text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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program hello
!$OMP parallel
print *, "Hello world"
!$OMP end parallel
end program hello
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/*
* Copyright (C) 2012-2015 Open Source Robotics Foundation
*
* 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... | {"hexsha": "cdfd76f6b02af5079e3da590c68e74eb831401fb", "size": 9474, "ext": "cc", "lang": "C++", "max_stars_repo_path": "gazebo/sensors/WirelessReceiver_TEST.cc", "max_stars_repo_name": "horikawahorikawa/gazebo-PR", "max_stars_repo_head_hexsha": "bdb99bec78b8adb95f8855057aae7c028f9e78c2", "max_stars_repo_licenses": ["E... |
// Copyright Abel Sinkovics (abel@sinkovics.hu) 2013.
// 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 <mpllibs/metamonad/try_c.hpp>
#include <mpllibs/metamonad/exception.hpp>
#include <mplli... | {"hexsha": "8414e10acfabc0165a2d11dab4be5029d32b63be", "size": 3190, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/metamonad/test/try_c.cpp", "max_stars_repo_name": "sabel83/mpllibs", "max_stars_repo_head_hexsha": "8e245aedcf658fe77bb29537aeba1d4e1a619a19", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
(*
Copyright 2016 Luxembourg University
Copyright 2017 Luxembourg University
This file is part of Velisarios.
Velisarios 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 Licen... | {"author": "vrahli", "repo": "Velisarios", "sha": "6fb353b18610cd79210755fcc90123536c367aaa", "save_path": "github-repos/coq/vrahli-Velisarios", "path": "github-repos/coq/vrahli-Velisarios/Velisarios-6fb353b18610cd79210755fcc90123536c367aaa/PBFT/PBFTlearns_or_knows_nv.v"} |
"""This model creates the ModelInterface for PyTorch."""
from contextlib import suppress
from copy import deepcopy
from typing import Optional, Tuple
import torch
import numpy as np
from ..helpers.model_interface import ModelInterface
from ..helpers import utils
class PyTorchModel(ModelInterface):
"""Interface ... | {"hexsha": "05ea239f48efd6005dc9763f35e3a69f4b812612", "size": 2824, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantus/helpers/pytorch_model.py", "max_stars_repo_name": "sebastian-lapuschkin/Quantus", "max_stars_repo_head_hexsha": "c3b8a9fb2018f34bd89ba38efa2b2b8c38128b3f", "max_stars_repo_licenses": ["MIT... |
struct InconsistentVRep{T, AT, D<:Polyhedra.FullDim} <: VRepresentation{T}
points::Polyhedra.PointsHull{T, AT, D}
rays::Polyhedra.RaysHull{T, AT, D}
function InconsistentVRep{T, AT, D}(d::Polyhedra.FullDim, points, lines,
rays) where {T, AT, D}
new{T, AT, D}(P... | {"hexsha": "d58b775c65d94c9906e1156a68b6d9cee3f21e0d", "size": 1258, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/inconsistentvrep.jl", "max_stars_repo_name": "mforets/Polyhedra.jl", "max_stars_repo_head_hexsha": "58013aefabcc3bbc71832ae8d9af495c613ab21a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import pandas as pd
import numpy as np
import copy as cp
import math
def compute_functions(index):
invocations = pd.read_csv(f"/media/soufianej/Transcend/Traces/Azure/invocations/invocations_per_function_md.anon.d0{index}.csv", index_col=False)
exec_times = pd.read_csv(f"/media/soufianej/Transcend/Traces/Azur... | {"hexsha": "319b6a93ad5e05f4ea1ef32f1193fc2f83a197b3", "size": 4311, "ext": "py", "lang": "Python", "max_stars_repo_path": "trace-generation/AzurePreProcessing.py", "max_stars_repo_name": "atlarge-research/opendc-serverless", "max_stars_repo_head_hexsha": "11c772bcb3fc7a7c2590d6ed6ab979b78cb9fec9", "max_stars_repo_lice... |
import enum
from typing import Optional
import numpy as np
import feast
class BqType(enum.Enum):
"""
BigQuery enum types. Used when dealing with types in any big query operation.
"""
FLOAT = 0
STRING = 1
DATETIME = 2
TIMESTAMP = 3
ARRAY = 4
BOOL = 5
STRUCT = 6
INTEGER = 7
c... | {"hexsha": "d1f456aa0e717d96a6684183de1672b0b27d83f5", "size": 3095, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/elemeno_ai_sdk/features/types.py", "max_stars_repo_name": "elemeno-ai/elemeno-ai-sdk", "max_stars_repo_head_hexsha": "2737b2c9c575119bd40efcd8c5e91a98f9d8b6b1", "max_stars_repo_licenses": ["Ap... |
module millerlocal
use common_types, only: flux_surface_type
implicit none
public :: init_local_defaults
public :: read_local_parameters
public :: communicate_parameters_multibox
public :: get_local_geo
public :: finish_local_geo
public :: local
private
integer :: nzed_local
real :... | {"hexsha": "bea47de47fae31f393715608faa1a5e5baa040fc", "size": 53935, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "geo/millerlocal.f90", "max_stars_repo_name": "AntonioG-Jerez/stella", "max_stars_repo_head_hexsha": "b9d0257ea3639218b7546116c1235ebd4a8e0752", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include <fstream>
#include <iostream>
#include <string>
#include <utility>
#include <vector>
#include <map>
#include <algorithm>
#include <stdint.h>
#include <boost/foreach.hpp>
#include <boost/property_tree/ptree.hpp>
#include <boost/property_tree/xml_parser.hpp>
using namespace boost::property_tree;
using namespace ... | {"hexsha": "814468f21591b04ad2ee72cd1e6e82b7f9c360ae", "size": 841, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "remodet_repository_wdh_part/tools/test_loadxml.cpp", "max_stars_repo_name": "UrwLee/Remo_experience", "max_stars_repo_head_hexsha": "a59d5b9d6d009524672e415c77d056bc9dd88c72", "max_stars_repo_license... |
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pandas as pd
import pylab as pl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing, metrics, tree
from io import StringIO
import pydotplus
... | {"hexsha": "2afcf783632f51465c9e2f2290e153e614378cd7", "size": 1816, "ext": "py", "lang": "Python", "max_stars_repo_path": "Decision Tree Classification/app.py", "max_stars_repo_name": "fiend361/IBM-Machine-Learning-with-Python-Course", "max_stars_repo_head_hexsha": "e0d5e078eb79837f957a6d0c332639259992c384", "max_star... |
from abc import ABCMeta, abstractmethod
from random import choice, random, uniform
from numpy import argmax, argmin
# Harmony Search class
# Taken from Solid library
# https://100.github.io/Solid/_modules/Solid/HarmonySearch.html
# Adapted for Python 3
# Class was not imported, as it contains python2 style prints
clas... | {"hexsha": "d7018461f5f1785c363cb9871a45171a5bba565a", "size": 5156, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/optimization/HarmonySearch.py", "max_stars_repo_name": "lyonva/Nue", "max_stars_repo_head_hexsha": "90680de00b0c76f6bfdbed71b785671e7c3a3f54", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | {"hexsha": "8abe030999e5409f36ccff8a48b245e0c639aeab", "size": 1854, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl_coach/exploration_policies/greedy.py", "max_stars_repo_name": "jl45621/coach", "max_stars_repo_head_hexsha": "9a895a1ac73aff44b2e6eb8e4d01e8ec35ceb084", "max_stars_repo_licenses": ["Apache-2.0"... |
%Program for creating CSV File
%Author : Athi Narayanan S
%M.E, Embedded Systems,
%K.S.R College of Engineering
%Erode, Tamil Nadu, India.
%http://sites.google.com/site/athisnarayanan/
%Program Description
%This program generates a CSV file containing the colors in the output image.
