text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
In order to connect to MongoDB on dicarlo5 server create an ssh tunnel using the
command below:
ssh -f -N -L 22334:localhost:22334 bashivan@dicarlo5.mit.edu
"""
from __future__ import print_function
import zmq
import sys
# sys.path.insert(0, '/Users/pouyabashivan/Dropbox (MIT)/Codes/Downloads/ThreeDWorld/Client... | {"hexsha": "3393dae959793d0736e4246c51011ddde43ff5f8", "size": 21145, "ext": "py", "lang": "Python", "max_stars_repo_path": "ClientTools/TDW_image_generator.py", "max_stars_repo_name": "neuroailab/ThreeDWorld", "max_stars_repo_head_hexsha": "62ca47c4030489b6986216000fe123c5e2367c3c", "max_stars_repo_licenses": ["Apache... |
\section{Conclusion and Future Work}
Within the described simple experiment we showed that expressive,
verbal surrogate models with high fidelity can be found for DNNs using
the developed methodology. We suggest that the approach is promising
and worth future research and optimization.
% In future work we would like ... | {"hexsha": "41888c79ce56351692cf33520d680deb48e12e38", "size": 3930, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper-tex/conclusion.tex", "max_stars_repo_name": "lthamm/concept-embeddings-and-ilp", "max_stars_repo_head_hexsha": "27592c6424147a2fbb54d7daebc92cd72b3f4a0c", "max_stars_repo_licenses": ["MIT"], "... |
n = rand(1:10)
A = matrixdepot("minij", n)
@test issym(A)
@test isposdef(A)
println("'minij' passed test...")
| {"hexsha": "e8a903109f406684357eabe5ee3aa905312ab982", "size": 110, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_minij.jl", "max_stars_repo_name": "JuliaPackageMirrors/MatrixDepot.jl", "max_stars_repo_head_hexsha": "86b9c9ce3ad7bf0ea8f282624696c9174c157bcc", "max_stars_repo_licenses": ["MIT"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2015-2016 European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# Permission is hereby gra... | {"hexsha": "a701066bfb118bb8841a3589a6acbc5b358fc2f7", "size": 26860, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyFAI/worker.py", "max_stars_repo_name": "vallsv/pyFAI", "max_stars_repo_head_hexsha": "64143652c2b219978ec370bf2fa215af01f937c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
#!/usr/bin/env python3
from pprint import pprint
import networkx as nx
from networkx.drawing.nx_pydot import read_dot, write_dot
import matplotlib.pyplot as plt
import numpy as np
def draw_graph(g, weights=False):
g = nx.DiGraph(g)
pos = nx.circular_layout(g)
edge_weights = nx.get_edge_attributes(g, 'wei... | {"hexsha": "5361c092f8cbfccdf2bd12045f83289929031273", "size": 3844, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "fabiocody/retiming", "max_stars_repo_head_hexsha": "63d0823fa895f0614cd9f859e3529a0956446da3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
[STATEMENT]
lemma FinalAllow_approximating_in_doubt_deny: "matcher_agree_on_exact_matches \<gamma> \<beta> \<Longrightarrow>
good_ruleset rs \<Longrightarrow>
(\<beta>, in_doubt_deny),p\<turnstile> \<langle>rs, Undecided\<rangle> \<Rightarrow>\<^sub>\<alpha> Decision FinalAllow \<Longrightarrow> \<Gamma>,\<gamma>... | {"llama_tokens": 5872, "file": "Iptables_Semantics_Semantics_Embeddings", "length": 12} |
/-
Copyright (c) 2016 Jeremy Avigad. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Andrew Zipperer, Jeremy Avigad
We provide two versions of the quoptient construction. They use the same names and notation:
one lives in the namespace 'quotient_group' and the other li... | {"author": "Bolt64", "repo": "lean2-aur", "sha": "1d7148e58a17b2d326b032ed1ebf8c5217320242", "save_path": "github-repos/lean/Bolt64-lean2-aur", "path": "github-repos/lean/Bolt64-lean2-aur/lean2-aur-1d7148e58a17b2d326b032ed1ebf8c5217320242/library/theories/group_theory/quotient.lean"} |
[STATEMENT]
lemma mult_le_mono2_hmset: "i \<le> j \<Longrightarrow> k * i \<le> k * j" for i j k :: hmultiset
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. i \<le> j \<Longrightarrow> k * i \<le> k * j
[PROOF STEP]
by (simp add: mult_left_mono) | {"llama_tokens": 107, "file": "Nested_Multisets_Ordinals_Syntactic_Ordinal", "length": 1} |
import numpy as np
import os
from statsmodels.tsa.arima_model import ARIMA
def housing_data_predict(destination_directory, paavo_housing_quarterly_df):
"""
Open Paavo quarterly housing price dataframe and predict the
quarterly prices between 2018 - 2020 with ARIMA(0, 1, 1) model
and save the predict... | {"hexsha": "f17d674f65890870b7942783a9c9974fe0303094", "size": 3798, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/modeling/housing_prediction.py", "max_stars_repo_name": "xiaoxiaobt/Reaktor-Data-Science-project", "max_stars_repo_head_hexsha": "c779eaa9e586ebe62929361bd4d1bc1c537e4e11", "max_stars_repo... |
clean_price <- function(data_price, file_price) {
data_price <- data_price %>%
mutate(date = str_replace_all(date, "\u00C3\u00a4", "\u00e4")) %>%
mutate(date = str_replace(date, "Jan", "J\u00e4n")) %>%
mutate(date = as.Date(date, format = "%d.%b.%Y")) %>%
mutate(price = as.numeric(price))
if (fil... | {"hexsha": "0a6e8874a9732af511a5c03d20dab6846bfe0196", "size": 581, "ext": "r", "lang": "R", "max_stars_repo_path": "clean_price.r", "max_stars_repo_name": "ha-pu/webscrap_ishares", "max_stars_repo_head_hexsha": "14c6e0cd7fd4358a503752225f3296f914165411", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
// =-=-=-=-=-=-=-
// legacy irods includes
#include "msParam.hpp"
#include "reGlobalsExtern.hpp"
#include "miscServerFunct.hpp"
// =-=-=-=-=-=-=-
//
#include "irods_resource_plugin.hpp"
#include "irods_file_object.hpp"
#include "irods_physical_object.hpp"
#include "irods_collection_object.hpp"
#include "irods_string_t... | {"hexsha": "0eee1af0dcd4f1e93454d196c9fa1d8514fd9255", "size": 47402, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "plugins/resources/roundrobin/libroundrobin.cpp", "max_stars_repo_name": "nesi/irods", "max_stars_repo_head_hexsha": "49eeaf76305fc483f21b1bbfbdd77d540b59cfd2", "max_stars_repo_licenses": ["BSD-3-Cl... |
import os
import datetime
import time
from collections.abc import Iterable
from glob import glob
import numpy as np
import netCDF4 as nc
import itertools
from .logger import get_log
from . import sario
log = get_log()
DATE_FMT = "%Y%m%d"
def find_igrams(directory=".", ext=".int", parse=True, filename=None):
"""... | {"hexsha": "a61260635b509061368fbf9838cc67c559a8a450", "size": 8244, "ext": "py", "lang": "Python", "max_stars_repo_path": "trodi/utils.py", "max_stars_repo_name": "scottstanie/trodi", "max_stars_repo_head_hexsha": "e359fbe65b4de27afdec093e2b41f0c63b665fe0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
# Include this startup file prior to running Julia code
# Add project module locations to path
push!(LOAD_PATH, abspath(joinpath("src","distributions")))
push!(LOAD_PATH, abspath(joinpath("src","samplers")))
| {"hexsha": "d686f9f83787a5a3a0a9d64842d654189b08a849", "size": 209, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/startup.jl", "max_stars_repo_name": "jgorham/stein_discrepancy", "max_stars_repo_head_hexsha": "addfe17ce04e6fec4be0c441c996e732b1f7abb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import mpmath
tpij = 2*mpmath.pi*1j
def eta(t):
q124 = mpmath.exp(tpij * t / 24)
q = mpmath.exp(tpij * t)
return q124*mpmath.qp(q,q)
mpmath.cplot( eta, re=[-1.1,1.1],im=[0.00001,0.5], points=1000000, verbose=True)
| {"hexsha": "8c5d62c614236d55a22ee323714a4a4369738a5b", "size": 231, "ext": "py", "lang": "Python", "max_stars_repo_path": "images/etaplot.py", "max_stars_repo_name": "rantonels/rantonels.github.io", "max_stars_repo_head_hexsha": "599804c02efa365c504c696cf0b5d22745bbb85b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# --------------
import time
import pandas as pd
import numpy as np
from nltk import pos_tag
import matplotlib.pyplot as plt
# code starts here
# Loading of data
df=pd.read_csv(path)
# Mapping of pos tags with nominees
tagged_titles = df['nominee'].str.split().map(pos_tag)
# Creating a dataframe
tagged_titles_df ... | {"hexsha": "dcd083a30a65d93d9f572244bcc2be82f30d7efb", "size": 2837, "ext": "py", "lang": "Python", "max_stars_repo_path": "EMMY-winner-analysis/code.py", "max_stars_repo_name": "hchaudhari73/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "42c0bf7b4bbeef10d187c74c8803b1fdca5d2cdd", "max_stars_repo_licenses": ["MI... |
import numpy as np
class DatasetsIndexingHelper:
def __init__(self, dataset_length_list):
self.dataset_length_list = dataset_length_list
self.length = sum(dataset_length_list)
def __getitem__(self, index: int):
for index_of_datasets, length in enumerate(self.dataset_length_list):
... | {"hexsha": "9eb09bde8b02f685a72a8501cd40b82fab431fbe", "size": 3217, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/tracking/sampler/_sampling_algos/sequence_picking/run_through/_server.py", "max_stars_repo_name": "zhangzhengde0225/SwinTrack", "max_stars_repo_head_hexsha": "526be17f8ef266cb924c6939bd8dda23... |
import numpy as np
import pandas as pd
import time
import xml.etree.ElementTree as ET
import os
from xml.dom import minidom
class Record:
def __init__(self, df=None, path=None):
self._df = df
self._path = path
@property
def df(self):
"""Gets the record as a dataframe.
