text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
Custom JSON serializer for log entries.
Handles Module types for now, more can be added later.
"""
struct LogEntrySerialization <: CommonSerialization end
show_json(io::StructuralContext, ::LogEntrySerialization, m::Module) = show_json(io, LogEntrySerialization(), string(m))
show_json(io::StructuralContext, ::LogE... | {"hexsha": "0af22348f1333c367f3f0b037b3f4d040f62c2a9", "size": 5016, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/log_utils.jl", "max_stars_repo_name": "xgdgsc/LogRoller.jl", "max_stars_repo_head_hexsha": "84ea509933f3e4a1c8e04a2f5488d921457ff34d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
from pysumma.pysumma.Plotting import Plotting
from pysumma.pysumma.hovmoller import hovmoller
from pysumma.pysumma.layers import layers
from pysumma.pysumma.spatial import spatial
#(4) Display Plotting.py
##1 Display plot from summa_plot created by andrew bennett from UW
import pandas as pd
import xarray as xr
import ... | {"hexsha": "635971c0371b0ab10933c6998ed6b3c642226c14", "size": 1902, "ext": "py", "lang": "Python", "max_stars_repo_path": "pysumma/pysumma/plot_sample.py", "max_stars_repo_name": "DavidChoi76/pysumma_alpha1", "max_stars_repo_head_hexsha": "c526ff85310524d07314ebfdc8699b61f9234087", "max_stars_repo_licenses": ["MIT"], ... |
from matplotlib import pyplot as plt, rcParams
from numpy import matlib
from scipy import signal
import json
import numpy as np
import warnings
warnings.filterwarnings("ignore") # Пока matplotlib < 3.3, будет рекомендовать
# use_line_collection в stem
def xcov(x, lags):
mean = np.mean(x)
return [
np.... | {"hexsha": "e799941a68432733a9fb7abe8321bf07ecefe743", "size": 12043, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lr_07.py", "max_stars_repo_name": "SqrtMinusOne/Digital_Signal_Processing", "max_stars_repo_head_hexsha": "fb746e771a33111e7f3df61beb282883c8d04b85", "max_stars_repo_licenses": ["Apache-2.0"]... |
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <yaml-cpp/yaml.h>
#include <a_star.hpp>
#include <algorithm>
#include <boost/functional/hash.hpp>
#include <boost/heap/fibonacci_heap.hpp>
#include <boost/program_options.hpp>
#include <iostream>
#include <numeric>
#include <vector>
using libMultiRobot... | {"hexsha": "6dc95806704dd9c8e4f359b97ee91ab467b86443", "size": 22977, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/train_env.cpp", "max_stars_repo_name": "zijinoier/mater", "max_stars_repo_head_hexsha": "7bdbd885debcaec0048c478187694a30edbc387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4.0, "... |
import numpy as np
import pandas as pd
from math import modf
format_slash = '%d/%m/%Y %H:%M'
format_dash = '%Y-%m-%d %H:%M:%S'
# 1.10.19 0:25
format_dots = '%d.%m.%Y %H:%M:%S'
# 30-11-20 18:59
format_dash_short = '%d-%m-%y %H:%M'
# unix epoch
epoch = pd.Timestamp("1970-01-01")
# just a reminder how to convert from ... | {"hexsha": "ba9d165ca178552eb3766135c79ce5b1425498b7", "size": 8311, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/handy.py", "max_stars_repo_name": "altanova/stuff", "max_stars_repo_head_hexsha": "d55a65e929d95bcb6729ea61f4a655dea6452d0d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
/*****************************************************************************
*
* Rokko: Integrated Interface for libraries of eigenvalue decomposition
*
* Copyright (C) 2012-2015 Rokko Developers https://github.com/t-sakashita/rokko
*
* Distributed under the Boost Software License, Version 1.0. (See accompanying
* fi... | {"hexsha": "2881fcbf2123dc437d8135c4d1d68a270ce844c7", "size": 4050, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "rokko/localized_matrix.hpp", "max_stars_repo_name": "wistaria/rokko", "max_stars_repo_head_hexsha": "7cd9d5155e82f038039a46c1dc8f382b3fe7e2b7", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_cou... |
(* Cyclone Semantics using TLC/LN in Coq Version 4 *)
(* "SAFE PROGRAMMING AT THE C LEVEL OF ABSTRACTION". Daniel Grossman, August 2003 *)
(* Lemmas for LN infrastructure *)
(* Brian Milnes 2016 *)
Set Implicit Arguments.
Require Export Cyclone_Type_Substitution Cyclone_LN_FV Cyclone_LN_LC_Body Cyclone_LN_Open_Close... | {"author": "briangmilnes", "repo": "CycloneCoqSemantics", "sha": "190c0fc57d5aebfde244efb06a119f108de7a150", "save_path": "github-repos/coq/briangmilnes-CycloneCoqSemantics", "path": "github-repos/coq/briangmilnes-CycloneCoqSemantics/CycloneCoqSemantics-190c0fc57d5aebfde244efb06a119f108de7a150/4.1/Cyclone_LN_Lemmas.v"} |
[STATEMENT]
lemma (in Corps) n_eq_val_eq_idealTr:
"\<lbrakk>distinct_pds K n P; x \<in> carrier (O\<^bsub>K P n\<^esub>); y \<in> carrier (O\<^bsub>K P n\<^esub>);
\<forall>j \<le> n. ((\<nu>\<^bsub>K (P j)\<^esub>) x) \<le> ((\<nu>\<^bsub>K (P j)\<^esub>) y)\<rbrakk> \<Longrightarrow> Rxa (O\<^bsub>K P n\<^esub>) y \... | {"llama_tokens": 13248, "file": "Valuation_Valuation2", "length": 20} |
"""Util for analysis of SIS/backselect data."""
import collections
import numpy as np
import os
import torch
import inference_util
import sis_util
from sufficient_input_subsets import sis
# Function to sort filenames by image index in path.
SR_SORT = lambda s: int(os.path.basename(s).split('_')[-1].split('.')[0])
... | {"hexsha": "e6e165bd1b989c69c5a73790d8de27f70d990d9d", "size": 4723, "ext": "py", "lang": "Python", "max_stars_repo_path": "sis_analysis_util.py", "max_stars_repo_name": "b-carter/overinterpretation", "max_stars_repo_head_hexsha": "211d25c83d97e3238109e5c611c1af696989d3b6", "max_stars_repo_licenses": ["MIT"], "max_star... |
from collections import namedtuple
import torch
from torch.autograd import Variable
import numpy as np
from const import *
def reps_pad(responses, max_len, evaluation):
x = np.array([resp + [PAD] * (max_len - len(resp)) for resp in responses])
if evaluation:
with torch.no_grad():
x = Var... | {"hexsha": "ca0f5e3767129199380baaf0decafeaf2dd27a44", "size": 3359, "ext": "py", "lang": "Python", "max_stars_repo_path": "retrieval-based-chatbots/data_loader.py", "max_stars_repo_name": "shinoyuki222/torch-light", "max_stars_repo_head_hexsha": "4799805d9bcae82a9f12a574dcf9fdd838c92ee9", "max_stars_repo_licenses": ["... |
CS REAL FUNCTION DAW(XX)
DOUBLE PRECISION FUNCTION DAW(XX)
C----------------------------------------------------------------------
C
C This function program evaluates Dawson's integral,
C
C 2 / x 2
C -x | t
C F(x) = e | e dt
C ... | {"hexsha": "e352f1fd315c58e776901c5a38baf132484dc8ff", "size": 13200, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/f2cl/packages/toms/715/daw.f", "max_stars_repo_name": "sbwhitecap/clocc-hg", "max_stars_repo_head_hexsha": "f6cf2591ceef8a3a80e04da9b414cdf60a25a90f", "max_stars_repo_licenses": ["MIT"], "max... |
%
% Showcase file to demonstrate the abilities of kLabCourse-template.
