text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
from os import path, makedirs
from shutil import rmtree
from datetime import datetime, timedelta
import platform
import pandas as pd
import numpy as np
import requests
from tinydb import TinyDB, Query
import pytest
import publicAPI.forecast_utils as forecast_utils
import publicAPI.exceptions as exceptions
import help... | {"hexsha": "e3cb3ebec22097cceb6548c63c20131d8ff6f362", "size": 8906, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_forecast_utils.py", "max_stars_repo_name": "EVEprosper/ProsperAPI", "max_stars_repo_head_hexsha": "2d25b9210d32ca777204b1dddb56848d7075dd85", "max_stars_repo_licenses": ["MIT"], "max_st... |
\documentclass[a4paper,11pt]{jctvcdoc}
\usepackage{geometry}[2010/02/12]
\usepackage{hyperref}
\hypersetup{colorlinks=true}
\usepackage{color,soul}
\usepackage[position=bottom]{subfig}
\captionsetup[subfloat]{position=top}
\usepackage{multirow}
\usepackage{dcolumn}
\newcolumntype{.}{D{.}{.}{-1}}
\usepackage{colortbl... | {"hexsha": "7c6b8917b113786bfb9016620ed4353bc771d7ce", "size": 58874, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/software-manual.tex", "max_stars_repo_name": "pargles/HM-12.0", "max_stars_repo_head_hexsha": "787b9c0c559d11e939f0d9fcd8f50387ca4ae5fa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import xarray as xr
import numpy as np
import dask.bag as db
import dask.array as da
from time import time
from scipy.interpolate import LinearNDInterpolator
from ..core import Instrument, Model
from .attenuation import calc_radar_atm_attenuation
from .psd import calc_mu_lambda
from ..core.instrument import ureg, quan... | {"hexsha": "0a5c69706d7949713bdbb39757af37c8ecba5896", "size": 47710, "ext": "py", "lang": "Python", "max_stars_repo_path": "emc2/simulator/radar_moments.py", "max_stars_repo_name": "columncolab/EMC2", "max_stars_repo_head_hexsha": "19dc8e25ebf477e1ad632d9e2d91692dcd80d3e4", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
#!/usr/bin/env python
import argparse
import gzip
import re
import sys
from collections import Counter
from functools import partial
from itertools import zip_longest
import faiss
import networkx as nx
import numpy as np
from chinese_whispers import chinese_whispers, aggregate_clusters
from gensim.models import Keyed... | {"hexsha": "3744a57a84991dd6606cc931be9d593a42805115", "size": 3498, "ext": "py", "lang": "Python", "max_stars_repo_path": "trihosg.py", "max_stars_repo_name": "uhh-lt/triframes", "max_stars_repo_head_hexsha": "1e87ad99d03055f652701ea2f8fea88a1786210d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_sta... |
import cv2
import numpy as np
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.data import MetadataCatalog
from detectron2.data import transforms as T
from detectron2.modeling import build_model
from detectron2.utils.visualizer import ColorMode, GenericMask, Visualizer, _create_text... | {"hexsha": "ed4e23b45720df872261c073a9e55f41611299cb", "size": 4426, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/IFC/demo/clip/predictor.py", "max_stars_repo_name": "sukjunhwang/IFC", "max_stars_repo_head_hexsha": "fb2ee4571dba4700eab3b52f10e147225b763e2a", "max_stars_repo_licenses": ["Apache-2.0"],... |
from warnings import warn
from numpy import asarray
from scipy.sparse import isspmatrix_csc, isspmatrix_csr, isspmatrix, \
SparseEfficiencyWarning, csc_matrix
import _superlu
noScikit = False
try:
import scikits.umfpack as umfpack
except ImportError:
import umfpack
noScikit = True
isUmfpack = ha... | {"hexsha": "73f3633ae2601e8a929e9c04a5a32dee8a647646", "size": 9090, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/sparse/linalg/dsolve/linsolve.py", "max_stars_repo_name": "mandli/scipy", "max_stars_repo_head_hexsha": "ce90df2874c39595ef69a586a3e7fdd9cb9b6f48", "max_stars_repo_licenses": ["BSD-3-Clause"... |
#!/usr/bin/env python
import numpy as np
from scipy.spatial.distance import pdist, squareform
### For matlab interface
# scipy.io.loadmat and scipy.io.savemat
# Create the following array where each row is a point in 2D space:
# [[0 1]
# [1 0]
# [2 0]]
x = np.array([[0, 1], [1, 0], [2, 0]])
print x
# Compute the... | {"hexsha": "ffeac8ee9e821b822e7f19bd551d0c69e8bc5870", "size": 630, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnn_python_tutorial/numpy/mat_sci.py", "max_stars_repo_name": "DeercoderPractice/python", "max_stars_repo_head_hexsha": "4a32cc8922f47baea390e8167e34f185f67ae0fd", "max_stars_repo_licenses": ["MIT"... |
from sympy import *
from sympy.solvers.solveset import linsolve
p0 = Symbol("p0", real=True)
p1 = Symbol("p1", real=True)
p2 = Symbol("p2", real=True)
p3 = Symbol("p3", real=True)
p4 = Symbol("p4", real=True)
p5 = Symbol("p5", real=True)
m = {
1: sympify("1 - 2 * 9 ** (-n) + (162 / 43) * (11 / 81) ** n + (96 / 4... | {"hexsha": "5d7bcc2f7fe0a8890f77f95c082936e0541a67bd", "size": 1105, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/hermann5_distribution.py", "max_stars_repo_name": "mmsbrggr/polar", "max_stars_repo_head_hexsha": "34348baf6992232e47cee7a4d56b5a96567c50b8", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import numpy
import sklearn
import condor
import borg
import borg.experiments.simulate_runs
logger = borg.get_logger(__name__, default_level = "INFO")
def simulate_run(run, maker, all_data, train_mask, test_mask, instances, independent, ... | {"hexsha": "535b13a09704e36ecc7e3745c34855aa0d9c81f8", "size": 4571, "ext": "py", "lang": "Python", "max_stars_repo_path": "borg/experiments/simulate_iid.py", "max_stars_repo_name": "borg-project/borg", "max_stars_repo_head_hexsha": "5140cff6c96de365b2eba9f07b7fc606b4b16c1b", "max_stars_repo_licenses": ["MIT"], "max_st... |
function [airway_mapped_image, airway_tree_root] = PTKMapAirwayCentrelineToImage(centreline_results, airway_image)
% PTKMapAirwayCentrelineToImage.
%
%
%
%
% Licence
% -------
% Part of the TD Pulmonary Toolkit. https://github.com/tomdoel/pulmonarytoolkit
% Author: To... | {"author": "tomdoel", "repo": "pulmonarytoolkit", "sha": "09688a006d548fb85795df0338d1ed4f4a010fb9", "save_path": "github-repos/MATLAB/tomdoel-pulmonarytoolkit", "path": "github-repos/MATLAB/tomdoel-pulmonarytoolkit/pulmonarytoolkit-09688a006d548fb85795df0338d1ed4f4a010fb9/Library/Airways/PTKMapAirwayCentrelineToImage.... |
# -*- coding: utf-8 -*-
"""
This module contains various tools used within Markov modeling / segmentation.