%The CSV format is as follows
%Pal... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/31687-color-quantization/K-Means-Color-Reduc... |
\documentclass{uofsthesis-cs}
% Documentation for the uofsthesis-cs class is given in uofsthesis-cs.dvi
%
% It is recommended that you read the CGSR thesis preparation
% guidelines before proceeding.
% They can be found at http://www.usask.ca/cgsr/thesis/index.htm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | {"hexsha": "32e4d9a48f2e86268bcb3d16341b2238034bc23b", "size": 11009, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "main.tex", "max_stars_repo_name": "kiranbandi/synvisio-thesis", "max_stars_repo_head_hexsha": "99a85c6c081c9c628a655eb9a9d841059f6f3e3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
import matplotlib.pyplot as plt
import sys
def extract_significant_data(input_file):
raw_table = np.genfromtxt(input_file, dtype=None, delimiter="\t",
encoding="UTF-8", usecols=(0, 3, 5))
polished_table = []
# print(raw_table)
for row in raw_table:
... | {"hexsha": "93d42295cb9c9629e222b69ecfed88e1e6de7a61", "size": 1815, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_processing/kraken/plot_single_sample_kraken_data.py", "max_stars_repo_name": "robymetallo/lferriphilum", "max_stars_repo_head_hexsha": "ab72a4e11bfd9b8947d1f2b2bb5fe1b852d313eb", "max_stars_r... |
""" A very simple FCFF NN intended to be used for comparing tensorflow to other
libraries. """
import tensorflow as tf
import numpy as np
class FeedforwardNetwork(object):
""" A simple, fully-connected feedforward neural network. """
def __init__(self, layers, outputs):
"""
Args:
layers: A list ... | {"hexsha": "31172cf09f43f6af000d5045b016f6e720d20257", "size": 4670, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/simple_feedforward.py", "max_stars_repo_name": "djpetti/rpinets", "max_stars_repo_head_hexsha": "8b6ebc969f3c75a0d0f5b414ed7faa7b65754892", "max_stars_repo_licenses": ["MIT"], "max_star... |
__precompile__(true)
module KernelDensityEstimate
using Gadfly, Colors, Cairo, Fontconfig
import Base: promote_rule, *, rand
export
kde!,
getPoints,
getBW,
root,
Npts,
Ndim,
getWeights,
marginal,
sample,
rand,
resample,
evaluateDualTree,
BallTree,
BallTreeDens... | {"hexsha": "65fe673d28aa72a5019507c4a4106d33f580e2b0", "size": 942, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/KernelDensityEstimate.jl", "max_stars_repo_name": "tkelman/KernelDensityEstimate.jl", "max_stars_repo_head_hexsha": "c44bdd9dcae8aa07a24f6f885c2ec8787a1dae10", "max_stars_repo_licenses": ["MIT"]... |
# Copyright 2019 Prashant Singh, Fredrik Wrede and Andreas Hellander
#
# 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 appl... | {"hexsha": "5d6af6cd0d94a697378f227de5e8ce0495c2a21b", "size": 7364, "ext": "py", "lang": "Python", "max_stars_repo_path": "sciope/tests/test_stochmet.py", "max_stars_repo_name": "sciope/sciope", "max_stars_repo_head_hexsha": "4da87ef9a1e1a5561286ce3eaffdb51183bf5c94", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
"""Correlation inferencer."""
import logging
import numpy as np
import sys
import scipy.sparse as ss
from scipy.special import betainc
from ..collections.graph import Graph
from .network_inferencer import NetworkInferencer
from ..utils.stats import CORRECTIONS_SIGNIFICANCE
logger = logging.getLogger(__name__.split('.'... | {"hexsha": "436dc4420ae48bf62ab9896620ce81d4fba8e7c8", "size": 2853, "ext": "py", "lang": "Python", "max_stars_repo_path": "cosifer/inferencers/correlation.py", "max_stars_repo_name": "C-nit/cosifer", "max_stars_repo_head_hexsha": "550b3ee1055bf1ceb8883ee8736c8d538ceb6ee4", "max_stars_repo_licenses": ["MIT"], "max_star... |
const RectilinearPointLoad{dim, T, N, M} = Union{PointLoadCantilever{dim, T, N, M}, HalfMBB{dim, T, N, M}, LBeam{T, N, M}}
@params struct ElementMatrix{T, TM <: AbstractMatrix{T}} <: AbstractMatrix{T}
matrix::TM
mask
meandiag::T
end
ElementMatrix(matrix, mask) = ElementMatrix(matrix, mask, sumdiag(matrix)/... | {"hexsha": "6591303f6d92d3325a1ce97c46bd4284db604908", "size": 13553, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TopOptProblems/matrices_and_vectors.jl", "max_stars_repo_name": "AlexanderBakerChris/TopOpt.jl", "max_stars_repo_head_hexsha": "eb9e8ee08f570a40d00eccef5c954353bffbad52", "max_stars_repo_licen... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import logging
import numpy as np
import nevergrad.common.typing as tp
from nevergrad.parametrization import pa... | {"hexsha": "0a6a76ff99417355a013d5f23dc21316984a26ad", "size": 10066, "ext": "py", "lang": "Python", "max_stars_repo_path": "nevergrad/optimization/multiobjective/nsga2.py", "max_stars_repo_name": "vishalbelsare/nevergrad", "max_stars_repo_head_hexsha": "f0fd681320609146e116322756cee9bf2388be9c", "max_stars_repo_licens... |
import numpy as np
from pymoo.algorithms.genetic_algorithm import GeneticAlgorithm
from pymoo.docs import parse_doc_string
from pymoo.model.survival import Survival
from pymoo.operators.crossover.simulated_binary_crossover import SimulatedBinaryCrossover
from pymoo.operators.mutation.polynomial_mutation import Polynom... | {"hexsha": "c3760ce3b65e8b6234e97b55c10e267df65c09be", "size": 3459, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymoo/algorithms/so_genetic_algorithm.py", "max_stars_repo_name": "gabicavalcante/pymoo", "max_stars_repo_head_hexsha": "1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846", "max_stars_repo_licenses": ["Apa... |
import numpy as np;
from .problem import Problem;
from ..utils.random import RandomGeneratable, RandomGenerator;
class TranslateProblem:
def __init__(self, problem_cls, spread= [100,None]):
self._problem = problem_cls;
self._spread = spread;
def random(self, random_state, dimension,**kwargs):... | {"hexsha": "f89895d1e5892265b73e5b8a9b3848a6c6f79cbe", "size": 1756, "ext": "py", "lang": "Python", "max_stars_repo_path": "learnedevolution/problems/translated.py", "max_stars_repo_name": "realtwister/LearnedEvolution", "max_stars_repo_head_hexsha": "2ec49b50a49acae9693cfb05ac114dfbcc4aa337", "max_stars_repo_licenses"... |
# ------------------------------------------------------------------------------
# @brief:
# ------------------------------------------------------------------------------
from .base_worker import base_worker
from mbbl.config import init_path
from mbbl.env.env_util import play_episode_with_env
from mbbl.util.common i... | {"hexsha": "42786ee4051e5c6dee1a647f277e1e88fa636d64", "size": 2866, "ext": "py", "lang": "Python", "max_stars_repo_path": "mbbl_envs/mbbl/worker/mf_worker.py", "max_stars_repo_name": "hbutsuak95/iv_rl", "max_stars_repo_head_hexsha": "0f72a8f077a238237027ea96b7d1160c35ac9959", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from gensim.models.word2vec import Word2Vec
from csv import reader
TRAIN_FILE = "SampleSetConditions.csv"
TEST_FILE = "TestSet.csv"
print("loading samples...",end="")
X, y = [], []
