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# coding: utf-8
# Copyright (c) Scanlon Materials Theory Group
# Distributed under the terms of the MIT License.
"""
Module containing helper functions for dealing with band structures.
todo:
* Extend get projections to allow specifying lm orbitals and atomic sites.
"""
import numpy as np
import itertools as it
fr... | {"hexsha": "1e658c937cddb2ce8fe9137e4f0b9de9b5732d59", "size": 10930, "ext": "py", "lang": "Python", "max_stars_repo_path": "sumo/electronic_structure/bandstructure.py", "max_stars_repo_name": "antisymmetric/sumo", "max_stars_repo_head_hexsha": "325c5d5bd6ac63f8a58b89a81cba0ca36842936a", "max_stars_repo_licenses": ["MI... |
/* 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": "95564877f80f53f74a6239a7431a7777290042c8", "size": 6494, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/vendor/mariadb-10.6.7/storage/columnstore/columnstore/utils/testbc/blockrequestprocessor.cpp", "max_stars_repo_name": "zettadb/zettalib", "max_stars_repo_head_hexsha": "3d5f96dc9e3e4aa255f4e6105... |
import logging
import pathlib
from unittest import mock
import boost_histogram as bh
import numpy as np
import pytest
from cabinetry import template_builder
def test__check_for_override():
# override exists for template
assert (
template_builder._check_for_override(
{"Up": {"setting": "v... | {"hexsha": "b1675a86aa5922f6573bcbbec1c772b8fce1e9ff", "size": 11344, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_template_builder.py", "max_stars_repo_name": "phinate/cabinetry", "max_stars_repo_head_hexsha": "10571e83155b0fd47796b93b85919975c3b364ed", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
from collections import defaultdict
from typing import Any, Dict, Sequence, Union
from attrdict import AttrDict
import numpy as np
import sys
import os
import copy
import time
sys.path.append("./ddetr")
import torch
import filelock
from determined.pytorch import (
DataLoader,
LRScheduler,
MetricReducer,
... | {"hexsha": "2a21312c1661dec7aafae7dc0baa6b78f547a463", "size": 9695, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/computer_vision/deformabledetr_coco_pytorch/model_def.py", "max_stars_repo_name": "gh-determined-ai/determined", "max_stars_repo_head_hexsha": "9a1ab33a3a356b69681b3351629fef4ab98ddb56", ... |
[STATEMENT]
lemma cdi_iff_no_strict_pd: \<open>i cd\<^bsup>\<pi>\<^esup>\<rightarrow> k \<longleftrightarrow> is_path \<pi> \<and> k < i \<and> \<pi> i \<noteq> return \<and> (\<forall> j \<in> {k..i}. \<not> (\<pi> k, \<pi> j) \<in> pdt)\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (i cd\<^bsup>\<pi>\<^es... | {"llama_tokens": 3709, "file": "IFC_Tracking_IFC", "length": 27} |
% The BLAST Algorithm
%
% Cartoonish introduction to the BLAST algorithm
\subsection{The BLAST Algorithm}
\begin{frame}
\frametitle{BLAST Is A Heuristic}
\begin{itemize}
\item<1-> BLAST does not use Needleman-Wunsch or Smith-Waterman
\item<1-> BLAST \emph{approximates} dynamic programming methods
\item... | {"hexsha": "ce699691bc7704a0f8185537a79fb33045702871", "size": 849, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "presentation/sections/subsection_blastintro.tex", "max_stars_repo_name": "peterjc/Teaching-Intro-to-Bioinf", "max_stars_repo_head_hexsha": "d642804aa73e80546e2326d2c2537c5727ac3ee8", "max_stars_repo_... |
# Chapter 2 End to End Machine Learning
import os
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = "datasets/housing/"
HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH
#-------------------------------------------------------------
# In[ Step 1. load csv data file
import pandas as ... | {"hexsha": "702677c23186cb1febf001b5e5cf73fa913594cf", "size": 12468, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ch 2/End-to-End Machine Learning Project.py", "max_stars_repo_name": "IESSC/Machine-Learning", "max_stars_repo_head_hexsha": "0866f09ee7ee671b49f0c7340383f5051dcacb49", "max_stars_repo_licenses":... |
# encoding: UTF-8
"""
这里的Demo是一个最简单的策略实现，并未考虑太多实盘中的交易细节，如：
1. 委托价格超出涨跌停价导致的委托失败
2. 委托未成交，需要撤单后重新委托
3. 断网后恢复交易状态
4. 等等
这些点是作者选择特意忽略不去实现，因此想实盘的朋友请自己多多研究CTA交易的一些细节，
做到了然于胸后再去交易，对自己的money和时间负责。
也希望社区能做出一个解决了以上潜在风险的Demo出来。
"""
from ctaStrategy.ctaBase import *
from ctaStrategy.ctaArbitrageTemplate import CtaArbitrageTempl... | {"hexsha": "86097a9c4b04c659e954d8cdd5f88a8a6f9d3901", "size": 12824, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctaStrategy/strategy/strategyArbitrageTest.py", "max_stars_repo_name": "JesseXu117/vn.trader_Arbitrage", "max_stars_repo_head_hexsha": "de3cd2f28c309d354b348e61a58f1a0178daa88b", "max_stars_repo_... |
import tensorflow as tf
from core.Log import log
from core import Measures, Extractions
from datasets import DataKeys
from forwarding.Forwarder import Forwarder
from forwarding.Util import apply_mask
import gc
import colorsys
import numpy as np
DETECTION_EXTRACTION_KEYS = (Extractions.DET_BOXES, Extractions.DET_PROB... | {"hexsha": "da715472dd6ced57105fa6b00dc405b4aaef3759", "size": 7837, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/TrackR-CNN/forwarding/RecurrentDetectionForwarder.py", "max_stars_repo_name": "Diego-Barbulo/TrackR-CNN", "max_stars_repo_head_hexsha": "12eeaca0bd1903ee28822bd2341456404b6e0af4", "max_... |
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 30 10:07:52 2021
@author: Uday Talwar
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 4800
def mandelbrotset(steps,threshold,msize,nsize,degree):
'''
The underlying function is of the form z_n+1 = ... | {"hexsha": "c041303e8460ad1acebb8db750171f60bb2de149", "size": 3202, "ext": "py", "lang": "Python", "max_stars_repo_path": "MandelbrotSets.py", "max_stars_repo_name": "ta1war/Mandelbrot_sets", "max_stars_repo_head_hexsha": "48ede6f4faac8e890d80e3bf11fc666c097c78e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Copyright 2020 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": "d9c623c15e6ae5e8a269ed1e3ae8439dce05c1bd", "size": 7312, "ext": "py", "lang": "Python", "max_stars_repo_path": "jax/_src/traceback_util.py", "max_stars_repo_name": "mariogeiger/jax", "max_stars_repo_head_hexsha": "7098088f4eb15cf750398889e4341dbc15cda1b3", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
!! generate an initial model for an arbitrary-mass, isothermal C WD
!! with an isentropic He envelope on the surface.
program init_1d
use bl_types
use bl_constants_module
use bl_error_module
use extern_probin_module, only: use_eos_coulomb
use eos_module, only: eos_input_rt, eos, eos_init
use eos_type_mod... | {"hexsha": "17aef7d2babb65f4622b3eff1e5cf2dd26099341", "size": 19522, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Util/initial_models/sub_chandra/init_1d.f90", "max_stars_repo_name": "sailoridy/MAESTRO", "max_stars_repo_head_hexsha": "f957d148d2028324a2a1076be244f73dad63fd67", "max_stars_repo_licenses": ["... |
## The inviscid Burgers' Equation - A different approach
#### Problem Setup
Setting $u(x,t) = \frac{x}{1+t}$, we expect $\nu = 0$ as a parameter.
Then $u_0(x) := u(x,0) = x$.
