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# ---
# title: 35. Search Insert Position
# id: problem35
# author: zhwang
# date: 2022-02-12
# difficulty: Easy
# categories: Array, Binary Search
# link: <https://leetcode.com/problems/search-insert-position/description/>
# hidden: true
# ---
#
# Given a sorted array of distinct integers and a target value, return t... | {"hexsha": "4548cbd2ba5220f4645ed1238a705baa9fedc0d7", "size": 1914, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-02/35.search-insert-position.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_r... |
[STATEMENT]
lemma sums_emeasure':
assumes [measurable]: "\<And>x. B x \<in> sets M"
assumes "\<And>x y. x \<noteq> y \<Longrightarrow> emeasure M (B x \<inter> B y) = 0"
shows "(\<lambda>x. emeasure M (B x)) sums emeasure M (\<Union>x. B x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x. emeasur... | {"llama_tokens": 2247, "file": "Minkowskis_Theorem_Minkowskis_Theorem", "length": 24} |
!* Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
!* See https://llvm.org/LICENSE.txt for license information.
!* SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
!* Attribute oriented initializations using intrinsic functions
program e10
interface
subroutine copy_str_to_re... | {"hexsha": "71d916f94c463b185c3b3adae3f07a24fcba8996", "size": 26536, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/f90_correct/src/ei10.f90", "max_stars_repo_name": "DominikAdamski/flang", "max_stars_repo_head_hexsha": "aa9a6ee997ff8a41e71ca7547481df69b6a2c7c6", "max_stars_repo_licenses": ["Apache-2.0"... |
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | {"hexsha": "8a527e72fb9ac806254d2c055fc283c938cc55b4", "size": 21415, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/transpiler/inference_transpiler.py", "max_stars_repo_name": "ysh329/Paddle", "max_stars_repo_head_hexsha": "50ad9046c9a440564d104eaa354eb9df83a35678", "max_stars_repo_licenses... |
import unittest
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator, clone
from sklearn.compose import make_column_transformer
from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.imputation.SimpleImputer import SimpleImputer
class TestSimpleIm... | {"hexsha": "540cfef9eaefb0ce4c7d217f3649bfed9fcb5863", "size": 9338, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_pipeline/components/test_imputers.py", "max_stars_repo_name": "LMZimmer/Auto-PyTorch_refactor", "max_stars_repo_head_hexsha": "ac7a9ce35e87a428caca2ac108b362a54d3b8f3a", "max_stars_repo_... |
\chapter{Distributed Shared Persistent Memory}
\input{hotpot/introduction}
\input{hotpot/motivation}
\input{hotpot/dspm}
\input{hotpot/design}
\input{hotpot/data}
\input{hotpot/xact}
\input{hotpot/crash}
\input{hotpot/network}
\input{hotpot/applications}
\input{hotpot/results}
\input{hotpot/related}
\input{hotpot/conc... | {"hexsha": "78404434dda1df9043e99f8ce0501c28e48e6456", "size": 325, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "hotpot.tex", "max_stars_repo_name": "lastweek/2022-UCSD-Thesis", "max_stars_repo_head_hexsha": "859886a5c8524aa73d7d0784d5d695ec60ff1634", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "... |
/**
* \author Sylvain Marsat, University of Maryland - NASA GSFC
*
* \brief C header for structures for EOBNRv2HM reduced order model (non-spinning version).
* See CQG 31 195010, 2014, arXiv:1402.4146 for details on the reduced order method.
* See arXiv:1106.1021 for the EOBNRv2HM model.
*
* Borrows from the S... | {"hexsha": "1fbb2f13689e693b9157c798fe9722a1df7a7f4e", "size": 6529, "ext": "h", "lang": "C", "max_stars_repo_path": "EOBNRv2HMROM/EOBNRv2HMROMstruct.h", "max_stars_repo_name": "titodalcanton/flare", "max_stars_repo_head_hexsha": "4ffb02977d19786ab8c1a767cc495a799d9575ae", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
function [f, df] = func_split_spcp(x,Y,params,errFcn)
% [f, df] = func_split_spcp(x,Y,params,errFunc)
% [errHist] = func_split_spcp();
% [S] = func_split_spcp(x,Y,params,'S');
%
% Compute function and gradient of split-SPCP objective
%
% lambda_L/2 (||U||_F^2 + ||V||_F^2) + phi(U,V)
%
% where
%
% phi(U,V) = min_S .... | {"author": "stephenbeckr", "repo": "fastRPCA", "sha": "44dfee56f142ebffe5a7003578868e84bd4330b7", "save_path": "github-repos/MATLAB/stephenbeckr-fastRPCA", "path": "github-repos/MATLAB/stephenbeckr-fastRPCA/fastRPCA-44dfee56f142ebffe5a7003578868e84bd4330b7/utilities/func_split_spcp.m"} |
import pandas as pd
import numpy as np
def load_titanic():
df = pd.read_csv('titanic.csv')
df.columns = df.columns.str.replace(" ", "_")
df.columns = df.columns.str.replace("/", "_")
df['Name'] = df['Name'].str.replace("'", " ")
return df
| {"hexsha": "5eb9fb15fdefffa192f838dd08a24b29890de7b7", "size": 265, "ext": "py", "lang": "Python", "max_stars_repo_path": "curriculum/unit-3-data-engineering/sprint-2-sql-and-databases/module2-sql-for-analysis/df_utils.py", "max_stars_repo_name": "BrianThomasRoss/lambda-school", "max_stars_repo_head_hexsha": "6140db5cb... |
# https://pytorch.org/docs/stable/notes/randomness.html
# 1. seed_everything
# 2. deterministic algorithm
# 3. seed worker_init (you should also set generator)
import torch
import random
import numpy as np
def seed_everything(seed, deterministic):
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(... | {"hexsha": "20fa89de7b825d6869a708d065d756d0d06f72bf", "size": 713, "ext": "py", "lang": "Python", "max_stars_repo_path": "pt_seed.py", "max_stars_repo_name": "hankyul2/PytorchSnippets", "max_stars_repo_head_hexsha": "ccfafbc3a5d0db18d2b162cad87d003a8396ac89", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
module Polymer
using LinearAlgebra
using ArgCheck
using REPL: symbol_latex
using LaTeXStrings
using YAML
include("utils.jl")
export
unicodesymbol2string,
reverse_dict
include("parameters.jl")
export
AbstractParameter,
PolymerParameter
export
χParam,
NParam,
χNParam,
fParam,
ϕParam... | {"hexsha": "f230cb3695af1df782818d16cf347365f473d028", "size": 1709, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Polymer.jl", "max_stars_repo_name": "liuyxpp/Polymer.jl", "max_stars_repo_head_hexsha": "a46e64d88f926b22af1f61c53bc2c612e614dc1c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
from manim2.animation.animation import Animation
from manim2.animation.composition import Succession
from manim2.mobject.types.vectorized_mobject import VMobject
from manim2.mobject.mobject import Group
from manim2.utils.bezier import integer_interpolate
from manim2.utils.config_ops import digest_config
from manim2.uti... | {"hexsha": "b0df1f0f317c4a77cb296184afe03a1fa0e990b2", "size": 10106, "ext": "py", "lang": "Python", "max_stars_repo_path": "manim2/animation/creation.py", "max_stars_repo_name": "tigerking/manim2", "max_stars_repo_head_hexsha": "93e8957e433b8e59acb5a5213a4074ee0125b823", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/env python
def load_velocity(filename):
import os
if not os.path.exists(filename):
return None
from numpy import zeros
from vtk import vtkPolyDataReader, vtkCellDataToPointData
reader = vtkPolyDataReader()
reader.SetFileName(filename)
reader.ReadAllVectorsOn()
rea... | {"hexsha": "063be454e6d367b7901846c8e4011a01cd9ebc91", "size": 2344, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot-vtk.py", "max_stars_repo_name": "mrklein/vtk-plot", "max_stars_repo_head_hexsha": "28c89fa03382e226f8c08e2da7d6c37e6388e69c", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 3, "... |
#!/usr/bin/env python
import argparse
import pickle
import h5py
from keras import optimizers
from keras.callbacks import ModelCheckpoint
from keras.layers import Activation, add, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Input, ZeroPadding2D
from keras.models import load_model, Model
from keras.regularizers... | {"hexsha": "8f66a163bf1e5878e2474fa634b0488a8aa1b816", "size": 3589, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "Markus-Goetz/block-prediction", "max_stars_repo_head_hexsha": "3f89d17d449f023d60fae5ec6bd712cb6cc8cb50", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma may_lock_unlock_lock_conv [simp]:
"has_lock l t \<Longrightarrow> may_lock (unlock_lock l) t = may_lock l t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. has_lock l t \<Longrightarrow> may_lock (unlock_lock l) t = may_lock l t
[PROOF STEP]
by(cases l)(auto split: if_split_asm nat.splits elim!: ... | {"llama_tokens": 140, "file": "JinjaThreads_Framework_FWLock", "length": 1} |
module TDAmeritrade
export TD_auth, price_history, get_quotes, get_movers, market_hours
using HTTP, JSON3, DelimitedFiles, Dates
using Pipe: @pipe
include("auth.jl")
include("movers.jl")
include("price_history.jl")
include("quotes.jl")
include("market_hours.jl")
function construct_api(path, query=NamedTuple())
... | {"hexsha": "311cc96ef868f000ff0531ff4bd0c7890f436186", "size": 748, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TDAmeritrade.jl", "max_stars_repo_name": "daryoush/TDAmeritrade.jl", "max_stars_repo_head_hexsha": "2724d16587ead7992487cc57c7aacf8f8d8a0612", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Implementation of the base classes for the ORSO header.
