text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
require("SymbolicLP")
using SymbolicLP
using GLPK
# Soft constraints
lpb = LPBlock([:x])
addconstraints(lpb, :(soft(x < -3, 1)))
z, x, flag = lpsolve(lpb)
@assert x[1] <= -3.0
lpb = LPBlock([:x])
addconstraints(lpb, :(soft(x > 3, 1)))
z, x, flag = lpsolve(lpb)
@assert x[1] >= 3.0
lpb = LPBlock([:x])
addconstraints(lpb... | {"hexsha": "3c319f4a2f1c3a21d775327b0890b061b1eeb0c3", "size": 1746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/symlp.jl", "max_stars_repo_name": "JuliaTagBot/SymbolicLP.jl", "max_stars_repo_head_hexsha": "d72a35dfd3d1015c96c3d6a13f80077901843a7a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from Scripts.PlayersProperties import defaultProperties
from Scripts.regression import Regression
import os
import sys
import time
from copy import deepcopy
class network:
def __init__(self):
self.count = 0
self.updateScore = False
self.TeamAScore = 0
self.TeamB... | {"hexsha": "69eae1c84798cdf190166041709e6566f35ceda9", "size": 17887, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfootball/Scripts/net.py", "max_stars_repo_name": "elishatofunmi/ReinEnv", "max_stars_repo_head_hexsha": "ad86203d3f4bddc7a8239cefdfa31c1a8e5e9af8", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import os
import tifffile
import numpy as np
from copy import copy
from merfishdecoder.util import imagereader
from merfishdecoder.core import dataset
class Frame(object):
def __init__(self,
dataSet,
fov: int = None,
zpos: float = None,
readoutName: str = None):
"""
... | {"hexsha": "a358bf3e7f70d08af51d7b9325d00ef60cc4f689", "size": 16021, "ext": "py", "lang": "Python", "max_stars_repo_path": "merfishdecoder/core/zplane.py", "max_stars_repo_name": "r3fang/MERlin", "max_stars_repo_head_hexsha": "2ee81a4e18c3fe406d91ee6ce65ca38b69ee9d68", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#include <szen/System/Config.hpp>
#include <szen/System/ErrorStream.hpp>
#include <json/json.h>
#include <boost/algorithm/string.hpp>
//#include <boost/filesystem.hpp>
/////////////////////////////////////////////////
// DEFAULTS
#define DEFAULT_FULLSCREEN 0
#define DEFAULT_RESOLUTION_FULLSCREEN Window::getOptim... | {"hexsha": "3625e408a5f6a29f1ae4bab882507bbbd9c54b5b", "size": 6851, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "code/szen/src/System/Config.cpp", "max_stars_repo_name": "Sonaza/scyori", "max_stars_repo_head_hexsha": "a894a9c7bd45a68ea1b6ff14877cdbe47ddd39cf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
/*
* This file is open source software, licensed to you under the terms
* of the Apache License, Version 2.0 (the "License"). See the NOTICE file
* distributed with this work for additional information regarding copyright
* ownership. You may not use this file except in compliance with the License.
*
* You may ... | {"hexsha": "5c3584da1d83bf706da6622e1cd2f9b66d24fba1", "size": 4396, "ext": "hh", "lang": "C++", "max_stars_repo_path": "src/fs/metadata_log_bootstrap.hh", "max_stars_repo_name": "rokinsky/seastar", "max_stars_repo_head_hexsha": "e935bc197c1acf81e7e0c3c6e78b735a95e8f2fa", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/python
# Copyright (c) 2019 Matthew Earl, Paulo Jarschel
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, c... | {"hexsha": "ac6dc45ecd7ece966d3f456cdc856c0a0a25ae9e", "size": 8968, "ext": "py", "lang": "Python", "max_stars_repo_path": "faceswaplib.py", "max_stars_repo_name": "pfjarschel/faceswapbot", "max_stars_repo_head_hexsha": "f8bc0f26874e04097a5e0a69f66dcdfcb2c4e31d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
\documentclass[12pt,a4paper]{article}
%\title{Lab6-Lucene3（自定义Similarity）}
\usepackage{ctex}
\usepackage{amsmath,amscd,amsbsy,amssymb,latexsym,url,bm,amsthm}
\usepackage{epsfig,graphicx,subfigure}
\usepackage{enumitem,balance}
\usepackage{wrapfig}
\usepackage{mathrsfs,euscript}
\usepackage[usenames]{xcolor}
\usepackage... | {"hexsha": "b45c75044e2dc069b64440a51d680c97ccc6f6f9", "size": 3864, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "homeworks/hw2/hw2.tex", "max_stars_repo_name": "MadCreeper/information-theory-notes", "max_stars_repo_head_hexsha": "3156594807acd53c32946581714a953d54045a43", "max_stars_repo_licenses": ["MIT"], "m... |
using Pkg
Pkg.activate(joinpath(@__DIR__, ".."))
using FoodCachingPlotting, Stipple, StippleUI, StipplePlotly, Genie, DataFrames,
Random, PlotlyJS, FoodCachingExperiments, FoodCachingModels
import FoodCachingExperiments: EXPERIMENTS, CLAYTON0103_EXPERIMENTS
const M = FoodCachingModels
Base.@kwdef mutable struct ... | {"hexsha": "a1ef5a3bf9dac1de7df61c98286061c86dfe44f9", "size": 12582, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/sim_dashboard.jl", "max_stars_repo_name": "jbrea/FoodCachingPlotting.jl", "max_stars_repo_head_hexsha": "0f95c0b93f507c16ca52f764c1b6239bff26b523", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
class MultinomialNaiveBayes:
def __init__(self):
self.initialize()
def initialize(self):
self.feature_probs = []
self.prior = []
self.classes = []
self.class_count = []
self.fitted = False
self.feature_count = []
self.n_featu... | {"hexsha": "36fd7ce57023605447571784303b59a407bbc7b4", "size": 3012, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNB.py", "max_stars_repo_name": "sebag90/spam_filter", "max_stars_repo_head_hexsha": "455309c02b6fa921b58111754a7fd2f3dd13271f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
# Write a function that takes as input a list of numbers, and returns
# the list of values given by the softmax function.
def softmax(L):
soft_max = []
denom = np.sum(np.exp(L))
for i in range(len(L)):
soft_max.append(np.exp(L[i])/denom)
return soft_max
| {"hexsha": "3ff8273be79ed673c11af379da98afd726453d62", "size": 298, "ext": "py", "lang": "Python", "max_stars_repo_path": "2_15_softmax.py", "max_stars_repo_name": "thejammerr/Deep-Learning-with-PyTorch", "max_stars_repo_head_hexsha": "393f7cee870ddb25b2e82457f102c3cc8fb9ef42", "max_stars_repo_licenses": ["MIT"], "max_... |
fir_filter_sim/dspba_library_package.vhd
fir_filter_sim/dspba_library.vhd
fir_filter_sim/auk_dspip_math_pkg_hpfir.vhd
fir_filter_sim/auk_dspip_lib_pkg_hpfir.vhd
fir_filter_sim/auk_dspip_avalon_streaming_controller_hpfir.vhd
fir_filter_sim/auk_dspip_avalon_streaming_sink_hpfir.vhd
fir_filter_sim/auk_dspip_avalon_streami... | {"hexsha": "785f4065feb8dc2a1ec575b9582e41a6404ee80f", "size": 813, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "de1soc/hw/ip/source/fir_filter_ip/fir_filter_sim.f", "max_stars_repo_name": "Seba-P/signal-recon-project", "max_stars_repo_head_hexsha": "73951b32bff79ab65af3a909ded933b0deb1d52e", "max_stars_repo_... |
import networkx as nx
import numpy as np
from HE2_ABC import Root
import HE2_tools as tools
from HE2_Solver import HE2_Solver
def evalute_network_fluids_wo_root(_G, x_dict):
assert not (Root in _G.nodes)
for u, v in _G.edges:
assert x_dict[(u, v)] >= 0
G = nx.DiGraph(_G)
for (u, v) in x_dict:... | {"hexsha": "11fad87f99192d91da6650adaa0c9c057084de53", "size": 3250, "ext": "py", "lang": "Python", "max_stars_repo_path": "KirchhoffSolver/code/HE2_MixFluids.py", "max_stars_repo_name": "khabibullinra/unifloc", "max_stars_repo_head_hexsha": "7338c12788e3f3340bf8d1cb1db15d0471b62434", "max_stars_repo_licenses": ["MIT"]... |
import numpy
import sympy
def max_coeff(poly):
""" Computes the maximum width of a polynomial's coefficients.
