content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import logging
import cloudinary
from django.conf import settings
L = logging.getLogger(__name__)
try:
cloudinary.config(
cloud_name=settings.CLOUDINARY_NAME,
api_key=settings.CLOUDINARY_API_KEY,
api_secret=settings.CLOUDINARY_API_SECRET
)
except AttributeError:
L.warning('Cloudinary settings attributes are missing and storage will not be available.')
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import setuptools
setuptools.setup(
name="inferactively",
version="0.0.1",
description=(
"A Python implementation of active inference for Markov Decision Processes"
),
license="Apache 2.0",
url="https://github.com/alec-tschantz/infer-actively",
packages=[
"inferactively",
"inferactively.core",
"inferactively.distributions",
"inferactively.agent",
"inferactively.envs",
],
)
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] | 2.367876 | 193 |
# Generated by Django 3.2.8 on 2021-10-17 16:26
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import uuid
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#!/usr/bin/python
from Tools.datetimetool import DateTimeTool
import logging
from DbAccess import DBHelper
SOCKET_TIMEOUT = 1
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from numpy import arange, pi, sin
from bokeh.models.annotations.labels import Label
from bokeh.plotting import figure, show
x = arange(-2*pi, 2*pi, 0.1)
y = sin(x)
p = figure(height=250, title=r"\[\sin(x)\text{ for }x\text{ between }-2\pi\text{ and }2\pi\]")
p.circle(x, y, alpha=0.6, size=7)
label = Label(
text="$$y = \sin(x)\$$",
x=150, y=130,
x_units="screen", y_units="screen",
)
p.add_layout(label)
p.yaxis.axis_label = r"\[\sin(x)\]"
p.xaxis.axis_label = r"\[x\pi\]"
show(p)
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7,
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] | 2.109705 | 237 |
import json
import logging
import swapper
from django.shortcuts import get_object_or_404
from django.utils.decorators import method_decorator
from django.views import View
from django.views.decorators.csrf import csrf_exempt
from .processor import PaymentProcessor
logger = logging.getLogger(__name__)
@method_decorator(csrf_exempt, name='dispatch')
class CallbackView(View):
"""
Dedicated callback view, since payNow does not support dynamic callback urls.
"""
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] | 3.178808 | 151 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2008 Sascha Steinbiss <[email protected]>
# Copyright (c) 2008 Center for Bioinformatics, University of Hamburg
#
# Permission to use, copy, modify, and 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.
#
from gt.dlload import gtlib
from gt.core.array import Array
from gt.core.error import Error, gterror
from gt.core.gtrange import Range
from gt.core.str_array import StrArray
from gt.extended.feature_node import FeatureNode
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] | 3.265896 | 346 |
from bs4 import BeautifulSoup as bs
import requests, json, pprint, pandas, csv, time
import psutil, asyncio, aiohttp
start_time = time.time()
headers = { # First line of defense
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Methods': 'GET',
'Access-Control-Allow-Headers': 'Content-Type',
'Access-Control-Max-Age': '3600',
'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'
}
# =============================================================
s_id = 1032500 # ||||||||||||||||||||||||||||||||||||||||||||| Starting ID
e_id = 1033000 # ||||||||||||||||||||||||||||||||||||||||||||| Ending ID
# =============================================================
# players = get_list_o_players(s_id, e_id)
players = asyncio.get_event_loop().run_until_complete(get_list_o_players(s_id, e_id))
print(players)
with open('players.csv', 'w', ) as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['Name', 'ID', 'LvL', 'Rank', 'Join Date', 'Join Time'])
for player in players:
writer.writerow([player.split('/')[0], player.split('/')[1], player.split('/')[2],
player.split('/')[3], player.split('/')[4], player.split('/')[5]])
pandas.set_option('display.max_columns', 6)
pandas.set_option('display.max_rows', 1000)
pandas.set_option('display.width', 1000)
print(pandas.read_csv('players.csv'))
print(f'---------------- Elapsed time {round(time.time() - start_time, 6)} seconds ----------------')
CPU_percent = psutil.cpu_percent()
RAM_percent = psutil.virtual_memory().percent
with open('benchmark.csv', 'a', ) as csvfile:
writer = csv.writer(csvfile)
# writer.writerow(['Players', 'Time','Cpu Used','RAM Used'])
writer.writerow([(e_id - s_id), round(time.time() - start_time, 6), CPU_percent, RAM_percent])
print(f'CPU Used: {CPU_percent}% , RAM Used: {RAM_percent}%')
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13931,
16718,
25,
1391,
24115,
62,
25067,
92,
4,
11537,
201,
198
] | 2.60241 | 747 |
import pandas as pd
from ._datasets import *
__all__ = [
"load_alicante_murcia", "load_barcelona", "load_madrid", "load_valencia"
"load_argentina", "load_burma", "load_china", "load_canada",
"load_djibouti", "load_egypt", "load_ireland", "load_finland",
"load_greece", "load_honduras", "load_italy", "load_japan",
"load_kazakhstan", "load_luxembourg", "load_morocco", "load_oman",
"load_nicaragua", "load_panama", "load_qatar", "load_rwanda", "load_sweden",
"load_tanzania", "load_uruguay", "load_vietnam", "load_sahara", "load_yemen",
"load_zimbabwe"
]
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1,
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198
] | 2.250951 | 263 |
import requests
import os
import shutil
from zipfile import ZipFile
| [
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# coding: utf-8
# In[3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().magic('matplotlib inline')
heat_map(filepath)
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import sys
import os
import csv
import json
import re
import math
from decimal import Decimal
from django.core.management.base import BaseCommand, CommandError
field_types = {
'date_ymd': 'DateField',
'number': 'FloatField',
'integer': 'IntegerField',
'email': 'EmailField',
'text': 'CharField',
'textarea': 'TextField',
'calc': 'FloatField',
'radio': 'CharField',
'select': 'CharField',
'checkbox': 'CharField',
'yesno': 'BooleanField',
'truefalse': 'BooleanField',
}
__project_name__ = ''
requires_model_validation = False
db_module = 'django.db'
args = 'file', 'jsonfile'
def generate_repeating_fixtures(self, line, form,
form_list, fixtures, pk_num,
pk_num_list, primary_key_counter,
additional_forms):
"""
This function generates the fixture dictionaries for a repeating form.
"""
num_repeats_all = 1
num_repeats_list = []
current_repeat_list = []
counter = 0
#populates current_repeat_list with 1s, amount equal to length of
#form_list - 1
current_repeat_list = [1] * len(form_list[1:])
#determines number of repeats from items in form_list
for item in form_list:
if len(item.split(' ')) > 1:
num_repeats_form = item.split(' ')[1]
num_repeats_list.insert(0, num_repeats_form)
num_repeats_all = int(num_repeats_all) * int(num_repeats_form)
for i in range(num_repeats_all):
primary_key_counter += 1
fixture_dict = {}
form_num_list = []
fk_index = len(form_list)-2
foreign_key = form_list[fk_index].lower().split(' ')[0].replace('_',
'')
if fk_index == 0:
fixture_dict[foreign_key] = ['', pk_num]
else:
fk_num = int(math.ceil(primary_key_counter /
float(num_repeats_list[0])))
fixture_dict[foreign_key] = ['', fk_num]
pk_num_list.append(primary_key_counter+additional_forms)
checkboxform = False
try:
if form['fields'][0]['field name'] == 'label' and \
form['fields'][1]['field name'] == 'value':
checkboxform = True
except IndexError:
pass
if checkboxform:
#clean_field_name = re.sub('\${d\}', '', form['form name'])
cb_field_name = form['form name'].split('~')[0].split(' ')[0]
cb_field_name = re.sub('\${d\}', '', cb_field_name)
if len(form_list[2:]) == 0:
base_field_name = cb_field_name
else:
base_field_name = get_field_name(self, form['fields'][1],
form_list[2:],
current_repeat_list,
cb_field_name).lower()
base_field_name = base_field_name[:-1]
field_names = get_field_names(self, form['fields'][1],
form_list[2:], base_field_name)
checked_lines = []
answered = False
for name in field_names:
try:
if line[name] == '1':
checked_lines.append(name[-1])
answered = True
elif line[name] == '0':
answered = True
except KeyError:
pass
checked_fixtures = []
for checked_line in checked_lines:
choices = form['fields'][1]['choices']
choice = choices.split('|')
"""
The number assigned to each choice by redcap might not start
at 1. This subtracts the starting num from the index so
an out of bounds error doesn't occur
"""
starts_at = choice[0].split(',')[0]
choice = choice[int(checked_line)-int(starts_at)]
choice = choice.split(',')
fixture_dict['label'] = [form['fields'][0], choice[1]]
fixture_dict['value'] = [form['fields'][1],
choice[0].strip(' ')]
checked_fixtures.append(dict(fixture_dict))
temp_primary = primary_key_counter
for i, fixture in enumerate(checked_fixtures):
if i < len(checked_fixtures)-1:
additional_forms = additional_forms + 1
clean_form_name = form['form name'].split(' ')[0] \
.replace('$', '')
fixtures.append([clean_form_name, fixture])
pk_num_list.append(temp_primary+additional_forms)
else:
for field in form['fields']:
clean_field_name = re.sub('\${d\}', '', field['field name'])
#form_list[0] and form_list[1] are both 'base forms'
#form_list[0] is record, form_list[1] is the form name
#given for each field without repeating
if len(form_list[2:]) == 0:
base_field_name = field['field name']
else:
base_field_name = get_field_name(self, field,
form_list[2:],
current_repeat_list) \
.lower()
if field['choices']:
field_names = get_field_names(self, field,
form_list[2:],
base_field_name)
checked_line = ''
answered = ''
for name in field_names:
try:
if len(field_names) > 1:
if line[name] == '1':
checked_line = name[-1]
answered = True
elif line[name] == '0':
answered = True
else:
if line[name]:
checked_line = line[name]
except KeyError:
#print 'ERROR: FIELD NOT FOUND ' + name
#print field
#print field_names
pass
#if the line is checked, the number of option is the answer
if checked_line:
fixture_dict[clean_field_name] = [field, checked_line]
elif answered is True:
fixture_dict[clean_field_name] = [field, '0']
else:
fixture_dict[clean_field_name] = [field, '']
elif '_summary' in field['field name']:
field_names = get_field_names_summary(self, field,
form_list[2:],
base_field_name[:-8])
checked_lines = []
answered = False
for name in field_names:
try:
if line[name] == '1':
checked_lines.append(name[-1])
answered = True
elif line[name] == '0':
answered = True
except KeyError:
pass
choices_str = ''
for checked_line in checked_lines:
choices = field['field note']
choice = choices.split('|')
"""
The number assigned to each choice might not start
at 1. This subtracts the starting num from the index
we check
"""
starts_at = choice[0].split(',')[0]
choice = choice[int(checked_line)-int(starts_at)]
choices_str = choices_str + ' ' + choice
fixture_dict[clean_field_name] = [field, choices_str]
else:
try:
fixture_dict[clean_field_name] = [field,
line[base_field_name]
]
except KeyError:
#print 'ERROR: NOT FOUND ' + base_field_name
#print field
#print base_field_name
pass
clean_form_name = form['form name'].split(' ')[0].replace('$', '')
fixtures.append([clean_form_name, fixture_dict])
cur_ind = len(current_repeat_list) - 1
update_current_repeats(self, num_repeats_list[::-1],
current_repeat_list, cur_ind)
return primary_key_counter, additional_forms
def get_field_name(self, field, form_list,
repeat_num_list, alt_field_name=None):
"""
Loops through a list of forms. All forms are prefix forms except for the
last form in form_list.
"""
prefix = ''
if alt_field_name:
field_name = alt_field_name
else:
field_name = field['field name']
for i in range(len(form_list)):
if i != len(form_list)-1:
str_split = form_list[i].split(' ')
name = str_split[0]
name = re.sub('\d$', '', name)
num_repeats = repeat_num_list[i]
prefix = prefix + name + str(num_repeats) + '_'
elif field_name.find('${d}') != -1:
new_field_name = re.sub('\$\{d\}',
str(repeat_num_list[-1]),
field_name)
else:
new_field_name = field_name + '' + str(repeat_num_list[-1])
new_field_name = prefix + new_field_name
return new_field_name
def find_related_forms(self, form_name, form_dict, foreign_forms=None):
"""
Finds the form_name value in the form_dict. If it is found, the function
will call itself using form_dict[form_name]. The form_dict is a dictionary
with the keys being a form name and the value being the name of the form
they have a foreign key relation with.
Ex: form_dict['Microarray 1'] = 'Prior Gen Testing'
This function will continue until no more related forms are found, and will
return a list of them, in order from highest to deepest form relation
"""
if foreign_forms is None:
foreign_forms = []
if form_name in form_dict and not form_name in foreign_forms:
foreign_forms.append(form_name)
find_related_forms(self, form_dict[form_name],
form_dict, foreign_forms)
return foreign_forms
def get_field_names(self, field, form_dict, field_name):
"""
Checkboxes and radio_other fields have multiple parts in the data csv,
usually something like name1 name2 name3 for each checkbox/radio button
that is pushable, but the info must be put into one field.
This method finds the fields in the data file that are related to the field
parameter. If it is a checkbox, it splits the possible choices and uses
that to find the fields.
If another special case for field names needs to be added, all that
needs to be done is add an elif statement with the field type or variable
it depends on.
"""
choices_field_names = []
if field['field type'] == 'checkbox' or \
field['field type'] == 'checkbox_other' or \
field['field type'] == 'checkbox_details':
choices = field['choices'].split('|')
for choice in choices:
choices_field_names.append(field_name.lower() + '___' +
choice.split(',')[0].strip(' '))
else:
choices_field_names.append(field_name.lower())
return choices_field_names
def update_current_repeats(self, form_list, current_repeats_list, cur_index):
"""
Updates the current_repeats_list depending on form_list([5,5,5] which
is a list of numbers indicating the max number of repeats needed) and
current_repeats_list([1,1,1] which is a list of numbers indicating what
iteration the repeating is on). When function is first called, cur_index
will be 0. Iterates the current_repeats_list like
[1,1,1][1,1,2][1,1,3][1,2,1][1,2,2][1,2,3]
if the element at cur_index in current_repeats_list is greater than or
equal to the element in form_list at cur_index (Both of these are ints),
then 'reset' the element in current_repeats_list.
if the cur_index - 1 is not negative (still in bounds + cur_index is
not first index) then recursively call update_current_repeats on
cur_index - 1
else add 1 to current_repeats_list[cur_index]
"""
if int(current_repeats_list[cur_index]) >= int(form_list[cur_index]):
current_repeats_list[cur_index] = 1
if cur_index - 1 >= 0:
cur_index -= 1
update_current_repeats(self, form_list,
current_repeats_list, cur_index)
else:
current_repeats_list[cur_index] += 1
def print_fixtures(self, fixtures_list, pk_list, fout):
"""
fixtures_list is a list of lists. Each element is a list of
[form name,fixture_dict]. Each element in fixture_dict
is [field, field_val]
function loops through each element in fixtures_list, then each
key(element) in fixtures_list[i][1](a fixture_dict) and determines if its
fields are blank. If they are not blank, the field is added to field_dict.
field_dict is then printed all fields in each fixture_dict has been checked
"""
all_json = []
first_fix = True
for i in range(len(fixtures_list)):
field_dict = {}
#if field has a value, print it
for key in fixtures_list[i][1]:
if fixtures_list[i][1][key]:
field = fixtures_list[i][1][key][0]
field_val = fixtures_list[i][1][key][1]
if field:
field_dict[key] = cast_field(self, field, field_val)
else:
#if it is just a foreign key
field_dict[key] = field_val
all_json.append({'model': __project_name__ + '.' +
fixtures_list[i][0].replace('_', '') + '',
'pk': pk_list[i],
'fields': field_dict
})
fout.write(json.dumps(all_json, indent=4, separators=(',', ': ')))
def get_field_type(self, field):
"""
Given the database connection, the table name, and the cursor row
description, this routine will return the given field type name,as well
as any additional keyword parameters and notes for the field.
"""
required = field['required']
validation_type = field['validation type']
field_type = field['field type']
try:
field_type = field_types.get(validation_type, field_types[field_type])
except KeyError:
field_type = 'TextField'
if not required:
if field_type is 'BooleanField':
field_type = 'NullBooleanField'
choices = None
if field['choices']:
try:
choices = [(int(v.strip()),
k.strip()) for v, k in [choice.split(',')
for choice in field['choices'].split('|')]]
field_type = 'IntegerField'
except (ValueError, TypeError):
pass
return field_type
def cast_field(self, field, field_val):
"""
Casts line[name] depending on the field_type
"""
field_type = get_field_type(self, field)
if field_type == 'CharField' or field_type == 'TextField':
return field_val
elif field_type == 'IntegerField':
if field_val and field_val.isdigit():
return int(field_val)
elif field_type == 'FloatField':
try:
return float(field_val)
except:
pass
elif field_type == 'NullBooleanField':
if field_val == '':
return None
elif field_val == '0':
return False
elif field_val == '1':
return True
else:
return field_val
elif field_type == 'BooleanField':
if field_val:
if field_val == '1':
return True
elif field_val == '0':
return False
else:
return field_val
elif field_type == 'DateField':
if field_val:
return field_val
else:
return field_val
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] | 1.876823 | 9,393 |
from . import base, messages, participants, rooms, users
| [
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1330,
2779,
11,
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11,
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198
] | 4.384615 | 13 |
from flask import Flask, render_template, url_for, redirect
from pathlib import Path
import config
from controller import ProgressController
controller = ProgressController()
app = Flask(__name__)
@app.route('/')
@app.route('/tech/<stem>')
def tech(stem):
"""
显示技术要求
:param stem:
:return:
"""
controller.activate(stem)
return render_template('text_label.html', view=TechView(controller))
@app.route('/change/<int:line_number>/<mark>')
@app.route('/view')
if __name__ == '__main__':
app.run(host=config.host, port=config.port)
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29,
14,
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13,
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7,
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28,
11250,
13,
4774,
11,
2493,
28,
11250,
13,
634,
8,
198
] | 2.621005 | 219 |
import multiprocessing.pool | [
11748,
18540,
305,
919,
278,
13,
7742
] | 3.857143 | 7 |
import json
import sys
batch_size = 2000
clusters = {
'eqiad': "search.svc.eqiad.wmnet",
'codfw': "search.svc.codfw.wmnet",
}
if __name__ == "__main__":
import math
import multiprocessing
if not len(sys.argv) == 2:
print("Usage: %s <wiki>\n" % (sys.argv[0]))
sys.exit(1)
wiki = sys.argv[1]
max_id = get_max_id(wiki) + 5000
min_per_process = batch_size * 10
num_processes = min(40, int(math.ceil(max_id / float(min_per_process))))
step = int(math.ceil(max_id/float(num_processes)))
q = multiprocessing.Queue()
workers = []
try:
listener = multiprocessing.Process(target=listen, args=(wiki, q))
listener.start()
for start in range(1, max_id, step):
args = (wiki, start, start + step, q)
worker = multiprocessing.Process(target=run, args=args)
workers.append(worker)
worker.start()
for w in workers:
w.join()
q.put_nowait(None)
listener.join()
except KeyboardInterrupt:
for w in workers:
w.terminate()
listener.terminate()
| [
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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11,
923,
1343,
2239,
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8,
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220,
220,
220,
220,
220,
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220,
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220,
220,
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220,
220,
220,
329,
266,
287,
3259,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
13,
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3419,
198,
220,
220,
220,
220,
220,
220,
220,
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13,
1996,
62,
2197,
4548,
7,
14202,
8,
198,
220,
220,
220,
220,
220,
220,
220,
24783,
13,
22179,
3419,
198,
220,
220,
220,
2845,
31973,
9492,
3622,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
266,
287,
3259,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
13,
23705,
378,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
24783,
13,
23705,
378,
3419,
198
] | 2.116883 | 539 |
from django.apps import AppConfig
from django.utils.translation import ugettext_lazy as _
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
198,
6738,
42625,
14208,
13,
26791,
13,
41519,
1330,
334,
1136,
5239,
62,
75,
12582,
355,
4808,
628
] | 3.5 | 26 |
#!/usr/bin/env python
# ctypes-opencv - A Python wrapper for OpenCV using ctypes
# Copyright (c) 2008, Minh-Tri Pham
# All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
# * Neither the name of ctypes-opencv's copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# For further inquiries, please contact Minh-Tri Pham at [email protected].
# ----------------------------------------------------------------------------
"""ctypes-opencv - A Python wrapper for OpenCV using ctypes
ctypes-opencv is a package that brings Intel's (now Willow Garage's) Open Source Computer Vision Library (OpenCV) to Python. OpenCV is a collection of algorithms and sample code for various computer vision problems. The goal of ctypes-opencv is to provide Python access to all documented functionality of OpenCV.
"""
DOCLINES = __doc__.split("\n")
from distutils.core import setup
CLASSIFIERS = """\
Development Status :: 5 - Production/Stable
Intended Audience :: Developers
Intended Audience :: End Users/Desktop
Intended Audience :: Information Technology
Intended Audience :: Science/Research
License :: OSI Approved :: BSD License
Natural Language :: English
Operating System :: OS Independent
Operating System :: Microsoft :: Windows
Operating System :: POSIX
Operating System :: Unix
Operating System :: MacOS
Programming Language :: Python
Programming Language :: Python :: 3
Topic :: Multimedia :: Graphics
Topic :: Multimedia :: Video
Topic :: Scientific/Engineering :: Artificial Intelligence
Topic :: Scientific/Engineering :: Human Machine Interfaces
Topic :: Software Development :: Libraries :: Python Modules
"""
setup(name = 'ctypes-opencv',
version = '0.8.0',
description = DOCLINES[0],
author = 'Minh-Tri Pham',
author_email = '[email protected]',
url = 'http://code.google.com/p/ctypes-opencv/',
license = 'New BSD License',
platforms = 'OS Independent, Windows, Linux, MacOS',
classifiers = filter(None, CLASSIFIERS.split('\n')),
long_description = "\n".join(DOCLINES[2:]),
packages = ['ctypes_opencv'],
data_files=[('doc/ctypes_opencv', ['AUTHORS', 'ChangeLog', 'COPYING', 'README', 'THANKS', 'TODO'])],
)
| [
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] | 3.696581 | 936 |
import requests
from steamapi.steamapikey import SteamAPIKey
#message = True
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# Generated by Django 3.2.7 on 2021-11-10 18:20
from django.db import migrations, models
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# -*- coding: utf-8 -*-
# Copyright 2018, IBM.
#
# This source code is licensed under the Apache License, Version 2.0 found in
# the LICENSE.txt file in the root directory of this source tree.
"""
Histogram visualization
"""
from string import Template
from collections import Counter
import sys
import time
import re
import numpy as np
if ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
try:
from IPython.core.display import display, HTML
except ImportError:
print("Error importing IPython.core.display")
def process_data(data, number_to_keep):
""" Prepare received data for representation.
