text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import os, sys
import numpy as np
from tqdm import tqdm
import tensorflow as tf
class MDN_Load():
def __init__(self, name):
self.name = name
if self.name == 'sample':
(self.x_train, self.y_train), (self.x_test, self.y_test) = self.get_sample(10000)
self.output_dim = 3
... | {"hexsha": "8495a6f03fa885c2b5045a91f772a919041433e5", "size": 2620, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/mdn_load.py", "max_stars_repo_name": "KNakane/tensorflow", "max_stars_repo_head_hexsha": "1e8c862b8f7928967b1c02c613df0222ab8c4cd2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
struct SnailfishNumber
triplets::Vector{Tuple{Int, Int, Int}} # value, depth, weight
end
SnailfishNumber(str::String) = parse(SnailfishNumber, str)
function Base.parse(::Type{SnailfishNumber}, str::String)
elements = eval(Meta.parse(str))
triplets = tripletize!(Tuple{Int, Int, Int}[], elements, 0, 1)
... | {"hexsha": "a5b13b22d980ceed2420c5f18f1570a78b8e33a5", "size": 2467, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "day18/solve.jl", "max_stars_repo_name": "nsgrantham/advent-of-code-2021", "max_stars_repo_head_hexsha": "d43d86fae014bbe72dc21283650d69d0cecb7691", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
theorem sup_state_Cons1:
"(G \<turnstile> (x#xt, a) <=s (yt, b)) =
(\<exists>y yt'. yt=y#yt' \<and> (G \<turnstile> x \<preceq> y) \<and> (G \<turnstile> (xt,a) <=s (yt',b)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. G \<turnstile> (x # xt, a) <=s (yt, b) = (\<exists>y yt'. yt = y # yt' \<and... | {"llama_tokens": 227, "file": null, "length": 1} |
"""
The tests here test the webapp by sending fake requests through a fake GH
object and checking that the right API calls were made.
Each fake request has just the API information currently needed by the webapp,
so if more API information is used, it will need to be added.
The GitHub API docs are useful:
- Pull req... | {"hexsha": "da9fbc6148d5f7d295049c7b9048a20e3659a952", "size": 75709, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy_bot/tests/test_webapp.py", "max_stars_repo_name": "asmeurer/sympy-bot-1", "max_stars_repo_head_hexsha": "08e16763f7c15f70366af91b8fb022aa6c962115", "max_stars_repo_licenses": ["BSD-3-Clause... |
from torch.nn import functional as F
from torch import nn
import torch
import numpy as np
from utils import layer
from radam import RAdam
from vpn import MVProp
import utils
from torch_critic import Critic as ClassicCritic
class CriticModel(nn.Module):
def __init__(self, env, layer_number, FLAGS):
super().... | {"hexsha": "57bc3a2a3181a83d1587e9b900724bcc8fa9fdc5", "size": 7752, "ext": "py", "lang": "Python", "max_stars_repo_path": "vpn_dqn_critic.py", "max_stars_repo_name": "christsa/hide-rl", "max_stars_repo_head_hexsha": "47dc3dfd93b817831473c07137a6a6e7f2eda549", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import matplotlib.pyplot as plt
import scipy.optimize as opt
import numpy as np
# Function
def func_exponential(x,g,n_0):
return n_0*np.power(1+g,x)
if __name__=="__main__":
# Data
x_samp = np.array([1,2,3,4,5,6])
y_samp = np.array([3,4,5,6,6,11])
# Estimate
w, _ = opt.curve_fit(func_grow, x... | {"hexsha": "ab4997310a543a92be5052bb61d90d3bbc71b0e2", "size": 804, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/func_exponential.py", "max_stars_repo_name": "yasirroni/myNafiun", "max_stars_repo_head_hexsha": "70f1eb56eb344d88fa6ea7cafe2d0925bfccc1d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os.path
import matplotlib.pyplot as plt
import scanpy as sc
import pandas as pd
import seaborn as sns
from outer_spacem.io import convert_name
import numpy as np
from pathlib import Path
from outer_spacem.pl import plot_distributions, plot_umap_top_n, volcano_plot
from singlecelltools.various import get_mole... | {"hexsha": "96ab967b14d01d4a9a44badb1bfdac6ce54de9a3", "size": 7615, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/gastrosome_processing/diff_express_analysis_old.py", "max_stars_repo_name": "abailoni/single-cell-analysis", "max_stars_repo_head_hexsha": "3fb68992a22249ab96178e173ceb552c037f25ba", "max... |
@testset "Parsimonious flux balance analysis with StandardModel" begin
model = test_toyModel()
d = parsimonious_flux_balance_analysis_dict(
model,
Tulip.Optimizer;
modifications = [
change_constraint("EX_m1(e)", lb = -10.0),
change_optimizer_attribute("IPM_Iterat... | {"hexsha": "341eb6c43787009e7d1150b74e88b9046e6a886b", "size": 726, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/analysis/parsimonious_flux_balance_analysis.jl", "max_stars_repo_name": "LCSB-BioCore/COBREXA.jl", "max_stars_repo_head_hexsha": "cfe20e2a9d5e98cd097cf9f62c5d32f07c1199b0", "max_stars_repo_lice... |
# -*- coding: utf-8 -*-
"""
@author: Quoc-Tuan Truong <tuantq.vnu@gmail.com>
"""
from scipy.sparse import csr_matrix, find
from collections import OrderedDict
import numpy as np
class TrainSet:
def __init__(self, uid_map, iid_map):
self._uid_map = uid_map
self._iid_map = iid_map
@property
... | {"hexsha": "1d8298560e21eec4a177353d582986e8d3111be2", "size": 7012, "ext": "py", "lang": "Python", "max_stars_repo_path": "cornac/data/trainset.py", "max_stars_repo_name": "Andrew-DungLe/cornac", "max_stars_repo_head_hexsha": "199ab9181f8b6387cc8748ccf8ee3e5c9df087fb", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
@testset "sparse_constructor" begin
A = sprand(10,10,0.1)
s = SparseTensor(A)
@test run(sess, s)≈A
I = [1;2;4;2;3;5]
J = [1;3;2;2;2;1]
V = rand(6)
A = sparse(I,J,V,6,5)
s = SparseTensor(I,J,V,6,5)
@test run(sess, s)≈A
indices = [I J]
s = SparseTensor(I,J,V,6,5)
@test run(... | {"hexsha": "ea3fc023013e994e576fcdd1e76f0e1785ffcf5b", "size": 7112, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/sparse.jl", "max_stars_repo_name": "EricDarve/ADCME.jl", "max_stars_repo_head_hexsha": "7eb334354e3ba5427a3f13a4a60e0f6ca5eec006", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
#!/usr/bin/env python3
import numpy as np
from org.mk.training.dl.rnn_cell import LSTMCell
from org.mk.training.dl.rnn import dynamic_rnn
#from org.mk.training.dl.rnn import compute_gradients
from org.mk.training.dl.rnn import print_gradients
from org.mk.training.dl.rnn import zero_state_initializer
from org.mk.train... | {"hexsha": "16a659b48667cb0626aea2381d7ff798e8923788", "size": 6654, "ext": "py", "lang": "Python", "max_stars_repo_path": "org/mk/training/dl/LSTMMainGraphbi.py", "max_stars_repo_name": "slowbreathing/Deep-Breathe", "max_stars_repo_head_hexsha": "bcc97cadfc53d3297317764ecfb2223e5e715fd1", "max_stars_repo_licenses": ["... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
def generate_seedmap(shape, speckle_density, speckle_size, randomseeds):
np.random.seed(randomseeds[0])
SpeckleSeedMap = np.random.rand(shape[0], shape[1]) < speckle_density
np.random.seed(randomseeds[1])
SpeckleDirectionMap = np.random.rand... | {"hexsha": "bed46c383a347c060a2f84a413e04fa9ffd641d2", "size": 4465, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/utils.py", "max_stars_repo_name": "GW-Wang-thu/Generator-of-Stereo-Speckle-images-with-displacement-labels", "max_stars_repo_head_hexsha": "6a920827bd7bbba3019c97c02c7382523b790449", "max_st... |