Arguments:
poly -- the rational polynomial to analyze
"""
dom = poly.domain
if isinstance(dom, sympy.polys.domains.IntegerRing):
# Finds maximal element.
return numpy.m... | {"hexsha": "d24cf8edc6290bf43395888943f449d1dd8620f8", "size": 1502, "ext": "py", "lang": "Python", "max_stars_repo_path": "symbaudio/utils/poly.py", "max_stars_repo_name": "ScottWe/symbaudio", "max_stars_repo_head_hexsha": "4797322acf340553cfd71ac3d63d62254735320e", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
[STATEMENT]
lemma nonpos_Ints_cases:
assumes "x \<in> \<int>\<^sub>\<le>\<^sub>0"
obtains n where "x = of_int n" "n \<le> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>n. \<lbrakk>x = of_int n; n \<le> 0\<rbrakk> \<Longrightarrow> thesis) \<Longrightarrow> thesis
[PROOF STEP]
using assms
[PROOF STATE]... | {"llama_tokens": 340, "file": null, "length": 3} |
type BernoulliArm <: BanditArm
p::Float64
end
function draw(arm::BernoulliArm)
if rand() > arm.p
0
else
1
end
end
| {"hexsha": "af01a24880417b589f18dc9ae45c892919b4abf7", "size": 131, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/arms/bernoulli.jl", "max_stars_repo_name": "rmminusrslash/BanditsBook", "max_stars_repo_head_hexsha": "b477b5a51c719f3725e0c0f05cb0ff7267bfc2f9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from configuration import configuration
from transformers import BertForMaskedLM, BertTokenizer, AutoConfig
import numpy as np
import torch
import argparse
from bias_utils import collate, how_many_tokens, find_mask_token
import pandas as pd
from model import Aligned_BERT
from tqdm import tqdm
parser = argparse.Argumen... | {"hexsha": "0d85fdee1656b7adba1d101d4f043dea5b6188b1", "size": 6574, "ext": "py", "lang": "Python", "max_stars_repo_path": "score.py", "max_stars_repo_name": "jaimeenahn/ethnic_bias", "max_stars_repo_head_hexsha": "a115eb7c3af7daf95309f3b4c1c7a564eb556190", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max... |
"""
Utilities for working with rating matrices.
"""
from collections import namedtuple
import logging
import warnings
import pandas as pd
import numpy as np
import scipy.sparse as sps
import numba as n
from numba import njit, jitclass, prange
from .util.array import swap
_logger = logging.getLogger(__name__)
Ratin... | {"hexsha": "0d2e1649a09b33ea74b9c1962e04b235d1f65cb6", "size": 18437, "ext": "py", "lang": "Python", "max_stars_repo_path": "lenskit/matrix.py", "max_stars_repo_name": "teej/lkpy", "max_stars_repo_head_hexsha": "06929c652319e5dede8d045a308a9721f03ab8a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
[STATEMENT]
lemma primepow_even_imp_primepow:
assumes "primepow_even n"
shows "primepow n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. primepow n
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. primepow n
[PROOF STEP]
from assms
[PROOF STATE]
proof (chain)
picking this:
primepow_even ... | {"llama_tokens": 914, "file": "Bertrands_Postulate_Bertrand", "length": 14} |
import segmentation_models_pytorch as smp
import numpy as np
import torch
from torchvision import datasets, transforms
import torchvision.models as models
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, random_split
from torchvision.transforms import ToTensor
from to... | {"hexsha": "b22242bf29288c2f6930a25d55a207b0600577dc", "size": 12402, "ext": "py", "lang": "Python", "max_stars_repo_path": "ensemble/train.py", "max_stars_repo_name": "RuixinGuo/Image-Segmentation", "max_stars_repo_head_hexsha": "77f4c08f37316ee0f1c9a3bd79b422df81ad4df8", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma measurable_liminf [measurable (raw)]:
assumes [measurable]: "\<And>n. A n \<in> sets M"
shows "liminf A \<in> sets M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. liminf A \<in> sets M
[PROOF STEP]
by (subst liminf_SUP_INF, auto) | {"llama_tokens": 107, "file": null, "length": 1} |
import pandas as pd
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import finterstellar as fs
pd.plotting.deregister_matplotlib_converters()
font = 'NanumSquareRound, AppleGothic, Malgun Gothic, DejaVu Sans'
class Visualize:
today = '(' + pd.to_datetime('today').date().strft... | {"hexsha": "92f69018505272edddd06f5d95902f1f4fc37178", "size": 25112, "ext": "py", "lang": "Python", "max_stars_repo_path": "w2/finterstellar/visualization.py", "max_stars_repo_name": "finterstellar/lecture", "max_stars_repo_head_hexsha": "fb14fb1c6a842e2ee2f79b0225ac9f4d11c3ca47", "max_stars_repo_licenses": ["MIT"], "... |
(*
File: Arithmetic_Summatory_Asymptotics.thy
Author: Manuel Eberl, TU München
*)
section \<open>Asymptotics of summatory arithmetic functions\<close>
theory Arithmetic_Summatory_Asymptotics
imports
Euler_MacLaurin.Euler_MacLaurin_Landau
Arithmetic_Summatory
Dirichlet_Series_Analysis
... | {"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/Dirichlet_Ser... |
'''
'''
import os
import pickle
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
from itertools import chain, combinations_with_replacement
# -- astropy --
import astropy.units as u
from astropy.time import Time
# -- specsim --
import specsim
from specsim.atmosphere import Moon
# -- fe... | {"hexsha": "7931a3722bf89c0a6df78883174ad969b8290e26", "size": 27204, "ext": "py", "lang": "Python", "max_stars_repo_path": "feasibgs/skymodel.py", "max_stars_repo_name": "changhoonhahn/feasiBGS", "max_stars_repo_head_hexsha": "b5f535f12cf64babc9e25bcec75edd45d8668f74", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma lincomb_0coeffs : "set ms \<subseteq> M \<Longrightarrow> \<forall>s\<in>set rs. s = 0 \<Longrightarrow> rs \<bullet>\<cdot> ms = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>set ms \<subseteq> M; \<forall>s\<in>set rs. s = (0::'r)\<rbrakk> \<Longrightarrow> rs \<bullet>\<cdot> ms = (... | {"llama_tokens": 351, "file": "Rep_Fin_Groups_Rep_Fin_Groups", "length": 2} |
v = read.table(file("results.log"))
t <- data.frame(readers=v[,1], writers=v[,2], distribution=v[,3], variant=v[,4], opss=v[,5], op=v[,7])
library(plyr)
t$writers = as.factor(t$writers)
t$readers = as.numeric(t$readers)
# split the data into tx/readers and tx/refresh
compare_impl = t[grep("evmap-refresh", t$variant, ... | {"hexsha": "5c22e41432979b4fe85a15dcd85a79d3f54ea9eb", "size": 2691, "ext": "r", "lang": "R", "max_stars_repo_path": "benchmark/plot.r", "max_stars_repo_name": "benbromhead/rust-evmap", "max_stars_repo_head_hexsha": "d4c12b8758c7e35145c40856f3d3474befe6ecda", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars... |