"""
import datetime
import json
import os.path
import pathlib
import re
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from copy import deepcopy
from dataclasses import (_FIELD, _FIELD_INITVAR, _FIELDS, _HAS_DEFAULT... | {"hexsha": "43253bde022ab708305f2554ce07354caee9c412", "size": 25222, "ext": "py", "lang": "Python", "max_stars_repo_path": "orsopy/fileio/base.py", "max_stars_repo_name": "arm61/orsopy", "max_stars_repo_head_hexsha": "b53aee761ff040e21a73937322b453a39bf3eb9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import scipy.io as sio
import numpy as np
import csv
def si(activity):
# activity [956, n]
nb_imgs = activity.shape[0]
nb_neurons = activity.shape[1]
si = []
for n in np.arange(nb_neurons):
sfr = [ np.power(fr,2)/nb_imgs for fr in activity[:,n]]
sfr = np.array(sfr)
num = np.... | {"hexsha": "a946b5c5ab451edde3d66ed8176ec0a41f762230", "size": 992, "ext": "py", "lang": "Python", "max_stars_repo_path": "pretrained_feature_extraction/selectivity.py", "max_stars_repo_name": "elijahc/ML_V1", "max_stars_repo_head_hexsha": "2bdc6edfe63530415852b9fd95780244470b087d", "max_stars_repo_licenses": ["MIT"], ... |
C @(#)ermisare.f 20.4 6/27/97
C****************************************************************
C
C File: ermisare.f
C Purpose: Routine to print out missing area message together with
C the three nearest candidates.
C
C Author: Walt Powell Date: 2 June 1993
C Called by:
C
C***********************... | {"hexsha": "e4172de2b1e81fefa0e2d9b004e97f02587d1b83", "size": 1574, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/ermisare.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14,... |
[STATEMENT]
lemma fv_DISJ: "finite \<Q> \<Longrightarrow> fv (DISJ \<Q>) \<subseteq> (\<Union>Q \<in> \<Q>. fv Q)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite \<Q> \<Longrightarrow> fv (DISJ \<Q>) \<subseteq> \<Union> (fv ` \<Q>)
[PROOF STEP]
by (auto simp: DISJ_def dest!: fv_cp[THEN set_mp] split: if_spli... | {"llama_tokens": 136, "file": "Safe_Range_RC_Relational_Calculus", "length": 1} |
#=
Sudoku is a puzzle where you're given a partially-filled 9 by 9 grid with digits. The objective is to fill the grid with the constraint that every row, column, and box (3 by 3 subgrid) must contain all of the digits from 1 to 9.
Implement an efficient sudoku solver.
=#
| {"hexsha": "0cb5cce3b943148d7f137b503def1f27905d80be", "size": 274, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Solutions/problem54_sudokusolver.jl", "max_stars_repo_name": "DominiqueCaron/daily-coding-problem", "max_stars_repo_head_hexsha": "41234497aa3a2c21c5dff43d86e9153d9582cced", "max_stars_repo_licenses... |
from builtins import zip
import numpy as np
import matplotlib.pyplot as plt
from rubin_sim.maf.utils import percentileClipping
from .plotHandler import BasePlotter
__all__ = ['OneDBinnedData']
class OneDBinnedData(BasePlotter):
def __init__(self):
self.plotType = 'BinnedData'
self.objectPlotter =... | {"hexsha": "35ac3138b50cd4632bb12fa9cde60e671b0a26aa", "size": 3908, "ext": "py", "lang": "Python", "max_stars_repo_path": "rubin_sim/maf/plots/onedPlotters.py", "max_stars_repo_name": "RileyWClarke/flarubin", "max_stars_repo_head_hexsha": "eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a", "max_stars_repo_licenses": ["MIT"], ... |
# Doing the necessary import
import numpy as np
import pandas as pd
import tensorflow
import matplotlib.pyplot as plt
from matplotlib.image import imread
import cv2
import os
from os import listdir
from PIL import Image
from sklearn.preprocessing import label_binarize, LabelBinarizer
from tensorflow.keras.preprocessin... | {"hexsha": "4c43368d584c39d35eeb9668802bb34b634b9f1a", "size": 6194, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/training.py", "max_stars_repo_name": "dipesg/Plant-Disease-Detection", "max_stars_repo_head_hexsha": "474dc480060c9e618e0cb64dc85bbbf0b9f39e05", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy import (abs, arctan2, asarray, cos, exp, floor, log, log10,
arange, pi, sign, sin, sqrt, sum, tan, tanh)
from .go_benchmark import Benchmark
class Hansen(Benchmark):
r"""
... | {"hexsha": "0edf324060ab56a0bcc6abfab0c3d2f3aa97f5e6", "size": 11401, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/benchmarks/go_benchmark_functions/go_funcs_H.py", "max_stars_repo_name": "xu-hong-/scipy", "max_stars_repo_head_hexsha": "f737001cf0a75654efe09a1de5cdf5d1895bda59", "max_stars_repo_lic... |
import csv
import pandas as pd
import numpy as np
import itertools
from sklearn import metrics
from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc
import matplotlib.pyplot as plt
import json
import multiprocessing
import os
from tqdm import tqdm
from pathlib import Path
os.environ['TF_CPP_MIN... | {"hexsha": "384005715e69ebf05ba6492f56f0759108091e37", "size": 6466, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/Ensemble-Face-Recognition.py", "max_stars_repo_name": "khawar512/deepface", "max_stars_repo_head_hexsha": "ec6a41013f49e35ad13a9af1c75f34fef733cb8f", "max_stars_repo_licenses": ["MIT"], "max... |
\section{Trim and linearisation}
\subsection{Trimming}
The first step when designing the control system of an aircraft is to study the behavior of the aircraft due to control inputs or external disturbances from an equilibrium condition. If the aircraft we're not to be in equilibrium, deviation from the initial condit... | {"hexsha": "144418f82c3317cd2a32c3560d1bf3036182884d", "size": 7459, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/1_trim_and_linearization.tex", "max_stars_repo_name": "aarondewindt/afcs_assignment", "max_stars_repo_head_hexsha": "ef8e368c9c81c3dcba4193bd2193a68d5e2bd2f6", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
import numpy as np
import cv2
from commonFunctions_v07 import get_info_from_logfile
from commonFunctions_v07 import flip_horizontally
from commonFunctions_v07 import visualize_loss_history
from commonFunctions_v07 import RGB2YUV
# History
# v01 : Start
# v02 : add nb_images to read parameter
# v... | {"hexsha": "7997c6cdaa231567348b0c1910b469de5b0d9c05", "size": 4291, "ext": "py", "lang": "Python", "max_stars_repo_path": "archiveOldVersions/model_v10.py", "max_stars_repo_name": "remichartier/014_selfDrivingCarND_BehavioralCloningProject", "max_stars_repo_head_hexsha": "1dcaa7c5a937929d4481e5efbf7ccc856c04c4ff", "ma... |
import glob
import logging
import os
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, KFold, GridSearchCV
from tqdm import tqdm
from sourced.ml.cmd.args import handle_input_arg
from sourced.ml.models import Id2Vec
def lo... | {"hexsha": "911e61359073fc9c9f2608302408bf25cdee72ea", "size": 4593, "ext": "py", "lang": "Python", "max_stars_repo_path": "sourced/ml/cmd/id2role_eval.py", "max_stars_repo_name": "chubbymaggie/ast2vec", "max_stars_repo_head_hexsha": "b9058681fdd30ad0a7f4fa90a24594b615340a90", "max_stars_repo_licenses": ["Apache-2.0"],... |
def swarpcom(imkey, listname='obj.list', path_save='.', path_obs = '/home/sonic/Research/table')
import os, glob
import numpy as np
from imsng import tool, phot
'''
imkey = 'Calib*-20180129-*0.fits'
path_save = '.'
