text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# -*- coding: utf-8 -*-
"""
Generate angle list and plot numbers of sensors for each angle on a cubemap
Allow to manually tune overlap for best coverage versus number of measurements
@author: Brice Dubost
Copyright 2020 Brice Dubost
Licensed under the Apache License, Version 2.0 (the "License");
you may not... | {"hexsha": "0baa63aca8a56f668d4a9e4b2642f016c784f973", "size": 8056, "ext": "py", "lang": "Python", "max_stars_repo_path": "robot_optimize_angles.py", "max_stars_repo_name": "brice-digilus/Infrared_Analysis", "max_stars_repo_head_hexsha": "614a14b832f130f9cca9ea3659c08e40fb3d1b1c", "max_stars_repo_licenses": ["Apache-2... |
import numpy as np
from numpy import ma
from scipy.optimize import bisect
# This code was modified slightly from superplot; some functionality was depreciated, so this filled in the blanks
# URL to original code is below:
# https://github.com/michaelhb/superplot/blob/master/superplot/statslib/two_dim.py
def posterior... | {"hexsha": "a2b26366ee20f007e34302188848b0f8ef062cbd", "size": 8019, "ext": "py", "lang": "Python", "max_stars_repo_path": "Plotting/plottingUtils/statUtils.py", "max_stars_repo_name": "jnhoward/SU2LDM_public", "max_stars_repo_head_hexsha": "67db9142cbb67946e273ac940d13906d0a39bf58", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma LIMSEQ_le_const2: "X \<longlonglongrightarrow> x \<Longrightarrow> \<exists>N. \<forall>n\<ge>N. X n \<le> a \<Longrightarrow> x \<le> a"
for a x :: "'a::linorder_topology"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<longlonglongrightarrow> x; \<exists>N. \<forall>n\<ge>N. X n \<l... | {"llama_tokens": 169, "file": null, "length": 1} |
import numpy as np
import pandas as pd
from munch import Munch
from plaster.run.prep import prep_fixtures
from plaster.run.prep.prep_worker import triangle_dytmat, dyt_to_seq
from plaster.run.priors import PriorsMLEFixtures, MLEPrior
from plaster.run.sim_v2 import sim_v2_worker
from plaster.run.sim_v2.sim_v2_params imp... | {"hexsha": "7c92f6f2420585a3c10ad1731b0110b8e74b7f7d", "size": 18488, "ext": "py", "lang": "Python", "max_stars_repo_path": "plaster/run/sim_v2/zests/zest_sim_v2_worker.py", "max_stars_repo_name": "erisyon/plaster", "max_stars_repo_head_hexsha": "20af32aed2365c6351fe3c26293308960099152b", "max_stars_repo_licenses": ["M... |
"""Copyright © 2020-present, Swisscom (Schweiz) AG.
All rights reserved."""
from subprocess import call
import numpy as np
from codi.codi_utils import create_speech_data, create_unlabelled_speech_data, save_ids
from codi.speech_trainer import SpeechTrainer
def train_codi(labelling='naive', threshold=None):
"""
... | {"hexsha": "5cb5b377224582a6e8dcf445da8eb77b5e8f8c9a", "size": 2784, "ext": "py", "lang": "Python", "max_stars_repo_path": "main_speech.py", "max_stars_repo_name": "swisscom/ai-research-data-valuation-repository", "max_stars_repo_head_hexsha": "bcb45b7d8b84674f12e0a3671260290d98257c9f", "max_stars_repo_licenses": ["Apa... |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy
import scipy
import os
import pylab
import networkx as nx
pylab.ion()
########################
# Computes which pairs are highly cross
# correlated and highly PLV (Phase Locking Value)
# correlated
########################
path=os.... | {"hexsha": "83157194aafba52d0d0584cdb04f78042ebcb5c0", "size": 4243, "ext": "py", "lang": "Python", "max_stars_repo_path": "TEST_1/Analysis/Classification_Synchronization_Types_Network.py", "max_stars_repo_name": "dmalagarriga/PLoS_2015_segregation", "max_stars_repo_head_hexsha": "949afedf96945c11ee84b1a6c9842e5257fb5b... |
Christianity is a relatively popular religion in town. Besides churches, there are a number of local businesses and services that either cater specifically to Christians or that operate under a Christian philosophy.
Retail
Davis Christian Bookroom
Integrity Windows & Doors
Christian Education
Davis Communi... | {"hexsha": "1df3e611faa09db06875d1679f029045ed86ae3b", "size": 469, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Christian_Businesses_and_Services.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT... |
From Categories Require Import Essentials.Notations.
From Categories Require Import Essentials.Types.
From Categories Require Import Essentials.Facts_Tactics.
From Categories Require Import Category.Main.
From Categories Require Import Functor.Functor Functor.Functor_Ops
Functor.Representable.Hom_Func.
From Cat... | {"author": "amintimany", "repo": "Categories", "sha": "1839108875df0107fa4f6061c654003decda2d49", "save_path": "github-repos/coq/amintimany-Categories", "path": "github-repos/coq/amintimany-Categories/Categories-1839108875df0107fa4f6061c654003decda2d49/KanExt/LocalFacts/ConesToHom.v"} |
#!/usr/bin/python
import argparse
import random
import os
import subprocess
import math
import sys
import time
import copy
from numpy.random import choice as choices
from WES_simulator import *
from snp_rate import *
def main():
parser = argparse.ArgumentParser(description='Simulator for WES or WGS data', \
forma... | {"hexsha": "5d4223c91f7bf46447cea8a9767abe84d0c7fee5", "size": 15517, "ext": "py", "lang": "Python", "max_stars_repo_path": "SECNVs.py", "max_stars_repo_name": "YJulyXing/SECNVs-SimulateCNVs-2.0-", "max_stars_repo_head_hexsha": "e1a4a7fe6ca4370e9fe3d7b92ecdf3ec5c55cbd4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
module Dh_allDs
! contains functions that compute the dh/dsigma matrices of the material model
! and one main (model-indepenent) function that calls all dh/dsigma functions of the model
! and returns the function values as a matrix
use constants
use material_info
use derived_types
implicit none
contains
... | {"hexsha": "493282eeec93523004b4a8c46ac828128d97f30c", "size": 1800, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "return_mapping/model_components_all/Dh_allDs.f90", "max_stars_repo_name": "yuyong1990/TsaiWu-Fortran", "max_stars_repo_head_hexsha": "a111ca1717adfbbaf3e9e34f4189a441e16441b8", "max_stars_repo_l... |
"""Dataloader for language generation"""
from collections import Counter
from itertools import chain
import numpy as np
from .._utils.unordered_hash import UnorderedSha256
from .._utils.file_utils import get_resource_file_path
from .dataloader import BasicLanguageGeneration
from ..metric import MetricChain, Perplexit... | {"hexsha": "b9c3d38a94d15c33de7b5812e5cf1089508886fd", "size": 7988, "ext": "py", "lang": "Python", "max_stars_repo_path": "contk/dataloader/language_generation.py", "max_stars_repo_name": "GentleSmile/contk", "max_stars_repo_head_hexsha": "14c86b16e064c3034f64f6c48a267a0a31f0c463", "max_stars_repo_licenses": ["Apache-... |
import math
from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch import Tensor
from data import GenericTranslationDataset, BATCH_SIZE
class EncoderDecoderTransformer(nn.Module):
def __init__(
self,
d_model: int,
... | {"hexsha": "ab7f6887d17a792025f739f1f1ac9b4bd8477fb8", "size": 8354, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/transformer.py", "max_stars_repo_name": "RyanElliott10/PyTorch-Transformer", "max_stars_repo_head_hexsha": "4ac9842712a9c0fa9a2396684ca92ef0d048fd05", "max_stars_repo_licenses": ["MIT"], "max_... |
from molsysmt._private_tools.exceptions import *
import numpy as np
from molsysmt.elements import entities
types = ["water", "ion", "cosolute", "protein", "peptide", "rna", "dna", "lipid", "small molecule"]
def _aux(item):
from molsysmt import get
from numpy import empty, full
entities = {}
n_entit... | {"hexsha": "871288dea14fbf1468e93863e963abf1ebecfc8a", "size": 9609, "ext": "py", "lang": "Python", "max_stars_repo_path": "molsysmt/elements/entity.py", "max_stars_repo_name": "uibcdf/MolSysMT", "max_stars_repo_head_hexsha": "9866a6fb090df9fff36af113a45164da4b674c09", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
from matplotlib import pyplot as plt
import advec_diff
plt.yscale("log")
runner = advec_diff.AdvecDiffRunner()
def case_sdc():
runner.variant = "sdc"
runner.coarse_factor = 1
runner.run()
t, r, rr, e, re = runner.results()
i = np.arange(0, len(r))
plt.plot(i, r, "^-", labe... | {"hexsha": "e90a66cad3d6944a78a3370ea749ebd49de583b5", "size": 1396, "ext": "py", "lang": "Python", "max_stars_repo_path": "advec_diff/coarse_factor.py", "max_stars_repo_name": "f-koehler/pfasst-analysis", "max_stars_repo_head_hexsha": "5a55fc6d4f5c7fd7ceec6c6c6354ad8231d361f3", "max_stars_repo_licenses": ["MIT"], "max... |
\documentclass{standalone}
\begin{document}
\chapter*{Conclusions}\addcontentsline{toc}{chapter}{Conclusions}
\markboth{Conclusions}{Conclusions}
In this work of thesis, I have developed, implemented and tested an automated pipeline for the identification of Ground Glass Opacities and Consolidation in chest CT sc... | {"hexsha": "5289b6277907cfcba1182c55ec98a7fa2251425e", "size": 2308, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/conclusions.tex", "max_stars_repo_name": "RiccardoBiondi/SCDthesis", "max_stars_repo_head_hexsha": "2506df1995e5ba239b28d2ca0b908ba55f81761b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module TestProject
using StaticArrays
function dot(x)
v = SVector(x...)
