text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import numpy as np
import matplotlib.pyplot as plt
from scipy.constants import gravitational_constant as G
from scipy.integrate import odeint
def star_positions_at(time, omega, r):
angle = omega*time
x1 = np.array([r*np.cos(angle), r*np.sin(angle)])
second_angle = angle + np.pi # on the otherside
x2 = ... | {"hexsha": "a878b5d026f2c7c04ccecfb322859c86e95c8ded", "size": 3318, "ext": "py", "lang": "Python", "max_stars_repo_path": "r3bp.py", "max_stars_repo_name": "cosmo-jana/numerics-physics-stuff", "max_stars_repo_head_hexsha": "f5fb35c00c84ca713877e20c1d8186e76883cd28", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import logging
import numpy as np
from luna.radiology.mirp.imageProcess import crop_image, get_supervoxels, get_supervoxel_overlap
from luna.radiology.mirp.utilities import extract_roi_names
def rotate_image(img_obj, settings=None, rot_angle=None, roi_list=None):
""" Rotation of image and rois """
if setti... | {"hexsha": "b8018a4305fb0626d8c02d374f16e073b296ec4a", "size": 9398, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyluna-radiology/luna/radiology/mirp/imagePerturbations.py", "max_stars_repo_name": "msk-mind/data-processing", "max_stars_repo_head_hexsha": "c016d218da2eca003d06b96f2c03f16b3ce97873", "max_stars... |
-- Challenge 229 Hard In Idris
-- | Reverses a number
reverse : Integer -> Integer
reverse = cast . reverse . show
solution : Integer -> Integer
solution max = solution' 0 0
where solution' : Integer -> Integer -> Integer
solution' n sum =
if nextN > max
then sum
else s... | {"hexsha": "4208f78bc0abf13feeb0967566a4fbf5c919c117", "size": 569, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Difficult/Challenge229/Challenge229.idr", "max_stars_repo_name": "RossMeikleham/DailyProgrammer", "max_stars_repo_head_hexsha": "1520436e57589c2da8bbdfa1cb732cd0948371f2", "max_stars_repo_licenses"... |
import os
import shutil
import numpy as np
import numpy.matlib as matl
import torch
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances
from torchvision import transforms
from tqdm import tqdm
class AverageMeter(object):
"""Computes and stores the average and current value"""
d... | {"hexsha": "f56eae26abb27ccbc9815658357dc9d4edd6accf", "size": 5482, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepml/utils/libs.py", "max_stars_repo_name": "bacnguyencong/Deep-metric-learning", "max_stars_repo_head_hexsha": "cac570966e3af0be3c90df9123af552216827322", "max_stars_repo_licenses": ["MIT"], "m... |
import torch
import torch.utils.data
import torch.cuda
import torch.backends.cudnn
import random
import numpy as np
from typing import Optional
def fix(offset: int = 0, fix_cudnn: bool = True):
random.seed(0x12345678 + offset)
torch.manual_seed(0x0DABA52 + offset)
torch.cuda.manual_seed(0x0DABA52 + 1 + of... | {"hexsha": "04bc30840486f98829e293c4cbc992559b587672", "size": 809, "ext": "py", "lang": "Python", "max_stars_repo_path": "framework/utils/seed.py", "max_stars_repo_name": "lukovnikov/transformer_generalization", "max_stars_repo_head_hexsha": "a538bfbba6877cd7a21e710f2535df2e9236ba52", "max_stars_repo_licenses": ["MIT"... |
export iauPn00
"""
Precession-nutation, IAU 2000 model: a multi-purpose function,
supporting classical (equinox-based) use directly and CIO-based
use indirectly.
This function is part of the International Astronomical Union's
SOFA (Standards Of Fundamental Astronomy) software collection.
Status: support function.
... | {"hexsha": "8bd8be9198d7a64485f498b91790be0d28ce0dd1", "size": 4757, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pn00.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SOFA.jl-ad3d3fd0-b5f2-51ee-b274-8cdbe62317e2", "max_stars_repo_head_hexsha": "528ba57a011551cac11c62712e69d03da6bebdef", "max_stars_repo_... |
%%% PLEASE RUN A SPELL CHECKER BEFORE COMMITTING YOUR CHANGES!
%%% PLEASE RUN A SPELL CHECKER BEFORE COMMITTING YOUR CHANGES!
%%% PLEASE RUN A SPELL CHECKER BEFORE COMMITTING YOUR CHANGES!
%%% PLEASE RUN A SPELL CHECKER BEFORE COMMITTING YOUR CHANGES!
%%% PLEASE RUN A SPELL CHECKER BEFORE COMMI... | {"hexsha": "4b25800b2a2122a11f8a280e4470cbe1d1286b7f", "size": 38531, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/version-history/8-4.history.tex", "max_stars_repo_name": "kurhula/HTCondor", "max_stars_repo_head_hexsha": "457d077e91900cd899b6254c4f0c8f582a70d0ea", "max_stars_repo_licenses": ["Apache-2.0"],... |
import numpy as np
import random as rand
# given a length n,
# return a random binary vector
def secretVector(n):
x = np.zeros(n, dtype = int)
for i in range(0, len(x) - 1):
r = rand.uniform(0, 1)
if r < 0.5:
x[i] = 0
else:
x[i] = 1
return x
# g... | {"hexsha": "af58ea3c6ee3fa708f6e9a07aa0222acf781926e", "size": 949, "ext": "py", "lang": "Python", "max_stars_repo_path": "attackUtils.py", "max_stars_repo_name": "johncmerfeld/BUCS_dataPrivacy", "max_stars_repo_head_hexsha": "788cb17fc0cef8135cdb2366526af00bc36c735b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright (c) 2018, Curious AI Ltd. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View... | {"hexsha": "8254d5c9cc751e430e883aabc02e5ca0b0f8e30e", "size": 5143, "ext": "py", "lang": "Python", "max_stars_repo_path": "ImbalancedSelfPU/mean_teacher/data.py", "max_stars_repo_name": "JSchweisthal/Positive-and-Unlabeled-Learning-from-Imbalanced-Data", "max_stars_repo_head_hexsha": "82e193842e4f6a7b4a0ef476f1104944e... |
import numpy as np
import os
import sys
from data_utils.prepare_dialogue_data import read_total_embeddings
def read_response_file(file_name, word2id):
#word_unk = "</s>"
word_unk = "<unk>"
word_unk_id = word2id[word_unk]
f = open(file_name, "r", encoding="utf-8")
response_data = []
for line ... | {"hexsha": "89622a725373a2ddfc7994c8ea1085908243b68c", "size": 5062, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics.py", "max_stars_repo_name": "AnonymousCIKM2021-2518/SAEDS", "max_stars_repo_head_hexsha": "165d081f6aacdefe16f5d04683c00d2ecd2b6a31", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import torch
import torch.nn as nn
import numpy as np
import warnings
warnings.filterwarnings("ignore")
torch.backends.cudnn.benchmark = True
def test(args, encoder, decoder, x, prev_hidden_temporal_list):
"""
Runs forward, computes loss and (if train mode) updates parameters
for the provided batch of in... | {"hexsha": "9fdf578e7cc43f2809fc70a5c85ccde1896719e0", "size": 5120, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/test.py", "max_stars_repo_name": "luuckiest/rvos", "max_stars_repo_head_hexsha": "f42a679c639270660201df687b04ff1c3e7a171f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 294... |
# Run tests on functions in src/by_reference_doc/tracks.jl
using Spotify.Tracks
@testset verbose = true "GET-request endpoints for tracks" begin
track_id = SpId()
@test ~isempty(tracks_get_audio_analysis(track_id)[1])
@test ~isempty(tracks_get_audio_features(track_id)[1])
# Cycle through different... | {"hexsha": "829a07ba83e5d408120422a64db09d607f3418b9", "size": 850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_tracks.jl", "max_stars_repo_name": "vnegi10/Spotify.jl", "max_stars_repo_head_hexsha": "fe93096d8871f385acf955888dfcd4af41f075e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6,... |
# Imports
import typhoon.api.hil as hil
from tests.utils import psim_export_netxml
from typhoon.api.schematic_editor import model
