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#!/usr/bin/env python
import os
import sys
import numpy
import scipy.optimize
import pyds9
import argparse
import astLib.astWCS as astWCS
import scipy.spatial
from astropy.io import fits
from astroquery.vizier import Vizier
import astropy.coordinates
from astropy.coordinates import Angle, FK5
import astropy.units ... | {"hexsha": "cfe824b09e54c2098124c4f1be442700091dd64c", "size": 15753, "ext": "py", "lang": "Python", "max_stars_repo_path": "hst_realign.py", "max_stars_repo_name": "rkotulla/hst_realign", "max_stars_repo_head_hexsha": "b3cb0e3351e7925b0280d65fdb9926a67945cdfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#pragma once
#include <grpc++/support/status.h>
#include <boost/exception/to_string.hpp>
#include <string>
namespace grpc {
std::string to_string(const StatusCode status);
std::string to_string(const Status &status);
} // namespace grpc
| {"hexsha": "abe44fa26700deb212acfcabe720fd94396b9aa3", "size": 242, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/bunsan/rpc/status.hpp", "max_stars_repo_name": "bunsanorg/rpc", "max_stars_repo_head_hexsha": "c0e0286e0a03ab9af183944819b06e4bb1cfeb00", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
""" Tutorial on the conversion models.
"""
import numpy as np
import lab
import pymc3 as pm
# Models
## Conversion probabilities
theta1 = 0.34
theta2 = 0.36
theta3 = 0.45
## Trials
trials_1 = 1101
trials_2 = 876
trials_3 = 1342
## Success
success_1 = np.sum(np.random.binomial(trials_1, theta1) == 0)
success_2 = n... | {"hexsha": "cbf6295e79c0d95118f514cc98f32277300de6fa", "size": 554, "ext": "py", "lang": "Python", "max_stars_repo_path": "lab/examples/conversion.py", "max_stars_repo_name": "rlouf/lab", "max_stars_repo_head_hexsha": "e8d6b41580215d090c91dc3edccfd12a3a6f21ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
[STATEMENT]
lemma lextup_lfinite[simp]: "lfinite xs \<Longrightarrow> lextup i xs = llist_of (extup i (list_of xs))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lfinite xs \<Longrightarrow> lextup i xs = llist_of (extup i (list_of xs))
[PROOF STEP]
by (simp add: lextup_def Lim_at'_lfinite) | {"llama_tokens": 119, "file": "Coinductive_Examples_LList_CCPO_Topology", "length": 1} |
# -*- coding: utf-8 -*-
# Copyright 1996-2015 PSERC. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
# Copyright (c) 2016-2018 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. ... | {"hexsha": "acd4f19dbbdca5317f1dbb041ed83558edc253e2", "size": 10631, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandapower/opf/make_objective.py", "max_stars_repo_name": "mathildebadoual/pandapower", "max_stars_repo_head_hexsha": "9ba4bcb78e84b644d2ba6df0c08e285c54af8ddc", "max_stars_repo_licenses": ["BSD-... |
args <- commandArgs(trailingOnly = TRUE)
xyz <- read.csv(file=args[1])
pdf(args[2])
scatterplot3d::scatterplot3d(xyz, color="blue", pch=19, xlab="Sample Size", ylab="Length of Simulation [s]", zlab="Match Percentage [%]", type="h")
| {"hexsha": "1dfdc48a34c3606e229ebabf5a963cdd63b3fb31", "size": 232, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/ideal-size-plotter.r", "max_stars_repo_name": "chris-wood/ccn-eavesdropper-simulator", "max_stars_repo_head_hexsha": "e291a1b06ab7f35c40e99f3b42cc7398908b2acb", "max_stars_repo_licenses": ["MIT"]... |
using Statistics, Dates
function test_ach()
@eval using Plots
gr()
nn = 1
ts = 10
ACh = fill(0.0, nn)
synt = 3 # or 8
Φ = 0.5
spikesequence = repeat([1, 0, 0, 0, 0], outer=2)
@show spikesequence
spt = sim_spikes(nn, ts, spikesequence)
p = scatter()
for t = 1:ts
update_ACh!(ACh, synt, Φ, t, spt[t])
... | {"hexsha": "8ad31f4ff6eff6818d25743589ad07c817f496f3", "size": 5884, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Tests2.jl", "max_stars_repo_name": "james-a-mcmanus/MushroomBody.jl", "max_stars_repo_head_hexsha": "eccf32598b1cfb2c3c8352bbca781a9b27a5b56a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/dc_tts
'''
from __future__ import print_function
from utils import load_spectrograms
import os
from data_load import load_data
import numpy as np
import tqdm
from hyperparams import Hyperparams a... | {"hexsha": "88088b7d727b9bea5468f8dd62d503d81c8e12cf", "size": 1224, "ext": "py", "lang": "Python", "max_stars_repo_path": "prepro.py", "max_stars_repo_name": "Leggerla/dc_tts", "max_stars_repo_head_hexsha": "08b7cdd940685ee96fa37464dbf5ea6709a6d1d5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, ... |
""" Calculate the zero-entropy GHD solution, for parameters """
""" defined in file 'parameters.py'. """
""" The files 'cont_tX.dat' contain the contour in """
""" phase-space at time X, and the files 'density_tX.dat' """
""" contain the particle density. """
""" Based on PRL 119, 195301 (2017) """
... | {"hexsha": "a6a5975fe46b78c5fba3549b786ace0411a965d0", "size": 1057, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "jdubail/Zero-entropy-GHD", "max_stars_repo_head_hexsha": "127e88c148afa114ab0ac89c878ae48c98d7048f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#
# Conditional Sampler
#
# chooses sampler based on values of a parent choice point
#
# implementation notes:
# * to facilitate EDAs using model-building (i.e. selection of parents), we implicitly constrain all the underlying samplers
# to be of the same type (i.e. only the parameters differ)
# * we permit only o... | {"hexsha": "b90ba8fc9fe0bc0d0a01d6ee13be6ba50ddf3862", "size": 13604, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/choice_model/samplers/modifying_samplers/conditional_sampler.jl", "max_stars_repo_name": "vserge/DataGenerators.jl", "max_stars_repo_head_hexsha": "10ca3368bb0dbc867e8a65d551df42175bddc42f", "... |
"""Utility module."""