:author: Jean-Baptiste Courbot - www.jb-courbot.fr
:date: Feb 23, 2018
"""
import numpy as np
from numpy import cos
def phi_theta(a,b):
"""
Weighting function to account for orientation in Ising models.... | {"hexsha": "89e2c44d37b394df9ebc1024d9266492e0756909", "size": 5115, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/fields_tools.py", "max_stars_repo_name": "courbot/otmf", "max_stars_repo_head_hexsha": "22015bb067c49c6b24645a4c0136bac2a7d40e95", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_count": n... |
#include <string>
#include <iostream>
#include <algorithm>
#include <boost/lambda/lambda.hpp>
#include <boost/lambda/casts.hpp>
#include <ctime>
#include <cstdlib>
using namespace boost::lambda ;
struct MyRandomizer {
char operator( )( ) {
return static_cast<char>( rand( ) % 256 ) ;
}
} ;
std::string dele... | {"hexsha": "cfb93ad5385ef760ba711c63c6a0ec4d33d5b842", "size": 1710, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lang/C++/strip-control-codes-and-extended-characters-from-a-string.cpp", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7... |
import numpy as np
from scipy.sparse import csc_matrix, diags
from scipy.sparse.linalg import splu
DOUBLE_EPS = 1e-14
SIZING_EPS = 1e-6
MIN_EDGE_LENGTH = 1e-2
MAX_RADIUS = 0.5
def axial_stiffness_matrix(L, A, E):
K = np.ones((2, 2))
K[0, 1] = -1.0
K[1, 0] = -1.0
L = max(L, MIN_EDGE_LENGTH) # nasty t... | {"hexsha": "685cfcded88d532ca3d7dd06352223f5c1cafca7", "size": 10880, "ext": "py", "lang": "Python", "max_stars_repo_path": "04/utils/analysis.py", "max_stars_repo_name": "danhaive/4.453x-resources", "max_stars_repo_head_hexsha": "789bcbac426bee12102fb7788f5b54dfa2773752", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
import random
import gym
import numpy as np
from stable_baselines3.common.env_checker import check_env
from plan_opt.demand import Demand
from plan_opt.envs.rampup2 import LEGAL_CHANGES
def env_health(config, env=None, first_step=False, random_steps=0, verbose=0):
if env is None:
... | {"hexsha": "a488215e8a64f7ffd04efa413f1750c6ca019ce8", "size": 2223, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plan_opt/env_health.py", "max_stars_repo_name": "sebas-seck/plan-opt", "max_stars_repo_head_hexsha": "bf95edc2c3609aea7572887097be0f2f75e19216", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Created by rahman at 14:51 2020-03-05 using PyCharm
import os
import random
import pandas as pd
import scipy
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
city = 'ny' #'ny'
DATAPATH = '../data/' + city + "/"
... | {"hexsha": "872c3164b902b3c7e37aee45754fc82e7de2e325", "size": 5341, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/shared_tools/utils.py", "max_stars_repo_name": "tahleen-rahman/all2friends", "max_stars_repo_head_hexsha": "156ba257677def409661e8b68ccdfb1e896ba721", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
# try to import numba
# or define dummy decorator
try:
from numba import autojit
except:
def autojit(func):
return func
# util functions for network simulation
def smooth_trace(trace, scale):
scale = int(scale)
if scale == 1 or scale == 0:
return trace
slen = int(... | {"hexsha": "f773d57aa74b4992ccc7a738ae5e36925acad880", "size": 1310, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/brian2/utils_net.py", "max_stars_repo_name": "caglorithm/stimulus_neural_populations", "max_stars_repo_head_hexsha": "58567901bed6f6bc17fc2975435138c33bb6be66", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma exL_exMap_lem:
fixes
f :: "Label -~> sterm" and
lz :: "Label -~> sterm" and f' :: "Label -~> sterm"
assumes "dom f = dom lz" and "dom f' = dom f"
shows
"\<forall>L1 L2. finite L1
\<longrightarrow> (\<forall>l\<in>dom f. \<forall>s p. s \<notin> L1 \<and> p \<notin> L1 \<and> s \<no... | {"llama_tokens": 83677, "file": "Locally-Nameless-Sigma_Sigma_ParRed", "length": 111} |
[STATEMENT]
lemma tensor_lookup:
assumes "\<And>is. is \<lhd> dims A \<Longrightarrow> lookup A is = e is"
shows "tensor_from_lookup (dims A) e = A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. tensor_from_lookup (dims A) e = A
[PROOF STEP]
using tensor_lookup_base lookup_def length_vec tensor_from_lookup_def
[PRO... | {"llama_tokens": 316, "file": "Deep_Learning_Tensor", "length": 2} |
import os
import uuid
from shutil import copytree
from tempfile import gettempdir
from uuid import uuid4
from os.path import join
import sys
from unittest import TestCase
from aequilibrae import Project
from aequilibrae.paths import path_computation, Graph
from aequilibrae.paths.results import PathResults
from aequili... | {"hexsha": "dbf6f07019c57530fe12105bb3e09e622e936b7a", "size": 7120, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/aequilibrae/paths/test_pathResults.py", "max_stars_repo_name": "Art-Ev/aequilibrae", "max_stars_repo_head_hexsha": "9f438278e09c875717779bfcc99bf7ba75ed1372", "max_stars_repo_licenses": ["MI... |
import cv2
import numpy as np
from scipy import ndimage
import os
# https://docs.opencv.org/3.4.1/d7/d4d/tutorial_py_thresholding.html
th = 127
max_val = 255
# for color do not forget to convert BGR to RBG
import cv2
cameraCapture = cv2.VideoCapture(0)
fps = 30
size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WID... | {"hexsha": "383b20b37551e82d4643debb4540cedfe3c59f4a", "size": 1186, "ext": "py", "lang": "Python", "max_stars_repo_path": "introduction/13_camera_thresholding.py", "max_stars_repo_name": "Tenjin0/python-opencv-base", "max_stars_repo_head_hexsha": "b9732f24de688547b6d45b9d796d0ff458902874", "max_stars_repo_licenses": [... |
import unicornhat as uh
import time
import colorsys
import math
from random import randint
import numpy
uh.set_layout(uh.PHAT)
uh.rotation(90)
uh.brightness(0.4)
width,height=uh.get_shape()
### Many of these were created by pimoroni and can be found here: https://github.com/pimoroni/unicorn-hat/tree/master/examples
#... | {"hexsha": "90f58957954e4db4ce72c15edc9fdaa07e9d5e02", "size": 33034, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/party.py", "max_stars_repo_name": "heckseven/partypi", "max_stars_repo_head_hexsha": "b3e827325b0b8411a881bcbb68dca3d4c4055e85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
import json
import os.path
from metric.bleu import moses_multi_bleu
import glob as glob
import numpy as np
import jsonlines
from tabulate import tabulate
from tqdm import tqdm
def compute_prf_SMD(gold, pred, global_entity_list):#, kb_plain=None):
# local_kb_word = [k[0] for k in kb_plain]
TP, FP, FN = 0, 0, 0
... | {"hexsha": "39c13f897040e3a68ceefd8d9593307b0bfbf279", "size": 3174, "ext": "py", "lang": "Python", "max_stars_repo_path": "metric/smd_scorer.py", "max_stars_repo_name": "andreamad8/FSB", "max_stars_repo_head_hexsha": "a81593590189fa5ad1cc37c5857f974effd9750a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 53,... |
[STATEMENT]
lemma hyps_for_collect: "fset (hyps_for n p) = {h . hyps n h = Some p}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fset (hyps_for n p) = {h. hyps n h = Some p}
[PROOF STEP]
by auto | {"llama_tokens": 93, "file": "Incredible_Proof_Machine_Incredible_Signatures", "length": 1} |
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
import os
import os.path
import time
prototypes = []
MAX_PROTOTYPES=20
lowe_ratio = 1.0
imgs = []
confidence_threshold = 0.7
durability = 0.01
Choice = True
LearnNewPrototypes = False
start_imgs_fns = []
start_imgs_fns.append("station2.jpg")
star... | {"hexsha": "29513135b5a3c22ec217216b6ea2aac7af040f16", "size": 6670, "ext": "py", "lang": "Python", "max_stars_repo_path": "Orb.py", "max_stars_repo_name": "PtrMan/21V1", "max_stars_repo_head_hexsha": "fbac4deb5bec3a5e50b81e1e91c4a8a9820d6aaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_s... |
