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# -*- coding: utf-8 -*-
"""
Created on Sun Nov 5 21:31:02 2017
@author: thuzhang
"""
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegres... | {"hexsha": "80ec147109819e66e84d7d1ec4c6fd44c7807724", "size": 1344, "ext": "py", "lang": "Python", "max_stars_repo_path": "MultiLevelForecast/TwoDayAhead/SeasonalPredict.py", "max_stars_repo_name": "AmateurZhang/EnjoyWithDataOnPowerSystems", "max_stars_repo_head_hexsha": "64227d0505012d2b5650874c65268e85d9751a17", "ma... |
(* --------------------------------------------------------------------
* Copyright (c) - 2006--2012 - IMDEA Software Institute
* Copyright (c) - 2006--2012 - Inria
* Copyright (c) - 2006--2012 - Microsoft Coprporation
*
* Distributed under the terms of the CeCILL-B-V1 license
* ----------------------------------... | {"author": "EasyCrypt", "repo": "certicrypt", "sha": "7b3cd2fe4a317aec38dfff9eec902b265c575587", "save_path": "github-repos/coq/EasyCrypt-certicrypt", "path": "github-repos/coq/EasyCrypt-certicrypt/certicrypt-7b3cd2fe4a317aec38dfff9eec902b265c575587/Examples/Indifferentiability/ECurve/PrimalityTest/PGroup.v"} |
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
class Handler:
def __init__(self):
self.name = __name__
def predict_transform(self, prediction, inp_dim, anchors, num_classes, CUDA=Tru... | {"hexsha": "24c0ad700cf5e4d0fef36c9ba9c63bc0851f1f2b", "size": 2510, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/core/models/internal/yolo/v3/util.py", "max_stars_repo_name": "romanovacca/detectioncollection", "max_stars_repo_head_hexsha": "98f43ceb4ddd51af39fd6566685a8dc57a1380b4", "max_stars_repo_licen... |
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
\file genScanNetData.py
\brief Code to train a segmentation network on the ScanNet dataset.
\copyright Copyright (c) 2018 Visual Computing group of Ulm University,
Germany. See the LICENSE file at the top-level... | {"hexsha": "e3cc2e3787c64522fd3b8dd127f2600d4c467adc", "size": 20702, "ext": "py", "lang": "Python", "max_stars_repo_path": "ScanNet/ScanNet.py", "max_stars_repo_name": "chenzhutian/MCCNN", "max_stars_repo_head_hexsha": "e28ca4a2deeecbfd1c8939ca666fcc010554fcbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9... |
FUNCTION MV_EI32 ( n, valin, valout )
C************************************************************************
C* MV_EI32 *
C* *
C* This function converts an array of IEEE 32-bit real numbers to *
C* IBM 32-bit real numbers. The input and output arrays may be the *
C* same. *
C* *
C* IN... | {"hexsha": "a0bae328a15919332cf819a08c83ae4695721a2a", "size": 2575, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/gemlib/mv/Linux/mvei32.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Claus... |
import pytest
import pandas as pd
import numpy as np
#from collections import namedtuple
#from collections import OrderedDict
import pyactms as ms
class TestActMS(object):
def test_getProtocol(self):
pass
| {"hexsha": "f954fcbc8fd929438a5efc8a210ff16dc5165a01", "size": 227, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_actms.py", "max_stars_repo_name": "seblum/pyactms", "max_stars_repo_head_hexsha": "46ddb59a9582b53e5fac805340e7fd56a7d7d133", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#include <boost/static_string/static_string.hpp>
int main()
{
boost::static_string<5> s1("UVXYZ", 3);
return s1 == "UVX" ? 0 : 1;
}
| {"hexsha": "914860644df7952d37cdfb0f1de7c0a2dbb0386b", "size": 137, "ext": "cc", "lang": "C++", "max_stars_repo_path": "test/static_string_test.cc", "max_stars_repo_name": "wxthon/rules_boost", "max_stars_repo_head_hexsha": "46916bd45a7ed0f1f64f5b5dff0fec5b87870d0a", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
/**
* @file VolumeBrickOctree.cpp
* @author Sebastian Maisch <sebastian.maisch@uni-ulm.de>
* @date 2015.08.17
*
* @brief Implementation of the layered octree.
*/
#include "VolumeBrickOctree.h"
#include "gfx/glrenderer/GLTexture3D.h"
#include "gfx/glrenderer/GLTexture.h"
#include "app/ApplicationBase.h"
#inc... | {"hexsha": "64102c0e774d3d96f66fa02544121c2b71ece6d5", "size": 14568, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OGLFramework_uulm/gfx/volumes/VolumeBrickOctree.cpp", "max_stars_repo_name": "dasmysh/OGLFramework_uulm", "max_stars_repo_head_hexsha": "b067b20ff2fc4350e4eea41493bda2c9a097921d", "max_stars_repo_l... |
[STATEMENT]
lemma rank_le_if_sorted_from_list:
assumes "sorted cmp'' ((v1,e1)#ys)" and "is_subtree (Node r0 {|(t0,e0)|}) (dtree_from_list v1 ys)"
shows "rank (rev r0) \<le> rank (rev (root t0))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rank (rev r0) \<le> rank (rev (dtree.root t0))
[PROOF STEP]
proof -
[PR... | {"llama_tokens": 1766, "file": "Query_Optimization_IKKBZ", "length": 17} |
# Autogenerated wrapper script for Tcl_jll for x86_64-linux-gnu
export libtcl
using Zlib_jll
JLLWrappers.@generate_wrapper_header("Tcl")
JLLWrappers.@declare_library_product(libtcl, "libtcl8.6.so")
function __init__()
JLLWrappers.@generate_init_header(Zlib_jll)
JLLWrappers.@init_library_product(
libtcl... | {"hexsha": "9086e17175f1b0ef3e86dba6082db841dd075735", "size": 450, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/x86_64-linux-gnu.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Tcl_jll.jl", "max_stars_repo_head_hexsha": "94fce4d69586523974ace7b99a6eea42b17a2f85", "max_stars_repo_licenses": ["MIT... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import arviz as az
az.style.use('arviz-darkgrid')
μ = 0.
σ = 1.
