bce3088b2af8ecd18357b50fb6a", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import scipy.optimize
import warnings
def calc_weights(cov, x0=None, options=None, scale_factor=10000,
pcr_tolerance=0.001, ignore_objective=False):
"""
Calculate the weights associated with the equal risk contribution
portfolio. Refer to "On the Properties of Equally-W... | {"hexsha": "201a23513d2e8d329c8c71a4b3c576d73ab3543d", "size": 4096, "ext": "py", "lang": "Python", "max_stars_repo_path": "erc/erc.py", "max_stars_repo_name": "matthewgilbert/erc", "max_stars_repo_head_hexsha": "0f1b2587570f67e89ad713d48947ca1d96a801c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_... |
\section{Croissant}
\label{croissant}
\setcounter{secnumdepth}{0}
Time: 9 hours (30 minutes prep, 7+ hours inactive rising and resting, 18 minutes baking)
Serves: 12 pastries, 6-12 people, depending on generosity
\begin{multicols}{2}
\subsection*{Ingredients}
\begin{itemize}
\item 1 recipe of \nameref{viennoiserie... | {"hexsha": "221fd875ad21bc05628dfc2bee14c815b037ad43", "size": 3478, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/breads/croissant.tex", "max_stars_repo_name": "calebwatt15/caleb-watt-cookbook", "max_stars_repo_head_hexsha": "abddcdb60e9422d63d945e7a9ec019c0288e34d7", "max_stars_repo_licenses": ["MIT"]... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 3 17:29:01 2021
@author: Luigi
"""
import allMethods as fz
#import funzioni_zeri as fz
import numpy as np
import sympy as sym
import sympy.utilities.lambdify
x = sym.symbols("x")
fx = x**3 + x**2 - 33*x + 63
dfx = sym.diff(fx, x, 1)
f = sym.lambdify(x... | {"hexsha": "ccbb73799dc8c2638ecea4fd0cfc8a6fe12a1b5f", "size": 720, "ext": "py", "lang": "Python", "max_stars_repo_path": "zeri_di_funzione/esercizi/4.py", "max_stars_repo_name": "luigi-borriello00/Metodi_SIUMerici", "max_stars_repo_head_hexsha": "cf1407c0ad432a49a96dcd08303213e48723c57a", "max_stars_repo_licenses": ["... |
__doc__ = """Timoshenko beam validation case, for detailed explanation refer to
Gazzola et. al. R. Soc. 2018 section 3.4.3 """
import numpy as np
import sys
# FIXME without appending sys.path make it more generic
sys.path.append("../../")
from elastica import *
from examples.TimoshenkoBeamCase.timoshenko_postproces... | {"hexsha": "7cf7d592f76dbfd03515e4ec2f84352e35568daf", "size": 2695, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/TimoshenkoBeamCase/timoshenko.py", "max_stars_repo_name": "zhidou2/PyElastica", "max_stars_repo_head_hexsha": "0f5502bc5349ab5e5dc794d8dfc82b7c2bd69eb6", "max_stars_repo_licenses": ["MIT"... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.montgomery64_2e416m2e208m1_7limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition add :
{ add : feBW_small -> feBW_small -> feBW_small
| forall a b, phiM_small (add a b) = F.add (phiM_small a)... | {"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... |
#!/usr/bin/env python
# -*- coding: latin1 -*-
import scipy as sp
import matplotlib.pyplot as plt
# Get data from external file
file = "./data/web_traffic.tsv"
data = sp.genfromtxt(file, delimiter="\t")
# all examples will have three classes in this file
colors = ['g', 'k', 'b', 'm', 'r']
linestyles = ['-', '-.', '-... | {"hexsha": "973b8fa1b6a0e5a4da26cfe49fd778a92a1c699a", "size": 1612, "ext": "py", "lang": "Python", "max_stars_repo_path": "web_traffic-2.py", "max_stars_repo_name": "aricarmona/machine-learning-python", "max_stars_repo_head_hexsha": "884cafaddc2b1e623c4701bfaa9fb6c9221f9e18", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
""" TODO:
"""
import numpy as np
from scipy import interpolate
def t_list(mb_solve, speed_of_light):
""" Return the time points shifted to the fixed (lab) frame of
reference given a speed-of-light.
Args:
mb_solve: An MBSolve object
speed_of_light:... | {"hexsha": "6a9cba2b3fc355c5a2caa274e95a2ab3bd067ffd", "size": 2813, "ext": "py", "lang": "Python", "max_stars_repo_path": "maxwellbloch/fixed.py", "max_stars_repo_name": "amcdawes/maxwellbloch", "max_stars_repo_head_hexsha": "48b5301ccfa24704a4240125d377b1448d5591d9", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Created by Dennis Willsch (d.willsch@fz-juelich.de)
# Modified by Gabriele Cavallaro (g.cavallaro@fz-juelich.de)
import os
import sys
import re
import numpy as np
import numpy.lib.recfunctions as rfn
import matplotlib.pyplot as plt
from utils import *
import shutil
import pickle
import numpy.lib.recfunctions as r... | {"hexsha": "2a1a6f64635f67ddfa87261639780f4a9d82f3cb", "size": 15189, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantum_SVM.py", "max_stars_repo_name": "GaIbatorix/Quantum-SVM", "max_stars_repo_head_hexsha": "30e2d7378ac6e19a4ba062b92970a9e8033ad525", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
MonteCarloModel(core, dates, paths)
A `MonteCarloModel` is the result of a simulation of a series of asset prices.
