content
stringlengths 1
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sequencelengths 1
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| token_count
int64 1
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|
|---|---|---|---|
from django.contrib import admin
from .models import Composition, Contest, Vote, Content
admin.site.register(Content)
admin.site.register(Composition)
admin.site.register(Contest)
admin.site.register(Vote)
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import threading
import typing
import nacl.signing
import time
import typing as tp
import logging.config
from .istation import IStation, StationData, STATION_VERSION, Measurement
from ..drivers.sds011 import SDS011_MODEL, SDS011
from collections import deque
from connectivity.config.logging import LOGGING_CONFIG
logging.config.dictConfig(LOGGING_CONFIG)
logger = logging.getLogger("sensors-connectivity")
class COMStation(IStation):
"""
Reads data from a serial port
"""
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from app.models.Model import Model
from flask_restful import abort
import sqlite3
"""
A class that factorizes the behavior of models used for the API
"""
# | [
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"""
* The Lock class controls multiple servos to latch the door shut before the rocket spins up.
* Author: Aaron Borger <[email protected] (307)534-6265>
"""
from devices.device import Device
import RPi.GPIO as GPIO
SERVO_PIN = 14
ZERO = 2.5
NINETY = 7.5
ONE_EIGHTY = 12.5
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'''
Generate features for outlier detection.
'''
import json
import sys
from certstream_analytics.analysers import WordSegmentation
from certstream_analytics.analysers import IDNADecoder
from certstream_analytics.analysers import FeaturesGenerator
def main(max_count=None):
'''
The record is assumed to be stored in a JSON file passed in as the first
parameter of the script.
'''
segmenter = WordSegmentation()
decoder = IDNADecoder()
generator = FeaturesGenerator()
with open(sys.argv[1]) as fhandle:
count = 0
for line in fhandle:
try:
record = json.loads(line.strip())
except json.decoder.JSONDecodeError:
continue
record = decoder.run(record)
record = segmenter.run(record)
record = generator.run(record)
print(json.dumps(record))
count += 1
if max_count and count > max_count:
break
if __name__ == '__main__':
main()
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220,
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198
] | 2.363218 | 435 |
import torch
import numpy as np
from torch.nn import functional as F
import torch.nn as nn
from torch.autograd import Variable
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# -*- coding: utf-8 -*-
import numpy
#only accept order = numpy.nan or 0
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from .storage import DatabaseManager | [
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] | 6 | 6 |
if __name__ == "__main__":
main()
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import ConfigParser
from helpers import get_abs_path
DEFAULT_CFG_FILEPATH = 'locust-swarm.cfg'
DEFAULT_MASTER_ROLE_NAME = 'locust-master'
DEFAULT_SLAVE_ROLE_NAME = 'locust-slave'
DEFAULT_MASTER_BOOTSTRAP_DIR = './bootstrap-master'
DEFAULT_SLAVE_BOOTSTRAP_DIR = './bootstrap-slave'
DEFAULT_NUM_SLAVES = 5
DEFAULT_CUSTOM_TAG_NAME = 'MachineRole'
get_config = _parse
# vim: filetype=python
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] | 2.425414 | 181 |
#-------by HYH -------#
import numpy as np
pCan=0.001
pNon=0.999
pPosCan=0.8
pPosNon=0.1
z='positive'
if 'positive'==z:
p=[pPosCan*pCan,pPosNon*pNon]
else:
p=[(1-pPosCan)*pCan,(1-pPosNon)*pNon]
p=p/np.sum(p)
print('The probability of having cancer given the %s test:\n'% z,'\n',p[0])
print('The probability of cancer free given the %s test:\n'%z,'\n',p[1]) | [
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import argparse, os, sys
from nit import generate_tile, generate_tile_from_initials, generate_initials_from_string
my_parser = argparse.ArgumentParser(prog="name initials tile generator",
usage="$(prog)s [options] name save_path",
description="Generate a name initials tile icon given name")
my_parser.add_argument("Name", metavar="name", type=str, help="Name to generate initials.")
my_parser.add_argument("Save_Path", metavar="save_path", type=str, help="Path where the generated tile should be saved.")
my_parser.add_argument("-bg", "--bg_color", type=str, help="Background color to be used in tile.")
my_parser.add_argument("-fg", "--fg_color", type=str, help="Color of the text to be used in tile.")
args = my_parser.parse_args()
if not os.path.isdir(os.path.split(args.Save_Path)[0]):
print("The path does not exist.")
sys.exit()
kwargs = dict(text=args.Name, save_path=args.Save_Path, bgColor=args.bg_color, fgColor=args.fg_color)
generate_tile_from_initials(**{k: v for k, v in kwargs.items() if v is not None})
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11,
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19570,
198,
1820,
62,
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13,
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62,
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12,
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1600,
366,
438,
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62,
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13,
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46265,
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8633,
7,
5239,
28,
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13,
5376,
11,
3613,
62,
6978,
28,
22046,
13,
16928,
62,
15235,
11,
275,
70,
10258,
28,
22046,
13,
35904,
62,
8043,
11,
277,
70,
10258,
28,
22046,
13,
40616,
62,
8043,
8,
198,
8612,
378,
62,
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62,
6738,
62,
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82,
7,
1174,
90,
74,
25,
410,
329,
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11,
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287,
479,
86,
22046,
13,
23814,
3419,
611,
410,
318,
407,
6045,
30072,
198
] | 2.641148 | 418 |
import pandas as pd
labels = pd.read_csv('../Fusion_dummy_His_MUT_joined.csv', header=0)
# line = pd.read_csv('../../Line1.csv', header=0)
line = pd.read_csv('../EC_cyclin_expression.csv', header=0)
# line['name'] = line['Proteomics_Participant_ID']
# line = line.drop(['Proteomics_Participant_ID', 'Histologic_type', 'Genomics_subtype', 'TP53_TP53'], axis=1)
# labels = labels.join(line.set_index('name'), on='name')
# labels['LINE1_ORF1p'] = (labels['LINE1_ORF1p'].dropna() > 0).astype(int)
# labels['RAD50-S635'] = (labels['RAD50-S635'].dropna() > 0).astype(int)
# labels['NBN-S343'] = (labels['NBN-S343'].dropna() > 0).astype(int)
# labels['ATR-T1989'] = (labels['ATR-T1989'].dropna() > 0).astype(int)
# labels['ATM-S1981'] = (labels['ATM-S1981'].dropna() > 0).astype(int)
line['name'] = line['Sample_ID'].str.slice(start=0, stop=9)
line = line.drop(['Sample_ID', 'Genomic_subtype'], axis=1)
labels = labels.join(line.set_index('name'), on='name')
labels['CCND1'] = (labels['CCND1'].dropna() > 0).astype(int)
labels['CCNE1'] = (labels['CCNE1'].dropna() > 0).astype(int)
labels['CCNA2'] = (labels['CCNA2'].dropna() > 0).astype(int)
labels['CCNB1'] = (labels['CCNB1'].dropna() > 0).astype(int)
labels.to_csv('../Fusion_dummy_His_MUT_joined.csv', index=False)
| [
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37,
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62,
44,
3843,
62,
46416,
13,
40664,
3256,
6376,
28,
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8,
198
] | 2.285199 | 554 |
#Ler o ano de nascimento de um jovem e verificar se ele está na hora de alistar, se está muito cedo
#para isso ou já passou do momento certo
from datetime import date
nascimento = int(input("Digite o ano do seu nascimento: "));
sexo = str(input("Você é homem ou mulher? Digite H para homem e M se for mulher: ")).upper().strip();
atual = date.today().year;
idade = (atual - nascimento);
if sexo == "H":
if idade < 18:
print(f"Você ainda tem \033[1:38m{idade}\033[m anos, ainda não está na hora de se alistar. Faltam \033[1:39m{18-idade}\033[m anos.");
print(f"Você deve se alistar em {nascimento + 18}")
elif idade == 18:
print(f"Você já tem \033[1:35m{idade}\033[m anos, chegou a hora! Aliste-se!");
elif idade > 18:
print(f"Você tem \033[1:34m{idade}\033[m anos, seu alistamento foi em \033[1:31m{nascimento + 18}\033[m. Verifique sua situação e caso necessário, regularize-a o mais rápido possível.");
else:
print("Como você é uma mulher, não precisa se alistar.");
| [
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] | 2.146091 | 486 |
from click.testing import CliRunner
import os
import py
import pytest
import builtins
import sys
from typing import Any
from tests import DATA_DIR
from xaitk_saliency.utils.bin.sal_on_coco_dets import sal_on_coco_dets
from importlib.util import find_spec
deps = ['kwcoco']
specs = [find_spec(dep) for dep in deps]
is_usable = all([spec is not None for spec in specs])
dets_file = os.path.join(DATA_DIR, 'test_dets.json')
config_file = os.path.join(DATA_DIR, 'config.json')
class TestSalOnCocoDetsNotUsable:
"""
These tests make use of the `tmpdir` fixture from `pytest`. Find more
information here: https://docs.pytest.org/en/6.2.x/tmpdir.html
"""
def test_warning(self, tmpdir: py.path.local) -> None:
"""
Test that proper warning is displayed when required dependencies are
not installed.
"""
output_dir = tmpdir.join('out')
runner = CliRunner()
if is_usable:
real_import = builtins.__import__
# mock import function that acts as if kwcoco is not installed
# monkeypatch import function
builtins.__import__ = mock_import
del sys.modules['xaitk_saliency.utils.bin.sal_on_coco_dets']
from xaitk_saliency.utils.bin.sal_on_coco_dets import sal_on_coco_dets as fail_sal_on_coco_dets
result = runner.invoke(fail_sal_on_coco_dets, [str(dets_file), str(output_dir), str(config_file)])
else:
result = runner.invoke(sal_on_coco_dets, [str(dets_file), str(output_dir), str(config_file)])
assert result.output == "This tool requires additional dependencies, please install 'xaitk-saliency[tools]'\n"
assert not output_dir.check(dir=1)
@pytest.mark.skipif(not is_usable, reason="Extra 'xaitk-saliency[tools]' not installed.")
class TestSalOnCocoDets:
"""
These tests make use of the `tmpdir` fixture from `pytest`. Find more
information here: https://docs.pytest.org/en/6.2.x/tmpdir.html
"""
def test_coco_sal_gen(self, tmpdir: py.path.local) -> None:
"""
Test saliency map generation with RandomDetector, RISEGrid, and
DRISEScoring.
"""
output_dir = tmpdir.join('out')
runner = CliRunner()
runner.invoke(sal_on_coco_dets, [str(dets_file), str(output_dir), str(config_file), "-v"])
# expected created directories for image saliency maps
img_dirs = [output_dir.join(d) for d in ["test_image1", "test_image2"]]
# detection ids that belong to each image
img_dets = [[1, 2, 3], [4, 5]]
assert sorted(output_dir.listdir()) == sorted(img_dirs)
for img_dir, det_ids in zip(img_dirs, img_dets):
map_files = [img_dir.join(f"det_{det_id}.jpeg") for det_id in det_ids]
assert sorted(img_dir.listdir()) == sorted(map_files)
def test_coco_sal_gen_img_overlay(self, tmpdir: py.path.local) -> None:
"""
Test saliency map generation with RandomDetector, RISEGrid, and
DRISEScoring with the overlay image option.
"""
output_dir = tmpdir.join('out')
runner = CliRunner()
runner.invoke(sal_on_coco_dets, [str(dets_file), str(output_dir), str(config_file), "--overlay-image"])
# expected created directories for image saliency maps
img_dirs = [output_dir.join(d) for d in ["test_image1", "test_image2"]]
# detection ids that belong to each image
img_dets = [[1, 2, 3], [4, 5]]
assert sorted(output_dir.listdir()) == sorted(img_dirs)
for img_dir, det_ids in zip(img_dirs, img_dets):
map_files = [img_dir.join(f"det_{det_id}.jpeg") for det_id in det_ids]
assert sorted(img_dir.listdir()) == sorted(map_files)
def test_config_gen(self, tmpdir: py.path.local) -> None:
"""
Test the generate configuration file option.
"""
output_dir = tmpdir.join('out')
output_config = tmpdir.join('gen_conf.json')
runner = CliRunner()
runner.invoke(sal_on_coco_dets, [str(dets_file), str(output_dir), str(config_file), "-g", str(output_config)])
# check that config file was created
assert output_config.check(file=1)
# check that no output was generated
assert not output_dir.check(dir=1)
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] | 2.372131 | 1,830 |
from setuptools import find_packages, setup
import pathlib as pl
DISTNAME = "gumbi"
DESCRIPTION = "Gaussian Process Model Building Interface"
AUTHOR = "John Goertz"
AUTHOR_EMAIL = ""
URL = "https://github.com/JohnGoertz/Gumbi"
LICENSE = "Apache 2.0"
PROJECT_ROOT = pl.Path(__file__).resolve().parent
REQUIREMENTS = PROJECT_ROOT / "requirements.txt"
README = PROJECT_ROOT / "README.md"
VERSION = PROJECT_ROOT / "VERSION"
with open(REQUIREMENTS) as f:
install_reqs = f.read().splitlines()
with open(README, 'r') as fh:
long_description = fh.read()
with open(VERSION, encoding="utf-8") as f:
version = f.read().strip()
classifiers = [
"Development Status :: 4 - Beta",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Mathematics",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
]
setup(
name=DISTNAME,
version=version,
author="John Goertz",
author_email="",
description=DESCRIPTION,
long_description_content_type="text/markdown",
long_description=long_description,
url=URL,
license=LICENSE,
python_requires='>=3.7',
packages=find_packages(),
include_package_data=True,
install_requires=install_reqs,
classifiers=classifiers,
#keywords=['python'],
)
| [
6738,
900,
37623,
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""" Cisco_IOS_XR_mpls_te_datatypes
This module contains a collection of generally useful
derived YANG data types.
Copyright (c) 2013\-2017 by Cisco Systems, Inc.
All rights reserved.
"""
from collections import OrderedDict
from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64
from ydk.filters import YFilter
from ydk.errors import YError, YModelError
from ydk.errors.error_handler import handle_type_error as _handle_type_error
class BfdReversePath(Enum):
"""
BfdReversePath (Enum Class)
Bfd reverse path
.. data:: bfd_reverse_path_binding_label = 1
BindingLabel
"""
bfd_reverse_path_binding_label = Enum.YLeaf(1, "bfd-reverse-path-binding-label")
class Ctype(Enum):
"""
Ctype (Enum Class)
Ctype
.. data:: ctype_null = 0
CTYPE NULL
.. data:: ctype_ipv4 = 1
CTYPE IPV4
.. data:: ctype_ipv4_p2p_tunnel = 7
CTYPE IPV4 P2P TUNNEL
.. data:: ctype_ipv6_p2p_tunnel = 8
CTYPE IPV6 P2P TUNNEL
.. data:: ctype_ipv4_uni = 9
CTYPE IPV4 UNI
.. data:: ctype_ipv4_p2mp_tunnel = 13
CTYPE IPV4 P2MP TUNNEL
.. data:: ctype_ipv6_p2mp_tunnel = 14
CTYPE IPV6 P2MP TUNNEL
"""
ctype_null = Enum.YLeaf(0, "ctype-null")
ctype_ipv4 = Enum.YLeaf(1, "ctype-ipv4")
ctype_ipv4_p2p_tunnel = Enum.YLeaf(7, "ctype-ipv4-p2p-tunnel")
ctype_ipv6_p2p_tunnel = Enum.YLeaf(8, "ctype-ipv6-p2p-tunnel")
ctype_ipv4_uni = Enum.YLeaf(9, "ctype-ipv4-uni")
ctype_ipv4_p2mp_tunnel = Enum.YLeaf(13, "ctype-ipv4-p2mp-tunnel")
ctype_ipv6_p2mp_tunnel = Enum.YLeaf(14, "ctype-ipv6-p2mp-tunnel")
class MplsTeAffinityValue(Enum):
"""
MplsTeAffinityValue (Enum Class)
Mpls te affinity value
.. data:: hex_value = 1
Affinity value in Hex number
.. data:: bit_position = 2
Affinity value by Bit-Position
"""
hex_value = Enum.YLeaf(1, "hex-value")
bit_position = Enum.YLeaf(2, "bit-position")
class MplsTeAttrSet(Enum):
"""
MplsTeAttrSet (Enum Class)
Mpls te attr set
.. data:: not_used = 0
Not used
.. data:: static = 1
Static
.. data:: lsp = 2
LSP
.. data:: unassigned = 3
Unassigned
.. data:: auto_backup = 4
Auto backup
.. data:: auto_mesh = 5
Auto mesh
.. data:: xro = 6
XRO
.. data:: p2mp_te = 7
P2MP TE
.. data:: otn_pp = 8
OTN Path Protection
.. data:: p2p_te = 9
P2P TE
"""
not_used = Enum.YLeaf(0, "not-used")
static = Enum.YLeaf(1, "static")
lsp = Enum.YLeaf(2, "lsp")
unassigned = Enum.YLeaf(3, "unassigned")
auto_backup = Enum.YLeaf(4, "auto-backup")
auto_mesh = Enum.YLeaf(5, "auto-mesh")
xro = Enum.YLeaf(6, "xro")
p2mp_te = Enum.YLeaf(7, "p2mp-te")
otn_pp = Enum.YLeaf(8, "otn-pp")
p2p_te = Enum.YLeaf(9, "p2p-te")
class MplsTeAutorouteMetric(Enum):
"""
MplsTeAutorouteMetric (Enum Class)
Mpls te autoroute metric
.. data:: relative = 1
Relative
.. data:: absolute = 2
Absolute
.. data:: constant = 3
Constant
"""
relative = Enum.YLeaf(1, "relative")
absolute = Enum.YLeaf(2, "absolute")
constant = Enum.YLeaf(3, "constant")
class MplsTeBackupBandwidthClass(Enum):
"""
MplsTeBackupBandwidthClass (Enum Class)
Mpls te backup bandwidth class
.. data:: class0 = 0
Class 0
.. data:: class1 = 1
Class 1
.. data:: any_class = 9
Any Class
"""
class0 = Enum.YLeaf(0, "class0")
class1 = Enum.YLeaf(1, "class1")
any_class = Enum.YLeaf(9, "any-class")
class MplsTeBackupBandwidthPool(Enum):
"""
MplsTeBackupBandwidthPool (Enum Class)
Mpls te backup bandwidth pool
.. data:: any_pool = 1
Any Pool
.. data:: global_pool = 2
Global Pool
.. data:: sub_pool = 4
Sub Pool
"""
any_pool = Enum.YLeaf(1, "any-pool")
global_pool = Enum.YLeaf(2, "global-pool")
sub_pool = Enum.YLeaf(4, "sub-pool")
class MplsTeBandwidthDste(Enum):
"""
MplsTeBandwidthDste (Enum Class)
Mpls te bandwidth dste
.. data:: standard_dste = 0
IETF-Standard DSTE
.. data:: pre_standard_dste = 1
Pre-Standard DSTE
"""
standard_dste = Enum.YLeaf(0, "standard-dste")
pre_standard_dste = Enum.YLeaf(1, "pre-standard-dste")
class MplsTeBandwidthLimit(Enum):
"""
MplsTeBandwidthLimit (Enum Class)
Mpls te bandwidth limit
.. data:: unlimited = 64
Unlimited
.. data:: limited = 128
Limited
"""
unlimited = Enum.YLeaf(64, "unlimited")
limited = Enum.YLeaf(128, "limited")
class MplsTeBandwidthPool(Enum):
"""
MplsTeBandwidthPool (Enum Class)
Mpls te bandwidth pool
.. data:: any_pool = 0
Any Pool
.. data:: sub_pool = 1
Sub Pool
"""
any_pool = Enum.YLeaf(0, "any-pool")
sub_pool = Enum.YLeaf(1, "sub-pool")
class MplsTeBfdSessionDownAction(Enum):
"""
MplsTeBfdSessionDownAction (Enum Class)
Mpls te bfd session down action
.. data:: re_setup = 1
Tear down and resetup
"""
re_setup = Enum.YLeaf(1, "re-setup")
class MplsTeIgpProtocol(Enum):
"""
MplsTeIgpProtocol (Enum Class)
Mpls te igp protocol
.. data:: none = 0
Not set
.. data:: isis = 1
IS IS
.. data:: ospf = 2
OSPF
"""
none = Enum.YLeaf(0, "none")
isis = Enum.YLeaf(1, "isis")
ospf = Enum.YLeaf(2, "ospf")
class MplsTeLogFrrProtection(Enum):
"""
MplsTeLogFrrProtection (Enum Class)
Mpls te log frr protection
.. data:: frr_active_primary = 1
Track only FRR active on primary LSP
.. data:: backup = 256
backup tunnel
.. data:: frr_ready_primary = 512
Track only FRR ready on primary LSP
.. data:: primary = 513
primary LSP
.. data:: all = 769
all
"""
frr_active_primary = Enum.YLeaf(1, "frr-active-primary")
backup = Enum.YLeaf(256, "backup")
frr_ready_primary = Enum.YLeaf(512, "frr-ready-primary")
primary = Enum.YLeaf(513, "primary")
all = Enum.YLeaf(769, "all")
class MplsTeOtnApsProtection(Enum):
"""
MplsTeOtnApsProtection (Enum Class)
Mpls te otn aps protection
.. data:: Y_1plus1_unidir_no_aps = 4
1PLUS1 UNIDIR NO APS
.. data:: Y_1plus1_unidir_aps = 8
1PLUS1 UNIDIR APS
.. data:: Y_1plus1_bdir_aps = 16
1PLUS1 BIDIR APS
"""
Y_1plus1_unidir_no_aps = Enum.YLeaf(4, "1plus1-unidir-no-aps")
Y_1plus1_unidir_aps = Enum.YLeaf(8, "1plus1-unidir-aps")
Y_1plus1_bdir_aps = Enum.YLeaf(16, "1plus1-bdir-aps")
class MplsTeOtnApsProtectionMode(Enum):
"""
MplsTeOtnApsProtectionMode (Enum Class)
Mpls te otn aps protection mode
.. data:: revertive = 1
Revertive
.. data:: non_revertive = 2
Non Revertive
"""
revertive = Enum.YLeaf(1, "revertive")
non_revertive = Enum.YLeaf(2, "non-revertive")
class MplsTeOtnApsRestorationStyle(Enum):
"""
MplsTeOtnApsRestorationStyle (Enum Class)
Mpls te otn aps restoration style
.. data:: keep_failed_lsp = 1
Keep Failed Lsp
.. data:: delete_failed_lsp = 2
Delete Failed Lsp
"""
keep_failed_lsp = Enum.YLeaf(1, "keep-failed-lsp")
delete_failed_lsp = Enum.YLeaf(2, "delete-failed-lsp")
class MplsTeOtnSncMode(Enum):
"""
MplsTeOtnSncMode (Enum Class)
Mpls te otn snc mode
.. data:: snc_n = 1
SNC N
.. data:: snc_i = 2
SNC I
.. data:: snc_s = 3
SNC S
"""
snc_n = Enum.YLeaf(1, "snc-n")
snc_i = Enum.YLeaf(2, "snc-i")
snc_s = Enum.YLeaf(3, "snc-s")
class MplsTePathDiversityConformance(Enum):
"""
MplsTePathDiversityConformance (Enum Class)
Mpls te path diversity conformance
.. data:: strict = 0
Strict
.. data:: best_effort = 1
Best effort
"""
strict = Enum.YLeaf(0, "strict")
best_effort = Enum.YLeaf(1, "best-effort")
class MplsTePathOption(Enum):
"""
MplsTePathOption (Enum Class)
Mpls te path option
.. data:: not_set = 0
Not Set
.. data:: dynamic = 1
Dynamic
.. data:: explicit_name = 3
Explicit, identified by name
.. data:: explicit_number = 4
Explicit, identified by number
.. data:: no_ero = 5
No ERO
.. data:: sr = 6
Segment routing
"""
not_set = Enum.YLeaf(0, "not-set")
dynamic = Enum.YLeaf(1, "dynamic")
explicit_name = Enum.YLeaf(3, "explicit-name")
explicit_number = Enum.YLeaf(4, "explicit-number")
no_ero = Enum.YLeaf(5, "no-ero")
sr = Enum.YLeaf(6, "sr")
class MplsTePathOptionProperty(Enum):
"""
MplsTePathOptionProperty (Enum Class)
Mpls te path option property
.. data:: none = 0
No property
.. data:: lockdown = 1
Path is not a canditate forreoptimization
.. data:: verbatim = 4
Explicit path does not require topology
database
.. data:: pce = 8
Dynamic path found by PCE server
.. data:: segment_routing = 16
Segment Routing path
"""
none = Enum.YLeaf(0, "none")
lockdown = Enum.YLeaf(1, "lockdown")
verbatim = Enum.YLeaf(4, "verbatim")
pce = Enum.YLeaf(8, "pce")
segment_routing = Enum.YLeaf(16, "segment-routing")
class MplsTePathOptionProtection(Enum):
"""
MplsTePathOptionProtection (Enum Class)
Mpls te path option protection
.. data:: active = 0
Active path
.. data:: protecting = 1
Protecting Path
"""
active = Enum.YLeaf(0, "active")
protecting = Enum.YLeaf(1, "protecting")
class MplsTePathSelectionInvalidationTimerExpire(Enum):
"""
MplsTePathSelectionInvalidationTimerExpire (Enum Class)
Mpls te path selection invalidation timer expire
.. data:: tunnel_action_tear = 1
Tear down tunnel.
