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#!/usr/bin/env python
# -*- coding: utf-8 -*-
##
## discrete_puzzl.py
##
## R.Hanai 2011.15. -
##
from numpy import *
import operator
import time
# unit vectors
exs = [array([1,0,0]),array([-1,0,0])]
eys = [array([0,1,0]),array([0,-1,0])]
ezs = [array([0,0,1]),array([0,0,-1])]
# piece原点: 3^3=27通り
def make_poss():
... | {"hexsha": "56a35e62851060948804ab59af13e2eb6e29c9f9", "size": 4582, "ext": "py", "lang": "Python", "max_stars_repo_path": "iv_scenario/src/discrete_puzzle.py", "max_stars_repo_name": "ryhanai/iv-plan-hironx", "max_stars_repo_head_hexsha": "2f89293a55df4608cb35e6a9676db97b9e486e7d", "max_stars_repo_licenses": ["BSD-3-C... |
[STATEMENT]
lemma rel_gpv_lift_spmf2: "rel_gpv A B gpv (lift_spmf q) \<longleftrightarrow> (\<exists>p. gpv = lift_spmf p \<and> rel_spmf A p q)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rel_gpv A B gpv (lift_spmf q) = (\<exists>p. gpv = lift_spmf p \<and> rel_spmf A p q)
[PROOF STEP]
by(subst gpv.rel_flip[sym... | {"llama_tokens": 199, "file": "CryptHOL_Generative_Probabilistic_Value", "length": 1} |
"""Utility functions for dealing with vertical coordinates."""
import logging
import numpy as np
import xarray as xr
from .._constants import GRAV_EARTH
from ..var import Var
from .. import internal_names
def to_radians(arr, is_delta=False):
"""Force data with units either degrees or radians to be radians."""
... | {"hexsha": "bb5ea66bd4dcc35093df898e25638f482539db1a", "size": 11880, "ext": "py", "lang": "Python", "max_stars_repo_path": "aospy/utils/vertcoord.py", "max_stars_repo_name": "spencerahill/aospy", "max_stars_repo_head_hexsha": "6c8df45705927476e140df903bcb88e5abadae22", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import tempfile
import pytest
from hypothesis import given
import astropy.units as u
import sunpy.net.dataretriever.sources.goes as goes
from sunpy.net import Fido
from sunpy.net import attrs as a
from sunpy.net.dataretriever.client import QueryResponse
from sunpy.net.tests.strategies import time_attr
from sunpy.tim... | {"hexsha": "82dbd6d4c69f752462e04e4c69edd1bf4e97e3de", "size": 9516, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunpy/net/dataretriever/sources/tests/test_goes_suvi.py", "max_stars_repo_name": "jmason86/sunpy", "max_stars_repo_head_hexsha": "d3339ca999d79c53ac984f4d3215b6dbefbeb734", "max_stars_repo_license... |
# Copyright (c) 2021. yoshida-lab. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
import re
import numpy as np
from pymatgen.core import Element
from pymatgen.analysis.local_env import VoronoiNN
from xenonpy.descriptor.base import BaseDes... | {"hexsha": "7db52d2420d9f4b3782f374b8fc5e281f0812c59", "size": 12500, "ext": "py", "lang": "Python", "max_stars_repo_path": "xenonpy/descriptor/structure.py", "max_stars_repo_name": "mori0711/XenonPy", "max_stars_repo_head_hexsha": "e36ca0ea112b45ee629cd980c88e80cd6c96c514", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
#! /usr/bin/env python
from obspy.core.stream import Stream
from numpy.testing import assert_equal
from geomagio.algorithm import Algorithm
def test_algorithm_process():
"""Algorithm_test.test_algorithm_process()
confirms that algorithm.process returns an obspy.core.stream object
"""
algorithm = Algo... | {"hexsha": "986a1512a3b061f551470c051e7ef7cb1126293a", "size": 978, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/algorithm_test/Algorithm_test.py", "max_stars_repo_name": "usgs/geomag-algorithms", "max_stars_repo_head_hexsha": "a83a0e36bed9307828e37b9130c25dbc26dd1bc9", "max_stars_repo_licenses": ["CC0-1... |
```python
from sympy import init_session
init_session()
```
IPython console for SymPy 1.5.1 (Python 3.6.10-64-bit) (ground types: gmpy)
These commands were executed:
>>> from __future__ import division
>>> from sympy import *
>>> x, y, z, t = symbols('x y z t')
>>> k, m, n = symbols('k m n... | {"hexsha": "db4a1c887cb9b0b0ba5017c4f1b98281869d54aa", "size": 42048, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/GTO_integrals/.ipynb_checkpoints/GTO_1D_S-checkpoint.ipynb", "max_stars_repo_name": "tsommerfeld/L2-methods_for_resonances", "max_stars_repo_head_hexsha": "acba48bfede415af... |
import numpy as np
import torch.nn as nn
import vsffdnet.basicblock as B
import torch
"""
# --------------------------------------------
# FFDNet (15 or 12 conv layers)
# --------------------------------------------
Reference:
@article{zhang2018ffdnet,
title={FFDNet: Toward a fast and flexible solution for CNN-based... | {"hexsha": "81121be00ca7deb50c351f0346ff75db787743fa", "size": 2595, "ext": "py", "lang": "Python", "max_stars_repo_path": "vsffdnet/network_ffdnet.py", "max_stars_repo_name": "NSQY/vs-ffdnet", "max_stars_repo_head_hexsha": "e770853e55840f4b4682ea5687d6a5b8d335f0eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
!
! Assemble_FTS_TauProfile
!
! Program to assemble the individual TauProfile datafiles into a single
! datafile for an FTS sensor.
!
!
! FILES ACCESSED:
! Input: - Sensor TauProfile netCDF data files for each profile and
! each molecule set.
!
! Output: - TauProfile netCDF data file combin... | {"hexsha": "afa2eededa40dd0b087014118a24962e8138cb52", "size": 15732, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/TauProd/Infrared/Assemble_FTS_TauProfile/Assemble_FTS_TauProfile.f90", "max_stars_repo_name": "hsbadr/crtm", "max_stars_repo_head_hexsha": "bfeb9955637f361fc69fa0b7af0e8d92d40718b1", "max_s... |
\section{\module{subprocess} --- Subprocess management}
\declaremodule{standard}{subprocess}
\modulesynopsis{Subprocess management.}
\moduleauthor{Peter \AA strand}{astrand@lysator.liu.se}
\sectionauthor{Peter \AA strand}{astrand@lysator.liu.se}
\versionadded{2.4}
The \module{subprocess} module allows you to spawn n... | {"hexsha": "509f283f6c32cdcbe5fe2ced84882549d79827cd", "size": 13403, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libsubprocess.tex", "max_stars_repo_name": "deadsnakes/python2.5", "max_stars_repo_head_hexsha": "d5dbcd8556f1e45094bd383b50727e248d9de1bf", "max_stars_repo_licenses": ["PSF-2.0"], "max_sta... |
import numpy as np
from keras.models import Model
from keras.optimizers import Adam
from keras.layers import Input, Conv2D, UpSampling2D, Dense, Flatten, Reshape
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from utils import dataIterator, sample_images
from tqdm import tqdm
... | {"hexsha": "06fe3a6155f3e22374337b417377cf200c869320", "size": 4689, "ext": "py", "lang": "Python", "max_stars_repo_path": "Keras/DCGAN.py", "max_stars_repo_name": "yotamin/GAN", "max_stars_repo_head_hexsha": "b12068c944a6d9e301d99ebbef844ec71e6d9182", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
open import Everything
{-
open import Oscar.Prelude
open import Oscar.Class.HasEquivalence
open import Oscar.Class.Symmetrical
open import Oscar.Data.Term
open import Oscar.Data.Substitunction
open import Oscar.Data.Surjcollation
open import Oscar.Data.Surjextenscollation
open import Oscar.Data.Proposequality
import ... | {"hexsha": "cb97bdeaf8356b7b980a769a243a1b4d0801f371", "size": 1732, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "archive/agda-3/src/Test/SymmetricalSubstitunction.agda", "max_stars_repo_name": "m0davis/oscar", "max_stars_repo_head_hexsha": "52e1cdbdee54d9a8eaee04ee518a0d7f61d25afb", "max_stars_repo_licenses"... |
\documentclass[12pt]{article}
\include{preamble}
\title{Math 341 / 650 Spring 2020 \\ Midterm Examination One}
\author{Professor Adam Kapelner}
\date{Thursday, February 27, 2020}
\begin{document}
\maketitle
\noindent Full Name \line(1,0){410}
\thispagestyle{empty}
\section*{Code of Academic Integrity}
\footnote... | {"hexsha": "c3ae6c943c1611f553383f5fd837d0ed1ee8bf27", "size": 9167, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "exams/midterm1/midterm1.tex", "max_stars_repo_name": "kapelner/QC_Math_341_Spring_2020", "max_stars_repo_head_hexsha": "ee25c45f90d707a0e04eae432ea930af93480529", "max_stars_repo_licenses": ["MIT"],... |
import pyhanabi
from rl_env import Agent
from random import randint
import numpy as np
import copy
class ProbAgent(Agent):
"""Agent that applies a simple heuristic."""
