text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import os
import fsspec
import numpy as np
import pandas as pd
import pytest
import scipy.sparse
from cirrocumulus.parquet_dataset import ParquetDataset
from cirrocumulus.prepare_data import PrepareData
from cirrocumulus.zarr_dataset import ZarrDataset
def read_and_diff(ds_reader, path, test_data, measures, dimensi... | {"hexsha": "e5529a3dbdd3da8ecdf66b520f0b955e045f72bf", "size": 3686, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_prepare_data.py", "max_stars_repo_name": "PfizerRD/cirrocumulus", "max_stars_repo_head_hexsha": "c7ce0c8c3c246282046e6d373d60442af55d3f09", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
import dace as dc
M, N = (dc.symbol(s, dtype=dc.int64) for s in ('M', 'N'))
@dc.program
def kernel(float_n: dc.float64, data: dc.float64[N, M]):
mean = np.mean(data, axis=0)
# stddev = np.std(data, axis=0)
stddev = np.sqrt(np.mean(np.subtract(data, mean)**2, axis=0))
stddev[stddev... | {"hexsha": "00761bd4d1bb5614c688927c5c6832ddc2432a3d", "size": 742, "ext": "py", "lang": "Python", "max_stars_repo_path": "npbench/benchmarks/polybench/correlation/correlation_dace.py", "max_stars_repo_name": "frahlg/npbench", "max_stars_repo_head_hexsha": "1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26", "max_stars_repo_lic... |
import time
import numpy as np
from spectra_gen import *
from to_rank import *
from utils import *
from datetime import datetime
from mask import *
from torch import sigmoid, tensor
import os
def to_explain(eobj):
print ('\n[To explain: SFL (Software Fault Localization) is used]')
print (' ### [Measures: {0}]'.fo... | {"hexsha": "a75e5aa7894a0ccc068b0d11bf12977f75e50459", "size": 8201, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/to_explain.py", "max_stars_repo_name": "gwenty/deepcover", "max_stars_repo_head_hexsha": "5cd4c9c0b60585bf30cf548c119f48d6faeb611f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
from dataclasses import dataclass, field
from operator import setitem
from isu.models.step import Step
from isu.models.section import Section
from isu.models.audio import Audio, SoundBite
from pathlib import Path
from PIL import Image
import cv2
import numpy as np
from enum import Enum
from typing import List, Optional... | {"hexsha": "9959cee9112186300b83f299c1cab0fc4c02ff99", "size": 11669, "ext": "py", "lang": "Python", "max_stars_repo_path": "isu/models/actions/animation.py", "max_stars_repo_name": "pecusys/isutils", "max_stars_repo_head_hexsha": "39fa92dc391cc430dcf1864f4c2f0212f0db58b6", "max_stars_repo_licenses": ["MIT"], "max_star... |
using LightGraphs
using LightGraphsMatching
using Test
using Cbc
using JuMP
using LinearAlgebra: I
@testset "LightGraphsMatching" begin
@testset "maximum_weight_matching" begin
g = complete_graph(3)
w = [
1 2 1
1 1 1
3 1 1
]
match = maximum_weight_matching(g, with_optimi... | {"hexsha": "97e426f83e53a3da57fce7fc52df04f4b6d0b05c", "size": 6446, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/LightGraphsMatching.jl-160ba089-64bf-5ba7-9e14-98ab1d9bcb0a", "max_stars_repo_head_hexsha": "80f15d9bc165c65b0db138fff7eaaf... |
# coding=utf-8
# 导入自己的函数包d2lzh_pytorch，注意要先将目标包的父路径添加到系统路径中
import sys
sys.path.append(r".")
from d2lzh_pytorch import data_process
from d2lzh_pytorch import layers
from d2lzh_pytorch import train
from collections import OrderedDict
import torch.nn as nn
import numpy as np
import torch
from torch.nn import init
"""
... | {"hexsha": "83e04fdf2a2e7c087d75e277063e8231966e237e", "size": 1606, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter3/Chapter3_7.py", "max_stars_repo_name": "ZFhuang/DiveIntoDLSketches", "max_stars_repo_head_hexsha": "cf0654d06ab6eeaefc35fa3bebd4937f1cbbb165", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
(* Default settings (from HsToCoq.Coq.Preamble) *)
Generalizable All Variables.
Unset Implicit Arguments.
Set Maximal Implicit Insertion.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Require Coq.Program.Tactics.
Require Coq.Program.Wf.
(* Converted imports: *)
Require BinNums.
Require Data.Either.
Req... | {"author": "plclub", "repo": "hs-to-coq", "sha": "e6401f6f054a2c1ff5e63a17ab8af2bcd5861c9c", "save_path": "github-repos/coq/plclub-hs-to-coq", "path": "github-repos/coq/plclub-hs-to-coq/hs-to-coq-e6401f6f054a2c1ff5e63a17ab8af2bcd5861c9c/examples/ghc/lib/DynFlags.v"} |
"""
MM1K(λ, μ, k)
Tạo mô hình `M/M/1/K`.
"""
struct MM1K{T} <: AbstractMMCK
λ::Union{T, Real}
μ::Union{T, Real}
k::Union{T, Integer}
ρ::Union{T, Real}
function MM1K(λ, μ, k)
T = Union{typeof(μ), typeof(λ)}
new{T}(λ, μ, k, λ/μ)
end
end
function pn(m::MM1K, n::Int)
ρ = m.ρ
k = m.k
if ρ == 1
1 / (k + 1)
... | {"hexsha": "b840d036b875d1df128933fcf4ecb21c39f8f531", "size": 712, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mm1k.jl", "max_stars_repo_name": "ndgnuh/MarkovAndQueueModels.jl", "max_stars_repo_head_hexsha": "30d0c3ecb9da61a466b95b791e8dbefc854ed38f", "max_stars_repo_licenses": ["Unlicense"], "max_stars_... |
[STATEMENT]
lemma finite_completion_lemma:
"finite I ==> (\<forall>i \<in> I. F \<in> (A i) leadsTo (A' i \<union> C)) -->
(\<forall>i \<in> I. F \<in> (A' i) co (A' i \<union> C)) -->
F \<in> (\<Inter>i \<in> I. A i) leadsTo ((\<Inter>i \<in> I. A' i) \<union> C)"
[PROOF... | {"llama_tokens": 2666, "file": null, "length": 6} |
#
# Copyright (c) 2017, UT-BATTELLE, LLC
# All rights reserved.
#
# This software is released under the BSD license detailed
# in the LICENSE file in the top level a-prime directory
#
###Work in Progress: Plot meridional averages for different fields in the same plot.