import numpy as np
import astropy.constants as const
import astropy.units as u
from scipy.interpolate import RectBivariateSpline
from typing import Sequence, Optional, Tuple, Union
import warnings
from .interpolators import Beam
def _get_bl_len_vec(bl_len_ns: Union[float, np.ndarray]) -> np.ndar... | {"hexsha": "b9192afeb5c3c1ae78773de94b9e2a5644e4190c", "size": 14933, "ext": "py", "lang": "Python", "max_stars_repo_path": "hera_sim/utils.py", "max_stars_repo_name": "hughbg/hera_sim", "max_stars_repo_head_hexsha": "b9f4fc39437f586f6ddfa908cf5c5f2e2a6d2231", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, ... |
import cv2
import numpy as np
from matplotlib import pyplot as plt
import torch
# required and recommended pytorch 0.4.0
print(torch.__version__)
def apply_mask(image, mask, out_shape=None):
applied_mask = np.zeros_like(image)
mask = np.argmax(mask, axis=2)
_, contours, _ = cv2.findContours(mask.astype(np... | {"hexsha": "202c56da7faadac7c81f51e4d921d13cfeccdca1", "size": 1075, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/simple_inference.py", "max_stars_repo_name": "serjik85kg/UnetV2-pytorch-segmentation", "max_stars_repo_head_hexsha": "c53598d00aeb0c79cb9dd52b6ab3b4976d127f21", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma hd_not_fwd: "\<not>forward (x#xs@[x]@ys)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> forward (x # xs @ [x] @ ys)
[PROOF STEP]
using hd_not_fwd_arcs forward_arcs_alt
[PROOF STATE]
proof (prove)
using this:
\<not> forward_arcs (?ys @ ?x # ?xs @ [?x])
forward ?xs = forward_arcs (rev ?xs)
g... | {"llama_tokens": 186, "file": "Query_Optimization_IKKBZ_Examples", "length": 2} |
#!/usr/bin/env python2
from sys import stdout, stderr, exit
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter as ADHF
from itertools import combinations, chain, imap
from collections import deque
from random import choice
import networkx as nx
DEFAULT_DELTA = 1
#
# XXX monkey-patching new networkX A... | {"hexsha": "245670131db76a7a75a399798a09a705a80c2d20", "size": 5954, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/graph_teams.py", "max_stars_repo_name": "danydoerr/GraphTeams", "max_stars_repo_head_hexsha": "4b4257275ded8248eb54748b5b833a9d2d21cf37", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright (C) 2021-present CompatibL
#
# 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 in ... | {"hexsha": "baadf6e422cfab266d4472de87fa8f1b5079c2de", "size": 2977, "ext": "py", "lang": "Python", "max_stars_repo_path": "oecd/oecd.py", "max_stars_repo_name": "compatibl/practical-machine-learning-workshop", "max_stars_repo_head_hexsha": "fd5279ab770e68dd14c933f34652c5b6ee32a058", "max_stars_repo_licenses": ["Apache... |
import numpy as np
import read_thres as thrs
def test_thr(check_thr):
data, x = thrs.ths_def(check_thr, threshd=1.E-5)
dat_nw = check_thr.drop(columns=["norm", "<x>", "<y>"])
x_nw = dat_nw.columns.values
assert len(x) == len(x_nw)
assert np.array_equal(x, x_nw)
assert data.equals(dat_nw)
| {"hexsha": "da0ca0a600bf57460ac16259e244376b4f62700f", "size": 315, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mod_stat_num/test/test_read_thres.py", "max_stars_repo_name": "jacquelinefedyk/team3", "max_stars_repo_head_hexsha": "0eada64eec2debceba6950650e9f9ea8c92fa6f8", "max_stars_repo_licenses": ["MIT... |
# coding: utf-8
# ## Elementwise Operations and Statistics.
# In this section I will cover on Elementwise operations, Basic reductions, Broadcasting, and Sorting data. Arrays are important because they enable you to express batch operations on data without writing any for loops. This is usually called vectorization. ... | {"hexsha": "7d34037e0c51774c74438429c53f5d14941b109e", "size": 3683, "ext": "py", "lang": "Python", "max_stars_repo_path": "All Python Codes/2017-23-11-so-stats-vector-math-numpy (1).py", "max_stars_repo_name": "bjfisica/MachineLearning", "max_stars_repo_head_hexsha": "20349301ae7f82cd5048410b0cf1f7a5f7d7e5a2", "max_st... |
import os
from typing import Any
import json
import base64
import math, sys
import numpy as np
from lux import game
from lux.game import Game
from lux.game_map import Cell, Position, RESOURCE_TYPES
from lux.constants import Constants
from lux.game_constants import GAME_CONSTANTS
from lux import annotate
from lux.game_o... | {"hexsha": "d6b1f0ca5d6dc68b90bd580f553225d6b9ff8664", "size": 26317, "ext": "py", "lang": "Python", "max_stars_repo_path": "rexai/agent.py", "max_stars_repo_name": "santomennam/LuxAI", "max_stars_repo_head_hexsha": "0696033d0a993896965a3391f539846adb60d3d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
# Copyright (c) 2015, Scott J Maddox. All rights reserved.
# Use of this source code is governed by the BSD-3-Clause
# license that can be found in the LICENSE file.
import os
import sys
fpath = os.path.abspath(os.path.join(os.path.dirname(__file__),
'../fdint/dfd.pyx'))
with open(... | {"hexsha": "ac0d2cd88b4ea2c8b5c2f17c2e8efa0f1e8fe583", "size": 1247, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/gen_dfd_pyx.py", "max_stars_repo_name": "jgukelberger/fdint", "max_stars_repo_head_hexsha": "0237323d6fd5d4161190ff7982811d8ae290f89e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit.Chem import rdmolfiles
from rdkit.Chem import rdmolops
from rdkit.Chem.rdchem import Mol
from sklearn.preprocessing import StandardScaler
from Datasets.Datasets import Dataset
class MolecularFeaturizer(object):
"""Abstract class for calcul... | {"hexsha": "699a906a28745e5db1ac0715fe4ce6468ac80ce1", "size": 2761, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/compoundFeaturization/baseFeaturizer.py", "max_stars_repo_name": "BioSystemsUM/DeepMol", "max_stars_repo_head_hexsha": "62904fac46f62ec6231543891efbe52ac7ea1cf1", "max_stars_repo_licenses": ["... |
import numpy as np
import scipy.signal
from frontend.acoustic.basic import smooth
from frontend.acoustic.pitch import read_tools_f0, write_tools_f0, hz_to_midi_note, midi_note_to_hz
from frontend.control import htk_lab_file, notes_file
import matplotlib.pyplot as plt # just for plots
def main():
f... | {"hexsha": "05c41ba2833b2ecc4b4d73bc4b52a335a30f039b", "size": 8745, "ext": "py", "lang": "Python", "max_stars_repo_path": "synth/utils/tuning_heuristic.py", "max_stars_repo_name": "MTG/content_choral_separation", "max_stars_repo_head_hexsha": "f3710fb9a15a88651d13ea2c07c6d0368f1cfb8f", "max_stars_repo_licenses": ["Apa... |
# -*- coding: utf-8 -*-
import numpy as np
import cv2, os
from PIL import Image
from cartoon import SAMPLE_IMG
from tqdm import tqdm
def load_net_in(img_fname=SAMPLE_IMG, desired_size=256):
input_image = Image.open(img_fname).convert("RGB")
input_image = input_image.resize((desired_size, desired_s... | {"hexsha": "c7c377301f250e7601993f39be03dd3acaf24c0d", "size": 2426, "ext": "py", "lang": "Python", "max_stars_repo_path": "cartoon/utils.py", "max_stars_repo_name": "penny4860/Keras-CartoonGan", "max_stars_repo_head_hexsha": "e6183c0450cc0a1371e9fb69387564c80f847ac0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
""" Result.Result class to wrap the simulation results."""
from __future__ import division, print_function # Python 2 compatibility
__author__ = "Lilian Besson"
__version__ = "0.9"
import numpy as np
class Result(object):
""" Result accumulators."""
# , delta_t_save=1):
def __i... | {"hexsha": "824740bc77ced5cade580c4c32edffb64ae85120", "size": 2105, "ext": "py", "lang": "Python", "max_stars_repo_path": "SMPyBandits/Environment/Result.py", "max_stars_repo_name": "balbok0/SMPyBandits", "max_stars_repo_head_hexsha": "c8ff765687989e0c20ab42c2e2e1d8440923225b", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright (c) 2016 Peter Eastman
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distr... | {"hexsha": "3d15b32a7330e56f01ebdc6e00ddbb6c63a55f68", "size": 25944, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules.py", "max_stars_repo_name": "peastman/synthulation", "max_stars_repo_head_hexsha": "79cfc880cad45bb8018e6cffe3ad72b11d0cb0c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
/*
Copyright (c) DataStax, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, so... | {"hexsha": "8285a4cfce3539a8e8f2ce5c83cb63cc4ce17ecc", "size": 5680, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/integration_tests/src/test_prepared_batch.cpp", "max_stars_repo_name": "kw217/cpp-driver", "max_stars_repo_head_hexsha": "58c17d15b7ade2a1e6fdf3ce05d6fbe3d90f1828", "max_stars_repo_licenses": [... |
"""Convert trained model for libwavernn
usage: convert_model.py [options] <checkpoint.pth>
options:
--output-dir=<dir> Output Directory [default: model_outputs]
-h, --help Show this help message and exit
"""