If ... | {"hexsha": "b68fda8246bdf8d100b956f0505793cfcb79fbb3", "size": 17541, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyhsmm/util/eaf_processing.py", "max_stars_repo_name": "dpaysan/pyhsmm", "max_stars_repo_head_hexsha": "2c9d57651f65d4a7b995ee7a1215da456bc410c8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
module Mod_nsm_ComputeQfactor
use typre
use Mod_PointerSetter
use Mod_nsm_BaseElmope
use Mod_nsm_InterpolateGradients
implicit none
private
public SetPointersComputeQFactor
type, extends(PointerSetter) :: SPComputeQFactor
contains
procedure :: SpecificSet => SpecificSetComputeQFactor
... | {"hexsha": "7632a7ce53bbe52ddcbb21784705386758a0aa90", "size": 2457, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/modules/nstinc/Elmopes/Mod_nsm_ComputeQfactor.f90", "max_stars_repo_name": "ciaid-colombia/InsFEM", "max_stars_repo_head_hexsha": "be7eb35baa75c31e3b175e95286549ccd84f8d40", "max_stars_r... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np, scipy as sp
import torch
from torch.nn.parameter import Parameter
import scipy.io
import matplotlib.pyplot as plt
# from visualdl import LogWriter
import argparse, sy... | {"hexsha": "0ab218d132d58af6f35ee17a1a5c9cd554e8e17d", "size": 3018, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyTorch/orthdAE.py", "max_stars_repo_name": "tkm646/orthogonal-denoising-autoencoder", "max_stars_repo_head_hexsha": "75f1c323d1fa18dcd28a4dcdf83916113a859abd", "max_stars_repo_licenses": ["Apache... |
import numpy as np
import pickle
import time
class policy_iteration:
def __init__(self,policy,state_name,action_name,prs,discount=None,theta=None,end_flag=None):
self.policy=policy
self.state_name=state_name
self.action_name=action_name
self.prs=prs
self.state_le... | {"hexsha": "7dec8e22ef26376759bdbbe15af824d69989c354", "size": 5757, "ext": "py", "lang": "Python", "max_stars_repo_path": "Note/RL/DP/policy_iteration.py", "max_stars_repo_name": "7NoteDancing/Note", "max_stars_repo_head_hexsha": "d1150c313aa695efb32181638b9b35fbad5f29ed", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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": "69d4059a3417fb51ebe3c8e6b71c4a2ea65513fe", "size": 11479, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindspore/nn/probability/distribution/log_normal.py", "max_stars_repo_name": "HappyKL/mindspore", "max_stars_repo_head_hexsha": "479cb89e8b5c9d859130891567038bb849a30bce", "max_stars_repo_license... |
[STATEMENT]
lemma sat_precond_as_proj_4:
fixes fm1 fm2 vs
assumes "fm2 \<subseteq>\<^sub>f fm1"
shows "(fmrestrict_set vs fm2 \<subseteq>\<^sub>f fm1)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fmrestrict_set vs fm2 \<subseteq>\<^sub>f fm1
[PROOF STEP]
using assms fmpred_restrict_set fmsubset_alt_def
[PRO... | {"llama_tokens": 269, "file": "Factored_Transition_System_Bounding_FmapUtils", "length": 2} |
% Project Specifications
\chapter{GEMM and $col2im$}
The previous chapter discussed different ways to view the convolution operation and its cousin,
the transposed convolution, both conceptually and implementationally. When it comes down to implementation,
we have seen that both operations can be implemented with a s... | {"hexsha": "e67844182539ac1740ea5b2825ef6be47b090d07", "size": 7818, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/gemm.tex", "max_stars_repo_name": "lambdalainen/metropolia-thesis-latex", "max_stars_repo_head_hexsha": "d7e705ad24f1f8065b2e7f026db5fdc90a7c8b3a", "max_stars_repo_licenses": ["CC-BY-4.0"],... |
import queue
import time
try:
import cupy as xp
except ImportError:
import numpy as xp
import numpy as np
from common import plot
import common.const
import comms.const
L_head = len(comms.const.PN_SEQ) + 1
L_msg = comms.const.MSG_BYTES * 8 // comms.const.SYMBOL_SIZE
L_plot = (L_head + L_msg) * comms.const.L_... | {"hexsha": "96f00797b640d5dbc571820c5afff18ba81670c2", "size": 2930, "ext": "py", "lang": "Python", "max_stars_repo_path": "hydrocode/modules/comms/corrplot.py", "max_stars_repo_name": "cuauv/software", "max_stars_repo_head_hexsha": "5ad4d52d603f81a7f254f365d9b0fe636d03a260", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
# -*- coding: utf-8 -*-
"""
Created on 2021/12/09 21:01:03
@File -> mi_partition.py
@Author: luolei
@Email: dreisteine262@163.com
@Describe: 基于离散化的互信息计算
"""
__doc__ = """
参考文献:
Georges A. Darbellay: Predictability: An Information-Theoretic Perspective, Signal Analysis \
and Prediction, 1998.
"""
... | {"hexsha": "c09bbdb4020b420e6655594adff7b75a5f64352d", "size": 9654, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/ref/mi_partition.py", "max_stars_repo_name": "Ulti-Dreisteine/data-information-measurement", "max_stars_repo_head_hexsha": "9ef777c28534867d07d9ab1a1b95d69a385043f1", "max_stars_repo_licenses... |
"""
Classes to solve canonical consumption-saving models with idiosyncratic shocks
to income. All models here assume CRRA utility with geometric discounting, no
bequest motive, and income shocks that are fully transitory or fully permanent.