###07/03/2017
import matplotlib as mpl
#changing t... | {"hexsha": "2ae22ebc1ff5af7ea983e4b1cf1879b8fdb9382c", "size": 9880, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/plot_meridional_avg_multiple_fields_climo.py", "max_stars_repo_name": "E3SM-Project/a-prime", "max_stars_repo_head_hexsha": "a8c084ab6f727904a2b38d8a93b9c83e2f978e3f", "max_stars_repo_licen... |
module RoboSimples
using PyCall
using AbstractActuators
export NRoboClient, NRoboTest
export move, moveX, moveY, moveZ, devposition, setreference
export rmove, rmoveX, rmoveY, rmoveZ
export positionX, positionY, positionZ
export setreferenceX, setreferenceY, setreferenceZ
export numaxes, axesnames, moveto
struct NR... | {"hexsha": "e53fe1d58dc6d8c12210666a63ab5416eb345e00", "size": 4238, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RoboSimples.jl", "max_stars_repo_name": "pjsjipt/RoboSimples.jl", "max_stars_repo_head_hexsha": "b30d59c223d6cde866937d8bbd1c4779b5c933db", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
include(joinpath("..", "common", "utils.jl"))
using LinearAlgebra: Diagonal, diag
⊗ = kron
abstract type AbstractProblem end
struct Isothermal{T} <: AbstractProblem
T₀::T
R::T
p₀::T
g::T
ρ₀::T
H::T
Cᵥ::T
γ::T
function Isothermal{T}(;
T₀ = 300,
R = 287,
p₀ = ... | {"hexsha": "1f373cfecf10ec18c0c1ebaa234bf83f986d01d3", "size": 4436, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "linear_energy/cg_sem.jl", "max_stars_repo_name": "jkozdon/clima_sem_vertical", "max_stars_repo_head_hexsha": "69c7d1743cb970ed699cb62a4dac347e178967bd", "max_stars_repo_licenses": ["MIT"], "max_sta... |
function KeggSpeciespKa = getKeggpKas(target_cids, target_inchi, n_pkas)
if nargin < 3
n_pkas = 20;
end
if ismac
cxcalc_cmd = '/Applications/ChemAxon/JChem/bin/cxcalc';
babel_cmd = '/usr/local/bin/babel';
else
cxcalc_cmd = 'cxcalc';
babel_cmd = 'babel';
end
[success, ~] = system(cxcalc_cmd);
if su... | {"author": "opencobra", "repo": "cobratoolbox", "sha": "e60274d127f65d518535fd0814d20c53dc530f73", "save_path": "github-repos/MATLAB/opencobra-cobratoolbox", "path": "github-repos/MATLAB/opencobra-cobratoolbox/cobratoolbox-e60274d127f65d518535fd0814d20c53dc530f73/src/analysis/thermo/trainingModel/new/getKeggpKas.m"} |
from os.path import dirname, join
from os import path
import fire
import random
import torch
import numpy as np
from tempfile import NamedTemporaryFile
from torch import nn
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score, f1_score, precision_recall_fscore_support, confusion_matrix, cla... | {"hexsha": "96ec781c0210333d0bc8e1d90f61401fefa4895e", "size": 19223, "ext": "py", "lang": "Python", "max_stars_repo_path": "relation_extraction.py", "max_stars_repo_name": "DFKI-NLP/lm-transformer-re", "max_stars_repo_head_hexsha": "650ac2c261ee6ed6113b3af1e9ee813c9952ad3c", "max_stars_repo_licenses": ["MIT"], "max_st... |
#== # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
#
# Tests related to TLE parser.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ==#
# File: ./src/orbit/tle.jl
# ========================
# Macros tle_str and tlenc_str
# ------------------... | {"hexsha": "31644a3d82f38927c0f6aeae6f88e1bf83061644", "size": 1872, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/orbit/tle.jl", "max_stars_repo_name": "FedericoStra/SatelliteToolbox.jl", "max_stars_repo_head_hexsha": "31b50dfc698f8f4d0309960ac95a51824b24283a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Copyright 2014-2019 The PySCF Developers. 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 appl... | {"hexsha": "41aea5d3a1fe79f193c99f48f0a7657128a4f777", "size": 4618, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/adc/uadc_ao2mo.py", "max_stars_repo_name": "azag0/pyscf", "max_stars_repo_head_hexsha": "1e3e27b61b3cfd22c9679d2c9851c13b3ebc5a1b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
[STATEMENT]
lemma alw_safe_combined2: "FullSpec s \<Longrightarrow> alw (holds safe_combined2) s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. FullSpec s \<Longrightarrow> alw (holds safe_combined2) s
[PROOF STEP]
apply (frule exch_alw_InvCapsNonneg)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>FullS... | {"llama_tokens": 349, "file": "Progress_Tracking_Combined", "length": 4} |
import numpy
from numpy import *
from math import sqrt
def rigid_transform_3D(A, B):
assert len(A) == len(B)
N = A.shape[0]; # total points
centroid_A = mean(A, axis=0)
centroid_B = mean(B, axis=0)
# centre the points
AA = A - tile(centroid_A, (N, 1))
BB = B - tile(centroid_B, (N, 1)... | {"hexsha": "0e533ade4103ecc822f4703bd579e09449858b34", "size": 1362, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "madeira-sustcer/ME336-Project2", "max_stars_repo_head_hexsha": "94334569418b4270a326453a000dfd53938d2c82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
function measure_mean!(tuning_run::Run, x::Configuration)
configurations = Array{Configuration}(undef, tuning_run.cost_evaluations)
fill!(configurations, deepcopy(x))
pmap_cost(x::Configuration) = tuning_run.cost(x, tuning_run.cost_arguments)
results = pmap(pmap_cost, configurations)
for i = 1:tun... | {"hexsha": "d9c883a4ee419f510349d8a9dc3247deba8299c9", "size": 662, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core/measurement/measure.jl", "max_stars_repo_name": "phrb/OPAL.jl", "max_stars_repo_head_hexsha": "5b0edb948892b5e4f3ad9df563c6216d7e550439", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu Nguyen" at 07:03, 18/03/2020 %
# ... | {"hexsha": "fbc48d6082fe62de07aa859f4dee50e2ce00ec99", "size": 15197, "ext": "py", "lang": "Python", "max_stars_repo_path": "mealpy/physics_based/EO.py", "max_stars_repo_name": "chenyuxiang0425/mealpy", "max_stars_repo_head_hexsha": "69e8dc727e15527e31ac5ace1debe92a0bc7d828", "max_stars_repo_licenses": ["MIT"], "max_st... |
import cv2 as cv
import numpy as np
# 载入手写数字图片
img = cv.imread('handwriting.jpg', 0)
# 将图像二值化
_, thresh = cv.threshold(img, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
contours, hierarchy = cv.findContours(thresh, 3, 2)
# 创建出两幅彩色图用于绘制
img_color1 = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
img_color2 = np.copy(img_color1... | {"hexsha": "7732da08c88a07847729007c2b4f425509b02273", "size": 1353, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/cv_tools.py", "max_stars_repo_name": "Monologuethl/yolov4-pytorch-16bit", "max_stars_repo_head_hexsha": "4411081a875ee779f9b7808dc922658a549dff8c", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""Work with a collection of moles."""
import argparse
import json
import math
import pathlib
import uuid
import numpy
import mel.lib.fs
import mel.lib.image
import mel.lib.math
import mel.rotomap.mask
KEY_IS_CONFIRMED = "is_uuid_canonical"
KEY_IS_UNCHANGED = "is_unchanged"
IGNORE_NEW_FILENAME = "ignore-new"
IGNO... | {"hexsha": "7a0b5bb0de522ec1628b53070e0612752a3a9f91", "size": 13361, "ext": "py", "lang": "Python", "max_stars_repo_path": "mel/rotomap/moles.py", "max_stars_repo_name": "aevri/mel", "max_stars_repo_head_hexsha": "4451b272ddc2095f757423ff519f36fc57708ec6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import os
import math
import pickle
import mxnet as mx
import matplotlib.pyplot as plt
from mxnet.gluon.data.vision import transforms
from .utils import *
from ..base.base_predictor import BasePredictor
from ...utils import save, load, tqdm
import warnings
import logging
import numpy as np
from mxnet.gluon import nn... | {"hexsha": "c8b265de4368b923a9f9f7730481f938f8c272e5", "size": 6574, "ext": "py", "lang": "Python", "max_stars_repo_path": "autogluon/task/object_detection/detector.py", "max_stars_repo_name": "awesome-archive/autogluon", "max_stars_repo_head_hexsha": "2fa500f1359e1909cfb5eac8c6ecdea6d4e2e9aa", "max_stars_repo_licenses... |
import tqdm
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from object_dataset import DatasetObjects
import numpy as np
labels_to_object = {
0: 'GoodGoal',
1: 'BadGoal',
2: 'GoodGoalMulti',
3: 'Wall',
4: 'Ramp',
5: 'Cylinder... | {"hexsha": "7b57bf1aceef87e5e4061910362b80167951f621", "size": 2324, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/object_detection_module/object_visualize_data.py", "max_stars_repo_name": "compsciencelab/ppo_D", "max_stars_repo_head_hexsha": "1870c908f498ceb29295e5625ff5598bed82cbb3", "max_stars_repo_lic... |
import obspy
import numpy as np
class Dataset(object):
""" Seismic data container
Basically, a list of obspy streams. Each stream corresponds to a
single seismic station and holds all the components recorded at that
station. Methods that help with data processing and metadata
ex... | {"hexsha": "4c1dd542f0f7544fa9518443ae80ca47d9e37269", "size": 5274, "ext": "py", "lang": "Python", "max_stars_repo_path": "mtuq/dataset/base.py", "max_stars_repo_name": "junxie01/mtuq", "max_stars_repo_head_hexsha": "6adf9e983c221e788daf1dec26d028bc406ce4e6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
"""
Implements the agent and game classes for the Sharing Game. Each class inherit
from the general agent and game class, respectively.