%
%
\documentclass[ngerman]{kLCReprt}
\usepackage{blindtext}
\usepackage{kLCTitle}
\reportAuthor[Zweiter Autor]{Erster Autor}
\reportAuthorMail[zweite.mail@mail.org]{email@mail.org}
\reportDate{25.02.2015}
\reportSubmissionDate{06.03.2015}
\repo... | {"hexsha": "a150ee071a63739f704516ac71cd40bc415ca184", "size": 766, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "showcase.tex", "max_stars_repo_name": "kzoch/kLabCourse-template", "max_stars_repo_head_hexsha": "efaffbde3bea7ea826357398414b35bf54b934c2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
@inline S_inner_to_outer(S_in, u, xi, phi0) =
1/u + xi - u * phi0*phi0/3 * (1 - u * xi) + u*u*u * S_in
@inline S_u_inner_to_outer(S_u_in, S_in, u, xi, phi0) =
-1/(u*u) - phi0*phi0/3 * (1 - u * xi) + u * xi * phi0*phi0/3 +
3 * u*u * S_in + u*u*u * S_u_in
@inline F_inner_to_outer(F_in, u) = u*u * F_in
@in... | {"hexsha": "2fb0b57d1828fc825bb4f070abf81f8fc0d078ad", "size": 866, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AdS5_3_1/inner_to_outer.jl", "max_stars_repo_name": "Mikel-Sanchez-Garitaonandia/Jecco.jl", "max_stars_repo_head_hexsha": "d1e030ed0e3534c6bbb7aeaba4e3904fc59a3c35", "max_stars_repo_licenses": [... |
import numpy as np
import nibabel
import pytest
from nilearn._utils.testing import write_tmp_imgs
from nilearn.decomposition.dict_learning import DictLearning
from nilearn.decomposition.tests.test_canica import _make_canica_test_data
from nilearn.image import iter_img, get_data
from nilearn.input_data import NiftiMask... | {"hexsha": "0cd3d411573f6592ef5c3e2f00306487d4f18fd7", "size": 6375, "ext": "py", "lang": "Python", "max_stars_repo_path": "nilearn/decomposition/tests/test_dict_learning.py", "max_stars_repo_name": "chouhanaryan/nilearn", "max_stars_repo_head_hexsha": "e26312be96fe5c0211da28889ddd3ab1bd0ddc49", "max_stars_repo_license... |
# Poisson distribution
export Poisson
import Base
using SpecialFunctions: logfactorial
@parameterized Poisson(λ) ≪ CountingMeasure(ℤ[0:∞])
Base.eltype(::Type{P}) where {P<:Poisson} = Int
function logdensity(d::Poisson{(:λ,)}, y)
λ = d.λ
return y * log(λ) - λ - logfactorial(y)
end
function logdensity(d::Poi... | {"hexsha": "a95d931da1635a5ab08a5bc5b1bd9e287e430f77", "size": 783, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parameterized/poisson.jl", "max_stars_repo_name": "jw3126/MeasureTheory.jl", "max_stars_repo_head_hexsha": "419d2f2fc3cb27c9b1d969d2e05022f3a4a01f66", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
from PIL import ImageGrab
import cv2
import time
def auto_canny(image, sigma=0.33):
# compute the median of the single channel pixel intensities
v = np.median(image)
# apply automatic Canny edge detection using the computed median
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min... | {"hexsha": "4a7a2579f4250a38824415ea4a9a931ae2462ff1", "size": 1990, "ext": "py", "lang": "Python", "max_stars_repo_path": "FilterTests.py", "max_stars_repo_name": "siddharths067/CNN-Based-Agent-Modelling-for-Humanlike-Driving-Simulaion", "max_stars_repo_head_hexsha": "42d79fc262d60ecc9eebbe0e77a1576a04979501", "max_st... |
%% Copyright (C) 2016 Lagu
%% Copyright (C) 2016, 2018-2019, 2022 Colin B. Macdonald
%%
%% This file is part of OctSymPy.
%%
%% OctSymPy is free software; you can redistribute it and/or modify
%% it under the terms of the GNU General Public License as published
%% by the Free Software Foundation; either version 3 of th... | {"author": "cbm755", "repo": "octsympy", "sha": "c1ecd1e08f027d5101d0f4250dfc496aa98c8bcd", "save_path": "github-repos/MATLAB/cbm755-octsympy", "path": "github-repos/MATLAB/cbm755-octsympy/octsympy-c1ecd1e08f027d5101d0f4250dfc496aa98c8bcd/inst/@sym/equationsToMatrix.m"} |
function E_slice_plot(E::ScalarField, dir, slice_location, t;
issliced=false,
save=false,
slice_dir=:x,
kwargs...)
if !issliced
E_slice = slice(E, slice_dir, slice_location)
else
E_slice = E
end
grid = getdomain(E_slice)
cl = ustrip(max(abs.(extrema(E_slice))...))
... | {"hexsha": "95ccb7c39ebf4e4fdbe18d50bc61e0f6a237c58a", "size": 869, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plots/fields.jl", "max_stars_repo_name": "ctp-fpub/SDFResultViewer.jl", "max_stars_repo_head_hexsha": "375a4f7a6cf87d32980b4af8a075c235d4e5ed3b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from scipy import stats
# ---------------------------
# Independent samples -------
# ---------------------------
def cles_ind(x1, x2):
"""Calc common language effect size
Interpret as the probability that a score sampled
at random from one distribution will be greater than
a score... | {"hexsha": "0e1f99837a5e22e89d0ce86e21f967c8a123fd63", "size": 3749, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_exploration/non_param_effect_size.py", "max_stars_repo_name": "0-b1t/spotify_insights", "max_stars_repo_head_hexsha": "a3c50b728c83139c53a21582fe6b867152356b8f", "max_stars_repo_licenses": ["... |
/////////1/////////2/////////3/////////4/////////5/////////6/////////7/////////8
// test_vector.cpp
// (C) Copyright 2002 Robert Ramey - http://www.rrsd.com .
// Use, modification and distribution is subject to the Boost Software
// License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www... | {"hexsha": "8aa301945fddd0aad817596ed93dbc482cddd0eb", "size": 2295, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "serialization/private/test/test_string_4g.cpp", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo_licens... |
[STATEMENT]
lemma real_sqrt_le_iff': "x \<ge> 0 \<Longrightarrow> y \<ge> 0 \<Longrightarrow> sqrt x \<le> y \<longleftrightarrow> x \<le> y ^ 2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>0 \<le> x; 0 \<le> y\<rbrakk> \<Longrightarrow> (sqrt x \<le> y) = (x \<le> y\<^sup>2)
[PROOF STEP]
using real_le_l... | {"llama_tokens": 303, "file": null, "length": 2} |
import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from .utils import image_and_pickle
from .utils import exponential_moving_average
def line_graph(values, filename, plotsdir, smoothing=None, title='', xname='', yname='', color='blue'):
if smoothing is not None:
values = expone... | {"hexsha": "6ab36904ef8437a34e2d026fd305e08d35aeb50e", "size": 962, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/visualizing/line_graph.py", "max_stars_repo_name": "isaachenrion/jets", "max_stars_repo_head_hexsha": "59aeba81788d0741af448192d9dfb764fb97cf8d", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
"""Event-based representation output interface."""
from operator import attrgetter, itemgetter
from typing import TYPE_CHECKING
import numpy as np
from numpy import ndarray
if TYPE_CHECKING:
from ..music import Music
def to_event_representation(
music: "Music",
use_single_note_off_event: bool = False,
... | {"hexsha": "b9645875979571596642105b81a1057acad5b373", "size": 4516, "ext": "py", "lang": "Python", "max_stars_repo_path": "muspy/outputs/event.py", "max_stars_repo_name": "jeremyjordan/muspy", "max_stars_repo_head_hexsha": "160cdcad10ece0618c0a71e75c3370622e786d9d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
using ViewerGL
GL = ViewerGL
points = rand(50,3)
GL.VIEW([
GL.GLPoints(points),
GL.GLHull(points,GL.Point4d(1,1,1,0.2)),
GL.GLAxis(GL.Point3d(0,0,0),GL.Point3d(1,1,1))
]);
| {"hexsha": "c406f77c9b8301112b4c1c07308048ec3f9ccc75", "size": 191, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/Convex.jl", "max_stars_repo_name": "cvdlab/ViewerGL.js", "max_stars_repo_head_hexsha": "ae28d7808699f9c34add4ad265b68a84bfa14842", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, ... |
"""
AutoML : Round 0
__author__ : abhishek thakur
"""
import numpy as np
from libscores import *
from sklearn import ensemble, linear_model, preprocessing
from sklearn import decomposition, metrics, cross_validation
np.set_printoptions(suppress=True)
train_data = np.loadtxt('cadata/cadata_train.data')
test_data = np... | {"hexsha": "a4209063b2c71ab4e98944d34394bf9bd3a6a726", "size": 962, "ext": "py", "lang": "Python", "max_stars_repo_path": "Phase0/cadata_main.py", "max_stars_repo_name": "abhishekkrthakur/AutoML", "max_stars_repo_head_hexsha": "ffdd8709081e1daecc84e0bce6a21ea32f22eeba", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
KeyGenerator
Can be used to generate a pair of matching secret and public keys. In addition, the `KeyGenerator`
provides functions to obtain relinearization keys (required after multiplication) and Galois keys
(needed for rotation).