return v'v
end
end # module
| {"hexsha": "e9b246345cc58a3e4375a4bae893646c14b2595e", "size": 111, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TestProject/src/TestProject.jl", "max_stars_repo_name": "jondeuce/MATDaemon.jl", "max_stars_repo_head_hexsha": "6a1de76acd835991e24f07067dd8c43c59e5e380", "max_stars_repo_licenses": ["MIT"], "m... |
using QMTK
using Compat.Test
@testset "Utils" begin
using QMTK.Consts.Pauli
@test kronprod(sigmax, sigmax, sigmai) == kron(kron(sigmax, sigmax), sigmai)
@test sigmax ⊗ sigmay ⊗ sigmaz == kron(kron(sigmax, sigmay), sigmaz)
h = @kron sigmax[1] ⊗ sigmaz[3] + sigmax[2] ⊗ sigmay[4]
ans = kronprod(σ₁, σ₀, σ₃, σ₀) + kronp... | {"hexsha": "626dde69536bfc9840e62ad0e82421daf7c5f9e8", "size": 622, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Utils.jl", "max_stars_repo_name": "Roger-luo/QMTK.jl", "max_stars_repo_head_hexsha": "90987261588fc8a4aefa73df2b1fb5d0c5a3f9d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_... |
# Copyright (c) Facebook, Inc. and its affiliates.import math
import os
import torch
import numpy as np
from tqdm import tqdm_notebook
import imageio
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from skimage import img_as_ubyte
import pdb
import glob
import natsort
from torch.au... | {"hexsha": "d0ed0a91b61572a0c16f1ccf22d91e901abef6a6", "size": 10443, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/visualize_data.py", "max_stars_repo_name": "ChicyChen/my_d3d", "max_stars_repo_head_hexsha": "efe3eb1f8c270ca371f628b1d6eface3042ac9a7", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import csv
import json
from glob import glob
from pprint import pprint
import pandas
from numpy import mean
files = glob('*.json')
results = {}
for file in files:
name = file.split(".")[0].split("_")
name = name[1] + " " + name[2]
data = json.load(open(file))
accuracy = mean([max(run["acc"]) for run i... | {"hexsha": "5dc9588f1fcd00666770295f6b042f9b947245d9", "size": 711, "ext": "py", "lang": "Python", "max_stars_repo_path": "nldrp/dnn/models/aggregate_scores.py", "max_stars_repo_name": "etzinis/nldrp", "max_stars_repo_head_hexsha": "3b6e24aa86a6d43bfd6f753b346739c00c282de3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import tensorflow as tf
import numpy as np
import logging
import hypertune
import argparse
import shutil
import os
def parse_tfrecord(example_data):
parsed = tf.io.parse_single_example(example_data, {
'size': tf.io.VarLenFeature(tf.int64),
'ref': tf.io.VarLenFeature(tf.float32),
'time': tf... | {"hexsha": "da8b97f0026747c44386e0560814c1c0bd681c13", "size": 6880, "ext": "py", "lang": "Python", "max_stars_repo_path": "02_data_representation/weather_search/wxsearch/train_autoencoder.py", "max_stars_repo_name": "fanchi/ml-design-patterns", "max_stars_repo_head_hexsha": "6f686601d2385a11a517f8394324062ec6094e14", ... |
import numpy as np
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
conf = SparkConf().setAppName("HW3").setMaster("local[2]")
sc = SparkContext(conf=conf)
# Map the data to a tuple of (hour, (project code, page name), page views)
# We combine project code and page name with a delimeter of... | {"hexsha": "e0d97f8f79b2ce209adda70eb053e48fe98fdb84", "size": 2276, "ext": "py", "lang": "Python", "max_stars_repo_path": "Spark/2a.py", "max_stars_repo_name": "bcspragu/Machine-Learning-Projects", "max_stars_repo_head_hexsha": "b6832cbb9bb27d7e8253300f97a3ab84b1a555dc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
// -*- mode:C++; tab-width:8; c-basic-offset:2; indent-tabs-mode:t -*-
// vim: ts=8 sw=2 smarttab
/*
* Ceph - scalable distributed file system
*
* Copyright (C) 2012 Inktank Storage, Inc.
*
* This is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* Lic... | {"hexsha": "f816ff41db0bd9a3f023e12fc055825880bae9af", "size": 8975, "ext": "cc", "lang": "C++", "max_stars_repo_path": "common/util.cc", "max_stars_repo_name": "liucxer/ceph-msg", "max_stars_repo_head_hexsha": "2e5c18c0c72253b283bfd3d0576033c0b515ce55", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
import numpy as np
import networkx as nx
from tqdm import tqdm
from numpy.linalg import inv
from sklearn.decomposition import TruncatedSVD
class BANE(object):
"""
Binarized Attributed Network Embedding Class (ICDM 2018).
"""
r"""An implementation of `"BANE" <https://arxiv.org/abs/1403.6652>`_
from ... | {"hexsha": "1e8e8aa92ef0e474d6cd5214d63750cd1bf77e4d", "size": 3955, "ext": "py", "lang": "Python", "max_stars_repo_path": "karateclub/node_embedding/attributed/bane.py", "max_stars_repo_name": "Laeyoung/ainized-karateclub", "max_stars_repo_head_hexsha": "26d8e10d9cb15a7ae6bf43db6ec338a6ae4f9aa0", "max_stars_repo_licen... |
import pickle
import numpy as np
import matplotlib.pyplot as plt
cumulative_rewards = pickle.load(open('cum_rewards_history-12.pkl', 'rb'))
epsilons = pickle.load(open('epsilon_history-12.pkl', 'rb'))
# Set general font size
plt.rcParams['font.size'] = '24'
ax = plt.subplot(211)
plt.title("Cumulative Rewards over E... | {"hexsha": "704f55e74a79f652ec4729e05812129ec5c77041", "size": 755, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulation/dqn-simulation/final/visualizer.py", "max_stars_repo_name": "pgabriela/dqn-jitsi-autoscaler", "max_stars_repo_head_hexsha": "b39eb335e584095ef66a9941dbe0b2ea21a02d4a", "max_stars_repo_li... |