"""
import numpy as np
from opinet import Agent, Game
class SharingAgent(Agent):
"""
Describes a agent in the Sharing Game
"""
def __init__(self, init_stances, alph... | {"hexsha": "c8c71bc90a1b765f66f5d9cd3bd5692572949f27", "size": 3228, "ext": "py", "lang": "Python", "max_stars_repo_path": "opinet/sharing.py", "max_stars_repo_name": "ryanwallace96/opinet", "max_stars_repo_head_hexsha": "72495ceef2382cb76f2318f970147cc7c6c8a45a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
""" logarithmic normal distribution: China most people
# ----
# License: BSD
# ----
# 0.1 - init version - 2018.4 - by Nick Qian
"""
from scipy.stats import lognorm
import matplotlib.pyplot as plt
import numpy as np
def logNrm_dist(s):
x = np.linspace(lognorm.ppf(0.01, s), lognorm.p... | {"hexsha": "fa65ca2d27772ab5838910a62f74cbc99d597bdd", "size": 921, "ext": "py", "lang": "Python", "max_stars_repo_path": "logNrm_dist.py", "max_stars_repo_name": "NickQian/pyIncome", "max_stars_repo_head_hexsha": "9aa3f17527d6903f893e2be58a34972946294394", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count"... |
from collections import namedtuple
import numpy as np
ValueRange = namedtuple('ValueRange', ['min', 'max'])
def determinerange(values):
"""Determine the range of values in each dimension"""
r = ValueRange(np.min(values, axis=0), np.max(values, axis=0))
if np.any(r.max - r.min < 1e-8):
r = ValueRa... | {"hexsha": "414cb1adc8e4683fe7bebf0e10f47d48922c328e", "size": 1105, "ext": "py", "lang": "Python", "max_stars_repo_path": "multiLevelCoSurrogates/utils/scaling.py", "max_stars_repo_name": "sjvrijn/multi-level-co-surrogates", "max_stars_repo_head_hexsha": "04a071eb4360bed6f1a517531690beec7857e3e5", "max_stars_repo_lice... |
import os
import json
import enum
import numpy as np
class LaneAssociation(enum.Enum):
LEFT = 0
CENTER = 1
RIGHT = 2
UNKNOWN = 3
NUM_VEHICLES = 6
NUM_ITERATIONS = 1_000
FPS = 25
VEHICLE_FILENAME = "vehicle_data.json"
VEHICLE_FILEPATH = os.path.join(
os.path.dirname(os.path.dirname(__file__)), ... | {"hexsha": "3db70ee060e786e9f7282e43479525101081331c", "size": 2093, "ext": "py", "lang": "Python", "max_stars_repo_path": "05_String/AdasInput/scripts/data.py", "max_stars_repo_name": "franneck94/UdemyCppExercises", "max_stars_repo_head_hexsha": "862d3e3df198ef8f3c7b850bbeead6161700f9d1", "max_stars_repo_licenses": ["... |
import numpy as np
from .topology import cellular_automaton2d
class Sandpile:
def __init__(self, rows, cols, is_closed_boundary=True):
self._K = 4 # this value is hard-coded because the neighbourhood type, "von Neumann", is fixed
self._network = cellular_automaton2d(rows=rows, cols=cols, neighbo... | {"hexsha": "b9d17f45d4acc4ad2438442832db93e93db4fca8", "size": 1317, "ext": "py", "lang": "Python", "max_stars_repo_path": "netomaton/sandpile.py", "max_stars_repo_name": "lantunes/netomaton", "max_stars_repo_head_hexsha": "fef60a787d031c9c7b1eb4ff990f7c12145579ef", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
#include <fstream>
#include <iostream>
#include <filesystem>
#include <string>
#include <unordered_map>
#include <algorithm>
#include <chrono>
#include <future>
#include <utility>
#include <boost/iostreams/filtering_streambuf.hpp>
#include <boost/iostreams/filter/gzip.hpp>
#include <xtensor/xarray.hpp>
#include <xtenso... | {"hexsha": "06dd99ea3ba0b780581cf63a9183c0147ed9a66a", "size": 20139, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/task/domain_probability_task.cpp", "max_stars_repo_name": "srom/nbias", "max_stars_repo_head_hexsha": "be8cf8dd623038dcf08d38ed3d19f635ee2dbeae", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Run this app with `python app.py` and
# visit http://127.0.0.1:8050/ in your web browser.
# Imports necessary libraries
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output
import numpy as np
import pandas as pd
from gensim import models, corp... | {"hexsha": "a13fa4b27a2533c0c36843e699a5a09d17f94536", "size": 8560, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "SatenikS/reddit-recommender-system-interesting-factoid", "max_stars_repo_head_hexsha": "dd0ba1ee3c3f5cfb4741ad9ce57a059ec4ade3f9", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma [smt_arith_multiplication]:
fixes A B :: real and p n :: int
assumes "A < B" "0 < n" "p > 0"
shows "(A / n) * p < (B / n) * p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. A / real_of_int n * real_of_int p < B / real_of_int n * real_of_int p
[PROOF STEP]
using assms
[PROOF STATE]
proof (pro... | {"llama_tokens": 223, "file": null, "length": 2} |
import pygame
import sys
from game.logic import SokobanLogic
import time
import os
import numpy as np
import torch
from solver.search_util.policy import Action_Predictior
from solver.search_util.value import Value_Predictior
from solver.solver_search import SokobanSolverSearch
from torch.utils.data import TensorDataset... | {"hexsha": "eb4b4a3dfd5e3977e7fb1716b5ed94ed80891b79", "size": 8916, "ext": "py", "lang": "Python", "max_stars_repo_path": "game/game.py", "max_stars_repo_name": "cyclone923/sokoban_pygame", "max_stars_repo_head_hexsha": "942ea41f4c0e160ab54e967f6dd9424ae3d0d243", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""This module contains helper functions and utilities for nelpy."""
