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175
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e8a482a9b3b788fc66ef4702abfdbbabb5a1cc73
|
e2509a9dd209911ff65bed4d30501186bb72323e
|
/client.py
|
242e34ff43e72e1bc585fb55355be44a4665154f
|
[
"MIT"
] |
permissive
|
pwr22/cloudflare_client
|
e9d270da36983c17b90c7b2d3f414cedcbf7e3ff
|
3b24b497bf7c18aed5acfa13521e378a43e88e39
|
refs/heads/master
| 2021-01-22T06:40:24.237325
| 2015-02-16T08:54:27
| 2015-02-16T08:54:27
| 30,859,748
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,466
|
py
|
# CloudFlare client API module
import requests
from upstream_error import UpstreamError
CF_URL = 'https://www.cloudflare.com/api_json.html'
# Specify object explicitly for Python 2 support
class Client (object):
def __init__(self, email, token):
'''Contruct a CloudFlare Client API object'''
self.__user = email
self.__key = token
def _makeCall(self, params):
'''Calls through to the CF API with params provided'''
# Splice in auth details, overriding existing
params.update({ 'email' : self.__user, 'tkn' : self.__key})
#TODO does requests raise on http errors?
res = requests.post(CF_URL, data = params).json()
if res['result'] == 'error':
# err_code could be missing
raise UpstreamError(res['msg'], res.get('err_code'))
return res
def __getattr__(self, callType):
'''Overridden to auto create methods wrapping CF API actions'''
# Generate wrapper
def callFunc(self, **params):
# Splice in the action, override anything existing
params.update({ 'a' : callType })
return self._makeCall(params)
# Avoid unnecessary redefinitions by installing in class
setattr(Client, callType, callFunc)
# Accessed this way to turn into method
# All these selfs because what if crazy inheritance?
return type(self).__dict__[callType].__get__(self, type(self))
|
[
"me+dev@peter-r.co.uk"
] |
me+dev@peter-r.co.uk
|
96cfff0c3cebbee5ce519d5146980a0dcec12ef5
|
1caff4eaf08d96c7a6f594499ec8664fc1b8cfe8
|
/test.py
|
c07ddacd8b2b2514c09930d0c90b3370d0cbdce5
|
[] |
no_license
|
ravikrranjan/learning-python
|
f94598454e0a58a6fcce79fb3eea75402e2eac4a
|
17d7628f12cec87913d79914d5c8b40b409bd371
|
refs/heads/master
| 2021-05-17T02:19:54.391310
| 2020-05-19T16:48:09
| 2020-05-19T16:48:09
| 250,573,197
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 16
|
py
|
print('Test');
|
[
"rkumar792@gmail.com"
] |
rkumar792@gmail.com
|
c38b951a2d54aed303e5d627666f8a9d612aee54
|
1c29d3282a1caabc4d3cac84319ca5d487725f61
|
/app.py
|
75169a967b52d772ef537deac97a13638f0b85ba
|
[] |
no_license
|
FrancoJigo/restaAPI
|
b5e03aa872a1aa6e59cf6af5f6257259ab48ce9d
|
17d184f817feb6babfa2f262645750a11063579a
|
refs/heads/master
| 2021-01-25T14:24:06.209598
| 2018-03-04T09:02:37
| 2018-03-04T09:02:37
| 123,695,140
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,786
|
py
|
from flask import Flask, url_for
from flask import request
from flask import json , Response , jsonify
from functools import wraps
app = Flask(__name__)
@app.route('/helloworld', methods=['GET'])
def indexhello():
if 'name' in request:
return 'Hello ! Welcome' + request.args['name']
else:
return 'Hello Amega'
@app.route('/echo', methods = ['GET', 'POST', 'PATCH', 'PUT', 'DELETE'])
def api_echo():
if request.method == 'GET':
return "ECHO: GET\n"
elif request.method == 'POST':
return "ECHO: POST\n"
elif request.method == 'PATCH':
return "ECHO: PATCH\n"
elif request.method == 'PUT':
return "ECHO: PUT\n"
elif request.method == 'DELETE':
return "ECHO: DELETE"
@app.route('/messages', methods = ['POST'])
def api_message():
if request.headers['Content-Type'] == 'text/plain':
return "Text Message: " + request.data
elif request.headers['Content-Type'] == 'application/json':
return "JSON Message: " + json.dumps(request.json)
elif request.headers['Content-Type'] == 'application/octet-stream':
f = open('./binary', 'wb')
f.write(request.data)
f.close()
return "Binary message written!"
else:
return "415 Unsupported Media Type ;)"
@app.route('/hello', methods = ['GET'])
def api_hello():
data = {
'hello' : 'world',
'number' : 3
}
js = json.dumps(data)
resp = jsonify(data)
resp.status_code = 200
resp.headers['Link'] = 'http://luisrei.com'
return resp
@app.errorhandler(404)
def not_found(error=None):
message = {
'status': 404,
'message': 'Not Found: ' + request.url,
}
resp = jsonify(message)
resp.status_code = 404
return resp
@app.route('/users/<userid>', methods = ['GET'])
def api_users(userid):
users = {'1':'jigu', '2':'franco', '3':'francojigo'}
if userid in users:
return jsonify({userid:users[userid]})
else:
return not_found()
def check_auth(username, password):
return username == 'admin' and password == 'password'
def authenticate():
message = {'message': "Authenticate."}
resp = jsonify(message)
resp.status_code = 401
resp.headers['WWW-Authenticate'] = 'Basic realm="Example"'
return resp
def requires_auth(f):
@wraps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth:
return authenticate()
elif not check_auth(auth.username, auth.password):
return authenticate()
return f(*args, **kwargs)
return decorated
@app.route('/secrets')
@requires_auth
def api_hello1():
return "Shhh this is top secret spy stuff!"
if __name__== '__main__':
app.run(debug=True)
|
[
"frankeejigzz@gmail.com"
] |
frankeejigzz@gmail.com
|
c64909b719e0c584c9a4cdc20015e2e2f8fcf9f0
|
0f66f175320140daf15c8c4f85e6af717ebaeec7
|
/test_supporting_commands.py
|
ca4d2c14c7d759a46a541d09f8c01312c3cb330c
|
[] |
no_license
|
PVSemk/IADPythonLab_1
|
50541536fd21812d5e28e427baa8c20bf3417d4d
|
520edcefd07499b51afaa0960ce723b00a32e735
|
refs/heads/master
| 2020-07-20T06:16:10.717534
| 2019-09-05T14:51:22
| 2019-09-05T14:51:22
| 206,588,312
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,001
|
py
|
import supporting_commands as func
# This file contains only testing for functions which are in supporting_commands.py
# In the file commands.py there is a number of functions which contain required input checking
# Which is not in supporting_commands.py (functions delete_person_by_number or search)
# However, principle is the same
# So I decided not to rewrite these segments
def test_check_value():
# If no birthday was entered, the list contains only number
# Due to transformations made in the main module
# That is why test ['Correct number', ''] is False
assert func.check_value(['']) == 0
assert func.check_value(['sadass']) == 0
assert func.check_value(['', 'asdsaad']) == 0
assert func.check_value(['+78005553535']) == 1
assert func.check_value(['+78005553535', '']) == 0
assert func.check_value(['+78005553535', ' ']) == 0
assert func.check_value(['88005553535']) == 1
assert func.check_value(['98006665656']) == 0
assert func.check_value(['890055565656']) == 0
assert func.check_value(['88005553535', 'asd']) == 0
assert func.check_value(['', '12/12/1989']) == 0
assert func.check_value(['8+7910345435', '12/12/1989']) == 0
assert func.check_value(['89103943410', '30/12/2045']) == 0
assert func.check_value(['+78005553535', '30/11/2018']) == 1
def test_check_bd_date():
assert func.check_bd_date('22/11/1986') == 1
assert func.check_bd_date('100/11/1986') == 0
assert func.check_bd_date('0/11/1986') == 0
assert func.check_bd_date('22/0/1986') == 0
assert func.check_bd_date('28/1/-100') == 0
assert func.check_bd_date('31/12/1486') == 1
assert func.check_bd_date('sdfdsfsdfsd') == 0
assert func.check_bd_date('sdf/dsfs/dfsd') == 0
assert func.check_bd_date('') == 0
assert func.check_bd_date('1233452345') == 0
assert func.check_bd_date('12/123') == 0
assert func.check_bd_date('ываыва') == 0
assert func.check_bd_date('0/0/0') == 0
assert func.check_bd_date('31/12/2018') == 0
assert func.check_bd_date('29/11/2045') == 0
def test_check_name_surname():
assert func.check_name_surname('Pavel Semkin') == 1
assert func.check_name_surname('pavel semkin') == 1
assert func.check_name_surname('Petr') == 0
assert func.check_name_surname('Sasd.sdfsdf sdf.sdf.sdfsdf') == 0
assert func.check_name_surname('123324646463452 12341321234') == 0
assert func.check_name_surname('_____ asdfasdfsdf _____') == 0
assert func.check_name_surname('____ _____') == 0
assert func.check_name_surname('Sasha1 Petrov') == 1
assert func.check_name_surname('1asha1 2etrov') == 0
assert func.check_name_surname('1asha1 petrov') == 0
assert func.check_name_surname('\n') == 0
def test_check_number():
assert func.check_number('89101418456') == 1
assert func.check_number('8910141845') == 0
assert func.check_number(func.number_format('+79101418456')) == 1
assert func.check_number(func.number_format('+79101418456123123')) == 0
assert func.check_number('Wrong input') == 0
assert func.check_number('89101sdffsdf') == 0
assert func.check_number('8+7+7+7+7+7+7+7+7+7+7') == 0
assert func.check_number('8910 123123 123123') == 0
assert func.check_number('99101412356') == 0
assert func.check_number('8910141845+7') == 0
assert func.check_number('891014184+7') == 0
assert func.check_number('\n') == 0
def test_check_choice():
assert func.check_choice(1, 6, '123') == -1
assert func.check_choice(1, 6, '1') == 1
assert func.check_choice(1, 6, '3') == 1
assert func.check_choice(1, 6, '6') == 0
assert func.check_choice(1, 6, 'asdasd') == -1
assert func.check_choice(1, 6, '') == -1
assert func.check_choice(1, 6, '12.5') == -1
assert func.check_choice(1, 6, '12/5') == -1
assert func.check_choice(1, 6, ' ') == -1
assert func.check_choice(1, 6, '\n') == -1
|
[
"pvsemk@mail.ru"
] |
pvsemk@mail.ru
|
c4c0b10223a78356469659f23378cbd2a4413a30
|
1bf423ffbd4e0f2b79224c3109dbb3da7317eb09
|
/disguise/forms.py
|
05eb03b77b38b43a0b84d44d9c32f06340cf661e
|
[] |
no_license
|
rahmaniaam/disguisely
|
09d189034450a8349b3249f916d048ec5cef0930
|
8fbfdbbf11015bdec009bf7902a8a2d4e8e28985
|
refs/heads/master
| 2022-12-04T11:24:20.677038
| 2020-03-21T01:51:55
| 2020-03-21T01:51:55
| 239,411,204
| 0
| 0
| null | 2022-11-22T05:24:40
| 2020-02-10T02:27:46
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 431
|
py
|
from django import forms
from .models import Disguise, Document
class DisguiseForm(forms.ModelForm):
class Meta:
model = Disguise
exclude = []
widgets = {
'dob': forms.DateInput(format=('%m/%d/%Y'), attrs={'class':'form-control', 'placeholder':'Select a date', 'type':'date'}),
}
class DocumentForm(forms.ModelForm):
class Meta:
model = Document
fields = '__all__'
|
[
"rahmania.astrid@ui.ac.id"
] |
rahmania.astrid@ui.ac.id
|
1fcb488242e10d0c03422d74916f668b21eb791b
|
0e69513ca0fda765b5f655c4405aafb209491389
|
/input/parse_pcm-dpc_it.py
|
4492610a839245b4948d341f93c7abb1d5d1c339
|
[] |
no_license
|
adrianrequena/covid19
|
57a54fdaec79c0d1d57de63810e3337513e87b2f
|
a13cb2c117a68de2740702831f84c17049aa95ab
|
refs/heads/master
| 2023-07-20T01:49:44.583897
| 2020-04-01T19:19:21
| 2020-04-01T19:19:21
| 252,279,864
| 0
| 0
| null | 2023-07-06T21:57:02
| 2020-04-01T20:28:35
|
Python
|
UTF-8
|
Python
| false
| false
| 1,687
|
py
|
#!/usr/bin/env python
import os
import sys
from pathlib import Path
from datetime import datetime, timedelta
import pandas
from utils import \
parse_level_args, github_raw_dataframe, github_raw_url, dataframe_output, merge_previous
# Root path of the project
ROOT = Path(os.path.dirname(__file__)) / '..'
# This script can parse both region-level and country-level data
is_region = parse_level_args(sys.argv[1:]).level == 'region'
if is_region:
df = github_raw_dataframe(
'pcm-dpc/COVID-19', 'dati-json/dpc-covid19-ita-regioni.json', orient='records')
else:
df = github_raw_dataframe(
'pcm-dpc/COVID-19', 'dati-json/dpc-covid19-ita-andamento-nazionale.json', orient='records')
df = df.rename(columns={
'data': 'Date',
'totale_casi': 'Confirmed',
'deceduti': 'Deaths',
'tamponi': 'Tested'
})
if is_region:
df['_RegionLabel'] = df['denominazione_regione']
# Parse date into a datetime object
df['Date'] = df['Date'].apply(lambda date: datetime.fromisoformat(date).date())
# Offset date by 1 day to match ECDC report
if not is_region:
df['RegionCode'] = None
df['Date'] = df['Date'].apply(lambda date: date + timedelta(days=1))
# Convert dates to ISO format
df['Date'] = df['Date'].apply(lambda date: date.isoformat())
# Add the country code to all records
df['CountryCode'] = 'IT'
# Merge the new data with the existing data (prefer new data if duplicates)
if not is_region:
filter_function = lambda row: row['CountryCode'] == 'IT' and pandas.isna(row['RegionCode'])
df = merge_previous(df, ['Date', 'CountryCode'], filter_function)
# Output the results
dataframe_output(df, ROOT, 'IT' if is_region else None)
|
[
"oscar@wahltinez.org"
] |
oscar@wahltinez.org
|
a139f38010032600fcc20c2f08653300a538e11f
|
e15f8e349c9f35fbce68e7c2aab2d3ca10f9cdf9
|
/cpss_vimeo/tests.py
|
1b19a01e58fb63ba4700d1d4664dc6021cbdbe5d
|
[
"Apache-2.0"
] |
permissive
|
xgdfalcon/django-cpss-vimeo
|
6059a9d8a8f29fe16bb9e03a35fb430c81cb69a4
|
fe474e4ab6306b4411df83a74ee8aecf8843a39a
|
refs/heads/master
| 2020-12-06T11:17:41.774940
| 2020-01-20T04:12:28
| 2020-01-20T04:12:28
| 232,449,912
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,374
|
py
|
#
# @license
# Copyright (c) 2020 XGDFalcon®. All Rights Reserved.
#
#
# XGDFalcon LLC retains all intellectual property rights to the code
# distributed as part of the Control Point System Software (CPSS) package.
#
"""
This python module provides the models for the video vault application.
Written by Larry Latouf (xgdfalcon@gmail.com)
"""
from django.test import TestCase
from django.test.client import RequestFactory
from .models import VimeoClientOption
import os
CLIENT_SECRET = os.environ['CLIENT_SECRET']
CLIENT_ID = os.environ['CLIENT_ID']
ACCESS_TOKEN = os.environ['ACCESS_TOKEN']
USER_ID = os.environ['USER_ID']
PROJECT_ID = os.environ['PROJECT_ID']
class VimeoDjangoTestCase(TestCase):
def setUp(self):
VimeoClientOption.objects.create(
vimeo_user_id=USER_ID,
vimeo_client_id=CLIENT_ID,
vimeo_client_secret=CLIENT_SECRET,
vimeo_access_token=ACCESS_TOKEN,
vimeo_project_id=PROJECT_ID)
def test_retrieve_project(self):
# response = self.client.get('/')
collection = VimeoClientOption.objects.get(vimeo_project_id=PROJECT_ID)
result = collection.get_folder_contents()
print(result)
def test_get_project(self):
rf = RequestFactory()
get_request = rf.get('project/'+PROJECT_ID)
print(get_request)
|
[
"xgdfalcon@gmail.com"
] |
xgdfalcon@gmail.com
|
ac6686f284a20fb047e983ae60f82972815b3f89
|
4ca55192b0cb2720c6ff6b221316bcc9cfa07ce1
|
/rememberme/responser.py
|
ef6d88b8508104813f253b6d135d2fdb5de04472
|
[] |
no_license
|
alexanderSvito/rememberme
|
603cadf1d6700cb9410856df00022bd361632cfb
|
9af7b93c627634a10d13160b369b52634a7dcbdd
|
refs/heads/master
| 2023-08-02T14:05:22.791466
| 2021-09-04T19:27:51
| 2021-09-04T19:27:51
| 181,683,424
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,975
|
py
|
import random
import importlib
from data.answer_schemas import *
class Responser:
def __init__(self, lang):
self.lang = lang
try:
msg = importlib.import_module('data.lang.{}'.format(lang))
except ImportError:
msg = importlib.import_module('data.lang.ru_ru')
self.messages = msg.__dict__
self.msg = msg
def get_default_message(self):
return self.msg.UNKNOWN_MSG
def get_help_response(self):
return self.msg.HELP
def get_guesser_start_response(self):
return self.msg.GUESSER_RULES
def get_cancel_response(self):
return self.msg.CANCEL
def get_not_a_game_response(self):
return None
def get_welcome_response(self):
return self.fill_schema(START_SCHEME)
def get_language_set_response(self):
return self.msg.LANGUAGE_SET
def get_ambiguous_language_response(self):
return self.msg.AMBIGUOUS_LANG
def get_error_msg(self):
return self.msg.PARSE_ERROR_MSG
def get_start_conj_response(self, term, translations, conj, round):
scheme = self.fill_schema(CONJ_START_SCHEME)
return scheme.format(
term=term,
translations=', '.join(translations),
conj=conj,
round=round
)
def get_add_pack_response(self, is_success):
if is_success:
return self.msg.ADD_PACK_SUCCESS_MSG
else:
return self.msg.PACK_NOT_FOUND_MSG
def get_list_packs_response(self, packs):
return '\n'.join([f'> {pack}' for pack in packs])
def get_translate_response(self, translations):
if translations:
return ', '.join(translations)
else:
raise ValueError(self.msg.TRANSLATION_ERROR_MSG)
def get_conj_response(self, conj):
if conj is None:
return self.msg.NO_CONJUGATION_MSG
res = ''
for time, data in conj.items():
res += f'*{data["russian_form"]}*\n'
for pronoun, form in data['conj'].items():
res += f'{pronoun}: _{form}_\n'
res += '\n'
return res
def get_start_guess_response(self, word):
if word is None:
return self.msg.NO_WORDS_MSG
scheme = self.fill_schema(GUESSER_START_SCHEME)
return scheme.format(
round=word
)
def get_same_word_response(self, anchor, response):
scheme = self.fill_schema(SAME_WORD_SCHEME)
return scheme.format(
anchor=anchor,
response=response
)
def get_created_response(self, anchor, response):
if anchor is None or response is None:
return self.msg.DATABASE_ERROR_MSG
scheme = self.fill_schema(CREATED_SCHEME)
return scheme.format(
anchor=anchor,
response=response
)
def get_guess_response(self, is_correct, is_finished, **kwargs):
if is_correct:
scheme = CORRECT_SCHEME + '\n'
else:
scheme = WRONG_SCHEME + '\n'
if is_finished:
scheme += GAME_OVER_SCHEME
else:
kwargs['round'] = kwargs['round'].capitalize()
scheme += NEXT_ROUND_SCHEME
scheme = self.fill_schema(scheme)
return scheme.format(**kwargs)
def get_conj_game_response(self,
is_correct,
is_finished,
**kwargs):
if is_correct:
scheme = CORRECT_SCHEME + '\n'
else:
scheme = WRONG_SCHEME + '\n'
if is_finished:
scheme += GAME_OVER_SCHEME
else:
kwargs['round'] = kwargs['round'].capitalize()
scheme += NEXT_CONJ_ROUND_SCHEME
scheme = self.fill_schema(scheme)
return scheme.format(**kwargs)
def get_edited_response(self, count, anchor, response):
if anchor is None or response is None:
return self.msg.DATABASE_ERROR_MSG
if count == 0:
return self.msg.NOT_FOUND
scheme = self.fill_schema(EDITED_SCHEME)
return scheme.format(
anchor=anchor,
response=response
)
def get_deleted_response(self, count, anchor, response):
if anchor is None or response is None:
return self.msg.DATABASE_ERROR_MSG
if count == 0:
return self.msg.NOT_FOUND
scheme = self.fill_schema(DELETED_SCHEME)
return scheme.format(
anchor=anchor,
response=response
)
def fill_schema(self, scheme, **kwargs):
messages = {
identifier: random.choice(options)
if isinstance(options, list) else options
for identifier, options
in self.messages.items()
}
static_scheme = scheme.format(**messages)
return static_scheme
|
[
"alexandervirk@gmail.com"
] |
alexandervirk@gmail.com
|
241c65e1a9bf79e74f5298faae186db101c10168
|
b0abf6ca5075554ca5b1e28c9e9881620670bffc
|
/evascrapy/pipelines.py
|
32043a1449d205291f1da00ecdc88c97f3c0bfd5
|
[] |
no_license
|
gianozdp/EvaScrapy
|
4ca3aacb1f48fa98386500e385accbc6b61bc432
|
c7782098501df0cb63f68bb233773710e86a78a1
|
refs/heads/master
| 2020-07-26T15:01:49.819957
| 2019-06-28T14:56:41
| 2019-06-28T14:56:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,743
|
py
|
import pathlib
import oss2
import ssl
import os
from urllib.parse import urlparse
from io import BytesIO
from kafka import KafkaProducer
from minio import Minio
from mns.account import Account
from mns.queue import Message
from evascrapy.items import QueueBasedItem, RawTextItem, TorrentFileItem
import logging
import urllib3
from elasticsearch import Elasticsearch
logger = logging.getLogger(__name__)
# default timeout for minio
urllib3.Timeout.DEFAULT_TIMEOUT = 5.0
# fix ValueError: Timeout value connect was <object object at 0x10db3a280>, but it must be an int, float or None.
def from_float(timeout):
return urllib3.Timeout(read=3, connect=3)
urllib3.Timeout.from_float = from_float
# fix ValueError: Timeout value connect was <object object at xxx>, but it must be an int, float or None.
class LocalFilePipeline(object):
def process_item(self, item: QueueBasedItem, spider) -> QueueBasedItem:
if not isinstance(item, QueueBasedItem):
return item
filepath = item.to_filepath(spider)
pathlib.Path(os.path.dirname(filepath)).mkdir(parents=True, exist_ok=True)
mode = 'w+' if isinstance(item, RawTextItem) else 'wb+'
with open(filepath, mode) as f:
f.write(
item.to_string() if isinstance(item, RawTextItem) else item.to_bytes()
)
return item
class AliyunOssPipeline(object):
_oss_bucket = None
def get_oss_bucket(self, settings: dict) -> oss2.Bucket:
if self._oss_bucket:
return self._oss_bucket
auth = oss2.Auth(settings['OSS_ACCESS_KEY_ID'], settings['OSS_ACCESS_KEY_SECRET'])
self._oss_bucket = oss2.Bucket(auth, settings['OSS_ENDPOINT'], settings['OSS_BUCKET'], connect_timeout=3.0)
return self._oss_bucket
def process_item(self, item: QueueBasedItem, spider) -> QueueBasedItem:
if not isinstance(item, QueueBasedItem):
return item
self.get_oss_bucket(
spider.settings
).put_object(
key=item.to_filepath(spider),
data=item.to_string() if isinstance(item, RawTextItem) else item.to_bytes()
)
return item
class AwsS3Pipeline(object):
_client = None
def get_client(self, settings) -> Minio:
if self._client:
return self._client
client = Minio(
settings['AWS_S3_ENDPOINT'],
access_key=settings['AWS_S3_ACCESS_KEY'],
secret_key=settings['AWS_S3_ACCESS_SECRET'],
region=settings['AWS_S3_REGION'],
secure=settings['AWS_S3_ACCESS_SECURE']
)
self._client = client
return client
def process_item(self, item: QueueBasedItem, spider) -> QueueBasedItem:
if not isinstance(item, QueueBasedItem):
return item
content = BytesIO(item.to_string().encode()) if isinstance(item, RawTextItem) else BytesIO(item.to_bytes())
self.get_client(
spider.settings
).put_object(
bucket_name=spider.settings['AWS_S3_DEFAULT_BUCKET'],
object_name=item.to_filepath(spider),
data=content,
length=content.getbuffer().nbytes,
metadata=item.get_meta(),
)
return item
class KafkaPipeline(object):
_kafka_producer = None
def get_producer(self, settings):
if self._kafka_producer:
return self._kafka_producer
if settings['KAFKA_SSL_ENABLE']:
context = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
context.verify_mode = ssl.CERT_REQUIRED
context.load_verify_locations(settings['KAFKA_SASL_CA_CERT_LOCATION'])
self._kafka_producer = KafkaProducer(
bootstrap_servers=settings['KAFKA_SERVER_STRING'].split(','),
sasl_mechanism=settings['KAFKA_SASL_MECHANISM'],
ssl_context=context,
security_protocol=settings['KAFKA_SECURITY_PROTOCOL'],
api_version=(0, 10),
retries=5,
sasl_plain_username=settings['KAFKA_SASL_PLAIN_USERNAME'],
sasl_plain_password=settings['KAFKA_SASL_PLAIN_PASSWORD']
)
else:
self._kafka_producer = KafkaProducer(
bootstrap_servers=settings['KAFKA_SERVER_STRING'].split(','),
api_version=(0, 10),
retries=5,
)
return self._kafka_producer
def process_item(self, item: QueueBasedItem, spider) -> QueueBasedItem:
if not isinstance(item, QueueBasedItem):
return item
future = self.get_producer(
spider.settings
).send(
spider.settings['KAFKA_TOPIC'],
item.to_kafka_message(spider)
)
future.get()
return item
class AliyunMnsPipeline(object):
_mns_producer = None
def get_producer(self, settings):
if self._mns_producer:
return self._mns_producer
account = Account(
host=settings['MNS_ACCOUNT_ENDPOINT'],
access_id=settings['MNS_ACCESSKEY_ID'],
access_key=settings['MNS_ACCESSKEY_SECRET'],
logger=logging.getLogger('evascrapy.pipelines.AliyunMnsPipeline'),
debug=False,
)
self._mns_producer = account.get_queue(settings['MNS_QUEUE_NAME'])
return self._mns_producer
def process_item(self, item, spider) -> QueueBasedItem:
if not isinstance(item, QueueBasedItem):
return item
msg = Message(item.to_mns_message(spider))
future = self.get_producer(spider.settings)
future.send_message(msg)
return item
class ElasticDupePipeline(object):
_es = None
def get_elastic(self, settings) -> Elasticsearch:
if self._es:
return self._es
url = urlparse(settings['TORRENT_FILE_ELASTIC_DUPE_URL'])
http_auth = (url.username, url.password) if url.username and url.password else None
self._es = Elasticsearch(
url.hostname,
http_auth=http_auth,
scheme=url.scheme,
port=url.port, )
return self._es
def process_item(self, item, spider) -> QueueBasedItem or None:
if not isinstance(item, TorrentFileItem):
return item
if self.get_elastic(spider.settings).exists(
index=spider.settings['TORRENT_FILE_ELASTIC_DUPE_INDICE'],
doc_type=spider.settings['TORRENT_FILE_ELASTIC_DUPE_DOCTYPE'],
id=item.get_info_hash(),
):
logger.info('Torrent item %s ignored by pipelines.ElasticDupePipeline', item.get_info_hash())
return None
return item
|
[
"allo.vince@gmail.com"
] |
allo.vince@gmail.com
|
1c332720219986262761e730eb3c9f28373b9757
|
8ca6a90b7db0cd0d7a54f98628359806bf18dcf4
|
/lstm/datain.py
|
0f42f87d3d8e190da173bab8feb0f66f3992c574
|
[] |
no_license
|
kateharline/buckler_lab_projects
|
39f47daeaf05925156a4a1db904941db7a06feef
|
904888fa836ba365329e66c331ae500e7a888195
|
refs/heads/master
| 2021-05-18T22:32:28.974481
| 2020-03-31T00:04:07
| 2020-03-31T00:04:07
| 251,437,497
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,559
|
py
|
# external libraries
import numpy as np
import pandas as pd
import os
import sklearn.preprocessing as sk
import pickfamily as pf
import platform
# import control datasets for testing
import control as c
####--------------making matrices-------------#############
def txt_to_csv(txt_file):
'''
convert txt file to csv to load as a dataframe etc
:param txt_file: str filename for the txt file to load
:return: NA, outputs file as csv
'''
with open(txt_file) as f:
csv_name = os.path.basename(txt_file).split()[0] + '.csv'
with open(csv_name, 'w') as out:
for line in f:
new_line = ','.join(line.split())
out.write(new_line)
out.write('\n')
##### make hydrophobicity matrix -- how to denote *
# column names for data frame
def make_hphob_matrix():
'''
compute differences in hydrophobicity between amino acids
:return: NA
'''
hydro_values = {'I': 4.92, 'L': 4.92, 'V': 4.04, 'P': 2.98, 'F': 2.98, 'M': 2.35,
'W': 2.33, 'A': 1.81, 'C': 1.28, 'G': 0.94, 'Y': -0.14, 'T': -2.57,
'S': -3.40, 'H': -4.66, 'Q': -5.54, 'K': -5.55, 'N': -6.64, 'E': -6.81,
'D': -8.72, 'R': -14.92}
csv_labels = list(hydro_values.keys())
def difference_matrix(values):
'''
compute pairwise differences for relational data
:param values: float values to find differences between
:return: matrix of differences (2D array)
'''
os.chdir('~/Desktop/buckler-lab/box-data')
vals = list(values.values())
matrix = np.zeros((20, 20))
for i in range(len(vals)):
for j in range(len(vals)):
matrix[i][j] = abs(vals[i] - vals[j]) / 20
matrix = pd.DataFrame(data=matrix, index=csv_labels, columns=csv_labels)
matrix['*'] = np.zeros((20))
return matrix
#compute matrix
matrix = difference_matrix(hydro_values)
matrix.to_csv(path_or_buf='protein_hphob.csv')
def float_to_rank():
'''
convert dictionary of protein hphob values to integers based on rank
:return: NA, output to txt file
'''
hphob_values = {'I': 4.92, 'L': 4.92, 'V': 4.04, 'P': 2.98, 'F': 2.98, 'M': 2.35,
'W': 2.33, 'A': 1.81, 'C': 1.28, 'G': 0.94, 'Y': -0.14, 'T': -2.57,
'S': -3.40, 'H': -4.66, 'Q': -5.54, 'K': -5.55, 'N': -6.64, 'E': -6.81,
'D': -8.72, 'R': -14.92, '*': float('-inf')}
########---------actual module code--------------############
def load_data(filename, delim=','):
'''
import data as pandas dataframe
:param filename: string name of the file
:param delim: string delimiter
:return: dataframe version of the given file
useful functions to check state of data
# print(data.head(10))
# print('data types '+str(data.dtypes))
'''
"""read in the csv of gene sequences and RNAseq expression values
returns the data as a pandas dataframe"""
data = pd.read_csv(filepath_or_buffer=filename, delimiter=delim)
return data
def my_max(seqs):
'''
determine the longest sequence
:param seqs: list of string sequences
:return: integer length of the longest sequence
'''
max_string = ''
max_len = len(max_string)
for seq in seqs:
if len(seq) > max_len:
max_string = seq
max_len = len(max_string)
return max_len
def base_to_one_hot(data, max, encode_dict):
'''
one hot helper function
:param data: datafrmae containing protein sequences as strings
:param max: int maximum length of sequence to use as array dimension or for padding
:param encode_dict: dataframe that can be used to convert sequence bases/residues to one hot vectors
:return: list of one hot encodings
'''
seqs = data['protein_sequence'].tolist()
d = encode_dict.to_dict('list')
newcol = np.zeros((len(seqs), max, 21))
for k, seq in enumerate(seqs):
# padding check
for i in range(len(seq)):
one_hot = d[seq[i]]
for j in range(0,21):
newcol[k][i][j] = one_hot[j]
return newcol
def encode_hphob(data):
'''
preprocess protein data for embedding, convert amino acid bases to integers based on hydrophobicity
:param data: dataframe with protein sequences and expression values
:return: the same dataframe with a new int array encoding of the proteins
'''
hphob_values = {'I': 4.92, 'L': 4.92, 'V': 4.04, 'P': 2.98, 'F': 2.98, 'M': 2.35,
'W': 2.33, 'A': 1.81, 'C': 1.28, 'G': 0.94, 'Y': -0.14, 'T': -2.57,
'S': -3.40, 'H': -4.66, 'Q': -5.54, 'K': -5.55, 'N': -6.64, 'E': -6.81,
'D': -8.72, 'R': -14.92, '*':0}
protein_seqs = data['protein_sequence'].tolist()
hphob_encoding = [[hphob_values[base] for base in seq ] for seq in protein_seqs]
data['hphob_encode'] = pd.Series(hphob_encoding).values
return data
def get_set(x_data, y_data, set):
'''
return the train, test or val subset of the x or y data
:param x_data: dataframe of x data
:param y_data: dataframe of y data
:param set: string subset of data to extract
:return: new dataframe ready for encoding, ids, sequences, expression values
'''
x_select = x_data.loc[x_data['group'] == set]
y_select = y_data.loc[y_data['group'] == set]
new_df = pd.concat([x_select, y_select], axis=1, join='inner')
return new_df
def extract_y(data, tissue, categorical):
'''
reformat dataframe values into usable np arrays
:param data: dataframe of sequence data and expression values
:param tissue: string tissue to select data from
:param categorical: bool make the data binary threshold [0, 1]
:return: np arrays of y data
'''
# slice out just one hot vectors and protein levels
slice = data.loc[:, [tissue]]
y = np.array(slice[tissue].values)
if categorical:
return binarize(y)
else:
return y
def binarize(y):
'''
create binary representation of expression levels 0: no expression 1: expression
:param y: np array of y data
:return: np array of binary y data
'''
bin_y = y.copy()
bin_y[bin_y > 0] = 1
bin_y[bin_y <= 0] = 0
return bin_y
def standardize(y_train, y_test, y_val):
'''
standardise the data between [0, 1] fit to train data
:param y_train: np array of y training exp values
:param y_test:np array of y test exp values
:param y_val: np array of y val exp values
:return: arrays scaled based on fit to y_train
'''
scaler = sk.MinMaxScaler.fit(y_train)
y_train_scaled = scaler.transform(y_train)
y_test_scaled = scaler.transform(y_test)
y_val_scaled = scaler.transform(y_val)
return y_train_scaled, y_test_scaled, y_val_scaled
def main(data_type='random', categorical=True, standardized=False):
'''
train/test synthetic data
# how long is the sequence and how many are there... for synthetic data
l = 400
n = 10000
synth = c.get_example('protein', n, l)
heavy_As = c.get_example('heavy_As', n, l)
encode_dict = load_data('box-data/protein_onehot.csv')
synth_encoded = encode_o_h(synth, encode_dict)
a_encoded = encode_o_h(heavy_As, encode_dict)
synth_encoded.to_csv('synth.csv')
a_encoded.to_csv('a_synth.csv')
'''
tissue = 'Protein_Leaf_Zone_3_Growth'
# pick x and y data based on family characteristics
x_data, y_data = pf.main(data_type)
# extract x and y values from dataframe based on set designation
train = get_set(x_data, y_data, 'train')
val = get_set(x_data, y_data, 'val')
test = get_set(x_data, y_data, 'test')
# one hot encode the x values and split based on set designation
encode_dict = load_data('protein_onehot.csv')
max = my_max(x_data['protein_sequence'].tolist())
train_encoded = base_to_one_hot(train, max, encode_dict)
val_encoded = base_to_one_hot(val, max, encode_dict)
test_encoded = base_to_one_hot(test, max, encode_dict)
# split the y values based on set designation
train = extract_y(train, tissue, categorical)
test = extract_y(test, tissue, categorical)
val = extract_y(val, tissue, categorical)
if standardized:
train, test, val = standardized(train, test, val)
return (train_encoded, train, test_encoded, test, val_encoded, val)
if __name__ == '__main__':
main()
|
[
"kharline@wustl.edu"
] |
kharline@wustl.edu
|
fe24ea8d36456e341af990a617aaf46b1ece5dac
|
323caaa8cbbe2b8d3cf3ae433d273a92457e61f7
|
/1，回归问题/回归问题.py
|
303f41c7c59f0b35df882af4b585356de8227ff1
|
[] |
no_license
|
1414003104/OldSheep_TensorFLow2.0_note
|
04e8b23d087295e18db829224cfad4999471012a
|
9c0241c01908c9df347d243420d84b292304c20d
|
refs/heads/main
| 2023-08-02T10:32:36.768385
| 2021-09-18T10:08:50
| 2021-09-18T10:08:50
| 407,482,324
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,859
|
py
|
import numpy as np
#y=wx+b
#损失计算公式
def compute_error_for_line_given_points(b,w,points):
totalError=0
for i in range(0,len(points)):
x=points[i,0]
y=points[i,1]
#computer mean-squared-error
totalError+=(y-(w*x+b))**2
#average loss for each point
return totalError/float(len(points))
def step_gradient(b_current,w_current,points,learningRate):
b_gradient=0
w_gradient=0
N=float(len(points))
for i in range(0,len(points)):
x=points[i,0]
y=points[i,1]
#grad_b=2(wx-b-y)
b_gradient+=(2/N)*((w_current*x+b_current)-y)
#grad_w=2(wx-b-y)x
w_gradient+=(2/N)*((w_current*x+b_current)-y)*x
#update w' 更新梯度
new_b=b_current-(learningRate*b_gradient)
new_w=w_current-(learningRate*w_gradient)
return [new_b,new_w]
def gradient_descent_runner(points,starting_b,starting_w,learning_rate,num_iterations):
b=starting_b
w=starting_w
#update for several times
for i in range(num_iterations):
b,w=step_gradient(b,w,np.array(points),learning_rate)
return [b,w]
def run():
points=np.genfromtxt("data.csv",delimiter=",")
learning_rate=0.0001
initial_b=0 #initial y-intercept guess
initial_w=0 #initial slope guess
num_iterations=1000#迭代次数
print("Starting gradient descent at b={0},w={1},error={2}"
.format(initial_b,initial_w,compute_error_for_line_given_points(initial_b,initial_w,points))
)
print("Running...")
[b,w]=gradient_descent_runner(points,initial_b,initial_w,learning_rate,num_iterations)
print("After{0} iterations b={1},w={2},error={3}"
.format(num_iterations,b,w,compute_error_for_line_given_points(b,w,points))
)
if __name__=='__main__':
run()
|
[
"noreply@github.com"
] |
noreply@github.com
|
71cdd0ba41024dbd952e1d2a8ec2527c9e2fb9c6
|
77fe4a852711bf024b4b481fc4d7f9fa5a6c96c4
|
/Project 2/Problem 4/problem_4.py
|
9f3f332fb423378ddbfa6499318a76d03176eb3e
|
[] |
no_license
|
Abdelaty/Data-Structure-and-Algorithms-Nanodegree
|
88bbd5d62c89c65d96152eec64acf17a8d434a96
|
52cb103084f37655701be3c9acb42888161541f4
|
refs/heads/master
| 2021-05-17T18:50:17.732854
| 2020-05-02T18:11:34
| 2020-05-02T18:11:34
| 250,926,399
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,768
|
py
|
class Group(object):
def __init__(self, _name):
self.name = _name
self.groups = []
self.users = []
def add_group(self, group):
self.groups.append(group)
def add_user(self, user):
self.users.append(user)
def get_groups(self):
return self.groups
def get_users(self):
return self.users
def get_name(self):
return self.name
def is_user_in_group(user, group):
"""
Return True if user is in the group, False otherwise.
Args:
user(str): user name/id
group(class:Group): group to check user membership against
"""
output = False
for u in group.get_users():
if u == user:
return True
for g in group.get_groups():
output |= is_user_in_group(user, g)
return output
"""
Tests
Group Structure:
Parent
|--Child1
| `--+--SubChild11
| `--SubChild12
`--Child2
`--+--SubChild21
`--SubChild22
Description:
Each Group contains 2 children
"""
#Groups
parent = Group("parent")
child1 = Group("child1")
child2 = Group("child2")
sub_child11 = Group("subchild11")
sub_child12 = Group("subchild12")
sub_child21 = Group("subchild21")
sub_child22 = Group("subchild22")
#Users
parent_user_1 = "parent_user_1"
child1_user_1 = "child1_user_1"
child2_user_1 = "child2_user_1"
sub_child11_user_1 = "sub_child11_user_1"
sub_child12_user_1 = "sub_child12_user_1"
sub_child21_user_1 = "sub_child21_user_1"
sub_child22_user_1 = "sub_child22_user_1"
parent_user_2 = "parent_user_2"
child1_user_2 = "child1_user_2"
child2_user_2 = "child2_user_2"
sub_child11_user_2 = "sub_child11_user_2"
sub_child12_user_2 = "sub_child12_user_2"
sub_child21_user_2 = "sub_child21_user_2"
sub_child22_user_2 = "sub_child22_user_2"
parent.add_user(parent_user_1)
parent.add_user(parent_user_2)
parent.add_group(child1)
child1.add_user(child1_user_1)
child1.add_user(child1_user_2)
parent.add_group(child2)
child2.add_user(child2_user_1)
child2.add_user(child2_user_2)
child1.add_group(sub_child11)
sub_child11.add_user(sub_child11_user_1)
sub_child11.add_user(sub_child11_user_2)
child1.add_group(sub_child12)
sub_child12.add_user(sub_child12_user_1)
sub_child12.add_user(sub_child12_user_2)
child2.add_group(sub_child21)
sub_child21.add_user(sub_child21_user_1)
sub_child21.add_user(sub_child21_user_2)
child2.add_group(sub_child22)
sub_child22.add_user(sub_child22_user_1)
sub_child22.add_user(sub_child22_user_2)
# Parent1 in Parent
print ("Pass" if (is_user_in_group(parent_user_1, parent) == True) else "Fail")
# Parent2 in Parent
print ("Pass" if (is_user_in_group(parent_user_2, parent) == True) else "Fail")
# Child1_1 in Parent
print ("Pass" if (is_user_in_group(child1_user_1, parent) == True) else "Fail")
# Child1_2 in Parent
print ("Pass" if (is_user_in_group(child1_user_2, parent) == True) else "Fail")
# Child2_1 in Parent
print ("Pass" if (is_user_in_group(child2_user_1, parent) == True) else "Fail")
# Child2_2 in Parent
print ("Pass" if (is_user_in_group(child2_user_2, parent) == True) else "Fail")
# SubChild11_1 in Parent
print ("Pass" if (is_user_in_group(sub_child11_user_1, parent) == True) else "Fail")
# SubChild11_2 in Parent
print ("Pass" if (is_user_in_group(sub_child11_user_2, parent) == True) else "Fail")
# SubChild22_1 in Parent
print ("Pass" if (is_user_in_group(sub_child21_user_1, parent) == True) else "Fail")
# SubChild22_2 in Parent
print ("Pass" if (is_user_in_group(sub_child21_user_2, parent) == True) else "Fail")
# Subchild22_2 in Child1
print ("Pass" if (is_user_in_group(sub_child22_user_2, child1) == False) else "Fail")
# Subchild11_1 in Child2
print ("Pass" if (is_user_in_group(sub_child11_user_1, child2) == False) else "Fail")
|
[
"Abdelaty.mohammedmagdi@gmail.com"
] |
Abdelaty.mohammedmagdi@gmail.com
|
31e8e82b6af220cc0d763eedf710a09f930658f0
|
fcf5a2d50638e24e225d62df740d88c686cbc39e
|
/else/01_PropagateRandomLed/line.py
|
e4cecefd4010de83653bbd2fa5c6b61241300adb
|
[] |
no_license
|
AndreaCrotti/wireless-sensors
|
001a50832f244b02924d7cdcaa10cf1ca752440e
|
33459c4a53c3ac01c742e65c9c72e63b25c08976
|
refs/heads/master
| 2016-09-10T18:52:46.156303
| 2010-06-14T11:56:08
| 2010-06-14T11:56:08
| 629,601
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 921
|
py
|
#!/usr/bin/python
# @author oscar.dustmann@rwth-aachen.de
#
# A simple test-scenario.
from TOSSIM import *
from random import *
import sys
t = Tossim([])
#t.addChannel("LedsC",sys.stdout)
t.addChannel("PrlC",sys.stdout)
t.addChannel("PrlC_l",sys.stdout)
nodes = 200
ndb = -1.0
### link setup
r = t.radio()
for i in range(0,nodes-1):
print "link " + str(i) + " <-> " + str(i+1)
r.add(i,i+1,ndb)
r.add(i+1,i,ndb)
### noise
noise = open("meyer-short.txt", "r")
lines = noise.readlines()
for line in lines:
s = line.strip()
if (s != ""):
val = int(s)
for i in range(0, nodes):
m = t.getNode(i)
m.addNoiseTraceReading(val)
### boot the nodes
for i in range(0,nodes):
n = t.getNode(i)
n.createNoiseModel();
n.bootAtTime(i*100)
## run the code
for i in range(0,10000000):
t.runNextEvent()
#while (m0.isOn() == 0):
# t.runNextEvent()
#while (m0.isOn() == 1):
# t.runNextEvent()
|
[
"oscar@querdenker.dyndns.org"
] |
oscar@querdenker.dyndns.org
|
1be16efded94fc0540fb6e7f42f81c7f9d858eec
|
c2983d006139ea69044233af9ae5ff29fe3640ba
|
/test/postgres_tests.py
|
59fc5330709e9ef47e1505a9b89fd8ef01e83bfe
|
[
"Apache-2.0"
] |
permissive
|
nkabir/pyschema
|
b0b608c6397dfe844876c05d25b67ef41b3dbcdf
|
d8e6de80f00a59c747beaf6ed53b8316e29762de
|
refs/heads/master
| 2021-01-14T09:42:15.671139
| 2015-04-27T15:38:44
| 2015-04-27T15:38:44
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 860
|
py
|
from unittest import TestCase
from pyschema import Record, no_auto_store
from pyschema.types import Integer, Text, Float, Boolean, Date, DateTime
from pyschema_extensions import postgres
@no_auto_store()
class MyItem(Record):
name = Text()
value = Integer()
dec = Float()
flag = Boolean()
date = Date()
datehour = DateTime()
class TestPostgres(TestCase):
def test_create_statement(self):
statement = postgres.create_statement(MyItem, 'my_table')
self.assertEquals("CREATE TABLE my_table (name TEXT, value INT, dec FLOAT, flag BOOLEAN, date DATE, datehour TIMESTAMP WITHOUT TIME ZONE)", statement)
statement = postgres.create_statement(MyItem)
self.assertEquals("CREATE TABLE my_item (name TEXT, value INT, dec FLOAT, flag BOOLEAN, date DATE, datehour TIMESTAMP WITHOUT TIME ZONE)", statement)
|
[
"freider@spotify.com"
] |
freider@spotify.com
|
bb46853c01135dbca52d0c9a878620334dfa657b
|
80a881f0ebb159d1dfe9fa10e93a7f2fcd7f3677
|
/planb/feedback/models.py
|
b5ad0abadca32394f000f47dbbe1908ae48d029a
|
[] |
no_license
|
Grapheme/mayak
|
42d874080f8233a1d0e709afe48cd2135ddaab98
|
4f26c290a7df060c65624328087c39a73a8540e7
|
refs/heads/master
| 2020-06-06T04:00:10.204791
| 2015-09-15T17:34:48
| 2015-09-15T17:34:48
| 27,764,461
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,375
|
py
|
# -*- coding: utf-8 -*-
from django.db import models
class FeedbackManager(models.Model):
"""
Адреса людей, которые должны получать на email записи из "обратной связи".
"""
email = models.EmailField()
class Meta:
verbose_name = u'менеджер обратной связи'
verbose_name_plural = u'менеджеры обратной связи'
ordering = ['email']
def __unicode__(self):
return self.email
class FeedbackFormManager(models.Model):
slug = models.SlugField(u'уникальный идентификатор формы', unique=True)
caption = models.CharField(u'название формы', max_length=200)
email = models.ManyToManyField(FeedbackManager, verbose_name=u'адреса')
class Meta:
verbose_name = u'форма обратной связи'
verbose_name_plural = u'формы обратной связи'
ordering = ['caption']
def email_flat_list(self):
result = ''
for e in self.email.all():
result += '<li>%s</li>' % (e.email)
result = '<ol>%s</ol>' % result
return result
email_flat_list.allow_tags = True
email_flat_list.short_description = u'Адреса'
def __unicode__(self):
return self.caption
|
[
"etomarat@gmail.com"
] |
etomarat@gmail.com
|
1be3440a397de19a2731a7caaf67ed5265d6bc63
|
dfa79b5a899b253df02f2bd1695c3a09458de7a4
|
/netquants.py~
|
f3d1adfaa41fcfb6108d606b375c0866eefd0ca3
|
[] |
no_license
|
DanielKoohmarey/netQuants
|
806fd9f09a7e52a4d61e9b1588c39b3919229ff6
|
d91cfaf2ac4de6521b2782ad007f1d69e5e9bc87
|
refs/heads/master
| 2020-12-25T10:49:44.228465
| 2013-05-07T00:28:56
| 2013-05-07T00:28:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,555
|
#!/usr/bin/env python
#
# Copyright 2009 Facebook
#
# 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 sys
sys.path.append("quant")
import quantPy as qp
import wIndicators as wI
import tornado.httpserver
import tornado.ioloop
import tornado.options
import tornado.web
from tornado.options import define, options
define("port", default=8888, help="run on the given port", type=int)
class MainHandler(tornado.web.RequestHandler):
def get(self):
result = qp.oneStock(wI.meanReversion, "ACE")
self.write(result.to_string())
class JsonServer(tornado.web.RequestHandler):
def get(self):
result = qp.oneStock(wI.meanReversion, "ACE")
self.write(result.to_string())
def main():
tornado.options.parse_command_line()
application = tornado.web.Application([
(r"/", MainHandler),
(r"/stock", JsonServer),
])
http_server = tornado.httpserver.HTTPServer(application)
http_server.listen(options.port)
tornado.ioloop.IOLoop.instance().start()
if __name__ == "__main__":
main()
|
[
"leeanna@berkeley.edu"
] |
leeanna@berkeley.edu
|
|
26dace9da5168c53db1423f65ab53c70e82b7187
|
d131ad1baf891a2918ae27b0dc57f3c0c1f99586
|
/blog/migrations/0001_initial.py
|
ec6923c8ffb8cbccaa6e420a5a387c7af1f5ae91
|
[] |
no_license
|
Alymbekov/TestProjectForDjangoForms
|
d3bf24844628136f9236d5222d32235e87f7aecd
|
ce3262e7565e293b691ea70b94b67155c15525bd
|
refs/heads/master
| 2020-04-10T05:35:19.516127
| 2018-12-07T14:24:05
| 2018-12-07T14:24:05
| 160,832,149
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 713
|
py
|
# Generated by Django 2.1 on 2018-11-18 08:19
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(db_index=True, max_length=150)),
('slug', models.SlugField(max_length=150, unique=True)),
('body', models.TextField(blank=True, db_index=True)),
('date_pub', models.DateTimeField(auto_now_add=True)),
],
),
]
|
[
"maxim.makarov.1997@mail.ru"
] |
maxim.makarov.1997@mail.ru
|
61525717db91dbf05797d7a00755affc2b82f2be
|
ff0d4899d63071d0517b6be0ffdd01579bc49fea
|
/quickgrab.py
|
a7c49d8903ca6f02f7e1a6cc56d9308bef60828c
|
[] |
no_license
|
kellyelton/octbot
|
0bd528e9fc68ddf83f67d228bdba079efba11606
|
3a50ce63b2dc7949fe4febe57b4ca5bb9668fda8
|
refs/heads/master
| 2021-01-15T16:15:06.890789
| 2013-05-07T01:54:42
| 2013-05-07T01:54:42
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,027
|
py
|
from PIL import ImageGrab
import os
import time
import win32gui
def screenGrab(box):
im = ImageGrab.grab(box)
im.save(os.getcwd() + '\\full_snap__' + str(int(time.time())) + '.png', 'PNG')
def winEnumHandler( hwnd, ctx ):
whereIsOctgn = ()
coords = ()
if win32gui.IsWindowVisible( hwnd ):
if win32gui.GetWindowText( hwnd ) == 'Octgn version : 3.1.16.99 : Android-Netrunner':
whereIsOctgn = hwnd
print (hex(whereIsOctgn))
coords = win32gui.GetWindowRect(whereIsOctgn)
print (coords)
win32gui.SetForegroundWindow(whereIsOctgn)
screenGrab(coords)
def winListHandler( hwnd, ctx ):
if win32gui.IsWindowVisible( hwnd ):
print (hex( hwnd ), win32gui.GetWindowText( hwnd ))
def listWindows():
win32gui.EnumWindows( winListHandler, None )
def screenGrabOctgn():
win32gui.EnumWindows( winEnumHandler, None )
def main():
screenGrabOctgn()
listWindows()
if __name__ == '__main__':
main()
|
[
"dan.h.johnson@gmail.com"
] |
dan.h.johnson@gmail.com
|
60d9422069f85a93dcee9aecd46120c3a7253c69
|
f4b60f5e49baf60976987946c20a8ebca4880602
|
/lib/python2.7/site-packages/acimodel-1.3_2j-py2.7.egg/cobra/modelimpl/tag/insttask.py
|
e2820118edd9b39d43659c40ce0995dfd34ecc0b
|
[] |
no_license
|
cqbomb/qytang_aci
|
12e508d54d9f774b537c33563762e694783d6ba8
|
a7fab9d6cda7fadcc995672e55c0ef7e7187696e
|
refs/heads/master
| 2022-12-21T13:30:05.240231
| 2018-12-04T01:46:53
| 2018-12-04T01:46:53
| 159,911,666
| 0
| 0
| null | 2022-12-07T23:53:02
| 2018-12-01T05:17:50
|
Python
|
UTF-8
|
Python
| false
| false
| 16,985
|
py
|
# coding=UTF-8
# **********************************************************************
# Copyright (c) 2013-2016 Cisco Systems, Inc. All rights reserved
# written by zen warriors, do not modify!
# **********************************************************************
from cobra.mit.meta import ClassMeta
from cobra.mit.meta import StatsClassMeta
from cobra.mit.meta import CounterMeta
from cobra.mit.meta import PropMeta
from cobra.mit.meta import Category
from cobra.mit.meta import SourceRelationMeta
from cobra.mit.meta import NamedSourceRelationMeta
from cobra.mit.meta import TargetRelationMeta
from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory
from cobra.model.category import MoCategory, PropCategory, CounterCategory
from cobra.mit.mo import Mo
# ##################################################
class InstTask(Mo):
"""
An instance task.
"""
meta = ClassMeta("cobra.model.tag.InstTask")
meta.moClassName = "tagInstTask"
meta.rnFormat = "tagInstTask-%(id)s"
meta.category = MoCategory.TASK
meta.label = "None"
meta.writeAccessMask = 0x1
meta.readAccessMask = 0x1
meta.isDomainable = False
meta.isReadOnly = True
meta.isConfigurable = False
meta.isDeletable = False
meta.isContextRoot = False
meta.parentClasses.add("cobra.model.action.TopomgrSubj")
meta.parentClasses.add("cobra.model.action.ObserverSubj")
meta.parentClasses.add("cobra.model.action.VmmmgrSubj")
meta.parentClasses.add("cobra.model.action.SnmpdSubj")
meta.parentClasses.add("cobra.model.action.ScripthandlerSubj")
meta.parentClasses.add("cobra.model.action.ConfelemSubj")
meta.parentClasses.add("cobra.model.action.EventmgrSubj")
meta.parentClasses.add("cobra.model.action.OspaelemSubj")
meta.parentClasses.add("cobra.model.action.VtapSubj")
meta.parentClasses.add("cobra.model.action.OshSubj")
meta.parentClasses.add("cobra.model.action.DhcpdSubj")
meta.parentClasses.add("cobra.model.action.ObserverelemSubj")
meta.parentClasses.add("cobra.model.action.DbgrelemSubj")
meta.parentClasses.add("cobra.model.action.VleafelemSubj")
meta.parentClasses.add("cobra.model.action.NxosmockSubj")
meta.parentClasses.add("cobra.model.action.DbgrSubj")
meta.parentClasses.add("cobra.model.action.AppliancedirectorSubj")
meta.parentClasses.add("cobra.model.action.OpflexpSubj")
meta.parentClasses.add("cobra.model.action.BootmgrSubj")
meta.parentClasses.add("cobra.model.action.AeSubj")
meta.parentClasses.add("cobra.model.action.PolicymgrSubj")
meta.parentClasses.add("cobra.model.action.ExtXMLApiSubj")
meta.parentClasses.add("cobra.model.action.OpflexelemSubj")
meta.parentClasses.add("cobra.model.action.PolicyelemSubj")
meta.parentClasses.add("cobra.model.action.IdmgrSubj")
meta.superClasses.add("cobra.model.action.RInst")
meta.superClasses.add("cobra.model.pol.ComplElem")
meta.superClasses.add("cobra.model.task.Inst")
meta.superClasses.add("cobra.model.action.Inst")
meta.rnPrefixes = [
('tagInstTask-', True),
]
prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("deleteAll", "deleteall", 16384)
prop._addConstant("deleteNonPresent", "deletenonpresent", 8192)
prop._addConstant("ignore", "ignore", 4096)
meta.props.add("childAction", prop)
prop = PropMeta("str", "data", "data", 52, PropCategory.REGULAR)
prop.label = "Data"
prop.isImplicit = True
prop.isAdmin = True
prop.range = [(0, 512)]
meta.props.add("data", prop)
prop = PropMeta("str", "descr", "descr", 33, PropCategory.REGULAR)
prop.label = "Description"
prop.isImplicit = True
prop.isAdmin = True
prop.range = [(0, 128)]
prop.regex = ['[a-zA-Z0-9\\!#$%()*,-./:;@ _{|}~?&+]+']
meta.props.add("descr", prop)
prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN)
prop.label = "None"
prop.isDn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("dn", prop)
prop = PropMeta("str", "endTs", "endTs", 15575, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("endTs", prop)
prop = PropMeta("str", "fail", "fail", 46, PropCategory.REGULAR)
prop.label = "Fail"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("fail", prop)
prop = PropMeta("str", "id", "id", 5642, PropCategory.REGULAR)
prop.label = "ID"
prop.isConfig = True
prop.isAdmin = True
prop.isCreateOnly = True
prop.isNaming = True
prop.defaultValue = 0
prop.defaultValueStr = "none"
prop._addConstant("ConfDef", "confdef", 4)
prop._addConstant("none", "none", 0)
meta.props.add("id", prop)
prop = PropMeta("str", "invErrCode", "invErrCode", 49, PropCategory.REGULAR)
prop.label = "Remote Error Code"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("ERR-FILTER-illegal-format", None, 1140)
prop._addConstant("ERR-FSM-no-such-state", None, 1160)
prop._addConstant("ERR-HTTP-set-error", None, 1551)
prop._addConstant("ERR-HTTPS-set-error", None, 1552)
prop._addConstant("ERR-MO-CONFIG-child-object-cant-be-configured", None, 1130)
prop._addConstant("ERR-MO-META-no-such-object-class", None, 1122)
prop._addConstant("ERR-MO-PROPERTY-no-such-property", None, 1121)
prop._addConstant("ERR-MO-PROPERTY-value-out-of-range", None, 1120)
prop._addConstant("ERR-MO-access-denied", None, 1170)
prop._addConstant("ERR-MO-deletion-rule-violation", None, 1107)
prop._addConstant("ERR-MO-duplicate-object", None, 1103)
prop._addConstant("ERR-MO-illegal-containment", None, 1106)
prop._addConstant("ERR-MO-illegal-creation", None, 1105)
prop._addConstant("ERR-MO-illegal-iterator-state", None, 1100)
prop._addConstant("ERR-MO-illegal-object-lifecycle-transition", None, 1101)
prop._addConstant("ERR-MO-naming-rule-violation", None, 1104)
prop._addConstant("ERR-MO-object-not-found", None, 1102)
prop._addConstant("ERR-MO-resource-allocation", None, 1150)
prop._addConstant("ERR-aaa-config-modify-error", None, 1520)
prop._addConstant("ERR-acct-realm-set-error", None, 1513)
prop._addConstant("ERR-add-ctrlr", None, 1574)
prop._addConstant("ERR-admin-passwd-set", None, 1522)
prop._addConstant("ERR-api", None, 1571)
prop._addConstant("ERR-auth-issue", None, 1548)
prop._addConstant("ERR-auth-realm-set-error", None, 1514)
prop._addConstant("ERR-authentication", None, 1534)
prop._addConstant("ERR-authorization-required", None, 1535)
prop._addConstant("ERR-connect", None, 1572)
prop._addConstant("ERR-create-domain", None, 1562)
prop._addConstant("ERR-create-keyring", None, 1560)
prop._addConstant("ERR-create-role", None, 1526)
prop._addConstant("ERR-create-user", None, 1524)
prop._addConstant("ERR-delete-domain", None, 1564)
prop._addConstant("ERR-delete-role", None, 1528)
prop._addConstant("ERR-delete-user", None, 1523)
prop._addConstant("ERR-domain-set-error", None, 1561)
prop._addConstant("ERR-http-initializing", None, 1549)
prop._addConstant("ERR-incompat-ctrlr-version", None, 1568)
prop._addConstant("ERR-internal-error", None, 1540)
prop._addConstant("ERR-invalid-args", None, 1569)
prop._addConstant("ERR-invalid-domain-name", None, 1582)
prop._addConstant("ERR-ldap-delete-error", None, 1510)
prop._addConstant("ERR-ldap-get-error", None, 1509)
prop._addConstant("ERR-ldap-group-modify-error", None, 1518)
prop._addConstant("ERR-ldap-group-set-error", None, 1502)
prop._addConstant("ERR-ldap-set-error", None, 1511)
prop._addConstant("ERR-missing-method", None, 1546)
prop._addConstant("ERR-modify-ctrlr-access", None, 1567)
prop._addConstant("ERR-modify-ctrlr-dvs-version", None, 1576)
prop._addConstant("ERR-modify-ctrlr-rootcont", None, 1575)
prop._addConstant("ERR-modify-ctrlr-scope", None, 1573)
prop._addConstant("ERR-modify-ctrlr-trig-inventory", None, 1577)
prop._addConstant("ERR-modify-domain", None, 1563)
prop._addConstant("ERR-modify-domain-encapmode", None, 1581)
prop._addConstant("ERR-modify-domain-enfpref", None, 1578)
prop._addConstant("ERR-modify-domain-mcastpool", None, 1579)
prop._addConstant("ERR-modify-domain-mode", None, 1580)
prop._addConstant("ERR-modify-role", None, 1527)
prop._addConstant("ERR-modify-user", None, 1525)
prop._addConstant("ERR-modify-user-domain", None, 1565)
prop._addConstant("ERR-modify-user-role", None, 1532)
prop._addConstant("ERR-no-buf", None, 1570)
prop._addConstant("ERR-passwd-set-failure", None, 1566)
prop._addConstant("ERR-provider-group-modify-error", None, 1519)
prop._addConstant("ERR-provider-group-set-error", None, 1512)
prop._addConstant("ERR-radius-global-set-error", None, 1505)
prop._addConstant("ERR-radius-group-set-error", None, 1501)
prop._addConstant("ERR-radius-set-error", None, 1504)
prop._addConstant("ERR-request-timeout", None, 1545)
prop._addConstant("ERR-role-set-error", None, 1515)
prop._addConstant("ERR-secondary-node", None, 1550)
prop._addConstant("ERR-service-not-ready", None, 1539)
prop._addConstant("ERR-set-password-strength-check", None, 1543)
prop._addConstant("ERR-store-pre-login-banner-msg", None, 1521)
prop._addConstant("ERR-tacacs-enable-error", None, 1508)
prop._addConstant("ERR-tacacs-global-set-error", None, 1507)
prop._addConstant("ERR-tacacs-group-set-error", None, 1503)
prop._addConstant("ERR-tacacs-set-error", None, 1506)
prop._addConstant("ERR-user-account-expired", None, 1536)
prop._addConstant("ERR-user-set-error", None, 1517)
prop._addConstant("ERR-xml-parse-error", None, 1547)
prop._addConstant("communication-error", "communication-error", 1)
prop._addConstant("none", "none", 0)
meta.props.add("invErrCode", prop)
prop = PropMeta("str", "invErrDescr", "invErrDescr", 50, PropCategory.REGULAR)
prop.label = "Remote Error Description"
prop.isImplicit = True
prop.isAdmin = True
prop.range = [(0, 128)]
prop.regex = ['[a-zA-Z0-9\\!#$%()*,-./:;@ _{|}~?&+]+']
meta.props.add("invErrDescr", prop)
prop = PropMeta("str", "invRslt", "invRslt", 48, PropCategory.REGULAR)
prop.label = "Remote Result"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "not-applicable"
prop._addConstant("capability-not-implemented-failure", "capability-not-implemented-failure", 16384)
prop._addConstant("capability-not-implemented-ignore", "capability-not-implemented-ignore", 8192)
prop._addConstant("capability-not-supported", "capability-not-supported", 32768)
prop._addConstant("capability-unavailable", "capability-unavailable", 65536)
prop._addConstant("end-point-failed", "end-point-failed", 32)
prop._addConstant("end-point-protocol-error", "end-point-protocol-error", 64)
prop._addConstant("end-point-unavailable", "end-point-unavailable", 16)
prop._addConstant("extend-timeout", "extend-timeout", 134217728)
prop._addConstant("failure", "failure", 1)
prop._addConstant("fru-identity-indeterminate", "fru-identity-indeterminate", 4194304)
prop._addConstant("fru-info-malformed", "fru-info-malformed", 8388608)
prop._addConstant("fru-not-ready", "fru-not-ready", 67108864)
prop._addConstant("fru-not-supported", "fru-not-supported", 536870912)
prop._addConstant("fru-state-indeterminate", "fru-state-indeterminate", 33554432)
prop._addConstant("fw-defect", "fw-defect", 256)
prop._addConstant("hw-defect", "hw-defect", 512)
prop._addConstant("illegal-fru", "illegal-fru", 16777216)
prop._addConstant("intermittent-error", "intermittent-error", 1073741824)
prop._addConstant("internal-error", "internal-error", 4)
prop._addConstant("not-applicable", "not-applicable", 0)
prop._addConstant("resource-capacity-exceeded", "resource-capacity-exceeded", 2048)
prop._addConstant("resource-dependency", "resource-dependency", 4096)
prop._addConstant("resource-unavailable", "resource-unavailable", 1024)
prop._addConstant("service-not-implemented-fail", "service-not-implemented-fail", 262144)
prop._addConstant("service-not-implemented-ignore", "service-not-implemented-ignore", 131072)
prop._addConstant("service-not-supported", "service-not-supported", 524288)
prop._addConstant("service-protocol-error", "service-protocol-error", 2097152)
prop._addConstant("service-unavailable", "service-unavailable", 1048576)
prop._addConstant("sw-defect", "sw-defect", 128)
prop._addConstant("task-reset", "task-reset", 268435456)
prop._addConstant("timeout", "timeout", 8)
prop._addConstant("unidentified-fail", "unidentified-fail", 2)
meta.props.add("invRslt", prop)
prop = PropMeta("str", "lcOwn", "lcOwn", 9, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "local"
prop._addConstant("implicit", "implicit", 4)
prop._addConstant("local", "local", 0)
prop._addConstant("policy", "policy", 1)
prop._addConstant("replica", "replica", 2)
prop._addConstant("resolveOnBehalf", "resolvedonbehalf", 3)
meta.props.add("lcOwn", prop)
prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("modTs", prop)
prop = PropMeta("str", "oDn", "oDn", 51, PropCategory.REGULAR)
prop.label = "Subject DN"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("oDn", prop)
prop = PropMeta("str", "operSt", "operSt", 15674, PropCategory.REGULAR)
prop.label = "Completion"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "scheduled"
prop._addConstant("cancelled", "cancelled", 3)
prop._addConstant("completed", "completed", 2)
prop._addConstant("crashsuspect", "crash-suspect", 7)
prop._addConstant("failed", "failed", 4)
prop._addConstant("indeterminate", "indeterminate", 5)
prop._addConstant("processing", "processing", 1)
prop._addConstant("ready", "ready", 8)
prop._addConstant("scheduled", "scheduled", 0)
prop._addConstant("suspended", "suspended", 6)
meta.props.add("operSt", prop)
prop = PropMeta("str", "originMinority", "originMinority", 54, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = False
prop.defaultValueStr = "no"
prop._addConstant("no", None, False)
prop._addConstant("yes", None, True)
meta.props.add("originMinority", prop)
prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN)
prop.label = "None"
prop.isRn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("rn", prop)
prop = PropMeta("str", "runId", "runId", 45, PropCategory.REGULAR)
prop.label = "ID"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("runId", prop)
prop = PropMeta("str", "startTs", "startTs", 36, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("startTs", prop)
prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("created", "created", 2)
prop._addConstant("deleted", "deleted", 8)
prop._addConstant("modified", "modified", 4)
meta.props.add("status", prop)
prop = PropMeta("str", "try", "try", 15574, PropCategory.REGULAR)
prop.label = "Try"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("try", prop)
prop = PropMeta("str", "ts", "ts", 47, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("ts", prop)
meta.namingProps.append(getattr(meta.props, "id"))
def __init__(self, parentMoOrDn, id, markDirty=True, **creationProps):
namingVals = [id]
Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps)
# End of package file
# ##################################################
|
[
"collinsctk@qytang.com"
] |
collinsctk@qytang.com
|
932e44b66fd1887560fc053e4a525c8d687dc93a
|
1b71a47534ec5262c6749e701e85320da523cec1
|
/Code/RealOffice/meeting/migrations/0007_auto_20170307_1854.py
|
4e84fa10a6abe68d2dd2cae8566409fb0f1d5f2a
|
[] |
no_license
|
kejriwalrahul/RealOffice
|
2a82fd07036e2452af923653e8a251730a67017e
|
077764f2d5ab62eba225557967a08b23a52567e9
|
refs/heads/master
| 2021-03-27T20:01:33.255446
| 2017-04-16T09:21:51
| 2017-04-16T09:21:51
| 80,219,099
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 488
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-03-07 18:54
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('meeting', '0006_auto_20170307_1854'),
]
operations = [
migrations.AlterField(
model_name='requirement',
name='orderDetails',
field=models.CharField(default=None, max_length=128, null=True),
),
]
|
[
"kejriwalrahul@outlook.com"
] |
kejriwalrahul@outlook.com
|
824d42429d0f17582b537a3d9045cc15c2c88584
|
78ec5fbacb0a22842e510eca0d8cf76fbb677af3
|
/api_example/languages/urls.py
|
7a8ac1f4e794e5ee0cd0d1868b639302e10ab3ba
|
[] |
no_license
|
FAVORK/Django-Rest
|
c7302843dff46fcdb339246fcc06c095dda8f1eb
|
8ec7411aa8972a7910dc06da424446cd4f08273f
|
refs/heads/master
| 2020-12-02T20:09:35.184824
| 2019-12-31T15:11:54
| 2019-12-31T15:11:54
| 231,105,640
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 230
|
py
|
from django.urls import path, include
from . import views
from rest_framework import routers
router = routers.DefaultRouter()
router.register('languages', views.LanguageView)
urlpatterns = [
path('', include(router.urls)),
]
|
[
"kamaukdan@gmail.com"
] |
kamaukdan@gmail.com
|
43a3171c18f24f3e5cf493bcf8576ddb6b9456b6
|
ebd2df05eae5875f3edd5c891442b9fe1f3d54ee
|
/empleados/views.py
|
3b8388bd33952007db18e34edaecbd69330d2a7c
|
[] |
no_license
|
gfcarbonell/app_navidad
|
06191ef3b084d40c7a5f387a60407406c2c89d54
|
fa290f8cf0b4b0d9237b555417fe38f879938adf
|
refs/heads/master
| 2020-12-24T11:54:10.514150
| 2016-11-16T15:37:09
| 2016-11-16T15:37:09
| 73,115,163
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,364
|
py
|
# -*- encoding: utf-8 -*-
from django.conf import settings
from django.views.generic import CreateView, UpdateView, ListView, DetailView
from .models import Empleado
from .forms import EmpleadoModelForm, EmpleadoUsuarioForm
from django.core.urlresolvers import reverse_lazy
from rest_framework import viewsets
from django.db.models import Q
import socket
from pure_pagination.mixins import PaginationMixin
from django.template.defaultfilters import slugify
from infos_sistemas.mixins import TipoPerfilUsuarioMixin
class EmpleadoCreateView(TipoPerfilUsuarioMixin, CreateView):
template_name = 'empleado_create.html'
form_class = EmpleadoUsuarioForm
model = Empleado
success_url = reverse_lazy('empleado:control')
def form_valid(self, form):
user = form['model_form_usuario'].save(commit=False)
user.usuario_creador = self.request.user
user.ultimo_usuario_editor = user.usuario_creador
try:
user.nombre_host = socket.gethostname()
user.ultimo_nombre_host = user.nombre_host
except:
user.nombre_host = 'localhost'
user.ultimo_nombre_host = user.nombre_host
user.direccion_ip = socket.gethostbyname(socket.gethostname())
user.ultimo_direccion_ip = socket.gethostbyname(socket.gethostname())
empleado = form['model_form_empleado'].save(commit=False)
empleado.tipo_persona = 'Natural'
if empleado.numero_hijo is None:
empleado.numero_hijo = 0
user.save()
empleado.usuario = user
empleado.usuario_creador = self.request.user
empleado.ultimo_usuario_editor = empleado.usuario_creador
try:
empleado.nombre_host = socket.gethostname()
empleado.ultimo_nombre_host = empleado.nombre_host
except:
empleado.nombre_host = 'localhost'
empleado.ultimo_nombre_host = empleado.nombre_host
empleado.direccion_ip = socket.gethostbyname(socket.gethostname())
empleado.ultimo_direccion_ip = socket.gethostbyname(socket.gethostname())
empleado.save()
return super(EmpleadoCreateView, self).form_valid(form)
class EmpleadoUpdate(TipoPerfilUsuarioMixin, UpdateView):
form_class = EmpleadoModelForm
success_url = reverse_lazy('empleado:control')
template_name = 'empleado_update.html'
queryset = Empleado.objects.all()
def form_valid(self, form):
self.object = form.save(commit=False)
if self.object.numero_hijo is None:
self.object.numero_hijo = 0
self.object.ultimo_usuario_editor = self.request.user
try:
self.object.ultimo_nombre_host = socket.gethostname()
except:
self.object.ultimo_nombre_host = 'localhost'
self.object.ultimo_direccion_ip = socket.gethostbyname(socket.gethostname())
self.object.save()
return super(EmpleadoUpdate, self).form_valid(form)
class EmpleadoUsuarioUpdateView(TipoPerfilUsuarioMixin, UpdateView):
form_class = EmpleadoUsuarioForm
success_url = reverse_lazy('empleado:control')
template_name = 'empleado_usuario_update.html'
queryset = Empleado.objects.all()
def get_context_data(self, **kwarg):
context = super(EmpleadoUpdateView, self).get_context_data(**kwarg)
empleado = self.queryset.get(slug__contains=self.kwargs['slug'])
data = {'empleado':empleado}
context.update(data)
return context
def get_form_kwargs(self):
kwargs = super(EmpleadoUpdateView, self).get_form_kwargs()
kwargs.update(instance={
'model_form_empleado': self.object,
'model_form_usuario': self.object.usuario,
})
return kwargs
def form_valid(self, form):
empleado = self.queryset.get(slug__contains=self.kwargs['slug'])
user = form['model_form_usuario'].save(commit=False)
user = empleado.usuario
user.ultimo_usuario_editor = self.request.user
try:
user.ultimo_nombre_host = user.nombre_host
except:
user.ultimo_nombre_host = user.nombre_host
user.ultimo_direccion_ip = socket.gethostbyname(socket.gethostname())
empleado = form['model_form_empleado'].save(commit=False)
empleado.tipo_persona = 'Natural'
if empleado.numero_hijo is None:
empleado.numero_hijo = 0
user.save()
empleado.usuario = user
empleado.ultimo_usuario_editor = self.request.user
try:
empleado.ultimo_nombre_host = empleado.nombre_host
except:
empleado.ultimo_nombre_host = empleado.nombre_host
empleado.ultimo_direccion_ip = socket.gethostbyname(socket.gethostname())
empleado.save()
return super(EmpleadoUpdateView, self).form_valid(form)
class EmpleadoDetailView(TipoPerfilUsuarioMixin, DetailView):
template_name = 'empleado_detail.html'
model = Empleado
queryset = Empleado.objects.all()
class EmpleadoControlListView(PaginationMixin, TipoPerfilUsuarioMixin, ListView):
model = Empleado
template_name = 'empleados.html'
paginate_by = 10
def get_context_data(self, **kwarg):
context = super(EmpleadoControlListView, self).get_context_data(**kwarg)
boton_menu = False
total_registro = self.model.objects.count()
data = {
'boton_menu' : boton_menu,
'total_registro': total_registro,
}
context.update(data)
return context
def get(self, request, *args, **kwargs):
if request.GET.get('search_registro', None):
self.object_list = self.get_queryset()
context = self.get_context_data()
return self.render_to_response(context)
else:
return super(EmpleadoControlListView, self).get(self, request, *args, **kwargs)
def get_queryset(self):
if self.request.GET.get('search_registro', None):
value = self.request.GET.get('search_registro', None)
queryset = self.model.objects.filter(Q(slug__icontains=slugify(value)))
else:
queryset = super(EmpleadoControlListView, self).get_queryset()
return queryset
|
[
"r.gian.f.carbonell.s@gmail.com"
] |
r.gian.f.carbonell.s@gmail.com
|
f77f5031d2828acf76391e5d475d5883c6790ee9
|
d7b4727e74d4a8a339c54dc5a15d8872319e7e7c
|
/playing_around/cv_test.py
|
011ceb3920e2b8e09354eb269026943c91a334d9
|
[] |
no_license
|
pussinboot/aal
|
fbd6a780cb18530f3f72d5b4c4cd9978f37ccfa4
|
53ea549d7f581bb3b8dd4c009925a89e6e9b924b
|
refs/heads/master
| 2022-01-02T19:35:04.817200
| 2015-10-28T19:40:46
| 2015-10-28T19:40:46
| 32,659,873
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,353
|
py
|
import numpy as np
import cv2
#image = cv2.imread("cv_test.jpg")
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#gray = cv2.GaussianBlur(gray, (3, 3), 0)
##cv2.imshow("Gray", gray)
#cv2.waitKey(0)
#edged = cv2.Canny(gray, 10, 100)
##cv2.imshow("Edged", edged)
##cv2.waitKey(0)
##cv2.imwrite("edgy.jpg",edged)
## construct and apply a closing kernel to 'close' gaps between 'white'
## pixels
#kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15))
#closed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)
##cv2.imshow("Closed", closed)
##cv2.waitKey(0)
##cv2.imwrite("closed.jpg",closed)
## find contours (i.e. the 'outlines') in the image and initialize the
## total number of books found
#( _, cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#
#cv2.drawContours(image, cnts, -1, (0,255,0), 3)
#
##total = 0
##
###print(len(cnts))
### loop over the contours
##for c in cnts:
### approximate the contour
## peri = cv2.arcLength(c, True)
## approx = cv2.approxPolyDP(c, 0.02 * peri, True)
##
### if the approximated contour has four points, then assume that the
### contour is a book -- a book is a rectangle and thus has four vertices
## if len(approx) == 4:
## cv2.drawContours(image, [approx], -1, (0, 255, 0), 4)
## total += 1
##
### display the output
##print("I found {0} books in that image".format(total))
#cv2.imshow("Output", image)
#cv2.waitKey(0)##
def thresh_callback(thresh):
edges = cv2.Canny(blur,thresh,thresh*2)
drawing = np.zeros(img.shape,np.uint8) # Image to draw the contours
_, contours, _ = cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
color = np.random.randint(0,255,(3)).tolist() # Select a random color
cv2.drawContours(drawing,[cnt],0,color,2)
#cv2.imshow('output',drawing)
fname = "./images/gif/gif2/" + str(thresh) + '.jpg'
cv2.imwrite(fname,drawing)
#cv2.imshow('input',img)
img = cv2.imread('cv_test_2.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(11,11),0)
#cv2.namedWindow('input',cv2.WINDOW_AUTOSIZE)
thresh = 100
max_thresh = 255
#cv2.createTrackbar('canny thresh:','input',thresh,max_thresh,thresh_callback)
#thresh_callback(thresh)
#if cv2.waitKey(0) == 27:
# cv2.destroyAllWindows()
for i in range(160):
thresh_callback(i)
|
[
""
] | |
556a49713f3c38e2aafd9f87264d58fdb55b1646
|
444c5e47a883bff0c116f00f08ade7f6c75ca235
|
/install_mac_package_managers
|
8e3e4cda322bc4ee7d19d5aea3cb9f2e517e3c06
|
[] |
no_license
|
poulh/p3-setup
|
86939b4cf69f2ea929cb4789bf7313516daeaa05
|
c3199a031b30bde802365624e7cc6400df624d4e
|
refs/heads/master
| 2020-03-07T19:07:53.842552
| 2019-12-01T23:16:49
| 2019-12-01T23:16:49
| 127,662,891
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,468
|
#!/usr/bin/env python
import argparse
import os
import subprocess
import sys
MANAGERS = [
{
'name':'xcode-select',
'cmds' : [
'xcode-select --install'
],
'check': 'xcode-select -p > /dev/null'
},{
'name': 'homebrew',
'cmds': [
'xcode-select --install',
'/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"',
],
'check': 'which brew > /dev/null',
}, {
'name': 'pip',
'cmds': [
'sudo easy_install pip',
],
'check': 'which pip > /dev/null',
}, {
'name': 'npm',
'cmds': [
'brew install npm',
],
'check': 'which npm > /dev/null',
'update':'brue update npm'
}, {
'name': 'ansible',
'cmds': ['brew install ansible'],
'check': 'which ansible-playbook > /dev/null',
'update':'brew upgrade ansible'
}]
def parse_args():
parser = argparse.ArgumentParser(
description='Installs various programs required for Symbiont development. Most using various package managers'
)
parser.add_argument(
'--dry-run',
action='store_true',
help='do not actually run the commands')
parser.add_argument(
'--force',
action='store_true',
help='run install command regardless if already installed')
for manager in MANAGERS:
name = manager['name']
parser.add_argument(
'--{}'.format(name),
action='append_const',
const=name,
dest='include',
help='Install just {}'.format(name))
parser.add_argument(
'--no-{}'.format(name),
action='append_const',
const=name,
dest='exclude',
help='Skip installing {}'.format(name))
args = parser.parse_args()
return args
def run_cmd(cmd, dry_run, verbose=True):
if verbose or dry_run:
print(cmd)
if dry_run == True:
return 0
else:
ret = subprocess.call(cmd, shell=True)
return ret
return 1
def create_check_cmd(name, info):
pkg = info['pkg']
if pkg == 'brew':
return 'brew {} ls {} &> /dev/null'.format(info.get('tap', ''), name)
elif pkg == 'pip':
return 'pip show {} &> /dev/null'.format(name)
elif pkg == 'npm':
return 'npm list --global {} &> /dev/null'.format(name)
elif pkg == 'docker':
return 'docker image inspect {} &>/dev/null'.format(name)
elif pkg == 'sh':
return info['check']
else:
raise Exception('unknown pkg {} for {}'.format(pkg, name))
def create_install_cmd(name, info):
pkg = info['pkg']
if pkg == 'brew':
return 'brew {} install {}'.format(info.get('tap', ''), name)
elif pkg == 'pip':
return 'pip install {}'.format(name)
elif pkg == 'npm':
return 'npm install --global {}'.format(name)
elif pkg == 'docker':
return 'docker pull {}'.format(name)
elif pkg == 'sh':
return info['install']
else:
raise Exception('unknown pkg {} for {}'.format(pkg, name))
def main():
args = parse_args()
if os.geteuid() == 0:
sys.exit(
"sudo/root detected. do not run this script as root. it will prompt you for sudo when/if needed"
)
for manager in MANAGERS:
name = manager['name']
# if the include array is not null, but the manager name isn't in the list, skip
if args.include and (name not in args.include):
continue
# if the exclude array is not null, and the manager name is in the list, skip
if args.exclude and (name in args.exclude):
continue
return_val = 1
app_installed = (0 == run_cmd(manager['check'], dry_run=False,
verbose=True))
if app_installed and 'update' in manager:
cmd = manager['update']
run_cmd(cmd, args.dry_run)
elif not app_installed or args.force:
for cmd in manager['cmds']:
run_cmd(cmd, args.dry_run)
else:
print('{} already installed'.format(manager['name']))
try:
main()
except Exception as e:
print(e.args)
|
[
"poulh@umich.edu"
] |
poulh@umich.edu
|
|
471b28b164af5875eb9670ed6bdea81faaa98ba6
|
9d1c9a81520437122d9f2f012c2737e4dd22713c
|
/src/td_clean.py
|
0b0e3a8e8ad9f059d56a6f5f5dd04748362a15f8
|
[
"MIT"
] |
permissive
|
geophysics-ubonn/crtomo_tools
|
136aa39a8a0d92061a739ee3723b6ef7879c57b8
|
aa73a67479c4e96bc7734f88ac7b35a74b5d158c
|
refs/heads/master
| 2023-08-24T01:55:29.517285
| 2023-08-08T13:03:46
| 2023-08-08T13:03:46
| 142,049,690
| 2
| 9
|
MIT
| 2019-06-06T12:46:42
| 2018-07-23T17:54:24
|
Standard ML
|
UTF-8
|
Python
| false
| false
| 1,791
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Clean a simulation directory of all modeling/inversion files
"""
import numpy as np
import os
import glob
def main():
rm_list = []
required_files_inversion = (
'exe/crtomo.cfg',
'grid/elem.dat',
'grid/elec.dat',
'mod/volt.dat')
clean_inv = np.all([os.path.isfile(x) for x in required_files_inversion])
if clean_inv:
rm_list += glob.glob('inv/*')
rm_list += [
'exe/error.dat',
'exe/crtomo.pid',
'exe/variogram.gnu',
'exe/inv.elecpositions',
'exe/inv.gstat',
'exe/inv.lastmod',
'exe/inv.lastmod_rho',
'exe/inv.mynoise_pha',
'exe/inv.mynoise_rho',
'exe/inv.mynoise_voltages',
'exe/tmp.kfak',
'overview.png',
]
required_files_modelling = (
'exe/crmod.cfg',
'grid/elem.dat',
'grid/elec.dat',
'config/config.dat',
'rho/rho.dat'
)
clean_mod = np.all([os.path.isfile(x) for x in required_files_modelling])
if clean_mod:
rm_list += glob.glob('mod/sens/*')
rm_list += glob.glob('mod/pot/*')
rm_list += ['mod/volt.dat', ]
rm_list += ['exe/crmod.pid', ]
for filename in rm_list:
if os.path.isfile(filename):
# print('Removing file {0}'.format(filename))
os.remove(filename)
plot_files = (
'rho.png',
'imag.png',
'real.png',
'phi.png',
'cov.png',
'fpi_imag.png',
'fpi_phi.png',
'fpi_real.png',
)
for filename in plot_files:
if os.path.isfile(filename):
os.remove(filename)
if __name__ == '__main__':
main()
|
[
"mweigand@geo.uni-bonn.de"
] |
mweigand@geo.uni-bonn.de
|
e3c7ab6be6aaf8422341c9f6c2da618e37250a0c
|
8eecff47b3165d91b91a29b311a9f105aa6a25c8
|
/combinations.py
|
de0baa72e882e7f3e6b2cf0ebcae3411dfd25bea
|
[] |
no_license
|
Temp-Nerd/All-d-Porgrams-in-d-wurld
|
dbdb394d4afa9cd4028bd406f4e95efc665f518f
|
23320f42013f9f5fbd9f1107ac0eea3cdfefb24d
|
refs/heads/main
| 2023-02-18T19:00:43.359747
| 2021-01-22T15:20:50
| 2021-01-22T15:20:50
| 331,982,184
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 415
|
py
|
def factorial (a) :
factorial=1
for i in range (a,1,-1) :
factorial*=i
return factorial
def combination (n,r) :
n_fact=factorial(n)
r_fact=factorial(r)
n_r_fact=factorial(n-r)
return(n_fact//(r_fact*n_r_fact))
x=int(input('Enter n :'))
y=int(input('Enter r :'))
print(f'The total no. of combinations of n taken r at a time is :{combination(x,y)}')
|
[
"noreply@github.com"
] |
noreply@github.com
|
4c21860e5affb23d0bbac4e1610f8389b5a570a8
|
750edcf8562215b588c0960c6a0d65857026ad14
|
/synonyms_fuse_model/tfidf_vec_generate.py
|
e6867548e9c29cc7cec3b4a82623fb98d16e38e5
|
[] |
no_license
|
laura-zhang-cn/natural_language_preprocessing
|
7b1b99129a83bf870a01d9ba8d5009ca46caa23c
|
45b400ba705de5499a5b9a0ac333224e03f94043
|
refs/heads/master
| 2021-06-04T12:31:13.321272
| 2021-05-20T01:10:28
| 2021-05-20T01:10:28
| 122,903,175
| 2
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,274
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 12 15:21:04 2018
@author: zhangyaxu
商品描述/标题等数据源 -> 品类上的 合并
训练词的tfidf向量并计算离散向量相似度，返回topn个，并存储到hive中
"""
from pyspark import SparkConf
from pyspark import SparkContext
from pyspark.sql import HiveContext
import gc
import pandas as pds
import numpy as npy
from pyspark.sql import functions as F
from pyspark.sql import Window
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
from corpus_richness_alg import term_doc_tf_r
from params import params
database_name=params['shared']['database_name']
def term_tfidf_weight(model,corpus,na_val=0.0,keep_type='appeared',out_type='split',term_keep_index=None,norm_type=None):
'''
model is a TfidfModel
corpus is a dictionary.doc2bow corpus which like [[(term_id,tf),..],..] [docs=>(terms=>tf)]
out_type : [split,aggr] return a datframe with vector as columns that split by doc_id or aggregate in one columns
norm_type :[None,'maxscale']
term_keep_index: list of term-id which should keep ,if None,keep all
return_val:['tfidf' ,'tf'], default 'tfidf'
'''
idf=pds.DataFrame(list(sorted(model.idfs.items())),columns=['term_id','idf'])
def per_doc_wd_tfidf_weight(x,keep_type='appeared'):
'''
x is a document's words-corpus which is list type like [(term_id,tf),...]
idf is term inverse-document-freq which is dataframe type like [(term_id,idf)]
keep_type: ['appeared','all'] . appeared :让文档仅保留出现在文档中的词,all:保留所有词,即使没在文档中出现,对应的值是0.0
return [(term_id,tf),..]