Using the forward Euler scheme, the equation can be re-written as:
and setting the factor $u_{n-1} = \mu_{n-1}$ (analogously to the previous... | {"hexsha": "60fdbe15742ecb52f4a337572bd18d0256038ee0", "size": 338458, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "burgers_equation/burgerseq_mod_fe.ipynb", "max_stars_repo_name": "ratnania/mlhiphy", "max_stars_repo_head_hexsha": "c75b5c4b5fbc557f77d234df001fe11b10681d7d", "max_stars_repo_licens... |
import torch
import numpy as np
from models.network_emb_study import UNet_Pretrained
from tutils import trans_init, trans_args, tfilename, save_script, CSVLogger
from tutils.trainer import LearnerModule, Trainer
class Learner(LearnerModule):
def __init__(self, logger, config, *args, **kwargs):
... | {"hexsha": "f200e74b06eaab531e7011130306e8983eb06276", "size": 746, "ext": "py", "lang": "Python", "max_stars_repo_path": "sc/inc/inc.py", "max_stars_repo_name": "Curli-quan/fewshot-select", "max_stars_repo_head_hexsha": "34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
# Copyright 2021 The NPLinker Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | {"hexsha": "56259635adda840009e2c8db5bd18ae878c2398e", "size": 4706, "ext": "py", "lang": "Python", "max_stars_repo_path": "prototype/nplinker/tests/test_data_linking_functions.py", "max_stars_repo_name": "louwenjjr/nplinker", "max_stars_repo_head_hexsha": "22e957d0f3326775ca5c1f850073067c6fb256d6", "max_stars_repo_lic... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 26 13:48:21 2017
@author: david
"""
from pyspark import SparkContext
import numpy as np
from time import time
class KMeansSpark():
def __init__(self, k, epsilon=1, max_iter=10):
self.k = k # numero de clusters
self.epsilon = epsilon # toleranc... | {"hexsha": "c36637f4a8fef7553b58b8808020e77aa8f2009e", "size": 3906, "ext": "py", "lang": "Python", "max_stars_repo_path": "ApacheSpark/KMeansSpark.py", "max_stars_repo_name": "davidRetana/TFG", "max_stars_repo_head_hexsha": "e04f758811c092a34a4e71cb3409a5a419e7b95a", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
from os.path import join
import numpy as np
from fury import actor, window, ui, pick
from fury.testing import assert_greater
import numpy.testing as npt
import itertools
from fury.data import DATA_DIR
def test_picking_manager():
xyz = 10 * np.random.rand(100, 3)
colors = np.random.rand(100, 4)
radii = np... | {"hexsha": "4402a209e446a1f3d6425b57c3f050450ece160f", "size": 8281, "ext": "py", "lang": "Python", "max_stars_repo_path": "fury/tests/test_pick.py", "max_stars_repo_name": "SunTzunami/fury", "max_stars_repo_head_hexsha": "39a28039fab8ba3070c0a7c1cdb1eed263f59971", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
from __future__ import print_function
import os, sys
import numpy as np
from algorithms import g_span as gSpan
from algorithms import load_graphs
filepath = os.path.dirname(os.path.abspath(__file__))
def main(filename='data/exampleG.txt', min_sup=2):
filename = os.path.join(filepath, filename)
graphs = load_gr... | {"hexsha": "a9ea1515adf64157b5e25a3dd3fa4223d37097d0", "size": 1345, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "LasseRegin/gSpan", "max_stars_repo_head_hexsha": "69df98dccf5d440723274e3c0e9c4fe335d93225", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_stars... |
"""IO methods for radar data from MYRORSS.
--- DEFINITIONS ---
MYRORSS = Multi-year Reanalysis of Remotely Sensed Storms
"""
import shutil
import os.path
import numpy
from gewittergefahr.gg_io import myrorss_and_mrms_io
from gewittergefahr.gg_utils import radar_utils
from gewittergefahr.gg_utils import myrorss_and_m... | {"hexsha": "d6353ba129825fd4d29cb725e6a9185fb7d39e5b", "size": 6522, "ext": "py", "lang": "Python", "max_stars_repo_path": "gewittergefahr/gg_io/myrorss_io.py", "max_stars_repo_name": "dopplerchase/GewitterGefahr", "max_stars_repo_head_hexsha": "4415b08dd64f37eba5b1b9e8cc5aa9af24f96593", "max_stars_repo_licenses": ["MI... |
MODULE vmec_params
USE stel_kinds, ONLY: rprec, dp
USE vparams, ONLY: mpold
C-----------------------------------------------
C L o c a l P a r a m e t e r s
C-----------------------------------------------
INTEGER, PARAMETER :: meven = 0, modd = 1
INTEGER, PARAMETER :: ndamp = 10
... | {"hexsha": "132bd8313de4e759e17a0ec7c185b766f147da89", "size": 9942, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "VMEC2000/Sources/General/vmec_params.f", "max_stars_repo_name": "joseluisvelasco/STELLOPT", "max_stars_repo_head_hexsha": "e064ebb96414d5afc4e205f43b44766558dca2af", "max_stars_repo_licenses": ["M... |
from torch.utils.data import Dataset, DataLoader
import numpy as np
import json
import jieba
import torch
class textDataset(Dataset):
def __init__(self, label_dir='../data/label.json', feature_dir='../data/feature_imgName.json', word_to_idx={},
index=None):
super().__init__()
with... | {"hexsha": "1e270d79cad9508bf7794a492aad9d6235cffe40", "size": 2255, "ext": "py", "lang": "Python", "max_stars_repo_path": "gaiic2022/dataset.py", "max_stars_repo_name": "Challyfilio/GAIIC2022-itmatch", "max_stars_repo_head_hexsha": "55eb1f762dec2d9fe859ed9caafd35cfcd125de6", "max_stars_repo_licenses": ["MIT"], "max_st... |
/**
* Author: Aravinth Panchadcharam
* Email: me@aravinth.info
* Date: 22/12/14.
* Project: Gesture Recogntion for Human-Robot Interaction
*/
#include <iostream>
#include <boost/asio.hpp>
#include "udp_server.h"
int main(){
try
{
boost::asio::io_service io_service;
udp_serve... | {"hexsha": "5c6332867d96dd1055a1a3670e5ea54246e7126a", "size": 512, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source/human-robot-interaction/src/udp-server-async/main.cpp", "max_stars_repo_name": "AravinthPanch/gesture-recognition-for-human-robot-interaction", "max_stars_repo_head_hexsha": "42effa14c0f7a03f4... |
#include <boost/spirit/include/qi_attr_cast.hpp>
| {"hexsha": "4a0af0e6a37b5acde69d96c60b0fd44b5eae3405", "size": 49, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_spirit_include_qi_attr_cast.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["B... |
'''
Created on Oct 30, 2015
@author: kashefy
'''
from nose.tools import assert_equal, assert_false, \
assert_true, assert_greater
import os
import tempfile
import shutil
import numpy as np
import lmdb
import caffe
from nideep.iow.lmdb_utils import IDX_FMT, MAP_SZ
import nideep.iow.to_lmdb as tol
import nideep.iow.... | {"hexsha": "eee4868a0dc085718faa2b3d35cbfc33e53fe076", "size": 9429, "ext": "py", "lang": "Python", "max_stars_repo_path": "nideep/iow/test_copy_lmdb.py", "max_stars_repo_name": "kashefy/caffe_sandbox", "max_stars_repo_head_hexsha": "31afc409df14fece0ac21707185e586dd2d625a9", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import warnings
warnings.filterwarnings('ignore')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from MulticoreTSNE import MulticoreTSNE as TSNE
from PIL import Image, Image... | {"hexsha": "93b205d02b30c971bb62733ca39101fe7f650442", "size": 4332, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/experiment3_clustering.py", "max_stars_repo_name": "arturandre/ssnp", "max_stars_repo_head_hexsha": "9c97ce96ee3f35b2db6294c2634034540e96f079", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from numpy import logical_and
import timeit
from skimage import io
import os
from os import listdir
from os.path import isfile, join
import sys
from exif import Image
def Locate_Columns(input_hologram,Standard_Deviation_First_Threshold,Begin_Time,time_slices):
#input_hologra... | {"hexsha": "2cf15b57a06e6295d31681b003b336bd382ffeee", "size": 8842, "ext": "py", "lang": "Python", "max_stars_repo_path": "Column_Thresholding.py", "max_stars_repo_name": "NikoRanta/DHM_Pipeline", "max_stars_repo_head_hexsha": "0c7790f71e940b42e9b362b9186cb53654f92a58", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pandas as pd