* `core`: a reference `CoreModel`
* `dates`: an `AbstractVector{Date}`
* `paths`: a matrix of the scenario paths: the rows are the scenarios, and the columns are the values at each date in `dates... | {"hexsha": "8619ef087b82a2967c3f992ac0b415b76c9ad3d3", "size": 4108, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/montecarlo.jl", "max_stars_repo_name": "alecloudenback/Miletus.jl", "max_stars_repo_head_hexsha": "5863620f254fe234001e815d03ef603df0204a1a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from tkinter import *
from tkinter import messagebox
import numpy as np
import pandas as pd
l1=['itching','skin_rash','nodal_skin_eruptions','continuous_sneezing','shivering','chills','joint_pain',
'stomach_pain','acidity','ulcers_on_tongue','muscle_wasting','vomiting','burning_micturition','spotting_ urination','... | {"hexsha": "c05366fff895779a929e9faff8e00292ba838a8c", "size": 8288, "ext": "py", "lang": "Python", "max_stars_repo_path": "disease_prediction.py", "max_stars_repo_name": "Reyuga/Reyuga", "max_stars_repo_head_hexsha": "d7c98fec6093f7540255a67de55bb765b3be8c06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
const GWPos = SVector{2,Int}
const TwoAgentPos = SVector{4,Int}
const dir = Dict(:up=>GWPos(0,1), :down=>GWPos(0,-1), :left=>GWPos(-1,0), :right=>GWPos(1,0), :stay=>GWPos(0,0),
:upleft=>GWPos(-1,1), :upright=>GWPos(1,1), :downright=>GWPos(1,-1), :downleft=>GWPos(-1,-1))
const aind = Dict(:up=>1, :down=... | {"hexsha": "f61b733a6755c19a2ac1f46e66eec652c2511b24", "size": 5626, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mdp.jl", "max_stars_repo_name": "ancorso/GridworldAdversary.jl", "max_stars_repo_head_hexsha": "3595bb7e0cee97cdaa50c00f0ae68228129799c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
"""
Test module for TwoPhaseFlow
"""
import pytest
import tables
import numpy as np
import proteus.defaults
from proteus import Context
from proteus import default_so
from proteus.iproteus import *
import os
import sys
Profiling.logLevel=1
Profiling.verbose=True
class TestTwoPhaseFlow(object):
... | {"hexsha": "13c277d453e012730f8117b53a24c917162d3595", "size": 3914, "ext": "py", "lang": "Python", "max_stars_repo_path": "proteus/tests/TwoPhaseFlow/test_TwoPhaseFlow.py", "max_stars_repo_name": "tridelat/proteus", "max_stars_repo_head_hexsha": "44d7c3cb2f992b109b30f14b4660235d90e9bdfb", "max_stars_repo_licenses": ["... |
using GeoFormatTypes, Test
using GeoFormatTypes: Geom, CRS, Extended, Unknown
@testset "Test construcors" begin
@test_throws ArgumentError ProjString("+lat_ts=56.5 +ellps=GRS80")
@test_throws ArgumentError ProjJSON(Dict("fype" => 1))
@test_throws ArgumentError ProjJSON("fype")
@test_throws ArgumentErro... | {"hexsha": "e413c6f2f26f3a3ac8d135c45fee9dd767ef780a", "size": 5779, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "rafaqz/CoordinateReferenceSystemsBase.jl", "max_stars_repo_head_hexsha": "7d9317ec03b02af6089449800d75265e42fb139c", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import scipy.sparse
def rcm(g):
'''Compute the reverse Cuthill-Mckee permutation of a graph. Note that the
method does NOT modify the graph, but rather just returns a permutation
vector that can be used by Graph.permute to achieve the actual reordering.
P... | {"hexsha": "c6ceb3b43f57d6fb05add8aa2d74a01819885620", "size": 599, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphdot/graph/reorder/rcm.py", "max_stars_repo_name": "yhtang/GraphDot", "max_stars_repo_head_hexsha": "3d5ed4fbb2f6912052baa42780b436da76979691", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"],... |
"""
Cart pole swing-up: Original version from:
https://github.com/zuoxingdong/DeepPILCO/blob/master/cartpole_swingup.py
Modified so that done=True when x is outside of -2.4 to 2.4
Reward is also reshaped to be similar to PyBullet/roboschool version
More difficult, since dt is 0.05 (not 0.01), and only 200 timesteps
"""... | {"hexsha": "901280a060221634c6a96c61adb8f03c3bb2fa7e", "size": 9585, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/cartpole-envs/cartpole_envs/envs/CartPoleSwingUpEnv.py", "max_stars_repo_name": "baimingc/casrl", "max_stars_repo_head_hexsha": "7567b6592ab1790c2231993fadb78a9b5933e125", "max_stars_repo_lic... |
# Copyright 2018 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | {"hexsha": "b48dc6a0ff2acfdda9e4ef5936bbab0a9cedee7a", "size": 10408, "ext": "py", "lang": "Python", "max_stars_repo_path": "cirq/optimizers/decompositions_test.py", "max_stars_repo_name": "muneerqu/Cirq", "max_stars_repo_head_hexsha": "729d993312467d8ea9127103f9e15ae2391e7d85", "max_stars_repo_licenses": ["Apache-2.0"... |
#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <exception>
#include <cstring>
#include <boost/algorithm/string.hpp>
#include <tao/pegtl.hpp>
#include "cli.h"
#include "../engine/engine.h"
#include "grammar_cli.h"
using std::endl;
using std::cin;
using std::cout;
using std::istream;
usi... | {"hexsha": "7225c3ef8c1ddf76da059791fce19b52ab9364cb", "size": 2165, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cli/cli.cpp", "max_stars_repo_name": "ThomasBuchinger/csv-query-language", "max_stars_repo_head_hexsha": "b6b65c3dcc033b8e8c7ed06440c387c6738d37bb", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
simplifyProject(P::Project) = map(P) do branch
map(branch) do solution
solution.data
end
end
#TODO actually convert(T, Project)
function complicateProject(V) #::Vector{Vector{Vector{Float64}}}
P = Project()
branches = map(V) do bData
branch = Branch(P)
solutions::Vector{Solution} = map(bData) do sData