import numpy as np
import os
import glob
import csv
import shutil
import tqdm as tq
from utils import *
# class Sensor(tb.IsDescription):
# number = tb.Int32Col()
# id = tb.StringCol(100)
# lat = tb.Float64Col()
# lon = tb.Float64Col()
rootPath = os.path.expanduser('~/data/storage/cense/confy/')
inputPath = ... | {"hexsha": "beefbb7976e0942ac42b6da2f37d33872701f0d3", "size": 2832, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepareDataNpy.py", "max_stars_repo_name": "mathieulagrange/confy", "max_stars_repo_head_hexsha": "dd6a7e05a19b2d2e465fd781853a90bf743adbcc", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
import pytz
from qstrader.signals.sma import SMASignal
@pytest.mark.parametrize(
'start_dt,lookbacks,prices,expected',
[
(
pd.Timestamp('2019-01-01 14:30:00', tz=pytz.utc),
[6, 12],
... | {"hexsha": "bc7b7b6b22c09427a3dd303a90f49e353e5e1f4d", "size": 1058, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/signals/test_sma.py", "max_stars_repo_name": "calumrussell/qstrader", "max_stars_repo_head_hexsha": "826d3eeb63b95b9d8587f5e2152c030f2c57bbba", "max_stars_repo_licenses": ["MIT"], "max_... |
################################################################################
#
# config.py (c) Cameron Liang
# University of Chicago
# jwliang@oddjob.uchicago.edu
#
# Read in and define all of the parameters for voigt profile based on a
# config file; see ./scripts/bvp_write_config.py for produci... | {"hexsha": "d7da858356db776daeab06cf2abc56095e22a5c4", "size": 22518, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayesvp/config.py", "max_stars_repo_name": "ais97/bayesvp", "max_stars_repo_head_hexsha": "dedf38dd7236f040301a84e5578f7202a2da07aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import numpy as np
import json
from ageta import *
vae,encoder,decoder=getvaeq()
def clean(q):
if type(q) is dict:
for key in q.keys():
q[key]=clean(q[key])
return q
if type(q) is list:
for i in range(len(q)):
q[i]=clean(q[i])
return q
return str(q)
def traf(q):
ll=q.layers
... | {"hexsha": "a820386e454abe1ca989a83cdfd00c96f9d64968", "size": 575, "ext": "py", "lang": "Python", "max_stars_repo_path": "not_so_weird/oneoff/exportmodel.py", "max_stars_repo_name": "psorus/anogen", "max_stars_repo_head_hexsha": "86afc22718aded00bbc05e4582fa0a9b6aa3ab25", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import logging
import time
import numpy as np
import matplotlib.pyplot as pt
from keysight_fpga.sd1.fpga_utils import print_fpga_info
from keysight_fpga.sd1.dig_iq import load_iq_image
from keysight_fpga.qcodes.M3202A_fpga import M3202A_fpga
from core_tools.drivers.M3102A import SD_DIG, OPERATION_MODES
from pulse_li... | {"hexsha": "6789350f3425b15883204101c0fd0a3f3895f015", "size": 6307, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/hvi2/test_single_shot_rf.py", "max_stars_repo_name": "peendebak/core_tools", "max_stars_repo_head_hexsha": "2e43edf0bbc1d7ceb7042559db499535e8f6a076", "max_stars_repo_licenses": ["BSD-2-C... |
module Splines
using EllipsisNotation
include("Utils.jl")
include("Polynomials.jl")
include("Visuals.jl")
import .Polynomials: bernstein, N_basis
import .Visuals: plot_curve, plot_nodes, plot_surface
import .Utils: get_curve_parameters, ∑, dimensions, nnodes
export plot_curve, plot_no... | {"hexsha": "f88b8af3c568470452293d6c2348dd1270a4d97c", "size": 12160, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Splines/src/Splines.jl", "max_stars_repo_name": "FedeClaudi/Splines", "max_stars_repo_head_hexsha": "a475aa4e423b4efe71363a5833a1709a7d59b6ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
### A Pluto.jl notebook ###
# v0.12.21
using Markdown
using InteractiveUtils
# ╔═╡ b6b1b198-7bd8-11eb-10f5-a98dd0a59fcf
begin
cd("..")
using Pkg
Pkg.activate(".")
end
# ╔═╡ 594227e6-7bd9-11eb-3ffb-ab8e6e7b4d95
begin
using ColabINAOE2021
using PyPlot
using SparseArrays
using LinearAlgebra
end
# ╔═╡ 2141105e-7... | {"hexsha": "53cb8a0c02cf08f4c49f99359bd61aa1eaf3bea4", "size": 3554, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/MArrays_Pluto.jl", "max_stars_repo_name": "rurz/ColabINAOE2021", "max_stars_repo_head_hexsha": "539bf6ea662a95706734958663dc5bca278d5298", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
module Functional
using LinearAlgebra: Matrix, zero, similar
using LinearAlgebra, Printf
# using Roots
# using Optim
using Quadmath
# using ProfileView
# using InteractiveUtils
using ..Discrete
# const Float = Float64
# const Float = BigFloat
const Float = Float128
include("kernel.jl")
include("findmax.jl")
# us... | {"hexsha": "f783d4cd0768c4d41e642ffb827fefc789bf23e5", "size": 10301, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/functional/builder.jl", "max_stars_repo_name": "numericalEFT/Lehmann.jl", "max_stars_repo_head_hexsha": "058a505d1bb8590bb20ffd053976798373d977de", "max_stars_repo_licenses": ["MIT"], "max_sta... |
function [priormeans,posteriormeans,covmatrix] = gpnddisimPredictRNAOnly(model,predtimes);
% GPASIMPREDICT Compute predictions (means and a covariance matrix)
% of RNA values for the GPASIM model, conditional on the existing
% RNA values in the model but not on any existing POL2 values.
%
% FORMAT
%-------------------... | {"author": "SheffieldML", "repo": "GPmat", "sha": "4b5914a38ecbad9fb7a13a3392970bfc28c9d911", "save_path": "github-repos/MATLAB/SheffieldML-GPmat", "path": "github-repos/MATLAB/SheffieldML-GPmat/GPmat-4b5914a38ecbad9fb7a13a3392970bfc28c9d911/gpsim/gpnddisimPredictRNAOnly.m"} |
# This example is written for the new interface
# This is the full COVID-19 model to be fitted to the RKI data
# see the PPT for details of the model design
import StateModeling as stm
import numpy as np
import matplotlib.pyplot as plt
# import tensorflow as tf
from Corona.LoadData import loadData, preprocessData
from... | {"hexsha": "edde5f9dc6804dd269fe2c5442240f4d18e04644", "size": 4906, "ext": "py", "lang": "Python", "max_stars_repo_path": "Examples/FitCorona.py", "max_stars_repo_name": "RainerHeintzmann/StateModeling", "max_stars_repo_head_hexsha": "051826783954c8421b0de341ba6241b33c43af79", "max_stars_repo_licenses": ["MIT"], "max_... |
import pickle
import numpy as np
import os
import sys
from collections import defaultdict
from tqdm import tqdm
sys.path.append('..')
from config import get_configs, experiment_path, data_path
sys.path.append('..')
