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tests/conftest.py | weiss-d/baby-crawler | 0 | 85086 | import pytest
@pytest.fixture
def checkup_html() -> str:
return {
"html": """<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Test Page Title</title>
</head>
<body>
<a href="https://google.com/">Root domain link</a>
<a href="https://google.com/1.html">Normal link 1</a>
<a href="https://google.com/2.html">Normal link 2</a>
<a href="https://google.com/2.html">Duplicate link 2</a>
<a href="https://google.com/3.html#test">Normal link 3 with anchor</a>
<a href="https://google.com/picture.png">Link to an image.</a>
<a href="https://mail.google.com/">Subdomain link</a>
<a href="/4.html">Relative link</a>
<a rel="nofollow" href="https://google.com/nofollow">Nofollow link</a>
<a href="https://yandex.ru">Foreign link</a>
<a href="mailto:<EMAIL>">Email link</a>
<a href="#fragment">Fragment link</a>
<a href="ftp://google.com">Non HTTP(S) link</a>
<a href="">Link with empty href</a>
<a>Link without a URL</a>
<title>Incorrect Title</title>
<p>Some stuff.</p>
</body>
</html>
""",
"title_nonempty_links": (
"Test Page Title",
{
"#fragment",
"/4.html",
"ftp://google.com",
"https://google.com/",
"https://google.com/1.html",
"https://google.com/2.html",
"https://google.com/3.html#test",
"https://google.com/picture.png",
"https://mail.google.com/",
"https://yandex.ru",
"mailto:<EMAIL>",
},
),
"filter_result_wo_subdomains": [
"https://google.com/1.html",
"https://google.com/2.html",
"https://google.com/3.html",
"https://google.com/4.html",
],
"filter_result_subdomains": [
"https://google.com/1.html",
"https://google.com/2.html",
"https://google.com/3.html",
"https://google.com/4.html",
"https://mail.google.com",
],
}
| [
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userprofile/utils.py | praekelt/django-userprofile | 0 | 167969 | from django.conf import settings
from django.db.models.loading import get_model
def get_profile_model():
"""
Returns configured user profile model or None if not found
"""
user_profile_module = getattr(settings, 'USER_PROFILE_MODULE', None)
if user_profile_module:
app_label, model_name = user_profile_module.split('.')
return get_model(app_label, model_name)
else:
return None
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Simple_AD/Simple_AD.py | elongstreet88/Python_Simple_AD | 1 | 60647 | from ldap3 import Server, Connection, ALL, NTLM, ALL_ATTRIBUTES, ALL_OPERATIONAL_ATTRIBUTES
class Simple_AD:
"""
A simple wrapper for ldap3 in an Active Directory Environment.
Examples:
0. Initilaize a connection
sad = Simple_AD(server_name="server1.mydomain.com", username="mydomain.com\\jsmith", password="<PASSWORD>")
1. Get group(s) by samaccountname
groups = sad.get_adgroup(samaccountname="Accounting")
2. Get users from a group fast
users_from_group = sad.expand_users_from_group(groups[0])
3. Get a user by samaccountname
user = sad.get_aduser(samaccountname="jsmith")
"""
def __init__(self, server_name, username, password, use_ssl=True, default_attributes=ALL_ATTRIBUTES):
"""
Class initilizer. Takes various ldap3 properties and passess them through
Args:
server_name (string): The name of the target ldap server
username (string): Username to authenticate in the format: {domain_name\\user_name}. Ex: [contoso\\jsmith]
password (string): <PASSWORD>
use_ssl (bool, optional): Binds to SSL on port 636 and should be used when possible. Defaults to True.
default_attributes: Sets the default attributes to get for an object during an LDAP query, optional. Defaults to ALL_ATTRIBUTES.
Can be set to:
ALL_OPERATIONAL_ATTRIBUTES - ldap3 operation attributes wrapper
[] - No attributes by default
["member","lastLogonDate"] - Custom attributes
"""
self.server = Server(server_name, use_ssl=use_ssl, get_info=ALL)
self.conn = Connection(self.server, auto_bind=True, user=username, password=password, authentication=NTLM)
self.base_ou = self.server.info.naming_contexts[0]
self.default_attributes = default_attributes
def get_adgroup(self, samaccountname="*", distinguishedname="*", attributes=False):
"""
Get an AD Group Object from a filter.
Can return multiple results using a wildcard '*'
Args:
samaccountname (str, optional): LDAP Property samaccountname. Defaults to "*".
distinguishedname (str, optional): LDAP Property distinguishedname.. Defaults to "*".
attributes (bool, optional): Custom attributes if not using defaults. Defaults to False.
Returns:
object: ldap3 attribute object of the group(s)
"""
if(attributes == False):
attributes = self.default_attributes
self.conn.search(
search_base=self.base_ou,
search_filter=f'(&(objectCategory=group)(samaccountname={samaccountname})(distinguishedname={distinguishedname}))',
search_scope='SUBTREE',
attributes = attributes
)
return self.conn.entries
def get_aduser(self, samaccountname="*", distinguishedname="*", attributes=False):
"""
Get an AD User Object from a filter.
Can return multiple results using a wildcard '*'
Args:
samaccountname (str, optional): LDAP Property samaccountname. Defaults to "*".
distinguishedname (str, optional): LDAP Property distinguishedname.. Defaults to "*".
attributes (bool, optional): Custom attributes if not using defaults. Defaults to False.
Returns:
object: ldap3 attribute object of the user(s)
"""
if(attributes == False):
attributes = self.default_attributes
self.conn.search(
search_base=self.base_ou,
search_filter=f'(&(objectCategory=user)(samaccountname={samaccountname})(distinguishedname={distinguishedname}))',
search_scope='SUBTREE',
attributes = attributes
)
return self.conn.entries
def expand_users_from_group(self, group, attributes=False):
"""
A handy function to quickly expand user attributes from a group object with the [member] attribute populated.
This is useful when you need to get additional attributes from a users in a group, but don't want to look them up for each.
This builds a single LDAP query for all users
Args:
group (group object): Group object with the [member] attribute populated
attributes (bool, optional): Custom attributes if not using defaults. Defaults to False.
Raises:
Exception: If [member] is not populated on the [group] argument, exception will be raised.
Returns:
object: List of AD users with their requested attributes
Examples:
1. Expand members from the group "Accounting"
sad = Simple_AD(server_name="server1.mydomain.com", username="mydomain.com\\jsmith", password="<PASSWORD>")
groups = sad.get_adgroup(samaccountname="Accounting")
users_from_group = sad.expand_users_from_group(groups[0])
"""
if not hasattr(group, 'member'):
raise Exception("Group must have attribute [member] populated.")
#Validate attributes to get
if(attributes == False):
attributes = self.default_attributes
#Build fast query in format: (|(distinguishedname=dn1)(distinguishedname=dn2)(...))
members_ldap_query = ""
for distinguishedname in group.member:
members_ldap_query += f"(distinguishedname={distinguishedname})"
members_ldap_query = "(|" + members_ldap_query + ")"
#Get the users
self.conn.search(
search_base=self.base_ou,
search_filter=members_ldap_query,
search_scope='SUBTREE',
attributes = attributes
)
#Return results
return self.conn.entries | [
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examples/plot_allpsd.py | butala/spectrum | 261 | 106429 | <filename>examples/plot_allpsd.py
"""
Spectral analysis of a two frequencies signal
==================================================
"""
###########################################################
# Context
# ----------
##############################################
# Example
# --------
#
# In the following example, we use most of the methods available to
# analyse an input signal made of the addition of two sinus and an
# additive gaussian noise
import numpy
import spectrum
from spectrum import tools
from numpy.testing import assert_array_almost_equal
import pylab
data = spectrum.marple_data
from pylab import *
nn = numpy.arange(200)
xx = cos(0.257*pi*nn) + sin(0.2*pi*nn) + 0.01*randn(size(nn));
def create_all_psd():
f = pylab.linspace(0, 1, 4096)
pylab.figure(figsize=(12,8))
# MA model
p = spectrum.pma(xx, 64,128); p(); p.plot()
"""
#ARMA 15 order
a, b, rho = spectrum.arma_estimate(data, 15,15, 30)
psd = spectrum.arma2psd(A=a,B=b, rho=rho)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15')
"""
# <NAME>
p = spectrum.pyule(xx, 7 , NFFT=4096, scale_by_freq=False); p.plot()
# equivalent to
# plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True)
#burg method
p = spectrum.pburg(xx, 7, scale_by_freq=False); p.plot()
#pcovar
p = spectrum.pcovar(xx, 7, scale_by_freq=False); p.plot()
#pmodcovar
p = spectrum.pmodcovar(xx, 7, scale_by_freq=False); p.plot()
# correlogram
p = spectrum.pcorrelogram(xx, lag=60, NFFT=512, scale_by_freq=False); p.plot()
# minvar
p = spectrum.pminvar(xx, 7, NFFT=256, scale_by_freq=False); p.plot()
# pmusic
p = spectrum.pmusic(xx, 10,4, scale_by_freq=False); p.plot()
# pmusic
p = spectrum.pev(xx, 10, 4, scale_by_freq=False); p.plot()
# periodogram
p = spectrum.Periodogram(xx, scale_by_freq=False); p.plot()
#
legend( ["MA 32", "pyule 7", "pburg 7", "pcovar", "pmodcovar", "correlogram",
"minvar", "pmusic", "pev", "periodgram"])
pylab.ylim([-80,80])
create_all_psd()
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src/LDPC/pycodes/build/lib.cygwin-1.5.10-i686-2.3/pycodes/tests/channel_coding/BEC/small_BEC_test.py | Horacehxw/Multi-label | 1 | 176049 |
import random
from pycodes.utils import CodeMaker
from pycodes.utils.channel_coding_perf import *
from pycodes.utils.channels import BSCOutput
seed = random.getstate()
random.setstate(seed)
print 'using seed',seed
col_w = 3
row_w = 6
N = 1002
K = N*col_w/row_w
E = (N-K)*row_w
L = CodeMaker.make_H_gallager(N,col_w,row_w)
erasureProb = [0.4, 0.375, 0.35, 0.325]
numTrials = [250]*len(erasureProb)
maxIter = 20
tester = Tester([CodeParams(N,K,E,L,'small test','erasures',erasureProb,
BECOutput,numTrials,maxIter,algorithm='default')])
tester.ReportStats(title='Small BEC test for code of length %i' % N,
reportFile='/tmp/small_BEC_test_results.py',
imgFile='/tmp/small_BEC_test_results.eps')
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recurrent_neural_networks/hopfield_network.py | sgalella/RecurrentNeuralNetworks | 0 | 41440 | import numpy as np
import matplotlib.pyplot as plt
from utils import get_state_vowel
class HopfieldNetwork:
"""
Creates a Hopfield Network.
"""
def __init__(self, patterns):
"""
Initializes the network.
Args:
patterns (np.array): Group of states to be memorized by the network.
"""
self.num_units = patterns.shape[1]
self.passes = 0
self.state_units = np.array([1 if 2 * np.random.random() - 1 >= 0 else 0 for _ in range(self.num_units)])
self.W = np.zeros((self.num_units, self.num_units))
for pattern in patterns:
self.W += np.dot(np.transpose((2 * patterns - 1)), (2 * patterns - 1))
np.fill_diagonal(self.W, 0)
self.energy = [-0.5 * np.dot(np.dot(self.state_units.T, self.W), self.state_units)]
def _generate_sequence_units(self):
""" Selects randomly the order to update states in the next iteration."""
return np.random.choice(self.num_units, self.num_units)
def run(self):
""" Runs the network until no updates occur. """
no_update = True
while True:
for unit in self._generate_sequence_units():
unit_activation = np.dot(self.W[unit, :], self.state_units)
if unit_activation >= 0 and self.state_units[unit] == 0:
self.state_units[unit] = 1
no_update = False
elif unit_activation < 0 and self.state_units[unit] == 1:
self.state_units[unit] = 0
no_update = False
self.energy.append(-0.5 * np.dot(np.dot(self.state_units.T, self.W), self.state_units))
self.passes += 1
if no_update:
break
else:
no_update = True
def main():
np.random.seed(1234)
patterns = np.array([get_state_vowel('A'),
get_state_vowel('E'),
get_state_vowel('I'),
get_state_vowel('O'),
get_state_vowel('U')])
net = HopfieldNetwork(patterns)
net.run()
# Plot patterns and output
plt.figure(figsize=(6, 3), tight_layout=True)
plt.subplot(2, 3, 1)
plt.imshow(np.reshape(patterns[0, :], (5, 5)), cmap="Greys_r")
plt.title("A")
plt.subplot(2, 3, 2)
plt.imshow(np.reshape(patterns[1, :], (5, 5)), cmap="Greys_r")
plt.title("E")
plt.subplot(2, 3, 3)
plt.imshow(np.reshape(patterns[2, :], (5, 5)), cmap="Greys_r")
plt.title("I")
plt.subplot(2, 3, 4)
plt.imshow(np.reshape(patterns[3, :], (5, 5)), cmap="Greys_r")
plt.title("O")
plt.subplot(2, 3, 5)
plt.imshow(np.reshape(patterns[4, :], (5, 5)), cmap="Greys_r")
plt.title("U")
plt.subplot(2, 3, 6)
plt.imshow(np.reshape(net.state_units, (5, 5)), cmap="Greys_r")
plt.title("Output")
# Plot energy over time
plt.figure(figsize=(4, 2))
plt.plot(net.energy)
plt.title("Energy")
plt.show()
if __name__ == "__main__":
main()
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Scripts/web_scraping_api.py | ElizaBedoyaZapata/Proyecto-Principal-PowerBI | 0 | 66626 | <gh_stars>0
import pandas as pd
from sodapy import Socrata
from selenium import webdriver
#from selenium.webdriver.support.ui import Select
# Links file
links = pd.read_excel('..\Inputs\enlaces.xlsx', engine='openpyxl')
# Chrome path to web scraping
chrome_path = r"C:\Users\kmili\Downloads\chromedriver_win32\chromedriver.exe"
driver = webdriver.Chrome(chrome_path)
# Download data: Fiscal - Tasa cupon cero
tasa_cupon_cero = links.loc[19, 'Fuente']
driver.get(tasa_cupon_cero)
driver.find_element_by_xpath("//*[@id='saw_766908_3']/option[text()='tableView!3']").click()
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LeetCode/0044_Wildcard_Matching.py | scott-au/PythonAlgorithms | 0 | 22273 | class Solution:
def isMatch(self, s: str, p: str) -> bool:
# this is a dynamic programming solution fot this
matrix = [[False for x in range(len(p) + 1)] for x in range(len(s) + 1)]
matrix[0][0] = True
for i in range(1, len(matrix[0])):
if p[i - 1] == "*":
matrix[0][i] = matrix[0][i - 1]
for i in range(1, len(matrix)):
for j in range(1, len(matrix[0])):
if s[i - 1] == p[j - 1] or p[j - 1] == "?":
matrix[i][j] = matrix[i - 1][j - 1]
elif p[j - 1] == "*":
matrix[i][j] = matrix[i][j - 1] or matrix[i - 1][j]
else:
matrix[i][j] = False
return matrix[len(s)][len(p)]
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src/main.py | Grant-Steinfeld/python-ubi-openshift | 7 | 20059 | from flask import Flask
from flask_restplus import Api, Resource, fields
from services.serviceHandler import convertCurrency, getCurrencyExchangeRates
from services.countryCurrencyCodeHandler import (
getCountryAndCurrencyCode,
getCurrencyNameAndCode,
)
app = Flask(__name__)
api = Api(
app,
version="1.0.0",
title="Bee Travels Currency Data Service",
description="This is a microservice that handles currency exchange rate data for Bee Travels",
)
currencyNS = api.namespace(
"Currency",
description="Operations associated with currency exchange rate conversions",
)
currencyNameOrCurrencyCode = api.model(
"currencyNameOrCurrencyCode",
{
"currencyCode": fields.String(
required=False, description="3 letter currency code"
),
"country": fields.String(required=False, description="country name"),
},
)
@currencyNS.route("/")
class CurrencyList(Resource):
"""Shows a list of currency ex rates"""
def get(self):
return getCurrencyExchangeRates()
# /currency/{currencyFromAmount}/{currencyFromCode}/{currencyToCode}
# /currency/10/EUR/USD
@currencyNS.route("/<int:currencyFromAmount>/<currencyFromCode>/<currencyToCode>")
@currencyNS.response(404, "Currency Code not found")
@currencyNS.param("currencyFromAmount", "currency to convert from value (float)")
@currencyNS.param("currencyFromCode", "currency (3 character code) to convert from")
@currencyNS.param("currencyToCode", "currency (3 character code) to convert to")
class Currency(Resource):
def get(self, currencyFromAmount, currencyFromCode, currencyToCode):
result = convertCurrency(
float(currencyFromAmount), currencyFromCode, currencyToCode
)
return {"result": result}
@currencyNS.route("/search")
@currencyNS.response(404, "Currency Code not found")
class Search(Resource):
@currencyNS.doc("search_currency_meta")
@currencyNS.expect(currencyNameOrCurrencyCode)
@currencyNS.marshal_with(currencyNameOrCurrencyCode, code=201)
def post(self):
if "currencyCode" in api.payload:
return getCountryAndCurrencyCode(api.payload["currencyCode"])
elif "country" in api.payload:
return getCurrencyNameAndCode(api.payload["country"])
else:
api.abort(400, "Pass in either the currencyCode or country name")
if __name__ == "__main__":
app.run(host="0.0.0.0", debug=True, port=7878)
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2011,
29922,
29955,
29947,
29955,
29947,
29897,
13,
2
] |
trpg/registration/utility.py | jffifa/trpg | 2 | 52037 | from django.conf import settings
def decrypt(text):
half_len = len(text)//2
return text[half_len:]
| [
1,
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corehq/apps/analytics/signals.py | kkrampa/commcare-hq | 1 | 17659 | <reponame>kkrampa/commcare-hq
from __future__ import absolute_import
from __future__ import unicode_literals
import six
from django.conf import settings
from django.contrib.auth.signals import user_logged_in
from corehq.apps.analytics.tasks import (
track_user_sign_in_on_hubspot,
HUBSPOT_COOKIE,
update_hubspot_properties,
identify,
update_subscription_properties_by_domain, get_subscription_properties_by_user)
from corehq.apps.analytics.utils import get_meta
from corehq.apps.registration.views import ProcessRegistrationView
from corehq.util.decorators import handle_uncaught_exceptions
from corehq.util.python_compatibility import soft_assert_type_text
from corehq.util.soft_assert import soft_assert
from django.dispatch import receiver
from django.urls import reverse
from corehq.apps.users.models import CouchUser
from corehq.apps.accounting.signals import subscription_upgrade_or_downgrade
from corehq.apps.domain.signals import commcare_domain_post_save
from corehq.apps.users.signals import couch_user_post_save
from corehq.apps.analytics.utils import get_instance_string
_no_cookie_soft_assert = soft_assert(to=['{}<EMAIL>('<EMAIL>', '<EMAIL>'),
<EMAIL>('<EMAIL>', '<EMAIL>'),
<EMAIL>('<EMAIL>', '<EMAIL>')],
send_to_ops=False)
@receiver(couch_user_post_save)
def user_save_callback(sender, **kwargs):
couch_user = kwargs.get("couch_user", None)
if couch_user and couch_user.is_web_user():
properties = {}
properties.update(get_subscription_properties_by_user(couch_user))
properties.update(get_domain_membership_properties(couch_user))
identify.delay(couch_user.username, properties)
update_hubspot_properties.delay(couch_user, properties)
@receiver(commcare_domain_post_save)
@receiver(subscription_upgrade_or_downgrade)
def domain_save_callback(sender, domain, **kwargs):
if isinstance(domain, six.string_types):
soft_assert_type_text(domain)
domain_name = domain
else:
domain_name = domain.name
update_subscription_properties_by_domain(domain_name)
def get_domain_membership_properties(couch_user):
env = get_instance_string()
return {
"{}number_of_project_spaces".format(env): len(couch_user.domains),
"{}project_spaces_list".format(env): '\n'.join(couch_user.domains),
}
@receiver(user_logged_in)
@handle_uncaught_exceptions(mail_admins=True)
def track_user_login(sender, request, user, **kwargs):
if settings.ANALYTICS_IDS.get('HUBSPOT_API_ID'):
couch_user = CouchUser.from_django_user(user)
if couch_user and couch_user.is_web_user():
if not request or HUBSPOT_COOKIE not in request.COOKIES:
# API calls, form submissions etc.
user_confirming = request.path.startswith(reverse(ProcessRegistrationView.urlname))
if user_confirming:
_no_cookie_soft_assert(False, 'User confirmed account but had no cookie')
else:
return
meta = get_meta(request)
track_user_sign_in_on_hubspot.delay(couch_user, request.COOKIES.get(HUBSPOT_COOKIE),
meta, request.path)
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] |
number-base.py | davejlin/py-checkio | 0 | 1601428 | <gh_stars>0
'''
Do you remember the radix and Numeral systems from math class?
Let's practice with it.
You are given a positive number as a string along with the radix for
it. Your function should convert it into decimal form. The radix is
less than 37 and greater than 1. The task uses digits and the letters
A-Z for the strings.
Watch out for cases when the number cannot be converted. For example:
"1A" cannot be converted with radix 9. For these cases your function
should return -1.
Input: Two arguments. A number as string and a radix as an integer.
Output: The converted number as an integer.
Example:
checkio("AF", 16) == 175
checkio("101", 2) == 5
checkio("101", 5) == 26
checkio("Z", 36) == 35
checkio("AB", 10) == -1
How it is used: Here you will learn how to work with the various
numeral systems and handle exceptions.
Precondition:
re.match("\A[A-Z0-9]\Z", str_number)
0 < len(str_number) ≤ 10
2 ≤ radix ≤ 36
'''
MAP = {
'0':0,'1':1,'2':2,'3':3,'4':4,'5':5,'6':6,'7':7,'8':8,'9':9,
'A':10,'B':11,'C':12,'D':13,'E':14,'F':15,'G':16,'H':17,'I':18,
'J':19,'K':20,'L':21,'M':22,'N':23,'O':24,'P':25,'Q':26,'R':27,
'S':28,'T':29,'U':30,'V':31,'W':32,'X':33,'Y':34,'Z':35
}
def checkio(str_number, radix):
digits = list(map(lambda x: MAP[x], list(str_number[::-1])))
if max(digits) >= radix: return -1
return sum([digit*radix**i for i, digit in enumerate(digits)])
def checkio_online_sln(str_number, radix):
try:
return int(str_number, radix)
except ValueError:
return -1
inputs = [
("1567", 10),
("AF", 16),
("101", 2),
("101", 5),
("Z", 36),
("AB", 10),
("DEADBEEF", 16)
]
outputs = map(lambda x: checkio_online_sln(x[0],x[1]), inputs)
print(list(outputs)) | [
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2
] |
nginx-flask/app/run.py | codenameyau/docker-cheatsheet | 1 | 1611776 | <filename>nginx-flask/app/run.py
from config import DEVELOPMENT
from routes import app
if __name__ == '__main__':
app.run(**DEVELOPMENT)
| [
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tests/test_config_middleware.py | cjwatson/pastedeploy | 18 | 95458 | import pytest
from paste.deploy.config import ConfigMiddleware
class Bug(Exception):
pass
def app_with_exception(environ, start_response):
def cont():
yield b"something"
raise Bug
start_response('200 OK', [('Content-type', 'text/html')])
return cont()
def test_error():
# This import is conditional due to Paste not yet working on py3k
try:
from paste.fixture import TestApp
except ImportError:
raise pytest.skip('unable to import TestApp')
wrapped = ConfigMiddleware(app_with_exception, {'test': 1})
test_app = TestApp(wrapped)
pytest.raises(Bug, test_app.get, '/')
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] |
preprocessing/preprocessing.py | sanujsriv/WTM | 0 | 83586 | ## Preprocessing.py
"""
Preprocessing Steps :-
1. Convert to lowercase
2. Remove punctuations with empty space
3. Remove digits
4. Apply lemmatization
5. Remove Stopwords
6. Remove words that do not have word embeddings
7. Remove words that have length < 3.
*** NOT APPLYING STEMMING (Instead applying lemmatization for better results)
Generating Preprocessed Docs Steps:
1. Apply Preprocessing Steps
2. Remove Documents that have length < 3
3. Apply CountVectorizer Data transform { with min_df =3 i.e. words that appear in less than 3 documents are removed }
4. Remove documents that are empty after countvectorization
5. Remove words in documents that are not in the vocab generated after countvectorization.
6. export the vocab words for which we have existing embeddings(Google Word2vec, GloVE etc.)
7. export the preprocessed data with its label.
8. (optional) Learn new embeddings using the preprocessed corpus
"""
from preprocessing_imports import *
from preprocessing_utils import *
from preprocessing_data import get_bbc_data
import gensim.downloader as api
# download and extract the GoogleNews-vectors-negative300.bin (https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz)
word2vec_model = models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
def get_data(data_to_get,dtype):
data_to_get = data_to_get.lower()
if data_to_get == 'bbc':
corpus,labels = get_bbc_data(dtype)
return corpus,labels
## Driver - Data Generation
all_data_to_get = ['bbc']
dtypes = ['short'] # short text
## Settings :-
min_doc_len = 3
min_word_length = 3
generate_skipgram_embeddings = 1 # Yes
home_dir = ''
for data_to_get in all_data_to_get:
if data_to_get == 'bbc': max_features = 2000
else: max_features = 4000
for dtype in dtypes:
os.chdir(home_dir+"/content")
docs,labels = get_data(data_to_get,dtype)
os.makedirs(home_dir+'/content/data_'+data_to_get+"/"+dtype,exist_ok=True)
os.chdir(home_dir+'/content/data_'+data_to_get+"/"+dtype)
with open(data_to_get+'_'+dtype+".txt", "a") as f:
f.write(data_to_get+" - "+dtype)
f.write("\n\n")
f.write('**'*50)
f.write("\n\n")
f.write(str(len(docs))+', '+str(len(labels))+'\n')
f.write('(labels,count): '+str(list(zip(*np.unique(labels, return_counts=True)))))
data_preprocessed,data_preprocessed_labels,embeddings,vocab = docs_labels_preprocessing(docs,labels,word2vec_model,min_doc_len,min_word_length,max_features)
f.write('\n\nlen of - \n data_preprocessed: '+str(len(data_preprocessed))+'\n data_preprocessed_labels: '+str(len(data_preprocessed_labels))+'\n vocab: '+str(len(vocab))+'\n embeddings : '+str(len(embeddings))+'\n')
len_docs = [len(d.split(" ")) for d in data_preprocessed]
f.write('\n min,mean,max docs len: '+str(np.min(len_docs))+', '+str(np.mean(len_docs).round(2))+', '+str(np.max(len_docs))+'\n\n')
save_obj(data_preprocessed,'data_preprocessed'+'_'+data_to_get+'_'+dtype)
save_obj(data_preprocessed_labels,'data_preprocessed_labels'+'_'+data_to_get+'_'+dtype)
save_obj(vocab,'vocab'+'_'+data_to_get+'_'+dtype)
save_obj(embeddings,'embeddings'+'_'+data_to_get+'_'+dtype)
f.write('**'*50)
if generate_skipgram_embeddings:
## Skipgram embeddings generation
gensim_prep_doc = [word_tokenize(d) for d in data_preprocessed]
model = gensim.models.Word2Vec(gensim_prep_doc, min_count=2, sg=1, size=300,iter=50, negative=10, window=4,workers=16)
vocab = list(model.wv.vocab)
# model.save(data_to_get+"_"+dtype+"_generated_word2vec.model")
generated_embeddings = {}
for v in list(model.wv.vocab):
vec = model.wv.__getitem__(v)
generated_embeddings[v] =vec
save_obj(generated_embeddings,'generated_embeddings_'+data_to_get+"_"+dtype)
# save_obj(vocab,'generated_vocab_'+data_to_get+"_"+dtype)
os.chdir(home_dir+'/content')
zipped_pickle_filename = data_to_get
os.system('zip -r '+zipped_pickle_filename+'_'+str(max_features)+'_.zip '+'/content/data_'+data_to_get+"/"+dtype)
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] |
tests/test_layers.py | SauravMaheshkar/enformer | 0 | 181617 | import pytest
import tensorflow as tf
from enformer.layers.activation_layers import GELU, SoftPlus
from enformer.layers.container_layers import Residual
from enformer.layers.pooling_layers import SoftmaxPooling1D
from .utils import Identity
@pytest.mark.layers
@pytest.mark.actions
def test_softplus():
layer = SoftPlus("softplus")
assert layer.name == "softplus"
outputs = layer(tf.range(0, 2, dtype=tf.float32)).numpy()
assert len(outputs) == 2
@pytest.mark.layers
@pytest.mark.actions
def test_gelu():
layer = GELU("gelu")
assert layer.name == "gelu"
outputs = layer(tf.range(0, 2, dtype=tf.float32)).numpy()
assert len(outputs) == 2
@pytest.mark.layers
@pytest.mark.actions
def test_residual():
layer = Residual(Identity(), name="residual")
assert layer.name == "residual"
outputs = layer(inputs=tf.ones((2, 2)))
assert outputs.shape == tf.TensorShape((2, 2))
@pytest.mark.layers
@pytest.mark.actions
def test_softmax_pooling():
layer = SoftmaxPooling1D()
assert layer.name == "softmax_pooling"
outputs = layer((tf.ones([3, 4, 5])))
assert outputs.shape == tf.TensorShape((3, 2, 5))
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2
] |
resist/types/models/message.py | an-dyy/Resist | 4 | 34903 | from __future__ import annotations
from typing import Literal, TypedDict, Union, final
from typing_extensions import NotRequired
from .asset import AssetData
class YoutubeLinkEmbedMetadata(TypedDict):
type: Literal["YouTube"]
id: str
timestamp: NotRequired[str]
class TwitchLinkEmbedMetadata(TypedDict):
type: Literal["Twitch"]
content_type: Literal["Channel", "Clip", "Video"]
id: str
class SpotifyLinkEmbedMetadata(TypedDict):
type: Literal["Spotify"]
content_type: str
id: str
SoundcloudLinkEmbedMetadata = TypedDict(
"SoundcloudLinkEmbedMetadata", {"type": Literal["Soundcloud"]}
)
class BandcampLinkEmbedMetadata(TypedDict):
type: Literal["Bandcamp"]
content_type: Literal["Album", "Track"]
id: str
class EmbedMediaData(TypedDict):
# base fields that both videos and images sent in embeds will have.
url: str
width: int
height: int
class EmbedImageData(EmbedMediaData):
# this contains the data about an image sent in an embed
# for example: a banner image in a URL's embed
size: Literal["Large", "Preview"]
class WebsiteEmbedData(TypedDict):
"""Represents the data of an embed for a URL."""
type: Literal["Website"]
url: NotRequired[str]
special: NotRequired[
YoutubeLinkEmbedMetadata
| SpotifyLinkEmbedMetadata
| TwitchLinkEmbedMetadata
| SoundcloudLinkEmbedMetadata
| BandcampLinkEmbedMetadata
]
title: NotRequired[str]
description: NotRequired[str]
image: NotRequired[EmbedImageData]
video: NotRequired[EmbedMediaData]
site_name: NotRequired[str]
icon_url: NotRequired[str]
colour: NotRequired[str]
class ImageEmbedData(EmbedImageData):
"""Represents the data of an image embed."""
type: Literal["Image"]
class TextEmbedData(TypedDict):
type: Literal["Text"]
icon_url: NotRequired[str]
url: NotRequired[str]
title: NotRequired[str]
description: NotRequired[str]
media: NotRequired[AssetData]
colour: NotRequired[str]
NoneEmbed = TypedDict("NoneEmbed", {"type": Literal["None"]})
@final
class SystemMessageContent(TypedDict):
type: Literal["text"]
content: str
@final
class UserActionSystemMessageContent(TypedDict):
type: Literal[
"user_added",
"user_remove",
"user_joined",
"user_left",
"user_kicked",
"user_banned",
]
id: str
by: NotRequired[str] # sent only with user_added and user_remove
@final
class ChannelActionSystemMessageContent(TypedDict):
type: Literal[
"channel_renamed", "channel_description_changed", "channel_icon_changed"
]
by: str
name: NotRequired[str] # sent only with channel_renamed
MessageEditedData = TypedDict("MessageEditedData", {"$date": str})
class MasqueradeData(TypedDict):
name: NotRequired[str]
avatar: NotRequired[str]
EmbedType = Union[WebsiteEmbedData, ImageEmbedData, TextEmbedData, NoneEmbed]
class MessageData(TypedDict):
_id: str
nonce: NotRequired[str]
channel: str
author: str
content: (
SystemMessageContent
| UserActionSystemMessageContent
| ChannelActionSystemMessageContent
| str
)
attachments: NotRequired[list[AssetData]]
edited: NotRequired[MessageEditedData]
embeds: NotRequired[list[EmbedType]]
mentions: NotRequired[list[str]]
replies: NotRequired[list[str]]
masquerade: NotRequired[MasqueradeData]
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] |
lemur/certificates/models.py | rajatsharma94/lemur | 2 | 193925 | """
.. module: lemur.certificates.models
:platform: Unix
:copyright: (c) 2018 by Netflix Inc., see AUTHORS for more
:license: Apache, see LICENSE for more details.
.. moduleauthor:: <NAME> <<EMAIL>>
"""
from datetime import timedelta
import arrow
from cryptography import x509
from cryptography.hazmat.primitives.asymmetric import rsa
from flask import current_app
from idna.core import InvalidCodepoint
from sqlalchemy import (
event,
Integer,
ForeignKey,
String,
PassiveDefault,
func,
Column,
Text,
Boolean,
Index,
)
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.orm import relationship
from sqlalchemy.sql.expression import case, extract
from sqlalchemy_utils.types.arrow import ArrowType
from werkzeug.utils import cached_property
from lemur.common import defaults, utils, validators
from lemur.constants import SUCCESS_METRIC_STATUS, FAILURE_METRIC_STATUS
from lemur.database import db
from lemur.domains.models import Domain
from lemur.extensions import metrics
from lemur.extensions import sentry
from lemur.models import (
certificate_associations,
certificate_source_associations,
certificate_destination_associations,
certificate_notification_associations,
certificate_replacement_associations,
roles_certificates,
pending_cert_replacement_associations,
)
from lemur.plugins.base import plugins
from lemur.policies.models import RotationPolicy
from lemur.utils import Vault
def get_sequence(name):
if "-" not in name:
return name, None
parts = name.split("-")
# see if we have an int at the end of our name
try:
seq = int(parts[-1])
except ValueError:
return name, None
# we might have a date at the end of our name
if len(parts[-1]) == 8:
return name, None
root = "-".join(parts[:-1])
return root, seq
def get_or_increase_name(name, serial):
certificates = Certificate.query.filter(Certificate.name == name).all()
if not certificates:
return name
serial_name = "{0}-{1}".format(name, hex(int(serial))[2:].upper())
certificates = Certificate.query.filter(Certificate.name == serial_name).all()
if not certificates:
return serial_name
certificates = Certificate.query.filter(
Certificate.name.ilike("{0}%".format(serial_name))
).all()
ends = [0]
root, end = get_sequence(serial_name)
for cert in certificates:
root, end = get_sequence(cert.name)
if end:
ends.append(end)
return "{0}-{1}".format(root, max(ends) + 1)
class Certificate(db.Model):
__tablename__ = "certificates"
__table_args__ = (
Index(
"ix_certificates_cn",
"cn",
postgresql_ops={"cn": "gin_trgm_ops"},
postgresql_using="gin",
),
Index(
"ix_certificates_name",
"name",
postgresql_ops={"name": "gin_trgm_ops"},
postgresql_using="gin",
),
)
id = Column(Integer, primary_key=True)
ix = Index(
"ix_certificates_id_desc", id.desc(), postgresql_using="btree", unique=True
)
external_id = Column(String(128))
owner = Column(String(128), nullable=False)
name = Column(String(256), unique=True)
description = Column(String(1024))
notify = Column(Boolean, default=True)
body = Column(Text(), nullable=False)
chain = Column(Text())
csr = Column(Text())
private_key = Column(Vault)
issuer = Column(String(128))
serial = Column(String(128))
cn = Column(String(128))
deleted = Column(Boolean, index=True, default=False)
dns_provider_id = Column(
Integer(), ForeignKey("dns_providers.id", ondelete="CASCADE"), nullable=True
)
not_before = Column(ArrowType)
not_after = Column(ArrowType)
not_after_ix = Index("ix_certificates_not_after", not_after.desc())
date_created = Column(ArrowType, PassiveDefault(func.now()), nullable=False)
signing_algorithm = Column(String(128))
status = Column(String(128))
bits = Column(Integer())
san = Column(String(1024)) # TODO this should be migrated to boolean
rotation = Column(Boolean, default=False)
user_id = Column(Integer, ForeignKey("users.id"))
authority_id = Column(Integer, ForeignKey("authorities.id", ondelete="CASCADE"))
root_authority_id = Column(
Integer, ForeignKey("authorities.id", ondelete="CASCADE")
)
rotation_policy_id = Column(Integer, ForeignKey("rotation_policies.id"))
notifications = relationship(
"Notification",
secondary=certificate_notification_associations,
backref="certificate",
)
destinations = relationship(
"Destination",
secondary=certificate_destination_associations,
backref="certificate",
)
sources = relationship(
"Source", secondary=certificate_source_associations, backref="certificate"
)
domains = relationship(
"Domain", secondary=certificate_associations, backref="certificate"
)
roles = relationship("Role", secondary=roles_certificates, backref="certificate")
replaces = relationship(
"Certificate",
secondary=certificate_replacement_associations,
primaryjoin=id == certificate_replacement_associations.c.certificate_id, # noqa
secondaryjoin=id
== certificate_replacement_associations.c.replaced_certificate_id, # noqa
backref="replaced",
)
replaced_by_pending = relationship(
"PendingCertificate",
secondary=pending_cert_replacement_associations,
backref="pending_replace",
viewonly=True,
)
logs = relationship("Log", backref="certificate")
endpoints = relationship("Endpoint", backref="certificate")
rotation_policy = relationship("RotationPolicy")
sensitive_fields = ("private_key",)
def __init__(self, **kwargs):
self.body = kwargs["body"].strip()
cert = self.parsed_cert
self.issuer = defaults.issuer(cert)
self.cn = defaults.common_name(cert)
self.san = defaults.san(cert)
self.not_before = defaults.not_before(cert)
self.not_after = defaults.not_after(cert)
self.serial = defaults.serial(cert)
# when destinations are appended they require a valid name.
if kwargs.get("name"):
self.name = get_or_increase_name(
defaults.text_to_slug(kwargs["name"]), self.serial
)
else:
self.name = get_or_increase_name(
defaults.certificate_name(
self.cn, self.issuer, self.not_before, self.not_after, self.san
),
self.serial,
)
self.owner = kwargs["owner"]
if kwargs.get("private_key"):
self.private_key = kwargs["private_key"].strip()
if kwargs.get("chain"):
self.chain = kwargs["chain"].strip()
if kwargs.get("csr"):
self.csr = kwargs["csr"].strip()
self.notify = kwargs.get("notify", True)
self.destinations = kwargs.get("destinations", [])
self.notifications = kwargs.get("notifications", [])
self.description = kwargs.get("description")
self.roles = list(set(kwargs.get("roles", [])))
self.replaces = kwargs.get("replaces", [])
self.rotation = kwargs.get("rotation")
self.rotation_policy = kwargs.get("rotation_policy")
self.signing_algorithm = defaults.signing_algorithm(cert)
self.bits = defaults.bitstrength(cert)
self.external_id = kwargs.get("external_id")
self.authority_id = kwargs.get("authority_id")
self.dns_provider_id = kwargs.get("dns_provider_id")
for domain in defaults.domains(cert):
self.domains.append(Domain(name=domain))
# Check integrity before saving anything into the database.
# For user-facing API calls, validation should also be done in schema validators.
self.check_integrity()
def check_integrity(self):
"""
Integrity checks: Does the cert have a valid chain and matching private key?