path_obs = '/home/sonic/Research/table'
listname = 'obj.list'
'''
imlist = glob.glob(imkey); i... | {"hexsha": "5ddd5356baa0a8107eb3be606838107e6ee26acd", "size": 1531, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/swarpcom.py", "max_stars_repo_name": "SilverRon/gppy", "max_stars_repo_head_hexsha": "0ee56ca270af62afe1702fce37bef30add14f12a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max... |
[STATEMENT]
lemma sparse_row_matrix_append: "sparse_row_matrix (arr@brr) = (sparse_row_matrix arr) + (sparse_row_matrix brr)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sparse_row_matrix (arr @ brr) = sparse_row_matrix arr + sparse_row_matrix brr
[PROOF STEP]
apply (induct arr)
[PROOF STATE]
proof (prove)
goal (... | {"llama_tokens": 291, "file": null, "length": 3} |
# -*- coding: utf-8 -*-
# pylint: disable=no-name-in-module
"""
Abstract predictor class\n
A predictor is the specific interface needed for using a predictive model\n
Predictors must:
- load an object or process in memory from a local file representing a model
- convert feature strings into the appropriate inp... | {"hexsha": "25605fc5592541136ccd613e740fc5fb5b8758de", "size": 4343, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict/predictors/base_predictor.py", "max_stars_repo_name": "gramhagen/ml-agent", "max_stars_repo_head_hexsha": "a0db7376a959d4039e6a1aca94aed6d9a86c7898", "max_stars_repo_licenses": ["Apache-2.... |
# Request handlers
function run_notification(conn, state::DebuggerState, params::NamedTuple{(:program,),Tuple{String}})
@debug "run_request"
state.debug_mode = :launch
put!(state.next_cmd, (cmd = :run, program = params.program))
end
function debug_notification(conn, state::DebuggerState, params::DebugAr... | {"hexsha": "11b9e4036229390b9c595c529b481b081dd0d07a", "size": 35173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/debugger_requests.jl", "max_stars_repo_name": "dpinol/DebugAdapter.jl", "max_stars_repo_head_hexsha": "a0151861de9d82c4129285018ebc779b9760df58", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Module: Potential
This module shall be used to implement subclasses of Potential. This module contains all available potentials.
"""
import typing as t
import numpy as np
import scipy.constants as const
import sympy as sp
from ensembler.potentials._basicPotentials import _potential1DCls, _potential1DClsPerturbed... | {"hexsha": "2ee4987a2f02c8649f1480803b1e39356ca6097a", "size": 59256, "ext": "py", "lang": "Python", "max_stars_repo_path": "ensembler/potentials/OneD.py", "max_stars_repo_name": "philthiel/Ensembler", "max_stars_repo_head_hexsha": "943efac3c673eb40165927e81336386788e3a19f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#! /usr/bin/env python
# coding: utf-8
# This is pos controller for like-car robot
import math
import numpy as np
import rospy
import tf
import tf2_ros
#import sensor_msgs.point_cloud2 as pc2
import laser_geometry.laser_geometry as lg
#from tf2_sensor_msgs.tf2_sensor_msgs import do_transform_cloud
from geometry_msgs... | {"hexsha": "2506f7f40c8e078749f4aae4b47691bed140cc5f", "size": 9386, "ext": "py", "lang": "Python", "max_stars_repo_path": "rc_potantial_field_planner/src/potential_fields_with_vector_group.py", "max_stars_repo_name": "taranraina/ros_capstone", "max_stars_repo_head_hexsha": "c9c4734fd22d6dc33e11723b5b2e681f21ae91d5", "... |
#!/usr/bin/env python3
import picamera
from PIL import Image, ImageFont, ImageDraw
import numpy as np
from time import sleep, time
import datetime
import subprocess
import os
import logging
logger = logging.getLogger("photobooth")
class Camera:
photo_w = 1920
photo_h = 1280
screen_w ... | {"hexsha": "0f1a15f3b2c900a750835c0fc91f06185d5392c7", "size": 12959, "ext": "py", "lang": "Python", "max_stars_repo_path": "Camera.py", "max_stars_repo_name": "Trekky12/photobooth", "max_stars_repo_head_hexsha": "f52cd48acff6fcebb50f336648e4ea4ea7df478a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Information sheet for the Matlab lab - Maths 6111 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\documentclass[10pt]{article}
\input ma_no_html_header
\use... | {"hexsha": "926b847c222fa8ec05d4c0703615b288a11ef95b", "size": 2561, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Worksheets/Worksheet6.tex", "max_stars_repo_name": "alistairwalsh/NumericalMethods", "max_stars_repo_head_hexsha": "fa10f9dfc4512ea3a8b54287be82f9511858bd22", "max_stars_repo_licenses": ["MIT"], "ma... |
from copy import deepcopy
from collections import OrderedDict
from numbers import Number
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from softlearning.utils.gym import is_continuous_space, is_discrete_space
from .rl_algorithm import RLAlgorithm
@tf.function(experimental_relax_sha... | {"hexsha": "5303beb940713a0690eed4f0f0ecc6bdb6b51970", "size": 11562, "ext": "py", "lang": "Python", "max_stars_repo_path": "softlearning/algorithms/sac.py", "max_stars_repo_name": "SilentEmber/softlearning", "max_stars_repo_head_hexsha": "e3ce4a019c2ebbfeab5ab036f19531523a709327", "max_stars_repo_licenses": ["MIT"], "... |
# +
""" plotting_utils.py
Helper functions for generating maps and plots
"""
import xarray as xr
import numpy as np
import pandas as pd
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from textwrap import wrap
import hvplot.xarray
import holoviews as hv
import matplotlib.pyplot as plt
from matplotli... | {"hexsha": "ca10367642bf63185bacad4c047681980cadbe23", "size": 21920, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/plotting_utils.py", "max_stars_repo_name": "akpetty/icesat2-book", "max_stars_repo_head_hexsha": "9e773dfc463c589a9150683bdb26dc3b4135f14a", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from typing import List
import numpy as np
from common.exceptionmanager import catch_error_exception
class MaskOperator(object):
_value_mask = 1
_value_backgrnd = 0
@classmethod
def binarise(cls, in_image: np.ndarray) -> np.ndarray:
return np.clip(in_image, cls._value_backgrnd, cls._value_m... | {"hexsha": "9464d23b1b447f2bcb242cff63d7b427e4fb2b91", "size": 3519, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/imageoperators/maskoperator.py", "max_stars_repo_name": "AntonioGUJ/AirwaySegmentation_Keras", "max_stars_repo_head_hexsha": "7da4c88dfde6f0dd2f8f181b2d3fd07dc2d28638", "max_stars_repo_license... |