... | {"hexsha": "a695d5d94a966bf01c54070c6619f637afc8a574", "size": 2236, "ext": "py", "lang": "Python", "max_stars_repo_path": "script.py", "max_stars_repo_name": "christocs/MiningTenemantsSVM", "max_stars_repo_head_hexsha": "b7f239b5a2f9d1a4b326b477922998dd4aa416e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Copyright 2018 Cognibit Solutions LLP.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | {"hexsha": "3941703025fe2beb80c23167be7d3cc82ac99a96", "size": 3317, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/classification_report.py", "max_stars_repo_name": "cognibit/Text-Normalization-Demo", "max_stars_repo_head_hexsha": "36355f4a2c5187948fe786b7318259151f9a9db6", "max_stars_repo_licenses": ["Apa... |
# Imports
import pyperf as perf
# import icclim
import numpy as np
x = np.array(np.random.rand(1000))
P = np.linspace(0.01, 0.99, 50)
def bench_argsort():
# np.quantile(x, P)
x.argsort().argsort() / len(x)
def bench_quantile():
np.quantile(x, P)
# x.argsort.argsort()
runner = perf.Runner()
runner... | {"hexsha": "046d0c0aa5f5ad346722d7895d0af1c85a1c7469", "size": 422, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/quantile_bench.py", "max_stars_repo_name": "Ouranosinc/xclim-benchmark", "max_stars_repo_head_hexsha": "72fbb5db0cd29df6f263c536529fe815ac3cca48", "max_stars_repo_licenses": ["MIT"], "max_s... |
theory SINVAR_NoRefl_impl
imports SINVAR_NoRefl "../TopoS_Interface_impl"
begin
code_identifier code_module SINVAR_NoRefl_impl => (Scala) SINVAR_NoRefl
subsubsection \<open>SecurityInvariant NoRefl List Implementation\<close>
fun sinvar :: "'v list_graph \<Rightarrow> ('v \<Rightarrow> node_config) \<Rightarrow> b... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Network_Security_Policy_Verification/Security_Invariants/SINVA... |
from pathlib import Path
from shutil import copyfile
import pandas as pd
import numpy as np
import unicodedata
from haversine import haversine
import time
import ast
from sklearn.metrics import average_precision_score
import statistics
"""
Evaluate ranking for MAP
"""
def find_closest_distance(altname, gsco... | {"hexsha": "a513cfd6f1e3fcb42c94f81ec475b123d68ccc2f", "size": 10931, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/evaluation_functions.py", "max_stars_repo_name": "Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching", "max_stars_repo_head_hexsha": "41fd23288b49cbd96d2ddd4ec763606e70e430a7", "... |
import argparse
import numpy as np
from gym_duckietown.envs import DuckietownEnv
import torch
import os
import sys
import cv2
import math
sys.path.append(os.path.join(os.path.dirname(__file__), "./gym-duckietown/learning/"))
sys.path.append(os.path.join(os.path.dirname(__file__), "./gym-duckietown/learning/reinforceme... | {"hexsha": "93e30175e24963af9ee34dca42b94662342c77f6", "size": 6768, "ext": "py", "lang": "Python", "max_stars_repo_path": "our_policy.py", "max_stars_repo_name": "lyf44/CS4278-5478-Project-Materials", "max_stars_repo_head_hexsha": "685419c65847e72450e99586e9e0f3794369b4a3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 5 23:00:34 2017
@author: shenda
"""
from collections import Counter
import numpy as np
import pandas as pd
import MyEval
import ReadData
import dill
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from... | {"hexsha": "0f34f0b0963ca26e207e03f39bf32182889db138", "size": 4835, "ext": "py", "lang": "Python", "max_stars_repo_path": "references/encase/code/TestXGB_cut.py", "max_stars_repo_name": "wenh06/cinc2020", "max_stars_repo_head_hexsha": "b3757f54df86c8470e8f22f3399b4aecd64dd5d1", "max_stars_repo_licenses": ["BSD-2-Claus... |
# Licensed with the 3-clause BSD license. See LICENSE for details.
from typing import List
import pytest
import numpy as np
from astropy.table import Table
from astropy.time import Time
import astropy.units as u
from astropy.tests.helper import remote_data
from ..ephemeris import (get_ephemeris_generator, set_ephem... | {"hexsha": "581ba08e84e488b3aa25b0c1c13add67c75a45a0", "size": 5019, "ext": "py", "lang": "Python", "max_stars_repo_path": "sbsearch/test/test_ephemeris.py", "max_stars_repo_name": "mkelley/sbsearch", "max_stars_repo_head_hexsha": "7569e473cf0b8f5df3cfc7332ae4287a780d28d2", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import OpenGL
from OpenGL.GL import *
from OpenGL.GLU import *
import numpy
import random
from math_utils import *
def drawOrigin():
glBegin(GL_LINES)
glColor(1,0,0)
glVertex3f(0,0,0)
glVertex3f(0,1000,0)
glColor(0,1,0)
glVertex3f(0,0,0)
glVertex3f(1000,0,0)
glColor(0,0,1)
glVertex... | {"hexsha": "1ab518a5c81a417a3b41fe367b304e866a25a77a", "size": 10009, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphics.py", "max_stars_repo_name": "arda-guler/miniLanding3D", "max_stars_repo_head_hexsha": "83c4884378e0f57853a1fb09b1fdbe36e15bacd7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
[STATEMENT]
lemma real_binomial_eq_mult_binomial_Suc:
assumes "k \<le> n"
shows "real(n choose k) = (n + 1 - k) / (n + 1) * (Suc n choose k)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. real (n choose k) = (real n + 1 - real k) / (real n + 1) * real (Suc n choose k)
[PROOF STEP]
using assms
[PROOF STATE]
proo... | {"llama_tokens": 249, "file": null, "length": 2} |
"""
Program to get average editing frequency from CrispEsso.
"""
import matplotlib
import pandas as pd
matplotlib.use('agg')
import matplotlib.pyplot as plt
import seaborn as sns
import string
import glob
import numpy as np
def revcomp(seq):
try: ## python2
tab = string.maketrans(b"ACTG", b"TG... | {"hexsha": "c9651f42d8f1d1f4b02e455062a477334640bb22", "size": 1848, "ext": "py", "lang": "Python", "max_stars_repo_path": "per_A_base_score/editing_frequency/average_model/get_average_frequency.py", "max_stars_repo_name": "YichaoOU/ABE_NonCoding_functional_score", "max_stars_repo_head_hexsha": "cef8dbbd74e5f9359feb6cf... |
From MetaCoq.Lob.Template.QuoteGround Require Export config utils Ast AstUtils Environment Primitive
LiftSubst UnivSubst EnvironmentTyping Reflect ReflectAst TermEquality WfAst.
From MetaCoq.Template Require Import Ast Typing.
#[export] Instance quote_isSort {T} : ground_quotable (isSort T) := ltac:(cbv [isSort];... | {"author": "JasonGross", "repo": "metacoq-lob", "sha": "acfc938eb79cac82c3c7d306f6d7010a4ad6492e", "save_path": "github-repos/coq/JasonGross-metacoq-lob", "path": "github-repos/coq/JasonGross-metacoq-lob/metacoq-lob-acfc938eb79cac82c3c7d306f6d7010a4ad6492e/theories/Template/QuoteGround/Typing.v"} |
import os
from pathlib import Path
from tqdm import tqdm
import numpy as np
import cv2
from PIL import Image
import torch
import torchvision as tv
from mycv.paths import IMAGENET_DIR
from mycv.utils.general import ANSI
from mycv.datasets.imcls import imcls_evaluate, get_input_normalization, get_tv_interpolation
def ... | {"hexsha": "c6c959d99b70cb24728fef6fab2f3fca740764e5", "size": 8448, "ext": "py", "lang": "Python", "max_stars_repo_path": "mycv/datasets/imagenet.py", "max_stars_repo_name": "duanzhiihao/mycv", "max_stars_repo_head_hexsha": "184b52f7a5c1b6f603122d4f4050952b65ba0ead", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Hack. :)
import site; site.addsitedir("/usr/local/lib/python2.7/site-packages")
# Standard imports
import cv2;
import numpy as np;
# print cv2.__version__;
# Read image
im = cv2.imread("test-images/still2.jpg", cv2.IMREAD_GRAYSCALE)
im = cv2.bitwise_not(im)