#================================
# RESEARCH GROUP PROJECT [RGP]
#================================
# This file is part of the COMP3096 Research Group Project.
# System
import logging
# Gym Imports
import gym
from gym.spaces import Box, Discrete, Tuple
# PySC2 Imports
from pysc2.lib.actions import FUNCTIONS, Function... | {"hexsha": "72a1ba23557b588ec99e22b25ee684eded2010a2", "size": 3929, "ext": "py", "lang": "Python", "max_stars_repo_path": "sc2g/sc2g/env/movement/multi_movement_directed.py", "max_stars_repo_name": "kiriphorito/COMP3096---MARL", "max_stars_repo_head_hexsha": "5e05413b0980d60f4a3f2a17123178c93bb0b763", "max_stars_repo_... |
# helpers to calc features / cols importance
import matplotlib.pyplot as plt
import numpy as np
def collapse_values(importance, features):
"""collapse cols w/ values (A_A, A_B, A_C for example into just A w/ sum(A_weights))"""
assert len(importance) == len(features)
assert abs(sum(importance) - 1) < 1e-1... | {"hexsha": "04ea84827feff513f1aacb388e05b3a23712457b", "size": 2361, "ext": "py", "lang": "Python", "max_stars_repo_path": "util_cols_importance.py", "max_stars_repo_name": "pbogomolov1967/ml_features_importance", "max_stars_repo_head_hexsha": "b3b60de2810bf6660a583bec24635593afb12507", "max_stars_repo_licenses": ["Apa... |
[STATEMENT]
lemma set_zip_tr[simp]: "(s, s') \<in> set (zip ss (tr_ss_f T ss)) \<longrightarrow> s' = tr_s_f T s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (s, s') \<in> set (zip ss (tr_ss_f T ss)) \<longrightarrow> s' = tr_s_f T s
[PROOF STEP]
by (induct ss, auto) | {"llama_tokens": 124, "file": "LightweightJava_Lightweight_Java_Equivalence", "length": 1} |
import numpy as np
from rl687.environments.gridworld import Gridworld
import matplotlib.pyplot as plt
import time
def problemA():
"""
Have the agent uniformly randomly select actions. Run 10,000 episodes.
Report the mean, standard deviation, maximum, and minimum of the observed
discounted returns.
... | {"hexsha": "4964ec04144fb812fb44dff61db3fa95c8b4b426", "size": 4757, "ext": "py", "lang": "Python", "max_stars_repo_path": "homeworks/homework1.py", "max_stars_repo_name": "anshuman1811/cs687-reinforcementlearning", "max_stars_repo_head_hexsha": "cf30cc0ab2b0e515cd4b643fc55c60cc5f38a481", "max_stars_repo_licenses": ["M... |
from __future__ import annotations
"""A module containing the core class to specify a Factor Graph."""
import collections
import copy
import functools
import inspect
import typing
from dataclasses import asdict, dataclass
from types import MappingProxyType
from typing import (
Any,
Callable,
Dict,
Fro... | {"hexsha": "c0b4f0960ae7b8b4173a8fb52f73303a0c3e3b30", "size": 41946, "ext": "py", "lang": "Python", "max_stars_repo_path": "pgmax/fg/graph.py", "max_stars_repo_name": "StannisZhou/PGMax", "max_stars_repo_head_hexsha": "58fbe9516342eb79eee7a12c99ba84bb91d97520", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import streamlit as st
# Import libraries | Standard
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
import os
import datetime
import warnings
warnings.filterwarnings("ignore") # ignoring annoying warnings
from time import time
from rich.progress import track
# Import ... | {"hexsha": "726b3a2a6aa1363a1c1c76f05594a0960db6280c", "size": 14570, "ext": "py", "lang": "Python", "max_stars_repo_path": "case/solution-1/data_eda_with_streamlit_app.py", "max_stars_repo_name": "7125messi/streamlit-web-ml", "max_stars_repo_head_hexsha": "903d528e561d045d5f6c1dabdb0b78b28e32191c", "max_stars_repo_lic... |
subroutine my_sub(input_file)
implicit none
character(len=*), intent(in) :: input_file
logical :: is_file
inquire(file=input_file, exist=is_file)
if (is_file.EQV..TRUE.) then
write(*,'(A)') "Input file: '"//trim(input_file)//"'"
else
write(*,'(A)') "Input file: '"//trim(input_fi... | {"hexsha": "30882ecd9b92730a62e84acc4789da4dc15217ed", "size": 731, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "src/template.f03", "max_stars_repo_name": "nathanielng/code-templates", "max_stars_repo_head_hexsha": "cb2aae1ec4462aaccdb313b8cd574ed57c685aa2", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
from __future__ import absolute_import, division, print_function, unicode_literals
import functools
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
fashion_mnist = keras.datasets.fashion_mni... | {"hexsha": "c226f4067199a303cefc23907f26a5707926e2ea", "size": 2281, "ext": "py", "lang": "Python", "max_stars_repo_path": "k_nearest_neighbors.py", "max_stars_repo_name": "NikPyth/KNN", "max_stars_repo_head_hexsha": "62c04b30fb24f135193d22986587a93b31acd212", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma support_upd[simp]: "support z A (f(x := z)) = support z A f - {x}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. support z A (f(x := z)) = support z A f - {x}
[PROOF STEP]
unfolding support_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {xa \<in> A. (f(x := z)) xa \<noteq> z} = {x \<in> A. ... | {"llama_tokens": 168, "file": null, "length": 2} |
import numpy as np
import support
from algorithm.algabc import GSA, Options
from problem.testfunc import TestFunction
# TODO: добавить воздможность выбора метода останова (по умолчанию - итерации) среднеквадратичное откл от лучшей точки
class GSAOptions(Options):
_alias_map = {
'g_idx': ['gi', 'g_index']... | {"hexsha": "2da003991e941459fde1ad4e6629475d2d1b85f0", "size": 8890, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithm/gsa.py", "max_stars_repo_name": "redb0/gotpy", "max_stars_repo_head_hexsha": "b3f2e12aff429e0bff0faa079a3694378293c974", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3... |
# -*- coding: latin-1 -*-
from __future__ import division
import ast
import numpy as np
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from functools import partial
import banco.bd_sensores as bd_sensores
import banco.bd_perf... | {"hexsha": "1f2406ab16fccfcd77b52639ecfc1ad6a39f4804", "size": 9936, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/graficos.py", "max_stars_repo_name": "Atzingen/controleForno-interface", "max_stars_repo_head_hexsha": "6a8968527f8b76c7d0c7ea26f8c8aca728fe4d2d", "max_stars_repo_licenses": ["MIT"], "max_s... |
import cv2
import time
import argparse
import numpy as np
def blur_face(image, face_detector):
"""
Runs the face detector, extracts face regions and blurs them
Args:
image: Input image or video frame
face_detector: Path to the face haarcascade file
Returns:
The processed image with face blurred
"""
... | {"hexsha": "d3f09b4c01847d61df4ada2940f948ae2b57f5f3", "size": 2533, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_blur.py", "max_stars_repo_name": "GSNCodes/Blur-Face", "max_stars_repo_head_hexsha": "63134c4e63052da464331a725c685422c1c633be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
// test/wwwc/css_syntax/parsing.cpp
#include <boost/test/unit_test.hpp>
#include <wordring/wwwc/css_syntax/parsing.hpp>
#include <wordring/wwwc/selectors/grammar.hpp>
#include <algorithm>
#include <any>
#include <iterator>
#include <string>
#include <typeindex>
#include <vector>
namespace
{
inline std::u32string... | {"hexsha": "1ccff2762e65369835cbef86aac440ecbdd47620", "size": 27588, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/wwwc/css_syntax/parsing.cpp", "max_stars_repo_name": "wordring/wordring", "max_stars_repo_head_hexsha": "e2c9c2ed66010537efd78694521312c5b63f0510", "max_stars_repo_licenses": ["Unlicense"], "m... |
// Copyright 2020, Beeri 15. All rights reserved.
// Author: Roman Gershman (romange@gmail.com)
//
#include "util/uring/http_handler.h"
#include <boost/beast/core.hpp> // for flat_buffer.
#include <boost/beast/http.hpp>
#include "base/logging.h"
namespace util {
using namespace http;
using namespace std;
using na... | {"hexsha": "e8058c24a2cb2c7ce057b47e9e08d36537a53280", "size": 3796, "ext": "cc", "lang": "C++", "max_stars_repo_path": "util/uring/http_handler.cc", "max_stars_repo_name": "ekatz-quotient/gaia", "max_stars_repo_head_hexsha": "63305f443416deccf96fd8ec2fb60bcb560e232b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
"""
..