.. data:: tunnel_action_drop = 2
Drop tunnel traffic.
"""
tunnel_action_tear = Enum.YLeaf(1, "tunnel-action-tear")
tunnel_action_drop = Enum.YLeaf(2, "tunnel-action-drop")
class MplsTePathSelectionMetric(Enum):
"""
MplsTePathSelectionMetric (Enum Class)
Mpls te path selection metric
.. data:: igp = 1
IGP Metric
.. data:: te = 2
TE Metric
.. data:: delay = 4
DELAY Metric
"""
igp = Enum.YLeaf(1, "igp")
te = Enum.YLeaf(2, "te")
delay = Enum.YLeaf(4, "delay")
class MplsTePathSelectionSegmentRoutingAdjacencyProtection(Enum):
"""
MplsTePathSelectionSegmentRoutingAdjacencyProtection (Enum Class)
Mpls te path selection segment routing adjacency
protection
.. data:: not_set = 0
Any segment can be used in a path.
.. data:: adj_unprotected = 1
Only unprotected adjacency segments can be used
in a path.
.. data:: adj_protected = 2
Only protected adjacency segments can be used
in a path.
"""
not_set = Enum.YLeaf(0, "not-set")
adj_unprotected = Enum.YLeaf(1, "adj-unprotected")
adj_protected = Enum.YLeaf(2, "adj-protected")
class MplsTePathSelectionTiebreaker(Enum):
"""
MplsTePathSelectionTiebreaker (Enum Class)
Mpls te path selection tiebreaker
.. data:: min_fill = 1
Prefer the path with the least-utilized links
.. data:: max_fill = 2
Prefer the path with the most-utilized links
.. data:: random = 3
Prefer a path with links utilized randomly
"""
min_fill = Enum.YLeaf(1, "min-fill")
max_fill = Enum.YLeaf(2, "max-fill")
random = Enum.YLeaf(3, "random")
class MplsTeSigNameOption(Enum):
"""
MplsTeSigNameOption (Enum Class)
Mpls te sig name option
.. data:: none = 0
None
.. data:: address = 1
Address
.. data:: name = 2
Name
"""
none = Enum.YLeaf(0, "none")
address = Enum.YLeaf(1, "address")
name = Enum.YLeaf(2, "name")
class MplsTeSwitchingCap(Enum):
"""
MplsTeSwitchingCap (Enum Class)
Mpls te switching cap
.. data:: psc1 = 1
PSC1
.. data:: lsc = 150
LSC
.. data:: fsc = 200
FSC
"""
psc1 = Enum.YLeaf(1, "psc1")
lsc = Enum.YLeaf(150, "lsc")
fsc = Enum.YLeaf(200, "fsc")
class MplsTeTunnelAffinity(Enum):
"""
MplsTeTunnelAffinity (Enum Class)
Mpls te tunnel affinity
.. data:: include = 1
Include Affinity
.. data:: include_strict = 2
Strictly Include Affinity
.. data:: exclude = 3
Exclude Affinity
.. data:: exclude_all = 4
Exclude All Affinities
.. data:: ignore = 5
Ignore Affinity
"""
include = Enum.YLeaf(1, "include")
include_strict = Enum.YLeaf(2, "include-strict")
exclude = Enum.YLeaf(3, "exclude")
exclude_all = Enum.YLeaf(4, "exclude-all")
ignore = Enum.YLeaf(5, "ignore")
class MplsTesrlgExclude(Enum):
"""
MplsTesrlgExclude (Enum Class)
Mpls tesrlg exclude
.. data:: mandatory = 1
SRLG Mandatory Exclude
.. data:: preferred = 2
SRLG Preferred Exclude
.. data:: weighted = 3
SRLG Weighted Exclude
"""
mandatory = Enum.YLeaf(1, "mandatory")
preferred = Enum.YLeaf(2, "preferred")
weighted = Enum.YLeaf(3, "weighted")
class PathInvalidationAction(Enum):
"""
PathInvalidationAction (Enum Class)
Path invalidation action
.. data:: tear = 1
Tear
.. data:: drop = 2
Drop
"""
tear = Enum.YLeaf(1, "tear")
drop = Enum.YLeaf(2, "drop")
class SrPrepend(Enum):
"""
SrPrepend (Enum Class)
Sr prepend
.. data:: none_type = 0
NoneType
.. data:: next_label = 1
Next Label
.. data:: bgp_n_hop = 2
BGP NHOP
"""
none_type = Enum.YLeaf(0, "none-type")
next_label = Enum.YLeaf(1, "next-label")
bgp_n_hop = Enum.YLeaf(2, "bgp-n-hop")
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11485,
1366,
3712,
2291,
62,
301,
2012,
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] | 2.19214 | 6,412 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2018-05-05 01:00
from __future__ import unicode_literals
from django.db import migrations
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"""
Advent of Code: Day 03 Part 1
tldr: most prevalent bit
"""
from collections import defaultdict
if __name__ == "__main__":
main()
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] | 2.959184 | 49 |
#!/usr/bin/env python3
#
# Scrape reports from https://crsreports.congress.gov.
#
# This site provides many of the same reports that
# are available through our own archive, but only as
# PDFs and only with versions as of the site launch
# date and going forward.
from collections import OrderedDict
import datetime
import hashlib
import json
import os
import re
import subprocess
import scrapelib
BASE_PATH = "incoming/crsreports.congress.gov"
# Create a scraper that automatically throttles our requests
# so that we don't overload the CRS server.
scraper = scrapelib.Scraper(
requests_per_minute=35,
retry_attempts=2,
retry_wait_seconds=10)
ProdTypeDisplayName = {
"R": "CRS Report",
"RS": "CRS Report",
"RL": "CRS Report",
"IN": "CRS Insight",
"IF": "CRS In Focus",
}
if __name__ == "__main__":
# Make the directories for the output files.
os.makedirs(BASE_PATH + "/documents", exist_ok=True)
os.makedirs(BASE_PATH + "/files", exist_ok=True)
scrape_report_listing()
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] | 2.961538 | 338 |
# Create a light and set its specular color to bright green.
#
import pyvista as pv
light = pv.Light()
light.specular_color = '#00FF00'
light.specular_color
# Expected:
## (0.0, 1.0, 0.0)
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import logging
from operator import attrgetter
from pathlib import Path
from typing import List
from opennem.db import get_database_engine
from opennem.db.views.queries import (
get_all_views_query,
get_query_drop_view,
get_view_unique_index_query,
)
from .continuous_aggregates import (
create_continuous_aggregation_query,
remove_continuous_aggregation_query,
)
from .schema import ContinuousAggregationPolicy, ViewDefinition
logger = logging.getLogger("opennem.db.views")
VIEW_PATH = Path(__file__).parent.parent / "fixtures" / "views"
AggregationPolicy30Minutes = ContinuousAggregationPolicy(
interval="30 minutes", start_interval="2 hours"
)
AggregationPolicy2Hours = ContinuousAggregationPolicy(
interval="2 hours", start_interval="6 hours", end_interval="2 hours"
)
AggregationPolicy6Hours = ContinuousAggregationPolicy(
interval="6 hours", start_interval="12 hours", end_interval="2 hours"
)
_VIEW_MAP = [
ViewDefinition(
priority=11,
name="mv_facility_all",
materialized=True,
filepath="mv_facility_all.sql",
primary_key=["trading_interval", "network_id", "code"],
indexes=[],
),
ViewDefinition(
priority=11,
name="mv_network_fueltech_days",
materialized=True,
filepath="mv_network_fueltech_days.sql",
primary_key=["trading_day", "network_id", "code"],
),
ViewDefinition(
priority=15,
name="mv_facility_45d",
materialized=True,
filepath="mv_facility_45d.sql",
primary_key=["trading_interval", "network_id", "code"],
),
ViewDefinition(
priority=20,
name="mv_region_emissions",
materialized=True,
filepath="mv_region_emissions.sql",
primary_key=["trading_interval", "network_id", "network_region"],
),
ViewDefinition(
priority=30,
name="mv_interchange_energy_nem_region",
materialized=True,
filepath="mv_interchange_energy_nem_region.sql",
primary_key=["trading_interval", "network_id", "network_region"],
),
ViewDefinition(
priority=40,
name="vw_region_flow_emissions",
materialized=False,
filepath="vw_region_flow_emissions.sql",
),
]
POSTGIS_VIEWS = ["geography_columns", "geometry_columns", "raster_columns", "raster_overviews"]
def purge_views() -> None:
"""Remove views that aren't in the view table"""
engine = get_database_engine()
all_views_query = get_all_views_query()
all_views = []
with engine.connect() as c:
result = list(c.execute(all_views_query))
# Dont drop postgis or mapped views
all_views = [i[0] for i in result if i[0] not in POSTGIS_VIEWS + [i.name for i in _VIEW_MAP]]
for view_name in all_views:
with engine.connect() as c:
c.execution_options(isolation_level="AUTOCOMMIT")
query = "drop materialized view if exists {} cascade;".format(view_name)
logger.info("Dropping view {}".format(view_name))
logger.debug(query)
try:
c.execute(query)
except Exception as e:
logger.error("Error dropping view: {}".format(e))
def init_database_views() -> None:
""" Initialize all the database view """
engine = get_database_engine()
views_sorted_by_priority = list(sorted(_VIEW_MAP, key=attrgetter("priority")))
for view in views_sorted_by_priority:
logger.info("Initializing view {}".format(view.name))
with engine.connect() as c:
c.execution_options(isolation_level="AUTOCOMMIT")
# drop
drop_query = get_query_drop_view(view)
logger.debug(drop_query)
try:
c.execute(drop_query)
except Exception as e:
logger.warn("Could not drop view {}".format(view.name))
# create
create_query = get_view_content(view)
logger.debug(create_query)
c.execute(create_query)
# index
index_create_query = get_view_unique_index_query(view)
if index_create_query:
logger.debug(index_create_query)
try:
c.execute(index_create_query)
except Exception as e:
logger.error("Error creating index: {}".format(e))
return None
def init_aggregation_policies() -> None:
""" Initializes the continuous aggregation policies """
# @TODO check what exists with query
engine = get_database_engine()
for view in _VIEW_MAP:
if not view.aggregation_policy:
logging.debug("Skipping {}".format(view.name))
continue
with engine.connect() as c:
drop_query = remove_continuous_aggregation_query(view)
try:
logger.debug(drop_query)
c.execute(drop_query)
except Exception:
logger.warn("Could not drop continuous aggregation query: {}".format(view.name))
pass
create_query = create_continuous_aggregation_query(view)
logger.debug(create_query)
try:
c.execute(create_query)
except Exception as e:
logger.warn("Could not create continuous aggregation query: {}".format(e))
def get_materialized_view_names() -> List[str]:
""" Returns a list of material view names in priority order """
return list(
v.name
for v in filter(
lambda x: x.materialized is True and x.aggregation_policy is None, _VIEW_MAP
)
)
def get_timescale_view_names() -> List[str]:
""" Returns a list of timescale view names in priority order """
return list(
v.name
for v in filter(lambda x: x.materialized is True and x.aggregation_policy, _VIEW_MAP)
)
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] | 2.323944 | 2,556 |
#!/usr/bin/env python
#
# Copyright 2017 Phedorabot
#
# 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, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import absolute_import, division, print_function, with_statement
# This is a script that shows how to handle the instant task execution post
# request from the Phedorabot server, you must ensure that this is done only
# with a post request
# import the webhook and the webhook excdeption class
from phedorabot.webhook import PhedorabotWebHookEngine
from phedorabot.webhook import PhedorabotWebHookException
# Wrap everything in a try/except block so we can deal with errors rightly
try:
# First your server should be able to read the headers sent in as dictionary
# and the raw body sent in
# Initialize the webhook engine
engine = PhedorabotWebHookEngine()
# set the headers as a raw dictionary from your server
engine.set_raw_header(server.request.headers)
# set the raw boy as string type this will be parsed by the engine
engine.set_raw_body(server.request.body)
# Next we need to ensure that we received this instant task execution payload
# from Phedorabot, before we can trust the payload enough to use it for
# any meaningful task execution
if engine.is_valid_task_execution():
# Ok this looks good we have a valid task execution otherwise the webhook
# will raise an exception for us
# At this point we have a valid task execution payload we need to get
# the public api key that is associated with this callback request data
# so that you can provide the corresponding api secret for verifying the
# integrity of the task payload
api_key = engine.get_api_key()
# Query for the corresponding api secret on your server, database or
# configuration storage using this api key
# after which set the below api secret to the corresponding secret
api_secret = ''
engine.set_api_secret(api_secret)
# Next verify the integrity of the task execution payload
if engine.verify_task_execution_payload():
# Getting this far means that the tash execution payload is valid
# and can be trusted.
# get the headers incase you passed customer headers when creating
# the task
headers = engine.get_headers()
# get the payload
payload = engine.get_payload()
# Now you can execute the task you want to execute here using the
# contents of the payload as well as the headers after that if you
# want to set customer status of the task execution you can call the
# engine.add_result() method, this expects a key and a value
# it will be registered on your Phedorabot task execution log so
# you can review it later
# e.g engine.add_result('status', 'Executed Successfully')
# TODO: task executtion here, after this part you are all done
# Note that Phedorabot server will give your server a 30 seconds
# window to get feed back from this callback scripts otherwise it
# will consider it a failure
except (Exception, PhedorabotWebHookException) as ex:
# if this is a Phedorabot Webhook exception we need to capture it
if hasattr(ex, 'what'):
engine.set_error(ex.get_what())
engine.set_error_description(ex.get_reason())
else:
engine.set_error('webhook_error')
engine.set_error_description(str(ex))
finally:
# send back response to Phedorabot so that you can see a log of how your
# callback script is executing
response = engine.get_response()
# Print this depending on your server type
print response
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] | 3.041369 | 1,402 |
#!/usr/bin/env python2.6
"""
Unit test of web.claims application as a complete Django WSGI web service.
"""
from __future__ import print_function
import unittest
import httplib
import urllib2, urllib
import logging, sys
import os.path
import datetime
import base64
import subprocess, time
import json
logger= logging.getLogger( __file__ )
class TestWS( unittest.TestCase ):
"""Exercise load and fetch operations.
The tests must be run in order to force the expected behavior.
"""
def setUpModule():
"""Spawn the test server process.
This should build a test database, load fixtures, and then provide
the Django-based services.
"""
global the_proc, the_log, the_err
command= ["/Library/Frameworks/Python.framework/Versions/2.6/bin/python2.6", "-m", "web.manage", "testserver",
'--addrport=18000', '--settings=web.settings',
'--noinput', '--verbosity=1',
'example837.json',
]
log_file= 'testserver.log'
err_file= 'testserver.err'
logger.info( '{0} >{1} 2>{2}'.format( ' '.join( command ), log_file, err_file ) )
the_log= open( log_file, 'w', 0 )
the_err= open( err_file, 'w', 0 )
the_proc = subprocess.Popen(command, shell=False, stdout=the_log, stderr=the_err)
time.sleep(6) # Wait for fixtures to load
status= the_proc.poll()
logger.info( 'PID %d, status %r', the_proc.pid, status )
logger.info( datetime.datetime.now() )
def tearDownModule():
"""Kill the server process."""
global the_proc
logger.info( "Stopping server" )
the_proc.kill()
logger.debug( "Waiting for %d to finally exit", the_proc.pid )
the_proc.wait()
logger.info( "PID %d, status %r", the_proc.pid, the_proc.returncode )
the_log.close()
the_err.close()
for f, p in (the_log, 'log>'), (the_err, 'err>'):
print()
with open( f.name, 'r' ) as source:
for line in source:
print( p, line, end='' )
print()
if __name__ == "__main__":
logging.basicConfig(
stream=sys.stderr,
level=logging.DEBUG,
)
if sys.version_info[:2] <= ( 2, 6 ):
#Python2.6 work-around
setUpModule()
tests= unittest.defaultTestLoader.loadTestsFromModule(__import__('__main__'))
result= unittest.TextTestRunner().run( tests )
tearDownModule()
sys.exit(not result.wasSuccessful())
#Python2.7
unittest.main()
| [
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555,
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] | 2.39961 | 1,026 |
# Generated by Django 2.1.7 on 2019-03-21 13:17
from django.db import migrations, models
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# -*- coding: utf-8 -*-
"""
Conversion factors and units, in cgs. To convert a given value, in cgs, to the desired units,
divide by that unit.
Example:
The speed of light in km·s⁻¹ would be
c_km = c / km
"""
# length
cm = 1.
m = 1e2
km = 1e5
AU = 1.4959787066e13
ly = 9.460730472e17
pc = 3.0856776e18
kpc = 1e3 * pc
Mpc = 1e6 * pc
Gpc = 1e9 * pc
mm = 1e-1
micron = 1e-4
um = micron
nm = 1e-7
angstrom = 1e-8
# mass
Msun = 1.9891e33
g = 1.
kg = 1e3
mg = 1e-3
# time
s = 1.
hr = 3600.
yr_Sidereal = 3.1558145e7
yr_Tropical = 3.155692519e7
yr_Gregorian = 3.1556952e7
yr_Julian = 3.15576e7
yr = yr_Julian
Myr = 1e6 * yr
Gyr = 1e9 * yr
# energy
eV = 1.6021765e-12 # one electron-volt, in erg
keV = 1e3 * eV
J = 1e-7 # one Joule, in erg | [
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1303,
530,
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293,
11,
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39035
] | 1.943734 | 391 |
import openml
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
| [
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''' Utilities for manipulating package-level settings. '''
import json
from pathlib import Path
import os
from io import open
import warnings
from .utils import listify
__all__ = ['set_option', 'set_options', 'get_option']
_config_name = 'pybids_config.json'
conf_path = str(Path(__file__).absolute().parent.joinpath('layout', 'config', '{}.json'))
_default_settings = {
'config_paths': {
name: conf_path.format(name) for name in ['bids', 'derivatives']},
# XXX 0.16: Remove
'extension_initial_dot': True,
}
def set_option(key, value):
""" Set a package-wide option.
Args:
key (str): The name of the option to set.
value (object): The new value of the option.
"""
if key not in _settings:
raise ValueError("Invalid pybids setting: '%s'" % key)
# XXX 0.16: Remove
elif key == "extension_initial_dot":
if value is not True:
raise ValueError(f"Cannot set {key!r} to {value!r} as of pybids 0.14. "
"This setting is always True, and will be removed "
"entirely in 0.16.")
warnings.warn("Setting 'extension_initial_dot' will be removed in pybids 0.16.",
FutureWarning)
_settings[key] = value
def set_options(**kwargs):
""" Set multiple package-wide options.
Args:
kwargs: Keyword arguments to pass onto set_option().
"""
for k, v in kwargs.items():
set_option(k, v)
def get_option(key):
""" Retrieve the current value of a package-wide option.
Args:
key (str): The name of the option to retrieve.
"""
if key not in _settings:
raise ValueError("Invalid pybids setting: '%s'" % key)
return _settings[key]
def from_file(filenames, error_on_missing=True):
""" Load package-wide settings from specified file(s).