from config import experiment_path
class Vocab(object):
def __init__(self, size):
... | {"hexsha": "2e2a75ad26814651eca858e2a6ddd4670f996a25", "size": 3628, "ext": "py", "lang": "Python", "max_stars_repo_path": "train/data.py", "max_stars_repo_name": "jiali-ms/JLM", "max_stars_repo_head_hexsha": "ca7325cfcc6ed56469a90e8eca59b1e79e0cba9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 128, "max_st... |
[STATEMENT]
lemma PO_m1_refines_m1a [iff]:
"refines R1a1 med1a1 m1a m1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. refines R1a1 med1a1 m1a m1
[PROOF STEP]
by (rule Refinement_basic) (auto del: subsetI) | {"llama_tokens": 104, "file": "Security_Protocol_Refinement_Key_establish_m1_ds", "length": 1} |
import numpy as np
import pandas as pd
import scipy
from sklearn import metrics
from FPMax import FPMax
from Apriori import Apriori
from scipy.cluster.hierarchy import fcluster
from scipy.cluster.hierarchy import linkage
# MASPC algorithm
class MASPC():
def __init__(self,demographic,dignosisCodes):
self.de... | {"hexsha": "1cbfc1362635b7e2fd966e15c95afae959cca98b", "size": 4031, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/MASPC.py", "max_stars_repo_name": "dominicamartinez/clustehr", "max_stars_repo_head_hexsha": "0ce893a666974674fad36591f0156bd720910b4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import keras
import librosa
import os
import cv2
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
from config import EXAMPLES_PATH
import os
os.environ["IMAGEIO_FFMPEG_EXE"] = "/usr/bin/ffmpeg"
import moviepy.editor
import sys
import shutil
import subprocess
class audio... | {"hexsha": "f973da6a1dc403a2e36dff98b491a63e252fefd2", "size": 4599, "ext": "py", "lang": "Python", "max_stars_repo_path": "multimodalDetection/multimodalPredictions.py", "max_stars_repo_name": "Rubik90/TFM_AG", "max_stars_repo_head_hexsha": "5e836245d0704122f2a0d47413e93bf53d966ca0", "max_stars_repo_licenses": ["MIT"]... |
(* Title: HOL/HOLCF/IOA/NTP/Correctness.thy
Author: Tobias Nipkow & Konrad Slind
*)
section \<open>The main correctness proof: Impl implements Spec\<close>
theory Correctness
imports Impl Spec
begin
definition
hom :: "'m impl_state => 'm list" where
"hom s = rq(rec(s)) @ (if rbit(rec s) = sbit(sen ... | {"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/NTP/Correctness.thy"} |
import numpy as np
class zigzag_tubes(object):
def __init__(self,nr_t,l,area):
self.A_tot = area*np.ones(nr_t, dtype=np.float)
self.l = l*np.ones(nr_t, dtype=np.float)
| {"hexsha": "335dcc2295901b6910db423f07c307edff1c7f73", "size": 194, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_dp/simulation/zigzag_tubes.py", "max_stars_repo_name": "amirdel/dispersion-continua", "max_stars_repo_head_hexsha": "2e1f7a3fbfcdc0b27c546cb0ae51a628a926ad60", "max_stars_repo_licenses": ["0BSD"... |
[STATEMENT]
lemma le_sup_equiv2: "(a \<le> b) = (a \<squnion> b = b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a \<le> b) = (a \<squnion> b = b)
[PROOF STEP]
by (rule sup1_dual.le_sup_equiv) | {"llama_tokens": 99, "file": "PseudoHoops_PseudoHoops", "length": 1} |
#=
test_collater:
- Julia version: 1.1.0
- Author: bramb
- Date: 2019-03-18
=#
using Test
using HyperCollate,MetaGraphs
include("util.jl")
@testset "collater" begin
@testset "collating 2 xml texts" begin
include("util.jl")
f_xml = """
<text>
<s>Hoe zoet moet nochtans zijn dit... | {"hexsha": "b2131baa2f1ee8ad6be7c22e9c594f25d9793015", "size": 1683, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_collater.jl", "max_stars_repo_name": "bleekere/HyperCollate.jl", "max_stars_repo_head_hexsha": "3caf51a4817dffa98b44d404639b0aab485eae32", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
#module LinearPredict
export linPred,linPred2
function Toeplitz(a::Array{Complex{Float64},1})
m=length(a)
T=[(k>=l) ? a[1+mod(k-l,m)] : conj(a[1+mod(l-k,m)]) for k=1:m,l=1:m]
return(T)
end
function autoCorr(a::Array{Complex{Float64},1},p::Int64)
c=zeros(Complex{Float64},p)
for... | {"hexsha": "cf38b3951130cde15a6b5f86099ce19c8fa71784", "size": 987, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LinearPredict.jl", "max_stars_repo_name": "marcel-utz/NMR", "max_stars_repo_head_hexsha": "3fddc374a9c3c5127fb3e8e16402fbbb91319215", "max_stars_repo_licenses": ["IJG"], "max_stars_count": null,... |
\documentclass[conference]{IEEEtran}
\pdfoutput=1
\usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
\usepackage{graphicx}
\usepackage{textcomp}
\usepackage{xcolor}
\usepackage{url}
\usepackage{flushend}
\def\BibTeX{{\rm
B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
T\kern-.1667em\lower.... | {"hexsha": "af237ab9e42956276f725b4a8b2b2b153f034d01", "size": 15444, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Leon-the-dark-side-of-unikernels.tex", "max_stars_repo_name": "leonm1/unikernel-research-2020", "max_stars_repo_head_hexsha": "abe09db995668dda8947efbe6a73d302a366a237", "max_stars_repo_licenses": ... |
function ecephyscache()
ecephys_project_cache.EcephysProjectCache.from_warehouse(manifest=ecephysmanifest)
end
# These will take a while to download
function getsessiontable()
@info "Please wait, this can take a few seconds"
CSV.read(IOBuffer(ecephyscache().get_session_table().to_csv()), DataFrame);
end
ex... | {"hexsha": "94640706bd54ac1e93082457bb18e90829050dfb", "size": 6517, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/EcephysCache.jl", "max_stars_repo_name": "brendanjohnharris/AllenNeuropixels.jl", "max_stars_repo_head_hexsha": "26156723c95e82cad6ceca2c5550d8790eb3a7bf", "max_stars_repo_licenses": ["MIT"], "... |
%
% API Documentation for QSTK
% Module QSTK.qstklearn.hmm
%
% Generated by epydoc 3.0.1
% [Mon Mar 5 00:49:20 2012]
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Module Description %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | {"hexsha": "29e4c4125d59b4f3a19c3fbff601c94d710695a4", "size": 12758, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Docs/pdf/QSTK.qstklearn.hmm-module.tex", "max_stars_repo_name": "elxavicio/QSTK", "max_stars_repo_head_hexsha": "4981506c37227a72404229d5e1e0887f797a5d57", "max_stars_repo_licenses": ["BSD-3-Clause... |
using ActorInterfaces.Classic
using Test
struct Spawner end
struct SpawnTree
childcount::UInt8
depth::UInt8
end
@ctx function (me::Spawner)(msg::SpawnTree)
if msg.depth > 0
for i = 1:msg.childcount
child = spawn(Spawner())
send(child, SpawnTree(msg.childcount, msg.depth - ... | {"hexsha": "c1769764e7f0af65a7a025079385ccb528eeeee7", "size": 964, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/suite/spawner.jl", "max_stars_repo_name": "JuliaActors/ActorInterfacesTests.jl", "max_stars_repo_head_hexsha": "0edf44b8a6e99f2707e908d430e3c622bc5101b0", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 1 19:22:38 2020
@author: sankha
"""
'''
Compute the following cluster validation indices:
Silhouette index, Dunne Index, Davies Bouldin Index
'''
# Import Libraries
import math
import random
from urllib.request import urlretrieve
import pandas... | {"hexsha": "c4c25f1c2b09ed3ef5e26ac11206c209123ae760", "size": 7406, "ext": "py", "lang": "Python", "max_stars_repo_path": "2nd.py", "max_stars_repo_name": "Sankha98/Data-Mining", "max_stars_repo_head_hexsha": "05f9e6bd9e88202231bb0a4add51da862f76cdcd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
from openmdao.api import Component, Group, IndepVarComp
import numpy as np
import pyframe3dd.frame3dd as frame3dd
from commonse.utilities import nodal2sectional
from commonse import gravity, eps, Tube, NFREQ
import commonse.UtilizationSupplement as util
import commonse.manufacturing as manufacture
from commonse.WindW... | {"hexsha": "b813242d5965386cb6046c1f42c22cda8e611be3", "size": 72320, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/floatingse/loading.py", "max_stars_repo_name": "mattEhall/FloatingSE", "max_stars_repo_head_hexsha": "f13e0f38a7742ea00a8f446a9ebf505dcf7acd42", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
"""
Fits PSPL model with parallax ground-based and satellite data
using EMCEE sampler. We're using photometry of OGLE-2014-BLG-0939 from:
Yee et al. 2015 ApJ 802, 76
https://ui.adsabs.harvard.edu/abs/2015ApJ...802...76Y/abstract
and explore only 1 of 4 degenerate models.