'''
tf_r=npy.array(list(dict(x).values()))*1.0/sum(dict(x).values()) #
tf=pds.DataFrame(list(zip(dict(x).keys(),tf_r.tolist())),columns=['term_id','tf'])
if keep_type=='all':
how_type='left'
else:
how_type='inner'
tf_idf=pds.merge(idf,tf,on='term_id',how=how_type).fillna(0.0)
tf_idf['tfidf']=tf_idf['tf']*tf_idf['idf'] # 包含大于1的值
tf_idf['tfidf']=tf_idf['tfidf']/tf_idf['tfidf'].max() # 去中心化 ,必须的，不然值都非常小
#print(tf_idf.dtypes)
return list(zip(tf_idf.term_id.values.tolist(),tf_idf.tfidf.values.tolist(),tf_idf.tf.values.tolist()))
# 文章下的词tfidf值
tfidf=list(map(lambda x : per_doc_wd_tfidf_weight(x=x,keep_type=keep_type),corpus)) # 文档层面的词向量表达 [[(term_id,tfidf),..],..] [docs=>(terms=>tfidf)]
ls_tfidf=[]
doc_id=0
# 展开文章，获得文章=>词=>tfidf值的dataframe
for doc_terms in tfidf:
ls_tfidf.extend([(doc_id,)+term_tfidf for term_tfidf in doc_terms])
doc_id=doc_id+1
del tfidf
df_tfidf=pds.DataFrame(ls_tfidf,columns=['doc_id','term_id','tfidf','tf'])
term_tfidf=df_tfidf.pivot(index='term_id',columns='doc_id',values='tfidf').fillna(na_val) # 词 => tfidf_vectory
if norm_type=='maxscale':
term_tfidf=term_tfidf.apply(lambda x : x/x.max(),axis=1) # 归一化
#del df_tfidf
#gc.collect()
if term_keep_index!=None:
term_tfidf=term_tfidf.loc[term_keep_index,:]
if out_type=='aggr':
#tfidf_vec=term_tfidf.apply(lambda x : x.tolist(),axis=1,result_type=None) # 仅 python3 # python2 不支持 result_type=None,默认是split，所以不可行
tfidf_vec=pds.DataFrame(list(zip(term_tfidf.index.values.tolist(),term_tfidf.values.tolist())),columns=['term_id','tfidf_vec']).set_index('term_id') # python2 & python3 都适用，也很快
return tfidf_vec
elif out_type=='split':
return term_tfidf
else:
return None
def tfidf_vec_generate_and_storage(cat_sts,na_val=0.0,norm_type='maxscale'):
'''
生成tfidf vec 并存储
提前去掉低频词，仅保留高频词，
对 稀疏向量 ，仅存储 非稀疏值 及对应位置，存储空间
'''
## 训练模型
dct = Dictionary(cat_sts)
corpus = [dct.doc2bow(line) for line in cat_sts]
model = TfidfModel(corpus)
tk2id = dct.token2id
# 去掉低频词，仅保留非低频词 term_id_keep 会去索引 tfidf-vec
tf_all=[]
for c in corpus:
tf_all.extend(c)
tf_all=pds.DataFrame(tf_all,columns=['term_id','tf']).groupby('term_id')['tf'].agg(['sum','count']).reset_index().rename(columns={'sum':'tf','count':'df'})
term_id_keep=tf_all.loc[tf_all.tf>3,:].term_id.values.tolist()
sqlContext.createDataFrame(tf_all).write.saveAsTable('{0}.term_id_tf_df'.format(database_name),mode='overwrite')
## transform: 词的tf-idf向量 ,需要获得词的向量表达,一般是 对想基于词对文档进行分类，或者计算词的相似度 ,比较快，3到5分钟
#term_tfidf=term_tfidf_weight(model=model,corpus=corpus,na_val=-1.0,keep_type='appeared',out_type='split') # 获得词的tfidf向量表达，pandas-dataframe方式传出，一行是一个词在各doc中的tfidf权重向量
tfidf_vec=term_tfidf_weight(model=model,corpus=corpus,na_val=na_val,keep_type='appeared',out_type='aggr',term_keep_index=term_id_keep,norm_type=norm_type)
#tfidf_vec=tfidf_vec
del model ,dct
gc.collect()
print(1)
def sparse_vec_disassemble(a,sparse_val=-1.0,return_part=1):
'''
获取疏向量 非稀疏部分的位置p，最终二者分别保留p位的值
a=npy.array([-1, -1, 0.2, 0.7, -1, -1])
则保留
a_u=[0.2,0.7]
return 保留向量
'''
pos_a=npy.array(range(len(a)))[a!=sparse_val].tolist()
a_u=a[pos_a]
if return_part==1:
return a_u.tolist()
elif return_part==2:
return pos_a
else:
return a_u.tolist(),pos_a
tfidf_vec2=tfidf_vec.apply(lambda x : sparse_vec_disassemble(a=npy.array(x[0]),sparse_val=na_val,return_part=1),axis=1).reset_index()
tfidf_vec2['pos']=tfidf_vec.apply(lambda x : sparse_vec_disassemble(a=npy.array(x[0]),sparse_val=na_val,return_part=2),axis=1).reset_index(drop=True)
tfidf_vec2.columns=['term_id','tfidf_vec','tfidf_vec_pos']
print(2)
#
tfidf_vec_df=sqlContext.createDataFrame(tfidf_vec2) # dataframe ，约10w条记录 ，rdd时计算
windowx=Window.orderBy('term_id')
tfidf_vec_df=tfidf_vec_df.withColumn('rankx',F.row_number().over(windowx))
#
#sqlContext.sql('drop table if exists {0}.term_id_tfidf_vec_in_cat '.format(database_name))
tfidf_vec_df.write.saveAsTable('{0}.term_id_tfidf_vec_in_cat'.format(database_name),mode='overwrite')
gc.collect()
tk_id0=pds.DataFrame(list(tk2id.items()),columns=['term_name','term_id']) # dataframe 方便操作join
tk_id=tk_id0.loc[(tk_id0.term_name.str.len()>1)&(tk_id0.term_name.str.contains(u'[\u4e00-\u9fa5a-zA-Z]')==True),:] # 剔除长度为1 和 仅数字或符号的 词
sqlContext.createDataFrame(tk_id).write.saveAsTable('{0}.term_id_name'.format(database_name),mode='overwrite')
return corpus
if __name__=='__main__':
confx=SparkConf().setAppName('3_word_semantic_similarity_on_prod_name')
sc=SparkContext(conf=confx)
sqlContext=HiveContext(sc)
sc.setLogLevel("WARN")
## 4 tfidf 相似度
topic_table=params['shared']['topic_cut_word_table']
df3=sqlContext.sql('select inputwds from {0}.{1}'.format(database_name,topic_table))
# 4.1 生成tfidf 向量，并存储
na_val=params['tfidf']['na_val']
norm_type=params['tfidf']['norm_type']
cat_sts=df3.select('inputwds').toPandas()['inputwds'].values.tolist() # 2G #cat_sts=[['ab','a','c'],['a','b','c','c']]
corpus=tfidf_vec_generate_and_storage(cat_sts,na_val=na_val,norm_type=norm_type) # NOT RETURN VALUE,BUT STORAGE TO TABLE : tfidf_vec_df ,tk_id
gc.collect()
# 4.2 计算相似度 建议这里重新开一个 py文件，这样可以释放内存 ，见 tfidf_vec_sim.py文件
# 4.2 term_id tf ration in each doc --> used in richness_coef
term_doc_tf_r(corpus=corpus,sqlContext=sqlContext)
|
[
"noreply@github.com"
] |
noreply@github.com
|
f282c11daf075504bc04c5b5e1c3abc69fdfe691
|
ac52ef481402457c9c967d8ed4930fb3c0b8cbdf
|
/projects/models.py
|
3d6cc872327ba8e52a849e8ad74a268deee9e644
|
[] |
no_license
|
ShakeelAhmad3/My_Portfolio
|
7a2e0f5991b25edb3d9f805effca88c24030a399
|
346ac5599b458d06735fec1af76c7d023e50190a
|
refs/heads/master
| 2023-09-02T17:31:00.809363
| 2021-11-20T11:31:27
| 2021-11-20T11:31:27
| 428,708,268
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 344
|
py
|
from django.db import models
# Create your models here.
class projects(models.Model):
title = models.CharField(max_length= 250)
body = models.TextField()
image = models.ImageField(upload_to='media/')
summary = models.CharField(max_length=250)
bub_date = models.DateField()
def __str__(self):
return self.title
|
[
"buneeri020@gmail.com"
] |
buneeri020@gmail.com
|
a4885e47195c762b6bbaf2e2a19dfdf4aa3f7fc1
|
2def74312f65dbe7950e9958070a175448d0dcff
|
/setup.py
|
6d8538691297cf80515acf61017b67e3b1b9c01b
|
[] |
no_license
|
JorgeJuarezM/pyqt-examples
|
9847353d93e94534d1193bb1bc3b33e4fc683f9a
|
586df9496314c5b62fe0e30d8959feb680a1500e
|
refs/heads/master
| 2021-07-20T18:53:33.081825
| 2017-10-30T04:29:45
| 2017-10-30T04:29:45
| 108,799,879
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 368
|
py
|
"""
This is a setup.py script generated by py2applet
Usage:
python setup.py py2app
"""
from setuptools import setup
APP = ['main.py']
DATA_FILES = []
OPTIONS = {'argv_emulation': True,
'iconfile': './icon.icns'}
setup(
app=APP,
name="Odoo Client",
data_files=DATA_FILES,
options={'py2app': OPTIONS},
setup_requires=['py2app'],
)
|
[
"contacto@jorgejuarez.net"
] |
contacto@jorgejuarez.net
|
e46fb5b9471464a721d150046158fc9f3b99a474
|
92e45c3f8460a1b61ba631072af30c1c5fa25c48
|
/src/chooseplan/urls.py
|
713acd3162b3d090fde22f6353eb15d2d5efa3bc
|
[] |
no_license
|
elmiraus/HealthcareNow
|
ad19bff6c7417dcdb3ed035fbc1bcdf1d5956df6
|
3f5877fce0cb6b1366dfe3528d5fc1c628b3ac51
|
refs/heads/master
| 2020-08-08T07:01:46.378703
| 2019-10-08T20:53:55
| 2019-10-08T20:53:55
| 213,767,892
| 0
| 0
| null | 2019-10-08T22:28:34
| 2019-10-08T22:28:33
| null |
UTF-8
|
Python
| false
| false
| 121
|
py
|
from django.urls import path
from . import views
urlpatterns = [
path('', views.chooseplan, name='chooseplan'),
]
|
[
"jacqueline.rollins@cgu.edu"
] |
jacqueline.rollins@cgu.edu
|
90bb35f751c04a00431dcc41c19d92be007cb65d
|
731a33f8bb92bad31ab233416d8ef6eb3a9f3fe0
|
/minlplib_instances/smallinvSNPr2b020-022.py
|
8348bb9863905664e9dffb15877a5b89b31156af
|
[] |
no_license
|
ChristophNeumann/IPCP
|
d34c7ec3730a5d0dcf3ec14f023d4b90536c1e31
|
6e3d14cc9ed43f3c4f6c070ebbce21da5a059cb7
|
refs/heads/main
| 2023-02-22T09:54:39.412086
| 2021-01-27T17:30:50
| 2021-01-27T17:30:50
| 319,694,028
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 167,363
|
py
|
# MINLP written by GAMS Convert at 02/15/18 11:44:29
#
# Equation counts
# Total E G L N X C B
# 4 0 2 2 0 0 0 0
#
# Variable counts
# x b i s1s s2s sc si
# Total cont binary integer sos1 sos2 scont sint
# 101 1 0 100 0 0 0 0
# FX 0 0 0 0 0 0 0 0
#
# Nonzero counts
# Total const NL DLL
# 401 301 100 0
from pyomo.environ import *
model = m = ConcreteModel()
m.i1 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i2 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i3 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i4 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i5 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i6 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i7 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i8 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i9 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i10 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i11 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i12 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i13 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i14 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i15 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i16 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i17 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i18 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i19 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i20 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i21 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i22 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i23 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i24 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i25 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i26 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i27 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i28 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i29 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i30 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i31 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i32 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i33 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i34 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i35 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i36 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i37 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i38 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i39 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i40 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i41 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i42 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i43 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i44 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i45 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i46 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i47 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i48 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i49 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i50 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i51 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i52 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i53 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i54 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i55 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i56 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i57 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i58 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i59 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i60 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i61 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i62 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i63 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i64 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i65 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i66 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i67 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i68 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i69 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i70 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i71 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i72 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i73 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i74 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i75 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i76 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i77 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i78 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i79 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i80 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i81 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i82 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i83 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i84 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i85 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i86 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i87 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i88 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i89 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i90 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i91 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i92 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i93 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i94 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i95 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i96 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i97 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i98 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i99 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.i100 = Var(within=Integers,bounds=(0,1E20),initialize=0)
m.x101 = Var(within=Reals,bounds=(None,None),initialize=0)
m.obj = Objective(expr=m.x101, sense=minimize)
m.c1 = Constraint(expr=0.00841507*m.i1**2 + 0.0222536*m.i2**2 + 0.0056479*m.i3**2 + 0.00333322*m.i4**2 + 0.00490963*m.i5
**2 + 0.0221034*m.i6**2 + 0.00509899*m.i7**2 + 0.049464*m.i8**2 + 0.0171508*m.i9**2 + 0.0064643*
m.i10**2 + 0.0218437*m.i11**2 + 0.00346366*m.i12**2 + 0.0458502*m.i13**2 + 0.0747061*m.i14**2 +
0.0196511*m.i15**2 + 0.014222*m.i16**2 + 0.0147535*m.i17**2 + 0.00398615*m.i18**2 + 0.00644484*
m.i19**2 + 0.0322232*m.i20**2 + 0.00887889*m.i21**2 + 0.0434025*m.i22**2 + 0.00981376*m.i23**2 +
0.0133193*m.i24**2 + 0.00471036*m.i25**2 + 0.00359843*m.i26**2 + 0.0112312*m.i27**2 + 0.00476479*
m.i28**2 + 0.00356255*m.i29**2 + 0.0730121*m.i30**2 + 0.00785721*m.i31**2 + 0.0243787*m.i32**2 +
0.0171188*m.i33**2 + 0.00439547*m.i34**2 + 0.00502594*m.i35**2 + 0.0580619*m.i36**2 + 0.0135984*
m.i37**2 + 0.00254137*m.i38**2 + 0.0153341*m.i39**2 + 0.109758*m.i40**2 + 0.0346065*m.i41**2 +
0.0127589*m.i42**2 + 0.011147*m.i43**2 + 0.0156318*m.i44**2 + 0.00556588*m.i45**2 + 0.00302864*
m.i46**2 + 0.0214898*m.i47**2 + 0.00499587*m.i48**2 + 0.00864393*m.i49**2 + 0.0228248*m.i50**2 +
0.0077726*m.i51**2 + 0.00992767*m.i52**2 + 0.0184506*m.i53**2 + 0.0113481*m.i54**2 + 0.0067583*
m.i55**2 + 0.0150416*m.i56**2 + 0.00324193*m.i57**2 + 0.00478196*m.i58**2 + 0.0132471*m.i59**2 +
0.00273446*m.i60**2 + 0.0282459*m.i61**2 + 0.0230221*m.i62**2 + 0.0240972*m.i63**2 + 0.00829946*
m.i64**2 + 0.00688665*m.i65**2 + 0.00858803*m.i66**2 + 0.00778038*m.i67**2 + 0.0082583*m.i68**2
+ 0.022885*m.i69**2 + 0.00568332*m.i70**2 + 0.0234021*m.i71**2 + 0.00924249*m.i72**2 +
0.00669675*m.i73**2 + 0.0109501*m.i74**2 + 0.00663385*m.i75**2 + 0.00328058*m.i76**2 + 0.0112814*
m.i77**2 + 0.00341076*m.i78**2 + 0.0400653*m.i79**2 + 0.00876827*m.i80**2 + 0.0138276*m.i81**2 +
0.00246987*m.i82**2 + 0.0406516*m.i83**2 + 0.00947194*m.i84**2 + 0.00647449*m.i85**2 + 0.0107715*
m.i86**2 + 0.00803069*m.i87**2 + 0.106502*m.i88**2 + 0.00815263*m.i89**2 + 0.0171707*m.i90**2 +
0.0163522*m.i91**2 + 0.00911726*m.i92**2 + 0.00287317*m.i93**2 + 0.00360309*m.i94**2 + 0.00699161
*m.i95**2 + 0.0340959*m.i96**2 + 0.00958446*m.i97**2 + 0.0147951*m.i98**2 + 0.0177595*m.i99**2 +
0.0208523*m.i100**2 + 0.00692522*m.i1*m.i2 + 0.00066464*m.i1*m.i3 + 0.00388744*m.i1*m.i4 +
0.001108218*m.i1*m.i5 + 0.0046712*m.i1*m.i6 + 0.00771824*m.i1*m.i7 + 0.0020653*m.i1*m.i8 +
0.001524626*m.i1*m.i9 + 0.00484724*m.i1*m.i10 + 0.00733242*m.i1*m.i11 + 0.00556218*m.i1*m.i12 +
0.0052571*m.i1*m.i13 + 0.0218926*m.i1*m.i14 + 0.01352862*m.i1*m.i15 + 0.00549784*m.i1*m.i16 +
0.00235342*m.i1*m.i17 + 0.00448206*m.i1*m.i18 + 0.0072148*m.i1*m.i19 + 0.00958894*m.i1*m.i20 +
0.00376328*m.i1*m.i21 + 0.0117501*m.i1*m.i22 + 0.00575998*m.i1*m.i23 - 0.000109147*m.i1*m.i24 +
0.000604944*m.i1*m.i25 + 0.00473296*m.i1*m.i26 + 0.000356572*m.i1*m.i27 - 0.001552262*m.i1*m.i28
+ 0.00119092*m.i1*m.i29 + 0.01373684*m.i1*m.i30 + 0.0059113*m.i1*m.i31 + 0.00623524*m.i1*m.i32
+ 0.00801204*m.i1*m.i33 + 0.00108736*m.i1*m.i34 + 0.001491474*m.i1*m.i35 + 0.01080356*m.i1*m.i36
+ 0.00559202*m.i1*m.i37 + 7.8057e-6*m.i1*m.i38 + 0.00831004*m.i1*m.i39 + 0.001096208*m.i1*m.i40
+ 0.001136658*m.i1*m.i41 + 0.0073715*m.i1*m.i42 + 0.000726938*m.i1*m.i43 + 0.00621872*m.i1*m.i44
+ 0.00646596*m.i1*m.i45 + 0.00441466*m.i1*m.i46 + 0.001262528*m.i1*m.i47 + 0.00567366*m.i1*m.i48
+ 0.00690472*m.i1*m.i49 + 0.01140754*m.i1*m.i50 + 0.00275514*m.i1*m.i51 + 0.00633434*m.i1*m.i52
+ 0.00842252*m.i1*m.i53 + 0.00674544*m.i1*m.i54 + 0.00577156*m.i1*m.i55 + 0.000723972*m.i1*m.i56
+ 0.00617654*m.i1*m.i57 + 0.00426758*m.i1*m.i58 + 0.00581362*m.i1*m.i59 + 0.00305964*m.i1*m.i60
+ 0.00915838*m.i1*m.i61 + 0.00408204*m.i1*m.i62 + 0.00526036*m.i1*m.i63 + 0.00641708*m.i1*m.i64
+ 0.001311362*m.i1*m.i65 + 0.00589896*m.i1*m.i66 + 0.001450664*m.i1*m.i67 + 0.0054669*m.i1*m.i68
+ 0.00759698*m.i1*m.i69 + 0.0069591*m.i1*m.i70 + 0.0023689*m.i1*m.i71 + 0.0026146*m.i1*m.i72 +
0.00520422*m.i1*m.i73 + 0.00959956*m.i1*m.i74 + 0.00799166*m.i1*m.i75 + 0.00256248*m.i1*m.i76 +
0.01210352*m.i1*m.i77 + 0.00469514*m.i1*m.i78 + 0.00329676*m.i1*m.i79 + 0.0068214*m.i1*m.i80 +
0.00190637*m.i1*m.i81 + 0.00256972*m.i1*m.i82 - 0.00577696*m.i1*m.i83 + 0.00245394*m.i1*m.i84 +
0.00585966*m.i1*m.i85 + 0.00330078*m.i1*m.i86 + 0.00362852*m.i1*m.i87 + 0.0064137*m.i1*m.i88 +
0.00375038*m.i1*m.i89 + 0.00666048*m.i1*m.i90 + 0.00942176*m.i1*m.i91 + 0.00379828*m.i1*m.i92 +
0.00246526*m.i1*m.i93 + 0.0029997*m.i1*m.i94 + 0.00592606*m.i1*m.i95 + 0.0136565*m.i1*m.i96 +
0.00562112*m.i1*m.i97 + 0.0031101*m.i1*m.i98 + 0.00328418*m.i1*m.i99 + 0.00992138*m.i1*m.i100 +
0.01159836*m.i2*m.i3 + 0.00432612*m.i2*m.i4 + 0.01055774*m.i2*m.i5 + 0.0235592*m.i2*m.i6 +
0.0053913*m.i2*m.i7 + 0.01748966*m.i2*m.i8 + 0.01322526*m.i2*m.i9 + 0.01103896*m.i2*m.i10 +
0.001420928*m.i2*m.i11 + 0.00303766*m.i2*m.i12 + 0.0325414*m.i2*m.i13 + 0.0528886*m.i2*m.i14 +
0.0344486*m.i2*m.i15 + 0.01889664*m.i2*m.i16 + 0.01085498*m.i2*m.i17 + 0.01133696*m.i2*m.i18 +
0.0105108*m.i2*m.i19 + 0.041965*m.i2*m.i20 + 0.01908526*m.i2*m.i21 + 0.0438608*m.i2*m.i22 +
0.01760436*m.i2*m.i23 + 0.0177692*m.i2*m.i24 + 0.01401386*m.i2*m.i25 + 0.01130076*m.i2*m.i26 +
0.0201926*m.i2*m.i27 + 0.00893526*m.i2*m.i28 + 0.01013464*m.i2*m.i29 + 0.0522552*m.i2*m.i30 +
0.00674062*m.i2*m.i31 + 0.0386894*m.i2*m.i32 + 0.01840562*m.i2*m.i33 + 0.0079061*m.i2*m.i34 +
0.01050574*m.i2*m.i35 + 0.038882*m.i2*m.i36 + 0.0209782*m.i2*m.i37 + 0.00569346*m.i2*m.i38 +
0.0259324*m.i2*m.i39 + 0.0472088*m.i2*m.i40 + 0.0282636*m.i2*m.i41 + 0.0225892*m.i2*m.i42 +
0.01104052*m.i2*m.i43 + 0.0218496*m.i2*m.i44 + 0.00682534*m.i2*m.i45 + 0.01022898*m.i2*m.i46 +
0.0273094*m.i2*m.i47 + 0.01045064*m.i2*m.i48 + 0.01767338*m.i2*m.i49 + 0.0311902*m.i2*m.i50 +
0.0126455*m.i2*m.i51 + 0.0206168*m.i2*m.i52 + 0.0261894*m.i2*m.i53 + 0.024527*m.i2*m.i54 +
0.01734138*m.i2*m.i55 + 0.01224052*m.i2*m.i56 + 0.01152072*m.i2*m.i57 + 0.01028864*m.i2*m.i58 +
0.01883544*m.i2*m.i59 + 0.00908648*m.i2*m.i60 + 0.0449708*m.i2*m.i61 + 0.0363664*m.i2*m.i62 +
0.01577062*m.i2*m.i63 + 0.01266282*m.i2*m.i64 + 0.01385216*m.i2*m.i65 + 0.00440902*m.i2*m.i66 +
0.01711764*m.i2*m.i67 + 0.0110787*m.i2*m.i68 + 0.0341778*m.i2*m.i69 + 0.0156542*m.i2*m.i70 +
0.01891112*m.i2*m.i71 + 0.0216326*m.i2*m.i72 + 0.01534328*m.i2*m.i73 + 0.01661334*m.i2*m.i74 +
0.01534594*m.i2*m.i75 + 0.01116732*m.i2*m.i76 + 0.01402982*m.i2*m.i77 + 0.00963242*m.i2*m.i78 +
0.0200668*m.i2*m.i79 + 0.01379116*m.i2*m.i80 + 0.01910046*m.i2*m.i81 + 0.0077605*m.i2*m.i82 -
0.000954558*m.i2*m.i83 + 0.01255918*m.i2*m.i84 + 0.0126639*m.i2*m.i85 + 0.0201936*m.i2*m.i86 +
0.017931*m.i2*m.i87 + 0.0389418*m.i2*m.i88 + 0.00845916*m.i2*m.i89 + 0.0267914*m.i2*m.i90 +
0.0193905*m.i2*m.i91 + 0.01261014*m.i2*m.i92 + 0.0069012*m.i2*m.i93 + 0.00876014*m.i2*m.i94 +
0.01829908*m.i2*m.i95 + 0.0373396*m.i2*m.i96 + 0.0211262*m.i2*m.i97 + 0.01549032*m.i2*m.i98 +
0.0247114*m.i2*m.i99 + 0.0324248*m.i2*m.i100 - 0.000720538*m.i3*m.i4 + 0.00453322*m.i3*m.i5 +
0.00638226*m.i3*m.i6 + 0.000938158*m.i3*m.i7 + 0.0035154*m.i3*m.i8 + 0.00681962*m.i3*m.i9 +
0.006345*m.i3*m.i10 + 0.00232904*m.i3*m.i11 - 0.00054599*m.i3*m.i12 + 0.01850556*m.i3*m.i13 +
0.01892336*m.i3*m.i14 + 0.00820906*m.i3*m.i15 + 0.00848796*m.i3*m.i16 + 0.0100743*m.i3*m.i17 +
0.00327798*m.i3*m.i18 + 0.000498452*m.i3*m.i19 + 0.01775572*m.i3*m.i20 + 0.00919688*m.i3*m.i21 +
0.01282772*m.i3*m.i22 + 0.00853066*m.i3*m.i23 + 0.00506148*m.i3*m.i24 + 0.004557*m.i3*m.i25 +
0.001737768*m.i3*m.i26 + 0.00560326*m.i3*m.i27 + 0.00374962*m.i3*m.i28 + 0.000427408*m.i3*m.i29
+ 0.01831098*m.i3*m.i30 + 0.00791496*m.i3*m.i31 + 0.01306*m.i3*m.i32 + 0.0143109*m.i3*m.i33 +
0.00324578*m.i3*m.i34 + 0.00289704*m.i3*m.i35 + 0.01899172*m.i3*m.i36 + 0.00855898*m.i3*m.i37 +
0.000764782*m.i3*m.i38 + 0.01045622*m.i3*m.i39 + 0.0241684*m.i3*m.i40 + 0.01022702*m.i3*m.i41 +
0.0096569*m.i3*m.i42 + 0.00605256*m.i3*m.i43 + 0.0087656*m.i3*m.i44 + 0.00231868*m.i3*m.i45 +
0.003075*m.i3*m.i46 + 0.00904418*m.i3*m.i47 + 0.00346386*m.i3*m.i48 + 0.00970054*m.i3*m.i49 +
0.0107517*m.i3*m.i50 + 0.00833706*m.i3*m.i51 + 0.00601022*m.i3*m.i52 + 0.00885472*m.i3*m.i53 +
0.0087269*m.i3*m.i54 + 0.00799796*m.i3*m.i55 + 0.0077742*m.i3*m.i56 + 0.00233028*m.i3*m.i57 +
0.00392772*m.i3*m.i58 + 0.00960436*m.i3*m.i59 + 0.000506858*m.i3*m.i60 + 0.01485036*m.i3*m.i61 +
0.01172454*m.i3*m.i62 + 0.00763564*m.i3*m.i63 + 0.00510368*m.i3*m.i64 + 0.00739458*m.i3*m.i65 +
0.00321864*m.i3*m.i66 + 0.00506992*m.i3*m.i67 + 0.001582392*m.i3*m.i68 + 0.0133327*m.i3*m.i69 +
0.00346984*m.i3*m.i70 + 0.00591914*m.i3*m.i71 + 0.0050918*m.i3*m.i72 + 0.00762942*m.i3*m.i73 +
0.0072567*m.i3*m.i74 + 0.0028432*m.i3*m.i75 + 0.00258746*m.i3*m.i76 + 0.00665946*m.i3*m.i77 +
0.001559716*m.i3*m.i78 + 0.0114221*m.i3*m.i79 + 0.00359546*m.i3*m.i80 + 0.00675946*m.i3*m.i81 +
0.001328782*m.i3*m.i82 + 0.00450512*m.i3*m.i83 + 0.00859628*m.i3*m.i84 + 0.00541618*m.i3*m.i85 +
0.01126372*m.i3*m.i86 + 0.00604642*m.i3*m.i87 + 0.01802074*m.i3*m.i88 + 0.0056414*m.i3*m.i89 +
0.00952436*m.i3*m.i90 + 0.00568388*m.i3*m.i91 + 0.0086732*m.i3*m.i92 + 0.001482822*m.i3*m.i93 +
0.0026677*m.i3*m.i94 + 0.00675394*m.i3*m.i95 + 0.01169216*m.i3*m.i96 + 0.0076724*m.i3*m.i97 +
0.00761804*m.i3*m.i98 + 0.01192344*m.i3*m.i99 + 0.01326866*m.i3*m.i100 + 0.00169903*m.i4*m.i5 +
0.00300136*m.i4*m.i6 + 0.00385392*m.i4*m.i7 + 0.00382362*m.i4*m.i8 + 0.00575034*m.i4*m.i9 +
0.00125203*m.i4*m.i10 + 0.000828078*m.i4*m.i11 + 0.00404896*m.i4*m.i12 - 0.001180878*m.i4*m.i13
+ 0.00956206*m.i4*m.i14 + 0.00571904*m.i4*m.i15 + 0.0047927*m.i4*m.i16 + 0.001736122*m.i4*m.i17
+ 0.001900434*m.i4*m.i18 + 0.00498296*m.i4*m.i19 + 0.0055112*m.i4*m.i20 + 0.00199047*m.i4*m.i21
+ 0.00302926*m.i4*m.i22 + 0.001107052*m.i4*m.i23 + 0.0032099*m.i4*m.i24 + 0.00202704*m.i4*m.i25
+ 0.0049441*m.i4*m.i26 + 0.00296714*m.i4*m.i27 + 0.001430786*m.i4*m.i28 + 0.00335542*m.i4*m.i29
+ 0.0072271*m.i4*m.i30 + 0.001983328*m.i4*m.i31 + 0.00263338*m.i4*m.i32 + 0.0034098*m.i4*m.i33
+ 0.001978102*m.i4*m.i34 + 0.00248436*m.i4*m.i35 + 0.001037234*m.i4*m.i36 + 0.001931824*m.i4*
m.i37 + 0.00154955*m.i4*m.i38 + 0.00293776*m.i4*m.i39 - 0.01282698*m.i4*m.i40 + 0.001937926*m.i4*
m.i41 + 0.0052959*m.i4*m.i42 + 0.001856036*m.i4*m.i43 + 0.000740384*m.i4*m.i44 + 0.00372246*m.i4*
m.i45 + 0.00362974*m.i4*m.i46 + 0.001687258*m.i4*m.i47 + 0.00297792*m.i4*m.i48 + 0.0024381*m.i4*
m.i49 + 0.00581304*m.i4*m.i50 + 0.000775592*m.i4*m.i51 + 0.00512872*m.i4*m.i52 + 0.00302932*m.i4*
m.i53 + 0.00451004*m.i4*m.i54 + 0.00355054*m.i4*m.i55 + 0.000365898*m.i4*m.i56 + 0.00396452*m.i4*
m.i57 + 0.00218522*m.i4*m.i58 + 0.001602712*m.i4*m.i59 + 0.00378946*m.i4*m.i60 + 0.00528342*m.i4*
m.i61 + 0.00345546*m.i4*m.i62 + 0.0072364*m.i4*m.i63 + 0.00460504*m.i4*m.i64 + 0.00362066*m.i4*
m.i65 + 0.00176825*m.i4*m.i66 + 0.00326082*m.i4*m.i67 + 0.00494324*m.i4*m.i68 + 0.00478058*m.i4*
m.i69 + 0.0047424*m.i4*m.i70 + 0.00406804*m.i4*m.i71 + 0.00356438*m.i4*m.i72 + 0.0039191*m.i4*
m.i73 + 0.00506266*m.i4*m.i74 + 0.005213*m.i4*m.i75 + 0.00334114*m.i4*m.i76 + 0.00410168*m.i4*
m.i77 + 0.00325268*m.i4*m.i78 + 0.000621396*m.i4*m.i79 + 0.00679868*m.i4*m.i80 + 0.001665408*m.i4
*m.i81 + 0.00231708*m.i4*m.i82 - 0.0025243*m.i4*m.i83 + 0.00277762*m.i4*m.i84 + 0.0040202*m.i4*
m.i85 + 0.001500566*m.i4*m.i86 + 0.001680814*m.i4*m.i87 + 0.00640404*m.i4*m.i88 + 0.00397656*m.i4
*m.i89 + 0.000508164*m.i4*m.i90 + 0.00565534*m.i4*m.i91 + 0.0031999*m.i4*m.i92 + 0.0007233*m.i4*
m.i93 + 0.001347788*m.i4*m.i94 + 0.00386662*m.i4*m.i95 + 0.0056032*m.i4*m.i96 + 0.00392786*m.i4*
m.i97 + 0.0032706*m.i4*m.i98 + 0.000716722*m.i4*m.i99 + 0.00200998*m.i4*m.i100 + 0.00725878*m.i5*
m.i6 + 0.000634496*m.i5*m.i7 + 0.0112129*m.i5*m.i8 + 0.006535*m.i5*m.i9 + 0.0076756*m.i5*m.i10 -
0.00455426*m.i5*m.i11 + 0.001111236*m.i5*m.i12 + 0.01473142*m.i5*m.i13 + 0.01556352*m.i5*m.i14 +
0.00889148*m.i5*m.i15 + 0.00833956*m.i5*m.i16 + 0.01155304*m.i5*m.i17 + 0.0044319*m.i5*m.i18 +
0.0061696*m.i5*m.i19 + 0.01660846*m.i5*m.i20 + 0.00921042*m.i5*m.i21 + 0.01240074*m.i5*m.i22 +
0.00930536*m.i5*m.i23 + 0.00636938*m.i5*m.i24 + 0.00582298*m.i5*m.i25 + 0.00314834*m.i5*m.i26 +
0.00569034*m.i5*m.i27 + 0.00513186*m.i5*m.i28 + 0.00443806*m.i5*m.i29 + 0.01398194*m.i5*m.i30 +
0.00649478*m.i5*m.i31 + 0.01579432*m.i5*m.i32 + 0.00734872*m.i5*m.i33 + 0.0056108*m.i5*m.i34 +
0.00623672*m.i5*m.i35 + 0.01544598*m.i5*m.i36 + 0.01144796*m.i5*m.i37 + 0.0024117*m.i5*m.i38 +
0.00970728*m.i5*m.i39 + 0.0182302*m.i5*m.i40 + 0.00790876*m.i5*m.i41 + 0.00731488*m.i5*m.i42 +
0.00543454*m.i5*m.i43 + 0.00647722*m.i5*m.i44 + 0.0035064*m.i5*m.i45 + 0.00307696*m.i5*m.i46 +
0.00716814*m.i5*m.i47 + 0.001828662*m.i5*m.i48 + 0.00846664*m.i5*m.i49 + 0.01292148*m.i5*m.i50 +
0.0081737*m.i5*m.i51 + 0.00647086*m.i5*m.i52 + 0.00609644*m.i5*m.i53 + 0.00842446*m.i5*m.i54 +
0.00619594*m.i5*m.i55 + 0.01114364*m.i5*m.i56 + 0.00464056*m.i5*m.i57 + 0.00294786*m.i5*m.i58 +
0.01085566*m.i5*m.i59 + 0.00324938*m.i5*m.i60 + 0.01321296*m.i5*m.i61 + 0.00956118*m.i5*m.i62 +
0.00799502*m.i5*m.i63 + 0.00255928*m.i5*m.i64 + 0.00635808*m.i5*m.i65 + 0.00425494*m.i5*m.i66 +
0.00743456*m.i5*m.i67 + 0.003997*m.i5*m.i68 + 0.01327542*m.i5*m.i69 + 0.00624764*m.i5*m.i70 +
0.00544782*m.i5*m.i71 + 0.00583882*m.i5*m.i72 + 0.00712322*m.i5*m.i73 + 0.00675538*m.i5*m.i74 +
0.00471928*m.i5*m.i75 + 0.00331686*m.i5*m.i76 + 0.0064726*m.i5*m.i77 + 0.0043073*m.i5*m.i78 +
0.01376458*m.i5*m.i79 + 0.00590054*m.i5*m.i80 + 0.00544478*m.i5*m.i81 + 0.00433406*m.i5*m.i82 +
0.0018936*m.i5*m.i83 + 0.00732892*m.i5*m.i84 + 0.00654804*m.i5*m.i85 + 0.00769986*m.i5*m.i86 +
0.00924248*m.i5*m.i87 + 0.01858866*m.i5*m.i88 + 0.00588762*m.i5*m.i89 + 0.00671372*m.i5*m.i90 +
0.00513832*m.i5*m.i91 + 0.00597632*m.i5*m.i92 + 0.0033572*m.i5*m.i93 + 0.00718978*m.i5*m.i94 +
0.00692006*m.i5*m.i95 + 0.0082357*m.i5*m.i96 + 0.00798976*m.i5*m.i97 + 0.00578018*m.i5*m.i98 +
0.00997244*m.i5*m.i99 + 0.00861536*m.i5*m.i100 + 0.00682146*m.i6*m.i7 + 0.00318158*m.i6*m.i8 +
0.01402384*m.i6*m.i9 + 0.01146794*m.i6*m.i10 + 0.00514562*m.i6*m.i11 + 0.001749894*m.i6*m.i12 +
0.0349226*m.i6*m.i13 + 0.0204032*m.i6*m.i14 + 0.0257432*m.i6*m.i15 + 0.01758104*m.i6*m.i16 +
0.01908054*m.i6*m.i17 + 0.00928378*m.i6*m.i18 + 0.00320468*m.i6*m.i19 + 0.0315536*m.i6*m.i20 +
0.01792788*m.i6*m.i21 + 0.0231518*m.i6*m.i22 + 0.01485588*m.i6*m.i23 + 0.01959078*m.i6*m.i24 +
0.01015748*m.i6*m.i25 + 0.00771848*m.i6*m.i26 + 0.0203708*m.i6*m.i27 + 0.00861336*m.i6*m.i28 +
0.00733064*m.i6*m.i29 + 0.0211284*m.i6*m.i30 + 0.01136376*m.i6*m.i31 + 0.0298052*m.i6*m.i32 +
0.01763386*m.i6*m.i33 + 0.01196962*m.i6*m.i34 + 0.00970124*m.i6*m.i35 + 0.0426536*m.i6*m.i36 +
0.0162704*m.i6*m.i37 + 0.00511032*m.i6*m.i38 + 0.0211034*m.i6*m.i39 + 0.0536216*m.i6*m.i40 +
0.0314338*m.i6*m.i41 + 0.0212846*m.i6*m.i42 + 0.01544516*m.i6*m.i43 + 0.0203852*m.i6*m.i44 +
0.00711214*m.i6*m.i45 + 0.01012528*m.i6*m.i46 + 0.0378006*m.i6*m.i47 + 0.00769828*m.i6*m.i48 +
0.01043538*m.i6*m.i49 + 0.0235092*m.i6*m.i50 + 0.00574084*m.i6*m.i51 + 0.01540822*m.i6*m.i52 +
0.01066192*m.i6*m.i53 + 0.01947344*m.i6*m.i54 + 0.01212224*m.i6*m.i55 + 0.01841288*m.i6*m.i56 +
0.00863178*m.i6*m.i57 + 0.0123986*m.i6*m.i58 + 0.01033934*m.i6*m.i59 + 0.00473636*m.i6*m.i60 +
0.0271978*m.i6*m.i61 + 0.0244978*m.i6*m.i62 + 0.0206042*m.i6*m.i63 + 0.0123061*m.i6*m.i64 +
0.00969592*m.i6*m.i65 + 0.0105285*m.i6*m.i66 + 0.01296694*m.i6*m.i67 + 0.00467684*m.i6*m.i68 +
0.0206522*m.i6*m.i69 + 0.01181216*m.i6*m.i70 + 0.034569*m.i6*m.i71 + 0.01713412*m.i6*m.i72 +
0.00997084*m.i6*m.i73 + 0.00934556*m.i6*m.i74 + 0.00446476*m.i6*m.i75 + 0.00591468*m.i6*m.i76 +
0.00902732*m.i6*m.i77 + 0.00684842*m.i6*m.i78 + 0.000346556*m.i6*m.i79 + 0.01344964*m.i6*m.i80 +
0.028585*m.i6*m.i81 + 0.00365848*m.i6*m.i82 + 0.0233826*m.i6*m.i83 + 0.01097966*m.i6*m.i84 +
0.01159854*m.i6*m.i85 + 0.0132315*m.i6*m.i86 + 0.00973116*m.i6*m.i87 + 0.01749474*m.i6*m.i88 +
0.00153948*m.i6*m.i89 + 0.01386412*m.i6*m.i90 + 0.01199914*m.i6*m.i91 + 0.0141917*m.i6*m.i92 +
0.001321806*m.i6*m.i93 + 0.00438272*m.i6*m.i94 + 0.01131596*m.i6*m.i95 + 0.01535776*m.i6*m.i96 +
0.01709068*m.i6*m.i97 + 0.024088*m.i6*m.i98 + 0.0176488*m.i6*m.i99 + 0.0244376*m.i6*m.i100 +
0.00488516*m.i7*m.i8 + 0.00626372*m.i7*m.i9 + 0.001990118*m.i7*m.i10 + 0.00360408*m.i7*m.i11 +
0.0044488*m.i7*m.i12 + 0.00345036*m.i7*m.i13 + 0.01022598*m.i7*m.i14 + 0.00914736*m.i7*m.i15 +
0.00744612*m.i7*m.i16 + 0.0041386*m.i7*m.i17 + 0.00439536*m.i7*m.i18 + 0.00478826*m.i7*m.i19 +
0.00946126*m.i7*m.i20 + 0.00383118*m.i7*m.i21 + 0.00577738*m.i7*m.i22 + 0.0023517*m.i7*m.i23 +
0.0050588*m.i7*m.i24 + 0.0021953*m.i7*m.i25 + 0.00304582*m.i7*m.i26 + 0.0025687*m.i7*m.i27 +
0.001019412*m.i7*m.i28 + 0.001803492*m.i7*m.i29 + 0.00840076*m.i7*m.i30 + 0.00405006*m.i7*m.i31
+ 0.00330894*m.i7*m.i32 + 0.00379124*m.i7*m.i33 + 0.00297878*m.i7*m.i34 + 0.00257924*m.i7*m.i35
+ 0.00710268*m.i7*m.i36 + 0.00290856*m.i7*m.i37 + 0.00084645*m.i7*m.i38 + 0.00616224*m.i7*m.i39
+ 0.00012188*m.i7*m.i40 + 0.00931498*m.i7*m.i41 + 0.00783*m.i7*m.i42 + 0.00769852*m.i7*m.i43 +
0.00783756*m.i7*m.i44 + 0.0049081*m.i7*m.i45 + 0.00379762*m.i7*m.i46 + 0.00691856*m.i7*m.i47 +
0.00516014*m.i7*m.i48 + 0.00525658*m.i7*m.i49 + 0.00529626*m.i7*m.i50 + 0.00103022*m.i7*m.i51 +
0.00545452*m.i7*m.i52 + 0.00609146*m.i7*m.i53 + 0.0066465*m.i7*m.i54 + 0.0057959*m.i7*m.i55 +
0.00384568*m.i7*m.i56 + 0.00518642*m.i7*m.i57 + 0.0049888*m.i7*m.i58 + 0.00240984*m.i7*m.i59 +
0.001870666*m.i7*m.i60 + 0.00856542*m.i7*m.i61 + 0.00433228*m.i7*m.i62 + 0.00926318*m.i7*m.i63 +
0.00802564*m.i7*m.i64 + 0.002679*m.i7*m.i65 + 0.00656044*m.i7*m.i66 + 0.00189873*m.i7*m.i67 +
0.00559974*m.i7*m.i68 + 0.0059088*m.i7*m.i69 + 0.00502274*m.i7*m.i70 + 0.00714092*m.i7*m.i71 +
0.00451814*m.i7*m.i72 + 0.0055096*m.i7*m.i73 + 0.0054579*m.i7*m.i74 + 0.00428152*m.i7*m.i75 +
0.00201372*m.i7*m.i76 + 0.00763776*m.i7*m.i77 + 0.001767634*m.i7*m.i78 - 0.00404984*m.i7*m.i79 +
0.00693072*m.i7*m.i80 + 0.00453578*m.i7*m.i81 + 0.001431356*m.i7*m.i82 + 0.001000832*m.i7*m.i83
+ 0.00363592*m.i7*m.i84 + 0.00399748*m.i7*m.i85 + 0.00244412*m.i7*m.i86 - 0.00038172*m.i7*m.i87
+ 0.00670104*m.i7*m.i88 + 0.00351634*m.i7*m.i89 + 0.000192176*m.i7*m.i90 + 0.00766242*m.i7*m.i91
+ 0.00431432*m.i7*m.i92 + 0.00099522*m.i7*m.i93 + 0.00215394*m.i7*m.i94 + 0.00467712*m.i7*m.i95
+ 0.00551306*m.i7*m.i96 + 0.00524514*m.i7*m.i97 + 0.00715168*m.i7*m.i98 + 0.00269474*m.i7*m.i99
+ 0.006577*m.i7*m.i100 + 0.01497394*m.i8*m.i9 + 0.0108969*m.i8*m.i10 + 0.00659842*m.i8*m.i11 +
0.00635336*m.i8*m.i12 + 0.0313098*m.i8*m.i13 + 0.0387588*m.i8*m.i14 + 0.01963812*m.i8*m.i15 +
0.00587206*m.i8*m.i16 + 0.0158028*m.i8*m.i17 + 0.00433344*m.i8*m.i18 + 0.01027216*m.i8*m.i19 +
0.0310764*m.i8*m.i20 + 0.01480666*m.i8*m.i21 + 0.0292324*m.i8*m.i22 + 0.01097454*m.i8*m.i23 +
0.01637932*m.i8*m.i24 + 0.0081932*m.i8*m.i25 + 0.00625414*m.i8*m.i26 + 0.01206926*m.i8*m.i27 +
0.00960586*m.i8*m.i28 + 0.00767454*m.i8*m.i29 + 0.0389634*m.i8*m.i30 + 0.01047056*m.i8*m.i31 +
0.0243166*m.i8*m.i32 + 0.01490526*m.i8*m.i33 + 0.0048023*m.i8*m.i34 + 0.00582726*m.i8*m.i35 +
0.0310084*m.i8*m.i36 + 0.01520046*m.i8*m.i37 + 0.00435652*m.i8*m.i38 + 0.01820518*m.i8*m.i39 +
0.028962*m.i8*m.i40 + 0.0236162*m.i8*m.i41 + 0.0089807*m.i8*m.i42 + 0.01679084*m.i8*m.i43 +
0.01575264*m.i8*m.i44 - 0.00596962*m.i8*m.i45 + 0.0045504*m.i8*m.i46 + 0.0135935*m.i8*m.i47 +
0.00528224*m.i8*m.i48 + 0.01215584*m.i8*m.i49 + 0.01116408*m.i8*m.i50 + 0.00976906*m.i8*m.i51 +
0.01011206*m.i8*m.i52 + 0.0224104*m.i8*m.i53 + 0.01007602*m.i8*m.i54 + 0.01583128*m.i8*m.i55 +
0.00761084*m.i8*m.i56 + 0.00804396*m.i8*m.i57 + 0.01038608*m.i8*m.i58 + 0.01602498*m.i8*m.i59 +
0.00380248*m.i8*m.i60 + 0.0227414*m.i8*m.i61 + 0.0208778*m.i8*m.i62 + 0.01278874*m.i8*m.i63 +
0.00882622*m.i8*m.i64 + 0.01253422*m.i8*m.i65 + 0.00938202*m.i8*m.i66 + 0.0132364*m.i8*m.i67 +
0.00341364*m.i8*m.i68 + 0.0217686*m.i8*m.i69 + 0.01082106*m.i8*m.i70 + 0.0109575*m.i8*m.i71 +
0.01032418*m.i8*m.i72 + 0.01203924*m.i8*m.i73 + 0.01820078*m.i8*m.i74 + 0.00454846*m.i8*m.i75 +
0.00699592*m.i8*m.i76 + 0.017175*m.i8*m.i77 + 0.00418326*m.i8*m.i78 + 0.003044*m.i8*m.i79 +
0.00913958*m.i8*m.i80 + 0.01058642*m.i8*m.i81 + 0.00609436*m.i8*m.i82 + 0.00939194*m.i8*m.i83 +
0.01860882*m.i8*m.i84 + 0.00544766*m.i8*m.i85 + 0.00672898*m.i8*m.i86 + 0.00847128*m.i8*m.i87 +
0.0399532*m.i8*m.i88 + 0.00230258*m.i8*m.i89 + 0.00647968*m.i8*m.i90 + 0.00663734*m.i8*m.i91 +
0.00723392*m.i8*m.i92 + 0.0028363*m.i8*m.i93 + 0.01094692*m.i8*m.i94 + 0.01122622*m.i8*m.i95 +
0.01922686*m.i8*m.i96 + 0.0178042*m.i8*m.i97 + 0.00987488*m.i8*m.i98 + 0.0201768*m.i8*m.i99 +
0.00916962*m.i8*m.i100 + 0.00380196*m.i9*m.i10 + 0.000241806*m.i9*m.i11 + 0.00422182*m.i9*m.i12
+ 0.01745366*m.i9*m.i13 + 0.01560378*m.i9*m.i14 + 0.01797116*m.i9*m.i15 + 0.0104377*m.i9*m.i16
+ 0.01789532*m.i9*m.i17 + 0.0058031*m.i9*m.i18 + 0.00524852*m.i9*m.i19 + 0.0217664*m.i9*m.i20 +
0.0137801*m.i9*m.i21 + 0.00556924*m.i9*m.i22 + 0.00707894*m.i9*m.i23 + 0.00383446*m.i9*m.i24 +
0.00797136*m.i9*m.i25 + 0.00671112*m.i9*m.i26 + 0.00962638*m.i9*m.i27 + 0.00548282*m.i9*m.i28 +
0.00537842*m.i9*m.i29 + 0.01125578*m.i9*m.i30 + 0.01033708*m.i9*m.i31 + 0.01741482*m.i9*m.i32 +
0.01282666*m.i9*m.i33 + 0.00490948*m.i9*m.i34 + 0.00344028*m.i9*m.i35 + 0.01643714*m.i9*m.i36 +
0.00871578*m.i9*m.i37 + 0.002884*m.i9*m.i38 + 0.01596496*m.i9*m.i39 + 0.0171071*m.i9*m.i40 +
0.0282184*m.i9*m.i41 + 0.0157083*m.i9*m.i42 + 0.01908622*m.i9*m.i43 + 0.01887462*m.i9*m.i44 +
0.00621506*m.i9*m.i45 + 0.00706654*m.i9*m.i46 + 0.01685764*m.i9*m.i47 + 0.0046064*m.i9*m.i48 +
0.01393082*m.i9*m.i49 + 0.01366172*m.i9*m.i50 + 0.00974224*m.i9*m.i51 + 0.01117786*m.i9*m.i52 +
0.0105042*m.i9*m.i53 + 0.01603942*m.i9*m.i54 + 0.01154502*m.i9*m.i55 + 0.0187017*m.i9*m.i56 +
0.0063051*m.i9*m.i57 + 0.01180982*m.i9*m.i58 + 0.01148738*m.i9*m.i59 + 0.0045111*m.i9*m.i60 +
0.01782442*m.i9*m.i61 + 0.01261594*m.i9*m.i62 + 0.0275116*m.i9*m.i63 + 0.01370986*m.i9*m.i64 +
0.01301448*m.i9*m.i65 + 0.00909146*m.i9*m.i66 + 0.00880956*m.i9*m.i67 + 0.00542126*m.i9*m.i68 +
0.0173699*m.i9*m.i69 + 0.0063573*m.i9*m.i70 + 0.01464082*m.i9*m.i71 + 0.01030184*m.i9*m.i72 +
0.01342364*m.i9*m.i73 + 0.01050302*m.i9*m.i74 + 0.00580926*m.i9*m.i75 + 0.00669824*m.i9*m.i76 +
0.0154461*m.i9*m.i77 + 0.00331996*m.i9*m.i78 - 0.00117976*m.i9*m.i79 + 0.0134427*m.i9*m.i80 +
0.01200946*m.i9*m.i81 + 0.00261992*m.i9*m.i82 + 0.01802554*m.i9*m.i83 + 0.01281546*m.i9*m.i84 +
0.00817562*m.i9*m.i85 + 0.01353278*m.i9*m.i86 + 0.0065419*m.i9*m.i87 + 0.0287756*m.i9*m.i88 +
0.00438656*m.i9*m.i89 + 0.006514*m.i9*m.i90 + 0.00948704*m.i9*m.i91 + 0.01460712*m.i9*m.i92 +
0.00442406*m.i9*m.i93 + 0.00525338*m.i9*m.i94 + 0.01080594*m.i9*m.i95 + 0.007284*m.i9*m.i96 +
0.01145784*m.i9*m.i97 + 0.01167366*m.i9*m.i98 + 0.01306896*m.i9*m.i99 + 0.01230056*m.i9*m.i100 +
0.00390108*m.i10*m.i11 + 0.00306506*m.i10*m.i12 + 0.0266658*m.i10*m.i13 + 0.027667*m.i10*m.i14 +
0.01278752*m.i10*m.i15 + 0.01031474*m.i10*m.i16 + 0.01126594*m.i10*m.i17 + 0.00489102*m.i10*m.i18
+ 0.00513038*m.i10*m.i19 + 0.01899656*m.i10*m.i20 + 0.01116072*m.i10*m.i21 + 0.0218888*m.i10*
m.i22 + 0.01101148*m.i10*m.i23 + 0.00938786*m.i10*m.i24 + 0.00495956*m.i10*m.i25 + 0.00409492*
m.i10*m.i26 + 0.00774196*m.i10*m.i27 + 0.00563678*m.i10*m.i28 + 0.00452506*m.i10*m.i29 +
0.0234496*m.i10*m.i30 + 0.00879878*m.i10*m.i31 + 0.01816086*m.i10*m.i32 + 0.01204676*m.i10*m.i33
+ 0.00474448*m.i10*m.i34 + 0.00478426*m.i10*m.i35 + 0.0297012*m.i10*m.i36 + 0.0151832*m.i10*
m.i37 + 0.00256504*m.i10*m.i38 + 0.01482468*m.i10*m.i39 + 0.0351312*m.i10*m.i40 + 0.00722204*
m.i10*m.i41 + 0.00911442*m.i10*m.i42 + 0.00459148*m.i10*m.i43 + 0.00643892*m.i10*m.i44 +
0.00232242*m.i10*m.i45 + 0.00525016*m.i10*m.i46 + 0.00918898*m.i10*m.i47 + 0.00604914*m.i10*m.i48
+ 0.00855226*m.i10*m.i49 + 0.01758968*m.i10*m.i50 + 0.00905476*m.i10*m.i51 + 0.0076611*m.i10*
m.i52 + 0.01159398*m.i10*m.i53 + 0.00933998*m.i10*m.i54 + 0.00932956*m.i10*m.i55 + 0.0077777*
m.i10*m.i56 + 0.00585234*m.i10*m.i57 + 0.00494612*m.i10*m.i58 + 0.01267098*m.i10*m.i59 +
0.0025072*m.i10*m.i60 + 0.01652258*m.i10*m.i61 + 0.0113132*m.i10*m.i62 + 0.00647572*m.i10*m.i63
+ 0.00509638*m.i10*m.i64 + 0.00796924*m.i10*m.i65 + 0.00671784*m.i10*m.i66 + 0.00876736*m.i10*
m.i67 + 0.00330284*m.i10*m.i68 + 0.0143256*m.i10*m.i69 + 0.00658518*m.i10*m.i70 + 0.00751304*
m.i10*m.i71 + 0.00447272*m.i10*m.i72 + 0.00707326*m.i10*m.i73 + 0.01022514*m.i10*m.i74 +
0.00629098*m.i10*m.i75 + 0.00437386*m.i10*m.i76 + 0.0069722*m.i10*m.i77 + 0.00631338*m.i10*m.i78
+ 0.01475202*m.i10*m.i79 + 0.00722624*m.i10*m.i80 + 0.00973154*m.i10*m.i81 + 0.00371556*m.i10*
m.i82 + 0.00253096*m.i10*m.i83 + 0.008833*m.i10*m.i84 + 0.00871744*m.i10*m.i85 + 0.0101816*m.i10*
m.i86 + 0.01000738*m.i10*m.i87 + 0.01974334*m.i10*m.i88 + 0.00587674*m.i10*m.i89 + 0.0124516*
m.i10*m.i90 + 0.00915752*m.i10*m.i91 + 0.00913708*m.i10*m.i92 + 0.00200378*m.i10*m.i93 +
0.00536928*m.i10*m.i94 + 0.00823672*m.i10*m.i95 + 0.01736144*m.i10*m.i96 + 0.01105742*m.i10*m.i97
+ 0.01023842*m.i10*m.i98 + 0.01685104*m.i10*m.i99 + 0.01457986*m.i10*m.i100 + 0.000833086*m.i11*
m.i12 + 0.00999478*m.i11*m.i13 + 0.01344484*m.i11*m.i14 + 0.0031808*m.i11*m.i15 + 0.01117228*
m.i11*m.i16 + 0.000697152*m.i11*m.i17 + 0.000585828*m.i11*m.i18 + 0.00585952*m.i11*m.i19 +
0.00859976*m.i11*m.i20 + 0.00502902*m.i11*m.i21 + 0.00447154*m.i11*m.i22 + 0.001969568*m.i11*
m.i23 + 0.0049358*m.i11*m.i24 - 0.00029705*m.i11*m.i25 + 0.0008833*m.i11*m.i26 + 0.00788936*m.i11
*m.i27 + 0.00223564*m.i11*m.i28 - 0.001370818*m.i11*m.i29 + 0.0148367*m.i11*m.i30 + 0.01084338*
m.i11*m.i31 + 0.000606756*m.i11*m.i32 + 0.00591896*m.i11*m.i33 - 0.00408456*m.i11*m.i34 -
0.002724*m.i11*m.i35 + 0.01495302*m.i11*m.i36 + 0.0001528802*m.i11*m.i37 + 0.000200858*m.i11*
m.i38 + 0.00843216*m.i11*m.i39 + 0.01341476*m.i11*m.i40 + 0.01160686*m.i11*m.i41 + 0.00464728*
m.i11*m.i42 + 0.00803576*m.i11*m.i43 + 0.00270742*m.i11*m.i44 - 0.00352162*m.i11*m.i45 +
0.000947796*m.i11*m.i46 + 0.00388898*m.i11*m.i47 + 0.00557236*m.i11*m.i48 + 0.00208008*m.i11*
m.i49 + 0.000931698*m.i11*m.i50 + 0.000654446*m.i11*m.i51 + 0.00650504*m.i11*m.i52 + 0.000501194*
m.i11*m.i53 + 0.00681518*m.i11*m.i54 + 0.00601122*m.i11*m.i55 - 0.00507122*m.i11*m.i56 +
0.000483176*m.i11*m.i57 + 0.00482018*m.i11*m.i58 + 0.0064067*m.i11*m.i59 - 0.000166498*m.i11*
m.i60 + 0.00575774*m.i11*m.i61 + 0.00725456*m.i11*m.i62 + 0.00219412*m.i11*m.i63 + 0.0084673*
m.i11*m.i64 + 0.000333436*m.i11*m.i65 + 0.00655332*m.i11*m.i66 - 0.00257168*m.i11*m.i67 +
0.01199786*m.i11*m.i68 + 0.0059299*m.i11*m.i69 + 0.001843394*m.i11*m.i70 + 0.01060724*m.i11*m.i71
+ 0.00647206*m.i11*m.i72 + 0.00231676*m.i11*m.i73 + 0.00580344*m.i11*m.i74 + 0.00620538*m.i11*
m.i75 - 0.000334258*m.i11*m.i76 + 0.00656424*m.i11*m.i77 - 0.001286316*m.i11*m.i78 + 0.00546106*
m.i11*m.i79 - 0.000202642*m.i11*m.i80 + 0.00426114*m.i11*m.i81 - 0.00204892*m.i11*m.i82 +
0.01117602*m.i11*m.i83 + 0.01034244*m.i11*m.i84 + 0.00449542*m.i11*m.i85 + 0.00797378*m.i11*m.i86
- 0.000792844*m.i11*m.i87 + 0.01939124*m.i11*m.i88 + 0.00432784*m.i11*m.i89 + 0.00204578*m.i11*
m.i90 + 0.021152*m.i11*m.i91 + 0.00283286*m.i11*m.i92 - 0.00407532*m.i11*m.i93 - 0.001198622*
m.i11*m.i94 + 0.0056114*m.i11*m.i95 + 0.00560696*m.i11*m.i96 + 0.00867776*m.i11*m.i97 +
0.01208222*m.i11*m.i98 + 0.00209588*m.i11*m.i99 + 0.0061276*m.i11*m.i100 + 0.00580036*m.i12*m.i13
+ 0.01674486*m.i12*m.i14 + 0.00758412*m.i12*m.i15 + 0.0061097*m.i12*m.i16 + 0.00406024*m.i12*
m.i17 + 0.00246134*m.i12*m.i18 + 0.00422294*m.i12*m.i19 + 0.00359302*m.i12*m.i20 + 0.0027503*
m.i12*m.i21 + 0.01042736*m.i12*m.i22 + 0.001094158*m.i12*m.i23 + 0.00410122*m.i12*m.i24 +
0.0025257*m.i12*m.i25 + 0.00319626*m.i12*m.i26 + 0.00241386*m.i12*m.i27 + 0.001365712*m.i12*m.i28
+ 0.00285332*m.i12*m.i29 + 0.01617908*m.i12*m.i30 + 0.00231724*m.i12*m.i31 + 0.00343892*m.i12*
m.i32 + 0.00256516*m.i12*m.i33 + 0.001014308*m.i12*m.i34 + 0.001643396*m.i12*m.i35 + 0.00879946*
m.i12*m.i36 + 0.00422942*m.i12*m.i37 + 0.001108756*m.i12*m.i38 + 0.0068803*m.i12*m.i39 -
0.00375268*m.i12*m.i40 + 0.0029422*m.i12*m.i41 + 0.00429146*m.i12*m.i42 + 0.00277958*m.i12*m.i43
+ 0.00284814*m.i12*m.i44 + 0.001633544*m.i12*m.i45 + 0.00422296*m.i12*m.i46 + 0.000606884*m.i12*
m.i47 + 0.0041981*m.i12*m.i48 + 0.00378962*m.i12*m.i49 + 0.00842602*m.i12*m.i50 + 0.002132*m.i12*
m.i51 + 0.00482062*m.i12*m.i52 + 0.00806126*m.i12*m.i53 + 0.00387284*m.i12*m.i54 + 0.0039366*
m.i12*m.i55 + 0.000612768*m.i12*m.i56 + 0.0044852*m.i12*m.i57 + 0.00284844*m.i12*m.i58 +
0.00336708*m.i12*m.i59 + 0.0030099*m.i12*m.i60 + 0.00693418*m.i12*m.i61 + 0.0046908*m.i12*m.i62
+ 0.00538386*m.i12*m.i63 + 0.00560854*m.i12*m.i64 + 0.00360994*m.i12*m.i65 + 0.00317544*m.i12*
m.i66 + 0.00443286*m.i12*m.i67 + 0.00420074*m.i12*m.i68 + 0.00506986*m.i12*m.i69 + 0.00415464*
m.i12*m.i70 + 0.00220046*m.i12*m.i71 + 0.00230386*m.i12*m.i72 + 0.00311708*m.i12*m.i73 +
0.00731294*m.i12*m.i74 + 0.0048156*m.i12*m.i75 + 0.00332812*m.i12*m.i76 + 0.00439802*m.i12*m.i77
+ 0.00371872*m.i12*m.i78 + 0.00601328*m.i12*m.i79 + 0.00749754*m.i12*m.i80 + 0.00280082*m.i12*
m.i81 + 0.00202854*m.i12*m.i82 + 0.001389608*m.i12*m.i83 + 0.00387764*m.i12*m.i84 + 0.00354982*
m.i12*m.i85 + 0.00265444*m.i12*m.i86 + 0.0022211*m.i12*m.i87 + 0.00666916*m.i12*m.i88 +
0.00412408*m.i12*m.i89 + 0.00421336*m.i12*m.i90 + 0.00306034*m.i12*m.i91 + 0.00210254*m.i12*m.i92
+ 0.001819242*m.i12*m.i93 + 0.0007903*m.i12*m.i94 + 0.00409078*m.i12*m.i95 + 0.00988156*m.i12*
m.i96 + 0.00522182*m.i12*m.i97 + 0.00482098*m.i12*m.i98 + 0.0042136*m.i12*m.i99 + 0.00408986*
m.i12*m.i100 + 0.0674968*m.i13*m.i14 + 0.0344974*m.i13*m.i15 + 0.0330226*m.i13*m.i16 + 0.0319354*
m.i13*m.i17 + 0.01218366*m.i13*m.i18 + 0.00519196*m.i13*m.i19 + 0.044536*m.i13*m.i20 + 0.0277772*
m.i13*m.i21 + 0.0622606*m.i13*m.i22 + 0.0259408*m.i13*m.i23 + 0.0302608*m.i13*m.i24 + 0.0163455*
m.i13*m.i25 + 0.0077583*m.i13*m.i26 + 0.0227636*m.i13*m.i27 + 0.01173702*m.i13*m.i28 + 0.00769116
*m.i13*m.i29 + 0.0709126*m.i13*m.i30 + 0.01974624*m.i13*m.i31 + 0.0471936*m.i13*m.i32 + 0.0320402
*m.i13*m.i33 + 0.0107856*m.i13*m.i34 + 0.00663924*m.i13*m.i35 + 0.0963608*m.i13*m.i36 + 0.0383208
*m.i13*m.i37 + 0.00629602*m.i13*m.i38 + 0.0436584*m.i13*m.i39 + 0.113305*m.i13*m.i40 + 0.030603*
m.i13*m.i41 + 0.0334486*m.i13*m.i42 + 0.0221094*m.i13*m.i43 + 0.0261022*m.i13*m.i44 + 0.00384036*
m.i13*m.i45 + 0.01393368*m.i13*m.i46 + 0.0390862*m.i13*m.i47 + 0.01408516*m.i13*m.i48 + 0.0200136
*m.i13*m.i49 + 0.0473844*m.i13*m.i50 + 0.0233922*m.i13*m.i51 + 0.0267544*m.i13*m.i52 + 0.0382128*
m.i13*m.i53 + 0.026998*m.i13*m.i54 + 0.0232812*m.i13*m.i55 + 0.0210468*m.i13*m.i56 + 0.01155576*
m.i13*m.i57 + 0.01460704*m.i13*m.i58 + 0.0315638*m.i13*m.i59 + 0.00606798*m.i13*m.i60 + 0.048913*
m.i13*m.i61 + 0.0422528*m.i13*m.i62 + 0.0227364*m.i13*m.i63 + 0.0218176*m.i13*m.i64 + 0.020181*
m.i13*m.i65 + 0.0171918*m.i13*m.i66 + 0.0231896*m.i13*m.i67 + 0.00653966*m.i13*m.i68 + 0.0386908*
m.i13*m.i69 + 0.01310368*m.i13*m.i70 + 0.0233574*m.i13*m.i71 + 0.01370986*m.i13*m.i72 +
0.01644046*m.i13*m.i73 + 0.0239108*m.i13*m.i74 + 0.01209114*m.i13*m.i75 + 0.00733894*m.i13*m.i76
+ 0.01831752*m.i13*m.i77 + 0.01361596*m.i13*m.i78 + 0.0349392*m.i13*m.i79 + 0.01738086*m.i13*
m.i80 + 0.0327952*m.i13*m.i81 + 0.00370036*m.i13*m.i82 + 0.0275306*m.i13*m.i83 + 0.0237408*m.i13*
m.i84 + 0.023854*m.i13*m.i85 + 0.0298082*m.i13*m.i86 + 0.01954408*m.i13*m.i87 + 0.0427146*m.i13*
m.i88 + 0.00800344*m.i13*m.i89 + 0.0379614*m.i13*m.i90 + 0.0237386*m.i13*m.i91 + 0.0280402*m.i13*
m.i92 + 0.00539152*m.i13*m.i93 + 0.00878456*m.i13*m.i94 + 0.0258544*m.i13*m.i95 + 0.0525716*m.i13
*m.i96 + 0.0324866*m.i13*m.i97 + 0.03178*m.i13*m.i98 + 0.0440898*m.i13*m.i99 + 0.0425102*m.i13*
m.i100 + 0.0526828*m.i14*m.i15 + 0.037439*m.i14*m.i16 + 0.0256328*m.i14*m.i17 + 0.0100326*m.i14*
m.i18 + 0.02287*m.i14*m.i19 + 0.05764*m.i14*m.i20 + 0.0305304*m.i14*m.i21 + 0.0790588*m.i14*m.i22
+ 0.0273134*m.i14*m.i23 + 0.0226144*m.i14*m.i24 + 0.01919436*m.i14*m.i25 + 0.01634394*m.i14*
m.i26 + 0.0200216*m.i14*m.i27 + 0.01187024*m.i14*m.i28 + 0.0175096*m.i14*m.i29 + 0.1303416*m.i14*
m.i30 + 0.01783484*m.i14*m.i31 + 0.0483706*m.i14*m.i32 + 0.0389666*m.i14*m.i33 + 0.00488422*m.i14
*m.i34 + 0.01045608*m.i14*m.i35 + 0.0811654*m.i14*m.i36 + 0.0367626*m.i14*m.i37 + 0.00522434*
m.i14*m.i38 + 0.05055*m.i14*m.i39 + 0.0849278*m.i14*m.i40 + 0.0341058*m.i14*m.i41 + 0.029549*
m.i14*m.i42 + 0.0119177*m.i14*m.i43 + 0.034956*m.i14*m.i44 + 0.0084943*m.i14*m.i45 + 0.01853266*
m.i14*m.i46 + 0.01893124*m.i14*m.i47 + 0.0205662*m.i14*m.i48 + 0.0326974*m.i14*m.i49 + 0.0610942*
m.i14*m.i50 + 0.0265816*m.i14*m.i51 + 0.0345152*m.i14*m.i52 + 0.0602904*m.i14*m.i53 + 0.0299894*
m.i14*m.i54 + 0.029724*m.i14*m.i55 + 0.00991024*m.i14*m.i56 + 0.0212834*m.i14*m.i57 + 0.01611994*
m.i14*m.i58 + 0.0349608*m.i14*m.i59 + 0.01544524*m.i14*m.i60 + 0.0660828*m.i14*m.i61 + 0.0517844*
m.i14*m.i62 + 0.0288716*m.i14*m.i63 + 0.02065*m.i14*m.i64 + 0.0285834*m.i14*m.i65 + 0.01348302*
m.i14*m.i66 + 0.0306592*m.i14*m.i67 + 0.01828946*m.i14*m.i68 + 0.0537368*m.i14*m.i69 + 0.0271944*
m.i14*m.i70 + 0.01793364*m.i14*m.i71 + 0.0206146*m.i14*m.i72 + 0.0281438*m.i14*m.i73 + 0.038653*
m.i14*m.i74 + 0.0322466*m.i14*m.i75 + 0.0212534*m.i14*m.i76 + 0.0336072*m.i14*m.i77 + 0.01910646*
m.i14*m.i78 + 0.0653414*m.i14*m.i79 + 0.0269972*m.i14*m.i80 + 0.0273492*m.i14*m.i81 + 0.01038358*
m.i14*m.i82 + 0.00619204*m.i14*m.i83 + 0.0273406*m.i14*m.i84 + 0.0211516*m.i14*m.i85 + 0.0382364*
m.i14*m.i86 + 0.0345294*m.i14*m.i87 + 0.1230516*m.i14*m.i88 + 0.032645*m.i14*m.i89 + 0.0494242*
m.i14*m.i90 + 0.030464*m.i14*m.i91 + 0.0229316*m.i14*m.i92 + 0.01328606*m.i14*m.i93 + 0.01219994*
m.i14*m.i94 + 0.0308436*m.i14*m.i95 + 0.0853596*m.i14*m.i96 + 0.0354032*m.i14*m.i97 + 0.0262134*
m.i14*m.i98 + 0.0473304*m.i14*m.i99 + 0.037143*m.i14*m.i100 + 0.01723066*m.i15*m.i16 + 0.0144032*
m.i15*m.i17 + 0.01011568*m.i15*m.i18 + 0.01071386*m.i15*m.i19 + 0.0363128*m.i15*m.i20 + 0.0200062
*m.i15*m.i21 + 0.0429276*m.i15*m.i22 + 0.01550086*m.i15*m.i23 + 0.01336936*m.i15*m.i24 +
0.01153424*m.i15*m.i25 + 0.01291552*m.i15*m.i26 + 0.01571376*m.i15*m.i27 + 0.0057752*m.i15*m.i28
+ 0.01132328*m.i15*m.i29 + 0.04615*m.i15*m.i30 + 0.0095472*m.i15*m.i31 + 0.0348208*m.i15*m.i32
+ 0.01999334*m.i15*m.i33 + 0.00687142*m.i15*m.i34 + 0.00887602*m.i15*m.i35 + 0.0412134*m.i15*
m.i36 + 0.0222294*m.i15*m.i37 + 0.0044452*m.i15*m.i38 + 0.0275012*m.i15*m.i39 + 0.0449902*m.i15*