Args:
filenames (str, list): Filename or list of filenames containing JSON
dictionary of settings.
error_on_missing (bool): If True, raises an error if a file doesn't
exist.
"""
filenames = listify(filenames)
for f in filenames:
if Path(f).exists():
settings = json.loads(Path(f).read_text(encoding='utf-8'))
_settings.update(settings)
elif error_on_missing:
raise ValueError("Config file '%s' does not exist." % f)
def reset_options(update_from_file=False):
""" Reset all options to the package defaults.
Args:
update_from_file (bool): If True, re-applies any config files found in
standard locations.
"""
global _settings
_settings = _default_settings.copy()
if update_from_file:
_update_from_standard_locations()
def _update_from_standard_locations():
""" Check standard locations for config files and update settings if found.
Order is user's home dir, environment variable ($PYBIDS_CONFIG), and then
current directory--with later files taking precedence over earlier ones.
"""
locs = [
Path.home() / _config_name,
Path('.') / _config_name
]
if 'PYBIDS_CONFIG' in os.environ:
locs.insert(1, os.environ['PYBIDS_CONFIG'])
from_file(locs, False)
_settings = {}
reset_options(True)
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62,
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8,
198
] | 2.481818 | 1,320 |
"""
Keyword extraction methods.
These accept lists of strings as arguments.
"""
from .pos import POSTokenizer
from .rake import RAKETokenizer
from .apriori import AprioriTokenizer
from .overkill import OverkillTokenizer
| [
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] | 3.683333 | 60 |
from pm4py.algo.filtering.log.attributes import attributes_filter
from pm4py.algo.filtering.log.end_activities import end_activities_filter
from pm4py.algo.filtering.log.start_activities import start_activities_filter
from pm4py.algo.filtering.log.variants import variants_filter
from pm4py.objects.conversion.log import factory as conversion_factory
from pm4py.objects.log.exporter.xes.versions.etree_xes_exp import export_log_as_string
from pm4py.objects.log.importer.xes import factory as xes_importer
from pm4py.objects.log.util import insert_classifier
from pm4py.objects.log.util import xes
from pm4py.statistics.traces.log import case_statistics
from pm4py.util import constants
from pm4pyws.handlers.xes.alignments import get_align
from pm4pyws.handlers.xes.cases import variants
from pm4pyws.handlers.xes.ctmc import transient
from pm4pyws.handlers.xes.filtering import factory as filtering_factory
from pm4pyws.handlers.xes.process_schema import factory as process_schema_factory
from pm4pyws.handlers.xes.sna import get_sna as sna_obtainer
from pm4pyws.handlers.xes.statistics import events_per_time, case_duration
from pm4pyws.util import casestats
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] | 3.07672 | 378 |
# coding: utf-8
from abc import ABCMeta, abstractmethod
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] | 3 | 20 |
# -*- coding: utf-8 -*-
import re
import scrapy
from scrapy.crawler import CrawlerProcess
from scrapy.linkextractors.lxmlhtml import LxmlLinkExtractor
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] | 2.903846 | 52 |
from wildfireassessment.ops import * #my package
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from skimage import morphology
from skimage.transform import resize
import pandas as pd
import geopandas as gpd
import pickle
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB, BernoulliNB
from sklearn import linear_model
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import confusion_matrix, recall_score, precision_score, accuracy_score, f1_score
from sklearn.externals import joblib
from rasterstats import zonal_stats
import fiona
from joblib import Parallel, delayed
import multiprocessing
import time
"""
def writeRasters():
#read in filepaths for data
print("Reading filepaths...")
filepath_post = Path("./data/Paradise/post")
filepath_pre = Path("./data/Paradise/pre")
#WorldView Post/Pre
fps_wv_post = sorted(list(filepath_post.glob("2*_clip.tif")))
fps_wv_pre = sorted(list(filepath_pre.glob("2*_clip.tif")))
#WorldView Post/Pre
fps_sent2_post = sorted(list((filepath_post / "clippedB08s").glob("B08_*.tif")))
fps_sent2_pre = sorted(list((filepath_pre / "clippedB08s").glob("B08_*.tif")))
print("Loading Model")
#LOAD model
rf_model = joblib.load(open("models/rf_grid_bin_precision.pkl", 'rb'))
print("Start reading images")
for i in range(len(fps_wv_post)):
print("Reading RGB...")
raster_src_post, rgb_post = readRGBImg(fps_wv_post[i])
raster_src_pre, rgb_pre = readRGBImg(fps_wv_pre[i])
print("Reading S2 B8...")
raster_src_post_b08, b08_post = readOneImg(fps_sent2_post[i])
raster_src_pre_b08, b08_pre = readOneImg(fps_sent2_pre[i])
print("Resizing B8 images")
b08_upscaled_post = resize(b08_post, raster_src_post.shape, anti_aliasing=True)
b08_upscaled_post = b08_upscaled_post * 255
b08_upscaled_post = b08_upscaled_post.astype(rasterio.uint8)
b08_upscaled_pre = resize(b08_pre, raster_src_pre.shape, anti_aliasing=True)
b08_upscaled_pre = b08_upscaled_pre * 255
b08_upscaled_pre = b08_upscaled_pre.astype(rasterio.uint8)
print("unravel rgb, b08")
#unravel
rgb_rav_post = {0 : rgb_post[:,:,0].ravel().astype(float),
1 : rgb_post[:,:,1].ravel().astype(float),
2 : rgb_post[:,:,2].ravel().astype(float)}
rgb_rav_pre = {0 : rgb_pre[:,:,0].ravel().astype(float),
1 : rgb_pre[:,:,1].ravel().astype(float),
2 : rgb_pre[:,:,2].ravel().astype(float)}
b08_rav_post = b08_upscaled_post.ravel().astype(float)
b08_rav_pre = b08_upscaled_pre.ravel().astype(float)
#release mem
b08_upscaled_post = None
b08_upscaled_pre = None
b08_post = None
b08_pre = None
rgb_pre = None
rgb_post = None
print("starting predictions with model")
def processInParallel(i):
X_chunk = makeChunkX(rgb_rav_post[2][i:i+100], rgb_rav_post[1][i:i+100], rgb_rav_post[0][i:i+100], b08_rav_post[i:i+100],
rgb_rav_pre[2][i:i+100], rgb_rav_pre[1][i:i+100], rgb_rav_pre[0][i:i+100], b08_rav_pre[i:i+100])
#impute by mean for missing values
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp.fit(X_chunk)
X_chunk_imp = imp.transform(X_chunk)
return rf_model.predict(X_chunk_imp)
start_time = time.time()
num_cores = multiprocessing.cpu_count()
pred_y = Parallel(n_jobs=num_cores, backend="multiprocessing")(delayed(processInParallel)(i) for i in range(0, len(b08_rav_post), 100))
print("--- %s seconds ---" % (time.time() - start_time))
print("Create mask")
#create mask
pred_y_rf = np.hstack(pred_y).reshape(raster_src_post.shape)
#clean mask
pred_y_rf_clean = morphology.remove_small_holes(pred_y_rf==1, 500)
pred_y_rf_clean = morphology.remove_small_objects(pred_y_rf_clean, 500)
fileNameMask = "../results/predict_mask_rf_" + fps_wv_post[i].name.split('_')[0] + ".tif"
print("Writing image mask to path:", fileNameMask)
metadata = {
'driver': 'GTiff',
'dtype': 'uint8',
'width': raster_src_post.meta['width'],
'height': raster_src_post.meta['height'],
'count': 1,
'crs': raster_src_post.meta['crs'],
'transform': raster_src_post.meta['transform']
}
with rasterio.open(fileNameMask, 'w', **metadata) as dst:
dst.write(pred_y_rf_clean.astype(np.uint8), 1)
def computeSI(b1, b2):
return (b1-b2)/(b1+b2)
def changedSI(SI_pre, SI_post):
return SI_pre - SI_post
def makeChunkX(b, g, r, n, b_p, g_p, r_p, n_p):
SI_gb = (computeSI(g, b), computeSI(g_p, b_p)) #(post, pre)
SI_rb = (computeSI(r, b), computeSI(r_p, b_p))
SI_rg = (computeSI(r, g), computeSI(r_p, g_p))
SI_nb = (computeSI(n, b), computeSI(n_p, b_p))
SI_ng = (computeSI(n, g), computeSI(n_p, g_p))
SI_nr = (computeSI(n, r), computeSI(n_p, r_p))
dSI_gb = changedSI(SI_gb[1], SI_gb[0])
dSI_rb = changedSI(SI_rb[1], SI_rb[0])
dSI_rg = changedSI(SI_rg[1], SI_rg[0])
dSI_nb = changedSI(SI_nb[1], SI_nb[0])
dSI_ng = changedSI(SI_ng[1], SI_ng[0])
dSI_nr = changedSI(SI_nr[1], SI_nr[0])
return np.dstack((b, b_p, g, g_p, r, r_p, n, n_p,
SI_gb[0], SI_rb[0], SI_rg[0], SI_nb[0], SI_ng[0], SI_nr[0],
SI_gb[1], SI_rb[1], SI_rg[1], SI_nb[1], SI_ng[1], SI_nr[1],
dSI_nb, dSI_rg, dSI_rb, dSI_gb, dSI_nr, dSI_ng))[0]
"""
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62,
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] | 2.055123 | 2,957 |
# -*- coding: utf-8 -*-
# !/usr/bin/env python3
"""
@author: zhuyuehui
@contact: [email protected]
@time: 2021/11/6 12:57 下午
"""
name = "PyAirwave"
from .PyAirwave import AirWave
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from ez_setup import use_setuptools
use_setuptools()
from setuptools import setup, find_packages
setup(name = 'Adafruit_CharLCD',
version = '1.0.0',
author = 'Tony DiCola',
author_email = '[email protected]',
description = 'Library to drive character LCD display and plate.',
license = 'MIT',
url = 'https://github.com/adafruit/Adafruit_Python_CharLCD/',
dependency_links = ['https://github.com/adafruit/Adafruit_Python_GPIO/tarball/master#egg=Adafruit-GPIO-0.4.0'],
install_requires = ['Adafruit-GPIO>=0.4.0'],
packages = find_packages())
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] | 2.408163 | 245 |
import explorerhat as eh
from time import sleep
CHAR_TABLE = [['1','2','3'],['4','5','6'],['7','8','9'],['*','0','#']]
while True:
ch = decode_key(key_pressed(), CHAR_TABLE)
print('%s pressed' % ch)
wait_for_release()
sleep(0.1)
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# Name: Linsey Schaap
# Student number: 11036109
"""
This script convert a csv file into a JSON format.
"""
import csv
import json
csvbestand = open("reizigerskilometers.csv", "r")
jsonbestand = open("reizigerskilometers.json", "w")
namen = ("Vervoerswijze", "Periode", "Provincie", "Afstand")
bestand = csv.DictReader(csvbestand, namen)
# Parse the CSV into JSON
out = json.dumps( [ regel for regel in bestand ] )
# Save the JSON
jsonbestand.write('{"data": ' + out + '}')
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] | 2.752874 | 174 |
from . import data, update_handlers | [
6738,
764,
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1366,
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62,
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8116
] | 3.888889 | 9 |
"""Unit tests for pyatv.protocols.mrp.variant."""
import pytest
from pyatv.support.variant import read_variant, write_variant
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#!/usr/bin/env python3'
# coding = utf-8
########################################################################################
##
## Maintainer: [email protected]
## Inspired by: https://github.com/somervda/ourbotmanager_ros.git
##
## Input: Analog potentiometer 1 + 2 + 3 (+4 )
## Output: micro-ROS node (ROS2) that publish topic /cmd_vel with msg.type twist_stamped
## Angular = X-axis = Pull stick Left/Right
## Linear = Y-axis = Pull stick Up/Down
## Twist = Z-axis = Turn/Twist stick (Not used right now)
##
## Behaviour:
## 1) Once: Read/Set all the parameters
## 2) Repeatedly: Read analog joystick via ADC
## 3) Repeatedly: Transform indata to a +/-100% values
## 4) Repeatedly: Map where the stick are => Depending om location, then adjust behivaiur.
## 5) Repeatedly: Publish ros-topic
##
## Prerequisite:
## $ sudo apt install i2c-tools
## $ sudo apt install python3-pip
## $ sudo pip3 install smbus2
## $ sudo pip3 install adafruit-ads1x15
## $ sudo i2cdetect -y 1
## $ sudo chmod a+rw /dev/i2c-1
##
## Hardware: KY-053 Analog Digital Converter (ADS1115, 16-bit) via default I2C adr.=0x48
## Hardware: Joystick with analog 10K resistors for X, Y and Z
## Host: Raspberry Pi 4(Ubuntu) via I2C
##
## Launch sequence:
## 1) $ ros2 run pet_mk_viii_joystick pet_potentiometer_node.py
##
# TODO: Get rid of time.sleep() with something more real time/concurrent and ROS2 friendly way of wait...
# Import the ROS2-stuff
import rclpy # TODO: IS this line neccesary. Due to the two following lines that importing "Node" and "Parameter"
from rclpy.node import Node
from rclpy.parameter import Parameter
from rcl_interfaces.msg import ParameterDescriptor
from std_msgs.msg import Int32
# Import the Ubuntu/Linux-hardware stuff
from smbus2 import SMBus
import Adafruit_ADS1x15
#from gpiozero import LED
# Import the common Ubuntu/Linux stuff
import sys
import time
import signal
class PotentiometerPublisher(Node):
'''
Analog potentiometer class
Read analog input -> Publish on ROS-topic
'''
# Keep track of last joystick values. Used due to reducing communication of equal values.
last_value_p0 = 0
last_value_p1 = 0
last_value_p2 = 0
last_value_p3 = 0
if __name__ == "__main__":
main()
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] | 2.967235 | 763 |
RAW_QUEUES = {
"example_timed_set": {
"job_factory": lambda rawparam: {
"path": "example.Print",
"params": {
"test": rawparam
}
}
}
} | [
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] | 1.965517 | 87 |
"""
Functions to download example data from public repositories.
"""
from .base import InputFiles, InputFilesWithSession
import os
import os.path as op
from pathlib import Path
def get_s3_register(subject_id, site, raw_keys, deriv_keys):
"""Get the S3 keys for a single subject's input files
Parameters
----------
subject_id : string
Subject ID on which to filter the s3 keys
site : string
Site ID from which to collect raw data
raw_keys : sequence
Sequence of raw data s3 keys to filter
deriv_keys : sequence
Sequence of derivative data s3 keys to filter
Returns
-------
InputFiles namedtuple
If all prerequisite s3 keys are present, return a namedtuple of
s3 keys. Otherwise, use the default None values.
"""
# Get only the s3 keys corresponding to this subject_id
sub_dwi_files = [k for k in raw_keys if subject_id in k and '/dwi/' in k]
sub_fmap_files = [k for k in raw_keys if subject_id in k and '/fmap/' in k]
sub_deriv_files = [k for k in deriv_keys if subject_id in k]
# Get the dwi files, bvec files, and bval files
dwi = [f for f in sub_dwi_files
if f.endswith('.nii.gz') and 'TRACEW' not in f]
bvec = [f for f in sub_dwi_files if f.endswith('.bvec')]
bval = [f for f in sub_dwi_files if f.endswith('.bval')]
epi_nii = [f for f in sub_fmap_files if f.endswith('epi.nii.gz')
and 'fMRI' not in f]
epi_json = [f for f in sub_fmap_files if f.endswith('epi.json')
and 'fMRI' not in f]
t1w = [f for f in sub_deriv_files if f.endswith('/T1w.nii.gz')]
freesurfer = [f for f in sub_deriv_files
if '/freesurfer/' in f]
# Use truthiness of non-empty lists to verify that all
# of the required prereq files exist in `s3_keys`
# TODO: If some of the files are missing, look farther up in the directory
# TODO: structure to see if there are files we should inherit
if all([dwi, bval, bvec, epi_nii, epi_json, t1w, freesurfer]):
return InputFiles(
subject=subject_id,
site=site,
valid=True,
files=dict(
dwi=dwi,
bvec=bvec,
bval=bval,
epi_nii=epi_nii,
epi_json=epi_json,
freesurfer=freesurfer,
t1w=t1w,
),
file_type='s3'
)
else:
return InputFiles(
subject=subject_id,
site=site,
valid=False,
files=None,
file_type='s3'
)
def get_s3_keys(prefix, s3_client, bucket='fcp-indi'):
"""Retrieve all keys in an S3 bucket that match the prefix and site ID
Parameters
----------
prefix : string
S3 prefix designating the S3 "directory" in which to search.
Do not include the site ID in the prefix.
s3_client : boto3 client object
from the get_s3_client() function
bucket : string
AWS S3 bucket in which to search
Returns
-------
list
All the keys matching the prefix and site in the S3 bucket
"""
# Avoid duplicate trailing slash in prefix
prefix = prefix.rstrip('/')
response = s3_client.list_objects_v2(
Bucket=bucket,
Prefix=prefix,
)
try:
keys = [d['Key'] for d in response.get('Contents')]
except TypeError:
raise ValueError(
'There are no subject files in the S3 bucket with prefix '
'{pfix:s}'.format(pfix=prefix)
)
while response['IsTruncated']:
response = s3_client.list_objects_v2(
Bucket=bucket,
Prefix=prefix,
ContinuationToken=response['NextContinuationToken']
)
keys += [d['Key'] for d in response.get('Contents')]
return keys
def keys_to_subject_register(keys, prefix, site):
"""Filter S3 keys based on data availability and return
Parameters
----------
keys : sequence
sequence of S3 keys
prefix : string
S3 prefix designating the S3 "directory" in which to search.
Do not include the site ID in the prefix.
site : string
Site ID from which to collect raw data
Returns
-------
list
List of `InputFiles` namedtuples for each valid subject
"""
deriv_keys = [
k for k in keys
if k.startswith(prefix + '/' + site + '/derivatives/sub-')
]
raw_keys = [
k for k in keys
if k.startswith(prefix + '/' + site + '/sub-')
]
subs_with_dwi = {
get_subject_id(k) for k in raw_keys
if '/dwi/' in k
}
subs_with_epi_nii = {
get_subject_id(k) for k in raw_keys
if (
k.endswith('epi.nii.gz')
and '/fmap/' in k
and 'fMRI' not in k
)
}
subs_with_epi_json = {
get_subject_id(k) for k in raw_keys
if (
k.endswith('epi.json')
and '/fmap/' in k
and 'fMRI' not in k
)
}
subs_with_freesurfer = {
get_subject_id(k) for k in deriv_keys
if '/freesurfer/' in k
}
subs_with_t1w = {
get_subject_id(k) for k in deriv_keys
if k.endswith('T1w.nii.gz')
}
valid_subjects = (
subs_with_dwi
& subs_with_epi_nii
& subs_with_epi_json
& subs_with_freesurfer
& subs_with_t1w
)
s3_registers = [
get_s3_register(subject_id=s, site=site, raw_keys=raw_keys,
deriv_keys=deriv_keys)
for s in valid_subjects
]
s3_registers = list(filter(
lambda sub: sub.valid,
s3_registers
))
return s3_registers
def download_register(subject_keys, s3_client,
bucket='fcp-indi', directory='./input',
overwrite=False):
"""
Parameters
----------
subject_keys : InputFiles namedtuple
Input s3 keys stored in namedtuple. Must have the fields
'subject': subjectID,
'site': siteID,
'files': dictionary of S3 keys
bucket : string
S3 bucket from which to extract files
directory : string
Local directory to which to save files
overwrite : bool
Flag to overwrite existing files
Returns
-------
files : InputFiles namedtuple
Input file paths stored in namedtuple. Has the fields
'subject': subjectID,
'site' : siteID,
'valid' : True,
'files' : local file paths,
'file_type' : 'local',
"""
subject = subject_keys.subject
site = subject_keys.site
input_files = InputFiles(
subject=subject,
site=site,
valid=True,
files={
k: [op.abspath(op.join(
directory, site, p.split('/' + site + '/')[-1]
)) for p in v] for k, v in subject_keys.files.items()
},
file_type='local'
)
s3keys = subject_keys.files
files = input_files.files
for ftype in s3keys.keys():
if isinstance(s3keys[ftype], str):
download_from_s3(fname_=files[ftype],
bucket_=bucket,
key_=s3keys[ftype])
elif all(isinstance(x, str) for x in s3keys[ftype]):
for key, fname in zip(s3keys[ftype], files[ftype]):
download_from_s3(fname_=fname, bucket_=bucket, key_=key)
else:
raise TypeError(
'This subject {sub:s} has {ftype:s} S3 keys that are neither '
'strings nor a sequence of strings. The S3 keys are {keys!s}'
''.format(sub=subject, ftype=ftype, keys=s3keys[ftype])
)
return input_files
def determine_directions(input_files,
input_type='s3',
bucket=None,
metadata_source='json',
json_key='PhaseEncodingDirection',
ap_value='j-', pa_value='j'):
"""Determine direction ['AP', 'PA'] of single subject's EPI nifty files
Use either metadata in associated json file or filename
Parameters
----------
input_files : InputFiles namedtuple
The local input files for the subject
input_type : "s3" or "local", default="s3"
The location of the input files, local or on S3
bucket : string or None, default=None
S3 Bucket where the input files are located.
If input_type == 's3', then bucket must not be None
metadata_source : "json" or "filename", default="json"
If "filename," look for the direction in the filename,
otherwise, use the json file and the other parameters
json_key : string, default="PhaseEncodingDirection"
The key that stores the direction information
ap_value : string, default="j-"
Metadata value to associate with dir-AP
pa_value : string, default="j"
Metadata value to associate with dir-PA
Returns
-------
InputFiles namedtuple
An InputFiles namedtuple where all fields match the `input_files`
namedtuple except that in the `files` field, the "epi_nii" and
"epi_json" keys have been replaced with "epi_ap" and "epi_pa."