# Setup SimpleBlobDetector parameters.
params = cv2.Simp... | {"hexsha": "d18ddab76e208621ca79c38c5b1338421cf1433e", "size": 1150, "ext": "py", "lang": "Python", "max_stars_repo_path": "Trial1.py", "max_stars_repo_name": "aliceyoung9/bubblewrap", "max_stars_repo_head_hexsha": "46066605f114f07bd78efec0740ff2b6c5b83189", "max_stars_repo_licenses": ["WTFPL"], "max_stars_count": null... |
module LPA
using Graphs
export nsdlpa, nsdlpa1, nmi, voi, modularity, avedegree, similarity, triangle, quadrangle, triquadrsim
include("labelpropagation.jl")
include("modularity.jl")
include("nmi.jl")
include("voi.jl")
include("utils.jl")
include("rankedge.jl")
end # module
| {"hexsha": "a5a38884d55d75b5b31cb54a793bd8e29dbd6138", "size": 278, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LPA.jl", "max_stars_repo_name": "afternone/LPA", "max_stars_repo_head_hexsha": "deefee07b004e26e1bd6a35419c43b5cf0a7b775", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
import numpy as np
import cv2
from ndu_gate_camera.api.video_source import VideoSource, log
from ndu_gate_camera.utility.ndu_utility import NDUUtility
# try:
# from picamera import PiCamera
# except ImportError:
# print("picamera library not found - installing...")
# if NDUUtility.install_package("picamer... | {"hexsha": "7fe45144fe04dea28cf17307131124cf42f50096", "size": 2143, "ext": "py", "lang": "Python", "max_stars_repo_path": "ndu_gate_camera/camera/video_sources/pi_camera_video_source.py", "max_stars_repo_name": "netcadlabs/ndu-gate", "max_stars_repo_head_hexsha": "f479c293284fa6582d8682c98abf88e3da33b406", "max_stars_... |
import collections
import torch
from torch.autograd import Variable
import numpy as np
NUMPY_RANDOM_STATE = np.random.RandomState()
def try_keys(input_dict, keys):
for k in keys:
try:
return input_dict[k]
except BaseException:
pass
return None
def ... | {"hexsha": "8f7f90ec90abf16de6479718c78a1a319be501f4", "size": 4679, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch_metric_learning/utils/common_functions.py", "max_stars_repo_name": "jacobdanovitch/pytorch_metric_learning", "max_stars_repo_head_hexsha": "dbcf2d49fffe92f7dc1221b939e182c214633520", "max_... |
#include <boost/test/unit_test.hpp>
#include <boost/concept_check.hpp>
#include "che/atom.h" // header to test
using namespace biosim;
BOOST_AUTO_TEST_SUITE(suite_atom)
BOOST_AUTO_TEST_CASE(atom_ctor) {
che::atom a;
BOOST_CHECK(a.get_identifier().empty());
BOOST_CHECK(a.get_position() == math::point());
st... | {"hexsha": "ff479e49c15d165d74e897b6adff5b1f0d3d4a5c", "size": 769, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/che/atom.cpp", "max_stars_repo_name": "shze/biosim", "max_stars_repo_head_hexsha": "e9e6d97de0ccf8067e1db15980eb600389fff6ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
from automon import SlackType, SyncType, AutomonCoordinator, RlvCoordinator
def _get_node(NodeClass, domain, d, node_idx, func_to_monitor, max_f_val=np.inf, min_f_val=-np.inf):
if max_f_val != np.inf or min_f_val != -np.inf:
node = NodeClass(idx=node_idx, d=d, domain=domain, func_to_mon... | {"hexsha": "5a5dd17a833456d25a0e3789c4a52982a51eb0e5", "size": 2112, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_utils/object_factory.py", "max_stars_repo_name": "hsivan/automon", "max_stars_repo_head_hexsha": "222b17651533bdb2abce7de36a80156ab7b9cc21", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of cond... | {"hexsha": "e61edba00e3cb50664b7e0127c25dc7e90563c38", "size": 7821, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/model/arithmetic.hpp", "max_stars_repo_name": "rbshi/timeloop", "max_stars_repo_head_hexsha": "434d15e85e7ec95a2b87f83fbf0f14885dc7fa75", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
"""
extract_col_feats(df, cols)
find mean, std, minimum, maximum in df[!, col]
default value of columns are all numeric columns except date
"""
function extract_col_statvals(df::DataFrame, cols::Array{Symbol, 1})
syms = []
types = []
vals = []
for col in cols
μ, σ = mean_and_std(skipmissing... | {"hexsha": "281d1c3c6caf7b6c27e889d97951695ecebcbdb0", "size": 5400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "appleparan/Mise.jl", "max_stars_repo_head_hexsha": "2b4d3d6012d830ac37edf60e276fb47d8bc3a493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
classdef ConstantDetectionProbabilityX < DetectionModelX
% ConstantDetectionProbabilityX class
%
% Summary of ConstantDetectionProbabilityX
% This is a class implementation of a detection model, described by a Poisson
% distributed false alarm rate and a Uniform spatial distribution.
%
% ConstantDetectionProbabilityX P... | {"author": "sglvladi", "repo": "TrackingX", "sha": "f737445c070f0d7d470f52f8a2b5540d5bb682da", "save_path": "github-repos/MATLAB/sglvladi-TrackingX", "path": "github-repos/MATLAB/sglvladi-TrackingX/TrackingX-f737445c070f0d7d470f52f8a2b5540d5bb682da/Models/Detection/ConstantDetectionProbabilityX/ConstantDetectionProbabi... |
using LinearAlgebra
using DynamicPolynomials
using SwitchOnSafety
using Combinatorics
using SparseArrays
using JuMP, Ipopt, MosekTools,NLopt
using SpecialFunctions
include("../src/RandomTrajectories.jl")
include("../src/AlgebraicLift.jl")
include("../src/ScenarioOpti.jl")
include("../src/Probabilistic... | {"hexsha": "9b3f2be00a65f03894dbd0114489b490b3c3c01a", "size": 10701, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/QuadvsSOSexample.jl", "max_stars_repo_name": "zhemingwang/DataDrivenSwitchControl", "max_stars_repo_head_hexsha": "2bb43ad448d77d52a8c1633a225549c3160f2eb7", "max_stars_repo_licenses": ["... |
"""The WaveBlocks Project
Plot the evolution of the relations between the parameters P and Q
homogeneous or inhomogeneous Hagedorn wavepacket during the
time propagation.
