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shelter/migrations/0024_auto_20201018_2225.py | kermox/schronisko-krakow | 1 | 163450 | # Generated by Django 3.0.5 on 2020-10-18 22:25
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('shelter', '0023_animal_adopted_by'),
]
operations = [
migrations.AlterField(
model_name='animal',
name='adopted_by',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='shelter.PetOwner'),
),
]
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tests/test_pycolor.py | edonyzpc/toolkitem | 3 | 30877 | from __future__ import print_function
import sys
sys.path.append('./')
from colorprinter.pycolor import PyColor
from colorprinter.pycolor import cprint
@PyColor('ured')
def printer(string):
a = 1
b = 2
print(str((a + b)**4) + string)
class TestClass(object):
def test_pycolor(self):
printer('edony')
def test_cprint(self):
cprint('ugreen', 'hello edony')
def test_setformat(self):
py_color = PyColor('green')
py_color.format = 'ucyan'
cprint(py_color.format, 'this is test')
def test_disableformat(self):
py_color = PyColor('ured')
cprint(py_color.format, 'this is test')
py_color.disable()
cprint(py_color.format, 'this is disable')
assert py_color.format is ''
def test_colorstr(self):
str1 = 'this is a test'
py_color = PyColor('green')
str2 = py_color.colorstr(str1)
str3 = py_color.colorstr(str1, 'red')
assert((str2 == '\033[0;32;40mthis is a test\033[0m') and
(str3 == '\033[0;31;40mthis is a test\033[0m'))
#if __name__ == "__main__":
# cprint('ugreen', 'hello edony')
# printer('edony')
# py_color = PyColor('green')
# py_color.format = 'ucyan'
# print(py_color.format)
# cprint(py_color.format, 'this is test')
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chat/models.py | nim65s/tutochan | 0 | 177902 | from django.conf import settings
from django.db import models
from ndh.models import Links, NamedModel, TimeStampedModel
class Chan(TimeStampedModel, NamedModel, Links):
pass
class Message(TimeStampedModel):
chan = models.ForeignKey(Chan, on_delete=models.CASCADE)
user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)
message = models.TextField()
def __str__(self):
return f'{self.chan} | {self.created:%x %X} - {self.user}: {self.message}'
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rbldap/accounts/uid.py | butlerx/rb-ldap-python | 0 | 196636 | <filename>rbldap/accounts/uid.py<gh_stars>0
"""uid functions"""
from .clients import LDAPConnection
async def find_available_uid(ldap_conn: LDAPConnection) -> int:
"""
Seach RB ldap for next available uid
Args:
ldap_conn: LDAP connection to use for searching
Returns:
next avilable uid
"""
res = await ldap_conn.search(
"ou=accounts,o=redbrick",
"(objectclass=posixAccount)",
attributes=["uidNumber"],
)
uids = [entry["attributes"]["uidNumber"][0] for entry in res]
uids.sort()
return int(uids[-1]) + 1
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tests/plugins/__init__.py | kyldery/streamlink | 1 | 166661 | from streamlink.plugin import Plugin
generic_negative_matches = [
"http://example.com/",
"https://example.com/",
"https://example.com/index.html",
]
class PluginCanHandleUrl:
__plugin__ = None
# A list of URLs that should match any of the plugin's URL regexes.
# ["https://foo", "https://bar"]
should_match = []
# A list of URL+capturegroup tuples, where capturegroup can be a dict (re.Match.groupdict()) or a tuple (re.Match.groups()).
# URLs defined in this list automatically get appended to the should_match list.
# Values in capturegroup dictionaries that are None get ignored when comparing and can be omitted in the test fixtures.
# [("https://foo", {"foo": "foo"}), ("https://bar", ("bar", None))]
should_match_groups = []
# A list of URLs that should not match any of the plugin's URL regexes.
# ["https://foo", "https://bar"]
should_not_match = []
def test_class_setup(self):
assert issubclass(self.__plugin__, Plugin), "Test has a __plugin__ that is a subclass of streamlink.plugin.Plugin"
assert len(self.should_match + self.should_match_groups) > 0, "Test has at least one positive URL"
def test_matchers(self):
should_match = self.should_match + [url for url, groups in self.should_match_groups]
assert all(
any(
matcher.pattern.match(url)
for url in should_match
)
for matcher in self.__plugin__.matchers
), "All plugin matchers should match"
# parametrized dynamically via conftest.py
def test_can_handle_url_positive(self, url):
assert any( # pragma: no branch
matcher.pattern.match(url)
for matcher in self.__plugin__.matchers
), "URL matches"
# parametrized dynamically via conftest.py
def test_can_handle_url_negative(self, url):
assert not any( # pragma: no branch
matcher.pattern.match(url)
for matcher in self.__plugin__.matchers
), "URL does not match"
# parametrized dynamically via conftest.py
def test_capture_groups(self, url, groups):
for matcher in self.__plugin__.matchers:
match = matcher.pattern.match(url)
if match: # pragma: no branch
res = (
# ignore None values in capture group dicts
{k: v for k, v in match.groupdict().items() if v is not None}
if type(groups) is dict else
# capture group tuples
match.groups()
)
assert res == groups, "URL capture groups match"
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gtk/ch6_header.py | ykyang/org.allnix.python | 0 | 192290 | """
https://python-gtk-3-tutorial.readthedocs.io/en/latest/layout.html#headerbar
@author: <NAME> <<EMAIL>>
"""
import gi
gi.require_version("Gtk", "3.0")
from gi.repository import Gtk, Gio
class HeaderBarWindow(Gtk.Window):
def __init__(self):
Gtk.Window.__init__(self, title="HeaderBar Demo")
self.set_border_width(10)
self.set_default_size(400, 200)
hb = Gtk.HeaderBar()
hb.set_show_close_button(True)
hb.props.title = "HeaderBar example"
self.set_titlebar(hb)
button = Gtk.Button()
icon = Gio.ThemedIcon(name="mail-send-receive-symbolic")
image = Gtk.Image.new_from_gicon(icon, Gtk.IconSize.BUTTON)
button.add(image)
hb.pack_end(button)
box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL)
Gtk.StyleContext.add_class(box.get_style_context(), "linked")
button = Gtk.Button()
button.add(
Gtk.Arrow(arrow_type=Gtk.ArrowType.LEFT, shadow_type=Gtk.ShadowType.NONE)
)
box.add(button)
button = Gtk.Button()
button = Gtk.Button.new_from_icon_name("pan-end-symbolic", Gtk.IconSize.MENU)
box.add(button)
hb.pack_start(box)
self.add(Gtk.TextView())
win = HeaderBarWindow()
win.connect("destroy", Gtk.main_quit)
win.show_all()
Gtk.main() | [
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OverFitting Simulation.py | escha2019/python_codes | 0 | 71767 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import normalize
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
class overfitting:
def __init__(self):
pass
def TrueTargetFunct(self):
pass
def AddNoiseTargetFunct(self):
pass
def plot(self, trueTarget, proposedTarget):
pass
def TrainTestSplit(self, X, y, degree, test_size = 0.20):
"""fit the data from exercise 4.2 using linear regression
"""
poly = PolynomialFeatures(degree)
data = poly.fit_transform(X)
data = pd.DataFrame(data, columns = poly.get_feature_names())
temp = poly.get_feature_names()
# Remove Cross interactions
[temp.remove(i) for i in poly.get_feature_names() if len(i.split())==2]
data = data[temp]
X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=test_size, random_state=42)
return X_train, X_test, y_train, y_test
def removeDataPoints(self, data, size):
"""data -- a list of tuples
E.g. [(x,y), (x,y), ...]
"""
return data.remove(np.random.Generator.choice(len(data)-1, replace=False, size = size))
def exerciseFourTwo(self, order, sigma, sampleSize):
"""Generate target function data with random noise using legendre polynomial
PARAMS:
order - order of polynomial
sigma - noise sigma for yn data
sampleSize - # of data points to generate
"""
# generate polynomical coefficient
c = np.random.normal(0, 1, order).reshape(-1, 1)
c = normalize(c, axis=0).flatten()
# generate uniform input/feature vector
x = np.random.uniform(-1, 1, sampleSize)
# generate Yi's using legendre polynomial
y = np.polynomial.legendre.legval(x, c = c)
# add noise to label/target
y = y + sigma*np.random.normal(0, 1, len(y))
return x.reshape(-1, 1), y.reshape(-1, 1)
def fitExercisefourTwo(self, X, y):
"""fit the data from exercise 4.2 using linear regression
"""
# run Regression
reg = LinearRegression().fit(X, y)
return reg
def predict(self, model, X, y):
error = np.mean(np.power(model.predict(X) - y, 2))
return error
def legendrePoly2(self, order, domain):
terms = [1, 1 + domain]
for i in range(2, order+1):
terms.append(((2*i - 1)/i)*domain*terms[i-1] - ((i - 1)/i)*terms[i-2])
return sum(terms), terms
# In[2]:
Qf = range(1, 51)
N = range(20, 125, 5)
sigma = np.arange(0, 2.05, 0.05)
# In[3]:
v = overfitting()
error = []
for leg in Qf:
for n in N:
for sig in sigma:
# generate data
X, y = v.exerciseFourTwo(order=leg, sigma=sig, sampleSize=n)
# train test split
X_train2, X_test2, y_train2, y_test2 = v.TrainTestSplit(X, y, 2)
X_train10, X_test10, y_train10, y_test10 = v.TrainTestSplit(X, y, 10)
# run two models
model2, model10 = v.fitExercisefourTwo(X_train2, y_train2),
v.fitExercisefourTwo(X_train10, y_train10)
# get in-sample error
errorTrainDeg2, errorTrainDeg10 = v.predict(model2, X_train2, y_train2),
v.predict(model10, X_train10, y_train10)
# get out-sample error
errorTestDeg2, errorTestDeg10 = v.predict(model2, X_test2, y_test2),
v.predict(model10, X_test10, y_test10)
# errors dictionary
error.append([errorTrainDeg2, errorTrainDeg10, errorTestDeg2,
errorTestDeg10, leg, n, sig])
pd.DataFrame(error, columns=['g2Train','g10Train', 'g2Test','g10Test',
'degLegredre', 'size', 'sigma']).to_excel("overfittingSimulation.xlsx", index=False)
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613,
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29922,
8824,
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13,
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2
] |
setup.py | gsoft-inc/github-secret-finder | 4 | 1615652 | <gh_stars>1-10
from setuptools import setup, find_packages
setup(
name='github_secret_finder',
version='2.0.0',
description='Script to monitor commits from Github users and organizations for secrets.',
url='https://github.com/gsoft-inc/github-secret-finder',
author='<NAME>',
author_email='<EMAIL>',
license='Apache',
packages=find_packages("src"),
package_dir={"": "src"},
include_package_data=True,
package_data={'github_secret_finder': ['data/*']},
install_requires=[
'unidiff',
'requests',
'detect_secrets',
'sqlitedict'
],
entry_points={
'console_scripts': ['github-secret-finder = github_secret_finder.main:main'],
},
)
| [
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261,
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3396,
29901,
3396,
7464,
13,
1678,
2981,
13,
29897,
13,
2
] |
backend/boards/admin.py | LucasSantosGuedes/App-Gestao | 142 | 108222 | from django.contrib import admin
from .models import Board, List, Item, Label, Comment, Attachment, Notification
admin.site.register(Board)
admin.site.register(List)
admin.site.register(Item)
admin.site.register(Label)
admin.site.register(Comment)
admin.site.register(Attachment)
admin.site.register(Notification)
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] |
ambari-metrics/ambari-metrics-host-monitoring/src/main/python/core/spnego_kerberos_auth.py | zyclove/ambari | 0 | 138588 | <filename>ambari-metrics/ambari-metrics-host-monitoring/src/main/python/core/spnego_kerberos_auth.py
#!/usr/bin/python2
'''
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.
'''
import logging
import httplib
import os
logger = logging.getLogger()
try:
import kerberos
except ImportError:
import krberr as kerberos
logger.warn('import kerberos exception: %s' % str(ImportError))
pass
class SPNEGOKerberosAuth:
def __init__(self):
self.krb_context = None
def authenticate_handshake (self, connection, method, service_url, body, headers, kinit_cmd, klist_cmd):
# kinit to ensure ticket valid
self.execute_kinit(kinit_cmd, klist_cmd)
try:
# Authenticate the client request
response = self.authenticate_client(connection, method, service_url, body, headers)
# Authenticate the response from the server
if response:
self.authenticate_server(response)
return response
finally:
# Clean the client context after the handshake
self.clean_client_context()
pass
def execute_kinit(self, kinit_cmd, klist_cmd):
exit_status = os.system(kinit_cmd)
logger.debug("kinit exit_status: {0}".format(exit_status))
logger.debug(os.system(klist_cmd))
return exit_status
def authenticate_client(self, connection, method, service_url, body, headers):
service = "HTTP@%s" % connection.host.lower()
logger.debug("connection: %s", connection)
logger.debug("service: %s", service)
auth_header = self.get_authorization_header(service)
logger.debug("Authorization: %s" % auth_header)
# Send 2nd HTTP request with authorization header
headers['Authorization'] = auth_header
try:
connection.request(method, service_url, body, headers)
response = connection.getresponse()
except Exception, e:
logger.warn('2nd HTTP request exception from server: %s' % str(e))
return None
pass
if response:
logger.debug("2nd HTTP response from server: retcode = {0}, reason = {1}"
.format(response.status, response.reason))
logger.debug(str(response.read()))
logger.debug("response headers: {0}".format(response.getheaders()))
return response
def get_authorization_header(self, service):
# Initialize the context object for client-side authentication with a service principal
try:
result, self.krb_context = kerberos.authGSSClientInit(service)
if result == -1:
logger.warn('authGSSClientInit result: {0}'.format(result))
return None
except kerberos.GSSError, e:
logger.warn('authGSSClientInit exception: %s' % str(e))
return None
pass
# Process the first client-side step with the context
try:
result = kerberos.authGSSClientStep(self.krb_context, "")
if result == -1:
logger.warn('authGSSClientStep result for authenticate client: {0}'.format(result))
return None
except kerberos.GSSError, e:
logger.warn('authGSSClientStep exception for authenticate client: %s' % str(e))
return None
pass
# Get the client response from the first client-side step
try:
negotiate_value = kerberos.authGSSClientResponse(self.krb_context)
logger.debug("authGSSClientResponse response:{0}".format(negotiate_value))
except kerberos.GSSError, e:
logger.warn('authGSSClientResponse exception: %s' % str(e))
return None
pass
# Build the authorization header
return "Negotiate %s" % negotiate_value
def authenticate_server(self, response):
auth_header = response.getheader('www-authenticate', None)
negotiate_value = self.get_negotiate_value(auth_header)
if negotiate_value == None:
logger.warn('www-authenticate header not found')
# Process the client-side step with the context and the negotiate value from 2nd HTTP response
try:
result = kerberos.authGSSClientStep(self.krb_context, negotiate_value)
if result == -1:
logger.warn('authGSSClientStep result for authenticate server: {0}'.format(result))
except kerberos.GSSError, e:
logger.warn('authGSSClientStep exception for authenticate server: %s' % str(e))
result = -1
pass
return result
def clean_client_context(self):
# Destroy the context for client-side authentication
try:
result = kerberos.authGSSClientClean(self.krb_context)
logger.debug("authGSSClientClean result:{0}".format(result))
except kerberos.GSSError, e:
logger.warn('authGSSClientClean exception: %s' % str(e))
result = -1
pass
return result
def get_hadoop_auth_cookie(self, set_cookie_header):
if set_cookie_header:
for field in set_cookie_header.split(";"):
if field.startswith('hadoop.auth='):
return field
else:
return None
return None
def get_negotiate_value(self, auth_header):
if auth_header:
for field in auth_header.split(","):
key, __, value = field.strip().partition(" ")
if key.lower() == "negotiate":
return value.strip()
else:
return None
return None
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src/os_detection/sequence_generation/sequence_generation.py | Deacllock/OSpy | 0 | 130584 | from scapy.all import *
import time
from os_detection.fingerprints import Result, FingerPrint
from os_detection.sequence_generation.seq import get_seq
from os_detection.sequence_generation.t1 import get_t1
SEQ_START = 22000
ACK_START = 44000
SPORT_START = 63000
TCP_PROBES_TIMES = []
# Send TCP probes with given parameters using scapy
def send_tcp_probe(i, dst, dport, window, options):
seq = SEQ_START + 10 * i
ack = ACK_START + 10 * i
sport = SPORT_START + i
syn = IP(dst=dst) / TCP(sport=sport, dport=dport, flags=0x02, seq=seq,
ack=ack, window=window, options=options)
send(syn)
# Send 6 TCP probes to Generate SEQ, OPS, WIN and TI tests Result.
# Total time shall be 500ms so that we can reliably detect the common 2Hz
# TCP timestamp sequences.
def send_tcp_probes(dst, dport):
timestamp = ('Timestamp', (0xFFFFFFFF, 0))
sack = ('SAckOK', '')
eol = ('EOL', '')
nop = ('NOP', '')
t1 = (1, [('WScale', 10), nop, ('MSS', 1460), timestamp, sack])
t2 = (63, [('MSS', 1400), ('WScale', 0), sack, timestamp, eol])
t3 = (4, [timestamp, nop, nop, ('WScale', 5), nop, ('MSS', 640)])
t4 = (4, [sack, timestamp, ('WScale', 10), eol])
t5 = (16, [('MSS', 536), sack, timestamp, ('WScale', 10), eol])
t6 = (512, [('MSS', 265), sack, timestamp])
tcp_probes = [t1, t2, t3, t4, t5, t6]
t0 = time.time()
i = 0
while (i < len(tcp_probes)):
if (time.time() - t0 >= 0.1 * i):
send_tcp_probe(i, dst, dport, *tcp_probes[i])
TCP_PROBES_TIMES.append(time.time())
i += 1
# Computes OPS Result as explained by nmap.org.
def get_ops(responses):
category, params = 'OPS', {}
opt2char = {
'EOL': 'L',
'NOP': 'N',
'MSS': 'M',
'Timestamp': 'T',
'SAckOK': 'S',
'WScale': 'W'}
for i in range(len(responses)):
p_name, p_val = ('O' + str(i + 1)), ''
if responses[i] is not None:
for opt in responses[i][TCP].options:
p_val += opt2char[opt[0]]
if (opt[0] == 'MSS' or opt[0] == 'WScale'):
p_val += hex(opt[1])[2:].upper()
if (opt[0] == 'Timestamp'):
for ts_val in opt[1]:
p_val += '0' if ts_val == 0 else '1'
params[p_name] = p_val
return Result(category, params)
# Computes WIN Result as explained by nmap.org
def get_win(responses):
category, params = 'WIN', {}
for i in range(len(responses)):
current = 'W' + str(i + 1)
if responses[i] is None:
params[current] = ''
else:
params['W' + str(i + 1)] = hex(responses[i]
[TCP].window)[2:].upper()
return Result(category, params)
# Given a TCP response list, computes SEQ, OPS, WIN and T1 Results.
# Return a Result list.
def parse_tcp_responses(tcp_responses, icmp_responses):
results = []
#results.append(get_seq(tcp_responses, icmp_responses, TCP_PROBES_TIMES))
results.append(get_ops(tcp_responses))
results.append(get_win(tcp_responses))
results.append(get_t1(tcp_responses[0]))
return results
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] |
framework/Decorators/Parallelization.py | rinelson456/raven | 1 | 74557 | <filename>framework/Decorators/Parallelization.py
# Copyright 2017 Battelle Energy Alliance, 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.
"""
Created on March 29, 2020
@author: alfoa
"""
#Internal Modules---------------------------------------------------------------
from utils import importerUtils as im
from utils.utils import Object
#Internal Modules End-----------------------------------------------------------
#External Modules---------------------------------------------------------------
# for internal parallel
## TODO: REMOVE WHEN RAY AVAILABLE FOR WINDOWOS
_remote = None
if im.isLibAvail("ray"):
from ray import remote as _remote
# end internal parallel module
#External Modules End-----------------------------------------------------------
class Parallel(object):
"""
RAVEN parallel decorator
It is used to mask ray parallel remote
decorator and to allow for direct call of
the underlying function (via _function attribute)
i.e. :
- if ray is available and needs to be used,
the decorated function (or class) will need to be called as following:
functionName.remote(*args, **kwargs)
- if ray is available and direct call to the function is needed,
the original function (or class) will need to be called as following:
functionName._function(*args, **kwargs)
- if ray is not available,
the original function (or class) will need to be called as following:
functionName(*args, **kwargs)
"""
def __init__(self):
"""
This is the constructor of the decorator for parallel execution
@ In, None (it uses the _remote global variable)
@ Out, None
"""
self.decorator = _remote
def __call__(self, func):
"""
This method mimic the "decorator" method
@ In, func, FunctionType or Class, the function or class to decorate
@ Out, decorated, FunctionType, or Class, the decorated function or class
"""
if self.decorator is None:
# Return the function decorated with a wrapper
# this is basically not decarate but we keep the same
# approach for accessing to the original underlying function
# in case of multi-threading
decorated = Object()
else:
# decorate the function
decorated = self.decorator(func)
decorated.__dict__['original_function'] = func
return decorated
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] |
tests/test_parser_identifier.py | mvisat/kopyt | 2 | 68913 | <reponame>mvisat/kopyt
import unittest
from kopyt import node
from . import TestParserBase
class TestParserSimpleIdentifier(TestParserBase):
def do_test(self, code: str) -> node.SimpleIdentifier:
return super().do_test("parse_simple_identifier", code,
node.SimpleIdentifier)
def test_parser_simple_identifier(self):
code = "a"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_simple_identifier_backtick(self):
code = "`~!@#$`"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_simple_identifier_underscore(self):
code = "_a_b_c"
result = self.do_test(code)
self.assertEqual(code, result.value)
class TestParserIdentifier(TestParserBase):
def do_test(self, code: str, test_str: bool = True) -> node.Identifier:
return super().do_test("parse_identifier",
code,
node.Identifier,
test_str=test_str)
def test_parser_identifier(self):
code = "a"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_identifier_multi(self):
code = "a.b.c"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_identifier_backtick(self):
code = "`~!@#$`._a_b"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_identifier_complex(self):
code = "_a_b_c.`~!@#$`.a"
result = self.do_test(code)
self.assertEqual(code, result.value)
def test_parser_identifier_newlines(self):
from re import compile
ws = compile(r"\s+")
codes = [
"a\n.b",
"a\r\n.b\n.c",
"a\n.`b`",
"`~!@#$`\r .a_b",
]
for code in codes:
with self.subTest(code=code):
result = self.do_test(code, False)
expected = ws.sub("", code)
self.assertEqual(expected, result.value)
if __name__ == "__main__":
unittest.main()
| [
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] |
setup.py | jsdnlb/Tangelo-challenge | 0 | 27530 | from setuptools import setup, find_packages
# Building and Distributing Packages with Setuptools
# Documentation here: https://setuptools.pypa.io/en/latest/setuptools.html
setup(name='app', version='1.0', packages=find_packages())
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src/operator/transformer/align.py | k-harada/abstraction-and-reasoning-challenge | 3 | 37801 | <filename>src/operator/transformer/align.py<gh_stars>1-10
from heapq import heappop, heappush
import numpy as np
from src.data import Problem, Case, Matter
from src.operator.mapper.map_connect import MapConnect
def align_xy(x0_arr: np.array, y0_arr: np.array, x1_arr: np.array, y1_arr: np.array, c_arr: np.array) -> np.array:
n = x0_arr.shape[0]
# x
heap_x = []
for i in range(n):
heappush(heap_x, (x0_arr[i], x1_arr[i], i))
x1_temp = 0
x_list_all = []
x_list_now = []
while len(heap_x):
x0, x1, i = heappop(heap_x)
if x0 >= x1_temp:
# new group
x1_temp = x1
if len(x_list_now) > 0:
x_list_all.append(x_list_now)
x_list_now = [i]
else:
x1_temp = max(x1_temp, x1)
x_list_now.append(i)
x_list_all.append(x_list_now)
# y
heap_y = []
for i in range(n):
heappush(heap_y, (y0_arr[i], y1_arr[i], i))
y1_temp = 0
y_list_all = []
y_list_now = []
while len(heap_y):
y0, y1, i = heappop(heap_y)
if y0 >= y1_temp:
# new group
y1_temp = y1
if len(y_list_now) > 0:
y_list_all.append(y_list_now)
y_list_now = [i]
else:
y1_temp = max(y1_temp, y1)
y_list_now.append(i)
y_list_all.append(y_list_now)
# find size
len_x_max = max([len(x) for x in x_list_all])
len_y_max = max([len(y) for y in y_list_all])
assert 0 < len_x_max * len_y_max <= n
# eval 158 is a good example
# try
xl, yl = 0, 0
for xl in range(len_x_max, n + 1):
if n % xl != 0:
continue
yl = n // xl
assert yl >= len_y_max # you must break before yl < len_y_max
# bind until xl
xl_temp = 0
for x in x_list_all:
xl_temp += len(x)
if xl_temp == xl:
xl_temp = 0
elif xl_temp > xl:
break
if xl_temp > 0:
continue
# success for x, bind until yl
yl_temp = 0
for y in y_list_all:
yl_temp += len(y)
if yl_temp == yl:
yl_temp = 0
elif yl_temp > yl:
break
if yl_temp > 0:
continue
# success for y
break
assert xl * yl > 0
# assign i, j
# bind until xl
i_arr = np.zeros(n, dtype=np.int)
row_id = 0
xl_temp = 0
for x_list in x_list_all:
xl_temp += len(x_list)
for i in x_list:
i_arr[i] = row_id
if xl_temp == xl:
xl_temp = 0
row_id += 1
# bind until yl
j_arr = np.zeros(n, dtype=np.int)
col_id = 0
yl_temp = 0
for y_list in y_list_all:
yl_temp += len(y_list)
for i in y_list:
j_arr[i] = col_id
if yl_temp == yl:
yl_temp = 0
col_id += 1
res_arr = -1 * np.ones((yl, xl), dtype=np.int)
for i in range(n):
assert res_arr[i_arr[i], j_arr[i]] == -1
res_arr[i_arr[i], j_arr[i]] = c_arr[i]
return res_arr
class Align:
def __init__(self):
pass
@classmethod
def case(cls, c: Case) -> Case:
m: Matter
x0_arr = np.array([m.x0 for m in c.matter_list if not m.is_mesh])
y0_arr = np.array([m.y0 for m in c.matter_list if not m.is_mesh])
x1_arr = np.array([m.x0 + m.shape[0] for m in c.matter_list if not m.is_mesh])
y1_arr = np.array([m.y0 + m.shape[1] for m in c.matter_list if not m.is_mesh])
non_mesh_list = [m for m in c.matter_list if not m.is_mesh]
c_arr = np.arange(len(non_mesh_list))
# same shape
assert len(non_mesh_list) >= 2
assert len({m.shape[0] for m in non_mesh_list}) == 1
assert len({m.shape[1] for m in non_mesh_list}) == 1
matter_shape = non_mesh_list[0].shape
res_arr = align_xy(x0_arr, y0_arr, x1_arr, y1_arr, c_arr)
new_case: Case = c.copy()
new_case.shape = res_arr.shape[0] * matter_shape[0], res_arr.shape[1] * matter_shape[1]
new_case.matter_list = []
for i in range(res_arr.shape[0]):
for j in range(res_arr.shape[1]):
m = non_mesh_list[res_arr[i, j]]
m_add = m.deepcopy()
m_add.x0 = i * matter_shape[0]
m_add.y0 = j * matter_shape[1]
new_case.matter_list.append(m_add)
return new_case
@classmethod
def problem(cls, p: Problem) -> Problem:
q: Problem = p.copy()
q.train_x_list = [cls.case(c) for c in p.train_x_list]
q.test_x_list = [cls.case(c) for c in p.test_x_list]
return q
if __name__ == "__main__":
x0_ = np.array([0, 0])
x1_ = np.array([1, 1])
y0_ = np.array([0, 2])
y1_ = np.array([1, 3])
c_ = np.array([4, 5])
res_arr_ = align_xy(x0_, y0_, x1_, y1_, c_)
print(res_arr_)
pp = Problem.load(158, "eval")
qq = MapConnect.problem(pp, allow_diagonal=True)
rr = Align.problem(qq)
print(rr)
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homeassistant/components/tradfri.py | automatemyhome/home-assistant-amh | 1 | 199147 | <reponame>automatemyhome/home-assistant-amh
"""
Support for Ikea Tradfri.
