content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import re
# Value selectors; aliases, tags, etc.
def tag(*tags):
"""Select a (list of) tag(s)."""
vtag = [t for t in tags]
return {"tag": vtag}
def tag_and(*tag_ands):
"""Select a (list of) tag_and(s)."""
vtag_and = [t for t in tag_ands]
return {"tag_and": vtag_and}
def tag_not(*tag_nots):
"""Select a (list of) tag_not(s)."""
vtag_not = [t for t in tag_nots]
return {"tag_not": vtag_not}
def alias(*alias):
"""Select a (list of) alias(es)."""
valias = [t for t in alias]
return {"alias": valias}
def registration_id(*reg_ids):
"""Select a (list of) registration_id(s)."""
vregistration_id = [t for t in reg_ids]
return {"registration_id": vregistration_id}
def segment(*segments):
"""Select a (list of) segment(s)."""
vsegment = [t for t in segments]
return {"segment": vsegment}
def abtest(*abtests):
"""Select a (list of) abtest(s)."""
vabtest = [t for t in abtests]
return {"abtest": vabtest}
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] | 2.414634 | 410 |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2018-07-30 14:11
from __future__ import unicode_literals
from django.db import migrations, models
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num_vezes = 0
soma_total = 0
maior_numero = None
menor_numero = None
while True:
num = input("Digite um número ou \"sair\" para encerrar o programa: ")
if num == "sair":
break
try:
numero = int(num)
num_vezes += 1
soma_total += numero
if maior_numero is None or numero > maior_numero:
maior_numero = numero
if menor_numero is None or numero < menor_numero:
menor_numero = numero
except:
print("Digite apenas números ou a palavra \"sair\", por favor.")
if maior_numero == None or menor_numero == None:
print("Você não digitou nenhum número. Portanto é impossível calcular o número de vezes, o somatório, o menor e o maior.")
print("Obrigado por utilizar o meu programa!")
else:
print("Números foram digitados " + str(num_vezes) + " vezes.")
print("A soma total dos números digitados é " + str(int(soma_total)) + ".")
print("O menor número digitado foi o número " + str(int(menor_numero)) + ".")
print("O maior número digitado foi o número " + str(int(maior_numero)) + ".")
print("Obrigado por utilizar o meu programa!")
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528,
283,
267,
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84,
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64,
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8,
198
] | 2.219417 | 515 |
from selenium import webdriver
import time
| [
6738,
384,
11925,
1505,
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3992,
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198,
11748,
640,
628
] | 4 | 11 |
"""
Filter classes
"""
from . import etree
from .base import CPIXComparableBase
def encode_bool(value):
"""Encode booleans to produce valid XML"""
if value:
return "true"
return "false"
class KeyPeriodFilter(CPIXComparableBase):
"""
KeyPeriodFilter element
Has single required attribute:
periodId
"""
def element(self):
"""Returns XML element"""
el = etree.Element("KeyPeriodFilter")
el.set("periodId", str(self.period_id))
return el
@staticmethod
def parse(xml):
"""
Parse XML and return KeyPeriodFilter
"""
if isinstance(xml, (str, bytes)):
xml = etree.fromstring(xml)
period_id = xml.attrib["periodId"]
return KeyPeriodFilter(period_id)
class LabelFilter(CPIXComparableBase):
"""
LabelFilter element
Not yet implemented
"""
class VideoFilter(CPIXComparableBase):
"""
VideoFilter element
Has optional attributes:
minPixels
maxPixels
hdr
wcg
minFps
maxFps
"""
def element(self):
"""Returns XML element"""
el = etree.Element("VideoFilter")
if self.min_pixels is not None:
el.set("minPixels", str(self.min_pixels))
if self.max_pixels is not None:
el.set("maxPixels", str(self.max_pixels))
if self.hdr is not None:
el.set("hdr", encode_bool(self.hdr))
if self.wcg is not None:
el.set("wcg", encode_bool(self.wcg))
if self.min_fps is not None:
el.set("minFps", str(self.min_fps))
if self.max_fps is not None:
el.set("maxFps", str(self.max_fps))
return el
@staticmethod
def parse(xml):
"""
Parse XML and return VideoFilter
"""
if isinstance(xml, (str, bytes)):
xml = etree.fromstring(xml)
min_pixels = None
max_pixels = None
hdr = None
wcg = None
min_fps = None
max_fps = None
if "minPixels" in xml.attrib:
min_pixels = xml.attrib["minPixels"]
if "maxPixels" in xml.attrib:
max_pixels = xml.attrib["maxPixels"]
if "hdr" in xml.attrib:
hdr = xml.attrib["hdr"]
if "wcg" in xml.attrib:
wcg = xml.attrib["wcg"]
if "minFps" in xml.attrib:
min_fps = xml.attrib["minFps"]
if "maxFps" in xml.attrib:
max_fps = xml.attrib["maxFps"]
return VideoFilter(min_pixels, max_pixels, hdr, wcg, min_fps, max_fps)
class AudioFilter(CPIXComparableBase):
"""
AudioFilter element
Has optional attributes:
minChannels
maxChannels
"""
def element(self):
"""Returns XML element"""
el = etree.Element("AudioFilter")
if self.min_channels:
el.set("minChannels", str(self.min_channels))
if self.max_channels:
el.set("maxChannels", str(self.max_channels))
return el
@staticmethod
def parse(xml):
"""
Parse XML and return AudioFilter
"""
if isinstance(xml, (str, bytes)):
xml = etree.fromstring(xml)
min_channels = None
max_channels = None
if "minChannels" in xml.attrib:
min_channels = xml.attrib["minChannels"]
if "maxChannels" in xml.attrib:
max_channels = xml.attrib["maxChannels"]
return AudioFilter(min_channels, max_channels)
class BitrateFilter(CPIXComparableBase):
"""
BitrateFilter element
Has optional attributes:
minBitrate
maxBitrate
"""
def element(self):
"""Returns XML element"""
el = etree.Element("BitrateFilter")
if self.min_bitrate:
el.set("minBitrate", str(self.min_bitrate))
if self.max_bitrate:
el.set("maxBitrate", str(self.max_bitrate))
return el
@staticmethod
def parse(xml):
"""
Parse XML and return BitrateFilter
"""
if isinstance(xml, (str, bytes)):
xml = etree.fromstring(xml)
min_bitrate = None
max_bitrate = None
if "minBitrate" in xml.attrib:
min_bitrate = xml.attrib["minBitrate"]
if "maxBitrate" in xml.attrib:
max_bitrate = xml.attrib["maxBitrate"]
return BitrateFilter(min_bitrate, max_bitrate)
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] | 2.079683 | 2,146 |
import base64
import httplib2
from email.mime.text import MIMEText
from apiclient.discovery import build
from oauth2client.client import flow_from_clientsecrets
from oauth2client.file import Storage
from oauth2client.tools import run_flow
from google_auth_oauthlib.flow import InstalledAppFlow
permiso = ['https://www.googleapis.com/auth/gmail.send']
memoria = Storage('gmail.storage')
IDOAuth = InstalledAppFlow.from_client_secrets_file("secreto_cliente_Gmail.json", scopes=permiso)
http = httplib2.Http()
credentials = memoria.get()
if credentials is None or credentials.invalid:
credentials = run_flow(IDOAuth, memoria, http=http)
Servicio=build('gmail', 'v1', credentials=credentials)
http = credentials.authorize(credentials)
message = MIMEText("Message")
message['to'] = "[email protected]"
message['from'] = "[email protected]"
message['subject'] = "Subject"
body = {'raw': base64.b64encode(message.as_bytes())}
Servicio.users().messages().send(userId="me",body=body).execute()
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] | 2.970326 | 337 |
import os
import shutil
import traceback
HOME_DIR = os.path.abspath(os.path.expanduser("~"))
PATH_DEFAULT_OBS = os.path.join(HOME_DIR, "videos", "obs")
DRY_RUN = False
def _is_video_file(file_path: str) -> bool:
"""Returns True if the given file is a video file."""
_, ext = os.path.splitext(file_path.lower())
return ext in [".mp4", ".mkv"]
def makedirs(new_dir: str, exist_ok: bool = False) -> None:
"""Make the given directory."""
print(f"make_dirs: {new_dir}")
if DRY_RUN:
return
os.makedirs(new_dir, exist_ok=exist_ok)
def movefile(src: str, dst: str) -> None:
"""Move the given file."""
print(f"movefile: {src} -> {dst}")
if DRY_RUN:
return
shutil.move(src, dst)
def organize(path: str = PATH_DEFAULT_OBS) -> None:
"""Organize the given path."""
paths = [os.path.join(path, p) for p in os.listdir(path) if _is_video_file(p)]
for p in paths:
try:
name_ext = os.path.basename(p)
name = os.path.splitext(name_ext)[0]
ext = os.path.splitext(name_ext)[1]
date_time = name.replace(" ", "_").split("_")
new_dir = os.path.join(path, date_time[0])
new_path = os.path.join(new_dir, f"{date_time[1]}{ext}")
makedirs(os.path.dirname(new_path), exist_ok=True)
movefile(p, new_path)
except Exception as e:
traceback.print_exc()
print(f"Could not process {p} because of {e}")
def main() -> None:
"""Main entry point."""
reply = input(
f"WARNING! This will organize all your videos in the obs path:\n {PATH_DEFAULT_OBS}\ncontinue? [y/n]: "
)
if reply.lower() != "y":
organize()
if __name__ == "__main__":
main()
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] | 2.164822 | 813 |
import math
x = float(input('Digite um ângulo: '))
tangente = math.tan(math.radians(x))
cos = math.acos(math.radians(x))
seno = math.asin(math.radians(x))
print(f'O cosseno de {x} é {cos:.2f}')
print(f'O seno de {x} é {seno:.2f}')
print(f'A tangente de {x} é {tangente:.2f}') | [
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] | 2.075188 | 133 |
#!/usr/local/bin/python
"""This demonstrates a minimal http upload cgi.
This allows a user to upload up to three files at once.
It is trivial to change the number of files uploaded.
This script has security risks. A user could attempt to fill
a disk partition with endless uploads.
If you have a system open to the public you would obviously want
to limit the size and number of files written to the disk.
"""
import cgi
import cgitb; cgitb.enable()
import os, sys
try: # Windows needs stdio set for binary mode.
import msvcrt
msvcrt.setmode (0, os.O_BINARY) # stdin = 0
msvcrt.setmode (1, os.O_BINARY) # stdout = 1
except ImportError:
pass
UPLOAD_DIR = "/tmp"
HTML_TEMPLATE = """<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head><title>File Upload</title>
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
</head><body><h1>File Upload</h1>
<form action="%(SCRIPT_NAME)s" method="POST" enctype="multipart/form-data">
File name: <input name="file_1" type="file"><br>
File name: <input name="file_2" type="file"><br>
File name: <input name="file_3" type="file"><br>
<input name="submit" type="submit">
</form>
</body>
</html>"""
def print_html_form ():
"""This prints out the html form. Note that the action is set to
the name of the script which makes this is a self-posting form.
In other words, this cgi both displays a form and processes it.
"""
print "content-type: text/html\n"
print HTML_TEMPLATE % {'SCRIPT_NAME':os.environ['SCRIPT_NAME']}
def save_uploaded_file (form_field, upload_dir):
"""This saves a file uploaded by an HTML form.
The form_field is the name of the file input field from the form.
For example, the following form_field would be "file_1":
<input name="file_1" type="file">
The upload_dir is the directory where the file will be written.
If no file was uploaded or if the field does not exist then
this does nothing.
"""
form = cgi.FieldStorage()
if not form.has_key(form_field): return
fileitem = form[form_field]
if not fileitem.file: return
fout = file (os.path.join(upload_dir, fileitem.filename), 'wb')
while 1:
chunk = fileitem.file.read(100000)
if not chunk: break
fout.write (chunk)
fout.close()
save_uploaded_file ("file_1", UPLOAD_DIR)
save_uploaded_file ("file_2", UPLOAD_DIR)
save_uploaded_file ("file_3", UPLOAD_DIR)
print_html_form ()
| [
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] | 2.730473 | 909 |
import argparse
import datetime
import os
import re
import sys
import unicodedata
import libs.header
import libs.unicode
import libs.utf8
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Parse Unicode codepoint database and write integration tests.')
parser.add_argument(
'-v', '--verbose',
dest = 'verbose',
action = 'store_true',
help = 'verbose output')
parser.add_argument(
'--casemapping',
dest = 'casemapping',
action = 'store_true',
help = 'write case mapping tests')
parser.add_argument(
'--normalization',
dest = 'normalization',
action = 'store_true',
help = 'write normalization tests')
parser.add_argument(
'--is-normalized',
dest = 'isnormalized',
action = 'store_true',
help = 'write is-normalized tests')
parser.add_argument(
'--casefolding',
dest = 'casefolding',
action = 'store_true',
help = 'write casefolding tests')
args = parser.parse_args()
if not args.casemapping and not args.normalization and not args.isnormalized and not args.casefolding:
all = True
else:
all = False
db = unicodedata.Database()
db.loadFromFiles(None)
if all or args.casemapping:
suite = CaseMappingIntegrationSuite(db)
suite.execute()
if all or args.normalization:
suite = NormalizationIntegrationSuite(db)
suite.execute()
if all or args.isnormalized:
suite = IsNormalizedIntegrationSuite(db)
suite.execute()
if all or args.casefolding:
suite = CaseFoldingIntegrationSuite(db)
suite.execute() | [
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7,
9945,
8,
201,
198,
197,
197,
2385,
578,
13,
41049,
3419
] | 2.567434 | 608 |
try:
from PyQt4.QtCore import QSettings
except ImportError:
from PyQt5.QtCore import QSettings
| [
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] | 2.454545 | 44 |
from re import search
from typing import List, Optional, Pattern
| [
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] | 4.714286 | 14 |
"""
__________________________________________________________________________________________________
:project: SiLA2_python
:details: Response data type in a SiLA Command, Property, Intermediate, ...
:file: data_type_response.py
:authors: Timm Severin
:date: (creation) 20190820
:date: (last modification) 20190820
__________________________________________________________________________________________________
**Copyright**:
This file is provided "AS IS" with NO WARRANTY OF ANY KIND,
INCLUDING THE WARRANTIES OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
For further Information see LICENSE file that comes with this distribution.
__________________________________________________________________________________________________
"""
# import library packages
from .data_type_parameter import ParameterDataType
class ResponseDataType(ParameterDataType):
"""
The class for responses.
This is essentially identical to a :class:`~.ParameterDataType`, however can be handled differently in the final
application and thus exists as its own class/object.
.. note:: When checking whether an object is a response or a parameter, note that
:func:`isinstance(obj, ParameterDataType)` will also return true if the object is a
:class:`ResponseDataType`, since they are derived from each other. Use ``type(obj) is ParameterDataType``
for a precise check.
"""
| [
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7,
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11,
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8,
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611,
262,
2134,
318,
257,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
4871,
25,
63,
31077,
6601,
6030,
47671,
1201,
484,
389,
10944,
422,
1123,
584,
13,
5765,
7559,
4906,
7,
26801,
8,
318,
25139,
2357,
6601,
6030,
15506,
198,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
329,
257,
7141,
2198,
13,
198,
220,
220,
220,
37227,
198
] | 3.801546 | 388 |
# ===============================================================
# Author: Rodolfo Ferro
# Email: [email protected]
# Twitter: @FerroRodolfo
#
# ABOUT COPYING OR USING PARTIAL INFORMATION:
# This script was originally created by Rodolfo Ferro, for
# his workshop in PythonDay Mexico 2018 at CUCEA in Gdl, Mx.
# Any explicit usage of this script or its contents is granted
# according to the license provided and its conditions.
# ===============================================================
# -*- coding: utf-8 -*-
import requests
import pprint
import json
def get_json(url, filename):
"""
Download JSON response url for testing.
"""
# Get response:
response = requests.get(url)
# If response's status is 200:
if response.status_code == requests.codes.ok:
# Pretty print response:
pprint.pprint(response.json())
# Save response into a JSON file:
with open(filename, 'wt') as output:
output.write(response.text)
return
def get_prediction(url, filename):
"""
Download JSON response url for prediction.
"""
# Set metadata:
headers = {'Content-type': 'application/json'}
input_values = {'sepal_length': 6.4,
'sepal_width': 3.2,
'petal_length': 4.5,
'petal_width': 1.5}
# Get response:
response = requests.post(url, json=input_values, headers=headers)
# If response's status is 200:
if response.status_code == requests.codes.ok:
# Pretty print response:
pprint.pprint(response.json())
# Save response into a JSON file:
with open(filename, 'wt') as output:
output.write(response.text)
return
if __name__ == '__main__':
# Try out our JSON response downloader:
get_json('http://localhost:5000/api/v0.0', 'response.json')
get_prediction('http://localhost:5000/api/v0.0/predict', 'response.json')
| [
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13,
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85,
15,
13,
15,
14,
79,
17407,
3256,
705,
26209,
13,
17752,
11537,
198
] | 2.719495 | 713 |
import pybullet_envs
from stable_baselines3 import SAC_LABER
model = SAC_LABER('MlpPolicy', 'HalfCheetahBulletEnv-v0', verbose=1, tensorboard_log="results/long_SAC_LABER_HalfCheetahBullet/")
model.learn(total_timesteps=3000000)
| [
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] | 2.516484 | 91 |
# -*- coding: utf-8 -*-
"""
testapplehealthdata.py: tests for the applehealthdata.py
Copyright (c) 2016 Nicholas J. Radcliffe
Licence: MIT
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import re
import shutil
import sys
import unittest
from collections import Counter
from applehealthdata import (HealthDataExtractor,
format_freqs, format_value,
abbreviate, encode)
CLEAN_UP = True
VERBOSE = False
def get_base_dir():
"""
Return the directory containing this test file,
which will (normally) be the applyhealthdata directory
also containing the testdata dir.
"""
return os.path.split(os.path.abspath(__file__))[0]
def get_testdata_dir():
"""Return the full path to the testdata directory"""
return os.path.join(get_base_dir(), 'testdata')
def get_tmp_dir():
"""Return the full path to the tmp directory"""
return os.path.join(get_base_dir(), 'tmp')
def remove_any_tmp_dir():
"""
Remove the temporary directory if it exists.
Returns its location either way.
"""
tmp_dir = get_tmp_dir()
if os.path.exists(tmp_dir):
shutil.rmtree(tmp_dir)
return tmp_dir
def make_tmp_dir():
"""
Remove any existing tmp directory.
Create empty tmp direcory.
Return the location of the tmp dir.
"""
tmp_dir = remove_any_tmp_dir()
os.mkdir(tmp_dir)
return tmp_dir
def copy_test_data():
"""
Copy the test data export6s3sample.xml from testdata directory
to tmp directory.
"""
tmp_dir = make_tmp_dir()
name = 'export6s3sample.xml'
in_xml_file = os.path.join(get_testdata_dir(), name)
out_xml_file = os.path.join(get_tmp_dir(), name)
shutil.copyfile(in_xml_file, out_xml_file)
return out_xml_file
if __name__ == '__main__':
unittest.main()
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198,
220,
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555,
715,
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13,
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3419,
198
] | 2.610372 | 752 |
import unittest
from datetime import date
from controller.books import Book, BookRead
if __name__ == "__main__":
unittest.main()
| [
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1298,
198,
220,
220,
220,
555,
715,
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] | 3.066667 | 45 |
from ixnetwork_restpy.base import Base
from ixnetwork_restpy.files import Files
| [
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] | 3.375 | 24 |
import pytest
@pytest.yield_fixture(scope="module")
@pytest.yield_fixture(scope="module")
@pytest.yield_fixture(scope="module")
| [
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] | 2.576923 | 52 |
import threading
import os.path
import time
from blueThread import MainBlue
# class myThread (threading.Thread):
# def __init__(self, threadID, name, counter):
# threading.Thread.__init__(self)
# self.threadID = threadID
# self.name = name
# self.counter = counter
# def run(self):
# print("Starting " + self.name)
# print_time(self.name, 5, self.counter)
# print("Exiting " + self.name)
run = True
foo = [False]
fileName = ""
def LookForFile(strToFind, path):
"""
function repeatedly look for a file
"""
while run:
MainBlue(foo)
time.sleep(1)
print("exiting file thread!")
def LookForStop(strToFind, path):
"""
function repeatedly look for a file
"""
global run
count = 0
filePath = path + strToFind
while run:
count += 1
if os.path.exists(filePath):
run = False
print("{0} FOUND {1} at {2} [{3}]".format(t2.getName(), strToFind, filePath, count))
else:
print("{0} not found {1} at {2} [{3}]".format(t2.getName(), strToFind, filePath, count))
time.sleep(1)
print("exiting stop thread!")
if __name__ == "__main__":
# creating thread
t1 = threading.Thread(target=LookForFile, name="THREAD_Finder", args=("rain","../"), daemon=True)
# t2 = threading.Thread(name="THREAD_Stopper", target=LookForStop, args=("stop","../"), daemon=True)
# starting thread 1
t1.start()
# starting thread 2
# t2.start()
# while run:
# print("doing nothing...")
# time.sleep(10)
input("Press Enter to flip foo")
if foo[0]:
foo[0] = False
else:
foo[0] = True
input("Press Enter to exit")
run = False
# wait until thread 1 is completely executed
t1.join()
# wait until thread 2 is completely executed
# t2.join()
# both threads completely executed
print("Done!") | [
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8
] | 2.299534 | 858 |
import time
from datetime import datetime
from datetime import timedelta
from uuid import uuid4 as uuid
from activitystreams import parse
from dino import environ
from dino.auth.redis import AuthRedis
from dino.cache.redis import CacheRedis
from dino.config import ApiActions, RedisKeys
from dino.config import ConfigKeys
from dino.config import SessionKeys
from dino.config import UserKeys
from dino.db.rdbms.handler import DatabaseRdbms
from dino.environ import ConfigDict
from dino.environ import GNEnvironment
from dino.exceptions import ChannelExistsException
from dino.exceptions import ChannelNameExistsException
from dino.exceptions import EmptyChannelNameException
from dino.exceptions import EmptyRoomNameException
from dino.exceptions import InvalidAclTypeException
from dino.exceptions import InvalidApiActionException
from dino.exceptions import NoSuchChannelException
from dino.exceptions import NoSuchRoomException
from dino.exceptions import NoSuchUserException
from dino.exceptions import RoomExistsException
from dino.exceptions import RoomNameExistsForChannelException
from dino.exceptions import UserExistsException
from dino.exceptions import ValidationException
from dino.validation.acl import AclDisallowValidator
from dino.validation.acl import AclIsAdminValidator
from dino.validation.acl import AclIsSuperUserValidator
from dino.validation.acl import AclRangeValidator
from dino.validation.acl import AclSameChannelValidator
from dino.validation.acl import AclSameRoomValidator
from dino.validation.acl import AclStrInCsvValidator
from test.base import BaseTest
| [
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198,
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] | 3.674365 | 433 |
"""migrate workbench state enum
Revision ID: cfd1c43b5d33
Revises: c8a7073deebb
Create Date: 2020-11-17 16:42:32.511722+00:00
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'cfd1c43b5d33'
down_revision = 'c8a7073deebb'
branch_labels = None
depends_on = None
| [
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13,
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67,
16,
66,
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1424,
796,
6045,
198,
10378,
2412,
62,
261,
796,
6045,
628,
198
] | 2.406015 | 133 |
import json
fn = open('../static/alderman.js', 'w')
#add alderman boundaries variable
json_file = open('../maps/alderman.geojson')
geo_json = json.load(json_file)
fn.write('var alderman_boundaries = ')
fn.write(json.dumps(geo_json))
fn.write(';\n\n')
json_file.close() | [
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# tables.py
class MortalityTable:
"""mortalitytable is a matrix, by age and duration."""
class MortalityImprovementTable:
"""MortalityImprovementTable is a matrix, by age and year."""
class RangeTable:
"""range table"""
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"""Mathematical helper functions."""
def normalize(array):
"""Normalize the array.
Set all the values betwwen 0 and 1.
0 corresponds to the min value and 1 the max.
If the normalization cannot occur, will return the array.
"""
min_ = min(array)
max_ = max(array)
return (
(array - min_) / (max_ - min_) # Normalize
if min_ != max_ else
array / (max_ if max_ > 0 else 1) # Avoid divide by 0
)
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nome = input('Insira nome completo: ').strip()
print('Possui "Silva"?', 'silva' in nome.lower())
input()
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import torch
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
def conv3x3(in_planes, out_planes, **kwargs):
"""3x3 convolution with padding"""
kwargs['kernel_size'] = 3
kwargs['padding'] = 1
kwargs['bias'] = False
return nn.Conv2d(in_planes, out_planes, **kwargs)
def conv1x1(in_planes, out_planes, **kwargs):
"""1x1 convolution"""
kwargs['kernel_size'] = 1
kwargs['bias'] = False
return nn.Conv2d(in_planes, out_planes, **kwargs)
class BasicBlock(nn.Module):
"""BasicBlock"""
expansion = 1
class Bottleneck(nn.Module):
"""Bottleneck"""
expansion = 4
class ResNet(nn.Module):
"""ResNet"""
def resnet18(num_classes=1000, **kwargs):
"""resnet18"""
return ResNet([2, 2, 2, 2], num_classes, BasicBlock)
def resnet34(num_classes=1000, **kwargs):
"""resnet34"""
return ResNet([3, 4, 6, 3], num_classes, BasicBlock)
def resnet50(num_classes=1000, **kwargs):
"""resnet50"""
return ResNet([3, 4, 6, 3], num_classes, Bottleneck)
def resnet101(num_classes=1000, **kwargs):
"""resnet101"""
return ResNet([3, 4, 23, 3], num_classes, Bottleneck)
def resnet152(num_classes=1000, **kwargs):
"""resnet152"""
return ResNet([3, 8, 36, 3], num_classes, Bottleneck) | [
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11,
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11,
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11,
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8
] | 2.398182 | 550 |
from modelon.impact.client import (
SimpleFMUExperimentDefinition,
SimpleModelicaExperimentDefinition,
Range,
Choices,
SimpleExperimentExtension,
)
import pytest
from modelon.impact.client import exceptions
from tests.impact.client.fixtures import *
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] | 3.223529 | 85 |
#coding:utf-8
import pymongo
import records
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# ou-tm351 - `nb_pub_utils`
#GOTCHA - Python on Mac logging in to Github: https://stackoverflow.com/a/42098127/454773
import click
import os
import shutil
import zipfile
import humanize
import datetime
import github
from tabulate import tabulate
from shlex import quote
import subprocess
def listify(item):
''' If presented with a string and a list is required, make a list... '''
item = [] if item is None else item
#We may be passed a tuple - in which case, listify...
item = list(item) if isinstance(item,(list,tuple)) else [item]
return item
def exclude_hidden_items(itemlist, exclude_hidden=True):
''' Exclude hidden items from ziplist '''
if exclude_hidden:
rmlist=[]
for x in itemlist:
if x.startswith('.'):
rmlist.append(x)
for x in rmlist:
itemlist.remove(x)
def exclude_items(itemlist, excludes, exclude_hidden=True, ipynb_only=False):
''' Exclude items from ziplist '''
for xd in set(itemlist).intersection(excludes):
itemlist.remove(xd)
if ipynb_only:
for i in [_i for _i in itemlist if not _i.endswith("ipynb")]:
itemlist.remove(i)
if exclude_hidden: exclude_hidden_items(itemlist)
def notebookTest(path=None, filename=None, dir_excludes=None, file_excludes=None):
''' Run notebook tests over explicitly named files and directories.