# --mel=<file> Mel file input for testing.
from docopt... | {"hexsha": "5c059bdd1760d82e0eeb82dd1721899167c362a6", "size": 10563, "ext": "py", "lang": "Python", "max_stars_repo_path": "convert_model.py", "max_stars_repo_name": "tiberiu44/WaveRNN-Pytorch", "max_stars_repo_head_hexsha": "1cc6cfc4458d144710b67b9ceeb2606cde683d33", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma is_RB_uptD2:
assumes "is_RB_upt d canon G u" and "v \<prec>\<^sub>t u" and "d (pp_of_term v) \<le> dgrad_max d"
and "component_of_term v < length fs"
shows "is_RB_in d canon G v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. is_RB_in d canon G v
[PROOF STEP]
using assms
[PROOF STATE]
proof... | {"llama_tokens": 410, "file": "Signature_Groebner_Signature_Groebner", "length": 3} |
#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# resamplers.py: Implementations of various resampling algorithms.
##
# © 2017, Chris Ferrie (csferrie@gmail.com) and
# Christopher Granade (cgranade@cgranade.com).
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted p... | {"hexsha": "80531fde56b5aa948404a5d3b61ec2ffaeaaafad", "size": 16000, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qinfer/resamplers.py", "max_stars_repo_name": "mikedeltalima/python-qinfer", "max_stars_repo_head_hexsha": "8170c84a0be1723f8c6b09e0d3c7a40a886f1fe3", "max_stars_repo_licenses": ["BSD-3-Claus... |
# Copyright The PyTorch Lightning team.
#
# 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 i... | {"hexsha": "f4f76ce31a45e9e54b9322cc75572f05b9b61d1c", "size": 5402, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/image/instance_segmentation/test_model.py", "max_stars_repo_name": "ar90n/lightning-flash", "max_stars_repo_head_hexsha": "61e1a2d3b72f8fbbffe6ace14fb5b5bb35c5f131", "max_stars_repo_licenses... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# play_phase.py
# aopy
#
# Created by Alexander Rudy on 2013-05-03.
# Copyright 2013 Alexander Rudy. All rights reserved.
#
from __future__ import (absolute_import, unicode_literals, division,
print_function)
import pyshell
from pyshell.u... | {"hexsha": "76651ea9a0b3d569a2e0883053f4afe2dfbfe847", "size": 13749, "ext": "py", "lang": "Python", "max_stars_repo_path": "aopy/wavefront/cli.py", "max_stars_repo_name": "alexrudy/aopy", "max_stars_repo_head_hexsha": "0242bdc81a10ac1a025e6e4cc447cfe90f16dd33", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
from effectlayer import EffectLayer
import numpy
import time
class Speck(EffectLayer):
def __init__(self, color, index=0):
self.index = index
self.color = numpy.array(color)
self.lifespan = 3.5
self.lastSwitch = time.time()
def render(self, model, params, frame):
if self.index < model.numLEDs: # if it's ... | {"hexsha": "5a9a9173767200c86d61e10b88d23cc0399c33d1", "size": 1671, "ext": "py", "lang": "Python", "max_stars_repo_path": "leds/jars/effects/specklayer.py", "max_stars_repo_name": "FlamingLotusGirls/Serenity", "max_stars_repo_head_hexsha": "c128182bfacc81ccc3782dc6a813bb30ba339d40", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/python
# This version by Johan Dahlin
import numpy as np
import scipy.weave as weave
#############################################################################################################################
# Resampling for SMC sampler: Continuous
########################################################... | {"hexsha": "514abe9ea6ec68f80810cdfa0c127b1180113e21", "size": 8517, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/weaveResampling.py", "max_stars_repo_name": "can-cs/pyResampling", "max_stars_repo_head_hexsha": "421323c798ef06c1102dc43de0a2e7fad53ab0f9", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/-
Copyright (c) 2022 Arthur Paulino. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Arthur Paulino
-/
import Mathlib.Tactic.Replace
set_option linter.unusedVariables false
-- tests with a explicitly named hypothesis
example (h : Int) : Nat := by
replace h : Nat :... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/test/Replace.lean"} |
#define CATCH_CONFIG_MAIN
#include "catch_amalgamated.hpp"
#include <potok/hpack/common.hpp>
#include <potok/hpack/encode.hpp>
#include <boost/system/error_code.hpp>
#include <limits>
#include <vector>
using potok::u64;
using potok::u8;
TEST_CASE("C.1.1. Example 1: Encoding 10 Using a 5-Bit Prefix")
{
// https:/... | {"hexsha": "037fef25d7bb3bdb73817f1701e07b2aadcab5f6", "size": 9909, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/hpack_encode_integer.cpp", "max_stars_repo_name": "LeonineKing1199/potok", "max_stars_repo_head_hexsha": "70affed2d9b51137adfe3d4f34f461fa5adc4061", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
import numpy as np
class Hypothesis:
def __init__(self, name, f=None):
self.name = name
self.f = f
def get_name(self):
return self.name
def train(self, x, y):
raise NotImplementedError
def predict(self, x):
raise NotImplementedError
class SkLearnHypothesis(... | {"hexsha": "2e882f031c6d8bcf4ae4d7cc024b3363a79e601f", "size": 1619, "ext": "py", "lang": "Python", "max_stars_repo_path": "hypothesis.py", "max_stars_repo_name": "TAU-MLwell/Rubust-Model-Compression", "max_stars_repo_head_hexsha": "072761dfe768929a0e94d51cdbdbf35a079d752b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import argparse
import numpy as np
import tensorflow as tf
import utils
from model_helper import las_model_fn
def parse_args():
parser = argparse.ArgumentParser(
description='Listen, Attend and Spell(LAS) implementation based on Tensorflow. '
'The model utilizes input pipeline and es... | {"hexsha": "76f6389a756b8cb0f3de79e7008fecf28baa40e4", "size": 2911, "ext": "py", "lang": "Python", "max_stars_repo_path": "infer.py", "max_stars_repo_name": "florianthonig/listen-attend-and-spell", "max_stars_repo_head_hexsha": "218dd4f200cd564d3052c550dbbfe1f2cd836008", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/env python.