def __init__(self, config, *args, **kwargs):
"""Initialize the agent."""
self.config = config
# Extract max info tokens or set defaul... | {"hexsha": "dc6eb196f6d780f55f8a776aad976632d457cef8", "size": 7740, "ext": "py", "lang": "Python", "max_stars_repo_path": "prob_based_agent.py", "max_stars_repo_name": "mwalton/hanabi-aaai20", "max_stars_repo_head_hexsha": "fa39a82c4845c233ed0b5e41370a0e1eaff3b0f9", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
"""
CrystalNets
Module for automatic reckognition of crystal net topologies.
To use as an executable, run the source file in a shell:
```bash
julia --project=$(normpath(@__DIR__, "..")) $(@__FILE__)
```
Otherwise, as a module, to try to reckognize the net underlying a crystal given in a
chemical file format called... | {"hexsha": "98074a02fcfea8280a593f3bbd753f5b955a7646", "size": 1818, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CrystalNets.jl", "max_stars_repo_name": "kjappelbaum/CrystalNets.jl", "max_stars_repo_head_hexsha": "a3ea0c02ad2125503b155dce1ec1499d842070ac", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# Face filters (Snapchat like) using OpenCV
# @author:- Webwares @2020
import cv2
import sys
import logging as log
import datetime as dt
from time import sleep
import numpy as np
import os
import subprocess
cascPath = "haarcascade_frontalface_default.xml" # for face detection
if not os.path.exists(cascPath):
su... | {"hexsha": "71ab8db66512393d36b912810db06acff09faf7e", "size": 3637, "ext": "py", "lang": "Python", "max_stars_repo_path": "snapchat.py", "max_stars_repo_name": "webwares/snapchat", "max_stars_repo_head_hexsha": "1b90d2de66a7acd36052b7ab7cb3fe0528ead506", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
[STATEMENT]
lemma (in cf_scospan) the_cf_scospan_ArrMap_app_\<bb>[cat_ss_cs_simps]:
assumes "f = \<bb>\<^sub>S\<^sub>S"
shows "\<langle>\<aa>\<rightarrow>\<gg>\<rightarrow>\<oo>\<leftarrow>\<ff>\<leftarrow>\<bb>\<rangle>\<^sub>C\<^sub>F\<^bsub>\<CC>\<^esub>\<lparr>ArrMap\<rparr>\<lparr>f\<rparr> = \<CC>\<lparr>CId\... | {"llama_tokens": 705, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_SS", "length": 2} |
#ASSUMES DATA WITH THROTTLING, NO DECOR STALL
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
class Cycle_Dump:
stat = None
supply_current = None
supply_voltage = None
def __init__(self, stats):
self.stats = stats
self.stats.readli... | {"hexsha": "d9de92d3f470460cdc3e3e6132d50f9b3ffe9e77", "size": 2979, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/jimmy_plot/deprecated/supply_curr+volt_over_time.py", "max_stars_repo_name": "JimmyZhang12/predict-T", "max_stars_repo_head_hexsha": "8ae818b0791104de20633ce91e6d633cda7445b3", "max_stars_r... |
# -*- coding: utf-8 -*-
import argparse
import inspect
import math
import numpy as np
import os
from pprint import pprint
import sys
# add parent directory to sys path to import relative modules
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentd... | {"hexsha": "2f6e6843c220eddc3fc955a146b36e8d772aaf8c", "size": 2513, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/grid.py", "max_stars_repo_name": "skratchdot/media-tools", "max_stars_repo_head_hexsha": "bca0c683fb637aeefda1c49454a118f809047d97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13... |
"""Classes for creating and augmenting Octrees"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
from ocnn.dataset.data_processor import DataProcessor
from ocnn.octree._octree import Octree
from ocnn.octree._octree import... | {"hexsha": "d8fa3614417ccaf439c31ea74c5da278582f67cc", "size": 1847, "ext": "py", "lang": "Python", "max_stars_repo_path": "ocnn/octree/python/ocnn/octree/octree_processor.py", "max_stars_repo_name": "FrozenSilent/O-CNN", "max_stars_repo_head_hexsha": "9527cd7670856229dfc3281bc05d2077a0553ec3", "max_stars_repo_licenses... |
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# MODEL POM - Princeton Ocean Model
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
#
# # ROUTINE: Profq
#
# DESCRIPTION
#
# ... | {"hexsha": "0cd8ca23284f257a9d4002157f1f6b449b2c2262", "size": 9597, "ext": "py", "lang": "Python", "max_stars_repo_path": "BFM17_POM1D_VrsFnl/src/pom/phys/profq1d.py", "max_stars_repo_name": "kyleniemeyer/pyPOM1D", "max_stars_repo_head_hexsha": "4eeb1ca16abe07d039c634f2338ebac395692362", "max_stars_repo_licenses": ["B... |
import argparse
import numpy as np
from sta663_project_lda.visualization.demo_topics import topic_viz
class LDASVI(object):
def __init__(self, datadir, K, alpha0=None, gamma0=None,
MB=256, kappa=0.5, tau0=256, eps=1e-3):
self.wordcnt_mat = np.load(datadir) # word-count matrix
self.... | {"hexsha": "d071ce4899ff1329f18e0657afdc4a2362c786c1", "size": 4238, "ext": "py", "lang": "Python", "max_stars_repo_path": "sta663_project_lda/algorithms/lda_svi.py", "max_stars_repo_name": "haofuml/sta663_project_lda", "max_stars_repo_head_hexsha": "d9d0253f61996fef48e9909aecf583e70e318aff", "max_stars_repo_licenses":... |
[STATEMENT]
lemma subst_poly_scaleRat: "subst_poly \<sigma> (r *R p) = r *R (subst_poly \<sigma> p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subst_poly \<sigma> (r *R p) = r *R subst_poly \<sigma> p
[PROOF STEP]
by (rule linear_poly_eqI, unfold valuate_scaleRat valuate_subst_poly, simp) | {"llama_tokens": 122, "file": "Farkas_Farkas", "length": 1} |
import re
import numpy as np
def clean(rows):
"""Cleans JSON file for each race. rows is a list of dictionaries.
"""
rows = map(handle_missing, rows)
rows = map(process_rider, rows)
return rows
def handle_missing(row):
"""Removes the Place column from a row if result was a DNF/DNP/DQ.
"""
... | {"hexsha": "4abc7b1a6fcda1b1fb8af3e6369691201367475d", "size": 1184, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess.py", "max_stars_repo_name": "physinet/road-results", "max_stars_repo_head_hexsha": "a55d9a54f9fc7b6e854de30777762df717d39d97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, ... |
/*
* Copyright 2014 Facebook, Inc.
