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# Copyright 2020 The TensorFlow Authors
#
# 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 i... | {"hexsha": "f8d9b21a1a7918ba9eb78aecc9be1941ce66944c", "size": 10437, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_graphics/math/interpolation/tests/slerp_test.py", "max_stars_repo_name": "Liang813/graphics", "max_stars_repo_head_hexsha": "71ab1775228a0a292427551350cbb62bfa8bd01a", "max_stars_repo_... |
\chapter{Holomorphic functions}
Throughout this chapter, we denote by $U$ an open subset of the complex plane,
and by $\Omega$ an open subset which is also simply connected.
The main references for this chapter were \cite{ref:dartmouth,ref:bak_ca}.
\section{The nicest functions on earth}
In high school you were told h... | {"hexsha": "c2fe223ddd6def66aff70ac2fd48f45c04943aed", "size": 29993, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "corpus/napkin/tex/complex-ana/holomorphic.tex", "max_stars_repo_name": "aDotInTheVoid/ltxmk", "max_stars_repo_head_hexsha": "ee461679e51e92a0e4b121f28ae5fe17d5e5319e", "max_stars_repo_licenses": ["... |
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from pathlib import Path
import random
from skimage.draw import circle
from skatingAI.Data.BODY_25_model import BODY_25
from skatingAI.Data.skating_dataset import get_data_names, get_pose_kp
path = f"{Path.cwd()}/Data"
def normali... | {"hexsha": "ada1c6f0f6586a5f8d4229f82cbc4ede10dc2220", "size": 5432, "ext": "py", "lang": "Python", "max_stars_repo_path": "skatingAI/Data/image_operations.py", "max_stars_repo_name": "na018/awesome.skating.ai", "max_stars_repo_head_hexsha": "50738d5a359dc7fd69ec676cfaa83471b8ffe2e5", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import threading
import multiprocessing
#As defined in the name this class wraps the processed data file.
#It provides methods for further processing of this data, for example inversion analysis.
#Potentially look at the RawDataWrapper first if this class is too confusing.
class ProcessedDataWrapper... | {"hexsha": "16b240e4777dadb9df966ebdeba34af8c06a5079", "size": 18729, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataModels/ProcessedDataWrapper.py", "max_stars_repo_name": "manuelulreich/TPDAnalysisToolkit", "max_stars_repo_head_hexsha": "ba3bf59658a113543fc5b70f36e38975e26675f3", "max_stars_repo_licenses"... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import trimesh
from os import path as osp
from .image_vis import (draw_camera_bbox3d_on_img, draw_depth_bbox3d_on_img,
draw_lidar_bbox3d_on_img)
def _write_obj(points, out_filename):
"""Write points into ``obj`... | {"hexsha": "295c28dbd9df24468739ea733a274da046b3d6e4", "size": 10811, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmdet3d/core/visualizer/show_result.py", "max_stars_repo_name": "MilkClouds/mmdetection3d", "max_stars_repo_head_hexsha": "772a7fd2a47b081b9445fba03c2f9f537328cb17", "max_stars_repo_licenses": ["... |
### tf-nightly==2.5.0-dev20210104
### https://google.github.io/flatbuffers/flatbuffers_guide_tutorial.html
#!/usr/bin/env python
# coding: utf-8
import os
import numpy as np
import json
import tensorflow.compat.v1 as tf
import tensorflow as tfv2
import shutil
from pathlib import Path
import pprint
os.environ['CUDA_... | {"hexsha": "71ad88189177ea071e54978b3e39b0f56406eabf", "size": 16832, "ext": "py", "lang": "Python", "max_stars_repo_path": "082_MediaPipe_Meet_Segmentation/02_segm_full_v679_tflite_to_pb_saved_model.py", "max_stars_repo_name": "IgiArdiyanto/PINTO_model_zoo", "max_stars_repo_head_hexsha": "9247b56a7dff37f28a8a7822a7ef4... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from datetime import datetime
from typing import List
import logging
def get_futures_chain(meta_data: pd.DataFrame, asofdate: datetime.date) -> pd.DataFrame:
"""
get current futures chain on asofdate
:param meta_data: data... | {"hexsha": "22e40b6c0f02b47113510502c8badf108c4081af", "size": 10057, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/futures_tools.py", "max_stars_repo_name": "Velocities/QuantResearch", "max_stars_repo_head_hexsha": "2435cf2d109a32c7cff51263bcd7d20ac4874d37", "max_stars_repo_licenses": ["MIT"], "max_star... |
/-
Copyright (c) 2018 Andreas Swerdlow. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Author: Andreas Swerdlow
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.algebra.module.basic
import Mathlib.ring_theory.ring_invo
import Mathlib.PostPort
unive... | {"author": "AurelienSaue", "repo": "Mathlib4_auto", "sha": "590df64109b08190abe22358fabc3eae000943f2", "save_path": "github-repos/lean/AurelienSaue-Mathlib4_auto", "path": "github-repos/lean/AurelienSaue-Mathlib4_auto/Mathlib4_auto-590df64109b08190abe22358fabc3eae000943f2/Mathlib/linear_algebra/sesquilinear_form.lean"} |
import numpy as np
class Zeros:
def __call__(self, shape):
return np.zeros(shape)
class Ones:
def __call__(self, shape):
return np.zeros(shape)
class RandomNormal:
def __init__(self, mean=0.0, sd=1.0, scale=0.01):
self.mean = mean
self.sd = sd
self.scale=scal... | {"hexsha": "4e35f2b943c625f544ddd2ea9482f64a69194887", "size": 1155, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepynets/initializers.py", "max_stars_repo_name": "akarsh-saxena/DeePyNets", "max_stars_repo_head_hexsha": "b7ea3687530305ccbf83a7374b7ccd4164489009", "max_stars_repo_licenses": ["MIT"], "max_sta... |
## Coordinates
#==========================================================================================#
wing_bounds(lead, trail) = permutedims([ lead trail ])
chop_leading_edge(obj :: HalfWing, span_num; y_flip = false) = chop_coordinates(leading_edge(obj, y_flip), span_num)
chop_trailing_edge(obj :: HalfWing, ... | {"hexsha": "6efefd77a7db516239a4a1140442fb0294f93b9a", "size": 13600, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Geometry/AircraftGeometry/Wings/mesh_wing.jl", "max_stars_repo_name": "HKUST-OCTAD-LAB/AeroMDAO.jl", "max_stars_repo_head_hexsha": "0ca9aa924f088cac59d04958eb5c6704b50feb18", "max_stars_repo_l... |
/*
* Copyright (c) CERN 2013
*
* Copyright (c) Members of the EMI Collaboration. 2010-2013
* See http://www.eu-emi.eu/partners for details on the copyright
* holders.
