text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
Created on 28 December 2020
@author: Peter Corke
"""
import numpy.testing as nt
import roboticstoolbox as rtb
import numpy as np
import spatialmath.base as sm
import unittest
from roboticstoolbox import Bug2, DXform, loadmat
from roboticstoolbox.mobile.bug2 import edgelist
from roboticstoolbox.mobile.landmarkmap... | {"hexsha": "b2ca5514c200ba98e3dc2de55a1d9ea658a0a118", "size": 14217, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_mobile.py", "max_stars_repo_name": "LabRobPL/robotics-toolbox-python", "max_stars_repo_head_hexsha": "4fe4d8a23bda77f5fde39c5d7b53dc953c2a07dd", "max_stars_repo_licenses": ["MIT"], "ma... |
Require Import Rupicola.Lib.Api.
Section Gallina.
Definition linkedlist A : Type := list A.
Definition ll_hd {A} : A -> linkedlist A -> A := hd.
Definition ll_next {A} : linkedlist A -> linkedlist A := tl.
End Gallina.
Section Separation.
Context {width: Z} {BW: Bitwidth width} {word: word.word width} {mem:... | {"author": "mit-plv", "repo": "rupicola", "sha": "3f59b3d2404ce425ddf4fd55ad2314996a573dc3", "save_path": "github-repos/coq/mit-plv-rupicola", "path": "github-repos/coq/mit-plv-rupicola/rupicola-3f59b3d2404ce425ddf4fd55ad2314996a573dc3/src/Rupicola/Examples/LinkedList/LinkedList.v"} |
!
! AtmProfile_to_LBLinput
!
! Program to convert AtmProfile data into LBL input files.
!
!
! CREATION HISTORY:
! Written by: Paul van Delst, 09-Jul-2010
! paul.vandelst@noaa.gov
!
PROGRAM AtmProfile_to_LBLinput
! ------------------
! Environment set up
! ------------------
! M... | {"hexsha": "3e6f60e3f9edb48473ceffe73fe2df9abb9d0010", "size": 2872, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/TauProd/LBL/input/AtmProfile_to_LBLinput/AtmProfile_to_LBLinput.f90", "max_stars_repo_name": "hsbadr/crtm", "max_stars_repo_head_hexsha": "bfeb9955637f361fc69fa0b7af0e8d92d40718b1", "max_sta... |
\documentclass{article}
\usepackage{enumerate}
\usepackage{amsmath, amsthm, amssymb}
\usepackage[margin=1in]{geometry}
\usepackage[parfill]{parskip}
\DeclareMathOperator*{\argmax}{arg\,max}
\title{Econ C103 Problem Set 5}
\author{Sahil Chinoy}
\date{February 28, 2017}
\begin{document}
\maketitle{}
\subsection*{Exerc... | {"hexsha": "fdd5864228cc169be8a783fe22f4e53afb664dc4", "size": 3390, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "hw5/hw5.tex", "max_stars_repo_name": "sahilchinoy/econ103", "max_stars_repo_head_hexsha": "ab2ecbb759eb811e953157e7f04f5a003066a62c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
import tclab
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import random
# Second order model of TCLab
# initial parameter guesses
Kp = 0.2
taus = 50.0
zeta = 1.2
# magnitude of step
M = 80
# overdamped 2nd order step response
def model(y0,t,M,Kp,... | {"hexsha": "e7468305579cbd7acdb6ac65a0025d268d04193c", "size": 5010, "ext": "py", "lang": "Python", "max_stars_repo_path": "0_Test_Device/Python/test_Second_Order.py", "max_stars_repo_name": "APMonitor/arduino", "max_stars_repo_head_hexsha": "f36e65a70dd7122d1829883899e40e56bf6c4279", "max_stars_repo_licenses": ["Apach... |
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage[letterpaper, margin=1in]{geometry}
\usepackage{amsmath}
\usepackage{amssymb }
\title{CS5820 HW9}
\author{Renhao Lu, NetID: rl839}
\begin{document}
\maketitle
\section{NP-Complete proof sketches}
\subsection{UNSAT}
The approach is not... | {"hexsha": "6b9eafc6577806b746b1258803ab8201d477c02c", "size": 9399, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "CS5820 HW-9.tex", "max_stars_repo_name": "lurenhaothu/CS4820", "max_stars_repo_head_hexsha": "a656d336e60e2f6b4416574cc518486945f1c93f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 17 11:33:55 2020
@author: nmei
"""
import os
from glob import glob
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
working_dir = '../confidence_results'
figure_dir = '../figures'
if not os.path.ex... | {"hexsha": "c2ad747d505a29c989e29cd196554964509fbb45", "size": 2046, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/4.2.plot_confidence.py", "max_stars_repo_name": "nmningmei/agent_models", "max_stars_repo_head_hexsha": "8380f1203e6d5a18f18f9adeb6bd36b23b2ae61b", "max_stars_repo_licenses": ["MIT"], "max... |
/*=============================================================================
Copyright (c) 2001-2009 Joel de Guzman
Copyright (c) 2001-2009 Hartmut Kaiser
http://spirit.sourceforge.net/
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at h... | {"hexsha": "13b36962d3d790db660978fdefa4bc15d39eff0d", "size": 2064, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/lib/boost/spirit/home/support/detail/sign.hpp", "max_stars_repo_name": "EricBoittier/vina-carb-docker", "max_stars_repo_head_hexsha": "e8730d1ef90395e3d7ed3ad00264702313b0766a", "max_stars_repo_... |
[STATEMENT]
lemma wf_wcode_double_case_le[intro]: "wf wcode_double_case_le"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. wf wcode_double_case_le
[PROOF STEP]
by(auto simp: wcode_double_case_le_def ) | {"llama_tokens": 92, "file": "Universal_Turing_Machine_UTM", "length": 1} |
# Library Imports
import numpy as np
from matplotlib import pyplot
from tensorflow import zeros
from numpy.random import randn
def generate_latent_points(latent_dim, no_of_samples):
# generate points in the latent space
latent = randn(latent_dim * no_of_samples)
# reshape into a batch of inputs ... | {"hexsha": "db7fc4863937738e510571cbfc08e430156036cd", "size": 1561, "ext": "py", "lang": "Python", "max_stars_repo_path": "Utils/fake_samples.py", "max_stars_repo_name": "platonic-realm/UM-PDD", "max_stars_repo_head_hexsha": "747171ec3d87e54fb70a2cf4472b8da9fbd2f67b", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
####################################################################
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
import sys
sys.path.append('./')
####################################################################
batch_size = 256
num_classes = 10
epochs = 30
image_shape = (32, 32, 3)
(x... | {"hexsha": "9c7f37d2e38a9076a216e186ee1e65ddb04fc30e", "size": 2559, "ext": "py", "lang": "Python", "max_stars_repo_path": "train-node/train-cifar-10.py", "max_stars_repo_name": "xAlpharax/NeuralODE-Notes-Projects", "max_stars_repo_head_hexsha": "4353e2aa800c1dded5388950b0cc338f41cae63c", "max_stars_repo_licenses": ["M... |
def calculate_taxonomic_resolution(TaXon_table_xlsx, path_to_outdirs, x_tax_res, y_tax_res, figure_type, template, theme, font_size, clustering_unit):
import glob
import PySimpleGUI as sg
import pandas as pd
from pandas import DataFrame
import numpy as np
import plotly.graph_objects as go