from typhoon.test.capture import start_capture, get_capture_results
import pytest
import typhoon.test.signals as sig
from typhoon.test.ranges import around
import numpy as np
import os
from ... | {"hexsha": "98b75d4526a4bfc0215594c406fc3c29b1ba7754", "size": 3080, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_switching_components/test_mosfet/test_mosfet.py", "max_stars_repo_name": "typhoon-hil/schematic-converter", "max_stars_repo_head_hexsha": "86846ca0851c0a7c814f414eed52bd6b41da9921", "ma... |
import importlib
import sys
import os
import pylbm
import pytest
import numpy as np
path = os.path.dirname(__file__) + '/../demo/3D'
path = os.path.abspath(path)
@pytest.fixture
def test3D_case_dir():
sys.path.append(path)
yield
sys.path.pop()
@pytest.mark.slow
@pytest.mark.h5diff(single_reference=True)... | {"hexsha": "0df35e49d4143c943ed7cf750eaf88499e8e9fcf", "size": 1137, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_demo_3d.py", "max_stars_repo_name": "bgraille/pylbm", "max_stars_repo_head_hexsha": "fd4419933e05b85be364232fddedfcb4f7275e1f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
Base.@deprecate contiguous_view ContiguousView
Base.@deprecate strided_view StridedView
Base.@deprecate unsafe_view unsafe_aview
Base.@deprecate view aview
| {"hexsha": "4935593e1acdbb1c39de650306c704c800bb045c", "size": 156, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/deprecates.jl", "max_stars_repo_name": "goretkin/ArrayViews.jl", "max_stars_repo_head_hexsha": "d32696431d85648cc5885440a6b45b8cdf23a334", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Author: Moises Henrique Pereira
# this class handles to train models and give the corresponding prediction
from numpy.lib.arraysetops import isin
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import IsolationForest
from .CounterfactualEngineEnums import CounterfactualEngineEnums
class C... | {"hexsha": "b912c1262fd3f27df93f0b069ce87b246898ed93", "size": 2091, "ext": "py", "lang": "Python", "max_stars_repo_path": "ui/CounterfactualEngine/CounterfactualEngine.py", "max_stars_repo_name": "MoisesHenr/OCEAN", "max_stars_repo_head_hexsha": "e99c853893adc89652794ace62fcc8ffa78aa7ac", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma nth_w2p: "bit ((2::'a::len word) ^ n) m \<longleftrightarrow> m = n \<and> m < LENGTH('a::len)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bit (2 ^ n) m = (m = n \<and> m < LENGTH('a))
[PROOF STEP]
by transfer (auto simp add: bit_exp_iff) | {"llama_tokens": 120, "file": "Word_Lib_More_Word", "length": 1} |
import numpy as np
import torch
import numpy.linalg as linalg
import scipy.stats as stats
import pickle
from sys import exit
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument("--m", type = int, default = 20)
parser.add_argument("--n", type = int, default = 40)
parser.add_argument("--K", ... | {"hexsha": "5c58402951c7eeea9272ab2b99fc664756a2821a", "size": 1135, "ext": "py", "lang": "Python", "max_stars_repo_path": "Bayesian_lasso_svd_mi/normal_distributed_noise/script/simulate_data.py", "max_stars_repo_name": "RuoshuiAqua/Bayesian_SVD_MI", "max_stars_repo_head_hexsha": "4b221ca86d909ba8a9f54bdda65c90045f10e9... |
function b = r8mat_gesl ( a, n, pivot, b, job )
%*****************************************************************************80
%
%% R8MAT_GESL solves a system factored by R8MAT_GEFA.
%
% Discussion:
%
% This is a simplified version of the LINPACK routine DGESL.
%
% Licensing:
%
% This code is distributed und... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_mat/r8mat_gesl.m"} |
[STATEMENT]
lemma dg_Funct_is_arrI:
assumes "\<NN> : \<FF> \<mapsto>\<^sub>C\<^sub>F\<^sub>.\<^sub>t\<^sub>m \<GG> : \<AA> \<mapsto>\<mapsto>\<^sub>C\<^sub>.\<^sub>t\<^sub>m\<^bsub>\<alpha>\<^esub> \<BB>"
shows "ntcf_arrow \<NN> : cf_map \<FF> \<mapsto>\<^bsub>dg_Funct \<alpha> \<AA> \<BB>\<^esub> cf_map \<GG>"
[P... | {"llama_tokens": 1688, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_DG_FUNCT", "length": 10} |
module debspkg
# Write your package code here.
using ForwardDiff
include("extra_file.jl")
export f,derivative_of_f1
end
| {"hexsha": "e27193ff788d31c2773c1d82d3b3b726646a5a53", "size": 124, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/debspkg.jl", "max_stars_repo_name": "krishna-vasudev/debspkg.jl", "max_stars_repo_head_hexsha": "dd788f253bd965bdadd0c28cf22c37f18011bf6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# (C) British Crown Copyright 2017-2021 Met Office.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | {"hexsha": "76d45a170bb770804bf2b109cac027aa88a33202", "size": 37639, "ext": "py", "lang": "Python", "max_stars_repo_path": "improver_tests/synthetic_data/test_set_up_test_cubes.py", "max_stars_repo_name": "cpelley/improver", "max_stars_repo_head_hexsha": "ebf77fe2adc85ed7aec74c26671872a2e4388ded", "max_stars_repo_lice... |
# 2-level system with oscillating detuning
## Imports
Start by importing the necessary packages
```python
%load_ext autoreload
%autoreload 2
import joblib
import matplotlib.pyplot as plt
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D
plt.style.use("ggplot")
import numpy as np
import qutip
... | {"hexsha": "3d8f8f6c9c1a26400ea279d66bee4c0a6c5d4040", "size": 248506, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "examples/Two-level system with oscillating detuning - no RWA.ipynb", "max_stars_repo_name": "otimgren/toy-systems", "max_stars_repo_head_hexsha": "017184e26ad19eb8497af7e7e4f3e7bb81... |
from math import log, sqrt
import numpy as np
class MCTSNode:
def __init__(self, parent, action, player, numberOfAgents):
self.parent = parent
self.action = action
self.children = []
self.explored_children = 0
self.visits = 0
self.value = np.zeros(numberOfA... | {"hexsha": "32ca34e5c16907d1f17192eacc6bb5cc70239cfa", "size": 487, "ext": "py", "lang": "Python", "max_stars_repo_path": "game_logic/mcts_util.py", "max_stars_repo_name": "matiasmv/MultiAgentes2020", "max_stars_repo_head_hexsha": "1c25216a077fd62491b16a533332866817242a25", "max_stars_repo_licenses": ["MIT"], "max_star... |
/**
* Copyright Soramitsu Co., Ltd. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0
*/
#include <gtest/gtest.h>
#include <boost/uuid/random_generator.hpp>
#include <boost/uuid/uuid.hpp>
#include <boost/uuid/uuid_io.hpp>
#include <boost/variant.hpp>
#include "backend/protobuf/query_responses/proto_query_r... | {"hexsha": "28282fe6d47eff9c34e05e08f03782f8a7be0338", "size": 5721, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/regression/regression_test.cpp", "max_stars_repo_name": "iceseer/iroha", "max_stars_repo_head_hexsha": "4a68cd50c24c8922dded85ee28872e07f636b32f", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
push!(LOAD_PATH, "/Users/eroesch/Documents/phd/neural-ode/")
using Flux, DiffEqFlux, DifferentialEquations, Plots
## Setup ODE to optimize
function lotka_volterra(du,u,p,t)
x, y = u
α, β, δ, γ = p
du[1] = dx = α*x - β*x*y
du[2] = dy = -δ*y + γ*x*y
end
u0 = [1.0,1.0]
tspan = (0.0,10.0)
p = [1.5,1.0,3.0,1.0]
pro... | {"hexsha": "36e5d965be7e2098d42d07e891766972ef4a5282", "size": 1320, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "other/diffeq/lis_two_stage.jl", "max_stars_repo_name": "LislPisl/model-zoo", "max_stars_repo_head_hexsha": "e0684ddcf027d9509461fc1024ee53327fb6a65d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
""" twomotors.py
Run two motors with a sinusoidal speed input.