import numpy as np
# import os
# from collections import OrderedDict
# import libs
# import libs.definition as lib_df
def KL(a, b):
a = np.asarray(a, dtype=np.float)
b = np.asarray(b, dtype=np.float)
return np.sum(np.where(a != 0, a * np.log(a / b), 0))
def getKL(proto_fields,lib... | {"hexsha": "24ba56bfe660fb38505b96d49fb2a90b3b10ed4f", "size": 1749, "ext": "py", "lang": "Python", "max_stars_repo_path": "postprocess/common.py", "max_stars_repo_name": "ampmap-cmu/AmpMap", "max_stars_repo_head_hexsha": "5f2d1e3fb9863315041d37a0727a829fce06c515", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "ma... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Compare output data to analytical solution
# Arguments: [<show_plots (0 or 1)>] [<filenames>]
#
import sys
import numpy as np
import csv
import collections
import copy
import os
from argparse import ArgumentParser
import time
#sys.path.append('../../sc... | {"hexsha": "453f474254d71b0d6a5c6c5106b0dfbfcb54328d", "size": 1963, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/electrophysiology/monodomain/pod/scripts/snapshots.py", "max_stars_repo_name": "maierbn/opendihu", "max_stars_repo_head_hexsha": "577650e2f6b36a7306766b0f4176f8124458cbf0", "max_stars_rep... |
%!TEX root = ../thesis.tex
\chapter{Scheduling}
\ifpdf
\graphicspath{{Chapters/Figs/Raster/}{Chapters/Figs/PDF/}{Chapters/Figs/}}
\else
\graphicspath{{Chapters/Figs/Vector/}{Chapters/Figs/}}
\fi
%********************************** % Intro *****************************************
In this chapter a scheduling... | {"hexsha": "53564cb92f8325176fa077fc6277bf9d797be837", "size": 23777, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapters/scheduling.tex", "max_stars_repo_name": "pantonante/EMC2-thesis", "max_stars_repo_head_hexsha": "47cf8aff592557c1ca990404dc7c079e09307262", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma append_cols_mult_left:
assumes A: "A \<in> carrier_mat n a"
and B: "B \<in> carrier_mat n b"
and P: "P \<in> carrier_mat n n"
shows "P * (A @\<^sub>c B) = (P*A) @\<^sub>c (P*B)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P * (A @\<^sub>c B) = P * A @\<^sub>c P * B
[PROOF STEP]
proof -
[PR... | {"llama_tokens": 3396, "file": "Smith_Normal_Form_SNF_Missing_Lemmas", "length": 21} |
[STATEMENT]
lemma carrier_possible_subset:
"carrier t \<subseteq> A \<Longrightarrow> possible t x \<Longrightarrow> x \<in> A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>carrier t \<subseteq> A; possible t x\<rbrakk> \<Longrightarrow> x \<in> A
[PROOF STEP]
by transfer force | {"llama_tokens": 103, "file": "Call_Arity_TTree", "length": 1} |
import numpy as np
from c3pO.icp._base import BaseICP
from c3pO.base import BaseScorer
from c3pO.utils import calc_p
class ICPClassifier(BaseICP):
"""Inductive conformal classifier.
Parameters
----------
nc_function : BaseModelNC
Nonconformity scorer object used to calculate nonconformity... | {"hexsha": "78349e6fa0613332d4f33273ebae993fb321fd5b", "size": 5302, "ext": "py", "lang": "Python", "max_stars_repo_path": "c3pO/icp/classifier.py", "max_stars_repo_name": "maclandrol/c3pO", "max_stars_repo_head_hexsha": "3f3be5132c02f1c313b9ef5abf8264cd3d1dda31", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
using PrettyTables
using StatsBase
function zscore_transform(data)
μ = mean(data)
σ = std(data)
z(d) = (d .- μ) ./ σ
unz(d) = d .* σ .+ μ
return z, unz
end
function meanlowerupper(data, PI = (0.055, 0.945))
m = mean.(eachrow(data))
lower = quantile.(eachrow(data), PI[1])
upper = quanti... | {"hexsha": "fd2599ffa9bc0b937834e73682e5d1eb0e4a485f", "size": 1963, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tools.jl", "max_stars_repo_name": "karajan9/statisticalrethinking", "max_stars_repo_head_hexsha": "e7516c468ca182c4b1c9cae0cfd0bb1feef7ed3c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
% Set onlycurpagenum when you do not want to show total page number.
% Set secheader if you want to show a headline with (sub)section names.
% Set infolines if you want to show a headline and a footline.
\documentclass[onlycurpagenum,infolines,xcolor=table]{beamer}
% Copyright 2017 Alick Zhao <alick9188@gmail.com>
%
% ... | {"hexsha": "12b755e55dad5fead3ae76bcbed1c54f81d9d641", "size": 9437, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "200+ beamer \u6a21\u677f\u5408\u96c6/beamerthemetamu(\u5fb7\u514b\u8428\u65afA&M\u5927\u5b66)/sample/beamerthemetamu.tex", "max_stars_repo_name": "lemoxiao/Awesome-Beamer-Collection", "max_stars_rep... |
# -*- coding: utf-8 -*-
# Modified by Yifan Gui from FAIR Detectron2, Apache 2.0 licence.
import argparse
import json
import multiprocessing as mp
import os
import time
import gc
import numpy as np
import pandas as pd
import skimage.io as io
from detectron2.config import get_cfg
from detectron2.utils.logger import set... | {"hexsha": "2464a1a359dbe9153238ddf1d1cbe909c06d0264", "size": 5196, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/detect.py", "max_stars_repo_name": "Jeff-Gui/PCNAdeep", "max_stars_repo_head_hexsha": "ed4effc07e330155905b73064435d444ac857c1d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import requests
import time
import json
import sys
import os
import numpy as np
import cv2 as cv
import io
URL = "http://192.168.1.243:8001"
colors = [
(250,0,0),
(0,0,120),
(0,0,0),
(0,250,0),
(220,220,0),
(107,142,35),
(152,251,152),
(70,130,180),
(220,20,60),
(255,0,0),
... | {"hexsha": "e634bfb1f56ec841ab6c10226556f9599de82f7e", "size": 2705, "ext": "py", "lang": "Python", "max_stars_repo_path": "client/detect_client.py", "max_stars_repo_name": "peterwa88/docker-fastrcnn", "max_stars_repo_head_hexsha": "8891a518c60c4f882834ad025e5b9295063a5a5a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma take_bit_numeral_bit0:
\<open>take_bit (numeral l) (numeral (Num.Bit0 k)) = take_bit (pred_numeral l) (numeral k) * 2\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. take_bit (numeral l) (numeral (num.Bit0 k)) = take_bit (pred_numeral l) (numeral k) * (2::'a)
[PROOF STEP]
by (simp add: tak... | {"llama_tokens": 155, "file": null, "length": 1} |
/*! \file
\brief A string input.
Copyright (C) 2019-2022 kaoru https://www.tetengo.org/
*/
#include <cstddef>
#include <memory>
#include <stdexcept>
#include <string>
#include <utility>
#include <boost/container_hash/hash.hpp>
#include <boost/core/noncopyable.hpp>
#include <tetengo/lattice/i... | {"hexsha": "170df32c481fe04f13ca626d954fe7d3f257abde", "size": 3416, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "library/lattice/cpp/src/tetengo.lattice.string_input.cpp", "max_stars_repo_name": "tetengo/tetengo", "max_stars_repo_head_hexsha": "66e0d03635583c25be4320171f3cc1e7f40a56e6", "max_stars_repo_license... |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "bdd0185dfe4abe9d9acecc5381ff82c54b8c0705", "size": 4786, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/compiler/tests/eager_test.py", "max_stars_repo_name": "tucaiyong/tensorflow", "max_stars_repo_head_hexsha": "3cc3c87f375f1bc292bd58db4928b810ac888bc6", "max_stars_repo_licenses": ["Apac... |
import cv2
import argparse
import numpy as np
import os
def get_noise(img,value=10):
noise = np.random.uniform(0,256,img.shape[0:2])
v = value *0.01
noise[np.where(noise<(256-v))]=0
k = np.array([ [0, 0.1, 0],
[0.1, 8, 0.1],
[0, 0.1, 0] ])
... | {"hexsha": "477153ae4398a29d4b1813c6b8afb90530247cc4", "size": 3043, "ext": "py", "lang": "Python", "max_stars_repo_path": "generators/OpenCV/rainy.py", "max_stars_repo_name": "JW9MsjwjnpdRLFw/RMT", "max_stars_repo_head_hexsha": "a877fd78639a8d4c534d0373b9d0ad023e0fa2dd", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
Require Import Explicit_sub.