See also: [`SecretKey`](@ref), [`PublicKey`](@ref), [`RelinKeys`](@ref)
"""
... | {"hexsha": "6dc656da83152469fead03318d278bdaf29f19cb", "size": 3247, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/keygenerator.jl", "max_stars_repo_name": "sloede/SEAL.jl", "max_stars_repo_head_hexsha": "68285d72db5c02d8ef692aa989bd02c651fd3ff2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "... |
PROGRAM A10Q2
!--
! A program to implement the `Sieve of Eratosthenes` algorithm
!--
IMPLICIT NONE
INTEGER, DIMENSION(1:4999) :: S, Sfinal
INTEGER :: i, j, jprev, p, pnew
REAL :: size
PRINT *, "Determines the list of prime numbers from 0-5000 using the 'Sieve of Eratosthenes' method"
!- initialize the arrays... | {"hexsha": "dc39dc4d61ef8d647ccd4d4c5547511689a26747", "size": 1090, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Assignment 10/A10Q2.f90", "max_stars_repo_name": "Chris-Drury/COMP3731", "max_stars_repo_head_hexsha": "59d70f4fe8354b7b50fd2911ec2d8e7aad8401bc", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
# coding: utf-8
# In[51]:
import cv2
import numpy as np
import matplotlib.pyplot as plt
import math
video_path = "360.mp4"
p_frame_thresh = 300000 # You may need to adjust this threshold
cap = cv2.VideoCapture(video_path)
# Read the first frame.
ret, prev_frame = cap.read()
alto,ancho,canales=prev_frame.shape
de... | {"hexsha": "aa81591877458ea4867a0fc611fab2bad7fa1e8b", "size": 1783, "ext": "py", "lang": "Python", "max_stars_repo_path": "oldies/futbol.py", "max_stars_repo_name": "jrodrigopuca/futbolpy", "max_stars_repo_head_hexsha": "4583e0ce3dfe1521e2f0b6f656407c640bbf3149", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
/* Copyright (C) 2019-2020 Thomas Jespersen, TKJ Electronics. All rights reserved.
*
* This program is free software: you can redistribute it and/or modify it
* under the terms of the MIT License
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the impli... | {"hexsha": "1d27d218e3e0f579cc5ff603bf6f2b8e7a5a9a5c", "size": 4802, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ros/jetsoncar_driver/src/test_node.cpp", "max_stars_repo_name": "mindThomas/JetsonCar", "max_stars_repo_head_hexsha": "74636d4da1f7f71ca9f2315a1b2347393b081eda", "max_stars_repo_licenses": ["MIT"], ... |
#%%
%load_ext autoreload
%autoreload 2
import jax
import jax.numpy as np
import numpy as onp
import distrax
import optax
import gym
from functools import partial
from env import Navigation2DEnv, Navigation2DEnv_Disc
import cloudpickle
import pathlib
import haiku as hk
from jax.config import config
config.update(... | {"hexsha": "916cb3d5f5d86cfe661925c45b9951221f6e545b", "size": 8100, "ext": "py", "lang": "Python", "max_stars_repo_path": "maml/maml.py", "max_stars_repo_name": "gebob19/rl_with_jax", "max_stars_repo_head_hexsha": "a30df06de3035c460e5339611974664a2130ca6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "ma... |
import os
import math
import shutil
import time
import collections
from pathlib import Path
import logging
import uuid
import numpy as np
from fmpy.fmi1 import FMU1Slave, FMU1Model
from fmpy.fmi2 import FMU2Slave, FMU2Model
from fmpy import read_model_description, extract
from energym.envs.env import Env
from energym... | {"hexsha": "fadf99d44a0b596dc80caf1030f1af116edb4527", "size": 24264, "ext": "py", "lang": "Python", "max_stars_repo_path": "energym/envs/env_fmu.py", "max_stars_repo_name": "bsl546/energym", "max_stars_repo_head_hexsha": "0133ca7a19d21352a427e1913755e1ebf6fd8bb6", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_c... |
module MessageRequest
export body_is_a_stream, body_was_streamed, setuseragent!, resource
import ..Layer, ..request
using ..IOExtras
using URIs
using ..Messages
import ..Messages: bodylength
import ..Headers
import ..Form, ..content_type
"""
"request-target" per https://tools.ietf.org/html/rfc7230#section-5.3
"""
r... | {"hexsha": "195318f588daadbb6e70fb301981c11a30137339", "size": 3310, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MessageRequest.jl", "max_stars_repo_name": "cmcaine/HTTP.jl", "max_stars_repo_head_hexsha": "7bf03e2f29b8a25eeffd7223a60a6352173fe1da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
import requests
from StringIO import StringIO
from matplotlib import image as img
import geopy
import yaml
# First hard-code what is needed for correct output of green_between()
class Map(object):
def __init__(self, latitude, longitude, satellite=True,
zoom=10, size=(400, 400)... | {"hexsha": "893e9dac63057a71f53c6ef3c807c319bce02cf0", "size": 3269, "ext": "py", "lang": "Python", "max_stars_repo_path": "greengraph/tests/fixtures/generate_green_between_fixtures.py", "max_stars_repo_name": "ddervs/GreenGraph", "max_stars_repo_head_hexsha": "bb65e5d9f2a34686add644e4fa1851aabf82c3c1", "max_stars_repo... |
# Lint as: python3
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | {"hexsha": "60fb14039f5e358bbe7152529716e95da31321bf", "size": 18283, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_quant_finance/experimental/dates/holiday_calendar.py", "max_stars_repo_name": "rajflume/tf-quant-finance", "max_stars_repo_head_hexsha": "5cb9474f6f2e74617735d38ef26aaef28ce69aff", "max_stars_... |
import numpy as np
import tensorflow as tf
import random
import tensorflow.layers as layer
from collections import deque
import random
import datetime
import time
from multiagent.environment import MultiAgentEnv
from multiagent.policy import InteractivePolicy
import multiagent.scenarios as scenarios
#################... | {"hexsha": "ecbd4eeb8c2fe3d3698258b5b65ffbbedfe0dde6", "size": 7254, "ext": "py", "lang": "Python", "max_stars_repo_path": "maddpg.py", "max_stars_repo_name": "170928/-Review-Multi-Agent-Actor-Critic-for-Mixed-Cooperative-Competitive-Environment", "max_stars_repo_head_hexsha": "db927493c9291686ca11033caf528be9c7a86058"... |
[STATEMENT]
lemma harm_pos: "n > 0 \<Longrightarrow> harm n > (0 :: 'a :: {real_normed_field,linordered_field})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < n \<Longrightarrow> (0::'a) < harm n
[PROOF STEP]
unfolding harm_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. 0 < n \<Longrightarrow> (0::'a) < ... | {"llama_tokens": 170, "file": null, "length": 2} |
//| This file is a part of the sferes2 framework.