import mdtraj
import numpy as np
def gmx_saxs(q, trajectory, topology):
intensity = np.zeros_like(q)
for chunk in mdtraj.iterload(trajectory, top=topology):
for c in chunk:
c1 = c.remove_solvent()
for i in range(c1.n_atoms):
rhoi = c1.topology.atom(i).element[0]... | {"hexsha": "37232836318fdc40c84c02d1f1583d7f1d253c44", "size": 1519, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mdscripts/saxs/saxs.py", "max_stars_repo_name": "awacha/mdscripts", "max_stars_repo_head_hexsha": "831bda06557fa2d5f0899fc2f6552c9e49146cef", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
import numpy as np
def compute_errors_over_time(Xtrain,
ytrain,
Xtest,
ytest,
theta,
feature_inds,
thresholds):
"""
The function `... | {"hexsha": "77ddf377612c64fc56a94ea27635c9d5a18809dd", "size": 1454, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/homework2/q5/errors_over_time.py", "max_stars_repo_name": "skymarshal/cs229-machine-learning-stanford-fall-2016", "max_stars_repo_head_hexsha": "3f9e0f4ea7d4fe73a50dc12c84fe47131bb0622a", "max... |
[STATEMENT]
lemma weakPsiCongSym:
fixes \<Psi> :: 'b
and P :: "('a, 'b, 'c) psi"
and Q :: "('a, 'b, 'c) psi"
assumes "\<Psi> \<rhd> P \<doteq> Q"
shows "\<Psi> \<rhd> Q \<doteq> P"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Psi> \<rhd> Q \<doteq> P
[PROOF STEP]
using assms
[PROOF STATE]
proo... | {"llama_tokens": 237, "file": "Psi_Calculi_Weak_Psi_Congruence", "length": 2} |
r"""
Graded Hopf algebras
"""
#*****************************************************************************
# Copyright (C) 2008 Teresa Gomez-Diaz (CNRS) <Teresa.Gomez-Diaz@univ-mlv.fr>
# Nicolas M. Thiery <nthiery at users.sf.net>
#
# Distributed under the terms of the GNU General Public License... | {"hexsha": "e8f4b9dc24d0cc2ee2ff6237631842b38eac1021", "size": 1512, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/categories/graded_hopf_algebras.py", "max_stars_repo_name": "bopopescu/sage-5", "max_stars_repo_head_hexsha": "9d85b34956ca2edd55af307f99c5d3859acd30bf", "max_stars_repo_licenses": ["BSL-... |
\vspace{-20pt}
\section{Procedure}
\label{sec:Procedure}
The first step is to find the minimal current value for the laser setup, where
it is still lasing. For this measurement the setup is changed to the configuration shown in
figure~\ref{fig:setup_current}. A voltmeter is connected to the laser current
monitor for a... | {"hexsha": "aedf8f2ee879c9985bf1385ec9c8e6d3f32ab753", "size": 5242, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Fortgeschrittenenpraktikum/Protokolle/V60_Diodenlaser/Auswertung.tex", "max_stars_repo_name": "smjhnits/Praktikum", "max_stars_repo_head_hexsha": "92c9df3ee7dfa2417f464036d18ac33b70765fdd", "max_sta... |
import numpy as np
import pandas as pd
from dtwknn import DtwKnn
min_window_size = 30
max_window_size = 100
threshold_mean = 0
threshold_std = 0.5
threshold_change = 1.5
model = DtwKnn(n_neighbors=1)
def segment_window(data, array, min_window_size, max_window_size):
index_segment = list()
prev_point = array... | {"hexsha": "264341a134db52878d1f65474f2659ac3fd387b8", "size": 4818, "ext": "py", "lang": "Python", "max_stars_repo_path": "bkm3t_DHBKHN/Cau4/service_predict/gesture.py", "max_stars_repo_name": "atheros98/OLP-FOSS-2018", "max_stars_repo_head_hexsha": "c3ba261a60e80a6e355da34b6015c767a4d69fba", "max_stars_repo_licenses"... |
import numpy as np
class Config:
MEMORY_START_ADDRESS = 0x200
FONT_SET_START_ADDRESS = 0x50
FONT_SET = np.array([
0xF0, 0x90, 0x90, 0x90, 0xF0,
0x20, 0x60, 0x20, 0x20, 0x70,
0xF0, 0x10, 0xF0, 0x80, 0xF0,
0xF0, 0x10, 0xF0, 0x10, 0xF0,
0x90, 0x90, 0xF0, 0x10, 0x10,
... | {"hexsha": "1c5b39d6b1c3cd61c5995ed3bf62415a34b72de6", "size": 759, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/cpu/config/memory_config.py", "max_stars_repo_name": "rafael-junio/JustAChip8PythonEmulator", "max_stars_repo_head_hexsha": "ff9c2d67aeaf4f87ff3b5fd6f0231702587455a7", "max_stars_repo_licenses... |
# ===============================================================================
# Copyright 2011 Jake Ross
#
# 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": "09cd794e3693a0f428c10079ab8eabcd5e9bc901", "size": 9485, "ext": "py", "lang": "Python", "max_stars_repo_path": "sandbox/count_time.py", "max_stars_repo_name": "ASUPychron/pychron", "max_stars_repo_head_hexsha": "dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# -*- coding: utf-8 -*-
from typing import NamedTuple, Optional, Tuple
import numpy as np
from signalworks import dsp
from signalworks.processors.processing import DefaultProgressTracker, Processor
from signalworks.tracking import TimeValue, Wave
class SpectralDiscontinuityEstimator(Processor):
name = "Spectral ... | {"hexsha": "24b496e3ffab545ac874bbfddde9d25eabd4a675", "size": 2799, "ext": "py", "lang": "Python", "max_stars_repo_path": "signalworks/processors/spectral_discontinutiy_estimator.py", "max_stars_repo_name": "lxkain/tracking", "max_stars_repo_head_hexsha": "00ed9a0b31c4880687a42df3bf9651e68e0c4360", "max_stars_repo_lic... |
import numpy as np
import pandas as pd
from datetime import datetime
from types import FunctionType
from pandapower.timeseries import OutputWriter
from pandahub.mongo_io_methods import MongoIOMethods
try:
import pplog
logger = pplog.getLogger(__name__)
except ImportError:
import logging
class OutputWrite... | {"hexsha": "53dd48af95296204f98a9e4ffd68253437994ba0", "size": 6835, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandahub/lib/timeseries/output_writer_mongodb.py", "max_stars_repo_name": "e2nIEE/pandahub", "max_stars_repo_head_hexsha": "4d4abb29f49d32d035120ebea99fb96ba3d44bfc", "max_stars_repo_licenses": ["... |
import pytest
import numpy as np
import scipy.sparse as sp
import warnings
from sklearn import clone
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils._testing import (
assert_array_almost_equal,
assert_array_equal,
assert_allclose_dense_s... | {"hexsha": "e1317acb978084675cb32d3ae204320b7235f285", "size": 15963, "ext": "py", "lang": "Python", "max_stars_repo_path": "sklearn/preprocessing/tests/test_discretization.py", "max_stars_repo_name": "huzq/scikit-learn", "max_stars_repo_head_hexsha": "f862129f36786acbae3d9f2d161bbb72d77b87ec", "max_stars_repo_licenses... |
import numpy as np
from PyMieScatt import RayleighMieQ
from scipy.special import jv, yv
def MieQ(m, wavelength, diameter, nMedium=1, asDict=False, asCrossSection=False):
# http://pymiescatt.readthedocs.io/en/latest/forward.html#MieQ
wavelength /= nMedium
m /= nMedium
x = np.pi*diameter/wavelength
if x==0:
... | {"hexsha": "da2901b897b2943ec59d19ed3b1d7de99ed797f4", "size": 2574, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyMieScatt/devmode/functionPrototyping.py", "max_stars_repo_name": "hmaarrfk/PyMieScatt", "max_stars_repo_head_hexsha": "81d152af85dad20963cdee2dffd9dfe9a8fc54a1", "max_stars_repo_licenses": ["MIT... |
Demo - Poisson equation 2D
=======================
Solve Poisson's equation in 2D with homogeneous Dirichlet bcs in one direction and periodicity in the other.