For more details about this component, please refer to the documentation at
https://home-assistant.io/components/ikea_tradfri/
"""
import asyncio
import logging
from uuid import uuid4
import voluptuous as vol
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers import discovery
from homeassistant.const import CONF_HOST
from homeassistant.components.discovery import SERVICE_IKEA_TRADFRI
from homeassistant.util.json import load_json, save_json
REQUIREMENTS = ['pytradfri==4.0.1',
'DTLSSocket==0.1.4',
'https://github.com/chrysn/aiocoap/archive/'
'3286f48f0b949901c8b5c04c0719dc54ab63d431.zip'
'#aiocoap==0.3']
DOMAIN = 'tradfri'
GATEWAY_IDENTITY = 'homeassistant'
CONFIG_FILE = '.tradfri_psk.conf'
KEY_CONFIG = 'tradfri_configuring'
KEY_GATEWAY = 'tradfri_gateway'
KEY_API = 'tradfri_api'
KEY_TRADFRI_GROUPS = 'tradfri_allow_tradfri_groups'
CONF_ALLOW_TRADFRI_GROUPS = 'allow_tradfri_groups'
DEFAULT_ALLOW_TRADFRI_GROUPS = True
CONFIG_SCHEMA = vol.Schema({
DOMAIN: vol.Schema({
vol.Inclusive(CONF_HOST, 'gateway'): cv.string,
vol.Optional(CONF_ALLOW_TRADFRI_GROUPS,
default=DEFAULT_ALLOW_TRADFRI_GROUPS): cv.boolean,
})
}, extra=vol.ALLOW_EXTRA)
_LOGGER = logging.getLogger(__name__)
def request_configuration(hass, config, host):
"""Request configuration steps from the user."""
configurator = hass.components.configurator
hass.data.setdefault(KEY_CONFIG, {})
instance = hass.data[KEY_CONFIG].get(host)
# Configuration already in progress
if instance:
return
@asyncio.coroutine
def configuration_callback(callback_data):
"""Handle the submitted configuration."""
try:
from pytradfri.api.aiocoap_api import APIFactory
from pytradfri import RequestError
except ImportError:
_LOGGER.exception("Looks like something isn't installed!")
return
identity = uuid4().hex
security_code = callback_data.get('security_code')
api_factory = APIFactory(host, psk_id=identity, loop=hass.loop)
# Need To Fix: currently entering a wrong security code sends
# pytradfri aiocoap API into an endless loop.
# Should just raise a requestError or something.
try:
key = yield from api_factory.generate_psk(security_code)
except RequestError:
configurator.async_notify_errors(hass, instance,
"Security Code not accepted.")
return
res = yield from _setup_gateway(hass, config, host, identity, key,
DEFAULT_ALLOW_TRADFRI_GROUPS)
if not res:
configurator.async_notify_errors(hass, instance,
"Unable to connect.")
return
def success():
"""Set up was successful."""
conf = load_json(hass.config.path(CONFIG_FILE))
conf[host] = {'identity': identity,
'key': key}
save_json(hass.config.path(CONFIG_FILE), conf)
configurator.request_done(instance)
hass.async_add_job(success)
instance = configurator.request_config(
"IKEA Trådfri", configuration_callback,
description='Please enter the security code written at the bottom of '
'your IKEA Trådfri Gateway.',
submit_caption="Confirm",
fields=[{'id': 'security_code', 'name': 'Security Code',
'type': 'password'}]
)
@asyncio.coroutine
def async_setup(hass, config):
"""Set up the Tradfri component."""
conf = config.get(DOMAIN, {})
host = conf.get(CONF_HOST)
allow_tradfri_groups = conf.get(CONF_ALLOW_TRADFRI_GROUPS)
known_hosts = yield from hass.async_add_job(load_json,
hass.config.path(CONFIG_FILE))
@asyncio.coroutine
def gateway_discovered(service, info,
allow_tradfri_groups=DEFAULT_ALLOW_TRADFRI_GROUPS):
"""Run when a gateway is discovered."""
host = info['host']
if host in known_hosts:
# use fallbacks for old config style
# identity was hard coded as 'homeassistant'
identity = known_hosts[host].get('identity', 'homeassistant')
key = known_hosts[host].get('key')
yield from _setup_gateway(hass, config, host, identity, key,
allow_tradfri_groups)
else:
hass.async_add_job(request_configuration, hass, config, host)
discovery.async_listen(hass, SERVICE_IKEA_TRADFRI, gateway_discovered)
if host:
yield from gateway_discovered(None,
{'host': host},
allow_tradfri_groups)
return True
@asyncio.coroutine
def _setup_gateway(hass, hass_config, host, identity, key,
allow_tradfri_groups):
"""Create a gateway."""
from pytradfri import Gateway, RequestError
try:
from pytradfri.api.aiocoap_api import APIFactory
except ImportError:
_LOGGER.exception("Looks like something isn't installed!")
return False
try:
factory = APIFactory(host, psk_id=identity, psk=key,
loop=hass.loop)
api = factory.request
gateway = Gateway()
gateway_info_result = yield from api(gateway.get_gateway_info())
except RequestError:
_LOGGER.exception("Tradfri setup failed.")
return False
gateway_id = gateway_info_result.id
hass.data.setdefault(KEY_API, {})
hass.data.setdefault(KEY_GATEWAY, {})
gateways = hass.data[KEY_GATEWAY]
hass.data[KEY_API][gateway_id] = api
hass.data.setdefault(KEY_TRADFRI_GROUPS, {})
tradfri_groups = hass.data[KEY_TRADFRI_GROUPS]
tradfri_groups[gateway_id] = allow_tradfri_groups
# Check if already set up
if gateway_id in gateways:
return True
gateways[gateway_id] = gateway
hass.async_add_job(discovery.async_load_platform(
hass, 'light', DOMAIN, {'gateway': gateway_id}, hass_config))
hass.async_add_job(discovery.async_load_platform(
hass, 'sensor', DOMAIN, {'gateway': gateway_id}, hass_config))
return True
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] |
app/tasker/tasks/models.py | jaredlockhart/django-htmx-todo | 4 | 142784 | from django.db import models
from django.db.models.query import QuerySet
from django.urls import reverse
class TaskList(models.Model):
name = models.CharField(max_length=255)
slug = models.CharField(max_length=255)
tasks: QuerySet["Task"]
class Meta:
verbose_name = "Task List"
verbose_name_plural = "Task Lists"
def __str__(self) -> str:
return self.name
def get_absolute_url(self) -> str:
return reverse("tasklist-detail", kwargs={"slug": self.slug})
@property
def is_complete(self) -> bool:
return not self.tasks.filter(is_done=False).exists()
@property
def complete_tasks(self) -> models.QuerySet["Task"]:
return self.tasks.filter(is_done=True)
@property
def incomplete_tasks(self) -> models.QuerySet["Task"]:
return self.tasks.filter(is_done=False)
class Task(models.Model):
task_list = models.ForeignKey(
TaskList,
related_name="tasks",
on_delete=models.CASCADE,
)
name = models.CharField(max_length=255)
is_done = models.BooleanField(default=False)
class Meta:
verbose_name = "Task Item"
verbose_name_plural = "Task Items"
def __str__(self) -> str:
return self.name
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optionstrader/script.py | Zaitsev11/Optionstrader | 6 | 89761 | # TODO
# Fix the importing
# import CustomName
# ...
# obj = CustomName()
# obj.account.get_balance()
# obj.parser.extract_symbols()
import __init__
from __init__ import Account
from __init__ import Webservice
from __init__ import Database
from __init__ import Parser
import sys, json
if __name__ == "__main__":
# TODO
# Rename or Depricate this code
myaccount = Account()
webservice = Webservice()
input_data = raw_input("Function to Process > ")
if input_data == "g":
print(myaccount.get_balance())
if input_data == "a":
money = raw_input("Amount of money > ")
myaccount.add_money_to_checking(money)
if input_data == "s":
money = raw_input("Amount of money > ")
myaccount.subtract_money_from_checking(money)
if input_data =='x':
parser = Parser()
#symbol = raw_input("Enter Symbol > ")
parser.extract_symbols()
if input_data =='l':
parser = Parser()
#symbol = raw_input("Enter Symbol > ")
parser.start_options_scan()
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] |
tenants/admin.py | ibowditch/polls | 0 | 176397 | <filename>tenants/admin.py
from django.db import models
from django.contrib import admin
from django_tenants.admin import TenantAdminMixin
from .models import Client, Domain
@admin.register(Client)
class ClientAdmin(TenantAdminMixin, admin.ModelAdmin):
list_display = ('name', 'paid_until')
# @admin.register(Domain)
# class DomainAdmin(TenantAdminMixin, admin.ModelAdmin):
# list_display = ('name', 'paid_until')
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Mundo 2/Ex46.py | legna7/Python | 2 | 65855 | <filename>Mundo 2/Ex46.py
from time import sleep
print(f'{"-=-" * 7}[CONTAGEM REGRESSIVA]{"-=-" * 7}')
print('''Vai estourar os fogos de artifício daqui a 10 segundos,
vamos começar a contar!''')
sleep(1)
for c in range(10, -1, -1):
print(c)
sleep(1)
print('BOOM! POOW! BOOM!')
print('AEEEEEE! OS FOGOS ESTOURARAM!!')
print('-=-' * 20)
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data/scaling/DataScaler.py | khoehlein/CNNs-for-Wind-Field-Downscaling | 5 | 175030 | import numpy as np
import torch
import torch.nn as nn
class DataScaler(nn.Module):
def __init__(
self,
bias=None, scale=None,
channels=None, dtype=None, device=None,
**kwargs
):
super(DataScaler, self).__init__()
if bias is None:
assert scale is None
assert channels is not None
bias = torch.zeros(1, channels, 1, 1, dtype=dtype, device=device)
scale = torch.ones(1, channels, 1, 1, dtype=dtype, device=device)
else:
assert isinstance(bias, torch.Tensor)
assert isinstance(scale, torch.Tensor)
assert bias.size() == scale.size()
assert bias.size(1) == channels or channels is None
assert bias.dtype == scale.dtype
assert bias.device == scale.device
channels = bias.size(1)
self.channels = channels
self.register_buffer('bias', bias)
self.bias.requires_grad = False
self.register_buffer('scale', scale)
self.scale.requires_grad = False
self.kwargs = kwargs
if device is not None or dtype is not None:
self.to(device=device, dtype=dtype)
@classmethod
def from_data(cls, data, **kwargs):
assert len(data.shape) == 4
channels = data.shape[1]
if isinstance(data, torch.Tensor):
dtype = data.dtype,
device = data.device
elif isinstance(data, np.ndarray):
dtype = torch.double
device = torch.device('cpu')
else:
raise Exception('[ERROR] Unknown data type.')
scaler = cls(channels=channels, dtype=dtype, device=device, **kwargs)
scaler.fit(data)
return scaler
@classmethod
def from_parameters(cls, bias, scale, **kwargs):
scaler = cls(bias=bias, scale=scale, **kwargs)
return scaler
def fit(self, data, **kwargs):
assert isinstance(data, (np.ndarray, torch.Tensor))
if len(kwargs) > 0:
self.kwargs.update(kwargs)
if isinstance(data, np.ndarray):
self.cpu()
bias, scale = self.compute_scalings_numpy(data, **self.kwargs)
self.bias = torch.tensor(bias)
self.scale = torch.tensor(scale)
else:
self.to(data.device)
bias, scale = self.compute_scalings_torch(data, **self.kwargs)
self.bias = bias
self.scale = scale
self.kwargs = kwargs
self.bias.requires_grad = False
self.scale.requires_grad = False
return self.bias, self.scale
def transform(self, data, offset=None):
assert isinstance(data, (np.ndarray, torch.Tensor))
if offset is None or self.scale.numel() == 1:
bias = self.bias
scale = self.scale
else:
assert offset.shape == (data.shape[0], 2)
patch_size = data.shape[-2:]
bias = torch.cat(
[self.bias[:, :, x[0]:(x[0] + patch_size[0]), x[1]:(x[1] + patch_size[1])] for x in offset],
dim=0
)
scale = torch.cat(
[self.scale[:, :, x[0]:(x[0] + patch_size[0]), x[1]:(x[1] + patch_size[1])] for x in offset],
dim=0
)
is_numpy_array = isinstance(data, np.ndarray)
if is_numpy_array:
if bias.numel() > 0:
bias = bias.cpu().numpy()
data = data - bias
if scale.numel() > 0:
scale = scale.cpu().numpy()
data = data / scale
else:
if bias.numel() > 0:
if data.device != bias.device:
bias = bias.to(data.device)
data = data - bias
if scale.numel() > 0:
if data.device != scale.device:
scale = scale.to(data.device)
data = data / scale
return data
def transform_back(self, data, offset=None):
assert isinstance(data, (np.ndarray, torch.Tensor))
if offset is None or self.scale.numel() == 1:
bias = self.bias
scale = self.scale
else:
assert offset.shape == (data.shape[0], 2)
patch_size = data.shape[-2:]
bias = torch.cat(
[self.bias[:, :, x[0]:(x[0] + patch_size[0]), x[1]:(x[1] + patch_size[1])] for x in offset],
dim=0
)
scale = torch.cat(
[self.scale[:, :, x[0]:(x[0] + patch_size[0]), x[1]:(x[1] + patch_size[1])] for x in offset],
dim=0
)
if isinstance(data, np.ndarray):
if scale.numel() > 0:
scale = scale.cpu().numpy()
data = data * scale
if bias.numel() > 0:
bias = bias.cpu().numpy()
data = data + bias
else:
if scale.numel() > 0:
if data.device != scale.device:
scale = scale.to(data.device)
data = data * scale
if bias.numel() > 0:
if data.device != bias.device:
bias = bias.to(data.device)
data = data + bias
return data
def forward(self, data, mode=None):
if mode == 'apply':
data = self.transform(data)
elif mode == 'revert':
data = self.transform_back(data)
else:
raise NotImplementedError('[ERROR] Operation mode not available.')
return data
def compute_scalings_numpy(self, data: np.ndarray, **kwargs):
raise NotImplementedError()
def compute_scalings_torch(self, data: torch.Tensor, **kwargs):
raise NotImplementedError() | [
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clusterval/validation.py | Nuno09/clusterval | 3 | 125697 | <filename>clusterval/validation.py
"""
Function evaluate does hierarchical clustering and validation of it's results.
Validation can be done with external or internal indices, or both.
"""
from scipy.cluster.hierarchy import dendrogram, fcluster, cut_tree
from fastcluster import linkage
from scipy.spatial.distance import pdist
from sklearn.cluster import KMeans
from sklearn.neighbors import NearestCentroid
from statistics import mean
import pandas as pd
from collections import defaultdict
import matplotlib.pyplot as plt
import itertools
import numpy as np
import math
import random
from clusterval.internal import calculate_internal
from clusterval.external import calculate_external
class Clusterval:
"""
min_k: int, optional
minimum number of clusters to test. Default 2.
max_k: int, optional
maximum number of clusters to test. Default 8.
algorithm: str, optional
clustering algorithm to use. Accepts 'single', 'ward'(default), 'complete', 'centroid', 'average' and 'kmeans
bootstrap_samples: int, optional
number of bootstrap samples simulated. Default 250
index: list of str or str, optional
what indices to be calculated. Accepts 'all' to calculate all, 'internal' to calculate only internal indices,
'external' to calculate external indices, or one of ['AR', 'FM', 'J', 'AW', 'VD', 'H', 'F', 'VI', 'K', 'Phi', 'RT', 'SS',
'CVNN', 'XB', 'SDbw', 'DB', 'S', 'SD', 'PBM', 'Dunn']. Default 'all'.
"""
def __init__(self, min_k=2, max_k=8, algorithm='ward', bootstrap_samples=250, index='all'):
external_indices = ['AR', 'FM', 'J', 'AW', 'VD', 'H', 'F', 'VI', 'K', 'Phi', 'RT', 'SS']
internal_indices = ['CVNN', 'XB', 'SDbw', 'DB', 'S', 'SD', 'PBM', 'Dunn']
min_indices = ['VD', 'VI', 'MS', 'CVNN', 'XB', 'SDbw', 'DB', 'SD']
idx_distance_matrix = ['XB', 'SDbw', 'DB', 'SD', 'PBM']
indices = {'AR': ['AR'], 'FM': ['FM'], 'J': ['J'], 'AW': ['AW'], 'VD': ['VD'], 'H': ['H'],
'F': ['F'], 'K': ['K'], 'Phi': ['Phi'], 'RT': ['RT'], 'SS': ['SS'],
'VI': ['VI'], 'CVNN': ['CVNN'], 'XB': ['XB'],
'SDbw': ['SDbw'], 'DB': ['DB'], 'S': ['S'], 'SD': ['SD'],'PBM': ['PBM'], 'Dunn': ['Dunn'],
'all': external_indices + internal_indices, 'external': external_indices,
'internal': internal_indices
}
if isinstance(index, str):
index = [x.strip() for x in index.split(',')]
for idx in index:
if idx not in indices.keys():
raise ValueError('{0} is not a valid index value, please check help(clusterval.Clusterval) to see acceptables indices'.format(
idx))
self._external_indices = external_indices
self._internal_indices = internal_indices
self._min_indices = min_indices
self._indices = indices
self._idx_distance_matrix = idx_distance_matrix
self.min_k = min_k
self.max_k = max_k
self.algorithm = algorithm
self.bootstrap_samples = bootstrap_samples
self.index = index
columns = []
for i in self.index:
columns = columns + self._indices[i]
self.output_df = pd.DataFrame(
index=range(self.min_k, self.max_k + 1),
columns=columns,
dtype=np.float64
)
self.final_k = None
self.count_choice = None
self.final_clusters = None
self.Z= None
self.long_info = None
def __repr__(self):
args = ['{}={}'.format(' ' + key, value) for key,value in self.__dict__.items() if key not in ['output_df',
'_external_indices',
'_internal_indices',
'_min_indices',
'_indices',
'_data',
'final_k',
'count_choice',
'final_clusters',
'long_info',
'Z',
'_idx_distance_matrix']]
args = ','.join(args)
return 'Clusterval(' + str(args) + ')\nfinal_k = {}'.format(self.final_k)
def __str__(self):
args = ['{} is {}'.format(' ' + key, value) for key, value in self.__dict__.items() if key not in ['output_df',
'_external_indices',
'_internal_indices',
'_min_indices',
'_indices',
'_data',
'final_k',
'count_choice',
'final_clusters',
'long_info',
'Z',
'_idx_distance_matrix']]
args = ';\n'.join(args)
return 'Clusterval: \n' + str(args) + '\n'
def _cluster_indices(self, cluster_assignments, idx):
'''
Transform cluster memebership array into array of clusters
:param cluster_assignments: array
:param idx: array with indices
:return: array of clusters
'''
n = int(cluster_assignments.max())
clusters = []
for cluster_number in range(0, n + 1):
aux = np.where(cluster_assignments == cluster_number)[0].tolist()
if aux:
cluster = list(idx[i] for i in aux)
clusters.append(cluster)
return clusters
def _distance_dict(self, data):
"""
Build dictionary that maps pairs to distance values
:param data: ndarray m * (m - 1)) // 2 like returned by pdist
:return: dictionary of tuples
"""
from itertools import combinations
from sympy import Symbol, solve
diss_matrix = False
#check if distances already calculated
if data.ndim == 1:
pairwise_distances = [i for i in data]
x = Symbol('x')
n = solve(x ** 2 - x - 2*data.shape[0])[1]
diss_matrix = True
else:
pairwise_distances = pdist(data)
n = len(data)
comb = list(combinations([i for i in range(0, n)], 2))
dist_dict = defaultdict(float)
for pair, dist in zip(comb,pairwise_distances):
dist_dict[pair] = dist
return dist_dict, diss_matrix
def _choose(self, choices_dict):
for metrics in self.index:
for metric in self._indices[metrics]:
if metric in self.output_df.columns.values:
if metric in self._min_indices:
value = self.output_df.loc[self.output_df[metric].idxmin()]
else:
value = self.output_df.loc[self.output_df[metric].idxmax()]
choices_dict[metric] = [value.name, value[metric]]
return choices_dict
def evaluate(self, data):
"""
Perform hierarchical clustering or Kmeans clustering on the data and calculate the validation indices
:param data: array-like, n_samples x n_features or 1-d dissimilarity array
Dataset to cluster
:return: Clusterval object
"""
columns = []
for i in self.index:
columns = columns + self._indices[i]
self.output_df = pd.DataFrame(
index=range(self.min_k, self.max_k + 1),
columns=columns,
dtype=np.float64
)
#convert data to numpy array
data = np.array(data)
#build dictionary with pairwise distances
distances, diss_matrix = self._distance_dict(data)
for metrics in self.index:
for idx in self._indices[metrics]:
if (diss_matrix) and (idx in self._idx_distance_matrix):
self.output_df = self.output_df.drop([idx], axis=1)
clustering = defaultdict(dict)
# dictionary with all mean values of the metrics for every k
choices_dict = defaultdict(list)
self.count_choice = defaultdict(int)
results = {k: {} for k in range(self.min_k, self.max_k + 1)}
# build dictionaries that hold the calculations
for k in range(self.min_k, self.max_k + 1):
for metrics in self.index:
for idx in self._indices[metrics]:
if (diss_matrix) and (idx in self._idx_distance_matrix):
continue
else:
results[k][idx] = []
if not any(self.output_df.columns.values):
raise ValueError('No CVIs being evaluated. If inputing distance matrix, please be aware that SD, XB, SDbw, PBM and DB are not possible to calculate.')
if self.algorithm in ['ward', 'single', 'complete', 'average', 'centroid']:
self.Z = linkage(data, method=self.algorithm)
for k in range(self.min_k, self.max_k + 1):
if self.algorithm in ['ward', 'single', 'complete', 'average', 'centroid']:
# builds a list of the hierarchical clusters
# with cut_tree
partition = cut_tree(self.Z, n_clusters=k)
clusters = self._cluster_indices(partition, [i for i in range(0, len(data))])
clf = NearestCentroid()
if not diss_matrix:
try:
clf.fit(data, list(itertools.chain.from_iterable(partition)))
#if only one cluster formed, try with fcluster - this happened with centroid
except ValueError as e:
if isinstance(e, ValueError):
partition = fcluster(self.Z, t=k, criterion='maxclust')
clusters = self._cluster_indices(partition, [i for i in range(0, len(data))])
clf.fit(data, list(itertools.chain(partition)))
centroids = clf.centroids_
else:
centroids = None
elif self.algorithm == 'kmeans':
if diss_matrix:
raise ValueError('k-means does not work with only pairwise distances as input. Please set another algorithm.')
partition = KMeans(n_clusters=k, random_state=0).fit(data)
clusters = self._cluster_indices(partition.labels_, [i for i in range(0, len(data))])
centroids = partition.cluster_centers_
else:
raise ValueError(self.algorithm + ' is not an acceptable clustering algorithm, please choose \'single\','
' \'ward\', \'centroid\', \'complete\', \'average\' or \'kmeans\'')
# dictionary of clustering of each 'k', to be used in internal validation
clustering[k] = {'clusters': clusters, 'centroids': centroids}
if (self.index[0] in ['all', 'external']) or (set(self.index).intersection(self._external_indices)):
# external validation step
for i in range(self.bootstrap_samples):
sample = random.sample(list(data), int(3 / 4 * len(data)))
#check if combinations are good in case of data == distance matrix
if diss_matrix:
NN = len(sample)
N = int(math.ceil(math.sqrt(NN*2)))
comb = (N*(N-1)//2)
if comb != NN:
sample = sample[:-(abs(comb - NN))]
if self.algorithm in ['ward', 'single', 'complete', 'average', 'centroid']:
Z_sample = linkage(sample, method=self.algorithm)
#clusters_sample = self._cluster_indices(fcluster(Z_sample, t=k, criterion='maxclust'), [i for i in range(0, len(sample))])
clusters_sample = self._cluster_indices(cut_tree(Z_sample, k), [i for i in range(0, len(sample))])
elif diss_matrix:
raise ValueError('k-means does not work with only pairwise distances as input. Please set another algorithm.')
else:
clusters_sample = self._cluster_indices(KMeans(n_clusters=k, random_state=0).fit(sample).labels_, [i for i in range(0, len(sample))])
external = calculate_external(clusters, clusters_sample, indices=self.index)
for key, metric in external.items():
if (key in self.index) or (self.index):
results[k][key].append(metric)
if (self.index[0] in ['all', 'internal']) or (set(self.index).intersection(self._internal_indices)):
# dictionary of distance between pairs of data points, e.g, {(pair1,pair2) : dist(pair1,pair2)}
# internal validation step
if diss_matrix:
internal = calculate_internal(clustering, data=data, distance_dict=distances, indices=['CVNN','S','Dunn'])
else:
internal = calculate_internal(clustering, data=data, distance_dict=distances, indices=self.index)
for cvi, k_clust in internal.items():
for n_c, val in k_clust.items():
results[n_c][cvi] = val
for k, keys in results.items():
for key, value in keys.items():
if key in self._external_indices:
self.output_df.loc[k, key] = mean(value)
else:
self.output_df.loc[k, key] = value
choices_dict = self._choose(choices_dict)
for values in choices_dict.values():
self.count_choice[str(values[0])] += 1
max_value = 0
for key, value in self.count_choice.items():
if value > max_value:
max_value = value
final_k = key
self.final_k = int(final_k)
if self.algorithm in ['ward', 'single', 'complete', 'average', 'centroid']:
#self.final_clusters = fcluster(self.Z, t=self.final_k, criterion='maxclust')
self.final_clusters = np.concatenate(np.asarray(cut_tree(self.Z, self.final_k)))
else:
self.final_clusters = KMeans(n_clusters=self.final_k, random_state=0).fit(data).labels_
self.long_info = self.print_results()
return self
def print_results(self):
'''
print detailed information on the clustering results conclusion
:return: str with the results
'''
output_str = '* Minimum number of clusters to test: ' + str(self.min_k) + '\n'
output_str += '* Maximum number of clusters to test: ' + str(self.max_k) + '\n'
output_str +='* Number of bootstrap samples generated: ' + str(self.bootstrap_samples) + '\n'
output_str += '* Clustering algorithm used: ' + str(self.algorithm) + '\n'
if self.index not in ['all', 'internal', 'external']:
idx = list(self.output_df.columns.values)
else:
idx = self.index
output_str +='\n* Validation Indices calculated: ' + str(idx) + '\n\n'
output_str +="* Among all indices: \n\n"
output_str +='\n\n* According to the majority rule, the best number of clusters is ' + str(self.final_k) + '\n\n\n'
for k_num in sorted(self.count_choice):
output_str +='* ' + str(self.count_choice[k_num]) + ' proposed ' + k_num + ' as the best number of clusters \n\n'
output_str +='\t\t\t***** Conclusion *****\t\t\t\n'
#Display all dataframe
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
output_str +=str(self.output_df) + '\n'
output_str +='\n* The best partition is:\n'
output_str +=str(self.final_clusters) + '\n'
return output_str
def _calculate_dendrogram(self, labels=None):
'''
Calculate dendrogram for object linkage product
:param labels: array-like, shape 1 x len(data.shape[0])
:return: dendrogram
'''
dend = dendrogram(
self.Z,
# truncate_mode = 'lastp',
# p=6,
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=10, # font size for the x axis labels
labels=labels
)
return dend
def plot(self, labels=None):
'''
print the hierarchical clustering dendrogram using matplotlib
:return: matplotlib image of dendrogram
'''
fig = plt.figure(figsize=(40, 20))
plt.title('Hierarchical Clustering Dendrogram - Linkage: %s, Metrics: %s' % (self.algorithm, str(self.index)),
fontsize=30)
plt.xlabel('data point index', labelpad=20, fontsize=30)
plt.ylabel('distance', labelpad=10, fontsize=30)
plt.xticks(size=40)
plt.yticks(size=40)
self._calculate_dendrogram(labels)
plt.show()
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auth-api/src/auth_api/resources/org_products.py | severinbeauvais/sbc-auth | 0 | 273 | <gh_stars>0
# Copyright © 2019 Province of British Columbia
#
# 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.
"""API endpoints for managing an Org resource."""
from flask import request
from flask_restplus import Namespace, Resource, cors
from auth_api import status as http_status
from auth_api.exceptions import BusinessException
from auth_api.jwt_wrapper import JWTWrapper
from auth_api.schemas import ProductSubscriptionSchema
from auth_api.schemas import utils as schema_utils
from auth_api.services import Product as ProductService
from auth_api.tracer import Tracer
from auth_api.utils.roles import Role
from auth_api.utils.util import cors_preflight
API = Namespace('products', description='Endpoints for products management')
TRACER = Tracer.get_instance()
_JWT = JWTWrapper.get_instance()
@cors_preflight('GET,POST,OPTIONS')
@API.route('', methods=['GET', 'POST', 'OPTIONS'])
class OrgProducts(Resource):
"""Resource for managing product subscriptions."""
@staticmethod
@TRACER.trace()
@cors.crossdomain(origin='*')
@_JWT.has_one_of_roles([Role.STAFF_CREATE_ACCOUNTS.value])
def post(org_id):
"""Post a new product subscription to the org using the request body."""
request_json = request.get_json()
valid_format, errors = schema_utils.validate(request_json, 'org_product_subscription')
if not valid_format:
return {'message': schema_utils.serialize(errors)}, http_status.HTTP_400_BAD_REQUEST
try:
subscriptions = ProductService.create_product_subscription(org_id, request_json)
if subscriptions is None:
response, status = {'message': 'Not authorized to perform this action'}, \
http_status.HTTP_401_UNAUTHORIZED
else:
response, status = {'subscriptions': ProductSubscriptionSchema().dump(subscriptions, many=True)}, \
http_status.HTTP_201_CREATED
except BusinessException as exception:
response, status = {'code': exception.code, 'message': exception.message}, exception.status_code
return response, status
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tests/game_utils_test.py | TylerYep/wolfbot | 3 | 102079 | <gh_stars>1-10
import pytest
from tests.conftest import override_input, verify_output_string
from wolfbot.enums import Role
from wolfbot.game_utils import (
find_all_player_indices,
get_center,
get_numeric_input,
get_player,
print_roles,
swap_characters,
)
class TestPrintRoles:
"""Tests for the print_roles function."""
@staticmethod
def test_print_roles(
caplog: pytest.LogCaptureFixture, small_game_roles: tuple[Role, ...]
) -> None:
"""Correctly print and format roles."""
shuffled_roles = [Role.SEER, Role.VILLAGER, Role.WOLF, Role.ROBBER]
print_roles(shuffled_roles, "Hidden")
expected = (
"[Hidden] Player roles: [Seer, Villager, Wolf]\n"
f"{' ' * 9}Center cards: [Robber]\n"
)
verify_output_string(caplog, expected)
@staticmethod
def test_print_wolfbot_guesses(
caplog: pytest.LogCaptureFixture, medium_game_roles: tuple[Role, ...]
) -> None:
"""Correctly print and format roles."""
print_roles(medium_game_roles, "WolfBot")
expected = (
"[WolfBot] Player roles: [Robber, Drunk, Wolf, Troublemaker, Seer]\n"
" Center cards: [Minion]\n"
)
verify_output_string(caplog, expected)
class TestSwapCharacters:
"""Tests for the swap_characters function."""
@staticmethod
def test_same_index_error(small_game_roles: tuple[Role, ...]) -> None:
"""Don't attempt to swap the same index."""
with pytest.raises(RuntimeError):
swap_characters(list(small_game_roles), 2, 2)
@staticmethod
def test_swap(small_game_roles: tuple[Role, ...]) -> None:
"""Correctly swap two players."""
roles = list(small_game_roles)
swap_characters(roles, 0, 2)
assert roles == [Role.ROBBER, Role.SEER, Role.VILLAGER]
class TestFindAllPlayerIndices:
"""Tests for the find_all_player_indices function."""
@staticmethod
def test_returns_correct_indices(large_game_roles: tuple[Role, ...]) -> None:
"""Don't attempt to swap the same index."""
result = find_all_player_indices(large_game_roles, Role.VILLAGER)
assert result == (1, 4, 11)
class TestGetPlayer:
"""Tests for the get_player function."""
@staticmethod
def test_generates_random_indices(large_game_roles: tuple[Role, ...]) -> None:
"""Generated indices should be random."""
result = [get_player(is_user=False) for _ in range(10)]
assert result == [6, 6, 0, 4, 8, 7, 6, 4, 7, 5]
@staticmethod
def test_random_excludes_values(large_game_roles: tuple[Role, ...]) -> None:
"""Generated indices should exclude specified values."""
exclude = (6, 7, 8)
result = [get_player(is_user=False, exclude=exclude) for _ in range(10)]
assert not set(result).intersection(exclude)
assert result == [9, 9, 0, 4, 11, 10, 9, 4, 10, 5]
@staticmethod
def test_user_input_indices(
monkeypatch: pytest.MonkeyPatch, large_game_roles: tuple[Role, ...]
) -> None:
"""Generated indices should be random."""
inputs = [0, 2, 20, 1]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_player(is_user=True) for _ in range(3)]
assert result == [0, 2, 1]
@staticmethod
def test_user_excludes_values(
monkeypatch: pytest.MonkeyPatch, large_game_roles: tuple[Role, ...]
) -> None:
"""Generated indices should exclude specified values."""
exclude = (6, 7, 8)
inputs = [0, 20, 4, 7, 5, 6]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_player(is_user=True, exclude=exclude) for _ in range(3)]
assert not set(result).intersection(exclude)
assert result == [0, 4, 5]
class TestGetCenter:
"""Tests for the get_center function."""
@staticmethod
def test_generates_random_indices(large_game_roles: tuple[Role, ...]) -> None:
"""Generated indices should be random."""
result = [get_center(is_user=False) for _ in range(10)]
assert result == [13, 13, 12, 13, 14, 13, 13, 13, 13, 13]
@staticmethod
def test_excludes_values(large_game_roles: tuple[str, ...]) -> None:
"""Generated indices should exclude specified values."""
exclude = (12, 13)
result = [get_center(is_user=False, exclude=exclude) for _ in range(10)]
assert not set(result).intersection(exclude)
assert result == [14] * 10
@staticmethod
def test_user_center_indices(
monkeypatch: pytest.MonkeyPatch, large_game_roles: tuple[Role, ...]
) -> None:
"""Generated indices should be random."""
inputs = [1, 2, 0, 2]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_center(is_user=True) for _ in range(3)]
assert result == [13, 14, 12]
@staticmethod
def test_user_excludes_values(
monkeypatch: pytest.MonkeyPatch, large_game_roles: tuple[Role, ...]
) -> None:
"""Generated indices should exclude specified values."""
exclude = (12, 13)
inputs = [2, 0, 4, 1, 2, 2]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_center(is_user=True, exclude=exclude) for _ in range(3)]
assert not set(result).intersection(exclude)
assert result == [14] * 3
class TestGetNumericInput:
"""Tests for the get_numeric_input function."""
@staticmethod
def test_numeric_input(monkeypatch: pytest.MonkeyPatch) -> None:
"""Generated indices should be random."""
inputs = [1, 7, 10, 15, 2, 0]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_numeric_input(10) for _ in range(4)]
assert result == [1, 7, 2, 0]
@staticmethod
def test_numeric_input_range(monkeypatch: pytest.MonkeyPatch) -> None:
"""Generated indices should be random."""
inputs = [1, 37, 30, 34, 0, 33]
monkeypatch.setattr("builtins.input", override_input([str(i) for i in inputs]))
result = [get_numeric_input(30, 34) for _ in range(2)]
assert result == [30, 33]
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Algorithms/5_Searching/11.py | abphilip-codes/Hackerrank_DSA | 1 | 39374 | <filename>Algorithms/5_Searching/11.py
# https://www.hackerrank.com/challenges/short-palindrome/problem
#!/bin/python3
import math
import os
import random
import re
import sys
#
# Complete the 'shortPalindrome' function below.
#
# The function is expected to return an INTEGER.
# The function accepts STRING s as parameter.
#
def shortPalindrome(s):
ans = mod = 10**9 + 7
a = [0] * 26 ** 1
b = [0] * 26 ** 2
c = [0] * 26 ** 3
for z in s:
k = ord(z)-97
x = 26*k-1
y = k-26
for w in range(26):
y+=26
x+=1
ans+=c[y]
c[x]+=b[x]
b[x]+=a[w]
a[k]+=1
return ans%mod
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
s = input()
result = shortPalindrome(s)
fptr.write(str(result) + '\n')
fptr.close() | [
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simulator/thruster_plotter.py | ErikRowe/Bachelor-E2205 | 1 | 48935 | <reponame>ErikRowe/Bachelor-E2205
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
lengths = np.array([
[0.156, 0.111, 0.085],
[0.156, -0.111, 0.085],
[-0.156, 0.111, 0.085],
[-0.156, -0.111, 0.085],
[0.12, 0.218, 0],
[0.12, -0.218, 0],
[-0.12, 0.218, 0],
[-0.12, -0.218, 0]])
names = np.array(["T1","T2","T3","T4","T5","T6","T7","T8"])
angles = np.array([-45, 45, -135, 135, 0, 0, 0, 0, 0])
directions = np.array([1, 1, -1, -1, 1, -1, -1, 1])
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111, projection='3d')
for i in range(4):
point = lengths[i]
x, y, z = point[0],-point[1],-point[2]
ax.text(x,y,z, names[i], color='black')
#ax.scatter(x, y, z, label = names[i])
if angles[i] == -45:
u = 1
v = 1
if angles[i] == 45:
u = 1
v = -1
if angles[i] == -135:
u = 1
v = -1
if angles[i] == 135:
u = 1
v = 1
w = 0
ax.quiver(x, y, z, u, v, w, length = 0.1, normalize = True)
for i in range(4,8):
point = lengths[i]
x, y, z = point[0],-point[1],-point[2]
ax.text(x,y,z, names[i], color='black')
#ax.scatter(x, y, z, label = names[i])
u = 0
v = 0
w = directions[i]
ax.quiver(x, y, z, u, v, w, length = 0.1, normalize = True)
ax.set_xlim([-0.2,0.2])
ax.set_ylim([-0.2,0.2])
ax.set_zlim([-0.2,0.2])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.quiver(0,0,0,1,0,0, length = .2, linewidth=2, color = "r")
ax.quiver(0,0,0,0,-1,0, length = .2, linewidth=2, color = "b")
ax.quiver(0,0,0,0,0,-1, length = .2, linewidth=2, color = "g")
ax.text(.25,0,0, 'x', color='r')
ax.text(0,-.25,0, 'y', color='b')
ax.text(0,0,-.25, 'z', color='g')
plt.show()
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homeassistant/components/tesla/device_tracker.py | domwillcode/home-assistant | 23 | 61446 | """Support for tracking Tesla cars."""
import logging
from homeassistant.components.device_tracker import SOURCE_TYPE_GPS
from homeassistant.components.device_tracker.config_entry import TrackerEntity
from . import DOMAIN as TESLA_DOMAIN, TeslaDevice
_LOGGER = logging.getLogger(__name__)
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Tesla binary_sensors by config_entry."""
entities = [
TeslaDeviceEntity(
device,
hass.data[TESLA_DOMAIN][config_entry.entry_id]["controller"],
config_entry,
)
for device in hass.data[TESLA_DOMAIN][config_entry.entry_id]["devices"][
"devices_tracker"
]
]
async_add_entities(entities, True)
class TeslaDeviceEntity(TeslaDevice, TrackerEntity):
"""A class representing a Tesla device."""
def __init__(self, tesla_device, controller, config_entry):
"""Initialize the Tesla device scanner."""
super().__init__(tesla_device, controller, config_entry)
self._latitude = None
self._longitude = None
self._attributes = {"trackr_id": self.unique_id}
self._listener = None
async def async_update(self):
"""Update the device info."""