//| Copyright 2009, ISIR / Universite Pierre et Marie Curie (UPMC)
//| Main contributor(s): Jean-Baptiste Mouret, mouret@isir.fr
//|
//| This software is a computer program whose purpose is to facilitate
//| experiments in evolutionary computation and evolutionary robot... | {"hexsha": "916f456a7dbd58c4f0733ae91f1006d2e32fda3b", "size": 4426, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "sferes/sferes/gen/cmaes.hpp", "max_stars_repo_name": "Evolving-AI-Lab/innovation-engine", "max_stars_repo_head_hexsha": "58c7fcc3cbe3d6f8f59f87d95bdb5f2302f425ba", "max_stars_repo_licenses": ["MIT"]... |
import os
import sys
import click
import cv2
import numpy as np
from utils.dataset.data_provider import load_annoataion
@click.command()
@click.option('--input', '-i', default='data/dataset/mlt_cmt')
@click.option('--name', '-n')
def process(input, name):
im_fn = os.path.join(input, "image", name)
im = cv2.im... | {"hexsha": "43b1705a88887d093a67451c1bfac49a06b12c70", "size": 980, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_split.py", "max_stars_repo_name": "deeplearningvn/text-detection", "max_stars_repo_head_hexsha": "63f9ec1766b8d66f33e6d073f5ae577402d45b19", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using Test
using QBase
@testset "./src/channels.jl" begin
@testset "repalacer_channel()" begin
@testset "simple qubit examples" begin
ρ = State([1 0;0 0])
σ = State([0 0;0 1])
r = replacer_channel(ρ, σ, 0.5)
@test r isa State
@test r == [0.5 0;0 0.5]
end
@testset ... | {"hexsha": "a6e0c8b1d91763d3eaa501fb8f3c58e5d48dbfec", "size": 2705, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/unit/channels.jl", "max_stars_repo_name": "ChitambarLab/QBase.jl", "max_stars_repo_head_hexsha": "cb30a84b784c61abdae8b007e1de691f3ccd4e4b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from thyme.trajectories import Trajectories
from thyme.trajectory import Trajectory
from thyme.filters.distance import e_filter
from thyme.filters.energy import sort_e
from thyme.routines.dist_plots.energy import multiple_plots as multiple_plots_e
from thyme.parsers.vasp import pack_folder_trj, get_childfolders, write
... | {"hexsha": "3772251b6178ea14af6d8f9346653f7e8213c77f", "size": 1236, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/formate_au_vasp/new_poscar.py", "max_stars_repo_name": "nw13slx/thyme", "max_stars_repo_head_hexsha": "b2a16aa1e6b0701adcfd2bd146f85b5c46b35254", "max_stars_repo_licenses": ["MIT"], "max_s... |
# @testset "AABB" begin
# c = SVector(99.0, 99.0, 99.0)
# e = SVector(1.0, 2.0, 3.0)
#
# aabb = AABB(c, e)
#
# @test aabb isa AABB
# @test aabb.c == c
# @test aabb.e == e
# @test AABB(aabb) == aabb
# end
@testset "OBB" begin
c = SVector(99.0, 99.0, 99.0)
e = SVector(1.0, 2.0, 3.0)... | {"hexsha": "5084b22615ed10845f422c1a7f1fa03b958e3d2f", "size": 651, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_obb/test_box_types.jl", "max_stars_repo_name": "UnofficialJuliaMirror/PressureFieldContact.jl-1a2887a7-38b6-5ebe-8978-3a02049ebf6f", "max_stars_repo_head_hexsha": "55d7b6f85771465de112171c... |
# Copyright (c) Byron Galbraith and Unlock contributors.
# 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. Redistributions of source code must retain the above copyright notice,
# this... | {"hexsha": "4c84a1d377388be208625380e0896ccd5672ca2e", "size": 6541, "ext": "py", "lang": "Python", "max_stars_repo_path": "unlock/state/scope_state.py", "max_stars_repo_name": "NeuralProsthesisLab/unlock", "max_stars_repo_head_hexsha": "0c4d95abdab288d3e657ca2db867b06f755f26ff", "max_stars_repo_licenses": ["BSD-3-Clau... |
# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2019
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
#... | {"hexsha": "76c48a1b83c25501b6100aa3c536b96e50cf7da3", "size": 7228, "ext": "py", "lang": "Python", "max_stars_repo_path": "pynet/transforms.py", "max_stars_repo_name": "HChegraoui/pynet", "max_stars_repo_head_hexsha": "3e26f7992e5b6954f637e3a68e4766f3886e2ce9", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_count... |
"""
Mathematica (Wolfram Alpha): integral_(-2)^2 sqrt(4 - x^2) (1./2 + x^3 cos(x/2)) dx = 3.14159
python workouts/integration_examples/free_wifi.py > outputs/integration_examples/free_wifi.log
"""
from qmcpy import *
from numpy import *
from time import time
def pi_problem(abs_tol=.01):
t0 = time()
distributi... | {"hexsha": "bea1776df1b51c7e0397faabff096f990122e056", "size": 978, "ext": "py", "lang": "Python", "max_stars_repo_path": "workouts/integration_examples/pi_problem.py", "max_stars_repo_name": "kachiann/QMCSoftware", "max_stars_repo_head_hexsha": "0ed9da2f10b9ac0004c993c01392b4c86002954c", "max_stars_repo_licenses": ["A... |
#T# improper fraction can be converted to and from mixed numbers
#T# to work with improper fractions and mixed numbers, the sympy package is used
import sympy
#T# create an improper fraction with the Rational constructor
num1 = sympy.Rational(7, 4) # 7/4
#T# the p, q attributes of a rational number contain the numer... | {"hexsha": "1ec559d4bf2a65d76f220e6c8f12c738f5583700", "size": 944, "ext": "py", "lang": "Python", "max_stars_repo_path": "Math/A01_Arithmetics_basics/Programs/S02_2/Improper_fractions.py", "max_stars_repo_name": "Polirecyliente/SGConocimiento", "max_stars_repo_head_hexsha": "560b08984236d7a10f50c6b5e6fb28844193d81b", ... |
import pandas as pd
import numpy as np
import operator
import collections
import logging
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
class StatisticEstimator:
def __init__(self):
self.counts = collections.defaultdict(lambda: co... | {"hexsha": "e75b70fd08d942971c2a3d89f77f45adc20696d6", "size": 4062, "ext": "py", "lang": "Python", "max_stars_repo_path": "citibike/stat_models.py", "max_stars_repo_name": "MaxenceHanin/5SDBD-Integ-E3", "max_stars_repo_head_hexsha": "dbf85386451b420da4132b69341155332cf56a53", "max_stars_repo_licenses": ["MIT"], "max_s... |
import torch
import numpy as np
from models.stylegan3.networks_stylegan3 import Generator
from utils.common import make_transform
class Expander:
def __init__(self, G: Generator):
self.G = G
def generate_expanded_image(self, ws=None, all_s=None, landmark_t=None,
pixe... | {"hexsha": "9e2edc43a08d2656ef1613c46db1dcbe4e8f17b4", "size": 5569, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/fov_expansion.py", "max_stars_repo_name": "ohhagr/stylegan3-editing-environment", "max_stars_repo_head_hexsha": "5f3602f4a6bb8036511b35aacc9b332d0ca5fa58", "max_stars_repo_licenses": ["MIT"]... |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "631dfdb05b7891c16abac3670b582806cd4c200b", "size": 7546, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/examples/train_cifar10/gen_tf_dnn.py", "max_stars_repo_name": "supernovaremnant/TensorflowOpenCL-GPU", "max_stars_repo_head_hexsha": "8d443c9afb49064c4bdf68fc0c5217986077d24c", "max_sta... |
#!/usr/bin/env python
'''Utility for testing sinex files equivalence based on comparison of values in the SOLUTION/ESTIMATE blocks. Ignores other blocks and header info.