# (c) 2011, 2012 Georgia Tech Research Corporation
# This source code is released under the New BSD license. Please see
# http://wiki.quantsoftware.org/index.php?title=QSTK_License
# for license details.
#
# Created on Month day, Year
#
# @author: Vishal Shekhar
# @contact: mailvishalshekhar@gmail.com
# @summary: ML A... | {"hexsha": "532a202153bf90aa81ad63a7632fe46256be3aed", "size": 2321, "ext": "py", "lang": "Python", "max_stars_repo_path": "QSTK/qstklearn/mldiagnostics.py", "max_stars_repo_name": "romanbsd/QuantSoftwareToolkit", "max_stars_repo_head_hexsha": "6b7e15fa3c0ba483a30674ff5acf30c77b91b877", "max_stars_repo_licenses": ["BSD... |
import dlib
import numpy as np
import face_recognition_models
import dlib.cuda as cuda
class FaceRec():
def __init__(self, gpu):
cuda.set_device(gpu)
face_detector = dlib.get_frontal_face_detector()
predictor_68_point_model = face_recognition_models.pose_predictor_model_location()
... | {"hexsha": "5ab147f71240458c49fcec350c8d83f03cbab22d", "size": 2684, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/src/libs/facerec.py", "max_stars_repo_name": "dropitlikecross/looking-to-listen", "max_stars_repo_head_hexsha": "bd2e4b272a4ee1424cf3077870886748c42c6a0c", "max_stars_repo_licenses": ["... |
import netsquid as ns
import numpy as np
import matplotlib.pyplot as plt
from qkd.networks import TwoPartyNetwork
from qkd.protocols.bb84 import KeySenderProtocol as BB84Sender, KeyReceiverProtocol as BB84Receiver
from qkd.protocols.e91 import KeySenderProtocol as E91Sender, KeyReceiverProtocol as E91Receiver
from qkd... | {"hexsha": "b768bb9ab5f20d2d4f3fee0412d4331b89b4ac43", "size": 9200, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_simulations/run_sim.py", "max_stars_repo_name": "stephendiadamo/qkd_error_recon", "max_stars_repo_head_hexsha": "cb21f4764ab4bd3dad4db7f0d61164d2457731c5", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import pandas as pd
import itertools as it
__version__ = 0.1
class Frame():
"""
Creates a `kadro.Frame` object out of a `pandas.DataFrame` object. Will ignore index.
Datastructure is immutable but reference to `pandas.DataFrame` is always kept.
<pre>Example:
import numpy as np
... | {"hexsha": "4e95b2da6a9199a4084b0f14adee6cf8aba3d7c8", "size": 11827, "ext": "py", "lang": "Python", "max_stars_repo_path": "kadro/Frame.py", "max_stars_repo_name": "koaning/kadro", "max_stars_repo_head_hexsha": "cbf993e5142d1ade26ac5922d7d15784d56b3db6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_... |
import pandas as pd
from scipy.stats import beta, norm
from scattertext.termranking.OncePerDocFrequencyRanker import OncePerDocFrequencyRanker
from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
class BetaPosterior(CorpusBasedTermScorer):
'''
Beta Posterior Scoring. Code adapted f... | {"hexsha": "a5f6a5ff15152f301187cd830a49c1a2de568e21", "size": 3888, "ext": "py", "lang": "Python", "max_stars_repo_path": "scattertext/termscoring/BetaPosterior.py", "max_stars_repo_name": "shettyprithvi/scattertext", "max_stars_repo_head_hexsha": "a15613b6feef3ddc56c03aadb8e1e629d28a427d", "max_stars_repo_licenses": ... |
import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets
from tqdm import tqdm
from pixelsnail import PixelSNAIL
def train(epoch, loader, model, optimizer, device):
loader = tqdm(loader)
criterion = nn.CrossEntropyLoss()
for i,... | {"hexsha": "5d5af244159bf5a614ce9d0553ca94e37ce00f00", "size": 1605, "ext": "py", "lang": "Python", "max_stars_repo_path": "pixelsnail_cifar.py", "max_stars_repo_name": "eyalbetzalel/vq-vae-2-pytorch", "max_stars_repo_head_hexsha": "464c683e5049377cba7101156542168d068cf507", "max_stars_repo_licenses": ["MIT"], "max_sta... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
@copyright 2016 J.T. Lapreste
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
... | {"hexsha": "9f1c391754116fa0455ebe2b21ef550f6fe87d13", "size": 1312, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/simd/function/if_zero_else_one.hpp", "max_stars_repo_name": "yaeldarmon/boost.simd", "max_stars_repo_head_hexsha": "561316cc54bdc6353ca78f3b6d7e9120acd11144", "max_stars_repo_licenses"... |
"""
IndicatorSOS1Bridge{T,S<:MOI.AbstractScalarSet}
The `IndicatorSOS1Bridge` replaces an indicator constraint of the following
form:
``z \\in \\mathbb{B}, z == 1 \\implies f(x) \\in S`` with a SOS1 constraint:
``z \\in \\mathbb{B}, slack \\text{ free}, f(x) + slack \\in S, SOS1(slack, z)``.
"""
struct IndicatorSO... | {"hexsha": "0a067a7e00b453f9185a5dbc761c82c3285ae652", "size": 5067, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Bridges/Constraint/indicator_sos.jl", "max_stars_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_stars_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_stars_repo_licens... |
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import os
import math
def undesired_objects(image):
image = image.astype('uint8')
nb_components, output, stats, centroids = cv.connectedComponentsWithStats(image, connectivity=4)
sizes = stats[:, -1]
max_label = 1
max_... | {"hexsha": "dfa1e9010884230981ab22df339c7bef3f05a71e", "size": 1987, "ext": "py", "lang": "Python", "max_stars_repo_path": "Helpers/connectedcomponents.py", "max_stars_repo_name": "AsianZeus/Digitalizing-Prescription-Image", "max_stars_repo_head_hexsha": "3e102e5fd5a6e8427153ae57c94a698b0abc06a6", "max_stars_repo_licen... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from numpy.testing import assert_allclose
from astropy.tests.helper import pytest
from ...utils.testing import requires_dependency
from ...utils.random im... | {"hexsha": "de9b6d05dd7f9cf02259b4fff3a7faeed38956c4", "size": 3448, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/stats/tests/test_fit_statistics.py", "max_stars_repo_name": "grburgess/gammapy", "max_stars_repo_head_hexsha": "609e460698caca7223afeef5e71826c7b32728d1", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma secureTT_iff_secure': "Orig.secureTT \<longleftrightarrow> Prime.secure"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Orig.secureTT = secure
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. Orig.secureTT \<Longrightarrow> secure
2. secure \<Longrightarrow> Orig.secureTT
[PRO... | {"llama_tokens": 3629, "file": "Bounded_Deducibility_Security_BD_Security_Triggers", "length": 34} |
! { dg-do compile }
! { dg-options "-std=f2003" }
program a
implicit none
integer n, m(3,3)
integer(kind=8) k
integer, allocatable :: i(:), j(:)
real, allocatable :: x(:)
n = 42
m = n
k = 1_8
allocate(i(4), source=42, source=n) ! { dg-error "Redundant SOURCE tag found" }
allocate(integer(4) :: ... | {"hexsha": "ee6c3635912dbe8d2805cf2779f1c6f0ca98c3f0", "size": 744, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/allocate_alloc_opt_4.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_... |
import pyrender
import os
import trimesh
import numpy as np
import cv2
import os
import torch
from scipy.spatial.transform import Rotation as R
os.environ['PYOPENGL_PLATFORM'] = 'egl'
def to_homo(rotation, translation):
transform = np.eye(4)
transform[:3, :3] = rotation
transform[:3, 3] = translation
... | {"hexsha": "3a4dc6e029a35e3d0c046bf603883d099d674367", "size": 8167, "ext": "py", "lang": "Python", "max_stars_repo_path": "render_swisscube.py", "max_stars_repo_name": "Komod0D/eccv18-rgb_pose_refinement", "max_stars_repo_head_hexsha": "1662e044e806b5d7d8fc0d09fd6f0da333dba663", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma sources_sinks_aux:
"sources_aux I D U xs = sinks_aux (I\<inverse>) D U (rev xs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sources_aux I D U xs = sinks_aux (I\<inverse>) D U (rev xs)
[PROOF STEP]
by (induction xs, simp_all) | {"llama_tokens": 107, "file": "Noninterference_Ipurge_Unwinding_IpurgeUnwinding", "length": 1} |
// -*- mode: c++; indent-tabs-mode: nil; -*-
//
// Copyright (c) 2009-2013 Illumina, Inc.