@author: R. Bourquin
@copyright: Copyright (C) 2010, 2011 R. Bourquin
@license: Modified BSD License
"""
import sys
from numpy import conj, abs
fr... | {"hexsha": "51b56ab06a3c4ea38a073f2ed8990e556628d499", "size": 2944, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plotters_simple/PlotPQRelation.py", "max_stars_repo_name": "WaveBlocks/WaveBlocks", "max_stars_repo_head_hexsha": "2af3730dcf27e54006ec602e696b4d4df25459d8", "max_stars_repo_licenses": ["BSD-3... |
import time
from typing import Callable, Union
import numpy as np
from .utils import time_fn
from .stopping_reason import StoppingReason
class Settings:
def __init__(self,
n_max_iterations=50,
damping_constant=0.0,
loss_stop_threshold=0.0,
grad_... | {"hexsha": "b898162c53987c297a15ddd28e30703408c68cc0", "size": 4168, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygauss_newton/gauss_newton.py", "max_stars_repo_name": "Daiver/pygauss_newton", "max_stars_repo_head_hexsha": "63dd741c6edaeb2891842dc4d0b714412b30a04c", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Tests sklearn Imputers: MissingIndicator and SimpleImputer
"""
import unittest
import warnings
import numpy as np
import torch
from sklearn.impute import MissingIndicator, SimpleImputer
try:
from sklearn.preprocessing import Imputer
except ImportError:
# Imputer was deprecate in sklearn >= 0.22
Imput... | {"hexsha": "d4e049e4c8eb759332ccca716aa561151db7386a", "size": 5777, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_sklearn_imputer_converter.py", "max_stars_repo_name": "vumichien/hummingbird", "max_stars_repo_head_hexsha": "8981e11ce2536167c329a5d9d20e81125a792fe4", "max_stars_repo_licenses": ["MIT... |
using Test
using Unitful: m, s, cm
using UnitfulRecipes: recipe!, UnitFormatter
import RecipesBase
Attributes = Dict{Symbol, Any}
@testset "One Array" begin
attr = Attributes()
ys_val = [1, 2.3]
ys = ys_val * m
ys_ret = recipe!(attr, ys)
@test ys_ret ≈ ys_val
@test attr[:yformatter] == UnitForm... | {"hexsha": "05d8ada9198fae44160d9ac24bd99ec25f8fdb8c", "size": 1537, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/UnitfulRecipes.jl-42071c24-d89e-48dd-8a24-8a12d9b8861f", "max_stars_repo_head_hexsha": "d7d1c530c2b5a60a7b0acad23c25445324d45dbb", "... |
import torch
import scipy
from torch_geometric.utils import add_self_loops
from torch_scatter import scatter_add
############################# Our model
def get_directed_adj(edge_index, num_nodes, dtype, edge_weight=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1),), dtype=dtype... | {"hexsha": "585f823bfdb591a7a40e49a20de9e52e5e175584", "size": 8357, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/get_adj.py", "max_stars_repo_name": "dfuttu1/AGNN", "max_stars_repo_head_hexsha": "579a28388ba3e28d3382ef71c4ab089bedb4705a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
""" Object to process a single raw image"""
import inspect
import numpy as np
from pypeit import msgs
from pypeit.core import procimg
from pypeit.core import flat
from pypeit.images import pypeitimage
from pypeit.par import pypeitpar
from IPython import embed
class ProcessRawImage(pypeitimage.PypeItImage):
""... | {"hexsha": "ec2c731d8c13c2458cd2bbc3c02aecc1c4970fa1", "size": 11177, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypeit/images/processrawimage.py", "max_stars_repo_name": "seib2/PypeIt", "max_stars_repo_head_hexsha": "18ce33aa8aa12b8ee51303ad87a723ec81e0e6f8", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
from coopihc.base.StateElement import StateElement
from coopihc.base.utils import (
StateNotContainedError,
StateNotContainedWarning,
)
from coopihc.base.elements import integer_set, box_space
import numpy
import pytest
import json
import copy
from tabulate import tabulate
def test_array_init_integer():
... | {"hexsha": "56d7927077bc5c01fd5071b79c41451ae9231fc8", "size": 22653, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/components/base/stateelement/test_statelement.py", "max_stars_repo_name": "jgori-ouistiti/CoopIHC", "max_stars_repo_head_hexsha": "0fe24c618a430517c1394625275faff3ce344f7f", "max_stars_repo_... |
import matplotlib.pyplot as plt
from numpy import matlib
from scipy.sparse.linalg import svds
import numpy as np
from scipy.sparse import csc_matrix, lil_matrix
import multiprocessing as mp
m, n = 3, 10
l = int(1.0 * m * n * m * n)
dist = np.random.normal
dist_par = (0.0, 1.0 / np.sqrt(m * n))
w_size = (m * n, m * n... | {"hexsha": "5f0f0eb95d202642e5a2da5a0b987c9194047181", "size": 3322, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/scripts/2main.py", "max_stars_repo_name": "joepatmckenna/fem", "max_stars_repo_head_hexsha": "18b3e3cb0b83f7a4eb464c84f09f00673ea2fcb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path as op
import sys
import re
import logging
from astropy.table import Table, Column
from maize.apps.base import AttrDict, str2bool, eprint, sh, mkdir, which
from maize.formats.base import must_open
from maize.formats.pbs import PbsJob, create_job_ch... | {"hexsha": "4d0d63e284dc6afaf8bb5b791110dc92a4efc766", "size": 7532, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/pipelines/dnaseq.py", "max_stars_repo_name": "orionzhou/biolib", "max_stars_repo_head_hexsha": "940fb66f1b2608d34a2d00ebdf41dc84c6381f42", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
import uuid
import nibabel as nb
import numpy as np
import pytest
from ..nibabel import MergeROIs
@pytest.fixture
def create_roi(tmp_path):
files = []
def _create_roi(affine, img_data, roi_index):
img_data[tuple(roi_index)] = 1
nii = nb.Nifti1Image(img_data, affine)
filename = tmp_p... | {"hexsha": "4b40d38420e7fc6d934cdb8a1bfe784b4b11749f", "size": 1824, "ext": "py", "lang": "Python", "max_stars_repo_path": "nibabies/interfaces/tests/test_nibabel.py", "max_stars_repo_name": "nipreps/nibabies", "max_stars_repo_head_hexsha": "55143215eaebca4924cefdc74d8008a347efea16", "max_stars_repo_licenses": ["Apache... |
"""Train the model"""
import argparse
import os
import tensorflow as tf
from model.input_fn import train_input_fn
from model.input_fn import test_input_fn
from model.model_fn import TripletLoss
from model.utils import Params
import random
from tqdm import tqdm
from numpy import savez_compressed
import model.multi_mo... | {"hexsha": "3b42a03acfa7cfb4cfa43a94235ae6735a77703e", "size": 5662, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "meryemmhamdi1/tensorflow-triplet-loss", "max_stars_repo_head_hexsha": "9c40ad45fddfb8d2b955faf2973385f6985f72aa", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#
from typing import List
import itertools as it
import difflib
import numpy as np
def to_string_seq(tour: List[int]) -> str:
"""Convert tour to a string sequence."""
return ' '.join(str(e) for e in tour)
def plan_to_string_seq(plan: List[List[int]]) -> str:
"""Convert tour plan represented as list of l... | {"hexsha": "fa52c36757218242abdfc44511cf416e5fb4662e", "size": 3580, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/utils/seq_match.py", "max_stars_repo_name": "jokofa/JAMPR_plus", "max_stars_repo_head_hexsha": "6500c7ef36e7aad5d00b6c7cf98266b8a1979955", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma perp_per_2:
assumes "A B Perp A C"
shows "Per B A C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Per B A C
[PROOF STEP]
by (simp add: Perp_perm assms perp_per_1) | {"llama_tokens": 88, "file": "IsaGeoCoq_Tarski_Neutral", "length": 1} |
import os
import torch
import torch.nn as nn
import numpy as np
from tensorboardX import SummaryWriter
import util
from config import Configuration
from dataset import Dataset
from models import ENAS, FPN #import ENAScontroller, ENAStrainer
def load_controller_and_trainer(args, logger, data):
if args.name == 'ENAS... | {"hexsha": "ebc917971beeb7af4830457061447e85d974d7a9", "size": 990, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "imhgchoi/Architecture-Search", "max_stars_repo_head_hexsha": "38027d91fc8928b5c5a6ccde0013e571527a5293", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma ereal_MInf_plus[simp]: "-\<infinity> + x = (if x = \<infinity> then \<infinity> else -\<infinity>::ereal)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - \<infinity> + x = (if x = \<infinity> then \<infinity> else - \<infinity>)
[PROOF STEP]
by simp | {"llama_tokens": 117, "file": "Lower_Semicontinuous_Lower_Semicontinuous", "length": 1} |
'''
Generating music
'''
import pickle
import numpy as np
from music21 import instrument,note, stream, chord
from keras.models import Sequential
from keras.layers import Activation, BatchNormalization, Dense, Dropout, LSTM
def generate():
with open('misc/notes','rb') as filepath:
notes = pickle.load(fi... | {"hexsha": "e45b16a1e2dda3412dbd04087d0502d76ce86bae", "size": 662, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate.py", "max_stars_repo_name": "7wikd/Jazz-LSTM", "max_stars_repo_head_hexsha": "9510ef1b4f427fde68d2afa8d72bcb7b4d96ad61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
import igraph as ig
import matplotlib.pyplot as plt
from collections import defaultdict
error = 0.0001 # constante utilizada como limite para considerar dois valores float como iguais
class Point:
"""
Representação de um ponto com coordenadas x, y.