The functionality is based on assert_frame_equal method (https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_frame_equal.html)'''
impo... | {"hexsha": "625e4cb2cd3564358dd38a018e8bde5c4ab675c8", "size": 2405, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/diffsnx.py", "max_stars_repo_name": "umma-zannat/ginan", "max_stars_repo_head_hexsha": "a4d1a3bb8696267f23d26e8c6a2f6080b87bb494", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# To run this script:
# pkg> build GoogleSheets
println("Instalation of GoogleSheets python package dependencies")
using PyCall
@pyimport pip
pip.main(["install","google-api-python-client","google-auth-httplib2","google-auth-oauthlib"]) | {"hexsha": "e0736c46245426a25165d9853ea9d972bdcdcc01", "size": 239, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/build.jl", "max_stars_repo_name": "Sixzero/GoogleSheets.jl", "max_stars_repo_head_hexsha": "bffecf8110143c8f10dfbc3ca554c3793d81917c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14,... |
@recipe function plot(l::AbstractLattice; bondcolor=:grey)
markershape --> :circle # if markershape is unset, make it :auto
markercolor --> :black
markersize --> 5
grid --> false
axis --> false
legend --> false
if ndims(l) == 3
showaxis --> false
end
# plot sites
... | {"hexsha": "7e0924199b0ee38a3b94907cd75e315bd2afa3f3", "size": 1392, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/recipes.jl", "max_stars_repo_name": "crstnbr/LatPhysPlottingPlots.jl", "max_stars_repo_head_hexsha": "119e1779e96860c430beb81243e58d97075b02ab", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
(** Binary trees the nodes of which are labelled with type A *)
Section Some_type_A.
Variable A: Type.
Inductive tree : Type :=
| leaf
| node (label: A)(left_son right_son : tree).
Inductive subtree (t:tree) : tree -> Prop :=
| subtree1 : forall t' (x:A), subtree t (node x t t')
| subtree2 : forall (... | {"author": "raduom", "repo": "coq-art", "sha": "092a8df8e74d7d7a90a2405e4eacf902e528d83a", "save_path": "github-repos/coq/raduom-coq-art", "path": "github-repos/coq/raduom-coq-art/coq-art-092a8df8e74d7d7a90a2405e4eacf902e528d83a/ch15_general_recursion/SRC/btreewf.v"} |
import numpy as np
import csv
import sys
fname=str(sys.argv[1])
ofname=str(sys.argv[2])
chr=str(sys.argv[3])
bin_size=int(sys.argv[4])
chr_size = {'chr1':249250621,
'chr2':243199373,
'chr3':198022430,
'chr4':191154276,
'chr5':180915260,
'chr6':171115067,
'chr7':159138663,
'chr8':146364022,
'chr9':141213431,
... | {"hexsha": "a6ec33a13052a71dbc92bad2f33036c8ea43032a", "size": 1578, "ext": "py", "lang": "Python", "max_stars_repo_path": "convert_coo_to_full_matrix2.py", "max_stars_repo_name": "tharvesh/preprocess_script", "max_stars_repo_head_hexsha": "a52d56442c4038a1af567c83773972f10078294e", "max_stars_repo_licenses": ["MIT"], ... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Problem statement
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\clearpage
\section{Problem Statement}
\label{sec:problem}
%% intro
In this section I will describe the store backend system examined ... | {"hexsha": "7ca11c5812dc4204c546890224fd3fe2f9445309", "size": 12681, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sec3_problem.tex", "max_stars_repo_name": "Sentri/Master-s-Thesis", "max_stars_repo_head_hexsha": "09846d109a45a07e28641891ee3b651228fc049e", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_co... |
//
// Copyright (c) 2020 Richard Hodges (hodges.r@gmail.com)
//
// 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)
//
// Official repository: https://github.com/madmongo1/webclient
//
// This project was made possible ... | {"hexsha": "48624a351941a436b8a9d8a9fdc9a92cccf84b54", "size": 2620, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/boost/webclient/async/future.spec.cpp", "max_stars_repo_name": "madmongo1/webclient", "max_stars_repo_head_hexsha": "7eb52899443a76ced83b6f286b0e0d688f02fc65", "max_stars_repo_licenses": ["BSL-1... |
% To compile this document
% graphics.off();rm(list=ls());library('knitr');knit('EDA-lab.Rnw'); for(i in 1:2) system('R CMD pdflatex EDA-lab.tex')
%detach(bodyfat);
% extract R-code
% purl('EDA-lab.Rnw')
%setwd("/Volumes/Macintosh Storage/Users/jbinder/Dropbox/Docs/Teaching/isb101/Visualization in R/Tutorial")
\d... | {"hexsha": "5c2d3b3a83a06bb749fdfbf38388816ad04cb320", "size": 66724, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "R/Visualization/Visualization in R/Tutorial/EDA-lab.tex", "max_stars_repo_name": "rolandkrause/isb101", "max_stars_repo_head_hexsha": "40be2fa62a6ca986e4ed9f1833382b2c10478039", "max_stars_repo_lic... |
import unittest
import numpy as np
from spartan import expr
from spartan.util import Assert
from spartan import util
import test_common
TEST_SIZE = 50
class TestReduce(test_common.ClusterTest):
def test_sum_3d(self):
x = expr.arange((TEST_SIZE, TEST_SIZE, TEST_SIZE), dtype=np.int64)
nx = np.arange(TEST_S... | {"hexsha": "60c539ec18f729b6747b16a427c8253dcc2c2d4b", "size": 3631, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_reduce.py", "max_stars_repo_name": "GabrielWen/spartan", "max_stars_repo_head_hexsha": "ce3bf7f2bb551d7f996a1884acef819b620cc854", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
using myJuliaUtils
using Test
using LinearAlgebra
using DelimitedFiles
using Statistics
# Testing both lag functions
@testset "testing lag functions" begin
# if vector
T = 100
P = rand(1:T - 1)
a = collect(1.0:1.0:T)
b = lag0(a, P)
@test b[1:P] == zeros(P)
@test T - P == b[end]
# if ma... | {"hexsha": "0b124edb4b9430fed61be8c433a1262dc8f2ee37", "size": 7920, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TestingExtfunc.jl", "max_stars_repo_name": "zymbuzz/myJuliaUtils", "max_stars_repo_head_hexsha": "bd18eff600ca8a5052575706dc3a4b494128c4a5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import matplotlib.pyplot as plt
import random
import seaborn as sns
sns.set_style("white")
from scipy.stats import norm
import time
from math import sqrt, log, exp, pi
from random import uniform
size = 500
set1 = np.random.normal(loc = 1, scale = 0.1, size = size)
set2 = np.random.normal(loc = 1.5,... | {"hexsha": "f126d726fe9f62d3b745225c6039cd6cb312e0f5", "size": 4959, "ext": "py", "lang": "Python", "max_stars_repo_path": "gmm.py", "max_stars_repo_name": "prakhardogra921/Clustering-using-Kmeans-Cmeans-and-GMM", "max_stars_repo_head_hexsha": "7fce1527a5362313aa45e5fd41f5369af3087cba", "max_stars_repo_licenses": ["MIT... |
From VST Require Import floyd.proofauto.
From CertiGC Require Import model.GCGraph.
From CertiGC Require Import vst.ast.env_graph_gc.
From CertiGC Require Import vst.clightgen.gc.
From CertiGC Require Import vst.cmodel.constants.
From CertiGC Require Import vst.cmodel.spatial_gcgraph.