Args:
data (dict): values to represent (ex. {'001' : 130})
number_to_keep (int): number of elements to show individually.
Returns:
dict: processed data to show.
"""
result = dict()
if number_to_keep != 0:
data_temp = dict(Counter(data).most_common(number_to_keep))
data_temp['rest'] = sum(data.values()) - sum(data_temp.values())
data = data_temp
labels = data
values = np.array([data[key] for key in labels], dtype=float)
pvalues = values / sum(values)
for position, label in enumerate(labels):
result[label] = round(pvalues[position], 5)
return result
def iplot_histogram(executions_results, options=None):
""" Create a histogram representation.
Graphical representation of the input array using a vertical bars
style graph.
Args:
executions_results (array): Array of dictionaries containing
- data (dict): values to represent (ex. {'001' : 130})
- name (string): name to show in the legend
- device (string): Could be 'real' or 'simulated'
options (dict): Representation settings containing
- width (integer): graph horizontal size
- height (integer): graph vertical size
- slider (bool): activate slider
- number_to_keep (integer): groups max values
- show_legend (bool): show legend of graph content
- sort (string): Could be 'asc' or 'desc'
"""
# HTML
html_template = Template("""
<p>
<div id="histogram_$divNumber"></div>
</p>
""")
# JavaScript
javascript_template = Template("""
<script>
requirejs.config({
paths: {
qVisualization: "https://qvisualization.mybluemix.net/q-visualizations"
}
});
require(["qVisualization"], function(qVisualizations) {
qVisualizations.plotState("histogram_$divNumber",
"histogram",
$executions,
$options);
});
</script>
""")
# Process data and execute
div_number = str(time.time())
div_number = re.sub('[.]', '', div_number)
if not options:
options = {}
if 'slider' in options and options['slider'] is True:
options['slider'] = 1
else:
options['slider'] = 0
if 'show_legend' in options and options['show_legend'] is False:
options['show_legend'] = 0
else:
options['show_legend'] = 1
if 'number_to_keep' not in options:
options['number_to_keep'] = 0
data_to_plot = []
for execution in executions_results:
data = process_data(execution['data'], options['number_to_keep'])
data_to_plot.append({'data': data})
html = html_template.substitute({
'divNumber': div_number
})
javascript = javascript_template.substitute({
'divNumber': div_number,
'executions': data_to_plot,
'options': options
})
display(HTML(html + javascript))
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] | 2.35723 | 1,646 |
"""
Utility functions for formatting
"""
from qcelemental import constants as qcc
from qcelemental import periodictable as ptab
from automol import geom
# Conversion factors
BOHR2ANG = qcc.conversion_factor('bohr', 'angstrom')
def determine_struct_type(geo):
""" determines the linear string
"""
# Remove dummy atoms
geo = [coords for coords in geo
if coords[0] != 'X']
if geom.is_atom(geo):
struct_type = 'Monoatomic'
else:
if geom.is_linear(geo):
struct_type = 'Linear'
else:
struct_type = 'Nonlinear'
return struct_type
def format_coords(geo):
""" format the coords section
"""
# Get the number of atoms
natoms = len(geo)
# Get the geometry information
symbols = geom.symbols(geo)
coordinates = geom.coordinates(geo)
masses = [int(ptab.to_mass(symbol)) for symbol in symbols]
# Build a string with the formatted coordinates string
if geom.is_atom(geo):
geo_str = '{0:<4s}{1:<6d}'.format(symbols[0], masses[0])
else:
geo_str = ''
for symbol, mass, coords in zip(symbols, masses, coordinates):
coords = [coord * BOHR2ANG for coord in coords]
coords_str = '{0:>14.8f}{1:>14.8f}{2:>14.8f}'.format(
coords[0], coords[1], coords[2])
geo_str += '{0:<4s}{1:<6d}{2}\n'.format(
symbol, mass, coords_str)
# Remove final newline character from the string
geo_str = geo_str.rstrip()
return natoms, geo_str
def format_values_string(coord, values, conv_factor=1.0):
""" format the values string for the divsur.inp file
"""
if values:
values = ', '.join('{0:.3f}'.format(val * conv_factor)
for val in values)
values_string = '{0} = ({1})'.format(coord, values)
else:
values_string = ''
return values_string
def format_pivot_xyz_string(idx, npivot, xyzP, phi_dependence=False):
""" format the pivot point xyz
"""
assert npivot in (1, 2)
atom_idx = idx
if idx == 1:
d_idx = 1
else:
d_idx = 2
if npivot == 1:
x_val = 'x{0} = {1:.3f}'.format(atom_idx, xyzP[0])
y_val = ' y{0} = {1:.3f}'.format(atom_idx, xyzP[1])
z_val = ' z{0} = {1:.3f}'.format(atom_idx, xyzP[2])
pivot_xyz_string = (x_val + y_val + z_val)
elif npivot > 1 and not phi_dependence:
x_val1 = 'x{0} = {1:.3f} + d{2}*cos(t{0})'.format(
atom_idx, xyzP[0], d_idx)
y_val1 = ' y{0} = {1:.3f} + d{2}*sin(t{0})'.format(
atom_idx, xyzP[1], d_idx)
z_val1 = ' z{0} = 0.000'.format(
atom_idx)
x_val2 = 'x{0} = {1:.3f} - d{2}*cos(t{0})'.format(
atom_idx+1, xyzP[0], d_idx)
y_val2 = ' y{0} = {1:.3f} - d{2}*sin(t{0})'.format(
atom_idx+1, xyzP[1], d_idx)
z_val2 = ' z{0} = 0.000'.format(
atom_idx+1)
pivot_xyz_string = (x_val1 + y_val1 + z_val1 + '\n' +
x_val2 + y_val2 + z_val2)
else:
# Not sure if this implementation is any good
x_val1 = 'x{0} = {1:.3f} + d{2}*sin(p{0})*cos(t{0})'.format(
atom_idx, xyzP[0], d_idx)
y_val1 = ' y{0} = {1:.3f} + d{2}*sin(p{0})*sin(t{0})'.format(
atom_idx, xyzP[1], d_idx)
z_val1 = ' z{0} = {1:.3f} + d{2}*cos(p{0})'.format(
atom_idx, xyzP[2], d_idx)
x_val2 = 'x{0} = {1:.3f} - d{2}*sin(p{0})*cos(t{0})'.format(
atom_idx+1, xyzP[0], d_idx)
y_val2 = ' y{0} = {1:.3f} - d{2}*sin(p{0})*sin(t{0})'.format(
atom_idx+1, xyzP[1], d_idx)
z_val2 = ' z{0} = {1:.3f} + d{2}*cos(p{0})'.format(
atom_idx+1, xyzP[2], d_idx)
pivot_xyz_string = (x_val1 + y_val1 + z_val1 + '\n' +
x_val2 + y_val2 + z_val2)
return pivot_xyz_string
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] | 1.822222 | 2,160 |
import os
import os.path as osp
import random
import shutil
import sys
import json
import glob
import xml.etree.ElementTree as ET
import argparse
# voc_dir = '/data/xiaowenjie/nas/datasets/urpc2021/new_urpc/before/' #remember to modify the path
# voc_annotations = voc_dir + 'boxes/'
# txt_dir = voc_dir + 'txt/'
# coco_ann_dir = voc_dir + 'coco_ann/'
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = None
def get_categories(xml_dir):
"""Generate category name to id mapping from a list of xml files.
Arguments:
Returns:
dict -- category name to id mapping.
"""
classes_names = []
xml_files = os.listdir(xml_dir)
xml_files.sort()
for xml_file in xml_files:
print('read kinds:', xml_file)
xml_file = osp.join(xml_dir, xml_file)
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall("object"):
classes_names.append(member[0].text)
classes_names = list(set(classes_names))
classes_names.sort()
return {name: i for i, name in enumerate(classes_names)}
if __name__ == '__main__':
parer = argparse.ArgumentParser()
parer.add_argument('voc_ann')
parer.add_argument('txt_dir')
parer.add_argument('coco_save')
arg = parer.parse_args()
voc_to_coco(arg.voc_ann, arg.coco_save, arg.txt_dir)
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62,
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] | 2.402154 | 557 |
# Generated by Django 3.1.6 on 2021-02-17 03:27
from django.db import migrations, models
| [
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] | 2.84375 | 32 |
import pytz
from datetime import datetime
from django.test import TestCase
from robber import expect
from data.constants import MEDIA_TYPE_DOCUMENT
from data.factories import (
AllegationFactory,
AllegationCategoryFactory,
AttachmentFileFactory,
OfficerFactory,
OfficerAllegationFactory,
VictimFactory,
)
from social_graph.serializers import SocialGraphCRDetailSerializer
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] | 3.361345 | 119 |
from discord import Color
# TODO: Place holder for now - can use __init__.py once dependencies such as
# data.ROLE_MAPPINGS_TABLE and rally_api.BASE_URL have been removed
"""
Constants useful for data module
"""
ROLE_MAPPINGS_TABLE = "mappings"
CHANNEL_MAPPINGS_TABLE = "channel_mappings"
RALLY_CONNECTIONS_TABLE = "rally_connections"
CHANNEL_PREFIXES_TABLE = "channel_prefixes"
DEFAULT_COIN_TABLE = "default_coin"
CONFIG_TABLE = "config"
USERS_TABLE = "users"
USERS_TOKEN_TABLE = "users_token"
COMMANDS_TABLE = "commands"
COIN_PRICE_TABLE = "coin_price"
GUILD_ID_KEY = "guildId"
PRICE_KEY = "priceInUSD"
REQUIRED_BALANCE_KEY = "requiredBalance"
ROLE_NAME_KEY = "roleName"
CHANNEL_NAME_KEY = "channel"
DISCORD_ID_KEY = "discordId"
RALLY_ID_KEY = "rallyId"
BOT_TOKEN_KEY = "botToken"
BOT_INSTANCES_KEY = "botInstances"
OWNER_ID_KEY = "ownerId"
TIME_ADDED_KEY = "timeAdded"
BOT_NAME_KEY = "botName"
BOT_AVATAR_KEY = "botAvatar"
BOT_ID_KEY = "botId"
AVATAR_TIMEOUT_KEY = "avatarTimeout"
NAME_TIMEOUT_KEY = "nameTimeout"
BOT_ACTIVITY_TEXT_KEY = "botActivityText"
BOT_ACTIVITY_TYPE_KEY = "botActivityType"
USERNAME_KEY = "username"
DISCRIMINATOR_KEY = "discriminator"
GUILDS_KEY = "guilds"
TOKEN_KEY = "token"
TIME_CREATED_KEY = "timeCreated"
NAME_KEY = "name"
DESCRIPTION_KEY = "description"
PREFIX_KEY = "prefix"
CONFIG_NAME_KEY = "configName"
PURCHASE_MESSAGE_KEY = "purchaseMessage"
DONATE_MESSAGE_KEY = "donateMessage"
"""
Constants useful for rally_api module
"""
COIN_KIND_KEY = "coinKind"
COIN_BALANCE_KEY = "coinBalance"
BASE_URL = "https://api.rally.io/v1"
COINGECKO_API_URL = "https://api.coingecko.com/api/v3"
DISCORD_API_URL = "https://discord.com/api"
"""
Constants useful for update_cog module
"""
UPDATE_WAIT_TIME = 600
"""
Miscellaneous constants
"""
ERROR_COLOR = Color(0xFF0000)
SUCCESS_COLOR = Color(0x0000FF)
WARNING_COLOR = Color(0xFFFF00)
GREEN_COLOR = Color(0x00FF00)
RED_COLOR = Color(0xFF0000)
WHITE_COLOR = Color(0xFFFFFE)
DARK_RED_COLOR = Color(0x800000)
DARK_GREEN_COLOR = Color(0x008000)
PRICE_GRADIENT_DEPTH = 5
DEFAULT_DONATE_MESSAGE = "You can donate to by going to - Your donation helps grow and support the community and creator - Plus, there are 10 tiers of Donation badges to earn to show off your support!"
DEFAULT_PURCHASE_MESSAGE = "You can purchase at by using a Credit/Debit card or a number of different Crypto Currencies! Buying earns rewards, supports the community, and you can even get VIP Status! (hint: there’s a secret VIP room for users who hold over X # of ;)"
DEFAULT_BOT_AVATAR_URL = "https://rallybot.app/img/space.5424f731.png"
API_TAGS_METADATA = [
{"name": "channels", "description": "Coin channel mappings"},
{"name": "coin", "description": "Default coin in guild"},
{"name": "commands", "description": "Get list of all available bot commands"},
{"name": "prefix", "description": "Command prefix in guild"},
{"name": "roles", "description": "Coin role mappings"},
{"name": "coins", "description": "Coin price data"},
{"name": "bot_instance", "description": "Bot instances"},
{"name": "bot_avatar", "description": "Configure bot avatar"},
{"name": "bot_name", "description": "Configure bot name"},
] | [
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366,
11213,
1298,
366,
16934,
495,
10214,
1438,
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60
] | 2.690635 | 1,196 |
import sublime
import sublime_plugin
from .core.configurations import is_supported_syntax
from .core.protocol import Request, Range, DocumentHighlightKind
from .core.registry import session_for_view, client_from_session
from .core.documents import get_document_position
from .core.settings import settings, client_configs
from .core.views import range_to_region
try:
from typing import List, Dict, Optional
assert List and Dict and Optional
except ImportError:
pass
SUBLIME_WORD_MASK = 515
NO_HIGHLIGHT_SCOPES = 'comment, string'
_kind2name = {
DocumentHighlightKind.Unknown: "unknown",
DocumentHighlightKind.Text: "text",
DocumentHighlightKind.Read: "read",
DocumentHighlightKind.Write: "write"
}
| [
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] | 3.164502 | 231 |
#! /usr/bin/env python3
'''
Problem 39 - Project Euler
http://projecteuler.net/index.php?section=problems&id=039
'''
import math
if __name__ == '__main__':
x = 5
pytha = []
while True:
a,b,c = getPythagoreanTriplet(x)
if a+b+c > 1000:
break
else:
if a != 0:
print((a,b,c),a+b+c)
pytha.append(a+b+c)
x+=1
maxp = (0, 0)
for i in set(pytha):
c = pytha.count(i)
if c > maxp[0]:
maxp = (c, i)
print(maxp)
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8,
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220,
220,
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3601,
7,
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79,
8,
198
] | 1.672783 | 327 |
"""
Created on April 19, 2020
@author: Yi Wang
"""
import datetime
from mylib.geosfp.gc_process import geosfp_add_variables
#######################
# Start user parameters
#
gc_root_dir = '/Dedicated/jwang-data/GCDATA/GEOS_2x2.5/GEOS_FP/'
new_root_dir = '/Dedicated/jwang-data/GCDATA/GEOS_2x2.5/GEOS_FP_soil_T/'
startDate = '20140820'
endDate = '20140825'
#
# End user parameters
#####################
currDate = startDate
currDate_D = datetime.datetime.strptime(currDate, '%Y%m%d')
endDate_D = datetime.datetime.strptime(endDate, '%Y%m%d')
while currDate_D <= endDate_D:
# year, month, and day
currDate = str(currDate_D)
yyyy = currDate[0:4]
mm = currDate[5:7]
dd = currDate[8:10]
yyyymmdd = yyyy + mm + dd
print('------ process ' + yyyymmdd + ' ------')
gc_dir = gc_root_dir + yyyy + '/' + mm + '/'
new_dir = new_root_dir + yyyy + '/' + mm + '/'
geosfp_add_variables(gc_dir, new_dir, yyyymmdd)
# go to next day
currDate_D = currDate_D + datetime.timedelta(days=1)
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220,
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81,
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35,
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35,
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4818,
8079,
13,
16514,
276,
12514,
7,
12545,
28,
16,
8,
198
] | 2.197452 | 471 |
import random
import pytest
import pandas as pd
from datetime import datetime, date, timedelta
from ..conftest import (
FauxJIRA as JIRA,
FauxIssue as Issue,
FauxChange as Change,
FauxFieldValue as Value
)
from ..querymanager import QueryManager
from ..utils import extend_dict
from .progressreport import (
throughput_range_sampler,
update_team_sampler,
calculate_team_throughput,
calculate_epic_target,
find_epics,
update_story_counts,
forecast_to_complete,
Outcome,
Team,
Epic,
ProgressReportCalculator
)
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
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13,
69,
9602,
198,
198,
31,
9078,
9288,
13,
69,
9602,
198
] | 2.647303 | 241 |
from setuptools import find_packages, setup
from setuptools.command.install import install
from setuptools import setup, find_packages
from distutils.command.install import install as _install
import os
PROJECT_DIR = os.path.dirname(__file__)
DEPENDENCIES = open(os.path.join(PROJECT_DIR, 'requirements.txt')).readlines()
setup(
name='model_fkeywords',
version='0.1.0',
description='A Natural Language Processing Library',
author='Eneas Rodrigues',
license='MIT',
packages=find_packages(include=['api_model']),
install_requires=[d for d in DEPENDENCIES if '://' not in d],
python_requires='==3.7.13',
#TO-DO: Fix dependency links : not working with bdist_wheel
dependency_links = ["git+https://github.com/explosion/spacy-models/releases/download/pt_core_news_sm-3.2.0/pt_core_news_sm-3.2.0.tar.gz"],
tests_require=['pytest', 'parameterized'],
zip_safe=False
)
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] | 2.797546 | 326 |
stable = 'scala-native-0.4.0-2.12'
latest = 'scala-native-0.4.1-SNAPSHOT-2.12' | [
31284,
796,
705,
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] | 1.857143 | 42 |
#!/usr/bin/python
# Written by Thomas York
# Imports
from flask import Flask
from flask_hashing import Hashing
from config import Config
from flask_sqlalchemy import SQLAlchemy
# Flask Setup
app = Flask(__name__)
app.config.from_object(Config)
hashing = Hashing(app)
db = SQLAlchemy(app)
# Route setup
from app.routes import *
# Flask Setup
if __name__ == "__main__":
app.run()
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198,
220,
220,
220,
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3419,
198
] | 2.954198 | 131 |
"""
Task 1
@author: Alexandr Mazanik
"""
import time
import turtle
main()
time.sleep(2)
| [
37811,
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2435,
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7,
17,
8,
198
] | 2.666667 | 36 |
#%%
import IPython.display as ipyd
#%%
from IPython.display import Markdown
ideas = open('./design/ideas.md')
output = ideas.read()
Markdown(output)
#%% [markdown]
# ### Problem type description template
#%% [markdown]
# ### Problem type title
#
# Description
#
# #### Problem generation
#
# Outline of algorithm for generating problem
#
# #### Problem template
#
# Form the problem takes
#
# #### Solution generation
#
# Outline of algorithm for generating solution
#
# #### Solution template
#
# Form the solution takes
#%% [markdown]
# ### Given $f(x)$, find $f(-x)$
#%%
from sympy import symbols, Poly, Rational, latex
from numpy import random, around, arange
from numpy.random import randint, rand
from problem import create_full_text_problem
from testbed_utils import render_debug_problem
from util import fmath
render_debug_problem(polynomial_function_find_negative())
#%% [markdown]
# ### Complete the square
#
# Generate an equation from a perfect square, then remove the term with the lowest degree and add some other number. Not sure what rule should be used when generating this other number.