Sol... | {"hexsha": "7734787feb3ac4c8c73f3af2f749741f4171b21f", "size": 1368, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "master/lib/ncmprojSAVEHELPER.jl", "max_stars_repo_name": "285714/ncm", "max_stars_repo_head_hexsha": "fcf289c7ef5f8500ebcb238e36c6a7ee9e054147", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# assume this is run after detect.py has been run, this means that the images in data/images
# have corresponding data in labels
from PIL import Image
import numpy as np
import pandas as pd
import os
import random
import sklearn
import skimage
import skimage.io
import matplotlib.pyplot as plt
import pathlib
PROJECT_DI... | {"hexsha": "d702d46ecc353dd76ae63eed6d847175a92b2fd8", "size": 1835, "ext": "py", "lang": "Python", "max_stars_repo_path": "crop_images_from_yolo_labels.py", "max_stars_repo_name": "AndrewLaird/ChessTutorModels", "max_stars_repo_head_hexsha": "c4fd960417d5b9918e430d040deb89fed3f4b73b", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
import pandas as pd
from glob import glob
dfs = list()
directoryPath = 'data/raw/data_for_november_2019_evaluation/south_sudan_data/IMF/'
filenames = glob(directoryPath + 'imf*.xlsx')
for filename in filenames:
df = pd.read_excel(filename)
df = df.transpose()
index_val = df.index.... | {"hexsha": "450f13f46de61c642325070826fad41fef1b5418", "size": 1740, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/data_processing/IMF.py", "max_stars_repo_name": "mikiec84/delphi", "max_stars_repo_head_hexsha": "2e517f21e76e334c7dfb14325d25879ddf26d10d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
"""
Test to exercise Small File Workload
Note:
This test is using the benchmark-operator and the elastic search, so it start
process with port forwarding on port 9200 from the host that run the test (localhost)
to the elastic-search within the open-shift cluster, so, if you host is listen to
port 9200, this test can n... | {"hexsha": "8a2b1262bae497c57d706316ce12859ffd71835d", "size": 26594, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/e2e/performance/io_workload/test_small_file_workload.py", "max_stars_repo_name": "annagitel/ocs-ci", "max_stars_repo_head_hexsha": "284fe04aeb6e3d6cb70c99e65fec8ff1b1ea1dd5", "max_stars_rep... |
import pandas as pd
import numpy as np
import random
import sys
import pathlib
import string
from datetime import datetime
# TODO:
# Ensure generated company names are unique
# OverflowError: int too large to convert to float
test_data = pd.DataFrame()
def string_generator(size):
chars = string.as... | {"hexsha": "43126adaba652688d76bf85401491c34bc676e50", "size": 4877, "ext": "py", "lang": "Python", "max_stars_repo_path": "RandoData.py", "max_stars_repo_name": "Midnitte/RandoData", "max_stars_repo_head_hexsha": "715cfbab261b0247b66b2131b226ee179973781d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
[STATEMENT]
lemma list_member_conv_member [simp]:
"equal_base.list_member (=) = List.member"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. equal_base.list_member (=) = List.member
[PROOF STEP]
proof(intro ext)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x xa. equal_base.list_member (=) x xa = List.me... | {"llama_tokens": 348, "file": "Containers_DList_Set", "length": 5} |
import sys
import json
import time
import array
import struct
import logging
import numpy as np
from copy import deepcopy
from pybleno import *
import wasatch
from wasatch.WasatchDevice import WasatchDevice
from wasatch.WasatchBus import WasatchBus
from wasatch import applog
logger = logging.getLogger(__name__)
###... | {"hexsha": "d1040f3c26c7ee1433adf949d96591306a74f63b", "size": 18456, "ext": "py", "lang": "Python", "max_stars_repo_path": "Bluetooth/Characteristics.py", "max_stars_repo_name": "WasatchPhotonics/RPi-Communication", "max_stars_repo_head_hexsha": "3dfb695e75f4a5fd84c4a00f8fc5c57519a7884e", "max_stars_repo_licenses": ["... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from PIL import Image
import numpy as np
#Returns numpy image at size imageSize*imageSize
def getProcessedData(img,imageSize):
img = img.resize((imageSize,imageSize), resample=Image.ANTIALIAS)
imgData = np.asarray(img, dtype=np.uint8).reshape(imageSize,imageSize,1)
... | {"hexsha": "35914257719d308fdec3ee722fcfc4a8472cff09", "size": 547, "ext": "py", "lang": "Python", "max_stars_repo_path": "CNN/imageFilesTools.py", "max_stars_repo_name": "jleapeMIT/danceable", "max_stars_repo_head_hexsha": "ddc5584214a334d38532c5a3d0160a5ba4edf118", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma reach_reach\<^sub>t_fst:
"reach \<Sigma> \<delta> q\<^sub>0 = fst ` reach\<^sub>t \<Sigma> \<delta> q\<^sub>0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. reach \<Sigma> \<delta> q\<^sub>0 = fst ` reach\<^sub>t \<Sigma> \<delta> q\<^sub>0
[PROOF STEP]
unfolding reach\<^sub>t_def reach_def imag... | {"llama_tokens": 274, "file": "LTL_to_DRA_DTS", "length": 2} |
import pandas as pd
import numpy as np
from pandas import Series
from pandas import DataFrame
from statsmodels import regression
def init(context):
context.hs300 = "000300.XSHG"
# window must larger than 64
context.WINDOW = 400
def handle_bar(context, bar_dict):
time_series = history_bars(context.h... | {"hexsha": "b62f2909b3fe4d1a903e38e979eb54591e394b75", "size": 4307, "ext": "py", "lang": "Python", "max_stars_repo_path": "rqalpha/strategy/hurst.py", "max_stars_repo_name": "quantModel/Rqalpha-myquant-learning", "max_stars_repo_head_hexsha": "5dc39c6d8f6d89bb89350ef64c860cb53c369c9f", "max_stars_repo_licenses": ["Apa... |
"""
MinOver algorithm to find a point inside a polytope.
Francesc Font-Clos
Oct 2018
"""
import numpy as np
class MinOver(object):
"""MinOver solver."""
def __init__(self, polytope, ):
"""
Create a MinOver solver.