From CertiGC Require Import vst.s... | {"author": "CertiGraph", "repo": "CertiGC", "sha": "ec0183449d5e7dc66d33c9bc2dd5759de0ebd877", "save_path": "github-repos/coq/CertiGraph-CertiGC", "path": "github-repos/coq/CertiGraph-CertiGC/CertiGC-ec0183449d5e7dc66d33c9bc2dd5759de0ebd877/theories/CertiGC/vst/verif/verif_resume.v"} |
import sys
from abc import ABC, abstractmethod
import glob
import os
import random
import shutil
import statistics
from imagecorruptions import corrupt
import cv2
import numpy as np
import pickle
from tqdm import tqdm
from mean_average_precision import MetricBuilder
import yoloPredictor
import imageDifferenceCalculat... | {"hexsha": "9141e0871f5d0559623d5c00fac5b1ce291cbefc", "size": 26941, "ext": "py", "lang": "Python", "max_stars_repo_path": "SelectSamples.py", "max_stars_repo_name": "johagge/DeepActiveLearning", "max_stars_repo_head_hexsha": "937b82710f7fa3c6c8e165ab0dc0f4d4d770499d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/python
#+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
#|R|a|s|p|b|e|r|r|y|P|i|.|c|o|m|.|t|w|
#+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# Copyright (c) 2017, raspberrypi.com.tw
# All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
# color_space.py
... | {"hexsha": "7283d6a7f29feba3528da9d81ae3e66ca0b50dde", "size": 949, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera-opencv/01-color_space/color_space.py", "max_stars_repo_name": "ReemDAlsh/camera-python-opencv", "max_stars_repo_head_hexsha": "6adb12b682907554645211217e970480685347b0", "max_stars_repo_lice... |
#BSD 3-Clause License
#
#Copyright (c) 2021, Florent Audonnet
#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. Redistributions of source code must retain the above copyright notice, this
# lis... | {"hexsha": "5e5d33bb0e972da6e7df6f5a8c7c7326f731d151", "size": 8972, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple_arm/simple_arm/vr_publish.py", "max_stars_repo_name": "09ubberboy90/lvl4-ros2-sim-comp", "max_stars_repo_head_hexsha": "c197c76b29a9d864a800b81332bc3a549ecaa7c3", "max_stars_repo_licenses":... |
[STATEMENT]
lemma span_minimal: "S \<subseteq> T \<Longrightarrow> subspace T \<Longrightarrow> span S \<subseteq> T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>S \<subseteq> T; subspace T\<rbrakk> \<Longrightarrow> span S \<subseteq> T
[PROOF STEP]
by (auto simp: span_explicit intro!: subspace_sum subs... | {"llama_tokens": 115, "file": null, "length": 1} |
from shor.gates import Hadamard, PauliX, CCNOT, SWAP, CRZ, CH, S, Sdg, T, Tdg, PauliY, PauliZ, ID, Cx, U1, U3, U2, Rx, Cz, Ry, Rz
from shor.layers import Qubits
from shor.operations import Measure
from shor.quantum import Circuit
from shor.backends import QuantumSimulator, QSession
import numpy as np
import math
def ... | {"hexsha": "abe9a24531ce86335ad7e74b3c3d8bc01ea3f9b2", "size": 7986, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/integration/test_gates_integration.py", "max_stars_repo_name": "jywyq/shor", "max_stars_repo_head_hexsha": "5e38c6875d68207a6d0e492d83f7b1f6ae0afb58", "max_stars_repo_licenses": ["MIT"], "ma... |
This organization is a successor to Davis Students Against War
Overview
Please Note:
in the past there has been an organization referred to as the Davis Students Against War, under the leadership of Karl Duesterberg. That organization has since fallen out and become inactive. THIS PAGE is not in reference to the ol... | {"hexsha": "003d261d00896288f42231d6eca5ec23385d2e36", "size": 3700, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Davis_Students_Against_War_Resource.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["... |
import numpy as np
from typing import Dict, Any
from dataset.dataset import Dataset
from utils.constants import INPUT_SHAPE, INPUTS, OUTPUT, SAMPLE_ID, INPUT_NOISE, SMALL_NUMBER
from utils.constants import INPUT_SCALER, NUM_OUTPUT_FEATURES, NUM_CLASSES, LABEL_MAP
class SingleDataset(Dataset):
def tensorize(self... | {"hexsha": "0eee1fac4ada0ab321a61e7d8beac621a6d14952", "size": 1296, "ext": "py", "lang": "Python", "max_stars_repo_path": "budget-rnn/src/dataset/single_dataset.py", "max_stars_repo_name": "tejaskannan/ml-models", "max_stars_repo_head_hexsha": "ad5acad2c0ce75773062ffcdff088a6fbe5ffc17", "max_stars_repo_licenses": ["Ap... |
from layers import layer, ABC, abstractmethod
import numpy as np
from numpy.lib.stride_tricks import as_strided
eps = 1e-8
class trainable(layer, ABC):
"""
The base class for layers with trainable parameters
"""
def set_params(self, *params):
"""
Generates a unique identifier for each... | {"hexsha": "9e2ca31f600aaa1a9f4222b843a211922dd03125", "size": 8808, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/trainable.py", "max_stars_repo_name": "nkarve/twistml", "max_stars_repo_head_hexsha": "59e168c0776e9a234037a973b608450c05ffa198", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max... |
"""
Unit Tests for CorfuncCalculator class
"""
from __future__ import division, print_function
import os.path
import unittest
import time
import numpy as np
from sas.sascalc.corfunc.corfunc_calculator import CorfuncCalculator
from sas.sascalc.dataloader.data_info import Data1D
def find(filename):
return os.pat... | {"hexsha": "3bb3dd9890b61d20333bf35eef274c9767ce6221", "size": 4850, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/corfunc/test/utest_corfunc.py", "max_stars_repo_name": "opendatafit/sasview", "max_stars_repo_head_hexsha": "c470220eecfc9f6d8a0e27e2ea8919dcb1b38e39", "max_stars_repo_licenses": ["BSD-3-Clau... |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
class PRUNE(nn.Module):
def __init__(self, nodeCount, n_latent=128, n_emb=128, n_prox=64):
super(PRUNE, self).__init__()
'''
Parameters
----------
n_late... | {"hexsha": "6ec27e001edbab5e005f2f24654bdb475c43e1b9", "size": 2386, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model.py", "max_stars_repo_name": "j40903272/PRUNE-pytorch", "max_stars_repo_head_hexsha": "2494adbda12a0bec8fbf69252d730d32d0f57996", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5,... |
//
// get_local_deleter_test2.cpp
//
// Copyright 2002, 2017 Peter Dimov
//
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
#include <boost/config.hpp>
#if defined( BOOST_NO_CXX11_RVALUE_REFERENCES ) || defi... | {"hexsha": "999fffe7465767e672985661f2e35dfeafef19bd", "size": 1993, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/smart_ptr/test/get_local_deleter_array_test2.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e... |
import os, sys
import numpy as np
def norm(v):
'''Normalizes a given vector and returns the normalized vector.
==========
Parameters
v: [array of floats] vector to be normalized
==========
'''
# numerical instability
# if ray goes through exact center, offset by ... | {"hexsha": "22ed29ef062b7ae83ed50c472a5114acadacb6a9", "size": 4549, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/lens_ray_tracing.py", "max_stars_repo_name": "masoncarney/stepped_luneburg", "max_stars_repo_head_hexsha": "728323f6331cc90cf8d97e9702a958ef553a284e", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import SimpleRN... | {"hexsha": "025cccd9d356751a7a8c5d7d9af450a17402282f", "size": 4550, "ext": "py", "lang": "Python", "max_stars_repo_path": "temp.py", "max_stars_repo_name": "bedirhanbuyukoz/IMDB-Sentiment-Analysis---Machine-Learning-RNN", "max_stars_repo_head_hexsha": "f6d9d0e4e19c8cf69b3a422ae52e4aaca1ed85e3", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 17 02:57:47 2015
1.
This script plots co-evolution history of galaxy stellar mass growth and
lambda_r evolution. Considering abrupt stellar mass growth as a consequence of
galaxy merger, the role of merger history on lambda_r might be understood.
2.