#
# #### Problem generation
#
# 1. Generate $c$ for $(x + c)$ within some predetermined bounds. $c$ can be negative.
# 2. Evaluate $(x + c)^2$ for an output of $x^2+xc+c^2$.
# 3. Optionally multiply this by an integer within some bounds, so an extra step has to be taken to factor when solving, for an output of $ax^2+axc+ac^2$
# 4. Remove $ac^2$ for an output of $ax^2+axc)$.
# 5. Add another number $n$ and make it equal zero for $ax^2+axc+n=0$
#
# $ax^2+axc+n=0$
#
# #### Problem template
#
# $ax^2+axc+n=0$
#
# Factor by completing the square.
#
# #### Solution generation
#
# $a(x+c)+n-ac^2=0$
#
# Solution should be in vertex form ($h$ and $k$ are only used because they haven't been defined in the algorithm. They serve only to show the shape of the expression):
#
# $a(x-h)^2+k=0$
#
# #### Solution template
#
# $a(x-h)^2+k=0$
#%%
from sympy import Poly, Rational, expand, simplify, latex, symbols
from numpy import random, around, arange
from numpy.random import rand
render_debug_problem(quadratic_function_find_vertex_intercept_form())
#%% [markdown]
# ### Finding vertex given quadratic
#
# $-\frac{b}{2a}$
#
# Quadratics generated by or provided to this type don't need to be neatly factored or have real x-intercepts.
#%%
from sympy import symbols, Rational, UnevaluatedExpr, Array
import numpy as np
from numpy import random
from math import gcd
from functools import reduce
coeff_bounds = (1, 10)
a, b, c = (gen_coeff(), gen_coeff(), gen_coeff())
x = symbols('x')
gcd = reduce(gcd, (a, b, c))
# Divide coefficients by gcd
a, b, c = Array(np.array([a, b, c])/gcd).applyfunc(lambda coeff: Rational(coeff))
factored_f = a*x**2+b*x+c
latex_expr = latex(factored_f)
f = factored_f*gcd
problem_content = f'{gcd}({latex_expr})' if gcd != 1 else latex_expr
vertex_x = Rational(-b, 2*a)
vertex_y = f.subs(x, vertex_x)
ipyd.Latex(f'problem: ${problem_content}$ solution: ${(vertex_x, vertex_y)}$, gcd:${gcd}$ {(a, b, c)}')
#%% [markdown]
# ### Determining possible number of real roots of quadratic function
#
# By analyzing discriminant
#
# 2, 1 (repeated), or 0
#
# **We need a method to generate a quadratic function that has an equal likelihood of having 0, 1, or 2 roots**
#%%
#
#%% [markdown]
# ### List transformations
#
# We're just doing this with $x^2$ right now, but there's not reason we couldn't do it with something else.
#
# #### Examples
#
# $x^2+1$: Up by 1
#
# $x^2-2$: Down by 2
#
# $(x+5)^2+1/2$: Left by 5, up by 1/2
#
# $5(x+2)^2+2$: Left by 2, stretch vertically by 5 (compress horizontally by 1/5), up by 2
#%%
# STUB: Do later
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] | 2.758315 | 1,353 |
import unittest
from signals.generators.ios.objc.parameters import ObjCParameter
from signals.parser.fields import Field
from signals.parser.schema import DataObject
from tests.utils import create_dynamic_schema
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"""Converts 1 hz RSA from a bedrock hazard map to soil values
using the NEHRP amplifcation factors
Reference: Borchedt 1994
RSA is then converted to MMI using the formuala of Atkinson and Kaka (2006)
Jonathan Griffin, AIFDR January 2011
"""
import sys, os
import numpy as np
from scipy.interpolate import interp1d
from scipy.stats import norm
from RSA2MMI import rsa2mmi9
def hazmap2amp(RSA1, NEHRP_class, period=1.0):
"""Function to amplify RSA amplifcation based on BS30 values and the NEHRP
amplification factors
"""
function = Amp_fns()
RSA_amp_list = []
for i in range(len(RSA1)):
if period <= 0.3: # Assumed value, not clear from paper
RSA_amp = function.short_period_dict[NEHRP_class[i]](RSA1[i])*RSA1[i]
RSA_amp_list.append(RSA_amp)
else:
RSA_amp = function.mid_period_dict[NEHRP_class[i]](RSA1[i])*RSA1[i]
RSA_amp_list.append(RSA_amp)
return RSA_amp_list
def read_data(infile):
"""Read data into numpy array
"""
f_in=open(infile, 'r')
header = f_in.readline()
RSA1 = []
vs30 = []
for line in f_in.readlines():
row = line.split(',')
RSA1.append(float(row[2]))
vs30.append(float(row[3]))
f_in.close()
return RSA1, vs30
def write_data(infile, outfile, NEHRP_class, RSA_amp_list, MMI):
"""Write output data to file
"""
f_in = open(infile, 'r')
header = f_in.readline()
f_out = open(outfile, 'w')
i = 0
# Write header
f_out.write('LONGITUDE,LATITUDE,BEDROCK_RSA1,VS30,SITE_CLASS,SOIL_RSA,MMI\n')
for line in f_in.readlines():
row = line.rstrip('\n').rstrip('\r')
row = row + ',' + NEHRP_class[i] + ',' + str(RSA_amp_list[i]) + ','\
+ str(MMI[i]) +'\n'
f_out.write(row)
i+=1
f_in.close()
f_out.close()
if __name__ == '__main__':
haz_NEHRP_class_file = sys.argv[1]
RSA1, vs30 = read_data(haz_NEHRP_class_file)
NEHRP_class = vs30_to_NEHRP_class(vs30)
RSA_amp_list = hazmap2amp(RSA1, NEHRP_class)
MMI = rsa2mmi9(RSA_amp_list, period = 1.0)
outfile = haz_NEHRP_class_file[:-4] + '_MMI.csv'
write_data(haz_NEHRP_class_file, outfile, NEHRP_class, RSA_amp_list, MMI)
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] | 2.00085 | 1,177 |
import json
import scrapy
from kingfisher_scrapy.base_spider import CompressedFileSpider
from kingfisher_scrapy.util import components, handle_http_error
class ArgentinaBuenosAires(CompressedFileSpider):
"""
Domain
Ciudad de Buenos Aires
API documentation
https://data.buenosaires.gob.ar/acerca/ckan
Bulk download documentation
https://data.buenosaires.gob.ar/dataset/buenos-aires-compras/archivo/2a3d077c-71b6-4ba7-8924-f3e38cf1b8fc
"""
name = 'argentina_buenos_aires'
data_type = 'release_package'
compressed_file_format = 'release_package'
# the data list service takes too long to be downloaded, so we increase the download timeout
download_timeout = 1000
@handle_http_error
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import requests, logging, json, sys
from Lib.GCS.http_calls import EdgeGridHttpCaller
from random import randint
from akamai.edgegrid import EdgeGridAuth
from Lib.GCS.config import EdgeGridConfig
import urllib
import socket
import subprocess
import os
import dns.resolver
import functools
class Wrapper:
"""
A simple wrapper for the API calls. Each call maps to a API URL and no tampering of the results is done within the class.
"""
def getGroups(self):
"""Return the group and contract details based on PAPI credentials.
Keyword arguments:
None
Return type:
List of groups
"""
if self.account:
params = 'accountSwitchKey={0}'.format(self.account)
else:
params = None
return self.httpCaller.getResult('/papi/v1/groups/',parameters=params)
def getContractNames(self):
"""
Returns the contract id and contract name for a given contract Id
Keyword arguments:
None
Return parameter:
Hash of contractId and contract name. Same as the output from the raw API call to "/papi/v1/groups/"
"""
if self.account:
params = 'accountSwitchKey={0}'.format(self.account)
else:
params = None
return self.httpCaller.getResult('/papi/v1/contracts/',parameters=params)
def getProducts(self, contractId):
"""
Returns the contract information for the contractId
Keyword arguments:
contractId
Return parameter:
Contract details
"""
if self.account:
params = 'accountSwitchKey={0}&contractId={1}'.format(self.account,contractId)
else:
params = 'contractId={0}'.format(contractId)
return self.httpCaller.getResult('/papi/v1/products/',parameters=params)
def getCPCodes(self, groupId, contractId):
"""
Return the CP Code details for a groupId-contractId combination
Keyword arguments:
groupId
contractId
Return parameter:
List of CP Codes
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/cpcodes/',parameters=params)
def getEdgeHostNames(self,groupId, contractId,version=None):
"""
Returns the edgehostnames by groupId. If all groups for an account are passed to this function, it will return all the Edge host names associated with the account.
Keyword arguments:
groupId
contractId
Return parameter:
List of edge hostnames
"""
if version == 'hapi':
endpoint = '/hapi/v1/edge-hostnames'
if self.account:
params = 'accountSwitchKey={0}'.format(self.account)
else:
endpoint = '/papi/v1/edgehostnames/'
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult(endpoint,parameters=params)
def getAppSecConfigurations(self):
"""
Keyword arguments:
None
Return parameter:
Lists available versions for the specified security configuration
https://developer.akamai.com/api/cloud_security/application_security/v1.html#getconfigurations
"""
endpoint = '/appsec/v1/configs'
params = None
if self.account:
params = 'accountSwitchKey={0}'.format(self.account)
return self.httpCaller.getResult(endpoint,parameters=params)
def getProperties(self, groupId, contractId):
"""
Returns the names of properties associated with a group. If all groups for an account are passed to this function, it will return all the properties associated with the account.
Keyword arguments:
groupId
contractId
Return parameter:
List of properties
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/',parameters=params)
def getPropertyVersions(self, propertyId, groupId, contractId):
"""
Returns the property versions. This can be used to find the audit trail details for a configuration
Keyword arguments:
propertId
groupId
contractId
Return parameters:
List of property versions
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/{0}/versions/'.format(propertyId),parameters=params)
def getavailableBehavior(self, propertyId,propertyVersion, contractId, groupId ):
"""
Returns a lists of set of behaviors you may apply within a property version’s rules. The available set is determined by the product under which you created the property,
and any additional modules enabled under your account.
Keyword arguments:
propertId
propertyVersion
contractId
groupId
Return parameters:
List of behaviors for
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/{0}/versions/{1}/available-behaviors'.format(propertyId,propertyVersion),parameters=params)
def getVersionDetails(self, propertyId, groupId, contractId, propertyVersion=1):
"""
Returns information about a specific property version
Keyword arguments:
propertyVersion: Default version is 1, the first version.
propertId
groupId
contractId
Return parameters:
Details on a specific property version
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/{0}/versions/{1}'.format(propertyId,propertyVersion),parameters=params)
def getLatestVersionDetails(self, propertyId, groupId, contractId):
"""
Returns information about a specific property version
Keyword arguments:
propertyVersion: Default version is 1, the first version.
propertId
groupId
contractId
Return parameters:
Details on a specific property version
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/latest/versions/{0}'.format(propertyId),parameters=params)
def getConfigRuleTree(self, propertyId, versionNumber, groupId, contractId):
"""
Returns all the Property Manager rule details. It will not retrieve advanced code.
Keyword arguments:
propertyId
versionNumber - Specific version for which we need the rules
groupId
contractId
Return parameters:
Configuration tree rule for a given configuration
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/{0}/versions/{1}/rules/'.format(propertyId,versionNumber),parameters=params)
def getPropertyHostNames(self, propertyId, versionNumber, groupId, contractId):
"""
Returns the host names associated with a configuration.
Keyword arguments:
propertyId
versionNumber - Specific version for which we need the rules
groupId
contractId
Return parameters:
List of host names belonging to the configuration
"""
if self.account:
params = 'accountSwitchKey={0}&groupId={1}&contractId={2}'.format(self.account,groupId,contractId)
else:
params = 'groupId={0}&contractId={1}'.format(groupId,contractId)
return self.httpCaller.getResult('/papi/v1/properties/{0}/versions/{1}/hostnames/'.format(propertyId,versionNumber),parameters=params)
def getEnrollements(self, contractId):
"""
Returns the enrollements associated with a contractId.
Keyword arguments:
contractId
Return parameters:
List of enrollments associated with a contractId
"""
if self.account:
params = 'accountSwitchKey={0}&contractId={1}'.format(self.account,contractId)
else:
params = 'contractId={0}'.format(contractId)
return self.httpCaller.getResult('/cps/v2/enrollments',parameters=params, headers='cps')
@functools.lru_cache()
def getCNAME(self, hostname):
"""
Runs a dig command to find the CNAME for a given host name.
If a CNAME is found, it returns it. Else returns a None.
Keyword arguments:
hostname: The host name for which we need the CNAME
"""
try:
return (dns.resolver.query(hostname, 'CNAME')).response.answer[0][0]
except (dns.resolver.NXDOMAIN, dns.resolver.NoAnswer):
return None
@functools.lru_cache()
@functools.lru_cache()
@functools.lru_cache()
@functools.lru_cache()
def checkIfCdnIP(self, ipaddress):
"""
Returns if an IP address blongs to Akamai or if it is not an Akamai IP. It uses the OS command "host" on systems
that supports it. Else, it uses the command nslookup.
Keyword arguments:
ipaddress
Return parameters:
A boolean flag based on whether the call returns a true or a false.
"""
result = False
try:
if os.name =="nt":
resp = str ( subprocess.check_output(['nslookup',ipaddress]) )
print(resp)
if resp.find('akamai'):
result=True
else:
resp = str( subprocess.check_output(['host', ipaddress]) )
print (resp)
resp = resp.split(' ')
if len(resp) >=5:
if resp[4].find('akamai') > -1:
result=True
except subprocess.CalledProcessError:
pass
return result
@functools.lru_cache()
if __name__=="__main__":
w = Wrapper()
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] | 2.788568 | 3,604 |
import time
from urllib import parse as url_parse
import pytest
from page_get import (
get_cont_of_weibo, get_page, get_profile)
from tasks.comment import crawl_comment_by_page
from tasks.repost import crawl_repost_by_page
from tests import REQUEST_INTERNAL
HOME_AJAX_URL = 'http://weibo.com/p/aj/v6/mblog/mbloglist?ajwvr=6&domain={}&pagebar={}&is_ori=1&id={}{}&page={}' \
'&pre_page={}&__rnd={}'
@pytest.mark.parametrize(
'mid', ['4158010915826421', '4159555900113636']
)
@pytest.mark.parametrize(
'uid, expect', [
('1371731565', 'Miss'),
('1642351362', 'angelababy')
])
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628
] | 2.235714 | 280 |
from glmnet import ElasticNet
import io
import numpy as np
import pandas as pd
import requests
from sklearn.preprocessing import StandardScaler
# Load data
url = 'https://raw.githubusercontent.com/CCS-Lab/easyml/master/Python/datasets/prostate.csv'
s = requests.get(url).content
prostate = pd.read_csv(io.StringIO(s.decode('utf-8')))
# Generate coefficients from data by hand
X, y = prostate.drop('lpsa', axis=1).values, prostate['lpsa'].values
sclr = StandardScaler()
X_preprocessed = sclr.fit_transform(X)
# no random state
coefficients = []
for i in range(10):
model = ElasticNet(alpha=1, standardize=False, cut_point=0.0, n_lambda=200)
print(id(model))
model.fit(X_preprocessed, y)
coefficients.append(np.asarray(model.coef_))
print(coefficients)
# seed set at outer level
np.random.seed(43210)
coefficients = []
for i in range(10):
model = ElasticNet(alpha=1, standardize=False, cut_point=0.0, n_lambda=200)
print(id(model))
model.fit(X_preprocessed, y)
coefficients.append(np.asarray(model.coef_))
print(coefficients)
# seed set at inner level
coefficients = []
for i in range(10):
np.random.seed(43210)
model = ElasticNet(alpha=1, standardize=False, cut_point=0.0, n_lambda=200)
print(id(model))
model.fit(X_preprocessed, y)
coefficients.append(np.asarray(model.coef_))
print(coefficients)
# seed set at function level
coefficients = []
for i in range(10):
random_state = np.random.RandomState(i)
model = ElasticNet(alpha=1, standardize=False, cut_point=0.0, n_lambda=200, random_state=random_state)
print(id(model))
model.fit(X_preprocessed, y)
coefficients.append(np.asarray(model.coef_))
print(coefficients)
coefficients = []
random_state = np.random.RandomState(43210)
for i in range(10):
model = ElasticNet(alpha=1, standardize=False, cut_point=0.0, n_lambda=200, random_state=random_state)
print(id(model))
model.fit(X_preprocessed, y)
coefficients.append(np.asarray(model.coef_))
print(coefficients)
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] | 2.655218 | 757 |
#!/usr/bin/env python3
# This script compares AppGene files for similiarty
# Run this script in terminal / command line to see the usage of arguments.
import os
import argparse
from sklearn.feature_extraction.text import HashingVectorizer
import json
import re
from sklearn.metrics.pairwise import cosine_similarity
import numpy
import subprocess
import time
from pprint import pprint
import itertools
import gc
import random
import hashlib
hashFeatureNumber = 2 ** 16
nGramRange = (16, 16)
tmpDir = os.path.curdir
jsonStopCharRe = re.compile(",|:|,|{|}|\"")
def getAllFilesOfExtension(rootDir, extension):
"""Traverse a directory tree and find all the files with a specified extension
Args:
rootDir: The directory to traverse
extension: The extension
Returns:
A list of files
"""
fileList = []
for (dirPath, dirNames, fileNames) in os.walk(rootDir):
for baseName in fileNames:
if baseName.endswith(extension):
fileList.append((dirPath, baseName))
return fileList
def customTokenizer(input):
"""Tokenise transformed Smali instructions.
Args:
input: Lines of transformed instructions
Returns:
An array of transformed instructions
"""
return re.split("\n", input)
def getHashVector(content):
"""Get hash vector of transformed Smali instructions.
Args:
input: Lines of transformed instructions
Returns:
Hash vector
"""
hashVectorizer = HashingVectorizer(n_features=hashFeatureNumber, tokenizer=customTokenizer,
ngram_range=nGramRange)
return hashVectorizer.transform([content])
def loadJSONFromFile(filename):
"""A generic function to read a JSON file.
Args:
filename: The full filename for the JSON file
Returns:
The object loaded from JSON
"""
jsonFile = open(filename, "r")
theObject = json.load(jsonFile)
jsonFile.close()
return theObject
def writeTextToBufferDir(baseFilename, text):
"""Write text to a file in the buffer directory.
Args:
baseFilename: Base filename of the target file
text: The text to be written to the file
Returns:
Full filename of the target file
"""
bufferFilename = os.path.join(tmpDir, baseFilename)
bufferFile = open(bufferFilename, "w")
bufferFile.write(text)
bufferFile.close()
return bufferFilename
def getTextSHA256(plaintext):
"""Get the SHA256 hash value of plaintext.
Args:
plaintext: The plaintext
Returns:
Hash value represneted in Hex string
"""
textHash = hashlib.sha256(plaintext)
return "%s" % textHash.hexdigest()
def getHashInArray(arr):
"""Get the SHA256 hash values of elements in an array.
Args:
arr: The array
Returns:
An array of hash values (represneted in Hex string)
"""
return [getTextSHA256(t.encode("utf-8")) for t in arr]
def diffContentPairAsFiles(file1Content, file2Content):
"""Use the operating system's wdiff utility to compare two files.
Args:
file1Content: Content of the first file
file2Content: Content of the second file
Returns:
Result object with properties: union, intersection and ratio
"""
diffResult = {"ratio": float(0), "intersection": float(0), "union": float(0)}
try:
tmpFilename1 = writeTextToBufferDir("_diff_tmp1_{}".format(time.time()), file1Content)
tmpFilename2 = writeTextToBufferDir("_diff_tmp2_{}".format(time.time()), file2Content)
diffOuput = subprocess.run("wdiff -s -1 -2 -3 {} {}".format(tmpFilename1, tmpFilename2), check=False,
stdout=subprocess.PIPE, shell=True).stdout.decode("utf-8")
# Parse the output of wdiff
diffOuput = diffOuput.replace(" word ", " words ").split("\n")
file1ResultSegments = diffOuput[0].split(" ")
file2ResultSegments = diffOuput[1].split(" ")
words = int(file1ResultSegments[file1ResultSegments.index("words") - 1])
if words > 0:
common = float(file1ResultSegments[file1ResultSegments.index("common") - 2])
file1Total = float(file1ResultSegments[file1ResultSegments.index("words") - 1])
file2Total = float(file2ResultSegments[file2ResultSegments.index("words") - 1])
diffResult["union"] = (file1Total + file2Total - common)
diffResult["intersection"] = common
diffResult["ratio"] = float(diffResult["intersection"]) / float(diffResult["union"])
os.remove(tmpFilename1)
os.remove(tmpFilename2)
except:
print("pair diff failed - {} {}".format(tmpFilename1, tmpFilename2))
gc.collect(2)
return diffResult
def diffMarkupPairs(content1, content2):
"""Compare two markup (XML) files by their common attribute-value pairs and common values.
Args:
content1: Extracted attribute-value pairs
content2: Extracted values
Returns:
Result object with properties: byAttributeValuePair and byValue (both of the same structure as the output from diffContentPairAsFiles)
"""
pairResult = {"byAttributeValuePair": None, "byValue": None}
content1 = jsonStopCharRe.sub("", content1)
content2 = jsonStopCharRe.sub("", content2)
if not ((not content1) and (not content2)):
pairResult["byAttributeValuePair"] = diffContentPairAsFiles(content1.replace(" ", "_").replace("\n", " "),
content2.replace(" ", "_").replace("\n", " "))
pairResult["byValue"] = diffContentPairAsFiles(content1.replace("\n", " "),
content2.replace("\n", " "))
return pairResult
def getJaccardSimilarity(arr1, arr2):
"""Get the Jaccard similarity of two arrays
Args:
arr1: The first array
arr1: The second array
Returns:
Result object with properties: union, intersection and ratio
"""
jaccardSimResult = {"ratio": float(0),
"intersection": float(len(numpy.intersect1d(arr1, arr2, assume_unique=True))),
"union": float(len(numpy.union1d(arr1, arr2)))}
if jaccardSimResult["union"] > float(0):
jaccardSimResult["ratio"] = jaccardSimResult["intersection"] / jaccardSimResult["union"]
return jaccardSimResult
def getEmptyJaccardResult():
"""Get as empty Jaccard similarity result object.
Returns:
Result object with properties: union, intersection and ratio (all values are 0)
"""
return {"ratio": 0, "intersection": 0, "union": 0}
def compareGenes(gene1, gene2):
"""Compare a pair of AppGene objects
Args:
gene1: The first AppGene object
gene2: The second AppGene object
Returns:
Result object with properties:
smali:
cosineSimilarity: The cosine similarity of the hash vectors of AppGene pairs
byLine: The union, intersection and ratio (Jaccard Similarity) of transformed Smali instructions by line
1-gram: The union, intersection and ratio (Jaccard Similarity) of transformed Smali instructions by opcode and argument
namespace: The union, intersection and ratio (Jaccard Similarity) of namespaces (code package names in full)
markup:
names: The union, intersection and ratio (Jaccard Similarity) of attribute names in markup (XML) files
values: The union, intersection and ratio (Jaccard Similarity) of attribute values in markup (XML) files
media:
exactDuplicates: The union, intersection and ratio (Jaccard Similarity) of pHash (perceptual hash) values of image files
nearDuplicates: The union, intersection and ratio (Jaccard Similarity) of SHA256 values of all other resource files
permission:
android: The union, intersection and ratio (Jaccard Similarity) of Android permissions
non-android: The union, intersection and ratio (Jaccard Similarity) of custom permissions
"""
result = {"smali": {}, "namespace": {}, "markup": {}, "media": {}, "permission": {}}
try:
print("Comparing vectorised code ...")
result["smali"]["cosineSimilarity"] = cosine_similarity(gene1["features"]["smaliVector"],
gene2["features"]["smaliVector"]).item(0)
except:
print("Failed to compare vectorised code")
result["smali"]["cosineSimilarity"] = 0
try:
print("Comparing disassembled code ...")
result["smali"]["byLine"] = diffContentPairAsFiles(gene1["smali"].replace(" ", "_"),
gene2["smali"].replace(" ", "_"))
result["smali"]["1-gram"] = diffContentPairAsFiles(gene1["smali"].replace("\n", " "),
gene2["smali"].replace("\n", " "))
except:
print("Failed to compare disassembled code")
result["smali"]["byLine"] = getEmptyJaccardResult()
result["smali"]["1-gram"] = getEmptyJaccardResult()
try:
print("Comparing permissions ...")
result["permission"] = {
"android": getJaccardSimilarity([pf for pf in gene1["permission-feature"] if pf.startswith("android.")],
[pf for pf in gene2["permission-feature"] if pf.startswith("android.")]),
"non-android": getJaccardSimilarity(
[pf for pf in gene1["permission-feature"] if not pf.startswith("android.")],
[pf for pf in gene2["permission-feature"] if not pf.startswith("android.")])}
except:
print("Failed to compare permissions")
result["permission"]["android"] = getEmptyJaccardResult()
result["permission"]["non-android"] = getEmptyJaccardResult()
try:
print("Comparing namespaces ...")
result["namespace"] = getJaccardSimilarity(gene1["namespace"], gene2["namespace"])
except:
print("Failed to compare namespaces")
result["namespace"] = getEmptyJaccardResult()
try:
print("Comparing media files ...")
result["media"]["exactDuplicates"] = getJaccardSimilarity(gene1["features"]["media_sha256"],
gene2["features"]["media_sha256"])
result["media"]["nearDuplicates"] = getJaccardSimilarity(gene1["features"]["media_phash"],
gene2["features"]["media_phash"])
except:
print("Failed to media files")
result["media"]["exactDuplicates"] = getEmptyJaccardResult()
result["media"]["nearDuplicates"] = getEmptyJaccardResult()
try:
print("Comparing markup files ...")
result["markup"]["names"] = getJaccardSimilarity(getHashInArray(gene1["markup"]["names"]),
getHashInArray(gene2["markup"]["names"]))
result["markup"]["values"] = getJaccardSimilarity(getHashInArray(gene1["markup"]["values"]),
getHashInArray(gene2["markup"]["values"]))
except:
print("Failed to markup files")
result["markup"]["names"] = getEmptyJaccardResult()
result["markup"]["values"] = getEmptyJaccardResult()
return result
def computeFeatures(geneObject):
"""Vectorise and Smali code and hash the resource files.