*
* 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... | {"hexsha": "742ccc0d27c7e2d8c5a8418d7c1d95b1fce54432", "size": 4853, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "thrift/lib/cpp2/test/HeaderClientChannelHttpTest.cpp", "max_stars_repo_name": "project-zerus/fbthrift", "max_stars_repo_head_hexsha": "fc092e2b645def21482c1772250a97a7cd003cee", "max_stars_repo_lice... |
# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
from scipy.stats import distributions
__all__ = ('Prior', 'UniformPrior', 'ExpPrior', 'InvGammaPrior', 'BetaPrior',
'LogPrior')
class Prior(object):
"""
Convenience class for handling prior distributions. Prior objects c... | {"hexsha": "4e3abf328c8f671bca7e608462a83cb3fcbc4d55", "size": 4322, "ext": "py", "lang": "Python", "max_stars_repo_path": "mtd/priors.py", "max_stars_repo_name": "jbernhard/mtd", "max_stars_repo_head_hexsha": "6326fdb44f071311ace7862371e658d609f43d08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_sta... |
Require Import Coq.Classes.Morphisms.
Require Import Coq.Classes.RelationClasses.
Require Import Logic.lib.Ensembles_ext.
Require Import Logic.GeneralLogic.Base.
Require Import Logic.GeneralLogic.ProofTheory.TheoryOfSequentCalculus.
Require Import Logic.MinimumLogic.Syntax.
Local Open Scope logic_base.
Local Open Scop... | {"author": "QinxiangCao", "repo": "LOGIC", "sha": "d1476d57345c87447ea500b3d5ea99ee6d0f6863", "save_path": "github-repos/coq/QinxiangCao-LOGIC", "path": "github-repos/coq/QinxiangCao-LOGIC/LOGIC-d1476d57345c87447ea500b3d5ea99ee6d0f6863/MinimumLogic/ProofTheory/TheoryOfSequentCalculus.v"} |
Probabilistic Programming
=====
and Bayesian Methods for Hackers
========
##### Version 0.1
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`
___
Welcome to *Bayesian Methods for Hackers*. The ... | {"hexsha": "cb82f53deed0467a015d6609b77ff2818b7325f7", "size": 359770, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb", "max_stars_repo_name": "jeremymiller00/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers", "max_stars_repo_head_hexsha"... |
"""
lvmspec.sky
============
Utility functions to compute a sky model and subtract it.
"""
import numpy as np
from lvmspec.resolution import Resolution
from lvmspec.linalg import cholesky_solve
from lvmspec.linalg import cholesky_solve_and_invert
from lvmspec.linalg import spline_fit
from lvmutil.log import get_logg... | {"hexsha": "b5ddfc7a58617b51521fba2b92a9687c3eebfffb", "size": 13479, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/lvmspec/sky.py", "max_stars_repo_name": "sdss/lvmspec", "max_stars_repo_head_hexsha": "befd6991537c4947fdf63ca262937f2bb845148f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
import networkx as nx
def get_neighbors(G, v):
neighbors = list()
for n in G.neighbors(v):
weight = 1
try:
weight = G[v][n]['weight']
except Exception as ex:
print(ex, 'Hier')
finally:
neighbors.append((n, weight))
return neighbors
def ... | {"hexsha": "1360ba258d67ad26628450818ad726336b2301b1", "size": 2882, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dijkstra_original.py", "max_stars_repo_name": "philippzabka/resilionator", "max_stars_repo_head_hexsha": "a51d38eec73bdb3fed3646a0234dc39308309273", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import argparse
import logging
import math
import os
from collections import Counter
from typing import Iterable, Iterator, NamedTuple, Tuple
import cv2
import librosa
import numpy as np
import optuna
import pandas as pd
import torch
import torch.nn as nn
from efficientnet_pytorch import EfficientNet
from sklearn imp... | {"hexsha": "fdac6c95c134e101ffe886ec723da7a7e341121c", "size": 7324, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/train_model.py", "max_stars_repo_name": "karmatarap/capstone_project", "max_stars_repo_head_hexsha": "16f2b60be9634efec772c7dffb6efb90c37880ec", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
from nibabel.affines import from_matvec
from nibabel.eulerangles import euler2mat
from ..patched import obliquity
def test_obliquity():
"""Check the calculation of inclination of an affine axes."""
from math import pi
aligned = np.diag([2.0, 2.0, 2.3, 1.0])
aligned[:-1, -1] = [-10, -... | {"hexsha": "f76293f7cf7a8e881c640a84378b82e308a196e4", "size": 639, "ext": "py", "lang": "Python", "max_stars_repo_path": "nitransforms/tests/test_affines.py", "max_stars_repo_name": "mgxd/nitransforms", "max_stars_repo_head_hexsha": "a922f3cb8ee1df5b484f617c34e1816a726e54e0", "max_stars_repo_licenses": ["MIT"], "max_s... |
Load LFindLoad.
From lfind Require Import LFind.
Require Import Arith.
From adtind Require Import goal49.
Require Import Extraction.
Extract Inductive nat => nat [ "(O)" "S" ].
Extract Inductive list => list [ "Nil" "Cons" ].
Definition lfind_example_1 := ( false).
Definition lfind_example_2... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal49_theorem0_158_eqb_refl/lfind_extr... |
import numpy as np
from catboost import Pool, CatBoostClassifier
from catboost.utils import read_cd
from gbdt_uncertainty.data import process_classification_dataset
from gbdt_uncertainty.assessment import prr_class, ood_detect, nll_class
from gbdt_uncertainty.uncertainty import entropy_of_expected_class, expected_... | {"hexsha": "e0d88e57eb435df59c2bb629273f86b7ebefc302", "size": 14638, "ext": "py", "lang": "Python", "max_stars_repo_path": "aggregate_results_classification.py", "max_stars_repo_name": "yandex-research/GBDT-uncertainty", "max_stars_repo_head_hexsha": "339264ee82c1ec2b22d4200d3b9c18fcce56bb0d", "max_stars_repo_licenses... |
# ******************************************************************************
# Copyright 2014-2018 Intel Corporation
#
# 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.apa... | {"hexsha": "46267768f4bcd3f0b219748b978c0cf1355ee310", "size": 24885, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_mergebroadcast_layer.py", "max_stars_repo_name": "rsketine/neon", "max_stars_repo_head_hexsha": "a10f90546d2ddae68c3671f59ba9b513158a91f1", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
theorem winding_number_cindex_pathE:
fixes g::"real \<Rightarrow> complex"
assumes "finite_ReZ_segments g z" and "valid_path g" "z \<notin> path_image g" and
loop: "pathfinish g = pathstart g"
shows "winding_number g z = - cindex_pathE g z / 2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. win... | {"llama_tokens": 55223, "file": "Winding_Number_Eval_Cauchy_Index_Theorem", "length": 623} |
import random
import numpy as np
def _lstsq_vector(A, b, constraints=None):
"""Minimize || A*x - b || subject to equality constraints x_i = c_i.