*
* Licensed under Apache License Version 2.0
*
*/
#include "HdfsNS.h"
#include <boost/algorithm/string/predicate.hpp>
using nam... | {"hexsha": "b57c1d82e1503c36fb75231a5f40f5f31603ba95", "size": 3022, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/HdfsUtil.cpp", "max_stars_repo_name": "andrea-manzi/dmlite-hdfs-plugin", "max_stars_repo_head_hexsha": "16a9909db7cbdf0328215d30b9df4ea5a752a9b6", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
using Protos
using Protos.Parsing
using Test
@testset "Protos.jl" begin
@testset "parsing" begin
testfile = joinpath(@__DIR__, "test.proto")
io = open(testfile)
parsed = parse_proto(io)
@show parsed
@test parsed.comment == "\nThis is a file-wide comment.\n"
@test par... | {"hexsha": "463721624e9d45f2b93d2ce49d156ba71ee220f3", "size": 1073, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tbreloff/Protos.jl", "max_stars_repo_head_hexsha": "6f154a39231268300ca4f9e0f71878cd769eea3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
import numpy as np
import matplotlib.pyplot as plt
def estimate_coef(x, y):
# number of observations/points
n = np.size(x)
# mean of x and y vector
m_x, m_y = np.mean(x), np.mean(y)
# calculating cross-deviation and deviation about x
SS_xy = np.sum(y*x) - n*m_y*m_x
SS_xx = np.sum(x*x) - n... | {"hexsha": "7c29ce63420201450e16d8972536698fa42db53c", "size": 1226, "ext": "py", "lang": "Python", "max_stars_repo_path": "Regression/SimpleLinearRegression.py", "max_stars_repo_name": "sum-coderepo/HadoopApp", "max_stars_repo_head_hexsha": "0e8d48c5d541b5935c9054fb1335d829d67d7b59", "max_stars_repo_licenses": ["Apach... |
"""Exercise 1
Usage:
$ CUDA_VISIBLE_DEVICES=2 python practico_1_train_petfinder.py --dataset_dir ../ --epochs 30 --dropout 0.1 0.1 --hidden_layer_sizes 200 100
To know which GPU to use, you can check it with the command
$ nvidia-smi
"""
import argparse
import os
import mlflow
import pickle
import numpy as np
impo... | {"hexsha": "0ebf6e6f4a1667f2d0b5238c117fa44dfca6f7c4", "size": 10203, "ext": "py", "lang": "Python", "max_stars_repo_path": "tercer_modelo.py", "max_stars_repo_name": "nahuelalmeira/deepLearning", "max_stars_repo_head_hexsha": "f1fcd06f5735c8be9272b0c8392b1ae467c08582", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from paralleldomain.decoding.decoder import DatasetDecoder
from paralleldomain.model.class_distribution import ClassDistribution
def test_from_dataset(decoder: DatasetDecoder):
dataset = decoder.get_dataset()
class_dist = ClassDistribution.from_dataset(dataset=dataset)
assert class_dis... | {"hexsha": "3e12232216043ab981613a802fd5bb80a1b54f17", "size": 805, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_paralleldomain/model/test_class_distibution.py", "max_stars_repo_name": "parallel-domain/pd-sdk", "max_stars_repo_head_hexsha": "20e3d052a5cb612a2dd84bda7b1b5487a6a60edc", "max_stars_repo_lice... |
# based on the keras documentation
#
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Conv1D,Activation
from keras import losses, optimizers
import keras.utils as keras_utils
import json
f = open('data_for_everything')
j = json.load(f)
# our parameters
nn_input_size = len(j[0][... | {"hexsha": "9b8f07e157c6588a775d6619b9a559fea0c384f1", "size": 1750, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nn/train.py", "max_stars_repo_name": "bahorn/BrumHack7", "max_stars_repo_head_hexsha": "cffa2484f63728e73d6dd2bbe6b24fbd12e1cd93", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": ... |
#
# Copyright (c) 2021, 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 appl... | {"hexsha": "a8613eae27f6f6740cd4054eb1d513606f97e1e9", "size": 4883, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/python/uff_ssd/utils/boxes.py", "max_stars_repo_name": "martellz/TensorRT", "max_stars_repo_head_hexsha": "f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4", "max_stars_repo_licenses": ["Apache-2.... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 12 11:00:56 2017
@author: 028375
"""
import pandas as pd
import numpy as np
begindate='20171001'
spotdate='20171018'
lastdate='20171017'
path0='F:\月结表\境内TRS\S201710\\'.decode('utf-8')
def TestTemplate(Status,Collateral,Position):
path1=('股衍境内TRS检验... | {"hexsha": "676b6ca831e438f7ac593289042c9e7e1e5beb4a", "size": 2602, "ext": "py", "lang": "Python", "max_stars_repo_path": "productcontrol/TRS_EQ.py", "max_stars_repo_name": "JulianGong/littleAccountingTools", "max_stars_repo_head_hexsha": "d315a70ed102b13d48b2df6968283c36934857bb", "max_stars_repo_licenses": ["MIT"], ... |
"""
Compare our results with FJ model (and upper bound)
If we directly modify k nodes' innate opinions, each one modify with at most epsilon,
how much will it influence the final measures?
Ref:
Gaitonde, Jason, Jon Kleinberg, and Eva Tardos.
"Adversarial perturbations of opinion dynamics in networks." Proce... | {"hexsha": "345ad8b0321082c7721b876529efcddc14ff0289", "size": 5663, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "compare/CompareFJ.jl", "max_stars_repo_name": "SijingTu/WebConf-22-Viral-Marketing-Opinion-Dynamics", "max_stars_repo_head_hexsha": "8f1cc37b0cf5b392aece17b45cca84e6121d26eb", "max_stars_repo_licen... |
from python_helper import log
from globals import newGlobalsInstance
globalsInstance = newGlobalsInstance(
__file__,
successStatus = True,
errorStatus = True,
infoStatus = True,
# debugStatus = True,
failureStatus = True
)
log.info(__name__, 'Importiong libraries')
import time
import numpy as ... | {"hexsha": "53b2e5e8e0577e88d1f1cd9fa686b55b1b1c86f3", "size": 5812, "ext": "py", "lang": "Python", "max_stars_repo_path": "appTest.py", "max_stars_repo_name": "SamuelJansen/central-point-query", "max_stars_repo_head_hexsha": "1cbf1d5fdab3efe653bfd4e24952d9a1883983ca", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Run Program: horovodrun -np 4 python3 pycylon_horovod_pytorch_example.py
"""
import argparse
import os
import socket
import horovod.torch as hvd
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from pycylon import CylonEnv
from p... | {"hexsha": "7c5e4f25c9657e0dbd6d852c3ff3fa8db8933e82", "size": 6895, "ext": "py", "lang": "Python", "max_stars_repo_path": "horovod/pycylon_horovod_pytorch_example.py", "max_stars_repo_name": "vibhatha/cylon_applications", "max_stars_repo_head_hexsha": "6af9bdf3b4738c6cc4cf2c3c181a5c60f049d113", "max_stars_repo_license... |
import cirq
import numpy as np
import pandas as pd
from typing import List
from qnn.qnlp.circuits_words import CircuitsWords
def get_overall_run_words(trial_result: cirq.TrialResult, num: int):
""" Takes the average of the measurements of a given qubit on a given circuit
(the results are on the form of a bitstring... | {"hexsha": "61338eafe37a3edfeddacf3a5680596929dc166e", "size": 5446, "ext": "py", "lang": "Python", "max_stars_repo_path": "qnn/qnlp/optimization_words.py", "max_stars_repo_name": "tomiock/QNNs", "max_stars_repo_head_hexsha": "9822aac1b56e617f92bc5e3670d06285047e5066", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# -*- coding: utf-8 -*-
import logging
import os
import sys
import matplotlib.pyplot as plt
import tempfile
import numpy as np
import wave
import subprocess
from PyQt4 import QtCore
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_... | {"hexsha": "55b8d8d88cb42117086883871b24eb24f0156212", "size": 4562, "ext": "py", "lang": "Python", "max_stars_repo_path": "wav_plot.py", "max_stars_repo_name": "QuantumEnergyE/wave_plot", "max_stars_repo_head_hexsha": "af2c03d90ca23ddca35051298ad9d1f4e514e932", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Wordle Solver"""
import json
import logging
from pathlib import Path
from collections import Counter
from functools import cached_property
import networkx as nx
from .vocab import Vocabulary
from .wordle import Wordle
from .defaults import COVERAGE_CACHE
###########... | {"hexsha": "50b299b723395ebfbc522d00056cc5baf88e3a98", "size": 7287, "ext": "py", "lang": "Python", "max_stars_repo_path": "wordle/solver.py", "max_stars_repo_name": "hrishikeshrt/python-wordle", "max_stars_repo_head_hexsha": "574f4476a0a3b35ebb0030babc8bd49d8107f34c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import networkx as nx
from tools import safe_sample
from nature import add_node
MIN_LAYERS, MAX_LAYERS = 1, 3
MIN_NODES, MAX_NODES = 1, 2
def Regulon(parent=None):
n_layers = safe_sample(MIN_LAYERS, MAX_LAYERS)
M, ids = nx.MultiDiGraph(), []
for layer_number in range(n_layers):
n_nodes = safe_sa... | {"hexsha": "54217b9f17adf3a2f406e0a6c57b26d895d39cab", "size": 1155, "ext": "py", "lang": "Python", "max_stars_repo_path": "nature/bricks/graph/evolve/regulon.py", "max_stars_repo_name": "bionicles/neuromax", "max_stars_repo_head_hexsha": "a53a17a1c033c11ac607a9e28f43b1f906e58aad", "max_stars_repo_licenses": ["MIT"], "... |
from fundopt.fundtsloader import load_funds
import pandas as pd
import numpy as np
import datetime as dt
import logging
from arctic import Arctic # pyright: reportMissingImports=false
from pymongo import MongoClient
import keyring
import ssl
client = MongoClient("localhost")
# client = MongoClient(keyring.get_p... | {"hexsha": "3b03ab83cb454909408ef5ae71b5b8c67c716b32", "size": 983, "ext": "py", "lang": "Python", "max_stars_repo_path": "load_fund.py", "max_stars_repo_name": "joshualee155/FundOptimizer", "max_stars_repo_head_hexsha": "da842de6c99f89c767d03c9ef1b392237b726a3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Simple linear regression example in TensorFlow
This program tries to predict the number of thefts from
the number of fire in the city of Chicago
"""
# pylint: disable=invalid-name
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import xlrd
DATA_FILE = 'data/fire_theft.xls'
LOG_FILE = 'l... | {"hexsha": "34ace8ced91080f8dd0809bca4b704466135ead1", "size": 3262, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw9/hw9_p2_normalized.py", "max_stars_repo_name": "shanaka-desoysa/tensorflow", "max_stars_repo_head_hexsha": "0effc668f42b64bd0712240ab2f5e8a8be42960f", "max_stars_repo_licenses": ["Apache-2.0"],... |
import numpy as np
from scipy.stats import f
class MVRCalculator:
"""
Class holds the calculations needed to perform the regression
on some data. Used to seperate out the data and calculations.