"""
if metadata_source not in ['filename', 'json']:
raise ValueError('metadata_source must be "filename" or "json".')
if input_type not in ['s3', 'local']:
raise ValueError('input_type must be "local" or "s3".')
if input_type == 's3' and bucket is None:
raise ValueError('If input_type is "s3," you must supply a bucket.')
epi_files = input_files.files['epi_nii']
json_files = input_files.files['epi_json']
if metadata_source == 'filename':
ap_files = [f for f in epi_files if 'dir-AP' in f]
pa_files = [f for f in epi_files if 'dir-PA' in f]
else:
# Confirm that each nifty file has a corresponding json file.
required_json = set([f.replace('.nii.gz', '.json') for f in epi_files])
if set(json_files) != required_json:
raise ValueError(
'There are nifty files without corresponding json files. We '
'failed to find the following expected files: {files!s}'
''.format(files=required_json - set(json_files))
)
ap_files = []
pa_files = []
for jfile in json_files:
metadata = get_json(jfile)
direction = metadata.get(json_key)
if direction == ap_value:
if 'dir-PA' in jfile:
mod_logger.warning(
'The key {key:s}={val:s} does not match the direction '
'suggested by the filename {fn:s}'.format(
key=json_key, val=direction, fn=jfile
)
)
ap_files.append(jfile.replace('.json', '.nii.gz'))
elif direction == pa_value:
if 'dir-AP' in jfile:
mod_logger.warning(
'The key {key:s}={val:s} does not match the direction '
'suggested by the filename {fn:s}'.format(
key=json_key, val=direction, fn=jfile
)
)
pa_files.append(jfile.replace('.json', '.nii.gz'))
elif direction is None:
mod_logger.warning(
'The key {key:s} does not exist in file {jfile:s}. '
'Falling back on filename to determine directionality.'
'\n\n'.format(key=json_key, jfile=jfile)
)
if 'dir-AP' in jfile:
ap_files.append(jfile.replace('.json', '.nii.gz'))
elif 'dir-PA' in jfile:
pa_files.append(jfile.replace('.json', '.nii.gz'))
else:
raise ValueError(
'The key {key:s} does not exist in file {jfile:s} and '
'the directionality could not be inferred from the '
'file name.'.format(key=json_key, jfile=jfile)
)
else:
mod_logger.warning(
'The metadata in file {jfile:s} does not match the dir-PA '
'or dir-AP values that you provided. {key:s} = {val:s}. '
'Falling back on filename to determine directionality.\n\n'
''.format(jfile=jfile, key=json_key, val=direction)
)
if 'dir-AP' in jfile:
ap_files.append(jfile.replace('.json', '.nii.gz'))
elif 'dir-PA' in jfile:
pa_files.append(jfile.replace('.json', '.nii.gz'))
else:
raise ValueError(
'The metadata for key {key:s} in file {jfile:s} does '
'not match the dir-PA or dir-AP values that you '
'provided. {key:s} = {val:s}. And the directionality '
'could not be inferred from the file name.'.format(
key=json_key,
jfile=jfile,
val=direction,
))
files = copy.deepcopy(input_files.files)
del files['epi_nii']
del files['epi_json']
files['epi_ap'] = ap_files
files['epi_pa'] = pa_files
return InputFiles(
subject=input_files.subject,
site=input_files.site,
valid=input_files.valid,
files=files,
file_type=input_files.file_type
)
def separate_sessions(input_files, multiples_policy='sessions',
assign_empty_sessions=True):
"""Separate input file register into different sessions
Parameters
----------
input_files : InputFiles namedtuple
multiples_policy : "sessions" or "concatenate"
Flag that dictates how to handle multiple files in a session.
If "sessions," treat multiples as different sessions and assign
to new session IDs. If "concatenate," concatenate multiples into
a single session
assign_empty_sessions : bool
If True, assign session IDs to files without a session ID in
their path
Returns
-------
list of InputFiles namedtuples
List of InputFiles namedtuples for each session ID.
"""
if multiples_policy not in ['sessions', 'concatenate']:
raise ValueError('`multiples_policy` must be either "sessions" or '
'"concatenate"')
# Take only the first of the T1W nifty files
if len(input_files.files['t1w']) > 1:
mod_logger.warning(
'Found more than one T1W file for subject {sub:s} at site {site:s}'
'. Discarding the others.\n\n'.format(sub=input_files.subject,
site=input_files.site)
)
t1w = input_files.files['t1w']
# Take only the first freesurfer directory
freesurfer_dirs = {
f.split('/freesurfer/')[0] for f in input_files.files['freesurfer']
}
if len(freesurfer_dirs) > 1:
mod_logger.warning(
'Found more than one freesurfer dir for subject {sub:s} at site '
'{site:s}. Discarding the others.\n\n'.format(
sub=input_files.subject, site=input_files.site
)
)
freesurfer_dir = freesurfer_dirs.pop()
freesurfer = [f for f in input_files.files['freesurfer']
if f.startswith(freesurfer_dir)]
# Organize the files by session ID
ftypes = ['dwi', 'bvec', 'bval', 'epi_ap', 'epi_pa']
sess_ids = {ft: {get_sess_id(fn) for fn in input_files.files[ft]}
for ft in ftypes}
if not all([s == list(sess_ids.values())[0] for s in sess_ids.values()]):
mod_logger.warning(
'Session numbers are inconsistent for subject {sub:s} at site '
'{site:s}. Sess-IDs: {sess_ids!s}.\nFiles: {files!s}\n\n'.format(
sub=input_files.subject,
site=input_files.site,
sess_ids=sess_ids,
files={k: (v) for k, v in input_files.files.items()
if k in ['dwi', 'bvec', 'bval', 'epi_ap', 'epi_pa']},
)
)
return [InputFilesWithSession(
subject=input_files.subject,
site=input_files.site,
session=None,
files=None,
file_type=None,
)]
# We just confirmed that all of the session ID sets are equal so we can
# pop one set of session IDs off of `sess_ids` and use it from now on
sess_ids = sess_ids[ftypes[0]]
# Collect files by session ID and then file type
files_by_session = {
sess: {
ft: [
f for f in input_files.files[ft] if get_sess_id(f) == sess
]
for ft in ftypes
}
for sess in sess_ids
}
output_files = []
# Loop over each session ID
for session, files in files_by_session.items():
# Confirm that the subject has an equal number of each type of file
n_files = {k: len(v) for k, v in files.items()
if k in ['dwi', 'bvec', 'bval', 'epi_ap', 'epi_pa']}
if len(set(n_files.values())) != 1:
mod_logger.warning(
'The number of files is inconsistent for subject {sub:s} at '
'site {site:s}. The file numbers are {n_files!s}\n\n'.format(
sub=input_files.subject,
site=input_files.site,
n_files=n_files
)
)
output_files.append(InputFilesWithSession(
subject=input_files.subject,
site=input_files.site,
session=None,
files=None,
file_type=None,
))
elif len(set(n_files.values())) == 1:
# There is only one set of files in this session. Append to output.
if session == 'null':
output_session = 'sess-01' if assign_empty_sessions else None
else:
output_session = session
output_files.append(InputFilesWithSession(
subject=input_files.subject,
site=input_files.site,
session=output_session,
files=dict(
dwi=input_files.files['dwi'],
bvec=input_files.files['bvec'],
bval=input_files.files['bval'],
epi_ap=input_files.files['epi_ap'],
epi_pa=input_files.files['epi_pa'],
t1w=t1w,
freesurfer=freesurfer,
),
file_type=input_files.file_type,
))
else:
# There are multiple copies of files for this one session ID.
if multiples_policy == 'concatenate':
# The multiple copies represent one session and should be
# concatenated
raise NotImplementedError('Concatenation of multiples not yet '
'implemented.')
else:
# The multiple copies represent multiple sessions and
# should be further subdivided into sessions
raise NotImplementedError('Session subdivision not yet '
'implemented.')
return output_files
def get_all_s3_registers(prefix, sites, bucket='fcp-indi'):
"""
Parameters
----------
prefix : string
S3 prefix designating the S3 "directory" in which to search.
Do not include the site ID in the prefix.
sites : sequence of strings
Site IDs from which to collect raw data
bucket : string
AWS S3 bucket in which to search
Returns
-------
dict
dict where the keys are site IDs and the values are
list of `InputFiles` namedtuples for each valid subject
at that site
"""
subjects = {}
for site in sites:
# Get all S3 keys
keys = get_s3_keys(prefix=prefix, site=site, bucket='fcp-indi')
# Get all registers (without the AP/PA directions)
regs = keys_to_subject_register(keys=keys, prefix=prefix, site=site)
# Assign the fmap files to either AP/PA
regs_pa_ap = [
determine_directions(input_files=reg,
input_type='s3',
bucket=bucket,
metadata_source='json',
json_key='PhaseEncodingDirection',
ap_value='j-', pa_value='j')
for reg in regs
]
# Separate each subject register into different sessions
regs_nested = [
separate_sessions(reg,
multiples_policy='sessions',
assign_empty_sessions=True)
for reg in regs_pa_ap
]
# But `separate_sessions` returns a list of namedtuples
# so `regs_nested` is nested and needs to be flattened
regs_flat = [item for sublist in regs_nested for item in sublist]
subjects[site] = [reg for reg in regs_flat if reg.files is not None]
return subjects
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77,
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13,
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11537,
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1782,
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4938,
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220,
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220,
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220,
220,
6352,
62,
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62,
67,
37686,
198,
220,
220,
220,
220,
220,
220,
220,
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1222,
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77,
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1222,
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62,
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1222,
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62,
69,
6037,
333,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1222,
6352,
62,
4480,
62,
83,
16,
86,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
264,
18,
62,
2301,
6223,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
651,
62,
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18,
62,
30238,
7,
32796,
62,
312,
28,
82,
11,
2524,
28,
15654,
11,
8246,
62,
13083,
28,
1831,
62,
13083,
11,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
16124,
62,
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28,
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62,
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8,
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220,
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220,
220,
220,
329,
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18,
62,
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7,
24455,
7,
198,
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220,
220,
220,
220,
220,
220,
37456,
850,
25,
850,
13,
12102,
11,
198,
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220,
220,
220,
220,
220,
220,
264,
18,
62,
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220,
15306,
628,
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220,
220,
1441,
264,
18,
62,
2301,
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628,
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4299,
4321,
62,
30238,
7,
32796,
62,
13083,
11,
264,
18,
62,
16366,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19236,
11639,
69,
13155,
12,
521,
72,
3256,
8619,
28,
4458,
14,
15414,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49312,
28,
25101,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
2426,
62,
13083,
1058,
23412,
25876,
3706,
83,
29291,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
264,
18,
8251,
8574,
287,
3706,
83,
29291,
13,
12039,
423,
262,
7032,
198,
220,
220,
220,
220,
220,
220,
220,
705,
32796,
10354,
2426,
2389,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
15654,
10354,
2524,
2389,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
16624,
10354,
22155,
286,
311,
18,
8251,
628,
220,
220,
220,
19236,
1058,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
311,
18,
19236,
422,
543,
284,
7925,
3696,
628,
220,
220,
220,
8619,
1058,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
10714,
8619,
284,
543,
284,
3613,
3696,
628,
220,
220,
220,
49312,
1058,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
19762,
284,
49312,
4683,
3696,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
3696,
1058,
23412,
25876,
3706,
83,
29291,
198,
220,
220,
220,
220,
220,
220,
220,
23412,
2393,
13532,
8574,
287,
3706,
83,
29291,
13,
7875,
262,
7032,
198,
220,
220,
220,
220,
220,
220,
220,
705,
32796,
10354,
2426,
2389,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
15654,
6,
1058,
2524,
2389,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
12102,
6,
1058,
6407,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
16624,
6,
1058,
1957,
2393,
13532,
11,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7753,
62,
4906,
6,
1058,
705,
12001,
3256,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
2426,
796,
2426,
62,
13083,
13,
32796,
198,
220,
220,
220,
2524,
796,
2426,
62,
13083,
13,
15654,
628,
220,
220,
220,
5128,
62,
16624,
796,
23412,
25876,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2426,
28,
32796,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2524,
28,
15654,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4938,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
3696,
34758,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
25,
685,
404,
13,
397,
2777,
776,
7,
404,
13,
22179,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8619,
11,
2524,
11,
279,
13,
35312,
10786,
14,
6,
1343,
2524,
1343,
31051,
11537,
58,
12,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15306,
329,
279,
287,
410,
60,
329,
479,
11,
410,
287,
2426,
62,
13083,
13,
16624,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
8964,
198,
220,
220,
220,
220,
220,
220,
220,
2393,
62,
4906,
11639,
12001,
6,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
264,
18,
13083,
796,
2426,
62,
13083,
13,
16624,
198,
220,
220,
220,
3696,
796,
5128,
62,
16624,
13,
16624,
198,
220,
220,
220,
329,
277,
4906,
287,
264,
18,
13083,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
82,
18,
13083,
58,
701,
2981,
4357,
965,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4321,
62,
6738,
62,
82,
18,
7,
69,
3672,
62,
28,
16624,
58,
701,
2981,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19236,
62,
28,
27041,
316,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
28,
82,
18,
13083,
58,
701,
2981,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
477,
7,
271,
39098,
7,
87,
11,
965,
8,
329,
2124,
287,
264,
18,
13083,
58,
701,
2981,
60,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
277,
3672,
287,
19974,
7,
82,
18,
13083,
58,
701,
2981,
4357,
3696,
58,
701,
2981,
60,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4321,
62,
6738,
62,
82,
18,
7,
69,
3672,
62,
28,
69,
3672,
11,
19236,
62,
28,
27041,
316,
11,
1994,
62,
28,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1212,
2426,
1391,
7266,
25,
82,
92,
468,
1391,
701,
2981,
25,
82,
92,
311,
18,
8251,
326,
389,
6159,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
37336,
4249,
257,
8379,
286,
13042,
13,
383,
311,
18,
8251,
389,
1391,
13083,
0,
82,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4458,
18982,
7,
7266,
28,
32796,
11,
277,
4906,
28,
701,
2981,
11,
8251,
28,
82,
18,
13083,
58,
701,
2981,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
1441,
5128,
62,
16624,
628,
198,
4299,
5004,
62,
12942,
507,
7,
15414,
62,
16624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5128,
62,
4906,
11639,
82,
18,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19236,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
62,
10459,
11639,
17752,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33918,
62,
2539,
11639,
35645,
27195,
7656,
35,
4154,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2471,
62,
8367,
11639,
73,
12,
3256,
14187,
62,
8367,
11639,
73,
6,
2599,
198,
220,
220,
220,
37227,
35,
2357,
3810,
4571,
37250,
2969,
3256,
705,
4537,
20520,
286,
2060,
2426,
338,
412,
11901,
47803,
3696,
628,
220,
220,
220,
5765,
2035,
20150,
287,
3917,
33918,
2393,
393,
29472,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
5128,
62,
16624,
1058,
23412,
25876,
3706,
83,
29291,
198,
220,
220,
220,
220,
220,
220,
220,
383,
1957,
5128,
3696,
329,
262,
2426,
628,
220,
220,
220,
5128,
62,
4906,
1058,
366,
82,
18,
1,
393,
366,
12001,
1600,
4277,
2625,
82,
18,
1,
198,
220,
220,
220,
220,
220,
220,
220,
383,
4067,
286,
262,
5128,
3696,
11,
1957,
393,
319,
311,
18,
628,
220,
220,
220,
19236,
1058,
4731,
393,
6045,
11,
4277,
28,
14202,
198,
220,
220,
220,
220,
220,
220,
220,
311,
18,
48353,
810,
262,
5128,
3696,
389,
5140,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1002,
5128,
62,
4906,
6624,
705,
82,
18,
3256,
788,
19236,
1276,
407,
307,
6045,
628,
220,
220,
220,
20150,
62,
10459,
1058,
366,
17752,
1,
393,
366,
34345,
1600,
4277,
2625,
17752,
1,
198,
220,
220,
220,
220,
220,
220,
220,
1002,
366,
34345,
553,
804,
329,
262,
4571,
287,
262,
29472,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4306,
11,
779,
262,
33918,
2393,
290,
262,
584,
10007,
628,
220,
220,
220,
33918,
62,
2539,
1058,
4731,
11,
4277,
2625,
35645,
27195,
7656,
35,
4154,
1,
198,
220,
220,
220,
220,
220,
220,
220,
383,
1994,
326,
7000,
262,
4571,
1321,
628,
220,
220,
220,
2471,
62,
8367,
1058,
4731,
11,
4277,
2625,
73,
21215,
198,
220,
220,
220,
220,
220,
220,
220,
3395,
14706,
1988,
284,
11602,
351,
26672,
12,
2969,
628,
220,
220,
220,
14187,
62,
8367,
1058,
4731,
11,
4277,
2625,
73,
1,
198,
220,
220,
220,
220,
220,
220,
220,
3395,
14706,
1988,
284,
11602,
351,
26672,
12,
4537,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
35656,
198,
220,
220,
220,
23412,
25876,
3706,
83,
29291,
198,
220,
220,
220,
220,
220,
220,
220,
1052,
23412,
25876,
3706,
83,
29291,
810,
477,
7032,
2872,
262,
4600,
15414,
62,
16624,
63,
198,
220,
220,
220,
220,
220,
220,
220,
3706,
83,
29291,
2845,
326,
287,
262,
4600,
16624,
63,
2214,
11,
262,
366,
538,
72,
62,
77,
4178,
1,
290,
198,
220,
220,
220,
220,
220,
220,
220,
366,
538,
72,
62,
17752,
1,
8251,
423,
587,
6928,
351,
366,
538,
72,
62,
499,
1,
290,
366,
538,
72,
62,
8957,
526,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
611,
20150,
62,
10459,
407,
287,
37250,
34345,
3256,
705,
17752,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
38993,
62,
10459,
1276,
307,
366,
34345,
1,
393,
366,
17752,
1911,
11537,
628,
220,
220,
220,
611,
5128,
62,
4906,
407,
287,
37250,
82,
18,
3256,
705,
12001,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
15414,
62,
4906,
1276,
307,
366,
12001,
1,
393,
366,
82,
18,
1911,
11537,
628,
220,
220,
220,
611,
5128,
62,
4906,
6624,
705,
82,
18,
6,
290,
19236,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
1532,
5128,
62,
4906,
318,
366,
82,
18,
553,
345,
1276,
5127,
257,
19236,
2637,
8,
628,
220,
220,
220,
2462,
72,
62,
16624,
796,
5128,
62,
16624,
13,
16624,
17816,
538,
72,
62,
77,
4178,
20520,
198,
220,
220,
220,
33918,
62,
16624,
796,
5128,
62,
16624,
13,
16624,
17816,
538,
72,
62,
17752,
20520,
198,
220,
220,
220,
611,
20150,
62,
10459,
6624,
705,
34345,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
2471,
62,
16624,
796,
685,
69,
329,
277,
287,
2462,
72,
62,
16624,
611,
705,
15908,
12,
2969,
6,
287,
277,
60,
198,
220,
220,
220,
220,
220,
220,
220,
14187,
62,
16624,
796,
685,
69,
329,
277,
287,
2462,
72,
62,
16624,
611,
705,
15908,
12,
4537,
6,
287,
277,
60,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7326,
2533,
326,
1123,
47803,
2393,
468,
257,
11188,
33918,
2393,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2672,
62,
17752,
796,
900,
26933,
69,
13,
33491,
7,
4458,
77,
4178,
13,
34586,
3256,
45302,
17752,
11537,
329,
277,
287,
2462,
72,
62,
16624,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
611,
900,
7,
17752,
62,
16624,
8,
14512,
2672,
62,
17752,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1858,
389,
47803,
3696,
1231,
11188,
33918,
3696,
13,
775,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
47904,
284,
1064,
262,
1708,
2938,
3696,
25,
1391,
16624,
0,
82,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4458,
18982,
7,
16624,
28,
35827,
62,
17752,
532,
900,
7,
17752,
62,
16624,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
2471,
62,
16624,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
14187,
62,
16624,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
474,
7753,
287,
33918,
62,
16624,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
796,
651,
62,
17752,
7,
73,
7753,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4571,
796,
20150,
13,
1136,
7,
17752,
62,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4571,
6624,
2471,
62,
8367,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
15908,
12,
4537,
6,
287,
474,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
953,
62,
6404,
1362,
13,
43917,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
464,
1994,
1391,
2539,
25,
82,
92,
34758,
2100,
25,
82,
92,
857,
407,
2872,
262,
4571,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
47811,
276,
416,
262,
29472,
1391,
22184,
25,
82,
92,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
28,
17752,
62,
2539,
11,
1188,
28,
37295,
11,
24714,
28,
73,
7753,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2471,
62,
16624,
13,
33295,
7,
73,
7753,
13,
33491,
7,
4458,
17752,
3256,
45302,
77,
4178,
13,
34586,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
4571,
6624,
14187,
62,
8367,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
15908,
12,
2969,
6,
287,
474,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
953,
62,
6404,
1362,
13,
43917,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
464,
1994,
1391,
2539,
25,
82,
92,
34758,
2100,
25,
82,
92,
857,
407,
2872,
262,
4571,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
47811,
276,
416,
262,
29472,
1391,
22184,
25,
82,
92,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
28,
17752,
62,
2539,
11,
1188,
28,
37295,
11,
24714,
28,
73,
7753,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14187,
62,
16624,
13,
33295,
7,
73,
7753,
13,
33491,
7,
4458,
17752,
3256,
45302,
77,
4178,
13,
34586,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
4571,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
953,
62,
6404,
1362,
13,
43917,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
464,
1994,
1391,
2539,
25,
82,
92,
857,
407,
2152,
287,
2393,
1391,
73,
7753,
25,
82,
27422,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
37,
9221,
736,
319,
29472,
284,
5004,
4571,
1483,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
59,
77,
59,
77,
4458,
18982,
7,
2539,
28,
17752,
62,
2539,
11,
474,
7753,
28,
73,
7753,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
15908,
12,
2969,
6,
287,
474,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2471,
62,
16624,
13,
33295,
7,
73,
7753,
13,
33491,
7,
4458,
17752,
3256,
45302,
77,
4178,
13,
34586,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
705,
15908,
12,
4537,
6,
287,
474,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14187,
62,
16624,
13,
33295,
7,
73,
7753,
13,
33491,
7,
4458,
17752,
3256,
45302,
77,
4178,
13,
34586,
6,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
464,
1994,
1391,
2539,
25,
82,
92,
857,
407,
2152,
287,
2393,
1391,
73,
7753,
25,
82,
92,
290,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1169,
4571,
1483,
714,
407,
307,
41240,
422,
262,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
7753,
1438,
2637,
13,
18982,
7,
2539,
28,
17752,
62,
2539,
11,
474,
7753,
28,
73,
7753,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
953,
62,
6404,
1362,
13,
43917,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
464,
20150,
287,
2393,
1391,
73,
7753,
25,
82,
92,
857,
407,
2872,
262,
26672,
12,
4537,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
273,
26672,
12,
2969,
3815,
326,
345,
2810,
13,
1391,
2539,
25,
82,
92,
796,
1391,
2100,
25,
82,
27422,
705,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
37,
9221,
736,
319,
29472,
284,
5004,
4571,
1483,
13,
59,
77,
59,
77,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
4458,
18982,
7,
73,
7753,
28,
73,
7753,
11,
1994,
28,
17752,
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628,
220,
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7481,
198
] | 2.038864 | 10,601 |
import os
import boto3
AMI = os.environ['AMI']
INSTANCE_TYPE = os.environ['INSTANCE_TYPE']
KEY_NAME = os.environ['KEY_NAME']
SUBNET_ID = os.environ['SUBNET_ID']
ec2 = boto3.resource('ec2')
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] | 2.358025 | 81 |
############################################################
# -*- coding: utf-8 -*-
#
# # # # # # #
# ## ## # ## # #
# # # # # # # # # # #
# # ## # ## ## ######
# # # # # # #
#
# Python-based Tool for interaction with the 10micron mounts
# GUI with PyQT5 for python
#
# written in python3, (c) 2019-2021 by mworion
#
# Licence APL2.0
#
###########################################################
# standard libraries
import pytest
import unittest.mock as mock
import platform
if not platform.system() == 'Windows':
pytest.skip("skipping windows-only tests", allow_module_level=True)
# external packages
from astropy.io import fits
from PyQt5.QtCore import QThreadPool, QObject, pyqtSignal
from skyfield.api import Angle, wgs84
import ctypes
# local import
from mountcontrol.mount import Mount
from logic.environment.skymeter import Skymeter
from logic.camera.cameraAscom import CameraAscom
from base.driverDataClass import Signals
from base.ascomClass import AscomClass
from base.loggerMW import setupLogging
setupLogging()
@pytest.fixture(autouse=True, scope='function')
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] | 2.891089 | 404 |
import re
import renderer
LINK_PATTERN = re.compile(r'(^|\s)(http(?:s)?://[^ \<]+\w)')
class LinkRenderer(renderer.RenderBase):
'''
Converts standalone http or https links in text into the Markdown
equivalent, so
lorem ipsum http://www.example.com, etc
becomes
lorem ipsum [http://www.example.com](http://www.example.com), etc
'''
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] | 2.420382 | 157 |
import niGraphParser
import niGraph
import sys
import glob
import os
import random
import time
import re
from matplotlib import pyplot
import networkx as nx
import networkx.algorithms.shortest_paths.dense as dense
import xml.etree.ElementTree as ET
import networkx.algorithms.simple_paths as nxPaths
graphsDir = "..\DataSets"
# print("Topological sort took " + str(durationTime) + " seconds")
# pyplot.loglog(sizes, times, "o")
# pyplot.xscale("log")
# pyplot.yscale("log")
# pyplot.show()
if __name__ == "__main__":
main()
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] | 2.661905 | 210 |
#------------------------------------------------------
# import
#------------------------------------------------------
import os
import cv2
#------------------------------------------------------
# global
#------------------------------------------------------
LINE_COLOR = (0, 0, 255)
LINE_THICKNESS = 10
FONT_STYLE = cv2.FONT_HERSHEY_SIMPLEX
FONT_SCALE = 1.0
FONT_THICKNESS = 2
FONT_COLOR = (0, 0, 0) #BGR
FONT_BACKGROUND_COLOR = (0, 0, 255)
#------------------------------------------------------
# function
#------------------------------------------------------
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] | 3.979167 | 144 |
import os
| [
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] | 3.666667 | 3 |
import csv
import argparse
parser = argparse.ArgumentParser(description='parameters for cleaning a csv file')
parser.add_argument(
'--columns',
type=int,
nargs="+",
default=[],
help="The columns to remove. Usage: --rows 0 1 10 25",
)
parser.add_argument(
'--file',
type=str,
default=None,
help="The csv file path/name.",
)
parser.add_argument(
'--rows',
type=int,
nargs="+",
default=[],
help="Rows to remove."