It currently solves three types of models:
1) A very basic "perfect foresi... | {"hexsha": "db6c8419d3d9f89b11c296587fd5acb1bf35f9da", "size": 119386, "ext": "py", "lang": "Python", "max_stars_repo_path": "HARK/ConsumptionSaving/ConsIndShockModel.py", "max_stars_repo_name": "nicksawhney/HARK", "max_stars_repo_head_hexsha": "f7608a96c3b491f9cf605472768dd996eb624f76", "max_stars_repo_licenses": ["Ap... |
import numpy as np
from sksfa.utils import ReceptiveRebuilder, ReceptiveSlicer
from sklearn.preprocessing import PolynomialFeatures
from sksfa import SFA
from time import time
class Flatten:
def fit(self, X, y=None):
pass
def partial(self, X, y=None):
pass
def transform(self, X):
... | {"hexsha": "056677079d2d8159fb50f0ba0ec501d6d0fc7696", "size": 12707, "ext": "py", "lang": "Python", "max_stars_repo_path": "sksfa/_hsfa.py", "max_stars_repo_name": "wiskott-lab/sklearn-sfa", "max_stars_repo_head_hexsha": "0db443a5df013627a0ca573ea8be9e7ef591ecd2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
from joblib import Parallel, delayed
from astropy.io import fits
import warnings
import glob
import os
warnings.filterwarnings('ignore')
########## USER PARAMETERS ######################
path = '/Users/felipegran/Desktop/Doctorado/ESO/m0.7m1.4pt1/' #with final /
datacube_name = 'm0.7m1.4pt1.fits' #image names will ... | {"hexsha": "6b69e726c96eeccb9d9358db295a02f17c36ade3", "size": 5406, "ext": "py", "lang": "Python", "max_stars_repo_path": "GoMUSEvPSFex.py", "max_stars_repo_name": "fegran/GoMUSEvPSFex", "max_stars_repo_head_hexsha": "d0b2ec4491b9214ee37956fd6bfa1f92a3cc8811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#pragma once
#include "BeastContext.hpp"
#include "BeastSocket.hpp"
#include <arepa/communication/Signal.hpp>
#include <boost/asio/ip/tcp.hpp>
#include <boost/beast.hpp>
#include <memory>
namespace arepa::networking::websocket {
/**
* A class that binds a TCP endpoint and accepts websockets.
* This uses boost::a... | {"hexsha": "9d0d3e43175b571e5b4cf2b651fa7e705221afe8", "size": 1599, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "module/networking_websocket/include/BeastSocketListener.hpp", "max_stars_repo_name": "selfeki/social-gaming-platform", "max_stars_repo_head_hexsha": "8d59512620470c57fa760998f3bcf1e4469130ef", "max_... |
[STATEMENT]
lemma "test (do {
tmp0 \<leftarrow> slots_fallback_document . getElementById(''test5'');
n \<leftarrow> createTestTree(tmp0);
tmp1 \<leftarrow> n . ''test5'';
removeWhiteSpaceOnlyTextNodes(tmp1);
tmp2 \<leftarrow> n . ''f2'';
tmp2 . remove();
tmp3 \<leftarrow> n . ''s1'';
tmp4 \<leftarrow> t... | {"llama_tokens": 980, "file": "Shadow_SC_DOM_tests_slots_fallback", "length": 1} |
\documentclass[letterpaper]{article}
%% Language and font encodings
\usepackage[english]{babel}
\usepackage[utf8x]{inputenc}
\usepackage[T1]{fontenc}
%% Sets page size and margins
\usepackage[letterpaper,top=2.5cm,bottom=2cm,left=2cm,right=2cm,marginparwidth=1.75cm]{geometry}
%% Useful packages
\usepackage{amsmath}
... | {"hexsha": "e13d3b68661b5a1b04075207c791838eacbd0d49", "size": 8091, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "a1/a1.tex", "max_stars_repo_name": "violetguos/intro_machine_learning", "max_stars_repo_head_hexsha": "744b7bfc586a8d629086c92248b9b9c2aa1eb071", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
! this is the first example in C.3.1
type id_numbers
integer ssn
integer employee_number
end type id_numbers
type person_id
character(len=30) last_name
character(len=1) middle_initial
character(len=30) first_name
type(id_numbers) number
end type person_id
type person
integer age
type(person_id... | {"hexsha": "b272695a438aab8f21391206f7dde10d01af67e1", "size": 477, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/annex_c/c_3_1_0.f90", "max_stars_repo_name": "OpenFortranProject/ofp-sdf", "max_stars_repo_head_hexsha": "202591cf4ac4981b21ddc38c7077f9c4d1c16f54", "max_stars_repo_licenses": ["BSD-3-Claus... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#HW2 for EECS 598 Motion Planning
#code based on the simplemanipulation.py example
import time
import openravepy
#### YOUR IMPORTS GO HERE ####
import astarTool as a
from copy import deepcopy
#### END OF YOUR IMPORTS ####
if not __openravepy_build_doc__:
from openrave... | {"hexsha": "79fafee12702ee18b7c490fc226601839bd64fcb", "size": 4802, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/HW2_astar.py", "max_stars_repo_name": "willsirius/DualTreeRRTStartMotionPlanning", "max_stars_repo_head_hexsha": "d3e6d2ec0cd7c38379d5b0ff42924b7216bd29cd", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib
from six import BytesIO
def add_figure_to_archive(fig, zipfile, filename):
bytes_buf = BytesIO()
plt.savefig(bytes_buf, format='png')
bytes_buf.seek(0)
zipfile.writestr(filename, bytes_buf.read())
by... | {"hexsha": "c1a4932bc1b4b6ccef801bc957fca395d6ab578e", "size": 9072, "ext": "py", "lang": "Python", "max_stars_repo_path": "pystq/plots.py", "max_stars_repo_name": "JuantonioMS/pyngs", "max_stars_repo_head_hexsha": "5c929e68c975aae94669d0a0ff29ceb462de9b5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
from sklearn.metrics import accuracy_score,classification_report,roc_curve,auc,confusion_matrix
import numpy as np
from sklearn.model_selection import cross_val_predict
class Ensemble(object):
def __init__(self, estimators):
self.estimators_names=[]
self.estimators=[]
self.result={}
... | {"hexsha": "812242f75ab06926d83d1da97c9dd144636ec6b0", "size": 6498, "ext": "py", "lang": "Python", "max_stars_repo_path": "birthplace/FeatureExtraction/ensemble.py", "max_stars_repo_name": "sakuranew/KGAttributesExtraction", "max_stars_repo_head_hexsha": "f4d796046ced6ff508442a802962549f4c4a51de", "max_stars_repo_lice... |
module TestDefaultlogger
using Historic.Internal: DefaultLogger
using Logging
using Test
function with_defaultlogger(body)
buffer = (main = IOBuffer(), fallback = IOBuffer())
context = (:displaysize => (5, 20),)
io = (
main = IOContext(buffer.main, context...),
fallback = IOContext(buffer.... | {"hexsha": "747a38e88a3d820a272de1ae2214f276f78f648b", "size": 1118, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/HistoricTests/src/test_defaultlogger.jl", "max_stars_repo_name": "JuliaConcurrent/Historic.jl", "max_stars_repo_head_hexsha": "e212319016f145bbd9fc136fac21a5a18ce4ffa4", "max_stars_repo_licens... |
#=
Character and string classification functions
Copyright 2017-2018 Gandalf Software, Inc., Scott P. Jones,
Licensed under MIT License, see LICENSE.md
=#
# Recommended by deprecate
@static if V6_COMPAT
text_width(str::AbstractString) = strwidth(str)
text_width(ch::Char) = charwidth(ch)
import Base: is_a... | {"hexsha": "55b5d27e53be0536209192c3e3d7a0a7b68625d7", "size": 9531, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unicode.jl", "max_stars_repo_name": "oxinabox/Strs.jl", "max_stars_repo_head_hexsha": "fe071fd34fc8de23076f2d8e7dcbae5efd8d9011", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# coding=utf-8
"""Given image and homography matrix, visualize the homograph."""
from __future__ import print_function
import argparse
import cv2
import os
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument("image")
parser.add_argument("homography")
parser.add_argument("new_image")
def read_... | {"hexsha": "b925baa05173cdfe96c43739efe80aeea964816e", "size": 2136, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/baselines/vis_homography.py", "max_stars_repo_name": "JunweiLiang/next-prediction", "max_stars_repo_head_hexsha": "0b7f78321fd43037fa1e6582715eb734000c2cf8", "max_stars_repo_licenses": ["Apac... |
! H0 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
! H0 X
! H0 X libAtoms+QUIP: atomistic simulation library
! H0 X
! H0 X Portions of this code were written by
! H0 X Albert Bartok-Partay, Silvia Cereda, Gabor Csanyi, James Kermode,
! H0 X Ivan Solt, Wojciech Szlachta, Csilla... | {"hexsha": "0328fec23af9a5f00c64aa90311677a820445b27", "size": 197231, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "src/libAtoms/minimization.f95", "max_stars_repo_name": "albapa/QUIP", "max_stars_repo_head_hexsha": "ecde1e332c6bd62c238d3cd90e31dba4fb390313", "max_stars_repo_licenses": ["NRL"], "max_stars_c... |
Require Import Category.Lib.