$$
\begin{align}
\nabla^2 u(x, y) &= f(x, y), \quad \forall \, (x, y) \in [-1, 1] \times [0, 2\pi]\\
u(\pm 1, y) &= 0 \\
u(x, 2\pi) &= u(x, 0)
\end{align}... | {"hexsha": "6cf8f2b229576db350872e786140e99682d68187", "size": 8375, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "binder/Poisson2D.ipynb", "max_stars_repo_name": "jaisw7/shenfun", "max_stars_repo_head_hexsha": "7482beb5b35580bc45f72704b69343cc6fc1d773", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from pandas.testing import assert_frame_equal
from woodwork.logical_types import (
Boolean,
Categorical,
Double,
Integer,
NaturalLanguage,
)
from blocktorch.pipelines.components import Impute... | {"hexsha": "d2edebf376b20b73300f85252055ba5ba2c24c68", "size": 20251, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_source/src/blocktorch/blocktorch/tests/component_tests/test_imputer.py", "max_stars_repo_name": "blocktorch/blocktorch", "max_stars_repo_head_hexsha": "044aa269813ab22c5fd27f84272e5fb540fc522b... |
#
# plot model parameters on a simplex
#
import sys, os
from argparse import ArgumentParser
import codecs
import numpy as np
from scipy.misc import logsumexp
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
from matplotlib.tri import UniformTriRefiner, Triangulation
sys.path.insert(1, os.path.join(... | {"hexsha": "79a968e1016a9d367cc677187c1c1ed6f26e9d40", "size": 5422, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/format_apics/simplex.py", "max_stars_repo_name": "murawaki/creole-mixture", "max_stars_repo_head_hexsha": "dfe585f2c8d698b24c022ec0933ce30925410cfe", "max_stars_repo_licenses": ["Apache-2.... |
module L1L2
using DataFrames
using LinearAlgebra
using ..DataMod, ..ManualModelMod
export l1l2
# Soft thresholding
function S(x::Float64, λ::Float64)::Float64
if x >= λ
return x - λ
elseif x <= -λ
return x + λ
else
return 0
e... | {"hexsha": "271c95439060ad32242a8b026f0f85ce922678e2", "size": 2395, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "incl/fs-l1l2reg.jl", "max_stars_repo_name": "KasperNooteboom/thesis-rvfl-fs", "max_stars_repo_head_hexsha": "31f8ee8ff58da5a8c1f505ef045c35ebbfe91255", "max_stars_repo_licenses": ["MIT"], "max_star... |
\documentclass[12pt]{article}
\title{Modern Parser Combinators in Python}
\date{\today}
\usepackage[sc,osf]{mathpazo}
\usepackage[T1]{fontenc}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{listings}
% \lstset{language=Python}
\lstset{escapechar=\!}
\usepackage[backend=bibtex8,style=authoryear]{biblatex}
\... | {"hexsha": "679d925a051b24876697556aabac4fc3ae53caa3", "size": 64923, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "combinators.tex", "max_stars_repo_name": "ceridwen/combinators", "max_stars_repo_head_hexsha": "a821b69a4382914792699bf10a84087cf251c78c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
export jumble_iter
function jumble_iter(text::String)
return SubwordIter(word_to_bag(text))
end
| {"hexsha": "5a25c7396bebb04cf2d9986550abc830c77fc4db", "size": 104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/jumble.jl", "max_stars_repo_name": "dpmerrell/Scrabble.jl", "max_stars_repo_head_hexsha": "61a3333e0983873b3e41e6c7e068850d37e4c4f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 27 15:49:43 2020
@author: Rapha
"""
import glm
import math
import numpy as np
import OpenGL.GL as gl
from cg.shader_programs.ShaderProgram import ShaderProgram
class MultiLightPhongShadingShaderProgram():
POINT_LIGHT = 3
DIRECTIONAL_LIGHT = 4
SPOTLIGHT ... | {"hexsha": "137747cb7c152a9e7d9baa130c3c8f2fa132a314", "size": 19110, "ext": "py", "lang": "Python", "max_stars_repo_path": "1S2020/cg/shader_programs/MultiLightPhongShadingShaderProgram.py", "max_stars_repo_name": "andre91998/EA979", "max_stars_repo_head_hexsha": "f3b82588ffaf20848a54b3a21b0332c1e72c54e8", "max_stars_... |
import numpy as np
import h5py
class CustomClass:
"""
An artificial custom class used to present
the serialization and deserialization with hdf5.
"""
def __init__(self, name: str, number: int, data: np.ndarray, nested_dict: dict):
self.name = name
self.number = number
self... | {"hexsha": "2211564d680d699c960cfdf7fedf504aa77e43c1", "size": 3508, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/custom_class.py", "max_stars_repo_name": "djeada/Hdf5", "max_stars_repo_head_hexsha": "6264d2d8063341ebed4ecec5fd766303fd018186", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
From iris.proofmode Require Import tactics.
From iris.algebra Require Import auth.
From Perennial.goose_lang Require Import proofmode notation.
From Perennial.program_logic Require Import recovery_weakestpre recovery_adequacy.
From Perennial.goose_lang Require Export recovery_lifting.
From Perennial.goose_lang Require ... | {"author": "mit-pdos", "repo": "perennial", "sha": "76dafee3cd47e1c5e5a6d5436f87738a06f13ee0", "save_path": "github-repos/coq/mit-pdos-perennial", "path": "github-repos/coq/mit-pdos-perennial/perennial-76dafee3cd47e1c5e5a6d5436f87738a06f13ee0/src/goose_lang/recovery_adequacy.v"} |
"""
DuckDB data chunk
"""
mutable struct DataChunk
handle::duckdb_data_chunk
function DataChunk(handle::duckdb_data_chunk, destroy::Bool)
result = new(handle)
if destroy
finalizer(_destroy_data_chunk, result)
end
return result
end
end
function get_column_count(c... | {"hexsha": "7ed25de4534948c736c69d6bc2895eb1b5f4e388", "size": 1704, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tools/juliapkg/src/data_chunk.jl", "max_stars_repo_name": "lokax/duckdb", "max_stars_repo_head_hexsha": "c2581dfebccaebae9468c924c2c722fcf0306944", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma locally_compact_homeomorphism_projection_closed:
assumes "locally compact S"
obtains T and f :: "'a \<Rightarrow> 'a :: euclidean_space \<times> 'b :: euclidean_space"
where "closed T" "homeomorphism S T f fst"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>T f. \<lbrakk>closed T; home... | {"llama_tokens": 4763, "file": null, "length": 48} |
\documentclass{beamer}
\usetheme{Antibes}
\useinnertheme{rectangles}
\useoutertheme{infolines}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
% patch the look of +, = in arev
\usefonttheme{serif}
\usepackage{arev}
\usepackage{amsmath}
\usepackage{amssymb}
\setbeamertemplate{footline}{%
\begin{beamercolorbox}[... | {"hexsha": "06cf8ec4745aeb75c3511679901ecfdf475c33a3", "size": 1708, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "templates/de/TeX/Beamer/Vorlage.tex", "max_stars_repo_name": "JohnBSmith/JohnBSmith.github.io", "max_stars_repo_head_hexsha": "5bb0fac7ec4d653be6bd71b4c7ab344c9615f1eb", "max_stars_repo_licenses": [... |
from numpy.lib.shape_base import expand_dims
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.nn.modules.activation import ReLU
def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, 2*(i//2) / np.float(d_model))
return pos * angle_rates
def positional_encoding(position, ... | {"hexsha": "2ecf86369b7de02ca08c0b4cf10deffdea15c894", "size": 3541, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/utils.py", "max_stars_repo_name": "pgr2015/transformer_pytorch", "max_stars_repo_head_hexsha": "192e5a0feddf3cd8106f6cba72113d01a873ac1c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Commented out IPython magic to ensure Python compatibility.