#
# xTAPP.py
#
# Interface to xTAPP (http://xtapp.cp.is.s.u-tokyo.ac.jp)
#
# Copyright (c) 2014-2020 Terumasa Tadano
#
# This file is distributed under the terms of the MIT license.
# Please see the file 'LICENCE.txt' in the root directory
# or http://opensource.org/licenses/mit-license.php for information.
#
import n... | {"hexsha": "7e432077aff93078bff6402883b3b371f261ba41", "size": 17760, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/interface/xTAPP.py", "max_stars_repo_name": "jochym/alamode", "max_stars_repo_head_hexsha": "128cc2315a661f2440a2f264f0b9dd75ed42dd39", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import os
import numpy as np
import shutil
import tensorflow as tf
def load_raw_data_list(data_dir, filelist):
obs_list = []
action_list = []
reward_list = []
domain_list = []
for i in range(len(filelist)):
filename = filelist[i]
raw_data = np.load(os.path.join(data_dir, filename)... | {"hexsha": "11429b6cf49ab14baf4bf1a9a3045aab691ebb6c", "size": 12652, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/misc/data_handler.py", "max_stars_repo_name": "Adaptive-RL/AdaRL-code", "max_stars_repo_head_hexsha": "493b1ee5a0f98a220c5a1e5ce2e2ce6572d02e9f", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
class MHC():
def __init__(self, shape=None, dtype=np.float32):
self.shape = shape
def compute(self, D, F, s):
# Compute reward for taking action on old state:
# Calculate permutation matrix for new state
P = self.permutationMatrix(s)
... | {"hexsha": "b3de089ebc723bd96aee6b5c7095450b2d5532f1", "size": 1276, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_flp/rewards/mhc.py", "max_stars_repo_name": "TejaswiniMedi/gym-flp", "max_stars_repo_head_hexsha": "97d1d1b510896ab5b871cfc9f591fbbffd830ff4", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
subroutine getggHWWamps(p,Mloop_bquark,Mloop_tquark)
c--- Returns a series of arrays representing the dressed amp[itudes
c--- for the process gg->Higgs->ZZ; there are:
c--- Mloop_bquark(h1,h2,h34,h56) top quark mass=mt
c--- Mloop_tquark(h1,h2,h34,h56) bottom quark mass=mb
c---
c--- The overall f... | {"hexsha": "0a8cb95c4f06a71a29d0c5db9f953de83f68403d", "size": 2660, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/VV/old/getggHWWamps.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.nn.utils.rnn import pad_sequence
class policy_net(nn.Module):
def __init__(self, game, args):
super(policy_net, self).__init__()
self.args=args
self.game=game
self.num_nodes=game.num_nod... | {"hexsha": "eadaf2217dab3bdaf35b24d3d7b588e3282c274f", "size": 7903, "ext": "py", "lang": "Python", "max_stars_repo_path": "network.py", "max_stars_repo_name": "xuewanqi/NSGZero", "max_stars_repo_head_hexsha": "c0ccdf94e4acc93ad475b6093f176ef2804a767c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
import matplotlib.colors as colors
import numpy as np
import seaborn as sns
import spectra
from matplotlib import colorbar
from matplotlib.patches import Patch
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
# from http://chris35wills.github.io/matpl... | {"hexsha": "d8b52fe64fac197d9fc97e65c31df554d5046a16", "size": 5844, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/aves/visualization/colors/base.py", "max_stars_repo_name": "sergioangulo/aves", "max_stars_repo_head_hexsha": "43a14ec9c82929136a39590b15fe7f92182aae20", "max_stars_repo_licenses": ["CC-BY-3.0... |
"""
Module to read ANSYS ASCII block formatted CDB files
USAGE
# load module
import pyansys
# load ANSYS cdb file
archive = pyansys.ReadArchive('example.cdb')
# Parse the raw data into a VTK unstructured grid
grid = archive.ParseVTK()
# Plot the result
grid.Plot()
"""
import warnings
import numpy as np
from pyan... | {"hexsha": "5ee80d3cf64442bab3890eac61f5668a67d444ce", "size": 7598, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyansys/archive_reader.py", "max_stars_repo_name": "J-Light/pyansys", "max_stars_repo_head_hexsha": "facdb69f5c83e55de2b789b629ebbcbfa507e339", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
'''
05_CliffWalk_Benchmark_alpha.py : replication of Figure 6.3
Cem Karaoguz, 2020
MIT License
'''
import numpy as np
import pylab as pl
from IRL.environments.Gridworlds import DeterministicGridWorld
from IRL.agents.TemporalDifferenceLearning import SARSA, QLearning, ExpectedSARSA, DoubleQLearning
def runExperiment... | {"hexsha": "90425e2cf2ccda12b02918bd93c933c32c950c81", "size": 5984, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter06/05_CliffWalk_Benchmark_alpha.py", "max_stars_repo_name": "cemkaraoguz/reinforcement-learning-an-introduction-second-edition", "max_stars_repo_head_hexsha": "735bfa6b66ffb52b7cf03966164e7... |
subroutine chand (xphi,xmuv,xmus,xtau
s ,xrray)
!*
!Description: routine used to compute the intrinsic reflectance of a pure
! molecular atmosphere.
!Input parameters:
! float xphi: relative azimuth angle: difference between the sun azimuth
! and the sensor azimuth (in degree)
! float xmuv: cosine of t... | {"hexsha": "1c74805b7d2547dadef93f4b33c1caaddb8e7800", "size": 2988, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ledaps/ledapsSrc/src/lndsr/CHAND.f", "max_stars_repo_name": "ldj01/espa-surface-reflectance", "max_stars_repo_head_hexsha": "d6f617095710883763734ff6d8943e80822d80ed", "max_stars_repo_licenses": [... |
import logging
from typing import Tuple, Union
import anndata
import numba
import numpy as np
import pandas as pd
import scipy.sparse as sp_sparse
logger = logging.getLogger(__name__)
def _compute_library_size(
data: Union[sp_sparse.spmatrix, np.ndarray]
) -> Tuple[np.ndarray, np.ndarray]:
sum_counts = data... | {"hexsha": "0a38a763ed715ec14e8291d02b00e92ef6debf2d", "size": 7171, "ext": "py", "lang": "Python", "max_stars_repo_path": "scvi/data/_utils.py", "max_stars_repo_name": "gordian-biotechnology/scvi-tools", "max_stars_repo_head_hexsha": "45cf390ccc0eef5665d2ff33b6fe2f2cea042baa", "max_stars_repo_licenses": ["BSD-3-Clause... |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "ab4f37d8e53180e631dde1916c1afcb20ebddf53", "size": 9256, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlenlp/ops/transformer/faster_transformer.py", "max_stars_repo_name": "BenfengXu/PaddleNLP", "max_stars_repo_head_hexsha": "eca87fde4a1814a8f028e0e900d1792cbaa5c700", "max_stars_repo_licenses":... |
from __future__ import print_function
import numpy as np
with open('form_factors_2body.dat') as f:
names = f.readline().split()[1:]
data = [[float(x) for x in line.split()] for line in f]
nvalues = len(data)
dtype = zip(names, [np.float64]*nvalues)
np_data = np.zeros(nvalues, dtype)
data = ... | {"hexsha": "f00571fa7a198d2b1114da92385b122b820e367f", "size": 845, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/dat2npy.py", "max_stars_repo_name": "latrocinia/saxstools", "max_stars_repo_head_hexsha": "8e88474f62466b745791c0ccbb07c80a959880f3", "max_stars_repo_licenses": ["Python-2.0", "OLDAP-2.7"], "m... |
# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Generator module in VITS.