This script loads l... | {"hexsha": "467c5b7b1c6cebd120ca74300a09f276dbb5b210", "size": 5900, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Rotation/plot_stellarmassgrowth_lambda_growth.py", "max_stars_repo_name": "Hoseung/pyRamAn", "max_stars_repo_head_hexsha": "f9386fa5a9f045f98590039988d3cd50bc488dc2", "max_stars_repo_licen... |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
os.chdir('../Datos//')
datos = pd.read_csv('Drug5.csv')
barras = pd.value_counts(datos['Drug'])
plt.figure()
N=len(barras)
plt.bar(np.arange(N), barras) # Gráfico de barras
plt.title('Drug') # Colocamos el título
plt.ylabel('Frecuenc... | {"hexsha": "5dba0058eb6328d82286d41ce291cb4fad71cf4d", "size": 402, "ext": "py", "lang": "Python", "max_stars_repo_path": "01_Diag_Barras.py", "max_stars_repo_name": "JosefinaMedina/Deep-Learning-2021-P1", "max_stars_repo_head_hexsha": "6abd77a55f065e0072a2923f0e395cb2cec11fcc", "max_stars_repo_licenses": ["Apache-2.0"... |
import sys
import gym
import numpy as np
from collections import defaultdict, deque
import matplotlib.pyplot as plt
import check_test
from plot_utils import plot_values
env = gym.make('CliffWalking-v0')
print(env.action_space)
print(env.observation_space)
# define the optimal state-value function
V_opt = np.zeros((4... | {"hexsha": "85a46f2fa41d628290fc6c4afe190d82b035ffd2", "size": 5955, "ext": "py", "lang": "Python", "max_stars_repo_path": "temporal-difference/temporal_difference.py", "max_stars_repo_name": "csggnn/deep-reinforcement-learning", "max_stars_repo_head_hexsha": "73795e831832590d252dd57b95b877715e84e2fc", "max_stars_repo_... |
/*
This file is part of solidity.
solidity is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
solidity is distributed in the hope that i... | {"hexsha": "13ad9f8caf4862eef72d9e1e01c3f224712bada8", "size": 1864, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libyul/Object.cpp", "max_stars_repo_name": "MrChico/solidity", "max_stars_repo_head_hexsha": "5b4ea1eb895d5edc9a24ee5c6f96d8580eceec08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
"""Tests coreml.data.data_module.py"""
from os.path import join, exists
import multiprocessing as mp
import torch
import numpy as np
import unittest
from coreml.config import DATA_ROOT
from coreml.data.data_module import DataModule
class DataModuleTestCase(unittest.TestCase):
"""Class to check the creation of Dat... | {"hexsha": "1ef73a5f879d2187962a8a9a5113c204972ca597", "size": 3985, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/data/test_data_module.py", "max_stars_repo_name": "core-ml/coreml", "max_stars_repo_head_hexsha": "c983b919ec9041a9a8d71c03829158ed41da7890", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#include <boost/algorithm/string.hpp>
#include "Conversion.hpp"
#include "ScoreFileEntry.hpp"
#include "Serialize.hpp"
#include "StringTable.hpp"
#include "TextKeys.hpp"
#include "TextMessages.hpp"
using namespace std;
// Default constructor
ScoreFileEntry::ScoreFileEntry()
: score(0), sex(CreatureSex::CREATURE_SEX_M... | {"hexsha": "faac4ac6f2e7c7ebca9dea6eeae3af3d7d20ef93", "size": 4125, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "engine/source/ScoreFileEntry.cpp", "max_stars_repo_name": "sidav/shadow-of-the-wyrm", "max_stars_repo_head_hexsha": "747afdeebed885b1a4f7ab42f04f9f756afd3e52", "max_stars_repo_licenses": ["MIT"], "m... |
c
c The small main program below is an example of how to compute field
c components with T89C.
c See GEOPACK.DOC for an example of field line tracing.
c
dimension parmod(10)
1 print *, ' enter x,y,z,ps,iopt'
read*, x,y,z,ps,iopt
call t89c(iopt,parmod,ps,x,y,z,bx,by,bz)
print *, bx,b... | {"hexsha": "d14aa3a9dfea685ad39706a1cb3065d7bfd06e5f", "size": 15257, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "fortran/T89.f", "max_stars_repo_name": "scivision/tsyganenko", "max_stars_repo_head_hexsha": "416f1366caf745ec28cf15a7263a07a249f5f587", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, ... |
#!/usr/bin/env python
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("h5file")
parser.add_argument("--pattern")
args = parser.parse_args()
import h5py
import rapprentice.cv_plot_utils as cpu
import numpy as np
import cv2
import fnmatch
hdf = h5py.File(args.h5file,"r")
all_imgnames = [(np.asar... | {"hexsha": "89b2a158f9095a8c38afa842e590197f4f3ec090", "size": 833, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/view_demos.py", "max_stars_repo_name": "wjchen84/rapprentice", "max_stars_repo_head_hexsha": "9232a6a21e2c80f00854912f07dcdc725b0be95a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
import numpy as np
import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.dates as mdates
import requests
import io
#hide
def load_timeseries(name,
base_url='https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covi... | {"hexsha": "82ced2b9e199cc6f46d1c293f966fe2034320c1f", "size": 3535, "ext": "py", "lang": "Python", "max_stars_repo_path": "_notebooks/lib/covid_data.py", "max_stars_repo_name": "caglorithm/notebooks", "max_stars_repo_head_hexsha": "6d9bb0f2e8483c39bc13dd3e435f7f05dd8f3bce", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import cv2
import numpy as np
import os
import glob
import math
def find(img, cap, DIM, K, D):
sift = cv2.xfeatures2d.SIFT_create()
kp_image, desc_image = sift.detectAndCompute(img,None)
img = cv2.drawKeypoints(img,kp_image,img)
#matching
index_params = dict(algorithm=0,trees = 5)
... | {"hexsha": "65ca92217a407afbe00f635744a583900794f194", "size": 4418, "ext": "py", "lang": "Python", "max_stars_repo_path": "ObjectFinder.py", "max_stars_repo_name": "thereturn932/Fly-n-Forget", "max_stars_repo_head_hexsha": "44e7585fd52679110a064824f278dab9319b725d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import argparse
import pickle
import numpy as np
import random
import torch
import torch.optim
"""
Utility functions for handling parsed arguments
"""
def get_args() -> argparse.Namespace:
parser = argparse.ArgumentParser('Train a ProtoTree')
parser.add_argument('--dataset',
... | {"hexsha": "6c3c3fd39c79755f52db0b78815889cfb1952cf9", "size": 14801, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/args.py", "max_stars_repo_name": "TristanGomez44/ProtoTree", "max_stars_repo_head_hexsha": "d9e77a90b47cb1efe19f1736c6701872a3c4a62e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Runs a one round screening simulation for experiment 4 - PstP.
Initial training data was sampled from PstP dataset using uniform random sampling or diversity (Tanimoto dissimilarity) sampling.
Experiment 4 - PstP: prospective screening of PstP target.
