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# Copyright (c) Hikvision Research Institute. All rights reserved.
import os
import numpy as np
from mmdet.datasets.pipelines import LoadAnnotations as MMDetLoadAnnotations
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadAnnotations(MMDetLoadAnnotations):
"""Load multiple types of annotat... | {"hexsha": "b5c7eed87d252e9caf34b725b78c89bdddb61801", "size": 2610, "ext": "py", "lang": "Python", "max_stars_repo_path": "opera/datasets/pipelines/loading.py", "max_stars_repo_name": "hikvisionresearch/opera", "max_stars_repo_head_hexsha": "0fb345a7ad0046c6fd674959c0ae19a65adeeacf", "max_stars_repo_licenses": ["Apach... |
using PackageCompiler
tutorials_file = joinpath("..", "12_n_site.jl")
PackageCompiler.create_sysimage(
["ITensors", "ITensorUnicodePlots", "ITensorGLMakie", "Zygote"];
sysimage_path="ITensor.so",
precompile_execution_file=tutorials_file,
)
# Then make an alias like:
#
# alias julia-itensor='julia -J/home/mfishm... | {"hexsha": "066d5b05e61b4bd4e641607cead5b297e6b042eb", "size": 418, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/package_compile/package_compile.jl", "max_stars_repo_name": "mtfishman/ITensorTutorials.jl", "max_stars_repo_head_hexsha": "dcbc1988299e6a7f3b612faeb31563da38ece3be", "max_stars_repo_licen... |
module RetryRequest
import ..HTTP
using ..Sockets
using ..IOExtras
using ..MessageRequest
using ..Messages
import ..@debug, ..DEBUG_LEVEL, ..sprintcompact
export retrylayer
"""
retrylayer(req) -> HTTP.Response
Retry the request if it throws a recoverable exception.
`Base.retry` and `Base.ExponentialBackOff` im... | {"hexsha": "1c495e6350ec20c3ee8b80418676ef9ec81ff6b1", "size": 2596, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RetryRequest.jl", "max_stars_repo_name": "NHDaly/HTTP.jl", "max_stars_repo_head_hexsha": "4b3c6336de1f7f4f55dc172e6bf247dd530611a7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# load data
file='data_sheet.xlsx'
data= pd.ExcelFile(file)
print(data.sheet_names)
# create data frame from data
df_col = data.parse('RD0557_ARC6_404_R3C4@lam940nm')
df_diffuser = data.parse('Diffuser_(RD0479_Jul20_OB6@940')
df_fanout = data.par... | {"hexsha": "a4ed9718d0672ff0d83b10c83457ba40ec00853d", "size": 1364, "ext": "py", "lang": "Python", "max_stars_repo_path": "analyis_plot.py", "max_stars_repo_name": "aki2020/DataAnalysis", "max_stars_repo_head_hexsha": "079c3bd8c2efadff26faf126d09d7b90d3d96a5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from numpy import exp, sqrt, zeros, maximum, log, abs
import matplotlib.pyplot as plt
from scipy.stats import norm
class BinomalTree(object):
"""
Class that computes the Cox, Ross & Rubinstein (CRR) binomial tree model.
"""
implemented_types = ['european', 'binary', 'american']
def __init__(self... | {"hexsha": "093368487ac8f10bd3da36dac89e6665ff45055c", "size": 10374, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantfin/options/pricing.py", "max_stars_repo_name": "gusamarante/QuantFin", "max_stars_repo_head_hexsha": "ba8ae75095692b3e6a922220ef8cefc1bea7c35e", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python
# coding: utf-8
# # Exploring factors that influence educational outcomes with hypertools
# ### Load in libraries needed for the project
# In[1]:
import pandas as pd
import numpy as np
import hypertools as hyp
import seaborn as sns
sns.set(style="darkgrid")
get_ipython().run_line_magic('matp... | {"hexsha": "bb7567afba34abb57e508ac7ae4fb53ab181d82d", "size": 3253, "ext": "py", "lang": "Python", "max_stars_repo_path": "myexample/education.py", "max_stars_repo_name": "johnson7788/hypertools", "max_stars_repo_head_hexsha": "17e737ceabd97f63d30a294615e12395ed096910", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
SUBROUTINE SPHBES(N,X,FJ)
IMPLICIT REAL*8(A-H,O-Z)
DIMENSION FJ(*)
DATA XLIM/1.D0/,HF/.5D0/,ZERO/0.D0/,ONE/1.D0/,TNHF/1.05D1/, &
FFT/1.5D1/,T25/1.D18/,TN25/1.D-18/,TN50/1.D-36/,two/2.D0/
IF(N.EQ.0) THEN
IF(X.EQ.0.D0) THEN
FJ(1)=1.D0
RETURN
END IF
FJ(1)=D... | {"hexsha": "3459d619e10babf8f66a28621456e9705abd99b8", "size": 2380, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/dftso/sphbes.f90", "max_stars_repo_name": "chanul13/EDMFTF", "max_stars_repo_head_hexsha": "967d85d898924991b31861b4e1f45129e3eff180", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import numpy as np
import pandas as pd
import pygmo
import xarray as xr
from .constants import DESIGN_ID
def xr_value_dims(name, value):
"""
Returns the value and dimensions/coordinates in the way we like 'em.
>>> import xarray as xr
>>> name = "foo"
>>> value = np.array([1, 2, 3])
>>> xr_va... | {"hexsha": "c0685601799ca0be5b09fe9f42a1cbfd3ab6107b", "size": 7381, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/scop/processing.py", "max_stars_repo_name": "ovidner/openmdao-scop", "max_stars_repo_head_hexsha": "040ed417f69a216ee3e1e69fb6b87feaf7c6cd17", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using FIGlet
using Test
@testset "FIGlet.jl" begin
iob = IOBuffer(b"flf2a", read=true);
@test FIGlet.readmagic(iob) == UInt8['f', 'l', 'f', '2', 'a']
@test FIGlet.FIGletHeader('$', 6, 5, 16, 15, 11) == FIGlet.FIGletHeader('$', 6, 5, 16, 15, 11, 0, 2, 0)
@test FIGlet.availablefonts() |> length == 680
... | {"hexsha": "ea035042eb5e09fe2c8a3dc4473c1b6638671934", "size": 1049, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "wookay/FIGlet.jl", "max_stars_repo_head_hexsha": "040fdc50742f7cb8906aa22815fab60b870abcfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
\chapter{Hyperbolic functions}
\section{Definitions}
\section{Differentiation and integration}
\section{Inverse hyperbolic functions} | {"hexsha": "77252677aade36aac96eb40e9ad99b789ca653c6", "size": 133, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "corpus/a-level-notes/furthermath/content/pure_core/hyperbolic.tex", "max_stars_repo_name": "aDotInTheVoid/ltxmk", "max_stars_repo_head_hexsha": "ee461679e51e92a0e4b121f28ae5fe17d5e5319e", "max_stars_... |
# LICENSE: Simplified BSD https://github.com/mmp2/megaman/blob/master/LICENSE
import numpy as np
from sklearn import neighbors
from scipy import sparse
from .cyflann.index import Index as CyIndex
from .utils import RegisterSubclasses
try:
import pyflann as pyf
PYFLANN_LOADED = True
except ImportError:
PY... | {"hexsha": "b2bc474b82732151e51dc78926237c253256e283", "size": 5737, "ext": "py", "lang": "Python", "max_stars_repo_path": "megaman/geometry/adjacency.py", "max_stars_repo_name": "jakevdp/Mmani", "max_stars_repo_head_hexsha": "681b6cdbd358b207e8b6c4a482262c84bea15bd7", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
import copy
import json
import os
import random
import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.utils.data import Subset
from torchvision import transforms
from matplotlib import pyplot as plt
from .datasets import ConceptDataset
from data import CUB200, MNIST
def get_tran... | {"hexsha": "beb1df0e76c02f5125443806460703fa8fbcc4cc", "size": 9729, "ext": "py", "lang": "Python", "max_stars_repo_path": "lens/utils/data.py", "max_stars_repo_name": "pietrobarbiero/logic_explained_networks", "max_stars_repo_head_hexsha": "238f2a220ae8fc4f31ab0cf12649603aba0285d5", "max_stars_repo_licenses": ["Apache... |
import numpy as np
print("######")
print("AND 1")
def AND_origianl(x1, x2):
w1, w2, theta = 0.5, 0.5, 0.7