'''
#Could probably define this recursively to handle mulitple paths/filenames...
sanitiser = """[regex1]
regex: <graphviz.files.Source at [^>]*>
replace: <graphviz.files.Source>
[regex2]
regex: CPU times: .*
replace: CPU times: CPUTIME
[regex3]
regex: Wall time: .*
replace: Wall time: WALLTIME
[regex4]
regex: .* per loop \(mean ± std. dev. of .* runs, .* loops each\)
replace: TIMEIT_REPORT
"""
#tmp_fn = "_sanitise_cfg.cfg"
#with open(tmp_fn, "w") as f:
# f.write(sanitiser)
#cmd=f'py.test --nbval-sanitize-with {tmp_fn} '
cmd=f'py.test '
file_excludes = listify(file_excludes)
for d in listify(dir_excludes):
cmd = cmd + ' --ignore={} '.format(quote(d))
print("*Not testing in directory: {}*".format(d))
cmd = cmd+' --nbval '
## WARNING - TO DO - if we are running this from a notebook, also exclude path=='.'
if path is None and filename is None:
#Process current directory
return cli_command(cmd)
elif filename:
#Process file(s) in directory
if isinstance(filename, list):
for _filename in filename:
cmd = '{cmd} {filename}'.format(cmd=cmd, filename=pathmaker(path, quote(_filename)))
resp=cli_command(cmd)
else:
cmd = '{cmd} {filename}'.format(cmd=cmd, filename=pathmaker(path, quote(filename)))
resp=cli_command(cmd)
return resp
else:
#Process files in path
#If we pass a directory name in then the test will be run over all files in the directory
#py.test accumulates the test responses
resps = []
for singlepath in listify(path):
for dirname, subdirs, files in os.walk(singlepath):
exclude_items(subdirs, dir_excludes)
exclude_items(files, file_excludes, ipynb_only=True)
print('Processing directory: {}'.format(dirname))
with click.progressbar(files) as bar:
for filename in bar:
filepathname=os.path.join(dirname, filename)
cmd = '{cmd} {path}'.format(cmd=cmd, path=quote(filepathname))
resps.append( cli_command(cmd) )
#for singlepath in listify(path):
# print("\nTesting in directory: {}".format(singlepath))
# if singlepath=='.':
# print('**DO NOT test in current directory from a notebook**')
# cmd = '{cmd} {path}'.format(cmd=cmd, path=quote(singlepath))
# resps.append( cli_command(cmd) )
os.unlink(tmp_fn)
return resps
def notebookProcessor(notebook, mode=None, outpath=None, outfile=None, inplace=True):
''' Clear notebook output cells.
Process a single notebook, clearing cell outputs running cells until
a warning, or running all cells despite warnings.
Processed notebooks can be written to a specified directory or rendered inplace.
'''
if mode is None: return (-1, 'Mode not specified.')
if outpath is not None and not os.path.exists(outpath):
os.makedirs(outpath)
if outfile is not None:
outpath = '/'.join([outpath,outfile]) if outpath is not None else outfile
cmd='jupyter nbconvert --to notebook'
if mode in ['clearOutput', 'clearOutputTest' ]:
cmd = '{cmd} --ClearOutputPreprocessor.enabled=True'.format(cmd=cmd)
elif mode == 'run':
cmd = '{cmd} --execute'.format(cmd=cmd)
elif mode == 'runWithErrors':
cmd = '{cmd} --ExecutePreprocessor.allow_errors=True --execute'.format(cmd=cmd)
else: return (-1, 'Mode not specified correctly.')
if outpath is None and inplace:
cmd='{cmd} --inplace'.format(cmd=cmd)
#Select file
cmd='{cmd} {notebook}'.format(cmd=cmd,notebook=quote(notebook))
#If output path not set, and --inplace is not set,
# nbformat will create a new file with same name ending: .nbformat.ipynb
if outpath is not None:
cmd ='{cmd} --output-dir {outpath}'.format(cmd=cmd, outpath=quote(outpath))
return cli_command(cmd)
def directoryProcessor(path,
mode=None, outpath=None, inplace=True,
include_hidden=False,
dir_excludes=None,
file_excludes=None, rmdir=False, currdir=False, subdirs=True,
reportlevel=1, logfile=None):
''' Process all the notebooks in one or more directories and
(optionally) in associated subdirectories.
Processed notebooks can be written to a specified directory or rendered inplace.
Path hierarchies to notebooks in multiple directories or subdirectories are
respected when writing to a specified output directory.
'''
def _process(outpath):
''' Process files associated with a particular directory '''
processfiles=[f for f in files if f.endswith('.ipynb')]
if subdirs:
print(dirname)
if outpath is not None:
outpath='/'.join([outpath, dirname])
if not os.path.exists(outpath):
os.makedirs(outpath)
if not mode == 'tests':
#print('About to process {}'.format(processfiles))
with click.progressbar(processfiles) as bar:
for filename in bar:
if not currdir and dirname=='.': continue
if reportlevel>1:
print("Processing >{}<".format('/'.join([dirname,filename])))
resp = notebookProcessor('/'.join([dirname,filename]), mode=mode, outpath=outpath, inplace=inplace )
if reportlevel>0 and resp and resp[0]!=0:
print("Error with {}".format('/'.join([dirname,filename])))
if logfile:
with open(logfile, "a") as out:
out.write(resp[1])
#if mode in ['tests', 'clearOutputTest']:
# #Tests need to run in original dir in case of file dependencies
# testreport = notebookTest(path=dirname,dir_excludes=dir_excludes)
# print('tested:',dirname)
# print(testreport[1])
#if mode == 'clearOutputTest':
# #If we are testing for warnings, need to test in original directory
# # in case there are file dependencies
# outpath=None
# inplace=True
if mode is None: return
if isinstance(path, list):
if rmdir:
shutil.rmtree(outpath, ignore_errors=True)
#Make sure we only delete the directory on the way in...
rmdir=False
for _path in path:
#When provided with multiple directories, process each one separately
#Note that subdirs for each directory can be handled automatically
directoryProcessor(_path, mode, '/'.join([outpath, _path]), inplace,
include_hidden, dir_excludes, file_excludes,
rmdir, currdir, subdirs, reportlevel, logfile)
return
#TO DO - simplify this so we just pass one exclusion type then detect if file or dir?
file_excludes = listify(file_excludes)
dir_excludes = listify(dir_excludes)
if outpath is not None and os.path.exists(outpath):
if rmdir:
print('\n***Deleting directory `{}` and all its contents....***\n\n'.format(outpath))
shutil.rmtree(outpath, ignore_errors=True)
else:
print('\nOutput directory `{}` already exists. Remove it first by setting: rmdir=True\n'.format(outpath))
#dir_excludes = [] if dir_excludes is None else dir_excludes
#file_excludes = [] if file_excludes is None else file_excludes
if os.path.isfile(path):
notebookProcessor(path, mode=mode, outpath=outpath, inplace=inplace )
elif subdirs:
for dirname, subdirs, files in os.walk(path):
exclude_items(subdirs, dir_excludes, not include_hidden)
exclude_items(files, file_excludes, not include_hidden)
_process(outpath)
# if passed a single file rather than directory path
else:
files=os.listdir(path)
exclude_items(files, file_excludes, not include_hidden)
dirname=path
_process(outpath)
#Running zipper with a file_processor will change the cell state in current dir
#That is, notebooks are processed in place then zipped
#The notebooks as seen in the dir will reflect those in the zipfile
#We could modify this behaviour so it does not affect original notebooks?
def zipper(dirtozip, zipfilename,
include_hidden=False,
dir_excludes=None,
file_excludes=None,
file_processor=None,
reportlevel=1, rmdir=False,
zip_append=False):
''' Zip the contents of a directory and its subdirectories '''
file_excludes = listify(file_excludes)
dir_excludes = listify(dir_excludes)
zip_permission = "a" if zip_append else "w"
#Create a new/replacement zip file, rather than append if zipfile already exists
zf = zipfile.ZipFile(zipfilename, zip_permission, compression=zipfile.ZIP_DEFLATED)
#Don't zip files of same name as the zip file we are creating
file_excludes.append(zipfilename)
# if we have just a single file to zip and not a dir, zip that
if os.path.isfile(dirtozip):
zf.write(dirtozip)
elif os.path.isdir(dirtozip):
#https://stackoverflow.com/a/31779538/454773
for dirname, subdirs, files in os.walk(dirtozip):
exclude_items(subdirs, dir_excludes, not include_hidden)
exclude_items(files, file_excludes, not include_hidden)
print('Processing directory: {}'.format(dirname))
zf.write(dirname)
with click.progressbar(files) as bar:
for filename in bar:
if reportlevel>1:print(filename)
filepathname=os.path.join(dirname, filename)
#There is no point using 'run': if there is an error, nbconvert will fail
if file_processor in ['clearOutput', 'runWithErrors'] and filename.endswith('.ipynb'):
#This introduces side effects - notebooks are processed in current path
#Should we do this in a tmpfile?
notebookProcessor(filepathname, mode=file_processor, inplace=True)
zf.write(filepathname)
zf.close()
#Is this too risky?!
#if rmdir: shutil.rmtree(dirtozip, ignore_errors=True)
return zipfilename
def insideZip(zfn, report=True):
''' Look inside a zip file.
The report contains four columns: file_size, file compressed size, datetime and filename.
Setting report=True returns a pretty printed report. '''
if not os.path.isfile(zfn):
print("\nHmm... {} doesn't seem to be a file?\n".format(zfn))
return
print('\nLooking inside zipfile: {}\n'.format(zfn))
fz=zipfile.ZipFile(zfn)
txt=[]
for fn in fz.infolist():
txt.append( [fn.file_size,
fn.compress_size,
datetime.datetime(*fn.date_time).isoformat(),
fn.filename] )
print('{}, {}, {}, {}'.format(fn.file_size,
fn.compress_size,
datetime.datetime(*fn.date_time).isoformat(),
fn.filename))
tabulate(txt, headers=['Full','Zip','Datetime','Path'],tablefmt="simple")
return txt
@click.command()
@click.option('--file-processor','-r', type=click.Choice(['clearOutput', 'runWithErrors']))
@click.option('--include-hiddenfiles', '-H', is_flag=True, help='Include hidden files')
@click.option('--exclude-dir', '-X', multiple=True, type=click.Path(resolve_path=False), help='Exclude specified directory')
@click.option('--exclude-file','-x', multiple=True,type=click.Path(resolve_path=False), help='Exclude specified file')
@click.option('--zip_append','-a', is_flag=True, help='Add to existing zip file')
@click.argument('path', type=click.Path(resolve_path=False))
#@click.argument('zipfile', type=click.File('wb'))
@click.argument('zipfile', type=click.Path())
def cli_zip(file_processor, include_hiddenfiles, exclude_dir, exclude_file, zip_append, path, zipfile):
"""Create a zip file from the contents of a specified directory.
The zipper can optionally run a notebook processor on notebooks before zipping them to check that all cells are run or all cells are cleared.
"""
print('You must be crazy using this...')
if not zip_append:
print(f"\nOverwriting any previous {zipfile} file\n")
else:
print(f"\nAppending zipped files to: {zipfile}\n")
fn = zipper(path, zipfile,
include_hidden=include_hiddenfiles,
dir_excludes=exclude_dir,
file_excludes=exclude_file,
file_processor=file_processor,
zip_append=zip_append)
print(f"\nZip file: {fn}\n")
@click.command()
@click.option('--quiet', '-q', is_flag=True, help='Suppress the report.')
@click.option('--warnings', '-w', is_flag=True, help='Display warnings')
@click.argument('filename', type=click.Path(resolve_path=True),nargs=-1)
def cli_zipview(filename, warnings, quiet):
"""List the contents of one or more specified zipfiles.
"""
zip_contents = []
for f in listify(filename):
zip_contents.append((f, insideZip(f)))
if warnings and zip_contents:
for (zn, item) in zip_contents:
print(f"\n\n====== Zip file quality report: {zn} ======\n")
for record in item:
if record[1] > 1e6:
print(f"WARNING: \"{record[3]}\" looks quite large file ({humanize.naturalsize(record[0])} unzipped, {humanize.naturalsize(record[1])} compressed)")
for _path in record[3].split('/'):
if len(_path) > 50:
print(f"ERROR: the filepath element \"{_path}\" in \"{record[3]}\" is too long (max. 50 chars)")
if _path.startswith("."):
print(f"WARNING: \"{record[3]}\" is a hidden file/directory (do you really need it in the zip file?)")
print("\n===========================\n\n")
@click.command()
@click.option('--exclude-dir','-X', multiple=True,type=click.Path(resolve_path=False), help='Do not recurse through specified directory when assembling tests.')
@click.option('--exclude-file','-x', multiple=True,type=click.Path(resolve_path=False), help='Exclude specified file')
@click.option('--outfile','-o', type=click.Path(resolve_path=False), help='Output report file. Leave this blank to display report on command line.')
@click.argument('testitems', type=click.Path(resolve_path=False),nargs=-1)
def cli_nbtest( exclude_dir, exclude_file, outfile, testitems):
"""Test specified notebooks and/or the notebooks in a specified directory or directories (`TESTITEMS`) using the `nbdime` plugin for `py.test`.
Running `tm351nbtest` without any specified directory or file will assemble tests recursively from the current directory down."""
testitems = testitems or '.'
_notebookTest(testitems, outfile, exclude_dir, exclude_file)
@click.command()
@click.option('--file-processor','-r', type=click.Choice(['clearOutput', 'runWithErrors']), help='File processor actions that can be applied to notebooks using `nbconvert`')
@click.option('--outpath', '-O', type=click.Path(resolve_path=False), help='path to output directory')
@click.option('--inplace/--no-inplace',default=True, help='Run processors on notebooks inplace')
@click.option('--exclude-dir', '-X', multiple=True, type=click.Path(resolve_path=False), help='Exclude specified directory')
@click.option('--exclude-file','-x', multiple=True,type=click.Path(resolve_path=False), help='Exclude specified file')
@click.option('--include-hidden/--no-include-hidden',default=False, help='Include hidden files')
@click.option('--rmdir/--no-rmdir',default=False, help='Check the output directory is empty before we use it')
@click.option('--currdir/--no-currdir',default=False, help='Process files in current directory')
@click.option('--subdirs/--no-subdirs',default=True, help='Process files in subdirectories')
@click.option('--reportlevel', default=1, help='Reporting level')
@click.argument('path',type=click.Path(resolve_path=False))
def cli_nbrun(file_processor, outpath, inplace, exclude_dir, exclude_file, include_hidden, rmdir, currdir, subdirs, reportlevel, path):
"""Directory processor for notebooks - allows the user to run nbconvert operations on notebooks, such as running all cells or clearing all cells.
To run tests, use: tm351nbtest
To zip folders (with the option or running notebook processors on zipped files), use: tm351zip
"""
directoryProcessor(path,
mode=file_processor, outpath=outpath, inplace=inplace,
include_hidden=include_hidden,
dir_excludes=exclude_dir,
file_excludes=exclude_file, rmdir=rmdir, currdir=currdir,
subdirs=subdirs,reportlevel=reportlevel)
from github import Github
import getpass
import base64
import logging
from github.GithubException import GithubException
def get_sha_for_tag(repository, tag):
"""
Returns a commit PyGithub object for the specified repository and tag.
"""
branches = repository.get_branches()
matched_branches = [match for match in branches if match.name == tag]
if matched_branches:
return matched_branches[0].commit.sha
tags = repository.get_tags()
matched_tags = [match for match in tags if match.name == tag]
if not matched_tags:
raise ValueError('No Tag or Branch exists with that name')
return matched_tags[0].commit.sha
def download_directory(repository, sha, server_path, outpath='gh_downloads', file_processor=None):
"""
Download all contents at server_path with commit tag sha in
the repository.
"""
contents = repository.get_dir_contents(server_path, ref=sha)
if not os.path.exists(outpath):
os.makedirs(outpath)
for content in contents:
print("Downloading: %s" % content.path)
if content.type == 'dir':
download_directory(repository, sha, content.path, '/'.join([outpath,content.name]))
else:
try:
path = content.path
file_content = repository.get_contents(path, ref=sha)
file_data = base64.b64decode(file_content.content)
outpathfile='/'.join([outpath,content.name])
file_out = open(outpathfile, "wb")
file_out.write(file_data)
file_out.close()
except (IOError, github.GithubException) as exc:
#If we fail over because of a large blog, use the data api for the download
ret,error=exc.args
if 'message' in error and error['message']=='Not Found':
print('Hmm... file not found? {}'.format(path))
elif 'errors' in error and error['errors'][0]['code']=='too_large':
#print('...large file, trying blob download instead...')
file_content = repository.get_git_blob(content.sha)
file_data = base64.b64decode(file_content.content)
file_out = open('/'.join([outpath,content.name]), "wb")
file_out.write(file_data)
file_out.close()
#logging.error('Error processing %s: %s', content.path, exc)
#if content.name.endswith('.ipynb') and file_processor in ['clearOutput', 'clearOutputTest','runWithErrors' ]:
# notebookProcessor(outpathfile, file_processor)
DEFAULT_REPO='undercertainty/tm351'
@click.command()
@click.option('--github-user', '-u', help="Your Github username.")
@click.option('--password', hide_input=True,
confirmation_prompt=False)
@click.option('--repo','-r', prompt='Repository ({})'.format(DEFAULT_REPO),
help='Repository name')
@click.option('--branch','-b',help='Branch or tag to download')
@click.option('--directory', help='Directory to download (or: all)')
@click.option('--savedir',type=click.Path(resolve_path=False),
help='Directory to download repo / repo dir into; default is dir name')
@click.option('--file-processor', type=click.Choice(['clearOutput', 'runWithErrors']), help='Optionally specify a file processor to be run against downloaded notebooks.')
@click.option('--zip/--no-zip', default=False, help='Optionally create a zip file of the downloaded repository/directory with the same name as the repository/directory.')
@click.option('--auth/--no-auth', default=True, help="By default, run with auth (prompt for credentials)")
@click.option('--with-tests','-t',is_flag=True, help="Run tests on notebooks after download")
@click.option('--logfile',type=click.Path(resolve_path=False), help='Path to logfile')
def cli_gitrepos(github_user, password, repo, branch, directory, savedir, file_processor, zip, auth, with_tests, logfile):
"""Download files from a specified branch in a particular git repository.
The download can also be limited to just the contents of a specified directory.
Don't worry that there look to be a lot of arguments - you will be prompted for them if you just run: tm351gitrepos
"""
if auth or github_user:
if not github_user: github_user = click.prompt('\nGithub username')
if not password: password = click.prompt('\nGithub password', hide_input=True)
github = Github(github_user, password)
#Show we're keeping no password...
password = None
auth = True
else: github = Github()
if auth:
user = github.get_user()
#organisations = github.get_user().get_orgs()
print('Logging into git as {} ({})'.format(github_user, user.name))
repo = repo or DEFAULT_REPO
repository = github.get_repo(repo)
if not branch:
print('\nBranches available:\n\t{}'.format('\n\t'.join(github_repo_branches(repository)) ))
branch = click.prompt('\nWhich branch? (master)')
branch_or_tag_to_download = branch or 'master'
sha = get_sha_for_tag(repository, branch_or_tag_to_download)
another = ''
while another!='-':
if not directory:
if branch!='master':
contents = repository.get_dir_contents('.', ref=sha)
else:
contents = repository.get_dir_contents('.')
print('\nYou can download all directories from this repo (all) or select one:\n\t{}'.format('\n\t'.join(github_repo_topdirs(contents))))
directory = click.prompt('Which directory? (all)')
directory_to_download = '.' if (not directory or directory=='all') else directory
outpath = savedir or directory_to_download
if outpath == '.' and savedir !='.': outpath=repo.replace('/','_')+'_files'
msg='\nOkay... downloading {}/{}'.format(repo,directory_to_download )
if file_processor is not None:
msg = msg + ' using notebook processor: {}'.format(file_processor)
else: msg = msg + ' with no notebook processing'
print(msg)
download_directory(repository, sha, directory_to_download, outpath,file_processor )
if file_processor in ['clearOutput', 'clearOutputTest','runWithErrors' ]:
click.echo('\nRunning notebook processor: {}'.format(file_processor))
directoryProcessor(outpath, mode=file_processor, subdirs=True,
reportlevel=1, logfile=logfile)
if logfile:
click.echo('\nLog written to {}'.format(logfile))
if with_tests:
click.echo('\nRunning notebook tests over: {}'.format(outpath))
if not logfile: logfile = 'tests.log'
_notebookTest(outpath, logfile )
click.echo('\nLog written to {}'.format(logfile))
if zip:
print('\nZipping into: {}/nYou may also want to delete the working directory ({}).'.format(repository, outpath) )
zipper(outpath,repository)
else:
print('\n\nTo zip the downloaded directory, run something like: {}'.format('tm351zip {o} {z}\n\nTo run a notebook processor (OPTIONS: runWithErrors, clearOutput) while zipping: tm351zip "{o}" {z} --file-processor OPTION\n'.format(o=outpath,z=repository.name)))
directory=''
another = click.prompt('\Download another directory from this branch? (To quit: -)')
#TODO
#print('\n\nTo run this command again: {}'.format())
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] | 2.404883 | 11,018 |
#!/usr/bin/env
"""
class definitions for standard 1 variable plots
class definitions for standard 2 variable plots
class definitions for standard 3 variable plots
History:
--------
2019-05-21: error in calculation used corrected udata to correct vdata
"""
# System Stack
import datetime
# science stack
import numpy as np
# Visual Stack
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.dates import (
YearLocator,
WeekdayLocator,
MonthLocator,
DayLocator,
HourLocator,
DateFormatter,
)
import matplotlib.ticker as ticker
class TimeseriesPorpertyPropertyPlot(object):
""" class to plot property vs property plots with density iso-contours"""
mpl.rcParams["svg.fonttype"] = "none"
mpl.rcParams["ps.fonttype"] = 42
mpl.rcParams["pdf.fonttype"] = 42
def __init__(
self, fontsize=10, labelsize=10, plotstyle="k-.", stylesheet="seaborn-whitegrid"
):
"""Initialize the timeseries with items that do not change.
This sets up the axes and station locations. The `fontsize` and `spacing`
are also specified here to ensure that they are consistent between individual
station elements.