"""
@staticmethod
def searchValue(f, target,
tolerance=0.000001, start=0, st... | {"hexsha": "0d273f529c35fcd4b1f7f0b7c8b1f48fd07a931b", "size": 6544, "ext": "py", "lang": "Python", "max_stars_repo_path": "mvr_calc.py", "max_stars_repo_name": "richhaar/multivariable-linear-regression", "max_stars_repo_head_hexsha": "af195fed6031e813da5614c0b77ab2a74190c8b1", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy
import csv
import matplotlib.pyplot as plt
import pprint
#change leafsize according to need, i think it was 2000
def kdtree( data, leafsize=10 ):
ndim = data.shape[0]
ndata = data.shape[1]
# find bounding hyper-rectangle
hrect = numpy.zeros((2,data.shape[0]))
hrect[0,:] = ... | {"hexsha": "10e91a18a52008ddce42682ab629418cd0f8526d", "size": 3555, "ext": "py", "lang": "Python", "max_stars_repo_path": "k-d_tree.py", "max_stars_repo_name": "smellycattt/Data-Mining-Project", "max_stars_repo_head_hexsha": "83f3873df9ccecbfc5bdfc8e1fc6766acb4a2c8e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Raised Cosine distribution
#
# Ref: http://en.wikipedia.org/wiki/Raised_cosine_distribution
#
immutable Cosine <: ContinuousUnivariateDistribution
μ::Float64
σ::Float64
Cosine(μ::Real, σ::Real) = (@check_args(Cosine, σ > zero(σ)); new(μ, σ))
Cosine(μ::Real) = new(μ, 1.0)
Cosine() = new(0.0, 1.0)... | {"hexsha": "9f3ee9364db998d12332eda28897ede401ff6b70", "size": 1235, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/univariate/continuous/cosine.jl", "max_stars_repo_name": "ericproffitt/Distributions.jl", "max_stars_repo_head_hexsha": "54daf6f7230c6cf1fa46d9a948a33ad68b5fd3b0", "max_stars_repo_licenses": ["... |
from flask import Flask, render_template
import plotly.figure_factory as ff
import json
import plotly
import pandas as pd
import numpy as np
import requests
app = Flask(__name__)
app.debug = True
@app.route('/')
def index():
info_data = requests.get("http://localhost:8000/test").json()
t_o_a = info_data[... | {"hexsha": "71e71b36fc938e4928ad678bdb96af9ffad4f5e5", "size": 3631, "ext": "py", "lang": "Python", "max_stars_repo_path": "servers/Radiation/plotly/app.py", "max_stars_repo_name": "arpitgogia/mars_city", "max_stars_repo_head_hexsha": "30cacd80487a8c2354bbc15b4fad211ed1cb4f9d", "max_stars_repo_licenses": ["BSD-2-Clause... |
import numpy as np
from RenderPy.Shape import Shape
from RenderPy.Tuple import Tuple
from RenderPy.Intersection import Intersection
# ---------------------
"""
Cone class helps to describe a cone with a center at point(0,0,0)
It inherits all elements from shape
Cone class contains the following functions:
__init__
l... | {"hexsha": "9c7c6acdf32febfc5fc729500846cb63026a8063", "size": 4928, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/RenderPy/Cone.py", "max_stars_repo_name": "woes-lynne/3DRenderPy", "max_stars_repo_head_hexsha": "44d9106b51ae4ce8307c794b85d4ec649751beb3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/* Legendre polynomials
An in-class exercise using equations for the Polynomials and solvers as
shown in the lecture/manuscript for Numerical Methods for CSE
by Prof. R. Hiptmair, ETH Zürich
Include the Eigen3 library as shown in documentation for Eigen3.
use piping to store the .m file. Example call:
legendr... | {"hexsha": "58bb6829d85013d3f24fa231f2aa1caebebfc428", "size": 13948, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "example2/legendre.cpp", "max_stars_repo_name": "pirminschmid/MatlabPlotter", "max_stars_repo_head_hexsha": "6cdc3954ee4a065d978c0248b00406366eafe237", "max_stars_repo_licenses": ["MIT"], "max_stars... |
Lose weight permanently
Have you lost weight, regained the weight, lost weight again and again and again? If you are like many people, weight management has been a life long struggle. Now is the time to stop the struggle and learn the skills you need in order to lose the weight and keep it off permanently.