//
// This software is provided under the terms and conditions of the
// Illumina Open Source Software License 1.
//
// You should have received a copy of the Illumina Open Source
// Software License 1 along with this program. If ... | {"hexsha": "5ac574749208f6dad00a719f278868733c239f44", "size": 3756, "ext": "hh", "lang": "C++", "max_stars_repo_path": "isaac_variant_caller/src/lib/strelka/position_somatic_snv.hh", "max_stars_repo_name": "sequencing/isaac_variant_caller", "max_stars_repo_head_hexsha": "ed24e20b097ee04629f61014d3b81a6ea902c66b", "max... |
/-
Copyright (c) 2020 Floris van Doorn. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Floris van Doorn
-/
import measure_theory.measure.measure_space
import measure_theory.measure.regular
import topology.opens
import topology.compacts
/-!
# Contents
In this file we ... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/measure_theory/measure/content.lean"} |
from numpy import array
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import random, math, copy, statistics
from IPython.display import clear_output
from time import sleep
import matplotlib.pyplot as plt
random.seed(1)
def F6(x,y):
return 0.5 - (((... | {"hexsha": "617339213e7168f5a457fcfee6fd167dcf081071", "size": 5692, "ext": "py", "lang": "Python", "max_stars_repo_path": "pso_f6_mod.py", "max_stars_repo_name": "CaioMM/f6_mod", "max_stars_repo_head_hexsha": "34f5a0fbc8f24d62a6b593939fa1b7296a8384dd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
"""Plot survey and synthetic matrices for France, Japan, and Shanghai, China as shown in figure 3."""
import numpy as np
import pandas as pd
import matplotlib as mplt
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
import matplotlib.font_manager as font_manager
import matpl... | {"hexsha": "9cdc6ec6e0758085fbf33008de8e3b7e53b43251", "size": 6496, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_results/scripts/fig_3b.py", "max_stars_repo_name": "DongxiaW/mixing-patterns", "max_stars_repo_head_hexsha": "e841a934b826ecc98bf443026c32e7c6b7aa75bc", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python
"""
DataProcParams class for importing, working with, and storing data processing
parameters (e.g., PINGU's V5 processing).
"""
from __future__ import absolute_import, division
from collections import Mapping, OrderedDict, Sequence
from copy import deepcopy
from itertools import izip
import os... | {"hexsha": "786f7e771f19ff4a5844458dcc442f8c09f38215", "size": 25724, "ext": "py", "lang": "Python", "max_stars_repo_path": "pisa/utils/data_proc_params.py", "max_stars_repo_name": "torkjellsdatter/pisa", "max_stars_repo_head_hexsha": "7b26b0ac40c873a87786286acfd1c96abf724a99", "max_stars_repo_licenses": ["Apache-2.0"]... |
[STATEMENT]
lemma "implc_get_offending_flows [ACL_not_with]
\<lparr> nodesL = [''A'', ''B'', ''C''], edgesL = [(''B'', ''A''), (''B'', ''C''), (''A'', ''B'')] \<rparr> =
[[(''B'', ''C'')], [(''A'', ''B'')]]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. implc_get_offending_flows [ACL_not_with] \<lparr>no... | {"llama_tokens": 246, "file": "Network_Security_Policy_Verification_Examples_Impl_List_Playground_ChairNetwork_statefulpolicy_example", "length": 1} |
#!/usr/bin/env python3
import astropy.time
import astropy.coordinates
locUK608 = astropy.coordinates.EarthLocation.from_geodetic(lat=51.143833512, lon=-1.433500703, height=176.028) # UK608 LBA
locIE613 = astropy.coordinates.EarthLocation.from_geocentric(3801633.528060000, -529021.899396000, 5076997.185, unit='m') # I... | {"hexsha": "4f8f16615caad5c78c00a0a90264992bc9606b86", "size": 1170, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/lst.py", "max_stars_repo_name": "griffinfoster/artemis", "max_stars_repo_head_hexsha": "bf445b7d2dc9127676503a4efd633d3492e6139a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
"""
somasnakes
===========
Original package is adjusted for soma detection by donghaozhang and siqiliu.
This soma submodule can be used for soma detection only, but this submodule is
currently embedded in rivuletpy. The soma mask can be generate by setting
its corresponding argument. Soma detec... | {"hexsha": "6a6240e4845befc3c4d0d115b447c4d47fcf80b2", "size": 24264, "ext": "py", "lang": "Python", "max_stars_repo_path": "rivuletpy/soma.py", "max_stars_repo_name": "holiday01/rivuletpy", "max_stars_repo_head_hexsha": "4adf06cbe5a16d34eb1d27df80ff658073484457", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
/-
Copyright (c) 2015 Microsoft Corporation. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Leonardo de Moura, Jeremy Avigad, Minchao Wu, Mario Carneiro
! This file was ported from Lean 3 source module data.finset.basic
! leanprover-community/mathlib commit 68cc421841... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Data/Finset/Basic.l... |
import collections
import itertools
import jax.numpy as np
from jax import jit
import numpy.random as rnd
import numpy
import mdp.search_spaces as search_spaces
def onehot(x, N):
return np.eye(N)[x]
def entropy(p):
return -np.sum(np.log(p+1e-8) * p)
def sigmoid(x):
return 1/(1+np.exp(-x))
def softmax(... | {"hexsha": "cf28613d94b294f2ea778de9e2ea1df9921fecde", "size": 6974, "ext": "py", "lang": "Python", "max_stars_repo_path": "mdp/utils.py", "max_stars_repo_name": "act65/mdps", "max_stars_repo_head_hexsha": "59f35467baa83b953ccdac5290acfcc31f33fd28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
!Author Huang Yihan
!This is the main program of this homework
!1.read inputs
!2.read resonance table
!3.calculate
Program main
use input_mod
use table_mod
use calculate_mod
implicit none
call read_inputs
call res_table_init
call U8_xs_init
call H1_xs_init
call calculat... | {"hexsha": "21af554bd53690dc8dfc72713fcb91744a46b348", "size": 411, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/main.f90", "max_stars_repo_name": "Huang-Yihan/Recentyl-Physicals-homework", "max_stars_repo_head_hexsha": "5569fa99987bf97982dbc4ac10faf45d5f3ce62b", "max_stars_repo_licenses": ["MIT"], "max... |
lemma {u} FOIL {R : Type u} [ring R] : ∀ a b c d : R, (a + b) * (c + d) = a*c + a*d + b*c + b*d :=
by { intros, rw [left_distrib, right_distrib, right_distrib], ac_refl }
lemma {u} FOIL_neg_square {R : Type u} [comm_ring R] : ∀ a b : R, (a - b) * (a - b) = a*a + (-(a*b)+ -(a*b)) + b*b :=
by { intros, rw sub_eq_add_neg... | {"author": "Shamrock-Frost", "repo": "FormalComplexAnalysis", "sha": "3cac79d9b603b1edf7df1bc7e948c74eb86a2cc0", "save_path": "github-repos/lean/Shamrock-Frost-FormalComplexAnalysis", "path": "github-repos/lean/Shamrock-Frost-FormalComplexAnalysis/FormalComplexAnalysis-3cac79d9b603b1edf7df1bc7e948c74eb86a2cc0/algebra.l... |