Args:
geneObject: AppGene object
Returns:
The AppGene object with a new property "features" with properties:
smaliVector: The hash vector of transformed Smali code
media_phash: List of pHash (perceptual hash) values of image files
media_sha256: List of SHA256 values of all other resource files
"""
geneObject["features"] = {}
geneObject["features"]["smaliVector"] = getHashVector(geneObject["smali"])
geneObject["features"]["media_phash"] = list(geneObject["media"]["phash"].keys())
geneObject["features"]["media_sha256"] = list(geneObject["media"]["sha256"].keys())
return geneObject
def dumpObjectAsJson(obj, filename):
"""Write/dump an object to a JSON file.
Args:
obj: The object to be dumped
filename: The full filename of the target file
Returns:
None
"""
outputFileHandler = open(filename, "w")
json.dump(obj, outputFileHandler, indent=4, ensure_ascii=False, sort_keys=True)
outputFileHandler.close()
def comparePair(geneFilename1, geneFilename2):
"""Load two AppGenes from JSON files and pass them to the compareGenes function.
Args:
geneFilename1: The object to be dumped
geneFilename2: The full filename for the target file
Returns:
The same result object as the compareGenes function, supplemented by a new property "pair": an array containing the ids and version codes of the two AppGenes (see the code below for the structure).
"""
result = None
print("About to process {} and {}".format(geneFilename1, geneFilename2))
try:
gene1 = loadJSONFromFile(geneFilename1)
gene1 = computeFeatures(gene1)
gene2 = loadJSONFromFile(geneFilename2)
gene2 = computeFeatures(gene2)
print("Comparing {} to {}".format(gene1["appID"], gene2["appID"]))
result = compareGenes(gene1, gene2)
result["pair"] = []
result["pair"].extend([{"id": gene1["appID"],
"version": gene1["appVersion"]},
{"id": gene2["appID"],
"version": gene2["appVersion"]}])
except:
print("Comparison failed")
gc.collect()
return result
def compareAppGenesInDir(geneFileList, shuffle):
"""Generate all combinations of AppGene pairs of AppGene files on a list and then compare them.
Args:
geneFileList: List of full filenames for AppGene files
shuffle: (bool) Whether the combinations are shuffled
Returns:
A list of results of compared pairs. (See comparePair and compareGenes functions for the strucutre of the results object for each pair.)
"""
result = []
pairList = list(itertools.combinations(geneFileList, 2))
if shuffle:
random.shuffle(pairList)
for genePair in pairList:
pairResult = comparePair(genePair[0], genePair[1])
if pairResult is not None:
result.append(pairResult)
dumpObjectAsJson(pairResult, os.path.join(tmpDir, "_tmp_pair_result_{}".format(time.time())))
else:
print("Failed to compare pair {} {}".format(genePair[0], genePair[1]))
return result
if __name__ == '__main__':
# The usage of arguemnts is self-explanatory as follows
argParser = argparse.ArgumentParser()
argParser.add_argument("--mode", choices=["single-pair", "all-pairs"], help="Mode of comparison", required=True)
argParser.add_argument("--appgene1", help="AppGene file 1 in single-pair mode")
argParser.add_argument("--appgene2", help="AppGene file 2 in single-pair mode")
argParser.add_argument("--geneDir", help="Directory containing AppGene files in all-pairs mode")
argParser.add_argument("--bufferDir", help="Directory for temporary files",
default=os.getenv("COMPARE_TEMP_DIR", os.path.curdir))
argParser.add_argument("--shuffle", help="Shuffle the order of AppGene pairs (applicable to all-pairs mode only)",
action="store_true")
argParser.add_argument("--outputFile", help="Result file to be saved", required=True)
args = argParser.parse_args()
tmpDir = os.path.realpath(args.bufferDir)
outputResult = {}
if args.mode == "single-pair":
outputResult = comparePair(os.path.realpath(args.appgene1), os.path.realpath(args.appgene2))
elif args.mode == "all-pairs":
geneRootDir = os.path.realpath(args.geneDir)
allAppGeneFiles = list(
os.path.join(geneFile[0], geneFile[1]) for geneFile in getAllFilesOfExtension(geneRootDir, ".appgene"))
outputResult = compareAppGenesInDir(allAppGeneFiles, args.shuffle)
pprint(outputResult)
dumpObjectAsJson(outputResult, args.outputFile)
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] | 2.422909 | 6,862 |
from corehq.const import SERVER_DATETIME_FORMAT
from corehq.util.timezones.conversions import PhoneTime, ServerTime
from corehq.util.timezones.utils import get_timezone
| [
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] | 3.035714 | 56 |
from typing import Dict
import pandas as pd
import warnings
from concurrent.futures import ThreadPoolExecutor, TimeoutError
from capiq_excel.downloader.timeout import TimeoutWrapper
from capiq_excel.fileops import get_path_of_failed_folder_add_if_necessary, move_file_to_failed_folder, get_path_of_additional_failed_folder_add_if_necessary
from capiq_excel.workbook.populate.main import populate_capiq_for_file
from exceldriver.tools import _start_excel_with_addins_and_attach, _get_excel_running_workbook, _restart_excel_with_addins_and_attach, NoExcelWorkbookException
from processfiles.files import FileProcessTracker
def populate_all_files_in_folder(folder, financial_data_items_dict: Dict[str, str],
market_data_items_dict: Dict[str, str], restart=True, timeout=240,
run_failed=False,):
"""
"""
_validate_populate_inputs(folder, restart, run_failed)
excel = _start_excel_with_addins_and_attach()
failed_folder = get_path_of_failed_folder_add_if_necessary(folder)
if run_failed:
# Set main folder as 'failed', then set failed folder as another failed folder inside the original
folder = failed_folder
failed_folder = get_path_of_additional_failed_folder_add_if_necessary(folder)
file_tracker = FileProcessTracker(folder=folder, restart=restart, file_types=('xlsx',))
with ThreadPoolExecutor(max_workers=1) as e:
for i, file in enumerate(file_tracker.file_generator()):
excel, successful = _try_to_get_result_if_fail_restart_excel(
e,
i,
file,
excel,
financial_data_items_dict=financial_data_items_dict,
market_data_items_dict=market_data_items_dict
)
if not successful:
move_file_to_failed_folder(file, failed_folder)
### Functions below to assist with multiprocessing/timeout handling
## END TEMP | [
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220,
220,
220,
220,
220,
220,
1445,
62,
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62,
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62,
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7,
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8,
628,
198,
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351,
18540,
305,
919,
278,
14,
48678,
9041,
628,
198,
2235,
23578,
309,
39494
] | 2.455665 | 812 |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
from requests import get, post
from bs4 import BeautifulSoup
from random import randint | [
2,
48443,
14629,
14,
8800,
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21015,
198,
2,
532,
9,
12,
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6738,
4738,
1330,
43720,
600
] | 3.093023 | 43 |
print('This program finds the number of times a letter appears in a text')
word = input('Enter a text: ')
char = input('Enter letter to find its number of times: ')
count(word,char) | [
4798,
10786,
1212,
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262,
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286,
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663,
1271,
286,
1661,
25,
705,
8,
198,
9127,
7,
4775,
11,
10641,
8
] | 3.62 | 50 |
import numpy as np
from tangles.tree_tangles import get_hard_predictions
from tangles.loading import load_GMM
from sklearn.metrics import pairwise_distances
def test_simple_gaussians_ab():
"""
AB test to make sure the tangles algorithm still does the exact same thing.
"""
X, _ = load_GMM([20, 20], np.array([[0, 0], [1, 1]]), [0.7, 0.7], 10)
idx_a = [0, 1, 5, 12, 20, 23, 34]
idx_b = [11, 21, 34, 35, 22, 7]
cuts = []
for a, b in zip(idx_a, idx_b):
cut = pivot_cut(X, X[a, :], X[b, :])
cuts.append(cut)
pred = get_hard_predictions(np.concatenate(cuts).T, 10)
res_ab = np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0])
assert np.all(pred == res_ab)
def test_simple_gaussians_performance():
"""
Makes sure that the tangles algorithm achieves 100% performance on a simple gaussian,
even if the AB test is not passed (so we might have some small implementation change).
"""
X, ys = load_GMM([20, 20], np.array([[0, 0], [1, 1]]), [0.2, 0.2], 10)
idx_a = [0, 1, 5, 12, 20, 23, 34]
idx_b = [11, 21, 34, 35, 22, 7]
cuts = []
for a, b in zip(idx_a, idx_b):
cut = pivot_cut(X, X[a, :], X[b, :])
cuts.append(cut)
pred = get_hard_predictions(np.concatenate(cuts).T, 10)
assert np.all(pred == ys)
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16,
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17,
11,
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17,
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8,
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6630,
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220,
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329,
257,
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287,
19974,
7,
312,
87,
62,
64,
11,
4686,
87,
62,
65,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
2005,
796,
30355,
62,
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7,
55,
11,
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58,
64,
11,
1058,
4357,
1395,
58,
65,
11,
1058,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
6630,
13,
33295,
7,
8968,
8,
198,
220,
220,
220,
2747,
796,
651,
62,
10424,
62,
28764,
9278,
7,
37659,
13,
1102,
9246,
268,
378,
7,
23779,
737,
51,
11,
838,
8,
198,
220,
220,
220,
6818,
45941,
13,
439,
7,
28764,
6624,
331,
82,
8,
198
] | 2.107143 | 672 |
"""Vconnex integration"""
import hashlib
import hmac
import base64
import json
import time
from types import SimpleNamespace
from typing import Any
from enum import Enum
import requests
import logging
API__TOKEN = "/auth/project-token"
logger = logging.getLogger(__name__)
| [
37811,
53,
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834,
8,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198
] | 2.841121 | 107 |
from django.contrib import admin
from .models import Image
# Register your models here.
@admin.register(Image)
| [
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
6738,
764,
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534,
4981,
994,
13,
198,
198,
31,
28482,
13,
30238,
7,
5159,
8,
628
] | 3.5625 | 32 |
import os
import ntpath
#EL PROGRAMA PUEDE TENER INPUT DE UNA URL
#extraer_sonido(ruta)
| [
11748,
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11748,
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198,
2,
26086,
263,
62,
1559,
17305,
7,
81,
29822,
8,
201,
198
] | 2.04 | 50 |
from validate_docbr import CPF
| [
6738,
26571,
62,
15390,
1671,
1330,
16932,
37,
198
] | 3.444444 | 9 |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
main() | [
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] | 2.076923 | 65 |
# Third party imports
import pytest
# pydatastructs imports
from pydatastructs.linkedlist import LinkedList
# Setup
# initialize _linkedlist in pytest fixture to be used in individual method tests
@pytest.fixture
# Executions
# append() adds a new instantiation of the Node class to the end of the linked list
# append() accepts any data type
# remove_head() removes and returns the head node from the linked list
# remove_head() shifts the head property pointer to the next node in the linked list
# remove_head() returns None if the linked list is empty
# find_node() finds and returns the first node that has the value provided
# find_node() returns None if no node contains the value provided | [
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] | 3.977273 | 176 |
import pymol
from pymol import cmd, util
from sys import argv
# usage: pymol -cq script_name.py -- path/to/fragments/
path = argv[1:][0]
print(f"Using path: {path} for structures")
# Set some nice CB friendly colours
cmd.set_color("cb_orange", [0.96, 0.41, 0.23])
# Add filter (ambient) and other misc settings
cmd.set("ambient", 0.4)
cmd.set("ambient_occlusion_mode", 1)
cmd.set("ambient_occlusion_scale", 15)
cmd.bg_colour("white")
cmd.set("antialias", 2)
cmd.set("ortho", 1)
cmd.set("ray_trace_mode", 0)
cmd.reset()
cmd.delete('all')
# Specify fragment codes from each series
amino_p1_prime = "Mpro-x12321_0A"
ugi_p1_prime = "Mpro-x2776_0A"
quin_p1_prime = "Mpro-P0008_0A"
benzo_p1_prime = "Mpro-x10871_0A"
fragments = [amino_p1_prime, ugi_p1_prime, quin_p1_prime, benzo_p1_prime]
# Load each fragment: ligand + apo protein
for i, fragment in enumerate(fragments):
cmd.load(path + f'{fragment}/' + f'{fragment}.sdf', f'p1prime-{i}-{fragment}-ligand')
cmd.load(path + f'{fragment}/' + f'{fragment}_apo-desolv.pdb', f'p1prime-{i}-{fragment}-protein')
# Sort out colours for each series
cmd.color("wheat", f"p1prime-0-*") # amino
cmd.color("palegreen", f"p1prime-1-*") # ugi
cmd.color("violet", f"p1prime-2-*") # quin
cmd.color("deepolive", f"p1prime-3-*") # benzo
# Retain non-carbon default colours
util.cnc(f"p1prime-*")
# remove waters
cmd.remove('resn HOH')
cmd.deselect()
# Show molecular representation
cmd.hide('all')
cmd.bg_color('white')
util.cbaw('*-protein')
cmd.show('sticks', f'*-protein and not hydrogen')
cmd.show('surface', f'*-protein and not hydrogen')
cmd.show('sticks', 'not hydrogen and *-ligand')
cmd.hide('sticks', 'hydrogen')
cmd.disable('*-protein')
cmd.disable('*-ligand')
cmd.set('surface_color', 'white')
# Sort transparency for surfaces and catalytic dyad
cmd.set('surface_mode', 3)
cmd.set('transparency', 0.15)
cmd.set('transparency', 1, 'resi 145 and resn CYS')
cmd.set('transparency', 1, 'resi 41 and resn HIS')
# Set the viewport and view
cmd.viewport(720,720)
cmd.set_view ("\
0.889428616, -0.456408978, 0.024401871,\
-0.099697880, -0.141634151, 0.984882414,\
-0.446053863, -0.878418088, -0.171478689,\
0.000357639, 0.000210542, -94.736244202,\
-21.802495956, 4.796146870, 28.488866806,\
81.561714172, 107.897544861, 20.000000000 ")
# Label MPro pockets
cmd.set("label_shadow_mode", 2)
# P1 prime
cmd.select("p1_prime", "resi 25+26+27")
cmd.set("surface_color", "cb_orange", "p1_prime")
cmd.show("surface", "p1_prime")
cmd.pseudoatom("p1_prime_label", "p1_prime")
cmd.set("label_color", "cb_orange", "p1_prime_label")
cmd.set("label_size", -0.8, "p1_prime_label")
cmd.set("label_font_id", 7, "p1_prime_label")
### hide psuedoatom
cmd.hide("everything", "p1_prime_label")
cmd.show("label", "p1_prime_label")
pymol.finish_launching()
# Create images
print("Creating images for P1 prime...")
for i, fragment in zip([0,1,2,3], fragments):
fragment_key = {0: "amino", 1:"ugi", 2: "quin", 3: "benzo"}
print(fragment_key[i], fragment)
cmd.enable(f'p1prime-{i}-{fragment}-ligand')
cmd.enable(f'p1prime-{i}-{fragment}-protein')
cmd.ray(720,720)
cmd.png(f"./p1_prime_flex_{fragment_key[i]}_{fragment}.png")
cmd.disable(f'p1prime-{i}-{fragment}-ligand')
cmd.disable(f'p1prime-{i}-{fragment}-protein')
| [
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] | 2.269518 | 1,473 |
import unittest
from mosaic.node import Node
''' def test_fully_expanded(self):
def a_func(): return 0
def b_func(): return 0
def c_func(): return 0
x1 = ListTask(is_ordered=False, name = "x1", tasks = ["x1__p1", "x1__p2"])
x2 = ListTask(is_ordered=True, name = "x2", tasks = ["x2__p1", "x2__p2"])
start = ChoiceScenario(name = "root", scenarios=[x1, x2])
sampler = { "x1__p1": Parameter("x1__p1", [0, 1], "uniform", "float"),
"x1__p2": Parameter("x1__p2", [1, 2, 3, 4, 5, 6, 7], "choice", "int"),
"x2__p1": Parameter("x2__p1", ["a", "b", "c", "d"], "choice", "string"),
"x2__p2": Parameter("x2__p2", [a_func, b_func, c_func], "choice", "int")
}
space = Space(scenario = start, sampler = sampler)
node = Node()
node.add_node(name="x1", parent_node = 0)
node.add_node(name="x2", parent_node = 0)
assert(node.fully_expanded(0, space))
assert(node.fully_expanded(1, space))
assert(node.fully_expanded(2, space))
node.add_node(name="x1__p1", parent_node = 1)
assert(node.fully_expanded(1, space))
node.set_attribute(1, "max_number_child", 2)
assert(node.fully_expanded(1, space))
assert(node.fully_expanded(3, space))
node.set_attribute(3, "max_number_child", 2)
node.add_node(name="x1__p2", value=1, parent_node = 3)
assert(node.fully_expanded(3, space))
node.add_node(name="x1__p2", value=2, parent_node = 3)
assert(node.fully_expanded(3, space))
''' | [
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] | 2.02372 | 801 |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 7 20:52:19 2019
@author: if715029
"""
### Crea los clusters de 5, 20, 40 y 125 días y los exporta a .sav
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from time import time
import pickle
t1 = time()
csv = ['AC','ALFAA','ALPEKA','ALSEA','ELEKTRA','IENOVA','MEXCHEM','PE&OLES','PINFRA','WALMEX']
data = []
for i in csv:
data.append(pd.read_csv('../Data/%s.csv'%i, index_col='0'))
#%%
#%%
ndias = [5,20,40,125]
vent = []
for i in ndias:
close_v = crear_ventanas(data[0]['Close'],i)
for j in range(1,len(data)):
close_v = np.concatenate((close_v, crear_ventanas(data[j]['Close'],i)))
vent.append(close_v)
#%%
cont = len(ndias)
for i in range(cont):
vent[i] = np.transpose((vent[i].transpose()-vent[i].mean(axis=1))/vent[i].std(axis=1))
#%% Función para la gráfica de codo
#%%
#for i in range(cont):
# grafica_codo_kmeans(vent[i],np.arange(1,16))
#%%
model_close = []
for i in range(cont):
model_close.append(KMeans(n_clusters=4,init='k-means++').fit(vent[i]))
#%% Función para dibujar los centroides del modelo
#%%
for i in range(cont):
ver_centroides(model_close[i].cluster_centers_)
#%%
pickle.dump(model_close,open('model_close.sav','wb')) | [
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10786,
19849,
62,
19836,
13,
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41707,
39346,
6,
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] | 2.216949 | 590 |
from django.db.models import (
BooleanField,
CharField,
Model,
TextField,
)
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11,
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8,
628
] | 2.384615 | 39 |
def countdown():
"""Write a generator that counts from 100 to 1"""
for i in reversed(range(1, 101)):
yield i
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from tf2recommender.config.local import ColumnNames
config = {
'training_args':
NcfTrainingArguments(
user_dim=10,
item_dim=6,
batch_size=16,
num_epochs=5,
hidden1_dim=8,
hidden2_dim=2),
'col_names':
ColumnNames(user_col='user', item_col='item', rating_col='rating')
}
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] | 2.074074 | 162 |
"""
进程控制类
"""
"""
PCB
进程控制块
"""
"""
进程控制块队列
"""
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from nonebot import on_command
from nonebot.rule import to_me
from nonebot.adapters.cqhttp import Bot,Event
from nonebot.adapters.cqhttp import MessageSegment as msg
from nonebot.log import logger
from .util import check_rss,update_rss,rss_db_init,rss_server,add_rss,query_user_rss,remove_rss
from .model import Rss
from christinaqqbot.utils.rule import _gruop_white_list
import threading
rss_db_init()
threading.Thread(target=rss_server).start()
RSS=on_command('rss',rule=to_me()&_gruop_white_list)
@RSS.handle()
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] | 2.872928 | 181 |
"""
Timer
======
This module measures the execution time and provides three ways to do this:
* **MeasureTime**: Measure time as decorator ``@MeasureTime``.
* **MeasureBlockTime**: Measure as ``with MeasureBlockTime("my_block") as my_block:``.
* **Timer**: Measure as instance ``timer = Timer()``
"""
from __future__ import annotations
import timeit
import datetime
from functools import wraps
from ml_dev_utils import log
def MeasureTime(function):
"""Measure the execution time as a decorator.
Returns:
: The return of the wrapped function
Example::
from ml_dev_utils.Timer import MeasureTime
@MeasureTime
def my_function():
print("my awesome code")
The measured duration time will be written to ``log.debug``. Therefore to
see the output, you need to set up a handler for logging.
Example with specific ``ml_dev_utils`` logger::
import logging
from ml_dev_utils import log
from ml_dev_utils.log_handler import console
from ml_dev_utils.Timer import MeasureTime
log.addHandler(console)
log.setLevel(logging.DEBUG)
@MeasureTime
def my_function():
print("my awesome code")
my_function()
"""
@wraps(function)
return _wrapper
class MeasureBlockTime():
"""Measure the execution time of a code block.
Args:
name: A name for the timer instance. Defaults to "(block)".
log_time: If true, it will write the execution time to ``log.debug``. Defaults to True.
**Attributes**
Attributes:
timer (ml_dev_utils.Timer.Timer): Keeps the measured time.
Example::
from ml_dev_utils.Timer import MeasureBlockTime
with MeasureBlockTime("my_block") as my_block:
print("my code here")
print(my_block.timer.time)
"""
class Timer():
"""Measures the execution time.