Parameters
----------
polytope: hitandrun.polytope
... | {"hexsha": "4c00ffcb169d0a7e43cb26f29e2ecbd87c2be0c3", "size": 2665, "ext": "py", "lang": "Python", "max_stars_repo_path": "hitandrun/minover.py", "max_stars_repo_name": "fontclos/hitandrun", "max_stars_repo_head_hexsha": "00c29424acfee685208301e5f16d2782325733ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
###################################
# Script :
# 1) Contains class to generate XL-MS
# plots
# 2) Inherits from CX class
#
# ganesans - Salilab - UCSF
# ganesans@salilab.org
###################################
import pandas as pd
import glob
import sys,os,math,itertools
import numpy as np
import pandas as pd
from val... | {"hexsha": "72334d33c606b6c1b4c00f77e141ced1f500fd6f", "size": 11434, "ext": "py", "lang": "Python", "max_stars_repo_path": "master/pyext/src/validation/cx_plots.py", "max_stars_repo_name": "salilab/IHMValidation", "max_stars_repo_head_hexsha": "ddf1a080a4b7f66c2f067312f5f4a5c6584848d1", "max_stars_repo_licenses": ["MI... |
import os,sys,glob,time
import obspy
import scipy
import pycwt
import pyasdf
import datetime
import numpy as np
import pandas as pd
from obspy.signal.invsim import cosine_taper
from obspy.signal.regression import linear_regression
from scipy.fftpack import fft,ifft,next_fast_len
from seisgo import stacking as stack
fro... | {"hexsha": "7d477ad53ca5dbf6849bd87968236bc309f33d40", "size": 47272, "ext": "py", "lang": "Python", "max_stars_repo_path": "seisgo/noise.py", "max_stars_repo_name": "xtyangpsp/SeisGo", "max_stars_repo_head_hexsha": "c445cdc7e760de957559af3e33e3a26489e3ee55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "... |
#include <boost/make_shared.hpp>
#include <boost/thread/locks.hpp>
#include <boost/thread/mutex.hpp>
#include <string>
#include <vector>
#include "caffe/array/array.hpp"
#include "caffe/array/math.hpp"
namespace caffe {
template<typename T>
Array<T>::Array(const Array & o) : ArrayMemory(o), ArrayBase<T>(o) { }
templ... | {"hexsha": "8ecbdec11e421bd9f689e62ec808589d53223b7f", "size": 6442, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "caffe/src/caffe/array/array.cpp", "max_stars_repo_name": "tinghuiz/learn-reflectance", "max_stars_repo_head_hexsha": "31ab326d344834e9cd8bb042551176bcf3114a9c", "max_stars_repo_licenses": ["MIT"], "... |
# This file is part of the Open Data Cube, see https://opendatacube.org for more information
#
# Copyright (c) 2015-2020 ODC Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import toolz
from ..model import Dataset
from ..storage import reproject_and_fuse, BandInfo
from ..storage._rio import Raste... | {"hexsha": "7cc852e1183d26d9cb3b3f80a93a76374673b188", "size": 11862, "ext": "py", "lang": "Python", "max_stars_repo_path": "datacube/testutils/io.py", "max_stars_repo_name": "agdc-research-trial/gdf", "max_stars_repo_head_hexsha": "82ed29c263eaf65f5c1fbb4e9207c99e9700b85c", "max_stars_repo_licenses": ["Apache-2.0"], "... |
"""
parse_options(kwargs)
Internal function. Takes the keyword arguments from the main function and parses it into a
usable Dict object
# Examples
```julia-repl
julia> parse_options(ex::Expr)
Dict{String,Any} with 2 entries:
"screen_name" => "jack"
...
```
"""
function parse_options(kwargs)
options = Dict... | {"hexsha": "9f5f4e34ff15fd5e28add2359b5cc43796388210", "size": 13336, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cursoring.jl", "max_stars_repo_name": "alexpkeil1/Twitter.jl", "max_stars_repo_head_hexsha": "a2c257aae4b37ba7a37ca2cfe0cfce0071cb2ad4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# In Pandas which is an open source BSD-licensed python library, easy to use data structures and data
# analysis tools for the python PL
# Pandas delase with three DS, Panel, Dataframe, series
# In Pandas DataFrame, .head(n=5) return the first n rows
# In Pandas DataFrame, .describe() generates descriptive statistics t... | {"hexsha": "fdde5abd13659df97f7f4a5e02887206a18ef59e", "size": 1348, "ext": "py", "lang": "Python", "max_stars_repo_path": "BuildModel-Keras.py", "max_stars_repo_name": "JoyeBright/Deep-Learning", "max_stars_repo_head_hexsha": "ba62cc8b3cbeeacc11b69f52999aac2bd0d7f018", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from sitator.dynamics import JumpAnalysis
from sitator.util import PBCCalculator
from sitator.network.merging import MergeSites
from sitator.util.mcl import markov_clustering
import logging
logger = logging.getLogger(__name__)
class MergeSitesByDynamics(MergeSites):
"""Merges sites using dyna... | {"hexsha": "798f42fb2f11f814b0c4df149cb89d6485826b30", "size": 6587, "ext": "py", "lang": "Python", "max_stars_repo_path": "sitator/dynamics/MergeSitesByDynamics.py", "max_stars_repo_name": "materials-DFT/sitator", "max_stars_repo_head_hexsha": "6755a71ccd975425b0f9e9df27585b618be3433a", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
# coding: utf-8
from keras.models import load_model
from keras.preprocessing.image import img_to_array, load_img
import sys
from urllib.request import urlopen
import numpy as np
# Base values
target_height = 180
target_width = 320
channels = 3
model = load_model('../models/human_not_human.h5')... | {"hexsha": "635d143a7438a4212077ecb0635552e8a9db6933", "size": 634, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/guess.py", "max_stars_repo_name": "jeffisadams/human-nothuman", "max_stars_repo_head_hexsha": "c67eba2b5ad5882ca0989bb17175bb6fbca19db4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
subroutine UpdateBladeVel(IFLG)
use configr
use blade
use wake
use wallsoln
integer :: i,ygcErr
real :: Point(3), dVel(3), dUdX
! Calculate the velocity induced on the blades by wake, wall, and freestream
if (iflg .eq. 0) then
! re-initialize uiwake viwake wiwake as we are b... | {"hexsha": "017731898877eeffb0526968056bc64a83e031b1", "size": 2458, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "src/UpdateBladeVel.f95", "max_stars_repo_name": "ebranlard/CACTUS", "max_stars_repo_head_hexsha": "6d89b48759fe78d1890a77656bafdbd1e703bbb2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
/*
* ====================================================================
* 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 license... | {"hexsha": "87d054fcb965ee928cf57a90d75217aca6fb26b8", "size": 6767, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "subversion/bindings/cxx/tests/test_strings.cpp", "max_stars_repo_name": "timgates42/subversion", "max_stars_repo_head_hexsha": "0f088f530747140c6783c2eeb77ceff8e8613c42", "max_stars_repo_licenses": ... |
import unittest
import numpy as np
import pandas as pd
from apollon.tools import time_stamp
from comsar.tracks import TimbreTrack
class TestTimbreTrack(unittest.TestCase):
def setUp(self):
self.track = TimbreTrack()
def test_nfeatures(self):
self.assertIsInstance(self.track.n_features, int)