)
parser.add_argument(
'--strip',
action="store_true",
help="Remove all leading and trailing white spaces."
)
args = parser.parse_args()
if __name__ == "__main__":
cleaner = Cleaner(args.file, args.columns, args.rows, args.strip) | [
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] | 2.537102 | 283 |
#! /usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = ["Rachel P. B. Moraes", "Igor Montagner", "Fabio Miranda"]
import rospy
import numpy as np
import tf
import math
import cv2
import time
from geometry_msgs.msg import Twist, Vector3, Pose
from nav_msgs.msg import Odometry
from sensor_msgs.msg import Image, CompressedImage
from cv_bridge import CvBridge, CvBridgeError
import smach
import smach_ros
face_cascade = cv2.CascadeClassifier('haarcascade_frontalcatface.xml')
bridge = CvBridge()
global cv_image
global dif_x
global media
global centro
global area1, area2
global p
cv_image = None
dif_x = None
area1, area2 = 0,0
atraso = 1.5E9
delay_miranda = 0.05
# Variáveis para permitir que o roda_todo_frame troque dados com a máquina de estados
media = 0
centro = 0
p = False
## Classes - estados
# main
if __name__ == '__main__':
main()
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###################################################
# 智能小车1.0 -- 舵机模块
#
# @author chenph
# @date 2018/5/15
###################################################
import RPi.GPIO as GPIO
import time
# 初始模块
# 舵机左转
# 舵机右转
if __name__ == "__main__":
try:
# 19,21,23
m = ServoModule(19)
m.turnLeft()
time.sleep(5)
m.turnRight()
except KeyboardInterrupt:
pass
GPIO.cleanup() | [
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# -*- coding: utf-8 -*-
import pytest
from darjeeling.core import (TestOutcome,
TestCoverage,
FileLine,
FileLineSet)
@pytest.fixture
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# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pretrain BERT"""
import os
import random
import math
import numpy as np
import torch
from arguments import get_args
from configure_data import configure_data
from fp16 import FP16_Module
from fp16 import FP16_Optimizer
from learning_rates import AnnealingLR
from model import BertModel
from model import get_params_for_weight_decay_optimization
from model import DistributedDataParallel as DDP
from optim import Adam
from utils import Timers, save_checkpoint, load_checkpoint, check_checkpoint, move_to_cuda
import pdb
def get_model(tokenizer, args):
"""Build the model."""
print('building BERT model ...')
model = BertModel(tokenizer, args)
print(' > number of parameters: {}'.format(
sum([p.nelement() for p in model.parameters()])), flush=True)
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
print("fp16 mode")
model = FP16_Module(model)
if args.fp32_embedding:
model.module.model.bert.embeddings.word_embeddings.float()
model.module.model.bert.embeddings.position_embeddings.float()
model.module.model.bert.embeddings.token_type_embeddings.float()
if args.fp32_tokentypes:
model.module.model.bert.embeddings.token_type_embeddings.float()
if args.fp32_layernorm:
for name, _module in model.named_modules():
if 'LayerNorm' in name:
_module.float()
# Wrap model for distributed training.
if args.world_size > 1:
model = DDP(model)
return model
def get_optimizer(model, args):
"""Set up the optimizer."""
# Build parameter groups (weight decay and non-decay).
while isinstance(model, (DDP, FP16_Module)):
model = model.module
layers = model.model.bert.encoder.layer
pooler = model.model.bert.pooler
lmheads = model.model.cls.predictions
nspheads = model.model.cls.seq_relationship
embeddings = model.model.bert.embeddings
param_groups = []
param_groups += list(get_params_for_weight_decay_optimization(layers))
param_groups += list(get_params_for_weight_decay_optimization(pooler))
param_groups += list(get_params_for_weight_decay_optimization(nspheads))
param_groups += list(get_params_for_weight_decay_optimization(embeddings))
param_groups += list(get_params_for_weight_decay_optimization(
lmheads.transform))
param_groups[1]['params'].append(lmheads.bias)
# Use Adam.
optimizer = Adam(param_groups,
lr=args.lr, weight_decay=args.weight_decay)
# Wrap into fp16 optimizer.
if args.fp16:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale,
dynamic_loss_scale=args.dynamic_loss_scale,
dynamic_loss_args={
'scale_window': args.loss_scale_window,
'min_scale': args.min_scale,
'delayed_shift': args.hysteresis})
return optimizer
def get_learning_rate_scheduler(optimizer, args):
"""Build the learning rate scheduler."""
# Add linear learning rate scheduler.
if args.lr_decay_iters is not None:
num_iters = args.lr_decay_iters
else:
num_iters = args.train_iters * args.epochs
init_step = -1
warmup_iter = args.warmup * num_iters
lr_scheduler = AnnealingLR(optimizer,
start_lr=args.lr,
warmup_iter=warmup_iter,
num_iters=num_iters,
decay_style=args.lr_decay_style,
last_iter=init_step)
return lr_scheduler
def setup_model_and_optimizer(args, tokenizer):
"""Setup model and optimizer."""
model = get_model(tokenizer, args)
optimizer = get_optimizer(model, args)
lr_scheduler = get_learning_rate_scheduler(optimizer, args)
criterion = torch.nn.CrossEntropyLoss(reduction='sum', ignore_index=-1)
args.continue_train = False
check_checkpoint(model, optimizer, lr_scheduler, args)
if args.load is not None and not args.continue_train:
print("| Resume checkpoints from {}".format(args.load))
epoch, i, total_iters = load_checkpoint(model, optimizer,
lr_scheduler, args)
if args.resume_dataloader:
args.epoch = epoch
args.mid_epoch_iters = i
args.total_iters = total_iters
return model, optimizer, lr_scheduler, criterion
def forward_step(data, model, tokenizer, criterion, args):
"""Forward step."""
sample = move_to_cuda(data, torch.cuda.current_device())
output, nsp, past = model(**sample["net_input"])
nsp_labels = sample["nsp_labels"]
target = sample["target"]
nsp_loss = criterion(nsp.view(-1, 3).contiguous().float(),
nsp_labels.view(-1).contiguous())
losses = criterion(output.view(-1, tokenizer.num_tokens).contiguous().float(),
target.contiguous().view(-1).contiguous())
# pdb.set_trace()
return losses, nsp_loss, sample["nsentences"], sample["ntokens"]
def backward_step(optimizer, model, lm_loss, nsp_loss, batch_size, batch_tokens, args):
"""Backward step."""
# Total loss.
loss = lm_loss / batch_tokens + nsp_loss / batch_size
# Backward pass.
optimizer.zero_grad()
if args.fp16:
optimizer.backward(loss, update_master_grads=False)
else:
loss.backward()
# Reduce across processes.
lm_loss_reduced = lm_loss
nsp_loss_reduced = nsp_loss
if args.world_size > 1:
batch_size = torch.Tensor([batch_size]).to(lm_loss.device)
batch_tokens = torch.Tensor([batch_tokens]).to(lm_loss.device)
reduced_losses = torch.cat((lm_loss.view(1), nsp_loss.view(1), batch_size, batch_tokens))
torch.distributed.all_reduce(reduced_losses.data)
# reduced_losses.data = reduced_losses.data / args.world_size
model.allreduce_params(reduce_after=False,
fp32_allreduce=args.fp32_allreduce)
lm_loss_reduced = reduced_losses[0]
nsp_loss_reduced = reduced_losses[1]
batch_size = reduced_losses[2].item()
batch_tokens = reduced_losses[3].item()
# Update master gradients.
if args.fp16:
optimizer.update_master_grads()
# Clipping gradients helps prevent the exploding gradient.
if args.clip_grad > 0:
if not args.fp16:
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)
else:
optimizer.clip_master_grads(args.clip_grad)
return lm_loss_reduced, nsp_loss_reduced, batch_size, batch_tokens
def train_step(input_data, model, tokenizer, criterion, optimizer, lr_scheduler, args):
"""Single training step."""
# Forward model for one step.
lm_loss, nsp_loss, batch_size, batch_tokens = forward_step(input_data, model, tokenizer, criterion, args)
# Calculate gradients, reduce across processes, and clip.
lm_loss_reduced, nsp_loss_reduced, batch_size, batch_tokens = backward_step(optimizer, model, lm_loss,
nsp_loss, batch_size, batch_tokens,
args)
# Update parameters.
optimizer.step()
# Update learning rate.
skipped_iter = 0
if not (args.fp16 and optimizer.overflow):
lr_scheduler.step()
else:
skipped_iter = 1
return lm_loss_reduced, nsp_loss_reduced, skipped_iter, batch_size, batch_tokens
def train_epoch(epoch, model, tokenizer, optimizer, train_data, val_data,
lr_scheduler, criterion, timers, args):
"""Train one full epoch."""
# Turn on training mode which enables dropout.
model.train()
# Tracking loss.
total_lm_loss = 0.0
total_nsp_loss = 0.0
# Iterations.
max_iters = len(train_data)
iteration = 0
update_num = 0
total_tokens = 0
total_batch = 0
skipped_iters = 0
data_iterator = iter(train_data)
if args.resume_dataloader:
iteration = args.mid_epoch_iters
comsume_data(iteration)
args.resume_dataloader = False
lr_scheduler.step(max_iters * (epoch-1) + iteration)
# Data iterator.
timers('interval time').start()
while iteration < max_iters:
lm_loss, nsp_loss, skipped_iter, batch_size, batch_tokens = train_step(next(data_iterator), model, tokenizer, criterion,optimizer, lr_scheduler, args)
update_num += 1
skipped_iters += skipped_iter
iteration += 1
args.cur_iteration = iteration
# Update losses.
total_lm_loss += lm_loss.data.detach().float().item()
total_nsp_loss += nsp_loss.data.detach().float().item()
if nsp_loss != 0.0:
total_batch += batch_size
total_tokens += batch_tokens
if total_batch < 1:
total_batch = 1
# Logging.
if iteration % args.log_interval == 0:
learning_rate = optimizer.param_groups[0]['lr']
avg_nsp_loss = total_nsp_loss / total_batch
avg_lm_loss = total_lm_loss / total_tokens
elapsed_time = timers('interval time').elapsed()
log_string = ' epoch{:2d} |'.format(epoch)
log_string += ' iteration {:8d}/{:8d} |'.format(iteration,
max_iters)
log_string += ' lm loss {:.3f} |'.format(avg_lm_loss)
log_string += ' lm ppl {:.3f} |'.format(math.exp(avg_lm_loss))
log_string += ' nsp loss {:.3f} |'.format(avg_nsp_loss)
log_string += ' batch size {} |'.format(batch_size)
log_string += ' learning rate {:.7f} |'.format(learning_rate)
log_string += ' tpi (ms): {:.2f} |'.format(
elapsed_time * 1000.0 / args.log_interval)
if args.fp16:
log_string += ' loss scale {:.3f} |'.format(
optimizer.loss_scale)
print(log_string, flush=True)
if iteration % args.valid_interval == 0:
lm_loss, nsp_loss = evaluate(val_data, model, tokenizer, criterion, args)
val_loss = lm_loss + nsp_loss
print('-' * 100)
print('| end of epoch {:3d} | valid loss {:.3f} | '
'valid LM Loss {:.3f} | valid LM PPL {:.3f} | valid NSP Loss {:.3f}'.format(
epoch, val_loss, lm_loss, math.exp(lm_loss), nsp_loss))
print('-' * 100)
if args.save:
checkpoints_path = "checkpoints_{}_{}.pt".format(epoch, iteration)
save_checkpoint(checkpoints_path, epoch, iteration, model,
optimizer, lr_scheduler, args)
checkpoints_path = "checkpoints-last.pt"
save_checkpoint(checkpoints_path, epoch, iteration, model,
optimizer, lr_scheduler, args)
if val_loss < evaluate.best_val_loss:
evaluate.best_val_loss = val_loss
if args.save:
best_path = 'checkpoints-best.pt'
print('saving best model to:',
os.path.join(args.save, best_path))
save_checkpoint(best_path, epoch, iteration, model,
optimizer, lr_scheduler, args)
if args.save:
final_path = 'checkpoints_{}.pt'.format(epoch)
print('saving final epoch model to:', os.path.join(args.save, final_path))
save_checkpoint(final_path, epoch + 1, 0, model, optimizer, lr_scheduler, args)
cur_path = 'checkpoints-last.pt'
save_checkpoint(cur_path, epoch + 1, 0, model, optimizer, lr_scheduler, args)
lm_loss, nsp_loss = evaluate(val_data, model, tokenizer, criterion, args)
val_loss = lm_loss + nsp_loss
if val_loss < evaluate.best_val_loss:
evaluate.best_val_loss = val_loss
if args.save:
best_path = 'checkpoints-best.pt'
print('saving best model to:',
os.path.join(args.save, best_path))
save_checkpoint(best_path, epoch+1, 0, model,
optimizer, lr_scheduler, args)
return iteration, skipped_iters
def evaluate(data_source, model, tokenizer, criterion, args):
"""Evaluation."""
# Turn on evaluation mode which disables dropout.
model.eval()
total_lm_loss = 0
total_nsp_loss = 0
total_batch_size = 0
total_batch_tokens = 0
for data_loader in data_source:
local_lm_loss = 0
local_batch_tokens = 0
max_iters = len(data_loader)
with torch.no_grad():
data_iterator = iter(data_loader)
iteration = 0
while iteration < max_iters:
# Forward evaluation.
lm_loss, nsp_loss, batch_size, batch_tokens = forward_step(next(data_iterator), model, tokenizer,criterion, args)
# Reduce across processes.
if isinstance(model, DDP):
batch_size = torch.Tensor([batch_size]).to(lm_loss.device)
batch_tokens = torch.Tensor([batch_tokens]).to(lm_loss.device)
reduced_losses = torch.cat((lm_loss.view(1), nsp_loss.view(1), batch_size, batch_tokens))
torch.distributed.all_reduce(reduced_losses.data)
# reduced_losses.data = reduced_losses.data / args.world_size
lm_loss = reduced_losses[0]
nsp_loss = reduced_losses[1]
batch_size = reduced_losses[2].item()
batch_tokens = reduced_losses[3].item()
if lm_loss == 0.0:
batch_size = 0
total_lm_loss += lm_loss.data.detach().float().item()
total_nsp_loss += nsp_loss.data.detach().float().item()
local_lm_loss += lm_loss.data.detach().float().item()
local_batch_tokens += batch_tokens
total_batch_size += batch_size
total_batch_tokens += batch_tokens
iteration += 1
local_lm_loss /= local_batch_tokens
print('| LOCAL valid LM Loss {:.3f} | valid LM PPL {:.3f}'.format(local_lm_loss, math.exp(local_lm_loss)))
# Move model back to the train mode.
model.train()
total_lm_loss /= total_batch_tokens
total_nsp_loss /= total_batch_size
return total_lm_loss, total_nsp_loss
def initialize_distributed(args):
"""Initialize torch.distributed."""
# Manually set the device ids.
device = args.rank % torch.cuda.device_count()
if args.local_rank is not None:
device = args.local_rank
torch.cuda.set_device(device)
# Call the init process
if args.world_size > 1:
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=args.distributed_backend,
world_size=args.world_size, rank=args.rank,
init_method=init_method)
suppress_output(args.rank == 0)
def suppress_output(is_master):
"""Suppress printing on the current device. Force printing with `force=True`."""
import builtins as __builtin__
builtin_print = __builtin__.print
__builtin__.print = print
def set_random_seed(seed):
"""Set random seed for reproducability."""
if seed is not None and seed > 0:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def main():
"""Main training program."""
print('Pretrain BERT model')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
set_random_seed(args.seed)
print(args)
# Data stuff.
data_config = configure_data()
data_config.set_defaults(data_set_type='BERT', transpose=False)
(train_data, val_data), tokenizer = data_config.apply(args)
args.train_iters = len(train_data)
evaluate.best_val_loss = float("inf")
# Model, optimizer, and learning rate.
model, optimizer, lr_scheduler, criterion = setup_model_and_optimizer(
args, tokenizer)
# evaluate(val_data, model, tokenizer, criterion, args)
# At any point you can hit Ctrl + C to break out of training early.
try:
total_iters = 0
skipped_iters = 0
start_epoch = 1
best_val_loss = float('inf')
# Resume data loader if necessary.
if args.resume_dataloader:
start_epoch = args.epoch
total_iters = args.total_iters
# For all epochs.
for epoch in range(start_epoch, args.epochs + 1):
timers = Timers()
# if args.shuffle:
# train_data.batch_sampler.sampler.set_epoch(epoch + args.seed)
timers('epoch time').start()
iteration, skipped = train_epoch(epoch, model, tokenizer, optimizer,
train_data, val_data, lr_scheduler,
criterion, timers, args)
elapsed_time = timers('epoch time').elapsed()
total_iters += iteration
skipped_iters += skipped
lm_loss, nsp_loss = evaluate(val_data, model, tokenizer, criterion, args)
val_loss = lm_loss + nsp_loss
print('-' * 100)
print('| end of epoch {:3d} | time: {:.3f}s | valid loss {:.3f} | '
'valid LM Loss {:.3f} | valid LM PPL {:.3f} | valid NSP Loss {:.3f}'.format(
epoch, elapsed_time, val_loss, lm_loss, math.exp(lm_loss), nsp_loss))
print('-' * 100)
if val_loss < evaluate.best_val_loss:
evaluate.best_val_loss = val_loss
if args.save:
best_path = 'checkpoints-best.pt'
print('saving best model to:',
os.path.join(args.save, best_path))
save_checkpoint(best_path, epoch + 1, 0, model, optimizer, lr_scheduler, args)
except KeyboardInterrupt:
print('-' * 100)
print('Exiting from training early')
if args.save:
cur_path = 'checkpoints-last.pt'
print('saving current model to:',
os.path.join(args.save, cur_path))
save_checkpoint(cur_path, epoch, args.cur_iteration, model, optimizer, lr_scheduler, args)
exit()
if __name__ == "__main__":
main()
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
@author: julian
Read (and write?) molden .freq files
"""
import numpy
import string
from molsys.util.constants import angstrom, kcalmol
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################################################################################
# Author: Fanyang Cheng
# Date: 04/07/2021
# Description: This program read the weekly gas average price txt file as input
# and draw a graph for to show the data.
################################################################################
import matplotlib.pyplot as plt
#read file
if __name__ == '__main__':
main()
plt.show()
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# JSON API classes
import brainspell
from article_helpers import *
from base_handler import *
from search_helpers import *
from user_account_helpers import *
# For GitHub OAuth
import requests
import urllib.parse
import os
import hashlib
REQ_DESC = "The fields to search through. 'x' is experiments, 'p' is PMID, 'r' is reference, and 't' is title + authors + abstract."