It is similar to example_06_fit_parallax_EMCE... | {"hexsha": "57f02c0d3bcec16d6b5d386575b1f1467349638b", "size": 4765, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example_12_fit_satellite_parallax_EMCEE.py", "max_stars_repo_name": "pmehta08/MulensModel", "max_stars_repo_head_hexsha": "261738c445a8d116d09c90e65f6e847cfc8a7ad8", "max_stars_repo_licen... |
(*-------------------------------------------*
| The Dining Mathematicians in CSP-Prover |
| August 2004 |
| December 2004 (modified) |
| November 2005 (modified) |
| A... | {"author": "yoshinao-isobe", "repo": "CSP-Prover", "sha": "806fbe330d7e23279675a2eb351e398cb8a6e0a8", "save_path": "github-repos/isabelle/yoshinao-isobe-CSP-Prover", "path": "github-repos/isabelle/yoshinao-isobe-CSP-Prover/CSP-Prover-806fbe330d7e23279675a2eb351e398cb8a6e0a8/DM/DM1_Imp_def.thy"} |
import QuantumOptics
const qo = QuantumOptics
using Test, IonSim
using Suppressor
@suppress_err begin
# setup system
C = Ca40(["S-1/2", "D-1/2"])
chain = LinearChain(
ions=[C, C], com_frequencies=(x=2,y=2,z=1), vibrational_modes=(x=[1], y=[], z=[1])
)
T = Trap(configuration=chain)
modes = get_vibrational_modes(... | {"hexsha": "9c57e296379b96bb1d8a8771b4f77ebf786e647b", "size": 4655, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_operators.jl", "max_stars_repo_name": "HaeffnerLab/IonSim.jl", "max_stars_repo_head_hexsha": "884b0196ace4623e91bcd957f293d26d6723c071", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#! /usr/bin/env python
DESCRIPTION = 'pyPanair: A pre / post processor for PANAIR'
DISTNAME = 'pyPanair'
MAINTAINER = 'STakanashi'
LICENSE = 'MIT'
DOWNLOAD_URL = 'https://github.com/SaTa999/pyPanair'
VERSION = '0.8.0'
try:
from setuptools import setup
_has_setuptools = True
except ImportError:
... | {"hexsha": "bc723b91ae8985647718dd439fc38030c3c6bedf", "size": 1592, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "SaTa999/pyPanair", "max_stars_repo_head_hexsha": "49c987d92a447903ac28cc19da8ad91aa5843466", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_star... |
```python
import numpy as np
import sympy as sym
import numba
import pydae.build as db
```
```python
```
### Electromechanical differential equations
\begin{eqnarray}
f_1 &=& \dot \delta = \Omega_b \left( \omega - \omega_s \right) \\
f_2 &=& \dot \omega = \frac{1}{2H} \left( p_m - p_e - D \left( \omega - \omega_... | {"hexsha": "f0ade8db3777cd0ee89114bfd0eb1830c560ff44", "size": 6883, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "examples/grids/smib_milano_ex8p1/iso_milano_ex8p1_4ord_uctrl/iso_milano_ex8p1_4ord_uctrl_builder.ipynb", "max_stars_repo_name": "pydae/pydae", "max_stars_repo_head_hexsha": "8076bcfeb... |
"""
Reimplements distillator.py with a callback-based interface.
Author: Dominic Jack
Date: May 2020
"""
import itertools
import time
from typing import Dict, Iterable, List, Optional, Sequence, Union
import numpy as np
import tensorflow as tf
import tqdm
from rlo import analytics
from rlo import distillator
from rlo... | {"hexsha": "6f53395648e9f1443e95177c10b7b63adb9d4cdc", "size": 9512, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlo/src/rlo/distillator_v2.py", "max_stars_repo_name": "tomjaguarpaw/knossos-ksc", "max_stars_repo_head_hexsha": "8fa75e67c0db8f632b135379740051cd10ff31f2", "max_stars_repo_licenses": ["MIT"], "ma... |
push!(LOAD_PATH, "Modules")
using Colors
using JuliRay
φ=MathConstants.φ
Base.@irrational ° 0.0174532925199432957692369076848861271344 (big(pi)/big(180))
function POLYGON2(vertices,O,R;r1=1.0,r2=0.05,r3=0.025)
n=length(vertices)
C=O+R*normalize(+(vertices...)/n-O)
V=rgbColor(csgUnion((p->Sphere(p,r2)).(v... | {"hexsha": "b43d7a9d3a24ec1ed2a16acdc9c4f9e6efe93cba", "size": 5566, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Examples/NetOfPolyhedra/NetOfPolyhedra.jl", "max_stars_repo_name": "hyrodium/JuliRay", "max_stars_repo_head_hexsha": "178ea03571ea0852294d24a2277ac26d630880b3", "max_stars_repo_licenses": ["MIT"], ... |
"""
_tmap(f, xs...)
An internal helper for `tmap` that handles the `basesize == 1` case.
"""
function _tmap(f, xss...)
# Set the number of threads that BLAS uses to 1 here in case it's using up too many
nthreads() > 1 && LinearAlgebra.BLAS.set_num_threads(1)
futures = map(xss...) do x...
Thread... | {"hexsha": "35409da04808fe172ec4f84177f8cf6d71c483ea", "size": 2290, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tmap.jl", "max_stars_repo_name": "invenia/Parallelism.jl", "max_stars_repo_head_hexsha": "d5b8a82828b68bf7823ca208987c0a03b23e81f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "m... |
#!/usr/bin/env python3
"""
Unit and regression test for data_proc.py
"""
import errno
import os
import sys
import unittest
from contextlib import contextmanager
from io import StringIO
import pandas as pd
import numpy as np
import logging
from otu_proj.data_proc import main, data_process_analysis
logging.basicConfig(l... | {"hexsha": "248cc213023d92886cef02702fc2ca9dad47ab89", "size": 5020, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_otu_proj.py", "max_stars_repo_name": "chechung/otu_proj", "max_stars_repo_head_hexsha": "dc68b9708c36901c696c61c84f8283f69f9c08ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import glob
from time import time
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.callbacks import TensorBoard
from keras.optimizers import RMSprop
from numpy.random import seed
from sklearn.model_selection import StratifiedKFold
from keras.models import load_model
from keras. models import Model... | {"hexsha": "982e4524cc5011a46f035a311e10998e867f3df6", "size": 12159, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "jaist-kotani-lab/DeepSaliencyInCrowd", "max_stars_repo_head_hexsha": "d9c47fc6e2acfc37ffbc50e473e8e48f854ff268", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
module Datasets
Commonly used datasets and utilities for creating data containers.
In the future, contents will be integrated into packages:
- FastAI datasets and data containers will be moved into MLDatasets.jl
- data container transformations will be moved to MLDataPattern.jl
This submodule will then reex... | {"hexsha": "749261b0d822c7b9bed04c6e1a7d4e653bd4def1", "size": 1549, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/Datasets.jl", "max_stars_repo_name": "inferential/FastAI.jl", "max_stars_repo_head_hexsha": "3a017af061a1125231fe7d7a4ec98da0a255d781", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""Generate the API for symbolic multicomponent enrichment using sympy.
Note that there is a bug in sympy v0.7.2 that prevents the cse() function
from being used with infinities. For a work around see [1].