X = stats.norm(μ, σ)
x = X.rvs(1500)
x[0:50]
x.mean()
mu_params = [-1, 0, 1]
sd_params = [0.5, 1, 1.5]
x = np.linspace(-7, 7, 2000)
_, ax = plt.subplots... | {"hexsha": "18858f828059bf503aa4f18f55ab6b20a0358be5", "size": 3387, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter01/.virtual_documents/01 Thinking Probabilistically.ipynb.py", "max_stars_repo_name": "Vedia-JerezDaniel/Bayesi_py", "max_stars_repo_head_hexsha": "3d598c5b740157eb4761989f3b4e357d1598b7cf"... |
# -*- coding: utf-8 -*-
import os
from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import numpy as np
"""
Utility functions for loading, saving,
processing and visualizing hdf5 timeseries files
""... | {"hexsha": "539f0ec3f512eee32138d060dfe370e64c5d130e", "size": 3727, "ext": "py", "lang": "Python", "max_stars_repo_path": "hdf5/utils_hdf5.py", "max_stars_repo_name": "agrija9/Wind-Turbine-Anomaly-Detection-VRAE", "max_stars_repo_head_hexsha": "91d3c95f2f19cc9a57f5b9ddff5934cabf0cea30", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: layers.py
# Author: Qian Ge <geqian1001@gmail.com>
import numpy as np
import tensorflow as tf
def Linear(inputs, out_dim, name='Linear', nl=tf.identity):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
inputs = batch_flatten(inputs)
in_dim... | {"hexsha": "bb18b7c77c755f1f4c314948972bca61e0795b86", "size": 1224, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/model/layers.py", "max_stars_repo_name": "kanedo/recurrent-attention-model", "max_stars_repo_head_hexsha": "8da82b7fbeb7c94859a1d4fd428ea112bde9a0c9", "max_stars_repo_licenses": ["MIT"], "max_... |
import logging
import mlflow
import numpy as np
import optuna
from omegaconf import DictConfig
from optuna.integration.mlflow import MLflowCallback
from optuna.trial import Trial
from sklearn.metrics import accuracy_score, log_loss, roc_auc_score
from sklearn.model_selection import TimeSeriesSplit, train_test_split
fr... | {"hexsha": "ca5286a8a92a5c8255a4bc04cf6a94f29293b3b3", "size": 3451, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/elo/step.py", "max_stars_repo_name": "AnH0ang/big-data-challenge", "max_stars_repo_head_hexsha": "aa9ba92e2355bc2838a037219d20d387e2eefdbb", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from astropy.tests.helper import remote_data
from ... import datasets
@remote_data
def test_load_catalog_atnf():
catalog = datasets.load_cata... | {"hexsha": "d4699f9bae5ac73f393daed3019e719918c9c21d", "size": 942, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/datasets/tests/test_catalogs.py", "max_stars_repo_name": "joleroi/gammapy", "max_stars_repo_head_hexsha": "c4e0c4bd74c79d30e0837559d18b7a1a269f70d9", "max_stars_repo_licenses": ["BSD-3-Clau... |
#pragma once
#include "Concepts.hpp"
#include <boost/hana/tuple.hpp>
#include <span>
namespace codys {
template <typename Tag, typename Unit_>
struct State {
using Unit = Unit_;
using depends_on = boost::hana::tuple<State<Tag, Unit>>;
template <class SystemType, std::size_t N>
constexpr static do... | {"hexsha": "df4fe66cd2af7f8a07e23890d2135c6bd5cf4851", "size": 467, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/codys/State.hpp", "max_stars_repo_name": "hansepp/codys", "max_stars_repo_head_hexsha": "987af24cca75745916f4dad03f75a3d0a05586fd", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count"... |
using Random
using MLJ
using SigmaRidgeRegression
id_design = BlockCovarianceDesign([IdentityCovarianceDesign(), IdentityCovarianceDesign()])
ps = [100; 100]
grp = GroupedFeatures(ps)
design = set_groups(id_design, grp)
αs = [1.0; 1.0]
ntest = 20_000
ntrain = 400
ridge_sim = GroupRidgeSimulationSettings(;
groups... | {"hexsha": "52ddac92d3a6255c0982cabd36c44e9555691566", "size": 1196, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_simulations.jl", "max_stars_repo_name": "nignatiadis/SigmaRidgeRegression.jl", "max_stars_repo_head_hexsha": "f9dc3300250c87200b66ddae17501e174c221f21", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
import numpy as np
from sys import argv
class Body:
def __init__(self, m, x_init, v_init):
self.m = m # in kg
self.x = x_init # in m
self.v = v_init # in m/s
def p_transfer(u1, u2, m1, m2):
# from conservation of energy and momentum
v1 = ((m1-m2)/(m1+m2))*u1... | {"hexsha": "f8e2ada27e916bf0c101dd88d5703645254deb50", "size": 2759, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python-implementation/collision-sim.py", "max_stars_repo_name": "physicodes/collision-sim", "max_stars_repo_head_hexsha": "aa90d7af9565d95996999b8f2dc4a1fe14d14308", "max_stars_repo_licenses": ["M... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE 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/lic... | {"hexsha": "da0f18e77c94a875f589bac0a6bb49c07b3135cb", "size": 4927, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/federatedml/federatedrec/factorization_machine/homo_factorization_machine/homo_fm_arbiter.py", "max_stars_repo_name": "showKstage/fut-fate", "max_stars_repo_head_hexsha": "77ef6fb84e9f8345a... |
/* Copyright (C) 2014 InfiniDB, Inc.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; version 2 of
the License.
This program is distributed in the hope that it will be useful,
but W... | {"hexsha": "2df56453e43407262a9afaa11427c953e62d94a0", "size": 2215, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/vendor/mariadb-10.6.7/storage/columnstore/columnstore/dbcon/dmlpackage/gramtest.cpp", "max_stars_repo_name": "zettadb/zettalib", "max_stars_repo_head_hexsha": "3d5f96dc9e3e4aa255f4e6105489758944... |
import sys
import numpy as np
from matplotlib import patches, collections, ticker, pyplot as plt
from skimage import io, morphology, measure
# width of vacuum opening
ROOMBA_WIDTH = 180
SCALING_FACTOR = 10 # mm/pixel
class Polygon():
def __init__(self, points, center=[0, 0], snap=90, scaling=1):
self.sn... | {"hexsha": "45e771d560e06b57abb239eae41a117a4f0b4743", "size": 14488, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyze/polygon.py", "max_stars_repo_name": "henrythasler/roomba", "max_stars_repo_head_hexsha": "ecb021b2726f80d269c463f4709b7910e4d1a229", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
#
# // SPDX-License-Identifier: BSD-3-CLAUSE
#
# (C) Copyright 2018, Xilinx, Inc.
#
#!/usr/bin/python
from __future__ import print_function
from progressbar import ProgressBar
import numpy as np
import tensorflow as tf
from xfdnn.rt.xdnn_util import make_list
from xfdnn.rt.xdnn_io import loadIm... | {"hexsha": "773d891a625d6c912670e01e14e1133b1d922244", "size": 3958, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tensorflow/utils.py", "max_stars_repo_name": "yarenty/ml-suite", "max_stars_repo_head_hexsha": "570202d904cf5980c2262b4cfc067eb8428ea8d0", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
from imgProcessor import *
from vehDetectFunc import *
import pickle
from scipy.ndimage.measurements import label
VIS_WINDOWS = False
VIS_BBOX = False
VIS_HEAT = False
TEST_ON_IMAGE =... | {"hexsha": "2232e1f627249e9387b9b7878b0b8c2027289bdb", "size": 11655, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/vehicle_detection_svm/vehicleDetection.py", "max_stars_repo_name": "towardsautonomy/towardsautonomy.github.com", "max_stars_repo_head_hexsha": "54088e8fa87ba265df2c2d660148996767c36668",... |
#include <stdlib.h>
#include <stdio.h>
#include <errno.h>
#include <math.h>
#include <gsl/gsl_integration.h>
double f1(double x, void *params) {
(void) (params); /* avoid unused parameter warning */
return pow(x, 2);
}
double fi1(double x) {
return pow(x, 3) / 3;
}
double f2(double x, void *params) {
... | {"hexsha": "34b658de8ad7a7103fa47acc2e6d8d3bdcbeed08", "size": 1714, "ext": "c", "lang": "C", "max_stars_repo_path": "lab4/zad4.c", "max_stars_repo_name": "mistyfiky/agh-mownit", "max_stars_repo_head_hexsha": "d88c21308b863942497c111d044e359ce220d421", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
roslaunch my_pkg ur5_bringup.launch
turn on external program on teach pendant!!
roslaunch usb_cam usb_cam-test.launch
roslaunch my_pkg single.launch
rosrun my_pkg ibvs.py
yxj 20210826
'''
from numpy import linalg
import rospy
from rospy.rostime import Duration
from s... | {"hexsha": "b29f5b9239e594ee912f69bf98e66b0e041d806a", "size": 9791, "ext": "py", "lang": "Python", "max_stars_repo_path": "ws_icra2022/src/my_pkg/ur5_eyeInhand/ibvs_only_task_space_Nori.py", "max_stars_repo_name": "yanseim/Vision-Based-Control", "max_stars_repo_head_hexsha": "4a92103d99703ac2a45d4ad8d01a663e29c0aa7d",... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# saov.py - Waqas Bhatti (wbhatti@astro.princeton.edu) - Jan 2017
'''
Contains the Schwarzenberg-Czerny Analysis of Variance period-search algorithm
implementation for periodbase.