Require Import FOTheory.
Require Import basic Omega.
Import BuildModel.
Import T J R.
Import CCM.
Import ZFind_basic.
Import ZFnats.
Fixpoint int_fotrm t:=
match t with
| Var i => Ref i
| Cst_0 => Zero
| Cst_1 => Succ Zero
| Df_Add u v => Add (int_fotrm u) (int_fotrm v)
end.
... | {"author": "barras", "repo": "cic-model", "sha": "dcc38f3104048aa50d230f819085131b16702d3d", "save_path": "github-repos/coq/barras-cic-model", "path": "github-repos/coq/barras-cic-model/cic-model-dcc38f3104048aa50d230f819085131b16702d3d/CCTnat.v"} |
function varargout = calculator(varargin)
% CALCULATOR M-file for calculator.fig
% CALCULATOR, by itself, creates a new CALCULATOR or raises the existing
% singleton*.
%
% H = CALCULATOR returns the handle to a new CALCULATOR or the handle to
% the existing singleton*.
%
% CALCULATOR('CALLBACK'... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/30454-calculator/calculator.m"} |
import pandas as pd
import os
import csv
import numpy as np
from itertools import cycle
from collections import Counter
def d5_1():
data = pd.read_csv("input_5.csv", header = None, delimiter = "->", engine = "python")
data[["x1", "y1"]] = data[0].str.split(",", expand = True).astype(int)
data[["x2", "y2"]]... | {"hexsha": "9048f82ead62442f834cedf992a94009b2559616", "size": 3544, "ext": "py", "lang": "Python", "max_stars_repo_path": "d5/d5.py", "max_stars_repo_name": "mwm021/Advent-of-Code-2021", "max_stars_repo_head_hexsha": "9908b95ea6503c1b31fa26845e8ee5d0ad474718", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#include "3dUnitTypeSerialization.hpp"
#include <boost/foreach.hpp>
#include "logger.hpp"
#include <boost/optional/optional.hpp>
#include <glm/gtx/euler_angles.hpp>
using namespace m3d;
void typeSerialization::serialize(boost::property_tree::ptree &pt, m3d::rawPointcloud &v, std::string id)
{
ptree p;
... | {"hexsha": "2188de68a56506dfa23563545764b7f0c329f178", "size": 6212, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/3dUnitTypeSerialization.cpp", "max_stars_repo_name": "michalpelka/m3d_unit_dr", "max_stars_repo_head_hexsha": "ecee744474bde24a4a50d4872b59a087c66ac677", "max_stars_repo_licenses": ["Unlicense"]... |
from typing import Dict
from numpy.lib.histograms import _ravel_and_check_weights
import datasets
from datasets import load_dataset, Dataset
import transformers
from transformers import AutoTokenizer
from copy import deepcopy
import pandas as pd
class DataFilter(object):
'''使用pandas来完成数据提取（复杂度有点感人，但只能选择相信pandas内置... | {"hexsha": "bc03540d690fbc61986e2c5fe847b9b5c68c898e", "size": 3522, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader.py", "max_stars_repo_name": "cogito233/ImmigrationBert", "max_stars_repo_head_hexsha": "d80c44351f795ec2f6156d57d2ee126aace2d1da", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#pragma once
#include <mumlib.hpp>
#include <log4cpp/Category.hh>
#include <boost/noncopyable.hpp>
#include <string>
#include <stdexcept>
// 0 = mumble users connected at start; 1 = connect at dial-in
// TODO: fix mumlib::TransportException when this option is enabled
#define MUM_DELAYED_CONNECT 0
namespace mumble... | {"hexsha": "66e62ed7977777c5be4e586f7286622357c1a49e", "size": 2916, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "MumbleCommunicator.hpp", "max_stars_repo_name": "hiro2233/mumsi", "max_stars_repo_head_hexsha": "19c604cbf3f19728163c1a84bc243f8a29c66786", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
from __future__ import print_function
import sympy as sm
x, y, a = sm.symbols('x y a')
f = a*x + y**2*sm.sin(y)
step1 = sm.Integral(f, x, y)
print(step1)
step2 = sm.Integral(sm.Integral(f, x).doit(), y)
print(step2)
step3 = step2.doit()
print(step3)
| {"hexsha": "f4bac67975580d83ae06eca350b46f30649290b2", "size": 251, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/src/pyembed/src/ex1.py", "max_stars_repo_name": "dutille/doconce", "max_stars_repo_head_hexsha": "f1d41ae065f7d3c24450180c2ce019b03edbaddc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#!/usr/bin/python
# Usage:
#
# Reference
#
import numpy as np
def loadMatrix():
def expectation():
def maximization():
def convergenceCheck():
def outputPrediction():
def main():
# configuration
inputTrain = "../dataset/train.txt"
inputTest = "../dataset/test.txt"
amountOfEigenv = 5
output... | {"hexsha": "0833f37303e83d4b7e6ed44de170856585219560", "size": 739, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignments/3/A/PCAReconstructDataset.bak.py", "max_stars_repo_name": "gypleon/codesCloud", "max_stars_repo_head_hexsha": "bc779fd3485b925ff5e5345e725a97d6ce262a2b", "max_stars_repo_licenses": ["Ap... |
#!/usr/bin/env python
# coding=utf-8
"""Tests based on the IOOS QARTOD manuals."""
import logging
import warnings
from typing import Dict, List, Tuple, Union, Sequence
from numbers import Real as N
from collections import namedtuple
import numpy as np
import pandas as pd
from ioos_qc.utils import (
isnan,
isf... | {"hexsha": "45ba4898da43628c0c4a6b34263ced451120a8a6", "size": 4940, "ext": "py", "lang": "Python", "max_stars_repo_path": "ioos_qc/axds.py", "max_stars_repo_name": "ocefpaf/ioos_qc", "max_stars_repo_head_hexsha": "b5412e2ad35e47d0d460afea963ecbffe260afe6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
# Copyright Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" ... | {"hexsha": "db1e76530eea51ef7032893e53eb5a8b7250f94d", "size": 3468, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/MXNetEnv/inference/inference_src/predict.py", "max_stars_repo_name": "erebus9856/sagemaker-battlesnake-ai", "max_stars_repo_head_hexsha": "b1799622e56cbc4e619f15806db62c5fb6f25993", "max_st... |
#redirect Gladys Valley Hall
| {"hexsha": "31674364d32f0d4e940604b4601b7b9504542c76", "size": 29, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Veterinary_Medicine_Instructional_Facility.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses... |
\documentclass[serif,xcolor=pdftex,dvipsnames,table,hyperref={bookmarks=false,breaklinks}]{beamer}
\input{../config.tex}
\settitlecard{3}{Classes and Representing Numbers}
\begin{document}
\maketitlepage
% \section{Preliminaries}
% \subsection{Foo}
%
%
% \begin{frame}[t]{Reminders}
% \begin{itemize... | {"hexsha": "baa2d1882a088e80e4e09837ca520864cba4e92f", "size": 9876, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/Lecture03/lecture.tex", "max_stars_repo_name": "royadams/intro_to_numerical_computing_with_python", "max_stars_repo_head_hexsha": "f31706f691b8a22ad8db19cdb950a0cb1df047f4", "max_stars_repo_lice... |
Rebol []
header-file: %include/glfw3.h
output-file: %glfw3.reds
do %common-init.r
ch_hexdigit: charset "0123456789ABCDEF"
echo output-file
print {Red/System [
Title: "Red/System glfw3 binding - A library for OpenGL/Vulkan, window and input"
Author: "Oldes"
File: %glfw3.reds
Rights: "Copyright (C) 2017 Da... | {"hexsha": "3b21b142c05a8c5f48118f4a5d66eb0b87a15e1e", "size": 7398, "ext": "r", "lang": "R", "max_stars_repo_path": "parse-glfw3.r", "max_stars_repo_name": "Oldes/parse-opengl-headers", "max_stars_repo_head_hexsha": "f13fab98e56da767108f9eb3c268703b2fea0fd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
"""Text and number formatting operations"""
from copy import deepcopy
import json
import numpy as np
def round_all_numbers_in_dict(d, rounding_digits=2, outplace=True):
""" Return a new version of dict d with all floats rounded to N digits."""