Parameters
----------
fontsize : int
The fontsize to use for drawing text
labelsize : int
The fontsize to use for labels
stylesheet : str
Choose a mpl stylesheet [u'seaborn-darkgrid',
u'seaborn-notebook', u'classic', u'seaborn-ticks',
u'grayscale', u'bmh', u'seaborn-talk', u'dark_background',
u'ggplot', u'fivethirtyeight', u'seaborn-colorblind',
u'seaborn-deep', u'seaborn-whitegrid', u'seaborn-bright',
u'seaborn-poster', u'seaborn-muted', u'seaborn-paper',
u'seaborn-white', u'seaborn-pastel', u'seaborn-dark',
u'seaborn-dark-palette']
"""
self.fontsize = fontsize
self.labelsize = labelsize
self.plotstyle = plotstyle
plt.style.use(stylesheet)
@staticmethod
def add_title(mooringid="", lat=-99.9, lon=-99.9, depth=9999, instrument=""):
"""Pass parameters to annotate the title of the plot
This sets the standard plot title using common meta information from PMEL/EPIC style netcdf files
Parameters
----------
mooringid : str
Mooring Identifier
lat : float
The latitude of the mooring
lon : float
The longitude of the mooring
depth : int
Nominal depth of the instrument
instrument : str
Name/identifier of the instrument plotted
"""
ptitle = (
"Plotted on: {time:%Y/%m/%d %H:%M} \n from {mooringid} Lat: {latitude:3.3f} Lon: {longitude:3.3f}"
" Depth: {depth}\n : {instrument}"
).format(
time=datetime.datetime.now(),
mooringid=mooringid,
latitude=lat,
longitude=lon,
depth=depth,
instrument=instrument,
)
return ptitle
@staticmethod
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] | 2.362253 | 1,314 |
"""
The tests for omit interaction feature
"""
import os
import sys
from collections import namedtuple
from pyplif_hippos import ParseConfig, hippos, similarity
def test_configuration_single_omit_interaction(tmpdir):
"""Test configuration for omitting specific interaction"""
# Arrange
config_file = tmpdir.mkdir("sub").join("config.txt")
config_file.write(
"""
docking_method plants # plants or vina
docking_conf plants-003.conf
similarity_coef tanimoto mcconnaughey
full_ref 00000100000000000000000000000000000100000000000001000000000000010000001000000000000000000001000000000000000000000000000000101000000000000000000101000000000010000 00010101000000000000000000000000000100000000000001010000000000010000001000000000000010000000000000000000000001011000001000001000000000000000000101000000000000000 00010101000000100000000000000000000100000000000001010100100000010000001000000000000010000001000000000000010000000000100000101010000000000000000001000000000000000
residue_name ARG116 GLU117 LEU132 LYS148 ASP149 ARG150 ARG154 TRP177 SER178 ILE221 ARG223 THR224 GLU226 ALA245 HIS273 GLU275 GLU276 ARG292 ASP294 GLY347 ARG374 TRP408 TYR409
residue_number 40 41 56 72 73 74 78 101 102 145 147 148 150 169 197 199 200 216 218 271 298 332 333
omit_interaction hydrophobic ARG223
full_outfile plants_full_ifp.csv
sim_outfile plants_similarity.csv
logfile plants.log
"""
)
arg = os.path.join(config_file.dirname, config_file.basename)
if len(sys.argv) > 1:
sys.argv[1] = arg
else:
sys.argv.append(arg)
# Act
hippos_config = ParseConfig()
hippos_config.parse_config()
omit_interaction = hippos_config.omit_interaction[0]
# Assert
assert omit_interaction.interaction_type == "hydrophobic"
assert omit_interaction.res_name == ["ARG223"]
def test_configuration_omit_multiple_residue_interaction(tmpdir):
"""Test configuration for omitting multiple residue interaction"""
# Arrange
config_file = tmpdir.mkdir("sub").join("config.txt")
config_file.write(
"""
docking_method plants # plants or vina
docking_conf plants-003.conf
similarity_coef tanimoto mcconnaughey
full_ref 00000100000000000000000000000000000100000000000001000000000000010000001000000000000000000001000000000000000000000000000000101000000000000000000101000000000010000 00010101000000000000000000000000000100000000000001010000000000010000001000000000000010000000000000000000000001011000001000001000000000000000000101000000000000000 00010101000000100000000000000000000100000000000001010100100000010000001000000000000010000001000000000000010000000000100000101010000000000000000001000000000000000
residue_name ARG116 GLU117 LEU132 LYS148 ASP149 ARG150 ARG154 TRP177 SER178 ILE221 ARG223 THR224 GLU226 ALA245 HIS273 GLU275 GLU276 ARG292 ASP294 GLY347 ARG374 TRP408 TYR409
residue_number 40 41 56 72 73 74 78 101 102 145 147 148 150 169 197 199 200 216 218 271 298 332 333
omit_interaction hydrophobic ARG150 TRP177 ARG223
full_outfile plants_full_ifp.csv
sim_outfile plants_similarity.csv
logfile plants.log
"""
)
arg = os.path.join(config_file.dirname, config_file.basename)
if len(sys.argv) > 1:
sys.argv[1] = arg
else:
sys.argv.append(arg)
# Act
hippos_config = ParseConfig()
hippos_config.parse_config()
omit_interaction = hippos_config.omit_interaction[0]
# Assert
assert omit_interaction.interaction_type == "hydrophobic"
assert omit_interaction.res_name == ["ARG150", "TRP177", "ARG223"]
def test_configuration_omit_multiple_interaction_type(tmpdir):
"""Test configuration for omitting multiple interaction type"""
# Arrange
config_file = tmpdir.mkdir("sub").join("config.txt")
config_file.write(
"""
docking_method plants # plants or vina
docking_conf plants-003.conf
similarity_coef tanimoto mcconnaughey
full_ref 00000100000000000000000000000000000100000000000001000000000000010000001000000000000000000001000000000000000000000000000000101000000000000000000101000000000010000 00010101000000000000000000000000000100000000000001010000000000010000001000000000000010000000000000000000000001011000001000001000000000000000000101000000000000000 00010101000000100000000000000000000100000000000001010100100000010000001000000000000010000001000000000000010000000000100000101010000000000000000001000000000000000
residue_name ARG116 GLU117 LEU132 LYS148 ASP149 ARG150 ARG154 TRP177 SER178 ILE221 ARG223 THR224 GLU226 ALA245 HIS273 GLU275 GLU276 ARG292 ASP294 GLY347 ARG374 TRP408 TYR409
residue_number 40 41 56 72 73 74 78 101 102 145 147 148 150 169 197 199 200 216 218 271 298 332 333
omit_interaction hydrophobic ARG223
omit_interaction h_bond ARG292
full_outfile plants_full_ifp.csv
sim_outfile plants_similarity.csv
logfile plants.log
"""
)
arg = os.path.join(config_file.dirname, config_file.basename)
if len(sys.argv) > 1:
sys.argv[1] = arg
else:
sys.argv.append(arg)
# Act
hippos_config = ParseConfig()
hippos_config.parse_config()
omit_interaction_1 = hippos_config.omit_interaction[0]
omit_interaction_2 = hippos_config.omit_interaction[1]
# Assert
assert omit_interaction_1.interaction_type == "hydrophobic"
assert omit_interaction_1.res_name == ["ARG223"]
assert omit_interaction_2.interaction_type == "h_bond"
assert omit_interaction_2.res_name == ["ARG292"]
def test_configuration_long_interaction_type(tmpdir):
"""Test configuration checking all long interaction_type"""
# Arrange
config_file = tmpdir.mkdir("sub").join("config.txt")
config_file.write(
"""
docking_method plants # plants or vina
docking_conf plants-003.conf
similarity_coef tanimoto mcconnaughey
full_ref 00000100000000000000000000000000000100000000000001000000000000010000001000000000000000000001000000000000000000000000000000101000000000000000000101000000000010000 00010101000000000000000000000000000100000000000001010000000000010000001000000000000010000000000000000000000001011000001000001000000000000000000101000000000000000 00010101000000100000000000000000000100000000000001010100100000010000001000000000000010000001000000000000010000000000100000101010000000000000000001000000000000000
residue_name ARG116 GLU117 LEU132 LYS148 ASP149 ARG150 ARG154 TRP177 SER178 ILE221 ARG223 THR224 GLU226 ALA245 HIS273 GLU275 GLU276 ARG292 ASP294 GLY347 ARG374 TRP408 TYR409
residue_number 40 41 56 72 73 74 78 101 102 145 147 148 150 169 197 199 200 216 218 271 298 332 333
omit_interaction hydrophobic ARG116
omit_interaction aromatic GLU117
omit_interaction h_bond LEU132
omit_interaction electrostatic LYS148
omit_interaction h_bond_donor ASP149
omit_interaction h_bond_acceptor ARG150
omit_interaction electrostatic_positive ARG154
omit_interaction electrostatic_negative TRP177
omit_interaction aromatic_facetoface SER178
omit_interaction aromatic_edgetoface ILE221
full_outfile plants_full_ifp.csv
sim_outfile plants_similarity.csv
logfile plants.log
"""
)
arg = os.path.join(config_file.dirname, config_file.basename)
if len(sys.argv) > 1:
sys.argv[1] = arg
else:
sys.argv.append(arg)
# Act
hippos_config = ParseConfig()
hippos_config.parse_config()
omit_interaction_1 = hippos_config.omit_interaction[0]
omit_interaction_2 = hippos_config.omit_interaction[1]
omit_interaction_3 = hippos_config.omit_interaction[2]
omit_interaction_4 = hippos_config.omit_interaction[3]
omit_interaction_5 = hippos_config.omit_interaction[4]
omit_interaction_6 = hippos_config.omit_interaction[5]
omit_interaction_7 = hippos_config.omit_interaction[6]
omit_interaction_8 = hippos_config.omit_interaction[7]
omit_interaction_9 = hippos_config.omit_interaction[8]
omit_interaction_10 = hippos_config.omit_interaction[9]
# Assert
assert omit_interaction_1.interaction_type == "hydrophobic"
assert omit_interaction_1.res_name == ["ARG116"]
assert omit_interaction_2.interaction_type == "aromatic"
assert omit_interaction_2.res_name == ["GLU117"]
assert omit_interaction_3.interaction_type == "h_bond"
assert omit_interaction_3.res_name == ["LEU132"]
assert omit_interaction_4.interaction_type == "electrostatic"
assert omit_interaction_4.res_name == ["LYS148"]
assert omit_interaction_5.interaction_type == "h_bond_donor"
assert omit_interaction_5.res_name == ["ASP149"]
assert omit_interaction_6.interaction_type == "h_bond_acceptor"
assert omit_interaction_6.res_name == ["ARG150"]
assert omit_interaction_7.interaction_type == "electrostatic_positive"
assert omit_interaction_7.res_name == ["ARG154"]
assert omit_interaction_8.interaction_type == "electrostatic_negative"
assert omit_interaction_8.res_name == ["TRP177"]
assert omit_interaction_9.interaction_type == "aromatic_facetoface"
assert omit_interaction_9.res_name == ["SER178"]
assert omit_interaction_10.interaction_type == "aromatic_edgetoface"
assert omit_interaction_10.res_name == ["ILE221"]
def test_configuration_short_interaction_type(tmpdir):
"""Test configuration checking all short interaction_type"""
# Arrange
config_file = tmpdir.mkdir("sub").join("config.txt")
config_file.write(
"""
docking_method plants # plants or vina
docking_conf plants-003.conf
similarity_coef tanimoto mcconnaughey
full_ref 00000100000000000000000000000000000100000000000001000000000000010000001000000000000000000001000000000000000000000000000000101000000000000000000101000000000010000 00010101000000000000000000000000000100000000000001010000000000010000001000000000000010000000000000000000000001011000001000001000000000000000000101000000000000000 00010101000000100000000000000000000100000000000001010100100000010000001000000000000010000001000000000000010000000000100000101010000000000000000001000000000000000
residue_name ARG116 GLU117 LEU132 LYS148 ASP149 ARG150 ARG154 TRP177 SER178 ILE221 ARG223 THR224 GLU226 ALA245 HIS273 GLU275 GLU276 ARG292 ASP294 GLY347 ARG374 TRP408 TYR409
residue_number 40 41 56 72 73 74 78 101 102 145 147 148 150 169 197 199 200 216 218 271 298 332 333
omit_interaction HPB ARG116
omit_interaction ARM GLU117
omit_interaction HBD LEU132
omit_interaction ELE LYS148
omit_interaction HBD_DON ASP149
omit_interaction HBD_ACC ARG150
omit_interaction ELE_POS ARG154
omit_interaction ELE_NEG TRP177
omit_interaction ARM_F2F SER178
omit_interaction ARM_E2F ILE221
full_outfile plants_full_ifp.csv
sim_outfile plants_similarity.csv
logfile plants.log
"""
)
arg = os.path.join(config_file.dirname, config_file.basename)
if len(sys.argv) > 1:
sys.argv[1] = arg
else:
sys.argv.append(arg)
# Act
hippos_config = ParseConfig()
hippos_config.parse_config()
omit_interaction_1 = hippos_config.omit_interaction[0]
omit_interaction_2 = hippos_config.omit_interaction[1]
omit_interaction_3 = hippos_config.omit_interaction[2]
omit_interaction_4 = hippos_config.omit_interaction[3]
omit_interaction_5 = hippos_config.omit_interaction[4]
omit_interaction_6 = hippos_config.omit_interaction[5]
omit_interaction_7 = hippos_config.omit_interaction[6]
omit_interaction_8 = hippos_config.omit_interaction[7]
omit_interaction_9 = hippos_config.omit_interaction[8]
omit_interaction_10 = hippos_config.omit_interaction[9]
# Assert
assert omit_interaction_1.interaction_type == "hydrophobic"
assert omit_interaction_1.res_name == ["ARG116"]
assert omit_interaction_2.interaction_type == "aromatic"
assert omit_interaction_2.res_name == ["GLU117"]
assert omit_interaction_3.interaction_type == "h_bond"
assert omit_interaction_3.res_name == ["LEU132"]
assert omit_interaction_4.interaction_type == "electrostatic"
assert omit_interaction_4.res_name == ["LYS148"]
assert omit_interaction_5.interaction_type == "h_bond_donor"
assert omit_interaction_5.res_name == ["ASP149"]
assert omit_interaction_6.interaction_type == "h_bond_acceptor"
assert omit_interaction_6.res_name == ["ARG150"]
assert omit_interaction_7.interaction_type == "electrostatic_positive"
assert omit_interaction_7.res_name == ["ARG154"]
assert omit_interaction_8.interaction_type == "electrostatic_negative"
assert omit_interaction_8.res_name == ["TRP177"]
assert omit_interaction_9.interaction_type == "aromatic_facetoface"
assert omit_interaction_9.res_name == ["SER178"]
assert omit_interaction_10.interaction_type == "aromatic_edgetoface"
assert omit_interaction_10.res_name == ["ILE221"]
def test_replace_bit_char():
"""Test bit replacement function for omitted residue"""
# Arrange
bitstring = "1000001"
omit_hydrophobic = [1, 0, 0, 0, 0, 0, 0]
omit_aromatic = [0, 1, 1, 0, 0, 0, 0]
omit_h_bond = [0, 0, 0, 1, 1, 0, 0]
omit_electrostatic = [0, 0, 0, 0, 0, 1, 1]
omit_h_bond_donor = [0, 0, 0, 1, 0, 0, 0]
omit_h_bond_acceptor = [0, 0, 0, 0, 1, 0, 0]
omit_electrostatic_positive = [0, 0, 0, 0, 0, 1, 0]
omit_electrostatic_negative = [0, 0, 0, 0, 0, 0, 1]
omit_aromatic_facetoface = [0, 1, 0, 0, 0, 0, 0]
omit_aromatic_edgetoface = [0, 0, 1, 0, 0, 0, 0]
# Act
bitstring_1 = hippos.replace_bit_char(bitstring, omit_hydrophobic)
bitstring_2 = hippos.replace_bit_char(bitstring, omit_aromatic)
bitstring_3 = hippos.replace_bit_char(bitstring, omit_h_bond)
bitstring_4 = hippos.replace_bit_char(bitstring, omit_electrostatic)
bitstring_5 = hippos.replace_bit_char(bitstring, omit_h_bond_donor)
bitstring_6 = hippos.replace_bit_char(bitstring, omit_h_bond_acceptor)
bitstring_7 = hippos.replace_bit_char(bitstring, omit_electrostatic_positive)
bitstring_8 = hippos.replace_bit_char(bitstring, omit_electrostatic_negative)
bitstring_9 = hippos.replace_bit_char(bitstring, omit_aromatic_facetoface)
bitstring_10 = hippos.replace_bit_char(bitstring, omit_aromatic_edgetoface)
# Assert
assert bitstring_1 == "n000001"
assert bitstring_2 == "1nn0001"
assert bitstring_3 == "100nn01"
assert bitstring_4 == "10000nn"
assert bitstring_5 == "100n001"
assert bitstring_6 == "1000n01"
assert bitstring_7 == "10000n1"
assert bitstring_8 == "100000n"
assert bitstring_9 == "1n00001"
assert bitstring_10 == "10n0001"
def test_cleanup_omitted_interaction():
"""Test for bitstring preparation prior to similarity calculation"""
# Arrange
refbit = "000001000101"
tgtbit = "11n00n000011"
# Act
clean_refbit, clean_tgtbit = similarity.clean_omitted_interactions(refbit, tgtbit)
# Assert
assert clean_refbit == "0000000101"
assert clean_tgtbit == "1100000011"
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] | 2.755917 | 5,408 |
#!/usr/bin/python3
import sys
from lib.demucs import demucs
from lib.demucs.demucs import model
from lib.demucs.demucs.audio import AudioFile
from lib.demucs.demucs.utils import apply_model, load_model
from pathlib import Path
from scipy.io import wavfile
# within the demucs directory
sys.modules['demucs.model'] = model
sys.modules['demucs'] = demucs
class DemucsService():
"""
def encode_mp3(wav, path, bitrate=320, verbose=False):
try:
import lameenc
except ImportError:
print("Failed to call lame encoder. Maybe it is not installed? "
"On windows, run `python.exe -m pip install -U lameenc`, "
"on OSX/Linux, run `python3 -m pip install -U lameenc`, "
"then try again.", file=sys.stderr)
sys.exit(1)
encoder = lameenc.Encoder()
encoder.set_bit_rate(bitrate)
encoder.set_in_sample_rate(44100)
encoder.set_channels(2)
encoder.set_quality(2) # 2-highest, 7-fastest
if not verbose:
encoder.silence()
mp3_data = encoder.encode(wav.tostring())
mp3_data += encoder.flush()
with open(path, "wb") as f:
f.write(mp3_data)
"""
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] | 2.162587 | 572 |
import pymysql
conn = pymysql.Connection(
host = '192.168.160.33',
port = 3306,
user = 'develop',
password='xs_dev',
database='test',
charset='utf8'
)
cursor = conn.cursor()
sql = """
select * from user1
"""
try:
cursor.execute(sql)
res = cursor.fetchall()
for row in res:
id = row[0]
fname=row[1]
lname=row[2]
age =row[3]
sex=row[4]
income=row[5]
print("id=%s,fname=%s,lname=%s,age=%s,sex=%s,income=%s" % (id, fname, lname, age, sex, income))
except Exception as e:
print(e)
# 关闭连接
conn.close() | [
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4,
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1,
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11,
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255,
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162,
236,
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] | 1.904153 | 313 |
from nfmanagementapi.models import ServiceObject
from nfmanagementapi.schemata import ServiceObjectSchema
from marshmallow.exceptions import ValidationError
from .BaseResource import BaseResource
from flask import request
from app import db
from uuid import uuid4
path = 'service_objects'
endpoint = 'service_objects'
| [
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] | 3.938272 | 81 |
# Generated by Django 3.1.6 on 2021-02-12 00:15
from django.db import migrations, models
import django.db.models.deletion
| [
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] | 2.818182 | 44 |
config = {
"frequency": 440.0,
"duration": 20.0,
"sampling_rate": 44100,
"filename": "test_v1.wav",
"overtones": [
[
440.0,
1.0
],
[
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0.1098108639573242
],
[
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0.843727285302496
],
[
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0.17496422223017305
],
[
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0.013028474684831037
],
[
20577.216573422433,
0.23529784971612777
],
[
21425.497754119715,
0.6436550795219932
],
[
11410.89145988607,
0.011826877382886125
]
],
"amp_ctrl_points": [
[
0.0,
0.0
],
[
20.0,
100.0
],
[
33.0,
20.0
],
[
47.0,
88.0
],
[
56.0,
45.0
],
[
76.0,
80.0
],
[
90.0,
5.0
],
[
100.0,
20.0
]
]
}
| [
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import datetime
import re
import os
import struct
from dataclasses import dataclass, field
from itertools import combinations, product
from typing import List, Dict
import pandas as pd
import numpy as np
import peakutils
from matplotlib import pyplot as plt
from scipy import signal as spsig
import plotly.graph_objs as go
from tqdm.autonotebook import tqdm
import networkx as nx
from ipywidgets import interactive, VBox, HBox
from lmfit.models import LinearModel
from pyspectools import routines
from pyspectools import figurefactory as ff
from pyspectools import fitting
from pyspectools.spectra import analysis
from pyspectools import parsers
def parse_spectrum(filename, threshold=20.0):
""" Function to read in a blackchirp or QtFTM spectrum from file """
dataframe = pd.read_csv(
filename, delimiter="\t", names=["Frequency", "Intensity"], skiprows=1
)
return dataframe[dataframe["Intensity"] <= threshold]
def center_cavity(dataframe, thres=0.3, verbose=True):
""" Finds the center frequency of a Doppler pair in cavity FTM measurements
and provides a column of offset frequencies.
Sometimes the peak finding threshold has to be tweaked to get the center
frequency correctly.
"""
# Find the peak intensities
center_indexes = peakutils.indexes(dataframe["Intensity"], thres=thres)
peak_frequencies = dataframe.iloc[center_indexes]["Frequency"]
# Calculate the center frequency as the average
center = np.average(peak_frequencies)
if verbose is True:
print("Center frequency at " + str(center))
dataframe["Offset Frequency"] = dataframe["Frequency"] - center
@dataclass
@dataclass
class Scan:
"""
DataClass for a Scan. Holds all of the relevant information that
describes a FT scan, such as the ID, what machine it was collected
on, and the experimental settings.
Has a few class methods that will make look ups easily such as
the date the scan was collected and the gases used.
"""
id: int
machine: str
fid: np.array
date: datetime.datetime
shots: int = 0
cavity_voltage: int = 0
cavity_atten: int = 0
cavity_frequency: float = 0.0
dr_frequency: float = 0.0
dr_power: int = 0
fid_points: int = 0
fid_spacing: float = 0.0
discharge: bool = False
magnet: bool = False
gases: Dict = field(default_factory=dict)
filter: List = field(default_factory=list)
exp: float = 0.0
zeropad: bool = False
window: str = ""
def __post_init__(self):
"""
Functions called after __init__ is called.
"""
# Perform FFT
self.process_fid()
def __deepcopy__(self):
"""
Dunder method to produce a deep copy - this will be used when
manipulating multiple Scan objects.
:return: A deep copy of the current Scan object
"""
new_scan = Empty()
new_scan.__class__ = self.__class__
new_scan.__dict__.update(self.__dict__)
return new_scan
def average(self, others):
"""
Dunder method to co-average two or more Scans in the time domain.
:param other: Scan object, or tuple/list
:return: A new Scan object with the co-added FID
"""
new_scan = self.__deepcopy__()
try:
new_scan.fid = np.average(others.extend(new_scan.fid), axis=0)
new_scan.average_ids = [scan.id for scan in others]
# If there is no extend method, then assume we're working with a
# single Scan
except AttributeError:
new_scan.fid = np.average([new_scan.fid, others.fid], axis=0)
new_scan.average_ids = [others.id]
new_scan.process_fid()
return new_scan
def __add__(self, other):
"""
Dunder method to co-add two or more Scans in the time domain.
:param other: Scan object, or tuple/list
:return: A new Scan object with the co-added FID
"""
new_scan = self.__deepcopy__()
new_scan.fid = np.sum([new_scan.fid, other.fid], axis=0)
new_scan.process_fid()
return new_scan
def __sub__(self, other):
"""
Dunder method to subtract another Scan from the current Scan in the time domain.
i.e. this scan - other scan
:param other: Scan object, or tuple/list
:return: A new Scan object with the subtracted FID
"""
new_scan = self.__deepcopy__()
new_scan.fid = np.subtract(new_scan.fid, other.fid)
new_scan.process_fid()
return new_scan
def subtract_frequency(self, other):
"""
Method to subtract another Scan from the current in the frequency domain.
:param other: Scan object to subtract with
:return: A new Scan object with the subtracted spectrum
"""
new_scan = self.__deepcopy__()
new_scan.spectrum["Intensity"] = (
new_scan.spectrum["Intensity"] - other.spectrum["Intensity"]
)
new_scan.subtracted = other.id
return new_scan
def add_frequency(self, other):
"""
Method to add another Scan from the current in the frequency domain.
:param other: Scan object to add with
:return: A new Scan object with the co-added spectrum
"""
new_scan = self.__deepcopy__()
new_scan.spectrum["Intensity"] = (
new_scan.spectrum["Intensity"] + other.spectrum["Intensity"]
)
new_scan.subtracted = other.id
return new_scan
@classmethod
def from_dict(cls, data_dict):
"""
Function to initialize a Scan object from a dictionary
of FT scan data collected from `parse_scan`.
:param data_dict: dict containing parsed data from FT
:return: Scan object
"""
scan_obj = cls(**data_dict)
return scan_obj
@classmethod
def from_qtftm(cls, filepath):
"""
Method to initialize a Scan object from a FT scan file.
Will load the lines into memory and parse the data into
a dictionary, which then gets passed into a Scan object.
:param filepath: str path to FID file
:return: Scan object
"""
with open(filepath) as read_file:
data_dict = parse_scan(read_file.readlines())
scan_obj = cls(**data_dict)
return scan_obj
@classmethod
def from_pickle(cls, filepath):
"""
Method to create a Scan object from a previously pickled
Scan.
:param filepath: path to the Scan pickle
:return: instance of the Scan object
"""
scan_obj = routines.read_obj(filepath)
if isinstance(scan_obj, Scan) is False:
raise Exception("File is not a Scan object; {}".format(type(scan_obj)))
else:
return scan_obj
@classmethod
def from_remote(cls, remote_path, ssh_obj=None):
"""
Method to initialize a Scan object from a remote server.
Has the option to pass an instance of a paramiko SSHClient, which would be
useful in a Batch. If none is supplied, an instance will be created.
:param remote_path: str remote path to the file
:param ssh_obj: optional argument to supply a paramiko SSHClient object
:return: Scan object from remote QtFTM file
"""
if ssh_obj is None:
default_keypath = os.path.join(os.path.expanduser("~"), ".ssh/id_rsa.pub")
hostname = input("Please provide remote hostname: ")
username = input("Please provide login: ")
ssh_settings = {"hostname": hostname, "username": username}
if os.path.isfile(default_keypath) is True:
ssh_settings["key_filename"] = default_keypath
else:
password = input("Please provide password: ")
ssh_settings["password"] = password
ssh_obj = routines.RemoteClient(**ssh_settings)
# Parse the scan data from remote file
data_dict = parse_scan(ssh_obj.open_remote(remote_path))
scan_obj = cls(**data_dict)
return scan_obj
def to_file(self, filepath, format="yaml"):
""" Method to dump data to YAML format.
Extensions are automatically decided, but
can also be supplied.
parameters:
--------------------
:param filepath - str path to yaml file
:param format - str denoting the syntax used for dumping. Defaults to YAML.
"""
if "." not in filepath:
if format == "json":
filepath += ".json"
else:
filepath += ".yml"
if format == "json":
writer = routines.dump_json
else:
writer = routines.dump_yaml
writer(filepath, self.__dict__)
def to_pickle(self, filepath=None, **kwargs):
"""
Pickles the Scan object with the joblib wrapper implemented
in routines.