Let A be a matrix of shape (m, n) and b a vector of length m. This function
solves the minimization problem || A*x - b || for x, subject to 0 <= r <= n
equ... | {"hexsha": "7c734063e137328a553aa2f23b3ef080c43c60d6", "size": 4771, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cocktail/utils.py", "max_stars_repo_name": "marcromani/nica-nmf", "max_stars_repo_head_hexsha": "0a83fd2d1b90d0715929496a7a646c434120f296", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
module SafeFlagPrimTrustMe where
open import Agda.Builtin.Equality
open import Agda.Builtin.TrustMe
| {"hexsha": "694748867df72877104d8d5c7a74d7f114e53ede", "size": 101, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Fail/SafeFlagPrimTrustMe.agda", "max_stars_repo_name": "bennn/agda", "max_stars_repo_head_hexsha": "f77b563d328513138d6c88bf0a3e350a9b91f8ed", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
from __future__ import division
import numpy as np
from sklearn.svm import SVC
from scipy.special import expit
import copy
from scipy.stats import norm
from background_check import BackgroundCheck
class OcDecomposition(object):
def __init__(self, base_estimator=BackgroundCheck(),
normalization=... | {"hexsha": "1181b6d93dd53d8b5a57092c1a20f57a6658ed1c", "size": 4953, "ext": "py", "lang": "Python", "max_stars_repo_path": "cwc/models/oc_decomposition.py", "max_stars_repo_name": "perellonieto/background_check", "max_stars_repo_head_hexsha": "a5b6549a62be276c7199e87e78a94a64af688ab9", "max_stars_repo_licenses": ["MIT"... |
### run using 'marc' conda env ###
import pandas as pd
import numpy as np
import pickle
# original ADMISSIONS
df = pd.read_pickle('/project/M-ABeICU176709/ABeICU/data/ADMISSIONS.pickle', compression = 'zip')
adm_original = len(df['ADMISSION_ID'].unique())
pt_original = len(df['PATIENT_ID'].unique())
print('original no... | {"hexsha": "97d85ac719d9639d2d41b5734e9dc85380ccab47", "size": 9579, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/revision/12_supplemental_3.py", "max_stars_repo_name": "data-intelligence-for-health-lab/delirium_prediction", "max_stars_repo_head_hexsha": "a0a25819ef6c98e32563b4e3b986c1a26fc30ed7", "max_s... |
"""configurator class allows to load any python file by its filename
and store the contents in a namespace
namespace elements are accessible throught both key access or member acess"""
import runpy
from os import path
from collections import OrderedDict
from weakref import WeakKeyDictionary
import numpy
meta = WeakK... | {"hexsha": "518997b15048ea4e603eabc100276f64915a3e0d", "size": 13652, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylib/gna/configurator.py", "max_stars_repo_name": "gnafit/gna", "max_stars_repo_head_hexsha": "c1a58dac11783342c97a2da1b19c97b85bce0394", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5... |
"""
insert_location(ex::Expr, location)
Insert a symbolic representation of `location` into the arguments of an `expression`.
Used in the `@at` macro for specifying the location of an `AbstractOperation`.
"""
function insert_location!(ex::Expr, location)
if ex.head === :call && ex.args[1] ∈ operators
... | {"hexsha": "7dd87f464360bd40b953a47df4dc7b8e6cccb833", "size": 961, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AbstractOperations/at.jl", "max_stars_repo_name": "ascheinb/Oceananigans.jl", "max_stars_repo_head_hexsha": "52bfeb09e3562f639deb32b8807f32a88e3a1cfa", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from .abstruct import Channel
from ..util import pishifts
class DepolarizingChannel(Channel):
def __init__(self, n, p, seed=None):
super().__init__(n, seed)
if not isinstance(p, (list, np.ndarray)):
p = [p]
self.param_len = len(p)
self._channel_parame... | {"hexsha": "8b758d736699a3b89ce194e0ebe3696c95c2474d", "size": 5731, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyqecc/channel/channel.py", "max_stars_repo_name": "shim98a/pyqecc", "max_stars_repo_head_hexsha": "69965b4cab947718bf42adfaf79f63b25da61f66", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 29 11:34:48 2020
@author: roume
"""
import os, cv2
import numpy as np
import matplotlib as plot
from skimage.measure import label, regionprops
from scipy.special import expit as sigmoid # used for numerical stability
inputPath = 'C:/Users/roume/PycharmProjects/ECE276A_P... | {"hexsha": "bdb951f06959d17340bf2f4be5aea7fbad654db2", "size": 1777, "ext": "py", "lang": "Python", "max_stars_repo_path": "pr1_code/stop_sign_test_imgs.py", "max_stars_repo_name": "roumenguha/Stop_Sign_Detection_Redux", "max_stars_repo_head_hexsha": "30e00b9a2726b81a531a5d51e007272c9b207171", "max_stars_repo_licenses"... |
import os
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from sklearn.datasets import make_spd_matrix
from sklearn.covariance import empirical_covariance
from sklearn.metrics import mean_squared_error
from torch.utils.data import DataLoader
import numpy as np
from synthetic import train_nn
from... | {"hexsha": "a2bc36165b2a8d929b6b36b9802511ed4fc804fe", "size": 3692, "ext": "py", "lang": "Python", "max_stars_repo_path": "synthetic_moments_ridge.py", "max_stars_repo_name": "veronicatozzo/distribution-network", "max_stars_repo_head_hexsha": "585a3294f09ade975d921e28576c24007e5a41de", "max_stars_repo_licenses": ["MIT... |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 11 02:15:26 2018
@author: AshwinAmbal
Description:The code below is used to extract 'k' similar location for a given
location based on the tf, df or idf values as specified in the sample inputs file.
"""
import xml.etree.ElementTree as ET
import pandas as pd
from scipy... | {"hexsha": "0249cc3ab41c3d7c20e088bbfbb9e8cb237aeab2", "size": 3122, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/Tasks/Task_3.py", "max_stars_repo_name": "ajayguna-96/Finding-Similarity-between-Vector-Models-of-images-using-Similarity-Scores", "max_stars_repo_head_hexsha": "c957024ed3be75e2e6bbe6ea790e2... |
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "7277394ffd0b908ebcb766a8095d4b73aa4fa763", "size": 3581, "ext": "py", "lang": "Python", "max_stars_repo_path": "moonlight/staves/staff_processor_test.py", "max_stars_repo_name": "lithomas1/moonlight", "max_stars_repo_head_hexsha": "cd22d6f47bbcf043a0027e91d342ae25dfc8a30a", "max_stars_repo_licenses": ["Apac... |
import os
import unittest
import aspecd.exceptions
import numpy as np
import trepr.exceptions
import trepr.processing
import trepr.dataset
ROOTPATH = os.path.split(os.path.abspath(__file__))[0]
class TestPretriggerOffsetCompensation(unittest.TestCase):
def setUp(self):
self.processing = trepr.processin... | {"hexsha": "0ffd498e451d84afc9042e69ea316d38cda7ea11", "size": 6347, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_processing.py", "max_stars_repo_name": "tillbiskup/trepr", "max_stars_repo_head_hexsha": "4d24cb9ce5b89bfd3f9ee2016c8c493c4d76ea7f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
# -*- coding: utf-8 -*-
"""
Dataset for Mask R-CNN
Configurations and data loading code for COCO format.
