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stringlengths 4
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stringlengths 6
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chexpert/ema.py | PJansson/Chexpert | 0 | 198403 | <reponame>PJansson/Chexpert
# Credits: https://github.com/Alibaba-MIIL/ASL
from copy import deepcopy
import torch
from torch import nn
class ModelEMA(nn.Module):
def __init__(self, model, decay=0.9995, device=None):
super().__init__()
self.module = deepcopy(model)
self.module.eval()
self.decay = decay
self.device = device
if self.device is not None:
self.module.to(device=device)
def _update(self, model, update_fn):
with torch.no_grad():
for ema_v, model_v in zip(
self.module.state_dict().values(), model.state_dict().values()
):
if self.device is not None:
model_v = model_v.to(device=self.device)
ema_v.copy_(update_fn(ema_v, model_v))
def update(self, model):
self._update(
model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m
)
def set(self, model):
self._update(model, update_fn=lambda e, m: m)
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custom_components/kweather_air365/__init__.py | KuddLim/KWeatherAir365 | 0 | 99408 | <filename>custom_components/kweather_air365/__init__.py
"""KWeather Air365 Sensor""" | [
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{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}/settings/base.py | drgarcia1986/cookiecutter-muffin | 3 | 28346 | import os
STATIC_FOLDERS = (
'{{cookiecutter.repo_name}}/common/static',
'{{cookiecutter.repo_name}}/users/static',
)
# Muffin Plugins
PLUGINS = (
'muffin_jinja2',
'muffin_peewee',
'muffin_session',
)
# Plugins configurations
SESSION_SECRET = 'SecretHere'
SESSION_LOGIN_URL = '/users/signin/'
JINJA2_TEMPLATE_FOLDERS = (
'{{cookiecutter.repo_name}}/common/templates',
'{{cookiecutter.repo_name}}/public/templates',
'{{cookiecutter.repo_name}}/users/templates'
)
PEEWEE_CONNECTION = os.environ.get('DATABASE_URL', 'sqlite:///{{cookiecutter.repo_name}}.sqlite')
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tracker.py | AImotion-Autonomous-Vehicles/AirSimTracking | 0 | 47263 | from .coordinates import Coordinates
from typing import List
import csv
import pandas as pd
class Tracker:
@staticmethod
def load(csv_path: str):
airsim_results = pd.read_csv(csv_path, encoding='utf-8')
tracker = Tracker()
for index, row in airsim_results.iterrows():
coordinates = Coordinates(row['x'], row['y'])
tracker.update_coordinates(coordinates)
return tracker
_initial_coordinates: Coordinates
_coordinates: List[Coordinates]
def __init__(self, initial_coordinates: Coordinates = Coordinates(0.0,0.0)) -> None:
self._initial_coordinates = initial_coordinates
self._coordinates = [Coordinates(0.0,0.0)]
def update_coordinates(self, coordinates: Coordinates) -> None:
coordinates.x -= self._initial_coordinates.x
coordinates.y -= self._initial_coordinates.y
if coordinates == self.__get_last_coordinates():
return
self._coordinates.append(coordinates)
def __get_last_coordinates(self):
return self._coordinates[len(self._coordinates)-1]
def get_coordinates(self) -> List[Coordinates]:
return self._coordinates
def save_coordinates(self, path: str) -> None:
with open(path,'w') as csv_file:
fieldnames = ['x', 'y', 'timestamp']
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, lineterminator = '\n')
writer.writeheader()
for coordinate in self._coordinates:
writer.writerow({
'x': str(coordinate.x),
'y': str(coordinate.y),
'timestamp': str(coordinate.timestamp)
})
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MCTS.py | alexandre-lavoie/MCTS | 0 | 179405 | import random
from typing import Generic, TypeVar
State = TypeVar('State')
Action = TypeVar('Action')
class Monte_Carlo_Tree(Generic[State]):
class Node(Generic[State, Action]):
def __init__(self, state: State, action: Action=None, parent=None):
self.score = 0
self.state = state
self.action = action
self.parent = parent
self.children = []
def update(self, result):
self.score += result
def __str__(self):
return self.__repr__()
def __repr__(self):
return str(self.state) + str(self.action) + str(self.score)
def __init__(self, init_state: State, player: object, simulate_step: lambda state, player, state_constraints: (Node, int), init_action: Action=None):
self.root = Monte_Carlo_Tree.Node(init_state, init_action)
self.player = player
self.simulate_step = simulate_step
def update(self, state: State):
""" Updates tree to current state. If the state already exist, simply change root else create new node. """
for child in self.root.children:
if child.state == state:
self.root = child