if outplace:
d = deepcopy(d)
for k, v in d.items():
... | {"hexsha": "f4a0e5e049b29afcb3e31c88724705d5ec085c0a", "size": 1378, "ext": "py", "lang": "Python", "max_stars_repo_path": "dnachisel/biotools/formatting_operations.py", "max_stars_repo_name": "simone-pignotti/DnaChisel", "max_stars_repo_head_hexsha": "b7f0f925c9daefcc5fec903a13cfa74c3b726a7a", "max_stars_repo_licenses... |
C>
C> \brief Calculate the LdR decomposition of the overlap matrix
C>
C> This subroutine is based on the function `wfn1_overlap_bo`.
C> Whereas `wfn1_overlap_bo` only calculates the overlap between
C> two determinants, this routine also calculates the transformation
C> matrices of the orbitals as well as the diagonal ... | {"hexsha": "a5d3aae79685d7bebbfc387ba321ec350c1126c2", "size": 5733, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/rdmft/recycling/wfn1/overlap.f", "max_stars_repo_name": "miroi/nwchem", "max_stars_repo_head_hexsha": "c252de495e545a244cf6a7b806566c33f0a225ed", "max_stars_repo_licenses": ["ECL-2.0"], "max_s... |
from typing import Dict, List, Tuple, Optional
from copy import copy
from functools import partial
from scipy import interpolate
from vnpy.event import Event, EventEngine
from vnpy.trader.engine import MainEngine
from vnpy.trader.ui import QtWidgets, QtCore, QtGui
from vnpy.trader.event import EVENT_TICK, EVENT_TIMER... | {"hexsha": "f7f41d3bfaf7babd616b4330fe7dbdbe20916963", "size": 33661, "ext": "py", "lang": "Python", "max_stars_repo_path": "vnpy_optionmaster/ui/manager.py", "max_stars_repo_name": "noranhe/vnpy_optionmaster", "max_stars_repo_head_hexsha": "180c85f92004d1092bc45032dc31585539de9768", "max_stars_repo_licenses": ["MIT"],... |
import warnings
import numpy as np
def generate_regular_latlon_grid(ndpts, grid_template):
""" Generate simple 1d lat, lon arrays for regular grid definition
"""
# earth_shape = grid_template[0] # 6 = sphere of 6,371,229.0 m
nlon = grid_template[7]
nlat = grid_template[8]
angle_scale = grid_... | {"hexsha": "3a2d6c12d9dd2097aab4cf0c30d3e6177eebc870", "size": 1449, "ext": "py", "lang": "Python", "max_stars_repo_path": "gripy/grids.py", "max_stars_repo_name": "abrammer/gripy", "max_stars_repo_head_hexsha": "1ce204a8f2f6a3015d3bc7cd1ce6e82d730673ac", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
from os import path
import numpy as np
import csv
def check_file_exits(predicted_test_Y_file_path):
if not path.exists(predicted_test_Y_file_path):
raise Exception("Couldn't find '" + predicted_test_Y_file_path +"' file")
def check_format(test_X_file_path, predicted_test_Y_file_path):
pred_... | {"hexsha": "48b5123838cecf3ef89c6b8def8edbfb66147493", "size": 1400, "ext": "py", "lang": "Python", "max_stars_repo_path": "decision_trees_passport/validate.py", "max_stars_repo_name": "Silent-faith/ML-Algorithim-coded-from-scratch", "max_stars_repo_head_hexsha": "a1eaa588bb507c9c8ef0ec4cedc7c24179901d29", "max_stars_r... |
@testset "Test total multi order indices" begin
# Nx = 1
midxs = totalorder([0])
@test midxs == reshape([0],(1,1))
midxs = totalorder([1])
@test midxs == reshape([0; 1],(2,1))
midxs = totalorder([3])
@test midxs == reshape([0; 1; 2; 3],(4,1))
#Nx = 2
midxs = totalorder([0; 0])
@test midxs == reshape([0; 0],(1,2))
... | {"hexsha": "1e4f31fb4697019d5a8e3437d4156f8b0fb92378", "size": 1210, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/margin/totalorder.jl", "max_stars_repo_name": "mleprovost/TransportBasedInference.jl", "max_stars_repo_head_hexsha": "bdcedf72e9ea23c24678fe6af7a00202c5f9d5d7", "max_stars_repo_licenses": ["MI... |
import os
import numpy as np
fileFolder = "D:\\DigKey\\"
mapPath = "d:\\DigKeyEnglish2Chinese.txt"
outputFilePath = "d:\\pariOutput.txt"
finalOutPath = "d:\\DigKeyGoodsPair.txt"
english2chinese = {}
chinese2english = {}
with open(mapPath,"r") as rh:
for line in rh:
eng = line.strip().split("... | {"hexsha": "39bce889c745a04783eb09495a9960c5d5c4238c", "size": 5245, "ext": "py", "lang": "Python", "max_stars_repo_path": "Correspone.py", "max_stars_repo_name": "zoubohao/Emotion-Classification", "max_stars_repo_head_hexsha": "ec6c52647581181a1a3c0231de01ecedaa2ceebd", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from sklearn import preprocessing
def GradientDescent(y, x):
'''a functon to calculate the coefficient of each feature with gradient
descent
return beta, which is a matrix of coefficients of all the control variables
x, y are python lists
'''
l = len(x[0]) # how many featur... | {"hexsha": "6935b2348a91a26da591aa1ff5592177e8be551d", "size": 1118, "ext": "py", "lang": "Python", "max_stars_repo_path": "gradient_descent.py", "max_stars_repo_name": "dingyunxing/James-code-paradise", "max_stars_repo_head_hexsha": "688a8e6c8b569bacb6ac9f6754f43a5a3a7eba7a", "max_stars_repo_licenses": ["MIT"], "max_s... |
import torch
import argparse
import numpy as np
from torch import nn
from torch import optim
from torch.utils import data
from torch.optim.lr_scheduler import StepLR
from torch.nn import functional as F
from collections import defaultdict
import time
import pickle
import energy_estimator.analyse as simul
class Net(n... | {"hexsha": "58ec1233be1613e96d09ef23c6724f8da030d617", "size": 8328, "ext": "py", "lang": "Python", "max_stars_repo_path": "energy_estimator/images_energy_estimator.py", "max_stars_repo_name": "iliaishacked/sponge_examples", "max_stars_repo_head_hexsha": "914ecb8c1ce06ad7177b34d907d419995c0734e6", "max_stars_repo_licen... |
#=
Jobs puzzle in ConstraintSolver.jl
This is a standard problem in Automatic Reasoning.
From http://www-unix.mcs.anl.gov/~wos/mathproblems/jobs.html
"""
Jobs Puzzle
There are four people: Roberta, Thelma, Steve, and Pete.
Among them, they hold eight different jobs.
Each holds exactly two jobs... | {"hexsha": "1de8b00cf1d18687f440ac582371b4d46fd1e047", "size": 6384, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/constraints/jobs_puzzle.jl", "max_stars_repo_name": "tias/hakank", "max_stars_repo_head_hexsha": "87b7f180c9393afce440864eb9e5fb119bdec1a4", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Copyright (c) 2012 Andre Martins
// All Rights Reserved.