@author: Mattia Brusamento
"""
import os
import sys
import time
import numpy as np
import json
# Download and install the Python coco tools from https://github.com/waleedka/coco
# That's a fork from the original... | {"hexsha": "1c1c7b0585a19297352558b99758edbabe1b30bc", "size": 5703, "ext": "py", "lang": "Python", "max_stars_repo_path": "materials_trash_detector/trash_dataset.py", "max_stars_repo_name": "Waste-NANDO/Mask_RCNN", "max_stars_repo_head_hexsha": "272fbeba35e62ce3a6772c9c70e62da9fcb4a40e", "max_stars_repo_licenses": ["M... |
import nose
import unittest
import numpy as np
from pandas import Series, date_range
import pandas.util.testing as tm
from pandas.tseries.util import pivot_annual, isleapyear
class TestPivotAnnual(unittest.TestCase):
"""
New pandas of scikits.timeseries pivot_annual
"""
def test_daily(self):
... | {"hexsha": "02a98858ed80852066036098cbeae6e252364d1f", "size": 1713, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tseries/tests/test_util.py", "max_stars_repo_name": "breisfeld/pandas", "max_stars_repo_head_hexsha": "f1fd50bb8e7603042fe93e01e862766673e33450", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 17 15:11:05 2020
@author: mlampert
"""
import os
import copy
import pandas
import numpy as np
import pickle
import flap
import flap_nstx
thisdir = os.path.dirname(os.path.realpath(__file__))
fn = os.path.join(thisdir,'../flap_nstx.cfg')
flap.co... | {"hexsha": "f3fc8d525f1c173731fc48ad09ca613915c4089c", "size": 22881, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/plot_elm_properties_vs_energy_drop.py", "max_stars_repo_name": "fusion-flap/flap_nstx_gpi", "max_stars_repo_head_hexsha": "cf7d4bdecea8fd7434f8f7eb64e1a7b13fc0f759", "max_stars_repo_lice... |
#!/usr/bin/env python
import numpy
from numpy.random import RandomState
from sklearn.datasets import make_friedman1
from sklearn.model_selection import train_test_split
from typing import Union
from backprop.network import Network
_demo_problem_num_train_samples: int = 1000
_demo_problem_num_test_samples: int = 100
... | {"hexsha": "a1cb9d20eceefda48810b2d69f5413ec1438b335", "size": 2907, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/backprop/backprop_demo.py", "max_stars_repo_name": "TedBrookings/backprop", "max_stars_repo_head_hexsha": "36c09b43e3c81f8506e806ede1435a0144d2f792", "max_stars_repo_licenses": ["Apache-2.0"],... |
{-# OPTIONS --without-K #-}
module PiG where
import Level as L
open import Data.Empty
open import Data.Unit
open import Data.Sum
open import Data.Product
open import Data.Nat
open import Function
open import Relation.Binary.PropositionalEquality
open import Relation.Binary
-----------------------------... | {"hexsha": "7e6281c0965cd8b17efa592abbe1101351f9a784", "size": 4722, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "PiG.agda", "max_stars_repo_name": "JacquesCarette/pi-dual", "max_stars_repo_head_hexsha": "003835484facfde0b770bc2b3d781b42b76184c1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count"... |
using PowerModelsReliability
using PowerModels
using InfrastructureModels
using Ipopt
using Mosek
using Juniper
using Cbc
using CPLEX
using Gurobi
using JuMP
using SCS
scs = JuMP.with_optimizer(SCS.Optimizer, max_iters=100000)
ipopt = JuMP.with_optimizer(Ipopt.Optimizer, tol=1e-6, print_level=0)
cplex = JuMP.with_opt... | {"hexsha": "a6016f962d8eb6f5b2b1375e9d6802101d80cf9f", "size": 3001, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/model/test_scopf.jl", "max_stars_repo_name": "frederikgeth/PowerModelsReliability.jl", "max_stars_repo_head_hexsha": "a3b53c5f93134e159cd1e1beac39b2e5a4a0c7ba", "max_stars_repo_licenses": ["BS... |
from datetime import datetime
from pdb import set_trace
from time import time
import numpy as np
import tensorflow as tf
import torch
from deep_lagrangian_networks.replay_memory import PyTorchReplayMemory
from deep_lagrangian_networks.utils import init_env, load_dataset
from DeLaN_tensorflow_ddq import DeepLagrangian... | {"hexsha": "4d80374c194805c4f534405be607264b61660ba9", "size": 6314, "ext": "py", "lang": "Python", "max_stars_repo_path": "DeLaN_train_ddq.py", "max_stars_repo_name": "BolunDai0216/deep_lagrangian_networks", "max_stars_repo_head_hexsha": "18fdc324669026b8bccccde94929bce0e068919c", "max_stars_repo_licenses": ["MIT"], "... |
import signal
import sys
import time
import thread
import numpy as np
from minicps.devices import PLC
class BasePLC(PLC):
# Pulls a fresh value from the local DB and updates the local CPPPO
def send_system_state(self):
values = []
# Send sensor values (may have gaussian noise)
for tag... | {"hexsha": "4f754fe705d9e269882faa188b0ac05adb7f1af9", "size": 1906, "ext": "py", "lang": "Python", "max_stars_repo_path": "dhalsim/python2/basePLC.py", "max_stars_repo_name": "afmurillo/WadiTwin", "max_stars_repo_head_hexsha": "80e2e260a99c02f93aa0a45c9037eef07a70a2f2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import pandas as pd
emails = pd.read_csv('./emails.csv')
#emails[:10]
def process_email(text):
text = text.lower()
return list(set(text.split()))
emails['words'] = emails['text'].apply(process_email)
num_emails = len(emails)
num_spam = sum(emails['spam'])
print("Number of emails:", num_emai... | {"hexsha": "ae89283f8f7255747f942a2bc60036846b7a9041", "size": 1992, "ext": "py", "lang": "Python", "max_stars_repo_path": "NaiveBayes.py", "max_stars_repo_name": "abhishekloni01/AIML-Lab-Termworks", "max_stars_repo_head_hexsha": "253a2ed8cb056281267acfe4c7e559b4cadd790a", "max_stars_repo_licenses": ["CC0-1.0"], "max_s... |
# coding: utf-8
# In[1]:
import sklearn.mixture as mix
import scipy as sp
import numpy as np
import copy
'''
num:データの個数
dim:データの特徴量次元
state = {'FEATURE', 'LABEL', 'CLUSTER', 'SCORE', 'GM'}:ディクショナリ
feature:選択した特徴量を表すリスト
label:データをクラスタリングした際のラベル
clusters:データをいくつのクラスタに分類するか。Boumanのアルゴリズムによって求める。
score:評価値
'''
def sc... | {"hexsha": "6b1b89aebbf93f57d181b0d23f0c8b29be11c4b5", "size": 14928, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_selection/FSSEM.py", "max_stars_repo_name": "Harmoware/Harmoware-SEC", "max_stars_repo_head_hexsha": "828e05116fee3804096ff6c89e211c03a0e98ac5", "max_stars_repo_licenses": ["Apache-2.0"],... |
import os
from data.base_dataset import BaseDataset, get_params, get_transform
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
class AlignedDataset(BaseDataset):
"""A dataset class for paired image dataset.
It assumes that the directory '/path/to/data/train' contains image... | {"hexsha": "29779f3225a87f2baf94553233060af232203f72", "size": 3855, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/aligned_dataset.py", "max_stars_repo_name": "laurencebho/pytorch-CycleGAN-and-pix2pix", "max_stars_repo_head_hexsha": "5539ea60645ccba0c76ab543d471512c20887d5c", "max_stars_repo_licenses": ["... |
"""Import tasks for the Pan-STARRS Survey for Transients
"""
import csv
import os
from astropy.time import Time as astrotime
from astrocats.catalog.utils import make_date_string, pbar
def do_psst(catalog):
task_str = catalog.get_current_task_str()
# 2016arXiv160204156S
file_path = os.path.join(
... | {"hexsha": "9674137fa65388718cdaa8e36ae7534cecc798a8", "size": 3382, "ext": "py", "lang": "Python", "max_stars_repo_path": "tasks/psst.py", "max_stars_repo_name": "astrocatalogs/tidaldisruptions", "max_stars_repo_head_hexsha": "87558308b09c4d08a20dec141e438ccbcddb491b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
## Isentropic Vortex
> WORK IN PROGRESS !!!
In this example we are going to solve the Euler equations for an isentropic two-dimensional vortex in a full-periodic square domain. Since the problem is not diffusive, the expected behavior is for the vortex to be convected unchanged forever. This is a useful example for ... | {"hexsha": "91b6bd0fa34d1860632f1377a4d474f0347c3312", "size": 91632, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "examples/06_vortex2d.ipynb", "max_stars_repo_name": "smatkovi/nangs", "max_stars_repo_head_hexsha": "b9ab6f32fe3632d9ee403f197742cc203670217d", "max_stars_repo_licenses": ["Apache-2.... |
from absl import app
from absl import flags
import os
import re
import numpy as np
import string
import tensorflow as tf
from tensorflow import keras
from pprint import pprint
from read_dbpedia import load_dbpedia
from read_imdb import load_imdb
from read_trec_50 import load_trec_50
from read_trec_6 import load_trec_... | {"hexsha": "1de9af6c10190e47035fc23608da296d49bd1e7d", "size": 9977, "ext": "py", "lang": "Python", "max_stars_repo_path": "defences/dp/classification/cnn-keras.py", "max_stars_repo_name": "JunW15/AdvMT", "max_stars_repo_head_hexsha": "4ec727199a810cd0b153c2d465b9660641e0f3f1", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Calculates daily zonal-mean yt of a given surface variable
for an aquaplanet simulation
"""
import numpy as np
import xarray as xr
from ds21grl.misc import get_dim_exp
from ds21grl.read_aqua import read_yt_zm_sfc_daily
from ds21grl.write_data import write_yt_zm_sfc_daily
from... | {"hexsha": "97c03a61ae27f90af7b681381916c374eaf1704d", "size": 1072, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/processing/12.0-calc-yt-zm-sfc-daily-aqua.py", "max_stars_repo_name": "edunnsigouin/ds21grl", "max_stars_repo_head_hexsha": "b6544cbc97529943da86e48a437ce68dc00e0f82", "max_stars_repo_license... |
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2015-2016 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to d... | {"hexsha": "9259d88114ce626cee9ab9e489eaf741cdc609c5", "size": 14468, "ext": "py", "lang": "Python", "max_stars_repo_path": "xsocs/gui/process/FitWidget.py", "max_stars_repo_name": "omserta/xsocs", "max_stars_repo_head_hexsha": "5e1cf1352233498c48f0566e0b819e18373e95e5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Models for causal set graphs.