START_DESC = "The offset of the articles to show; e.g., start = 10 would return results 11 - 20."
assert "github_frontend_client_id" in os.environ \
and "github_frontend_client_secret" in os.environ, \
"You need to set the 'github_frontend_client_id' and 'github_frontend_client_secret' environment variables."
assert "github_frontend_dev_client_id" in os.environ \
and "github_frontend_dev_client_secret" in os.environ, \
"You need to set the 'github_frontend_dev_client_id' and 'github_frontend_dev_client_secret' environment variables."
class ListEndpointsEndpointHandler(BaseHandler):
""" Return a list of all JSON API endpoints.
Do not include /help pages, or aliases. """
parameters = {}
endpoint_type = Endpoint.PULL_API
# BEGIN: Authentication endpoints
class GithubOauthProductionEndpointHandler(BaseHandler):
""" GitHub login authentication. Return the GitHub token and
Brainspell API key. """
parameters = {
"code": {
"type": str,
"description": "The code returned after GitHub OAuth."
}
}
endpoint_type = Endpoint.PULL_API
client_id_key = "github_frontend_client_id"
client_secret_key = "github_frontend_client_secret"
class GithubOauthDevelopmentEndpointHandler(
GithubOauthProductionEndpointHandler):
""" Endpoint for development OAuth. """
client_id_key = "github_frontend_dev_client_id"
client_secret_key = "github_frontend_dev_client_secret"
# BEGIN: search API endpoints
class QueryEndpointHandler(BaseHandler):
""" Endpoint to handle search queries. Return 10 results at a time. """
parameters = {
"q": {
"type": str,
"default": "",
"description": "The query to search for."
},
"start": {
"type": int,
"default": 0,
"description": START_DESC
},
"req": {
"type": str,
"default": "t",
"description": REQ_DESC
}
}
endpoint_type = Endpoint.PULL_API
class CoordinatesEndpointHandler(BaseHandler):
"""
API endpoint to fetch coordinates from all articles that match a query.
Return 200 sets of coordinates at a time.
"""
parameters = {
"q": {
"type": str,
"default": "",
"description": "The search query to return the coordinates for."
},
"start": {
"type": int,
"default": 0,
"description": START_DESC
},
"req": {
"type": str,
"default": "t",
"description": REQ_DESC
}
}
endpoint_type = Endpoint.PULL_API
class RandomQueryEndpointHandler(BaseHandler):
""" Return five random articles (for use on Brainspell's front page) """
parameters = {}
endpoint_type = Endpoint.PULL_API
class AddArticleFromPmidEndpointHandler(BaseHandler):
""" Add an article to our database via PMID (for use on the search page) """
parameters = {
"new_pmid": {
"type": str,
"description": PMID_DESC
}
}
endpoint_type = Endpoint.PUSH_API
# BEGIN: article API endpoints
class ArticleEndpointHandler(BaseHandler):
"""
Return the contents of an article, given a PMID.
Called by the view-article page.
"""
parameters = {
"pmid": {
"type": str
}
}
endpoint_type = Endpoint.PULL_API
class BulkAddEndpointHandler(BaseHandler):
"""
Add a large number of articles to our database at once,
by parsing a file that is sent to us in a JSON format.
"""
parameters = {}
endpoint_type = Endpoint.PUSH_API
class SetArticleAuthorsEndpointHandler(BaseHandler):
""" Edit the authors of an article. """
parameters = {
"pmid": {
"type": str
},
"authors": {
"type": str,
"description": "The string to set as the 'authors' for this article."
}
}
endpoint_type = Endpoint.PUSH_API
class ToggleStereotaxicSpaceVoteEndpointHandler(BaseHandler):
""" Toggle a user's vote for the stereotaxic space of an article. """
parameters = {
"pmid": {
"type": str
},
"space": {
"type": str,
"description": "Must be 'mni' or 'talairach' without quotes."
}
}
endpoint_type = Endpoint.PUSH_API
class NumberOfSubjectsVoteEndpointHandler(BaseHandler):
""" Place a vote for the number of subjects for an article. """
parameters = {
"pmid": {
"type": str
},
"subjects": {
"type": int,
"description": "The number of subjects that should be set for this article."
}
}
endpoint_type = Endpoint.PUSH_API
class AddExperimentsTableViaTextEndpointHandler(BaseHandler):
"""
Add a table of experiment coordinates via text.
Used on the view-article page.
"""
parameters = {
"values": {
"type": str,
"description": "Takes a CSV formatted string of coordinates; i.e., x, y, z separated by commas, and each coordinate separated by a newline."
},
"pmid": {
"type": str
}
}
endpoint_type = Endpoint.PUSH_API
class ToggleUserVoteEndpointHandler(BaseHandler):
""" Endpoint for a user to vote on an article tag. """
parameters = {
"topic": {
"type": str,
"description": "The name of the tag to place a vote for."
},
"pmid": {
"type": str
},
"direction": {
"type": str,
"description": "The direction that the user clicked in. Will toggle; i.e., if the user votes up on an article they've already upvoted, then it will clear the vote."
}
}
endpoint_type = Endpoint.PUSH_API
# BEGIN: table API endpoints
class ToggleUserTagOnArticleEndpointHandler(BaseHandler):
""" Toggle a user tag on an article in our database. """
parameters = {
"pmid": {
"type": str
},
"tag_name": {
"type": str,
"description": "The name of the tag to add."
}
}
endpoint_type = Endpoint.PUSH_API
class UpdateTableVoteEndpointHandler(BaseHandler):
""" Update the vote on a tag for an experiment table. """
parameters = {
"tag_name": {
"type": str
},
"direction": {
"type": str
},
"experiment": {
"type": int
},
"pmid": {
"type": str
},
"column": {
"type": str,
"description": "The column to place the vote under. Options are 'T' for tasks, 'B' for behavioral, and 'C' for cognitive."
}
}
endpoint_type = Endpoint.PUSH_API
class FlagTableEndpointHandler(BaseHandler):
""" Flag a table as inaccurate. """
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
}
}
endpoint_type = Endpoint.PUSH_API
class EditTableTitleCaptionEndpointHandler(BaseHandler):
""" Edit the title and caption for an experiment table. """
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
},
"title": {
"type": str
},
"caption": {
"type": str,
"default": ""
}
}
endpoint_type = Endpoint.PUSH_API
class DeleteRowEndpointHandler(BaseHandler):
""" Delete a row of coordinates from an experiment table. """
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
},
"row_number": {
"type": int
}
}
endpoint_type = Endpoint.PUSH_API
class SplitTableEndpointHandler(BaseHandler):
"""
Split a table of coordinates for an experiment into two
separate tables.
"""
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
},
"row_number": {
"type": int
}
}
endpoint_type = Endpoint.PUSH_API
class UpdateRowEndpointHandler(BaseHandler):
""" Update a row of coordinates in an experiment table. """
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
},
"coordinates": {
"type": json.loads,
"description": "Takes a JSON array of three or four coordinates. (The fourth is z-effective.)"
},
"row_number": {
"type": int
}
}
endpoint_type = Endpoint.PUSH_API
class AddRowEndpointHandler(BaseHandler):
""" Add a single row of coordinates to an experiment table. """
parameters = {
"pmid": {
"type": str
},
"experiment": {
"type": int
},
"coordinates": {
"type": json.loads,
"description": "Takes a JSON array of three or four coordinates. (The fourth is z-effective.)"
},
"row_number": {
"type": int,
"default": -1,
"description": "The index that this row should be located at in the table. Defaults to the end of the table."
}
}
endpoint_type = Endpoint.PUSH_API
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] | 2.314132 | 4,288 |
#!/usr/bin/env python3
# This file is covered by the LICENSE file in the root of this project.
import imp
import os
import time
import numpy as np
from matplotlib import pyplot as plt
import torch
import torch.backends.cudnn as cudnn
from torch import nn
import __init__ as booger
from tasks.semantic.modules.segmentator import *
from tasks.semantic.postproc.KNN import KNN
| [
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628
] | 3.071429 | 126 |
import random
import settings
import finance_utils
import asyncio
import aiohttp
from time import strftime
from datetime import date
from sanic import Sanic, response
from sanic.response import json
app = Sanic()
@app.route('/vitruvina', methods=['POST'])
if __name__ == '__main__':
app.run(host='0.0.0.0', port=80) | [
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# flake8: noqa
"""
Copyright 2020 - Present Okta, 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, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# AUTO-GENERATED! DO NOT EDIT FILE DIRECTLY
# SEE CONTRIBUTOR DOCUMENTATION
from okta.okta_object import OktaObject
class ApplicationAccessibility(
OktaObject
):
"""
A class for ApplicationAccessibility objects.
"""
| [
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13,
198,
220,
220,
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37227,
198
] | 3.517241 | 232 |
#Import necessary modules
import arcpy
from arcpy.sa import *
import numpy
#Check-out necessary extensions
arcpy.CheckOutExtension('Spatial')
#Set input parameters
elevation_raster = arcpy.GetParameterAsText(0)
conRL = arcpy.GetParameter(1)
conRL_ouRaster = arcpy.GetParameterAsText(2)
conH2OSat = arcpy.GetParameter(3)
conH2OSat_outRaster = arcpy.GetParameterAsText(4)
#Set up workspace
scratchWS = arcpy.env.scratchWorkspace
scratchGDB = arcpy.env.scratchGDB
#output cell size and processing extent should be the same as elevation raster
arcpy.env.cellSize = elevation_raster
output_cell_size = arcpy.env.cellSize
arcpy.env.extent = elevation_raster
extent = arcpy.env.extent
arcpy.env.overwriteOutput = True
arcpy.env.parallelProcessingFactor = "75%"
arcpy.Delete_management("in_memory")
#Get coordinate system information
desc = arcpy.Describe(elevation_raster)
coordSystem = desc.spatialReference
arcpy.AddMessage("Creating constant roughness length raster")
rlConstant = CreateConstantRaster(conRL, "FLOAT", output_cell_size, extent)
arcpy.DefineProjection_management(rlConstant, coordSystem)
rlConstant.save(conRL_ouRaster)
arcpy.AddMessage("Creating constant liquid water saturation raster")
waterConstant = CreateConstantRaster(conH2OSat, "FLOAT", output_cell_size, extent)
arcpy.DefineProjection_management(waterConstant, coordSystem)
waterConstant.save(conH2OSat_outRaster) | [
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] | 3.037118 | 458 |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
if __name__ == "__main__":
import unittest
unittest.main()
| [
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] | 3.080808 | 99 |
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import llnl.util.tty as tty
import llnl.util.tty.color as color
import spack.paths
def shell_init_instructions(cmd, equivalent):
"""Print out instructions for users to initialize shell support.
Arguments:
cmd (str): the command the user tried to run that requires
shell support in order to work
equivalent (str): a command they can run instead, without
enabling shell support
"""
shell_specific = "{sh_arg}" in equivalent
msg = [
"`%s` requires spack's shell support." % cmd,
"",
"To set up shell support, run the command below for your shell.",
"",
color.colorize("@*c{For bash/zsh/sh:}"),
" . %s/setup-env.sh" % spack.paths.share_path,
"",
color.colorize("@*c{For csh/tcsh:}"),
" source %s/setup-env.csh" % spack.paths.share_path,
"",
color.colorize("@*c{For fish:}"),
" source %s/setup-env.fish" % spack.paths.share_path,
"",
"Or, if you do not want to use shell support, run " + (
"one of these" if shell_specific else "this") + " instead:",
"",
]
if shell_specific:
msg += [
equivalent.format(sh_arg="--sh ") + " # bash/zsh/sh",
equivalent.format(sh_arg="--csh ") + " # csh/tcsh",
equivalent.format(sh_arg="--fish") + " # fish",
]
else:
msg += [" " + equivalent]
msg += ['']
tty.error(*msg)
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19662,
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] | 2.294679 | 733 |
"""
MIT License
Copyright (c) 2018 Samuel Wilder
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, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Contains several utilities for parsing with the Pyparsing library as well as
all of the parsers for all the compilers.
"""
from pyparsing import *
from red_utils import fix_hex_num
'''
Simple function to return a lambda to replace 'tokens' with 'replace'.
Usage:
>>> op_exponent = Literal('**').setParseAction(ReplaceWith('^'))
'''
ReplaceWith = lambda replace: lambda string, loc, tokens: replace
'''
Convenience function to shorten the syntax necessary to use the 'ReplaceWith'
lambda.
Usage A vs. B:
A. op_exponent = Replace(Literal('**'), '^') # <- Way shorter
B. op_exponent = Literal('**').setParseAction(ReplaceWith('^'))
'''
Replace = lambda parser, string: parser.setParseAction(ReplaceWith(string))
'''
Kills the storage specifiers of C integers because they cannot be compiled in
Red/System.
'''
IntegerSuffix = (
CaselessLiteral('ui8').suppress()
| CaselessLiteral('ui16').suppress()
| CaselessLiteral('ui32').suppress()
| CaselessLiteral('ui64').suppress()
| CaselessLiteral('ull').suppress()
| CaselessLiteral('ul').suppress()
| CaselessLiteral('u').suppress()
| CaselessLiteral('ll').suppress()
| CaselessLiteral('l').suppress()
| CaselessLiteral('i8').suppress()
| CaselessLiteral('i16').suppress()
| CaselessLiteral('i32').suppress()
| CaselessLiteral('i64').suppress()
)
'''
Kills the storage specifiers of C floats because they cannot be compiled in
Red/System.
'''
FloatSuffix = (
CaselessLiteral('f8').suppress()
| CaselessLiteral('f16').suppress()
| CaselessLiteral('f32').suppress()
| CaselessLiteral('f64').suppress()
| CaselessLiteral('f').suppress()
)
'''
Parses a hex literal and automatically changes it to the Red/System equivalent.
'''
HexNumber = Combine(
Literal('0x') +
Word(nums + 'abcdefABCDEF') +
Optional(IntegerSuffix)
# Make sure that the replacement of the '0x' to 'h' happens here
).setParseAction(lambda s, l, tokens: fix_hex_num(tokens[0]))('HexNumber')
'''
Parses a C integer.
'''
Integer = Combine(
Optional(Literal('-')) +
(
Word(nums) + CaselessLiteral('e') + (
Literal('+') | Literal('-')
) +
Word(nums) + Optional(IntegerSuffix)
| Word(nums) + Optional(IntegerSuffix)
)
)('Integer')
'''
Parses a C floating point decimal.
'''
FloatNumber = Combine(
Optional(Literal('-')) + (
Optional(Word(nums)) + Literal('.') + Word(nums) + CaselessLiteral('e') + (Literal('+') | Literal('-')) + Word(nums) + Optional(FloatSuffix | IntegerSuffix)
| Optional(Word(nums)) + Literal('.') + Word(nums) + Optional(FloatSuffix | IntegerSuffix)
)
)('FloatNumber')
'''
Parses any type of C integer literal.
'''
Number = FloatNumber | HexNumber | Integer
'''
Identifier:
age
_123
__abc123
th1s1samaz3box
'''
Identifier = Word(alphas + '_', bodyChars=alphanums + '_')
'''
Parses a C pound define.
'''
PoundDefine = (
Keyword('#define') +
Identifier +
Optional(
OneOrMore(
Number
| quotedString
| Identifier
| Keyword('()')
| Keyword('( )')
| Literal('(')
| Literal(')')
| Literal(',')
| Keyword('...')
| Word('!@#$%^&*-=+|.')
)
)
)
'''
Parses a C macro.
'''
Macro = (
Keyword('#define').suppress() +
Identifier +
Literal('(').suppress() +
Group(
ZeroOrMore(
Identifier
| Literal(',')
| Keyword('...')
)
) +
Literal(')').suppress()
)
'''
Parses a C prefix such as a function return type. Replaces any occurance of a
specific storage type with a single type so it can be ingested by RGB.
'''
Prefix = OneOrMore(
Keyword('__declspec(dllimport)').suppress()
| Keyword('__declspec(dllexport)').suppress()
| Keyword('__declspec(noreturn)').suppress()
| Keyword('__stdcall').suppress()
| Keyword('__cdecl').suppress()
| Keyword('unsigned').suppress()
| Keyword('signed').suppress()
| Keyword('long long unsigned int').setParseAction(ReplaceWith('int'))
| Keyword('long long signed int').setParseAction(ReplaceWith('int'))
| Keyword('long long int').setParseAction(ReplaceWith('int'))
| Keyword('long long').setParseAction(ReplaceWith('long'))
| Keyword('long unsinged int').setParseAction(ReplaceWith('int'))
| Keyword('long signed int').setParseAction(ReplaceWith('int'))
| Keyword('long int').setParseAction(ReplaceWith('int'))
| Keyword('long double').setParseAction(ReplaceWith('double'))
| Keyword('short int').setParseAction(ReplaceWith('int'))
| Keyword('const').suppress()
| Identifier
)
'''
Parses a C function pointer.
'''
FunctionPtr = (
Keyword('typedef void').suppress() +
Literal('(').suppress() +
Optional(Keyword('__stdcall').suppress() | Keyword('__cdecl').suppress()) +
Literal('*').suppress() +
Identifier +
Literal(')').suppress() +
Literal('(').suppress() +
Group(
ZeroOrMore(
(Prefix | Literal('*')) +
Optional(Literal(','))
)
) +
Literal(')').suppress() +
Literal(';').suppress()
)
'''
Any C type. Filters out simple unacceptable occurances.
'''
Types = OneOrMore(
Keyword('unsigned').suppress()
| Keyword('signed').suppress()
| Replace(Keyword('long long unsigned int'), 'int')
| Replace(Keyword('long long signed int'), 'int')
| Replace(Keyword('long long int'), 'int')
| Replace(Keyword('long long'), 'long')
| Replace(Keyword('long unsigned int'), 'int')
| Replace(Keyword('long signed int'), 'int')
| Replace(Keyword('long int'), 'int')
| Replace(Keyword('long double'), 'double')
| Replace(Keyword('short int'), 'int')
| Keyword('const').suppress()
| Identifier
)
'''
Parses a C typedef.
'''
Typedef = (
Keyword('typedef').suppress() +
OneOrMore(Types)
)
'''
Parses a C function.
'''
Function = (
Group(OneOrMore(Prefix) + Optional(OneOrMore(Literal('*'))) + Optional(Prefix)) +
Literal('(').suppress() +
Group(ZeroOrMore(
Prefix
| Literal('*')
| Literal(',')
| Keyword('...')
)) +
Literal(')').suppress() +
Literal(';').suppress()
)
'''
Parses a C global variable.
'''
GlobalVar = (
Keyword('extern').suppress() +
OneOrMore(Prefix | Literal('*')) +
Literal(';').suppress()
)
'''
Parses a C struct prefix.
'''
StructPrefix = OneOrMore(
Keyword('__declspec(dllimport)').suppress()
| Keyword('__declspec(dllexport)').suppress()
| Keyword('__declspec(noreturn)').suppress()
| Keyword('__stdcall').suppress()
| Keyword('__cdecl').suppress()
| Keyword('unsigned').suppress()
| Keyword('signed').suppress()
| Keyword('long long unsigned int').setParseAction(ReplaceWith('int'))
| Keyword('long long signed int').setParseAction(ReplaceWith('int'))
| Keyword('long long int').setParseAction(ReplaceWith('int'))
| Keyword('long long').setParseAction(ReplaceWith('long'))
| Keyword('long unsinged int').setParseAction(ReplaceWith('int'))
| Keyword('long signed int').setParseAction(ReplaceWith('int'))
| Keyword('long int').setParseAction(ReplaceWith('int'))
| Keyword('long double').setParseAction(ReplaceWith('double'))
| Keyword('short int').setParseAction(ReplaceWith('int'))
| Keyword('const').suppress()
)
'''
Parses a variable declaration within a struct.
'''
Decl = OneOrMore(
StructPrefix
| Identifier
| Literal('*')
) + Literal(';').suppress()
'''
Parses the start of a C struct.
'''
StructStart = (
Optional(Keyword('typedef').suppress()) +
Keyword('struct').suppress() +
Identifier
)
'''
Parses the end of a C struct.
'''
StructEnd = (
Literal('}').suppress() +
Group(
ZeroOrMore(Identifier +
Optional(Literal(','))) +
Literal(';').suppress()
))
'''
Parses a C enum.