This example is an extension of `motor_one.py`. Its purpose is to show how
to use list comprehensions to access and run the two motors. Note also that
the current time (`tcurr`) will have different values for each motor. Using
the correct time stamp for ... | {"hexsha": "e9ea500f8514c0482330389180acf21f1fcba796", "size": 2012, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/twomotors.py", "max_stars_repo_name": "EduardoNigro/pyev3", "max_stars_repo_head_hexsha": "dbb84a3eee85b6733014ef9a54e5f2270f5ef6a6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
%%%%%%%%%%%%%%%%%%%%%definitions%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{../../doc/related_pages/header.tex}
\input{../../doc/related_pages/newcommands.tex}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%DOCUMENT%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
\title{
The full-F electromagnetic model in toroidal geometr... | {"hexsha": "6ced081c16ddb68f04a078e9d795d4265bdaf34a", "size": 96116, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/feltor/feltor.tex", "max_stars_repo_name": "RaulGerru/FELTOR_Raul", "max_stars_repo_head_hexsha": "a566f8a9003ade437e093334877f839f3dfd0260", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
@testset "Genus" begin
Qx, x = FlintQQ["x"]
K, a = NumberField(x - 1, "a", cached = false)
OK = maximal_order(K)
rlp = real_places(K)
sig = Dict(rlp[1] => 2)
p2 = prime_decomposition(OK, 2)[1][1]
p3 = prime_decomposition(OK, 3)[1][1]
p5 = prime_decomposition(OK, 5)[1][1]
@test length(Hecke.local_gen... | {"hexsha": "450651b268237933e6b7261534a74e75b6e44b82", "size": 1976, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/QuadForm/Quad/Genus.jl", "max_stars_repo_name": "tthsqe12/Hecke.jl", "max_stars_repo_head_hexsha": "ced5350865aeaaa471a42f0300b1a7c459a73f09", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
#!/usr/bin/env python
"""
pyOpt_objective
Holds the representation of a pyOptSparse objective
Copyright (c) 2008-2013 by pyOpt Developers
All rights reserved.
Developers:
-----------
- Dr. Gaetan K.W. Kenway (GKK)
History
-------
v. 1.0 - Initial Class Creation (GKK, 2013)
"""
import numpy
# ==================... | {"hexsha": "2dfd8d27374f815a4a39a31df64553aef2eba15f", "size": 1278, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyoptsparse/pyOpt_objective.py", "max_stars_repo_name": "robfalck/pyoptsparse", "max_stars_repo_head_hexsha": "c99f4bfe8961492d0a1879f9ecff7a2fbb3c8c1d", "max_stars_repo_licenses": ["CNRI-Python"]... |
import holoviews as hv
import numpy as np
import param
# from ...models import Interface
from ..abstract_plot_models import InterfacePlottingBase
class GeneCountOverTime(InterfacePlottingBase):
"""
For each treatment group:
+ Curve: mean / median / trend
+ Points: given by count_variable.
... | {"hexsha": "af40c722dd21f9350c8589162531fdce66c04fe8", "size": 6228, "ext": "py", "lang": "Python", "max_stars_repo_path": "GSForge/plots/gem/_gene_count_vs_time.py", "max_stars_repo_name": "SystemsGenetics/GEMprospector", "max_stars_repo_head_hexsha": "7c5c7f5003a4b12feffe8d94325da96d53cc17f6", "max_stars_repo_license... |
import sys
import numpy as np
from scipy.stats import multivariate_normal
sys.path.append('./../../')
from src.HMC.hmcparameter import HMCParameter
class VelParam(HMCParameter):
"""
This class implements a velocity parameter for an HMC parameter, with a Gaussian distribution.
"""
def __init__(self, ... | {"hexsha": "5eb27ea62a63d14de5fa20f0e224c40aae5a5b54", "size": 1286, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/FullModel/vel_param.py", "max_stars_repo_name": "noashin/local_global_attention_model", "max_stars_repo_head_hexsha": "531e6a4cc1dc364a6a4168de1b9f972727a8aeb1", "max_stars_repo_licenses": ["M... |
from typing import List
import spacy
nlp = spacy.load("en_core_web_sm")
# example text
# text = """First of all, my obvious answer is Grave of The Fireflies. The emotional labor--no thank you. Second of all, the less obvious answer is A Whisker Away, because it was so well-made and relatable in my opinion that I was c... | {"hexsha": "82d160810f4be004cb5948112bd56865d44387ff", "size": 4938, "ext": "py", "lang": "Python", "max_stars_repo_path": "entity_recognition.py", "max_stars_repo_name": "singinzrain/codechella-spoiler-detection", "max_stars_repo_head_hexsha": "acc3339a91e0e36a5a761bc2bc4d20d132074fd8", "max_stars_repo_licenses": ["Ap... |
import numpy as np
import pandas as pd
class Dictionary:
def __init__(self, dict_dir):
'''
Hash Table or dict()
'''
self.data = pd.read_csv(dict_dir, encoding="UTF8")
print("Building Dictionary from ", dict_dir)
self._word2idx = {}
self._idx2word = {}
... | {"hexsha": "af01e2d3443cefe1509d8d575bfcd9d351828e93", "size": 1594, "ext": "py", "lang": "Python", "max_stars_repo_path": "law/dictionary.py", "max_stars_repo_name": "Evangeline98/Legal-AI", "max_stars_repo_head_hexsha": "4d6e01e7956f2f1940f803bc7c9599581a992a2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import contextlib
import datetime
import io
import json
import logging
import numpy as np
import os
import shutil
import pycocotools.mask as mask_util
from fvcore.common.timer import Timer
from iopath.common.file_io import file_lock
from PIL import Image
import glob
import pdb
from pathlib import Path
from detectron2.... | {"hexsha": "cc78e49393997e5cf67491e076333f3f2f2e5776", "size": 5192, "ext": "py", "lang": "Python", "max_stars_repo_path": "detectron2/data/datasets/tdw_playroom.py", "max_stars_repo_name": "honglin-chen/detectron2_panoptic_deeplab", "max_stars_repo_head_hexsha": "9e31751a3dbd3a1223fb6fed11cec7800ab5b4ea", "max_stars_r... |
#!/usr/bin/env python
import os, sys
import logging
import subprocess
import shutil
from setuptools import setup, find_packages
#from setuptools.command.build_py import build_py
#from setuptools.extension import Extension
#import numpy as np
#from Cython.Build import cythonize
#import cmake
# Set up the logging... | {"hexsha": "5a6c490debe9aedceb157fb95242bfeaae6bec2d", "size": 2953, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "paul-krug/Target-Approximation-Model", "max_stars_repo_head_hexsha": "17406423ba45a4b001661b0364023ce8b5b78fbc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# -*- coding: utf-8 -*-
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from astropy.units import cds
from sloscillations import generate_data
cds.enable()
if __name__=="__main__":
metadata = pd.read_csv('metadata.csv')
dat = generate_data.... | {"hexsha": "a27ae86eb19168834acec6a3478a6887f1c2480e", "size": 588, "ext": "py", "lang": "Python", "max_stars_repo_path": "sloscillations/tests/test_datagen.py", "max_stars_repo_name": "jsk389/RGB-PowerSpectra-v2", "max_stars_repo_head_hexsha": "47ca7cae256ad09a7e5a40fe9da82d48c32ff7cc", "max_stars_repo_licenses": ["MI... |
import rdkit
import rdkit.Chem as Chem
import numpy as np
import random
elem_list = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr', 'Pt',... | {"hexsha": "2970eac8c0105d16dd6a88e987ecaa05d540f5df", "size": 15329, "ext": "py", "lang": "Python", "max_stars_repo_path": "USPTO-15K/rank-diff-wln/mol_graph.py", "max_stars_repo_name": "wengong-jin/nips17-rexgen", "max_stars_repo_head_hexsha": "fb7dea369b0721b88cd0133a7d66348d244f65d3", "max_stars_repo_licenses": ["M... |
import os, cv2
import numpy as np
from .utils import normalize
from typing import Tuple, Dict
def dividir_dataset_em_treinamento_e_teste(dataset: np.ndarray, divisao=(80,20)):
"""
Divisão representa a porcentagem entre conj. de treinamento e conj. de teste.