f... | {"hexsha": "d24393a26dfeda9363ce8e026cf4afd98df6de84", "size": 4736, "ext": "py", "lang": "Python", "max_stars_repo_path": "taxontabletools/calculate_taxonomic_resolution.py", "max_stars_repo_name": "TillMacher/TaxonTableTools", "max_stars_repo_head_hexsha": "9b5c6356acd9890465d39f16b671eb346ceec25a", "max_stars_repo_l... |
from scipy.io import loadmat
from numpy import transpose
import skimage.io as sio
from utils import visualize
import numpy as np
import os
import argparse
def main(args):
detection = loadmat('/home/tju/pytorch-pose/evaluation/data/detections.mat')
det_idxs = detection['RELEASE_img_index']
debug = 1
thr... | {"hexsha": "5c13ce59918b7883d20faaf08d00c830578a06fc", "size": 3954, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualization/plot_mpii-1.py", "max_stars_repo_name": "mobeixiaoxin/deep-high-resolution-net.pytorch", "max_stars_repo_head_hexsha": "081dea8b60726cc0db45ef907f49b4748ac7bba2", "max_stars_repo_lic... |
import unittest
from ..utils import stft, get_samplerate
from ..nodes.nonstat import *
from golem import DataSet
from scipy import signal
import matplotlib.pyplot as plt
class TestSlowSphere(unittest.TestCase):
def setUp(self):
np.random.seed(0)
def test_slowsphere(self):
'''
When the input is sta... | {"hexsha": "6b1ff2a8bf1fe2b9af6d2cf9a628ca9431be7cf7", "size": 1850, "ext": "py", "lang": "Python", "max_stars_repo_path": "psychic/tests/testnonstat.py", "max_stars_repo_name": "breuderink/psychic", "max_stars_repo_head_hexsha": "a89dd821b801e315df785f633e88689f4f5a93bf", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
[STATEMENT]
lemma prime_exp: "prime (p ^ n) \<longleftrightarrow> n = 1 \<and> prime p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. prime (p ^ n) = (n = 1 \<and> prime p)
[PROOF STEP]
by auto2 | {"llama_tokens": 86, "file": "Auto2_HOL_HOL_Primes_Ex", "length": 1} |
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi,np.pi,300)
plt.axes([.1,.1,0.8,0.8])
plt.plot(x,np.sin(x), color='r')
plt.axes([.3,.15,0.4,0.3])
plt.plot(x,np.cos(x), color='g')
plt.show() | {"hexsha": "5aef3c2519578de589fd346bdce3df6895a7e939", "size": 217, "ext": "py", "lang": "Python", "max_stars_repo_path": "Week4/12. axes.py", "max_stars_repo_name": "HawkingLaugh/Data-Processing-Using-Python", "max_stars_repo_head_hexsha": "6c4d7e09317aee41684731d5611f2f0dab217b2b", "max_stars_repo_licenses": ["MIT"],... |
"""Functions to perform registration between all hybridizations.
register_final_images(folder, gene='Nuclei',
sub_pic_frac=0.2, use_MPI=False,
apply_to_corners=True, apply_warping = True)
-- Register stitched images an in all HDF5 file in the folder
find_reg_final_i... | {"hexsha": "a267fd0b5eb6fa653d610e9a0790d9f6cc60df2f", "size": 38007, "ext": "py", "lang": "Python", "max_stars_repo_path": "pysmFISH/stitching_package/hybregistration.py", "max_stars_repo_name": "ambrosejcarr/pysmFISH", "max_stars_repo_head_hexsha": "0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb", "max_stars_repo_licenses"... |
import re
from copy import deepcopy
from shutil import copyfileobj
import f90nml
import numpy as np
import pandas as pd
from f90nml.namelist import Namelist
from six import StringIO
from pymagicc.definitions import (
DATA_HIERARCHY_SEPARATOR,
convert_magicc6_to_magicc7_variables,
convert_magicc7_to_opensc... | {"hexsha": "b005c8f55c53e86e7c7f3e21dceb31fd96c8f3cb", "size": 28180, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymagicc/io/base.py", "max_stars_repo_name": "gabriel-abrahao/pymagicc", "max_stars_repo_head_hexsha": "c9bdd610f97aa832dd879796aa6b9bfe11fa0f07", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
section\<open>Guard-Based Encodings\<close>
theory G
imports T_G_Prelim Mcalc2C
begin
subsection\<open>The guard translation\<close>
text\<open>The extension of the function symbols with type witnesses:\<close>
datatype ('fsym,'tp) efsym = Oldf 'fsym | Wit 'tp
text\<open>The extension of the predicate symbols with... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Sort_Encodings/G.thy"} |
import numpy as np
from scipy.ndimage.interpolation import rotate as scipyrotate
import torch
from torch.utils.data import Dataset
import torch.nn.functional as F
from torchvision import transforms, datasets
def get_dataset(dataset, data_path):
if dataset == 'MNIST':
channel = 1
im_size = (28, 28)... | {"hexsha": "a65fdad6988dd07a0a2e7924592e3d70b2a66e8d", "size": 6627, "ext": "py", "lang": "Python", "max_stars_repo_path": "data.py", "max_stars_repo_name": "dhkim2810/MaskedDatasetCondensation", "max_stars_repo_head_hexsha": "f52144e9cd68e46b4ebdbcaf96829edb732b79ae", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import torch
import numpy as np
import torch.utils.data as data
class Subset(data.Dataset):
def __init__(self, dataset, indices=None):
"""
Subset of dataset given by indices.
"""
super(Subset, self).__init__()
self.dataset = dataset
self.indices = indices
i... | {"hexsha": "281cba6842977a0192d221f346105fcac415da6b", "size": 1925, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/data/utils.py", "max_stars_repo_name": "moeraza/ali-g", "max_stars_repo_head_hexsha": "342e24e139fc1e75f4bf576d0784ed886f305cf8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import numpy as np
from kcsd import csd_profile as CSD
from kcsd import KCSD2D
from scipy.integrate import simps
from scipy.interpolate import griddata
from figure_properties import *
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
def fetch_folder(csd_type='... | {"hexsha": "903f92a92454061ee54c317672e0c78ef5d349c7", "size": 7108, "ext": "py", "lang": "Python", "max_stars_repo_path": "figures/kCSD_properties/tutorial_broken_electrodes.py", "max_stars_repo_name": "rdarie/kCSD-python", "max_stars_repo_head_hexsha": "5b9e1b1dce2ff95c0d981c2c4015b7a75199de9a", "max_stars_repo_licen... |
#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "a3b701427a6b11ee1550d2ecceb5bf6996bbf924", "size": 8473, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl_coach/agents/dnec_agent.py", "max_stars_repo_name": "dnishio/NEC-RP", "max_stars_repo_head_hexsha": "3e1a5601f559f7ad0abe3b28ad341e6cda278383", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
include(joinpath(pwd(), "src/constraints/contact.jl"))
# include(joinpath(pwd(), "src/constraints/contact_no_slip.jl"))
# Simulate contact model one step
function step_contact(model::Model{<: Integration, FixedTime}, x1, u1, w1, h;
tol_c = 1.0e-5, tol_opt = 1.0e-5, tol_s = 1.0e-4, nlp = :ipopt)
# Horizon
... | {"hexsha": "c03cb9588a957e7315c4bb3583d504b63d0325f7", "size": 1510, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/contact_simulator/simulator.jl", "max_stars_repo_name": "jmichaux/motion_planning", "max_stars_repo_head_hexsha": "9a36f394261ff11ca8325d8a5e9d8a79f18b2744", "max_stars_repo_licenses": ["MIT"],... |