This code is based on https://github.com/jaywalnut310/vits.
"""
import math
from typing import List
from typing import Optional
from typing import Tuple
import numpy as np
import torch
import t... | {"hexsha": "b17e1ea958af5c13a75bd6314051e8d31ce9c7ed", "size": 23143, "ext": "py", "lang": "Python", "max_stars_repo_path": "espnet2/gan_tts/vits/generator.py", "max_stars_repo_name": "Emrys365/espnet", "max_stars_repo_head_hexsha": "d90eb2a806b2c9d1bb80a7708a94b6b645504f47", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import json
import os
from datetime import datetime
import numpy
def get_current_time():
return datetime.now().strftime("%Y-%m-%d-at-%H-%M")
class CalibrationImportError(RuntimeError):
pass
def import_json(target, json_file):
'''Maps imported json to variables of the instance'''
try:
with... | {"hexsha": "a37197cfd2aaf04a039ef37f30c7606f026dd284", "size": 4299, "ext": "py", "lang": "Python", "max_stars_repo_path": "program/program/CalibrationResults.py", "max_stars_repo_name": "JankaSvK/thesis", "max_stars_repo_head_hexsha": "c440ab8242b058f580fdf9d5a1d00708a1696561", "max_stars_repo_licenses": ["MIT"], "max... |
\chapter[Stative/non-stative distinction and change as a
lexico-semantic concept]{The stative/non-stative distinction and
change as a lexico-semantic concept}\label{sec:4}\label{ch:4}
\section{Introduction}\label{sec:4.0}
In this chapter I will discuss Change as the abstract semantic concept
associated with the not... | {"hexsha": "037c56b63d08f391c3fce45d614d9985b1d6a8a7", "size": 56504, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters.old/04.tex", "max_stars_repo_name": "langsci/80", "max_stars_repo_head_hexsha": "5730903ca905631e7c55cdf842684a28790ff675", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": nul... |
[STATEMENT]
lemma incrtm0: "Itm vs (x#bs) (incrtm0 t) = Itm vs bs t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Itm vs (x # bs) (incrtm0 t) = Itm vs bs t
[PROOF STEP]
by (induct t rule: decrtm0.induct) simp_all | {"llama_tokens": 106, "file": null, "length": 1} |
"""Contains code for generating plots describing the neural network's performance.
@since 0.6.1
"""
# pylint: disable=C0413
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt # noqa
import numpy as np # noqa
from . import constants # noqa
from .logger import debug, trace # noqa... | {"hexsha": "de1d08a266378848ec99624ad8f9a278969c25d0", "size": 8093, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_rnn/plotter.py", "max_stars_repo_name": "ffrankies/tf-rnn", "max_stars_repo_head_hexsha": "23400d4deb775841a1b8aae2831c09cc043b8263", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
```python
from __future__ import print_function
from sympy import symbols, log, exp, limit, KroneckerDelta, diff, \
Product, factor, Pow, Symbol, simplify, Limit, Mul, expand, init_printing, latex, collect, Add
from optionloop import OptionLoop
from IPython.display import Latex, Math
init_printing()
def __get_dc... | {"hexsha": "770ebb30a204fade5fb087999b9a4aa740753be3", "size": 11545, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "derivations/limittest/Untitled.ipynb", "max_stars_repo_name": "arghdos/SPyJac-paper", "max_stars_repo_head_hexsha": "7f65253a3acd3a93141e673c2cdd5810ecc6a0ca", "max_stars_repo_licens... |
from numpy import *
from scipy import *
from scipy.signal import remez, resample
from .halfbandfir import halfbandfir
from fractions import gcd
from .upfirdn import upfirdn
def resample_cascade(x, fs_start, fs_end, N=42):
"""
Resample a signal from one sampling frequency to another, using a halfband
filte... | {"hexsha": "7caa0bf727702d9a3432c3c2b4d8a198e9c273ab", "size": 1994, "ext": "py", "lang": "Python", "max_stars_repo_path": "libsquiggly/resampling/resampling.py", "max_stars_repo_name": "staticfloat/libsquiggly", "max_stars_repo_head_hexsha": "79c63c119a60e2e9c558aefcda6b1c1ac413a47a", "max_stars_repo_licenses": ["MIT"... |
import pytest
import librosa
import torch
from scipy.signal import chirp, sweep_poly
import sys
sys.path.insert(0, "./")
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
from nnAudio.Spectrogram import *
from parameters import *
import warnings
gpu_idx = 0 # Choose which GPU to use
# If GPU is av... | {"hexsha": "a3d4465e4bf2d611a7faeff5b04018ff6f28c358", "size": 9228, "ext": "py", "lang": "Python", "max_stars_repo_path": "Installation/tests/test_cqt.py", "max_stars_repo_name": "shaun95/nnAudio", "max_stars_repo_head_hexsha": "744fab12497a5316153978de2e97422c9c7389e0", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
export ModiaProblem, ModiaSolve
function ModiaProblem(m1::ModiaLang.SimulationModel{FloatType1,ParType,EvaluatedParType,FloatType1},
m2::ModiaLang.SimulationModel{FloatType2,ParType,EvaluatedParType,FloatType2};
p, merge=nothing, kwargs...) where {FloatType1,ParType,Evaluat... | {"hexsha": "741cd14b815820e78436fc6320707435c0c9066f", "size": 4474, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReverseDiffInterface.jl", "max_stars_repo_name": "ModiaSim/ModiaLang", "max_stars_repo_head_hexsha": "6f8fc420f86f9af51eb897cfd9d7069c6ccc9659", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\documentclass[papersize=a4,paper=landscape,11pt]{scrartcl}
\usepackage[top=0.5cm, bottom=2.5cm, left=0.5cm, right=0.5cm]{geometry}
\usepackage[default]{sourcesanspro}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{lipsum}
\usepackage{multicol}
\usepackage{tabularx}
\usepackage[table]{xcolor}
\defi... | {"hexsha": "1ee24c407eac6855ea6726351ca168ea35c93413", "size": 8100, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "cheatsheet/cheatsheet.tex", "max_stars_repo_name": "thillux/AuPUtils", "max_stars_repo_head_hexsha": "8ca03b320a13172faa4d7711918ea8456bc82e82", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
[STATEMENT]
lemma cp_OclNotEmpty: "X->notEmpty\<^sub>S\<^sub>e\<^sub>t() \<tau> = ((\<lambda>_. X \<tau>)->notEmpty\<^sub>S\<^sub>e\<^sub>t()) \<tau>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. X->notEmpty\<^sub>S\<^sub>e\<^sub>t() \<tau> = \<lambda>_. X \<tau>->notEmpty\<^sub>S\<^sub>e\<^sub>t() \<tau>
[PROOF S... | {"llama_tokens": 362, "file": "Featherweight_OCL_collection_types_UML_Set", "length": 3} |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as md
import datetime as dt
from matplotlib.ticker import Formatter, FormatStrFormatter, MaxNLocator
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
from matplotlib.axes im... | {"hexsha": "7f8900ed8ac40a305eca9678c518e330bbd1fd42", "size": 26638, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyxlma/plot/xlma.py", "max_stars_repo_name": "vbalderdash/xlma-python", "max_stars_repo_head_hexsha": "41d7f8614d0b3596d293b5e9caca911794e04364", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
import numpy as np
import pytorch3d
# Util function for loading meshes
from pytorch3d.io import load_objs_as_meshes
# Data structures and functions for rendering
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
... | {"hexsha": "2dbc41cdea03b4450644a84316f1f862c46d2d4d", "size": 15637, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphs/render/render_base.py", "max_stars_repo_name": "THU-luvision/SurRF", "max_stars_repo_head_hexsha": "581b9fa5392cb6cceffcd5f13876e3de6c125ac0", "max_stars_repo_licenses": ["MIT"], "max_star... |
import warnings
import numpy as np
def Rt2Trans(R, t):
"""Constructs a 4x4 transformation matrix from a rotation matrix
and a translation vector.