(* Title: HOL/HOLCF/IOA/Seq.thy
Author: Olaf Müller
*)
section \<open>Partial, Finite and Infinite Sequences (lazy lists), modeled as domain\<close>
theory Seq
imports HOLCF
begin
default_sort pcpo
domain (unsafe) 'a seq = nil ("nil") | cons (HD :: 'a) (lazy TL :: "'a seq") (infixr "##" 65)
inducti... | {"author": "seL4", "repo": "isabelle", "sha": "e1ab32a3bb41728cd19541063283e37919978a4c", "save_path": "github-repos/isabelle/seL4-isabelle", "path": "github-repos/isabelle/seL4-isabelle/isabelle-e1ab32a3bb41728cd19541063283e37919978a4c/src/HOL/HOLCF/IOA/Seq.thy"} |
#Author : Zoumpekas Athanasios
#codename : thzou
import os
import numpy as np
import pandas as pd
import pickle
import quandl
import datetime
import time
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import sys
import io
from itertools import product
import warnings
from plotly import tools
import ... | {"hexsha": "3bb36b49c612f1750363628f6edd4b7cc219e495", "size": 5753, "ext": "py", "lang": "Python", "max_stars_repo_path": "python_scripts/data_acquisition.py", "max_stars_repo_name": "thzou/crypto_analysis", "max_stars_repo_head_hexsha": "7eb4f4c988e4d5a94d36ed61a002041e44d72c10", "max_stars_repo_licenses": ["MIT"], "... |
import argparse, random, sys, os
# import librosa
import numpy as np
import soundfile
def main(args):
y, sr = soundfile.read(args.filename, always_2d=True)
print(f"File loaded with y {y.shape}, sr = {sr}")
# multichannel
if y.shape[1] > 1:
y = y.mean(axis=1, keepdims=True)
print(f"File... | {"hexsha": "8cd26954f74e2432d71f478e52c6c3f1b3207b0b", "size": 1552, "ext": "py", "lang": "Python", "max_stars_repo_path": "playground/audio_slicer.py", "max_stars_repo_name": "x75/smp_audio", "max_stars_repo_head_hexsha": "6e293d6419132e34e38efde90efb58cabb9c623b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
(************************************************************************)
(* v * The Coq Proof Assistant / The Coq Development Team *)
(* <O___,, * INRIA - CNRS - LIX - LRI - PPS - Copyright 1999-2010 *)
(* \VV/ **************************************************************)
(* // * Th... | {"author": "mattam82", "repo": "Coq-misc", "sha": "60bc3cbe72083f4fa1aa759914936e4fa3d6b42e", "save_path": "github-repos/coq/mattam82-Coq-misc", "path": "github-repos/coq/mattam82-Coq-misc/Coq-misc-60bc3cbe72083f4fa1aa759914936e4fa3d6b42e/plugins/setoid_ring/Ring2_tac.v"} |
import logging
import numpy as np
import os
try:
import matplotlib.pyplot as plt
from matplotlib import gridspec
is_matplotlib = True
except:
is_matplotlib = False
from pystella.util.phys_var import phys
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
__author__ = 'bakl'
eve_el... | {"hexsha": "7ab7519fe6f9117ac1ad56dbbd92861e31bd11c6", "size": 41252, "ext": "py", "lang": "Python", "max_stars_repo_path": "pystella/model/sn_eve.py", "max_stars_repo_name": "cradesto/pystella", "max_stars_repo_head_hexsha": "f6f44ed12d9648585a52a09e15d494daa4c70c59", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#-*-coding:utf-8-*-
import numpy as np
def coarse_forecast(SimRes, Bath, idx_list, lowlim, highlim):
'''
Takes wave height from coarse forecast, crops at lowlim bathymetry value and forecasts using Green's Law to highlim.
SimRes: Coarse Grid simulation results
Bath: Coarse Grid Bathymetry
idx_list: Index List of s... | {"hexsha": "064e0ad8c2fd3eee46194771690c4374420b9b13", "size": 2399, "ext": "py", "lang": "Python", "max_stars_repo_path": "transfer_function/src/coarse_forecast.py", "max_stars_repo_name": "DanGiles/Localised-Tsunami-Response", "max_stars_repo_head_hexsha": "33b041c94b55aec66b7940d3979e9e4f788cd702", "max_stars_repo_l... |
import numpy as np
import tensorflow as tf
from stable_baselines.common.tf_layers import linear
from tensorflow.python.ops import math_ops
from gym import spaces
class ProbabilityDistribution(object):
def __init__(self):
super(ProbabilityDistribution, self).__init__()
def flatparam(self):
ra... | {"hexsha": "7488eabde4b2ee9ad1d32e477efd51215fc13424", "size": 13016, "ext": "py", "lang": "Python", "max_stars_repo_path": "stable_baselines/common/distributions.py", "max_stars_repo_name": "emadboctorx/stable-baselines", "max_stars_repo_head_hexsha": "9bce185538e8bf69836371286e23919fd85eec64", "max_stars_repo_license... |
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import inf_def
def main():
inf_net = inf_def.InferenceNetwork()
targets = tf.placeholder(tf.float32, [None, 10])
correct_prediction = tf.equal(tf.argmax(inf_net.logits, 1), tf.argmax(targets, 1))
accu... | {"hexsha": "82f8851a05eb881ff55df5cbf5bbfb6911119946", "size": 768, "ext": "py", "lang": "Python", "max_stars_repo_path": "commons/mnist/inf/test_inf_net.py", "max_stars_repo_name": "hamedhaghighi/Usupervised_Image_Restoration", "max_stars_repo_head_hexsha": "a3fefbf54891b9e984987fe15bd6b434b59fec3c", "max_stars_repo_l... |
# # unfished
# add_theme(:ggplot2_base,
# bglegend = _invisible,
# fg = :white,
# fglegend = _invisible,
# fgguide = :black)
#
# add_theme(:ggplot2,
# base = :ggplot2_base,
# bginside = :lightgray,
# fg = :lightgray,
# fgtext = :gray,
# fg... | {"hexsha": "c071fe6a1793cf3e06d71b9a581dae400929bcd6", "size": 891, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ggplot2.jl", "max_stars_repo_name": "greimel/PlotThemes.jl", "max_stars_repo_head_hexsha": "8e7f69e05369f570ffdcb125144b46f754b92d05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <boost/intrusive/list.hpp>
| {"hexsha": "3781a4261a26b4a86a9eae1397afa18273d9d79c", "size": 36, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_intrusive_list.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
#!/usr/bin/python -u
import numpy as np
import os, sys, random
import cv2
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.models import load_m... | {"hexsha": "11eda219f5512cb86e70c99cabf59941692357d5", "size": 4584, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_train.py", "max_stars_repo_name": "mashgin/basic_deep_learning_keras", "max_stars_repo_head_hexsha": "4d6597c29ee9e929f7e4b17e146266fbdcf848a6", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright (c) 2021-2022, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | {"hexsha": "7cec56e1c798fc354c776f88e311e342be5d47e8", "size": 1803, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/cugraph-pytorch/cugraph-local/data_loader/read_cora.py", "max_stars_repo_name": "wangxiaoyunNV/dgl", "max_stars_repo_head_hexsha": "abcf92cdee4c571b72e9347595e8b8f158ff66d4", "max_stars_r... |
@testset "ch04_sim02" begin