Available methods:
minkowski_interval(N, D)
de_sitter_interval(N, D, eta_0, eta_1)
causal_set_graph(R, p)
"""
# Copyright (C) 2016 by
# James Clough <james.clough91@gmail.com>
# All rights reserved.
# BSD license.
__author__ = "\n".join(["James Clough (james.clough91@gm... | {"hexsha": "7d40a25450e27be1232e321dc8440f942735e73b", "size": 6024, "ext": "py", "lang": "Python", "max_stars_repo_path": "dagology/generators/causal_set.py", "max_stars_repo_name": "JamesClough/dagology", "max_stars_repo_head_hexsha": "5421fd0ad439e70a61d0408eb1cacebaa403f671", "max_stars_repo_licenses": ["MIT"], "ma... |
'''
This is a sample class for a model. You may choose to use it as-is or make any changes to it.
This has been provided just to give you an idea of how to structure your model class.
'''
import cv2
import numpy as np
import os
from openvino.inference_engine import IECore,IENetwork,IEPlugin
class FaceDetectionModel:
... | {"hexsha": "fbb6d2622f351da3ee625bbc1a24da833688b544", "size": 3274, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/face_detection.py", "max_stars_repo_name": "DhruvKinger/Pointer-Controller", "max_stars_repo_head_hexsha": "ba82623987cdc6f4748e761743f207b154db95dd", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Copyright 2021 Max-Planck-Gesellschaft
Code author: Jan Achterhold, jan.achterhold@tuebingen.mpg.de
Embodied Vision Group, Max Planck Institute for Intelligent Systems, Tübingen
This source code is licensed under the MIT license found in the
LICENSE.md file in the root directory of this source tree or at
https://o... | {"hexsha": "38c8ef6c63406a773f2e81190dbbb116bb7ce916", "size": 5862, "ext": "py", "lang": "Python", "max_stars_repo_path": "context_exploration/evaluation/quadrant_pendulum_evaluation.py", "max_stars_repo_name": "EmbodiedVision/explorethecontext", "max_stars_repo_head_hexsha": "d4bd0d4af980de16ede642c987878a55e089f736"... |
(* This Isabelle theory is produced using the TIP tool offered at the following website:
https://github.com/tip-org/tools
This file was originally provided as part of TIP benchmark at the following website:
https://github.com/tip-org/benchmarks
Yutaka Nagashima at CIIRC, CTU changed the TIP output th... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/UR/TIP/TIP15/TIP15/TIP_sort_nat_ISortPermutes.thy"} |
!==========================================================================
elemental subroutine gsw_specvol_second_derivatives_wrt_enthalpy (sa, ct, &
p, v_sa_sa, v_sa_h, v_h_h, iflag)
! =========================================================================
!
! Calculates ... | {"hexsha": "1a63b500144698ceaa192280556ba30d7af31244", "size": 3535, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "MOM6-interface/MOM6/pkg/GSW-Fortran/toolbox/gsw_specvol_second_derivatives_wrt_enthalpy.f90", "max_stars_repo_name": "minsukji/ci-debug", "max_stars_repo_head_hexsha": "3e8bbbe6652b702b61d289661... |
function p = hexagon_shape_2d ( angle )
%*****************************************************************************80
%
%% HEXAGON_SHAPE_2D returns points on the unit regular hexagon in 2D.
%
% Diagram:
%
% 120_____60
% / \
% 180/ \0
% \ /
% \_____/
% 240 300
%
... | {"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/geometry/hexagon_shape_2d.m"} |
import warnings, logging, sys
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.models import model_from_json
import pickle
import numpy as np
import matplotlib.pyplot as plt
import ... | {"hexsha": "4ad9049daa7bef929a189b4d1555c2781267cf0f", "size": 9368, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNIST/RobustnessTest.py", "max_stars_repo_name": "matthewwicker/StatisticalGuarenteesForBNNs", "max_stars_repo_head_hexsha": "1f585636c152b8489e331641c743ff628c2b7cc7", "max_stars_repo_licenses": ... |
import numpy as np
class Transform:
"""
Positional data for an object, Magnebot, body part, etc.
"""
def __init__(self, position: np.array, rotation: np.array, forward: np.array):
"""
:param position: The position vector of the object as a numpy array.
:param rotation: The rot... | {"hexsha": "d4c823a8bde45ea8c34e59f11709e32811830ecd", "size": 1209, "ext": "py", "lang": "Python", "max_stars_repo_path": "magnebot/transform.py", "max_stars_repo_name": "neuroailab/magnebot", "max_stars_repo_head_hexsha": "3f537fcd95685efeadf7200208a310a4c6a2f10c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
cc ------------ dpmjet3.4 - authors: S.Roesler, R.Engel, J.Ranft -------
cc -------- phojet1.12-40 - authors: S.Roesler, R.Engel, J.Ranft -------
cc - oct'13 -------
cc ----------- pythia-6.4 - authors: Torbjorn Sjostrand, Lund'10 -------
cc -------------------------... | {"hexsha": "7005f2f086d353baaa983d9e44f4f94921625702", "size": 10804, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/pythia/pyinit.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
module PointCloud
using jInv.Mesh
using jInv.Utils
using jInv.InverseSolve
using ShapeReconstructionPaLS.Utils
using ShapeReconstructionPaLS.ParamLevelSet
using MAT
using SparseArrays
using Distributed
import jInv.ForwardShare.getData
import jInv.ForwardShare.getSensTMatVec
import jInv.ForwardShare.getSensMatVec
import... | {"hexsha": "715f19ff0f7466674fbebb1a58874618b4bd1576", "size": 2965, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PointCloud/PointCloud.jl", "max_stars_repo_name": "BGUCompSci/ShapeReconstructionPaLS", "max_stars_repo_head_hexsha": "725cfa2a2ab357b4f2ed564eb2227158efc07f7f", "max_stars_repo_licenses": ["MI... |
## Automatically adapted for numpy.oldnumeric Jun 27, 2008 by -c
#
# Copyright (C) 2000-2008 greg Landrum
#
""" Training algorithms for feed-forward neural nets
Unless noted otherwise, algorithms and notation are taken from:
"Artificial Neural Networks: Theory and Applications",
Dan W. Patterson, Prentice H... | {"hexsha": "0459edcf8046da769d854385ea4a34ecdd7ae4a9", "size": 8146, "ext": "py", "lang": "Python", "max_stars_repo_path": "rdkit/ML/Neural/Trainers.py", "max_stars_repo_name": "darkreactions/rdkit", "max_stars_repo_head_hexsha": "0c388029c1f9386d832f6c321e59a11589c373d8", "max_stars_repo_licenses": ["PostgreSQL"], "ma... |
\documentclass[]{article}
%opening
\title{Learning neural Question Answering Systems for Low-resource Langauges}
\author{C.W, R.H}
\usepackage{graphicx}
\begin{document}
\begin{titlepage} % Suppresses headers and footers on the title page
\centering % Centre everything on the title page
%---------------------... | {"hexsha": "ecde47e087ae6a9bc12ea6e158fa68a5fd6eb5d1", "size": 76381, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ideas/thesis.tex", "max_stars_repo_name": "Mithrillion/BiQA", "max_stars_repo_head_hexsha": "f61bea95521f5b2ffd838aa60aecaad568de6564", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import math
from scipy.special import logsumexp
def logsumexp_list(lst):
while len(lst)>1:
a = lst.pop(0)
b = lst.pop(0)
c = b + math.log10(math.exp(a - b) + 1)
lst.insert(0,c)
return lst[0]
def forward(X):
K = 2
F0_1 = []
F0_2 = []
E = [[1/6,1/6,1/6,1/6,1/6,1/6... | {"hexsha": "813ab8a321d21fb5863ce98b2ff1093adbdf4c4f", "size": 2114, "ext": "py", "lang": "Python", "max_stars_repo_path": "bio-info/bioinfo_6.py", "max_stars_repo_name": "kyamada101/Python", "max_stars_repo_head_hexsha": "a9be850b1818fb4784cb84e86b20cf2c61784e38", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""This class is used whenever a security evaluation job is requested. It creates the security
evaluation job starting from the parameters of the request."""