import os
import math
import numpy as np
import pandas as pd
from dotenv import load_dotenv
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from ... | {"hexsha": "9cf93d2bc6f28d28e0146820362144feb15cc565", "size": 7251, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipeline/scripts/LR_classifier.py", "max_stars_repo_name": "larrylawl/auto-github-issue-labeller-action", "max_stars_repo_head_hexsha": "3bf15b39ebddda551472f6de4c74083194ee8271", "max_stars_repo_... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% Sample examination layout for %%%%%
%%%%% dit_maths_exam.sty %%%%%
%%%%% %%%%%
%%%%% V3 September 2015 %%%%%
%%%%% - Use new sty file to mirror CoSH template %%%%%
%%%%% - include bw option for black & white logo ... | {"hexsha": "70e684540ce8a40703624b23dc00172793ad43ff", "size": 12728, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter 5- Regression/06_ANOVA/Linear Regression.tex", "max_stars_repo_name": "john-s-butler-dit/Probability_and_Statistical_Inference", "max_stars_repo_head_hexsha": "7351ae85320065a691ba2d7a5239e... |
import numpy as np
from importlib import import_module, reload
from lmfit import minimize, Parameters, Minimizer, report_fit
from scipy.stats import gmean # geometric mean
# import csv
# import pandas as pd
# indices for weight, magnitude, and phase lists
M, P, W = 0, 1, 2
# Set max for model weighting. Minimum is... | {"hexsha": "01bbff2ba05414b148b3b8fd1095824a700d42f7", "size": 14762, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyZfit/zfit_modelcore_cli.py", "max_stars_repo_name": "tinix84/utils", "max_stars_repo_head_hexsha": "3deda887f99bb70d7131b0ba17cfa46082ca3f8d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
from .Board import Board
from .Board import _1D_to_2D_board, _1D_to_2D_coord, DIRECTIONS, MOVE_LEN
from .CheckersLogic import CheckersLogic
from string import ascii_uppercase
DIM = 8
coordinates_list = list(ascii_uppercase)[:DIM]
class HumanPlayer():
def __init__(self, game):
self.game... | {"hexsha": "2031ee18d379e13075fe7d6b036f6117946b2f4c", "size": 1190, "ext": "py", "lang": "Python", "max_stars_repo_path": "checkers/HumanPlayer.py", "max_stars_repo_name": "DomFC/alpha-zero-general", "max_stars_repo_head_hexsha": "865a09b397a7776d2b0e07022bc840c293843f22", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2016 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the Licen... | {"hexsha": "12c467b216f7b6775ef1021aade70f848c2fae2c", "size": 2822, "ext": "py", "lang": "Python", "max_stars_repo_path": "openquake.hazardlib/openquake/hazardlib/tests/gsim/sadigh_1997_test.py", "max_stars_repo_name": "rainzhop/ConvNetQuake", "max_stars_repo_head_hexsha": "a3e6de3f7992eac72f1b9883fec36b8c7fdefd48", "... |
import doctest
import unittest
import numpy as np
import logging
import os
import pandas as pd
import tempfile
import shutil
import pysnptools.util as pstutil
from pysnptools.snpreader import Bed, DistributedBed
from pysnptools.util.filecache import LocalCache
from pysnptools.util.filecache import PeerToPeer
from pysn... | {"hexsha": "131ac9aea6c7df3011ce3beba9d994c16898cf69", "size": 11563, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastlmm/association/tests/test_single_snp_scale.py", "max_stars_repo_name": "HealthML/FaST-LMM", "max_stars_repo_head_hexsha": "3c502ce1c693a934b5f2ff7b63a9577e892cb716", "max_stars_repo_licenses... |
# /usr/bin/env python3
import numpy as np
#Broadcasting: forma de disfusion en python
def centrar():
y= np.random.random(size=(8,9))
ymean=y.mean(axis=0)
ycentered= y-ymean
print(ycentered)
print(ycentered.mean(axis=0))
if __name__=="__main__":
centrar() | {"hexsha": "58cd4877c921633ca0c6413f786d0e50df24d410", "size": 280, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numpy/CalculoMatrices/broadcasting.py", "max_stars_repo_name": "Jovamih/PythonProyectos", "max_stars_repo_head_hexsha": "cc87cc98001fba528c3a03fe9a0edd290b83ec60", "max_stars_repo_licenses": ["MIT"... |
/*
* OSMDatabaseBuilder.hpp
*
* Created on: Jun 8, 2015
* Author: jcassidy
*/
#ifndef OSMDATABASEBUILDER_HPP_
#define OSMDATABASEBUILDER_HPP_
#include "OSMEntity.hpp"
#include "OSMWay.hpp"
#include "OSMNode.hpp"
#include "OSMRelation.hpp"
#include "OSMDatabase.hpp"
#include <boost/container/flat_map.hpp>
... | {"hexsha": "9de43b8cd01c2e4f02001d96ecd3e10efac1f810", "size": 3661, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "OSMDatabaseBuilder.hpp", "max_stars_repo_name": "jeffreycassidy/osm2bin", "max_stars_repo_head_hexsha": "7a6751d1a5045d8fb260aaa723ae85da193b4c93", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
"""
Practice finding contours (continuous lines) in a given image
"""
import numpy as np
import cv2
img = cv2.imread('images/149.jpg') # read image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)
contours, h = cv2.findContours(
thresh, cv2.RETR_EXTER... | {"hexsha": "7461bcbfd0eec6d684936390a69c8bc372e3d4c1", "size": 1048, "ext": "py", "lang": "Python", "max_stars_repo_path": "OpenCV-play/ContourShapes.py", "max_stars_repo_name": "jerdavies/opencv-play", "max_stars_repo_head_hexsha": "39be017e7ec7b649597cc618c05511c83e8efaee", "max_stars_repo_licenses": ["MIT"], "max_st... |
extractargs!(arguments::Vector{Symbol}, defined::Set, sym, mod) = nothing
function extractargs!(arguments::Vector{Symbol}, defined::Set, sym::Symbol, mod)
if ((sym ∉ defined) && sym ∉ (:nothing, :(+), :(*), :(-), :(/), :(÷), :(<<), :(>>), :(>>>), :zero, :one)) && !Base.isdefined(mod, sym)
push!(defined, sym)
... | {"hexsha": "eed899b7acdd580bebaa8dab4c9d407de3de2cf2", "size": 8269, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/closure.jl", "max_stars_repo_name": "ChrisRackauckas/Polyester.jl", "max_stars_repo_head_hexsha": "a15c5d44acbd3e8794b8d6d3aa3e09391c6142d7", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
!>@author
!>Netlib 2019
!>@copyright Public Domain
!>@brief
!> Find a minimum between bounds (single precision version)
!>Downloaded from Netlib, 2019,
!>@param[in] ax: lower bound
!>@param[in] bx: upper bound
!>@param[in] f: function to minimize
!>@param[in]... | {"hexsha": "69b6d66e38720f8fa22be8f76f58e1ca6aaeb854", "size": 4353, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "sfmin.f", "max_stars_repo_name": "UoM-maul1609/open-source-numerical-functions", "max_stars_repo_head_hexsha": "35201f021bbdcc41fcda8cccfd0639b8d1f6445f", "max_stars_repo_licenses": ["MIT"], "max_... |
# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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": "1d04634957ca46a9855555b61ec6eb3e8a8ca0fd", "size": 2320, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/st/ops/gpu/test_softmax_cross_entropy_with_logits_op.py", "max_stars_repo_name": "unseenme/mindspore", "max_stars_repo_head_hexsha": "4ba052f0cd9146ac0ccc4880a778706f1b2d0af8", "max_stars_re... |
import data.real.basic data.analysis.filter
open lattice multiset
universes u v
-- `I` is a "set" (actually a type) that is finite and nonempty
variables {I : Type} [fintype I] [nonempty I] [decidable_eq I]
-- `S` is a "set family", a function which produces (sub)sets of ℝ
variables (S : I → set ℝ)
-- Since `I` is ... | {"author": "MonoidMusician", "repo": "lean-math-stuff", "sha": "56e6ae80b4a634f23a90989a7156ce053a012acf", "save_path": "github-repos/lean/MonoidMusician-lean-math-stuff", "path": "github-repos/lean/MonoidMusician-lean-math-stuff/lean-math-stuff-56e6ae80b4a634f23a90989a7156ce053a012acf/src/sup_sum.lean"} |
# -*- coding: utf-8 -*-
from collections import defaultdict
from os.path import dirname
import os
import networkx as nx
""" Given a set of simulation runs and a threshold graph (output from Tills tool
gml2tg) for a arbitrary threshold and weight, generate one gml file with
networkx for each unique complex =... | {"hexsha": "d83072dbb8a60fa8354930539ba1005b30fc0cc6", "size": 3651, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/write_unique_complexes.py", "max_stars_repo_name": "BiancaStoecker/complex-similarity-evaluation", "max_stars_repo_head_hexsha": "58ac04b943122660b1973656a620077392a5df16", "max_stars_repo... |
from scipy.constants import pi
from scipy.constants import speed_of_light
import numpy as np
class Linear_Antenna:
def __init__(self, frequency=300e6, radius=1e-4, lenght_factor=1/2, source_voltage=1) -> None:
# antenna characteristics
self.lbd = speed_of_light/frequency
self.w = 2 * pi * frequency
self.k ... | {"hexsha": "1ec8e32928821016a44eb1f0a75b06d08d61a369", "size": 4185, "ext": "py", "lang": "Python", "max_stars_repo_path": "antennas.py", "max_stars_repo_name": "mfbsouza/AntennasPy", "max_stars_repo_head_hexsha": "1005db54550ce8dde5a50b41b66e3caa8925be92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
using DiffEqFlux, OrdinaryDiffEq, DiffEqSensitivity
using CUDA, Test, Zygote, Random, LinearAlgebra
CUDA.allowscalar(false)
H = CuArray(rand(Float32, 2, 2))
ann = FastChain(FastDense(1, 4, tanh))
p = initial_params(ann)
function func(x, p, t)
ann([t],p)[1]*H*x
end
x0 = CuArray(rand(Float32, 2))
x1 = CuArray(ran... | {"hexsha": "5ac14f2987c8d90bd1aad4b3188fe2582c16897e", "size": 1456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/gpu/mixed_gpu_cpu_adjoint.jl", "max_stars_repo_name": "SciML/DiffEqSensitivity.jl", "max_stars_repo_head_hexsha": "9da3a808229173ccc73c7237d1b051ef9bb3e0de", "max_stars_repo_licenses": ["MIT"]... |
import tqdm
import numpy as np
from paddlenlp.datasets import load_dataset
from paddlenlp.data import Stack, Tuple, Pad
import paddle
from functools import partial
from paddlenlp.transformers import BertTokenizer
import time
import paddle
#加载tokenized数据集
def read(data_src,data_tgt, max_len=512):
data_src = open(da... | {"hexsha": "680bafcb9199597e11ec60a7229514613e81683c", "size": 4924, "ext": "py", "lang": "Python", "max_stars_repo_path": "ProphetNet_paddle/train.py", "max_stars_repo_name": "zhangliu55/ProphetNet-paddle", "max_stars_repo_head_hexsha": "90271830d92594910439afc603562c3c29bf378a", "max_stars_repo_licenses": ["Unlicense... |
[STATEMENT]
lemma min_maxsimpchainD_maxsimpchain:
assumes "min_maxsimpchain xs"
shows "maxsimpchain xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. maxsimpchain xs
[PROOF STEP]
proof (cases xs rule: list_cases_Cons_snoc)
[PROOF STATE]
proof (state)
goal (3 subgoals):
1. xs = [] \<Longrightarrow> maxsimpcha... | {"llama_tokens": 1318, "file": "Buildings_Simplicial", "length": 18} |
from utils2 import *
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
#Define input parameters
dx = 1.
L = 5.e6
x = np.arange(0.,L,dx)
R = .02+0.*x
#Solve equations
Hsg,S2,Msg = solvePW(x,R)
Hth,T1,FTint,T0 = solveAW(x,Msg)
#Get plotcolors
red,blu,pur = getcols()
#Make variable plot
pret... | {"hexsha": "570068954d474a1a5f8382043bca10372f371ce9", "size": 2680, "ext": "py", "lang": "Python", "max_stars_repo_path": "draftplot_reference.py", "max_stars_repo_name": "erwinlambert/alphabeta", "max_stars_repo_head_hexsha": "04dd03de3521556067c997c2deaf0d6560124b7b", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 29 11:07:18 2020
@author: Tobias Faiss
The code snippet was provided by IBM's DV0101EN "Visualizing Data with Python" course on edX.org
URL: https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%20... | {"hexsha": "3a7d7e408e71caf478300ca4a0ab34b0ba159f4a", "size": 4695, "ext": "py", "lang": "Python", "max_stars_repo_path": "waffle_chart.py", "max_stars_repo_name": "tfaiss/python-snipptes", "max_stars_repo_head_hexsha": "c41d4f61e91efcb49a2053e561fdc65cc9aedb8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
'''Tests for Siemens rda format conversion.
Copyright William Clarke, University of Oxford 2022
Subject to the BSD 3-Clause License.
'''
import subprocess
from pathlib import Path
import json
import numpy as np
from .io_for_tests import read_nifti_mrs
# Data paths
siemens_path = Path(__file__).parent / 'spec2nii_t... | {"hexsha": "1e739e4957eff1e699d0a8a44ff351c0d37f7805", "size": 988, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_siemens_rda.py", "max_stars_repo_name": "alexcraven/spec2nii", "max_stars_repo_head_hexsha": "81d4364efcd8e75ea52b5d06c7b18cac234061fd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
from __future__ import print_function, absolute_import
import os
import sys
import time
import pickle
import random
import argparse
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.distributed as dist
import torch.backends.cudnn as cudnn
import numpy as np
fro... | {"hexsha": "53cea642ed97f787b75fc66867959fbd93bd88a7", "size": 4700, "ext": "py", "lang": "Python", "max_stars_repo_path": "old/extract_features.py", "max_stars_repo_name": "czyczyyzc/GATES", "max_stars_repo_head_hexsha": "53e2e45d6cd3ec3af1f9389f30bc34c9b04265fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import scipy as sp
import numpy as np
import pylab as pl
import scipy.integrate as spi
RMars=3.4E6 # Radius of Mars
msat=260 # Mass of Satellite
G=6.67E-11 # Gravitational Constant
M=6.4E23 # Mass of Mars
def f(initial,t):
x=initial[0] #x position as initial condition 1
y=initial[1] #y position ... | {"hexsha": "4fc8e4afad36034c284d5302ffb404be426c43cd", "size": 3322, "ext": "py", "lang": "Python", "max_stars_repo_path": "Satellite Displacement.py", "max_stars_repo_name": "EuniceChen1/ComputingProject2013", "max_stars_repo_head_hexsha": "467418fad48cc7f7e92c39e4bed3195f9315e740", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env python
'''Batch Processing Classes.
'''
import cPickle as pickle
import gzip
import os
import sys
import time
import numpy as np
class Scaler(object):
def __init__(self, offset, scale):
self.__offset = offset
self.__scale = scale
def scale_input(self, y):
return y / se... | {"hexsha": "3cef925c29dec9e1189c2858cd5ffeb1717f69a4", "size": 7784, "ext": "py", "lang": "Python", "max_stars_repo_path": "final-project/code/batch.py", "max_stars_repo_name": "wclark3/machine-learning", "max_stars_repo_head_hexsha": "f4f09d6d1efa022d9c34647883e49ae8e2f1fe6c", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
class Opt:
def __init__(self):
self.arch='mesh_ae'
self.batch_size=16
self.checkpoints_dir='./checkpoints'
self.dataroot='G:/dataset/MCB_B/MCB_B/'
self.mode='autoencoder'
self.export_folder=''
self.fc_n=100
self.feature_dir='./featu... | {"hexsha": "7d763528599a9ce19f0e3178ed18090f1c32283b", "size": 1353, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/options/opt.py", "max_stars_repo_name": "anglixjtu/MeshCNN_", "max_stars_repo_head_hexsha": "83826e66d8989ed4967047c2ed6d099568c5781c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
# Copyright 2018 The TensorFlow Probability 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 o... | {"hexsha": "b536893a8bd4c5b3908ce2099a2436e6fc1d7838", "size": 3693, "ext": "py", "lang": "Python", "max_stars_repo_path": "discussion/fun_mcmc/util_tf.py", "max_stars_repo_name": "bolcom/probability", "max_stars_repo_head_hexsha": "4a11efad1ecd8a1336e4c9fdb0105efbf2375ad7", "max_stars_repo_licenses": ["Apache-2.0"], "... |
# generate face rois by picking 80 max voxels in each sphere
import os, math
import nibabel as nib
import numpy as np
# initialize parameters
### work_directory = '/Users/chloe/Documents/'
### all_subjects = ['sub-02', 'sub-19', 'sub-20']
### all_masks_dir = '/Users/chloe/Documents/kanparcel_nii/'
work_directory = '/... | {"hexsha": "266c1d5a150eb8d8cb2d4ea99e2d9c65ed5ecf06", "size": 6310, "ext": "py", "lang": "Python", "max_stars_repo_path": "roi/roi_gen_face_ROIs.py", "max_stars_repo_name": "yl3506/iMVPD_dev", "max_stars_repo_head_hexsha": "3e08dbdf45b964a459a7dd1cb2f468c6df72d64b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# -*- coding: utf-8 -*-
# @Time : 2017/7/12 下午8:28
# @Author : play4fun
# @File : 凸包-凸性检测-边界矩形-最小外接圆-拟合.py
# @Software: PyCharm
"""
凸包-凸性检测-边界矩形-最小外接圆-拟合.py:
"""
import cv2
import numpy as np
img=cv2.imread('../data/lightning.png',0)
image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_... | {"hexsha": "e8ecb338c9a2475b478c5655c94cb27470b4a0df", "size": 2154, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch21-\u8f6e\u5ed3Contours/\u51f8\u5305-\u51f8\u6027\u68c0\u6d4b-\u8fb9\u754c\u77e9\u5f62-\u6700\u5c0f\u5916\u63a5\u5706-\u62df\u5408.py", "max_stars_repo_name": "makelove/OpenCV-Python-Tutorial", ... |
------------------------------------------------------------------------
-- The extensional sublist relation over decidable setoid equality.