Alguns métodos foram... | {"hexsha": "76303996b5600869db689522c5d3412ad3fad62f", "size": 5702, "ext": "py", "lang": "Python", "max_stars_repo_path": "instance_generation.py", "max_stars_repo_name": "carolmb/ia_part1", "max_stars_repo_head_hexsha": "95b94e6570834cb60703b1d79180c21ab218b52d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
Yolo Property Management is a residential property management company that manages over 300 units across Davis. Their most well known communities include Aspen Village, Glacier Point and Saratoga West Apartments located in West Davis. Yolo Property Management has recently partnered with the University to create Grad... | {"hexsha": "5c8a31d05827a66a5b67a85cb470475cf0703ecf", "size": 930, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Yolo_Property_Management.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_... |
# Copyright 2017 Hugh Salimbeni
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | {"hexsha": "a16ea6150ea9619cadbcbfa4a67dae69369ed67f", "size": 22808, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/uci_exps/bayesian_benchmarks/data.py", "max_stars_repo_name": "b4thesunrise/drbayes", "max_stars_repo_head_hexsha": "9bc827aea2c7f084fb1ee77a4bd9f3c9726ecf8c", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------------
#
# TITLE :
# AUTHOR :
# PROJECT :
#
# ----------------------------------------------------------------------------
# Docstring
"""GC Orbit Solution Script.
Convert the sky coordinates, distances, mean PM and li... | {"hexsha": "5ac7d7104956548987c97732b8b6831ed8aebdca", "size": 10825, "ext": "py", "lang": "Python", "max_stars_repo_path": "jas1101finalproject/scripts/get_globular_clusters/run_orbits.py", "max_stars_repo_name": "nstarman/jas1101_project", "max_stars_repo_head_hexsha": "f54620b715eb2f7dbe7bd39d4a1e21e50bc06541", "max... |
import numpy as np
import pytest
from ermaket.utils import Singleton
_created = 0
class Dummy(metaclass=Singleton):
def __init__(self):
global _created
_created += 1
self.info = np.random.random()
def test_create():
global _created
_created = 0
a = Dummy()
b = Dummy()
... | {"hexsha": "b9eeb3a88fc6a0854872d822b27c34a4b6c45cd4", "size": 846, "ext": "py", "lang": "Python", "max_stars_repo_path": "ermaket/tests/test_singleton.py", "max_stars_repo_name": "SqrtMinusOne/ERMaket_Experiment", "max_stars_repo_head_hexsha": "c4a7b61651edd15a619d9b690e2aaeaab4de282d", "max_stars_repo_licenses": ["Ap... |
[STATEMENT]
lemma I_def'_rl': "Der_1b \<D> \<Longrightarrow> \<forall>A p. (\<I> A p) \<longleftarrow> (\<exists>E. (\<I> E p) \<and> E \<^bold>\<preceq> A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Der_1b \<D> \<Longrightarrow> \<forall>A. contains (\<I> A) (\<lambda>p. nonEmpty (\<lambda>E. \<I> E p \<and> c... | {"llama_tokens": 297, "file": "Topological_Semantics_topo_derivative_algebra", "length": 2} |
import os
from gtts import gTTS
import numpy as np
def to_speech(text):
tts = gTTS(text=text, lang = 'en')
return tts
def save(tts, filename):
tts.save(filename + '.mp3')
data = {'A':158, 'E':9307, 'M':1318, 'R':576, 'T':637, 'N':5707}
labels = ['A','E','M','R','T','N']
values = [158, 9307, 1318, 576, 637, 570... | {"hexsha": "994d9143b80f597bf70c1d9d9b23540a35923855", "size": 866, "ext": "py", "lang": "Python", "max_stars_repo_path": "to_speech.py", "max_stars_repo_name": "10DarkShadow01/Emotional-Intelligence-alpha-", "max_stars_repo_head_hexsha": "60542236f02c7955a786401e00a24b1a2853532d", "max_stars_repo_licenses": ["Apache-2... |
# SPDX-FileCopyrightText: 2021 Division of Intelligent Medical Systems, DKFZ
# SPDX-FileCopyrightText: 2021 Janek Groehl
# SPDX-License-Identifier: MIT
import os
import numpy as np
import pandas as pd
def read_rxt_file(file_path: str) -> (np.ndarray, np.ndarray, np.ndarray, float):
if not os.path.exists(file_pat... | {"hexsha": "50bac01b0515507a676e5b3ac3d4cb506185bb02", "size": 1254, "ext": "py", "lang": "Python", "max_stars_repo_path": "simpa_tests/manual_tests/test_with_experimental_measurements/utils.py", "max_stars_repo_name": "IMSY-DKFZ/simpa", "max_stars_repo_head_hexsha": "b8bddcf43a4bff2564f0ec208dc511b82e49bfb4", "max_sta... |
#! /usr/bin/python
# -*- coding: utf8 -*-
import struct
import os
import StringIO
import tempfile
import numpy as np
#from scipy.signal import butter
from eegpy.formats.iobase import (MemoryMappedBinaryDataFile,
EEGfiltered)
fmtF32header = "= 21p 7p i i H i d d d 13p i H f f 931p"
#Erklaerung
# =... | {"hexsha": "698ae822015eca40ba0b4257f4199d2b7cb0b707", "size": 9607, "ext": "py", "lang": "Python", "max_stars_repo_path": "eegpy/formats/f32.py", "max_stars_repo_name": "thorstenkranz/eegpy", "max_stars_repo_head_hexsha": "0f9461456999874abbb774896ca832eb27740a9d", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], ... |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 19 18:05:04 2017
@author: David Jarron
"""
# !/usr/bin/python
import cv2
import numpy as np
import matplotlib.pyplot as plt
#import hough_line_linker as hll
img = cv2.imread('IMG_3380.JPG')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
height, width = gr... | {"hexsha": "9578e0025ee2ec5268d1fd092a3474b3145f85be", "size": 2870, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencvtestfile.py", "max_stars_repo_name": "dmjarron/2D_3D", "max_stars_repo_head_hexsha": "5da8760b2df751f19c04fc2c1eeeeb8e6ad73e67", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
import geopandas as gpd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
stations_list = ['SJOSC120',
'SJOSC119',
'SJOSC118',
'SJOSC117',
'SJOSC116',
'SJOSC115',
'SJO SC29',
'SJ... | {"hexsha": "66ef2ddb5eb06a2d490516b59104f5e0e03b0791", "size": 3800, "ext": "py", "lang": "Python", "max_stars_repo_path": "task_scripts/task2.py", "max_stars_repo_name": "collincr/ini_team_13", "max_stars_repo_head_hexsha": "dca3d88fc31515ec0127cfd03963ce0b5ed735d8", "max_stars_repo_licenses": ["Unlicense"], "max_star... |
"""
Modified From https://github.com/OpenNMT/OpenNMT-tf/blob/r1/examples/library/minimal_transformer_training.py
MIT License
Copyright (c) 2017-present The OpenNMT Authors.