m.i40 + 0.0316194*m.i15*m.i41 + 0.021335*m.i15*m.i42 + 0.01203424*m.i15*m.i43 + 0.0250958*m.i15*
m.i44 + 0.00747774*m.i15*m.i45 + 0.01208838*m.i15*m.i46 + 0.0258298*m.i15*m.i47 + 0.01217868*
m.i15*m.i48 + 0.0181139*m.i15*m.i49 + 0.0324096*m.i15*m.i50 + 0.01156602*m.i15*m.i51 + 0.01869794
*m.i15*m.i52 + 0.0276488*m.i15*m.i53 + 0.0230496*m.i15*m.i54 + 0.0171536*m.i15*m.i55 + 0.01527606
*m.i15*m.i56 + 0.01288824*m.i15*m.i57 + 0.014014*m.i15*m.i58 + 0.01657292*m.i15*m.i59 + 0.0080112
*m.i15*m.i60 + 0.0380938*m.i15*m.i61 + 0.0298954*m.i15*m.i62 + 0.0218266*m.i15*m.i63 + 0.01580514
*m.i15*m.i64 + 0.01327226*m.i15*m.i65 + 0.01171988*m.i15*m.i66 + 0.01749552*m.i15*m.i67 +
0.00958228*m.i15*m.i68 + 0.02991*m.i15*m.i69 + 0.01687722*m.i15*m.i70 + 0.0214718*m.i15*m.i71 +
0.0177952*m.i15*m.i72 + 0.01429134*m.i15*m.i73 + 0.01835742*m.i15*m.i74 + 0.014413*m.i15*m.i75 +
0.01215492*m.i15*m.i76 + 0.01888264*m.i15*m.i77 + 0.01135654*m.i15*m.i78 + 0.01419354*m.i15*m.i79
+ 0.01589948*m.i15*m.i80 + 0.01996746*m.i15*m.i81 + 0.00616376*m.i15*m.i82 + 0.00905236*m.i15*
m.i83 + 0.01329424*m.i15*m.i84 + 0.01265054*m.i15*m.i85 + 0.01743812*m.i15*m.i86 + 0.01662354*
m.i15*m.i87 + 0.0326642*m.i15*m.i88 + 0.00648876*m.i15*m.i89 + 0.0255582*m.i15*m.i90 + 0.01710528
*m.i15*m.i91 + 0.01530604*m.i15*m.i92 + 0.00729364*m.i15*m.i93 + 0.00786908*m.i15*m.i94 +
0.0169034*m.i15*m.i95 + 0.034265*m.i15*m.i96 + 0.0206426*m.i15*m.i97 + 0.01574576*m.i15*m.i98 +
0.0251768*m.i15*m.i99 + 0.0302234*m.i15*m.i100 + 0.0180502*m.i16*m.i17 + 0.00797572*m.i16*m.i18
+ 0.00993386*m.i16*m.i19 + 0.0236072*m.i16*m.i20 + 0.01425014*m.i16*m.i21 + 0.0269392*m.i16*
m.i22 + 0.01322908*m.i16*m.i23 + 0.01719786*m.i16*m.i24 + 0.00995474*m.i16*m.i25 + 0.00544834*
m.i16*m.i26 + 0.01319632*m.i16*m.i27 + 0.00695148*m.i16*m.i28 + 0.00568042*m.i16*m.i29 + 0.045082
*m.i16*m.i30 + 0.01190474*m.i16*m.i31 + 0.01955462*m.i16*m.i32 + 0.0138212*m.i16*m.i33 +
0.00642106*m.i16*m.i34 + 0.00665524*m.i16*m.i35 + 0.0380492*m.i16*m.i36 + 0.01602708*m.i16*m.i37
+ 0.00369958*m.i16*m.i38 + 0.0220792*m.i16*m.i39 + 0.0304262*m.i16*m.i40 + 0.01843444*m.i16*
m.i41 + 0.021247*m.i16*m.i42 + 0.01518988*m.i16*m.i43 + 0.01406774*m.i16*m.i44 + 0.0051723*m.i16*
m.i45 + 0.0080675*m.i16*m.i46 + 0.0176419*m.i16*m.i47 + 0.0090298*m.i16*m.i48 + 0.0126196*m.i16*
m.i49 + 0.025967*m.i16*m.i50 + 0.01140228*m.i16*m.i51 + 0.01900414*m.i16*m.i52 + 0.01781402*m.i16
*m.i53 + 0.0194748*m.i16*m.i54 + 0.01211848*m.i16*m.i55 + 0.01166912*m.i16*m.i56 + 0.00870972*
m.i16*m.i57 + 0.00719416*m.i16*m.i58 + 0.01574372*m.i16*m.i59 + 0.00725944*m.i16*m.i60 +
0.0294988*m.i16*m.i61 + 0.0260914*m.i16*m.i62 + 0.01974094*m.i16*m.i63 + 0.01434116*m.i16*m.i64
+ 0.00954816*m.i16*m.i65 + 0.0087947*m.i16*m.i66 + 0.01216302*m.i16*m.i67 + 0.01307338*m.i16*
m.i68 + 0.023669*m.i16*m.i69 + 0.01061826*m.i16*m.i70 + 0.01531198*m.i16*m.i71 + 0.01282252*m.i16
*m.i72 + 0.01136194*m.i16*m.i73 + 0.01289612*m.i16*m.i74 + 0.0111961*m.i16*m.i75 + 0.00467394*
m.i16*m.i76 + 0.0120207*m.i16*m.i77 + 0.00634502*m.i16*m.i78 + 0.0272842*m.i16*m.i79 + 0.01354848
*m.i16*m.i80 + 0.01491878*m.i16*m.i81 + 0.00372788*m.i16*m.i82 + 0.01347184*m.i16*m.i83 +
0.01367452*m.i16*m.i84 + 0.01430584*m.i16*m.i85 + 0.01662228*m.i16*m.i86 + 0.01019354*m.i16*m.i87
+ 0.031864*m.i16*m.i88 + 0.01389622*m.i16*m.i89 + 0.01404588*m.i16*m.i90 + 0.01898344*m.i16*
m.i91 + 0.01310136*m.i16*m.i92 + 0.00293122*m.i16*m.i93 + 0.00548746*m.i16*m.i94 + 0.01674526*
m.i16*m.i95 + 0.0263504*m.i16*m.i96 + 0.0187966*m.i16*m.i97 + 0.0198675*m.i16*m.i98 + 0.0160833*
m.i16*m.i99 + 0.01885334*m.i16*m.i100 + 0.00599666*m.i17*m.i18 + 0.0047675*m.i17*m.i19 +
0.0265872*m.i17*m.i20 + 0.01628802*m.i17*m.i21 + 0.01871884*m.i17*m.i22 + 0.01233104*m.i17*m.i23
+ 0.01365522*m.i17*m.i24 + 0.00989432*m.i17*m.i25 + 0.00330258*m.i17*m.i26 + 0.0116841*m.i17*
m.i27 + 0.0079471*m.i17*m.i28 + 0.0045994*m.i17*m.i29 + 0.0254766*m.i17*m.i30 + 0.01659406*m.i17*
m.i31 + 0.0220846*m.i17*m.i32 + 0.01861566*m.i17*m.i33 + 0.00948066*m.i17*m.i34 + 0.0090429*m.i17
*m.i35 + 0.0337978*m.i17*m.i36 + 0.01595384*m.i17*m.i37 + 0.00235078*m.i17*m.i38 + 0.0201494*
m.i17*m.i39 + 0.0342284*m.i17*m.i40 + 0.0277738*m.i17*m.i41 + 0.01731318*m.i17*m.i42 + 0.01753214
*m.i17*m.i43 + 0.01978996*m.i17*m.i44 + 0.00369934*m.i17*m.i45 + 0.00718436*m.i17*m.i46 +
0.01949342*m.i17*m.i47 + 0.00499956*m.i17*m.i48 + 0.01707236*m.i17*m.i49 + 0.0203004*m.i17*m.i50
+ 0.01279548*m.i17*m.i51 + 0.011643*m.i17*m.i52 + 0.01115602*m.i17*m.i53 + 0.01587576*m.i17*
m.i54 + 0.010193*m.i17*m.i55 + 0.0217498*m.i17*m.i56 + 0.0064957*m.i17*m.i57 + 0.00989022*m.i17*
m.i58 + 0.01554654*m.i17*m.i59 + 0.00382894*m.i17*m.i60 + 0.01868378*m.i17*m.i61 + 0.01822302*
m.i17*m.i62 + 0.0270002*m.i17*m.i63 + 0.01054316*m.i17*m.i64 + 0.01114578*m.i17*m.i65 + 0.010706*
m.i17*m.i66 + 0.01057722*m.i17*m.i67 + 0.00541042*m.i17*m.i68 + 0.022045*m.i17*m.i69 + 0.00933892
*m.i17*m.i70 + 0.0217256*m.i17*m.i71 + 0.010527*m.i17*m.i72 + 0.01245986*m.i17*m.i73 + 0.01462496
*m.i17*m.i74 + 0.00471612*m.i17*m.i75 + 0.00385082*m.i17*m.i76 + 0.0150046*m.i17*m.i77 +
0.00469912*m.i17*m.i78 + 0.01570408*m.i17*m.i79 + 0.01238884*m.i17*m.i80 + 0.0167981*m.i17*m.i81
+ 0.00275656*m.i17*m.i82 + 0.0264668*m.i17*m.i83 + 0.01754616*m.i17*m.i84 + 0.0104241*m.i17*
m.i85 + 0.0155118*m.i17*m.i86 + 0.00992204*m.i17*m.i87 + 0.0334656*m.i17*m.i88 + 0.0100102*m.i17*
m.i89 + 0.00830234*m.i17*m.i90 + 0.00830522*m.i17*m.i91 + 0.01347376*m.i17*m.i92 + 0.00371114*
m.i17*m.i93 + 0.00721878*m.i17*m.i94 + 0.01197232*m.i17*m.i95 + 0.01097582*m.i17*m.i96 +
0.0153446*m.i17*m.i97 + 0.01911512*m.i17*m.i98 + 0.0158341*m.i17*m.i99 + 0.01647016*m.i17*m.i100
+ 0.0038501*m.i18*m.i19 + 0.01438424*m.i18*m.i20 + 0.00575166*m.i18*m.i21 + 0.01286738*m.i18*
m.i22 + 0.0072269*m.i18*m.i23 + 0.00577628*m.i18*m.i24 + 0.00353166*m.i18*m.i25 + 0.00406754*
m.i18*m.i26 + 0.00586712*m.i18*m.i27 + 0.00246394*m.i18*m.i28 + 0.00208424*m.i18*m.i29 +
0.00868042*m.i18*m.i30 + 0.00488392*m.i18*m.i31 + 0.01139774*m.i18*m.i32 + 0.00652178*m.i18*m.i33
+ 0.00514824*m.i18*m.i34 + 0.00420068*m.i18*m.i35 + 0.01314078*m.i18*m.i36 + 0.00738678*m.i18*
m.i37 + 0.00212172*m.i18*m.i38 + 0.00767338*m.i18*m.i39 + 0.01491396*m.i18*m.i40 + 0.00689198*
m.i18*m.i41 + 0.00941516*m.i18*m.i42 + 0.00703674*m.i18*m.i43 + 0.00623926*m.i18*m.i44 +
0.0042213*m.i18*m.i45 + 0.00377366*m.i18*m.i46 + 0.01005392*m.i18*m.i47 + 0.00385304*m.i18*m.i48
+ 0.0061538*m.i18*m.i49 + 0.00828744*m.i18*m.i50 + 0.00452496*m.i18*m.i51 + 0.00647618*m.i18*
m.i52 + 0.00595912*m.i18*m.i53 + 0.00909974*m.i18*m.i54 + 0.00683082*m.i18*m.i55 + 0.00696058*
m.i18*m.i56 + 0.00489492*m.i18*m.i57 + 0.00399036*m.i18*m.i58 + 0.0071619*m.i18*m.i59 +
0.00282566*m.i18*m.i60 + 0.01253118*m.i18*m.i61 + 0.01017836*m.i18*m.i62 + 0.0054806*m.i18*m.i63
+ 0.00679494*m.i18*m.i64 + 0.00492774*m.i18*m.i65 + 0.00294036*m.i18*m.i66 + 0.00302154*m.i18*
m.i67 + 0.00492864*m.i18*m.i68 + 0.01002278*m.i18*m.i69 + 0.00498708*m.i18*m.i70 + 0.00467346*
m.i18*m.i71 + 0.00622154*m.i18*m.i72 + 0.0060522*m.i18*m.i73 + 0.00606086*m.i18*m.i74 +
0.00435108*m.i18*m.i75 + 0.00246578*m.i18*m.i76 + 0.00518572*m.i18*m.i77 + 0.00318624*m.i18*m.i78
+ 0.00460288*m.i18*m.i79 + 0.007017*m.i18*m.i80 + 0.00647242*m.i18*m.i81 + 0.00407958*m.i18*
m.i82 - 0.000888864*m.i18*m.i83 + 0.00537106*m.i18*m.i84 + 0.00634694*m.i18*m.i85 + 0.00514234*
m.i18*m.i86 + 0.00350408*m.i18*m.i87 - 0.00202898*m.i18*m.i88 + 0.001751682*m.i18*m.i89 +
0.0065019*m.i18*m.i90 + 0.007451*m.i18*m.i91 + 0.0035437*m.i18*m.i92 + 0.001995674*m.i18*m.i93 +
0.00436006*m.i18*m.i94 + 0.00715274*m.i18*m.i95 + 0.00776482*m.i18*m.i96 + 0.00710082*m.i18*m.i97
+ 0.00609606*m.i18*m.i98 + 0.00652362*m.i18*m.i99 + 0.01247386*m.i18*m.i100 + 0.01204848*m.i19*
m.i20 + 0.00628788*m.i19*m.i21 + 0.00938206*m.i19*m.i22 + 0.00540152*m.i19*m.i23 + 0.00366816*
m.i19*m.i24 + 0.00424804*m.i19*m.i25 + 0.00443146*m.i19*m.i26 + 0.00550836*m.i19*m.i27 +
0.00441186*m.i19*m.i28 + 0.00464964*m.i19*m.i29 + 0.0215394*m.i19*m.i30 + 0.00534434*m.i19*m.i31
+ 0.01089826*m.i19*m.i32 + 0.00384858*m.i19*m.i33 + 0.00271286*m.i19*m.i34 + 0.00459438*m.i19*
m.i35 + 0.00753494*m.i19*m.i36 + 0.00675858*m.i19*m.i37 + 0.00330138*m.i19*m.i38 + 0.01012594*
m.i19*m.i39 + 0.00097236*m.i19*m.i40 + 0.00697634*m.i19*m.i41 + 0.0055734*m.i19*m.i42 +
0.00439042*m.i19*m.i43 + 0.00466626*m.i19*m.i44 + 0.0056599*m.i19*m.i45 + 0.00343664*m.i19*m.i46
+ 0.00191227*m.i19*m.i47 + 0.00409474*m.i19*m.i48 + 0.00728426*m.i19*m.i49 + 0.0118005*m.i19*
m.i50 + 0.00439032*m.i19*m.i51 + 0.00819602*m.i19*m.i52 + 0.00683532*m.i19*m.i53 + 0.00927236*
m.i19*m.i54 + 0.00638082*m.i19*m.i55 + 0.0049778*m.i19*m.i56 + 0.0064092*m.i19*m.i57 + 0.00332368
*m.i19*m.i58 + 0.00797006*m.i19*m.i59 + 0.00515114*m.i19*m.i60 + 0.0140857*m.i19*m.i61 +
0.00824548*m.i19*m.i62 + 0.00645382*m.i19*m.i63 + 0.00492056*m.i19*m.i64 + 0.0040063*m.i19*m.i65
+ 0.00621702*m.i19*m.i66 + 0.00486474*m.i19*m.i67 + 0.01089728*m.i19*m.i68 + 0.01064856*m.i19*
m.i69 + 0.00763898*m.i19*m.i70 + 0.00304924*m.i19*m.i71 + 0.00746516*m.i19*m.i72 + 0.0073895*
m.i19*m.i73 + 0.008372*m.i19*m.i74 + 0.0096269*m.i19*m.i75 + 0.00403824*m.i19*m.i76 + 0.00896868*
m.i19*m.i77 + 0.00369816*m.i19*m.i78 + 0.01338638*m.i19*m.i79 + 0.00702566*m.i19*m.i80 +
0.00204776*m.i19*m.i81 + 0.0040369*m.i19*m.i82 - 0.00617474*m.i19*m.i83 + 0.00664876*m.i19*m.i84
+ 0.00640014*m.i19*m.i85 + 0.00537574*m.i19*m.i86 + 0.00744762*m.i19*m.i87 + 0.0288232*m.i19*
m.i88 + 0.0089059*m.i19*m.i89 + 0.00438344*m.i19*m.i90 + 0.01192674*m.i19*m.i91 + 0.00326376*
m.i19*m.i92 + 0.00330764*m.i19*m.i93 + 0.00649262*m.i19*m.i94 + 0.0076392*m.i19*m.i95 +
0.01075072*m.i19*m.i96 + 0.00749846*m.i19*m.i97 + 0.00563188*m.i19*m.i98 + 0.00430788*m.i19*m.i99
+ 0.00505074*m.i19*m.i100 + 0.026993*m.i20*m.i21 + 0.0407142*m.i20*m.i22 + 0.0262048*m.i20*m.i23
+ 0.0233804*m.i20*m.i24 + 0.01566388*m.i20*m.i25 + 0.01254316*m.i20*m.i26 + 0.0230746*m.i20*
m.i27 + 0.01228074*m.i20*m.i28 + 0.01141404*m.i20*m.i29 + 0.046979*m.i20*m.i30 + 0.01956928*m.i20
*m.i31 + 0.0444886*m.i20*m.i32 + 0.0345924*m.i20*m.i33 + 0.01450852*m.i20*m.i34 + 0.01607032*
m.i20*m.i35 + 0.0534276*m.i20*m.i36 + 0.027915*m.i20*m.i37 + 0.00446976*m.i20*m.i38 + 0.0310128*
m.i20*m.i39 + 0.0617194*m.i20*m.i40 + 0.0418284*m.i20*m.i41 + 0.0284554*m.i20*m.i42 + 0.0202322*
m.i20*m.i43 + 0.0309222*m.i20*m.i44 + 0.00850138*m.i20*m.i45 + 0.01226594*m.i20*m.i46 + 0.0355744
*m.i20*m.i47 + 0.01044628*m.i20*m.i48 + 0.0261968*m.i20*m.i49 + 0.0353182*m.i20*m.i50 +
0.01768812*m.i20*m.i51 + 0.0227266*m.i20*m.i52 + 0.0229416*m.i20*m.i53 + 0.0285392*m.i20*m.i54 +
0.024215*m.i20*m.i55 + 0.0227*m.i20*m.i56 + 0.01349126*m.i20*m.i57 + 0.01576804*m.i20*m.i58 +
0.0251472*m.i20*m.i59 + 0.00678918*m.i20*m.i60 + 0.0460104*m.i20*m.i61 + 0.0362612*m.i20*m.i62 +
0.0246576*m.i20*m.i63 + 0.01897386*m.i20*m.i64 + 0.021042*m.i20*m.i65 + 0.01449872*m.i20*m.i66 +
0.01901978*m.i20*m.i67 + 0.01289314*m.i20*m.i68 + 0.04318*m.i20*m.i69 + 0.0192612*m.i20*m.i70 +
0.0319956*m.i20*m.i71 + 0.0241418*m.i20*m.i72 + 0.0231068*m.i20*m.i73 + 0.0232748*m.i20*m.i74 +
0.01394672*m.i20*m.i75 + 0.01233534*m.i20*m.i76 + 0.0250086*m.i20*m.i77 + 0.01003866*m.i20*m.i78
+ 0.01782134*m.i20*m.i79 + 0.0175231*m.i20*m.i80 + 0.0266842*m.i20*m.i81 + 0.00899148*m.i20*
m.i82 + 0.01916166*m.i20*m.i83 + 0.0237898*m.i20*m.i84 + 0.01674726*m.i20*m.i85 + 0.0243836*m.i20
*m.i86 + 0.0205712*m.i20*m.i87 + 0.0526016*m.i20*m.i88 + 0.01299922*m.i20*m.i89 + 0.0223216*m.i20
*m.i90 + 0.0221722*m.i20*m.i91 + 0.0200512*m.i20*m.i92 + 0.00605128*m.i20*m.i93 + 0.01422172*
m.i20*m.i94 + 0.0209666*m.i20*m.i95 + 0.0316224*m.i20*m.i96 + 0.0278754*m.i20*m.i97 + 0.0266692*
m.i20*m.i98 + 0.032317*m.i20*m.i99 + 0.0372718*m.i20*m.i100 + 0.0225584*m.i21*m.i22 + 0.01330824*
m.i21*m.i23 + 0.01120138*m.i21*m.i24 + 0.00988644*m.i21*m.i25 + 0.0053562*m.i21*m.i26 +
0.01171726*m.i21*m.i27 + 0.0075308*m.i21*m.i28 + 0.0062293*m.i21*m.i29 + 0.028151*m.i21*m.i30 +
0.01116532*m.i21*m.i31 + 0.024731*m.i21*m.i32 + 0.01403094*m.i21*m.i33 + 0.0053378*m.i21*m.i34 +
0.0062169*m.i21*m.i35 + 0.0322338*m.i21*m.i36 + 0.0173092*m.i21*m.i37 + 0.00310282*m.i21*m.i38 +
0.01943686*m.i21*m.i39 + 0.0397312*m.i21*m.i40 + 0.0227668*m.i21*m.i41 + 0.01402322*m.i21*m.i42
+ 0.01184862*m.i21*m.i43 + 0.01574106*m.i21*m.i44 + 0.00351088*m.i21*m.i45 + 0.00692094*m.i21*
m.i46 + 0.01710158*m.i21*m.i47 + 0.00581758*m.i21*m.i48 + 0.013985*m.i21*m.i49 + 0.0205976*m.i21*
m.i50 + 0.01286968*m.i21*m.i51 + 0.01222018*m.i21*m.i52 + 0.01492284*m.i21*m.i53 + 0.01502328*
m.i21*m.i54 + 0.01279528*m.i21*m.i55 + 0.01443928*m.i21*m.i56 + 0.00711002*m.i21*m.i57 +
0.00897148*m.i21*m.i58 + 0.0175601*m.i21*m.i59 + 0.00366562*m.i21*m.i60 + 0.0240206*m.i21*m.i61
+ 0.01871124*m.i21*m.i62 + 0.01471548*m.i21*m.i63 + 0.00910326*m.i21*m.i64 + 0.01121548*m.i21*
m.i65 + 0.0093615*m.i21*m.i66 + 0.0129081*m.i21*m.i67 + 0.0055548*m.i21*m.i68 + 0.0214638*m.i21*
m.i69 + 0.00932128*m.i21*m.i70 + 0.01654162*m.i21*m.i71 + 0.01150414*m.i21*m.i72 + 0.01130758*
m.i21*m.i73 + 0.01195864*m.i21*m.i74 + 0.00685764*m.i21*m.i75 + 0.00673976*m.i21*m.i76 +
0.01092518*m.i21*m.i77 + 0.00610126*m.i21*m.i78 + 0.0166491*m.i21*m.i79 + 0.00973956*m.i21*m.i80
+ 0.01360816*m.i21*m.i81 + 0.00413938*m.i21*m.i82 + 0.01295166*m.i21*m.i83 + 0.01359658*m.i21*
m.i84 + 0.0100056*m.i21*m.i85 + 0.01591198*m.i21*m.i86 + 0.01302584*m.i21*m.i87 + 0.0321888*m.i21
*m.i88 + 0.0069057*m.i21*m.i89 + 0.01467542*m.i21*m.i90 + 0.0104985*m.i21*m.i91 + 0.01203108*
m.i21*m.i92 + 0.00438602*m.i21*m.i93 + 0.0064228*m.i21*m.i94 + 0.0109577*m.i21*m.i95 + 0.01683074
*m.i21*m.i96 + 0.01510662*m.i21*m.i97 + 0.013665*m.i21*m.i98 + 0.01994166*m.i21*m.i99 + 0.0184821
*m.i21*m.i100 + 0.01713984*m.i22*m.i23 + 0.0290628*m.i22*m.i24 + 0.01659484*m.i22*m.i25 +
0.01330504*m.i22*m.i26 + 0.0220338*m.i22*m.i27 + 0.0096401*m.i22*m.i28 + 0.01336178*m.i22*m.i29
+ 0.0794522*m.i22*m.i30 + 0.00912184*m.i22*m.i31 + 0.0466568*m.i22*m.i32 + 0.0203942*m.i22*m.i33
+ 0.00695226*m.i22*m.i34 + 0.0125215*m.i22*m.i35 + 0.0728992*m.i22*m.i36 + 0.0354588*m.i22*m.i37
+ 0.00691112*m.i22*m.i38 + 0.037201*m.i22*m.i39 + 0.0756082*m.i22*m.i40 + 0.0292772*m.i22*m.i41
+ 0.0266054*m.i22*m.i42 + 0.01269282*m.i22*m.i43 + 0.0230306*m.i22*m.i44 + 0.000804368*m.i22*
m.i45 + 0.01545384*m.i22*m.i46 + 0.0296748*m.i22*m.i47 + 0.0193381*m.i22*m.i48 + 0.0200644*m.i22*
m.i49 + 0.0450946*m.i22*m.i50 + 0.01567104*m.i22*m.i51 + 0.0202574*m.i22*m.i52 + 0.0456018*m.i22*
m.i53 + 0.024727*m.i22*m.i54 + 0.01871804*m.i22*m.i55 + 0.01574656*m.i22*m.i56 + 0.01426746*m.i22
*m.i57 + 0.0112117*m.i22*m.i58 + 0.0237092*m.i22*m.i59 + 0.01100176*m.i22*m.i60 + 0.0484136*m.i22
*m.i61 + 0.0477626*m.i22*m.i62 + 0.01715072*m.i22*m.i63 + 0.01569402*m.i22*m.i64 + 0.0163363*
m.i22*m.i65 + 0.00819194*m.i22*m.i66 + 0.0250362*m.i22*m.i67 + 0.01191736*m.i22*m.i68 + 0.0445474
*m.i22*m.i69 + 0.0208408*m.i22*m.i70 + 0.0196514*m.i22*m.i71 + 0.01993902*m.i22*m.i72 +
0.01317816*m.i22*m.i73 + 0.0290184*m.i22*m.i74 + 0.022028*m.i22*m.i75 + 0.01241074*m.i22*m.i76 +
0.01467528*m.i22*m.i77 + 0.0179883*m.i22*m.i78 + 0.040464*m.i22*m.i79 + 0.01646476*m.i22*m.i80 +
0.0251454*m.i22*m.i81 + 0.00665554*m.i22*m.i82 - 0.00094782*m.i22*m.i83 + 0.01809638*m.i22*m.i84
+ 0.01658492*m.i22*m.i85 + 0.0242392*m.i22*m.i86 + 0.0215874*m.i22*m.i87 + 0.0229098*m.i22*m.i88
+ 0.01114584*m.i22*m.i89 + 0.046945*m.i22*m.i90 + 0.0230318*m.i22*m.i91 + 0.01381346*m.i22*m.i92
+ 0.0100301*m.i22*m.i93 + 0.00837496*m.i22*m.i94 + 0.0250054*m.i22*m.i95 + 0.0620424*m.i22*m.i96
+ 0.0302296*m.i22*m.i97 + 0.0248336*m.i22*m.i98 + 0.0372288*m.i22*m.i99 + 0.0441042*m.i22*m.i100
+ 0.00618108*m.i23*m.i24 + 0.00567144*m.i23*m.i25 + 0.0048866*m.i23*m.i26 + 0.00839514*m.i23*
m.i27 + 0.00487436*m.i23*m.i28 + 0.004356*m.i23*m.i29 + 0.024299*m.i23*m.i30 + 0.00996842*m.i23*
m.i31 + 0.0204928*m.i23*m.i32 + 0.01726232*m.i23*m.i33 + 0.00564344*m.i23*m.i34 + 0.00506272*
m.i23*m.i35 + 0.027322*m.i23*m.i36 + 0.01648718*m.i23*m.i37 + 0.001813512*m.i23*m.i38 + 0.0143408
*m.i23*m.i39 + 0.0410642*m.i23*m.i40 + 0.00822668*m.i23*m.i41 + 0.01397884*m.i23*m.i42 +
0.00751294*m.i23*m.i43 + 0.01081252*m.i23*m.i44 + 0.00375058*m.i23*m.i45 + 0.00488444*m.i23*m.i46
+ 0.01210078*m.i23*m.i47 + 0.0050334*m.i23*m.i48 + 0.01042672*m.i23*m.i49 + 0.01834872*m.i23*
m.i50 + 0.0122672*m.i23*m.i51 + 0.01291522*m.i23*m.i52 + 0.01243908*m.i23*m.i53 + 0.01372984*
m.i23*m.i54 + 0.0114482*m.i23*m.i55 + 0.0105593*m.i23*m.i56 + 0.00644542*m.i23*m.i57 + 0.00648944
*m.i23*m.i58 + 0.01543002*m.i23*m.i59 + 0.0037869*m.i23*m.i60 + 0.0214726*m.i23*m.i61 +
0.01495998*m.i23*m.i62 + 0.00692592*m.i23*m.i63 + 0.00648514*m.i23*m.i64 + 0.00794602*m.i23*m.i65
+ 0.00558232*m.i23*m.i66 + 0.0093087*m.i23*m.i67 + 0.000819996*m.i23*m.i68 + 0.01512186*m.i23*
m.i69 + 0.0070338*m.i23*m.i70 + 0.00840292*m.i23*m.i71 + 0.00668858*m.i23*m.i72 + 0.00956292*
m.i23*m.i73 + 0.00972254*m.i23*m.i74 + 0.00409738*m.i23*m.i75 + 0.00544566*m.i23*m.i76 +
0.01207296*m.i23*m.i77 + 0.00561846*m.i23*m.i78 + 0.01639358*m.i23*m.i79 + 0.00769632*m.i23*m.i80
+ 0.01062502*m.i23*m.i81 + 0.0060578*m.i23*m.i82 + 0.00866906*m.i23*m.i83 + 0.00707332*m.i23*
m.i84 + 0.01006612*m.i23*m.i85 + 0.01147664*m.i23*m.i86 + 0.0127172*m.i23*m.i87 + 0.01718458*
m.i23*m.i88 + 0.00499896*m.i23*m.i89 + 0.01300446*m.i23*m.i90 + 0.00824348*m.i23*m.i91 +
0.01100222*m.i23*m.i92 + 0.00359882*m.i23*m.i93 + 0.00760194*m.i23*m.i94 + 0.01026304*m.i23*m.i95
+ 0.01748628*m.i23*m.i96 + 0.01222018*m.i23*m.i97 + 0.00656104*m.i23*m.i98 + 0.01929844*m.i23*
m.i99 + 0.01526792*m.i23*m.i100 + 0.01061256*m.i24*m.i25 + 0.00390748*m.i24*m.i26 + 0.0176534*
m.i24*m.i27 + 0.00973526*m.i24*m.i28 + 0.00580416*m.i24*m.i29 + 0.0308904*m.i24*m.i30 +
0.00564094*m.i24*m.i31 + 0.0202996*m.i24*m.i32 + 0.00846578*m.i24*m.i33 + 0.00878324*m.i24*m.i34
+ 0.0092725*m.i24*m.i35 + 0.0386418*m.i24*m.i36 + 0.01405906*m.i24*m.i37 + 0.0050169*m.i24*m.i38
+ 0.01753958*m.i24*m.i39 + 0.0277342*m.i24*m.i40 + 0.0200538*m.i24*m.i41 + 0.0160148*m.i24*m.i42
+ 0.01157484*m.i24*m.i43 + 0.0097945*m.i24*m.i44 + 0.00047637*m.i24*m.i45 + 0.0074696*m.i24*
m.i46 + 0.0232922*m.i24*m.i47 + 0.0064693*m.i24*m.i48 + 0.0076863*m.i24*m.i49 + 0.01970906*m.i24*
m.i50 + 0.00539232*m.i24*m.i51 + 0.01285448*m.i24*m.i52 + 0.0120141*m.i24*m.i53 + 0.0124346*m.i24
*m.i54 + 0.00898946*m.i24*m.i55 + 0.00726448*m.i24*m.i56 + 0.0065436*m.i24*m.i57 + 0.00501008*
m.i24*m.i58 + 0.01101314*m.i24*m.i59 + 0.00470396*m.i24*m.i60 + 0.0237074*m.i24*m.i61 + 0.0228986
*m.i24*m.i62 + 0.01228188*m.i24*m.i63 + 0.01100376*m.i24*m.i64 + 0.00915078*m.i24*m.i65 +
0.0069269*m.i24*m.i66 + 0.01206108*m.i24*m.i67 + 0.00908652*m.i24*m.i68 + 0.0217466*m.i24*m.i69
+ 0.00887002*m.i24*m.i70 + 0.022452*m.i24*m.i71 + 0.0139555*m.i24*m.i72 + 0.00715706*m.i24*m.i73
+ 0.01096546*m.i24*m.i74 + 0.00744888*m.i24*m.i75 + 0.0028668*m.i24*m.i76 + 0.0036177*m.i24*
m.i77 + 0.00580328*m.i24*m.i78 + 0.0086669*m.i24*m.i79 + 0.00929752*m.i24*m.i80 + 0.01854944*
m.i24*m.i81 + 0.0023229*m.i24*m.i82 + 0.01207648*m.i24*m.i83 + 0.01205652*m.i24*m.i84 + 0.0096674
*m.i24*m.i85 + 0.0108503*m.i24*m.i86 + 0.00597266*m.i24*m.i87 + 0.0190243*m.i24*m.i88 +
0.00640978*m.i24*m.i89 + 0.01034642*m.i24*m.i90 + 0.01193214*m.i24*m.i91 + 0.00822214*m.i24*m.i92
+ 0.00070224*m.i24*m.i93 + 0.00307244*m.i24*m.i94 + 0.01092084*m.i24*m.i95 + 0.0203774*m.i24*
m.i96 + 0.01743418*m.i24*m.i97 + 0.0232524*m.i24*m.i98 + 0.01437366*m.i24*m.i99 + 0.01998814*
m.i24*m.i100 + 0.00270846*m.i25*m.i26 + 0.00878244*m.i25*m.i27 + 0.00564506*m.i25*m.i28 +
0.00404084*m.i25*m.i29 + 0.0227806*m.i25*m.i30 + 0.00477484*m.i25*m.i31 + 0.016725*m.i25*m.i32 +
0.00496432*m.i25*m.i33 + 0.00361518*m.i25*m.i34 + 0.00462338*m.i25*m.i35 + 0.0204146*m.i25*m.i36
+ 0.01087624*m.i25*m.i37 + 0.00256388*m.i25*m.i38 + 0.01236456*m.i25*m.i39 + 0.01769162*m.i25*
m.i40 + 0.01576792*m.i25*m.i41 + 0.00928236*m.i25*m.i42 + 0.00793946*m.i25*m.i43 + 0.00966756*
m.i25*m.i44 + 0.00248138*m.i25*m.i45 + 0.00485932*m.i25*m.i46 + 0.0122764*m.i25*m.i47 + 0.0023089
*m.i25*m.i48 + 0.00859364*m.i25*m.i49 + 0.01421118*m.i25*m.i50 + 0.00733214*m.i25*m.i51 +
0.00816206*m.i25*m.i52 + 0.00960248*m.i25*m.i53 + 0.00866518*m.i25*m.i54 + 0.00692386*m.i25*m.i55
+ 0.00882586*m.i25*m.i56 + 0.00434948*m.i25*m.i57 + 0.0041589*m.i25*m.i58 + 0.01055232*m.i25*
m.i59 + 0.00330494*m.i25*m.i60 + 0.01561392*m.i25*m.i61 + 0.0126551*m.i25*m.i62 + 0.00815092*
m.i25*m.i63 + 0.00612506*m.i25*m.i64 + 0.0070869*m.i25*m.i65 + 0.00424002*m.i25*m.i66 +
0.00879504*m.i25*m.i67 + 0.0058829*m.i25*m.i68 + 0.01439048*m.i25*m.i69 + 0.00610238*m.i25*m.i70
+ 0.01131906*m.i25*m.i71 + 0.00889538*m.i25*m.i72 + 0.00612414*m.i25*m.i73 + 0.00846104*m.i25*
m.i74 + 0.0057198*m.i25*m.i75 + 0.00393476*m.i25*m.i76 + 0.00432972*m.i25*m.i77 + 0.00446968*
m.i25*m.i78 + 0.0141591*m.i25*m.i79 + 0.00681524*m.i25*m.i80 + 0.00839778*m.i25*m.i81 +
0.00242412*m.i25*m.i82 + 0.0061299*m.i25*m.i83 + 0.00821362*m.i25*m.i84 + 0.0059951*m.i25*m.i85
+ 0.01036166*m.i25*m.i86 + 0.0075501*m.i25*m.i87 + 0.0208316*m.i25*m.i88 + 0.00461656*m.i25*
m.i89 + 0.01024232*m.i25*m.i90 + 0.00541446*m.i25*m.i91 + 0.0058998*m.i25*m.i92 + 0.00419408*
m.i25*m.i93 + 0.0034525*m.i25*m.i94 + 0.00742618*m.i25*m.i95 + 0.01117296*m.i25*m.i96 +
0.00976304*m.i25*m.i97 + 0.01005714*m.i25*m.i98 + 0.00997578*m.i25*m.i99 + 0.01119052*m.i25*
m.i100 + 0.0054348*m.i26*m.i27 + 0.00158545*m.i26*m.i28 + 0.00507804*m.i26*m.i29 + 0.01115184*
m.i26*m.i30 + 0.00280118*m.i26*m.i31 + 0.0103351*m.i26*m.i32 + 0.00796856*m.i26*m.i33 +
0.00322344*m.i26*m.i34 + 0.00410686*m.i26*m.i35 + 0.00922294*m.i26*m.i36 + 0.00708292*m.i26*m.i37
+ 0.00218796*m.i26*m.i38 + 0.00667316*m.i26*m.i39 + 0.00604564*m.i26*m.i40 + 0.00774532*m.i26*
m.i41 + 0.00814596*m.i26*m.i42 + 0.0026451*m.i26*m.i43 + 0.00582206*m.i26*m.i44 + 0.00332382*
m.i26*m.i45 + 0.00451686*m.i26*m.i46 + 0.00733916*m.i26*m.i47 + 0.00476946*m.i26*m.i48 +
0.00485772*m.i26*m.i49 + 0.0100103*m.i26*m.i50 + 0.00280844*m.i26*m.i51 + 0.00687248*m.i26*m.i52
+ 0.00732458*m.i26*m.i53 + 0.00815206*m.i26*m.i54 + 0.00612236*m.i26*m.i55 + 0.00307146*m.i26*
m.i56 + 0.0049056*m.i26*m.i57 + 0.00412472*m.i26*m.i58 + 0.0040935*m.i26*m.i59 + 0.0040596*m.i26*
m.i60 + 0.01138906*m.i26*m.i61 + 0.00976836*m.i26*m.i62 + 0.0087752*m.i26*m.i63 + 0.00574374*
m.i26*m.i64 + 0.00539202*m.i26*m.i65 + 0.0020772*m.i26*m.i66 + 0.00535872*m.i26*m.i67 + 0.0041987
*m.i26*m.i68 + 0.00941624*m.i26*m.i69 + 0.00708368*m.i26*m.i70 + 0.00623148*m.i26*m.i71 +
0.0059506*m.i26*m.i72 + 0.00509138*m.i26*m.i73 + 0.00640786*m.i26*m.i74 + 0.00599214*m.i26*m.i75
+ 0.00535234*m.i26*m.i76 + 0.0061449*m.i26*m.i77 + 0.0049639*m.i26*m.i78 + 0.00212662*m.i26*
m.i79 + 0.00709762*m.i26*m.i80 + 0.00556936*m.i26*m.i81 + 0.0033022*m.i26*m.i82 - 0.0001706112*
m.i26*m.i83 + 0.0042184*m.i26*m.i84 + 0.00533878*m.i26*m.i85 + 0.00407216*m.i26*m.i86 + 0.0050287
*m.i26*m.i87 + 0.00492458*m.i26*m.i88 + 0.00236614*m.i26*m.i89 + 0.0069424*m.i26*m.i90 +
0.00767098*m.i26*m.i91 + 0.00534286*m.i26*m.i92 + 0.001624812*m.i26*m.i93 + 0.00309366*m.i26*
m.i94 + 0.00617648*m.i26*m.i95 + 0.01108742*m.i26*m.i96 + 0.0068572*m.i26*m.i97 + 0.00411952*
m.i26*m.i98 + 0.00653102*m.i26*m.i99 + 0.00944332*m.i26*m.i100 + 0.01236278*m.i27*m.i28 +
0.00615174*m.i27*m.i29 + 0.0284656*m.i27*m.i30 + 0.00531366*m.i27*m.i31 + 0.0227234*m.i27*m.i32
+ 0.01239532*m.i27*m.i33 + 0.00873604*m.i27*m.i34 + 0.01006162*m.i27*m.i35 + 0.0244272*m.i27*
m.i36 + 0.01206064*m.i27*m.i37 + 0.00764146*m.i27*m.i38 + 0.01638042*m.i27*m.i39 + 0.0281728*
m.i27*m.i40 + 0.0236864*m.i27*m.i41 + 0.01394576*m.i27*m.i42 + 0.01151236*m.i27*m.i43 +
0.00967762*m.i27*m.i44 + 0.0001345884*m.i27*m.i45 + 0.00656542*m.i27*m.i46 + 0.0226088*m.i27*
m.i47 + 0.00665866*m.i27*m.i48 + 0.00867994*m.i27*m.i49 + 0.01519986*m.i27*m.i50 + 0.00516678*
m.i27*m.i51 + 0.01290734*m.i27*m.i52 + 0.00750112*m.i27*m.i53 + 0.015481*m.i27*m.i54 + 0.00918208
*m.i27*m.i55 + 0.01133662*m.i27*m.i56 + 0.00655584*m.i27*m.i57 + 0.00645326*m.i27*m.i58 +
0.01022706*m.i27*m.i59 + 0.00655942*m.i27*m.i60 + 0.0230718*m.i27*m.i61 + 0.0200196*m.i27*m.i62
+ 0.01214952*m.i27*m.i63 + 0.00996324*m.i27*m.i64 + 0.00982212*m.i27*m.i65 + 0.00606814*m.i27*
m.i66 + 0.00854006*m.i27*m.i67 + 0.00819936*m.i27*m.i68 + 0.01608286*m.i27*m.i69 + 0.00821942*
m.i27*m.i70 + 0.0230626*m.i27*m.i71 + 0.01648106*m.i27*m.i72 + 0.00833058*m.i27*m.i73 + 0.0119455
*m.i27*m.i74 + 0.0073591*m.i27*m.i75 + 0.00553444*m.i27*m.i76 + 0.00629646*m.i27*m.i77 +
0.00406434*m.i27*m.i78 + 0.00760068*m.i27*m.i79 + 0.00662478*m.i27*m.i80 + 0.0198678*m.i27*m.i81
+ 0.0044671*m.i27*m.i82 + 0.01205228*m.i27*m.i83 + 0.0106948*m.i27*m.i84 + 0.00763694*m.i27*
m.i85 + 0.01122432*m.i27*m.i86 + 0.00899094*m.i27*m.i87 + 0.0237458*m.i27*m.i88 + 0.00548044*
m.i27*m.i89 + 0.01135562*m.i27*m.i90 + 0.01131762*m.i27*m.i91 + 0.00767916*m.i27*m.i92 +
0.00281062*m.i27*m.i93 + 0.00450634*m.i27*m.i94 + 0.01029564*m.i27*m.i95 + 0.01573164*m.i27*m.i96
+ 0.01494338*m.i27*m.i97 + 0.01900252*m.i27*m.i98 + 0.01470772*m.i27*m.i99 + 0.01866828*m.i27*
m.i100 + 0.00362518*m.i28*m.i29 + 0.01640256*m.i28*m.i30 + 0.00349192*m.i28*m.i31 + 0.0129237*
m.i28*m.i32 + 0.00538584*m.i28*m.i33 + 0.00533474*m.i28*m.i34 + 0.00643216*m.i28*m.i35 +
0.01292206*m.i28*m.i36 + 0.00798078*m.i28*m.i37 + 0.0054977*m.i28*m.i38 + 0.00885966*m.i28*m.i39
+ 0.016828*m.i28*m.i40 + 0.01167374*m.i28*m.i41 + 0.00549216*m.i28*m.i42 + 0.00692364*m.i28*
m.i43 + 0.00370672*m.i28*m.i44 + 0.000284348*m.i28*m.i45 + 0.00277668*m.i28*m.i46 + 0.00936392*
m.i28*m.i47 + 0.00267238*m.i28*m.i48 + 0.00522892*m.i28*m.i49 + 0.00779258*m.i28*m.i50 +
0.0043462*m.i28*m.i51 + 0.00591302*m.i28*m.i52 + 0.00320368*m.i28*m.i53 + 0.00698682*m.i28*m.i54
+ 0.00560018*m.i28*m.i55 + 0.0075828*m.i28*m.i56 + 0.00361162*m.i28*m.i57 + 0.00229658*m.i28*
m.i58 + 0.00780328*m.i28*m.i59 + 0.0033416*m.i28*m.i60 + 0.01168298*m.i28*m.i61 + 0.0082366*m.i28
*m.i62 + 0.00465746*m.i28*m.i63 + 0.00328332*m.i28*m.i64 + 0.00685966*m.i28*m.i65 + 0.00386632*
m.i28*m.i66 + 0.0053142*m.i28*m.i67 + 0.00432904*m.i28*m.i68 + 0.00791276*m.i28*m.i69 + 0.0040137
*m.i28*m.i70 + 0.01081358*m.i28*m.i71 + 0.00841874*m.i28*m.i72 + 0.00534694*m.i28*m.i73 +
0.00677544*m.i28*m.i74 + 0.00391198*m.i28*m.i75 + 0.00308942*m.i28*m.i76 + 0.00250778*m.i28*m.i77
+ 0.00189916*m.i28*m.i78 + 0.00856184*m.i28*m.i79 + 0.00337182*m.i28*m.i80 + 0.00959416*m.i28*
m.i81 + 0.00329038*m.i28*m.i82 + 0.00388664*m.i28*m.i83 + 0.00685968*m.i28*m.i84 + 0.00406002*
m.i28*m.i85 + 0.00658126*m.i28*m.i86 + 0.00646838*m.i28*m.i87 + 0.0218548*m.i28*m.i88 +
0.00541992*m.i28*m.i89 + 0.00503116*m.i28*m.i90 + 0.00418236*m.i28*m.i91 + 0.0040874*m.i28*m.i92
+ 0.0022624*m.i28*m.i93 + 0.00392254*m.i28*m.i94 + 0.00482686*m.i28*m.i95 + 0.00726382*m.i28*
m.i96 + 0.00767472*m.i28*m.i97 + 0.01066418*m.i28*m.i98 + 0.00883358*m.i28*m.i99 + 0.0070211*
m.i28*m.i100 + 0.0147917*m.i29*m.i30 + 0.001068816*m.i29*m.i31 + 0.0105712*m.i29*m.i32 +
0.00407766*m.i29*m.i33 + 0.00300076*m.i29*m.i34 + 0.00524794*m.i29*m.i35 + 0.01016322*m.i29*m.i36
+ 0.00841674*m.i29*m.i37 + 0.00258632*m.i29*m.i38 + 0.00698836*m.i29*m.i39 + 0.01223674*m.i29*
m.i40 + 0.01128912*m.i29*m.i41 + 0.00481604*m.i29*m.i42 + 0.00316394*m.i29*m.i43 + 0.00690116*
m.i29*m.i44 + 0.00082418*m.i29*m.i45 + 0.00343988*m.i29*m.i46 + 0.00660586*m.i29*m.i47 +
0.00315994*m.i29*m.i48 + 0.004109*m.i29*m.i49 + 0.01072766*m.i29*m.i50 + 0.00295018*m.i29*m.i51
+ 0.00574084*m.i29*m.i52 + 0.00735384*m.i29*m.i53 + 0.00646518*m.i29*m.i54 + 0.00437712*m.i29*
m.i55 + 0.0050201*m.i29*m.i56 + 0.00428602*m.i29*m.i57 + 0.00339284*m.i29*m.i58 + 0.00395186*
m.i29*m.i59 + 0.00369852*m.i29*m.i60 + 0.01069104*m.i29*m.i61 + 0.00877524*m.i29*m.i62 +
0.00780122*m.i29*m.i63 + 0.00319846*m.i29*m.i64 + 0.00522668*m.i29*m.i65 + 0.00318906*m.i29*m.i66
+ 0.00765554*m.i29*m.i67 + 0.00353436*m.i29*m.i68 + 0.0090668*m.i29*m.i69 + 0.0062235*m.i29*
m.i70 + 0.00879038*m.i29*m.i71 + 0.00661754*m.i29*m.i72 + 0.00355728*m.i29*m.i73 + 0.0041974*
m.i29*m.i74 + 0.00530048*m.i29*m.i75 + 0.00543652*m.i29*m.i76 + 0.00436164*m.i29*m.i77 +
0.00450742*m.i29*m.i78 + 0.00725294*m.i29*m.i79 + 0.00491692*m.i29*m.i80 + 0.00689594*m.i29*m.i81
+ 0.00288614*m.i29*m.i82 + 0.005327*m.i29*m.i83 + 0.00356482*m.i29*m.i84 + 0.00320232*m.i29*
m.i85 + 0.00401206*m.i29*m.i86 + 0.00746968*m.i29*m.i87 + 0.01484586*m.i29*m.i88 + 0.00405332*
m.i29*m.i89 + 0.00646554*m.i29*m.i90 + 0.00398186*m.i29*m.i91 + 0.0045419*m.i29*m.i92 +
0.00249602*m.i29*m.i93 + 0.00344506*m.i29*m.i94 + 0.0046313*m.i29*m.i95 + 0.01012898*m.i29*m.i96
+ 0.00666118*m.i29*m.i97 + 0.00510452*m.i29*m.i98 + 0.00865974*m.i29*m.i99 + 0.00556162*m.i29*
m.i100 + 0.01432038*m.i30*m.i31 + 0.048762*m.i30*m.i32 + 0.03246*m.i30*m.i33 + 0.00510162*m.i30*
m.i34 + 0.00990812*m.i30*m.i35 + 0.0782504*m.i30*m.i36 + 0.0336068*m.i30*m.i37 + 0.00740496*m.i30
*m.i38 + 0.0520556*m.i30*m.i39 + 0.0689666*m.i30*m.i40 + 0.0338084*m.i30*m.i41 + 0.0303886*m.i30*
m.i42 + 0.01530392*m.i30*m.i43 + 0.0286584*m.i30*m.i44 + 0.001838718*m.i30*m.i45 + 0.01735792*
m.i30*m.i46 + 0.0257124*m.i30*m.i47 + 0.01952576*m.i30*m.i48 + 0.0285968*m.i30*m.i49 + 0.0597966*
m.i30*m.i50 + 0.0235442*m.i30*m.i51 + 0.0356002*m.i30*m.i52 + 0.056815*m.i30*m.i53 + 0.031993*
m.i30*m.i54 + 0.0256864*m.i30*m.i55 + 0.012682*m.i30*m.i56 + 0.01927838*m.i30*m.i57 + 0.0132181*
m.i30*m.i58 + 0.0308396*m.i30*m.i59 + 0.01646776*m.i30*m.i60 + 0.0691402*m.i30*m.i61 + 0.0539688*
m.i30*m.i62 + 0.0253122*m.i30*m.i63 + 0.0217306*m.i30*m.i64 + 0.0238236*m.i30*m.i65 + 0.01199066*
m.i30*m.i66 + 0.0301278*m.i30*m.i67 + 0.0209952*m.i30*m.i68 + 0.0484514*m.i30*m.i69 + 0.0226726*
m.i30*m.i70 + 0.02153*m.i30*m.i71 + 0.023498*m.i30*m.i72 + 0.0217474*m.i30*m.i73 + 0.0363548*
m.i30*m.i74 + 0.0290864*m.i30*m.i75 + 0.01738014*m.i30*m.i76 + 0.0248066*m.i30*m.i77 + 0.01560782
*m.i30*m.i78 + 0.0735134*m.i30*m.i79 + 0.0216582*m.i30*m.i80 + 0.030706*m.i30*m.i81 + 0.00888388*
m.i30*m.i82 + 0.00819988*m.i30*m.i83 + 0.02421*m.i30*m.i84 + 0.01903928*m.i30*m.i85 + 0.0384208*
m.i30*m.i86 + 0.0308632*m.i30*m.i87 + 0.112101*m.i30*m.i88 + 0.0313082*m.i30*m.i89 + 0.0480838*
m.i30*m.i90 + 0.0265036*m.i30*m.i91 + 0.0219052*m.i30*m.i92 + 0.01243318*m.i30*m.i93 + 0.00866336
*m.i30*m.i94 + 0.0318698*m.i30*m.i95 + 0.0809696*m.i30*m.i96 + 0.0362056*m.i30*m.i97 + 0.0307602*
m.i30*m.i98 + 0.0452826*m.i30*m.i99 + 0.0359652*m.i30*m.i100 + 0.01352968*m.i31*m.i32 +
0.01461656*m.i31*m.i33 + 0.00410226*m.i31*m.i34 + 0.00308616*m.i31*m.i35 + 0.0221942*m.i31*m.i36
+ 0.0095014*m.i31*m.i37 + 0.0001894118*m.i31*m.i38 + 0.01328104*m.i31*m.i39 + 0.0207254*m.i31*
m.i40 + 0.01363894*m.i31*m.i41 + 0.01129202*m.i31*m.i42 + 0.0108266*m.i31*m.i43 + 0.01097008*
m.i31*m.i44 + 0.00461712*m.i31*m.i45 + 0.00463752*m.i31*m.i46 + 0.00929264*m.i31*m.i47 +
0.00473752*m.i31*m.i48 + 0.0114599*m.i31*m.i49 + 0.0117742*m.i31*m.i50 + 0.0088573*m.i31*m.i51 +
0.0075837*m.i31*m.i52 + 0.00658756*m.i31*m.i53 + 0.0113218*m.i31*m.i54 + 0.00930362*m.i31*m.i55
+ 0.01063604*m.i31*m.i56 + 0.00432704*m.i31*m.i57 + 0.00804616*m.i31*m.i58 + 0.01180986*m.i31*
m.i59 + 0.0009047*m.i31*m.i60 + 0.01200762*m.i31*m.i61 + 0.00940268*m.i31*m.i62 + 0.01417994*
m.i31*m.i63 + 0.0076164*m.i31*m.i64 + 0.00575322*m.i31*m.i65 + 0.00834872*m.i31*m.i66 +
0.00454676*m.i31*m.i67 + 0.00544346*m.i31*m.i68 + 0.0132866*m.i31*m.i69 + 0.00553084*m.i31*m.i70
+ 0.01147094*m.i31*m.i71 + 0.00577578*m.i31*m.i72 + 0.00887008*m.i31*m.i73 + 0.01059428*m.i31*
m.i74 + 0.0040723*m.i31*m.i75 + 0.00207936*m.i31*m.i76 + 0.01175316*m.i31*m.i77 + 0.00278464*
m.i31*m.i78 + 0.00880162*m.i31*m.i79 + 0.0087823*m.i31*m.i80 + 0.00669872*m.i31*m.i81 +
0.001695732*m.i31*m.i82 + 0.01128974*m.i31*m.i83 + 0.0131319*m.i31*m.i84 + 0.00861518*m.i31*m.i85
+ 0.01080682*m.i31*m.i86 + 0.00523332*m.i31*m.i87 + 0.0207656*m.i31*m.i88 + 0.00591302*m.i31*
m.i89 + 0.00439716*m.i31*m.i90 + 0.0115743*m.i31*m.i91 + 0.00995262*m.i31*m.i92 + 0.000428388*
m.i31*m.i93 + 0.00464012*m.i31*m.i94 + 0.00813868*m.i31*m.i95 + 0.00570582*m.i31*m.i96 +
0.00954936*m.i31*m.i97 + 0.01038358*m.i31*m.i98 + 0.00920842*m.i31*m.i99 + 0.01146966*m.i31*
m.i100 + 0.0209668*m.i32*m.i33 + 0.0108011*m.i32*m.i34 + 0.01248282*m.i32*m.i35 + 0.0530038*m.i32
*m.i36 + 0.0301486*m.i32*m.i37 + 0.00760388*m.i32*m.i38 + 0.0317898*m.i32*m.i39 + 0.0642986*m.i32
*m.i40 + 0.0332684*m.i32*m.i41 + 0.0235182*m.i32*m.i42 + 0.0143552*m.i32*m.i43 + 0.0235288*m.i32*
m.i44 + 0.00682838*m.i32*m.i45 + 0.01137478*m.i32*m.i46 + 0.0318282*m.i32*m.i47 + 0.00984204*
m.i32*m.i48 + 0.0207836*m.i32*m.i49 + 0.0371082*m.i32*m.i50 + 0.01715818*m.i32*m.i51 + 0.0184894*
m.i32*m.i52 + 0.0241264*m.i32*m.i53 + 0.0254814*m.i32*m.i54 + 0.01913224*m.i32*m.i55 + 0.0212986*
m.i32*m.i56 + 0.01167336*m.i32*m.i57 + 0.01191892*m.i32*m.i58 + 0.0246844*m.i32*m.i59 +
0.00772776*m.i32*m.i60 + 0.0424102*m.i32*m.i61 + 0.0330624*m.i32*m.i62 + 0.0190237*m.i32*m.i63 +
0.01185726*m.i32*m.i64 + 0.01593976*m.i32*m.i65 + 0.00931156*m.i32*m.i66 + 0.01976096*m.i32*m.i67
+ 0.00940704*m.i32*m.i68 + 0.0353824*m.i32*m.i69 + 0.01637874*m.i32*m.i70 + 0.0234414*m.i32*
m.i71 + 0.01981882*m.i32*m.i72 + 0.01518934*m.i32*m.i73 + 0.0206944*m.i32*m.i74 + 0.01368518*
m.i32*m.i75 + 0.01085922*m.i32*m.i76 + 0.0142422*m.i32*m.i77 + 0.01225292*m.i32*m.i78 + 0.025216*
m.i32*m.i79 + 0.01581384*m.i32*m.i80 + 0.0226748*m.i32*m.i81 + 0.0078489*m.i32*m.i82 + 0.00488232
*m.i32*m.i83 + 0.01715432*m.i32*m.i84 + 0.01617784*m.i32*m.i85 + 0.0224728*m.i32*m.i86 +
0.0213528*m.i32*m.i87 + 0.0404024*m.i32*m.i88 + 0.00700416*m.i32*m.i89 + 0.0284686*m.i32*m.i90 +
0.01764584*m.i32*m.i91 + 0.01747106*m.i32*m.i92 + 0.00781272*m.i32*m.i93 + 0.01173676*m.i32*m.i94
+ 0.01901852*m.i32*m.i95 + 0.032411*m.i32*m.i96 + 0.0238232*m.i32*m.i97 + 0.021198*m.i32*m.i98
+ 0.0300116*m.i32*m.i99 + 0.0354006*m.i32*m.i100 + 0.0090127*m.i33*m.i34 + 0.00772724*m.i33*
m.i35 + 0.0313702*m.i33*m.i36 + 0.01413346*m.i33*m.i37 + 0.001835906*m.i33*m.i38 + 0.01789618*
m.i33*m.i39 + 0.0342932*m.i33*m.i40 + 0.0203234*m.i33*m.i41 + 0.01859662*m.i33*m.i42 + 0.00949822
*m.i33*m.i43 + 0.0173394*m.i33*m.i44 + 0.00462026*m.i33*m.i45 + 0.0076766*m.i33*m.i46 + 0.0195887
*m.i33*m.i47 + 0.00677792*m.i33*m.i48 + 0.01593666*m.i33*m.i49 + 0.0205366*m.i33*m.i50 +
0.01028686*m.i33*m.i51 + 0.01380638*m.i33*m.i52 + 0.0139701*m.i33*m.i53 + 0.016589*m.i33*m.i54 +
0.0139115*m.i33*m.i55 + 0.01339328*m.i33*m.i56 + 0.00706492*m.i33*m.i57 + 0.01010916*m.i33*m.i58
+ 0.0112109*m.i33*m.i59 + 0.0038394*m.i33*m.i60 + 0.0232104*m.i33*m.i61 + 0.01960694*m.i33*m.i62
+ 0.01805454*m.i33*m.i63 + 0.01327968*m.i33*m.i64 + 0.0135282*m.i33*m.i65 + 0.0101248*m.i33*
m.i66 + 0.00800254*m.i33*m.i67 + 0.0030849*m.i33*m.i68 + 0.0205056*m.i33*m.i69 + 0.00997944*m.i33
*m.i70 + 0.01867754*m.i33*m.i71 + 0.01023414*m.i33*m.i72 + 0.01414764*m.i33*m.i73 + 0.01623304*
m.i33*m.i74 + 0.00580254*m.i33*m.i75 + 0.00688906*m.i33*m.i76 + 0.01955742*m.i33*m.i77 +
0.0043617*m.i33*m.i78 + 0.0110714*m.i33*m.i79 + 0.00837212*m.i33*m.i80 + 0.0186224*m.i33*m.i81 +
0.0038599*m.i33*m.i82 + 0.01828456*m.i33*m.i83 + 0.01460176*m.i33*m.i84 + 0.00984126*m.i33*m.i85
+ 0.01375926*m.i33*m.i86 + 0.01081848*m.i33*m.i87 + 0.0294078*m.i33*m.i88 + 0.00904426*m.i33*
m.i89 + 0.01335384*m.i33*m.i90 + 0.00944562*m.i33*m.i91 + 0.01586856*m.i33*m.i92 + 0.00253356*
m.i33*m.i93 + 0.00579828*m.i33*m.i94 + 0.01264366*m.i33*m.i95 + 0.0212436*m.i33*m.i96 + 0.014968*
m.i33*m.i97 + 0.01459146*m.i33*m.i98 + 0.01990882*m.i33*m.i99 + 0.020898*m.i33*m.i100 + 0.0078456
*m.i34*m.i35 + 0.01102212*m.i34*m.i36 + 0.00676724*m.i34*m.i37 + 0.00365266*m.i34*m.i38 +
0.00595098*m.i34*m.i39 + 0.01153866*m.i34*m.i40 + 0.01058304*m.i34*m.i41 + 0.00838326*m.i34*m.i42
+ 0.00601354*m.i34*m.i43 + 0.00621002*m.i34*m.i44 + 0.00388646*m.i34*m.i45 + 0.00291464*m.i34*
m.i46 + 0.01279302*m.i34*m.i47 + 0.001590652*m.i34*m.i48 + 0.00546164*m.i34*m.i49 + 0.00756668*
m.i34*m.i50 + 0.00255946*m.i34*m.i51 + 0.00586752*m.i34*m.i52 - 0.0001086844*m.i34*m.i53 +
0.00756758*m.i34*m.i54 + 0.00472132*m.i34*m.i55 + 0.0090114*m.i34*m.i56 + 0.00404276*m.i34*m.i57
+ 0.00259172*m.i34*m.i58 + 0.0043188*m.i34*m.i59 + 0.00265148*m.i34*m.i60 + 0.00988174*m.i34*
m.i61 + 0.00773706*m.i34*m.i62 + 0.00871216*m.i34*m.i63 + 0.0051719*m.i34*m.i64 + 0.005674*m.i34*
m.i65 + 0.0042472*m.i34*m.i66 + 0.0029352*m.i34*m.i67 + 0.00380488*m.i34*m.i68 + 0.00782908*m.i34
*m.i69 + 0.00528678*m.i34*m.i70 + 0.01141144*m.i34*m.i71 + 0.00731358*m.i34*m.i72 + 0.00557996*
m.i34*m.i73 + 0.00428558*m.i34*m.i74 + 0.00214164*m.i34*m.i75 + 0.001888024*m.i34*m.i76 +
0.00450712*m.i34*m.i77 + 0.001974898*m.i34*m.i78 + 0.000555542*m.i34*m.i79 + 0.004826*m.i34*m.i80
+ 0.01009798*m.i34*m.i81 + 0.00342408*m.i34*m.i82 + 0.0066259*m.i34*m.i83 + 0.00557372*m.i34*
m.i84 + 0.00493326*m.i34*m.i85 + 0.0033431*m.i34*m.i86 + 0.00355798*m.i34*m.i87 + 0.0070914*m.i34
*m.i88 + 0.00319452*m.i34*m.i89 + 0.001165088*m.i34*m.i90 + 0.00330168*m.i34*m.i91 + 0.00487072*
m.i34*m.i92 + 0.001039364*m.i34*m.i93 + 0.00462638*m.i34*m.i94 + 0.00474964*m.i34*m.i95 +
0.00307738*m.i34*m.i96 + 0.00634158*m.i34*m.i97 + 0.0093911*m.i34*m.i98 + 0.00479968*m.i34*m.i99
+ 0.00945466*m.i34*m.i100 + 0.00886108*m.i35*m.i36 + 0.008324*m.i35*m.i37 + 0.0042517*m.i35*
m.i38 + 0.0063195*m.i35*m.i39 + 0.00897334*m.i35*m.i40 + 0.01438534*m.i35*m.i41 + 0.00707384*
m.i35*m.i42 + 0.00524994*m.i35*m.i43 + 0.00729354*m.i35*m.i44 + 0.00231104*m.i35*m.i45 +
0.00317018*m.i35*m.i46 + 0.01095322*m.i35*m.i47 + 0.00256082*m.i35*m.i48 + 0.0066693*m.i35*m.i49
+ 0.00896786*m.i35*m.i50 + 0.00243944*m.i35*m.i51 + 0.00542922*m.i35*m.i52 + 0.001853016*m.i35*
m.i53 + 0.0080304*m.i35*m.i54 + 0.004194*m.i35*m.i55 + 0.00944224*m.i35*m.i56 + 0.0044097*m.i35*
m.i57 + 0.00234874*m.i35*m.i58 + 0.0045055*m.i35*m.i59 + 0.00387194*m.i35*m.i60 + 0.01070194*
m.i35*m.i61 + 0.01020854*m.i35*m.i62 + 0.00869604*m.i35*m.i63 + 0.0038381*m.i35*m.i64 +
0.00566828*m.i35*m.i65 + 0.00392276*m.i35*m.i66 + 0.00493806*m.i35*m.i67 + 0.00543634*m.i35*m.i68
+ 0.01090284*m.i35*m.i69 + 0.00744802*m.i35*m.i70 + 0.01323476*m.i35*m.i71 + 0.00994186*m.i35*
m.i72 + 0.00554564*m.i35*m.i73 + 0.00631474*m.i35*m.i74 + 0.00456554*m.i35*m.i75 + 0.00357674*
m.i35*m.i76 + 0.00520436*m.i35*m.i77 + 0.0030095*m.i35*m.i78 + 0.0057729*m.i35*m.i79 + 0.00411204
*m.i35*m.i80 + 0.00953392*m.i35*m.i81 + 0.00378046*m.i35*m.i82 + 0.00572152*m.i35*m.i83 +
0.00613732*m.i35*m.i84 + 0.00382166*m.i35*m.i85 + 0.00356476*m.i35*m.i86 + 0.00634394*m.i35*m.i87
+ 0.0111758*m.i35*m.i88 + 0.00567884*m.i35*m.i89 + 0.00368822*m.i35*m.i90 + 0.00382434*m.i35*
m.i91 + 0.00295216*m.i35*m.i92 + 0.00261056*m.i35*m.i93 + 0.00538486*m.i35*m.i94 + 0.00508518*
m.i35*m.i95 + 0.00571674*m.i35*m.i96 + 0.00749186*m.i35*m.i97 + 0.00986618*m.i35*m.i98 +
0.00565378*m.i35*m.i99 + 0.0094721*m.i35*m.i100 + 0.0440606*m.i36*m.i37 + 0.0069763*m.i36*m.i38
+ 0.0493166*m.i36*m.i39 + 0.121634*m.i36*m.i40 + 0.0358136*m.i36*m.i41 + 0.0380066*m.i36*m.i42
+ 0.0240066*m.i36*m.i43 + 0.0315302*m.i36*m.i44 + 0.00778714*m.i36*m.i45 + 0.01711478*m.i36*
m.i46 + 0.0433014*m.i36*m.i47 + 0.01592312*m.i36*m.i48 + 0.0219624*m.i36*m.i49 + 0.0584382*m.i36*
m.i50 + 0.0237454*m.i36*m.i51 + 0.030079*m.i36*m.i52 + 0.0450814*m.i36*m.i53 + 0.0285826*m.i36*
m.i54 + 0.0266392*m.i36*m.i55 + 0.01830758*m.i36*m.i56 + 0.01364522*m.i36*m.i57 + 0.01568*m.i36*
m.i58 + 0.0359108*m.i36*m.i59 + 0.00643528*m.i36*m.i60 + 0.056249*m.i36*m.i61 + 0.0503568*m.i36*
m.i62 + 0.0221574*m.i36*m.i63 + 0.023432*m.i36*m.i64 + 0.0219264*m.i36*m.i65 + 0.01946022*m.i36*
m.i66 + 0.0301552*m.i36*m.i67 + 0.00986666*m.i36*m.i68 + 0.0496472*m.i36*m.i69 + 0.0177644*m.i36*
m.i70 + 0.0308856*m.i36*m.i71 + 0.01899074*m.i36*m.i72 + 0.01805938*m.i36*m.i73 + 0.0273694*m.i36
*m.i74 + 0.01662774*m.i36*m.i75 + 0.00832596*m.i36*m.i76 + 0.0203852*m.i36*m.i77 + 0.0174271*
m.i36*m.i78 + 0.039217*m.i36*m.i79 + 0.0232082*m.i36*m.i80 + 0.0357644*m.i36*m.i81 + 0.00331724*
m.i36*m.i82 + 0.0276304*m.i36*m.i83 + 0.0267904*m.i36*m.i84 + 0.02756*m.i36*m.i85 + 0.0320374*
m.i36*m.i86 + 0.0222598*m.i36*m.i87 + 0.0496644*m.i36*m.i88 + 0.01118028*m.i36*m.i89 + 0.0432572*
m.i36*m.i90 + 0.027434*m.i36*m.i91 + 0.0293774*m.i36*m.i92 + 0.0055352*m.i36*m.i93 + 0.00852418*
m.i36*m.i94 + 0.028037*m.i36*m.i95 + 0.0642512*m.i36*m.i96 + 0.0386458*m.i36*m.i97 + 0.040981*
m.i36*m.i98 + 0.04604*m.i36*m.i99 + 0.0478424*m.i36*m.i100 + 0.00525362*m.i37*m.i38 + 0.0212576*
m.i37*m.i39 + 0.0543916*m.i37*m.i40 + 0.018282*m.i37*m.i41 + 0.01700698*m.i37*m.i42 + 0.00953368*
m.i37*m.i43 + 0.0147155*m.i37*m.i44 + 0.00425042*m.i37*m.i45 + 0.00777022*m.i37*m.i46 +
0.01646346*m.i37*m.i47 + 0.00740598*m.i37*m.i48 + 0.01274586*m.i37*m.i49 + 0.0282742*m.i37*m.i50
+ 0.01506898*m.i37*m.i51 + 0.01409464*m.i37*m.i52 + 0.01916222*m.i37*m.i53 + 0.01572296*m.i37*
m.i54 + 0.01361714*m.i37*m.i55 + 0.01302042*m.i37*m.i56 + 0.00807862*m.i37*m.i57 + 0.00701644*
m.i37*m.i58 + 0.0201438*m.i37*m.i59 + 0.00497496*m.i37*m.i60 + 0.0259544*m.i37*m.i61 + 0.01982096
*m.i37*m.i62 + 0.01082904*m.i37*m.i63 + 0.00909066*m.i37*m.i64 + 0.0112364*m.i37*m.i65 +
0.0089483*m.i37*m.i66 + 0.01522148*m.i37*m.i67 + 0.00459152*m.i37*m.i68 + 0.0214858*m.i37*m.i69
+ 0.01075074*m.i37*m.i70 + 0.0132224*m.i37*m.i71 + 0.00980738*m.i37*m.i72 + 0.00885252*m.i37*
m.i73 + 0.01427422*m.i37*m.i74 + 0.00903996*m.i37*m.i75 + 0.00768272*m.i37*m.i76 + 0.0103221*
m.i37*m.i77 + 0.01082002*m.i37*m.i78 + 0.0248284*m.i37*m.i79 + 0.01098172*m.i37*m.i80 +
0.01335848*m.i37*m.i81 + 0.00545734*m.i37*m.i82 + 0.00921544*m.i37*m.i83 + 0.0110069*m.i37*m.i84
+ 0.01385998*m.i37*m.i85 + 0.01437348*m.i37*m.i86 + 0.01621552*m.i37*m.i87 + 0.01981332*m.i37*
m.i88 + 0.00549314*m.i37*m.i89 + 0.0210958*m.i37*m.i90 + 0.0116061*m.i37*m.i91 + 0.01444326*m.i37
*m.i92 + 0.00631646*m.i37*m.i93 + 0.00847398*m.i37*m.i94 + 0.0132838*m.i37*m.i95 + 0.0257442*
m.i37*m.i96 + 0.01746728*m.i37*m.i97 + 0.01331586*m.i37*m.i98 + 0.0246618*m.i37*m.i99 + 0.0231186
*m.i37*m.i100 + 0.00427726*m.i38*m.i39 + 0.00960742*m.i38*m.i40 + 0.00588794*m.i38*m.i41 +
0.0040899*m.i38*m.i42 + 0.00370486*m.i38*m.i43 + 0.001581616*m.i38*m.i44 + 0.00157779*m.i38*m.i45
+ 0.001517842*m.i38*m.i46 + 0.00577098*m.i38*m.i47 + 0.00184948*m.i38*m.i48 + 0.001412132*m.i38*
m.i49 + 0.00473326*m.i38*m.i50 + 0.001265572*m.i38*m.i51 + 0.00389392*m.i38*m.i52 + 0.00195541*
m.i38*m.i53 + 0.0045747*m.i38*m.i54 + 0.003024*m.i38*m.i55 + 0.00322834*m.i38*m.i56 + 0.00240162*
m.i38*m.i57 + 0.000494648*m.i38*m.i58 + 0.0035117*m.i38*m.i59 + 0.00302272*m.i38*m.i60 +
0.0067192*m.i38*m.i61 + 0.00576934*m.i38*m.i62 + 0.00236514*m.i38*m.i63 + 0.00208302*m.i38*m.i64
+ 0.00359594*m.i38*m.i65 + 0.001590092*m.i38*m.i66 + 0.00239398*m.i38*m.i67 + 0.00302224*m.i38*
m.i68 + 0.00326928*m.i38*m.i69 + 0.00302294*m.i38*m.i70 + 0.0049377*m.i38*m.i71 + 0.00553496*
m.i38*m.i72 + 0.00229972*m.i38*m.i73 + 0.00318332*m.i38*m.i74 + 0.00325074*m.i38*m.i75 +
0.001803886*m.i38*m.i76 + 0.000902562*m.i38*m.i77 + 0.001651326*m.i38*m.i78 + 0.0039935*m.i38*
m.i79 + 0.00233242*m.i38*m.i80 + 0.00546644*m.i38*m.i81 + 0.00223454*m.i38*m.i82 - 0.001681894*
m.i38*m.i83 + 0.0025273*m.i38*m.i84 + 0.0032781*m.i38*m.i85 + 0.001557044*m.i38*m.i86 +
0.00327138*m.i38*m.i87 + 0.00674346*m.i38*m.i88 + 0.0020784*m.i38*m.i89 + 0.00343958*m.i38*m.i90
+ 0.00324954*m.i38*m.i91 + 0.00206404*m.i38*m.i92 + 0.00161462*m.i38*m.i93 + 0.00247166*m.i38*
m.i94 + 0.00341238*m.i38*m.i95 + 0.00585902*m.i38*m.i96 + 0.00423638*m.i38*m.i97 + 0.00566634*
m.i38*m.i98 + 0.00315378*m.i38*m.i99 + 0.00449598*m.i38*m.i100 + 0.0491892*m.i39*m.i40 +
0.0262408*m.i39*m.i41 + 0.0205234*m.i39*m.i42 + 0.01409356*m.i39*m.i43 + 0.0195666*m.i39*m.i44 +
0.00525174*m.i39*m.i45 + 0.01076856*m.i39*m.i46 + 0.0216478*m.i39*m.i47 + 0.01097136*m.i39*m.i48
+ 0.0178672*m.i39*m.i49 + 0.0324104*m.i39*m.i50 + 0.0147971*m.i39*m.i51 + 0.01855664*m.i39*m.i52
+ 0.0250992*m.i39*m.i53 + 0.0213078*m.i39*m.i54 + 0.01575182*m.i39*m.i55 + 0.01438592*m.i39*
m.i56 + 0.0105253*m.i39*m.i57 + 0.01177712*m.i39*m.i58 + 0.0207946*m.i39*m.i59 + 0.00650454*m.i39
*m.i60 + 0.036126*m.i39*m.i61 + 0.0278076*m.i39*m.i62 + 0.0206546*m.i39*m.i63 + 0.01499036*m.i39*
m.i64 + 0.01276412*m.i39*m.i65 + 0.0125414*m.i39*m.i66 + 0.01617824*m.i39*m.i67 + 0.010394*m.i39*
m.i68 + 0.0290228*m.i39*m.i69 + 0.01190924*m.i39*m.i70 + 0.01824964*m.i39*m.i71 + 0.014012*m.i39*
m.i72 + 0.01408568*m.i39*m.i73 + 0.0192582*m.i39*m.i74 + 0.01283914*m.i39*m.i75 + 0.00757714*
m.i39*m.i76 + 0.0157748*m.i39*m.i77 + 0.00886562*m.i39*m.i78 + 0.0226622*m.i39*m.i79 + 0.01506442
*m.i39*m.i80 + 0.01868878*m.i39*m.i81 + 0.00371016*m.i39*m.i82 + 0.01245306*m.i39*m.i83 +
0.01693888*m.i39*m.i84 + 0.0145704*m.i39*m.i85 + 0.0207926*m.i39*m.i86 + 0.01487822*m.i39*m.i87
+ 0.0465058*m.i39*m.i88 + 0.01052428*m.i39*m.i89 + 0.0220072*m.i39*m.i90 + 0.01887928*m.i39*
m.i91 + 0.01597714*m.i39*m.i92 + 0.00531126*m.i39*m.i93 + 0.00658506*m.i39*m.i94 + 0.01713092*
m.i39*m.i95 + 0.0328166*m.i39*m.i96 + 0.0213542*m.i39*m.i97 + 0.0210286*m.i39*m.i98 + 0.0255336*
m.i39*m.i99 + 0.0274274*m.i39*m.i100 + 0.0504412*m.i40*m.i41 + 0.0336102*m.i40*m.i42 + 0.0294804*
m.i40*m.i43 + 0.0424704*m.i40*m.i44 + 0.0030095*m.i40*m.i45 + 0.01146224*m.i40*m.i46 + 0.0507426*
m.i40*m.i47 + 0.01585054*m.i40*m.i48 + 0.0217164*m.i40*m.i49 + 0.0491478*m.i40*m.i50 + 0.0317926*
m.i40*m.i51 + 0.0284682*m.i40*m.i52 + 0.0468934*m.i40*m.i53 + 0.0309254*m.i40*m.i54 + 0.028626*
m.i40*m.i55 + 0.0309698*m.i40*m.i56 + 0.01062184*m.i40*m.i57 + 0.01987174*m.i40*m.i58 + 0.0429952
*m.i40*m.i59 + 0.00300922*m.i40*m.i60 + 0.0574936*m.i40*m.i61 + 0.0496304*m.i40*m.i62 +
0.01678646*m.i40*m.i63 + 0.0153295*m.i40*m.i64 + 0.0230176*m.i40*m.i65 + 0.0200972*m.i40*m.i66 +
0.0274442*m.i40*m.i67 - 0.00465404*m.i40*m.i68 + 0.0404524*m.i40*m.i69 + 0.01243058*m.i40*m.i70
+ 0.0333654*m.i40*m.i71 + 0.01847532*m.i40*m.i72 + 0.01863464*m.i40*m.i73 + 0.01865328*m.i40*
m.i74 + 0.0086314*m.i40*m.i75 + 0.0107773*m.i40*m.i76 + 0.0203618*m.i40*m.i77 + 0.01445046*m.i40*
m.i78 + 0.0410886*m.i40*m.i79 + 0.01194082*m.i40*m.i80 + 0.044529*m.i40*m.i81 + 0.00528742*m.i40*
m.i82 + 0.0445722*m.i40*m.i83 + 0.0229102*m.i40*m.i84 + 0.0241064*m.i40*m.i85 + 0.0368384*m.i40*
m.i86 + 0.0327072*m.i40*m.i87 + 0.0612044*m.i40*m.i88 + 0.0029601*m.i40*m.i89 + 0.0534994*m.i40*
m.i90 + 0.0258428*m.i40*m.i91 + 0.0317582*m.i40*m.i92 + 0.00965728*m.i40*m.i93 + 0.01437522*m.i40
*m.i94 + 0.0249652*m.i40*m.i95 + 0.0605768*m.i40*m.i96 + 0.0345084*m.i40*m.i97 + 0.0313726*m.i40*
m.i98 + 0.064674*m.i40*m.i99 + 0.0504464*m.i40*m.i100 + 0.0211266*m.i41*m.i42 + 0.0280268*m.i41*
m.i43 + 0.0396958*m.i41*m.i44 + 0.00245084*m.i41*m.i45 + 0.00955952*m.i41*m.i46 + 0.0396834*m.i41
*m.i47 + 0.0061862*m.i41*m.i48 + 0.02227*m.i41*m.i49 + 0.0217142*m.i41*m.i50 + 0.00978418*m.i41*
m.i51 + 0.01479238*m.i41*m.i52 + 0.016171*m.i41*m.i53 + 0.0243916*m.i41*m.i54 + 0.01422356*m.i41*
m.i55 + 0.0283342*m.i41*m.i56 + 0.00801394*m.i41*m.i57 + 0.01783044*m.i41*m.i58 + 0.01283818*
m.i41*m.i59 + 0.00500652*m.i41*m.i60 + 0.0289002*m.i41*m.i61 + 0.0313062*m.i41*m.i62 + 0.0372108*
m.i41*m.i63 + 0.0192516*m.i41*m.i64 + 0.0152555*m.i41*m.i65 + 0.01848886*m.i41*m.i66 + 0.01396382
*m.i41*m.i67 + 0.01323774*m.i41*m.i68 + 0.0319484*m.i41*m.i69 + 0.01505338*m.i41*m.i70 +
0.0464724*m.i41*m.i71 + 0.0275962*m.i41*m.i72 + 0.01531976*m.i41*m.i73 + 0.0159052*m.i41*m.i74 +
0.00897454*m.i41*m.i75 + 0.00931212*m.i41*m.i76 + 0.01958562*m.i41*m.i77 + 0.00344582*m.i41*m.i78
+ 0.00874906*m.i41*m.i79 + 0.01063594*m.i41*m.i80 + 0.02994*m.i41*m.i81 + 0.000668906*m.i41*
m.i82 + 0.0436128*m.i41*m.i83 + 0.0233408*m.i41*m.i84 + 0.00754018*m.i41*m.i85 + 0.01805636*m.i41
*m.i86 + 0.01281402*m.i41*m.i87 + 0.0523726*m.i41*m.i88 + 0.00844562*m.i41*m.i89 + 0.01302218*
m.i41*m.i90 + 0.01396562*m.i41*m.i91 + 0.01458222*m.i41*m.i92 + 0.0072903*m.i41*m.i93 +
0.00709746*m.i41*m.i94 + 0.01473562*m.i41*m.i95 + 0.01085782*m.i41*m.i96 + 0.021406*m.i41*m.i97
+ 0.0295828*m.i41*m.i98 + 0.01994264*m.i41*m.i99 + 0.0263314*m.i41*m.i100 + 0.01525376*m.i42*
m.i43 + 0.01763084*m.i42*m.i44 + 0.00749008*m.i42*m.i45 + 0.00916454*m.i42*m.i46 + 0.0235102*
m.i42*m.i47 + 0.00921988*m.i42*m.i48 + 0.01347394*m.i42*m.i49 + 0.0247352*m.i42*m.i50 +
0.01120346*m.i42*m.i51 + 0.01858118*m.i42*m.i52 + 0.01723882*m.i42*m.i53 + 0.0208142*m.i42*m.i54
+ 0.01360838*m.i42*m.i55 + 0.0118194*m.i42*m.i56 + 0.00860676*m.i42*m.i57 + 0.00935934*m.i42*
m.i58 + 0.01516418*m.i42*m.i59 + 0.0068076*m.i42*m.i60 + 0.028779*m.i42*m.i61 + 0.0258494*m.i42*
m.i62 + 0.0233604*m.i42*m.i63 + 0.01573382*m.i42*m.i64 + 0.01049188*m.i42*m.i65 + 0.00740748*