------------------------------------------------------------------------
{-# OPTIONS --without-K --safe #-}
open import Relation.Binary
module Data.List.Relation.Binary.Subset.... | {"hexsha": "198472e46fc6fcfddb940499ab182ad07aabfaf7", "size": 5012, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Data/List/Relation/Binary/Subset/DecSetoid.agda", "max_stars_repo_name": "banacorn/bidirectional", "max_stars_repo_head_hexsha": "0c9a6e79c23192b28ddb07315b200a94ee900ca6", "max_stars_repo_license... |
"""
generate noisy data with various noise files
"""
import os
import sys
import numpy as np
import scipy.io.wavfile as wav
import librosa
from pathlib import Path
import soundfile
#######################################################################
# data info setting ... | {"hexsha": "4fb51e60eb070fda9d53d0370c2c2a600ccd7e75", "size": 6784, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_noisy_data.py", "max_stars_repo_name": "seorim0/DNN-based-Speech-Enhancement-in-the-frequency-domain", "max_stars_repo_head_hexsha": "ed54e8c0eaea1f063c4db8e7a475ea3eb6e2f836", "max_stars... |
"""
Classic cart-pole system implemented by Rich Sutton et al.
Copied from http://incompleteideas.net/sutton/book/code/pole.c
permalink: https://perma.cc/C9ZM-652R
"""
import math
from typing import Optional, Union
import numpy as np
import gym
from gym import logger, spaces
from gym.utils import seeding
class Cart... | {"hexsha": "7a58c3cb763d68c2753e9e73d80a1afe563951ce", "size": 10317, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_cartpole.py", "max_stars_repo_name": "drib861204/deep-q-learning", "max_stars_repo_head_hexsha": "216ef85f6bb94f9de9b26fa13a51980e948a2136", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
## Model Generators for 2D Gaussian PSFs
using SpecialFunctions
"""
PSF_airy2D <: PSF
Contains the parameter used to calculate an Airy Pattern PSF
The Airy PSF is
I(r)=ν²/(4π)(2*J₁(ν*r)/(ν*r))²
where
ν=πD/(λf)=2*π*nₐ/λ
!!! note
The Gaussian approximation is σ = 0.42*π/ν
"""
struct PSF_airy2... | {"hexsha": "a3802c39fd1902c2ce8e703fe648aea37b8911d3", "size": 1313, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/airy2D.jl", "max_stars_repo_name": "LidkeLab/BAMF.jl", "max_stars_repo_head_hexsha": "58566fbe5af7acfd76bc8b435979095bfd030bf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
\documentclass{article}
\input{../../preamble.tex}
\pagestyle{main}
\renewcommand{\leftmark}{Lab Report 1 (Wave Motion)}
\begin{document}
\section{Wave Amplitude vs. Velocity}
\textbf{Materials}: I used the \href{https://phet.colorado.edu/sims/html/wave-on-a-string/latest/wave-on-a-string_en.html}{Wave on a Stri... | {"hexsha": "15852944c720c7c5495fa6da8dc782c78d30eabd", "size": 5975, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "LabReports/LabReport1/labreport1.tex", "max_stars_repo_name": "shadypuck/PHYS13300Notes", "max_stars_repo_head_hexsha": "61c7dcb457b6ce79feba5d9a46e991c88cdcde68", "max_stars_repo_licenses": ["CC-BY... |
"""
fully tested with feature change, norm-feature change, feature sloc, norm-feature sloc
"""
import re
import os
import matplotlib.pyplot as plt
import re
import numpy as np
import math
from scipy.stats.stats import kendalltau
import scipy
from matplotlib.patches import Rectangle
from scipy import stats
import se... | {"hexsha": "0b62f03e4e7a211652856e923f13c5cd7f241393", "size": 7680, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/results-past/figure-4-b.py", "max_stars_repo_name": "shaifulcse/codemetrics-with-context-replication", "max_stars_repo_head_hexsha": "9f0fe6e840d204b70efc9610e6887a64f9a51ce7", "max_stars_rep... |
from albumentations import RandomScale, DualTransform, PadIfNeeded
from albumentations.pytorch import ToTensorV2
import random
import cv2
import numpy as np
__all__ = ['RandomDiscreteScale',
'ToTensor',
'ConstantPad']
class RandomDiscreteScale(RandomScale):
def __init__(self, scales, interp... | {"hexsha": "e5c6eabc8a0f6758b69ef0b17f6247e5e62f8630", "size": 2592, "ext": "py", "lang": "Python", "max_stars_repo_path": "simplecv/_impl/preprocess/albu.py", "max_stars_repo_name": "Bobholamovic/SimpleCV", "max_stars_repo_head_hexsha": "f4edacf088d0155725a469e227de847820bdfa53", "max_stars_repo_licenses": ["MIT"], "m... |
lst = readlines("data/unixdict.txt")
filter!(issorted, lst)
filter!(x -> length(x) == maximum(length, lst), lst)
println.(lst)
| {"hexsha": "4594386cdf525962b750d725c974830254cc661f", "size": 127, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/ordered-words-2.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 23 10:07:59 2020
@author: chaos
"""
import os
import sys
sys.path.append('../..')
import matrixslow as ms
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# 读取图像，归一化
pic = matplotlib.image.imread(os.path.abspath('../../data/mondrian.jpg')) / 255
# ... | {"hexsha": "e4ae9e154c0afbff2f6637bd945d81ea9eb57be1", "size": 1419, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/ch08/sobel.py", "max_stars_repo_name": "nuaalixu/MatrixSlow", "max_stars_repo_head_hexsha": "490b7114130919b3d0f0320018308313951f8478", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# Author: Pengcheng He (penhe@microsoft.com)
# Date: 05/15/2019
#
""" FP16 optimizer wrapper
"""
from collections import defaultdict
import numpy as np
import math
import torch
import pdb
im... | {"hexsha": "689a3d49a05bdc9248edd88763841239e8401694", "size": 9229, "ext": "py", "lang": "Python", "max_stars_repo_path": "DeBERTa/optims/fp16_optimizer.py", "max_stars_repo_name": "tirkarthi/DeBERTa", "max_stars_repo_head_hexsha": "c558ad99373dac695128c9ec45f39869aafd374e", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
aggregate2
Aggregate data to yearly, monthly, daily, hourly or ONE minute samples.