Diet... | {"hexsha": "963108133cd517075ce8c71135a947a2a52f56b3", "size": 1060, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Weight_No_More.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Retrieves either NZTA or NZS1170.5 code values
for the given locations
"""
from pathlib import Path
import argparse
import multiprocessing as mp
from typing import Sequence
import numpy as np
import pandas as pd
import sha_calc as sha
import gmhazard_calc as sc
DEFAULT_RETURN_PERIODS = np.array([20, 25, 50, 100... | {"hexsha": "f1b2b9beee3aefdc21321a7e4f4b69cbb2281c3d", "size": 6933, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/gmhazard_scripts/one_off/nz_code_retrieval.py", "max_stars_repo_name": "ucgmsim/gmhazard", "max_stars_repo_head_hexsha": "d3d90b4c94b3d9605597a3efeccc8523a1e50c0e", "max_stars_repo_licenses"... |
"""
Class Features
Name: drv_data_hs_geo
Author(s): Francesco Avanzi (francesco.avanzi@cimafoundation.org), Fabio Delogu (fabio.delogu@cimafoundation.org)
Date: '20210525'
Version: '1.0.0'
"""
#######################################################################################
# Library... | {"hexsha": "4ed51360827cd95217c5f1af218cd870fb934ffb", "size": 14168, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/ground_network/hs/drv_data_hs_geo.py", "max_stars_repo_name": "c-hydro/hyde", "max_stars_repo_head_hexsha": "3a3ff92d442077ce353b071d5afe726fc5465201", "max_stars_repo_licenses": ["MIT"], "m... |
SUBROUTINE struct_sizes(nat,nsym,ndif,lattic,AA,BB,CC,alpha,structf)
IMPLICIT NONE
CHARACTER*80, intent(in):: structf
INTEGER, intent(out) :: nat, nsym, ndif
CHARACTER*4, intent(out):: lattic
REAL*8, intent(out) :: AA,BB,CC,alpha(3)
!----------- local variables ---------------
CHARACTER*4 :: irel... | {"hexsha": "4123d0ce547ba761f7021db0938ace1c110750d4", "size": 21305, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/downfold/latgen.f90", "max_stars_repo_name": "chanul13/EDMFTF", "max_stars_repo_head_hexsha": "967d85d898924991b31861b4e1f45129e3eff180", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
"""
Optimization algorithms
"""
import numpy as np
import numpy.ma as ma
import numpy.linalg as LA
import copy
from tqdm import tqdm
from scipy.spatial import distance
from sklearn.neighbors import NearestNeighbors
import torch
import torch.nn as nn
"""
def grad_free_optimizer(initial_sequence, oracle, N):
... | {"hexsha": "7e931af73a686790cb4cc3d33eccddff4b8b466f", "size": 12490, "ext": "py", "lang": "Python", "max_stars_repo_path": "relso/optim/optim_algs.py", "max_stars_repo_name": "ec1340/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers", "max_stars_repo_head_hexsha": "2320b3ebd97df908474b23e7a4395b8fa... |
%% SECTION HEADER /////////////////////////////////////////////////////////////////////////////////////
\section{The Time Integration}
\label{sec:time}
%% SECTION CONTENT ////////////////////////////////////////////////////////////////////////////////////
Similar to the \ac{fem}, the time solution of the governing equ... | {"hexsha": "3de91fbfb7be95d4ee531956dc775fccf2651798", "size": 5671, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/proposal/Dissertation/Chapters/Chapter4/sec:time.tex", "max_stars_repo_name": "pfiborek/model-hc", "max_stars_repo_head_hexsha": "9e49fe23117fd320be14214e5ff6bafd2b1fc1a3", "max_stars_repo_lice... |
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import math
import logging
from numpy.linalg import norm
import astropy.units as u
import astropy.constants as const
from naima.models import (
ExponentialCutoffPowerLaw,
ExponentialCutoffBrokenPowerLaw,
Synchrotron,
InverseCompton
... | {"hexsha": "b8f897557f45d60778171a62fb662b04a5a6b3fc", "size": 22939, "ext": "py", "lang": "Python", "max_stars_repo_path": "tgblib/fit_results.py", "max_stars_repo_name": "RaulRPrado/tev-binaries-model", "max_stars_repo_head_hexsha": "c60959caaffbcdf3398914b03531647f95e97da0", "max_stars_repo_licenses": ["Apache-2.0"]... |
# -*- coding: utf-8 -*-
"""Creating sets, variables, constraints and parts of the objective function
for Flow objects.
SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
SPDX-FileCopyrightText: Simon Hilpert
SPDX-FileCopyrightText: Cord Kaldemeyer
SPDX-FileCopyrightText: Patrik Schönfeldt
SPDX-FileCopyrightText:... | {"hexsha": "4a18d63bf253d6255e1ed516047eae50cffccf8e", "size": 10574, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/oemof/solph/blocks/flow.py", "max_stars_repo_name": "lensum/oemof-solph", "max_stars_repo_head_hexsha": "75789b1578035d0b658c4b97fcc41fc3ca61638e", "max_stars_repo_licenses": ["MIT"], "max_st... |
from collections.abc import Iterable
import re
import warnings
import numpy as np
import h5py
import openmc
import openmc.checkvalue as cv
from openmc.region import Region
_VERSION_SUMMARY = 6
class Summary(object):
"""Summary of geometry, materials, and tallies used in a simulation.
Attributes
------... | {"hexsha": "866dd8288a563efc647b483a6c684116727dc610", "size": 7627, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmc/summary.py", "max_stars_repo_name": "johnnyliu27/openmc", "max_stars_repo_head_hexsha": "d7359f151cc9eece99fb155e80f73a1b3393f7f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
struct VanDerWaals{S,T,A} <: CubicModel
type::S
tc::T
pc::T
ω::T
mw::T
vc::Union{T,Nothing}
_a::T
_b::T
aij::A
function VanDerWaals(tc,pc,ω,mw,vc=nothing,aij = nothing)
if length(tc) == 1
type = SINGLE()
else
type = MULTI()
end
... | {"hexsha": "b04fa3e681a09102461ea07f4f2692e657be422e", "size": 5013, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/cubic/vdw.jl", "max_stars_repo_name": "longemen3000/ThermoModels.jl", "max_stars_repo_head_hexsha": "a817ba1677c0b1bed39fe5a695d3ada63b673f40", "max_stars_repo_licenses": ["MIT"], "max_s... |
from keras.preprocessing.text import Tokenizer #this is used to assign some numeric value to every word that appear in the training set
from keras.preprocessing.sequence import pad_sequences
import pandas as pd
import nltk
import numpy as np
import re
from sklearn.model_selection import train_test_split
import s... | {"hexsha": "bc782cb6d120284a38090ecc4a7d567ec4d2453c", "size": 4982, "ext": "py", "lang": "Python", "max_stars_repo_path": "RNN.py", "max_stars_repo_name": "SimralPimenta20/personality-detection-using-social-media-messages", "max_stars_repo_head_hexsha": "f841266fcd2d9e1fd9de7d8e33abe0e9fcd2661c", "max_stars_repo_licen... |
##### OVERRIDES FOR EFFICIENCY / CORRECTNESS
function add_vertices!(g::AbstractSimpleWeightedGraph, n::Integer)
T = eltype(g)
U = weighttype(g)
(nv(g) + one(T) <= nv(g)) && return false # test for overflow
emptycols = spzeros(U, nv(g) + n, n)
g.weights = hcat(g.weights, emptycols[1:nv(g), :])... | {"hexsha": "eb6a19d43fdc50b99df9f4ec51718272b942c868", "size": 4663, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/overrides.jl", "max_stars_repo_name": "scheidan/SimpleWeightedGraphs.jl-1", "max_stars_repo_head_hexsha": "e500596d906193d2de8d052f72ceeb750c1de1bf", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import scipy.stats.distributions as sc_dist
from itertools import compress
def aggarwal_limits(mu, alpha=0.68268949):
"""Get Poissonian limits for specified contour levels
Parameters
----------
pdfs : array_like
The expected number of events (Poisson mean) in each observabl... | {"hexsha": "e098f3ba8bc8d407daa2610883cca99591581e8a", "size": 26216, "ext": "py", "lang": "Python", "max_stars_repo_path": "disteval/visualization/comparison_plotter/functions/calc_funcs.py", "max_stars_repo_name": "jebuss/pydisteval", "max_stars_repo_head_hexsha": "52c1c21cd5568b640732deb29f4216d881d6dd53", "max_star... |
[STATEMENT]
lemma ex_gt_count_imp_le_multiset:
"(\<forall>y :: 'a :: order. y \<in># M + N \<longrightarrow> y \<le> x) \<Longrightarrow> count M x < count N x \<Longrightarrow> M < N"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<forall>y. y \<in># M + N \<longrightarrow> y \<le> x; count M x < count ... | {"llama_tokens": 311, "file": null, "length": 2} |
(* infotheo: information theory and error-correcting codes in Coq *)
(* Copyright (C) 2020 infotheo authors, license: LGPL-2.1-or-later *)
From mathcomp Require Import all_ssreflect ssralg fingroup finalg matrix.
Require Import Reals Lra.
From mathcomp Require Import Rstruct.