break
else:
self.root = Monte_Carlo_Tree.Node(state)
def search(self, node: Node=None):
""" Searches tree using Monte Carlo Tree Search. """
if node == None:
node = self.root
for _ in range(100):
next_node, next_state = self.expand(self.select(node))
node.children.append(next_node)
next_node.parent = node
if next_state == 0:
last_node, last_state = self.simulate(next_node)
self.backpropagate(last_node, last_state)
def current_state(self) -> State:
""" Returns the current state of the tree. """
return self.root.state
def best_action(self) -> Action:
""" Returns the next best action from the root node and updates the root node. """
self.search()
node = self.best_node()
self.root = node
return node.action
def best_node(self, node: Node=None) -> Node:
""" Returns the best child node relative to current node. """
if node == None:
node = self.root
if len(node.children) == 0:
return node
else:
return max(node.children, key=lambda child: child.score)
def select(self, node: Node=None) -> Node:
""" Selects the next best node to expand on the tree. """
if node == None:
node = self.root
if len(node.children) == 0:
return node
return self.select(node.children[0])
def expand(self, node: Node=None) -> (Node, int):
""" Expands a node considering current node child constraints. """
if node == None:
node = self.root
next_state = self.simulate_step(node.state, self.player, [child.state for child in node.children])
return next_state
def simulate(self, node: Node=None) -> (Node, int):
""" Simulates current node using simulate_step until completion. Returns final node. """
if node == None:
node = self.root
next_node, state = self.simulate_step(node.state, self.player)
next_node.parent = node
node.children.append(next_node)
if state == 0:
return self.simulate(next_node)
else:
return (next_node, state)
def backpropagate(self, node: Node, result = None):
""" Backpropagates the result from the current node to the parent nodes. """
if node != None:
if result == None:
result = node.score
else:
node.update(result)
self.backpropagate(node.parent, result) | [
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U8-optics.py | mahnooranjum/Clustering_MachineLearning | 0 | 67578 | <gh_stars>0
##############################################################################
# <NAME>
# References:
# Official Documentation
##############################################################################
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
# iloc gets data via numerical indexes
# .values converts from python dataframe to numpy object
dataset = pd.read_csv('Moons.csv')
X = dataset.iloc[0:1000, 1:3].values
y = dataset.iloc[0:1000, 3].values
plt.scatter(X[:,0], X[:,1])
plt.show()
from sklearn.cluster import OPTICS
model = OPTICS(min_cluster_size = 0.3)
y_pred = model.fit_predict(X)
# Visualising the clusters
clusters = np.unique(y_pred)
for cluster in clusters:
# get row indexes for samples with this cluster
row_idx = np.where(y_pred == cluster)
plt.scatter(X[row_idx, 0], X[row_idx, 1])
plt.show()
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tests/test_temporary_package.py | Thom1729/package_util | 0 | 52961 | from unittest import TestCase
from sublime_lib import ResourcePath
from package_util import TemporaryPackage
class TestTemporaryPackage(TestCase):
def test_temporary_package_name(self):
expected_resource_path = ResourcePath('Packages/TemporaryPackageTest')
expected_file_path = expected_resource_path.file_path()
name = 'TemporaryPackageTest'
with TemporaryPackage(name) as path:
self.assertEquals(path.name, name)
self.assertEquals(path, expected_resource_path)
self.assertTrue(expected_file_path.is_dir())
self.assertFalse(expected_file_path.exists())
def test_temporary_package_prefix_suffix(self):
prefix = 'TemporaryPackage'
suffix = 'Test'
with TemporaryPackage(prefix=prefix, suffix=suffix) as path:
self.assertTrue(path.name.startswith(prefix))
self.assertTrue(path.name.endswith(suffix))
def test_temporary_package_arguments_error(self):
with self.assertRaises(ValueError):
TemporaryPackage('TemporaryPackageTest', prefix='foo')
with self.assertRaises(ValueError):
TemporaryPackage('TemporaryPackageTest', suffix='foo')
def test_temporary_package_exclusive(self):
with TemporaryPackage('TemporaryPackageTest'):
with self.assertRaises(FileExistsError):
with TemporaryPackage('TemporaryPackageTest'):
pass
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tests/common.py | joshuagl/in-toto | 1 | 130245 | #!/usr/bin/env python
"""
<Program Name>
common.py
<Author>
<NAME> <<EMAIL>>
<Started>
Feb 6, 2018
<Copyright>
See LICENSE for licensing information.