Copyright (c) 2014-2017, Magni developers.
All rights reserved.
See LICENSE.rst for further information.
Module providing utilities for control of plotting using `matplotlib`.
The module has a number of public attributes which provide settings for
colormap cycles, linestyle cycles, and marker cycle... | {"hexsha": "c4f23e73523d15d6fb896f9510f070e11dd67e63", "size": 9480, "ext": "py", "lang": "Python", "max_stars_repo_path": "magni/utils/plotting.py", "max_stars_repo_name": "SIP-AAU/Magni", "max_stars_repo_head_hexsha": "6328dc98a273506f433af52e6bd394754a844550", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_... |
import numpy as np
AGGREGATE_MAP = {
'mean': np.mean,
'min': np.min,
'median': np.median,
'max': np.max,
}
| {"hexsha": "b2600b1f17ce40988a6f406209f5ebd2c1205d89", "size": 124, "ext": "py", "lang": "Python", "max_stars_repo_path": "summarizer/util.py", "max_stars_repo_name": "stungkit/bert-extractive-summarizer", "max_stars_repo_head_hexsha": "84f27333aef33629444589c24933b76448777d4f", "max_stars_repo_licenses": ["MIT"], "max... |
@testset "Test time series data application" begin
sys = PSB.build_system(PSB.PSITestSystems, "c_sys5")
pmi_data = PMI.get_pm_data(sys)
mn_data =
PMI.apply_time_series(pmi_data, sys, last(PSY.get_forecast_initial_times(sys)), 3:5)
@test mn_data["multinetwork"]
@test length(mn_data["nw"]) ... | {"hexsha": "33e4b6ff9f2b80b6781c2d1297f27186a70ea5aa", "size": 862, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_apply_time_series.jl", "max_stars_repo_name": "NREL-SIIP/PowerModelsInterface", "max_stars_repo_head_hexsha": "b4b589db5e276d71973ba169db29437fb9b5cb14", "max_stars_repo_licenses": ["BSD-3... |
import streamlit as st
import pandas as pd
from pyvis.network import Network
import networkx as nx
import matplotlib.pyplot as plt
import bz2
import pickle
import _pickle as cPickle
# Load any compressed pickle file
def decompress_pickle(file):
data = bz2.BZ2File(file, 'rb')
data = cPickle.load(data)
return data
... | {"hexsha": "69e9b6ac19b84d4d1032896ae0625c658ace113a", "size": 4779, "ext": "py", "lang": "Python", "max_stars_repo_path": "streamlit/Autodidact/myapp_old.py", "max_stars_repo_name": "rts1988/IntelligentTutoringSystem_Experiments", "max_stars_repo_head_hexsha": "b2f797a5bfff18fb37c7a779a19a72a75db7eeef", "max_stars_rep... |
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
# Load a text file as a string.
with open('hound.txt') as infile:
text = infile.read()
# Load an image as a NumPy array.
mask = np.array(Image.open('holmes.png'))
# Get stop words as a set and add ... | {"hexsha": "0c013e5790891e8d52ad2a6bd2d348ade5bf44be", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter_3/wc_hound.py", "max_stars_repo_name": "Soccertanker/Real_World_Python", "max_stars_repo_head_hexsha": "5a0671ec11e5b5522c8ee4683bac880b92d8ac12", "max_stars_repo_licenses": ["FTL"], "max_... |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 18 11:49:51 2017
@author: Jalen Morgan, Taylor Paskett
"""
import numpy as np
import sympy
from stablab.finite_difference_code import pde
from sympy import Matrix
from stablab.finite_difference_code import approximate
"""Used for both pdes and odes"""
def ... | {"hexsha": "b626a8ffbd0ec12f9200254a44e603ed3984a212", "size": 12004, "ext": "py", "lang": "Python", "max_stars_repo_path": "stablab/finite_difference.py", "max_stars_repo_name": "nonlinear-waves/stablab_python", "max_stars_repo_head_hexsha": "101724f8bcefc34e90cf70d0813919188e08cb8a", "max_stars_repo_licenses": ["MIT"... |
# -*- coding: utf-8 -*-
from math import exp, factorial
import numpy as np
from numpy.linalg import matrix_power
from scipy.stats import poisson
from scipy.linalg import norm, null_space, solve, solve_sylvester, expm, inv
import matplotlib.pyplot as plt
import sys, warnings
from tqdm import tqdm
'''
̄W comb... | {"hexsha": "042b6c618e166c57fe87055bb5450f3bd936d13d", "size": 12472, "ext": "py", "lang": "Python", "max_stars_repo_path": "queue-def.py", "max_stars_repo_name": "Gravifer/queue-sdp", "max_stars_repo_head_hexsha": "0541d2460e4cfd7a75d3578378d19cb0926bfbfe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# -*- coding: utf-8 -*-
from __future__ import print_function
# identifying str and unicode on Python 2, or str on Python 3
from six import string_types, text_type
import os, sys
import time
from abc import ABCMeta, abstractmethod
import re
import itertools
import glob
import csv
from text_unidecode import unidecode
... | {"hexsha": "99fbaeca5bffcdbb792214eb480fa143ad07c827", "size": 131423, "ext": "py", "lang": "Python", "max_stars_repo_path": "femlAlgorithms.py", "max_stars_repo_name": "athenarc/ToponymPairClassification", "max_stars_repo_head_hexsha": "41dd487ad5f9ba4c0f60342d1af0a1c56c3e4136", "max_stars_repo_licenses": ["MIT"], "ma... |
context("Immutable")
# Create smaller subset of baseball data (for speed)
bsmall <- subset(baseball, id %in% sample(unique(baseball$id), 20))[, 1:5]
bsmall$id <- factor(bsmall$id)
bsmall <- bsmall[sample(rownames(bsmall)), ]
rownames(bsmall) <- NULL
test_that("idf is immutable", {
#Since idf are constructed by scr... | {"hexsha": "176338a30edc07c2dadb6593018157b22815a3d1", "size": 2191, "ext": "r", "lang": "R", "max_stars_repo_path": "source/gdaexperience6/plyr/tests/testthat/test-idf.r", "max_stars_repo_name": "lalaithan/developer-immersion-data", "max_stars_repo_head_hexsha": "b48d291ad5a03d56c0228d00e0b290b638d50194", "max_stars_r... |
from typing import Any, Callable, Dict, List, Optional, Tuple
import configparser
import logging
import os
# from packaging.version import parse, Version
import torch
from catalyst.tools.frozen_class import FrozenClass
logger = logging.getLogger(__name__)
IS_CUDA_AVAILABLE = torch.cuda.is_available()
NUM_CUDA_DEVIC... | {"hexsha": "078340ff999026914d1535eabf04e34cb8752000", "size": 17426, "ext": "py", "lang": "Python", "max_stars_repo_path": "catalyst/settings.py", "max_stars_repo_name": "stjordanis/catalyst-1", "max_stars_repo_head_hexsha": "93eedf0b9520bf1f83f63b13d6818df2a1e85b33", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
module TwoDGridWorldUtils
using SparseArrays
using Distributions
import ..TMazeCumulantSchedules
import ..ContGridWorldParams
import ..ContGridWorld
import ..Learner
import ..check_goal
import ..range_check
import ..get_action_probs
import ..GVFHordes
import ..update
import ..Curiosity
import ..GVFSRHordes
import ..S... | {"hexsha": "b912089d2940bc1ea0ce8b49ed73d6989e6f1ecd", "size": 12956, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/2d-gridworld.jl", "max_stars_repo_name": "MatthewMcLeod/curiosity", "max_stars_repo_head_hexsha": "7b452cb296c36a2e7b2f01763177c097bae8011c", "max_stars_repo_licenses": ["MIT"], "max_sta... |
class DataAveraging:
@staticmethod
def add_sensor_data_to_total(sensor_data, previous_total):
new_total = previous_total
for i in range(0,len(sensor_data)):
data_point = sensor_data[i]
if type(data_point) == int or type(data_point) == float:
new_total[i] ... | {"hexsha": "d1b1a658f60e42e0c81c15d0dfb3c3fd161f1c9f", "size": 4634, "ext": "py", "lang": "Python", "max_stars_repo_path": "house_code/main_programs/PSUPozyx/modules/data_averaging.py", "max_stars_repo_name": "mukobi/Pozyx-Gabe", "max_stars_repo_head_hexsha": "a8b444c2013b1df5043cd25106b72562409b5130", "max_stars_repo_... |
from scipy.sparse import csr_matrix, coo_matrix, diags
from scipy.sparse import isspmatrix
import random
class WordSaladMatrixBuilder():
"""Aids in the construction of a WordSaladMatrix. The WordSaladMatrix object
has some finicky requirements and this object helps construct one in a
reasonably efficient ... | {"hexsha": "5177f3b273ed335088934e0af031e3e6d9383613", "size": 8170, "ext": "py", "lang": "Python", "max_stars_repo_path": "markovchaintest.py", "max_stars_repo_name": "skurmedel/wordsalad", "max_stars_repo_head_hexsha": "5feaf29bf8b9c88624b783cd087a6589ea0ab48a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Export DynamicLinks API
export DynamicLinks,
getindex, endof, length, start, next, done, eltype,
handle, header
# Export Dynamic Link API
export DynamicLink,
DynamicLinks, handle, path
# Export RPath API
export RPath,
handle, rpaths, canonical_rpaths, find_library
"""
... | {"hexsha": "950155fe8d24e55163a292250a6d59621e505ffc", "size": 4425, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Abstract/DynamicLink.jl", "max_stars_repo_name": "Keno/ObjectFile.jl", "max_stars_repo_head_hexsha": "98d7b327448456df024ab87243520f972c715e16", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\lab{Object-Oriented Programming}{Object Oriented Programming}
\label{lab:OOP}
\objective{Teach object-oriented programming in Python.}
\section*{Introduction}
Writing readable code is an important skill for computer programmers.
