text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
Implementation of DDPG - Deep Deterministic Policy Gradient
Algorithm and hyperparameter details can be found here:
http://arxiv.org/pdf/1509.02971v2.pdf
The algorithm is tested on the Pendulum-v0 OpenAI gym task
and developed with tflearn + Tensorflow
Author: Vamshi Kumar Kurva
improved upon the original code... | {"hexsha": "e14d019ded81124258dd53232c868d65e9b82cf7", "size": 22821, "ext": "py", "lang": "Python", "max_stars_repo_path": "5_Deep_Deterministic_Policy_Gradients/DDPG/DDPG.py", "max_stars_repo_name": "vaisakh-shaj/DeepReinforcementLearning", "max_stars_repo_head_hexsha": "99f62d9eee6626ac70c3410b72e0a3a151ec375f", "ma... |
program cmd_args
implicit none
character(len=32) :: arg_matrix_size_str ! matrix size
integer :: arg_matrix_size
if ( command_argument_count() .ne. 1 ) then
write(*,*) 'Error, only one argument is required for matrix size. Aborting'
stop
endif
! re... | {"hexsha": "3365c98fbb32a8f1b7c50f757f16d23b60c1f7d7", "size": 569, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "misc/cmd_args.f90", "max_stars_repo_name": "eusojk/fortran-programs", "max_stars_repo_head_hexsha": "60fe727a341615153e044e7ac7deabc435444e39", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os
import open3d as o3d
import numpy as np
import copy
import math
NOISE_BOUND = 0.05
FRAG1_COLOR =[0, 0.651, 0.929]
FRAG2_COLOR = [1, 0.706, 0]
GT_COLOR =[0, 1, 0]
def load_all_gt_pairs(gt_log_path):
"""
Load all possible pairs from GT
"""
with open(gt_log_path) as f:
content = f.readl... | {"hexsha": "bd906b31dbc206afb197334e8959a06946482e24", "size": 3670, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/teaser_python_3dsmooth/bench_utils.py", "max_stars_repo_name": "plusk01/TEASER-plusplus", "max_stars_repo_head_hexsha": "0d497521d261b3fa35c4ca29eb86ba7cf9558f9f", "max_stars_repo_license... |
# Copyright (c) 2017 VisualDL Authors. All Rights Reserve.
#
# 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... | {"hexsha": "4d1777ca945b54bdb5cacfee3fc4c7e3d1fc9c87", "size": 5812, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/paddle/paddle_cifar10.py", "max_stars_repo_name": "nepeplwu/VisualDL", "max_stars_repo_head_hexsha": "a6928902ca0802419fa337236b71d2db8e669e13", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
module FiniteHorizonPOMDPs
using POMDPs
using POMDPModelTools
using Random: Random, AbstractRNG
export
HorizonLength,
FiniteHorizon,
InfiniteHorizon,
horizon,
stage,
stage_states,
stage_stateindex,
ordered_stage_states,
stage_observations,
stage_obsindex,
ordered_stage_obs... | {"hexsha": "3fb56624cdfbf1868eebdf37a98973991136ea58", "size": 409, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FiniteHorizonPOMDPs.jl", "max_stars_repo_name": "JuliaPOMDP/FiniteHorizonPOMDPs.jl", "max_stars_repo_head_hexsha": "6579a0dcaf95d2e403a48af08465e9fc901be62e", "max_stars_repo_licenses": ["MIT"],... |
# using AutomotiveDrivingModels
# using NearestNeighbors
# import AutomotiveDrivingModels: get_actions!, observe!, action_context, get_name
# import Base.rand
# import PyPlot
# export
# HRHC,
# curveDist,
# wrap_to_π,
# kdProject,
# generateObstacleMap,
# updateObstacleMap!,
# generateMoti... | {"hexsha": "fa747786bce953096263b4e957791a5c54026bb0", "size": 30390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/controllers/HierarchicalRecedingHorizonController.jl", "max_stars_repo_name": "kylejbrown17/LevelKRacing.jl", "max_stars_repo_head_hexsha": "2e66f89cbe2afe571f23030ad04bc48de77c5e98", "max_sta... |
import os
# set number of threads - this should be optimized for your compute instance
mynt="16"
os.environ["TF_NUM_INTEROP_THREADS"] = mynt
os.environ["TF_NUM_INTRAOP_THREADS"] = mynt
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = mynt
import os.path
from os import path
import glob as glob
import math
import t... | {"hexsha": "aeff7eca9c84b69f000d9a72136695d0ace2c84f", "size": 6719, "ext": "py", "lang": "Python", "max_stars_repo_path": "applications/evaluate_DKT_dice_overlap_cv.py", "max_stars_repo_name": "stnava/superiq", "max_stars_repo_head_hexsha": "a13befe5f525bbef02cd095031952db62c5d054e", "max_stars_repo_licenses": ["Apach... |
import rospy
import cv2
import numpy as np
from sensor_msgs.msg import Image
from std_msgs.msg import String
from cv_bridge import CvBridge
import iamangrynow
def image_callback(data):
img = bridge.imgmsg_to_cv2(data, 'bgr8') # OpenCV image
iamangrynow.recognize_digit(img)
#imgStack = stackImages(1.0, ... | {"hexsha": "8ce506a482a8bbcfc99f252c348d9630e37671ba", "size": 757, "ext": "py", "lang": "Python", "max_stars_repo_path": "clover_simulation/src/static_test/digit_test.py", "max_stars_repo_name": "SailorTheMan/NTI_PoROSiata", "max_stars_repo_head_hexsha": "2eb2fe56ee67714492cf9c6e7bce258ccf9b9d8b", "max_stars_repo_lice... |
immutable WindowsPath <: AbstractPath
parts::Tuple{Vararg{String}}
drive::String
root::String
end
WindowsPath() = WindowsPath(tuple(), "", "")
WindowsPath(parts::Tuple) = WindowsPath(parts, "", "")
function WindowsPath(str::AbstractString)
if isempty(str)
return WindowsPath(tuple("."), "", ""... | {"hexsha": "6853429156ec37c54247c2cdc0da703c5ce56bbe", "size": 2194, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/windows.jl", "max_stars_repo_name": "vtjnash/FilePaths.jl", "max_stars_repo_head_hexsha": "a480e3c1c8b0239acb0f3320486f8be4e9b7a127", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from paper_1.data.data_loader import load_val_data, load_train_data, sequential_data_loader, random_data_loader
from paper_1.utils import read_parameter_file, create_experiment_directory
from paper_1.evaluation.eval_utils import init_metrics_object
from paper_1.baseline.main import train as baseline_train
from paper_1.... | {"hexsha": "7d6ad190979d6481b1c2985d3daa77d4ce6fbfd1", "size": 5689, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/paper_1/curriculum/main.py", "max_stars_repo_name": "ludwigflo/paper1", "max_stars_repo_head_hexsha": "13202febdb01a76bbf115435ce9676f6b82e1393", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Bizzaro Francesco