:param filepath: optional argument to pickle to. Defaults to the id.pkl
:param kwargs: additional settings for the pickle operation
"""
if filepath is None:
filepath = "{}.pkl".format(self.id)
routines.save_obj(self, filepath, **kwargs)
def process_fid(self, **kwargs):
"""
Perform an FFT on the FID to yield the frequency domain spectrum.
Kwargs are passed into the FID processing, which will override the
Scan attributes.
:param kwargs: Optional keyword arguments for processing the FID
"""
# Calculate the frequency bins
frequencies = np.linspace(
self.cavity_frequency, self.cavity_frequency + 1.0, len(self.fid)
)
# Calculate the time bins
time = np.linspace(0.0, self.fid_spacing * self.fid_points, self.fid_points)
process_list = ["window", "filter", "exp", "zeropad"]
process_dict = {
key: value for key, value in self.__dict__.items() if key in process_list
}
# Override with user settings
process_dict.update(**kwargs)
temp_fid = np.copy(self.fid)
self.spectrum = fid2fft(
temp_fid, 1.0 / self.fid_spacing, frequencies, **process_dict
)
self.fid_df = pd.DataFrame({"Time (us)": time * 1e6, "FID": temp_fid})
def within_time(self, date_range):
"""
Function for determining of the scan was taken between
a specified date range in month/day/year, in the format
04/09/08 for April 9th, 2008.
:param date_range: list containing the beginning and end date strings
:return: bool - True if within range, False otherwise
"""
try:
early = datetime.datetime.strptime(date_range[0], "%m/%d/%y")
except:
early = datetime.datetime(1, 1, 1)
try:
late = datetime.datetime.strptime(date_range[1], "%m/%d/%y")
except:
late = datetime.datetime(9999, 1, 1)
return early <= self.date <= late
def is_depleted(self, ref, roi=None, depletion=None):
"""
Function for determining if the signal in this Scan is less
than that of another scan. This is done by a simple comparison
of the average of 10 largest intensities in the two spectra. If
the current scan is less intense than the reference by the
expected depletion percentage, then it is "depleted".
This function can be used to determine if a scan if depleted
in DR/magnet/discharge assays.
TODO - implement a chi squared test of sorts to determine if a
depletion is statistically significant
:param ref: second Scan object for comparison
:param depletion: percentage of depletion expected of the reference
:return: bool - True if signal in this Scan is less intense than the reference
"""
y_ref = ref.spectrum["Intensity"].values
y_obs = self.spectrum["Intensity"].values
self.ref_freq = ref.fit.frequency
self.ref_id = ref.id
if roi:
y_ref = y_ref[roi]
y_obs = y_obs[roi]
# This doesn't work, or is not particularly discriminating.
# chisq, p_value = chisquare(
# y_obs, y_ref
# )
if depletion is None:
sigma = np.std(y_obs, axis=0) * 16.0
else:
sigma = depletion
expected = np.sum(y_ref, axis=0) - sigma
return np.sum(y_obs, axis=0) <= expected
def scatter_trace(self):
"""
Create a Plotly Scattergl trace. Called by the Batch function, although
performance-wise it takes forever to plot up ~3000 scans.
:return trace: Scattergl object
"""
text = "Scan ID: {}<br>Cavity: {}<br>DR: {}<br>Magnet: {}<br>Attn: {}".format(
self.id,
self.cavity_frequency,
self.dr_frequency,
self.magnet,
self.cavity_atten,
)
trace = go.Scattergl(
x=np.linspace(self.id, self.id + 1, len(self.spectrum["Intensity"])),
y=self.spectrum["Intensity"],
text=text,
marker={"color": "rgb(43,140,190)"},
hoverinfo="text",
)
return trace
def fit_cavity(self, plot=True, verbose=False):
"""
Perform a fit to the cavity spectrum. Uses a paired Gaussian model
that minimizes the number of fitting parameters.
:param plot: bool specify whether a Plotly figure is made
:return: Model Fit result
"""
y = self.spectrum["Intensity"].dropna().values
x = self.spectrum["Frequency (MHz)"].dropna().values
model = fitting.PairGaussianModel()
result = model.fit_pair(x, y, verbose=verbose)
self.spectrum["Fit"] = result.best_fit
self.fit = result
self.fit.frequency = self.fit.best_values["x0"]
if plot is True:
fig = go.FigureWidget()
fig.layout["xaxis"]["title"] = "Frequency (MHz)"
fig.layout["xaxis"]["tickformat"] = ".2f"
fig.add_scatter(x=x, y=y, name="Observed")
fig.add_scatter(x=x, y=result.best_fit, name="Fit")
return result, fig
else:
return result
def parse_scan(filecontents):
"""
Function for extracting the FID data from an FT scan. The data
is returned as a dictionary, which can be used to initialize a
Scan object.
:param filecontents: list of lines from an FID file
:return: dict containing parsed data from FID
"""
data = {"gases": dict()}
# FID regex
fid_regex = re.compile(r"^fid\d*", re.M)
# Regex to find gas channels
gas_regex = re.compile(r"^#Gas \d name", re.M)
flow_regex = re.compile(r"^#Gas \d flow", re.M)
# Regex to detect which channel is set to the discharge
dc_regex = re.compile(r"^#Pulse ch \d name\s*DC", re.M)
dc_channel = None
for index, line in enumerate(filecontents):
if "#Scan" in line:
split_line = line.split()
data["id"] = int(split_line[1])
try:
data["machine"] = split_line[2]
except IndexError:
data["machine"] = "FT1"
if "#Probe freq" in line:
data["cavity_frequency"] = float(line.split()[2])
if "#Shots" in line:
data["shots"] = int(line.split()[-1])
if "#Date" in line:
strip_targets = ["#Date", "\t", "\n"]
data["date"] = datetime.datetime.strptime(
re.sub("|".join(strip_targets), "", line), "%a %b %d %H:%M:%S %Y"
)
if "#Cavity Voltage" in line:
data["cavity_voltage"] = int(line.split()[2])
if "#Attenuation" in line:
data["cavity_atten"] = int(line.split()[1])
if "#DR freq" in line:
data["dr_frequency"] = float(line.split()[2])
if "#DR power" in line:
data["dr_power"] = int(line.split()[2])
if "#FID spacing" in line:
data["fid_spacing"] = float(re.findall(r"\de[+-]?\d\d", line)[0])
if "#FID points" in line:
data["fid_points"] = int(line.split()[-1])
# Get the name of the gas
if gas_regex.match(line):
split_line = line.split()
# Only bother parsing if the channel is used
gas_index = int(split_line[1])
try:
data["gases"][gas_index] = {"gas": " ".join(split_line[3:])}
except IndexError:
data["gases"][gas_index] = {"gas": ""}
# Get the flow rate for channel
if flow_regex.match(line):
split_line = line.split()
gas_index = int(split_line[1])
data["gases"][gas_index]["flow"] = float(split_line[3])
if "#Magnet enabled" in line:
data["magnet"] = bool(int(line.split()[2]))
# Find the channel the discharge is set to and compile a regex
# to look for the channel
if dc_regex.match(line):
dc_index = line.split()[2]
dc_channel = re.compile(r"^#Pulse ch {} enabled".format(dc_index), re.M)
# Once the discharge channel index is known, start searching for it
if dc_channel:
if dc_channel.match(line):
data["discharge"] = bool(int(line.split()[-1]))
# Find when the FID lines start popping up
if fid_regex.match(line):
fid = filecontents[index + 1 :]
fid = [float(value) for value in fid]
data["fid"] = np.array(fid)
return data
def perform_fft(fid, spacing, start=0, stop=-1, window="boxcar"):
"""
Perform an FFT on an FID to get the frequency domain spectrum.
All of the arguments are optional, and provide control over how the FFT is performed, as well as post-processing
parameters like window functions and zero-padding.
This is based on the FFT code by Kyle Crabtree, with modifications to fit this dataclass.
Parameters
----------
fid - Numpy 1D array
Array holding the values of the FID
spacing - float
Time spacing between FID points in microseconds
start - int, optional
Starting index for the FID array to perform the FFT
stop - int, optional
End index for the FID array to perform the FFT
zpf - int, optional
Pad the FID with zeros to nth nearest power of 2
window - str
Specify the window function used to process the FID. Defaults to boxcar, which is effectively no filtering.
The names of the window functions available can be found at:
https://docs.scipy.org/doc/scipy/reference/signal.windows.html
Returns
-------
"""
fid = np.copy(fid)
if window is not None and window in spsig.windows.__all__:
window_f = spsig.windows.get_window(window, fid.size)
fid *= window_f
else:
raise Exception("Specified window function is not implemented in SciPy!")
# Set values to zero up to starting index
fid[:start] = 0.0
if stop < 0:
# If we're using negative indexes
fid[fid.size + stop :] = 0.0
else:
# Otherwise, index with a positive number
fid[stop:] = 0.0
# Perform the FFT
fft = np.fft.rfft(fid)
read_length = len(fid) // 2 + 1
df = 1.0 / fid.size / spacing
# Generate the frequency array
frequency = np.linspace(0.0, self.header["sideband"] * df, read_length)
frequency += self.header["probe_freq"]
fft[(frequency >= f_max) & (frequency <= f_min)] = 0.0
fft *= 1000.0
return frequency, fft
def fid2fft(fid, rate, frequencies, **kwargs):
"""
Process an FID by performing an FFT to yield the frequency domain
information. Kwargs are passed as additional processing options,
and are implemented as some case statements to ensure the settings
are valid (e.g. conforms to sampling rate, etc.)
:param fid: np.array corresponding to the FID intensity
:param rate: sampling rate in Hz
:param frequencies: np.array corresponding to the frequency bins
:param kwargs: signal processing options:
delay - delays the FID processing by setting the start
of the FID to zero
zeropad - Toggles whether or not the number of sampled
points is doubled to get artificially higher
resolution in the FFT
window - Various window functions provided by `scipy.signal`
exp - Specifies an exponential filter
filter - 2-tuple specifying the frequency cutoffs for a
band pass filter
:return: freq_df - pandas dataframe with the FFT spectrum
"""
# Remove DC
new_fid = fid - np.average(fid)
if "delay" in kwargs:
delay = int(kwargs["delay"] / (1.0 / rate) / 1e6)
new_fid[:delay] = 0.0
# Zero-pad the FID
if "zeropad" in kwargs:
if kwargs["zeropad"] is True:
# Pad the FID with zeros to get higher resolution
fid = np.append(new_fid, np.zeros(len(new_fid)))
# Since we've padded with zeros, we'll have to update the
# frequency array
frequencies = spsig.resample(frequencies, len(frequencies) * 2)
# Apply a window function to the FID
if "window" in kwargs:
if kwargs["window"] in spsig.windows.__all__:
new_fid *= spsig.get_window(kwargs["window"], new_fid.size)
# Apply an exponential filter on the FID
if "exp" in kwargs:
if kwargs["exp"] > 0.0:
new_fid *= spsig.exponential(len(new_fid), tau=kwargs["exp"])
# Apply a bandpass filter on the FID
if ("filter" in kwargs) and (len(kwargs["filter"]) == 2):
low, high = sorted(kwargs["filter"])
if low < high:
new_fid = apply_butter_filter(new_fid, low, high, rate)
# Perform the FFT
fft = np.fft.rfft(new_fid)
# Get the real part of the FFT, and only the non-duplicated side
real_fft = np.abs(fft[: int(len(new_fid) / 2)]) / len(new_fid) * 1e3
frequencies = spsig.resample(frequencies, real_fft.size)
# For some reason, resampling screws up the frequency ordering...
real_fft = real_fft[np.argsort(frequencies)]
frequencies = np.sort(frequencies)
# Package into a pandas dataframe
freq_df = pd.DataFrame({"Frequency (MHz)": frequencies, "Intensity": real_fft})
return freq_df
def butter_bandpass(low, high, rate, order=1):
"""
A modified version of the Butterworth bandpass filter described here,
adapted for use with the FID signal.
http://scipy-cookbook.readthedocs.io/items/ButterworthBandpass.html
The arguments are:
:param low The low frequency cut-off, given in kHz.
:param high The high frequency cut-off, given in kHz.
:param rate The sampling rate, given in Hz. From the FIDs, this means that
the inverse of the FID spacing is used.
:return bandpass window
"""
# Calculate the Nyquist frequency
nyq = 0.5 * (rate / (2.0 * np.pi))
low = (low * 1e3) / nyq
high = (high * 1e3) / nyq
if high > 1.0:
raise Exception("High frequency cut-off exceeds the Nyquist frequency.")
b, a = spsig.butter(order, [low, high], btype="band", analog=False)
return b, a
def apply_butter_filter(data, low, high, rate, order=1):
"""
A modified Butterworth bandpass filter, adapted from the Scipy cookbook.
The argument data supplies the FID, which then uses the scipy signal
processing function to apply the digital filter, and returns the filtered
FID.
See the `butter_bandpass` function for additional arguments.
"""
b, a = butter_bandpass(low, high, rate, order=order)
y = spsig.lfilter(b, a, data)
return y
def generate_ftb_line(frequency, shots, **kwargs):
""" Function that generates an FTB file for a list of
frequencies, plus categorization tests.
kwargs are passed as additional options for the ftb
batch. Keywords are:
magnet: bool
dipole: float
atten: int
skiptune: bool
drfreq: float
drpower: int
cal
parameters:
---------------
:param frequency: float for frequency in MHz
:param shots: int number of shots to integrate for
returns:
---------------
:return ftbline: str
"""
line = "ftm:{:.4f} shots:{}".format(frequency, shots)
for key, value in kwargs.items():
line += " {}:{}".format(key, value)
line += "\n"
return line
def neu_categorize_frequencies(frequencies, intensities=None, nshots=50, **kwargs):
"""
Routine to generate an FTB batch file for performing a series of tests
on frequencies.
"""
ftb_string = ""
if intensities:
norm_int = intensities / np.max(intensities)
shotcounts = np.round(nshots / norm_int).astype(int)
else:
shotcounts = np.full(len(frequencies), nshots, dtype=int)
# default settings for all stuff
param_dict = {
"dipole": 1.0,
"magnet": "false",
"drpower": "10",
"skiptune": "false",
}
param_dict.update(kwargs)
for freq, shot in zip(frequencies, shotcounts):
ftb_string += generate_ftb_str(freq, shot, **param_dict)
if "magnet" in kwargs:
param_dict["magnet"] = "true"
ftb_string += generate_ftb_str(freq, shot, **param_dict)
def categorize_frequencies(
frequencies,
nshots=50,
intensities=None,
power=None,
attn_list=None,
dipole=None,
attn=None,
magnet=False,
dr=False,
discharge=False,
):
"""
Function that will format an FT batch file to perform categorization
tests, with some flexibility on how certain tests are performed.
"""
ftb_str = ""
if intensities is None:
shots = np.full(len(frequencies), nshots, dtype=int)
else:
shots = np.sqrt(nshots / intensities).astype(int)
if dipole:
if attn is None:
# If dipole test requested, but no attenuation
# supplied do the default sweep
dipole_test = [0.01, 0.1, 1.0, 3.0, 5.0]
dipole_flag = "dipole"
else:
# Otherwise run specific attenuations
dipole_test = attn_list
dipole_flag = "atten"
if dr is True:
freq_list = combinations(frequencies, 2)
print(list(freq_list))
else:
freq_list = frequencies
# loop over each frequency and number of shots
for value, shotcount in zip(freq_list, shots):
if dr is True:
freq, dr_freq = value
else:
freq = value
# Generate normal observation
try:
freq = float(freq)
shotcount = int(shotcount)
if dr is True:
dr_freq = float(dr_freq)
ftb_str += generate_ftb_line(freq, shotcount, **{"skiptune": "false"})
if dr is True:
ftb_str += generate_ftb_line(
freq, shotcount, **{"skiptune": "true", "drfreq": dr_freq}
)
if dipole is True:
for dipole_value in dipole_test:
ftb_str += generate_ftb_line(
freq, shotcount, **{dipole_flag: dipole_value}
)
if magnet is True:
ftb_str += generate_ftb_line(freq, shotcount, **{"magnet": "true"})
if discharge is True:
# Toggle the discharge stack on and off
ftb_str += generate_ftb_line(
freq, shotcount, **{"pulse,1,enabled": "false"}
)
ftb_str += generate_ftb_line(
freq, shotcount, **{"pulse,1,enabled": "true"}
)
except ValueError:
print("Error with " + str(value))
return ftb_str
def calculate_integration_times(intensity, nshots=50):
"""
Method for calculating the expected integration time
in shot counts based on the intensity; either theoretical
line strengths or SNR.
parameters:
---------------
intensity - array of intensity metric; e.g. SNR
nshots - optional int number of shots used for the strongest line
returns:
---------------
shot_counts - array of shot counts for each frequency
"""
norm_int = intensity / np.max(intensity)
shot_counts = np.round(nshots / norm_int).astype(int)
return shot_counts
@dataclass
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220,
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220,
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1058,
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318,
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79,
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8,
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220,
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31878,
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8,
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62,
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35006,
281,
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319,
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284,
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10958,
13,
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220,
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389,
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1303,
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378,
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198,
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19998,
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45941,
13,
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220,
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13,
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13,
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1429,
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14631,
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1600,
366,
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1600,
366,
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324,
8973,
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1429,
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198,
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220,
220,
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220,
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220,
220,
220,
220,
1994,
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1988,
329,
1994,
11,
1988,
287,
2116,
13,
834,
11600,
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13,
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3419,
611,
1994,
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62,
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198,
220,
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1782,
198,
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1303,
3827,
13154,
351,
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220,
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1429,
62,
11600,
13,
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8,
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62,
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312,
796,
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13,
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13,
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312,
8,
198,
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13,
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49909,
17,
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11,
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13,
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13,
69,
312,
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11,
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11,
12429,
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62,
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198,
220,
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13,
69,
312,
62,
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796,
279,
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19778,
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357,
385,
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640,
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68,
21,
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62,
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312,
30072,
628,
220,
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220,
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62,
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11,
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62,
9521,
2599,
198,
220,
220,
220,
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37227,
198,
220,
220,
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220,
15553,
329,
13213,
286,
262,
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373,
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198,
220,
220,
220,
220,
220,
220,
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257,
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3128,
2837,
287,
1227,
14,
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14,
1941,
11,
287,
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5794,
198,
220,
220,
220,
220,
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220,
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14,
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2919,
329,
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860,
400,
11,
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13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
3128,
62,
9521,
25,
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262,
3726,
290,
886,
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13042,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
20512,
532,
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611,
1626,
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11,
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4306,
198,
220,
220,
220,
220,
220,
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37227,
198,
220,
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1949,
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220,
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220,
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1903,
796,
4818,
8079,
13,
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8079,
13,
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457,
524,
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58,
15,
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76,
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88,
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198,
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1903,
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4818,
8079,
13,
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16,
11,
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11,
352,
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198,
220,
220,
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1949,
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198,
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2739,
796,
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8079,
13,
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13,
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457,
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58,
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76,
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88,
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220,
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198,
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2739,
796,
4818,
8079,
13,
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24214,
11,
352,
11,
352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1903,
19841,
2116,
13,
4475,
19841,
2739,
628,
220,
220,
220,
825,
318,
62,
10378,
33342,
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944,
11,
1006,
11,
686,
72,
28,
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11,
42435,
28,
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2599,
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37227,
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15553,
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428,
20937,
318,
1342,
198,
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220,
220,
220,
220,
220,
220,
621,
326,
286,
1194,
9367,
13,
770,
318,
1760,
416,
257,
2829,
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198,
220,
220,
220,
220,
220,
220,
220,
286,
262,
2811,
286,
838,
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17509,
871,
287,
262,
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5444,
430,
13,
1002,
198,
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220,
220,
220,
220,
220,
220,
262,
1459,
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318,
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8157,
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4941,
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198,
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220,
220,
220,
220,
220,
2938,
42435,
5873,
11,
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340,
318,
366,
10378,
33342,
1911,
628,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
460,
307,
973,
284,
5004,
611,
257,
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611,
34069,
198,
220,
220,
220,
220,
220,
220,
220,
287,
10560,
14,
19726,
3262,
14,
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10136,
840,
592,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16926,
46,
532,
3494,
257,
33166,
44345,
1332,
286,
10524,
284,
5004,
611,
257,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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42435,
318,
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628,
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220,
220,
220,
220,
220,
1058,
17143,
1006,
25,
1218,
20937,
2134,
329,
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198,
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220,
220,
220,
220,
220,
220,
1058,
17143,
42435,
25,
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286,
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286,
262,
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198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
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532,
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6737,
287,
428,
20937,
318,
1342,
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621,
262,
4941,
198,
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220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
331,
62,
5420,
796,
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13,
4443,
6582,
14692,
5317,
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1,
4083,
27160,
198,
220,
220,
220,
220,
220,
220,
220,
331,
62,
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796,
2116,
13,
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1,
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198,
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220,
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220,
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2116,
13,
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62,
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80,
796,
1006,
13,
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13,
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198,
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220,
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220,
220,
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220,
2116,
13,
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312,
796,
1006,
13,
312,
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220,
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331,
62,
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58,
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72,
60,
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220,
220,
220,
220,
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1303,
770,
1595,
470,
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11,
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318,
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13,
198,
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1303,
442,
271,
80,
11,
279,
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796,
442,
271,
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198,
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220,
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1303,
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331,
62,
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331,
62,
5420,
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220,
220,
220,
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1303,
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611,
42435,
318,
6045,
25,
198,
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220,
220,
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264,
13495,
796,
45941,
13,
19282,
7,
88,
62,
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11,
16488,
28,
15,
8,
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1467,
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15,
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220,
220,
220,
220,
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2073,
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264,
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532,
264,
13495,
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220,
1441,
45941,
13,
16345,
7,
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62,
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11,
16488,
28,
15,
8,
19841,
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628,
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220,
220,
825,
41058,
62,
40546,
7,
944,
2599,
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220,
220,
220,
220,
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37227,
198,
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220,
220,
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13610,
257,
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306,
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13,
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347,
963,
2163,
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2854,
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340,
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37227,
198,
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220,
220,
220,
220,
220,
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2420,
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33351,
4522,
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27,
1671,
29,
34,
615,
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25,
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27,
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29,
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25,
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27,
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29,
13436,
3262,
25,
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27,
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29,
8086,
77,
25,
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18982,
7,
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2116,
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220,
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220,
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19726,
3262,
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198,
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220,
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220,
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2116,
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66,
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62,
41769,
11,
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220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
12854,
796,
467,
13,
3351,
1436,
4743,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
28,
37659,
13,
21602,
10223,
7,
944,
13,
312,
11,
2116,
13,
312,
1343,
352,
11,
18896,
7,
944,
13,
4443,
6582,
14692,
5317,
6377,
8973,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
28,
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13,
4443,
6582,
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5317,
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33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
2420,
28,
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11,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
18364,
28,
4895,
8043,
1298,
366,
81,
22296,
7,
3559,
11,
15187,
11,
19782,
16725,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20599,
10951,
2625,
5239,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
12854,
628,
220,
220,
220,
825,
4197,
62,
66,
615,
414,
7,
944,
11,
7110,
28,
17821,
11,
15942,
577,
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13,
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220,
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49909,
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22468,
7,
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8,
329,
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1366,
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11,
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28,
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2245,
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16,
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3524,
7718,
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12,
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220,
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21048,
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330,
31172,
13,
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220,
40117,
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220,
220,
24200,
438,
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220,
49909,
532,
399,
32152,
352,
35,
7177,
198,
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220,
220,
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220,
220,
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15690,
4769,
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3815,
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31050,
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15744,
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400,
16936,
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362,
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220,
220,
4324,
532,
965,
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220,
220,
220,
220,
220,
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18291,
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4324,
2163,
973,
284,
1429,
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2389,
13,
2896,
13185,
284,
3091,
7718,
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543,
318,
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25431,
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383,
3891,
286,
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4324,
5499,
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307,
1043,
379,
25,
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220,
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3740,
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21602,
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15,
13,
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11,
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4299,
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17,
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7,
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11,
2494,
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19998,
11,
12429,
46265,
22046,
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37227,
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1321,
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290,
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617,
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220,
389,
4938,
357,
68,
13,
70,
13,
17216,
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284,
19232,
2494,
11,
3503,
2014,
628,
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220,
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25,
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1058,
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25,
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220,
220,
1058,
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19998,
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13,
18747,
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41701,
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1058,
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86,
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3689,
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220,
220,
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220,
220,
220,
220,
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5711,
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262,
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2389,
7587,
416,
4634,
262,
923,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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286,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
263,
404,
324,
532,
309,
48549,
1771,
393,
407,
262,
1271,
286,
35846,
198,
220,
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220,
220,
220,
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220,
220,
220,
220,
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220,
220,
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220,
220,
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220,
220,
220,
220,
220,
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220,
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2173,
318,