xlim = [-5.0, 5.0]
ylim = [-5.0, 5.0]
world = World(xlim, ylim)
circlings = Array{Agent,1}(undef, 0)
robots = Array{RealRobot,1}(undef, 0)
for i = 1:10
circling = Agent(0.2, 10.0 / 180 * pi)
robot =
RealRobot([0.0, 0.0, 0.0], circling, noth... | {"hexsha": "e64a7b70515c37399b7148c269ea7e389d35a2cf", "size": 9564, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ch04_test.jl", "max_stars_repo_name": "soblin/JuliaProbo", "max_stars_repo_head_hexsha": "bb206e19dd350af7f82b90e7c5062e5a088eff2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
\section{Intermediate Language}~\label{sec:il}
Each prover first translates the Copilot specification into an
intermediate representation best suited for model checking. Two
representations are available :
\begin{itemize}
\item
The \textbf{IL} format : a list
of quantifier-free equations over integer sequences, i... | {"hexsha": "deda03ca042cc519ede7c46b769a6c65a8c69af0", "size": 33522, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "co-kind-report/IL.tex", "max_stars_repo_name": "Copilot-Language/copilot-discussion", "max_stars_repo_head_hexsha": "caccad918b23dae991095344a845827ddccd6047", "max_stars_repo_licenses": ["BSD-3-Cl... |
(* Copyright 2021 (C) Mihails Milehins *)
section\<open>Simple semicategories\<close>
theory CZH_SMC_Simple
imports
CZH_DG_Simple
CZH_SMC_NTSMCF
begin
subsection\<open>Background\<close>
text\<open>
The section presents a variety of simple semicategories, such as the empty
semicategory \<open>0\<close>... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/CZH_Foundatio... |
#!/usr/bin/env python
#-------------------------------------------------------------------------------
# Copyright 2019 Pivotal Software Inc
#
# 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
... | {"hexsha": "4c51d992304c0daab3a140d65867d8cb5107e8fc", "size": 12608, "ext": "py", "lang": "Python", "max_stars_repo_path": "RTS4MADlib/samples/CreditCardTransactionGenerator/transactionGenerator.py", "max_stars_repo_name": "pivotal/Realtime-scoring-for-MADlib", "max_stars_repo_head_hexsha": "4ed6500bdd3422c3d090d8c6c6... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import print_function
import os
import requests
from numpy import testing as npt
from astropy.tests.helper import pytest
from astropy.table import Table
import astropy.coordinates as coord
import astropy.units as u
from ...exceptions impo... | {"hexsha": "91bb00fc01a33b363a273f6e3b54bfeba82876d3", "size": 10110, "ext": "py", "lang": "Python", "max_stars_repo_path": "astroquery/ned/tests/test_ned.py", "max_stars_repo_name": "hamogu/astroquery", "max_stars_repo_head_hexsha": "9a2d1a2ecc4dbfafa6a39cf7a180bcf831a6266a", "max_stars_repo_licenses": ["BSD-3-Clause"... |
from dolfin import * # @UnusedWildImport
import logging
import numpy as np
logging.getLogger('FFC').setLevel(logging.warnings)
logging.basicConfig(level=logging.DEBUG)
logging.getLogger('UFL').setLevel(logging.warnings)
set_log_level(WARNING)
def kl(y, N, order=1):
deg = order
mesh = UnitSquareMesh(int(N), in... | {"hexsha": "4248c56cd0bf1ef03712df9325ce0828b110508f", "size": 2247, "ext": "py", "lang": "Python", "max_stars_repo_path": "smolyak/applications/pde/kl.py", "max_stars_repo_name": "mbaudin47/smolyak", "max_stars_repo_head_hexsha": "c12d7cbae57d9b1cf2e026547b78bcd9c8d63a6c", "max_stars_repo_licenses": ["MIT"], "max_star... |
using ControlBenchmarks
using ControlSystems
benchmarkProb = controlbenchmark( JonesMorari() )
@test ControlSystems.nstates( benchmarkProb.sys ) == 4
@test ControlSystems.ninputs( benchmarkProb.sys ) == 2
| {"hexsha": "6532e32e4da0aa34c6fb63f16ca3f51ace5dd29b", "size": 207, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/benchmarks/jonesMorari.jl", "max_stars_repo_name": "imciner2/ControlBenchmarks.jl", "max_stars_repo_head_hexsha": "f11f4a98bd8ab2923771b72e7f0d4c60302207c8", "max_stars_repo_licenses": ["MIT"],... |
import torch
import os
import argparse
import numpy as np
import sys
sys.path.append('./')
from pipelines import config
from pipelines.utils.point_utils import read_point_ply
parser = argparse.ArgumentParser(description='Extract meshes from occupancy process.')
parser.add_argument('--config', default='configs/lig/lig_... | {"hexsha": "6472385358bc1c46afc0f566ed1ad6ca79ae58a5", "size": 3313, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/run_lig.py", "max_stars_repo_name": "wnbzhao/Local-Implicit-Grid-Pytorch", "max_stars_repo_head_hexsha": "d45da37beda52653f0066f9ba0f0500c54402e13", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy
import pickle
from tensorflow.python.framework import dtypes
from sklearn.model_selection import train_test_split
import tensorflow as tf
from enum import Enum
import scipy.ndimage
import scipy.misc
from sklearn.utils import shuffle
class TrafficDataProvider(object):
"""
provide data to neural ne... | {"hexsha": "1247c724e4a13c7e240a8b423429cf4cd01cd03b", "size": 9579, "ext": "py", "lang": "Python", "max_stars_repo_path": "traffic/traffic_data.py", "max_stars_repo_name": "JamesLuoau/Traffic-Sign-Recognition-with-Deep-Learning-CNN", "max_stars_repo_head_hexsha": "e73a892a17db71121861b746861d57643d34ea30", "max_stars_... |
#!/usr/bin/env python
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
import numpy as np
import network
slim = tf.contrib.slim
import os
import json
import cv2
import signal
import sys
class Inferer:
def __init__(self, model_num):
model_name = str(model_num)
log_folder =... | {"hexsha": "94a82be4f453680103ce86fa486955ca721ad2b9", "size": 2131, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros_workspace/src/deep_segmentation/src/inferer.py", "max_stars_repo_name": "NVIDIA-Jetson/Foursee-Navigation", "max_stars_repo_head_hexsha": "673b4a8bcf5774cf23d2564bada68709d28c850e", "max_stars... |
%----------------------------------------------------------------------------------------
% VARIOUS REQUIRED PACKAGES
%----------------------------------------------------------------------------------------
\usepackage{titlesec} % Allows customization of titles
\usepackage[top=3cm,bottom=3cm,left=3.2cm,right=3.2cm,h... | {"hexsha": "52305bea64ac3ac0ddd2d29ee58e8ee00a2f691a", "size": 16911, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "usr/src/uts/common/io/qede/579xx/drivers/ecore/documentation/structure.tex", "max_stars_repo_name": "AsahiOS/gate", "max_stars_repo_head_hexsha": "283d47da4e17a5871d9d575e7ffb81e8f6c52e51", "max_st... |
function keep(varargin);
%KEEP keeps the caller workspace variables of your choice and clear the rest.
% Its usage is just like "clear" but only for variables.