| {"hexsha": "9dabb3279c757efac6a1a761f984e3a040bccfd6", "size": 320, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tracks/test_timbre.py", "max_stars_repo_name": "ifsm/comsar", "max_stars_repo_head_hexsha": "aeb45d03409e223ff417d8d9345e7b128fc3a3af", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import numpy as np
from spn.algorithms.Inference import EPSILON, add_node_likelihood
from spn.structure.leaves.spmnLeaves.SPMNLeaf import Utility
from spn.structure.leaves.histogram.Inference import histogram_likelihood
def utility_value(node, data=None, dtype=np.float64):
uVal = np.ones((data.shape[0], 1), dty... | {"hexsha": "33e46ddc5f47ba00d0ce72c8e946d69f0df3fc82", "size": 591, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/spn/structure/leaves/spmnLeaves/Inference.py", "max_stars_repo_name": "radum2275/SPFlow", "max_stars_repo_head_hexsha": "4ba05aef644b66fc8621991c78e426cef408b985", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma rev_nth_snoc: \<open>(xs @ [x]) !. Suc v = Some y \<Longrightarrow> xs !. v = Some y\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (xs @ [x]) !. Suc v = Some y \<Longrightarrow> xs !. v = Some y
[PROOF STEP]
by (induct xs) auto | {"llama_tokens": 106, "file": "Hybrid_Logic_Hybrid_Logic", "length": 1} |
/* ****************************************************************** **
** OpenSees - Open System for Earthquake Engineering Simulation **
** Pacific Earthquake Engineering Research Center **
** **
** ... | {"hexsha": "e1b08c2866cd678bb79db3f3966f8fe279315fba", "size": 2971, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OpenSees/SRC/analysis/algorithm/eigenAlgo/EigenAlgorithm.cpp", "max_stars_repo_name": "kuanshi/ductile-fracture", "max_stars_repo_head_hexsha": "ccb350564df54f5c5ec3a079100effe261b46650", "max_stars... |
import pandas as pd
import numpy as np
from tqdm import tqdm
import argparse
from datetime import datetime
parser = argparse.ArgumentParser()
parser.add_argument("--data", default='../data_cleaned/time_evolution_10_levels_natural.csv', \
help="filename.", type=str)
parser.add_argument("--maxlevel"... | {"hexsha": "e98735b7ac0a6c46066d22750f67b6fbab5d1088", "size": 5176, "ext": "py", "lang": "Python", "max_stars_repo_path": "order_flow_imbalance/ofi_computation.py", "max_stars_repo_name": "nicolezattarin/LOB-feature-analysis", "max_stars_repo_head_hexsha": "c73735d887c146a85f24267de7789d689a6c4311", "max_stars_repo_li... |
import speech_recognition as sr
from tkinter import *
from tkinter import ttk
from tkinter import filedialog
import threading
import time
import os
import numpy as np
import librosa.display
import copy
from sklearn.externals import joblib
from winsound import *
from numpy import array, zeros, argmin, inf, ndim
from sci... | {"hexsha": "aab09dc792e7265cfbfbfa5570887a4edbd5e9f1", "size": 27498, "ext": "py", "lang": "Python", "max_stars_repo_path": "Final_app_of_DTW-58-word_Recognizer_using_MFCC-DTW/Speech_Recognizer.py", "max_stars_repo_name": "mayank-kumar-giri/Automatic-Speech-Recognition-for-Chhattisgarhi", "max_stars_repo_head_hexsha": ... |
import os
import argparse
from DFLIMG import DFLIMG, DFLPNG
from pathlib import Path
from PIL import Image
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--upscale_factor', type=int, default=1)
parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.pt... | {"hexsha": "1af2dcd7991e91e4af1e874d081278611a039fba", "size": 6776, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference_gfpgan_full.py", "max_stars_repo_name": "chuanli11/GFPGAN", "max_stars_repo_head_hexsha": "4adbf820cef782c7d33113be35e5f1a49f2a3793", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
"""
Luis Eduardo Sánchez González
Universidad Autonoma de Coahuila
Facultad de Ciencias Físico Matemáticas
mié 03 feb 2021 13:10:46 CST
"""
import numpy as np
class Difference:
def __init__(self, f):
if callable(f):
self.f = f
else:
raise ValueError("La derivada es igual a cero.")
def InitialCon... | {"hexsha": "216af2fc6dad9fff071d014fca00bf1ec068ff34", "size": 814, "ext": "py", "lang": "Python", "max_stars_repo_path": "PhysicsPy/Derivation.py", "max_stars_repo_name": "Luis2501/physicspy", "max_stars_repo_head_hexsha": "003069affc641726ebb4e167f6603033b98919b0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#pragma once
#include <boost/filesystem.hpp>
namespace rai
{
boost::filesystem::path AppPath();
void SetStdinEcho(bool);
std::string PemPath();
} | {"hexsha": "b408c81ea59023bff400124cfdf1089919e0d97e", "size": 147, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "rai/secure/plat.hpp", "max_stars_repo_name": "gokoo/Raicoin", "max_stars_repo_head_hexsha": "494be83a1e29106d268f71e613fac1e4033a82f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 94.0, "m... |
"""
Simulated devices for documentation and testing
"""
import collections
import itertools
import os
import tempfile
import threading
import time
from bluesky.utils import short_uid
import numpy as np
from ophyd import Signal, Device, Component, DeviceStatus, Staged
from ophyd.sim import new_uid
import scipy.special
... | {"hexsha": "39872afd408df462523d187202f163af4348e208", "size": 4447, "ext": "py", "lang": "Python", "max_stars_repo_path": "bluesky_darkframes/sim.py", "max_stars_repo_name": "tacaswell/bluesky-darkframes", "max_stars_repo_head_hexsha": "8922eacd7316b3b93112e969376268f2772523a7", "max_stars_repo_licenses": ["BSD-3-Clau... |
'''
Record Linkage Testing Script using Logistic Regression Method over Graph Embeddings generated using TransH
'''
import numpy as np
import pandas as pd
import random
import re
import recordlinkage
import unittest
import xml.etree.ElementTree
from common import get_logger, log_quality_results, InformationRetriev... | {"hexsha": "634479ced5b2780f56d8bdd8e38ffedbb1a1b99a", "size": 7466, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/erer/test_logistic_transh.py", "max_stars_repo_name": "bhaskargautam/record-linkage", "max_stars_repo_head_hexsha": "01eb29f8b7fb4dd1625187232f2dafe47f24cddf", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma list_rel_induct[induct set,consumes 1, case_names Nil Cons]:
assumes "(l,l')\<in>\<langle>R\<rangle> list_rel"
assumes "P [] []"
assumes "\<And>x x' l l'. \<lbrakk> (x,x')\<in>R; (l,l')\<in>\<langle>R\<rangle>list_rel; P l l' \<rbrakk>
\<Longrightarrow> P (x#l) (x'#l')"
shows "P l l'"
[PR... | {"llama_tokens": 643, "file": "Automatic_Refinement_Parametricity_Relators", "length": 4} |
[STATEMENT]
lemma observable_io_target_unique_target :
assumes "observable M"
and "io_targets M q1 io = {q2}"
and "path M (io || tr) q1"
and "length io = length tr"
shows "target (io || tr) q1 = q2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. target (io || tr) q1 = q2
[PROOF STEP]
using assms
... | {"llama_tokens": 216, "file": "Adaptive_State_Counting_FSM_FSM", "length": 2} |
df = DataFrame()
df[:A] = 1:numData
lamb_grid = [10. .^(-7:1)]
c_grid = linspace(1, 5, 6) # This choice of c_grid yields no distinguishable difference. Try: c_grid = 2. .^(1:5)
deg_grid = [2:6] #2 is a pretty meaningless choice. drop to 3.