Ex: (80,20) representa 80% para treino e 20% para... | {"hexsha": "e774b5def425d232e18e62416736614348ac3bb7", "size": 3990, "ext": "py", "lang": "Python", "max_stars_repo_path": "denoising/pre_processing.py", "max_stars_repo_name": "darlannakamura/denoising-dbt", "max_stars_repo_head_hexsha": "19171cda784b1b4beb1c8fb030102b8b8e98e27b", "max_stars_repo_licenses": ["MIT"], "... |
"""Constraints module.
Classes for constraints and lists of constraints.
"""
import numpy as np
import casadi as cs
from enum import Enum
from safe_control_gym.envs.benchmark_env import BenchmarkEnv
class ConstrainedVariableType(str, Enum):
"""Allowable constraint type specifiers.
"""
STATE = 'STATE... | {"hexsha": "e5e4ece62ac543b619da4fc026544d592ff67e35", "size": 21862, "ext": "py", "lang": "Python", "max_stars_repo_path": "safe_control_gym/envs/constraints.py", "max_stars_repo_name": "gokhanalcan/safe-control-gym", "max_stars_repo_head_hexsha": "e9086e102663a60a66f2cc9c8cd7610888744056", "max_stars_repo_licenses": ... |
/* Classes for Boost.Flyweight key-value tests.
*
* Copyright 2006-2009 Joaquin M Lopez Munoz.
* 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)
*
* See http://www.boost.org/libs/flyweight for library home page... | {"hexsha": "11a97d0e99593db88c82c10cb4dad33403fc529f", "size": 2800, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/libs/flyweight/test/heavy_objects.hpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses": ["BSD... |
SUBROUTINE readsrc(srcfile,iexit)
!-------------------------------------------------------------
!
! Reads the source maps based on the format = 0/1
!
!-------------------------------------------------------------
USE invar
USE totvar
IMPLICIT NONE
INTEGER, INTENT(INOUT) :: iexit
INTEGER :: form, a, ni, i, j, g, xs, ... | {"hexsha": "09d77455ccb2e5838048365d0e6269c2dda8d79a", "size": 2227, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "variations-old/anisdd-2d/readsrc.f90", "max_stars_repo_name": "lanl/pidots", "max_stars_repo_head_hexsha": "9b1bf717337ce84ea20b82cfb358eadb04532d6c", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
#!/usr/bin/env python
# coding: utf-8
# Render metrics graphs
import csv
import logging
import os
import glob
import socket
from argparse import ArgumentParser
from collections import defaultdict
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import torch
from mpl_toolkits.axe... | {"hexsha": "c775a86cbabb3cc785e614a537a1a8c928065c22", "size": 7389, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/create_metric_graphs.py", "max_stars_repo_name": "TomFrederik/grok", "max_stars_repo_head_hexsha": "dee13b05ad60c097c90ddfdb4a61eaa33df312b7", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <boost/test/unit_test.hpp>
#include <hotplate/hotplate.h>
BOOST_AUTO_TEST_SUITE(hotplate_test_suite)
BOOST_AUTO_TEST_CASE(hotplate_test_1)
{
hotplate::hotplate hp(42);
BOOST_REQUIRE(true);
}
BOOST_AUTO_TEST_SUITE_END()
| {"hexsha": "386a292bfa9387fcbb48512361ce3df3685a2b16", "size": 233, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/hotplate_boost_test/hotplate_boost_test.cpp", "max_stars_repo_name": "samwarring/hotplate", "max_stars_repo_head_hexsha": "2176054ffe315023208a085777682bc4ebccdbe7", "max_stars_repo_licenses": ["... |
import tensorflow as tf
import numpy as np
import nltk
import pickle
import random
import Helper
import argparse
# parse cli arguments
ap = argparse.ArgumentParser(description="Tensorflow RNN for text generation")
ap.add_argument('-t', '--train', help = 'Set this flag to train the RNN from scratch', action='store_true... | {"hexsha": "6237984a454f7d5d7dc135e8c13ae3b344787242", "size": 11928, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lil JadenBot/word-lstm.py", "max_stars_repo_name": "oduwa/pyRNN", "max_stars_repo_head_hexsha": "d6c60724da68b76e2e9cf941431e6aa67ee0f329", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from library.c4_bbox_iou import bbox_overlaps
def demo_bbox_iou():
pred_bbox3 = np.array([[10, 20, 60, 65, 0.6],
[10, 27, 60, 75, 0.8],
[50, 90, 90, 95, 0.45],
[10, 90, 90, 95, 0.8],
[20,... | {"hexsha": "1ab0b81400327221ca01d9175981fb6a9817f5d6", "size": 576, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/c4_bbox_iou.py", "max_stars_repo_name": "hhaAndroid/deep_learning_codesegment", "max_stars_repo_head_hexsha": "2b09654b9cb2aec9f534c496819ad19d69734897", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 08:55:25 2020
@author: Miguel
"""
import sys
sys.path.append('C:/Users/mahom/Documents/Python Scripts/UNM/utils')
import numpy as np
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn import gaussian_process
f... | {"hexsha": "801af5eaab64e4ffdcda1a5095e3f9bc18f1941d", "size": 6224, "ext": "py", "lang": "Python", "max_stars_repo_path": "GP24I_v4.py", "max_stars_repo_name": "Miguel-Hombrados/GPK-pytorch", "max_stars_repo_head_hexsha": "ef2addc5b40fa94a9ff1d8b650ee02d9044790c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module Generic.Lib.Data.Product where
open import Data.Product renaming (map to pmap; zip to pzip) hiding (map₁; map₂) public
open import Generic.Lib.Intro
open import Generic.Lib.Category
infixl 4 _,ᵢ_
first : ∀ {α β γ} {A : Set α} {B : Set β} {C : A -> Set γ}
-> (∀ x -> C x) -> (p : A × B) -> C (proj₁ p) × ... | {"hexsha": "efb22caced340ab9533d17509d9db817f87dde22", "size": 1462, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Generic/Lib/Data/Product.agda", "max_stars_repo_name": "turion/Generic", "max_stars_repo_head_hexsha": "e102b0ec232f2796232bd82bf8e3906c1f8a93fe", "max_stars_repo_licenses": ["MIT"], "max_star... |
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from gym import spaces
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
from stable_baselines3.common.vec_env.stacked_observations import StackedDictObservations, StackedObservations
class VecFrameStack(VecE... | {"hexsha": "e06d5125e0f4b666af20a1b86ef53c63b635c1b9", "size": 2694, "ext": "py", "lang": "Python", "max_stars_repo_path": "stable_baselines3/common/vec_env/vec_frame_stack.py", "max_stars_repo_name": "SwapnilPande/stable-baselines3", "max_stars_repo_head_hexsha": "93f62cef7c44295f7e82c751a8a7b23653361313", "max_stars_... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from data_utils.task_def import TaskType
from module.san import SANClassifier
#------------------------------
# The Discriminator
# https://www.aclweb.org/anthology/2020.acl-main.191/
# https://github.com/crux82/ganbert
#------... | {"hexsha": "8940367ef3206544b82465569ea98170ebd1ff9a", "size": 6467, "ext": "py", "lang": "Python", "max_stars_repo_path": "mttransformer/tasks/__init__.py", "max_stars_repo_name": "sicilyg/ganbert-test", "max_stars_repo_head_hexsha": "5bb14573a2330573ea9c932827ec1b462842a530", "max_stars_repo_licenses": ["Apache-2.0"]... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TEMPLATE: Define bibliography options
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define the options for bibliography and load biblatex package
\RequirePackage... | {"hexsha": "dece7ce135c44bc56c8e2550ea3ca93c168cb7d1", "size": 16581, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "support/templates/tech/template-tech-format.tex", "max_stars_repo_name": "jcarpi/nasa-latex-docs", "max_stars_repo_head_hexsha": "9400faf9d26c8dd58489d5fecc3046d2183ccc6e", "max_stars_repo_licenses... |