_LOGGER.debug("Updating device position: %s", self.name)
await super().async_update()
location = self.tesla_device.get_location()
if location:
self._latitude = location["latitude"]
self._longitude = location["longitude"]
self._attributes = {
"trackr_id": self.unique_id,
"heading": location["heading"],
"speed": location["speed"],
}
@property
def latitude(self) -> float:
"""Return latitude value of the device."""
return self._latitude
@property
def longitude(self) -> float:
"""Return longitude value of the device."""
return self._longitude
@property
def should_poll(self):
"""Return whether polling is needed."""
return True
@property
def source_type(self):
"""Return the source type, eg gps or router, of the device."""
return SOURCE_TYPE_GPS
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sandbox/grist/migrations.py | gristlabs/grist-core | 2,667 | 79126 | <filename>sandbox/grist/migrations.py
import json
import re
import six
from six.moves import xrange
import actions
import identifiers
import schema
import summary
import table_data_set
import logger
log = logger.Logger(__name__, logger.INFO)
# PHILOSOPHY OF MIGRATIONS.
#
# We should probably never remove, modify, or rename metadata tables or columns.
# Instead, we should only add.
#
# We can mark old columns/tables as deprecated, which should be ignored except to prevent us from
# adding same-named entities in the future.
#
# If we change the meaning of a column, we have to create a new column with a new name.
#
# This should make it at least barely possible to share documents by people who are not all on the
# same Grist version (even so, it will require more work). It should also make it somewhat safe to
# upgrade and then open the document with a previous version.
all_migrations = {}
def noop_migration(_all_tables):
return []
# Each migration function includes a .need_all_tables attribute. See migration() decorator.
noop_migration.need_all_tables = False
def create_migrations(all_tables, metadata_only=False):
"""
Creates and returns a list of DocActions needed to bring this document to
schema.SCHEMA_VERSION.
all_tables: all tables or just the metadata tables (those named with _grist_ prefix) as a
dictionary mapping table name to TableData.
metadata_only: should be set if only metadata tables are passed in. If ALL tables are
required to process migrations, this method will raise a "need all tables..." exception.
"""
try:
doc_version = all_tables['_grist_DocInfo'].columns["schemaVersion"][0]
except Exception:
doc_version = 0
# We create a TableDataSet, and populate it with the subset of the current schema that matches
# all_tables. For missing items, we make up tables and incomplete columns, which should be OK
# since we would not be adding new records to deprecated columns.
# Note that this approach makes it NOT OK to change column types.
tdset = table_data_set.TableDataSet()
# For each table in the provided metadata tables, create an AddTable action.
user_schema = schema.build_schema(all_tables['_grist_Tables'],
all_tables['_grist_Tables_column'],
include_builtin=False)
for t in six.itervalues(user_schema):
tdset.apply_doc_action(actions.AddTable(t.tableId, schema.cols_to_dict_list(t.columns)))
# For each old table/column, construct an AddTable action using the current schema.
new_schema = {a.table_id: a for a in schema.schema_create_actions()}
for table_id, data in sorted(six.iteritems(all_tables)):
# User tables should already be in tdset; the rest must be metadata tables.
# (If metadata_only is true, there is simply nothing to skip here.)
if table_id not in tdset.all_tables:
new_col_info = {}
if table_id in new_schema:
new_col_info = {c['id']: c for c in new_schema[table_id].columns}
# Use an incomplete default for unknown (i.e. deprecated) columns; some uses of the column
# would be invalid, such as adding a new record with missing values.
col_info = sorted([new_col_info.get(col_id, {'id': col_id}) for col_id in data.columns],
key=lambda c: list(six.iteritems(c)))
tdset.apply_doc_action(actions.AddTable(table_id, col_info))
# And load in the original data, interpreting the TableData object as BulkAddRecord action.
tdset.apply_doc_action(actions.BulkAddRecord(*data))
migration_actions = []
for version in xrange(doc_version + 1, schema.SCHEMA_VERSION + 1):
migration_func = all_migrations.get(version, noop_migration)
if migration_func.need_all_tables and metadata_only:
raise Exception("need all tables for migration to %s" % version)
migration_actions.extend(all_migrations.get(version, noop_migration)(tdset))
# Note that if we are downgrading versions (i.e. doc_version is higher), then the following is
# the only action we include into the migration.
migration_actions.append(actions.UpdateRecord('_grist_DocInfo', 1, {
'schemaVersion': schema.SCHEMA_VERSION
}))
return migration_actions
def get_last_migration_version():
"""
Returns the last schema version number for which we have a migration defined.
"""
return max(all_migrations)
def migration(schema_version, need_all_tables=False):
"""
Decorator for migrations that associates the decorated migration function with the given
schema_version. This decorated function will be run to migrate forward to schema_version.
Migrations are first attempted with only metadata tables, but if any required migration function
is marked with need_all_tables=True, then the migration will be retried with all tables.
NOTE: new migrations should NOT set need_all_tables=True; it would require more work to process
very large documents safely (incuding those containing on-demand tables).
"""
def add_migration(migration_func):
migration_func.need_all_tables = need_all_tables
all_migrations[schema_version] = migration_func
return migration_func
return add_migration
# A little shorthand to make AddColumn actions more concise.
def add_column(table_id, col_id, col_type, *args, **kwargs):
return actions.AddColumn(table_id, col_id,
schema.make_column(col_id, col_type, *args, **kwargs))
# Another shorthand to only add a column if it isn't already there.
def maybe_add_column(tdset, table_id, col_id, col_type, *args, **kwargs):
if col_id not in tdset.all_tables[table_id].columns:
return add_column(table_id, col_id, col_type, *args, **kwargs)
return None
# Returns the next unused row id for the records of the table given by table_id.
def next_id(tdset, table_id):
row_ids = tdset.all_tables[table_id].row_ids
return max(row_ids) + 1 if row_ids else 1
# Parses a json string, but returns an empty object for invalid json.
def safe_parse(json_str):
try:
return json.loads(json_str)
except ValueError:
return {}
@migration(schema_version=1)
def migration1(tdset):
"""
Add TabItems table, and populate based on existing sections.
"""
doc_actions = []
# The very first migration is extra-lax, and creates some tables that are missing in some test
# docs. That's only because we did not distinguish schema version before migrations were
# implemented. Other migrations should not need such conditionals.
if '_grist_Attachments' not in tdset.all_tables:
doc_actions.append(actions.AddTable("_grist_Attachments", [
schema.make_column("fileIdent", "Text"),
schema.make_column("fileName", "Text"),
schema.make_column("fileType", "Text"),
schema.make_column("fileSize", "Int"),
schema.make_column("timeUploaded", "DateTime")
]))
if '_grist_TabItems' not in tdset.all_tables:
doc_actions.append(actions.AddTable("_grist_TabItems", [
schema.make_column("tableRef", "Ref:_grist_Tables"),
schema.make_column("viewRef", "Ref:_grist_Views"),
]))
if 'schemaVersion' not in tdset.all_tables['_grist_DocInfo'].columns:
doc_actions.append(add_column('_grist_DocInfo', 'schemaVersion', 'Int'))
doc_actions.extend([
add_column('_grist_Attachments', 'imageHeight', 'Int'),
add_column('_grist_Attachments', 'imageWidth', 'Int'),
])
view_sections = actions.transpose_bulk_action(tdset.all_tables['_grist_Views_section'])
rows = sorted({(s.tableRef, s.parentId) for s in view_sections})
if rows:
values = {'tableRef': [r[0] for r in rows],
'viewRef': [r[1] for r in rows]}
row_ids = list(xrange(1, len(rows) + 1))
doc_actions.append(actions.ReplaceTableData('_grist_TabItems', row_ids, values))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=2)
def migration2(tdset):
"""
Add TableViews table, and populate based on existing sections.
Add TabBar table, and populate based on existing views.
Add PrimaryViewId to Tables and populated using relatedViews
"""
# Maps tableRef to viewRef
primary_views = {}
# Associate each view with a single table; this dict includes primary views.
views_to_table = {}
# For each table, find a view to serve as the primary view.
view_sections = actions.transpose_bulk_action(tdset.all_tables['_grist_Views_section'])
for s in view_sections:
if s.tableRef not in primary_views and s.parentKey == "record":
# The view containing this section is a good candidate for primary view.
primary_views[s.tableRef] = s.parentId
if s.parentId not in views_to_table:
# The first time we see a (view, table) combination, associate the view with that table.
views_to_table[s.parentId] = s.tableRef
def create_primary_views_action(primary_views):
row_ids = sorted(primary_views.keys())
values = {'primaryViewId': [primary_views[r] for r in row_ids]}
return actions.BulkUpdateRecord('_grist_Tables', row_ids, values)
def create_tab_bar_action(views_to_table):
row_ids = list(xrange(1, len(views_to_table) + 1))
return actions.ReplaceTableData('_grist_TabBar', row_ids, {
'viewRef': sorted(views_to_table.keys())
})
def create_table_views_action(views_to_table, primary_views):
related_views = sorted(set(views_to_table.keys()) - set(primary_views.values()))
row_ids = list(xrange(1, len(related_views) + 1))
return actions.ReplaceTableData('_grist_TableViews', row_ids, {
'tableRef': [views_to_table[v] for v in related_views],
'viewRef': related_views,
})
return tdset.apply_doc_actions([
actions.AddTable('_grist_TabBar', [
schema.make_column('viewRef', 'Ref:_grist_Views'),
]),
actions.AddTable('_grist_TableViews', [
schema.make_column('tableRef', 'Ref:_grist_Tables'),
schema.make_column('viewRef', 'Ref:_grist_Views'),
]),
add_column('_grist_Tables', 'primaryViewId', 'Ref:_grist_Views'),
create_primary_views_action(primary_views),
create_tab_bar_action(views_to_table),
create_table_views_action(views_to_table, primary_views)
])
@migration(schema_version=3)
def migration3(tdset):
"""
There is no longer a "Derived" type for columns, and summary tables use the type suitable for
the column being summarized. For old documents, convert "Derived" type to "Any", and adjust the
usage of "lookupOrAddDerived()" function.
"""
# Note that this is a complicated migration, and mainly acceptable because it is before our very
# first release. For a released product, a change like this should be done in a backwards
# compatible way: keep but deprecate 'Derived'; introduce a lookupOrAddDerived2() to use for new
# summary tables, but keep the old interface as well for existing ones. The reason is that such
# migrations are error-prone and may mess up customers' data.
doc_actions = []
tables = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables']))
tables_map = {t.id: t for t in tables}
columns = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables_column']))
# Convert columns from type 'Derived' to type 'Any'
affected_cols = [c for c in columns if c.type == 'Derived']
if affected_cols:
doc_actions.extend(
actions.ModifyColumn(tables_map[c.parentId].tableId, c.colId, {'type': 'Any'})
for c in affected_cols
)
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables_column',
[c.id for c in affected_cols],
{'type': ['Any' for c in affected_cols]}
))
# Convert formulas of the form '.lookupOrAddDerived($x,$y)' to '.lookupOrAddDerived(x=$x,y=$y)'
formula_re = re.compile(r'(\w+).lookupOrAddDerived\((.*?)\)')
arg_re = re.compile(r'^\$(\w+)$')
def replace(match):
args = ", ".join(arg_re.sub(r'\1=$\1', arg.strip()) for arg in match.group(2).split(","))
return '%s.lookupOrAddDerived(%s)' % (match.group(1), args)
formula_updates = []
for c in columns:
new_formula = c.formula and formula_re.sub(replace, c.formula)
if new_formula != c.formula:
formula_updates.append((c, new_formula))
if formula_updates:
doc_actions.extend(
actions.ModifyColumn(tables_map[c.parentId].tableId, c.colId, {'formula': f})
for c, f in formula_updates
)
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables_column',
[c.id for c, f in formula_updates],
{'formula': [f for c, f in formula_updates]}
))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=4)
def migration4(tdset):
"""
Add TabPos column to TabBar table
"""
doc_actions = []
row_ids = tdset.all_tables['_grist_TabBar'].row_ids
doc_actions.append(add_column('_grist_TabBar', 'tabPos', 'PositionNumber'))
doc_actions.append(actions.BulkUpdateRecord('_grist_TabBar', row_ids, {'tabPos': row_ids}))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=5)
def migration5(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Views', 'primaryViewTable', 'Ref:_grist_Tables',
formula='_grist_Tables.lookupOne(primaryViewId=$id)', isFormula=True),
])
@migration(schema_version=6)
def migration6(tdset):
# This undoes the previous migration, since primaryViewTable is now a formula private to the
# sandbox rather than part of the document schema.
return tdset.apply_doc_actions([
actions.RemoveColumn('_grist_Views', 'primaryViewTable'),
])
@migration(schema_version=7)
def migration7(tdset):
"""
Add summarySourceTable/summarySourceCol fields to metadata, and adjust existing summary tables
to correspond to the new style.
"""
# Note: this migration has some faults.
# - It doesn't delete viewSectionFields for columns it removes (if a user added some special
# columns manually.
# - It doesn't fix types of Reference columns that refer to old-style summary tables
# (if the user created some such columns manually).
doc_actions = [action for action in [
maybe_add_column(tdset, '_grist_Tables', 'summarySourceTable', 'Ref:_grist_Tables'),
maybe_add_column(tdset, '_grist_Tables_column', 'summarySourceCol', 'Ref:_grist_Tables_column')
] if action]
# Maps tableRef to Table object.
tables_map = {t.id: t for t in actions.transpose_bulk_action(tdset.all_tables['_grist_Tables'])}
# Maps tableName to tableRef
table_name_to_ref = {t.tableId: t.id for t in six.itervalues(tables_map)}
# List of Column objects
columns = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables_column']))
# Maps columnRef to Column object.
columns_map_by_ref = {c.id: c for c in columns}
# Maps (tableRef, colName) to Column object.
columns_map_by_table_colid = {(c.parentId, c.colId): c for c in columns}
# Set of all tableNames.
table_name_set = set(table_name_to_ref.keys())
remove_cols = [] # List of columns to remove
formula_updates = [] # List of (column, new_table_name, new_formula) pairs
table_renames = [] # List of (table, new_name) pairs
source_tables = [] # List of (table, summarySourceTable) pairs
source_cols = [] # List of (column, summarySourceColumn) pairs
# Summary tables used to be named as "Summary_<SourceName>_<ColRef1>_<ColRef2>". This regular
# expression parses that.
summary_re = re.compile(r'^Summary_(\w+?)((?:_\d+)*)$')
for t in six.itervalues(tables_map):
m = summary_re.match(t.tableId)
if not m or m.group(1) not in table_name_to_ref:
continue
# We have a valid summary table.
source_table_name = m.group(1)
source_table_ref = table_name_to_ref[source_table_name]
groupby_colrefs = [int(x) for x in m.group(2).strip("_").split("_")]
# Prepare a new-style name for the summary table. Be sure not to conflict with existing tables
# or with each other (i.e. don't rename multiple tables to the same name).
new_name = summary.encode_summary_table_name(source_table_name)
new_name = identifiers.pick_table_ident(new_name, avoid=table_name_set)
table_name_set.add(new_name)
log.warn("Upgrading summary table %s for %s(%s) to %s" % (
t.tableId, source_table_name, groupby_colrefs, new_name))
# Remove the "lookupOrAddDerived" column from the source table (which is named using the
# summary table name for its colId).
remove_cols.extend(c for c in columns
if c.parentId == source_table_ref and c.colId == t.tableId)
# Upgrade the "group" formula in the summary table.
expected_group_formula = "%s.lookupRecords(%s=$id)" % (source_table_name, t.tableId)
new_formula = "table.getSummarySourceGroup(rec)"
formula_updates.extend((c, new_name, new_formula) for c in columns
if (c.parentId == t.id and c.colId == "group" and
c.formula == expected_group_formula))
# Schedule a rename of the summary table.
table_renames.append((t, new_name))
# Set summarySourceTable fields on the metadata.
source_tables.append((t, source_table_ref))
# Set summarySourceCol fields in the metadata. We need to find the right summary column.
groupby_cols = set()
for col_ref in groupby_colrefs:
src_col = columns_map_by_ref.get(col_ref)
sum_col = columns_map_by_table_colid.get((t.id, src_col.colId)) if src_col else None
if sum_col:
groupby_cols.add(sum_col)
source_cols.append((sum_col, src_col.id))
else:
log.warn("Upgrading summary table %s: couldn't find column %s" % (t.tableId, col_ref))
# Finally, we have to remove all non-formula columns that are not groupby-columns (e.g.
# 'manualSort'), because the new approach assumes ALL non-formula columns are for groupby.
remove_cols.extend(c for c in columns
if c.parentId == t.id and c not in groupby_cols and not c.isFormula)
# Create all the doc actions from the arrays we prepared.
# Process remove_cols
doc_actions.extend(
actions.RemoveColumn(tables_map[c.parentId].tableId, c.colId) for c in remove_cols)
doc_actions.append(actions.BulkRemoveRecord(
'_grist_Tables_column', [c.id for c in remove_cols]))
# Process table_renames
doc_actions.extend(
actions.RenameTable(t.tableId, new) for (t, new) in table_renames)
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables', [t.id for t, new in table_renames],
{'tableId': [new for t, new in table_renames]}
))
# Process source_tables and source_cols
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables', [t.id for t, ref in source_tables],
{'summarySourceTable': [ref for t, ref in source_tables]}
))
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables_column', [t.id for t, ref in source_cols],
{'summarySourceCol': [ref for t, ref in source_cols]}
))
# Process formula_updates. Do this last since recalculation of these may cause new records added
# to summary tables, so we should have all the tables correctly set up by this time.
doc_actions.extend(
actions.ModifyColumn(table_id, c.colId, {'formula': f})
for c, table_id, f in formula_updates)
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables_column', [c.id for c, t, f in formula_updates],
{'formula': [f for c, t, f in formula_updates]}
))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=8)
def migration8(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Tables_column', 'untieColIdFromLabel', 'Bool'),
])
@migration(schema_version=9)
def migration9(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Tables_column', 'displayCol', 'Ref:_grist_Tables_column'),
add_column('_grist_Views_section_field', 'displayCol', 'Ref:_grist_Tables_column'),
])
@migration(schema_version=10)
def migration10(tdset):
"""
Add displayCol to all reference cols, with formula $<ref_col_id>.<visible_col_id>
(Note that displayCol field was added in the previous migration.)
"""
doc_actions = []
tables = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables']))
columns = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables_column']))
# Maps tableRef to tableId.
tables_map = {t.id: t.tableId for t in tables}
# Maps tableRef to sets of colIds in the tables. Used to prevent repeated colIds.
table_col_ids = {t.id: set(tdset.all_tables[t.tableId].columns.keys()) for t in tables}
# Get the next sequential column row id.
row_id = next_id(tdset, '_grist_Tables_column')
for c in columns:
# If a column is a reference with an unset display column, add a display column.
if c.type.startswith('Ref:') and not c.displayCol:
# Get visible_col_id. If not found, row id is used and no display col is necessary.
visible_col_id = ""
try:
visible_col_id = json.loads(c.widgetOptions).get('visibleCol')
if not visible_col_id:
continue
except Exception:
continue # If invalid widgetOptions, skip this column.
# Set formula to use the current visibleCol in widgetOptions.
formula = ("$%s.%s" % (c.colId, visible_col_id))
# Get a unique colId for the display column, and add it to the set of used ids.
used_col_ids = table_col_ids[c.parentId]
display_col_id = identifiers.pick_col_ident('gristHelper_Display', avoid=used_col_ids)
used_col_ids.add(display_col_id)
# Add all actions to the list.
doc_actions.append(add_column(tables_map[c.parentId], 'gristHelper_Display', 'Any',
formula=formula, isFormula=True))
doc_actions.append(actions.AddRecord('_grist_Tables_column', row_id, {
'parentPos': 1.0,
'label': 'gristHelper_Display',
'isFormula': True,
'parentId': c.parentId,
'colId': 'gristHelper_Display',
'formula': formula,
'widgetOptions': '',
'type': 'Any'
}))
doc_actions.append(actions.UpdateRecord('_grist_Tables_column', c.id, {'displayCol': row_id}))
# Increment row id to the next unused.
row_id += 1
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=11)
def migration11(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Views_section', 'embedId', 'Text'),
])
@migration(schema_version=12)
def migration12(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Views_section', 'options', 'Text')
])
@migration(schema_version=13)
def migration13(tdset):
# Adds a basketId to the entire document to take advantage of basket functionality.
# From this version on, embedId is deprecated.
return tdset.apply_doc_actions([
add_column('_grist_DocInfo', 'basketId', 'Text')
])
@migration(schema_version=14)
def migration14(tdset):
# Create the ACL table AND also the default ACL groups, default resource, and the default rule.
# These match the actions applied to new document by 'InitNewDoc' useraction (as of v14).
return tdset.apply_doc_actions([
actions.AddTable('_grist_ACLMemberships', [
schema.make_column('parent', 'Ref:_grist_ACLPrincipals'),
schema.make_column('child', 'Ref:_grist_ACLPrincipals'),
]),
actions.AddTable('_grist_ACLPrincipals', [
schema.make_column('userName', 'Text'),
schema.make_column('groupName', 'Text'),
schema.make_column('userEmail', 'Text'),
schema.make_column('instanceId', 'Text'),
schema.make_column('type', 'Text'),
]),
actions.AddTable('_grist_ACLResources', [
schema.make_column('colIds', 'Text'),
schema.make_column('tableId', 'Text'),
]),
actions.AddTable('_grist_ACLRules', [
schema.make_column('aclFormula', 'Text'),
schema.make_column('principals', 'Text'),
schema.make_column('resource', 'Ref:_grist_ACLResources'),
schema.make_column('aclColumn', 'Ref:_grist_Tables_column'),
schema.make_column('permissions', 'Int'),
]),
# Set up initial ACL data.
actions.BulkAddRecord('_grist_ACLPrincipals', [1,2,3,4], {
'type': ['group', 'group', 'group', 'group'],
'groupName': ['Owners', 'Admins', 'Editors', 'Viewers'],
}),
actions.AddRecord('_grist_ACLResources', 1, {
'tableId': '', 'colIds': ''
}),
actions.AddRecord('_grist_ACLRules', 1, {
'resource': 1, 'permissions': 0x3F, 'principals': '[1]'
}),
])
@migration(schema_version=15)
def migration15(tdset):
# Adds a filter JSON property to each field.
# From this version on, filterSpec in _grist_Views_section is deprecated.
doc_actions = [
add_column('_grist_Views_section_field', 'filter', 'Text')
]
# Get all section and field data to move section filter data to the fields
sections = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Views_section']))
fields = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Views_section_field']))
specs = {s.id: safe_parse(s.filterSpec) for s in sections}
# Move filter data from sections to fields
for f in fields:
# If the field belongs to the section and the field's colRef is in its filterSpec,
# pull the filter setting from the section.
filter_spec = specs.get(f.parentId)
if filter_spec and str(f.colRef) in filter_spec:
doc_actions.append(actions.UpdateRecord('_grist_Views_section_field', f.id, {
'filter': json.dumps(filter_spec[str(f.colRef)])
}))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=16)
def migration16(tdset):
# Add visibleCol to columns and view fields, and set it from columns' and fields' widgetOptions.
doc_actions = [
add_column('_grist_Tables_column', 'visibleCol', 'Ref:_grist_Tables_column'),
add_column('_grist_Views_section_field', 'visibleCol', 'Ref:_grist_Tables_column'),
]
# Maps tableId to table, for looking up target table as listed in "Ref:*" types.
tables = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables']))
tables_by_id = {t.tableId: t for t in tables}
# Allow looking up columns by ref or by (tableRef, colId)
columns = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables_column']))
columns_by_ref = {c.id: c for c in columns}
columns_by_id = {(c.parentId, c.colId): c.id for c in columns}
# Helper which returns the {'visibleCol', 'widgetOptions'} update visibleCol should be set.
def convert_visible_col(col, widget_options):
if not col.type.startswith('Ref:'):
return None
# To set visibleCol, we need to know the target table. Skip if we can't find it.
target_table = tables_by_id.get(col.type[len('Ref:'):])
if not target_table:
return None
try:
parsed_options = json.loads(widget_options)
except Exception:
return None # If invalid widgetOptions, skip this column.
visible_col_id = parsed_options.pop('visibleCol', None)
if not visible_col_id:
return None
# Find visible_col_id as the column name in the appropriate table.
target_col_ref = (0 if visible_col_id == 'id' else
columns_by_id.get((target_table.id, visible_col_id), None))
if target_col_ref is None:
return None
# Use compact separators without whitespace, to match how JS encodes JSON.
return {'visibleCol': target_col_ref,
'widgetOptions': json.dumps(parsed_options, separators=(',', ':')) }
for c in columns:
new_values = convert_visible_col(c, c.widgetOptions)
if new_values:
doc_actions.append(actions.UpdateRecord('_grist_Tables_column', c.id, new_values))
fields = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Views_section_field']))
for f in fields:
c = columns_by_ref.get(f.colRef)
if c:
new_values = convert_visible_col(c, f.widgetOptions)
if new_values:
doc_actions.append(actions.UpdateRecord('_grist_Views_section_field', f.id, new_values))
return tdset.apply_doc_actions(doc_actions)
# This is actually the only migration that requires all tables because it modifies user data
# (specifically, any columns of the deprecated "Image" type).
@migration(schema_version=17, need_all_tables=True)
def migration17(tdset):
"""
There is no longer an "Image" type for columns, as "Attachments" now serves as a
display type for arbitrary files including images. Convert "Image" columns to "Attachments"
columns.
"""
doc_actions = []
tables = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables']))
tables_map = {t.id: t for t in tables}
columns = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables_column']))
# Convert columns from type 'Image' to type 'Attachments'
affected_cols = [c for c in columns if c.type == 'Image']
conv = lambda val: [val] if isinstance(val, int) and val > 0 else []
if affected_cols:
# Update the types in the data tables
doc_actions.extend(
actions.ModifyColumn(tables_map[c.parentId].tableId, c.colId, {'type': 'Attachments'})
for c in affected_cols
)
# Update the values to lists
for c in affected_cols:
if c.isFormula:
# Formula columns don't have data stored in DB, should not have data changes.
continue
table_id = tables_map[c.parentId].tableId
table = tdset.all_tables[table_id]
doc_actions.append(
actions.BulkUpdateRecord(table_id, table.row_ids,
{c.colId: [conv(val) for val in table.columns[c.colId]]})
)
# Update the types in the metadata tables
doc_actions.append(actions.BulkUpdateRecord(
'_grist_Tables_column',
[c.id for c in affected_cols],
{'type': ['Attachments' for c in affected_cols]}
))
return tdset.apply_doc_actions(doc_actions)
@migration(schema_version=18)
def migration18(tdset):
return tdset.apply_doc_actions([
add_column('_grist_DocInfo', 'timezone', 'Text'),
# all documents prior to this migration have been created in New York
actions.UpdateRecord('_grist_DocInfo', 1, {'timezone': 'America/New_York'})
])
@migration(schema_version=19)
def migration19(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Tables', 'onDemand', 'Bool'),
])
@migration(schema_version=20)
def migration20(tdset):
"""
Add _grist_Pages table and populate based on existing TableViews entries, ie: tables are sorted
alphabetically by their `tableId` and views are gathered within their corresponding table and
sorted by their id.
"""
tables = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Tables']))
table_map = {t.id: t for t in tables}
table_views = list(actions.transpose_bulk_action(tdset.all_tables['_grist_TableViews']))
# Old docs may include "Other views", not associated with any table. Don't include those in
# table_views_map: they'll get included but not sorted or grouped by tableId.
table_views_map = {tv.viewRef: table_map[tv.tableRef].tableId
for tv in table_views if tv.tableRef in table_map}
views = list(actions.transpose_bulk_action(tdset.all_tables['_grist_Views']))
def view_key(view):
"""
Returns ("Table1", 2) where "Table1" is the view's tableId and 2 the view id. For
primary view (ie: not referenced in _grist_TableViews) returns ("Table1", -1). Useful
to get the list of views sorted in the same way as in the Table side pane. We use -1
for primary view to make sure they come first among all the views of the same table.