"""
if self.private_key:
validators.verify_private_key_match(
utils.parse_private_key(self.private_key),
self.parsed_cert,
error_class=AssertionError,
)
if self.chain:
chain = [self.parsed_cert] + utils.parse_cert_chain(self.chain)
validators.verify_cert_chain(chain, error_class=AssertionError)
@cached_property
def parsed_cert(self):
assert self.body, "Certificate body not set"
return utils.parse_certificate(self.body)
@property
def active(self):
return self.notify
@property
def organization(self):
return defaults.organization(self.parsed_cert)
@property
def organizational_unit(self):
return defaults.organizational_unit(self.parsed_cert)
@property
def country(self):
return defaults.country(self.parsed_cert)
@property
def state(self):
return defaults.state(self.parsed_cert)
@property
def location(self):
return defaults.location(self.parsed_cert)
@property
def distinguished_name(self):
return self.parsed_cert.subject.rfc4514_string()
@property
def key_type(self):
if isinstance(self.parsed_cert.public_key(), rsa.RSAPublicKey):
return "RSA{key_size}".format(
key_size=self.parsed_cert.public_key().key_size
)
@property
def validity_remaining(self):
return abs(self.not_after - arrow.utcnow())
@property
def validity_range(self):
return self.not_after - self.not_before
@property
def subject(self):
return self.parsed_cert.subject
@property
def public_key(self):
return self.parsed_cert.public_key()
@hybrid_property
def expired(self):
if self.not_after <= arrow.utcnow():
return True
@expired.expression
def expired(cls):
return case([(cls.not_after <= arrow.utcnow(), True)], else_=False)
@hybrid_property
def revoked(self):
if "revoked" == self.status:
return True
@revoked.expression
def revoked(cls):
return case([(cls.status == "revoked", True)], else_=False)
@hybrid_property
def has_private_key(self):
return self.private_key is not None
@has_private_key.expression
def has_private_key(cls):
return case([(cls.private_key.is_(None), True)], else_=False)
@hybrid_property
def in_rotation_window(self):
"""
Determines if a certificate is available for rotation based
on the rotation policy associated.
:return:
"""
now = arrow.utcnow()
end = now + timedelta(days=self.rotation_policy.days)
if self.not_after <= end:
return True
@in_rotation_window.expression
def in_rotation_window(cls):
"""
Determines if a certificate is available for rotation based
on the rotation policy associated.
:return:
"""
return case(
[(extract("day", cls.not_after - func.now()) <= RotationPolicy.days, True)],
else_=False,
)
@property
def extensions(self):
# setup default values
return_extensions = {"sub_alt_names": {"names": []}}
try:
for extension in self.parsed_cert.extensions:
value = extension.value
if isinstance(value, x509.BasicConstraints):
return_extensions["basic_constraints"] = value
elif isinstance(value, x509.SubjectAlternativeName):
return_extensions["sub_alt_names"]["names"] = value
elif isinstance(value, x509.ExtendedKeyUsage):
return_extensions["extended_key_usage"] = value
elif isinstance(value, x509.KeyUsage):
return_extensions["key_usage"] = value
elif isinstance(value, x509.SubjectKeyIdentifier):
return_extensions["subject_key_identifier"] = {"include_ski": True}
elif isinstance(value, x509.AuthorityInformationAccess):
return_extensions["certificate_info_access"] = {"include_aia": True}
elif isinstance(value, x509.AuthorityKeyIdentifier):
aki = {"use_key_identifier": False, "use_authority_cert": False}
if value.key_identifier:
aki["use_key_identifier"] = True
if value.authority_cert_issuer:
aki["use_authority_cert"] = True
return_extensions["authority_key_identifier"] = aki
elif isinstance(value, x509.CRLDistributionPoints):
return_extensions["crl_distribution_points"] = {
"include_crl_dp": value
}
# TODO: Not supporting custom OIDs yet. https://github.com/Netflix/lemur/issues/665
else:
current_app.logger.warning(
"Custom OIDs not yet supported for clone operation."
)
except InvalidCodepoint as e:
sentry.captureException()
current_app.logger.warning(
"Unable to parse extensions due to underscore in dns name"
)
except ValueError as e:
sentry.captureException()
current_app.logger.warning("Unable to parse")
current_app.logger.exception(e)
return return_extensions
def __repr__(self):
return "Certificate(name={name})".format(name=self.name)
@event.listens_for(Certificate.destinations, "append")
def update_destinations(target, value, initiator):
"""
Attempt to upload certificate to the new destination
:param target:
:param value:
:param initiator:
:return:
"""
destination_plugin = plugins.get(value.plugin_name)
status = FAILURE_METRIC_STATUS
try:
if target.private_key or not destination_plugin.requires_key:
destination_plugin.upload(
target.name,
target.body,
target.private_key,
target.chain,
value.options,
)
status = SUCCESS_METRIC_STATUS
except Exception as e:
sentry.captureException()
raise
metrics.send(
"destination_upload",
"counter",
1,
metric_tags={
"status": status,
"certificate": target.name,
"destination": value.label,
},
)
@event.listens_for(Certificate.replaces, "append")
def update_replacement(target, value, initiator):
"""
When a certificate is marked as 'replaced' we should not notify.
:param target:
:param value:
:param initiator:
:return:
"""
value.notify = False
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] |
python/testing/parameterized/test_parameterized.py | amitsaha/playground | 4 | 73425 | <filename>python/testing/parameterized/test_parameterized.py
import unittest
from nose_parameterized import parameterized, param
@parameterized([param(2), param(3)])
def test_square(x):
assert x**2 == x**2
class AddTestCase(unittest.TestCase):
@parameterized.expand([
("string1", "string1"),
("string2", "string2")
])
def test_check_string(self, _, s):
assert isinstance(s, str)
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] |
geoip_test/__init__.py | soutys/geoip_test | 0 | 157236 | # -*- coding: utf-8 -*-
'''Package info module
'''
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
with_statement,
)
import os
WORK_DIR = os.path.dirname(os.path.abspath(__file__))
__author__ = 'soutys <<EMAIL>>'
__version__ = open(os.path.join(WORK_DIR, 'VERSION'), 'r').read().strip()
__classifiers__ = [
# 'Development Status :: 1 - Planning',
# 'Development Status :: 2 - Pre-Alpha',
'Development Status :: 3 - Alpha',
# 'Development Status :: 4 - Beta',
# 'Development Status :: 5 - Production/Stable',
# 'Development Status :: 6 - Mature',
# 'Development Status :: 7 - Inactive',
'Environment :: Other Environment',
'Intended Audience :: Developers',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.4',
'Topic :: Utilities',
]
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parser/team27/G-27/execution/symbol/table.py | Lenin201/tytus | 1 | 25962 | class Table(object):
def __init__(self, name):
self.name = name
self.columns = []
def createColumn(self, column):
self.columns.append(column)
def readColumn(self, name):
for value in self.columns:
if value.name == name:
return value
def updateColumn(self, name, column):
for i in range(0,len(self.columns)):
if self.columns[i].name == name:
self.columns[i] = column
break
def deleteColumn(self, name):
for i in range(0, len(self.columns)):
if self.columns[i].name == name:
del self.columns[i]
break
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flask_user/tests/test_multiple_emails.py | 80pctsols/Flask-User | 0 | 138386 | <filename>flask_user/tests/test_multiple_emails.py
"""
tests.test_all_forms.py
---------------------------
Flask-User automated tests:
Tests posting a sequence of valid forms for all possible config combinations
:copyright: (c) 2013 by <NAME>
:author: <NAME> (<EMAIL>)
:license: Simplified BSD License, see LICENSE.txt for more details.
"""
from __future__ import print_function
import datetime
from flask import current_app, url_for
# **********************
# ** Global Variables **
# **********************
# Using global variable for speed
user1 = None
# ********************************
# ** Automatically called Tests **
# ********************************
# The 'client' and 'app' parameters are set up in conftest.py
# Functions that start with 'test' will be run automatically by the test suite runner (py.test)
def test_multiple_emails(app, db, client):
"""
Test 'multiple emails per user' feature
"""
# Set Flask-User settings
um = current_app.user_manager
um.enable_register = True
um.enable_username = False
um.enable_email = True
um.enable_confirm_email = True
um.enable_change_username = False
um.enable_change_password = False
um.enable_forgot_password = False
um.enable_multiple_emails = True
um.enable_retype_password = False
# Adjust DbAdapter settings
um.db_adapter.UserEmailClass = app.UserEmailClass
# Adjust URL routes
app.add_url_rule(um.email_action_url, 'user.email_action', um.email_action_view_function)
app.add_url_rule(um.manage_emails_url, 'user.manage_emails', um.manage_emails_view_function, methods=['GET', 'POST'])
# constants
EMAIL1 = '<EMAIL>'
EMAIL2 = '<EMAIL>'
PASSWORD = '<PASSWORD>'
# Register user
response = client.post_valid_form(url_for('user.register'), email=EMAIL1, password=PASSWORD)
user_email1 = um.db_adapter.UserEmailClass.query.filter(um.db_adapter.UserEmailClass.email==EMAIL1).first()
assert user_email1 != None
# Confirm email
confirmation_token = um.generate_token(user_email1.id)
client.get_valid_page(url_for('user.confirm_email', token=confirmation_token))
# Log in using email1
client.login(email=EMAIL1, password=PASSWORD)
# Visit manage emails page
response = client.get_valid_page(url_for('user.manage_emails'))
assert response.data.find(EMAIL1) >= 0
# Add an email
response = client.post_valid_form(url_for('user.manage_emails'), email=EMAIL2)
assert response.data.find(EMAIL1) >= 0
assert response.data.find(EMAIL2) >= 0
user_email2 = um.db_adapter.UserEmailClass.query.filter(um.db_adapter.UserEmailClass.email==EMAIL2).first()
assert user_email2 != None
# Confirm email
confirmation_token = um.generate_token(user_email2.id)
client.get_valid_page(url_for('user.confirm_email', token=confirmation_token))
# Logout
client.logout()
# Log in using email1
client.login(email=EMAIL1, password=PASSWORD)
# Logout
client.logout()
# Log in using email2
client.login(email=EMAIL2, password=PASSWORD)
# Confirm
response = client.get_valid_page(url_for('user.email_action', id=user_email2.id, action='confirm'))
# Make primary
response = client.get_valid_page(url_for('user.email_action', id=user_email2.id, action='make-primary'))
# Delete
response = client.get_valid_page(url_for('user.email_action', id=user_email1.id, action='delete'))
# Logout
client.logout()
# Restore settings
um.enable_multiple_emails = False
um.enable_confirm_email = True
um.enable_retype_password = True
um.db_adapter.UserEmailClass = None
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] |
catkin_ws/src/00-infrastructure/duckietown/include/duckietown_utils_tests/colors.py | yxiao1996/dev | 2 | 188687 | <gh_stars>1-10
from comptests.registrar import comptest, run_module_tests
from termcolor import colored
from duckietown_utils.text_utils import (get_length_on_screen, format_table_plus,
remove_escapes, make_row_red)
@comptest
def test_color_sizes():
s1 = 'one'
s2 = colored(s1, 'magenta')
# print(s1.__repr__())
# print(s2.__repr__())
l1 = get_length_on_screen(s1)
l2 = get_length_on_screen(s2)
# print l1, l2
assert l1 == l2
def get_test_table():
table = []
for i in range(3):
row = []
for j in range(5):
row.append('*' * i * j)
table.append(row)
return table
@comptest
def test_table():
table = get_test_table()
r1 = format_table_plus(table)
table[1] = make_row_red(table[1])
r2 = format_table_plus(table)
#print r1
#print r2
r2e = remove_escapes(r2)
#print r2e
assert r1 == r2e
if __name__ == '__main__':
run_module_tests()
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anaf/documents/migrations/0001_initial.py | tovmeod/anaf | 2 | 18751 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import anaf.documents.models
import anaf.documents.files
class Migration(migrations.Migration):
dependencies = [
('core', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Document',
fields=[
('object_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='core.Object')),
('title', models.CharField(max_length=255)),
('body', models.TextField(null=True, blank=True)),
],
options={
'ordering': ['-last_updated'],
},
bases=('core.object',),
),
migrations.CreateModel(
name='File',
fields=[
('object_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='core.Object')),
('name', models.CharField(max_length=255)),
('content', models.FileField(storage=anaf.documents.files.FileStorage(), upload_to=anaf.documents.models.generate_filename)),
],
options={
'ordering': ['-last_updated'],
},
bases=('core.object',),
),
migrations.CreateModel(
name='Folder',
fields=[
('object_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='core.Object')),
('name', models.CharField(max_length=255)),
('parent', models.ForeignKey(related_name='child_set', blank=True, to='documents.Folder', null=True)),
],
options={
},
bases=('core.object',),
),
migrations.CreateModel(
name='WebLink',
fields=[
('object_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='core.Object')),
('title', models.CharField(max_length=255)),
('url', models.CharField(max_length=255)),
('folder', models.ForeignKey(to='documents.Folder')),
],
options={
'ordering': ['-last_updated'],
},
bases=('core.object',),
),
migrations.AddField(
model_name='file',
name='folder',
field=models.ForeignKey(to='documents.Folder'),
preserve_default=True,
),
migrations.AddField(
model_name='document',
name='folder',
field=models.ForeignKey(to='documents.Folder'),
preserve_default=True,
),
]
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ibeis/algo/graph/mixin_groundtruth.py | brmscheiner/ibeis | 0 | 199584 | <filename>ibeis/algo/graph/mixin_groundtruth.py
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import utool as ut
import numpy as np
import vtool as vt
import pandas as pd
from ibeis.algo.graph.nx_utils import ensure_multi_index
from ibeis.algo.graph.state import POSTV, NEGTV, INCMP
print, rrr, profile = ut.inject2(__name__)
class Groundtruth(object):
def is_comparable(infr, aid_pairs, allow_guess=True):
"""
Guesses by default when real comparable information is not available.
"""
if infr.ibs is not None:
return infr.ibeis_is_comparable(aid_pairs, allow_guess)
is_comp = list(infr.gen_edge_values('gt_comparable', edges=aid_pairs,
default=True,
on_missing='default'))
return np.array(is_comp)
def is_photobomb(infr, aid_pairs):
if infr.ibs is not None:
return infr.ibeis_is_photobomb(aid_pairs)
return np.array([False] * len(aid_pairs))
def is_same(infr, aid_pairs):
if infr.ibs is not None:
return infr.ibeis_is_same(aid_pairs)
node_dict = ut.nx_node_dict(infr.graph)
nid1 = [node_dict[n1]['orig_name_label']
for n1, n2 in aid_pairs]
nid2 = [node_dict[n2]['orig_name_label']
for n1, n2 in aid_pairs]
return np.equal(nid1, nid2)
def apply_edge_truth(infr, edges=None):
if edges is None:
edges = list(infr.edges())
edge_truth_df = infr.match_state_df(edges)
edge_truth = edge_truth_df.idxmax(axis=1).to_dict()
infr.set_edge_attrs('truth', edge_truth)
infr.edge_truth.update(edge_truth)
def match_state_df(infr, index):
""" Returns groundtruth state based on ibeis controller """
index = ensure_multi_index(index, ('aid1', 'aid2'))
aid_pairs = np.asarray(index.tolist())
aid_pairs = vt.ensure_shape(aid_pairs, (None, 2))
is_same = infr.is_same(aid_pairs)
is_comp = infr.is_comparable(aid_pairs)
match_state_df = pd.DataFrame.from_items([
(NEGTV, ~is_same & is_comp),
(POSTV, is_same & is_comp),
(INCMP, ~is_comp),
])
match_state_df.index = index
return match_state_df
def match_state_gt(infr, edge):
if edge in infr.edge_truth:
truth = infr.edge_truth[edge]
elif hasattr(infr, 'dummy_verif'):
truth = infr.dummy_verif._get_truth(edge)
else:
aid_pairs = np.asarray([edge])
is_same = infr.is_same(aid_pairs)[0]
is_comp = infr.is_comparable(aid_pairs)[0]
match_state = pd.Series(dict([
(NEGTV, ~is_same & is_comp),
(POSTV, is_same & is_comp),
(INCMP, ~is_comp),
]))
truth = match_state.idxmax()
return truth
def edge_attr_df(infr, key, edges=None, default=ut.NoParam):
""" constructs DataFrame using current predictions """
edge_states = infr.gen_edge_attrs(key, edges=edges, default=default)
edge_states = list(edge_states)
if isinstance(edges, pd.MultiIndex):
index = edges
else:
if edges is None:
edges_ = ut.take_column(edge_states, 0)
else:
edges_ = ut.lmap(tuple, ut.aslist(edges))
index = pd.MultiIndex.from_tuples(edges_, names=('aid1', 'aid2'))
records = ut.itake_column(edge_states, 1)
edge_df = pd.Series.from_array(records)
edge_df.name = key
edge_df.index = index
return edge_df
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2
] |
codetest.py | Shannon-NJIT/PyCharmsHW | 0 | 42501 | #adding two numbers
num1 = 2
num2 = 3
sum = num1 + num2
print("The sum is:", sum) | [
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2
] |
scripts/acled2locations.py | ynxue/FabFlee | 1 | 180693 | <filename>scripts/acled2locations.py
import pandas as pd
import warnings
import sys
import os
import calendar as cal
from datetime import datetime
def month_convert(month):
name_to_number = {name: number
for number, name in enumerate(cal.month_name) if number
} # dict month : month_num
month_num = name_to_number.get(month) # month number in int form
return month_num
def date_format(in_date):
# converting date from textbased to dd-mm-yyyy format
split_date = in_date.split()
month_num = month_convert(split_date[1])
out_date = split_date[0] + "-" + str(month_num) + "-" + split_date[2]
return out_date
def between_date(d1, d2):
# Gets difference between two dates in string format "dd-mm-yy"
d1list = d1.split("-")
d2list = d2.split("-")
date1 = datetime(int(d1list[2]), int(d1list[1]), int(d1list[0]))
date2 = datetime(int(d2list[2]), int(d2list[1]), int(d2list[0]))
return abs((date1 - date2).days) + 1 # Maybe add +1
def date_verify(date):
date_format = "%d-%m-%Y"
try:
date_obj = datetime.strptime(date, date_format)
return True
except ValueError:
print("Incorrect data format please input dd-mm-yyyy")
return False
def drop_rows(inputdata, columnname, dropparameter):
removedrows = inputdata.index[
inputdata[columnname] == dropparameter].tolist()
outputdata = inputdata.drop(removedrows)
return outputdata
def filter_table(df, colname, adminlevel):
if adminlevel == "admin1":
adminlist = df.admin1.unique()
elif adminlevel == "location":
adminlist = df.location.unique()
else:
adminlist = df.admin2.unique()
newdf = pd.DataFrame(columns=df.columns)
for admin in adminlist:
tempdf = df.loc[df[adminlevel] == admin]
tempdf.sort_values(colname, ascending=True)
newdf = newdf.append(tempdf.tail(1))
print(newdf)
return newdf
def find_csv(country):
path_to_dir = os.getcwd()
print(path_to_dir)
filename = country + "-acled.csv"
locations = os.path.join(
"config_files", country, "source_data", filename
)
print(locations)
return locations
# Takes path to acled csv file, a start date in dd-mm-yyyy format, and a
# filter (First occurence or highest fatalities)
def acled2locations(fab_flee_loc, country, start_date,
filter_opt, admin_level):
warnings.filterwarnings('ignore')
input_file = os.path.join(fab_flee_loc, "config_files",
country,
"acled.csv")
print("Current Path: ", input_file)
try:
tempdf = pd.read_csv(input_file)
except:
print("Runtime Error: File Cannot be found")
df = tempdf[["event_date", "country", "admin1", "admin2",
"location", "latitude", "longitude", "fatalities"]]
# event_date is given in incorrect format, so formatting to dd-mm-yyyy
# required
event_dates = df["event_date"].tolist()
formatted_event_dates = [date_format(date) for date in event_dates]
conflict_dates = [between_date(d, start_date)
for d in formatted_event_dates]
# replacing event_date
df.loc[:, "event_date"] = conflict_dates
df.rename(columns={'event_date': 'conflict_date'}, inplace=True)
df = drop_rows(df, 'fatalities', 0)
if filter_opt == 'earliest':
filter_opt = 'conflict_date'
try:
df = filter_table(df, filter_opt, admin_level)
except:
print("Runtime error: filter_opt value must be earliest or fatalities")
# Exporting CSV to locations.csv
output_df = df[['location', 'admin1', 'country',
'latitude', 'longitude', 'conflict_date']]
output_df.rename(columns={'location': '#name',
'admin1': 'region'}, inplace=True)
output_df["location_type"] = "conflict_zone"
output_df["population"] = "0"
output_df = output_df[
['#name', 'region', 'country', 'latitude',
'longitude', 'location_type', 'conflict_date',
'population']
]
output_file = os.path.join(fab_flee_loc, "config_files",
country, "input_csv",
"locations.csv")
try:
output_df.to_csv(output_file, index=False, mode='x')
except FileExistsError:
print("File Already exists, saving as new_locations.csv")
output_file = os.path.join(fab_flee_loc, "config_files",
country, "input_csv",
"new_locations.csv")
output_df.to_csv(output_file, index=False, mode='x')
if __name__ == '__main__':
fabflee = sys.argv[1]
country = sys.argv[2]
start_date = sys.argv[3]
filter_opt = sys.argv[4]
adminlevel = sys.argv[5]
acled2locations(fabflee, country, start_date, filter_opt, adminlevel)
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supermamas/areas/service.py | oasalonen/supermamas | 0 | 43538 | from flask_babel import gettext
from dateutil import rrule, parser
from datetime import datetime, date, timedelta
from supermamas.areas.districts import District
from supermamas.areas.cities import City
class Service:
__instance = None
def __new__(cls, district_repository=None, city_repository=None):
if not Service.__instance:
Service.__instance = object.__new__(cls)
Service.__instance.district_repository = district_repository
Service.__instance.city_repository = city_repository
return Service.__instance
def _district_repository(self):
return Service.__instance.district_repository
def _city_repository(self):
return Service.__instance.city_repository
def cities(self):
return self._city_repository().get_all()
def districts(self):
return self._district_repository().get_all()
def get_city(self, city_id):
return self._city_repository().get(city_id)
def get_district(self, district_id):
return self._district_repository().get(district_id)
def get_districts_in_same_city_by_district(self, district_id):
city = self._city_repository().get_by_district(district_id)
return city.districts
def add_city(self, city_name):
errors = {}
if not city_name:
errors["city_name"] = gettext(u"City name is missing")
if not errors:
city = City()
city.name = city_name
city = self._city_repository().insert(city)
else:
city = None
return (city, errors)
def add_district(self, district_name):
errors = {}
if not district_name:
errors["district_name"] = gettext(u"District name is missing")
if not errors:
district = District()
district.name = district_name
district = self._district_repository().insert(district)
else:
district = None
return (district, errors)
def delete_city(self, city_id):
self._city_repository().remove(city_id)
def delete_district(self, district_id):
self._district_repository().remove(district_id) | [
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tests/highlevel_functions/test_highlevel_functions.py | meta00/vital_sqi | 4 | 173700 | <gh_stars>1-10
import pytest
import os
import numpy as np
from vital_sqi.data.signal_io import PPG_reader
from vital_sqi.highlevel_functions.highlevel import \
compute_SQI,compute_multiple_SQIs,get_clean_signals,\
get_cutpoints,basic_ecg_pipeline,basic_ppg_pipeline,segment_PPG_SQI_extraction
class TestSignalPreProcess(object):
def test_on_signal_preprocess(self):
pass
# file_name = os.path.abspath('tests/test_data/ppg_smartcare.csv')
# mock_data = PPG_reader(file_name, signal_idx=['PLETH'],
# timestamp_idx=['TIMESTAMP_MS'],
# info_idx=['PULSE_BPM', 'SPO2_PCT',
# 'PERFUSION_INDEX'],
# sampling_rate=100,
# start_datetime='2020/12/30 10:00:00')
# mock_signal = mock_data.signals
# with pytest.raises(Exception) as exc_info:
# signal_preprocess()
# assert exc_info.match("No signal provided")
# out_data = signal_preprocess(mock_signal)
# assert len(mock_signal)==len(out_data)
class TestComputeAllSQI(object):
def test_on_compute_all_SQI(self):
pass
# file_name = os.path.abspath('tests/test_data/ppg_smartcare.csv')
# mock_data = PPG_reader(file_name, signal_idx=['PLETH'],
# timestamp_idx=['TIMESTAMP_MS'],
# info_idx=['PULSE_BPM', 'SPO2_PCT',
# 'PERFUSION_INDEX'],
# sampling_rate=100,
# start_datetime='2020/12/30 10:00:00')
# mock_signal = mock_data.signals[0]
# with pytest.raises(Exception) as exc_info:
# compute_all_SQI(signal=None,segments=[],raw_signal=[])
# assert exc_info.match("Signal or segments not provided, can't extract all SQI's")
# with pytest.raises(Exception) as exc_info:
# compute_all_SQI(signal=[],segments=None,raw_signal=[])
# assert exc_info.match("Signal or segments not provided, can't extract all SQI's")
# with pytest.raises(Exception) as exc_info:
# compute_all_SQI(signal=[],segments=[],raw_signal=None)
# assert exc_info.match("Signal or segments not provided, can't extract all SQI's")
# mock_segment_length = 1000
# mock_segment = np.arange(np.floor(len(mock_signal)/mock_segment_length))\
# *mock_segment_length
# mock_segment = [int(idx) for idx in mock_segment]
# mock_sqi_list = compute_all_SQI(signal=mock_signal,segments=mock_segment,
# raw_signal=mock_signal)
# assert len(mock_sqi_list) > 0
# mock_sqi_dict = mock_sqi_list[0]
# assert type(mock_sqi_dict) is dict
# assert len(mock_sqi_dict.keys()) > 0
# for key in mock_sqi_dict.keys():
# assert mock_sqi_dict[key] is not None
class TestComputeMultipleSQIs(object):
def test_on_compute_multiple_SQIs(self):
pass
class TestMakeRuleSet(object):
def test_on_make_rule_set(self):
pass
class TestGetCutPoints(object):
def test_on_get_cutpoints(self):
pass
class TestGetCleanSignals(object):
def test_on_get_clean_signals(self):
pass
class TestBasicPpgPipeline(object):
def test_on_basic_ppg_pipeline(self):
pass
class TestBasicEcgPipeline(object):
def test_on_basic_ecg_pipeline(self):
pass
| [
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] |
itests/pages/permissions.py | bonniech3n/merou | 0 | 164935 | from typing import TYPE_CHECKING
from itests.pages.base import BaseElement, BasePage
if TYPE_CHECKING:
from typing import List
class PermissionsPage(BasePage):
@property
def has_create_permission_button(self):
# type: () -> bool
return self.find_elements_by_class_name("create-permission") != []
@property
def permission_rows(self):
# type: () -> List[PermissionRow]
all_permission_rows = self.find_elements_by_class_name("permission-row")
return [PermissionRow(row) for row in all_permission_rows]
@property
def limit_label(self):
# type: () -> str
return self.find_element_by_class_name("dropdown-toggle").text.strip()
def click_show_all_button(self):
# type: () -> None
button = self.find_element_by_class_name("show-all")
button.click()
def click_show_audited_button(self):
# type: () -> None
button = self.find_element_by_class_name("show-audited")
button.click()
def click_sort_by_date(self):
# type: () -> None
heading = self.find_element_by_class_name("col-date")
link = heading.find_element_by_tag_name("a")
self.root.get(link.get_attribute("href"))
class PermissionRow(BaseElement):
@property
def name(self):
# type: () -> str
return self.find_element_by_class_name("permission-name").text
@property
def description(self):
# type: () -> str
return self.find_element_by_class_name("permission-description").text
@property
def created_on(self):
# type: () -> str
return self.find_element_by_class_name("permission-created-on").text
class PermissionGrantRow(BaseElement):
@property
def name(self):
return self.find_element_by_class_name("permission-name").text
@property
def href(self):
name = self.find_element_by_class_name("permission-name")
link = name.find_element_by_tag_name("a")
return link.get_attribute("href")
@property
def argument(self):
return self.find_element_by_class_name("permission-argument").text
@property
def source(self):
return self.find_element_by_class_name("permission-source").text
@property
def granted_on(self):
return self.find_element_by_class_name("permission-granted-on").text
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] |
tests/test_main.py | newvicx/pybatch | 0 | 125459 | <gh_stars>0
import os
from datetime import datetime
from typing import List, Union
import pandas as pd
import pytest
from dotenv import load_dotenv
from pypibatch import NoBatchesFound, PIBatch, PIBatchError
load_dotenv()
SERVER = os.getenv("PI_SERVER")
UNIT_W_SUB = os.getenv("PI_UNIT_W_SUB")
UNIT_NO_SUB = os.getenv("PI_UNIT_NO_SUB")
TEST_BATCH = os.getenv("TEST_BATCH_NO_SUB")
pi = PIBatch(SERVER)
def test_unable_to_connect_to_server():
"""
PIBatchError is raised when unable to connect to server
"""
with pytest.raises(PIBatchError):
PIBatch("UnknownServer")
def test_no_batches_found():
"""
NoBatchesFound is raised when query returns no unit batches
"""
with pytest.raises(NoBatchesFound):
pi.search("UnknownUnit")
def test_search():
"""
Query should return unit batches and sub batches as non
empty DataFrame's
"""
unit_batches, sub_batches = pi.search(UNIT_W_SUB)
assert (
isinstance(unit_batches, pd.DataFrame) and
isinstance(sub_batches, pd.DataFrame)
)
assert (
not unit_batches.empty and
not sub_batches.empty
)
def test_search_no_sub_batches():
"""
Query can return non empty unit batch and empty
sub batches
"""
unit_batches, sub_batches = pi.search(
UNIT_NO_SUB,
start_time=datetime(year=2022, month=1, day=1),
batch_id=TEST_BATCH
)
assert (
isinstance(unit_batches, pd.DataFrame) and
isinstance(sub_batches, pd.DataFrame)
)
assert (
not unit_batches.empty and
sub_batches.empty
)
@pytest.mark.parametrize(
[
"start_time",
"end_time",
"batch_id",
"product",
"procedure",
"sub_batches",
"expected"
],
[
(
datetime(2022, 1, 1),
datetime(2022, 1, 2),
["*123*", "456"],
["prod1", "prod2"],
["proc1", "proc2", "proc3"],
["sb1"],
(
"2022-01-01T00:00:00",
"2022-01-02T00:00:00",
"*123*,456",
"prod1,prod2",
"proc1,proc2,proc3",
"sb1"
)
),
(
"*-60d",
"*-5d",
"789",
"prod1",
"proc1",
"sb1",
(
"*-60d",
"*-5d",
"789",
"prod1",
"proc1",
"sb1",
)
)
]
)
def test_query_param_conversion(
start_time: Union[datetime, str],
end_time: Union[datetime, str],
batch_id: Union[List[str], str],
product: Union[List[str], str],
procedure: Union[List[str], str],
sub_batches: Union[List[str], str],
expected: tuple
):
pi = PIBatch(SERVER)
params = pi._prep_search_criteria(
start_time,
end_time,
batch_id,
product,
procedure,
sub_batches
)
assert params == expected
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] |
tests/unit/cirrus/quality_control_test.py | Maxsparrow/cirrus | 12 | 55405 | '''
qc command tests
'''
import mock
import unittest
from cirrus.quality_control import main, run_linters, build_parser
class QCCommandTests(unittest.TestCase):
"""
test coverage for qc command module
"""
def test_build_parser(self):
"""test build_parser call"""
args = [
'--include-files', 'file1', 'file2',
'--exclude-files', 'file3', 'file4',
'--exclude-dirs', 'dir1',
'--linters', 'Pep8', 'Pyflakes'
]
qc_conf = {}
opts = build_parser(args, qc_conf)
self.assertEqual(opts.include_files, ['file1', 'file2'])
self.assertEqual(opts.exclude_files, ['file3', 'file4'])
self.assertEqual(opts.exclude_dirs, ['dir1'])
self.assertEqual(opts.linters, ['Pep8', 'Pyflakes'])
qc_conf = {'exclude_dirs': ['dir3', 'dir4']}
args = [
'--include-files', 'file1', 'file2',
'--exclude-files', 'file3', 'file4',
'--linters', 'Pep8', 'Pyflakes'
]
opts = build_parser(args, qc_conf)
self.assertEqual(opts.exclude_dirs, ['dir3', 'dir4'])
@mock.patch("cirrus.quality_control.load_configuration")
@mock.patch("cirrus.quality_control.FACTORY")
def test_run_linters(self, mock_factory, mock_conf):
"""test pass case"""
mock_linter = mock.Mock()
mock_linter.test_mode = False
mock_linter.check = mock.Mock()
mock_linter.errors = None
mock_factory.return_value = mock_linter
mock_factory.registry = {
'Pep8': None,
'Pylint': None
}
opts = mock.Mock()
opts.test_only = False
opts.linters = ['Pep8', 'Pylint']
run_linters(opts, mock_conf, {})
self.assertTrue(mock_linter.check.called)
self.assertEqual(mock_linter.check.call_count, 2)
@mock.patch("cirrus.quality_control.load_configuration")
@mock.patch("cirrus.quality_control.FACTORY")
def test_run_linters_fail(self, mock_factory, mock_conf):
"""test fail case"""
mock_linter = mock.Mock()
mock_linter.test_mode = False
mock_linter.check = mock.Mock()
mock_linter.errors = 100
mock_factory.return_value = mock_linter
mock_factory.registry = {
'Pep8': None,
'Pylint': None
}
opts = mock.Mock()
opts.test_only = False
opts.linters = ['Pep8', 'Pylint']
self.assertRaises(RuntimeError, run_linters, opts, mock_conf, {})
@mock.patch("cirrus.quality_control.load_configuration")
@mock.patch("cirrus.quality_control.FACTORY")
def test_bad_linter_name(self, mock_factory, mock_conf):
"""test fail case"""
mock_linter = mock.Mock()
mock_linter.test_mode = False
mock_linter.check = mock.Mock()
mock_linter.errors = 100
mock_factory.return_value = mock_linter
mock_factory.registry = {
'Pep8': None,
'Pylint': None
}
opts = mock.Mock()
opts.test_only = False
opts.linters = ['WOMP']
self.assertRaises(RuntimeError, run_linters, opts, mock_conf, {})
@mock.patch("cirrus.quality_control.load_configuration")
@mock.patch("cirrus.quality_control.build_parser")
@mock.patch("cirrus.quality_control.run_linters")
@mock.patch("cirrus.quality_control.get_diff_files")
def test_main(self, mock_diffs, mock_rl, mock_bp, mock_conf):
mock_qc = {}
mock_conf.quality_control = mock.Mock(return_value=mock_qc)
mock_opts = mock.Mock()
mock_opts.only_changes = False
mock_bp.return_value = mock_opts
main()
self.assertTrue(mock_rl.called)
@mock.patch("cirrus.quality_control.load_configuration")
@mock.patch("cirrus.quality_control.build_parser")
@mock.patch("cirrus.quality_control.run_linters")
@mock.patch("cirrus.quality_control.get_diff_files")
def test_main_diffs(self, mock_diffs, mock_rl, mock_bp, mock_conf):
mock_qc = {}
mock_conf.quality_control = mock.Mock(return_value=mock_qc)
mock_opts = mock.Mock()
mock_opts.only_changes = True
mock_opts.incude_files = None
mock_bp.return_value = mock_opts
mock_diffs.return_value = ['DIFF1.py', 'DIFF2.py']
main()
self.assertTrue(mock_rl.called)
self.assertEqual(mock_opts.include_files, mock_diffs.return_value)
mock_diffs.return_value = []
self.assertRaises(SystemExit, main)
if __name__ == '__main__':
unittest.main()
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tests/UnitTests.py | MattBussing/DistancePi | 0 | 88730 | <reponame>MattBussing/DistancePi
# This is the experimental testing suite for this app
import json
import re
import sys
import unittest
import requests
from main import Device
from custom_threads import MyThread
# TODO delete all the messages added
class TestAdd(unittest.TestCase):
url = 'https://distance-pi.herokuapp.com'
postUrl = url + '/'
test = 'test'
delUrl = url + '/' + test
delJson = {'message': 'value'}
postJson = {'message': 'value', '_to': 'testing'}
def deleteMessage(self):
return requests.delete(url=self.delUrl, json=self.delJson)
def postMessage(self, postData):
return requests.post(url=self.postUrl, json=postData)
def device(self, d):
m = d.main()
print('uploading test data')
self.messageList = ['lol', 'sadfads', 'i hate lol', 'asdfasdfasdf']
for i in self.messageList:
postProcess = MyThread(self.postMessage, postData={
'message': i, '_to': self.test})
postProcess.start()
def test_normal_run(self):
print("\n############normal_run")
# test a normal run
d = Device(name="UnitTests.py", tests=True,
onComputer=True)
self.device(d)
self.assertEqual(d.main().sort(), self.messageList.sort())
def test_sleep_variable(self):
print("\n##############sleep_variable")
d = Device(name="UnitTests.py", tests=True,
sleepOn=True, onComputer=True, verbose=True)
self.device(d)
print(self.messageList)
self.assertEqual(d.main().sort(), self.messageList.sort())
def test_rpi(self):
print("rpi")
d = Device(name="UnitTests.py", tests=True)
self.device(d)
self.assertEqual(d.main().sort(), self.messageList.sort())
if __name__ == '__main__':
unittest.main()
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Mundo 3/ex105.py | erickeloi/ExerciciosTreino | 0 | 35905 | <reponame>erickeloi/ExerciciosTreino
# Exercício Python 105: Analisando e gerando Dicionários
# Faça um programa que tenha uma função notas() que pode receber várias notas de alunos
# e vai retornar um dicionário com as seguintes informações:
#
# - Quantidade de notas
# - A maior nota
# - A menor nota
# - A média da turma
# - A situação (opcional)
#
# Adicione também as docstrings dessa função para consulta pelo desenvolvedor.
def notas(notas, situacao=True):
"""
-> Recebe várias notas de uma turma, faz uma análise dessas informações e retorna um Dicionário com elas.