//
// This file is part of AD3 2.0.
//
// AD3 2.0 is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at... | {"hexsha": "dae0cf069b987f1d3129d6a3e83846f2fb73eea4", "size": 23305, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_stars_repo_name": "zgdahai/graphlabapi", "max_stars_repo_head_hexsha": "7d66bbda82d4d44cded35f9438e1c9359b0ca64e", "max_stars_repo_licenses": ["ECL-2.0",... |
/*!@file
* @copyright This code is licensed under the 3-clause BSD license.
* Copyright ETH Zurich, Laboratory of Physical Chemistry, Reiher Group.
* See LICENSE.txt for details.
*/
#include <boost/test/unit_test.hpp>
#include "Molassembler/Temple/Adaptors/All.h"
#include "Molassembler/Temple/Functional.h"
#i... | {"hexsha": "a16984e1dd0ec35c7e4170081f22ed04dd77c07a", "size": 10023, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/Temple/Adaptors.cpp", "max_stars_repo_name": "qcscine/molassembler", "max_stars_repo_head_hexsha": "3b72168477b2d1dee55812517e49d9c3285c50ba", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
/-5 .8 Summary of New Constructs
Lemmas:
dec_trivial -- decidable truth (e.g., a true closed executable expression)
funext -- functional extensionality
propext -- propositional extensionality
Tactics:
generalize -- replaces a term by a fresh variable defined by a new hypothesis
linarith -- applies... | {"author": "mathprocessing", "repo": "lean_mathlib_examples", "sha": "743c6456c0a3219dd1722efdd31ee6f3a113818a", "save_path": "github-repos/lean/mathprocessing-lean_mathlib_examples", "path": "github-repos/lean/mathprocessing-lean_mathlib_examples/lean_mathlib_examples-743c6456c0a3219dd1722efdd31ee6f3a113818a/src/full_... |
#!/usr/bin/env python3
"""
.. module:: classify
:platform: Unix
:synopsis: Classify output from MothNet model.
.. moduleauthor:: Adam P. Jones <ajones173@gmail.com>
"""
from sklearn.metrics import confusion_matrix, roc_curve, auc
import numpy as _np
from scipy import interp as _interp
def roc_multi(true_cla... | {"hexsha": "15784d7373b16aa2c1485f4edb7f61ecf6304b39", "size": 14127, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymoth/modules/classify.py", "max_stars_repo_name": "meccaLeccaHi/pymoth", "max_stars_repo_head_hexsha": "92189887e316e0d17426952625dd33746d2657cf", "max_stars_repo_licenses": ["MIT"], "max_stars... |
/*!
@file
Defines `boost::hana::Traversable::traverse_mcd`.
@copyright Louis Dionne 2014
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#ifndef BOOST_HANA_TRAVERSABLE_TRAVERSE_MCD_HPP
#define BOOST_HANA_TRAVERSABLE_TRAVERSE... | {"hexsha": "97ef9a2578693e7eedef4f6cf6dcd7ad2fd5cd9c", "size": 767, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/hana/traversable/traverse_mcd.hpp", "max_stars_repo_name": "rbock/hana", "max_stars_repo_head_hexsha": "2b76377f91a5ebe037dea444e4eaabba6498d3a8", "max_stars_repo_licenses": ["BSL-1.0"]... |
"""
=============
Interpolation
=============
Provides interpolation algorithms across tabular data for ``vivarium``
simulations.
"""
import pandas as pd
import numpy as np
from typing import Union, List, Tuple
ParameterType = Union[List[List[str]], List[Tuple[str, str, str]]]
class Interpolation:
"""A callabl... | {"hexsha": "90cafbacc7ae8ca19eff0bd0a99e5e52037dee75", "size": 12461, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/vivarium/interpolation.py", "max_stars_repo_name": "ihmeuw/vivarium", "max_stars_repo_head_hexsha": "77393d2e84ff2351c926f65b33272b7225cf9628", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import abc
import os
from enum import Enum
from typing import Optional, Union, TypeVar, List, Tuple, Callable, Generator
import gym
import numpy as np
from gym import spaces
import sap.battle as battle
import sap.game as game
import sap.pet as pet
import sap.pet_impl as pet_impl
import sap.player as player
import sap... | {"hexsha": "8be3a3d2e9e798420c01ddfebb12aac38961b8df", "size": 13239, "ext": "py", "lang": "Python", "max_stars_repo_path": "sap/envs/sap_random_versus_env.py", "max_stars_repo_name": "steveryb/super-auto-pets-ai", "max_stars_repo_head_hexsha": "f86e033473885e2c7a0312c598f1870bf8edce3c", "max_stars_repo_licenses": ["MI... |
#EasyPlotQwt base types & core functions
#-------------------------------------------------------------------------------
#==Constants
===============================================================================#
#TODO: Support transparent, or *read* plot background color somehow.
const COLOR_BACKGROUND = EasyPlot... | {"hexsha": "bbdbaa6c17a7b61e8bca1700fbf434d6c205cc10", "size": 6354, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "subpkgs/EasyPlotQwt/src/base.jl", "max_stars_repo_name": "ma-laforge/CMDimData.jl", "max_stars_repo_head_hexsha": "f543265841b81dc20f5dca18a37cd642371a0a34", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import yaml
import pgl
import time
import copy
import numpy as np
import os.path as osp
from pgl.utils.logger import log
from pgl.graph import Graph
from pgl import graph_kernel
from pgl.sampling.custom import subgraph
from ogb.lsc import MAG240MDataset, MAG240MEvaluator
from dataset.base_dataset import BaseD... | {"hexsha": "75a7d943b6a04f9073c5bdec88f17ba16db5e24a", "size": 2740, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/kddcup2021/MAG240M/r_unimp/check_cv_get_test_result.py", "max_stars_repo_name": "zbmain/PGL", "max_stars_repo_head_hexsha": "dbded6a1543248b0a33c05eb476ddc513401a774", "max_stars_repo_lic... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 18 16:55:14 2017
@author: ajaver
Get food contour using a pre-trained neural network
"""
# %%
import tables
import os
import numpy as np
import cv2
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', categor... | {"hexsha": "70b4fe2b2ca4ff083955694a19c323f38069a6ad", "size": 14687, "ext": "py", "lang": "Python", "max_stars_repo_path": "tierpsy/analysis/food_cnt/getFoodContourNN.py", "max_stars_repo_name": "JGNieto/tierpsy-tracker", "max_stars_repo_head_hexsha": "0c1565bc1ed4d54f9fe196931eecafb663a68e3f", "max_stars_repo_license... |
#! /usr/bin/env python3
# coding=utf-8
import datetime
#import matplotlib
#matplotlib.use('Agg')
#import numpy as np
#import matplotlib.pyplot as plt
#import sys, os
import peakTree
import peakTree.helpers as h
import logging
log = logging.getLogger('peakTree')
log.setLevel(logging.INFO)
log.addHandler(... | {"hexsha": "7ed0613bc4ee250d6491868ee287dfd229607900", "size": 930, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_conversion_joyrad.py", "max_stars_repo_name": "martin-rdz/peakTree", "max_stars_repo_head_hexsha": "f37764b80faa5ead14835c2d03fda46c64b94e4d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python
################################################################################
# rel_locs_RPs.py
#
# Takes genome wide locations and converts into relative locations graphs
#
################################################################################
import pylab as P
import matplotlib.pyp... | {"hexsha": "c7fe6c687c3646fd98820b5eeb9c879ad98abce6", "size": 13650, "ext": "py", "lang": "Python", "max_stars_repo_path": "TFBS_scoring/rel_locs_RPs.py", "max_stars_repo_name": "trevorsorrells/RPG_evolution", "max_stars_repo_head_hexsha": "17b76230459919aea07fd733a06548477d3c67ee", "max_stars_repo_licenses": ["MIT"],... |
import pytest
from cuxfilter.assets import datetime as dt
from cuxfilter.charts.constants import CUDF_DATETIME_TYPES
import datetime
import pandas as pd
import cudf
import numpy as np
@pytest.mark.parametrize(
"date, _type, factor",
[
(