"""
if view.id in table_views_map:
return (table_views_map[view.id], view.id)
# the name of primary view's is the same as the tableId
return (view.name, -1)
views.sort(key=view_key)
row_ids = list(xrange(1, len(views) + 1))
return tdset.apply_doc_actions([
actions.AddTable('_grist_Pages', [
schema.make_column('viewRef', 'Ref:_grist_Views'),
schema.make_column('pagePos', 'PositionNumber'),
schema.make_column('indentation', 'Int'),
]),
actions.ReplaceTableData('_grist_Pages', row_ids, {
'viewRef': [v.id for v in views],
'pagePos': row_ids,
'indentation': [1 if v.id in table_views_map else 0 for v in views]
})
])
@migration(schema_version=21)
def migration21(tdset):
return tdset.apply_doc_actions([
add_column('_grist_ACLRules', 'aclFormulaParsed', 'Text'),
add_column('_grist_ACLRules', 'permissionsText', 'Text'),
add_column('_grist_ACLRules', 'rulePos', 'PositionNumber'),
add_column('_grist_ACLRules', 'userAttributes', 'Text'),
])
@migration(schema_version=22)
def migration22(tdset):
return tdset.apply_doc_actions([
add_column('_grist_Tables_column', 'recalcWhen', 'Int'),
add_column('_grist_Tables_column', 'recalcDeps', 'RefList:_grist_Tables_column'),
])
@migration(schema_version=23)
def migration23(tdset):
return tdset.apply_doc_actions([
add_column('_grist_DocInfo', 'documentSettings', 'Text'),
actions.UpdateRecord('_grist_DocInfo', 1, {'documentSettings': '{"locale":"en-US"}'})
])
@migration(schema_version=24)
def migration24(tdset):
return tdset.apply_doc_actions([
actions.AddTable('_grist_Triggers', [
schema.make_column("tableRef", "Ref:_grist_Tables"),
schema.make_column("eventTypes", "ChoiceList"),
schema.make_column("isReadyColRef", "Ref:_grist_Tables_column"),
schema.make_column("actions", "Text"), # JSON
]),
])
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] |
tools/make_folders.py | leonzucchini/recipes | 8 | 31509 | <filename>tools/make_folders.py
import os
import sys
import shutil
import re
def make_output_folder(folder_path, debug=False):
""" Make folder for output, checking for previous results """
# Skip if debug (avoids replace prompt)
if debug:
print "FolderSetup warning: Not creating directory because debug = True"
pass
else:
# If destination folder does not exist then create it
if not os.path.exists(folder_path):
os.mkdir(folder_path)
else:
# Otherwise give a choice to replace (overwrite), use, or exit
confirm_prompt = "The following folder exists:" + "\n" + \
str(folder_path) + "\n" + \
"Would you like to add to it ('a'), overwrite ('o'), or exit ('e'): "
confirm = raw_input(confirm_prompt)
# Prompt for correctly formatted input (y/n)
while not re.search(r'[aeo]', confirm):
confirm_prompt = "Please confirm what you want to do." + "\n" + \
"Would you like to add to it ('a'), overwrite ('o'), or exit ('e'):"
confirm = raw_input(confirm_prompt)
# If exit
if confirm == "e":
print "OK exiting."
sys.exit(1)
# Else if overwrite
elif confirm == "o":
# Make folder path
shutil.rmtree(folder_path)
os.mkdir(folder_path)
print "Created output folder: %s" %(folder_path)
# Else if add
elif confirm == "a":
print "OK adding to folder"
return None | [
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] |
bounties_api/std_bounties/migrations/0003_bountystate_event.py | tenthirtyone/BountiesAPI | 45 | 173151 | <reponame>tenthirtyone/BountiesAPI
# -*- coding: utf-8 -*-
# Generated by Django 1.11.11 on 2018-04-18 22:15
from __future__ import unicode_literals
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('std_bounties', '0002_auto_20180409_2309'),
]
operations = [
migrations.CreateModel(
name='BountyState',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('bountyStage', models.IntegerField()),
('change_date', models.DateTimeField()),
('bounty', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='std_bounties.Bounty')),
],
options={
'get_latest_by': 'change_date',
},
),
migrations.CreateModel(
name='Event',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('event', models.CharField(max_length=128)),
('fulfillment_id', models.IntegerField(null=True)),
('transaction_hash', models.CharField(max_length=128)),
('transaction_from', models.CharField(max_length=128)),
('contract_inputs', django.contrib.postgres.fields.jsonb.JSONField(null=True)),
('event_date', models.DateTimeField()),
('bounty', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='std_bounties.Bounty')),
],
),
]
| [
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] |
script/organiseTaskList.py | shawnwun/woz | 13 | 86374 | ######################################################################
######################################################################
# Copyright <NAME>, Cambridge Dialogue Systems Group, 2017 #
######################################################################
######################################################################
import sys
import os
import operator
import json
import random
random.seed(0)
tasklist = sorted(os.listdir('tempTasks'))
fin = file('tmp/finishedList')
finished = [l.replace('\n','') for l in fin.readlines()][1:]
fin.close()
todos = []
for filename in tasklist:
print filename
fin = file('tempTasks/'+filename)
task= json.load(fin)
fin.close()
if 'eod' in task['goal'] and task['goal']['eod'] and len(task['log'])!=0:
# indicating it is finished
if (len(task['log'])<10 and 'MUL' in filename) or\
(len(task['log'])<4 and 'SNG' in filename) or\
len(task['log'])%2==1:
# if length is too short, not finished
todos.append(filename)
elif filename in finished:
# if already in finished list
pass
else:
# if cannot decide
for i in range(len(task['log'])):
print task['log'][i]['text']
ans = raw_input('%s is finished? (y/n), %d ' % (filename,len(task['log'])))
print
if ans=='n':
# not finished
todos.append(filename)
if ans=='y':
# finished
finished.append(filename)
else:
todos.append(filename)
fout = file('tmp/taskList','w')
fout.write('0\n')
fout.write('\n'.join(todos))
fout.close()
fout = file('tmp/finishedList','w')
fout.write('\n'.join(['0']+finished))
fout.close()
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controller/controller_client.py | ZeBraHack0/ATP-Controller | 1 | 1617122 | <gh_stars>1-10
from socket import *
import argparse
parser = argparse.ArgumentParser(description='P4ML User Client')
parser.add_argument('--model', type=str, default='vgg16',
help='model to train')
parser.add_argument('--dataset', type=str, default='benchmark',
help='dataset to train')
parser.add_argument('--gpus', type=int, default=1,
help='number of training gpus')
parser.add_argument('--num_iters', type=int, default=10,
help='number of training iterations')
args = parser.parse_args()
host = '10.0.0.10'
port = 8888
bufsize = 1024
addr = (host, port)
client = socket(AF_INET, SOCK_STREAM)
client.connect((host, port))
print("client up, enter your job")
try:
cmd = "control_" + str(args.gpus) + "_" + args.dataset + "_" + args.model + "_" + str(args.num_iters)
client.send(cmd.encode('utf-8'))
print("submit job successfully!")
print("waiting for job finishing...")
data = client.recv(bufsize)
print(data.decode('utf-8'))
except Exception as e:
print(e)
client.close() | [
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focal_mech/demo/test6.py | blasscoc/FocalMechClassifier | 12 | 12361 | from numpy import array, rad2deg, pi, mgrid, argmin
from matplotlib.pylab import contour
import matplotlib.pyplot as plt
import mplstereonet
from obspy.imaging.beachball import aux_plane
from focal_mech.lib.classify_mechanism import classify, translate_to_sphharm
from focal_mech.io.read_hash import read_demo, read_hash_solutions
from focal_mech.util.hash_routines import hash_to_classifier
from focal_mech.lib.sph_harm import get_sph_harm
from focal_mech.lib.correlate import corr_shear
hash_solns = read_hash_solutions("example1.out")
# we want solutions that are symetric
polarity_data = read_demo("north1.phase", "scsn.reverse", reverse=True)
inputs = hash_to_classifier(polarity_data, parity=1)
event = 3146815
result = classify(*inputs[event], kernel_degree=2)
Alm = translate_to_sphharm(*result, kernel_degree=2)
coeffs = array([Alm[0,0],
Alm[1,-1], Alm[1,0], Alm[1,1],
Alm[2,-2], Alm[2,-1], Alm[2,0], Alm[2,1], Alm[2,2]])
svm_soln, f = corr_shear(Alm)
resolution = (200,400)
longi, lati, Z = get_sph_harm(resolution=resolution)
mech = coeffs.dot(Z).real
longi.shape = resolution
lati.shape = resolution
mech.shape = resolution
c = contour(longi, lati, mech, [0])
pth1 = c.collections[0].get_paths()[0].vertices
pth1 = rad2deg(pth1)
pth2 = c.collections[0].get_paths()[1].vertices
pth2 = rad2deg(pth2)
hash_focal = rad2deg(hash_solns[event])
event2 = 3158361
result = classify(*inputs[event2], kernel_degree=2)
Alm = translate_to_sphharm(*result, kernel_degree=2)
coeffs = array([Alm[0,0],
Alm[1,-1], Alm[1,0], Alm[1,1],
Alm[2,-2], Alm[2,-1], Alm[2,0], Alm[2,1], Alm[2,2]])
svm_soln2, f = corr_shear(Alm)
resolution = (200,400)
longi, lati, Z = get_sph_harm(resolution=resolution)
mech = coeffs.dot(Z).real
longi.shape = resolution
lati.shape = resolution
mech.shape = resolution
c = contour(longi, lati, mech, [0])
pth3 = c.collections[0].get_paths()[0].vertices
pth3 = rad2deg(pth3)
pth4 = c.collections[0].get_paths()[1].vertices
pth4 = rad2deg(pth4)
hash_focal2 = rad2deg(hash_solns[event2])
event3 = 3153955
result = classify(*inputs[event3], kernel_degree=2)
Alm = translate_to_sphharm(*result, kernel_degree=2)
coeffs = array([Alm[0,0],
Alm[1,-1], Alm[1,0], Alm[1,1],
Alm[2,-2], Alm[2,-1], Alm[2,0], Alm[2,1], Alm[2,2]])
svm_soln3, f = corr_shear(Alm)
resolution = (200,400)
longi, lati, Z = get_sph_harm(resolution=resolution)
mech = coeffs.dot(Z).real
longi.shape = resolution
lati.shape = resolution
mech.shape = resolution
c = contour(longi, lati, mech, [0])
pth5 = c.collections[0].get_paths()[0].vertices
pth5 = rad2deg(pth5)
pth6 = c.collections[0].get_paths()[1].vertices
pth6 = rad2deg(pth6)
hash_focal3 = rad2deg(hash_solns[event3])
fig = plt.figure(facecolor="white", figsize=(10,20))
ax = fig.add_subplot(221, projection='stereonet')
ax.rake(pth1[:,0], pth1[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
ax.rake(pth2[:,0], pth2[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
strike, dip, rake = svm_soln
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = aux_plane(*svm_soln)
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = hash_focal
ax.plane(strike-90, dip, 'g-', linewidth=2)
strike, dip, rake = aux_plane(*hash_focal)
ax.plane(strike-90, dip,'g-', linewidth=2)
azi = rad2deg(polarity_data[event][:,0])
toa = rad2deg(polarity_data[event][:,1])
polarity = polarity_data[event][:,2]
for a, t, p in zip(azi, toa, polarity):
if p > 0:
ax.pole(a, t,'o', markeredgecolor='red', markerfacecolor='red')
else:
ax.pole(a, t,'o', markeredgecolor='blue', markerfacecolor='white')
ax.grid()
ax = fig.add_subplot(222, projection='stereonet')
ax.rake(pth3[:,0], pth3[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
ax.rake(pth4[:,0], pth4[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
strike, dip, rake = svm_soln2
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = aux_plane(*svm_soln2)
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = hash_focal2
ax.plane(strike-90, dip, 'g-', linewidth=2)
strike, dip, rake = aux_plane(*hash_focal2)
ax.plane(strike-90, dip,'g-', linewidth=2)
azi = rad2deg(polarity_data[event2][:,0])
toa = rad2deg(polarity_data[event2][:,1])
polarity = polarity_data[event2][:,2]
for a, t, p in zip(azi, toa, polarity):
if p > 0:
ax.pole(a, t,'o', markeredgecolor='red', markerfacecolor='red')
else:
ax.pole(a, t,'o', markeredgecolor='blue', markerfacecolor='white')
ax.grid()
ax = fig.add_subplot(224, projection='stereonet')
ax.rake(pth5[:,0], pth5[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
ax.rake(pth6[:,0], pth6[:,1] +90.0, 90.0, ':', color='red', linewidth=3)
strike, dip, rake = svm_soln3
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = aux_plane(*svm_soln3)
ax.plane(strike, dip, '-r', linewidth=2)
strike, dip, rake = hash_focal3
ax.plane(strike-90, dip, 'g-', linewidth=2)
strike, dip, rake = aux_plane(*hash_focal3)
ax.plane(strike-90, dip,'g-', linewidth=2)
azi = rad2deg(polarity_data[event3][:,0])
toa = rad2deg(polarity_data[event3][:,1])
polarity = polarity_data[event3][:,2]
for a, t, p in zip(azi, toa, polarity):
if p > 0:
ax.pole(a, t,'o', markeredgecolor='red', markerfacecolor='red')
else:
ax.pole(a, t,'o', markeredgecolor='blue', markerfacecolor='white')
ax.grid()
plt.tight_layout(pad=4.0, h_pad=20.0)
plt.show()
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] |
tests/test_Methods.py | davidjsanders/python_cowbull_webapp | 0 | 125004 | <filename>tests/test_Methods.py
import io
import logging
import os
import unittest
from app import app
from GameSPA.GameSPA import GameSPA
from initialization_package.set_config import set_config
from unittest import TestCase
class TestMethods(TestCase):
def setUp(self):
# Setup the environment to ensure any running cowbull
# game server is ignored.
os.environ['COWBULL_SERVER'] = '0'
os.environ['COWBULL_PORT'] = '0'
# Import the app and set the config.
from app import app
app.config['TESTING'] = True
app.config['WTF_CSRF_ENABLED'] = False
app.config['DEBUG'] = False
self.app = app.test_client()
def tearDown(self):
pass
def test_set_config(self):
# Note: PASS the real app not the test app
set_config(self.app.application)
def test_set_config_custom_config(self):
# Note: PASS the real app not the test app
os.environ['COWBULL_PROTOCOL'] = "https"
os.environ['COWBULL_SERVER'] = "foobar"
os.environ['COWBULL_PORT'] = "8"
os.environ['COWBULL_VERSION'] = "v2"
os.environ['COWBULL_ENVIRONMENT'] = "UnitTest"
os.environ['NAVBAR_COLOUR'] = "BLUE"
os.environ['BUILD_NUMBER'] = "1.2.3.4"
os.environ['FLASK_PORT'] = "80"
os.environ['FLASK_HOST'] = "172.16.17.32"
set_config(self.app.application)
self.assertEqual(self.app.application.config['cowbull_protocol'], "https")
self.assertEqual(self.app.application.config['cowbull_server'], "foobar")
self.assertEqual(self.app.application.config['cowbull_port'], "8")
self.assertEqual(self.app.application.config['cowbull_version'], "v2")
self.assertEqual(
self.app.application.config['cowbull_url'],
"https://foobar:8/v2"
)
self.assertEqual(
self.app.application.config['cowbull_modes_url'],
"https://foobar:8/v2/modes"
)
self.assertEqual(
self.app.application.config['cowbull_game_url'],
"https://foobar:8/v2/game"
)
self.assertEqual(self.app.application.config['environment'], "UnitTest")
self.assertEqual(self.app.application.config['navbar_colour'], "BLUE")
self.assertEqual(self.app.application.config['build_number'], "1.2.3.4")
self.assertEqual(self.app.application.config['FLASK_PORT'], 80)
self.assertEqual(self.app.application.config['FLASK_HOST'], '4.3.2.1')
def test_set_config_bad_port(self):
# Note: PASS the real app not the test app
os.environ['FLASK_PORT'] = "BADINT"
set_config(self.app.application)
self.assertEqual(self.app.application.config['FLASK_PORT'], 8001)
def test_set_config_value_error(self):
with self.assertRaises(ValueError):
set_config()
# Expect the game to fail in unit test. Cowbull server should NOT be running
# def test_no_game(self):
# response = self.app.get('/', follow_redirects=True)
# response_expected = "Game is unavailable"
# self.assertTrue(response_expected in str(response.get_data()))
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setup.py | viktorsapozhok/kubernetes-cronjob-tutorial | 0 | 132236 | from setuptools import setup
def get_requirements():
r = []
with open("requirements.txt") as fp:
for line in fp.read().split("\n"):
if not line.startswith("#"):
r += [line.strip()]
return r
setup(
name="app",
packages=["app"],
include_package_data=True,
zip_safe=False,
install_requires=get_requirements(),
entry_points={
"console_scripts": [
"myapp=app.cli:main",
]
},
)
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] |
stubs/ssm.py | claytonbrown/troposphere | 0 | 157658 | <reponame>claytonbrown/troposphere
from . import AWSObject, AWSProperty
from .validators import *
from .constants import *
# -------------------------------------------
class SSMParameterValues(AWSProperty):
"""# ParameterValues - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-parametervalues.html",
"Properties": {
"ParameterValues": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-parametervalues.html#cfn-ssm-association-parametervalues-parametervalues",
"DuplicatesAllowed": false,
"PrimitiveItemType": "String",
"Required": true,
"Type": "List",
"UpdateType": "Mutable"
}
}
}
"""
props = {
'ParameterValues': ([basestring], True, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-parametervalues.html#cfn-ssm-association-parametervalues-parametervalues')
}
# -------------------------------------------
class SSMTarget(AWSProperty):
"""# Target - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-target.html",
"Properties": {
"Key": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-target.html#cfn-ssm-association-target-key",
"PrimitiveType": "String",
"Required": true,
"UpdateType": "Immutable"
},
"Values": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-target.html#cfn-ssm-association-target-values",
"DuplicatesAllowed": false,
"PrimitiveItemType": "String",
"Required": true,
"Type": "List",
"UpdateType": "Immutable"
}
}
}
"""
props = {
'Key': (basestring, True, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-target.html#cfn-ssm-association-target-key'),
'Values': ([basestring], True, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ssm-association-target.html#cfn-ssm-association-target-values')
}
# -------------------------------------------
class SSMDocument(AWSObject):
"""# AWS::SSM::Document - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-document.html",
"Properties": {
"Content": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-document.html#cfn-ssm-document-content",
"PrimitiveType": "Json",
"Required": true,
"UpdateType": "Immutable"
},
"DocumentType": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-document.html#cfn-ssm-document-documenttype",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Immutable"
}
}
}
"""
resource_type = "AWS::SSM::Document"
props = {
'Content': ((basestring, dict), True, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-document.html#cfn-ssm-document-content'),
'DocumentType': (basestring, False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-document.html#cfn-ssm-document-documenttype')
}
# -------------------------------------------
class SSMParameter(AWSObject):
"""# AWS::SSM::Parameter - CloudFormationResourceSpecification version: 1.4.0
{
"Attributes": {
"Type": {
"PrimitiveType": "String"
},
"Value": {
"PrimitiveType": "String"
}
},
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html",
"Properties": {
"Description": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-description",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Mutable"
},
"Name": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-name",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Immutable"
},
"Type": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-type",
"PrimitiveType": "String",
"Required": true,
"UpdateType": "Mutable"
},
"Value": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-value",
"PrimitiveType": "String",
"Required": true,
"UpdateType": "Mutable"
}
}
}
"""
resource_type = "AWS::SSM::Parameter"
props = {
'Type': (basestring, True, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-type'),
'Description': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-description'),
'Value': (basestring, True, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-value'),
'Name': (basestring, False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-parameter.html#cfn-ssm-parameter-name')
}
# -------------------------------------------
class SSMAssociation(AWSObject):
"""# AWS::SSM::Association - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html",
"Properties": {
"DocumentVersion": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-documentversion",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Mutable"
},
"InstanceId": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-instanceid",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Immutable"
},
"Name": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-name",
"PrimitiveType": "String",
"Required": true,
"UpdateType": "Immutable"
},
"Parameters": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-parameters",
"DuplicatesAllowed": false,
"ItemType": "ParameterValues",
"Required": false,
"Type": "Map",
"UpdateType": "Mutable"
},
"ScheduleExpression": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-scheduleexpression",
"PrimitiveType": "String",
"Required": false,
"UpdateType": "Mutable"
},
"Targets": {
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-targets",
"DuplicatesAllowed": false,
"ItemType": "Target",
"Required": false,
"Type": "List",
"UpdateType": "Immutable"
}
}
}
"""
resource_type = "AWS::SSM::Association"
props = {
'DocumentVersion': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-documentversion'),
'InstanceId': (basestring, False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-instanceid'),
'Name': (basestring, True, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-name'),
'Parameters': (dict, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-parameters'),
'ScheduleExpression': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-scheduleexpression'),
'Targets': ([Target], False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ssm-association.html#cfn-ssm-association-targets')
}
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] |
Python Fundamentals/Data types and Variables/Exercise/Task04.py | IvanTodorovBG/SoftUni | 1 | 178390 | n = int(input())
total_sum = 0
for x in range(1, n + 1):
ascii_code = input()
total_sum += ord(ascii_code)
print(f"The sum equals: {total_sum}") | [
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app/views/v1/search.py | daghan/Ostrich | 0 | 17222 | from app import webapp, mysql
from app.models import Search , Utils, Collection, WebUtils
from flask import request, jsonify
from flask.ext.jsonpify import jsonify as jsonp
import json
'''
Generic search call
@params
q: search query
page: the page number of search results (default 0)
type: type of search: {default: free(all fields), category, isbn}
@response
List of search result objects(ES)
'''
@webapp.route('/search')
def searchString():
response = {'status': 'False'}
results = {}
query = Utils.getParam(request.args, 'q')
page = Utils.getParam(request.args, 'page', var_type='int', default=1)
search_type = Utils.getParam(request.args, 'type', default='free')
user_id = Utils.getParam(request.args, 'userId', 'int')
flow = Utils.getParam(request.args, 'flow', default='borrow')
gcm_id = Utils.getParam(request.args, 'gcm_id', default=None)
uuid = Utils.getParam(request.args, 'distinct_id', default=None)
ref = Utils.getParam(request.args, 'ref', default='mobile')
if not query:
return Utils.errorResponse(response, 'HTTP_STATUS_CODE_DATA_MISSING')
if ref == 'web':
return jsonify(WebUtils.fetchSearchResults(query, search_type, page))
user_info = {'user_id': user_id, 'gcm_id': gcm_id, 'uuid': uuid}
search = Search(query, user_info, flow)
if search_type == 'free':
results = search.basicSearch(page=page-1)
elif search_type == 'category':
results = search.categorySearch(page=page-1)
elif search_type == 'collections':
results = search.collectionsSearch(page=page-1)
elif search_type == 'isbn':
results = search.isbnSearch(page=page-1)
elif search_type == 'auto':
results = search.autoComplete()
elif search_type == 'custom':
results = search.customQuery()
return results
#log
if user_id not in Utils.getAdmins():
Search.logSearch({_:request.args.get(_) for _ in request.args}, search_type)
return jsonify(results) if flow != 'admin' else jsonp(results)
@webapp.route('/getCategories')
def getCategories():
categories = Search.getSearchCategoriesForApp()
return jsonify(categories)
@webapp.route('/getCollectionCategory')
def getCollectionCategory():
return jsonify(Collection.getByCategory())
@webapp.route('/searchFail', methods=['POST'])
def searchFail():
#NOTE deprecated. Done directly from backend
return jsonify(status='true')
user_id = Utils.getParam(request.form, 'user_id', 'int')
q = Utils.getParam(request.form, 'q')
q_type = Utils.getParam(request.form,'type')
flow = Utils.getParam(request.form, 'flow', default='borrow')
Search(q, {'user_id': user_id}, flow).reportFail(True,True,q_type)
return jsonify(status='true')
@webapp.route('/recommended', methods=['GET'])
def recommended():
return jsonify(Search([]).mostRecommended())
@webapp.route('/mostSearched', methods=['GET'])
def mostSearched():
return jsonify(Search([]).mostSearched())
@webapp.route('/getMultiplePanels')
def getMultiplePanels():
cursor = mysql.connect().cursor()
cursor.execute("""SELECT collection_id FROM collections WHERE active = 1 AND
partial_order = 1 ORDER BY collection_id DESC""")
panels = []
for col_id in cursor.fetchall():
panels.append(Collection(col_id).getObj())
return jsonify(panels)
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backtester.py | iamthejao/portfolioTools | 0 | 176612 | from portfolio import Portfolio, PM
import datetime as dt
from collections import OrderedDict
import utility
import copy
import numpy as np
class Backtester:
def __init__(self, universeObj, start=None, end=None):
if start is None:
start = universeObj.dateRange[0]
if end is None:
end = universeObj.dateRange[1]
self.universe = universeObj
# Can still be None, None, which will be checked later
self.dateRange = [start, end]
if self.dateRange[0] is None or self.dateRange[1] is None:
start, end = PM.getPortfolioDateRange(universeObj.assets)
if self.dateRange[0] is None:
self.dateRange[0] = start
if self.dateRange[1] is None:
self.dateRange[1] = end
self.dateRange = tuple(copy.deepcopy(self.dateRange))
def load(self):
self.universe.make()
self.originals = copy.deepcopy(self.universe.data)
def reduce(self, frame):
return frame.loc[(frame.index >= self.visibility[0]) & (frame.index <= self.visibility[1])]
def reduceVisibility(self, dateRange):
self.visibility = (np.datetime64(dateRange[0]), np.datetime64(dateRange[1]))
for key in self.universe.data.keys():
self.universe.data[key] = self.reduce(self.originals[key].copy())
def backtest(self, strategy, frequency, daysBeforeVisibility=360, inverse=False):
"""
Strategy function is a function that receives universeObj, start and end and should return
assets, allocation, direction vectors
frequency is the jump
daysBeforeVisibility is the start and currentDay the end of which the strategy function should see
"""
history = OrderedDict()
firstDayData = self.dateRange[0] - dt.timedelta(days=daysBeforeVisibility)
self.universe.dateRange = (firstDayData, self.dateRange[1])
print("Loading universe data from {0} to {1}.".format(self.universe.dateRange[0], self.universe.dateRange[1]))
self.load()
print("Backtesting from {0} to {1}".format(self.dateRange[0], self.dateRange[1]))
prevPi = None
kwdict = {}
currentDay = self.dateRange[0]
while currentDay < self.dateRange[1]:
startVisibility = currentDay - dt.timedelta(days=daysBeforeVisibility)
self.reduceVisibility((startVisibility, currentDay))
print("")
print('[BACKTESTER]: Running strategy with data from: \n {0} to {1}'.format(startVisibility, currentDay))
assets, allocation, direction, kwdict = strategy(self.universe, kwdict)
if inverse:
direction *= -1.0
nextRun = min(utility.next_weekday(currentDay + dt.timedelta(days=frequency)), self.dateRange[1])
print('[BACKTESTER]: Checking Portfolio from: \n {0} to {1}'.format(currentDay, nextRun))
print("")
pi = Portfolio(assets, allocation, direction, self.universe.riskFree)
if prevPi is None:
pi.setDateRange(currentDay, nextRun)
else:
pi.setDateRange(prevPi.pReturn.index.max(), nextRun)
pi.factors = self.universe.factors
pi.make()
pi.summarize()
history[currentDay] = {'assets': assets,
'n_assets': len(assets),
'allocation': allocation,
'direction': direction,
'pi': pi}
currentDay = nextRun
prevPi = pi
return history
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self driving/controls/kalman/EKF_dynamic.py | Systemx-ai/Pilot-X | 1 | 131569 | """ Code for the EKF Refactored """
import sympy
from sympy import atan, pi, tan
from sympy import symbols, Matrix
from math import sqrt, tan, cos, sin, atan2
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import randn
from filterpy.kalman import ExtendedKalmanFilter as EKF
from numpy import array, sqrt
class RobotEKF(EKF):
def __init__(self, dt, wheelbase, std_vel, std_steer):
EKF.__init__(self, 3, 2, 2)
self.dt = dt
self.wheelbase = wheelbase
self.std_vel = std_vel
self.std_steer = std_steer
a, x, y, v, w, theta, time = symbols(
'a, x, y, v, w, theta, t')
d = v*time
beta = (d/w)*sympy.tan(a)
r = w/sympy.tan(a)
self.fxu = Matrix(
[[x-r*sympy.sin(theta)+r*sympy.sin(theta+beta)],
[y+r*sympy.cos(theta)-r*sympy.cos(theta+beta)],
[theta+beta]])
self.F_j = self.fxu.jacobian(Matrix([x, y, theta]))
self.V_j = self.fxu.jacobian(Matrix([v, a]))
# save dictionary and it's variables for later use
self.subs = {x: 0, y: 0, v:0, a:0,
time:dt, w:wheelbase, theta:0}
self.x_x, self.x_y, = x, y
self.v, self.a, self.theta = v, a, theta
def predict(self, u):
self.x = self.move(self.x, u, self.dt)
self.subs[self.theta] = self.x[2, 0]
self.subs[self.v] = u[0]
self.subs[self.a] = u[1]
F = array(self.F_j.evalf(subs=self.subs)).astype(float)
V = array(self.V_j.evalf(subs=self.subs)).astype(float)
# covariance of motion noise in control space
M = array([[self.std_vel*u[0]**2, 0],
[0, self.std_steer**2]])
self.P = np.dot(F, self.P).dot(F.T) + np.dot(V, M).dot(V.T)
def move(self, x, u, dt):
hdg = x[2, 0]
vel = u[0]
steering_angle = u[1]
dist = vel * dt
if abs(steering_angle) > 0.001: # is robot turning?
beta = (dist / self.wheelbase) * tan(steering_angle)
r = self.wheelbase / tan(steering_angle) # radius
dx = np.array([[-r*sin(hdg) + r*sin(hdg + beta)],
[r*cos(hdg) - r*cos(hdg + beta)],
[beta]])
else: # moving in straight line
dx = np.array([[dist*cos(hdg)],
[dist*sin(hdg)],
[0]])
return x + dx
##MIT license | [
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] |
examples/intro-example/dags/billomat-test.py | lukasdraheim/airflow-tutorial | 0 | 142050 | from datetime import timedelta
import airflow
from airflow.models import DAG
from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
from airflow.operators.bash_operator import BashOperator
import random
import requests
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': airflow.utils.dates.days_ago(2),
'email': ['<EMAIL>'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
# 'wait_for_downstream': False,
# 'dag': dag,
# 'adhoc':False,
# 'sla': timedelta(hours=2),
# 'execution_timeout': timedelta(seconds=300),
# 'on_failure_callback': some_function,
# 'on_success_callback': some_other_function,
# 'on_retry_callback': another_function,
# 'trigger_rule': u'all_success'
}
dag = DAG('billomat', default_args=default_args, schedule_interval=None)
def billomat_run():
url = 'https://helpcheck.billomat.net/api/invoices?api_key=1c06f5590c6c626b45a26ce30ebff7cd&format=json'
r = requests.get(url)
if r.status_code == 200:
print('Success!')
elif r.status_code == 404:
print('Not Found.')
else :
print('No Success')
def billomat_print():
print(r)
with dag:
run_billomat_run = PythonOperator(
task_id='extract_json_from_billomat',
python_callable=billomat_run
# provide_context=True,
# retries=10,
# retry_delay=timedelta(seconds=10)
)
run_billomat_print = PythonOperator(
task_id='transform_json',
python_callable=billomat_print
)
run_billomat_run >> run_billomat_print
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29918,
29890,
453,
290,
271,
29918,
2158,
13,
2
] |
analysis/copy_cluster_mat.py | JaerongA/lman-deafening | 0 | 138646 | <gh_stars>0
"""
This program copy/pastes SpkInfo.mat from each cell root to the destination folder (InformationAnalysis)
"""
import os
from summary import load
project_path = load.project(load.config()) # load cluster summary file
summary_df, nb_cluster = load.summary(load.config()) # load cluster summary file
# Make a folder to save files
# # save_path = 'InformationAnalysis'
# # save.make_save_dir(save_path)
save_path = os.path.join(project_path,
r'Analysis\InformationAnalysis') # the data folder where SAP feature values are stored
if not os.path.exists(save_path):
os.mkdir(save_path)
def copy_cluster_mat(summary_df):
import shutil
for cluster_run in range(0, nb_cluster):
cluster = load.cluster(summary_df, cluster_run)
if int(cluster.AnalysisOK):
# print(cluster)
session_id, cell_id, session_path, cell_path = load.cluster_info(cluster)
# print('Accessing... ' + cell_root)
os.chdir(cell_path)
mat_file = [file for file in os.listdir(cell_path) if file.endswith('SpkInfo.mat')][0]
# print(mat_file)
# Make a new folder for individual neurons
new_save_path = os.path.join(save_path, cell_id)
print(new_save_path)
if not os.path.exists(new_save_path):
os.mkdir(new_save_path)
shutil.copy(mat_file, new_save_path)
if __name__ == '__main__':
copy_cluster_mat(summary_df)
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zSPA/zSPA/spa/views.py | hawwestin/MSR.DjangoByExample | 1 | 79578 | from django.shortcuts import render
from decouple import config
from .models import Images
# Create your views here.
def index(request, **kwargs):
"""
Render single page application and provide context data.
:param request:
:param kwargs:
:return:
"""
context = {
'images': Images.objects.filter(is_active=True),
'googleForm': config('googleForm', default=''),
'googleMaps': config('googleMaps', default=''),
}
return render(request, 'spa/main_SPA.html', context)
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avkapi/vk.py | JMarkin/aVKapi | 7 | 77978 | import asyncio
import logging
from aiohttp import ClientSession
from .methods import Messages
from .utils import json
logger = logging.getLogger(__name__)
API_URL = 'https://api.vk.com/method/'
class VK:
def __init__(self, confirmation_code=None, secret_key=None, access_token=None, loop=None):
"""
:type confirmation_code: str
"""
self.confirmation_code = confirmation_code
self.secret_key = secret_key
self.access_token = access_token
self.api_version = '5.80'
# asyncio loop instance
if loop is None:
loop = asyncio.get_event_loop()
self.loop = loop
self._session = ClientSession(loop=self.loop, json_serialize=json.dumps)
self.messages = Messages(access_token=access_token, session=self._session, api_version=self.api_version)
async def get_session(self):
if not self._session:
self._session = ClientSession()
async def api_request(self, method_name, parameters):
link = f'{API_URL}{method_name}?{parameters}'
params = {
'access_token': self.access_token,
'v': self.api_version
}
params.update(**parameters)
async with self._session.post(link) as resp:
status = resp.status
text = await resp.text()
logger.info(f'Response: {status}, {text}')
async def close(self):
if isinstance(self._session, ClientSession) and not self._session.closed:
await self._session.close()
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src/beefore/diff.py | pybee/beefore | 24 | 80134 | <reponame>pybee/beefore
import os
import re
FILE_START = re.compile(r"diff --git a/(.*) b/(.*)")
LINE_RANGE = re.compile(r"@@ -(\d+),(\d+) \+(\d+),(\d+) @@")
def positions(directory, diff_content):
"""Given a diff payload, look for all the line ranges described for a given file.
Returns a map of source file lines, mapped to the position in the diff
that the line is referenced.