# March 2020
#
# This script can generate random
# Symbolic Regression problem instances.
import random
import json
import math
import numpy as np
def f1(x):
return 3+1/(x+1)+math.pow(x,2)
def f2(x):
return x*math.sin(3*x)
def f3(x):
return math.cos(math.sin(x))+0.5*x
def f4(x):
... | {"hexsha": "0d1ea2f2b06fe8306629d2ff55e41e7cbe57b745", "size": 744, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-GAs/SymbolicRegression/generator.py", "max_stars_repo_name": "D33pBlue/Study-on-Genetic-Algorithms", "max_stars_repo_head_hexsha": "456f2ac93c307320ddee0ceded7f735f9e8e93a2", "max_stars_repo... |
import tensorflow as tf
import numpy as np
workers = ['127.0.0.1:50001', '127.0.0.2:50002', '127.0.0.2:50003']
cluster_spec = tf.train.ClusterSpec({'workers': workers})
server = tf.train.Server(cluster_spec, job_name='workers', task_index=0)
server.join()
| {"hexsha": "e19d2db97302805724d10251ea87870615f765c0", "size": 259, "ext": "py", "lang": "Python", "max_stars_repo_path": "Stage1-SimpleConcept/TF-distribute-server1.py", "max_stars_repo_name": "markliou/DistributedTensorflow", "max_stars_repo_head_hexsha": "5b8c78fea2e8a0e061129a24144289aa55509077", "max_stars_repo_li... |
double complex function BSYA1fggpppp(e1,p2,p3,e4,za,zb,zab,zba)
implicit none
C-----Authors: John Campbell and Keith Ellis, March 2012
C---- arXiv:1101.5947 [hep-ph], Eq. (100),fully Badger-compliant
C---- (These are twiddle functions, c.f.arXiv:1101.5947[hep-ph],Eq.(91))
include 'constants.f'
i... | {"hexsha": "f698ea5bec083aff158a13156bcd795125f865b4", "size": 684, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/TopdkBSY/BSYA1fggpppp.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0... |
# =============================================================================
# IMPORT SCIPY MODULES
# =============================================================================
import numpy as np
from tabulate import tabulate
from numba import jit
class RotationHelper:
def transformCompl(self,S,th,**kwargs):... | {"hexsha": "9b9c31864bf3f39b0f7fa766799ce5fb6d95c771", "size": 5454, "ext": "py", "lang": "Python", "max_stars_repo_path": "AeroComBAT/Utilities.py", "max_stars_repo_name": "bennames/AeroComBAT-Project", "max_stars_repo_head_hexsha": "ddc7194d5ccc0b8bf09b73cc0c2c3d64adf4a472", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
from pathlib import Path
import os
import sys
import time
import cv2
import numpy as np
import pandas as pd
from cova.dnn import infer, metrics
from cova.motion import object_crop as crop
from cova.motion.motion_detector import merge_overlapping_boxes, re... | {"hexsha": "8686d0858527651ace7a73d19f8943ec170c79bd", "size": 20075, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/bgs_infer.py", "max_stars_repo_name": "danirivas/cova-tuner", "max_stars_repo_head_hexsha": "e7eaf7e75f0c15ce35c449fb67529c9c73386817", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
C @(#)swapxai.f 20.3 2/13/96
subroutine swapxai (i,j)
C This subroutine exchanges two "I" intertie entities OARCINT(*,I)
c and OARCINT(*,J).
include 'ipfinc/parametr.inc'
include 'ipfinc/alt_case.inc'
character tempc*10
tempc = oarcint(1,i)
oarcint... | {"hexsha": "8208d42fb38bfd62ebfe03724641416c2672840a", "size": 577, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/swapxai.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "... |
[STATEMENT]
lemma (in padic_integers) Zp_residue_eq:
assumes "a \<in> carrier Zp"
assumes "b \<in> carrier Zp"
assumes "val_Zp (a \<ominus> b) > k"
shows "(a k) = (b k)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a k = b k
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. a k = b k
[P... | {"llama_tokens": 1585, "file": "Padic_Ints_Padic_Integers", "length": 13} |
subroutine cmo_addatt_cmo(imsgin,xmsgin,cmsgin,msgtype,nwds,
* ierror_return)
C
C
C#######################################################################
C
C PURPOSE -
C
C This Routine Adds Attributes to an existing Mesh Object.
C
C INPUT ARGUMENTS -
C
C im... | {"hexsha": "c48737d4297e7bc5f008e59dd2173337085ac772", "size": 12066, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/cmo_addatt_cmo.f", "max_stars_repo_name": "millerta/LaGriT-1", "max_stars_repo_head_hexsha": "511ef22f3b7e839c7e0484604cd7f6a2278ae6b9", "max_stars_repo_licenses": ["CNRI-Python"], "max_stars... |
[STATEMENT]
lemma subdegree_minus_commute [simp]:
"subdegree (f-(g::('a::group_add) fps)) = subdegree (g - f)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subdegree (f - g) = subdegree (g - f)
[PROOF STEP]
proof (-, cases "g-f=0")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. g - f = 0 \<Longrightarrow> su... | {"llama_tokens": 1298, "file": null, "length": 16} |
\chapter{\ac{GPU} Programs}
There's a broad range of shader languages and \ac{API}s like \ac{GLSL}, \ac{HLSL} and Cg. Therefore, when designing the \ac{GPU} program interfaces for PLRenderer, one design goal was to be able to implement as many \ac{GPU} program backends as possible - and this without producing to much i... | {"hexsha": "2d0bd419b5a6564afa616718a624968bcfe1de36", "size": 4194, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Docs/PixelLightBase/PLRenderer/GPUPrograms.tex", "max_stars_repo_name": "ktotheoz/pixellight", "max_stars_repo_head_hexsha": "43a661e762034054b47766d7e38d94baf22d2038", "max_stars_repo_licenses": ["... |
"""
解AVSb的方程
"""
import os
import multiprocessing
import argparse
import numpy
from basics import get_procs_num
from fermi.avsb import shift_kv, get_von_hove_patches
from fermi.avsb import d1_disp, p2_disp
from fermi.avsb import intra_band_u, inter_band_uprime
import flowequ.mulitband_hubbard as hubbard
from helpers.e... | {"hexsha": "eb464c2d28d4ca8c2410ef2f683a066e615c2166", "size": 3754, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/avsb/solution.py", "max_stars_repo_name": "maryprimary/frg", "max_stars_repo_head_hexsha": "e789439f599eb884a6220ae5b471cf610b0c2b2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# reading image (storing image in 'img' variable) and writeing image (saving the image in detination folder)
# image location (relative path) -> "res/lena.jpg"
# destination to save images -> "result/*.jpg"
# importing OpenCV, Numpy, Matplotlib.Pyplot
import cv2
import numpy as np
import matplotlib.pyplot as pl... | {"hexsha": "f3448cc49d67ff16b2de5b5e902c8ba0fba1c927", "size": 853, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/class01.py", "max_stars_repo_name": "sarveswar1/AlgoBook", "max_stars_repo_head_hexsha": "7e1692ee768cc84f581c9f33151869e8d0d18550", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import datetime
import numpy as np
import os
import random
import sys
import time
import torch
import torch.nn as nn
import torchvision.utils as vutils
from torch.backends import cudnn
import utils
from sagan_models import Generator, Discriminator
class Trainer(object):
def __init__(self, config):
# I... | {"hexsha": "84cc055cc400efa720b5f9c240966bb272530424", "size": 13574, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer.py", "max_stars_repo_name": "christopher-beckham/self-attention-GAN-pytorch", "max_stars_repo_head_hexsha": "9b10ecfd6957633bc6fde099dc9674acf1c222e4", "max_stars_repo_licenses": ["MIT"],... |
# ms_mint/io.py
import pandas as pd
import numpy as np
import io
import pymzml
from pathlib import Path as P
from datetime import date
from pyteomics import mzxml, mzml
def ms_file_to_df(fn):
fn = str(fn)
if fn.lower().endswith('.mzxml'):
df = mzxml_to_df(fn)
elif fn.lower().endswith('.mzml'):
... | {"hexsha": "e9a47a5a0ac3da24b3598e49798067b6bf496938", "size": 4948, "ext": "py", "lang": "Python", "max_stars_repo_path": "ms_mint/io.py", "max_stars_repo_name": "luis-ponce/ms-mint", "max_stars_repo_head_hexsha": "cefd0d455c6658bf8c737160bd7253bb147c9c14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
"""
This script was made by Nick at 19/07/20.
To implement code for inference with your model.
"""
from argparse import ArgumentParser, Namespace
import os
import matplotlib.pyplot as plt
import numpy as np
import pytorch_lightning as pl
import torch
from src.utils import Config, get_dataloader
pl.seed_every... | {"hexsha": "127ff7df37ff42b84b26a441171af0497fd7e3f8", "size": 2098, "ext": "py", "lang": "Python", "max_stars_repo_path": "infer.py", "max_stars_repo_name": "HephaestusProject/seq2seq-att", "max_stars_repo_head_hexsha": "383f72d8bb46bdd4d66f0f7838f39c94eeb069b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
chapter \<open>Future Work\<close>
theory %invisible Future_Work
imports Main
begin
text \<open>\label{chap:future}\<close>
section \<open>Populating the Framework\<close>
text \<open>\label{sec:populate}\<close>
text \<open>Pop-refinement provides a framework,
which must be populated with re-usable
concepts, meth... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Pop_Refinement/Future_Work.thy"} |
"""
This module provides R style pairs plotting functionality.