:param notas: (Obrigatório) Várias Notas de alunos podem ser digitadas para a análise
:param situacao: (Opcional) True ou False, False por padrão
você escolhe se deseja que o dicionário contenha a análise subjetiva da turma
:return: Retorna um Dicionário com as informações:
- Maior Nota
- Menor Nota
- Média da Turma
- A Situação da Turma (Opcional): Ruim, Regular, Boa
"""
dicionario_de_alunos = dict()
sit = ""
maior = menor = media = total = 0
for contador, nota in enumerate(notas):
if contador == 0:
menor = nota
maior = nota
if nota > maior:
maior = nota
if nota < menor:
menor = nota
total += nota
media = total / len(notas)
dicionario_de_alunos = {
"Quantidade de Notas": len(notas),
"Maior Nota": maior,
"Menor Nota": menor,
"Média da Turma": media
}
if media >= 7:
sit = "Boa"
elif 5 <= media < 7:
sit = "Regular"
elif media < 5:
sit = "Ruim"
if situacao == False:
return dicionario_de_alunos
if situacao == True:
dicionario_de_alunos["Situação"] = sit
return dicionario_de_alunos
notas_alunos = list()
while True:
numero = float(input("Digite as notas dos alunos (999 para parar): "))
if numero == 999:
break
notas_alunos.append(numero)
situacao = str(input("Quer Mostrar a Situação das notas ? [S/N]")).strip().upper()[0]
while situacao not in 'SN':
situacao = str(input("Quer Mostrar a Situação das notas ? [S/N]")).strip().upper()[0]
if situacao == 'S':
situacao = True
elif situacao == 'N':
situacao = False
print(notas(notas_alunos, situacao))
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dvc/output/gs.py | Abrosimov-a-a/dvc | 0 | 78428 | <reponame>Abrosimov-a-a/dvc
from __future__ import unicode_literals
from dvc.output.s3 import OutputS3
from dvc.remote.gs import RemoteGS
class OutputGS(OutputS3):
REMOTE = RemoteGS
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websites/gamefaqs/gameparser.py | jb262/completewasteoftime | 6 | 115305 | <gh_stars>1-10
'''
This module contains all necessary functions to scrape
both base info (description, release date, etc.) and
advanced info (versions, add-ons, etc.) for a video game
provided a BeautifulSoup object containing the corresponding
base/advance data page.
'''
import re
def get_name(info_page):
'''
Returns the title of the game from both base and advanced info page.
:param info_page: BeautifulSoup object containing the base/advanced info page
'''
return info_page.find('h1', class_='page-title').text
def get_description(base_info_page):
'''
Returns the description of the game from the base info page.
:param base_info_page: BeautifulSoup object containing the baseinfo page
'''
return base_info_page.find('div', class_='desc').text
def get_user_ratings(base_info_page):
'''
Returns the user ratings (owned, rating, difficulty, length, completed) from the base info page.
The user ratings are stored in a fieldset having the class mygames_section.
The title of the single categories is stored in div-containers, conveniently having the class
subsection-title. Besides the category title, this element also stores the rating itself.
The votes on which the rating is based is stored in a separate paragraph with the class rate.
The owned-statistic is the only entry without a corresponding votes-statistic, thus resulting
in an error, if the text-attribute is accessed. This case is caught by assigning None to the
votes in this case. There is also a disabled element, which can be accessed by signed in users.
This element is skipped as it does not contain any relevant info.
:param base_info_page: BeautifulSoup object containing the base info page.
'''
result = dict()
user_ratings_fieldsets = base_info_page.find_all('fieldset', class_='mygames_section')
if user_ratings_fieldsets:
categories = list()
ratings = list()
votes = list()
for user_rating_fieldset in user_ratings_fieldsets:
if not 'disabled' in user_rating_fieldset.attrs:
subsection = user_rating_fieldset.find('div', class_='subsection-title')
category, rating = subsection.text.split(':')
try:
vote = subsection.find('p', 'rate').text
except AttributeError:
vote = None
categories.append(category)
ratings.append(rating)
votes.append(vote)
for category, rating, vote in zip(categories, ratings, votes):
result[category] = {
'Rating': rating,
'Votes': vote}
return result
def get_base_info(base_info_page):
'''
Returns the basic info on the game provided on the base info page
(platforms, developer, release date, genre, franchise, metacritic score, ESRB rating).
Franchise, ESRB rating and metacritic score can be missing, the latter two especially
when data of older games is scraped.
This data is stored in a div container at the upper right corner, having the class
pod_gameinfo. Inside this container is an unordered list. The list itself has a very heterogenic
structure: The core platform, ESRB rating and Metacritic score have their own unique classes,
while the other list items do not. The developing/publishing company and, if provided, the number
of DLCs/Add-Ons do not even have a label in contrast to release date, other platforms and franchise,
resulting in this long if-elif-statement.
In case of the ESRB-rating and the Metacritic score, the obtained result sets are passed to dedicated
parsing methods.
:param base_info_page: BeautifulSoup object containing the base info page
'''
result = dict()
base_info = base_info_page.find('div', class_='pod_gameinfo').find('ul')
if base_info:
categories = list()
values = list()
for info in base_info.find_all('li'):
if not 'class' in info.attrs:
contents = info.find('b')
if contents:
category = re.sub(r'\s', '-', contents.text[:-1])
value = [val.text for val in info.find_all('a')]
if len(value) == 1:
value = value[0]
else:
if re.search(r'#dlc', info.find('a')['href']):
category = 'DLC'
elif re.search(r'/company/', info.find('a')['href']):
category = 'Company'
value = info.text
categories.append(category)
elif 'core-platform' in info['class']:
categories.append('Core-Platform')
value = info.text
elif 'boxshot' in info['class']:
continue
elif 'esrb' in info['class']:
result['ESRB'] = __get_esrb_rating(info)
continue
elif 'metacritic' in info['class']:
result['Metacritic'] = __get_metacritic_score(info)
continue
else:
base_data = info.text.split(':')
categories.append(base_data[0].strip())
value = base_data[1].strip()
values.append(value)
for category, value in zip(categories, values):
result[category] = value
return result
def get_full_base_info(base_info_page):
'''
Returns the full base info on the game provided on the base info page
(base info, description, user ratings, name).
This method simply calls all available operations on the base info page
and stores the results in a dictionary.
:param base_info_page: BeautifulSoup object containing the base info page
'''
result = get_base_info(base_info_page)
result['Description'] = get_description(base_info_page)
result['User-Ratings'] = get_user_ratings(base_info_page)
result['Name'] = get_name(base_info_page)
return result
def get_advanced_info(advanced_info_page):
'''
Returns the full info on the game provided on the advanced info page.
(title data, versions, add-ons)
This method simply calls all available perations on the advanced info page
and stores the results in a dictionary.
:param advanced_info_page: BeautifulSoup object containing the advanced info page
'''
result = dict()
result['Title-Data'] = get_title_data(advanced_info_page)
result['Versions'] = get_versions(advanced_info_page)
result['DLC'] = get_dlc(advanced_info_page)
return result
def get_title_data(advanced_info_page):
'''
Returns the title data of the game provided on the advanced info page
(developer, genres, ESRB-descriptors, Wikipedia (EN) link, multiplayers, etc. ).
The retrieved data can differ from game to game given the data provided.
Example: Tales of Berseria: Genre, Developer, Local Players, Online Players, Wiki
The Sims: Genre, Developer, ESRB-Descriptors, Wiki
The title data is stored in description list, which itself is nested into a
div-container with the unique class pod-titledata.
The tags and contents are parsed separately and stored as keys and values in
a dictionary.
:param advanced_info_page: BeautifulSoup object containing the advanced info page
'''
result = dict()
title_data = advanced_info_page.find('div', 'pod_titledata')
if title_data:
categories = title_data.find_all('dt')
values = title_data.find_all('dd')
for category, value in zip(categories, values):
key = re.sub(r'\s', '-', re.sub(r'[()]', '', category.text))
value = re.sub(r' >', ',', value.text).split(',')
if len(value) > 1:
value = [val.strip() for val in value]
result[key[:-1]] = value
return result
def get_versions(advanced_info_page):
'''
Returns all published versions of the game (region, publisher, product ID, barcode, rating).
The data is stored in a table without any unique identifiers or classes. The cells in a single
table row all have unique classes corresponding to their contents, except for the product ID and
barcode cells sharing the same class. This can be bypassed by the simple rule that all elements
in the result set with an even index are product IDs and all elements with an odd index are
barcodes. This rule is valid since, even if there is no product ID or barcode provided,
parsing returns None as their values, guaranteeing elements at all indexes.
:param advanced_info_page: BeautifulSoup object containing the advanced info page
'''
result = list()
regions = advanced_info_page.find_all('td', class_='cregion')
publishers = advanced_info_page.find_all('td', class_='datacompany')
ids = advanced_info_page.find_all('td', class_='datapid')
product_ids = ids[::2]
barcodes = ids[1::2]
release_dates = advanced_info_page.find_all('td', class_='cdate')
ratings = advanced_info_page.find_all('td', class_='datarating')
if regions and publishers and product_ids and barcodes and release_dates and ratings:
for (region, publisher, product_id, barcode, release_date, rating) in zip(
regions, publishers, product_ids, barcodes, release_dates, ratings):
result.append(
{'Region': region.text.strip(),
'Publisher': publisher.text.strip(),
'Product-Id': product_id.text.strip(),
'Barcode': barcode.text.strip(),
'Release-Date': release_date.text.strip(),
'Rating': rating.text.strip()})
return result
def get_dlc(advance_info_page):
'''
Returns all released add-ons/DLCs of the game (name, gamefaqs-link).
The DLC section is very incomplete. A lot of extensions to the games are missing (example:
The Sims actually having at least 3,013,553 purchasable add-ons, while only having two according
to gamefaqs). The data itself is stored in a table with the ID dlc, making the parsing straightforward.
:param advanced_info_page: BeautifulSoup object containing the advanced info page
'''
result = list()
dlcs = advance_info_page.find('div', id='dlc')
if dlcs:
for dlc in dlcs.find_all('a', href=True):
result.append({
'Name': dlc.text,
'Link': dlc['href']})
return result
def get_questions(questions_page):
'''
Returns all asked questions either from the answered questions page or the unresolved questions page including
link and answer count.
Both answered and unresolved questions pages have the same structure: The information is stored in a container,
which holds the questions, the links to the questions and the answer count in tables with the classes qna_table
separated by topic (e.g. Enemy/Boss Help, Technical Help, etc.). The parsing is then straighforward.
If one of these sections does not have a header (i.e. a description for the topic), the section is skipped.
This line of code was only added for the sake of completeness, as this situation should not ever occur.
'''
result = list()
questions = questions_page.find_all('table', class_='qna_table')
if questions:
for topics in questions:
try:
topic = topics.find('th', class_='question').text
except AttributeError:
continue
topic_dict = {
'Topic': topic,
'Questions': list()}
questions_texts = topics.find_all('a', href=True)
answer_count = topics.find_all('td', class_='count')
for question, count in zip(questions_texts, answer_count):
topic_dict['Questions'].append({
'Question': question.text,
'Link': question['href'],
'Count': int(count.text)})
result.append(topic_dict)
return result
def get_question_details(details_page):
'''
Returns question details (i.e. the full question text) and, if any, its answers including up- and downvotes.
The container with the left span of the main content has to be selected first, otherwise the aside element
will be parsed as well, leading to nonsensical results.
Both questions and answers are stored in containers with the class friend_info. There is no strict separation
between the question and its answers in the container´s body, both are stored in spans with the class name. The only
way to distinguish them is to check, if there are any up- or downvotes stored in the container as well.
'''
result = dict()
main_content = details_page.find('div', class_='main_content').find('div', class_='span8')
details = main_content.find_all('div', class_='friend_info')
if details:
answers = list()
for detail in details:
full_text = detail.find('span', class_='name').text
upvotes = detail.find('span', class_='up')
downvotes = detail.find('span', class_='down')
if not upvotes or not downvotes:
result['Full-Question'] = full_text
else:
answers.append({
'Answer': full_text,
'Upvotes': int(upvotes.text),
'Downvotes': int(downvotes.text)})
result['Answers'] = answers
return result
def get_all_games(page):
'''
Returns a list of the next 100 games, including gamefaqs links, from an `all-games-page` of
a given console.
The info is stored in the rows of a table with the unique class results. The relevant information
- name and gamefaqs link of the game - are stored in a table data cell with the unique class
rtitle. Thus parsing is straighforward.
:param page: `All-games-page` of a given console.
'''
result = list()
games = page.find('table', class_='results')
if games:
rows = games.find_all('td', class_='rtitle')
if rows:
for row in rows:
link = row.find('a', href=True)
link_text = link['href']
game = link.text
result.append({
'Name': game,
'Link': link_text})
return result
def __get_metacritic_score(base_info_pod):
'''
Returns the average metacritic score and the number of reviews.
The score itself is stored in a div-container with the unique class score.
The total count of reviews needs to be retrieved from a link text below, from which
the actual number has to be extracted using a regular expression.
:param base_info_pod: BeautifulSoup object containing the base info box provided in the upper
right of the base info page.
'''
result = dict()
metacritic = base_info_pod.find('div', class_='review_link')
if metacritic:
metacritic_reviews = metacritic.text
try:
numbers = re.compile(r'\d+')
metacritic_review_count = int(numbers.findall(metacritic_reviews)[0])
except IndexError:
metacritic_review_count = None
result['Reviews'] = metacritic_review_count
try:
metacritic_score = int(base_info_pod.find('div', class_='score').text)
except AttributeError:
metacritic_score = None
result['Score'] = metacritic_score
return result
def __get_esrb_rating(base_info_pod):
'''
Returns the ESRB rating and the description of the rating.
The ESRB info is stored in a list entry (of the unordered list of base information) with the
unique class esrb. While hidden behind the logo on the visible website, the actual rating and
its rating can easily be retrieved by a splitting its content on a -.
:param base_info_pod: BeautifulSoup object containing the base info box provided in the upper
right of the base info page.
'''
result = dict()
if base_info_pod:
rating, description = base_info_pod.text.split("-")
rating, description = rating.strip(), description.strip()
result['Rating'] = rating
result['Description'] = description
return result | [
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] |
code/examples/example_binomial_and_log_normal_abtest.py | hugopibernat/BayesianABTestAnalysis | 0 | 3658 | #################################################
####### Author: <NAME> #######
####### Contact: <EMAIL> #######
####### Date: April 2014 #######
#################################################
from bayesianABTest import sampleSuccessRateForBinomial, sampleMeanForLogNormal, probabilityOfABetterThanB
from numpy.random import lognormal
from numpy import mean, concatenate, zeros
# Generate Log-Normal data
A_actuals = lognormal(mean=4.10, sigma=1.0, size=100)
B_actuals = lognormal(mean=4.00, sigma=1.0, size=100)
# Plus some zeros
A_data = concatenate([A_actuals,zeros(10000)])
B_data = concatenate([B_actuals,zeros(10000)])
# Modeling conversions with a binomial variable
A_purchases = sum(A_data > 0)
A_sessions = len(A_data)
B_purchases = sum(B_data > 0)
B_sessions = len(B_data)
A_CR = sampleSuccessRateForBinomial(A_sessions,A_purchases)
B_CR = sampleSuccessRateForBinomial(B_sessions,B_purchases)
# Modeling the spend with a log-normal
A_non_zero_data = A_data[A_data > 0]
B_non_zero_data = B_data[B_data > 0]
A_spend = sampleMeanForLogNormal(A_non_zero_data)
B_spend = sampleMeanForLogNormal(B_non_zero_data)
# Combining the two
A_rps = A_CR*A_spend
B_rps = B_CR*B_spend
# Result:
print probabilityOfABetterThanB(A_rps,B_rps) | [
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] |
v2/audio/audio_reader.py | safgen/deep-speaker | 0 | 40741 | <reponame>safgen/deep-speaker<filename>v2/audio/audio_reader.py
from time import time
import dill
import librosa
import numpy as np
import os
import re
from glob import glob
from random import shuffle, randint, choice
from tqdm import tqdm
from helpers.logger import Logger
logger = Logger.instance()
TMP_DIR = '/tmp/speaker-change-detection/'
if not os.path.exists(TMP_DIR):
os.makedirs(TMP_DIR)
SENTENCE_ID = 'sentence_id'
SPEAKER_ID = 'speaker_id'
FILENAME = 'filename'
class PreMergeProcessor:
@staticmethod
def ramp(sound_1, sound_2):
mask_1 = np.linspace(1, 0, len(sound_1))
mask_2 = np.linspace(0, 1, len(sound_2))
return np.expand_dims(np.multiply(mask_1, sound_1.flatten()), axis=1), \
np.expand_dims(np.multiply(mask_2, sound_2.flatten()), axis=1)
class SoundMerger:
@staticmethod
def default_merger(sound_1, sound_2):
return 0.5 * sound_1 + 0.5 * sound_2
@staticmethod
def clip_merger(sound_1, sound_2):
return np.clip(sound_1 + sound_2, -1, 1)
@staticmethod
def add_merger(sound_1, sound_2):
return sound_1 + sound_2
def find_files(directory, pattern='**/*.wav'):
"""Recursively finds all files matching the pattern."""
return sorted(glob(os.path.join(directory, pattern), recursive=True))
def read_audio_from_filename(filename, sample_rate):
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio.reshape(-1, 1)
return audio, filename
def load_generic_audio(files, sample_rate):
for filename in files:
audio, filename = read_audio_from_filename(filename, sample_rate)
yield audio, filename
def load_vctk_audio(directory, sample_rate):
"""Generator that yields audio waveforms from the VCTK dataset, and
additionally the ID of the corresponding speaker."""
files = find_files(directory)
speaker_re = re.compile(r'p([0-9]+)_([0-9]+)\.wav')
for filename in files:
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio.reshape(-1, 1)
matches = speaker_re.findall(filename)[0]
speaker_id, recording_id = [int(id_) for id_ in matches]
yield audio, speaker_id
def overlap(sound1, sound2, overlap_length,
merge=SoundMerger.add_merger,
pre_merge_processor=PreMergeProcessor.ramp):
tmp = np.array(sound1)
l1 = len(sound1)
l2 = len(sound2)
# print('-> l1 = {}, l2 = {}'.format(l1, l2))
if overlap_length >= l1 and overlap_length >= l2:
overlap_length = min(l1, l2)
elif overlap_length >= l2:
overlap_length = l2
elif overlap_length >= l1:
overlap_length = l1
overlap_1, overlap_2 = pre_merge_processor(tmp[-overlap_length:], sound2[:overlap_length])
overlap_part = merge(overlap_1, overlap_2)
assert len(overlap_part) == overlap_length
assert np.max(overlap_part) <= 1.0
assert np.min(overlap_part) >= -1.0
tmp[-overlap_length:] = overlap_part
out = np.concatenate((tmp, sound2[overlap_length:]))
assert len(out) == l1 + l2 - overlap_length
out = np.clip(out, -1, 1)
mid_overlap_point_offset = int(l1)
# mid_overlap_point_offset = int(l1 - overlap_length * 0.5)
return out, mid_overlap_point_offset
def trim_silence(audio, threshold):
"""Removes silence at the beginning and end of a sample."""
energy = librosa.feature.rmse(audio)
frames = np.nonzero(np.array(energy > threshold))
indices = librosa.core.frames_to_samples(frames)[1]
# Note: indices can be an empty array, if the whole audio was silence.
audio_trim = audio[0:0]
left_blank = audio[0:0]
right_blank = audio[0:0]
if indices.size:
audio_trim = audio[indices[0]:indices[-1]]
left_blank = audio[:indices[0]] # slice before.
right_blank = audio[indices[-1]:] # slice after.
return audio_trim, left_blank, right_blank
def extract_speaker_id(filename):
return filename.split('/')[-2]
def extract_sentence_id(filename):
return filename.split('/')[-1].split('_')[1].split('.')[0]
class AudioReader(object):
def __init__(self,
audio_dir,
sample_rate,
speakers_sub_list=None):
self.audio_dir = os.path.expanduser(audio_dir) # for the ~/
self.sample_rate = sample_rate
self.metadata = dict() # small cache <SPEAKER_ID -> SENTENCE_ID, filename>
self.cache = dict() # big cache <filename, data:audio librosa, blanks.>
logger.debug('Initializing AudioReader()')
logger.debug('audio_dir = {}'.format(self.audio_dir))
logger.debug('sample_rate = {}'.format(sample_rate))
logger.debug('speakers_sub_list = {}'.format(speakers_sub_list))
st = time()
if len(find_files(TMP_DIR, pattern='*.pkl')) == 0: # generate all the pickle files.
logger.debug('Nothing found at {}. Generating all the caches now.'.format(TMP_DIR))
files = find_files(self.audio_dir)
assert len(files) != 0, 'Generate your cache please.'
logger.debug('Found {} files in total in {}.'.format(len(files), self.audio_dir))
if speakers_sub_list is not None:
files = list(
filter(lambda x: any(word in extract_speaker_id(x) for word in speakers_sub_list), files))
logger.debug('{} files correspond to the speaker list {}.'.format(len(files), speakers_sub_list))
assert len(files) != 0
bar = tqdm(files)
for filename in bar:
bar.set_description(filename)
try:
speaker_id = extract_speaker_id(filename)
audio, _ = read_audio_from_filename(filename, self.sample_rate)
energy = np.abs(audio[:, 0])
silence_threshold = np.percentile(energy, 95)
offsets = np.where(energy > silence_threshold)[0]
left_blank_duration_ms = (1000.0 * offsets[0]) // self.sample_rate # frame_id to duration (ms)
right_blank_duration_ms = (1000.0 * (len(audio) - offsets[-1])) // self.sample_rate
# _, left_blank, right_blank = trim_silence(audio[:, 0], silence_threshold)
logger.info('_' * 100)
logger.info('left_blank_duration_ms = {}, right_blank_duration_ms = {}, '
'audio_length = {} frames, silence_threshold = {}'.format(left_blank_duration_ms,
right_blank_duration_ms,
len(audio),
silence_threshold))
obj = {'audio': audio,
'audio_voice_only': audio[offsets[0]:offsets[-1]],
'left_blank_duration_ms': left_blank_duration_ms,
'right_blank_duration_ms': right_blank_duration_ms,
FILENAME: filename}
cache_filename = filename.split('/')[-1].split('.')[0] + '_cache'
tmp_filename = os.path.join(TMP_DIR, cache_filename) + '.pkl'
with open(tmp_filename, 'wb') as f:
dill.dump(obj, f)
logger.debug('[DUMP AUDIO] {}'.format(tmp_filename))
# commit to metadata dictionary when you're sure no errors occurred during processing.
if speaker_id not in self.metadata:
self.metadata[speaker_id] = {}
sentence_id = extract_sentence_id(filename)
if sentence_id not in self.metadata[speaker_id]:
self.metadata[speaker_id][sentence_id] = []
self.metadata[speaker_id][sentence_id] = {SPEAKER_ID: speaker_id,
SENTENCE_ID: sentence_id,
FILENAME: filename}
except librosa.util.exceptions.ParameterError as e:
logger.error(e)
logger.error('[DUMP AUDIO ERROR SKIPPING FILENAME] {}'.format(filename))
dill.dump(self.metadata, open(os.path.join(TMP_DIR, 'metadata.pkl'), 'wb'))
logger.debug(
'Using the generated files at {}. Using them to load the cache. Be sure to have enough memory.'.format(
TMP_DIR))
self.metadata = dill.load(open(os.path.join(TMP_DIR, 'metadata.pkl'), 'rb'))
pickle_files = find_files(TMP_DIR, pattern='*.pkl')
for pkl_file in tqdm(pickle_files, desc='reading cache'):
if 'metadata' not in pkl_file:
with open(pkl_file, 'rb') as f:
obj = dill.load(f)
self.cache[obj[FILENAME]] = obj
logger.debug('Cache took {0:.2f} seconds to load. {1:} keys.'.format(time() - st, len(self.cache)))
def get_speaker_list(self):
return sorted(list(self.metadata.keys()))
def sample_speakers(self, speaker_list, num_speakers):
if speaker_list is None:
speaker_list = self.get_speaker_list()
all_speakers = list(speaker_list)
shuffle(all_speakers)
speaker_list = all_speakers[0:num_speakers]
return speaker_list
def define_random_mix(self, num_sentences=3, num_speakers=3, speaker_ids_to_choose_from=None):
speaker_ids_to_choose_from = self.sample_speakers(speaker_ids_to_choose_from, num_speakers)
targets = []
for i in range(num_sentences):
speaker_id = choice(speaker_ids_to_choose_from)
sentence_id = choice(list(self.metadata[speaker_id].keys()))
targets.append({SPEAKER_ID: speaker_id,
SENTENCE_ID: sentence_id,
FILENAME: self.metadata[speaker_id][sentence_id]['filename']})
return targets
def define_mix(self, mix_sequence):
targets = []
for speaker_sentence_id in mix_sequence:
speaker_id, sentence_id = speaker_sentence_id
if isinstance(speaker_id, int):
speaker_id = 'p' + str(speaker_id)
if isinstance(sentence_id, int):
sentence_id = str(sentence_id).zfill(3)
targets.append({SPEAKER_ID: speaker_id,
SENTENCE_ID: sentence_id,
FILENAME: self.metadata[speaker_id][sentence_id]['filename']})
return targets
def generate_mix_with_voice_only(self, targets):
if len(targets) == 0:
return [], None
audio_dict = {}
for i, target in enumerate(targets):
audio_dict[i] = self.cache[target[FILENAME]]
output = audio_dict[0]['audio_voice_only']
targets[0]['offset'] = 0 # in ms
for i in range(1, len(targets)):
offset_pos = -1
beg = targets[i - 1]['offset']
while offset_pos <= beg:
offset_pos = len(output)
output = np.concatenate((output, audio_dict[i]['audio_voice_only']))
targets[i]['offset'] = offset_pos
return targets, output
def generate_mix(self, targets):
audio_dict = {}
for i, target in enumerate(targets):
audio_dict[i] = self.cache[target[FILENAME]]
output = audio_dict[0]['audio']
targets[0]['offset'] = 0 # in ms
for i in range(1, len(targets)):
# example: sound 1 - 600 ms at the end.
dur_right_blank_1 = audio_dict[i - 1]['right_blank_duration_ms']
# example: sound 2 - 25 ms at the beginning (for silence).
dur_left_blank_2 = audio_dict[i]['left_blank_duration_ms']
blank_duration_ms = int(dur_right_blank_1 + dur_left_blank_2)
# example: sample from U[1, 600+25ms] 1 and not 0 because otherwise we have a shape error.
# ValueError: operands could not be broadcast together with shapes (723639,1) (0,1)
offset_pos = -1
beg = targets[i - 1]['offset']
while offset_pos <= beg:
# print('-> append : {}'.format(audio_dict[i][FILENAME]))
if blank_duration_ms <= 1:
rand_val = 1
else:
rand_val = int(randint(1, blank_duration_ms) * 0.8) + 1
# print('-> [{}] generated from U[1,{}]'.format(rand_val, blank_duration_ms))
output, offset_pos = overlap(output, audio_dict[i]['audio'], rand_val)
targets[i]['offset'] = offset_pos
# print(beg)
# print(offset_pos)
# print('___')
# librosa.output.write_wav('toto_{}.wav'.format(i), output[beg:offset_pos], sr=self.sample_rate)
# librosa.output.write_wav('toto.wav', output, sr=self.sample_rate)
# print('buffer length = {}'.format(len(output)))
# pprint.pprint(targets)
return targets, output
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scripts/plot.py | dfvella/vtol | 0 | 47069 | <reponame>dfvella/vtol
#!/usr/bin/env python3
import sys
import matplotlib.pyplot as plt
try:
filename = sys.argv[1]
except IndexError:
print('error: data file not specified')
exit(1)
#response = [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]
#response = [ 0.2, 0.2, 0.2, 0.2, 0.1, 0.05, 0.02, 0.02, 0.01, 0.0 ]
#response = [ 0.4, 0.3, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] # delay <20 ms
response = [ 0.3, 0.3, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] # delay ~25 ms
buffer = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
assert(abs(sum(response) - 1) < 0.001)
assert(len(response) == len(buffer))
def filter(data: float, buff: list, resp: list) -> float:
buff.insert(0, data)
buff.pop()
res = 0
for data, weight in zip(buff, resp):
res += data * weight
return res
tList = []
xList = []
dList = []
fList = []
with open(filename, 'r') as f:
for line in f:
try:
t, x = line.split()
except TypeError:
print('error: failed to parse line')
exit(1)
if len(tList) > 1:
d = (xList[-1] - xList[-2]) / (tList[-1] - tList[-2])
else:
d = 0
tList.append(float(t))
xList.append(float(x))
dList.append(float(d))
fList.append(float(filter(d, buffer, response)))
plt.plot(tList, xList, label='x')
plt.plot(tList, dList, label='dx/dt')
plt.plot(tList, fList, label='filtered')
plt.title(filename)
plt.ylabel('x')
plt.xlabel('t')
plt.legend()
plt.show()
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plugins/modules/tag.py | robertcsapo/dnacenter-ansible | 0 | 90261 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: (c) 2021, Cisco Systems
# GNU General Public License v3.0+ (see LICENSE or https://www.gnu.org/licenses/gpl-3.0.txt)
DOCUMENTATION = r"""
---
module: tag
short_description: Manage Tag objects of Tag
description:
- Returns the Tags for given filter criteria.
- Creates Tag with specified Tag attributes.
- Updates a Tag specified by id.
- Deletes a Tag specified by id.
- Returns Tag specified by Id.
- Returns Tag count.
version_added: '1.0.0'
author: <NAME> (@racampos)
options:
additional_info_attributes:
description:
- AdditionalInfo.attributes query parameter.
type: str
additional_info_name_space:
description:
- AdditionalInfo.nameSpace query parameter.
type: str
field:
description:
- Available field names are 'name,id,parentId,type,additionalInfo.nameSpace,additionalInfo.attributes'.
type: str
level:
description:
- Level query parameter.
type: str
limit:
description:
- Limit query parameter.
type: str
name:
description:
- Tag name is mandatory when filter operation is used.
- TagDTO's name.
- Name query parameter.
type: str
offset:
description:
- Offset query parameter.
type: str
order:
description:
- Available values are asc and des.
type: str
size:
description:
- Size in kilobytes(KB).
type: str
sort_by:
description:
- Only supported attribute is name. SortyBy is mandatory when order is used.
type: str
system_tag:
description:
- SystemTag query parameter.
type: str
description:
description:
- TagDTO's description.
type: str
dynamicRules:
description:
- TagDTO's dynamicRules (list of objects).
type: list
elements: dict
suboptions:
memberType:
description:
- It is the Tag's memberType.
type: str
rules:
description:
- It is the Tag's rules.
type: dict
suboptions:
items:
description:
- It is the Tag's items.
type: list
name:
description:
- It is the Tag's name.
type: str
operation:
description:
- It is the Tag's operation.
type: str
value:
description:
- It is the Tag's value.
type: str
values:
description:
- It is the Tag's values.
type: list
id:
description:
- TagDTO's id.
- Tag ID.
- Required for states query and absent.
type: str
instanceTenantId:
description:
- TagDTO's instanceTenantId.
type: str
systemTag:
description:
- TagDTO's systemTag.
type: bool
attribute_name:
description:
- AttributeName query parameter.
type: str
name_space:
description:
- NameSpace query parameter.
type: str
count:
description:
- If true gets the number of objects.
- Required for state query.
type: bool
requirements:
- dnacentersdk
seealso:
# Reference by module name
- module: cisco.dnac.plugins.module_utils.definitions.tag
# Reference by Internet resource
- name: Tag reference
description: Complete reference of the Tag object model.
link: https://developer.cisco.com/docs/dna-center/api/1-3-3-x
# Reference by Internet resource
- name: Tag reference
description: SDK reference.
link: https://dnacentersdk.readthedocs.io/en/latest/api/api.html#v2-1-1-summary
"""
EXAMPLES = r"""
- name: get_tag
cisco.dnac.tag:
state: query # required
additional_info_attributes: SomeValue # string
additional_info_name_space: SomeValue # string
field: SomeValue # string
level: SomeValue # string
limit: SomeValue # string
name: SomeValue # string
offset: SomeValue # string
order: SomeValue # string
size: SomeValue # string
sort_by: SomeValue # string
system_tag: SomeValue # string
register: nm_get_tag
- name: create_tag
cisco.dnac.tag:
state: present # required
description: SomeValue # string
dynamicRules:
- memberType: SomeValue # string
rules:
values:
- SomeValue # string
items: None
operation: SomeValue # string
name: SomeValue # string
value: SomeValue # string
id: SomeValue # string
instanceTenantId: SomeValue # string
name: SomeValue # string
systemTag: True # boolean
- name: update_tag
cisco.dnac.tag:
state: present # required
description: SomeValue # string
dynamicRules:
- memberType: SomeValue # string
rules:
values:
- SomeValue # string
items: None
operation: SomeValue # string
name: SomeValue # string
value: SomeValue # string
id: SomeValue # string
instanceTenantId: SomeValue # string
name: SomeValue # string
systemTag: True # boolean
- name: delete_tag
cisco.dnac.tag:
state: absent # required
id: SomeValue # string, required
- name: get_tag_by_id
cisco.dnac.tag:
state: query # required
id: SomeValue # string, required
register: nm_get_tag_by_id
- name: get_tag_count
cisco.dnac.tag:
state: query # required
count: True # boolean, required
attribute_name: SomeValue # string
level: SomeValue # string
name: SomeValue # string
name_space: SomeValue # string
size: SomeValue # string
system_tag: SomeValue # string
register: nm_get_tag_count
"""
RETURN = r"""
dnac_response:
description: A dictionary with the response returned by the DNA Center Python SDK
returned: always
type: dict
sample: {"response": 29, "version": "1.0"}
sdk_function:
description: The DNA Center SDK function used to execute the task
returned: always
type: str
sample: tag.create_tag
missing_params:
description: Provided arguments do not comply with the schema of the DNA Center Python SDK function
returned: when the function request schema is not satisfied
type: list
sample:
"""
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] |
bin/extract_shared_or_unique_otuids.py | bhawan1/phylotoast | 19 | 1609176 | <filename>bin/extract_shared_or_unique_otuids.py<gh_stars>10-100
#! /usr/bin/env python
"""
Abstract: Get unique OTUIDs present in each category of your dataset.