"""
directory = os.path.abspath(directory)
mappings = {}
current_file = None
file_start = None
block_start = None
block_offset = None
for i, d in enumerate(diff_content):
match = FILE_START.match(d)
if match:
current_file = {}
filename = os.path.abspath(match.group(2))[len(directory)+1:]
mappings[filename] = current_file
# Start of a new file; so reset the file andd block offsets.
file_start = None
block_start = None
block_offset = None
elif current_file is not None:
match = LINE_RANGE.match(d)
if match:
# Found the start of a new range in the file
first_line = int(match.group(3))
# If this is the first line range for a file,
# then the line counter is also the starting index
# for diff offsets.
if file_start is None:
file_start = i
block_start = i + 1
block_offset = 1 if match.group(1) == '0' else 0
elif block_start and len(d) > 0:
if d[0] == '-':
block_offset += 1
else:
current_file[i - block_start - block_offset + first_line] = i - file_start
return mappings
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] |
main.py | laMia482/tensorflow-example | 0 | 178151 | <reponame>laMia482/tensorflow-example<filename>main.py
from config import cfg
import kernel
import tensorflow as tf
def main(_):
if cfg.is_train is True:
kernel.train()
if cfg.is_eval is True:
kernel.eval()
if __name__ == '__main__':
tf.app.run()
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src_py/hat/syslog/server/ui.py | hat-open/hat-syslog | 1 | 142420 | """Web server implementation"""
from pathlib import Path
import functools
import logging
import typing
import urllib
from hat import aio
from hat import juggler
from hat.syslog.server import common
import hat.syslog.server.backend
mlog: logging.Logger = logging.getLogger(__name__)
"""Module logger"""
package_path: Path = Path(__file__).parent
"""Python package path"""
ui_path: Path = package_path / 'ui'
"""UI directory path"""
max_results_limit: int = 200
"""Max results limit"""
autoflush_delay: float = 0.2
"""Juggler autoflush delay"""
async def create_web_server(addr: str,
pem: typing.Optional[Path],
backend: hat.syslog.server.backend.Backend
) -> 'WebServer':
"""Create web server"""
addr = urllib.parse.urlparse(addr)
host = addr.hostname
port = addr.port
server = WebServer()
server._backend = backend
server._srv = await juggler.listen(host, port, server._on_connection,
static_dir=ui_path,
pem_file=pem,
autoflush_delay=autoflush_delay)
return server
class WebServer(aio.Resource):
@property
def async_group(self) -> aio.Group:
"""Async group"""
return self._srv.async_group
def _on_connection(self, conn):
self.async_group.spawn(self._connection_loop, conn)
async def _connection_loop(self, conn):
change_queue = aio.Queue()
conn.async_group.spawn(_change_loop, self._backend, conn, change_queue)
try:
with self._backend.register_change_cb(change_queue.put_nowait):
with conn.register_change_cb(
functools.partial(change_queue.put_nowait, [])):
await conn.wait_closing()
finally:
conn.close()
async def _change_loop(backend, conn, change_queue):
try:
filter_json = _sanitize_filter(conn.remote_data)
first_id = backend.first_id
last_id = backend.last_id
filter_changed = True
new_entries_json = []
while True:
if filter_changed:
filter = common.filter_from_json(filter_json)
while not change_queue.empty():
change_queue.get_nowait()
entries = await backend.query(filter)
entries_json = [common.entry_to_json(entry)
for entry in entries]
elif new_entries_json:
previous_id = entries_json[0]['id'] if entries_json else 0
entries_json = [*_filter_entries(filter_json, previous_id,
new_entries_json),
*entries_json]
entries_json = entries_json[:filter.max_results]
conn.set_local_data({'filter': filter_json,
'entries': entries_json,
'first_id': first_id,
'last_id': last_id})
new_entries = await change_queue.get()
new_entries_json = [common.entry_to_json(entry)
for entry in new_entries]
first_id = backend.first_id
last_id = backend.last_id
new_filter_json = _sanitize_filter(conn.remote_data)
filter_changed = new_filter_json != filter_json
filter_json = new_filter_json
finally:
conn.close()
def _sanitize_filter(filter_json):
if not filter_json:
filter_json = common.filter_to_json(
common.Filter(max_results=max_results_limit))
if (filter_json['max_results'] is None or
filter_json['max_results'] > max_results_limit):
filter_json = dict(filter_json, max_results=max_results_limit)
return filter_json
def _filter_entries(filter_json, previous_id, entries_json):
for i in entries_json:
if i['id'] <= previous_id:
continue
if (filter_json['last_id'] is not None
and i['id'] > filter_json['last_id']):
continue
if (filter_json['entry_timestamp_from'] is not None
and i['timestamp'] < filter_json['entry_timestamp_from']):
continue
if (filter_json['entry_timestamp_to'] is not None
and i['timestamp'] > filter_json['entry_timestamp_to']):
continue
if (filter_json['facility'] is not None and
i['msg']['facility'] != filter_json['facility']):
continue
if (filter_json['severity'] is not None and
i['msg']['severity'] != filter_json['severity']):
continue
if not _match_str_filter(filter_json['hostname'],
i['msg']['hostname']):
continue
if not _match_str_filter(filter_json['app_name'],
i['msg']['app_name']):
continue
if not _match_str_filter(filter_json['procid'],
i['msg']['procid']):
continue
if not _match_str_filter(filter_json['msgid'],
i['msg']['msgid']):
continue
if not _match_str_filter(filter_json['msg'],
i['msg']['msg']):
continue
yield i
def _match_str_filter(filter, value):
return not filter or filter in value
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] |
lstchain/visualization/camera.py | misabelber/cta-lstchain | 0 | 14556 | import numpy as np
from ..reco.disp import disp_vector
import astropy.units as u
import matplotlib.pyplot as plt
from ctapipe.visualization import CameraDisplay
__all__ = [
'overlay_disp_vector',
'overlay_hillas_major_axis',
'overlay_source',
'display_dl1_event',
]
def display_dl1_event(event, camera_geometry, tel_id=1, axes=None, **kwargs):
"""
Display a DL1 event (image and pulse time map) side by side
Parameters
----------
event: ctapipe event
tel_id: int
axes: list of `matplotlib.pyplot.axes` of shape (2,) or None
kwargs: kwargs for `ctapipe.visualization.CameraDisplay`
Returns
-------
axes: `matplotlib.pyplot.axes`
"""
if axes is None:
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
image = event.dl1.tel[tel_id].image
peak_time = event.dl1.tel[tel_id].peak_time
if image is None or peak_time is None:
raise Exception(f"There is no calibrated image or pulse time map for telescope {tel_id}")
d1 = CameraDisplay(camera_geometry, image, ax=axes[0], **kwargs)
d1.add_colorbar(ax=axes[0])
d2 = CameraDisplay(camera_geometry, peak_time, ax=axes[1], **kwargs)
d2.add_colorbar(ax=axes[1])
return axes
def overlay_source(display, source_pos_x, source_pos_y, **kwargs):
"""
Display the source (event) position in the camera
Parameters
----------
display: `ctapipe.visualization.CameraDisplay`
source_pos_x: `astropy.units.Quantity`
source_pos_y: `astropy.units.Quantity`
kwargs: args for `matplotlib.pyplot.scatter`
Returns
-------
`matplotlib.pyplot.axes`
"""
kwargs['marker'] = 'x' if 'marker' not in kwargs else kwargs['marker']
kwargs['color'] = 'red' if 'color' not in kwargs else kwargs['color']
display.axes.scatter(source_pos_x, source_pos_y, **kwargs)
def overlay_disp_vector(display, disp, hillas, **kwargs):
"""
Overlay disp vector on a CameraDisplay
Parameters
----------
display: `ctapipe.visualization.CameraDisplay`
disp: `DispContainer`
hillas: `ctapipe.containers.HillasParametersContainer`
kwargs: args for `matplotlib.pyplot.quiver`
"""
assert np.isfinite([hillas.x.value, hillas.y.value]).all()
if not np.isfinite([disp.dx.value, disp.dy.value]).all():
disp_vector(disp)
display.axes.quiver(hillas.x, hillas.y,
disp.dx, disp.dy,
units='xy', scale=1*u.m,
angles='xy',
**kwargs,
)
display.axes.quiver(hillas.x.value, hillas.y.value, disp.dx.value, disp.dy.value, units='xy', scale=1)
def overlay_hillas_major_axis(display, hillas, **kwargs):
"""
Overlay hillas ellipse major axis on a CameraDisplay.
Parameters
----------
display: `ctapipe.visualization.CameraDisplay`
hillas: `ctapipe.containers.HillaParametersContainer`
kwargs: args for `matplotlib.pyplot.plot`
"""
kwargs['color'] = 'black' if 'color' not in kwargs else kwargs['color']
length = hillas.length * 2
x = -length + 2 * length * np.arange(10) / 10
display.axes.plot(hillas.x + x * np.cos(hillas.psi.to(u.rad).value),
hillas.y + x * np.sin(hillas.psi.to(u.rad).value),
**kwargs,
)
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db_creator.py | Ksusha1319/stud_projects | 0 | 71319 | import datetime
from sqlalchemy import create_engine, ForeignKey
from sqlalchemy import Column, Date, Integer, String, DateTime, func
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, backref
engine = create_engine('sqlite:///test.db', echo=True)
Base = declarative_base()
class Nickname(Base):
__tablename__ = "nicknames"
id = Column(Integer, primary_key=True)
name = Column(String)
def __repr__(self):
return "<Nickname: {}>".format(self.name)
class Report(Base):
""""""
__tablename__ = "reports"
id = Column(Integer, primary_key=True)
created_date = Column(DateTime(timezone=True), default=func.now())
comment = Column(String)
incident_date = Column(String)
incident_type = Column(String)
game_type = Column(String)
priority = Column(String)
status = Column(String)
resolution = Column(String)
comment_soc = Column(String)
service_login = Column(String)
username = Column(String)
ticket = Column(String)
nickname_id = Column(Integer, ForeignKey("nicknames.id"))
nickname = relationship("Nickname", backref=backref(
"reports", order_by=id))
# create tables
class Users(Base):
__tablename__ = "Users"
id = Column(Integer, primary_key=True)
created_date = Column(DateTime(timezone=True), default=func.now())
username = Column(String)
pass_hash = Column(String)
email = Column(String)
role = Column(String)
about = Column(String)
Base.metadata.create_all(engine)
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app.py | tangingw/python_spotify | 0 | 59212 | <gh_stars>0
import hashlib
import json
import requests
from flask import Flask
from flask import request
from flask import jsonify
from flask import redirect
from flask import url_for
from utils import Handler
from utils import auth
from utils import config
BASE_URL = "https://api.spotify.com"
app = Flask(__name__)
handler = Handler()
@app.route("/")
def hello_world():
return "Hello World!"
@app.route("/authorize")
def authorize():
client_id, _ = auth()
config_content = config()
authorize_url = "https://accounts.spotify.com/authorize/"
params = {
"client_id": client_id,
"response_type": "code",
"redirect_uri": config_content["callback_url"],
"state": hashlib.sha256(config_content["state_msg"].encode('utf-8')).hexdigest(),
"scope": config_content["scope"]
}
response = requests.get(authorize_url, params=params)
return redirect(response.url)
@app.route("/callback/")
def callback():
callback_url = config()["callback_url"]
callback_params = request.args
if "error" in callback_params:
return jsonify(
error=callback_params.get("error"),
state=callback_params.get("state")
)
post_data = {
"grant_type": "authorization_code",
"code": callback_params.get("code"),
"redirect_uri": callback_url,
}
return handler.write_file_handler(post_data)
@app.route("/refresh_token")
def refresh_token():
with open("token.json", "r") as file:
data = json.loads(file.read())
if "refresh_token" not in data:
return redirect(url_for("authorize"))
post_data = {
"grant_type": "refresh_token",
"refresh_token": data["refresh_token"]
}
return handler.write_file_handler(post_data)
@app.route("/spotify")
def get_spotify():
player_info = handler.generic_handler(BASE_URL + "/v1/me/player", request.path)
if player_info:
return player_info
return redirect(url_for("get_my_devices"))
@app.route("/spotify/me/devices")
def get_my_devices():
return handler.generic_handler(BASE_URL + "/v1/me/player/devices", request.path)
@app.route("/spotify/me")
def get_spotify_me():
return handler.generic_handler(BASE_URL + "/v1/me", request.path)
@app.route("/spotify/me/following")
def get_my_follow():
params = {"type": "artist"}
return handler.generic_handler(BASE_URL + "/v1/me/following", request.path, params=params)
@app.route("/spotify/me/playlists")
def get_my_playlist():
return handler.generic_handler(BASE_URL + "/v1/me/playlists", request.path)
@app.route("/spotify/search")
def search_spotify():
callback_params = request.args
return handler.generic_handler(BASE_URL + "/v1/search", request.path, params=callback_params)
if __name__ == "__main__":
app.run(host='0.0.0.0', port=8080)
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clkhash/benchmark.py | data61/clkhash | 28 | 79255 | <filename>clkhash/benchmark.py<gh_stars>10-100
import os
import tempfile
from timeit import default_timer as timer
from clkhash.clk import generate_clk_from_csv
from clkhash.randomnames import NameList
def compute_hash_speed(num: int, quiet: bool = False, max_workers=None) -> float:
""" Hash time.
"""
namelist = NameList(num)
os_fd, tmpfile_name = tempfile.mkstemp(text=True)
schema = NameList.SCHEMA
header_row = ','.join([f.identifier for f in schema.fields])
with open(tmpfile_name, 'wt') as f:
f.write(header_row)
f.write('\n')
for person in namelist.names:
print(','.join([str(field) for field in person]), file=f)
with open(tmpfile_name, 'rt') as f:
start = timer()
generate_clk_from_csv(f, 'secret', schema, progress_bar=not quiet, max_workers=max_workers)
end = timer()
os.close(os_fd)
os.remove(tmpfile_name)
elapsed_time = end - start
if not quiet:
print("{:6d} hashes in {:.6f} seconds. {:.2f} KH/s".format(num, elapsed_time, num / (1000 * elapsed_time)))
return num / elapsed_time
if __name__ == '__main__':
for max_workers in [1, 2, 4, 8, 16]:
print()
if max_workers == 1:
print("Without multiprocessing")
sizes = [10_000]
else:
print(f"Using up to {max_workers} workers")
sizes = [10_000, 50_000, 100_000]
for n in sizes:
compute_hash_speed(n, max_workers=max_workers)
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polynomial_mixture/polynomial_mixture.py | sremes/polynomial-mixture | 0 | 151879 | """Bayesian polynomial mixture model."""
# pylint: disable=invalid-name
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
class BayesianPolynomialMixture: # pylint: disable=too-few-public-methods
"""Handles creation of a polynomial mixture model."""
def __init__(self, num_components=5, polynomial_degree=3):
"""Creates polynomial mixture with given mixture components of given degree."""
self.num_components = num_components
self.polynomial_degree = polynomial_degree
self.coefficient_precisions = [10.0 ** x for x in range(self.polynomial_degree + 1)]
self.concentration = np.array([0.1 for _ in range(self.num_components)])
self.wishart_df = self.polynomial_degree + 2.0
self.student_df = 2
def create_model(self, X):
"""Defines the joint distribution of the mixture model."""
precision_scale = np.repeat(np.expand_dims(self.coefficient_precisions, 0), self.num_components, axis=0)
joint_distribution = tfd.JointDistributionNamed(
dict(
precision=tfd.Independent(
tfd.WishartLinearOperator(
df=self.wishart_df,
scale=tf.linalg.LinearOperatorDiag(precision_scale),
input_output_cholesky=True,
name="precision",
),
reinterpreted_batch_ndims=1,
),
coefficients=lambda precision: tfd.Independent(
tfd.MultivariateNormalTriL(
loc=0, scale_tril=tfb.MatrixInverseTriL()(precision), name="coefficients"
),
reinterpreted_batch_ndims=1,
),
scale=tfd.HalfCauchy(loc=np.float64(0.0), scale=np.float64(1.0), name="noise_scale"),
mixture_probs=tfd.Dirichlet(concentration=self.concentration, name="mixture_probs"),
mixture=lambda mixture_probs, coefficients, scale: tfd.Sample(
tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(probs=mixture_probs, name="mixture_distribution"),
components_distribution=tfd.StudentT(
df=self.student_df,
loc=tf.linalg.matmul(X, coefficients, transpose_b=True),
scale=scale,
name="sample_likelihood",
),
name="mixture_components",
),
sample_shape=1,
),
),
name="joint_distribution",
)
return joint_distribution
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yatube/posts/views.py | Ecmek/yatube_project | 1 | 188706 | <filename>yatube/posts/views.py
from django.core.paginator import Paginator
from django.contrib.auth.decorators import login_required
from django.shortcuts import get_object_or_404, redirect, render
from django.views.decorators.cache import cache_page
from django.http import HttpResponseRedirect
from .models import Follow, Post, Group, User, Ip
from .forms import CommentForm, PostForm
from .decorators import counted
paginator_pages = 10
def get_client_ip(request):
x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')
if x_forwarded_for:
ip = x_forwarded_for.split(',')[0]
else:
ip = request.META.get('REMOTE_ADDR')
return ip
# @cache_page(5)
@counted
def index(request):
latest = Post.objects.select_related('author', 'group').prefetch_related(
'comments', 'views')
paginator = Paginator(latest, paginator_pages)
page_number = request.GET.get('page')
page = paginator.get_page(page_number)
return render(request, 'index.html', {'page': page})
@counted
def group_posts(request, slug):
group = get_object_or_404(Group, slug=slug)
posts = group.group_posts.select_related('author').prefetch_related(
'comments', 'views')
paginator = Paginator(posts, paginator_pages)
page_number = request.GET.get('page')
page = paginator.get_page(page_number)
return render(request, 'group.html', {'group': group, 'page': page})
@counted
def profile(request, username):
author = get_object_or_404(User, username=username)
posts = author.posts.select_related('group').prefetch_related(
'comments', 'views')
paginator = Paginator(posts, paginator_pages)
page_number = request.GET.get('page')
page = paginator.get_page(page_number)
following = None
if request.user.is_authenticated:
following = author.following.filter(user=request.user).exists()
context = {
'author': author,
'page': page,
'following': following,
}
return render(request, 'profile.html', context)
@counted
def post_view(request, username, post_id):
post = get_object_or_404(Post, author__username=username, id=post_id)
comments = post.comments.select_related('author')
author = post.author
form = CommentForm()
ip = get_client_ip(request)
Ip.objects.get_or_create(ip=ip)
post.views.add(Ip.objects.get(ip=ip))
following = None
if request.user.is_authenticated:
following = author.following.filter(user=request.user).exists()
context = {
'author': author,
'post': post,
'form': form,
'comments': comments,
'following': following,
}
return render(request, 'post.html', context)
@login_required
def add_comment(request, username, post_id):
post = get_object_or_404(Post, author__username=username, id=post_id)
form = CommentForm(request.POST or None)
if form.is_valid():
comment = form.save(commit=False)
comment.post = post
comment.author = request.user
comment.save()
return redirect('posts:post', username, post_id)
@login_required
def new_post(request):
form = PostForm(request.POST or None, files=request.FILES or None)
if form.is_valid():
post = form.save(commit=False)
post.author = request.user
post.save()
return redirect('posts:index')
context = {
'header': 'Добавить запись',
'submit_text': 'Добавить',
'form': form,
}
return render(request, 'new_post.html', context)
def post_edit(request, username, post_id):
if request.user.username != username:
return redirect('posts:post', username, post_id)
post = get_object_or_404(Post, author__username=username, id=post_id)
if not post.is_recently_pub:
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
form = PostForm(
request.POST or None, files=request.FILES or None, instance=post
)
if form.is_valid():
post = form.save(commit=False)
post.save()
return redirect('posts:post', username, post_id)
context = {
'header': 'Редактировать запись',
'submit_text': 'Сохранить',
'form': form,
'post': post,
}
return render(request, 'new_post.html', context)
@login_required
@counted
def follow_index(request):
posts = Post.objects.filter(
author__following__user=request.user
).select_related('author', 'group').prefetch_related('comments')
paginator = Paginator(posts, paginator_pages)
page_number = request.GET.get('page')
page = paginator.get_page(page_number)
return render(request, 'follow.html', {'page': page})
@login_required
def profile_follow(request, username):
if request.user.username == username:
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
author = get_object_or_404(User, username=username)
Follow.objects.get_or_create(user=request.user, author=author)
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
@login_required
def profile_unfollow(request, username):
author = get_object_or_404(User, username=username)
Follow.objects.filter(user=request.user, author=author).delete()
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
@login_required
def post_delete(request, username, post_id):
if request.user.username != username:
return redirect('posts:post', username, post_id)
post = get_object_or_404(Post, author__username=username, id=post_id)
post.delete()
return HttpResponseRedirect(request.META.get('HTTP_REFERER'))
def page_not_found(request, exception):
return render(request, 'misc/404.html', {'path': request.path}, status=404)
def server_error(request):
return render(request, 'misc/500.html', status=500)
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app/controllers/web/admin/dashboard.py | Clivern/Kraven | 3 | 1612842 | """
Dashboard Web Controller
"""
# standard library
import os
# Django
from django.views import View
from django.shortcuts import render
from django.utils.translation import gettext as _
# local Django
from app.modules.core.context import Context
from app.modules.core.decorators import login_if_not_authenticated
class Dashboard(View):
template_name = 'templates/admin/dashboard.html'
__context = Context()
@login_if_not_authenticated
def get(self, request):
self.__context.autoload_options()
self.__context.autoload_user(request.user.id if request.user.is_authenticated else None)
self.__context.push({
"page_title": _("Dashboard · %s") % self.__context.get("app_name", os.getenv("APP_NAME", "Kraven"))
})
return render(request, self.template_name, self.__context.get())
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Integrator.py | ganesh-cc/ticket_update | 0 | 112320 |
import endpointGenerator
import freshdeskService
import processData
import slackService
from collections import defaultdict
fd = defaultdict(list)
def integrator_func(team):
tag = endpointGenerator.url_generator_multiple(team)
global fd
for k,v in tag.items():
fd.clear()
print('\n' + '\n' +'\n' +k)
if k == "closed":
fd = freshdeskService.trigger_filter(v)
if fd != 0:
fd = processData.dataMapper(fd)
else:
fd = freshdeskService.trigger(v)
if fd != 0:
fd = processData.dataMapper(fd)
#slackService.post_slack(team, k, str(fd))
print(team, k, fd)
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] |
src/get/download_nino34.py | jdossgollin/2018-paraguay-floods | 2 | 1607353 | <filename>src/get/download_nino34.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Download raw NINO 3.4 data from
http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO34
and parse it
"""
import argparse
import os
import pandas as pd
import numpy as np
from datetime import datetime
parser = argparse.ArgumentParser() # pylint: disable=C0103
parser.add_argument("--syear", help="the first year to retain")
parser.add_argument("--eyear", help="the last year to retain")
parser.add_argument("--outfile", help="the filename of the data to save")
def download_data(sdate, edate, outfile):
"""Load in the NINO 3.4 Data
"""
url = "http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/.NINO34/gridtable.tsv"
col_name = "nino_34"
nino_34 = pd.read_table(
url,
delim_whitespace=True,
index_col=None,
skiprows=2,
names=["time", "{}".format(col_name)],
)
# the times don't make sense, parse them in
nino_34["time"] = np.int_(np.floor(nino_34["time"]))
nino_34["year"] = 1960 + nino_34["time"] // 12
nino_34["month"] = 1 + nino_34["time"] % 12
nino_34["day"] = 1
nino_34["time"] = pd.to_datetime(nino_34[["year", "month", "day"]])
nino_34.set_index("time", inplace=True)
nino_34 = nino_34[["{}".format(col_name)]]
nino_34 = nino_34.loc[sdate:edate]
nino_34 = nino_34.to_xarray()
# save to file
if os.path.isfile(outfile):
os.remove(outfile)
nino_34.to_netcdf(outfile, format="NETCDF4", mode="w")
def main():
"""Parse the command line arguments and run download_data().
"""
args = parser.parse_args()
outfile = os.path.abspath(args.outfile)
sdate = datetime(int(args.syear), 1, 1)
edate = datetime(int(args.eyear), 12, 31)
download_data(sdate=sdate, edate=edate, outfile=outfile)
if __name__ == "__main__":
main()
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] |
nnet/run/srl/run.py | SaidehNK/sr | 193 | 178992 | from nnet.models.srl import *
from nnet.run.runner import *
from nnet.ml.voc import *
from nnet.run.srl.run import *
from nnet.run.srl.util import *
from nnet.run.srl.decoder import *
from functools import partial
from nnet.run.srl.read_dependency import get_adj
import nnet.run.srl.conll09_evaluation.eval
def make_local_voc(labels):
return {i: label for i, label in enumerate(labels)}
def bio_reader(record):
dbg_header, sent, pos_tags, dep_parsing, degree, frame, target, f_lemmas, f_targets, labels_voc, labels = record.split(
'\t')
labels_voc = labels_voc.split(' ')
frame = [frame] * len(labels_voc)
words = []
for word in sent.split(' '):
words.append(word)
pos_tags = pos_tags.split(' ')
labels = labels.split(' ')
assert (len(words) == len(labels))
local_voc = {v: k for k, v in make_local_voc(labels_voc).items()}
labels = [local_voc[label] for label in labels]
dep_parsing = dep_parsing.split()
dep_parsing = [p.split('|') for p in dep_parsing]
dep_parsing = [(p[0], int(p[1]), int(p[2])) for p in dep_parsing]
f_lemmas = f_lemmas.split(' ')
f_targets = f_targets.split(' ')
return dbg_header, words, pos_tags, dep_parsing, np.int32(degree), frame, \
np.int32(target), f_lemmas, np.int32(f_targets), labels_voc, labels
class BioSrlErrorComputer:
def __init__(self, converter, do_labeling, data_partition,
eval_dir):
self.converter = converter
self.do_labeling = do_labeling
self.data_partition = data_partition
self.labels1 = []
self.labels2 = []
self.predictions = []
self.eval_dir = eval_dir
self.do_eval = nnet.run.srl.conll09_evaluation.eval.do_eval
def pprint(self, record, voc, predictions, true_labels):
n = 2
sent = record[1]
predictions = [p[:len(voc.items())] for p in predictions.tolist()]
predictions = predictions[:len(sent)]
constraints = []
best_labeling = constrained_decoder(voc, predictions, 100, constraints)
info = list()
for word, prediction, true_label, best in zip(sent, predictions,
true_labels,
best_labeling):
nbest = sorted(range(len(prediction)),
key=lambda x: -prediction[x])
nbest = nbest[:n]
probs = [prediction[l] for l in nbest]
labels = [voc[label] for label in nbest if label in voc]
labels = ' '.join(labels)
info.append((word, labels, probs, voc[true_label], best))
return info
def compute(self, model, batch):
errors, errors_w = 0, 0.0
record_ids, batch = zip(*batch)
model.test_mode_on()
model_input = self.converter(batch)
sent, \
p_sent, \
pos_tags,\
sent_mask, \
targets, \
frames, \
labels_voc, \
labels_voc_mask, \
freq, \
region_mark, sent_pred_lemmas_idx, \
adj_arcs_in, adj_arcs_out, adj_lab_in, adj_lab_out, \
mask_in, mask_out, mask_loop, \
true_labels \
= model_input
predictions = model.predict(*model_input[:-1])
labels = np.argmax(predictions, axis=1)
labels = np.reshape(labels, sent.shape)
predictions = np.reshape(predictions,
(sent.shape[0], sent.shape[1],
labels_voc.shape[1]))
for i, sent_labels in enumerate(labels):
labels_voc = batch[i][-2]
local_voc = make_local_voc(labels_voc)
info = self.pprint(batch[i], local_voc, predictions[i],
true_labels[i])
self.labels1.append([x[4] for x in info])
self.labels2.append([x[3] for x in info])
if self.do_labeling:
sentence = []
for word, label, probs, true, best in info:
single_pred = "%10s\t%10s\t%20s\t%40s\t%10s\t%10s\t%10s" % (
word, best, label, probs, true, batch[i][4],
batch[i][3][0])
sentence.append(
(word, best, label, probs, true, batch[i][4],
batch[i][3][0]))
print(single_pred)
print('\n')
self.predictions.append(sentence)
else:
sentence = []
for word, label, probs, true, best in info:
sentence.append(
(word, best, label, probs, true, batch[i][4], batch[i][3][0]))
self.predictions.append(sentence)
for word, label, probs, true, best in info:
if true != best:
errors += 1
errors_w += 1 / len(info)
loss = np.sum(model.compute_loss(*model_input))
model.test_mode_off()
return errors, errors, errors_w
def final(self):
if self.do_eval:
results = self.do_eval(self.data_partition, self.predictions,
self.eval_dir)
else:
evaluate(self.labels1, self.labels2)
self.predictions = []
self.labels1, self.labels2 = [], []
return results[2]
class SRLRunner(Runner):
def __init__(self):
super(SRLRunner, self).__init__()
self.word_voc = create_voc('file', self.a.word_voc)
self.word_voc.add_unks()
self.freq_voc = frequency_voc(self.a.freq_voc)
self.p_word_voc = create_voc('file', self.a.p_word_voc)
self.p_word_voc.add_unks()
self.role_voc = create_voc('file', self.a.role_voc)
self.frame_voc = create_voc('file', self.a.frame_voc)
self.pos_voc = create_voc('file', self.a.pos_voc)
def add_special_args(self, parser):
parser.add_argument(
"--word-voc", required=True)
parser.add_argument(
"--p-word-voc", required=True)
parser.add_argument(
"--freq-voc", required=True)
parser.add_argument(
"--role-voc", required=True)
parser.add_argument(
"--frame-voc", required=True)
parser.add_argument(
"--pos-voc", required=True
)
parser.add_argument(
"--word-embeddings", required=True
)
parser.add_argument(
"--data_partition", required=True
)
parser.add_argument(
"--hps", help="model hyperparams", required=False
)
parser.add_argument(
"--eval-dir", help="path to dir with eval data and scripts",
required=True
)
def get_parser(self):
return partial(bio_reader)
def get_reader(self):
return simple_reader
def get_converter(self):
def bio_converter(batch):
headers, sent_, pos_tags, dep_parsing, degree, frames, \
targets, f_lemmas, f_targets, labels_voc, labels = list(
zip(*batch))
sent = [self.word_voc.vocalize(w) for w in sent_]
p_sent = [self.p_word_voc.vocalize(w) for w in sent_]
freq = [[self.freq_voc[self.word_voc.direct[i]] if
self.word_voc.direct[i] != '_UNK' else 0 for i in w] for
w
in sent]
pos_tags = [self.pos_voc.vocalize(w) for w in pos_tags]
frames = [self.frame_voc.vocalize(f) for f in frames]
labels_voc = [self.role_voc.vocalize(r) for r in labels_voc]
lemmas_idx = [self.frame_voc.vocalize(f) for f in f_lemmas]
adj_arcs_in, adj_arcs_out, adj_lab_in, adj_lab_out, \
mask_in, mask_out, mask_loop = get_adj(dep_parsing, degree)
sent_batch, sent_mask = mask_batch(sent)
p_sent_batch, _ = mask_batch(p_sent)
freq_batch, _ = mask_batch(freq)
freq_batch = freq_batch.astype(dtype='float32')
pos_batch, _ = mask_batch(pos_tags)
labels_voc_batch, labels_voc_mask = mask_batch(labels_voc)
labels_batch, _ = mask_batch(labels)
frames_batch, _ = mask_batch(frames)
region_mark = np.zeros(sent_batch.shape, dtype='float32')
hps = eval(self.a.hps)
rm = hps['rm']
if rm >= 0:
for r, row in enumerate(region_mark):
for c, column in enumerate(row):
if targets[r] - rm <= c <= targets[r] + rm:
region_mark[r][c] = 1
sent_pred_lemmas_idx = np.zeros(sent_batch.shape, dtype='int32')
for r, row in enumerate(sent_pred_lemmas_idx):
for c, column in enumerate(row):
for t, tar in enumerate(f_targets[r]):
if tar == c:
sent_pred_lemmas_idx[r][c] = lemmas_idx[r][t]
sent_pred_lemmas_idx = np.array(sent_pred_lemmas_idx, dtype='int32')
assert (sent_batch.shape == sent_mask.shape)
assert (
frames_batch.shape == labels_voc_batch.shape == labels_voc_mask.shape)
assert (labels_batch.shape == sent_batch.shape)
return sent_batch, p_sent_batch, pos_batch, sent_mask, targets, frames_batch, \
labels_voc_batch, \
labels_voc_mask, freq_batch, \
region_mark, \
sent_pred_lemmas_idx,\
adj_arcs_in, adj_arcs_out, adj_lab_in, adj_lab_out, \
mask_in, mask_out, mask_loop, \
labels_batch
return bio_converter
def get_tester(self):
converter = self.get_converter()
computer = BioSrlErrorComputer(
converter, self.a.test_only, self.a.data_partition,
self.a.eval_dir)
corpus = Corpus(
parser=partial(bio_reader),
batch_size=self.a.batch,
path=self.a.test,
reader=self.get_reader()
)
return ErrorRateTester(computer, self.a.out, corpus)
def load_model(self):
hps = eval(self.a.hps)
hps['vframe'] = self.frame_voc.size()
hps['vword'] = self.word_voc.size()
hps['vbio'] = self.role_voc.size()
hps['vpos'] = self.pos_voc.size()
hps['word_embeddings'] = parse_word_embeddings(self.a.word_embeddings)
hps['in_arcs']=True
hps['out_arcs']=True
return BioSRL(hps)
if __name__ == '__main__':
SRLRunner().run()
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images/splunk/deployment/init_dmc.py | halr9000/docker-splunk-cluster | 0 | 84697 | def configurations():
return {
"components": {
"kvstore": False,
"web": True,
"indexing": False,
"dmc": True
}
} | [
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windows/winproxy/apis/shlwapi.py | IMULMUL/PythonForWindows | 479 | 182921 | import ctypes
import windows
import windows.generated_def as gdef
from ..apiproxy import ApiProxy, NeededParameter, is_implemented
from ..error import fail_on_zero
class ShlwapiProxy(ApiProxy):
APIDLL = "Shlwapi"
default_error_check = staticmethod(fail_on_zero)
@ShlwapiProxy()
def StrStrIW(pszFirst, pszSrch):
return StrStrIW.ctypes_function(pszFirst, pszSrch)
@ShlwapiProxy()
def StrStrIA(pszFirst, pszSrch):
return StrStrIA.ctypes_function(pszFirst, pszSrch)
@ShlwapiProxy()
def IsOS(dwOS):
if not is_implemented(IsOS) and windows.system.version[0] < 6:
# Before Vista:
# If so use ordinal 437 from DOCUMENTATION
# https://docs.microsoft.com/en-us/windows/desktop/api/shlwapi/nf-shlwapi-isos#remarks
IsOS.proxy.func_name = 437
return IsOS.ctypes_function(dwOS)
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tutorial/simple_flowsheet.py | Nukleon84/pypedream | 9 | 181624 | #Hacked Path to find package. Would no be needed when package is installed via pip
import sys
import os
sys.path.append(os.path.abspath('../pypedream'))
from pypedream import MaterialStream, Flowsheet, ThermodynamicSystem
import pypedream.database.purecomponents as pcdb
sys= ThermodynamicSystem("Test", "NRTL")
sys.addComponent(pcdb.Water())
sys.addComponent(pcdb.Ethanol())
sys.addComponent(pcdb.Methanol())
sys.addComponent(pcdb.Acetone())
sys.addComponent(pcdb.Isopropanol())
sys.addComponent(pcdb.Benzene())
sys.addComponent(pcdb.Toluene())
sys.fill()
f= Flowsheet("Test",sys)
S001=f.mstr("S001")
#f.mstr("S001").fvpz(100, 0.5,1000,[ ("Water",0.5), ("Methanol",0.5) ])
print("\nFix Temperature\n\n")
f.mstr("S001").ftpz(100, 82.5,1000,[ ("Water",0.5), ("Methanol",0.5) ])
f.solve()
for v in S001.variables.values():
print(f"{v.fullName()} = {v.displayValue()} {v.displayUnit}")
print("\nBubble Point\n\n")
f.mstr("S001").fpx(100, 1000,[ ("Water",0.5), ("Methanol",0.5) ])
f.solve()
for v in S001.variables.values():
print(f"{v.fullName()} = {v.displayValue()} {v.displayUnit}")
print("\nDew Point\n\n")
f.mstr("S001").fpy(100, 1000,[ ("Water",0.5), ("Methanol",0.5) ])
f.solve()
for v in S001.variables.values():
print(f"{v.fullName()} = {v.displayValue()} {v.displayUnit}")
print("\nFix Temperature 25°C\n\n")
f.mstr("S001").ftpz(100,25,1000,[ ("Water",0.5), ("Methanol",0.5) ])
f.solve()
for v in S001.variables.values():
print(f"{v.fullName()} = {v.displayValue()} {v.displayUnit}")
for T in range(25,120):
f.mstr("S001").ftpz(100,T,1000,[ ("Water",0.5), ("Methanol",0.5) ])
f.solve(silent=True)
print(f"T = {S001.getVar('T').quantity()}, VF = {S001.getVar('VF').value}")
#print(S001)
'''
f= Flowsheet("Test",sys)
F01= f.unit("F01", "Flash", [f.mstr("S001")],[f.mstr("S002"),f.mstr("S003")])
f.mstr("S001").ftpz(100, 25,1,[ ("Water",0.5), ("Methanol",0.5) ])
f.unit("F01").spec([ ("VF",0.5,SI.none), ("P",1,METRIC.bar) ])
f.init()
f.solve()
print(f.report())
print(f.streamtable())
''' | [
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5461,
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3101,
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13,
12008,
2
] |
Python/Code/Thermal_Models/models/__init__.py | mhasoba/TPCFitting | 0 | 96436 | #!/usr/bin/env python3
"""
This directory contains classes representing each growth model.