import logging
import numpy as np
import esutil as eu
from metadetect.detect import MEDSifier
from .util import get_masked_frac
from .fofs import get_fofs
from .mof_fitter import MOFFitter
from .metacal_fitter import MetacalFitter, METACAL_TYPES
logger = logging.getLogger(__name__)
class MetacalPlusMOF(object):
... | {"hexsha": "750faa95def216210014902063d8e0c7fd920511", "size": 3661, "ext": "py", "lang": "Python", "max_stars_repo_path": "mdetsims/metacal/metacal_mof_fitter.py", "max_stars_repo_name": "kaiwen-kakuiii/metadetect-sims", "max_stars_repo_head_hexsha": "a0fd133ca5bc946c6ce769e8657ef2ce10226953", "max_stars_repo_licenses... |
import os
# import bct
# import igraph
import numpy as np
import pylab as pl
from copy import copy
import networkx as nx
from random import shuffle
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from mpl_toolkits.axes_grid1 import make_axes_locatable
class Drawing(object):
"""
draw ... | {"hexsha": "6f40f1212c9a61c1f32960fc9e91381e6fe059c4", "size": 5930, "ext": "py", "lang": "Python", "max_stars_repo_path": "itng/drawing.py", "max_stars_repo_name": "SaeedTaghavi/itng_toolbox", "max_stars_repo_head_hexsha": "baae7abbea78cbeec3b5b8b29fad7d748dff3c20", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
#############################################################################
##
# This file is part of Taurus
##
# http://taurus-scada.org
##
# Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain
##
# Taurus is free software: you can redistribute it and/or modify
# it under the terms of t... | {"hexsha": "0746a5d8eebdd53926ac823e6faa0e2380333890", "size": 3596, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/taurus/core/util/test/test_codecs.py", "max_stars_repo_name": "MikeFalowski/taurus", "max_stars_repo_head_hexsha": "ef041bf35dd847caf08a7efbe072f4020d35522e", "max_stars_repo_licenses": ["CC-B... |
from mne.decoding import CSP
import numpy as np
import pandas as pd
from scipy.signal import iirfilter, sosfilt
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_selection import SelectKBest, mutual_info_classif
FILTERS_YK2019 = [
(7.5, 14), (11, 13), (10, 14), (9, 12), (19, 22), (16, ... | {"hexsha": "76222e0db18b28563d525493e251f754ab12b1b7", "size": 4856, "ext": "py", "lang": "Python", "max_stars_repo_path": "moabb/pipelines/fbcsp_selectandcov.py", "max_stars_repo_name": "m2-farzan/moabb", "max_stars_repo_head_hexsha": "a8be50d2508f28ba4d272b0f111328a75c42adab", "max_stars_repo_licenses": ["BSD-3-Claus... |
\documentclass[british]{article}
\usepackage[T1]{fontenc}
\usepackage[latin9]{luainputenc}
\usepackage[a4paper]{geometry}
\geometry{verbose,tmargin=2cm,bmargin=2cm,lmargin=2cm,rmargin=2cm}
\usepackage{fancyhdr}
\pagestyle{fancy}
\usepackage{babel}
\usepackage{setspace}
\usepackage{microtype}
\onehalfspacing
\usepackage... | {"hexsha": "0a46ff11477ce3c4995b27cfa2230748f18d19d3", "size": 12733, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Algorithm_Basics_Notes/Algorithm_Basics_Notes.tex", "max_stars_repo_name": "Yuvvi01/Notes", "max_stars_repo_head_hexsha": "42f22fcb564b8dfab1443b00ec6f75327f86a87e", "max_stars_repo_licenses": ["MI... |
import numpy as np
import thermoplotting as tp
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
#Use structure score and lattice score to bin structures as FCC, BCC or HCP.
#Uses struclat_scores.txt, generated through
#casm query -k 'struc_score(PRIM,basis_score)' 'struc_score(PRIM,lattice_score)'... | {"hexsha": "44683245477cd1ae28b01f8f590795e89d60411a", "size": 2695, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/scripts/strucbin.py", "max_stars_repo_name": "Van-der-Ven-Group/thermoplotting", "max_stars_repo_head_hexsha": "d826d728f406896b7a56207f3f4e9b4176de0e97", "max_stars_repo_licenses": ["MIT"], "... |
#! /usr/bin/env python
import unittest
import numpy as np
from mushi import histories
from mushi import utils
class TestMushi(unittest.TestCase):
def test_constant_history(self):
u"""test expected SFS under constant demography and mutation rate
against the analytic formula from Fu (1995)
... | {"hexsha": "6bc1a3aca3fb17cb5c517a9ab5ce1c0c0234f4fd", "size": 839, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_mushi.py", "max_stars_repo_name": "Lukez-pi/mushi_lz", "max_stars_repo_head_hexsha": "a60d9d44834de56b764940c83562451b2db9e245", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma id_closes:
shows "(id \<Gamma>) closes \<Gamma>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Lam_ml.id \<Gamma> closes \<Gamma>
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Lam_ml.id \<Gamma> closes \<Gamma>
[PROOF STEP]
{
[PROOF STATE]
proof (state)
goal (1 subgoal):... | {"llama_tokens": 1008, "file": "Lam-ml-Normalization_Lam_ml", "length": 14} |
from client import Client
import numpy as np
import configparser
from utilities import *
# Handle configuration file
config = configparser.ConfigParser()
config.read('config.ini')
binance_config = config['Binance Futures']
order_layout_mode = binance_config['OrderLayoutMode']
if order_layout_mode not in ('constant', '... | {"hexsha": "d6f0c9d59cdc57b235eae80f2cedf19b7b3e1bca", "size": 4603, "ext": "py", "lang": "Python", "max_stars_repo_path": "place-order.py", "max_stars_repo_name": "TurtleARM/CryptoLayering", "max_stars_repo_head_hexsha": "d544ce2ac63a3a42edd16092d1d3db8266639daf", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Bilinear Bayer CFA Demosaicing
==============================
*Bayer* CFA (Colour Filter Array) bilinear demosaicing.
References
----------
- :cite:`Losson2010c` : Losson, O., Macaire, L., & Yang, Y. (2010).
Comparison of Color Demosaicing Methods. In Advances in Imaging and
Elec... | {"hexsha": "92a830162f6dc973b89de5842e3aa66570434c6e", "size": 3657, "ext": "py", "lang": "Python", "max_stars_repo_path": "colour_demosaicing/bayer/demosaicing/bilinear.py", "max_stars_repo_name": "maaleske/colour-demosaicing", "max_stars_repo_head_hexsha": "5470a538323adfc14779e4444e36c3af69945dd7", "max_stars_repo_l... |
#data set class
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
import glob
class brain_dataset(Dataset):
def __init__(self, instance_list, label_list):
self.instance_list = instance_list
self.instance_label = label_list
def __getitem__(self, index):
... | {"hexsha": "3d9e132164cc56eef94799f93598812d4ef1896e", "size": 4371, "ext": "py", "lang": "Python", "max_stars_repo_path": "helpers/brain_data.py", "max_stars_repo_name": "tufts-ml/fNIRS-mental-workload-classifiers", "max_stars_repo_head_hexsha": "b5199d6184e659152d1fe650db48eba53a221186", "max_stars_repo_licenses": ["... |
from __future__ import print_function
import matplotlib
matplotlib.use('tkAgg')
import matplotlib.pyplot as plt
from scipy.sparse import csr_matrix
from dolfin import *
import scipy
import numpy as np
import pyshtools
from deepsphere import utils
# Test for PETSc and SLEPc
if not has_linear_algebra_backend("PETSc... | {"hexsha": "e51c433593e5b461574c97b1deb05a42083eebab", "size": 5106, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/03.FEM_laplacian/equiangular/normal/16_check_reordering_mask.py", "max_stars_repo_name": "MartMilani/PDM", "max_stars_repo_head_hexsha": "cca07a8485c6933361536286279ae6c7e14d7fa1", "max_star... |