m.i42*m.i66 + 0.01082116*m.i42*m.i67 + 0.00777482*m.i42*m.i68 + 0.0240088*m.i42*m.i69 +
0.01102072*m.i42*m.i70 + 0.01820862*m.i42*m.i71 + 0.01298112*m.i42*m.i72 + 0.01234456*m.i42*m.i73
+ 0.0141652*m.i42*m.i74 + 0.00934936*m.i42*m.i75 + 0.00505832*m.i42*m.i76 + 0.01458566*m.i42*
m.i77 + 0.00728638*m.i42*m.i78 + 0.0099359*m.i42*m.i79 + 0.01486474*m.i42*m.i80 + 0.01668502*
m.i42*m.i81 + 0.00373442*m.i42*m.i82 + 0.01190258*m.i42*m.i83 + 0.01201006*m.i42*m.i84 +
0.0151776*m.i42*m.i85 + 0.0145938*m.i42*m.i86 + 0.00824462*m.i42*m.i87 + 0.0160982*m.i42*m.i88 +
0.006593*m.i42*m.i89 + 0.01418496*m.i42*m.i90 + 0.01803698*m.i42*m.i91 + 0.0159653*m.i42*m.i92 +
0.00291508*m.i42*m.i93 + 0.00538746*m.i42*m.i94 + 0.01644022*m.i42*m.i95 + 0.0250208*m.i42*m.i96
+ 0.018306*m.i42*m.i97 + 0.01797718*m.i42*m.i98 + 0.01649756*m.i42*m.i99 + 0.025412*m.i42*m.i100
+ 0.01762524*m.i43*m.i44 + 0.0026577*m.i43*m.i45 + 0.00500594*m.i43*m.i46 + 0.01987672*m.i43*
m.i47 + 0.00486026*m.i43*m.i48 + 0.01054502*m.i43*m.i49 + 0.00887754*m.i43*m.i50 + 0.00693606*
m.i43*m.i51 + 0.01006578*m.i43*m.i52 + 0.01002454*m.i43*m.i53 + 0.0138188*m.i43*m.i54 +
0.00975298*m.i43*m.i55 + 0.01686962*m.i43*m.i56 + 0.00490722*m.i43*m.i57 + 0.00949952*m.i43*m.i58
+ 0.01032096*m.i43*m.i59 + 0.00313858*m.i43*m.i60 + 0.01509816*m.i43*m.i61 + 0.0162044*m.i43*
m.i62 + 0.01875628*m.i43*m.i63 + 0.01240346*m.i43*m.i64 + 0.0085184*m.i43*m.i65 + 0.0097536*m.i43
*m.i66 + 0.00601436*m.i43*m.i67 + 0.0069333*m.i43*m.i68 + 0.01534648*m.i43*m.i69 + 0.00585324*
m.i43*m.i70 + 0.01833662*m.i43*m.i71 + 0.01219044*m.i43*m.i72 + 0.00997222*m.i43*m.i73 +
0.00950324*m.i43*m.i74 + 0.00395808*m.i43*m.i75 + 0.00230734*m.i43*m.i76 + 0.01177946*m.i43*m.i77
+ 0.00120913*m.i43*m.i78 + 0.00451336*m.i43*m.i79 + 0.0087064*m.i43*m.i80 + 0.01415418*m.i43*
m.i81 + 0.00158382*m.i43*m.i82 + 0.01934448*m.i43*m.i83 + 0.01332798*m.i43*m.i84 + 0.0073079*
m.i43*m.i85 + 0.01024086*m.i43*m.i86 + 0.00333288*m.i43*m.i87 + 0.01697646*m.i43*m.i88 +
0.00457426*m.i43*m.i89 + 0.00557218*m.i43*m.i90 + 0.0103559*m.i43*m.i91 + 0.00897022*m.i43*m.i92
+ 0.00315402*m.i43*m.i93 + 0.00504118*m.i43*m.i94 + 0.01075858*m.i43*m.i95 + 0.00678594*m.i43*
m.i96 + 0.01260626*m.i43*m.i97 + 0.0163881*m.i43*m.i98 + 0.01009846*m.i43*m.i99 + 0.01154306*
m.i43*m.i100 + 0.00483446*m.i44*m.i45 + 0.00652268*m.i44*m.i46 + 0.0242272*m.i44*m.i47 +
0.00478826*m.i44*m.i48 + 0.01685648*m.i44*m.i49 + 0.020425*m.i44*m.i50 + 0.00923526*m.i44*m.i51
+ 0.01214276*m.i44*m.i52 + 0.01807778*m.i44*m.i53 + 0.01714928*m.i44*m.i54 + 0.0117815*m.i44*
m.i55 + 0.01675568*m.i44*m.i56 + 0.0065756*m.i44*m.i57 + 0.01226174*m.i44*m.i58 + 0.0107529*m.i44
*m.i59 + 0.00316098*m.i44*m.i60 + 0.0237412*m.i44*m.i61 + 0.023095*m.i44*m.i62 + 0.0261176*m.i44*
m.i63 + 0.01217274*m.i44*m.i64 + 0.01008618*m.i44*m.i65 + 0.0100818*m.i44*m.i66 + 0.01058518*
m.i44*m.i67 + 0.00547734*m.i44*m.i68 + 0.0242058*m.i44*m.i69 + 0.01131642*m.i44*m.i70 + 0.0238346
*m.i44*m.i71 + 0.01469328*m.i44*m.i72 + 0.01153818*m.i44*m.i73 + 0.0107527*m.i44*m.i74 +
0.00664436*m.i44*m.i75 + 0.00643936*m.i44*m.i76 + 0.01819866*m.i44*m.i77 + 0.00401038*m.i44*m.i78
+ 0.00860378*m.i44*m.i79 + 0.01052694*m.i44*m.i80 + 0.01791956*m.i44*m.i81 + 0.001302356*m.i44*
m.i82 + 0.024415*m.i44*m.i83 + 0.01318656*m.i44*m.i84 + 0.00691488*m.i44*m.i85 + 0.0134211*m.i44*
m.i86 + 0.01005166*m.i44*m.i87 + 0.036692*m.i44*m.i88 + 0.00614716*m.i44*m.i89 + 0.0120958*m.i44*
m.i90 + 0.00884752*m.i44*m.i91 + 0.01296164*m.i44*m.i92 + 0.00513894*m.i44*m.i93 + 0.00596534*
m.i44*m.i94 + 0.01196692*m.i44*m.i95 + 0.01664976*m.i44*m.i96 + 0.01462126*m.i44*m.i97 +
0.0157382*m.i44*m.i98 + 0.01533824*m.i44*m.i99 + 0.0188597*m.i44*m.i100 + 0.00317774*m.i45*m.i46
+ 0.00420624*m.i45*m.i47 + 0.00199361*m.i45*m.i48 + 0.0050265*m.i45*m.i49 + 0.00894044*m.i45*
m.i50 + 0.00284776*m.i45*m.i51 + 0.00547162*m.i45*m.i52 + 0.00269966*m.i45*m.i53 + 0.0064379*
m.i45*m.i54 + 0.00472118*m.i45*m.i55 + 0.0042126*m.i45*m.i56 + 0.00394074*m.i45*m.i57 +
0.00265196*m.i45*m.i58 + 0.00448504*m.i45*m.i59 + 0.001797504*m.i45*m.i60 + 0.00867806*m.i45*
m.i61 + 0.00322858*m.i45*m.i62 + 0.00607352*m.i45*m.i63 + 0.00436738*m.i45*m.i64 + 0.00237578*
m.i45*m.i65 + 0.0044976*m.i45*m.i66 + 0.00181419*m.i45*m.i67 + 0.00495262*m.i45*m.i68 +
0.00570214*m.i45*m.i69 + 0.00422674*m.i45*m.i70 + 0.001748284*m.i45*m.i71 + 0.00347868*m.i45*
m.i72 + 0.00586478*m.i45*m.i73 + 0.00333902*m.i45*m.i74 + 0.0046385*m.i45*m.i75 + 0.001228842*
m.i45*m.i76 + 0.00595824*m.i45*m.i77 + 0.0027183*m.i45*m.i78 + 0.00108409*m.i45*m.i79 +
0.00761658*m.i45*m.i80 + 0.0005468*m.i45*m.i81 + 0.001647768*m.i45*m.i82 - 0.00572218*m.i45*m.i83
+ 0.00291394*m.i45*m.i84 + 0.00667112*m.i45*m.i85 + 0.00283124*m.i45*m.i86 + 0.00214236*m.i45*
m.i87 + 0.00913532*m.i45*m.i88 + 0.0031579*m.i45*m.i89 + 0.001671266*m.i45*m.i90 + 0.007457*m.i45
*m.i91 + 0.00539294*m.i45*m.i92 + 0.001548892*m.i45*m.i93 + 0.00325768*m.i45*m.i94 + 0.00415906*
m.i45*m.i95 + 0.00472416*m.i45*m.i96 + 0.00257908*m.i45*m.i97 + 0.00311904*m.i45*m.i98 -
0.00028754*m.i45*m.i99 + 0.00641254*m.i45*m.i100 + 0.00936266*m.i46*m.i47 + 0.00551424*m.i46*
m.i48 + 0.00665328*m.i46*m.i49 + 0.01254298*m.i46*m.i50 + 0.00457552*m.i46*m.i51 + 0.00723508*
m.i46*m.i52 + 0.01013924*m.i46*m.i53 + 0.00835722*m.i46*m.i54 + 0.00612552*m.i46*m.i55 +
0.00568528*m.i46*m.i56 + 0.00506602*m.i46*m.i57 + 0.00547684*m.i46*m.i58 + 0.00630834*m.i46*m.i59
+ 0.0034076*m.i46*m.i60 + 0.01269782*m.i46*m.i61 + 0.01056202*m.i46*m.i62 + 0.00905674*m.i46*
m.i63 + 0.00727642*m.i46*m.i64 + 0.0053986*m.i46*m.i65 + 0.00499194*m.i46*m.i66 + 0.00693256*
m.i46*m.i67 + 0.00384534*m.i46*m.i68 + 0.01113952*m.i46*m.i69 + 0.00571676*m.i46*m.i70 +
0.00918194*m.i46*m.i71 + 0.00582038*m.i46*m.i72 + 0.00587208*m.i46*m.i73 + 0.00927628*m.i46*m.i74
+ 0.00540062*m.i46*m.i75 + 0.00399822*m.i46*m.i76 + 0.00599102*m.i46*m.i77 + 0.00478388*m.i46*
m.i78 + 0.0052496*m.i46*m.i79 + 0.0080323*m.i46*m.i80 + 0.00786638*m.i46*m.i81 + 0.001854684*
m.i46*m.i82 + 0.00407872*m.i46*m.i83 + 0.00621788*m.i46*m.i84 + 0.00606418*m.i46*m.i85 +
0.00669516*m.i46*m.i86 + 0.00483036*m.i46*m.i87 + 0.00889994*m.i46*m.i88 + 0.00341184*m.i46*m.i89
+ 0.00883678*m.i46*m.i90 + 0.00699852*m.i46*m.i91 + 0.00577214*m.i46*m.i92 + 0.00238288*m.i46*
m.i93 + 0.001681122*m.i46*m.i94 + 0.00660328*m.i46*m.i95 + 0.0125098*m.i46*m.i96 + 0.00829924*
m.i46*m.i97 + 0.00843732*m.i46*m.i98 + 0.00930502*m.i46*m.i99 + 0.01141018*m.i46*m.i100 +
0.00622806*m.i47*m.i48 + 0.01275134*m.i47*m.i49 + 0.0219686*m.i47*m.i50 + 0.00559252*m.i47*m.i51
+ 0.014742*m.i47*m.i52 + 0.01293552*m.i47*m.i53 + 0.0202408*m.i47*m.i54 + 0.01276622*m.i47*m.i55
+ 0.0211842*m.i47*m.i56 + 0.00751862*m.i47*m.i57 + 0.01167596*m.i47*m.i58 + 0.0096102*m.i47*
m.i59 + 0.00476024*m.i47*m.i60 + 0.0291008*m.i47*m.i61 + 0.0293252*m.i47*m.i62 + 0.0218568*m.i47*
m.i63 + 0.01597818*m.i47*m.i64 + 0.01230724*m.i47*m.i65 + 0.01074494*m.i47*m.i66 + 0.01192482*
m.i47*m.i67 + 0.0072756*m.i47*m.i68 + 0.0259978*m.i47*m.i69 + 0.01196354*m.i47*m.i70 + 0.0346772*
m.i47*m.i71 + 0.01997802*m.i47*m.i72 + 0.0109755*m.i47*m.i73 + 0.01126216*m.i47*m.i74 +
0.00543986*m.i47*m.i75 + 0.00507998*m.i47*m.i76 + 0.01031016*m.i47*m.i77 + 0.0051788*m.i47*m.i78
+ 0.001275304*m.i47*m.i79 + 0.00993436*m.i47*m.i80 + 0.0302174*m.i47*m.i81 + 0.0025327*m.i47*
m.i82 + 0.0227778*m.i47*m.i83 + 0.01358392*m.i47*m.i84 + 0.01015524*m.i47*m.i85 + 0.01402648*
m.i47*m.i86 + 0.00789154*m.i47*m.i87 + 0.0151434*m.i47*m.i88 + 0.001278866*m.i47*m.i89 +
0.0158996*m.i47*m.i90 + 0.01154264*m.i47*m.i91 + 0.01393698*m.i47*m.i92 + 0.00304714*m.i47*m.i93
+ 0.00512466*m.i47*m.i94 + 0.01429612*m.i47*m.i95 + 0.01681572*m.i47*m.i96 + 0.01931984*m.i47*
m.i97 + 0.0267484*m.i47*m.i98 + 0.01797768*m.i47*m.i99 + 0.0282598*m.i47*m.i100 + 0.00546656*
m.i48*m.i49 + 0.01037534*m.i48*m.i50 + 0.00353598*m.i48*m.i51 + 0.00756044*m.i48*m.i52 +
0.01216498*m.i48*m.i53 + 0.00967664*m.i48*m.i54 + 0.00647364*m.i48*m.i55 + 0.00302706*m.i48*m.i56
+ 0.0053717*m.i48*m.i57 + 0.00577622*m.i48*m.i58 + 0.00544272*m.i48*m.i59 + 0.00352554*m.i48*
m.i60 + 0.01442968*m.i48*m.i61 + 0.0109524*m.i48*m.i62 + 0.00913756*m.i48*m.i63 + 0.00640136*
m.i48*m.i64 + 0.00303604*m.i48*m.i65 + 0.00380586*m.i48*m.i66 + 0.00547728*m.i48*m.i67 +
0.00370642*m.i48*m.i68 + 0.00883124*m.i48*m.i69 + 0.00549652*m.i48*m.i70 + 0.00566248*m.i48*m.i71
+ 0.00467596*m.i48*m.i72 + 0.00529964*m.i48*m.i73 + 0.00953518*m.i48*m.i74 + 0.00623786*m.i48*
m.i75 + 0.00402142*m.i48*m.i76 + 0.00662892*m.i48*m.i77 + 0.004711*m.i48*m.i78 + 0.001686804*
m.i48*m.i79 + 0.00761384*m.i48*m.i80 + 0.0057658*m.i48*m.i81 + 0.00181049*m.i48*m.i82 -
0.00054847*m.i48*m.i83 + 0.0048793*m.i48*m.i84 + 0.00598068*m.i48*m.i85 + 0.00652398*m.i48*m.i86
+ 0.0036324*m.i48*m.i87 + 0.00674584*m.i48*m.i88 + 0.00354232*m.i48*m.i89 + 0.00923644*m.i48*
m.i90 + 0.01247554*m.i48*m.i91 + 0.00613734*m.i48*m.i92 + 0.000820814*m.i48*m.i93 + 0.001893008*
m.i48*m.i94 + 0.00690274*m.i48*m.i95 + 0.01623126*m.i48*m.i96 + 0.00810288*m.i48*m.i97 +
0.00702362*m.i48*m.i98 + 0.01027006*m.i48*m.i99 + 0.01224198*m.i48*m.i100 + 0.01829412*m.i49*
m.i50 + 0.0119479*m.i49*m.i51 + 0.01038228*m.i49*m.i52 + 0.01375438*m.i49*m.i53 + 0.01480194*
m.i49*m.i54 + 0.01103368*m.i49*m.i55 + 0.01464938*m.i49*m.i56 + 0.00724638*m.i49*m.i57 +
0.00857364*m.i49*m.i58 + 0.0149174*m.i49*m.i59 + 0.00407556*m.i49*m.i60 + 0.0214208*m.i49*m.i61
+ 0.01655784*m.i49*m.i62 + 0.01832206*m.i49*m.i63 + 0.0099515*m.i49*m.i64 + 0.01025382*m.i49*
m.i65 + 0.00862324*m.i49*m.i66 + 0.00863512*m.i49*m.i67 + 0.0076467*m.i49*m.i68 + 0.0220404*m.i49
*m.i69 + 0.0095053*m.i49*m.i70 + 0.01307838*m.i49*m.i71 + 0.01047408*m.i49*m.i72 + 0.01294838*
m.i49*m.i73 + 0.01471132*m.i49*m.i74 + 0.00851398*m.i49*m.i75 + 0.00575748*m.i49*m.i76 +
0.0145716*m.i49*m.i77 + 0.00460678*m.i49*m.i78 + 0.01570596*m.i49*m.i79 + 0.00985226*m.i49*m.i80
+ 0.01023644*m.i49*m.i81 + 0.00369278*m.i49*m.i82 + 0.00860988*m.i49*m.i83 + 0.01393008*m.i49*
m.i84 + 0.00839504*m.i49*m.i85 + 0.01483048*m.i49*m.i86 + 0.01071222*m.i49*m.i87 + 0.0344974*
m.i49*m.i88 + 0.00962838*m.i49*m.i89 + 0.01169418*m.i49*m.i90 + 0.01045396*m.i49*m.i91 +
0.0095482*m.i49*m.i92 + 0.00539536*m.i49*m.i93 + 0.00663516*m.i49*m.i94 + 0.01120512*m.i49*m.i95
+ 0.01484196*m.i49*m.i96 + 0.0127009*m.i49*m.i97 + 0.01167858*m.i49*m.i98 + 0.01477446*m.i49*
m.i99 + 0.01842494*m.i49*m.i100 + 0.01663076*m.i50*m.i51 + 0.021828*m.i50*m.i52 + 0.029083*m.i50*
m.i53 + 0.0230518*m.i50*m.i54 + 0.01639088*m.i50*m.i55 + 0.01308142*m.i50*m.i56 + 0.01225642*
m.i50*m.i57 + 0.0094199*m.i50*m.i58 + 0.0222192*m.i50*m.i59 + 0.00884396*m.i50*m.i60 + 0.0415716*
m.i50*m.i61 + 0.032076*m.i50*m.i62 + 0.021259*m.i50*m.i63 + 0.01432872*m.i50*m.i64 + 0.01445944*
m.i50*m.i65 + 0.01098896*m.i50*m.i66 + 0.0219658*m.i50*m.i67 + 0.01066588*m.i50*m.i68 + 0.0354768
*m.i50*m.i69 + 0.01575178*m.i50*m.i70 + 0.01775054*m.i50*m.i71 + 0.01436852*m.i50*m.i72 +
0.01353572*m.i50*m.i73 + 0.01936092*m.i50*m.i74 + 0.01665002*m.i50*m.i75 + 0.00971184*m.i50*m.i76
+ 0.01642836*m.i50*m.i77 + 0.01382168*m.i50*m.i78 + 0.0341934*m.i50*m.i79 + 0.01843884*m.i50*
m.i80 + 0.01940942*m.i50*m.i81 + 0.00527464*m.i50*m.i82 + 0.00829608*m.i50*m.i83 + 0.0138699*
m.i50*m.i84 + 0.01840912*m.i50*m.i85 + 0.0210266*m.i50*m.i86 + 0.0205286*m.i50*m.i87 + 0.0451728*
m.i50*m.i88 + 0.01361116*m.i50*m.i89 + 0.0277252*m.i50*m.i90 + 0.01783032*m.i50*m.i91 +
0.01982086*m.i50*m.i92 + 0.00668064*m.i50*m.i93 + 0.00765962*m.i50*m.i94 + 0.01980832*m.i50*m.i95
+ 0.043863*m.i50*m.i96 + 0.0241266*m.i50*m.i97 + 0.0216094*m.i50*m.i98 + 0.0284306*m.i50*m.i99
+ 0.0308476*m.i50*m.i100 + 0.01058872*m.i51*m.i52 + 0.01279448*m.i51*m.i53 + 0.0112444*m.i51*
m.i54 + 0.00990216*m.i51*m.i55 + 0.00896022*m.i51*m.i56 + 0.00513818*m.i51*m.i57 + 0.00543454*
m.i51*m.i58 + 0.01870256*m.i51*m.i59 + 0.00309084*m.i51*m.i60 + 0.01767624*m.i51*m.i61 +
0.01208918*m.i51*m.i62 + 0.01086364*m.i51*m.i63 + 0.00670046*m.i51*m.i64 + 0.00877154*m.i51*m.i65
+ 0.00557174*m.i51*m.i66 + 0.00887856*m.i51*m.i67 + 0.00260902*m.i51*m.i68 + 0.01536338*m.i51*
m.i69 + 0.00483316*m.i51*m.i70 + 0.00448378*m.i51*m.i71 + 0.0043601*m.i51*m.i72 + 0.00929772*
m.i51*m.i73 + 0.00989476*m.i51*m.i74 + 0.00528028*m.i51*m.i75 + 0.00446022*m.i51*m.i76 +
0.00845848*m.i51*m.i77 + 0.00509916*m.i51*m.i78 + 0.0204202*m.i51*m.i79 + 0.00800384*m.i51*m.i80
+ 0.00529538*m.i51*m.i81 + 0.0038846*m.i51*m.i82 + 0.00772216*m.i51*m.i83 + 0.009979*m.i51*m.i84
+ 0.010097*m.i51*m.i85 + 0.0139755*m.i51*m.i86 + 0.01131734*m.i51*m.i87 + 0.02533*m.i51*m.i88 +
0.00621034*m.i51*m.i89 + 0.01160734*m.i51*m.i90 + 0.00843408*m.i51*m.i91 + 0.00995326*m.i51*m.i92
+ 0.00455616*m.i51*m.i93 + 0.00533468*m.i51*m.i94 + 0.00929878*m.i51*m.i95 + 0.0142337*m.i51*
m.i96 + 0.01066822*m.i51*m.i97 + 0.00526832*m.i51*m.i98 + 0.01737382*m.i51*m.i99 + 0.01465192*
m.i51*m.i100 + 0.01484222*m.i52*m.i53 + 0.0171371*m.i52*m.i54 + 0.01181392*m.i52*m.i55 +
0.00600344*m.i52*m.i56 + 0.00840878*m.i52*m.i57 + 0.0071463*m.i52*m.i58 + 0.01536778*m.i52*m.i59
+ 0.0071369*m.i52*m.i60 + 0.0280962*m.i52*m.i61 + 0.0210708*m.i52*m.i62 + 0.01590808*m.i52*m.i63
+ 0.01317442*m.i52*m.i64 + 0.0091774*m.i52*m.i65 + 0.0068045*m.i52*m.i66 + 0.01047574*m.i52*
m.i67 + 0.00882116*m.i52*m.i68 + 0.01759098*m.i52*m.i69 + 0.00927774*m.i52*m.i70 + 0.01307496*
m.i52*m.i71 + 0.0115876*m.i52*m.i72 + 0.01090888*m.i52*m.i73 + 0.0112976*m.i52*m.i74 + 0.00919952
*m.i52*m.i75 + 0.00611904*m.i52*m.i76 + 0.0126521*m.i52*m.i77 + 0.0063454*m.i52*m.i78 +
0.01337936*m.i52*m.i79 + 0.01210696*m.i52*m.i80 + 0.01264942*m.i52*m.i81 + 0.00476554*m.i52*m.i82
+ 0.01346924*m.i52*m.i83 + 0.01007318*m.i52*m.i84 + 0.0127267*m.i52*m.i85 + 0.01394736*m.i52*
m.i86 + 0.0099746*m.i52*m.i87 + 0.0311922*m.i52*m.i88 + 0.0079236*m.i52*m.i89 + 0.01182038*m.i52*
m.i90 + 0.01651678*m.i52*m.i91 + 0.01241554*m.i52*m.i92 + 0.0030009*m.i52*m.i93 + 0.00533038*
m.i52*m.i94 + 0.0132025*m.i52*m.i95 + 0.0243106*m.i52*m.i96 + 0.01594256*m.i52*m.i97 + 0.01260958
*m.i52*m.i98 + 0.0156343*m.i52*m.i99 + 0.01771086*m.i52*m.i100 + 0.0153737*m.i53*m.i54 +
0.01383672*m.i53*m.i55 + 0.00715324*m.i53*m.i56 + 0.00943676*m.i53*m.i57 + 0.00990018*m.i53*m.i58
+ 0.01573366*m.i53*m.i59 + 0.00657884*m.i53*m.i60 + 0.0319944*m.i53*m.i61 + 0.029398*m.i53*m.i62
+ 0.01378922*m.i53*m.i63 + 0.01107682*m.i53*m.i64 + 0.01095454*m.i53*m.i65 + 0.00681218*m.i53*
m.i66 + 0.01767184*m.i53*m.i67 + 0.00360916*m.i53*m.i68 + 0.0271974*m.i53*m.i69 + 0.01108326*
m.i53*m.i70 + 0.00659666*m.i53*m.i71 + 0.00877032*m.i53*m.i72 + 0.01135242*m.i53*m.i73 +
0.01814298*m.i53*m.i74 + 0.01264072*m.i53*m.i75 + 0.00851402*m.i53*m.i76 + 0.01433306*m.i53*m.i77
+ 0.00973382*m.i53*m.i78 + 0.025286*m.i53*m.i79 + 0.01345344*m.i53*m.i80 + 0.01259382*m.i53*
m.i81 + 0.0027805*m.i53*m.i82 + 0.000307752*m.i53*m.i83 + 0.0107134*m.i53*m.i84 + 0.01054482*
m.i53*m.i85 + 0.0158905*m.i53*m.i86 + 0.01354224*m.i53*m.i87 + 0.0304602*m.i53*m.i88 + 0.0090225*
m.i53*m.i89 + 0.0279162*m.i53*m.i90 + 0.01259072*m.i53*m.i91 + 0.01154418*m.i53*m.i92 +
0.00696904*m.i53*m.i93 + 0.0036836*m.i53*m.i94 + 0.01605638*m.i53*m.i95 + 0.0430698*m.i53*m.i96
+ 0.01780592*m.i53*m.i97 + 0.01137144*m.i53*m.i98 + 0.0256234*m.i53*m.i99 + 0.0212362*m.i53*
m.i100 + 0.01304758*m.i54*m.i55 + 0.01398616*m.i54*m.i56 + 0.00915664*m.i54*m.i57 + 0.01070596*
m.i54*m.i58 + 0.01499*m.i54*m.i59 + 0.0070249*m.i54*m.i60 + 0.0302542*m.i54*m.i61 + 0.0244214*
m.i54*m.i62 + 0.0228504*m.i54*m.i63 + 0.01378888*m.i54*m.i64 + 0.00915648*m.i54*m.i65 + 0.0089268
*m.i54*m.i66 + 0.010488*m.i54*m.i67 + 0.00997224*m.i54*m.i68 + 0.0229576*m.i54*m.i69 + 0.01077794
*m.i54*m.i70 + 0.01825372*m.i54*m.i71 + 0.01517784*m.i54*m.i72 + 0.01258444*m.i54*m.i73 +
0.01361126*m.i54*m.i74 + 0.01029832*m.i54*m.i75 + 0.00657472*m.i54*m.i76 + 0.01463254*m.i54*m.i77
+ 0.00613474*m.i54*m.i78 + 0.01201368*m.i54*m.i79 + 0.013126*m.i54*m.i80 + 0.01505614*m.i54*
m.i81 + 0.00467872*m.i54*m.i82 + 0.01050702*m.i54*m.i83 + 0.01265914*m.i54*m.i84 + 0.01318044*
m.i54*m.i85 + 0.01473222*m.i54*m.i86 + 0.01110614*m.i54*m.i87 + 0.0261814*m.i54*m.i88 +
0.00783796*m.i54*m.i89 + 0.01294844*m.i54*m.i90 + 0.0192808*m.i54*m.i91 + 0.0139507*m.i54*m.i92
+ 0.00351228*m.i54*m.i93 + 0.0068612*m.i54*m.i94 + 0.01527036*m.i54*m.i95 + 0.0205052*m.i54*
m.i96 + 0.01688726*m.i54*m.i97 + 0.01524852*m.i54*m.i98 + 0.0174601*m.i54*m.i99 + 0.0244266*m.i54
*m.i100 + 0.00673562*m.i55*m.i56 + 0.00707698*m.i55*m.i57 + 0.00734322*m.i55*m.i58 + 0.01405048*
m.i55*m.i59 + 0.00334038*m.i55*m.i60 + 0.0222096*m.i55*m.i61 + 0.01523028*m.i55*m.i62 + 0.0102055
*m.i55*m.i63 + 0.01002768*m.i55*m.i64 + 0.01048288*m.i55*m.i65 + 0.00635712*m.i55*m.i66 +
0.00874464*m.i55*m.i67 + 0.00593524*m.i55*m.i68 + 0.01648812*m.i55*m.i69 + 0.0080135*m.i55*m.i70
+ 0.00887592*m.i55*m.i71 + 0.00847214*m.i55*m.i72 + 0.01055314*m.i55*m.i73 + 0.01129422*m.i55*
m.i74 + 0.00699156*m.i55*m.i75 + 0.00627446*m.i55*m.i76 + 0.01024268*m.i55*m.i77 + 0.00531432*
m.i55*m.i78 + 0.0098513*m.i55*m.i79 + 0.01065934*m.i55*m.i80 + 0.00967318*m.i55*m.i81 +
0.00462964*m.i55*m.i82 + 0.00334858*m.i55*m.i83 + 0.01100528*m.i55*m.i84 + 0.00975296*m.i55*m.i85
+ 0.01214742*m.i55*m.i86 + 0.00846042*m.i55*m.i87 + 0.0242638*m.i55*m.i88 + 0.0054702*m.i55*
m.i89 + 0.01124098*m.i55*m.i90 + 0.0118002*m.i55*m.i91 + 0.01077996*m.i55*m.i92 + 0.00250778*
m.i55*m.i93 + 0.00555816*m.i55*m.i94 + 0.01037364*m.i55*m.i95 + 0.0175302*m.i55*m.i96 +
0.01283314*m.i55*m.i97 + 0.01054116*m.i55*m.i98 + 0.01565736*m.i55*m.i99 + 0.01643682*m.i55*
m.i100 + 0.00563824*m.i56*m.i57 + 0.00909602*m.i56*m.i58 + 0.0103611*m.i56*m.i59 + 0.00370386*
m.i56*m.i60 + 0.01345496*m.i56*m.i61 + 0.01240364*m.i56*m.i62 + 0.01894134*m.i56*m.i63 +
0.00842246*m.i56*m.i64 + 0.00913306*m.i56*m.i65 + 0.0128603*m.i56*m.i66 + 0.00789202*m.i56*m.i67
+ 0.0049437*m.i56*m.i68 + 0.0172921*m.i56*m.i69 + 0.00742364*m.i56*m.i70 + 0.0201228*m.i56*m.i71
+ 0.0118952*m.i56*m.i72 + 0.01088666*m.i56*m.i73 + 0.0107701*m.i56*m.i74 + 0.00409754*m.i56*
m.i75 + 0.00366002*m.i56*m.i76 + 0.01236854*m.i56*m.i77 + 0.00300872*m.i56*m.i78 + 0.0135613*
m.i56*m.i79 + 0.00480806*m.i56*m.i80 + 0.01596128*m.i56*m.i81 + 0.00309564*m.i56*m.i82 +
0.01777436*m.i56*m.i83 + 0.01193038*m.i56*m.i84 + 0.00565974*m.i56*m.i85 + 0.01170688*m.i56*m.i86
+ 0.01022376*m.i56*m.i87 + 0.0163427*m.i56*m.i88 + 0.00612568*m.i56*m.i89 + 0.01115784*m.i56*
m.i90 + 0.00381802*m.i56*m.i91 + 0.0089326*m.i56*m.i92 + 0.0075443*m.i56*m.i93 + 0.00818402*m.i56
*m.i94 + 0.00966992*m.i56*m.i95 + 0.00265106*m.i56*m.i96 + 0.01019204*m.i56*m.i97 + 0.01329902*
m.i56*m.i98 + 0.01411634*m.i56*m.i99 + 0.0138779*m.i56*m.i100 + 0.00474894*m.i57*m.i58 +
0.00767974*m.i57*m.i59 + 0.0043561*m.i57*m.i60 + 0.01478228*m.i57*m.i61 + 0.00989558*m.i57*m.i62
+ 0.00895424*m.i57*m.i63 + 0.0066828*m.i57*m.i64 + 0.00578744*m.i57*m.i65 + 0.00498864*m.i57*
m.i66 + 0.00614268*m.i57*m.i67 + 0.0054738*m.i57*m.i68 + 0.01078148*m.i57*m.i69 + 0.00688352*
m.i57*m.i70 + 0.0068114*m.i57*m.i71 + 0.00628102*m.i57*m.i72 + 0.00701898*m.i57*m.i73 +
0.00848154*m.i57*m.i74 + 0.0066742*m.i57*m.i75 + 0.00450208*m.i57*m.i76 + 0.0074907*m.i57*m.i77
+ 0.00457588*m.i57*m.i78 + 0.00668368*m.i57*m.i79 + 0.00806954*m.i57*m.i80 + 0.00702352*m.i57*
m.i81 + 0.0038917*m.i57*m.i82 + 0.000255196*m.i57*m.i83 + 0.00565464*m.i57*m.i84 + 0.00629044*
m.i57*m.i85 + 0.00649918*m.i57*m.i86 + 0.00619514*m.i57*m.i87 + 0.01578988*m.i57*m.i88 +
0.00523946*m.i57*m.i89 + 0.00717944*m.i57*m.i90 + 0.0080494*m.i57*m.i91 + 0.00534064*m.i57*m.i92
+ 0.00276512*m.i57*m.i93 + 0.00412012*m.i57*m.i94 + 0.00715034*m.i57*m.i95 + 0.01300638*m.i57*
m.i96 + 0.00826382*m.i57*m.i97 + 0.0068466*m.i57*m.i98 + 0.00897648*m.i57*m.i99 + 0.01037138*
m.i57*m.i100 + 0.00646004*m.i58*m.i59 + 0.00186599*m.i58*m.i60 + 0.01246886*m.i58*m.i61 +
0.00999352*m.i58*m.i62 + 0.01381952*m.i58*m.i63 + 0.00855014*m.i58*m.i64 + 0.00465434*m.i58*m.i65
+ 0.00825376*m.i58*m.i66 + 0.00576402*m.i58*m.i67 + 0.00273548*m.i58*m.i68 + 0.01035762*m.i58*
m.i69 + 0.004824*m.i58*m.i70 + 0.01355144*m.i58*m.i71 + 0.00700278*m.i58*m.i72 + 0.00707718*m.i58
*m.i73 + 0.00851974*m.i58*m.i74 + 0.00330912*m.i58*m.i75 + 0.00401842*m.i58*m.i76 + 0.00999942*
m.i58*m.i77 + 0.00277578*m.i58*m.i78 - 0.000989722*m.i58*m.i79 + 0.00742188*m.i58*m.i80 +
0.00901096*m.i58*m.i81 + 0.000981242*m.i58*m.i82 + 0.01290728*m.i58*m.i83 + 0.0083181*m.i58*m.i84
+ 0.00517936*m.i58*m.i85 + 0.00723458*m.i58*m.i86 + 0.0044253*m.i58*m.i87 + 0.0137847*m.i58*
m.i88 + 0.001547694*m.i58*m.i89 + 0.00582604*m.i58*m.i90 + 0.00844516*m.i58*m.i91 + 0.00776542*
m.i58*m.i92 + 0.00182761*m.i58*m.i93 + 0.0023829*m.i58*m.i94 + 0.00628056*m.i58*m.i95 +
0.00690478*m.i58*m.i96 + 0.00802988*m.i58*m.i97 + 0.0076502*m.i58*m.i98 + 0.01085276*m.i58*m.i99
+ 0.0112764*m.i58*m.i100 + 0.00476864*m.i59*m.i60 + 0.025812*m.i59*m.i61 + 0.01805478*m.i59*
m.i62 + 0.0109551*m.i59*m.i63 + 0.00938908*m.i59*m.i64 + 0.01178962*m.i59*m.i65 + 0.0076335*m.i59
*m.i66 + 0.01177666*m.i59*m.i67 + 0.0070214*m.i59*m.i68 + 0.0221478*m.i59*m.i69 + 0.007972*m.i59*
m.i70 + 0.0074733*m.i59*m.i71 + 0.0088486*m.i59*m.i72 + 0.01271666*m.i59*m.i73 + 0.0141508*m.i59*
m.i74 + 0.00914726*m.i59*m.i75 + 0.00537448*m.i59*m.i76 + 0.01084216*m.i59*m.i77 + 0.0073258*
m.i59*m.i78 + 0.0246694*m.i59*m.i79 + 0.01112936*m.i59*m.i80 + 0.00816652*m.i59*m.i81 +
0.00597972*m.i59*m.i82 + 0.00662172*m.i59*m.i83 + 0.01458364*m.i59*m.i84 + 0.01429256*m.i59*m.i85
+ 0.01882618*m.i59*m.i86 + 0.01439702*m.i59*m.i87 + 0.034478*m.i59*m.i88 + 0.0080275*m.i59*m.i89
+ 0.01623632*m.i59*m.i90 + 0.01482176*m.i59*m.i91 + 0.01127396*m.i59*m.i92 + 0.00550568*m.i59*
m.i93 + 0.00798042*m.i59*m.i94 + 0.01294416*m.i59*m.i95 + 0.0212862*m.i59*m.i96 + 0.01627426*
m.i59*m.i97 + 0.0106876*m.i59*m.i98 + 0.021021*m.i59*m.i99 + 0.0210024*m.i59*m.i100 + 0.01016558*
m.i60*m.i61 + 0.00950624*m.i60*m.i62 + 0.00759926*m.i60*m.i63 + 0.00405624*m.i60*m.i64 +
0.00408766*m.i60*m.i65 + 0.001012866*m.i60*m.i66 + 0.00434698*m.i60*m.i67 + 0.00457798*m.i60*
m.i68 + 0.0080193*m.i60*m.i69 + 0.0054101*m.i60*m.i70 + 0.0046192*m.i60*m.i71 + 0.00570946*m.i60*
m.i72 + 0.00452172*m.i60*m.i73 + 0.00634618*m.i60*m.i74 + 0.00624388*m.i60*m.i75 + 0.0033187*
m.i60*m.i76 + 0.00483228*m.i60*m.i77 + 0.00344686*m.i60*m.i78 + 0.0083673*m.i60*m.i79 +
0.00518592*m.i60*m.i80 + 0.00542166*m.i60*m.i81 + 0.0031059*m.i60*m.i82 - 0.001025068*m.i60*m.i83
+ 0.0028835*m.i60*m.i84 + 0.00445296*m.i60*m.i85 + 0.00423572*m.i60*m.i86 + 0.0051822*m.i60*
m.i87 + 0.01112192*m.i60*m.i88 + 0.00500464*m.i60*m.i89 + 0.0062184*m.i60*m.i90 + 0.00602*m.i60*
m.i91 + 0.00246398*m.i60*m.i92 + 0.00288384*m.i60*m.i93 + 0.00278724*m.i60*m.i94 + 0.00626372*
m.i60*m.i95 + 0.01170704*m.i60*m.i96 + 0.00615192*m.i60*m.i97 + 0.00462302*m.i60*m.i98 +
0.00471294*m.i60*m.i99 + 0.00588256*m.i60*m.i100 + 0.0418718*m.i61*m.i62 + 0.0230598*m.i61*m.i63
+ 0.01842282*m.i61*m.i64 + 0.01721234*m.i61*m.i65 + 0.00990124*m.i61*m.i66 + 0.0216044*m.i61*
m.i67 + 0.01473812*m.i61*m.i68 + 0.0394464*m.i61*m.i69 + 0.01716988*m.i61*m.i70 + 0.0195513*m.i61
*m.i71 + 0.0219932*m.i61*m.i72 + 0.01943214*m.i61*m.i73 + 0.020134*m.i61*m.i74 + 0.0174732*m.i61*
m.i75 + 0.01174406*m.i61*m.i76 + 0.01834496*m.i61*m.i77 + 0.01109086*m.i61*m.i78 + 0.0264464*
m.i61*m.i79 + 0.01965936*m.i61*m.i80 + 0.0227546*m.i61*m.i81 + 0.00831452*m.i61*m.i82 +
0.00631004*m.i61*m.i83 + 0.01801602*m.i61*m.i84 + 0.01882322*m.i61*m.i85 + 0.026381*m.i61*m.i86
+ 0.0201168*m.i61*m.i87 + 0.0582994*m.i61*m.i88 + 0.01420784*m.i61*m.i89 + 0.0279352*m.i61*m.i90
+ 0.0260044*m.i61*m.i91 + 0.01994278*m.i61*m.i92 + 0.00558188*m.i61*m.i93 + 0.0100806*m.i61*
m.i94 + 0.0228614*m.i61*m.i95 + 0.0472894*m.i61*m.i96 + 0.0277624*m.i61*m.i97 + 0.0233414*m.i61*
m.i98 + 0.0320998*m.i61*m.i99 + 0.037788*m.i61*m.i100 + 0.0226754*m.i62*m.i63 + 0.01497022*m.i62*
m.i64 + 0.0138219*m.i62*m.i65 + 0.00559668*m.i62*m.i66 + 0.01850946*m.i62*m.i67 + 0.01131414*
m.i62*m.i68 + 0.0392412*m.i62*m.i69 + 0.01609634*m.i62*m.i70 + 0.0216048*m.i62*m.i71 + 0.0216526*
m.i62*m.i72 + 0.0150155*m.i62*m.i73 + 0.01738604*m.i62*m.i74 + 0.01374744*m.i62*m.i75 +
0.00779326*m.i62*m.i76 + 0.01429558*m.i62*m.i77 + 0.0081994*m.i62*m.i78 + 0.024889*m.i62*m.i79 +
0.01494124*m.i62*m.i80 + 0.0229898*m.i62*m.i81 + 0.00445144*m.i62*m.i82 + 0.01114552*m.i62*m.i83
+ 0.01793036*m.i62*m.i84 + 0.01444614*m.i62*m.i85 + 0.01879448*m.i62*m.i86 + 0.01466504*m.i62*
m.i87 + 0.0326604*m.i62*m.i88 + 0.01169144*m.i62*m.i89 + 0.0254028*m.i62*m.i90 + 0.01965996*m.i62
*m.i91 + 0.01132102*m.i62*m.i92 + 0.0046546*m.i62*m.i93 + 0.00635342*m.i62*m.i94 + 0.0209304*
m.i62*m.i95 + 0.040751*m.i62*m.i96 + 0.0251822*m.i62*m.i97 + 0.0238578*m.i62*m.i98 + 0.0225858*
m.i62*m.i99 + 0.0313134*m.i62*m.i100 + 0.01545704*m.i63*m.i64 + 0.01086358*m.i63*m.i65 +
0.00996396*m.i63*m.i66 + 0.00982328*m.i63*m.i67 + 0.00892944*m.i63*m.i68 + 0.024956*m.i63*m.i69
+ 0.0125295*m.i63*m.i70 + 0.0274234*m.i63*m.i71 + 0.0136346*m.i63*m.i72 + 0.0143589*m.i63*m.i73
+ 0.01281966*m.i63*m.i74 + 0.009889*m.i63*m.i75 + 0.00617316*m.i63*m.i76 + 0.0195622*m.i63*m.i77
+ 0.00502572*m.i63*m.i78 + 0.00153262*m.i63*m.i79 + 0.01706792*m.i63*m.i80 + 0.01790944*m.i63*
m.i81 + 0.001490592*m.i63*m.i82 + 0.0267338*m.i63*m.i83 + 0.01586496*m.i63*m.i84 + 0.01166282*
m.i63*m.i85 + 0.01568614*m.i63*m.i86 + 0.00753188*m.i63*m.i87 + 0.0417782*m.i63*m.i88 + 0.0112216
*m.i63*m.i89 + 0.00371206*m.i63*m.i90 + 0.01829192*m.i63*m.i91 + 0.01841964*m.i63*m.i92 +
0.00206622*m.i63*m.i93 + 0.00505172*m.i63*m.i94 + 0.01487174*m.i63*m.i95 + 0.01414348*m.i63*m.i96
+ 0.0156802*m.i63*m.i97 + 0.01823426*m.i63*m.i98 + 0.01258764*m.i63*m.i99 + 0.01994098*m.i63*
m.i100 + 0.00854654*m.i64*m.i65 + 0.01079866*m.i64*m.i66 + 0.00602732*m.i64*m.i67 + 0.00921276*
m.i64*m.i68 + 0.01464414*m.i64*m.i69 + 0.00664932*m.i64*m.i70 + 0.0144736*m.i64*m.i71 +
0.00978338*m.i64*m.i72 + 0.00959208*m.i64*m.i73 + 0.0112566*m.i64*m.i74 + 0.00671142*m.i64*m.i75
+ 0.00408206*m.i64*m.i76 + 0.01167568*m.i64*m.i77 + 0.00375274*m.i64*m.i78 + 0.00404336*m.i64*
m.i79 + 0.00963238*m.i64*m.i80 + 0.0122908*m.i64*m.i81 + 0.001806772*m.i64*m.i82 + 0.01577266*
m.i64*m.i83 + 0.01128074*m.i64*m.i84 + 0.0095111*m.i64*m.i85 + 0.0097723*m.i64*m.i86 + 0.00346618
*m.i64*m.i87 + 0.01289324*m.i64*m.i88 + 0.00453186*m.i64*m.i89 + 0.0078486*m.i64*m.i90 +
0.01310134*m.i64*m.i91 + 0.00985686*m.i64*m.i92 + 0.00257788*m.i64*m.i93 + 0.00260324*m.i64*m.i94
+ 0.0108877*m.i64*m.i95 + 0.01349616*m.i64*m.i96 + 0.01306042*m.i64*m.i97 + 0.01405114*m.i64*
m.i98 + 0.0115142*m.i64*m.i99 + 0.01728302*m.i64*m.i100 + 0.0048225*m.i65*m.i66 + 0.00871696*
m.i65*m.i67 + 0.00504014*m.i65*m.i68 + 0.01673796*m.i65*m.i69 + 0.00728674*m.i65*m.i70 +
0.00969202*m.i65*m.i71 + 0.0082057*m.i65*m.i72 + 0.0103704*m.i65*m.i73 + 0.00998004*m.i65*m.i74
+ 0.00672722*m.i65*m.i75 + 0.00633346*m.i65*m.i76 + 0.00774852*m.i65*m.i77 + 0.00440922*m.i65*
m.i78 + 0.01343946*m.i65*m.i79 + 0.00798994*m.i65*m.i80 + 0.01225132*m.i65*m.i81 + 0.00444398*
m.i65*m.i82 + 0.00673302*m.i65*m.i83 + 0.0109598*m.i65*m.i84 + 0.00683186*m.i65*m.i85 +
0.01183874*m.i65*m.i86 + 0.0090907*m.i65*m.i87 + 0.0283952*m.i65*m.i88 + 0.00785096*m.i65*m.i89
+ 0.01125058*m.i65*m.i90 + 0.00510526*m.i65*m.i91 + 0.00837574*m.i65*m.i92 + 0.00385798*m.i65*
m.i93 + 0.00464904*m.i65*m.i94 + 0.00896456*m.i65*m.i95 + 0.0160694*m.i65*m.i96 + 0.0113557*m.i65
*m.i97 + 0.01155766*m.i65*m.i98 + 0.01443876*m.i65*m.i99 + 0.01238186*m.i65*m.i100 + 0.00548536*
m.i66*m.i67 + 0.00630564*m.i66*m.i68 + 0.00939978*m.i66*m.i69 + 0.00431468*m.i66*m.i70 +
0.01542742*m.i66*m.i71 + 0.0071665*m.i66*m.i72 + 0.00755022*m.i66*m.i73 + 0.00838922*m.i66*m.i74
+ 0.00386922*m.i66*m.i75 + 0.001951058*m.i66*m.i76 + 0.01146338*m.i66*m.i77 + 0.001980078*m.i66*
m.i78 + 0.00444902*m.i66*m.i79 + 0.00356762*m.i66*m.i80 + 0.00956806*m.i66*m.i81 - 0.00023183*
m.i66*m.i82 + 0.01703884*m.i66*m.i83 + 0.01002452*m.i66*m.i84 + 0.0062546*m.i66*m.i85 +
0.00563304*m.i66*m.i86 + 0.00514984*m.i66*m.i87 + 0.01908326*m.i66*m.i88 + 0.00457928*m.i66*m.i89
+ 0.003995*m.i66*m.i90 + 0.0080501*m.i66*m.i91 + 0.00810108*m.i66*m.i92 + 0.00328186*m.i66*m.i93
+ 0.00369064*m.i66*m.i94 + 0.0058103*m.i66*m.i95 + 0.00438208*m.i66*m.i96 + 0.00867896*m.i66*
m.i97 + 0.0114927*m.i66*m.i98 + 0.01103938*m.i66*m.i99 + 0.00981454*m.i66*m.i100 + 0.00310364*
m.i67*m.i68 + 0.0195756*m.i67*m.i69 + 0.00833924*m.i67*m.i70 + 0.01122*m.i67*m.i71 + 0.00862168*
m.i67*m.i72 + 0.00711248*m.i67*m.i73 + 0.00958304*m.i67*m.i74 + 0.00671208*m.i67*m.i75 +
0.00667666*m.i67*m.i76 + 0.00639998*m.i67*m.i77 + 0.00746068*m.i67*m.i78 + 0.0164696*m.i67*m.i79
+ 0.00952472*m.i67*m.i80 + 0.01054908*m.i67*m.i81 + 0.00295206*m.i67*m.i82 + 0.00786538*m.i67*
m.i83 + 0.00812566*m.i67*m.i84 + 0.00774908*m.i67*m.i85 + 0.01084866*m.i67*m.i86 + 0.01179554*
m.i67*m.i87 + 0.022894*m.i67*m.i88 + 0.00619526*m.i67*m.i89 + 0.01517056*m.i67*m.i90 + 0.00567344
*m.i67*m.i91 + 0.00901318*m.i67*m.i92 + 0.00388018*m.i67*m.i93 + 0.0036956*m.i67*m.i94 + 0.008896
*m.i67*m.i95 + 0.021896*m.i67*m.i96 + 0.01327636*m.i67*m.i97 + 0.0109*m.i67*m.i98 + 0.0178563*
m.i67*m.i99 + 0.01328366*m.i67*m.i100 + 0.01361686*m.i68*m.i69 + 0.00764086*m.i68*m.i70 +
0.00794036*m.i68*m.i71 + 0.01077146*m.i68*m.i72 + 0.00701056*m.i68*m.i73 + 0.00764336*m.i68*m.i74
+ 0.01085638*m.i68*m.i75 + 0.00267198*m.i68*m.i76 + 0.00622086*m.i68*m.i77 + 0.0026961*m.i68*
m.i78 + 0.01283914*m.i68*m.i79 + 0.00651186*m.i68*m.i80 + 0.00444824*m.i68*m.i81 + 0.00245108*
m.i68*m.i82 - 0.000724804*m.i68*m.i83 + 0.01001432*m.i68*m.i84 + 0.00659112*m.i68*m.i85 +
0.00798872*m.i68*m.i86 + 0.00378278*m.i68*m.i87 + 0.0249894*m.i68*m.i88 + 0.00935338*m.i68*m.i89
+ 0.00406214*m.i68*m.i90 + 0.01547864*m.i68*m.i91 + 0.0026383*m.i68*m.i92 + 0.001956366*m.i68*
m.i93 + 0.00433104*m.i68*m.i94 + 0.0086862*m.i68*m.i95 + 0.00871594*m.i68*m.i96 + 0.00917804*
m.i68*m.i97 + 0.01147728*m.i68*m.i98 + 0.000904318*m.i68*m.i99 + 0.0095902*m.i68*m.i100 +
0.01716134*m.i69*m.i70 + 0.0210128*m.i69*m.i71 + 0.01970512*m.i69*m.i72 + 0.01824406*m.i69*m.i73
+ 0.0202038*m.i69*m.i74 + 0.0166321*m.i69*m.i75 + 0.0080034*m.i69*m.i76 + 0.01785698*m.i69*m.i77
+ 0.00956708*m.i69*m.i78 + 0.0273938*m.i69*m.i79 + 0.01578286*m.i69*m.i80 + 0.01986548*m.i69*
m.i81 + 0.00472512*m.i69*m.i82 + 0.0064477*m.i69*m.i83 + 0.0205866*m.i69*m.i84 + 0.01485404*m.i69
*m.i85 + 0.0219926*m.i69*m.i86 + 0.01726592*m.i69*m.i87 + 0.044296*m.i69*m.i88 + 0.01519388*m.i69
*m.i89 + 0.0245318*m.i69*m.i90 + 0.019668*m.i69*m.i91 + 0.01322886*m.i69*m.i92 + 0.00622812*m.i69
*m.i93 + 0.00886068*m.i69*m.i94 + 0.0207946*m.i69*m.i95 + 0.0369544*m.i69*m.i96 + 0.0252064*m.i69
*m.i97 + 0.0246794*m.i69*m.i98 + 0.0240826*m.i69*m.i99 + 0.0322226*m.i69*m.i100 + 0.01122678*
m.i70*m.i71 + 0.00985708*m.i70*m.i72 + 0.00817346*m.i70*m.i73 + 0.01042594*m.i70*m.i74 +
0.0087512*m.i70*m.i75 + 0.00587552*m.i70*m.i76 + 0.00956692*m.i70*m.i77 + 0.00604702*m.i70*m.i78
+ 0.01012786*m.i70*m.i79 + 0.00894572*m.i70*m.i80 + 0.00937532*m.i70*m.i81 + 0.0040741*m.i70*
m.i82 + 0.001290572*m.i70*m.i83 + 0.00820512*m.i70*m.i84 + 0.00683756*m.i70*m.i85 + 0.00768078*
m.i70*m.i86 + 0.00827048*m.i70*m.i87 + 0.01990564*m.i70*m.i88 + 0.007123*m.i70*m.i89 + 0.00998564
*m.i70*m.i90 + 0.00953688*m.i70*m.i91 + 0.00558782*m.i70*m.i92 + 0.00342686*m.i70*m.i93 +
0.00568486*m.i70*m.i94 + 0.00914938*m.i70*m.i95 + 0.01630104*m.i70*m.i96 + 0.01110616*m.i70*m.i97
+ 0.010247*m.i70*m.i98 + 0.00833958*m.i70*m.i99 + 0.01265252*m.i70*m.i100 + 0.0220254*m.i71*
m.i72 + 0.0095213*m.i71*m.i73 + 0.01209936*m.i71*m.i74 + 0.00527094*m.i71*m.i75 + 0.00557218*
m.i71*m.i76 + 0.01262004*m.i71*m.i77 + 0.0037353*m.i71*m.i78 - 0.000223588*m.i71*m.i79 +
0.00801532*m.i71*m.i80 + 0.0286786*m.i71*m.i81 + 0.000788336*m.i71*m.i82 + 0.0387752*m.i71*m.i83
+ 0.01552284*m.i71*m.i84 + 0.00720994*m.i71*m.i85 + 0.01148132*m.i71*m.i86 + 0.00870698*m.i71*
m.i87 + 0.028675*m.i71*m.i88 + 0.00544718*m.i71*m.i89 + 0.00673884*m.i71*m.i90 + 0.01008984*m.i71
*m.i91 + 0.01241834*m.i71*m.i92 + 0.0025495*m.i71*m.i93 + 0.00280272*m.i71*m.i94 + 0.00947552*
m.i71*m.i95 + 0.0070495*m.i71*m.i96 + 0.0170916*m.i71*m.i97 + 0.0269036*m.i71*m.i98 + 0.01506306*
m.i71*m.i99 + 0.0206782*m.i71*m.i100 + 0.00925426*m.i72*m.i73 + 0.00967792*m.i72*m.i74 +
0.00847338*m.i72*m.i75 + 0.005213*m.i72*m.i76 + 0.00908662*m.i72*m.i77 + 0.00316872*m.i72*m.i78
+ 0.00898138*m.i72*m.i79 + 0.0069179*m.i72*m.i80 + 0.0151281*m.i72*m.i81 + 0.00348424*m.i72*
m.i82 + 0.01111986*m.i72*m.i83 + 0.01165966*m.i72*m.i84 + 0.0064802*m.i72*m.i85 + 0.00959246*
m.i72*m.i86 + 0.0084611*m.i72*m.i87 + 0.0240956*m.i72*m.i88 + 0.00687054*m.i72*m.i89 + 0.0094553*
m.i72*m.i90 + 0.0110757*m.i72*m.i91 + 0.00543508*m.i72*m.i92 + 0.0037306*m.i72*m.i93 + 0.00500972
*m.i72*m.i94 + 0.01005818*m.i72*m.i95 + 0.01294332*m.i72*m.i96 + 0.01344022*m.i72*m.i97 +
0.01593132*m.i72*m.i98 + 0.0093216*m.i72*m.i99 + 0.01640118*m.i72*m.i100 + 0.01198598*m.i73*m.i74
+ 0.00750544*m.i73*m.i75 + 0.0050216*m.i73*m.i76 + 0.01285904*m.i73*m.i77 + 0.00339452*m.i73*
m.i78 + 0.00891788*m.i73*m.i79 + 0.00948614*m.i73*m.i80 + 0.00944098*m.i73*m.i81 + 0.00409826*
m.i73*m.i82 + 0.00488372*m.i73*m.i83 + 0.01210326*m.i73*m.i84 + 0.00827726*m.i73*m.i85 +
0.0117403*m.i73*m.i86 + 0.00812428*m.i73*m.i87 + 0.031499*m.i73*m.i88 + 0.00909586*m.i73*m.i89 +
0.00829156*m.i73*m.i90 + 0.0112196*m.i73*m.i91 + 0.00781298*m.i73*m.i92 + 0.00380884*m.i73*m.i93
+ 0.00624296*m.i73*m.i94 + 0.01005016*m.i73*m.i95 + 0.01437472*m.i73*m.i96 + 0.011277*m.i73*
m.i97 + 0.01058622*m.i73*m.i98 + 0.0121454*m.i73*m.i99 + 0.01373088*m.i73*m.i100 + 0.01080198*
m.i74*m.i75 + 0.00656182*m.i74*m.i76 + 0.01437682*m.i74*m.i77 + 0.00746976*m.i74*m.i78 +
0.0158128*m.i74*m.i79 + 0.01161714*m.i74*m.i80 + 0.01098286*m.i74*m.i81 + 0.00409892*m.i74*m.i82
+ 0.00263806*m.i74*m.i83 + 0.01368742*m.i74*m.i84 + 0.00966578*m.i74*m.i85 + 0.0126469*m.i74*
m.i86 + 0.0097362*m.i74*m.i87 + 0.0236752*m.i74*m.i88 + 0.0087263*m.i74*m.i89 + 0.01653132*m.i74*
m.i90 + 0.01259886*m.i74*m.i91 + 0.0074886*m.i74*m.i92 + 0.00612882*m.i74*m.i93 + 0.00553384*
m.i74*m.i94 + 0.01256076*m.i74*m.i95 + 0.022837*m.i74*m.i96 + 0.01489052*m.i74*m.i97 + 0.0138654*
m.i74*m.i98 + 0.01608016*m.i74*m.i99 + 0.0185439*m.i74*m.i100 + 0.00483222*m.i75*m.i76 +
0.0084646*m.i75*m.i77 + 0.00605234*m.i75*m.i78 + 0.01627408*m.i75*m.i79 + 0.00784142*m.i75*m.i80
+ 0.00564276*m.i75*m.i81 + 0.00324588*m.i75*m.i82 - 0.00767236*m.i75*m.i83 + 0.00699372*m.i75*
m.i84 + 0.00737608*m.i75*m.i85 + 0.00954642*m.i75*m.i86 + 0.00823136*m.i75*m.i87 + 0.0262748*
m.i75*m.i88 + 0.00948902*m.i75*m.i89 + 0.01252876*m.i75*m.i90 + 0.01423104*m.i75*m.i91 +
0.00521492*m.i75*m.i92 + 0.00397698*m.i75*m.i93 + 0.00422896*m.i75*m.i94 + 0.01025216*m.i75*m.i95
+ 0.021456*m.i75*m.i96 + 0.01000128*m.i75*m.i97 + 0.00860654*m.i75*m.i98 + 0.0079023*m.i75*m.i99
+ 0.01223272*m.i75*m.i100 + 0.00508036*m.i76*m.i77 + 0.00440326*m.i76*m.i78 + 0.00722936*m.i76*
m.i79 + 0.00592748*m.i76*m.i80 + 0.00543106*m.i76*m.i81 + 0.00352072*m.i76*m.i82 + 0.00282876*
m.i76*m.i83 + 0.00421804*m.i76*m.i84 + 0.00327576*m.i76*m.i85 + 0.00605002*m.i76*m.i86 +
0.00724932*m.i76*m.i87 + 0.01581762*m.i76*m.i88 + 0.00366428*m.i76*m.i89 + 0.00812736*m.i76*m.i90
+ 0.00388382*m.i76*m.i91 + 0.0047062*m.i76*m.i92 + 0.00287772*m.i76*m.i93 + 0.00297876*m.i76*
m.i94 + 0.00459654*m.i76*m.i95 + 0.01070758*m.i76*m.i96 + 0.0061617*m.i76*m.i97 + 0.00324936*
m.i76*m.i98 + 0.00970994*m.i76*m.i99 + 0.00690694*m.i76*m.i100 + 0.00393188*m.i77*m.i78 +
0.00648912*m.i77*m.i79 + 0.00880144*m.i77*m.i80 + 0.00990674*m.i77*m.i81 + 0.00277832*m.i77*m.i82
+ 0.01369158*m.i77*m.i83 + 0.01108874*m.i77*m.i84 + 0.00804488*m.i77*m.i85 + 0.0100624*m.i77*
m.i86 + 0.00868852*m.i77*m.i87 + 0.0320896*m.i77*m.i88 + 0.00865688*m.i77*m.i89 + 0.00846622*
m.i77*m.i90 + 0.01262084*m.i77*m.i91 + 0.01111726*m.i77*m.i92 + 0.00462154*m.i77*m.i93 +
0.00718072*m.i77*m.i94 + 0.01147082*m.i77*m.i95 + 0.0176254*m.i77*m.i96 + 0.01224072*m.i77*m.i97
+ 0.00939734*m.i77*m.i98 + 0.012534*m.i77*m.i99 + 0.01295098*m.i77*m.i100 + 0.00907912*m.i78*
m.i79 + 0.00720642*m.i78*m.i80 + 0.00426806*m.i78*m.i81 + 0.0028332*m.i78*m.i82 - 0.001354666*
m.i78*m.i83 + 0.00318608*m.i78*m.i84 + 0.00627032*m.i78*m.i85 + 0.00574778*m.i78*m.i86 +
0.00663794*m.i78*m.i87 + 0.00493084*m.i78*m.i88 + 0.00225816*m.i78*m.i89 + 0.01063042*m.i78*m.i90
+ 0.0063342*m.i78*m.i91 + 0.00541402*m.i78*m.i92 + 0.00268782*m.i78*m.i93 + 0.00290288*m.i78*
m.i94 + 0.00588184*m.i78*m.i95 + 0.01436716*m.i78*m.i96 + 0.00728756*m.i78*m.i97 + 0.00442972*
m.i78*m.i98 + 0.00924454*m.i78*m.i99 + 0.00979098*m.i78*m.i100 + 0.0060581*m.i79*m.i80 +
0.00755126*m.i79*m.i81 + 0.00637932*m.i79*m.i82 - 0.00105651*m.i79*m.i83 + 0.01349704*m.i79*m.i84
+ 0.01178354*m.i79*m.i85 + 0.0220208*m.i79*m.i86 + 0.0245836*m.i79*m.i87 + 0.0524002*m.i79*m.i88
+ 0.0230428*m.i79*m.i89 + 0.0314514*m.i79*m.i90 + 0.00636018*m.i79*m.i91 + 0.0061917*m.i79*m.i92
+ 0.01207768*m.i79*m.i93 + 0.00753416*m.i79*m.i94 + 0.01719794*m.i79*m.i95 + 0.0367202*m.i79*
m.i96 + 0.01636496*m.i79*m.i97 + 0.01053626*m.i79*m.i98 + 0.0223148*m.i79*m.i99 + 0.0125583*m.i79
*m.i100 + 0.00731124*m.i80*m.i81 + 0.0043053*m.i80*m.i82 + 0.00250064*m.i80*m.i83 + 0.00942746*
m.i80*m.i84 + 0.01109824*m.i80*m.i85 + 0.0094077*m.i80*m.i86 + 0.00584688*m.i80*m.i87 +
0.01773876*m.i80*m.i88 + 0.00587054*m.i80*m.i89 + 0.0073899*m.i80*m.i90 + 0.01217556*m.i80*m.i91
+ 0.0092825*m.i80*m.i92 + 0.001672258*m.i80*m.i93 + 0.00403362*m.i80*m.i94 + 0.01001412*m.i80*
m.i95 + 0.01641906*m.i80*m.i96 + 0.01159292*m.i80*m.i97 + 0.01062798*m.i80*m.i98 + 0.00967468*
m.i80*m.i99 + 0.0140493*m.i80*m.i100 + 0.00288116*m.i81*m.i82 + 0.022981*m.i81*m.i83 + 0.01105584
*m.i81*m.i84 + 0.00722284*m.i81*m.i85 + 0.01178602*m.i81*m.i86 + 0.00945868*m.i81*m.i87 +
0.024973*m.i81*m.i88 + 0.00575624*m.i81*m.i89 + 0.01415098*m.i81*m.i90 + 0.0066048*m.i81*m.i91 +
0.01072344*m.i81*m.i92 + 0.00322326*m.i81*m.i93 + 0.00351188*m.i81*m.i94 + 0.01127788*m.i81*m.i95
+ 0.01956074*m.i81*m.i96 + 0.01617428*m.i81*m.i97 + 0.0227228*m.i81*m.i98 + 0.01855842*m.i81*
m.i99 + 0.01991896*m.i81*m.i100 - 0.00333172*m.i82*m.i83 + 0.00228114*m.i82*m.i84 + 0.00336158*
m.i82*m.i85 + 0.00354748*m.i82*m.i86 + 0.00514572*m.i82*m.i87 + 0.00636398*m.i82*m.i88 +
0.00276272*m.i82*m.i89 + 0.00394504*m.i82*m.i90 + 0.00242814*m.i82*m.i91 + 0.00151634*m.i82*m.i92
+ 0.00205258*m.i82*m.i93 + 0.00416174*m.i82*m.i94 + 0.0036601*m.i82*m.i95 + 0.00573294*m.i82*
m.i96 + 0.0040347*m.i82*m.i97 + 0.001040396*m.i82*m.i98 + 0.00519918*m.i82*m.i99 + 0.00479088*
m.i82*m.i100 + 0.01497528*m.i83*m.i84 + 0.0032291*m.i83*m.i85 + 0.01011148*m.i83*m.i86 +
0.00471364*m.i83*m.i87 + 0.0246434*m.i83*m.i88 + 0.000996878*m.i83*m.i89 - 0.00262512*m.i83*m.i90
- 0.000789784*m.i83*m.i91 + 0.01304756*m.i83*m.i92 + 0.000531142*m.i83*m.i93 - 0.000443948*m.i83
*m.i94 + 0.00279848*m.i83*m.i95 - 0.0065326*m.i83*m.i96 + 0.01221224*m.i83*m.i97 + 0.01799712*
m.i83*m.i98 + 0.0158385*m.i83*m.i99 + 0.0071337*m.i83*m.i100 + 0.00892568*m.i84*m.i85 +
0.01364388*m.i84*m.i86 + 0.0072533*m.i84*m.i87 + 0.0326884*m.i84*m.i88 + 0.00896504*m.i84*m.i89
+ 0.00823562*m.i84*m.i90 + 0.0125821*m.i84*m.i91 + 0.00787816*m.i84*m.i92 + 0.00249586*m.i84*
m.i93 + 0.00519262*m.i84*m.i94 + 0.01044988*m.i84*m.i95 + 0.01107886*m.i84*m.i96 + 0.0139867*
m.i84*m.i97 + 0.01596046*m.i84*m.i98 + 0.01218826*m.i84*m.i99 + 0.01543212*m.i84*m.i100 +
0.00990954*m.i85*m.i86 + 0.00725662*m.i85*m.i87 + 0.0133432*m.i85*m.i88 + 0.00507396*m.i85*m.i89
+ 0.00930526*m.i85*m.i90 + 0.01462284*m.i85*m.i91 + 0.01055408*m.i85*m.i92 + 0.00190258*m.i85*
m.i93 + 0.00468802*m.i85*m.i94 + 0.0107648*m.i85*m.i95 + 0.01646608*m.i85*m.i96 + 0.01215728*
m.i85*m.i97 + 0.01028698*m.i85*m.i98 + 0.01183266*m.i85*m.i99 + 0.01660366*m.i85*m.i100 +
0.0120373*m.i86*m.i87 + 0.0422718*m.i86*m.i88 + 0.00969238*m.i86*m.i89 + 0.01765146*m.i86*m.i90
+ 0.01429788*m.i86*m.i91 + 0.0124585*m.i86*m.i92 + 0.0040945*m.i86*m.i93 + 0.0046898*m.i86*m.i94
+ 0.01232074*m.i86*m.i95 + 0.0222548*m.i86*m.i96 + 0.0145479*m.i86*m.i97 + 0.0128277*m.i86*m.i98
+ 0.0192244*m.i86*m.i99 + 0.01947568*m.i86*m.i100 + 0.032904*m.i87*m.i88 + 0.0084843*m.i87*m.i89
+ 0.01591916*m.i87*m.i90 + 0.0059879*m.i87*m.i91 + 0.00789644*m.i87*m.i92 + 0.00607862*m.i87*
m.i93 + 0.00667478*m.i87*m.i94 + 0.0088746*m.i87*m.i95 + 0.01963916*m.i87*m.i96 + 0.01115822*
m.i87*m.i97 + 0.0065973*m.i87*m.i98 + 0.01821046*m.i87*m.i99 + 0.01269924*m.i87*m.i100 + 0.04164*
m.i88*m.i89 + 0.01700894*m.i88*m.i90 + 0.0282218*m.i88*m.i91 + 0.0247666*m.i88*m.i92 + 0.00860626
*m.i88*m.i93 + 0.0146832*m.i88*m.i94 + 0.0207292*m.i88*m.i95 + 0.0482992*m.i88*m.i96 + 0.026772*
m.i88*m.i97 + 0.0300758*m.i88*m.i98 + 0.0329128*m.i88*m.i99 + 0.01375988*m.i88*m.i100 +
0.00594302*m.i89*m.i90 + 0.00801468*m.i89*m.i91 + 0.00437824*m.i89*m.i92 + 0.00302882*m.i89*m.i93
+ 0.0041304*m.i89*m.i94 + 0.00803522*m.i89*m.i95 + 0.01620516*m.i89*m.i96 + 0.00836644*m.i89*
m.i97 + 0.01022328*m.i89*m.i98 + 0.0069101*m.i89*m.i99 + 0.00464412*m.i89*m.i100 + 0.01014268*
m.i90*m.i91 + 0.00890216*m.i90*m.i92 + 0.00857494*m.i90*m.i93 + 0.00416286*m.i90*m.i94 +
0.01435266*m.i90*m.i95 + 0.038709*m.i90*m.i96 + 0.01593092*m.i90*m.i97 + 0.0108455*m.i90*m.i98 +
0.0247362*m.i90*m.i99 + 0.0239224*m.i90*m.i100 + 0.01172504*m.i91*m.i92 - 3.25928e-5*m.i91*m.i93
+ 0.00582154*m.i91*m.i94 + 0.01455814*m.i91*m.i95 + 0.0217724*m.i91*m.i96 + 0.01520358*m.i91*
m.i97 + 0.01361584*m.i91*m.i98 + 0.01107608*m.i91*m.i99 + 0.0218082*m.i91*m.i100 + 0.000834202*
m.i92*m.i93 + 0.00361846*m.i92*m.i94 + 0.00964536*m.i92*m.i95 + 0.01621624*m.i92*m.i96 +
0.01139352*m.i92*m.i97 + 0.01032652*m.i92*m.i98 + 0.01663626*m.i92*m.i99 + 0.01551254*m.i92*
m.i100 + 0.00302326*m.i93*m.i94 + 0.0039602*m.i93*m.i95 + 0.0070366*m.i93*m.i96 + 0.0035814*m.i93
*m.i97 + 0.00156313*m.i93*m.i98 + 0.00599576*m.i93*m.i99 + 0.00427812*m.i93*m.i100 + 0.00550244*
m.i94*m.i95 + 0.00558508*m.i94*m.i96 + 0.0059384*m.i94*m.i97 + 0.00357124*m.i94*m.i98 + 0.0064057
*m.i94*m.i99 + 0.00623724*m.i94*m.i100 + 0.0227304*m.i95*m.i96 + 0.01445112*m.i95*m.i97 +
0.01257804*m.i95*m.i98 + 0.01368382*m.i95*m.i99 + 0.01773414*m.i95*m.i100 + 0.0257114*m.i96*m.i97
+ 0.01933344*m.i96*m.i98 + 0.0317874*m.i96*m.i99 + 0.0306278*m.i96*m.i100 + 0.01873902*m.i97*
m.i98 + 0.01912542*m.i97*m.i99 + 0.0219022*m.i97*m.i100 + 0.01388668*m.i98*m.i99 + 0.0207524*
m.i98*m.i100 + 0.0256994*m.i99*m.i100 - m.x101 <= 0)
m.c2 = Constraint(expr= 0.00311438*m.i1 - 0.0196628*m.i2 - 0.0134176*m.i3 - 0.00687102*m.i4 - 0.0147519*m.i5
- 0.0184501*m.i6 - 0.0153449*m.i7 - 0.136908*m.i8 + 0.0173991*m.i9 - 0.00159102*m.i10
- 0.0468625*m.i11 + 0.00163166*m.i12 - 0.00431355*m.i13 - 0.0377972*m.i14 - 0.0149845*m.i15
- 0.0104868*m.i16 + 0.0238532*m.i17 - 0.0104023*m.i18 + 0.0013017*m.i19 - 0.0474684*m.i20
- 0.00693531*m.i21 - 0.00667252*m.i22 - 0.0063525*m.i23 - 0.0205131*m.i24 - 0.00639281*m.i25
- 0.00085931*m.i26 - 0.0202418*m.i27 - 0.0104094*m.i28 - 0.00728791*m.i29 - 0.0650481*m.i30
+ 0.00379685*m.i31 - 0.00873524*m.i32 - 0.0191879*m.i33 - 0.0262863*m.i34 - 0.0148439*m.i35
- 0.0185713*m.i36 - 0.0097821*m.i37 - 0.0169321*m.i38 - 0.0126042*m.i39 + 0.0147787*m.i40
- 0.0212007*m.i41 - 0.0136018*m.i42 - 0.00404129*m.i43 - 0.01093*m.i44 - 0.0138447*m.i45
- 0.00281865*m.i46 - 0.0168853*m.i47 - 0.00610726*m.i48 - 0.00313898*m.i49 - 0.031707*m.i50
+ 0.00048868*m.i51 - 0.0135947*m.i52 - 0.00571196*m.i53 - 0.0158213*m.i54 - 0.00551418*m.i55
+ 7.4592E-5*m.i56 - 0.00372748*m.i57 + 0.00092127*m.i58 - 0.00743836*m.i59 + 0.00559625*m.i60
- 0.0170773*m.i61 - 0.0321089*m.i62 - 0.0230835*m.i63 - 0.0133205*m.i64 - 0.00788571*m.i65
- 0.0339356*m.i66 + 0.00227885*m.i67 - 0.010863*m.i68 - 0.0171333*m.i69 - 0.00515196*m.i70
- 0.0244616*m.i71 - 0.00205996*m.i72 + 0.00281383*m.i73 - 0.00173674*m.i74 - 0.0179568*m.i75
- 0.00659808*m.i76 - 0.0108104*m.i77 - 0.00557398*m.i78 - 0.0427583*m.i79 + 0.00183802*m.i80
- 0.0178204*m.i81 - 0.00328309*m.i82 - 0.0207823*m.i83 - 0.0110875*m.i84 - 0.0128258*m.i85
- 0.00442073*m.i86 - 0.00903049*m.i87 + 0.0203439*m.i88 - 0.0223604*m.i89 - 0.0149007*m.i90
- 0.0193623*m.i91 - 0.013037*m.i92 - 0.00297365*m.i93 - 0.0112456*m.i94 - 0.00469496*m.i95
- 0.00682019*m.i96 - 0.00327006*m.i97 - 0.0258562*m.i98 - 0.0215847*m.i99 - 0.0231142*m.i100
>= 0)
m.c3 = Constraint(expr= 52.59*m.i1 + 28.87*m.i2 + 29.19*m.i3 + 46.55*m.i4 + 24.26*m.i5 + 42.53*m.i6 + 40.53*m.i7
+ 79.56*m.i8 + 108.9*m.i9 + 79.06*m.i10 + 20.15*m.i11 + 35.64*m.i12 + 39.55*m.i13 + 14.32*m.i14
+ 26.41*m.i15 + 62.48*m.i16 + 254.3*m.i17 + 32.42*m.i18 + 24.84*m.i19 + 10.1*m.i20 + 21.2*m.i21
+ 40.25*m.i22 + 17.32*m.i23 + 60.92*m.i24 + 54.73*m.i25 + 78.62*m.i26 + 49.24*m.i27
+ 68.19*m.i28 + 50.3*m.i29 + 3.83*m.i30 + 18.27*m.i31 + 59.67*m.i32 + 12.21*m.i33 + 38.09*m.i34
+ 71.72*m.i35 + 23.6*m.i36 + 70.71*m.i37 + 56.98*m.i38 + 34.47*m.i39 + 10.23*m.i40 + 59.19*m.i41
+ 58.61*m.i42 + 445.29*m.i43 + 131.69*m.i44 + 34.24*m.i45 + 43.11*m.i46 + 25.18*m.i47 + 28*m.i48
+ 19.43*m.i49 + 14.33*m.i50 + 28.41*m.i51 + 74.5*m.i52 + 36.54*m.i53 + 38.99*m.i54 + 43.15*m.i55
+ 199.55*m.i56 + 59.07*m.i57 + 123.55*m.i58 + 20.55*m.i59 + 66.72*m.i60 + 37.95*m.i61
+ 27.62*m.i62 + 23.21*m.i63 + 36.09*m.i64 + 23.09*m.i65 + 46.54*m.i66 + 67.89*m.i67
+ 34.83*m.i68 + 11.96*m.i69 + 45.77*m.i70 + 32.91*m.i71 + 77.37*m.i72 + 21.46*m.i73
+ 53.11*m.i74 + 14.29*m.i75 + 61.13*m.i76 + 32.79*m.i77 + 59.84*m.i78 + 6.59*m.i79 + 14.06*m.i80
+ 55.29*m.i81 + 33.33*m.i82 + 4.24*m.i83 + 23.21*m.i84 + 47.85*m.i85 + 48.99*m.i86 + 57.46*m.i87
+ 28.87*m.i88 + 24.6*m.i89 + 22.26*m.i90 + 28.31*m.i91 + 26.67*m.i92 + 48.1*m.i93 + 28.01*m.i94
+ 64.85*m.i95 + 25.54*m.i96 + 31.47*m.i97 + 18.31*m.i98 + 35.06*m.i99 + 8.06*m.i100 >= 2000)
m.c4 = Constraint(expr= 52.59*m.i1 + 28.87*m.i2 + 29.19*m.i3 + 46.55*m.i4 + 24.26*m.i5 + 42.53*m.i6 + 40.53*m.i7
+ 79.56*m.i8 + 108.9*m.i9 + 79.06*m.i10 + 20.15*m.i11 + 35.64*m.i12 + 39.55*m.i13 + 14.32*m.i14
+ 26.41*m.i15 + 62.48*m.i16 + 254.3*m.i17 + 32.42*m.i18 + 24.84*m.i19 + 10.1*m.i20 + 21.2*m.i21
+ 40.25*m.i22 + 17.32*m.i23 + 60.92*m.i24 + 54.73*m.i25 + 78.62*m.i26 + 49.24*m.i27
+ 68.19*m.i28 + 50.3*m.i29 + 3.83*m.i30 + 18.27*m.i31 + 59.67*m.i32 + 12.21*m.i33 + 38.09*m.i34
+ 71.72*m.i35 + 23.6*m.i36 + 70.71*m.i37 + 56.98*m.i38 + 34.47*m.i39 + 10.23*m.i40 + 59.19*m.i41
+ 58.61*m.i42 + 445.29*m.i43 + 131.69*m.i44 + 34.24*m.i45 + 43.11*m.i46 + 25.18*m.i47 + 28*m.i48
+ 19.43*m.i49 + 14.33*m.i50 + 28.41*m.i51 + 74.5*m.i52 + 36.54*m.i53 + 38.99*m.i54 + 43.15*m.i55
+ 199.55*m.i56 + 59.07*m.i57 + 123.55*m.i58 + 20.55*m.i59 + 66.72*m.i60 + 37.95*m.i61
+ 27.62*m.i62 + 23.21*m.i63 + 36.09*m.i64 + 23.09*m.i65 + 46.54*m.i66 + 67.89*m.i67
+ 34.83*m.i68 + 11.96*m.i69 + 45.77*m.i70 + 32.91*m.i71 + 77.37*m.i72 + 21.46*m.i73
+ 53.11*m.i74 + 14.29*m.i75 + 61.13*m.i76 + 32.79*m.i77 + 59.84*m.i78 + 6.59*m.i79 + 14.06*m.i80
+ 55.29*m.i81 + 33.33*m.i82 + 4.24*m.i83 + 23.21*m.i84 + 47.85*m.i85 + 48.99*m.i86 + 57.46*m.i87
+ 28.87*m.i88 + 24.6*m.i89 + 22.26*m.i90 + 28.31*m.i91 + 26.67*m.i92 + 48.1*m.i93 + 28.01*m.i94
+ 64.85*m.i95 + 25.54*m.i96 + 31.47*m.i97 + 18.31*m.i98 + 35.06*m.i99 + 8.06*m.i100 <= 2200)
|
[
"christoph.neumann@kit.edu"
] |
christoph.neumann@kit.edu
|
fa2af2256e992f5dea361ca6dc8422c6d97e35d1
|
43ab33b2f50e47f5dbe322daa03c86a99e5ee77c
|
/rcc/models/study_events.py
|
73804683abfe9626a9ff78782d4aa06520a3ae77
|
[] |
no_license
|
Sage-Bionetworks/rcc-client
|
c770432de2d2950e00f7c7bd2bac22f3a81c2061
|
57c4a621aecd3a2f3f9faaa94f53b2727992a01a
|
refs/heads/main
| 2023-02-23T05:55:39.279352
| 2021-01-21T02:06:08
| 2021-01-21T02:06:08
| 331,486,099
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,338
|
py
|
# coding: utf-8
"""
nPhase REST Resource
REDCap REST API v.2 # noqa: E501
The version of the OpenAPI document: 2.0
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from rcc.configuration import Configuration
class StudyEvents(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'study_event': 'list[StudyEvent]'
}
attribute_map = {
'study_event': 'studyEvent'
}
def __init__(self, study_event=None, local_vars_configuration=None): # noqa: E501
"""StudyEvents - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._study_event = None
self.discriminator = None
if study_event is not None:
self.study_event = study_event
@property
def study_event(self):
"""Gets the study_event of this StudyEvents. # noqa: E501
:return: The study_event of this StudyEvents. # noqa: E501
:rtype: list[StudyEvent]
"""
return self._study_event
@study_event.setter
def study_event(self, study_event):
"""Sets the study_event of this StudyEvents.
