text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import Lean
syntax (name := test) "test%" ident : command
open Lean.Elab
open Lean.Elab.Command
@[commandElab test] def elabTest : CommandElab := fun stx => do
let id ← resolveGlobalConstNoOverloadWithInfo stx[1]
liftTermElabM none do
IO.println (repr (← Lean.Meta.Match.getEquationsFor id))
return ()
def f... | {"author": "Kha", "repo": "lean4-nightly", "sha": "b4c92de57090e6c47b29d3575df53d86fce52752", "save_path": "github-repos/lean/Kha-lean4-nightly", "path": "github-repos/lean/Kha-lean4-nightly/lean4-nightly-b4c92de57090e6c47b29d3575df53d86fce52752/tests/lean/run/matchEqs.lean"} |
#! /usr/bin/env python
import sys
import numpy as np
from matplotlib import pyplot as plt
import ogr
import gdal
import geolib
import extract_profile
#Input DEM
dem_fn = sys.argv[1]
dem_ds = gdal.Open(dem_fn)
dem_srs = geolib.get_srs(dem_ds)
#TLS location
tls_srs = geolib.wgs_srs
tls_coord = (0,0,0)
tls_height = 2... | {"hexsha": "1b62be35ef8af26e49d96558c7848e1769329b1b", "size": 1743, "ext": "py", "lang": "Python", "max_stars_repo_path": "tls_planner.py", "max_stars_repo_name": "dshean/tls_tools", "max_stars_repo_head_hexsha": "1bfdb1ea80a8106e797deed046a5cc35d685beb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#
# Copyright 2021 Johannes Hörmann
# 2020-2021 Lars Pastewka
#
# ### MIT license
#
# 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... | {"hexsha": "13515c271987da1bcfeae12fba0ee766ba8b7aa6", "size": 7984, "ext": "py", "lang": "Python", "max_stars_repo_path": "dtool_lookup_gui/widgets/graph_widget.py", "max_stars_repo_name": "IMTEK-Simulation/dtool-lookup-gui", "max_stars_repo_head_hexsha": "60e0824b2e883d756e57d933e4657645dfa7c6d4", "max_stars_repo_lic... |
import os
import argparse
import json
import numpy as np
from torch import device
from torch.cuda import is_available
if is_available():
DEVICE = device("cuda")
else:
DEVICE = device("cpu")
from multiml import logger
def main(opts):
logger.set_level(opts.loglevel)
global DEVICE
from utils impor... | {"hexsha": "3bf66d953c6f3fc96549411bf3d3611db4c64af2", "size": 7871, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pytorch/run_multi_connection_asngnas.py", "max_stars_repo_name": "UTokyo-ICEPP/multiml_htautau", "max_stars_repo_head_hexsha": "5f926c2291a55f57419aa0130d07e2a793fc7353", "max_stars_repo_... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implemetation of the tracker described in paper
% "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization",
% Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014
%
% Copyright (C) 2014 Jianming Zhang
%
% This program is f... | {"author": "flyers", "repo": "drone-tracking", "sha": "c42e1833acfb858ac8f4ec69fa04ab02ac4c19ad", "save_path": "github-repos/MATLAB/flyers-drone-tracking", "path": "github-repos/MATLAB/flyers-drone-tracking/drone-tracking-c42e1833acfb858ac8f4ec69fa04ab02ac4c19ad/trackers/MEEM/expert_ensemble/expertsDo.m"} |
# maintained by rajivak@utexas.edu
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
# from mpi4py import MPI
import sys
import math
###########################################################################################################... | {"hexsha": "33dba23ed516bf17018a5d8a26ee5e8070d824aa", "size": 5607, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/mmd/MMD-critic/mmd.py", "max_stars_repo_name": "sthagen/christophM-interpretable-ml-book", "max_stars_repo_head_hexsha": "d8b82b8e6ab82c78d95de784a601e71025621ab2", "max_stars_repo_license... |
! test with processor lattice.
program cc
integer n, i, j
parameter (n=100)
real a(n,n)
!hpf$ dynamic a
!hpf$ template t(n,n)
!hpf$ processors p(2,2)
!hpf$ align with t:: a
!hpf$ distribute t(cyclic,cyclic) onto p
a(1,1) = 1.0
!hpf$ realign a(i,j) with t(j,i)
print *, a(1,1)
... | {"hexsha": "20b556a9c58c78c689ce16213023bc3385a3f90d", "size": 326, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/Hpfc/cc.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_stars_repo_licenses": ["MIT"], "max_st... |
#=
Copyright (c) 2015, Intel Corporation
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice,
this list of conditions and the followi... | {"hexsha": "fba023882bff31613e72fbc755dd640cff77dec6", "size": 2692, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/constant_fold.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/CompilerTools.jl-98f049d2-a028-5a73-bd4d-a8c50ff59ab5", "max_stars_repo_head_hexsha": "ee7f80e5dc8c6b6dfefc1b8b5b037ea3... |
program conditionalStatements
implicit none
integer a, b, c, result;
print *, "Enter value of a :";
read *, a;
print *, "Enter value of b :";
read *, b;
print *, "Enter value of c :";
read *, c;
if( a < b) then
if(a < c) then
result = a;
... | {"hexsha": "82e4df3c4960d055200f390165ba20a5f480a73b", "size": 570, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "III-sem/NumericalMethod/FortranProgram/Practice/if-else.f95", "max_stars_repo_name": "ASHD27/JMI-MCA", "max_stars_repo_head_hexsha": "61995cd2c8306b089a9b40d49d9716043d1145db", "max_stars_repo_li... |
#! Demonstrates the usage of parameterized test fixtures.
#:include 'fytest.fypp'
#:block TEST_SUITE('parameterized2')
use mymath
implicit none
type :: fact_calc_t
integer :: val
integer :: expresult
end type fact_calc_t
#! This will contain the parameters of the tests, once TEST_SUITE_INIT() has ... | {"hexsha": "5b4a581585f0f399d0ee6e894ae6b476e33d356b", "size": 1726, "ext": "fpp", "lang": "FORTRAN", "max_stars_repo_path": "examples/serial/test/test_parameterized2.fpp", "max_stars_repo_name": "aradi/fytest", "max_stars_repo_head_hexsha": "9133d5dab5b582161f4fb4c4b127d7f97133e3e7", "max_stars_repo_licenses": ["BSD-2... |
"""
This file contains some utility functions for using ImageNet dataset and L-OBS
Author: Chen Shangyu (schen025@e.ntu.edu.sg)
"""
import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
imp... | {"hexsha": "cf6fabaa6eabb0a4a608dbdbd8384fec7b09f6c1", "size": 7059, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyTorch/ImageNet/utils.py", "max_stars_repo_name": "csyhhu/L-OBS", "max_stars_repo_head_hexsha": "346e67977955f34b10b0461ab4d60ef8d35dc145", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# flake8: noqa
import pytest
import embedding.training_process
import numpy
import tempfile
from pathlib import Path
import unittest
import ai_training as ait
pytestmark = pytest.mark.asyncio
async def mock_w2v_call(payload, endpoint='words'):
if endpoint == "words":
return {'vectors': {"word1": [1.1, 1... | {"hexsha": "3c7cd95cc149af3c8b1a2108a6a2a912d3f88be8", "size": 6387, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/embedding/tests/test_embedding_training.py", "max_stars_repo_name": "hutomadotAI/qamatcher", "max_stars_repo_head_hexsha": "0ece9bc354aea0c104cce7f3f372aa8e83a7601b", "max_stars_repo_licenses"... |
#! /usr/bin/python3
# -*- coding: utf-8 -*-
import numpy as np
import sys
from matplotlib import pyplot as plt
""" Change Layout Default Settings"""
def setRcParams():
params = {'axes.facecolor' : 'white',
'axes.labelsize' : 'x-large', # xx-large
'axes.titlesize' : 'x-large', # xx-large
... | {"hexsha": "fae47b9aad467b49c40c33a5a15d839b30738e2d", "size": 3339, "ext": "py", "lang": "Python", "max_stars_repo_path": "performanceTests/results/visualize.py", "max_stars_repo_name": "formelfritz/mallocMC", "max_stars_repo_head_hexsha": "d1dba808abf63de39db56587d65b9f497f4e7a41", "max_stars_repo_licenses": ["MIT"],... |