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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,
6323,
287,
262,
376,
9792,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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4324,
532,
26386,
4324,
5499,
2810,
416,
4600,
1416,
541,
88,
13,
12683,
282,
63,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1033,
532,
18291,
6945,
281,
39682,
8106,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8106,
532,
362,
12,
83,
29291,
31577,
262,
8373,
2005,
8210,
329,
257,
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,
4097,
1208,
8106,
198,
220,
220,
220,
1058,
7783,
25,
2030,
80,
62,
7568,
532,
19798,
292,
1366,
14535,
351,
262,
376,
9792,
10958,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
17220,
6257,
198,
220,
220,
220,
649,
62,
69,
312,
796,
49909,
532,
45941,
13,
23913,
7,
69,
312,
8,
198,
220,
220,
220,
611,
366,
40850,
1,
287,
479,
86,
22046,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5711,
796,
493,
7,
46265,
22046,
14692,
40850,
8973,
1220,
357,
16,
13,
15,
1220,
2494,
8,
1220,
352,
68,
21,
8,
198,
220,
220,
220,
220,
220,
220,
220,
649,
62,
69,
312,
58,
25,
40850,
60,
796,
657,
13,
15,
198,
220,
220,
220,
1303,
12169,
12,
15636,
262,
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2389,
198,
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220,
220,
611,
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9107,
404,
324,
1,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
15744,
262,
376,
2389,
351,
1976,
27498,
284,
651,
2440,
6323,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49909,
796,
45941,
13,
33295,
7,
3605,
62,
69,
312,
11,
45941,
13,
9107,
418,
7,
11925,
7,
3605,
62,
69,
312,
22305,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4619,
356,
1053,
44582,
351,
1976,
27498,
11,
356,
1183,
423,
284,
4296,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8373,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19998,
796,
599,
82,
328,
13,
411,
1403,
7,
69,
8897,
3976,
11,
18896,
7,
69,
8897,
3976,
8,
1635,
362,
8,
198,
220,
220,
220,
1303,
27967,
257,
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2163,
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11201,
8973,
8,
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1303,
27967,
257,
4097,
6603,
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319,
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8,
290,
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7,
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22046,
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8973,
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6624,
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649,
62,
69,
312,
796,
4174,
62,
4360,
353,
62,
24455,
7,
3605,
62,
69,
312,
11,
1877,
11,
1029,
11,
2494,
8,
198,
220,
220,
220,
1303,
35006,
262,
376,
9792,
198,
220,
220,
220,
277,
701,
796,
45941,
13,
487,
83,
13,
81,
487,
83,
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3605,
62,
69,
312,
8,
198,
220,
220,
220,
1303,
3497,
262,
1103,
636,
286,
262,
376,
9792,
11,
290,
691,
262,
1729,
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646,
489,
3474,
1735,
198,
220,
220,
220,
1103,
62,
487,
83,
796,
45941,
13,
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487,
83,
58,
25,
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11925,
7,
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62,
69,
312,
8,
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362,
8,
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18896,
7,
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62,
69,
312,
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1635,
352,
68,
18,
198,
220,
220,
220,
19998,
796,
599,
82,
328,
13,
411,
1403,
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69,
8897,
3976,
11,
1103,
62,
487,
83,
13,
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198,
220,
220,
220,
1303,
1114,
617,
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11,
581,
321,
11347,
23742,
510,
262,
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16216,
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198,
220,
220,
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1103,
62,
487,
83,
796,
1103,
62,
487,
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58,
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13,
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3976,
15437,
198,
220,
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19998,
796,
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13,
30619,
7,
69,
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3976,
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198,
220,
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1303,
15717,
656,
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19798,
292,
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14535,
198,
220,
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2030,
80,
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796,
279,
67,
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19778,
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357,
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19998,
11,
366,
5317,
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1298,
1103,
62,
487,
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30072,
198,
220,
220,
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1441,
2030,
80,
62,
7568,
628,
198,
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9215,
62,
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11,
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11,
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11,
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28,
16,
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198,
220,
220,
220,
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198,
220,
220,
220,
220,
220,
220,
220,
317,
9518,
2196,
286,
262,
18971,
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4097,
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3417,
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11,
198,
220,
220,
220,
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220,
220,
220,
16573,
329,
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351,
262,
376,
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13,
198,
220,
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220,
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198,
220,
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383,
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389,
25,
628,
220,
220,
220,
220,
220,
220,
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1058,
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383,
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2005,
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11,
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287,
37597,
13,
198,
220,
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220,
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1058,
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1029,
383,
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2005,
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11,
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287,
37597,
13,
198,
220,
220,
220,
220,
220,
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1058,
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2494,
383,
19232,
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11,
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287,
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13,
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262,
376,
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428,
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326,
198,
220,
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31050,
318,
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13,
198,
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1058,
7783,
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198,
220,
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198,
220,
220,
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1303,
27131,
378,
262,
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30062,
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198,
220,
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299,
88,
80,
796,
657,
13,
20,
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13,
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198,
220,
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1877,
796,
357,
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1635,
352,
68,
18,
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80,
198,
220,
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1029,
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68,
18,
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80,
198,
220,
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611,
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13,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
35528,
7203,
11922,
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2005,
12,
2364,
21695,
262,
17735,
30062,
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19570,
198,
220,
220,
220,
275,
11,
257,
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599,
82,
328,
13,
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353,
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11,
685,
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11,
1029,
4357,
275,
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2625,
3903,
1600,
15075,
28,
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198,
220,
220,
220,
1441,
275,
11,
257,
628,
198,
4299,
4174,
62,
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11,
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11,
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11,
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11,
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28,
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198,
220,
220,
220,
37227,
198,
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317,
9518,
18971,
9268,
4097,
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11,
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422,
262,
1446,
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88,
4255,
2070,
13,
628,
220,
220,
220,
220,
220,
220,
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383,
4578,
1366,
9416,
262,
376,
2389,
11,
543,
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3544,
262,
629,
541,
88,
6737,
198,
220,
220,
220,
220,
220,
220,
220,
7587,
2163,
284,
4174,
262,
4875,
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11,
290,
5860,
262,
29083,
198,
220,
220,
220,
220,
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220,
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376,
2389,
13,
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220,
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4091,
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63,
2163,
329,
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13,
198,
220,
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198,
220,
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275,
11,
257,
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9215,
62,
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11,
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28,
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198,
220,
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331,
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82,
328,
13,
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220,
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1441,
331,
628,
198,
198,
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62,
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65,
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11,
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11,
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2599,
198,
220,
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15553,
326,
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281,
19446,
33,
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329,
257,
1351,
286,
198,
220,
220,
220,
220,
220,
220,
220,
19998,
11,
5556,
17851,
1634,
5254,
13,
628,
220,
220,
220,
220,
220,
220,
220,
479,
86,
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389,
3804,
355,
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329,
262,
10117,
65,
198,
220,
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15458,
13,
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10879,
389,
25,
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220,
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220,
220,
220,
220,
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19972,
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20512,
198,
220,
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19550,
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12178,
198,
220,
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220,
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31919,
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493,
198,
220,
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1341,
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25,
20512,
198,
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220,
1553,
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80,
25,
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198,
220,
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220,
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1553,
6477,
25,
493,
198,
220,
220,
220,
220,
220,
220,
220,
2386,
628,
220,
220,
220,
220,
220,
220,
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10007,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
24305,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
8373,
25,
12178,
329,
8373,
287,
19805,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
6934,
25,
493,
1271,
286,
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284,
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329,
628,
220,
220,
220,
220,
220,
220,
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5860,
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198,
220,
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220,
24305,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
10117,
65,
1370,
25,
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198,
220,
220,
220,
37227,
198,
220,
220,
220,
1627,
796,
366,
701,
76,
29164,
25,
13,
19,
69,
92,
6934,
29164,
92,
1911,
18982,
7,
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11,
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198,
220,
220,
220,
329,
1994,
11,
1988,
287,
479,
86,
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13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1627,
15853,
366,
23884,
29164,
92,
1911,
18982,
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2539,
11,
1988,
8,
198,
220,
220,
220,
1627,
15853,
37082,
77,
1,
198,
220,
220,
220,
1441,
1627,
628,
198,
4299,
497,
84,
62,
66,
47467,
1096,
62,
69,
8897,
3976,
7,
69,
8897,
3976,
11,
17509,
871,
28,
14202,
11,
299,
20910,
28,
1120,
11,
12429,
46265,
22046,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
371,
28399,
284,
7716,
281,
19446,
33,
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2393,
329,
9489,
257,
2168,
286,
5254,
198,
220,
220,
220,
220,
220,
220,
220,
319,
19998,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
10117,
65,
62,
8841,
796,
13538,
198,
220,
220,
220,
611,
17509,
871,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2593,
62,
600,
796,
17509,
871,
1220,
45941,
13,
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600,
641,
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8,
198,
220,
220,
220,
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2823,
9127,
82,
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13,
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1220,
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62,
600,
737,
459,
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198,
220,
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2073,
25,
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2823,
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82,
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13,
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69,
8897,
3976,
828,
299,
20910,
11,
288,
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28,
600,
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628,
220,
220,
220,
1303,
4277,
6460,
329,
477,
3404,
198,
220,
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62,
11600,
796,
1391,
198,
220,
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67,
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352,
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19726,
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1600,
198,
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457,
1726,
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198,
220,
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1782,
628,
220,
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62,
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13,
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8,
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329,
2030,
80,
11,
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287,
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11,
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82,
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198,
220,
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10117,
65,
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15853,
7716,
62,
701,
65,
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80,
11,
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11,
12429,
17143,
62,
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198,
220,
220,
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220,
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611,
366,
19726,
3262,
1,
287,
479,
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25,
198,
220,
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220,
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5772,
62,
11600,
14692,
19726,
3262,
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796,
366,
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198,
220,
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10117,
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80,
11,
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11,
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17143,
62,
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8,
628,
198,
4299,
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1096,
62,
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198,
220,
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19998,
11,
198,
220,
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299,
20910,
28,
1120,
11,
198,
220,
220,
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17509,
871,
28,
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11,
198,
220,
220,
220,
1176,
28,
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198,
220,
220,
220,
708,
77,
62,
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28,
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198,
220,
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19550,
2305,
28,
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11,
198,
220,
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708,
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28,
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198,
220,
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19972,
28,
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220,
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1553,
28,
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11,
198,
220,
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17655,
28,
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198,
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220,
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37227,
198,
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15553,
326,
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5794,
281,
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284,
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1634,
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351,
617,
13688,
319,
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198,
220,
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10117,
65,
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2536,
796,
13538,
198,
220,
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220,
611,
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871,
318,
6045,
25,
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6934,
796,
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13,
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288,
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6934,
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13,
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60,
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198,
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198
] | 2.307527 | 12,555 |
import json
from dataclasses import dataclass
import omitempty
from dnsimple.struct import Struct
class DomainRenewRequest(dict):
"""DomainRenewRequest represents the attributes you can pass to a renew API request."""
@dataclass
class DomainRenewal(Struct):
"""Represents the result of a domain renewal call."""
id = None
"""The domain registration ID in DNSimple"""
domain_id = None
"""The associated domain ID"""
state = None
"""The state of the renewal"""
period = None
"""The number of years the domain was registered for"""
created_at = None
"""When the domain renewal was created in DNSimple"""
updated_at = None
"""When the domain renewal was last updated in DNSimple"""
| [
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22901,
373,
938,
6153,
287,
18538,
320,
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] | 3.337838 | 222 |
class RequestAdapter(object):
"""
RequestAdapters bridge transmute's
representation of a request, with the framework's
implementation.
implement the unimplemented methods.
"""
@property
def body(self):
""" return the request body. """
raise NotImplementedError()
def _get_framework_args(self):
"""
often, a framework provides specific variables that are passed
into the handler function (e.g. the request object in
aiohttp). return a dictionary of these arguments, which will be
added to the function arguments if they appear.
"""
raise NotImplementedError()
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] | 2.938596 | 228 |
# python3
""" Task: Count the number of inversions of a given sequence """
tot_count = 0
n = int ( input () )
seq = [ int ( i ) for i in input ().split () ]
mergesort ( seq )
print ( tot_count )
| [
2,
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18,
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419,
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62,
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1267,
198
] | 2.985075 | 67 |
import numpy as np
import scipy.misc
import os
import time
# from PIL import Image
DATA_DIR = '/home/ubuntu/lsun/bedrooms/'
NEW_DATA_DIR = '/home/ubuntu/lsun/bedrooms_128/'
# with open(DATA_DIR+'files.txt', 'r') as f:
# files = [l[:-1] for l in f]
# # images = np.zeros((batch_size, 3, 256, 256), dtype='int32')
# random_state = np.random.RandomState(42)
# random_state.shuffle(files)
# z = 1729468
# for i, path in enumerate(files):
# if i < 1729500:
# continue
# try:
# image = scipy.misc.imread(
# os.path.normpath(os.path.join(DATA_DIR, path))
# )
# # try:
# # image = image.transpose(2,0,1)
# offset_y = (image.shape[0]-256)/2
# offset_x = (image.shape[1]-256)/2
# image = image[offset_y:offset_y+256, offset_x:offset_x+256]
# image = image[::2,::2]+image[1::2,::2]+image[::2,1::2]+image[1::2,1::2]
# image = image / 4
# # image = image.astype('int32')
# # im = Image.fromarray(image)
# # p = os.path.normpath(os.path.join(NEW_DATA_DIR, path))
# # try:
# # os.makedirs(os.path.dirname(p))
# # except:
# # pass
# scipy.misc.imsave(NEW_DATA_DIR+'{}.jpg'.format(z), image)
# # im.save(p[:-4]+'jpg')
# if z % 100 == 0:
# print z
# z += 1
# except:
# print "skip"
# # if i > 0 and i % batch_size == 0:
# # if downscale:
# # downscaled_images = images[:,:,::2,::2] + images[:,:,1::2,::2] + images[:,:,::2,1::2] + images[:,:,1::2,1::2]
# # downscaled_images = downscaled_images / 4.
# # yield (downscaled_images.astype('int32'),)
# # else:
# # yield (images,)
# # except Exception as ex:
# # print ex
# # print "warning data preprocess failed for path {}".format(path)
if __name__ == '__main__':
train_gen = load(64)
t0 = time.time()
for i, batch in enumerate(train_gen(), start=1):
print "{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0])
if i == 1000:
break
t0 = time.time()
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220,
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220,
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256,
15,
796,
640,
13,
2435,
3419,
198
] | 1.919326 | 1,128 |
from django.db.utils import IntegrityError
from django.db.models import Q
from rest_framework import serializers
from core.models import FavoriteThing
from core.models import Category
from .helper import reorder_rankings, reorder_rankings_subtract
| [
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] | 3.772727 | 66 |
# Tests for the Genomics Data Quality Pipeline
import mock, datetime, pytz
from rdr_service import clock
from rdr_service.api_util import open_cloud_file
from rdr_service.genomic_enums import GenomicJob, GenomicSubProcessStatus, GenomicSubProcessResult, \
GenomicManifestTypes, GenomicIncidentCode
from tests.helpers.unittest_base import BaseTestCase
from rdr_service.genomic.genomic_job_controller import DataQualityJobController
from rdr_service.genomic.genomic_data_quality_components import ReportingComponent
| [
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] | 3.503356 | 149 |
# Time limit exceeded
while True:
try:
A = input()
B = input()
lA = len(A)
lB = len(B)
biggest = ""
shortest = ""
lshortest = 0
if max(lA, lB) == lA:
biggest, shortest = A, B
lbiggest = lA
lshortest = lB
else:
biggest, shortest = B, A
lbiggest = lB
lshortest = lA
bSub = 0
currentSub = 0
for k in range(lshortest):
for w in range(lbiggest):
if shortest[k] == biggest[w]:
currentSub = 1
q = w+1
for p in range(k+1,lshortest):
if q >= lbiggest:
break
if shortest[p] == biggest[q]:
currentSub += 1
q += 1
else:
break
if currentSub >= bSub:
bSub = currentSub
print(bSub)
except:
break | [
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197,
198,
197,
16341,
25,
198,
197,
197,
9032
] | 1.929919 | 371 |
import os, urllib, requests, json
priority = 1
| [
11748,
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571,
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11,
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] | 3.133333 | 15 |
list1 = [1, 4, 8, 2, 9]
print len(list1)
print max(list1), min(list1)
print list1[-2]
print list1[-5:3]
print list1[-3:]
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] | 2 | 62 |
from matplotlib import pyplot as plt
from script import sales_times1
from script import sales_times2
# normed=True This command divides the height of each column by
# a constant such that the total shaded area of the histogram sums
# to 1
plt.hist(sales_times1, bins=20, alpha=0.4, normed=True)
plt.hist(sales_times2, bins=20, alpha=0.4, normed=True)
plt.show()
#%%
from matplotlib import pyplot as plt
exam_scores1 = [62.58, 67.63, 81.37, 52.53, 62.98, 72.15, 59.05, 73.85, 97.24, 76.81, 89.34, 74.44, 68.52, 85.13, 90.75, 70.29, 75.62, 85.38, 77.82, 98.31, 79.08, 61.72, 71.33, 80.77, 80.31, 78.16, 61.15, 64.99, 72.67, 78.94]
exam_scores2 = [72.38, 71.28, 79.24, 83.86, 84.42, 79.38, 75.51, 76.63, 81.48,78.81,79.23,74.38,79.27,81.07,75.42,90.35,82.93,86.74,81.33,95.1,86.57,83.66,85.58,81.87,92.14,72.15,91.64,74.21,89.04,76.54,81.9,96.5,80.05,74.77,72.26,73.23,92.6,66.22,70.09,77.2]
# Make your plot here
plt.figure(figsize=(10,8))
plt.hist(exam_scores1,bins=12,normed=True,
histtype='step',linewidth=2)
plt.hist(exam_scores2,bins=12,normed=True,
histtype='step',linewidth=2)
legends=["1st Yr Teaching","2nd Yr Teaching"]
plt.legend(legends)
plt.title("Final Exam Score Distribution")
plt.xlabel("Percentage")
plt.ylabel("Frequency")
plt.savefig("my_histogram.png")
#%%
import numpy as np
import pandas as pd
# Import matplotlib pyplot
from matplotlib import pyplot as plt
# Read in transactions data
greatest_books = pd.read_csv("top-hundred-books.csv")
# Save transaction times to a separate numpy array
author_ages = greatest_books['Ages']
# Use numpy to calculate the average age of the top 100 authors
average_age = np.average(author_ages)
print("The average age of the 100 greatest authors, according to Le Monde is: " + str(average_age))
# Plot the figure
plt.hist(author_ages, range=(10, 80), bins=14, edgecolor='black')
plt.title("Age of Top 100 Novel Authors at Publication")
plt.xlabel("Publication Age")
plt.ylabel("Count")
plt.axvline(average_age, color='r', linestyle='solid', linewidth=2, label="Mean")
plt.legend()
plt.show()
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437,
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] | 2.376 | 875 |
# print(123456)
# print('Kaic', 'Pierre', 'Outra Coisa')
# print('Kaic', 'Pierre', sep='-', end='')
# print('Testando', 'Outras', 'Coisas', sep='-', end='')
print('428', '330', '048', sep='.', end='-')
print('93')
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] | 2.404494 | 89 |
import sys
sys.path.append("../")
# KoBERT 모델
import config
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import gluonnlp as nlp
from tqdm import tqdm, tqdm_notebook
from KoBERT.kobert.utils import get_tokenizer
from KoBERT.kobert.pytorch_kobert import get_pytorch_kobert_model
from transformers import AdamW
# from transformers.optimization import WarmupLinearSchedule
from transformers import get_linear_schedule_with_warmup
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
bertmodel, vocab = get_pytorch_kobert_model()
# 토크나이저 메서드를 tokenizer에 호출
# 코퍼스를 토큰으로 만드는 과정을 수행, 이 때 토크나이저는 kobert 패키지에 있는 get_tokenizer()를 사용하고,
# 토큰화를 위해 필요한 단어 사전은 kobert의 vocab을 사용함.
# uncased로 투입해야 하므로 lower = False
tokenizer = get_tokenizer()
tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower = False)
print(f'device using: {device}')
model_config=config.model_config
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0, path='checkpoint.pt', trace_func=print):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 7
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
delta (float): Minimum change in the monitored quantity to qualify as an improvement.
Default: 0
path (str): Path for the checkpoint to be saved to.
Default: 'checkpoint.pt'
trace_func (function): trace print function.
Default: print
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
self.delta = delta
self.path = path
self.trace_func = trace_func
def save_checkpoint(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
self.trace_func(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), self.path)
self.val_loss_min = val_loss
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] | 2.075623 | 1,243 |
import urllib.parse
from .saucenao import get_saucenao_detail, SauceNAOError
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] | 2.724138 | 29 |
A = True
B = False
print(A and B)
print(A or B)
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] | 2.181818 | 22 |
#!/usr/bin/env python
# Author : Pierre Schnizer
"""
Collection of Callbacks systems for pygsl. They follow the GSL definitions as
close as possible. Instead os a struct python classes are used.
All solvers accept a C void pointer, which is passed to the callback. In Pygsl
this is an abitrary python object. See the doc strings of the various classes
for further detail.
"""
from . import _callback
class gsl_function(_gsl_function):
"""
This class defines the callbacks known as gsl_function to
gsl.
e.g to supply the function f:
def f(x, params):
a = params[0]
b = parmas[1]
c = params[3]
return a * x ** 2 + b * x + c
to some solver, use
function = gsl_function(f, params)
"""
initfunc = _callback.gsl_function_init
freefunc = _callback.gsl_function_free
class gsl_function_fdf(_gsl_function_fdf):
"""
This class defines the callbacks known as gsl_function_fdf to
gsl.
e.g to supply the function f:
def f(x, None):
return exp(2 * x)
def df(x, None):
return 2 * exp(2 * x)
def fdf(x, None):
myf = f(x, None)
mydf = df(x, None)
return myf, mydf
to some solver, accepting gsl_function_fdf, use
function = gsl_function_fdf(f, df, fdf, params)
"""
initfunc = _callback.gsl_function_init_fdf
freefunc = _callback.gsl_function_free_fdf
class gsl_multiroot_function(_gsl_function):
"""
This class defines the callbacks for gsl_multiroot_function.
To supply the function rosenbrock define the function:
def rosenbrock_f(x, params):
a = params[0]
b = params[1]
y = copy.copy(x)
y[0] = a * (1 - x[0])
y[1] = b * (x[1] - x[0] * x[0])
return y
sys = multiroots.gsl_multiroot_function(rosenbrock_f, params, 2)
"""
initfunc = _callback.gsl_multiroot_function_init
freefunc = _callback.gsl_multiroot_function_free
class gsl_multiroot_function_fdf(_gsl_function_fdf):
"""
This class defines the callbacks for gsl_multiroot_function.
To supply the function rosenbrock define the functions:
def rosenbrock_f(x, params):
a = params[0]
b = params[1]
y = copy.copy(x)
y[0] = a * (1 - x[0])
y[1] = b * (x[1] - x[0] * x[0])
return y
def rosenbrock_df(x, params):
a = params[0]
b = params[1]
df = Numeric.zeros((x.shape[0], x.shape[0]), Numeric.Float)
df[0,0] = -a
df[0,1] = 0
df[1,0] = -2 * b * x[0]
df[1,1] = b
return df
def rosenbrock_fdf(x, params):
f = rosenbrock_f(x, params)
df = rosenbrock_df(x, params)
return f, df
# dimension of x
size = 2
sys = multiroots.gsl_multiroot_function(rosenbrock_f, rosenbrock_df,
rosenbrock_fdf, params, size)
"""
initfunc = _callback.gsl_multiroot_function_init_fdf
freefunc = _callback.gsl_multiroot_function_free_fdf
class gsl_multifit_function(_gsl_function):
"""
This class defines the callbacks for gsl_multimin_function.
To fit a exponential function to data write the following function:
def exp_f(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t= params[0]
yi = params[1]
sigma = params[2]
Yi = A * exp(-lambda_ * t) + b
f = yi - Yi / sigma
return f
# Number of data samples
n = len(data)
# Number of paramters
p = 3
multifit_nlin.gsl_multifit_function(exp_f, data, n, p)
"""
initfunc = _callback.gsl_multifit_function_init
freefunc = _callback.gsl_multifit_function_free
class gsl_multifit_function_fdf(_gsl_function_fdf):
"""
This class defines the callbacks for gsl_multimin_function.
def exp_f(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t= params[0]
yi = params[1]
sigma = params[2]
Yi = A * exp(-lambda_ * t) + b
f = yi - Yi / sigma
return f
def exp_df(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t= params[0]
yi = params[1]
sigma = params[2]
e = exp(-lambda_ * t)
e_s = e/sigma
df = Numeric.array((e_s, -t * A * e_s, 1/sigma))
df = Numeric.transpose(df)
print df.shape
return df
def exp_fdf(x, params):
f = exp_f(x, params)
df = exp_df(x, params)
return f, df
# Number of data samples
n = len(data)
# Number of paramters
p = 3
multifit_nlin.gsl_multifit_function_fdf(exp_f, exp_df, exp_fdf, data, n, p)
"""
initfunc = _callback.gsl_multifit_function_init_fdf
freefunc = _callback.gsl_multifit_function_free_fdf
class gsl_multimin_function(gsl_multiroot_function):
"""
This class defines the callbacks for gsl_multimin_function.
The following example function defines a simple paraboloid with two
parameters.
To supply the system define the function:
def my_f(v, params):
x = v[0]
y = v[1]
dp = params
t1 = (x - dp[0])
t2 = (y - dp[1])
f = 10.0 * t1 * t1 + 20.0 * t2 * t2 + 30.0
return f
# dimension of x
size = 2
sys = multimin.gsl_multifit_function(my_f, params, 2)
"""
initfunc = _callback.gsl_multimin_function_init
freefunc = _callback.gsl_multimin_function_free
class gsl_multimin_function_fdf(gsl_multiroot_function_fdf):
"""
This class defines the callbacks for gsl_multimin_function_fdf.
The following example function defines a simple paraboloid with two
parameters.
To supply the system define the function:
def my_f(v, params):
x = v[0]
y = v[1]
dp = params
t1 = (x - dp[0])
t2 = (y - dp[1])
f = 10.0 * t1 * t1 + 20.0 * t2 * t2 + 30.0
return f
def my_df(v, params):
x = v[0]
y = v[1]
df = Numeric.zeros(v.shape, Numeric.Float)
dp = params
df[0] = 20. * (x - dp[0])
df[1] = 40. * (y - dp[1])
return df
def my_fdf(v, params):
f = my_f(v, params)
df = my_df(v,params)
return f, df
# dimension of x
size = 2
sys = multimin.gsl_multifit_function(my_f, my_df, my_fdf, params, size)
"""
initfunc = _callback.gsl_multimin_function_init_fdf
freefunc = _callback.gsl_multimin_function_free_fdf
class gsl_monte_function(gsl_multiroot_function):
"""
This class defines the callbacks for gsl_monte_function.
"""
initfunc = _callback.gsl_monte_function_init
freefunc = _callback.gsl_monte_function_free
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] | 2.066425 | 3,312 |
import argparse
if __name__ == "__main__":
main() | [
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] | 2.545455 | 22 |
from collections import Counter
def partial_digest(distances):
'''Returns a set whose positive pairwise differences generate 'distances'.'''