N = length(lamb_grid) * length(c_grid) * length(deg_grid)
res = Array(Flo... | {"hexsha": "0a67f6b7fe0f49a724d837e00ddab2fbf96bd093", "size": 934, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia_files/trafficCvalEnd.jl", "max_stars_repo_name": "jingzbu/InverseVITraffic", "max_stars_repo_head_hexsha": "c0d33d91bdd3c014147d58866c1a2b99fb8a9608", "max_stars_repo_licenses": ["MIT"], "max_... |
import geometry.tarski_2
open classical set
namespace Euclidean_plane
variables {point : Type} [Euclidean_plane point]
local attribute [instance, priority 0] prop_decidable
-- Right Angles
def R (a b c : point) : Prop := eqd a c a (S b c)
theorem R.symm {a b c : point} : R a b c → R c b a :=
begin
intro h,
have h1 ... | {"author": "ImperialCollegeLondon", "repo": "xena-UROP-2018", "sha": "b111fb87f343cf79eca3b886f99ee15c1dd9884b", "save_path": "github-repos/lean/ImperialCollegeLondon-xena-UROP-2018", "path": "github-repos/lean/ImperialCollegeLondon-xena-UROP-2018/xena-UROP-2018-b111fb87f343cf79eca3b886f99ee15c1dd9884b/src/Geometry/tar... |
using HarwellRutherfordBoeing
using Krylov
using LinearOperators
# using ProfileView
# M = HarwellBoeingMatrix("data/illc1033.rra");
M = HarwellBoeingMatrix("data/illc1850.rra");
A = M.matrix;
(m, n) = size(A);
@printf("System size: %d rows and %d columns\n", m, n);
# Define a linear operator with preallocation.
Ap =... | {"hexsha": "e471f69d58cb0a28d00dd25ae1a3e9b498f07bf1", "size": 1068, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/test_lsqr.jl", "max_stars_repo_name": "abelsiqueira/Krylov.jl", "max_stars_repo_head_hexsha": "dc0ca5466f7f1f7e65958fe016e3a06b858e3df0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | {"hexsha": "0e57569e0da77d69da74031c9a26bb6efc835074", "size": 20161, "ext": "py", "lang": "Python", "max_stars_repo_path": "ShapeVariationAnalyzer/Resources/Classifier/generation_shapes.py", "max_stars_repo_name": "lbumbolo/ShapeVariationAnalyzer", "max_stars_repo_head_hexsha": "976e22cbacc87fb593d92e24cbdbba6c99a6406... |
/**
@file
@author Alexander Sherikov
@copyright 2017 Alexander Sherikov. Licensed under the Apache License,
Version 2.0. (see LICENSE or http://www.apache.org/licenses/LICENSE-2.0)
@brief
*/
#include "utf_common.h"
#include <boost/mpl/vector.hpp>
#include <qpmad/solver.h>
#include <qpmad/testi... | {"hexsha": "96d46a7a4af4b0affbadb75ed9c261c8a80116cf", "size": 5950, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/resolve.cpp", "max_stars_repo_name": "Aerobotics/qpmad", "max_stars_repo_head_hexsha": "b687654e2eb97f121c6161fe05abbc7594d7c7d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
from abc import ABC, abstractmethod
from collections import Counter
from functools import reduce
from typing import List, Tuple
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
class Scorer(ABC):
precision: float
recall: float
def get_scores(self, predicted_chains: List[... | {"hexsha": "b85af3d096aa994d215bdfe67dea5b5a58fc89aa", "size": 6745, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/scorers.py", "max_stars_repo_name": "tugas-akhir-nlp/coreference-resolution-cnn-v2", "max_stars_repo_head_hexsha": "b112893b3bd7b893e3830e183aa79acff8af9896", "max_stars_repo_licenses": ["MI... |
! This test checks lowering of OpenMP threadprivate Directive.
// RUN: not flang-new -fc1 -emit-fir -fopenmp %s 2>&1 | FileCheck %s
program main
integer, save :: x, y
// CHECK: not yet implemented: OpenMPThreadprivate
!$omp threadprivate(x, y)
end
| {"hexsha": "da1981a299b71c7e48a6d61914385ecb03e9e81c", "size": 255, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "flang/test/Lower/OpenMP/Todo/omp-threadprivate.f90", "max_stars_repo_name": "ornata/llvm-project", "max_stars_repo_head_hexsha": "494913b8b4e4bce0b3525e5569d8e486e82b9a52", "max_stars_repo_licens... |
import utils
import sklearn
import tensorflow.compat.v1 as tf
import numpy as np
def tf_dataset(batch_pc_gen):
while True:
yield next(batch_pc_gen)
def get_dataset(batch_pc_gen, batch_size):
with tf.device('/device:CPU:0'):
ds = tf.data.Dataset.from_generator(lambda: tf_dataset(batch_pc_gen)... | {"hexsha": "94178e5727f84851a6d7bb83c9989a52416c0940", "size": 2578, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/80_input.py", "max_stars_repo_name": "mauriceqch/pcc_attr_folding", "max_stars_repo_head_hexsha": "2fc37de7fb146a724ebada2e39df51de272fa01a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Copyright (c) 2009-2010 Satoshi Nakamoto
// Copyright (c) 2009-2017 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <pow.h>
#include <arith_uint256.h>
#include <boost/multiprecision/cpp... | {"hexsha": "329b4e896627e9b388ef65d40ee4c7a56ad87f9a", "size": 6982, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/pow.cpp", "max_stars_repo_name": "ecoinchain/ecoin", "max_stars_repo_head_hexsha": "c46f87f04c0e66df5f035baf21acc00dcd009037", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5.0, "max_st... |
import numpy as np
from pyKriging.krige import kriging
class MyKriging(kriging):
def __init__(self,*args,**kwargs):
kriging.__init__(self,*args,**kwargs)
def kdata(self):
# Create a set of data to plot
plotgrid = 61
x = np.linspace(0, 1, num=plotgrid)
y = np.linspace(0, ... | {"hexsha": "e2b5d21a82704688d019b5c097786ea67c548541", "size": 940, "ext": "py", "lang": "Python", "max_stars_repo_path": "sanrr/metamodel.py", "max_stars_repo_name": "ddfabbro/SANRR", "max_stars_repo_head_hexsha": "aa5b71b1e8ac1e0471828922ff50e098d550a157", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import random
import numpy as np
import gym
import imageio # write env render to mp4
import datetime
from collections import deque
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Input, Conv2D, Flatten
from tensorflow.keras.optimizers import Adam