import os
import tarfile
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split # TensorDataset
import torchvision
from torchvision.datasets import ImageFolder # MNIST, CIFAR10 e... | {"hexsha": "2b686b56900b147e63b5e33a9ad64001285c2958", "size": 10352, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pytorch/resnet_onecyclepolicy.py", "max_stars_repo_name": "VladimerKhasia/ML", "max_stars_repo_head_hexsha": "7b7a6075458a8e9ac275a803f0fd89fb606294ae", "max_stars_repo_licenses": ["MIT"], "max_s... |
import pandas as pd
import matplotlib.pyplot as plt
import scipy
import seaborn as sns
df = pd.read_csv('lifespan40All.csv')
heatmapData = pd.pivot_table(df, values='commentators', index=['all'], columns='year')
plt.figure(figsize = (20, 4))
plot = sns.heatmap(heatmapData, cmap='BuPu', xticklabels=100, yticklabels=F... | {"hexsha": "9386f14b733b0aaaac688522c288d1e25a42843c", "size": 503, "ext": "py", "lang": "Python", "max_stars_repo_path": "allCommentariesCreateHeatmap.py", "max_stars_repo_name": "lwcvl/Plotting-All-Hadith-Commentaries", "max_stars_repo_head_hexsha": "71d22f5815d86943e7e5ce72f84363e4fc3610eb", "max_stars_repo_licenses... |
import numpy as np
import pandas as pd
def read_and_merge_data(*, base_path="../../data/raw/"):
df = pd.read_excel(base_path + 'RVMS_Current_Property_and_BIZ_Owner_List - vCurrent (1).xlsx',
sheet_name='Biz & Prop Owner MAIN list')
naics = pd.read_excel(base_path + '2-6 digit_2017_Code... | {"hexsha": "fd63f9a341a00bfbd2c3fdf80df94de200b2d967", "size": 1110, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/make_dataset.py", "max_stars_repo_name": "tlittrell/BusinessEngagementMatrix", "max_stars_repo_head_hexsha": "1367493cc01d28da52b0959d51f760c4205109f7", "max_stars_repo_licenses": ["MIT"]... |
function S=tria(A)
%%TRIA Square root matrix triangularization. Given a rectangular square
% root matrix, obtain a lower-triangular square root matrix that is
% square.
%
%INPUTS: A A numRowXnumCol matrix that is generally not square.
%
%OUTPUTS: S A lower-triangular matrix such that S*S'=A*A'. If
% ... | {"author": "USNavalResearchLaboratory", "repo": "TrackerComponentLibrary", "sha": "9f6e329de5be06a371757c4b853200beb6def2d0", "save_path": "github-repos/MATLAB/USNavalResearchLaboratory-TrackerComponentLibrary", "path": "github-repos/MATLAB/USNavalResearchLaboratory-TrackerComponentLibrary/TrackerComponentLibrary-9f6e3... |
# Problem 2 - Project Euler
# http://projecteuler.net/index.php?section=problems&id=2
function fibevensum(a, b, sum, xmax)
if a >= xmax
sum
elseif a % 2 == 0
fibevensum(b, a + b, sum + a, xmax)
else
fibevensum(b, a + b, sum, xmax)
end
end
println(fibevensum(1,2,0, 4000000))
| {"hexsha": "85966f09735f532af7b92b02f6e75e75ff05907d", "size": 316, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia/problem002.jl", "max_stars_repo_name": "emergent/ProjectEuler", "max_stars_repo_head_hexsha": "ec1c92cc47fde80efddeb0346d9b0fa511df1f00", "max_stars_repo_licenses": ["Unlicense"], "max_stars_c... |
# Simple 1D GP classification example
import time
import numpy as np
import matplotlib.pyplot as plt
import GPpref
import plot_tools as ptt
from active_learners import ActiveLearner, UCBLatent, PeakComparitor, LikelihoodImprovement, ABSThresh, UCBAbsRel
import test_data
import pickle
class Learner(object):
def __i... | {"hexsha": "51566ce1e85f63e81c5759927d8d6d425be44987", "size": 7118, "ext": "py", "lang": "Python", "max_stars_repo_path": "active_statruns.py", "max_stars_repo_name": "nrjl/GPN", "max_stars_repo_head_hexsha": "c7bd98d69e075ef05bcb2a443c02a71a916a71f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
#include <boost/algorithm/string.hpp>
#include <ros/time.h>
#include <tf/tf.h>
#include <tf_conversions/tf_eigen.h>
#include <geometry_msgs/TwistStamped.h>
#include <geometry_msgs/Pose.h>
#include <pluginlib/class_list_macros.h>
#include <cnr_logger/cnr_logger_macros.h>
#include <cnr_cartesian_velocity_controller/cnr_... | {"hexsha": "073085ae122f47684e7799b4101434ccd69f7cc0", "size": 10668, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cnr_cartesian_velocity_controller/src/cnr_cartesian_velocity_controller/cnr_cartesian_velocity_controller.cpp", "max_stars_repo_name": "CNR-STIIMA-IRAS/cnr_ros_controllers", "max_stars_repo_head_he... |
# -*- coding: utf-8 -*-
"""
computes the spectral decrease from the magnitude spectrum
Args:
X: spectrogram (dimension FFTLength X Observations)
f_s: sample rate of audio data
Returns:
vsk spectral decrease
"""
import numpy as np
def FeatureSpectralDecrease(X,f_s):
# compute index... | {"hexsha": "21752373668147f6f3afc92577cc1ed8172ca426", "size": 695, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyACA/FeatureSpectralDecrease.py", "max_stars_repo_name": "RichardYang40148/pyACA-1", "max_stars_repo_head_hexsha": "870d100ed232cca5a890570426116f70cd0736c8", "max_stars_repo_licenses": ["MIT"], "... |
# ----------------------------------------
# create fastapi app
# ----------------------------------------
from fastapi import FastAPI, File ,UploadFile
app = FastAPI()
# ----------------------------------------
# setup templates folder
# ----------------------------------------
from fastapi.templating import Jinja2... | {"hexsha": "e3c42f09b3e36856f3a206e30d2a43a1b1c960d5", "size": 4060, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "rahulct-commits/ocr.pytorch", "max_stars_repo_head_hexsha": "edc766312a3953f225bbb329f5efa75c3b253210", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import sys
import os
import socket
HOME = os.environ['HOME']
sys.path.insert(1, HOME + '/github/StreamingSVM')
import numpy as np
from operations import Print
import time
from comms import Communication
from distributed import DistributedDataLoader
from api import Constant
from api import ExperimentObjectPSGDItems
# ... | {"hexsha": "b9cab939e820fba1a7e407fa320b63cbd91b7ca5", "size": 5470, "ext": "py", "lang": "Python", "max_stars_repo_path": "psgd/PSGDAdamMulti.py", "max_stars_repo_name": "vibhatha/PSGDSVMPY", "max_stars_repo_head_hexsha": "69ed88f5db8d9a250ee944f44b88e54351f8696f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import cv2
import numpy as np
import matplotlib.pyplot as plt
import modi
import time
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
def make_coordinates(image, line_parameters):
slope, intercept = line_parameters
y1 = image.shape[0]
y2 = int(y1*(2/5))
x1 = in... | {"hexsha": "8ea2258dcd55ac6e9632304c06e8245a982d66cc", "size": 6985, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/EyeCar.py", "max_stars_repo_name": "TheStarkor/Eye-Car", "max_stars_repo_head_hexsha": "e0962cd36effa24cc90935b4364dadf47e1ef2d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# encoding='utf-8'
import cv2
import os
import numpy as np
import random
'''
挑选几张不同颜色的汽车背景，切成小图，将生成的车牌贴在小图上，使生成的车牌更真实
'''
def show(img, title='无标题'):
"""
本地测试时展示图片
:param img:
:param name:
:return:
"""
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontPropert... | {"hexsha": "9985890285ae53fdd5a704f806b4b2302c565169", "size": 2659, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/cut.py", "max_stars_repo_name": "mymsimple/plate_generator", "max_stars_repo_head_hexsha": "cbea92aff070a8691a0394263e8f4b20b3f2c839", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Provides the Station class.