"""
from .linear_models import boltzmann_arrhenius, \
lm
from .schoolfield_models import schoolfield_two_factor, \
schoolfield_two_factor_tpk, \
schoolfield_full, \
schoolfield_full_tpk
from .weighted_models import boltzmann_arrhenius_two_weights_linear, \
boltzmann_arrhenius_two_weights_non_linear, \
boltzmann_arrhenius_two_weights_normalise_only, \
boltzmann_arrhenius_one_weight_linear, \
boltzmann_arrhenius_one_weight_non_linear | [
1,
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650,
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7915,
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5464,
29918,
10660,
2
] |
tests/utilities/test_fetching.py | PeppeSaccardi/pytorch-lightning | 1 | 94574 | # Copyright The PyTorch Lightning team.
#
# 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 pytest
from torch import tensor
from torch.utils.data import DataLoader, IterableDataset
from pytorch_lightning.trainer.supporters import CombinedLoader
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.fetching import DataFetcher
@pytest.mark.parametrize("use_combined_loader", [False, True])
def test_prefetch_iterator(use_combined_loader):
"""Test the DataFetcher with PyTorch IterableDataset."""
class IterDataset(IterableDataset):
def __iter__(self):
yield 1
yield 2
yield 3
for prefetch_batches in range(0, 4):
if use_combined_loader:
loader = CombinedLoader([DataLoader(IterDataset()), DataLoader(IterDataset())])
expected = [
([tensor([1]), tensor([1])], False),
([tensor([2]), tensor([2])], False),
([tensor([3]), tensor([3])], True),
]
else:
loader = DataLoader(IterDataset())
expected = [(1, False), (2, False), (3, True)]
iterator = DataFetcher(prefetch_batches=prefetch_batches)
prefetch_batches += 1
assert iterator.prefetch_batches == prefetch_batches
iterator.setup(loader)
def generate():
generated = []
for idx, data in enumerate(iterator, 1):
if iterator.done:
assert iterator.fetched == 3
else:
assert iterator.fetched == (idx + prefetch_batches)
generated.append(data)
return generated
assert generate() == expected
# validate reset works properly.
assert generate() == expected
assert iterator.fetched == 3
class EmptyIterDataset(IterableDataset):
def __iter__(self):
return iter([])
dataloader = DataLoader(EmptyIterDataset())
iterator = DataFetcher()
iterator.setup(dataloader)
assert list(iterator) == []
def test_misconfiguration_error():
fetcher = DataFetcher()
with pytest.raises(
MisconfigurationException, match="The `dataloader_iter` isn't available outside the __iter__ context."
):
loader = DataLoader(range(10))
fetcher.setup(loader)
assert fetcher.loaders[0] == loader
fetcher.loader_iters
iter(fetcher)
assert fetcher.loader_iters
| [
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setup.py | zuokangbo/strack-connect | 0 | 168586 | # :coding: utf-8
# :copyright: Copyright (c) 2021 strack
"""Describe the distribution to distutils."""
# Import third-party modules
import os
from setuptools import find_packages
from setuptools import setup
ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
README_PATH = os.path.join(ROOT_PATH, 'README.md')
requires = [
'dayu_widgets >= 0.0.1',
'strack-api >=1.0.0',
'PyYAML >= 5.0'
]
setup(
name='strack-connect',
author='weijer',
url='https://github.com/cgpipline/strack-connect',
license='Apache License (2.0)',
version='0.0.1',
author_email='<EMAIL>',
description=('Connect for strack.'),
long_description=open(README_PATH, 'r', encoding='UTF-8').read(),
classifiers=[
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: Implementation'
],
packages=find_packages(),
include_package_data=True,
zip_safe=False,
install_requires=requires,
setup_requires=['pytest-runner'],
tests_require=['pytest']
)
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robot/migrations/0011_auto_20200105_1702.py | Misschl/wechat | 1 | 183238 | <filename>robot/migrations/0011_auto_20200105_1702.py
# Generated by Django 2.1.5 on 2020-01-05 17:02
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('robot', '0010_auto_20200105_1342'),
]
operations = [
migrations.AlterModelOptions(
name='appmodel',
options={'verbose_name': '对接应用', 'verbose_name_plural': '对接应用'},
),
migrations.AlterField(
model_name='message',
name='is_at',
field=models.BooleanField(default=False, null=True),
),
migrations.AlterField(
model_name='wxuser',
name='friend',
field=models.BooleanField(null=True, verbose_name='我的好友'),
),
migrations.AlterField(
model_name='wxuser',
name='is_friend',
field=models.BooleanField(null=True, verbose_name='和我有好友关系'),
),
migrations.AlterField(
model_name='wxuser',
name='puid',
field=models.CharField(help_text='微信用户的外键', max_length=15, primary_key=True, serialize=False),
),
]
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Python/maximum-candies-you-can-get-from-boxes.py | RideGreg/LeetCode | 1 | 73255 | <gh_stars>1-10
# Time: O(n^2)
# Space: O(n)
# 1298 weekly contest 168 12/21/2019
# Given n boxes, each box is given in the format [status, candies, keys, containedBoxes] where:
#
# status[i]: an integer which is 1 if box[i] is open and 0 if box[i] is closed.
# candies[i]: an integer representing the number of candies in box[i].
# keys[i]: an array contains the indices of the boxes you can open with the key in box[i].
# containedBoxes[i]: an array contains the indices of the boxes found in box[i].
# You will start with some boxes given in initialBoxes array. You can take all the candies in any open box and you can use the keys in it to open new boxes and you also can use the boxes you find in it.
#
# Return the maximum number of candies you can get following the rules above.
# Each box is contained in one box at most.
# 1 <= status.length <= 1000
import collections
class Solution(object):
def maxCandies(self, status, candies, keys, containedBoxes, initialBoxes):
"""
:type status: List[int]
:type candies: List[int]
:type keys: List[List[int]]
:type containedBoxes: List[List[int]]
:type initialBoxes: List[int]
:rtype: int
"""
result = 0
q = collections.deque(initialBoxes)
while q:
changed = False
sz = len(q)
for _ in range(sz):
box = q.popleft()
if not status[box]:
q.append(box)
else:
changed = True
result += candies[box]
for contained_key in keys[box]:
status[contained_key] = 1
q.extend(containedBoxes[box])
if not changed:
break
return result
def maxCandies_ming(self, status, candies, keys, containedBoxes, initialBoxes):
ans, cur, cont = 0, initialBoxes, True
while cont:
cont = False
nxt = []
for i in cur:
if status[i]:
ans += candies[i]
nxt.extend(containedBoxes[i])
for k in keys[i]:
status[k] = 1
cont = True
else:
nxt.append(i) # 没打开的box统统留到下次处理
cur = nxt
return ans
'''
#已经意识到一次处理不完，试图作第二次处理。其实仍然不够，应该无限次循环加个退出条件
#key的作用就是改为open状态，维护myKey较麻烦，不如更新状态
oBox, cBox, myKey = [], [], set()
q = collections.deque(initialBoxes)
while q:
i = q.popleft()
if status[i] or i in myKey:
oBox.append(i)
myKey.update(keys[i])
for j in containedBoxes[i]:
q.append(j)
else:
cBox.append(i)
oBox2, cBox2 = [], []
q = collections.deque(oBox+cBox)
while q:
i = q.popleft()
if status[i] or i in myKey:
oBox2.append(i)
myKey.update(keys[i])
else:
cBox2.append(i)
return sum(candies[i] for i in oBox2)
'''
print(Solution().maxCandies([1,0,1,0], [7,5,4,100], [[],[],[1],[]], [[1,2],[3],[],[]], [0])) # 16
print(Solution().maxCandies([1,0,0,0,0,0], [1,1,1,1,1,1], [[1,2,3,4,5],[],[],[],[],[]],
[[1,2,3,4,5],[],[],[],[],[]], [0])) # 6
print(Solution().maxCandies([1,1,1], [100,1,100], [[],[0,2],[]], [[],[],[]], [1])) # 1
print(Solution().maxCandies([1], [100], [[]], [[]], [])) # 0
print(Solution().maxCandies([1,1,1], [2,3,2], [[],[],[]], [[],[],[]], [2,1,0])) # 7
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] |
model/data_module.py | ricardorei/lightning-convai | 5 | 65786 | # -*- coding: utf-8 -*-
r"""
DataModule
==========
The DataModule encapsulates all the steps needed to process data:
- Download / tokenize
- Save to disk.
- Apply transforms (tokenize, pad, batch creation, etc…).
- Load inside Dataset.
- Wrap inside a DataLoader.
The most important function to understand inside the DataModule is the `build_input` which
is responsible by building the inputs that will be used to train the PersonaGPT2 Model.
This function receives a tokenized `persona`, `history` and `reply`, concatenates everything
and builds the language model targets. It also keeps track of the possition of the token used
to represent the entire sequence (the last one).
Example:
>>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
>>> DataModule.build_input(
tokenizer=tokenizer,
persona=[[72, 588], [1820, 318]],
history=[[5303, 1804], [316, 20023]],
reply=[276, 764],
lm_labels=False
)
{'input_ids': [50258, 72, 588, 1820, 318, 50260, 5303, 1804, 50261, 316, 20023, 50260,
276, 764, 50258], 'token_type_ids': [50260, 50260, 50260, 50260, 50260, 50261, 50261,
50261, 50260, 50260, 50260, 50261, 50261, 50261, 50261], 'mc_token_ids': 14, 'lm_labels':
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]}
>>> DataModule.build_input(
tokenizer=tokenizer,
persona=[[72, 588], [1820, 318]],
history=[[5303, 1804], [316, 20023]],
reply=[276, 764],
lm_labels=True
)
{'input_ids': [50258, 72, 588, 1820, 318, 50260, 5303, 1804, 50261, 316, 20023, 50260,
276, 764, 50258], 'token_type_ids': [50260, 50260, 50260, 50260, 50260, 50261, 50261,
50261, 50260, 50260, 50260, 50261, 50261, 50261, 50261], 'mc_token_ids': 14, 'lm_labels':
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 276, 764, 50258]}
"""
import hashlib
import json
import multiprocessing
import os
from argparse import Namespace
from collections import defaultdict
from itertools import chain
from typing import Dict, List
import click
import torch
from torch.utils.data import DataLoader, TensorDataset
import pytorch_lightning as pl
from model.tokenizer import Tokenizer
from torchnlp.download import download_file_maybe_extract
PERSONACHAT_URL = "https://s3.amazonaws.com/datasets.huggingface.co/personachat/personachat_self_original.json"
PADDED_INPUTS = ["input_ids", "lm_labels", "token_type_ids"]
MODEL_INPUTS = ["input_ids", "mc_token_ids", "lm_labels", "mc_labels", "token_type_ids"]
class DataModule(pl.LightningDataModule):
"""PyTorch Lightning DataModule.
:param hparams: Namespace with data specific arguments.
:param tokenizer: Model Tokenizer.
"""
def __init__(self, hparams: Namespace, tokenizer: Tokenizer):
super().__init__()
self.hparams = hparams
self.tokenizer = tokenizer
@classmethod
def build_input(
cls,
tokenizer: Tokenizer,
persona: List[List[int]],
history: List[List[int]],
reply: List[int] = [],
lm_labels: bool = False,
) -> Dict[str, List[int]]:
"""Builds a model input.
:param persona: List of persona sentences tokenized.
:param history: List of history sentences tokenizes.
:param reply: Tokenized answer.
:param lm_labels: Flag to build LM labels for ground-truth replies.
:return: Dictionary with model inputs.
"""
bos, eos, speaker1, speaker2 = (
tokenizer.bos_index,
tokenizer.eos_index,
tokenizer.speaker1_index,
tokenizer.speaker2_index,
)
sequence = (
[[bos] + list(chain(*persona))] # concats all persona sentences
+ history # concats history
+ [reply + [eos]] # concats reply
)
sequence = [sequence[0]] + [
[speaker2 if (len(sequence) - i) % 2 else speaker1] + s
for i, s in enumerate(sequence[1:])
]
instance = {
"input_ids": list(chain(*sequence)),
"token_type_ids": [
speaker2 if i % 2 else speaker1
for i, s in enumerate(sequence)
for _ in s
],
}
instance["mc_token_ids"] = len(instance["input_ids"]) - 1
instance["lm_labels"] = [-100] * len(instance["input_ids"])
if lm_labels:
instance["lm_labels"] = (
([-100] * sum(len(s) for s in sequence[:-1]))
+ [-100]
+ sequence[-1][1:]
)
return instance
def _tokenize(self, obj):
if isinstance(obj, str):
return self.tokenizer.encode(obj)
if isinstance(obj, dict):
return dict((k, self._tokenize(o)) for k, o in obj.items())
return list(self._tokenize(o) for o in obj)
def _get_dataset(
self,
dataset_path: str = "",
data_folder: str = "data/",
):
"""Downloads PersonaChat corpus from S3 if no dataset_path is provided.
:param dataset_path: Path to a json file containing the train and validation dataset.
:param data_folder: Folder used to store data.
:return: Returns a dictionary with the training and validation data.
"""
if not os.path.isfile(dataset_path):
click.secho(f"Download dataset from {PERSONACHAT_URL}", fg="yellow")
dataset_file = download_file_maybe_extract(
PERSONACHAT_URL,
directory=data_folder,
check_files=["personachat_self_original.json"],
)
dataset_path = "data/personachat_self_original.json"
dataset_hash = (
int(hashlib.sha256(dataset_path.encode("utf-8")).hexdigest(), 16) % 10 ** 8
)
# To avoid using cache for different models
# split(/) for microsoft/DialoGPT-small
pretrained_model = (
self.hparams.pretrained_model.split("/")[1]
if "/" in self.hparams.pretrained_model
else self.hparams.pretrained_model
)
dataset_cache = data_folder + ".dataset_" + str(dataset_hash) + pretrained_model
if os.path.isfile(dataset_cache):
click.secho(f"Loading tokenized dataset from cache: {dataset_cache}.")
return torch.load(dataset_cache)
else:
dataset_file = dataset_path
with open(dataset_file, "r", encoding="utf-8") as f:
dataset = json.loads(f.read())
click.secho("Running tokenization: This might take some time!", fg="yellow")
dataset = self._tokenize(dataset)
torch.save(dataset, dataset_cache)
return dataset
@classmethod
def pad_dataset(
cls, dataset: dict, padding: int = 0, padded_inputs: List[str] = PADDED_INPUTS
):
"""
Pad the dataset.
NOTE: This could be optimized by defining a Dataset class and
padding at the batch level, but this is simpler.
:param dataset: Dictionary with sequences to pad.
:param padding: padding index.
:param padded_inputs:
"""
max_l = max(len(x) for x in dataset["input_ids"])
for name in padded_inputs:
dataset[name] = [
x + [padding if name != "lm_labels" else -100] * (max_l - len(x))
for x in dataset[name]
]
return dataset
def prepare_data(self):
"""
Lightning DataModule function that will be used to load/download data,
build inputs with padding and to store everything as TensorDatasets.
"""
personachat = self._get_dataset(self.hparams.dataset_path)
click.secho("Building inputs and labels.", fg="yellow")
datasets = {"train": defaultdict(list), "valid": defaultdict(list)}
for dataset_name, dataset in personachat.items():
num_candidates = len(dataset[0]["utterances"][0]["candidates"])
if self.hparams.num_candidates > 0 and dataset_name == "train":
num_candidates = min(self.hparams.num_candidates, num_candidates)
for dialog in dataset:
persona = dialog["personality"].copy()
for _ in range(self.hparams.personality_permutations):
for utterance in dialog["utterances"]:
history = utterance["history"][
-(2 * self.hparams.max_history + 1) :
]
for j, candidate in enumerate(
utterance["candidates"][-num_candidates:]
):
lm_labels = bool(j == num_candidates - 1)
instance = self.build_input(
self.tokenizer, persona, history, candidate, lm_labels
)
for input_name, input_array in instance.items():
datasets[dataset_name][input_name].append(input_array)
datasets[dataset_name]["mc_labels"].append(num_candidates - 1)
datasets[dataset_name]["n_candidates"] = num_candidates
persona = [persona[-1]] + persona[:-1] # permuted personalities
click.secho("Padding inputs and building tensors.", fg="yellow")
tensor_datasets = {"train": [], "valid": []}
for dataset_name, dataset in datasets.items():
dataset = self.pad_dataset(dataset, padding=self.tokenizer.pad_index)
for input_name in MODEL_INPUTS:
tensor = torch.tensor(dataset[input_name])
# MC labels contain the labels within the batch!
# Thats why we have to split the data according to those batches.
if input_name != "mc_labels":
tensor = tensor.view(
(-1, datasets[dataset_name]["n_candidates"]) + tensor.shape[1:]
)
tensor_datasets[dataset_name].append(tensor)
self.train_dataset = TensorDataset(*tensor_datasets["train"])
self.valid_dataset = TensorDataset(*tensor_datasets["valid"])
click.secho(
"Train dataset (Batch, Candidates, Seq length): {}".format(
self.train_dataset.tensors[0].shape
),
fg="yellow",
)
click.secho(
"Valid dataset (Batch, Candidates, Seq length): {}".format(
self.valid_dataset.tensors[0].shape
),
fg="yellow",
)
def train_dataloader(self) -> DataLoader:
""" Function that loads the train set. """
return DataLoader(
self.train_dataset,
batch_size=self.hparams.batch_size,
shuffle=True,
num_workers=multiprocessing.cpu_count(),
)
def val_dataloader(self) -> DataLoader:
""" Function that loads the validation set. """
return DataLoader(
self.valid_dataset,
batch_size=self.hparams.batch_size,
shuffle=False,
num_workers=multiprocessing.cpu_count(),
)
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Ex_Files_Python_EssT/Exercise Files/Chap01/rockPaperScissors.py | cinthiatengan/Basic-Python | 0 | 39773 | <reponame>cinthiatengan/Basic-Python
""" Rock Paper Scissors
--------------------------------------------------------------
"""
import random
import os
import re
os.system('cls' if os.name=='nt' else clear)
while (1< 2):
print ("\n")
print ("Rock, Paper, Scissors - Shoot!")
userChoice = input("Choose your weapon [R]ock, [P]aper, or [S]cissors: ")
if not re.match("[SsRrPp]", userChoice):
print ("Please choose a letter: ")
print ("[R]ock, [P]aper, or [S]cissors.")
continue
# Echo the user's choice
print ("You choose: " + userChoice)
choices = ['R', 'P', 'S']
opponentChoice = random.choice(choices)
print ("I choose: " + opponentChoice)
if opponentChoice == str.upper(userChoice):
print ("Tie!")
elif opponentChoice == 'R' and userChoice.upper() == 'S':
print ("Rock beats Scissors, I lose")
continue
elif opponentChoice == 'S' and userChoice.upper() == 'P':
print ("Scissors beats paper, I lose")
continue
elif opponentChoice == 'P' and userChoice.upper() == 'R':
print ("Paper beats rock, I lose")
continue
else:
print ("I win!") | [
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algDev/visualization/plot_indicators.py | ajmal017/ralph-usa | 0 | 34953 | from models.indicators import Indicators
import numpy as np
import matplotlib.pyplot as plt
def plot_prices(ax, prices, line_style):
i = np.arange(len(prices))
ax.plot(ax, prices, line_style)
return ax
def plot_macd(ax, prices, slow_period, fast_period, line_style='k-'):
macd = Indicators.macd(prices, slow_period, fast_period)[slow_period - 1:]
i = np.arange(len(prices))[slow_period-1:]
ax.plot(i, macd, line_style)
return ax
def plot_ema(ax, prices, period, line_style='k-'):
ema = Indicators.ema(prices, period)[period-1:]
i = np.arange(len(prices))[period-1:]
ax.plot(i, ema, line_style)
return ax
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serve.py | uwmisl/purpledrop-driver | 0 | 27038 |
from gevent import monkey
monkey.patch_all()
import sys
import purpledrop.server as server
from purpledrop.purpledrop import list_purpledrop_devices, PurpleDropDevice, PurpleDropController
devices = list_purpledrop_devices()
if(len(devices) == 0):
print("No PurpleDrop USB device found")
sys.exit(1)
elif len(devices) > 1:
print("Multiple PurpleDrop devices found. Please ammend software to allow selection by serial number")
for d in devices:
print(f"{d.device}: Serial {d.serial_number}")
sys.exit(1)
dev = PurpleDropDevice(devices[0].device)
controller = PurpleDropController(dev)
server.run_server(controller, "localhost:5000")
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pybabylonjs/babylonjs.py | TileDB-Inc/TileDB-PyBabylonJS | 2 | 50179 | <filename>pybabylonjs/babylonjs.py<gh_stars>1-10
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) TileDB, Inc..
# Distributed under the terms of the Modified BSD License.
"""
BabylonJS Jupyter Widget
"""
from ipywidgets import DOMWidget
import json
import os
from traitlets import CInt, Float, Dict, List, TraitError, Unicode, validate
from ._frontend import module_name, module_version
class BabylonJS(DOMWidget):
"""BabylonJS"""
_model_name = Unicode("BabylonJSModel").tag(sync=True)
_model_module = Unicode(module_name).tag(sync=True)
_model_module_version = Unicode(module_version).tag(sync=True)
_view_name = Unicode("BabylonJSView").tag(sync=True)
_view_module = Unicode(module_name).tag(sync=True)
_view_module_version = Unicode(module_version).tag(sync=True)
extents = List().tag(sync=True)
width = CInt(700).tag(sync=True)
height = CInt(500).tag(sync=True)
query = Dict().tag(sync=True)
token = Unicode(os.getenv("TILEDB_REST_TOKEN", "")).tag(sync=True)
uri = Unicode().tag(sync=True)
value = Dict().tag(sync=True)
wheel_precision = Float(50.0).tag(sync=True)
z_scale = Float(0.5).tag(sync=True)
@validate("value")
def _valid_value(self, proposal):
if proposal.value:
reqd = ["X", "Y", "Z", "Red", "Green", "Blue"]
if not all(key in proposal.value.keys() for key in reqd):
raise TraitError(f"Missing one of {reqd} in input")
data = {}
data["X"] = proposal.value["X"].tolist()
data["Y"] = proposal.value["Y"].tolist()
data["Z"] = proposal.value["Z"].tolist()
data["Red"] = proposal.value["Red"].tolist()
data["Green"] = proposal.value["Green"].tolist()
data["Blue"] = proposal.value["Blue"].tolist()
self.extents = [
min(data["X"]),
max(data["X"]),
min(data["Y"]),
max(data["Y"]),
min(data["Z"]),
max(data["Z"]),
]
return json.dumps(data)
else:
return proposal.value
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pylearn2/sampling/replace_samples.py | CKehl/pylearn2 | 0 | 38827 | <gh_stars>0
'''
Created on Apr 16, 2015
@author: christian
'''
from optparse import OptionParser
import numpy
import os
from os import listdir
from os.path import isfile, join
import cPickle
import glob
def unpickle(file):
fo = open(file, 'rb')
dictionary = cPickle.load(fo)
fo.close()
return dictionary
if __name__ == '__main__':
optionParser = OptionParser("usage: %prog -i INPUT_FILE -m META_FILE -t SOURCE_TAG -r DESTINATION_TAG")
optionParser.add_option("-i", "--input", action="store", dest="input", type="string", metavar="FILE", help="pickled python dataset file")
optionParser.add_option("-m", "--meta", action="store", dest="meta", type="string", metavar="FILE", help="pickled python metadata file")
optionParser.add_option("-t", "--tag", action="store", dest="tag", type="int", help="selected tag to add the image", default = 0)
optionParser.add_option("-r", "--replace_tag", action="store", dest="rtag", type="int", help="replacement tag number for <tag>", default = 0)
(options, args) = optionParser.parse_args()
my_obj = dict()
meta_obj = dict()
label_str = ''
meta_label_str = ''
test_img_array = None
test_array = []
test_classes = []
test_obj = dict()
if options.input != None:
my_obj = unpickle(options.input)
in_dir = os.path.dirname(options.input)
test_obj = unpickle(os.path.join(in_dir, "test"))
else:
exit()
ds = 0
if("fine_labels" in my_obj.keys()):
ds = 1 #CIFAR-100
label_str = 'fine_labels'
meta_label_str = 'fine_label_names'
else:
ds = 0 #CIFAR-10 and combined
label_str = 'labels'
meta_label_str = 'label_names'
meta_inputs = []
if(options.meta == None) or (options.meta == ""):
meta_inputs = glob.glob(os.path.dirname(options.input)+os.path.sep+"*meta*")
else:
meta_inputs.append(options.meta)
meta_obj = unpickle(meta_inputs[0])
num_img_base_array = [0]*(len(my_obj[label_str]))
img_base_array = [[0]*3072]*(my_obj['data'].shape[0])
img_array = numpy.array(img_base_array, dtype=numpy.uint8)
class_array = [0]*(my_obj['data'].shape[0])
for i in range(0, my_obj['data'].shape[0]):
data_entry = my_obj['data'][i]
tag_no = my_obj[label_str][i]
img_array[i] = data_entry
class_array[i] = tag_no
num_img_base_array[tag_no]+=1
# Test array generation
tcursor_point = 0
#print "Test Data fieldsize: "+str(test_obj['data'].shape[0])
for i in range(0, test_obj['data'].shape[0]):
data_entry = test_obj['data'][i]
tag_no = test_obj['labels'][i]
#print "Test Image: "+str(i)+" => Tag: "+str(tag_no)
test_array.append(data_entry.tolist())
test_classes.append(tag_no)
tcursor_point+=1
tag_img_number = num_img_base_array[options.tag]
img_of_tag = []
for i in range(0, len(class_array)):
if(class_array[i] == options.tag):
img_of_tag.append(i)
print "Data with selected tag: "+str(img_of_tag)+" ("+str(tag_img_number)+")"
print "Dataset size before replacement: "+str(len(class_array))+" | "+str(img_array.shape[0])
for i in range(0, len(img_of_tag)):
class_array[img_of_tag[i]]=options.rtag
del num_img_base_array[options.tag]
print "Dataset size after replacement: "+str(len(class_array))+" | "+str(img_array.shape[0])
print "Label dictionary before replacement: "+str(len(meta_obj[meta_label_str]))
del meta_obj[meta_label_str][options.tag]
print "Label dictionary after replacement: "+str(len(meta_obj[meta_label_str]))
# re-adapt mapping
for i in range(0, len(class_array)):
if(class_array[i] > options.tag):
class_array[i]-=1
################
# TESTING DATA #
################
del img_of_tag[:]
for i in range(0, len(test_classes)):
if(test_classes[i] == options.tag):
img_of_tag.append(i)
for i in range(0, len(img_of_tag)):
test_classes[img_of_tag[i]]=options.rtag
# re-adapt mapping
for i in range(0, len(test_classes)):
if(test_classes[i] > options.tag):
test_classes[i]-=1
out_obj = dict()
out_obj['data']=img_array
out_obj['labels']=class_array
out_dir = os.path.dirname(options.input)
#fo = open(os.path.join(options.output,"experiment"), 'wb')
cPickle.dump(out_obj, open(os.path.join(out_dir,"experiment_rp"), "wb"), protocol=2)
test_n_obj = dict()
test_img_array = numpy.array(test_array, dtype=numpy.uint8)
test_n_obj['data']=test_img_array
test_n_obj['labels']=test_classes
cPickle.dump(test_n_obj, open(os.path.join(out_dir,"test_rp"), "wb"), protocol=2)
meta_obj_out = dict()
meta_obj_out['label_names'] = meta_obj[meta_label_str]
cPickle.dump(meta_obj_out, open(os.path.join(out_dir,"meta_rp"), "wb"), protocol=2)
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app/__init__.py | VirginiaNdungu1/ugatuzi | 1 | 190352 | from flask import Flask
from flask_bootstrap import Bootstrap
from flask_sqlalchemy import SQLAlchemy
from config import config_options
from flask_login import LoginManager
bootstrap = Bootstrap()
db = SQLAlchemy()
login_manager = LoginManager()
login_manager.session_protection = 'strong'
login_manager.login_message = "You must be logged in to access this page."