1. https://groups.google.com/forum/#!msg/sympy/YL1R_hR6OKQ/axKrCsCSMQsJ
"""
from __future__ import print_functio... | {"hexsha": "bc7ecb72c3fc1f63353e3a51e74c97fe3cd27cd1", "size": 14636, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyne/apigen/enrich_multi_sym.py", "max_stars_repo_name": "ypark234/pyne", "max_stars_repo_head_hexsha": "b7c4932c0399e6a0881aea943b392fb97cd0b6bd", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
"""~
@author: yanral
"""
import sabs_pkpd
import pints
import numpy as np
import os
# Select the folder in which this repo is downloaded in the line below
os.chdir('The/location/of/the/root/folder/of/this/repo')
# Set the parallelisation of the script
parallel = True
# Load the MMT model for... | {"hexsha": "9378b8b32de2486e23f9941f35dc01ba39bea67d", "size": 10493, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Fitting ORd-CiPA/Fitting with OHara initial conditions.py", "max_stars_repo_name": "CardiacModelling/Gamma_0", "max_stars_repo_head_hexsha": "05c7e16088656b343d3aa347df88fd3dd58cea87", "m... |
using CUDA
using ExaPF
using KernelAbstractions
using Test
using TimerOutputs
import ExaPF: PowerSystem
import ExaPF.PowerSystem: ParsePSSE
const PS = PowerSystem
@testset "Powerflow residuals and Jacobian" begin
local_case = "case14.raw"
# read data
to = TimerOutputs.TimerOutput()
datafile = joinpat... | {"hexsha": "139953173222ec3b6583fcac408fea9c241d6b4e", "size": 5265, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/powersystem.jl", "max_stars_repo_name": "exanauts/ExaPF.jl", "max_stars_repo_head_hexsha": "cd1bcb8a0782fe448d46a10816f82c5d28c3854e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 43... |
#
# This file is part of the profilerTools suite (see
# https://github.com/mssm-labmmol/profiler).
#
# Copyright (c) 2020 mssm-labmmol
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software withou... | {"hexsha": "582c9d2c15d8117cacb1330014852274d2bb068d", "size": 3515, "ext": "py", "lang": "Python", "max_stars_repo_path": "profilerTools/randomizer.py", "max_stars_repo_name": "mssm-labmmol/profiler", "max_stars_repo_head_hexsha": "6c4a508cd656a8cdf05ab3db19680e6239296a88", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""@package Methods.Machine.MagnetType14.build_geometry
MagnetType14 build_geometry method
@date Created on Wed Dec 17 16:09:15 2014
@copyright (C) 2014-2015 EOMYS ENGINEERING.
@author pierre_b
"""
from numpy import angle, exp
from pyleecan.Classes.Arc1 import Arc1
from pyleecan.Classes.Segmen... | {"hexsha": "4f267f205b0da9bcd03d483ca4b23cebaff285a2", "size": 3363, "ext": "py", "lang": "Python", "max_stars_repo_path": "Methods/Machine/MagnetType14/build_geometry.py", "max_stars_repo_name": "Superomeg4/pyleecan", "max_stars_repo_head_hexsha": "2b695b5f39e77475a07aa0ea89489fb0a9659337", "max_stars_repo_licenses": ... |
import numpy as np
from scipy import optimize
class SteelSection:
"""a"""
def __init__(self, code, props):
"""a"""
self.code = code
self.area = props[0]
self.mass = props[1]
self.ixx = props[2]
self.zxx = props[3]
self.sxx = props[4]
self.rx = ... | {"hexsha": "2058e6dfa06a6d37ba1151e86ebe8b4288cd7899", "size": 8703, "ext": "py", "lang": "Python", "max_stars_repo_path": "steeldesign/sections.py", "max_stars_repo_name": "robbievanleeuwen/steeldesign", "max_stars_repo_head_hexsha": "4a2351c7d922a4f0f379b413ecca17b672be1c6e", "max_stars_repo_licenses": ["MIT"], "max_... |
# -*- coding: utf-8 -*-
"""
trim image to create sub images
Author :
Yuki Kumon
Last Update :
2019-11-15
"""
import sys
sys.path.append('.')
from misc.dependences.attr_dict import AttributeDict
from misc.dependences.File_util import File_util
import yaml
import os
import csv
import numpy as np
import cv2
... | {"hexsha": "880872fa96a054fc0bed76b9a4570287c9fcde81", "size": 2628, "ext": "py", "lang": "Python", "max_stars_repo_path": "misc/create_subimage.py", "max_stars_repo_name": "Yuki-Kumon/PSMNet", "max_stars_repo_head_hexsha": "c47a9e6edb885e76fc6a6d399f5ec42c65133d85", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import kmeans
import common
import naive_em
import em
X = np.loadtxt("toy_data.txt")
# TODO: Your code here
# for K in [1,2,3,4]:
# for seed in [0,1,2,3,4]:
# mixture,post=common.init(X, K, seed)
# mixture, post, cost=kmeans.run(X,mixture,post)
# common.plot(X,mixture... | {"hexsha": "2a96b605630a5e4d56fbbd6a6840963b201cc254", "size": 4145, "ext": "py", "lang": "Python", "max_stars_repo_path": "mit-ml/netflix/main.py", "max_stars_repo_name": "stepinski/machinelearning", "max_stars_repo_head_hexsha": "1f84883a25616da4cd76bb4655267efd3421e561", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
#-------------------------------------------------------------------------------
# Name: vis
# Purpose:
#
# Authors: Maik Heistermann
#
# Created: 2015-11-6
# Copyright: (c) Maik Heistermann
# Licence: The MIT License
#---------------------------------------------------... | {"hexsha": "eefae7f85bb930e309056ca8415bb325fc25d608", "size": 11938, "ext": "py", "lang": "Python", "max_stars_repo_path": "trmmlib/vis.py", "max_stars_repo_name": "heistermann/trmmlib", "max_stars_repo_head_hexsha": "b32cf623737285073e4c61bd0e01a0fe8b26c329", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 11 09:55:19 2017
@author: Elizabeth Ferriss
Show all FTIR spectra and baselines used for H diffusion in olivine GCA paper
"""
from olivine.SanCarlos import SanCarlos_spectra as SC
from olivine.KilaueaIki import Kiki_spectra as kiki
from matplotlib.backends.backend_pdf im... | {"hexsha": "be19fc93fc80994bbdc309114662a311e0112e11", "size": 8528, "ext": "py", "lang": "Python", "max_stars_repo_path": "olivine/old_and_exploratory/Ferriss_Supplement_estimates.py", "max_stars_repo_name": "EFerriss/olivine", "max_stars_repo_head_hexsha": "78cb3c7664d4dce284cf5096775cdf1e40463f91", "max_stars_repo_l... |
import os, sys
import cv2
import numpy as np
import torch
from PIL import Image
from sklearn.cluster import DBSCAN
from dface import MTCNN, FaceNet
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_frames(video):
frames = []
vid = cv2.VideoCapture(video)
total = int(vid.get(cv2.CAP_PROP_FRAME_COUNT... | {"hexsha": "7d75f8bc4e056436bf037066b43c9ad0360a4e2f", "size": 2234, "ext": "py", "lang": "Python", "max_stars_repo_path": "example.py", "max_stars_repo_name": "deepware/dFace", "max_stars_repo_head_hexsha": "3471ce8f40fe41d17669f67dba3f88768cde0815", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 13, ... |
/*!
* @file
* This file contains unit tests for `boost::mpl::sequence_facade`.
*/
#include <boost/mpl/sequence_facade.hpp>
#include <boost/mpl/begin.hpp>
#include <boost/mpl/empty.hpp>
#include <boost/mpl/end.hpp>
#include <boost/mpl/front.hpp>
#include <boost/mpl/size.hpp>
#include <boost/mpl/vector.hpp>
#include ... | {"hexsha": "bfb9bb757e6e7fce8187a828124636274c13956a", "size": 903, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/sequence_facade.cpp", "max_stars_repo_name": "ldionne/mpl_extensions", "max_stars_repo_head_hexsha": "ca728992567b96dad884be1658b0822a955174cc", "max_stars_repo_licenses": ["BSL-1.0"], "max_star... |
import numpy as np
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from PyBASC.basc import (
group_dim_reduce,
nifti_individual_stability,
map_group_stability_random_bootstrap,
join_group_stability,
individual_group_clustered_maps,
post_analysis,
ndarray_to_vol
... | {"hexsha": "84b2e76d84ae55abf3ea46d895bd76be77edbf30", "size": 28722, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyBASC/pipeline.py", "max_stars_repo_name": "AkiNikolaidis/BASC", "max_stars_repo_head_hexsha": "07ac80c1a22df84db8bdd30b09b881cecc8caf1d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 30 17:11:02 2021
@author: qcao
sketch of rod plate model and skeleton code for PyChrono
Useful links:
https://github.com/projectchrono/chrono/blob/03931da6b00cf276fc1c4882f43b2fd8b3c6fa7e/src/chrono/fea/ChElementShellBST.cpp
"""
# the u... | {"hexsha": "d9d5b80dd180c43f90d3d6c41e0986812dcd51d4", "size": 8942, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/test_20211130_rod_plate_models.py", "max_stars_repo_name": "qiancao/BoneBox", "max_stars_repo_head_hexsha": "0d10dac7c93f16f0643bebc62c63be2f4bd099f6", "max_stars_repo_licenses": ["BSD-3-... |
%function ind = MS_nearest(x,tau,v);
%
% returns the row vector containing the indicies of the nearest
% neighbours to each of the columns of x. Each point and its tau
% temporal neighbours are excluded from the search.