Require Import Category.Instance.Lambda.Ltac.
Require Import Category.Instance.Lambda.Ty.
Require Import Category.Instance.Lambda.Exp.
Require Import Category.Instance.Lambda.Sub.
From Equations Require Import Equations.
Set Equations With UIP.
Generalizable All Variables.
Section Log.
... | {"author": "jwiegley", "repo": "category-theory", "sha": "5376e32a4eeace4a84674820083bc2985a2a593f", "save_path": "github-repos/coq/jwiegley-category-theory", "path": "github-repos/coq/jwiegley-category-theory/category-theory-5376e32a4eeace4a84674820083bc2985a2a593f/Instance/Lambda/Log.v"} |
program phase_iterative
implicit none
!! # Grid parameters
integer maxmx, maxmy
parameter(maxmx = 2**10, maxmy = 2**10)
!! # Input parameters
double precision ax_in, ay_in, dx_in, dy_in, t_in, tstart
double precision domain_length
integer mx_in, my_in, nstp, izero, nchar
character*100 ... | {"hexsha": "13eae360a390429df695c5684fc59d0a18b273c1", "size": 12883, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "applications/elliptic/phasefield/fishpack/phase_iterative.f90", "max_stars_repo_name": "ECLAIRWaveS/ForestClaw", "max_stars_repo_head_hexsha": "0a18a563b8c91c55fb51b56034fe5d3928db37dd", "max_s... |
import numpy as np
import torch
import torch.nn as nn
from torch.nn import Parameter
class Self_Attn(nn.Module):
""" Self attention Layer"""
'''
https://github.com/heykeetae/Self-Attention-GAN/blob/master/sagan_models.py
'''
def __init__(self, in_dim, activation, with_attention=False):
... | {"hexsha": "809354fc2954f9dd4c49ce41ab1dbfda91208a64", "size": 8249, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/modules/modules.py", "max_stars_repo_name": "7568/Shift-Net_pytorch", "max_stars_repo_head_hexsha": "4863127301862457030cb8027cbe567c33aa90b2", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Originally by adamb70 from https://github.com/adamb70/Python-Spherical-Projection
# Modified to be used with Source Engine cubemaps.
# Converted to numpy to achieve reasonable performance.
import numpy
from numpy import ndarray
from enum import IntEnum
from typing import Tuple
def spherical_coordinates(i: ndarray,... | {"hexsha": "407b6e4b8455e58a330ae6b3135d49e3be5ec388", "size": 4365, "ext": "py", "lang": "Python", "max_stars_repo_path": "io_import_vmf/cube2equi.py", "max_stars_repo_name": "lasa01/io_import_vmf", "max_stars_repo_head_hexsha": "3341b8e2d0be77cba8f3ec30812f6c859f9b9a83", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#ifndef JHMI_UTILITY_LOAD_PROTOBUF_HPP_NRC_20160520
#define JHMI_UTILITY_LOAD_PROTOBUF_HPP_NRC_20160520
#include "utility/scope_exit.hpp"
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/gzip_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <boost/filesystem.hpp>
#ifnd... | {"hexsha": "fb516aa9975e27d293503aed46262e522b48afe2", "size": 1253, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utility/load_protobuf.hpp", "max_stars_repo_name": "ncrookston/liver_source", "max_stars_repo_head_hexsha": "9876ac4e9ea57d8e23767af9be061a9b10c6f1e5", "max_stars_repo_licenses": ["BSL-1.0"], "max_s... |
import numpy as np
from gluonts.evaluation.backtest import make_evaluation_predictions
from gluonts.evaluation import Evaluator, MultivariateEvaluator
import warnings
warnings.filterwarnings('ignore')
from src.data_handler.sharable_dataset import SharableListDataset, SharableMultiVariateDataset
def evaluation(train, t... | {"hexsha": "34814a3abe839235174629b7a67a78885ac14121", "size": 2391, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluator.py", "max_stars_repo_name": "jeffrey82221/gluonts_fund_price_forecast", "max_stars_repo_head_hexsha": "fed7c484c4dba663201f9cf96aa86ca98119b54c", "max_stars_repo_licenses": ["MIT"], ... |
% main.tex
\documentclass{report}
\setcounter{secnumdepth}{5}
\begin{document}
\chapter{A}
a
\section{AA}
aa
\subsection{AAA}
\subsubsection*{AAAA}
aaaa
\paragraph{AAAAA}
aaaaa
\subparagraph{AAAAAA}
aaaaaa
\end{document} | {"hexsha": "cdd1f4c548ede2e0bbaf0564d90e51095f06e500", "size": 220, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/test_misc/main copy 5.tex", "max_stars_repo_name": "imagingbook/latextree", "max_stars_repo_head_hexsha": "272ee1594b3bdea39a043fb2ac2b86ac9a1728e8", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""Automatic verification of ANUGA flows.
See functions exercised by this wrapper for more details
"""
import unittest
import os
import numpy
import anuga
indent = anuga.indent
args = anuga.get_args()
verbose = args.verbose
class Test_results(unittest.TestCase):
def setUp(self):
for file in os.listdir('... | {"hexsha": "6dd50f3cf112e72664e53084bc911f4d072e1b10", "size": 3499, "ext": "py", "lang": "Python", "max_stars_repo_path": "validation_tests/analytical_exact/avalanche_dry/validate_avalanche_dry.py", "max_stars_repo_name": "samcom12/anuga_core", "max_stars_repo_head_hexsha": "f4378114dbf02d666fe6423de45798add5c42806", ... |
#!/usr/bin/env python3
import struct
import time
import numpy as np
import pandas as pd
from getpass import getpass
from bluepy.btle import Peripheral, DefaultDelegate
addr = 'C0:98:E5:51:EE:C5'
if len(addr) != 17:
raise ValueError("Invalid address supplied")
# Define UUIDs for BLE connection
SERVICE_UUID = "... | {"hexsha": "bb712194c0716a240b35cfed02e4de622db63532", "size": 4059, "ext": "py", "lang": "Python", "max_stars_repo_path": "software/apps/romi_sysid/robot_control.py", "max_stars_repo_name": "aparande/Robo-AR", "max_stars_repo_head_hexsha": "797b7375e59e049b9b2c21ff926cc42286d39b05", "max_stars_repo_licenses": ["MIT"],... |
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy.linalg as linalg
import numpy as np
import pathlib
path = pathlib.Path().absolute()
from E import *
def plot_matrices(N,J,h,PBC = True):
H = HeisenbergHamiltonian(N,J,h,PBC = PBC)
T = get... | {"hexsha": "d95e30c2dff576a13bd5d0962eeac8bc0be6ac6e", "size": 3109, "ext": "py", "lang": "Python", "max_stars_repo_path": "matrix_plot.py", "max_stars_repo_name": "giopolykra/Transverse_Ising", "max_stars_repo_head_hexsha": "1d8bf96112ae6e89fecc35d0f9c4bd820804da07", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include "VariableSpace.h"
#include "ContainerImpl.h"
#include "../API/Yap/VdfParser.h"
#include <vector>
#include <sstream>
#include <boost/lexical_cast.hpp>
using namespace Yap;
using namespace std;
#define IMPLEMENT_VARIABLE_NO_ENABLE public: \
virtual int GetType() const override { return _type;} \
virtual con... | {"hexsha": "2d3b42f5e7e3459763421b5da1a2a8e962b70447", "size": 26698, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Shared/Implement/VariableSpace.cpp", "max_stars_repo_name": "yangshadip/YAP-SELF", "max_stars_repo_head_hexsha": "c715baa61c9504304629f28c05fd0f70b629f32a", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma lcp_ext_right_conv: "\<not> r \<bowtie> r' \<Longrightarrow> (r \<cdot> u) \<and>\<^sub>p (r' \<cdot> v) = r \<and>\<^sub>p r'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> r \<bowtie> r' \<Longrightarrow> r \<cdot> u \<and>\<^sub>p r' \<cdot> v = r \<and>\<^sub>p r'
[PROOF STEP]
by (induc... | {"llama_tokens": 151, "file": "Combinatorics_Words_CoWBasic", "length": 1} |
!==============================================================================!