#! /usr/bin/python
from tools import *
import numpy
import random
import math
import time
import sys
from solvers import dynamicKnapsack
'''
Parametric optimizers for 0/1 knapsack -
* Simulated annealer
* Random Search
'''
# Simulated annealer - optimizing solutions to 0/1
# knapsack - climber can get close to opti... | {"hexsha": "7bb8e949bb8fff29d92d8a55ec1a8e41330dfdd9", "size": 1787, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/parametric.py", "max_stars_repo_name": "lbenning/Knapsack", "max_stars_repo_head_hexsha": "1b06409bafc04210837b984fb638804794faada6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, ... |
import tensorflow as tf
import numpy as np
EPSILON = 1e-6
class BN_Conv(object):
def __init__(self, var_scope, is_training, filter_width, in_channels, out_channels, dilation = 1):
self.var_scope = var_scope
self.filter_width = filter_width
self.in_channels = in_channels
self.out_channels = out_channels
sel... | {"hexsha": "ed1c014c81f500fd0da0501b2680997a244452fe", "size": 8427, "ext": "py", "lang": "Python", "max_stars_repo_path": "BN_layers.py", "max_stars_repo_name": "wanglabcumc/VariationalHomologEncoder", "max_stars_repo_head_hexsha": "b2ae5244bd651042fbe29e1a3769c07122c8c145", "max_stars_repo_licenses": ["MIT"], "max_st... |
from couplib.myreportservice import *
from couplib.constants import *
from configuration import *
from math import *
import numpy as np
#-------------------------------------------------------------------------------
class AtomInterface():
"""Interface class for the ionfromation about atoms (primarely read from PDB f... | {"hexsha": "9c3f589d0bc8ae11c54cb56221ffbf854333066e", "size": 9047, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/interfaces.py", "max_stars_repo_name": "DKosenkov/PyFREC", "max_stars_repo_head_hexsha": "a578f649b1309f1e23412e3695cce2b9e48fda3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
def multiclass_accuracy(prediction, ground_truth):
"""
Computes metrics for multiclass classification
Arguments:
prediction, np array of int (num_samples) - model predictions
ground_truth, np array of int (num_samples) - true labels
Returns:
accuracy - ratio of accurate... | {"hexsha": "6add0c161a5ae57b20e87863390c4964d2fdb403", "size": 551, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignments/assignment2/metrics.py", "max_stars_repo_name": "DenisYarullin/dlcourse_ai", "max_stars_repo_head_hexsha": "3e29c0c0ae59479424a14c391dca0948682d7fa5", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# nnutil2 - Tensorflow utilities for training neural networks
# Copyright (c) 2019, Abdó Roig-Maranges <abdo.roig@gmail.com>
#
# This file is part of 'nnutil2'.
#
# This file may be modified and distributed under the terms of the 3-clause BSD
# license. See the LICENSE fi... | {"hexsha": "c56f071f33f99f5b560e8d112d59df536553c39a", "size": 1167, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/data_merge.py", "max_stars_repo_name": "aroig/nnutil2", "max_stars_repo_head_hexsha": "1fc77df351d4eee1166688e25a94287a5cfa27c4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
[STATEMENT]
lemma RSubmodule_RSpan_single :
assumes "m \<in> M"
shows "RSubmodule (RSpan [m])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. RSubmodule (RSpan [m])
[PROOF STEP]
proof (rule RSubmoduleI)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. VecGroup.Subgroup (RSpan [m])
2. \<And>r n. \<lbrakk>r \... | {"llama_tokens": 1802, "file": "Rep_Fin_Groups_Rep_Fin_Groups", "length": 20} |
# import some common detectron2 utilities
from detectron2.engine import DefaultPredictor, DefaultTrainer
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets import register_coco_instances
from ... | {"hexsha": "be251b45e14d0b39587d2e1d3dbed1b6dfaa1ac0", "size": 6166, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/demo_train_middle_fusion.py", "max_stars_repo_name": "Jamie725/RGBT-detection", "max_stars_repo_head_hexsha": "e7741bf0a8bdfb940794248a6d3247e4a5025dc4", "max_stars_repo_licenses": ["Apache-2... |
##################################################################################
# Woldenberg a la Bonica
# This code runs a Bonica-like algorithm to provide a dynamic view of Woldenberg's
# IFE. The first run has already been saved ("posterior-samples/wold23-window-results-compress.RData")
# March 19, 2013: Add par... | {"hexsha": "ff2f73f984106fc8d71fac239c54160cc75b960e", "size": 19677, "ext": "r", "lang": "R", "max_stars_repo_path": "code/ifeJagsDynWoldenbergBonica.r", "max_stars_repo_name": "grosasballina/ife-update", "max_stars_repo_head_hexsha": "174e3bfdffa6e84bff9fe70defe1e8afff05c7d6", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import pytest
from napari.components.layerlist import LayerList
from napari.layers import Image, Points
@pytest.fixture
def layer_list():
return LayerList()
@pytest.fixture
def points_layer():
return Points()
@pytest.fixture
def image_layer():
data = np.ones((10, 10))
data[::2,... | {"hexsha": "b8f0f3efa562b00339650897422361c6530fc0a9", "size": 353, "ext": "py", "lang": "Python", "max_stars_repo_path": "napari/utils/context/_tests/conftest.py", "max_stars_repo_name": "Napari/napari", "max_stars_repo_head_hexsha": "2dc5aa659f875c353bfbde3b20d8f07a664ed8a8", "max_stars_repo_licenses": ["BSD-3-Clause... |
import matplotlib
matplotlib.use('gtk')
import matplotlib.pyplot as plt
import numpy as np
import gdfmm
import skimage.io
import pdb
rgb = skimage.io.imread('/home/daniel/nyu_label/rgb/r-1294439283.377657-2381571548.png')
dep = skimage.io.imread('/home/daniel/nyu_label/rawdepth/r-1294439283.377657-2381571548.png')
dep... | {"hexsha": "b074203152da53b9fe6070d33864b8305ca5663b", "size": 433, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/testgdfmm.py", "max_stars_repo_name": "xkjyeah/gifmm", "max_stars_repo_head_hexsha": "78ceeb08136abf902c37260df2366d9e85d156f1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count... |
using CrystallographyBase: Lattice
using LinearAlgebra: I
export distortby, distort, strainstate
# See https://link.springer.com/content/pdf/10.1007%2F978-3-7091-0382-1_7.pdf and https://doi.org/10.2138/am-1997-1-207
distortby(lattice::Lattice, strain::TensorStrain) =
Lattice((I + strain.data) * lattice.data)
dis... | {"hexsha": "1990bb61cac7b78083d71384df354f2fe732af3e", "size": 559, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/distort.jl", "max_stars_repo_name": "MineralsCloud/LinearElasticity.jl", "max_stars_repo_head_hexsha": "351d7df008063d9445193cc1038840ac64c9b8bf", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Title : SVM - draw the diagrammatic sketch
# Objective : draw the diagrammatic sketch for SVM. Note that it is not the code for SVM
# Created by: Wu Shangbin
# Created on: 2021/12/8
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScale... | {"hexsha": "cfa6107bed80aed481ef53a382cd846f597189e0", "size": 1507, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/SVM_draw_diagrammatic_sketch.py", "max_stars_repo_name": "595666666/tripping", "max_stars_repo_head_hexsha": "f448300c31de96089b855ee9774068d748b0cd31", "max_stars_repo_licenses": ["BSD-3-C... |
(** * Induced functors between comma categories *)
Require Import Category.Core Functor.Core NaturalTransformation.Core.
Require Import Category.Dual.
Require Import Category.Prod.
Require Import NaturalTransformation.Identity.
Require Import FunctorCategory.Core Cat.Core.