from tensorfl... | {"hexsha": "7b97ee40a99f7e0082ec1b8f42a119d9c3db0221", "size": 9711, "ext": "py", "lang": "Python", "max_stars_repo_path": "DQN_Drones.py", "max_stars_repo_name": "Abluceli/Multi-agent-Reinforcement-Learning-Algorithms", "max_stars_repo_head_hexsha": "15810a559e2f2cf9e5fcb158c083f9e9dd6012fc", "max_stars_repo_licenses"... |
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import numpy as np
from PIL import ImageFilter, Image
from tqdm import tqdm
import pandas as pd
import random
from typing import Callable, Optional
import os
class ImageNetSubset(datasets.Imag... | {"hexsha": "2f76fc3ec6ccb98fc6412b378aed665e0a85064c", "size": 3789, "ext": "py", "lang": "Python", "max_stars_repo_path": "custom_datasets.py", "max_stars_repo_name": "UCDvision/low-budget-al", "max_stars_repo_head_hexsha": "32f927da55ac20561938147e126c9faf6c113234", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using Weiqi
import Weiqi: empty, black, white, magnitude, cb
# Chinese rules https://www.cs.cmu.edu/~wjh/go/rules/Chinese.html
abstract type Player end
struct Blackplayer <: Player end
struct Whiteplayer <: Player end
mutable struct NewPosition{T<:Player}
player::T
coords::Tuple{Int64, Int64}
stone::Sto... | {"hexsha": "13955a6d429225061f7bd1d32aa8822b1a96c5ce", "size": 2506, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/chineserules.jl", "max_stars_repo_name": "hpoit/Weiqi.jl", "max_stars_repo_head_hexsha": "be2533a50bf2e2cb48d3efabfb4fe000034d5c94", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
[STATEMENT]
lemma sig_red_tail_lt_rep_list: "sig_red sing_reg (\<prec>) F p q \<Longrightarrow> punit.lt (rep_list q) = punit.lt (rep_list p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sig_red sing_reg (\<prec>) F p q \<Longrightarrow> punit.lt (rep_list q) = punit.lt (rep_list p)
[PROOF STEP]
by (auto simp: si... | {"llama_tokens": 150, "file": "Signature_Groebner_Signature_Groebner", "length": 1} |
# Copyright 2018 Samuel Payne sam_payne@byu.edu
# 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 ... | {"hexsha": "5a7f0ab9644a474dbc324d7f835605279627cb40", "size": 4907, "ext": "py", "lang": "Python", "max_stars_repo_path": "cptac/pancan/harmonized.py", "max_stars_repo_name": "old-rob/cptac", "max_stars_repo_head_hexsha": "9b33893dd11c9320628a751c8840783a6ce81957", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# Mecánica con SymPy
_Si SymPy te ha parecido hasta ahora un CAS decente e incluso interesante (nada como tener los resultados en $\LaTeX$ incrustados en el notebook y la sintaxis de Python para hacer cálculo simbólico) entonces espera a ver el paquete `mechanics`. Con él, podremos manipular velocidades y aceleracio... | {"hexsha": "fd7e85ca60af7a8e2a3937723b9823428e007c57", "size": 55685, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks_completos/041-SymPy-Mecanica.ipynb", "max_stars_repo_name": "diegoomataix/Curso_AeroPython", "max_stars_repo_head_hexsha": "c2cf71a938062bc70dbbf7c2f21e09653fa2cedd", "max_... |
import pandas as pd
import numpy as np
import os
import datetime
from typing import Any, Dict, Optional, Union, Dict, List, Callable
import warnings
import logging
import copy
from qualipy.backends.pandas_backend.generator import BackendPandas
from qualipy.backends.sql_backend.generator import BackendSQL
... | {"hexsha": "2db5736a31b682de36ea05a3f9d4e6980c2f0a64", "size": 18521, "ext": "py", "lang": "Python", "max_stars_repo_path": "qualipy/run.py", "max_stars_repo_name": "baasman/qualipy", "max_stars_repo_head_hexsha": "e246a44ea3a5dcc92291983c52a89189338f808f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
// This file is part of snark, a generic and flexible library for robotics research
// Copyright (c) 2011 The University of Sydney
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 1. Redistr... | {"hexsha": "703677029b3a4a60a4d4761b496d9c6ba9db4737", "size": 5578, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "batteries/ocean/battery.cpp", "max_stars_repo_name": "mission-systems-pty-ltd/snark", "max_stars_repo_head_hexsha": "2bc8a20292ee3684d3a9897ba6fee43fed8d89ae", "max_stars_repo_licenses": ["BSD-3-Cla... |
from numpy.distutils.core import Extension, setup
ext = Extension(name='finite_diff', sources=['finite_diff.f90'])
setup(
name="kdv",
description="Python version of the KdV solver",
install_requires=['scipy', 'matplotlib'],
ext_modules=[ext],
script_name='setup.py',
script_args=['build_ext', '... | {"hexsha": "f67717338dea946e0add61ad8a6d6300e1d2e087", "size": 334, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/setup.py", "max_stars_repo_name": "ashwinvis/kdv-compact", "max_stars_repo_head_hexsha": "065f6a543692f3a3c7848bf7b7bd02cb1451b253", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
[STATEMENT]
lemma ascii_of_idem:
"ascii_of c = c" if "\<not> digit7 c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ascii_of c = c
[PROOF STEP]
using that
[PROOF STATE]
proof (prove)
using this:
\<not> digit7 c
goal (1 subgoal):
1. ascii_of c = c
[PROOF STEP]
by (cases c) simp | {"llama_tokens": 133, "file": null, "length": 2} |
# Copyright (c) 2013, Aakvatech and contributors
# For license information, please see license.txt
import frappe
from frappe import msgprint, _
import pandas as pd
import numpy as np
def execute(filters=None):
columns = get_columns(filters)
data = []
lab_details = get_lab_results(filters)
if not lab... | {"hexsha": "2db4a62cb6087900bb12bb57bc2c873aca68e231", "size": 3425, "ext": "py", "lang": "Python", "max_stars_repo_path": "hms_tz/hms_tz/report/lab_report_chart/lab_report_chart.py", "max_stars_repo_name": "av-dev2/hms_tz", "max_stars_repo_head_hexsha": "a36dbe8bfacf6a770913b1bfa000d43edd2cd87a", "max_stars_repo_licen... |
\chapter{Conclusion}