'''
#############
## LOGGING ##
#############
import logging
from astrobase import log_su... | {"hexsha": "19ea7f67666b74a5e4f11aec38378385b5a6197d", "size": 19850, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrobase/periodbase/saov.py", "max_stars_repo_name": "pierfra-ro/astrobase", "max_stars_repo_head_hexsha": "b9f62c59a3ab9cdc1388d409fa281c26f1e6db6c", "max_stars_repo_licenses": ["MIT"], "max_st... |
""""
# DeepCTR
https://github.com/shenweichen/DeepCTR
https://deepctr-doc.readthedocs.io/en/latest/Examples.html#classification-criteo
DeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based CTR models
along with lots of core components layers which can be used to easily build cust... | {"hexsha": "770f6b5804b9bc6a897592000b9e23c073268893", "size": 23712, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlmodels/model_keras/01_deepctr.py", "max_stars_repo_name": "whitetiger1002/mlmodels", "max_stars_repo_head_hexsha": "f70f1da7434e8855eed50adc67b49cc169f2ea24", "max_stars_repo_licenses": ["MIT"]... |
[STATEMENT]
lemma simple_cg_insert_invar_with_conv :
assumes "observable M1" and "observable M2"
shows "convergence_graph_insert_invar M1 M2 simple_cg_lookup_with_conv simple_cg_insert"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. convergence_graph_insert_invar M1 M2 simple_cg_lookup_with_conv simple_cg_inse... | {"llama_tokens": 694, "file": "FSM_Tests_EquivalenceTesting_Simple_Convergence_Graph", "length": 4} |
module scalar_projection_routines
use spherepack_precision, only: &
wp, & ! working precision
ip, & ! integer precision
PI, &
MACHINE_EPSILON
use type_SpherepackUtility, only: &
SpherepackUtility
use gaussian_latitudes_and_weights_routines, only: &
compute_... | {"hexsha": "29a366b44f3695b84a092ee9f72a169e5f53b8a1", "size": 5746, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/scalar_projection_routines.f90", "max_stars_repo_name": "jlokimlin/spherepack4.1", "max_stars_repo_head_hexsha": "e80da29462c5987162a0884baaab7434d0215467", "max_stars_repo_licenses": ["MIT"... |
module DataAndPlotting
using HDF5
using Plots
# Wave parameters
const ki = 3
const kj = 3
# Grid size
const ni = 300
const nj = 200
# i ranges from 1...ni
# i-1 ranges from 0...ni-1
# x ranges from 0...1
function index2coord(i, ni)
@assert ni > 1
@assert 1 <= i <= ni
(i - 1) / (ni - 1)
end
# Set up... | {"hexsha": "8422c14bc5421ac3d8896c6d76b19fd7fe3f6435", "size": 999, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DataAndPlotting.jl", "max_stars_repo_name": "eschnett/DataAndPlotting.jl", "max_stars_repo_head_hexsha": "19e03405a448d13e908b7e5eddd4e05b6287949b", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# Requires: a limb darkening information file (with fits of intensity w.r.t surface inclination)
# Outputs: a plot that compares different gravity interpolation schemes
import math
import numpy as np
import pickle
import pa.lib.star as st
import pa.lib.util as ut
import matplotlib.pyplot as plt
import matplotlib
from ... | {"hexsha": "a32eb82f38a39cfd07fa4658f1dbde4f5b279b5e", "size": 5389, "ext": "py", "lang": "Python", "max_stars_repo_path": "pa/usr/05b_gravity_interpolation.py", "max_stars_repo_name": "mlipatov/paint_atmospheres", "max_stars_repo_head_hexsha": "75b494010a728e0682645de739752ff20a47d717", "max_stars_repo_licenses": ["MI... |
subroutine pl_nupd
!! ~ ~ ~ PURPOSE ~ ~ ~
!! This subroutine calculates plant nitrogen demand
!! ~ ~ ~ INCOMING VARIABLES ~ ~ ~
!! name |units |definition
!! ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
!! bio_n1(:) |none |1st shape ... | {"hexsha": "c712577ee4d03b56beb08fd8210dd4d5b62aca2f", "size": 2633, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/data/program_analysis/multifile_multimod/mfmm_02/pl_nupd.f90", "max_stars_repo_name": "mikiec84/delphi", "max_stars_repo_head_hexsha": "2e517f21e76e334c7dfb14325d25879ddf26d10d", "max_star... |
# -*- coding: utf-8 -*-
#to get these to print all threads in spyder :
# Run > Configuration per file > Execute in an external system terminal
# And then add a read at the end of the program to prevent the console of closing
"""
Created on Fri Jan 10 16:19:58 2020
This script uses the Amadeus framework on an artificia... | {"hexsha": "accdf90eb0eedbe1409c74f038e57708fa3b75c4", "size": 5957, "ext": "py", "lang": "Python", "max_stars_repo_path": "Main_averageModelScript.py", "max_stars_repo_name": "DannyVanpoucke/Amadeus", "max_stars_repo_head_hexsha": "6604cbdca43a1e2c8c0df72d251dcfbbd116e11e", "max_stars_repo_licenses": ["Apache-2.0"], "... |
function u = poisson(varargin)
%POISSON Poisson solver in the unit ball with Dirichlet or Neumann
% boundary conditions.
% U = POISSON(F, BC, M, N, P) is the solution to the Poisson equation in
% the unit ball with right-hand side F and Dirichlet boundary data
% U(1,lambda,theta) = @(lambda,theta) BC(l... | {"author": "chebfun", "repo": "chebfun", "sha": "8c49396a55e46ddd57a1d108c6a8f32e37536d54", "save_path": "github-repos/MATLAB/chebfun-chebfun", "path": "github-repos/MATLAB/chebfun-chebfun/chebfun-8c49396a55e46ddd57a1d108c6a8f32e37536d54/@ballfun/poisson.m"} |
"""
Module for unit tests that check if estimated embeddings for toy examples satisfy basic intuitions
@author Siddharth Reddy <sgr45@cornell.edu>
"""
import unittest
import logging
import pandas as pd
import numpy as np
from lentil import models
from lentil import est
from lentil import toy
logging.basicConfig()... | {"hexsha": "3ab5bb06409ff9849caf6a7358ec5164b5411aa6", "size": 13865, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_models.py", "max_stars_repo_name": "Knewton/lentil", "max_stars_repo_head_hexsha": "957227de8c48b6c3c5dfd754da30a259b7409084", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
requiredTypes = [ "AbstractLevel" ]
for reqType in requiredTypes
if !isdefined( Symbol( uppercase( string( reqType[ 1 ] ) ) * reqType[ 2:end ] ) )
include( reqType * ".jl" )
end # if !isdefined( Symbol( ...