%
% Xiaoning (David) Yang xyang@lanl.gov 1998
% Revision based on comments from Michael McPartland,
% michael@gaitalf.mgh.harvard.edu,... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/181-keep/keep.m"} |
import math
import numpy as np
from itertools import chain
import load_subjects as ls
# Given a list X, returns a list of changepoints
def get_changepoints(X):
return X[:-1] != X[1:]
# Construct numpy array from jagged data by filling ends of short rows with NaNs
def jagged_to_numpy(jagged):
aligned = np.ones((le... | {"hexsha": "13a18f70287406317fca507d451a086a2908f71a", "size": 10479, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_code/util.py", "max_stars_repo_name": "sss1/behavioral_eyetracking", "max_stars_repo_head_hexsha": "945268e564b9e9031be0afb40054d6ab35059633", "max_stars_repo_licenses": ["CC-BY-4.0"], "... |
""" Import libraries """
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
import getopt
from decimal import Decimal
from keras.models import Model
from keras.layers import LSTM
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
import os
import rando... | {"hexsha": "24f24056aebd19d8a9539d8a9e9e35e0336216c6", "size": 5055, "ext": "py", "lang": "Python", "max_stars_repo_path": "Convolutional_autoencoder/reduce.py", "max_stars_repo_name": "EPantelaios/LSTM-Neural-Networks", "max_stars_repo_head_hexsha": "fc0490a79a237f12d56d52d9b33585c79d15d9ce", "max_stars_repo_licenses"... |
from collections import defaultdict
import tensorflow_hub as hub
import cv2
from matplotlib import pyplot as plt
from matplotlib import patches
from pathlib import Path
import numpy as np
label_map = { 1: "person", 2: "bicycle", 3: "car", 4: "motorcycle", 5: "airplane", 6: "bus", 7: "train", 8:... | {"hexsha": "4f7a2cedd4f60ebb2e5d4d8405a992429397767f", "size": 4084, "ext": "py", "lang": "Python", "max_stars_repo_path": "question3/main.py", "max_stars_repo_name": "wechat-haibei/3D-deeplearning-processing", "max_stars_repo_head_hexsha": "9acf5900b24e29b03cb358c992d3d1e429288279", "max_stars_repo_licenses": ["MIT"],... |
const ignorefirst = 10 # cm
const bigturn = π/3 # 60°
# const smallturn = π/93 # 60°
const s = 500
const Point = SVector{2, Float64}
point(::Missing) = missing
point(x::Instantaneous)= Point(x.data[1], x.data[2])
point(x::Point) = x
_getv(spl, k) = SVector{2, Float64}(derivative(spl, k))
function gettpindex(spl, ks... | {"hexsha": "f7ebbc166036c16d487c3682e1fab1f276829033", "size": 2726, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/track.jl", "max_stars_repo_name": "yakir12/DungBase.jl", "max_stars_repo_head_hexsha": "1144c221d8e3552e1d4bfac23059f2ed61688959", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
.SH "Installing \*(PN"
.PP
.II installation
To install \*(PN on your system, following the directions in
the appropriate sub-section:
either for the VAX, or for the Intel development system.
.Sh "Installing \*(PN on the VAX"
.PP
To install \*(PN on the VAX, do the following:
.nr l1 0
\*i
Create a directory in which the... | {"hexsha": "ee2c1279e078a029b5f0b496f229d6bbeaf36c85", "size": 3161, "ext": "r", "lang": "R", "max_stars_repo_path": "doc/mwc/doc/mwc/text/isisinstall.r", "max_stars_repo_name": "gspu/Coherent", "max_stars_repo_head_hexsha": "299bea1bb52a4dcc42a06eabd5b476fce77013ef", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import pandas as pd
import numpy as np
import os
from sklearn.metrics import *
results_directory = '/sb-personal/cvqa/results/c2vqa-verbs-results-final'
output_directory = '/sb-personal/cvqa/src/c2vqa-verbs/analysis'
output_joined_file = os.path.join(output_directory, "all_models_test_results.csv")
if os.path.exists... | {"hexsha": "65a721719034cbf82de5983f99f50dbdf11f9ea6", "size": 2935, "ext": "py", "lang": "Python", "max_stars_repo_path": "c2vqa-verbs/analysis/relevance-example-results.py", "max_stars_repo_name": "andeeptoor/qar-qae", "max_stars_repo_head_hexsha": "60d61b0c6b5686dda471c727227cee2cc365f836", "max_stars_repo_licenses"... |
import os
import numpy as np
from nobos_commons.data_structures.constants.dataset_part import DatasetPart
from nobos_commons.data_structures.dimension import ImageSize
from nobos_commons.utils.file_helper import get_create_path
from nobos_torch_lib.datasets.action_recognition_datasets.ehpi_dataset import NormalizeEhpi... | {"hexsha": "d33c4f0cc31943aaa298a35c28cecd64171894e4", "size": 4367, "ext": "py", "lang": "Python", "max_stars_repo_path": "ehpi_action_recognition/paper_reproduction_code/evaluations/lstm/test_its_journal_2019.py", "max_stars_repo_name": "steuwe/ehpi_action_recognition", "max_stars_repo_head_hexsha": "4318e82e541c9b42... |
# -*- coding: utf-8 -*-
"""
This module contains the Branch class (one branch of the tree) and the Nodes class
"""
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
from scipy.spatial import cKDTree
pool = ThreadPool(16)
class Branch:
"""Class that contains a branch of the fractal tree
... | {"hexsha": "c5d77e22cf5d73077f91c367ba1ecd410952cca7", "size": 10545, "ext": "py", "lang": "Python", "max_stars_repo_path": "Branch3D.py", "max_stars_repo_name": "adsche/fractal-tree", "max_stars_repo_head_hexsha": "9d33a91cbdcdbad40b082a1b936e5cb133a8892a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "m... |
[STATEMENT]
lemma finite_is_class: "finite {C. is_class P C}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite {C. is_class P C}
[PROOF STEP]
(*<*)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite {C. is_class P C}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. finite {C. is_clas... | {"llama_tokens": 435, "file": "JinjaDCI_Common_Decl", "length": 7} |
export ode_order_lowering
function lower_varname(var::Variable, idv, order)
order == 0 && return var
name = Symbol(var.name, :_, string(idv.name)^order)
return Variable(name; known = var.known)
end
function ode_order_lowering(sys::ODESystem)
eqs_lowered, _ = ode_order_lowering(sys.eqs, sys.iv)
OD... | {"hexsha": "8dcbc12758f313def6f171760cf2afa10f9a3e1d", "size": 1515, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/systems/diffeqs/first_order_transform.jl", "max_stars_repo_name": "shashi/ModelingToolkit.jl", "max_stars_repo_head_hexsha": "600ea214f19ed5b9d8c5d355e15e7ff4d9e9115b", "max_stars_repo_licenses... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
different transformation functions
in a neural network
synthetic classifiaction problem
possibility to restrain information
python 3.7.7
numpy 1.19.2
scikit-learn 0.24.1
tensorflow 2.0.0
keras 2.3.1
matplitlib 3.3.2
author: adrienne bohlmann
"""
import numpy as np
... | {"hexsha": "c56006cb37616bee24e30821ee280943df5b49ed", "size": 9070, "ext": "py", "lang": "Python", "max_stars_repo_path": "synth_classifiaction_NN_transform.py", "max_stars_repo_name": "adriennebohlmann/synthPlay", "max_stars_repo_head_hexsha": "1496119348369fe5d1485f37f632ef2e3e7623e6", "max_stars_repo_licenses": ["C... |
% SPDX-License-Identifier: MIT
% Copyright (c) 2017-2020 Forschungszentrum Juelich GmbH
% This code is licensed under MIT license (see the LICENSE file for details)
%
\documentclass[
t, % align text inside frame to t=top, b=bottom, c=center
10pt, % 8pt, 9pt, 10pt, 11pt, 12pt, 14pt, 17pt, 20pt available as text font
asp... | {"hexsha": "a4c298e52fec0255418591dd6c35ae38ff6d5801", "size": 6499, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tutorial/tutorial.tex", "max_stars_repo_name": "SFKV/Hackathon-Talks", "max_stars_repo_head_hexsha": "ca7040b8877e934ad030bd5d29fa7d449120706c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/* bst/gsl_bst_avl.h
*
* Copyright (C) 2018 Patrick Alken
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or (at
* your option) any later version.
* ... | {"hexsha": "6560777ce27a479a16e81f1842ea1e9d67cf318d", "size": 2461, "ext": "h", "lang": "C", "max_stars_repo_path": "include/gsl/gsl_bst_avl.h", "max_stars_repo_name": "vinej/sml", "max_stars_repo_head_hexsha": "115c007926ca80d51a37cdf887b5252338d8bc8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
\section{Use of Recursive Functions}
\begin{lstlisting}[language=Haskell]
module ProgExercises.FS_2019_ProgExer03Prob_V01 where
-- Develop some functions using recursion over lists.