This paper uses the Gibbs sampler with a Metropolis-Hastings step to generate new samples from a NHPP. Test statistics are used to check if the new samples are from the NHPP. The NHPP used has a rate function which is a combination of a log-linear function and a power-law function. By testing the s... | {"hexsha": "84929ea6bd073ca98547d614a10b74a59431fe98", "size": 500, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Thesis/Thesis/chapters/concludingremarks.tex", "max_stars_repo_name": "mariufa/ProsjektOppgave", "max_stars_repo_head_hexsha": "3ef2fda314c55322de20f19ca861e4268a5e2d08", "max_stars_repo_licenses": [... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import astropy.units as u
from astropy.constants import h, c, k_B
from astropy.visualization import quantity_support
from .chemistry import chemistry
from .opacity import kappa
__all__ = [
'dashboard'
]
def dashboard(
... | {"hexsha": "ebf8c6e2f2a9c1c5a2fc2587e345a5418a5412cf", "size": 4716, "ext": "py", "lang": "Python", "max_stars_repo_path": "frei/plot.py", "max_stars_repo_name": "bmorris3/frei", "max_stars_repo_head_hexsha": "ab81b494da131dd24cb796d0bae6b45ab08f5b3c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": nul... |
import cv2
import numpy as np
frontal_face = cv2.CascadeClassifier('classifier/haarcascade_frontalface_default.xml')
#eye_cascade = cv2.CascadeClassifier('classifier/eye_pair_big.xml')
#eye_cascade = cv2.CascadeClassifier('classifier/eye_pair_small.xml')
eye_cascade = cv2.CascadeClassifier('classifier/haarcascade_eye.... | {"hexsha": "296b873cabb51a302315e5d102f9031c507796b1", "size": 4983, "ext": "py", "lang": "Python", "max_stars_repo_path": "eye_tracking.py", "max_stars_repo_name": "batbat99/eye_tracker", "max_stars_repo_head_hexsha": "571cca789623a45d200ece8113d7e42e876711ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
{- Byzantine Fault Tolerant Consensus Verification in Agda, version 0.9.
Copyright (c) 2021, Oracle and/or its affiliates.
Licensed under the Universal Permissive License v 1.0 as shown at https://opensource.oracle.com/licenses/upl
-}
open import LibraBFT.Impl.OBM.Logging.Logging
open import LibraBFT.ImplShared... | {"hexsha": "f2780f47725ac4082fedbc51f2ad1e7bfca2f2e4", "size": 757, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/LibraBFT/Impl/Types/Ledger2WaypointConverter.agda", "max_stars_repo_name": "LaudateCorpus1/bft-consensus-agda", "max_stars_repo_head_hexsha": "a4674fc473f2457fd3fe5123af48253cfb2404ef", "max_st... |
# have all phylogenies in one file
# have a file with the chromosome and window for each tree in the correct order
# have a popmap with the individual names and groupings wished to test
# have the outgroup labeled once in the popmap as "outgroup"
library(ape)
library(phytools)
options(scipen=999)
# read in trees, in... | {"hexsha": "cc6704ea0a28cf05bddcc1176ce25f00e4487c5c", "size": 5037, "ext": "r", "lang": "R", "max_stars_repo_path": "08e_gsi.r", "max_stars_repo_name": "jdmanthey/certhia_phylogeography", "max_stars_repo_head_hexsha": "7830f496419252149f5e4f1a657ce19c80ec05da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/usr/bin/env python
from setuptools import setup
from setuptools.command.build_ext import build_ext as _build_ext
class build_ext(_build_ext):
def finalize_options(self):
_build_ext.finalize_options(self)
# Prevent numpy from thinking it is still in its setup process:
__builtins__.__NUMPY... | {"hexsha": "875008626982638d5fdfc701f011c21c62054103", "size": 1312, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "jmatuskey/exoctk", "max_stars_repo_head_hexsha": "bfd7e5100014048f73baf23c964598381f691ffd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null... |
\chapter{Related Works} \label{ch:review}
In this chapter, I select the most outstanding studies based on a self-defined criteria (either published in a set of pre-selected venues or performed the highest impact by receiving at least fifty citations). To better introduce these papers in a well-organized manner, I cat... | {"hexsha": "db0d95b793c28e9b96dc41bf48d057646cb3b368", "size": 1384, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapter2/chapter2.tex", "max_stars_repo_name": "RoyZhengGao/thesis", "max_stars_repo_head_hexsha": "b73b473d5b8a5d948080420edeb899c60d88c9e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import pickle
import sys
import warnings
from collections import OrderedDict
import biosppy.signals.tools as st
import numpy as np
import wfdb
from biosppy.signals.ecg import correct_rpeaks, hamilton_segmenter
from hrv.classical import frequency_domain, time_domain
from scipy.signal import medfilt... | {"hexsha": "959aea6673bc315fd2a49870629b49b87e1b393a", "size": 4634, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "JackAndCole/Detection-of-sleep-apnea-from-single-lead-ECG-signal-using-a-time-window-artificial-neural-network", "max_stars_repo_head_hexsha": "692bb7d96... |
import os
import torch
import torch.nn.functional as F
import random
import numpy as np
import pandas as pd
from config import Config
from dataset import THUMOSInferenceDataset, inference_collate_fn
from model import SSAD
from utils import post_process, temporal_nms
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
... | {"hexsha": "258481eae800585be3fbe02a71f65c1b76f3ba26", "size": 4062, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference.py", "max_stars_repo_name": "Rheelt/SSAD_pytorch", "max_stars_repo_head_hexsha": "785ec81432b706f393fef276ff55485b71fa3eb2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "m... |
(* -------------------------------------------------------------------- *)
From mathcomp Require Import all_ssreflect all_algebra bigenough.
(* ------- *) Require Import finmap boolp reals.
(* ------- *) Require (*--*) Setoid.
(* -------------------------------------------------------------------- *)
Set Implicit Ar... | {"author": "ejgallego", "repo": "coq-alternate-reals", "sha": "8e1ad799ae9ae80d3c1d97d0a5f5b6d772eb6e01", "save_path": "github-repos/coq/ejgallego-coq-alternate-reals", "path": "github-repos/coq/ejgallego-coq-alternate-reals/coq-alternate-reals-8e1ad799ae9ae80d3c1d97d0a5f5b6d772eb6e01/src/discrete.v"} |
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