Usage:
python experiment_ors_pstp... | {"hexsha": "f47d117689b419bd5ab6ace58a798a366f439622", "size": 4307, "ext": "py", "lang": "Python", "max_stars_repo_path": "chtc_runners/experiment_ors_pstp_runner.py", "max_stars_repo_name": "gitter-lab/active-learning-drug-discovery", "max_stars_repo_head_hexsha": "b24004a359037b3a1175a61c181ec231b711c797", "max_star... |
import math
import numpy as np
from knn_robustness.utils import top_k_min_indices
from knn_robustness.utils import KnnPredictor
from knn_robustness.utils import QpSolver
class ExactSolver:
def __init__(
self, X_train, y_train, qp_solver: QpSolver,
n_pos_for_screen, bounded, upper=1., low... | {"hexsha": "9cf1da28ea3ccaeff5014962b9a949d6ae7f377f", "size": 4639, "ext": "py", "lang": "Python", "max_stars_repo_path": "knn_robustness/nn_only/exact.py", "max_stars_repo_name": "wangwllu/knn_robustness", "max_stars_repo_head_hexsha": "9a02f92bf00febf900b2817c1b7230284816b21b", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma ClassI [intro, simp]:
"(a, b) \<in> E \<Longrightarrow> a \<in> Class b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a, b) \<in> E \<Longrightarrow> a \<in> Class b
[PROOF STEP]
unfolding Class_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a, b) \<in> E \<Longrightarrow> a \<in> (\<l... | {"llama_tokens": 179, "file": "Jacobson_Basic_Algebra_Set_Theory", "length": 2} |
import numpy as np
import nltk
def wordcount_fn(file_uri):
nparr_words = open(file_uri, 'r')
text_as_str = nparr_words.read() #for small textfiles
text_as_list = text_as_str.split()
counts = nltk.FreqDist(text_as_list).items()
for key, val in counts:
print(str(val) + " " + str(key))
... | {"hexsha": "17b3e687ac48a4380d8c09fd50b9472cf230cedd", "size": 443, "ext": "py", "lang": "Python", "max_stars_repo_path": "term-frequency/wordcounts.py", "max_stars_repo_name": "paulowe/python-data-programming", "max_stars_repo_head_hexsha": "96fdb3f888a554ac66e69e1f6958f3e0ef5b1075", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
piOvr4 = np.pi/4
def get_2d_rot(angle=0):
return np.array([[np.cos(angle),-np.sin(angle)],
[np.sin(angle),np.cos(angle)]])
def get_2d_refl(angle=0):
return np.array([[-np.cos(angle),np.sin(angle)],
[np.sin(angle),np.cos(angle)]])
def make_ngon(nside... | {"hexsha": "d5e8cd20e39de0c1fafa55fc5aed3d8b6ce0d719", "size": 1262, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/helpers.py", "max_stars_repo_name": "hakonanes/morphops", "max_stars_repo_head_hexsha": "10b77498e444d2e5a64268b418e5c686041868d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22,... |
import os
import numpy as np
from pgimp.GimpFile import GimpFile
from pgimp.util import file
from pgimp.util.TempFile import TempFile
if __name__ == '__main__':
img_path = file.relative_to(__file__, '../../../doc/source/_static/img')
png_file = os.path.join(img_path, 'mask_applied.png')
height = 100
... | {"hexsha": "77ffb883cad3f3c8dddd79ed42e9d6cd67079ed8", "size": 1659, "ext": "py", "lang": "Python", "max_stars_repo_path": "pgimp/doc/examples/multilayer_to_npz.py", "max_stars_repo_name": "netogallo/pgimp", "max_stars_repo_head_hexsha": "bb86254983e1673d702e1fa2ed207166fd15ec65", "max_stars_repo_licenses": ["MIT"], "m... |
import pyparsing as pp
#from pyparsing import (
#Suppress, Group, Optional, Word, ZeroOrMore, White, Combine,
#Dict, Literal, OneOrMore, Regex,
#alphas, alphanums, nums, oneOf, delimitedList, quotedString
#)
pword = pp.Word(pp.alphas).setName('word')
pword_underscore = pp.Word(pp.alphas + '_').setName('wor... | {"hexsha": "1429f13167720315e90f3ffa01b9761ae36e7c09", "size": 14371, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyNastran/converters/dev/vrml/parsing_help.py", "max_stars_repo_name": "luzpaz/pyNastran", "max_stars_repo_head_hexsha": "939e9eefdc87a3bf67939a23dc09f155b93969a0", "max_stars_repo_licenses": ["B... |
"""
author: Antoine Spahr
date : 04.11.2020
----------
TO DO :
"""
import sys
sys.path.append('../../')
import click
import os
import pandas as pd
import numpy as np
import nibabel as nib
import skimage
import skimage.io
from src.utils.ct_utils import window_ct
from src.utils.print_utils import print_progessbar
@c... | {"hexsha": "59dd9292a8b71a7583d19bdc94080615ce7dd392", "size": 4010, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/scripts/data_preparation/generate_2DBrainDataset.py", "max_stars_repo_name": "antoine-spahr/Label-Efficient-Volumetric-Deep-Semantic-Segmentation-of-ICH", "max_stars_repo_head_hexsha": "61e74... |
# import standard modules
# import third party modules
import numpy as np
# import project related modules
class CostFunction(object):
"""
Parent class for all cost Functions within NumpyNet. It is used to reduce the amount of code for initialization
since all cost functions share the same attributes.
... | {"hexsha": "bf3bfaceb31706229d5a6a96b8213121e002951c", "size": 2822, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple_numpy_net/loss.py", "max_stars_repo_name": "Perledition/GeneticNumpyNet", "max_stars_repo_head_hexsha": "f175fc71f63e9e4f79c8c63f080c853bb5b96f4a", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import albumentations as A
import cv2
import numpy as np
import pandas as pd
import torch
from pandas import DataFrame
from torch.utils.data import Dataset, DataLoader
from constants import DFDC
from training.datasets.transform import create_train_transform, create_val_test_transform
class DFDCDataset(Dat... | {"hexsha": "3617f982c083055d0e9bd0d4ad633a2356f8365a", "size": 3429, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/datasets/dfdc_dataset.py", "max_stars_repo_name": "joizhang/sifdnet", "max_stars_repo_head_hexsha": "9b3efa8c709bcea502a3989f6c62389f74099bae", "max_stars_repo_licenses": ["MIT"], "max_st... |
subroutine qqb_wbjet(p,msq)
implicit none
c--- R.K. Ellis, 8/3/04
c--- matrix element squared and averaged over initial colours and spins
c q(-p1) + b(-p2) --> W^+ + b(p5) + f(p6)
c |
c --> nu(p3) + e^+(p4)
c---or
c q(-p1) + b(-p2) --> W^- + b(p5) + f... | {"hexsha": "6a1b3677eaaa139ce28c21f3a9711acd0c6b8fe3", "size": 4838, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/Wbjet/qqb_wbjet.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"], "... |
import abc
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
from .models import model
from .parsers.common_parser import CommonParser
from . import checks
class Tester:
def __init__(self, metric_name="MeanF1Score", border=0.5,
invert_list=None):
"""
... | {"hexsha": "b1a0f3ef16f768e82f7a0d5c44c0fb5f7ce32f43", "size": 9757, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlalgorithms/tester.py", "max_stars_repo_name": "robot-lab/tinkoff-optimization-of-procurement", "max_stars_repo_head_hexsha": "6f34ede84f8b51520a6a269d4a4cefae4d8f54c1", "max_stars_repo_licenses"... |
#
# Copyright John Reid 2006
#
import numpy, numpy.random
from _maths import *
def reverse_complement( s ):
result = numpy.zeros_like( s )
for i in xrange( len( s ) ):
result[ len(s) - i - 1 ] = 3 - s[i]
return result
class GappedPssm( object ):
def __init__(
self,
phi... | {"hexsha": "4850e2a79c23b43fe54e13e7ed514345ec6f30a4", "size": 6206, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/biopsy/gapped_pssms/_generate.py", "max_stars_repo_name": "JohnReid/biopsy", "max_stars_repo_head_hexsha": "1eeb714ba5b53f2ecf776d865d32e2078cbc0338", "max_stars_repo_licenses": ["MIT"], "m... |
import unittest
import numpy as np
import image_stitching as stit
class TestImageStitching(unittest.TestCase):
def test_stitching(self):
# prepare input files
paths_to_input_file = [
'/home/yuthon/Workspace/image-stitching/assets/data/test2/test_cam_1.mp4',
'/home/yuthon/Wo... | {"hexsha": "2d80b318e9a9cac09174082aba3087c4a13a2f6e", "size": 2456, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_python_bindings.py", "max_stars_repo_name": "corenel/image-stitching", "max_stars_repo_head_hexsha": "844af322999f47868d71d250b77d9ca0bc8b143f", "max_stars_repo_licenses": ["MIT"], "max... |
% ******************************* Thesis Appendix A ****************************
\chapter{Source Code}
Some of our implementations are originally written and some are modified from previous open source projects. So we upload related codes to GitHub in my personal repositories as \url{https://github.com/SeleneLI}. It i... | {"hexsha": "ef9e77fd5f28feed2752bdb939eec63b8a704c96", "size": 1256, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Appendix1/appendix1.tex", "max_stars_repo_name": "SeleneLI/YueLI_thesis", "max_stars_repo_head_hexsha": "f2ae3525afe1e4f5be42daca2e932addbc66e00d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
module test_julia_int
using Test
@test UInt8(0b11111111) == 0xff
@test UInt8(0b01111111) == 0x7f
@test UInt8(0b00111111) == 0x3f
@test UInt8(0b00011111) == 0x1f
@test UInt8(0b00001111) == 0x0f
@test UInt8(0b00000111) == 0x07
@test UInt8(0b00000011) == 0x03
@test UInt8(0b00000001) == 0x01
@test UInt8(0b00000000) == 0x... | {"hexsha": "259c8212828f11ad0c41838ad74c7d9b596198a6", "size": 1515, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/julia/int.jl", "max_stars_repo_name": "wookay/TestJulia07.jl", "max_stars_repo_head_hexsha": "17f139763d96e456fdb4b59fbb7964273523cb00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
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