# ==========================
# Computing a facet integral
# ==========================
#
# In this demo, we look at how Basix can be used to compute the integral
# of the normal derivative of a basis function over a triangular facet
# of a tetrahedral cell.
#
# As an example, we integrate the normal derivative of the f... | {"hexsha": "82cdf7f106d5f1ee168f80e1cb07d3f6dbed15d1", "size": 3254, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/demo_facet_integral.py", "max_stars_repo_name": "FEniCS/basix", "max_stars_repo_head_hexsha": "da150e039a5188ac0edd31c8d126e4c8ee4fce6d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma oprod_minus_Id2:
"r' \<le> Id \<Longrightarrow> r *o r' - Id \<le> {((x1,y), (x2,y)). (x1, x2) \<in> (r - Id) \<and> y \<in> Field r'}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. r' \<subseteq> Id \<Longrightarrow> r *o r' - Id \<subseteq> {((x1, y), x2, y). (x1, x2) \<in> r - Id \<and> y \<i... | {"llama_tokens": 336, "file": null, "length": 2} |
module attribute_value_pair_m
implicit none
private
type, public :: attribute_value_pair_t
character(len=80) :: the_attribute
character(len=80) :: the_value
end type attribute_value_pair_t
contains
end module attribute_value_pair_m
| {"hexsha": "e766a4d5eb217775ab447c9f786b4b9988c8f1f9", "size": 267, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "attribute_value_pair_m.f90", "max_stars_repo_name": "bceverly/FABS", "max_stars_repo_head_hexsha": "97d2db030eb4cdcc14da6e50f22fbef140c10091", "max_stars_repo_licenses": ["BSD-3-Clause-No-Nuclear... |
from model import Model
# from view import View
# import pygame
import numpy as np
import time
_LOSE_ = -9999999
mapper = np.array([\
[64 ,16 ,2 ,1],
[16 ,2 ,1 ,-2],
[2 ,1 ,-2 ,-16],
[1 ,-2 ,-16 ,-64]
])
class Node:
def __init__(self, move, grid):
self.score = 0
self.... | {"hexsha": "5551ffe8e82419a270240e1d3fc84f58e5c6504b", "size": 2478, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/minimax.py", "max_stars_repo_name": "sihrc/2048-Python-ML", "max_stars_repo_head_hexsha": "c61a85deb9dcb3a4183c59fb33ef5ca9c9b5a824", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
SUBROUTINE XGRAT
C
C ------------------------------------------------
C ROUTINE NO. ( 219) VERSION (A8.6) 24:NOV:86
C ------------------------------------------------
C
C THIS DRAWS AN X-GRATICULE WITH NO ANNOTATION,
C SETTING THE AXIS INTERVALS AUTOMATICALLY.
C
C... | {"hexsha": "f4c2ce78f778f407642533635fdad6b6c550a423", "size": 1646, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/xgrat.f", "max_stars_repo_name": "ZedThree/GHOST", "max_stars_repo_head_hexsha": "cba30b43bdcc73fb87cff0724337a7d3a1bd7812", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
##
# \file binary_mask_from_mask_srr_estimator.py
# \brief Class to estimate binary mask from mask SRR stack
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date January 2019
#
import os
import re
import numpy as np
import SimpleITK as sitk
import pysitk.simple_itk_helper as sitkh
import ni... | {"hexsha": "8f69bdedd7b03fdb5d03747a959c36ecd1f94cea", "size": 1751, "ext": "py", "lang": "Python", "max_stars_repo_path": "niftymic/utilities/binary_mask_from_mask_srr_estimator.py", "max_stars_repo_name": "martaranzini/NiftyMIC", "max_stars_repo_head_hexsha": "6bd3c914dad8f2983e84ef009b944c429e1fafb3", "max_stars_rep... |
%==============================================================
%\newpage
\chapter{Implementation}\label{implementationdet}
The implementation consists of two separate parts. The first contains the feature extraction and preparation of data from the audio files. The results are stored into feature files. In the second... | {"hexsha": "bf3c64780cbf4863973da7d84cb6b76c0d6935c5", "size": 21533, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "CH5.tex", "max_stars_repo_name": "oObqpdOo/MusicSimilarity", "max_stars_repo_head_hexsha": "223ea464015608ce23c9e856963b4a3702617f73", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": n... |
import torch
import numpy as np
from dltranz.trx_encoder import PaddedBatch
from dltranz.seq_encoder.rnn_encoder import RnnSeqEncoderDistributionTarget
def get_data():
payload = {'amount': torch.arange(4*10).view(4, 10).float(),
'event_time': torch.arange(4*10).view(4, 10).float(),
... | {"hexsha": "ba92ad9f2cab4d6b2e1673c168ef13f07a8e3a35", "size": 1998, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/dltranz_tests/test_seq_encoder/test_rnn_seq_distribution_targets_encoder.py", "max_stars_repo_name": "KirillVladimirov/pytorch-lifestream", "max_stars_repo_head_hexsha": "83005b950d41de8afc1... |
"""
$(SIGNATURES)
Check a scalar: should be real, finite, not nan, in optional bounds.
Real is implied by being `AbstractFloat`.
"""
function check_float(x :: T1; lb = nothing, ub = nothing) where T1 <: AbstractFloat
isValid = true;
if !isfinite(x)
isValid = false;
@warn "Not finite"
e... | {"hexsha": "36f83c50f69cb565615398ad94ed0e32867aca5f", "size": 2559, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/check.jl", "max_stars_repo_name": "hendri54/CommonLH", "max_stars_repo_head_hexsha": "aba46201434da0c3fec6476b66de750eb1f7e493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
#include <boost/graph/make_maximal_planar.hpp>
| {"hexsha": "74bf7a788a3096740d4cbf55c9f54c5a93e820be", "size": 47, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_graph_make_maximal_planar.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL... |
[STATEMENT]
lemma fst_in_eclose [simp]: "x \<^bold>\<in> eclose \<langle>x, y\<rangle>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<^bold>\<in> eclose \<langle>x, y\<rangle>
[PROOF STEP]
by (metis eclose_hinsert hmem_hinsert hpair_def hunion_iff) | {"llama_tokens": 109, "file": "HereditarilyFinite_Rank", "length": 1} |
[STATEMENT]
lemma hd_lq_conv_nth: assumes "u <p v" shows "hd(u\<inverse>\<^sup>>v) = v!\<^bold>|u\<^bold>|"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. hd (u\<inverse>\<^sup>>v) = v ! \<^bold>|u\<^bold>|
[PROOF STEP]
using prefix_length_less[OF assms, THEN hd_drop_conv_nth]
[PROOF STATE]
proof (prove)
using this:... | {"llama_tokens": 335, "file": "Combinatorics_Words_CoWBasic", "length": 3} |
Require Import Semantics_ConvAppl.
Require Import ReferencesImpl.
Require Import CapabilitiesAppl.
Require Import IndicesImpl.
Require Import ObjectsAppl.
Require Import SystemStateAppl.
Require Import SemanticsDefinitionsAppl.
Require Import SemanticsAppl.
Require Import ExecutionAppl.
Require Import AccessEdgeAppl.