tmp = x1*w1 + x2*w2
if tmp <= theta:
return 0
else:
return 1
print(AND_origianl(0,0))
print(AND_origianl(1,0))
print(AND_origianl(0,1))
print(AND_origianl(1,1))
print("######")
p... | {"hexsha": "fe2857b3e2d9e9bbbd95ae583f9e5bef691f5521", "size": 1352, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/deep-learning-from-scratch/chapter2.py", "max_stars_repo_name": "tomothumb/sandbox", "max_stars_repo_head_hexsha": "0bca792ea1e97ee045accdc7cd6b4fa0c424785b", "max_stars_repo_licenses": ["M... |
'''
ANKIT KHANDELWAL
15863
Exercise 8
'''
from math import exp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
blur = pd.read_csv("blur.txt", sep=' ', header=None)
def G(x, y):
return exp(-(x ** 2 + y ** 2) / (2 * 25 ** 2))
gauss = np.ones((1024, 1024))
for i in range(1024):
for j ... | {"hexsha": "d2c136426aaa87e04e7421345bd139faebe01a70", "size": 1218, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ankit Khandelwal_HW4/Exercise 8/exercise 8.py", "max_stars_repo_name": "ankit27kh/Computational-Physics-PH354", "max_stars_repo_head_hexsha": "d37c93e430a0f282251a814456890acb4a2961fe", "max_stars... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 23 17:09:02 2020
@author: minjie
"""
import globalvar as gl
import os.path as osp
from config import cfg
from tqdm import tqdm
import torchvision
import torch
import numpy as np
from collections import Counter
from data.transforms.build import g... | {"hexsha": "becc364b125e8bcf6b65c89f326a62db28bf8885", "size": 7617, "ext": "py", "lang": "Python", "max_stars_repo_path": "proc_hawaii_flower_feat.py", "max_stars_repo_name": "zyxwvu321/Classifer_SSL_Longtail", "max_stars_repo_head_hexsha": "e6c09414c49e695b0f4221a3c6245ae3929a1788", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
class Annuity:
def __init__(self, annuity, rate, year, period=12):
self.annuity = annuity
self.rate = rate / 100
self.year = year
self.period = period
def get_fv(self):
pass
def get_pv_ordinary_annuity(self):
item_1_numerator = 1 - (1 / ... | {"hexsha": "83143cb8dcfb375e7ca3b6129f492e9ad993211c", "size": 780, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Annuity.py", "max_stars_repo_name": "lancejchen/FM-Python-Caculator", "max_stars_repo_head_hexsha": "f64b9c717fe458c2854c84cca4157a53ef871632", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/***************************************************************************
*
* Copyright (c) 2013 Baidu.com, Inc. All Rights Reserved
*
**************************************************************************/
/**
* @file ThriftTracker.cpp
*
* @author liuming03
* @date 2013-9-12
* @brief
*/
#incl... | {"hexsha": "b04408963ed3aa3df194e18aee40cf4ea7972885", "size": 7837, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gko-src/src/ThriftTracker.cpp", "max_stars_repo_name": "JohnnyFang0515/gingko", "max_stars_repo_head_hexsha": "33f289b36d6d8176fa7be67275dda93d40efbfd7", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
"""
AbstractEstimationMethod
Abstract type for defining statistical estimation methods.
"""
abstract type AbstractEstimationMethod end
"""
GradientDescentEstimation <: AbstractEstimationMethod
Method for estimation using gradient descent.
"""
struct GradientDescentEstimation <: AbstractEstimationMethod end
... | {"hexsha": "e20166e2a7e2b509d7da466ff9b051d619d17db1", "size": 39263, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/statistics.jl", "max_stars_repo_name": "r-dornig/Manifolds.jl", "max_stars_repo_head_hexsha": "c40e31ddc5507caff2e9346ac889d8f741740100", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from matplotlib import pyplot as plt
from matplotlib import mlab as mlab
import matplotlib.animation as animation
from rtlsdr import RtlSdr
#import numpy as np
import time
sdr = RtlSdr()
# configure device
sdr.sample_rate = 2.4e6 # min is 1e6 // max is 3.2e6 // std is 2.4e6 (Hz)
sdr.center_freq = 94.7e6 ... | {"hexsha": "a00310051764afe9a44b303b422bfc6c99953ffa", "size": 1576, "ext": "py", "lang": "Python", "max_stars_repo_path": "spektrum_live_speedtest.py", "max_stars_repo_name": "MxFxM/DIY_SDR", "max_stars_repo_head_hexsha": "6c60da87ca62049073afba0e0e85bef6bc606b6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
Require Import SpecDeps.
Require Import RData.
Require Import EventReplay.
Require Import MoverTypes.
Require Import Constants.
Require Import CommonLib.
Require Import AbsAccessor.Spec.
Local Open Scope Z_scope.
Section SpecLow.
Definition find_lock_rec_spec0 (g_rd: Pointer) (rec_list: Pointer) (rec_idx: Z64) (ad... | {"author": "columbia", "repo": "osdi-paper196-ae", "sha": "6df8f9e5b7b1e508a22327fd4a8c0c9be6c56496", "save_path": "github-repos/coq/columbia-osdi-paper196-ae", "path": "github-repos/coq/columbia-osdi-paper196-ae/osdi-paper196-ae-6df8f9e5b7b1e508a22327fd4a8c0c9be6c56496/proof/PSCIAux/LowSpecs/find_lock_rec.v"} |
import numpy as np
import bokeh.plotting as bkp
data = np.load('prices2018.npy')
data = data[np.argsort(data[:, 2]), :]
p = 3
c = ((np.log(data[:,2]) - np.log(data[:,2]).min()) / (np.log(data[:,2]).max() - np.log(data[:,2]).min()))**p
#c = np.linspace(0, 1, data.shape[0])
colors = ['#%02x%02x%02x' % ... | {"hexsha": "904d8df34bb11bfa660534b01bb4000341f291fe", "size": 531, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/data/plot_housing_prices.py", "max_stars_repo_name": "tinnguyen96/bayesian-coresets", "max_stars_repo_head_hexsha": "1d9930f58dbf6464f2a1ecb18267c04d865ea258", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma tm_weak_copy_correct13:
"\<lbrace>\<lambda>tap. tap = ([], [Bk,Bk]@r) \<rbrace> tm_weak_copy \<lbrace>\<lambda>tap. tap = ([Bk,Bk], r) \<rbrace>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrace>\<lambda>tap. tap = ([], [Bk, Bk] @ r)\<rbrace> tm_weak_copy \<lbrace>\<lambda>tap. tap = ([Bk,... | {"llama_tokens": 849, "file": "Universal_Turing_Machine_WeakCopyTM", "length": 7} |
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 02 17:26:23 2017
@author: lansford
"""
"""Vibrational modes."""
from ase.data import covalent_radii as CR
from ase.io import read
import numpy as np
def get_geometric_data(CONTCAR, cutoff=1.25, crystal_type='fcc'):
""" Obtain important geometric data for all atoms
... | {"hexsha": "9399002f766d8f6d83f2816ca02bc9a9fcda7f54", "size": 7516, "ext": "py", "lang": "Python", "max_stars_repo_path": "pdos_overlap/coordination.py", "max_stars_repo_name": "JLans/pdos_overlap", "max_stars_repo_head_hexsha": "575b3f7aeb8d329dc2b5d07755aa167bc71b2c77", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import pytest
import numpy as np
import loupe
def test_zernike_rho_theta():
with pytest.raises(ValueError):
loupe.zernike(mask=1, index=1, normalize=True, rho=1, theta=None)
def test_zernike_positive():
with pytest.raises(ValueError):
loupe.zernike(mask=1, index=-1)
def test_zernike_basis(... | {"hexsha": "03bd0ae2c6fc76795498bd05e2a06c3e0b8757c9", "size": 1186, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_zernike.py", "max_stars_repo_name": "andykee/loupe", "max_stars_repo_head_hexsha": "8b10781598973aac7c129e190209acad7e5a9559", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
/-
Copyright (c) 2019 Scott Morrison. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Scott Morrison
-/
import algebra.category.CommRing.basic
import topology.category.Top.basic
import topology.algebra.ring
/-!