__all__ = ['spatial_information',
'frange',
'swap_cols',
'swap_rows',
'pairwise',
'is_sorted',
'linear_merge',
'PrettyDuration',
'ddt_asa',
'get_contig... | {"hexsha": "37f19f659d9ef143b2408f934266bdcc951f5ade", "size": 73603, "ext": "py", "lang": "Python", "max_stars_repo_path": "nelpy/utils.py", "max_stars_repo_name": "IsaacBusaleh/nelpy", "max_stars_repo_head_hexsha": "f2663cf6f028c9bd0e630fbf8a527c236f4e0f41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
\section{Experiment}
While the modified Count-min sketch algorithm provides an error bound for the
estimated movie averages, what we are really interested in is how much it
affects the ordering of the movies when sorted by these estimated averages
compared to the real averages. In this section, we evaluate the modified... | {"hexsha": "19bc34169638c127f728387081bc32b56305a1f2", "size": 4193, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "experiment.tex", "max_stars_repo_name": "Bladtman242/SAD2_project", "max_stars_repo_head_hexsha": "6bc06598b2dd676a0c6ef6860c6e4b69ccff3e85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script includes the remote computations for brainage prediction using
decentralized SVR with FNC as features
"""
import json
import sys
import numpy as np
from core import common_functions as cf
OUTPUT_KEY_LIST = ['w_local', 'intercept_local', 'n_train_samples_... | {"hexsha": "5bcba636dc5c9ba8b824f719227d66d5c7992da4", "size": 7716, "ext": "py", "lang": "Python", "max_stars_repo_path": "fnc_scripts/remote.py", "max_stars_repo_name": "trendscenter/decentralized_brainage_paper", "max_stars_repo_head_hexsha": "20ddcc537aa50a4576ff33949f6b3cbd053a1521", "max_stars_repo_licenses": ["M... |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df_full = pd.read_csv('/Users/mac/Documents/GitHub/my_shots/throw-the-ball/data/kobe/data.csv')
df_sample = pd.read_csv('/Users/mac/Documents/GitHub/my_shots/throw-the-ball/data/kobe/sample_submission.csv')
print('\n Full d... | {"hexsha": "28966744604725b28258763e9ed2f5ad313e4329", "size": 4484, "ext": "py", "lang": "Python", "max_stars_repo_path": "NBA/player_analysis/exploring_kobe_shots.py", "max_stars_repo_name": "blmendes/basketball-shot-mechanics", "max_stars_repo_head_hexsha": "bcc02e65b66c6523187ae17a28652c8bdb5574b4", "max_stars_repo... |
import dgl
import ogb
import math
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
device_id=0 # GPU 的使用 id
n_layers=3 # 输入层 + 隐藏层 + 输出层的数量
n_hiddens=256 # 隐藏层节点的数量
dropout=0.5
lr=0.01
epochs=300
runs=10 ... | {"hexsha": "5e40851ca744124524a841fe130cbc8e6d0a61c4", "size": 5648, "ext": "py", "lang": "Python", "max_stars_repo_path": "GNN/GCN.py", "max_stars_repo_name": "jaykay233/tensorflow_models", "max_stars_repo_head_hexsha": "5b60b2adfa5e2d82c59189da6398388ba58c6c33", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import numpy as np
def L2_distance_1(a, b):
if a.shape != b.shape:
raise ValueError("The dimensions of a and b don't agree")
if a.shape[0] == 1:
a = np.concatenate((a, np.zeros(a.shape)), axis=0)
b = np.concatenate((b, np.zeros(b.shape)), axis=0)
elif len(a.shape) == 1:
... | {"hexsha": "90c9a6f40667be05bb9060f03c480b59e41f8c67", "size": 733, "ext": "py", "lang": "Python", "max_stars_repo_path": "SIMLR/src/L2_distance_1.py", "max_stars_repo_name": "5966466/SIMLR-python", "max_stars_repo_head_hexsha": "0ceb42ea4e766fd1a1bcbb1ee17af369dbc890c9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import os, shutil
import pickle as pkl
from dummylearning.utilities.info import Info
from dummylearning.plots.survival import Plots
from dummylearning.analysis.survival import Analysis
class Report(Info):
def __init__(self, model, verbose = True):
super().__init__(verbose)
... | {"hexsha": "89ef9ea90017ff6745e8b933cd06afc569dfd4d3", "size": 4118, "ext": "py", "lang": "Python", "max_stars_repo_path": "dummylearning/reports/survival.py", "max_stars_repo_name": "JuantonioMS/dummylearning", "max_stars_repo_head_hexsha": "1780a7ba0f38633bcbfbacd4f35a31cce4c94a87", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/python
# encoding: utf-8
M=float
t=str
X=list
g=False
b=dict
mV=True
mE=print
mF=any
import os
G=os.path
import sys
u=sys.path
u.append('/Users/luoyonggui/PycharmProjects/mayiutils_n1/mayiutils/db')
from pymongo_wrapper import PyMongoWrapper
import pandas as pd
N=pd.date_range
R=pd.to_datetime
L=pd.read_pick... | {"hexsha": "aeabc8d2397cb5bcd3d1e5c05fa93aa0fa7399e7", "size": 3838, "ext": "py", "lang": "Python", "max_stars_repo_path": "engineering/test_obfuscate1.py", "max_stars_repo_name": "mayi140611/mayiexamples", "max_stars_repo_head_hexsha": "221cf9e8916d81198df7355894ec59dc334ae0af", "max_stars_repo_licenses": ["Apache-2.0... |
# standard libraries
import argparse
import pathlib
# dependent packages
import numpy as np
import matplotlib.pyplot as plt
from astropy import table
from astropy.modeling import models, fitting
# module settings
plt.style.use("seaborn-darkgrid")
plt.style.use("seaborn-muted")
# command line arguments
parser = argpa... | {"hexsha": "674bc126b38412bb43447e69a800ac450f923fd3", "size": 1541, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipelines/subrefxy_fit.py", "max_stars_repo_name": "deshima-dev/qlook-pipeline", "max_stars_repo_head_hexsha": "90f520e101a58ae1dc1ffd9317ad16035e8efe0c", "max_stars_repo_licenses": ["MIT"], "max_... |
import argparse
import functools
import importlib
import logging
import random
import shutil
import subprocess
import sys
import time
from distutils.util import strtobool
from pathlib import Path
import numpy as np
from tensorboardX import SummaryWriter
curPath = Path(__file__).resolve()
sys.path.append(str(curPath.p... | {"hexsha": "024f928a459af92bbb9b35aef4e4f5025f5cb936", "size": 6262, "ext": "py", "lang": "Python", "max_stars_repo_path": "synthesis/main.py", "max_stars_repo_name": "AryaGuo/cadical", "max_stars_repo_head_hexsha": "159af5b21fbe090e5c1514d7659d2bee92b3402a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
{"mathlib_filename": "Mathlib.Algebra.Order.Field.Defs", "llama_tokens": 0} | |
#ifndef VENTURA_PROCESS_HANDLE_HPP
#define VENTURA_PROCESS_HANDLE_HPP
#include <silicium/error_or.hpp>
#include <silicium/get_last_error.hpp>
#include <boost/swap.hpp>
#ifndef _WIN32
#include <sys/wait.h>
#endif
namespace ventura
{
#ifdef _WIN32
struct process_handle
{
process_handle() BOOST_NOEXCEPT... | {"hexsha": "199f39ebde8daeb39b46dad0d741e1ba91e03dcf", "size": 3094, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ventura/process_handle.hpp", "max_stars_repo_name": "TyRoXx/ventura", "max_stars_repo_head_hexsha": "e261d3a0589819f0709e43211974312be8a4e935", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import time
import numpy
import json
import random
import matplotlib.pyplot as plt
class ActionSpace:
def __init__(self):
self.data = numpy.array([0, 1, 2, 3, 4], dtype="int8")
self.dict = {0: "A", 1: "W", 2: "D", 3: "S", 4: "B"} # int -> Left, Up, Right, Down, Bomb
self.size = ... | {"hexsha": "a8c2bec604d5366a017a8e8789075e215c398af0", "size": 7885, "ext": "py", "lang": "Python", "max_stars_repo_path": "asset/genius/q_learning/environment.py", "max_stars_repo_name": "haok1402/BomBot", "max_stars_repo_head_hexsha": "2c26da303f06d7a2968fecd14ed1a541538ef889", "max_stars_repo_licenses": ["MIT"], "ma... |
import scipy.optimize
import numpy as np
import torch
from ..functional import nac_weight, sparsity_error
from ..abstract import ExtendedTorchModule
from ._abstract_recurrent_cell import AbstractRecurrentCell
class PosNACLayer(ExtendedTorchModule):
"""Implements the NAC (Neural Accumulator)
Arguments:
... | {"hexsha": "51326de5e20d952ade2e9683ab97da6d732a2d71", "size": 1635, "ext": "py", "lang": "Python", "max_stars_repo_path": "stable_nalu/layer/pos_nac.py", "max_stars_repo_name": "wlm2019/Neural-Arithmetic-Units", "max_stars_repo_head_hexsha": "f9de9d004bb2dc2ee28577cd1760d0a00c185836", "max_stars_repo_licenses": ["MIT"... |
""" Shared fixtures for tests """
import logging
import pytest
import numpy as np
import pandas as pd
from resqpy.model import Model, new_model
from resqpy.organize import WellboreFeature, WellboreInterpretation
from resqpy.well import Trajectory, MdDatum, WellboreFrame
from resqpy.crs import Crs
@pytest.fixture(a... | {"hexsha": "adf0cb90c1128059d773bdf79d26dd16edd5eba0", "size": 2656, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/conftest.py", "max_stars_repo_name": "seryozni/resqpy", "max_stars_repo_head_hexsha": "2fab32f0db02b40f6cacbc620372bbd0cd27fd0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
""" Module containing classes and routines used in training of policies.