Args:
name: A name for the timer instance. Defaults to "unnamed".
**Attributes**
Attributes:
name (str): The name of the Timer.
time (datetime.timedelta): The measured duration time.
start_time (float): The start time of the measurement.
Example::
from ml_dev_utils.Timer import Timer
timer = Timer().start()
print(timer.end())
"""
def start(self) -> Timer:
"""Starts the measurement.
Set's the start time in the ``start_time`` instance variable.
Returns:
Returns itself for fluent initialization.
"""
self.start_time = timeit.default_timer()
return self
def end(self) -> datetime.timedelta:
"""Stops the measurement.
Measures the time duration and set's it to the ``time`` instance variable.
Returns:
The measured time duration
"""
self.time = timeit.default_timer() - self.start_time
self.time = _format_time(self.time)
return self.time
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220,
220,
220,
220,
220,
1441,
2116,
13,
2435,
628,
198
] | 2.665477 | 1,121 |
from regression_tests import *
| [
6738,
20683,
62,
41989,
1330,
1635,
198
] | 4.428571 | 7 |
"""
BAD CODE
The problem is in the data structure. Loading the movie database as a list
of lists causes the function movies() to find the movies of a user to be
of linear complexity O(1) and the function shared() to find shared movies to be
of quadratic complexity O(m*m).
The good code has constant complexity O(1) for movies() and
linear complexity O(m) for shared(). For a very small movie database the
difference will be small but for a larger database it will be dramatic.
"""
db = load_db('movies.csv')
print(db)
print(movies('user42', db))
print(shared('user42', 'user13', db))
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10786,
7220,
3682,
3256,
705,
7220,
1485,
3256,
20613,
4008,
198
] | 3.529762 | 168 |
from .motifprogram import MotifProgram
import os
import shutil
from subprocess import Popen, PIPE
from gimmemotifs.motif import Motif
class Gadem(MotifProgram):
"""
Predict motifs using GADEM.
Reference:
"""
def _run_program(self, bin, fastafile, params=None):
"""
Run GADEM and predict motifs from a FASTA file.
Parameters
----------
bin : str
Command used to run the tool.
fastafile : str
Name of the FASTA input file.
params : dict, optional
Optional parameters. For some of the tools required parameters
are passed using this dictionary.
Returns
-------
motifs : list of Motif instances
The predicted motifs.
stdout : str
Standard out of the tool.
stderr : str
Standard error of the tool.
"""
default_params = {}
if params is not None:
default_params.update(params)
new_file = os.path.join(self.tmpdir, "gadem_in.fa")
shutil.copy(fastafile, new_file)
fastafile = new_file
pfmfile = fastafile + ".pwm"
outfile = fastafile + ".out"
current_path = os.getcwd()
os.chdir(self.tmpdir)
cmd = "%s -fseq %s -fpwm %s -fout %s" % (bin, fastafile, pfmfile, outfile)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
motifs = []
if os.path.exists(pfmfile):
with open(pfmfile) as f:
motifs = self.parse(f)
os.chdir(current_path)
return motifs, stdout, stderr
def parse(self, fo):
"""
Convert GADEM output to motifs
Parameters
----------
fo : file-like
File object containing GADEM output.
Returns
-------
motifs : list
List of Motif instances.
"""
motifs = []
nucs = {"A": 0, "C": 1, "G": 2, "T": 3}
lines = fo.readlines()
for i in range(0, len(lines), 5):
align = []
pwm = []
pfm = []
m_id = ""
line = lines[i].strip()
m_id = line[1:]
number = m_id.split("_")[0][1:]
if os.path.exists("%s.seq" % number):
with open("%s.seq" % number) as f:
for line in f:
if "x" not in line and "n" not in line:
line = line.strip().upper()
align.append(line)
if not pfm:
pfm = [[0 for x in range(4)] for x in range(len(line))]
for p in range(len(line)):
pfm[p][nucs[line[p]]] += 1
m = [
line.strip().split(" ")[1].split("\t") for line in lines[i + 1 : i + 5]
]
pwm = [[float(m[x][y]) for x in range(4)] for y in range(len(m[0]))]
motifs.append(Motif(pwm))
motifs[-1].id = "{}_{}".format(self.name, m_id)
if align:
motifs[-1].pfm = pfm
motifs[-1].align = align
return motifs
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
38353,
58,
79,
7131,
77,
1229,
82,
58,
1370,
58,
79,
11907,
60,
15853,
352,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
285,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1627,
13,
36311,
22446,
35312,
7203,
366,
38381,
16,
4083,
35312,
7203,
59,
83,
4943,
329,
1627,
287,
3951,
58,
72,
1343,
352,
1058,
1312,
1343,
642,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
26377,
796,
16410,
22468,
7,
76,
58,
87,
7131,
88,
12962,
329,
2124,
287,
2837,
7,
19,
15437,
329,
331,
287,
2837,
7,
11925,
7,
76,
58,
15,
60,
4008,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32702,
82,
13,
33295,
7,
47733,
361,
7,
79,
26377,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32702,
82,
58,
12,
16,
4083,
312,
796,
45144,
92,
23330,
92,
1911,
18982,
7,
944,
13,
3672,
11,
285,
62,
312,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
10548,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32702,
82,
58,
12,
16,
4083,
79,
38353,
796,
279,
38353,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32702,
82,
58,
12,
16,
4083,
31494,
796,
10548,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
32702,
82,
198
] | 1.859898 | 1,763 |
"""
C, R (= 열, 행) = 7, 6일 때
[6, 7, 8, 9, 10, 11, 12]
[5, 26, 27, 28, 29, 30, 13]
[4, 25, 38, 39, 40, 31, 14]
[3, 24, 37, 42, 41, 32, 15]
[2, 23, 36, 35, 34, 33, 16]
[1, 22, 21, 20, 19, 18, 17]
"""
C, R = map(int, input().split())
grid = [[0]* C for _ in range(R)]
count, offset = 0, 0
max_size = C*R
while R > 0 and C > 0:
for i in range(offset+R-1, offset-1, -1):
if count >= max_size:
break
count += 1
grid[i][offset] = count
for j in range(offset+1, offset+C):
if count >= max_size:
break
count += 1
grid[offset][j] = count
for i in range(offset+1, offset+R):
if count >= max_size:
break
count += 1
grid[i][offset+C-1] = count
for j in range(offset+C-2, offset, -1):
if count >= max_size:
break
count += 1
grid[offset+R-1][j] = count
offset +=1
R -=2
C -=2
for row in grid:
print(row)
| [
37811,
198,
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23821,
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371,
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17,
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1640,
5752,
287,
10706,
25,
198,
220,
220,
220,
3601,
7,
808,
8,
198
] | 1.862595 | 524 |
import sys
from .parent_parser import ParentParser
from .common_parser import CommonParser
class CreateSiteUsersParser:
"""
Parser for createsiteusers command
"""
@staticmethod
def create_site_user_parser():
"""Method to parse create site users arguments passed by the user"""
parent_parser = ParentParser()
parser = parent_parser.parent_parser_with_global_options()
subparsers = parser.add_subparsers()
create_site_users_parser = subparsers.add_parser('createsiteusers',
parents=[parser])
create_site_users_parser.add_argument('--role', '-r',
default="Unlicensed",
help='name of site')
args = create_site_users_parser.parse_args(sys.argv[3:])
csv_lines = CommonParser.read_file(sys.argv[2])
if args.site is None or args.site == "Default":
args.site = ''
return csv_lines, args
| [
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6624,
366,
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1298,
198,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
26498,
13,
15654,
796,
10148,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
269,
21370,
62,
6615,
11,
26498,
198
] | 2.159751 | 482 |
""" "Stickfix" (c) by Ignacio Slater M.
"Stickfix" is licensed under a
Creative Commons Attribution 4.0 International License.
You should have received a copy of the license along with this
work. If not, see <http://creativecommons.org/licenses/by/4.0/>.
"""
from bot.utils.logger import StickfixLogger
class StickfixException(Exception):
"""
Base class for exceptions in this module
Attributes:
err_message -- message sent by the error.
err_cause -- reason that caused the exception.
"""
class InputException(StickfixException):
"""
Exception raised when the arguments passed as input to the bot are incorrect.
"""
class NoStickerException(StickfixException):
"""
Exception raised when the bot can't find a sticker in a message.
"""
class WrongContextException(StickfixException):
"""
Exception raised when the bot tries to execute a command from the wrong context.
For example, this exception should be raised if a command can only be called from a private
chat and is being
called from a group chat.
"""
class InsufficientPermissionsException(StickfixException):
"""
Exception raised when a user tries to call a command without the appropriate permissions.
"""
class Databasexception(StickfixException):
"""
Exception raised when a database operation fails.
"""
def unexpected_error(e: Exception, a_logger: StickfixLogger):
""" Logs an unhandled exception. """
a_logger.critical("Unexpected error")
a_logger.critical(str(type(e)))
a_logger.critical(str(e.args))
| [
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198
] | 3.222222 | 504 |
#!/usr/bin/env python
# coding: utf-8
# <div class="alert alert-block alert-info">
# <b><h1>ENGR 1330 Computational Thinking with Data Science </h1></b>
# </div>
#
# Copyright © 2021 Theodore G. Cleveland and Farhang Forghanparast
#
# Last GitHub Commit Date:
#
# # 34: KNN Applications
# - application
#
# In[ ]:
# ## References
# In[ ]:
| [
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] | 2.611511 | 139 |
from __future__ import absolute_import
from proteus import *
from proteus.default_p import *
from math import *
from .vortex import *
from proteus.mprans import RDLS
from . import ls_vortex_3d_p
name=soname+"_rdls"
LevelModelType = RDLS.LevelModel
coefficients = RDLS.Coefficients(applyRedistancing=applyRedistancing,
epsFact=epsFactRedistance,
nModelId=0,
rdModelId=1)
#now define the Dirichlet boundary conditions
dirichletConditions = {0:getDBC}
if LevelModelType == RDLS.LevelModel:
weakDirichletConditions = {0:RDLS.setZeroLSweakDirichletBCs}
#weakDirichletConditions = {0:RDLS.setZeroLSweakDirichletBCsSimple}
else:
weakDirichletConditions = {0:coefficients.setZeroLSweakDirichletBCs}
#weakDirichletConditions = {0:coefficients.setZeroLSweakDirichletBCs2}
#weakDirichletConditions = None
initialConditions = ls_vortex_3d_p.initialConditions
fluxBoundaryConditions = {0:'noFlow'}
advectiveFluxBoundaryConditions = {}
diffusiveFluxBoundaryConditions = {0:{}}
| [
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49646,
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] | 2.373102 | 461 |
from xapitrader.core.data.stream.base import StreamDataInterface
from xapitrader.types import types
from xapitrader.types import utils
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] | 3.214286 | 42 |
import pytest
from mxnet.util import use_np
from mxnet.gluon.data import DataLoader
import numpy as np
import numpy.testing as npt
import tempfile
import pickle
import os
from sklearn.model_selection import train_test_split
from autogluon.core.utils.loaders import load_pd
from autogluon.text.text_prediction.mx.preprocessing import MultiModalTextFeatureProcessor,\
base_preprocess_cfg, MultiModalTextBatchify, get_cls_sep_id, auto_shrink_max_length
from autogluon.text.text_prediction.infer_types import infer_column_problem_types
TEST_CASES = [
['melbourne_airbnb_sample',
'https://autogluon-text-data.s3.amazonaws.com/test_cases/melbourne_airbnb_sample_1000.pq',
'price_label'],
['women_clothing_rating',
'https://autogluon-text-data.s3.amazonaws.com/test_cases/women_clothing_sample.pq',
'Rating']
]
@use_np
@pytest.mark.parametrize('dataset_name,url,label_column', TEST_CASES)
@pytest.mark.parametrize('backbone_name', ['google_electra_small',
'google_albert_base_v2'])
@pytest.mark.parametrize('all_to_text', [False, True])
@use_np
@pytest.mark.parametrize('dataset_name,url,label_column', TEST_CASES)
@pytest.mark.parametrize('backbone_name', ['google_electra_small',
'google_albert_base_v2',
'fairseq_roberta_base'])
@pytest.mark.parametrize('all_to_text', [False, True])
@pytest.mark.parametrize('insert_sep', [False, True])
@pytest.mark.parametrize('stochastic_chunk', [False, True])
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220,
220,
220,
220,
220,
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62,
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3477,
62,
354,
2954,
3256,
685,
25101,
11,
6407,
12962,
198
] | 2.271802 | 688 |
"""__init.py__"""
import sys
MIN_PYTHON_VERSION = (3, 8)
if sys.version_info < MIN_PYTHON_VERSION:
version_str = '.'.join(map(str, MIN_PYTHON_VERSION))
raise EnvironmentError("Python version needs to be at least " + version_str)
| [
37811,
834,
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] | 2.597826 | 92 |
from bokeh.plotting import show, output_file
from bokeh.models import Plot, Title, Circle, ColumnDataSource, DataRange1d, LinearAxis, Range1d
xdr = DataRange1d()
ydr = DataRange1d()
p = Plot(
title=None, toolbar_location=None,
x_range=xdr, y_range=ydr,
plot_width=800, plot_height=800,
min_border=30,
background_fill_color="#F0F0F0",
border_fill_color="lightgray")
p.extra_x_ranges["x"] = Range1d(0, 100)
p.extra_y_ranges["y"] = Range1d(0, 100)
source = ColumnDataSource(dict(x=[1.0, 2.0, 3.0], y=[1.0, 2.0, 3.0]))
p.add_layout(LinearAxis(axis_label="x_label"), "above")
p.add_layout(LinearAxis(x_range_name="x"), "above")
p.add_layout(LinearAxis(axis_label="x_label"), "below")
p.add_layout(LinearAxis(x_range_name="x"), "below")
p.add_layout(LinearAxis(axis_label="y_label"), "left")
p.add_layout(LinearAxis(y_range_name="y"), "left")
p.add_layout(LinearAxis(axis_label="y_label"), "right")
p.add_layout(LinearAxis(y_range_name="y"), "right")
gly = Circle(x="x", y="y", size=10)
p.add_glyph(source, gly)
add_title("Title A1", "above", "red")
add_title("Title A2", "above", "green")
add_title("Title A3", "above", "lightblue")
add_title("Title A4", "above", "pink")
add_title("Title B1", "below", "red")
add_title("Title B2", "below", "green")
add_title("Title L1", "left", "red")
add_title("Title L2", "left", "green")
add_title("Title R1", "right", "red")
add_title("Title R2", "right", "green")
add_title("Title R3", "right", "lightblue")
add_title("Title R4", "right", "pink")
output_file("panels.html")
show(p)
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] | 2.352496 | 661 |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by 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.
# ==============================================================================
"""Utils to be used in testing DNN estimators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import tempfile
import numpy as np
import six
from tensorflow.core.framework import summary_pb2
from tensorflow.python.client import session as tf_session
from tensorflow.python.feature_column import feature_column
from tensorflow.python.feature_column import feature_column_v2
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import test
from tensorflow.python.summary import summary as summary_lib
from tensorflow.python.summary.writer import writer_cache
from tensorflow.python.training import checkpoint_utils
from tensorflow.python.training import gradient_descent
from tensorflow.python.training import monitored_session
from tensorflow.python.training import optimizer as optimizer_lib
from tensorflow.python.training import saver
from tensorflow.python.training import session_run_hook
from tensorflow.python.training import training_util
from tensorflow_estimator.python.estimator import estimator
from tensorflow_estimator.python.estimator import model_fn
from tensorflow_estimator.python.estimator.canned import head as head_lib
from tensorflow_estimator.python.estimator.canned import metric_keys
from tensorflow_estimator.python.estimator.canned import prediction_keys
from tensorflow_estimator.python.estimator.inputs import numpy_io
from tensorflow_estimator.python.estimator.mode_keys import ModeKeys
# pylint rules which are disabled by default for test files.
# pylint: disable=invalid-name,protected-access,missing-docstring
# Names of variables created by model.
LEARNING_RATE_NAME = 'dnn/regression_head/dnn/learning_rate'
HIDDEN_WEIGHTS_NAME_PATTERN = 'dnn/hiddenlayer_%d/kernel'
HIDDEN_BIASES_NAME_PATTERN = 'dnn/hiddenlayer_%d/bias'
BATCH_NORM_BETA_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/beta'
BATCH_NORM_GAMMA_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/gamma'
BATCH_NORM_MEAN_NAME_PATTERN = 'dnn/hiddenlayer_%d/batchnorm_%d/moving_mean'
BATCH_NORM_VARIANCE_NAME_PATTERN = (
'dnn/hiddenlayer_%d/batchnorm_%d/moving_variance')
LOGITS_WEIGHTS_NAME = 'dnn/logits/kernel'
LOGITS_BIASES_NAME = 'dnn/logits/bias'
OCCUPATION_EMBEDDING_NAME = ('dnn/input_from_feature_columns/input_layer/'
'occupation_embedding/embedding_weights')
CITY_EMBEDDING_NAME = ('dnn/input_from_feature_columns/input_layer/'
'city_embedding/embedding_weights')
# This is so that we can easily switch between feature_column and
# feature_column_v2 for testing.
feature_column.numeric_column = feature_column._numeric_column
feature_column.categorical_column_with_hash_bucket = feature_column._categorical_column_with_hash_bucket # pylint: disable=line-too-long
feature_column.categorical_column_with_vocabulary_list = feature_column._categorical_column_with_vocabulary_list # pylint: disable=line-too-long
feature_column.categorical_column_with_vocabulary_file = feature_column._categorical_column_with_vocabulary_file # pylint: disable=line-too-long
feature_column.embedding_column = feature_column._embedding_column
def create_checkpoint(weights_and_biases,
global_step,
model_dir,
batch_norm_vars=None):
"""Create checkpoint file with provided model weights.
Args:
weights_and_biases: Iterable of tuples of weight and bias values.
global_step: Initial global step to save in checkpoint.
model_dir: Directory into which checkpoint is saved.
batch_norm_vars: Variables used for batch normalization.
"""
weights, biases = zip(*weights_and_biases)
if batch_norm_vars:
assert len(batch_norm_vars) == len(weights_and_biases) - 1
(bn_betas, bn_gammas, bn_means, bn_variances) = zip(*batch_norm_vars)
model_weights = {}
# Hidden layer weights.
for i in range(0, len(weights) - 1):
model_weights[HIDDEN_WEIGHTS_NAME_PATTERN % i] = weights[i]
model_weights[HIDDEN_BIASES_NAME_PATTERN % i] = biases[i]
if batch_norm_vars:
model_weights[BATCH_NORM_BETA_NAME_PATTERN % (i, i)] = bn_betas[i]
model_weights[BATCH_NORM_GAMMA_NAME_PATTERN % (i, i)] = bn_gammas[i]
model_weights[BATCH_NORM_MEAN_NAME_PATTERN % (i, i)] = bn_means[i]
model_weights[BATCH_NORM_VARIANCE_NAME_PATTERN % (i, i)] = bn_variances[i]
# Output layer weights.
model_weights[LOGITS_WEIGHTS_NAME] = weights[-1]
model_weights[LOGITS_BIASES_NAME] = biases[-1]
with ops.Graph().as_default():
# Create model variables.
for k, v in six.iteritems(model_weights):
variables_lib.Variable(v, name=k, dtype=dtypes.float32)
# Create non-model variables.
global_step_var = training_util.create_global_step()
# Initialize vars and save checkpoint.
with tf_session.Session() as sess:
variables_lib.global_variables_initializer().run()
global_step_var.assign(global_step).eval()
saver.Saver().save(sess, os.path.join(model_dir, 'model.ckpt'))
def mock_head(testcase, hidden_units, logits_dimension, expected_logits):
"""Returns a mock head that validates logits values and variable names."""
hidden_weights_names = [(HIDDEN_WEIGHTS_NAME_PATTERN + '/part_0:0') % i
for i in range(len(hidden_units))]
hidden_biases_names = [(HIDDEN_BIASES_NAME_PATTERN + '/part_0:0') % i
for i in range(len(hidden_units))]
expected_var_names = (
hidden_weights_names + hidden_biases_names +
[LOGITS_WEIGHTS_NAME + '/part_0:0', LOGITS_BIASES_NAME + '/part_0:0'])
head = test.mock.NonCallableMagicMock(spec=head_lib._Head)
head.logits_dimension = logits_dimension
head._create_tpu_estimator_spec = test.mock.MagicMock(
wraps=_create_tpu_estimator_spec)
head.create_estimator_spec = test.mock.MagicMock(
wraps=_create_estimator_spec)
return head
def mock_optimizer(testcase, hidden_units, expected_loss=None):
"""Creates a mock optimizer to test the train method.
Args:
testcase: A TestCase instance.
hidden_units: Iterable of integer sizes for the hidden layers.
expected_loss: If given, will assert the loss value.
Returns:
A mock Optimizer.