Date: 06/09/2016
"""
import sys
import argparse
import os.path as osp
from itertools import combinations
from collections import defaultdict
from phylotoast import otu_calc as oc, util
try:
import biom
except ImportError as ie:
sys.exit("Please install missing module: {}.".format(ie))
try:
import pandas as pd
except ImportError as ie:
sys.exit("Please install missing module: {}.".format(ie))
def sample_group(sid, groups):
"""
Iterate through all categories in an OrderedDict and return category name if SampleID
present in that category.
:type sid: str
:param sid: SampleID from dataset.
:type groups: OrderedDict
:param groups: Returned dict from phylotoast.util.gather_categories() function.
:return type: str
:return: Category name used to classify `sid`.
"""
for name in groups:
if sid in groups[name].sids:
return name
def combine_sets(*sets):
"""
Combine multiple sets to create a single larger set.
"""
combined = set()
for s in sets:
combined.update(s)
return combined
def unique_otuids(groups):
"""
Get unique OTUIDs of each category.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on category name and unique OTUIDs as values.
"""
uniques = {key: set() for key in groups}
for i, group in enumerate(groups):
to_combine = groups.values()[:i]+groups.values()[i+1:]
combined = combine_sets(*to_combine)
uniques[group] = groups[group].difference(combined)
return uniques
def shared_otuids(groups):
"""
Get shared OTUIDs between all unique combinations of groups.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on group combination and their shared OTUIDs as values.
"""
for g in sorted(groups):
print("Number of OTUs in {0}: {1}".format(g, len(groups[g].results["otuids"])))
number_of_categories = len(groups)
shared = defaultdict()
for i in range(2, number_of_categories+1):
for j in combinations(sorted(groups), i):
combo_name = " & ".join(list(j))
for grp in j:
# initialize combo values
shared[combo_name] = groups[j[0]].results["otuids"].copy()
"""iterate through all groups and keep updating combo OTUIDs with set
intersection_update"""
for grp in j[1:]:
shared[combo_name].intersection_update(groups[grp].results["otuids"])
return shared
def write_uniques(path, prefix, uniques):
"""
Given a path, the method writes out one file for each group name in the
uniques dictionary with the file name in the pattern
PATH/prefix_group.txt
with each file containing the unique OTUIDs found when comparing that group
to all the other groups in uniques.
:type path: str
:param path: Output files will be saved in this PATH.
:type prefix: str
:param prefix: Prefix name added in front of output filename.
:type uniques: dict
:param uniques: Output from unique_otus() function.
"""
for group in uniques:
fp = osp.join(path, "{}_{}.txt".format(prefix, group))
with open(fp, "w") as outf:
outf.write("\n".join(uniques[group]))
def handle_program_options():
parser = argparse.ArgumentParser(description="Parse a BIOM format file and obtain a "
"list of unique OTUIDs found in each category in "
"mapping file.")
parser.add_argument("input_biom_fp", help="BIOM format file path.")
parser.add_argument("output_dir",
help="Path to save category unique OTUIDs.")
parser.add_argument("mapping_file", help="Mapping file with category information.")
parser.add_argument("category_column", help="Column in mapping file specifying the "
"category/condition of all samples.")
parser.add_argument("-p", "--prefix", default="unique", help="Provide specific text "
"to prepend the output file names. By default, the 'unique' will "
"be added in front of output filenames, for uniques calculations "
"only.")
parser.add_argument("-r", "--reverse",
help="Get shared OTUIDs among all unique combinations of groups "
"and write out the results to path provided to this option.")
return parser.parse_args()
def main():
args = handle_program_options()
try:
# Load biom format file
biomf = biom.load_table(args.input_biom_fp)
except TypeError as te:
sys.exit("The data in the path does not appear to be a BIOM format table. "
"Error: {}.".format(te))
# Determine OTUIDs present in each sample
sample_otus = oc.assign_otu_membership(biomf)
try:
# Parse mapping file
header, imap = util.parse_map_file(args.mapping_file)
except ValueError as ve:
sys.exit("Error: {}.".format(ve))
# Get relevant category information
group_data = util.gather_categories(imap, header, [args.category_column])
# Initialize results dict in group_data with {"otuids": set()} for each category
for group in group_data:
group_data[group].results["otuids"] = set()
# Collect all OTUIDs present in each category
for sid in sample_otus:
group = sample_group(sid, group_data)
group_data[group].results["otuids"].update(sample_otus[sid])
if args.reverse:
# Get shared OTUIDs
shared = shared_otuids(group_data)
# Write out shared OTUIDs results
shared_df = pd.DataFrame.from_dict(shared, orient="index").T
shared_df.to_csv(args.reverse, sep="\t", index=False)
# Create input for unique_otus
group_otuids = {group: group_data[group].results["otuids"] for group in group_data}
# Write out unique OTUIDs to file
write_uniques(args.output_dir, args.prefix, unique_otuids(group_otuids))
if __name__ == "__main__":
sys.exit(main())
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my_ip/migrations/0007_alter_project_project_image.py | OscarMugendi/Django-week3-IP | 0 | 112760 | <reponame>OscarMugendi/Django-week3-IP
# Generated by Django 3.2.7 on 2021-09-24 05:37
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('my_ip', '0006_auto_20210924_0111'),
]
operations = [
migrations.AlterField(
model_name='project',
name='project_image',
field=models.ImageField(blank=True, default=0, upload_to='images/projects/'),
),
]
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bedrock/sqlalchemy_test.py | hieast/bedrock | 0 | 165297 |
from ark.db import Database
from sqlalchemy import Column, Integer
from sqlalchemy.dialects import postgresql
db = Database('postgresql://oceanuse:[email protected]:5432/ocean')
class Test(db.Model):
__tablename__ = 'test'
id = Column(Integer, primary_key=True)
query = db.session.query(Test).limit(10).offset(-1)
print query.all()
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average_screen_color.py | Animenosekai/AverageColor | 0 | 137936 | ### Code by <NAME>
###
###
import pyautogui
import PIL
def average_image_color(image):
"""
Code by olooney on GitHub
> https://gist.github.com/olooney/1246268
Returns the average color from a given image (PIL)
"""
i = image
h = i.histogram()
# split into red, green, blue
r = h[0:256]
g = h[256:256*2]
b = h[256*2: 256*3]
# perform the weighted average of each channel:
# the *index* is the channel value, and the *value* is its weight
return [
sum( i*w for i, w in enumerate(r) ) / sum(r),
sum( i*w for i, w in enumerate(g) ) / sum(g),
sum( i*w for i, w in enumerate(b) ) / sum(b)
]
if __name__ == '__main__':
import sys
import numpy as np
import matplotlib.pyplot as plt
import os
os.system('cls' if os.name == 'nt' else 'clear')
if len(sys.argv) > 1:
refresh_rate = int(sys.argv[1])
else:
refresh_rate = 10
print('Chosen refresh rate: ' + str(refresh_rate))
plt.ion()
fig = plt.gcf()
fig.show()
fig.canvas.draw()
iteration = 0
plt.title('Average Screen Color')
while True:
iteration += 1
image = pyautogui.screenshot()
rgb = average_image_color(image)
#VIZUALIZATION
spikes = np.random.random(1) #To show a rectangle
plt.xlabel('Average Color (RGB): ' + str(rgb))
plt.eventplot(spikes, orientation='horizontal', linelengths=0.9, linewidths=1000, color = [(rgb[0]/255, rgb[1]/255, rgb[2]/255)])
if iteration > refresh_rate:
iteration = 0
plt.clf()
plt.title('Average Screen Color')
fig.canvas.draw()
plt.pause(0.000001)
def get_average_screen_color():
'''
Returns the current screen average color.
'''
image = pyautogui.screenshot()
rgb = average_image_color(image)
return rgb | [
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package/__init__.py | jfcrenshaw/python-package | 0 | 146261 | __version__ = "0.1.0"
from .module import *
| [
1,
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334,
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2
] |
tolteca/simu/__init__.py | dennis-l/tolteca | 2 | 164661 | #!/usr/bin/env python
import astropy.units as u
from typing import Union
from dataclasses import dataclass, field, is_dataclass
from cached_property import cached_property
import copy
from typing import ClassVar
from schema import Or
from tollan.utils.dataclass_schema import add_schema
from tollan.utils.log import get_logger, logit, log_to_file
from tollan.utils.fmt import pformat_yaml
from tollan.utils import rupdate
from ..utils.common_schema import PhysicalTypeSchema
from ..utils.config_registry import ConfigRegistry
from ..utils.config_schema import add_config_schema
from ..utils.runtime_context import RuntimeContext, RuntimeContextError
from ..utils import config_from_cli_args
__all__ = ['SimulatorRuntime', 'SimulatorRuntimeError']
@add_schema
@dataclass
class ObsParamsConfig(object):
"""The config class for ``simu.obs_params``."""
t_exp: Union[u.Quantity, None] = field(
default=None,
metadata={
'description': 'The duration of the observation to simulate.',
'schema': Or(PhysicalTypeSchema('time'), None),
}
)
f_smp_mapping: u.Quantity = field(
default=12. << u.Hz,
metadata={
'description': 'The sampling frequency to '
'evaluate mapping models.',
'schema': PhysicalTypeSchema("frequency"),
}
)
f_smp_probing: u.Quantity = field(
default=120. << u.Hz,
metadata={
'description': 'The sampling frequency '
'to evaluate detector signals.',
'schema': PhysicalTypeSchema("frequency"),
}
)
class Meta:
schema = {
'ignore_extra_keys': False,
'description': 'The parameters related to observation.'
}
@add_schema
@dataclass
class PerfParamsConfig(object):
"""The config class for ``simu.pef_params``."""
chunk_len: u.Quantity = field(
default=10 << u.s,
metadata={
'description': 'Chunk length to split the simulation to '
'reduce memory footprint.',
'schema': PhysicalTypeSchema("time"),
}
)
catalog_model_render_pixel_size: u.Quantity = field(
default=0.5 << u.arcsec,
metadata={
'description': 'Pixel size to render catalog source model.',
'schema': PhysicalTypeSchema("angle"),
}
)
mapping_eval_interp_len: Union[u.Quantity, None] = field(
default=None,
metadata={
'description': 'Interp length to speed-up mapping evaluation.',
'schema': PhysicalTypeSchema("time"),
}
)
mapping_erfa_interp_len: u.Quantity = field(
default=300 << u.s,
metadata={
'description': 'Interp length to speed-up AltAZ to '
'ICRS coordinate transformation.',
'schema': PhysicalTypeSchema("time"),
}
)
aplm_eval_interp_alt_step: u.Quantity = field(
default=60 << u.arcmin,
metadata={
'description': (
'Interp altitude step to speed-up '
'array power loading model eval.'),
'schema': PhysicalTypeSchema("angle"),
}
)
pre_eval_sky_bbox_padding_size: u.Quantity = field(
default=3. << u.arcmin,
metadata={
'description': (
'Padding size to add to the sky bbox for '
'pre-eval calculations.'),
'schema': PhysicalTypeSchema("angle"),
}
)
pre_eval_t_grid_size: int = field(
default=100,
metadata={
'description': 'Size of time grid used for pre-eval calculations.',
'schema': PhysicalTypeSchema("angle"),
}
)
anim_frame_rate: u.Quantity = field(
default=300 << u.s,
metadata={
'description': 'Frame rate for plotting animation.',
'schema': PhysicalTypeSchema("frequency"),
}
)
class Meta:
schema = {
'ignore_extra_keys': False,
'description': 'The parameters related to performance tuning.'
}
mapping_registry = ConfigRegistry.create(
name='MappingConfig',
dispatcher_key='type',
dispatcher_description='The mapping type.'
)
"""The registry for ``simu.mapping``."""
instrument_registry = ConfigRegistry.create(
name='InstrumentConfig',
dispatcher_key='name',
dispatcher_description='The instrument name.'
)
"""The registry for ``simu.instrument``."""
sources_registry = ConfigRegistry.create(
name='SourcesConfig',
dispatcher_key='type',
dispatcher_description='The simulator source type.'
)
"""The registry for ``simu.sources``."""
plots_registry = ConfigRegistry.create(
name='PlotsConfig',
dispatcher_key='type',
dispatcher_description='The plot type.'
)
"""The registry for ``simu.plots``."""
exports_registry = ConfigRegistry.create(
name='ExportsConfig',
dispatcher_key='type',
dispatcher_description='The export type.'
)
"""The registry for ``simu.exports``."""
# Load submodules here to populate the registries
from . import mapping as _ # noqa: F401, E402, F811
from . import sources as _ # noqa: F401, E402, F811
from . import plots as _ # noqa: F401, E402, F811
from . import exports as _ # noqa: F401, E402, F811
from . import toltec as _ # noqa: F401, E402, F811
# from . import lmt as _ # noqa: F401, E402, F811
@add_config_schema
@add_schema
@dataclass
class SimuConfig(object):
"""The config for `tolteca.simu`."""
jobkey: str = field(
metadata={
'description': 'The unique identifier the job.'
}
)
instrument: dict = field(
metadata={
'description': 'The dict contains the instrument setting.',
'schema': instrument_registry.schema,
'pformat_schema_type': f'<{instrument_registry.name}>',
})
mapping: dict = field(
metadata={
'description': "The simulator mapping trajectory config.",
'schema': mapping_registry.schema,
'pformat_schema_type': f'<{mapping_registry.name}>'
}
)
obs_params: ObsParamsConfig = field(
metadata={
'description': 'The dict contains the observation parameters.',
})
sources: list = field(
default_factory=list,
metadata={
'description': 'The list contains input sources for simulation.',
'schema': list(sources_registry.item_schemas),
'pformat_schema_type': f"[<{sources_registry.name}>, ...]"
})
perf_params: PerfParamsConfig = field(
default_factory=PerfParamsConfig,
metadata={
'description': 'The dict contains the performance related'
' parameters.',
})
plots: list = field(
default_factory=list,
metadata={
'description': 'The list contains config for plotting.',
'schema': list(plots_registry.item_schemas),
'pformat_schema_type': f"[<{plots_registry.name}>, ...]"
})
exports: list = field(
default_factory=list,
metadata={
'description': 'The list contains config for exporting.',
'schema': list(exports_registry.item_schemas),
'pformat_schema_type': f"[<{exports_registry.name}>, ...]"
})
plot_only: bool = field(
default=False,
metadata={
'description': 'Make plots of those defined in `plots`.'
})
export_only: bool = field(
default=False,
metadata={
'description': 'Export the simu config as defined in `exports`.'
})
class Meta:
schema = {
'ignore_extra_keys': True,
'description': 'The config dict for the simulator.'
}
config_key = 'simu'
logger: ClassVar = get_logger()
def get_or_create_output_dir(self):
logger = get_logger()
rootpath = self.runtime_info.config_info.runtime_context_dir
output_dir = rootpath.joinpath(self.jobkey)
if not output_dir.exists():
with logit(logger.debug, 'create output dir'):
output_dir.mkdir(parents=True, exist_ok=True)
return output_dir
def get_log_file(self):
return self.runtime_info.logdir.joinpath('simu.log')
@cached_property
def mapping_model(self):
return self.mapping(self)
@cached_property
def source_models(self):
return [s(self) for s in self.sources]
@cached_property
def simulator(self):
return self.instrument(self)
@cached_property
def t_simu(self):
"""The length of the simulation.
It equals `obs_params.t_exp` when set, otherwise ``t_pattern``
of the mapping pattern is used.
"""
t_simu = self.obs_params.t_exp
if t_simu is None:
t_pattern = self.mapping_model.t_pattern
self.logger.debug(f"mapping pattern time: {t_pattern}")
t_simu = t_pattern
self.logger.info(f"use t_simu={t_simu} from mapping pattern")
else:
self.logger.info(f"use t_simu={t_simu} from obs_params")
return t_simu
class SimulatorRuntimeError(RuntimeContextError):
"""Raise when errors occur in `SimulatorRuntime`."""
pass
class SimulatorRuntime(RuntimeContext):
"""A class that manages the runtime context of the simulator.
This class drives the execution of the simulator.
"""
config_cls = SimuConfig
logger = get_logger()
@cached_property
def simu_config(self):
"""Validate and return the simulator config object..
The validated config is cached. :meth:`SimulatorRuntime.update`
should be used to update the underlying config and re-validate.
"""
return self.config_cls.from_config(
self.config, rootpath=self.rootpath,
runtime_info=self.runtime_info)
def update(self, config):
self.config_backend.update_override_config(config)
if 'simu_config' in self.__dict__:
del self.__dict__['simu_config']
def cli_run(self, args=None):
"""Run the simulator with CLI as save the result.
"""
if args is not None:
_cli_cfg = config_from_cli_args(args)
# note the cli_cfg is under the namespace simu
cli_cfg = {self.config_cls.config_key: _cli_cfg}
if _cli_cfg:
self.logger.info(
f"config specified with commandline arguments:\n"
f"{pformat_yaml(cli_cfg)}")
self.update(cli_cfg)
cfg = self.simu_config.to_config()
# here we recursively check the cli_cfg and report
# if any of the key is ignored by the schema and
# throw an error
def _check_ignored(key_prefix, d, c):
if isinstance(d, dict) and isinstance(c, dict):
ignored = set(d.keys()) - set(c.keys())
ignored = [f'{key_prefix}.{k}' for k in ignored]
if len(ignored) > 0:
raise SimulatorRuntimeError(
f"Invalid config items specified in "
f"the commandline: {ignored}")
for k in set(d.keys()).intersection(c.keys()):
_check_ignored(f'{key_prefix}{k}', d[k], c[k])
_check_ignored('', cli_cfg, cfg)
return self.run()
def run(self):
cfg = self.simu_config
self.logger.debug(
f"run simu with config dict: "
f"{pformat_yaml(cfg.to_config())}")
if cfg.plot_only:
return self._run_plot()
if cfg.export_only:
return self._run_export()
return self._run_simu()
def _run_plot(self):
cfg = self.simu_config
self.logger.info(
f"make simu plots:\n"
f"{pformat_yaml(cfg.to_dict()['plots'])}")
results = []
for plotter in cfg.plots:
result = plotter(cfg)
results.append(result)
if plotter.save:
# TODO handle save here
pass
return results
def _run_export(self):
cfg = self.simu_config
self.logger.info(
f"export simu:\n"
f"{pformat_yaml(cfg.to_dict()['exports'])}")
results = []
for exporter in cfg.exports:
result = exporter(cfg)
results.append(result)
return results
def _run_simu(self):
"""Run the simulator."""
cfg = self.simu_config
simu = cfg.simulator
t_simu = cfg.t_simu
mapping_model = cfg.mapping_model
source_models = cfg.source_models
output_dir = cfg.get_or_create_output_dir()
self.logger.debug(
f'run {simu} with:{{}}\n'.format(
pformat_yaml({
'obs_params': cfg.obs_params.to_dict(),
'perf_params': cfg.perf_params.to_dict(),
})))
self.logger.debug(
'mapping:\n{}\n\nsources:\n{}\n'.format(
mapping_model,
'\n'.join(str(s) for s in source_models)
)
)
self.logger.debug(
f'simu output dir: {output_dir}\nsimu length={t_simu}'
)
# run the simulator
log_file = cfg.get_log_file()
self.logger.info(f'setup logging to file {log_file}')
with log_to_file(
filepath=log_file,
level='DEBUG',
disable_other_handlers=False
):
output_ctx = simu.output_context(dirpath=output_dir)
with output_ctx.open():
self.logger.info(
f"write output to {output_ctx.rootpath}")
# save the config file as YAML
config_filepath = output_ctx.make_output_filename(
'tolteca', '.yaml')
with open(config_filepath, 'w') as fo:
config = copy.deepcopy(self.config)
rupdate(config, self.simu_config.to_config())
self.yaml_dump(config, fo)
with simu.iter_eval_context(cfg) as (iter_eval, t_chunks):
# save mapping model meta
output_ctx.write_mapping_meta(
mapping=mapping_model, simu_config=cfg)
# save simulator meta
output_ctx.write_sim_meta(simu_config=cfg)
# run simulator for each chunk and save the data
n_chunks = len(t_chunks)
for ci, t in enumerate(t_chunks):
self.logger.info(
f"simulate chunk {ci}/{n_chunks} "
f"t_min={t.min()} t_max={t.max()}")
output_ctx.write_sim_data(iter_eval(t))
return output_dir
def plot(self, type, **kwargs):
"""Make plot of type `type`."""
if type not in plots_registry:
raise ValueError(
f"Invalid plot type {type}. "
f"Available types: {plots_registry.keys()}")
plotter = plots_registry[type].from_dict(kwargs)
return plotter(self.simu_config)
# make a list of all simu config item types
_locals = list(locals().values())
simu_config_item_types = list()
for v in _locals:
if is_dataclass(v) and hasattr(v, 'schema'):
simu_config_item_types.append(v)
elif isinstance(v, ConfigRegistry):
simu_config_item_types.append(v)
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21707,
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3972,
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9933,
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411,
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554,
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421,
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7529,
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29918,
4548,
29952,
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731,
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6467,
4954,
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11037,
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10417,
4766,
338,
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3601,
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29918,
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4397,
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13,
2
] |
ticket/migrations/0011_ticketmeantimes.py | aessex24/rindr | 1 | 186681 | <reponame>aessex24/rindr
# Generated by Django 3.2.8 on 2021-11-01 19:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('ticket', '0010_ticket_archived'),
]
operations = [
migrations.CreateModel(
name='TicketMeanTimes',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('mean_response', models.DurationField(verbose_name='Mean Time to Response')),
('ticket_count', models.IntegerField(verbose_name='Ticket Count')),
],
options={
'db_table': 'ticket_meantimes',
'managed': False,
},
),
]
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] |
Python-Advanced/multidimensional_elements_exercise/2x2_squares_in_matrix.py | Xamaneone/SoftUni-Intro | 0 | 143428 | <gh_stars>0
def read_matrix():
(rows, columns) = map(int, input().split(" "))
matrix = []
for r in range(rows):
row = input().split(" ")
matrix.append(row)
return matrix, rows, columns
def subsqares_count(matrix,subsquare, rows, columns):
count = 0
for r in range(rows - (subsquare - 1)):
for c in range(columns - (subsquare - 1)):
data = set()
for r2 in range(0, subsquare):
for c2 in range(0, subsquare):
data.add(matrix[r + r2][c + c2])
if len(data) == 1:
count += 1
return count
matrix, rows, columns = read_matrix()
subsquare = 2
print(subsqares_count(matrix, subsquare, rows, columns))
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2
] |
test.py | markmelnic/Car-Indexes-Scraper | 2 | 54542 |
import os
import json
import subprocess
import mobile_de
import autoscout24_ch
import anibis_ch
if __name__ == '__main__':
try:
#autoscout24_ch.scrape_makes()
#autoscout24_ch.scrape_models()
#mobile_de.scrape_makes()
#mobile_de.scrape_models()
anibis_ch.scrape_makes()
# check "models": []
except KeyboardInterrupt:
exit(0)
except json.decoder.JSONDecodeError:
os.remove('makes.json')
subprocess.run('python test.py')
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] |
oldcode/guestbook111013.py | mdreid/dinkylink | 1 | 16638 | import os
import urllib
from google.appengine.api import users
from google.appengine.ext import ndb
import jinja2
import webapp2
from sys import argv
import datetime
import pickle
import sys
sys.path.insert(0, 'libs')
import BeautifulSoup
from bs4 import BeautifulSoup
import requests
import json
JINJA_ENVIRONMENT = jinja2.Environment(
loader=jinja2.FileSystemLoader(os.path.dirname(__file__)),
extensions=['jinja2.ext.autoescape', 'jinja2.ext.loopcontrols'],
autoescape=True)
url = 'http://www.njtransit.com/sf/sf_servlet.srv?hdnPageAction=TrainSchedulesFrom'
pu_code = "124_PRIN"
ny_code = "105_BNTN"
prs = "Princeton"
nyp = "New York Penn Station"
# get date
today = datetime.date.today()
str_date = today.__format__("%m/%d/%Y")
# trip info
toNY_dict = {'selOrigin': pu_code, 'selDestination': ny_code, 'datepicker': str_date, 'OriginDescription': prs, 'DestDescription': nyp}
toPU_dict = {'selOrigin': ny_code, 'selDestination': pu_code, 'datepicker': str_date, 'OriginDescription': nyp, 'DestDescription': prs}
# get to webpage with data for the day
with requests.Session() as re:
toNY = re.post(url, data=toNY_dict)
toPU = re.post(url, data=toPU_dict)
toPUhtml = toPU.text
toNYhtml = toNY.text
#Reads in html file and name of destination and outputs csv file with comma spliced file of train information
def scrape(html,destination):
title = str(today) + str(destination)
soup = BeautifulSoup(html)
# Improvements: instead of being so hacky with 10 search for td
# Gather all lines in table
table1 = soup.find_all("tr")
table2 = table1[10] #table1[10] contains the table of interest
table3 = table2.find_all('span')
# Create 7 lists
origin = [] #Times for departure at origin
origintrain = []
transferarrive = [] #Times for arrival at transfer
transferdepart = [] #Time for departure at transfer
transfertrain = [] #Train or bus number
destination = [] #Time of arrival at destination
total = [] #Total time of Travel
#Create 3 Columns of Text File
origin.append("Origin Departure") #Times for departure at origin
origintrain.append("Origin Train")
transferarrive.append("Transfer Arrival") #Times for arrival at transfer
transferdepart.append("Transfer Departure") #Time for departure at transfer
transfertrain.append("Transfer Train or Bus")
destination.append("Destination Arrival") #Time of arrival at destination
total.append("Total Travel Time") #Total time of Travel
#Store 4 columns into 4 lists
#Regex and pull approapriate data
for i in range(4, len(table3)-3, 4):
#origin.append(str(table3[i].text)[0:len(table3[i].text)])
origin.append(str(table3[i].text)[0:8])
origintrain.append(str(table3[i].text)[-5:])
transferarrive.append(str(table3[i+1].text)[7:15])
transferdepart.append(str(table3[i+1].text)[39:48])
transfertrain.append(str(table3[i+1].text)[-5:])
destination.append(str(table3[i+2].text)[0:len(table3[i+2].text)])
total.append(str(table3[i+3].text)[0:len(table3[i+3].text)])
#text_file = open(str(title) + ".csv", "w")
Dict = {'origin': origin[1:], 'transferarrive' : transferarrive[1:], 'transferdepart': transferdepart[1:], 'destination':destination[1:]}
return Dict
#Create csv files for to Princeton and to New York
toPUDict = scrape(toPUhtml, 'PU')
toNYDict = scrape(toNYhtml, 'NY')
class njdata(ndb.Model):
"""Models an individual Guestbook entry with author, content, and date."""
originstring = ndb.StringProperty(repeated = True)
transferarrivestring = ndb.StringProperty(repeated = True)
transferdepartstring = ndb.StringProperty(repeated = True)
destinationstring = ndb.StringProperty(repeated = True)
date = ndb.DateTimeProperty(auto_now_add=True) #Need date to get most recent data
globalPUDict = {}
class Test123(webapp2.RequestHandler):
def get(self):
toPUdata = njdata()
#toNYdata = njdata()
#toPUdata.content = pickle.dumps(toPUDict)
toPUdata.originstring = toPUDict['origin']
toPUdata.transferarrivestring = toPUDict['transferarrive']
toPUdata.transferdepartstring = toPUDict['transferdepart']
toPUdata.destinationstring = toPUDict['destination']
#Save data into data models
toPUdata.put()
#toNYdata.put()
toPUdata_query = toPUdata.query().order(-njdata.date)
a = toPUdata_query.fetch(1)
global globalPUDict
globalPUDict = {'origin': a[0].originstring, 'transferarrive': a[0].transferarrivestring, 'transferdepart': a[0].transferdepartstring, 'destination': a[0].destinationstring}
self.response.write(globalPUDict)
self.response.write(toPUDict)
class MainPage(webapp2.RequestHandler):
def get(self):
template = JINJA_ENVIRONMENT.get_template('index.html')
self.response.write(template.render())
class ToNY(webapp2.RequestHandler):
def get(self):
template = JINJA_ENVIRONMENT.get_template('toNY.html')
self.response.write(template.render(toNYDict))
class ToPU(webapp2.RequestHandler):
def get(self):
self.response.write(globalPUDict)
template = JINJA_ENVIRONMENT.get_template('toPU.html')
self.response.write(template.render(globalPUDict))
application = webapp2.WSGIApplication([
('/', MainPage),
('/toNY', ToNY),
('/toPU', ToPU),
('/test', Test123),
], debug=True)
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] |
q2/1_Graph/pair_count.py | PostQuantum/Buckyball-Ising-Model | 8 | 108937 | import numpy as np
def count(LM):
co = 0
if LM.shape[0]>2:
index = np.argwhere(LM==1)[:5]
for it in index:
lm_ = np.delete(LM,it[0],0)
lm_ = np.delete(lm_,it[1]-1,0)
lm_ = np.delete(lm_,it[0],1)
lm = np.delete(lm_,it[1]-1,1)
LM[it[0],it[1]] = 0
LM[it[1],it[0]] = 0
co += count(lm)
elif LM.shape[0]==2:
if LM[0,0]==0 and LM[1,1]==0 and LM[0,1]==1 and LM[1,0]==1:
co = 1
else:
co = 0
return co
if __name__ == "__main__":
LMn = [[0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1],
[1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1],
[1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0],
[1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0],
[0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1],
[1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0],
[1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1],
[0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0]]
LMn = np.array(LMn)
print("pair_count = ",count(LMn))
| [
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2
] |
webrecorder/test/test_colls_api.py | ssteo/webrecorder | 1,217 | 150207 | from datetime import datetime
import os
from .testutils import FullStackTests
# ============================================================================
class TestWebRecCollsAPI(FullStackTests):
@classmethod
def setup_class(cls):
super(TestWebRecCollsAPI, cls).setup_class()
def test_create_anon_coll(self):
res = self.testapp.post_json('/api/v1/collections?user={user}'.format(user=self.anon_user), params={'title': 'Temp'})
assert self.testapp.cookies['__test_sesh'] != ''
assert res.json['collection']['id'] == 'temp'
assert res.json['collection']['title'] == 'Temp'
def test_create_anon_coll_dup_error(self):
res = self.testapp.post_json('/api/v1/collections?user={user}'.format(user=self.anon_user),
params={'title': 'Temp'}, status=400)
assert self.testapp.cookies['__test_sesh'] != ''
assert res.json == {'error': 'duplicate_name'}
def test_get_anon_coll(self):
res = self.testapp.get('/api/v1/collection/temp?user={user}'.format(user=self.anon_user))
assert res.json['collection']
coll = res.json['collection']
assert coll['size'] == 0
assert coll['id'] == 'temp'
assert coll['slug'] == 'temp'
assert coll['title'] == 'Temp'
assert coll['slug_matched'] == True
assert coll['created_at'] <= datetime.utcnow().isoformat()
assert self.ISO_DT_RX.match(coll['created_at'])
assert self.ISO_DT_RX.match(coll['updated_at'])
assert coll['recordings'] == []
assert coll['public'] == False
assert coll['public_index'] == False
def test_get_anon_coll_wrong_user(self):
res = self.testapp.get('/api/v1/collection/temp?user=temp-ABC', status=404)
assert res.json['error'] == 'no_such_user'
def test_list_anon_collections(self):
res = self.testapp.get('/api/v1/collections?user={user}'.format(user=self.anon_user))
colls = res.json['collections']
assert len(colls) == 1
colls.sort(key=lambda x: x['id'])
assert colls[0]['id'] == 'temp'
assert colls[0]['title'] == 'Temp'
assert 'pages' not in colls[0]
assert 'recordings' not in colls[0]
assert 'lists' not in colls[0]
#assert colls[0]['download_url'] == 'http://localhost:80/{user}/temp/$download'.format(user=self.anon_user)
def test_error_no_title(self):
res = self.testapp.post_json('/api/v1/collections?user={user}'.format(user=self.anon_user), status=400)
assert res.json['error'] == 'invalid_coll_name'
def test_error_invalid_temp_title(self):
res = self.testapp.post_json('/api/v1/collections?user={user}'.format(user=self.anon_user),
params={'title': 'new'}, status=400)
assert res.json['error'] == 'invalid_temp_coll_name'
def test_error_external_not_allowed(self):
params = {'external': True,
'title': 'temp'
}
res = self.testapp.post_json('/api/v1/collections?user={user}'.format(user=self.anon_user), params=params,
status=403)
assert res.json == {'error': 'external_not_allowed'}
def test_error_no_such_coll(self):
res = self.testapp.get('/api/v1/collection/blah@$?user={user}'.format(user=self.anon_user), status=404)
assert res.json == {'error': 'no_such_collection'}
def test_error_missing_user_coll(self):
res = self.testapp.post_json('/api/v1/collections', params={'title': 'Recording'}, status=400)
assert res.json == {'error': 'no_user_specified'}
def test_error_invalid_user_coll(self):
res = self.testapp.post_json('/api/v1/collections?user=user', params={'title': 'Example'}, status=404)
assert res.json == {'error': 'no_such_user'}
def test_error_invalid_user_coll_2(self):
res = self.testapp.post_json('/api/v1/collections?user=temp$123', params={'title': 'Example'}, status=404)
assert res.json == {'error': 'no_such_user'}
def test_delete_coll(self):
res = self.testapp.delete('/api/v1/collection/temp?user={user}'.format(user=self.anon_user))
assert res.json == {'deleted_id': 'temp'}
| [
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2754,
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CangJie/CTC/RFAG/Module/sym/net/WCRLSTM.py | bigdata-ustc/CangJie | 2 | 1607491 | <reponame>bigdata-ustc/CangJie
# coding: utf-8
# 2020/1/2 @ tongshiwei
from longling.ML.MxnetHelper.gallery.layer.attention import \
DotProductAttentionCell
import mxnet as mx
from mxnet import gluon
from .net import EmbeddingLSTM
class WCRLSTM(EmbeddingLSTM):
def __init__(self,
net_type,
class_num, embedding_dim,
lstm_hidden=None, embedding_size=None,
embed_dropout=0.5, fc_dropout=0.5,
**kwargs):
r"""Our method: 包含词和字，以及字、词部首的网络模型"""
super(WCRLSTM, self).__init__(**kwargs)
self.word_length = None
self.character_length = None
self.lstm_hidden = lstm_hidden if lstm_hidden is not None else embedding_dim
self.net_type = net_type
with self.name_scope():
self.embedding = WCREmbedding(
word_embedding_size=embedding_size["w"],
word_radical_embedding_size=embedding_size["rw"],
char_embedding_size=embedding_size["c"],
char_radical_embedding_size=embedding_size["rc"],
embedding_dim=embedding_dim,
dropout=embed_dropout,
)
for i in range(4):
if self.net_type in ("bilstm", "bilstm_att"):
setattr(self, "rnn%s" % i,
gluon.rnn.BidirectionalCell(
gluon.rnn.LSTMCell(self.lstm_hidden),
gluon.rnn.LSTMCell(self.lstm_hidden))
)
elif self.net_type == "lstm":
setattr(
self, "rnn%s" % i, gluon.rnn.LSTMCell(self.lstm_hidden),
)
else:
raise TypeError(
"net_type should be lstm, bilstm or bilstm_att,"
" now is %s" % self.net_type
)
if self.net_type == "bilstm_att":
self.word_attention = DotProductAttentionCell(
units=self.lstm_hidden, scaled=False
)
self.char_attention = DotProductAttentionCell(
units=self.lstm_hidden, scaled=False
)
self.dropout = gluon.nn.Dropout(fc_dropout)
self.fc = gluon.nn.Dense(class_num)
def hybrid_forward(self, F, w, rw, c, rc, word_mask, charater_mask, *args, **kwargs):
word_length = self.word_length if self.word_length else len(w[0])
character_length = self.character_length if self.character_length else len(
c[0])
word_embedding, word_radical_embedding, character_embedding, character_radical_embedding = self.embedding(w, rw,
c, rc)
merge_outputs = True
if F is mx.symbol:
# 似乎 gluon 有问题， 对symbol
word_mask, charater_mask = None, None
if self.net_type == "bilstm_att":
w_e, (w_lo, w_ls, w_ro, w_rs) = getattr(self, "rnn0").unroll(
word_length, word_embedding,
merge_outputs=merge_outputs,
valid_length=word_mask
)
word_radical_embedding = \
self.word_attention(word_radical_embedding, w_e,
mask=word_mask
)[0]
wr_e, (wr_lo, wr_ls, wr_ro, wr_rs) = getattr(self, "rnn1").unroll(
word_length, word_radical_embedding,
begin_state=(w_ls, w_ls, w_rs, w_rs),
merge_outputs=merge_outputs,
valid_length=word_mask
)
c_e, (c_lo, c_ls, c_ro, c_rs) = getattr(self, "rnn2").unroll(
character_length, character_embedding,
merge_outputs=merge_outputs,
valid_length=charater_mask
)
character_radical_embedding = \
self.char_attention(character_radical_embedding, c_e,
mask=charater_mask
)[0]
cr_e, (cr_lo, cr_ls, cr_ro, cr_rs) = getattr(self, "rnn3").unroll(
character_length, character_radical_embedding,
begin_state=(c_ls, c_ls, c_rs, c_rs),
merge_outputs=merge_outputs,
valid_length=charater_mask
)
attention = F.concat(
w_lo, w_ro, wr_lo, wr_ro, c_lo, c_ro, cr_lo, cr_ro
)
elif self.net_type == "bilstm":
w_e, (w_lo, w_ls, w_ro, w_rs) = getattr(self, "rnn0").unroll(
word_length, word_embedding,
merge_outputs=merge_outputs,
valid_length=word_mask
)
wr_e, (wr_lo, wr_ls, wr_ro, wr_rs) = getattr(self, "rnn1").unroll(
word_length, word_radical_embedding,
merge_outputs=merge_outputs,
valid_length=word_mask
)
c_e, (c_lo, c_ls, c_ro, c_rs) = getattr(self, "rnn2").unroll(
character_length, character_embedding,
merge_outputs=merge_outputs,
valid_length=charater_mask
)
cr_e, (cr_lo, cr_ls, cr_ro, cr_rs) = getattr(self, "rnn3").unroll(
character_length, character_radical_embedding,
merge_outputs=merge_outputs,
valid_length=charater_mask
)
attention = F.concat(
w_lo, w_ro, wr_lo, wr_ro, c_lo, c_ro, cr_lo, cr_ro
)
elif self.net_type == "lstm":
w_e, (w_o, w_s) = getattr(self, "rnn0").unroll(
word_length,
word_embedding,
merge_outputs=merge_outputs,
valid_length=word_mask
)
wr_e, (wr_o, wr_s) = getattr(self, "rnn1").unroll(
word_length,
word_radical_embedding,
merge_outputs=merge_outputs,
valid_length=word_mask
)
c_e, (c_o, c_s) = getattr(self, "rnn2").unroll(
character_length,
character_embedding,
merge_outputs=merge_outputs,
valid_length=charater_mask
)
cr_e, (cr_o, cr_s) = getattr(self, "rnn3").unroll(
character_length, character_radical_embedding,
merge_outputs=merge_outputs,
valid_length=charater_mask)
attention = F.concat(w_o, wr_o, c_o, cr_o)
else:
raise TypeError(
"net_type should be lstm, bilstm or bilstm_att,"
" now is %s" % self.net_type
)
attention = self.dropout(attention)
# attention = self.dropout(
# F.Activation(self.nn(attention), act_type='softrelu')
# )
# fc_in = self.layers_attention(attention)
fc_in = attention
return self.fc(fc_in)
def set_network_unroll(self, word_length, character_length):
self.word_length = word_length
self.character_length = character_length
class WCREmbedding(gluon.HybridBlock):
def __init__(self, word_embedding_size, word_radical_embedding_size,
char_embedding_size, char_radical_embedding_size,
embedding_dim, dropout=0.5, prefix=None,
params=None):
super(WCREmbedding, self).__init__(prefix, params)
with self.name_scope():
self.word_embedding = gluon.nn.Embedding(
word_embedding_size, embedding_dim
)
self.word_radical_embedding = gluon.nn.Embedding(
word_radical_embedding_size, embedding_dim
)
self.char_embedding = gluon.nn.Embedding(
char_embedding_size, embedding_dim
)
self.char_radical_embedding = gluon.nn.Embedding(
char_radical_embedding_size, embedding_dim
)
self.word_dropout = gluon.nn.Dropout(dropout)
self.char_dropout = gluon.nn.Dropout(dropout)
def hybrid_forward(self,
F, word_seq, word_radical_seq,
character_seq, character_radical_seq,
*args, **kwargs
):
word_embedding = self.word_embedding(word_seq)
word_radical_embedding = self.word_radical_embedding(word_radical_seq)
character_embedding = self.char_embedding(character_seq)
character_radical_embedding = self.char_radical_embedding(
character_radical_seq
)
word_embedding = self.word_dropout(word_embedding)
word_radical_embedding = self.word_dropout(word_radical_embedding)
character_embedding = self.char_dropout(character_embedding)
character_radical_embedding = self.char_dropout(
character_radical_embedding
)
return word_embedding, word_radical_embedding, character_embedding, character_radical_embedding
def set_weight(self, we, rwe, ce, rce):
self.word_embedding.weight.set_data(we)
self.word_radical_embedding.weight.set_data(rwe)
self.char_embedding.weight.set_data(ce)
self.char_radical_embedding.weight.set_data(rce)
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] |
samples/snippets/test_export_to_bigquery.py | renovate-bot/python-contact-center-insights | 4 | 12518 | # Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import uuid
import google.auth
from google.cloud import bigquery
import pytest
import export_to_bigquery
GCLOUD_TESTS_PREFIX = "python_samples_tests"
@pytest.fixture
def project_id():
_, project_id = google.auth.default()
return project_id
@pytest.fixture
def unique_id():
uuid_hex = uuid.uuid4().hex[:8]
return f"{GCLOUD_TESTS_PREFIX}_{uuid_hex}"
@pytest.fixture
def bigquery_resources(project_id, unique_id):