# Initialize variables.
X = {0}
width = max(distances)
# Create lambda functions for multiset operations.
new_dist = lambda y, S: Counter(abs(y-s) for s in S)
containment = lambda a, b: all(a[x] <= b[x] for x in a)
# Create the multiset which generates 'distances'.
while len(distances) > 0:
y = max(distances)
if containment(new_dist(y, X), distances):
X |= {y}
distances -= new_dist(y, X)
else:
X |= {width - y}
distances -= new_dist(width - y, X)
return X
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Read the input data.
with open('data/data.dat') as input_data:
distances = Counter(map(int,input_data.read().strip().split()))
# Get the partial digest.
X = sorted(list(partial_digest(distances)))
# Print and save the answer.
print ' '.join(map(str, X))
if __name__ == '__main__':
main() | [
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6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419
] | 2.501109 | 451 |
if __name__ == '__main__':
text = input("Give words: ")
print(pig_latin(text))
| [
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7,
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62,
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198
] | 2.225 | 40 |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 15 13:35:23 2018
@author: Victor Onink
Here we create a figure that has the 24h, and the 3h flow field densities
for the North Pacific
"""
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from scipy import io
import pandas as pd
# cbar=my_map.colorbar(density)
# cbar.ax.tick_params(labelsize=12)
# cbar.set_label("Plastic Counts ($10^{-3}$ # km$^{-2}$)", rotation=90,fontsize=12)
#%%
location='D:\Desktop\Thesis\ParcelsFigData\Data\North Pacific\OutputFiles\Onink et al\Densities/'
File=['NorthPacificTotalDensity24h','NorthPacificStokesTotalDensity24h',
'NorthPacificTotalDensity3h','NorthPacificStokesTotalDensity3h']
axeslabelsize=14
textsize=12
fig,axes=plt.subplots(nrows=2, ncols=1,figsize=(10*2,8*1))
for i in range(len(File)):
density=np.load(location+File[i])
density[np.isnan(density)]=0
meanFinalYear=np.sum(density[-183:,:,:]/density[-183:,:,:].shape[0],axis=0)
meanFinalYear[meanFinalYear==0]=np.nan
latD=np.linspace(-80,80,160)
lonD=np.linspace(0,359,360)
plt.subplot(2,2,i+1)
density=plotDensity(i,lonD,latD,meanFinalYear)
fig.subplots_adjust(right=0.9)
cbar_ax = fig.add_axes([0.93, 0.12, 0.02, 0.74])
cbar=fig.colorbar(density,cax=cbar_ax)
cbar.ax.tick_params(labelsize=textsize)
cbar.set_label("Plastic Counts ($10^{-3}$ # km$^{-2}$)", rotation=90,fontsize=axeslabelsize)
cbar.ax.set_yticklabels(['<0.1','0.3','0.5','0.7','0.9','1.1','1.3','1.5','1.7','1.9<'])
plt.subplots_adjust(wspace=0.06)
plt.savefig('D:\Desktop\Thesis\ParcelsFigData\Data\North Pacific\Figures\NorthPacificTimeStepDensities.jpg',
bbox_inches='tight')
| [
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] | 2.212005 | 783 |
from settings import *
BASE_URL = os.getenv('OPENDUTY_BASE_URL', "http://localhost")
XMPP_SETTINGS = {
'user': os.getenv('OPENDUTY_XMPP_USER'),
'password': os.getenv('OPENDUTY_XMPP_PASS'),
'server': os.getenv('OPENDUTY_XMPP_SERVER', 'xmpp'),
'port': os.getenv('OPENDUTY_XMPP_PORT', 5222),
}
EMAIL_SETTINGS = {
'user': os.getenv('OPENDUTY_EMAIL_USER'),
'password': os.getenv('OPENDUTY_EMAIL_PASS'),
}
'''
TWILIO_SETTINGS = {
'SID': "TWILIO_ACCOUNT_SID",
'token': "TWILIO_ACCOUNT_TOKEN",
'phone_number': "your_twilio_phone_number",
'sms_number': "your_twilio_sms_number",
'twiml_url': "http://www.website.org/voice.xml"
}
'''
SLACK_SETTINGS = {
'apikey': os.getenv('OPENDUTY_SLACK_APIKEY', "YOUR_SLACK_API_KEY")
}
'''
PROWL_SETTINGS = {
'priority': 0
'application': 'openduty'
}
'''
DATABASES = {
'default': {
'ENGINE': os.getenv('OPENDUTY_DATABASE_ENGINE', 'django.db.backends.mysql'),
'NAME': os.getenv('OPENDUTY_DATABASE_NAME', 'openduty'),
'USER': os.getenv('OPENDUTY_DATABASE_USER', 'openduty'),
'PASSWORD': os.getenv('OPENDUTY_DATABASE_PASS', 'dutyfree'),
'HOST': os.getenv('OPENDUTY_DATABASE_HOST', 'db'),
'PORT': os.getenv('OPENDUTY_DATABASE_PORT', '3306')
}
}
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.getenv('OPENDUTY_SECRET_KEY', 'yoursecretkey')
ALLOWED_HOSTS = ['your.dutyfree.host']
DEBUG = os.getenv('OPENDUTY_DEBUG', False)
TEMPLATE_DEBUG = os.getenv('OPENDUTY_TEMPLATE_DEBUG', False)
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] | 2.086782 | 749 |
# Conventional Machine Learning Algorithms
# Test Script for Class of "NaiveBayes".
# Author: Qixun Qu
# Create on: 2018/04/24
# Modify on: 2018/04/25
# ,,, ,,,
# ;" '; ;' ",
# ; @.ss$$$$$$s.@ ;
# `s$$$$$$$$$$$$$$$'
# $$$$$$$$$$$$$$$$$$
# $$$$P""Y$$$Y""W$$$$$
# $$$$ p"$$$"q $$$$$
# $$$$ .$$$$$. $$$$'
# $$$DaU$$O$$DaU$$$'
# '$$$$'.^.'$$$$'
# '&$$$$$&'
from __future__ import division
from __future__ import print_function
from utils import *
from NaiveBayes import *
from sklearn.datasets import make_hastie_10_2
# Basic settings
random_state = 9527
n_samples = 10000
test_size = 0.2
# Generate Dataset for training and testing
# Obtain all samples
X, y = make_hastie_10_2(n_samples=n_samples,
random_state=random_state)
# Split dataset
X_train, y_train, X_test, y_test = split_dataset(X, y, test_size,
random_state)
# Normalize dataset
X_train_scaled, X_test_scaled = scale_dataset(X_train, X_test)
# Train Gaussian Naive Bayes Classifier
nb = NaiveBayes(alpha=1)
nb.fit(X_train_scaled, y_train, cont_feat_idx="all")
# Predict test set and evaluate results
y_pred = nb.predict(X_test_scaled)
print("Accuracy of test set:", accuracy(y_pred, y_test))
# Accuracy can reach 0.9765.
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] | 2.20302 | 596 |
from Bridge import Proxy2Server
import os
from DataTypes import Packet, A_Packet_Class
from DataTypes import VarInt, Output_Streamer, Bytes_Streamer, Socket_Streamer
import time
output = Output_Streamer()
input = Bytes_Streamer()
login_packets = A_Packet_Class()
SOCK = Socket_Streamer('connect.2b2t.org', 25565, login_packets)
handshake = Packet(login_packets)
handshake.set(['VarInt', 'VarInt', 'String', 'Ushort', 'VarInt'])
status = Packet(login_packets)
status.set(['VarInt', 'String'])
request = Packet(login_packets)
request.set(['VarInt'])
ping_pong = Packet(login_packets)
ping_pong.set(['VarInt', 'Long'])
encryption_req = Packet(login_packets)
encryption_req.set(['VarInt', 'String', 'String', 'String'])
encryption_res = Packet(login_packets)
encryption_res.set(['VarInt', 'String', 'String'])
login_success = Packet(login_packets)
login_success.set(['VarInt', 'String', 'String'])
set_compression = Packet(login_packets)
set_compression.set(['VarInt', 'VarInt'])
login_packets.map_pack(pack_0)
login_packets.map_unpack(unpack_0)
# data = handshake.pack([0x00, 340, b'2b2t.org', 25565, 1])
# server_sock.write(data)
# data = request.pack([0x00])
# server_sock.write(data)
# status.unpack(server_sock, output)
input.write(handshake.pack([0x00, 340, b'2b2t.org', 25565, 2]))
SOCK.write(input)
input.write(status.pack([0x00, b'ThBlitz']))
SOCK.write(input)
SOCK.read(input)
encryption_req.unpack(input, output)
print(f'encryption_req : {output.getvalue()}')
data = output.getvalue()
login_packets.server_id = data[1]
login_packets.server_public_key = data[2]
login_packets.verification_token = data[3]
import secrets
login_packets.aes_key = secrets.randbits(128).to_bytes(16, 'big')
hash , ver_token , shared_secret = login_packets.get_hash()
import mojang_api
uuid , name , token , login_data = mojang_api.login_through_microsoft()
res = mojang_api.join_server(token, uuid, hash)
print(f'response from mojang : {res}')
input.reset()
input.write(encryption_res.pack([0x01, shared_secret, ver_token]))
SOCK.write(input)
login_packets.encryption_enabled = True
SOCK.read(input)
set_compression.unpack(input, output)
login_packets.compression_threshold = output.getvalue()[1]
login_packets.compression_enabled = True
print(f'compression_packet : {output.getvalue()}')
SOCK.read(input)
login_success.unpack(input, output)
print(f'login_success : {output.getvalue()}')
SOCK.read(input)
status.unpack(input, output)
print(input.getvalue())
while True:
SOCK.read(input)
print(hex(VarInt.unpack(input)))
print(input.read())
time.sleep(1)
# t
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] | 2.585149 | 1,010 |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 17 13:35:39 2021
@author: ejgen
------ What is this file? ------
This script targets the files goodreads_reviews_cleaned.csv and
review_sentences_analyzed.csv, calculating summary statistics such as
review length and sentiment score.
This script targets the following files:
../../data/cleaned/goodreads_reviews_cleaned.csv
../../data/analysis_results/review_sentences_analyzed.csv
The resulting csv file is located at:
../../data/analysis_results/goodreads_reviews_analyzed.csv
"""
#%% --- Import required packages ---
import os
from pathlib import Path # To wrap around filepaths
import pandas as pd
#%% --- Set proper directory to assure integration with doit ---
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
#%% --- Import data ---
#goodreads_reviews_cleaned
import_fp = Path("../../data/cleaned/goodreads_reviews_cleaned.csv")
goodreads_reviews = pd.read_csv(import_fp, encoding = "utf-8", index_col = False)
#review_sentences_analyzed
import_fp = Path("../../data/analysis_results/review_sentences_analyzed.csv")
sentences_analyzed = pd.read_csv(import_fp, encoding = "utf-8")
#%% --- Prepare data ---
sentences_analyzed = sentences_analyzed.loc[:,["review_id",
"sentence_id",
"sent_mentions_original",
"sent_mentions_trans",
"length_in_words",
"VADER_score_compound"]]
# Take a subset of goodreads reviews to include only reviews whose review no
# appear in sentences_analyzed.
rid_mask = goodreads_reviews["review_id"].isin(sentences_analyzed["review_id"])
goodreads_reviews = goodreads_reviews.loc[rid_mask, :]
#%% --- Analyze: review length in sentences and words. ---
length_per_review = (sentences_analyzed
.groupby("review_id")
["length_in_words"]
.agg(["sum","count"])
.rename({"sum" : "total_length_in_words",
"count" : "total_length_in_sentences"},
axis = 1))
goodreads_reviews = (goodreads_reviews
.merge(length_per_review,
how = "left",
on = "review_id"))
#%% --- Analyze: mention ratios for explicit translation/author mentions
orig_mention_mask = sentences_analyzed["sent_mentions_original"] == True
trans_mention_mask = sentences_analyzed["sent_mentions_trans"] == True
only_orig_mention_mask = (orig_mention_mask & ~trans_mention_mask)
only_trans_mention_mask = (~orig_mention_mask & trans_mention_mask)
both_mention_mask = (orig_mention_mask & trans_mention_mask)
masks = {"share_of_only_trans_mentions" : only_trans_mention_mask,
"share_of_trans_mentions" : trans_mention_mask,
"share_of_only_orig_mentions": only_orig_mention_mask,
"share_of_orig_mentions": orig_mention_mask}
for prefix, mask in masks.items():
calc = (sentences_analyzed[mask].
groupby("review_id")
["length_in_words"]
.agg(["count"])
.rename({"count": prefix},
axis = 1)
.reset_index())
goodreads_reviews = (goodreads_reviews.merge(calc,
how = "left",
on = "review_id")
.fillna(value = 0,
axis = 0))
goodreads_reviews[prefix] = ((goodreads_reviews[prefix]
/ goodreads_reviews["total_length_in_sentences"])
* 100)
#%% --- Analyze: VADER score for the whole review ---
VADER_score_per_review = (sentences_analyzed
.groupby("review_id")
["VADER_score_compound"]
.agg(["sum","count"])
.reset_index())
VADER_score_per_review["avg_VADER_score"] = (VADER_score_per_review["sum"]
/ VADER_score_per_review["count"])
VADER_score_per_review = VADER_score_per_review.drop(labels = ["sum","count"],
axis = "columns")
goodreads_reviews = goodreads_reviews.merge(VADER_score_per_review,
how = "left",
on = "review_id")
#%% --- Export data ---
export_fp = Path("../../data/analysis_results/goodreads_reviews_analyzed.csv")
goodreads_reviews.to_csv(export_fp, encoding = "utf-8", index = False)
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] | 1.998347 | 2,420 |
import cv2, json, sys, datetime
import tensorflow as tf
import numpy as np
from face_filter import c_face_filter
from mtcnn_detect import c_MTCNNDetect
from face_attr import c_face_attr_reader
standard_face_size = 160 # 160(weight) * 160(height)
detect_resolution = 80 # 80(weight) * 80(height)
the_face_attrs_reader = c_face_attr_reader(standard_face_size)
the_filter = c_face_filter()
face_detect = c_MTCNNDetect(tf.Graph(), scale_factor=2) #scale_factor, rescales image for faster detection
vs = cv2.VideoCapture(0)
ret = 0
while ret >= 0:
ret = record_single_face() | [
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] | 2.814634 | 205 |
from pandac import PandaModules as PM
from direct.directnotify import DirectNotifyGlobal
from direct.showbase.PythonUtil import list2dict, uniqueElements
import string
import LevelConstants
import types
if __dev__:
import os
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#!/usr/bin/env python
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Copyright (c) 2017 Jamf. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
# * Neither the name of the Jamf 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 JAMF SOFTWARE, LLC "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 JAMF SOFTWARE, LLC 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.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# This script was modified from Andrina Kelly's version presented at JNUC2013 for allowing
# a user to elevate their privelages to administrator once per day for 60 minutes. After
# the 60 minutes if a user created a new admin account that account will have admin rights
# also revoked.
#
# To accomplish this the following will be performed:
# - A launch daemon will be put in place in order to remove admin rights
# - Log will be written to tempAdmin.log
# - This policy in Jamf will be set to only be allowed once per day
#
# REQUIREMENTS:
# - Jamf Pro
# - Policy for enabling tempAdmin via Self Service
# - Policy to remove tempAdmin via custom trigger
# - tempAdmin.sh & removeTempAdmin.sh Scripts
#
#
# Written by: Joshua Roskos | Professional Services Engineer | Jamf
#
# Created On: June 20th, 2017
# Updated On: July 26th, 2017
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# IMPORTS
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import os, plistlib, pwd, grp, subprocess, sys
from SystemConfiguration import SCDynamicStoreCopyConsoleUser
from datetime import datetime
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# VARIABLES
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
userName = (SCDynamicStoreCopyConsoleUser(None, None, None) or [None])[0] # get the logged in user's name
workingDir = '/usr/local/jamfps/' # working directory for script
launchdFile = 'com.jamfps.adminremove.plist' # launch daemon file name
launchdLabel = launchdFile.replace('.plist', '') # launch daemon label
plistFile = 'MakeMeAdmin.plist' # settings file name
tempAdminLog = 'tempAdmin.log' # script log file
adminTimer = 3600 # how long should they have admin rights for (in seconds)
policyCustomTrigger = 'adminremove' # custom trigger specified for removeTempAdmin.py policy
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# LAUNCH DAEMON
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# place launchd plist to call JSS policy to remove admin rights.
print 'Creating LaunchDaemon...'
launchDaemon = { 'Label':launchdLabel,
'LaunchOnlyOnce':True,
'ProgramArguments':['/usr/local/jamf/bin/jamf', 'policy', '-trigger', policyCustomTrigger],
'StartInterval':adminTimer,
'UserName':'root',
}
plistlib.writePlist(launchDaemon, '/Library/LaunchDaemons/' + launchdFile)
# set the permission on the file just made.
userID = pwd.getpwnam("root").pw_uid
groupID = grp.getgrnam("wheel").gr_gid
os.chown('/Library/LaunchDaemons/' + launchdFile, userID, groupID)
os.chmod('/Library/LaunchDaemons/' + launchdFile, 0644)
# load the removal plist timer.
print 'Loading LaunchDaemon...'
subprocess.call(["launchctl", "load", "-w", '/Library/LaunchDaemons/' + launchdFile])
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# APPLICATION
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# build log files
if not os.path.exists(workingDir):
os.makedirs(workingDir)
# record user that will need to have admin rights removed
# record current existing admins
print 'Retrieving List of Current Admins...'
currentAdmins = grp.getgrnam('admin').gr_mem
print 'Updating Plist...'
plist = { 'User2Remove':userName,
'CurrentAdminUsers':currentAdmins}
plistlib.writePlist(plist, workingDir + plistFile)
# give current logged user admin rights
subprocess.call(["dseditgroup", "-o", "edit", "-a", userName, "-t", "user", "admin"])
# add log entry
log = open(workingDir + tempAdminLog, "a+")
log.write("{} - MakeMeAdmin Granted Admin Rights for {}\r\n".format(datetime.now(), userName))
log.close()
print 'Granted Admin Right to ' + userName
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32053,
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] | 2.492737 | 2,547 |
import hashlib
message = input()
print(hashlib.sha256(message.encode()).hexdigest()) | [
11748,
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] | 2.965517 | 29 |
# Generated by Django 3.1.6 on 2021-04-17 11:19
import django.contrib.postgres.fields
from django.db import migrations, models
| [
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] | 2.931818 | 44 |
# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
import param
import pytest
from abex.common.generic_parsing import GenericConfig, IntTuple
def test_overridable_parameter() -> None:
"""
Test to check overridable parameters are correctly identified.
"""
param_dict = ParamClass.get_overridable_parameters()
assert "name" in param_dict
assert "flag" in param_dict
assert "seed" in param_dict
assert "number" in param_dict
assert "integers" in param_dict
assert "optional_int" in param_dict
assert "optional_float" in param_dict
assert "tuple1" in param_dict
assert "int_tuple" in param_dict
assert "enum" in param_dict
assert "readonly" not in param_dict
assert "_non_override" not in param_dict
assert "constant" not in param_dict
def test_create_parser() -> None:
"""
Check that parse_args works as expected, with both non default and default values.
"""
check(["--name=foo"], "name", "foo")
check(["--seed", "42"], "seed", 42)
check(["--seed", ""], "seed", 42)
check(["--number", "2.17"], "number", 2.17)
check(["--number", ""], "number", 3.14)
check(["--integers", "1,2,3"], "integers", [1, 2, 3])
check(["--optional_int", ""], "optional_int", None)
check(["--optional_int", "2"], "optional_int", 2)
check(["--optional_float", ""], "optional_float", None)
check(["--optional_float", "3.14"], "optional_float", 3.14)
check(["--tuple1", "1,2"], "tuple1", (1, 2.0))
check(["--int_tuple", "1,2,3"], "int_tuple", (1, 2, 3))
check(["--enum=2"], "enum", ParamEnum.EnumValue2)
check(["--floats=1,2,3.14"], "floats", [1.0, 2.0, 3.14])
check(["--integers=1,2,3"], "integers", [1, 2, 3])
check(["--flag"], "flag", True)
# Check that default values are created as expected, and that the non-overridable parameters
# are omitted.
defaults = vars(ParamClass.create_argparser().parse_args([]))
assert defaults["seed"] == 42
assert defaults["tuple1"] == (1, 2.3)
assert defaults["int_tuple"] == (1, 1, 1)
assert defaults["enum"] == ParamEnum.EnumValue1
assert "readonly" not in defaults
assert "constant" not in defaults
assert "_non_override" not in defaults
# We can't test if all invalid cases are handled because argparse call sys.exit
# upon errors.
def test_apply_overrides() -> None:
"""
Test that overrides are applied correctly, ond only to overridable parameters,
"""
m = ParamClass()
overrides = {"name": "newName", "int_tuple": (0, 1, 2)}
actual_overrides = m.apply_overrides(overrides)
assert actual_overrides == overrides
assert all([x == i and isinstance(x, int) for i, x in enumerate(m.int_tuple)])
assert m.name == "newName"
# Attempt to change seed and constant, but the latter should be ignored.
change_seed: Dict[str, Any] = {"seed": 123}
old_constant = m.constant
changes2 = m.apply_overrides({**change_seed, "constant": "Nothing"})
assert changes2 == change_seed
assert m.seed == 123
assert m.constant == old_constant
@pytest.mark.parametrize("value_idx_0", [1.0, 1])
@pytest.mark.parametrize("value_idx_1", [2.0, 2])
@pytest.mark.parametrize("value_idx_2", [3.0, 3])
def test_int_tuple_validation(value_idx_0: Any, value_idx_1: Any, value_idx_2: Any) -> None:
"""
Test integer tuple parameter is validated correctly.
"""
m = ParamClass()
val = (value_idx_0, value_idx_1, value_idx_2)
if not all([isinstance(x, int) for x in val]):
with pytest.raises(ValueError):
m.int_tuple = (value_idx_0, value_idx_1, value_idx_2)
else:
m.int_tuple = (value_idx_0, value_idx_1, value_idx_2)
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62,
312,
87,
62,
16,
11,
1988,
62,
312,
87,
62,
17,
8,
198
] | 2.676509 | 1,524 |
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class VirtualNetworkConfiguration(Model):
"""Configuration of a virtual network to which API Management service is
deployed.
Variables are only populated by the server, and will be ignored when
sending a request.
:ivar vnetid: The virtual network ID. This is typically a GUID. Expect a
null GUID by default.
:vartype vnetid: str
:ivar subnetname: The name of the subnet.
:vartype subnetname: str
:param subnet_resource_id: The full resource ID of a subnet in a virtual
network to deploy the API Management service in.
:type subnet_resource_id: str
"""
_validation = {
'vnetid': {'readonly': True},
'subnetname': {'readonly': True},
'subnet_resource_id': {'pattern': r'^/subscriptions/[^/]*/resourceGroups/[^/]*/providers/Microsoft.(ClassicNetwork|Network)/virtualNetworks/[^/]*/subnets/[^/]*$'},
}
_attribute_map = {
'vnetid': {'key': 'vnetid', 'type': 'str'},
'subnetname': {'key': 'subnetname', 'type': 'str'},
'subnet_resource_id': {'key': 'subnetResourceId', 'type': 'str'},
}
| [
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3256,
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4906,
10354,
705,
2536,
6,
5512,
198,
220,
220,
220,
1782,
198
] | 3.099617 | 522 |
import django.shortcuts
def main(request):
"""
request handler for '/'.
"""
return django.shortcuts.render(request, 'app_website/index.html', {})
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# global error handlers for app_website
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def error_400(request, exception):
"""
request handler for a 400 error.
"""
context = {
'err': '[400 Bad Request] Path: ' + request.path,
}
return django.shortcuts.render(request, 'app_website/error.html', context)
def error_403(request, exception):
"""
request handler for a 403 error.
"""
context = {
'err': '[403 Permission Denied] Path: ' + request.path,
}
return django.shortcuts.render(request, 'app_website/error.html', context)
def error_404(request, exception):
"""
request handler for a 404 error.
"""
context = {
'err': '[404 Page Not Found] Path: ' + request.path,
}
return django.shortcuts.render(request, 'app_website/error.html', context)
def error_500(request):
"""
request handler for a 500 error.
"""
context = {
'err': '[500 Server Error] Path: ' + request.path,
}
return django.shortcuts.render(request, 'app_website/error.html', context)
| [
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329,
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13,
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198,
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4732,
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220,
220,
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44438,
4059,
9652,
13047,
60,
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25,
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1343,
2581,
13,
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11,
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220,
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1441,
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7,
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11,
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1324,
62,
732,
12485,
14,
18224,
13,
6494,
3256,
4732,
8,
198
] | 3.2225 | 400 |
from .SculptASequenceView import SculptASequenceView
| [
6738,
764,
50,
3129,
457,
1921,
4853,
594,
7680,
1330,
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13327,
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594,
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198
] | 3.117647 | 17 |
# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright (c) 2015 Peter Sprygada, <[email protected]>
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * 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.