R... | {"author": "doerrie", "repo": "confinement-proof", "sha": "db7bfb3522990d0820de64f13baa97b67e694c44", "save_path": "github-repos/coq/doerrie-confinement-proof", "path": "github-repos/coq/doerrie-confinement-proof/confinement-proof-db7bfb3522990d0820de64f13baa97b67e694c44/AccessExecutionAppl.v"} |
from utilscc import*
from time import*
import numpy as np
from qpsolvers import *
def build_initial_graph(Y, m):
# if well understood create the m nearest neighboor directed graph
n = Y.shape[0]
A = np.zeros([n, n])
E = pairwise_matrix_rownorm(Y)
for i in np.arange(0, n):
sorted_index = np.... | {"hexsha": "7c82d9421c0d73a8306d9b2c98ab89e336c9d669", "size": 2577, "ext": "py", "lang": "Python", "max_stars_repo_path": "constrLaplacianRank.py", "max_stars_repo_name": "cmiras/spectralGraphTopology", "max_stars_repo_head_hexsha": "ecf8ef089fd5fc6f746e25a89e0fbcd3dd4efb19", "max_stars_repo_licenses": ["MIT"], "max_s... |
''' remove salt and pepper noise from an image
by applying median filter of size 3x3 and 5x5 '''
import cv2
import numpy as np
img = cv2.imread('sap.png',0)
new_img = cv2.imread('sap.png', 0)
prop = img.shape
#we take a window and find the median of intensity values
#in that window and assigned it to the curren... | {"hexsha": "f67923a53624671e76feb08b8674ebdfe01f1b77", "size": 1109, "ext": "py", "lang": "Python", "max_stars_repo_path": "Day6/salt_pepper_noise.py", "max_stars_repo_name": "susantabiswas/Digital-Image-Processing", "max_stars_repo_head_hexsha": "4808e6ddb42c82acafb5eb5d58b41f9fd495483f", "max_stars_repo_licenses": ["... |
\documentclass[12pt, a4paper]{article}
\usepackage[dutch]{babel}
\usepackage{graphicx}
\usepackage{fullpage}
\usepackage{fancyhdr}
\usepackage{setspace}
\usepackage{color}
\usepackage{float}
\usepackage[parfill]{parskip}
\usepackage{epstopdf}
\usepackage{tabularx}
\usepackage{ctable}
\doublespacing
\pagestyle{fancyplai... | {"hexsha": "52d1faa3511bfe084d2f888cbfe77806f0e48ceb", "size": 11030, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "programmeertalen-project/Report.tex", "max_stars_repo_name": "FlashYoshi/UGentProjects", "max_stars_repo_head_hexsha": "5561ce3bb73d5bc5bf31bcda2be7e038514c7072", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python3
import numpy as np
from collections import Counter
from collections import defaultdict
import sys
import pickle
import re
if (len(sys.argv) < 2):
print("Usage: generate.py [filename] [n]")
exit()
n = int(sys.argv[2]) if len(sys.argv) >= 3 else 20
file = open(sys.argv[1])
text = file.r... | {"hexsha": "4624cb741fb7a0b4a862f5dc8fcc97830ddcb317", "size": 1765, "ext": "py", "lang": "Python", "max_stars_repo_path": "charles-university/2017-npfl092/hw02/generate.py", "max_stars_repo_name": "Hyperparticle/lct-master", "max_stars_repo_head_hexsha": "8acb0ca8fe14bb86305f235e3fec0a595acae2de", "max_stars_repo_lice... |
import json
from os.path import join as _join
import numpy as np
from wepppy.nodb import Ron, Wepp
def get_wd(runid):
return _join('/geodata/weppcloud_runs', runid)
def combined_watershed_viewer_generator(runids, title, units=None, varopts=None, varname=None, asjson=False):
if units is None:
units... | {"hexsha": "ae9c608843dfde8769913541263eefb51b5f3211", "size": 3455, "ext": "py", "lang": "Python", "max_stars_repo_path": "wepppy/weppcloud/combined_watershed_viewer_generator.py", "max_stars_repo_name": "hwbeeson/wepppy", "max_stars_repo_head_hexsha": "6358552df99853c75be8911e7ef943108ae6923e", "max_stars_repo_licens... |
import argparse
import time
import cv2
import numpy as np
import tensorflow as tf
import pygame
from pygame.locals import *
from OpenGL.GL import *
from OpenGL.GLU import *
from models import vnect_model as vnect_model
import utils.utils as utils
parser = argparse.ArgumentParser()
parser.add_argument('--device', def... | {"hexsha": "ed05e8ab32f5c803ac45bfe8e40c7b11e5685416", "size": 9907, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_tf_gl.py", "max_stars_repo_name": "rrbarioni/VNect-tensorflow", "max_stars_repo_head_hexsha": "2137172dd61df5f83ce3fbe0cf972950b3cb23f7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
/* -*- C++ -*-; c-basic-offset: 4; indent-tabs-mode: nil */
/*
* Implementation for rpc connection class.
*
* Copyright (c) 2015 Cisco Systems, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you
* may not use this file except in compliance with the License. You
* may obtain a copy of th... | {"hexsha": "328c30fbff582a78e42cf69b3cc920748ee46675", "size": 8421, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/rpc_connection.cpp", "max_stars_repo_name": "readams/throng", "max_stars_repo_head_hexsha": "b87742707eb13dc6103cf7dda3b1a91f1bd96034", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import pandas as pd
import numpy as np
from collections import defaultdict, Counter
import json
# ----------------------------------------------------------------------------------------
# Load data
result_final=pd.read_csv("/Users/chiarasemenzin/Desktop/LSCP/SLT/result_final_lisa.csv") #file with segment info and l... | {"hexsha": "29b5c125f57b409249d5e30b6e4076c16224e2af", "size": 3566, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_analyses/code/write_seg_data.py", "max_stars_repo_name": "LAAC-LSCP/zoo-babble-validation", "max_stars_repo_head_hexsha": "efcc26cc994293b8fabf553a60833962b3cdc0f7", "max_stars_repo_licenses"... |
from nose.tools import assert_equal, assert_true, assert_false
from numpy.testing import assert_array_equal
from pbcore import data
from StringIO import StringIO
from pbcore.io.FastqIO import *
# Test QV <-> string conversion routines
class TestQvConversion(object):
def setup(self):
self.ascii = \
... | {"hexsha": "2ce3a480c0b9344bfebd1f1d597b57e0b9ea5619", "size": 7182, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_pbcore_io_FastqIO.py", "max_stars_repo_name": "yoshihikosuzuki/pbcore", "max_stars_repo_head_hexsha": "956c45dea8868b5cf7d9b8e9ce98ac8fe8a60150", "max_stars_repo_licenses": ["BSD-3-Clau... |
import cv2 as cv
import numpy as np
#边缘保留滤波(EPF)
#高斯双边模糊（磨皮）
def bi_demo(image):
dest = cv.bilateralFilter(image,0,100,15)
cv.imshow("bi_demo",dest)
#均值迁移（边缘过度模糊）油画
def shfit_demo(image):
dest = cv.pyrMeanShiftFiltering(image,0,10,50)
cv.imshow("shfit_demo",dest)
src = cv.imread("C:/1/1.jpg")
cv.n... | {"hexsha": "327ac5e85624c783f8164ec5c46517cc1bf84009", "size": 451, "ext": "py", "lang": "Python", "max_stars_repo_path": "8_Filter.py", "max_stars_repo_name": "Sanduoo/OpenCV-Python", "max_stars_repo_head_hexsha": "8356782f65918a5193b0d2d2618d0bcb32e831d2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
[STATEMENT]
lemma inv_end5_loop_Oc_Oc_drop[elim]:
"\<lbrakk>0 < x; inv_end5_loop x (b, Oc # list); b \<noteq> []; hd b = Oc\<rbrakk> \<Longrightarrow>
inv_end5_loop x (tl b, Oc # Oc # list)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>0 < x; inv_end5_loop x (b, Oc # list); b \<noteq> []; hd b = Oc\<... | {"llama_tokens": 1141, "file": "Universal_Turing_Machine_WeakCopyTM", "length": 6} |
import numpy as np
import tensorflow as tf
__author__ = 'Otilia Stretcu'
def print_metrics_dict(metrics):
for name, val in metrics.items():
print('--------------', name, '--------------')
if isinstance(val, tf.Tensor):
val = val.numpy()
if name == 'confusion':
prin... | {"hexsha": "22285fbb0c12c3b2de59f3b37eb85aa074061176", "size": 410, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/printing.py", "max_stars_repo_name": "otiliastr/coarse-to-fine-curriculum", "max_stars_repo_head_hexsha": "00fe0ad58dd2a5871958307f7791d93003b6310b", "max_stars_repo_licenses": ["Apache-2... |
[STATEMENT]
lemma Standard_of_int [simp]: "of_int z \<in> Standard"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. of_int z \<in> Standard
[PROOF STEP]
by (simp add: star_of_int_def) | {"llama_tokens": 78, "file": null, "length": 1} |
import random
import numpy as np
from src.utils.articulation_points import articulationPoints
from src.connectivity import can_lose
from src.districts import district_populations, is_frontier
def fix_pop_equality(state, partition, n_districts, tolerance=.10, max_iters=10000):
assert 0 < tolerance < 1.0
ideal_... | {"hexsha": "2e42567435ea898d3de03e0ebf1a0b1419908f61", "size": 3600, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimize/src/constraints.py", "max_stars_repo_name": "joel-simon/Antimander", "max_stars_repo_head_hexsha": "ec2058a76f172e0941d5e4558776831050a00c2a", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
/-
Copyright (c) 2022 Floris van Doorn. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Floris van Doorn, Patrick Massot
-/
import topology.basic
/-!