# Create a BigQuery dataset.
bigquery_client = bigquery.Client()
dataset_id = unique_id
table_id = unique_id
dataset = bigquery.Dataset(f"{project_id}.{dataset_id}")
dataset.location = "US"
bigquery_client.create_dataset(dataset, timeout=30)
# Create a BigQuery table under the created dataset.
table = bigquery.Table(f"{project_id}.{dataset_id}.{table_id}")
bigquery_client.create_table(table)
yield dataset_id, table_id
# Delete the BigQuery dataset and table.
bigquery_client.delete_dataset(dataset_id, delete_contents=True)
def test_export_data_to_bigquery(capsys, project_id, bigquery_resources):
dataset_id, table_id = bigquery_resources
export_to_bigquery.export_to_bigquery(project_id, project_id, dataset_id, table_id)
out, err = capsys.readouterr()
assert "Exported data to BigQuery" in out
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] |
tests/functional_tests/identity_tests/test_properties.py | lycantropos/lz | 7 | 72823 | from typing import Any
from hypothesis import given
from lz.functional import (curry,
identity)
from tests import strategies
@given(strategies.scalars)
def test_currying(object_: Any) -> None:
curried = curry(identity)
result = curried(object_)
assert result is object_
| [
1,
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orm_database/create.py | SamuelSSan28/camara_bot_2.0 | 0 | 186562 | <gh_stars>0
from models import Projetos
projeto_1 = {
'protocolo':1402,
'processo':"1394 /2021",
'data':'----',
'titulo': '---',
'vereador': "---",
'situacao':'---',
'tipo':'----'
}
projetos = [projeto_1]
# Inserimos os quatro projetos na tabela 'Projetos'
Projetos.insert_many(projetos).execute()
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__init__.py | ModEngineer/pygraphutils | 1 | 143199 | #Copyright 2020 ModEngineer
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import warnings
from copy import deepcopy
class GraphError(Exception):
"""Error class for this module"""
pass
class Auto:
"""NoneType-like object that represents the automatic value"""
def __repr__(self):
return "Auto"
def __str__(self):
return "Auto"
class ExitContainer():
"""Error container class that can be used to raise the error or to get an exit code. 0 is a generic, non-error code, negative codes should represent errors, and positive codes should represent non-error program states."""
class ExitContainerWarning(Warning):
pass
def __init__(self, exception=None, code=Auto()):
#Exit code processing with exception checking
self.exception = exception
#Auto handling
if type(code) is Auto or code is Auto:
if exception == None:
self.code = 0
else:
self.code = -0x1
else:
self.code = code
#Positive code handling and exception warning
if type(code) is int:
if code >= 0:
if exception != None:
warnings.warn(
"ExitContainer with an exception should not have a positive code.",
category=ExitContainer.ExitContainerWarning)
#Negative code handling and exception warning
elif code < 0:
if exception == None:
warnings.warn(
"ExitContainer without an exception should not have a negative code.",
category=ExitContainer.ExitContainerWarning)
def raise_exception(self):
"""A function that raises the ExitContainer's exception if it has an exception stored"""
if self.exception != None:
raise self.exception
#Graph validation function. Checks graph connections, endpoint count, and nonexistent nodes listed as connections.
def validate_graph(graphData):
try:
endpointCount = 0
for node in graphData:
if type(graphData[node]) not in [list, tuple]:
return ExitContainer(
TypeError(
"Type " + repr(type(graphData[node])) +
" is not a list or a tuple. The graphData argument requires that connections are stored in lists or tuples."
), -0x2)
try:
for connectedNode in graphData[node]:
if not node in graphData[connectedNode]:
return ExitContainer(
GraphError(
"Nodes " + repr(node) + " and " +
repr(connectedNode) +
" do not list each other in their connections."
), -0x3)
except KeyError as exc:
try:
raise GraphError("Node " + repr(exc.args[0]) +
" does not exist.") from exc
except GraphError as exc2:
return ExitContainer(exc2, -0x4)
if len(graphData[node]) % 2 == 1:
endpointCount += 1
if endpointCount > 2:
return ExitContainer(
GraphError(
"Graph cannot contain more than 2 nodes of odd degree."
), -0x5)
if not is_connected(graphData):
return ExitContainer(GraphError("Graph is not connected."), -0x6)
return ExitContainer()
except Exception as e:
return ExitContainer(e)
#Node remover
def remove_node(node, graphData):
for connection in graphData[node]:
graphData[connection].remove(node)
del graphData[node]
#Edge remover
def remove_edge(node1, node2, graphData):
graphData[node1].remove(node2)
graphData[node2].remove(node1)
#Node creator
def add_node(node, graphData):
if node in graphData.keys():
raise GraphError("Node already in graph")
graphData[node] = []
#Edge creator
def add_edge(node1, node2, graphData):
if not node1 in graphData.keys():
add_node(node1, graphData)
if not node2 in graphData.keys():
add_node(node2, graphData)
if not node1 in graphData[node2]:
graphData[node2].append(node1)
if not node2 in graphData[node1]:
graphData[node1].append(node2)
def dfs(graphData, vertex=Auto()):
s = []
discovered = []
if type(vertex) is Auto or vertex is Auto:
vertex = list(graphData.keys())[0]
s.append(vertex)
while s:
vertex = s.pop()
if not vertex in discovered:
discovered.append(vertex)
for w in graphData[vertex]:
s.append(w)
return discovered
def is_connected(graphData):
discovered = dfs(graphData)
#DFS, returns a list of discovered nodes after picking an arbitrary node
#Loop that checks if any node isn't discovered
for key in graphData.keys():
if not key in discovered:
return False
return True
#Bridge-finding.
def find_bridges(graphData):
if not is_connected(graphData):
raise GraphError("Unconnected graph cannot be tested for bridges")
#Init
processed = []
bridges = []
#Loop that removes an edge temporarily, runs DFS, and checks if the graph is still connected
for start in graphData.keys():
for end in graphData[start]:
if (start, end) in processed or (end, start) in processed:
continue
processed.append((start, end))
tempGData = deepcopy(graphData)
remove_edge(start, end, tempGData)
if not is_connected(tempGData):
bridges.append((start, end))
return bridges
#Function that collapses a list of lists into a single list
def __collapse_list_list(listList, recursive=False):
outList = []
for item in listList:
if type(item) == list:
if recursive:
item = __collapse_list_list(deepcopy(item), True)
outList += item
else:
outList.append(item)
return outList
#Function that checks if all items in subList are in containerList
def __list_contains_list(subList, containerList):
for item in subList:
if item not in containerList:
return False
return True
#Graph splitter
def split_unconnected(graphData):
if is_connected(graphData):
return [graphData]
dfsresults = []
while not __list_contains_list(graphData.keys(),
__collapse_list_list(dfsresults)):
dfsresults.append(
dfs(graphData,
vertex=[
vert for vert in graphData.keys()
if not vert in __collapse_list_list(dfsresults)
][0]))
outputGraphs = []
for result in dfsresults:
currentGraph = {}
for node in result:
currentGraph[node] = graphData[node]
outputGraphs.append(currentGraph)
return outputGraphs
def split_at_bridge(splitNode, node2, graphData):
bridgeList = find_bridges(graphData)
if not ((splitNode, node2) in bridgeList or
(node2, splitNode) in bridgeList):
raise GraphError("Cannot split graph on non-bridge")
outGraphs = []
outGraphs.append(deepcopy(graphData))
outGraphs.append(deepcopy(graphData))
for connection in outGraphs[0][splitNode]:
if connection != node2:
remove_edge(splitNode, connection, outGraphs[0])
bridgeGraphDFS = dfs(outGraphs[0], splitNode)
loopDict = deepcopy(outGraphs[0])
for node in loopDict:
if not node in bridgeGraphDFS:
remove_node(node, outGraphs[0])
loopDict = deepcopy(outGraphs[1])
for node in loopDict:
if node in bridgeGraphDFS and node != splitNode:
remove_node(node, outGraphs[1])
return outGraphs
#Fleury implementation based on Wikipedia's pseudocode
def fleury(graphData, checkPath=True):
"""Fleury's algorithm for finding a Eulerian path in an undirected graph"""
#Init
tempGData = deepcopy(graphData)
endpoints = []
#Endpoint finding
for node in tempGData:
if len(tempGData[node]) % 2 == 1:
endpoints.append(node)
if len(endpoints) == 0:
currentNode = list(tempGData.keys())[0]
else:
currentNode = endpoints[0]
path = [currentNode]
#Mainloop
while tempGData[currentNode]:
#Next-edge-finder. Prefers non-bridges
bridges = find_bridges(tempGData)
nonBridgeConnections = [
connection for connection in tempGData[currentNode]
if not connection in bridges
]
if nonBridgeConnections:
nextNode = nonBridgeConnections[0]
else:
nextNode = tempGData[currentNode][0]
#Next-node-queuing and path building
remove_edge(currentNode, nextNode, tempGData)
if not tempGData[currentNode]:
remove_node(currentNode, tempGData)
path.append(nextNode)
currentNode = nextNode
#Path validation
if checkPath:
for node in graphData:
for connection in graphData[node]:
listContainedList = False
for index in range(len(path) - 1):
if __list_contains_list([node, connection],
[path[index], path[index + 1]]):
listContainedList = True
break
if not listContainedList:
return None
return path
# Automatic splitting of graph into multiple graphs with Eulerian paths (NOT FUNCTIONAL)
# def auto_split(graphData, returnGraphs=True, returnPaths=False):
# assert returnGraphs or returnPaths, "auto_split must have a selected return value."
# output = {}
# if returnGraphs:
# output["graphs"] = []
# if returnPaths:
# output["paths"] = []
# fleuryTest = fleury(graphData)
# if fleuryTest:
# if returnGraphs:
# output["graphs"] = [graphData]
# if returnPaths:
# output["paths"] = [fleuryTest]
# return output
# eulerianParts = []
# tempGData = deepcopy(graphData)
# while tempGData:
# currentFleury = fleury(tempGData, False)
# eulerianParts.append(currentFleury)
# if returnGraphs:
# output["graphs"].append({})
# for index, node1 in enumerate(currentFleury[:-1]):
# node2 = currentFleury[index + 1]
# remove_edge(node1, node2, tempGData)
# if returnGraphs:
# add_edge(node1, node2, output["graphs"][-1])
# tempTempGData = deepcopy(tempGData)
# for node in tempTempGData:
# if not tempGData[node]:
# if returnGraphs and not node in __collapse_list_list(
# eulerianParts):
# output["graphs"].append([node])
# remove_node(node, tempGData)
# del tempTempGData
# if returnPaths:
# output["paths"] = eulerianParts
# return output | [
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2
] |
main.py | wdsrocha/anime-recommender | 0 | 144203 | from src import preprocessing, telegram_bot
if __name__ == "__main__":
preprocessing.main()
telegram_bot.main()
| [
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13,
2
] |
direct_gd_predict/hash-profile.py | wac/meshop | 1 | 12609 | <reponame>wac/meshop<gh_stars>1-10
import sys
import heapq
import optparse
from bitcount2 import bitcount
hasher={}
profile={}
key_list=[]
key_col=0
def usage():
print sys.argv[0]," [profile_file]"
print " Load the profile lines from profile_file"
print " Hash function uses the features listed in profile_file"
print " and tests for p-value greater/less than or equal (0/1)"
print " Hash all the profiles from stdin"
exit(1)
def do_hash(hasher, p, key_list):
hashval=""
# for k, v in hasher.iteritems():
for k in key_list:
v=hasher[k]
if k in p and p[k] < v:
hashval=hashval+"1"
else:
hashval=hashval+"0"
return hashval
sep='|'
key_col=0
#feature_col=1
#score_col=6
in_feature_col=0
in_score_col=1
process_feature_col=1
process_score_col=6
parser = optparse.OptionParser()
#parser.add_option("-n", dest="heapsize",
# default=50, action="store", type="int")
#parser.add_option("-R", "--random", dest="use_random",
# default=False, action="store_true")
(options, args) = parser.parse_args(sys.argv)
if (len(args) > 1):
profile_filename=args[1]
else:
usage()
for line in open(profile_filename):
if line[0]=='#':
continue
tuples=line.strip().split(sep)
key=tuples[in_feature_col]
key_list.append(key)
hasher[key]=tuples[in_score_col]
curr_profile={}
old_key=""
for line in sys.stdin:
line=line.strip()
if line[0]=='#':
print line
continue
tuples=line.split(sep)
curr_key=tuples[key_col]
if not old_key:
old_key=curr_key
if not old_key==curr_key:
hashval=do_hash(hasher, curr_profile, key_list)
hashval_int=int(hashval, 2)
print old_key+sep+hashval+sep+str(hashval_int)+sep+str(bitcount(hashval_int))
curr_profile={}
old_key=curr_key
curr_profile[tuples[process_feature_col]]=tuples[process_score_col]
hashval=do_hash(hasher, curr_profile, key_list)
hashval_int=int(hashval, 2)
print old_key+sep+hashval+sep+str(hashval_int)+sep+str(bitcount(hashval_int))
| [
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2
] |
test/integration/broken/maint/test_vpc_host_maintenance.py | elShiaLabeouf/cloudstack | 1 | 134915 | <filename>test/integration/broken/maint/test_vpc_host_maintenance.py
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""" Component tests VM life cycle in VPC network functionality
"""
#Import Local Modules
from nose.plugins.attrib import attr
from marvin.cloudstackTestCase import cloudstackTestCase
import unittest
from marvin.cloudstackAPI import migrateSystemVm
from marvin.lib.utils import cleanup_resources
from marvin.lib.base import (Host,
VirtualMachine,
ServiceOffering,
VPC,
VpcOffering,
Router,
Network,
NetworkOffering,
Cluster,
Account)
from marvin.lib.common import (get_domain,
get_zone,
get_template,
wait_for_cleanup)
import time
class Services:
"""Test VM life cycle in VPC network services
"""
def __init__(self):
self.services = {
"account": {
"email": "<EMAIL>",
"firstname": "Test",
"lastname": "User",
"username": "test",
# Random characters are appended for unique
# username
"password": "password",
},
"service_offering": {
"name": "Tiny Instance",
"displaytext": "Tiny Instance",
"cpunumber": 1,
"cpuspeed": 100,
"memory": 64,
},
"service_offering_1": {
"name": "Tiny Instance- tagged host 1",
"displaytext": "Tiny off-tagged host2",
"cpunumber": 1,
"cpuspeed": 100,
"memory": 64,
"hosttags": "hosttag1"
},
"service_offering_2": {
"name": "Tiny Instance- tagged host 2",
"displaytext": "Tiny off-tagged host2",
"cpunumber": 1,
"cpuspeed": 100,
"memory": 64,
"hosttags": "hosttag2"
},
"network_offering": {
"name": 'VPC Network offering',
"displaytext": 'VPC Network off',
"guestiptype": 'Isolated',
"supportedservices": 'Dhcp,Dns,SourceNat,PortForwarding,Lb,UserData,StaticNat,NetworkACL',
"traffictype": 'GUEST',
"availability": 'Optional',
"useVpc": 'on',
"serviceProviderList": {
"Dhcp": 'VpcVirtualRouter',
"Dns": 'VpcVirtualRouter',
"SourceNat": 'VpcVirtualRouter',
"PortForwarding": 'VpcVirtualRouter',
"Lb": 'VpcVirtualRouter',
"UserData": 'VpcVirtualRouter',
"StaticNat": 'VpcVirtualRouter',
"NetworkACL": 'VpcVirtualRouter'
},
},
"network_offering_no_lb": {
"name": 'VPC Network offering',
"displaytext": 'VPC Network off',
"guestiptype": 'Isolated',
"supportedservices": 'Dhcp,Dns,SourceNat,PortForwarding,UserData,StaticNat,NetworkACL',
"traffictype": 'GUEST',
"availability": 'Optional',
"useVpc": 'on',
"serviceProviderList": {
"Dhcp": 'VpcVirtualRouter',
"Dns": 'VpcVirtualRouter',
"SourceNat": 'VpcVirtualRouter',
"PortForwarding": 'VpcVirtualRouter',
"UserData": 'VpcVirtualRouter',
"StaticNat": 'VpcVirtualRouter',
"NetworkACL": 'VpcVirtualRouter'
},
},
"network_off_shared": {
"name": 'Shared Network offering',
"displaytext": 'Shared Network offering',
"guestiptype": 'Shared',
"traffictype": 'GUEST',
"availability": 'Optional',
"useVpc": 'on',
"specifyIpRanges": True,
"specifyVlan": True
},
"vpc_offering": {
"name": 'VPC off',
"displaytext": 'VPC off',
"supportedservices": 'Dhcp,Dns,SourceNat,PortForwarding,Lb,UserData,StaticNat',
},
"vpc": {
"name": "TestVPC",
"displaytext": "TestVPC",
"cidr": '10.0.0.1/24'
},
"network": {
"name": "Test Network",
"displaytext": "Test Network",
"netmask": '255.255.255.0',
"limit": 5,
# Max networks allowed as per hypervisor
# Xenserver -> 5, VMWare -> 9
},
"lbrule": {
"name": "SSH",
"alg": "leastconn",
# Algorithm used for load balancing
"privateport": 22,
"publicport": 2222,
"openfirewall": False,
"startport": 2222,
"endport": 2222,
"protocol": "TCP",
"cidrlist": '0.0.0.0/0',
},
"natrule": {
"privateport": 22,
"publicport": 22,
"startport": 22,
"endport": 22,
"protocol": "TCP",
"cidrlist": '0.0.0.0/0',
},
"fw_rule": {
"startport": 1,
"endport": 6000,
"cidr": '0.0.0.0/0',
# Any network (For creating FW rule)
"protocol": "TCP"
},
"http_rule": {
"startport": 80,
"endport": 80,
"cidrlist": '0.0.0.0/0',
"protocol": "ICMP"
},
"virtual_machine": {
"displayname": "Test VM",
"username": "root",
"password": "password",
"ssh_port": 22,
"hypervisor": 'XenServer',
# Hypervisor type should be same as
# hypervisor type of cluster
"privateport": 22,
"publicport": 22,
"protocol": 'TCP',
"userdata": 'This is sample data',
},
"ostype": 'CentOS 5.3 (64-bit)',
# Cent OS 5.3 (64 bit)
"sleep": 60,
"timeout": 30,
"mode": 'advanced'
}
class TestVMLifeCycleHostmaintenance(cloudstackTestCase):
@classmethod
def setUpClass(cls):
cls.testClient = super(TestVMLifeCycleHostmaintenance, cls).getClsTestClient()
cls.api_client = cls.testClient.getApiClient()
cls.services = Services().services
# Get Zone, Domain and templates
cls.domain = get_domain(cls.api_client)
cls.zone = get_zone(cls.api_client, cls.testClient.getZoneForTests())
cls.template = get_template(
cls.api_client,
cls.zone.id,
cls.services["ostype"]
)
cls.hypervisor = cls.testClient.getHypervisorInfo()
if cls.hypervisor.lower() in ['lxc']:
raise unittest.SkipTest("Template creation from root volume is not supported in LXC")
cls.services["virtual_machine"]["zoneid"] = cls.zone.id
cls.services["virtual_machine"]["template"] = cls.template.id
clusterWithSufficientHosts = None
clusters = Cluster.list(cls.api_client, zoneid=cls.zone.id)
for cluster in clusters:
cls.hosts = Host.list(cls.api_client, clusterid=cluster.id)
if len(cls.hosts) >= 2:
clusterWithSufficientHosts = cluster
break
if clusterWithSufficientHosts is None:
raise unittest.SkipTest("No Cluster with 2 hosts found")
Host.update(cls.api_client, id=cls.hosts[0].id, hosttags="hosttag1")
Host.update(cls.api_client, id=cls.hosts[1].id, hosttags="hosttag2")
cls.service_offering_1 = ServiceOffering.create(
cls.api_client,
cls.services["service_offering_1"]
)
cls.service_offering_2 = ServiceOffering.create(
cls.api_client,
cls.services["service_offering_2"]
)
cls.vpc_off = VpcOffering.create(
cls.api_client,
cls.services["vpc_offering"]
)
cls.vpc_off.update(cls.api_client, state='Enabled')
cls.account = Account.create(
cls.api_client,
cls.services["account"],
admin=True,
domainid=cls.domain.id
)
cls.vpc_off = VpcOffering.create(
cls.api_client,
cls.services["vpc_offering"]
)
cls.vpc_off.update(cls.api_client, state='Enabled')
cls.services["vpc"]["cidr"] = '10.1.1.1/16'
cls.vpc = VPC.create(
cls.api_client,
cls.services["vpc"],
vpcofferingid=cls.vpc_off.id,
zoneid=cls.zone.id,
account=cls.account.name,
domainid=cls.account.domainid
)
cls.nw_off = NetworkOffering.create(
cls.api_client,
cls.services["network_offering"],
conservemode=False
)
# Enable Network offering
cls.nw_off.update(cls.api_client, state='Enabled')
# Creating network using the network offering created
cls.network_1 = Network.create(
cls.api_client,
cls.services["network"],
accountid=cls.account.name,
domainid=cls.account.domainid,
networkofferingid=cls.nw_off.id,
zoneid=cls.zone.id,
gateway='10.1.1.1',
vpcid=cls.vpc.id
)
cls.nw_off_no_lb = NetworkOffering.create(
cls.api_client,
cls.services["network_offering_no_lb"],
conservemode=False
)
# Enable Network offering
cls.nw_off_no_lb.update(cls.api_client, state='Enabled')
# Creating network using the network offering created
cls.network_2 = Network.create(
cls.api_client,
cls.services["network"],
accountid=cls.account.name,
domainid=cls.account.domainid,
networkofferingid=cls.nw_off_no_lb.id,
zoneid=cls.zone.id,
gateway='10.1.2.1',
vpcid=cls.vpc.id
)
# Spawn an instance in that network
cls.vm_1 = VirtualMachine.create(
cls.api_client,
cls.services["virtual_machine"],
accountid=cls.account.name,
domainid=cls.account.domainid,
serviceofferingid=cls.service_offering_1.id,
networkids=[str(cls.network_1.id)]
)
# Spawn an instance in that network
cls.vm_2 = VirtualMachine.create(
cls.api_client,
cls.services["virtual_machine"],
accountid=cls.account.name,
domainid=cls.account.domainid,
serviceofferingid=cls.service_offering_1.id,
networkids=[str(cls.network_1.id)]
)
cls.vm_3 = VirtualMachine.create(
cls.api_client,
cls.services["virtual_machine"],
accountid=cls.account.name,
domainid=cls.account.domainid,
serviceofferingid=cls.service_offering_2.id,
networkids=[str(cls.network_2.id)]
)
routers = Router.list(
cls.api_client,
account=cls.account.name,
domainid=cls.account.domainid,
listall=True
)
if isinstance(routers, list):
cls.vpcvr = routers[0]
cls._cleanup = [
cls.service_offering_1,
cls.service_offering_2,
cls.nw_off,
cls.nw_off_no_lb,
]
return
@classmethod
def tearDownClass(cls):
try:
#Delete the host tags
Host.update(cls.api_client, id=cls.hosts[0].id, hosttags="")
Host.update(cls.api_client, id=cls.hosts[1].id, hosttags="")
cls.account.delete(cls.api_client)
wait_for_cleanup(cls.api_client, ["account.cleanup.interval"])
#Cleanup resources used
cleanup_resources(cls.api_client, cls._cleanup)
cls.vpc_off.delete(cls.api_client)
except Exception as e:
raise Exception("Warning: Exception during cleanup : %s" % e)
return
def setUp(self):
self.apiclient = self.testClient.getApiClient()
self.dbclient = self.testClient.getDbConnection()
self.debug("Check the status of VPC virtual router")
routers = Router.list(
self.apiclient,
networkid=self.network_1.id,
listall=True
)
if not isinstance(routers, list):
raise Exception("No response from list routers API")
self.router = routers[0]
if self.router.state == "Running":
self.debug("Verified that the Router is in Running State")
self.cleanup = []
return
def tearDown(self):
try:
#Clean up, terminate the created network offerings
cleanup_resources(self.apiclient, self.cleanup)
except Exception as e:
raise Exception("Warning: Exception during cleanup : %s" % e)
return
def validate_vm_deployment(self):
"""Validates VM deployment on different hosts"""
vms = VirtualMachine.list(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid,
networkid=self.network_1.id,
listall=True
)
self.assertEqual(
isinstance(vms, list),
True,
"List VMs shall return a valid response"
)
host_1 = vms[0].hostid
self.debug("Host for network 1: %s" % vms[0].hostid)
vms = VirtualMachine.list(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid,
networkid=self.network_2.id,
listall=True
)
self.assertEqual(
isinstance(vms, list),
True,
"List VMs shall return a valid response"
)
host_2 = vms[0].hostid
self.debug("Host for network 2: %s" % vms[0].hostid)
self.assertNotEqual(
host_1,
host_2,
"Both the virtual machines should be deployed on diff hosts "
)
return
@attr(tags=["advanced", "intervlan"])
def test_01_enable_maintenance_with_vpc_nw(self):
""" Test enable Maintenance Mode on Hosts which have VPC elements
"""
# Validate the following
# 1. Create a VPC with cidr - 10.1.1.1/16
# 2. Add network1(10.1.1.1/24) and network2(10.1.2.1/24) to this VPC.
# 3. Deploy vm1 and vm2 in network1 and vm3 and vm4 in network2. Make
# sure vm1 and vm3 are deployed on one host in the cluster while
# vm2 and vm4 are deployed on the other host in the cluster. This
# can be done using host's tags & service offerings with host tags
# Steps:
# 1.Enable Maintenance on one of host on which VPCVR is present
# Validations:
# 1. Successfully push the host into maintenance mode.
# 2. VMs present on the above host should successfully migrate to the
# other host present in the cluster
Host.update(self.apiclient, id=self.hosts[0].id, hosttags="hosttag1,hosttag2")
Host.update(self.apiclient, id=self.hosts[1].id, hosttags="hosttag1,hosttag2")
self.validate_vm_deployment()
self.debug("Stop the host on which the VPC virtual router is running")
try:
Host.enableMaintenance(self.apiclient, id=self.vpcvr.hostid)
except Exception as e:
self.fail("Failed to enable maintenance mode on host: %s" % e)
self.debug(
"Check if all instances belonging to the account are up again?")
routers = Router.list(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid,
listall=True
)
self.assertEqual(
isinstance(routers, list),
True,
"List routers shall return a valid VPCVR for account"
)
router = routers[0]
try:
timeout = self.services["timeout"]
self.debug("Timeout Value %d : " % timeout)
while True:
list_router_response = Router.list(
self.apiclient,
id = router.id,
state = "Running"
)
if list_router_response is not None:
break
elif timeout == 0:
raise Exception("Router state should be running after migration")
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug("Waiting for %d seconds - %d tries left" % (self.services["sleep"],timeout))
self.debug("Verified that the Router is in Running State")
except Exception as e:
self.fail("Failed to find the Router in Running state %s " % e)
vms = VirtualMachine.list(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid,
listall=True
)
self.assertEqual(
isinstance(vms, list),
True,
"VM response should return instances running for account"
)
for vm in vms:
self.assertEqual(
vm.state,
"Running",
"Vm state should be running after migration"
)
return
@attr(tags=["advanced", "intervlan"])
def test_02_cancel_maintenance(self):
""" Test cancel Maintenance Mode on the above Hosts + Migrate VMs Back
"""
# Steps
# 1. Cancel Maintenance Mode on the host.
# 2. Migrate the VMs back onto the host on which Maintenance mode is
# cancelled.
# Validate the following
# 1. Successfully cancel the Maintenance mode on the host.
# 2. Migrate the VMs back successfully onto the host.
# 3. Check that the network connectivity exists with the migrated VMs.
try:
timeout = self.services["timeout"]
while True:
list_host_response = Host.list(
self.apiclient,
id=self.vpcvr.hostid,
resourcestate="Maintenance")
if list_host_response is not None:
break
elif timeout == 0:
raise Exception("Failed to list the Host in Maintenance State")
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug("Verified that the Host is in Maintenance State")
except:
self.fail("Failed to find the Host in maintenance state")
self.debug("Cancel host maintenence on which the VPCVR is running")
try:
Host.cancelMaintenance(self.apiclient, id=self.vpcvr.hostid)
timeout = self.services["timeout"]
while True:
list_host_response = Host.list(
self.apiclient,
id=self.vpcvr.hostid,
state="Up")
if list_host_response is not None:
break
elif timeout == 0:
raise Exception("Failed to list the Host in Up State after Canceling Maintenance Mode")
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug("Verified that the Host is in Up State after Canceling Maintenance Mode")
except Exception as e:
self.fail("Failed to cancel maintenance mode on host: %s" % e)
self.debug(
"Migrating the instances back to the host: %s" %
self.vpcvr.hostid)
try:
cmd = migrateSystemVm.migrateSystemVmCmd()
cmd.hostid = self.vpcvr.hostid
cmd.virtualmachineid = self.vpcvr.id
self.apiclient.migrateSystemVm(cmd)
except Exception as e:
self.fail("Failed to migrate VPCVR back: %s" % e)
self.debug("Check the status of router after migration")
routers = Router.list(
self.apiclient,
id=self.vpcvr.id,
listall=True
)
self.assertEqual(
isinstance(routers, list),
True,
"List routers shall return the valid response"
)
self.assertEqual(
routers[0].state,
"Running",
"Router state should be running"
)
# TODO: Check for the network connectivity
return
@attr(tags=["advanced", "intervlan"])
def test_03_reconnect_host(self):
""" Test reconnect Host which has VPC elements
"""
# Steps:
# 1.Reconnect one of the host on which VPC Virtual Router is present.
# Validate the following
# 1. Host should successfully reconnect.
# 2. Network connectivity to all the VMs on the host should not be
# effected due to reconnection.
try:
timeout = self.services["timeout"]
while True:
list_host_response = Host.list(
self.apiclient,
id=self.vpcvr.hostid,
resourcestate="Enabled")
if list_host_response is not None:
break
elif timeout == 0:
raise Exception("Failed to list the Host in Up State")
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug("Verified that the Host is in Up State")
except:
self.fail("Failed to find the Host in Up State")
self.debug("Reconnecting the host where VPC VR is running")
try:
Host.reconnect(self.apiclient, id=self.vpcvr.hostid)
except Exception as e:
self.fail("Failed to reconnect to host: %s" % e)
self.debug("Check the status of router after migration")
routers = Router.list(
self.apiclient,
id=self.vpcvr.id,
listall=True
)
self.assertEqual(
isinstance(routers, list),
True,
"List routers shall return the valid response"
)
self.assertEqual(
routers[0].state,
"Running",
"Router state should be running"
)
# TODO: Check for the network connectivity
return
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trac/Scripts/pygmentize-script.py | thinkbase/PortableTrac | 2 | 180713 | #!"E:\PortableTrac\Portable Python 2.7.3.1\App\python.exe"
# EASY-INSTALL-ENTRY-SCRIPT: 'Pygments==1.5','console_scripts','pygmentize'
__requires__ = 'Pygments==1.5'
import sys
from pkg_resources import load_entry_point
sys.exit(
load_entry_point('Pygments==1.5', 'console_scripts', 'pygmentize')()
)
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rest_framework_sudo/expiration.py | Nekmo/djangorestframework-sudo | 1 | 95412 | <gh_stars>1-10
import datetime
from functools import wraps
from rest_framework.exceptions import PermissionDenied
from rest_framework_sudo import settings
from django.contrib.auth.models import AbstractUser
from django.utils import timezone
from rest_framework_sudo.utils import get_request_argument
def get_user_remaning_time(user: AbstractUser):
expired_time = datetime.timedelta(seconds=0)
if not user.last_login:
return expired_time
now = timezone.now()
expiration_time = user.last_login - now + settings.REST_FRAMEWORK_SUDO_EXPIRATION
if expiration_time.total_seconds() < 0:
return expired_time
return expiration_time
def expire_now(user: AbstractUser) -> None:
if not get_user_remaning_time(user):
return
user.last_login = timezone.now() - settings.REST_FRAMEWORK_SUDO_EXPIRATION - datetime.timedelta(seconds=1)
user.save()
def get_expires_at(user: AbstractUser):
return timezone.now() + get_user_remaning_time(user)
def validate_sudo(request):
if not get_user_remaning_time(request.user):
raise PermissionDenied('You need to authenticate again to perform this action.')