"""
from __future__ import annotations
import os
from typing import TYPE_CHECKING
import numpy as np
from tensorflow.keras.layers import Dense, Dropout, Input, Dot
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimi... | {"hexsha": "5d67eb9254d81158b8226981983e826721087e2a", "size": 8066, "ext": "py", "lang": "Python", "max_stars_repo_path": "aizynthfinder/training/keras_models.py", "max_stars_repo_name": "cambiegroup/aizynthfinder", "max_stars_repo_head_hexsha": "f5bafb2ac4749284571c05ae6df45b6f45cccd30", "max_stars_repo_licenses": ["... |
from pathlib import Path, PurePath
import os
from fnmatch import fnmatch
import sys
import csv
sys.path.append("c:\\Users\\kpdav\\machine_learning\\projects\\PGA-portfolio-optimizer\\config")
import config
from pgatour_metrics import get_espn_tournaments
import time
from csv import DictWriter
from concurrent.futures ... | {"hexsha": "3d3ab40a01fac9dd909986742122546a28b82820", "size": 41199, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/historical_data.py", "max_stars_repo_name": "kevin-persaud-davis/PGA-portfolio-optimizer", "max_stars_repo_head_hexsha": "e37a7a760c2ba1747a835a09da91733ca1bb8cdf", "max_stars_repo_licen... |
import argparse
import sys
import tensorflow as tf
import numpy as np
import skimage.io as io
from skimage.transform import rescale
# Prepare image to network input format
def prep(im):
if len(im.shape)==3:
return np.transpose(im,[2,0,1]).reshape((1,3,112,112))*2-1
elif len(im.shape)==4:
return... | {"hexsha": "8ae686f85448a9c16818b74e02681e53fa5b7151", "size": 2120, "ext": "py", "lang": "Python", "max_stars_repo_path": "Attack/cos_tf.py", "max_stars_repo_name": "cclauss/advhat", "max_stars_repo_head_hexsha": "ae996265a2d8481ecf7cec02d641af71668cec17", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 315, "m... |
"""
Read an audio as watermark signal (hidden voice), and another as carrier signal,
then the voices are composited, and finally the watermark signal is extracted from the composite signal
"""
import numpy as np
from scipy.io import wavfile
from scipy.signal import resample, hilbert, firwin
from tools import MaxMinNo... | {"hexsha": "b60bf564efb969438c2bff0038a5cc2191362493", "size": 3684, "ext": "py", "lang": "Python", "max_stars_repo_path": "hide_with_file.py", "max_stars_repo_name": "lsccccc/SSB-voice-hidden", "max_stars_repo_head_hexsha": "0abe2084c0b46dea5029930a6d15e48fde0cfede", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import sys
import pydart
import numpy as np
print('Example: rigidChain')
class DampingController:
""" Add damping force to the skeleton """
def __init__(self, skel):
self.skel = skel
def compute(self):
damping = -0.01 * self.skel.qdot
for i in range(1, self.skel.ndofs, 3):
... | {"hexsha": "fa8c22d676fde570d69767f7d6eed83fe4c5b8c3", "size": 1095, "ext": "py", "lang": "Python", "max_stars_repo_path": "DartFootProject/rigidChain/main.py", "max_stars_repo_name": "hpgit/HumanFoot", "max_stars_repo_head_hexsha": "f9a1a341b7c43747bddcd5584b8c98a0d1ac2973", "max_stars_repo_licenses": ["Apache-2.0"], ... |
/*
* Copyright (c) 2018 Ryan Berryhill, University of Toronto
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy,... | {"hexsha": "e1186a153dea4f056fe79d67219eee04d67a5059", "size": 3313, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/test_clause_database.cpp", "max_stars_repo_name": "ryanberryhill/pme", "max_stars_repo_head_hexsha": "416be2d52c920d285cc686a56d2f30bfab66bc51", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# __all__ = ['growing_degree_day', 'root_zone_water', 'check_groundwater_table', 'root_development', 'pre_irrigation',
# 'drainage', 'rainfall_partition', 'irrigation', 'infiltration', 'capillary_rise', 'germination',
# 'growth_stage', 'water_stress', 'cc_development', 'cc_required_time', 'adjust_... | {"hexsha": "a3b5f124926ff337162b40c8f915ebb870ee872f", "size": 153103, "ext": "py", "lang": "Python", "max_stars_repo_path": "aquacrop/solution.py", "max_stars_repo_name": "arongergely/aquacrop", "max_stars_repo_head_hexsha": "a6799ad81f46ebe9b48721f9cb6c55143663fe60", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# Remove nans from textfile output of dmstack and only extract few columns
# Author: Bhishan Poudel
#
# Filtering:
# 1. flag calib_psfCandidate==False
# 2. column deblend_nChild==0
# 3. ellipticity e = sqrt(e1^2 + e2^2) < 1.5
# 4. choose only few columns given below
# 5. remove nans from all these columns
# 6. chan... | {"hexsha": "a0ea37960887bd93e6c81e3d450baff05430b52b", "size": 2790, "ext": "py", "lang": "Python", "max_stars_repo_path": "IMPORTANT_scripts/remove_nans_dmstack.py", "max_stars_repo_name": "bpRsh/shear_analysis_after_dmstack", "max_stars_repo_head_hexsha": "bfe8cffbf36c2adfb4c6db79f46e7d0949ba148c", "max_stars_repo_li... |
#
# Copyright 2013 Y12Studio
#
# 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, ... | {"hexsha": "291e6a93edbae015a8d0af0c96ce36da19ce9eac", "size": 1835, "ext": "py", "lang": "Python", "max_stars_repo_path": "testonly/mpush2/t_foo.py", "max_stars_repo_name": "y12studio/pi", "max_stars_repo_head_hexsha": "c815b0b2a2421036ec99e085ffa92b1e3b5145f9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
[STATEMENT]
lemma admS_POR_lf [intro, simp]:
"POR_lf_rep r \<in> admS"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. POR_lf_rep r \<in> admS
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<bottom> \<in> POR_lf_rep r
2. adm (\<lambda>x. x \<in> POR_lf_rep r)
[PROOF STEP]
show "\<bottom> \<... | {"llama_tokens": 838, "file": "PCF_PCF", "length": 10} |
"""
OpenMDAO Wrapper for Flops
Automatically generated from flops.scriptWrapper with parse_phoenixwrapper.
This wrapper is based on the ModelCenter Java wrapper, version 2.00 Beta
"""
# pylint: disable-msg=E0611,F0401,E1101
from numpy import int64 as numpy_int64
from numpy import float64 as numpy_float64
from numpy i... | {"hexsha": "7cb1cc7995d8f7890757e20eecfb73987fe59034", "size": 322272, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/flops_wrapper/flops_wrapper.py", "max_stars_repo_name": "OpenMDAO-Plugins/flops_wrapper", "max_stars_repo_head_hexsha": "da03b981b88682426038dce14e4fa992ec27d158", "max_stars_repo_licenses":... |
[STATEMENT]
lemma JF_cind: "sbis UNIV UNIV dtor1 dtor2 R1 R2 \<Longrightarrow> R1 \<subseteq> Id \<and> R2 \<subseteq> Id"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sbis UNIV UNIV dtor1 dtor2 R1 R2 \<Longrightarrow> R1 \<subseteq> Id \<and> R2 \<subseteq> Id
[PROOF STEP]
apply (rule rev_mp)
[PROOF STATE]
proof ... | {"llama_tokens": 57879, "file": "BNF_Operations_GFP", "length": 76} |
[STATEMENT]
lemma even_bit_succ_iff:
\<open>bit (1 + a) n \<longleftrightarrow> bit a n \<or> n = 0\<close> if \<open>even a\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bit ((1::'a) + a) n = (bit a n \<or> n = 0)
[PROOF STEP]
using that
[PROOF STATE]
proof (prove)
using this:
even a
goal (1 subgoal):
... | {"llama_tokens": 203, "file": null, "length": 2} |
"""
==============
Rotating a Map
==============
How to rotate a map.