# Category of topological commutative rings
We introduce ... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/topology/category/TopCommRing.lean"} |
#!/usr/bin/env python3.6
# -*- Coding: UTF-8 -*-
"""
Block Matching Algorithm.
------------------------
According to [cuevs2013]_ in a block matching (BM) approach:
'...image frames in a video sequence are divided into blocks. For each
block in the current frame, the best matching block is identified inside a
... | {"hexsha": "08c2e237f9be30f51f065b2a0a21b78e092f47c1", "size": 12088, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/blockmatching/blockmatching.py", "max_stars_repo_name": "duducosmos/blockmatching", "max_stars_repo_head_hexsha": "c37949cce79b58ae658d5814ba3cff906dc4ab4e", "max_stars_repo_licenses": ["Apac... |
#!/usr/bin/env python
'''
Calculates the total number of character occurances at each position within the set of sequences passed.
'''
from __future__ import division
import argparse
import numpy as np
import sys
import pandas as pd
import mpathic.qc as qc
import mpathic.io as io
from mpathic import SortSeqError
def m... | {"hexsha": "45bf3a73b6ba7244802a660eb52c0305608f7fef", "size": 4082, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/profile_ct.py", "max_stars_repo_name": "irelandb/mpathic_for_cluster", "max_stars_repo_head_hexsha": "bacdbf4d9227a8035cd4de2b4d19aeefe82093c6", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
#coding=utf8
from __future__ import print_function, division
import os,time,datetime
import numpy as np
import datetime
from math import ceil
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from utils.utils import LossRecord
import pdb
def dt():
return datet... | {"hexsha": "811ffe9cd2a1ee7193e6865492ed8fed2904695e", "size": 3027, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/eval_model.py", "max_stars_repo_name": "leowangzi/DCL", "max_stars_repo_head_hexsha": "85f27ed29f5cda461b3dce0f2bbb3f11844ed7bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
/-
Copyright (c) 2019 Kenny Lau. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kenny Lau
-/
import algebra.module.basic
import algebra.gcd_monoid.basic
import algebra.group_ring_action
import group_theory.group_action.defs
/-!
# Instances on punit
This file collect... | {"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/punit_instances.lean"} |
from glob import glob
import sys #can be used to perform sys.exit()
import cv2
import numpy as np
import os
import yaml
import logging
import tensorflow as tf
import pandas as pd
from facenet.src import facenet
from facenet.src.align import detect_face
from logging.handlers import TimedRotatingFileHandler
i... | {"hexsha": "fcdb923d784ee178e4c187a09edac8aa54e1b4e6", "size": 5060, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_localize_feature_extract.py", "max_stars_repo_name": "Nikhil-Jain-2002/Final_PSI_Project", "max_stars_repo_head_hexsha": "f7ca109201e9bc6bc537382df9134ea63a2bdabd", "max_stars_repo_licenses":... |
[STATEMENT]
lemma (in start_context) semi: "semilat (A, r, f)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. semilat (A, r, f)
[PROOF STEP]
apply (insert semilat_JVM[OF wf])
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>mxs mxl. semilat (JVM_SemiType.sl P mxs mxl)) \<Longrightarrow> semilat (A, r, f)
[PR... | {"llama_tokens": 346, "file": "Jinja_BV_TF_JVM", "length": 4} |
MODULE coilsnamin
USE modular_coils
USE saddle_coils
USE saddle_surface
USE vf_coils
USE tf_coils
USE bcoils_mod
USE bnorm_mod
USE control_mod
USE Vcoilpts
IMPLICIT NONE
NAMELIST /coilsin/ nmod_coils_per_period, nf_phi, nf_rho, epsfcn,
1 lv... | {"hexsha": "2f984775bf0b710caead9342da5f9821e16f0f61", "size": 9329, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/LIBSTELL/Sources/Modules/coilsnamin.f", "max_stars_repo_name": "jonathanschilling/VMEC_8_49", "max_stars_repo_head_hexsha": "9f1954d83b2db13f4f4b58676badda4425caeeee", "max_stars_repo_licenses... |
function variance = bradford_variance ( a, b, c )
%*****************************************************************************80
%
%% BRADFORD_VARIANCE returns the variance of the Bradford PDF.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 03 September 2004
%
% A... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/prob/bradford_variance.m"} |
#TODO merge into StatsBase.jl & potentially make types there abstract
using Random
using StatsBase: make_alias_table!
struct OneToInf <: AbstractVector{Int} end
Base.size(::OneToInf) = (typemax(Int),)
Base.getindex(::OneToInf, x::Integer) = x
Base.iterate(::OneToInf, state=1) = (state, state+1)
Base.eltype(::Type{One... | {"hexsha": "a8f99b8e64228e7c4c8ca39c450808ded6787bae", "size": 2948, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/alias_table.jl", "max_stars_repo_name": "LilithHafner/FBD", "max_stars_repo_head_hexsha": "422c7743ad94348fe10d5495915ed33da06fc433", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 18 12:58:11 2020
@author: Ashish
"""
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20,1000)
y = np.sin(x)+.2*x
plt.plot(x,y)
plt.xlabel("input")
plt.ylabel("output")
plt.title("my plot")
plt.show()
# Scatterplt
X = np.random... | {"hexsha": "18704c6d52c2808ec92fa37d6ab0adec709d34c5", "size": 703, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/fundamentals/matplotlib_basic_plots.py", "max_stars_repo_name": "duttashi/learnpy", "max_stars_repo_head_hexsha": "c08b76b173b06d66187e51a6939d55d5dd12cb5a", "max_stars_repo_licenses": ["MI... |
Require Export List. Export ListNotations.
Require Import ZArith.
Local Open Scope Z_scope.
Require Import VST.floyd.sublist.
Fixpoint repeat_op_nat{T: Type}(n: nat)(start: T)(op: T -> T): T := match n with
| O => start
| S m => op (repeat_op_nat m start op)
end.
Definition repeat_op{T: Type}(n: Z)(start: T)(op: T ->... | {"author": "princeton-vl", "repo": "CoqGym", "sha": "0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e", "save_path": "github-repos/coq/princeton-vl-CoqGym", "path": "github-repos/coq/princeton-vl-CoqGym/CoqGym-0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e/coq_projects/VST/aes/list_utils.v"} |
#!/usr/bin/env python
# coding: utf-8
# In[61]:
from datetime import datetime
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold, cross_val_score
from sklearn.metrics import mean_squared_error , make_scorer, mean_absolute_error
from sklearn.linear_model import ElasticNetCV, Lass... | {"hexsha": "88a88997bb409c0487778d1be55147685892b6f7", "size": 5220, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ml_module/model_regression.py", "max_stars_repo_name": "gprasanthkumar/ML_Pipeline_Example", "max_stars_repo_head_hexsha": "be866cfe9c1981452d490c8f2fc4ddb7a4f985f4", "max_stars_repo_licenses"... |
import cv2
import numpy as np
import time
# A required callback method that goes into the trackbar function.
def nothing(x):
pass
# Initializing the webcam feed.
cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
# Create a window named trackbars.
cv2.namedWindow("Trackbars")
# Now create 6 trackbars ... | {"hexsha": "7932b9304272cf76074f30b3518e61c38c4a0315", "size": 2863, "ext": "py", "lang": "Python", "max_stars_repo_path": "pensetup.py", "max_stars_repo_name": "dipghoshraj/virtual-pen", "max_stars_repo_head_hexsha": "ffc63af834ec3b495d98ba0de85b125580b2f025", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, ... |
//-----------------------------------------------------------------------------
// Copyright (c) 2017-2018 Benjamin Buch
//
// https://github.com/bebuch/disposer
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt)
/... | {"hexsha": "bb4622bbf8964bd55e08b4316c5151c9625d6c9c", "size": 3217, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/disposer/core/component_ref.hpp", "max_stars_repo_name": "bebuch/disposer", "max_stars_repo_head_hexsha": "8d065cb5cdcbeaecdbe457f5e4e60ff1ecc84105", "max_stars_repo_licenses": ["BSL-1.0"], ... |
'''
Implements a linear soft-constrait MPC controller with stability guarantees.