Require Import ssrZ ... | {"author": "affeldt-aist", "repo": "infotheo", "sha": "5f9efb859dbadcbcae2330e2e21e76f9b632d879", "save_path": "github-repos/coq/affeldt-aist-infotheo", "path": "github-repos/coq/affeldt-aist-infotheo/infotheo-5f9efb859dbadcbcae2330e2e21e76f9b632d879/information_theory/joint_typ_seq.v"} |
%--- help for dsge_solver_h ---
%
% H1 line
%
% ::
%
%
% Args:
%
% Returns:
% :
%
% Note:
%
% Example:
%
% See also:
%
% Other functions named dsge_solver_h
%
% dsge/dsge_solver_h
% | {"author": "jmaih", "repo": "RISE_toolbox", "sha": "1b2edfa27830c6d522f9d7d2335d33c3e4d84285", "save_path": "github-repos/MATLAB/jmaih-RISE_toolbox", "path": "github-repos/MATLAB/jmaih-RISE_toolbox/RISE_toolbox-1b2edfa27830c6d522f9d7d2335d33c3e4d84285/classes/models/@dsge/private/+deprecated/dsge_solver_h.m"} |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | {"hexsha": "0c324ba8ee9aa2d16fadfd68e8b19e9e9a3a9abf", "size": 8208, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/distributed_passes/test_dist_gradient_merge_pass.py", "max_stars_repo_name": "DevilCarp/Paddle", "max_stars_repo_head_hexsha": "04325d2cbefb029a4478bdc069d3279c... |
function part1(input)
risk = reduce(hcat, (parse.(Int, collect(line))
for line in eachline(input)))
return shortest_path(risk)
end
function part2(input)
risk = reduce(hcat, (parse.(Int, collect(line))
for line in eachline(input)))
h, w = size(risk)
... | {"hexsha": "69921da7f1e26638dc3c87a4bf1f6962629399a1", "size": 1214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2021/day15.jl", "max_stars_repo_name": "GunnarFarneback/AdventOfCode.jl", "max_stars_repo_head_hexsha": "2f60011747bfe5d27e954f914f39b4ea2f7b0722", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Jacob Kaplan
kmeans.py
"""
import sys
import cv2 as cv
import numpy as np
def scale(img):
"""
Take in image
Reshape it to have width of 600 pixels
Use OpenCV mean shift to recolor each pixel by shifting it towards
the mode of a given radius of pixels
Return recolored image
""... | {"hexsha": "15f38747372dd6255c68200bfe4f5def584572b2", "size": 1640, "ext": "py", "lang": "Python", "max_stars_repo_path": "kmeans/kmeans.py", "max_stars_repo_name": "jcolekaplan/computer_vision", "max_stars_repo_head_hexsha": "48d39b081a7b6b699019052eeae36ab703bb34eb", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
The Medical Sciences Building (really four buildings known as Med Sci 1A, 1B, 1C, or 1D), is address(located, 38.533564, 121.763794) near the Genome and Biomedical Sciences Facility building in the Health Sciences Complex which is near the western edge of the core campus. Med Sci 1A is more commonly known as Tupper Hal... | {"hexsha": "c22df86b572de95fb6ef17618c76682fe44775a7", "size": 326, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Medical_Sciences.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
from ctypes import *
import numpy as np
import time
import math
class Point(object):
x = 0
y = 0
def __init__(self, x=0, y=0):
self.x = x
self.y = y
# 判断速度是否发生突变，每判断一次消耗2us
def speed_jump(c1, c2, c3, inter_time):
'''
1、去除每辆车的前10帧，防止刚... | {"hexsha": "e9bd113adef90afbacb4a4e62a3a7a4cd4ba87ad", "size": 1109, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/speed_jump.py", "max_stars_repo_name": "chiyukunpeng/traffic-accident-detection", "max_stars_repo_head_hexsha": "f0e33744d6bd2d634c22ef2d561558c4b10105d6", "max_stars_repo_licenses": ["MIT"... |
"""
Inferring a binomial proportion via exact mathematical analysis.
"""
import sys
import numpy as np
from scipy.stats import beta
from scipy.special import beta as beta_func
import matplotlib.pyplot as plt
import matplotlib.patches as patches
#from HDIofICDF import *
from scipy.optimize import fmin
#from scipy.stats ... | {"hexsha": "49017e15b4bb53e3727fa01344c68fc1443af428", "size": 4896, "ext": "py", "lang": "Python", "max_stars_repo_path": "second-round-intreview/parcoord-brushing/backend/src/code/parallelcoord/ParCoordKmeans.py", "max_stars_repo_name": "halilagin/parcoord-brushing", "max_stars_repo_head_hexsha": "71dde2d9b24038afb51... |
# ¿Cómo se mueve un péndulo?
> Calificaciones: https://docs.google.com/spreadsheets/d/1X8sAHmrIErYgoAjTocclAFS0Mx_EA8BDWlp6DgYyBVo/edit?usp=sharing
> Se dice que un sistema cualquiera, mecánico, eléctrico, neumático, etc., es un oscilador armónico si, cuando se deja en libertad fuera de su posición de equilibrio, v... | {"hexsha": "df61ce55d3084f4b4cf4798e2d45b8f1d441b003", "size": 279998, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Modulo3/Clase15_OsciladorArmonico.ipynb", "max_stars_repo_name": "DiegoBAL23/simmatp2021", "max_stars_repo_head_hexsha": "238e88b58cf0481de444ffd14a8b46dbdfae6066", "max_stars_repo_... |
from numpy.testing import assert_array_equal
import numpy as np
from tadataka.depth import compute_depth_mask
def test_compute_depth_mask():
depths = np.array([
[-1, 4, 2, 3, -4],
[-8, 5, 1, 0, 2]
])
assert_array_equal(
compute_depth_mask(depths, min_depth=0.0),
[False, Tr... | {"hexsha": "ac2bda56c04edc0618fe3ff1558ac96c7719e099", "size": 475, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_depth.py", "max_stars_repo_name": "IshitaTakeshi/Tadataka", "max_stars_repo_head_hexsha": "852c7afb904503005e51884408e1492ef0be836f", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import pathlib
import shutil
import numpy as np
from text_recognizer.datasets.emnist_lines import EmnistLinesDataset
import text_recognizer.util as util
SUPPORT_DIRNAME = pathlib.Path(__file__).parents[0].resolve() / 'emnist_lines'
def create_emnist_lines_support_files():
shutil.rmtree(SUPPORT_DIRNAME, ignore... | {"hexsha": "be342a8bbc96def54ceff5970ed2eaabc92e0627", "size": 822, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab6_sln/text_recognizer/tests/support/create_emnist_lines_support_files.py", "max_stars_repo_name": "sergeyk/fsdl-text-recognizer-project", "max_stars_repo_head_hexsha": "8083e181f830f9f493f15b9e8... |
[STATEMENT]
lemma convol_apply: "BNF_Def.convol f g x = (f x, g x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. BNF_Def.convol f g x = (f x, g x)
[PROOF STEP]
unfolding convol_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (f x, g x) = (f x, g x)
[PROOF STEP]
.. | {"llama_tokens": 137, "file": null, "length": 2} |
[STATEMENT]
lemma (in ring) indexed_const_index_free: "index_free (indexed_const k) i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. index_free (indexed_const k) i
[PROOF STEP]
unfolding index_free_def indexed_const_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>m. i \<in># m \<longrightarrow> (if m... | {"llama_tokens": 154, "file": null, "length": 2} |
import argparse
import csv
import random
import sys
from pathlib import Path
import functools
import ipdb
import numpy as np
import torch
import torch.nn as nn
from box import Box
from tqdm import tqdm
from BERT.dataset import create_data_loader
from BERT.train import Model
from BERT.common.losses import CrossEntropy... | {"hexsha": "4aee44294099ecdf03678447434cc7149b3d1216", "size": 7210, "ext": "py", "lang": "Python", "max_stars_repo_path": "part2/BERT/predict.py", "max_stars_repo_name": "peter850706/Contextual-embeddings-for-sequence-classification", "max_stars_repo_head_hexsha": "e26ba68f6aa30ec07319dcd37a04a8f56e07d7b0", "max_stars... |
************************************************************************
*
* Subroutine MLELOAD2 Called by: MLELOAD
*
*
* Estimate loads using MLE. Bias correction is done by the
* method of Bradu and Mundlak(1970). An estimate of the variance
* of the load is obtained... | {"hexsha": "aaba927c3707dd27dbfe4cdfcb45a4a1f61f2143", "size": 2781, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "codeNexampls/[loadest]/loadsrc/loadest/source_USGS/mleload2.f", "max_stars_repo_name": "lthiamodelers/baseflow-coefficients", "max_stars_repo_head_hexsha": "183fef49548fa2e1bf0bb8cff57e96e75d62976... |
# Import Flask
from flask import Flask, jsonify
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
import datetime as dt
import numpy as np
import pandas as pd
# set up datab... | {"hexsha": "c1246cae51a213a086ec7fc42e5c8f3dd6d8de6e", "size": 4363, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "etarakci/sqlalchemy-challenge", "max_stars_repo_head_hexsha": "aba9b7b9215755f9f751f99dd6979e89e41a3851", "max_stars_repo_licenses": ["ADSL"], "max_stars_count": n... |
#!/usr/bin/env python
import rogata_library as rgt
import cv2
import cv2.aruco as aruco
import numpy as np
import sys
def calibrate_colors(image):
"""Utility to calibrate the colors for contour detection
Allows the visual calibration of contours which can be saved by pressing the s key
Colors are defined ... | {"hexsha": "21fcaeff19924a8fd805965dc42556cced7f3953", "size": 2797, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/calibrate_scene.py", "max_stars_repo_name": "liquidcronos/RoGaTa-Engine", "max_stars_repo_head_hexsha": "3704bbd85c9d07f180f7e7516e282468ac76b557", "max_stars_repo_licenses": ["MIT"], "max... |
"""This is the actual code we use to score people's solutions
server-side. The interfaces here are not yet stable, but we include
them so that people can reproduce our scoring calculations
independently.