'''
Enum = (
Keyword('enum').suppress() +
Identifier +
Literal('{').suppress() +
Group(OneOrMore(
Identifier + Replace(Literal('='), ': ') + Word(alphanums + '_-.\'"') + Literal(',')
| Identifier + Replace(Literal('='), ': ') + Word(alphanums + '_-.\'"')
| Identifier + Literal(',')
| Identifier
)) +
Literal('}').suppress() +
Optional(Identifier) +
Literal(';').suppress()
)
| [
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198,
36393,
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66,
8,
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318,
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286,
262,
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364,
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262,
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279,
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25,
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18608,
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198,
8,
198
] | 2.71064 | 3,252 |
# %%
# %%
import pandas as pd
import numpy as np
import pathlib
import matplotlib
import matplotlib.pyplot as plt
from our_plot_config import derived_dir, fig_dir, raw_dir, setplotstyle
from kappas import do_one_period
setplotstyle()
# %%
# Input files
f_cereal = raw_dir / 'cereal.parquet'
f_airlines = raw_dir / 'airlines.parquet'
f_firm_info = derived_dir / 'firm-info.parquet'
f_kappas = derived_dir / 'official-kappas.parquet'
# Figure outputs
fig_both = fig_dir / 'figure16_airlines_cereal_banks.pdf'
# %%
# ### Read in the (Cleaned) Parquet File of Beta's
# - Read in stata file
# - Create the "quarter" variable
# - Apply the $\kappa$ calculations period by period
# - Save the output to a new parquet file
# - Write a Stata file.
# %%
# read in, create quarter and drop kappa_ff
df_cereal = process_df(f_cereal)
# Clean up airlines a bit more
df_airlines = process_df(f_airlines)
df_airlines = df_airlines[df_airlines.kappa < 4].copy()
df_firms = pd.read_parquet(f_firm_info)
df_firms2 = df_firms.loc[df_firms['siccd'] ==
6021, ['permno', 'quarter', 'comnam']].copy()
df_k = pd.read_parquet(f_kappas)
df_banks = pd.merge(pd.merge(df_k[df_k['from'] != df_k['to']], df_firms2, left_on=['quarter', 'from'], right_on=['quarter', 'permno']),
df_firms2, left_on=['quarter', 'to'], right_on=['quarter', 'permno'])
# %%
df_tot = pd.concat([df_cereal.groupby(['quarter'])['kappa'].median(), df_airlines.groupby(
['quarter'])['kappa'].median(), df_banks.groupby(['quarter'])['kappa'].median()], axis=1)
# %%
df_tot[df_tot.index >
'1999-01-01'].plot(figsize=(20, 10), color=['navy', 'maroon', 'darkgreen'])
plt.legend(['RTE Cereal', 'Airlines', 'Banks'])
plt.ylabel(r"Median Pairwise Profit Weights $(\kappa)$")
plt.xlabel("")
plt.ylim(0, 1)
plt.savefig(fig_both, bbox_inches='tight')
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198
] | 2.355781 | 787 |
# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
class Module:
"""
Callable class from which user-defined layers can inherit.
The #build method should be overriden and should build the subgraph.
The benefit of inheriting from this class rather than passing a function is
that you can save input tensors as fields on `self`, then later when you call
the subgraph, you can pass a mapping from the input tensor ids to the
corresponding parent tensor you wish to pass.
"""
| [
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] | 3.944444 | 126 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import cv2
import os
import time
import oxuva
from scripts import *
if __name__ == '__main__':
main()
| [
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] | 3.287671 | 73 |
# Import classes from your brand new package
from naive_tree import GeneTree
from naive_tree import GeneNetwork
# Create an object of Mammals class & call a method of it
myTree = GeneTree()
myTree.build_network()
myTree.print_gene_network_summary()
myTree.gene_network.print_test_edge(4140)
# then get network between source and list-of-leaves.
vs = myTree.get_subcomponent(source='CXCL10', target=['PTGDR2','PTGDR'])
print('subcomponent length')
print(len(vs))
# and we can prune the tree to just nodes
# reachable from the source
myTree.prune_tree(vs)
# doing a search starting from the root to make sure we can
# reach all nodes
vs = myTree.pruned_graph.bfs(
myTree.pruned_graph.vs.find(name='CXCL10').index,
mode='out')
print('len vs: ' + str(len(vs[0])))
# and we can create a spanning tree based on
# edge weights.
myTree.get_spanning_tree()
myTree.compute_conditionals() | [
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] | 2.855346 | 318 |
#!/usr/bin/python
import numpy as np
from scipy import optimize
from sympy import *
import matplotlib.pyplot as plt
import pdb
import random
import os
# Symbolic function to evaluate shape functions
shape_functions=lambda x: np.matrix([(1-x)/DX,x/DX])
xn = np.array([0.,1.])
DX = 1.
## required for plotting residual
CFL=np.linspace(0.,1.,100.)
samples=1000
number_prev = Rand(1, 4, samples)
position_prev = RandPosition(number_prev)
number_curr = Rand(1, 4, samples)
position_curr = RandPosition(number_curr)
if not os.path.exists('eulerRandom.npy'):
eulerSolution=[]
rk2Solution=[]
eulerSolution_id=[]
rk2Solution_id=[]
for i in range(samples):
print "Computing critical CFL for sample ",i,": ",number_curr[i]," particles"
shapes_prev=shape_functions(position_prev[i])
shapes_curr=shape_functions(position_curr[i])
solution_euler=[]
solution_rk2=[]
solution_euler_id=[]
solution_rk2_id=[]
for k in range(number_curr[i]):
# if number_curr[i]<number_prev[i] :
# print "Attention ca va merder !!!!!!"
# else:
# print "Ca va le faire..."
res=residual(k,position_curr[i],position_prev[i],1)
solution_euler.append(gridSearch(res))
res=residual(k,position_curr[i],position_curr[i],1)
solution_euler_id.append(gridSearch(res))
res=residual(k,position_curr[i],position_prev[i],2)
solution_rk2.append(gridSearch(res))
res=residual(k,position_curr[i],position_curr[i],2)
solution_rk2_id.append(gridSearch(res))
eulerSolution.append(min(solution_euler))
rk2Solution.append(min(solution_rk2))
eulerSolution_id.append(min(solution_euler_id))
rk2Solution_id.append(min(solution_rk2_id))
np.save('eulerRandom.npy',eulerSolution)
np.save('rk2Random.npy',rk2Solution)
np.save('eulerRandom_id.npy',eulerSolution_id)
np.save('rk2Random_id.npy',rk2Solution_id)
else :
eulerSolution=np.load('eulerRandom.npy')
rk2Solution=np.load('rk2Random.npy')
eulerSolution_id=np.load('eulerRandom_id.npy')
rk2Solution_id=np.load('rk2Random_id.npy')
import statistics
print "Mean CFL for euler periodic: ", statistics.mean(eulerSolution_id)
print "Mean CFL for euler non-periodic: ", statistics.mean(eulerSolution)
print "Mean CFL for rk2 periodic: ", statistics.mean(rk2Solution_id)
print "Mean CFL for rk2 non-periodic: ", statistics.mean(rk2Solution)
print " "
print "Median CFL for euler periodic: ", statistics.median(eulerSolution_id)
print "Median CFL for euler non-periodic: ", statistics.median(eulerSolution)
print "Median CFL for rk2 periodic: ", statistics.median(rk2Solution_id)
print "Median CFL for rk2 non-periodic: ", statistics.median(rk2Solution)
pdb.set_trace()
barsEuler=np.histogram(eulerSolution,bins=np.linspace(0.,1.,11))
barsRk2=np.histogram(rk2Solution,bins=np.linspace(0.,1.,11))
export2DTeXFile('cflStatistics.tex',barsEuler[1],np.array([barsEuler[0]/float(samples),barsRk2[0]/float(samples)]),['Euler','RK2'])
barsEuler2=np.histogram(eulerSolution_id,bins=np.linspace(0.,1.,11))
barsRk22=np.histogram(rk2Solution_id,bins=np.linspace(0.,1.,11))
pdb.set_trace()
export2DTeXFile('cflStatistics_id.tex',barsEuler2[1],np.array([barsEuler2[0]/float(samples),barsRk22[0]/float(samples)]),['Euler','RK2'])
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] | 2.268082 | 1,507 |
# Copyright (C) 2018 Alpha Griffin
# @%@~LICENSE~@%@
from . import config, TokenError
from bitcash.format import public_key_to_address
from os import path
import sqlite3
from hashlib import sha256
| [
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import json
from configparser import ConfigParser
from subprocess import PIPE, Popen
from .archivesspace import ArchivesSpaceClient
from .helpers import create_tag, format_aspace_date, get_closest_dates
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import pytest
from conftest import twitter_session, BASE_URL
from utils import get_home_tweets
# status list to tweet
status_list = {"We welcome you to MSD family :)", "Hello World !!"}
@pytest.mark.run(order=1) ## ording test cases -- make tweet first as first test case
@pytest.mark.parametrize("status_text", status_list) ## making it parametrized with the iterable "status text"
def test_make_tweet(twitter_session, status_text):
'''
Test Case for the creation of a tweet.
Args:
twitter_session - the OAuth1Session from the pytest fixture.
status_text - the text which will be dumped in the tweet created for testing.
'''
# making API call to post the tweet with the status_text provide
resp = twitter_session.post(f"{BASE_URL}/statuses/update.json", params={'status': status_text})
print (f"\nTweet Response - {resp.text}") ## response shall be captured from std
# Assert to confirm if the tweet is made successfully
assert resp.status_code == 200
# Assert to Confirm if the tweet made is having correct data
assert resp.json()['text'] == status_text
@pytest.mark.run(order=4) ## ordering test cases -- delete the tweet after all the test cases are done
def test_delete_tweet(twitter_session):
'''
Test Case for the deletion of a tweet.
This test case is executed post creation.
We will be searching for the tweet from the home timeline and deleting it.
Args:
twitter_session - the OAuth1Session from the pytest fixture.
'''
# loop through the tweets made as part of test case
for tweet in get_home_tweets(twitter_session, tweet_count=len(status_list)):
# verifing if its the same tweet we had made, before deleting
if tweet['text'] in status_list:
# API call to delete the tweet
resp = twitter_session.post(f"{BASE_URL}/statuses/destroy/{tweet['id']}.json")
print (f"\nDelete tweet Response - {resp.text}") ## response shall be captured from std
# Assert to confirm if the request made successfully
assert resp.status_code == 200
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939,
198
] | 3.011445 | 699 |
import pickle
import operator
import re
prog = re.compile( r"^([\^\~\&\|])\((\w+),?(\w+)?\)$" )
with open("sha_decoded.pp", "r") as file:
_h, _REFERENCES_, _DEPENDENCY_ ,_INV_DEP_, _COST_ = pickle.load( file )
inv_refs = {v: k for k, v in _REFERENCES_.iteritems()}
#sorted_COST = sorted(_COST_.items(), key=operator.itemgetter(1))
opsByDiv = {}
# CONSTRAINTS = {}
# for i in range(2):
# for j in _h[i]:
# CONSTRAINTS[ _h[i][j] ] = 0
for i in _COST_.items():
if( i[1] not in opsByDiv ):
opsByDiv[i[1]]=[]
opsByDiv[i[1]].append(i[0])
csv = open("sha2562hash.csv","w")
csv.write("\n"*10)
opsResultCell = {}
for n in opsByDiv:
line = []
c = 0
for opRef in (opsByDiv[n]):
opsResultCell[opRef] = toCell(n+10,c)
op = inv_refs[opRef]
a,b = getElements( op )
if(a[0]=='r'):
cella = opsResultCell[a]
elif( a[0] == 't'):
cella = '1'
elif( a[0] == 'f' ):
cella = '0'
elif( a[0]=='b'):
cella = toCell(1,int(a[1:]))
else:
cella = a
if( b!= None and b[0]=='r' ):
cellb = opsResultCell[b]
elif( b!= None and b[0] == 't'):
cellb = '1'
elif( b!= None and b[0] == 'f' ):
cellb = '0'
elif( b!= None and b[0]=='b'):
cellb = toCell(1,int(b[1:]))
else:
cellb = b
if(op[0]=='^'):
line.append("=XOR("+cella+";"+cellb+")")
elif(op[0]=='|'):
line.append("=OR("+cella+";"+cellb+")")
elif(op[0]=='&'):
line.append("=AND("+cella+";"+cellb+")")
elif(op[0]=='~'):
line.append("=NOT("+cella+")")
c+=1
csv.write('\t'.join(line)+"\n")
csv.close() | [
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198,
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40664,
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] | 1.847375 | 819 |
import os
from channels.routing import ChannelNameRouter, ProtocolTypeRouter
from django.core.asgi import get_asgi_application
from notifications import consumers
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'notifs.settings')
application = ProtocolTypeRouter(
{
'http': get_asgi_application(),
'channel': ChannelNameRouter(
{
'django_notifs': consumers.DjangoNotifsConsumer.as_asgi(),
}
),
}
)
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] | 2.38191 | 199 |
import threading
# pls thinking about if there not use mutex...
# you must try it, comment the line 12 and the line 14...
mutex = threading.Lock()
num = 0
if __name__ == "__main__":
main()
| [
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] | 2.756757 | 74 |
import contextlib
import pathlib
import shutil
import sys
import tempfile
import typing as ty
import click
import click_pathlib
import jsonlines
import numpy as np
import spacy
import ujson as json
from typing import Any, Dict, List, Literal, Optional, TextIO
from typing_extensions import TypedDict
from decofre.formats import formats
from decofre import detmentions, score, clusterize
spacy.tokens.Doc.set_extension("clusters", default=None)
spacy.tokens.Span.set_extension("cluster", default=None)
spacy.tokens.Span.set_extension("singleton", default=True)
@contextlib.contextmanager
def smart_open(
filename: str, mode: str = "r", *args, **kwargs
) -> ty.Generator[ty.IO, None, None]:
"""Open files and i/o streams transparently."""
if filename == "-":
if "r" in mode:
stream = sys.stdin
else:
stream = sys.stdout
if "b" in mode:
fh = stream.buffer # type: ty.IO
else:
fh = stream
close = False
else:
fh = open(filename, mode, *args, **kwargs)
close = True
try:
yield fh
finally:
if close:
try:
fh.close()
except AttributeError:
pass
@contextlib.contextmanager
def dir_manager(
path: ty.Optional[ty.Union[pathlib.Path, str]] = None, cleanup=None
) -> ty.Generator[pathlib.Path, None, None]:
"""A context manager to deal with a directory, default to a self-destruct temp one."""
if path is None:
d_path = pathlib.Path(tempfile.mkdtemp())
if cleanup is None:
cleanup = True
else:
d_path = pathlib.Path(path).resolve()
d_path.mkdir(parents=True, exist_ok=True)
if cleanup is None:
cleanup = False
elif cleanup:
if d_path.glob("*"):
raise ValueError(f"{d_path} is not empty.")
try:
yield d_path
finally:
if cleanup:
shutil.rmtree(d_path)
def antecedents_from_mentions(
mentions: ty.Iterable[ty.Dict[str, ty.Any]],
max_candidates: int = 128,
distance_buckets: ty.Sequence[int] = (1, 2, 3, 4, 5, 7, 15, 32, 63),
) -> ty.Dict[str, ty.Dict[str, AntecedentFeaturesDict]]:
"""Extract an antecedent dataset from a list of detected mentions."""
sorted_mentions = sorted(mentions, key=lambda m: (m["start"], m["end"]))
if len(sorted_mentions) < 2:
return dict()
# The first mention in a document has no antecedent candidates
res = dict()
for i, mention in enumerate(sorted_mentions[1:], start=1):
mention_content_set = set(mention["content"])
antecedent_candidates = sorted_mentions[max(0, i - max_candidates) : i]
antecedents: ty.Dict[str, AntecedentFeaturesDict] = dict()
for j, candidate in enumerate(antecedent_candidates):
candidate_content_set = set(candidate["content"])
w_distance = int(
np.digitize(
mention["start"] - candidate["end"],
bins=distance_buckets,
right=True,
)
)
u_distance = int(
np.digitize(
mention["sentence"] - candidate["sentence"],
bins=distance_buckets,
)
)
m_distance: int = int(
np.digitize(
len(antecedent_candidates) - j,
bins=distance_buckets,
right=True,
)
)
spk_agreement = mention.get("speaker") == candidate.get("speaker")
intersect = len(mention_content_set.intersection(candidate_content_set))
token_incl_ratio = int(
10
* intersect
/ min(len(mention_content_set), len(candidate_content_set))
)
token_com_ratio = int(
10 * intersect / len(mention_content_set.union(candidate_content_set))
)
overlap = mention["start"] < candidate["end"]
antecedents[candidate["span_id"]] = {
"w_distance": w_distance,
"u_distance": u_distance,
"m_distance": m_distance,
"spk_agreement": spk_agreement,
"overlap": overlap,
"token_incl": token_incl_ratio,
"token_com": token_com_ratio,
}
res[mention["span_id"]] = antecedents
return res
@click.command(help="End-to-end coreference resolution")
@click.argument(
"detect-model",
type=click_pathlib.Path(exists=True, dir_okay=False),
)
@click.argument(
"coref-model",
type=click_pathlib.Path(exists=True, dir_okay=False),
)
@click.argument(
"input_file",
type=click.File("r"),
)
@click.argument(
"output_file",
type=click.File("w", atomic=True),
default="-",
)
@click.option(
"--from",
"input_format",
type=click.Choice(formats.keys()),
default="raw_text",
help="The input format",
show_default=True,
)
@click.option(
"--intermediary-dir",
"intermediary_dir_path",
type=click_pathlib.Path(resolve_path=True, file_okay=False),
help="A path to a directory to use for intermediary files, defaults to a self-destructing temp dir",
)
@click.option(
"--lang",
default="fr_core_news_lg",
help="A spaCy model handle for the document.",
show_default=True,
)
@click.option(
"--to",
"output_format",
type=click.Choice(["latex", "prodigy", "sacr", "text"]),
default="text",
help="Output formats (experimental)",
)
if __name__ == "__main__":
main_entry_point()
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] | 2.124861 | 2,691 |
"""
Migration script for the job state history table
"""
from __future__ import print_function
import datetime
import logging
from sqlalchemy import Column, DateTime, ForeignKey, Integer, MetaData, String, Table
from galaxy.model.custom_types import TrimmedString
from galaxy.model.migrate.versions.util import create_table, drop_table
now = datetime.datetime.utcnow
log = logging.getLogger(__name__)
metadata = MetaData()
JobStateHistory_table = Table("job_state_history", metadata,
Column("id", Integer, primary_key=True),
Column("create_time", DateTime, default=now),
Column("update_time", DateTime, default=now, onupdate=now),
Column("job_id", Integer, ForeignKey("job.id"), index=True),
Column("state", String(64), index=True),
Column("info", TrimmedString(255)))
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29201,
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29201,
7203,
10951,
1600,
833,
320,
1150,
10100,
7,
13381,
22305,
628,
198
] | 2.360494 | 405 |
#!/usr/bin/env python3
"""
Deploy DST configuration using Ansible.
Copyright (c) 2020, Copyright (c) 2020, Cisco Systems, Inc. or its affiliates
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from __future__ import print_function
from builtins import input
from dst_topology import DSTTopology
import argparse
import sys
import subprocess
from dst_utils import *
import time
import tempfile
import os
import re
from yaml import load, dump
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
if __name__ == "__main__":
main()
| [
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263,
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355,
360,
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12331,
25,
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360,
15829,
628,
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6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 3.571429 | 567 |
#######################################################################
# Godot RST File to MediaWiki converter #
#######################################################################
import re
import pandas as pd
import sys
source=sys.argv[1]
with open(source) as file:
file_contents = file.read()
class_name=file_contents.splitlines()[8]
print("<tr><td><a target=_blank href='http://godotestarrive.ovh/index.php?title="+class_name+"_GD&action=edit'>Wiki "+class_name+"</a></td>")
print("<td><a target=_new href='mw/"+class_name+".mw'>"+class_name+" MW File</a></td></tr>")
| [
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76,
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10,
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6,
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] | 2.630081 | 246 |
# Yu Zhang yz2729
# Lab 2 Date: 09/23/21
import RPi.GPIO as GPIO
import pygame # Import pygame graphics library
import time
import os # for OS calls
CODERUN = True
GPIO.setmode(GPIO.BCM)
GPIO.setup(17,GPIO.IN,pull_up_down = GPIO.PUD_UP)
GPIO.add_event_detect(17, GPIO.FALLING, callback=GPIO17_callback, bouncetime=300)
# Environment Setting
os.putenv('SDL_VIDEODRIVER', 'fbcon') # Display on piTFT
os.putenv('SDL_FBDEV', '/dev/fb0')
pygame.init()
# Screen Setting
size = (width, height) = (320, 240)
# size = (width, height) = (800, 800)
screen = pygame.display.set_mode(size)
black = 0, 0, 0
FPS = 40
clock = pygame.time.Clock()
# Big Ball
speed_big = [1,1]
ball_big = pygame.image.load("magic_ball.png")
ballrect_big = ball_big.get_rect()
ballrect_big.left = 192
ballrect_big.bottom = 128
# Small Ball
speed_small = [-2,-2]
ball_small = pygame.image.load("soccer-ball.png")
ballrect_small = ball_small.get_rect()
ballrect_small.right = 50
ballrect_small.bottom = 240
start_time = time.time()
while (time.time() - start_time <= 360) and CODERUN:
# time.sleep(0.02)
clock.tick(FPS)
ballrect_big = ballrect_big.move(speed_big)
if ballrect_big.left < 0 or ballrect_big.right > width:
speed_big[0] = -speed_big[0]
if ballrect_big.top < 0 or ballrect_big.bottom > height:
speed_big[1] = -speed_big[1]
ballrect_small= ballrect_small.move(speed_small)
if ballrect_small.left < 0 or ballrect_small.right > width:
speed_small[0] = -speed_small[0]
if ballrect_small.top < 0 or ballrect_small.bottom > height:
speed_small[1] = -speed_small[1]
if ballrect_big.colliderect(ballrect_small):
# tmp = speed_big
speed_big[0] = - speed_big[0]
speed_big[1] = - speed_big[1]
speed_small[0] = - speed_small[0]
speed_small[1] = - speed_small[1]
screen.fill(black) # Erase the Work space
screen.blit(ball_big, ballrect_big) # Combine Ball surface with workspace surface
screen.blit(ball_small, ballrect_small)
pygame.display.flip() # display workspace on screen
GPIO.cleanup() | [
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44573,
319,
3159,
198,
198,
16960,
9399,
13,
27773,
929,
3419
] | 2.271784 | 964 |
from abc import ABCMeta, abstractmethod
import numpy as np
_epsilon = np.finfo('double').eps
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6738,
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] | 2.941176 | 34 |
# This file will consist of some wrapper for using MySQL
# It is mainly used for preparing and calling mysql cli
import logging
from mysql_autoxtrabackup.general_conf import path_config
from mysql_autoxtrabackup.general_conf.generalops import GeneralClass
from mysql_autoxtrabackup.process_runner.process_runner import ProcessRunner
logger = logging.getLogger(__name__)
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] | 3.73 | 100 |
# Faça um programa que leia 5 valores e guarde-os em uma lista, no final mostre qual é o maior e o menor valor e qual
# A sua posição na lista
valores = list() # cria uma lista
for c in range(0, 5):
valores.append(int(input(f'Digite um valor para a posição {c}: '))) # inserindo os números dentro da lista
print(f'Os valores digitadores foram {valores}') # lista com os números inseridos
print(f'O maior valor digitado foi {max(valores)} na posição: ', end='') # maior valor e a posição do mesmo
for posição in range(0, 5):
if valores[posição] == max(valores):
print(posição, end=' ')
print(f'\nO menor valor digitado foi {min(valores)} na posição: ', end='') # menor valor e a posição do mesmo
for posição in range(0, 5):
if valores[posição] == min(valores):
print(posição, end=' ')
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198
] | 2.321023 | 352 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.1.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # S_EllipsoidTestSVI [<img src="https://www.arpm.co/lab/icons/icon_permalink.png" width=30 height=30 style="display: inline;">](https://www.arpm.co/lab/redirect.php?code=S_EllipsoidTestSVI&codeLang=Python)