end
export SubJob
type SubJob <: AbstractLevel
# Name of the SubJob ex: X, A, B
Na... | {"hexsha": "5f078212dd5acbaf37cee1333d8794596cc18126", "size": 732, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/types/SubJob.jl", "max_stars_repo_name": "Omazaria/CareerPathBeDef", "max_stars_repo_head_hexsha": "da5b1cc1a70382444a348da82725d4cfe670d3c9", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import time
import numpy as np
import py_aca
import pytest
from test_tde import setup_matrix_test
import cutde.fullspace as FS
import cutde.halfspace as HS
from cutde.aca import call_clu_aca
@pytest.mark.slow
@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
@pytest.mark.parametrize("F... | {"hexsha": "66d489f29f76e189686655eea27459943b58cf0b", "size": 4898, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_aca.py", "max_stars_repo_name": "tbenthompson/cutde", "max_stars_repo_head_hexsha": "7b263e2ac16de536d23d8e4d3165705e2ec9a31f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17... |
#version 330 core
in vec2 uvCoords;
in vec3 normalCameraSpace;
in vec3 toEye;
in vec3 lightDirection;
in float distance;
out vec3 color;
uniform sampler2D albedoTex;
uniform sampler2D metallicTex;
uniform sampler2D roughnessTex;
uniform sampler2D iblbrdf;
const vec3 lightColor = vec3(1.0,1.0,1.0);
const float light... | {"hexsha": "75da120e96343b367f89c0c59440634eff230040", "size": 2410, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "shaders/pbr.f", "max_stars_repo_name": "Faaux/cbLib", "max_stars_repo_head_hexsha": "968f327cee0f92d06724b493c3745f3f35d64038", "max_stars_repo_licenses": ["BSD-3-Clause", "MIT-0", "MIT"], "max_st... |
"""
Solver for a Quordle puzzle
https://www.quordle.com/#/
"""
import os
import pickle
import numpy as np
import helper
import solver
__author__ = "Z Feng"
max_attempts = 9
def accept_quordle_test_result():
"""
Let the user input the word guessed and the results as 4 matching patterns
:return: guess, ... | {"hexsha": "3e1c28479203e1fee8da706ee72b08f40e761f3c", "size": 4692, "ext": "py", "lang": "Python", "max_stars_repo_path": "quordle_solver.py", "max_stars_repo_name": "fxh90/WordleSolver", "max_stars_repo_head_hexsha": "7467093b19f09811596fe53a8dad1beedb4c8d09", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import pandas as pd
from random import random
flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
pdata = pd.DataFrame({"a":flow, "b":flow})
pdata.b = pdata.b.shift(9)
data = pdata.iloc[10:] * random() # some noise
import numpy as np
def _load_data(data, n_prev = 100):
"""
data should be pd.DataFrame()
... | {"hexsha": "4bb4a13bb24798984b89603ceaf4ad28e694868a", "size": 1767, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ground-Truth-Skeletons/Uddeshya/LSTM_test1.py", "max_stars_repo_name": "93TEI/3D_Action_Recognition", "max_stars_repo_head_hexsha": "b648f4cd8e479872c0cd9488120ada18bc64e5ad", "max_stars_repo_lice... |
type FractalDict
data::Dict{AbstractString,Function}
end
FractalDict()=FractalDict(Dict{AbstractString,Function}())
const frac_dict=FractalDict()
function add_frac!(fracname::AbstractString,f::Function)
frac_dict.data[fracname]=f
end
fractal_names() = keys(frac_dict.data)
function create_fractal(fractal_name::Abstr... | {"hexsha": "1be98f98e5bcef3a9bcef3c2dd700cc732d2199f", "size": 3699, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/fractal_variations.jl", "max_stars_repo_name": "jblondin/Fractal", "max_stars_repo_head_hexsha": "b2ff056dd747f81400e687d0091b3b120f113b22", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
/*
* test_NDFileHDF5ExtraDimensions.cpp
*
* Created on: 10 Nov 2015
* Author: gnx91527
*/
#include <stdio.h>
#include "boost/test/unit_test.hpp"
// AD dependencies
#include <NDPluginDriver.h>
#include <NDArray.h>
#include <asynDriver.h>
#include <string.h>
#include <stdint.h>
#include <deque>
#include <... | {"hexsha": "037a2fc434edfb59a6df63af66aadd59767ac52c", "size": 25487, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ADApp/pluginTests/test_NDFileHDF5ExtraDimensions.cpp", "max_stars_repo_name": "gregoryrd/ADCore", "max_stars_repo_head_hexsha": "b4be366dec26efa362a2675521123446d90b961a", "max_stars_repo_licenses"... |
# -*- coding: utf-8 -*-
"""Unit tests for sktime classifier compatability with sklearn interfaces."""
__author__ = ["MatthewMiddlehurst"]
__all__ = [
"test_sklearn_cross_validation",
"test_sklearn_cross_validation_iterators",
"test_sklearn_parameter_tuning",
"test_sklearn_composite_classifiers",
]
imp... | {"hexsha": "4dd6025db53e41d65b111e71180c935daad483d5", "size": 4816, "ext": "py", "lang": "Python", "max_stars_repo_path": "sktime/classification/tests/test_sklearn_compatability.py", "max_stars_repo_name": "biologioholic/sktime", "max_stars_repo_head_hexsha": "9d0391a04b11d22bd783b452f01aa5b4529b41a2", "max_stars_repo... |
/**
* The MIT License (MIT)
*
* Copyright © 2019 Ruben Van Boxem
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to us... | {"hexsha": "9286be102837112c957c8b89fb5642cf1d8e72e7", "size": 3631, "ext": "h++", "lang": "C++", "max_stars_repo_path": "css/grammar/position.h++", "max_stars_repo_name": "rubenvb/skui", "max_stars_repo_head_hexsha": "5bda2d73232eb7a763ba9d788c7603298767a7d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19.... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//===========================... | {"hexsha": "0b48f3bdaedf4c199280117f601f3a7dc4746970", "size": 1187, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/function/log2.hpp", "max_stars_repo_name": "xmar/pythran", "max_stars_repo_head_hexsha": "dbf2e8b70ed1e4d4ac6b5f26ead4add940a72592", "max_stars_repo_licenses": ["BSD-3-Clause"... |
%!TEX root = ../thesis.tex
%*******************************************************************************
%****************************** Second Chapter *********************************
%*******************************************************************************
\chapter{Results of Modification of Physical Prop... | {"hexsha": "515236e7022a8a87318093b73aefb17f875a7ebd", "size": 130042, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter5/chapter5.tex", "max_stars_repo_name": "ErpanArkin/phd-thesis", "max_stars_repo_head_hexsha": "155d4f8f8c4e87dea894403cdc58739ba5580aef", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import logging
import numpy as np
import math
import psutil
import time
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from autogluon.core.constants import REGRESSION
from autogluon.core.utils.exceptions import NotEnoughMemoryError
from .knn_utils import FAISSNeighborsClassifier, FAISSNeighb... | {"hexsha": "7ca7bb1c7c276436dab6cdc0d1172130a9e106c0", "size": 7492, "ext": "py", "lang": "Python", "max_stars_repo_path": "tabular/src/autogluon/tabular/models/knn/knn_model.py", "max_stars_repo_name": "jrzaurin/autogluon", "max_stars_repo_head_hexsha": "b7dbed302c4181f9160b2f8f88f73c1fa37941cd", "max_stars_repo_licen... |
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 7 19:50:39 2019
@author: Brandon
"""
import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
def walk(N):
rand.seed()
x = [0.0]
y = [0.0]
for n in range(N):
x.append(x[-1] + (rand.random() - 0.5)*2.0)... | {"hexsha": "a4adcb45af59385d5bb81ae2c86166c62d4daa1b", "size": 1653, "ext": "py", "lang": "Python", "max_stars_repo_path": "Week 03/Exercise_06_Q4_BSW.py", "max_stars_repo_name": "bswood9321/PHYS-3210", "max_stars_repo_head_hexsha": "d780cac166688338ce91099cba4a4f6628430647", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python
import cv2
import numpy as np
#listdir:for fetching data from a directory
from os import listdir
from os.path import isfile, join
from flask import render_template
import warnings
data_path = 'C:\\Users\\hp\\PycharmProjects\\faces\\'
onlyfiles = [f for f in listdir(data_path) if isfile(join(data... | {"hexsha": "cd8647c57b58f0129f661414c80083db518ce1a5", "size": 4464, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera.py", "max_stars_repo_name": "akshaya26/face_recognition", "max_stars_repo_head_hexsha": "f81b68f86c63b0364aa1c44056183a3ef82257e2", "max_stars_repo_licenses": ["Unlicense"], "max_stars_coun... |
[STATEMENT]
lemma partition_on_mset_add_single:
assumes "partition_on_mset A P"
shows "partition_on_mset (add_mset a A) (add_mset {#a#} P)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. partition_on_mset (add_mset a A) (add_mset {#a#} P)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
partition... | {"llama_tokens": 209, "file": "Design_Theory_Multisets_Extras", "length": 2} |
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import plotnine as pn
from scipy.stats import skew
from pandas.api.types import CategoricalDtype
from sklearn.base import BaseEstimator, TransformerMixin
from minepy import MINE
from scipy import stats
from dcor import distance_correla... | {"hexsha": "fd9a28676719f9455afe29f5a6fd660bf771126c", "size": 30156, "ext": "py", "lang": "Python", "max_stars_repo_path": "exploretransform/_exploretransform.py", "max_stars_repo_name": "bxp151/exploretransform", "max_stars_repo_head_hexsha": "f67039f22f30319586c8b4861d21b7bf147cecb9", "max_stars_repo_licenses": ["MI... |
import numpy as np
import nept
task_times = dict()
task_times['on_track'] = nept.Epoch(np.array([2421.1, 4816.9]))
experiment_times = dict()
experiment_times['left_trials'] = nept.Epoch(np.array([[2436.3, 2487.4],
[3398.8, 3423.7],
... | {"hexsha": "aa27758ee13e57a04c42f24cfc7c3f4291f58d13", "size": 1400, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/r042d5.py", "max_stars_repo_name": "vandermeerlab/hc_hyperalign", "max_stars_repo_head_hexsha": "f140aca8c9b1c4196ea2c63326fbbbded22bbcc1", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# In[1]:
import numpy as np
import pandas as pd
# In[2]:
def generate_population():
chromosome = [[i, j, k, l, m, n, o, p]
for i in range(1, 9)
for j in range(1, 9)
for k in range(1, 9)
for l in range(1, 9)
for m in range(1, 9)
for n in range(1... | {"hexsha": "a81c82f1816f0c4b6bcb3fbbc63e59c1fb9698bc", "size": 5550, "ext": "py", "lang": "Python", "max_stars_repo_path": "8_Queens/8queen.py", "max_stars_repo_name": "aminizahra/Evolutionary-Computing", "max_stars_repo_head_hexsha": "0dab8222fccb9bd1efca9141481e5aebc73de5ea", "max_stars_repo_licenses": ["Unlicense"],... |
!======================================================================!