"""
hidden_weights_names = [(HIDDEN_WEIGHTS_NAME_PATTERN + '/part_0:0') % i
for i in range(len(hidden_units))]
hidden_biases_names = [(HIDDEN_BIASES_NAME_PATTERN + '/part_0:0') % i
for i in range(len(hidden_units))]
expected_var_names = (
hidden_weights_names + hidden_biases_names +
[LOGITS_WEIGHTS_NAME + '/part_0:0', LOGITS_BIASES_NAME + '/part_0:0'])
def _minimize(loss, global_step=None, var_list=None):
"""Mock of optimizer.minimize."""
trainable_vars = var_list or ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES)
testcase.assertItemsEqual(expected_var_names,
[var.name for var in trainable_vars])
# Verify loss. We can't check the value directly, so we add an assert op.
testcase.assertEquals(0, loss.shape.ndims)
if expected_loss is None:
if global_step is not None:
return state_ops.assign_add(global_step, 1).op
return control_flow_ops.no_op()
assert_loss = assert_close(
math_ops.to_float(expected_loss, name='expected'),
loss,
name='assert_loss')
with ops.control_dependencies((assert_loss,)):
if global_step is not None:
return state_ops.assign_add(global_step, 1).op
return control_flow_ops.no_op()
optimizer_mock = test.mock.NonCallableMagicMock(
spec=optimizer_lib.Optimizer,
wraps=optimizer_lib.Optimizer(use_locking=False, name='my_optimizer'))
optimizer_mock.minimize = test.mock.MagicMock(wraps=_minimize)
return optimizer_mock
class BaseDNNModelFnTest(object):
"""Tests that _dnn_model_fn passes expected logits to mock head."""
def _test_logits(self, mode, hidden_units, logits_dimension, inputs,
expected_logits):
"""Tests that the expected logits are passed to mock head."""
with ops.Graph().as_default():
training_util.create_global_step()
head = mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = self._dnn_model_fn(
features={'age': constant_op.constant(inputs)},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
self._fc_impl.numeric_column(
'age', shape=np.array(inputs).shape[1:])
],
optimizer=mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def test_one_dim_logits(self):
"""Tests one-dimensional logits.
input_layer = [[10]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]]
= [[relu(2.38), relu(-0.12)]] = [[2.38, 0]]
logits = [[-1*2.38 +1*0 +0.3]] = [[-2.08]]
"""
base_global_step = 100
create_checkpoint(
(([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),), base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=1,
inputs=[[10.]],
expected_logits=[[-2.08]])
def test_multi_dim_logits(self):
"""Tests multi-dimensional logits.
input_layer = [[10]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]]
= [[relu(2.38), relu(-0.12)]] = [[2.38, 0]]
logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38]]
= [[-2.08, 2.08, 1.19]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]],
[.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10.]],
expected_logits=[[-2.08, 2.08, 1.19]])
def test_multi_example_multi_dim_logits(self):
"""Tests multiple examples and multi-dimensional logits.
input_layer = [[10], [5]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)],
[relu(0.6*5 +0.1), relu(0.5*5 -0.1)]]
= [[6.1, 4.9], [3.1, 2.4]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)],
[relu(1*3.1 -0.8*2.4 +0.2), relu(0.8*3.1 -1*2.4 -0.1)]]
= [[2.38, 0], [1.38, 0]]
logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38],
[-1*1.38 +0.3, 1*1.38 -0.3, 0.5*1.38]]
= [[-2.08, 2.08, 1.19], [-1.08, 1.08, 0.69]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]],
[.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10.], [5.]],
expected_logits=[[-2.08, 2.08, 1.19], [-1.08, 1.08, .69]])
def test_multi_dim_input_one_dim_logits(self):
"""Tests multi-dimensional inputs and one-dimensional logits.
input_layer = [[10, 8]]
hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]]
= [[1.3, 0.9]]
hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]]
= [[0.78, relu(-0.06)]] = [[0.78, 0]]
logits = [[-1*0.78 +1*0 +0.3]] = [[-0.48]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),), base_global_step,
self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=1,
inputs=[[10., 8.]],
expected_logits=[[-0.48]])
def test_multi_dim_input_multi_dim_logits(self):
"""Tests multi-dimensional inputs and multi-dimensional logits.
input_layer = [[10, 8]]
hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]]
= [[1.3, 0.9]]
hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]]
= [[0.78, relu(-0.06)]] = [[0.78, 0]]
logits = [[-1*0.78 + 0.3, 1*0.78 -0.3, 0.5*0.78]] = [[-0.48, 0.48, 0.39]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10., 8.]],
expected_logits=[[-0.48, 0.48, 0.39]])
def test_multi_feature_column_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
with ops.Graph().as_default():
training_util.create_global_step()
head = mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = self._dnn_model_fn(
features={
'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])
},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
self._fc_impl.numeric_column('age'),
self._fc_impl.numeric_column('height')
],
optimizer=mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def test_multi_feature_column_mix_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
create_checkpoint((
([[.6, .5], [-.6, -.5]], [.1, -.1]),
([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),
), base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
with ops.Graph().as_default():
training_util.create_global_step()
head = mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
estimator_spec = self._dnn_model_fn(
features={
'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])
},
labels=constant_op.constant([[1]]),
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age'),
feature_column_v2.numeric_column('height')
],
optimizer=mock_optimizer(self, hidden_units))
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
if mode == ModeKeys.TRAIN:
sess.run(estimator_spec.train_op)
elif mode == ModeKeys.EVAL:
sess.run(estimator_spec.loss)
elif mode == ModeKeys.PREDICT:
sess.run(estimator_spec.predictions)
else:
self.fail('Invalid mode: {}'.format(mode))
def test_features_tensor_raises_value_error(self):
"""Tests that passing a Tensor for features raises a ValueError."""
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[0, 0, 0]]
with ops.Graph().as_default():
training_util.create_global_step()
head = mock_head(
self,
hidden_units=hidden_units,
logits_dimension=logits_dimension,
expected_logits=expected_logits)
with self.assertRaisesRegexp(ValueError, 'features should be a dict'):
self._dnn_model_fn(
features=constant_op.constant(inputs),
labels=constant_op.constant([[1]]),
mode=ModeKeys.TRAIN,
head=head,
hidden_units=hidden_units,
feature_columns=[
self._fc_impl.numeric_column(
'age', shape=np.array(inputs).shape[1:])
],
optimizer=mock_optimizer(self, hidden_units))
class BaseDNNLogitFnTest(object):
"""Tests correctness of logits calculated from _dnn_logit_fn_builder."""
def _test_logits(self,
mode,
hidden_units,
logits_dimension,
inputs,
expected_logits,
batch_norm=False):
"""Tests that the expected logits are calculated."""
with ops.Graph().as_default():
# Global step needed for MonitoredSession, which is in turn used to
# explicitly set variable weights through a checkpoint.
training_util.create_global_step()
# Use a variable scope here with 'dnn', emulating the dnn model_fn, so
# the checkpoint naming is shared.
with variable_scope.variable_scope('dnn'):
input_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=0, min_slice_size=64 << 20))
logit_fn = self._dnn_logit_fn_builder(
units=logits_dimension,
hidden_units=hidden_units,
feature_columns=[
self._fc_impl.numeric_column(
'age', shape=np.array(inputs).shape[1:])
],
activation_fn=nn.relu,
dropout=None,
input_layer_partitioner=input_layer_partitioner,
batch_norm=batch_norm)
logits = logit_fn(
features={'age': constant_op.constant(inputs)}, mode=mode)
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
self.assertAllClose(expected_logits, sess.run(logits))
def test_one_dim_logits(self):
"""Tests one-dimensional logits.
input_layer = [[10]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]]
= [[relu(2.38), relu(-0.12)]] = [[2.38, 0]]
logits = [[-1*2.38 +1*0 +0.3]] = [[-2.08]]
"""
base_global_step = 100
create_checkpoint(
(([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),), base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=1,
inputs=[[10.]],
expected_logits=[[-2.08]])
def test_one_dim_logits_with_batch_norm(self):
"""Tests one-dimensional logits.
input_layer = [[10]]
hidden_layer_0 = [[relu(0.6*10 +1), relu(0.5*10 -1)]] = [[7, 4]]
hidden_layer_0 = [[relu(0.6*20 +1), relu(0.5*20 -1)]] = [[13, 9]]
batch_norm_0, training (epsilon = 0.001):
mean1 = 1/2*(7+13) = 10,
variance1 = 1/2*(3^2+3^2) = 9
x11 = (7-10)/sqrt(9+0.001) = -0.999944449,
x21 = (13-10)/sqrt(9+0.001) = 0.999944449,
mean2 = 1/2*(4+9) = 6.5,
variance2 = 1/2*(2.5^2+.2.5^2) = 6.25
x12 = (4-6.5)/sqrt(6.25+0.001) = -0.99992001,
x22 = (9-6.5)/sqrt(6.25+0.001) = 0.99992001,
logits = [[-1*(-0.999944449) + 2*(-0.99992001) + 0.3],
[-1*0.999944449 + 2*0.99992001 + 0.3]]
= [[-0.699895571],[1.299895571]]
batch_norm_0, not training (epsilon = 0.001):
moving_mean1 = 0, moving_variance1 = 1
x11 = (7-0)/sqrt(1+0.001) = 6.996502623,
x21 = (13-0)/sqrt(1+0.001) = 12.993504871,
moving_mean2 = 0, moving_variance2 = 1
x12 = (4-0)/sqrt(1+0.001) = 3.998001499,
x22 = (9-0)/sqrt(1+0.001) = 8.995503372,
logits = [[-1*6.996502623 + 2*3.998001499 + 0.3],
[-1*12.993504871 + 2*8.995503372 + 0.3]]
= [[1.299500375],[5.297501873]]
"""
base_global_step = 100
create_checkpoint(
(
([[.6, .5]], [1., -1.]),
([[-1.], [2.]], [.3]),
),
base_global_step,
self._model_dir,
batch_norm_vars=([[0, 0], # beta.
[1, 1], # gamma.
[0, 0], # moving mean.
[1, 1], # moving variance.
],))
self._test_logits(
ModeKeys.TRAIN,
hidden_units=[2],
logits_dimension=1,
inputs=[[10.], [20.]],
expected_logits=[[-0.699895571], [1.299895571]],
batch_norm=True)
for mode in [ModeKeys.EVAL, ModeKeys.PREDICT]:
self._test_logits(
mode,
hidden_units=[2],
logits_dimension=1,
inputs=[[10.], [20.]],
expected_logits=[[1.299500375], [5.297501873]],
batch_norm=True)
def test_multi_dim_logits(self):
"""Tests multi-dimensional logits.
input_layer = [[10]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)]] = [[6.1, 4.9]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)]]
= [[relu(2.38), relu(-0.12)]] = [[2.38, 0]]
logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38]]
= [[-2.08, 2.08, 1.19]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]],
[.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10.]],
expected_logits=[[-2.08, 2.08, 1.19]])
def test_multi_example_multi_dim_logits(self):
"""Tests multiple examples and multi-dimensional logits.
input_layer = [[10], [5]]
hidden_layer_0 = [[relu(0.6*10 +0.1), relu(0.5*10 -0.1)],
[relu(0.6*5 +0.1), relu(0.5*5 -0.1)]]
= [[6.1, 4.9], [3.1, 2.4]]
hidden_layer_1 = [[relu(1*6.1 -0.8*4.9 +0.2), relu(0.8*6.1 -1*4.9 -0.1)],
[relu(1*3.1 -0.8*2.4 +0.2), relu(0.8*3.1 -1*2.4 -0.1)]]
= [[2.38, 0], [1.38, 0]]
logits = [[-1*2.38 +0.3, 1*2.38 -0.3, 0.5*2.38],
[-1*1.38 +0.3, 1*1.38 -0.3, 0.5*1.38]]
= [[-2.08, 2.08, 1.19], [-1.08, 1.08, 0.69]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5]], [.1, -.1]), ([[1., .8], [-.8, -1.]],
[.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10.], [5.]],
expected_logits=[[-2.08, 2.08, 1.19], [-1.08, 1.08, .69]])
def test_multi_dim_input_one_dim_logits(self):
"""Tests multi-dimensional inputs and one-dimensional logits.
input_layer = [[10, 8]]
hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]]
= [[1.3, 0.9]]
hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]]
= [[0.78, relu(-0.06)]] = [[0.78, 0]]
logits = [[-1*0.78 +1*0 +0.3]] = [[-0.48]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1.], [1.]], [.3]),), base_global_step,
self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=1,
inputs=[[10., 8.]],
expected_logits=[[-0.48]])
def test_multi_dim_input_multi_dim_logits(self):
"""Tests multi-dimensional inputs and multi-dimensional logits.
input_layer = [[10, 8]]
hidden_layer_0 = [[relu(0.6*10 -0.6*8 +0.1), relu(0.5*10 -0.5*8 -0.1)]]
= [[1.3, 0.9]]
hidden_layer_1 = [[relu(1*1.3 -0.8*0.9 + 0.2), relu(0.8*1.3 -1*0.9 -0.2)]]
= [[0.78, relu(-0.06)]] = [[0.78, 0]]
logits = [[-1*0.78 + 0.3, 1*0.78 -0.3, 0.5*0.78]] = [[-0.48, 0.48, 0.39]]
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
self._test_logits(
mode,
hidden_units=(2, 2),
logits_dimension=3,
inputs=[[10., 8.]],
expected_logits=[[-0.48, 0.48, 0.39]])
def test_multi_feature_column_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
create_checkpoint((([[.6, .5], [-.6, -.5]],
[.1, -.1]), ([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),),
base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
with ops.Graph().as_default():
# Global step needed for MonitoredSession, which is in turn used to
# explicitly set variable weights through a checkpoint.
training_util.create_global_step()
# Use a variable scope here with 'dnn', emulating the dnn model_fn, so
# the checkpoint naming is shared.
with variable_scope.variable_scope('dnn'):
input_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=0, min_slice_size=64 << 20))
logit_fn = self._dnn_logit_fn_builder(
units=logits_dimension,
hidden_units=hidden_units,
feature_columns=[
self._fc_impl.numeric_column('age'),
self._fc_impl.numeric_column('height')
],
activation_fn=nn.relu,
dropout=None,
input_layer_partitioner=input_layer_partitioner,
batch_norm=False)
logits = logit_fn(
features={
'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])
},
mode=mode)
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
self.assertAllClose(expected_logits, sess.run(logits))
def test_multi_feature_column_mix_multi_dim_logits(self):
"""Tests multiple feature columns and multi-dimensional logits.
All numbers are the same as test_multi_dim_input_multi_dim_logits. The only
difference is that the input consists of two 1D feature columns, instead of
one 2D feature column.
"""
base_global_step = 100
create_checkpoint((
([[.6, .5], [-.6, -.5]], [.1, -.1]),
([[1., .8], [-.8, -1.]], [.2, -.2]),
([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]),
), base_global_step, self._model_dir)
hidden_units = (2, 2)
logits_dimension = 3
inputs = ([[10.]], [[8.]])
expected_logits = [[-0.48, 0.48, 0.39]]
for mode in [
ModeKeys.TRAIN, ModeKeys.EVAL,
ModeKeys.PREDICT
]:
with ops.Graph().as_default():
# Global step needed for MonitoredSession, which is in turn used to
# explicitly set variable weights through a checkpoint.
training_util.create_global_step()
# Use a variable scope here with 'dnn', emulating the dnn model_fn, so
# the checkpoint naming is shared.
with variable_scope.variable_scope('dnn'):
input_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=0, min_slice_size=64 << 20))
logit_fn = self._dnn_logit_fn_builder(
units=logits_dimension,
hidden_units=hidden_units,
feature_columns=[
feature_column.numeric_column('age'),
feature_column_v2.numeric_column('height')
],
activation_fn=nn.relu,
dropout=None,
input_layer_partitioner=input_layer_partitioner,
batch_norm=False)
logits = logit_fn(
features={
'age': constant_op.constant(inputs[0]),
'height': constant_op.constant(inputs[1])
},
mode=mode)
with monitored_session.MonitoredTrainingSession(
checkpoint_dir=self._model_dir) as sess:
self.assertAllClose(expected_logits, sess.run(logits))
class _SummaryHook(session_run_hook.SessionRunHook):
"""Saves summaries every N steps."""
def _assert_checkpoint(
testcase, global_step, input_units, hidden_units, output_units, model_dir):
"""Asserts checkpoint contains expected variables with proper shapes.
Args:
testcase: A TestCase instance.
global_step: Expected global step value.
input_units: The dimension of input layer.
hidden_units: Iterable of integer sizes for the hidden layers.
output_units: The dimension of output layer (logits).
model_dir: The model directory.
"""
shapes = {
name: shape
for (name, shape) in checkpoint_utils.list_variables(model_dir)
}
# Global step.
testcase.assertEqual([], shapes[ops.GraphKeys.GLOBAL_STEP])
testcase.assertEqual(
global_step,
checkpoint_utils.load_variable(
model_dir, ops.GraphKeys.GLOBAL_STEP))
# Hidden layer weights.
prev_layer_units = input_units
for i in range(len(hidden_units)):
layer_units = hidden_units[i]
testcase.assertAllEqual(
(prev_layer_units, layer_units),
shapes[HIDDEN_WEIGHTS_NAME_PATTERN % i])
testcase.assertAllEqual(
(layer_units,),
shapes[HIDDEN_BIASES_NAME_PATTERN % i])
prev_layer_units = layer_units
# Output layer weights.
testcase.assertAllEqual((prev_layer_units, output_units),
shapes[LOGITS_WEIGHTS_NAME])
testcase.assertAllEqual((output_units,),
shapes[LOGITS_BIASES_NAME])
def _assert_simple_summary(testcase, expected_values, actual_summary):
"""Assert summary the specified simple values.
Args:
testcase: A TestCase instance.
expected_values: Dict of expected tags and simple values.
actual_summary: `summary_pb2.Summary`.
"""
testcase.assertAllClose(expected_values, {
v.tag: v.simple_value
for v in actual_summary.value if (v.tag in expected_values)
})
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] | 2.034389 | 17,593 |
# Copyright The IETF Trust 2007, All Rights Reserved
# Django settings for ietf project.
# BASE_DIR and "settings_local" are from
# http://code.djangoproject.com/wiki/SplitSettings
import os
try:
import syslog
syslog.openlog("datatracker", syslog.LOG_PID, syslog.LOG_USER)
except ImportError:
pass
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# a place to put ajax logs if necessary.
LOG_DIR = '/var/log/datatracker'
import sys
sys.path.append(os.path.abspath(BASE_DIR + "/.."))
import datetime
from ietf import __version__
DEBUG = False
TEMPLATE_DEBUG = DEBUG
# Valid values:
# 'production', 'test', 'development'
# Override this in settings_local.py if it's not the desired setting:
SERVER_MODE = 'production'
# Domain name of the IETF
IETF_DOMAIN = 'ietf.org'
ADMINS = (
('IETF Django Developers', 'django-project@' + IETF_DOMAIN),
('GMail Tracker Archive', '[email protected]'),
('Henrik Levkowetz', '[email protected]'),
('Robert Sparks', '[email protected]'),
('Ole Laursen', '[email protected]'),
('Ryan Cross', '[email protected]'),
)
ALLOWED_HOSTS = [".ietf.org", ".ietf.org.", "209.208.19.216", "4.31.198.44", ]
# Server name of the tools server
TOOLS_SERVER = 'tools.' + IETF_DOMAIN
# Override this in the settings_local.py file:
SERVER_EMAIL = 'Django Server <django-project@' + TOOLS_SERVER + '>'
DEFAULT_FROM_EMAIL = 'IETF Secretariat <ietf-secretariat-reply@' + IETF_DOMAIN + '>'
MANAGERS = ADMINS
DATABASES = {
'default': {
'NAME': 'ietf_utf8',
'ENGINE': 'django.db.backends.mysql',
'USER': 'ietf',
#'PASSWORD': 'ietf',
#'OPTIONS': {},
},
# 'legacy': {
# 'NAME': 'ietf',
# 'ENGINE': 'django.db.backends.mysql',
# 'USER': 'ietf',
# #'PASSWORD': 'ietf',
# },
}
DATABASE_TEST_OPTIONS = {
# Comment this out if your database doesn't support InnoDB
'init_command': 'SET storage_engine=InnoDB',
}
# Local time zone for this installation. Choices can be found here:
# http://www.postgresql.org/docs/8.1/static/datetime-keywords.html#DATETIME-TIMEZONE-SET-TABLE
# although not all variations may be possible on all operating systems.
# If running in a Windows environment this must be set to the same as your
# system time zone.
TIME_ZONE = 'PST8PDT'
# Language code for this installation. All choices can be found here:
# http://www.w3.org/TR/REC-html40/struct/dirlang.html#langcodes
# http://blogs.law.harvard.edu/tech/stories/storyReader$15
LANGUAGE_CODE = 'en-us'
SITE_ID = 1
# If you set this to False, Django will make some optimizations so as not
# to load the internationalization machinery.
USE_I18N = False
USE_TZ = False
MEDIA_URL = 'https://www.ietf.org/'
# Absolute path to the directory static files should be collected to.
# Example: "/var/www/example.com/static/"
SERVE_CDN_FILES_LOCALLY_IN_DEV_MODE = True
# URL to use when referring to static files located in STATIC_ROOT.
#if SERVER_MODE != 'production' and SERVE_CDN_FILES_LOCALLY_IN_DEV_MODE:
STATIC_URL = "/static/"
STATIC_ROOT = os.path.abspath(BASE_DIR + "/../static/")
#else:
# STATIC_URL = "https://www.ietf.org/lib/dt/%s/"%__version__
# STATIC_ROOT = "/a/www/www6s/lib/dt/%s/"%__version__
# Destination for components handled by djangobower
COMPONENT_ROOT = BASE_DIR + "/externals/static/"
COMPONENT_URL = STATIC_URL
# List of finder classes that know how to find static files in
# various locations.
STATICFILES_FINDERS = (
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
'ietf.utils.bower_storage.BowerStorageFinder',
)
WSGI_APPLICATION = "ietf.wsgi.application"
AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', )
#DATABASE_ROUTERS = ["ietf.legacy_router.LegacyRouter"]
# -------------------------------sh -----------------------------------------
# Django/Python Logging Framework Modifications
# enable HTML error emails
from django.utils.log import DEFAULT_LOGGING
LOGGING = DEFAULT_LOGGING.copy()
LOGGING['handlers']['mail_admins']['include_html'] = True
# Filter out "Invalid HTTP_HOST" emails
# Based on http://www.tiwoc.de/blog/2013/03/django-prevent-email-notification-on-suspiciousoperation/
from django.core.exceptions import SuspiciousOperation
LOGGING['filters']['skip_suspicious_operations'] = {
'()': 'django.utils.log.CallbackFilter',
'callback': skip_suspicious_operations,
}
LOGGING['handlers']['mail_admins']['filters'] += [ 'skip_suspicious_operations' ]
# Filter out UreadablePostError:
from django.http import UnreadablePostError
LOGGING['filters']['skip_unreadable_posts'] = {
'()': 'django.utils.log.CallbackFilter',
'callback': skip_unreadable_post,
}
LOGGING['handlers']['mail_admins']['filters'] += [ 'skip_unreadable_posts' ]
# End logging
# ------------------------------------------------------------------------
#SESSION_COOKIE_AGE = 60 * 60 * 24 * 7 * 2 # Age of cookie, in seconds: 2 weeks.