Supports ellipsoidal termina sets only. Uses Casadi with Opti stack
'''
from sys import path
path.append(r"./casadi-py27-v3.5.5")
from casadi import *
import numpy as np
from scipy.linalg import fractional_matrix_power
import mat... | {"hexsha": "1dc4d0ca33dacf76a6a9016c95e3d5c07df8478d", "size": 12506, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/core/StabilizingController.py", "max_stars_repo_name": "ziyadsheeba/soft-safety-filter", "max_stars_repo_head_hexsha": "700f6e43eb1bae6e1f022aee6a4ca3b8b68de661", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma bit_nat_iff [bit_simps]:
\<open>bit (nat k) n \<longleftrightarrow> k \<ge> 0 \<and> bit k n\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bit (nat k) n = (0 \<le> k \<and> bit k n)
[PROOF STEP]
proof (cases \<open>k \<ge> 0\<close>)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. 0 \... | {"llama_tokens": 1275, "file": null, "length": 16} |
#!/usr/bin/env python3
import os
import sys
sys.path.append( '..' )
import MotifTable
import pickle
import distributions
import GenomeBindingTable as gbt
import FragExtract as Frag
import ChipSeq
import PCR
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
cbcolors = {'sky blue': (8... | {"hexsha": "b89188184e3bf3eaba15dae396d4560b6b19b261", "size": 23418, "ext": "py", "lang": "Python", "max_stars_repo_path": "paper/chip_scratch.py", "max_stars_repo_name": "vishakad/animate", "max_stars_repo_head_hexsha": "0da18be596bf7e462d47bef86aff4c71fc9cffbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from nilabels.tools.aux_methods.utils_nib import set_new_data
def cut_4d_volume_with_a_1_slice_mask(data_4d, data_mask):
"""
Fist slice maks is applied to all the timepoints of the volume.
:param data_4d:
:param data_mask:
:return:
"""
assert data_4d.shape[:3] == data_m... | {"hexsha": "496f6fb9ee2c7e006966d33b35e6fe6a7a2c001a", "size": 2263, "ext": "py", "lang": "Python", "max_stars_repo_path": "nilabels/tools/image_colors_manipulations/cutter.py", "max_stars_repo_name": "nipy/nilabels", "max_stars_repo_head_hexsha": "b065febc611eef638785651b4642d53bb61f1321", "max_stars_repo_licenses": [... |
# Gaussian mixture model suign PyMC3
# Based on https://github.com/aloctavodia/BAP/blob/master/code/Chp6/06_mixture_models.ipynb
import pymc3 as pm
import numpy as np
import scipy.stats as stats
import pandas as pd
import theano.tensor as tt
import matplotlib.pyplot as plt
import arviz as az
np.random.seed(42)
#url... | {"hexsha": "53ae951a1fd4c47b938d74e6bef08c9979e83c7c", "size": 2084, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/gmm_identifiability_pymc3.py", "max_stars_repo_name": "always-newbie161/pyprobml", "max_stars_repo_head_hexsha": "eb70c84f9618d68235ef9ba7da147c009b2e4a80", "max_stars_repo_licenses": ["MI... |
"""
tests desispec.sky
"""
import unittest
import numpy as np
from desispec.sky import compute_sky, subtract_sky
from desispec.resolution import Resolution
from desispec.frame import Frame
import desispec.io
import desispec.scripts.sky as skyscript
class TestSky(unittest.TestCase):
#- Create unique test f... | {"hexsha": "506384f8132f0dc2d9aecc5a4d0a5c9ab8d0a3c6", "size": 4590, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/desispec/test/test_sky.py", "max_stars_repo_name": "echaussidon/desispec", "max_stars_repo_head_hexsha": "8a8bd59653861509dd630ffc8e1cd6c67f6cdd51", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import cv2
import copy
import numpy as np
import scipy.io as sio
from utils.common_utils import interp, BFconsistCheck, \
FBconsistCheck, consistCheck, get_KeySourceFrame_flowNN_gradient
def get_flowNN_gradient(args,
... | {"hexsha": "5d34327fd0c9559f8b4de384278e57c483a073cd", "size": 26056, "ext": "py", "lang": "Python", "max_stars_repo_path": "tool/get_flowNN_gradient_test.py", "max_stars_repo_name": "co-develop-drv/FGVC", "max_stars_repo_head_hexsha": "60d91f85ee48d757dd070e66984ea57d7e60f668", "max_stars_repo_licenses": ["MIT"], "max... |
/* ----------------------------------------------------------------
Copyright (c) Coding Nerd
Licensed under the Apache License, Version 2.0
See LICENSE in the project root for license information.
---------------------------------------------------------------- */
#pragma once
#include <boost/property_... | {"hexsha": "21dd036e5e9bad423446858f4b2ff4639a2fc79c", "size": 1012, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "common/inc/property_manager.hpp", "max_stars_repo_name": "c6supper/unify-nmon", "max_stars_repo_head_hexsha": "8570f70de640ccb2ea502585883aefa5a93849b9", "max_stars_repo_licenses": ["Apache-2.0"], "... |
Require Import compcert.common.Memory.
Require Import VST.msl.Coqlib2.
Require Import VST.msl.eq_dec.
Require Import VST.msl.seplog.
Require Import VST.msl.ageable.
Require Import VST.msl.age_to.
Require Import VST.veric.coqlib4.
Require Import VST.veric.juicy_mem.
Require Import VST.veric.compcert_rmaps.