def sudo_required(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
request = get_request_argument(args, kwargs)
if request is None:
raise AttributeError('request is not available')
validate_sudo(request)
return fn(*args, **kwargs)
return wrapper
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src/schemas/serlializers/bright.py | rena2damas/healthcheck-service | 0 | 116669 | from marshmallow import Schema, fields
class HealthCheckSchema(Schema):
name = fields.String()
status = fields.String(attribute="status.name")
node = fields.String()
seconds_ago = fields.Integer()
timestamp = fields.Integer()
info = fields.String()
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migration/management/commands/migratedata.py | dixonary/uwcs-zarya | 7 | 29797 | from django.core.management.base import BaseCommand
from django.contrib.auth import get_user_model
from django.template.defaultfilters import slugify, linebreaks, date, truncatechars
from wagtail.core.models import Page
from wagtail.core.rich_text import RichText
from migration.models import *
from accounts.models import CompsocUser, ShellAccount, DatabaseAccount
from blog.models import BlogPage
from events.models import EventSignup, EventPage, EventType
import time
COMMS_DICT = {
'NL': 'Newsletter',
'M': 'Meeting Minutes',
'N': 'News Item'
}
def migrate_compsoc_memberinfo():
"""
Amalgamates the old user detail objects into the new CompsocUser and other models
"""
websites = WebsiteDetails.objects.using('old_data').all()
nicks = NicknameDetails.objects.using('old_data').all()
shell_accounts = OldShellAccount.objects.using('old_data').all()
db_accounts = OldDatabaseAccount.objects.using('old_data').all()
userinfo = {}
# Handle shell accounts
for account in shell_accounts:
user = get_user_model().objects.filter(id=account.user_id).first()
new_account = ShellAccount(name=account.name, user=user, status=account.status)
new_account.save()
# Handle DB accounts
for account in db_accounts:
user = get_user_model().objects.filter(id=account.user_id).first()
new_account = DatabaseAccount(name=account.name, user=user, status=account.status)
new_account.save()
# Handle transfer of Nickname info to CompsocUser model
for nick in nicks:
user_id = nick.user_id
userinfo[user_id] = {
'nickname': nick.nickname,
'website_title': '',
'website_url': ''
}
# Handle transfer of Website info to CompsocUser model
for site in websites:
user_id = site.user_id
if user_id in userinfo:
userinfo[user_id]['website_title'] = site.websiteTitle
userinfo[user_id]['website_url'] = site.websiteUrl
else:
userinfo[user_id] = {
'nickname': '',
'website_title': site.websiteTitle,
'website_url': site.websiteUrl
}
# Save new CompsocUser info
for uid, details in userinfo.items():
user = get_user_model().objects.filter(id=uid).first()
new_userinfo = CompsocUser(nickname=details['nickname'], website_title=details['website_title'],
website_url=details['website_url'], user=user)
print('Restoring {user}'.format(user=new_userinfo))
new_userinfo.save()
def migrate_old_posts():
"""
Converts all old posts from a simple page format to one Wagtail accepts
"""
# id=4 is the specific page ID for the news index page
index = Page.objects.get(id=4).specific
old_posts = Communication.objects.using('old_data').all().order_by('date')
user = get_user_model().objects.get(id=1)
for post in old_posts:
if post.title:
title = post.title
else:
title = 'Archived item from {date}'.format(date=date(post.date, 'D jS F Y'))
slug = slugify('{title} - {rand}'.format(title=title, rand=int(round(time.time() * 1000))))
if len(post.text) > 512:
intro = post.text[:512] + '...'
else:
intro = post.text
page = BlogPage(
search_description='',
seo_title=title,
show_in_menus=False,
slug=slug,
title=title,
date=post.date,
first_published_at=post.date,
intro=linebreaks(intro),
)
page.body.stream_data = [
('paragraph', RichText('<p>{body}</p>'.format(body=linebreaks(post.text))))
]
page.tags.add(COMMS_DICT[post.type])
print('Restoring article from {date}'.format(date=post.date))
index.add_child(instance=page)
revision = page.save_revision(
user=user,
submitted_for_moderation=False
)
revision.publish()
page.save()
def migrate_events():
event_index = Page.objects.get(id=6).specific
user = get_user_model().objects.get(id=1)
old_events = OldEvent.objects.using('old_data').all()
# Migrate events
for old_event in old_events:
old_event_type = old_event.type
try:
# We don't actually care about this - its a test to migrate the event across
event_type = EventType.objects.get(name=old_event_type.name, target=old_event_type.target)
except EventType.DoesNotExist:
event_type = EventType(name=old_event_type.name, target=old_event_type.target)
event_type.save()
title = '{type} on {date}'.format(type=old_event_type.name, date=date(old_event.start, 'D jS F Y'))
slug = slugify('{title} - {rand}'.format(title=title, rand=int(round(time.time() * 1000))))
if old_event.shortDescription:
description = old_event.shortDescription
else:
if old_event_type.info:
description = old_event_type.info
else:
description = old_event_type.name
new_event = EventPage(
title=title.strip(),
slug=slug,
description=description.strip(),
start=old_event.start,
finish=old_event.finish,
cancelled=old_event.cancelled,
category=event_type,
location=old_event.location.name
)
new_event.body.stream_data = [
('paragraph', RichText('<p>{body}</p>'.format(body=linebreaks(old_event.longDescription))))
]
print('Restoring event {type} from {date}'.format(type=old_event.type.name, date=old_event.start))
event_index.add_child(instance=new_event)
revision = new_event.save_revision(
user=user,
submitted_for_moderation=False
)
revision.publish()
new_event.save()
# Deal with signups
old_signups = Signup.objects.using('old_data').filter(event_id=old_event.id)
for old_signup in old_signups:
print('Restoring signup for {type} from {date}'.format(type=old_event.type.name, date=old_event.start))
new_signup = EventSignup(comment=truncatechars(old_signup.comment, 1024),
member=get_user_model().objects.get(id=old_signup.user_id),
event_id=new_event.id, signup_created=old_signup.time)
new_signup.save()
class Command(BaseCommand):
def handle(self, *args, **options):
migrate_compsoc_memberinfo()
migrate_old_posts()
migrate_events()
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] |
main.py | maxBombrun/lipidDroplets | 0 | 38511 | <reponame>maxBombrun/lipidDroplets<gh_stars>0
import os
import csv
import multiprocessing
import settings
import segmentNucAndGFP
import cellProfilerGetRelation
import measureGFPSize
import plotFeatures
import fusionCSV
import clusterDroplets
import computeZprime
settings.init()
CPPath=settings.pathList[0]
inputDataPath=settings.pathList[1]
resultPath=settings.pathList[2]
outputDetPath=settings.pathList[3]
inputCellProfilerPath=settings.pathList[4]
outputCellProfilerPath=settings.pathList[5]
nProc = multiprocessing.cpu_count()
listPlates= [x for x in os.listdir(inputDataPath) if os.path.isdir(inputDataPath+x) and x.startswith('plate')]
csv.register_dialect('unixpwd', delimiter=',',quotechar = '"', doublequote = True, skipinitialspace = False,lineterminator = '\n', quoting = csv.QUOTE_NONE)
print listPlates
## Segmentation of the nuclei and the lipid droplets
segmentNucAndGFP.segmentFatDroplet(listPlates)
## Cells approximation based on the previous segmentation
## and features extraction through CellProfiler
cellProfilerGetRelation.runCellProfilerRelationship()
## Individual lipid droplet measurements
## Creation of size distribution vectors
measureGFPSize.measureGFP()
## Classification of the cells based on the vectors
clusterDroplets.getClusterOfDroplets(nbClass=2)
## Creation of CSV output, summarizing the measurements per-cell and per-well
fusionCSV.getPerImageMeasurements()
## Plotting of the features
plotFeatures.plotFeat()
## Validation of the features through the computation of the Zprime factor
computeZprime.getZprime()
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] |
examples/tasks/dtw-energy-plus-models-data/code.py | dburian/ivis-core | 2 | 14106 | <gh_stars>1-10
import sys
import os
import json
from elasticsearch import Elasticsearch, helpers
from datetime import datetime, timezone
import numpy as np
from dtw import dtw
# Get parameters and set up elasticsearch
data = json.loads(sys.stdin.readline())
es = Elasticsearch([{'host': data['es']['host'], 'port': int(data['es']['port'])}])
state = data.get('state')
params= data['params']
entities= data['entities']
# Get ES index and fields
sensor_set = entities['signalSets'][params['sensors']]
sensor_ts = entities['signals'][params['sensors']][params['ts']]
sensor_co2 = entities['signals'][params['sensors']][params['co2']]
limit_val = float(params['limitValue'])
limit = limit_val
if state is None or state.get('index') is None:
ns = sensor_set['namespace']
msg = {}
msg['type'] = 'sets'
# Request new signal set creation
msg['sigSet'] = {
"cid" : "e_plus_mod",
"name" : "E+ comparison" ,
"namespace": ns,
"description" : "Comparison of Energy+ models" ,
"aggs" : "0"
}
signals= []
signals.append({
"cid": "ts",
"name": "ts",
"description": "timestamp",
"namespace": ns,
"type": "date",
"indexed": False,
"settings": {}
})
signals.append({
"cid": "mod",
"name": "mod",
"description": "mod",
"namespace": ns,
"type": "keyword",
"indexed": False,
"settings": {}
})
signals.append({
"cid": "model",
"name": "model",
"description": "Closest model's cid",
"namespace": ns,
"type": "keyword",
"indexed": False,
"settings": {}
})
msg['sigSet']['signals'] = signals
ret = os.write(3,(json.dumps(msg) + '\n').encode())
state = json.loads(sys.stdin.readline())
error = state.get('error')
if error:
sys.stderr.write(error+"\n")
sys.exit(1)
else:
store_msg = {}
store_msg["type"] = "store"
store_msg["state"] = state
ret = os.write(3,(json.dumps(store_msg) + '\n').encode())
def get_co2_values(index,ts_field, co2_field):
# sensor data query
query = {
'_source': [co2_field, ts_field],
'sort': [{ts_field: 'asc'}],
'query': {
"range" : {
ts_field : {
"gt" : "now-180m/m",
"lt" : "now/m"
}
}
}
}
results = es.search(index=index, body=query)
sensor_data = []
for item in results['hits']['hits']:
val = item["_source"][co2_field]
if val is not None:
sensor_data.append(val)
else:
continue
return sensor_data
sensor_data = get_co2_values(sensor_set['index'], sensor_ts['field'], sensor_co2['field'])
if not sensor_data:
print('No sensor data to measure on')
exit()
sensor_np = np.array(sensor_data, dtype=float).reshape(-1, 1)
euclidean_norm = lambda x, y: np.abs(x - y)
min_model={}
min_distance=float("inf")
for model in params['models']:
ts =entities['signals'][model['sigSet']][model['ts']]['field']
co2 =entities['signals'][model['sigSet']][model['co2']]['field']
sig_set = entities['signalSets'][model['sigSet']]['index']
model_data = get_co2_values(sig_set, ts,co2)
if not model_data:
print(f'No data for signal set {sig_set}')
continue
# Calculate for all models
model_np = np.array(model_data, dtype=float).reshape(-1, 1)
# Calculate for all models
d, cost_matrix, acc_cost_matrix, path = dtw(sensor_np, model_np, dist=euclidean_norm)
if d<min_distance:
min_distance = d
min_model['name'] = entities["signalSets"][model["sigSet"]]["name"]
min_model['cid'] = model["sigSet"]
min_model['ts'] = ts
min_model['co2'] = co2
min_model['index'] = sig_set
# Do something with closest model
if not min_model:
print('No model found')
exit()
print(f'Closest model is: {min_model["name"]}')
# Query prediction
query = {
'_source': [min_model['co2'], min_model['ts']],
'sort': [{min_model['ts']: 'asc'}],
"aggs" : {
"max_co2" : { "max" : { "field" : min_model['co2'] } }
},
'query': {
"range" : {
min_model['ts'] : {
"gt" : "now/m",
"lt" : "now+60m/m"
}
}
}
}
results = es.search(index=min_model['index'], body=query)
max_co2 = results['aggregations']['max_co2']['value']
# Get current mode
# TODO this will probably change later on to take data from the actual system
query = {
'size': 1,
'_source': [state['fields']['mod']],
'sort': [{state['fields']['ts']: 'desc'}],
'query': {
"match_all": {}
}
}
results = es.search(index=state['index'], body=query)
mod = results['hits']['hits'][0]['_source'][state['fields']['mod']] if results['hits']['total'] > 0 else 'mod1'
# If currently over limit or going to be according to models data, open more
if sensor_data[-1] > limit or max_co2 > limit:
mod = 'mod2'
elif sensor_data[-1] < limit - 200:
mod = 'mod1'
print(f'Chosen: {mod}')
ts = datetime.now(timezone.utc).astimezone()
doc = {
state['fields']['ts']: ts,
state['fields']['model']: min_model['cid'],
state['fields']['mod']: mod
}
res = es.index(index=state['index'], doc_type='_doc', id=ts, body=doc)
#prediction_data = []
#for item in results['hits']['hits']:
# val = item["_source"][min_model['co2']]
# if val is not None:
# prediction_data.append(val)
# else:
# continue
#print (prediction_data)
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src/lib/detectors/ddd.py | nerminsamet/houghnet | 161 | 67088 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from src.lib.external.nms import soft_nms
from src.lib.models.decode import ddd_decode
from src.lib.models.utils import flip_tensor
from src.lib.utils.image import get_affine_transform
from src.lib.utils.post_process import ddd_post_process
from src.lib.utils.debugger import Debugger
from src.lib.utils.ddd_utils import compute_box_3d, project_to_image, alpha2rot_y
from src.lib.utils.ddd_utils import draw_box_3d, unproject_2d_to_3d
from .base_detector import BaseDetector
class DddDetector(BaseDetector):
def __init__(self, opt):
super(DddDetector, self).__init__(opt)
self.calib = np.array([[707.0493, 0, 604.0814, 45.75831],
[0, 707.0493, 180.5066, -0.3454157],
[0, 0, 1., 0.004981016]], dtype=np.float32)
def pre_process(self, image, scale, calib=None):
height, width = image.shape[0:2]
inp_height, inp_width = self.opt.input_h, self.opt.input_w
c = np.array([width / 2, height / 2], dtype=np.float32)
if self.opt.keep_res:
s = np.array([inp_width, inp_height], dtype=np.int32)
else:
s = np.array([width, height], dtype=np.int32)
trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height])
resized_image = image #cv2.resize(image, (width, height))
inp_image = cv2.warpAffine(
resized_image, trans_input, (inp_width, inp_height),
flags=cv2.INTER_LINEAR)
inp_image = (inp_image.astype(np.float32) / 255.)
inp_image = (inp_image - self.mean) / self.std
images = inp_image.transpose(2, 0, 1)[np.newaxis, ...]
calib = np.array(calib, dtype=np.float32) if calib is not None \
else self.calib
images = torch.from_numpy(images)
meta = {'c': c, 's': s,
'out_height': inp_height // self.opt.down_ratio,
'out_width': inp_width // self.opt.down_ratio,
'calib': calib}
return images, meta
def process(self, images, return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output = self.model(images)[-1]
output['hm'] = output['hm'].sigmoid_()
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
wh = output['wh'] if self.opt.reg_bbox else None
reg = output['reg'] if self.opt.reg_offset else None
torch.cuda.synchronize()
forward_time = time.time()
dets = ddd_decode(output['hm'], output['rot'], output['dep'],
output['dim'], wh=wh, reg=reg, K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def post_process(self, dets, meta, scale=1):
dets = dets.detach().cpu().numpy()
detections = ddd_post_process(
dets.copy(), [meta['c']], [meta['s']], [meta['calib']], self.opt)
self.this_calib = meta['calib']
return detections[0]
def merge_outputs(self, detections):
results = detections[0]
for j in range(1, self.num_classes + 1):
if len(results[j] > 0):
keep_inds = (results[j][:, -1] > self.opt.peak_thresh)
results[j] = results[j][keep_inds]
return results
def debug(self, debugger, images, dets, output, scale=1):
dets = dets.detach().cpu().numpy()
img = images[0].detach().cpu().numpy().transpose(1, 2, 0)
img = ((img * self.std + self.mean) * 255).astype(np.uint8)
pred = debugger.gen_colormap(output['hm'][0].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hm')
debugger.add_ct_detection(
img, dets[0], show_box=self.opt.reg_bbox,
center_thresh=self.opt.vis_thresh, img_id='det_pred')
def show_results(self, debugger, image, results):
debugger.add_3d_detection(
image, results, self.this_calib,
center_thresh=self.opt.vis_thresh, img_id='add_pred')
debugger.add_bird_view(
results, center_thresh=self.opt.vis_thresh, img_id='bird_pred')
debugger.show_all_imgs(pause=self.pause) | [
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biserici_inlemnite/biserici/migrations/0047_auto_20210804_1305.py | ck-tm/biserici-inlemnite | 0 | 184213 | <reponame>ck-tm/biserici-inlemnite<gh_stars>0
# Generated by Django 3.1.13 on 2021-08-04 10:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biserici', '0046_auto_20210804_1253'),
]
operations = [
migrations.AddField(
model_name='componentaartistica',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='descriere',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='finisaj',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='fotografii',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='historicalcomponentaartistica',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='historicaldescriere',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='historicalfinisaj',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='historicalfotografii',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='historicalidentificare',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
migrations.AddField(
model_name='identificare',
name='missing_fields',
field=models.JSONField(blank=True, null=True),
),
]
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hyperparameter_tuning/mxnet_gluon_cifar10_random_search/random_tuner.py | zanhsieh/amazon-sagemaker-examples | 3 | 197064 | <gh_stars>1-10
import random
import time
import boto3
import re
import pandas as pd
import numpy as np
#################
# Hyperparameters
#################
class CategoricalParameter():
'''
Class for categorical hyperparameters.
Takes one argument which is a list of possible hyperparameter values.
'''
def __init__(self, values):
self.values = values
def get_value(self):
return random.choice(self.values)
class IntegerParameter():
'''
Class for integer hyperparameters.
Takes two arguments: min_value and then max_value.
'''
def __init__(self, min_value, max_value):
self.min_value = min_value
self.max_value = max_value
def get_value(self):
return random.randint(self.min_value, self.max_value)
class ContinuousParameter():
'''
Class for continuous hyperparameters.
Takes two arguments: min_value and then max_value.
'''
def __init__(self, min_value, max_value):
self.min_value = min_value
self.max_value = max_value
def get_value(self):
return random.uniform(self.min_value, self.max_value)
###############
# Random search
###############
def _get_random_hyperparameter_values(hyperparameters):
'''
Converts a dict using hyperparameter classes to a dict of hyperparameter values.
'''
hps = {}
for hp, definition in hyperparameters.items():
if isinstance(definition, (CategoricalParameter, IntegerParameter,
ContinuousParameter)):
hps[hp] = definition.get_value()
else:
hps[hp] = definition
return hps
def random_search(train_fn,
hyperparameters,
base_name=None,
max_jobs=100,
max_parallel_jobs=100):
'''
Runs random search for hyperparameters.
Takes in:
train_fn: A function that kicks off a training job based on two positional arguments-
job name and hyperparameter dictionary. Note, wait must be set to False if using .fit()
hyperparameters: A dictonary of hyperparameters defined with hyperparameter classes.
base_name: Base name for training jobs. Defaults to 'random-hp-<timestamp>'.
max_jobs: Total number of training jobs to run.
max_parallel_jobs: Most training jobs to run concurrently. This does not affect the quality
of search, just helps stay under account service limits.
Returns a dictionary of max_jobs job names with associated hyperparameter values.
'''
if base_name is None:
base_name = 'random-hp-' + time.strftime('%Y-%m-%d-%H-%M-%S-%j', time.gmtime())
client = boto3.client('sagemaker')
jobs = {}
running_jobs = {}
for i in range(max_jobs):
job = base_name + '-' + str(i)
hps = _get_random_hyperparameter_values(hyperparameters)
jobs[job] = hps.copy()
train_fn(job, hps)
running_jobs[job] = True
while len(running_jobs) == max_parallel_jobs:
for job in running_jobs.keys():
if client.describe_training_job(TrainingJobName=job)['TrainingJobStatus'] != 'InProgress':
running_jobs.pop(job)
time.sleep(20)
return jobs
################
# Analyze output
################
def get_metrics(jobs, regex):
'''
Gets CloudWatch metrics for training jobs
Takes in:
jobs: A dictionary where training job names are keys.
regex: a regular expression string to parse the objective metric value.
Returns a dictionary of training job names as keys and corresponding list
which contains the objective metric from each log stream.
'''
job_metrics = {}
for job in jobs.keys():
client = boto3.client('logs')
streams = client.describe_log_streams(logGroupName='/aws/sagemaker/TrainingJobs',
logStreamNamePrefix=job + '/')
streams = [s['logStreamName'] for s in streams['logStreams']]
stream_metrics = []
for stream in streams:
events = client.get_log_events(logGroupName='/aws/sagemaker/TrainingJobs',
logStreamName=stream)['events']
message = [e['message'] for e in events]
metrics = []
for m in message:
try:
metrics.append(re.search(regex, m).group(1))
except:
pass
stream_metrics.extend(metrics)
job_metrics[job] = stream_metrics
return job_metrics
def table_metrics(jobs, metrics):
'''
Returns Pandas DataFrame of jobs, hyperparameter values, and objective metric value
'''
job_metrics = jobs.copy()
for job in job_metrics.keys():
objective = float(metrics[job][-1]) if len(metrics[job]) > 0 else np.nan
job_metrics[job].update({'objective': objective,
'job_number': int(job.split('-')[-1])})
return pd.DataFrame.from_dict(job_metrics, orient='index')
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] |
tracardi/service/wf/domain/work_flow.py | Tracardi/tracardi | 153 | 182186 | from typing import Tuple, List
from tracardi.domain.event import Event
from tracardi.domain.payload.tracker_payload import TrackerPayload
from tracardi.domain.profile import Profile
from tracardi.domain.session import Session
from .debug_info import DebugInfo
from .flow import Flow
from .flow_history import FlowHistory
from ..utils.dag_error import DagGraphError
from ..utils.dag_processor import DagProcessor
from ..utils.flow_graph_converter import FlowGraphConverter
from tracardi.service.plugin.domain.console import Log
class WorkFlow:
def __init__(self, flow_history: FlowHistory, tracker_payload: TrackerPayload = None):
self.tracker_payload = tracker_payload
self.flow_history = flow_history
async def invoke(self, flow: Flow, event: Event, profile, session, ux: list, debug=False) -> Tuple[DebugInfo, List[Log], 'Event', 'Profile', 'Session']:
"""
Invokes workflow and returns DebugInfo and list of saved Logs.
"""
if event is None:
raise DagGraphError(
"Flow `{}` has no context event defined.".format(
flow.id))
if not flow.flowGraph:
raise DagGraphError("Flow {} is empty".format(flow.id))
if self.flow_history.is_acyclic(flow.id):
# Convert Editor graph to exec graph
converter = FlowGraphConverter(flow.flowGraph.dict())
dag_graph = converter.convert_to_dag_graph()
dag = DagProcessor(dag_graph)
try:
exec_dag = dag.make_execution_dag(debug=debug)
except DagGraphError as e:
raise DagGraphError("Flow `{}` returned the following error: `{}`".format(flow.id, str(e)))
# Init and run with event
await exec_dag.init(flow,
self.flow_history,
event,
session,
profile,
self.tracker_payload,
ux)
debug_info, log_list, profile, session = await exec_dag.run(
payload={},
flow=flow,
event=event,
profile=profile,
session=session
)
await exec_dag.close()
return debug_info, log_list, event, profile, session
raise RuntimeError("Workflow has circular reference.")
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imgapp/management/commands/create_token.py | Thakursim/dodjapp | 0 | 126334 | <reponame>Thakursim/dodjapp<filename>imgapp/management/commands/create_token.py
import getpass
from django.core.management import BaseCommand
from django.contrib.auth.models import User
from django.db import IntegrityError
from rest_framework.authtoken.models import Token
#The class must be named Command, and subclass BaseCommand
class Command(BaseCommand):
# Show this when the user types help
help = "Command to create user and get token in response"
# A command must define handle()
def handle(self, *args, **options):
success = True
while success:
try:
username = str(raw_input('Username: '))
password = getpass.getpass('Password: ')
user = User.objects.create(username=username, password=password)
api_key=Token.objects.create(user=user)
success = False
print "User Created Successfully with this token -> %s" % api_key.key
except IntegrityError as e:
if 'UNIQUE constraint failed: auth_user.username' in e.message:
print "Username already exists, Please try again with different username"
else:
print (e)
except Exception as e:
print(e) | [
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] |
examples/trivial_post_input.py | ajCameron/mewbot | 0 | 183169 | #!/usr/bin/env python3
"""
Implementing a new Input - as it happens to be the first input,
a few other things are going to have to go on
- like defining some behaviors
- and actually building a bot
- and fixing any weird type errors
"""
from __future__ import annotations
from typing import Set, Type, Dict, Any
from mewbot.bot import Bot
from mewbot.behaviour import Behaviour
from mewbot.core import InputEvent, OutputEvent
from mewbot.api.v1 import Trigger, Condition, Action
from mewbot.http_post import PostInput, PostIOConfig, PostInputEvent
class TrivialTrigger(Trigger):
"""
Nothing fancy - just fires whenever there is an PostInputEvent.
Will be used in the PrintBehavior.
"""
@staticmethod
def consumes_inputs() -> Set[Type[InputEvent]]:
return {
PostInputEvent,
}
def matches(self, event: InputEvent) -> bool:
return True
class TrivialCondition(Condition):
"""
Allows every input
"""
@staticmethod
def consumes_inputs() -> Set[Type[InputEvent]]:
return {
PostInputEvent,
}
def allows(self, event: InputEvent) -> bool:
return True
class PrintAction(Action):
"""
Print every InputEvent.
"""
@staticmethod
def consumes_inputs() -> Set[Type[InputEvent]]:
return {
PostInputEvent,
}
@staticmethod
def produces_outputs() -> Set[Type[OutputEvent]]:
return set()
async def act(self, event: InputEvent, state: Dict[str, Any]) -> None:
print(event)
if __name__ == "__main__":
# - Behavior
# Construct the print_behavior - which just prints every input event
print_behavior = Behaviour()
# Triggers on all PostInput events
print_behavior.add(TrivialTrigger())
# Passes all incoming events
print_behavior.add(TrivialCondition())
# Prints all the events which make their way into the bot
print_behavior.add(PrintAction())
# - IOConfig
post_ioconfig = PostIOConfig()
post_ioconfig.inputs.append(PostInput())
test_bot = Bot()
test_bot.name = "TrivialWebhookExample"
test_bot.add_io_config(post_ioconfig)
test_bot.add_behaviour(print_behavior)
test_bot.run()
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face.py | justinham/rasp | 0 | 48965 | <reponame>justinham/rasp<filename>face.py
import os
import glob
import numpy as np
import cv2
import tensorflow as tf
from fr_utils import *
from inception_blocks_v2 import *
from keras import backend as K
from easyfacenet.simple import facenet
def triplet_loss(y_true, y_pred, alpha = 0.3):
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
return loss
def prepare_database():
database = {}
for file in glob.glob("images/*"):
identity = os.path.splitext(os.path.basename(file))[0]
database[identity] = img_path_to_encoding(file, FRmodel)
return database
def who_is_it(image, database, model):
encoding = img_to_encoding(image, model)
min_dist = 100
identity = None
# Loop over the database dictionary's names and encodings.
for (name, db_enc) in database.items():
dist = np.linalg.norm(db_enc - encoding)
print('distance for %s is %s' %(name, dist))
if dist < min_dist:
min_dist = dist
identity = name
if min_dist > 0.52:
return None
else:
return identity
K.set_image_data_format('channels_first')
FRmodel = faceRecoModel(input_shape=(3, 96, 96))
FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
load_weights_from_FaceNet(FRmodel)
images = ['images/image1.jpg', 'images/image2.jpg', 'images/image3.jpg']
aligned = facenet.align_face(images)
embeddings = facenet.embedding(aligned)
comparisons = facenet.compare(aligned)
print("Is image 1 and 2 similar? ", bool(comparisons[0][1])) | [
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Python_Projects/numeric/lossofsignificance.py | arifBurakDemiray/TheCodesThatIWrote | 1 | 18869 | # -*- coding: utf-8 -*-
"""
Created on Mon Apr 13 13:35:33 2020
"""
#for finding loss of significances
x=1e-1
flag = True
a=0
while (flag):
print (((2*x)/(1-(x**2))),"......",(1/(1+x))-(1/(1-x)))
x= x*(1e-1)
a=a+1
if(a==25):
flag=False
| [
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] |
09-mock-challenge/my_library/module.py | haneyr/pytest-examples | 0 | 124652 | import pandas as pd
def is_sum_of_series_big(series):
# Lets check if this dataframe has over a thousand rows
if series.sum() > 1000:
return True
else:
return False
if __name__ == "__main__":
s1 = pd.Series([1000, 1000])
print(s1)
print(is_sum_of_series_big(s1))
s2 = pd.Series([1, 1])
print(s2)
print(is_sum_of_series_big(s2))
| [
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] |
synmock/sphere.py | bengranett/synmock | 1 | 167923 | <filename>synmock/sphere.py<gh_stars>1-10
import numpy as np
import healpy
ic = 180/np.pi
def xyz2lonlat(x,y,z,norm=True,getr=False):
""" """
if norm:
r = np.sqrt(x*x+y*y+z*z)
else:
r = np.ones(x.shape)
ii = r > 0
lat = np.zeros(len(r), dtype=float)
lon = np.zeros(len(r), dtype=float)
lat[ii] = np.arcsin(z[ii]/r[ii])*ic
lon[ii] = np.arctan2(y[ii],x[ii])*ic
lon = lon%360
if getr:
return lon,lat,r
return lon,lat
def expand_mask(mask, fwhm=1, threshold=0.1):
""" """
mask_sm = healpy.smoothing(mask, fwhm=fwhm*np.pi/180)
mask_out = mask * 0
sel = mask_sm > threshold
mask_out[sel] = 1
return mask_out | [
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api/subscription_api/serializers.py | vmariiechko/sport-club | 0 | 103890 | <reponame>vmariiechko/sport-club<filename>api/subscription_api/serializers.py
from django.utils import timezone
from rest_framework import serializers
from ..accounts_api.models import Member
from ..cards_api.models import Pass
from .models import Subscription
class SubscriptionSerializer(serializers.ModelSerializer):
member = serializers.SlugRelatedField(queryset=Member.objects.all(), slug_field='email')
card = serializers.SlugRelatedField(queryset=Pass.objects.all(), slug_field='name')
class Meta:
model = Subscription
fields = ('member', 'card', 'visits_count', 'purchased', 'expires')
extra_kwargs = {
'visits_count': {'read_only': True},
'purchased': {'read_only': True},
'expires': {'read_only': True}
}
def validate_member(self, value):
if Subscription.objects.filter(member=value, expires__gt=timezone.now(), visits_count__gt=0).exists():
raise serializers.ValidationError("You already have an active subscription")
return value
def create(self, validated_data):
instance = super().create(validated_data)
instance.visits_count = validated_data['card'].visits_count
instance.save()
return instance
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tests/rororo/test_openapi.py | fajfer/rororo | 105 | 43711 | import datetime
import io
import json
import zipfile
from pathlib import Path
import pyrsistent
import pytest
import yaml
from aiohttp import web
from openapi_core.shortcuts import create_spec
from yarl import URL
from rororo import (
BaseSettings,
get_openapi_context,
get_openapi_schema,
get_openapi_spec,
openapi_context,
OperationTableDef,
setup_openapi,
setup_settings_from_environ,
)
from rororo.annotations import DictStrAny
from rororo.openapi import get_validated_data
from rororo.openapi.exceptions import (
ConfigurationError,
OperationError,
validation_error_context,
ValidationError,
)
ROOT_PATH = Path(__file__).parent
INVALID_OPENAPI_JSON_PATH = ROOT_PATH / "invalid-openapi.json"
INVALID_OPENAPI_YAML_PATH = ROOT_PATH / "invalid-openapi.yaml"
OPENAPI_JSON_PATH = ROOT_PATH / "openapi.json"
OPENAPI_YAML_PATH = ROOT_PATH / "openapi.yaml"
TEST_NESTED_OBJECT = {
"uid": "6fccda1b-0873-4c8a-bceb-a2acfe5851da",
"type": "nested-object",
"data": {
"data_item": {"key": "value1", "any_data": {}},
"data_items": [
{"key": "value2", "any_data": {"two": 2}},
{"key": "value3", "any_data": {"three": 3}},
],
"str_items": ["1", "2", "3"],
},
"any_data": {"key1": "value1", "key2": "value2", "list": [1, 2, 3]},
}
operations = OperationTableDef()
invalid_operations = OperationTableDef()
def custom_json_loader(content: bytes) -> DictStrAny:
return json.load(io.BytesIO(content))
def custom_yaml_loader(content: bytes) -> DictStrAny:
return yaml.load(content, Loader=yaml.SafeLoader)
@invalid_operations.register("does-not-exist")
async def does_not_exist(request: web.Request) -> web.Response:
return web.Response(text="Hello, world!")