:param study_event: The study_event of this StudyEvents. # noqa: E501
:type: list[StudyEvent]
"""
self._study_event = study_event
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
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, StudyEvents):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, StudyEvents):
return True
return self.to_dict() != other.to_dict()
|
[
"thomas.yu@sagebase.org"
] |
thomas.yu@sagebase.org
|
07025217cb00bf91a6ba23c519d15a6c2bff30ad
|
82a9077bcb5a90d88e0a8be7f8627af4f0844434
|
/google-cloud-sdk/lib/tests/unit/api_lib/compute/instances/ops_agents/exceptions_test.py
|
6315b0e3b6f56b2dd728bee1157215665d21febe
|
[
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] |
permissive
|
piotradamczyk5/gcloud_cli
|
1ae2553595e569fad6ce84af62b91a7ee5489017
|
384ece11040caadcd64d51da74e0b8491dd22ca3
|
refs/heads/master
| 2023-01-01T23:00:27.858583
| 2020-10-21T04:21:23
| 2020-10-21T04:21:23
| 290,238,061
| 0
| 0
| null | 2020-10-19T16:43:36
| 2020-08-25T14:31:00
|
Python
|
UTF-8
|
Python
| false
| false
| 1,861
|
py
|
# -*- coding: utf-8 -*- #
# Copyright 2020 Google LLC. 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.
"""Unit Tests for ops_agents.exceptions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from googlecloudsdk.api_lib.compute.instances.ops_agents import exceptions
from tests.lib import test_case
import six
ERROR_MESSAGE_1 = 'At most one agent with type [logging] is allowed.'
ERROR_MESSAGE_2 = (
'The agent version [1] is not allowed. Expected values: [latest], '
'[current-major], or anything in the format of '
'[MAJOR_VERSION.MINOR_VERSION.PATCH_VERSION] or [MAJOR_VERSION.*.*].')
ERROR_MESSAGE_3 = (
'An agent can not be pinned to the specific version [5.3.1] when '
'[enable-autoupgrade] is set to true for that agent.')
MULTI_ERROR_MESSAGE = '{} | {} | {}'.format(
ERROR_MESSAGE_1, ERROR_MESSAGE_2, ERROR_MESSAGE_3)
class PolicyValidationMultiErrorTest(test_case.TestCase):
def testErrorMessage(self):
errors = [
exceptions.PolicyValidationError(ERROR_MESSAGE_1),
exceptions.PolicyValidationError(ERROR_MESSAGE_2),
exceptions.PolicyValidationError(ERROR_MESSAGE_3),
]
multi_error = exceptions.PolicyValidationMultiError(errors)
self.assertEqual(MULTI_ERROR_MESSAGE, six.text_type(multi_error))
|
[
"code@bootstraponline.com"
] |
code@bootstraponline.com
|
84ed64371f199639424fba91bfd98c0c5eec0792
|
bdff2f51d12aa4329df511ec1f5564c0cb9b14fe
|
/tests/integration/adapters/test_mongo_projects_repository.py
|
8aebb4543a985a1f7445567d2c6bc077072b3526
|
[] |
no_license
|
jdgillespie91/projects-api
|
caec3e5af8979e512100545c4f799f3ccff4e287
|
b1df9447dedfd3fe9d875f4372160b9dd770548c
|
refs/heads/master
| 2021-04-29T06:36:30.004810
| 2018-06-03T14:40:04
| 2018-06-07T19:24:18
| 77,964,889
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,242
|
py
|
from pymongo import MongoClient
from pytest import fixture
from projects.adapters.mongo_projects_repository import MongoProjectsRepository
from projects.entities.project import ProjectSchema
@fixture(scope='module')
def database():
client = MongoClient(
'mongodb://mongo:27017/',
socketTimeoutMS=3000,
connectTimeoutMS=3000,
serverSelectionTimeoutMS=3000
)
client.drop_database('projects')
db = client.projects
collection = db.projects
collection.insert_one({
'id': 'a9c1fff4-09b1-4668-b94b-a301f21efdde',
'title': 'some title',
'description': 'some description',
'status': 'some status',
'links': {
'homepage': 'https://some.url'
}
})
def test_get(database):
expected_projects = [
ProjectSchema().load({
'id': 'a9c1fff4-09b1-4668-b94b-a301f21efdde',
'title': 'some title',
'description': 'some description',
'status': 'some status',
'links': {
'homepage': 'https://some.url'
}
})
]
repo = MongoProjectsRepository()
actual_projects = repo.get()
assert expected_projects == actual_projects
|
[
"jdgillespie91@gmail.com"
] |
jdgillespie91@gmail.com
|
80ffd316b9bbc8a682e4c8e9e842d3020e7a8472
|
545536daea315e31e01e388326e21a317f73dc6c
|
/Guddu on a Date.py
|
f390db81dd0b921ac0e786f7bc984075e63bfca0
|
[] |
no_license
|
calkikhunt/CODE_CHEF
|
3cd4db7d2231dc31a045645da08c52a78edda6b6
|
81bb90368822bc77e70582ab3eae1a4244e6c80f
|
refs/heads/master
| 2022-04-18T08:43:23.900118
| 2020-01-29T09:31:35
| 2020-01-29T09:31:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 805
|
py
|
t=int(input())
for i in range(t):
ctrcopy=19
n=int(input())
ptr=0
while ptr<(n):
ctr=ctrcopy
check=str(ctrcopy)
doublecheck=str(ctrcopy+19)
sumdigi=0
while ctr>0:
use=ctr%10
ctr=ctr//10
sumdigi+=use
if sumdigi%10==0 and check[len(check)-1]!='0':
ptr+=1
if ptr>=n:
break
ctrcopy+=9
elif sumdigi%10==0 and check[len(check)-1]=='0' and check[0]==doublecheck[0]:
ptr+=1
if ptr>=n:
break
ctrcopy+=19
elif sumdigi%10==0 and check[len(check)-1]=='0' and check[0]!=doublecheck[0]:
ptr+=1
if ptr>=n:
break
ctrcopy+=18
print(ctrcopy)
|
[
"wimpywarlord@gmail.com"
] |
wimpywarlord@gmail.com
|
a17d7cd9fdcdc856d383afb6531cce96e9bb9932
|
1ff376da81912600e0f8b3d45ea061d9418a654c
|
/backend/weeklypulls/apps/series/models.py
|
219c094f4f48347bc1312ed8e9e5114862031b13
|
[] |
no_license
|
rkuykendall/weeklypulls
|
9c3448665b3a18cc0375ad40a60ad71008bb4e89
|
e8300a6f28f6ce959130865e8bcf8c365033b2ce
|
refs/heads/master
| 2021-01-17T19:51:43.702126
| 2017-12-18T12:16:28
| 2017-12-18T12:16:28
| 61,999,182
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,704
|
py
|
import os
from django.db import models
from django.contrib.postgres.fields import ArrayField
import marvelous
from weeklypulls.apps.marvel.models import DjangoCache
class Series(models.Model):
series_id = models.IntegerField(unique=True)
read = ArrayField(models.IntegerField(), default=list)
skipped = ArrayField(models.IntegerField(), default=list)
created_at = models.DateTimeField(auto_now_add=True)
class Meta:
verbose_name_plural = "series"
def __str__(self):
try:
return '{} ({})'.format(self.api['title'], self.series_id)
except Exception:
return 'Series {} (api error)'.format(self.series_id)
@property
def api(self):
public_key = os.environ['MAPI_PUBLIC_KEY']
private_key = os.environ['MAPI_PRIVATE_KEY']
cache = DjangoCache()
marvel_api = marvelous.api(public_key, private_key, cache=cache)
series = marvel_api.series(self.series_id)
response = {
'title': series.title,
'comics': [],
'series_id': self.series_id,
}
series_args = {
'format': "comic",
'formatType': "comic",
'noVariants': True,
'limit': 100,
}
for comic in series.comics(series_args):
response['comics'].append({
'id': comic.id,
'title': comic.title,
'read': (comic.id in self.read),
'skipped': (comic.id in self.skipped),
'on_sale': comic.dates.on_sale,
'series_id': comic.series.id,
'images': comic.images,
})
return response
|
[
"robert@rkuykendall.com"
] |
robert@rkuykendall.com
|
f349e2ae3c868492cbe120dac5a23192b4e8183c
|
a2aef0303eceb97e121392c6e23704bc42cf606a
|
/venv/Scripts/easy_install-script.py
|
bda2ce6ee9eccc23161dedbf05d9743be3e7f841
|
[] |
no_license
|
khisomovkomron/bdd-test-framework
|
3dded546d388f54ad765028af605d36f4e1142b3
|
94aa00236336b270bdb0b85484bbfef781068363
|
refs/heads/master
| 2023-02-04T10:26:05.847056
| 2020-12-25T14:06:15
| 2020-12-25T14:06:15
| 324,369,459
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 456
|
py
|
#!C:\Users\komro\PycharmProjects\BDD_Framework\venv\Scripts\python.exe
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install'
__requires__ = 'setuptools==40.8.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install')()
)
|
[
"normok_9595@bk.ru"
] |
normok_9595@bk.ru
|
774b67059eddcf1cedf719cb61af7c2ced0de7fa
|
8ecf4930f9aa90c35e5199d117068b64a8d779dd
|
/TopQuarkAnalysis/SingleTop/test/crabs44/SingleTopMC_TTBarQ2upFall11_cfg.py
|
da0b2fabbd1a6100f2c5fce7261928493357cfcf
|
[] |
no_license
|
fabozzi/ST_44
|
178bd0829b1aff9d299528ba8e85dc7b7e8dd216
|
0becb8866a7c758d515e70ba0b90c99f6556fef3
|
refs/heads/master
| 2021-01-20T23:27:07.398661
| 2014-04-14T15:12:32
| 2014-04-14T15:12:32
| 18,765,529
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 13,424
|
py
|
import FWCore.ParameterSet.Config as cms
process = cms.Process("SingleTop")
ChannelName = "TTBarQ2up";
process.load("FWCore.MessageLogger.MessageLogger_cfi")
process.options = cms.untracked.PSet(
wantSummary = cms.untracked.bool(True),
FailPath = cms.untracked.vstring('ProductNotFound','Type Mismatch')
)
process.MessageLogger.cerr.FwkReport.reportEvery = 1000
#from PhysicsTools.PatAlgos.tools.cmsswVersionTools import run36xOn35xInput
# conditions ------------------------------------------------------------------
print "test "
#process.load("Configuration.StandardSequences.MixingNoPileUp_cff")
process.load("Configuration.StandardSequences.Geometry_cff")
process.load("Configuration.StandardSequences.FrontierConditions_GlobalTag_cff")
process.load("Configuration.StandardSequences.MagneticField_AutoFromDBCurrent_cff") ### real data
#process.GlobalTag.globaltag = cms.string('START42_V17::All')
process.GlobalTag.globaltag = cms.string('START44_V13::All')
#from Configuration.PyReleaseValidation.autoCond import autoCond
#process.GlobalTag.globaltag = autoCond['startup']
process.load("TopQuarkAnalysis.SingleTop.SingleTopSequences_cff")
process.load("SelectionCuts_Skim_cff");################<----------
#From <<ysicsTools.PatAlgos.tools.cmsswVersionTools import *
#larlaun42xOn3yzMcInput(process)
#run36xOn35xInput(process)
# Get a list of good primary vertices, in 42x, these are DAF vertices
from PhysicsTools.SelectorUtils.pvSelector_cfi import pvSelector
process.goodOfflinePrimaryVertices = cms.EDFilter(
"PrimaryVertexObjectFilter",
filterParams = pvSelector.clone( minNdof = cms.double(4.0), maxZ = cms.double(24.0) ),
src=cms.InputTag('offlinePrimaryVertices')
)
# require physics declared
process.load('HLTrigger.special.hltPhysicsDeclared_cfi')
process.hltPhysicsDeclared.L1GtReadoutRecordTag = 'gtDigis'
#dummy output
process.out = cms.OutputModule("PoolOutputModule",
fileName = cms.untracked.string('dummy.root'),
outputCommands = cms.untracked.vstring(""),
)
#rocess.load("PhysicsTools.HepMCCandAlgos.flavorHistoryPaths_cfi")
#mytrigs=["HLT_Mu9"]
mytrigs=["*"]
from HLTrigger.HLTfilters.hltHighLevel_cfi import *
#if mytrigs is not None :
# process.hltSelection = hltHighLevel.clone(TriggerResultsTag = 'TriggerResults::HLT', HLTPaths = mytrigs)
# process.hltSelection.throw = False
#
# getattr(process,"pfNoElectron"+postfix)*process.kt6PFJets
# set the dB to the beamspot
process.patMuons.usePV = cms.bool(False)
process.patElectrons.usePV = cms.bool(False)
#inputJetCorrLabel = ('AK5PFchs', ['L1FastJet', 'L2Relative', 'L3Absolute'])
# Configure PAT to use PF2PAT instead of AOD sources
# this function will modify the PAT sequences. It is currently
# not possible to run PF2PAT+PAT and standart PAT at the same time
from PhysicsTools.PatAlgos.tools.pfTools import *
from PhysicsTools.PatAlgos.tools.trigTools import *
postfix = ""
#usePF2PAT(process,runPF2PAT=True, jetAlgo='AK5', runOnMC=True, postfix=postfix, jetCorrections = inputJetCorrLabel)
usePF2PAT(process,runPF2PAT=True, jetAlgo='AK5', runOnMC=True, postfix=postfix)
switchOnTriggerMatchEmbedding(process,triggerMatchers = ['PatJetTriggerMatchHLTIsoMuBTagIP','PatJetTriggerMatchHLTIsoEleBTagIP'])
process.pfPileUp.Enable = True
process.pfPileUp.checkClosestZVertex = cms.bool(False)
process.pfPileUp.Vertices = cms.InputTag('goodOfflinePrimaryVertices')
process.pfJets.doAreaFastjet = True
process.pfJets.doRhoFastjet = False
#process.pfJets.Rho_EtaMax = cms.double(4.4)
#Compute the mean pt per unit area (rho) from the
#PFchs inputs
from RecoJets.JetProducers.kt4PFJets_cfi import kt4PFJets
process.kt6PFJets = kt4PFJets.clone(
rParam = cms.double(0.6),
src = cms.InputTag('pfNoElectron'+postfix),
doAreaFastjet = cms.bool(True),
doRhoFastjet = cms.bool(True),
# voronoiRfact = cms.double(0.9),
# Rho_EtaMax = cms.double(4.4)
)
process.patJetCorrFactors.rho = cms.InputTag("kt6PFJets", "rho")
coneOpening = cms.double(0.4)
defaultIsolationCut = cms.double(0.2)
#coneOpening = process.coneOpening
#defaultIsolationCut = process.coneOpening
#Muons
#applyPostfix(process,"isoValMuonWithNeutral",postfix).deposits[0].deltaR = coneOpening
#applyPostfix(process,"isoValMuonWithCharged",postfix).deposits[0].deltaR = coneOpening
#applyPostfix(process,"isoValMuonWithPhotons",postfix).deposits[0].deltaR = coneOpening
#electrons
#applyPostfix(process,"isoValElectronWithNeutral",postfix).deposits[0].deltaR = coneOpening
#applyPostfix(process,"isoValElectronWithCharged",postfix).deposits[0].deltaR = coneOpening
#applyPostfix(process,"isoValElectronWithPhotons",postfix).deposits[0].deltaR = coneOpening
applyPostfix(process,"pfIsolatedMuons",postfix).combinedIsolationCut = defaultIsolationCut
applyPostfix(process,"pfIsolatedElectrons",postfix).combinedIsolationCut = defaultIsolationCut
#applyPostfix(process,"pfIsolatedMuons",postfix).combinedIsolationCut = cms.double(0.125)
#applyPostfix(process,"pfIsolatedElectrons",postfix).combinedIsolationCut = cms.double(0.125)
#postfixQCD = "ZeroIso"
# Add the PV selector and KT6 producer to the sequence
getattr(process,"patPF2PATSequence"+postfix).replace(
getattr(process,"pfNoElectron"+postfix),
getattr(process,"pfNoElectron"+postfix)*process.kt6PFJets )
#Residuals (Data)
#process.patPFJetMETtype1p2Corr.jetCorrLabel = 'L2L3Residual'
process.patseq = cms.Sequence(
# process.patElectronIDs +
process.goodOfflinePrimaryVertices *
process.patElectronIDs *
getattr(process,"patPF2PATSequence"+postfix) #*
# process.producePatPFMETCorrections
# getattr(process,"patPF2PATSequence"+postfixQCD)
)
process.pfIsolatedMuonsZeroIso = process.pfIsolatedMuons.clone(combinedIsolationCut = cms.double(float("inf")))
process.patMuonsZeroIso = process.patMuons.clone(pfMuonSource = cms.InputTag("pfIsolatedMuonsZeroIso"), genParticleMatch = cms.InputTag("muonMatchZeroIso"))
# use pf isolation, but do not change matching
tmp = process.muonMatch.src
adaptPFMuons(process, process.patMuonsZeroIso, "")
process.muonMatch.src = tmp
process.muonMatchZeroIso = process.muonMatch.clone(src = cms.InputTag("pfIsolatedMuonsZeroIso"))
process.pfIsolatedElectronsZeroIso = process.pfIsolatedElectrons.clone(combinedIsolationCut = cms.double(float("inf")))
process.patElectronsZeroIso = process.patElectrons.clone(pfElectronSource = cms.InputTag("pfIsolatedElectronsZeroIso"))
#####################
#Adaptpfelectrons (process, process.patElectronsZeroIso, "")
#Add the PF type 1 corrections to MET
#process.load("PhysicsTools.PatUtils.patPFMETCorrections_cff")
#process.selectedPatJetsForMETtype1p2Corr.src = cms.InputTag('selectedPatJets')
#process.selectedPatJetsForMETtype2Corr.src = cms.InputTag('selectedPatJets')
#process.patPFJetMETtype1p2Corr.type1JetPtThreshold = cms.double(10.0)
#process.patPFJetMETtype1p2Corr.skipEM = cms.bool(False)
#process.patPFJetMETtype1p2Corr.skipMuons = cms.bool(False)
#process.patPF2PATSequence.remove(process.patPF2PATSequence.FastjetJetProducer)
process.pathPreselection = cms.Path(
process.patseq #+ process.producePatPFMETCorrections
)
process.ZeroIsoLeptonSequence = cms.Path(
process.pfIsolatedMuonsZeroIso +
process.muonMatchZeroIso +
process.patMuonsZeroIso +
process.pfIsolatedElectronsZeroIso +
process.patElectronsZeroIso
)
#process.looseLeptonSequence.remove(process.muonMatchLoose)
#getattr(process,"pfNoPileUp"+postfix).enable = True
#getattr(process,"pfNoMuon"+postfix).enable = True
#getattr(process,"pfNoElectron"+postfix).enable = True
#getattr(process,"pfNoTau"+postfix).enable = False
#Getattr (process,"pfNoJet"+postfix).enable = True
process.pfNoTau.enable = False
#process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1000) )
process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) )
process.source = cms.Source ("PoolSource",
fileNames = cms.untracked.vstring (
#'file:/tmp/oiorio/F81B1889-AF4B-DF11-85D3-001A64789DF4.root'
#'file:/tmp/oiorio/EC0EE286-FA55-E011-B99B-003048F024F6.root'
#'file:/tmp/oiorio/D0B32FD9-6D87-E011-8572-003048678098.root'
#'file:/tmp/oiorio/149E3017-B799-E011-9FA9-003048F118C2.root'
#'file:/tmp/oiorio/FE4EF257-A3AB-E011-9698-00304867915A.root',
#'file:/tmp/oiorio/50A31B1A-8AAB-E011-835B-0026189438F5.root'
#'file:/tmp/oiorio/TTJetsLocalFall11.root',
#'file:/tmp/oiorio/',
#'file:/tmp/oiorio/00012F91-72E5-DF11-A763-00261834B5F1.root',
#'/store/mc/Fall11/T_TuneZ2_t-channel_7TeV-powheg-tauola/AODSIM/PU_S6_START44_V9B-v1/0000/CA7C6394-CE32-E111-9125-003048FFD796.root'
#'/store/mc/Fall11/Tbar_TuneZ2_t-channel_7TeV-powheg-tauola/AODSIM/PU_S6_START44_V9B-v1/0000/B81B1A7D-6E2A-E111-A1C1-0018F3D096EC.root'
#'/store/mc/Fall11/T_TuneZ2_t-channel_7TeV-powheg-tauola/AODSIM/PU_S6_START44_V9B-v1/0000/DE6B0050-3133-E111-B437-003048FFD736.root'
#'/store/mc/Fall11/T_TuneZ2_s-channel_7TeV-powheg-tauola/AODSIM/PU_S6_START44_V9B-v1/0000/440369A6-A23C-E111-9B5B-E0CB4E19F9AF.root'
#'file:/afs/cern.ch/work/m/mmerola/FC1035C0-2E32-E111-86D1-001A92971BD6_tchannelFall11_44X.root'
),
#eventsToProcess = cms.untracked.VEventRange('1:2807840-1:2807840'),
duplicateCheckMode = cms.untracked.string('noDuplicateCheck')
)
#process.TFileService = cms.Service("TFileService", fileName = cms.string("/tmp/oiorio/"+ChannelName+"_pt_bmode.root"))
process.TFileService = cms.Service("TFileService", fileName = cms.string("pileupdistr_"+ChannelName+".root"))
process.pileUpDumper = cms.EDAnalyzer("SingleTopPileUpDumper",
channel = cms.string(ChannelName),
)
#process.WLightFilter = process.flavorHistoryFilter.clone(pathToSelect = cms.int32(11))
#process.WccFlter = process.flavorHistoryFilter.clone(pathToSelect = cms.int32(6))
#process.WbbFilter = process.flavorHistoryFilter.clone(pathToSelect = cms.int32(5))
#process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","REDIGI38X")
#process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","REDIGI37X")
#process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","REDIGI")
#process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","REDIGI311X")
#process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","HLT")
process.hltFilter.TriggerResultsTag = cms.InputTag("TriggerResults","","HLT")
process.hltFilter.HLTPaths = mytrigs
process.countLeptons.doQCD = cms.untracked.bool(False)
process.baseLeptonSequence = cms.Path(
# process.pileUpDumper +
process.basePath
)
process.selection = cms.Path (
process.preselection +
process.nTuplesSkim
)
from TopQuarkAnalysis.SingleTop.SingleTopNtuplizers_cff import saveNTuplesSkimLoose
from TopQuarkAnalysis.SingleTop.SingleTopNtuplizers_cff import saveNTuplesSkimMu
savePatTupleSkimLoose = cms.untracked.vstring(
'drop *',
'keep patMuons_selectedPatMuons_*_*',
'keep patElectrons_selectedPatElectrons_*_*',
'keep patJets_selectedPatJets_*_*',
'keep patMETs_patMETs_*_*',
'keep *_patPFMet_*_*',
'keep *_patType1CorrectedPFMet_*_*',
'keep *_PVFilterProducer_*_*',
'keep patJets_selectedPatJetsTriggerMatch_*_*',
"keep *_TriggerResults_*_*",#Trigger results
"keep *_PatJetTriggerMatchHLTIsoMuBTagIP_*_*",#Trigger matches
"keep *_patTrigger_*_*",
"keep *_patTriggerEvent_*_*",
'keep *_pfJets_*_*',
'keep patJets_topJetsPF_*_*',
'keep patMuons_looseMuons_*_*',
'keep patElectrons_looseElectrons_*_*',
'keep patMuons_tightMuons_*_*',
'keep patElectrons_tightElectrons_*_*',
'keep *_PDFInfo_*_*',
'keep *_patElectronsZeroIso_*_*',
'keep *_patMuonsZeroIso_*_*',
'keep *_PVFilterProducer_*_*',
'keep *_cFlavorHistoryProducer_*_*',
'keep *_bFlavorHistoryProducer_*_*',
)
## Output module configuration
process.singleTopNTuple = cms.OutputModule("PoolOutputModule",
# fileName = cms.untracked.string('rfio:/CST/cern.ch/user/o/oiorio/SingleTop/SubSkims/WControlSamples1.root'),
# fileName = cms.untracked.Bstring('/tmp/oiorio/edmntuple_tchannel_big.root'),
# fileName = cms.untracked.string('/tmp/oiorio/edmntuple_'+ChannelName+'.root'),
fileName = cms.untracked.string('edmntuple_'+ChannelName+'.root'),
SelectEvents = cms.untracked.PSet( SelectEvents = cms.vstring('selection')),
outputCommands = saveNTuplesSkimLoose,
)
process.singleTopPatTuple = cms.OutputModule("PoolOutputModule",
# fileName = cms.untracked.string('rfio:/CST/cern.ch/user/o/oiorio/SingleTop/SubSkims/WControlSamples1.root'),
fileName = cms.untracked.string('pattuple_'+ChannelName+'.root'),
SelectEvents = cms.untracked.PSet( SelectEvents = cms.vstring('selection')),
outputCommands = savePatTupleSkimLoose
)
process.singleTopNTuple.dropMetaData = cms.untracked.string("ALL")
process.outpath = cms.EndPath(
process.singleTopNTuple #+
# process.singleTopPatTuple
)
|
[
"Francesco.Fabozzi@cern.ch"
] |
Francesco.Fabozzi@cern.ch
|
b859862094592ded8b548969d924bdcdce8f980c
|
bf46b65866c1179b49c07e1d428c5a8d6dbdd9c6
|
/forecasting_revenue.py
|
6be1403fdc743e52ca57c2fbfa86456850307e3a
|
[] |
no_license
|
goldiekapur/Time-Series-Analysis-Bike-Sharing-Demand-Forecasting
|
cd3a28775207ba220cf9f2d9851d125ee02a8385
|
7d52c48ff88f7db498c9ecb917c1c3b1b65d1802
|
refs/heads/master
| 2021-01-09T03:08:35.691840
| 2019-04-24T23:23:38
| 2019-04-24T23:23:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,415
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 29 17:25:47 2019
@author: mayank
"""
import pandas as pd
import numpy as np
from fbprophet import Prophet
from datetime import datetime
from sklearn.model_selection import train_test_split
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from math import sqrt
df = pd.read_excel('C:/Users/tusha/Desktop/newtable.xlsx',sheet_name='newtable')
df.head()
df.dtypes
df['start_time']=pd.to_datetime(df['start_time'],format="%d/%m/%Y %I:%M:%S %p")
df['end_time']=pd.to_datetime(df['end_time'],format="%d/%m/%Y %I:%M:%S %p")
#calculating the revenue from each trip not tsking the membership fees into consideration
def revenue(passtype,duration,time):
perthirty=1.75
if(time<date(year=2018,month=7,day=12)):
perthirty=3.5
cost=0
qty=int(duration/30)
extra=duration %30
if(extra!=0):
qty=qty+1
if(passtype== 'Monthly Pass' or passtype== 'Annual Pass' or passtype == 'One Day Pass' or passtype == 'Flex Pass'):
qty=qty-1
if(qty<0):
qty=0
cost=perthirty*qty
return cost
rev=list()
for passtype,duration,time in zip(df['passholder_type'],df['trip_duration'],df['start_time']):
rev.append(revenue(passtype,duration,time.date()))
df['revenue']=rev
# df['end_time'].head(10)
df_d = pd.pivot_table(df[['revenue','start_time']],aggfunc='sum',index=df['start_time'].dt.date,fill_value=0) #date
df_d.index = pd.to_datetime(df_d.index)
## imported from the cycling event url:https://www.ciclavia.org/events_history
#rem=['2016-08-14','2018-10-16','2017-03-26','2017-05-11','2017-08-13','2017-10-08',\
# '2017-12-10','2018-04-22','2018-06-24','2018-09-30','2018-12-02']
#
#df_d['temp']=df_d.index # creating a column of indexes
#
#for l in rem:
# df_d=df_d[df_d.temp!=l] # removing the rows with rem values as these are the outliers
#
#
#df_d.drop('temp',axis=1,inplace=True) # removing the index column
df_o=pd.DataFrame()
mse_trn=[]
mse_tst=[]
for i,v in enumerate(df_d.columns):
#i=0
#v=('start_time', 3005)
def prophet_inputize(df,column_num = 0):
df_1 = pd.DataFrame()
df_1['ds'] = df.index
df_1['y'] = list(df.iloc[:,i])
return(df_1)
def do_something(df_d,sample ='D',test =-184):
#df_d = df_d.resample(sample).sum()
# imported from the cycling event url:https://www.ciclavia.org/events_history
rem=['2016-08-14','2018-10-16','2017-03-26','2017-05-11','2017-08-13','2017-10-08',\
'2017-12-10','2018-04-22','2018-06-24','2018-09-30','2018-12-02']
df_d['temp']=df_d.index # creating a column of indexes
for l in rem:
df_d=df_d[df_d.temp!=l] # removing the rows with rem values as these are the outliers
df_d.drop('temp',axis=1,inplace=True) # removing the index column
df_d_trn = pd.DataFrame()
df_d_tst = pd.DataFrame()
df_d_trn = df_d.loc[df_d.index[:test]]#int(train_prop*len(df_d.index))]]
df_d_tst = df_d.loc[df_d.index[test:]]#int(train_prop*len(df_d.index)):]]
df_d_trn = prophet_inputize(df_d_trn,i)
df_d_tst = prophet_inputize(df_d_tst,i)
# df_d_trn.plot(x='ds',y='y')
# df_d_tst.plot(x='ds',y='y')
return(df_d_trn,df_d_tst)
sample_freq = 'D'
trn, tst = do_something(df_d,sample_freq,-184)
m = Prophet(yearly_seasonality=True,weekly_seasonality= False,daily_seasonality=False)
m.fit(trn)
future = m.make_future_dataframe(periods=len(tst)+90+3,freq=sample_freq) # till 31st march 2019 q1 2019
forecast = m.predict(future)
df_o[v]=forecast['yhat']
# df_o['ds']=forecast['ds']
# fig1 = m.plot(forecast)
# print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
# print(tst.tail())
y_actual_tst=tst['y']
y_predicted_tst=df_o[v][df_o.index[-184:]]
mse_tst.append(sqrt(mean_squared_error(y_actual_tst, y_predicted_tst)))
y_actual_trn=trn['y']
y_predicted_trn=df_o[v][df_o.index[:-184-93]] ## removing q1 2019 dates
mse_trn.append(sqrt(mean_squared_error(y_actual_trn, y_predicted_trn)))
df_o['ds']=forecast['ds']
### now run this
# imported from the cycling event url:https://www.ciclavia.org/events_history
rem=['2016-08-14','2018-10-16','2017-03-26','2017-05-11','2017-08-13','2017-10-08',\
'2017-12-10','2018-04-22','2018-06-24','2018-09-30','2018-12-02']
df_d['ds']=df_d.index # creating a column of indexes
for l in rem:
df_d=df_d[df_d.ds!=l] # removing the rows with rem values as these are the outliers
df_d=df_d.reset_index(drop=True) # reset index to zero
mse_trn=pd.DataFrame(mse_trn)
mse_tst=pd.DataFrame(mse_tst)
#import statistics
#import math
#statistics.mean(mse)
with pd.ExcelWriter('outputY_Revenue.xlsx') as writer: # doctest: +SKIP
df_d.to_excel(writer,sheet_name='actual(Y)')
df_o.to_excel(writer,sheet_name='predicted(Y)')
mse_trn.to_excel(writer,sheet_name='mse_trn(Y)')
mse_tst.to_excel(writer,sheet_name='mse_tst(Y)')
|
[
"noreply@github.com"
] |
noreply@github.com
|
6ac0c907489a203ecfc7642cfbef6fc7477c2e62
|
1287456060aa52a0338ab3928c300a14779f9a30
|
/SRP/tasks.py
|
f054f4a1446e36b72b5887a09929672704735830
|
[] |
no_license
|
maxm11/Full-Stack-Senior-Research-Project
|
66a9dc0e817556c0a0806740aba65535adeca9ef
|
68e5d80343881bdee0335f39e95063897bc4e8d6
|
refs/heads/master
| 2021-03-27T08:31:03.530687
| 2018-12-05T06:28:55
| 2018-12-05T06:28:55
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,219
|
py
|
# Create your tasks here
from __future__ import absolute_import, unicode_literals
from .models import Entity, Experience, Sentence, Noun
from decimal import Decimal
from .libs.nlp import tone
from django.http import HttpResponse
from django.views.decorators.csrf import csrf_exempt
from background_task import background
from textblob import TextBlob
from django_dandelion.datatxt import EntityExtraction
import requests
# Sample Tasks
def add(x, y):
return x + y
def div(x, y):
return x / y
def xsum(numbers):
return sum(numbers)
@background(schedule=1, queue="entity")
def entity_bg(ent_id):
entity_id = int(ent_id)
entity = Entity.objects.filter(pk=entity_id)[0]
if entity.current_process:
try:
sent = Sentence.objects.filter(entity_id=entity.id, process_t=-1)[0]
ee = EntityExtraction()
text = sent.content
ee.params = 'text', text
ee.params = 'lang', 'en'
ee.params = 'country', 'US'
ee.params = 'min_confidence', '0.5'
a = ee.analyze()
for note in a.annotations:
n = Noun()
n.noun = note['id']
n.joy = sent.joy
n.sadness = sent.sadness
n.fear = sent.fear
n.anger = sent.anger
n.analytical = sent.analytical
n.confident = sent.confident
n.tentative = sent.tentative
n.entity_id = entity.id
n.experience_id = sent.experience_id
n.sentence_id = sent.id
n.save()
sent.process_t = entity.current_t
sent.save()
entity.current_t += 1
entity.joy = ((sent.joy + entity.joy)/2)
entity.sadness = ((sent.sadness + entity.sadness)/2)
entity.fear = ((sent.fear + entity.fear)/2)
entity.anger = ((sent.anger + entity.anger)/2)
entity.analytical = ((sent.analytical + entity.analytical)/2)
entity.confident = ((sent.confident + entity.confident)/2)
entity.tentative = ((sent.tentative + entity.tentative)/2)
entity.save()
except IndexError:
entity.current_process = False
entity.save()
@background(schedule=1, queue="experience")
def experience_intake(exp_id, time):
experience_id = int(exp_id)
experience = Experience.objects.filter(pk=experience_id)[0]
# Take in Experience
experience_content = experience.content
# Run Text Sentiment
# Output : sent_score, sent_mag, sentences[list]
analysis = tone(experience_content)
# Document Sentiment
for t in analysis['document_tone']['tones']:
tid = t['tone_id']
score = t['score']
exec("experience." + tid + "= score")
# Save Experience
experience.process_t = time
experience.save()
# Breakdown the sentences and save them to the database
if analysis['sentences_tone']:
for sent in analysis['sentences_tone']:
content = sent['text']
s = Sentence(content=content, experience_id=experience.id, entity_id=experience.entity_id, create_t=time)
for t in sent['tones']:
tid = t['tone_id']
score = t['score']
exec("s." + tid + "= score")
s.save()
def noun_display(search, entity_id):
# Establish Context
context = dict()
ee = EntityExtraction()
text = search
ee.params = 'text', text
ee.params = 'lang', 'en'
ee.params = 'country', 'US'
ee.params = 'min_confidence', '0.01'
a = ee.analyze()
if a.annotations:
concept = a.annotations[0]
concept_title = concept['title']
concept_id = concept['id']
concept_confidence = concept['confidence']
try:
params = {'action':'query', 'titles': concept_title, 'prop':'pageimages', 'format':'json', 'pithumbsize':'256'}
wiki_request = requests.post('https://en.wikipedia.org/w/api.php', params)
j = wiki_request.json()
concept_img = next( iter( (j['query']['pages'].values())))['thumbnail']['source']
except:
concept_img = ""
try:
params = {'action':'query', 'titles': concept_title, 'prop':'extracts', 'format':'json', 'exsentences':'2', 'explaintext':''}
r = requests.post('https://en.wikipedia.org/w/api.php', params)
j = r.json()
concept_desc = next( iter( (j['query']['pages'].values())))['extract']
except:
concept_desc = ""
else:
context.update(search_error=(search + " is not a recognized concept."))
return context
# Get list of nouns
nounlist = Noun.objects.filter(entity_id=entity_id, noun=concept_id)
if nounlist:
# Average Scores
avg_joy = Decimal()
avg_sadness = Decimal()
avg_fear = Decimal()
avg_anger = Decimal()
avg_analytical = Decimal()
avg_confident = Decimal()
avg_tentative = Decimal()
for rec in nounlist:
avg_joy += rec.joy
avg_sadness += rec.sadness
avg_fear += rec.fear
avg_anger += rec.anger
avg_analytical += rec.analytical
avg_confident += rec.confident
avg_tentative += rec.tentative
avg_joy /= nounlist.count()
avg_sadness /= nounlist.count()
avg_fear /= nounlist.count()
avg_anger /= nounlist.count()
avg_analytical /= nounlist.count()
avg_confident /= nounlist.count()
avg_tentative /= nounlist.count()
context.update(avg_joy = avg_joy, avg_sadness = avg_sadness, avg_fear = avg_fear, avg_anger = avg_anger, avg_analytical = avg_analytical, avg_confident = avg_confident, avg_tentative = avg_tentative, concept_title=concept_title, concept_confidence=concept_confidence, concept_img=concept_img, concept_desc=concept_desc)
return context
else:
context.update(search_error=(concept_title + " is a recognized concept but was not present in the selected entity."))
return context
|
[
"maxmrphy@gmail.com"
] |
maxmrphy@gmail.com
|
c83b1d17781b0f8bb755ab8da5af3e52437121b5
|
dc397dcb1f6210c6e1bde1c8650428ab84239e72
|
/sandbox/mooncake2.py
|
9cafb0fcea00859b571a86dcaedb5e2cbc3bc774
|
[] |
no_license
|
ccurro/font-bakers
|
963a939736ececa9ab8ceb0756fd9507c285de7e
|
9e0a7e726a7c2c46e56a8a79168afe3efc4648fb
|
refs/heads/master
| 2020-04-07T01:03:24.898430
| 2019-04-29T21:31:49
| 2019-04-29T21:31:49
| 157,929,015
| 5
| 0
| null | 2019-04-29T21:32:20
| 2018-11-16T22:38:06
|
Python
|
UTF-8
|
Python
| false
| false
| 9,070
|
py
|
#!/bin/python
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
BATCH_SIZE = 512
NUM_BLOCKS = 16
NUM_TRAIN_ITERATIONS = 10000
SEED_LENGTH = 20
class CausalConv1d(nn.Conv1d):
# From Alex Rogozhnikov:
# https://github.com/pytorch/pytorch/issues/1333#issuecomment-400338207
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
bias=True,
):
self.__padding = (kernel_size - 1) * dilation
super(CausalConv1d, self).__init__(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=self.__padding,
dilation=dilation,
groups=groups,
bias=bias,
)
def forward(self, input):
result = super(CausalConv1d, self).forward(input)
if self.__padding != 0:
return result[:, :, :-self.__padding]
return result
def generate_sinewaves(batch_size,
num_periods=2,
variance=0.1,
max_num_samples=200):
"""
Returns samples of one period of a sinewave with random frequency, phase
and number of samples.
Parameters
----------
batch_size : int
Batch size.
num_periods : int
Number of periods of the sinewave to generate before stopping.
variance : float
Variance of the AWGN to add to the sinewave.
max_num_samples : int
The maximum length of the stopped sinewave.
Returns
-------
sinewave : [batch_size, 1, max_num_samples]
"""
sinewaves = []
for i in range(batch_size):
n = np.random.randint(50, 150)
f = np.random.uniform(0.8, 1.2)
phi = np.random.uniform(0.1, 0.5)
x = np.linspace(0, num_periods / f, n, dtype=np.float32)
sinewave = np.sin(2 * np.pi * f * (x + phi))
pad_length = max_num_samples - len(sinewave)
sinewave = np.pad(
sinewave, (0, pad_length), "constant", constant_values=0)
sinewave += variance * np.random.randn(max_num_samples)
sinewaves.append(sinewave)
return torch.tensor(np.stack(sinewaves, axis=0)).unsqueeze(1).to(DEVICE)
def negative_log_prob(x, pi, mu, sigma):
"""
Negative log probability of predictive distribution of x_{k+1} from x_1,
..., x_k.
Parameters
----------
x : [batch_size, num_channels, max_num_samples]
Sinewave.
pi, mu, sigma : [batch_size, num_components (+1 for pi), max_num_samples]
Mixture weights, means and standard deviations of each Gaussian
component, respectively.
Returns
-------
[batch_size, max_num_samples - 1]
"""
# Index appropriately to compute nlogp of predicted next value.
vals = x[..., 1:]
pi = pi[:, 1:, :-1]
mu = mu[..., :-1]
sigma = sigma[..., :-1]
negative_densities = (0.5 * np.log(2 * np.pi) + torch.log(sigma) -
torch.log(pi) + 0.5 * (vals - mu)**2 / sigma**2)
return negative_densities.sum(dim=1)
class ResidualBlock(nn.Module):
"""
|-------------------------------------|
| |
| |-- tanh --| |
----|-> dilated_conv * --- 1x1 -- + -->
|-- sigm --| |
|
|
----------------------------------> + -------->
"""
def __init__(self, num_channels, kernel_size, dilation):
super(ResidualBlock, self).__init__()
self.num_channels = num_channels
self.conv1 = CausalConv1d(
num_channels,
2 * num_channels,
kernel_size=kernel_size,
dilation=dilation)
self.conv2 = nn.Conv1d(
num_channels, num_channels, kernel_size=1, dilation=1)
def forward(self, x):
a = self.conv1(x)
b = torch.tanh(a[:, :self.num_channels, :])
c = torch.sigmoid(a[:, self.num_channels:, :])
return self.conv2(b * c) + x
class Mooncake(nn.Module):
def __init__(
self,
in_channels=1,
max_num_samples=200,
num_channels=4,
num_blocks=8,
kernel_size=2,
dilations=[1, 2, 4, 8, 16, 32, 64, 128],
num_components=1,
):
super(Mooncake, self).__init__()
if len(dilations) != num_blocks:
msg = ("Number of dilations must be equal to number of residual "
"blocks.")
raise ValueError(msg)
self.max_num_samples = max_num_samples
self.num_channels = num_channels
self.num_blocks = num_blocks
# Coordconv adds 1 to `in_channels`
self.conv1 = CausalConv1d(
in_channels + 1, num_channels, kernel_size=kernel_size, dilation=1)
self.conv2 = nn.Conv1d(num_channels, 2 * num_channels, kernel_size=1)
self.blocks = nn.ModuleList([
ResidualBlock(
num_channels, kernel_size=kernel_size,
dilation=dilations[i]).to(DEVICE)
for i in range(self.num_blocks)
])
self.conv_pi = nn.Conv1d(
2 * num_channels, num_components + 1, kernel_size=1)
self.conv_mu = nn.Conv1d(
2 * num_channels, num_components, kernel_size=1)
self.conv_sigma = nn.Conv1d(
2 * num_channels, num_components, kernel_size=1)
def forward(self, x):
"""
Parameters
----------
x : [batch_size, num_channels, num_samples]
Input.
Returns
-------
pi, mu, sigma : [batch_size, num_components (+1 for pi), num_samples]
Mixture weights, means and standard deviations of each Gaussian
component, respectively.
"""
batch_size = x.shape[0]
num_samples = x.shape[2]
linspace = torch.tensor(
np.tile(
np.linspace(0, num_samples / self.max_num_samples,
num_samples),
[batch_size, 1])).unsqueeze(1).float().to(DEVICE)
x = torch.cat([x, linspace], dim=1)
taps = [self.conv1(x)]
for i in range(self.num_blocks):
tap = self.blocks[i](taps[i])
taps.append(tap)
aggregated_blocks = F.relu(torch.stack(taps).mean(dim=0))
z = self.conv2(aggregated_blocks)
pi = F.softmax(self.conv_pi(z), dim=1)
mu = 2 * torch.tanh(self.conv_mu(z) / 2)
sigma = F.softplus(self.conv_sigma(z))
return pi, mu, sigma
if __name__ == "__main__":
np.random.seed(1618)
torch.manual_seed(1618)
dilations = [2**i for i in range(NUM_BLOCKS)]
mooncake = Mooncake(num_blocks=NUM_BLOCKS, dilations=dilations).to(DEVICE)
optimizer = optim.Adam(mooncake.parameters(), lr=0.002)
num_trainable_params = sum(
p.numel() for p in mooncake.parameters() if p.requires_grad)
print("# trainable parameters: {}".format(num_trainable_params))
# Train
mooncake.train()
for i in range(NUM_TRAIN_ITERATIONS):
# Generate one sinewave and make a batch out of it.
sinewaves = generate_sinewaves(BATCH_SIZE)
pi, mu, sigma = mooncake(sinewaves)
# Update
optimizer.zero_grad()
nlogp = negative_log_prob(sinewaves, pi, mu, sigma).mean()
nlogp.backward()
optimizer.step()
if i % 10 == 0:
print("[{}] nlogp: {}".format(i,
nlogp.cpu().detach().numpy().item()))
del nlogp
if i % 500 == 499:
# Infer
mooncake.eval()
fig, ax = plt.subplots()
for c in ['r', 'b', 'g']:
with torch.no_grad():
ground_truth = generate_sinewaves(1)
inferred = torch.zeros_like(ground_truth)
inferred[..., :SEED_LENGTH] = ground_truth[..., :
SEED_LENGTH]
for j in range(SEED_LENGTH, inferred.shape[-1]):
pi, mu, sigma = mooncake(inferred[..., :j])
pi = pi[:, 1:, -1]
mu = mu[..., -1]
sigma = sigma[..., -1]
inferred[..., j] = (pi * mu).sum(dim=1)
ax.plot(
ground_truth.cpu().detach().numpy().squeeze(),
color=c,
linestyle='dashed',
alpha=0.3)
ax.plot(
inferred.cpu().detach().numpy().squeeze(),
color=c,
linestyle='solid')
plt.savefig("inference_{}.png".format(i))
|
[
"noreply@github.com"
] |
noreply@github.com
|
34b4dcbd61262a45e923027007b9cb5f120328f2
|
1391c61927d4074254525950c71d9a2b9a63d2c9
|
/My_second_Project/My_second_Project/settings.py
|
318f94b331fbe5103e34c7ab11d02b992a45e540
|
[] |
no_license
|
bridgecrew-perf7/django-deployment-21
|
69a372d3bb69bb17459d5b6d2e2bc5fcfee66e43
|
4427466274723e66fedabfc94df188b871b620ed
|
refs/heads/main
| 2023-07-13T20:57:58.883444
| 2021-08-15T10:38:28
| 2021-08-15T10:38:28
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,399
|
py
|
"""
Django settings for My_second_Project project.
Generated by 'django-admin startproject' using Django 3.1.7.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
import os
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
TEMPLATES_DIR = os.path.join(BASE_DIR, 'templates')
STATIC_DIR = os.path.join(BASE_DIR, 'static')
MEDIA_DIR = os.path.join(BASE_DIR, 'media')
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'u!3oz9fa7u%f%&*!%frn3o09_e-)lpjfbo-*s8y&!#d$3%=+_v'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'Login_app'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'My_second_Project.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'My_second_Project.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/media/'
LOGIN_URL = '/login/'
STATICFILES_DIRS = [STATIC_DIR]
MEDIA_ROOT = MEDIA_DIR
|
[
"tareqhasan2007@gmail.com"
] |
tareqhasan2007@gmail.com
|
f414faf29603d9e40eddaabc2774538b2a0c5f56
|
0e6f16fe472c164134048f4356662cd91e1ad37c
|
/DJANGO_PROJECT/settings.py
|
bfdbc920d1c168d5bfa691afd0f58d913913dd66
|
[] |
no_license
|
neel0812/quick
|
e35d1ec7ff809f4a9a9c9734ed0496f6152e7cde
|
142d9e6429ade89f6d43a563dd31cd00a275b316
|
refs/heads/master
| 2022-12-23T04:30:38.670543
| 2020-10-01T05:32:12
| 2020-10-01T05:32:12
| 300,156,149
| 0
| 0
| null | 2020-10-01T05:27:50
| 2020-10-01T05:27:49
| null |
UTF-8
|
Python
| false
| false
| 2,183
|
py
|
import os
BASE_DIR = os.path.dirname(
os.path.dirname(os.path.abspath(__file__))
)
SECRET_KEY = "o7fa-3u*pqnf@9_@@-d-)$4@*f56-j+4#cv25_3h3h=5u7)ah%"
DEBUG = True
ALLOWED_HOSTS = []
INSTALLED_APPS = [
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
"crispy_forms", # pip install django-crispy-forms
"todo",
]
MIDDLEWARE = [
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
]
ROOT_URLCONF = "DJANGO_PROJECT.urls"
TEMPLATES = [
{
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.debug",
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
],
},
},
]
WSGI_APPLICATION = "DJANGO_PROJECT.wsgi.application"
DATABASES = {
"default": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": os.path.join(BASE_DIR, "db.sqlite3"),
}
}
AUTH_PASSWORD_VALIDATORS = [
{
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
},
{
"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
},
{
"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
},
{
"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
},
]
LANGUAGE_CODE = "en-us"
TIME_ZONE = "UTC"
USE_I18N = True
USE_L10N = True
USE_TZ = True
STATIC_URL = "/static/"
CRISPY_TEMPLATE_PACK = "bootstrap4"
|
[
"srpatel980@gmail.com"
] |
srpatel980@gmail.com
|
90e2130d02e322b29d8fe681c1460a2e4fcaf0bc
|
2a27a5ad947eeb9dafbe7dc11e6233932d977d4c
|
/crack.py
|
aaaec7a50564d286b525d32b906d2ad4071320ec
|
[] |
no_license
|
Boy-Tolkit/s-crack
|
daab342732b69602850db58b275ec3e5073668da
|
1e4550ce2e01abe889efd379f3da5f10c15750b6
|
refs/heads/main
| 2023-04-27T22:35:28.077169
| 2021-05-16T23:35:37
| 2021-05-16T23:35:37
| 368,006,435
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,076,317
|
py
|
# ECRYPT BY Boy HamzaH
# Subscribe Cok Chanel YouTube Gua Anjing
# Dan Jangan Lupa Follow Github Gua
exec(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 39, 109, 97, 114, 115, 104, 97, 108, 39, 41, 46, 108, 111, 97, 100, 115],chr))(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 34, 122, 108, 105, 98, 34, 41, 46, 100, 101, 99, 111, 109, 112, 114, 101, 115, 115],chr))(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 34, 98, 97, 115, 101, 54, 52, 34, 41, 46, 98, 54, 52, 100, 101, 99, 111, 100, 101],chr))(b'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|
[
"noreply@github.com"
] |
noreply@github.com
|
2233f57c3679133af081bb703969e9eb6bbad208
|
710f7ad3af10c79aabb0cf0f64203d968e0057d8
|
/add_data.py
|
a8595e4a9deeb7e872ac759e7cf4414f35164720
|
[] |
no_license
|
tentotal/telegram-bot
|
dc986e79c01fe249c6a0cc16b8cdae8e3f4934d6
|
97bf882c572c4ec22841542bb5d252541bad9a70
|
refs/heads/master
| 2020-03-07T22:17:29.129819
| 2018-04-02T12:03:36
| 2018-04-02T12:03:36
| 127,750,135
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,973
|
py
|
import sqlite3
conn = sqlite3.connect('data.db')
c = conn.cursor()
def create_table():
c.execute("CREATE TABLE IF NOT EXISTS BlueCheese (mood TEXT, url TEXT, file_id TEXT, caption TEXT)")
def add(mood, url, file_id, caption):
conn = sqlite3.connect('data.db')
c = conn.cursor()
c.execute("INSERT INTO BlueCheese (mood, url, file_id, caption) VALUES (?,?,?,?)",
(mood, url, file_id, caption))
conn.commit()
c.close()
conn.close()
# create_table()
add("Fresh Tunes",
"https://itunes.apple.com/ru/playlist/urban-vibes/pl.0a6e08e1248a441284c3eb5a355adfc6?l=en",
"AgADAgAD_6cxG3ELKUgZPsTzNvG3TYfCDw4ABAN1KgPtW4gJzsQCAAEC",
"Playlist")
add("Fresh Tunes",
"https://itunes.apple.com/ru/playlist/new-hip-hop/pl.4355fef8c209446f82fe6fdf9fa97e03?l=en",
"AgADAgAEqDEbcQspSKwMIDi7gIsmwsUPDgAEmW8z883ZMAnLwAIAAQI",
"Playlist")
add("Essentials",
"https://itunes.apple.com/ru/playlist/chill/pl.6d2f03aab577450cb9f357f63020f7a3?l=en",
"AgADAgADiqgxG3ELIUgzNG0rKMyElCQWSw0ABPvjY1ajkkJN2MgRAAEC",
"Playlist")
add("Essentials",
"https://itunes.apple.com/ru/playlist/mood/pl.daa2a689923d4562bf5650a96809f929?l=en",
"AgADAgADi6gxG3ELIUjV-epUrFpYulgYMw4ABDwY-GMpHR1ofWcAAgI",
"Playlist")
add("Essentials",
"https://itunes.apple.com/ru/playlist/late-night-hip-hop/pl.c15a5391c65e44759efc3083463f88c4?l=en",
"AgADAgADAagxG3ELKUik62c-KGM8FwYxSw0ABBHtf-EagwUbxNkRAAEC",
"Playlist")
add("Essentials",
"https://itunes.apple.com/ru/playlist/onrepeat/pl.426a1044619f47d6b1f86b3f79ecf857?l=en",
"AgADAgADAqgxG3ELKUh8ZKtOfe0rYcIaMw4ABGOFUItGU30tCWsAAgI",
"Playlist")
add("Chill",
"https://itunes.apple.com/ru/album/88glam/1308490281?l=en",
"AgADAgADwKgxG4S8GEg62sqbtY3uyijPDw4ABI5eLSFTYLyXJcACAAEC",
"88GLAM - 88GLAM")
add("Chill",
"https://itunes.apple.com/ru/album/stoney-deluxe/1170616610?l=en",
"AgADAgADwagxG4S8GEhbhRYoC-HKugUIMw4ABE_uIIFNvSQ3H2EAAgI",
"Post Malone - Stoney")
add("Chill",
"https://itunes.apple.com/ru/album/welcome-to-gazi/1118065829?l=en",
"AgADAgADwqgxG4S8GEjaczKYtIh2Pn0OMw4ABI2Pf79lDv-fIWIAAgI",
"A.CHAL - Welcome to GAZI")
add("Chill",
"https://itunes.apple.com/ru/album/blonde/1146195596?l=en",
"AgADAgADxagxG4S8GEiNbTq6U3jKnCQ7Sw0ABEtuOO9B2CxhetoRAAEC",
"Frank Ocean - Blonde")
add("Chill",
"https://itunes.apple.com/ru/album/lil-boat/1130017345?l=en",
"AgADAgADw6gxG4S8GEgx4Di9V7xwcjv-Mg4ABCwZXaMSS8K4zGAAAgI",
"Lil Yachty - Lil Boat")
add("Chill",
"https://itunes.apple.com/ru/album/worlds/886037928?l=en",
"AgADAgADxKgxG4S8GEiD5TyUisfvKNLaDw4ABOUErDdmaJt50cECAAEC",
"Porter Robinson - Worlds")
add("All The Way Up",
"https://itunes.apple.com/ru/album/issa-album/1254351754?l=en",
"AgADAgADxqgxG4S8GEgFH-YR1QkdhhLODw4ABHGTF7rVjx9MhL0CAAEC",
"21 Savage - Issa Album")
add("All The Way Up",
"https://itunes.apple.com/ru/album/birds-in-the-trap-sing-mcknight/1150135681?l=en",
"AgADAgADx6gxG4S8GEiPb3HCf_QFkIkAATMOAAR-1J-Jh-tGjbRgAAIC",
"Travis Scott - Birds in the Trap Sing McKnight")
add("All The Way Up",
"https://itunes.apple.com/ru/album/damn/1223618217?l=en",
"AgADAgADyKgxG4S8GEjyxuafRAmREyvNDw4ABD_4VF4gy19HcL8CAAEC",
"Kendrick Lamar - DAMN.")
add("All The Way Up",
"https://itunes.apple.com/ru/album/still-striving/1266713355?l=en",
"AgADAgADBKgxG3ELKUigHYANxgP-XO_BDw4ABGIJsWJfQQahvsYCAAEC",
"A$AP Ferg - Still Striving")
add("All The Way Up",
"https://itunes.apple.com/ru/album/at-long-last-a%24ap/994727168?l=en",
"AgADAgADBagxG3ELKUg0YO9ORtd-rjMcMw4ABFbMeVPQKum4gmoAAgI",
"A$AP Rocky - AT.LONG.LAST.A$AP")
add("All The Way Up",
"https://itunes.apple.com/ru/album/more-life/1216996902?l=en",
"AgADAgADA6gxG3ELKUhwirTDO8N3WmsKMw4ABGcPj8CwkPr332kAAgI",
"Drake - More Life")
|
[
"noreply@github.com"
] |
noreply@github.com
|
f8881798d5ff65d89336d5d349a7c1f28b288ccd
|
1275fe3e7cfe893c9a5f922c60fa4426eb155dbb
|
/legacy/cuda-convnet2/python_util/util.py
|
7aeec4217ef87546f6414f399ec375ad38272839
|
[
"Apache-2.0",
"MIT"
] |
permissive
|
elhuhdron/emdrp
|
5f4b057986580139ce4de9a3a01083d717b90541
|
0c48f3325dd255d0ae06a89033e34cdc958ac4ab
|
refs/heads/master
| 2021-12-28T20:45:41.418547
| 2021-10-21T13:33:26
| 2021-10-21T13:33:26
| 47,223,300
| 5
| 1
|
MIT
| 2021-09-24T14:47:36
| 2015-12-01T23:11:46
|
Python
|
UTF-8
|
Python
| false
| false
| 2,867
|
py
|
# Copyright 2014 Google Inc. 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 re
#import cPickle as myPickle
import pickle as myPickle
import os
from cStringIO import StringIO
class UnpickleError(Exception):
pass
GPU_LOCK_NO_SCRIPT = -2
GPU_LOCK_NO_LOCK = -1
def pickle(filename, data):
fo = filename
if type(filename) == str:
fo = open(filename, "w")
myPickle.dump(data, fo, protocol=myPickle.HIGHEST_PROTOCOL)
fo.close()
def unpickle(filename):
if not os.path.exists(filename):
raise UnpickleError("Path '%s' does not exist." % filename)
fo = open(filename, 'r')
z = StringIO()
file_size = os.fstat(fo.fileno()).st_size
# Read 1GB at a time to avoid overflow
while fo.tell() < file_size:
z.write(fo.read(1 << 30))
fo.close()
dict = myPickle.loads(z.getvalue())
z.close()
return dict
def is_intel_machine():
VENDOR_ID_REGEX = re.compile('^vendor_id\s+: (\S+)')
f = open('/proc/cpuinfo')
for line in f:
m = VENDOR_ID_REGEX.match(line)
if m:
f.close()
return m.group(1) == 'GenuineIntel'
f.close()
return False
# Returns the CPUs associated with a given GPU
def get_cpus_for_gpu(gpu):
#proc = subprocess.Popen(['nvidia-smi', '-q', '-i', str(gpu)], stdout=subprocess.PIPE)
#lines = proc.communicate()[0]
#lines = subprocess.check_output(['nvidia-smi', '-q', '-i', str(gpu)]).split(os.linesep)
with open('/proc/driver/nvidia/gpus/%d/information' % gpu) as f:
for line in f:
if line.startswith('Bus Location'):
bus_id = line.split(':', 1)[1].strip()
bus_id = bus_id[:7] + ':' + bus_id[8:]
ff = open('/sys/module/nvidia/drivers/pci:nvidia/%s/local_cpulist' % bus_id)
cpus_str = ff.readline()
ff.close()
cpus = [cpu for s in cpus_str.split(',') for cpu in range(int(s.split('-')[0]),int(s.split('-')[1])+1)]
return cpus
return [-1]
def get_cpu():
if is_intel_machine():
return 'intel'
return 'amd'
def is_windows_machine():
return os.name == 'nt'
def tryint(s):
try:
return int(s)
except:
return s
def alphanum_key(s):
return [tryint(c) for c in re.split('([0-9]+)', s)]
|
[
"pwatkins@gmail.com"
] |
pwatkins@gmail.com
|
481ae39bdd81c05407a95d88b256471c8e60c9a3
|
d7327e6f2a68da73da2f2a99128da0e8a4a1b5d1
|
/cache/.mako.tmp/post_helper.tmpl.py
|
8989eeefff5685cabf849085b186c2f1957c7e3b
|
[] |
no_license
|
ryandkerr/nikola-ryandkerr
|
62d1d12aa65a05a8ea0aa304431a4e22109e130e
|
8c402e7f453948df66d9b4bdb4b2c661ff5213fa
|
refs/heads/master
| 2021-01-23T13:49:47.540182
| 2015-06-30T02:01:15
| 2015-06-30T02:01:15
| 37,417,504
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 9,719
|
py
|
# -*- coding:utf-8 -*-
from mako import runtime, filters, cache
UNDEFINED = runtime.UNDEFINED
__M_dict_builtin = dict
__M_locals_builtin = locals
_magic_number = 10
_modified_time = 1435629633.56992
_enable_loop = True
_template_filename = u'/home/ryan/.virtualenvs/nikola-web/local/lib/python2.7/site-packages/nikola/data/themes/base/templates/post_helper.tmpl'
_template_uri = u'post_helper.tmpl'
_source_encoding = 'utf-8'
_exports = ['html_tags', 'html_pager', 'twitter_card_information', 'meta_translations', 'mathjax_script', 'open_graph_metadata']
def render_body(context,**pageargs):
__M_caller = context.caller_stack._push_frame()
try:
__M_locals = __M_dict_builtin(pageargs=pageargs)
__M_writer = context.writer()
__M_writer(u'\n')
__M_writer(u'\n\n')
__M_writer(u'\n\n')
__M_writer(u'\n\n')
__M_writer(u'\n\n')
__M_writer(u'\n\n')
__M_writer(u'\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_html_tags(context,post):
__M_caller = context.caller_stack._push_frame()
try:
_link = context.get('_link', UNDEFINED)
hidden_tags = context.get('hidden_tags', UNDEFINED)
__M_writer = context.writer()
__M_writer(u'\n')
if post.tags:
__M_writer(u' <ul itemprop="keywords" class="tags">\n')
for tag in post.tags:
if tag not in hidden_tags:
__M_writer(u' <li><a class="tag p-category" href="')
__M_writer(unicode(_link('tag', tag)))
__M_writer(u'" rel="tag">')
__M_writer(unicode(tag))
__M_writer(u'</a></li>\n')
__M_writer(u' </ul>\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_html_pager(context,post):
__M_caller = context.caller_stack._push_frame()
try:
messages = context.get('messages', UNDEFINED)
__M_writer = context.writer()
__M_writer(u'\n')
if post.prev_post or post.next_post:
__M_writer(u' <ul class="pager">\n')
if post.prev_post:
__M_writer(u' <li class="previous">\n <a href="')
__M_writer(unicode(post.prev_post.permalink()))
__M_writer(u'" rel="prev" title="')
__M_writer(filters.html_escape(unicode(post.prev_post.title())))
__M_writer(u'">')
__M_writer(unicode(messages("Previous post")))
__M_writer(u'</a>\n </li>\n')
if post.next_post:
__M_writer(u' <li class="next">\n <a href="')
__M_writer(unicode(post.next_post.permalink()))
__M_writer(u'" rel="next" title="')
__M_writer(filters.html_escape(unicode(post.next_post.title())))
__M_writer(u'">')
__M_writer(unicode(messages("Next post")))
__M_writer(u'</a>\n </li>\n')
__M_writer(u' </ul>\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_twitter_card_information(context,post):
__M_caller = context.caller_stack._push_frame()
try:
twitter_card = context.get('twitter_card', UNDEFINED)
__M_writer = context.writer()
__M_writer(u'\n')
if twitter_card and twitter_card['use_twitter_cards']:
__M_writer(u' <meta name="twitter:card" content="')
__M_writer(filters.html_escape(unicode(twitter_card.get('card', 'summary'))))
__M_writer(u'">\n')
if 'site:id' in twitter_card:
__M_writer(u' <meta name="twitter:site:id" content="')
__M_writer(unicode(twitter_card['site:id']))
__M_writer(u'">\n')
elif 'site' in twitter_card:
__M_writer(u' <meta name="twitter:site" content="')
__M_writer(unicode(twitter_card['site']))
__M_writer(u'">\n')
if 'creator:id' in twitter_card:
__M_writer(u' <meta name="twitter:creator:id" content="')
__M_writer(unicode(twitter_card['creator:id']))
__M_writer(u'">\n')
elif 'creator' in twitter_card:
__M_writer(u' <meta name="twitter:creator" content="')
__M_writer(unicode(twitter_card['creator']))
__M_writer(u'">\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_meta_translations(context,post):
__M_caller = context.caller_stack._push_frame()
try:
lang = context.get('lang', UNDEFINED)
translations = context.get('translations', UNDEFINED)
len = context.get('len', UNDEFINED)
__M_writer = context.writer()
__M_writer(u'\n')
if len(translations) > 1:
for langname in translations.keys():
if langname != lang and post.is_translation_available(langname):
__M_writer(u' <link rel="alternate" hreflang="')
__M_writer(unicode(langname))
__M_writer(u'" href="')
__M_writer(unicode(post.permalink(langname)))
__M_writer(u'">\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_mathjax_script(context,post):
__M_caller = context.caller_stack._push_frame()
try:
__M_writer = context.writer()
__M_writer(u'\n')
if post.is_mathjax:
__M_writer(u' <script type="text/x-mathjax-config">\n MathJax.Hub.Config({tex2jax: {inlineMath: [[\'$latex \',\'$\'], [\'\\\\(\',\'\\\\)\']]}});</script>\n <script src="/assets/js/mathjax.js"></script>\n')
return ''
finally:
context.caller_stack._pop_frame()
def render_open_graph_metadata(context,post):
__M_caller = context.caller_stack._push_frame()
try:
lang = context.get('lang', UNDEFINED)
permalink = context.get('permalink', UNDEFINED)
url_replacer = context.get('url_replacer', UNDEFINED)
striphtml = context.get('striphtml', UNDEFINED)
abs_link = context.get('abs_link', UNDEFINED)
blog_title = context.get('blog_title', UNDEFINED)
use_open_graph = context.get('use_open_graph', UNDEFINED)
__M_writer = context.writer()
__M_writer(u'\n')
if use_open_graph:
__M_writer(u' <meta property="og:site_name" content="')
__M_writer(striphtml(unicode(blog_title)))
__M_writer(u'">\n <meta property="og:title" content="')
__M_writer(filters.html_escape(unicode(post.title()[:70])))
__M_writer(u'">\n <meta property="og:url" content="')
__M_writer(unicode(abs_link(permalink)))
__M_writer(u'">\n')
if post.description():
__M_writer(u' <meta property="og:description" content="')
__M_writer(filters.html_escape(unicode(post.description()[:200])))
__M_writer(u'">\n')
else:
__M_writer(u' <meta property="og:description" content="')
__M_writer(filters.html_escape(unicode(post.text(strip_html=True)[:200])))
__M_writer(u'">\n')
if post.previewimage:
__M_writer(u' <meta property="og:image" content="')
__M_writer(unicode(url_replacer(permalink, post.previewimage, lang, 'absolute')))
__M_writer(u'">\n')
__M_writer(u' <meta property="og:type" content="article">\n')
if post.date.isoformat():
__M_writer(u' <meta property="article:published_time" content="')
__M_writer(unicode(post.date.isoformat()))
__M_writer(u'">\n')
if post.tags:
for tag in post.tags:
__M_writer(u' <meta property="article:tag" content="')
__M_writer(unicode(tag))
__M_writer(u'">\n')
return ''
finally:
context.caller_stack._pop_frame()
"""
__M_BEGIN_METADATA
{"source_encoding": "utf-8", "line_map": {"15": 0, "20": 2, "21": 11, "22": 23, "23": 40, "24": 69, "25": 85, "26": 93, "32": 13, "38": 13, "39": 14, "40": 15, "41": 16, "42": 17, "43": 18, "44": 18, "45": 18, "46": 18, "47": 18, "48": 21, "54": 25, "59": 25, "60": 26, "61": 27, "62": 28, "63": 29, "64": 30, "65": 30, "66": 30, "67": 30, "68": 30, "69": 30, "70": 33, "71": 34, "72": 35, "73": 35, "74": 35, "75": 35, "76": 35, "77": 35, "78": 38, "84": 71, "89": 71, "90": 72, "91": 73, "92": 73, "93": 73, "94": 74, "95": 75, "96": 75, "97": 75, "98": 76, "99": 77, "100": 77, "101": 77, "102": 79, "103": 80, "104": 80, "105": 80, "106": 81, "107": 82, "108": 82, "109": 82, "115": 3, "122": 3, "123": 4, "124": 5, "125": 6, "126": 7, "127": 7, "128": 7, "129": 7, "130": 7, "136": 87, "140": 87, "141": 88, "142": 89, "148": 42, "159": 42, "160": 43, "161": 44, "162": 44, "163": 44, "164": 45, "165": 45, "166": 46, "167": 46, "168": 47, "169": 48, "170": 48, "171": 48, "172": 49, "173": 50, "174": 50, "175": 50, "176": 52, "177": 53, "178": 53, "179": 53, "180": 55, "181": 60, "182": 61, "183": 61, "184": 61, "185": 63, "186": 64, "187": 65, "188": 65, "189": 65, "195": 189}, "uri": "post_helper.tmpl", "filename": "/home/ryan/.virtualenvs/nikola-web/local/lib/python2.7/site-packages/nikola/data/themes/base/templates/post_helper.tmpl"}
__M_END_METADATA
"""
|
[
"ryankerr@college.harvard.edu"
] |
ryankerr@college.harvard.edu
|
faa5ddf2e6adabec7e26614f98bcb1343fb7633b
|
d18626d8f6f023e6e583f13dafea677c01b002bc
|
/test-wi-sub-pipeline.py
|
58386666c85cdccfde266d66ee5e0df348e4aeb3
|
[] |
no_license
|
kmsmith137/rf_pipelines
|
b3c596e1977ff9821a906cd279d128baf05edd68
|
4ecf6f9a909ef185cc03335339829f2b438cd1c0
|
refs/heads/master
| 2022-03-08T13:49:09.400064
| 2020-09-06T22:57:53
| 2020-09-06T22:57:53
| 61,664,765
| 2
| 6
| null | 2022-03-03T18:05:58
| 2016-06-21T20:25:17
|
C++
|
UTF-8
|
Python
| false
| false
| 5,939
|
py
|
#!/usr/bin/env python
#
# Tests wi_sub_pipeline, in special case Dt=1 for now.
# Also indirectly tests jsonize/from_json() for a few transforms.
#
# FIXME cleanup: combine with test-cpp-python-equivalence.py
import numpy as np
import numpy.random as rand
import rf_pipelines
def make_random_transform():
transform_type = rand.randint(0,3)
if transform_type == 0:
axis = 'freq' # FIXME generalize later
nbins = rand.randint(1, 5)
nt_chunk = 8 * rand.randint(5, 11)
epsilon = rand.uniform(3.0e-4, 1.0e-3)
return rf_pipelines.spline_detrender(nt_chunk, axis, nbins, epsilon)
elif transform_type == 1:
# intensity_clipper
axis = rand.randint(0,2) if (rand.uniform() < 0.66) else None
Df = 2**rand.randint(0,4)
Dt = 2**rand.randint(0,4)
sigma = rand.uniform(1.3, 1.7)
niter = rand.randint(1,5)
iter_sigma = rand.uniform(1.8, 2.0)
nt_chunk = Dt * 8 * rand.randint(1,8)
two_pass = True if rand.randint(0,2) else False
return rf_pipelines.intensity_clipper(nt_chunk, axis, sigma, niter, iter_sigma, Df, Dt, two_pass)
else:
# std_dev_clipper
axis = rand.randint(0,2)
Df = 2**rand.randint(0,4)
Dt = 2**rand.randint(0,4)
sigma = rand.uniform(1.3, 1.7)
nt_chunk = Dt * 8 * rand.randint(1,8)
two_pass = True if rand.randint(0,2) else False
return rf_pipelines.std_dev_clipper(nt_chunk, axis, sigma, Df, Dt, two_pass)
def make_random_pipeline():
n = rand.randint(1, 5)
return rf_pipelines.pipeline([ make_random_transform() for i in xrange(n) ])
def make_random_pipeline_json():
p = make_random_pipeline()
j = p.jsonize()
# throw in this test of jsonize()/from_json()
jj = rf_pipelines.pipeline_object.from_json(j).jsonize()
assert j == jj
return j
####################################################################################################
class initial_stream(rf_pipelines.wi_stream):
def __init__(self, intensity_arr, weights_arr, nt_chunk=None):
assert intensity_arr.ndim == 2
assert intensity_arr.shape == weights_arr.shape
if nt_chunk is None:
nt_chunk = rand.randint(10,20)
rf_pipelines.wi_stream.__init__(self, 'initial_stream')
self.nfreq = intensity_arr.shape[0]
self.nt_chunk = nt_chunk
self.nt_tot = intensity_arr.shape[1]
self.intensity_arr = intensity_arr
self.weights_arr = weights_arr
def _fill_chunk(self, intensity, weights, pos):
intensity[:,:] = 0.
weights[:,:] = 0.
if pos >= self.nt_tot:
return False
n = min(self.nt_tot - pos, self.nt_chunk)
intensity[:,:n] = self.intensity_arr[:,pos:(pos+n)]
weights[:,:n] = self.weights_arr[:,pos:(pos+n)]
return True
class final_transform(rf_pipelines.wi_transform):
def __init__(self, nt_chunk=None):
if nt_chunk is None:
nt_chunk = rand.randint(10,20)
rf_pipelines.wi_transform.__init__(self, "final_transform")
self.nt_chunk = nt_chunk
self.intensity_chunks = [ ]
self.weight_chunks = [ ]
def _process_chunk(self, intensity, weights, pos):
self.intensity_chunks.append(np.copy(intensity))
self.weight_chunks.append(np.copy(weights))
def get_results(self):
intensity = np.concatenate(self.intensity_chunks, axis=1)
weights = np.concatenate(self.weight_chunks, axis=1)
return (intensity, weights)
def run_pipeline(pipeline_json, intensity_arr, weights_arr):
# Just for fun, randomize 'nt_chunk'.
p0 = initial_stream(intensity_arr, weights_arr)
p1 = rf_pipelines.pipeline_object.from_json(pipeline_json)
p2 = final_transform()
p = rf_pipelines.pipeline([p0,p1,p2])
p.run(outdir=None, verbosity=0, debug=True)
(intensity, weights) = p2.get_results()
return (intensity, weights)
####################################################################################################
def maxdiff(a1, a2):
assert a1.shape == a2.shape
return np.max(np.abs(a1-a2))
def run_test():
Df = 2**rand.randint(0,5)
nfreq = Df * 8 * rand.randint(10, 20)
nt_tot = 8 * rand.randint(150, 500)
input_intensity = rand.standard_normal(size=(nfreq,nt_tot))
input_weights = rand.uniform(0.5, 1.0, size=(nfreq,nt_tot))
p0_json = make_random_pipeline_json()
p1_json = make_random_pipeline_json()
p2_json = make_random_pipeline_json()
# First run
(i0,w0) = run_pipeline(p0_json, input_intensity, input_weights)
(i0,w0) = (i0[:,:nt_tot], w0[:,:nt_tot])
(i1,w1) = rf_pipelines.wi_downsample(i0, w0, Df, 1)
(i2,w2) = run_pipeline(p1_json, i1, w1)
(i2,w2) = (i2[:,:nt_tot], w2[:,:nt_tot])
rf_pipelines.weight_upsample(w0, w2)
(i3,w3) = run_pipeline(p2_json, i0, w0)
# Second run
si = initial_stream(input_intensity, input_weights)
p0 = rf_pipelines.pipeline_object.from_json(p0_json)
p1 = rf_pipelines.pipeline_object.from_json(p1_json)
ps = rf_pipelines.wi_sub_pipeline(p1, Df=Df, Dt=1)
p2 = rf_pipelines.pipeline_object.from_json(p2_json)
tf = final_transform()
p = rf_pipelines.pipeline([ si, p0, ps, p2, tf ])
p.run(outdir=None, verbosity=0, debug=True)
(i4,w4) = tf.get_results()
eps_i = maxdiff((i3*w3)[:,:nt_tot],(i4*w4)[:,:nt_tot])
eps_w = maxdiff(w3[:,:nt_tot], w4[:,:nt_tot])
assert eps_i < 1.0e-5
assert eps_w < 1.0e-5
assert np.all(w3[:,nt_tot:] == 0.0)
assert np.all(w4[:,nt_tot:] == 0.0)
####################################################################################################
niter = 100
for iter in xrange(100):
if iter % 10 == 0:
print 'test-wi-sub-pipeline: iteration %d/%d' % (iter, niter)
run_test()
print 'test-wi-sub-pipeline: pass'
|
[
"kmsmith@perimeterinstitute.ca"
] |
kmsmith@perimeterinstitute.ca
|
b0707b9174477ff856490eef4c8f850d69768242
|
76dc1118958fdd709a27b826457fede99498a88d
|
/miner/address.py
|
9e610104d7dfe30ac07012f2ed5f64d8dac3c57e
|
[
"MIT"
] |
permissive
|
JesseEmond/pickaxe
|
5246301e0af1c6f573ea509f7524e40757ed690d
|
73b5eebbe00d658dc37a23b5bfc2eb0c2e48b2a4
|
refs/heads/master
| 2020-12-01T11:40:49.336817
| 2016-04-29T04:12:06
| 2016-04-29T04:12:06
| 66,162,585
| 1
| 0
| null | 2016-08-20T18:44:34
| 2016-08-20T18:44:34
| null |
UTF-8
|
Python
| false
| false
| 558
|
py
|
from base58 import b58decode_check
def p2pkh_address_to_pubkey_hash(address):
"""
Takes a P2PKH address (starting with a 1, m or n symbol) and extracts its
HASH160 hash (used as a public key hash).