This example demonstrates how to train a standard Transformer model using
OpenNMT-tf as a library in about 200 lines of code. While relatively s... | {"hexsha": "5e2f1137ea89458380c94e9002f795712109dcc6", "size": 22291, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tensorflow/decoding/translate_example.py", "max_stars_repo_name": "hieuhoang/FasterTransformer", "max_stars_repo_head_hexsha": "440695ccac874574b1d2e1121788e8fa674b4381", "max_stars_repo... |
import numpy as np
import pandas as pd
import argparse
def load_csv(data_dir, filename):
"""
Loades a pandas DataFrame df inside a data_dir folder with a filename.csv extension
Robust for all OS because of pathlib module
"""
import pandas as pd
from pathlib import Path
return pd.read_csv(Pa... | {"hexsha": "e8c19df01ed6e09ad1639d9045308af6b66156b1", "size": 3557, "ext": "py", "lang": "Python", "max_stars_repo_path": "tweet_classifier.py", "max_stars_repo_name": "codatmo/Brazil-Tweet-Classifier", "max_stars_repo_head_hexsha": "5aab126323df748e15189dd67f7e39a6ed3e678d", "max_stars_repo_licenses": ["BSD-3-Clause"... |
No push, lets try to reduce deprecated support in this.
Keep NEEDS just in case.
--- boost/system/config.hpp.orig 2016-09-21 17:33:27.000000000 +0300
+++ boost/system/config.hpp
@@ -10,6 +10,12 @@
#ifndef BOOST_SYSTEM_CONFIG_HPP
#define BOOST_SYSTEM_CONFIG_HPP
+#if defined(__DragonFly__) && !defi... | {"hexsha": "690b7684d710d1fc7089efdf003db66f4a00afa4", "size": 595, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ports/devel/boost-libs/dragonfly/patch-boost_system_config.hpp", "max_stars_repo_name": "liweitianux/DeltaPorts", "max_stars_repo_head_hexsha": "b907de0ceb9c0e46ae8961896e97b361aa7c62c0", "max_stars_... |
"""
Print out a length distribution for used WAV files.
TODO: This module is not updated to the current TXT to CSV changes.
"""
import os
import pickle
import sys
from multiprocessing import Pool, Lock, cpu_count
import numpy as np
from scipy.io import wavfile
from tqdm import tqdm
from asr.params import MIN_EXAMPL... | {"hexsha": "69ef2841d8c99da3b389749e15e48d9403af6284", "size": 5356, "ext": "py", "lang": "Python", "max_stars_repo_path": "asr/dataset/wav_lengths.py", "max_stars_repo_name": "aflyingwolf/ctc-asr", "max_stars_repo_head_hexsha": "6f1a17366b942c1a70230e170da4d0ae15fa52da", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
include("../src/nn.jl")
using Test
Random.seed!(1)
"""
charge distribution -> potential (Poisson's eqn), electric field (Gauss's law)
"""
# input Scalar field
inranks = [0]
# output scalar field, vector field
outranks = [0, 1]
sz=(8,8,8)
dx = 0.1
dV=dx^3
rmax = 2dx
lmax = 1
# charge distribution
x = [zeros(sz...)]... | {"hexsha": "57cfffa3198336379f7baf305a8af3edccae577b", "size": 1625, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "aced-differentiate/equivariant-operators-website", "max_stars_repo_head_hexsha": "62d65317d0f96c6fc8490a22d76ef307e585e972", "max_stars_repo_licenses": ["... |
import pandas as pd
import panel as pn
import numpy as np
from .config import filters, datastyles, categories, plot_data_path
def get_numbers(df, filt, datastyle):
if df is None:
return []
numbers = list(df.query(f'filt == "{filt}" and datastyle == "{datastyle}"').number.unique())
numbers.sort()... | {"hexsha": "237c5b0dbdea2130fb115090f6aab9dff8694caf", "size": 1003, "ext": "py", "lang": "Python", "max_stars_repo_path": "navigator/gui.py", "max_stars_repo_name": "timothydmorton/pipe-analysis-navigator", "max_stars_repo_head_hexsha": "85b05053324f693386a0c12b292ab38d452a01db", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from flask import Flask
from flask import render_template,request
from sklearn.externals import joblib
from forms import CarForm
from config import Config
app = Flask("Car Price Prediction")
app.config.from_object(Config)
@app.route("/", methods=["GET"])
def home():
car_form = CarForm()
i... | {"hexsha": "5295b32d2293fe5eca60d4e39c1e0332034ecca2", "size": 1928, "ext": "py", "lang": "Python", "max_stars_repo_path": "Day 17/src/app.py", "max_stars_repo_name": "vgaurav3011/100-Days-of-ML", "max_stars_repo_head_hexsha": "ec302b03fd492c459cff2592b3a4f5e38f9c9d72", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import matplotlib.pyplot as plt
from imageio import imwrite
import matplotlib.patches as patches
from sklearn.cluster import DBSCAN
import json
from PIL import Image
def read_json_coords(label):
'''Read a json file containing bounding boxes into coordinate arrays '''
coords, centres = [], []... | {"hexsha": "ad7cbdd00daf97d63c4c09e57caa08f371c266ec", "size": 4422, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/preprocessing/aerialdataprocessing.py", "max_stars_repo_name": "KMacfarlaneGreen/mres_whales", "max_stars_repo_head_hexsha": "11b96a824d9d302afbcb192d24f5087e77721a30", "max_stars_repo_license... |
import warnings
warnings.simplefilter(action='ignore',category=FutureWarning)
import cv2 ## openCV
import os
import numpy as np
import matplotlib.pyplot as plt
import operator
from IPython.display import Markdown, display
def printmd(string, color=None):
colorstr = "<span style='color:{}'>{}</span>".format(color,... | {"hexsha": "6ebf67c7c073223c5e6799beaef1c7cad9d142a1", "size": 8293, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper_module.py", "max_stars_repo_name": "sungsujaing/Insight_workshop", "max_stars_repo_head_hexsha": "a951903794cc37fb21d1228a38a3ad6798a7987f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""Tasks for generating Sample Similarity results."""
import numpy as np
from sklearn.manifold import TSNE
from app.extensions import celery
from app.display_modules.utils import persist_result_helper, scrub_category_val
from app.tool_results.kraken import KrakenResultModule
from app.tool_results.krakenhll import Kra... | {"hexsha": "9242b175e301bf9df4123201f6c613fd105d8e78", "size": 6071, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/display_modules/sample_similarity/tasks.py", "max_stars_repo_name": "MetaGenScope/metagenscope-server", "max_stars_repo_head_hexsha": "609cd57c626c857c8efde8237a1f22f4d1e6065d", "max_stars_rep... |
from __future__ import print_function
import time
import sys
from io import StringIO
import os
import shutil
import argparse
import csv
import json
import numpy as np
import pandas as pd
import logging
from sklearn.compose import ColumnTransformer
from sklearn.externals import joblib
from sklearn.impute import Simp... | {"hexsha": "4610395b3205173ac6dd5ae978bde489e6cbbe2c", "size": 9424, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "gonsoomoon-ml/churn-prediction-workshop2", "max_stars_repo_head_hexsha": "54d80cd3df42868ec26e5b28f9a15fd32ae80a3e", "max_stars_repo_licenses": ["MIT"], ... |
################################################################################
# Script: sp.py
# Description: This script is for preparing all the fields for sample points
# All the cities should run this script first to get the pre-prepared sample points
# before running the aggregation.
# Two major outputs:
# 1. a... | {"hexsha": "40588f1859fe952e0d5af42b67d7ec702bd69af7", "size": 13778, "ext": "py", "lang": "Python", "max_stars_repo_path": "process/sp.py", "max_stars_repo_name": "carlhiggs/global-indicators", "max_stars_repo_head_hexsha": "c2973aaf372fde3ae2290ad75f0766f6263a4e18", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from SkateUtils.KeyPoseState import State
import numpy as np
import pydart2 as pydart
import pickle
if __name__ == '__main__':
pydart.init()
world = pydart.World(1./1200., '../data/skel/skater_3dof_with_ground.skel')
skel = world.skeletons[1]
pelvis_pos_y = skel.dof_indices(["j_pelvis_pos_y"])
pe... | {"hexsha": "c551df35159f9b0c82577d91165fdbe764a29441", "size": 3146, "ext": "py", "lang": "Python", "max_stars_repo_path": "swizzle/make_skate_keyframe.py", "max_stars_repo_name": "snumrl/skate", "max_stars_repo_head_hexsha": "a57ec2dc81dc2502da8886b92b870d2c8d65b838", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy.testing as npt
from cvdm.score import fremantle, Fremantle
def test_fremantle():
tmp = fremantle(59, True, True, 8, 0.92, 0.79, True, False)
npt.assert_almost_equal(tmp, 0.062, decimal=3)
def test_fremantle_json():
fr = Fremantle()
tmp = fr.score({"index_age": 59,
... | {"hexsha": "77f00e3a539aa81f026dfacc448f5c9db7dba2b4", "size": 624, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvdm/score/tests/test_fremantle.py", "max_stars_repo_name": "joyceho/cvdm", "max_stars_repo_head_hexsha": "df386290221fd1388bef06104db0dd07978f91d9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
// This file is part of libigl, a simple c++ geometry processing library.