#!/usr/bin/env python2
##########################################################
#
# Script: smooth_mesh_2d.py
#
# Description: Smooths a 2D region of an AWP-12 mesh for
# each k value along the z axis.
#
# The interpolation boundaries are points on
# the x-axis.
#
# Eg: M8 80m C... | {"hexsha": "13b272ea7dd8e7c19ad21d1f7919e4f81ae8c9d4", "size": 4286, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scripts/mesh/smooth_mesh_2d.py", "max_stars_repo_name": "baagaard-usgs/UCVMC", "max_stars_repo_head_hexsha": "53d3c136ea32bceaa02a89c351fee7e8155a5af5", "max_stars_repo_licenses": ["Apach... |
from story.utils import result_replace
import numpy as np
class GPT2Generator:
def __init__(self, **kwargs):
pass
def generate(self, prompt, debug_print=False):
if debug_print:
print("******DEBUG******")
print("Prompt is: ", repr(prompt))
for _ in range(5):
... | {"hexsha": "8322aa95dc774fcb51fc571d0472f424035143ad", "size": 644, "ext": "py", "lang": "Python", "max_stars_repo_path": "generator/gpt2/gpt2_dummy.py", "max_stars_repo_name": "Ezekial711/AIDungeon", "max_stars_repo_head_hexsha": "0915796c5b5a1729cf1a4cf221aec97b0a1a82cf", "max_stars_repo_licenses": ["MIT"], "max_star... |
import time
import numpy as np
import oneflow as flow
class Logger(object):
def __init__(self, rank):
self.rank = rank
self.step = 0
self.metrics = dict()
def register_metric(
self, metric_name, metric, print_format=None, reset_after_print=False
):
if metric_name i... | {"hexsha": "1e3dbe222ecd6b9006dfa85a06ab344a70873a5f", "size": 4369, "ext": "py", "lang": "Python", "max_stars_repo_path": "GPT/oneflow_gpt/logger.py", "max_stars_repo_name": "mls1999725/models", "max_stars_repo_head_hexsha": "77b3a9d727cb7cf3a14a75d8fdb0d17bb411bd02", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
"""
Algorithm for solving Physics-Informed Neural Networks problems.
Arguments:
* `dx` is the discretization of the grid
* `chain` is a Flux.jl chain with a d-dimensional input and a 1-dimensional output,
* `init_params` is the initial parameter of the neural network,
* `phi` is a trial solution,
* `autodiff` is a boo... | {"hexsha": "09ebb02ae44422f9d0073f2fc016d39e6f472c47", "size": 14990, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pinns_pde_solve.jl", "max_stars_repo_name": "smibayy/NeuralPDE.jl", "max_stars_repo_head_hexsha": "0721880667f56957c668ed26736118cc8626b82b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import pulp
import pandas as pd
from data3_mrmcsp import *
from scipy.spatial import distance
"""
This is a calculation of routes between origins and destinations
it append to a dictionary (keys are a origin-destination pair id)
Example:
example = ['o0_s0_s1_s2_s9_d0', 'o0_s2_s3_s8_d0'] is a set of routes between... | {"hexsha": "1088dfe9e8b01d3ca3c5c179d860fe612b619614", "size": 5111, "ext": "py", "lang": "Python", "max_stars_repo_path": "mrmcsp/mrmcsp.py", "max_stars_repo_name": "adaj/patrol-plan", "max_stars_repo_head_hexsha": "25c603b50d2a56b7163756aa3a8c4f66f39a3e0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
(*Theorem DivergesAt_Transfer:
forall n lookahead against matched,
MatchFrontAlign lookahead matched
-> MatchFrontAlign against matched
-> n < length lookahead
-> ~(EqDivergesAt lookahead against n).
Proof.
intros ?; induction n; intros ? ?.
intros ? HFAlm HFAam HL [HTA HD];
do 2 rewrite -> skipn_O in *... | {"author": "blainehansen", "repo": "traya", "sha": "aafacf52221f234e0d1616d0d6a966fbe73935fd", "save_path": "github-repos/coq/blainehansen-traya", "path": "github-repos/coq/blainehansen-traya/traya-aafacf52221f234e0d1616d0d6a966fbe73935fd/scratch.v"} |
import os
import torch
import h5py
from torchvision import transforms
from PIL import Image
from torch.autograd import Variable
from DataAugment import DataAugment
import numpy as np
import nibabel as nib
import pandas as pd
augment = DataAugment()
transform = {'MinMax': True, 'ZScore' : True, 'Resize': 82, 'TenCrop':... | {"hexsha": "eefe9d0afdf50c8c309c40adc4ce787a707fc403", "size": 3056, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/infer.py", "max_stars_repo_name": "koriavinash1/PAC18", "max_stars_repo_head_hexsha": "7ee1439cab112aab5af40451b32f4d79f9c499aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max... |
From Coqprime Require Import PocklingtonRefl.
Local Open Scope positive_scope.
Lemma primo81:
prime 4732254109989697->
prime 46357161274830326863.
Proof.
intro H.
apply (Pocklington_refl
(Ell_certif
46357161274830326863
9796
((4732254109989697,1)::nil)
0
711828
117
... | {"author": "mukeshtiwari", "repo": "Formally_Verified_Verifiable_Group_Generator", "sha": "e80e8d43e81b5201d6ab82a8ebc07a5cef03476b", "save_path": "github-repos/coq/mukeshtiwari-Formally_Verified_Verifiable_Group_Generator", "path": "github-repos/coq/mukeshtiwari-Formally_Verified_Verifiable_Group_Generator/Formally_Ve... |
[STATEMENT]
lemma vD_nsqn_sqn [elim, simp]:
assumes "ip\<in>vD(rt)"
shows "nsqn rt ip = sqn rt ip"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nsqn rt ip = sqn rt ip
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. nsqn rt ip = sqn rt ip
[PROOF STEP]
from \<open>ip\<in>vD(rt)\<close>
[... | {"llama_tokens": 477, "file": "AODV_variants_c_gtobcast_C_Fresher", "length": 7} |
'''
Created on Apr 23, 2018
@author: rsepulveda3
'''
#import sys
#sys.path.append('C:\ProgramData\Anaconda3\Lib\site-packages')
import datetime
import matplotlib.pyplot as plt
from sklearn import tree
import graphviz
from sklearn.datasets import load_iris
from sklearn.datasets import load_wine
import sklearn.dat... | {"hexsha": "dd7e57945b2327a509679406a898a676bc3296a5", "size": 3305, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/decision_tree/tree_test.py", "max_stars_repo_name": "SirLaughalot/pythonTest", "max_stars_repo_head_hexsha": "a74e49f670e026877b3daa1f0ec9714d054ff9af", "max_stars_repo_licenses": ["BSD-3-Clau... |
# Copyright 2021 Sony Semiconductors Israel, Inc. 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 b... | {"hexsha": "aca5b98427bd09b42819dd098e51665058f719a3", "size": 4802, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_compression_toolkit/keras/visualization/nn_visualizer.py", "max_stars_repo_name": "haihabi/model_optimization", "max_stars_repo_head_hexsha": "97372a9596378bb2287c59f1180b5059f741b2d6", "max... |
Require Import Reals Psatz Omega ClassicalEpsilon.
From discprob.basic Require Export base Series_Ext order bigop_ext sval Reals_ext.
From Coquelicot Require Export Rcomplements Rbar Series Lim_seq Hierarchy Markov Continuity.
From stdpp Require Export base.
Definition le_prop {X} (P1 P2: X → Prop) :=
∀ x, P1 x → P2... | {"author": "jtassarotti", "repo": "coq-proba", "sha": "11d69b2286940ff532421252a7d9b1384c2f674a", "save_path": "github-repos/coq/jtassarotti-coq-proba", "path": "github-repos/coq/jtassarotti-coq-proba/coq-proba-11d69b2286940ff532421252a7d9b1384c2f674a/theories/measure/sets.v"} |
/*
* Copyright (c) 2011-2019, The DART development contributors
* All rights reserved.