:copyright:
Lion Krischer (krischer@geophysik.uni-muenchen.de), 2013
:license:
GNU Lesser General Public License, Version 3
(https://www.gnu.org/copyleft/lesser.html)
"""
from __future__ import (absolute_import, division, print_f... | {"hexsha": "4dc96053390501c70e60f45fb152e67eeaaf8830", "size": 21281, "ext": "py", "lang": "Python", "max_stars_repo_path": "IRIS_data_download/IRIS_download_support/obspy/core/inventory/station.py", "max_stars_repo_name": "earthinversion/Fnet_IRIS_data_automated_download", "max_stars_repo_head_hexsha": "09a6e0c992662f... |
[STATEMENT]
lemma test_star [simp]: "`p\<^sup>\<star> = 1`"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<iota> p\<^sup>\<star> = (1::'b)
[PROOF STEP]
by (metis star_subid test_iso test_top top_greatest) | {"llama_tokens": 94, "file": "KAT_and_DRA_TwoSorted_KAT2", "length": 1} |
import numpy as np
from .sh import SH
class TestSH:
"""Test sequential halving policy"""
def test_simple_run(self):
arm_num = 5
budget = 20
learner = SH(arm_num=arm_num, budget=budget)
learner.reset()
while True:
actions = learner.actions()
if actions is None:
break
... | {"hexsha": "6b315f791d4f55c17972fbf49acb034ebd49f28d", "size": 448, "ext": "py", "lang": "Python", "max_stars_repo_path": "banditpylib/learners/ordinary_fbbai_learner/sh_test.py", "max_stars_repo_name": "XiGYmax/banditpylib", "max_stars_repo_head_hexsha": "07698a1c6b17720a8199dea76580546fe3dfb9be", "max_stars_repo_lice... |
__author__ = 'diegopinheiro'
__email__ = 'diegompin@gmail.com'
__github__ = 'https://github.com/diegompin'
from src.training_strategies.search_strategy import GridSearchStrategy, RandomizedSearchStrategy
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from scipy.stats import randint as sp_randi... | {"hexsha": "18d4f40736100515b4fa34ac8fc3abc04d6a5060", "size": 3108, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/algorithm_strategies/random_forest_strategy.py", "max_stars_repo_name": "rionbr/smm4h", "max_stars_repo_head_hexsha": "6009ed7800884ab37b7080c8c825c30f501b6942", "max_stars_repo_licenses": ["M... |
import re
import requests
import io
import sys
import json
import urllib
from bs4 import BeautifulSoup
import sqlite3
import time
import fitz
from PIL import ImageDraw,ImageFont
from PIL import Image
import random
import numpy as np
#import cv2
#sys.stdout = io.TextIOWrapper(sys.stdout.buffer,encodin... | {"hexsha": "0ec347958b97ae810cd1bafde26c964702ad33dc", "size": 5996, "ext": "py", "lang": "Python", "max_stars_repo_path": "ydk2list.py", "max_stars_repo_name": "i82Security/ydkparser", "max_stars_repo_head_hexsha": "f5a6c833d074347bc783eeac8a1ca861e2c0a665", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
"""
Normals Interface Class
Meteorological data provided by Meteostat (https://dev.meteostat.net)
under the terms of the Creative Commons Attribution-NonCommercial
4.0 International Public License.
The code is licensed under the MIT license.
"""
from copy import copy
from typing import Union
from datetime import dat... | {"hexsha": "d7d21dc1db0aa3091bc0d23d5e85e05f03bc7906", "size": 9765, "ext": "py", "lang": "Python", "max_stars_repo_path": "meteostat/interface/normals.py", "max_stars_repo_name": "meteoDaniel/meteostat-python", "max_stars_repo_head_hexsha": "69ea4206e402f42bc47e3e909923fe5744d92814", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''eclipses.py - Waqas Bhatti (wbhatti@astro.princeton.edu) - Oct 2017
This contains a double gaussian model for first order modeling of eclipsing
binaries.
'''
import numpy as np
from numpy import nan as npnan, sum as npsum, abs as npabs, \
roll as nproll, isfinit... | {"hexsha": "3bb14f11ddc5dc2c9aa20e6d92e897c59cac503e", "size": 4689, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrobase/lcmodels/eclipses.py", "max_stars_repo_name": "adrn/astrobase", "max_stars_repo_head_hexsha": "7af71167deec58dffc8f668c0b34cb75ed44ae6a", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
using Documenter, GibbsTypePriors
makedocs(
modules = [GibbsTypePriors],
format = Documenter.HTML(; prettyurls = get(ENV, "CI", nothing) == "true"),
authors = "konkam",
sitename = "GibbsTypePriors.jl",
pages = Any["index.md"]
# strict = true,
# clean = true,
# checkdocs = :exports,
)
d... | {"hexsha": "e7086edfbbd598aff8e11b3cbec32b725b669162", "size": 412, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "konkam/GibbsTypePriors", "max_stars_repo_head_hexsha": "f923ed8a365261c34f4749b75005764279e63c94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import numpy as np
from typing import Union
__all__ = ['sum', 'mean', 'var', 'std', 'mean_std', 'quantile', 'median', 'ratio']
def sum(obs: np.ndarray) -> np.float:
return obs.sum(axis=0)
def mean(obs: np.ndarray) -> np.float:
return np.divide(obs.sum(axis=0), obs.shape[0])
def demeaned(obs: np.ndarray)... | {"hexsha": "1c712baef6b541001c8f3b4230fc65c8bbfcd885", "size": 991, "ext": "py", "lang": "Python", "max_stars_repo_path": "abito/lib/stats/plain.py", "max_stars_repo_name": "avito-tech/abito", "max_stars_repo_head_hexsha": "9071eecd9526ee5c268cfacd7ac9a49b6ee185e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import unittest
import numpy as np
from numpy.testing import assert_almost_equal as almost_equal
from thimbles.spectrographs import SamplingModel
import thimbles as tmb
class TestSamplingMatrixhModel(unittest.TestCase):
min_wv = 100
max_wv = 200
npts_spec = 30
npts_model = 100
def setUp(se... | {"hexsha": "cf35d068811d4a4714436659f3ba09c6b7f847ef", "size": 1403, "ext": "py", "lang": "Python", "max_stars_repo_path": "thimbles/tests/test_spectrograph.py", "max_stars_repo_name": "quidditymaster/thimbles", "max_stars_repo_head_hexsha": "b122654a012f0eb4f043d1ee757f884707c97615", "max_stars_repo_licenses": ["MIT"]... |
-- Intuitionistic propositional calculus.
-- Hilbert-style formalisation of syntax.
-- Nested terms.
module IPC.Syntax.Hilbert where
open import IPC.Syntax.Common public
-- Derivations.
infix 3 _⊢_
data _⊢_ (Γ : Cx Ty) : Ty → Set where
var : ∀ {A} → A ∈ Γ → Γ ⊢ A
app : ∀ {A B} → Γ ⊢ A ▻ B → Γ ⊢ A → Γ... | {"hexsha": "e4b9e5d13db8e76eb1c1b1361d7464cb459b085a", "size": 7564, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "IPC/Syntax/Hilbert.agda", "max_stars_repo_name": "mietek/hilbert-gentzen", "max_stars_repo_head_hexsha": "fcd187db70f0a39b894fe44fad0107f61849405c", "max_stars_repo_licenses": ["X11"], "max_stars_... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
from numpy.testing import assert_almost_equal, assert_array_almost_equal
import pytest
from PyDSTool import PyDSTool_ValueError, PyDSTool_TypeError
from PyDSTool.Generator import Vode_ODEsystem
@pytest.fixture()
de... | {"hexsha": "6c2889b1036c01d3f258e3f69e5e65d679cd7ce7", "size": 3369, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/generator/test_odesystem_set_through_vode.py", "max_stars_repo_name": "yuanz271/PyDSTool", "max_stars_repo_head_hexsha": "886c143cdd192aea204285f3a1cb4968c763c646", "max_stars_repo_licenses"... |
import numpy as np
from sympy.utilities.iterables import multiset_permutations
import networkx as nx
import itertools
from context import *
from utils.graph_utils import rand_permute_adj_matrix, is_isomorphic_from_adj
def generate_automorphism_dict(num_nodes, edges_range, directed=False, dtype=np.float64):
"""
... | {"hexsha": "d3ec240672b4d405539ef927af3f051cdee5abdd", "size": 7364, "ext": "py", "lang": "Python", "max_stars_repo_path": "embedding/iso_nn_data_util.py", "max_stars_repo_name": "BrunoKM/rhoana_graph_tools", "max_stars_repo_head_hexsha": "7150f4bc6337ecf51dd9123cf03561a57d655160", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