:see: https://en.bitcoin.it/wiki/List_of_address_prefixes
:param address: P2PKH public address
:returns: HASH160 hash of the public key
"""
decoded = b58decode_check(address)
# check that it is a mainnet or testnet P2PKH address
assert(decoded[0] in [0x00, 0x6F])
return decoded[1:] # skip version byte
|
[
"emond.jesse@gmail.com"
] |
emond.jesse@gmail.com
|
dd17b924d8c1cdced32a20a58454603aebae7f7e
|
dea39b5d71a51923b0690ad2663371f863e56d92
|
/app/__init__.py
|
eb88621efb0d0498123ef26e697cb1983e2c9a9a
|
[] |
no_license
|
kamillacrozara/flask-base
|
c41b4a32dd5923e2f414e4d8af475189c1be7cfc
|
88efcaaeb8138bbedf7ffecd94fee883977d8a1d
|
refs/heads/master
| 2021-01-21T02:01:28.089332
| 2016-06-15T21:45:25
| 2016-06-15T21:45:25
| 61,230,056
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 428
|
py
|
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from config import config
db = SQLAlchemy()
def create_app(config_name):
app = Flask(__name__)
app.config.from_object(config[config_name])
config[config_name].init_app(app)
db.init_app(app)
# attach routes and custom error pages here
from .main import main as main_blueprint
app.register_blueprint(main_blueprint)
return app
|
[
"holanda.kamilla@gmail.com"
] |
holanda.kamilla@gmail.com
|
fee7f65b768ca3c7ee0d20fcf3e77badd3499824
|
ed496f92c738f3d6f169b48d9c6f47390a2693b8
|
/EasyOrders/wsgi.py
|
01ef649a12cbc712df630619cf50a142834744c4
|
[] |
no_license
|
yatharta/EasyOrders
|
642074db6c03ff00ba28e3dc12ffce3ded1c54cd
|
729e3574a82b13b96f9b60a84640e10fe9c664bc
|
refs/heads/master
| 2023-06-19T17:07:14.151804
| 2021-04-17T17:12:06
| 2021-04-17T17:12:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 397
|
py
|
"""
WSGI config for EasyOrders project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'EasyOrders.settings')
application = get_wsgi_application()
|
[
"60061690+tiwari1302@users.noreply.github.com"
] |
60061690+tiwari1302@users.noreply.github.com
|
dac834b379278ddf5e2bc0403e4ac406d9aea1e4
|
4f6ad7cdea2cab5fe89df34f6e5158e4b77837c3
|
/server/dvaapp/serializers.py
|
746c7a13a61f2e3b5f38663e2f1bf6dacfb29986
|
[
"BSD-3-Clause",
"MIT",
"Apache-2.0"
] |
permissive
|
ginusxiao/DeepVideoAnalytics
|
7194d83b518976340cd834e4e6a8ab9b164a2e3f
|
52c38c729b1a114cc46e641943e3e28a68428e25
|
refs/heads/master
| 2020-03-18T21:40:31.811272
| 2018-05-29T10:16:20
| 2018-05-29T10:16:20
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 26,156
|
py
|
from rest_framework import serializers, viewsets
from django.contrib.auth.models import User
from models import Video, Frame, Region, DVAPQL, QueryResults, TEvent, IndexEntries, \
Tube, Segment, Label, VideoLabel, FrameLabel, RegionLabel, \
SegmentLabel, TubeLabel, TrainedModel, Retriever, SystemState, QueryRegion,\
QueryRegionResults, Worker, TrainingSet
import os, json, logging, glob
from collections import defaultdict
from django.conf import settings
from StringIO import StringIO
from rest_framework.parsers import JSONParser
class UserSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = User
fields = ('url', 'username', 'email', 'password')
extra_kwargs = {
'password': {'write_only': True},
}
# def create(self, validated_data):
# user = User.objects.create_user(**validated_data)
# return user
#
# def update(self, instance, validated_data):
# if 'password' in validated_data:
# password = validated_data.pop('password')
# instance.set_password(password)
# return super(UserSerializer, self).update(instance, validated_data)
class VideoSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = Video
fields = '__all__'
class RetrieverSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = Retriever
fields = '__all__'
class TrainedModelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = TrainedModel
fields = '__all__'
class TrainingSetSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = TrainingSet
fields = '__all__'
class LabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = Label
fields = '__all__'
class FrameLabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = FrameLabel
fields = '__all__'
class RegionLabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = RegionLabel
fields = '__all__'
class SegmentLabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = SegmentLabel
fields = '__all__'
class VideoLabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = VideoLabel
fields = '__all__'
class TubeLabelSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = TubeLabel
fields = '__all__'
class FrameLabelExportSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = FrameLabel
fields = '__all__'
class RegionLabelExportSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = RegionLabel
fields = '__all__'
class SegmentLabelExportSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = SegmentLabel
fields = '__all__'
class VideoLabelExportSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = VideoLabel
fields = '__all__'
class WorkerSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Worker
fields = ('queue_name', 'id')
class TubeLabelExportSerializer(serializers.ModelSerializer):
class Meta:
model = TubeLabel
fields = '__all__'
class FrameSerializer(serializers.HyperlinkedModelSerializer):
media_url = serializers.SerializerMethodField()
def get_media_url(self,obj):
return "{}{}/frames/{}.jpg".format(settings.MEDIA_URL,obj.video_id,obj.frame_index)
class Meta:
model = Frame
fields = ('url','media_url', 'video', 'frame_index', 'keyframe', 'w', 'h', 't',
'name', 'subdir', 'id', 'segment_index')
class SegmentSerializer(serializers.HyperlinkedModelSerializer):
media_url = serializers.SerializerMethodField()
def get_media_url(self,obj):
return "{}{}/segments/{}.mp4".format(settings.MEDIA_URL,obj.video_id,obj.segment_index)
class Meta:
model = Segment
fields = ('video','segment_index','start_time','end_time','metadata',
'frame_count','start_index','start_frame','end_frame','url','media_url', 'id')
class RegionSerializer(serializers.HyperlinkedModelSerializer):
media_url = serializers.SerializerMethodField()
def get_media_url(self,obj):
if obj.materialized:
return "{}{}/regions/{}.jpg".format(settings.MEDIA_URL,obj.video_id,obj.pk)
else:
return None
class Meta:
model = Region
fields = ('url','media_url','region_type','video','user','frame','event','frame_index',
'segment_index','text','metadata','full_frame','x','y','h','w',
'polygon_points','created','object_name','confidence','materialized','png', 'id')
class TubeSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = Tube
fields = '__all__'
class QueryRegionSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = QueryRegion
fields = '__all__'
class SystemStateSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = SystemState
fields = '__all__'
class QueryResultsSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = QueryResults
fields = '__all__'
class QueryRegionResultsSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = QueryRegionResults
fields = '__all__'
class QueryResultsExportSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = QueryResults
fields = '__all__'
class QueryRegionResultsExportSerializer(serializers.ModelSerializer):
class Meta:
model = QueryRegionResults
fields = '__all__'
class QueryRegionExportSerializer(serializers.ModelSerializer):
query_region_results = QueryRegionResultsExportSerializer(source='queryregionresults_set', read_only=True, many=True)
class Meta:
model = QueryRegion
fields = ('id','region_type','query','event','text','metadata','full_frame','x','y','h','w','polygon_points',
'created','object_name','confidence','png','query_region_results')
class TaskExportSerializer(serializers.ModelSerializer):
query_results = QueryResultsExportSerializer(source='queryresults_set', read_only=True, many=True)
query_regions = QueryRegionExportSerializer(source='queryregion_set', read_only=True, many=True)
class Meta:
model = TEvent
fields = ('started','completed','errored','worker','error_message','video','operation','queue',
'created','start_ts','duration','arguments','task_id','parent','parent_process',
'imported','query_results', 'query_regions', 'id')
class TEventSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = TEvent
fields = '__all__'
class IndexEntriesSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField()
class Meta:
model = IndexEntries
fields = '__all__'
class RegionExportSerializer(serializers.ModelSerializer):
class Meta:
model = Region
fields = '__all__'
class FrameExportSerializer(serializers.ModelSerializer):
region_list = RegionExportSerializer(source='region_set', read_only=True, many=True)
class Meta:
model = Frame
fields = ('region_list', 'video', 'frame_index', 'keyframe', 'w', 'h', 't',
'name', 'subdir', 'id', 'segment_index')
class IndexEntryExportSerializer(serializers.ModelSerializer):
class Meta:
model = IndexEntries
fields = '__all__'
class TEventExportSerializer(serializers.ModelSerializer):
class Meta:
model = TEvent
fields = '__all__'
class TubeExportSerializer(serializers.ModelSerializer):
class Meta:
model = Tube
fields = '__all__'
class SegmentExportSerializer(serializers.ModelSerializer):
class Meta:
model = Segment
fields = '__all__'
class DVAPQLSerializer(serializers.HyperlinkedModelSerializer):
tasks = TaskExportSerializer(source='tevent_set', read_only=True, many=True)
query_image_url = serializers.SerializerMethodField()
def get_query_image_url(self,obj):
if obj.process_type == DVAPQL.QUERY:
return "{}queries/{}.png".format(settings.MEDIA_URL,obj.uuid)
else:
return None
class Meta:
model = DVAPQL
fields =('process_type','query_image_url','created', 'user', 'uuid', 'script', 'tasks',
'results_metadata', 'results_available', 'completed','id')
class VideoExportSerializer(serializers.ModelSerializer):
frame_list = FrameExportSerializer(source='frame_set', read_only=True, many=True)
segment_list = SegmentExportSerializer(source='segment_set', read_only=True, many=True)
index_entries_list = IndexEntryExportSerializer(source='indexentries_set', read_only=True, many=True)
event_list = TEventExportSerializer(source='tevent_set', read_only=True, many=True)
tube_list = TubeExportSerializer(source='tube_set', read_only=True, many=True)
frame_label_list = FrameLabelExportSerializer(source='framelabel_set', read_only=True, many=True)
region_label_list = RegionLabelExportSerializer(source='regionlabel_set', read_only=True, many=True)
tube_label_list = TubeLabelExportSerializer(source='tubelabel_set', read_only=True, many=True)
segment_label_list = SegmentLabelExportSerializer(source='segmentlabel_set', read_only=True, many=True)
video_label_list = VideoLabelExportSerializer(source='videolabel_set', read_only=True, many=True)
class Meta:
model = Video
fields = ('name', 'length_in_seconds', 'height', 'width', 'metadata', 'frames', 'created', 'description',
'uploaded', 'dataset', 'uploader', 'segments', 'url','frame_list', 'segment_list',
'event_list', 'tube_list', 'index_entries_list', 'frame_label_list', 'region_label_list',"stream",
'tube_label_list', 'segment_label_list', 'video_label_list')
def serialize_video_labels(v):
serialized_labels = {}
sources = [FrameLabel.objects.filter(video_id=v.pk), VideoLabel.objects.filter(video_id=v.pk),
SegmentLabel.objects.filter(video_id=v.pk), RegionLabel.objects.filter(video_id=v.pk),
TubeLabel.objects.filter(video_id=v.pk)]
for source in sources:
for k in source:
if k.label_id not in serialized_labels:
serialized_labels[k.label_id] = {'id':k.label.id,'name':k.label.name,'set':k.label.set}
return serialized_labels.values()
def import_frame_json(f,frame_index,event_id,video_id,w,h):
regions = []
df = Frame()
df.video_id = video_id
df.event_id = event_id
df.w = w
df.h = h
df.frame_index = frame_index
df.name = f['path']
for r in f.get('regions',[]):
regions.append(import_region_json(r,frame_index,video_id,event_id))
return df,regions
def import_region_json(r,frame_index,video_id,event_id,segment_index=None,frame_id=None):
dr = Region()
dr.frame_index = frame_index
dr.video_id = video_id
dr.event_id = event_id
dr.object_name = r['object_name']
dr.region_type = r.get('region_type', Region.ANNOTATION)
dr.full_frame = r.get('full_frame', False)
if segment_index:
dr.segment_index = segment_index
if frame_id:
dr.frame_id = frame_id
dr.x = r.get('x', 0)
dr.y = r.get('y', 0)
dr.w = r.get('w', 0)
dr.h = r.get('h', 0)
dr.confidence = r.get('confidence', 0.0)
if r.get('text', None):
dr.text = r['text']
else:
dr.text = ""
dr.metadata = r.get('metadata', None)
return dr
def create_event(e, v):
de = TEvent()
de.imported = True
de.started = e.get('started', False)
de.start_ts = e.get('start_ts', None)
de.completed = e.get('completed', False)
de.errored = e.get('errored', False)
de.error_message = e.get('error_message', "")
de.video_id = v.pk
de.operation = e.get('operation', "")
de.created = e['created']
if 'seconds' in e:
de.duration = e.get('seconds', -1)
else:
de.duration = e.get('duration', -1)
de.arguments = e.get('arguments', {})
de.task_id = e.get('task_id', "")
return de
class VideoImporter(object):
def __init__(self, video, json, root_dir):
self.video = video
self.json = json
self.root = root_dir
self.region_to_pk = {}
self.frame_to_pk = {}
self.event_to_pk = {}
self.segment_to_pk = {}
self.label_to_pk = {}
self.tube_to_pk = {}
self.name_to_shasum = {'inception':'48b026cf77dfbd5d9841cca3ee550ef0ee5a0751',
'facenet':'9f99caccbc75dcee8cb0a55a0551d7c5cb8a6836',
'vgg':'52723231e796dd06fafd190957c8a3b5a69e009c'}
def import_video(self):
if self.video.name is None or not self.video.name:
self.video.name = self.json['name']
self.video.frames = self.json['frames']
self.video.height = self.json['height']
self.video.width = self.json['width']
self.video.segments = self.json.get('segments', 0)
self.video.stream = self.json.get('stream',False)
self.video.dataset = self.json['dataset']
self.video.description = self.json['description']
self.video.metadata = self.json['metadata']
self.video.length_in_seconds = self.json['length_in_seconds']
self.video.save()
if not self.video.dataset:
old_video_path = [fname for fname in glob.glob("{}/video/*.mp4".format(self.root))][0]
new_video_path = "{}/video/{}.mp4".format(self.root, self.video.pk)
os.rename(old_video_path, new_video_path)
self.import_events()
self.import_segments()
self.bulk_import_frames()
self.convert_regions_files()
self.import_index_entries()
self.import_labels()
self.import_region_labels()
self.import_frame_labels()
self.import_segment_labels()
self.import_tube_labels()
self.import_video_labels()
def import_labels(self):
for l in self.json.get('labels', []):
dl, _ = Label.objects.get_or_create(name=l['name'],set=l.get('set',''))
self.label_to_pk[l['id']] = dl.pk
def import_region_labels(self):
region_labels = []
for rl in self.json.get('region_label_list', []):
drl = RegionLabel()
drl.frame_id = self.frame_to_pk[rl['frame']]
drl.region_id = self.region_to_pk[rl['region']]
drl.video_id = self.video.pk
if 'event' in rl:
drl.event_id = self.event_to_pk[rl['event']]
drl.frame_index = rl['frame_index']
drl.segment_index = rl['segment_index']
drl.label_id = self.label_to_pk[rl['label']]
region_labels.append(drl)
RegionLabel.objects.bulk_create(region_labels,1000)
def import_frame_labels(self):
frame_labels = []
for fl in self.json.get('frame_label_list', []):
dfl = FrameLabel()
dfl.frame_id = self.frame_to_pk[fl['frame']]
dfl.video_id = self.video.pk
if 'event' in fl:
dfl.event_id = self.event_to_pk[fl['event']]
dfl.frame_index = fl['frame_index']
dfl.segment_index = fl['segment_index']
dfl.label_id = self.label_to_pk[fl['label']]
frame_labels.append(dfl)
FrameLabel.objects.bulk_create(frame_labels,1000)
def import_segment_labels(self):
segment_labels = []
for sl in self.json.get('segment_label_list', []):
dsl = SegmentLabel()
dsl.video_id = self.video.pk
if 'event' in sl:
dsl.event_id = self.event_to_pk[sl['event']]
dsl.segment_id = self.segment_to_pk[sl['segment']]
dsl.segment_index = sl['segment_index']
dsl.label_id = self.label_to_pk[sl['label']]
segment_labels.append(dsl)
SegmentLabel.objects.bulk_create(segment_labels,1000)
def import_video_labels(self):
video_labels = []
for vl in self.json.get('video_label_list', []):
dvl = VideoLabel()
dvl.video_id = self.video.pk
if 'event' in vl:
dvl.event_id = self.event_to_pk[vl['event']]
dvl.label_id = self.label_to_pk[vl['label']]
video_labels.append(dvl)
VideoLabel.objects.bulk_create(video_labels,1000)
def import_tube_labels(self):
tube_labels = []
for tl in self.json.get('tube_label_list', []):
dtl = TubeLabel()
dtl.video_id = self.video.pk
if 'event' in tl:
dtl.event_id = self.event_to_pk[tl['event']]
dtl.label_id = self.label_to_pk[tl['label']]
dtl.tube_id = self.tube_to_pk[tl['tube']]
tube_labels.append(dtl)
TubeLabel.objects.bulk_create(tube_labels,1000)
def import_segments(self):
old_ids = []
segments = []
for s in self.json.get('segment_list', []):
old_ids.append(s['id'])
segments.append(self.create_segment(s))
segment_ids = Segment.objects.bulk_create(segments, 1000)
for i, k in enumerate(segment_ids):
self.segment_to_pk[old_ids[i]] = k.id
def create_segment(self,s):
ds = Segment()
ds.video_id = self.video.pk
ds.segment_index = s.get('segment_index', '-1')
ds.start_time = s.get('start_time', 0)
ds.framelist = s.get('framelist', {})
ds.end_time = s.get('end_time', 0)
ds.metadata = s.get('metadata', "")
if s.get('event', None):
ds.event_id = self.event_to_pk[s['event']]
ds.frame_count = s.get('frame_count', 0)
ds.start_index = s.get('start_index', 0)
return ds
def import_events(self):
old_ids = []
children_ids = defaultdict(list)
events = []
for e in self.json.get('event_list', []):
old_ids.append(e['id'])
if 'parent' in e:
children_ids[e['parent']].append(e['id'])
events.append(create_event(e, self.video))
event_ids = TEvent.objects.bulk_create(events, 1000)
for i, k in enumerate(event_ids):
self.event_to_pk[old_ids[i]] = k.id
for old_id in old_ids:
parent_id = self.event_to_pk[old_id]
for child_old_id in children_ids[old_id]:
ce = TEvent.objects.get(pk=self.event_to_pk[child_old_id])
ce.parent_id = parent_id
ce.save()
def convert_regions_files(self):
if os.path.isdir('{}/detections/'.format(self.root)):
source_subdir = 'detections' # temporary for previous version imports
os.mkdir('{}/regions'.format(self.root))
else:
source_subdir = 'regions'
convert_list = []
for k, v in self.region_to_pk.iteritems():
dd = Region.objects.get(pk=v)
original = '{}/{}/{}.jpg'.format(self.root, source_subdir, k)
temp_file = "{}/regions/d_{}.jpg".format(self.root, v)
converted = "{}/regions/{}.jpg".format(self.root, v)
if dd.materialized or os.path.isfile(original):
try:
os.rename(original, temp_file)
convert_list.append((temp_file, converted))
except:
raise ValueError, "could not copy {} to {}".format(original, temp_file)
for temp_file, converted in convert_list:
os.rename(temp_file, converted)
def import_index_entries(self):
# previous_transformed = set()
for i in self.json['index_entries_list']:
di = IndexEntries()
di.video = self.video
di.algorithm = i['algorithm']
# defaults only for backward compatibility
if 'indexer_shasum' in i:
di.indexer_shasum = i['indexer_shasum']
elif i['algorithm'] in self.name_to_shasum:
di.indexer_shasum = self.name_to_shasum[i['algorithm']]
else:
di.indexer_shasum = 'UNKNOWN'
if 'approximator_shasum' in i:
di.approximator_shasum = i['approximator_shasum']
di.count = i['count']
di.contains_detections = i['contains_detections']
di.contains_frames = i['contains_frames']
di.approximate = i['approximate']
di.created = i['created']
di.features_file_name = i['features_file_name']
if 'entries_file_name' in i:
entries = json.load(file('{}/indexes/{}'.format(self.root, i['entries_file_name'])))
else:
entries = i['entries']
di.detection_name = i['detection_name']
di.metadata = i.get('metadata',{})
transformed = []
for entry in entries:
entry['video_primary_key'] = self.video.pk
if 'detection_primary_key' in entry:
entry['detection_primary_key'] = self.region_to_pk[entry['detection_primary_key']]
if 'frame_primary_key' in entry:
entry['frame_primary_key'] = self.frame_to_pk[entry['frame_primary_key']]
transformed.append(entry)
di.entries =transformed
di.save()
def bulk_import_frames(self):
frame_regions = defaultdict(list)
frames = []
frame_index_to_fid = {}
for i, f in enumerate(self.json['frame_list']):
frames.append(self.create_frame(f))
frame_index_to_fid[i] = f['id']
if 'region_list' in f:
for a in f['region_list']:
ra = self.create_region(a)
if ra.region_type == Region.DETECTION:
frame_regions[i].append((ra, a['id']))
else:
frame_regions[i].append((ra, None))
elif 'detection_list' in f or 'annotation_list' in f:
raise NotImplementedError, "Older format no longer supported"
bulk_frames = Frame.objects.bulk_create(frames)
regions = []
regions_index_to_rid = {}
region_index = 0
bulk_regions = []
for i, k in enumerate(bulk_frames):
self.frame_to_pk[frame_index_to_fid[i]] = k.id
for r, rid in frame_regions[i]:
r.frame_id = k.id
regions.append(r)
regions_index_to_rid[region_index] = rid
region_index += 1
if len(regions) == 1000:
bulk_regions.extend(Region.objects.bulk_create(regions))
regions = []
bulk_regions.extend(Region.objects.bulk_create(regions))
regions = []
for i, k in enumerate(bulk_regions):
if regions_index_to_rid[i]:
self.region_to_pk[regions_index_to_rid[i]] = k.id
def create_region(self, a):
da = Region()
da.video_id = self.video.pk
da.x = a['x']
da.y = a['y']
da.h = a['h']
da.w = a['w']
da.vdn_key = a['id']
if 'text' in a:
da.text = a['text']
elif 'metadata_text' in a:
da.text = a['metadata_text']
if 'metadata' in a:
da.metadata = a['metadata']
elif 'metadata_json' in a:
da.metadata = a['metadata_json']
da.materialized = a.get('materialized', False)
da.png = a.get('png', False)
da.region_type = a['region_type']
da.confidence = a['confidence']
da.object_name = a['object_name']
da.full_frame = a['full_frame']
if a.get('event', None):
da.event_id = self.event_to_pk[a['event']]
if 'parent_frame_index' in a:
da.frame_index = a['parent_frame_index']
else:
da.frame_index = a['frame_index']
if 'parent_segment_index' in a:
da.segment_index = a.get('parent_segment_index', -1)
else:
da.segment_index = a.get('segment_index', -1)
return da
def create_frame(self, f):
df = Frame()
df.video_id = self.video.pk
df.name = f['name']
df.frame_index = f['frame_index']
df.subdir = f['subdir']
df.h = f.get('h', 0)
df.w = f.get('w', 0)
df.t = f.get('t', 0)
if f.get('event', None):
df.event_id = self.event_to_pk[f['event']]
df.segment_index = f.get('segment_index', 0)
df.keyframe = f.get('keyframe', False)
return df
def import_tubes(tubes, video_obj):
"""
:param segments:
:param video_obj:
:return:
"""
# TODO: Implement this
raise NotImplementedError
|
[
"akshayubhat@gmail.com"
] |
akshayubhat@gmail.com
|
e47f403cff42f8e7b4e57a819f1862876c988f13
|
23414270f524b36972140bd9044300ada3a28136
|
/密码体制算法实现/密码体制---ElGamal/ElGamal.py
|
9f44a89c9204be14e6ef8501456a57da819a8bd7
|
[] |
no_license
|
Jing0607101510/CryptoAlgorithms
|
421f463f5dc3e4701e8d1a5c7fbea6f772e92367
|
a0a78b37b1fd07db75ea7e5ef88c2c9cfee95ced
|
refs/heads/master
| 2021-10-09T06:57:25.105848
| 2018-12-23T06:46:20
| 2018-12-23T06:46:20
| 162,868,862
| 2
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,331
|
py
|
from PyQt5.QtWidgets import QApplication, QWidget
import sys
from ElGamal_ui import Ui_Form
import random
class ElGamal(QWidget, Ui_Form):
def __init__(self):
super(ElGamal, self).__init__()
self.setupUi(self)
self.setupSignal()
self.setupData()
def setupSignal(self):
self.encry.clicked.connect(self.onEncryptionClicked)
self.decry.clicked.connect(self.onDecryptionClicked)
self.clear1.clicked.connect(self.onClear1Clicked)
self.clear2.clicked.connect(self.onClear2Clicked)
self.gen_key.clicked.connect(self.genKey)
def onClear1Clicked(self):
self.textEdit_1.clear()
self.textBrowser_1.clear()
def onClear2Clicked(self):
self.textBrowser_2.clear()
self.textEdit_2.clear()
def setupData(self):
self.prime = int(self.lineEdit_1.text())
self.prime_root = int(self.lineEdit_2.text())
self.xa = random.randint(1,1000000000)
self.lineEdit_3.setText(str(self.xa))
self.public_a = self.gen_pub_key(self.xa)
self.lineEdit_7.setText(str(self.public_a))
def genKey(self):
self.xa = random.randint(1,1000000000)
self.lineEdit_3.setText(str(self.xa))
self.public_a = self.gen_pub_key(self.xa)
self.lineEdit_7.setText(str(self.public_a))
def gen_pub_key(self, x):
return self.fast_exp_mode(self.prime_root, x, self.prime)
def fast_exp_mode(self, a, b, c):
res = 1
a = a % c
while b != 0:
if b % 2 == 1:
res = (res * a) % c
b >>= 1
a = (a * a) % c
return res
def split_plainText(self, text):
if len(text) % 3 != 0:
text += '\0'*(3-len(text)%3)
n = 0
i = 0
res = []
while i < len(text):
n = (n << 8) | ord(text[i])
n = (n << 8) | ord(text[i+1])
n = (n << 8) | ord(text[i+2])
res.append(n)
n = 0
i += 3
return res
def onEncryptionClicked(self):
plain_text = self.textEdit_1.toPlainText()
if plain_text:
blocks = self.split_plainText(plain_text)
result = ''
for block in blocks:
result += self.encryption(block)
self.textBrowser_1.setText(result)
def calc(self, a, key):
return self.fast_exp_mode(a, key, self.prime)
def encryption(self, block):
k = random.randint(1, self.prime-1)
K = self.calc(self.public_a, k)
c1 = self.calc(self.prime_root, k)
c2 = ((K%self.prime)*(block%self.prime))%self.prime
return '%08x%08x'%(c1, c2)
def split_enText(self, text):
if len(text) % 16 != 0:
text += '0' * (16 - len(text) % 16)
result = []
i = 0
while i < len(text):
c1 = text[i: i+8]
c2 = text[i+8: i+16]
i += 16
c1 = int(c1, 16)
c2 = int(c2, 16)
result.append([c1, c2])
return result
def onDecryptionClicked(self):
en_text = self.textEdit_2.toPlainText()
if en_text:
result = ''
blocks = self.split_enText(en_text)
for block in blocks:
result += self.decryption(block)
self.textBrowser_2.setText(result)
def decryption(self, block):
c1 = block[0]
c2 = block[1]
K = self.calc(c1, self.xa)
K_inverse = self.get_inverse(K, self.prime)
M = (c2 * (K_inverse % self.prime)) % self.prime
m = ''
for i in range(3):
m += chr(M&0x0ff)
M >>= 8
return m[::-1]
def get_inverse(self, x, mod):
x1 = 1
x2 = 0
x3 = mod
y1 = 0
y2 = 1
y3 = (x%mod+mod)%mod
while y3 != 1:
q = x3 // y3
t1 = x1 - q * y1
t2 = x2 - q * y2
t3 = x3 - q * y3
x1 = y1
x2 = y2
x3 = y3
y1 = t1
y2 = t2
y3 = t3
return y2
if __name__ == "__main__":
app = QApplication(sys.argv)
elgamal = ElGamal()
elgamal.show()
sys.exit(app.exec_())
|
[
"1293521172@qq.com"
] |
1293521172@qq.com
|
21426abe1f48a898a33972d629c9120481bac87b
|
c59e65267ca6b2cea83cc00a136cd4e1a18da0a1
|
/PyBuildingData/PyBuildingData.py
|
755af6db79d9301d457890386955c27e25452430
|
[
"MIT"
] |
permissive
|
victorcalixto/FOSS-BIM-Experiments
|
c46bb4cd6a0f1e2d240f98f86296735bcd6748cb
|
9a4a126b7ba4bff43dec21fa1560b4d22ae34558
|
refs/heads/main
| 2023-08-14T20:20:01.833767
| 2021-10-01T09:02:13
| 2021-10-01T09:02:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 21,510
|
py
|
# Helpers
#Primitives
def PointXY(x,y)
def PointXYZ(x,y,z)
def Line2D(PointXY,PointXY)
def Arc2D(PointXY,PointXY,PointXY)
def PolyCurve
sqrt2 = 1.414213562 # Squareroot of number 2
def find_in_list_of_list(mylist, char):
for sub_list in mylist:
if char in sub_list:
return (mylist.index(sub_list))
raise ValueError("'{char}' is not in list".format(char=char))
# Py Building Data
def PyBData.Common
#PyBData.Common.Framing
#PyBData.Common.Section
#Describe Parametric Profiles
#def Section
#Aluminium
#Steel
def parameters
def C-channel_parallel_flange(Section)
Description = "C-channel with parallel flange"
ID = "C_PF"
#parameters
b = Section.b #width
h = Sectopm.h #height
tf = Section.tf #flange thickness
tw = Section.tw #web thickness
r = Section.r #web fillet
e = Section.e #centroid horizontal
#describe points
p1 = [-e,-h/2] #left bottom
p2 = [b-e,-h/2] #right bottom
p3 = [b-e,-h/2+tf]
p4 = [-e+tw+r,-h/2+tf] #start arc
p5 = [-e+tw+r-r1,-h/2+tf+r-r1] #second point arc
p6 = [-e+tw,-h/2+tf+r] #end arc
p7 = [-e+tw,h/2-tf-r] #start arc
p8 = [-e+tw+r-r1,h/2-tf-r+r1] #second point arc
p9 = [-e+tw+r,h/2-tf] #end arc
p10 = [b-e,h/2-tf]
p11 = [b-e,h/2] #right top
p12 = [-e,h/2] #left top
#describe curves
l1 = line2D(p1,p2)
l2 = line2D(p2,p3)
l3 = line2D(p3,p4)
l3 = arc2D(p4,p5,p6)
l4 = line2D(p6,p7)
l5 = arc2D(p7,p8,p9)
l6 = line2D(p9,p10)
l7 = line2D(p10,p11)
l8 = line2D(p11,p12)
l9 = line2D(p12,p1)
curve = [l1,l2,l3,l4,l5,l6,l7,l8,l9]
def C-channel_sloped_flange(Section)
Description = "C-channel with sloped flange"
ID = "C_SF"
#parameters
b = Section.b #width
h = Sectopm.h #height
tf = Section.tf #flange thickness
tw = Section.tw #web thickness
r1 = Section.r1 #web fillet
r11 = r1/sqrt2
r2 = Section.r2 #flange fillet
r21 = r2/sqrt2
tl = Section.tl #flange thickness location from right
sa = Section.sa #the angle of sloped flange in degrees
e = Section.e #centroid horizontal
#describe points
#describe points
p1 = [-e,-h/2] #left bottom
p2 = [b-e,-h/2] #right bottom
p3 = [b-e,-h/2+tf-math.tan(sa)*tl-r2] #start arc
p4 = [b-e-r2+r21,-h/2+tf-math.tan(sa)*tl-r2+r21] #second point arc
p5 = [b-e-r2+math.sin(sa)*r2,-h/2+tf-math.tan(sa)*(tl-r2)] #end arc
p6 = [-e+tw+r1-math.sin(sa)*r1,-h/2+tf+math.tan(sa)*(b-tl-tw-r1)] #start arc
p7 = [-e+tw+r1-r11,-h/2+tf+math.tan(sa)*(b-tl-tw-r1)+r1-r11] #second point arc
p8 = [-e+tw,-h/2+tf+math.tan(sa)*(b-tl-tw)+r1] #end arc
p9 = [p8[0],-p8[1]] #start arc
p10 = [p7[0],-p7[1]] #second point arc
p11 = [p6[0],-p6[1]] #end arc
p12 = [p5[0],-p5[1]] #start arc
p13 = [p4[0],-p4[1]] #second point arc
p14 = [p3[0],-p3[1]] #end arc
p15 = [p2[0],-p2[1]] #right top
p16 = [p1[0],-p1[1]] #left top
#describe curves
l1 = line2D(p1,p2)
l2 = line2D(p2,p3)
l3 = arc2D(p3,p4,p5)
l4 = line2D(p5,p6)
l5 = arc2D(p6,p7,p8)
l6 = line2D(p8,p9)
l7 = arc2D(p9,p10,p11)
l8 = line2D(p11,p12)
l9 = arc2D(p12,p13,p14)
l10 = line2D(p14,p15)
l11 = line2D(p15,p16)
l12 = line2D(p16,p1)
curve = [l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12]
def I-shape_parallel_flange(Section)
Description = "I Shape profile with parallel flange"
ID = "I_PF"
#parameters
b = Section.b #width
h = Section.h #height
tf = Section.tf #flange thickness
tw = Section.tw #web thickness
r = Section.r #web fillet
r1 = r/sqrt2
#describe points
p1 = [b/2,-h/2] #right bottom
p2 = [b/2,-h/2+tf]
p3 = [tw/2+r,-h/2+tf] #start arc
p4 = [tw/2+r-r1,(-h/2+tf+r-r1)] #second point arc
p5 = [tw/2,-h/2+tf+r] #end arc
p6 = [tw/2,h/2-tf-r] #start arc
p7 = [tw/2+r-r1,h/2-tf-r+r1] #second point arc
p8 = [tw/2+r,h/2-tf] #end arc
p9 = [b/2,h/2-tf]
p10 = [b/2),(h/2] #right top
p11 = [-p10[0],p10[1]] #left top
p12 = [-p9[0],p9[1]]
p13 = [-p8[0],p8[1]] #start arc
p14 = [-p7[0],p7[1]] #second point arc
p15 = [-p6[0],p6[1]] #end arc
p16 = [-p5[0],p5[1]] #start arc
p17 = [-p4[0],p4[1]] #second point arc
p18 = [-p3[0],p3[1]] #end arc
p19 = [-p2[0],p2[1]]
p20 = [-p1[0],p1[1]]
#describe curves
l1 = line2D(p1,p2)
l2 = line2D(p2,p3)
l3 = arc2D(p3,p4,p5)
l4 = line2D(p5,p6)
l5 = arc2D(p6,p7,p8)
l6 = line2D(p8,p9)
l7 = line2D(p9,p10)
l8 = line2D(p10,p11)
l9 = line2D(p11,p12)
l10 = line2D(p12,p13)
l11 = arc2D(p13,p14,p15)
l12 = line2D(p15,p16)
l13 = arc2D(p16,p17,p18)
l14 = line2D(p18,p19)
l15 = line2D(p19,p20)
l16 = line2D(p20,p1)
curve = [l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12,l13,l14,l15,l16]
("steelprofilename", "h", "bf", "tf", "tw", "r", "I-shape parallel flange"),
def L_angle(Section)
Description = "L-angle""
ID = "L"
#parameters
b = Section.b #width
h = Section.h #height
tw = Section.tw #wall nominal thickness
tf = tw
r1 = Section.r1 #inner fillet
r11 = r1/math.sqrt(2)
r2 = Section.r2 #outer fillet
r21 = r2/math.sqrt(2)
ex = obj.CentroidHorizontal.Value #from left
ey = obj.CentroidVertical.Value #from bottom
#describe points
p1 = [-ex,-ey] #left bottom
p2 = [b-ex,-ey] #right bottom
p3 = [b-ex,-ey+tf-r2] #start arc
p4 = [b-ex-r2+r21,-ey+tf-r2+r21] #second point arc
p5 = [b-ex-r2,-ey+tf] #end arc
p6 = [-ex+tf+r1,-ey+tf] #start arc
p7 = [-ex+tf+r1-r11,-ey+tf+r1-r11] #second point arc
p8 = [-ex+tf,-ey+tf+r1] #end arc
p9 = [-ex+tf,h-ey-r2] #start arc
p10 = [-ex+tf-r2+r21,h-ey-r2+r21] #second point arc
p11 = [-ex+tf-r2,h-ey] #end arc
p12 = [-ex,h-ey] #left top
#describe curves
l1 = line2D(p1,p2)
l2 = line2D(p2,p3)
l3 = arc2D(p3,p4,p5)
l4 = line2D(p5,p6)
l5 = arc2D(p6,p7,p8)
l6 = line2D(p8,p9)
l7 = arc2D(p9,p10,p11)
l8 = line2D(p11,p12)
l9 = line2D(p12,p1)
curve = [l1,l2,l3,l4,l5,l6,l7,l8,l9]
def rectangle_hollow_section(Section)
Description = "rectangle hollow section"
ID = "RHS"
#parameters
b = Section.b #width
h = Section.h #height
t = Section.t #wall nominal thickness
r1 = Section.r1 #inner fillet
r2 = Section.r2 #outer fillet
#describe points
#outer curve
p1 = [b/2-r1,-h/2] #right bottom start arc
p2 = [b/2-r1+r11,-h/2+r1-r11] #right bottom second point arc
p3 = [b/2,-h/2+r1] #right bottom end arc
p4 = [p3[0],-p3[1]] #right top start arc
p5 = [p2[0],-p2[1]] #right top second point arc
p6 = [p1[0],-p1[1]] #right top end arc
p7 = [-p6[0],p6[1]] #left top start arc
p8 = [-p5[0],p5[1]] #left top second point arc
p9 = [-p4[0],p4[1]] #left top end arc
p10 = [p9[0],-p9[1]] #left bottom start arc
p11 = [p8[0],-p8[1]] #left bottom second point arc
p12 = [p7[0],-p7[1]] #left bottom end arc
#inner curve
q1 = [b/2-t-r2,-h/2+t] #right bottom start arc
q2 = [b/2-t-r2+r21,-h/2+t+r2-r21] #right bottom second point arc
q3 = [b/2-t,-h/2+t+r2] #right bottom end arc
q4 = [q3[0],-q3[1]] #right top start arc
q5 = [q2[0],-q2[1]] #right top second point arc
q6 = [q1[0],-q1[1]] #right top end arc
q7 = [-q6[0],q6[1]] #left top start arc
q8 = [-q5[0],q5[1]] #left top second point arc
q9 = [-q4[0],q4[1]] #left top end arc
q10 = [q9[0],-q9[1]] #left bottom start arc
q11 = [q8[0],-q8[1]] #left bottom second point arc
q12 = [q7[0],-q7[1]] #left bottom end arc
#CURVES TO ADD
#ConcreteCastInPlace
#ConcretePrecast
#Wood
SectionDatabase #Database of steelsections, concretesections and wood dimensions
# Concrete
def concrete_shapes(shapename):
shape_data = ["rectangle shape",
"round shape",
"H-shape",
"U-shape",
"L-shape",
"T-shape",
"RHS-shape",
"CHS-shape",
"cross-shape"
]
return "test"
# Steel
# profile means for coldformed steel
# otherwise a section is hotrolled or welded
def steel_profiles(profilename):
shape_data = [("C-profile"),
("C-profile with fold"),
("C-profile with lips"),
("C-channel parallel flange"), #done
("C-channel sloped flange"), #done
("I-shape parallel flange"), #done
("I-shape sloped flange"),
("I-shape welded"),
("I-split parallel flange"),
("I-split sloped flange"),
("L-profile"), #done
("L-profile with lips"),
("L-angle"),
("pipe standard"),
("rectangle bar"),
("rectangle hollow section"),
("round"),
("round hollow section",),
("sigma profile"),
("sigma profile with fold"),
("sigma profile with lips"),
("T-shape"),
("Z-profile"),
("Z-profile with lips")
]
steelprofile_data =[("HEA100",96,100,5,8,12,"I-shape parallel flange"),
("HEA120",114,120,5,8,12,"I-shape parallel flange"),
("HEA140",133,140,6,9,12,"I-shape parallel flange"),
("HEA160",152,160,6,9,15,"I-shape parallel flange"),
("HEA180",171,180,6,10,15,"I-shape parallel flange"),
("HEA200",190,200,7,10,18,"I-shape parallel flange"),
("HEA220",210,220,7,11,18,"I-shape parallel flange"),
("HEA240",230,240,8,12,21,"I-shape parallel flange"),
("HEA260",250,260,8,13,24,"I-shape parallel flange"),
("HEA280",270,280,8,13,24,"I-shape parallel flange"),
("HEA300",290,300,9,14,27,"I-shape parallel flange"),
("HEA320",310,300,9,16,27,"I-shape parallel flange"),
("HEA360",350,300,10,18,27,"I-shape parallel flange"),
("HEA400",390,300,11,19,27,"I-shape parallel flange"),
("HEA450",440,300,12,21,27,"I-shape parallel flange"),
("HEA500",490,300,12,23,27,"I-shape parallel flange"),
("HEA550",540,300,13,24,27,"I-shape parallel flange"),
("HEA600",590,300,13,25,27,"I-shape parallel flange"),
("HEA650",640,300,14,26,27,"I-shape parallel flange"),
("HEA700",690,300,15,27,27,"I-shape parallel flange"),
("HEA800",790,300,15,28,30,"I-shape parallel flange"),
("HEA900",890,300,16,30,30,"I-shape parallel flange"),
("HEA1000",990,300,17,31,30,"I-shape parallel flange"),
("HEB100",100,100,6,10,12,"I-shape parallel flange"),
("HEB120",120,120,7,11,12,"I-shape parallel flange"),
("HEB140",140,140,7,12,12,"I-shape parallel flange"),
("HEB160",160,160,8,13,15,"I-shape parallel flange"),
("HEB180",180,180,9,14,15,"I-shape parallel flange"),
("HEB200",200,200,9,15,18,"I-shape parallel flange"),
("HEB220",220,220,10,16,18,"I-shape parallel flange"),
("HEB240",240,240,10,17,21,"I-shape parallel flange"),
("HEB260",260,260,10,18,24,"I-shape parallel flange"),
("HEB280",280,280,11,18,24,"I-shape parallel flange"),
("HEB300",300,300,11,19,27,"I-shape parallel flange"),
("HEB320",320,300,12,21,27,"I-shape parallel flange"),
("HEB340",340,300,12,22,27,"I-shape parallel flange"),
("HEB360",360,300,13,23,27,"I-shape parallel flange"),
("HEB400",400,300,14,24,27,"I-shape parallel flange"),
("HEB450",450,300,14,26,27,"I-shape parallel flange"),
("HEB500",500,300,15,28,27,"I-shape parallel flange"),
("HEB550",550,300,15,29,27,"I-shape parallel flange"),
("HEB600",600,300,16,30,27,"I-shape parallel flange"),
("HEB650",650,300,16,31,27,"I-shape parallel flange"),
("HEB700",700,300,17,32,27,"I-shape parallel flange"),
("HEB800",800,300,18,33,30,"I-shape parallel flange"),
("HEB900",900,300,19,35,30,"I-shape parallel flange"),
("HEB1000",1000,300,19,36,30,"I-shape parallel flange"),
("HEM100",120,106,12,20,12,"I-shape parallel flange"),
("HEM120",140,126,13,21,12,"I-shape parallel flange"),
("HEM140",160,146,13,22,12,"I-shape parallel flange"),
("HEM160",180,166,14,23,15,"I-shape parallel flange"),
("HEM180",200,186,15,24,15,"I-shape parallel flange"),
("HEM200",220,206,15,25,18,"I-shape parallel flange"),
("HEM220",240,226,16,26,18,"I-shape parallel flange"),
("HEM240",270,248,18,32,21,"I-shape parallel flange"),
("HEM260",290,268,18,33,24,"I-shape parallel flange"),
("HEM280",310,288,19,33,24,"I-shape parallel flange"),
("HEM300",340,310,21,39,27,"I-shape parallel flange"),
("HEM320",359,309,21,40,27,"I-shape parallel flange"),
("HEM340",377,309,21,40,27,"I-shape parallel flange"),
("HEM360",395,308,21,40,27,"I-shape parallel flange"),
("HEM400",432,307,21,40,27,"I-shape parallel flange"),
("HEM450",478,307,21,40,27,"I-shape parallel flange"),
("HEM500",524,306,21,40,27,"I-shape parallel flange"),
("HEM550",572,306,21,40,27,"I-shape parallel flange"),
("HEM600",620,305,21,40,27,"I-shape parallel flange"),
("HEM650",668,305,21,40,27,"I-shape parallel flange"),
("HEM700",716,304,21,40,27,"I-shape parallel flange"),
("HEM800",814,303,21,40,30,"I-shape parallel flange"),
("HEM900",910,302,21,40,30,"I-shape parallel flange"),
("HEM1000",1008,302,21,40,30,"I-shape parallel flange"),
("IPE80",80,3.8,46,5.2,5,"I-shape parallel flange"),
("IPE100",100,4.1,55,5.7,7,"I-shape parallel flange"),
("IPE120",120,4.4,64,6.3,7,"I-shape parallel flange"),
("IPE140",140,4.7,73,6.9,7,"I-shape parallel flange"),
("IPE160",160,5,82,7.4,9,"I-shape parallel flange"),
("IPE180",180,5.3,91,8,9,"I-shape parallel flange"),
("IPE200",200,5.6,100,8.5,12,"I-shape parallel flange"),
("IPE220",220,5.9,110,9.2,12,"I-shape parallel flange"),
("IPE240",240,6.2,120,9.8,15,"I-shape parallel flange"),
("IPE270",270,6.6,135,10.2,15,"I-shape parallel flange"),
("IPE300",300,7.1,150,10.7,15,"I-shape parallel flange"),
("IPE330",330,7.5,160,11.5,18,"I-shape parallel flange"),
("IPE360",360,8,170,12.7,18,"I-shape parallel flange"),
("IPE400",400,8.6,180,13.5,21,"I-shape parallel flange"),
("IPE450",450,9.4,190,14.6,21,"I-shape parallel flange"),
("IPE500",500,10.2,200,16,21,"I-shape parallel flange"),
("IPE550",550,11.1,210,17.2,24,"I-shape parallel flange"),
("IPE600",600,12,220,19,24,"I-shape parallel flange"),
("UNP80",80,45,6,8,"C-channelslopedflange"),
("UNP100",100,50,6,9,"C-channelslopedflange"),
("UNP120",120,55,7,9,"C-channelslopedflange"),
("UNP140",140,60,7,10,"C-channelslopedflange"),
("UNP160",160,65,8,11,"C-channelslopedflange"),
("UNP180",180,70,8,11,"C-channelslopedflange"),
("UNP200",200,75,9,12,"C-channelslopedflange"),
("UNP220",220,80,9,13,"C-channelslopedflange"),
("UNP240",240,85,10,13,"C-channelslopedflange"),
("UNP260",260,90,10,14,"C-channelslopedflange"),
("UNP280",280,95,10,15,"C-channelslopedflange"),
("UNP300",300,100,10,16,"C-channelslopedflange"),
("UNP320",320,100,14,18,"C-channelslopedflange"),
("UNP350",350,100,14,16,"C-channelslopedflange"),
("UNP380",380,102,14,16,"C-channelslopedflange"),
("UNP400",400,110,14,18,"C-channelslopedflange"),
("UPE80",80,50,4.5,8,10,"C-channelparallelflange"),
("UPE100",100,55,5,8.5,10,"C-channelparallelflange"),
("UPE120",120,60,5.5,9,10,"C-channelparallelflange"),
("UPE140",140,65,6,9.5,10,"C-channelparallelflange"),
("UPE160",160,70,6.5,10,12,"C-channelparallelflange"),
("UPE180",180,75,7,10.5,12,"C-channelparallelflange"),
("UPE200",200,80,7.5,11,12,"C-channelparallelflange"),
("UPE220",220,85,8,12,12,"C-channelparallelflange"),
("UPE240",240,90,8.5,13,15,"C-channelparallelflange"),
("UPE270",270,95,9,14,15,"C-channelparallelflange"),
("UPE300",300,100,9.5,15,15,"C-channelparallelflange"),
("UPE330",330,105,11,16,18,"C-channelparallelflange"),
("UPE360",360,110,12,17,18,"C-channelparallelflange"),
("UPE400",400,115,13.5,18,18,"C-channelparallelflange")
]
steelprofile_sublist = steelprofile_data[find_in_list_of_list(steelprofile_data, profilename)]
parameternames_sublist = shape_data[find_in_list_of_list(shape_data, steelprofile_sublist[-1])]
return steelprofile_sublist, parameternames_sublist
name_profile = "HEA200"
profile_data = steel_profiles(name_profile)[0]
profile_name = profile_data[0]
b = profile_data[2]
h = profile_data[1]
tw = profile_data[4]
tf = profile_data[3]
r = profile_data[5]
print(b)
print(h)
print(tw)
print(tf)
print(r)
|
[
"30430941+DutchSailor@users.noreply.github.com"
] |
30430941+DutchSailor@users.noreply.github.com
|
6197cdabb7c4583ac32673f476142d255aaa856f
|
d65499ebd34c4fb8095294b12619104efbbd8ee4
|
/Airflow Writing/main_code.py
|
2c2325b4f3e04e8e185fae5014555c6833c0d61d
|
[] |
no_license
|
ashishsingh99/AirFlow-Writing
|
3135a93a95c0b22e97ca4a8a91584ddaaea9fe3f
|
696b66ab44751f5b256b1fe8591bc17abb8d6ebe
|
refs/heads/main
| 2023-05-29T03:14:36.446736
| 2021-06-13T13:28:24
| 2021-06-13T13:28:24
| 376,550,471
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,451
|
py
|
import cv2
import numpy as np
#### global ####
x,y,k = 200,200,-1
cap = cv2.VideoCapture(0)
################################################
############# func def #########################
def take_inp(event, x1, y1, flag, param):
global x, y, k
if event == cv2.EVENT_LBUTTONDOWN:
x = x1
y = y1
k = 1
cv2.namedWindow("enter_point")
cv2.setMouseCallback("enter_point", take_inp)
##### taking input point ######################
while True:
_, inp_img = cap.read()
inp_img = cv2.flip(inp_img, 1)
gray_inp_img = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
cv2.imshow("enter_point", inp_img)
if k == 1 or cv2.waitKey(30) == 27:
cv2.destroyAllWindows()
break
##############################################
stp = 0
########## opical flow starts here ###########
old_pts = np.array([[x, y]], dtype=np.float32).reshape(-1,1,2)
mask = np.zeros_like(inp_img)
while True:
_, new_inp_img = cap.read()
new_inp_img = cv2.flip(new_inp_img, 1)
new_gray = cv2.cvtColor(new_inp_img, cv2.COLOR_BGR2GRAY)
new_pts,status,err = cv2.calcOpticalFlowPyrLK(gray_inp_img,
new_gray,
old_pts,
None, maxLevel=1,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
15, 0.08))
for i, j in zip(old_pts, new_pts):
x,y = j.ravel()
a,b = i.ravel()
if cv2.waitKey(2) & 0xff == ord('q'):
stp = 1
elif cv2.waitKey(2) & 0xff == ord('w'):
stp = 0
elif cv2.waitKey(2) == ord('n'):
mask = np.zeros_like(new_inp_img)
if stp == 0:
mask = cv2.line(mask, (a,b), (x,y), (0,0,255), 6)
cv2.circle(new_inp_img, (x,y), 6, (0,255,0), -1)
new_inp_img = cv2.addWeighted(mask, 0.3, new_inp_img, 0.7, 0)
cv2.putText(mask, "'q' to gap 'w' - start 'n' - clear", (10,50),
cv2.FONT_HERSHEY_PLAIN, 2, (255,255,255))
cv2.imshow("ouput", new_inp_img)
cv2.imshow("result", mask)
gray_inp_img = new_gray.copy()
old_pts = new_pts.reshape(-1,1,2)
if cv2.waitKey(1) & 0xff == ord("a"):
break
cv2.destroyAllWindows()
cap.release()
|
[
"noreply@github.com"
] |
noreply@github.com
|
23b938e1ab8367b219527b79832098965d421692
|
b306123b0b6a7751357bfd553763f2d9cb62689d
|
/transformers/authors_transformer.py
|
21690931b1659e814655a1338a37a909c5e6e461
|
[] |
no_license
|
aascode/detecting-deception-in-political-debates
|
cc3e326493199fbc7fa94d8cf06821dce5d52c10
|
5f7c007e82fef1792aa7982bfb630cd0ddf057aa
|
refs/heads/master
| 2022-03-22T22:05:47.891021
| 2020-01-05T04:07:59
| 2020-01-05T05:26:16
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 522
|
py
|
from transformers import BaseTransformer
class AuthorsTransformer(BaseTransformer):
def __init__(self, data):
self.names = ['AUTHOR_TRUMP', 'AUTHOR_CLINTON', 'AUTHOR_OTHER']
transformed = []
for author in data:
is_trump = 1 if 'TRUMP' in author else 0
is_clinton = 1 if 'CLINTON' in author else 0
is_other = 1 if is_trump == 0 and is_clinton == 0 else 0
transformed.append([is_trump, is_clinton, is_other])
self.features = transformed
|
[
"34142780+fire0@users.noreply.github.com"
] |
34142780+fire0@users.noreply.github.com
|
9aec856b0fb1eb94d3f55b1249194e4c210932aa
|
1f3f0dc8799dac1e7974b1b05211c2bb863db787
|
/Asakura/3set/part26.py
|
f09145e1e0d00d37716d85d01ebdf63eb69112a2
|
[] |
no_license
|
m-note/100knock2015
|
665bb27bc84a0eacaa795523b5e65a5b64c426ac
|
84cd1d0617b0b5c15f64e593dd2e0ae21a4dcef7
|
refs/heads/master
| 2021-01-18T19:41:54.111994
| 2015-07-28T16:15:53
| 2015-07-28T16:15:53
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,156
|
py
|
#!usr/bin/python
#--*--coding:utf-8--*--
#強調マークアップの除去：025の処理時に、テンプレートの値からMediawikiの強調マークアップを除去してテキストに変換せよ
import sys
import re
if __name__ == '__main__':
inputfile = open(sys.argv[1],'r')
re_start = re.compile('\{\{基礎情報')
re_end = re.compile('\}\}')
re_temp = re.compile('\|(.+?) = (.+)')
re_ref = re.compile('(.*)(<ref>|<ref.*)')
re_impact = re.compile('\'\'+')
mydict = {}
flag = False
for line in inputfile:
if re_start.match(line) is not None:
flag = True
continue
if re_end.match(line) is not None:
flag = False
break
if flag:
result = re_temp.search(line)
if result is not None:
key = result.group(1)
ref = re_ref.search(result.group(2))
if ref is not None:
value = ref.group(1)
else:
value = result.group(2)
value = re_impact.sub('',value)
mydict[key] = value
for key,value in sorted(mydict.items()):
print '%s = %s' % (key,value)
|
[
"tennisabc562@gmail.com"
] |
tennisabc562@gmail.com
|
e05b8c8908428641797fabbd3dc891fe97237cdb
|
8b30f1b8bcee0e8428a183e944ab01d4bd8912a3
|
/Trees/_tree_abstract.py
|
deecacfa9ed3ad4cfc86f7fe9aa76b3fc90a00f5
|
[] |
no_license
|
hayleymathews/data_structures_and_algorithms
|
1cd2bb4358e8f8a9681b79e2cf862dc51be4a4b6
|
ef89e4c89cb014d0acea1669f927cadc6af70225
|
refs/heads/master
| 2020-09-02T13:27:15.083968
| 2018-01-27T20:42:46
| 2018-01-27T20:42:46
| 219,231,977
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,872
|
py
|
"""python implementation of abstract class for ADT Tree"""
from abc import ABC, abstractmethod
from Queues.linked_queue import LinkedQueue
class Tree(ABC):
"""
abstract class representing a tree structure
"""
class Node:
def __init__(self, value):
self.value = value
def __repr__(self):
return "Node: {}".format(self.value)
def __init__(self):
self.root = None
def __iter__(self):
"""
generate an iteration of the tree's elements
"""
for p in self.positions():
yield p.element()
@abstractmethod
def __len__(self):
"""
return total number of elements in tree
"""
pass
@abstractmethod
def add_root(self, e):
"""
add Element e as tree's root
"""
pass
def get_root(self):
"""
return Position representing tree's root or None if empty
"""
return self.root
@abstractmethod
def parent(self, p):
"""
return Position representing p's paren of None if p is root
"""
pass
@abstractmethod
def num_children(self, p):
"""
return number of children Position p has
"""
pass
@abstractmethod
def children(self, p):
"""
generate an iteration of Positions representing p's children
"""
pass
def is_root(self, p):
"""
return True if Position p represents root of tree O(1)
"""
return self.get_root() == p
def is_leaf(self, p):
"""
return True if Position p has no children O(1)
"""
return self.num_children(p) == 0
def is_empty(self):
"""
return True if tree is empty
"""
return len(self) == 0
def depth(self, p):
"""
return number of levels separating Position p from root
"""
if self.is_root(p):
return 0
else:
return 1 + self.depth(self.parent(p))
def positions(self):
"""
generate an iteration of the tree's positions
"""
return self.preorder()
def preorder(self):
"""
generate a preorder iteration of positions in the tree
"""
if not self.is_empty():
for p in self._subtree_preorder(self.root):
yield p
def _subtree_preorder(self, p):
"""
generate a preorder iteration of positions in subtree rooted at p
"""
yield p
for c in self.children(p):
for other in self._subtree_preorder(c):
yield other
def postorder(self):
"""
generate a postorder iteration of positions in the tree
"""
if not self.is_empty():
for p in self._subtree_postorder(self.root):
yield p
def _subtree_postorder(self, p):
"""
genereate a postorder iteration of positions in subtree rooted at p
"""
for c in self.children(p):
for other in self._subtree_postorder(c):
yield other
yield p
def breadth_first(self):
"""
generate a breadth-first iteratorion of the positions of the tree
"""
if not self.is_empty():
fringe = LinkedQueue()
fringe.enqueue(self.root)
while not fringe.is_empty():
p = fringe.dequeue()
yield p
for c in self.children(p):
fringe.enqueue(c)
def preorder_indent(self, T, p, d):
"""
print preorder representation of subtree of T rooted at p at depth d
"""
print(2*d*' ' + str(p.element()))
for c in T.children(p):
self.preorder_indent(T, c, d+ 1)
|
[
"hmathews.tulane@gmail.com"
] |
hmathews.tulane@gmail.com
|
e7e5c5a12d2160bfbdb3aa8eb09df7b667911baf
|
0a8619f073dd199f054eff1947d3d5a66f0f160c
|
/4.py
|
cabb6877cdb802c615eceb7b09bd24fcfd2db1e3
|
[] |
no_license
|
rainmayecho/applemunchers
|
70fc858eb6d9086365398b1515abac9e3fd265dd
|
cd1e92836eac53a781597bf316d80bcf0cba9dfb
|
refs/heads/master
| 2021-01-18T22:24:48.087329
| 2013-09-10T16:55:47
| 2013-09-10T16:55:47
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 381
|
py
|
def func():
i=900
palindrome = 0
for x in range(i,1000):
for y in range(i+1,1000):
if is_palindrome(str(x*y)) and x*y > palindrome:
palindrome = x*y
return palindrome
def is_palindrome(input):
string = list(input)
string.reverse()
if list(input) == string:
return True
return False
|
[
"sanguinex9@gmail.com"
] |
sanguinex9@gmail.com
|
dc87d52a4a3efca7e2c41d6882afd2891afdd885
|
1b622808bd714e3c770c811bfa6aed0b36693928
|
/30.py
|
1453dce7785baf4be61fffc89a73d01df55b6983
|
[] |
no_license
|
dinob0t/project_euler
|
a4d9a28b2994b64ea6ad064b05553f13ad38fc6d
|
b0fed278ae2bfc1bfe44043f2b02482ebc210a56
|
refs/heads/master
| 2020-09-12T14:07:16.137051
| 2014-09-01T16:16:49
| 2014-09-01T16:16:49
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 675
|
py
|
def sum_power_digits(num,power):
num_str = str(num)
num_sum = 0
for i in range(len(num_str)):
num_sum = num_sum + int(num_str[i])**power
return num_sum
def find_max(power):
nine_list = []
nine_list.append('9')
nines = int("".join(nine_list))
while nines < sum_power_digits(nines,power):
nine_list.append('9')
nines = int("".join(nine_list))
return nines
def find_numbers(power):
max_test = find_max(power)
success_sum = 0
for i in range(2,max_test):
spd = sum_power_digits(i, power)
if spd == i:
success_sum = success_sum + i
return success_sum
if __name__ == "__main__":
#print sum_power_digits(99999,4)
print find_numbers(5)
|
[
"dean.hillan@gmail.com"
] |
dean.hillan@gmail.com
|
5037790cae63e5d0725dbad341711f5906ac6bd6
|
705a1a6b909cb8456780e10e28cb255fc17acde5
|
/Jubilacion.py
|
f3ef2e2c421b3909d50656af5bd7d505ec8ef870
|
[] |
no_license
|
danistenia/Calculadora-Pensiones-AFP
|
7b321ce4ea8beedde3436547d4acabc886d2663e
|
145c20778f975e53e4b3a6fddda0cd427efdc358
|
refs/heads/master
| 2022-12-06T15:21:50.362452
| 2020-08-15T20:56:35
| 2020-08-15T20:56:35
| 287,823,353
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,844
|
py
|
import streamlit as st
from enum import Enum
from typing import List
def jubilacion(sueldo_clp,tasa,tiempo_años,capital_actual,sexo):
for i in range(0,(tiempo_años*12)+1):
if i==0:
aporte = ((sueldo_clp/0.83)*(1+0.01/12))*0.1 + capital_actual
else:
aporte = ((sueldo_clp/0.83)*(1+0.01/12))*0.1 + monto_ganado
monto_ganado = aporte * (1+tasa/1200)
#print(monto_ganado)
if sexo=='Hombre':
pension_mensual = monto_ganado/(20*12)
else:
pension_mensual = monto_ganado/(30*12)
return f'{round(pension_mensual):,}',monto_ganado
def jubilacion_total(sueldo_clp,tasa,tiempo_años,capital_actual):
for i in range(0,(tiempo_años*12)+1):
if i==0:
aporte = ((sueldo_clp/0.83)*(1+0.01/12))*0.1 + capital_actual
else:
aporte = ((sueldo_clp/0.83)*(1+0.01/12))*0.1 + monto_ganado
monto_ganado = aporte * (1+tasa/1200)
print(monto_ganado)
return f'{round(monto_ganado):,}'
st.write("""
# AFP y Jubilación """)
st.sidebar.header('Parámetros')
sexo = st.sidebar.selectbox("Sexo", ('Hombre','Mujer'))
sueldo_liquido = st.sidebar.number_input('Cuánto es su sueldo líquido?')
rentabilidad = st.sidebar.number_input('Tasa de Rentabilidad')
tiempo_jubilacion = st.sidebar.number_input('Cuánto le queda para Jubilar?')
capital_actual = st.sidebar.number_input('Cuánto es su capital actual?')
pensound = jubilacion(sueldo_liquido,rentabilidad,int(tiempo_jubilacion),capital_actual, sexo)
pensound_2 = jubilacion_total(sueldo_liquido,rentabilidad,int(tiempo_jubilacion),capital_actual)
st.write( "Su pensión mensual será **{}** y su monto total acumulado es **{}**.".format(pensound,pensound_2))
|
[
"noreply@github.com"
] |
noreply@github.com
|
9b7d397ba307c03c0cd50292f30ea2770a2a8816
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p02623/s581456736.py
|
db739a5bab8b529088885d50f94a895ce4eb8e86
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102
| 2021-05-13T17:27:22
| 2021-05-13T17:27:22
| 367,112,348
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 713
|
py
|
n, m, k = map(int, input().split())
a = list(map(int, input().split()))
b = list(map(int, input().split()))
a_num = 0
b_num = 0
book_num = 0
passed_k = 0
for i in range(n):
if a[i] + passed_k <= k:
a_num += 1
passed_k += a[i]
else:
break
for i in range(m):
if b[i] + passed_k <= k:
b_num += 1
passed_k += b[i]
else:
break
book_num = a_num + b_num
while a_num > 0:
passed_k -= a[a_num - 1]
a_num -= 1
while b_num < m:
if passed_k + b[b_num] <= k:
passed_k += b[b_num]
b_num += 1
else:
break
book_num = max(book_num, a_num + b_num)
if b_num == m:
break
print(book_num)
|
[
"66529651+Aastha2104@users.noreply.github.com"
] |
66529651+Aastha2104@users.noreply.github.com
|
7ae8008a08ca52e7b57bd92704d3e8870be2f0c6
|
97c6ea9a1e561d9a8ac250c90b15ecf3cda6af44
|
/models/pointnet2_seg.py
|
68db21bd2f9cb874105111d5f93db8c56ad04153
|
[] |
no_license
|
li1901/Pointnet2.PyTorch
|
4d216aa92526e294ce38469b48025913a2d5350f
|
1b98042fa286ce13db5cbfeb498f0f64dc1487b4
|
refs/heads/master
| 2023-01-08T23:15:51.981693
| 2020-11-14T03:02:12
| 2020-11-14T03:02:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,406
|
py
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.set_abstraction import PointNet_SA_Module, PointNet_SA_Module_MSG
from utils.feature_propagation import PointNet_FP_Module
class pointnet2_seg_ssg(nn.Module):
def __init__(self, in_channels, nclasses):
super(pointnet2_seg_ssg, self).__init__()
self.pt_sa1 = PointNet_SA_Module(M=512, radius=0.2, K=32, in_channels=in_channels, mlp=[64, 64, 128], group_all=False)
self.pt_sa2 = PointNet_SA_Module(M=128, radius=0.4, K=64, in_channels=131, mlp=[128, 128, 256], group_all=False)
self.pt_sa3 = PointNet_SA_Module(M=None, radius=None, K=None, in_channels=259, mlp=[256, 512, 1024], group_all=True)
self.pt_fp1 = PointNet_FP_Module(in_channels=1024+256, mlp=[256, 256], bn=True)
self.pt_fp2 = PointNet_FP_Module(in_channels=256 + 128, mlp=[256, 128], bn=True)
self.pt_fp3 = PointNet_FP_Module(in_channels=128 + 6, mlp=[128, 128, 128], bn=True)
self.conv1 = nn.Conv1d(128, 128, 1, stride=1, bias=False)
self.bn1 = nn.BatchNorm1d(128)
self.dropout1 = nn.Dropout(0.5)
self.cls = nn.Conv1d(128, nclasses, 1, stride=1)
def forward(self, l0_xyz, l0_points):
l1_xyz, l1_points = self.pt_sa1(l0_xyz, l0_points)
l2_xyz, l2_points = self.pt_sa2(l1_xyz, l1_points)
l3_xyz, l3_points = self.pt_sa3(l2_xyz, l2_points)
l2_points = self.pt_fp1(l2_xyz, l3_xyz, l2_points, l3_points)
l1_points = self.pt_fp2(l1_xyz, l2_xyz, l1_points, l2_points)
l0_points = self.pt_fp3(l0_xyz, l1_xyz, torch.cat([l0_points, l0_xyz], dim=-1), l1_points)
net = l0_points.permute(0, 2, 1).contiguous()
net = self.dropout1(F.relu(self.bn1(self.conv1(net))))
net = self.cls(net)
return net
class seg_loss(nn.Module):
def __init__(self):
super(seg_loss, self).__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, pred, label):
'''
:param pred: shape=(B, N, C)
:param label: shape=(B, N)
:return:
'''
loss = self.loss(pred, label)
return loss
if __name__ == '__main__':
in_channels = 6
n_classes = 50
l0_xyz = torch.randn(4, 1024, 3)
l0_points = torch.randn(4, 1024, 3)
model = pointnet2_seg_ssg(in_channels, n_classes)
net = model(l0_xyz, l0_points)
print(net.shape)
|
[
"lifazhu@deepglint.com"
] |
lifazhu@deepglint.com
|
d0ab779d19449025bfcd4a9b8f4ae12d101f3ed3
|
a63d907ad63ba6705420a6fb2788196d1bd3763c
|
/src/api/dataflow/modeling/job/job_driver.py
|
fb79d2645b77791a5e854507749137ec274c3ec8
|
[
"MIT"
] |
permissive
|
Tencent/bk-base
|
a38461072811667dc2880a13a5232004fe771a4b
|
6d483b4df67739b26cc8ecaa56c1d76ab46bd7a2
|
refs/heads/master
| 2022-07-30T04:24:53.370661
| 2022-04-02T10:30:55
| 2022-04-02T10:30:55
| 381,257,882
| 101
| 51
|
NOASSERTION
| 2022-04-02T10:30:56
| 2021-06-29T06:10:01
|
Python
|
UTF-8
|
Python
| false
| false
| 10,200
|
py
|
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available.
Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
BK-BASE 蓝鲸基础平台 is licensed under the MIT License.
License for BK-BASE 蓝鲸基础平台:
--------------------------------------------------------------------
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.
"""
import json
import uuid
from dataflow.batch.api.api_helper import BksqlHelper
from dataflow.batch.handlers.processing_batch_info import ProcessingBatchInfoHandler
from dataflow.batch.handlers.processing_job_info import ProcessingJobInfoHandler
from dataflow.modeling.api.api_helper import ModelingApiHelper
from dataflow.modeling.job.jobnavi_register_modeling import ModelingJobNaviRegister
from dataflow.modeling.settings import PARSED_TASK_TYPE, TABLE_TYPE
from dataflow.shared.log import modeling_logger as logger
from dataflow.shared.meta.result_table.result_table_helper import ResultTableHelper
from dataflow.shared.storekit.storekit_helper import StorekitHelper
from dataflow.udf.functions import function_driver
def register_schedule(job_id, schedule_time, created_by, is_restart=False):
# 离线已将相关操作重新封装整理，因此这里可以进行很大的简化
jobnavi_register = ModelingJobNaviRegister(job_id, created_by, is_restart)
return jobnavi_register.register_jobnavi(schedule_time)
def get_output_info(node_info):
# 注意，这里是从前端传递的内容解析出输出字段
# 此时真正的物理表可能还不存在 ，所以这里不能使用ResultTableHelper等相关的请求来获取请求的storage等信息
output_table = None
output_fields = []
output_alias = None
for table_id in node_info["output"]:
output_table = table_id
for field in node_info["output"][table_id]["fields"]:
logger.info(field)
output_field = {
"field": field["field_name"],
"type": field["field_type"],
"description": field["field_alias"],
"origin": [],
}
output_fields.append(output_field)
output_alias = node_info["output"][table_id]["table_alias"]
return {"name": output_table, "fields": output_fields, "alias": output_alias}
def get_input_info(dependence_info):
input_table = None
input_fileds = []
input = {}
for table_id in dependence_info:
input_table = table_id
result_table_fields = ResultTableHelper.get_result_table_fields(input_table)
for filed_info in result_table_fields:
input_table_field = {
"field": filed_info["field_name"],
"type": filed_info["field_type"],
"origin": "",
"description": filed_info["field_alias"],
}
input_fileds.append(input_table_field)
result_table_storage = ResultTableHelper.get_result_table_storage(input_table, "hdfs")["hdfs"]
input["type"] = "hdfs"
input["format"] = result_table_storage["data_type"]
result_table_connect = json.loads(result_table_storage["storage_cluster"]["connection_info"])
input["path"] = "{hdfs_url}/{hdfs_path}".format(
hdfs_url=result_table_connect["hdfs_url"],
hdfs_path=result_table_storage["physical_table_name"],
)
input["table_type"] = TABLE_TYPE.RESULT_TABLE.value
if input["format"] == "iceberg":
iceberg_hdfs_config = StorekitHelper.get_hdfs_conf(input_table)
iceberg_config = {
"physical_table_name": result_table_storage["physical_table_name"],
"hdfs_config": iceberg_hdfs_config,
}
input["iceberg_config"] = iceberg_config
return {"name": input_table, "fields": input_fileds, "info": input}
def get_window_info(input_table, dependence_info, node_info):
# 由于计算真正的数据路径时需要用到当前节点的周期配置（schedule_info）以及依赖表的配置(dependence)
# 所以这里将两者合并在一起成为window_info，每个依赖表都有一个window信息
# 所以在window的信息中有两部分，一部分是每个source都不一样的dependence 以及每个 source内值都一样的schedule_info
# schedule_info表示的是当前节点的调试信息，与父任务无关，这里需要注意理解
window_info = {}
window_info.update(dependence_info[input_table])
window_info.update(node_info["schedule_info"])
return window_info
def update_process_and_job(table_name, processor_logic, submit_args):
batch_processing_info = ProcessingBatchInfoHandler.get_proc_batch_info_by_prefix(table_name)
for processing_info in batch_processing_info:
# 上述两者要更新加Processing
ProcessingBatchInfoHandler.update_proc_batch_info_logic(processing_info.processing_id, processor_logic)
ProcessingBatchInfoHandler.update_proc_batch_info_submit_args(processing_info.processing_id, submit_args)
processing_job_info_list = ProcessingJobInfoHandler.get_proc_job_info_by_prefix(table_name)
for processing_job_info in processing_job_info_list:
job_config = json.loads(processing_job_info.job_config)
job_config["submit_args"] = json.dumps(submit_args)
job_config["processor_logic"] = json.dumps(processor_logic)
ProcessingJobInfoHandler.update_proc_job_info_job_config(processing_job_info.job_id, job_config)
return table_name
def get_sub_query_task(sql, sub_sql, target_entity_name, geog_area_code):
"""
将子查询的相关信息封装为一个临时的task
@param sql: 源mlsql
@param sub_sql: 子查询
@param target_entity_name: mlsql生成实例名称
@param geog_area_code: area code
@return: 临时的任务信息
"""
uuid_str = str(uuid.uuid4())
processing_id = target_entity_name + "_" + uuid_str[0 : uuid_str.find("-")]
# 解析所有用到的udf
udf_data = function_driver.parse_sql(sub_sql, geog_area_code)
logger.info("udf data result:" + json.dumps(udf_data))
udf_name_list = []
for udf in udf_data:
udf_name_list.append(udf["name"])
# 解析用到的所有表
sub_query_table_names = ModelingApiHelper.get_table_names_by_mlsql_parser({"sql": sql})
logger.info("sub query table names:" + json.dumps(sub_query_table_names))
spark_sql_propertiies = {
"spark.input_result_table": sub_query_table_names,
"spark.bk_biz_id": target_entity_name[0 : target_entity_name.find("_")],
"spark.dataflow_udf_function_name": ",".join(udf_name_list),
"spark.result_table_name": processing_id,
}
# 使用sparksql解析子查询的输出
spark_sql_parse_result_list = BksqlHelper.spark_sql(sub_sql, spark_sql_propertiies)
logger.info("spark sql result:" + json.dumps(spark_sql_parse_result_list))
spark_sql_parse_result = spark_sql_parse_result_list[0]
sub_query_fields = spark_sql_parse_result["fields"]
sparksql_query_sql = spark_sql_parse_result["sql"]
tmp_sub_query_fields = []
for field in sub_query_fields:
tmp_sub_query_fields.append(
{
"field": field["field_name"],
"type": field["field_type"],
"description": field["field_alias"],
"index": field["field_index"],
"origins": [""],
}
)
# 解析子查询的输入中用到的所有列
sql_columns_result = ModelingApiHelper.get_columns_by_mlsql_parser({"sql": sub_sql})
logger.info("sql column result:" + json.dumps(sql_columns_result))
processor = {
"args": {
"sql": sparksql_query_sql,
"format_sql": sql_columns_result["sql"], # 含有通配字符的sql
},
"type": "untrained-run",
"name": "tmp_processor",
}
# 解析子查询用到的数据区间（目前暂无应用）
sql_query_source_result = ModelingApiHelper.get_mlsql_query_source_parser({"sql": sub_sql})
logger.info("sql query source result:" + json.dumps(sql_query_source_result))
processor["args"]["time_range"] = sql_query_source_result
# todo:根据所有输入表检查输入列是否存在,去掉不存在的输入列（这里认为不存在的即为经过as或其它重新命名得到的列）这里可以进一步优化
sql_all_columns = sql_columns_result["columns"]
sql_exist_columns = []
for table in sub_query_table_names:
table_fields = ResultTableHelper.get_result_table_fields(table)
for field in table_fields:
sql_exist_columns.append(field["field_name"])
processor["args"]["column"] = list(set(sql_all_columns).intersection(set(sql_exist_columns)))
tmp_subquery_task = {
"table_name": processing_id,
"fields": tmp_sub_query_fields,
"parents": sub_query_table_names,
"processor": processor,
"interpreter": [],
"processing_id": processing_id,
"udfs": udf_data,
"task_type": PARSED_TASK_TYPE.SUB_QUERY.value,
}
return tmp_subquery_task
|
[
"terrencehan@tencent.com"
] |
terrencehan@tencent.com
|
2a55fd29b450411bb3c2a75e589f5359a2f3f9bf
|
d580e171f80540923e56fce88655b5c6b42af6c1
|
/python_code.py
|
76654fcf64c5aa00c0a28167bd11f8e1f80af8d3
|
[] |
no_license
|
trushadesign/github-example
|
b81a8276d4a1ff2cb97b6bc30c172cd669921045
|
4d8f75421a0a75b74b3dbddfa7cee82a5e598bc8
|
refs/heads/master
| 2020-11-29T11:07:42.071586
| 2019-12-25T12:57:26
| 2019-12-25T12:57:26
| 230,099,784
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 35
|
py
|
print("Hello Github!")
git status
|
[
"serhii.vichev@MacBook-Air-Serhii.local"
] |
serhii.vichev@MacBook-Air-Serhii.local
|
adf0cfbbc71169a4d6fecca777b0ca1f2df1a9b5
|
c17635a2b05a96ea75d3b7dc76b63e1326606b64
|
/test/pexpect/__init__.py
|
dcf360f9dd8485a50f63f6111081d921d207fa0c
|
[] |
no_license
|
HamzaAnis/SimpleShell
|
8efde65c624f4024a9944cdce4cd66600a85cb99
|
afc642c2a4e029332ea535c9b0d445027c9f3d21
|
refs/heads/master
| 2020-03-28T19:21:45.654387
| 2018-09-19T15:50:28
| 2018-09-19T15:50:28
| 148,967,947
| 5
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 87,780
|
py
|
'''Pexpect is a Python module for spawning child applications and controlling
them automatically. Pexpect can be used for automating interactive applications
such as ssh, ftp, passwd, telnet, etc. It can be used to a automate setup
scripts for duplicating software package installations on different servers. It
can be used for automated software testing. Pexpect is in the spirit of Don
Libes' Expect, but Pexpect is pure Python. Other Expect-like modules for Python
require TCL and Expect or require C extensions to be compiled. Pexpect does not
use C, Expect, or TCL extensions. It should work on any platform that supports
the standard Python pty module. The Pexpect interface focuses on ease of use so
that simple tasks are easy.
There are two main interfaces to the Pexpect system; these are the function,
run() and the class, spawn. The spawn class is more powerful. The run()
function is simpler than spawn, and is good for quickly calling program. When
you call the run() function it executes a given program and then returns the
output. This is a handy replacement for os.system().
For example::
pexpect.run('ls -la')
The spawn class is the more powerful interface to the Pexpect system. You can
use this to spawn a child program then interact with it by sending input and
expecting responses (waiting for patterns in the child's output).
For example::
child = pexpect.spawn('scp foo user@example.com:.')
child.expect('Password:')
child.sendline(mypassword)
This works even for commands that ask for passwords or other input outside of
the normal stdio streams. For example, ssh reads input directly from the TTY
device which bypasses stdin.
Credits: Noah Spurrier, Richard Holden, Marco Molteni, Kimberley Burchett,
Robert Stone, Hartmut Goebel, Chad Schroeder, Erick Tryzelaar, Dave Kirby, Ids
vander Molen, George Todd, Noel Taylor, Nicolas D. Cesar, Alexander Gattin,
Jacques-Etienne Baudoux, Geoffrey Marshall, Francisco Lourenco, Glen Mabey,
Karthik Gurusamy, Fernando Perez, Corey Minyard, Jon Cohen, Guillaume
Chazarain, Andrew Ryan, Nick Craig-Wood, Andrew Stone, Jorgen Grahn, John
Spiegel, Jan Grant, and Shane Kerr. Let me know if I forgot anyone.
Pexpect is free, open source, and all that good stuff.
http://pexpect.sourceforge.net/
PEXPECT LICENSE
This license is approved by the OSI and FSF as GPL-compatible.
http://opensource.org/licenses/isc-license.txt
Copyright (c) 2012, Noah Spurrier <noah@noah.org>
PERMISSION TO USE, COPY, MODIFY, AND/OR DISTRIBUTE THIS SOFTWARE FOR ANY
PURPOSE WITH OR WITHOUT FEE IS HEREBY GRANTED, PROVIDED THAT THE ABOVE
COPYRIGHT NOTICE AND THIS PERMISSION NOTICE APPEAR IN ALL COPIES.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
try:
import os
import sys
import time
import select
import re
import struct
import resource
import types
import pty
import tty
import termios
import fcntl
import errno
import traceback
import signal
import codecs
import stat
except ImportError: # pragma: no cover
err = sys.exc_info()[1]
raise ImportError(str(err) + '''
A critical module was not found. Probably this operating system does not
support it. Pexpect is intended for UNIX-like operating systems.''')
__version__ = '3.3'
__revision__ = ''
__all__ = ['ExceptionPexpect', 'EOF', 'TIMEOUT', 'spawn', 'spawnu', 'run', 'runu',
'which', 'split_command_line', '__version__', '__revision__']
PY3 = (sys.version_info[0] >= 3)
# Exception classes used by this module.
class ExceptionPexpect(Exception):
'''Base class for all exceptions raised by this module.
'''
def __init__(self, value):
super(ExceptionPexpect, self).__init__(value)
self.value = value
def __str__(self):
return str(self.value)
def get_trace(self):
'''This returns an abbreviated stack trace with lines that only concern
the caller. In other words, the stack trace inside the Pexpect module
is not included. '''
tblist = traceback.extract_tb(sys.exc_info()[2])
tblist = [item for item in tblist if 'pexpect/__init__' not in item[0]]
tblist = traceback.format_list(tblist)
return ''.join(tblist)
class EOF(ExceptionPexpect):
'''Raised when EOF is read from a child.
This usually means the child has exited.'''
class TIMEOUT(ExceptionPexpect):
'''Raised when a read time exceeds the timeout. '''
##class TIMEOUT_PATTERN(TIMEOUT):
## '''Raised when the pattern match time exceeds the timeout.
## This is different than a read TIMEOUT because the child process may
## give output, thus never give a TIMEOUT, but the output
## may never match a pattern.
## '''
##class MAXBUFFER(ExceptionPexpect):
## '''Raised when a buffer fills before matching an expected pattern.'''
def run(command, timeout=-1, withexitstatus=False, events=None,
extra_args=None, logfile=None, cwd=None, env=None):
'''
This function runs the given command; waits for it to finish; then
returns all output as a string. STDERR is included in output. If the full
path to the command is not given then the path is searched.