subroutine Cpu_Timer_Mod_Stop(f_name)
!------------------------------------------------------------------------------!
implicit none
!-----------------------------------[Locals]-----------------------------------!
character(len=*) ::... | {"hexsha": "329666661bf2235eface8f6d085a53acc7a7107b", "size": 1378, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Sources/Shared/Cpu_Timer_Mod/Stop.f90", "max_stars_repo_name": "Dundj/Convex_Geomotry", "max_stars_repo_head_hexsha": "38507824d97270b3e4ead194a16148ff6158b59f", "max_stars_repo_licenses": ["MIT... |
"""
Title :Base_tester.py
Description :Base class for dataset benchmarks
Author :Ilke Cugu
Date Created :16-01-2020
Date Modified :02-05-2020
version :1.0.2
python_version :3.6.6
"""
import keras
import numpy as np
class Base_tester:
def __init__(self, wait=False):
... | {"hexsha": "f882a0fcbc55e17a48728478895cc7d1c5be48b5", "size": 2916, "ext": "py", "lang": "Python", "max_stars_repo_path": "testers/Base_tester.py", "max_stars_repo_name": "cuguilke/psykedelic", "max_stars_repo_head_hexsha": "ecdcd0c4fd1ed1316c4e98f42aae0c1bc38d337d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
// Copyright Abel Sinkovics (abel@sinkovics.hu) 2015.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <boost/metaparse/v1/impl/next_digit.hpp>
#include <boost/mpl/assert.hpp>
#include <boos... | {"hexsha": "ca7be64124e4e0089e7944ab6bf357fc93382b11", "size": 787, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/libs/metaparse/test/next_digit.cpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_l... |
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import time
# Converts a Tensor into an image array (numpy)
# |imtype|: the desired type of the converted numpy array
def tensor2im(input_image, imtype=np.uint8):
if isinstance(input_image, torch.Tensor):
... | {"hexsha": "1e14248506f744064ed0fa14883747215e11725b", "size": 2677, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/util.py", "max_stars_repo_name": "VMReyes/keypointgan", "max_stars_repo_head_hexsha": "17b6f6f43430d532603d25edb2f42c087119986e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
# General imports and utility functions
from imports import *
from utils import *
# Training environment
from parameters import par, update_dependencies
from stimulus import Stimulus
from optimizers import Standard, AdamOpt
import plotting_functions as pf
import copy
import cupy.linalg as LA
# Network/cell model fun... | {"hexsha": "bc6b421270fe175b21e6b89852526e458ea5184c", "size": 19142, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "gdgrant/Spiking-RNN", "max_stars_repo_head_hexsha": "47c1e822f20096080ff35692dc8a4de673d4222a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3,... |
function _is_cell_done(cell)
if cell.running_disabled
return true
else
return !cell.queued && !cell.running
end
end
"""
_is_notebook_done(notebook::Notebook)
Return whether all cells in the `notebook` have executed.
This method is more reliable than using `notebook.executetoken` becaus... | {"hexsha": "5fe0dd0fdaf6a3b6fe0e500642dc3caee6717c5f", "size": 1906, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/build.jl", "max_stars_repo_name": "ctrekker/PlutoStaticHTML.jl", "max_stars_repo_head_hexsha": "7ec26a63af6bb32feb3b05f22496b0cec74a3445", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
While named after our neighbors to the north, the Canada Goose is a variety of geese goose that spend quite a bit of their lives around the Davis area. There are a few places where these Birds and Bird Watching birds seem to have taken up year round residence.
The California National Primate Research Center Primate C... | {"hexsha": "ecfa396cc825677c9fc26421b17d6b730bf570fb", "size": 724, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Canada_Geese.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#importing libraries
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
#importing data set
dataset = pd.read_csv('Data.csv')
x = dataset.iloc[:,:-1].values
y = dataset.iloc[:,3].values
#handling missing values
imputer= SimpleImputer(missing_values=np.nan, strategy='mean')
imputer=impute... | {"hexsha": "7198099921d87af7efcc07439f8dfe2e2c9de591", "size": 374, "ext": "py", "lang": "Python", "max_stars_repo_path": "Handling Missing values/Imputer.py", "max_stars_repo_name": "Anish-AV/Data-Preprocessing", "max_stars_repo_head_hexsha": "0b3165dc2934c2d9578de1d1629a583d6479d3ba", "max_stars_repo_licenses": ["MIT... |
import numpy as np
import cv2
def flo(img):
fimg = np.fft.fft2(img)
fsimg = np.fft.fftshift(fimg)
afsimg = np.abs(fsimg) / np.max(np.abs(fsimg))
return fsimg, afsimg
def getpic(x_coefficient, y_coefficient, linenum):
pic = np.ones([256, 256])
for x in range(pic.shape[0]):
... | {"hexsha": "2e70e5115802873a9bd396fe24fd71a8ced69101", "size": 1128, "ext": "py", "lang": "Python", "max_stars_repo_path": "MoireTest/Moirefft.py", "max_stars_repo_name": "YuLingFengSCNU2017/MoireFitting", "max_stars_repo_head_hexsha": "8fb72f4892e8cf2eb5bdb03474c42dedebfd8640", "max_stars_repo_licenses": ["MIT"], "max... |
(*
Author: René Thiemann
License: LGPL
*)
section \<open>Show for Complex Numbers\<close>
text \<open>We print complex numbers as real and imaginary parts. Note that by transitivity, this theory
demands that an implementations for \textit{show-real} is available, e.g., by using
one of the theo... | {"author": "glimonta", "repo": "thesis", "sha": "1ef0e434ea7e98c4eb29ffe7bde668cb1951e4ed", "save_path": "github-repos/isabelle/glimonta-thesis", "path": "github-repos/isabelle/glimonta-thesis/thesis-1ef0e434ea7e98c4eb29ffe7bde668cb1951e4ed/src/Lib/Show/Show_Complex.thy"} |
# Model brings together the network, the loss function, the feed of
# training images, and a training loop
import tensorflow as tf
from PIL import Image
import numpy as np
import os
from feed import Feed
from architecture import GAN
from utils import pixels01, pixels11, tile
# print and flush
def printnow(x, end='... | {"hexsha": "078f35c9ac565bfbf84ec370c729386c5ed2f189", "size": 10780, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "ReidWilliams/GANs", "max_stars_repo_head_hexsha": "e04cc40953bb9d2a173f9f1c066081beed95f563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_sta... |
Threads.nthreads() == 20 ||
error("Doc build on Noctua should be run with 20 Julia threads!")
println("--- :julia: Instantiating project")
using Pkg
Pkg.activate("..")
Pkg.instantiate()
Pkg.activate(".")
Pkg.instantiate()
push!(LOAD_PATH, joinpath(@__DIR__, ".."))
deleteat!(LOAD_PATH, 2)
println("+++ :julia: Buildi... | {"hexsha": "74ecc416cd02ebda16953619c93ed9da7de609a9", "size": 357, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/build_docs.jl", "max_stars_repo_name": "carstenbauer/Pinning.jl", "max_stars_repo_head_hexsha": "61735eea175eb28f810892dda84e93cfec8bd074", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
CallbackFunction()
Set a generic Xpress callback function.