-- Higher-order functions are not required yet.
toBeImplemented = undefined
--delDups deletes duplicates from a list
testDelDups =... | {"hexsha": "5c4e4ccdfe230ab8babd60dabaa91edfed8ca0de", "size": 3023, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "TSM_AdvPrPa/Excercises/Haskell/06_RecursiveFunctions.tex", "max_stars_repo_name": "nortismo/mse-documentations", "max_stars_repo_head_hexsha": "cc67637785237d630f077a863edcd5f49aa52b59", "max_stars_... |
import pandas as pd
pd.options.mode.chained_assignment = None
from pkg_resources import parse_version
import warnings
from ete3 import NCBITaxa
import numpy as np
import argparse
import tarfile
import re
import math
# Helper function to import tables
def safely_read_csv(path, **kwargs):
try:
return pd.rea... | {"hexsha": "6beb3bd75e5066b6b53e5f18534ba109b21c4b3c", "size": 5972, "ext": "py", "lang": "Python", "max_stars_repo_path": "blast/taxonomy/wrapper.py", "max_stars_repo_name": "avilab/vs-wrappers", "max_stars_repo_head_hexsha": "65524b3566969da7909e0d08c423b6eecadea039", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#================================AxionFuncs.py=================================#
# Written by C. O'Hare
# Contains:
# Functions for calculating Solar Axion fluxes for photon & electron coupling
# Functions for calculating X-ray spectra in a haloscope
# Functions to smear X-ray spectra by an angular resolution
# Script ... | {"hexsha": "4bc77d7318342c7d906e522b5cd4ed33bf434843", "size": 7611, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/AxionFuncs.py", "max_stars_repo_name": "cajohare/IAXOmass", "max_stars_repo_head_hexsha": "978306f2a504f5f05562a4f5fcecf55b789e8289", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
\begin{publications}
\section*{已发表论文}
\begin{enumerate}
\item
\textbf{Xuda~Zhou}, Zidong~Du, Shijin~Zhang, Lei~Zhang, Huiying~Lan, Shaoli~Liu, Ling~Li, Qi~Guo, Tianshi~Chen, Yunji~Chen: Addressing Sparsity in Deep Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018.
\... | {"hexsha": "67f650a18cb36e036fa8ae0791b0b6f9f0d8bbaf", "size": 598, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/publications.tex", "max_stars_repo_name": "ustcanycall/graduate", "max_stars_repo_head_hexsha": "4c92658dfd4069b3697b1590a0b2b9b61ef35019", "max_stars_repo_licenses": ["LPPL-1.3c"], "max_sta... |
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0, 2 * np.pi, 150)
x1, y1 = np.cos(t), np.sin(t)
x2, y2 = 2 * x1, 2 * y1
colors = ['darkred', 'darkgreen']
fig, ax = plt.subplots()
ax.plot(x1, y1, color=colors[0], label='Inner', linewidth=3)
ax.plot(x2, y2, color=colors[1], label='Outer', linewidth... | {"hexsha": "2dda7635941960c988c96a63a49aba3f21b24b63", "size": 383, "ext": "py", "lang": "Python", "max_stars_repo_path": "anatomy-of-matplotlib-scipyconf-2018/solutions/4.1-legends_and_scaling.py", "max_stars_repo_name": "dlimpid/np-mpl-pd-nims-moderator-2019-04", "max_stars_repo_head_hexsha": "7b48116d9e4449e05b46b5f... |
# -*- coding: utf-8 -*-
"""
Objective: create an airfoil with a leading edge restriction, same upper length
restriction, othogonal upper spars and constant thicknesses in four places
Created on Mon Oct 17 10:36:34 2016
@author: Pedro
"""
from __future__ import print_function
import os
import math
import n... | {"hexsha": "7cec37a6ddd26682a094b85c93c0b04c7e4024ca", "size": 26018, "ext": "py", "lang": "Python", "max_stars_repo_path": "aeropy/morphing/twist_3D.py", "max_stars_repo_name": "belac626/AeroPy", "max_stars_repo_head_hexsha": "4f045306427e08b742237b7393ce9602f1072d60", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
function scan_data = rdprfile(ufilename,plotmode,verbose)
%RDPRFILE Reads data from a profilometer data file
%
% DATA = RDPRFILE(UFILENAME,VERBOSE)
%
% RDPRFILE examines the header in a profilometer data file and attempts to
% determine the type of data file. If a supported type is found, it then
% uses the appropria... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/15096-rd-scripts/rd_scripts_v13/rdprfile.m"} |
[STATEMENT]
lemma Nil_rsp2 [quot_respect]:
shows "(list_all2 (\<approx>) OOO (\<approx>)) Nil Nil"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (list_all2 (\<approx>) OOO (\<approx>)) [] []
[PROOF STEP]
by (rule compose_list_refl, rule list_eq_equivp) | {"llama_tokens": 107, "file": null, "length": 1} |
from __future__ import division
import numpy as np
from numpy.core.numeric import NaN
from scipy.signal import spectrogram
from . import timbral_util
import tensorflow as tf
import tensorflow.keras.backend as K
def timbral_depth(fname, fs=0, dev_output=False, phase_correction=False, clip_output=False, threshold_db=-6... | {"hexsha": "e14f3935b4de13c3295b4569aa280e76768370a9", "size": 31467, "ext": "py", "lang": "Python", "max_stars_repo_path": "timbral_models/Timbral_Depth.py", "max_stars_repo_name": "ALavault/tf_timbral_models", "max_stars_repo_head_hexsha": "302923f48e4bb09d7aaa3197758cd7764ac71d47", "max_stars_repo_licenses": ["Apach... |
[STATEMENT]
lemma hd_reach_all_if_nfwd_app_fwd:
"\<lbrakk>\<not>forward_arcs (y#xs); forward_arcs (y#ys@xs); x \<in> set (y#ys@xs)\<rbrakk>
\<Longrightarrow> hd (rev (y#ys@xs)) \<rightarrow>\<^sup>*\<^bsub>T\<^esub> x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<not> forward_arcs (y # xs); forwar... | {"llama_tokens": 498, "file": "Query_Optimization_IKKBZ_Examples", "length": 2} |
import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
from torch.optim.lr_scheduler import CosineAnnealingLR
class BiC(nn.Module):
def __init__(self, lr, scheduling, lr_decay_factor, weight_decay, batch_size, epochs):
super(BiC, self).__init__()
self.beta = torch.nn.... | {"hexsha": "1e1bc81b0252fee832ffe5c3966d78728bd575af", "size": 5074, "ext": "py", "lang": "Python", "max_stars_repo_path": "inclearn/convnet/imbalance.py", "max_stars_repo_name": "Danden1/DER-ClassIL.pytorch", "max_stars_repo_head_hexsha": "66ccdb45890d3da335f4dcb841160cbea8719c15", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
...
"""
import urllib2
import time
import csv, cStringIO
import numpy as np
import matplotlib.pyplot as plt
log_file = "Yun_Log_BatteryDisCharging.log"
fmt_print = "%s, %14.3f, %9.3f s, %9.3f s, %9.3f V, %9.3f A, %9.3f Ohm, %9.3f W, %9.3f mAh, %9.3f J"
fmt_write = "%s,... | {"hexsha": "4a8ffa657c652841fe438e788c361f49c6e34f6c", "size": 3989, "ext": "py", "lang": "Python", "max_stars_repo_path": "Yun_Log_BatteryDisCharging/Yun_Log_BatteryDisCharging.py", "max_stars_repo_name": "drtrigon/sketchbook", "max_stars_repo_head_hexsha": "fa58bf767cce2537c86e5ce1638771a164a5b118", "max_stars_repo_l... |
\documentclass[letterpaper,10pt]{article}
\usepackage[margin=2cm]{geometry}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{multicol}
\usepackage{listings}
\usepackage{color}
\definecolor{mygreen}{rgb}{0,0.6,0}
\definecolor{mygray}{rgb}{0.5,0.5,0.5}
\definecolor{mymau... | {"hexsha": "43c29213f84471efdd405ffdd14fbf9119f6f008", "size": 12331, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "18794_Pattern_Recognition_Theory/Homeworks/HW2/document.tex", "max_stars_repo_name": "MengwenHe-CMU/Courses", "max_stars_repo_head_hexsha": "6cd9a9469b573ff76f70ceff6a0aa6103f7cdf3e", "max_stars_re... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.