*
* The list of contributors can be found at:
* https://github.com/dartsim/dart/blob/master/LICENSE
*
* This file is provided under the following "BSD-style" License:
* Redistribution and use in source and binary forms, w... | {"hexsha": "f6689c0bd4c89464c2dfd0b905ac2794dea5308c", "size": 15179, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "unittests/comprehensive/test_SoftDynamics.cpp", "max_stars_repo_name": "jyf588/nimblephysics", "max_stars_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_stars_repo_licenses": [... |
from os import path
import sys
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from pymapf.decentralized.velocity_obstacle.velocity_obstacle import (
MultiAgentVelocityObstacle,
)
from pymapf.decentralized.position import Position
import numpy as np
sim = MultiAgentVelocityObstacle(simulation_... | {"hexsha": "4d92baab726189f74e6d6db7afc93b8369628262", "size": 772, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/switch_positions_vel_obstacles.py", "max_stars_repo_name": "APLA-Toolbox/pymapf", "max_stars_repo_head_hexsha": "255df006925401e5ccdf82afc7dac339221574ba", "max_stars_repo_licenses": ["MIT"... |
\section{Data preprocessing}
\subsection{Resizing and Normalization}
Before applying the classification models we preprocessed our image data. We started off by resizing each image to a window of pixels of \(64 \times 64\), forming a square. Although all the images had the same size before this step, we decided to res... | {"hexsha": "c5394e81b7561ea3aee9c86b0fed3d6ea9e09b79", "size": 2634, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/sections/data-preprocessing.tex", "max_stars_repo_name": "vascoalramos/image-face-detection", "max_stars_repo_head_hexsha": "c6aced3864343481dea27882a164134890fb001a", "max_stars_repo_license... |
import numpy as np
from foolbox import set_seeds
from foolbox.attacks import LocalSearchAttack as Attack
def test_attack(bn_model, bn_criterion, bn_images, bn_labels):
set_seeds(22)
attack = Attack(bn_model, bn_criterion)
advs = attack(bn_images, bn_labels, unpack=False, d=1, t=20, R=250)
for adv in ... | {"hexsha": "16cce2137e2825dd36de696fceff4527d1514df8", "size": 1051, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnns/foolbox/foolbox_2_3_0/tests/attacks/test_batch_attacks_localsearch.py", "max_stars_repo_name": "anonymous-user-commits/perturb-net", "max_stars_repo_head_hexsha": "66fc7c4a1234fa34b92bcc85751... |
"""Image utility functions.
"""
# standard imports
from typing import Union, Tuple, List
from pathlib import Path
import logging
# third party imports
import numpy as np
# toolbox imports
from ..base.image import Imagelike
from ..base.image import ImageReader, ImageWriter, ImageDisplay, ImageResizer
# logging
LOG =... | {"hexsha": "cb19af9b42ff79a6085770026691505a38024d24", "size": 7392, "ext": "py", "lang": "Python", "max_stars_repo_path": "dltb/util/image.py", "max_stars_repo_name": "Petr-By/qtpyvis", "max_stars_repo_head_hexsha": "0b9a151ee6b9a56b486c2bece9c1f03414629efc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
subroutine ktsol(n,ns,nv,a,b,x,ktilt,maxeq)
!-----------------------------------------------------------------------
! Solution of a system of linear equations by gaussian elimination with
! partial pivoting. Several right hand side matrices and several
! variables are allowed.
! NOTE: All input... | {"hexsha": "9de5510dc9c6ff1ee61596224a75c206a1da343a", "size": 5215, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "for_code/kt3d/gslib/ktsol.f90", "max_stars_repo_name": "cmrajan/pygslib", "max_stars_repo_head_hexsha": "acdf96d9ec17658f18fe9f078104c6259b479f52", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import pandas as pd
import numpy as np
path="./RAWCSV/2021-10-25"
STATE_NAMES = {
# 'AP': 'Andhra Pradesh',
'AR': 'Arunachal Pradesh',
'AS': 'Assam',
'BR': 'Bihar',
'CT': 'Chhattisgarh',
'GA': 'Goa',
'GJ': 'Gujarat',
'HR': 'Haryana',
'HP': 'Himachal Pradesh',
'JH': 'Jharkhand',
'KA': 'Karnataka... | {"hexsha": "998bf181715f83df4542ab8c91081999b01fea72", "size": 3188, "ext": "py", "lang": "Python", "max_stars_repo_path": "json_creation/DataminJSON-code.py", "max_stars_repo_name": "incovid19/incovid19", "max_stars_repo_head_hexsha": "57bdb7fe52a6dea699c77117e9c36696b88bb059", "max_stars_repo_licenses": ["MIT"], "max... |
from json import load
import queue
from database import *
import sklearn.preprocessing
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from queue import Queue
import loader
available = False
last_centroids = None
centroids_queue = Queue(100)
query = f"""
SELECT block_hash, wallets.*
... | {"hexsha": "b084de28cfcec1da263af56e083ae9fe509d2fb6", "size": 3376, "ext": "py", "lang": "Python", "max_stars_repo_path": "wallet_clustering.py", "max_stars_repo_name": "di3go-sona/bitcoin-viz", "max_stars_repo_head_hexsha": "dd70b21ea9e5371318b28683f08994a14fe74011", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
import os
import numpy as np
import pandas as pd
from CHECLabPy.plotting.setup import Plotter
from scipy.stats import norm
class PedestalSpread(Plotter):
def plot(self, eped_sigma):
mean = np.mean(eped_sigma)
std = np.std(eped_sigma)
x = np.linspace(eped_sigma.min(), eped_sigma.max(), 100... | {"hexsha": "c39a61191d9f5a57bc00f7bfe3a5ab509be7e3b3", "size": 1329, "ext": "py", "lang": "Python", "max_stars_repo_path": "sstcam_sandbox/old/mc_config/fadc_pedestal_variation.py", "max_stars_repo_name": "watsonjj/CHECLabPySB", "max_stars_repo_head_hexsha": "91330d3a6f510a392f635bd7f4abd2f77871322c", "max_stars_repo_l... |
import numpy as np
import time
tests = np.arange(1, 11) * 1000
timings = np.zeros(tests.size)
for idx, N in enumerate(tests):
a = np.linspace(0, 2 * np.pi, N)
k = 100
A = a[:, np.newaxis]
start_time = time.time()
M = np.exp(1j * k * np.sqrt(A ** 2 + A.T ** 2))
timings[idx] = time.time() - sta... | {"hexsha": "8f27a697d1fb37d566b7aeabb424859e71ff3286", "size": 343, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "clavigne/julia-numpy-fortran-test", "max_stars_repo_head_hexsha": "1d56454c411eb7d25e29c3dab263c890a156bec3", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import torch
from torch import nn
import torch.nn.functional as nnFunc
import numpy as np
class TorusPadding(nn.Module):
'''
Padding Module thats pad the images as it is on a torus
'''
def __init__(self, padSize):
super(TorusPadding, self).__init__()
self.padSize = padSize
def forwa... | {"hexsha": "31b7ebc558d19061201a77c1db87ead7bf7bde24", "size": 4322, "ext": "py", "lang": "Python", "max_stars_repo_path": "gotex/vgg.py", "max_stars_repo_name": "ahoudard/GOTEX", "max_stars_repo_head_hexsha": "3dad19918ae8e72904d0b47f9eca2a5cf5434fca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
# -----------------------------------------------------------
# Test lead lag and multi-delayed transformations
#
# (C) 2020 Kevin Schlegel, Oxford, United Kingdom
# Released under Apache License, Version 2.0
# email kevinschlegel@cantab.net
# -----------------------------------------------------------
import numpy as ... | {"hexsha": "db90b55ac9595e814b6260620eb4fea9811d6e18", "size": 3040, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_leadlag.py", "max_stars_repo_name": "WeixinYang/PSFDataset", "max_stars_repo_head_hexsha": "f29b37489c580ad3c677bb9385a721cc57da60e4", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# forecast models