Require Impor... | {"author": "ildyria", "repo": "coq-verif-tweetnacl", "sha": "8181ab4406cefd03ab0bd53d4063eb1644a2673d", "save_path": "github-repos/coq/ildyria-coq-verif-tweetnacl", "path": "github-repos/coq/ildyria-coq-verif-tweetnacl/coq-verif-tweetnacl-8181ab4406cefd03ab0bd53d4063eb1644a2673d/packages/coq-vst/coq-vst.2.0/veric/aging... |
import collections
import logging
import numpy as np
import platform
import random
from typing import List, Dict
# Import ray before psutil will make sure we use psutil's bundled version
import ray # noqa F401
import psutil # noqa E402
from ray.rllib.execution.segment_tree import SumSegmentTree, MinSegmentTree
from... | {"hexsha": "07c46e97a6b2b25f8a807ab2291d68aa44eb51bf", "size": 18294, "ext": "py", "lang": "Python", "max_stars_repo_path": "rllib/execution/replay_buffer.py", "max_stars_repo_name": "77loopin/ray", "max_stars_repo_head_hexsha": "9322f6aab53f4ca5baf5a3573e1ffde12feae519", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# https://www.kaggle.com/sreevishnudamodaran/tpu-hubmap-double-u-net-model-augmentation
import os
import glob
import tensorflow as tf
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from patho.cfg.config import path_cfg,img_proc_cfg
from patho.utils.utils import read_singl... | {"hexsha": "12f30821cfae4bfe32ecd1218e08f65f8d0e4dc2", "size": 3942, "ext": "py", "lang": "Python", "max_stars_repo_path": "patho/api/datasets/gen_tfrecords.py", "max_stars_repo_name": "vankhoa21991/patho_toolbox", "max_stars_repo_head_hexsha": "11489e958500505d501bcd11ea5c944d7629ea0f", "max_stars_repo_licenses": ["MI... |
import torch
import numpy as np
import argparse
import pickle
import time
import torch.nn as nn
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from transformers import BertModel, BertTokenizer
MAX_LEN = 100
class BERTClassifier(nn.Module):
def __init__(self, trg_voc... | {"hexsha": "4c526987ce4468254ec5d6a3098bff82c98023f5", "size": 3300, "ext": "py", "lang": "Python", "max_stars_repo_path": "ext_feats/infer_bert/text_bert_tweet.py", "max_stars_repo_name": "yuewang-cuhk/CMKP", "max_stars_repo_head_hexsha": "4e8348283fad9a2a4c605959c79bc2922171b753", "max_stars_repo_licenses": ["MIT"], ... |
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2017-2022 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to dea... | {"hexsha": "14c89f44739b0b2dcc7011d3ab4e12d6d447de46", "size": 25179, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/modules/hip/handlehip.cpp", "max_stars_repo_name": "shobana-mcw/rpp", "max_stars_repo_head_hexsha": "e4a5eb622b9abd0a5a936bf7174a84a5e2470b59", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from tictactoe_env import TicTacToe
import pdb
import numpy as np
import matplotlib.pyplot as plt
import itertools
import random
if __name__ == "__main__":
env = TicTacToe()
num_episodes = 1000
learning_rate = 0.6
epsilon = 0.2
number_of_actions = 9
... | {"hexsha": "3231ecc63824f56c5ed41ff9b73276e17bfd0cf2", "size": 4238, "ext": "py", "lang": "Python", "max_stars_repo_path": "Q-learning/Tic-tac-toe/training.py", "max_stars_repo_name": "nipunbhanot/Q-Learning", "max_stars_repo_head_hexsha": "50102cd4fd904105f2a246f74a9fffb0eadd39df", "max_stars_repo_licenses": ["MIT"], ... |
import warnings
from typing import Dict, Optional, Tuple, Union
import numpy as np
import pandas as pd
from autotabular.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
from autotabular.pipeline.components.base import AutotabularPreprocessingAlgorithm
from autotabular.pipeline.constants import DENSE, ... | {"hexsha": "35f624b303e5c1a22a27d16557166dcd8a800d77", "size": 3263, "ext": "py", "lang": "Python", "max_stars_repo_path": "autotabular/pipeline/components/data_preprocessing/exclude_miss_target/exclude_missing_target.py", "max_stars_repo_name": "jianzhnie/AutoTabular", "max_stars_repo_head_hexsha": "fb407300adf97532a2... |
from galearn import settings
from sklearn.model_selection import cross_val_score
import numpy as np
# having this here allows some functions to be called directly outside of simulate
rng = settings.rng
fitness_function = settings.fitness_function
estimator = settings.estimator
gene_pool = settings.gene_pool
gnp_window... | {"hexsha": "1255390ae59fa819bfa92aeb7bff98fe5f0eb3c2", "size": 13960, "ext": "py", "lang": "Python", "max_stars_repo_path": "galearn/galearn.py", "max_stars_repo_name": "aegonwolf/galearn", "max_stars_repo_head_hexsha": "1e069c90e49d151066d199333e25fb80e745f635", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
from spada.bio.switch import IsoformSwitch, LiteSwitch
from spada.io import io
from spada.network.network import Network
import abc
import numpy as np
import operator
import random
class GeneNetwork(Network):
"""docstring for GeneNetwork
GeneNetwork contains a network of genes.
Node information:
id(str) G... | {"hexsha": "89ca7cca37cfa073df042d5517d72f390b6ce94d", "size": 6783, "ext": "py", "lang": "Python", "max_stars_repo_path": "spada/network/gene_network.py", "max_stars_repo_name": "hclimente/spada", "max_stars_repo_head_hexsha": "c9584df9e25a597c70b67a6a0b7ce63283a11839", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# --------------------------------------------------
# Anvil Uplink Server
# --------------------------------------------------
# Author : Tom Eleff
# Version : 1_0
# Date : 23JAN22
# --------------------------------------------------
import schedule
import time
import os
import sys
import json
import d... | {"hexsha": "42c3509fe19df087207eafeffca69f225e8841a8", "size": 17622, "ext": "py", "lang": "Python", "max_stars_repo_path": "server_code/AnvilServer.py", "max_stars_repo_name": "thomaseleff/Crypto-Price-Estimator", "max_stars_repo_head_hexsha": "92dea0122f86f024f7cd324039a7d9d093382b59", "max_stars_repo_licenses": ["MI... |
import pandas as pd
import pandas as pd
import numpy as np
from nltk.stem import PorterStemmer
ps = PorterStemmer()
import nltk
import string
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metri... | {"hexsha": "46cb1b293ef61ea820165c06aad1fd06ceb5b5a3", "size": 1262, "ext": "py", "lang": "Python", "max_stars_repo_path": "The_Django_Website/RedditFlair/webapp/modd/pls_work.py", "max_stars_repo_name": "basketcase03/The_Reddit_Flair_Detection", "max_stars_repo_head_hexsha": "b735924ce1eafc767382ff60482881d4eab1a498",... |
import data.padics
import for_mathlib.ideal_operations
import for_mathlib.normed_spaces
import for_mathlib.nnreal
import for_mathlib.padics
import adic_space
/-!
# The p-adics form a Huber ring
In this file we show that ℤ_[p] and ℚ_[p] are Huber rings.
They are the fundamental examples of Huber rings.
We also show... | {"author": "leanprover-community", "repo": "lean-perfectoid-spaces", "sha": "95a6520ce578b30a80b4c36e36ab2d559a842690", "save_path": "github-repos/lean/leanprover-community-lean-perfectoid-spaces", "path": "github-repos/lean/leanprover-community-lean-perfectoid-spaces/lean-perfectoid-spaces-95a6520ce578b30a80b4c36e36ab... |
# Copyright 2020, The TensorFlow Authors.
#
# 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 t... | {"hexsha": "4c80f49a9fdb6f75150da9773fbf96853b7052e7", "size": 7840, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack_test.py", "max_stars_repo_name": "jagielski/privacy", "max_stars_repo_head_hexsha": "e468af41dd11176f3f04eabc42cf... |
import numpy as np
import matplotlib.pyplot as plt
import os
from deeperwin.dispatch import load_from_file
import re
import pandas as pd
_REFERENCE_ENERGIES = {'He': -2.90372, 'Li': -7.478067, 'Be': -14.66733, 'B': -24.65371, 'C': -37.84471, 'N': -54.58882,
'O': -75.06655, 'F': -99.7329, 'Ne': -... | {"hexsha": "113074bbbf22b44c836f3f5ad22ec7c171f5f23a", "size": 2575, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/analysis/analyze_ferminet.py", "max_stars_repo_name": "deepqmc/deeperwin", "max_stars_repo_head_hexsha": "1c514c972ee1df3cc5c8c3ecaf9408befd157341", "max_stars_repo_licenses": ["MIT"], "max_st... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from ..utils import one_hot
from model.models import GeneralizedFewShotModel
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
... | {"hexsha": "78fdbd959a611a4b8b52bf088b13c6dc73aedc88", "size": 7654, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/models/acastle.py", "max_stars_repo_name": "Sha-Lab/CASTLE", "max_stars_repo_head_hexsha": "212cb7aaad1bfae7041c90143220286bde24db33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# %% markdown
# # run_importance_sampler
# sets up the data matrix (number of samples x 6 columns) and the 'analysis_settings' struct with algorithm parameters
#
# **USAGE**:
# - Specify parameters within this script and then execute `run_importance_sampler()`
#
# **INPUTS**:
# - None
#
# **OUTPUTS**:
# - None
# %%
fr... | {"hexsha": "de949b134e644217dc8288842521775e756f2c12", "size": 10141, "ext": "py", "lang": "Python", "max_stars_repo_path": "toreorganize/run_importance_sampler.py", "max_stars_repo_name": "githubpsyche/PCITpy", "max_stars_repo_head_hexsha": "be1b68fcfdb2eaaf653eabd94ba6d893d0be1fb8", "max_stars_repo_licenses": ["Apach... |
# -*- coding: utf-8 -*-
"""
Created on Mon May 7 22:15:54 2018
@author: Steven
"""
import numpy as np
def variance(q, AS, f):
"""
Determines the variance of the number of counts in each channel
i for the qth iteration, where:
-q is the iteration number
-AS is either the estimated activit... | {"hexsha": "c6e24954d12ce0478e0dbe1055cccc0dda3b5f7d", "size": 1023, "ext": "py", "lang": "Python", "max_stars_repo_path": "radioxenon_ml/solve/variance.py", "max_stars_repo_name": "manninosi/radioxenon_ml", "max_stars_repo_head_hexsha": "e901a2465bcbe491184cefc58db021a9321b9555", "max_stars_repo_licenses": ["MIT"], "m... |
from __future__ import absolute_import, division, print_function
import math
import random
from collections import deque
from os.path import exists, join
import cv2
import numpy as np
import torch
import re
from PIL import Image, ImageOps, ImageEnhance, ImageDraw
from torchvision.transforms import (ColorJitter, Compos... | {"hexsha": "fbf623ac113de0c8316c4dae27b64da2f8b09fda", "size": 54029, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchreid/data/transforms.py", "max_stars_repo_name": "daniil-lyakhov/deep-object-reid", "max_stars_repo_head_hexsha": "b0f7d6a2d4cff8c417a66d82c09d16788d81ec67", "max_stars_repo_licenses": ["Apa... |
# %%
import os
import subprocess
import json
import numpy as np
import pdal
from osgeo import gdal
from osgeo_utils.auxiliary.util import GetOutputDriverFor
import pandas as pd
import geopandas as gpd
import folium
import dask.dataframe as dd
from dask import delayed, compute
from dask.diagnostics import ProgressBar
... | {"hexsha": "b6a0c39b36a9b42bb4c20478baf5e5374718bae5", "size": 2772, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics_raster.py", "max_stars_repo_name": "kulpojke/powerline", "max_stars_repo_head_hexsha": "d380d4a53039bb21b4cf0d3d3d1c81be179476aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright(c) 2018 Senscape Corporation.