@operations.register("create-post")
async def create_post(request: web.Request) -> web.Response:
data = get_validated_data(request)
published_at: datetime.datetime = data["published_at"]
with validation_error_context("body", "published_at"):
if published_at.tzinfo is None:
raise ValidationError(message="Invalid value")
return web.json_response(
{**data, "id": 1, "published_at": data["published_at"].isoformat()},
status=201,
)
@operations.register
async def hello_world(request: web.Request) -> web.Response:
with openapi_context(request) as context:
name = context.parameters.query.get("name") or "world"
email = context.parameters.query.get("email") or "<EMAIL>"
return web.json_response(
{"message": f"Hello, {name}!", "email": email}
)
@operations.register
async def retrieve_any_object_from_request_body(
request: web.Request,
) -> web.Response:
return web.json_response(pyrsistent.thaw(get_validated_data(request)))
@operations.register
async def retrieve_array_from_request_body(
request: web.Request,
) -> web.Response:
with openapi_context(request) as context:
return web.json_response(pyrsistent.thaw(context.data))
@operations.register
async def retrieve_empty(request: web.Request) -> web.Response:
context = get_openapi_context(request)
return web.Response(
status=204, headers={"X-API-Key": context.security.get("apiKey") or ""}
)
@operations.register
async def retrieve_invalid_response(request: web.Request) -> web.Response:
return web.json_response({})
@operations.register
async def retrieve_post(request: web.Request) -> web.Response:
context = get_openapi_context(request)
return web.json_response(
{"id": context.parameters.path["post_id"], "title": "The Post"}
)
@operations.register
async def retrieve_nested_object_from_request_body(
request: web.Request,
) -> web.Response:
with openapi_context(request) as context:
data = pyrsistent.thaw(context.data)
data["uid"] = str(data["uid"])
return web.json_response(
data,
headers={
"X-Data-Type": str(type(context.data)),
"X-Data-Data-Data-Items-Type": str(
type(context.data["data"]["data_items"])
),
"X-Data-Data-Str-Items-Type": str(
type(context.data["data"]["str_items"])
),
"X-Data-UID-Type": str(type(context.data["uid"])),
},
)
@operations.register
async def retrieve_zip(request: web.Request) -> web.Response:
output = io.BytesIO()
with zipfile.ZipFile(output, "w") as handler:
handler.writestr("hello.txt", "Hello, world!")
output.seek(0)
return web.Response(
body=output,
content_type="application/zip",
headers={"Content-Disposition": "attachment; filename=hello.zip"},
)
@operations.register
async def upload_image(request: web.Request) -> web.Response:
return web.Response(
body=get_openapi_context(request).data,
content_type=request.content_type,
status=201,
)
@operations.register
async def upload_text(request: web.Request) -> web.Response:
return web.Response(
text=get_openapi_context(request).data,
content_type=request.content_type,
status=201,
)
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_any_object_request_body(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url=URL("/api/")
)
client = await aiohttp_client(app)
response = await client.post("/api/any-object", json=TEST_NESTED_OBJECT)
assert response.status == 200
assert await response.json() == TEST_NESTED_OBJECT
@pytest.mark.parametrize(
"data, expected_status, expected_response",
(
(
{},
422,
{"detail": [{"loc": ["body"], "message": "[] is too short"}]},
),
(
[],
422,
{"detail": [{"loc": ["body"], "message": "[] is too short"}]},
),
(
[""],
422,
{"detail": [{"loc": ["body", 0], "message": "'' is too short"}]},
),
(["Hello", "world!"], 200, ["Hello", "world!"]),
),
)
async def test_array_request_body(
aiohttp_client, data, expected_status, expected_response
):
app = setup_openapi(
web.Application(),
OPENAPI_YAML_PATH,
operations,
server_url=URL("/api"),
)
client = await aiohttp_client(app)
response = await client.post("/api/array", json=data)
assert response.status == expected_status
assert await response.json() == expected_response
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_create_post_201(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
published_at = "2020-04-01T12:00:00+02:00"
client = await aiohttp_client(app)
response = await client.post(
"/api/create-post",
json={
"title": "Post",
"slug": "post",
"content": "Post Content",
"published_at": published_at,
},
)
assert response.status == 201
assert await response.json() == {
"id": 1,
"title": "Post",
"slug": "post",
"content": "Post Content",
"published_at": published_at,
}
@pytest.mark.parametrize(
"schema_path, invalid_data, expected_detail",
(
(
OPENAPI_JSON_PATH,
{},
[
{"loc": ["body", "title"], "message": "Field required"},
{"loc": ["body", "slug"], "message": "Field required"},
{"loc": ["body", "content"], "message": "Field required"},
{"loc": ["body", "published_at"], "message": "Field required"},
],
),
(
OPENAPI_YAML_PATH,
{"title": "Title"},
[
{"loc": ["body", "slug"], "message": "Field required"},
{"loc": ["body", "content"], "message": "Field required"},
{"loc": ["body", "published_at"], "message": "Field required"},
],
),
(
OPENAPI_JSON_PATH,
{"title": "Title", "slug": "slug"},
[
{"loc": ["body", "content"], "message": "Field required"},
{"loc": ["body", "published_at"], "message": "Field required"},
],
),
(
OPENAPI_YAML_PATH,
{"title": "Title", "slug": "slug", "content": "Content"},
[{"loc": ["body", "published_at"], "message": "Field required"}],
),
),
)
async def test_create_post_422(
aiohttp_client, schema_path, invalid_data, expected_detail
):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url=URL("/dev-api"),
)
client = await aiohttp_client(app)
response = await client.post("/dev-api/create-post", json=invalid_data)
assert response.status == 422
assert (await response.json())["detail"] == expected_detail
@pytest.mark.parametrize(
"schema_path, schema_loader",
(
(OPENAPI_JSON_PATH, custom_json_loader),
(OPENAPI_YAML_PATH, custom_yaml_loader),
),
)
def test_custom_schema_loader(schema_path, schema_loader):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url="/api/",
schema_loader=schema_loader,
)
assert isinstance(get_openapi_schema(app), dict)
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_email_format(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.get(
"/api/hello", params={"email": "<EMAIL>"}
)
assert response.status == 200
assert (await response.json())["email"] == "<EMAIL>"
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_invalid_parameter_format(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.get("/api/posts/not-an-integer")
assert response.status == 422
assert await response.json() == {
"detail": [
{
"loc": ["parameters", "post_id"],
"message": "'not-an-integer' is not a type of 'integer'",
}
]
}
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_invalid_parameter_value(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.get("/api/posts/0")
assert response.status == 422
assert await response.json() == {
"detail": [
{
"loc": ["parameters", "post_id"],
"message": "0 is less than the minimum of 1",
}
]
}
def test_get_openapi_schema_no_schema():
with pytest.raises(ConfigurationError):
get_openapi_schema(web.Application())
def test_get_openapi_spec_no_spec():
with pytest.raises(ConfigurationError):
get_openapi_spec(web.Application())
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_multiple_request_errors(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.get("/api/hello?name=&email=")
assert response.status == 422
assert await response.json() == {
"detail": [
{
"loc": ["parameters", "name"],
"message": "Empty parameter value",
},
{
"loc": ["parameters", "email"],
"message": "Empty parameter value",
},
]
}
@pytest.mark.parametrize(
"schema_path, query_string, expected_message",
(
(OPENAPI_JSON_PATH, None, "Hello, world!"),
(OPENAPI_JSON_PATH, "?name=Name", "Hello, Name!"),
(str(OPENAPI_JSON_PATH), None, "Hello, world!"),
(str(OPENAPI_JSON_PATH), "?name=Name", "Hello, Name!"),
(OPENAPI_YAML_PATH, None, "Hello, world!"),
(OPENAPI_YAML_PATH, "?name=Name", "Hello, Name!"),
(str(OPENAPI_YAML_PATH), None, "Hello, world!"),
(str(OPENAPI_YAML_PATH), "?name=Name", "Hello, Name!"),
),
)
async def test_openapi(
aiohttp_client, schema_path, query_string, expected_message
):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api"
)
client = await aiohttp_client(app)
url = "/api/hello"
response = await client.get(
f"{url}{query_string}" if query_string is not None else url
)
assert response.status == 200
assert (await response.json())["message"] == expected_message
@pytest.mark.parametrize("is_enabled", (False, True))
async def test_openapi_validate_response(aiohttp_client, is_enabled):
app = web.Application()
setup_openapi(
app,
OPENAPI_YAML_PATH,
operations,
server_url="/api",
is_validate_response=is_enabled,
)
client = await aiohttp_client(app)
response = await client.get("/api/hello")
assert response.status == 200
assert await response.json() == {
"message": "Hello, world!",
"email": "<EMAIL>",
}
@pytest.mark.parametrize(
"has_openapi_schema_handler, url, expected_status",
(
(True, "/api/openapi.json", 200),
(False, "/api/openapi.yaml", 404),
(True, "/api/openapi.yaml", 200),
(False, "/api/openapi.yaml", 404),
(True, "/api/openapi.txt", 500),
(False, "/api/openapi.txt", 404),
),
)
async def test_openapi_schema_handler(
aiohttp_client, has_openapi_schema_handler, url, expected_status
):
app = web.Application()
setup_openapi(
app,
OPENAPI_YAML_PATH,
operations,
server_url=URL("/api"),
has_openapi_schema_handler=has_openapi_schema_handler,
)
client = await aiohttp_client(app)
response = await client.get(url)
assert response.status == expected_status
@pytest.mark.parametrize(
"schema_path, headers, expected",
(
(OPENAPI_JSON_PATH, {}, ""),
(OPENAPI_JSON_PATH, {"X-API-Key": "apiKey"}, "apiKey"),
(OPENAPI_YAML_PATH, {}, ""),
(OPENAPI_YAML_PATH, {"X-API-Key": "apiKey"}, "apiKey"),
),
)
async def test_optional_security_scheme(
aiohttp_client, schema_path, headers, expected
):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.get("/api/empty", headers=headers)
assert response.status == 204
assert response.headers["X-API-Key"] == expected
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_request_body_nested_object(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api/"
)
client = await aiohttp_client(app)
response = await client.post("/api/nested-object", json=TEST_NESTED_OBJECT)
assert response.status == 200
assert response.headers["X-Data-Type"] == "<class 'pyrsistent._pmap.PMap'>"
assert (
response.headers["X-Data-Data-Data-Items-Type"]
== "<class 'pvectorc.PVector'>"
)
assert (
response.headers["X-Data-Data-Str-Items-Type"]
== "<class 'pvectorc.PVector'>"
)
assert response.headers["X-Data-UID-Type"] == "<class 'uuid.UUID'>"
assert await response.json() == TEST_NESTED_OBJECT
@pytest.mark.parametrize(
"schema_path, loader",
(
(OPENAPI_JSON_PATH, custom_json_loader),
(OPENAPI_YAML_PATH, custom_yaml_loader),
),
)
async def test_setup_openapi_schema_and_spec(
aiohttp_client, schema_path, loader
):
schema = loader(schema_path.read_bytes())
spec = create_spec(schema)
app = setup_openapi(
web.Application(),
operations,
schema=schema,
spec=spec,
server_url="/api/",
)
client = await aiohttp_client(app)
response = await client.get("/api/hello")
assert response.status == 200
assert await response.json() == {
"message": "Hello, world!",
"email": "<EMAIL>",
}
@pytest.mark.parametrize(
"schema_path, loader",
(
(OPENAPI_JSON_PATH, custom_json_loader),
(OPENAPI_YAML_PATH, custom_yaml_loader),
),
)
async def test_setup_openapi_schema_and_path_ignore_invalid_schema_path(
aiohttp_client, schema_path, loader
):
schema = loader(schema_path.read_bytes())
spec = create_spec(schema)
setup_openapi(
web.Application(),
INVALID_OPENAPI_JSON_PATH,
operations,
schema=schema,
spec=spec,
server_url="/api/",
)
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
def test_setup_openapi_invalid_operation(schema_path):
with pytest.raises(OperationError):
setup_openapi(
web.Application(),
schema_path,
invalid_operations,
server_url="/api",
)
def test_setup_openapi_invalid_path():
with pytest.raises(ConfigurationError):
setup_openapi(
web.Application(), ROOT_PATH / "does-not-exist.yaml", operations
)
def test_setup_openapi_invalid_file():
with pytest.raises(ConfigurationError):
setup_openapi(web.Application(), ROOT_PATH / "settings.py", operations)
@pytest.mark.parametrize(
"schema_path", (INVALID_OPENAPI_JSON_PATH, INVALID_OPENAPI_YAML_PATH)
)
def test_setup_openapi_invalid_spec(schema_path):
with pytest.raises(ConfigurationError):
setup_openapi(web.Application(), schema_path, operations)
@pytest.mark.parametrize(
"schema_path, level, url, expected_status",
(
(OPENAPI_JSON_PATH, "test", "/api/hello", 200),
(OPENAPI_JSON_PATH, "test", "/dev-api/hello", 404),
(OPENAPI_YAML_PATH, "test", "/api/hello", 200),
(OPENAPI_YAML_PATH, "test", "/dev-api/hello", 404),
(OPENAPI_JSON_PATH, "dev", "/api/hello", 404),
(OPENAPI_JSON_PATH, "dev", "/dev-api/hello", 200),
(OPENAPI_YAML_PATH, "dev", "/api/hello", 404),
(OPENAPI_YAML_PATH, "dev", "/dev-api/hello", 200),
),
)
async def test_setup_openapi_server_url_from_settings(
monkeypatch, aiohttp_client, schema_path, level, url, expected_status
):
monkeypatch.setenv("LEVEL", level)
app = setup_openapi(
setup_settings_from_environ(web.Application(), BaseSettings),
schema_path,
operations,
)
client = await aiohttp_client(app)
response = await client.get(url)
assert response.status == expected_status
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
def test_setup_openapi_server_url_invalid_level(monkeypatch, schema_path):
monkeypatch.setenv("LEVEL", "prod")
with pytest.raises(ConfigurationError):
setup_openapi(
setup_settings_from_environ(web.Application(), BaseSettings),
schema_path,
operations,
)
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
def test_setup_openapi_server_url_does_not_set(schema_path):
with pytest.raises(ConfigurationError):
setup_openapi(web.Application(), schema_path, operations)
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_upload_image(aiohttp_client, schema_path):
blank_png = (Path(__file__).parent / "data" / "blank.png").read_bytes()
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api"
)
client = await aiohttp_client(app)
response = await client.post(
"/api/upload-image",
data=blank_png,
headers={"Content-Type": "image/png"},
)
assert response.status == 201
assert await response.read() == blank_png
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_upload_text(aiohttp_client, schema_path):
text = "Hello, world! And other things..."
app = setup_openapi(
web.Application(), schema_path, operations, server_url="/api"
)
client = await aiohttp_client(app)
response = await client.post(
"/api/upload-text",
data=text.encode("utf-8"),
headers={"Content-Type": "text/plain"},
)
assert response.status == 201
assert await response.text() == text
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_validate_binary_response(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url="/api",
is_validate_response=True,
)
client = await aiohttp_client(app)
response = await client.get("/api/download.zip")
assert response.status == 200
assert response.content_type == "application/zip"
content = io.BytesIO(await response.read())
with zipfile.ZipFile(content) as handler:
with handler.open("hello.txt") as item:
assert item.read() == b"Hello, world!"
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_validate_empty_response(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url="/api",
is_validate_response=True,
)
client = await aiohttp_client(app)
response = await client.get("/api/empty")
assert response.status == 204
@pytest.mark.parametrize(
"schema_path, is_validate_response, expected_status",
(
(OPENAPI_JSON_PATH, False, 200),
(OPENAPI_JSON_PATH, True, 422),
(OPENAPI_YAML_PATH, False, 200),
(OPENAPI_JSON_PATH, True, 422),
),
)
async def test_validate_response(
aiohttp_client, schema_path, is_validate_response, expected_status
):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url="/api",
is_validate_response=is_validate_response,
)
client = await aiohttp_client(app)
response = await client.get("/api/invalid-response")
assert response.status == expected_status
@pytest.mark.parametrize("schema_path", (OPENAPI_JSON_PATH, OPENAPI_YAML_PATH))
async def test_validate_response_error(aiohttp_client, schema_path):
app = setup_openapi(
web.Application(),
schema_path,
operations,
server_url="/api",
is_validate_response=True,
)
client = await aiohttp_client(app)
response = await client.get("/api/invalid-response")
assert response.status == 422
assert await response.json() == {
"detail": [
{"loc": ["response", "uid"], "message": "Field required"},
{"loc": ["response", "type"], "message": "Field required"},
{"loc": ["response", "data"], "message": "Field required"},
{"loc": ["response", "any_data"], "message": "Field required"},
]
}
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] |
src/rnn/text_classification_train.py | jorgemf/kaggle_redefining_cancer_treatment | 20 | 52106 | import tensorflow as tf
import csv
import time
from datetime import timedelta
import sys
import numpy as np
from tensorflow.python.training import training_util
from tensorflow.contrib import slim
from tensorflow.python.ops import variables as tf_variables
from ..configuration import *
from .. import trainer, evaluator, metrics
from ..task_spec import get_task_spec
from .text_classification_dataset import TextClassificationDataset
def _load_embeddings(vocabulary_size, embeddings_size,
filename_prefix='embeddings', from_dir=DIR_DATA_WORD2VEC):
embeddings = []
embeddings_file = '{}_{}_{}'.format(filename_prefix, vocabulary_size, embeddings_size)
with open(os.path.join(from_dir, embeddings_file), 'r') as file:
reader = csv.reader(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
for row in reader:
embeddings.append([float(r) for r in row])
return embeddings
class TextClassificationTrainer(trainer.Trainer):
"""
Helper class to run the training and create the model for the training. See trainer.Trainer for
more details.
"""
def __init__(self, dataset, text_classification_model, log_dir=DIR_TC_LOGDIR,
use_end_sequence=False, task_spec=None, max_steps=None):
self.text_classification_model = text_classification_model
self.use_end_sequence = use_end_sequence
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
super(TextClassificationTrainer, self).__init__(log_dir=log_dir, dataset=dataset,
task_spec=task_spec, max_steps=max_steps,
monitored_training_session_config=config)
def model(self, input_text_begin, input_text_end, gene, variation, expected_labels, batch_size,
vocabulary_size=VOCABULARY_SIZE, embeddings_size=EMBEDDINGS_SIZE, output_classes=9):
# embeddings
embeddings = _load_embeddings(vocabulary_size, embeddings_size)
# global step
self.global_step = training_util.get_or_create_global_step()
# model
with slim.arg_scope(self.text_classification_model.model_arg_scope()):
outputs = self.text_classification_model.model(input_text_begin, input_text_end,
gene, variation, output_classes,
embeddings=embeddings,
batch_size=batch_size)
# loss
targets = self.text_classification_model.targets(expected_labels, output_classes)
self.loss = self.text_classification_model.loss(targets, outputs)
tf.summary.scalar('loss', self.loss)
# learning rate
self.optimizer, self.learning_rate = \
self.text_classification_model.optimize(self.loss, self.global_step)
if self.learning_rate is not None:
tf.summary.scalar('learning_rate', self.learning_rate)
# metrics
self.metrics = metrics.single_label(outputs['prediction'], targets)
# saver to save the model
self.saver = tf.train.Saver()
# check a nan value in the loss
self.loss = tf.check_numerics(self.loss, 'loss is nan')
return None
def create_graph(self, dataset_tensor, batch_size):
input_text_begin, input_text_end, gene, variation, expected_labels = dataset_tensor
if not self.use_end_sequence:
input_text_end = None
return self.model(input_text_begin, input_text_end, gene, variation, expected_labels, batch_size)
def step(self, session, graph_data):
lr, _, loss, step, metrics = \
session.run([self.learning_rate, self.optimizer, self.loss, self.global_step,
self.metrics])
if self.is_chief and time.time() > self.print_timestamp + 5 * 60:
self.print_timestamp = time.time()
elapsed_time = str(timedelta(seconds=time.time() - self.init_time))
m = 'step: {} loss: {:0.4f} learning_rate = {:0.6f} elapsed seconds: {} ' \
'precision: {} recall: {} accuracy: {}'
logging.info(m.format(step, loss, lr, elapsed_time,
metrics['precision'], metrics['recall'], metrics['accuracy']))
def after_create_session(self, session, coord):
self.init_time = time.time()
self.print_timestamp = time.time()
class TextClassificationTest(evaluator.Evaluator):
"""Evaluator for distributed training"""
def __init__(self, dataset, text_classification_model, output_path, log_dir=DIR_TC_LOGDIR,
use_end_sequence=False,max_steps=None):
self.use_end_sequence = use_end_sequence
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
super(TextClassificationTest, self).__init__(checkpoints_dir=log_dir, dataset=dataset,
output_path=output_path, max_steps=max_steps,
singular_monitored_session_config=config)
self.text_classification_model = text_classification_model
self.eval_writer = tf.summary.FileWriter(log_dir)
def model(self, input_text_begin, input_text_end, gene, variation, expected_labels, batch_size,
vocabulary_size=VOCABULARY_SIZE, embeddings_size=EMBEDDINGS_SIZE, output_classes=9):
# embeddings
embeddings = _load_embeddings(vocabulary_size, embeddings_size)
# model
with slim.arg_scope(self.text_classification_model.model_arg_scope()):
outputs = self.text_classification_model.model(input_text_begin, input_text_end,
gene, variation, output_classes,
embeddings=embeddings,
batch_size=batch_size,
training=False)
# loss
targets = self.text_classification_model.targets(expected_labels, output_classes)
loss = self.text_classification_model.loss(targets, outputs)
self.accumulated_loss = tf.Variable(0.0, dtype=tf.float32, name='accumulated_loss',
trainable=False)
self.accumulated_loss = tf.assign_add(self.accumulated_loss, loss)
step = tf.Variable(0, dtype=tf.int32, name='eval_step', trainable=False)
step_increase = tf.assign_add(step, 1)
self.loss = self.accumulated_loss / tf.cast(step_increase, dtype=tf.float32)
tf.summary.scalar('loss', self.loss)
# metrics
self.metrics = metrics.single_label(outputs['prediction'], targets, moving_average=False)
return None
def create_graph(self, dataset_tensor, batch_size):
input_text_begin, input_text_end, gene, variation, expected_labels = dataset_tensor
if not self.use_end_sequence:
input_text_end = None
graph_data = self.model(input_text_begin, input_text_end, gene, variation,
expected_labels, batch_size)
return graph_data
def step(self, session, graph_data, summary_op):
summary, self.loss_result, self.metrics_results = \
session.run([summary_op, self.loss, self.metrics])
return summary
def end(self, session):
super(TextClassificationTest, self).end(session)
chk_step = int(self.lastest_checkpoint.split('-')[-1])
m = 'step: {} loss: {:0.4f} precision: {} recall: {} accuracy: {}'
logging.info(m.format(chk_step, self.loss_result, self.metrics_results['precision'],
self.metrics_results['recall'], self.metrics_results['accuracy']))
def after_create_session(self, session, coord):
# checkpoints_file = os.path.join(self.checkpoints_dir, 'checkpoint')
# alt_checkpoints_dir = '{}_tp'.format(self.checkpoints_dir)
# import glob
# files = glob.glob('{}/model.ckpt-*.data-*'.format(alt_checkpoints_dir))
# chk_step = 0
# for f in files:
# num = f.split('model.ckpt-')[1].split('.')[0]
# num = int(num)
# if chk_step == 0 or num < chk_step:
# chk_step = num
# if chk_step != 0:
# ckpt_files = glob.glob('{}/model.ckpt-{}.data-*'.format(alt_checkpoints_dir, chk_step))
# ckpt_files = [x.split('/')[-1] for x in ckpt_files]
# for f in ckpt_files + ['model.ckpt-{}.index', 'model.ckpt-{}.meta']:
# f = f.format(chk_step)
# os.rename(os.path.join(alt_checkpoints_dir, f), os.path.join(self.checkpoints_dir, f))
# with open(checkpoints_file, 'wb') as f:
# f.write('model_checkpoint_path: "./model.ckpt-{}"\n'.format(chk_step))
# f.write('all_model_checkpoint_paths: "./model.ckpt-{}"\n'.format(chk_step))
super(TextClassificationTest, self).after_create_session(session, coord)
# with open(checkpoints_file, 'wb') as f:
# f.write('model_checkpoint_path: "./model.ckpt-"\n')
# f.write('all_model_checkpoint_paths: "./model.ckpt-"\n')
class TextClassificationEval(evaluator.Evaluator):
"""Evaluator for text classification"""
def __init__(self, dataset, text_classification_model, output_path, log_dir=DIR_TC_LOGDIR,
use_end_sequence=False):
self.use_end_sequence = use_end_sequence
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
super(TextClassificationEval, self).__init__(checkpoints_dir=log_dir,
output_path=output_path,
infinite_loop=False,
singular_monitored_session_config=config)
self.dataset = dataset
self.text_classification_model = text_classification_model
def model(self, input_text_begin, input_text_end, gene, variation, batch_size,
vocabulary_size=VOCABULARY_SIZE, embeddings_size=EMBEDDINGS_SIZE, output_classes=9):
# embeddings
embeddings = _load_embeddings(vocabulary_size, embeddings_size)
# global step
self.global_step = training_util.get_or_create_global_step()
self.global_step = tf.assign_add(self.global_step, 1)
# model
with tf.control_dependencies([self.global_step]):
with slim.arg_scope(self.text_classification_model.model_arg_scope()):
self.outputs = self.text_classification_model.model(input_text_begin, input_text_end,
gene, variation, output_classes,
embeddings=embeddings,
batch_size=batch_size,
training=False)
# restore only the trainable variables
self.saver = tf.train.Saver(var_list=tf_variables.trainable_variables())
return self.outputs
def create_graph(self, dataset_tensor, batch_size):
input_text_begin, input_text_end, gene, variation = dataset_tensor
if not self.use_end_sequence:
input_text_end = None
return self.model(input_text_begin, input_text_end, gene, variation, batch_size)
def after_create_session(self, session, coord):
super(TextClassificationEval, self).after_create_session(session, coord)
print('ID,class1,class2,class3,class4,class5,class6,class7,class8,class9')
def step(self, session, graph_data, summary_op):
step, predictions = session.run([self.global_step, self.outputs['prediction']])
predictions = predictions[0]
predictions = [p + 0.01 for p in predictions] # penalize less the mistakes
sum = np.sum(predictions)
predictions = [p / sum for p in predictions]
print('{},{}'.format(step, ','.join(['{:.3f}'.format(x) for x in predictions])))
return None
import logging
def main(model, name, sentence_split=False, end_sequence=USE_END_SEQUENCE, batch_size=TC_BATCH_SIZE):
"""
Main method to execute the text_classification models
:param ModelSimple model: object model based on ModelSimple
:param str name: name of the model
:param bool sentence_split: whether to split the dataset in sentneces or not,
only used for hatt model
:param bool end_sequence: whether to use or not the end of the sequences in the models
:param int batch_size: batch size of the models
"""
logging.getLogger().setLevel(logging.INFO)
log_dir = '{}_{}'.format(DIR_TC_LOGDIR, name)
if len(sys.argv) > 1 and sys.argv[1] == 'test':
# execute the test with the train dataset
dataset = TextClassificationDataset(type='train', sentence_split=sentence_split)
tester = TextClassificationTest(dataset=dataset, text_classification_model=model,
log_dir=log_dir,
output_path=os.path.join(log_dir, 'test_trainset'),
use_end_sequence=end_sequence)
tester.run()
elif len(sys.argv) > 1 and sys.argv[1] == 'validate':
dataset = TextClassificationDataset(type='val', sentence_split=sentence_split)
tester = TextClassificationTest(dataset=dataset, text_classification_model=model,
log_dir=log_dir,
output_path=os.path.join(log_dir, 'validate'),
use_end_sequence=end_sequence)
tester.run()
elif len(sys.argv) > 1 and sys.argv[1] == 'eval':
# evaluate the data of the test dataset. We submit this output to kaggle
dataset = TextClassificationDataset(type='test', sentence_split=sentence_split)
evaluator = TextClassificationEval(dataset=dataset, text_classification_model=model,
log_dir=log_dir,
output_path=os.path.join(log_dir, 'test'),
use_end_sequence=end_sequence)
evaluator.run()
elif len(sys.argv) > 1 and sys.argv[1] == 'eval_stage2':
# evaluate the data of the test dataset. We submit this output to kaggle
dataset = TextClassificationDataset(type='stage2_test', sentence_split=sentence_split)
evaluator = TextClassificationEval(dataset=dataset, text_classification_model=model,
log_dir=log_dir,
output_path=os.path.join(log_dir, 'test_stage2'),
use_end_sequence=end_sequence)
evaluator.run()
else:
# training
task_spec = get_task_spec(with_evaluator=USE_LAST_WORKER_FOR_VALIDATION)
if task_spec.join_if_ps():
# join if it is a parameters server and do nothing else
return
with(tf.gfile.Open(os.path.join(DIR_DATA_TEXT_CLASSIFICATION, 'train_set'))) as f:
max_steps = int(TC_EPOCHS * len(f.readlines()) / batch_size)
if task_spec.is_evaluator():
dataset = TextClassificationDataset(type='val', sentence_split=sentence_split)
# evaluator running in the last worker
tester = TextClassificationTest(dataset=dataset, text_classification_model=model,
log_dir=log_dir,
output_path=os.path.join(log_dir, 'val'),
use_end_sequence=end_sequence,
max_steps=max_steps)
tester.run()
else:
dataset = TextClassificationDataset(type='train', sentence_split=sentence_split)
trainer = TextClassificationTrainer(dataset=dataset, text_classification_model=model,
log_dir=log_dir, use_end_sequence=end_sequence,
task_spec=task_spec, max_steps=max_steps)
trainer.run(epochs=TC_EPOCHS, batch_size=batch_size)
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tests/test_middleware/test_pure_asgi_middleware.py | adamantike/starlette-context | 242 | 73327 | <reponame>adamantike/starlette-context
from starlette import status
from starlette.applications import Starlette
from starlette.middleware import Middleware
from starlette.requests import Request
from starlette.responses import JSONResponse
from starlette.testclient import TestClient
from starlette_context import context, plugins
from starlette_context.header_keys import HeaderKeys
from starlette_context.middleware import RawContextMiddleware
plugins_to_use = (
plugins.CorrelationIdPlugin(),
plugins.RequestIdPlugin(),
plugins.UserAgentPlugin(),
plugins.ForwardedForPlugin(),
plugins.DateHeaderPlugin(),
)
middleware = [
Middleware(
RawContextMiddleware,
plugins=plugins_to_use,
)
]
app = Starlette(middleware=middleware)
client = TestClient(app)
@app.route("/")
async def index(request: Request) -> JSONResponse:
return JSONResponse(content=context.data)
def test_valid_request():
resp = client.get("/")
assert resp.status_code == status.HTTP_200_OK
for plugin in plugins_to_use:
assert plugin.key in resp.text
assert HeaderKeys.correlation_id in resp.headers
assert HeaderKeys.request_id in resp.headers
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corehq/ex-submodules/casexml/apps/phone/fixtures.py | johan--/commcare-hq | 0 | 196102 | <gh_stars>0
from collections import namedtuple
from casexml.apps.case.xml import V1
from django.conf import settings
from corehq.apps.users.models import CommCareUser
from dimagi.utils.modules import to_function
import itertools
class FixtureGenerator(object):
"""
The generator object, which gets fixtures from your config file that should
be included when OTA restoring.
See: https://bitbucket.org/javarosa/javarosa/wiki/externalinstances
To use, add the following to your settings.py
FIXTURE_GENERATORS = {
'group1': [
"myapp.fixturegenerators.gen1",
"myapp.fixturegenerators.gen2",
...
],
...
}
The values in the file should be paths to objects that
implement the following API:
provider(user, version, last_sync) --> [list of fixture objects]
provider.id --> the ID of the fixture
If the provider generates multiple fixtures it should use an ID format as follows:
"prefix:dynamic"
In this case 'provider.id' should just be the ID prefix.
The function should return an empty list if there are no fixtures
"""
def __init__(self):
self._generator_providers = {}
if hasattr(settings, "FIXTURE_GENERATORS"):
for group, func_paths in settings.FIXTURE_GENERATORS.items():
self._generator_providers[group] = filter(None, [
to_function(func_path) for func_path in func_paths
])
def _get_fixtures(self, group, fixture_id, user, version, last_sync):
if version == V1:
return [] # V1 phones will never use or want fixtures
if getattr(user, "_hq_user", False):
user = user._hq_user
if not isinstance(user, CommCareUser):
return []
if group:
providers = self._generator_providers.get(group, [])
else:
providers = itertools.chain(*self._generator_providers.values())
if fixture_id:
full_id = fixture_id
prefix = fixture_id.split(':', 1)[0]
def provider_matches(provider):
# some providers generate fixtures with dynamic ID's e.g. item-list:my-item-list
# in which case provider.id is just the prefix.
return provider.id == full_id or provider.id == prefix
providers = [provider for provider in providers if provider_matches(provider)]
return itertools.chain(*[provider(user, version, last_sync)
for provider in providers])
def get_fixture_by_id(self, fixture_id, user, version, last_sync=None):
"""
Only get fixtures with the specified ID.
"""
fixtures = self._get_fixtures(None, fixture_id, user, version, last_sync)
for fixture in fixtures:
if fixture.attrib.get("id") == fixture_id:
return fixture
def get_fixtures(self, user, version, last_sync=None, group=None):
"""
Gets all fixtures associated with an OTA restore operation
"""
return self._get_fixtures(group, None, user, version, last_sync)
generator = FixtureGenerator()
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src/modax/layers/network.py | GJBoth/modax | 2 | 36463 | from typing import Callable
from jax import lax
from flax import linen as nn
class MultiTaskDense(nn.Module):
features: int
n_tasks: int
kernel_init: Callable = nn.initializers.lecun_normal()
bias_init: Callable = nn.initializers.zeros
@nn.compact
def __call__(self, inputs):
kernel = self.param(
"kernel", self.kernel_init, (self.n_tasks, inputs.shape[-1], self.features)
)
y = lax.dot_general(
inputs, kernel, dimension_numbers=(((2,), (1,)), ((0,), (0,)))
)
bias = self.param("bias", self.bias_init, (self.n_tasks, 1, self.features))
y = y + bias
return y
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] |
mmdet/models/backbones/cspresnet.py | jie311/miemiedetection | 65 | 120322 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.ops import get_act_fn
from mmdet.models.custom_layers import ShapeSpec
class ConvBNLayer(nn.Module):
def __init__(self,
ch_in,
ch_out,
filter_size=3,
stride=1,
groups=1,
padding=0,
act=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias=False)
self.bn = nn.BatchNorm2d(ch_out)
self.act = get_act_fn(act) if act is None or isinstance(act, (
str, dict)) else act
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
return x
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
if isinstance(self.conv, torch.nn.Conv2d):
if self.conv.weight.requires_grad:
param_group_conv = {'params': [self.conv.weight]}
param_group_conv['lr'] = base_lr * 1.0
param_group_conv['base_lr'] = base_lr * 1.0
param_group_conv['weight_decay'] = base_wd
param_group_conv['need_clip'] = need_clip
param_group_conv['clip_norm'] = clip_norm
param_groups.append(param_group_conv)
if self.bn is not None:
if self.bn.weight.requires_grad:
param_group_norm_weight = {'params': [self.bn.weight]}
param_group_norm_weight['lr'] = base_lr * 1.0
param_group_norm_weight['base_lr'] = base_lr * 1.0
param_group_norm_weight['weight_decay'] = 0.0
param_group_norm_weight['need_clip'] = need_clip
param_group_norm_weight['clip_norm'] = clip_norm
param_groups.append(param_group_norm_weight)
if self.bn.bias.requires_grad:
param_group_norm_bias = {'params': [self.bn.bias]}
param_group_norm_bias['lr'] = base_lr * 1.0
param_group_norm_bias['base_lr'] = base_lr * 1.0
param_group_norm_bias['weight_decay'] = 0.0
param_group_norm_bias['need_clip'] = need_clip
param_group_norm_bias['clip_norm'] = clip_norm
param_groups.append(param_group_norm_bias)
class RepVggBlock(nn.Module):
def __init__(self, ch_in, ch_out, act='relu'):
super(RepVggBlock, self).__init__()
self.ch_in = ch_in
self.ch_out = ch_out
self.conv1 = ConvBNLayer(
ch_in, ch_out, 3, stride=1, padding=1, act=None)
self.conv2 = ConvBNLayer(
ch_in, ch_out, 1, stride=1, padding=0, act=None)
self.act = get_act_fn(act) if act is None or isinstance(act, (
str, dict)) else act
def forward(self, x):
if hasattr(self, 'conv'):
y = self.conv(x)
else:
y = self.conv1(x) + self.conv2(x)
y = self.act(y)
return y
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
if hasattr(self, 'conv'):
self.conv.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
else:
self.conv1.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
self.conv2.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
def convert_to_deploy(self):
if not hasattr(self, 'conv'):
self.conv = nn.Conv2d(
in_channels=self.ch_in,
out_channels=self.ch_out,
kernel_size=3,
stride=1,
padding=1,
groups=1)
kernel, bias = self.get_equivalent_kernel_bias()
self.conv.weight.copy_(kernel)
self.conv.bias.copy_(bias)
self.__delattr__('conv1')
self.__delattr__('conv2')
def get_equivalent_kernel_bias(self):
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
return kernel3x3 + self._pad_1x1_to_3x3_tensor(
kernel1x1), bias3x3 + bias1x1
def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
def _fuse_bn_tensor(self, branch):
if branch is None:
return 0, 0
kernel = branch.conv.weight
running_mean = branch.bn._mean
running_var = branch.bn._variance
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn._epsilon
std = (running_var + eps).sqrt()
t = (gamma / std).reshape((-1, 1, 1, 1))
return kernel * t, beta - running_mean * gamma / std
class BasicBlock(nn.Module):
def __init__(self, ch_in, ch_out, act='relu', shortcut=True):
super(BasicBlock, self).__init__()
assert ch_in == ch_out
self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=act)
self.conv2 = RepVggBlock(ch_out, ch_out, act=act)
self.shortcut = shortcut
def forward(self, x):
y = self.conv1(x)
y = self.conv2(y)
if self.shortcut:
return x + y
else:
return y
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
self.conv1.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
self.conv2.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
class EffectiveSELayer(nn.Module):
""" Effective Squeeze-Excitation
From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
"""
def __init__(self, channels, act='hardsigmoid'):
super(EffectiveSELayer, self).__init__()
self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
self.act = get_act_fn(act) if act is None or isinstance(act, (
str, dict)) else act
def forward(self, x):
x_se = x.mean((2, 3), keepdim=True)
x_se = self.fc(x_se)
return x * self.act(x_se)
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
if isinstance(self.fc, torch.nn.Conv2d):
if self.fc.weight.requires_grad:
param_group_conv_weight = {'params': [self.fc.weight]}
param_group_conv_weight['lr'] = base_lr * 1.0
param_group_conv_weight['base_lr'] = base_lr * 1.0
param_group_conv_weight['weight_decay'] = base_wd
param_group_conv_weight['need_clip'] = need_clip
param_group_conv_weight['clip_norm'] = clip_norm
param_groups.append(param_group_conv_weight)
if self.fc.bias.requires_grad:
param_group_conv_bias = {'params': [self.fc.bias]}
param_group_conv_bias['lr'] = base_lr * 1.0
param_group_conv_bias['base_lr'] = base_lr * 1.0
param_group_conv_bias['weight_decay'] = base_wd
param_group_conv_bias['need_clip'] = need_clip
param_group_conv_bias['clip_norm'] = clip_norm
param_groups.append(param_group_conv_bias)
class CSPResStage(nn.Module):
def __init__(self,
block_fn,
ch_in,
ch_out,
n,
stride,
act='relu',
attn='eca'):
super(CSPResStage, self).__init__()
ch_mid = (ch_in + ch_out) // 2
if stride == 2:
self.conv_down = ConvBNLayer(
ch_in, ch_mid, 3, stride=2, padding=1, act=act)
else:
self.conv_down = None
self.conv1 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
self.conv2 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
self.blocks = nn.Sequential(*[
block_fn(
ch_mid // 2, ch_mid // 2, act=act, shortcut=True)
for i in range(n)
])
if attn:
self.attn = EffectiveSELayer(ch_mid, act='hardsigmoid')
else:
self.attn = None
self.conv3 = ConvBNLayer(ch_mid, ch_out, 1, act=act)
def forward(self, x):
if self.conv_down is not None:
x = self.conv_down(x)
y1 = self.conv1(x)
y2 = self.blocks(self.conv2(x))
y = torch.cat([y1, y2], 1)
if self.attn is not None:
y = self.attn(y)
y = self.conv3(y)
return y
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
if self.conv_down is not None:
self.conv_down.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
self.conv1.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
self.conv2.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
for layer in self.blocks:
layer.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
if self.attn is not None:
self.attn.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
self.conv3.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
class CSPResNet(nn.Module):
__shared__ = ['width_mult', 'depth_mult', 'trt']
def __init__(self,
layers=[3, 6, 6, 3],
channels=[64, 128, 256, 512, 1024],
act='swish',
return_idx=[0, 1, 2, 3, 4],
depth_wise=False,
use_large_stem=False,
width_mult=1.0,
depth_mult=1.0,
freeze_at=-1,
trt=False):
super(CSPResNet, self).__init__()
channels = [max(round(c * width_mult), 1) for c in channels]
layers = [max(round(l * depth_mult), 1) for l in layers]
act = get_act_fn(
act, trt=trt) if act is None or isinstance(act,
(str, dict)) else act
if use_large_stem:
self.stem = nn.Sequential()
self.stem.add_module('conv1', ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act))
self.stem.add_module('conv2', ConvBNLayer(channels[0] // 2, channels[0] // 2, 3, stride=1, padding=1, act=act))
self.stem.add_module('conv3', ConvBNLayer(channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act))
else:
self.stem = nn.Sequential()
self.stem.add_module('conv1', ConvBNLayer(3, channels[0] // 2, 3, stride=2, padding=1, act=act))
self.stem.add_module('conv2', ConvBNLayer(channels[0] // 2, channels[0], 3, stride=1, padding=1, act=act))
n = len(channels) - 1
self.stages = nn.Sequential()
for i in range(n):
self.stages.add_module(str(i), CSPResStage(BasicBlock, channels[i], channels[i + 1], layers[i], 2, act=act))
self._out_channels = channels[1:]
self._out_strides = [4, 8, 16, 32]
self.return_idx = return_idx
if freeze_at >= 0:
self._freeze_parameters(self.stem)
for i in range(min(freeze_at + 1, n)):
self._freeze_parameters(self.stages[i])
def _freeze_parameters(self, m):
for p in m.parameters():
p.requires_grad_(False)
def forward(self, inputs):
x = self.stem(inputs)
outs = []
for idx, stage in enumerate(self.stages):
x = stage(x)
if idx in self.return_idx:
outs.append(x)
return outs
def add_param_group(self, param_groups, base_lr, base_wd, need_clip, clip_norm):
for layer in self.stem:
layer.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
for idx, stage in enumerate(self.stages):
stage.add_param_group(param_groups, base_lr, base_wd, need_clip, clip_norm)
@property
def out_shape(self):
return [
ShapeSpec(
channels=self._out_channels[i], stride=self._out_strides[i])
for i in self.return_idx
]
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src/OTLMOW/OTLModel/Classes/DwarseMarkeringVerschuind.py | davidvlaminck/OTLClassPython | 2 | 22698 | # coding=utf-8
from OTLMOW.OTLModel.BaseClasses.OTLAttribuut import OTLAttribuut
from OTLMOW.OTLModel.Classes.DwarseMarkeringToegang import DwarseMarkeringToegang
from OTLMOW.OTLModel.Datatypes.KlDwarseMarkeringVerschuindCode import KlDwarseMarkeringVerschuindCode
from OTLMOW.OTLModel.Datatypes.KlDwarseMarkeringVerschuindSoort import KlDwarseMarkeringVerschuindSoort
from OTLMOW.OTLModel.Datatypes.KwantWrdInDecimaleGraden import KwantWrdInDecimaleGraden
from OTLMOW.OTLModel.Datatypes.KwantWrdInVierkanteMeter import KwantWrdInVierkanteMeter
# Generated with OTLClassCreator. To modify: extend, do not edit
class DwarseMarkeringVerschuind(DwarseMarkeringToegang):
"""Een schuine markering dwars op de weg aangebracht om het verkeer te waarschuwen, informeren of regelen."""