# Neighborhoods of a set
In this file we define the filter `𝓝ˢ s` or `nhds_set s` consisting of all neighborhoods of a... | {"author": "Mel-TunaRoll", "repo": "Lean-Mordell-Weil-Mel-Branch", "sha": "4db36f86423976aacd2c2968c4e45787fcd86b97", "save_path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch", "path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch/Lean-Mordell-Weil-Mel-Branch-4db36f86423976aacd2c2968c4e4... |
import tactic.slim_check
import .mk_slim_check_test
example : true :=
begin
have : ∀ i j : ℕ, i < j → j < i,
success_if_fail_with_msg
{ slim_check { random_seed := some 257 } }
"
===================
Found problems!
i := 0
j := 1
guard: 0 < 1 (by construction)
issue: 1 < 0 does not hold
(0 shrinks)
-----------... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/test/slim_check.lean"} |
import numpy
num_datasets = 0 #Number of Datasets
num_parameters = 6 #Number of Parameters
projects = [] #Names of the projects
parameters = { ... | {"hexsha": "c3f08da6da026ef43e6fecd5dd834a4d9be45b63", "size": 3159, "ext": "py", "lang": "Python", "max_stars_repo_path": "ridge_analyze/ridge-firebase-python/Rank.py", "max_stars_repo_name": "vsthakur101/DevCommunity_MAJOR", "max_stars_repo_head_hexsha": "164f2444d7cfa7330513a226fd14429c9e4c0dad", "max_stars_repo_lic... |
import numpy as np
from copy import deepcopy
from express import settings
from express.properties.utils import eigenvalues
from express.properties.non_scalar import NonScalarProperty
class BandGaps(NonScalarProperty):
"""
The minimum energy difference between the highest occupied (valence) band and the lowes... | {"hexsha": "cf4f956fc4392386eb83fe34ee10cbb17b56c481", "size": 6505, "ext": "py", "lang": "Python", "max_stars_repo_path": "express/properties/non_scalar/bandgaps.py", "max_stars_repo_name": "Exabyte-io/exabyte-express", "max_stars_repo_head_hexsha": "579cc1ad3666352848e0ac8eeec84cb410a9a9c7", "max_stars_repo_licenses"... |
```python
j
```
```python
import sympy as sp
import numpy as np
import altair as alt
```
# Solving steady state expression for λ
```python
# Define the efficiencies
gamma_max = sp.Symbol('{{\gamma^{(max)}}}')
nu_max = sp.Symbol(r'{{\nu^{(max)}}}')
phi_R = sp.Symbol('{{\phi_R}}')
Kd = sp.Symbol('{{K_D^{(c_{AA})... | {"hexsha": "4934a1044b8989f4995627f70a7569f0ad2b008a", "size": 34807, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "code/analysis/symbolics.ipynb", "max_stars_repo_name": "gchure/modelling_growth", "max_stars_repo_head_hexsha": "764d7aee4d0d562cd5e1b6e21b534ab465d1d672", "max_stars_repo_licenses":... |
"""Reference path extraction
"""
import math
from typing import Optional, List, Tuple
from dataclasses import dataclass
from itertools import product
import matplotlib as mpl
from matplotlib.axes import Axes
import matplotlib.pyplot as plt
import geopandas as gpd
import numpy as np
from shapely import geometry, ops
f... | {"hexsha": "0cdf3c8dad869ae81881ccac56b288f567a0a2a9", "size": 3522, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/path_extraction.py", "max_stars_repo_name": "kopytjuk/opendd-analysis", "max_stars_repo_head_hexsha": "72c94a39e5fef6dc1af4e0bcf62768229d165296", "max_stars_repo_licenses": ["MIT"], "max_stars... |
/-
Copyright (c) 2021 Eric Weiser. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Eric Wieser
! This file was ported from Lean 3 source module algebra.algebra.subalgebra.pointwise
! leanprover-community/mathlib commit b2c707cd190a58ea0565c86695a19e99ccecc215
! Please ... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/Mathlib/Algebra/Algebra/Subalgebra/Pointwis... |
# -*- coding: utf-8 -*-
'''
test.py
'''
import os
import logging
from chainer import Variable
import numpy as np
import pandas as pd
import sklearn.metrics
def test_network(test, model, output_path='', batch_size=100, device_id=-1):
'''
Test.
Args:
dataset (chainer.dataset): Dataset
mode... | {"hexsha": "9f6f6b6e2d781120026c9613945aacc8a1cda8e9", "size": 1036, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/run/test.py", "max_stars_repo_name": "yizumi1012xxx/cookpad", "max_stars_repo_head_hexsha": "48bafd2e5dc7d99fc79df43d95ae46a7cb08bb14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
from flask import Flask, jsonify
#################################################
# Database Setup
#################################################
eng... | {"hexsha": "3f14c1e51d7afeb8044d1e27560dc5643d7b6fcc", "size": 4549, "ext": "py", "lang": "Python", "max_stars_repo_path": "hawaiiApp.py", "max_stars_repo_name": "rfindlater/sqlalchemy-challenge", "max_stars_repo_head_hexsha": "7fae34b9df31bd1415471fc662b852e7b6b5b508", "max_stars_repo_licenses": ["ADSL"], "max_stars_c... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import Any
__author__ = "Christian Heider Nielsen"
import numpy
__all__ = ["sample"]
def sample(iter_set: iter) -> Any:
"""
@param iter_set:
@type iter_set:
@return:
@rtype:
"""
a = list(iter_set)
if len(a):
idx = n... | {"hexsha": "1b7e2c4c936e9025920ffcb03453da9102b4949c", "size": 373, "ext": "py", "lang": "Python", "max_stars_repo_path": "neodroidagent/utilities/exploration/sampling/set_sampling.py", "max_stars_repo_name": "gitter-badger/agent", "max_stars_repo_head_hexsha": "3f53eaa7ebdee3ab423c7b58785d584fe1a6ae11", "max_stars_rep... |
[STATEMENT]
lemma dgrad_max_0: "d 0 \<le> dgrad_max d"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. d (0::'a) \<le> dgrad_max d
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. d (0::'a) \<le> dgrad_max d
[PROOF STEP]
from finite_Keys
[PROOF STATE]
proof (chain)
picking this:
finite ?A \<Long... | {"llama_tokens": 899, "file": "Signature_Groebner_Signature_Groebner", "length": 12} |
"""
SpectralNeuralOperator{F,L,F1,F2,P}
A type representing a Neural Operator whose forward pass is of the form
y(t) = σ((B*x)(t) - λ(t)*(B*x)(t) + b(t))
where `x`, `λ` and `b` are functions, and `B` is a linear operator.