"""
struct CallbackFunction <: MOI.AbstractCallback end
function MOI.set(model::Optimizer, ::CallbackFunction, ::Nothing)
if model.callback_data !== nothing
removecboptnode(model.inner, C_NULL, C_NULL)
model.callback_data = nothin... | {"hexsha": "f200b9a85a00cac2f8495809d8659c384c43baae", "size": 9050, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MOI/MOI_callbacks.jl", "max_stars_repo_name": "vfegger/Xpress.jl", "max_stars_repo_head_hexsha": "0f5f1bb8f3df535a6e88a9628d5708eafca50bd2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
The model inference code in this file is modified from
https://gist.github.com/fyr91/83a392ffd22342d4e5f8866b01fafb30 Thanks to the
original authur: fyr91
"""
from onnx_tf.backend import prepare
import cv2
import numpy as np
import onnx
import onnxruntime as ort
def area_of(left_top, right_bottom):
... | {"hexsha": "fa89991638d1a4078a068da92613bbbb0205b185", "size": 5793, "ext": "py", "lang": "Python", "max_stars_repo_path": "face-tracking/face_detection.py", "max_stars_repo_name": "rossning92/rpi-robot", "max_stars_repo_head_hexsha": "c92ae66e27533a5f80ba6ac3ecfe2351337681fa", "max_stars_repo_licenses": ["MIT"], "max_... |
import matplotlib.pyplot as plt
import numpy as np
import os
def write_data(file):
data = []
for line in file:
list_line = line.split(" ")
if list_line[0] == "Time":
data.append(float(list_line[2]))
return np.array(data)
my_path = os.path.abspath(os.path.dirname(__file__))
path... | {"hexsha": "b524807989e02ec4c96bcc09792887d8906847c3", "size": 3816, "ext": "py", "lang": "Python", "max_stars_repo_path": "plots.py", "max_stars_repo_name": "bubogunz/AAlab1", "max_stars_repo_head_hexsha": "254d11deb69973c9fa6a3ef77300f93c5345eeb6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "... |
# Authors: Pearu Peterson, Pauli Virtanen, John Travers
"""
First-order ODE integrators.
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
... | {"hexsha": "76efa8c617de3bacaae4d2bd792b4f984c976560", "size": 25735, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/integrate/ode.py", "max_stars_repo_name": "lesserwhirls/scipy-cwt", "max_stars_repo_head_hexsha": "ee673656d879d9356892621e23ed0ced3d358621", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
#include "f1_datalogger/udp_logging/common/rebroadcast_handler_2018.h"
#include <boost/bind.hpp>
#include <iostream>
deepf1::RebroadcastHandler2018::RebroadcastHandler2018()
{
std::cout << "Constructing Rebroadcast Handler" << std::endl;
}
deepf1::RebroadcastHandler2018::~RebroadcastHandler2018()
{
}
void handle_send(... | {"hexsha": "6a48efdeb8c835a275c5a30e97472aff987466ca", "size": 5223, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "data-logger/src/udp_logging/common/rebroadcast_handler_2018.cpp", "max_stars_repo_name": "linklab-uva/deepracing", "max_stars_repo_head_hexsha": "fc25c47658277df029e7399d295d97a75fe85216", "max_star... |
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.math import sum as _sum
from chainer.utils import array
from chainer.utils import type_check
class BatchL2NormSquared(function_node.FunctionNode):
def check_type_forward(self, in_typ... | {"hexsha": "8a7df8e3f5c62150cb3912ea62d7a8c636b6dbad", "size": 2593, "ext": "py", "lang": "Python", "max_stars_repo_path": "chainer/functions/math/batch_l2_norm_squared.py", "max_stars_repo_name": "LuoYuanke/PrivChainer", "max_stars_repo_head_hexsha": "758d765c7903f6913cfd58c21db069d5f2a12203", "max_stars_repo_licenses... |
'''
FastText Recommender Module
'''
import numpy as np
from gensim.models import FastText
from gensim import matutils
class Recommender:
'''FastText Recommender Class'''
def __init__(self, path):
self.model = FastText.load(path)
def doc2words(self, doc, num=10):
'''
입력된 토큰, 토큰 리스트... | {"hexsha": "aae8aa41787421dadfadaac7d19f7ede02338bb7", "size": 2605, "ext": "py", "lang": "Python", "max_stars_repo_path": "SIGNUS/modules/recommender/fasttext/__init__.py", "max_stars_repo_name": "837477/SIGNUS", "max_stars_repo_head_hexsha": "cd395dfd45d2c36d09ec9a8069e6e52e19f058e8", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma degree_leI:
assumes "(\<And>i. pdevs_apply y i = 0 \<Longrightarrow> pdevs_apply x i = 0)"
shows "degree x \<le> degree y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. degree x \<le> degree y
[PROOF STEP]
proof cases
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. ?P \<Longrightarrow> degr... | {"llama_tokens": 709, "file": "Affine_Arithmetic_Affine_Form", "length": 9} |
"""Handle Fast Fourier Transform (FFT) for filter parameterization."""
import numpy as np
from astropy import units as u
from astropy.modeling.models import custom_model, Sine1D
from astropy.table import Table
from synphot.compat import NUMPY_LT_1_17
from synphot.models import Empirical1D
from synphot.spectrum import... | {"hexsha": "909ec88904e91ae26c9a405270f56b01e4ed488d", "size": 7798, "ext": "py", "lang": "Python", "max_stars_repo_path": "synphot/filter_parameterization/filter_fft.py", "max_stars_repo_name": "spacetelescope/pysynphot_DONOTUSE", "max_stars_repo_head_hexsha": "2a382d7bdf29cc4a1e6b69e59d5c1d0f82dabffc", "max_stars_rep... |
Require Import Algebra.Utils Algebra.SetoidCat Algebra.Monad Algebra.Monoid Algebra.Alternative Algebra.NearSemiRing Algebra.Monad.ContT Algebra.Alternative Algebra.Functor Algebra.Applicative PairUtils SetoidUtils Tactics Algebra.SetoidCat.UnitUtils Algebra.Monoid.ArrUtils Algebra.Monad.Utils.
Require Import Relat... | {"author": "xu-hao", "repo": "CertifiedQueryArrow", "sha": "8db512e0ebea8011b0468d83c9066e4a94d8d1c4", "save_path": "github-repos/coq/xu-hao-CertifiedQueryArrow", "path": "github-repos/coq/xu-hao-CertifiedQueryArrow/CertifiedQueryArrow-8db512e0ebea8011b0468d83c9066e4a94d8d1c4/Algebra/Monad/StoreHeap.v"} |
import numpy as np
import pandas as pd
import unittest
import io
import sys
from context import grama as gr
## Test cohort shapley
##################################################
class TestCohortShapley(unittest.TestCase):
def setUp(self):
pass
def test_cohort_shapley(self):
df_data = gr.d... | {"hexsha": "e3be4d6b0e42ab23fbfd8796d6326d40ba3e4c67", "size": 633, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_shapley.py", "max_stars_repo_name": "natalia-rubio/py_grama", "max_stars_repo_head_hexsha": "968c1c0238d7165de3b1b96534791feacc4aa960", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Written by Jimmy Zhong (zhongj2@carleton.edu), Carleton '23 under Professor Rika Anderson; date: August 31nd 2021
'''
cd /workspace/data/zhongj/Transposase_Project/integron_finder_tara_contig
python3
exec(open('find_cassette_function_and_pnps_august24.py').read())
'''
import glob
import os
import subprocess
import sy... | {"hexsha": "040938787749717596c96c1494d950570e71d00a", "size": 11411, "ext": "py", "lang": "Python", "max_stars_repo_path": "pn-ps-and-mapping/metagenome_function_and_pnps.py", "max_stars_repo_name": "carleton-spacehogs/transposase-deep-ocean", "max_stars_repo_head_hexsha": "cf782acec39f902c563ff83f6e74c2200bf7f743", "... |
SUBROUTINE SET_DIR( DIR_OUT )
!***********************************************************************
!* Sets an Output Directory if Specified or if Local is Read-Only
!*
!* Language: Fortran
!*
!* Platform: Windows
!*
!* Compiler: Fortran
!*
!* Author: Stuart G. Mentzer
!* Andrew Orndorff
!*
!* Date: 2... | {"hexsha": "50acf9a5642dbd1e30d41d25faff3ad848720626", "size": 3822, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/Windows/set_dir.for", "max_stars_repo_name": "DeadParrot/NHTSA-Tools", "max_stars_repo_head_hexsha": "e8de2d5aa3d6de96a858ae70ecc4e75fa3d80ac4", "max_stars_repo_licenses": ["MIT"], "max_... |
import torch.nn as nn
import numpy as np
from box import Box
from pathlib import Path
from src.model.nets.base_net import BaseNet
class MyNet(BaseNet):
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = ou... | {"hexsha": "9d89da2616af9b9edb554d87417c37343b677cc7", "size": 846, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/model/test_net.py", "max_stars_repo_name": "Tung-I/nips2019_template", "max_stars_repo_head_hexsha": "a1fcf35b7633d192d2706a533731cb8c457ac230", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#! /usr/bin/python3
from numpy.random import rand
from mat_util import load, save, save_text
newmat = True;
datadir = 'data'