SESSION_COOKIE_AGE = 60 * 60 * 24 * 365 * 50 # Age of cookie, in seconds: 50 years
SESSION_EXPIRE_AT_BROWSER_CLOSE = False
SESSION_SERIALIZER = 'django.contrib.sessions.serializers.PickleSerializer'
TEMPLATE_LOADERS = (
('django.template.loaders.cached.Loader', (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
)),
'ietf.dbtemplate.template.Loader',
)
MIDDLEWARE_CLASSES = (
'django.middleware.csrf.CsrfViewMiddleware',
'django.middleware.common.CommonMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.http.ConditionalGetMiddleware',
'ietf.middleware.SQLLogMiddleware',
'ietf.middleware.SMTPExceptionMiddleware',
'ietf.middleware.RedirectTrailingPeriod',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'ietf.middleware.UnicodeNfkcNormalization',
# 'debug_toolbar.middleware.DebugToolbarMiddleware', #
)
ROOT_URLCONF = 'ietf.urls'
TEMPLATE_DIRS = (
BASE_DIR + "/templates",
BASE_DIR + "/secr/templates",
)
TEMPLATE_CONTEXT_PROCESSORS = (
'django.contrib.auth.context_processors.auth',
'django.core.context_processors.debug',
'django.core.context_processors.i18n',
'django.core.context_processors.request',
'django.core.context_processors.media',
'django.contrib.messages.context_processors.messages',
'ietf.context_processors.server_mode',
'ietf.context_processors.revision_info',
'ietf.secr.context_processors.secr_revision_info',
'ietf.context_processors.rfcdiff_base_url',
)
# Additional locations of static files (in addition to each app's static/ dir)
STATICFILES_DIRS = (
os.path.join(BASE_DIR, 'static'),
os.path.join(BASE_DIR, 'secr/static'),
os.path.join(BASE_DIR, 'externals/static'),
)
INSTALLED_APPS = (
# Django apps
'django.contrib.admin',
'django.contrib.admindocs',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.humanize',
'django.contrib.messages',
'django.contrib.sessions',
'django.contrib.sitemaps',
'django.contrib.sites',
'django.contrib.staticfiles',
# External apps
'bootstrap3',
'djangobwr',
'form_utils',
'tastypie',
'widget_tweaks',
# IETF apps
'ietf.api',
'ietf.community',
'ietf.dbtemplate',
'ietf.doc',
'ietf.group',
'ietf.idindex',
'ietf.iesg',
'ietf.ietfauth',
'ietf.ipr',
'ietf.liaisons',
'ietf.mailinglists',
'ietf.mailtrigger',
'ietf.meeting',
'ietf.message',
'ietf.name',
'ietf.nomcom',
'ietf.person',
'ietf.redirects',
'ietf.release',
'ietf.submit',
'ietf.sync',
'ietf.utils',
# IETF Secretariat apps
'ietf.secr.announcement',
'ietf.secr.areas',
'ietf.secr.drafts',
'ietf.secr.groups',
'ietf.secr.meetings',
'ietf.secr.proceedings',
'ietf.secr.roles',
'ietf.secr.rolodex',
'ietf.secr.sreq',
'ietf.secr.telechat',
)
# Settings for django-bootstrap3
# See http://django-bootstrap3.readthedocs.org/en/latest/settings.html
BOOTSTRAP3 = {
# Label class to use in horizontal forms
'horizontal_label_class': 'col-md-2',
# Field class to use in horiozntal forms
'horizontal_field_class': 'col-md-10',
# Set HTML required attribute on required fields
'set_required': True,
# Set placeholder attributes to label if no placeholder is provided
'set_placeholder': False,
# Class to indicate required
'form_required_class': 'bootstrap3-required',
# Class to indicate error
'form_error_class': 'bootstrap3-error',
}
INTERNAL_IPS = (
# AMS servers
'64.170.98.32',
'64.170.98.86',
# local
'127.0.0.1',
'::1',
)
# no slash at end
IDTRACKER_BASE_URL = "https://datatracker.ietf.org"
RFCDIFF_BASE_URL = "https://www.ietf.org/rfcdiff"
# The name of the method to use to invoke the test suite
TEST_RUNNER = 'ietf.utils.test_runner.IetfTestRunner'
# Fixtures which will be loaded before testing starts
GLOBAL_TEST_FIXTURES = [ 'names','ietf.utils.test_data.make_immutable_base_data','nomcom_templates' ]
TEST_DIFF_FAILURE_DIR = "/tmp/test/failure/"
TEST_GHOSTDRIVER_LOG_PATH = "ghostdriver.log"
TEST_MATERIALS_DIR = "tmp-meeting-materials-dir"
TEST_BLUESHEET_DIR = "tmp-bluesheet-dir"
# These are regexes
TEST_URL_COVERAGE_EXCLUDE = [
"^\^admin/",
]
# Tese are filename globs
TEST_CODE_COVERAGE_EXCLUDE = [
"*/tests*",
"*/admin.py",
"*/migrations/*",
"ietf/settings*",
"ietf/utils/test_runner.py",
]
TEST_COVERAGE_MASTER_FILE = os.path.join(BASE_DIR, "../release-coverage.json.gz")
TEST_COVERAGE_LATEST_FILE = os.path.join(BASE_DIR, "../latest-coverage.json")
TEST_CODE_COVERAGE_CHECKER = None
if SERVER_MODE != 'production':
import coverage
TEST_CODE_COVERAGE_CHECKER = coverage.Coverage(source=[ BASE_DIR ], cover_pylib=False, omit=TEST_CODE_COVERAGE_EXCLUDE)
TEST_CODE_COVERAGE_REPORT_PATH = "coverage/"
TEST_CODE_COVERAGE_REPORT_URL = os.path.join(STATIC_URL, TEST_CODE_COVERAGE_REPORT_PATH, "index.html")
TEST_CODE_COVERAGE_REPORT_DIR = os.path.join(BASE_DIR, "static", TEST_CODE_COVERAGE_REPORT_PATH)
TEST_CODE_COVERAGE_REPORT_FILE = os.path.join(TEST_CODE_COVERAGE_REPORT_DIR, "index.html")
# WG Chair configuration
MAX_WG_DELEGATES = 3
DATE_FORMAT = "Y-m-d"
DATETIME_FORMAT = "Y-m-d H:i T"
# Override this in settings_local.py if needed
# *_PATH variables ends with a slash/ .
DOCUMENT_PATH_PATTERN = '/a/www/ietf-ftp/{doc.type_id}/'
INTERNET_DRAFT_PATH = '/a/www/ietf-ftp/internet-drafts/'
INTERNET_DRAFT_PDF_PATH = '/a/www/ietf-datatracker/pdf/'
RFC_PATH = '/a/www/ietf-ftp/rfc/'
CHARTER_PATH = '/a/www/ietf-ftp/charter/'
CONFLICT_REVIEW_PATH = '/a/www/ietf-ftp/conflict-reviews'
STATUS_CHANGE_PATH = '/a/www/ietf-ftp/status-changes'
AGENDA_PATH = '/a/www/www6s/proceedings/'
IPR_DOCUMENT_PATH = '/a/www/ietf-ftp/ietf/IPR/'
IESG_TASK_FILE = '/a/www/www6/iesg/internal/task.txt'
IESG_ROLL_CALL_FILE = '/a/www/www6/iesg/internal/rollcall.txt'
IESG_MINUTES_FILE = '/a/www/www6/iesg/internal/minutes.txt'
IESG_WG_EVALUATION_DIR = "/a/www/www6/iesg/evaluation"
# Move drafts to this directory when they expire
INTERNET_DRAFT_ARCHIVE_DIR = '/a/www/www6s/draft-archive'
# The following directory contains linked copies of all drafts, but don't
# write anything to this directory -- its content is maintained by ghostlinkd:
INTERNET_ALL_DRAFTS_ARCHIVE_DIR = '/a/www/www6s/archive/id'
MEETING_RECORDINGS_DIR = '/a/www/audio'
# Mailing list info URL for lists hosted on the IETF servers
MAILING_LIST_INFO_URL = "https://www.ietf.org/mailman/listinfo/%(list_addr)s"
# Liaison Statement Tool settings (one is used in DOC_HREFS below)
LIAISON_UNIVERSAL_FROM = 'Liaison Statement Management Tool <lsmt@' + IETF_DOMAIN + '>'
LIAISON_ATTACH_PATH = '/a/www/ietf-datatracker/documents/LIAISON/' # should end in a slash
LIAISON_ATTACH_URL = 'https://www.ietf.org/lib/dt/documents/LIAISON/' # should end in a slash, location should have a symlink to LIAISON_ATTACH_PATH
# Ideally, more of these would be local -- but since we don't support
# versions right now, we'll point to external websites
DOC_HREFS = {
"charter": "https://www.ietf.org/charter/{doc.name}-{doc.rev}.txt",
"draft": "https://www.ietf.org/archive/id/{doc.name}-{doc.rev}.txt",
"slides": "https://www.ietf.org/slides/{doc.name}-{doc.rev}",
"conflrev": "https://www.ietf.org/cr/{doc.name}-{doc.rev}.txt",
"statchg": "https://www.ietf.org/sc/{doc.name}-{doc.rev}.txt",
"liaison": "%s{doc.external_url}" % LIAISON_ATTACH_URL,
"liai-att": "%s{doc.external_url}" % LIAISON_ATTACH_URL,
}
MEETING_DOC_HREFS = {
"agenda": "/meeting/{meeting}/agenda/{doc.group.acronym}/",
"minutes": "https://www.ietf.org/proceedings/{meeting}/minutes/{doc.external_url}",
"slides": "https://www.ietf.org/proceedings/{meeting}/slides/{doc.external_url}",
"recording": "{doc.external_url}",
}
# Override this in settings_local.py if needed
CACHE_MIDDLEWARE_SECONDS = 300
CACHE_MIDDLEWARE_KEY_PREFIX = ''
# The default with no CACHES setting is 'django.core.cache.backends.locmem.LocMemCache'
# This setting is possibly overridden further down, after the import of settings_local
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
'LOCATION': '127.0.0.1:11211',
}
}
IPR_EMAIL_FROM = '[email protected]'
IANA_EVAL_EMAIL = "[email protected]"
# Put real password in settings_local.py
IANA_SYNC_PASSWORD = "secret"
IANA_SYNC_CHANGES_URL = "https://datatracker.iana.org:4443/data-tracker/changes"
IANA_SYNC_PROTOCOLS_URL = "https://www.iana.org/protocols/"
RFC_TEXT_RSYNC_SOURCE="ftp.rfc-editor.org::rfcs-text-only"
RFC_EDITOR_SYNC_PASSWORD="secret"
RFC_EDITOR_SYNC_NOTIFICATION_URL = "https://www.rfc-editor.org/parser/parser.php"
RFC_EDITOR_QUEUE_URL = "https://www.rfc-editor.org/queue2.xml"
RFC_EDITOR_INDEX_URL = "https://www.rfc-editor.org/rfc/rfc-index.xml"
# NomCom Tool settings
ROLODEX_URL = ""
NOMCOM_PUBLIC_KEYS_DIR = '/a/www/nomcom/public_keys/'
NOMCOM_FROM_EMAIL = '[email protected]'
OPENSSL_COMMAND = '/usr/bin/openssl'
DAYS_TO_EXPIRE_NOMINATION_LINK = ''
DEFAULT_FEEDBACK_TYPE = 'offtopic'
NOMINEE_FEEDBACK_TYPES = ['comment', 'questio', 'nomina']
# ID Submission Tool settings
IDSUBMIT_FROM_EMAIL = 'IETF I-D Submission Tool <[email protected]>'
IDSUBMIT_ANNOUNCE_FROM_EMAIL = '[email protected]'
IDSUBMIT_ANNOUNCE_LIST_EMAIL = '[email protected]'
# Days from meeting to day of cut off dates on submit -- cutoff_time_utc is added to this
IDSUBMIT_DEFAULT_CUTOFF_DAY_OFFSET_00 = 13
IDSUBMIT_DEFAULT_CUTOFF_DAY_OFFSET_01 = 13
IDSUBMIT_DEFAULT_CUTOFF_TIME_UTC = datetime.timedelta(hours=23, minutes=59, seconds=59)
IDSUBMIT_DEFAULT_CUTOFF_WARNING_DAYS = datetime.timedelta(days=21)
IDSUBMIT_REPOSITORY_PATH = INTERNET_DRAFT_PATH
IDSUBMIT_STAGING_PATH = '/a/www/www6s/staging/'
IDSUBMIT_STAGING_URL = '//www.ietf.org/staging/'
IDSUBMIT_IDNITS_BINARY = '/a/www/ietf-datatracker/scripts/idnits'
IDSUBMIT_FILE_TYPES = (
'txt',
'xml',
'pdf',
'ps',
)
IDSUBMIT_MAX_DRAFT_SIZE = {
'txt': 6*1024*1024, # Max size of txt draft file in bytes
'xml': 10*1024*1024, # Max size of xml draft file in bytes
'pdf': 10*1024*1024,
'ps' : 10*1024*1024,
}
IDSUBMIT_MAX_DAILY_SAME_DRAFT_NAME = 20
IDSUBMIT_MAX_DAILY_SAME_DRAFT_NAME_SIZE = 50 # in MB
IDSUBMIT_MAX_DAILY_SAME_SUBMITTER = 50
IDSUBMIT_MAX_DAILY_SAME_SUBMITTER_SIZE = 150 # in MB
IDSUBMIT_MAX_DAILY_SAME_GROUP = 150
IDSUBMIT_MAX_DAILY_SAME_GROUP_SIZE = 450 # in MB
IDSUBMIT_MAX_DAILY_SUBMISSIONS = 1000
IDSUBMIT_MAX_DAILY_SUBMISSIONS_SIZE = 2000 # in MB
XML_LIBRARY = "/www/tools.ietf.org/tools/xml2rfc/web/public/rfc/"
MEETING_MATERIALS_SUBMISSION_START_DAYS = -90
MEETING_MATERIALS_SUBMISSION_CUTOFF_DAYS = 26
MEETING_MATERIALS_SUBMISSION_CORRECTION_DAYS = 50
INTERNET_DRAFT_DAYS_TO_EXPIRE = 185
DOT_BINARY = '/usr/bin/dot'
UNFLATTEN_BINARY= '/usr/bin/unflatten'
PS2PDF_BINARY = '/usr/bin/ps2pdf'
RSYNC_BINARY = '/usr/bin/rsync'
# Account settings
DAYS_TO_EXPIRE_REGISTRATION_LINK = 3
HTPASSWD_COMMAND = "/usr/bin/htpasswd2"
HTPASSWD_FILE = "/www/htpasswd"
# Generation of bibxml files for xml2rfc
BIBXML_BASE_PATH = '/a/www/ietf-ftp/xml2rfc'
# Timezone files for iCalendar
TZDATA_ICS_PATH = BASE_DIR + '/../vzic/zoneinfo/'
CHANGELOG_PATH = BASE_DIR + '/../changelog'
SECR_BLUE_SHEET_PATH = '/a/www/ietf-datatracker/documents/blue_sheet.rtf'
SECR_BLUE_SHEET_URL = '//datatracker.ietf.org/documents/blue_sheet.rtf'
SECR_INTERIM_LISTING_DIR = '/a/www/www6/meeting/interim'
SECR_MAX_UPLOAD_SIZE = 40960000
SECR_PROCEEDINGS_DIR = '/a/www/www6s/proceedings/'
SECR_PPT2PDF_COMMAND = ['/usr/bin/soffice','--headless','--convert-to','pdf','--outdir']
USE_ETAGS=True
PRODUCTION_TIMEZONE = "America/Los_Angeles"
PYFLAKES_DEFAULT_ARGS= ["ietf", ]
VULTURE_DEFAULT_ARGS= ["ietf", ]
# Automatic Scheduling
#
# how much to login while running, bigger numbers make it more verbose.
BADNESS_CALC_LOG = 0
#
# these penalties affect the calculation of how bad the assignments are.
BADNESS_UNPLACED = 1000000
# following four are used only during migrations to setup up ConstraintName
# and penalties are taken from the database afterwards.
BADNESS_BETHERE = 200000
BADNESS_CONFLICT_1 = 100000
BADNESS_CONFLICT_2 = 10000
BADNESS_CONFLICT_3 = 1000
BADNESS_TOOSMALL_50 = 5000
BADNESS_TOOSMALL_100 = 50000
BADNESS_TOOBIG = 100
BADNESS_MUCHTOOBIG = 500
# do not run SELENIUM tests by default
SELENIUM_TESTS = False
SELENIUM_TESTS_ONLY = False
# Domain which hosts draft and wg alias lists
DRAFT_ALIAS_DOMAIN = IETF_DOMAIN
GROUP_ALIAS_DOMAIN = IETF_DOMAIN
# Path to the email alias lists. Used by ietf.utils.aliases
DRAFT_ALIASES_PATH = "/a/postfix/draft-aliases"
DRAFT_VIRTUAL_PATH = "/a/postfix/draft-virtual"
# Set debug apps in DEV_APPS settings_local
DEV_APPS = ()
DRAFT_VIRTUAL_DOMAIN = "virtual.ietf.org"
GROUP_ALIASES_PATH = "/a/postfix/group-aliases"
GROUP_VIRTUAL_PATH = "/a/postfix/group-virtual"
GROUP_VIRTUAL_DOMAIN = "virtual.ietf.org"
POSTCONFIRM_PATH = "/a/postconfirm/test-wrapper"
USER_PREFERENCE_DEFAULTS = {
"expires_soon" : "14",
"new_enough" : "14",
"full_draft" : "off",
"left_menu" : "on",
}
# Put the production SECRET_KEY in settings_local.py, and also any other
# sensitive or site-specific changes. DO NOT commit settings_local.py to svn.
from settings_local import * # pyflakes:ignore
# Add DEV_APPS to INSTALLED_APPS
INSTALLED_APPS += DEV_APPS
INSTALLED_APPS += CODESTAND_APPS
# We provide a secret key only for test and development modes. It's
# absolutely vital that django fails to start in production mode unless a
# secret key has been provided elsewhere, not in this file which is
# publicly available, for instance from the source repository.
if SERVER_MODE != 'production':
# stomp out the cached template loader, it's annoying
TEMPLATE_LOADERS = tuple(l for e in TEMPLATE_LOADERS for l in (e[1] if isinstance(e, tuple) and "cached.Loader" in e[0] else (e,)))
CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.dummy.DummyCache',
}
}
if 'SECRET_KEY' not in locals():
SECRET_KEY = 'PDwXboUq!=hPjnrtG2=ge#N$Dwy+wn@uivrugwpic8mxyPfHka'
ALLOWED_HOSTS = ['*',]
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] | 2.413188 | 8,098 |
# Logistic regression
#%%
# Importing libraries
import numpy as np
import random
from matplotlib import pyplot as plt
#%%
#%%
# Generating training data
x = np.linspace(1, 10, 1000)[:, np.newaxis].T
y = np.hstack((np.zeros((1,500)), np.ones((1,500))))
# Adding some randomness
y[0,random.randint(0,500)] = 1
y[0,random.randint(500,1000)] = 0
print("X is", x.shape)
print("y is", y.shape)
#%%
# Creating and training the model
model = LogisticRegression()
model.train(x, y)
#%%
# Generating a test example
x1 = np.array([[0.05]])
# Predicting the output
y1 = model.predict(x1)
print("Prediction of", x1, " is", y1)
#%%
# Generating predictions for all input examples X
x_test = np.linspace(1, 10, 100)[:,np.newaxis].T
y_p = model.predict(x_test)
#%%
# Plotting training data
plt.figure(0)
plt.plot(x, y, "r+")
# plt.show()
# Plotting predictions
print("x is", x.shape, "and y_p is", y_p.shape)
print("max of y_p:", np.max(y_p))
plt.plot(x_test, y_p, "bo")
plt.show() | [
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] | 2.448363 | 397 |
import os
import unittest
from recipe_scrapers.whatsgabycooking import WhatsGabyCooking
| [
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# -*- coding: utf-8 -*-
#
# michael a.g. aïvázis
# orthologue
# (c) 1998-2019 all rights reserved
#
# superclasses
from .Communicator import Communicator
# declaration
class Cartesian(Communicator):
"""
An encapsulation of Cartesian communicators
"""
# per-instance public data
axes = None
periods = None
coordinates = None
# meta methods
# end of file
| [
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] | 2.821429 | 140 |
from configparser import ConfigParser
from pymongo import MongoClient
from math import pow, sqrt
__author__ = 'MuhamadNoorZainal MuhamadZabidi'
__version__ = '1.5'
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] | 2.428571 | 77 |
from typing import Dict, Tuple, List, Any
import logging
from overrides import overrides
from conllu import parse_incr, string_to_file
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import Field, TextField, SequenceLabelField, MetadataField
from allennlp.data.instance import Instance
from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenIndexer
from allennlp.data.tokenizers import Token, Tokenizer
from .enhanced_universal_dependencies_oracle import get_oracle_actions
logger = logging.getLogger(__name__)
METADATA_PARSERS = {
"sent_id": lambda key, value: (key, value),
"text": lambda key, value: (key, value),
}
@DatasetReader.register("enhanced_universal_dependencies")
class EnhancedUniversalDependenciesDatasetReader(DatasetReader):
"""
Reads a file in the conllu Universal Dependencies format.
# Parameters
token_indexers : `Dict[str, TokenIndexer]`, optional (default=`{"tokens": SingleIdTokenIndexer()}`)
The token indexers to be applied to the words TextField.
tokenizer : `Tokenizer`, optional, default = None
A tokenizer to use to split the text. This is useful when the tokens that you pass
into the model need to have some particular attribute. Typically it is not necessary.
"""
@overrides
@overrides
def text_to_instance(
self, # type: ignore
words: List[str],
annotation: List[Dict[str,Any]],
gold_actions: List[str] = None,
multiwords: List[Dict[str,str]]=None,
sent_id: str = None,
text: str = None,
) -> Instance:
"""
# Parameters
words : `List[str]`, required.
The words in the sentence to be encoded.
upos_tags : `List[str]`, required.
The universal dependencies POS tags for each word.
dependencies : `List[Tuple[str, int]]`, optional (default = None)
A list of (head tag, head index) tuples. Indices are 1 indexed,
meaning an index of 0 corresponds to that word being the root of
the dependency tree.
# Returns
An instance containing words, upos tags, dependency head tags and head
indices as fields.