We correspondly do not currently import this module.
"""
import numpy as np
import requests
import gym
def score... | {"hexsha": "bc9edf5e1a40e3666cf199b44dd7218e26b7821d", "size": 7066, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym/scoreboard/scoring.py", "max_stars_repo_name": "soochyboy/openaiattempt", "max_stars_repo_head_hexsha": "2933a95ec5cc7efd282ed793d5efd6af6f2ce00f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import os
import tensorflow as tf
import datetime
import numpy as np
import pandas as pd
from tensorflow.keras.callbacks import ModelCheckpoint
import utils as ut
class ConvBlock:
def __init__(self, n_filters=64, filter_size=(3, 3), strides=(1, 1), padding='same', activation='elu', use_bn=True):
self.n_fi... | {"hexsha": "16f76a1abf15b199e113967f4156ad16b4b93127", "size": 13803, "ext": "py", "lang": "Python", "max_stars_repo_path": "training.py", "max_stars_repo_name": "tldrafael/FaceReconstructionWithVAEAndFaceMasks", "max_stars_repo_head_hexsha": "a7ec6a424142167e5e68cb2f09552f7a84706362", "max_stars_repo_licenses": ["MIT"... |
//
// Copyright (C) 2009-2012 Artyom Beilis (Tonkikh)
//
// 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)
//
#define BOOSTER_SOURCE
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <proce... | {"hexsha": "b652aa3ead410c9ace00087e2d6f50d9e6685ed2", "size": 7379, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "booster/lib/thread/src/thread_winapi.cpp", "max_stars_repo_name": "gatehouse/cppcms", "max_stars_repo_head_hexsha": "61da055ffeb349b4eda14bc9ac393af9ce842364", "max_stars_repo_licenses": ["MIT"], "m... |
#!/usr/bin/env python3
"""
@summary: for the jupyter notebooks: tools, column creators, diagramming routines, etc.
@version: v40 (29/November/2018)
@since: 26/June/2018
@organization:
@author: https://github.com/drandreaskrueger
@see: https://github.com/drandreaskrueger/chainhammer for updates
@TODO: this... | {"hexsha": "1c41dffec20d92c8af4de2b7464ed7d62eef7243", "size": 23885, "ext": "py", "lang": "Python", "max_stars_repo_path": "reader/blocksDB_diagramming.py", "max_stars_repo_name": "nairobi222/chainhammer", "max_stars_repo_head_hexsha": "94ab5269a9a9c751d355b41f90ac244026ccf46b", "max_stars_repo_licenses": ["MIT"], "ma... |
"""
cubeset - Defines a CubeSet class that contains code to handle operations
on several IFU data cubes, e.g., coaddition
"""
import os
import numpy as np
from matplotlib import pyplot as plt
from astropy.io import ascii
from astropy.io import fits as pf
from .oscube import OsCube
class CubeSet(list):
... | {"hexsha": "d283ad58515b8f34b747e30d5a0d4d0b7f651b04", "size": 11676, "ext": "py", "lang": "Python", "max_stars_repo_path": "keckcode/osiris/cubeset.py", "max_stars_repo_name": "cdfassnacht/keck_code", "max_stars_repo_head_hexsha": "a952b3806b3e64eef70deec2b2d1352e6ef6dfa0", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import os
import time
import torch
import numpy as np
from hparam import hparam as hp
from speech_embedder_net import get_cossim, R2Plus1DNet
import sys
def extract(model_path,dataset):
device = torch.device("cpu")
embedder_net = R2Plus1DNet([2,2,2,2]).to(device)
embedder_net.load_state_dict(torch.load(mode... | {"hexsha": "0e297a37e5a0140a1c34e09296d61fd8ff1be08c", "size": 1158, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract.py", "max_stars_repo_name": "amadeusuzx/PyTorch_Speaker_Verification", "max_stars_repo_head_hexsha": "0ad5b01822cbd88da82258cd1930d024c04109f6", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
using Oceananigans.Utils: work_layout
using Oceananigans.Architectures: device
using Oceananigans.TimeSteppers: store_tracer_tendency!
import Oceananigans.TimeSteppers: store_tendencies!
""" Store source terms for `uh`, `vh`, and `h`. """
@kernel function store_solution_tendencies!(G⁻, grid, G⁰)
i, j, k = @index(... | {"hexsha": "cd9dd427a0f441ce35d50eb1d6fef86834857b6d", "size": 1588, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Models/ShallowWaterModels/store_shallow_water_tendencies.jl", "max_stars_repo_name": "ali-ramadhan/OceanDispatch.jl", "max_stars_repo_head_hexsha": "65b8851d37052e90ca4a3e0c4a1c20398b0ee09a", "... |
function [compartmentReactions] = findRxnFromCompartment(model, compartment)
% Finds all the reactions and their identifiers in a compartment of interest.
%
% USAGE:
%
% [compartmentReactions] = findRxnFromCompartment(model,Compartment)
%
% INPUTS:
% model: COBRA model strcture
% compartmen... | {"author": "opencobra", "repo": "cobratoolbox", "sha": "e60274d127f65d518535fd0814d20c53dc530f73", "save_path": "github-repos/MATLAB/opencobra-cobratoolbox", "path": "github-repos/MATLAB/opencobra-cobratoolbox/cobratoolbox-e60274d127f65d518535fd0814d20c53dc530f73/src/analysis/exploration/findRxnFromCompartment.m"} |
% Conclusions
\chapter{Conclusions}
\label{ch:conclusion}
\gls{spirit} is a novel robotics teleoperation system which overlays the current position and orientation of a vehicle onto previously acquired images.
This research focuses on developing a \gls{spirit}-based user interface for aerial robots.