Simple aggregation = sum, mean, maximum or minimum can be applied during re-sampling.
Missing (NaN) data can be either kept or replaced during re-sampling.
**Input**
* data: DataFrame where at least one column contains DateTime
* ... | {"hexsha": "a16dadd26fb33eb490b663f8a82f6b19c4409b9b", "size": 7044, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "resampledata.jl", "max_stars_repo_name": "tehrandavis/data_management_tools", "max_stars_repo_head_hexsha": "3c531c78f85f4de3be20dc4ac35696721fe77290", "max_stars_repo_licenses": ["MIT"], "max_star... |
function initialise_Q(gp::GPBase)
# TODO: Use PDMats for the below
V = cov(gp.kernel, gp.x, gp.data)
Ω = inv(V)
K = deepcopy(Ω)
m = mean(gp.mean, gp.x)
Q = Approx(m, V)
return Q, V, K
end
function update_Q!(Q, m, V)
Q.m = m
Q.V = V
end
function elbo(y::AbstractArray, μ::AbstractA... | {"hexsha": "1413d18e00a68feda6c4a67820d30f38a05d8c8a", "size": 4552, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/vi.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/GaussianProcesses.jl-891a1506-143c-57d2-908e-e1f8e92e6de9", "max_stars_repo_head_hexsha": "47fb1820729a11c2ad6edf0a14641289d28b4b1... |
[STATEMENT]
lemma ent_wandI:
assumes IMP: "Q*P \<Longrightarrow>\<^sub>A R"
shows "P \<Longrightarrow>\<^sub>A (Q -* R)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P \<Longrightarrow>\<^sub>A Q -* R
[PROOF STEP]
unfolding entails_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>h. h \<Turnstile... | {"llama_tokens": 1840, "file": "Van_Emde_Boas_Trees_Separation_Logic_Imperative_HOL_Assertions", "length": 17} |
# ===========================================================================
# rscanvas.py ---------------------------------------------------------
# ===========================================================================
# import ------------------------------------------------------------------
# ---------... | {"hexsha": "08a7446d747350793307b631edf8e5cd249f1dfa", "size": 3163, "ext": "py", "lang": "Python", "max_stars_repo_path": "rsvis/tools/rscanvasframe.py", "max_stars_repo_name": "Tom-Hirschberger/DataVisualization", "max_stars_repo_head_hexsha": "1aec6a85e2af7ba62ba47e6ee93dc9a7d99c6221", "max_stars_repo_licenses": ["M... |
[STATEMENT]
lemma cf_brcomp_ObjMap_vdomain[cat_cs_simps]:
assumes "\<SS> : \<CC> \<times>\<^sub>C \<CC> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
shows "\<D>\<^sub>\<circ> (cf_brcomp \<SS>\<lparr>ObjMap\<rparr>) = (\<CC> \<times>\<^sub>C\<^sub>3 \<CC> \<times>\<^sub>C\<^sub>3 \<CC>)\<lparr>Obj\<rpa... | {"llama_tokens": 948, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_PCategory", "length": 8} |
"""
Author: Alberto Purpura
Copyright: (C) 2019-2020 <http://www.dei.unipd.it/
Department of Information Engineering> (DEI), <http://www.unipd.it/ University of Padua>, Italy
License: <http://www.apache.org/licenses/LICENSE-2.0 Apache License, Version 2.0>
"""
from tqdm import tqdm
import data_utils ... | {"hexsha": "726354528a3ee3fc995ce8501a431a493a032727", "size": 15071, "ext": "py", "lang": "Python", "max_stars_repo_path": "MP_WN_WE/evaluation.py", "max_stars_repo_name": "albpurpura/PE4IR", "max_stars_repo_head_hexsha": "54c5d471181cdb64225ecd738577b9f1f94c8d24", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
/-
Copyright (c) 2016 Microsoft Corporation. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Leonardo de Moura
Converter monad for building simplifiers.
-/
prelude
import init.meta.tactic init.meta.simp_tactic init.meta.interactive
import init.meta.congr_lemma init.met... | {"author": "subfish-zhou", "repo": "N2Lean", "sha": "8e858cc5b01f1ad921094dc355db3cb9473a42fd", "save_path": "github-repos/lean/subfish-zhou-N2Lean", "path": "github-repos/lean/subfish-zhou-N2Lean/N2Lean-8e858cc5b01f1ad921094dc355db3cb9473a42fd/library/init/meta/converter/conv.lean"} |
import numpy as np
import pandas as pd
__all__ = ['volParkinson', 'volVanilla']
def difs ( close ):
return np.log(close).diff()
def volParkinson ( highs, lows ):
"""Estimates the historical volatility for series using the Parkinson method.
Arguments:
highs:
numpy array or pandas series of ... | {"hexsha": "51da2659f86666d2bf760a3133050b24a290f67f", "size": 1466, "ext": "py", "lang": "Python", "max_stars_repo_path": "rraider/estimate.py", "max_stars_repo_name": "alienbrett/rraider", "max_stars_repo_head_hexsha": "f587858227a807216bd798d2f1feb13feb6894df", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
PROGRAM RYLATEST
C
double precision al
double complex y,rc(2,2)
C
AL = 1.D00
YI = 0.
DO NR = 1,20
YR = 2. + .1*NR
Y = CMPLX(YR,YI)
CALL RYLA(Y,AL,RC)
PRINT*,YR,YI,AL
PRINT*,(RC(I,1),I=1,2)
PRINT*,(RC(I,2),I=1,2)
ENDDO
STOP
END
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC... | {"hexsha": "50910cb94719f10017fc6a3d6107768d26234e14", "size": 6865, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "WAVES_VMS_Fortran/PJK_Fortran/waves_dir/rylatest_8.for", "max_stars_repo_name": "lynnbwilsoniii/Wind_Decom_Code", "max_stars_repo_head_hexsha": "ef596644fe0ed3df5ff3b462602e7550a04323e2", "max_s... |
[STATEMENT]
lemma ocomplete_no_cast [simp]:
"((\<sigma>, s), R:*cast(m), (\<sigma>', s')) \<notin> ocnet_sos T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ((\<sigma>, s), R:*cast(m), \<sigma>', s') \<notin> ocnet_sos T
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. ((\<sigma>, s), R:*cast... | {"llama_tokens": 614, "file": "AWN_OAWN_SOS", "length": 7} |
[STATEMENT]
lemma flopped_half_chamber_systems_fg: "\<C>-f\<turnstile>\<C> = g\<turnstile>\<C>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. folding_g.\<C> - f \<turnstile> folding_g.\<C> = g \<turnstile> folding_g.\<C>
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. folding_g.\<C> - f \<turn... | {"llama_tokens": 1735, "file": "Buildings_Chamber", "length": 10} |
# Copyright 2020 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "fb53abb1a9e15a5d5a040be42213f325ab345163", "size": 8820, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/delf/delf/python/delg/perform_retrieval.py", "max_stars_repo_name": "raijin0704/models", "max_stars_repo_head_hexsha": "6906bfbdbf2ad628bb6aeca9989dc04f605b6a60", "max_stars_repo_licenses... |
#Emera Tagging
project.library( 'aegis', 'bio.snowcrab')
dn = file.path(project.datadirectory("bio.snowcrab"),'data','tagging','Emera')
a = dir(dn)
b = a[grep('tags',a)]
a = a[grep('meta',a)]
a = read.csv(file.path(dn,a),header=T)
out = NULL
for(i in b) {
h = read.csv(file.path(dn,i),header=T)
out =... | {"hexsha": "f896753b55173e5719b24d41e0b7cd675ff95f92", "size": 1034, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/13.Emera.Tagging.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using ExprTools
@testset "parsing" begin
# kwargs
let kwargs = splitdef(:( foo(a; b=2, c::Int=1, d::Int, e) = a + b + c))[:kwargs]
@test Pretend.arg_names(kwargs) == [:b, :c, :d, :e]
end
end
| {"hexsha": "afb87db7f66b4683c271b0a5bdfe359041814751", "size": 214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_misc.jl", "max_stars_repo_name": "tk3369/Pretend.jl", "max_stars_repo_head_hexsha": "e45237f4c5faf4e2c339688e2aec52d33a41b680", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "m... |
SUBROUTINE SEARCH(XPARAM,ALPHA,SIG,NVAR,GMIN,OKC,OKF, FUNCT)
IMPLICIT DOUBLE PRECISION (A-H,O-Z)
INCLUDE 'SIZES'
DIMENSION XPARAM(*), SIG(*)
************************************************************************
*
* SEARCH PERFORMS A LINE SEARCH FOR POWSQ. IT MINIMIZES THE NORM OF
* THE... | {"hexsha": "7770552449e2f38e753e22db359e1be699b8830e", "size": 4641, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "1989_MOPAC5/search.f", "max_stars_repo_name": "openmopac/MOPAC-archive", "max_stars_repo_head_hexsha": "01510e44246de34a991529297a10bcf831336038", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
/-
Copyright (c) 2020 Hanting Zhang. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Hanting Zhang
-/
import data.polynomial.splits
import ring_theory.mv_polynomial.symmetric
/-!