# License: Apache 2.0
import os
os.chdir(os.path.dirname(os.path.realpath(__file__)))
import sys
sys.path.append('../SungemSDK-Python')
import hsapi as hs # pylint: disable=E0401
import cv2
import numpy
import time
import RPi.GP... | {"hexsha": "841d5b9fbd2839df0ba6397d32d47a76c4f534ef", "size": 3794, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/rpi-car/main.py", "max_stars_repo_name": "HornedSungem/SungemAppZoo", "max_stars_repo_head_hexsha": "4f04d27e1c5e7bd36d73c3830985e9f3cdb1ebc2", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
"""Pd2dBackground class."""
import numpy
import scipy
import scipy.interpolate
from typing import NoReturn, Union
from cryspy.B_parent_classes.cl_1_item import ItemN
def inversed_hessian_to_correlation(inv_hessian):
"""Calculate correlation matrix and sigmas for inversed hessian matrix."""
np_sigma_sq = numpy... | {"hexsha": "fc6b62042d2e0bd0e01b1aa97aa3b77d4a926c6c", "size": 8204, "ext": "py", "lang": "Python", "max_stars_repo_path": "cryspy/C_item_loop_classes/cl_1_inversed_hessian.py", "max_stars_repo_name": "ikibalin/rhochi", "max_stars_repo_head_hexsha": "1ca03f18dc72006322a101ed877cdbba33ed61e7", "max_stars_repo_licenses":... |
"""
test_callbacks
----------------
"""
import numpy as np
from numpy.testing import assert_allclose
import pytest
from elastica.wrappers import CallBacks
from elastica.wrappers.callbacks import _CallBack
class TestCallBacks:
@pytest.fixture(scope="function")
def load_callback(self, request):
return ... | {"hexsha": "7cb05565f585d0f32e4b5a990f1e8523e3ef2052", "size": 6394, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_wrappers/test_callbacks.py", "max_stars_repo_name": "merozlab/PyElastica", "max_stars_repo_head_hexsha": "1d8c354a25846f63073809e3a809f27a150c3b9c", "max_stars_repo_licenses": ["MIT"], ... |
from scipy import sparse as sps
from sklearn.linear_model import ElasticNet
from ..definitions import InteractionMatrix
from .base import BaseSimilarityRecommender
def slim_weight(X: InteractionMatrix, alpha: float, l1_ratio: float) -> sps.csr_matrix:
model = ElasticNet(
fit_intercept=False,
posi... | {"hexsha": "f09003d8f8014adc6d596042539d7cf5ec84c700", "size": 1476, "ext": "py", "lang": "Python", "max_stars_repo_path": "irspack/recommenders/slim.py", "max_stars_repo_name": "wararaki/irspack", "max_stars_repo_head_hexsha": "650cc012924d46b3ecb87f1a6f806aee735a9559", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
#with open('C:/Users/Admin/Chh/chembl_smiles.txt ', 'r') as f:
with open('C:/Users/Admin/CharRNN/chembl_smiles.txt','r') as f:
text = f.read()
# Showing the first 100 characters
text[:100]
# encoding the text and map each charact... | {"hexsha": "c11c32de70ebe4df55c5c5874ceae8782a3432d6", "size": 2562, "ext": "py", "lang": "Python", "max_stars_repo_path": "charRNN/dataloader.py", "max_stars_repo_name": "Sunitach10/CHARRNN", "max_stars_repo_head_hexsha": "edb6fa53712b6f3e7ad6820eb1e1491167f61927", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
__name__ = "fastpdb"
__author__ = "Patrick Kunzmann"
__all__ = ["PDBFile"]
__version__ = "1.0.1"
import numpy as np
import biotite
import biotite.structure as struc
import biotite.structure.io.pdb as pdb
from .fastpdb import PDBFile as RustPDBFile
class PDBFile(biotite.TextFile):
r"""
This class represents a... | {"hexsha": "2c32809e4969cb7f831d0181282c839576affea9", "size": 16199, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-src/fastpdb/__init__.py", "max_stars_repo_name": "padix-key/fastpdb", "max_stars_repo_head_hexsha": "016c0211743fcbf4366fdaf6fb5579ff073c6274", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import argparse
from pathlib import Path
from typing import List
import numpy as np
import pandas as pd
def categorize_by_label_distribution(group: pd.DataFrame,
label: str,
dif_threshold: float = 0.1,
top_... | {"hexsha": "ffe28a109d074834ea105180163b0662b5c8ed3d", "size": 7792, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/categorize_target_by_template_quality.py", "max_stars_repo_name": "yutake27/HMDM", "max_stars_repo_head_hexsha": "a16c6e77cae9509ccf49140171797680068709aa", "max_stars_repo_licenses": ["MIT"],... |
import argparse
import logging
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
def find_k_nearest(source, vectors, k):
norm1 = np.linalg.norm(source)
norm2 = np.linalg.norm(vectors, axis=1)
cosine_similarity = np.sum(source * vectors, axis=1) / norm1 / norm2
r... | {"hexsha": "41a42095da61ab98352acf2bbc7c40d687077910", "size": 3535, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo_sl.py", "max_stars_repo_name": "seamusl/word_embedding", "max_stars_repo_head_hexsha": "1f1da2e135c7459e402b08fae52b51e59388353d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""Utility functions."""