Args:
R (np.ndarray): 3x3 rotation matrix.
t (np.ndarray): (3,) translation vector.
Returns:
np.ndarray: 4x4 transformation vecto... | {"hexsha": "f0c400ebadd0b8a1be2f16057d5ae09f4af84152", "size": 5089, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rote/rote.py", "max_stars_repo_name": "SebastianGrans/Rote", "max_stars_repo_head_hexsha": "37c4cc6859f2c90fe37e90b6fc604b7bdbef4fa3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
/*
* Copyright 2017 Maarten de Vries <maarten@de-vri.es>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and... | {"hexsha": "8ba526f81e1576e0044e620be02962c2b240be60", "size": 7109, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/ldapxx/connection.hpp", "max_stars_repo_name": "de-vri-es/ldapxx", "max_stars_repo_head_hexsha": "51ac152b79068e0ff7fe207c6ec941f567085231", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
import pandas as pd
import numpy as np
import shutil
import os
import glob
class DataCleaner:
"""
Cleans up the data recorded by the Simulator, and presents the data as a zip file that can be downloaded
by the Udacity workspace while creating a model
"""
def __init__(self, data_root):
... | {"hexsha": "cde0097c29ee10ba7720b58c36d8107aeb11edcd", "size": 5704, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_cleaner.py", "max_stars_repo_name": "samiriff/Behavioral-Cloning", "max_stars_repo_head_hexsha": "207cd33a842b2d973e765a6cdfbd0c27a07ebb26", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
from collections import OrderedDict
from functools import partial
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pytest
from storefact import get_store_from_url
from plateau.core.dataset import DatasetMetadata
from plateau.core.uuid import ... | {"hexsha": "9b04cdb3dbb02b7f8f67d9a0cae68f39a2cd7503", "size": 18697, "ext": "py", "lang": "Python", "max_stars_repo_path": "plateau/io/testing/write.py", "max_stars_repo_name": "data-engineering-collective/plateau", "max_stars_repo_head_hexsha": "ab87282a2f66c4f847654f28f8a2b0df33cb4d62", "max_stars_repo_licenses": ["... |
#!/usr/bin/python
from __future__ import print_function
import numpy as np
import sys
import os
from .parameters import get_params
from .partition import partition
from .score_structure import score_structure
from .util.constants import KT_IN_KCAL
from scipy.optimize import check_grad
def calc_dG_gap( training_example... | {"hexsha": "dcd090f773ea74ecce8d94b550f7bf4a1d3b3b34", "size": 4097, "ext": "py", "lang": "Python", "max_stars_repo_path": "zetafold/training.py", "max_stars_repo_name": "rickyHong/Zetafold-repl", "max_stars_repo_head_hexsha": "7a325bb65f242d8951c5d257cafa351a789a6f37", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
def xyz2uvd(pts, paras, flip=1):
# paras: [fx, fy, fu, fv]
pts_uvd = pts.copy()
pts_uvd = pts_uvd.reshape(-1, 3)
pts_uvd[:, 1] *= flip
pts_uvd[:, :2] = pts_uvd[:, :2] * paras[:2] / pts_uvd[:, 2:] + paras[2:]
return pts_uvd.reshape(pts.shape).astype(np.float32)
def uvd2xyz(... | {"hexsha": "8b83460ec48a0592cacd11aa914171d6be0c24ca", "size": 598, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/util.py", "max_stars_repo_name": "LiderMyHand/AWR-Adaptive-Weighting-Regression", "max_stars_repo_head_hexsha": "81c4c98edd98cd03d423d820ca1fe9e01dbbb242", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma contains_pred_eq: "contains_pred \<equiv> \<lambda>A x. Predicate.Pred (\<lambda>y. contains A x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. contains_pred \<equiv> \<lambda>A x. pred.Pred (\<lambda>y. contains A x)
[PROOF STEP]
by(rule eq_reflection)(auto simp add: contains_pred_def fun_eq_iff... | {"llama_tokens": 127, "file": null, "length": 1} |
function B = ndim_expand(A, v)
%NDIM_EXPAND expand an array in a new dimension by multiplying it with a vector
% B = NDIM_EXPAND(A, v)
%
% A - multidimensional array (can be vector or matrix)
% v - vector
% B - add one new dimension to A as: [A.*v(1) | A.*v(2) | ... | A.*v(n)]
%
% eg. ndim_expand(ones(2,3), [2 3 4])
... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/25514-tp-tool/tptool/array/ndim_expand.m"} |
[STATEMENT]
lemma list_distinct_prefix :
assumes "\<And> i . i < length xs \<Longrightarrow> xs ! i \<notin> set (take i xs)"
shows "distinct xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. distinct xs
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. distinct xs
[PROOF STEP]
have "\<And>... | {"llama_tokens": 2716, "file": "FSM_Tests_Util", "length": 42} |
import matplotlib.pyplot as plt
import pandas as pd
from PIL import Image
import numpy as np
def make_AMR_legend(color, temp_dir):
for i in color.index:
plt.scatter([],[],marker='s',s=300,c= '#' + color.loc[i,1])
plt.axis('off')
plt.rcParams["legend.loc"] = "lower left"
plt.rcParams["font.size... | {"hexsha": "47bef794e8814d9ca7ac63c4e496d6c0f91fe3f2", "size": 3536, "ext": "py", "lang": "Python", "max_stars_repo_path": "BeMAp_package/mapping/make_legend.py", "max_stars_repo_name": "yusuketsuda/BeMAp", "max_stars_repo_head_hexsha": "b64608730e5a819f83170e34c72a7b3d609ff12c", "max_stars_repo_licenses": ["MIT"], "ma... |
## Following Qt
abstract Object
abstract Widget <: Object
abstract Observable <: Object
abstract AbstractModel <: Observable
abstract Container <: Widget
abstract Layout <: Object
abstract Control <: Widget
abstract WidgetModel <: Control
abstract WidgetVectorModel <: WidgetModel
abstract Style <: Widget
#... | {"hexsha": "71d14bebce6819e0c1e868a2733693435702b04e", "size": 443, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/types.jl", "max_stars_repo_name": "jverzani/JGUI.jl", "max_stars_repo_head_hexsha": "39779b825125387758b2112b15509ca2239f9673", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_sta... |
subroutine rdcrc(xc,yc,r)
C rdcrc 01/06/75 01/01/80
common/gfgel/gf(28)
if(gf(9).ge.0.) call strmod(0)
i=abs(gf(9))
write(5,1)i
1 format(' ',i3)
if(gf(i).lt.2.) go to 5
xc=gf(i+1)
yc=gf(i+2)
r=gf(i+3)
go to 6
5 call gffals(6)
6 if(gf(9).gt... | {"hexsha": "412543b0a4f7e62e45621f9594126c335ee02d52", "size": 363, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ftn/rdcrc.f", "max_stars_repo_name": "sergev/grafor", "max_stars_repo_head_hexsha": "2b7b244d84b739bdcdf1717824ee895eb015c4a0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4, "max... |
//==============================================================================
//
// (c) Copyright, 2013 University Corporation for Atmospheric Research (UCAR).
// All rights reserved.
// Do not copy or distribute without authorization.