# Vieta's Formula
The main result is `multiset.prod_X_add_C_eq_sum_esymm`, which shows th... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/ring_theory/polynomial/vieta.lean"} |
import os
import time
import random
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
from torchnet import meter
from dataset import DataSet
from network import Network
from config import config
from utils.logger import logger
from dataset import data_loader
def setup_seed(seed):
torch... | {"hexsha": "097805fcb10db4c9bf8f0fe0523440df928b96e7", "size": 5231, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "BruceHan98/Chinese-Character-Recognition", "max_stars_repo_head_hexsha": "4bf1d4f69b892871da02f0d078530c929d320ed0", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
\sekshun{Interoperability}
\label{Interoperability}
\index{interoperability}
Chapel's interoperability features support cooperation between Chapel
and other languages. They provide the ability to create software
systems that incorporate both Chapel and non-Chapel components.
Thus, they support the reuse of existing s... | {"hexsha": "21bcdeac3ee10e9ddcea9cbc727025aae979c27e", "size": 20464, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "spec/Interoperation.tex", "max_stars_repo_name": "KING-SID/chapel", "max_stars_repo_head_hexsha": "8fe143dff7395a9600794ec0c3921038d8c81784", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"], "... |
import numpy as np
import pytest
from fracdiff.sklearn.stat import StatTester
class TestStat:
"""
Test `StatTester`.
"""
def _make_stationary(self, seed, n_samples):
np.random.seed(seed)
return np.random.randn(n_samples)
def _make_nonstationary(self, seed, n_samples):
np... | {"hexsha": "940530b7bfc9369db47db3e820c5d252ff98f0a1", "size": 1231, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/sklearn/test_stat.py", "max_stars_repo_name": "jmrichardson/fracdiff", "max_stars_repo_head_hexsha": "983e1f7c64e717799330c528b034f32544c0a417", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
from brl_gym.wrapper_envs.bayes_env import BayesEnv
from brl_gym.envs.mujoco.wam_find_obj import WamFindObjEnv
from brl_gym.estimators.mujoco.ekf_wam_find_obj_estimator import EKFWamFindObjEstimator
import gym
from gym import utils
from gym.spaces import Box
import numpy as np
class BayesWamFindObj(BayesEnv):
de... | {"hexsha": "9866ce0583f560a54a817e6d3619b2ca67a6d2c8", "size": 2185, "ext": "py", "lang": "Python", "max_stars_repo_path": "brl_gym/wrapper_envs/mujoco/wrapper_wam_find_obj.py", "max_stars_repo_name": "gilwoolee/brl_gym", "max_stars_repo_head_hexsha": "9c0784e9928f12d2ee0528c79a533202d3afb640", "max_stars_repo_licenses... |
[STATEMENT]
lemma powr_mono:
fixes x :: real
assumes "a \<le> b" and "1 \<le> x" shows "x powr a \<le> x powr b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x powr a \<le> x powr b
[PROOF STEP]
using assms less_eq_real_def
[PROOF STATE]
proof (prove)
using this:
a \<le> b
1 \<le> x
(?x \<le> ?y) = (?x < ?y \<... | {"llama_tokens": 191, "file": null, "length": 2} |
__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import io
from typing import Dict
import numpy as np
from PIL import Image
from jina.executors.encoders.frameworks import BaseMindsporeEncoder
from jina.executors.crafters import BaseCrafter
from .lenet.src.lenet impo... | {"hexsha": "816a5ef40d7a35fd2a8bf2058cf0320d5f9dc11e", "size": 2449, "ext": "py", "lang": "Python", "max_stars_repo_path": "lenet-chinese_mnist/__init__.py", "max_stars_repo_name": "leonwanghui/mindspore-jina-apps", "max_stars_repo_head_hexsha": "e2912d9a93689c69005345758e3b7a2f8ba6133e", "max_stars_repo_licenses": ["A... |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 2 15:36:11 2020
@author: Timothe
"""
import sys, os, time
import random
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg')
from matplotlib.backends.qt_compat import QtCore, QtWidgets
from matplotlib.backends.backend_qt5agg import (
FigureCanvas, Navig... | {"hexsha": "6a1d3e45a478bdc6b57ba0d8edb14258180de418", "size": 4788, "ext": "py", "lang": "Python", "max_stars_repo_path": "LibrairieVideoAna/Unclean/Qt_Explore.py", "max_stars_repo_name": "JostTim/custom_libs", "max_stars_repo_head_hexsha": "8f9e3f45c6f5f7e47b6582e072d09a8910efddd3", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from tsfresh.transformers import RelevantFeatureAugmenter
from tsfresh.transformers import FeatureAugmenter
from tsfresh.feature_extraction im... | {"hexsha": "d3a77a944e66bc6e57e66e9817586ab80e0f6610", "size": 3560, "ext": "py", "lang": "Python", "max_stars_repo_path": "old_pipeline.py", "max_stars_repo_name": "MichaelSheely/RegionPredictionFromTemperature", "max_stars_repo_head_hexsha": "e5da3f727f28069aa2744d476a472df98d9945b0", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma tm_cf_diagonal_is_functor'[cat_cs_intros]:
assumes "tiny_category \<alpha> \<JJ>"
and "category \<alpha> \<CC>"
and "\<alpha>' = \<alpha>"
and "\<AA> = \<CC>"
and "\<BB> = cat_Funct \<alpha> \<JJ> \<CC>"
shows "\<Delta>\<^sub>C\<^sub>F\<^sub>.\<^sub>t\<^sub>m \<alpha> \<JJ> \<CC> ... | {"llama_tokens": 512, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_FUNCT", "length": 3} |
# %% [markdown]
# # Imports
import os
import pickle
import warnings
from operator import itemgetter
from pathlib import Path
from timeit import default_timer as timer
import colorcet as cc
import matplotlib.colors as mplc
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
impo... | {"hexsha": "1be0bf54a9e2c5a8dfec48aeb3e481377300ff82", "size": 21877, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/56.0-BDP-LR-homogenize.py", "max_stars_repo_name": "zeou1/maggot_models", "max_stars_repo_head_hexsha": "4e1b518c2981ab1ca9607099c3813e8429d94ca4", "max_stars_repo_licenses": ["BSD-3-Cl... |
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