Well-written code is easy to understand and modify.
An important part of readable code i... | {"hexsha": "1c6c36fda53e6290a3ed336685f077f2ddcb1d23", "size": 14721, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Python/OOP/ObjectOriented.tex", "max_stars_repo_name": "jessicaleete/numerical_computing", "max_stars_repo_head_hexsha": "cc71f51f35ca74d00e617af3d1a0223e19fb9a68", "max_stars_repo_licenses": ["CC-... |
import faiss
import random
import os
import torch
import numpy as np
import torch.nn.functional as F
from tqdm.auto import tqdm
from torch.utils.data import DataLoader, TensorDataset, SequentialSampler
from datasets import load_from_disk
from transformers import AdamW, get_linear_schedule_with_warmup
class DenseRetr... | {"hexsha": "dc4b231d162f705a1f11a66d69c0a6e40cc2bbc5", "size": 11191, "ext": "py", "lang": "Python", "max_stars_repo_path": "deprecated/dpr/code/dense_retrieval.py", "max_stars_repo_name": "eunaoeh/mrc-level2-nlp-01", "max_stars_repo_head_hexsha": "caa893ca7d689200b3528377901d59fa9ca452ac", "max_stars_repo_licenses": [... |
\documentclass[a4paper,10pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[margin=1in]{geometry}
\usepackage{graphicx}
\usepackage{listings}
\usepackage{hyperref}
\begin{document}
\begin{titlepage}
\begin{center}
\vspace*{1cm}
\huge{\textbf{Music Genre Classification}}
\vspace{0.5cm}
CS725 Project
\vspace{3.5cm... | {"hexsha": "89fa80bf5674f66cab58161bae938280a5e2419d", "size": 2800, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Project Proposal/Music Genre Classification.tex", "max_stars_repo_name": "anshulgupta0803/ipl-match-prediction", "max_stars_repo_head_hexsha": "955cf539b307c1a2b0d93f6dfc7036f379902678", "max_stars_... |
"""Tests for writers.record."""
import numpy as np
import tensorflow as tf
import pytest
import deepr as dpr
@pytest.mark.parametrize("shape", [[1], [2], [2, 3], [None, 3], [2, 3, 4], [None, 3, 4]])
@pytest.mark.parametrize("dtype", [tf.int64, tf.float32])
@pytest.mark.parametrize("chunk_size", [None, 2])
def test_... | {"hexsha": "b9f2ea2ffe8c4b176390b015e564ee938e8d13f0", "size": 1485, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/writers/test_writers_record.py", "max_stars_repo_name": "drohde/deepr", "max_stars_repo_head_hexsha": "672772ea3ce9cf391f9f8efc7ae9c9d438957817", "max_stars_repo_licenses": ["Apache-2.0... |
[STATEMENT]
lemma merge_eq: "xs\<noteq>[] \<or> ys\<noteq>[] \<Longrightarrow> merge xs ys = (
if ys=[] \<or> (xs\<noteq>[] \<and> hd xs < hd ys) then hd xs # merge (tl xs) ys
else hd ys # merge xs (tl ys)
)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. xs \<noteq> [] \<or> ys \<noteq> [] \<Longrightarrow> m... | {"llama_tokens": 215, "file": "IMP2_doc_Examples", "length": 1} |
\SecDef{minimal}{Minimal and Maximal Zero-Sum Sets}
In this section we study zero-sum sets of particular rank $n$ and prove results on their existence. We are particularly interested in the smallest of such sets, defined in the following sense.
\begin{definition}
We denote by $\minzs{n}{d}$ the minimum number $m \in \... | {"hexsha": "6cf1fc677cc55b968b28d956350edbe4bbf6c99f", "size": 27122, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "thesis-source/9niLinear/4minimal.tex", "max_stars_repo_name": "hellman/thesis", "max_stars_repo_head_hexsha": "6ba1c2b241e63c07cf76108481c1b67f21a50f12", "max_stars_repo_licenses": ["MIT"], "max_st... |
#ifndef BOOST_NETWORK_PROTOCOL_HTTP_MESSAGE_MODIFIERS_VERSION_HPP_20100608
#define BOOST_NETWORK_PROTOCOL_HTTP_MESSAGE_MODIFIERS_VERSION_HPP_20100608
// Copyright 2010 (c) Dean Michael Berris
// Copyright 2010 (c) Sinefunc, Inc.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LI... | {"hexsha": "bb16abb0129c076f6bcf765de6ae563b9892f3ef", "size": 1302, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/network/protocol/http/message/modifiers/version.hpp", "max_stars_repo_name": "yhager/cpp-netlib", "max_stars_repo_head_hexsha": "540ed7622be3f9534709036522f86bde1e84829f", "max_stars_repo_lice... |
function gauss_quadrature(y::Array{<:Number},GLweights::AbstractArray{<:Real,1})
# Assume that y is evaluated at the zeros
# I = Σ wi*y(zi) where zi are the roots of Pl of the appropriate order
return squeeze(sum(GLweights.*y,1),1) :: Array{<:Number}
end
function clenshaw_curtis_quadrature(y::Array{<:Real})::Array{... | {"hexsha": "45970cc45c30e90663af764aeba2c086ba02f0af", "size": 4672, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/periodic_integrals.jl", "max_stars_repo_name": "jishnub/NumericallyIntegrateArrays.jl", "max_stars_repo_head_hexsha": "96d11436753909b8d0c992055cb1696503b92b24", "max_stars_repo_licenses": ["MI... |
# -*- coding: UTF-8 -*-
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 颜色变换（色调，明暗，直方图和Gamma曲线）
def img_color(imgPath):
original_img = cv2.imread(imgPath)
img = cv2.resize(original_img,None,fx=0.8,fy=0.8,
interpolation=cv2.INTER_AREA) # 图像缩小
Make_border_im... | {"hexsha": "6f18e52a0d0215bcebedd70876bcae19b7964808", "size": 2161, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python-opencv/001-base/test_004_change_color.py", "max_stars_repo_name": "bjlhx15/python-algorithm", "max_stars_repo_head_hexsha": "bbd162e194359a01806922d73b709fe64fcfa422", "max_stars_repo_l... |
"""
Class to manage building, loading, and saving features for an action
recognition CNN over the NTURGB dataset
Features Implemented
--------------------
- 3D voxel flow
- 3D image as voxel grid
"""
import sys, os
import numpy as np
from ntu_rgb import NTU
from sysu_dataset import SYSU
from tqdm import tqdm, trang... | {"hexsha": "c73366d5a1ea9b216f335a781eda6d2c320685c0", "size": 4517, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_manager.py", "max_stars_repo_name": "mpeven/ntu_rgb", "max_stars_repo_head_hexsha": "4a8b43c521500907d2f241e4b440381cf8c62350", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "... |
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @author : East
# @time : 2019/7/14 18:03
# @file : 02.2d_laplace.py
# @project : fempy
# software : PyCharm
import numpy as np
import matplotlib.pyplot as plt
from fempy.mesh import Mesh2D
from fempy.fem2d import FEM2D
from fempy.femplot.plot2d import tri_mesh, tri_tri... | {"hexsha": "58ccf255a3fd7c4ef454db9e7e8ae67a6f364a14", "size": 5067, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/02.2d_laplace/02.2d_laplace.py", "max_stars_repo_name": "EastMagica/fempy", "max_stars_repo_head_hexsha": "5f16fe458a63ede5a49925a691924fbbbea767ec", "max_stars_repo_licenses": ["MIT"], "... |
import sys
# insert at 1, 0 is the script path (or '' in REPL)
sys.path.insert(1, '/home/austin/Github/natural-selection-simulator/lib/')