By default, `y` is projected in the Chebyshev polynomial basis before outputing. Thi... | {"hexsha": "530499e9c90bbace08d299b82c7150c2eaf522fb", "size": 3207, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/neural_operators/spectral_neural_operator.jl", "max_stars_repo_name": "csimal/SpectralLearning.jl", "max_stars_repo_head_hexsha": "4999657700a0d84dfff470a52ddb3e3b37a44aae", "max_stars_repo_lic... |
from showml.deep_learning.layers import Activation
import numpy as np
class Sigmoid(Activation):
"""A layer which applies the Sigmoid operation to an input.
"""
def forward(self, X: np.ndarray) -> np.ndarray:
return 1 / (1 + np.exp(-X))
def backward(self, X: np.ndarray) -> np.ndarray:
... | {"hexsha": "f000ee48db8cbe86fcd0f21c9a1a2ab552c8f5f9", "size": 1012, "ext": "py", "lang": "Python", "max_stars_repo_path": "showml/deep_learning/activations.py", "max_stars_repo_name": "shubhomoy/ShowML", "max_stars_repo_head_hexsha": "9fbc366941ad910f1fbd7d91da823616c34fd400", "max_stars_repo_licenses": ["MIT"], "max_... |
str = """Hello,
world.
"""
print(str)
| {"hexsha": "5ad1a4c74fdad704b3f52409cd58d9dcb40a8368", "size": 56, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/literals-string-4.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "... |
"""
Combine two linguistic terms.
a and b are functions of two sets of the same domain.
Since these combinators are used directly in the Set class to implement logic operations,
the most obvious use of this module is when subclassing Set to make use of specific combinators
for special circumstances.
Most functions... | {"hexsha": "86959711192b60e8792e05af0d6a88c8fbb81040", "size": 4990, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/fuzzylogic/combinators.py", "max_stars_repo_name": "katerss1/fuzzylogic", "max_stars_repo_head_hexsha": "e5bfe90d706ae8f5981a0aaf13542ee751b84abd", "max_stars_repo_licenses": ["MIT"], "max_sta... |
/-
Copyright (c) 2022 Jannis Limperg. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jannis Limperg
-/
import Aesop
example {n m k : Nat} (h : n < m) (h₂ : m < k) : n < k := by
apply Nat.lt_trans <;> aesop
| {"author": "JLimperg", "repo": "aesop", "sha": "c68fb1d5a9172498230d81d95c61f6461bea6722", "save_path": "github-repos/lean/JLimperg-aesop", "path": "github-repos/lean/JLimperg-aesop/aesop-c68fb1d5a9172498230d81d95c61f6461bea6722/tests/run/MVarsInInitialGoal.lean"} |
"""
Multiple Brain classes that extend sb.Brain to enable attacks.
"""
import logging
import time
import warnings
import importlib
import os
from shutil import which
from xml.dom import NotFoundErr
import speechbrain as sb
import torch
from speechbrain.dataio.dataloader import LoopedLoader
from speechb... | {"hexsha": "8ee547216b64b15bf6a2aade682e7eb3ef8d2a94", "size": 42084, "ext": "py", "lang": "Python", "max_stars_repo_path": "robust_speech/adversarial/brain.py", "max_stars_repo_name": "RaphaelOlivier/robust_speech", "max_stars_repo_head_hexsha": "f02e16408c1ba9926749eafc3cbf45c74828f756", "max_stars_repo_licenses": ["... |
import numpy as np
import tensorflow.compat.v1 as tf
from t3f.tensor_train import TensorTrain
from t3f.tensor_train_batch import TensorTrainBatch
from t3f import ops
from t3f import shapes
from t3f import initializers
class _TTTensorTest():
def testFullTensor2d(self):
np.random.seed(1)
for rank in [1, 2]:... | {"hexsha": "6f5a458ac30d1686a6cc21fa7f8a12838f1d6b4a", "size": 44285, "ext": "py", "lang": "Python", "max_stars_repo_path": "t3f/ops_test.py", "max_stars_repo_name": "aiboyko/t3f", "max_stars_repo_head_hexsha": "0361b80f36a06eb5aa5d536650eef9e006289139", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# This file is part of pyunicorn.
# Copyright (C) 2008--2017 Jonathan F. Donges and pyunicorn authors
# URL: <http://www.pik-potsdam.de/members/donges/software>
# License: BSD (3-clause)
"""
Provides classes for analyzing spatially embedded complex networks, handling
multiva... | {"hexsha": "f7ade3f1b9e165f5e51ea75adedb068e559b9a93", "size": 203147, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyunicorn/core/network.py", "max_stars_repo_name": "etzinis/pyunicorn", "max_stars_repo_head_hexsha": "151e1640c3ae71549c96c461b1377642743dbf01", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
import numpy as np
from scipy.linalg import solve_banded
from randomvars import Cont, Disc
import randomvars._utils as utils
# %% Functions
def y_from_xp(x, p, coeff):
dx = np.diff(x)
dx_lead = np.concatenate([dx, [0]])
dx_lag = np.concatenate([[0], dx])
banded_matrix = 0.5 * np.array(
[dx_la... | {"hexsha": "001e5559c008b980e7c232286b94ced138eab336", "size": 6049, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/retype.py", "max_stars_repo_name": "echasnovski/randomvars", "max_stars_repo_head_hexsha": "15417b0e3ecd27f185b70471102c158f60d51c28", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import FloatingArray
@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
# np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
@pytest.mark.filterwarnings("ignore:invalid value enco... | {"hexsha": "6f53b447769007d4278da443249b1df2bca062a4", "size": 6365, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tests/arrays/integer/test_function.py", "max_stars_repo_name": "RakhithJK/pandas", "max_stars_repo_head_hexsha": "0eeda645212c240d6cbdef8e3ba4834c3763553b", "max_stars_repo_licenses": ["PSF... |
# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
import pytest # noqa: F401
import numpy as np # noqa: F401
import awkward as ak # noqa: F401
def test():
record = ak._v2.with_name(ak._v2.Record({"x": 10.0}), "X")
assert ak._v2.parameters(record) == {"__record__": "X"... | {"hexsha": "0bad83e47ba93da79bf839431ae2868e9341f71d", "size": 322, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/v2/test_1400-with_name-record.py", "max_stars_repo_name": "douglasdavis/awkward-1.0", "max_stars_repo_head_hexsha": "f00775803a5568efb0a8e2dae3b1a4f23228fa40", "max_stars_repo_licenses": ["BS... |
#!/usr/bin/env python3
import argparse
from collections import OrderedDict
import numpy as np
def build_vocab(train_paths, output_path):
"""
Builds the vocabulary.
Compatible with Nematus build_dict function, but does not
output frequencies and special symbols.
:param train_paths:
... | {"hexsha": "b2a11b224aa957fefddbe411561bdd4918d9c028", "size": 2098, "ext": "py", "lang": "Python", "max_stars_repo_path": "github/joeynmt/scripts/build_vocab.py", "max_stars_repo_name": "shania3322/joeynmt", "max_stars_repo_head_hexsha": "5afe9d00930f19949b2078141771bf4621f6e9ae", "max_stars_repo_licenses": ["Apache-2... |
import numpy as np
from tabulate import tabulate
class TruncatedDisplay(object):
""" Performs similar functionality as less command in unix OS where stdout is chunked up into a set number of
lines and user needs to provide input to continue displaying lines """
def __init__(self, num_lines=10):
... | {"hexsha": "3cf1468487a7a1548626ba234552ae4f6965c509", "size": 2990, "ext": "py", "lang": "Python", "max_stars_repo_path": "coronacli/display.py", "max_stars_repo_name": "adaros92/coronacli", "max_stars_repo_head_hexsha": "a760933f3d8f009ab7b414c30d9a93db19baaf0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy
import os
def make_pattern(sprites):
"""Convert an array of sprites to PPU pattern data.