import bisect
import os
from typing import List, Union
import numpy as np
import torch
from .classification.attack_classification import AttackClassification, S... | {"hexsha": "547b411f84459cdc55c4e1e86a633f08597522ed", "size": 12171, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/adv/evaluation_manager.py", "max_stars_repo_name": "pralab/pandavision", "max_stars_repo_head_hexsha": "7a76f333127d5cbdf5a0af5a202cec50a2041c6d", "max_stars_repo_licenses": ["MIT"], "max_sta... |
\documentclass[a4paper,11pt]{article}
\title{Example 3}
\author{My name}
\date{2011-01-05}
\begin{document}
\maketitle
\section{What's here}
This is our
second document.
It contains two paragraphs. The first line of a paragraph will be
indented, but not when it follows a heading.
% Here’s a comme... | {"hexsha": "629f4001369e7f5114054ea0c293c06cef5fe52f", "size": 415, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter02/9867_02_22.tex", "max_stars_repo_name": "eagleqian/LaTeX-Beginner-s-Guide", "max_stars_repo_head_hexsha": "49f6c9c8e0c9f7a6554e720c8a82978a5f5d1042", "max_stars_repo_licenses": ["MIT"], "ma... |
From mathcomp Require Import ssreflect.
From Category.Base Require Import Logic Category Functor NatTran.
From Category.Instances Require Import NatTranComp FunctorCategory Product.ProductCategory.
Set Universe Polymorphism.
(* Currying *)
Module Currying.
Section Currying.
Context {C D E : Category}.
Var... | {"author": "k27c8ff627uxz", "repo": "category_theory", "sha": "d5568b2ba04120a4f0e5bc7f2d61297c3cf42b9e", "save_path": "github-repos/coq/k27c8ff627uxz-category_theory", "path": "github-repos/coq/k27c8ff627uxz-category_theory/category_theory-d5568b2ba04120a4f0e5bc7f2d61297c3cf42b9e/src/Instances/Product/FunctorCurrying.... |
#default implementation
@impl begin
struct ScoreGetLogScore end
function get_log_score(sf::Score{I}, i::I)::AbstractFloat where {I}
return log(get_score(sf, i))
end
end
| {"hexsha": "b043e16e03b2e8fc4c1352d3e602c0b096265d24", "size": 190, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sfuncs/score/score.jl", "max_stars_repo_name": "p2t2/Scruff.jl", "max_stars_repo_head_hexsha": "e6d42e1c9bb427f33d01f443ebaf3ef243fa0bde", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import argparse
import os
import re
import cv2
import fnmatch
import numpy as np
from sift import SIFT
from surf import SURF
from vlad import VLAD
from vgg import VGG
from superpoint import SuperPointLocalFeature
from pca_global_descriptor import PCAGlobalDescriptor
def parse_args():
parser = argparse.ArgumentPar... | {"hexsha": "47307138e6af5ba3ceaab7e6fe709f76d6426a84", "size": 5646, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval.py", "max_stars_repo_name": "osmr/imgret", "max_stars_repo_head_hexsha": "28ac6461de815e37539f1893c29d4af6d1c1647d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_stars_repo... |
Require Import ucos_include.
Open Scope code_scope.
Open Scope Z_scope.
Open Scope int_scope.
Lemma rh_tcbls_mqls_p_getmsg_hold:
forall mqls tcbls ct a v vl qmax wl,
RH_TCBList_ECBList_P mqls tcbls ct ->
EcbMod.get mqls a =
Some
(absmsgq (v:: vl) qmax, wl) ->
RH_TCBList_ECBList_P (EcbMod.set ... | {"author": "brightfu", "repo": "CertiuCOS2", "sha": "1b7e588056a23bc32a9e442a240de3002b16eefb", "save_path": "github-repos/coq/brightfu-CertiuCOS2", "path": "github-repos/coq/brightfu-CertiuCOS2/CertiuCOS2-1b7e588056a23bc32a9e442a240de3002b16eefb/coqimp/certiucos/ucos_lib/OSQAcceptPure.v"} |
import numpy as np
import scipy.misc
import scipy.signal
import neurokit2 as nk
import os
from sklearn import preprocessing
import pandas as pd
from numpy import genfromtxt
import pandas as pd
import matplotlib.pyplot as plt
# import timesynth as ts
from neurokit2.misc import NeuroKitWarning, listify
from itertools i... | {"hexsha": "8296f804769685c4b1a3dd7cd47b323717c49980", "size": 9565, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_synth_signal.py", "max_stars_repo_name": "akazako1/CollinsLab_ScrunchingAnalysis", "max_stars_repo_head_hexsha": "91509671fdada9b59f0e3e027b989afc53e5d45d", "max_stars_repo_licenses": ["M... |
# Define server logic to read selected file ----
server <- function(input, output,session) {
#install.packages("readtext")
library("readtext")
create_readData_file<-"D:/RWebProject/ShinyApp-ver1/ver03/readData.txt"
algorithm_file_path<-"D:/RWebProject/ShinyApp-ver1/ver03/action.R"
create_Result_file... | {"hexsha": "c2a3185711a673734e0211be60f371bfe51272b3", "size": 3511, "ext": "r", "lang": "R", "max_stars_repo_path": "fakeNewsFrontEnd/server.r", "max_stars_repo_name": "ShiMarinho/MachineLearning-FakeNews", "max_stars_repo_head_hexsha": "e20d983923deb1380ba231177fe8131d60dfe506", "max_stars_repo_licenses": ["MIT"], "m... |
"""BERT embedding."""
import argparse
import io
import logging
import os
import numpy as np
import mxnet as mx
from mxnet.gluon.data import DataLoader
import gluonnlp
from gluonnlp.data import BERTTokenizer, BERTSentenceTransform
from gluonnlp.base import get_home_dir
try:
from data.embedding import BertEmbeddi... | {"hexsha": "728b885e51c7e363e4a86a6d138390dd19416a5a", "size": 9631, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/bert/embedding.py", "max_stars_repo_name": "faramarzmunshi/gluon-nlp", "max_stars_repo_head_hexsha": "218661c71b62b025d636975d2e71a0a4c2ea9f76", "max_stars_repo_licenses": ["Apache-2.0"], ... |
"""
Title: Random forest digital twin template
Authors: Blagoj Delipetrev, Mattia Santoro, Nicholas Spadaro
Date created: 2020/11/09
Last modified: 2020/11/09
Description: Templates for creation and execution through the VLAB on DestinationEarth VirtualCloud of random forest based digital twins.