login_manager.login_view = 'auth.login'
def create_app(config_name):
app = Flask(__name__)
# creating configurations
app.config.from_object(config_options[config_name])
# config_options[config_name].init_app(app)
# initializing flask extensions
bootstrap.init_app(app)
db.init_app(app)
login_manager.init_app(app)
# registering main blueprint
from .main import main as main_blueprint
app.register_blueprint(main_blueprint)
# registering auth blueprint
from .auth import auth as auth_blueprint
app.register_blueprint(auth_blueprint, url_prefix='/authenticate')
# # registering api blueprint
# from .api import api as api_blueprint
# app.register_blueprint(api_blueprint)
return app
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] |
server.py | palmpilot71/CMPUT404-assignment-webserver | 0 | 113360 | <reponame>palmpilot71/CMPUT404-assignment-webserver
# coding: utf-8
import SocketServer, os
# Copyright 2013 <NAME>, <NAME>, <NAME>
#
# 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.
#
#
# Furthermore it is derived from the Python documentation examples thus
# some of the code is Copyright © 2001-2013 Python Software
# Foundation; All Rights Reserved
#
# http://docs.python.org/2/library/socketserver.html
#
# run: python freetests.py
# try: curl -v -X GET http://127.0.0.1:8080/
class MyWebServer(SocketServer.BaseRequestHandler):
def _check_resource_location(self, requested_resource):
current_directory = os.path.abspath(os.curdir)
data_directory = current_directory + "/www"
return os.path.abspath(requested_resource).startswith(data_directory)
def _adjust_resource(self, requested_resource):
current_directory = os.path.abspath(os.curdir)
requested_resource = current_directory + "/www" + requested_resource
return requested_resource
def _create_headers(self):
return "HTTP/1.1 200 OK\r\n"
def _send_404(self, requested_resource):
headers = "HTTP/1.1 404 Not Found\r\n"
close = "Connection: close\r\n\r\n"
body = ("<html>\n<body>404 - Could not find resource at "
+ requested_resource
+ " </body>\n</html>")
self.request.send(headers + close + body)
def _get_mime(self, adjusted_resource):
if (adjusted_resource[-4:] == ".css"):
return "Content-Type: text/css\r\n"
elif (adjusted_resource[-5:] == ".html"):
return "Content-Type: text/html\r\n"
else:
return "Content Type: \r\n"
def _send_302(self, requested_resource):
headers = "HTTP/1.1 302 Found\r\n"
location = "Location: http://127.0.0.1:8080" + requested_resource + "index.html\r\n"
close = "Connection: close\r\n\r\n"
self.request.send(headers + location + close)
def _handle_directory_redirect(self, adjusted_resource):
return os.path.isdir(adjusted_resource)
def handle(self):
self.data = self.request.recv(1024).strip()
#print("Got a request of: %s\n" % self.data)
if (self.data):
split_data = self.data.split(" ")
else:
return
requested_resource = split_data[1]
adjusted_resource = self._adjust_resource(requested_resource)
if (adjusted_resource.endswith("/")):
self._send_302(requested_resource)
elif (self._check_resource_location(adjusted_resource)):
if (self._handle_directory_redirect(adjusted_resource)):
self._send_302(requested_resource + "/")
else:
try:
resource = open(adjusted_resource).read()
headers = self._create_headers()
content_type = self._get_mime(adjusted_resource)
close = "Connection: close\r\n\r\n"
self.request.send(headers + content_type + close + resource)
except:
self._send_404(requested_resource)
else:
self._send_404(requested_resource)
if __name__ == "__main__":
HOST, PORT = "localhost", 8080
SocketServer.TCPServer.allow_reuse_address = True
# Create the server, binding to localhost on port 8080
server = SocketServer.TCPServer((HOST, PORT), MyWebServer)
# Activate the server; this will keep running until you
# interrupt the program with Ctrl-C
server.serve_forever()
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] |
tests/test_timeout.py | MattBBaker/ucx-py | 0 | 123999 | import pytest
import asyncio
import ucp
import time
async def talk_to_server(ip, port, timeout):
try:
ep = await ucp.get_endpoint(ip, port, timeout)
except TimeoutError:
pass
@pytest.mark.asyncio
async def test_timeout():
ucp.init()
ip = ucp.get_address()
await asyncio.gather(talk_to_server(ip.encode(), 9999, timeout=0.1))
ucp.fin()
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instrumentserver/apps.py | Coughman058/instrumentserver | 0 | 177766 | import os
import argparse
import logging
from . import QtWidgets, QtCore
from .log import setupLogging
from .server.application import startServerGuiApplication
from .server.core import startServer
from .client import Client
from .gui import widgetDialog
from .gui.instruments import ParameterManagerGui
setupLogging(addStreamHandler=True,
logFile=os.path.abspath('instrumentserver.log'))
logger = logging.getLogger('instrumentserver')
logger.setLevel(logging.DEBUG)
def server(port, user_shutdown):
app = QtCore.QCoreApplication([])
server, thread = startServer(port, user_shutdown)
thread.finished.connect(app.quit)
return app.exec_()
def serverWithGui(port):
app = QtWidgets.QApplication([])
window = startServerGuiApplication(port)
return app.exec_()
def serverScript() -> None:
parser = argparse.ArgumentParser(description='Starting the instrumentserver')
parser.add_argument("--port", default=5555)
parser.add_argument("--gui", default=True)
parser.add_argument("--allow_user_shutdown", default=False)
args = parser.parse_args()
if args.gui:
serverWithGui(args.port)
else:
server(args.port, args.allow_user_shutdown)
def parameterManagerScript() -> None:
parser = argparse.ArgumentParser(description='Starting a parameter manager instrument GUI')
parser.add_argument("--name", default="parameter_manager")
parser.add_argument("--port", default=5555)
args = parser.parse_args()
app = QtWidgets.QApplication([])
# open a client to a server using default address (localhost) and port.
cli = Client(port=args.port)
if args.name in cli.list_instruments():
pm = cli.get_instrument(args.name)
else:
pm = cli.create_instrument(
'instrumentserver.params.ParameterManager', args.name)
pm.fromFile()
_ = widgetDialog(ParameterManagerGui(pm))
app.exec_()
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common/Request_Package.py | Four-sun/Requests_Load | 0 | 44479 | <gh_stars>0
# -*- coding: utf-8 -*-
"""
Created: on 2018-04-11
@author: Four
Project: common\Request_Package.py
Request重新封装
"""
import json
import time
from common.log import Logger
Logger_Message = Logger()
send_time=time.strftime("%Y-%m-%d-%H_%M_%S",time.localtime(time.time())) #获取当前时间
def send_requests(s, testdata, cookie=None,):
'''封装requests请求'''
method = testdata["method"]
description = testdata["description"]
url = testdata["url"]
# url后面的params参数d
try:
params = eval(testdata["params"])
except:
params = None
# 请求头部headers
try:
headers = eval(testdata["headers"])
except:
headers = None
# post请求body类型
type = testdata["type"]
test_nub = testdata['caseid']
Logger_Message.loginfo("-------------正在执行用例：%s 描述：%s -------------" % (test_nub,description))
print("-------------正在执行用例：%s 描述：%s -------------" % (test_nub,description))
print("%s\n 请求头部：%s \n 请求方式：%s\n 请求url:%s \n 请求参数：%s" % (send_time,headers,method, url,params))
Logger_Message.loginfo("%s\n 请求头部：%s \n 请求方式：%s\n 请求url:%s \n 请求参数：%s" % (send_time,headers,method, url,params))
# post请求body内容
try:
bodydata = eval(testdata["body"])
except:
bodydata = {}
# post上传files内容
try:
files = eval(testdata["files"])
except:
files = {}
# 判断传data数据还是json
if type == "data":
body = bodydata
elif type == "json":
body = json.dumps(bodydata)
else:
body = bodydata
if method == "post":
if type == "files":
print("%s\n post请求body类型为：%s \tfiles内容为：%s" % (send_time, type, files))
Logger_Message.loginfo("%s\n post请求body类型为：%s \tfiles内容为：%s" % (send_time,type, files))
else:
print("%s\n post请求body类型为：%s \tbody内容为：%s" % (send_time, type, body))
Logger_Message.loginfo("%s\n post请求body类型为：%s \tbody内容为：%s" % (send_time,type, body))
verify = False # 是否检验
res = {} # 接受返回数据
try:
r = s.request(method=method,
cookies=cookie,
url=url,
params=params,
headers=headers,
data=body,
verify=verify)
print("页面返回信息：%s" % r.content.decode("utf-8"))
Logger_Message.loginfo("页面返回信息：%s" % r.content.decode("utf-8"))
res["statuscode"] = str(r.status_code) # 状态码转成str
res["text"] = r.content.decode("utf-8")
res["times"] = str(r.elapsed.total_seconds()) # 接口请求时间转str
if res["statuscode"] != "200":
res["error"] = res["text"]
return False
else:
res["error"] = ""
res["msg"] = ""
if testdata["checkpoint"] in res["text"]:
res["result"] = "pass"
res["text"] = r.content.decode("utf-8")
print("%s \t接口请求时间：%ss \t用例测试结果:%s---->%s " % (send_time,res["times"],test_nub, res["result"]))
Logger_Message.loginfo("%s \t接口请求时间：%ss \t用例测试结果:%s---->%s " % (send_time,res["times"],test_nub, res["result"]))
return True
else:
res["result"] = "fail"
print("%s \t 接口请求时间：%ss \t用例测试结果:%s---->%s %s Not in %s" % (send_time,res["times"],test_nub, res["result"],testdata["checkpoint"],res["text"]))
Logger_Message.loginfo("%s \t 接口请求时间：%ss \t用例测试结果:%s---->%s %s Not in %s" % (send_time,res["times"],test_nub, res["result"],testdata["checkpoint"],res["text"]))
return False
except Exception as msg:
res["msg"] = str(msg)
Logger_Message.logwarning("%s\t%s" % (send_time, msg))
return res
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] |
searchBar/basicapp/tests.py | rajvijen/Industry-Visit-Planning-and-Booking | 0 | 128868 | <filename>searchBar/basicapp/tests.py
from django.test import TestCase ,Client
from django.http import HttpRequest
from django.urls import reverse
from .models import UserProfileInfo
from django.contrib.auth.models import User
class ListView_Testing(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
cls.user = User.objects.create_user(username = "hemanth",email='<EMAIL>', password='<PASSWORD>')
cls.profile = UserProfileInfo.objects.create(user = cls.user,name = "hemanth",gender = "M")
def test_view_uses_correct_template_index(self):
response = self.client.get(reverse('index'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'searchBarApp/index.html')
def test_view_register(self):
response = self.client.get(reverse('basicapp:register'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/registration.html')
def test_user_login(self):
response = self.client.get(reverse('basicapp:user_login'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/login.html')
def test_user_profile(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:profile'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/profile.html')
def test_user_profile_update(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:update'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/update_profile.html')
def test_visited_industries(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:visited_industries'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/visited.html')
def test_Booked_industries(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:booked_to_visit'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/booked_to_visit.html')
'''
def test_charge1(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:charge1'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/booking_charge.html')
def test_charge2(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:charge2'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/booking_charge.html')
def test_Cancelticket(self):
self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:cancel_ticket'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/booked_to_visit.html')
def test_activate(self,**kwargs,**kwargs1):
response = self.client.get(reverse('basicapp:activate',**kwargs,**kwargs1))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'searchBarApp/index.html')
class Forms_Testing(TestCase):
def test_UserProfileInfoForms(self):
invalid_data = {
'username':"Hemanth",
'email':"<EMAIL>",
'password':<PASSWORD>,
'name':'hemanth',
'gender':"K"
}
form = UserProfileInfo(data = invalid_data)
form.is_valid()
self.assertTrue(form.errors)
'''
#URL-Testing
class URL_testing(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
cls.user = User.objects.create_user(username = "hemanth",email='<EMAIL>', password='<PASSWORD>')
cls.profile = UserProfileInfo.objects.create(user = cls.user,name = "hemanth",gender = "M")
def test_URL_register(self):
response = self.client.get(reverse('basicapp:register'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/registration.html')
def test_URL_user_login(self):
response = self.client.get(reverse('basicapp:user_login'))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'basicapp/login.html')
def test_URL_profile(self):
#self.client.force_login(self.user)
response = self.client.get(reverse('basicapp:profile'))
self.assertEqual(response.status_code, 302)
#self.assertTemplateUsed(response, 'basicapp/profile.html')
def test_URL_profile_update(self):
response = self.client.get(reverse('basicapp:update'))
self.assertEqual(response.status_code, 302)
def test_URL_visited_industries(self):
response = self.client.get(reverse('basicapp:visited_industries'))
self.assertEqual(response.status_code, 302)
def test_URL_bookinglist_update(self):
response = self.client.get(reverse('basicapp:booked_to_visit'))
self.assertEqual(response.status_code, 302)
'''
def test_URL_charge1(self):
response = self.client.get(reverse('basicapp:charge1'))
self.assertEqual(response.status_code, 302)
def test_URL_charge2(self):
response = self.client.get(reverse('basicapp:charge2'))
self.assertEqual(response.status_code, 302)
''' | [
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examples/canvas/stencil_canvas.py | Sentient07/kivy | 0 | 1610690 | '''
Stencil demo
============
This is a test of the stencil graphics instruction inside the stencil view
widget. When you use a stencil, every draw outside the bounding box of the
stencil view will be avoid. All the graphics will draw only in the stencil view.
You can "draw" a stencil view by touch & draw. The touch down will set the
position, and the drag will set the size.
'''
from kivy.app import App
from kivy.core.window import Window
from kivy.graphics import Color, Rectangle
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.button import Button
from kivy.uix.label import Label
from kivy.uix.stencilview import StencilView
from random import random as r
from functools import partial
class StencilTestWidget(StencilView):
'''Drag to define stencil area
'''
def on_touch_down(self, touch):
self.pos = touch.pos
self.size = (1, 1)
def on_touch_move(self, touch):
self.size = (touch.x - touch.ox, touch.y - touch.oy)
class StencilCanvasApp(App):
def add_rects(self, label, wid, count, *largs):
label.text = str(int(label.text) + count)
with wid.canvas:
for x in range(count):
Color(r(), 1, 1, mode='hsv')
Rectangle(pos=(r() * wid.width + wid.x,
r() * wid.height + wid.y), size=(10, 10))
def reset_stencil(self, wid, *largs):
wid.pos = (0, 0)
wid.size = Window.size
def reset_rects(self, label, wid, *largs):
label.text = '0'
wid.canvas.clear()
def build(self):
wid = StencilTestWidget(size_hint=(None, None), size=Window.size)
label = Label(text='0')
btn_add500 = Button(text='+ 200 rects')
btn_add500.bind(on_press=partial(self.add_rects, label, wid, 200))
btn_reset = Button(text='Reset Rectangles')
btn_reset.bind(on_press=partial(self.reset_rects, label, wid))
btn_stencil = Button(text='Reset Stencil')
btn_stencil.bind(on_press=partial(self.reset_stencil, wid))
layout = BoxLayout(size_hint=(1, None), height=50)
layout.add_widget(btn_add500)
layout.add_widget(btn_reset)
layout.add_widget(btn_stencil)
layout.add_widget(label)
root = BoxLayout(orientation='vertical')
rfl = FloatLayout()
rfl.add_widget(wid)
root.add_widget(rfl)
root.add_widget(layout)
return root
if __name__ == '__main__':
StencilCanvasApp().run()
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models/base.py | neural-reckoning/decoding_sound_location | 1 | 129979 | <reponame>neural-reckoning/decoding_sound_location
from brian import *
from brian.hears import *
from matplotlib import cm, rcParams
import sys, os, glob
sys.path = [os.path.normpath(os.path.join(os.path.split(__file__)[0], '../'))]+sys.path
from common import *
| [
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identipy_app/migrations/0016_auto_20191025_1622.py | levitsky/identipy_server | 0 | 94735 | <gh_stars>0
# -*- coding: utf-8 -*-
# Generated by Django 1.11.25 on 2019-10-25 13:22
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('identipy_app', '0015_auto_20190719_2316'),
]
operations = [
migrations.AlterField(
model_name='resimagefile',
name='imgtype',
field=models.CharField(choices=[(b'S', b'PSM'), (b'P', b'Peptide'), (b'R', b'Protein'), (b'O', b'Feature')], default=b'O', max_length=1),
),
migrations.AlterField(
model_name='searchrun',
name='status',
field=models.CharField(choices=[(b'W', b'Waiting'), (b'R', b'Running'), (b'V', b'Postsearch processing'), (b'F', b'Finished'), (b'D', b'Dead'), (b'E', b'Error')], default=b'D', max_length=1),
),
]
| [
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] |
common_logger/utils1.py | NathanKr/python-logger-playground | 0 | 183955 | <reponame>NathanKr/python-logger-playground
import logging
def add(num1 : float,num2 : float)->float:
logging.warning(f'args : {num1} , {num2}')
return num1+num2 | [
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python/python_challenge/25/25.py | yunyu2019/blog | 0 | 27951 | <reponame>yunyu2019/blog<filename>python/python_challenge/25/25.py<gh_stars>0
#!/usr/bin/python
# -*- coding: utf-8 -*-
# @Date : 2016-05-17 16:36:18
# @Author : Yunyu2019 (<EMAIL>)
# @Link : http://www.pythonchallenge.com/pc/hex/lake.html
import os
import wave
import time
import Image
import requests
def download(urls):
filename=os.path.basename(urls)
try:
req=requests.get(urls,auth=('butter','fly'))
fp=open(filename,'wb')
fp.write(req.content)
fp.close()
print 'download:%s' % filename
except:
print 'fail download:%s' % filename
def getdatas(files):
fp=open(files,'rb')
data=fp.read()[44:]
fp.close()
img=Image.new('RGB',(60,60))
img.fromstring(data)
return img
"""
for i in range(1,26):
urls='http://www.pythonchallenge.com/pc/hex/lake%s.wav' % i
download(urls)
time.sleep(1)
"""
img=Image.new('RGB',(300,300))
for i in range(25):
y,x=divmod(i,5)
files='lake{0}.wav'.format(i+1)
pices=getdatas(files)
img.paste(pices,(x*60,y*60))
img.save('lake.jpg') | [
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nvmexplorer_src/input_defs/nvsim_interface.py | lpentecost/NVMExplorer | 5 | 87048 | <reponame>lpentecost/NVMExplorer
#initialize class for nvsim output class, to be extracted from parsed nvsim results and pickled as input to the analytical model
#per-technology specifications can be inherit
from nvmexplorer_src.input_defs.cell_cfgs import *
class NVSimInputConfig:
def __init__(self,
mem_cfg_file_path="../../data/mem_cfgs/test_SRAM.cfg", #path to nvsim cfg input
process_node=45, #chosen node in nm
opt_target="ReadLatency", #optimization target
word_width=64, #word width in bits
capacity=4, #capacity in MB
cell_type=SRAMCellConfig() #pass the cell configuration
):
self.mem_cfg_file_path = mem_cfg_file_path
self.process_node = process_node
self.opt_target = opt_target
self.word_width = word_width
self.capacity = capacity
self.cell_type = cell_type
def generate_mem_cfg(self):
""" Creates a memory config file using characteristics of :class:`NVSimInputConfig` object
to be used as an input to NVSim
"""
cfg_file = open(self.mem_cfg_file_path, "w+")
cfg_file.write(self.cell_type.mem_cfg_base)
cfg_file.write("-ProcessNode: %d\n" % self.process_node+"\n")
cfg_file.write("-OptimizationTarget: "+self.opt_target+"\n")
cfg_file.write("-WordWidth (bit): %d\n" % self.word_width+"\n")
cfg_file.write("-Capacity (MB): %d\n" % self.capacity+"\n")
if self.cell_type.mlc > 1:
cell_levels = 2**(self.cell_type.mlc)
cfg_file.write("-CellLevels: %d\n" % cell_levels+"\n")
cfg_file.close()
class NVSimOutputConfig: #initialized to 16nm SRAM, 4MB
def __init__(self,
exp_name="default", #name or ID
#retain relevant NVSim configs
input_cfg = NVSimInputConfig(),
#also define all relevant outputs
read_latency=-1, #ns
read_bw=-1, #GB/s
read_energy=-1, #pJ/access
write_latency=-1, #ns
write_bw=-1, #GB/s
write_energy=-1, #nJ/access
leakage_power=-1, #mW
area=-1, #mm^2
area_efficiency=-1 #percentage
):
# define all parameters
self.exp_name = exp_name
self.input_cfg = input_cfg
self.read_latency = read_latency
self.read_bw = read_bw
self.read_energy = read_energy
self.write_latency = write_latency
self.write_bw = write_bw
self.write_energy = write_energy
self.leakage_power = leakage_power
self.area = area
self.area_efficiency = area_efficiency
def print_summary(self):
""" Prints a summary of the parsed NVSim output results
"""
print("Experiment Name: "+self.exp_name)
print("Read Latency (ns): %f" % self.read_latency)
print("Read BW (GB/s): %f" % self.read_bw)
print("Write Latency (ns): %f" % self.write_latency)
print("Write BW (GB/s): %f" % self.write_bw)
print("Read Energy (pJ): %f" % self.read_energy)
print("Write Energy (pJ): %f" % self.write_energy)
print("Leakage Power (mW): %f" % self.leakage_power)
print("Area (mm^2): %f" % self.area)
print("Area Efficiency (percent): %f" % self.area_efficiency)
def parse_nvsim_output(filepath='output_examples/sram_0', input_cfg=NVSimInputConfig()):
""" Returns a :class:`NVSimOutputConfig` object which gets populated with the output results in
parsed from file_path.
:param filepath: path to NVSim output file
:type filepath: String
:param input_cfg: :class:`NVSimIntputConfig` object that was used to create the NVSim output
:type input_cfg: :class:`NVSimInputConfig` object
:return: :class:`NVSimOutputConfig` object containing parsed NVSim results
:rtype: :class:`NVSimOutputConfig`
"""
#initialize base
base = NVSimOutputConfig(input_cfg=input_cfg)
with open(filepath, 'r') as f:
lines = f.readlines()
# Get rid of new lines
lines = map(lambda x: x.rstrip(), lines)
for line in lines:
if ' - Total Area =' in line and (base.area == -1):
# sloppy fix, skip to end of line
if "=" in line[line.index("=")+1:]:
line = line[line.index("=")+1:]
base.area = float(line[line.index("=")+1:][:-4])
if line[-4:] == "um^2":
base.area = base.area / (1000.)**2
elif ' - Read Latency' in line and (base.read_latency == -1):
base.read_latency = float(line[line.index("=")+1:][:-2])
if line[-2:] == "us": #scale to ns
base.read_latency = base.read_latency * 1000.
if line[-2:] == "ps": #scale to ns
base.read_latency = base.read_latency / 1000.
if line[-2:] == "ms": #scale to ns (yikes!)
base.read_latency = base.read_latency * 1000000.
elif ' - Area Efficiency =' in line:
base.area_efficiency = float(line[line.index("=")+1:][:-1])
elif ' - Read Bandwidth' in line:
base.read_bw = float(line[line.index("=")+1:][:-4])
if line[-4:] == "MB/s": #scale to GB/s
base.read_bw = base.read_bw / 1024.
if line[-4:] == "KB/s": #scale to GB/s
base.read_bw = base.read_bw / 1024. / 1024.
elif ' - Write Bandwidth' in line:
base.write_bw = float(line[line.index("=")+1:][:-4])
if line[-4:] == "MB/s": #scale to GB/s
base.write_bw = base.write_bw / 1024.
if line[-4:] == "KB/s": #scale to GB/s
base.write_bw = base.write_bw / 1024. / 1024.
elif ' - Read Dynamic Energy ' in line:
base.read_energy = float(line[line.index("=")+1:][:-2])
if line[-2:] == "nJ": #scale to pJ
base.read_energy = base.read_energy * 1000.
if line[-2:] == "uJ": #scale to pJ
base.read_energy = base.read_energy * 1000. * 1000.
elif ' - Leakage Power ' in line:
base.leakage_power = float(line[line.index("=")+1:][:-2])
if line[-2:] == "uW": #scale to mW
base.leakage_power = base.leakage_power / 1000.
elif ' - Write Latency' in line and (base.write_latency == -1):
base.write_latency = float(line[line.index("=")+1:][:-2])
if line[-2:] == "us": #scale to ns
base.write_latency = base.write_latency * 1000.
if line[-2:] == "ps": #scale to ns
base.write_latency = base.write_latency / 1000.
if line[-2:] == "ms": #scale to ns (yikes!)
base.write_latency = base.write_latency * 1000000.
elif ' - Write Dynamic Energy ' in line:
base.write_energy = float(line[line.index("=")+1:][:-2])
if line[-2:] == "nJ": #scale to pJ
base.write_energy = base.write_energy * 1000.
if line[-2:] == "uJ": #scale to pJ
base.write_energy = base.write_energy * 1000. * 1000.
#separately detect any tech-specific quantities
if input_cfg.cell_type.mem_cell_type == "SRAM":
continue
#FIXME set vs. reset for RRAM, support for other cells
else:
if ' - SET Latency' in line:
base.write_latency = float(line[line.index("=")+1:][:-2])
if line[-2:] == "us": #scale to ns
base.write_latency = base.write_latency * 1000.
if line[-2:] == "ps": #scale to ns
base.write_latency = base.write_latency / 1000.
if line[-2:] == "ms": #scale to ns (yikes!)
base.write_latency = base.write_latency * 1000000.
elif ' - SET Dynamic Energy ' in line:
base.write_energy = float(line[line.index("=")+1:][:-2])
if line[-2:] == "nJ": #scale to pJ
base.write_energy = base.write_energy * 1000.
if line[-2:] == "uJ": #scale to pJ
base.write_energy = base.write_energy * 1000. * 1000.
return base
if __name__ == '__main__':
#test mem cfg definition and output parsing
test_input_cfg = NVSimInputConfig()
test_input_cfg.cell_type.generate_cell_file()
test_input_cfg.cell_type.append_cell_file()
test_input_cfg.generate_mem_cfg()
print("Memory config generation test complete.")
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396,
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2626,
274,
16434,
5023,
322,
1962,
13755,
13,
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1243,
29918,
2080,
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16859,
353,
405,
29963,
8942,
4290,
3991,
580,
13,
29871,
1243,
29918,
2080,
29918,
16859,
29889,
3729,
29918,
1853,
29889,
17158,
29918,
3729,
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1445,
580,
13,
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29918,
2080,
29918,
16859,
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3729,
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13,
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1596,
703,
16015,
2295,
12623,
1243,
4866,
23157,
13,
13,
13,
13,
2
] |
labdrivers/labdrivers/lakeshore/__init__.py | RMUlti/Alex | 0 | 54807 | <gh_stars>0
from .ls332 import Ls332
| [
1,
529,
12443,
29918,
303,
1503,
29958,
29900,
13,
3166,
869,
3137,
29941,
29941,
29906,
1053,
365,
29879,
29941,
29941,
29906,
30004,
13,
2
] |
python/thread/writing_thread.py | y2ghost/study | 0 | 190687 | <gh_stars>0
import random
import time
from threading import Thread
class WritingThread(Thread):
def __init__(self, filename: str, number_of_lines: int,
work_time: int = 1) -> None:
Thread.__init__(self)
self.filename = filename
self.number_of_lines = number_of_lines
self.work_time = work_time
def run(self) -> None:
"""
Run the thread
"""
print(f'Writing {self.number_of_lines} lines of text to '
f'{self.filename}')
with open(self.filename, 'w') as f:
for line in range(self.number_of_lines):
text = f'This is line {line+1}\n'
f.write(text)
time.sleep(self.work_time)
print(f'Finished writing {self.filename}')
if __name__ == '__main__':
files = [f'test{x}.txt' for x in range(1, 6)]
for filename in files:
work_time = random.choice(range(1, 3))
number_of_lines = random.choice(range(5, 20))
thread = WritingThread(filename, number_of_lines, work_time)
thread.start()
| [
1,
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29918,
303,
1503,
29958,
29900,
13,
5215,
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292,
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4770,
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1649,
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1649,
3396,
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518,
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29912,
29916,
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3945,
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921,
297,
3464,
29898,
29896,
29892,
29871,
29953,
4638,
13,
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363,
10422,
297,
2066,
29901,
13,
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664,
29918,
2230,
353,
4036,
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16957,
29898,
3881,
29898,
29896,
29892,
29871,
29941,
876,
13,
4706,
1353,
29918,
974,
29918,
9012,
353,
4036,
29889,
16957,
29898,
3881,
29898,
29945,
29892,
29871,
29906,
29900,
876,
13,
4706,
3244,
353,
28676,
4899,
29898,
9507,
29892,
1353,
29918,
974,
29918,
9012,
29892,
664,
29918,
2230,
29897,
13,
4706,
3244,
29889,
2962,
580,
13,
2
] |
src/hcb/artifacts/make_threshold_breakdown_plots.py | Strilanc/honeycomb-boundaries | 0 | 164636 | import pathlib
import sys
from typing import List, Tuple, Dict, Any
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
from hcb.artifacts.make_lambda_plots import DesiredLineFit, project_intersection_of_both_observables
from hcb.artifacts.make_threshold_plots import make_threshold_plots
from hcb.tools.analysis.collecting import MultiStats
OUT_DIR = pathlib.Path(__file__).parent.parent.parent.parent / "out"
MARKERS = "ov*sp^<>8PhH+xXDd|" * 100
COLORS = list(mcolors.TABLEAU_COLORS) * 3
def main():
if len(sys.argv) == 1:
raise ValueError("Specify csv files to include as command line arguments.")
csvs = []
for path in sys.argv[1:]:
p = pathlib.Path(path)
if p.is_dir():
csvs.extend(p.glob("*.csv"))
else:
csvs.append(p)
all_data = MultiStats.from_recorded_data(*csvs).filter(lambda e: 'PC3' not in e.circuit_style)
gate_sets = {
'SD6': 'SD6',
'SI1000': 'SI1000',
'EM3': 'EM3_v3',
}
layouts = {
('planar honeycomb code\n(correlated MWPM decoding)', 'bounded_honeycomb_memory', 'internal_correlated', 'H'),
('planar honeycomb code\n(correlated MWPM decoding)', 'bounded_honeycomb_memory', 'internal_correlated', 'V'),
('planar honeycomb code\n(MWPM decoding)', 'bounded_honeycomb_memory', 'internal', 'H'),
('planar honeycomb code\n(MWPM decoding)', 'bounded_honeycomb_memory', 'internal', 'V'),
}
groups = {
gate_set_caption: [
DesiredLineFit(
legend_caption=layout_caption,
marker=MARKERS[k],
color=COLORS[k],
observable=obs,
filter_circuit_style=f"{gate_set_prefix}_{gate_set_suffix}",
filter_decoder=decoder,
)
for k, (layout_caption, gate_set_prefix, decoder, obs) in enumerate(layouts)
]
for gate_set_caption, gate_set_suffix in gate_sets.items()
}
fig, _ = make_threshold_plots(data=all_data, groups=groups)
fig.set_size_inches(18, 18)
fig.savefig(OUT_DIR / "threshold_breakdown.png", bbox_inches='tight', dpi=200)
plt.show()
if __name__ == '__main__':
main()
| [
1,
1053,
2224,
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13,
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6471,
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13155,
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13,
1678,
2537,
29889,
842,
29918,
2311,
29918,
262,
6609,
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29896,
29947,
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29947,
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2537,
29889,
7620,
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376,
386,
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8690,
3204,
29889,
2732,
613,
289,
1884,
29918,
262,
6609,
2433,
29873,
523,
742,
270,
1631,
29922,
29906,
29900,
29900,
29897,
13,
13,
1678,
14770,
29889,
4294,
580,
13,
13,
13,
361,
4770,
978,
1649,
1275,
525,
1649,
3396,
1649,
2396,
13,
1678,
1667,
580,
13,
2
] |
examples/test_feasibility.py | OllieBoyne/sslap | 7 | 24581 | """Check the feasibility of a bipartite graph by using SSLAP's feasibility module"""
import numpy as np
from sslap import hopcroft_solve
# All 3 methods will use the same input bipartite graph:
# i = 0 connects to j = 0, 1
# i = 1 connects to j = 1, 2
# i = 2 connects to j = 1, 4
# i = 3 connects to j = 2
# i = 4 connects to j = 3
# which has a maximum matching of 5
# eg i:j of 0:0, 1:1, 2:4, 3:2, 4:3
def dict_usage():
lookup = {0: [0, 1], 1: [1, 2], 2: [1, 4], 3: [2], 4: [3]}
res = hopcroft_solve(lookup=lookup)
print(res)
def mat_usage():
mat = - np.ones((5, 5)) # all invalid, except
mat[[0, 0, 1, 1, 2, 2, 3, 4], [0, 1, 1, 2, 1, 4, 2, 3]] = 1 # for valid edges
res = hopcroft_solve(mat=mat)
print(res)
def loc_usage():
loc = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [2, 1], [2, 4], [3, 2], [4, 3]]) # (i, j) for each edge
res = hopcroft_solve(loc=loc)
print(res)
if __name__ == "__main__":
dict_usage()
mat_usage()
loc_usage()
| [
1,
9995,
5596,
278,
28326,
4127,
310,
263,
289,
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568,
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491,
773,
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432,
353,
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demo/part_1.py | eric6239/HeartsEnv | 6 | 101256 | <reponame>eric6239/HeartsEnv
from gym import spaces
table_space = spaces.Tuple([
spaces.Discrete(13), # n_round
spaces.Discrete(4), # start_pos
spaces.Discrete(4), # cur_pos
spaces.Discrete(1), # exchanged
spaces.Discrete(1), # heart_occured
spaces.Discrete(100), # n_games
spaces.Tuple([ # board
spaces.MultiDiscrete([13, 4])
] * 4),
spaces.Tuple([ # first_draw
spaces.MultiDiscrete([13, 4])
]),
spaces.Tuple([ # bank
spaces.Tuple([
spaces.MultiDiscrete([13, 4])
] * 3),
] * 4)
])
player_space = spaces.Tuple([
spaces.Discrete(200), # score
spaces.Tuple([ # hand
spaces.MultiDiscrete([13, 4])
] * 13),
spaces.Tuple([ # income
spaces.MultiDiscrete([13, 4])
] * 52),
] * 4)
p_space = spaces.Tuple([
spaces.Discrete(200), # score
spaces.Tuple([ # hand
spaces.MultiDiscrete([13, 4])
] * 13),
spaces.Tuple([ # income
spaces.MultiDiscrete([13, 4])
] * 52),
])
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Quiron/users/admin.py | CesarNav/Quiron | 0 | 91209 | # Django
from django.contrib import admin
# Import our models to the admin
from users.models import Profile
# Register the model profile in the admin
@admin.register(Profile)
class ProfileAdmin(admin.ModelAdmin):
list_display = ['pk', 'user','prof_register','picture']
list_display_links = ('pk', 'prof_register',)
list_editable = ('picture',)
search_fields = ('user__first_name', 'user__last_name', )
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jccli/jc_api_v1.py | zaro0508/jccli | 0 | 57510 | # -*- coding: utf-8 -*-
"""
.. currentmodule:: jccli.jc_api_v1.py
.. moduleauthor:: zaro0508 <<EMAIL>>
This is a utility library for the jumpcloud version 1 api
.. note::
To learn more about the jumpcloud api 1
`project website <https://github.com/TheJumpCloud/jcapi-python/tree/master/jcapiv1>`_.