"""
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
# from . import plotting_util
from .plotting_util import (LegendEnum, get_color,
prepare_pairs_data, make... | {"hexsha": "0cc11918ffc4b7ec1d9204d44385af88a7baff8d", "size": 19001, "ext": "py", "lang": "Python", "max_stars_repo_path": "ema_workbench/analysis/pairs_plotting.py", "max_stars_repo_name": "quaquel/EMAworkbench", "max_stars_repo_head_hexsha": "b16a454d734465bb163ea9ff1c52536cd945563e", "max_stars_repo_licenses": ["BS... |
import numpy as np
import pandas as pd
ops = ["mean", "sum", "median", "std", "skew", "kurt", "mad", "prod", "sem", "var"]
class FrameOps:
params = [ops, ["float", "int"], [0, 1], [True, False]]
param_names = ["op", "dtype", "axis", "use_bottleneck"]
def setup(self, op, dtype, axis, use_bottleneck):
... | {"hexsha": "ed5ebfa61594ec56483a2881cb36412d9d6f4dd9", "size": 4912, "ext": "py", "lang": "Python", "max_stars_repo_path": "asv_bench/benchmarks/stat_ops.py", "max_stars_repo_name": "LauraCollard/pandas", "max_stars_repo_head_hexsha": "b1c3a9031569334cafc4e8d45d35408421f7dea4", "max_stars_repo_licenses": ["BSD-3-Clause... |
From Coq Require Vector List.
Require Import Rupicola.Lib.Core.
Require Import Rupicola.Lib.Notations.
Require Import Rupicola.Lib.Loops.
Require Export bedrock2.ArrayCasts.
Open Scope list_scope.
Module VectorArray.
Section VectorArray.
Context {K: Type}.
Context {Conv: Convertible K nat}.
Open Scope n... | {"author": "mit-plv", "repo": "rupicola", "sha": "3f59b3d2404ce425ddf4fd55ad2314996a573dc3", "save_path": "github-repos/coq/mit-plv-rupicola", "path": "github-repos/coq/mit-plv-rupicola/rupicola-3f59b3d2404ce425ddf4fd55ad2314996a573dc3/src/Rupicola/Lib/Arrays.v"} |
import os
import glob
from calibrator import Calibrator
import cv2
import numpy as np
try:
import python.modules.tf_calib
except ImportError:
pass
def calibrate(path: str, filter: str, nrows: int, ncols: int):
calibrator = Calibrator(1)
objp = np.zeros((nrows * ncols, 3), np.float32)
objp[:, :2] ... | {"hexsha": "15866c6023d1934d6843b277056b1da1c9a6a806", "size": 1316, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "vahagnIV/tf_calib", "max_stars_repo_head_hexsha": "24088b593c41d9bc2123e39f3d0523b0762a761e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_star... |
*DECK TRBAK1
SUBROUTINE TRBAK1 (NM, N, A, E, M, Z)
C***BEGIN PROLOGUE TRBAK1
C***PURPOSE Form the eigenvectors of real symmetric matrix from
C the eigenvectors of a symmetric tridiagonal matrix formed
C by TRED1.
C***LIBRARY SLATEC (EISPACK)
C***CATEGORY D4C4
C***TYPE SINGLE PRECIS... | {"hexsha": "00b8ac94afd762068f2c1159c69e776ce29b7123", "size": 3566, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "slatec/src/trbak1.f", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_coun... |
#!/usr/bin/env python
import os
import subprocess
# import science modules
import numpy as np
import astropy.units as u
from astropy.time import Time
from numpy.linalg import norm
from scipy.interpolate import interp1d
from scipy.optimize import leastsq
from astropy.coordinates import SkyCoord, EarthLocation, \
g... | {"hexsha": "7c414addef2ca601c5565b43689a4c1e5e338a5f", "size": 33786, "ext": "py", "lang": "Python", "max_stars_repo_path": "trajectory_utilities.py", "max_stars_repo_name": "desertfireballnetwork/DFN_darkflight", "max_stars_repo_head_hexsha": "f41d2a2b82ce96f380f26acfe278c0afa536b9cd", "max_stars_repo_licenses": ["MIT... |
import numba
from numba import deferred_type
from numba.experimental import jitclass
from morpyneural.Genetic.JitElementClass import JitElement, JitElementListType
@jitclass([
('elements', JitElementListType)
])
class JitPopulation(object):
def __init__(self):
self.elements = [JitElement()]
se... | {"hexsha": "2ba32dcf358f5425f7bb1de55f8f5e5e8e5f2dc0", "size": 1716, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/morpyneural/Genetic/JitPopulationClass.py", "max_stars_repo_name": "Morgiver/neural-network", "max_stars_repo_head_hexsha": "b5c4a600bfe8032bc7ad859bb7286efdac90e74d", "max_stars_repo_licenses... |
using DSP
"""
highlow_butterworth_filter(data,sampling_rate; low_pass=30, high_pass=1, bw_n_pole=5, offset=true)
Applies a high and low-pass filter of butterworth design (n pole 5). For altering the
threshold values for filters, change add keyword arguments low_pass for low pass filter cut-off
(default=30) and hi... | {"hexsha": "e9bfd6aaa008b08e86e8fb5a6e10e69c9dde8d3b", "size": 2546, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/filters.jl", "max_stars_repo_name": "ElectronicTeaCup/MegTools", "max_stars_repo_head_hexsha": "fef50fdcc6261fc645fee54c847d51b6c05d7f6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
def cross_entropy(y, y_net):
n = y.shape[0]
return -1 / n * (y * np.log(y_net) + (1 - y) * np.log(1 - y_net)).sum(axis=0)
def sigmoid(x):
ex = np.exp(x)
return ex / (1 + ex)
class NeuralNet:
def __init__(self, in_size, hl_size, out_size, dna=None):
if dna is not None... | {"hexsha": "175ce68d72ed111345600f8e5f493a2d757dd08c", "size": 1270, "ext": "py", "lang": "Python", "max_stars_repo_path": "snakipy/neuro.py", "max_stars_repo_name": "gab50000/PySnake", "max_stars_repo_head_hexsha": "22ec382d7aa3d957897d6f85ce65b2e52a05b863", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
Job Board Job hunting can be a royal pain in the youknowwhat. This page may help streamline that process. Note that many places that have online applications also have paper applications available instore.
Retail
Bookstores
Bring in resume:
Avid Reader
OffCampus Books
Newsbeat
Other bookstores (please indi... | {"hexsha": "6c161255e2b5d7faf364be47ef507ef2fbd3021b", "size": 11071, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Job_Applications.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
""" Subclass from Abstract Base Class featureExtractor that outputs features of the raw data that are required for machine learning models """
import numpy as np
from prosi3d.meta.featureExtractor import FeatureExtractor
class Nircamera (FeatureExtractor):
"""
Attribute:
xxx: xxx.