"""
import matplotlib.pyplot as plt
import astropy.units as u
import sunpy.data.sample
import sunpy.map
###############################################################################
# We start with the sample data
aia_map = sunpy.map.Map(sunpy.... | {"hexsha": "5a543acae103964fe3d0c5a7578a3d18f615ef65", "size": 1117, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/map/map_rotation.py", "max_stars_repo_name": "johan12345/sunpy", "max_stars_repo_head_hexsha": "56e1ab0c2c992f99e0fe3e6bff468b731a51228c", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
"""
Mattsson2014
Coefficients of the SBP operators given in
Mattsson (2014)
Diagonal-norm summation by parts operators for fiite difference approximations
of third and fourth derivatives.
Journal of Computational Physics 274, pp. 432-454.
"""
struct Mattsson2014 <: SourceOfCoefficients end
function Bas... | {"hexsha": "7144f9eac7ebf0f143ad1867b4e35203675b496a", "size": 55101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SBP_coefficients/Mattsson2014.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SummationByPartsOperators.jl-9f78cca6-572e-554e-b819-917d2f1cf240", "max_stars_repo_head_hexsha": "9d6... |
import torch
import numpy as np
import random
def stackmix(x, y, alpha, prob, nframes=64):
if prob < 0:
raise ValueError('prob must be a positive value')
k = random.random()
if k > 1 - prob:
batch_size = x.size()[0]
batch_idx = torch.randperm(batch_size)
lam = np.random... | {"hexsha": "38e63ebae576344c83d3c02c5cbc4c03a73bd192", "size": 3928, "ext": "py", "lang": "Python", "max_stars_repo_path": "videomix.py", "max_stars_repo_name": "jayChung0302/videomix", "max_stars_repo_head_hexsha": "528052cce1ee5a21a755e5d963e3fe58a308a0ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "... |
import pybullet as p
import matplotlib.pyplot as plt
import numpy as np
p.connect(p.PhysX)
p.loadURDF('urdf/laikago_description/laikago_foot.urdf', [0,0,0.47])
p.loadURDF('urdf/plane/plane.urdf')
p.setGravity(0,0, -9.81)
p.loadPlugin('eglRendererPlugin')
_,_, img, _,_ = p.getCameraImage(1920, 1080)
img = img[:,:,:3... | {"hexsha": "82ec98c23ce34a89bc1cfd525f80ae658ff881b7", "size": 350, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tools/simple_load_urdf.py", "max_stars_repo_name": "Yunaik/drl_env", "max_stars_repo_head_hexsha": "d284e79847c59daa6ccb222f30fc7e2a86375546", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import os
import numpy as np
import pytest
import qcodes as qc
import scipy.fft as fp
from qcodes.dataset.experiment_container import (experiments,
load_last_experiment)
from qcodes.dataset.sqlite.database import (conn_from_dbpath_or_conn, connect,
... | {"hexsha": "866b483729589663db88c8ab25316ea257107857", "size": 6963, "ext": "py", "lang": "Python", "max_stars_repo_path": "nanotune/tests/data/test_data_export.py", "max_stars_repo_name": "theatlasroom/nanotune", "max_stars_repo_head_hexsha": "444edb47b34739db82e1c58a6c963cb14b223398", "max_stars_repo_licenses": ["MIT... |
import json
import logging
from enum import Enum
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
logger = logging.getLogger(__name__)
class JsonSerializable(object):
""" Interface for serializable classes."""
def toJson(self):
return json.dumps(self, default=lambda o:... | {"hexsha": "2c9f00350cd12ccaa68a4de92a735c833c58104a", "size": 3437, "ext": "py", "lang": "Python", "max_stars_repo_path": "expan/core/util.py", "max_stars_repo_name": "andompesta/expan", "max_stars_repo_head_hexsha": "705081b28bb5cbc5bf93b9c1689aa45308e595f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 320... |
push!(LOAD_PATH,"../src/")
using Documenter, Quiqbox
makedocs(
sitename="Quiqbox.jl",
modules = [Quiqbox],
authors="Weishi Wang",
pages=[
"Home"=>"index.md"
"Manual"=>[
"basis.md"
"SCF.md"
"optimization.md"
]
"Base"=>[
"cor... | {"hexsha": "a4bb32ede575ab9533aa6c57a454c3e15991f018", "size": 642, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "frankwswang/Quiqbox.jl", "max_stars_repo_head_hexsha": "e3c137d1017235c68db6389ff4a902e789cfa376", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "m... |
import numpy as np
from pyfibre.model.tools.filters import (
gaussian, tubeness, hysteresis, derivatives,
form_structure_tensor, form_nematic_tensor
)
from pyfibre.tests.pyfibre_test_case import PyFibreTestCase
class TestFilters(PyFibreTestCase):
def setUp(self):
self.image = np.ones((5, 5))
... | {"hexsha": "80682add72803564eb3683dc5fcbf93506123870", "size": 4563, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfibre/model/tools/tests/test_filters.py", "max_stars_repo_name": "franklongford/ImageCol", "max_stars_repo_head_hexsha": "96f0db337a203c5634bebcbae10a6d85789dff2c", "max_stars_repo_licenses": ["... |
import logging
import math
import numpy
import matplotlib.pyplot as plt
from dalesdata import dataslice
# Matplotlib plotting backend for dalesview.
log = logging.getLogger(__name__)
class Mpl4Dales(object):
def __init__(self):
pass
# Plotting interface method, entry point of the class
@static... | {"hexsha": "d48134f1436ff15c99e1ebb7f7725a9ada972624", "size": 4807, "ext": "py", "lang": "Python", "max_stars_repo_path": "dalesview/mpl4dales.py", "max_stars_repo_name": "CloudResolvingClimateModeling/dalesview", "max_stars_repo_head_hexsha": "9e1d7a96bcdadde5d5681d0478a2af9d93a8cc69", "max_stars_repo_licenses": ["Ap... |
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.patches as mpatches
def plot_rewards(rewards, file_name, display_interval=10):
"""Plot average reward for each time step
:param rewards: reward received at each step
:param file_name: the file where... | {"hexsha": "700992f14b780e3367472f2118950bdca7cf4a4c", "size": 3136, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter06/plot_utils.py", "max_stars_repo_name": "roiyeho/drl-book", "max_stars_repo_head_hexsha": "1db635fd508e5b17ef8bfecbe49a79f55503a1f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#ifndef ASLAM_GRID_CALIBRATION_TARGET_DESIGN_VARIABLE_CONTAINER_HPP
#define ASLAM_GRID_CALIBRATION_TARGET_DESIGN_VARIABLE_CONTAINER_HPP
#include <boost/shared_ptr.hpp>
#include <aslam/targets.hpp>
#include <aslam/backend/MappedEuclideanPoint.hpp>
namespace aslam {
class GridCalibrationTargetDesignVariableContainer {... | {"hexsha": "402eb4815bd9be2aea403b3c349c268f925af12a", "size": 1334, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "aslam_cv/aslam_cv_backend/include/aslam/GridCalibrationTargetDesignVariableContainer.hpp", "max_stars_repo_name": "PushyamiKaveti/kalibr", "max_stars_repo_head_hexsha": "d8bdfc59ee666ef854012becc935... |
import os
import logging
import pickle
import copy
from turtle import update
import numpy as np
from tqdm import tqdm
import gensim
import torch
import torch.nn.functional as F
import torch.optim as optim
from .lm import MWMLNetLMFineGrind,MWMLNetLMClassifier
from ..data import Dictionary
logger = logging.getLogger(... | {"hexsha": "2ea0c412f48b8f8dda4acbaaf960ae93fb3d7d69", "size": 10241, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model/base.py", "max_stars_repo_name": "MobtgZhang/MWMLNet", "max_stars_repo_head_hexsha": "125bb39935916b6b4be505c51cb6a04eb49b96d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% GEANT manual in LaTeX form %
% %
% Michel Goossens (for translation into LaTeX) ... | {"hexsha": "ab201a43d4cc4c56e02a6dfd48355f5048b8d6da", "size": 3782, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "geant/base020.tex", "max_stars_repo_name": "berghaus/cernlib-docs", "max_stars_repo_head_hexsha": "76048db0ca60708a16661e8494e1fcaa76a83db7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_cou... |
import connexion
import six
from swagger_server.models.prediction import PREDICTION # noqa: E501
from swagger_server import util
from subprocess import Popen, PIPE
from re import split
from sys import stdout
import subprocess
import numpy as np
import pandas as pd
#import seaborn as sns
from statsmodels.nonparamet... | {"hexsha": "9152b14717b1f3d3601beebb4eb949fd72dc5a4d", "size": 8236, "ext": "py", "lang": "Python", "max_stars_repo_path": "PredictBigMartDataset/project/default_controller.py", "max_stars_repo_name": "arijitsinha80/PYworld", "max_stars_repo_head_hexsha": "ece0ddb1233452da2442405d3c40bd24aef77af8", "max_stars_repo_lice... |