np.datetime64("2018-10-07").astype("datetime64[ns]"... | {"hexsha": "6ad04aefcde0c735364cf7c3993b677372c03965", "size": 3211, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cuxfilter/tests/assets/test_datetime.py", "max_stars_repo_name": "kkraus14/cuxfilter", "max_stars_repo_head_hexsha": "99d7cf67802270d24db0051162df4feb798f2e15", "max_stars_repo_licenses": [... |
include("euler/euler.jl")
using .Calculus: triangular_numbers
function compute(n::Int)::Int
limit = trunc(Int, √n)
triangles = triangular_numbers(limit)
min_difference = n
nearest = 0
for i ∈ 1:limit
for j ∈ 1:i
rectangles = triangles[i] * triangles[j]
difference = a... | {"hexsha": "9c78dbfb7bc552b4b9c27860eb8314273db70ed3", "size": 557, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "problems/0085/compute.jl", "max_stars_repo_name": "Dynortice/Project-Euler", "max_stars_repo_head_hexsha": "99a0201b5d5f147eab77fc52d9db8995045cded0", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
## Analyzing lowered code
function add_docexpr!(docexprs::AbstractDict{Module,V}, mod::Module, ex) where V
docexs = get(docexprs, mod, nothing)
if docexs === nothing
docexs = docexprs[mod] = V()
end
push!(docexs, ex)
return docexprs
end
function lookup_callexpr(frame, stmt)
fargs = Jul... | {"hexsha": "ab40c7a472e7cbd7941c340e45cd70bdf40874c8", "size": 22568, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lowered.jl", "max_stars_repo_name": "t-bltg/Revise.jl", "max_stars_repo_head_hexsha": "165433a5e4fc5cdcee1d2ce20843ad476f1c5a83", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import torch
import numpy as np
import sys,os
import torch.nn.functional as F
#from Common.Const import GPU
#GPU = 50
sys.path.append(os.path.join(os.getcwd(),"metrics"))
from CD_EMD.emd_ import emd_module
from CD_EMD.cd.chamferdist import ChamferDistance as CD
class PointcloudMixup(object):
def __init__(self):... | {"hexsha": "0881a05b8d3abb54d9780d38aecd1a0c32f75d24", "size": 10874, "ext": "py", "lang": "Python", "max_stars_repo_path": "Common/data_utils.py", "max_stars_repo_name": "JiazeWang/SP-GAN", "max_stars_repo_head_hexsha": "455003f78b1160ebe0a2056005b069808c0df35b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
\section{Homotopy groups of types}
\subsection{Pointed types}
\begin{defn}
\begin{enumerate}
\item A pointed type consists of a type $X$ equipped with a base point $x:X$. We will write $\UU_\ast$ for the type $\sm{X:\UU}X$ of all pointed types.
\item Let $(X,\ast_X)$ be a pointed type. A \define{pointed family} over ... | {"hexsha": "767a6eb8dd957cb101440837648c36096dd308eb", "size": 14934, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Book/homotopy_groups.tex", "max_stars_repo_name": "tadejpetric/HoTT-Intro", "max_stars_repo_head_hexsha": "f4228d6ecfc6cdb119c6e8b0e711fea05b98b2d5", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_... |
# See https://github.com/TuringLang/Turing.jl/issues/1199
ChainRulesCore.@non_differentiable push!!(
vi::VarInfo, vn::VarName, r, dist::Distribution, gidset::Set{Selector}
)
ChainRulesCore.@non_differentiable updategid!(
vi::AbstractVarInfo, vn::VarName, spl::Sampler
)
# https://github.com/TuringLang/Turing.j... | {"hexsha": "edcac7874a44e6c7845a82ec253519bf7c53f899", "size": 771, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/compat/ad.jl", "max_stars_repo_name": "TuringLang/MicroPPL", "max_stars_repo_head_hexsha": "d222316a7a2fd5afe6ec74a7ec2a50c6f08c1d00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
// ==========================================================================
// Munin Text Document.
//
// Copyright (C) 2010 Matthew Chaplain, All Rights Reserved.
//
// Permission to reproduce, distribute, perform, display, and to prepare
// derivitive works from this file under the following conditions:
//
... | {"hexsha": "75554b3a76e2fd91e642484af6caa3a752b68bdd", "size": 11488, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "munin/include/munin/text/document.hpp", "max_stars_repo_name": "KazDragon/paradice9", "max_stars_repo_head_hexsha": "bb89ce8bff2f99d2526f45b064bfdd3412feb992", "max_stars_repo_licenses": ["MIT"], "... |
# standard library
import argparse
import os
import glob
# third-party libraries
from scipy.io.wavfile import write
import numpy as np
# project libraries
from speech.utils.wave import array_from_wave, wav_duration
from speech.utils import convert
def main(audio_dir:str, use_extend:bool, use_resample:bool) -> None:... | {"hexsha": "c785bcbbd5e36ab415e4a21dacd84f15781a2a88", "size": 4517, "ext": "py", "lang": "Python", "max_stars_repo_path": "speech/utils/process_noise.py", "max_stars_repo_name": "dzubke/speech-lite", "max_stars_repo_head_hexsha": "65f83ac2b7551650820f079ce5152741f2a6fdb8", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import _init_paths
from fast_rcnn.train import get_training_roidb, train_net
from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from datasets.factory import get_imdb
import datasets.imdb
import caffe
import argparse
import pprint
import numpy as np
import sys
import zl_config as C
from fast_... | {"hexsha": "5a0a4953d9562e1533157b38a142a61f34a1faf3", "size": 3032, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/debug_iccv_gt.py", "max_stars_repo_name": "yjy941124/PPR-FCN", "max_stars_repo_head_hexsha": "1eba5515b37e7b32413efdf14bb0c22a2e46fee9", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import sys
sys.path.append("../../")
from mushroom_env import fill_volume_env_3D_multiple_obj_3Dac
from mushroom_env import fill_volume_env_3D_multiple_obj_3Dac_4
from mushroom_env import fill_volume_env_3D_multiple_obj_3Dac_wrap_robot
from mushroom_env import fill_volume_env_3D_multiple_obj_3Dac_wrap_robot_1
from mush... | {"hexsha": "11d87a1ae7fc3ad007db3babf9c5a43912730a7a", "size": 33231, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/boxes_3D_dqn__fill_multidim.py", "max_stars_repo_name": "nifunk/GNNMushroomRL", "max_stars_repo_head_hexsha": "d0d8eefdc10bca62e7cb536d65ea619607be755b", "max_stars_repo_licenses": ["MIT... |
# -*- coding: utf-8 -*-
'''
Created on 30 mars 2014
@author: Mohamed
'''
import sys
import numpy as np
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import scipy.sparse.linalg as la
import math
from opencavity.utilsfunc import UtilsFunc
class Help(object):
"""... | {"hexsha": "ce985cc8eba932e424d1a0c1ad633033395b82d3", "size": 29538, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/opencavity/modesolver.py", "max_stars_repo_name": "giomalt/SLM_hologram_generation", "max_stars_repo_head_hexsha": "74ad38be8fe17c710856b2508389cd8c9f1ee77a", "max_stars_repo_licenses": ["MIT... |
import numpy as np
from typing import Dict, List, Tuple
import logging
from functools import lru_cache
from mypy.types import Type
from . import register
from ..shortcuts import (is_int, is_bool, is_float, bool_type, int_type,
float_type, is_ndarray_of_bools, is_ndarray_of_floats,
... | {"hexsha": "6eefd0cd93f5860a2c71c970ae8814c2b83aaab2", "size": 2190, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy_plugin/typefunctions/ufuncs.py", "max_stars_repo_name": "rmcgibbo/numpy-mypy", "max_stars_repo_head_hexsha": "4f63fbd82bc7067753975bfc887b061be5c2b30f", "max_stars_repo_licenses": ["BSD-2-Cl... |
def example(Simulator):
import numpy as np
from csdl import Model
import csdl
class ExampleTensorSummationSparse(Model):
def define(self):
# Shape of Tensor
shape3 = (2, 4, 3)
c = np.arange(24).reshape(shape3)
# Declaring tensor
... | {"hexsha": "71c769b73bef0329752412f9093e1feb21baf745", "size": 890, "ext": "py", "lang": "Python", "max_stars_repo_path": "csdl/examples/valid/ex_einsum_old_tensor_summation_sparse.py", "max_stars_repo_name": "LSDOlab/csdl", "max_stars_repo_head_hexsha": "04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3", "max_stars_repo_licen... |
"""Synthetic observation data testing.