# For details, see [here](https://www.arpm.co/lab/redirect.php?permalink=ExerSVIiid).
# ## Prepare the environment
# +
import os
import os.path as path
import sys
sys.path.append(path.abspath('../../functions-legacy'))
from numpy import diff
from scipy.io import loadmat
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
plt.style.use('seaborn')
from CONFIG import GLOBAL_DB, TEMPORARY_DB
from ARPM_utils import save_plot
from autocorrelation import autocorrelation
from InvarianceTestEllipsoid import InvarianceTestEllipsoid
# -
# ## Load the database generated by script S_FitSVI
# +
try:
db = loadmat(os.path.join(GLOBAL_DB, 'db_FitSVI'), squeeze_me=True)
except FileNotFoundError:
db = loadmat(os.path.join(TEMPORARY_DB, 'db_FitSVI'), squeeze_me=True)
theta = db['theta']
# -
# ## Compute increments and autocorrelations
# +
lag_ = 10
# preallocating variables
delta_theta = {}
acf_delta_theta = {}
for k in range(6):
delta_theta[k] = diff(theta[[k],:]) # increments
acf_delta_theta[k] = autocorrelation(delta_theta[k], lag_) # autocorrelations
# -
# ## IID test for SVI parameters
# +
lag = 10 # lag to be printed
ell_scale = 2 # ellipsoid radius coefficient
fit = 0 # fitting
pos = [] # use default settings for plot positions
# names of figures
name = {}
name[0]=r'Invariance test(increments of $\theta_1$)'
name[1]=r'Invariance test(increments of $\theta_2$)'
name[2]=r'Invariance test(increments of $\theta_3$)'
name[3]=r'Invariance test(increments of $\theta_4$)'
name[4]=r'Invariance test(increments of $\theta_5$)'
name[5]=r'Invariance test(increments of $\theta_6$)'
for k in range(6):
f = figure(figsize=(12,6))
InvarianceTestEllipsoid(delta_theta[k], acf_delta_theta[k][0,1:], lag, fit, ell_scale, pos, name[k]);
# save_plot(ax=plt.gca(), extension='png', scriptname=os.path.basename('.')[:-3], count=plt.get_fignums()[-1])
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1136,
62,
69,
570,
5700,
3419,
58,
12,
16,
12962,
198
] | 2.442211 | 995 |
from gssa.core import search
from gssa.graph_search import breadth_first_search
from gssa.secretenv import author_first, author_last, vancouver_author
author = author_first + ' ' + author_last
publist = search(author, nres=100, overwrite=False)
breadth_first_search(publist[:100], levels=2, filters=(has_author, special))
| [
6738,
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] | 3.12381 | 105 |
from . import data
from . import cube
from . import rss
from . import spectrum1d
from . import ssplibrary
from . import parameters
from . import fit_profile
from . import header
import copyreg as copy_reg
from types import *
copy_reg.pickle(MethodType, _pickle_method, _unpickle_method)
| [
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] | 3.356322 | 87 |
import math
| [
11748,
10688,
201
] | 4 | 3 |
from random import choice, gauss, random
from neat.config import ConfigParameter
# TODO: There is probably a lot of room for simplification of these classes using metaprogramming.
| [
6738,
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] | 4.181818 | 44 |
import numpy as np
import cv2
import os
import caffe
from scipy.spatial.distance import cosine
image_folder = './images'
output_folder = './features'
model = './train/code/sphereface_deploy.prototxt'
weights = './train/result/sphereface_model.caffemodel'
net = caffe.Net(model, weights, caffe.TEST)
if __name__ == '__main__':
#save_feature_vectors()
print(detect_from_img('./Aaron_Peirsol_0003.jpg'))
#img_feature = extract_deep_feature('./Aaron_Peirsol_0003.jpg', net)
| [
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] | 2.554974 | 191 |
""" Common helpers in the grid package """
__all__ = ['isNestedInstance', 'pretty_size_print']
def isNestedInstance(obj, cl):
""" Test for sub-classes types
I could not find a universal test
keywords
--------
obj: object instance
object to test
cl: Class
top level class to test
returns
-------
r: bool
True if obj is indeed an instance or subclass instance of cl
"""
tree = []
for k in cl.__subclasses__():
tree += k.__subclasses__()
tree += cl.__subclasses__() + [ cl ]
return issubclass(obj.__class__, tuple(tree))
def pretty_size_print(num_bytes):
"""
Output number of bytes in a human readable format
keywords
--------
num_bytes: int
number of bytes to convert
returns
-------
output: str
string representation of the size with appropriate unit scale
"""
if num_bytes is None:
return
KiB = 1024
MiB = KiB * KiB
GiB = KiB * MiB
TiB = KiB * GiB
PiB = KiB * TiB
EiB = KiB * PiB
ZiB = KiB * EiB
YiB = KiB * ZiB
if num_bytes > YiB:
output = '%.3g YB' % (num_bytes / YiB)
elif num_bytes > ZiB:
output = '%.3g ZB' % (num_bytes / ZiB)
elif num_bytes > EiB:
output = '%.3g EB' % (num_bytes / EiB)
elif num_bytes > PiB:
output = '%.3g PB' % (num_bytes / PiB)
elif num_bytes > TiB:
output = '%.3g TB' % (num_bytes / TiB)
elif num_bytes > GiB:
output = '%.3g GB' % (num_bytes / GiB)
elif num_bytes > MiB:
output = '%.3g MB' % (num_bytes / MiB)
elif num_bytes > KiB:
output = '%.3g KB' % (num_bytes / KiB)
else:
output = '%.3g Bytes' % (num_bytes)
return output
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220,
220,
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62,
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1875,
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33,
25,
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220,
220,
220,
220,
220,
220,
5072,
796,
705,
7225,
18,
70,
10771,
6,
4064,
357,
22510,
62,
33661,
1220,
13756,
33,
8,
198,
220,
220,
220,
1288,
361,
997,
62,
33661,
1875,
21927,
33,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
796,
705,
7225,
18,
70,
14204,
6,
4064,
357,
22510,
62,
33661,
1220,
21927,
33,
8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
796,
705,
7225,
18,
70,
2750,
4879,
6,
4064,
357,
22510,
62,
33661,
8,
628,
220,
220,
220,
1441,
5072,
198
] | 2.178528 | 829 |
from deepjets.generate import generate_events
for event in generate_events('w_vincia.config', 1, write_to='vincia.hepmc', shower='vincia', random_state=1, verbosity=0):
pass
for event, weight in generate_events('w.config', 1, write_to='dire.hepmc', shower='dire', random_state=1, verbosity=0):
print weight
| [
6738,
2769,
73,
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13,
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] | 2.756522 | 115 |
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import division
import unittest
import numpy as np
from singa import loss
from singa import tensor
if __name__ == '__main__':
unittest.main()
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13,
198,
2,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
198,
11748,
555,
715,
395,
198,
11748,
299,
32152,
355,
45941,
198,
198,
6738,
1702,
64,
1330,
2994,
198,
6738,
1702,
64,
1330,
11192,
273,
628,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.804781 | 251 |
import Sticks
import stick
import time
import pygame
pygame.init()
board = pygame.display.set_mode((400, 400))
while 1:
for e in pygame.event.get():
if e.type == pygame.KEYDOWN and e.key == pygame.K_TAB:
print("pressed tab")
"""
# ///---=== FIND STICK TEST ===---
ex1 = Sticks.Sticks()
ex1.new_sticks(10)
a = [i.length for i in ex1.sticks]
print(a)
print([ex1.find_stick(i, 0).location for i in a])
print([ex1.find_stick(i, 0).location for i in a])
# ///---=== FIND STICK TEST ===---
"""
"""
# ///---=== SWAP STICK TEST ===---
s1 = stick.Stick(20, 130)
s2 = stick.Stick(13, 1)
s3 = stick.Stick(103, 58)
print("s1 nesnesinin id'si: %d konumu: %d" % (s1.o_id, s1.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s2.o_id, s2.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s3.o_id, s3.location))
Sticks.Sticks().swap_stick_locations(s1, s2)
print("\nKonumlar değiştirildi (s1, s2)!!\n")
print("s1 nesnesinin id'si: %d konumu: %d" % (s1.o_id, s1.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s2.o_id, s2.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s3.o_id, s3.location))
Sticks.Sticks().swap_stick_locations(s1, s3)
print("\nKonumlar değiştirildi (s1, s3)!!\n")
print("s1 nesnesinin id'si: %d konumu: %d" % (s1.o_id, s1.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s2.o_id, s2.location))
print("s2 nesnesinin id'si: %d konumu: %d" % (s3.o_id, s3.location))
# ///---=== SWAP STICK TEST ===---
"""
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18,
13,
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4008,
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220,
220,
220,
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34013,
6329,
18604,
12672,
2969,
3563,
11860,
43001,
24844,
6329,
198,
37811,
198
] | 2.024423 | 737 |
# Generated by Django 3.1.2 on 2020-11-30 02:52
from django.db import migrations, models
| [
2,
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12,
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198,
6738,
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628
] | 2.84375 | 32 |
'''This plots the results of the parameter sweep for the OOHI
example.
'''
from os import mkdir
from os.path import isdir
from pickle import load
from numpy import arange, array, atleast_2d, hstack, sum, where, zeros
from matplotlib.pyplot import close, colorbar, imshow, set_cmap, subplots
from mpl_toolkits.axes_grid1 import make_axes_locatable
from seaborn import heatmap
if isdir('plots/oohi') is False:
mkdir('plots/oohi')
with open('outputs/oohi/results.pkl','rb') as f:
(vuln_peaks,
vuln_end,
iso_peaks,
cum_iso,
iso_method_range,
iso_rate_range,
iso_prob_range) = load(f)
vp_min = vuln_peaks.min()
vp_max = vuln_peaks.max()
ve_min = vuln_end.min()
ve_max = vuln_end.max()
ip_min = iso_peaks.min()
ip_max = iso_peaks.max()
ci_min = cum_iso.min()
ci_max = cum_iso.max()
fig, (ax1, ax2) = subplots(1,2,sharex=True)
axim=ax1.imshow(vuln_peaks[0,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=vp_min,
vmax=vp_max)
ax1.set_xlabel('Detection rate')
ax1.set_ylabel('Adherence probability')
ax2.imshow(vuln_peaks[1,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=vp_min,
vmax=vp_max)
ax2.set_xlabel('Detection rate')
ax2.get_yaxis().set_ticks([])
divider = make_axes_locatable(ax2)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = colorbar(axim,
label="Peak % prevalence in vulnerable population",
cax=cax)
fig.savefig('plots/oohi/vuln_peaks.png',
bbox_inches='tight',
dpi=300)
close()
fig, (ax1, ax2) = subplots(1,2,sharex=True)
axim=ax1.imshow(vuln_end[0,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ve_min,
vmax=ve_max)
ax1.set_xlabel('Detection rate')
ax1.set_ylabel('Adherence probability')
ax2.imshow(vuln_end[1,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ve_min,
vmax=ve_max)
ax2.set_xlabel('Detection rate')
ax2.get_yaxis().set_ticks([])
divider = make_axes_locatable(ax2)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = colorbar(axim,
label="Cumulative % infected in vulnerable population",
cax=cax)
fig.savefig('plots/oohi/cum_vuln_cases.png',
bbox_inches='tight',
dpi=300)
close()
fig, (ax1, ax2) = subplots(1,2,sharex=True)
axim=ax1.imshow(iso_peaks[0,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ip_min,
vmax=ip_max)
ax1.set_xlabel('Detection rate')
ax1.set_ylabel('Adherence probability')
ax2.imshow(iso_peaks[1,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ip_min,
vmax=ip_max)
ax2.set_xlabel('Detection rate')
ax2.get_yaxis().set_ticks([])
divider = make_axes_locatable(ax2)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = colorbar(axim,
label="Peak % population isolating",
cax=cax)
fig.savefig('plots/oohi/iso_peak.png',
bbox_inches='tight',
dpi=300)
close()
fig, (ax1, ax2) = subplots(1,2,sharex=True)
axim=ax1.imshow(cum_iso[0,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ci_min,
vmax=ci_max)
ax1.set_xlabel('Detection rate')
ax1.set_ylabel('Adherence probability')
ax2.imshow(cum_iso[1,:,:],
origin='lower',
extent=(iso_rate_range[0],iso_rate_range[1],0,1),
vmin=ci_min,
vmax=ci_max)
ax2.set_xlabel('Detection rate')
ax2.get_yaxis().set_ticks([])
divider = make_axes_locatable(ax2)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = colorbar(axim,
label="Cumulative % isolating",
cax=cax)
fig.savefig('plots/oohi/cum_iso.png',
bbox_inches='tight',
dpi=300)
close()
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220,
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288,
14415,
28,
6200,
8,
198,
19836,
3419,
198
] | 1.92015 | 2,129 |
from DHT import DHT_Read
import paho.mqtt.client as mqtt
import json
import requests
import time
try:
file=open("config.json", "r")
json_str = file.read()
file.close()
except:
raise KeyError("Error opening config file. Please check.")
config_json = json.loads(json_str)
url = config_json["catalog"]["url"]
ID = config_json["zoneID"]
response = requests.get(url+"/broker")
brokerData=response.json()
broker = brokerData["IP"]
port = brokerData["port"]
del brokerData
del response
updateTime = 1
sensor = DHT_Read(17)
publisher = DHT_Pub("DHT11", broker, port)
publisher.start()
while True:
val=sensor.read()
if val is not None:
jsonDic=json.loads(val)
print jsonDic
#Publish Temperature
temp='{"temperature": ' + str(jsonDic["temperature"])+', "time": '+str(jsonDic["time"])+'}'
publisher.publish("/"+ID+"/temperature",temp)
#Publish Humidity
hum='{"humidity": ' + str(jsonDic["humidity"])+', "time": '+str(jsonDic["time"])+'}'
publisher.publish("/"+ID+"/humidity",hum)
else:
print "Error reading from sensor"
time.sleep(updateTime)
| [
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1,
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7,
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8,
198
] | 2.478842 | 449 |
from django.shortcuts import render
import json
from urllib.error import HTTPError
import urllib
# Create your views here.
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"""The module for loading MANE Transcript mappings to genes."""
from typing import Dict, Optional, List
import pandas as pd
from uta_tools import MANE_SUMMARY_PATH, logger
class MANETranscriptMappings:
"""The MANE Transcript mappings class."""
def __init__(self, mane_data_path: str = MANE_SUMMARY_PATH) -> None:
"""Initialize the MANE Transcript mappings class.
:param str mane_data_path: Path to RefSeq MANE summary data
"""
self.mane_data_path = mane_data_path
self.df = self._load_mane_transcript_data()
def _load_mane_transcript_data(self) -> pd.core.frame.DataFrame:
"""Load RefSeq MANE data file into DataFrame.
:return: DataFrame containing RefSeq MANE Transcript data
"""
return pd.read_csv(self.mane_data_path, delimiter="\t")
def get_gene_mane_data(self, gene_symbol: str) -> Optional[List[Dict]]:
"""Return MANE Transcript data for a gene.
:param str gene_symbol: HGNC Gene Symbol
:return: MANE Transcript data (Transcript accessions,
gene, and location information)
"""
data = self.df.loc[self.df["symbol"] == gene_symbol.upper()]
if len(data) == 0:
logger.warning(f"Unable to get MANE Transcript data for gene: "
f"{gene_symbol}")
return None
# Ordering: MANE Plus Clinical (If it exists), MANE Select
data = data.sort_values("MANE_status")
return data.to_dict("records")
def get_mane_from_transcripts(self, transcripts: List[str]) -> List[Dict]:
"""Get mane transcripts from a list of transcripts
:param List[str] transcripts: RefSeq transcripts on c. coordinate
:return: MANE data
"""
mane_rows = self.df["RefSeq_nuc"].isin(transcripts)
result = self.df[mane_rows]
if len(result) == 0:
return []
return result.to_dict("records")
| [
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4943,
198
] | 2.371671 | 826 |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Tianheng Cheng([email protected])
# ------------------------------------------------------------------------------
from .aflw import AFLW
from .cofw import COFW
from .cofwsd import COFWSD
from .face300w import Face300W
from .face300wsd import Face300WSD
from .wflw import WFLW
from .wflwsd import WFLWSD
from .wflwe70 import WFLWE70
from .free import FreeData
__all__ = ['AFLW', 'COFW', 'Face300W', 'WFLW', 'get_dataset']
| [
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] | 3.53012 | 166 |
import tensorflow as tf
from merlin.spec import Spec, Default
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] | 3.555556 | 18 |
"""Digest tests."""
import unittest
from ocfl.ntree import Ntree
class TestAll(unittest.TestCase):
"""TestAll class to run tests."""
def test01_encode(self):
"""Test encode."""
nt = Ntree()
self.assertEqual(nt.encode(''), '')
self.assertEqual(nt.encode('a'), 'a')
self.assertEqual(nt.encode('a/b:?'), 'a=b+^3f')
def test02_decode(self):
"""Test decode."""
nt = Ntree()
self.assertEqual(nt.decode(''), '')
self.assertEqual(nt.decode('a'), 'a')
self.assertEqual(nt.decode('a=b+^3f'), 'a/b:?')
def test03_identifier_to_path(self):
"""Test path creation."""
nt = Ntree(n=2, encapsulate=False)
self.assertEqual(nt.identifier_to_path(''), '')
self.assertEqual(nt.identifier_to_path('a'), 'a')
self.assertEqual(nt.identifier_to_path('ab'), 'ab')
self.assertEqual(nt.identifier_to_path('abc'), 'ab/c')
self.assertEqual(nt.identifier_to_path('abcde'), 'ab/cd/e')
nt = Ntree(n=3, encapsulate=False)
self.assertEqual(nt.identifier_to_path('abcdefg'), 'abc/def/g')
self.assertEqual(nt.identifier_to_path('abcdefgh'), 'abc/def/gh')
self.assertEqual(nt.identifier_to_path('abcdefghi'), 'abc/def/ghi')
nt = Ntree(n=2)
self.assertEqual(nt.identifier_to_path(''), '')
self.assertEqual(nt.identifier_to_path('a'), 'a/a')
self.assertEqual(nt.identifier_to_path('ab'), 'ab/ab')
self.assertEqual(nt.identifier_to_path('abc'), 'ab/c/abc')
self.assertEqual(nt.identifier_to_path('abcde'), 'ab/cd/e/abcde')
nt = Ntree(n=3)
self.assertEqual(nt.identifier_to_path('abcdefg'), 'abc/def/g/abcdefg')
self.assertEqual(nt.identifier_to_path('abcdefgh'), 'abc/def/gh/abcdefgh')
self.assertEqual(nt.identifier_to_path('abcdefghi'), 'abc/def/ghi/abcdefghi')
def test03_path_to_identifier(self):
"""Test path interpretation."""
nt = Ntree(n=2, encapsulate=False)
self.assertEqual(nt.path_to_identifier(''), '')
self.assertEqual(nt.path_to_identifier('a'), 'a')
self.assertEqual(nt.path_to_identifier('ab'), 'ab')
self.assertEqual(nt.path_to_identifier('ab/c'), 'abc')
self.assertEqual(nt.path_to_identifier('ab/cd/e'), 'abcde')
nt = Ntree(n=3, encapsulate=False)
self.assertEqual(nt.path_to_identifier('abc/def/g'), 'abcdefg')
self.assertEqual(nt.path_to_identifier('abc/def/gh'), 'abcdefgh')
self.assertEqual(nt.path_to_identifier('abc/def/ghi'), 'abcdefghi')
nt = Ntree(n=2)
self.assertEqual(nt.path_to_identifier(''), '')
self.assertEqual(nt.path_to_identifier('a/a'), 'a')
self.assertEqual(nt.path_to_identifier('ab/ab'), 'ab')
self.assertEqual(nt.path_to_identifier('ab/c/abc'), 'abc')
self.assertEqual(nt.path_to_identifier('ab/cd/e/abcde'), 'abcde')
nt = Ntree(n=3)
self.assertEqual(nt.path_to_identifier('abc/def/g/abcdefg'), 'abcdefg')
self.assertEqual(nt.path_to_identifier('abc/def/gh/abcdefgh'), 'abcdefgh')
self.assertEqual(nt.path_to_identifier('abc/def/ghi/abcdefghi'), 'abcdefghi')
# Bad ones
self.assertRaises(Exception, nt.path_to_identifier, 'abc/def/g/a-diff-g')
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10210,
14,
68,
14,
39305,
2934,
33809,
705,
39305,
2934,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
299,
83,
796,
399,
21048,
7,
77,
28,
18,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
30493,
36,
13255,
7,
429,
13,
6978,
62,
1462,
62,
738,
7483,
10786,
39305,
14,
4299,
14,
70,
14,
39305,
4299,
70,
33809,
705,
39305,
4299,
70,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
30493,
36,
13255,
7,
429,
13,
6978,
62,
1462,
62,
738,
7483,
10786,
39305,
14,
4299,
14,
456,
14,
39305,
4299,
456,
33809,
705,
39305,
4299,
456,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
30493,
36,
13255,
7,
429,
13,
6978,
62,
1462,
62,
738,
7483,
10786,
39305,
14,
4299,
14,
456,
72,
14,
39305,
4299,
456,
72,
33809,
705,
39305,
4299,
456,
72,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7772,
3392,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
30493,
21762,
2696,
7,
16922,
11,
299,
83,
13,
6978,
62,
1462,
62,
738,
7483,
11,
705,
39305,
14,
4299,
14,
70,
14,
64,
12,
26069,
12,
70,
11537,
198
] | 2.052142 | 1,611 |
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