SUBROUTINE Probe1D
!----------------------------------------------------------------------!
! This program finds the coordinate of cell-centers in non-homogeneous
! direction and write them in file name.1D
!--------------------------------------... | {"hexsha": "7e8f4b1a83fa2926c50c2ac8b92b1aba8da9a8fe", "size": 2634, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Library/Probe1D.f90", "max_stars_repo_name": "palkinev/T-FlowS-old-compressible", "max_stars_repo_head_hexsha": "7aa7219aa5526ac270b39d52ba8c12847e1d71a2", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
from src.Map.Map import Map
from src.StateUtils import pad_centered
from src.base.BaseState import BaseState
class DHMultiState(BaseState):
def __init__(self, map_init: Map, num_agents: int):
super().__init__(map_init)
self.device_list = None
self.device_map = None # Fl... | {"hexsha": "cf824b9f4008a55c155eecd5d901818337e15de5", "size": 5311, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/DHMulti/State.py", "max_stars_repo_name": "fgwy/uavSim", "max_stars_repo_head_hexsha": "a58dc3149a52fc407c288c281eedb176738bd558", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
import skimage
import skimage.data as data
from skimage.viewer import ImageViewer
from numpy.testing import assert_equal, assert_allclose
def setup_line_profile(image):
from skimage.viewer.plugins.lineprofile import LineProfile
viewer = ImageViewer(skimage.img_as_float(image))
plugin = LineProfile()
v... | {"hexsha": "6b0fd48f017fccf3c188620c7d1025674bba3cf3", "size": 1511, "ext": "py", "lang": "Python", "max_stars_repo_path": "skimage/viewer/tests/test_viewer.py", "max_stars_repo_name": "jeysonmc/scikit-image", "max_stars_repo_head_hexsha": "d652a9ce9a71a3f6f13cb23ee7c5f25148457b24", "max_stars_repo_licenses": ["BSD-3-C... |
""" Unit tests for fitness.py"""
# Author: Genevieve Hayes
# License: BSD 3 clause
import unittest
import numpy as np
from mlrose import (OneMax, FlipFlop, FourPeaks, SixPeaks, ContinuousPeaks,
Knapsack, TravellingSales, Queens, MaxKColor,
CustomFitness)
from mlrose.fitness imp... | {"hexsha": "f7aeb47ad84db5974f92fb76382c93411aeae5ca", "size": 7150, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_fitness.py", "max_stars_repo_name": "dreadn0ught/mlrose", "max_stars_repo_head_hexsha": "2a9d604ea464cccc48f30b8fe6b81fe5c4337c80", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
#ifndef POG_GENERIC_HPP
#define POG_GENERIC_HPP
#include <cstddef>
#include <iostream>
#include <type_traits>
#include <boost/mpl/range_c.hpp>
#include <boost/fusion/include/mpl.hpp>
#include <boost/fusion/include/at_c.hpp>
#include <boost/fusion/include/value_at.hpp>
#include <boost/fusion/include/for_each.hpp>
#in... | {"hexsha": "ee8d6e6cd8953acbd872944559bdf3080cabae0b", "size": 2618, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "generic.hpp", "max_stars_repo_name": "daniel-j-h/argparse-generic", "max_stars_repo_head_hexsha": "5746829953cd10ab20c57bf0596549535f73cac6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
//F# Compiler for F# 4.1
open System
printfn "Hello, World! How is it going?"
| {"hexsha": "a846e1195c36a81e775bcf8fb650ab3d47f324b3", "size": 79, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "HelloWorld(F#).f", "max_stars_repo_name": "sharma18526/Hello-World-1", "max_stars_repo_head_hexsha": "d4e59182b71782e5c5731c4d02b371827ed4b090", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma normalize_positive_dst_ports_normalized_n_primitive:
assumes n: "normalized_nnf_match m"
and noneg: "\<not> has_disc_negated is_Dst_Ports False m"
shows "\<forall>m' \<in> set (normalize_positive_dst_ports m). normalized_dst_ports m'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<fo... | {"llama_tokens": 675, "file": "Iptables_Semantics_Primitive_Matchers_Ports_Normalize", "length": 4} |
/-
Copyright (c) 2019 Sébastien Gouëzel. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Sébastien Gouëzel, Yury Kudryashov
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.topology.metric_space.emetric_space
import Mathlib.PostPort
univers... | {"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/topology/G_delta_auto.lean"} |
import networkx as nx
import itertools
G=nx.Graph()
import time
start_time=time.time()
steps=0
def countVertexDegree(matrix): #Crea una lista con los grados de cada vrtice en el grafo que recibe
global steps
degree=[]
deg=0
for row in matrix:
for bit in row:
if bit==1:
... | {"hexsha": "dd729cfec6a41a9049befec52f290e2732b2f290", "size": 10526, "ext": "py", "lang": "Python", "max_stars_repo_path": "subgraphIsomorphism.py", "max_stars_repo_name": "rJuarez96/subgraph", "max_stars_repo_head_hexsha": "3164e4d6b2dacd670a8f8e158ae1bcace23eff99", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# that's still a very lame function, but might be useful once more sampling