<Purpose>
Common code for in-toto unittests, import like so:
`import tests.common`
Tests importing this module, should be run from the project root, e.g.:
`python tests/test_in_toto_run.py`
or using the aggregator script (preferred way):
`python tests/runtests.py`.
"""
import os
import sys
import inspect
import unittest
if sys.version_info >= (3, 3):
from unittest.mock import patch # pylint: disable=no-name-in-module,import-error
else:
from mock import patch # pylint: disable=import-error
def run_with_portable_scripts(decorated):
print("patching...")
scripts_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "scripts")
print("scripts are located in {}".format(scripts_path))
@patch.dict(os.environ, {"PATH": "{};{}".format(scripts_path, os.environ['PATH'])})
class Patched(decorated):
pass
return Patched
class CliTestCase(unittest.TestCase):
"""TestCase subclass providing a test helper that patches sys.argv with
passed arguments and asserts a SystemExit with a return code equal
to the passed status argument.
Subclasses of CliTestCase require a class variable that stores the main
function of the cli tool to test as staticmethod, e.g.:
```
import tests.common
from in_toto.in_toto_run import main as in_toto_run_main
class TestInTotoRunTool(tests.common.CliTestCase):
cli_main_func = staticmethod(in_toto_run_main)
...
```
"""
cli_main_func = None
def __init__(self, *args, **kwargs):
"""Constructor that checks for the presence of a callable cli_main_func
class variable. And stores the filename of the module containing that
function, to be used as first argument when patching sys.argv in
self.assert_cli_sys_exit.
"""
if not callable(self.cli_main_func):
raise Exception("Subclasses of `CliTestCase` need to assign the main"
" function of the cli tool to test using `staticmethod()`: {}"
.format(self.__class__.__name__))
file_path = inspect.getmodule(self.cli_main_func).__file__
self.file_name = os.path.basename(file_path)
super(CliTestCase, self).__init__(*args, **kwargs)
def assert_cli_sys_exit(self, cli_args, status):
"""Test helper to mock command line call and assert return value.
The passed args does not need to contain the command line tool's name.
This is assessed from `self.cli_main_func`
"""
with patch.object(sys, "argv", [self.file_name]
+ cli_args), self.assertRaises(SystemExit) as raise_ctx:
self.cli_main_func() # pylint: disable=not-callable
self.assertEqual(raise_ctx.exception.code, status)
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brownies/LANL/toACE/energyAngular.py | brown170/fudge | 14 | 1605028 | # <<BEGIN-copyright>>
# Copyright 2021, Lawrence Livermore National Security, LLC.
# See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
# <<END-copyright>>
"""
This module adds the method toACE to the classes in the fudge.productData.distributions.energyAngularMC module.