//
// File: $RCSfile: config_reader.cc,v $
// Version: ... | {"hexsha": "dc97af11ab66fca2726bf8c8470ecc812168b343", "size": 4635, "ext": "cc", "lang": "C++", "max_stars_repo_path": "apps/cdf_to_csv_dicast/config_reader.cc", "max_stars_repo_name": "OSADP/Pikalert-Vehicle-Data-Translator-", "max_stars_repo_head_hexsha": "295da604408f6f13af0301b55476a81311459386", "max_stars_repo_l... |
import networkx as nx
import matplotlib.pyplot as plt
from edge import algorithms, graphics, base, generator
def compare_algorithms_one_iteration():
g = generator.GraphGenerator(n=5).euclidean_graph()
pos = nx.spring_layout(g)
plt.subplot(221)
plt.title('Original Graph')
nx.draw(g, pos=pos, **g... | {"hexsha": "7af1046eecbf8d888233efd48bd208ab894be945", "size": 1084, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/compare_and_display_graphs.py", "max_stars_repo_name": "lucasdavid/edge", "max_stars_repo_head_hexsha": "c7d9cf7e2803cc8d49abbe3ddb9f16eb130c1b01", "max_stars_repo_licenses": ["MIT"], "max... |
import paddle
import unittest
import numpy as np
import interpretdl as it
from paddle.utils.download import get_weights_path_from_url
from tutorials.assets.vision_transformer import ViT_base_patch16_224
from tests.utils import assert_arrays_almost_equal
class TestRollout(unittest.TestCase):
def set_paddle_model... | {"hexsha": "4ee6d435e1225f6b11a4a33fe252e3144705651b", "size": 2183, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/interpreter/test_rollout.py", "max_stars_repo_name": "Christophe-Jia/InterpretDL", "max_stars_repo_head_hexsha": "5736cb880d3c9bd79241d2ea6cb0490d9e8b089d", "max_stars_repo_licenses": ["Apac... |
import numpy as np
from SERGIO.SERGIO.gene import gene
from scipy.stats import ttest_rel, ttest_ind, ranksums
import sys
import csv
import networkx as nx
#from scipy.stats import wasserstein_distance
class sergio (object):
def __init__(self,number_genes, number_bins, number_sc, noise_params,\
noise_type, deca... | {"hexsha": "be3bae55ce80fbdb3476c0f570d510ea48a06f97", "size": 46495, "ext": "py", "lang": "Python", "max_stars_repo_path": "SERGIO/SERGIO/sergio.py", "max_stars_repo_name": "Harshs27/GRNUlar", "max_stars_repo_head_hexsha": "7e38fd85b65210219724c03cccb020555f388059", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import json
import logging
import os
from PIL import Image
import albumentations as A
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from ..tools.utils import mask2bbox, rle_encoding
class SESModel:
def __init__(self, model_path):
self.model_path = model_path
... | {"hexsha": "4199435831c7ac8b809fe626fe04ec5ce7ff3b28", "size": 2892, "ext": "py", "lang": "Python", "max_stars_repo_path": "service/serving/ses_model.py", "max_stars_repo_name": "hasty-ai/docker-inference-example", "max_stars_repo_head_hexsha": "f5e8bcccff8011b783c25c9795771be1fd4f732d", "max_stars_repo_licenses": ["MI... |
from pyboost_ipc import managed_shared_memory, open_or_create
from pyboost_ipc_tests import create_struct_with_offset_ptr_in_shared_memory
def test_exports_buffer_interface():
import numpy as np
shmem = managed_shared_memory(open_or_create, 'MySharedMemory', 1024)
s = create_struct_with_offset_ptr_in_sh... | {"hexsha": "c4b4fd4fa9334410bc3f329ad8fc8ca1ea3be13c", "size": 510, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/python/_tests/test_offset_ptr.py", "max_stars_repo_name": "sandeep-gh/pyboost_ipc", "max_stars_repo_head_hexsha": "8e26106f1ea93085acc9fa2f68bb048da53cc8e7", "max_stars_repo_licenses": ["MIT... |
#!/usr/bin/env python
import os
import numpy as np
import sys
import csv
sys.path.insert(0, "../src")
ART_NET_PATH = "../networks"
import auxilary_functions as functions
from generation_algorithm import *
import networkx as nx
from argparse import ArgumentParser
import json
def load_ffl_based_component():
with o... | {"hexsha": "cbbad1ed7ebaf440a498f3ba5da2d6a9d3ff2033", "size": 3894, "ext": "py", "lang": "Python", "max_stars_repo_path": "snippets/parameter_space_exploration.py", "max_stars_repo_name": "zhivkoplias/network_generation_algo", "max_stars_repo_head_hexsha": "3ee2493443acb8773f54bc70d11469a43a87a973", "max_stars_repo_li... |
import cv2
import numpy as np
import time
import glob
def colorMasks(inputImage):
hsv = cv2.cvtColor(inputImage, cv2.COLOR_BGR2HSV)
redLow = np.array([0,120,70])
redHigh = np.array([10,255,255])
redLowMask = cv2.inRange(hsv, redLow, redHigh)
redLow = np.array([170,120,70])
redHigh = np.array(... | {"hexsha": "46fac423165e43a643a9ca56cf2b996a83a847e5", "size": 1837, "ext": "py", "lang": "Python", "max_stars_repo_path": "robotVision.py", "max_stars_repo_name": "AnujSaharan/Wimpbot-Vision", "max_stars_repo_head_hexsha": "15c54104aaf3387c192a7668791b55d82b0e970a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# Copyright 2021 The Deluca 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 w... | {"hexsha": "98b96ac1332c3fa4d739ed6ed2d73e02e29bbb97", "size": 3685, "ext": "py", "lang": "Python", "max_stars_repo_path": "deluca/tests/lung/core_test.py", "max_stars_repo_name": "google/deluca", "max_stars_repo_head_hexsha": "626ade7bfa44afc52e6ffb9a9e6e94258b4dc024", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python
# Copyright 2014-2020 The PySCF Developers. 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
#
# U... | {"hexsha": "3b004f37a9346549e9a929934d25e83a31827ef2", "size": 52235, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/pbc/df/rsdf_helper.py", "max_stars_repo_name": "umamibeef/pyscf", "max_stars_repo_head_hexsha": "1263d54b02914caf4476a3ed9a2de5e0c848954c", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""
Tests for YUI DataTable renderings.