Require Import InitialTerminalCategory.Core In... | {"author": "CPP21-Universal-Algebra-in-HoTT", "repo": "Universal-Algebra-in-HoTT", "sha": "7228b5b88684abff3c26a7eed07e1222b04fd8de", "save_path": "github-repos/coq/CPP21-Universal-Algebra-in-HoTT-Universal-Algebra-in-HoTT", "path": "github-repos/coq/CPP21-Universal-Algebra-in-HoTT-Universal-Algebra-in-HoTT/Universal-A... |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "71a69bf85d98a33ca2cadf8bf6026881a1b4fbd3", "size": 16870, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlescience/pde/pde_navier_stokes.py", "max_stars_repo_name": "Liu-xiandong/PaddleScience", "max_stars_repo_head_hexsha": "5e667a4fe6138c22e0ff54af81d83a0b7cae4572", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
'''Ray implementation - for general raytracing
David Dunn
Jan 2017 - created by splitting off from dGraph
ALL UNITS ARE IN METERS
ie 1 cm = .01
www.qenops.com
'''
__author__ = ('David Dunn')
__version__ = '1.6'
import numpy as np
from numpy import dot, cross
from numpy.linalg import norm... | {"hexsha": "719ebb5b90605b323272e1c462473f0ce1ae44a8", "size": 8390, "ext": "py", "lang": "Python", "max_stars_repo_path": "ray.py", "max_stars_repo_name": "qenops/dGraph", "max_stars_repo_head_hexsha": "b67c835bf60f1627a79d3e22183301f34431c5b3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_sta... |
# Case I example: Bad diamond II
# Claudia January 2015
# types in "types.jl"
if !isdefined(:individualtest) individualtest = false; end
if(individualtest)
include("../src/types.jl")
include("../src/functions.jl")
end
tree = "((((8,10))#H1,7),6,(4,#H1));" # Case I Bad diamond II
#f = open("prueba_tree.txt",... | {"hexsha": "db07b4f65f769ec5d2c70e2eb9a6a8a7c8521ebc", "size": 416, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/case_i_example.jl", "max_stars_repo_name": "kwsparks/PhyloNetworks.jl", "max_stars_repo_head_hexsha": "f466c13fa599cd8bc546e5ad4f2f0e5e64756805", "max_stars_repo_licenses": ["MIT"], "max_st... |
/-
Copyright (c) 2020 Markus Himmel. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Markus Himmel
-/
import category_theory.abelian.exact
import category_theory.over
/-!
# Pseudoelements in abelian categories
A *pseudoelement* of an object `X` in an abelian category ... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/category_theory/abelian/pseudoelements.lean"} |
import os
import pickle
import numpy as np
from sklearn.metrics import precision_score, recall_score, f1_score, precision_recall_curve
import matplotlib.pyplot as plt
from utils.preprocess import down_sample
def point_adjust_k(scores, targets, thres, k=20):
"""
:param scores: anomaly score
:param targets... | {"hexsha": "49c26c5d68347c85a3230a7f1fc730f1d9769d09", "size": 6707, "ext": "py", "lang": "Python", "max_stars_repo_path": "pa_k_experiments.py", "max_stars_repo_name": "kj21choi/LATAD", "max_stars_repo_head_hexsha": "80d91e0f251ad0225342ee30e2461a39fa9cca97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
cc ------------ dpmjet3.4 - authors: S.Roesler, R.Engel, J.Ranft -------
cc -------- phojet1.12-40 - authors: S.Roesler, R.Engel, J.Ranft -------
cc - oct'13 -------
cc ----------- pythia-6.4 - authors: Torbjorn Sjostrand, Lund'10 -------
cc -------------------------... | {"hexsha": "3a13a7bd13803b9571f0c8c6eeb0ed293c288bd0", "size": 1509, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/pythia/pymemx.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
[STATEMENT]
lemma rQuot_empty[simp]: "rQuot a {} = {}"
and rQuot_epsilon[simp]: "rQuot a {[]} = {}"
and rQuot_char[simp]: "rQuot a {[b]} = (if a = b then {[]} else {})"
and rQuot_union[simp]: "rQuot a (A \<union> B) = rQuot a A \<union> rQuot a B"
and rQuot_inter[simp]: "rQuot a (A \<inter> B) = rQuot ... | {"llama_tokens": 344, "file": "MSO_Regex_Equivalence_Pi_Regular_Set", "length": 1} |
[STATEMENT]
lemma powser_times_n_limit_0:
fixes x :: "'a::{real_normed_div_algebra,banach}"
assumes "norm x < 1"
shows "(\<lambda>n. of_nat n * x ^ n) \<longlonglongrightarrow> 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>n. of_nat n * x ^ n) \<longlonglongrightarrow> (0::'a)
[PROOF STEP]
proo... | {"llama_tokens": 3103, "file": null, "length": 32} |
"""
Tests for constraint module
"""
import numpy as np
import pytest
import quta.constraints as cons
def test_concatenation():
"""
Test for checking that concatenation of sets
of linear constraints works.
"""
C_0 = np.eye(3)
b_0 = np.zeros(3)
n_0 = 0
C_1 = np.eye(3) * 4
b_1 = np.o... | {"hexsha": "7297406175673b656ec7b0499765d40974a47d59", "size": 2899, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/constraint_test.py", "max_stars_repo_name": "freol35241/quota", "max_stars_repo_head_hexsha": "8fafbe10d474cd18d8d18d48b497ecfe7d786189", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pandas as pd
import scipy as sp
from scipy.stats import t
import numpy as np
# CREDITS: this script is written by github user <glesserd> (https://github.com/glesserd)
# Originally available at <https://gist.github.com/glesserd/406519a4a79a49efb2353cfe05bcc6ee>
#from: http://www.cookbook-r.com/Graphs/Plotting_... | {"hexsha": "0137abbbd5da17dec089f2b2039f88e624ddfa82", "size": 5540, "ext": "py", "lang": "Python", "max_stars_repo_path": "summary.py", "max_stars_repo_name": "saurabhr/psypy", "max_stars_repo_head_hexsha": "65c5c4a4603dd310d1875c7f2247882e938bc19f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
[GOAL]
C : Type u₁
inst✝² : Category.{v, u₁} C
D : Type u₂
inst✝¹ : Category.{v, u₂} D
e : C ≌ D
inst✝ : WellPowered C
X : D
⊢ EssentiallySmall (MonoOver ((Equivalence.symm e).functor.obj X))
[PROOFSTEP]
infer_instance
| {"mathlib_filename": "Mathlib.CategoryTheory.Subobject.WellPowered", "llama_tokens": 109} |
[STATEMENT]
lemma cs_in_initial_state_implies_not_snapshotted:
assumes
"trace init t final" and
"snd (cs (S t i) cid) = NotStarted" and
"channel cid = Some (p, q)"
shows
"~ has_snapshotted (S t i) q"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> ps (S t i) q \<noteq> None
[PROOF STEP]
pro... | {"llama_tokens": 7093, "file": "Chandy_Lamport_Snapshot", "length": 71} |
#=
Copyright (c) 2018-2022 Chris Coey, Lea Kapelevich, and contributors
This Julia package Hypatia.jl is released under the MIT license; see LICENSE
file in the root directory or at https://github.com/chriscoey/Hypatia.jl
see description in native.jl
=#
using SparseArrays
struct MatrixCompletionJuMP{T <: Real} <: E... | {"hexsha": "ffd1628391a3ae8dd2e6bf56224b956636c7e55f", "size": 1182, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/matrixcompletion/JuMP.jl", "max_stars_repo_name": "chriscoey/2021.0177-1", "max_stars_repo_head_hexsha": "a3f6258d332d2e2edc9b0e3fcd1ae9614ea9499f", "max_stars_repo_licenses": ["MIT"], "ma... |
import pickle
import pandas as pd
import quandl
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
from statistics import mean
style.use("seaborn-dark-palette")
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
def create_labels(cur_hpi, fut_hpi):
if fut_hpi > cur_hp... | {"hexsha": "49bd0e0f8c763549f8f8d1210c2819c614f4fa2f", "size": 1451, "ext": "py", "lang": "Python", "max_stars_repo_path": "sentdex_data_analysis/pandas_mappingFunctions.py", "max_stars_repo_name": "yull1860outlook/Data-Analysis", "max_stars_repo_head_hexsha": "b777d9a75eb1acc4c899946d547e5585469a83ae", "max_stars_repo... |
import awkward as ak
import numpy as np
import fastjet._ext # noqa: F401, E402
class _classsingleevent:
def __init__(self, data, jetdef):
self.jetdef = jetdef
self.data = self.single_to_jagged(data)
px, py, pz, E, offsets = self.extract_cons(self.data)
px = self.correct_byteorder... | {"hexsha": "4fe2b3139e68f9c9004d147ba2d528b75651d323", "size": 15014, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/fastjet/_singleevent.py", "max_stars_repo_name": "scikit-hep/fastjet", "max_stars_repo_head_hexsha": "e5aebdc66167400472cd29a36f4af0f2a789a992", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
program big_integer
!! this gets to 20! with int64 or 12! with int32
use, intrinsic :: iso_fortran_env, only : int64, real128
implicit none (type, external)
integer(int64) :: n, i, fac
integer :: ios
character(2) :: argv
n = 10
call get_command_argument(1, argv, status=ios)
if (ios==0) read(argv,'(i2)') n
if (n<0) ... | {"hexsha": "d65522ab61bd551eda4fb5df2cfbb448d344e3b8", "size": 478, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "standard/big_integer.f90", "max_stars_repo_name": "supershushu/fortran2018-examples", "max_stars_repo_head_hexsha": "f0dc03b80326bc7c06fa31945b6e7406a60c1fa8", "max_stars_repo_licenses": ["MIT"],... |
#include "platform/i_platform.h"
#include "network/client_system.h"
#include <boost/timer.hpp>
#include "core/program_state.h"
#include "messsage_holder.h"
#include <portable_oarchive.hpp>
#include <iosfwd>
#include "my_name_message.h"
#include "engine/engine.h"
#include "main/window.h"
#include "platform/settings.h"
n... | {"hexsha": "8fd706ef41362e83f8521a1d6b9e5e45edc79ae3", "size": 8185, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/network/client_system.cpp", "max_stars_repo_name": "MrPepperoni/Reaping2-1", "max_stars_repo_head_hexsha": "4ffef3cca1145ddc06ca87d2968c7b0ffd3ba3fd", "max_stars_repo_licenses": ["MIT"], "max_st... |
Require Export Bedrock.Word.