##
#
# This is a quick script to plot the trajectories resulting from different
# methods on the same plot. We can also do this with comparison.py, but this
# way allows us to compare directly with MILP, which we don't implement here.
#
##
import numpy as np
import matplotlib.pyplot as plt
from... | {"hexsha": "02a8e864a6227411b8fe521b29b56a846f7e553e", "size": 5668, "ext": "py", "lang": "Python", "max_stars_repo_path": "stl_optimization_results_plot.py", "max_stars_repo_name": "vincekurtz/STL_optimization", "max_stars_repo_head_hexsha": "05993a497b4fa68e43f0db5f86b7312ae5e7afc2", "max_stars_repo_licenses": ["MIT"... |
#include <demo/color.h>
#include <demo/memcpy.h>
#include <boost/simd.hpp>
#include <boost/simd/function/load.hpp>
#include <boost/simd/function/store.hpp>
#include <intrin.h>
#include <omp.h>
#include <cstdint>
#include <cstring>
#include <memory>
#include <fstream>
bench::time_point bench::start_;
int main(int, ... | {"hexsha": "a7024cad11acf2cb8b5e684a8bd6d183c5e8384e", "size": 4511, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source/main.cpp", "max_stars_repo_name": "chronos38/low_latency_demo", "max_stars_repo_head_hexsha": "de0d0d3dcebff23ba77c06c6c368b9d1c3d2c648", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Reinforcement learning via policy gradients
"""
import random, math, pickle, time
import interface, move, utils
import numpy as np
from agent import Agent
from rl import RLAlgorithm
from collections import defaultdict
from utils import progressBar
from copy import deepcopy
from sklearn.neural_network import MLPReg... | {"hexsha": "138c85a1fe2c03c16ea92e8b36db6f2bcadfa468", "size": 5700, "ext": "py", "lang": "Python", "max_stars_repo_path": "policy_gradients.py", "max_stars_repo_name": "sds-dubois/snake.ai", "max_stars_repo_head_hexsha": "b5ee2a91b210055397e7942c7e24a51a5e583834", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from chaco.api import AbstractPlotData, ArrayPlotData, Plot, ArrayDataSource
from traits.api import Dict, Instance, Str
from pandas import DataFrame
class PandasPlotData(AbstractPlotData):
''' Chaco requires a PlotData interface to manage plot/data mapping; however, pandas
already is its o... | {"hexsha": "980849a71f1504bb5e3af04a0a12d8a75831d25b", "size": 7500, "ext": "py", "lang": "Python", "max_stars_repo_path": "skspec/chaco_interface/pandasplotdata.py", "max_stars_repo_name": "hugadams/scikit-spectra", "max_stars_repo_head_hexsha": "c451be6d54080fbcc2a3bc5daf8846b83b7343ee", "max_stars_repo_licenses": ["... |
include("test_data.jl")
function test_cmp(io::Union{Nothing,IO} = nothing)
map(TestData.test_cmp_data) do x
ic = IntcodeMachine(x)
run_intcode!(ic)
res = fetch(ic)
isnothing(res) && return @error "Intcode failed CMP '$x'"
@info something(res)
!isnothing(io) && flush(... | {"hexsha": "3d6879fc3a4e4d12a290c82c76917ad89fce5e31", "size": 966, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "events/2019/day-05/Intcode/test/test_cmp_jmp.jl", "max_stars_repo_name": "myrddin89/advent-of-code", "max_stars_repo_head_hexsha": "1401484be662794841c0ac5b863c0fda28e2fe06", "max_stars_repo_license... |
# Load necessary packages
library(tidyverse)
# Use readr to read the raw .tab file from GitHub
# Skip the lengthy metadata.
bfd <- read_tsv("https://raw.githubusercontent.com/phjacobs/foram_sdm/master/Data/Raw/BFD.tab", skip=1326)
# Remove '[m]', '[#]' and spaces
names(bfd) <- gsub(x = names(bfd), pattern = " \\[m\\... | {"hexsha": "7f92aa6f7b3e7ffa26b79957284dc177d6755393", "size": 1189, "ext": "r", "lang": "R", "max_stars_repo_path": "bfd_raw_to_tidy.r", "max_stars_repo_name": "phjacobs/tidyflow", "max_stars_repo_head_hexsha": "f2461c83c5ed69ea0d160447018d0f03494db177", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
__author__ = 'lucabasa'
__version__ = '1.0.1'
__status__ = 'development'
import numpy as np
import pandas as pd
def clean_cols(data, col_list):
df = data.copy()
for col in col_list:
try:
del df[col]
except KeyError:
pass
return df
def flag_missing(data, col_li... | {"hexsha": "af4c435bab7fba30fe4776381d507fd320b3cdce", "size": 1455, "ext": "py", "lang": "Python", "max_stars_repo_path": "titanic/processing.py", "max_stars_repo_name": "lucabasa/kaggle_competitions", "max_stars_repo_head_hexsha": "15296375dc303218093aa576533fb809a4540bb8", "max_stars_repo_licenses": ["Apache-2.0"], ... |
using Colors, Gadfly, RDatasets
set_default_plot_size(5inch,4inch)
iris = dataset("datasets","iris")
p = plot(iris, x=:SepalLength, y=:PetalLength, color=:Species, Geom.point,
layer(Stat.smooth(method=:lm, levels=[0.90, 0.99]), Geom.line, Geom.ribbon),
Theme(alphas=[0.6], key_position=:inside)
)
| {"hexsha": "8cf0bd32852ec3b58fc3968350efc629436079df", "size": 309, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testscripts/stat_smooth.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Gadfly.jl-c91e804a-d5a3-530f-b6f0-dfbca275c004", "max_stars_repo_head_hexsha": "d180d5760c758863f24e27e2bc42d... |
"""
Block and braile rendering of julia arrays, for terminal graphics.
"""
module UnicodeGraphics
export blockize, brailize, blockize!, brailize!
"""
brailize(a, cutoff=0)
Convert an array to a block unicode string, filling values above the cutoff point.
"""
blockize(a, cutoff=0) = blockize!(initblock(size(a)), ... | {"hexsha": "9add9cc2e1b07678b7fb271ce92c325a0cec4d7d", "size": 2767, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/UnicodeGraphics.jl", "max_stars_repo_name": "rafaqz/UnicodeGraphics.jl", "max_stars_repo_head_hexsha": "b1adbf1b0c13e50c0c8fba5e02db87a9f8b9f8ea", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import mxnet as mx
from mxnet.gluon import nn
from mxnet import nd
import numpy as np
from mxnet.base import numeric_types
from mxnet import symbol
class Reconstruction2D(nn.HybridBlock):
def __init__(self, in_channels = 1, block_grad = False, **kwargs):
super().__init__(**kwargs)
self.in_channels = in_channels
... | {"hexsha": "04a5590b9a4f51f5964e332ace8e16c9016fec95", "size": 5381, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_flow/MaskFlownet/network/layer.py", "max_stars_repo_name": "yamaru12345/DF-VO2", "max_stars_repo_head_hexsha": "ed7359deeb38c36099cf8198f88e9a74dfa2403a", "max_stars_repo_licenses": ["MIT"], ... |
###########################################################################################################################
# SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN SINAN #
########################################################################... | {"hexsha": "ae18a7418ef7510bbc1a48a8a2b4071ee7bf50d0", "size": 13850, "ext": "py", "lang": "Python", "max_stars_repo_path": "pegasus/dados/transform/prepare_SINAN.py", "max_stars_repo_name": "SecexSaudeTCU/PegaSUS", "max_stars_repo_head_hexsha": "0e24c00595e8a7376680dfb2e5aa42e1e9eb7770", "max_stars_repo_licenses": ["M... |
import numpy as np
def generate(model,
bpe,
texts,
length=100,
top_k=1,
temperature=1.0):
"""Generate text after the given contexts.
:param model: The trained model.
:param bpe: Byte pair encoding object.
:param texts: A list of texts.
... | {"hexsha": "7e9e50cd5a57cd7a2842811692d83548b98f3f86", "size": 1653, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_gpt_2/gen.py", "max_stars_repo_name": "Pimax1/keras-gpt-2", "max_stars_repo_head_hexsha": "0a4adaad651a5a51e8a9c647c50cc01c3e51055c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# (c) Copyright IBM Corporation 2020.