"""
import copy
import unittest
import numpy
import tigernet
from .network_objects import network_lattice_1x1_geomelem
from .network_objects import network_empirical_simplified
import platform
os = platform.platform()[:7].lower()
if os == "windows":
WINDOWS = True
DECI... | {"hexsha": "d7b0bbc122ba2a9f6ca919d6d3d5a3e9717e6399", "size": 25773, "ext": "py", "lang": "Python", "max_stars_repo_path": "tigernet/tests/test_observations_synthetic.py", "max_stars_repo_name": "martinfleis/tigernet", "max_stars_repo_head_hexsha": "c3bee46bdd79bb9aa8a98356b4f95f5a1fb4c2b4", "max_stars_repo_licenses":... |
# Make our document distances work with the TextAnalysis package
const DefaultDocDistance = WordMoversDistance()
# Find the closest token with an embedding in a DocDistance. If the
# token itself has no embedding we will try to "clean" it until we find
# an embedding which is likely to carry very similar meaning.
fun... | {"hexsha": "5b695ff3e6852c3c96322e83611fad5510529126", "size": 4209, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/textanalysis_interface.jl", "max_stars_repo_name": "robertfeldt/DocumentDistances.jl", "max_stars_repo_head_hexsha": "2545b15e0907c23be011351f200b34b397e008ae", "max_stars_repo_licenses": ["MIT... |
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import tqdm
import random
import copy
from collections import defaultdict
import re
# %% Global Constants
TOL = 1e-5
USE_CUDA = torch.cuda.is_available()
RANDOM_GROUP_ORDER = True
INTERMEDIATE_ACTIVATION = "relu"
WEIGHT_DECAY = 0
... | {"hexsha": "02eea5dc036869aecd4a6a94d178fd8383357c73", "size": 18014, "ext": "py", "lang": "Python", "max_stars_repo_path": "sgin_model.py", "max_stars_repo_name": "BeibinLi/SGIN", "max_stars_repo_head_hexsha": "e73f64c9111b0841f3324459114c396847b9ccbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
```python
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from simplified_monorotor import Monorotor
import plotting
import testing
import trajectories
pylab.rcParams['figure.fig... | {"hexsha": "1057a359c1336750feee56326c32130539e5b8a0", "size": 108260, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Course03/12-4. PD with FF.ipynb", "max_stars_repo_name": "thhuang/notes-fcnd", "max_stars_repo_head_hexsha": "c5b0ec7d99df3cb60a850308d16ccc6c096c7931", "max_stars_repo_licenses": [... |
"""
Analyze TCIA-GBM annotations
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data_root = 'D:/Datasets/TCIA-GBM/'
def homogeneous(df):
df[['good', 'ugly', 'bad']].loc[~df[['good', 'ugly', 'bad']].isnull()] = 1
df[['good', 'ugly', 'bad']].loc[df[['... | {"hexsha": "b41a5ae55a8ff1de47d264d0f7436da2ff333f1e", "size": 3182, "ext": "py", "lang": "Python", "max_stars_repo_path": "sandbox/analyze_tcia_annotations.py", "max_stars_repo_name": "ashish-code/MI-DQA", "max_stars_repo_head_hexsha": "1d4d5c2fa148aa7229cc9089adc982ab0d33e631", "max_stars_repo_licenses": ["MIT"], "ma... |
function sub_sst = extract_sst(sst,t1,t2,edge_mode)
%EXTRACT_SST: Extract event SST (Start/Stop Times) between t1 and t2
%
%USAGE: sub_sst = extract_sst(sst,t1,t2,edge_mode)
%
%INPUTS: sst - Event Start/Stop Times [Nx2 array of numeric time values]
% t1 - time value, extract all events in sst between t1 (start... | {"author": "geoscience-community-codes", "repo": "GISMO", "sha": "a4eafca9d2ac85079253510005ef00aa9998d030", "save_path": "github-repos/MATLAB/geoscience-community-codes-GISMO", "path": "github-repos/MATLAB/geoscience-community-codes-GISMO/GISMO-a4eafca9d2ac85079253510005ef00aa9998d030/deprecated/@helicorder/private/SS... |
[STATEMENT]
lemma And_append_syntactic:
"xs \<noteq> [] \<Longrightarrow> And (xs @ ys) = And ((And xs)#ys)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. xs \<noteq> [] \<Longrightarrow> And (xs @ ys) = And (And xs # ys)
[PROOF STEP]
by (induction xs rule: list_nonempty_induct) simp+ | {"llama_tokens": 117, "file": "LTL_to_DRA_LTL_FGXU", "length": 1} |
/-
Copyright (c) 2022 Kevin Buzzard. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Author : Kevin Buzzard
-/
import tactic -- imports all the Lean tactics
import data.real.basic -- imports the real numbers
/-
# Figuring out how to use the reals
## The `library_search` tact... | {"author": "ImperialCollegeLondon", "repo": "formalising-mathematics-2022", "sha": "af5f176b3b881b7bc0ae89b55befe48c9d4ab790", "save_path": "github-repos/lean/ImperialCollegeLondon-formalising-mathematics-2022", "path": "github-repos/lean/ImperialCollegeLondon-formalising-mathematics-2022/formalising-mathematics-2022-a... |
#### GitHub Actions
abstract type GitHubAction end
"""
DocBuild
# Fields
$(DocStringExtensions.FIELDS)
"""
Base.@kwdef struct DocBuild <: GitHubAction
"Build name"
build_name::String
"Julia version"
julia_version::String
end
function yaml(gha::DocBuild)
return "name: Documentation
on:
pus... | {"hexsha": "2734342526e58d2710cd0230251967a91bb969e3", "size": 4021, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/github_actions.jl", "max_stars_repo_name": "charleskawczynski/PkgTemplates", "max_stars_repo_head_hexsha": "40415cfa561d50618a02ce4eca1da981af511bb9", "max_stars_repo_licenses": ["MIT"], "max_s... |
from setuptools import setup, Extension, dist
import setuptools
import sys
dist.Distribution().fetch_build_eggs(['Cython>=0.15.1', 'numpy>=1.10'])
try:
from Cython.Build import cythonize
USE_CYTHON = True
except ImportError:
sys.exit("""Could not import Cython, which is required to build benepar extension... | {"hexsha": "0ef0013470b4a5cb4d405de1f60ccf84d34c4bec", "size": 1826, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "samghelms/self-attentive-parser", "max_stars_repo_head_hexsha": "2d6c9dc0fc95a0c46264a91a225fd16a788496f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 11 17:01:28 2021
@author: gawe
Functions dealing with rectangular patch antenna.
"""
import math
import numpy as np
from math import cos, sin, sqrt, pi, log10, atan2, acos, radians
from scipy import integrate
import scipy.integrate
import json
import matplotlib.pyplo... | {"hexsha": "6538dcc9cf1a9bbe54b3620fd8a8778a17bb8815", "size": 55899, "ext": "py", "lang": "Python", "max_stars_repo_path": "PatchAntenna.py", "max_stars_repo_name": "gmweir/QuasiOptics", "max_stars_repo_head_hexsha": "0974178984f845597c5209217613c26edf931ed0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
[STATEMENT]
lemma no_unacquired_write_to_read_only_reads':
assumes no_wrt: "no_write_to_read_only_memory \<S> sb"
assumes non_vol: "non_volatile_owned_or_read_only True \<S> \<O> sb"
assumes consis: "sharing_consistent \<S> \<O> sb"
shows "read_only_reads \<O> sb \<inter> outstanding_refs is_Write\<^sub>s\<^sub... | {"llama_tokens": 400, "file": "Store_Buffer_Reduction_ReduceStoreBuffer", "length": 2} |
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