Require Import
Fiat.Narcissus.Examples.NetworkStack.EthernetHeader
Fiat.Narcissus.Examples.NetworkStack.ARPPacket
Fiat.Narcissus.Examples.NetworkStack.IPv4Header
Fiat.Narcissus.Examples.NetworkStack.TCP_Packet
Fiat.Narcissus.Examples.NetworkStack.UDP... | {"author": "mit-plv", "repo": "fiat", "sha": "4c78284c3a88db32051bdba79202f40c645ffb7f", "save_path": "github-repos/coq/mit-plv-fiat", "path": "github-repos/coq/mit-plv-fiat/fiat-4c78284c3a88db32051bdba79202f40c645ffb7f/src/Narcissus/Examples/NetworkStack/TestInfrastructure.v"} |
# !python3
# -*- coding: utf-8 -*-
# author: flag
import numpy as np
import scipy.io
# load the data from matlab of .mat
def loadMatlabIdata(filename=None):
data = scipy.io.loadmat(filename, mat_dtype=True, struct_as_record=True) # variable_names='CATC'
return data
def get_data2multi_scale(equation_name=N... | {"hexsha": "60f42957bc1b1e6311f687c49e102cadaa77c1ae", "size": 1380, "ext": "py", "lang": "Python", "max_stars_repo_path": "matData2multi_scale.py", "max_stars_repo_name": "xuzhiqin1990/MSDNN2ellipticPDEs", "max_stars_repo_head_hexsha": "ddaee034474c18bc23b51824fb6a00539c07d52c", "max_stars_repo_licenses": ["MIT"], "ma... |
/*
** Author(s):
** - Herve Cuche <hcuche@aldebaran-robotics.com>
**
** Copyright (C) 2010, 2012 Aldebaran Robotics
*/
#include <future>
#include <vector>
#include <string>
#include <gtest/gtest.h>
#include <qi/session.hpp>
#include <qi/anyobject.hpp>
#include <qi/type/dynamicobjectbuilder.hpp>
#include <qi/ty... | {"hexsha": "5ec872579265d7f17d0b7499a4b1837b54db136f", "size": 21691, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/messaging/test_service.cpp", "max_stars_repo_name": "yumilceh/libqi", "max_stars_repo_head_hexsha": "f094bcad506bcfd5a8dcfa7688cbcce864b0765b", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import pandas as pd
import os
import yaml
from yaml.loader import SafeLoader
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Open the file and load the file
with open('C:\\Users\\jonah\\Desktop\\Projects\\Programming\\Personal\\sliceSLAM\\config.YAML') as f:
cfg = yaml.load(f,... | {"hexsha": "dd8fc590267f2c5d0315ae1988366ab4b3daded1", "size": 1981, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelling/plots.py", "max_stars_repo_name": "Jonah1234567/sliceSLAM", "max_stars_repo_head_hexsha": "be64f5bdabfb282cbf128a65d82609b49174750e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include "graph.hpp"
#include <fstream>
#include <stack>
#include <boost/filesystem.hpp>
#include <boost/graph/graphviz.hpp>
namespace fs = boost::filesystem;
//#####################################################################################################################
struct VertexWriter
{
Ve... | {"hexsha": "77d085167a351e8404e6adedbd0d82261b0081b5", "size": 6432, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "graph.cpp", "max_stars_repo_name": "5cript/include-graph", "max_stars_repo_head_hexsha": "be6b2b1278ec25edfb3c66bbd45be72c9deae3ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
export RBMSplit
struct RBMSplit{VT,MT} <: MatrixNeuralNetwork
ar::VT
ac::VT
b::VT
Wr::MT
Wc::MT
end
@treelike RBMSplit
"""
RBMSplit([T=Complex{STD_REAL_PREC}], N, α, [initW, initb, inita])
Constructs a Restricted Bolzmann Machine to encode a vectorised density matrix,
with weights of type `T`... | {"hexsha": "d4945a3fadc4f8a4a7e484336fff995c872edb60", "size": 4273, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Networks/RBMSplit.jl", "max_stars_repo_name": "TheorieMPQ/NeuralQuantum.jl", "max_stars_repo_head_hexsha": "e78e2f44f83da2217965e6f4404eb29f1b87d321", "max_stars_repo_licenses": ["MIT"], "max_s... |
/-
Copyright (c) 2018 Simon Hudon. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Simon Hudon, Patrick Massot
-/
import tactic.pi_instances
import algebra.group.pi
import algebra.hom.ring
/-!
# Pi instances for ring
This file defines instances for ring, semiring and ... | {"author": "nick-kuhn", "repo": "leantools", "sha": "567a98c031fffe3f270b7b8dea48389bc70d7abb", "save_path": "github-repos/lean/nick-kuhn-leantools", "path": "github-repos/lean/nick-kuhn-leantools/leantools-567a98c031fffe3f270b7b8dea48389bc70d7abb/src/algebra/ring/pi.lean"} |
"""
struct CXXScopeSpec <: Any
Hold a pointer to a `clang::CXXScopeSpec` object.