#!/usr/bin/env python
# Simple example of Wiener deconvolution in Python.
# We use a fixed SNR across all frequencies in this example.
#
# Written 2015 by Dan Stowell. Public domain.
import matplotlib
import matplotlib.cm as cm
# matplotlib.use('PDF') # http://www.astrobetter.com/plotting-to-a-file-in-python/
import... | {"hexsha": "76d78e1ca5491c952c65fd161882d0f168d1b604", "size": 3111, "ext": "py", "lang": "Python", "max_stars_repo_path": "rapidtide/wiener2.py", "max_stars_repo_name": "bbfrederick/delaytools", "max_stars_repo_head_hexsha": "190d79ae4c19317dfce38a528e43fd05459f29a5", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
'''
Finetuning Huggingface's models for question-answering on Natural Questions (NQ) datasety by Google
For the full list of options, type python run_nq.py -h
'''
from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import glob
import sy... | {"hexsha": "63510479caa3549d5f7203f24f9fa3a20917c558", "size": 33348, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_nq.py", "max_stars_repo_name": "valemore/nq_transformers", "max_stars_repo_head_hexsha": "fcc31f06989fc7b83cec10477e56573aebd1a0eb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import numpy as np
from boirlscenarios.irlobject import IRLObject
from boirlscenarios.configurations import Configurations
from tqdm import tqdm
import GPyOpt
import os
import boirlscenarios.constants as constants
import matplotlib.pyplot as plt
from tabulate import tabulate
import time
def exp_moving_ave... | {"hexsha": "bc4da2ef94537d9209255e5e277396633df0e190", "size": 34193, "ext": "py", "lang": "Python", "max_stars_repo_path": "boirlscenarios/boirlmain.py", "max_stars_repo_name": "clear-nus/BOIRL", "max_stars_repo_head_hexsha": "cc872111fda3c7b8118e1a864831013c30f63948", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma locate_locate_pred_unique:
assumes "\<And> a. a \<in> set al \<Longrightarrow> (0::nat) < f a"
and "locate_pred f al i n_j"
shows "n_j = locate f al i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n_j = locate f al i
[PROOF STEP]
unfolding locate_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
... | {"llama_tokens": 681, "file": "Probabilistic_Noninterference_Language_Semantics", "length": 6} |
from . import colour_functions as cf
import matplotlib.pyplot as plt
import progressbar
from scipy.interpolate import interp2d
from pathlib import Path
from PIL import Image
import time
import numpy as np
from .backend_functions import backend as bd
m = 1.
cm = 1e-2
mm = 1e-3
um = 1e-6
nm = 1e-9
class Polychromat... | {"hexsha": "b2eebe4434b8f18373d9f8eec5f620387f7526d8", "size": 9615, "ext": "py", "lang": "Python", "max_stars_repo_path": "diffractsim/polychromatic_simulator.py", "max_stars_repo_name": "villadsegede/Diffraction-Simulations--Angular-Spectrum-Method", "max_stars_repo_head_hexsha": "35a875711bba8f00ab060cea211aeb3f36c3... |
using Test
using FixedEffects
using StatsBase
using PooledArrays, CategoricalArrays
import Base: ==
==(x::FixedEffect{R,I}, y::FixedEffect{R,I}) where {R,I} =
x.refs == y.refs && x.interaction == y.interaction && x.n == y.n
@testset "FixedEffect" begin
fe1 = FixedEffect(1:10)
@test sprint(show, fe1) == "... | {"hexsha": "666cbac2cf290ecc2ea7e3e31ddee7f14966c874", "size": 2199, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/types.jl", "max_stars_repo_name": "FixedEffects/FixedEffects.jl", "max_stars_repo_head_hexsha": "6a5874c8cccb4c766977c33492ff9f271b97cef6", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma mod_int_wlog [consumes 1, case_names modulo]:
fixes P :: "int \<Rightarrow> bool"
assumes "b > 0"
assumes "\<And>k. 0 \<le> k \<Longrightarrow> k < b \<Longrightarrow> n mod b = k \<Longrightarrow> P n"
shows "P n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P n
[PROOF STEP]
using \<op... | {"llama_tokens": 233, "file": "Probabilistic_Prime_Tests_Jacobi_Symbol", "length": 2} |
from __future__ import annotations
__copyright__ = "Copyright (C) 2021 Kaushik Kulkarni"
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without l... | {"hexsha": "5df0a13c9d355c673ce926689b65abd3c6b9d015", "size": 19298, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytato/utils.py", "max_stars_repo_name": "alexfikl/pytato", "max_stars_repo_head_hexsha": "6ece6bc8bc35b22fe000a60ded74e8845883d30b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
''' Imports '''
import numpy as np
import tensorflow as tf
import os
import argparse
from Var import Var
class DataLoader:
def __init__(self, num_frames, use_arm, m_score):
self.num_frames = num_frames
self.use_arm = use_arm
se... | {"hexsha": "3028cb127612a4daae4d235a7ae9e8a53974286d", "size": 5111, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataLoader.py", "max_stars_repo_name": "bhardwajRahul/Gesture-Recognition", "max_stars_repo_head_hexsha": "e4722ecb58e5c65f34f92a21058eae81ed3c84e0", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import cv2 as cv
import numpy as np
img = cv.imread('sudoku.png')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
edges = cv.Canny(gray, 50, 150, apertureSize=3)
lines = cv.HoughLines(edges, 1, np.pi/180, 200)
for line in lines :
rho, theta = line[0]
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0... | {"hexsha": "06a0224f7c681e662cd69929a13bae578e3b8a69", "size": 606, "ext": "py", "lang": "Python", "max_stars_repo_path": "55_hough_line_basic.py", "max_stars_repo_name": "EnesMercan/Computer-Vision-Python", "max_stars_repo_head_hexsha": "21ba2f5125b56c17fa72e8b032cbe943af1f36fb", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
theorem (in itrace_top) alpern_schneider:
assumes notempty: "A \<noteq> {}"
and Psub: "P \<subseteq> A\<^sup>\<omega>"
shows "\<exists> S L. infsafety A S \<and> infliveness A L \<and> P = S \<inter> L"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>S L. infsafety A S \<and> inflivenes... | {"llama_tokens": 1470, "file": "Topology_LList_Topology", "length": 19} |
\documentclass{beamer}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
% \usepackage{amscd, amsfonts, amsmath, amssymb, amstext, amsthm, caption, epsfig, fancyhdr, float, graphicx, latexsym, mathtools, multicol, multirow, algorithm, chngcntr}
\usepackage[english]{babel}
\usepackage{booktabs}
\usepackage{amsmath,a... | {"hexsha": "8063ba9db8121f8f78442f88bc91cb345b3b6ac6", "size": 30809, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Slides/Seminar/seminar.tex", "max_stars_repo_name": "LaGauffre/BLOCKASTICS", "max_stars_repo_head_hexsha": "4087304a4fb6fe55b5e8746315f524eddedc72e8", "max_stars_repo_licenses": ["CC0-1.0"], "max_s... |
import pandas as pd
import numpy as np
from pymongo import MongoClient
import json
filename = 'PaperCitationContexts.txt'
key_paper_id = 'PaperId'