xxx: xxx... | {"hexsha": "22e0e3a56a81b9f4f3cd5e954dff822dd60c27ed", "size": 598, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/prosi3d/sensors/nircamera.py", "max_stars_repo_name": "pzimbrod/prosi-3d", "max_stars_repo_head_hexsha": "6eaa5b9cdb7192f542417429b1775c3e61a9bc60", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma expands_to_powr_nat_0_0:
assumes "eventually (\<lambda>x. f x = 0) at_top" "eventually (\<lambda>x. g x = 0) at_top"
"basis_wf basis" "length basis = expansion_level TYPE('a :: multiseries)"
shows "((\<lambda>x. powr_nat (f x) (g x)) expands_to (const_expansion 1 :: 'a)) basis"
[PROOF ... | {"llama_tokens": 595, "file": null, "length": 5} |
# histogramPlotter.py
# Input is a file containing a single column of data (going to be using this for BLEU scores)
# Output is a histogram of the data.
#
# Expects 2 arguments:
# --input_data /path/to/test/dataset.csv
# --output_file /path/to/output/file.jpg
#
# Dylan Auty, 31/05/16
import argparse, json
import ... | {"hexsha": "b385d0a8417346b0b2bacc11bb327ab735c8ebd2", "size": 1175, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/evaluation/histogramPlotter.py", "max_stars_repo_name": "DylanAuty/torch-rnn-constrained", "max_stars_repo_head_hexsha": "49ac085ca5dc3ef68741b8fbabe4804eb6e19fc2", "max_stars_repo_license... |
import warnings
import pickle
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import sys
import os
import math
import bisect
import tensorflow as tf
import warnings
# if you run python inside the folder, then:
sys.path.insert(0, '../lib')
print(sys.path)
from cde.data_collector import ParquetData... | {"hexsha": "268cd10e1ef2a4a624cf1c3425d6977fabc73ea5", "size": 4222, "ext": "py", "lang": "Python", "max_stars_repo_path": "latency_prediction/validation/validate_model_dual.py", "max_stars_repo_name": "samiemostafavi/data-driven-dvp-prediction", "max_stars_repo_head_hexsha": "a6f4ac16f047f677dca532ba1303521628a053fe",... |
# parse readable numeric strings
parse_readable(::Type{T}, s::String, ch::Char) where {T <: Union{Integer, AbstractFloat}} =
Base.parse(T, join(split(s,ch),""))
parse_readable(::Type{T}, s::String, ch1::Char, ch2::Char) where {T <: AbstractFloat} =
Base.parse(T, join(split(s,(ch1,ch2)),""))
"""
how many tim... | {"hexsha": "a96fd88d980b0050320f178599f1fc99382dd718", "size": 487, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parse.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ReadableNumbers.jl-7774933c-dd73-5de8-a8c3-ca082e6dff1c", "max_stars_repo_head_hexsha": "16e65bed68cad3d1674db547a40ef1b174e870e... |
#=
Copyright 2020 INSIGNEO Institute for in silico Medicine
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 agree... | {"hexsha": "a178f1b514c39722403d8cf4c633d17bb8742f22", "size": 4257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/openBF.jl", "max_stars_repo_name": "ibenemerito88/openBF_workshop", "max_stars_repo_head_hexsha": "a63a6fbd1ef8528890fb1072730124e054875008", "max_stars_repo_licenses": ["Zlib", "Apache-2.0"], ... |
# coding: utf-8
# ************************************
# Author: Ziqin Wang
# Email: ziqin.wang.edu@gmail.com
# Github: https://github.com/Storife
# ************************************
import argparse
from math import log10
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Vari... | {"hexsha": "78f6e231536513fd6fab6ef5e9bcab6d6a418969", "size": 3376, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/RANet.py", "max_stars_repo_name": "cvmlarun/RANet", "max_stars_repo_head_hexsha": "3f67a3f36aaacd9cc7fb98ec79f77db8f1ebdc60", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1,... |
! mimic NWChem tgt_sd_t_s1_1 kernel
! RL: do not redefine simd clause to be schedule(static, 1)
! RL: make the schedule clause usage be explicit
implicit integer (a-z)
l1 = 1; l2 = 1; l3 = 1; l4 = 1; l5 = 1; l6 = 1;
u1 = 24; u2 = 24; u3 = 24; u4 = 24; u5 = 24; u6 = 24;
call tgt_sd_t_s1_1(l1,l2,l3,l4,l5,l6, u1,... | {"hexsha": "dc9392db449392194738161bf6684d1652acc438", "size": 996, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/smoke-fails/nwchem-s1_1/nwchem-s1_1.f90", "max_stars_repo_name": "raramakr/aomp", "max_stars_repo_head_hexsha": "9a224fe01ca8eff4209b8b79aa1fa15a18da65db", "max_stars_repo_licenses": ["Apach... |
"""
Core module for methods related to flat fielding.
.. include common links, assuming primary doc root is up one directory
.. include:: ../include/links.rst
"""
import inspect
import copy
import os
import numpy as np
from scipy import interpolate, ndimage
from matplotlib import pyplot as plt
from IPython import em... | {"hexsha": "7e79d680bfbaa3bc1a117b445bacdd05bff384bc", "size": 22312, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypeit/core/flat.py", "max_stars_repo_name": "ykwang1/PypeIt", "max_stars_repo_head_hexsha": "a96cff699f1284905ce7ef19d06a9027cd333c63", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# coding: utf-8
# DO NOT EDIT
# Autogenerated from the notebook tsa_arma_0.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT
# # Autoregressive Moving Average (ARMA): Sunspots data
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot... | {"hexsha": "e1f31f28b4b6252c3c15f8b0b26b60d702ce1e7b", "size": 4589, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/python/tsa_arma_0.py", "max_stars_repo_name": "madhushree14/statsmodels", "max_stars_repo_head_hexsha": "04f00006a7aeb1c93d6894caa420698400da6c33", "max_stars_repo_licenses": ["BSD-3-Clau... |
# Affine coupling layer from Dinh et al. (2017)
# Includes 1x1 convolution from in Putzky and Welling (2019)
# Author: Philipp Witte, pwitte3@gatech.edu
# Date: January 2020
export LearnedCouplingLayerSLIM
"""
CS = LearnedCouplingLayerSLIM(nx1, nx2, nx_in, ny1, ny2, ny_in, n_hidden, batchsize;
logdet::Bo... | {"hexsha": "c71889406f44dabb7d5d1a75b754f60a4e179380", "size": 4765, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/layers/invertible_layer_slim_learned.jl", "max_stars_repo_name": "alisiahkoohi/InvertibleNetworks.jl", "max_stars_repo_head_hexsha": "719f788bcd12909496dfd5322d9b8b953996fc57", "max_stars_repo_... |
import numpy as np
import radvel.kepler
def timetrans_to_timeperi(tc, per, ecc, omega):
"""
Convert Time of Transit to Time of Periastron Passage
Args:
tc (float): time of transit
per (float): period [days]
ecc (float): eccentricity
omega (float): longitude of periastron ... | {"hexsha": "281ea81f45d87d6eb914e6ce2bc35f0d17c23bce", "size": 2607, "ext": "py", "lang": "Python", "max_stars_repo_path": "radvel/orbit.py", "max_stars_repo_name": "spencerhurt/radvel", "max_stars_repo_head_hexsha": "05a1a1e020d239bf7cba8575b68a6d83ec0b3a5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49, ... |
MODULE m_emp_init
contains
SUBROUTINE emp_init (EMP_0_coef)
! ----------------------------------------------------------------------
! SUBROUTINE: emp_init
! ----------------------------------------------------------------------
! Purpose: Initialize the EMP model
! -------------------------------------... | {"hexsha": "58edf866a983d97fbcce25596addd45da346c798", "size": 2798, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "src/fortran/m_emp_init.f03", "max_stars_repo_name": "RodrigoNaves/ginan-bitbucket-update-tests", "max_stars_repo_head_hexsha": "4bd5cc0a9dd0e94b1c2d8b35385e128404009b0c", "max_stars_repo_license... |
import warnings
warnings.filterwarnings("ignore")
import sys
sys.path.append("./")
from pathlib import Path
from multiprocessing import Pool
import numpy as np
import pandas as pd
import statistics as st
import cProfile
from matplotlib import pyplot as plt
from numpy import array, polyfit, poly1d
from corems.mass_... | {"hexsha": "9ac4bb7ea5ccd7a8a5cf9a8fea4764f40825b111", "size": 1992, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scripts/Loss_Finder.py", "max_stars_repo_name": "Kzra/CoreMS", "max_stars_repo_head_hexsha": "88bef42e3cf5d11c04ad13b4c58d8a366f7844a7", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
[STATEMENT]
lemma systemIN_noOUT:
assumes "systemIN x i"
shows "\<not> systemOUT x i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> systemOUT x i
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
systemIN x i
goal (1 subgoal):
1. \<not> systemOUT x i
[PROOF STEP]
by (simp add: systemI... | {"llama_tokens": 140, "file": "ComponentDependencies_DataDependencies", "length": 2} |
# -*- coding: utf-8 -*-
""" Tests for the `CRA` module."""