"""
from xData import standards as standardsModule
from fudge.productData.distributions import energyAngularMC as energyAngularMCModule
def toACE( self, label, offset, weight, **kwargs ) :
header = [ 0, 61, offset + len( weight ) + 4 ] + weight
energyData = self.energy.data
energyAngularData = self.energyAngular.data
INTE = -1
interpolation = energyData.interpolation
if( interpolation == standardsModule.interpolation.flatToken ) :
INTE = 1
elif( interpolation == standardsModule.interpolation.linlinToken ) :
INTE = 2
if( INTE == -1 ) : raise Exception( 'Interpolation "%s" not supported for incident energy' % interpolation )
if( energyData.interpolationQualifier == standardsModule.interpolation.unitBaseToken ) : INTE += 20
INTT = -1
interpolation = energyData[0].interpolation
if( interpolation == standardsModule.interpolation.flatToken ) :
INTT = 1
elif( interpolation == standardsModule.interpolation.linlinToken ) :
INTT = 2
if( INTT == -1 ) : raise Exception( 'Interpolation "%s" not supported for outgoing energy' % interpolation )
NE = len( energyData )
e_ins = []
Ls = []
EpData = []
offset += len( header ) + 3 + 1 + 2 * NE + 1 # header length plus NR, NE, Es, Ls, (1-based).
for i1, _EpData in enumerate( energyData ) :
e_ins.append( _EpData.outerDomainValue )
Ls.append( offset + len( EpData ) )
LCs = []
muPData = []
NP = len( _EpData )
offset_LC = Ls[-1] + 1 + 4 * NP + 1
for i2, _muData in enumerate( energyAngularData[i1] ) :
LCs.append( offset_LC )
mus = _muData.xs.values.values
interpolation = { standardsModule.interpolation.flatToken : 1, standardsModule.interpolation.linlinToken : 2 }[_muData.interpolation]
muData = [ interpolation, len( mus ) ] + mus + _muData.pdf.values.values + _muData.cdf.values.values
offset_LC += len( muData )
muPData += muData
EpData += [ INTT, NP ] + _EpData.xs.values.values + _EpData.pdf.values.values + _EpData.cdf.values.values + LCs + muPData
return( header + [ 1, NE, INTE, NE ] + e_ins + Ls + EpData )
energyAngularMCModule.form.toACE = toACE
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] |
chat/user.py | gdw439/python | 1 | 97341 | <gh_stars>1-10
import sys
import time
import socket
import pymysql
import threading
import PyQt5.QtCore
import PyQt5.QtGui
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
#addr = ('172.16.17.32', 3300)
addr = ('127.0.0.1', 3300)
class Login(QDialog):
# 登录窗口
def __init__(self):
super().__init__()
self.setFixedSize(420, 240)
self.setWindowFlags(Qt.FramelessWindowHint)
palette1 = QPalette()
palette1.setBrush(self.backgroundRole(), QBrush(QPixmap('login.png')))
self.setPalette(palette1)
self.headlab = QLabel(self)
self.nameldt = QLineEdit(self)
self.keysldt = QLineEdit(self)
self.logbtn = QPushButton('登录', self)
self.regbtn = QPushButton('注册', self)
self.headlab.setPixmap(QPixmap('logo.png'))
# 排列控件
self.headlab.move(130, 50)
self.logbtn.move(105, 200)
self.regbtn.move(245, 200)
self.nameldt.move(105, 120)
self.keysldt.move(105, 160)
self.logbtn.setFixedSize(70, 27)
self.regbtn.setFixedSize(70, 27)
self.nameldt.setFixedSize(210, 27)
self.keysldt.setFixedSize(210, 27)
self.nameldt.setPlaceholderText('你的昵称')
self.keysldt.setPlaceholderText('你的密码')
self.keysldt.setEchoMode(QLineEdit.Password)
self.show()
# 登录数据检查
def formatcheck(self):
name = self.nameldt.text()
keyword = self.keysldt.text()
if name == '':
print('请输入用户名')
return False
elif keyword == '':
print('请输入密码')
return False
else:
return True
def mousePressEvent(self, event):
self.last = event.globalPos()
def mouseMoveEvent(self, event):