key_paper_ref = 'PaperReferenceId'
key_citation_context = 'CitationContext'
header = [key_paper_id,key_paper_ref,key_citation_context]
client = MongoClient('localhost', ... | {"hexsha": "3b69e864b7aa3f8331cbd262b99040780f746919", "size": 867, "ext": "py", "lang": "Python", "max_stars_repo_path": "extraction/mag/dump_to_mongo/citation_to_mongo.py", "max_stars_repo_name": "dhjournals/code", "max_stars_repo_head_hexsha": "8e85744325f2938786b88a3143a8ed7ae39f1992", "max_stars_repo_licenses": ["... |
import numpy as np
import numpy.random as npr
import scipy.stats as sps
import sklearn.ensemble
import sklearn.ensemble.forest
from spearmint import util
from sklearn.externals.joblib import Parallel, delayed
def init(expt_dir, arg_string):
args = util.unpack_args(arg_string)
return RandomForestEIChoo... | {"hexsha": "2112e02cbca4e16c48bb75b03a1ed463a9dccaf8", "size": 2940, "ext": "py", "lang": "Python", "max_stars_repo_path": "spearmint-win/spearmint/chooser/RandomForestEIChooser.py", "max_stars_repo_name": "TudorParas/Practical-Bayesian-Optimization", "max_stars_repo_head_hexsha": "db13b509e07e1283c9f37a31cae11d9c9940e... |
import time
import traceback
from io import StringIO
from typing import List, Tuple, Type, Dict, Any, Optional, Iterator
from pathlib import Path
import math
from dataclasses import dataclass, field
import warnings
import sys
import os
import torch
from torch import nn as nn
import numpy as np
import tqdm
from rtg i... | {"hexsha": "151290e4d01237cbde0856db758077e9bc899e77", "size": 30155, "ext": "py", "lang": "Python", "max_stars_repo_path": "rtg/module/decoder.py", "max_stars_repo_name": "isi-vista/rtg", "max_stars_repo_head_hexsha": "149415f424f2a6585cbe0d97f0007b8b0b53d164", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
import os
import sys
import numpy as np
import codecs
if __name__ == "__main__":
if len(sys.argv)!=3:
print('Usage: python src/prepare_txt_done_data_file.py <meta_file> <utts.data>\n')
sys.exit(0)
meta_file = sys.argv[1]
out_file = sys.argv[2]
out_f = open(out_file,'w')
with ope... | {"hexsha": "6c3e00ef6300dd57d6d3de652c50581a3f9bb91d", "size": 552, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/prepare_txt_done_data_file.py", "max_stars_repo_name": "AvashnaGovender/Tacotron", "max_stars_repo_head_hexsha": "b4d710ffb0f9e7ef0096d1993b8a24cae4f0d557", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma lock_okE:
"\<lbrakk> lock_ok ls ts;
\<forall>t. ts t = None \<longrightarrow> (\<forall>l. has_locks (ls $ l) t = 0) \<Longrightarrow> Q;
\<forall>t e x ln. ts t = \<lfloor>((e, x), ln)\<rfloor> \<longrightarrow> (\<forall>l. has_locks (ls $ l) t + ln $ l = expr_locks e l) \<Longrightarrow... | {"llama_tokens": 304, "file": "JinjaThreads_J_Threaded", "length": 1} |
// Copyright (C) 2013 Eurodecision
// Authors: Guillaume Pinot
//
// 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/property_map/compose_property_map.hpp>
#include <iostream>
int main()
{
cons... | {"hexsha": "ed809632a35c78f6027759f9008aaf7e0975c00f", "size": 889, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/property_map/example/compose_property_map_example.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676d... |
import cv2
import sys
import numpy
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=3, minSize=(10, 10), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
def draw_rects(img, rects, col... | {"hexsha": "1b8be3fc882eb9be27c95ef49065f6b51a3f99b1", "size": 1410, "ext": "py", "lang": "Python", "max_stars_repo_path": "Image/test.py", "max_stars_repo_name": "pection/InternshipProject", "max_stars_repo_head_hexsha": "2b39cc244e7ac989795d3aba5c1a11bb6c9a57b7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
function varargout = Read_FrictionmodeGUI(varargin)
% Last Modified by GUIDE v2.5 11-Feb-2019 18:03:10
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Read_Fric... | {"author": "TUMFTM", "repo": "sim_vehicle_dynamics", "sha": "df2ae95dbeb6f8e4591f31ee378acac8e812f358", "save_path": "github-repos/MATLAB/TUMFTM-sim_vehicle_dynamics", "path": "github-repos/MATLAB/TUMFTM-sim_vehicle_dynamics/sim_vehicle_dynamics-df2ae95dbeb6f8e4591f31ee378acac8e812f358/vehicle_environment/variable_fric... |
import time
import click
import gym
import numpy as np
from .agent import Agent
from .utils import KinematicConstraint, Rate, tf
from ..scene import Body, VR, Marker
class VRAgent(Agent):
def __init__(self, env, timescale=1):
super(VRAgent, self).__init__(env)
scene = env.unwrapped.scene
... | {"hexsha": "294a5c51d691729f2c1a7795e769886f1ae9b741", "size": 4849, "ext": "py", "lang": "Python", "max_stars_repo_path": "mime/agent/vr_agent.py", "max_stars_repo_name": "rjgpinel/mime-release", "max_stars_repo_head_hexsha": "26a850c4ba5b702b86d068995614163338fb01df", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 3 22:49:52 2020
@author: lutra
Phage_like_plasmids_SSU5_P1_D6_12Nov20
CREATE TABLE "Phage_like_plasmids_SSU5_P1_D6_12Nov20" (
"id" INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
"project_ID" INTEGER,
"project_ID_number" INTEGER,
"nucleotid... | {"hexsha": "e58e36d3b2e67c49e77b0e94b567f15f0567f324", "size": 6872, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_scripts_for_PLP_project/9_2_PLP_prepare_TableS2.py", "max_stars_repo_name": "snvlab18/phage_like_plasmids_project_2020", "max_stars_repo_head_hexsha": "f594debd821cafe7be2a8f0827379bf0c95cc... |
cd(@__DIR__); include("setups/simpletree.jl")
gr(dpi = 200)
## build frames
SGWT = sgwt_frame(Matrix(W); nf = 6)
SGWT = reshape(SGWT, (N, :))
SGWT_dual = (SGWT * SGWT') \ SGWT
distROT = natural_eigdist(𝚽, 𝛌, Q; α = 1.0, input_format = :pmf1, distance = :ROT)
rNGWF, dic_l2x = rngwf_all_vectors(distROT, 𝚽; σ = 0.1 *... | {"hexsha": "e3acf9efbf3e173dd2a03b7e176abe7ef728b671", "size": 1189, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dissertations/htli/scripts/Figure10.10.jl", "max_stars_repo_name": "BoundaryValueProblems/MTSG.jl", "max_stars_repo_head_hexsha": "8cf8e2b3035876b5ceda45109b0847a60b581a7c", "max_stars_repo_li... |
import os,shutil,sys
import numpy as np
from mpi4py import MPI
import pandas as pd
from collections import OrderedDict
from pypospack.pyposmat.data import PyposmatConfigurationFile
from pypospack.pyposmat.data import PyposmatDataAnalyzer
# from pypospack.pyposmat.engines import PyposmatMonteCarloSampler
from pypospack.... | {"hexsha": "6b7e8f30d15999147961fbf54adbb42d190f7402", "size": 59530, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/cluster_sampling/dev__cluster_sampling/mc_sampler_iterate_w_cluster.py", "max_stars_repo_name": "eragasa/pypospack", "max_stars_repo_head_hexsha": "21cdecaf3b05c87acc532d992be2c04d85bfbc22", ... |
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