Note that lines are terminated by CR/LF (\\r\\n) combination even on
UNIX-like systems because this is the standard for pseudottys. If you set
'withexitstatus' to true, then run will return a tuple of (command_output,
exitstatus). If 'withexitstatus' is false then this returns just
command_output.
The run() function can often be used instead of creating a spawn instance.
For example, the following code uses spawn::
from pexpect import *
child = spawn('scp foo user@example.com:.')
child.expect('(?i)password')
child.sendline(mypassword)
The previous code can be replace with the following::
from pexpect import *
run('scp foo user@example.com:.', events={'(?i)password': mypassword})
**Examples**
Start the apache daemon on the local machine::
from pexpect import *
run("/usr/local/apache/bin/apachectl start")
Check in a file using SVN::
from pexpect import *
run("svn ci -m 'automatic commit' my_file.py")
Run a command and capture exit status::
from pexpect import *
(command_output, exitstatus) = run('ls -l /bin', withexitstatus=1)
The following will run SSH and execute 'ls -l' on the remote machine. The
password 'secret' will be sent if the '(?i)password' pattern is ever seen::
run("ssh username@machine.example.com 'ls -l'",
events={'(?i)password':'secret\\n'})
This will start mencoder to rip a video from DVD. This will also display
progress ticks every 5 seconds as it runs. For example::
from pexpect import *
def print_ticks(d):
print d['event_count'],
run("mencoder dvd://1 -o video.avi -oac copy -ovc copy",
events={TIMEOUT:print_ticks}, timeout=5)
The 'events' argument should be a dictionary of patterns and responses.
Whenever one of the patterns is seen in the command out run() will send the
associated response string. Note that you should put newlines in your
string if Enter is necessary. The responses may also contain callback
functions. Any callback is function that takes a dictionary as an argument.
The dictionary contains all the locals from the run() function, so you can
access the child spawn object or any other variable defined in run()
(event_count, child, and extra_args are the most useful). A callback may
return True to stop the current run process otherwise run() continues until
the next event. A callback may also return a string which will be sent to
the child. 'extra_args' is not used by directly run(). It provides a way to
pass data to a callback function through run() through the locals
dictionary passed to a callback.
'''
return _run(command, timeout=timeout, withexitstatus=withexitstatus,
events=events, extra_args=extra_args, logfile=logfile, cwd=cwd,
env=env, _spawn=spawn)
def runu(command, timeout=-1, withexitstatus=False, events=None,
extra_args=None, logfile=None, cwd=None, env=None, **kwargs):
"""This offers the same interface as :func:`run`, but using unicode.
Like :class:`spawnu`, you can pass ``encoding`` and ``errors`` parameters,
which will be used for both input and output.
"""
return _run(command, timeout=timeout, withexitstatus=withexitstatus,
events=events, extra_args=extra_args, logfile=logfile, cwd=cwd,
env=env, _spawn=spawnu, **kwargs)
def _run(command, timeout, withexitstatus, events, extra_args, logfile, cwd,
env, _spawn, **kwargs):
if timeout == -1:
child = _spawn(command, maxread=2000, logfile=logfile, cwd=cwd, env=env,
**kwargs)
else:
child = _spawn(command, timeout=timeout, maxread=2000, logfile=logfile,
cwd=cwd, env=env, **kwargs)
if events is not None:
patterns = list(events.keys())
responses = list(events.values())
else:
# This assumes EOF or TIMEOUT will eventually cause run to terminate.
patterns = None
responses = None
child_result_list = []
event_count = 0
while True:
try:
index = child.expect(patterns)
if isinstance(child.after, child.allowed_string_types):
child_result_list.append(child.before + child.after)
else:
# child.after may have been a TIMEOUT or EOF,
# which we don't want appended to the list.
child_result_list.append(child.before)
if isinstance(responses[index], child.allowed_string_types):
child.send(responses[index])
elif isinstance(responses[index], types.FunctionType):
callback_result = responses[index](locals())
sys.stdout.flush()
if isinstance(callback_result, child.allowed_string_types):
child.send(callback_result)
elif callback_result:
break
else:
raise TypeError('The callback must be a string or function.')
event_count = event_count + 1
except TIMEOUT:
child_result_list.append(child.before)
break
except EOF:
child_result_list.append(child.before)
break
child_result = child.string_type().join(child_result_list)
if withexitstatus:
child.close()
return (child_result, child.exitstatus)
else:
return child_result
class spawn(object):
'''This is the main class interface for Pexpect. Use this class to start
and control child applications. '''
string_type = bytes
if PY3:
allowed_string_types = (bytes, str)
@staticmethod
def _chr(c):
return bytes([c])
linesep = os.linesep.encode('ascii')
crlf = '\r\n'.encode('ascii')
@staticmethod
def write_to_stdout(b):
try:
return sys.stdout.buffer.write(b)
except AttributeError:
# If stdout has been replaced, it may not have .buffer
return sys.stdout.write(b.decode('ascii', 'replace'))
else:
allowed_string_types = (basestring,) # analysis:ignore
_chr = staticmethod(chr)
linesep = os.linesep
crlf = '\r\n'
write_to_stdout = sys.stdout.write
encoding = None
def __init__(self, command, args=[], timeout=30, maxread=2000,
searchwindowsize=None, logfile=None, cwd=None, env=None,
ignore_sighup=True, echo=True):
'''This is the constructor. The command parameter may be a string that
includes a command and any arguments to the command. For example::
child = pexpect.spawn('/usr/bin/ftp')
child = pexpect.spawn('/usr/bin/ssh user@example.com')
child = pexpect.spawn('ls -latr /tmp')
You may also construct it with a list of arguments like so::
child = pexpect.spawn('/usr/bin/ftp', [])
child = pexpect.spawn('/usr/bin/ssh', ['user@example.com'])
child = pexpect.spawn('ls', ['-latr', '/tmp'])
After this the child application will be created and will be ready to
talk to. For normal use, see expect() and send() and sendline().
Remember that Pexpect does NOT interpret shell meta characters such as
redirect, pipe, or wild cards (``>``, ``|``, or ``*``). This is a
common mistake. If you want to run a command and pipe it through
another command then you must also start a shell. For example::
child = pexpect.spawn('/bin/bash -c "ls -l | grep LOG > logs.txt"')
child.expect(pexpect.EOF)
The second form of spawn (where you pass a list of arguments) is useful
in situations where you wish to spawn a command and pass it its own
argument list. This can make syntax more clear. For example, the
following is equivalent to the previous example::
shell_cmd = 'ls -l | grep LOG > logs.txt'
child = pexpect.spawn('/bin/bash', ['-c', shell_cmd])
child.expect(pexpect.EOF)
The maxread attribute sets the read buffer size. This is maximum number
of bytes that Pexpect will try to read from a TTY at one time. Setting
the maxread size to 1 will turn off buffering. Setting the maxread
value higher may help performance in cases where large amounts of
output are read back from the child. This feature is useful in
conjunction with searchwindowsize.
The searchwindowsize attribute sets the how far back in the incoming
seach buffer Pexpect will search for pattern matches. Every time
Pexpect reads some data from the child it will append the data to the
incoming buffer. The default is to search from the beginning of the
incoming buffer each time new data is read from the child. But this is
very inefficient if you are running a command that generates a large
amount of data where you want to match. The searchwindowsize does not
affect the size of the incoming data buffer. You will still have
access to the full buffer after expect() returns.
The logfile member turns on or off logging. All input and output will
be copied to the given file object. Set logfile to None to stop
logging. This is the default. Set logfile to sys.stdout to echo
everything to standard output. The logfile is flushed after each write.
Example log input and output to a file::
child = pexpect.spawn('some_command')
fout = file('mylog.txt','w')
child.logfile = fout
Example log to stdout::
child = pexpect.spawn('some_command')
child.logfile = sys.stdout
The logfile_read and logfile_send members can be used to separately log
the input from the child and output sent to the child. Sometimes you
don't want to see everything you write to the child. You only want to
log what the child sends back. For example::
child = pexpect.spawn('some_command')
child.logfile_read = sys.stdout
To separately log output sent to the child use logfile_send::
self.logfile_send = fout
If ``ignore_sighup`` is True, the child process will ignore SIGHUP
signals. For now, the default is True, to preserve the behaviour of
earlier versions of Pexpect, but you should pass this explicitly if you
want to rely on it.
The delaybeforesend helps overcome a weird behavior that many users
were experiencing. The typical problem was that a user would expect() a
"Password:" prompt and then immediately call sendline() to send the
password. The user would then see that their password was echoed back
to them. Passwords don't normally echo. The problem is caused by the
fact that most applications print out the "Password" prompt and then
turn off stdin echo, but if you send your password before the
application turned off echo, then you get your password echoed.
Normally this wouldn't be a problem when interacting with a human at a
real keyboard. If you introduce a slight delay just before writing then
this seems to clear up the problem. This was such a common problem for
many users that I decided that the default pexpect behavior should be
to sleep just before writing to the child application. 1/20th of a
second (50 ms) seems to be enough to clear up the problem. You can set
delaybeforesend to 0 to return to the old behavior. Most Linux machines
don't like this to be below 0.03. I don't know why.
Note that spawn is clever about finding commands on your path.
It uses the same logic that "which" uses to find executables.
If you wish to get the exit status of the child you must call the
close() method. The exit or signal status of the child will be stored
in self.exitstatus or self.signalstatus. If the child exited normally
then exitstatus will store the exit return code and signalstatus will
be None. If the child was terminated abnormally with a signal then
signalstatus will store the signal value and exitstatus will be None.
If you need more detail you can also read the self.status member which
stores the status returned by os.waitpid. You can interpret this using
os.WIFEXITED/os.WEXITSTATUS or os.WIFSIGNALED/os.TERMSIG.
The echo attribute may be set to False to disable echoing of input.
As a pseudo-terminal, all input echoed by the "keyboard" (send()
or sendline()) will be repeated to output. For many cases, it is
not desirable to have echo enabled, and it may be later disabled
using setecho(False) followed by waitnoecho(). However, for some
platforms such as Solaris, this is not possible, and should be
disabled immediately on spawn.
'''
self.STDIN_FILENO = pty.STDIN_FILENO
self.STDOUT_FILENO = pty.STDOUT_FILENO
self.STDERR_FILENO = pty.STDERR_FILENO
self.stdin = sys.stdin
self.stdout = sys.stdout
self.stderr = sys.stderr
self.searcher = None
self.ignorecase = False
self.before = None
self.after = None
self.match = None
self.match_index = None
self.terminated = True
self.exitstatus = None
self.signalstatus = None
# status returned by os.waitpid
self.status = None
self.flag_eof = False
self.pid = None
# the child file descriptor is initially closed
self.child_fd = -1
self.timeout = timeout
self.delimiter = EOF
self.logfile = logfile
# input from child (read_nonblocking)
self.logfile_read = None
# output to send (send, sendline)
self.logfile_send = None
# max bytes to read at one time into buffer
self.maxread = maxread
# This is the read buffer. See maxread.
self.buffer = self.string_type()
# Data before searchwindowsize point is preserved, but not searched.
self.searchwindowsize = searchwindowsize
# Delay used before sending data to child. Time in seconds.
# Most Linux machines don't like this to be below 0.03 (30 ms).
self.delaybeforesend = 0.05
# Used by close() to give kernel time to update process status.
# Time in seconds.
self.delayafterclose = 0.1
# Used by terminate() to give kernel time to update process status.
# Time in seconds.
self.delayafterterminate = 0.1
self.softspace = False
self.name = '<' + repr(self) + '>'
self.closed = True
self.cwd = cwd
self.env = env
self.echo = echo
self.ignore_sighup = ignore_sighup
_platform = sys.platform.lower()
# This flags if we are running on irix
self.__irix_hack = _platform.startswith('irix')
# Solaris uses internal __fork_pty(). All others use pty.fork().
self.use_native_pty_fork = not (
_platform.startswith('solaris') or
_platform.startswith('sunos'))
# inherit EOF and INTR definitions from controlling process.
try:
from termios import VEOF, VINTR
fd = sys.__stdin__.fileno()
self._INTR = ord(termios.tcgetattr(fd)[6][VINTR])
self._EOF = ord(termios.tcgetattr(fd)[6][VEOF])
except (ImportError, OSError, IOError, termios.error):
# unless the controlling process is also not a terminal,
# such as cron(1). Fall-back to using CEOF and CINTR.
try:
from termios import CEOF, CINTR
(self._INTR, self._EOF) = (CINTR, CEOF)
except ImportError:
# ^C, ^D
(self._INTR, self._EOF) = (3, 4)
# Support subclasses that do not use command or args.
if command is None:
self.command = None
self.args = None
self.name = '<pexpect factory incomplete>'
else:
self._spawn(command, args)
@staticmethod
def _coerce_expect_string(s):
if not isinstance(s, bytes):
return s.encode('ascii')
return s
@staticmethod
def _coerce_send_string(s):
if not isinstance(s, bytes):
return s.encode('utf-8')
return s
@staticmethod
def _coerce_read_string(s):
return s
def __del__(self):
'''This makes sure that no system resources are left open. Python only
garbage collects Python objects. OS file descriptors are not Python
objects, so they must be handled explicitly. If the child file
descriptor was opened outside of this class (passed to the constructor)
then this does not close it. '''
if not self.closed:
# It is possible for __del__ methods to execute during the
# teardown of the Python VM itself. Thus self.close() may
# trigger an exception because os.close may be None.
try:
self.close()
# which exception, shouldnt' we catch explicitly .. ?
except:
pass
def __str__(self):
'''This returns a human-readable string that represents the state of
the object. '''
s = []
s.append(repr(self))
s.append('version: ' + __version__)
s.append('command: ' + str(self.command))
s.append('args: %r' % (self.args,))
s.append('searcher: %r' % (self.searcher,))
s.append('buffer (last 100 chars): %r' % (self.buffer)[-100:],)
s.append('before (last 100 chars): %r' % (self.before)[-100:],)
s.append('after: %r' % (self.after,))
s.append('match: %r' % (self.match,))
s.append('match_index: ' + str(self.match_index))
s.append('exitstatus: ' + str(self.exitstatus))
s.append('flag_eof: ' + str(self.flag_eof))
s.append('pid: ' + str(self.pid))
s.append('child_fd: ' + str(self.child_fd))
s.append('closed: ' + str(self.closed))
s.append('timeout: ' + str(self.timeout))
s.append('delimiter: ' + str(self.delimiter))
s.append('logfile: ' + str(self.logfile))
s.append('logfile_read: ' + str(self.logfile_read))
s.append('logfile_send: ' + str(self.logfile_send))
s.append('maxread: ' + str(self.maxread))
s.append('ignorecase: ' + str(self.ignorecase))
s.append('searchwindowsize: ' + str(self.searchwindowsize))
s.append('delaybeforesend: ' + str(self.delaybeforesend))
s.append('delayafterclose: ' + str(self.delayafterclose))
s.append('delayafterterminate: ' + str(self.delayafterterminate))
return '\n'.join(s)
def _spawn(self, command, args=[]):
'''This starts the given command in a child process. This does all the
fork/exec type of stuff for a pty. This is called by __init__. If args
is empty then command will be parsed (split on spaces) and args will be
set to parsed arguments. '''
# The pid and child_fd of this object get set by this method.
# Note that it is difficult for this method to fail.
# You cannot detect if the child process cannot start.
# So the only way you can tell if the child process started
# or not is to try to read from the file descriptor. If you get
# EOF immediately then it means that the child is already dead.
# That may not necessarily be bad because you may have spawned a child
# that performs some task; creates no stdout output; and then dies.
# If command is an int type then it may represent a file descriptor.
if isinstance(command, type(0)):
raise ExceptionPexpect('Command is an int type. ' +
'If this is a file descriptor then maybe you want to ' +
'use fdpexpect.fdspawn which takes an existing ' +
'file descriptor instead of a command string.')
if not isinstance(args, type([])):
raise TypeError('The argument, args, must be a list.')
if args == []:
self.args = split_command_line(command)
self.command = self.args[0]
else:
# Make a shallow copy of the args list.
self.args = args[:]
self.args.insert(0, command)
self.command = command
command_with_path = which(self.command)
if command_with_path is None:
raise ExceptionPexpect('The command was not found or was not ' +
'executable: %s.' % self.command)
self.command = command_with_path
self.args[0] = self.command
self.name = '<' + ' '.join(self.args) + '>'
assert self.pid is None, 'The pid member must be None.'
assert self.command is not None, 'The command member must not be None.'
if self.use_native_pty_fork:
try:
self.pid, self.child_fd = pty.fork()
except OSError: # pragma: no cover
err = sys.exc_info()[1]
raise ExceptionPexpect('pty.fork() failed: ' + str(err))
else:
# Use internal __fork_pty
self.pid, self.child_fd = self.__fork_pty()
# Some platforms must call setwinsize() and setecho() from the
# child process, and others from the master process. We do both,
# allowing IOError for either.
if self.pid == pty.CHILD:
# Child
self.child_fd = self.STDIN_FILENO
# set default window size of 24 rows by 80 columns
try:
self.setwinsize(24, 80)
except IOError as err:
if err.args[0] not in (errno.EINVAL, errno.ENOTTY):
raise
# disable echo if spawn argument echo was unset
if not self.echo:
try:
self.setecho(self.echo)
except (IOError, termios.error) as err:
if err.args[0] not in (errno.EINVAL, errno.ENOTTY):
raise
# Do not allow child to inherit open file descriptors from parent.
max_fd = resource.getrlimit(resource.RLIMIT_NOFILE)[0]
os.closerange(3, max_fd)
if self.ignore_sighup:
signal.signal(signal.SIGHUP, signal.SIG_IGN)
if self.cwd is not None:
os.chdir(self.cwd)
if self.env is None:
os.execv(self.command, self.args)
else:
os.execvpe(self.command, self.args, self.env)
# Parent
try:
self.setwinsize(24, 80)
except IOError as err:
if err.args[0] not in (errno.EINVAL, errno.ENOTTY):
raise
self.terminated = False
self.closed = False
def __fork_pty(self):
'''This implements a substitute for the forkpty system call. This
should be more portable than the pty.fork() function. Specifically,
this should work on Solaris.
Modified 10.06.05 by Geoff Marshall: Implemented __fork_pty() method to
resolve the issue with Python's pty.fork() not supporting Solaris,
particularly ssh. Based on patch to posixmodule.c authored by Noah
Spurrier::
http://mail.python.org/pipermail/python-dev/2003-May/035281.html
'''
parent_fd, child_fd = os.openpty()
if parent_fd < 0 or child_fd < 0:
raise ExceptionPexpect("Could not open with os.openpty().")
pid = os.fork()
if pid == pty.CHILD:
# Child.
os.close(parent_fd)
self.__pty_make_controlling_tty(child_fd)
os.dup2(child_fd, self.STDIN_FILENO)
os.dup2(child_fd, self.STDOUT_FILENO)
os.dup2(child_fd, self.STDERR_FILENO)
else:
# Parent.
os.close(child_fd)
return pid, parent_fd
def __pty_make_controlling_tty(self, tty_fd):
'''This makes the pseudo-terminal the controlling tty. This should be
more portable than the pty.fork() function. Specifically, this should
work on Solaris. '''
child_name = os.ttyname(tty_fd)
# Disconnect from controlling tty, if any. Raises OSError of ENXIO
# if there was no controlling tty to begin with, such as when
# executed by a cron(1) job.
try:
fd = os.open("/dev/tty", os.O_RDWR | os.O_NOCTTY)
os.close(fd)
except OSError as err:
if err.errno != errno.ENXIO:
raise
os.setsid()
# Verify we are disconnected from controlling tty by attempting to open
# it again. We expect that OSError of ENXIO should always be raised.
try:
fd = os.open("/dev/tty", os.O_RDWR | os.O_NOCTTY)
os.close(fd)
raise ExceptionPexpect("OSError of errno.ENXIO should be raised.")
except OSError as err:
if err.errno != errno.ENXIO:
raise
# Verify we can open child pty.
fd = os.open(child_name, os.O_RDWR)
os.close(fd)
# Verify we now have a controlling tty.
fd = os.open("/dev/tty", os.O_WRONLY)
os.close(fd)
def fileno(self):
'''This returns the file descriptor of the pty for the child.
'''
return self.child_fd
def close(self, force=True):
'''This closes the connection with the child application. Note that
calling close() more than once is valid. This emulates standard Python
behavior with files. Set force to True if you want to make sure that
the child is terminated (SIGKILL is sent if the child ignores SIGHUP
and SIGINT). '''
if not self.closed:
self.flush()
os.close(self.child_fd)
# Give kernel time to update process status.
time.sleep(self.delayafterclose)
if self.isalive():
if not self.terminate(force):
raise ExceptionPexpect('Could not terminate the child.')
self.child_fd = -1
self.closed = True
#self.pid = None
def flush(self):
'''This does nothing. It is here to support the interface for a
File-like object. '''
pass
def isatty(self):
'''This returns True if the file descriptor is open and connected to a
tty(-like) device, else False.
On SVR4-style platforms implementing streams, such as SunOS and HP-UX,
the child pty may not appear as a terminal device. This means
methods such as setecho(), setwinsize(), getwinsize() may raise an
IOError. '''
return os.isatty(self.child_fd)
def waitnoecho(self, timeout=-1):
'''This waits until the terminal ECHO flag is set False. This returns
True if the echo mode is off. This returns False if the ECHO flag was
not set False before the timeout. This can be used to detect when the
child is waiting for a password. Usually a child application will turn
off echo mode when it is waiting for the user to enter a password. For
example, instead of expecting the "password:" prompt you can wait for
the child to set ECHO off::
p = pexpect.spawn('ssh user@example.com')
p.waitnoecho()
p.sendline(mypassword)
If timeout==-1 then this method will use the value in self.timeout.
If timeout==None then this method to block until ECHO flag is False.
'''
if timeout == -1:
timeout = self.timeout
if timeout is not None:
end_time = time.time() + timeout
while True:
if not self.getecho():
return True
if timeout < 0 and timeout is not None:
return False
if timeout is not None:
timeout = end_time - time.time()
time.sleep(0.1)
def getecho(self):
'''This returns the terminal echo mode. This returns True if echo is
on or False if echo is off. Child applications that are expecting you
to enter a password often set ECHO False. See waitnoecho().
Not supported on platforms where ``isatty()`` returns False. '''
try:
attr = termios.tcgetattr(self.child_fd)
except termios.error as err:
errmsg = 'getecho() may not be called on this platform'
if err.args[0] == errno.EINVAL:
raise IOError(err.args[0], '%s: %s.' % (err.args[1], errmsg))
raise
self.echo = bool(attr[3] & termios.ECHO)
return self.echo
def setecho(self, state):
'''This sets the terminal echo mode on or off. Note that anything the
child sent before the echo will be lost, so you should be sure that
your input buffer is empty before you call setecho(). For example, the
following will work as expected::
p = pexpect.spawn('cat') # Echo is on by default.
p.sendline('1234') # We expect see this twice from the child...
p.expect(['1234']) # ... once from the tty echo...
p.expect(['1234']) # ... and again from cat itself.
p.setecho(False) # Turn off tty echo
p.sendline('abcd') # We will set this only once (echoed by cat).
p.sendline('wxyz') # We will set this only once (echoed by cat)
p.expect(['abcd'])
p.expect(['wxyz'])
The following WILL NOT WORK because the lines sent before the setecho
will be lost::
p = pexpect.spawn('cat')
p.sendline('1234')
p.setecho(False) # Turn off tty echo
p.sendline('abcd') # We will set this only once (echoed by cat).
p.sendline('wxyz') # We will set this only once (echoed by cat)
p.expect(['1234'])
p.expect(['1234'])
p.expect(['abcd'])
p.expect(['wxyz'])
Not supported on platforms where ``isatty()`` returns False.
'''
errmsg = 'setecho() may not be called on this platform'
try:
attr = termios.tcgetattr(self.child_fd)
except termios.error as err:
if err.args[0] == errno.EINVAL:
raise IOError(err.args[0], '%s: %s.' % (err.args[1], errmsg))
raise
if state:
attr[3] = attr[3] | termios.ECHO
else:
attr[3] = attr[3] & ~termios.ECHO
try:
# I tried TCSADRAIN and TCSAFLUSH, but these were inconsistent and
# blocked on some platforms. TCSADRAIN would probably be ideal.
termios.tcsetattr(self.child_fd, termios.TCSANOW, attr)
except IOError as err:
if err.args[0] == errno.EINVAL:
raise IOError(err.args[0], '%s: %s.' % (err.args[1], errmsg))
raise
self.echo = state
def _log(self, s, direction):
if self.logfile is not None:
self.logfile.write(s)
self.logfile.flush()
second_log = self.logfile_send if (direction=='send') else self.logfile_read
if second_log is not None:
second_log.write(s)
second_log.flush()
def read_nonblocking(self, size=1, timeout=-1):
'''This reads at most size characters from the child application. It
includes a timeout. If the read does not complete within the timeout
period then a TIMEOUT exception is raised. If the end of file is read
then an EOF exception will be raised. If a log file was set using
setlog() then all data will also be written to the log file.
If timeout is None then the read may block indefinitely.
If timeout is -1 then the self.timeout value is used. If timeout is 0
then the child is polled and if there is no data immediately ready
then this will raise a TIMEOUT exception.
The timeout refers only to the amount of time to read at least one
character. This is not effected by the 'size' parameter, so if you call
read_nonblocking(size=100, timeout=30) and only one character is
available right away then one character will be returned immediately.
It will not wait for 30 seconds for another 99 characters to come in.
This is a wrapper around os.read(). It uses select.select() to
implement the timeout. '''
if self.closed:
raise ValueError('I/O operation on closed file.')
if timeout == -1:
timeout = self.timeout
# Note that some systems such as Solaris do not give an EOF when
# the child dies. In fact, you can still try to read
# from the child_fd -- it will block forever or until TIMEOUT.
# For this case, I test isalive() before doing any reading.
# If isalive() is false, then I pretend that this is the same as EOF.
if not self.isalive():
# timeout of 0 means "poll"
r, w, e = self.__select([self.child_fd], [], [], 0)
if not r:
self.flag_eof = True
raise EOF('End Of File (EOF). Braindead platform.')
elif self.__irix_hack:
# Irix takes a long time before it realizes a child was terminated.
# FIXME So does this mean Irix systems are forced to always have
# FIXME a 2 second delay when calling read_nonblocking? That sucks.
r, w, e = self.__select([self.child_fd], [], [], 2)
if not r and not self.isalive():
self.flag_eof = True
raise EOF('End Of File (EOF). Slow platform.')
r, w, e = self.__select([self.child_fd], [], [], timeout)
if not r:
if not self.isalive():
# Some platforms, such as Irix, will claim that their
# processes are alive; timeout on the select; and
# then finally admit that they are not alive.
self.flag_eof = True
raise EOF('End of File (EOF). Very slow platform.')
else:
raise TIMEOUT('Timeout exceeded.')
if self.child_fd in r:
try:
s = os.read(self.child_fd, size)
except OSError as err:
if err.args[0] == errno.EIO:
# Linux-style EOF
self.flag_eof = True
raise EOF('End Of File (EOF). Exception style platform.')
raise
if s == b'':
# BSD-style EOF
self.flag_eof = True
raise EOF('End Of File (EOF). Empty string style platform.')
s = self._coerce_read_string(s)
self._log(s, 'read')
return s
raise ExceptionPexpect('Reached an unexpected state.') # pragma: no cover
def read(self, size=-1):
'''This reads at most "size" bytes from the file (less if the read hits
EOF before obtaining size bytes). If the size argument is negative or
omitted, read all data until EOF is reached. The bytes are returned as
a string object. An empty string is returned when EOF is encountered
immediately. '''
if size == 0:
return self.string_type()
if size < 0:
# delimiter default is EOF
self.expect(self.delimiter)
return self.before
# I could have done this more directly by not using expect(), but
# I deliberately decided to couple read() to expect() so that
# I would catch any bugs early and ensure consistant behavior.
# It's a little less efficient, but there is less for me to
# worry about if I have to later modify read() or expect().
# Note, it's OK if size==-1 in the regex. That just means it
# will never match anything in which case we stop only on EOF.
cre = re.compile(self._coerce_expect_string('.{%d}' % size), re.DOTALL)
# delimiter default is EOF
index = self.expect([cre, self.delimiter])
if index == 0:
### FIXME self.before should be ''. Should I assert this?
return self.after
return self.before
def readline(self, size=-1):
'''This reads and returns one entire line. The newline at the end of
line is returned as part of the string, unless the file ends without a
newline. An empty string is returned if EOF is encountered immediately.
This looks for a newline as a CR/LF pair (\\r\\n) even on UNIX because
this is what the pseudotty device returns. So contrary to what you may
expect you will receive newlines as \\r\\n.
If the size argument is 0 then an empty string is returned. In all
other cases the size argument is ignored, which is not standard
behavior for a file-like object. '''
if size == 0:
return self.string_type()
# delimiter default is EOF
index = self.expect([self.crlf, self.delimiter])
if index == 0:
return self.before + self.crlf
else:
return self.before
def __iter__(self):
'''This is to support iterators over a file-like object.
'''
return iter(self.readline, self.string_type())
def readlines(self, sizehint=-1):
'''This reads until EOF using readline() and returns a list containing
the lines thus read. The optional 'sizehint' argument is ignored.
Remember, because this reads until EOF that means the child
process should have closed its stdout. If you run this method on
a child that is still running with its stdout open then this
method will block until it timesout.'''
lines = []
while True:
line = self.readline()
if not line:
break
lines.append(line)
return lines
def write(self, s):
'''This is similar to send() except that there is no return value.
'''
self.send(s)
def writelines(self, sequence):
'''This calls write() for each element in the sequence. The sequence
can be any iterable object producing strings, typically a list of
strings. This does not add line separators. There is no return value.
'''
for s in sequence:
self.write(s)
def send(self, s):
'''Sends string ``s`` to the child process, returning the number of
bytes written. If a logfile is specified, a copy is written to that
log. '''
time.sleep(self.delaybeforesend)
s = self._coerce_send_string(s)
self._log(s, 'send')
return self._send(s)
def _send(self, s):
return os.write(self.child_fd, s)
def sendline(self, s=''):
'''Wraps send(), sending string ``s`` to child process, with os.linesep
automatically appended. Returns number of bytes written. '''
n = self.send(s)
n = n + self.send(self.linesep)
return n
def sendcontrol(self, char):
'''Helper method that wraps send() with mnemonic access for sending control
character to the child (such as Ctrl-C or Ctrl-D). For example, to send
Ctrl-G (ASCII 7, bell, '\a')::
child.sendcontrol('g')
See also, sendintr() and sendeof().
'''
char = char.lower()
a = ord(char)
if a >= 97 and a <= 122:
a = a - ord('a') + 1
return self.send(self._chr(a))
d = {'@': 0, '`': 0,
'[': 27, '{': 27,
'\\': 28, '|': 28,
']': 29, '}': 29,
'^': 30, '~': 30,
'_': 31,
'?': 127}
if char not in d:
return 0
return self.send(self._chr(d[char]))
def sendeof(self):
'''This sends an EOF to the child. This sends a character which causes
the pending parent output buffer to be sent to the waiting child
program without waiting for end-of-line. If it is the first character
of the line, the read() in the user program returns 0, which signifies
end-of-file. This means to work as expected a sendeof() has to be
called at the beginning of a line. This method does not send a newline.
It is the responsibility of the caller to ensure the eof is sent at the
beginning of a line. '''
self.send(self._chr(self._EOF))
def sendintr(self):
'''This sends a SIGINT to the child. It does not require
the SIGINT to be the first character on a line. '''
self.send(self._chr(self._INTR))
def eof(self):
'''This returns True if the EOF exception was ever raised.
'''
return self.flag_eof
def terminate(self, force=False):
'''This forces a child process to terminate. It starts nicely with
SIGHUP and SIGINT. If "force" is True then moves onto SIGKILL. This
returns True if the child was terminated. This returns False if the
child could not be terminated. '''
if not self.isalive():
return True
try:
self.kill(signal.SIGHUP)
time.sleep(self.delayafterterminate)
if not self.isalive():
return True
self.kill(signal.SIGCONT)
time.sleep(self.delayafterterminate)
if not self.isalive():
return True
self.kill(signal.SIGINT)
time.sleep(self.delayafterterminate)
if not self.isalive():
return True
if force:
self.kill(signal.SIGKILL)
time.sleep(self.delayafterterminate)
if not self.isalive():
return True
else:
return False
return False
except OSError:
# I think there are kernel timing issues that sometimes cause
# this to happen. I think isalive() reports True, but the
# process is dead to the kernel.
# Make one last attempt to see if the kernel is up to date.
time.sleep(self.delayafterterminate)
if not self.isalive():
return True
else:
return False
def wait(self):
'''This waits until the child exits. This is a blocking call. This will
not read any data from the child, so this will block forever if the
child has unread output and has terminated. In other words, the child
may have printed output then called exit(), but, the child is
technically still alive until its output is read by the parent. '''
if self.isalive():
pid, status = os.waitpid(self.pid, 0)
else:
raise ExceptionPexpect('Cannot wait for dead child process.')
self.exitstatus = os.WEXITSTATUS(status)
if os.WIFEXITED(status):
self.status = status
self.exitstatus = os.WEXITSTATUS(status)
self.signalstatus = None
self.terminated = True
elif os.WIFSIGNALED(status):
self.status = status
self.exitstatus = None
self.signalstatus = os.WTERMSIG(status)
self.terminated = True
elif os.WIFSTOPPED(status): # pragma: no cover
# You can't call wait() on a child process in the stopped state.
raise ExceptionPexpect('Called wait() on a stopped child ' +
'process. This is not supported. Is some other ' +
'process attempting job control with our child pid?')
return self.exitstatus
def isalive(self):
'''This tests if the child process is running or not. This is
non-blocking. If the child was terminated then this will read the
exitstatus or signalstatus of the child. This returns True if the child
process appears to be running or False if not. It can take literally
SECONDS for Solaris to return the right status. '''
if self.terminated:
return False
if self.flag_eof:
# This is for Linux, which requires the blocking form
# of waitpid to get the status of a defunct process.
# This is super-lame. The flag_eof would have been set
# in read_nonblocking(), so this should be safe.
waitpid_options = 0
else:
waitpid_options = os.WNOHANG
try:
pid, status = os.waitpid(self.pid, waitpid_options)
except OSError:
err = sys.exc_info()[1]
# No child processes
if err.errno == errno.ECHILD:
raise ExceptionPexpect('isalive() encountered condition ' +
'where "terminated" is 0, but there was no child ' +
'process. Did someone else call waitpid() ' +
'on our process?')
else:
raise err
# I have to do this twice for Solaris.
# I can't even believe that I figured this out...
# If waitpid() returns 0 it means that no child process
# wishes to report, and the value of status is undefined.
if pid == 0:
try:
### os.WNOHANG) # Solaris!
pid, status = os.waitpid(self.pid, waitpid_options)
except OSError as e: # pragma: no cover
# This should never happen...
if e.errno == errno.ECHILD:
raise ExceptionPexpect('isalive() encountered condition ' +
'that should never happen. There was no child ' +
'process. Did someone else call waitpid() ' +
'on our process?')
else:
raise
# If pid is still 0 after two calls to waitpid() then the process
# really is alive. This seems to work on all platforms, except for
# Irix which seems to require a blocking call on waitpid or select,
# so I let read_nonblocking take care of this situation
# (unfortunately, this requires waiting through the timeout).
if pid == 0:
return True
if pid == 0:
return True
if os.WIFEXITED(status):
self.status = status
self.exitstatus = os.WEXITSTATUS(status)
self.signalstatus = None
self.terminated = True
elif os.WIFSIGNALED(status):
self.status = status
self.exitstatus = None
self.signalstatus = os.WTERMSIG(status)
self.terminated = True
elif os.WIFSTOPPED(status):
raise ExceptionPexpect('isalive() encountered condition ' +
'where child process is stopped. This is not ' +
'supported. Is some other process attempting ' +
'job control with our child pid?')
return False
def kill(self, sig):
'''This sends the given signal to the child application. In keeping
with UNIX tradition it has a misleading name. It does not necessarily
kill the child unless you send the right signal. '''
# Same as os.kill, but the pid is given for you.
if self.isalive():
os.kill(self.pid, sig)
def _pattern_type_err(self, pattern):
raise TypeError('got {badtype} ({badobj!r}) as pattern, must be one'
' of: {goodtypes}, pexpect.EOF, pexpect.TIMEOUT'\
.format(badtype=type(pattern),
badobj=pattern,
goodtypes=', '.join([str(ast)\
for ast in self.allowed_string_types])
)
)
def compile_pattern_list(self, patterns):
'''This compiles a pattern-string or a list of pattern-strings.
Patterns must be a StringType, EOF, TIMEOUT, SRE_Pattern, or a list of
those. Patterns may also be None which results in an empty list (you
might do this if waiting for an EOF or TIMEOUT condition without
expecting any pattern).
This is used by expect() when calling expect_list(). Thus expect() is
nothing more than::
cpl = self.compile_pattern_list(pl)
return self.expect_list(cpl, timeout)
If you are using expect() within a loop it may be more
efficient to compile the patterns first and then call expect_list().
This avoid calls in a loop to compile_pattern_list()::
cpl = self.compile_pattern_list(my_pattern)
while some_condition:
...
i = self.expect_list(clp, timeout)
...
'''
if patterns is None:
return []
if not isinstance(patterns, list):
patterns = [patterns]
# Allow dot to match \n
compile_flags = re.DOTALL
if self.ignorecase:
compile_flags = compile_flags | re.IGNORECASE
compiled_pattern_list = []
for idx, p in enumerate(patterns):
if isinstance(p, self.allowed_string_types):
p = self._coerce_expect_string(p)
compiled_pattern_list.append(re.compile(p, compile_flags))
elif p is EOF:
compiled_pattern_list.append(EOF)
elif p is TIMEOUT:
compiled_pattern_list.append(TIMEOUT)
elif isinstance(p, type(re.compile(''))):
compiled_pattern_list.append(p)
else:
self._pattern_type_err(p)
return compiled_pattern_list
def expect(self, pattern, timeout=-1, searchwindowsize=-1):
'''This seeks through the stream until a pattern is matched. The
pattern is overloaded and may take several types. The pattern can be a
StringType, EOF, a compiled re, or a list of any of those types.
Strings will be compiled to re types. This returns the index into the
pattern list. If the pattern was not a list this returns index 0 on a
successful match. This may raise exceptions for EOF or TIMEOUT. To
avoid the EOF or TIMEOUT exceptions add EOF or TIMEOUT to the pattern
list. That will cause expect to match an EOF or TIMEOUT condition
instead of raising an exception.
If you pass a list of patterns and more than one matches, the first
match in the stream is chosen. If more than one pattern matches at that
point, the leftmost in the pattern list is chosen. For example::
# the input is 'foobar'
index = p.expect(['bar', 'foo', 'foobar'])
# returns 1('foo') even though 'foobar' is a "better" match
Please note, however, that buffering can affect this behavior, since
input arrives in unpredictable chunks. For example::
# the input is 'foobar'
index = p.expect(['foobar', 'foo'])
# returns 0('foobar') if all input is available at once,
# but returs 1('foo') if parts of the final 'bar' arrive late
After a match is found the instance attributes 'before', 'after' and
'match' will be set. You can see all the data read before the match in
'before'. You can see the data that was matched in 'after'. The
re.MatchObject used in the re match will be in 'match'. If an error
occurred then 'before' will be set to all the data read so far and
'after' and 'match' will be None.
If timeout is -1 then timeout will be set to the self.timeout value.
A list entry may be EOF or TIMEOUT instead of a string. This will
catch these exceptions and return the index of the list entry instead
of raising the exception. The attribute 'after' will be set to the
exception type. The attribute 'match' will be None. This allows you to
write code like this::
index = p.expect(['good', 'bad', pexpect.EOF, pexpect.TIMEOUT])
if index == 0:
do_something()
elif index == 1:
do_something_else()
elif index == 2:
do_some_other_thing()
elif index == 3:
do_something_completely_different()
instead of code like this::
try:
index = p.expect(['good', 'bad'])
if index == 0:
do_something()
elif index == 1:
do_something_else()
except EOF:
do_some_other_thing()
except TIMEOUT:
do_something_completely_different()
These two forms are equivalent. It all depends on what you want. You
can also just expect the EOF if you are waiting for all output of a
child to finish. For example::
p = pexpect.spawn('/bin/ls')
p.expect(pexpect.EOF)
print p.before
If you are trying to optimize for speed then see expect_list().
'''
compiled_pattern_list = self.compile_pattern_list(pattern)
return self.expect_list(compiled_pattern_list,
timeout, searchwindowsize)
def expect_list(self, pattern_list, timeout=-1, searchwindowsize=-1):
'''This takes a list of compiled regular expressions and returns the
index into the pattern_list that matched the child output. The list may
also contain EOF or TIMEOUT(which are not compiled regular
expressions). This method is similar to the expect() method except that
expect_list() does not recompile the pattern list on every call. This
may help if you are trying to optimize for speed, otherwise just use
the expect() method. This is called by expect(). If timeout==-1 then
the self.timeout value is used. If searchwindowsize==-1 then the
self.searchwindowsize value is used. '''
return self.expect_loop(searcher_re(pattern_list),
timeout, searchwindowsize)
def expect_exact(self, pattern_list, timeout=-1, searchwindowsize=-1):
'''This is similar to expect(), but uses plain string matching instead
of compiled regular expressions in 'pattern_list'. The 'pattern_list'
may be a string; a list or other sequence of strings; or TIMEOUT and
EOF.
This call might be faster than expect() for two reasons: string
searching is faster than RE matching and it is possible to limit the
search to just the end of the input buffer.
This method is also useful when you don't want to have to worry about
escaping regular expression characters that you want to match.'''
if (isinstance(pattern_list, self.allowed_string_types) or
pattern_list in (TIMEOUT, EOF)):
pattern_list = [pattern_list]
def prepare_pattern(pattern):
if pattern in (TIMEOUT, EOF):
return pattern
if isinstance(pattern, self.allowed_string_types):
return self._coerce_expect_string(pattern)
self._pattern_type_err(pattern)
try:
pattern_list = iter(pattern_list)
except TypeError:
self._pattern_type_err(pattern_list)
pattern_list = [prepare_pattern(p) for p in pattern_list]
return self.expect_loop(searcher_string(pattern_list),
timeout, searchwindowsize)
def expect_loop(self, searcher, timeout=-1, searchwindowsize=-1):
'''This is the common loop used inside expect. The 'searcher' should be
an instance of searcher_re or searcher_string, which describes how and
what to search for in the input.
See expect() for other arguments, return value and exceptions. '''
self.searcher = searcher
if timeout == -1:
timeout = self.timeout
if timeout is not None:
end_time = time.time() + timeout
if searchwindowsize == -1:
searchwindowsize = self.searchwindowsize
try:
incoming = self.buffer
freshlen = len(incoming)
while True:
# Keep reading until exception or return.
index = searcher.search(incoming, freshlen, searchwindowsize)
if index >= 0:
self.buffer = incoming[searcher.end:]
self.before = incoming[: searcher.start]
self.after = incoming[searcher.start: searcher.end]
self.match = searcher.match
self.match_index = index
return self.match_index
# No match at this point
if (timeout is not None) and (timeout < 0):
raise TIMEOUT('Timeout exceeded in expect_any().')
# Still have time left, so read more data
c = self.read_nonblocking(self.maxread, timeout)
freshlen = len(c)
time.sleep(0.0001)
incoming = incoming + c
if timeout is not None:
timeout = end_time - time.time()
except EOF:
err = sys.exc_info()[1]
self.buffer = self.string_type()
self.before = incoming
self.after = EOF
index = searcher.eof_index
if index >= 0:
self.match = EOF
self.match_index = index
return self.match_index
else:
self.match = None
self.match_index = None
raise EOF(str(err) + '\n' + str(self))
except TIMEOUT:
err = sys.exc_info()[1]
self.buffer = incoming
self.before = incoming
self.after = TIMEOUT
index = searcher.timeout_index
if index >= 0:
self.match = TIMEOUT
self.match_index = index
return self.match_index
else:
self.match = None
self.match_index = None
raise TIMEOUT(str(err) + '\n' + str(self))
except:
self.before = incoming
self.after = None
self.match = None
self.match_index = None
raise
def getwinsize(self):
'''This returns the terminal window size of the child tty. The return
value is a tuple of (rows, cols). '''
TIOCGWINSZ = getattr(termios, 'TIOCGWINSZ', 1074295912)
s = struct.pack('HHHH', 0, 0, 0, 0)
x = fcntl.ioctl(self.child_fd, TIOCGWINSZ, s)
return struct.unpack('HHHH', x)[0:2]
def setwinsize(self, rows, cols):
'''This sets the terminal window size of the child tty. This will cause
a SIGWINCH signal to be sent to the child. This does not change the
physical window size. It changes the size reported to TTY-aware
applications like vi or curses -- applications that respond to the
SIGWINCH signal. '''
# Some very old platforms have a bug that causes the value for
# termios.TIOCSWINSZ to be truncated. There was a hack here to work
# around this, but it caused problems with newer platforms so has been
# removed. For details see https://github.com/pexpect/pexpect/issues/39
TIOCSWINSZ = getattr(termios, 'TIOCSWINSZ', -2146929561)
# Note, assume ws_xpixel and ws_ypixel are zero.
s = struct.pack('HHHH', rows, cols, 0, 0)
fcntl.ioctl(self.fileno(), TIOCSWINSZ, s)
def interact(self, escape_character=chr(29),
input_filter=None, output_filter=None):
'''This gives control of the child process to the interactive user (the
human at the keyboard). Keystrokes are sent to the child process, and
the stdout and stderr output of the child process is printed. This
simply echos the child stdout and child stderr to the real stdout and
it echos the real stdin to the child stdin. When the user types the
escape_character this method will stop. The default for
escape_character is ^]. This should not be confused with ASCII 27 --
the ESC character. ASCII 29 was chosen for historical merit because
this is the character used by 'telnet' as the escape character. The
escape_character will not be sent to the child process.
You may pass in optional input and output filter functions. These
functions should take a string and return a string. The output_filter
will be passed all the output from the child process. The input_filter
will be passed all the keyboard input from the user. The input_filter
is run BEFORE the check for the escape_character.
Note that if you change the window size of the parent the SIGWINCH
signal will not be passed through to the child. If you want the child
window size to change when the parent's window size changes then do
something like the following example::
import pexpect, struct, fcntl, termios, signal, sys
def sigwinch_passthrough (sig, data):
s = struct.pack("HHHH", 0, 0, 0, 0)
a = struct.unpack('hhhh', fcntl.ioctl(sys.stdout.fileno(),
termios.TIOCGWINSZ , s))
global p
p.setwinsize(a[0],a[1])
# Note this 'p' global and used in sigwinch_passthrough.
p = pexpect.spawn('/bin/bash')
signal.signal(signal.SIGWINCH, sigwinch_passthrough)
p.interact()
'''
# Flush the buffer.
self.write_to_stdout(self.buffer)
self.stdout.flush()
self.buffer = self.string_type()
mode = tty.tcgetattr(self.STDIN_FILENO)
tty.setraw(self.STDIN_FILENO)
if PY3:
escape_character = escape_character.encode('latin-1')
try:
self.__interact_copy(escape_character, input_filter, output_filter)
finally:
tty.tcsetattr(self.STDIN_FILENO, tty.TCSAFLUSH, mode)
def __interact_writen(self, fd, data):
'''This is used by the interact() method.
'''
while data != b'' and self.isalive():
n = os.write(fd, data)
data = data[n:]
def __interact_read(self, fd):
'''This is used by the interact() method.
'''
return os.read(fd, 1000)
def __interact_copy(self, escape_character=None,
input_filter=None, output_filter=None):
'''This is used by the interact() method.
'''
while self.isalive():
r, w, e = self.__select([self.child_fd, self.STDIN_FILENO], [], [])
if self.child_fd in r:
try:
data = self.__interact_read(self.child_fd)
except OSError as err:
if err.args[0] == errno.EIO:
# Linux-style EOF
break
raise
if data == b'':
# BSD-style EOF
break
if output_filter:
data = output_filter(data)
if self.logfile is not None:
self.logfile.write(data)
self.logfile.flush()
os.write(self.STDOUT_FILENO, data)
if self.STDIN_FILENO in r:
data = self.__interact_read(self.STDIN_FILENO)
if input_filter:
data = input_filter(data)
i = data.rfind(escape_character)
if i != -1:
data = data[:i]
self.__interact_writen(self.child_fd, data)
break
self.__interact_writen(self.child_fd, data)
def __select(self, iwtd, owtd, ewtd, timeout=None):
'''This is a wrapper around select.select() that ignores signals. If
select.select raises a select.error exception and errno is an EINTR
error then it is ignored. Mainly this is used to ignore sigwinch
(terminal resize). '''
# if select() is interrupted by a signal (errno==EINTR) then
# we loop back and enter the select() again.
if timeout is not None:
end_time = time.time() + timeout
while True:
try:
return select.select(iwtd, owtd, ewtd, timeout)
except select.error:
err = sys.exc_info()[1]
if err.args[0] == errno.EINTR:
# if we loop back we have to subtract the
# amount of time we already waited.
if timeout is not None:
timeout = end_time - time.time()
if timeout < 0:
return([], [], [])
else:
# something else caused the select.error, so
# this actually is an exception.
raise
##############################################################################
# The following methods are no longer supported or allowed.
def setmaxread(self, maxread): # pragma: no cover
'''This method is no longer supported or allowed. I don't like getters
and setters without a good reason. '''
raise ExceptionPexpect('This method is no longer supported ' +
'or allowed. Just assign a value to the ' +
'maxread member variable.')
def setlog(self, fileobject): # pragma: no cover
'''This method is no longer supported or allowed.
'''
raise ExceptionPexpect('This method is no longer supported ' +
'or allowed. Just assign a value to the logfile ' +
'member variable.')
##############################################################################
# End of spawn class
##############################################################################
class spawnu(spawn):
"""Works like spawn, but accepts and returns unicode strings.
Extra parameters:
:param encoding: The encoding to use for communications (default: 'utf-8')
:param errors: How to handle encoding/decoding errors; one of 'strict'
(the default), 'ignore', or 'replace', as described
for :meth:`~bytes.decode` and :meth:`~str.encode`.
"""
if PY3:
string_type = str
allowed_string_types = (str, )
_chr = staticmethod(chr)
linesep = os.linesep
crlf = '\r\n'
else:
string_type = unicode
allowed_string_types = (unicode, )
_chr = staticmethod(unichr)
linesep = os.linesep.decode('ascii')
crlf = '\r\n'.decode('ascii')
# This can handle unicode in both Python 2 and 3
write_to_stdout = sys.stdout.write
def __init__(self, *args, **kwargs):
self.encoding = kwargs.pop('encoding', 'utf-8')
self.errors = kwargs.pop('errors', 'strict')
self._decoder = codecs.getincrementaldecoder(self.encoding)(errors=self.errors)
super(spawnu, self).__init__(*args, **kwargs)
@staticmethod
def _coerce_expect_string(s):
return s
@staticmethod
def _coerce_send_string(s):
return s
def _coerce_read_string(self, s):
return self._decoder.decode(s, final=False)
def _send(self, s):
return os.write(self.child_fd, s.encode(self.encoding, self.errors))
class searcher_string(object):
'''This is a plain string search helper for the spawn.expect_any() method.
This helper class is for speed. For more powerful regex patterns
see the helper class, searcher_re.
Attributes:
eof_index - index of EOF, or -1
timeout_index - index of TIMEOUT, or -1
After a successful match by the search() method the following attributes
are available:
start - index into the buffer, first byte of match
end - index into the buffer, first byte after match
match - the matching string itself
'''
def __init__(self, strings):
'''This creates an instance of searcher_string. This argument 'strings'
may be a list; a sequence of strings; or the EOF or TIMEOUT types. '''
self.eof_index = -1
self.timeout_index = -1
self._strings = []
for n, s in enumerate(strings):
if s is EOF:
self.eof_index = n
continue
if s is TIMEOUT:
self.timeout_index = n
continue
self._strings.append((n, s))
def __str__(self):
'''This returns a human-readable string that represents the state of
the object.'''
ss = [(ns[0], ' %d: "%s"' % ns) for ns in self._strings]
ss.append((-1, 'searcher_string:'))
if self.eof_index >= 0:
ss.append((self.eof_index, ' %d: EOF' % self.eof_index))
if self.timeout_index >= 0:
ss.append((self.timeout_index,
' %d: TIMEOUT' % self.timeout_index))
ss.sort()
ss = list(zip(*ss))[1]
return '\n'.join(ss)
def search(self, buffer, freshlen, searchwindowsize=None):
'''This searches 'buffer' for the first occurence of one of the search
strings. 'freshlen' must indicate the number of bytes at the end of
'buffer' which have not been searched before. It helps to avoid
searching the same, possibly big, buffer over and over again.
See class spawn for the 'searchwindowsize' argument.
If there is a match this returns the index of that string, and sets
'start', 'end' and 'match'. Otherwise, this returns -1. '''
first_match = None
# 'freshlen' helps a lot here. Further optimizations could
# possibly include:
#
# using something like the Boyer-Moore Fast String Searching
# Algorithm; pre-compiling the search through a list of
# strings into something that can scan the input once to
# search for all N strings; realize that if we search for
# ['bar', 'baz'] and the input is '...foo' we need not bother
# rescanning until we've read three more bytes.
#
# Sadly, I don't know enough about this interesting topic. /grahn
for index, s in self._strings:
if searchwindowsize is None:
# the match, if any, can only be in the fresh data,
# or at the very end of the old data
offset = -(freshlen + len(s))
else:
# better obey searchwindowsize
offset = -searchwindowsize
n = buffer.find(s, offset)
if n >= 0 and (first_match is None or n < first_match):
first_match = n
best_index, best_match = index, s
if first_match is None:
return -1
self.match = best_match
self.start = first_match
self.end = self.start + len(self.match)
return best_index
class searcher_re(object):
'''This is regular expression string search helper for the
spawn.expect_any() method. This helper class is for powerful
pattern matching. For speed, see the helper class, searcher_string.
Attributes:
eof_index - index of EOF, or -1
timeout_index - index of TIMEOUT, or -1
After a successful match by the search() method the following attributes
are available:
start - index into the buffer, first byte of match
end - index into the buffer, first byte after match
match - the re.match object returned by a succesful re.search
'''
def __init__(self, patterns):
'''This creates an instance that searches for 'patterns' Where
'patterns' may be a list or other sequence of compiled regular
expressions, or the EOF or TIMEOUT types.'''
self.eof_index = -1
self.timeout_index = -1
self._searches = []
for n, s in zip(list(range(len(patterns))), patterns):
if s is EOF:
self.eof_index = n
continue
if s is TIMEOUT:
self.timeout_index = n
continue
self._searches.append((n, s))
def __str__(self):
'''This returns a human-readable string that represents the state of
the object.'''
#ss = [(n, ' %d: re.compile("%s")' %
# (n, repr(s.pattern))) for n, s in self._searches]
ss = list()
for n, s in self._searches:
try:
ss.append((n, ' %d: re.compile("%s")' % (n, s.pattern)))
except UnicodeEncodeError:
# for test cases that display __str__ of searches, dont throw
# another exception just because stdout is ascii-only, using
# repr()
ss.append((n, ' %d: re.compile(%r)' % (n, s.pattern)))
ss.append((-1, 'searcher_re:'))
if self.eof_index >= 0:
ss.append((self.eof_index, ' %d: EOF' % self.eof_index))
if self.timeout_index >= 0:
ss.append((self.timeout_index, ' %d: TIMEOUT' %
self.timeout_index))
ss.sort()
ss = list(zip(*ss))[1]
return '\n'.join(ss)
def search(self, buffer, freshlen, searchwindowsize=None):
'''This searches 'buffer' for the first occurence of one of the regular
expressions. 'freshlen' must indicate the number of bytes at the end of
'buffer' which have not been searched before.
See class spawn for the 'searchwindowsize' argument.
If there is a match this returns the index of that string, and sets
'start', 'end' and 'match'. Otherwise, returns -1.'''
first_match = None
# 'freshlen' doesn't help here -- we cannot predict the
# length of a match, and the re module provides no help.
if searchwindowsize is None:
searchstart = 0
else:
searchstart = max(0, len(buffer) - searchwindowsize)
for index, s in self._searches:
match = s.search(buffer, searchstart)
if match is None:
continue
n = match.start()
if first_match is None or n < first_match:
first_match = n
the_match = match
best_index = index
if first_match is None:
return -1
self.start = first_match
self.match = the_match
self.end = self.match.end()
return best_index
def is_executable_file(path):
"""Checks that path is an executable regular file (or a symlink to a file).
This is roughly ``os.path isfile(path) and os.access(path, os.X_OK)``, but
on some platforms :func:`os.access` gives us the wrong answer, so this
checks permission bits directly.
"""
# follow symlinks,
fpath = os.path.realpath(path)
# return False for non-files (directories, fifo, etc.)
if not os.path.isfile(fpath):
return False
# On Solaris, etc., "If the process has appropriate privileges, an
# implementation may indicate success for X_OK even if none of the
# execute file permission bits are set."
#
# For this reason, it is necessary to explicitly check st_mode
# get file mode using os.stat, and check if `other',
# that is anybody, may read and execute.
mode = os.stat(fpath).st_mode
if mode & stat.S_IROTH and mode & stat.S_IXOTH:
return True
# get current user's group ids, and check if `group',
# when matching ours, may read and execute.
user_gids = os.getgroups() + [os.getgid()]
if (os.stat(fpath).st_gid in user_gids and
mode & stat.S_IRGRP and mode & stat.S_IXGRP):
return True
# finally, if file owner matches our effective userid,
# check if `user', may read and execute.
user_gids = os.getgroups() + [os.getgid()]
if (os.stat(fpath).st_uid == os.geteuid() and
mode & stat.S_IRUSR and mode & stat.S_IXUSR):
return True
return False
def which(filename):
'''This takes a given filename; tries to find it in the environment path;
then checks if it is executable. This returns the full path to the filename
if found and executable. Otherwise this returns None.'''
# Special case where filename contains an explicit path.
if os.path.dirname(filename) != '' and is_executable_file(filename):
return filename
if 'PATH' not in os.environ or os.environ['PATH'] == '':
p = os.defpath
else:
p = os.environ['PATH']
pathlist = p.split(os.pathsep)
for path in pathlist:
ff = os.path.join(path, filename)
if is_executable_file(ff):
return ff
return None
def split_command_line(command_line):
'''This splits a command line into a list of arguments. It splits arguments
on spaces, but handles embedded quotes, doublequotes, and escaped
characters. It's impossible to do this with a regular expression, so I
wrote a little state machine to parse the command line. '''
arg_list = []
arg = ''
# Constants to name the states we can be in.
state_basic = 0
state_esc = 1
state_singlequote = 2
state_doublequote = 3
# The state when consuming whitespace between commands.
state_whitespace = 4
state = state_basic
for c in command_line:
if state == state_basic or state == state_whitespace:
if c == '\\':
# Escape the next character
state = state_esc
elif c == r"'":
# Handle single quote
state = state_singlequote
elif c == r'"':
# Handle double quote
state = state_doublequote
elif c.isspace():
# Add arg to arg_list if we aren't in the middle of whitespace.
if state == state_whitespace:
# Do nothing.
None
else:
arg_list.append(arg)
arg = ''
state = state_whitespace
else:
arg = arg + c
state = state_basic
elif state == state_esc:
arg = arg + c
state = state_basic
elif state == state_singlequote:
if c == r"'":
state = state_basic
else:
arg = arg + c
elif state == state_doublequote:
if c == r'"':
state = state_basic
else:
arg = arg + c
if arg != '':
arg_list.append(arg)
return arg_list
# vim: set shiftround expandtab tabstop=4 shiftwidth=4 ft=python autoindent :
|
[
"theaccountname@yahoo.com"
] |
theaccountname@yahoo.com
|
1548e71eb3e56b1454fba2ebb60d6ebcd1105cfe
|
4f03a65a6af608a8fb2d0049f6b1237532585925
|
/src/apconf/mixins/cms.py
|
347eaec1e8516b1276f09a493216a5910b076ab1
|
[] |
no_license
|
pymallorca/pymallorca
|
865f0cfa1fa693b7b488330236691eb8c6c4bf2b
|
048d41e02c305d6ccbe67ebf99ca519af6698109
|
refs/heads/master
| 2020-06-09T04:08:37.359739
| 2014-11-24T23:30:49
| 2014-11-24T23:30:49
| 26,458,565
| 2
| 0
| null | 2014-11-22T11:36:36
| 2014-11-10T22:20:06
|
Python
|
UTF-8
|
Python
| false
| false
| 918
|
py
|
# -*- coding: utf-8 -*-
from apconf import Options
opts = Options()
def get(value, default):
return opts.get(value, default, section='CMS')
class CMSMixin(object):
CMS_SEO_FIELDS = True
CMS_REDIRECTS = True
CMS_SOFTROOT = False
CMS_TEMPLATE_INHERITANCE = True
CMS_MENU_TITLE_OVERWRITE = True
CMS_USE_TINYMCE = False
CMS_PERMISSION = True
@property
def CMS_LANGUAGES(self):
lang_dict = lambda code, name: {
'code': code,
'name': name,
'hide_untranslated': code == self.LANGUAGE_CODE,
'redirect_on_fallback': not (code == self.LANGUAGE_CODE),
}
langs_list = [lang_dict(code, name) for code, name in self.LANGUAGES]
return {
self.SITE_ID: langs_list,
'default': {
'fallbacks': [self.LANGUAGE_CODE, ]
}
}
|
[
"gshark@gmail.com"
] |
gshark@gmail.com
|
e49f2894a3ab9c1a654ba0df022d7398b7113dc5
|
85455c059499f6df9648defd7bbf44d9a3963a6a
|
/app/models.py
|
40fa912bd68ea0ad93da96bccf4cd1d190150dcb
|
[] |
no_license
|
zoneneo/hanzi
|
6951024bf99eead7cace58f78b12daa21f610268
|
6f35785120a3733b656c3baef12518112d6df4b6
|
refs/heads/main
| 2023-02-20T19:40:35.767078
| 2021-01-26T10:05:35
| 2021-01-26T10:05:35
| 327,545,310
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 9,543
|
py
|
# -*- coding: utf-8 -
from .exts import db
from sqlalchemy import Column, text, func, Index, outerjoin, and_
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy import distinct
from sqlalchemy import desc
from sqlalchemy import update
from sqlalchemy.orm import aliased
from sqlalchemy.types import TypeDecorator, VARCHAR
import sqlalchemy.types as types
from sqlalchemy.ext.mutable import Mutable,MutableDict,MutableList
import json
# lett=db.Enum()
# a=ord('a')
# lett.enums=[chr(i) for i in range(a,a+26)]
class JSONEncodedDict(TypeDecorator):
"""Represents an immutable structure as a json-encoded string."""
impl = VARCHAR
def process_bind_param(self, value, dialect):
if value is not None:
value = json.dumps(value)
return value
def process_result_value(self, value, dialect):
if value is not None:
value = json.loads(value)
return value
#MutableDict.associate_with(JSONEncodedDict)
def tab_name(val,pre='c'):
seq=pre+val
return ''.join([c.isupper() and '_'+c or c for c in seq]).lower()
class Base(db.Model):
__abstract__ = True
__table_args__ = {'mysql_engine': 'InnoDB'}
# create_time = db.Column(db.TIMESTAMP(True),server_default=text('CURRENT_TIMESTAMP'))
@declared_attr
def __tablename__(cls):
return tab_name(cls.__name__)
def save(self, flush = True):
result = db.session.add(self)
if flush:
#db.session.flush()
db.session.commit()
return result
def upsert(self):
db.session.merge(self)
return db.session.commit()
def destroy(self):
result = db.session.delete(self)
return result
def to_dict(self):
d = dict()
for c in self.__table__.columns:
v = getattr(self, c.name)
if v:
d[c.name] = v
return d
@classmethod
def rollback(cls):
return db.session.rollback()
@classmethod
def update(cls,sid,**row):
cls.query.filter_by(id=sid).update(row)
return db.session.commit()
@classmethod
def _get(cls, key):
return cls.query.get(key)
@classmethod
def remove(cls, kid):
one = cls.query.get(kid)
db.session.delete(one)
return db.session.commit()
@classmethod
def delete(cls, ids):
result = (cls.query
.filter(cls.id.in_(ids))
.delete(synchronize_session=False))
return result
@classmethod
def _count(cls, search = None):
result = db.session.query(func.count(cls.id))
return result.scalar()
@classmethod
def commit(cls):
db.session.commit()
@classmethod
def _search(cls, page, size, **search):
is_desc = search.pop('is_desc',False)
if search:
key, val = search.popitem()
if key in cls.__table__.columns:
field = getattr(cls, key)
query = cls.query.filter(field.like("%" + val + "%"))
else:
query = cls.query
else:
query = cls.query
if is_desc:
query = query.order_by(desc(cls.id))
else:
query = query.order_by(cls.id)
return query.paginate(page, per_page=size, error_out=False)
@classmethod
def _page(cls, page, size, **kwargs):
is_desc = kwargs.pop('is_desc',False)
if kwargs:
conditions={}
for key in kwargs:
if key in cls.__table__.columns:
conditions[key]=kwargs[key]
query = cls.query.filter_by(**conditions)
else:
query = cls.query
if is_desc:
query = query.order_by(desc(cls.id))
else:
query = query.order_by(cls.id)
return query.paginate(page, per_page=size, error_out=False)
@classmethod
def _all(cls):
return cls.query.all()
class Common(Base):
key = db.Column(db.String(128), primary_key=True)
value = db.Column(db.String(1024))
class User(Base):
id = db.Column(db.Integer,autoincrement=True,primary_key=True)
username = db.Column(db.String(100),comment= '姓名')
password = db.Column(db.String(128),comment= '密码')
email = db.Column(db.String(128),comment= '邮箱')
role = db.Column(db.String(6),comment= '角色')
class Customer(Base):
id = db.Column(db.Integer,autoincrement=True,primary_key=True)
name = db.Column(db.String(100),comment= '姓名')
age = db.Column(db.String(100),comment= '年龄')
sex = db.Column(db.String(100),comment= '性别')
birthday = db.Column(db.Integer,comment= '出生日期')
nation = db.Column(db.String(100),comment= '民族')
phone = db.Column(db.String(16),comment= '联系电话')
email = db.Column(db.String(128),comment= '邮箱')
address = db.Column(db.String(64),comment= '联系地址')
idcard = db.Column(db.String(20),comment= '身份证号码')
native_place = db.Column(db.String(64),comment= '籍贯')
remark = db.Column(db.String(128),comment= '备注')
class Study(Base):
id = db.Column(db.Integer,autoincrement=True,primary_key=True)
class Students(Base):
id = db.Column(db.Integer,autoincrement=True,primary_key=True)
#汉语字词
class Words(Base):
id = db.Column(db.Integer, primary_key=True)
gbk = db.Column(db.String(8), comment='编码')
spell = db.Column(db.String(50), comment='拼写')
word = db.Column(db.String(4), comment='汉字')
tone = db.Column(db.String(50), comment='拼音')
freq = db.Column(db.Integer, comment='频率')
#四字成语
class Idiom(Base):
id = db.Column(db.Integer, primary_key=True)
#格言谚语
class Proverb(Base):
id = db.Column(db.Integer, primary_key=True)
#词组短语
class Phrase(Base):
id = db.Column(db.Integer, primary_key=True)
gbk=db.Column(db.String(16),unique=True, comment='编码')
score = db.Column(db.Boolean(0), comment='评分')
length = db.Column(db.Integer, comment='词组长度')
spell = db.Column(db.String(128), comment='拼音')
words = db.Column(db.String(32), comment='词组')
class Chapter(Base):
id = db.Column(db.Integer, primary_key=True)
grade = db.Column(db.Integer, comment='年级')
chapter = db.Column(db.Integer, comment='章节')
subject = db.Column(db.String(64), comment='题目')
content = db.Column(db.Text, comment='课文')
class Section(Base):
id = db.Column(db.Integer, primary_key=True)
grade = db.Column(db.Integer, comment='年级')
chapter = db.Column(db.Integer, comment='章节')
know = db.Column(db.String(512), comment='识字表')
word = db.Column(db.String(512), comment='写字表')
phrase = db.Column(db.Text, comment='词语表')
class Dictation(Base):
id = db.Column(db.Integer, primary_key=True)
book_id = db.Column(db.Integer, comment='课本id')
grade = db.Column(db.Integer, comment='年级')
chapter = db.Column(db.String(64), comment='章节')
words = db.Column(db.String(64), comment='写字表')
know = db.Column(db.String(64), comment='识字表')
phrase = db.Column(db.String(512), comment='词组')
class Courses(Base):
id = db.Column(db.Integer, primary_key=True)
book_id = db.Column(db.Integer, comment='课本id')
grade = db.Column(db.Integer, comment='年级')
chapter = db.Column(db.String(64), comment='章节')
words = db.Column(db.String(64), comment='写字表')
know = db.Column(db.String(64), comment='识字表')
phrase = db.Column(db.String(512), comment='词组')
class Article(Base):
id = db.Column(db.Integer, primary_key=True)
tag = db.Column(db.String(64), comment='标签')
book_id = db.Column(db.Integer, comment='书本id')
category = db.Column(db.String(64), comment='分类')
author = db.Column(db.String(32), comment='作者')
subject = db.Column(db.String(64), comment='主题')
content = db.Column(db.Text, comment='内容')
class TextBook(Base):
id = db.Column(db.Integer, primary_key=True)
#publication_date = db.Column(db.TIMESTAMP(True), server_default=text('CURRENT_TIMESTAMP'))
title =db.Column(db.String(64), comment='书本名称')
course = db.Column(db.String(32), comment='科目')#语文
level = db.Column(db.String(64), comment='级别')#小初中高级
grade = db.Column(db.Integer, comment='年级')
volume = db.Column(db.Integer, comment='上中下册')
edition=db.Column(db.String(64), comment='版本')
editor=db.Column(db.String(128), comment='主编')
abstract=db.Column(db.Text, comment='摘要')
isbn = db.Column(db.String(32), comment='版本')
press=db.Column(db.Integer, comment='出版商id')
province = db.Column(db.Integer, comment='省份')
class Publisher(Base):
id = db.Column(db.Integer, primary_key=True)
publishing_house = db.Column(db.String(128), comment='出版商')
serial_number = db.Column(db.String(8), comment='编号')
province = db.Column(db.Integer, comment='省份')
class Links(Base):
id = db.Column(db.Integer, primary_key=True)
used = db.Column(db.Boolean(0), comment='己使用')
grade = db.Column(db.Integer, comment='年级')
chapter = db.Column(db.Integer, comment='课文')
subject = db.Column(db.String(64), comment='课文题目')
link = db.Column(db.String(512), comment='链接')
tag = db.Column(db.String(16), comment='标签')
|
[
"zoneneo@hotmail.com"
] |
zoneneo@hotmail.com
|
881bf26ac89b923944c31b113c5a4250cb30de70
|
780c45da6388931381d911499723c5afa8a44036
|
/run_test_c30.py
|
ce1a8a664e0893aa42c5eaf89ed0835150c1a6ad
|
[
"Apache-2.0"
] |
permissive
|
daitouli/metaheuristics
|
f9157bd700957072a69c0be03d8d34378533581c
|
9d885e4c9e9f39ad22baa9ea5d263d5daa276f88
|
refs/heads/master
| 2021-02-04T18:40:47.387347
| 2019-09-30T06:51:26
| 2019-09-30T06:51:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,520
|
py
|
import pandas as pd
from models.multiple_solution.swarm_based.ABC import *
from models.multiple_solution.swarm_based.BMO import *
from models.multiple_solution.swarm_based.BOA import *
from models.multiple_solution.swarm_based.EPO import *
from models.multiple_solution.swarm_based.HHO import *
from models.multiple_solution.swarm_based.NMR import *
from models.multiple_solution.swarm_based.PFA import *
from models.multiple_solution.swarm_based.PSO import *
from models.multiple_solution.swarm_based.SFO import *
from models.multiple_solution.swarm_based.SOA import *
from models.multiple_solution.swarm_based.WOA import *
from utils.FunctionUtil import *
## Setting parameters
root_paras = {
"problem_size": 100,
"domain_range": [-100, 100],
"print_train": True,
"objective_func": C30
}
abc_paras = {
"epoch": 500,
"pop_size": 100,
"couple_bees": [16, 4], # number of bees which provided for good location and other location
"patch_variables": [5.0, 0.985], # patch_variables = patch_variables * patch_factor (0.985)
"sites": [3, 1], # 3 bees (employed bees, onlookers and scouts), 1 good partition
}
bmo_paras = {
"epoch": 500,
"pop_size": 100,
"bm_teams": 10
}
boa_paras = {
"epoch": 500,
"pop_size": 100,
"c": 0.01,
"p": 0.8,
"alpha": [0.1, 0.3]
}
epo_paras = {
"epoch": 500,
"pop_size": 100
}
hho_paras = {
"epoch": 500,
"pop_size": 100
}
nmr_paras = {
"pop_size": 100,
"epoch": 500,
"bp": 0.75, # breeding probability
}
pfa_paras = {
"epoch": 500,
"pop_size": 100
}
pso_paras = {
"epoch": 500,
"pop_size": 100,
"w_minmax": [0.4, 0.9], # [0-1] -> [0.4-0.9] Weight of bird
"c_minmax": [1.2, 1.2] # [(1.2, 1.2), (0.8, 2.0), (1.6, 0.6)] Effecting of local va global
}
isfo_paras = {
"epoch": 500,
"pop_size": 100, # SailFish pop size
"pp": 0.1 # the rate between SailFish and Sardines (N_sf = N_s * pp) = 0.25, 0.2, 0.1
}
soa_paras = {
"epoch": 500,
"pop_size": 100,
}
woa_paras = {
"epoch": 500,
"pop_size": 100
}
## Run model
name_model = {
'BaseABC': BaseABC(root_algo_paras=root_paras, abc_paras=abc_paras),
'BaseBMO': BaseBMO(root_algo_paras=root_paras, bmo_paras=bmo_paras),
"AdaptiveBOA": AdaptiveBOA(root_algo_paras=root_paras, boa_paras=boa_paras),
"BaseEPO": BaseEPO(root_algo_paras=root_paras, epo_paras=epo_paras),
"BaseHHO": BaseHHO(root_algo_paras=root_paras, hho_paras=hho_paras),
"LevyNMR": LevyNMR(root_algo_paras=root_paras, nmr_paras=nmr_paras),
"IPFA": IPFA(root_algo_paras=root_paras, pfa_paras=pfa_paras),
"BasePSO": BasePSO(root_algo_paras=root_paras, pso_paras=pso_paras),
"ImprovedSFO": ImprovedSFO(root_algo_paras=root_paras, isfo_paras=isfo_paras),
"BaseSOA": BaseSOA(root_algo_paras=root_paras, soa_paras=soa_paras),
"BaoWOA": BaoWOA(root_algo_paras=root_paras, woa_paras=woa_paras)
}
### 1st: way
# list_loss = []
# for name, model in name_model.items():
# _, loss = model._train__()
# list_loss.append(loss)
# list_loss = np.asarray(list_loss)
# list_loss = list_loss.T
# np.savetxt("run_test_c30.csv", list_loss, delimiter=",", header=str(name_model.keys()))
### 2nd: way
list_loss = {}
for name, model in name_model.items():
_, loss = model._train__()
list_loss[name] = loss
df = pd.DataFrame(list_loss)
df.to_csv('c30_results.csv') # saving the dataframe
|
[
"nguyenthieu2102@gmail.com"
] |
nguyenthieu2102@gmail.com
|
6a7aa84cda5df60cadffb73eb06bb8813fea8c4c
|
0d1548f0fc2eeabfb663b3523e062df4413cd7ae
|
/manage.py
|
b3be62c86cff2966d667623c28727ccca9a80445
|
[] |
no_license
|
rachanabhagwat15/Stock-Market-Prediction
|
3f6ab7ea3c919cade1e9effa09e79522f82e6447
|
bae1b983b846501114228f520dbf9cda0df8caad
|
refs/heads/main
| 2023-02-15T20:41:19.433982
| 2021-01-17T04:47:50
| 2021-01-17T04:47:50
| 330,315,606
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 683
|
py
|
#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Stock.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
|
[
"noreply@github.com"
] |
noreply@github.com
|
a13082ba9e21bf1a732e1cfa9a5f593917aa62c5
|
84ac452582ba1f2a5ba48f490a21ef62ecd502d5
|
/build/android/tombstones.py
|
39fa5050e3a92dc3e847dc5f0152493dc613b183
|
[] |
no_license
|
stanislavalbreht/sandbox_test_2016
|
e215a45a48be6b31873c1ad5510f232ee80107aa
|
0e6e0c265d6af23f6eeac510d57271d6aa0de5c4
|
refs/heads/master
| 2021-01-10T03:26:10.886636
| 2016-01-27T08:29:27
| 2016-01-27T08:29:27
| 50,448,216
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,533
|
py
|
#!/usr/bin/env python
#
# Copyright 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
# Find the most recent tombstone file(s) on all connected devices
# and prints their stacks.
#
# Assumes tombstone file was created with current symbols.
import datetime
import logging
import multiprocessing
import os
import re
import subprocess
import sys
import optparse
import devil_chromium
from devil.android import device_blacklist
from devil.android import device_errors
from devil.android import device_utils
from devil.utils import run_tests_helper
_TZ_UTC = {'TZ': 'UTC'}
def _ListTombstones(device):
"""List the tombstone files on the device.
Args:
device: An instance of DeviceUtils.
Yields:
Tuples of (tombstone filename, date time of file on device).
"""
try:
if not device.PathExists('/data/tombstones', timeout=60, retries=3):
return
# TODO(perezju): Introduce a DeviceUtils.Ls() method (crbug.com/552376).
lines = device.RunShellCommand(
['ls', '-a', '-l', '/data/tombstones'],
as_root=True, check_return=True, env=_TZ_UTC, timeout=60)
for line in lines:
if 'tombstone' in line:
details = line.split()
t = datetime.datetime.strptime(details[-3] + ' ' + details[-2],
'%Y-%m-%d %H:%M')
yield details[-1], t
except device_errors.CommandFailedError:
logging.exception('Could not retrieve tombstones.')
except device_errors.CommandTimeoutError:
logging.exception('Timed out retrieving tombstones.')
def _GetDeviceDateTime(device):
"""Determine the date time on the device.
Args:
device: An instance of DeviceUtils.
Returns:
A datetime instance.
"""
device_now_string = device.RunShellCommand(
['date'], check_return=True, env=_TZ_UTC)
return datetime.datetime.strptime(
device_now_string[0], '%a %b %d %H:%M:%S %Z %Y')
def _GetTombstoneData(device, tombstone_file):
"""Retrieve the tombstone data from the device
Args:
device: An instance of DeviceUtils.
tombstone_file: the tombstone to retrieve
Returns:
A list of lines
"""
return device.ReadFile(
'/data/tombstones/' + tombstone_file, as_root=True).splitlines()
def _EraseTombstone(device, tombstone_file):
"""Deletes a tombstone from the device.
Args:
device: An instance of DeviceUtils.
tombstone_file: the tombstone to delete.
"""
return device.RunShellCommand(
['rm', '/data/tombstones/' + tombstone_file],
as_root=True, check_return=True)
def _DeviceAbiToArch(device_abi):
# The order of this list is significant to find the more specific match (e.g.,
# arm64) before the less specific (e.g., arm).
arches = ['arm64', 'arm', 'x86_64', 'x86_64', 'x86', 'mips']
for arch in arches:
if arch in device_abi:
return arch
raise RuntimeError('Unknown device ABI: %s' % device_abi)
def _ResolveSymbols(tombstone_data, include_stack, device_abi):
"""Run the stack tool for given tombstone input.
Args:
tombstone_data: a list of strings of tombstone data.
include_stack: boolean whether to include stack data in output.
device_abi: the default ABI of the device which generated the tombstone.
Yields:
A string for each line of resolved stack output.
"""
# Check if the tombstone data has an ABI listed, if so use this in preference
# to the device's default ABI.
for line in tombstone_data:
found_abi = re.search('ABI: \'(.+?)\'', line)
if found_abi:
device_abi = found_abi.group(1)
arch = _DeviceAbiToArch(device_abi)
if not arch:
return
stack_tool = os.path.join(os.path.dirname(__file__), '..', '..',
'third_party', 'android_platform', 'development',
'scripts', 'stack')
proc = subprocess.Popen([stack_tool, '--arch', arch], stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
output = proc.communicate(input='\n'.join(tombstone_data))[0]
for line in output.split('\n'):
if not include_stack and 'Stack Data:' in line:
break
yield line
def _ResolveTombstone(tombstone):
lines = []
lines += [tombstone['file'] + ' created on ' + str(tombstone['time']) +
', about this long ago: ' +
(str(tombstone['device_now'] - tombstone['time']) +
' Device: ' + tombstone['serial'])]
logging.info('\n'.join(lines))
logging.info('Resolving...')
lines += _ResolveSymbols(tombstone['data'], tombstone['stack'],
tombstone['device_abi'])
return lines
def _ResolveTombstones(jobs, tombstones):
"""Resolve a list of tombstones.
Args:
jobs: the number of jobs to use with multiprocess.
tombstones: a list of tombstones.
"""
if not tombstones:
logging.warning('No tombstones to resolve.')
return
if len(tombstones) == 1:
data = [_ResolveTombstone(tombstones[0])]
else:
pool = multiprocessing.Pool(processes=jobs)
data = pool.map(_ResolveTombstone, tombstones)
for tombstone in data:
for line in tombstone:
logging.info(line)
def _GetTombstonesForDevice(device, options):
"""Returns a list of tombstones on a given device.