import pytest
from pytest import approx
import numpy as np
import scipy.sparse as sp
from deepburn.CRAM import CRA, CRAC, cras_literature, CRA_ODEsolver
def test_init():
crasolver = CRA_ODEsolver()
assert isinstance(crasolver._cra, CRA)
def test_b... | {"hexsha": "be2da162909230db52fe40925209f2909e60124d", "size": 1508, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/CRAM/test_CRAsolve.py", "max_stars_repo_name": "gvdeynde/deepburn", "max_stars_repo_head_hexsha": "1af3d62ec0e70b82250bce31342326adcf561002", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import sys
import numpy as np
class Graph(object):
def __init__(self):
nodes = np.loadtxt("assets/nodes.csv", dtype=str, delimiter=',')
matrix = np.genfromtxt("assets/AM.csv", delimiter=',', filling_values=1000)
init_graph = {}
for node in nodes:
init_graph[... | {"hexsha": "6c98082f5d714e13c0ff621e3628b4913de276f7", "size": 4412, "ext": "py", "lang": "Python", "max_stars_repo_path": "Graph.py", "max_stars_repo_name": "bitsPleaseHacked22/UofaPathfinder", "max_stars_repo_head_hexsha": "0676b667cc88c9435e0658e7aa7e5609d8691d41", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
MODULE m_pulses_init
! ----------------------------------------------------------------------
! MODULE: m_pulses_init
! ----------------------------------------------------------------------
! Purpose:
! Module for calling the pulses_force subroutine
! --------------------------------------------------------... | {"hexsha": "4188865640a9cb1c511595ba36a42c66d8a5e9d9", "size": 6574, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "src/fortran/m_pulses_init.f03", "max_stars_repo_name": "RodrigoNaves/ginan-bitbucket-update-tests", "max_stars_repo_head_hexsha": "4bd5cc0a9dd0e94b1c2d8b35385e128404009b0c", "max_stars_repo_lice... |
using VirulenceEvolution
using VirulenceEvolution:getindex, setindex!
using Test
# test ind2sub
t = reshape(1:8, 2, 2, 2)
@test t[VirulenceEvolution.ind2sub(axes(t), 6)...] == 6
# test Dynamics
d = Dynamics(0, 1)
@test d.history == [(0, 1)]
record!(d, 1, 2)
@test d.history == [(0, 1), (1, 2)]
# test zeroatdiag
m = [... | {"hexsha": "10e9345d709912c8fc4594a85aa63d11aa101b48", "size": 982, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "wangl-cc/VirulenceEvolution.jl", "max_stars_repo_head_hexsha": "a765e3e17e7c40e2900b1606dfdce6c0470f83d8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
module Plot
import PyPlot; plt = PyPlot
plt.svg(true)
using PyCall
export plt, make_bar_plot
"""
make_bar_plot(lookup_value, groups, keys; ...)
Create a bar plot.
# Examples
```julia
fig, ax = make_bar_plot(
["A", "B", "C", "D", "E"], ["1", "2", "3", "4"],
) do group, key
rand()
end
ax.set_title("...... | {"hexsha": "a41254deaae0b9498dfc54a66566f04568684585", "size": 2238, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Plot.jl", "max_stars_repo_name": "cduck/Tweaks.jl", "max_stars_repo_head_hexsha": "c9dc3fdb866d5a3a05c23e9bfff1839c5565d6d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
import pdb
def get_run_info(runtimes_info_path):
runtimes_info = open(runtimes_info_path, "r").read()
max_index = len(runtimes_info) -1
next_index = 0
runtime_vals = []
max_ram_vals = []
while next_index <= max_index:
try:
pos_before_index = runtimes_info.... | {"hexsha": "b01e896c4a0b2b29eec4cd0db304292ac71ee66d", "size": 2247, "ext": "py", "lang": "Python", "max_stars_repo_path": "runtime_testing/triadsim_runtimes.py", "max_stars_repo_name": "greggj2016/regens", "max_stars_repo_head_hexsha": "763413891f41068830b5e711ad3f16917e7771cf", "max_stars_repo_licenses": ["MIT"], "ma... |
# -*- coding: utf-8 -*-
import numpy
import scipy.linalg
import sklearn.cross_decomposition
import sklearn.metrics
class LinearCCA(object):
def __init__(self, n_components):
self._n_components = n_components
self._wx = None
self._wy = None
def fit(self, X, Y):
""" fit the mode... | {"hexsha": "aacc454bd71c3eb4f9b97cf3493d4dfb77cb8423", "size": 5663, "ext": "py", "lang": "Python", "max_stars_repo_path": "cca.py", "max_stars_repo_name": "t-aritake/KernelCCA.py", "max_stars_repo_head_hexsha": "68b219d096b344409f5d8bc7ff97102304565664", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": ... |
from pylab import *
####################################
# Load of Input Data (Daten laden) #
####################################
def load_RKI(filename, LandkreisID, state_name ='Bavaria'):
'''
Reads file of the RKI database and selects the relevant data for the specific county.
Input
=====
... | {"hexsha": "d5fb0db1a53baa222fb521a565df296367ee6885", "size": 40508, "ext": "py", "lang": "Python", "max_stars_repo_path": "cov19_local.py", "max_stars_repo_name": "koepferl/CoV19Dahoam", "max_stars_repo_head_hexsha": "0c4ea2db3d1cfc759b53f3e7a3dc84eb4a551c0f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
from cv2 import cv2
import numpy as np
from matplotlib import pyplot as plt
# 灰度化
def rgb2gray(src):
height = src.shape[0]
width = src.shape[1]
red_channel, green_channel, blue_channel = cv2.split(src)
dst = np.zeros(red_channel.shape, red_channel.dtype)
for h in range(height):
f... | {"hexsha": "049ad30b2bfb884fb8fdfce14b8e8ee00f3b9bb2", "size": 2858, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_4.py", "max_stars_repo_name": "Believas/ISBN-barcode-recognition", "max_stars_repo_head_hexsha": "69ab87ec9faa114666d8a651980913d9ee6402ca", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/*
* Copyright (c) 2020 International Business Machines
* All rights reserved.
*
* SPDX-License-Identifier: BSD-3-Clause
*
* Authors: Kornilios Kourtis (kou@zurich.ibm.com, kornilios@gmail.com)
*
*/
// vim: set expandtab softtabstop=4 tabstop:4 shiftwidth:4:
#ifndef TRT_SYNC_ABSTRACT_H__
#define TRT_SYNC_... | {"hexsha": "6a5daa03b2970c0b9b41826c602c3d50f01182ba", "size": 6013, "ext": "hh", "lang": "C++", "max_stars_repo_path": "trt/src/trt/sync_base_types.hh", "max_stars_repo_name": "nik-io/uDepot", "max_stars_repo_head_hexsha": "06b94b7f2438b38b46572ede28072e24997e40c6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#include <string.h>
#include <stdlib.h>
#include <arpa/inet.h>
#include <boost/thread/mutex.hpp>
#include "./../Game/cmdtypes.h"
#include "./../Game/log.h"
#include "./../utils/stringbuilder.hpp"
#include "./../Game/getdefinevalue.h"
#include "./../server.h"
#include "./../Monster/monster.h"
#include "diskdbmanage... | {"hexsha": "d690da9bd844b711a0e20d942bc8f6e092ab3a16", "size": 53523, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "memManage/diskdbmanager.cpp", "max_stars_repo_name": "ycsoft/FatCat-Server", "max_stars_repo_head_hexsha": "fe01d3278927437c04977f3009154537868cc354", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from openvino.inference_engine import IECore
import cv2
import sys
import time
import argparse
from decode_np import Decode
def build_argparser():
parser = argparse.ArgumentParser(description='')
parser.add_argument("-t", "--tiny", action="store_true",
help='store_tr... | {"hexsha": "84f444ab36f55a8f88f01b2712ab27e6fbb90205", "size": 5323, "ext": "py", "lang": "Python", "max_stars_repo_path": "detect.py", "max_stars_repo_name": "PieceZhang/face_detect_yolov4_yolov4tiny_ssd_openvino", "max_stars_repo_head_hexsha": "7e55ca610862b7c2dd1552be007a39153a8c20dc", "max_stars_repo_licenses": ["A... |
"""
Example use of vixutil to plot the term structure.