% v is an array (not necessarily logical) indicating which columns of x to
% use or, the relative i... | {"author": "benfulcher", "repo": "hctsa", "sha": "919f2aed7cc8e1a3a03304c1ade573fa664c73f8", "save_path": "github-repos/MATLAB/benfulcher-hctsa", "path": "github-repos/MATLAB/benfulcher-hctsa/hctsa-919f2aed7cc8e1a3a03304c1ade573fa664c73f8/Toolboxes/Michael_Small/MS_nearest.m"} |
[STATEMENT]
lemma termination_0_1:
assumes p: "\<And>s. guard s \<Longrightarrow> p \<le> weight_spmf (while s)"
and p_pos: "0 < p"
and lossless: "\<And>s. guard s \<Longrightarrow> lossless_spmf (body s)"
shows "lossless_spmf (while s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lossless_spmf (local... | {"llama_tokens": 11727, "file": "Probabilistic_While_While_SPMF", "length": 74} |
import configparser
import os
import sys
import netCDF4 as nc
import numpy as np
from helperFunctions import temperature_array_from_result
from helperFunctions import surface_temperature_array_from_result
DEPTH = 10
def print_results(section, temp_mean, temp_max, temp_min, temp_std_dev):
print('Mean temp of {}:... | {"hexsha": "3c41b50cf636908653e4cc66f362e7103c1fa25c", "size": 13733, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/MRIDataPBHEqnFDM/postProcessing.py", "max_stars_repo_name": "nih23/MRIDrivenHeatSimulation", "max_stars_repo_head_hexsha": "de6d16853df1faf44c700d1fc06584351bf6c816", "max_stars_repo_lic... |
import numpy as np
import pandas as pd
class Population():
"""docstring for Population"""
def __init__(self, population=None, #можно передать конкретную популяцию
population_size=10, #размер популяции
random_state=42, #фиксируем seed, если инициализируется с... | {"hexsha": "b37d796668e70639a263dd6347a0ff50f9176a09", "size": 6359, "ext": "py", "lang": "Python", "max_stars_repo_path": "memetic_algorithm/memetic_algorithm.py", "max_stars_repo_name": "tsyploff/rossiya-airlines", "max_stars_repo_head_hexsha": "3d0dc97f8712f197da2179e5c3836349785ebb45", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
#A Basic Example 一个基本的例子
'''
Keras is a powerful and easy-to-use deep learning library for Theano and TensorFlow
that provides a high-level neural networks API to develop and evaluate deep learning models.
'''
import num... | {"hexsha": "ecfb1815a85034f3cccc249a196dad68aa4c89fa", "size": 740, "ext": "py", "lang": "Python", "max_stars_repo_path": "kerasTest/kerasBasicExample.py", "max_stars_repo_name": "Dongzhixiao/newRecognize", "max_stars_repo_head_hexsha": "d87fa7752e2a805ddb2f8c0435383de47b687f6b", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import sys
sys.path.append('../')
import gridemic
import numpy as np
from scipy.optimize import root_scalar
R0 = float(sys.argv[1])
print(R0)
# Common settings
Seed = 5772 # A seed for the RNG. None lets numpy.random select a seed randomly
N = 3162 # Grid size. N x N = P
NIi = 100 # N... | {"hexsha": "8b3f64c3523c8239c8f057cc847536bc14c4e89f", "size": 1973, "ext": "py", "lang": "Python", "max_stars_repo_path": "scarselli_etal_2021/run2b.py", "max_stars_repo_name": "burakbudanur/gridemic", "max_stars_repo_head_hexsha": "7429fd53555a1abd21614e1cce104f043576e05d", "max_stars_repo_licenses": ["MIT"], "max_st... |
function fx = p57_fun ( n, x )
%*****************************************************************************80
%
%% P57_FUN evaluates the integrand for problem 57.
%
% Interval:
%
% 0 <= x <= 1
%
% Integrand:
%
% x^(3/2)
%
% Antiderivative:
%
% (2/5) * x^(5/2)
%
% Exact Integral:
%
% 0.4
%
% Licensin... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_int/p57_fun.m"} |
function D = hmm_kl (hmm_p,hmm_q)
% Computes Kullback-Leibler divergence between two Hidden Markov Model
% distributions, through an approximation (an upper bound) as proposed in
% M. Do (2003). IEEE Signal Processing Letters 10
%
% Author: Diego Vidaurre, OHBA, University of Oxford (2018)
K = length(hmm_p.Pi);
if K~=... | {"author": "OHBA-analysis", "repo": "HMM-MAR", "sha": "bb0433b75482e473980791a2b30afe2012cf6578", "save_path": "github-repos/MATLAB/OHBA-analysis-HMM-MAR", "path": "github-repos/MATLAB/OHBA-analysis-HMM-MAR/HMM-MAR-bb0433b75482e473980791a2b30afe2012cf6578/utils/math/hmm_kl.m"} |
# Referenced by S. Jahnavi Prasad's detect_mask_video.py
#(https://github.com/jahnavi-prasad)
# import the necessary packages
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
import torchvision
from torchvision import transforms, datasets, models
import torch
from PIL import Image
import matplotli... | {"hexsha": "967a54009c50d48723c4bb58601e62534b3f2fde", "size": 3029, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/inference.py", "max_stars_repo_name": "hwanseung2/Image_processing", "max_stars_repo_head_hexsha": "6b4415ed6ccde274900cfd376d50dc3093d1f849", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from collections import Counter
from numpy import log
from sklearn.base import BaseEstimator, ClassifierMixin
from data.data_examination import make_sig_words
from data.pipelines import (tokenize_pipe,
lower_pipe,
stem_pipe,
lemmatize... | {"hexsha": "fcd09872979249de9f4967d4d9e2bf53550bb116", "size": 3820, "ext": "py", "lang": "Python", "max_stars_repo_path": "Predictors/probabilistic.py", "max_stars_repo_name": "ALT-G-AI/skynet-learns-to-write", "max_stars_repo_head_hexsha": "47009c1b375b9893cfd9c6b47a5ab41e28b25144", "max_stars_repo_licenses": ["MIT"]... |
(* File reduced by coq-bug-finder from original input, then from 7421 lines to 6082 lines, then from 5860 lines to 5369 lines, then from 5300 lines to 165 lines, then from 111 lines to 38 lines *)
Set Implicit Arguments.