import math
import os
import sys
import warnings
import weakref
from pathlib import Path
from time import perf_counter as clock
import numpy as np
from .flavor import array_of_flavor
# The map between byteorders in NumPy and PyTables
byteorders = {
'>': 'big',
'<': 'little',
'='... | {"hexsha": "78df25af6dff26277253c73426d9c76096a9df84", "size": 13717, "ext": "py", "lang": "Python", "max_stars_repo_path": "tables/utils.py", "max_stars_repo_name": "xmatthias/PyTables", "max_stars_repo_head_hexsha": "da01cf8908c2d8c2b07e8a35685f0811807453f6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
'''
Linear Layer in a deep learning network. three key things:
- backprop
- forward
- drivative matrix of thetas(weights)
'''
from typechecker import accept
from numpy.matlib import rand, matrix
from numpy import ndarray
class LinearLayer():
@accept(LinearLayer, int, int)
def __init__(self, nInputs, nOutputs... | {"hexsha": "0ccf75cca52766b4d09aada2b948da96f0aaddcf", "size": 2974, "ext": "py", "lang": "Python", "max_stars_repo_path": "proto/LinearLayer.py", "max_stars_repo_name": "cjackie/JensorFlow", "max_stars_repo_head_hexsha": "6b928ba2d0c64325cbdbf46ae4ced12821260843", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
module num_parthd
contains
function num_parthds()
integer :: num_parthds
! use omp_lib
!!$omp parallel
! num_parthds = omp_get_num_threads()
num_parthds = 8
! num_parthds = 6
! num_parthds = 4
!!$omp end parallel
return
end function
end module num_parthd
| {"hexsha": "0ba731ff96e69f0864f7bd5d8303bda6067133d3", "size": 302, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "GMAO_stoch/num_parthd.f90", "max_stars_repo_name": "GEOS-ESM/GMAO_Shared", "max_stars_repo_head_hexsha": "022af23abbc7883891006b57379be96d9a50df23", "max_stars_repo_licenses": ["NASA-1.3", "ECL-2... |
from . import utils
from . import anchor_base
from . import anchor_explanation
import numpy as np
import json
import os
import string
import sys
from io import open
# Python3 hack
try:
UNICODE_EXISTS = bool(type(unicode))
except NameError:
def unicode(s):
return s
def id_generator(size=15):
"""He... | {"hexsha": "0fa57a44ed1805bdb5b6fd8b0f14a4727b6bb2b2", "size": 6909, "ext": "py", "lang": "Python", "max_stars_repo_path": "anchor/anchor_text.py", "max_stars_repo_name": "JannisBush/anchor", "max_stars_repo_head_hexsha": "e4f758e99cb5eb1f14e51948e7b0d4c6efe48b6f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
'''
Created on Sep 30, 2013
modified on Mars 3, 2020
@author: J. Akeret, S. Birrer
'''
from __future__ import print_function, division, absolute_import, unicode_literals
from copy import copy
from math import floor
import math
import multiprocessing
import numpy
class ParticleSwarmOptimizer(object):
'''
Opt... | {"hexsha": "73bf074e876a14810acdb33e44e9635b36df42da", "size": 8848, "ext": "py", "lang": "Python", "max_stars_repo_path": "mpipso/pso.py", "max_stars_repo_name": "ajshajib/mpipso", "max_stars_repo_head_hexsha": "4433f9c213795c39a6b1f43e6284d952dfcd5c1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_s... |
"""Core module for stacking related operations"""
from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.model_selection import KFold
import numpy as np
import pickle
import os
class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, ... | {"hexsha": "1c1e5d36ae30841719a250ee0a96e0895fba7347", "size": 2849, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/stacking.py", "max_stars_repo_name": "matheushent/clfs-models", "max_stars_repo_head_hexsha": "e2f95064cc036289c203080e75718964873a5d8a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from collections import OrderedDict
import unittest
import numpy as np
from mock import Mock
from annotypes import Serializable
from malcolm.core import BlockModel, StringMeta, Alarm, \
AlarmSeverity, AlarmStatus, TimeStamp, VMeta, TableMeta, StringArrayMeta, \
NumberMeta, NumberArrayMeta, MethodModel, Choice... | {"hexsha": "5d3e361ac660a5c9175721fc4a8b66d5c81a6b64", "size": 23743, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_core/test_models.py", "max_stars_repo_name": "dinojugosloven/pymalcolm", "max_stars_repo_head_hexsha": "0b856ee1113efdb42f2f3b15986f8ac5f9e1b35a", "max_stars_repo_licenses": ["Apache-2... |
% RGBTOGRAY.m
% -------------------------------------------------------------------
%
% Date: 27/04/2013
% Last modified: 27/04/2013
% -------------------------------------------------------------------
function gray = RGBTOGRAY(img)
if size(img, 3) ~= 3,
error('The input should be RGB');
end
... | {"author": "thfylsty", "repo": "Classic-and-state-of-the-art-image-fusion-methods", "sha": "5d9457df396f1ea6921e1b9b3703995205940862", "save_path": "github-repos/MATLAB/thfylsty-Classic-and-state-of-the-art-image-fusion-methods", "path": "github-repos/MATLAB/thfylsty-Classic-and-state-of-the-art-image-fusion-methods/Cl... |
import tensorflow as tf
import numpy as np
import os
import sys
import urllib
import tarfile
from utils.generic_utils import split_apply_concat
tfgan = tf.contrib.gan
MODEL_DIR = './inception/'
INCEPTION_GRAPH_NAME = 'inceptionv1_for_inception_score.pb'
INCEPTION_INPUT = 'Mul:0'
INCEPTION_OUTPUT = 'logits:0'
INCEPTIO... | {"hexsha": "801538d56c4a48129e113578d457a35b3234e3b5", "size": 7440, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/inception.py", "max_stars_repo_name": "kshmelkov/gan_evaluation", "max_stars_repo_head_hexsha": "8583837a8fa796ae8ec4bb503a159a9e0a40cbf7", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
from SimpleCV import Image, ImageSet, Camera, VirtualCamera, ROI, Color, LineScan
import numpy as np
import scipy.signal as sps
import warnings
import time as time
class TemporalColorTracker:
"""
**SUMMARY**
The temporal color tracker attempts to find and periodic color
signal in an roi or arbitrary fu... | {"hexsha": "4f5f5326a630dc74e94100fc23070a4c257110a8", "size": 15935, "ext": "py", "lang": "Python", "max_stars_repo_path": "SimpleCV/MachineLearning/TemporalColorTracker.py", "max_stars_repo_name": "M93Pragya/SimpleCV", "max_stars_repo_head_hexsha": "6c4d61b6d1d9d856b471910107cad0838954d2b2", "max_stars_repo_licenses"... |
#!/usr/bin/env python
from __future__ import division
import rospy
import tf
import scipy.linalg as la
import numpy as np
from math import *
import mavros_msgs.srv
from mavros_msgs.msg import AttitudeTarget
from nav_msgs.msg import Odometry
from std_msgs.msg import *
from geometry_msgs.msg import *
from mavros_msgs.msg... | {"hexsha": "f44f574c6ab130bf97c7cbd3a5d6950d5dc79bfa", "size": 19404, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadcopter/script/single_UAV.py", "max_stars_repo_name": "Bibbidi-Babbidi-Boo/SDRE-based-Cooperative-UAV-Landing-on-High-speed-targets", "max_stars_repo_head_hexsha": "515fb38120990cb707521da1a58... |
import pandas as pd
import numpy as np
#***********************From dict of Series or dicts********************
#dictionary takes key:value
dict = {"Name":pd.Series(["Nahid", "Rafi", "Meem"]),
"Age":pd.Series([21,22,21]),
"Weight":pd.Series([48,75,76]),
"Height":pd.Series([5.3, 5.8, 5.6])}
df =... | {"hexsha": "70f34f045d8586ab11d1b82d5117d99c5cbf5089", "size": 5131, "ext": "py", "lang": "Python", "max_stars_repo_path": "python3/pandas/Pandas_DataFrameDS.py", "max_stars_repo_name": "Nahid-Hassan/code-snippets", "max_stars_repo_head_hexsha": "24bd4b81564887822a0801a696001fcbeb6a7a75", "max_stars_repo_licenses": ["M... |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 19 14:59:39 2018