"""
struct CXXScopeSpec
ptr::CXCXXScopeSpec
end
Base.unsafe_convert(::Type{CXCXXScopeSpec}, x::CXXScopeSpec) = x.ptr
Base.cconvert(::Type{CXCXXScopeSpec}, x::CXXScopeSpec) = x
| {"hexsha": "678645c4fbc66c39e6152ef508f4df99414af874", "size": 266, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/clang/core/Sema/DeclSpec.jl", "max_stars_repo_name": "vchuravy/ClangCompiler.jl", "max_stars_repo_head_hexsha": "47080072b059465f8176349c6e67bc678fa238d2", "max_stars_repo_licenses": ["MIT"], "m... |
from torchvision import datasets, transforms
from base import BaseDataLoader
import numpy as np
import cv2
import random
from utils import util
import torch.utils.data as data
import os
import torch
import torch.utils.data.sampler as sampler
def safe_crop(mat, x, y, crop_size=(320, 320)):
crop_h, crop_w = crop_si... | {"hexsha": "bfdf69444e0f748e2604822822bc2f7b8762c706", "size": 10767, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_loader/data_loaders.py", "max_stars_repo_name": "wyk0517/image-matting-experiment", "max_stars_repo_head_hexsha": "1b86bdf241468f65f3b551b48db72d277b8163db", "max_stars_repo_licenses": ["MIT... |
#ifndef SAMPML_INCLUDE_FEATURE_VECTOR_HPP
#define SAMPML_INCLUDE_FEATURE_VECTOR_HPP
#include <dlib/matrix.h>
#include "common.hpp"
namespace SAMPML_NAMESPACE {
template <typename T, std::size_t N>
using feature_vector = dlib::matrix<T, N, 1>;
}
#endif /* SAMPML_INCLUDE_COMMON_HPP */ | {"hexsha": "34d2e620d55beabd8e51895298e817a401ed32f9", "size": 295, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "sampml/sampml/feature_vector.hpp", "max_stars_repo_name": "YashasSamaga/sampml", "max_stars_repo_head_hexsha": "dc84110b53b120caeeb4c0234fcfd6ab16793c59", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Loads the given model and data and predicts (labels and probabilities).
import pandas as pd
import numpy as np
import pickle
import joblib
import argparse
import sys
import os
from sklearn.ensemble import RandomForestClassifier as RFC
### Configuration
_datasettypes = ['covid19_infected_0','hospital_admission_12','i... | {"hexsha": "e92dc2bb2a7fe36405ed658d8364a2d9d1d3d138", "size": 3791, "ext": "py", "lang": "Python", "max_stars_repo_path": "cope.py", "max_stars_repo_name": "StephanLorenzen/Covid19PredictionEngine", "max_stars_repo_head_hexsha": "af9550a01110175989bc295006cfff1bd0babfac", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# -*- coding: utf-8 -*-
# Author: HowkeWayne
# Date: 2019/4/18 - 9:11
"""
File Description...
lenet-5 网络测试实验
LeNet5 implements on tensorflow
"""
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from tensorflow.examples.tutorials... | {"hexsha": "251c4a38de1966b823a9c4488f52bc5c4a5004e9", "size": 11531, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lenet-5.py", "max_stars_repo_name": "HowkeWayne/G-program", "max_stars_repo_head_hexsha": "e96df9d8c890ced88027d5eeb0734fcff7fb7ad7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
# coding: utf-8
# A [Mandelbrot set](https://en.wikipedia.org/wiki/Mandelbrot_set) is the set of complex numbers c where:
# $$
# \begin{array}{c}
# c \in \mathbb{C} \\\
# z_0 = 0 \\\
# z_{n+1} = z_n^2 + c \\\
# \lim_{n\to \infty} \lvert z_{n+1}\rvert \le 2
# \end{array}
# $$
#
# In[1]:
get_ipython().magic('matplot... | {"hexsha": "42d20803449fce8088cc65e281e4ddff7686b1d1", "size": 5476, "ext": "py", "lang": "Python", "max_stars_repo_path": "mandelbrot_perf.py", "max_stars_repo_name": "stelmod/python__num_perf", "max_stars_repo_head_hexsha": "44aa6d785c67ec4b2d32638a6aee412fbf9fda6d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma M_ne_policy[intro]: "is_policy p \<Longrightarrow> s \<in> space (prob_algebra Ms) \<Longrightarrow> space M \<noteq> {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>is_policy p; s \<in> space (prob_algebra Ms)\<rbrakk> \<Longrightarrow> space M \<noteq> {}
[PROOF STEP]
using space_K0 p... | {"llama_tokens": 322, "file": "MDP-Rewards_MDP_cont", "length": 2} |
import cv2
import mediapipe as mp
import numpy as np
class HandDetector():
def __init__(self, window_shape):
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(max_num_hands=2)
self.mpDraw = mp.solutions.drawing_utils
self.center = {"pitch": (0, 0),
... | {"hexsha": "e6f9f6e9b260bee29d3da32576e0a70254fa14a2", "size": 2316, "ext": "py", "lang": "Python", "max_stars_repo_path": "handdetector.py", "max_stars_repo_name": "realfolkcode/theremin", "max_stars_repo_head_hexsha": "d3694f633c91a493e64e7897ded871a68dfb5ded", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy
import pandas
import statsmodels.api as sm
def custom_heuristic(file_path):
'''
You are given a list of Titantic passengers and their associated
information. More information about the data can be seen at the link below:
http://www.kaggle.com/c/titanic-gettingStarted/data
For this exe... | {"hexsha": "55efae8390b08133c43b22a306e42aaabe959cd1", "size": 3096, "ext": "py", "lang": "Python", "max_stars_repo_path": "aula 2/03_quiz_your_custom_heuristic.py", "max_stars_repo_name": "RichardPSilva/Udacity-Intro-to-Data-Science", "max_stars_repo_head_hexsha": "36820b186c670a4b022a623eacc21e4c18a10235", "max_stars... |
# Suppress warnings
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import os, sys, gc, random
import datetime
import dateutil.relativedelta
import argparse
# Machine learning
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn... | {"hexsha": "4f7d9ecde5cdd2f0a9e178c8ac49d3756019272b", "size": 7223, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/src/inference.py", "max_stars_repo_name": "bsm8734/BC_stage2_Tabular_data_Classification", "max_stars_repo_head_hexsha": "e421360f3f6f9016c58bfff2dd20485206e4a365", "max_stars_repo_licenses":... |
import functools
import numpy as np
from scipy.stats import norm as ndist
import nose.tools as nt
from ..lasso import lasso, full_targets
from ...tests.instance import gaussian_instance
def test_onedim_lasso(n=50000, W=1.5, signal=2., sigma=1, randomizer_scale=1):
beta = np.array([signal])
while True:
... | {"hexsha": "df3aea08da908cf48376120a95f767f513fb0b94", "size": 10485, "ext": "py", "lang": "Python", "max_stars_repo_path": "selectinf/randomized/tests/test_selective_MLE_onedim.py", "max_stars_repo_name": "selective-inference/Python-software", "max_stars_repo_head_hexsha": "e906fbb98946b129eb6713e8956bde7a080181f4", "... |
import numpy
def ToyObjective(objective):
def __init__(self):
pass
def numVar(objective):
pass
def evaluate(objective):
pass
class Igo(object):
def __init__(self,objective):
self.objective = objective
self.numVar_ = objective.numVar()
self.mean ... | {"hexsha": "d6983730fd1c069d2aca01425251df5575e79c1a", "size": 2899, "ext": "py", "lang": "Python", "max_stars_repo_path": "seglibpython/seglib/igo/igo_opt.py", "max_stars_repo_name": "DerThorsten/seglib", "max_stars_repo_head_hexsha": "4655079e390e301dd93e53f5beed6c9737d6df9f", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
from pusion.core.combiner import UtilityBasedCombiner
from pusion.util.constants import *
class ComplementaryOutputCombiner(UtilityBasedCombiner):
"""
The :class:`ComplementaryOutputCombiner` combines fully complementary decision outputs by concatenating individual
decisions across cl... | {"hexsha": "c1935686495fd702deb9836682ba4ed1aa2da4ba", "size": 2153, "ext": "py", "lang": "Python", "max_stars_repo_path": "pusion/core/complementary_output_combiner.py", "max_stars_repo_name": "IPVS-AS/pusion", "max_stars_repo_head_hexsha": "58ef24b602f611192430f6005ecf5305f878f412", "max_stars_repo_licenses": ["MIT"]... |
struct TimeDateZone <: NanosecondBasis
timestamp::TimeDate
inzone::AkoTimeZone # one of {FixedTimeZone, VariableTimeZone}
atzone::FixedTimeZone
# ensure other constructors will be give explictly
function TimeDateZone(attime::Time, ondate::Date, inzone::VariableTimeZone, atzone::Fixed... | {"hexsha": "0dd18cfc9234417dae0a2f6a2fab95f10769d371", "size": 4295, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TimeDateZone.jl", "max_stars_repo_name": "UnofficialJuliaMirror/TimesDates.jl-bdfc003b-8df8-5c39-adcd-3a9087f5df4a", "max_stars_repo_head_hexsha": "1480b658d20532692e4b19ca70bbe50e701681a6", "m... |
from mapoca.trainers.subprocess_env_manager import worker
import numpy as np
from typing import List, Tuple, Optional, Mapping as MappingType
from mlagents_envs.base_env import (
BehaviorSpec,
ObservationSpec,
DimensionProperty,
ObservationType,
ActionSpec,
DecisionSteps,
TerminalSteps,
... | {"hexsha": "d58b88457eda13b8e21a53dc05d704111d471bc1", "size": 7356, "ext": "py", "lang": "Python", "max_stars_repo_path": "ma-poca/mapoca/mapoca/particles_env.py", "max_stars_repo_name": "Unity-Technologies/paper-ml-agents", "max_stars_repo_head_hexsha": "885144ee25e86b929c5acee90b9b8dc059bcb9af", "max_stars_repo_lice... |
#include <ripple/app/ledger/LedgerMaster.h>
#include <ripple/app/main/Application.h>
#include <ripple/app/misc/NetworkOPs.h>
#include <ripple/app/misc/SHAMapStore.h>
#include <ripple/protocol/jss.h>
#include <ripple/rpc/Context.h>
#include <ripple/beast/core/LexicalCast.h>
#include <boost/algorithm/string/case_conv.h... | {"hexsha": "1463ad699af3f71aea27f691eca567cb55e6193a", "size": 2548, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ripple/rpc/handlers/CanDelete.cpp", "max_stars_repo_name": "DEEPSPACE007/DsDeFi-Exchange", "max_stars_repo_head_hexsha": "777486b799bae42a4297f9524f3ff30e0b149ef7", "max_stars_repo_licenses": ["... |
# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Free University
# Berlin, 14195 Berlin, Germany.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source ... | {"hexsha": "8cdae65e3882a66f1ccef9dd201ff7948faf151b", "size": 7587, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyemma/coordinates/tests/test_featurereader.py", "max_stars_repo_name": "clonker/PyEMMA", "max_stars_repo_head_hexsha": "a36534ce2ec6a799428dfbdef0465c979e6c68aa", "max_stars_repo_licenses": ["BSD... |
# -*- coding: utf-8 -*-
"""Stacking (meta ensembling). See http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
for more information.