typeURI = 'https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind'
"""De URI van het object volgens https://www.w3.org/2001/XMLSchema#anyURI."""
def __init__(self):
super().__init__()
self._basisoppervlakte = OTLAttribuut(field=KwantWrdInVierkanteMeter,
naam='basisoppervlakte',
label='oppervlakte',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind.basisoppervlakte',
definition='De basisoppervlakte van de dwarse markering in vierkante meter.',
owner=self)
self._code = OTLAttribuut(field=KlDwarseMarkeringVerschuindCode,
naam='code',
label='code',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind.code',
definition='De (COPRO/BENOR) code van dwarse markering.',
owner=self)
self._hoek = OTLAttribuut(field=KwantWrdInDecimaleGraden,
naam='hoek',
label='hoek',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind.hoek',
definition='De hoek van de verschuinde dwarsmarkering in decimale graden.',
owner=self)
self._oppervlakte = OTLAttribuut(field=KwantWrdInVierkanteMeter,
naam='oppervlakte',
label='oppervlakte',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind.oppervlakte',
definition='De oppervlakte van een dwarsmarkering na verschuining.',
owner=self)
self._soortOmschrijving = OTLAttribuut(field=KlDwarseMarkeringVerschuindSoort,
naam='soortOmschrijving',
label='soort omschrijving',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DwarseMarkeringVerschuind.soortOmschrijving',
definition='De soort en tevens de omschrijving van dwarse markering.',
owner=self)
@property
def basisoppervlakte(self):
"""De basisoppervlakte van de dwarse markering in vierkante meter."""
return self._basisoppervlakte.get_waarde()
@basisoppervlakte.setter
def basisoppervlakte(self, value):
self._basisoppervlakte.set_waarde(value, owner=self)
@property
def code(self):
"""De (COPRO/BENOR) code van dwarse markering."""
return self._code.get_waarde()
@code.setter
def code(self, value):
self._code.set_waarde(value, owner=self)
@property
def hoek(self):
"""De hoek van de verschuinde dwarsmarkering in decimale graden."""
return self._hoek.get_waarde()
@hoek.setter
def hoek(self, value):
self._hoek.set_waarde(value, owner=self)
@property
def oppervlakte(self):
"""De oppervlakte van een dwarsmarkering na verschuining."""
return self._oppervlakte.get_waarde()
@oppervlakte.setter
def oppervlakte(self, value):
self._oppervlakte.set_waarde(value, owner=self)
@property
def soortOmschrijving(self):
"""De soort en tevens de omschrijving van dwarse markering."""
return self._soortOmschrijving.get_waarde()
@soortOmschrijving.setter
def soortOmschrijving(self, value):
self._soortOmschrijving.set_waarde(value, owner=self)
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] |
titan/files/mixins/versions_views.py | GoogleCloudPlatform/titan | 19 | 109441 | <filename>titan/files/mixins/versions_views.py
#!/usr/bin/env python
# Copyright 2012 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Handlers for Titan Files versions service."""
import json
import logging
import webapp2
from titan import files
from titan.common import handlers
from titan.files.mixins import versions
class ChangesetHandler(handlers.BaseHandler):
"""RESTful handler for versions.Changeset."""
def post(self):
"""POST handler."""
try:
namespace = self.request.get('namespace', None)
vcs = versions.VersionControlService()
changeset = vcs.new_staging_changeset(namespace=namespace)
self.write_json_response(changeset)
self.response.set_status(201)
except (TypeError, ValueError):
self.error(400)
logging.exception('Bad request:')
class ChangesetCommitHandler(handlers.BaseHandler):
"""RESTful handler for VersionControlService.Commit."""
def post(self):
"""POST handler."""
try:
namespace = self.request.get('namespace', None)
save_manifest = self.request.get('save_manifest', 'true') == 'true'
vcs = versions.VersionControlService()
staging_changeset = versions.Changeset(
int(self.request.get('changeset')), namespace=namespace)
force = bool(self.request.get('force', False))
manifest = self.request.POST.get('manifest', None)
if not force and not manifest or force and manifest:
self.error(400)
logging.error('Exactly one of "manifest" or "force" params is required')
return
# If a client has full knowledge of the files uploaded to a changeset,
# the "manifest" param may be given to ensure a strongly consistent
# commit. If given, associate the files to the changeset and finalize it.
if manifest:
manifest = json.loads(manifest)
for path in manifest:
titan_file = files.File(path, changeset=staging_changeset,
namespace=namespace, _internal=True)
staging_changeset.associate_file(titan_file)
staging_changeset.finalize_associated_files()
final_changeset = vcs.commit(
staging_changeset, force=force, save_manifest=save_manifest)
self.write_json_response(final_changeset)
self.response.set_status(201)
except (TypeError, ValueError):
self.error(400)
logging.exception('Bad request:')
ROUTES = (
('/_titan/files/versions/changeset', ChangesetHandler),
('/_titan/files/versions/changeset/commit', ChangesetCommitHandler),
)
application = webapp2.WSGIApplication(ROUTES, debug=False)
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x_tef2/profiles.py | parkermac/LiveOcean | 4 | 126096 | <filename>x_tef2/profiles.py
"""
Make profiles of salt or density vs. depth at each section.
Goal is to explore time evolution of ds/dx at places like Admiralty Inlet.
"""
# setup
import netCDF4 as nc
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
import scipy.stats as stats
import pandas as pd
import os
import sys
sys.path.append(os.path.abspath('../alpha'))
import Lfun
import zfun
Ldir = Lfun.Lstart()
indir0 = Ldir['LOo'] + 'tef2/'
# choose the tef extraction to process
#item = Lfun.choose_item(indir0)
year = 2017
item = 'cas6_v3_lo8b_'+str(year)+'.01.01_'+str(year)+'.12.31'
indir0 = indir0 + item + '/'
indir = indir0 + 'extractions/'
sect_list_raw = os.listdir(indir)
sect_list_raw.sort()
sect_list = [item for item in sect_list_raw if ('.nc' in item)]
testing = True
if testing:
sect_list = ['ai1.nc','ai2.nc','ai3.nc']
else:
pass
outdir = indir0 + 'profiles/'
Lfun.make_dir(outdir)
if testing:
plt.close('all')
# create z edges and bins
zedges = np.linspace(-100, 0, 26) # original was 1001 used 5001 for Willapa
zbins = zedges[:-1] + np.diff(zedges)/2
for fn in sect_list:
if testing:
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
print(fn)
df = pd.DataFrame(index=zbins , columns = range(1,13,1))
# load fields
ds = nc.Dataset(indir + fn)
s = ds['salt'][:]
mask = s[0,:,:].squeeze().mask
th = ds['temp'][:]
z0 = ds['z0'][:]
ot = ds['ocean_time'][:]
gtagex = ds.gtagex
ds0 = ds.date_string0
ds1 = ds.date_string1
ds.close()
dt = []
mo = []
for t in ot:
this_dt = Lfun.modtime_to_datetime(t)
this_mo = this_dt.month
dt.append(this_dt)
mo.append(this_mo)
mo[-1] = 12 # fix last point
mo = np.array(mo)
if mask.size > 1:
z0m = z0[~mask].data
else:
z0m = z0.flatten()
for mm in (range(1,13,1)):
i0 = np.argwhere(mo==mm)[0][0]
i1 = np.argwhere(mo==mm)[-1][0]
si = s[i0:i1+1,:,:].mean(axis=0)
if mask.size > 1:
sm = si[~mask].data
else:
sm = si.flatten()
ss = stats.binned_statistic(z0m, sm, statistic='mean', bins=zedges)
sb = ss.statistic
df.loc[:,mm] = sb
if testing:
df.plot(ax=ax, title=fn)
df.to_pickle(outdir + fn.replace('.nc','.p'))
plt.show()
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forte/multipack_pipeline.py | huzecong/forte | 0 | 186819 | <reponame>huzecong/forte
import logging
from typing import Dict, List, Optional
import yaml
from texar.torch import HParams
from forte.base_pipeline import BasePipeline
from forte.data.multi_pack import MultiPack
from forte.data.selector import Selector
from forte.utils import get_class
logger = logging.getLogger(__name__)
__all__ = [
"MultiPackPipeline"
]
# pylint: disable=attribute-defined-outside-init
class MultiPackPipeline(BasePipeline[MultiPack]):
"""
The pipeline consists of a list of predictors.
"""
def __init__(self):
super().__init__()
self._selectors: List[Selector] = []
@property
def selectors(self):
return self._selectors
def init_from_config(self, configs: Dict):
"""
Parse the configuration sections from the input config,
into a list of [processor, config]
Initialize the pipeline with the configurations
"""
if "Reader" not in configs or configs["Reader"] is None:
raise KeyError('No reader in the configuration')
reader_config = configs["Reader"]
reader, _ = create_class_with_kwargs(
class_name=reader_config["type"],
class_args=reader_config.get("kwargs", {}),
)
self.set_reader(reader)
# HParams cannot create HParams from the inner dict of list
if "Processors" in configs and configs["Processors"] is not None:
for processor_configs in configs["Processors"]:
p, processor_hparams = create_class_with_kwargs(
class_name=processor_configs["type"],
class_args=processor_configs.get("kwargs", {}),
h_params=processor_configs.get("hparams", {}),
)
selector_hparams = processor_hparams.selector
selector_class = get_class(selector_hparams['type'])
selector_kwargs = selector_hparams["kwargs"]
selector = selector_class(**selector_kwargs)
self.add_processor(p, processor_hparams, selector)
self.initialize()
def create_class_with_kwargs(
class_name: str, class_args: Dict, h_params: Optional[Dict] = None):
cls = get_class(class_name)
if not class_args:
class_args = {}
obj = cls(**class_args)
p_params: Dict = {}
if h_params is not None and \
"config_path" in h_params and \
h_params["config_path"] is not None:
filebased_hparams = yaml.safe_load(open(h_params["config_path"]))
else:
filebased_hparams = {}
p_params.update(filebased_hparams)
if h_params is not None:
p_params.update(h_params.get("overwrite_configs", {}))
default_processor_hparams = cls.default_hparams()
processor_hparams = HParams(p_params,
default_processor_hparams)
return obj, processor_hparams
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13,
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13,
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13,
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565,
298,
29918,
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338,
451,
6213,
29901,
13,
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282,
29918,
7529,
29889,
5504,
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29882,
29918,
7529,
29889,
657,
703,
957,
3539,
29918,
2917,
29879,
613,
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876,
13,
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2322,
29918,
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29918,
29882,
7529,
353,
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29879,
29889,
4381,
29918,
29882,
7529,
580,
13,
13,
1678,
21433,
29918,
29882,
7529,
353,
379,
9629,
29898,
29886,
29918,
7529,
29892,
13,
462,
18884,
2322,
29918,
26482,
29918,
29882,
7529,
29897,
13,
13,
1678,
736,
5446,
29892,
21433,
29918,
29882,
7529,
13,
2
] |
vjezba1/UDP_client.py | vmilkovic/primjena-blockchain-tehnologije | 0 | 133187 | <filename>vjezba1/UDP_client.py
import socket
HOST = '127.0.0.1'
PORT = 6444
poruka = input("unesi: ")
poruka = poruka.encode()
with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
s.sendto(poruka, (HOST, PORT))
data = s.recv(1024)
print('Received from server ', data)
| [
1,
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29958,
29894,
1324,
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13,
1678,
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877,
29816,
515,
1923,
13420,
848,
29897,
13,
2
] |
code/Built-in-Types/set.py | zhangsucheng/Python-pracive | 0 | 179481 | set1 = set()
set1.add(1)
set2 = {1, 2, 3}
print(set1)
print(set1 | set2)
print(set1 & set2)
| [
1,
731,
29896,
353,
731,
580,
13,
842,
29896,
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1202,
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842,
29896,
669,
731,
29906,
29897,
13,
2
] |
timeeval_experiments/algorithms/mstamp.py | HPI-Information-Systems/TimeEval | 2 | 190720 | <reponame>HPI-Information-Systems/TimeEval
from durations import Duration
from typing import Any, Dict, Optional
from timeeval import Algorithm, TrainingType, InputDimensionality
from timeeval.adapters import DockerAdapter
from timeeval.params import ParameterConfig
import numpy as np
from timeeval.utils.window import ReverseWindowing
# post-processing for left_stampi
def post_mstamp(scores: np.ndarray, args: dict) -> np.ndarray:
window_size = args.get("hyper_params", {}).get("anomaly_window_size", 50)
return ReverseWindowing(window_size=window_size).fit_transform(scores)
_mstamp_parameters: Dict[str, Dict[str, Any]] = {
"anomaly_window_size": {
"defaultValue": 50,
"description": "Size of the sliding windows",
"name": "anomaly_window_size",
"type": "int"
},
"random_state": {
"defaultValue": 42,
"description": "Seed for the random number generator",
"name": "random_state",
"type": "int"
}
}
def mstamp(params: ParameterConfig = None, skip_pull: bool = False, timeout: Optional[Duration] = None) -> Algorithm:
return Algorithm(
name="mSTAMP",
main=DockerAdapter(
image_name="registry.gitlab.hpi.de/akita/i/mstamp",
skip_pull=skip_pull,
timeout=timeout,
group_privileges="akita",
),
preprocess=None,
postprocess=post_mstamp,
param_schema=_mstamp_parameters,
param_config=params or ParameterConfig.defaults(),
data_as_file=True,
training_type=TrainingType.UNSUPERVISED,
input_dimensionality=InputDimensionality("multivariate")
)
| [
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29918,
12531,
537,
29922,
4290,
16142,
8180,
537,
703,
4713,
27432,
403,
1159,
13,
1678,
1723,
13,
2
] |
test/integration/objectstore/test_mixed_store_by.py | beatrizserrano/galaxy | 0 | 85116 | <reponame>beatrizserrano/galaxy<gh_stars>0
"""Integration tests for mixing store_by."""
import os
import re
import string
from galaxy.util import is_uuid
from ._base import (
BaseObjectStoreIntegrationTestCase,
files_count,
)
DISTRIBUTED_OBJECT_STORE_CONFIG_TEMPLATE = string.Template(
"""<?xml version="1.0"?>
<object_store type="distributed" id="primary" order="0">
<backends>
<backend id="files1" type="disk" weight="1" store_by="uuid">
<files_dir path="${temp_directory}/files1"/>
<extra_dir type="temp" path="${temp_directory}/tmp1"/>
<extra_dir type="job_work" path="${temp_directory}/job_working_directory1"/>
</backend>
<backend id="files2" type="disk" weight="1" store_by="id">
<files_dir path="${temp_directory}/files2"/>
<extra_dir type="temp" path="${temp_directory}/tmp2"/>
<extra_dir type="job_work" path="${temp_directory}/job_working_directory2"/>
</backend>
</backends>
</object_store>
"""
)
TEST_INPUT_FILES_CONTENT = "1 2 3"
class MixedStoreByObjectStoreIntegrationTestCase(BaseObjectStoreIntegrationTestCase):
# setup by _configure_object_store
files1_path: str
files2_path: str
@classmethod
def handle_galaxy_config_kwds(cls, config):
cls._configure_object_store(DISTRIBUTED_OBJECT_STORE_CONFIG_TEMPLATE, config)
def test_both_types(self):
"""Test each object store configures files correctly."""
i = 0
with self.dataset_populator.test_history() as history_id:
# Loop breaks once each object store has at least once file of each type.
while True:
hda = self.dataset_populator.new_dataset(history_id, content=TEST_INPUT_FILES_CONTENT, wait=True)
content = self.dataset_populator.get_history_dataset_content(history_id, hda["id"])
assert content.strip() == TEST_INPUT_FILES_CONTENT
files1_count = files_count(self.files1_path)
files2_count = files_count(self.files2_path)
if files1_count and files2_count:
break
i += 1
if i > 50:
raise Exception(
"Problem with logic of test, randomly each object store should have at least one file by now"
)
def strip_to_id(x):
match = re.match(r"dataset_(.*)\.dat", os.path.basename(x))
assert match
return match.group(1)
files1_paths = [strip_to_id(p) for p in _get_datasets_files_in_path(self.files1_path)]
files2_paths = [strip_to_id(p) for p in _get_datasets_files_in_path(self.files2_path)]
for files1_path in files1_paths:
assert is_uuid(files1_path)
for files2_path in files2_paths:
assert files2_path.isdigit()
def _get_datasets_files_in_path(directory):
files = []
for path, _, filename in os.walk(directory):
for f in filename:
if f.endswith(".dat"):
files.append(os.path.join(path, f))
return files
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] |
src/pyrin/cli/core/template/__init__.py | wilsonGmn/pyrin | 0 | 41891 | <filename>src/pyrin/cli/core/template/__init__.py
# -*- coding: utf-8 -*-
"""
cli core template package.
"""
from pyrin.packaging.base import Package
class CLICoreTemplatePackage(Package):
"""
cli core template package class.
"""
NAME = __name__
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jreast.py | setomits/jreaststatus | 0 | 48265 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from lxml import html
class JREast:
URL = 'http://traininfo.jreast.co.jp/train_info/{}.aspx'
lines = {
'kanto': ('東海道線', '京浜東北線', '横須賀線', '南武線', '横浜線',
'伊東線', '相模線', '鶴見線',
'宇都宮線', '高崎線', '京浜東北線', '埼京線', '川越線',
'武蔵野線', '上越線', '信越本線', '吾妻線', '烏山線',
'八高線', '日光線', '両毛線',
'中央線快速電車', '中央・総武各駅停車', '中央本線',
'武蔵野線', '五日市線', '青梅線', '八高線', '小海線',
'常磐線', '常磐線快速電車', '常磐線各駅停車', '水郡線',
'水戸線',
'総武快速線', '総武本線', '中央・総武各駅停車', '京葉線',
'武蔵野線', '内房線', '鹿島線', '久留里線', '外房線',
'東金線', '成田線',
'山手線',
'上野東京ライン', '湘南新宿ライン'),
'tohoku': ('羽越本線', '奥羽本線', '奥羽本線（山形線）', '常磐線',
'仙山線', '仙石線', '仙石東北ライン', '東北本線',
'磐越西線', '左沢線', '石巻線', '大船渡線',
'大船渡線ＢＲＴ', '大湊線', '男鹿線', '釜石線', '北上線',
'気仙沼線', '気仙沼線ＢＲＴ', '五能線', '水郡線',
'田沢湖線', '只見線', '津軽線', '八戸線', '花輪線',
'磐越東線', '山田線', '米坂線', '陸羽西線', '陸羽東線'),
'shinetsu': ('羽越本線', '信越本線', '上越線', '篠ノ井線', '中央本線',
'白新線', '磐越西線', '飯山線', '越後線', '大糸線',
'小海線', '只見線', '弥彦線', '米坂線')
}
def _extract_status(self, th):
s = {}
td = th.getnext()
s['status'] = td.xpath('img')[0].attrib['alt']
if s['status'] != '平常運転':
td = td.getnext()
s['publishedAt'] = td.text.strip()
td = th.getparent().getnext().xpath('td[@class="cause"]')[0]
s['detail'] = td.text.strip()
return s
def status(self, line):
self.area = None
for area in JREast.lines:
for l in JREast.lines[area]:
if l == line:
self.area = area
break
if self.area:
break
m = {'area': self.area, 'line': line, 'statuses': []}
if self.area:
url = JREast.URL.format(self.area)
else:
return m
dom = html.parse(url)
th = None
for node in dom.xpath('//th[@class="text-tit-xlarge"]'):
if node.text.strip() == line:
m['statuses'].append(self._extract_status(node))
return m
if __name__ == '__main__':
import pprint
jr = JREast()
pprint.pprint(jr.status('山田線'))
pprint.pprint(jr.status('京葉線'))
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] |
server/util/stringutil.py | rleir/MediaCloud-Web-Tools | 0 | 193733 | <filename>server/util/stringutil.py
import logging
from datetime import datetime
from server import mc
logger = logging.getLogger(__name__)
def ids_from_comma_separated_str(comma_separated_string):
id_list = []
if len(comma_separated_string) > 0:
id_list = [int(cid) for cid in comma_separated_string.split(",") if len(cid) > 0]
return id_list
def trim_solr_date(date_str):
return datetime.strptime(date_str, mc.SENTENCE_PUBLISH_DATE_FORMAT).strftime("%Y-%m-%d")
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smartsheet/models/alternate_email.py | Funtimes-Smarts/Python-import-Smart | 0 | 1613839 | # pylint: disable=C0111,R0902,R0904,R0912,R0913,R0915,E1101
# Smartsheet Python SDK.
#
# Copyright 2016 Smartsheet.com, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"): you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import absolute_import
from ..types import TypedList
from ..util import prep
from datetime import datetime
import json
import logging
import six
class AlternateEmail(object):
"""Smartsheet AlternateEmail data model."""
def __init__(self, props=None, base_obj=None):
"""Initialize the AlternateEmail model."""
self._base = None
if base_obj is not None:
self._base = base_obj
self._pre_request_filter = None
self._log = logging.getLogger(__name__)
self._confirmed = False
self._email = None
self.__id = None
if props:
# account for alternate variable names from raw API response
if 'confirmed' in props:
self.confirmed = props['confirmed']
if 'email' in props:
self.email = props['email']
# read only
if 'id' in props:
self._id = props['id']
# requests package Response object
self.request_response = None
self.__initialized = True
def __getattr__(self, key):
if key == 'id':
return self._id
else:
raise AttributeError(key)
@property
def confirmed(self):
return self._confirmed
@confirmed.setter
def confirmed(self, value):
if isinstance(value, bool):
self._confirmed = value
@property
def email(self):
return self._email
@email.setter
def email(self, value):
if isinstance(value, six.string_types):
self._email = value
@property
def _id(self):
return self.__id
@_id.setter
def _id(self, value):
if isinstance(value, six.integer_types):
self.__id = value
@property
def pre_request_filter(self):
return self._pre_request_filter
@pre_request_filter.setter
def pre_request_filter(self, value):
self._pre_request_filter = value
def to_dict(self, op_id=None, method=None):
obj = {
'confirmed': prep(self._confirmed),
'email': prep(self._email),
'id': prep(self.__id)}
return self._apply_pre_request_filter(obj)
def _apply_pre_request_filter(self, obj):
if self.pre_request_filter == 'add_alternate_email':
permitted = ['email']
all_keys = list(obj.keys())
for key in all_keys:
if key not in permitted:
self._log.debug(
'deleting %s from obj (filter: %s)',
key, self.pre_request_filter)
del obj[key]
return obj
def to_json(self):
return json.dumps(self.to_dict(), indent=2)
def __str__(self):
return json.dumps(self.to_dict())
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] |
backend/Layer/layer/validation/param_check.py | cvc-Fujii/line-api-use-case-reservation-Restaurant | 8 | 160324 | <gh_stars>1-10
import datetime
class ParamCheck():
"""
パラメータチェックを実施するクラス。
"""
def __init__(self):
"""
初期化を実施する。
"""
pass
def check_required(self, columns, column_name):
"""
必須チェックを実施する。
Parameters
----------
columns : obj
必須チェックをする項目
column_name: str
項目名
Returns
-------
str
エラー内容
"""
columns_replaced = str(columns).replace(' ', '')
if columns is None or not columns_replaced:
return '必須入力エラー:' + column_name
def check_length(self, columns, column_name, min, max):
"""
文字数チェックを実施する。
Parameters
----------
columns : obj
文字数チェックをする項目
column_name: str
項目名
min : int
最小桁数
max : int
最大桁数
Returns
-------
str
エラー内容
"""
if type(columns) is int:
columns = str(columns)
if min and int(min) > len(columns):
return f'文字数エラー（最小文字数[{min}]未満）:{column_name}'
if max and int(max) < len(columns):
return f'文字数エラー（最大文字数[{max}]超過）:{column_name}'
def check_int(self, columns, column_name):
"""
int型チェックを実施する。
Parameters
----------
columns : obj
int型チェックをする項目
column_name: str
項目名
Returns
-------
str
エラー内容
"""
if isinstance(columns, int):
columns_replaced = True
else:
columns_replaced = columns.isnumeric()
if columns is None or not columns_replaced:
return 'int型チェックエラー:' + column_name
def check_year_month(self, columns, column_name):
"""
年月の形式をチェックする。
Parameters
----------
columns : obj
形式チェックをする項目
column_name: str
項目名
Returns
-------
str
エラー内容
"""
# 日付のハイフンとスラッシュ区切りに対応
columns_replaced = columns.replace('-', '').replace('/', '')
try:
datetime.datetime.strptime(columns_replaced, "%Y%m")
except ValueError:
return f'年月形式エラー : {column_name}({columns})'
def check_year_month_day(self, columns, column_name):
"""
年月日の形式をチェックする。
Parameters
----------
columns : obj
形式チェックをする項目
column_name: str
項目名
Returns
-------
str
エラー内容
"""
# 日付のハイフンとスラッシュ区切りに対応
columns_replaced = columns.replace('-', '').replace('/', '')
try:
datetime.datetime.strptime(columns_replaced, "%Y%m%d")
except ValueError:
return f'年月日形式エラー : {column_name}({columns})'
def check_time_format(self, columns, column_name, time_format):
"""
時間の形式をチェックする。
Parameters
----------
columns : obj
形式チェックをする項目
column_name: str
項目名
time_format: str
チェックしたいフォーマット
Returns
-------
str
エラー内容
"""
# 日付のハイフンとスラッシュ区切りに対応
columns_replaced = columns.replace(':', '')
try:
datetime.datetime.strptime(columns_replaced, time_format)
except ValueError:
return f'時間形式エラー : {column_name}({columns})'
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] |
tests/networks/test_enas_net.py | marsggbo/hyperbox | 1 | 1602836 |
import torch
from hyperbox.networks.enas import ENASMicroNetwork
from hyperbox.mutator.random_mutator import RandomMutator
if __name__ == '__main__':
net = ENASMicroNetwork(
num_layers=2,
num_nodes=3,
).cuda()
m = RandomMutator(net)
m.reset()
x = torch.rand(2,3,64,64).cuda()
output = net(x)
print(output.shape)
pass | [
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] |
Anylink-server/add_user.py | orike122/anylink | 3 | 57188 | from config import Configuration
if __name__ == "__main__":
config = Configuration("/home/orikeidar01/config.json", "anylink")
config.database.add_user("<EMAIL>",
"ECD71870D1963316A97E3AC3408C9835AD8CF0F3C1BC703527C30265534F75AE", "anylink")
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symbols/grucnn.py | gkfthddk/keras | 0 | 145928 | <reponame>gkfthddk/keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, Reshape, Concatenate
from keras.layers import Input, Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers import SimpleRNN, LSTM, GRU
from keras.optimizers import SGD
import cv2, numpy as np
def get_symbol(input_shape1,input_shape2,weights_path=None):
input1 = Input(shape=input_shape1)
input2 = Input(shape=input_shape2)
# group 1
padd1_1=ZeroPadding2D((1,0))(input1)
conv1_1=Conv2D(64, (3, 1), activation='relu')(padd1_1)
padd1_2=ZeroPadding2D((0,1))(conv1_1)
conv1_2=Conv2D(64, (1, 3), activation='relu')(padd1_2)
pool1=MaxPooling2D((2,2), strides=(2,2))(conv1_2)
# group 2
padd2_1=ZeroPadding2D((1,0))(pool1)
conv2_1=Conv2D(128, (3, 1), activation='relu')(padd2_1)
padd2_2=ZeroPadding2D((0,1))(conv2_1)
conv2_2=Conv2D(128, (1, 3), activation='relu')(padd2_2)
padd2_3=ZeroPadding2D((1,0))(conv2_2)
conv2_3=Conv2D(128, (3, 1), activation='relu')(padd2_3)
padd2_4=ZeroPadding2D((0,1))(conv2_3)
conv2_4=Conv2D(128, (1, 3), activation='relu')(padd2_4)
pool2=MaxPooling2D((2,2), strides=(2,2))(conv2_4)
# group 3
flat1=Flatten()(pool2)
dens1=Dense(1024, activation='relu')(flat1)
drop1=Dropout(0.5)(dens1)
rnn=GRU(units=128,dropout=0.2,return_sequences=True)(input2)
flatr=Flatten()(rnn)
densr=Dense(256,activation='relu')(flatr)
dropr=Dropout(0.5)(densr)
concat=Concatenate(axis=1)([drop1,dropr])
dens2=Dense(1024, activation='relu')(concat)
drop2=Dropout(0.5)(dens2)
out=Dense(2,activation='softmax')(drop2)
if weights_path:
model.load_weights(weights_path)
return Model([input1,input2],out)
def rc():
return "rc"
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speedysvc/web_monitor/WebServiceManager.py | mcyph/shmrpc | 4 | 50905 | import sys
from datetime import datetime
LColours = [
'red', 'green', 'blue',
'purple', 'orange', 'brown',
'pink', 'cyan', 'magenta',
'yellow'
]
class WebServiceManager:
def __init__(self, jinja2_env):
self.jinja2_env = jinja2_env
self.DServices = {} # TODO: Replace me, directly using the Services instance!
def set_services(self, services):
self.services = services
def set_logger_parent(self, logger_parent):
"""
Set the logger parent (a FIFOJSONLog instance)
:param logger_parent:
:return:
"""
# TODO: Move this somewhere more appropriate!
self.logger_parent = logger_parent
def iter_services_by_name(self):
"""
:return:
"""
for service in sorted(
self.DServices.values(),
key=lambda service: service.name.lower()
):
yield service
def iter_services_by_port(self):
"""
:return:
"""
for service in sorted(
self.DServices.values(),
key=lambda service: service.port
):
yield service
def iter_service_ports(self):
"""
:return:
"""
for port in sorted(self.DServices):
yield port
#=====================================================================#
# Manage Web Services #
#=====================================================================#
def add_service(self, service):
"""
:param service:
:return:
"""
self.DServices[service.original_server_methods.port] = service
def remove_service(self, port):
"""
:param port:
:return:
"""
del self.DServices[port]
def start_service(self, port):
"""
:param port:
:return:
"""
self.services.start_service_by_port(port)
def stop_service(self, port):
"""
:param port:
:return:
"""
self.services.stop_service_by_port(port)
#=====================================================================#
# Get All Service Status/Stats #
#=====================================================================#
def get_overall_log(self, offset=None):
"""
Get the "overall" log for all services
:param offset:
:return:
"""
offset, LHTML = self.logger_parent.get_html_log(
offset=offset
)
return offset, '<br>'.join(LHTML) + ('<br>' if LHTML else '')
def get_overall_table_html(self, add_links=True):
"""
:param add_links:
:return:
"""
return self.jinja2_env.from_string(
'{% from "service_macros.html" import service_status_table %}\n'
'{{ service_status_table(LServiceTable, add_links) }}'
).render(
LServiceTable=self.get_overall_service_table(),
add_links=add_links
)
def get_overall_service_table(self):
"""
:return:
"""
L = []
for service in self.iter_services_by_name():
L.append(self.get_D_service_info(service.original_server_methods.port))
return L
def get_overall_service_methods(self, max_methods=15):
"""
Get a summary of the methods from all services, sorted
by total time the method has taken over all calls
:param max_methods:
:return:
"""
L = []
for service in self.iter_services_by_name():
DMethodStats = service.get_D_method_stats()
L.extend([
(
service.original_server_methods.port,
service.original_server_methods.name,
method_name,
D['num_calls'],
D['avg_call_time'],
D['total_time'])
for method_name, D
in DMethodStats.items()
])
L.sort(key=lambda i: -i[-1])
if max_methods is not None:
L = L[:max_methods]
return L
def get_overall_service_methods_html(self, max_methods=15):
L = self.get_overall_service_methods(max_methods)
return self.jinja2_env.from_string(
'{% from "service_macros.html" import overall_method_stats_html %}\n'
'{{ overall_method_stats_html(LMethodStats) }}'
).render(
LMethodStats=L
)
#=====================================================================#
# Get Single Service Status/Stats #
#=====================================================================#
def get_D_service_info(self, port, console_offset=None):
"""
:param port:
:param console_offset:
:return:
"""
service = self.DServices[port]
stsd = service.service_time_series_data
recent_values = stsd.get_recent_values()
offset, LHTML = service.fifo_json_log.get_html_log(
offset=console_offset
)
method_stats_html = self.get_method_stats_html(port)
D = {
"graphs": self.__get_D_graphs(recent_values),
"console_text": '\n'.join(LHTML),
"console_offset": offset,
"method_stats_html": method_stats_html
}
D.update(self.__get_D_table_info(port, recent_values))
D["table_html"] = self.__get_table_html(D)
return D
def get_method_stats_html(self, port):
"""
:param port:
:return:
"""
DMethodStats = self.DServices[port].get_D_method_stats()
LMethodStats = [
(method_name, D['num_calls'], D['avg_call_time'], D['total_time'])
for method_name, D
in DMethodStats.items()
]
return self.jinja2_env.from_string(
'{% from "service_macros.html" import method_stats_html %}\n'
'{{ method_stats_html(LMethodStats) }}'
).render(
LMethodStats=LMethodStats
)
def __get_D_table_info(self, port, recent_values):
"""
:param port:
:param recent_values:
:return:
"""
service = self.DServices[port]
return {
"port": port,
"name": service.original_server_methods.name,
"bound_to_tcp": service.tcp_bind,
"status": service.get_service_status(),
'workers': len(service.LPIDs), # TODO: MAKE BASED ON INTERFACE, NOT IMPLEMENTATION!
'physical_mem': recent_values[-1]['physical_mem'] // 1024 // 1024,
# We'll average over 3 iterations, as this can spike pretty quickly.
# Note that recent_values is actually reversed for displaying on the graph rtl
'cpu': round(sum([recent_values[-x]['cpu_usage_pc'] for x in range(3)]) / 3)
}
def __get_D_graphs(self, recent_values):
"""
:param recent_values:
:return:
"""
labels = [
datetime.utcfromtimestamp(D['timestamp']).strftime(
'%H:%M:%S' # %m/%d
) for D in recent_values
]
return {
"labels": labels,
"ram": self.__get_data_for_keys(
recent_values,
*(
('shared_mem', 'physical_mem', 'virtual_mem')
if sys.platform != 'win32'
else ('physical_mem', 'virtual_mem')
),
divisor=1024 * 1024
),
"io": self.__get_data_for_keys(
recent_values,
'io_read', 'io_written',
divisor=1024 * 1024
),
"cpu": self.__get_data_for_keys(
recent_values,
'cpu_usage_pc'
),
}
def __get_data_for_keys(self, values, *keys, divisor=None):
"""
:param values:
:param keys:
:param divisor:
:return:
"""
LData = []
for x, key in enumerate(keys):
LOut = []
for D in values:
i = D[key]
if divisor is not None:
i //= divisor
LOut.append(i)
LData.append([key, LOut, LColours[x]])
return LData
def __get_table_html(self, DService):
"""
:param DService:
:return:
"""
return self.jinja2_env.from_string(
'{% from "service_macros.html" import service_status_table %}\n'
'{{ service_status_table([DService]) }}'
).render(
DService=DService
)
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2
] |
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