dot = event.globalPos()
dx = dot.x() - self.last.x()
dy = dot.y() - self.last.y()
self.last = dot
self.move(self.x() + dx, self.y() + dy)
def mouseReleaseEvent(self, event):
dot = event.globalPos()
dx = dot.x() - self.last.x()
dy = dot.y() - self.last.y()
self.move(self.x() + dx, self.y() + dy)
class Box(QWidget):
# 构建窗口
row = 0
listnum = {}
def __init__(self):
super().__init__()
self.BoxInit()
def flushmessage(self):
word = self.oute.text()
self.oute.clear()
# 显示在信息主面板
self.inte.setAlignment(Qt.AlignRight)
self.inte.setTextColor(Qt.gray)
self.inte.insertPlainText(':我\n')
self.inte.setTextColor(Qt.blue)
self.inte.insertPlainText(word + ' ' * 4 + '\n')
def showmessage(self, name, mess):
# ? 光标问题处理
self.inte.setAlignment(Qt.AlignLeft)
self.inte.setTextColor(Qt.gray)
self.inte.insertPlainText(name + ':\n')
self.inte.setTextColor(Qt.red)
self.inte.insertPlainText(' ' * 4 + mess + '\n')
def flushlist(self, namelist):
for name in namelist:
self.listnum[name] = self.row
self.row += 1
self.lit.addItems(namelist)
def addlist(self, name):
self.lit.addItem(name)
self.listnum[name] = self.row
self.row += 1
def sublist(self, name):
self.lit.takeItem(self.listnum[name])
self.listnum.pop(name)
self.row -= 1
def BoxInit(self):
self.resize(500, 300)
self.setFixedSize(self.width(), self.height())
self.setWindowTitle('Vortex')
self.sendbtn = QPushButton()
self.sendbtn.setText('发送')
self.lab = QLabel()
self.lab.setText('在线好友')
self.lit = QListWidget()
self.lit.setFixedSize(100, 250)
self.inte = QTextEdit()
self.oute = QLineEdit()
grid = QGridLayout()
grid.addWidget(self.lab, 0, 0, 1, 1)
grid.addWidget(self.lit, 1, 0, 9, 1)
grid.addWidget(self.inte, 1, 1, 8, 2)
grid.addWidget(self.oute, 9, 1, 1, 1)
grid.addWidget(self.sendbtn, 9, 2, 1, 1)
self.setLayout(grid)
qr = self.frameGeometry()
cp = QDesktopWidget().availableGeometry().center()
qr.moveCenter(cp)
self.move(qr.topLeft())
class Chat:
myname = ''
user_list = []
def __init__(self):
self.box = Box()
self.login = Login()
self.login.logbtn.clicked.connect(self.loginemit)
self.login.regbtn.clicked.connect(self.registeremit)
self.sk = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
t = threading.Thread(target=self.hostlink)
t.start()
# 连接服务器
def hostlink(self):
self.login.logbtn.setDisabled(True)
self.login.regbtn.setDisabled(True)
while True:
try:
self.sk.connect(addr)
break
except:
time.sleep(1)
print('再次尝试连接')
print("已连接服务器")
self.login.logbtn.setDisabled(False)
self.login.regbtn.setDisabled(False)
def hostchat(self):
while True:
data = self.sk.recv(1024).decode('utf-8')
# 删除好友列表
if data.find('$$DEL:') != -1:
name = data.split(':')[1]
self.box.sublist(name)
# 添加好友列表
elif data.find('$$ADD:') != -1:
name = data.split(':')[1]
self.box.addlist(name)
# 接收好友信息
else:
time.sleep(0.5)
name = data.split('$')[1]
mess = data.split('$')[2]
self.box.showmessage(name, mess)
# 执行登录命令
def loginemit(self):
if self.login.formatcheck():
name = self.login.nameldt.text()
keyword = self.login.keysldt.text()
if self.loginhost(name, keyword) == True:
print('登录成功')
self.myname = name
self.login.close()
self.box.show()
self.box.sendbtn.clicked.connect(self.emitmessage)
self.box.flushlist(self.user_list)
else:
print('输入格式错误')
# 执行注册命令
def registeremit(self):
if self.login.formatcheck():
name = self.login.nameldt.text()
keyword = self.login.keysldt.text()
if self.registeruser(name, keyword) == True:
self.myname = name
print('登录成功')
self.login.close()
self.box.show()
self.box.sendbtn.clicked.connect(self.emitmessage)
self.box.flushlist(self.user_list)
else:
print('输入格式错误')