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause
import warnings
import numpy as np
from sklearn.linear_model import LogisticRegression
from ... | {"hexsha": "01c681a57f8dbe6fe83792ae7798d75aacb5c746", "size": 10251, "ext": "py", "lang": "Python", "max_stars_repo_path": "combo/models/classifier_stacking.py", "max_stars_repo_name": "vishalbelsare/combo", "max_stars_repo_head_hexsha": "229d578de498b47ae03cf2580472aceebf8c2766", "max_stars_repo_licenses": ["BSD-2-Cl... |
#include <iostream>
#include <string>
#include <vector>
#define BOOST_TEST_MAIN
#include <boost/test/included/unit_test.hpp>
#include "pillowtalk.h"
using namespace std;
int gNumberOfHeartbeats = 0;
static int callback_non_cont(pt_node_t* node)
{
string document = "http://127.0.0.1:5984/pt_test/";
pt_printout(n... | {"hexsha": "2197903b6bac0b29d56baa653d4006058345dfe4", "size": 1960, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_changes_feed.cpp", "max_stars_repo_name": "kuhlmannmarkus/pillowtalk", "max_stars_repo_head_hexsha": "eb752d148b9bdde6739294e5b30d30523caf4085", "max_stars_repo_licenses": ["MIT"], "max_st... |
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_pkg.sv
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_types.sv
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_utils.sv
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_if.sv
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_if_connector.sv
${TNOC_HOME}/rtl/axi_adapter/tnoc_axi_byte_counter.sv
${TNOC_HOME}/rtl/axi_adapter/... | {"hexsha": "b9b3fb2c9d843bf8df46e3fb08cdb44231b2b103", "size": 926, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "third_party/tests/Tnoc/cores/tnoc/rtl/axi_adapter/compile.f", "max_stars_repo_name": "little-blue/Surelog", "max_stars_repo_head_hexsha": "1c2459f841f6e6d923b336feacd22ccfb9aea845", "max_stars_repo... |
From Coq Require Import Arith.Arith.
From Coq Require Import Bool.Bool.
From Coq Require Import Arith.PeanoNat.
From Coq Require Import micromega.Lia.
From Coq Require Import Lists.List.
From Coq Require Import Reals.Reals. Import Rdefinitions. Import RIneq.
From Coq Require Import ZArith.Int. Import Znat.
From Coq Req... | {"author": "ChezJrk", "repo": "verified-scheduling", "sha": "e9876602147114e4378f10ac1402bd5705c0cef0", "save_path": "github-repos/coq/ChezJrk-verified-scheduling", "path": "github-repos/coq/ChezJrk-verified-scheduling/verified-scheduling-e9876602147114e4378f10ac1402bd5705c0cef0/src/Im2col.v"} |
from xcal3d import *
import numpy
if __name__ == "__main__":
import sys
import os
f = sys.argv[1]
ext = os.path.splitext(f)[1]
if ext != ".xsf":
print "unsupported skeleton file, only xsf"
source = open(f,"rb")
pa = SkelParser()
xml.sax.parse(source, pa)
sk = pa.target
db = dict([(b.name,b) for b in ... | {"hexsha": "49bde1ed589401c373a2d67b12accdfd2c9ff9ec", "size": 919, "ext": "py", "lang": "Python", "max_stars_repo_path": "xcal3d/showbodyinfo.py", "max_stars_repo_name": "eruffaldi/pyxcal3d", "max_stars_repo_head_hexsha": "443fb7c0c9168dbd2dc39816198933172682bde2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
SUBROUTINE GSTURB ( szturb, ituwid, iret )
C************************************************************************
C* GSTURB *
C* *
C* This subroutine sets the turbulence symbol attributes. *
C* *
C* GSTURB ( SZTURB, ITUWID, IRET ) *
C* *
C* Input parameters: *
C* SZTURB RE... | {"hexsha": "82acff732308c9b14f304f8331f2608589aa4e8d", "size": 1355, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/gplt/attribute/gsturb.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause... |
from pdb import set_trace as st
import os
import numpy as np
import cv2
import argparse
from sklearn.model_selection import train_test_split
import glob
parser = argparse.ArgumentParser('create subdirectories for trainin deblur gan')
parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A, ex. ... | {"hexsha": "38dc462b7b66daa60197d70c13aabe32a9338839", "size": 3099, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/split_train_test_val.py", "max_stars_repo_name": "piperod/DeblurGAN", "max_stars_repo_head_hexsha": "a008b9fa94f49b351a68fafaac864619f0b7d569", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
from decoding.strategy_utils import generate_step_with_prob, assign_single_value_long, assign_single_value_byte, assign_multi_value_long, convert_tokens
import models.Constants as Constants
import torch
from tqdm import tqdm
import numpy as np
import json
import math
import matplotlib.pyplot as plt
from matplotlib imp... | {"hexsha": "059ea08906ffd0f53cb04aada0ed87c979beb381", "size": 13299, "ext": "py", "lang": "Python", "max_stars_repo_path": "decoding/decoding/mask_predict.py", "max_stars_repo_name": "ybCliff/VideoCaptioning", "max_stars_repo_head_hexsha": "93fc3b095c970e51e1e24909163a827df98d6ef3", "max_stars_repo_licenses": ["MIT"],... |
using MathOptInterface
using ParametricOptInterface
using BenchmarkTools
const MOI = MathOptInterface
const POI = ParametricOptInterface
import Pkg
function moi_add_variables(N::Int)
model = MOI.Utilities.Model{Float64}()
MOI.add_variables(model, N)
return nothing
end
function poi_add_variables(N::Int)
... | {"hexsha": "0195067bfcc5bd5199713201078111f02ca34e83", "size": 16433, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/run_benchmarks.jl", "max_stars_repo_name": "tomasfmg/ParametricOptInterface.jl", "max_stars_repo_head_hexsha": "9f4a7c969374aa2dd16187d8d8c280684e606577", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma (in cpx_sq_mat) spectrum_to_pm_idx_bij:
assumes "hermitian A"