# Author: Christian Hubbs
# Email: christiandhubbs@gmail.com
# Date: 03.05.2019
import numpy as np
from calendar import monthrange
import warnings
from .demand_utils import *
# List of forecast commands that can be called
# forecast_options = [False, 'UNIFORM', 'UNIFORM_HORIZON',
# 'AGGREGATE_HORI... | {"hexsha": "b223cec7d0cffc24b1b5a0315bdcca915d046660", "size": 24792, "ext": "py", "lang": "Python", "max_stars_repo_path": "ada/environments/demand_models/forecast.py", "max_stars_repo_name": "hubbs5/public_drl_sc", "max_stars_repo_head_hexsha": "139ee12a4b498e3b55afe7b813c59538df2193f6", "max_stars_repo_licenses": ["... |
import vrep
import time
import numpy as np
import scipy.linalg as sla
M=[]
s=[]
def skew3(arr):
a=arr[0]
b=arr[1]
c=arr[2]
mat = np.array([[0,-c,b],[c,0,-a],[-b,a,0]])
return mat
def skew6(arr):
a = arr[0]
b = arr[1]
c = arr[2]
d = arr[3]
e = arr[4]
f ... | {"hexsha": "247415dd82ff6c607686631b5190575732a79737", "size": 14404, "ext": "py", "lang": "Python", "max_stars_repo_path": "week4/in.py", "max_stars_repo_name": "AngelosGuan/ECE470FinalProj", "max_stars_repo_head_hexsha": "eaac5e11b22e8e61046934dc0b14c9f959a7ff05", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
\documentclass[12pt]{article}
\usepackage[margin=0.75in, top=1in, bottom=1in, a4paper]{geometry}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{pgfplots}
\usepackage{mathtools}
\usepackage{booktabs}
\usepackage{indentfirst}
\newcommand{\mo}[1]{\lvert #1 \rvert}
\newcommand{\mos}[1]{\lvert #... | {"hexsha": "560132e3f92822084cf9b5a22d5aead8f6c82e1e", "size": 5200, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "homework-1/homework-1.tex", "max_stars_repo_name": "davidmaamoaix/convex-opt", "max_stars_repo_head_hexsha": "9a47c6f3241a72aac2b0df90e7f85869ccb09dd7", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class FullyConnectedNetwork(nn.Module):
def __init__(self, state_size, output_size, hidden_size, output_gate=None):
super(FullyConnectedNetwork, self).__init__()
self.linear1 = n... | {"hexsha": "96a4c9fbbabacd9c83d690cbef61698db14b4d88", "size": 1812, "ext": "py", "lang": "Python", "max_stars_repo_path": "p2_continuous-control/model.py", "max_stars_repo_name": "royveshovda/deep-reinforcement-learning", "max_stars_repo_head_hexsha": "64ba7ef5ab44f095b7e8b29f6c4ff1585025981a", "max_stars_repo_license... |
[STATEMENT]
lemma rawpsubst2_fresh_switch:
assumes "r \<in> atrm" "t \<in> trm" "s \<in> trm" "x \<in> var" "y \<in> var"
and "x \<noteq> y" "x \<notin> FvarsT s" "y \<notin> FvarsT t"
shows "rawpsubstT r ([(s,y),(t,x)]) = rawpsubstT r ([(t,x),(s,y)])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. rawpsubstT r [(... | {"llama_tokens": 340, "file": "Syntax_Independent_Logic_Syntax_Arith", "length": 2} |
import sys
import numpy as np
n, a, *x = map(int, sys.stdin.read().split())
def main():
m = 2500
dp = np.zeros((n + 1, m + 1), dtype=np.int64)
dp[0, 0] = 1
for i in range(n):
dp[1:, x[i] :] += dp[:-1, : -x[i]].copy()
i = np.arange(1, n + 1)
print(dp[i, i * a].sum())
... | {"hexsha": "86820c8f92f571b3192707e4118eae2e51c3f54f", "size": 362, "ext": "py", "lang": "Python", "max_stars_repo_path": "jp.atcoder/abc044/arc060_a/11773307.py", "max_stars_repo_name": "kagemeka/atcoder-submissions", "max_stars_repo_head_hexsha": "91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma alpha_Tree_supp_rel:
assumes "t1 =\<^sub>\<alpha> t2"
shows "supp_rel (=\<^sub>\<alpha>) t1 = supp_rel (=\<^sub>\<alpha>) t2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. supp_rel (=\<^sub>\<alpha>) t1 = supp_rel (=\<^sub>\<alpha>) t2
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using... | {"llama_tokens": 11549, "file": "Modal_Logics_for_NTS_FL_Formula", "length": 36} |
from .bananas import *
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench
| {"hexsha": "6613d0e976d007e91b6d34ab3fb6b6898054cc6c", "size": 101, "ext": "py", "lang": "Python", "max_stars_repo_path": "bananas/__init__.py", "max_stars_repo_name": "bccp/bananaplots", "max_stars_repo_head_hexsha": "dbfe107207e07351c7d7125430fde16fb2731cc2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
# USAGE
# python skindetector.py
# python skindetector.py --video video/skin_example.mov
# import the necessary packages
from pyimagesearch import imutils
import numpy as np
import argparse
import cv2
def has_hand(image, image_path="result.JPG"):
# define the upper and lower boundaries of the HSV pixel
# int... | {"hexsha": "e57a324175b22eb08d23acd1f7687597b368dc53", "size": 1854, "ext": "py", "lang": "Python", "max_stars_repo_path": "skindetector.py", "max_stars_repo_name": "minhkhang1795/iKrfatR", "max_stars_repo_head_hexsha": "2c8375cfe7decb068cc092b2bd57ccf622253f97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import math, operator
import random
class Sample(object):
def __init__(self, id, image, label):
self.id = id
self.image = image
self.label = label
class MiniBatch(object):
def __init__(self):
self.ids = []
self.images = []
self.labels = []
... | {"hexsha": "59fd2128d8b80c20f6a651f1834d993ecc9fc9c4", "size": 5141, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/reader/batch_patcher_online.py", "max_stars_repo_name": "kaist-dmlab/Ada-Boundary", "max_stars_repo_head_hexsha": "abdefe4a6009ab862f6ed0467462b0900b921c3a", "max_stars_repo_licenses": ["MIT"]... |
import numpy
import numpy_demo
a = numpy.array([1.0, 3.5, 8.4, 2.3, 6.6, 4.1], "d")
numpy_demo.sum_of_squares(a)
| {"hexsha": "46a1fb0c93b3be7b996bd98ac963449c7b31c7a2", "size": 114, "ext": "py", "lang": "Python", "max_stars_repo_path": "Demos/run_numeric_demo.py", "max_stars_repo_name": "smok-serwis/cython", "max_stars_repo_head_hexsha": "e551a3a348888bd89d4aad809916709a634af1fb", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
from tensorflow import keras
'''
Build the model
'''
model = keras.models.Sequential()
model.add(keras.layers.Conv2D())
| {"hexsha": "ae23e8083c0e38a47450773937d9f2773eb78d07", "size": 224, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_feature_extraction_TF2.0/CNN_structure_tf20.py", "max_stars_repo_name": "rechardchen123/trjaectory_image_process_from_frequency_domain", "max_stars_repo_head_hexsha": "b3af5eed3d66e0bda569b480... |
% based on example 7 in pythontex_gallery
% https://github.com/gpoore/pythontex/
\documentclass[12pt]{mmalatex}
\usepackage{examples}
\begin{document}
\section*{Step-by-step integration}
This is another nice example drawn from the Pythontex gallery, see
\ \url{https://github.com/gpoore/pythontex}.
It shows the ste... | {"hexsha": "de35a8ddc3b251e1016e5c172da76b352a855388", "size": 2317, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "mathematica/examples/example-03.tex", "max_stars_repo_name": "leo-brewin/hybrid-latex", "max_stars_repo_head_hexsha": "2debaf3f97eb551928d08dc4baded7ef7a4ab29a", "max_stars_repo_licenses": ["MIT"], ... |
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