# 执行发送命令
def emitmessage(self):
word = self.box.oute.text()
self.box.flushmessage()
name = self.box.lit.currentItem().text()
if name != None:
data = name + '$' + self.myname + '$' + word
self.sk.send(data.encode('utf-8'))
# 登录服务器时发送数据
def loginhost(self, name, keyword):
data = '$$INFO,' + name + ',' + keyword + ',' + 'Login'
self.sk.send(data.encode('utf-8'))
try:
data = self.sk.recv(1024).decode('utf-8')
if data == '$$USERAT':
print('存在用户冲突')
return False
elif data == '$$UNREG':
print('该用户未注册')
return False
elif data == '$$KEYERR':
print('密码不匹配')
return False
elif data == '$$LIST':
print('正在接收名单')
while True:
data = self.sk.recv(1024).decode('utf-8')
if data == '$$END':
print('接收完毕')
t = threading.Thread(target=self.hostchat)
t.start()
return True
break
print(data)
self.user_list.append(data)
else:
print('无意义数据')
except:
print('回应异常')
return False
# 注册时发送给服务器
def registeruser(self, name, keyword):
data = '$$INFO,' + name + ',' + keyword + ',' + 'Regis'
self.sk.send(data.encode('utf-8'))
try:
data = self.sk.recv(1024).decode('utf-8')
if data == '$$USERAT':
print('注册失败')
return False
elif data == '$$LIST':
print('正在接收名单')
while True:
data = self.sk.recv(1024).decode('utf-8')
if data == '$$END':
print('接收完毕')
return True
break
print(data)
self.user_list.append(data)
except:
print('等待回应异常')
return False
# 主函数
if __name__ == '__main__':
app = QApplication(sys.argv)
m = Chat()
sys.exit(app.exec_())
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samples/smoketest/smoke_test_async.py | lmcarreiro/azure-sdk-for-python | 0 | 135312 | # ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import asyncio
from key_vault_secrets_async import KeyVaultSecrets
from key_vault_keys_async import KeyVaultKeys
from key_vault_certificates_async import KeyVaultCertificates
from event_hubs_async import EventHub
print("")
print("==========================================")
print(" AZURE TRACK 2 SDKs SMOKE TEST ASYNC")
print("==========================================")
async def main():
await KeyVaultSecrets().run()
await KeyVaultKeys().run()
await KeyVaultCertificates().run()
await EventHub().run()
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
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knn2cat.py | carlosking19/python_library | 0 | 63498 | <gh_stars>0
#!/usr/bin/env python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans, SpectralClustering
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, minmax_scale, QuantileTransformer , Normalizer, PowerTransformer
from sklearn.model_selection import train_test_split
import os
import pickle
import urllib.request
code = "https://github.com/carlosking19/python_library/blob/c0210e1fa2c385a851f847faab299bc47e581983/knn_5n_model.sav"
response = urllib.request.urlopen(code)
data = response.read()
exec(data)
filename = (os.path.join(os.path.dirname(__file__),data))
loaded_model = pickle.load(open(filename, 'rb'))
#print('KNN training score {:.3f}'.format(grid_search.score(X_train, y_train)))
# print('KNN testing score {:.3f}'.format(grid_search.score(X_test, y_test)))
import pip
import mysql.connector
mydb = mysql.connector.connect(host = "localhost", user = "root", passwd = "", database = "grading_db")
mycursor = mydb.cursor()
mycursor.execute("SELECT * FROM student_descriptive")
myresult = pd.DataFrame(mycursor,columns = ['section id', 'section sis id', 'section name', 'course id',
'course sis id', 'course name', 'term id', 'term sis id', 'term name',
'enrollment type', 'user id', 'user sis id', 'user name',