and "A\<in> fc_mats"
shows "bij_betw (spectrum_to_pm_idx A) (spectrum A) {..< card (spectrum A)}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bij_betw (spectrum_to_pm_idx A) (spectrum A) {..<card (spectrum A)}
[PROOF STEP]
proof -
[... | {"llama_tokens": 4381, "file": "Projective_Measurements_Projective_Measurements", "length": 40} |
# !/usr/bin/env python3
import os
from glob import glob
import numpy as np
import random
import time
class Vocabulary(object):
def __init__(self, filename, vadidate_file=False):
self._id_to_word = []
self._word_to_id = {}
self._unk = -1
self._bos = -1
self._eos = -1
... | {"hexsha": "ae22f7e11bba11883e19ecaaab8507746ec1847e", "size": 7828, "ext": "py", "lang": "Python", "max_stars_repo_path": "subword/bilm/data.py", "max_stars_repo_name": "searobbersduck/ELMo_Chin", "max_stars_repo_head_hexsha": "5d9b2f0759ee3a46a4a1e20c08cc26109b7b90c9", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
module #6 where
open import Level
open import Data.Bool
open import Relation.Binary.PropositionalEquality
{-
Exercise 1.6. Show that if we define A × B :≡ ∏(x:2) rec2(U, A, B, x), then we can give a
definition of indA×B for which the definitional equalities stated in §1.5 hold propositionally (i.e. using equality
t... | {"hexsha": "b72a8d0874fdb73cce0fdb715f55eaa80b9ebfbb", "size": 1292, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Chapter1/#6.agda", "max_stars_repo_name": "CodaFi/HoTT-Exercises", "max_stars_repo_head_hexsha": "3411b253b0a49a5f9c3301df175ae8ecdc563b12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from itertools import combinations
import numpy as np
class FastCausalInference:
def __init__(self, data, conditional_independence_test):
self.data = data
sample_counts = [len(data[key]) for key in data]
for sample_count in sample_counts:
if sample_count != sample_counts[0]:
... | {"hexsha": "d336204a251e847bdfe13e90facfa570debc66ca", "size": 12109, "ext": "py", "lang": "Python", "max_stars_repo_path": "causal_inference.py", "max_stars_repo_name": "valerK/causal_discovery", "max_stars_repo_head_hexsha": "e3fbd3d221387f343b2ff0961d0f2faf581daeef", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
/*! \file
\brief A node constraint element.
Copyright (C) 2019-2021 kaoru https://www.tetengo.org/
*/
#include <any>
#include <iterator>
#include <string_view>
#include <utility>
#include <vector>
#include <boost/preprocessor.hpp>
#include <boost/scope_exit.hpp>
#include <boost/test/unit_test.... | {"hexsha": "f99cd2b0dbed9997080dc232b5258570925e7db1", "size": 9417, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "library/lattice/test/src/test_tetengo.lattice.node_constraint_element.cpp", "max_stars_repo_name": "kaorut/tetengo", "max_stars_repo_head_hexsha": "3360cce3e3f4c92b18154927685986c1fa7b4e8e", "max_st... |
from sympy.abc import x
from sympy import factor
result = factor(x**2 + 3*x)
print(result)
result1 = factor(x**2 - 9)
print(result1)
result2 = factor(x**2 - 4 * x + 4)
print(result2)
| {"hexsha": "3b6b524cf20fbf7b345b7cf455c27dfb4b79f358", "size": 187, "ext": "py", "lang": "Python", "max_stars_repo_path": "fat_polynomial.py", "max_stars_repo_name": "maiconloure/Learning_Python", "max_stars_repo_head_hexsha": "2999508909ace5f8ca0708cdea93b82abaaeafb2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from lib import common
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
Vmax = 10
Vmin = -10
N_ATOMS = 51
DELTA_Z = (Vmax - Vmin) / (N_ATOMS - 1)
def save_distr(vec, name):
plt.cla()
p = np.arange(Vmin, Vmax+DELTA_Z, DELTA_Z)
plt.bar(p, vec, width=0.5)
plt... | {"hexsha": "514b0cb7d25dabc7bd868fba4803ef18ffc138a5", "size": 3201, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/rainbow/distr_test.py", "max_stars_repo_name": "ChengUVa/ptan", "max_stars_repo_head_hexsha": "f9b3ef2680ff64fad52e600d73ff2bf42eee310d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from builtins import str
from builtins import range
import argparse
import glob
import lsst.afw.image as afwImage
import lsst.afw.geom as afwGeom
import lsst.afw.display.ds9 as ds9
import lsst.afw.math as afwMath
import numpy as np
#generate counts vs. exposure time data for a directory of flat fields
def linearity(... | {"hexsha": "928dbdbb113a0e8cf92e08da254afee94e61c2b4", "size": 2645, "ext": "py", "lang": "Python", "max_stars_repo_path": "Attic/linearity.py", "max_stars_repo_name": "tguillemLSST/eotest", "max_stars_repo_head_hexsha": "c6f150984fa5dff85b9805028645bf46fc846f11", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "max_... |
/* Copyright (c) 2017, United States Government, as represented by the
* Administrator of the National Aeronautics and Space Administration.
*
* All rights reserved.
*
* The Astrobee platform is licensed under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with t... | {"hexsha": "b4f3ff4cd54d2c1a5b96dbbea7d93017c42b26ad", "size": 16915, "ext": "cc", "lang": "C++", "max_stars_repo_path": "mobility/mobility/tools/teleop.cc", "max_stars_repo_name": "algprasad/astrobee", "max_stars_repo_head_hexsha": "a5697d71e0c86598b3a762cadf94e8da826171c1", "max_stars_repo_licenses": ["Apache-2.0"], ... |
[STATEMENT]
lemma fls_regpart_of_int [simp]:
"fls_regpart (of_int i) = (of_int i :: 'a::ring_1 fps)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fls_regpart (of_int i) = of_int i
[PROOF STEP]
by (simp add: fls_of_int fps_of_int) | {"llama_tokens": 115, "file": null, "length": 1} |
""" src.py.spad
"""
import numpy as np
def spad_timg(rates, eps=1e-6):
return 1.0 / np.clip(rates, eps, None)
def spad_logtimg(rates, eps=1e-6):