The proposed metho... | {"hexsha": "531a0ef80a4225ff40f046a1b68ec1ddfce08b07", "size": 4277, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "reports/thesis/conclusions.tex", "max_stars_repo_name": "masasin/spirit", "max_stars_repo_head_hexsha": "c8366e649eb105a8a579fb7a47dcc5aaeae6a0d8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
% Part: first-order-logic
% Chapter: model-theory
% Section: nonstandard-arithmetic
\documentclass[../../../include/open-logic-section]{subfiles}
\begin{document}
\olfileid{mod}{bas}{nsa}
\section{Non-standard Models of Arithmetic}
\begin{defn}
Let $\Lang{L_N}$ be the !!{language} of arithmetic, comprising a
!!{con... | {"hexsha": "451377a942b1eccb2c60383f7648dd35a41e7099", "size": 11089, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/model-theory/basics/nonstandard-arithmetic.tex", "max_stars_repo_name": "GKerfImf/OpenLogic", "max_stars_repo_head_hexsha": "5791905d3149f68e05885290f448054b98a0e51b", "max_stars_repo_licen... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.integrate as spi
#total no. agents
n = 50
#fraction of cooperators initial
fc0 = 0.7
#amount of resource available initial
R0 = 100
# Maximum amount of resource
Rmax = 200
# Social parameters
ec = 0.483/n #level of effort (cooperators)
ed = 1.826/n #lev... | {"hexsha": "4e07feb302fca2de47098470c8c9dc032eb44638", "size": 1656, "ext": "py", "lang": "Python", "max_stars_repo_path": "TSLModel2.py", "max_stars_repo_name": "adrianhindes/network-tsl", "max_stars_repo_head_hexsha": "70de88f3dde801e99481b3e4365b1c0461e54db3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
import pandas as pd
import os, copy
import joblib, logging
import skfuzzy as fuzz
import difflib, random, pickle
from deap import base, creator, tools, algorithms
from itertools import repeat
from collections import Sequence
import re
from sklearn.linear_model import LinearRegression, ElasticNetCV
fr... | {"hexsha": "2fb15c25f6dfb16e4fc6c0aae7cca0fe6a5c8526", "size": 34313, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fuzzy_clustering/version3/FuzzyClusteringManager/Clusterer_optimize_deep.py", "max_stars_repo_name": "joesider9/forecasting_library", "max_stars_repo_head_hexsha": "db07ff8f0f2693983058d49004f2fc... |
import os.path
import h5py
import numpy as np
import PIL
import PIL.Image
import pyx
from pyxutil import *
def make_fig_microstructure(name):
L = 3
a = 0.75*L
dim_shift = 0.6
c = pyx.canvas.canvas()
attrs = [pyx.style.linewidth.normal, pyx.deco.earrow()]
c.stroke(pyx.path.line(-DIM_LEG, 0, ... | {"hexsha": "8ac64846e779c1880dd9b7a550a08666334fd88f", "size": 2899, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphinx/tutorials/square_basic/make_pyx_figs.py", "max_stars_repo_name": "sbrisard/janus", "max_stars_repo_head_hexsha": "a6196a025fee6bf0f3eb5e636a6b2f895ca6fbc9", "max_stars_repo_licenses": ["BSD... |
import shutil
import time
import numpy
import pickle
from pathlib import Path
from trsfile.common import Header, SampleCoding
from trsfile.engine.engine import Engine
from trsfile.parametermap import TraceParameterMap
from trsfile.trace import Trace
from trsfile.traceparameter import ByteArrayParameter
class FileEng... | {"hexsha": "7d76f3da6d73ba8e71be7d25e83a5b68bfd44163", "size": 8477, "ext": "py", "lang": "Python", "max_stars_repo_path": "trsfile/engine/file.py", "max_stars_repo_name": "StefanD986/python-trsfile", "max_stars_repo_head_hexsha": "228df9d1cf1f2e18912c68d5c11c45c1493a2ece", "max_stars_repo_licenses": ["BSD-3-Clause-Cle... |
# extract_features.r - part of TF2
# Purpose: Take appropriately formatted (what is that?) DNase-1 signal and 'extract features' (binary). 0 means 'missing data', 1 'no', 2 'yes'.
# Warning: parallelising this means the peaks might (requires testing) get all messed up, order-wise, so you need to sort-bed on this afterw... | {"hexsha": "f5c8ac6c955a27e436e12ffc9d7db904a621b79e", "size": 3103, "ext": "r", "lang": "R", "max_stars_repo_path": "preprocess/extract_features.r", "max_stars_repo_name": "corcra/tf2", "max_stars_repo_head_hexsha": "46013e22f627f14bfbfa735f1d4b6e8e0a201d8f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
C @(#)chk_aredta.f 20.3 12/21/96
integer function chk_aredta(ptr, field, count, out_buffer)
integer ptr, field, count
character out_buffer(10)*120
C This subroutine checks AREA(*,PTR) extensively for data errors.
include 'ipfinc/parametr.inc'
include 'ipfinc/blank.inc'
incl... | {"hexsha": "a04811e6641abd973c8fd53f555d1fe95ca70631", "size": 790, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/chk_aredta.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14... |
import torch
import torch.optim as optim
from network import resnet,HRnet,PB_resnet,PB_net
from tool.dataset import VOC_Dataset
import argparse
from torchvision import transforms
from torch.utils.data import DataLoader
import torch.nn as nn
from sklearn.metrics import average_precision_score
import torch.nn.fu... | {"hexsha": "d060f652b0f47ff1eae00d3f38cbfea7d3c21445", "size": 2369, "ext": "py", "lang": "Python", "max_stars_repo_path": "Adaptive-Spatial-Feature-Pooling/weight_mask.py", "max_stars_repo_name": "code6levels/Adaptive-Spatial-Feature-Pooling", "max_stars_repo_head_hexsha": "200e10ae5fd99a1a13d7525beedc10912fdb2397", "... |
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, learning_curve, validation_curve, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model impor... | {"hexsha": "c7e0857cc681dd1bdeea70dbe1323674c8ad0030", "size": 10683, "ext": "py", "lang": "Python", "max_stars_repo_path": "06_Hyperparameter_Metrics.py", "max_stars_repo_name": "OscarDing/Oscar-s-Machine-Learning-in-Python", "max_stars_repo_head_hexsha": "d9dd359a178f5435b405235821147e5ea8a73c80", "max_stars_repo_lic... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os, argparse, itertools
import numpy as np
from mCNN.processing import read_csv, save_data_array
from scipy.spatial.distance import pdist, squareform
def main():
parser = argparse.ArgumentParser()
parser.description = 'A script to calculate mCSM features'
... | {"hexsha": "b3acef18a475ef0086e67361648369cb86a97607", "size": 6755, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Spatial/mCSM.py", "max_stars_repo_name": "ruiyangsong/mCNN", "max_stars_repo_head_hexsha": "889f182245f919fb9c7a8d97965b11576b01a96c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
\chapter{``N'' Standard Extension for User-Level Interrupts, Version 1.1}
\label{chap:n}
\begin{commentary}
This is a placeholder for a more complete writeup of the N
extension, and to form a basis for discussion.
\end{commentary}
This chapter presents a proposal for adding RISC-V user-level
interrupt and excepti... | {"hexsha": "00812a7bba9854d8ca4bc2a2f1d8b6ed4917c798", "size": 4206, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/n.tex", "max_stars_repo_name": "T-J-Teru/riscv-isa-manual", "max_stars_repo_head_hexsha": "ebeb14b4259e078097a2fb07aa25bdecc2e9e4d6", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count":... |
Christopher Civil is a fourth year student at UC Davis, majoring in Political Science. He is currently the PR chair in Phi Alpha Delta, the International PreLaw Professional fraternity. Chris is also an intern at the UC Davis News Service UC Davis Campus News Service, where he compiles a daily collection of news articl... | {"hexsha": "4a7531bf4e5e650aa6cb4b12df866f4b0bc6ed63", "size": 573, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/ChristopherCivil.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import datetime
import math
import time
import cv2
import numpy as np
ESC_KEY = 27
width = 0
height = 0
ContadorVerde = 0
ContadorAmarelo = 0
AreaContornoLimiteMin = 3000
OffsetLinhasRef = 260
cap = cv2.VideoCapture(0)
def TestaInterseccao(y, CoordenadaYLinha):
DiferencaAbsoluta = abs(y - CoordenadaYLinha)
... | {"hexsha": "4dfb71d8c6807d67c8a99cc94228131d22ce36d9", "size": 4754, "ext": "py", "lang": "Python", "max_stars_repo_path": "Identificador banana/Identificador+Contador.py", "max_stars_repo_name": "Lucas-Marcelino/CV_Pi-VII", "max_stars_repo_head_hexsha": "a7fdc0955e9710f351a7d16278de2093e9e84c69", "max_stars_repo_licen... |
import wandb
import pytest
import numpy as np
import datetime
def test_basic_ndx():