# LICENSE: Apache License 2.0 (Apache-2.0)
# http://www.apache.org/licenses/LICENSE-2.0
import numpy as np
import gc
from lrtc_lib.active_learning.strategies import ActiveLearningStrategies
from lrtc_lib.active_learning.core.strategy.perceptron_ensemble import PerceptronEnsemble... | {"hexsha": "28055a6ffbd5947aa71b1f35f8621c3d294f3e19", "size": 1467, "ext": "py", "lang": "Python", "max_stars_repo_path": "lrtc_lib/active_learning/core/strategy/perceptron_dropout.py", "max_stars_repo_name": "MovestaDev/low-resource-text-classification-framework", "max_stars_repo_head_hexsha": "4380755a65b35265e84ecb... |
import pandas as pd
import numpy as np
def ReadJenkins():
Headers=["Num","VComp","Object","Longitude","Latitude","Vmag", "SpType","SpType_ref","E(B-V)","Distance","Z","Lower_log(NHI)","log(NHI)","Flag_log(NHI)","Upper_log(NHI)","ref_log(NHI)","Lower_log(NH2)","log(NH2)","Upper_log(NH2)","ref_log(NH2)"]
row... | {"hexsha": "516b12558239f6eec564c39772f11529b1833166", "size": 729, "ext": "py", "lang": "Python", "max_stars_repo_path": "edibles/data/sightline_data/ReadJenkins_data.py", "max_stars_repo_name": "jancami/edibles", "max_stars_repo_head_hexsha": "51263b24c5e8aef786692011289b906a810ad2f7", "max_stars_repo_licenses": ["MI... |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def muscle(path):
"""Effect of Calcium Chloride on Muscle Contraction in ... | {"hexsha": "307b2b46257e5524d4dbac068d2b616712aa793a", "size": 2168, "ext": "py", "lang": "Python", "max_stars_repo_path": "observations/r/muscle.py", "max_stars_repo_name": "hajime9652/observations", "max_stars_repo_head_hexsha": "2c8b1ac31025938cb17762e540f2f592e302d5de", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma simplicial_simplex_simplex_cone:
assumes f: "simplicial_simplex p S f"
and T: "\<And>x u. \<lbrakk>0 \<le> u; u \<le> 1; x \<in> S\<rbrakk> \<Longrightarrow> (\<lambda>i. (1 - u) * v i + u * x i) \<in> T"
shows "simplicial_simplex (Suc p) T (simplex_cone p v f)"
[PROOF STATE]
proof (prove)
goa... | {"llama_tokens": 8653, "file": null, "length": 78} |
import argparse
import chainer
from chainer import cuda
import fcn
import numpy as np
import tqdm
from models.fcn8 import FCN8s
def evaluate():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--file', type=str, help='model file path')
args = parser.p... | {"hexsha": "e7e9bb1af802ad63828fd7602013209de9a50100", "size": 1447, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate.py", "max_stars_repo_name": "juliocamposmachado/gain2", "max_stars_repo_head_hexsha": "cd1cb0ac021078ed42fe3c1456040c00622e79d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24... |
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import warnings
import os
warnings.filterwarnings("ignore")
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
dataPath = "temp/"
if not os.path.exists(dataPath):
os.makedirs(d... | {"hexsha": "3b53bee0d511279513b937d275c284243aacab56", "size": 700, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter03/MNIST/Explore_MNIST.py", "max_stars_repo_name": "tongni1975/Deep-Learning-with-TensorFlow-Second-Edition", "max_stars_repo_head_hexsha": "6964bf3bf11c1b38113a0459b4d1a9dac416ed39", "max_s... |
[STATEMENT]
lemma OclAny_allInstances_at_post_oclIsTypeOf\<^sub>O\<^sub>c\<^sub>l\<^sub>A\<^sub>n\<^sub>y2:
"\<exists>\<tau>. (\<tau> \<Turnstile> not (OclAny .allInstances()->forAll\<^sub>S\<^sub>e\<^sub>t(X|X .oclIsTypeOf(OclAny))))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>\<tau>. \<tau> \<Turnstil... | {"llama_tokens": 360, "file": "Featherweight_OCL_examples_Employee_Model_Analysis_Analysis_UML", "length": 2} |
import os,sys,io,shutil,csv
from decimal import Decimal
import numpy as np
def unit_vector(vector):
""" Returns the unit vector of the vector."""
return vector / np.linalg.norm(vector)
def angle_between(v1, v2):
"""Finds angle between two vectors"""
v1_u = unit_vector(v1)
v2_u = unit_vector(v2)
return np.... | {"hexsha": "f0b4d77833aa6a3bf969fef9e426561d392961fb", "size": 8479, "ext": "py", "lang": "Python", "max_stars_repo_path": "Rotate.py", "max_stars_repo_name": "AntonyPapadakis/HumanActionRecognitionCnns", "max_stars_repo_head_hexsha": "55b73afeadccfedc84892e5e6b56644e7bd58b58", "max_stars_repo_licenses": ["MIT"], "max_... |
import matplotlib as mpl
import make_colormap as mc
import matplotlib
import matplotlib.cm as cm
from matplotlib import gridspec
import sys
sys.path.insert(1, '../sglv_timeseries')
import sglv_timeseries.glv.Timeseries
from matplotlib.colors import Normalize
from make_colormap import *
import pandas as pd
import n... | {"hexsha": "002bff78293efc3fddfb9fc1260ed2cdb5736a9d", "size": 23482, "ext": "py", "lang": "Python", "max_stars_repo_path": "noise_properties_plotting.py", "max_stars_repo_name": "lanadescheemaeker/logistic_models", "max_stars_repo_head_hexsha": "9e10e6e631c91adc8e85e8a4130caf9eca835d85", "max_stars_repo_licenses": ["B... |
[STATEMENT]
lemma kruskal_exchange_acyclic_inv_2:
assumes "acyclic w"
and "injective w"
and "d \<le> w"
and "bijective (d\<^sup>T * top)"
and "bijective (e * top)"
and "d \<le> top * e\<^sup>T * w\<^sup>T\<^sup>\<star>"
and "w * e\<^sup>T * top = bot"
shows "acyclic ((w \<sqint... | {"llama_tokens": 16571, "file": "Stone_Kleene_Relation_Algebras_Kleene_Relation_Algebras", "length": 109} |
[STATEMENT]
lemma Contra: "insert (Neg A) H \<turnstile> A \<Longrightarrow> H \<turnstile> A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. insert (Neg A) H \<turnstile> A \<Longrightarrow> H \<turnstile> A
[PROOF STEP]
by (metis Peirce Imp_I) | {"llama_tokens": 101, "file": "Goedel_HFSet_Semanticless_SyntaxN", "length": 1} |
[STATEMENT]
lemma f''_imp_f':
fixes f :: "real \<Rightarrow> real"
assumes "convex C"
and f': "\<And>x. x \<in> C \<Longrightarrow> DERIV f x :> (f' x)"
and f'': "\<And>x. x \<in> C \<Longrightarrow> DERIV f' x :> (f'' x)"
and pos: "\<And>x. x \<in> C \<Longrightarrow> f'' x \<ge> 0"
and x: "x \<in>... | {"llama_tokens": 11830, "file": null, "length": 114} |
%!TEX root = ../paper.tex
\section{Conclusion and Future Work}
\label{sec:conclusion}
Nowadays, everyone can write a blog post fairly easily.
Thus, the amount of blog posts and authors increases quickly, making the task of identifying an exact author hard to conclude.