from pylive.pylive import live_plotter
import numpy as np
size = 100
x_vec = np.linspace(0,1,size+1)[0:-1]
y_vec = np.random.randn(len(x_vec))
line1 = []
while True:
rand_val = ... | {"hexsha": "b16b485f47bb21c3d1b7d40016bfd1044175d024", "size": 444, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pylive_test.py", "max_stars_repo_name": "austinmdillow/natural-selection-simulator", "max_stars_repo_head_hexsha": "01c7d3ba310a3629a04f1a7e67c04a1b87ee4f09", "max_stars_repo_licenses": ["MIT... |
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Method to merge all .csv files into one, so we can enter all data
def merge_data():
data = [files for files in os.listdir(os.path.join('..', '..', 'data','raw')) if files.endswith('.csv')]
dataFrames = []
fo... | {"hexsha": "8ec07d175cf607d7cd81be96dd888cb4eaa5f5f0", "size": 2400, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/scripts/project_functions.py", "max_stars_repo_name": "data301-2020-winter2/course-project-group_1007", "max_stars_repo_head_hexsha": "3918515b3f6622d732d10cfae7d0c7bc30a2b449", "max_star... |
import matplotlib.pyplot as plt
import numpy as np
def MB_speed(v, m, T):
"""
:param v: velocidades das moléculas
:param m: massa da molécula estudada
:param T: temperatura da atmosfera
:return: função de densidade de probabilidade
Calcula a distribuição de velocidades para um gás ideal
co... | {"hexsha": "5d0d85a593e2df880376a4f35024ad2311982560", "size": 1288, "ext": "py", "lang": "Python", "max_stars_repo_path": "dist-Maxwell-Boltzmann.py", "max_stars_repo_name": "Gabriel-Scheffel/dist-vel-Maxwell-Boltsmann", "max_stars_repo_head_hexsha": "6e45e255c79c7b8aa745e7330ebf5e226a1bc1fa", "max_stars_repo_licenses... |
# Copyright 2019 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | {"hexsha": "ba26fd8bf682a846ba26effcd170c57fb911a27c", "size": 4203, "ext": "py", "lang": "Python", "max_stars_repo_path": "cirq/ops/linear_combinations.py", "max_stars_repo_name": "rickyHong/Quantum-cirq-repl", "max_stars_repo_head_hexsha": "fca994bb8184be96354c0c4bc64dbcad6df517f1", "max_stars_repo_licenses": ["Apach... |
"""
Structure featurizers generating a matrix for each structure.
Most matrix structure featurizers contain the ability to flatten matrices to be dataframe-friendly.
"""
import numpy as np
import scipy.constants as const
from sklearn.exceptions import NotFittedError
from pymatgen.core import Structure
from pymatgen.co... | {"hexsha": "df49ba793e6d458e21db40b9ccc898c1237e7245", "size": 18794, "ext": "py", "lang": "Python", "max_stars_repo_path": "matminer/featurizers/structure/matrix.py", "max_stars_repo_name": "ncfrey/matminer", "max_stars_repo_head_hexsha": "5a688de8f2c7eaf5109d34d58ab7875cfe980e48", "max_stars_repo_licenses": ["BSD-3-C... |
import pickle
import librosa
import numpy as np
from tensorflow import keras
class ser:
def mfcc(self):
file = "./Audios/Dataset/1092_Help_FEA_XX.wav"
data, sampling_rate = librosa.load(file)
X = []
mfcc_feature = np.mean(librosa.feature.mfcc(y=data, sr=sampling_rate, ... | {"hexsha": "da46238f108dfcfb858d6698dab931a501014d4f", "size": 977, "ext": "py", "lang": "Python", "max_stars_repo_path": "Source Code/Speech Emotion Dection/ser.py", "max_stars_repo_name": "GALI-SAI-SHANKAR/Threat-Alert-AI", "max_stars_repo_head_hexsha": "f50743c23c05684d6e32ff52799dc4cc24dcd98f", "max_stars_repo_lice... |
import pickle
import glob
import numpy as np
def print_stats(data):
returns = []
path_lengths = []
print("num trajectories", len(data))
for path in data:
rewards = path["rewards"]
returns.append(np.sum(rewards))
path_lengths.append(len(rewards))
print("returns")
print... | {"hexsha": "ce20ffe52de5597a4618268be84969ec69785382", "size": 3393, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/ashvin/icml2020/process_data/consolidate.py", "max_stars_repo_name": "Asap7772/railrl_evalsawyer", "max_stars_repo_head_hexsha": "baba8ce634d32a48c7dfe4dc03b123e18e96e0a3", "max_stars_... |
#! /usr/bin/env python
#
# File Name : generate_grid_mrf_model.py
# Created By : largelymfs
# Creation Date : [2016-01-20 14:42]
# Last Modified : [2016-01-20 14:50]
# Description : the pyscripts to generate mrf grid model
#
def output_2d... | {"hexsha": "79cd0f819d43c93f8a9c2e5c308bde2f2f576e0a", "size": 1188, "ext": "py", "lang": "Python", "max_stars_repo_path": "Grid_MRF/generate_grid_mrf_model.py", "max_stars_repo_name": "YoungLew/NoiseContrastiveLearning", "max_stars_repo_head_hexsha": "2abff09651e17af4370319ca63a4c090097f914f", "max_stars_repo_licenses... |
# Copyright 2019-2021 Huawei Technologies Co., Ltd
#
# 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 agre... | {"hexsha": "9f600b2cd72d5db5ac46f4312b17f0ad0c1c69fe", "size": 3215, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/common/test_run/ascend/equal_count_run.py", "max_stars_repo_name": "tianjiashuo/akg", "max_stars_repo_head_hexsha": "a9cbf642063fb1086a93e8bc6be6feb145689817", "max_stars_repo_licenses": ["A... |
[STATEMENT]
lemma is_pseudonatural_equivalence:
shows "pseudonatural_equivalence V\<^sub>C H\<^sub>C \<a>\<^sub>C \<i>\<^sub>C src\<^sub>C trg\<^sub>C V\<^sub>D H\<^sub>D \<a>\<^sub>D \<i>\<^sub>D src\<^sub>D trg\<^sub>D
F \<Phi>\<^sub>F H \<Phi>\<^sub>H map\<^sub>0 map\<^sub>1"
[PROOF STATE]
proof (pr... | {"llama_tokens": 261, "file": "Bicategory_PseudonaturalTransformation", "length": 1} |
#!/usr/bin/env python
import sys
import re
# plotting
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
net_re = re.compile('^net:.*/train_val_(.*)\.prototxt')
model_re1 = re.compile('Finetuning from models/bvlc_reference_... | {"hexsha": "4a93015f558880a2c2ec8fd1a30e572c00ab26ba", "size": 3170, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/bvlc_reference_caffenet/plot_accuracy_sparsity.py", "max_stars_repo_name": "IntelLabs/SkimCaffe", "max_stars_repo_head_hexsha": "27df6a8796a012da722c3e2673739350133c1779", "max_stars_repo_l... |
import numpy as np
from io import BytesIO
from typing import List
def bytes2numpy(data: bytes) -> np.ndarray:
'''
TODO: Annotation
'''
nda_bytes = BytesIO(data)
nda = np.load(nda_bytes, allow_pickle=False)
return nda
def numpy2bytes(data: np.ndarray) -> bytes:
'''
TODO: Annotation
... | {"hexsha": "9a636f72c17a81ff647429f88d4cea62389aeaf7", "size": 716, "ext": "py", "lang": "Python", "max_stars_repo_path": "rls/distribute/utils/numpy.py", "max_stars_repo_name": "yisuoyanyudmj/RLs-1", "max_stars_repo_head_hexsha": "a336b57e804507bca23cbadc3b5af1924c80d942", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
"""
This code is used for plotting annual anomalies of radiative fluxes for the model mean of CMIP5 and CMIP6 models.