#
# 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.
#
import itertools
import re
from ansible.module_utils.six import string_types
from ansible.module_utils.six.moves import zip, zip_longest
DEFAULT_COMMENT_TOKENS = ['#', '!', '/*', '*/']
| [
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6738,
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856,
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62,
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198,
6738,
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62,
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3256,
705,
0,
3256,
705,
15211,
3256,
705,
16208,
20520,
628,
198
] | 3.416819 | 547 |
import os
import pytest
from dvc.ignore import DvcIgnore
from dvc.main import main
@pytest.mark.parametrize(
"file,ret,output", [("ignored", 0, True), ("not_ignored", 1, False)]
)
@pytest.mark.parametrize(
"file,ret,output",
[
("file", 0, "{}:1:f*\tfile\n".format(DvcIgnore.DVCIGNORE_FILE)),
("foo", 0, "{}:2:!foo\tfoo\n".format(DvcIgnore.DVCIGNORE_FILE)),
(
os.path.join("dir", "foobar"),
0,
"{}:1:foobar\t{}\n".format(
os.path.join("dir", DvcIgnore.DVCIGNORE_FILE),
os.path.join("dir", "foobar"),
),
),
],
)
@pytest.mark.parametrize("non_matching", [True, False])
@pytest.mark.parametrize(
"args",
[
["-n", "file"],
["-a", "file"],
["-q", "-d", "file"],
["--stdin", "file"],
[],
],
)
@pytest.mark.parametrize("path,ret", [({"dir": {}}, 0), ({"dir": "files"}, 1)])
@pytest.mark.parametrize(
"file,ret,output", [("ignored", 0, True), ("not_ignored", 1, False)]
)
| [
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1600,
685,
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570,
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11,
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828,
5855,
1662,
62,
570,
1850,
1600,
352,
11,
10352,
15437,
198,
8,
198
] | 1.910394 | 558 |
import os
import threading
import time
from networktables import NetworkTables
from PIL import Image
from PIL.ImageColor import getcolor, getrgb
from PIL.ImageOps import grayscale
from StreamDeck.DeviceManager import DeviceManager
from StreamDeck.ImageHelpers import PILHelper
ASSETS_PATH = os.path.join(os.path.dirname(__file__), "assets")
ASSETS_PATH = os.path.join(os.path.dirname(__file__), "icons")
# As a client to connect to a robot
NetworkTables.initialize(server="10.11.89.2")
# NetworkTables.initialize(server="127.0.0.1")
time.sleep(3)
sd = NetworkTables.getTable("StreamDeck/0")
# a = [
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# "default",
# ]
# sd.putStringArray("Icons", a)
buttons = []
for i in range(0, 15):
sd.putBoolean(f"Action/{i}", False)
sd.putBoolean(f"Status/{i}", False)
button = Button(i)
buttons.append(button)
deck = DeviceManager().enumerate()[0]
deck.open()
deck.reset()
print(
"Opened '{}' device (serial number: '{}')".format(
deck.deck_type(), deck.get_serial_number()
)
)
# Set initial screen brightness to 30%.
deck.set_brightness(30)
# Set initial key images.
# for key in range(deck.key_count()):
# update_key_image(deck, key, False)
# Register callback function for when a key state changes.
deck.set_key_callback(key_change_callback)
while True:
for button in buttons:
button.update(deck)
# Wait until all application threads have terminated (for this example,
# this is when all deck handles are closed).
for t in threading.enumerate():
if t is threading.currentThread():
continue
if t.is_alive():
t.join()
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62,
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425,
33529,
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220,
220,
220,
220,
220,
220,
220,
256,
13,
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3419,
198
] | 2.577031 | 714 |
from dragonfly import (Grammar, CompoundRule, Text, MappingRule, Dictation, Function, Choice)
from macro_utilities import (replace_in_text, comment_choice, execute_with_dictation)
from vim.rules.letter import (camel_case, proper)
comparison_choice_map = {
"equal": "==",
"not equal": "/=",
"less or equal": "<=",
"greater or equal": ">=",
"less": "<",
"greater": ">",
}
stack_command_choice_map = {
"build fast": "build --fast",
"build": "build",
"shell": "repl",
"shall": "repl",
"test": "test",
"test fast": "test --fast",
"run": "run",
"install": "install",
}
# The main Curry grammar rules are activated here
curryBootstrap = Grammar("curry bootstrap")
curryBootstrap.add_rule(CurryEnabler())
curryBootstrap.load()
curryGrammar = Grammar("curry grammar")
curryGrammar.add_rule(CurryUtilities())
curryGrammar.add_rule(CurryDisabler())
curryGrammar.load()
curryGrammar.disable()
| [
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220,
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2,
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66,
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7279,
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66,
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13,
2220,
3419,
198,
66,
16682,
38,
859,
3876,
13,
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3419,
628
] | 2.578249 | 377 |
from deluge.plugins.init import PluginInitBase
VERSION = (0, 1, 8)
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6738,
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] | 3 | 24 |
from django.shortcuts import reverse
from django.views.generic import UpdateView
from applications.users.forms.profile import ProfileForm
from applications.users.layouts.profile import ProfileLayout
from applications.users.mixins.authenticated import AuthenticatedMixin
from applications.common.mixins.add_message import AddMessageMixin
from applications.common.mixins.add_request_to_form import AddRequestToFormMixin
Profile = ProfileCBV.as_view()
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198
] | 3.93913 | 115 |
# -*- coding: utf-8 -*-
import csv
import os
import cv2
import numpy as np
from flask import render_template, request, redirect, url_for
from flask import jsonify
from app.main import main
from app.utils.frame.frame import base64_to_png
from app.utils.frame.site import Site
from app.utils.frame.sub import PictureSub
from config import Config
import json
@main.route('/')
@main.route('/picture/', methods=['GET', 'POST'])
# INFO 2019/12/25 15:18 liliangbin 背景图片设置
@main.route('/background/', methods=['GET', 'POST'])
# TODO 2020/1/4 15:13 liliangbin 返回的地址应该是画框的位置（视频名字和时间位置）通过前端设置了
@main.route('/site/', methods=['GET', 'POST'])
# TODO 2020/6/12 15:50 liliangbin 代码可以优化一波
@main.route('/change_datas/', methods=['GET', 'POST'])
# INFO 2020/6/12 15:51 liliangbin 获取用户
@main.route("/site_get/", methods=['GET', 'POST'])
@main.route('/video_location/', methods=['POST'])
| [
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6,
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14,
15588,
62,
24886,
14,
3256,
5050,
28,
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32782,
6,
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198
] | 2.119617 | 418 |
# django imports
from django.forms import ModelForm
# lfs imports
from lfs.discounts.models import Discount
class DiscountForm(ModelForm):
"""
Form to manage discount data.
"""
| [
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# MIT License
#
# Copyright (c) 2020 SCL team at Red Hat
#
# 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.
from contextlib import contextmanager
import logging
import shutil
import os
import json
import jinja2
import subprocess
from pathlib import Path
from betka.constants import HOME
logger = logging.getLogger(__name__)
def run_cmd(cmd, return_output=False, ignore_error=False, shell=False, **kwargs):
"""
Run provided command on host system using the same user as invoked this code.
Raises subprocess.CalledProcessError if it fails.
:param cmd: list or str
:param return_output: bool, return output of the command
:param ignore_error: bool, do not fail in case nonzero return code
:param shell: bool, run command in shell
:param kwargs: pass keyword arguments to subprocess.check_* functions; for more info,
please check `help(subprocess.Popen)`
:return: None or str
"""
logger.debug("command: %r", cmd)
try:
if return_output:
return subprocess.check_output(
cmd,
stderr=subprocess.STDOUT,
universal_newlines=True,
shell=shell,
**kwargs,
)
else:
return subprocess.check_call(cmd, shell=shell, **kwargs)
except subprocess.CalledProcessError as cpe:
if ignore_error:
if return_output:
return cpe.output
else:
return cpe.returncode
else:
logger.error(f"failed with code {cpe.returncode} and output:\n{cpe.output}")
raise cpe
def text_from_template(template_dir, template_filename, template_data):
"""
Create text based on template in path template_dir/template_filename
:param template_dir: string, directory containing templates
:param template_filename: template for text in jinja
:param template_data: dict, data for substitution in template
:return: string
"""
if not os.path.exists(os.path.join(template_dir, template_filename)):
raise FileNotFoundError("Path to template not found.")
template_loader = jinja2.FileSystemLoader(searchpath=template_dir)
template_env = jinja2.Environment(loader=template_loader)
template = template_env.get_template(template_filename)
output_text = template.render(template_data=template_data)
logger.debug("Text from template created:")
logger.debug(output_text)
return output_text
def copy_upstream2downstream(src_parent: Path, dest_parent: Path):
"""Copies content from upstream repo to downstream repo
Copies all files/dirs/symlinks from upstream source to dist-git one by one,
while removing previous if exists.
:param src_parent: path to source directory
:param dest_parent: path to destination directory
"""
for f in src_parent.iterdir():
if f.name.startswith(".git"):
continue
dest = dest_parent / f.name
src = src_parent / f.name
logger.debug(f"Copying {str(src)} to {str(dest)}.")
# First remove the dest only if it is not symlink.
if dest.is_dir() and not dest.is_symlink():
logger.debug("rmtree %s", dest)
shutil.rmtree(dest)
else:
if dest.exists():
dest.unlink()
# Now copy the src to dest
if src.is_symlink() or not src.is_dir():
logger.debug("cp %s %s", src, dest)
shutil.copy2(src, dest, follow_symlinks=False)
else:
logger.debug("cp -r %s %s", src, dest)
shutil.copytree(src, dest, symlinks=True)
def clean_directory(path: Path):
"""
Function cleans directory except itself
:param path: directory path which is cleaned
"""
for d in path.iterdir():
src = path / d
if src.is_dir():
logger.debug("rmtree %s", str(src))
shutil.rmtree(src)
else:
src.unlink()
def list_dir_content(dir_name: Path):
"""
Lists all content of dir_name
:param dir_name: Directory for showing files
"""
logger.info("Look for a content in '%s' directory", str(dir_name))
for f in dir_name.rglob("*"):
if str(f).startswith(".git"):
continue
logger.debug(f"{f.parent / f.name}")
@contextmanager
def cwd(path):
"""
Switch to Path directory and once action is done
returns back
:param path:
:return:
"""
prev_cwd = Path.cwd()
os.chdir(path)
try:
yield
finally:
os.chdir(prev_cwd)
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] | 2.603809 | 2,153 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by 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.
import numpy as np
from deepspeech.frontend.utility import IGNORE_ID
from deepspeech.io.utility import pad_sequence
from deepspeech.utils.log import Log
__all__ = ["SpeechCollator"]
logger = Log(__name__).getlog()
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] | 3.6 | 230 |
# -*-python-*-
#
# Copyright (C) 1999-2018 The ViewCVS Group. All Rights Reserved.
#
# By using this file, you agree to the terms and conditions set forth in
# the LICENSE.html file which can be found at the top level of the ViewVC
# distribution or at http://viewvc.org/license-1.html.
#
# For more information, visit http://viewvc.org/
#
# -----------------------------------------------------------------------
"Version Control lib driver for remotely accessible Subversion repositories."
import vclib
import sys
import os
import re
import tempfile
import time
import urllib
from svn_repos import Revision, SVNChangedPath, _datestr_to_date, \
_compare_paths, _path_parts, _cleanup_path, \
_rev2optrev, _fix_subversion_exception, \
_split_revprops, _canonicalize_path
from svn import core, delta, client, wc, ra
### Require Subversion 1.3.1 or better. (for svn_ra_get_locations support)
if (core.SVN_VER_MAJOR, core.SVN_VER_MINOR, core.SVN_VER_PATCH) < (1, 3, 1):
raise Exception, "Version requirement not met (needs 1.3.1 or better)"
### BEGIN COMPATABILITY CODE ###
try:
SVN_INVALID_REVNUM = core.SVN_INVALID_REVNUM
except AttributeError: # The 1.4.x bindings are missing core.SVN_INVALID_REVNUM
SVN_INVALID_REVNUM = -1
### END COMPATABILITY CODE ###
def cat_to_tempfile(svnrepos, path, rev):
"""Check out file revision to temporary file"""
temp = tempfile.mktemp()
stream = core.svn_stream_from_aprfile(temp)
url = svnrepos._geturl(path)
client.svn_client_cat(core.Stream(stream), url, _rev2optrev(rev),
svnrepos.ctx)
core.svn_stream_close(stream)
return temp
| [
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7,
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198
] | 2.631415 | 643 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import io
import pathlib
import sys
import tempfile
from multiprocessing import Pool, cpu_count
import PyPDF2 as PyPDF2
import click
import pdfminer.pdftypes as pdftypes
import pdfminer.settings
from fpdf import FPDF
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams, LTAnno, LTContainer, LTText, LTTextBox
from pdfminer.pdfdocument import PDFDocument, PDFNoOutlines
from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
from pdfminer.pdfpage import PDFPage
from pdfminer.pdfparser import PDFParser
from pdfminer.psparser import PSLiteral, PSLiteralTable
from tqdm import tqdm
pdfminer.settings.STRICT = False
SUBSTITUTIONS = {
u'ff': 'ff',
u'fi': 'fi',
u'fl': 'fl',
u'’': "'",
}
ANNOT_SUBTYPES = set(['Text', 'Highlight', 'Squiggly', 'StrikeOut', 'Underline'])
DEBUG_BOXHIT = False
OUTDIR = ""
@click.command()
@click.option('--outdir', default="", help='Specify output directory')
@click.argument('files', nargs=-1)
if __name__ == "__main__":
main()
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] | 2.671569 | 408 |
import torch
def heatmap_focal_loss(preds, gt_heatmap, alpha, gamma, eps=1e-3):
"""
Params:
preds: Tensor[num_classes, height, width]
gt_heatmap: Tensor[num_classes, height, width]
alpha:
gamma: how much you want to reduce penalty around the ground truth locations
eps: add small number to prevent inf error
Returns:
loss: Tensor[]
"""
# See CornerNet paper for detail https://arxiv.org/abs/1808.01244
loss = -torch.where(
gt_heatmap == 1,
(1 - preds)**alpha * torch.log(preds + eps), # Loss for positive locations
(1 - gt_heatmap) ** gamma * (preds)**alpha * torch.log(1 - preds - eps) # loss for negative locations
).sum()
return loss
def dice_loss(inputs, targets, smooth=1.0):
"""
Params:
inputs: arbitrary size of Tensor
targets: arbitrary size of Tensor
smooth: smoothing factor
Returns:
loss: Tensor[]
"""
inputs = inputs.view(-1)
targets = targets.view(-1)
# Squred denominator version of Dice loss
dice = (2 * (inputs*targets).sum() + smooth) / ((inputs**2).sum() + (targets**2).sum() + smooth)
return 1 - dice
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16,
8,
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8,
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2196,
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2994,
198,
220,
220,
220,
17963,
796,
357,
17,
1635,
357,
15414,
82,
9,
83,
853,
1039,
737,
16345,
3419,
1343,
7209,
8,
1220,
14808,
15414,
82,
1174,
17,
737,
16345,
3419,
1343,
357,
83,
853,
1039,
1174,
17,
737,
16345,
3419,
1343,
7209,
8,
628,
220,
220,
220,
1441,
352,
532,
17963,
198
] | 2.406439 | 497 |
import pygments.lexers.hdl as lexers
from multiprocessing import Process
import helpers.common as common
tokenizer = lexers.VerilogLexer()
| [
11748,
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45117,
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628
] | 3.414634 | 41 |
"""
.. module:: dj-stripe.tests.test_event_handlers
:synopsis: dj-stripe Event Handler Tests.
.. moduleauthor:: Alex Kavanaugh (@kavdev)
.. moduleauthor:: Lee Skillen (@lskillen)
"""
from copy import deepcopy
import decimal
from django.contrib.auth import get_user_model
from django.test import TestCase
from mock import patch
from djstripe.models import Event, Charge, Transfer, Account, Plan, Customer, InvoiceItem, Invoice, Card, Subscription
from tests import (FAKE_CARD, FAKE_CHARGE, FAKE_CHARGE_II, FAKE_CUSTOMER, FAKE_CUSTOMER_II,
FAKE_EVENT_CHARGE_SUCCEEDED, FAKE_EVENT_CUSTOMER_CREATED,
FAKE_EVENT_CUSTOMER_DELETED, FAKE_EVENT_CUSTOMER_SOURCE_CREATED,
FAKE_EVENT_CUSTOMER_SOURCE_DELETED, FAKE_EVENT_CUSTOMER_SOURCE_DELETED_DUPE,
FAKE_EVENT_CUSTOMER_SUBSCRIPTION_CREATED, FAKE_EVENT_CUSTOMER_SUBSCRIPTION_DELETED,
FAKE_EVENT_INVOICE_CREATED, FAKE_EVENT_INVOICE_DELETED, FAKE_EVENT_INVOICEITEM_CREATED,
FAKE_EVENT_INVOICEITEM_DELETED, FAKE_EVENT_PLAN_CREATED, FAKE_EVENT_PLAN_DELETED,
FAKE_EVENT_TRANSFER_CREATED, FAKE_EVENT_TRANSFER_DELETED, FAKE_INVOICE, FAKE_INVOICE_II,
FAKE_INVOICEITEM, FAKE_PLAN, FAKE_SUBSCRIPTION, FAKE_SUBSCRIPTION_III, FAKE_TRANSFER)
| [
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11,
9677,
7336,
62,
5446,
15037,
24302,
8,
628,
628,
628,
628
] | 2.114105 | 631 |
import common
import json
import logging
import os
import subprocess
import time
from dateutil import parser
head_vault_hosts = 'OLD_IFS=${IFS};IFS=\',\' read -r -a VAULT_HOSTS <<< \"$STRING_VAULT_HOST\";IFS=${OLD_IFS};'
source_kms_utils = '. /usr/sbin/kms_utils.sh;'
global vault_token
global vault_accessor
global MAX_PERCENTAGE_EXPIRATION
vault_token = os.getenv('VAULT_TOKEN', '')
vault_accessor = os.getenv('ACCESSOR_TOKEN','')
MIN_PERCENTAGE_EXPIRATION = 0.2
logger = None
| [
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198,
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796,
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] | 2.5 | 194 |
"Introducing the sys Module"
import sys
print(sys.platform)
print(sys.maxsize)
print(sys.version)
if sys.platform[:3] == 'win': print('hello windows')
| [
1,
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] | 2.90566 | 53 |
from .orion import parse_orion
| [
6738,
764,
273,
295,
1330,
21136,
62,
273,
295,
198
] | 3.1 | 10 |
with open("./day09.input") as file:
data = [int(line.strip()) for line in file.readlines()]
p1 = get_first_not_matching(25)
print(p1)
p2 = get_contiguous_ns_that_add_to(p1)
print(p2) | [
4480,
1280,
7,
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8,
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8
] | 2.253012 | 83 |
import json
import os
import nibabel as nib
import numpy as np
import pandas as pd
ROOT = "./"
DATA = os.path.join(ROOT, "data/")
| [
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] | 2.64 | 50 |
import matplotlib
import random
import operator
import csv
import drunkframework
import matplotlib.animation
import matplotlib.pyplot
"""WARNING!!!!!"""
"""This code was tested using Spyder 5.0.4, should any problems be encountered using older
models please try """
#creates a new empty list for what will be the csv environment data, see https://docs.python.org/3/library/csv.html for more
environment = []
#drunks adapted from agents from GUI's practical replacing "agents"
drunks = []
#density is an empty list which will track agent movement independent of the movement process
density= []
#specifies number of drunks/agents
num_of_drunks = 25
#outlines the number of iterations the line 64-78 code will undergo
num_of_iterations = 100
#sets the dimensions for the matplotlib plots
fig = matplotlib.pyplot.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1])
f = open('drunk.txt', newline='')
#Note that the correct directory must be navigated to in the terminal else the full file path will be needed
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
#Used for testing purposes to ascertain the lay of the environment
#matplotlib.pyplot.xlim(0, 300)
#matplotlib.pyplot.ylim(0, 300)
#matplotlib.pyplot.imshow(environment)
for row in reader:
rowlist =[]
for value in row:
rowlist.append(value)
environment.append(rowlist)
f.close()
#print (rowlist) Used this to check list structure
#Code on lines 46-50 appends the density list output to a 300x300 grid, this code is needed
#to prevent the error "IndexError: list index out of range"
for i in range(300):
rowlist = []
for j in range(300):
rowlist.append(0)
density.append(rowlist)
#matplotlib.pyplot.imshow(environment) run this in isolation to check the environment is
#correct
## Make drunks and assign them with an identification number.
for i in range(num_of_drunks):
identification = ((1+i)*10)
# print(identification) #this should print 10-250 giving each of the drunks an identification number, later to be matched up with houses
drunks.append(drunkframework.Drunk(environment, drunks, identification))
#This is is supposed to work whereby if the co-ordinates of stilldrunk match their identification number they are home
#In the prototype density of the environment changed throughout the iterations, as such the drunks would
#often stop in areas which were not their home. The work around this was seperating the process of track
#and move through the creation of the density list. Track is left in but commented.
for i in range (num_of_drunks):
stilldrunk = drunks[i]
for j in range(num_of_iterations):
while environment [stilldrunk._y][stilldrunk._x] != stilldrunk.identification:
density[drunks[i]._y][drunks[i]._x]+=1
drunks[i].move()
#drunks[i].track() omitted from the final iteration of the application
#saves density list (see lines 68 to 73)
with open('density.txt', 'w', newline='') as f:
csvwriter = csv.writer(f, delimiter=',', quoting=csv.QUOTE_NONNUMERIC)
for row in density:
csvwriter.writerow(row)
#lines 79 to 90 serve the purpose of display the density and drunks in relation
#to their finishing position within the environment
matplotlib.pyplot.xlim(0, 300)
matplotlib.pyplot.ylim(0, 300)
matplotlib.pyplot.imshow(density)
matplotlib.pyplot.xlim(0, 300)
matplotlib.pyplot.ylim(0, 300)
matplotlib.pyplot.show(drunks)
matplotlib.pyplot.xlim(0, 300)
matplotlib.pyplot.ylim(0, 300)
matplotlib.pyplot.imshow(environment)
#Code below just prints we're home for each of the 25 agents following a resolution of
#the code
for i in range(num_of_drunks):
matplotlib.pyplot.scatter(drunks[i]._x, drunks[i]._y)
print("we're home!")
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"""Loading a .caffemodel and figure out the encoding.
Author: Yuhuang Hu
Email : [email protected]
"""
from __future__ import absolute_import
from __future__ import print_function
import os
# from keras.utils.visualize_util import plot
from keras.datasets import mnist as dataset
from keras.utils import np_utils
import transcaffe as tc
batch_size = 128
nb_classes = 10
nb_epoch = 40
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
# color channels
chnls = 1
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = dataset.load_data()
X_train = X_train.reshape(X_train.shape[0], chnls, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], chnls, img_rows, img_cols)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# define model for testing
data_path = os.environ["TRANSCAFFE_DATA"]
# model_str = os.path.join(data_path,
# "VGG_ILSVRC_16_layers_deploy.prototxt.txt")
model_str = os.path.join(data_path, "lenet.prototxt.txt")
model_bin = os.path.join(data_path, "lenet_iter_10000.caffemodel")
model = tc.load(model_str, model_bin, target_lib="keras")
model.compile(loss='categorical_crossentropy', optimizer='adadelta',
metrics=['accuracy'])
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
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from typing import Dict, List, Optional
from kubernetes import client
from tlaunch.lp_k8s.resource import Resource
from tlaunch.lp_k8s.util import map_opt
DEFAULT_PORT = 8001
DEFAULT_NAME = 'launchpad'
REVERB_IMAGE = 'reg.real-ai.cn/launchpad/reverb'
DEFAULT_COMMAND = ['python3', '-u', '-mlaunchpad_kubernetes.process_entry']
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"""Retry downloading files that caused errors in http_downloader.
We can find files to try downloading again by parsing the err.txt file for error messages.
Error log lines we are interested in look like:
09-04-2017 12:45:17..Error_http_downloader 'exports/CalStateTEACH Term 1/grios/Schedule/Mentor Info.docx', 'https://ourdomain.instructure.com/files/8080/download?download_frd=1&verifier=zVZdnkpTmmJIGYAg2U0PaDqESrJBFLi0Xsm73Eldu'
A regex string that captures the file name & URL looks like:
[0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]\.\.Error_http_downloader '(.*)', '(.*)'$
09.04.2017 tps Created.
09.17.2018 tps Change bad global Null reference to None.
"""
import script_logging
import http_downloader
import os
import re
import shutil
######### Constants #########
# Regex pattern for extracting file download details from error log.
REGEX_PATTERN = "[0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9] [0-9][0-9]:[0-9][0-9]:[0-9][0-9]\.\.Error_http_downloader '(.*)', '(.*)'$"
def make_cp_file_name():
"""Create a unique file that looks like "retry000.txt", "retry001.txt",
"retry002.txt", etc.
"""
cp_file_name = None # Function return variable
n = 0
while True:
cp_file_name = 'retry%03d.txt' % n
if (not os.path.exists(cp_file_name)):
break
else:
n = n + 1
continue
return cp_file_name
######### Stand-Alone Execution #########
if __name__ == "__main__":
load_errors()
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] | 2.370313 | 640 |
from nltk import RegexpTokenizer
# Common stopwords in french and english
# Clean text or sentence, removing stopwords
# return list
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import random
from enum import Enum
import numpy as np
from custom_decorators import profile
from shapes import Box
from shared_constants import BBREG_MULTIPLIERS, DEFAULT_ANCHORS
from util import calc_iou, cross_ious, get_reg_params, get_bbox_coords
POS_OVERLAP = 0.7
NEG_OVERLAP = 0.3
SAMPLE_SIZE = 256
MAX_POS_SAMPLES = 128
class RpnTrainingManager:
"""
Encapsulates the details of generating training inputs for a region proposal network for a given image.
"""
def __init__(self, calc_conv_dims, stride, preprocess_func, anchor_dims=DEFAULT_ANCHORS):
"""
:param calc_conv_dims: function that accepts a tuple of the image's height and width in pixels and returns the
height and width of the convolutional layer prior to the rpn layers.
:param stride: positive integer, the cumulative stride at the convolutional layer prior to the rpn layers.
:param preprocess_func: function that applies the same transformation to the image's pixels as used for Imagenet
training. Otherwise the Imagenet pre-trained weights will be mismatched.
:param anchor_dims: list of lists of positive integers, one height and width pair for each anchor.
"""
self._cache = {}
self.calc_conv_dims = calc_conv_dims
self.stride = stride
self.preprocess_func = preprocess_func
self.anchor_dims = anchor_dims
@profile
def batched_image(self, image):
"""
Returns the image data to be fed into the network.