Version: 0.1
"""... | {"hexsha": "fd4a7c53c9a875541d694c347b0957ad8b539412", "size": 9301, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "snicholas/RandomForestDT", "max_stars_repo_head_hexsha": "f8fd1d181e27592f5b4d15c9906d250081b1fd02", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import math
from typing import Optional, Union, List, Type
import numpy as np
import torch
import torch.multiprocessing
from falkon.sparse.sparse_tensor import SparseTensor
__all__ = (
"select_dim_over_nm",
"select_dim_over_nd",
"select_dim_over_nm_v2",
"calc_gpu_block_sizes",
"choose_fn",
"... | {"hexsha": "dc6721abfb41ecc54bdd719b71ebb745bd1f4d36", "size": 6964, "ext": "py", "lang": "Python", "max_stars_repo_path": "falkon/utils/helpers.py", "max_stars_repo_name": "Akatsuki96/falkon", "max_stars_repo_head_hexsha": "380ffc2fdd831955255f5ead297f0e729b1af147", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
## --- test Interpolations.jl
# Interpolation
@test linterp1(1:10, 1:10, 5.5) == 5.5
@test linterp1(1:10, collect(1:10.), 3:7) == 3:7
@test linterp1(1:10,21:30,5:0.5:6) == [25.0, 25.5, 26.0]
@test linterp1s(10:-1:1,21:30,5:0.5:6) == [26.0, 25.5, 25.0]
@test linterp_at_index(1:100,10) == 10
... | {"hexsha": "91d94bc17301020a9968e8b82360017b54ba7f1c", "size": 1037, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testInterpolations.jl", "max_stars_repo_name": "brenhinkeller/StatGeochemBase", "max_stars_repo_head_hexsha": "ead6e2c124191b43de7a3aecd49479bdd6512599", "max_stars_repo_licenses": ["MIT"], "m... |
SST_PATH = "/Users/ccolley/Documents/Research/SparseSymmetricTensors.jl/src/SparseSymmetricTensors.jl" # local path
#SST_PATH = "/homes/ccolley/Documents/Software/SparseSymmetricTensors.jl/src/SparseSymmetricTensors.jl" #Nilpotent path
include(SST_PATH)
using Main.SparseSymmetricTensors
#============================... | {"hexsha": "f5da7e40eee7976c5a29fbd198d815ed95254310", "size": 18825, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SparseSymmetricTensorCode.jl", "max_stars_repo_name": "chuckcol/LambdaTAME", "max_stars_repo_head_hexsha": "5c2192270def06a078cc4821c52b7905b937c0fa", "max_stars_repo_licenses": ["MIT"], "max_... |
import random
from typing import Dict, List, Optional, Tuple, Union
import networkx
import networkx as nx
import numpy as np
import pandas as pd
from ipycytoscape import CytoscapeWidget
import halerium.core as hal
def show_hal_graph(g: hal.Graph) -> CytoscapeWidget:
deps = dependencies_from_hal_graph(g)
retu... | {"hexsha": "cb8e599c2d0d4c8395a72a32e900426f2ed176fd", "size": 7095, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "Magier/HalGA", "max_stars_repo_head_hexsha": "db560ab219824f0e0556450becc9226d3b4949f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
from sympy.combinatorics import Permutation
from sympy.core import Basic
from sympy.combinatorics.permutations import perm_af_mul, \
_new_from_array_form, perm_af_commutes_with, perm_af_invert, perm_af_muln
from random import randrange, choice
from sympy.functions.combinatorial.factorials import factorial
from math im... | {"hexsha": "825ac29f5bb69200474bb3b9fd3cd72d174a5c3e", "size": 115660, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/combinatorics/perm_groups.py", "max_stars_repo_name": "sn6uv/sympy", "max_stars_repo_head_hexsha": "5b149c2f72847e4785c65358b09d99b29f101dd5", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import torch
import numpy as np
def compute_gradient(x):
# compute gradients of deformation fields x =[u, v]
# x: deformation field with 2 channels as x- and y- dimensional displacements
# du/dx = (u(x+1)-u(x-1)/2
bsize, csize, height, width = x.size()
xw = torch.cat((torch.zeros(bsize, csize, hei... | {"hexsha": "4be183fdd9bfd705cddfdcba77aa521dad1c5ba1", "size": 1361, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "cq615/Biomechanics-informed-motion-tracking", "max_stars_repo_head_hexsha": "e01419b31d67a87fc410292f253f05afe7935f7d", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from collections import defaultdict
np.random.seed(7)
class Agent:
def __init__(self, nA=6, alpha = 0.5, gamma = 0.85, start_epsilon = 1):
""" Initialize agent.
Params
======
- nA (int): number of actions available to the agent
- alpha (float): step-siz... | {"hexsha": "2936149721c975f0bf53936471c266984e358257", "size": 2566, "ext": "py", "lang": "Python", "max_stars_repo_path": "p_lab-taxi/Sarsamax/agent.py", "max_stars_repo_name": "and-buk/Udacity-DRLND", "max_stars_repo_head_hexsha": "c796137734e8d848da37a2f9be38f2fb1fdc176d", "max_stars_repo_licenses": ["MIT"], "max_st... |
# ----------------------------------------------------------------------------
# Copyright 2014 Nervana Systems Inc.
# 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.o... | {"hexsha": "c47da064cd6a32c4460f9f84f1423c42f043b843", "size": 5005, "ext": "py", "lang": "Python", "max_stars_repo_path": "neon/datasets/cifar10.py", "max_stars_repo_name": "kashif/neon", "max_stars_repo_head_hexsha": "d4d8ed498ee826b67f5fda1746d2d65c8ce613d2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
""" Generate molecular conformations from atomic pairwise distance matrices. """
import itertools
import numpy as np
import os
from rdkit import Chem
from rdkit.Chem import AllChem
# noinspection PyPackageRequirements
from tap import Tap
class Args(Tap):
"""
System arguments.
"""
data_... | {"hexsha": "5a36c523730318f94015acf76991d88b91da2fd4", "size": 2194, "ext": "py", "lang": "Python", "max_stars_repo_path": "conformation/distmat_to_conf.py", "max_stars_repo_name": "ks8/conformation", "max_stars_repo_head_hexsha": "f470849d5b7b90dc5a65bab8a536de1d57c1021a", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
Es 7
QR piu stabile
R è maggiorata dalla radice di n + max di aij
"""
import numpy as np
import numpy.linalg as npl
import scipy.linalg as sci
import funzioni_Sistemi_lineari as fz
import matplotlib.pyplot as plt
def Hankel(n):
A = np.zeros((n,n), dtype = float)
for ... | {"hexsha": "1744286c1a2b415c9f0075292d2299477f0cd975", "size": 1270, "ext": "py", "lang": "Python", "max_stars_repo_path": "sistemi_lineari/esercizi/Test7.py", "max_stars_repo_name": "luigi-borriello00/Metodi_SIUMerici", "max_stars_repo_head_hexsha": "cf1407c0ad432a49a96dcd08303213e48723c57a", "max_stars_repo_licenses"... |
def mapc2p(xc,yc):
"""
specifies the mapping to curvilinear coordinates -- should be consistent
with mapc2p.f
"""
from numpy import abs
xp = xc + (abs(yc+.2)+ .8)/2
yp = yc
return xp,yp
| {"hexsha": "e6b3fd15d7224f6bd0ada4ea35a459796902d08f", "size": 218, "ext": "py", "lang": "Python", "max_stars_repo_path": "book/chap23/acoustics/mapc2p.py", "max_stars_repo_name": "geoflows/geoclaw-4.x", "max_stars_repo_head_hexsha": "c8879d25405017b38392aa3b1ea422ff3e3604ea", "max_stars_repo_licenses": ["BSD-3-Clause"... |
#!/usr/bin/python
import pickle
from rdkit import Chem
import os,re,glob,sys
import numpy as np
import math
from rdkit.Chem import AllChem, rdmolops
from rdkit.Chem.Descriptors import MolWt
from xyz2mol import xyz2mol
import sys
import numpy as np
np.set_printoptions(threshold=sys.maxsize)
import subprocess
import ... | {"hexsha": "9f8de001a096c3d79267f2c8ba407494d538a305", "size": 33549, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycbh/utils.py", "max_stars_repo_name": "colliner/pyCBH", "max_stars_repo_head_hexsha": "29fb86d81cf45a343cb8e7e071d183bf74bb1c21", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# Routines for an unstructured mesh that is contained in a hierarchical, rectangularly
# partitioned mesh data structure
mutable struct HierarchicalRectangularlyPartitionedMesh
name::String
rect::Rectangle_2D
mesh::Ref{UnstructuredMesh_2D}
parent::Ref{HierarchicalRectangularlyPartitionedMesh}
childr... | {"hexsha": "9dd7ed9b20e917afefe5cf46ba7887ce5bde7d4e", "size": 14368, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mesh/HierarchicalRectangularlyPartitionedMesh.jl", "max_stars_repo_name": "KyleVaughn/MOCNeutronTransport", "max_stars_repo_head_hexsha": "6de0f5987c2b37c3c3039d073b63c223ff6cd5f7", "max_stars... |
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