"""
from distutils.util import strtobool
import jcapiv1
from jcapiv1 import Systemuserput
from jcapiv1.rest import ApiException
from jccli.errors import SystemUserNotFoundError
from jccli.helpers import class_to_dict
# pylint: disable=too-many-arguments
class JumpcloudApiV1:
"""
Wrapper for Jumpcloud API v1
"""
def __init__(self, api_key):
configuration = jcapiv1.Configuration()
configuration.api_key['x-api-key'] = api_key
self.system_users_api = jcapiv1.SystemusersApi(jcapiv1.ApiClient(configuration))
self.search_api = jcapiv1.SearchApi(jcapiv1.ApiClient(configuration))
def retrieve_users(self, user_ids=[]):
"""
Retrieve a list of users corresponding to ids
"""
# FIXME: This is not an ideal way to do this, but search_systemusers_post doesn't seem to allow filtering on ID
all_users = self.get_users()
return [user for user in all_users if user['id'] in user_ids]
def search_users(self, filter={}):
"""
Search for users on jumpcloud.
:param filter: (dict) an object used to filter search results for various fields. E.g.: `{"firstname": "David"}`
:return:
"""
query_filter = {'and': []}
for field, value in filter.items():
query_filter['and'].append({field: value})
if not filter:
query_filter = None
try:
api_response = self.search_api.search_systemusers_post(
content_type='application/json',
accept='application/json',
body={
'filter': query_filter
}
)
users = [user.to_dict() for user in api_response.results]
return users
except ApiException as error:
raise "Exception when calling SystemusersApi->systemusers_list: %s\n" % error
def get_users(self, limit='100', skip=0, search='', filter='', sort='', fields=''):
"""
Get users from jumpcloud
:param limit:
:param skip:
:param search:
:param filter:
:param sort:
:param fields:
:return: a list of users with dict of settings
"""
try:
api_response = self.system_users_api.systemusers_list(content_type='application/json',
accept='application/json',
limit=limit,
skip=skip,
sort=sort,
fields=fields,
x_org_id='',
search=search,
filter=filter)
users = [user.to_dict() for user in class_to_dict(api_response.results)]
return users
except ApiException as error:
raise "Exception when calling SystemusersApi->systemusers_list: %s\n" % error
def create_user(self, systemuser):
"""
Create a new user in jumpcloud
:param systemuser: a dictoionary of Systemuser properties
https://github.com/TheJumpCloud/jcapi-java/blob/master/jcapiv1/docs/Systemuser.md
:return: The api response
"""
body = jcapiv1.Systemuserputpost(username=systemuser['username'],
email=systemuser['email'],
firstname=systemuser.get('firstname', ''),
lastname=systemuser.get('lastname', ''),
allow_public_key=strtobool(
systemuser.get('allow_public_key', 'True')),
ldap_binding_user=strtobool(
systemuser.get('ldap_binding_user', 'False')),
passwordless_sudo=strtobool(
systemuser.get('passwordless_sudo', 'False')),
sudo=strtobool(systemuser.get('sudo', 'False')))
try:
api_response = self.system_users_api.systemusers_post(content_type='application/json',
accept='application/json',
body=body,
x_org_id='')
return api_response.to_dict()
except ApiException as error:
# FIXME: What should this behavior actually be?
raise Exception("Exception when calling SystemusersApi->systemusers_post: %s\n" % error)
def delete_user(self, username):
"""
Delete a user from jumpcloud
:param id: The jumpcloud id of the user
:return:
"""
user_id = self.get_user_id(username)
if user_id is None:
raise SystemUserNotFoundError(f"System user {username} not found")
try:
api_response = self.system_users_api.systemusers_delete(user_id,
content_type='application/json',
accept='application/json',
x_org_id='')
return api_response
except ApiException as error:
raise "Exception when calling SystemusersApi->systemusers_post: %s\n" % error
def get_user_id(self, username):
"""
Get the jumpcloud user id from the user name
:param username
:return: the user id
"""
users = self.get_users(limit='', fields="username")
for user in users:
if user['username'] == username:
return user['id']
raise SystemUserNotFoundError('No user found for username: %s' % (username,))
def get_user(self, username):
"""
Get detail view of a user object.
:param user_id:
:return: user properties dict
"""
# FIXME: As soon as we figure out how the `filter` parameter works on systemusers_list(), we should start
# filtering based on username
users = self.system_users_api.systemusers_list(
accept='application/json',
content_type='application/json'
).results
for user in users:
if user.username == username:
return user.to_dict()
raise SystemUserNotFoundError('No user found for username: %s' % (username,))
def set_user(self, username, attributes):
user_id = self.get_user_id(username)
api_response = self.system_users_api.systemusers_put(
accept='application/json',
content_type='application/json',
id=user_id,
body=Systemuserput(**attributes)
)
return api_response.to_dict()
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] |
lib/spack/spack/test/cache_fetch.py | LiamBindle/spack | 2,360 | 8325 | # Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import os
import pytest
from llnl.util.filesystem import mkdirp, touch
import spack.config
from spack.fetch_strategy import CacheURLFetchStrategy, NoCacheError
from spack.stage import Stage
@pytest.mark.parametrize('_fetch_method', ['curl', 'urllib'])
def test_fetch_missing_cache(tmpdir, _fetch_method):
"""Ensure raise a missing cache file."""
testpath = str(tmpdir)
with spack.config.override('config:url_fetch_method', _fetch_method):
fetcher = CacheURLFetchStrategy(url='file:///not-a-real-cache-file')
with Stage(fetcher, path=testpath):
with pytest.raises(NoCacheError, match=r'No cache'):
fetcher.fetch()
@pytest.mark.parametrize('_fetch_method', ['curl', 'urllib'])
def test_fetch(tmpdir, _fetch_method):
"""Ensure a fetch after expanding is effectively a no-op."""
testpath = str(tmpdir)
cache = os.path.join(testpath, 'cache.tar.gz')
touch(cache)
url = 'file:///{0}'.format(cache)
with spack.config.override('config:url_fetch_method', _fetch_method):
fetcher = CacheURLFetchStrategy(url=url)
with Stage(fetcher, path=testpath) as stage:
source_path = stage.source_path
mkdirp(source_path)
fetcher.fetch()
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] |
plus_factory.py | dlb123/pipebend-caculate | 0 | 179133 | <filename>plus_factory.py
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'plus_factory.ui'
#
# Created by: PyQt5 UI code generator 5.15.2
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(664, 379)
Dialog.setFixedSize(664, 379)
#Dialog.setWindowModify(QtCore.Qt.ApplicationModal)
self.buttonBox = QtWidgets.QDialogButtonBox(Dialog)
self.buttonBox.setGeometry(QtCore.QRect(240, 320, 191, 31))
self.buttonBox.setOrientation(QtCore.Qt.Horizontal)
self.buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Cancel|QtWidgets.QDialogButtonBox.Ok)
self.buttonBox.setObjectName("buttonBox")
self.verticalLayoutWidget = QtWidgets.QWidget(Dialog)
self.verticalLayoutWidget.setGeometry(QtCore.QRect(10, 10, 331, 161))
self.verticalLayoutWidget.setObjectName("verticalLayoutWidget")
self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget)
self.verticalLayout.setContentsMargins(10, 10, 10, 10)
self.verticalLayout.setObjectName("verticalLayout")
self.horizontalLayout = QtWidgets.QHBoxLayout()
self.horizontalLayout.setContentsMargins(20, -1, 20, -1)
self.horizontalLayout.setObjectName("horizontalLayout")
self.label = QtWidgets.QLabel(self.verticalLayoutWidget)
self.label.setObjectName("label")
self.horizontalLayout.addWidget(self.label)
self.lineEdit = QtWidgets.QLineEdit(self.verticalLayoutWidget)
self.lineEdit.setObjectName("lineEdit")
self.horizontalLayout.addWidget(self.lineEdit)
self.verticalLayout.addLayout(self.horizontalLayout)
self.horizontalLayout_2 = QtWidgets.QHBoxLayout()
self.horizontalLayout_2.setContentsMargins(20, -1, 20, -1)
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.label_2 = QtWidgets.QLabel(self.verticalLayoutWidget)
self.label_2.setObjectName("label_2")
self.horizontalLayout_2.addWidget(self.label_2)
self.lineEdit_2 = QtWidgets.QLineEdit(self.verticalLayoutWidget)
self.lineEdit_2.setObjectName("lineEdit_2")
self.horizontalLayout_2.addWidget(self.lineEdit_2)
self.verticalLayout.addLayout(self.horizontalLayout_2)
self.horizontalLayout_4 = QtWidgets.QHBoxLayout()
self.horizontalLayout_4.setContentsMargins(20, -1, 20, -1)
self.horizontalLayout_4.setObjectName("horizontalLayout_4")
self.label_4 = QtWidgets.QLabel(self.verticalLayoutWidget)
self.label_4.setObjectName("label_4")
self.horizontalLayout_4.addWidget(self.label_4)
self.lineEdit_4 = QtWidgets.QLineEdit(self.verticalLayoutWidget)
self.lineEdit_4.setObjectName("lineEdit_4")
self.horizontalLayout_4.addWidget(self.lineEdit_4)
self.verticalLayout.addLayout(self.horizontalLayout_4)
self.verticalLayoutWidget_2 = QtWidgets.QWidget(Dialog)
self.verticalLayoutWidget_2.setGeometry(QtCore.QRect(460, 10, 181, 111))
self.verticalLayoutWidget_2.setObjectName("verticalLayoutWidget_2")
self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.verticalLayoutWidget_2)
self.verticalLayout_2.setContentsMargins(10, 10, 10, 10)
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.horizontalLayout_3 = QtWidgets.QHBoxLayout()
self.horizontalLayout_3.setObjectName("horizontalLayout_3")
self.horizontalLayout_5 = QtWidgets.QHBoxLayout()
self.horizontalLayout_5.setContentsMargins(10, 10, 10, 10)
self.horizontalLayout_5.setObjectName("horizontalLayout_5")
self.label_5 = QtWidgets.QLabel(self.verticalLayoutWidget_2)
self.label_5.setObjectName("label_5")
self.horizontalLayout_5.addWidget(self.label_5)
self.lineEdit_3 = QtWidgets.QLineEdit(self.verticalLayoutWidget_2)
self.lineEdit_3.setObjectName("lineEdit_3")
self.horizontalLayout_5.addWidget(self.lineEdit_3)
self.horizontalLayout_3.addLayout(self.horizontalLayout_5)
self.verticalLayout_2.addLayout(self.horizontalLayout_3)
self.horizontalLayout_6 = QtWidgets.QHBoxLayout()
self.horizontalLayout_6.setContentsMargins(10, 10, 10, 10)
self.horizontalLayout_6.setObjectName("horizontalLayout_6")
self.label_6 = QtWidgets.QLabel(self.verticalLayoutWidget_2)
self.label_6.setObjectName("label_6")
self.horizontalLayout_6.addWidget(self.label_6)
self.lineEdit_5 = QtWidgets.QLineEdit(self.verticalLayoutWidget_2)
self.lineEdit_5.setObjectName("lineEdit_5")
self.horizontalLayout_6.addWidget(self.lineEdit_5)
self.verticalLayout_2.addLayout(self.horizontalLayout_6)
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(490, 130, 61, 28))
self.pushButton.setObjectName("pushButton")
self.tableWidget = QtWidgets.QTableWidget(Dialog)
self.tableWidget.setGeometry(QtCore.QRect(40, 200, 581, 97))
self.tableWidget.setRowCount(2)
self.tableWidget.setObjectName("tableWidget")
self.tableWidget.setColumnCount(0)
self.pushButton_2 = QtWidgets.QPushButton(Dialog)
self.pushButton_2.setGeometry(QtCore.QRect(330, 130, 61, 28))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_3 = QtWidgets.QPushButton(Dialog)
self.pushButton_3.setGeometry((QtCore.QRect(560, 130, 61, 28)))
self.pushButton_3.setObjectName('清除')
self.retranslateUi(Dialog)
self.buttonBox.accepted.connect(Dialog.accept)
self.buttonBox.rejected.connect(Dialog.reject)
QtCore.QMetaObject.connectSlotsByName(Dialog)
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
self.label.setText(_translate("Dialog", "设备型号:"))
self.label_2.setText(_translate("Dialog", "模具规格:"))
self.label_4.setText(_translate("Dialog", " 材质 :"))
self.label_5.setText(_translate("Dialog", "理论值:"))
self.label_6.setText(_translate("Dialog", "修正值:"))
self.pushButton.setText(_translate("Dialog", "添加"))
self.pushButton_2.setText(_translate("Dialog", "确认"))
self.pushButton_3.setText(_translate("Dialog", "清除")) | [
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] |
auth/tests/test_views.py | asb29/Redundant | 0 | 3689 | from django.test import TestCase
from django.test import Client
class RegisterTestCase(TestCase):
def test_register(self):
c = Client()
# on success redirects to /
response = c.post('/accounts/register/', {
'username': 'asdas',
'password1': '<PASSWORD>',
'password2': '<PASSWORD>'
})
self.assertRedirects(response, '/')
# passwords don't match
response = c.post('/accounts/register/', {
'username': 'asdasdasd1',
'password1': '<PASSWORD>',
'password2': '<PASSWORD>'
})
self.assertEquals(response.status_code, 200)
# username is empty
response = c.post('/accounts/register/', {
'username': '',
'password1': '<PASSWORD>',
'password2': '<PASSWORD>'
})
self.assertEquals(response.status_code, 200)
# no password
response = c.post('/accounts/register/', {
'username': 'asdasdasd',
'password1': '',
'password2': ''
})
self.assertEquals(response.status_code, 200)
# username and password are similar
response = c.post('/accounts/register/', {
'username': 'asdasdasd0',
'password1': '<PASSWORD>',
'password2': '<PASSWORD>'
})
self.assertEquals(response.status_code, 200)
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scripts/misc/redis_test.py | cclauss/archai | 344 | 45288 | import redis
redis_client = redis.StrictRedis(host="127.0.0.1", port=6379)
input("") | [
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clickhouse_proxy/auth.py | kfzteile24/clickhouse-proxy | 1 | 126043 | <reponame>kfzteile24/clickhouse-proxy<filename>clickhouse_proxy/auth.py
from clickhouse_proxy.config import config
from typing import Dict
def is_ipv6(ip: str) -> bool:
return ip.find(':') > -1
def ip_to_bits(ip: str) -> int:
# See what kind of IP this is and set parsing parameters
if is_ipv6(ip):
separator = ':'
chunks = [''] * 8
bytes_per_chunk = 2
base = 16
else:
separator = '.'
chunks = [''] * 4
bytes_per_chunk = 1
base = 10
# tokenize
str_chunks = ip.split(separator)
src_chunk_id = 0
for i in range(0, len(chunks) - 1):
chunk = str_chunks[src_chunk_id]
if chunk:
chunks[i] = chunk
src_chunk_id += 1
else:
chunks[i] = '0'
chunks[-1] = str_chunks[-1] or 0
# fmt is just for fancy viewing
# fmt = f'{{:0>{bytes_per_chunk * 2}}}'
# fancy_viewing = [fmt.format(c) for c in chunks]
int_chunks = [int(c, base) for c in chunks]
bit_ip: int = 0
for chunk in int_chunks:
bit_ip = bit_ip << (8 * bytes_per_chunk)
bit_ip = bit_ip | chunk
return bit_ip
def address_in_network(ip: str, net: str) -> bool:
"""Is an address in a network
"""
# If IP and net mask are not compatible, return False
if is_ipv6(ip) != is_ipv6(net):
return False
# Convert ips to int
i_ip = ip_to_bits(ip)
total_bits = 32 if not is_ipv6(ip) else 128
netparts = net.split('/')
netaddr = netparts[0]
bits = int(netparts[1]) if len(netparts) == 2 else total_bits
i_net = ip_to_bits(netaddr)
# get a mask of bits of "1" of the length of total_bits
mask_all = (2 << total_bits) - 1
# Compute significant bitmask (each bit is 1 except for the last, insignificant ones)
bits = total_bits - int(bits)
bitmask = mask_all ^ ((1 << bits) - 1)
# Check whether significant bits match
return i_ip & bitmask == i_net & bitmask
def authorize(params: Dict[str, str], remote_addr: str) -> str:
user = params.get('user', 'default')
if user not in config.users:
return f"User '{user}' is not authorised to access the ClickHouse ODBC Proxy"
authorized = False
for ip in config.users[user].get('ips', []):
if address_in_network(remote_addr, ip):
authorized = True
break
if not authorized:
return f"User '{user}' is not authorised to access the ClickHouse ODBC Proxy from IP {remote_addr}"
return ''
if __name__ == '__main__':
# TODO: need more tests maybe
assert is_ipv6('fd00:a516:7c1b:17cd:6d81:2137:bd2a:2c5b')
assert not is_ipv6('127.0.0.1')
assert address_in_network('192.0.0.0.129', '192.0.0.0.128/31')
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everything_db.py | muondu/Nesh-hotel | 1 | 1605545 | import sqlite3
donn = sqlite3.connect('bill.db')
d = donn.cursor()
conn = sqlite3.connect('everything.db')
c = conn.cursor()
def rooms():
c.execute('CREATE TABLE IF NOT EXISTS hotel_rooms(hotelnumber INTEGER)')
c.execute('INSERT INTO hotel_rooms VALUES(15)')
c.execute('INSERT INTO hotel_rooms VALUES(13)')
c.execute('INSERT INTO hotel_rooms VALUES(10)')
c.execute('INSERT INTO hotel_rooms VALUES(23)')
c.execute('SELECT * FROM hotel_rooms')
# print(c.fetchall())
rooms()
def bill():
d.execute('CREATE TABLE IF NOT EXISTS breakfast_bill(price INTEGER)')
d.execute('CREATE TABLE IF NOT EXISTS lunch_bill(price INTEGER)')
d.execute('CREATE TABLE IF NOT EXISTS supper_bill(price INTEGER)')
d.execute('CREATE TABLE IF NOT EXISTS room_bill(price INTEGER)')
bill()
def breakfast():
c.execute('CREATE TABLE IF NOT EXISTS breakfast(name TEXT)')
c.execute('INSERT INTO breakfast VALUES("Eggs")')
c.execute('INSERT INTO breakfast VALUES("Bread")')
breakfast()
def lunch():
c.execute('CREATE TABLE IF NOT EXISTS lunch(name TEXT)')
c.execute('INSERT INTO lunch VALUES("Chips")')
c.execute('INSERT INTO lunch VALUES("Fish")')
lunch()
def supper():
c.execute('CREATE TABLE IF NOT EXISTS dinner(name TEXT)')
c.execute('INSERT INTO dinner VALUES("Rice")')
c.execute('INSERT INTO dinner VALUES("Ugali")')
supper()
def desert():
c.execute('CREATE TABLE IF NOT EXISTS desert(name TEXT)')
c.execute('INSERT INTO desert VALUES("Ice cream")')
c.execute('INSERT INTO desert VALUES("Cake")')
desert()
c.execute('CREATE TABLE IF NOT EXISTS movies(name TEXT)')
c.execute('INSERT INTO movies VALUES("Megamind")')
c.execute('INSERT INTO movies VALUES("Nesh movie")')
c.execute('CREATE TABLE IF NOT EXISTS sport(name TEXT)')
c.execute('INSERT INTO sport VALUES("Tennis")')
c.execute('INSERT INTO sport VALUES("Golf")')
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] |
setup.py | meeas/sqlite | 1 | 66041 | #!/usr/bin/env python3
"""
SQLite Clinet written in python3
"""
# Always prefer setuptools over distutils
import re
import sys
# To use a consistent encoding
from codecs import open
from os import path
from setuptools import find_packages, setup
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='sqlite',
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# https://packaging.python.org/en/latest/single_source_version.html
version='0.2.0',
description='SQLite Clinet written in python3',
long_description=long_description,
# The project's main homepage.
url='https://github.com/nl253/SQLiteREPL',
# Author details
author='<NAME>',
author_email='<EMAIL>',
# Choose your license
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Topic :: Database :: Front-Ends',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
keywords='database sqllite3 sqlite clinet SQLite prompt-toolkit prompt_toolkit',
packages=find_packages(),
# List run-time dependencies here. These will be installed by pip when
# your project is installed. For an analysis of "install_requires" vs pip's
# requirements files see:
# https://packaging.python.org/en/latest/requirements.html
install_requires=['prompt_toolkit', 'tabulate', 'pygments'],
entry_points={
'console_scripts': ['sqlite = sqlite.main:main']
})
| [
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] |
tools/go_generics/defs.bzl | Exhorder6/gvisor | 12,536 | 90835 | <reponame>Exhorder6/gvisor<gh_stars>1000+
"""Generics support via go_generics.
A Go template is similar to a go library, except that it has certain types that
can be replaced before usage. For example, one could define a templatized List
struct, whose elements are of type T, then instantiate that template for
T=segment, where "segment" is the concrete type.
"""
TemplateInfo = provider(
"Information about a go_generics template.",
fields = {
"unsafe": "whether the template requires unsafe code",
"types": "required types",
"opt_types": "optional types",
"consts": "required consts",
"opt_consts": "optional consts",
"deps": "package dependencies",
"template": "merged template source file",
},
)
def _go_template_impl(ctx):
srcs = ctx.files.srcs
template = ctx.actions.declare_file(ctx.label.name + "_template.go")
args = ["-o=%s" % template.path] + [f.path for f in srcs]
ctx.actions.run(
inputs = srcs,
outputs = [template],
mnemonic = "GoGenericsTemplate",
progress_message = "Building Go template %s" % ctx.label,
arguments = args,
executable = ctx.executable._tool,
)
return [TemplateInfo(
types = ctx.attr.types,
opt_types = ctx.attr.opt_types,
consts = ctx.attr.consts,
opt_consts = ctx.attr.opt_consts,
deps = ctx.attr.deps,
template = template,
)]
go_template = rule(
implementation = _go_template_impl,
attrs = {
"srcs": attr.label_list(doc = "the list of source files that comprise the template", mandatory = True, allow_files = True),
"deps": attr.label_list(doc = "the standard dependency list", allow_files = True, cfg = "target"),
"types": attr.string_list(doc = "the list of generic types in the template that are required to be specified"),
"opt_types": attr.string_list(doc = "the list of generic types in the template that can but aren't required to be specified"),
"consts": attr.string_list(doc = "the list of constants in the template that are required to be specified"),
"opt_consts": attr.string_list(doc = "the list of constants in the template that can but aren't required to be specified"),
"_tool": attr.label(executable = True, cfg = "host", default = Label("//tools/go_generics/go_merge")),
},
)
def _go_template_instance_impl(ctx):
info = ctx.attr.template[TemplateInfo]
output = ctx.outputs.out
# Check that all required types are defined.
for t in info.types:
if t not in ctx.attr.types:
fail("Missing value for type %s in %s" % (t, ctx.attr.template.label))
# Check that all defined types are expected by the template.
for t in ctx.attr.types:
if (t not in info.types) and (t not in info.opt_types):
fail("Type %s is not a parameter to %s" % (t, ctx.attr.template.label))
# Check that all required consts are defined.
for t in info.consts:
if t not in ctx.attr.consts:
fail("Missing value for constant %s in %s" % (t, ctx.attr.template.label))
# Check that all defined consts are expected by the template.
for t in ctx.attr.consts:
if (t not in info.consts) and (t not in info.opt_consts):
fail("Const %s is not a parameter to %s" % (t, ctx.attr.template.label))
# Build the argument list.
args = ["-i=%s" % info.template.path, "-o=%s" % output.path]
if ctx.attr.package:
args.append("-p=%s" % ctx.attr.package)
if len(ctx.attr.prefix) > 0:
args.append("-prefix=%s" % ctx.attr.prefix)
if len(ctx.attr.suffix) > 0:
args.append("-suffix=%s" % ctx.attr.suffix)
args += [("-t=%s=%s" % (p[0], p[1])) for p in ctx.attr.types.items()]
args += [("-c=%s=%s" % (p[0], p[1])) for p in ctx.attr.consts.items()]
args += [("-import=%s=%s" % (p[0], p[1])) for p in ctx.attr.imports.items()]
if ctx.attr.anon:
args.append("-anon")
ctx.actions.run(
inputs = [info.template],
outputs = [output],
mnemonic = "GoGenericsInstance",
progress_message = "Building Go template instance %s" % ctx.label,
arguments = args,
executable = ctx.executable._tool,
)
return [DefaultInfo(
files = depset([output]),
)]
go_template_instance = rule(
implementation = _go_template_instance_impl,
attrs = {
"template": attr.label(doc = "the label of the template to be instantiated", mandatory = True),
"prefix": attr.string(doc = "a prefix to be added to globals in the template"),
"suffix": attr.string(doc = "a suffix to be added to globals in the template"),
"types": attr.string_dict(doc = "the map from generic type names to concrete ones"),
"consts": attr.string_dict(doc = "the map from constant names to their values"),
"imports": attr.string_dict(doc = "the map from imports used in types/consts to their import paths"),
"anon": attr.bool(doc = "whether anoymous fields should be processed", mandatory = False, default = False),
"package": attr.string(doc = "the package for the generated source file", mandatory = False),
"out": attr.output(doc = "output file", mandatory = True),
"_tool": attr.label(executable = True, cfg = "host", default = Label("//tools/go_generics")),
},
)
| [
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] |
help_functions.py | markozeman/TransformerSuperposition | 0 | 131508 | <reponame>markozeman/TransformerSuperposition<gh_stars>0
import numpy as np
import torch
from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix
from sklearn.metrics import average_precision_score
'''
def load_data(dataset):
"""
Load ELMo embeddings for 'dataset'.
:param dataset: string name of the datast
:return: X_train, y_train, X_test, y_test
"""
X_train = np.load('ELMo_embeddings/%s/X_train_all.npy' % dataset, allow_pickle=True)
y_train = np.load('ELMo_embeddings/%s/train_labels.npy' % dataset, allow_pickle=True)
X_test = np.load('ELMo_embeddings/%s/X_test_all.npy' % dataset, allow_pickle=True)
y_test = np.load('ELMo_embeddings/%s/test_labels.npy' % dataset, allow_pickle=True)
return X_train, y_train, X_test, y_test
'''
def count_trainable_parameters(model):
"""
Count the number of trainable model parameters.
:param model: torch model instance
:return: the number of trainable model parameters
"""
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def get_stats(outputs, y):
"""
Get statistics for given model outputs and true predictions 'y'.
:param outputs: list of model predictions, each element of the list has a size (batch_size, 2)
:param y: tensor of true labels (size equals to number of samples)
:return: (accuracy, AUROC, AUPRC)
"""
outputs = torch.cat(outputs, dim=0)
probs = torch.squeeze(torch.softmax(outputs, dim=1))
probs = probs.cpu().detach().numpy()
true = y.cpu().detach().numpy()
auroc = roc_auc_score(true, probs[:, 1])
auprc = average_precision_score(true, probs[:, 1])
predicted = np.argmax(outputs.cpu().detach().numpy(), axis=1).ravel()
acc = np.sum(true == predicted) / true.shape[0]
return acc, auroc, auprc
def remove_empty_values(metric_epoch, num_tasks, num_epochs):
"""
Remove empty values from 'metric_epoch', where there are zeros at some epochs (because of early stopping).
:param metric_epoch: array of size (num_runs, num_tasks * num_epochs) with 0 values where early stopping occured
:param num_tasks: number of tasks
:param num_epochs: number of epochs per task
:return: array of size (num_runs, num_tasks) with final metric value for each task in each run
"""
epoch_no0 = []
for row_i in range(len(metric_epoch)):
row = metric_epoch[row_i]
epoch_no0_row = []
for task_i in range(num_tasks):
index = ((task_i + 1) * num_epochs) - 1 # index of the last measured metric for each task
while row[index] == 0:
index -= 1
epoch_no0_row.append(row[index])
epoch_no0.append(epoch_no0_row)
return epoch_no0
def get_model_outputs(model, batch_X, batch_mask, use_MLP, use_PSP, contexts, task_index):
"""
Get model outputs with the forward pass of the batch of input data (batch_X).
:param model: torch model instance
:param batch_X: a batch of input data
:param batch_mask: a batch of input data masks if used
:param use_MLP: boolean - if True use MLP, else use Transformer
:param use_PSP: boolean - if True, PSP method is used, meaning we need set of contexts for each task (including the first)
:param contexts: contexts: binary context vectors
:param task_index: index of the current task, which is being learned
:return: batch model output
"""
if use_PSP:
if use_MLP:
return model.forward(batch_X, use_PSP, contexts, task_index)
else:
return model.forward(batch_X, batch_mask, use_PSP, contexts, task_index)
else:
if use_MLP:
return model.forward(batch_X)
else:
return model.forward(batch_X, batch_mask)
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] |
malaysiaflights/tests/test_mas.py | azam-a/malaysiaflights | 2 | 80377 | <gh_stars>1-10
import unittest
import datetime
import httpretty as HP
import json
from malaysiaflights.mas import MAS
class MASRequestTests(unittest.TestCase):
def url_helper(self, from_, to, date):
host = 'https://flymh.mobi'
path = ("/TravelAPI/travelapi/shop/1/mh/"
"{from_}/{to}/1/0/0/Economy/{date}/").format(from_=from_,
to=to, date=date)
headers = {'X-apiKey': '52e6d6d613d3a3e825ac02253fe6b5a4',
'Accept': 'application/json'}
return host, path, headers
@HP.activate
def test_search_calls_api_using_correct_path_and_headers(self):
host, path, headers = self.url_helper('KUL', 'TGG', '2015-06-15')
HP.register_uri(HP.GET, host+path, status=200)
d = datetime.datetime(2015, 6, 15)
MAS.search('KUL', 'TGG', d)
mocked_request = HP.last_request()
self.assertEqual(path, mocked_request.path)
self.assertEqual(headers['X-apiKey'],
mocked_request.headers['X-apiKey'])
self.assertEqual(headers['Accept'],
mocked_request.headers['Accept'])
class ResponseExtractionTests(unittest.TestCase):
def fixture_loader(self, path):
prefix = 'malaysiaflights/fixtures/'
with open(prefix + path, 'r') as file_:
return json.loads(file_.read())
def setUp(self):
self.single = self.fixture_loader('mas-single-econ.json')
self.zero = self.fixture_loader('mas-no-flights.json')
self.connecting = self.fixture_loader('mas-connecting.json')
def test_get_number_of_results_for_valid_response(self):
json = self.single
actual = MAS.get_number_of_results(json)
self.assertEqual(2, actual)
def test_get_number_of_results_for_no_flights_on_date(self):
json = self.zero
actual = MAS.get_number_of_results(json)
self.assertEqual(0, actual)
def test_get_flight_details_using_index_0_should_return_results(self):
json = self.single
expected = {
'flight_number': 'MH1326',
'departure_airport': 'KUL',
'arrival_airport': 'TGG',
'departure_time': '2015-06-15T07:25:00.000+08:00',
'arrival_time': '2015-06-15T08:20:00.000+08:00',
'total_fare': '255.45',
'fare_currency': 'MYR'}
actual = MAS.get_direct_flight_details(json, 0)
self.assertEqual(expected, actual)
def test_get_flight_details_using_index_1_should_return_results(self):
json = self.single
expected = {
'flight_number': 'MH1336',
'departure_airport': 'KUL',
'arrival_airport': 'TGG',
'departure_time': '2015-06-15T14:40:00.000+08:00',
'arrival_time': '2015-06-15T15:35:00.000+08:00',
'total_fare': '255.45',
'fare_currency': 'MYR'}
actual = MAS.get_direct_flight_details(json, 1)
self.assertEqual(expected, actual)
def test_is_connecting_flights_should_return_true_for_connecting(self):
json = self.connecting
actual = MAS.is_connecting_flights(json, 0)
self.assertTrue(actual)
def test_is_connecting_flights_should_return_false_for_direct(self):
json = self.connecting
actual = MAS.is_connecting_flights(json, 2)
self.assertFalse(actual)
def test_get_connecting_flights_details_return_results(self):
json = self.connecting
expected = {
'flight_number': 'MH5305 + MH5396',
'departure_airport': 'KBR',
'arrival_airport': 'PEN',
'departure_time': '2015-06-20T13:25:00.000+08:00',
'arrival_time': '2015-06-20T18:10:00.000+08:00',
'total_fare': '313.20',
'fare_currency': 'MYR'}
actual = MAS.get_connecting_flight_details(json, 5)
self.assertEqual(expected, actual)
class TimeConversionTest(unittest.TestCase):
def test_convert_to_api_format_returns_correct_output(self):
date_object = datetime.datetime(2015, 9, 25)
expected = '2015-09-25'
actual = MAS.to_api(date_object)
self.assertEqual(expected, actual)
def test_convert_extracted_time_to_datetime_returns_correct_object(self):
offset = datetime.timedelta(hours=8)
expected = datetime.datetime(2015, 6, 15, 14, 40,
tzinfo=datetime.timezone(offset))
actual = MAS.to_datetime('2015-06-15T14:40:00.000+08:00')
self.assertEqual(expected, actual)
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] |
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