The input should have shape (n,8,8) or (n,16,8) and type uint8.
"""
if sprites.dtype != numpy.uint8:
raise TypeError('bad sprite type: {}'.format(sprites.dtype))
n, h, w = sprites.shape
... | {"hexsha": "ecb84c983439cdef6438a2140c113999dc3c6443", "size": 3442, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/nes.py", "max_stars_repo_name": "depp/ctnes", "max_stars_repo_head_hexsha": "bc313db4333c87ce94adc20a44cb02439a9925ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_r... |
from tempfile import TemporaryDirectory
from typing import Any, Dict
import datasets
import flair
import numpy as np
import pytest
import torch
from flair.data import Corpus
from embeddings.data.data_loader import HuggingFaceDataLoader
from embeddings.data.dataset import HuggingFaceDataset
from embeddings.pipeline.ev... | {"hexsha": "3052650a86cb3f62d1fd40ed4ff645be173e59d5", "size": 3392, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_no_dev_training.py", "max_stars_repo_name": "CLARIN-PL/embeddings", "max_stars_repo_head_hexsha": "49fb59b796475ca92bc262ec2bc6def1d89a10e0", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import os
import tensorflow as tf
import keras.backend as K
import pickle
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
from keras.models import Model
from keras.layers import Input, Lambda
from keras.optimizers import RMSprop
from test_getinpu... | {"hexsha": "1fd64432c3726e52aff3205eca89e1f9383fc37d", "size": 1305, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "gustavomiguelsa/VGGVox-Verif-Keras-V2", "max_stars_repo_head_hexsha": "26f3d2e093716088952766a425e7d9ba20850908", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import skimage
from skimage import data
from skimage import exposure
# squeeze image intensities to lower image contrast
test_img = data.camera() / 5 + 100
def test_equalize_ubyte():
img_eq = exposure.equalize(test_img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check... | {"hexsha": "22339de7540b38133ba66c3e3aa5d129978b5b06", "size": 857, "ext": "py", "lang": "Python", "max_stars_repo_path": "skimage/exposure/tests/test_exposure.py", "max_stars_repo_name": "ogrisel/scikits-image", "max_stars_repo_head_hexsha": "cfc1dfa16b1e94dba42f2f5766a41cbbede03f7d", "max_stars_repo_licenses": ["BSD-... |
#################################################################################
# The Institute for the Design of Advanced Energy Systems Integrated Platform
# Framework (IDAES IP) was produced under the DOE Institute for the
# Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021
# by the softwar... | {"hexsha": "1c06352d2a1365dab6a45e559c004e5847785fd1", "size": 12936, "ext": "py", "lang": "Python", "max_stars_repo_path": "idaes/models_extra/power_generation/costing/tests/test_NGCC_costing.py", "max_stars_repo_name": "OOAmusat/idaes-pse", "max_stars_repo_head_hexsha": "ae7d3bb8e372bc32822dcdcb75e9fd96b78da539", "ma... |
\chapter{Code Information and Access}
\label{app:code}
The code for Conlangtionary is available at \url{https://github.com/whereswaldon/conlangtionary}. Conlangtionary contains an estimated 8,709 lines of original code on top of the Laravel framework. | {"hexsha": "778c5ca5e584c973cfd37b995d2b245fd24f5f21", "size": 252, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "0a_appendix_platforms.tex", "max_stars_repo_name": "whereswaldon/conlangtionary-thesis", "max_stars_repo_head_hexsha": "e011b07e654746581380cf5f76549706a368009c", "max_stars_repo_licenses": ["MIT"], ... |
import unittest
import numpy as np
import scipy.stats as ss
import calibration.sample as sample
class TestSamples(unittest.TestCase):
def test_consistent_targets(self):
N = 10000 # number of target samples
batch_size = 100 # batch size
# for probability vectors of different sizes
... | {"hexsha": "45a65fe888783bad9e8cb66639e59aff9ed38fb7", "size": 3122, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_samples.py", "max_stars_repo_name": "uu-sml/calibration", "max_stars_repo_head_hexsha": "7bd1a2407f96f87e37d81eadaea7efeb14bb8a83", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from legume.backend import backend as bd
import legume.utils as utils
from .shapes import Shape, Circle, Poly, Square
class Layer(object):
"""
Class for a single layer in the potentially multi-layer PhC.
"""
def __init__(self, lattice, z_min: float=0, z_max: float=0):
"""Init... | {"hexsha": "4c2d602f449be2e346b9e04968074ceb6a845de7", "size": 8465, "ext": "py", "lang": "Python", "max_stars_repo_path": "legume/phc/layer.py", "max_stars_repo_name": "ENPH-2113-Silicon-Photonics/legume", "max_stars_repo_head_hexsha": "45c64b78f09b7d8779f7a4e12f204f5778b08b0b", "max_stars_repo_licenses": ["MIT"], "ma... |
# Copyright (c) Puyuan Liu
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from decoding_algorithms.ctc_decoder_base import CTCDecoderBase
class CTCScopeSearchLengthControlDecoder(CTCDecoderBase):
"""
... | {"hexsha": "bb9dc4cedcd1d478d7430f82aa4a2baaa31e87e0", "size": 21602, "ext": "py", "lang": "Python", "max_stars_repo_path": "decoding_algorithms/ctc_scope_search_length_control.py", "max_stars_repo_name": "MANGA-UOFA/NAUS", "max_stars_repo_head_hexsha": "8c0c0815a280d0661adf588302848c7f1ecc56da", "max_stars_repo_licens... |
#!/usr/bin/env python
# This file is part of the pyMOR project (https://www.pymor.org).
# Copyright pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (https://opensource.org/licenses/BSD-2-Clause)
"""Burgers demo with different applications of Dynamic Mode Decomposition."""