# TODO: see if this name is confusing?
# mean(logtimgs) or log(meantimgs)?
# and which one do we need?
return np.log(spad_timg(rates, eps=eps))
def inve... | {"hexsha": "54085fbc80744b80634b65f04309142a8e72c6fd", "size": 2741, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/py/spad.py", "max_stars_repo_name": "shantanu-gupta/spad-timg-denoise", "max_stars_repo_head_hexsha": "3c9e5ae004dc3175ae796499ac7827e9bc2b4573", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
from scipy.stats import ttest_ind, ttest_1samp, ttest_rel, mannwhitneyu, norm
from collections import OrderedDict
from numpy.random import randint
import matplotlib.gridspec as gridspec
from matplotlib.lines import Line2D
from matplotlib.ticker import AutoMinorLocator, MultipleLocator, MaxNLocator, FixedLocator, AutoLo... | {"hexsha": "1b0eaa19094dac05c6fca80ba4ce893f2cc74d7d", "size": 23194, "ext": "py", "lang": "Python", "max_stars_repo_path": "bootstrap_contrast/old__/sandbox.py", "max_stars_repo_name": "josesho/bootstrap-contrast", "max_stars_repo_head_hexsha": "94fa42a5dc4622be016e2e522d1f07b19ba23a8d", "max_stars_repo_licenses": ["M... |
import ContinuumArrays: apply, MulQuasiMatrix
@testset "$(rpad("Bernstein Basis Tests",80))" begin
l = Bernstein(2)
d = Derivative(axes(l,1))
@test apply(*,d,l) isa BernsteinDerivative
@test d*l isa BernsteinDerivative
@test l' isa BernsteinDerivative
@test basis(l) == l.b
@test nbasis(l... | {"hexsha": "77a4bbd137fb6eeca6544503fb6586318df1ad01", "size": 2125, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/bernstein_tests.jl", "max_stars_repo_name": "JuliaGNI/CompactBasisFunctions.jl", "max_stars_repo_head_hexsha": "5a76714aca25c399d0856643aff3683d8e0f103a", "max_stars_repo_licenses": ["MIT"], "... |
#! /usr/bin/env python3
""" Full-Monty Python3 and the Holy Grail """
__copyright__ = "Copyright (C) 2009, Innovations Anonymous"
__version__ = "4.0"
__license__ = "Public Domain"
__status__ = "Development"
__author__ = "Brahmjot Singh"
__maintainer__ = "Brahmjot Singh"
__email__ = "InnovAnon-Inc@... | {"hexsha": "1fb62e2b26c45176a943b4eed2e26c5e3c1fa4f5", "size": 64587, "ext": "py", "lang": "Python", "max_stars_repo_path": "cgc.py", "max_stars_repo_name": "InnovAnon-Inc/ProgramSynthesis", "max_stars_repo_head_hexsha": "e7132c144cba34ef167de981c063b71c23075456", "max_stars_repo_licenses": ["Unlicense"], "max_stars_co... |
from __future__ import division
import pickle, random
import numpy as np
from itertools import cycle
import torch
from torch.autograd import Variable
all_feature_lengths = {'v_enc_onehot': 100,
'v_enc_embedding': 300,
'v_enc_dim300': 300,
'v_enc_dim2': 2,
'v_enc_dim10':... | {"hexsha": "7bfa672c5f69325048135decce88820e4d40f2a5", "size": 10192, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/science/dataset.py", "max_stars_repo_name": "YilunZhou/path-naturalness-prediction", "max_stars_repo_head_hexsha": "dec384a58297e1cd88f44eb31771d0251e0b06d4", "max_stars_repo_licenses": ["MI... |
import csv
import os
import re
from collections import defaultdict, OrderedDict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
from smartva.data.common_data import MALE, FEMALE, ADULT, CHILD, NEONATE
from smartva.grapher_prep import Graphe... | {"hexsha": "ff2c13772d8dcae20508bb7ba01015c162ce7b8d", "size": 5972, "ext": "py", "lang": "Python", "max_stars_repo_path": "smartva/cause_grapher.py", "max_stars_repo_name": "rileyhazard/SmartVA-Analyze-1", "max_stars_repo_head_hexsha": "0573eeff27d03f54e7506db4f1631c0cd9f54bbb", "max_stars_repo_licenses": ["MIT"], "ma... |
from abc import abstractmethod
from typing import List
import numpy as np
from reinvent_chemistry.link_invent.linker_descriptors import LinkerDescriptors
from reinvent_scoring.scoring.component_parameters import ComponentParameters
from reinvent_scoring.scoring.score_components import BaseScoreComponent
from reinvent... | {"hexsha": "b7616bdf8923f69af474b3d82a140925f9871d85", "size": 1654, "ext": "py", "lang": "Python", "max_stars_repo_path": "reinvent_scoring/scoring/score_components/link_invent/base_link_invent_component.py", "max_stars_repo_name": "MolecularAI/reinvent-scoring", "max_stars_repo_head_hexsha": "f7e052ceeffd29e17e1672c3... |
"""
Adapted from the swiss_roll.py example
packaged with Scikit-Learn.
"""
import matplotlib.pyplot as plt
import retina.core.axes
import retina.nldr as nldr
import numpy as np
from matplotlib import gridspec
from sklearn import manifold, datasets
class EventSystem(object):
def __init__(self, fig):
self.fi... | {"hexsha": "b27ec238bfcc54f45894e75fc8a55b694e8f321a", "size": 2943, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/nldr/swiss_roll.py", "max_stars_repo_name": "mcneela/Retina", "max_stars_repo_head_hexsha": "a2a671f6372848ac3bb3b304e681394cc6d90e85", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import random as rnd
import networkx
class GraphGenerator:
"""
Basic graph generator class.
build(size) is to be override by inheritors, called by TheGame class at the initiation
There are lots of possible network configuration. The following is implemented:
- GraphGeneratorSync.py
- GraphG... | {"hexsha": "a32923fd249e2a86430729cc85f4dc96ce65c8a9", "size": 2666, "ext": "py", "lang": "Python", "max_stars_repo_path": "Graph/GraphGenerator.py", "max_stars_repo_name": "wolf-null/resource-network-sim", "max_stars_repo_head_hexsha": "45662a84b03156047ac9441c0e1c8c0b57b6cefe", "max_stars_repo_licenses": ["CC0-1.0"],... |
"""
SkyLib astrometric reduction package
Built around the local Astrometry.net engine binding. Users must create an
Astrometry.net solver using :func:`create_solver`, which loads indexes, and
then use :func:`solve_field` to obtain an :class:`astropy.wcs.WCS` instance
given a list of XY positions of field stars.
"""
f... | {"hexsha": "3c42ad3270452d842e167e3f86d9b16312d8b99a", "size": 11125, "ext": "py", "lang": "Python", "max_stars_repo_path": "skylib/astrometry/main.py", "max_stars_repo_name": "SkynetRTN/skylib", "max_stars_repo_head_hexsha": "58fe57053db6a048f8a72d7b453ae411a2302545", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
(**************************************************************************)
(* This is part of STATES, it is distributed under the terms of the *)
(* GNU Lesser General Public License version 3 *)
(* (see file LICENSE for more details) *)
(* ... | {"author": "ekiciburak", "repo": "impex-on-decorated-logic", "sha": "cdfd22e36e6e0c4b001d23f0cf30c73a2c6867bd", "save_path": "github-repos/coq/ekiciburak-impex-on-decorated-logic", "path": "github-repos/coq/ekiciburak-impex-on-decorated-logic/impex-on-decorated-logic-cdfd22e36e6e0c4b001d23f0cf30c73a2c6867bd/Decorations... |
#include "crab_llvm/config.h"
/**
* Heap abstraction based on sea-dsa (https://github.com/seahorn/sea-dsa).
*/
#include "llvm/IR/Module.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/Value.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/InstIterator.h"
#include "llvm/Support/raw_ostream.h"
#include "sea... | {"hexsha": "406de96007ccded92c6790940ae62890ed9f6b4b", "size": 16168, "ext": "cc", "lang": "C++", "max_stars_repo_path": "lib/CrabLlvm/SeaDsaHeapAbstraction.cc", "max_stars_repo_name": "kuhar/crab-llvm", "max_stars_repo_head_hexsha": "fa548efd6c6c104d509d48d2ae7af09b7b7f1576", "max_stars_repo_licenses": ["Apache-2.0"],... |
from keras.applications.resnet50 import ResNet50
from keras import activations
from keras.preprocessing import image
from keras.layers import Input,Flatten,Dense
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.applications.resnet50 import preprocess_input
import numpy... | {"hexsha": "edbf5ffa15be1298b588336e467b183f7359c1a4", "size": 1849, "ext": "py", "lang": "Python", "max_stars_repo_path": "inceptionv3_highfive_model.py", "max_stars_repo_name": "alexandrosstergiou/Inception_v3_TV_Human_Interactions", "max_stars_repo_head_hexsha": "524ad7b5a0630d05b3aa4f2d5636bf097bd4d7a7", "max_stars... |
%----------------------------------------------------------------------------------------
% PACKAGES AND THEMES
%----------------------------------------------------------------------------------------
\documentclass[aspectratio=169,xcolor=dvipsnames]{beamer}
\usetheme{SimplePlus}
\usepackage{hyperref}
\usepackage{gra... | {"hexsha": "05e57697b9b1b3459458e217e67b9ef9405034e7", "size": 2592, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "slides/figures.tex", "max_stars_repo_name": "Klepac-Ceraj-Lab/ResonanceAnalysis", "max_stars_repo_head_hexsha": "bc29d9a2085b441a7d2ccea5a290cce0b285eec5", "max_stars_repo_licenses": ["MIT"], "max_s... |
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