//
// Copyright (C) 2014 Daniele Panozzo <daniele.panozzo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla Public License
// v. 2.0. If a copy of the MPL was not distributed with this file, You can
// obtain one at ht... | {"hexsha": "4f9a657ca510983950ca9f27d025dfdc11a8215e", "size": 13443, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "igl/opengl/ViewerCore.cpp", "max_stars_repo_name": "chenhadad/EngineI_GL_new_Final", "max_stars_repo_head_hexsha": "31fd37c617a6d82117e36676786bac8c0f04c278", "max_stars_repo_licenses": ["Apache-2.... |
'''Uses pushshift to pull data from farther back than Reddit allows us to go'''
import sys
import requests
import numpy as np
from datetime import datetime as dt
from dateutil import tz
joke_file_base = 'data_%%%%.csv'
record_file_base = 'data_%%%%.txt'
base_URL = 'https://api.pushshift.io/reddit/submission/search/?... | {"hexsha": "8414094985ec435ee2afb60a91886c207d27e867", "size": 4729, "ext": "py", "lang": "Python", "max_stars_repo_path": "subreddits/pushshift.py", "max_stars_repo_name": "jojordan3/dad-joke-ai", "max_stars_repo_head_hexsha": "a5e955fc78807e8810256d3fb758d3b3a089c136", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
import numpy as np
import de421
from time import time
from jplephem import Ephemeris
from jplephem.spk import SPK
def main():
for size in 10, 1000, 100000:
jd = np.linspace(2414992.5, 2471184.50, size)
kernel = SPK.open('de421.bsp')
ephem = Ephemeris(de421)
ma... | {"hexsha": "9361df73e58a22f40f9e9ef28bcfae07500f1a80", "size": 906, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/benchmark.py", "max_stars_repo_name": "jayvdb/python-jplephem", "max_stars_repo_head_hexsha": "331f0fff156ddd83acd5d5a6d8e3be5e07d9dd04", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# TODO: error calculation
import numpy as np
def get_mse(feature_data, gt, function, *params):
prediction = function(feature_data, *params)
squared_difference = np.square(np.subtract(gt, prediction))
return squared_difference.mean() | {"hexsha": "d4ad67cdaac3abcb2d07d7248fba278ed49049cd", "size": 245, "ext": "py", "lang": "Python", "max_stars_repo_path": "error_calc.py", "max_stars_repo_name": "Alice-OSENSE/feature_err_analysis", "max_stars_repo_head_hexsha": "39b0d79b9bd5b60a94851fc6a5b458c4d28427dc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Model
include_model("hopper")
# Dimensions
nq = 4 # configuration dimension
nu = 2 # control dimension
nc = 1 # number of contact points
nf = 2 # number of faces for friction cone
nb = nc * nf
ns = nq
# Parameters
g = 9.81 # gravity
μ = 1.0 # coefficient of friction
mb = 10.0 # body mass
ml = 1.0 # leg mass
Jb = ... | {"hexsha": "f5c7ed84c34606d25bf3101fe6020915cc7a74a4", "size": 7329, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/development/hopper_flip_al.jl", "max_stars_repo_name": "jmichaux/motion_planning", "max_stars_repo_head_hexsha": "9a36f394261ff11ca8325d8a5e9d8a79f18b2744", "max_stars_repo_licenses": ["MI... |
# Copyright 2016 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": "e00f8c810ed28f442f17a44888c59b7bb723866c", "size": 31037, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/debug/cli/analyzer_cli.py", "max_stars_repo_name": "RMORIOKA/tensorflow", "max_stars_repo_head_hexsha": "6886eb9c73940fd3b4dfadc3d6964ae9aa71eef6", "max_stars_repo_licenses": ["... |
""" OSC server ofr BITalino R-IoT
"""
import argparse
import math
from tornado import websocket, web, ioloop
import _thread as thread
import asyncio
import websockets
import json
import signal
import numpy
import sys, traceback, os, time, platform
import subprocess
#from os.path import expanduser
from pythonosc impo... | {"hexsha": "83d95cc63191408d778eda62faf583aefb4761ce", "size": 15748, "ext": "py", "lang": "Python", "max_stars_repo_path": "CEG_pyr/xriot_serverBIT.py", "max_stars_repo_name": "malfarasplux/sigaloud", "max_stars_repo_head_hexsha": "cf35dd51aaa332e9938db57c6aaf19470cc7675e", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import numpy as np
# ----------
# Functions to compute in log-domain.
# ----------
def logzero():
return -np.inf
def safe_log(x):
if x == 0:
return logzero()
return np.log(x)
def logsum_pair(logx, logy):
"""
Return log(x+y), avoiding arithmetic underflow/overflow.
logx: log(x)
... | {"hexsha": "22519b691fc9ad1a1c452a70a1f93b516915b9ae", "size": 1474, "ext": "py", "lang": "Python", "max_stars_repo_path": "lxmls/sequences/log_domain.py", "max_stars_repo_name": "mtreviso/lxmls-toolkit", "max_stars_repo_head_hexsha": "7b135d98c8bde592649fface8e6f24f112939937", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma convex_same_rel_interior_closure_straddle:
fixes S :: "'n::euclidean_space set"
shows "\<lbrakk>convex S; convex T\<rbrakk>
\<Longrightarrow> rel_interior S = rel_interior T \<longleftrightarrow>
rel_interior S \<subseteq> T \<and> T \<subseteq> closure S"
[PROOF STATE]
proof... | {"llama_tokens": 202, "file": null, "length": 1} |
using LinearAlgebra
using MAT
using Plots
using Statistics
file = matread("Data2.mat");
X = file["data"];
# Visualize the first two dimension of the data
scatter(X[:,1], X[:,2], aspect_ratio=:equal, leg=false)
# input: X - data points
# output: E - distance matrix
#
function get_E(X)
n = size(X,1); # number of p... | {"hexsha": "fc64a7c2288153876714b3cb69be5ce4991afad0", "size": 2533, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/diffuse_local.jl", "max_stars_repo_name": "teshenglin/diffusion_maps", "max_stars_repo_head_hexsha": "ee9ffff7773ebfa1953a833235e41eb13ddff9d7", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#! /usr/bin/env python
# by weil
# Sep 16, 2020
import pandas as pd
import numpy as np
import Cell_BLAST as cb
import scipy
import os
import scanpy as sc
from anndata import AnnData
from utils import construct_dataset
# expr_mat
# choose to use raw read counts, not processed data
expr_mat=pd.read_csv("../download/Luk... | {"hexsha": "f9eaa036053edfdded4984ad53d7bd19e51f399e", "size": 1300, "ext": "py", "lang": "Python", "max_stars_repo_path": "Datasets/collect/collect_lukowski.py", "max_stars_repo_name": "gao-lab/Cell_BLAST", "max_stars_repo_head_hexsha": "45b14bbd3385b8a7be0b48ef5ab42bc946f3558f", "max_stars_repo_licenses": ["MIT"], "m... |
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