Be sure to run vixutil -r first to download the data.
"""
import vixutil as vutil
import pandas as pd
import logging as logging
import asyncio
import sys
pd.set_option('display.max_rows', 10)
#need over two months
pd.set_option('display.min_rows', 10)
pd.set_op... | {"hexsha": "8eca75cca8e499dbc8934aea4a8f4519b449250b", "size": 2492, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/vix_utils/example_plot_vix_term_structure.py", "max_stars_repo_name": "MichaelWS/vix_utils", "max_stars_repo_head_hexsha": "c7a73a0c4013f7eb2329cfe27eb012028fa31cdd", "max_stars_repo_licenses"... |
import six
import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
from matplotlib.collections import LineCollection
from move_direction import angle_clockwise
from data_to_segments import angle_to_segments
from patterns import find_substr_idx
from helpers import s... | {"hexsha": "ffb786cbacac0e91da2a36ed18dddec6489b6f18", "size": 6157, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/plot_helpers.py", "max_stars_repo_name": "taneta/patterns", "max_stars_repo_head_hexsha": "b00e5e3466e467992795f183f12b3a0101bd238b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5... |
/// \file \brief This file disables some warnings produced by the library
///
/// \warning This file has no include guards (it is supposed to be included
/// multiple times) and should always be paired with a:
///
/// #include <boost/v3/detail/re_enable_warnings.hpp>
///
/// The following warnings are disabled by this ... | {"hexsha": "c87dd8f5daf36eb281237c0676be3f02350b6292", "size": 453, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/range/v3/detail/disable_warnings.hpp", "max_stars_repo_name": "CornedBee/range-v3", "max_stars_repo_head_hexsha": "99a9f5f70e65dfcf6bbc8894bf2a22d8f5d4552a", "max_stars_repo_licenses": ["MIT"... |
#! Demonstrates a failing test suite initializer
#:include 'fytest.fypp'
#:block TEST_SUITE('failing_suite')
use mymath
implicit none
#:contains
#! Using the test suite initializer to initialize suite.
#! Since it will fail, none of the tests in the suite will be run.
#:block TEST_SUITE_INIT
call rand... | {"hexsha": "0727fe8f3d33befd601e7d05ce464eace0783444", "size": 1823, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "examples/serial/test/test_failing_suite.fpp", "max_stars_repo_name": "aradi/fytest", "max_stars_repo_head_hexsha": "9133d5dab5b582161f4fb4c4b127d7f97133e3e7", "max_stars_repo_licenses": ["BSD-2-... |
import numpy as np
from sklearn.decomposition import PCA
def identity(data):
""" no transformation """
return data
def l2(data):
return np.asarray(data)/(np.linalg.norm(data, axis=0) + 1e-4)
def l1(data):
return np.asarray(data)/(np.linalg.norm(data, axis=0, ord=1) + 1e-4)
def pca_whitening_30d(... | {"hexsha": "55bcfe92dc287bb42567f2965ee01d7760871353", "size": 504, "ext": "py", "lang": "Python", "max_stars_repo_path": "Animator/normalization_methods.py", "max_stars_repo_name": "oronnir/CAST", "max_stars_repo_head_hexsha": "c2b095a516e5ad0cdfec8b13196045549cbd3f4c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright (c) 2018 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 applicabl... | {"hexsha": "13bc5768740ece00bbe285a0b47d82bb8a42d2c7", "size": 9502, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/test_im2sequence_op.py", "max_stars_repo_name": "jerrywgz/Paddle", "max_stars_repo_head_hexsha": "85c4912755b783dd7554a9d6b9dae4a7e40371bc", "max_stars_repo_lic... |
[STATEMENT]
lemma double_swap_qSwap:
assumes "good X"
shows "qGood (((pick X) #[[x \<and> y]]_zs) #[[x' \<and> y']]_zs') \<and>
((X #[x \<and> y]_zs) #[x' \<and> y']_zs') = asTerm (((pick X) #[[x \<and> y]]_zs) #[[x' \<and> y']]_zs')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. qGood (pick X #[[x \<and> y]... | {"llama_tokens": 267, "file": "Binding_Syntax_Theory_Transition_QuasiTerms_Terms", "length": 1} |
library(fitdistrplus)
######################################################################################
library(httk) # High-Throughput Toxicokinetics
library(sensitivity) # Sensitivity Analysis
######################################################################################
# devtools::install_github(... | {"hexsha": "126927b8d813e23ee1180249fe0534ac7f2131bd", "size": 9862, "ext": "r", "lang": "R", "max_stars_repo_path": "pbkm_modeling/R_code/runs.r", "max_stars_repo_name": "alfcrisci/michyf", "max_stars_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
SUBROUTINE INPTT4
C
C THIS INPTT4 UTILITY MODULE WILL READ USER-SUPPLIED TAPE (OR DISC
C FILE), AS GENERATED FROM OUTPUT4 OR FROM MSC/OUTPUTi MODULES (i=1,
C
C THIS MODULE HANDLES ONLY MATRICES, AND NOT TABLES
C
C COSMIC/OUTPUT4 AND MSC/OUTPUT4 ARE IDENTICAL (BINARY ONLY)
C COSMIC/INPU... | {"hexsha": "34f70b330d86baeac844658c916aa567ad060919", "size": 4913, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/inptt4.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
# _calculateSNR.py
# Module providing the calculateSNR function
# Copyright 2013 Giuseppe Venturini
# This file is part of python-deltasigma.
#
# python-deltasigma is a 1:1 Python replacement of Richard Schreier's
# MATLAB delta sigma toolbox (aka "delsigma"), upon which it is heavily based.
# T... | {"hexsha": "92ce7ea4632bed32eb6b82f2c054ccd637b26dcf", "size": 2139, "ext": "py", "lang": "Python", "max_stars_repo_path": "deltasigma/_calculateSNR.py", "max_stars_repo_name": "michi7x7/python-deltasigma", "max_stars_repo_head_hexsha": "029e97eb6de748744f62840114ae6725ec5a721b", "max_stars_repo_licenses": ["OLDAP-2.6"... |
from __future__ import division
import theano.tensor as tt
import theano
import numpy as np
from VIMCO import VIMCO
from utils import sigmoid, replicate_batch
class SBN(VIMCO):
def __init__(self, layers, batch_size, b1, b2, lam):
super(SBN, self).__init__(batch_size, b1, b2, lam)
self.layers = ... | {"hexsha": "72c8bfb650d83abb89d06d8c9a33dc70f24f5e25", "size": 5664, "ext": "py", "lang": "Python", "max_stars_repo_path": "SBN.py", "max_stars_repo_name": "y0ast/VIMCO", "max_stars_repo_head_hexsha": "62420d90d27656621f6ca47d90a55d051e9a5934", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_stars_repo_... |
import numpy as np
import keras
import csv
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Conv2D, MaxPooling2D, Flatten
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import CSVLogger
from keras.preprocessing.image import ImageDataGenerato... | {"hexsha": "88e224442772d6b8227747c0821ff349d84f9a7f", "size": 2371, "ext": "py", "lang": "Python", "max_stars_repo_path": "Balencing/CNN_Balacing(LeNet).py", "max_stars_repo_name": "kctoayo88/MLP_CNN_Comparison_Test", "max_stars_repo_head_hexsha": "8cd7d1222b432394223f2dacf0e906578ea2f4cf", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
from collections import defaultdict
import re
import numpy as np
from pyfr.readers import BaseReader, NodalMeshAssembler
from pyfr.readers.nodemaps import GmshNodeMaps
def msh_section(mshit, section):
endln = '$End{}\n'.format(section)
endix = int(next(mshit)) - 1
for i, l in e... | {"hexsha": "93162a2be83d4a32945d947bbd5f1a2645032e31", "size": 9075, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfr/readers/gmsh.py", "max_stars_repo_name": "synthetik-technologies/PyFR", "max_stars_repo_head_hexsha": "9d4d5e96a8a9d5ca47970ec197b251ae8b0ecdda", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
# -*- coding: utf-8 -*-
#
from __future__ import division
import numpy
import sympy
from .helpers import _symm_r_0, _z, _symm_s_t
from ..helpers import untangle
class Maxwell(object):
"""
J.C. Maxwell,
On Approximate Multiple Integration between Limits by Summation.