p = 23 # number of helper processes
# we use a tall thin matrix.
if newmat:
m = p*500 # p helpers will each solve an mxn system each iteration
n = 150
A = rand(m,n)
b = rand(m,1... | {"hexsha": "379720672b2331f3599823a380a4c734bda59c13", "size": 806, "ext": "py", "lang": "Python", "max_stars_repo_path": "admm/pre_proc.py", "max_stars_repo_name": "ddrake/convex_m", "max_stars_repo_head_hexsha": "6e506133c03bb1e0cf38143a907ac595082d524c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#
__doc__ = """
Defines miscellaneous functions
"""
import numpy as np
from configobj import ConfigObj
import copy
import os
import sys
import inspect
import re
import string
import random
import yaml
import datetime
from collections import OrderedDict
# FILE ------------------------------------------
def assert_fi... | {"hexsha": "671fe39283e3eb71255b0c1c6843245b8ae9e690", "size": 12394, "ext": "py", "lang": "Python", "max_stars_repo_path": "DaVE/dave/common/misc.py", "max_stars_repo_name": "upscale-project/hslink_phy", "max_stars_repo_head_hexsha": "741f78da673d2e633da05d292aa6645125ebae32", "max_stars_repo_licenses": ["BSD-3-Clause... |
Bulk Mailing is a incredibly complicated process that companies and the odd individual go through to send people things in the mail at discounted prices.
To do a Bulk Mailing you first need a license: http://pe.usps.com/businessmail101/postage/mailingPermit.htm
You then need to decide what kind of bulk mailing best ... | {"hexsha": "25d9391dec4475c83203bedc3b6f819d0fb43568", "size": 1845, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Bulk_Mail_Center.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
! ###################################################################
! Copyright (c) 2013-2022, Marc De Graef Research Group/Carnegie Mellon University
! All rights reserved.
!
! Redistribution and use in source and binary forms, with or without modification, are
! permitted provided that the following conditions are ... | {"hexsha": "1c1485567a2ec78ac9ad7eb523c7abaa8fc0b131", "size": 88577, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/EMsoftOOLib/mod_kvectors.f90", "max_stars_repo_name": "EMsoft-org/EMsoftOO", "max_stars_repo_head_hexsha": "052aefc32a603b0dcb830901fcf14535afc15676", "max_stars_repo_licenses": ["Unlice... |
import numpy as np
from scipy.io import loadmat
from scipy.optimize import minimize
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sn
from sklearn.svm import SVC
import timeit
def preprocess():
"""
Input:
Although this function doesn't have any input, you are required to load... | {"hexsha": "703288d7d0444dd1eca8aa7f2d2fc07f8d1b391f", "size": 16740, "ext": "py", "lang": "Python", "max_stars_repo_path": "SVM and Logistic Regression/script.py", "max_stars_repo_name": "Muthu2093/Machine-Learning-Applications", "max_stars_repo_head_hexsha": "bb171ff2bfdcb5af64403ae7f63fe96572d63963", "max_stars_repo... |
#######################################################################
# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
# Permission given to modify the code as long as you keep this #
# declaration at the top #
################################... | {"hexsha": "ed0c3ab8b493b3820e04082d138f2337e696311d", "size": 2213, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_rl/utils/normalizer.py", "max_stars_repo_name": "Louis-Bagot/DeepRL", "max_stars_repo_head_hexsha": "0b152c52bbba90362c8276c223fee3f9a464eb32", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#pragma once
#include <polyfem/Types.hpp>
#include <Eigen/Dense>
#include <Eigen/Sparse>
namespace polyfem {
// Show some stats about the matrix M: det, singular values, condition number, etc
void show_matrix_stats(const Eigen::MatrixXd &M);
template<typename T>
T determinant(const Eigen::Matrix<T, Eigen::Dyn... | {"hexsha": "036ebd67b368d514d2aac3e11560f7f49294cd4b", "size": 949, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/utils/MatrixUtils.hpp", "max_stars_repo_name": "ldXiao/polyfem", "max_stars_repo_head_hexsha": "d4103af16979ff67d461a9ebe46a14bbc4dc8c7c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
'''
Organize nav curves of multiple funds into the multi-timeseries objects offered by gluonts.
'''
import os
import inspect
import sys
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from gluonts.dataset.commo... | {"hexsha": "a330ae777fcf35f5219a4dd18cb0c852d3719fde", "size": 1571, "ext": "py", "lang": "Python", "max_stars_repo_path": "reference/multivariate_dataset_examples.py", "max_stars_repo_name": "jeffrey82221/gluonts_fund_price_forecast", "max_stars_repo_head_hexsha": "fed7c484c4dba663201f9cf96aa86ca98119b54c", "max_stars... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np
import iris
from matplotlib.path import Path
from lagranto.trajectory import load
import datetime
from mymodule import grid, convert, interpolate
import matplotlib.pyplot as plt
from lagranto import caltra,... | {"hexsha": "33c1ccfefd57f8693e96e5e20cf48d4c60426045", "size": 19459, "ext": "py", "lang": "Python", "max_stars_repo_path": "wcb_outflow/jakeb_old_code/July1.py", "max_stars_repo_name": "LSaffin/wcb_airmass", "max_stars_repo_head_hexsha": "996a8907fb2eaedb3e9e27e182fca19e5c2db9bd", "max_stars_repo_licenses": ["MIT"], "... |
/**
* Copyright (C) 2015 Dato, Inc.
* All rights reserved.
*
* This software may be modified and distributed under the terms
* of the BSD license. See the LICENSE file for details.
*/
#ifndef GRAPHLAB_SFRAME_SARRAY_FILE_FORMAT_V1_HPP
#define GRAPHLAB_SFRAME_SARRAY_FILE_FORMAT_V1_HPP
#include <string>
#include <me... | {"hexsha": "7fed8b278d0758412f70f7a585f3054c490ee11a", "size": 24257, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "oss_src/sframe/sarray_file_format_v1.hpp", "max_stars_repo_name": "parquette/ParFrame", "max_stars_repo_head_hexsha": "0522aa6afdf529b3e91505b70e918f1500aae886", "max_stars_repo_licenses": ["BSD-3-... |
C Copyright(C) 1999-2020 National Technology & Engineering Solutions
C of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with
C NTESS, the U.S. Government retains certain rights in this software.