# algorithms are implemented (or not...)
function sample(model::Model, algorithm)
run(algorithm, model)
end
| {"hexsha": "2aaa9d8950752d9925a61934fb07819cb22daa88", "size": 189, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sampling/sample.jl", "max_stars_repo_name": "matthieubulte/Probabilistic.jl", "max_stars_repo_head_hexsha": "8eb68f7c9372570657efb8362ff5bc7b09bca62b", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
import numpy as np
import dask.array as da
from numcodecs import Zlib
from pathlib import Path
import argparse
def tile_zarr(
zarr_pyramid_base, max_level=None, compressor=None, dtype=None,
):
img = da.from_zarr(zarr_pyramid_base)
pyramid_path = Path(zarr_pyramid_base).parent
... | {"hexsha": "b5ae5e2c60c1c5c802036d130ae4d387c820b9eb", "size": 2341, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/tile_zarr_base.py", "max_stars_repo_name": "haniffalab/vitessce-data", "max_stars_repo_head_hexsha": "9f70405bae373783f9e6ca32f88b6b19b7325fea", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import csv
import numpy as np
import os
from sklearn.model_selection import train_test_split
import urllib.request
from sklearn.cluster import MiniBatchKMeans
import cv2
def get_filepaths_in_folder(folder):
directory = os.fsencode(folder)
filenames = []
for file in os.listdir(directory):... | {"hexsha": "1eaf212ed5b7c3a2a6fc1889866960a7e8d3c46d", "size": 4115, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/before2after/preprocessing.py", "max_stars_repo_name": "b-yogesh/pytorch-CycleGAN-and-pix2pix", "max_stars_repo_head_hexsha": "52ceb9b2918ee8cae81ebddfaf38e0f90e85f333", "max_stars_repo_li... |
from comsyl.autocorrelation.CompactAFReader import CompactAFReader
import numpy as np
from srxraylib.plot.gol import plot_image, plot
import matplotlib.pylab as plt
#
# auxiliary functions
#
def test_equal(af1,af2):
np.testing.assert_almost_equal(1e-8*np.real(af1.eigenvalue(5)),1e-8*np.real(af2.eigenvalue(5))... | {"hexsha": "aab25081cbd5adf3f3400a5c2fa56ba4775cbcd8", "size": 6373, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/autocorrelation/CompactAFReaderTest.py", "max_stars_repo_name": "s-sajid-ali/comsyl", "max_stars_repo_head_hexsha": "f2a5d984b1e870d203a9152bbeca804c4304850e", "max_stars_repo_licenses": ["M... |
import cv2
import numpy as np
from mabopy.config.load_config import LoadConfig
import cvlib
conf = LoadConfig("config.toml").config
capture = cv2.VideoCapture(conf["app"]["camera_uri"])
i = 0
pt1 = (conf["app"]["crop_start"][0],conf["app"]["crop_start"][1])
w = conf["app"]["corp_width"]
pt2 = (pt1[0]+w,pt1[1]+w... | {"hexsha": "64d0d37bc8a24773d73261de809c25454488b9b7", "size": 1536, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/vision/vision25/dc4.py", "max_stars_repo_name": "mabotech/mabo.io", "max_stars_repo_head_hexsha": "7f646db9d5ee3cd0b137866bf8eaf295890f134c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
{- Byzantine Fault Tolerant Consensus Verification in Agda, version 0.9.
Copyright (c) 2021, Oracle and/or its affiliates.
Licensed under the Universal Permissive License v 1.0 as shown at https://opensource.oracle.com/licenses/upl
-}
open import LibraBFT.Base.Types
import LibraBFT.Impl.Consensus.Consensus... | {"hexsha": "0d7df635d46c8cce0d46316206a62f825ebff5f9", "size": 3051, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/LibraBFT/Impl/Consensus/ConsensusTypes/ProposalMsg.agda", "max_stars_repo_name": "LaudateCorpus1/bft-consensus-agda", "max_stars_repo_head_hexsha": "a4674fc473f2457fd3fe5123af48253cfb2404ef", ... |
import tvm, tvm.relay.testing
import tvm.contrib.graph_runtime as runtime
import os, argparse
import numpy as np
from tvm import autotvm, relay, auto_scheduler
from tvm.autotvm.graph_tuner import DPTuner, PBQPTuner
from tvm.contrib.utils import tempdir
from tvm.contrib.debugger import debug_runtime
from tvm.relay.testi... | {"hexsha": "b2f3102de96aa66e396cbeeaae13479636474299", "size": 14025, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_test.py", "max_stars_repo_name": "moderato/LayerFusion", "max_stars_repo_head_hexsha": "3acdbe18e9575eee6e4ab3b60293393a40eedac5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# import the necessary packages
import os
import cv2
import imutils
import numpy as np
def align_images(image, template, maxFeatures=700, keepPercent=0.2,
debug=False, output_dir=''):
'''
Aligns image to template via the steps:
1. ORB keypoint detection
2. Brute force hamming... | {"hexsha": "0fae1c16eba77ad9b589a00f13b358d84884ac64", "size": 4129, "ext": "py", "lang": "Python", "max_stars_repo_path": "vcv/pyimagesearch/alignment/align_images.py", "max_stars_repo_name": "mhudnell/vaccination-card-verification", "max_stars_repo_head_hexsha": "f2db657e80ac77b2845192cc606b0d1e9a66e8a9", "max_stars_... |
# -*- coding: utf-8 -*-
# ## Grafting models together
# Much of scientific thought and scholarship involves reading papers and textbooks with different models and synthesizing a novel model from components found in existing, well studied models.
#
# We refer to this as model grafting, where a component is taking from o... | {"hexsha": "782e2cf845d35994fca66b3d1c7acaf49ee40951", "size": 6710, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/odegraft.jl", "max_stars_repo_name": "mikiec84/SemanticModels.jl", "max_stars_repo_head_hexsha": "f81baf0789cc547375f300429d0fd49c866d5339", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
// Copyright (C) 2015-2019 Internet Systems Consortium, Inc. ("ISC")
//
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <config.h>
#include <asiolink/io_add... | {"hexsha": "b44aba1a305dfd1f8506a42956a52c2c01ad4205", "size": 20638, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/lib/dhcpsrv/tests/ncr_generator_unittest.cc", "max_stars_repo_name": "kphf1995cm/kea", "max_stars_repo_head_hexsha": "2f6940ef5ed697f3f683035ed7a16046253add4d", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import matplotlib.pyplot as plt
def plot_confusion(conf, norm_conf, true_classes,
pred_classes, cmap=plt.cm.jet):
"""
A function to plot the confusion matrix for a given dataset.
The number of points given the [i, j] label is the value in each
box, and the normali... | {"hexsha": "696b9f6d92bc33f922a18d64a8146e5bf135f35e", "size": 3492, "ext": "py", "lang": "Python", "max_stars_repo_path": "clustering/utils.py", "max_stars_repo_name": "ebridge2/ams_446", "max_stars_repo_head_hexsha": "2c7d8db96c5875ee5b4c0e2d44dad80812ac4140", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
[GOAL]
α : Type u_1
β : Type u_2
γ : Type u_3
δ : Type u_4
r : α → α → Prop
s : β → β → Prop
inst✝¹ : IsIrrefl α r
inst✝ : IsIrrefl β s
⊢ ∀ (a : α ⊕ β), ¬LiftRel r s a a
[PROOFSTEP]
rintro _ (⟨h⟩ | ⟨h⟩)
[GOAL]
case inl
α : Type u_1
β : Type u_2
γ : Type u_3
δ : Type u_4
r : α → α → Prop
s : β → β → Prop
inst✝¹ : IsIrre... | {"mathlib_filename": "Mathlib.Data.Sum.Order", "llama_tokens": 24017} |
SUBROUTINE MB03OD( JOBQR, M, N, A, LDA, JPVT, RCOND, SVLMAX, TAU,
$ RANK, SVAL, DWORK, LDWORK, INFO )
C
C SLICOT RELEASE 5.7.
C
C Copyright (c) 2002-2020 NICONET e.V.
C
C PURPOSE
C
C To compute (optionally) a rank-revealing QR factorization of a
C real general M-by-N mat... | {"hexsha": "70f6f0d85b6592c0d139a8ee11b30b7211e5bbc2", "size": 11412, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/MB03OD.f", "max_stars_repo_name": "bnavigator/SLICOT-Reference", "max_stars_repo_head_hexsha": "7b96b6470ee0eaf75519a612d15d5e3e2857407d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
import sys
sys.path.append("..")
import utils
from utils import *
import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse as sparse
def augment_feature_vector(X):
"""
Adds the x[i][0] = 1 feature for each data point x[i].