In W. Niven (Ed.), The Scientific... | {"hexsha": "833f525ef4a3413a772193d97efbe81f8683b702", "size": 1159, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadpy/quadrilateral/maxwell.py", "max_stars_repo_name": "gdmcbain/quadpy", "max_stars_repo_head_hexsha": "c083d500027d7c1b2187ae06ff2b7fbdd360ccc7", "max_stars_repo_licenses": ["MIT"], "max_stars... |
(* ================================================================== *)
Section EX.
Variables (A:Set) (P : A->Prop).
Variable Q:Prop.
(* Check the type of an expression. *)
Check P.
Lemma trivial : forall x:A, P x -> P x.
Proof.
intros.
assumption.
Qed.
(* Prints the definition of an identifier. *)
Print tr... | {"author": "melpereira7", "repo": "VF_2122", "sha": "cbac6daa9e4640a095cfadc06ad5fa5722d4bbfd", "save_path": "github-repos/coq/melpereira7-VF_2122", "path": "github-repos/coq/melpereira7-VF_2122/VF_2122-cbac6daa9e4640a095cfadc06ad5fa5722d4bbfd/Exerc\u00edcios/Coq/lesson1.v"} |
Require Import Coq.Program.Equality.
Require Import List.
Import ListNotations.
Require Import IFOL.Util.List_index.
Require Import IFOL.Util.HVec.
Require Import IFOL.Util.Witness.
Fixpoint RHVec {X} (Y : X -> Type) (xs : list X) : Type :=
match xs with
| [] => unit
| x :: xs' => Y x * RHVec Y xs'
end.
Fixp... | {"author": "emarzion", "repo": "IFOL", "sha": "135d188c9c899350e8726f97891101b46d8a7c2f", "save_path": "github-repos/coq/emarzion-IFOL", "path": "github-repos/coq/emarzion-IFOL/IFOL-135d188c9c899350e8726f97891101b46d8a7c2f/src/Util/RHVec.v"} |
using Libdl
const shared_lib = "./ocaml.so"
function start_ocaml()
lib = Libdl.dlopen(shared_lib)
ccall(("ocaml_jl_start", shared_lib), Cvoid, ())
end
start_ocaml()
fn = Main.mycaml_fn
Main.mycaml_fn(x=1, y=2)
println(fn((1, "foo", [1.2, "bar"])))
for i in 1:3
println(fn(i, "foo", [1.2, "bar"]))
end
fn2... | {"hexsha": "6aa337aa20aef798621907a8bce5445531498d3d", "size": 518, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/generic_test.jl", "max_stars_repo_name": "LaurentMazare/ocaml.jl", "max_stars_repo_head_hexsha": "1be77de8caa1da5610afd8d6c49d3359b5cbed25", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
//Copyright (c) 2013 Singapore-MIT Alliance for Research and Technology
//Licensed under the terms of the MIT License, as described in the file:
// license.txt (http://opensource.org/licenses/MIT)
#pragma once
#include "util/LangHelpers.hpp"
#include "metrics/Length.hpp"
#include <map>
#include <vector>
#includ... | {"hexsha": "d4e313b288df39a6e72780e678d0c6b8ae37a420", "size": 4617, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "dev/Basic/shared/geospatial/streetdir/GridStreetDirectoryImpl.hpp", "max_stars_repo_name": "gusugusu1018/simmobility-prod", "max_stars_repo_head_hexsha": "d30a5ba353673f8fd35f4868c26994a0206a40b6", ... |
#!/usr/bin/env python
# AUTHOR: Shane Gordon
# ROLE: TODO (some explanation)
# CREATED: 2015-06-06 13:12:10
import os
import re
import sys
import logging
import argparse
import subprocess
from datetime import datetime
import numpy as np
import shutil
import seaborn as sns
import time
import matplotlib.pyplot as... | {"hexsha": "86a3c9df4bf0bb47f31b835dda5651f4edd77693", "size": 23702, "ext": "py", "lang": "Python", "max_stars_repo_path": "cdgo/__main__.py", "max_stars_repo_name": "s-gordon/CDGo", "max_stars_repo_head_hexsha": "7bd1b3a6780f70f1237a7f0cac5e112c6b804100", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
[STATEMENT]
lemma sphere_cball [simp,intro]: "sphere z r \<subseteq> cball z r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sphere z r \<subseteq> cball z r
[PROOF STEP]
by force | {"llama_tokens": 73, "file": null, "length": 1} |
import pandas
# import scipy
import numpy
from numpy import array
from numpy.linalg import inv
from sklearn.preprocessing import Normalizer
# load CSV using Pandas
filename = 'mockData.csv'
names = ['name', 'sem1', 'sem2', 'sem3', 'sem4', 'sem5', 'sem6', 'sem7', 'sem8', 'dist', 'hour', 'tuition', 'hobby', 'ge... | {"hexsha": "76a84dea44db88e1db5052d1d64286fe0e87cda4", "size": 1526, "ext": "py", "lang": "Python", "max_stars_repo_path": "linear regression/linearRegression.py", "max_stars_repo_name": "harmitsampat96/Machine-Learning", "max_stars_repo_head_hexsha": "f7b6bf1ae07a9bc53cdb79660068011452eb1731", "max_stars_repo_licenses... |
'''
'''
import numpy as np
import fusilib.io.spikes2 as iospikes
time_locked_events_matrix = iospikes.time_locked_spike_matrix
def time_locked_delay_matrix(event_ids,
oldtimes,
newtimes,
dt,
delay_windo... | {"hexsha": "fed41b160b0cb50339d2cbb80cdad1869c8ac2fa", "size": 6944, "ext": "py", "lang": "Python", "max_stars_repo_path": "fusilib/io/events.py", "max_stars_repo_name": "anwarnunez/fusi", "max_stars_repo_head_hexsha": "c15ea2567e9fca92b1a6a1130eb396825d0f76cf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
[STATEMENT]
lemma bv_sub_length: "length (bv_sub w1 w2) \<le> Suc (max (length w1) (length w2))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (bv_sub w1 w2) \<le> Suc (max (length w1) (length w2))
[PROOF STEP]
proof (cases "bv_to_int w2 = 0")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. bv_to_int w2 =... | {"llama_tokens": 8809, "file": "RSAPSS_Word", "length": 84} |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import json
import numpy as np
import os
import torch
import datasets.registry
from foundations import paths
from fou... | {"hexsha": "0ea598a00282904dd5382783896916a98f270f10", "size": 7967, "ext": "py", "lang": "Python", "max_stars_repo_path": "lottery/test/test_runner.py", "max_stars_repo_name": "sbam13/open_lth", "max_stars_repo_head_hexsha": "d8c8d450cc8229afed54b26f77b91c3fe0c3f339", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma no_repetition_list:
assumes "set ws \<subseteq> {a,b}"
and not_per: "\<not> ws \<le>p [a,b] \<cdot> ws" "\<not> ws \<le>p [b,a] \<cdot> ws"
and not_square: "\<not> [a,a] \<le>f ws" and "\<not> [b,b] \<le>f ws"
shows False
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. False
[PROOF ... | {"llama_tokens": 4442, "file": "Combinatorics_Words_Equations_Basic", "length": 47} |
from os import listdir, path
import numpy as np
import sklearn.neighbors as neighbors
import vtk
from vtk.util.numpy_support import vtk_to_numpy
def extract_line(filename):
# Read the VTP file
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName(filename)
reader.Update()
# Extract the polygon ... | {"hexsha": "034fb574066f26bd308342ca2b24ad09de33c961", "size": 1238, "ext": "py", "lang": "Python", "max_stars_repo_path": "trench_automation/util.py", "max_stars_repo_name": "yozoon/TrenchDepositionAutomation", "max_stars_repo_head_hexsha": "4eb1dd9fbabe7a782aa2070de144240616c00472", "max_stars_repo_licenses": ["MIT"]... |
#include <StdInc.h>
#include "TrType.h"
#include "TrTopLevel.h"