C
C See packages/seacas/LICENSE for details
C==============================================================... | {"hexsha": "af74de7fc0729321ada721e6c4fa871b38c01976", "size": 2145, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/grepos/gp_mapev.f", "max_stars_repo_name": "jschueller/seacas", "max_stars_repo_head_hexsha": "14c34ae08b757cba43a3a03ec0f129c8a168a9d3", "max_stars_repo_licenses": ["... |
__author__ = 'Tadas'
import cv2
import numpy as np
import glob
# where to find the results.mat:
yt_dir = r"C:\Users\Tadas\Dropbox\AAM\test data\ytceleb_annotations_CVPR2014"
vids = ["0035_02_003_adam_sandler",
"0042_02_010_adam_sandler",
"0292_02_002_angelina_jolie",
"0293_02_003_angelina_jo... | {"hexsha": "1906fb09028ba1ebb5abec4939c4161fc1889dda", "size": 938, "ext": "py", "lang": "Python", "max_stars_repo_path": "CLM-framework/matlab_runners/yt_prep/extract_vids.py", "max_stars_repo_name": "OAkyildiz/falcon549A", "max_stars_repo_head_hexsha": "69d91a1c3729e537ce606ecda87be70f566dc536", "max_stars_repo_licen... |
from pylearn2.train_extensions import TrainExtension
from pylearn2.datasets.preprocessing import CentralWindow
from pylearn2.utils.rng import make_np_rng
from skimage.transform import AffineTransform, warp, resize
import skimage
import numpy as np
from pylearn2.datasets import preprocessing
import random
import... | {"hexsha": "f020436143fdca6485b2bea4e9290c1d07ec68f1", "size": 4298, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/external/repositories_2to3/132209/datasciencebowl-master/realtime_augment.py", "max_stars_repo_name": "Keesiu/meta-kaggle", "max_stars_repo_head_hexsha": "87de739aba2399fd31072ee81b391f9b7a63... |
# This test code was written by the `hypothesis.extra.ghostwriter` module
# and is provided under the Creative Commons Zero public domain dedication.
import numpy as np
import pytest
import torch
from hypothesis import assume, given, settings
from hypothesis import strategies as st
from hypothesis.extra.numpy import ar... | {"hexsha": "b5460e1d4d85203f2d6270592e18087bd5355a76", "size": 2040, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/PrivacyRaven/tests/test_utils_query.py", "max_stars_repo_name": "paragrapharamus/msdp", "max_stars_repo_head_hexsha": "7b91d7a7f1ccf8e3bcd21a0c2a5b55746b2d5cb6", "max_stars_repo_licenses": ["... |
import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
def make_upconv_net(in_channels, in_h, upconv_specs):
upconv_list = nn.ModuleList()
kernel_sizes = upconv_specs['kernel_sizes']
num_channels = upconv_specs['num_channels'... | {"hexsha": "dbaa2e4afcec1663746b07e9f0de657e75a541c4", "size": 2440, "ext": "py", "lang": "Python", "max_stars_repo_path": "gen_models/__init__.py", "max_stars_repo_name": "yifan-you-37/rl_swiss", "max_stars_repo_head_hexsha": "8b0ee7caa5c1fa93860916004cf4fd970667764f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
__author__ = "Md. Ahsan Ayub"
__license__ = "GPL"
__credits__ = ["Ayub, Md. Ahsan", "Johnson, Will",
"Siraj, Ambareen"]
__maintainer__ = "Md. Ahsan Ayub"
__email__ = "mayub42@students.tntech.edu"
__status__ = "Prototype"
# Modular function to apply decision tree classifier
def DT_classifier(X, Y, numFo... | {"hexsha": "54678f16985ecc2243e82a108caf9920e75abc1d", "size": 17268, "ext": "py", "lang": "Python", "max_stars_repo_path": "supervised_learning.py", "max_stars_repo_name": "duyndh/adversarial_ml_ids", "max_stars_repo_head_hexsha": "d963303b02dc52ad1233602abc71278a45e69341", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#coding=utf-8
import rospy
from std_msgs.msg import Header
from sensor_msgs.msg import Image, NavSatFix
from map_generator.msg import tjy
from nav_msgs.msg import Path
import numpy as np
import time
from googleplaces import GooglePlaces
import googlemaps
import time
import sys
import math
from math import cos,sin,tan,s... | {"hexsha": "c10e6776d7422824d2c9657228b1b2eac16126fe", "size": 17565, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/nearbyGPS.py", "max_stars_repo_name": "germal/Semantic_SLAM-1", "max_stars_repo_head_hexsha": "0284b3f832ca431c494f9c134fe46c40ec86ee38", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
% !TEX program = xelatex
\documentclass{resume}
\usepackage{zh_CN-Adobefonts_external} % Simplified Chinese Support using external fonts (./fonts/zh_CN-Adobe/)
\usepackage{zh_CN-Adobefonts_internal} % Simplified Chinese Support using system fonts
\begin{document}
\pagenumbering{gobble} % suppress displaying page numb... | {"hexsha": "dbc1be6627f91d262b0af216b0a0439579a986a5", "size": 4369, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "resume-zh_CN.tex", "max_stars_repo_name": "wuyifan18/resume", "max_stars_repo_head_hexsha": "5fa002530cb40b5540d424fbebcf59707d6f5430", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# 扩散映射(DiffusionMaps)
## 符号定义
|符号|概念|
|:--:|:--:|
|$\pmb{x}$|样本点|
|$X$|样本集合|
|$N$|样本总数|
|$G$|有限图|
|$S$|有限图元素集合|
|$W$|权重矩阵|
|$D$|度矩阵|
|$P$|转移矩阵|
|$M$|距离矩阵|
|$d$|距离|
|$m$|降维后维度|
## 概念
ISOMAP通过替换欧氏距离为最短路径距离实现了比较好的降维效果，但是ISOMAP有一个非常明显的缺陷：对噪声较敏感。噪声很有可能改变两个点之间的最短路径以至于影响相当多样本对的距离度量，从而得到错误的降维结果。
为了抵抗噪声的影响，一个非常简单的思路就是取多条路径的... | {"hexsha": "009d25d93c40efd85ddf494fe423bcdca9e2b6f3", "size": 6923, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "10_DiffusionMaps/DiffusionMaps.ipynb", "max_stars_repo_name": "koolo233/dimensionality_reduction_python", "max_stars_repo_head_hexsha": "452a927772c546f68d6a63e96cdb017b23e4077c", "ma... |
from flask import Flask, request
from gensim import corpora, models, similarities
import csv
import numpy as np
import logging
import os
import sys
import gzip
import pkg_resources
from pkg_resources import DistributionNotFound
import pathlib
logging.basicConfig(
handlers=[logging.FileHandler(__file__ + ".log", "... | {"hexsha": "a123178fcbbae70143c6ca1d867f644b951ca7c4", "size": 23132, "ext": "py", "lang": "Python", "max_stars_repo_path": "rdf2vec/src/main/resources/python_server.py", "max_stars_repo_name": "EDAO-Project/DBpediaEmbedding", "max_stars_repo_head_hexsha": "457b66beab5bbfc37b55cab7534ab66c7d00c7bf", "max_stars_repo_lic... |
import os
import sys
sys.path.append(os.getcwd())
from model import ValueNetwork
from env import JointState
import torch
import numpy as np
def test_rotate():
vn = ValueNetwork(14, [150, 150, 100], kinematic=False)
state = JointState(2, 2, 0, 1, 0.3, 2, 4, 1, 0, 4, 2, 2, 0, 0.3)
state = torch.Tensor(state... | {"hexsha": "d6d4bca7889a87f778231654c84e8dd55ca25e8a", "size": 896, "ext": "py", "lang": "Python", "max_stars_repo_path": "CADRL-master/test/test_model.py", "max_stars_repo_name": "NeuEIRG/Collision-Avoidance-with-DRL", "max_stars_repo_head_hexsha": "7b641410eba6fe7cba9ada29307c4a2a73e3d0d8", "max_stars_repo_licenses":... |
(*
File: Anonymous_PAPP.thy
Author: Manuel Eberl, University of Innsbruck
*)
section \<open>Anonymous Party Approval Rules\<close>
theory Anonymous_PAPP
imports Complex_Main "Randomised_Social_Choice.Order_Predicates" PAPP_Multiset_Extras
begin
text \<open>
In this section we will define (anonymous) P-A... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/PAPP_Impossib... |
"""
parameters lat/long bounding box (top/left bottom/right) and base
convert to row/col limits - get decimal lat long min /max multiple y 3 and round to integer
use numpy to read in the hgt files
extract the subarrays required, merge into a single array, add base, flip and output to stdout
"""
import s... | {"hexsha": "d4b8bac27acac3900472c658dc41ad4415511661", "size": 2351, "ext": "py", "lang": "Python", "max_stars_repo_path": "extract_strm90.py", "max_stars_repo_name": "KitWallace/terrain", "max_stars_repo_head_hexsha": "c9a1a7e11fbcc470e659c4b2c5b46d3e4e2c93cc", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count"... |
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