"""
fields: Dict[str, Field] = {}
text_field = TextField([Token(t) for t in words], self._token_indexers)
meta_dict = {"words": words, "annotation":annotation, "multiwords": multiwords, 'sent_id':sent_id, 'text':text}
fields["words"] = text_field
if gold_actions is not None:
meta_dict["gold_actions"] = gold_actions
fields["gold_actions"] = TextField([Token(a) for a in gold_actions], self._action_indexers)
fields["metadata"] = MetadataField(meta_dict)
return Instance(fields)
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] | 2.67313 | 1,083 |
import os
import time
import traceback
import numpy as np
import math
import cv2
from pathlib import Path
from osr2mp4 import logger
from osr2mp4.global_var import videoextensions
from osr2mp4.Exceptions import CannotCreateVideo, FourccIsNotExtension, WrongFourcc, LibAvNotFound
### TODO: MOVE THIS TO ITS OWN FILE
###
| [
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] | 3.19802 | 101 |
import pandas as pd
from sklearn.exceptions import NotFittedError
from pytorch_widedeep.wdtypes import * # noqa: F403
# This class does not represent any sctructural advantage, but I keep it to
# keep things tidy, as guidance for contribution and because is useful for the
# check_is_fitted function
class BasePreprocessor:
"""Base Class of All Preprocessors."""
def check_is_fitted(
estimator: BasePreprocessor,
attributes: List[str] = None,
all_or_any: str = "all",
condition: bool = True,
):
r"""Checks if an estimator is fitted
Parameters
----------
estimator: ``BasePreprocessor``,
An object of type ``BasePreprocessor``
attributes: List, default = None
List of strings with the attributes to check for
all_or_any: str, default = "all"
whether all or any of the attributes in the list must be present
condition: bool, default = True,
If not attribute list is passed, this condition that must be True for
the estimator to be considered as fitted
"""
estimator_name: str = estimator.__class__.__name__
error_msg = (
"This {} instance is not fitted yet. Call 'fit' with appropriate "
"arguments before using this estimator.".format(estimator_name)
)
if attributes is not None and all_or_any == "all":
if not all([hasattr(estimator, attr) for attr in attributes]):
raise NotFittedError(error_msg)
elif attributes is not None and all_or_any == "any":
if not any([hasattr(estimator, attr) for attr in attributes]):
raise NotFittedError(error_msg)
elif not condition:
raise NotFittedError(error_msg)
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25,
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220,
220,
220,
220,
220,
220,
220,
5298,
1892,
37,
2175,
12331,
7,
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62,
19662,
8,
198
] | 2.833613 | 595 |
from __future__ import annotations
from typing import NamedTuple
| [
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198,
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6738,
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] | 4.25 | 16 |
#!/usr/bin/env python3
""" Run a single-purpose HTTP server.
Server takes all GET requests and redirects them to a new host
if the request URI starts with SUBPATH, otherwise returns 404.
Requests are redirected to the URL provided by --baseurl. """
import socketserver
import http.server
import argparse
import sys
CHALLENGE_HOST = None
SUBPATH = "/.well-known/acme-challenge"
class ReusableServer(socketserver.TCPServer):
""" Allow TCPServer to reuse host address.
Without setting 'allow_reuse_address', we can get stuck in
TIME_WAIT after being killed and the stale state stops a new
server from attaching to the port."""
allow_reuse_address = True
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Redirect all URIs with matching prefix to another host")
parser.add_argument(
'--baseurl',
dest='baseurl',
required=True,
help="Destination URL for all matching URIs on this server")
args = parser.parse_args()
CHALLENGE_HOST = args.baseurl
if not CHALLENGE_HOST.startswith("http"):
print("Redirect URL must be a full URL starting with http")
sys.exit(1)
# If user gave us a trailing slash URL, remove slash.
if CHALLENGE_HOST[-1] == "/":
CHALLENGE_HOST = CHALLENGE_HOST[:-1]
serverAddress = ('', 80)
# Note: if running remotely by an SSH command, you MUST launch with '-t':
# > ssh -t me@otherhost leforward.py --baseurl http://otherserver.com
# If you omit '-t' the listening server won't terminate when you kill the
# ssh session, which probably isn't what you want.
with ReusableServer(serverAddress, RedirectChallenges) as httpd:
httpd.serve_forever()
| [
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2638,
67,
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2655,
303,
62,
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332,
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] | 2.884298 | 605 |
#############################################################
# 2016-09-26: ParameterType.py
# Author: Jeremy M. Gibson (State Archives of North Carolina)
#
# Description: Implementation of the parameter-type
##############################################################
from lxml.ElementInclude import etree
class Parameter:
""""""
def __init__(self, name=None, value=None):
"""Constructor for Parameter"""
self.name = name # type: str
self.value = value # type: str
def render(self, parent):
"""
:type parent: xml.etree.ElementTree.Element
:param parent:
:return:
"""
child = etree.SubElement(parent, "Parameter")
child1 = etree.SubElement(child, "Name")
child1.text = self.value
child2 = etree.SubElement(child, "Value")
child2.text = self.value | [
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796,
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] | 2.765079 | 315 |
#from server import db
#class User(db.Model):
# __tablename__ = 'users'
| [
2,
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] | 2.516129 | 31 |
import math
import numpy as np
from typing import Tuple, List | [
11748,
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198,
198,
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] | 3.647059 | 17 |
import logging
import numpy as np
import pickle
from copy import deepcopy
from ase.atoms import Atoms
from thyme.utils.cell import convert_cell_format
from thyme.utils.savenload import save_file, load_file
from thyme.utils.atomic_symbols import species_to_order_label
from ._key import *
| [
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import pygame
pygame.init()
ROWS = 45
COLS = 50
SQUARE_SIZE = 8
WIDTH, HEIGHT = 400, 400
win = pygame.display.set_mode((WIDTH, HEIGHT))
win.fill("white")
pygame.display.set_caption("PyPaint")
COLORS = ["red", "green", "blue", "yellow", "purple", "black"]
drawing_color = COLORS[5]
grid_squares = []
buttons = []
clear = pygame.image.load('eraser.png')
for i in range(COLS):
for j in range(ROWS):
square = GridSquare(i*SQUARE_SIZE, j*SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE)
grid_squares.append(square)
for i, color in enumerate(COLORS):
button = ColorButton(4*i+20*(i+1), 375, 20, 20, color)
buttons.append(button)
clock = pygame.time.Clock()
running = True
drawing = False
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
if event.type == pygame.MOUSEBUTTONDOWN:
drawing = True
if event.type == pygame.MOUSEBUTTONUP:
drawing = False
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE:
reset(grid_squares)
# Render the drawing grid
for square in grid_squares:
square.draw(win)
if draw_on_grid(drawing, square):
square.color = drawing_color
# Render the color buttons
for button in buttons:
button.draw(win)
if pick_color(button):
drawing_color = button.color
# Render the clear button
clear_rect = clear.get_rect(topleft = (188, 375))
win.blit(clear, clear_rect)
if pick_clear(clear_rect):
drawing_color = "white"
clock.tick(60)
pygame.display.update()
pygame.quit() | [
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] | 2.276567 | 734 |
"""Tests for the tucuxi.s3 Module using some fixtures from conftest.py"""
import logging
from typing import Dict
from typing import List
from tucuxi import S3
logger = logging.getLogger(__name__)
def test_set_get_object(s3_name: str, objs: List[Dict[str, str]]) -> None:
"""[summary]
Args:
s3_name (str): [description]
objs (List[Dict[str, str]]): [description]
"""
s3 = S3(s3_name)
logger.info("Testing set_object and get_object")
s3.set_object(objs[0]["key"], objs[0]["data"])
assert objs[0]["data"] == s3.get_object(objs[0]["key"]).decode("utf8")
def test_list_objects(s3_name: str, objs: List[Dict[str, str]]) -> None:
"""[summary]
Args:
s3_name (str): [description]
objs (List[Dict[str, str]]): [description]
"""
s3 = S3(s3_name)
logger.info("Testing get_all_s3_objects")
s3.set_object(objs[1]["key"], objs[1]["data"])
result = list(s3.list_objects("T"))
assert result == [objs[0]["key"], objs[1]["key"]]
def test_list_objects_prefix(s3_name: str, objs: List[Dict[str, str]]) -> None:
"""[summary]
Args:
s3_name (str): [description]
objs (List[Dict[str, str]]): [description]
"""
s3 = S3(s3_name)
logger.info("Testing get_by prefix")
assert objs[0]["key"][:-4] == next(s3.list_objects("T", "."))
def test_get_size(s3_name: str, objs: List[Dict[str, str]]) -> None:
"""[summary]
Args:
s3_name (str): [description]
objs (List[Dict[str, str]]): [description]
"""
s3 = S3(s3_name)
logger.info("Testing get_size")
assert 9 == s3.get_size(objs[0]["key"])
def test_view_tree(s3_name: str, objs: List[Dict[str, str]]) -> None:
"""[summary]
Args:
s3_name (str): [description]
objs (List[Dict[str, str]]): [description]
"""
s3 = S3(s3_name)
logger.info("Testing view_tree")
s3.view_tree()
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220,
220,
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62,
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3419,
198
] | 2.156708 | 887 |
from app import db
from models import Flaskr
# Create the database, and the table within.
db.create_all()
# Commit the changes.
db.session.commit()
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] | 3.409091 | 44 |
import pytest
from encoded.tests.features.conftest import app, app_settings, index_workbook
from pyramid.exceptions import HTTPBadRequest
pytestmark = [
pytest.mark.indexing,
pytest.mark.usefixtures('index_workbook'),
]
| [
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] | 3.012821 | 78 |
import torch
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
import numpy as np
import math
import os
import Audio
from text import text_to_sequence
from utils import process_text, pad_1D, pad_2D
import hparams
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class LightSpeechDataset(Dataset):
""" LJSpeech """
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] | 3.080645 | 124 |
from django.db import models
from django.contrib.auth.models import User
from aspc.activityfeed.signals import new_activity, delete_activity
from aspc.courses.models import Course
from amazon.api import AmazonAPI
import datetime
from aspc.settings import AMAZON_ACCESS_KEY, AMAZON_SECRET_KEY, AMAZON_ASSOC_TAG
import json
import logging
logger = logging.getLogger(__name__)
amazon = AmazonAPI(AMAZON_ACCESS_KEY, AMAZON_SECRET_KEY, AMAZON_ASSOC_TAG) | [
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] | 3.013423 | 149 |
# -*- coding: utf-8 -*-
#/usr/bin/env python
import numpy as np
import matplotlib.pylab as plt
from timer import Timer
from chebpy import ETDRK4FxCy, ETDRK4FxCy2, BC, ETDRK4
from chebpy import ROBIN, DIRICHLET
def test_etdrk4fxcy():
'''
The test function is
u = e^[f(x,y) - t]
where
f(x,y) = h(x) + g(y)
Assume it is the solution of following PDE
du/dt = (d^2/dx^2 + d^2/dy^2) u - w(x,y)u
in the domain [0,Lx]x[0,Ly] for time t=0 to t=1,
with boundary conditions
u(x+Lx,y,t) = u(x,y,t) # periodic in x direction
d/dy[u(x,y=0,t)] = ka u(y=0)
d/dy[u(x,y=Ly,t)] = kb u(y=Ly)
To generate a suitable solution, we assume
h(x) = sin(x)
h_x = dh/dx = cos(x)
h_xx = d^2h/dx^2 = -sin(x)
since it is periodic in x direction.
The corresponding w(x,y) is
w(x,y) = h_xx + g_yy + (h_x)^2 + (g_y)^2 + 1
1. For homogeneous NBC (ka=kb=0), a suitable g(y) is
g(y) = Ay^2(2y-3)/6
g_y = A(y^2-y) # g_y(y=0)=0, g_y(y=1)=0
g_yy = A(2*y-1)
where A is a positive constant.
Lx = 2*pi, Ly = 1.0, Nx = 64, Ny =32, Ns = 21
is a good parameter set. Note the time step ds = 1/(Ns-1) = 0.05 is very
large.
2. For homogeneous DBC, an approximate g(y) is
g(y) = -A(y-1)^2
g_y = -2A(y-1)
g_yy = -2A
where A is a positive and large constant.
Lx = 2*pi, Ly = 2.0, Nx = 64, Ny =32, Ns = 101
is a good parameter set.
3. For RBC, g(y) is given by
g(y) = -Ay
g_y = -A # ka=kb=-A
g_yy = 0
A is a positive constant.
Numerical result is different than the analytical one.
'''
Lx = 2*np.pi # x [0, Lx]
Nx = 64
Ly = 1.0 # y [0, Ly]
Ny = 127
Ns = 101
ds = 1. / (Ns - 1)
# Periodic in x direction, Fourier
xx = np.arange(Nx) * Lx / Nx
# Non-periodic in y direction, Chebyshev
ii = np.arange(Ny+1)
yy = np.cos(np.pi * ii / Ny) # yy [-1, 1]
yy = 0.5 * (yy + 1) * Ly # mapping to [0, Ly]
w = np.zeros([Nx,Ny+1])
A = 1.0
q = np.zeros([Ns, Nx, Ny+1])
q_exact = np.zeros([Nx, Ny+1])
#q[0] = 1.
for i in xrange(Nx):
for j in xrange(Ny+1):
x = xx[i]
y = yy[j]
# RBC
#q_exact[i,j] = np.exp(-A*y + np.sin(x) - 1)
#q[0,i,j] = np.exp(-A*y + np.sin(x))
#w[i,j] = np.cos(x)**2 - np.sin(x) + A**2 + 1
# homogeneous NBC
q_exact[i,j] = np.exp(A*y**2*(2*y-3)/6 + np.sin(x) - 1)
q[0,i,j] = np.exp(A*y**2*(2*y-3)/6 + np.sin(x))
w[i,j] = (A*y*(y-1))**2 + np.cos(x)**2 - np.sin(x) + A*(2*y-1) + 1
# homogeneous DBC
#q[0,i,j] = np.exp(-A*(y-1)**2 + np.sin(x))
#q_exact[i,j] = np.exp(-A*(y-1)**2 + np.sin(x) + 1)
#w[i, j] = np.cos(x)**2 - np.sin(x) + 4*A**2 + (2*A*(y-1))**2 + 1
# Fredrickson
#sech = 1. / np.cosh(0.25*(6*y[j]-3*Ly))
#w[i,j] = (1 - 2*sech**2)*(np.sin(2*np.pi*x[i]/Lx)+1)
#w[i,j] = (1 - 2*sech**2)
x = xx; y = yy
plt.imshow(w)
plt.xlabel('w')
plt.show()
plt.plot(x,w[:,Ny/2])
plt.xlabel('w(x)')
plt.show()
plt.plot(y,w[Nx/4,:])
plt.xlabel('w(y)')
plt.show()
# DBC
#lbc = BC(DIRICHLET, [0.0, 1.0, 0.0])
#rbc = BC(DIRICHLET, [0.0, 1.0, 0.0])
# RBC
#lbc = BC(ROBIN, [1.0, A, 0.0])
#rbc = BC(ROBIN, [1.0, A, 0.0])
# NBC
lbc = BC(ROBIN, [1.0, 0, 0.0])
rbc = BC(ROBIN, [1.0, 0, 0.0])
#q_solver = ETDRK4FxCy(Lx, Ly, Nx, Ny, Ns, h=ds, lbc=lbc, rbc=rbc)
q_solver = ETDRK4FxCy2(Lx, Ly, Nx, Ny, Ns, h=ds, lbc=lbc, rbc=rbc)
M = 100 # Took 1117.6 x 4 seconds for cpu one core
with Timer() as t:
for m in xrange(M):
q1 = q_solver.solve(w, q[0], q)
print "100 runs took ", t.secs, " seconds."
print 'Error =', np.max(np.abs(q1-q_exact))
plt.imshow(q[0])
plt.xlabel('q_0')
plt.show()
plt.imshow(q1)
plt.xlabel('q_solution')
plt.show()
plt.imshow(q_exact)
plt.xlabel('q_exact')
plt.show()
plt.plot(x,q[0,:,Ny/2], label='q0')
plt.plot(x,q1[:,Ny/2], label='q_solution')
plt.plot(x,q_exact[:,Ny/2], label='q_exact')
plt.legend(loc='best')
plt.xlabel('q[:,Ny/2]')
plt.show()
plt.plot(y,q[0,Nx/4,:], label='q0')
plt.plot(y,q1[Nx/4,:], label='q_solution')
plt.plot(y,q_exact[Nx/4,:], label='q_exact')
plt.legend(loc='best')
plt.xlabel('q[Nx/4,:]')
plt.show()
plt.plot(y,q[0,Nx*3/4,:], label='q0')
plt.plot(y,q1[Nx*3/4,:], label='q_solution')
plt.plot(y,q_exact[Nx*3/4,:], label='q_exact')
plt.legend(loc='best')
plt.xlabel('q[Nx*3/4,:]')
plt.show()
exit()
# Check with ETDRK4
sech = 1. / np.cosh(0.25*(6*y-3*Ly))
w1 = 1 - 2*sech**2
plt.plot(y,w1)
plt.show()
q = np.zeros([Ns, Ny+1])
q[0] = 1.
q_solver = ETDRK4(Ly,Ny,Ns,h=ds,lbc=lbc,rbc=rbc)
q1, y = q_solver.solve(w1, q[0], q)
plt.plot(y,q1)
plt.show()
def test_etdrk4():
'''
The test case is according to R. C. Daileda Lecture notes.
du/dt = (1/25) u_xx , x@(0,3)
with boundary conditions:
u(0,t) = 0
u_x(3,t) = -(1/2) u(3,t)
u(x,0) = 100*(1-x/3)
Conclusion:
We find that the numerical solution is much more accurate than the five
term approximation of the exact analytical solution.
'''
Nx = 64
Lx = 3
t = 1.
Ns = 101
ds = t/(Ns - 1)
ii = np.arange(Nx+1)
x = np.cos(np.pi * ii / Nx) # yy [-1, 1]
x = 0.5 * (x + 1) * Lx # mapping to [0, Ly]
w = np.zeros(Nx+1)
q = np.zeros([Ns, Nx+1])
q[0] = 100*(1-x/3)
# The approximation of exact solution by first 5 terms
q_exact = 47.0449*np.exp(-0.0210*t)*np.sin(0.7249*x) + \
45.1413*np.exp(-0.1113*t)*np.sin(1.6679*x) + \
21.3586*np.exp(-0.2872*t)*np.sin(2.6795*x) + \
19.3403*np.exp(-0.5505*t)*np.sin(3.7098*x) + \
12.9674*np.exp(-0.9015*t)*np.sin(4.7474*x)
lbc = BC(DIRICHLET, [0,1,0])
rbc = BC(ROBIN, [1.,0.5,0])
q_solver = ETDRK4(Lx,Nx,Ns,h=ds,c=1./25,lbc=lbc,rbc=rbc)
q1, x = q_solver.solve(w, q[0], q)
plt.plot(x, q[0], label='q_0')
plt.plot(x, q1, label='q_solution')
plt.plot(x, q_exact, label='q_exact')
plt.legend(loc='best')
plt.show()
def check(u):
'''
The PDE is
du/dt = u_xx + u_yy - wu
Calculate the residual using FD scheme.
R(x) = (u(x+h)
'''
pass
if __name__ == '__main__':
test_etdrk4fxcy()
#test_etdrk4()
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1303,
9288,
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316,
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628
] | 1.653196 | 4,083 |
# O(n) time, O(n) space | [
2,
440,
7,
77,
8,
640,
11,
440,
7,
77,
8,
2272
] | 1.916667 | 12 |
#!/usr/bin/python
# Written by Heiko 2019.02.07
# Will return the password change interval from Active Directory
import subprocess
result = subprocess.check_output( 'dsconfigad -show | grep \'Password change interval\' | awk \'{print $5}\' ', shell=True).strip()
print '<result>' + result + '</result>' | [
2,
48443,
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29,
6,
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1255,
1343,
705,
3556,
20274,
29,
6
] | 3.377778 | 90 |
#!/usr/bin/env python
import getpass
import time
from web3 import Web3, HTTPProvider
from eth_account import Account # will use directly instead of through web3 provider
from schedule import chainids, txs
print(len(txs), 'transactions in schedule.')
with open('infura.key') as keyfile:
infurakey = keyfile.read()
w3s = {
net: Web3(HTTPProvider('https://' + net + '.infura.io/v3/' + infurakey))
for net in chainids.keys()
}
# https://web3py.readthedocs.io/en/latest/middleware.html#geth-style-proof-of-authority
if 'rinkeby' in w3s.keys():
from web3.middleware import geth_poa_middleware
w3s['rinkeby'].middleware_onion.inject(geth_poa_middleware, layer=0)
# UGLY: assume Goerli is used, too
w3s['goerli'].middleware_onion.inject(geth_poa_middleware, layer=0)
# only ask for password once
with open('ethereum.key') as keyfile:
privkey = Account.decrypt(keyfile.read(), getpass.getpass())
acct = Account.privateKeyToAccount(privkey)
# get nonces on all chains
chainnonces = {}
for net, w3 in w3s.items():
print(net, 'block', w3.eth.getBlock('latest')['number'])
chainnonces[net] = w3.eth.getTransactionCount(acct.address) - 1
print('Nonces present:', chainnonces)
# print('Starting run in 10 seconds!')
# time.sleep(10)
print('Starting run:', time.ctime())
for nonce, tx in txs.items():
# don't even consider nonces present on all chains
if nonce < min(chainnonces.values()):
continue
if nonce == min(chainnonces.values()):
print('Nonces up to (and including)', nonce, 'present on all chains - skipping...')
continue
# populate "missing" key
tx['nonce'] = nonce
for net, w3 in w3s.items():
# skip txs already in _this_ chain
if tx['nonce'] <= chainnonces[net]:
print('Transaction with nonce', tx['nonce'], 'already included on', net, '- skipping...')
continue
# infura doesn't like chainId==0, so be explicit
tx['chainId'] = chainids[net]
# TODO: other ways to specify?..
if 'gasPrice' not in tx.keys():
tx['gasPrice'] = w3.eth.gasPrice
signed = acct.signTransaction(tx)
try:
txhash = w3.eth.sendRawTransaction(signed.rawTransaction)
print(net, 'tx with nonce', tx['nonce'], 'txhash', Web3.toHex(txhash))
except ValueError as e:
errorcode = e.args[0]['code']
# 'invalid sender' (everywhere?..) and 'transaction already imported' (kovan)
if errorcode != -32000 and errorcode != -32010:
raise e
else:
print('Transaction with nonce', tx['nonce'], 'already submitted to', net)
| [
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198
] | 2.431408 | 1,108 |
#!/usr/bin/env python
"""Build documentation and api."""
import os
EPYDOC = "python c:/programmi/python23/scripts/epydoc.py"
PSYCOPG = "c:/programmi/python23/lib/site-packages/psycopg2"
os.system("python ext2html.py ../doc/extensions.rst > ../doc/extensions.html")
os.system("%s "
"-o ../doc/api "
"--css ../doc/api-screen.css "
"--docformat restructuredtext "
"%s"
% (EPYDOC,PSYCOPG,))
| [
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] | 2.102804 | 214 |
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