:param image: shapes.Image object.
:return: 4-d numpy array with a single batch of the image, should can be used as a Keras model input.
"""
return np.expand_dims(self.preprocess_func(image.data), axis=0)
@profile
@profile
def rpn_y_true(self, image):
"""
Takes an image and returns the Keras model inputs to train with.
:param image: shapes.Image object to generate training inputs for.
:return: tuple where the first element is a numpy array of the ground truth network output for whether each
anchor overlaps with an object, and the second element is a numpy array of the ground truth network output for the
bounding box transformation parameters to transform each anchor into an object's bounding box.
"""
'''
Consider removing caching - added when self.process was taking 0.4s to run. Since then, optimized it down to
0.02s locally, 0.003s on aws so the cache isn't too useful anymore.
'''
if image.cache_key not in self._cache:
self._process(image)
results = self._cache[image.cache_key]
# TODO: why is the cached result being deleted? Investigate whether restoring it improves training time.
del self._cache[image.cache_key]
can_use = _apply_sampling(results['is_pos'], results['can_use'])
conv_rows, conv_cols = self.calc_conv_dims(image.height, image.width)
is_pos = np.reshape(results['is_pos'], (conv_rows, conv_cols, len(self.anchor_dims)))
can_use = np.reshape(can_use, (conv_rows, conv_cols, len(self.anchor_dims)))
selected_is_pos = np.logical_and(is_pos, can_use)
# combine arrays with whether or not to use for the loss function
y_class = np.concatenate([can_use, is_pos], axis=2)
bbreg_can_use = np.repeat(selected_is_pos, 4, axis = 2)
bbreg_targets = np.reshape(results['bbreg_targets'], (conv_rows, conv_cols, 4 * len(self.anchor_dims)))
y_bbreg = np.concatenate([bbreg_can_use, bbreg_targets], axis = 2)
y_class = np.expand_dims(y_class, axis=0)
y_bbreg = np.expand_dims(y_bbreg, axis=0)
return y_class, y_bbreg
def _idx_to_conv(idx, conv_width, anchors_per_loc):
"""
Converts an anchor box index in a 1-d numpy array to its corresponding 3-d index representing its convolution
position and anchor index.
:param idx: non-negative integer, the position in a 1-d numpy array of anchors.
:param conv_width: the number of possible horizontal positions the convolutional layer's filters can occupy, i.e.
close to the width in pixels divided by the cumulative stride at that layer.
:param anchors_per_loc: positive integer, the number of anchors at each convolutional filter position.
:return: tuple of the row, column, and anchor index of the convolutional filter position for this index.
"""
divisor = conv_width * anchors_per_loc
y, remainder = idx // divisor, idx % divisor
x, anchor_idx = remainder // anchors_per_loc, remainder % anchors_per_loc
return y, x, anchor_idx
@profile
def _get_conv_center(conv_x, conv_y, stride):
"""
Finds the center of this convolution position in the image's original coordinate space.
:param conv_x: non-negative integer, x coordinate of the convolution position.
:param conv_y: non-negative integer, y coordinate of the convolution position.
:param stride: positive integer, the cumulative stride in pixels at this layer of the network.
:return: tuple of positive integers, the x and y coordinates of the center of the convolution position.
"""
x_center = stride * (conv_x + 0.5)
y_center = stride * (conv_y + 0.5)
return int(x_center), int(y_center)
@profile
@profile
@profile
@profile
# this function was a huge bottleneck so threw away box abstractions to optimize performance
@profile
def _get_all_anchor_coords(conv_rows, conv_cols, anchor_dims, stride):
"""
Given the shape of a convolutional layer and the anchors to generate for each position, return all anchors.
:param conv_rows: positive integer, height of this convolutional layer.
:param conv_cols: positive integer, width of this convolutional layer.
:param anchor_dims: list of lists of positive integers, one height and width pair for each anchor.
:param stride: positive integer, cumulative stride of this anchor position in pixels.
:return: 2-d numpy array with one row for each anchor box containing its [x1, y1, x2, y2] coordinates.
"""
num_boxes = conv_rows * conv_cols * len(anchor_dims)
y, x, anchor_idxs = _num_boxes_to_conv_np(num_boxes, conv_cols, len(anchor_dims))
x_center, y_center = _get_conv_center_np(x, y, stride)
anchor_coords = np.zeros((num_boxes, 4), dtype=np.float32)
anchor_height = anchor_dims[anchor_idxs, 0]
anchor_width = anchor_dims[anchor_idxs, 1]
anchor_coords[:, 0] = x_center - anchor_width // 2
anchor_coords[:, 1] = y_center - anchor_height // 2
anchor_coords[:, 2] = anchor_coords[:, 0] + anchor_width
anchor_coords[:, 3] = anchor_coords[:, 1] + anchor_height
return anchor_coords
@profile
@profile
def _apply_sampling(is_pos, can_use):
"""
Applies the sampling logic described in the Faster R-CNN paper to determine which anchors should be evaluated in the
loss function.
:param is_pos: 1-d numpy array of booleans for whether each anchor is a true positive for some object.
:param can_use: 1-d numpy array of booleans for whether each anchor can be used at all in the loss function.
:return: 1-d numpy array of booleans of which anchors were chosen to be used in the loss function.
"""
# extract [0] due to np.where returning a tuple
pos_locs = np.where(np.logical_and(is_pos == 1, can_use == 1))[0]
neg_locs = np.where(np.logical_and(is_pos == 0, can_use == 1))[0]
num_pos = len(pos_locs)
num_neg = len(neg_locs)
# cap the number of positive samples per batch to no more than half the batch size
if num_pos > MAX_POS_SAMPLES:
locs_off = random.sample(range(num_pos), num_pos - MAX_POS_SAMPLES)
can_use[pos_locs[locs_off]] = 0
num_pos = MAX_POS_SAMPLES
# fill remaining portion of the batch size with negative samples
if num_neg + num_pos > SAMPLE_SIZE:
locs_off = random.sample(range(num_neg), num_neg + num_pos - SAMPLE_SIZE)
can_use[neg_locs[locs_off]] = 0
return can_use
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# coding: utf-8
"""
OpenAPI Petstore
This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501
The version of the OpenAPI document: 1.0.0
Generated by: https://openapi-generator.tech
"""
import pprint # noqa: F401
import re # noqa: F401
import six # noqa: F401
from petstore_api.exceptions import ( # noqa: F401
ApiKeyError,
ApiTypeError,
ApiValueError,
)
from petstore_api.model_utils import ( # noqa: F401
ModelNormal,
ModelSimple,
check_allowed_values,
check_validations,
date,
datetime,
file_type,
get_simple_class,
int,
model_to_dict,
none_type,
str,
type_error_message,
validate_and_convert_types
)
class XmlItem(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
openapi_types (dict): The key is attribute name
and the value is attribute type.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
attribute_map = {
'attribute_string': 'attribute_string', # noqa: E501
'attribute_number': 'attribute_number', # noqa: E501
'attribute_integer': 'attribute_integer', # noqa: E501
'attribute_boolean': 'attribute_boolean', # noqa: E501
'wrapped_array': 'wrapped_array', # noqa: E501
'name_string': 'name_string', # noqa: E501
'name_number': 'name_number', # noqa: E501
'name_integer': 'name_integer', # noqa: E501
'name_boolean': 'name_boolean', # noqa: E501
'name_array': 'name_array', # noqa: E501
'name_wrapped_array': 'name_wrapped_array', # noqa: E501
'prefix_string': 'prefix_string', # noqa: E501
'prefix_number': 'prefix_number', # noqa: E501
'prefix_integer': 'prefix_integer', # noqa: E501
'prefix_boolean': 'prefix_boolean', # noqa: E501
'prefix_array': 'prefix_array', # noqa: E501
'prefix_wrapped_array': 'prefix_wrapped_array', # noqa: E501
'namespace_string': 'namespace_string', # noqa: E501
'namespace_number': 'namespace_number', # noqa: E501
'namespace_integer': 'namespace_integer', # noqa: E501
'namespace_boolean': 'namespace_boolean', # noqa: E501
'namespace_array': 'namespace_array', # noqa: E501
'namespace_wrapped_array': 'namespace_wrapped_array', # noqa: E501
'prefix_ns_string': 'prefix_ns_string', # noqa: E501
'prefix_ns_number': 'prefix_ns_number', # noqa: E501
'prefix_ns_integer': 'prefix_ns_integer', # noqa: E501
'prefix_ns_boolean': 'prefix_ns_boolean', # noqa: E501
'prefix_ns_array': 'prefix_ns_array', # noqa: E501
'prefix_ns_wrapped_array': 'prefix_ns_wrapped_array' # noqa: E501
}
openapi_types = {
'attribute_string': (str,), # noqa: E501
'attribute_number': (float,), # noqa: E501
'attribute_integer': (int,), # noqa: E501
'attribute_boolean': (bool,), # noqa: E501
'wrapped_array': ([int],), # noqa: E501
'name_string': (str,), # noqa: E501
'name_number': (float,), # noqa: E501
'name_integer': (int,), # noqa: E501
'name_boolean': (bool,), # noqa: E501
'name_array': ([int],), # noqa: E501
'name_wrapped_array': ([int],), # noqa: E501
'prefix_string': (str,), # noqa: E501
'prefix_number': (float,), # noqa: E501
'prefix_integer': (int,), # noqa: E501
'prefix_boolean': (bool,), # noqa: E501
'prefix_array': ([int],), # noqa: E501
'prefix_wrapped_array': ([int],), # noqa: E501
'namespace_string': (str,), # noqa: E501
'namespace_number': (float,), # noqa: E501
'namespace_integer': (int,), # noqa: E501
'namespace_boolean': (bool,), # noqa: E501
'namespace_array': ([int],), # noqa: E501
'namespace_wrapped_array': ([int],), # noqa: E501
'prefix_ns_string': (str,), # noqa: E501
'prefix_ns_number': (float,), # noqa: E501
'prefix_ns_integer': (int,), # noqa: E501
'prefix_ns_boolean': (bool,), # noqa: E501
'prefix_ns_array': ([int],), # noqa: E501
'prefix_ns_wrapped_array': ([int],), # noqa: E501
}
validations = {
}
additional_properties_type = None
discriminator = None
def __init__(self, _check_type=True, _from_server=False, _path_to_item=(), _configuration=None, **kwargs): # noqa: E501
"""XmlItem - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_from_server (bool): True if the data is from the server
False if the data is from the client (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
attribute_string (str): [optional] # noqa: E501
attribute_number (float): [optional] # noqa: E501
attribute_integer (int): [optional] # noqa: E501
attribute_boolean (bool): [optional] # noqa: E501
wrapped_array ([int]): [optional] # noqa: E501
name_string (str): [optional] # noqa: E501
name_number (float): [optional] # noqa: E501
name_integer (int): [optional] # noqa: E501
name_boolean (bool): [optional] # noqa: E501
name_array ([int]): [optional] # noqa: E501
name_wrapped_array ([int]): [optional] # noqa: E501
prefix_string (str): [optional] # noqa: E501
prefix_number (float): [optional] # noqa: E501
prefix_integer (int): [optional] # noqa: E501
prefix_boolean (bool): [optional] # noqa: E501
prefix_array ([int]): [optional] # noqa: E501
prefix_wrapped_array ([int]): [optional] # noqa: E501
namespace_string (str): [optional] # noqa: E501
namespace_number (float): [optional] # noqa: E501
namespace_integer (int): [optional] # noqa: E501
namespace_boolean (bool): [optional] # noqa: E501
namespace_array ([int]): [optional] # noqa: E501
namespace_wrapped_array ([int]): [optional] # noqa: E501
prefix_ns_string (str): [optional] # noqa: E501
prefix_ns_number (float): [optional] # noqa: E501
prefix_ns_integer (int): [optional] # noqa: E501
prefix_ns_boolean (bool): [optional] # noqa: E501
prefix_ns_array ([int]): [optional] # noqa: E501
prefix_ns_wrapped_array ([int]): [optional] # noqa: E501
"""
self._data_store = {}
self._check_type = _check_type
self._from_server = _from_server
self._path_to_item = _path_to_item
self._configuration = _configuration
for var_name, var_value in six.iteritems(kwargs):
self.__set_item(var_name, var_value)
def __setitem__(self, name, value):
"""this allows us to set values with instance[field_name] = val"""
self.__set_item(name, value)
def __getitem__(self, name):
"""this allows us to get a value with val = instance[field_name]"""
return self.__get_item(name)
@property
def attribute_string(self):
"""Gets the attribute_string of this XmlItem. # noqa: E501
Returns:
(str): The attribute_string of this XmlItem. # noqa: E501
"""
return self.__get_item('attribute_string')
@attribute_string.setter
def attribute_string(self, value):
"""Sets the attribute_string of this XmlItem. # noqa: E501
"""
return self.__set_item('attribute_string', value)
@property
def attribute_number(self):
"""Gets the attribute_number of this XmlItem. # noqa: E501
Returns:
(float): The attribute_number of this XmlItem. # noqa: E501
"""
return self.__get_item('attribute_number')
@attribute_number.setter
def attribute_number(self, value):
"""Sets the attribute_number of this XmlItem. # noqa: E501
"""
return self.__set_item('attribute_number', value)
@property
def attribute_integer(self):
"""Gets the attribute_integer of this XmlItem. # noqa: E501
Returns:
(int): The attribute_integer of this XmlItem. # noqa: E501
"""
return self.__get_item('attribute_integer')
@attribute_integer.setter
def attribute_integer(self, value):
"""Sets the attribute_integer of this XmlItem. # noqa: E501
"""
return self.__set_item('attribute_integer', value)
@property
def attribute_boolean(self):
"""Gets the attribute_boolean of this XmlItem. # noqa: E501
Returns:
(bool): The attribute_boolean of this XmlItem. # noqa: E501
"""
return self.__get_item('attribute_boolean')
@attribute_boolean.setter
def attribute_boolean(self, value):
"""Sets the attribute_boolean of this XmlItem. # noqa: E501
"""
return self.__set_item('attribute_boolean', value)
@property
def wrapped_array(self):
"""Gets the wrapped_array of this XmlItem. # noqa: E501
Returns:
([int]): The wrapped_array of this XmlItem. # noqa: E501
"""
return self.__get_item('wrapped_array')
@wrapped_array.setter
def wrapped_array(self, value):
"""Sets the wrapped_array of this XmlItem. # noqa: E501
"""
return self.__set_item('wrapped_array', value)
@property
def name_string(self):
"""Gets the name_string of this XmlItem. # noqa: E501
Returns:
(str): The name_string of this XmlItem. # noqa: E501
"""
return self.__get_item('name_string')
@name_string.setter
def name_string(self, value):
"""Sets the name_string of this XmlItem. # noqa: E501
"""
return self.__set_item('name_string', value)
@property
def name_number(self):
"""Gets the name_number of this XmlItem. # noqa: E501
Returns:
(float): The name_number of this XmlItem. # noqa: E501
"""
return self.__get_item('name_number')
@name_number.setter
def name_number(self, value):
"""Sets the name_number of this XmlItem. # noqa: E501
"""
return self.__set_item('name_number', value)
@property
def name_integer(self):
"""Gets the name_integer of this XmlItem. # noqa: E501
Returns:
(int): The name_integer of this XmlItem. # noqa: E501
"""
return self.__get_item('name_integer')
@name_integer.setter
def name_integer(self, value):
"""Sets the name_integer of this XmlItem. # noqa: E501
"""
return self.__set_item('name_integer', value)
@property
def name_boolean(self):
"""Gets the name_boolean of this XmlItem. # noqa: E501
Returns:
(bool): The name_boolean of this XmlItem. # noqa: E501
"""
return self.__get_item('name_boolean')
@name_boolean.setter
def name_boolean(self, value):
"""Sets the name_boolean of this XmlItem. # noqa: E501
"""
return self.__set_item('name_boolean', value)
@property
def name_array(self):
"""Gets the name_array of this XmlItem. # noqa: E501
Returns:
([int]): The name_array of this XmlItem. # noqa: E501
"""
return self.__get_item('name_array')
@name_array.setter
def name_array(self, value):
"""Sets the name_array of this XmlItem. # noqa: E501
"""
return self.__set_item('name_array', value)
@property
def name_wrapped_array(self):
"""Gets the name_wrapped_array of this XmlItem. # noqa: E501
Returns:
([int]): The name_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__get_item('name_wrapped_array')
@name_wrapped_array.setter
def name_wrapped_array(self, value):
"""Sets the name_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__set_item('name_wrapped_array', value)
@property
def prefix_string(self):
"""Gets the prefix_string of this XmlItem. # noqa: E501
Returns:
(str): The prefix_string of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_string')
@prefix_string.setter
def prefix_string(self, value):
"""Sets the prefix_string of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_string', value)
@property
def prefix_number(self):
"""Gets the prefix_number of this XmlItem. # noqa: E501
Returns:
(float): The prefix_number of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_number')
@prefix_number.setter
def prefix_number(self, value):
"""Sets the prefix_number of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_number', value)
@property
def prefix_integer(self):
"""Gets the prefix_integer of this XmlItem. # noqa: E501
Returns:
(int): The prefix_integer of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_integer')
@prefix_integer.setter
def prefix_integer(self, value):
"""Sets the prefix_integer of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_integer', value)
@property
def prefix_boolean(self):
"""Gets the prefix_boolean of this XmlItem. # noqa: E501
Returns:
(bool): The prefix_boolean of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_boolean')
@prefix_boolean.setter
def prefix_boolean(self, value):
"""Sets the prefix_boolean of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_boolean', value)
@property
def prefix_array(self):
"""Gets the prefix_array of this XmlItem. # noqa: E501
Returns:
([int]): The prefix_array of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_array')
@prefix_array.setter
def prefix_array(self, value):
"""Sets the prefix_array of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_array', value)
@property
def prefix_wrapped_array(self):
"""Gets the prefix_wrapped_array of this XmlItem. # noqa: E501
Returns:
([int]): The prefix_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_wrapped_array')
@prefix_wrapped_array.setter
def prefix_wrapped_array(self, value):
"""Sets the prefix_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_wrapped_array', value)
@property
def namespace_string(self):
"""Gets the namespace_string of this XmlItem. # noqa: E501
Returns:
(str): The namespace_string of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_string')
@namespace_string.setter
def namespace_string(self, value):
"""Sets the namespace_string of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_string', value)
@property
def namespace_number(self):
"""Gets the namespace_number of this XmlItem. # noqa: E501
Returns:
(float): The namespace_number of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_number')
@namespace_number.setter
def namespace_number(self, value):
"""Sets the namespace_number of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_number', value)
@property
def namespace_integer(self):
"""Gets the namespace_integer of this XmlItem. # noqa: E501
Returns:
(int): The namespace_integer of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_integer')
@namespace_integer.setter
def namespace_integer(self, value):
"""Sets the namespace_integer of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_integer', value)
@property
def namespace_boolean(self):
"""Gets the namespace_boolean of this XmlItem. # noqa: E501
Returns:
(bool): The namespace_boolean of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_boolean')
@namespace_boolean.setter
def namespace_boolean(self, value):
"""Sets the namespace_boolean of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_boolean', value)
@property
def namespace_array(self):
"""Gets the namespace_array of this XmlItem. # noqa: E501
Returns:
([int]): The namespace_array of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_array')
@namespace_array.setter
def namespace_array(self, value):
"""Sets the namespace_array of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_array', value)
@property
def namespace_wrapped_array(self):
"""Gets the namespace_wrapped_array of this XmlItem. # noqa: E501
Returns:
([int]): The namespace_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__get_item('namespace_wrapped_array')
@namespace_wrapped_array.setter
def namespace_wrapped_array(self, value):
"""Sets the namespace_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__set_item('namespace_wrapped_array', value)
@property
def prefix_ns_string(self):
"""Gets the prefix_ns_string of this XmlItem. # noqa: E501
Returns:
(str): The prefix_ns_string of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_string')
@prefix_ns_string.setter
def prefix_ns_string(self, value):
"""Sets the prefix_ns_string of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_string', value)
@property
def prefix_ns_number(self):
"""Gets the prefix_ns_number of this XmlItem. # noqa: E501
Returns:
(float): The prefix_ns_number of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_number')
@prefix_ns_number.setter
def prefix_ns_number(self, value):
"""Sets the prefix_ns_number of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_number', value)
@property
def prefix_ns_integer(self):
"""Gets the prefix_ns_integer of this XmlItem. # noqa: E501
Returns:
(int): The prefix_ns_integer of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_integer')
@prefix_ns_integer.setter
def prefix_ns_integer(self, value):
"""Sets the prefix_ns_integer of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_integer', value)
@property
def prefix_ns_boolean(self):
"""Gets the prefix_ns_boolean of this XmlItem. # noqa: E501
Returns:
(bool): The prefix_ns_boolean of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_boolean')
@prefix_ns_boolean.setter
def prefix_ns_boolean(self, value):
"""Sets the prefix_ns_boolean of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_boolean', value)
@property
def prefix_ns_array(self):
"""Gets the prefix_ns_array of this XmlItem. # noqa: E501
Returns:
([int]): The prefix_ns_array of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_array')
@prefix_ns_array.setter
def prefix_ns_array(self, value):
"""Sets the prefix_ns_array of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_array', value)
@property
def prefix_ns_wrapped_array(self):
"""Gets the prefix_ns_wrapped_array of this XmlItem. # noqa: E501
Returns:
([int]): The prefix_ns_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__get_item('prefix_ns_wrapped_array')
@prefix_ns_wrapped_array.setter
def prefix_ns_wrapped_array(self, value):
"""Sets the prefix_ns_wrapped_array of this XmlItem. # noqa: E501
"""
return self.__set_item('prefix_ns_wrapped_array', value)
def to_dict(self):
"""Returns the model properties as a dict"""
return model_to_dict(self, serialize=False)
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, XmlItem):
return False
if not set(self._data_store.keys()) == set(other._data_store.keys()):
return False
for _var_name, this_val in six.iteritems(self._data_store):
that_val = other._data_store[_var_name]
types = set()
types.add(this_val.__class__)
types.add(that_val.__class__)
vals_equal = this_val == that_val
if (not six.PY3 and
len(types) == 2 and unicode in types): # noqa: F821
vals_equal = (
this_val.encode('utf-8') == that_val.encode('utf-8')
)
if not vals_equal:
return False
return True
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
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825,
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37227,
35561,
2081,
611,
1111,
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389,
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4961,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
407,
2116,
6624,
584,
198
] | 2.241857 | 10,684 |
"""
Die Modelle für Projektweite Daten: Nutzer/Profile
"""
from django.db import models
from django.contrib.auth.models import AbstractUser
from django.conf import settings
from django.utils.translation import ugettext as _
from userena.models import UserenaBaseProfile
from django.core.validators import RegexValidator
import random, string
from django.template.defaultfilters import slugify
from django.urls import reverse
def knoepfe_kopf(user):
""" gibt Knöpfe für Kopfleiste als Liste von Tupeln zurück """
anmelden = (reverse('userena_signin'), 'Anmelden')
registrieren = (reverse('userena_signup'), 'Registrieren')
abmelden = (reverse('userena_signout'), 'Abmelden')
profil = lambda nutzer: (reverse('userena_profile_detail',
kwargs={'username': nutzer.username}), 'Profil')
spam = ('spam', 'spam')
admin = ('/admin/', 'admin')
if user.username == 'admin':
liste = [abmelden, profil(user), spam]
elif user.is_authenticated():
liste = [abmelden, profil(user)]
else:
liste = [anmelden, registrieren]
if user.is_staff and user.get_all_permissions():
liste.append(admin)
return liste
def knoepfe_menü(user):
""" gibt Knöpfe für Menüleiste als Liste von Tupeln zurück """
alle = {
'index': ('/', 'Startseite'),
'olymp': (reverse('Wettbewerbe:index'), 'Wettbewerbe'),
'ehemalige': (reverse('Ehemalige:index'), 'Ehemalige'),
'impressum': (reverse('impressum'), 'Impressum'),
'db': ('https://olymp.piokg.de/static/db.pdf', 'Datenbanklayout'), # quick and very dirty :)
'todo': ('/todo/', 'ToDo-Liste'),
}
if user.username == 'admin':
return [alle[name] for name in ('index', 'olymp', 'ehemalige', 'todo', 'db')]
else:
return [alle[name] for name in ('index', 'olymp', 'db', 'impressum')]
class Nutzer(AbstractUser):
""" Nutzer-Klasse """
def knoepfe_kopf(nutzer):
""" soll Liste von Paaren für Knöpfe der Kopfleiste ausgeben
Nutzt im Moment die module-fkt gleichen Namens, könnte später vll
die Gruppenzugehörigkeit heranziehen, etc, ist flexibel """
return knoepfe_kopf(nutzer)
def knoepfe_menü(self):
""" soll Liste von Paaren für Knöpfe der Menüleiste ausgeben
Nutzt im Moment die module-fkt gleichen Namens, könnte später vll
die Gruppenzugehörigkeit heranziehen, etc, ist flexibel """
return knoepfe_menü(self)
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8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
220,
198
] | 2.281982 | 1,110 |
import numpy as np
import cv2
import sys
import torch
sys.path.append('..')
from torch.utils import data
from torch.utils.data import DataLoader
if __name__ == '__main__':
file_list = './data/test_data/list.txt'
wlfwdataset = WLFWDatasets(file_list)
dataloader = DataLoader(wlfwdataset, batch_size=256, shuffle=True, num_workers=0, drop_last=False)
for img, landmark, attribute, euler_angle in dataloader:
print("img shape", img.shape)
print("landmark size", landmark.size())
print("attrbute size", attribute)
print("euler_angle", euler_angle.size())
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62,
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1600,
304,
18173,
62,
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13,
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28955,
198
] | 2.541322 | 242 |
import os
import numpy as np
import pytest
from nexusformat.nexus.tree import NXfield, NXgroup, NXroot, nxload
@pytest.mark.parametrize("save", ["False", "True"])
| [
11748,
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8973,
8,
198
] | 2.87931 | 58 |
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