Args:
device: An instance of DeviceUtils.
options: command line arguments from OptParse
"""
ret = []
all_tombstones = list(_ListTombstones(device))
if not all_tombstones:
logging.warning('No tombstones.')
return ret
# Sort the tombstones in date order, descending
all_tombstones.sort(cmp=lambda a, b: cmp(b[1], a[1]))
# Only resolve the most recent unless --all-tombstones given.
tombstones = all_tombstones if options.all_tombstones else [all_tombstones[0]]
device_now = _GetDeviceDateTime(device)
try:
for tombstone_file, tombstone_time in tombstones:
ret += [{'serial': str(device),
'device_abi': device.product_cpu_abi,
'device_now': device_now,
'time': tombstone_time,
'file': tombstone_file,
'stack': options.stack,
'data': _GetTombstoneData(device, tombstone_file)}]
except device_errors.CommandFailedError:
for line in device.RunShellCommand(
['ls', '-a', '-l', '/data/tombstones'],
as_root=True, check_return=True, env=_TZ_UTC, timeout=60):
logging.info('%s: %s', str(device), line)
raise
# Erase all the tombstones if desired.
if options.wipe_tombstones:
for tombstone_file, _ in all_tombstones:
_EraseTombstone(device, tombstone_file)
return ret
def main():
custom_handler = logging.StreamHandler(sys.stdout)
custom_handler.setFormatter(run_tests_helper.CustomFormatter())
logging.getLogger().addHandler(custom_handler)
logging.getLogger().setLevel(logging.INFO)
parser = optparse.OptionParser()
parser.add_option('--device',
help='The serial number of the device. If not specified '
'will use all devices.')
parser.add_option('--blacklist-file', help='Device blacklist JSON file.')
parser.add_option('-a', '--all-tombstones', action='store_true',
help="""Resolve symbols for all tombstones, rather than just
the most recent""")
parser.add_option('-s', '--stack', action='store_true',
help='Also include symbols for stack data')
parser.add_option('-w', '--wipe-tombstones', action='store_true',
help='Erase all tombstones from device after processing')
parser.add_option('-j', '--jobs', type='int',
default=4,
help='Number of jobs to use when processing multiple '
'crash stacks.')
options, _ = parser.parse_args()
devil_chromium.Initialize()
blacklist = (device_blacklist.Blacklist(options.blacklist_file)
if options.blacklist_file
else None)
if options.device:
devices = [device_utils.DeviceUtils(options.device)]
else:
devices = device_utils.DeviceUtils.HealthyDevices(blacklist)
# This must be done serially because strptime can hit a race condition if
# used for the first time in a multithreaded environment.
# http://bugs.python.org/issue7980
tombstones = []
for device in devices:
tombstones += _GetTombstonesForDevice(device, options)
_ResolveTombstones(options.jobs, tombstones)
if __name__ == '__main__':
sys.exit(main())
|
[
"fatalerr@yandex-team.ru"
] |
fatalerr@yandex-team.ru
|
7d57e61792fab366f859e0ad676e16d0a883424a
|
d51735cfc00ea4e536c44bf8309db5e28f704972
|
/myutils.py
|
984dda6f6df5a6640dc986d6aac510503c35aa28
|
[] |
no_license
|
bhavyagera10/Attn-to-FC
|
23733adec8d658788f229aaf17a84f1694ef42ec
|
ba421d16e317b4b94a2c828f285689a5e2537e97
|
refs/heads/master
| 2022-11-21T05:54:33.982663
| 2020-07-27T21:30:31
| 2020-07-27T21:30:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 25,178
|
py
|
import sys
import javalang
from timeit import default_timer as timer
import keras
import numpy as np
import tensorflow as tf
import networkx as nx
import random
# do NOT import keras in this header area, it will break predict.py
# instead, import keras as needed in each function
# TODO refactor this so it imports in the necessary functions
dataprep = '/nfs/projects/attn-to-fc/data/standard'
sys.path.append(dataprep)
import tokenizer
start = 0
end = 0
def init_tf(gpu, horovod=False):
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config = tf.ConfigProto(log_device_placement=False)
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = gpu
set_session(tf.Session(config=config))
def prep(msg):
global start
statusout(msg)
start = timer()
def statusout(msg):
sys.stdout.write(msg)
sys.stdout.flush()
def drop():
global start
global end
end = timer()
sys.stdout.write('done, %s seconds.\n' % (round(end - start, 2)))
sys.stdout.flush()
def index2word(tok):
i2w = {}
for word, index in tok.w2i.items():
i2w[index] = word
return i2w
def seq2sent(seq, tokenizer):
sent = []
check = index2word(tokenizer)
for i in seq:
sent.append(check[i])
return(' '.join(sent))
class batch_gen(keras.utils.Sequence):
def __init__(self, seqdata, tt, config, training=True):
self.comvocabsize = config['comvocabsize']
self.tt = tt
self.batch_size = config['batch_size']
self.seqdata = seqdata
self.allfids = list(seqdata['dt%s' % (tt)].keys())
self.num_inputs = config['num_input']
self.config = config
self.training = training
random.shuffle(self.allfids) # actually, might need to sort allfids to ensure same order
def __getitem__(self, idx):
start = (idx*self.batch_size)
end = self.batch_size*(idx+1)
batchfids = self.allfids[start:end]
return self.make_batch(batchfids)
def make_batch(self, batchfids):
if self.config['batch_maker'] == 'datsonly':
return self.divideseqs(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'ast':
return self.divideseqs_ast(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'ast_threed':
return self.divideseqs_ast_threed(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'threed':
return self.divideseqs_threed(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'graphast':
return self.divideseqs_graphast(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'graphast_threed':
return self.divideseqs_graphast_threed(batchfids, self.seqdata, self.comvocabsize, self.tt)
elif self.config['batch_maker'] == 'pathast_threed':
return self.divideseqs_pathast_threed(batchfids, self.seqdata, self.comvocabsize, self.tt)
else:
return None
def __len__(self):
#if self.num_inputs == 4:
return int(np.ceil(len(list(self.seqdata['dt%s' % (self.tt)]))/self.batch_size))
#else:
# return int(np.ceil(len(list(self.seqdata['d%s' % (self.tt)]))/self.batch_size))
def on_epoch_end(self):
random.shuffle(self.allfids)
def divideseqs(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
datseqs = list()
comseqs = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wdatseq = seqdata['dt%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wdatseq = wdatseq[:self.config['tdatlen']]
if not self.training:
fiddat[fid] = [wdatseq, wcomseq]
else:
for i in range(len(wcomseq)):
datseqs.append(wdatseq)
comseq = wcomseq[:i]
comout = keras.utils.to_categorical(wcomseq[i], num_classes=comvocabsize)
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(np.asarray(comseq))
comouts.append(np.asarray(comout))
datseqs = np.asarray(datseqs)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
return [[datseqs, comseqs], comouts]
def divideseqs_ast(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
datseqs = list()
comseqs = list()
smlseqs = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wdatseq = seqdata['dt%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wsmlseq = seqdata['s%s' % (tt)][fid]
wdatseq = wdatseq[:self.config['tdatlen']]
if not self.training:
fiddat[fid] = [wdatseq, wcomseq, wsmlseq]
else:
for i in range(0, len(wcomseq)):
datseqs.append(wdatseq)
smlseqs.append(wsmlseq)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
datseqs = np.asarray(datseqs)
smlseqs = np.asarray(smlseqs)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
return [[datseqs, comseqs, smlseqs], comouts]
def divideseqs_ast_threed(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
tdatseqs = list()
sdatseqs = list()
comseqs = list()
smlseqs = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wtdatseq = seqdata['dt%s' % (tt)][fid]
wsdatseq = seqdata['ds%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wsmlseq = seqdata['s%s' % (tt)][fid]
wtdatseq = wtdatseq[:self.config['tdatlen']]
# the dataset contains 20+ functions per file, but we may elect
# to reduce that amount for a given model based on the config
newlen = self.config['sdatlen']-len(wsdatseq)
if newlen < 0:
newlen = 0
wsdatseq = wsdatseq.tolist()
for k in range(newlen):
wsdatseq.append(np.zeros(self.config['stdatlen']))
for i in range(0, len(wsdatseq)):
wsdatseq[i] = np.array(wsdatseq[i])[:self.config['stdatlen']]
wsdatseq = np.asarray(wsdatseq)
wsdatseq = wsdatseq[:self.config['sdatlen'],:]
wsmlseq = wsmlseq[:self.config['smllen']]
if not self.training:
fiddat[fid] = [wtdatseq, wsdatseq, wcomseq, wsmlseq]
else:
for i in range(0, len(wcomseq)):
tdatseqs.append(wtdatseq)
sdatseqs.append(wsdatseq)
smlseqs.append(wsmlseq)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
tdatseqs = np.asarray(tdatseqs)
sdatseqs = np.asarray(sdatseqs)
smlseqs = np.asarray(smlseqs)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
if self.config['num_output'] == 2:
return [[tdatseqs, sdatseqs, comseqs, smlseqs], [comouts, comouts]]
else:
return [[tdatseqs, sdatseqs, comseqs, smlseqs], comouts]
def divideseqs_threed(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
tdatseqs = list()
sdatseqs = list()
comseqs = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wtdatseq = seqdata['dt%s' % (tt)][fid]
wsdatseq = seqdata['ds%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wtdatseq = wtdatseq[:self.config['tdatlen']]
# the dataset contains 20+ functions per file, but we may elect
# to reduce that amount for a given model based on the config
newlen = self.config['sdatlen']-len(wsdatseq)
if newlen < 0:
newlen = 0
wsdatseq = wsdatseq.tolist()
for k in range(newlen):
wsdatseq.append(np.zeros(self.config['stdatlen']))
for i in range(0, len(wsdatseq)):
wsdatseq[i] = np.array(wsdatseq[i])[:self.config['stdatlen']]
wsdatseq = np.asarray(wsdatseq)
wsdatseq = wsdatseq[:self.config['sdatlen'],:]
if not self.training:
fiddat[fid] = [wtdatseq, wsdatseq, wcomseq]
else:
for i in range(0, len(wcomseq)):
tdatseqs.append(wtdatseq)
sdatseqs.append(wsdatseq)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
tdatseqs = np.asarray(tdatseqs)
sdatseqs = np.asarray(sdatseqs)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
if self.config['num_output'] == 2:
return [[tdatseqs, sdatseqs, comseqs], [comouts, comouts]]
else:
return [[tdatseqs, sdatseqs, comseqs], comouts]
def divideseqs_graphast(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
tdatseqs = list()
comseqs = list()
smlnodes = list()
smledges = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wtdatseq = seqdata['dt%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wsmlnodes = seqdata['s%s_nodes' % (tt)][fid]
wsmledges = seqdata['s%s_edges' % (tt)][fid]
# crop/expand ast sequence
wsmlnodes = wsmlnodes[:self.config['maxastnodes']]
tmp = np.zeros(self.config['maxastnodes'], dtype='int32')
tmp[:wsmlnodes.shape[0]] = wsmlnodes
wsmlnodes = np.int32(tmp)
# crop/expand ast adjacency matrix to dense
wsmledges = np.asarray(wsmledges.todense())
wsmledges = wsmledges[:self.config['maxastnodes'], :self.config['maxastnodes']]
tmp = np.zeros((self.config['maxastnodes'], self.config['maxastnodes']), dtype='int32')
tmp[:wsmledges.shape[0], :wsmledges.shape[1]] = wsmledges
wsmledges = np.int32(tmp)
# crop tdat to max tdat len specified by model config
wtdatseq = wtdatseq[:self.config['tdatlen']]
if not self.training:
fiddat[fid] = [wtdatseq, wcomseq, wsmlnodes, wsmledges]
else:
for i in range(0, len(wcomseq)):
if(self.config['use_tdats']):
tdatseqs.append(wtdatseq)
smlnodes.append(wsmlnodes)
smledges.append(wsmledges)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
if(self.config['use_tdats']):
tdatseqs = np.asarray(tdatseqs)
smlnodes = np.asarray(smlnodes)
smledges = np.asarray(smledges)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
if self.config['num_output'] == 2:
return [[tdatseqs, comseqs, smlnodes, smledges], [comouts, comouts]]
else:
if(self.config['use_tdats']):
return [[tdatseqs, comseqs, smlnodes, smledges], comouts]
else:
return [[comseqs, smlnodes, smledges], comouts]
def divideseqs_graphast_threed(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
tdatseqs = list()
sdatseqs = list()
comseqs = list()
smlnodes = list()
smledges = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wtdatseq = seqdata['dt%s' % (tt)][fid]
wsdatseq = seqdata['ds%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wsmlnodes = seqdata['s%s_nodes' % (tt)][fid]
wsmledges = seqdata['s%s_edges' % (tt)][fid]
# crop/expand ast sequence
wsmlnodes = wsmlnodes[:self.config['maxastnodes']]
tmp = np.zeros(self.config['maxastnodes'], dtype='int32')
tmp[:wsmlnodes.shape[0]] = wsmlnodes
wsmlnodes = np.int32(tmp)
# crop/expand ast adjacency matrix to dense
wsmledges = np.asarray(wsmledges.todense())
wsmledges = wsmledges[:self.config['maxastnodes'], :self.config['maxastnodes']]
tmp = np.zeros((self.config['maxastnodes'], self.config['maxastnodes']), dtype='int32')
tmp[:wsmledges.shape[0], :wsmledges.shape[1]] = wsmledges
wsmledges = np.int32(tmp)
# crop tdat to max tdat len specified by model config
wtdatseq = wtdatseq[:self.config['tdatlen']]
# the dataset contains 20+ functions per file, but we may elect
# to reduce that amount for a given model based on the config
newlen = self.config['sdatlen']-len(wsdatseq)
if newlen < 0:
newlen = 0
wsdatseq = wsdatseq.tolist()
for k in range(newlen):
wsdatseq.append(np.zeros(self.config['stdatlen']))
for i in range(0, len(wsdatseq)):
wsdatseq[i] = np.array(wsdatseq[i])[:self.config['stdatlen']]
wsdatseq = np.asarray(wsdatseq)
wsdatseq = wsdatseq[:self.config['sdatlen'],:]
if not self.training:
fiddat[fid] = [wtdatseq, wsdatseq, wcomseq, wsmlnodes, wsmledges]
else:
for i in range(0, len(wcomseq)):
if(self.config['use_tdats']):
tdatseqs.append(wtdatseq)
sdatseqs.append(wsdatseq)
smlnodes.append(wsmlnodes)
smledges.append(wsmledges)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
if(self.config['use_tdats']):
tdatseqs = np.asarray(tdatseqs)
sdatseqs = np.asarray(sdatseqs)
smlnodes = np.asarray(smlnodes)
smledges = np.asarray(smledges)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
if self.config['num_output'] == 2:
return [[tdatseqs, sdatseqs, comseqs, smlnodes, smledges], [comouts, comouts]]
else:
if(self.config['use_tdats']):
return [[tdatseqs, sdatseqs, comseqs, smlnodes, smledges], comouts]
else:
return [[sdatseqs, comseqs, smlnodes, smledges], comouts]
def idx2tok(self, nodelist, path):
out = list()
for idx in path:
out.append(nodelist[idx])
return out
def divideseqs_pathast_threed(self, batchfids, seqdata, comvocabsize, tt):
import keras.utils
tdatseqs = list()
sdatseqs = list()
comseqs = list()
smlpaths = list()
comouts = list()
fiddat = dict()
for fid in batchfids:
wtdatseq = seqdata['dt%s' % (tt)][fid]
wsdatseq = seqdata['ds%s' % (tt)][fid]
wcomseq = seqdata['c%s' % (tt)][fid]
wsmlnodes = seqdata['s%s_nodes' % (tt)][fid]
wsmledges = seqdata['s%s_edges' % (tt)][fid]
# crop/expand ast sequence
#wsmlnodes = wsmlnodes[:self.config['maxastnodes']]
#tmp = np.zeros(self.config['maxastnodes'], dtype='int32')
#tmp[:wsmlnodes.shape[0]] = wsmlnodes
#wsmlnodes = np.int32(tmp)
# crop/expand ast adjacency matrix to dense
#wsmledges = np.asarray(wsmledges.todense())
#wsmledges = wsmledges[:self.config['maxastnodes'], :self.config['maxastnodes']]
#tmp = np.zeros((self.config['maxastnodes'], self.config['maxastnodes']), dtype='int32')
#tmp[:wsmledges.shape[0], :wsmledges.shape[1]] = wsmledges
#wsmledges = np.int32(tmp)
g = nx.from_numpy_matrix(wsmledges.todense())
astpaths = nx.all_pairs_shortest_path(g, cutoff=self.config['pathlen'])
wsmlpaths = list()
for astpath in astpaths:
source = astpath[0]
if len([n for n in g.neighbors(source)]) > 1:
continue
for path in astpath[1].values():
if len([n for n in g.neighbors(path[-1])]) > 1:
continue # ensure only terminals as in Alon et al
if len(path) > 1 and len(path) <= self.config['pathlen']:
newpath = self.idx2tok(wsmlnodes, path)
tmp = [0] * (self.config['pathlen'] - len(newpath))
newpath.extend(tmp)
wsmlpaths.append(newpath)
random.shuffle(wsmlpaths) # Alon et al stipulate random selection of paths
wsmlpaths = wsmlpaths[:self.config['maxpaths']] # Alon et al use 200, crop/expand to size
if len(wsmlpaths) < self.config['maxpaths']:
wsmlpaths.extend([[0]*self.config['pathlen']] * (self.config['maxpaths'] - len(wsmlpaths)))
wsmlpaths = np.asarray(wsmlpaths)
# crop tdat to max tdat len specified by model config
wtdatseq = wtdatseq[:self.config['tdatlen']]
# the dataset contains 20+ functions per file, but we may elect
# to reduce that amount for a given model based on the config
newlen = self.config['sdatlen']-len(wsdatseq)
if newlen < 0:
newlen = 0
wsdatseq = wsdatseq.tolist()
for k in range(newlen):
wsdatseq.append(np.zeros(self.config['stdatlen']))
for i in range(0, len(wsdatseq)):
wsdatseq[i] = np.array(wsdatseq[i])[:self.config['stdatlen']]
wsdatseq = np.asarray(wsdatseq)
wsdatseq = wsdatseq[:self.config['sdatlen'],:]
if not self.training:
fiddat[fid] = [wtdatseq, wsdatseq, wcomseq, wsmlpaths]
else:
for i in range(0, len(wcomseq)):
if(self.config['use_tdats']):
tdatseqs.append(wtdatseq)
sdatseqs.append(wsdatseq)
smlpaths.append(wsmlpaths)
# slice up whole comseq into seen sequence and current sequence
# [a b c d] => [] [a], [a] [b], [a b] [c], [a b c] [d], ...
comseq = wcomseq[0:i]
comout = wcomseq[i]
comout = keras.utils.to_categorical(comout, num_classes=comvocabsize)
# extend length of comseq to expected sequence size
# the model will be expecting all input vectors to have the same size
for j in range(0, len(wcomseq)):
try:
comseq[j]
except IndexError as ex:
comseq = np.append(comseq, 0)
comseqs.append(comseq)
comouts.append(np.asarray(comout))
if(self.config['use_tdats']):
tdatseqs = np.asarray(tdatseqs)
if(self.config['use_sdats']):
sdatseqs = np.asarray(sdatseqs)
smlpaths = np.asarray(smlpaths)
comseqs = np.asarray(comseqs)
comouts = np.asarray(comouts)
if not self.training:
return fiddat
else:
if self.config['num_output'] == 2:
return [[tdatseqs, sdatseqs, comseqs, smlpaths], [comouts, comouts]]
else:
if(self.config['use_tdats'] and self.config['use_sdats']):
return [[tdatseqs, sdatseqs, comseqs, smlpaths], comouts]
elif(self.config['use_tdats'] and not self.config['use_sdats']):
return [[tdatseqs, comseqs, smlpaths], comouts]
elif(not self.config['use_tdats'] and self.config['use_sdats']):
return [[sdatseqs, comseqs, smlpaths], comouts]
elif(not self.config['use_tdats'] and not self.config['use_sdats']):
return [[comseqs, smlpaths], comouts]
|
[
"shaque@nd.edu"
] |
shaque@nd.edu
|
2101e8141de93508a3429d18ac2ef38c874c6d79
|
99df1b7e2323ced0f2b2c9d6e2696136ef7521d7
|
/medproject/medapp/migrations/0002_auto_20210126_2242.py
|
a9c1f27b9f95d15ec1e8c42c71e1a60c9ab7180f
|
[] |
no_license
|
Resa-Obamwonyi/med_appointment
|
f3d6abe0e88d38b863a9ebf3eb6dff150b45da68
|
0bd30c267d7726d030c81679b1bd592f6f05708b
|
refs/heads/main
| 2023-02-24T14:35:42.106402
| 2021-01-27T19:32:09
| 2021-01-27T19:32:09
| 333,034,260
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 815
|
py
|
# Generated by Django 3.1.2 on 2021-01-26 22:42
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('medapp', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='appointment',
name='end_time',
field=models.TimeField(),
),
migrations.AlterField(
model_name='appointment',
name='start_time',
field=models.TimeField(),
),
migrations.AlterField(
model_name='availability',
name='end_time',
field=models.TimeField(),
),
migrations.AlterField(
model_name='availability',
name='start_time',
field=models.TimeField(),
),
]
|
[
"theresaobamwonyi@gmail.com"
] |
theresaobamwonyi@gmail.com
|
4aab067f7718e6227ed17f2f3bc3df1da3500b6b
|
227da5bdfc09fdbe6cea7d694a58470c26aaf94a
|
/auctions/views.py
|
0db9b0d7bac07d817fe2ef4c3e477a9203d95aaa
|
[
"MIT"
] |
permissive
|
amogyisabogy1/Filesharing
|
28db40bba5a909c83fc7142cdc9590a93c76952d
|
bc1d318af015cd3ede4710ca7c2f1ed823c205e4
|
refs/heads/master
| 2023-08-25T13:33:57.759426
| 2021-10-11T01:38:12
| 2021-10-11T01:38:12
| 415,744,000
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 11,701
|
py
|
from django.contrib.auth import authenticate, login, logout
from django.db import IntegrityError
from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import render
from django.urls import reverse
import datetime
from annoying.functions import get_object_or_None
from django.contrib.auth.decorators import login_required
from .models import *
# this is the default view
def index(request):
return render(request, "auctions/index.html")
# this is the view for login
def login_view(request):
if request.method == "POST":
# Attempt to sign user in
username = request.POST["username"]
password = request.POST["password"]
user = authenticate(request, username=username, password=password)
# Check if authentication successful
if user is not None:
login(request, user)
return HttpResponseRedirect(reverse("index"))
# if not authenticated
else:
return render(request, "auctions/login.html", {
"message": "Invalid username and/or password.",
"msg_type": "danger"
})
# if GET request
else:
return render(request, "auctions/login.html")
# view for logging out
def logout_view(request):
logout(request)
return HttpResponseRedirect(reverse("index"))
# view for registering
def register(request):
if request.method == "POST":
username = request.POST["username"]
email = request.POST["email"]
# Ensure password matches confirmation
password = request.POST["password"]
confirmation = request.POST["confirmation"]
if password != confirmation:
return render(request, "auctions/register.html", {
"message": "Passwords must match.",
"msg_type": "danger"
})
if not username:
return render(request, "auctions/register.html", {
"message": "Please enter your username.",
"msg_type": "danger"
})
if not email:
return render(request, "auctions/register.html", {
"message": "Please enter your email.",
"msg_type": "danger"
})
# Attempt to create new user
try:
user = User.objects.create_user(username, email, password)
user.save()
except IntegrityError:
return render(request, "auctions/register.html", {
"message": "Username already taken.",
"msg_type": "danger"
})
login(request, user)
return HttpResponseRedirect(reverse("index"))
# if GET request
else:
return render(request, "auctions/register.html")
# view for dashboard
@login_required(login_url='/login')
def dashboard(request):
winners = Winner.objects.filter(winner=request.user.username)
# checking for watchlist
lst = Watchlist.objects.filter(user=request.user.username)
# list of products available in WinnerModel
present = False
prodlst = []
i = 0
if lst:
present = True
for item in lst:
product = Listing.objects.get(id=item.listingid)
prodlst.append(product)
print(prodlst)
return render(request, "auctions/dashboard.html", {
"product_list": prodlst,
"present": present,
"products": winners
})
# view for showing the active lisitngs
@login_required(login_url='/login')
def activelisting(request):
# list of products available
products = Listing.objects.all()
# checking if there are any products
empty = False
if len(products) == 0:
empty = True
return render(request, "auctions/activelisting.html", {
"products": products,
"empty": empty
})
# view to create a lisiting
@login_required(login_url='/login')
def createlisting(request):
# if user submitted the create listing form
if request.method == "POST":
# item is of type Listing (object)
item = Listing()
# assigning the data submitted via form to the object
item.pdf = request.FILES["document"]
item.seller = request.user.username
item.title = request.POST.get('title')
item.description = request.POST.get('description')
item.category = request.POST.get('category')
# submitting data of the image link is optional
if request.POST.get('image_link'):
item.image_link = request.POST.get('image_link')
else:
item.image_link = "https://www.aust-biosearch.com.au/wp-content/themes/titan/images/noimage.gif"
# saving the data into the database
item.save()
# retrieving the new products list after adding and displaying
products = Listing.objects.all()
empty = False
if len(products) == 0:
empty = True
return render(request, "auctions/activelisting.html", {
"products": products,
"empty": empty
})
# if request is get
else:
return render(request, "auctions/createlisting.html")
# view to display all the categories
@login_required(login_url='/login')
def categories(request):
return render(request, "auctions/categories.html")
# view to display individual listing
@login_required(login_url='/login')
def viewlisting(request, product_id):
# if the user submits his bid
comments = Comment.objects.filter(listingid=product_id)
if request.method == "POST":
item = Listing.objects.get(id=product_id)
newbid = int(request.POST.get('newbid'))
# checking if the newbid is greater than or equal to current bid
if item.starting_bid >= newbid:
product = Listing.objects.get(id=product_id)
return render(request, "auctions/viewlisting.html", {
"product": product,
"message": "Your Bid should be higher than the Current one.",
"msg_type": "danger",
"comments": comments
})
# if bid is greater then updating in Listings table
else:
item.starting_bid = newbid
item.save()
# saving the bid in Bid model
bidobj = Bid.objects.filter(listingid=product_id)
if bidobj:
bidobj.delete()
obj = Bid()
obj.user = request.user.username
obj.title = item.title
obj.listingid = product_id
obj.bid = newbid
obj.save()
product = Listing.objects.get(id=product_id)
return render(request, "auctions/viewlisting.html", {
"product": product,
"message": "Your Bid is added.",
"msg_type": "success",
"comments": comments
})
# accessing individual listing GET
else:
product = Listing.objects.get(id=product_id)
added = Watchlist.objects.filter(
listingid=product_id, user=request.user.username)
return render(request, "auctions/viewlisting.html", {
"product": product,
"added": added,
"comments": comments
})
# View to add or remove products to watchlists
@login_required(login_url='/login')
def addtowatchlist(request, product_id):
obj = Watchlist.objects.filter(
listingid=product_id, user=request.user.username)
comments = Comment.objects.filter(listingid=product_id)
# checking if it is already added to the watchlist
if obj:
# if its already there then user wants to remove it from watchlist
obj.delete()
# returning the updated content
product = Listing.objects.get(id=product_id)
added = Watchlist.objects.filter(
listingid=product_id, user=request.user.username)
return render(request, "auctions/viewlisting.html", {
"product": product,
"added": added,
"comments": comments
})
else:
# if it not present then the user wants to add it to watchlist
obj = Watchlist()
obj.user = request.user.username
obj.listingid = product_id
obj.save()
# returning the updated content
product = Listing.objects.get(id=product_id)
added = Watchlist.objects.filter(
listingid=product_id, user=request.user.username)
return render(request, "auctions/viewlisting.html", {
"product": product,
"added": added,
"comments": comments
})
# view for comments
@login_required(login_url='/login')
def addcomment(request, product_id):
obj = Comment()
obj.comment = request.POST.get("comment")
obj.user = request.user.username
obj.listingid = product_id
obj.save()
# returning the updated content
print("displaying comments")
comments = Comment.objects.filter(listingid=product_id)
product = Listing.objects.get(id=product_id)
added = Watchlist.objects.filter(
listingid=product_id, user=request.user.username)
return render(request, "auctions/viewlisting.html", {
"product": product,
"added": added,
"comments": comments
})
# view to display all the active listings in that category
@login_required(login_url='/login')
def category(request, categ):
# retieving all the products that fall into this category
categ_products = Listing.objects.filter(category=categ)
empty = False
if len(categ_products) == 0:
empty = True
return render(request, "auctions/category.html", {
"categ": categ,
"empty": empty,
"products": categ_products
})
# view when the user wants to close the bid
@login_required(login_url='/login')
def closebid(request, product_id):
winobj = Winner()
listobj = Listing.objects.get(id=product_id)
obj = get_object_or_None(Bid, listingid=product_id)
if not obj:
message = "Deleting Bid"
msg_type = "danger"
else:
bidobj = Bid.objects.get(listingid=product_id)
winobj.owner = request.user.username
winobj.winner = bidobj.user
winobj.listingid = product_id
winobj.winprice = bidobj.bid
winobj.title = bidobj.title
winobj.save()
message = "Bid Closed"
msg_type = "success"
# removing from Bid
bidobj.delete()
# removing from watchlist
if Watchlist.objects.filter(listingid=product_id):
watchobj = Watchlist.objects.filter(listingid=product_id)
watchobj.delete()
# removing from Comment
if Comment.objects.filter(listingid=product_id):
commentobj = Comment.objects.filter(listingid=product_id)
commentobj.delete()
# removing from Listing
listobj.delete()
# retrieving the new products list after adding and displaying
# list of products available in WinnerModel
winners = Winner.objects.all()
# checking if there are any products
empty = False
if len(winners) == 0:
empty = True
return render(request, "auctions/closedlisting.html", {
"products": winners,
"empty": empty,
"message": message,
"msg_type": msg_type
})
# view to see closed listings
@login_required(login_url='/login')
def closedlisting(request):
# list of products available in WinnerModel
winners = Winner.objects.all()
# checking if there are any products
empty = False
if len(winners) == 0:
empty = True
return render(request, "auctions/closedlisting.html", {
"products": winners,
"empty": empty
})
|
[
"amoghganjikunta@gmail.com"
] |
amoghganjikunta@gmail.com
|
fda5dcaafbc12d591a25e04eff5dae783cabda9c
|
588fc7a74ec467f708a6e0684870929cf4c99234
|
/30.py
|
35cbe344ed9066e19a2c3529531b46c502dfd342
|
[] |
no_license
|
petitviolet/project_euler
|
87710cd1b5138d6fea15a563c5a23b23c2b3c870
|
03f62a400708bd5cd4370456178c2ee63fb717db
|
refs/heads/master
| 2020-05-27T10:56:32.187374
| 2013-05-16T00:16:38
| 2013-05-16T00:16:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 512
|
py
|
# -*- encoding:utf-8 -*-
def sum_order(n):
if n == 1:
return False
str_n = str(n)
order = [int(s) for s in str_n]
result = 0
for o in order:
if o == 0:
pass
else:
result += o ** 5
if n == result:
return True
else:
return False
def main():
ans = 0
for i in xrange(1000000):
if sum_order(i):
print i
ans += i
i += 1
print ans
if __name__ == '__main__':
main()
|
[
"violethero0820@gmail.com"
] |
violethero0820@gmail.com
|
e232ea8556be487081ad7ae17a32d47bd88efdad
|
31e6ca145bfff0277509dbd7c4b44b8deddf3334
|
/LeetCode/Graph/combination-sum.py
|
1bad4a940655a4357b9828e4c8a4c2eb18a168a3
|
[] |
no_license
|
brillantescene/Coding_Test
|
2582d6eb2d0af8d9ac33b8e829ff8c1682563c42
|
0ebc75cd66e1ccea3cedc24d6e457b167bb52491
|
refs/heads/master
| 2023-08-31T06:20:39.000734
| 2021-10-15T10:51:17
| 2021-10-15T10:51:17
| 254,366,460
| 3
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 459
|
py
|
class Solution:
def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
result = []
def dfs(csum, index, path):
if csum < 0:
return
if csum == 0:
result.append(path)
return
for i in range(index, len(candidates)):
dfs(csum-candidates[i], i, path+[candidates[i]])
dfs(target, 0, [])
return result
|
[
"glisteneugene@gmail.com"
] |
glisteneugene@gmail.com
|
f26e7859405297b5aea6a72fdb7d7d3d9f8143c0
|
60e277d924751aeda0162060b7d04e455e3de3c9
|
/rcnfq/receive_video_zmq.py
|
910388461bd21d10d5feec3ce7bf28eec1576817
|
[
"MIT"
] |
permissive
|
cosmoharrigan/rc-nfq
|
0700e69af9f5af79b04c108190edf04517577bf3
|
0b18d27d0b95644bded258d5a9fbb5cb4f895c91
|
refs/heads/master
| 2021-07-12T18:10:08.884411
| 2021-03-17T18:25:37
| 2021-03-17T18:25:37
| 53,664,411
| 15
| 8
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,018
|
py
|
'''Receive streaming video from the robot using ZeroMQ
'''
import io
import socket
import struct
from PIL import Image
from scipy.misc import imread
import numpy as np
import zmq
import time
# Configure the following parameter:
IP_ADDRESS = "192.168.0.56"
# Setup SUBSCRIBE socket
context = zmq.Context()
zmq_socket = context.socket(zmq.SUB)
zmq_socket.setsockopt(zmq.SUBSCRIBE, b'')
zmq_socket.setsockopt(zmq.CONFLATE, 1)
zmq_socket.connect("tcp://{}:5557".format(IP_ADDRESS))
i = 0
try:
while True:
# Construct a stream to hold the image data and read the image
# data from the connection
image_stream = io.BytesIO()
payload = zmq_socket.recv()
image_stream.write(payload)
# Rewind the stream, open it as an image with PIL and do some
# processing on it
image_stream.seek(0)
image = imread(image_stream)
# Do something with the image
print(np.round(np.mean(image), 0))
i += 1
finally:
pass
|
[
"cosmo.harrigan@singularityu.org"
] |
cosmo.harrigan@singularityu.org
|
6349c63eb24b738a6bd6f609809d3db6989a179c
|
230a760662c8e2641dbe834bc41996fb9a7e644d
|
/app/fac/urls.py
|
ecb2febbb10c21a9b29c7baa01c2c3465a19d71c
|
[] |
no_license
|
OrlandoGareca/sistema-de-compra-y-facturacion
|
99a37762905bdf216558c5282705e2df4235f235
|
8b3cb64d2f9ebcbe085dd37b40fbc3b3b408dfee
|
refs/heads/master
| 2021-04-17T18:05:53.723207
| 2020-04-13T18:14:05
| 2020-04-13T18:14:05
| 249,464,579
| 0
| 0
| null | 2021-03-19T23:18:23
| 2020-03-23T15:10:38
|
HTML
|
UTF-8
|
Python
| false
| false
| 1,038
|
py
|
from django.urls import path, include
from app.fac.views import ClienteView,ClienteNew,ClienteEdit,clienteInactivar,\
FacturaView, facturas,\
ProductoView,\
borrar_detalle_factura
from app.fac.reportes import imprimir_factura_recibo
urlpatterns = [
path('clientes/',ClienteView.as_view(), name="cliente_list"),
path('clientes/new',ClienteNew.as_view(), name="cliente_new"),
path('clientes/<int:pk>',ClienteEdit.as_view(), name="cliente_edit"),
path('clientes/estado/<int:id>', clienteInactivar, name="cliente_inactivar"),
path('facturas/',FacturaView.as_view(), name="factura_list"),
path('facturas/new',facturas, name="factura_new"),
path('facturas/edit/<int:id>', facturas,name="factura_edit"),
path('facturas/buscar-producto', ProductoView.as_view(),name="factura_producto"),
path('facturas/borrar-detalle/<int:id>', borrar_detalle_factura ,name="factura_borrar_detalle"),
path('facturas/imprimir/<int:id>', imprimir_factura_recibo ,name="factura_imprimir_one"),
]
|
[
"orlando.dilmar.gareca.pena@gmail.com"
] |
orlando.dilmar.gareca.pena@gmail.com
|
91c38c6e741d31665a613aefbe52b741dad9f2d3
|
e2f133885cfcea86a3c06bba2f1d4d165e50c823
|
/api_test/main.py
|
eb2d68962d74199d1e2afd00f96adc2b336a3364
|
[] |
no_license
|
JR1QQ4/app_test
|
e0d9dc25ea03060d17dc7f29f30706ec4b8c16ea
|
1c2ab9a5601e94a28f9bfe485e615d22511bb79b
|
refs/heads/main
| 2023-05-25T14:55:53.326377
| 2021-06-08T14:33:52
| 2021-06-08T14:33:52
| 349,760,345
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,417
|
py
|
#!/usr/bin/python
# -*- coding:utf-8 -*-
from time import sleep
from appium import webdriver
from appium.webdriver.common.mobileby import MobileBy
from appium.webdriver.common.touch_action import TouchAction
from appium.webdriver.extensions.android.gsm import GsmCallActions
from appium.webdriver.webdriver import WebDriver
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
class Main:
_driver: WebDriver
_appPackage = "com.xueqiu.android"
_appActivity = ".view.WelcomeActivityAlias"
# _appActivity = ".common.MainActivity"
# 搜索框
_search_input = (MobileBy.ID, "com.xueqiu.android:id/tv_search")
_search_text = (MobileBy.ID, "com.xueqiu.android:id/search_input_text")
# 搜索到的内容
_search_result = (MobileBy.XPATH, '//*[@resource-id="com.xueqiu.android:id/name" and @text="$value"]')
_search_result_first = (MobileBy.ID, 'com.xueqiu.android:id/name')
_result_item = (MobileBy.XPATH, '//*[@resource-id="com.xueqiu.android:id/ll_stock_result_view"]'
'//*[@text="$value"]/../..')
_result_item_code = (MobileBy.XPATH, '//*[@text="$code"]')
_result_price = (MobileBy.XPATH, '//*[@resource-id="com.xueqiu.android:id/ll_stock_result_view"]'
'//*[@text="$value"]/../..//*[@resource-id="com.xueqiu.android:id/current_price"]')
_result_price_with_code = (MobileBy.XPATH, '//*[@text="$code"]/../../..'
'//*[@resource-id="com.xueqiu.android:id/current_price"]')
# 取消搜索
_close_search = (MobileBy.ID, 'com.xueqiu.android:id/action_close')
# tab导航
_tab = (MobileBy.XPATH, '//*[@resource-id="android:id/tabs"]//*[@text="$tab"]/..')
def __init__(self, driver: WebDriver = None):
if driver is None:
opts = ["http://127.0.0.1:4723/wd/hub",
{
"platformName": "Android",
"platformVersion": "6.0",
"deviceName": "127.0.0.1:7555",
"automationName": "UiAutomator2",
"appPackage": self._appPackage, # adb shell dumpsys activity top
"appActivity": self._appActivity,
"noRest": True,
"unicodeKeyBoard": True,
"resetKeyBoard": True,
# "avd": "Pixel_23_6", # 启动模拟器
"dontStopAppOnRest": True, # 首次启动 app 时不停止 app（可以调试或者运行的时候提升运行速度）
"skipDeviceInitialization": True, # 跳过安装，权限设置等操作（可以调试或者运行的时候提升运行速度）
# "newCommandTimeout": 300, # 每一条命令执行的间隔时间
# "uuid": "", # 用于
# "autoGrantPermissions": True, # 用于权限管理，设置了这个，就不需要设置 noRest
"chromedriverExecutable": "C:\\webdriver\\chromedriver.exe" # 用于测试 webview 页面
}
]
self._driver = webdriver.Remote(*opts)
else:
self._driver.start_activity(self._appPackage, self._appActivity)
self._driver.implicitly_wait(10)
def find(self, locator):
WebDriverWait(self._driver, 10).until(EC.visibility_of_element_located(locator))
return self._driver.find_element(*locator)
def click(self, locator):
ele = WebDriverWait(self._driver, 10).until(EC.visibility_of_element_located(locator))
ele.click()
def text(self, locator, value=""):
WebDriverWait(self._driver, 10).until(EC.visibility_of_element_located(locator))
if value != "":
self._driver.find_element(*locator).send_keys(value)
else:
return self._driver.find_element(*locator).text
def search(self, value="阿里巴巴"):
self.click(self._search_input)
self.text(self._search_text, value)
def search_and_get_price(self, value="阿里巴巴"):
self.click(self._search_input)
self.text(self._search_text, value)
self.click((self._search_result[0], self._search_result[1].replace("$value", "阿里巴巴")))
return float(self.text((self._result_price[0], self._result_price[1].replace("$value", "阿里巴巴"))))
def search_and_show_attribute(self):
ele = self.find(self._search_input)
search_enabled = ele.is_enabled()
print(ele.text) # 搜索股票/组合/用户/讨论
print(ele.location) # {'x': 219, 'y': 60}
print(ele.size) # {'height': 36, 'width': 281}
if search_enabled:
ele.click()
self.text(self._search_text, "alibaba")
ali_ele = self.find((self._search_result[0], self._search_result[1].replace("$value", "阿里巴巴")))
# ali_ele.is_displayed()
print(ali_ele.get_attribute("displayed")) # true
def move_to(self, cur=None, target=None):
sleep(3)
action = TouchAction(self._driver)
# action.press(x=cur["x"], y=cur["y"]).wait(200).move_to(x=target["x"], y=target["y"]).release().perform()
print(self._driver.get_window_rect())
action.press(x=360, y=1000).wait(200).move_to(x=360, y=280).release().perform()
def scroll_and_search_with_android_selector(self):
loc = (MobileBy.ANDROID_UIAUTOMATOR, 'new UiSelector().text("关注")')
WebDriverWait(self._driver, 10).until(EC.visibility_of_element_located(loc))
self._driver.find_element_by_android_uiautomator('new UiSelector().text("关注")').click()
self._driver.find_element_by_android_uiautomator('new UiScrollable(new UiSelector().'
'scrollable(true).instance(0)).'
'scrollIntoView(new UiSelector().text("玉山落雨").'
'instance(0));').click()
sleep(5)
def toast(self):
print(self._driver.page_source)
def clear(self, locator):
self.find(locator).clear()
def search_get_price(self, value, code):
self.click(self._search_input)
self.text(self._search_text, value)
self.click(self._search_result_first)
price = self.text((self._result_price_with_code[0], self._result_price_with_code[1].replace("$code", code)))
self.click(self._close_search)
return price
def mobile_call(self, phone_number="13883256868", action=GsmCallActions.CALL):
"""mumu 模拟器不支持，需要使用原生的"""
# action:
# GsmCallActions.CALL
# GsmCallActions.ACCEPT
# GsmCallActions.CANCEL
# GsmCallActions.HOLD
self._driver.make_gsm_call(phone_number, action)
def msg(self, phone_number="13537773695", message="Hello world!"):
"""mumu 模拟器不支持，需要使用原生的"""
self._driver.send_sms(phone_number, message)
def network(self, connection_type=1):
self._driver.set_network_connection(connection_type)
sleep(3)
self._driver.set_network_connection(6)
sleep(3)
def screenshot_as_file(self, path="./photos/img.png"):
self._driver.get_screenshot_as_file(path)
def webview(self):
self.click((self._tab[0], self._tab[1].replace("$tab", "交易")))
sleep(10)
print(self._driver.contexts)
# 立即开户，切换到 webview
self._driver.switch_to.context(self._driver.contexts[-1])
sleep(10)
# print(self._driver.window_handles)
loc1 = (MobileBy.XPATH, "//*[id='Layout_app_3V4']/div/div/ul/li[1]/div[2]/h1")
WebDriverWait(self._driver, 10).until(EC.element_to_be_clickable(loc1))
self.click(loc1)
sleep(10)
handle = self._driver.window_handles[-1]
self._driver.switch_to.window(handle)
# 开户信息填写
loc2 = (MobileBy.ID, "phone-number")
loc3 = (MobileBy.ID, "code")
loc4 = (MobileBy.CSS_SELECTOR, ".btn-submit")
self.text(loc2, "13810120202")
self.text(loc3, "6666")
self.click(loc4)
|
[
"chenjunrenyx@163.com"
] |
chenjunrenyx@163.com
|
ed530e3765c93ad395a073bdba2ebcf9db8a922e
|
2069ec66ace2e8fb5d55502d1c3ce7fd89f3cdcc
|
/fp2/example/write.py
|
2835c40effeaaa01280a975bc1037885b60af898
|
[] |
no_license
|
caimingA/ritsumeiPython
|
6812a0233456cf3d5346a63d890f4201160593c5
|
bb9c39726dd26fe53f7a41f5367bdab60c36a057
|
refs/heads/master
| 2022-11-16T22:28:50.274374
| 2020-07-13T14:53:51
| 2020-07-13T14:53:51
| 279,294,544
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 301
|
py
|
f = open("yuki.txt", mode="w", encoding="utf-8")
f.write("或冬曇りの午後、わたしは中央線の汽車の窓に一列の山脈を眺めてゐた。")
f.write("山脈は勿論まつ白だつた。")
f.write("が、それは雪と言ふよりも山脈の皮膚に近い色をしてゐた。")
|
[
"caiming106@sina.com"
] |
caiming106@sina.com
|
ca7494f0ff6984b0e87384fffe98c9b39bfd9b3e
|
4db4276f7e05c16bc6719b48deb58e40f74230ca
|
/wordcount.py
|
832ac70c7f9a6b322e9e1de8ab38f594d958de59
|
[] |
no_license
|
dmrsh/hello-world
|
ea6e00aa31401f48c46abbd48b35ab1c717530a0
|
2e33d0982e8fb40f8bce87b7b8141f2dc96f7d7e
|
refs/heads/master
| 2016-09-06T15:16:00.456635
| 2015-07-27T20:22:17
| 2015-07-27T20:22:17
| 39,794,669
| 0
| 0
| null | 2015-07-27T20:22:17
| 2015-07-27T19:52:28
|
Python
|
UTF-8
|
Python
| false
| false
| 463
|
py
|
import os
cwd = os.getcwd()
wds = open(cwd+'\subdir\muchText.txt')
d = dict()
for line in wds:
for word in line.split():
tok = word.strip().lower()
d[tok] = d.get(tok, 0) + 1
sd = []
for tok in d:
sd.append((d[tok], tok))
sd.sort(reverse=True)
for word in sd:
count = word[0]
if(count > 1):
print(word[1], '\t', count)
ld = dict()
for word in sd:
ld[word[0]] = ld.get(word[0], ()) + (word[1],)
for key in ld:
if(key > 1):
print(key, ld[key])
|
[
"s99bf19a@yahoo.co.uk"
] |
s99bf19a@yahoo.co.uk
|
cc83e1f6b8d5643656dcc69ec20fd57b14143940
|
c79d32b780dccf68c66a693ccc93969bae805d9c
|
/FP/Treino Exame/ex 3.py
|
466640675603564afb5d5eff19f3d11d320333c2
|
[] |
no_license
|
Tiburso/Trabalhos-IST
|
1ac065bd1328f37ff8d3d90960cae25ddafd5d27
|
2e473c3bf8a536899304c58b24e45f433962f2a4
|
refs/heads/master
| 2021-01-15T00:56:00.603234
| 2021-01-03T15:51:34
| 2021-01-03T15:51:34
| 242,820,217
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 362
|
py
|
def codifica(num):
n_f = 0
i = 0
while num > 0:
if (num % 10) % 2 == 0:
if num % 10 != 8:
n_f += (num % 10 + 2)*10**i
else:
if num % 10 == 1:
n_f += 9*10**i
else:
n_f += (num%10-2)*10**i
i += 1
num //= 10
return n_f
|
[
"noreply@github.com"
] |
noreply@github.com
|
5588a9b58bb4811699015d008966309f1b432923
|
76a01339f7ca19536a07d66e18ff427762157a2a
|
/codeforces/Python/serval_and_bus.py
|
49a999fb8f0c58c2e96f04c61667f1b963aee56a
|
[] |
no_license
|
shaarangg/CP-codes
|
75f99530921a380b93d8473a2f2a588dc35b0beb
|
94fc49d0f20c02da69f23c74e26c974dfe122b2f
|
refs/heads/main
| 2023-07-19T21:31:40.011853
| 2021-09-07T05:22:28
| 2021-09-07T05:22:28
| 332,644,437
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 282
|
py
|
n,t = map(int,input().split())
m=10**9
j=0
for i in range(n):
s,d = map(int,input().split())
if(t<=s):
a=s-t
else:
a=t-s
if(a%d==0):
a=0
else:
a = (a//d + 1)*d -t + s
if(m>a):
m=a
j=i+1
print(j)
|
[
"shaaranggsingh@gmail.com"
] |
shaaranggsingh@gmail.com
|
613ab8ac9d59b0f20df16241ff4defdd691ee164
|
79b6e625fb9fd2533a0e492d0e23fb5bb18f4f18
|
/app/config/__init__.py
|
2fc2af08e1789ef2352c15615fe8b6bea8adf792
|
[] |
no_license
|
schulzsebastian/vdata
|
93d57491c1b2553e42539ef5772cd845b1ee9075
|
999270b32ff60f0f17cd13eb0c8b391069bcdfba
|
refs/heads/master
| 2021-01-11T06:51:28.611857
| 2016-11-03T19:40:19
| 2016-11-03T19:40:19
| 71,816,268
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 194
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
try:
from .local_config import LocalConfig
current_config = LocalConfig
except:
from .config import Config
current_config = Config
|
[
"schulz.siwy@gmail.com"
] |
schulz.siwy@gmail.com
|
e4c41ba497ad969a0d1063fd764a5b856b1015ec
|
fdf96c2a5d0c044dcb9eea210434dfc5448cf2e9
|
/el.py
|
69d20ce3363d30b857e9ad995a268486ff20be78
|
[] |
no_license
|
joquizon/Python-Class
|
84f8db181c0c37385d18f4c9b9d616365b96972f
|
2341589e9cc63262c5424189a19b48772add5972
|
refs/heads/master
| 2021-05-21T13:58:36.812773
| 2021-01-29T16:02:56
| 2021-01-29T16:02:56
| 252,673,470
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,158
|
py
|
from Newdatin import Ndataenter
from Editdatin import Bigeditinput
from Edinfo import editinputFo
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
#_____________________________________________________________________________________________________________
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>mode select
def modeset():
mission = input("1 for entry or 2 for output: ")
if mission== '1':
print("A new employee! coo'coo :)")
Ndataenter()
elif mission== '2':
Bigeditinput()
#run search function for editing employee info
elif mission == '3':
editinputFo()
elif mission == '4':
quit()
else:
print('boopbeep Error!')
modeset()
modeset()
|
[
"josequizon@jcqfolio.com"
] |
josequizon@jcqfolio.com
|
c68fc822bcbcb1801f923e29bd5a518e6dfe4dcd
|
74db1485d7560f087b7798a816a1262f1aff97d8
|
/keepfit_api/keepfit/test.py
|
66293ce9f5d3e0bbb46d28b9d772f4077922595c
|
[] |
no_license
|
NirvaanReddy/SportsApp
|
d14c80b537c50f7900cda7f70c7ae945dd59ceda
|
632b20bde7877925cd0f1667208d42a0e8344d3c
|
refs/heads/main
| 2023-05-02T07:17:09.253553
| 2021-05-10T02:38:08
| 2021-05-10T02:38:08
| 337,168,832
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,011
|
py
|
from django.test import TestCase
from django.shortcuts import render
from rest_framework.decorators import api_view
from rest_framework.response import Response
from django.db import models
from .s import *
from .user import *
from django.core.files import File
from django.http import HttpResponse
from .user_endpoints import photos_path
from .user import *
from .user_endpoints import *
from .workout_endpoints import *
from .search_endpoints import *
class UserCreatedSuccesfully(TestCase):
def setUp(self):
pass
def createUserVerify(self):
# to verify that we are correctly making users
items = { "id": "whatever" ,"sex": "Male", "pounds":170,
"inches": 170, "shortBiography": "My name is Jason Gomez :)",
"birthdate" : 2.3 , "username" : "jjjj" , "password": "stringstring"
}
json_string = json.dumps(items)
result = create_user(json_string)
self.assertEqual("Hello", 'The lion says "roar"')
|
[
"nirvaanreddy@gmail.com"
] |
nirvaanreddy@gmail.com
|
9e373589be438679c33b4a580fe89b840e5164f3
|
a8ebc4cccbf1ba346b1de4779dee7d4c803c7106
|
/display_pixels/cap_symmetrize_velfield.py
|
ad419157b76b0cabc104f2eff3144a8894c45ca7
|
[] |
no_license
|
Treibeis/python
|
6b4680b167e8da7dffdcbe31704f18b0bd4f31fb
|
e5f467f1db96c99bb8085229f915592f90932802
|
refs/heads/master
| 2023-07-25T09:18:40.941330
| 2023-07-15T05:51:43
| 2023-07-15T05:51:43
| 143,747,936
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,828
|
py
|
#######################################################################
#
# Copyright (C) 2004-2015, Michele Cappellari
# E-mail: michele.cappellari_at_physics.ox.ac.uk
#
# This software is provided as is without any warranty whatsoever.
# Permission to use, for non-commercial purposes is granted.
# Permission to modify for personal or internal use is granted,
# provided this copyright and disclaimer are included unchanged
# at the beginning of the file. All other rights are reserved.
#
#######################################################################
#
# NAME:
# symmetrize_velfield()
#
# PURPOSE:
# This routine generates a bi-symmetric ('axisymmetric')
# version of a given set of kinematical measurements.
# PA: is the angle in degrees, measured counter-clockwise,
# from the vertical axis (Y axis) to the galaxy major axis.
# SYM: by-simmetry: is 1 for (V,h3,h5) and 2 for (sigma,h4,h6)
#
# HISTORY:
# V1.0.0: Michele Cappellari, Vicenza, 21 May 2004
# V1.0.1: Added MISSING keyword to TRIGRID call. Flipped velocity sign.
# Written basic documentation. MC, Leiden, 25 May 2004
# V1.1.0: Included point-symmetric case. Remco van den Bosch, Leiden, 18 January 2005
# V1.1.1: Minor code revisions. MC, Leiden, 23 May 2005
# V1.1.2: Important: changed definition of PA to be measured counterclockwise
# with respect to the positive Y axis, as in astronomical convention and
# consistently with my FIND_GALAXY routine. MC, Leiden, 1 June 2005
# V1.1.3: Added optional keyword TRIANG. Corrected rare situation with w=-1.
# MC, Leiden, 2 June 2005
# V1.1.4: Added prefix SYMM_ to internal functions to prevent conflicts
# with external functions with the same name. MC, Oxford, 11 May 2007
# V2.0.0 : Completely rewritten without any loop. MC, Oxford, 8 October 2013
# V2.0.1: Uses TOLERANCE keyword of TRIANGULATE to try to avoid IDL error
# "TRIANGULATE: Points are co-linear, no solution." MC, Oxford, 2 December 2013
# V3.0.0: Translated from IDL into Python. MC, Oxford, 14 February 2014
# V3.0.1: Fixed rare case where interpolated value at boundary becomes
# NaN due to numerical accuracy. MC, Oxford, 20 May 2015
#
#######################################################################
import numpy as np
from scipy import interpolate
#----------------------------------------------------------------------
# Michele cappellari, Paranal, 10 November 2013
def _rotate_points(x, y, ang):
"""
Rotates points counter-clockwise by an angle ANG-90 in degrees.
"""
theta = np.radians(ang - 90.)
xNew = x*np.cos(theta) - y*np.sin(theta)
yNew = x*np.sin(theta) + y*np.cos(theta)
return xNew, yNew
#----------------------------------------------------------------------
def symmetrize_velfield(xbin, ybin, velBin, sym=2, pa=90.):
"""
This routine generates a bi-symmetric ('axisymmetric')
version of a given set of kinematical measurements.
PA: is the angle in degrees, measured counter-clockwise,
from the vertical axis (Y axis) to the galaxy major axis.
SYM: by-simmetry: is 1 for (V,h3,h5) and 2 for (sigma,h4,h6)
"""
xbin, ybin, velBin = map(np.asarray, [xbin, ybin, velBin])
x, y = _rotate_points(xbin, ybin, -pa) # Negative PA for counter-clockwise
xyIn = np.column_stack([x, y])
xout = np.hstack([x,-x, x,-x])
yout = np.hstack([y, y,-y,-y])
xyOut = np.column_stack([xout, yout])
velOut = interpolate.griddata(xyIn, velBin, xyOut)
velOut = velOut.reshape(4, xbin.size)
velOut[0, :] = velBin # see V3.0.1
if sym == 1:
velOut[[1, 3], :] *= -1.
velSym = np.nanmean(velOut, axis=0)
return velSym
#----------------------------------------------------------------------
|
[
"[treibeis1995@gmail.com]"
] |
[treibeis1995@gmail.com]
|
77fd709015fd652698b0f4af3bad2db95658244b
|
9766c2e479e99cca5bf7cc834c949fc4d5286275
|
/TEST/GUI/00190_page_bdyanalysis/cleanup.py
|
016dd73ae3dc45790df8a484acfe062a7795a6de
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
UstbCmsPjy/OOF2
|
4c141e8da3c7e3c5bc9129c2cb27ed301455a155
|
f8539080529d257a02b8f5cc44040637387ed9a1
|
refs/heads/master
| 2023-05-05T09:58:22.597997
| 2020-05-28T23:05:30
| 2020-05-28T23:05:30
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 26
|
py
|
removefile('bdyanal.log')
|
[
"lnz5@rosie.nist.gov"
] |
lnz5@rosie.nist.gov
|
e093d502ae8e843bd99621c4dbb2bdd8f510aca7
|
c265cb56c7f6a5bc532a506188e61cb4d6ac3358
|
/example/get_face_enhancement_v1.py
|
029237fc791914bc3ec24b1c0dbb6e03fa4672ef
|
[
"MIT"
] |
permissive
|
tranhoangnguyen03/MobileFace
|
00ae926053494d1668004621cebbf5ed0575471b
|
c94aa16bf5f1fb3b74b67e5685fb39061cf10ef4
|
refs/heads/master
| 2020-06-25T22:40:46.715046
| 2019-07-29T11:51:51
| 2019-07-29T11:51:51
| 199,442,953
| 0
| 0
|
MIT
| 2019-07-29T11:50:43
| 2019-07-29T11:50:43
| null |
UTF-8
|
Python
| false
| false
| 2,818
|
py
|
import os, sys
import argparse
import cv2
from matplotlib import pyplot as plt
import numpy as np
sys.path.append(os.path.abspath(os.path.dirname(__file__)) + os.sep + '../MobileFace_Enhancement/')
from mobileface_enhancement import MobileFaceEnhance
def parse_args():
parser = argparse.ArgumentParser(description='Test MobileFace Makeup.')
parser.add_argument('--image-dir', type=str, default='./light',
help='Test images directory.')
parser.add_argument('--result-dir', type=str, default='./light_result',
help='Result images directory.')
parser.add_argument('--dark-th', type=int, default=80,
help='Black pixel threshold whith typical values from 50 to 100.')
parser.add_argument('--bright-th', type=int, default=200,
help='White pixel threshold whith typical values from 180 to 220.')
parser.add_argument('--dark-shift', type=float, default=0.4,
help='Gamma shift value for gamma correction to brighten the face. \
The typical values are from 0.3 to 0.5.')
parser.add_argument('--bright-shift', type=float, default=2.5,
help='Gamma shift value for gamma correction to darken the face. \
The typical values are from 2.0 to 3.0.')
args = parser.parse_args()
return args
def main():
args = parse_args()
enhance_tool = MobileFaceEnhance()
img_list = os.listdir(args.image_dir)
for img_name in img_list:
im_path = os.path.join(args.image_dir, img_name)
img = cv2.imread(im_path)
gamma, hist = enhance_tool.hist_statistic(img,
dark_th = args.dark_th,
bright_th = args.bright_th,
dark_shift = args.dark_shift,
bright_shift = args.bright_shift)
img_gamma = enhance_tool.gamma_trans(img, gamma)
if not os.path.exists(args.result_dir):
os.makedirs(args.result_dir)
rst_path = os.path.join(args.result_dir, 'rst_' + img_name)
cv2.imwrite(rst_path, img_gamma)
img_stack = np.hstack((img, img_gamma))
plt.figure(figsize=(5, 5))
ax1 = plt.subplot2grid((2,1),(0, 0))
ax1.set_title('Grayscale Histogram')
ax1.set_xlabel("Bins")
ax1.set_ylabel("Num of Pixels")
ax1.plot(hist)
ax1.set_xlim([0, 256])
ax1 = plt.subplot2grid((2, 1), (1, 0), colspan=3, rowspan=1)
ax1.set_title('Enhance Comparison')
ax1.imshow(img_stack[:,:,::-1])
plt.tight_layout()
plt.show()
if __name__ == "__main__":
main()
|
[
"helloai777@gmail.com"
] |
helloai777@gmail.com
|
a20ec095f9065df80a1ba32f675716abe0875c05
|
26c4426d2c9cd10fd7d4a73609512e69e31b64ba
|
/justone/mayflower/products/forms.py
|
452a35e79f1ecaab5846dfb47812af7c3869b763
|
[] |
no_license
|
KirillUdod/html2exc
|
550761213eb6edd7d3ea4787938cce65584606c3
|
60569f01822a15b2e5b6884a42774cd428953700
|
refs/heads/master
| 2021-01-15T17:07:05.906492
| 2016-01-06T11:51:38
| 2016-01-06T11:51:38
| 34,809,072
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,453
|
py
|
from django import forms
from products.models import Bouquet
class DependenciesForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(DependenciesForm, self).__init__(*args, **kwargs)
instance = getattr(self, 'instance', None)
dependencies = getattr(self.Meta.model, 'dependencies', {})
if isinstance(dependencies, dict):
for (depend_field, depend_field_value), fields in dependencies.iteritems():
if not isinstance(self.fields[depend_field], forms.BooleanField)\
and not getattr(self.fields[depend_field], 'choices', None):
raise ValueError()
if not isinstance(fields, (list, tuple)):
fields = [fields]
required = False
if self.data:
post_value = self.data.get(self.add_prefix(depend_field))
if post_value == 'on' and isinstance(depend_field_value, bool):
post_value = 'True'
if post_value == unicode(depend_field_value):
required = True
elif instance and getattr(instance, depend_field, None) == depend_field_value:
required = True
for field in fields:
self.fields[field].required = required
class BouquetAdminForm(DependenciesForm):
class Meta:
model = Bouquet
|
[
"kirilludod@gmail.com"
] |
kirilludod@gmail.com
|
03e8e0bae9c7c05a0bbd236400a40a4707d76ea3
|
15a1d05d48953ddaa835b94c8fb93ca69c02488d
|
/catkin_ws/src/initialize_particles/src/init_particles_caller.py
|
bcd4d11372cc28044b7bf35873ded9ddf54332b9
|
[] |
no_license
|
ok-kewei/ROS-Navigation
|
81b895cccbe65287be98da6e62967ae44dee4ab6
|
5218fe366db3638efc179cc6e55fe368842a1c20
|
refs/heads/master
| 2023-01-06T15:08:19.822330
| 2020-11-05T20:41:03
| 2020-11-05T20:41:03
| 304,449,087
| 3
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 329
|
py
|
#! /usr/bin/env python
import rospy
from std_srvs.srv import Empty, EmptyRequest
import sys
rospy.init_node('service_client')
rospy.wait_for_service('/global_localization')
disperse_particles_service = rospy.ServiceProxy('/global_localization', Empty)
msg = EmptyRequest()
result = disperse_particles_service(msg)
print result
|
[
"43628709+ok-kewei@users.noreply.github.com"
] |
43628709+ok-kewei@users.noreply.github.com
|
71b3ff2955147e25fe13059d258d1b76d5638d8c
|
6989347289b52b16c5c7af1f73851d734245bf61
|
/draw_sim_tree_with_matrix.py
|
9994b33b11058e7842418a9e3bb7bc6dc10ee783
|
[] |
no_license
|
cfriedline/bayesiansimulation
|
4abecf292a1d5361ac3951f2c367db4680cbf355
|
a74dbb7b39a10efc70bbb72086d45ca9fbaf5927
|
refs/heads/master
| 2021-01-01T19:20:51.001407
| 2014-06-05T07:00:00
| 2014-06-05T07:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,608
|
py
|
__author__ = 'chris'
import os
import sys
from ete2 import Tree, TreeNode, TreeStyle, AttrFace, TextFace, ClusterTree, ClusterNode, ProfileFace, CircleFace, NodeStyle, PhyloTree
import numpy
import tempfile
def get_color(data, row, col):
val = float(data[row, col])
coldata = get_column(data, col)
colsum = numpy.sum(coldata)
colmed = numpy.median(coldata)
colavg = numpy.average(coldata)
colsd = numpy.std(coldata)
colmin = numpy.min(coldata)
colptp = numpy.ptp(coldata)
n = (val-colmin)/colptp
if n >= 0.5:
first = int(round(n*255))
first = hex(first)[2:]
third = "00"
else:
third = int(round(n*255))
third = hex(third)[2:]
first = "00"
color = "#%s%s%s" % (first, "00", third)
return color
def get_tree_style(tree_file, abund, rownames):
with open("matrix.txt", "w") as temp:
cols = len(abund[0])
header = "#Names"
for i in xrange(cols):
header += "\tOTU%d" % i
temp.write("%s\n" % header)
for i, row in enumerate(abund):
temp.write("%s\t%s\n" % (rownames[i], '\t'.join([str(i) for i in row])))
t = Tree(tree_file)
t.convert_to_ultrametric(10)
assert isinstance(abund, numpy.ndarray)
assert isinstance(rownames, numpy.ndarray)
ts = TreeStyle()
ts.mode = "r"
ts.show_leaf_name = False
ts.show_scale = False
ts.show_branch_length = False
ts.branch_vertical_margin = 20
ts.force_topology = True
ts.optimal_scale_level = "full"
ts.scale = 50
ts.draw_guiding_lines = True
ts.guiding_lines_type = 0
ts.guiding_lines_color = "black"
for n in t.traverse():
if not n.is_leaf():
nstyle = NodeStyle()
n.set_style(nstyle)
nstyle['size'] = 0
nstyle['hz_line_width'] = 3
nstyle['vt_line_width'] = 3
else:
nstyle = NodeStyle()
n.set_style(nstyle)
nstyle['size'] = 0
nstyle['hz_line_width'] = 3
nstyle['vt_line_width'] = 3
nstyle['fgcolor'] = "Black"
nstyle['shape'] = "square"
name_face = AttrFace("name", fsize=14, ftype="Arial", fgcolor="black", penwidth=10, text_prefix=" ", text_suffix=" ")
n.add_face(name_face, column=0, position="aligned")
row_index = rownames.tolist().index(n.name)
col = 1
for i in xrange(10):
col += 1
n.add_face(CircleFace(5, color=get_color(abund, row_index, i)), column=col, position="aligned")
return t, ts
def get_tree(tree_file, abund, rownames):
t, ts = get_tree_style(tree_file, abund, rownames)
return t, ts
def read_data_file(file):
data = []
rownames = []
f = open(file)
for line in f:
d = line.rstrip().split("\t")
rownames.append(d[0])
data.append([int(i) for i in d[1:]])
return numpy.array(data), numpy.array(rownames)
def get_column(matrix, i):
return numpy.array([int(row[i]) for row in matrix])
def main():
dir = "/Users/chris/projects/bsim4/test/"
tree_file = os.path.join(dir, "tree_8_1000_0.txt")
abund_file = os.path.join(dir, "abund_1000_0_0.txt")
gap_file = os.path.join(dir, "gap_1000_0_0.txt")
abund, abund_names = read_data_file(abund_file)
gap, gap_names = read_data_file(gap_file)
tree, tree_style = get_tree(tree_file, abund, abund_names)
# tree.show(tree_style=tree_style)
tree.render("sim_tree.pdf", tree_style=tree_style)
if __name__ == '__main__':
main()
|
[
"cfriedline@vcu.edu"
] |
cfriedline@vcu.edu
|
f13581b5b5453ebc6de7b1a6fb333b7aef052b4f
|
5685c24b2d955a00861b4082c10e3b479923f3ea
|
/week4/8.5_lists.py
|
c817beb147b1f49da90b21a17531fd087de02d84
|
[
"MIT"
] |
permissive
|
itsanshulverma/py-data-structures-umich
|
2fe837faa38eaa07e7f357c2731fcebe7500743e
|
5cd4f2a87de8205d551057b32e8586405079f92c
|
refs/heads/master
| 2022-12-05T00:11:17.304705
| 2020-08-31T11:16:19
| 2020-08-31T11:16:19
| 291,684,537
| 4
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 846
|
py
|
#by Anshul Verma
#8.5 Open the file mbox-short.txt and read it line by line.
#When you find a line that starts with 'From ' like the following line:
# From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16 2008
#You will parse the From line using split() and
#print out the second word in the line (i.e. the entire address of the person who sent the message).
#Then print out a count at the end.
#Hint: make sure not to include the lines that start with 'From:'.
#You can download the sample data at
#http://www.py4e.com/code3/mbox-short.txt
fname = input("Enter the file name: ")
if len(fname) < 1 : fname = "mbox-short.txt"
fh = open(fname)
count = 0
for line in fh:
if line.startswith('From '):
count = count + 1
words = line.split()
print(words[1])
print("There were", count, "lines in the file with From as the first word")
|
[
"itsanshulverma@gmail.com"
] |
itsanshulverma@gmail.com
|
dd68bda41f4ec18bcbffcfb66e2ae4ecf26a052d
|
a661e08f57b572b6b4e0b3f54b6bc6db81f689aa
|
/requestget.py
|
739aafc3585aec2ab3fd35be36e36201caeecd12
|
[] |
no_license
|
absentfriend/Channel
|
ca7f8f336b85a2ca0db2bd12ac496627d474f883
|
0bec6d447632ecb05edcd2fd019a900dab8d0bd7
|
refs/heads/master
| 2022-07-09T14:35:15.697345
| 2022-06-21T22:39:28
| 2022-06-21T22:39:28
| 194,481,970
| 0
| 0
| null | 2019-06-30T06:13:21
| 2019-06-30T06:13:21
| null |
UTF-8
|
Python
| false
| false
| 555
|
py
|
import requests
headers = {'Referer': 'http://m.91kds.org/jiemu_sdqdtv1.html',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36'}
def geturl(url,headers):
r=requests.get(url,headers=headers)
r.encoding='utf-8'
#r.encoding='GB2312'
r=r.text
#print(r)
return r
def posturl(url,data,headers=headers):
r=requests.post(url,json=data,headers=headers)
r.encoding='utf-8'
#r.encoding='GB2312'
r=r.text
#print(r)
return r
|
[
"43053658+YaozhiweiGit@users.noreply.github.com"
] |
43053658+YaozhiweiGit@users.noreply.github.com
|
dc6dfa3596c32270087a4264ab9fb490ee5ddcba
|
089a6a7c51af5ca5b28282e9370bd1fc97ff94b5
|
/superhero_database/heroes/views.py
|
a2614fcd574ebbe99743b2e8002c8b43a032cc59
|
[] |
no_license
|
plumtree87/superheroes
|
95bedd4c354373d15bcea5eae770e4ea368adb35
|
0192a304676cb63de859a61ef9493d8473e7c5c9
|
refs/heads/main
| 2023-03-29T10:02:12.859584
| 2021-04-08T15:26:53
| 2021-04-08T15:26:53
| 355,323,644
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,399
|
py
|
from django.shortcuts import render
from django.http import HttpResponse, HttpResponseRedirect
from .models import Hero
from django.urls import reverse
# Create your views here.
def index(request):
all_heroes = Hero.objects.all()
context = {
'all_heroes': all_heroes
}
return render(request, 'heroes/index.html', context)
def detail(request, hero_id):
details = Hero.objects.get(pk=hero_id)
context = {
'details': details
}
return render(request, 'heroes/detail.html', context)
def create(request):
if request.method == 'POST':
name = request.POST.get('name')
alter_ego = request.POST.get('alter_ego')
primary_ability = request.POST.get('primary_ability')
secondary_ability = request.POST.get('secondary_ability')
catch_phrase = request.POST.get('catch_phrase')
new_hero = Hero(name=name, alter_ego=alter_ego, primary_ability=primary_ability, secondary_ability=secondary_ability, catch_phrase=catch_phrase)
new_hero.save()
return HttpResponseRedirect(reverse('heroes:index'))
else:
return render(request, 'heroes/create.html')
def edit(request, hero_id):
if request.method == 'POST':
details = Hero.objects.get(pk=hero_id)
name = request.POST.get('name')
alter_ego = request.POST.get('alter_ego')
primary_ability = request.POST.get('primary_ability')
secondary_ability = request.POST.get('secondary_ability')
catch_phrase = request.POST.get('catch_phrase')
details.name = name
details.alter_ego = alter_ego
details.primary_ability = primary_ability
details.secondary_ability = secondary_ability
details.catch_phrase = catch_phrase
details.save()
return HttpResponseRedirect(reverse('heroes:index'))
else:
details = Hero.objects.get(pk=hero_id)
context = {
'details': details
}
return render(request, 'heroes/edit.html', context)
def delete(request, hero_id):
if request.method == 'POST':
details = Hero.objects.get(pk=hero_id)
details.delete()
return HttpResponseRedirect(reverse('heroes:index'))
else:
details = Hero.objects.get(pk=hero_id)
context = {
'details': details
}
return render(request, 'heroes/delete.html', context)
|
[
"plumtree87@protonmail.com"
] |
plumtree87@protonmail.com
|
569d33d3a9b9c3cd4c1091378db6fb048a872c82
|
76931db41954de1e7314ee0d3f4ce296e69a2f00
|
/src/main.py
|
16e4ceeee88e14b0404de8b1a76ddaac5f201391
|
[] |
no_license
|
graciehao25/Insight_coding_practice
|
6b0c9e39f01ecad993258fc29b65dc5f9b3e6957
|
5267856066dc05ab65f786426e7b8b9b31f37b44
|
refs/heads/master
| 2020-04-03T13:39:04.533029
| 2018-10-30T21:59:02
| 2018-10-30T21:59:02
| 155,291,892
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,186
|
py
|
import csv
import sys
from collections import Counter
from get_idx import get_idx
from get_feature_list import get_feature_list
from a_list_of_top_X_sorted_feature_dict import a_list_of_top_X_sorted_feature_dict
from write_output_csv import write_output_csv
def main(INPUT, OUTPUT0, OUTPUT1):
"""
Main function contains six steps:
1. figure out the column indices of the a list of features
2. filter the dataframe by status, create a list for each feature.
3. Create frequency dictionary for each feature
4. Sort dictionary by vaule(desc) and alphabet(asc)
5. crop the dictionary and keep only TOP X
6. Save the output
Inputs of the main function:
1. an INPUT csv file of interests
2. OUTPUT0 named 'top_10_occupations.txt'
3. OUTPUT1 named 'top_10_states.txt'
The main funciton will call two other functions I wrote stored under the src folder:
1. get_idx to get the indices for the filter variable and features
2. feature_list to filter the dataframe by status, create a list for each feature.
"""
# Specs user can customize
X=10
filter_str="STATUS"
filter_condition="CERTIFIED"
features_list=["SOC_NAME","WORKSITE_STATE"]
a_list_of_output_paths = [OUTPUT0, OUTPUT1]
output_cols_1=["TOP_OCCUPATIONS", "NUMBER_CERTIFIED_APPLICATIONS", "PERCENTAGE"]
output_cols_2=["TOP_STATES", "NUMBER_CERTIFIED_APPLICATIONS", "PERCENTAGE"]
output_cols= [output_cols_1,output_cols_2]
"""
idx in the original data file
assuming the filter variable is "STATUS", and st
get the filter variable index and a list of feature indices
"""
filter_idx,features_idx=get_idx(INPUT,filter_str,features_list)
"""
iterate through the list of features user provided in main.py
"""
num_of_entries, a_list_of_top_x_dicts=a_list_of_top_X_sorted_feature_dict(INPUT,filter_idx,filter_condition,features_idx,X)
write_output_csv(num_of_entries,a_list_of_top_x_dicts,a_list_of_output_paths)
if __name__ == '__main__':
INPUT = sys.argv[1]
OUTPUT0 = sys.argv[2]
OUTPUT1 = sys.argv[3]
main(INPUT, OUTPUT0, OUTPUT1)
|
[
"graciehao25@gmail.com"
] |
graciehao25@gmail.com
|
2ce099f6946ba532a45dc0075b0918ffcbeff559
|
bd50532e0c0ca5fd6d5c1f389a5f98787736e170
|
/Symmetric Tree.py
|
b0a05dc2fcad4bbe3156161354e99b4fa56b5535
|
[
"MIT"
] |
permissive
|
chyavan-mc/My-Solutions-to-Leetcode-problems-using-Python-3
|
eec8bce6456e33776e3565ced8e87b94e75b1f19
|
b78779bd3f5313ab4752f9e9a23cb4a93805aff6
|
refs/heads/master
| 2022-08-29T16:49:30.227034
| 2019-01-17T05:47:54
| 2019-01-17T05:47:54
| 145,867,402
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 666
|
py
|
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def isSymmetric(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
ptr = root
if(ptr==None):
return True
return self.CheckSym(ptr,ptr)
def CheckSym(self,x,y):
if(x==None and y==None):
return True
elif(x!=None and y!=None):
if(x.val==y.val):
return self.CheckSym(x.left,y.right) and self.CheckSym(x.right,y.left)
return False
|
[
"noreply@github.com"
] |
noreply@github.com
|
702f47e3a48767738613e267a62b0f5477021049
|
0a68928bfcada2bb4803068ffe1175556aa06883
|
/Code Examples/doc_replacement.py
|
3bcab05b05cddf602d1acbd73c8b57e666dd8687
|
[
"Apache-2.0"
] |
permissive
|
shannonsands/StoryNode
|
a487a94ea7dc101f1209bdf548a3ed7a57607b5e
|
e863e68e4b95b92a074554c2399492dbfb54cbab
|
refs/heads/main
| 2023-04-24T16:06:10.473740
| 2021-05-16T23:10:16
| 2021-05-16T23:10:16
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 746
|
py
|
import spacy
from spacy.matcher import Matcher
import sys
def main(argv):
nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab)
# Add match ID "HelloWorld" with no callback and one pattern
pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
matcher.add("HelloWorld", [pattern])
doc = nlp("Hello, world! Hello world!")
matches = matcher(doc)
for match_id, start, end in matches:
string_id = nlp.vocab.strings[match_id] # Get string representation
span = doc[start:end] # The matched span
print(match_id, string_id, start, end, span.text)
for token in doc:
print(token)
print(doc[0:5])
if __name__ == "__main__":
main(sys.argv)
|
[
"seadav.17@gmail.com"
] |
seadav.17@gmail.com
|
3adc95673cbed3a1842979da20f0907a4b031f28
|
bfafc91d672a1ea2bff6178038d93ee3b0d39c3e
|
/bioinformatics/rosalind/NEED/need.py
|
674ed759d673c6b16ac06aea31783b7dd0f2b662
|
[] |
no_license
|
danshea/python
|
c2567644f4e18b98c30b4e908b6e96f5475e6fd6
|
66bd667d525c64dbb23a4248772cb3094c3ae930
|
refs/heads/master
| 2021-08-06T06:02:05.094272
| 2021-06-21T20:24:28
| 2021-06-21T20:24:28
| 4,727,109
| 2
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,148
|
py
|
#!/usr/bin/env python
################################################################################
#
# Name: need.py
# Date: 2014-04-25
# Author: dshea
# Description:
# Problem
#
# An online interface to EMBOSS's Needle tool for aligning DNA and RNA strings
# can be found here.
#
# Use:
#
# The DNAfull scoring matrix; note that DNAfull uses IUPAC notation for
# ambiguous nucleotides. Gap opening penalty of 10. Gap extension penalty of 1.
#
# For our purposes, the "pair" output format will work fine; this format shows
# the two strings aligned at the bottom of the output file beneath some
# statistics about the alignment.
#
# Given: Two GenBank IDs.
#
# Return: The maximum global alignment score between the DNA strings associated
# with these IDs. Sample Dataset
#
# JX205496.1 JX469991.1
#
# Sample Output
#
# 257
#
################################################################################
import os.path
import sys
from Bio.Emboss.Applications import NeedleCommandline
from Bio import Entrez
from Bio import SeqIO
# GLOBAL
Entrez.email = "shea.d@husky.neu.edu"
def getFasta(genbank_id):
filename = '{0:s}.fa'.format(genbank_id)
if not os.path.isfile(filename):
handle = Entrez.efetch(db='nucleotide', rettype='fasta', retmode='text', id=genbank_id)
seq_record = SeqIO.read(handle, 'fasta')
handle.close()
SeqIO.write(seq_record, filename, format='fasta')
return(filename)
def main():
if len(sys.argv) != 3:
print 'usage {0:s} genbank_id1 genbank_id2'.format(sys.argv[0])
sys.exit(1)
genbank_a = sys.argv[1]
genbank_b = sys.argv[2]
sequence_a = getFasta(genbank_a)
sequence_b = getFasta(genbank_b)
needle_cline = NeedleCommandline('/usr/local/bin/needle',
asequence=sequence_a, bsequence=sequence_b,
gapopen=10, gapextend=1, endweight=True, endopen=10, endextend=1,
outfile='{0:s}_{1:s}_needle.txt'.format(genbank_a,genbank_b))
stdout, stderr = needle_cline()
sys.exit(0)
if __name__ == '__main__':
main()
|
[
"daniel.john.shea@gmail.com"
] |
daniel.john.shea@gmail.com
|
1353f3961676958e592cf32649e52eeefe94fb13
|
45100ef7950cbac271695d3dc7fe80ae30dec437
|
/NSRDB_analysis.py
|
de2e361d277fc3841ef34f349ecf1dcf5bf1baeb
|
[] |
no_license
|
shafferpr/solar_radiation_database
|
635e628700c69b6dff258e95e310e7952629b88d
|
113a80bf25a1cd4db0a8d9124d70b867b38cca51
|
refs/heads/master
| 2021-01-23T05:44:49.555014
| 2017-05-31T20:04:22
| 2017-05-31T20:04:22
| 92,983,996
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,218
|
py
|
#! /usr/local/bin/python
import os
import sys
import string
import requests
import json
import numpy as np
import pandas as ps
import math
from bokeh.plotting import figure, output_file, show
from dateutil.parser import parse
def createDateTimeFigure(repoString):
payload = {}
r=requests.get('https://api.github.com/repos/'+repoString+'/commits', auth=('shafferpr@gmail.com','chem1633'), params=payload)
myList = []
for i in range (len(r.json())):
myList.append(r.json()[i]['sha'])
q=[]
for i in range (len(myList)):
q.append(requests.get('https://api.github.com/repos/'+repoString+'/commits/'+myList[i],auth=('shafferpr@gmail.com','chem1633'), params=payload))
x=[]
for i in range(len(q)):
x.append(parse(q[i].json()['commit']['author']['date']))
y=[]
for i in range(len(q)):
y.append(q[i].json()['stats']['total'])
output_file("./static/plot.html")
p = figure(title="commit size over time", x_axis_label='date of commit', y_axis_label='size of commit', x_axis_type="datetime")
p.line(x, y, legend=repoString, line_width=2, line_color="blue")
show(p)
return p
#createDateTimeFigure("tensorflow/tensorflow")
|
[
"shafferpr@gmail.com"
] |
shafferpr@gmail.com
|
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