# Base Case
table_a = wandb.Table(columns=["b"], data=[["a"], ["b"]])
table = wandb.Table(columns=["fi", "c"])
for _ndx, row in table_a.iterrows():
table.add_data(_ndx, "x")
assert all([row[0]._table == ta... | {"hexsha": "13baa3400aa8b18627c3423f66560e19fa5a05c8", "size": 7922, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_tables.py", "max_stars_repo_name": "borisgrafx/client", "max_stars_repo_head_hexsha": "c079f7816947a3092b500751eb920fda3866985f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os,sys,inspect
sys.path.insert(1, os.path.join(sys.path[0], '../../../'))
import datetime
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import pdb
import torch
import core.datasets.utils as utils
import core.datasets.fastmri.transforms as transforms
import co... | {"hexsha": "592b7167030a33f117f2a36cc2411914cf33082e", "size": 6637, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/datasets/fastmri/FastMRIDataset.py", "max_stars_repo_name": "aangelopoulos/im2im-uq", "max_stars_repo_head_hexsha": "b95c3620b4741c09e7104a24fc5e87d77249971c", "max_stars_repo_licenses": ["MI... |
import json
import os
import re
import pandas as pd
import numpy as np
from python_lib.errors import ExtensionError
path_exp = "./results_server/results/210612_004737/"
class CompilePheWAS_Results():
def __init__(self,
path_exp):
self.path_logs_stats = os.path.join(path_exp, "logs_... | {"hexsha": "24b27442d9f43d0229db0f3981f3c199a56fe6d6", "size": 13890, "ext": "py", "lang": "Python", "max_stars_repo_path": "compile_run_PheWAS.py", "max_stars_repo_name": "hms-dbmi/BDC_HarmonizedVars_PheWAS", "max_stars_repo_head_hexsha": "e5bf15cfbd7a9e329e5760d1427d3debc8290bc5", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import math
import tensorflow as tf
def tf_1d_to_ndarray(data, datatype=tf.float64):
with tf.Session() as sess:
data = sess.run(data)
return data
def tf_to_ndarray(data, datatype=tf.float32):
data = tf.image.convert_image_dtype(data[0, ..., 0], dtype=datatype)
with tf.Se... | {"hexsha": "5d29420169d1f35c21e9cc9558e9efafce6f66b7", "size": 2012, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tensorflow_wavelets/utils/cast.py", "max_stars_repo_name": "simonsimon006/tensorflow-wavelets", "max_stars_repo_head_hexsha": "21a095bf0048ae2488ca5ae4961d2cbfe94263a9", "max_stars_repo_licens... |
\documentclass[11pt, a4paper, oneside]{article}
\pagenumbering{arabic}
\usepackage{amssymb,amsmath}
\usepackage[utf8]{inputenc}
\usepackage[unicode=true]{hyperref}
\usepackage{titling} % configure maketitle
\usepackage{longtable,booktabs,lscape}
\usepackage[margin=2.5cm]{geometry}
\PassOptionsToPackage{usenames,dvipsn... | {"hexsha": "87c59f1717289b4f4ea2d5c9a9eb8967464e7246", "size": 5339, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "templates/chroma-titling.tex", "max_stars_repo_name": "cpmpercussion/chroma-template", "max_stars_repo_head_hexsha": "b8f00e934d15b0e1de0c6a52f12500b6df4f1ba1", "max_stars_repo_licenses": ["Unlicens... |
\section*{Work Experience}
\begin{entrylist}
\entry
{March 2021\\ Ongoing}
{Software Developer}
{Multimedia Srl}
{Developed software for very large sanitary institutions, following all the needed security measures to make sure sensitive data stays protected.
I had to work under pressure to quickly... | {"hexsha": "237a87513e9e7baf92cf1ec9a18d188d00187cbb", "size": 697, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/experience.tex", "max_stars_repo_name": "LLoyderino/Curriculum-Vitae", "max_stars_repo_head_hexsha": "2f6c5159f1afa42f99265ab8c47fc048600fecca", "max_stars_repo_licenses": ["MIT"], "max_star... |
import torch
import torch.nn.functional as F
import argparse
import cv2
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
from collections import OrderedDict
import os
from copy import copy
# embedding vector
Z_dim = 128
# L1 reconstruction loss balance
reconstruction_loss_lambda = 1.
# to avo... | {"hexsha": "3fd81c41dc642dcb79554216d38a94fee743a460", "size": 16000, "ext": "py", "lang": "Python", "max_stars_repo_path": "Question_imageGenerate/answers/alphaGAN_cifar10_pytorch.py", "max_stars_repo_name": "OverHall27/DLMugenKnock", "max_stars_repo_head_hexsha": "f08553213028e90baff7b4de3c640b51485f4a15", "max_stars... |
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
def cross_entropy_2d(predict, target):
"""
Args:
predict:(n, c, h, w)
target:(n, h, w)
"""
assert not target.requires_grad
assert predict.dim() == 4
assert target.dim() == 3
... | {"hexsha": "fc4db4d8a9c7ab30f62a7c96bdf8313459193349", "size": 1307, "ext": "py", "lang": "Python", "max_stars_repo_path": "advent/utils/loss.py", "max_stars_repo_name": "MLIA/ESL", "max_stars_repo_head_hexsha": "86679fd25d03667880379d59bc73194e7d8d03e3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17... |
import json
#import lmdb
import pickle
import os
from numpy import random
from PIL import Image
import sys
# import torchwordemb
def calc_f(tp,fp,fn):
precision = tp/(tp*fp)
recall = tp/(tp+fn)
f = 2 * precision * recall / (precision + recall)
print('presision = ', precision, '\nrecall = '... | {"hexsha": "a8a446c65ad190080de7c9aeb6f171c6f8e9818e", "size": 4040, "ext": "py", "lang": "Python", "max_stars_repo_path": "small_tools.py", "max_stars_repo_name": "paisuygoda/im2ingr", "max_stars_repo_head_hexsha": "b69de9a5a0a7fd42f64c091d681803bc501817eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
/*
* The MIT License (MIT)
*
* Copyright (c) <2015> <Stephan Gatzka>
*
* 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 t... | {"hexsha": "b93138b80fed61a60ece219d637954dbceb8c10e", "size": 2882, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tests/router_test.cpp", "max_stars_repo_name": "mloy/cjet", "max_stars_repo_head_hexsha": "6645cefebb21bad577ea3792cd3b5c77c61f408a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.0, ... |
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import torch
from tabulate import tabulate
import torchvision.utils as tu
c=[[i*j for i in range (20)] for j in range(10)]
b=torch.rand(120,40,3)*255
a = SummaryWriter(log_dir= "tb_test")
for i in range(10):
a.add_text("tester",tabulate(c),i)
... | {"hexsha": "235d563c161bdb16df3ac3350fd7593036791625", "size": 411, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/tensorb.py", "max_stars_repo_name": "neelabh17/SegmenTron", "max_stars_repo_head_hexsha": "69a4d1da858aba9222994847000f9945be3f4cd5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
"""
Solvers for over-identified systems.
@author : davidrpugh
"""
from scipy import optimize
from . import solvers
class LeastSquaresSolver(solvers.Solver):
def solve(self, basis_kwargs, boundary_points, coefs_array, nodes, problem,
**solver_options):
"""
Solve a boundary value p... | {"hexsha": "7a4a92f9a41e74925756fa6daa7d7b80c45b38c7", "size": 1495, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycollocation/solvers/over_identified.py", "max_stars_repo_name": "davidrpugh/bvp-solver", "max_stars_repo_head_hexsha": "9376f3488a992dc416cfd2a4dbb396d094927569", "max_stars_repo_licenses": ["MI... |
import numpy as np
import os
class _ADE_proto(object):
def __init__(self):
curr_path = os.path.dirname(os.path.abspath(__file__))
colors = np.load(os.path.join(curr_path, 'color150.npy'))
self.palette = np.full((256, 3), 255, np.uint8)
for i, c in enumerate(colors):
self... | {"hexsha": "2130e4df1610faf4c5a5878b71a338199abd3088", "size": 379, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/utils/dataset_tools/ade.py", "max_stars_repo_name": "js-fan/MCIC", "max_stars_repo_head_hexsha": "a98927e2d88452d96f1fba99a5dc25a5f518caa8", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import Base: IteratorSize, HasLength, IsInfinite, length
struct FinitePeriodTrigger <: AbstractFiniteTrigger
td::Dates.Period
n::Int
end
struct InfinitePeriodTrigger <: AbstractInfiniteTrigger
td::Dates.Period
end
"""
PeriodTrigger(t::Dates.Time[, n=number_of_times])
A trigger which should trigger ... | {"hexsha": "88ac040e9fc417d929fcf9a5ac8130deafeffff1", "size": 1130, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/triggers/period.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ExtensibleScheduler.jl-6837a093-145e-5c9b-b5ad-3b557e31aa31", "max_stars_repo_head_hexsha": "6cf6ab918d8924154e110426... |
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