#include "Module.h"
#include "GlobalContext.h"
#include "Nest/Utils/Diagnostic.hpp"
#include "Nest/Utils/cppif/StringRef.hpp"
#include "Nest/Utils/cppif/Type.hpp"
#include "Nest/Api/Type.h"
#include "Nest/Api/Node.h"
#include "Feather/Api/Feather.h"
#inc... | {"hexsha": "337c246d645ce5d428612641f608a47f69789a1c", "size": 4723, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/LLVMBackend/Tr/TrType.cpp", "max_stars_repo_name": "CristianDragu/sparrow", "max_stars_repo_head_hexsha": "49844c2329ac001c3a0779baae7a2f02743c4494", "max_stars_repo_licenses": ["MIT"], "max_sta... |
\documentclass[11pt]{article}
\usepackage[english]{babel}
\usepackage{a4}
\usepackage{latexsym}
\usepackage[
colorlinks,
pdftitle={IGV solutions week 10},
pdfsubject={Werkcollege Inleiding Gegevensverwerking week 10},
pdfauthor={Laurens Bronwasser, Martijn Vermaat}
]{hyperref}
\title{IGV solutions week 10}
\author... | {"hexsha": "21bb886cf3cf34959d041c996f1a45836e0cee66", "size": 6240, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "vu/igv/igv2003_10.tex", "max_stars_repo_name": "martijnvermaat/documents", "max_stars_repo_head_hexsha": "42483b7c4bf94ed708e2893c3ea961d025a10b5e", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_st... |
#Packages
import sys
import numpy
import matplotlib
import pandas
import sklearn
#version check
print('Python:{}'.format(sys.version))
print('Numpy:{}'.format(numpy.__version__))
print('matplotlib:{}'.format(matplotlib.__version__))
print('pandas:{}'.format(pandas.__version__))
print('sklearn:{}'.format(s... | {"hexsha": "13c5b083985f5e11f433fa1879aae6df7c19fd36", "size": 2901, "ext": "py", "lang": "Python", "max_stars_repo_path": "BreastCancerDetection.py", "max_stars_repo_name": "firoj998/Breast-Cancer-Detection", "max_stars_repo_head_hexsha": "bf041fba24ba0f2bbb379f64e7b7e56f11aa3245", "max_stars_repo_licenses": ["MIT"], ... |
import matplotlib.pyplot as plt
import numpy as np
#valores do grafico#
y = np.array([35, 25, 25, 15])
#intens do gafricO#
mylabels = ['Maçãs', 'Banana', 'Laranja', 'Melancia']
#espaços ente fatias#
myexplode = [0.2, 0, 0, 0]
plt.pie(y, labels=mylabels, explode=myexplode, shadow=True)
plt.show()
... | {"hexsha": "45336227bfbb0d479278647aa4c9f7849e3e915b", "size": 325, "ext": "py", "lang": "Python", "max_stars_repo_path": "gafricoPizza/grafico.py", "max_stars_repo_name": "lucasDEV20/GafricoPizzaEmPython", "max_stars_repo_head_hexsha": "1cd668e87db12cc36e679cbd33f324eacb2dc0da", "max_stars_repo_licenses": ["MIT"], "ma... |
import argparse
from design_search import RobotDesignEnv, make_graph, build_normalized_robot, presimulate, simulate
import mcts
import numpy as np
import os
import pyrobotdesign as rd
import random
import tasks
import time
class CameraTracker(object):
def __init__(self, viewer, sim, robot_idx):
self.viewer = vie... | {"hexsha": "7751683e8665719d88fd0fa7121e8e021261d214", "size": 8912, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/design_search/viewer.py", "max_stars_repo_name": "ONLYA/RoboGrammar", "max_stars_repo_head_hexsha": "4b9725739b24dc9df4049866c177db788b1e458f", "max_stars_repo_licenses": ["MIT"], "max_st... |
! { dg-do run }
! { dg-options "-fdump-tree-original" }
!
! PR fortran/56845
!
module m
type t
integer ::a
end type t
contains
subroutine sub
type(t), save, allocatable :: x
class(t), save,allocatable :: y
if (.not. same_type_as(x,y)) STOP 1
end subroutine sub
subroutine sub2
type(t), save, allocatable :: a(:)
... | {"hexsha": "d2514772a0386da798a040a098d24979db7349dc", "size": 715, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/class_allocate_14.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_rep... |
/*
Copyright 2010 Kenneth Riddile
Use, modification and distribution are subject to 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).
*/
/*************************************************************************************... | {"hexsha": "82fb791adbce4e43b549433f215cd748f4007a4d", "size": 4977, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdparty/boost/boost/gil/extension/io_new/formats/targa/write.hpp", "max_stars_repo_name": "Greentwip/windy", "max_stars_repo_head_hexsha": "4eb8174f952c5b600ff004827a5c85dbfb013091", "max_stars_rep... |
from __future__ import print_function
import mxnet as mx
import numpy as np
import argparse
import re
import sys
from convert_symbol import proto2symbol
caffe_flag = True
try:
import caffe
except ImportError:
import caffe_parse.parse_from_protobuf as parse
caffe_flag = False
def get_caffe_iter(layer_n... | {"hexsha": "a648530087518cb2c76141641c1c0636f310a1c0", "size": 7341, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/caffe_converter/convert_model.py", "max_stars_repo_name": "dmmiller612/mxnet", "max_stars_repo_head_hexsha": "3f410c23cb02df64625d7c8f9f299b580236f6a5", "max_stars_repo_licenses": ["Apache-2... |
# -*- coding: utf-8 -*-
"""
Tonemapping Operators Plotting
==============================
Defines the tonemapping operators plotting objects:
- :func:`colour_hdri.plotting.plot_tonemapping_operator_image`
"""
import matplotlib
import matplotlib.ticker
import numpy as np
from colour.plotting import (CONSTANTS_COLO... | {"hexsha": "c668bc0b3cb84e358e297010a86271c5f3464560", "size": 2800, "ext": "py", "lang": "Python", "max_stars_repo_path": "colour_hdri/plotting/tonemapping.py", "max_stars_repo_name": "colour-science/colour-hdri", "max_stars_repo_head_hexsha": "3a97c4ad8bc328e2fffabf84ac8b56d795dbeb82", "max_stars_repo_licenses": ["BS... |
import numpy as np
import cv2
# Identify pixels above the threshold
# Threshold of RGB > 160 does a nice job of identifying ground pixels only
def color_thresh(img, rgb_thresh=(160, 160, 160)):
# Create an array of zeros same xy size as img, but single channel
color_select = np.zeros_like(img[:,:,0])
# Req... | {"hexsha": "6030ff153a1b9b172d9345b5f8989d6d2d63945d", "size": 6428, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/perception.py", "max_stars_repo_name": "northerncat/RoboND-Rover-Project", "max_stars_repo_head_hexsha": "52030b8eaae3cde912d79bee42dced50ec81a6af", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/python
# md.py - An Event Driven Molecular Dynamics (EDMD) Simulator
#
# This script performs a simple, event-drive molecular dynamics
# simulation on a pygame canvas
#
# Dependencies:
# - pygame
# - numpy
# - particle.py (EDMD project)
# - event.py (EDMD project)
#
# Andrew D. McGuire 2017
# a.mcguire227@gm... | {"hexsha": "ad2cbb5190d16c6d9bdf91cf7fe6e40531bf3cba", "size": 10626, "ext": "py", "lang": "Python", "max_stars_repo_path": "md.py", "max_stars_repo_name": "adm78/EDMD", "max_stars_repo_head_hexsha": "1d6ba28841ac6917a31fe513505032c13f6b092a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo... |
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