"""
from mysite import settings
from FileVersion.versioned_file import VersionedFile
import Tree.named_tree as named_tree
import scisheets.core.helpers.api_util as api_util
from scisheets.core.column import Column
from scisheets.core.helpers.api_util \
import readObjectFrom... | {"hexsha": "8072251019af52e02d2ac46f656abfc4fb06de20", "size": 6790, "ext": "py", "lang": "Python", "max_stars_repo_path": "mysite/scisheets/ui/test_dt_table.py", "max_stars_repo_name": "ScienceStacks/JViz", "max_stars_repo_head_hexsha": "c8de23d90d49d4c9bc10da25f4a87d6f44aab138", "max_stars_repo_licenses": ["Artistic-... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('Agg')
import Bio.SeqUtils
import matplotlib.pyplot as plt
import numpy as np
import sys
import argparse
import re
##plot 2 energy profiles in one plot to compare them
# argparse for information
parser = argparse.ArgumentParser()
parser.... | {"hexsha": "b17c7dbe6346518f2defb02cc9e3e42afd308a74", "size": 5433, "ext": "py", "lang": "Python", "max_stars_repo_path": "Energy_Profile/plot_eps_and_snps.py", "max_stars_repo_name": "Twinstar2/Phython_scripts", "max_stars_repo_head_hexsha": "19f88420bca64014585e87747d01737afe074400", "max_stars_repo_licenses": ["MIT... |
module probdata_module
! Ensman test variables -- we set the defaults here
use amrex_fort_module, only : rt => amrex_real
real(rt) , save :: rho0 = 5.4588672836e-13_rt
real(rt) , save :: T0 = 100.e0_rt
real(rt) , save :: v0 = 235435.371882e0_rt
real(rt) , save :: rho1 = 1.2479... | {"hexsha": "8e8adc9b5d870569473a92179451f08cfd548ad8", "size": 603, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Exec/radiation_tests/Rad2Tshock/probdata.f90", "max_stars_repo_name": "yingtchen/Castro", "max_stars_repo_head_hexsha": "5e9bd2f7a699a45447b92a1c9c3064f6c2e3552c", "max_stars_repo_licenses": ["BS... |
import theano.tensor as T
import numpy as np
__all__ = ['var']
def var(name, label=None, observed=False, const=False, vector=False, lower=None, upper=None):
if vector and not observed:
raise ValueError('Currently, only observed variables can be vectors')
if observed and const:
raise ValueErr... | {"hexsha": "b80cc5c7ac6bc6b04c25cbb507c81770c6eaf2ee", "size": 645, "ext": "py", "lang": "Python", "max_stars_repo_path": "mle/variable.py", "max_stars_repo_name": "vezeli/python-mle", "max_stars_repo_head_hexsha": "cc03adc0ba4b16a81843063d73635fd8eeb00980", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 121, "... |
det22(a,b,c,d) = a*d-c*b
det33(a,b,c) = @inbounds a[1]*det22(b[2],c[2],b[3],c[3]) - b[1]*det22(a[2],c[2],a[3],c[3]) + c[1]*det22(a[2],b[2],a[3],b[3])
"""
solid_angle(topo, P, he, p)
the solid angle for the signed surface area of the triangle of he projected onto unit sphere centred at p
"""
function solid_angle... | {"hexsha": "d9ec6246f392135489d2779303c52891ba911955", "size": 12721, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/WindingNumbers.jl", "max_stars_repo_name": "mewertd2/HalfEdges.jl", "max_stars_repo_head_hexsha": "3f489ada015dd03fa2d82242d362d157214a1aa0", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
!! Copyright (C) Stichting Deltares, 2012-2016.
!!
!! This program is free software: you can redistribute it and/or modify
!! it under the terms of the GNU General Public License version 3,
!! as published by the Free Software Foundation.
!!
!! This program is distributed in the hope that it will be useful,
!! b... | {"hexsha": "274b39afb5911d1016efc7505128ecb950a7c172", "size": 3388, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/waq/packages/waq_kernel/src/waq_kernel/dlwq63.f", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02a... |
#************************
# Deep Residual Learning
# for DNA Methylation
# Fei Tan
# ft54@njit.edu
#************************
#*************************
# import modules
#
#*************************
import os
#os.environ['THEANO_FLAGS'] = "device=gpu0"
import sys
sys.setrecursionlimit(15000)
import numpy as np
impo... | {"hexsha": "98b3e3c2f3b7d860072b2e31fd5362c8519992b1", "size": 9496, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/DeepM6A/DeepM6A.py", "max_stars_repo_name": "tanfei2007/DeepM6A", "max_stars_repo_head_hexsha": "ac8b5543db292516ce10cf42b7506004140d4d41", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
%% *************************************************************************
%% cjaa.tex
%% CJAA Ver. 1.0, LaTeX class for Chinese Journal of Astronomy & Astrophysics
%% demonstration file
%% (C) Chin. J. Astron. Astrophy... | {"hexsha": "26b39ec07830794a50fbe5c57992d168ac5e09bd", "size": 33538, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "benchmark/src/test-data/0501/astro-ph0501298/review_5mq.tex", "max_stars_repo_name": "e-sim/pdf-text-extraction-benchmark", "max_stars_repo_head_hexsha": "42eede9867e5795a6fc040b0a7ce92da3ddd3120",... |
module ContinuumArrays
using IntervalSets, LinearAlgebra, LazyArrays, FillArrays, BandedMatrices, QuasiArrays
import Base: @_inline_meta, @_propagate_inbounds_meta, axes, getindex, convert, prod, *, /, \, +, -, ==,
IndexStyle, IndexLinear, ==, OneTo, tail, similar, copyto!, copy,
first, ... | {"hexsha": "1ef0c4f7cf20ce51b564c91dfe384ce340532ab6", "size": 5872, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ContinuumArrays.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ContinuumArrays.jl-7ae1f121-cc2c-504b-ac30-9b923412ae5c", "max_stars_repo_head_hexsha": "b116c5f83202062aacdf17cb3eb8... |
"""
*i64*
"""
import jax.numpy as jnp
from .._datatype import Datatype
from ._integer import Integer
__all__ = ["i64"]
class i64(
jnp.int64,
Integer,
Datatype,
):
def __init__(
self,
value: int,
):
super(i64, self).__init__(
self,
value,
... | {"hexsha": "ccb675ade1f473e807cd12712a8d15c172333f9b", "size": 327, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tensor/datatype/integer/x64.py", "max_stars_repo_name": "jedhsu/tensor", "max_stars_repo_head_hexsha": "3b2fe21029fa7c50b034190e77d79d1a94ea5e8f", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# Calculate test-retest reliability in these 40 subjects across global brain
from os.path import join as pjoin
from ATT.algorithm import tools
import framework_rt as fr
import cifti
import numpy as np
parpath = '/nfs/s2/userhome/huangtaicheng/hworkingshop/hcp_test'
with open(pjoin(parpath, 'tables', 'sessid_trt'), '... | {"hexsha": "ca84bc77a4596d798f3d92db627076e059f6635a", "size": 1119, "ext": "py", "lang": "Python", "max_stars_repo_path": "trt_reliability_subj.py", "max_stars_repo_name": "helloTC/Rest_activation_prediction", "max_stars_repo_head_hexsha": "f67cfe221d9f63afd67a2a5ef6330b8519ca7641", "max_stars_repo_licenses": ["MIT"],... |
// Copyright 2011-2017 Ryan Curtin (http://www.ratml.org/)
// Copyright 2017 National ICT Australia (NICTA)
//
// 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/licens... | {"hexsha": "a8271f31afbf5b167dae3538cfa1994e0fa1190e", "size": 1460, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "external/armadillo-10.1.2/tests2/sprow.cpp", "max_stars_repo_name": "hb407/libnome", "max_stars_repo_head_hexsha": "cf11c6e34e6d147e28bfc6f54dd3ca81d2443438", "max_stars_repo_licenses": ["MIT"], "ma... |
\chapter{Chemical Bonding}
\section{Introduction}
A \textit{chemical bond} is a mutual attraction between the nuclei and valence
electrons of different atoms that binds the atoms together. Atoms form chemical
bonds to gain a greater stability.
In the formation of a bond, atoms share, or give up, their valenece elect... | {"hexsha": "4471d423e89eacc2da439dc8a06373349c0caadf", "size": 7784, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "2015/chemistry/sections/chemical_bonding.tex", "max_stars_repo_name": "ttaylorr/midterms", "max_stars_repo_head_hexsha": "fdde0fd1a66eb5242d0dfa04a5201c3ab6d6b7eb", "max_stars_repo_licenses": ["MIT"... |
module chemistry_mod
implicit none
integer, parameter :: atom_recl = 100
type :: atom_t
character :: symbol*2, name*10
real :: mass
end type atom_t
end module chemistry_mod
!
program direct_access
use chemistry_mod, only: atom_t, atom_recl
implicit none
integer, parameter :: outu = 10, inu = 11, ... | {"hexsha": "e79e51bb2800660a33bad311aaa45d0ce4481276", "size": 1260, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "xxdirect_access.f90", "max_stars_repo_name": "awvwgk/FortranTip", "max_stars_repo_head_hexsha": "3810038667e3d5d2ab33c39d4bd0f41870025420", "max_stars_repo_licenses": ["Unlicense"], "max_stars_c... |
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