Args:
X - a NumPy matrix of n data points, each with d - 1 ... | {"hexsha": "907dc1d12c4e4f966c28bfeddc871d89e9b3bf0c", "size": 8158, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ml_starter/mnist/softmax.py", "max_stars_repo_name": "pi-projects/ml-starter", "max_stars_repo_head_hexsha": "b3de630d68d9fe879cef626189fc07dee8568851", "max_stars_repo_licenses": ["MIT"], "ma... |
import torch
import librosa
import numpy as np
import mir_eval
import separation_model as md
import data_set as dts
from shutil import copyfile
import os
class AudioSeparator:
r"""Implements a framework for using a SeparationModel to produce separated source for all files in the
validation set and meas... | {"hexsha": "6f7b5287bdaa69ad3c7f2b006fbfc4ad8b0d9db0", "size": 17449, "ext": "py", "lang": "Python", "max_stars_repo_path": "separator.py", "max_stars_repo_name": "4p0pt0Z/Audio_blind_source_separation", "max_stars_repo_head_hexsha": "a91851172680c86f3faea0a7ad31eedb023e172f", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python3
# Copyright <2019> <Chen Wang [https://chenwang.site], Carnegie Mellon University>
# Redistribution and use in source and binary forms, with or without modification, are
# permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyr... | {"hexsha": "3187a5cef52f91a0cc4eaafdac9db81a130090f9", "size": 7325, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_interest.py", "max_stars_repo_name": "castacks/interestingness", "max_stars_repo_head_hexsha": "b614818ab11dcc15c5fe6b55fe993882add3e8e6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
from __future__ import unicode_literals
from __future__ import division
from __future__ import print_function
from collections import Counter
import os
import pkg_resources
import matplotlib.pyplot as plt
import numpy as np
import pandas
qDiptab_file = pkg_resources.resource_filename('modality', 'data/qDiptab.csv')... | {"hexsha": "1b8d8407b206fc4def1a410fbb57a825089d510c", "size": 16853, "ext": "py", "lang": "Python", "max_stars_repo_path": "modality/diptest.py", "max_stars_repo_name": "tompollard/modality", "max_stars_repo_head_hexsha": "dbe6d407a44fa6b1b93fba1433d62cb0b2c4a132", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(Project_Test_Suite)
BOOST_AUTO_TEST_SUITE(Module_Test_Suite)
BOOST_AUTO_TEST_CASE(DummyTest_Passes) {
BOOST_CHECK_EQUAL(1,1);
BOOST_CHECK(true);
}
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE_END() | {"hexsha": "b98dbe53a5dfb6b50c522a13fbd2fcfa7d9e048e", "size": 313, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/module/module_test.cpp", "max_stars_repo_name": "przestaw/Cpp-CMake-template", "max_stars_repo_head_hexsha": "ce71db7b9635bd57e51ec812e0bfe04dac2355be", "max_stars_repo_licenses": ["MIT"], "max_... |
# Helper function returning a customized URDF of the 1R planar manipulator
import numpy as np
def generate_custom_urdf(joint_ang_bias: tuple = (0, 0, 0, 0, 0, 0, 0),
joint_pos_bias: tuple = ((0,0,0),(0,0,0),(0,0,0),(0,0,0),(0,0,0),
(0,0,0)... | {"hexsha": "0c7ca66879a2bfc22af1393dd38dcef86cb11e50", "size": 13159, "ext": "py", "lang": "Python", "max_stars_repo_path": "crisp/robots/panda_urdf_randomizer.py", "max_stars_repo_name": "traversaro/CRiSP-for-Misspecified-Robot-Model", "max_stars_repo_head_hexsha": "6b5e0c28dc52463ac3b1c72c61fea0c5f7ac21f8", "max_star... |
import tensorflow as tf
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LeakyReLU
##
import pandas as pd
import pdb
import numpy as np
import random
import math
import argparse
import tensorflow as tf
import matplotlib.pyplot as plt
from keras i... | {"hexsha": "23adce8124adb328422444d56f065feccfa5d939", "size": 2920, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nn.py", "max_stars_repo_name": "plug-ml/engine", "max_stars_repo_head_hexsha": "d713a0f4c10690667f68906b6fc6241c77d172a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
# Copyright 2014 Uri Laserson
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | {"hexsha": "2b2238427295a70caf4a34b16d537f29e39b4f36", "size": 14490, "ext": "py", "lang": "Python", "max_stars_repo_path": "ulutil/mplextensions.py", "max_stars_repo_name": "churchlab/ulutil", "max_stars_repo_head_hexsha": "7f9a427274acd99ae4d2dfe35123feb7c2dc9625", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# coding: utf-8
"""
Name: low_pressure_detecter.py
detecting low pressure.
Usage: python3 low_pressure_detecter.py
Author: Ryosuke Tomita
Date: 2021/12/19
"""
import argparse
from os.path import abspath, dirname, join
import sys
import numpy as np
from scipy import ndimage
import japanmap
sys.path.append(join(abspat... | {"hexsha": "215fe4b8ffaf81e1a7ce57bf0f89846d0dca3d59", "size": 4159, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyze_tool/low_pressure_detecter.py", "max_stars_repo_name": "RyosukeDTomita/cyclone_track", "max_stars_repo_head_hexsha": "91238a7a8af2e23b3ae697c35f3b6c40516dc80a", "max_stars_repo_licenses": ... |
import numpy as np
from model.filters.dotproduct import DotProduct
class Sepia(DotProduct):
def __init__(self):
pass
def apply(self, matrix):
kernel = np.array([[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]])
... | {"hexsha": "e5dc2923bb78cadd0fb74b89659a5f12627ef110", "size": 366, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/filters/sepia.py", "max_stars_repo_name": "DennisPing/image-processor-mvc", "max_stars_repo_head_hexsha": "b687185500404a84f21e16b6c56937be4afc5a1d", "max_stars_repo_licenses": ["MIT"], "max_... |
from __future__ import division
import os, sys, shutil, time, random, math
import argparse
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import torch
from torch.optim import SGD
import torch.backends.cudnn as cudnn
import torch.nn.parallel
import torch.distributed as dist
import torch.multiproc... | {"hexsha": "08eed8391cd5f53e911b94467b6e0b7c752448ed", "size": 20648, "ext": "py", "lang": "Python", "max_stars_repo_path": "reproduction/finetune_face.py", "max_stars_repo_name": "thudzj/BayesAdapter.github.io", "max_stars_repo_head_hexsha": "243b8b8686e2c9f1ea0bcda5ede317ab98405845", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# Software License Agreement (MIT License)
#
# Copyright (c) 2020, tri_star
# All rights reserved.
#
# 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... | {"hexsha": "a35b6499dcac0e3cf0582224369bd74c40106bf5", "size": 16852, "ext": "py", "lang": "Python", "max_stars_repo_path": "tri_star/src/nodes/simulator_interaction.py", "max_stars_repo_name": "ScazLab/Frontiers_Robot_Tool_Use", "max_stars_repo_head_hexsha": "ebace49e88562c18b3b967ec5360a4cec4f8fe56", "max_stars_repo_... |
\documentclass{article}
\usepackage{enumitem}
\usepackage{hyperref}
\usepackage{ulem}
\title{Markademic}
\author{Mike Chung}
\begin{document}
\maketitle
\section{Introduction}
Wouldn't it be great if you can write academic papers using markdown instead of latex?