In this paper, we presented an approach of dividi... | {"hexsha": "b6863ba234c0437ba8fa01d21095caae535e096b", "size": 3906, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/sections/06_conclusion.tex", "max_stars_repo_name": "tabergma/similar_author_identification", "max_stars_repo_head_hexsha": "15ca2bd44f1ff19bf62317f7b146f501a2e60699", "max_stars_repo_licenses... |
# Objective: Find the size of the components of the neutral network of a goal.
# Methodology: Do multiple neutral random walks starting at a random circuit that maps to the goal.
# neutral_walk() accumulate all neighbors of all neutral circuits encountered on the random neutral walk.
# Then run_neutral_walk() combine... | {"hexsha": "67c46a27c797e050d595abca0deafeeb3f83c3f1", "size": 20961, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/neutral_walk_connectivity.jl", "max_stars_repo_name": "ahalwright/CGP.jl", "max_stars_repo_head_hexsha": "73952fcb08d2e6ee39e4df142e14ea34e4c09226", "max_stars_repo_licenses": ["MIT"], "max_st... |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 6 11:36:46 2019
@author: CatOnTour
"""
import mpmath as mp
import numpy as np
import matplotlib.pyplot as plt
from sympy.abc import s
from sympy.integrals.transforms import inverse_laplace_transform
from sympy import symbols, lambdify
from sympy import *
from scipy impo... | {"hexsha": "3bf561d3c68d810ed10a72a02dfdf7cef6121e7d", "size": 3036, "ext": "py", "lang": "Python", "max_stars_repo_path": "solving_ODE_configurations.py", "max_stars_repo_name": "xi2pi/cardioLPN", "max_stars_repo_head_hexsha": "34759fea55f73312ccb8fb645ce2d04a0e2dddea", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/-
Copyright (c) 2021 Yaël Dillies, Bhavik Mehta. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yaël Dillies, Bhavik Mehta
-/
import combinatorics.simplicial_complex.extreme
open_locale classical affine big_operators
open set
--TODO: Generalise to LCTVS
variables {E ... | {"author": "mmasdeu", "repo": "brouwerfixedpoint", "sha": "548270f79ecf12d7e20a256806ccb9fcf57b87e2", "save_path": "github-repos/lean/mmasdeu-brouwerfixedpoint", "path": "github-repos/lean/mmasdeu-brouwerfixedpoint/brouwerfixedpoint-548270f79ecf12d7e20a256806ccb9fcf57b87e2/src/combinatorics/simplicial_complex/intrinsic... |
"""Module representing the Stroop Test protocol."""
# from typing import Dict, Tuple, Union, Optional, Sequence
from typing import Optional, Sequence
# import pandas as pd
# import numpy as np
# import matplotlib.pyplot as plt
# import matplotlib.ticker as mticks
# import seaborn as sns
#
# import biopsykit.colors as ... | {"hexsha": "5d54c5703b28cffca127f09fc79a529355fbf76b", "size": 30083, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/biopsykit/protocols/stroop.py", "max_stars_repo_name": "mad-lab-fau/BioPsyK", "max_stars_repo_head_hexsha": "8ed7a2949e9c03c7d67b9ac6d17948ae218d94c1", "max_stars_repo_licenses": ["MIT"], "ma... |
// Copyright 2018 The Simons Foundation, 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 requi... | {"hexsha": "41e98f5b3270c4cb842b90606be168d3dca26e8d", "size": 3867, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Sources/Machine/rbm_multival.hpp", "max_stars_repo_name": "stubbi/netket", "max_stars_repo_head_hexsha": "7391466077a4694e8f12c649730a81bf634f695e", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import numpy as np
import pandas as pd
import yfinance as yf
# Using yfinance
'''
tickerSymbol = 'GOOG'
tickerData = yf.Ticker(tickerSymbol)
tickerDf = tickerData.history(period='1d', start='2010-1-1', end='2021-4-25')
tickerDf.plot(y='Open')
'''
# calling Yahoo finance API and requesting to get data for the last 1 ... | {"hexsha": "a2034dd3abfa3bb58067bbf14f92e325d7dcadc3", "size": 3006, "ext": "py", "lang": "Python", "max_stars_repo_path": "30DayChartChallenge/20210428-uncertainties-future.py", "max_stars_repo_name": "vivekparasharr/Challenges-and-Competitions", "max_stars_repo_head_hexsha": "c99d67838a0bb14762d5f4be4993dbcce6fe0c5a"... |
(* Title: HOL/Auth/n_mutualExSimp_lemma_inv__4_on_rules.thy
Author: Yongjian Li and Kaiqiang Duan, State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
Copyright 2016 State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
*)
header{*T... | {"author": "lyj238Gmail", "repo": "newParaVerifier", "sha": "5c2d49bf8e6c46c60efa53c98b0ba5c577d59618", "save_path": "github-repos/isabelle/lyj238Gmail-newParaVerifier", "path": "github-repos/isabelle/lyj238Gmail-newParaVerifier/newParaVerifier-5c2d49bf8e6c46c60efa53c98b0ba5c577d59618/examples/n_mutualExSimp/n_mutualEx... |
from __future__ import print_function
import os
import pdb
import torch
import utils
import numpy as np
def has_checkpoint(checkpoint_path, rb_path):
"""check if a checkpoint exists"""
if not (os.path.exists(checkpoint_path) and os.path.exists(rb_path)):
return False
if 'model.pyth' not in os.list... | {"hexsha": "c53ca8819c69771b8c18df340d4c92dff489b159", "size": 5686, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/checkpoint.py", "max_stars_repo_name": "yangfanthu/modular-rl", "max_stars_repo_head_hexsha": "25c599bab641a7e732dbaf116cd240fa2358f113", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 2 20:32:21 2017
@author: linkw
"""
import pandas as pd
import numpy as np
#read indto df. take care of missing values and combine text.
data=pd.read_csv("D:\\Datasets\\kickstarter\\train.csv")
data.iloc[:,2].replace(np.NAN, '---', inplace=True)
data.iloc[:,4].replace(n... | {"hexsha": "c5168e800459d023f9eb5b4d8ce16b3f3c29271b", "size": 7269, "ext": "py", "lang": "Python", "max_stars_repo_path": "kickstarter_xgb.py", "max_stars_repo_name": "linkwithkk/kickstarter", "max_stars_repo_head_hexsha": "bd3b60aaedc3f88ececc484f00dde1414f011310", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
\documentclass[margin,line]{res}
\usepackage{fancyhdr}
\usepackage{wasysym}
\usepackage{textcomp}
%\usepackage{hyperref}
\usepackage{url}
\usepackage{marvosym}
%\usepackage[misc]{ifsym}
%
%\usepackage[margin=1in]{geometry}
\addtolength{\textwidth}{0.20cm}
\addtolength{\evensidemargin}{0.1cm}
\addtolength{\oddsidemar... | {"hexsha": "b6a2df32e7dcfd3dfb04cde10f97d9863c263a46", "size": 25785, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "public/cv/mycv_latest/OLD/CV_YuZhang.tex", "max_stars_repo_name": "joshtai/yzhangweb", "max_stars_repo_head_hexsha": "d113f20927c17f11f681d6a8eb67e57b9ecd5984", "max_stars_repo_licenses": ["MIT"], ... |
from copy import deepcopy
import imageio
import numpy as np
import os
from PIL import Image
# Local Modules
from simulation import SphereBody, Simulation, State, SystemState
ANIM_OUT_DIR = "animation_out"
ANIM_VID_FILENAME = ANIM_OUT_DIR + "/animation.mp4"
MAX_QUALITY = 95
V0 = np.array([35, 0, 0], dtype=float)
cl... | {"hexsha": "3775a2a7b11d471b1a8274a63efae84d3ecaa56f", "size": 4810, "ext": "py", "lang": "Python", "max_stars_repo_path": "animation.py", "max_stars_repo_name": "thinhnguyenuit/sombra", "max_stars_repo_head_hexsha": "5176d264508dd5cce780dc63f1dd948d66b189e8", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
import numpy as np
import math
import time
from sklearn.cluster import MiniBatchKMeans, KMeans
class Top_Down(object):
def __init__(self,n_classes):
self.subcls = math.ceil(math.sqrt(n_classes))
self.top_K = KMeans(n_clusters=self.subcls,n_init=10,max_iter=300,n_jobs=-1,verbose=0,init='random')
... | {"hexsha": "487a130ac2be452fbc4e1c7460ddeb38dd35a76e", "size": 2711, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/AIC2018_iamai/ReID/clustering.py", "max_stars_repo_name": "gordonjun2/CenterTrack", "max_stars_repo_head_hexsha": "358f94c36ef03b8ae7d15d8a48fbf70fff937e79", "max_stars_repo_licenses": ["MIT"]... |
import argparse
import os
import pdb
import pyproj
import numpy as np
from glob import glob
from tqdm import tqdm
from scipy.spatial.transform import Rotation as R
def config_parser():
parser = argparse.ArgumentParser(
description='Semantic label sampling script.',
formatter_class=argparse.Argume... | {"hexsha": "0fe2dbcdf94649c3d18a19e0675bd786612a2938", "size": 7721, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/tools/scan_npy_pointcloud.py", "max_stars_repo_name": "Shanci-Li/TOPO-DataGen", "max_stars_repo_head_hexsha": "bc2be65bbcca4cb415e2f7d19cb3c3d620279ddc", "max_stars_repo_licenses": ["MIT"]... |
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