"""
import matplotlib.pyplot as plt
import xarray as xr
import numpy as np
import seaborn as sns
import pandas as pd
import scipy as sc
#=== Import SEB Anomalies ====
#from seasonal_SEB_components im... | {"hexsha": "18ef83c1f753fe7adab926cd75f2860562adb06f", "size": 17854, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_scripts/SEB_rad_flux_annual.py", "max_stars_repo_name": "idunnam/Thesis", "max_stars_repo_head_hexsha": "a567a25aa037c949de285158804a6ee396fc0e6c", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "9c5bff552a8ce2f0f9911588c5e77a7b2ea7d1b5", "size": 7447, "ext": "py", "lang": "Python", "max_stars_repo_path": "jax/experimental/compilation_cache/compilation_cache.py", "max_stars_repo_name": "manifest/jax", "max_stars_repo_head_hexsha": "d82341d95f418fe2a03cfe691b21813e4309eff7", "max_stars_repo_licenses"... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Unit tests for finite_diff library
"""
import unittest
import random
from math import pi
import numpy as np
import finitediff
class TestFiniteDiff(unittest.TestCase):
"""Unit test class for the finitediff library"""
order = 4 # Order of the derivatives
... | {"hexsha": "a65f39ef854ba9a4bb94c8cf08a568806963b135", "size": 7416, "ext": "py", "lang": "Python", "max_stars_repo_path": "finitediff/unit_tests.py", "max_stars_repo_name": "jolyonb/finitediff", "max_stars_repo_head_hexsha": "fb6d05490fcf8a7a7603e68aec165b9fb931ba3a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# custom libs
import utils
import model_funcs as mf
class BasicBlockWOutput(nn.Module):
expansion = 1
def __init__(self, in_channels, channels, params, stride=1):
super(BasicBlockWOutput, self).__init__... | {"hexsha": "2401076cdf2cf6907b78559096b790608881b365", "size": 8996, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks/SDNs/ResNet_SDN.py", "max_stars_repo_name": "Sanghyun-Hong/DeepSloth", "max_stars_repo_head_hexsha": "92b3d0d3ef3f974d8bce7b4b4a1828776227e3c6", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import random
import cv2
import numpy as np
from yolox.utils import adjust_box_anns
from ..data_augment import box_candidates, random_perspective
from .datasets_wrapper import Dataset
class MosaicDetection(Dataset):
... | {"hexsha": "0c9aa6ee46f2c4ca782f44660535fa0de1e29423", "size": 8829, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolox/data/datasets/mosaicdetection.py", "max_stars_repo_name": "vghost2008/YOLOX", "max_stars_repo_head_hexsha": "37b3cba0756907679ff25a4e5cc96eaad3b6f988", "max_stars_repo_licenses": ["Apache-2.... |
from sklearn.linear_model import LogisticRegression
import sklearn
from sklearn.model_selection import cross_val_score
from scipy.sparse import lil_matrix
import numpy as np
import json
from time import time
import sklearn
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import random
colorset = dict(... | {"hexsha": "7659812b5b963fb7ebf0e41af5ec83822dddbd7d", "size": 2411, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/experiment/network visualization.py", "max_stars_repo_name": "MGitHubL/MNCI", "max_stars_repo_head_hexsha": "6651a59ba30dd8c588aa26580411d8d01571c296", "max_stars_repo_licenses": ["MIT"], "ma... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 16 12:12:57 2020
@author: ssterl
"""
###################################
######### REVUB core code #########
###################################
# REVUB model © 2019 CIREG project
# Author: Sebastian Sterl, Vrije Universiteit Brussel
# This code accompanies the paper "T... | {"hexsha": "727975efd0c13eaca12b7ae369e2e5d571d283c7", "size": 133662, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/REVUB_Python_B_Suriname_Sterl_etal_2020.py", "max_stars_repo_name": "VUB-HYDR/2020_Sterl_etal_RSER", "max_stars_repo_head_hexsha": "672adf4f7676cb2e8be77e441a8a407f59930437", "max_stars_rep... |
# -*- coding: utf-8 -*-
# Author: Daniel Yang <daniel.yj.yang@gmail.com>
#
# License: BSD 3 clause
#from ..datasets import public_dataset
from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfidfTra... | {"hexsha": "4adec05c97f6517f1bad563b0a2f16966d4b9e6f", "size": 33248, "ext": "py", "lang": "Python", "max_stars_repo_path": "machlearn/naive_bayes/_naive_bayes.py", "max_stars_repo_name": "daniel-yj-yang/pyml", "max_stars_repo_head_hexsha": "2328ae1d73eab39f2774331fcfaa10e8fa2fc0de", "max_stars_repo_licenses": ["BSD-3-... |
"""decodes and serializes frames from vidoes in a given directory
into TFRecord files to improve parallelized I/O and provide
prefetching benefits.
The program expects the folder containing the videos to have the following
structure:
-- class_name_1
-- video_1.mp4
-- video_2.mp4
-- cl... | {"hexsha": "8523d8bd49c628a29704d22e2ad03b227939f5a0", "size": 7968, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/create_tfrecords.py", "max_stars_repo_name": "Chianugoogidi/X3D-tf", "max_stars_repo_head_hexsha": "45935c227896b83492b3c923af37d9746ab8a3c0", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from .OBJET import OBJET
import numpy as np
class Objet(object):
"""OBJET"""
def __init__(self, path_to_meta_json, width=500, height=500):
self._OBJET = OBJET(path_to_meta_json, width, height)
self.width = width
self.height = height
def draw(self, ):
self._OBJET.Draw()
... | {"hexsha": "87ac45215be36e7193a736c3acd7ac26e8d704b8", "size": 1189, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyobjet/objet.py", "max_stars_repo_name": "MahanFathi/Objet", "max_stars_repo_head_hexsha": "c6e2366327852c18b30dbf2f439931860dc26bf9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
abstract type AbstractScheduler end
struct StepDecay <: AbstractScheduler
xmax
xmin
Δ
T
end
(d::StepDecay)(t) = max(d.xmin, d.xmax - div(t, d.T) * d.Δ)
d = StepDecay(1.0, 0.1, 0.1, 5)
plot(1:100, d.(1:100))
struct ExponentialDecay <: AbstractScheduler
xmax
xmin
ρ
end
(d::ExponentialDe... | {"hexsha": "ecdd89f5d69a4d0178d65283371d3ada85e191ab", "size": 419, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/schedulers.jl", "max_stars_repo_name": "cswaney/Minerva.jl", "max_stars_repo_head_hexsha": "5a925de2d2b483c317efd286eb81aa2d64d9a5a6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
/* Copyright (C) 2012-2019 IBM Corp.