Record PreCategory :=
{ object :> Type;
morphism : object -> object -> Type;
identity : f... | {"author": "princeton-vl", "repo": "CoqGym", "sha": "0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e", "save_path": "github-repos/coq/princeton-vl-CoqGym", "path": "github-repos/coq/princeton-vl-CoqGym/CoqGym-0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e/coq/test-suite/bugs/closed/3505.v"} |
# Copyright (c) 2009,2016,2019 MetPy Developers.
# Copyright (c) 2021 Nathan Wendt.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Tools for reading GEMPAK files."""
from collections import namedtuple
import struct
import numpy as np
class NamedStruct(struct.St... | {"hexsha": "eb4ee35d43dc8c27294410ca46f8fe199f0c0583", "size": 6625, "ext": "py", "lang": "Python", "max_stars_repo_path": "gempakio/tools.py", "max_stars_repo_name": "nawendt/gem2py", "max_stars_repo_head_hexsha": "d0bf086dec84d7cf28549177b8b9e402e29860db", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
# Import the required libraries needed
# for the application to run via Flask
# and the Machine learning Model
import os
import pickle
import numpy as np
from flask import Flask
from flask import render_template
from flask import url_for
from flask import request
# Initialize the Application
app = Flask(__name__)
#... | {"hexsha": "b39a92dfc0b51ca844c969a694237be867b5fd33", "size": 1812, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/app.py", "max_stars_repo_name": "jim-nnamdi/Loanapplicant-salaryPredictor", "max_stars_repo_head_hexsha": "8f01bd70804a21050ea38fc24c6a0b18efacb4b7", "max_stars_repo_licenses": ["bzip2-1.0.6"]... |
"""Very fancy downsampling."""
import numpy
from gewittergefahr.gg_utils import target_val_utils
from gewittergefahr.gg_utils import error_checking
from gewittergefahr.deep_learning import deep_learning_utils as dl_utils
LARGE_INTEGER = int(1e12)
FRACTION_UNINTERESTING_TIMES_TO_OMIT = 0.8
def _report_class_fraction... | {"hexsha": "a61e6775624d50be2281d903a041f3417ed657b0", "size": 11107, "ext": "py", "lang": "Python", "max_stars_repo_path": "gewittergefahr/deep_learning/fancy_downsampling.py", "max_stars_repo_name": "dopplerchase/GewitterGefahr", "max_stars_repo_head_hexsha": "4415b08dd64f37eba5b1b9e8cc5aa9af24f96593", "max_stars_rep... |
[STATEMENT]
lemma vec1_index: assumes j: "j < n"
shows "vec1I ze on n i ! j = (if i = j then on else ze)" (is "_ = ?r")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vec1I ze on n i ! j = (if i = j then on else ze)
[PROOF STEP]
unfolding vec1I_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (replicate i ze ... | {"llama_tokens": 2980, "file": "Matrix_Matrix_Legacy", "length": 36} |
names = readdir(".")
for name=names
if startswith(name, "test_") && endswith(name, ".jl")
println("Run $name")
run(`julia $name`)
end
end
| {"hexsha": "d81fe06d4023e9006664ace2db455e531c59f0b6", "size": 163, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "tkelman/QuantumOptics.jl", "max_stars_repo_head_hexsha": "1ccd79e1bb8775796703b8d2c407c1c74429e291", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import hashlib
from itertools import cycle
from random import Random
import numpy as np
from numpy import linalg
class SubsCipherAlgo:
def __init__(self, key):
self.key = key
self.table = list()
self.clean_rand = Random()
def gen_table(self, bytes_number, cols):
r = Random()
... | {"hexsha": "6a7d3b4fd9c311f0e676a23d7e81631b0f983cb4", "size": 7573, "ext": "py", "lang": "Python", "max_stars_repo_path": "ciplib.py", "max_stars_repo_name": "Kamkas/Block-cipher", "max_stars_repo_head_hexsha": "69eb5b71a63e013515485bf0bc9cb4610fcf17cf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
from manimlib import *
import numpy as np
# To watch one of these scenes, run the following:
# python -m manim example_scenes.py SquareToCircle
# Use -s to skip to the end and just save the final frame
# Use -w to write the animation to a file
# Use -o to write it to a file and open it once done
# Use -n <number> to s... | {"hexsha": "4af663609a6576171a26b2c27acd2df920b9a152", "size": 24605, "ext": "py", "lang": "Python", "max_stars_repo_path": "example_scenes.py", "max_stars_repo_name": "net-oil-man/Oilman-Manim-Tools", "max_stars_repo_head_hexsha": "f93d54d586fcae69932c4f0820de96d83cdb9c5d", "max_stars_repo_licenses": ["Apache-2.0"], "... |
[STATEMENT]
lemma Array_Object_widen1_trancl:
assumes wf: "wf_prog wmdc P"
and itA: "is_type P (A\<lfloor>\<rceil>)"
shows "P \<turnstile> A\<lfloor>\<rceil> <\<^sup>+ Class Object"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P \<turnstile> A\<lfloor>\<rceil> <\<^sup>+ Class Object
[PROOF STEP]
using itA
[P... | {"llama_tokens": 6626, "file": "JinjaThreads_Common_SemiType", "length": 30} |
""" Train network on the Fluorescence Microscopy Dataset (FMD) """
import argparse
import glob
import os
from os import listdir
from os.path import join
import keras
import numpy as np
import skimage
import tensorflow as tf
from imageio import imread
from keras import backend as K
from keras.callbacks import LambdaC... | {"hexsha": "76556de9e7613bb235cb8b35a468629b554720d6", "size": 5050, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_fmd.py", "max_stars_repo_name": "JRice15/self-supervised-poisson-gaussian", "max_stars_repo_head_hexsha": "4c9bf851194454eae17b5673a28a6131a8bd0ce5", "max_stars_repo_licenses": ["MIT"], "max... |
import xarray as xr
import salem
import geopandas
import geojson
from shapely.geometry import shape
import numpy as np
from glob import glob
import pandas as pd
import os
from subprocess import check_call
def subset_to_west(da, lon_range, lat_range, roi):
da = da.sel(latitude=slice(lat_range[0], lat_range[1]),
... | {"hexsha": "6631ae00195333f19ae626554784f86403b22e3c", "size": 4446, "ext": "py", "lang": "Python", "max_stars_repo_path": "compound_extremes/scripts/preprocess_JRA55.py", "max_stars_repo_name": "karenamckinnon/compound_extremes", "max_stars_repo_head_hexsha": "26542ed55af86fdfd3486e949f475b18f03165c4", "max_stars_repo... |
//==================================================================================================
/*!
@file
Forward declaration of common components
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICEN... | {"hexsha": "a558ddf65d0de827c22a777a30fe68d135019c8a", "size": 939, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/forward.hpp", "max_stars_repo_name": "SylvainCorlay/pythran", "max_stars_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_stars_repo_licenses": ["BSD-3-Claus... |
### ----------------- IMPORTS ----------------- ###
import os
from beartype import beartype
import numpy as np
import pandas as pd
from backend.adi_parse import AdiParse
from backend import search_function
from backend.get_all_comments import GetComments
### ------------------------------------------- ###
@beartype
d... | {"hexsha": "defa5636b177097e8af817a6ca015cfbf3b3e448", "size": 14647, "ext": "py", "lang": "Python", "max_stars_repo_path": "backend/filter_table.py", "max_stars_repo_name": "SAKEverse/sake-plan", "max_stars_repo_head_hexsha": "48162b9a4f4cd6fefd557dc09f723912155f6581", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import cv2
import numpy as np
import os
import glob
#img_dir = "/home/mcaadmin/ALL_IDB1/im" # Enter Directory of all images
#data_path = os.path.join(img_dir,'*g')
#files = glob.glob(data_path)
#for f1 in files:
image = cv2.imread("/home/mcaadmin/infected/Im001_1.png")
#while(1):
# Convert BGR to HSV
hsv = cv2.c... | {"hexsha": "4cff97a4ae0d512839dbefdd84232365e27a4afc", "size": 1029, "ext": "py", "lang": "Python", "max_stars_repo_path": "conversion.py", "max_stars_repo_name": "JessyRajan/wbcdiseases", "max_stars_repo_head_hexsha": "f1ad6630252d65e296265b58e19a99c85025fd7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#check (.=.)
-- #print (.=.)
-- #print (.=.)
#check (.==.)
#check Eq.refl
#check @Eq.refl
#check Eq.refl 5
#check @Eq.symm
#check @Eq.trans
#check @Eq.subst
#print Eq
def mod7Rel (x y : Nat) : Prop :=
x % 7 = y % 7
#check mod7Rel
theorem thm_1: mod7Rel 3 10 := rfl
#print thm_1
theorem thm_2: mod7Rel 3 10 := by
... | {"author": "shinsa82", "repo": "lean-devcontainer", "sha": "7edc7f0ad9aee38f686f161b6b65be805f2f538d", "save_path": "github-repos/lean/shinsa82-lean-devcontainer", "path": "github-repos/lean/shinsa82-lean-devcontainer/lean-devcontainer-7edc7f0ad9aee38f686f161b6b65be805f2f538d/quot.lean"} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.