I present markademic, a pipeline for creating an a... | {"hexsha": "b6960fed6b81905e6cef405fd093a4fc5054db08", "size": 810, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/draft.tex", "max_stars_repo_name": "mjyc/markademic", "max_stars_repo_head_hexsha": "c0e6a5e12138fb5f0dd2edc6f6f5f812ef25b778", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [TITLE] Discussion
%
% [AUTHOR] Chen Zhang (CSISS, GMU)
% [E-MAIL] czhang11@gmu.edu
%
% [PROJECT PAGE] https://github.com/czhang11/latex-manuscript-template
% [PROJECT DESC] ___edit_description_here___
%
% [FILE NAME] 4-dis... | {"hexsha": "31973b69b1117f28228afc98ef11fe2f77977664", "size": 1020, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "4-discussion.tex", "max_stars_repo_name": "czhang11/latex-manuscript-template", "max_stars_repo_head_hexsha": "4aaaa4c961db4cb19c97f391ad882baef4380521", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import distutils.spawn
import numpy as np
import pytest
from ..yorick import PDBReader
SKIP_YORICK = distutils.spawn.find_executable("yorick") is None
SKIP_YORICK_REASON = "Could not find yorick executable. Skipping tests."
@pytest.fixture
def pdb():
return PDBReader("data/galaxies-040.pdb")
def walker_helpe... | {"hexsha": "560c2abecea571eb7b99dd5f25a7c68668c9045a", "size": 3236, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrophysics_toolset/io/tests/test_io.py", "max_stars_repo_name": "cphyc/astrophysics_toolset", "max_stars_repo_head_hexsha": "36be3f459a1bbca73af6f39f0957bfac0cb122eb", "max_stars_repo_licenses":... |
from __future__ import print_function
from clBeergame import *
from utilities import *
import numpy as np
#from clGeneralParameters import generalParameters
import random
from config import get_config, update_config
import tensorflow as tf
config = None
#def main(config, beerGame):
def main(config):
random.seed(10... | {"hexsha": "e4c50bc04e1803705da51a1efb61960e174ddd34", "size": 3822, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "OptMLGroup/DeepBeerInventory-RL", "max_stars_repo_head_hexsha": "ca2bb90a5ee3a45fa89cfadf56354369a62bf5a8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
# This file is a part of AstroLib.jl. License is MIT "Expat".
"""
true_obliquity(jd) -> t_eps
### Purpose ###
Return the true obliquity of the ecliptic for a given Julian date
### Explanation ###
The function is used by the [`co_aberration`](@ref) procedure.
### Arguments ###
* `jd`: Julian date.
### Output... | {"hexsha": "67b4ac92eda2e221230270e3ace06a574e743866", "size": 696, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/true_obliquity.jl", "max_stars_repo_name": "UnofficialJuliaMirror/AstroLib.jl-c7932e45-9af1-51e7-9da9-f004cd3a462b", "max_stars_repo_head_hexsha": "fb2ef587a2ac68a1c864bf251e8d9c3601ec4719", "ma... |
function title = p07_title ( title )
%*****************************************************************************80
%
%% P07_TITLE returns the title of problem 7.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 06 May 2011
%
% Author:
%
% John Burkardt
%
% Param... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_zero/p07_title.m"} |
# encoding: utf-8
"""
evolsim.brain
-------------
class definition of animal brain from its DNA.
"""
# imports
# built-in
from enum import Enum
# local
# 3rd-party
import numpy as np
class SensorType(Enum):
AGE = 1,
XPOS = 2,
YPOS = 3,
class OutputType(Enum):
XMOVE = 1,
YMOVE = 2,
ATTACK... | {"hexsha": "7cdd981fdf3ce413a8feae58aa8b675e6b53f0f4", "size": 3168, "ext": "py", "lang": "Python", "max_stars_repo_path": "evolsim-source/brain.py", "max_stars_repo_name": "labrunhosarodrigues/evolsim", "max_stars_repo_head_hexsha": "e24288b4ed396d6c80ca7818ef49e2ac56a21d34", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""Utilities supporting pydmmt."""
import ast
import math
import numpy
import operator as op
import re
import string
class YAMLError(ValueError):
"""YAML error"""
class TextBased():
def __repr__(self):
return self.original_string
def __str__(self):
return self.original_string
def _... | {"hexsha": "4731ca476ad89b9fd7a7efca4cefbb0c84292af2", "size": 10926, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydmmt/util.py", "max_stars_repo_name": "Lordmzn/pydmmt", "max_stars_repo_head_hexsha": "a3155eb480b333023c2da20acf1f11a156387cd2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
from autofit.non_linear.samples.pdf import quantile
import autogalaxy as ag
import numpy as np
def test__quantile_1d_profile():
profile_1d_0 = np.array([1.0, 2.0, 3.0])
profile_1d_1 = np.array([1.0, 2.0, 3.0])
profile_1d_list = [profile_1d_0, profile_1d_1]
median_profile_1d = ag.util.er... | {"hexsha": "ea319b174e521eedd1eb8788136bfce6b273b847", "size": 1982, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_autogalaxy/util/test_error_util.py", "max_stars_repo_name": "caoxiaoyue/PyAutoGalaxy", "max_stars_repo_head_hexsha": "ad2b4b27404f5bf0f65ba9a0cd7c3ee6570e2d05", "max_stars_repo_licenses": ["M... |
*----------------------------------------------------------------------*
subroutine topo_make_unique(ireo,vtx,topo,xlines,nvtx,nj)
*----------------------------------------------------------------------*
* (unrestricted) sort of vertices, topo matrix, and xline matrix
* to ascending sequence as needed for... | {"hexsha": "e084dfca3bb640be8f736e1578f50725817ad3c2", "size": 6826, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "formula/topo_make_unique.f", "max_stars_repo_name": "ak-ustutt/GeCCo-public", "max_stars_repo_head_hexsha": "8d43a6c9323aeba7eb54625b95553bfd4b2418c6", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from time import time
import cv2
import argparse
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import pdb
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
from sklearn.linear_model import OrthogonalMatchingPu... | {"hexsha": "5ddee8496aa47858cb3d4e100444d83a2f05ba23", "size": 3352, "ext": "py", "lang": "Python", "max_stars_repo_path": "ImageDenoising/sparse_encoding.py", "max_stars_repo_name": "koriavinash1/ImageDenoising", "max_stars_repo_head_hexsha": "d598cd55ba230f72dca9e800286cccae2dce853b", "max_stars_repo_licenses": ["MIT... |
import ..list ..logic data.rat
instance rat.inhabited : inhabited rat := ⟨0⟩
inductive term_dlo : Type
| var : nat → term_dlo
| cst : rat → term_dlo
notation `V'` := term_dlo.var
notation `C'` r := term_dlo.cst r
instance term_dlo.decidable_eq : decidable_eq term_dlo := by tactic.mk_dec_eq_instance
-- def term... | {"author": "skbaek", "repo": "cooper", "sha": "812afc6b158821f2e7dac9c91d3b6123c7a19faf", "save_path": "github-repos/lean/skbaek-cooper", "path": "github-repos/lean/skbaek-cooper/cooper-812afc6b158821f2e7dac9c91d3b6123c7a19faf/dlo/formula.lean"} |
r"""
Solve Poisson equation in 1D with mixed Dirichlet and Neumann bcs
\nabla^2 u = f,
The equation to solve is
(\nabla^2 u, v) = (f, v)
Use any combination of Dirichlet and Neumann boundary conditions.
"""
import os
import sympy as sp
import numpy as np
from shenfun import inner, div, grad, TestFunction,... | {"hexsha": "6bfadcbadbf3d0b48f1ba203db7acc62522447a2", "size": 2717, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/mixedneumanndirichlet_poisson1D.py", "max_stars_repo_name": "jaisw7/shenfun", "max_stars_repo_head_hexsha": "7482beb5b35580bc45f72704b69343cc6fc1d773", "max_stars_repo_licenses": ["BSD-2-Clau... |
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