impor... | {"hexsha": "3bf972c7af3036ef81668c7e86bc57d4e12289c9", "size": 3346, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pymordemos/burgers_dmd.py", "max_stars_repo_name": "kinnala/pymor", "max_stars_repo_head_hexsha": "9d2a8ee5f7a71482e62952257332d269d50678e9", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
from dash import dcc, html, Input, Output, callback
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import dash_bootstrap_components as dbc
import mplcursors as mplcursors
import numpy as np
from sklearn import linear_model
layout = html.Div(
[
html.H2('B... | {"hexsha": "ff49af6f95376d15b0237cac23fd2ae6f5666b7f", "size": 1777, "ext": "py", "lang": "Python", "max_stars_repo_path": "pages/btc_fair_value.py", "max_stars_repo_name": "WilsonBreton5/cryptoCharts", "max_stars_repo_head_hexsha": "1aeaf0578aff18d7d60d71d16a73bffb6bed3939", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Spectral Estimation of Random Signals
*This jupyter/Python notebook is part of a [collection of notebooks](../index.ipynb) in the masters module [Digital Signal Processing](http://www.int.uni-rostock.de/Digitale-Signalverarbeitung.48.0.html), Comunications Engineering, Universität Rostock. Please direct questions an... | {"hexsha": "6617a38fc2f6e3901f06c97299c55dd6e8f6bd47", "size": 102794, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "spectral_estimation_random_signals/parametric_methods.ipynb", "max_stars_repo_name": "ZeroCommits/digital-signal-processing-lecture", "max_stars_repo_head_hexsha": "e1e65432a5617a30... |
import logging # use instead of print for more control
import inspect # get signature of functions (e.g. to pass kwargs)
from pathlib import Path # filesystem related stuff
import numpy as np # numerical computations
import re
import matplotlib.pyplot as plt
from astropy.io impor... | {"hexsha": "4271494aead982733d3c8e844f2db9568849e90d", "size": 16999, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pnlf/detection.py", "max_stars_repo_name": "fschmnn/pymuse", "max_stars_repo_head_hexsha": "91e97d03a3eb1ccc02131f4e731e6bb5df66772c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
import numpy as np
import tempfile
import pytest
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
try:
import tensorflow as tf
IMPORT_TF = True
except ImportError:
IMPORT_TF = False
else:
from umap.parametric_umap import ParametricUMAP, load_ParametricUMAP
... | {"hexsha": "6a6518f02493448993dbadf0d62c6c1e97fd35af", "size": 4229, "ext": "py", "lang": "Python", "max_stars_repo_path": "umap/tests/test_parametric_umap.py", "max_stars_repo_name": "worldbeater/umap", "max_stars_repo_head_hexsha": "eb8c4b2bbb08c1fc9b6a983af8d50a8d03468735", "max_stars_repo_licenses": ["BSD-3-Clause"... |
from behave import *
from hamcrest import assert_that, equal_to
from vec3 import Vec3, vec3
from vec4 import Vec4, point, vector
from base import equal, normalize, transform, ray, lighting
import numpy as np
from shape import material, sphere, test_shape, normal_at, set_transform, intersect, glass_sphere, point_light
f... | {"hexsha": "eee9f9f542f197693a4587a809d1d13007ab6153", "size": 8391, "ext": "py", "lang": "Python", "max_stars_repo_path": "features/steps/zz_08_materials_steps.py", "max_stars_repo_name": "tewarfel/RayTracerChallenge_1", "max_stars_repo_head_hexsha": "736cc5d159c267c9bcc14d42abb03eedc2f7e5f1", "max_stars_repo_licenses... |
Stephanie Jordan graduated in December 2007 and received a BA in Psychology from UC Davis. She is from Sacramento and went to Loretto High School. She is a former ASUCD External Affairs Commission External Affairs Commissioner. She also served as an ASUCD representative to the Academic Senates Undergraduate Council a... | {"hexsha": "7aef063f6afa625c2e9b01aa23a73c8465594eb2", "size": 833, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/StephanieJordan.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 24 18:08:05 2019
@author: lealp
"""
import pandas as pd
pd.set_option('display.width', 50000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
from shapely.geometry import Point
import geopandas as gpd
import numpy as np
class Base_cla... | {"hexsha": "fab1c447fe0a5f25544c08fba8abfb02807e721c", "size": 4390, "ext": "py", "lang": "Python", "max_stars_repo_path": "teleconnection/utils/netcdf_gdf_setter.py", "max_stars_repo_name": "PhilipeRLeal/teleconnection", "max_stars_repo_head_hexsha": "01c1e5f8e8c63e0e2223711e8a78654549145b0b", "max_stars_repo_licenses... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function,
unicode_literals, annotations)
from collections import defaultdict
from multiprocessing import Pool
import numpy as np
import psutil
num_cpus = psutil.cpu_count(logical=False)
def... | {"hexsha": "6fe6aa5e2add1e4a96bff34247542c3e59728881", "size": 1470, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/fasterparallel/benchmark2multi.py", "max_stars_repo_name": "imjoseangel/100DaysOfCode", "max_stars_repo_head_hexsha": "bff90569033e2b02a56e893bd45727125962aeb3", "max_stars_repo_licenses": ... |
program isubregion
use mod_za ! HYCOM I/O interface
use mod_zb ! HYCOM I/O interface for subregion
implicit none
c
c create a finer-grid subregion from a full region archive file.
c
c subregion grid must be an integer multiple of the original grid,
c with co-located p-grid points.
... | {"hexsha": "4022d2cf75f7800a37726c8333977f7021a73a74", "size": 37178, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "subregion/src/isubregion.f", "max_stars_repo_name": "TillRasmussen/HYCOM-tools", "max_stars_repo_head_hexsha": "7d26b60ce65ac9d785e0e36add36aca05c0f496d", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
theorem summatory_totient_asymptotics'':
"sum_upto (\<lambda>n. real (totient n)) \<sim>[at_top] (\<lambda>x. 3 / pi\<^sup>2 * x\<^sup>2)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sum_upto (\<lambda>n. real (totient n)) \<sim>[at_top] (\<lambda>x. 3 / pi\<^sup>2 * x\<^sup>2)
[PROOF STEP]
proof -
... | {"llama_tokens": 1196, "file": "Dirichlet_Series_Arithmetic_Summatory_Asymptotics", "length": 10} |
#!/usr/bin/env python3
# makes a plot for the flow loss case
# note: you need to comment out the header on the csv file
import matplotlib.pyplot as pl
from matplotlib import rc
import numpy as np
# directory with output data:
outdir = ('/home/gav/projects/moltres/problems/'
'LOFA/')
data = np.loadtxt(outdir... | {"hexsha": "662398fafbcd096657a8e0a87bcee81cd9e099df", "size": 1408, "ext": "py", "lang": "Python", "max_stars_repo_path": "flowLossPlot.py", "max_stars_repo_name": "arfc/uiuc-arfc-2017-01", "max_stars_repo_head_hexsha": "99e79735bc9ce977df4c4135e7b59bbb2a6b759c", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_co... |
// Copyright (C) 2016-2019 Internet Systems Consortium, Inc. ("ISC")
//
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <config.h>
#include <http/request_p... | {"hexsha": "3ebf824840967ae55ab4e1523315558339c1cd9e", "size": 17867, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/lib/http/request_parser.cc", "max_stars_repo_name": "kphf1995cm/kea", "max_stars_repo_head_hexsha": "2f6940ef5ed697f3f683035ed7a16046253add4d", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
def newton(f,fprime,x0,epsilon=1.0e-6, LOUD=False):
"""Find the root of the function f via Newton-Raphson method
Args:
f: function to find root of
fprime: derivative of f
x0: initial guess
epsilon: tolerance
Returns:
estimate of root
"... | {"hexsha": "a0e114265660b761416632234d30c69a7f1be700", "size": 3742, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch13.py", "max_stars_repo_name": "DrRyanMc/CompNucEng", "max_stars_repo_head_hexsha": "55d36abea64c9298092dee0b539bfaccae3f49a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_sta... |
# Copyright (c) 2017 Arup Pty. Ltd.
# Distributed under the terms of the MIT License.
"""Methods for Independent storms.
"""
import numpy as np
from .utils import least_squares
def peaks_over_threshold(max_storm_gusts, no_years, min_threshold=None, max_threshold=None):
"""Build a function that estimates the gus... | {"hexsha": "0303e667ab6835399f1ad333e1ef2d9458c5491b", "size": 2113, "ext": "py", "lang": "Python", "max_stars_repo_path": "extremevaluemethod/independentstorms.py", "max_stars_repo_name": "aldiamond/extreme-value-analysis", "max_stars_repo_head_hexsha": "b46487a2e51a687bc07b758ab8edf60b3e1772ef", "max_stars_repo_licen... |
import logging
from typing import List
import numpy as np
from rtree import index
class MagicDict(dict):
"""Dictionary, but content is accessible like property."""
def __deepcopy__(self, memo):
import copy
cls = self.__class__
result = cls.__new__(cls)
memo[id(self)] = resul... | {"hexsha": "e5d32cfea471f2bdf34ca6d13b7ab50b89994d60", "size": 10910, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/common.py", "max_stars_repo_name": "jkwang1992/sbp-env", "max_stars_repo_head_hexsha": "929a88c30e0056cce55ef22f74bfa218c1e78cc8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, ... |
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