@author: yiyuezhuo
"""
# The data is from Battle of Gettysburg, HPS
import numpy as np
Confederate = np.array(
[[24, 48],
[29, 10],
[33, 1],
[35, 28],
[36, 37],
[50, 42],
[60, 39],
[72, 45]])
Un... | {"hexsha": "6b1cdbb70a3c464bec64d3969b596d0d66d258f8", "size": 1468, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayestorch/data/gettysburg.py", "max_stars_repo_name": "yiyuezhuo/bayes-torch", "max_stars_repo_head_hexsha": "eba1e7bbff7e4c15560b3dc7e83bb6c5784c1bee", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import matplotlib.pyplot as plt
def partition(arr, start, end):
i = start - 1
pivot = arr[end]
for j in range(start, end):
if arr[j] <= pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]
arr[i + 1], arr[end] = arr[end], arr[i + 1]
return i + 1
def ... | {"hexsha": "315cdbbea44c4956115c49ff018010f51d4bb30b", "size": 792, "ext": "py", "lang": "Python", "max_stars_repo_path": "Sorting/algos/quick.py", "max_stars_repo_name": "CandleStein/VAlg", "max_stars_repo_head_hexsha": "43aecdd351954d316f132793cf069b70bf2e5cc2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
@testset "Test freecf $mat, $obj" for mat in ["her", "rec"], obj in ["kurt", "ent"]
# test for the hermitian matrices
if mat == "her"
for idx = 1: 5
# set up
N = 300
G1, G2 = randn(N, N), randn(N, 2N);
X1, X2 = (G1 + G1') / sqrt(2*N), (G2 * G2') / (2*N)
... | {"hexsha": "4e52b686aa2208954aecfbde9567471046fcdc5b", "size": 2815, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/fcf.jl", "max_stars_repo_name": "UnofficialJuliaMirror/FCA.jl-9ac0d1d1-ab1e-5a44-888c-d389b307372d", "max_stars_repo_head_hexsha": "47ed4f7e1ac9b764b2753bef0138f8961b6559ee", "max_stars_repo_l... |
[STATEMENT]
theorem no_type_error:
fixes \<sigma> :: jvm_state
assumes welltyped: "wf_jvm_prog\<^bsub>\<Phi>\<^esub> P" and conforms: "P,\<Phi> \<turnstile> \<sigma> \<surd>"
shows "exec_d P \<sigma> \<noteq> TypeError"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. exec_d P \<sigma> \<noteq> TypeError
[PROOF ... | {"llama_tokens": 144416, "file": "JinjaDCI_BV_BVNoTypeError", "length": 202} |
[STATEMENT]
lemma ws_update_foreach_refine[refine]:
assumes FIN: "finite (E``{u})"
assumes WSS: "dom ws \<subseteq> V"
assumes ID: "(E',E)\<in>Id" "(u',u)\<in>Id" "(p',p)\<in>Id" "(V',V)\<in>Id" "(ws',ws)\<in>Id"
shows "ws_update_foreach E' u' p' V' ws' \<le> \<Down>Id (ws_update E u p V ws)"
[PROOF STA... | {"llama_tokens": 3772, "file": "Gabow_SCC_Find_Path_Impl", "length": 6} |
// (C) Copyright Mac Murrett 2001.
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
// See http://www.boost.org for most recent version.
#ifndef BOOST_REMOTE_CALLS_MJM012... | {"hexsha": "8619f2aa7d70e346103877b157a9381d3c67470e", "size": 6891, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Framewave/sdk/boost_1_34_0/libs/thread/src/mac/remote_calls.hpp", "max_stars_repo_name": "dbremner/framewave", "max_stars_repo_head_hexsha": "94babe445689538e6c3b44b1575cca27893b9bb4", "max_stars_re... |
[STATEMENT]
lemma leadsETo_cancel1:
"[| F \<in> A leadsTo[CC] (B Un A'); F \<in> B leadsTo[CC] B' |]
==> F \<in> A leadsTo[CC] (B' Un A')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>F \<in> A leadsTo[CC] (B \<union> A'); F \<in> B leadsTo[CC] B'\<rbrakk> \<Longrightarrow> F \<in> A leadsTo[CC... | {"llama_tokens": 309, "file": null, "length": 3} |
import random
import cv2
import numpy as np
from fly_class import Fly
class SmartFlies:
def __init__(self, n_flies=50, n_obstacles=1, n_generations=10, mate_rate=0.25, mutate_rate=0.05,
lifespan=500, course_dims=(400, 600)):
self.target = (np.random.randint(5, course_dims[0] - 5),
... | {"hexsha": "5df47521902ad7da1fd29bf270e6fdbaf76cbe26", "size": 4422, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_smart_flies/smart_fly_class.py", "max_stars_repo_name": "AdamSpannbauer/misc_code_fun", "max_stars_repo_head_hexsha": "782b6aa1c6c3b5e881a4745564ca1faeca53d5fd", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 07:35:47 2017
@author: davidpvilaca
"""
import matplotlib.pyplot as plt
import numpy as np
import cv2
def intersection(L1, L2):
D = L1[0] * L2[1] - L1[1] * L2[0]
Dx = L1[2] * L2[1] - L1[1] * L2[2]
Dy = L1[0] * L2[2] - L1[2] * L2[0]
... | {"hexsha": "908cad4ab3be5ef30aaa324eede5c8f8a78c46fd", "size": 4720, "ext": "py", "lang": "Python", "max_stars_repo_path": "aula5/bordas.py", "max_stars_repo_name": "davidpvilaca/TEP", "max_stars_repo_head_hexsha": "decbf61a96863d76e1b84dc097aa37b12038aa75", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
# Python _for fun and profit_
###### Juan Luis Cano Rodríguez
###### Madrid, 2016-05-13 @ ETS Asset Management Factory
## Outline
* Introduction
* Python for Data Science
* Python for IT
* General advice
* Conclusions
## Outline
* Introduction
* Python for Data Science
* Interactive computation with Jupyter
... | {"hexsha": "be60b18c903c897f42af4824fe7e88ed52053399", "size": 769437, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Python for fun and profit.ipynb", "max_stars_repo_name": "Juanlu001/python-fun-and-profit", "max_stars_repo_head_hexsha": "ac9be81e9a151024be7d3123901f031665d9e766", "max_stars_repo... |
"""
Make figure 6, which includes
1. A plot of the centerpoints of all states
2. A plot of the top three latent state maps
3. A plot of the true and reconstructed locations
"""
import os
import cPickle
import gzip
from collections import namedtuple
import numpy as np
from scipy.io import loadmat
import matplotlib
im... | {"hexsha": "2e1f5821a53faad0836928e0b1392654fc4818db", "size": 7402, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/make_figure5.py", "max_stars_repo_name": "slinderman/pyhsmm-spiketrains", "max_stars_repo_head_hexsha": "462d8d2c59bd2e7c39d20d624bd8b289a31baaa2", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma except_keys_Int [simp]: "except p (keys p \<inter> U) = except p U"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. except p (keys p \<inter> U) = except p U
[PROOF STEP]
by (rule poly_mapping_eqI) (simp add: in_keys_iff lookup_except) | {"llama_tokens": 100, "file": "Polynomials_Power_Products", "length": 1} |
CHAPTER 2
Data Structure Access
The following functions allow one to create and manipu-
late the various types of lisp data structures. Refer to
1.2 for details of the data structures known to FRANZ LISP.
2.1. Lists
The following func... | {"hexsha": "a005b85a18e939c9ea8543225884af3538b69e30", "size": 62791, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch2.r", "max_stars_repo_name": "omasanori/franz-lisp", "max_stars_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_star... |
#!/usr/bin/env python
import pydot
import networkx as nx
import libsystemflowgraph as sys_fg
import copy
def build_dot_graph(flow_graph, cluster_system=False):
dot_graph = pydot.Dot(graph_type='digraph')
dot_subgraph = pydot.Cluster(graph_name='system_server')
node_dic = {}
for node in flow_graph.nodes():
node_... | {"hexsha": "af2200c6d83612d8989ecc4224454d0e1894a29c", "size": 1651, "ext": "py", "lang": "Python", "max_stars_repo_path": "grodddroid/AnalyseAndroBlareTools/logparser/graphtodot.py", "max_stars_repo_name": "demirdagemir/thesis", "max_stars_repo_head_hexsha": "4a48bddf815c91729e27484548bb7bbf7ddeda64", "max_stars_repo_... |
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