* This program is 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 a... | {"hexsha": "590fbbc29a6577b81fa544ca21977aed63372e58", "size": 8862, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/PermNetwork.cpp", "max_stars_repo_name": "patrick-schwarz/HElib", "max_stars_repo_head_hexsha": "cd267e2ddc6e92886b89f3aa51c416d5c1d2dc59", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""
```
set_regime_val!(p::Parameter{S},
i::Int, v::S; override_bounds::Bool = false) where S <: Real
set_regime_val!(p::Parameter{S},
model_regime::Int, v::S, d::AbstractDict{Int, Int}; override_bounds::Bool = false) where S <: Real
```
sets the value in regime `i` of `p` to be `v`. By default, we enforce
t... | {"hexsha": "0ecb5c56d7f4636f6278b5067c5270df710260d0", "size": 16984, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/regimes.jl", "max_stars_repo_name": "FRBNY-DSGE/ModelConstructors", "max_stars_repo_head_hexsha": "3c8e6ebbfd3a1c1ed8851bd84e876e595f3a9145", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
import os
from typing import Tuple, Sequence, Callable
import csv
import cv2
import numpy as np
import pandas as pd
from PIL import Image
from sklearn.model_selection import KFold
import torch
import torch.optim as optim
from torch import nn, Tensor
from torch.nn import functional as F
from torch.utils.data import Dat... | {"hexsha": "d76a24d4b71e0652608398790a1c77acbfc07ad0", "size": 7153, "ext": "py", "lang": "Python", "max_stars_repo_path": "kaggle/leaves/tta.py", "max_stars_repo_name": "ioyy900205/PyTorch_mess-around", "max_stars_repo_head_hexsha": "90d255e17158699fd7902f7746b35fa18975112e", "max_stars_repo_licenses": ["MIT"], "max_s... |
import logging
import re
import numpy as np
import torch
from datasets import Metric, load_metric
from transformers import PreTrainedTokenizer
__all__ = [
"CodeGenerationEvaluator"
]
# From https://github.com/neulab/external-knowledge-codegen/blob/datasets/conala/conala_eval.py#L94
special_chars = re.compile(r'(... | {"hexsha": "3b83d694b4ca68c6c4e7d1e85f86876cb5400d8e", "size": 5410, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluation/seq_to_seq.py", "max_stars_repo_name": "jhk16/stackoverflow-encourages-cheating", "max_stars_repo_head_hexsha": "425fa92e7defc783d34f4bd3366cd96990d3c037", "max_stars_repo_licenses"... |
!
! Copyright (c) 2019, NVIDIA CORPORATION. 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 app... | {"hexsha": "00657496791229ce8c40efc924cd1d021209d259", "size": 1956, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "indevelopment/module8/English/Fortran/cublas/Solution/main.f90", "max_stars_repo_name": "mozhgan-kch/openacc-training-materials", "max_stars_repo_head_hexsha": "099643c50f41415943035daa64a698db7... |
from astropy.table import Table
import yaml
import os
from db_tables import load_connection, open_settings
SETTINGS = yaml.load(open(os.path.join(os.environ['HOME'], 'dd_configure.yaml')))
print SETTINGS
Session, engine = load_connection(SETTINGS['CONNECTION_STRING'], echo=False)
results = engine.execute("""SELECT
... | {"hexsha": "67a2ba11699dd45bcf12448d269edf764d04fc91", "size": 1663, "ext": "py", "lang": "Python", "max_stars_repo_path": "pull.py", "max_stars_repo_name": "justincely/disk_detective_data", "max_stars_repo_head_hexsha": "5e8ceadf5708a525287357f83ca927edd519b0dd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
from xml.dom import minidom
import re
import numpy as np
from BezierCurve import GetBezierPoints as bp
import pygame
import time
import triangulate
import stl
from stl import mesh
import copy
import earcut
# import geopandas as gpd
from shapely.geometry import Polygon
import pathlib
# read the SVG file
file_name = "ne... | {"hexsha": "4c1081c73b13dcd9367e522df99a7efdb5a3283a", "size": 21275, "ext": "py", "lang": "Python", "max_stars_repo_path": "SvgToStl.py", "max_stars_repo_name": "daekwon00/SvgToStl", "max_stars_repo_head_hexsha": "170d81ea689b505dbba9e2293b2dbe0e723da6ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
import pandas as pd
import seaborn as sns
from abc import ABC, abstractmethod
from matplotlib import pyplot as plt
from typing import Generic, TypeVar, List, Union, Dict, Sequence, Optional
from ...util.string import ToStringMixin, dictString
from ...vector_model import VectorModel
# Note: in the 2... | {"hexsha": "48ee2cc91cc5a2948143eea2e7037d786c47e9dc", "size": 8955, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sensai/evaluation/eval_stats/eval_stats_base.py", "max_stars_repo_name": "schroedk/sensAI", "max_stars_repo_head_hexsha": "a2d6d7c6ab7bed9ccd5eac216dd988c49d69aec7", "max_stars_repo_licenses":... |
function [ f,qualMeasOut] = PCSD(proj,geo,angles,maxiter,varargin)
%PCSD solves the reconstruction problem using projection-controlled steepest descent method
%
% PCSD(PROJ,GEO,ALPHA,NITER) solves the reconstruction problem using
% the projection data PROJ taken over ALPHA angles, corresponding to the
% geometry ... | {"author": "CERN", "repo": "TIGRE", "sha": "8df632662228d1b1c52afd95c90d0f7a9f8dc4b3", "save_path": "github-repos/MATLAB/CERN-TIGRE", "path": "github-repos/MATLAB/CERN-TIGRE/TIGRE-8df632662228d1b1c52afd95c90d0f7a9f8dc4b3/MATLAB/Algorithms/PCSD.m"} |
[STATEMENT]
lemma start_end_implies_terminating:
assumes "has_start_points x"
and "has_end_points x"
shows "terminating x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. terminating x
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
has_start_points x
has_end_points x
goal (1 subgoal):
... | {"llama_tokens": 141, "file": "Relational_Paths_Paths", "length": 2} |
## testing function (for notebooks e.g.)
function __plot_check(dfcart,plotdir,plotfile, showplot=true)
cart= DataFrame(X=dfcart.data[1,:], Y=dfcart.data[2,:], Z=dfcart.data[3,:])
println("## check plot subtraction ...")
PyPlot.plt.figure(figsize=(9.0,8.0))
PyPlot.plt.subplot(1, 1, 1 , xlim=[-100,100]... | {"hexsha": "8898ddea603d38d60f53559b05a388c7870e4a83", "size": 1032, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/testing.jl", "max_stars_repo_name": "bosscha/gaia-shock", "max_stars_repo_head_hexsha": "61327854c998651e16a9a020a6008439e2217620", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import pytest
from cfl.density_estimation_methods.condExpMod import CondExpMod
import tensorflow as tf
# tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
import visual_bars.generate_visual_bars_data as vbd
from cfl.dataset import Dataset
import os
import numpy as np
############################### SETUP... | {"hexsha": "b494882e0a6c6e7c4f37f9834831d3cbf83f95b9", "size": 7156, "ext": "py", "lang": "Python", "max_stars_repo_path": "testing/test_condExpBase.py", "max_stars_repo_name": "eberharf/cfl", "max_stars_repo_head_hexsha": "077b99a05824f1371ac47d76dfed6bb160222668", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import numpy as np
from deformations.utility.mesh3d import mesh3d
from deformations.utility.bernstein import get_bernstein_polynomial
def get_min_max(x, *args, **kwargs):
return np.min(x, *args, **kwargs), np.max(x, *args, **kwargs)
def stu_to_xyz(stu_points, stu_origin, stu_axes):
return stu_origin + s... | {"hexsha": "fd4624f972eff8e01efaf0e95613b6060436638e", "size": 2393, "ext": "py", "lang": "Python", "max_stars_repo_path": "implementation/deformations/utility/deform.py", "max_stars_repo_name": "saurabbhsp/mesh-3d-reconstruction", "max_stars_repo_head_hexsha": "c52312bce7e3121643189f6b67192ffe28b08565", "max_stars_rep... |
import Data.Vect
-- Exercise 1
myPlusCommutes : (n : Nat) -> (m : Nat) -> n + m = m + n
myPlusCommutes Z m = rewrite plusZeroRightNeutral m in Refl
myPlusCommutes (S k) m = rewrite myPlusCommutes k m in
rewrite plusSuccRightSucc m k in Refl
-- Exercise 2
reverseProof_nil : (acc : Vect n1 a) ... | {"hexsha": "9926e25b80dc673445dabbadb28dddda0f881ae9", "size": 778, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "TypeDrivenDevelopment/Chapter08/Exercises/ex_8_2.idr", "max_stars_repo_name": "lambdaxymox/type-driven-development-with-idris", "max_stars_repo_head_hexsha": "e5e55715cd7418f3e6fab8e5658d7518da3fdc... |
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