'content type', 'content', 'times viewed', 'times participated',
'last viewed'])
myresult["user id"] = myresult["user id"].astype("float")
myresult["times viewed"] = myresult["times viewed"].astype("float")
myresult
pivot_df = myresult.pivot_table(values='times viewed', index=['user id'], columns='content type', aggfunc=pd.Series.sum)
pivot_df = pd.DataFrame(pivot_df)
pivot_df.fillna(0)
pivot_df.insert(4, 'assignment sum',pivot_df['assignment']+pivot_df['assignments'])
pivot_df.drop(['assignment','assignments'], axis = 1, inplace=True)
pivot_df.insert(6, 'collaboration',pivot_df['collaborations'])
pivot_df.drop(['collaborations'], axis = 1, inplace=True)
# pivot_df.columns
pivot_df = pivot_df.fillna(0)
pivot_df['user id'] = pivot_df.index
pivot_df.reset_index(drop=True, inplace=True)
pivot_df = pivot_df[['user id', 'announcements', 'assignment sum', 'discussion', 'files', 'grades', 'modules', 'quizzes', 'quizzesquiz', 'topics', 'wiki']]
pivot_df
#Convert Index User ID into column
x_no_out = pivot_df.loc[:,['announcements', 'assignment sum', 'discussion', 'files', 'grades', 'modules', 'quizzes', 'quizzesquiz', 'topics', 'wiki']].values
scaler = StandardScaler()
x_no_out_scaled = scaler.fit_transform(x_no_out)
# print(X_no_out.shape)
x_no_out = pivot_df.loc[:,['announcements', 'assignment sum', 'discussion', 'files', 'grades', 'modules', 'quizzes', 'quizzesquiz', 'topics', 'wiki']].values
y_test_hat=loaded_model.predict(x_no_out_scaled)
y_test_hat
# df = pd.DataFrame(mlp_predict, columns = ['Predicted_Grade'])
# print(df)
pivot_df['PREDICTED_STATUS'] = y_test_hat
pivot_df[["PREDICTED_STATUS"]].value_counts()
pivot_df = pivot_df.replace({'PREDICTED_STATUS': {'Pass': 'PASS', 'Fail': 'FAIL'}})
#pivot_df.drop (['Predicted_Grade'], axis=1, inplace=True)
predict_val = pivot_df[['user id', 'PREDICTED_STATUS']].to_dict()
import pymysql
import pandas as pd
import mysql.connector
mydb = mysql.connector.connect(host = "localhost", user = "root", passwd = "", database = "grading_db")
# create cursor
mycursor = mydb.cursor()
#sql = f" UPDATE student_predictive SET PREDICTED_STATUS = 'try' "
for i in range(len(predict_val['user id'])):
sql = "UPDATE student_predictive SET PREDICTED_STATUS = %s WHERE USER_ID = %s"
mycursor.execute(sql, [predict_val['PREDICTED_STATUS'][i], predict_val['user id'][i]])
# predict_val['PREDICTED_STATUS'][i]
# {predict_val['user id'][i]
mydb.commit()
print("KNN 2cat")
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] |
hw/hw09/tests/multiples_3.py | AnthonyNg404/61A | 0 | 67248 | test = {
'name': 'multiples_3',
'points': 1,
'suites': [
{
'cases': [
{
'code': r"""
scm> (car multiples-of-three)
3
scm> (list? (cdr multiples-of-three)) ; Check to make sure variable contains a stream
#f
scm> (list? (cdr (cdr-stream multiples-of-three))) ; Check to make sure rest of stream is a stream
#f
scm> (equal? (first-k multiples-of-three 5) '(3 6 9 12 15))
#t
scm> (equal? (first-k multiples-of-three 10) '(3 6 9 12 15 18 21 24 27 30))
#t
scm> (length (first-k multiples-of-three 100))
100
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
scm> (load-all ".")
scm> (define (first-k s k) (if (or (null? s) (= k 0)) nil (cons (car s) (first-k (cdr-stream s) (- k 1)))))
scm> (define (length lst) (if (null? lst) 0 (+ 1 (length (cdr lst)))))
""",
'teardown': '',
'type': 'scheme'
}
]
}
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in_progress/GiRaFFEfood_NRPy/GiRaFFEfood_NRPy_Aligned_Rotator.py | fedelopezar/nrpytutorial | 1 | 12865 | #!/usr/bin/env python
# coding: utf-8
# <a id='top'></a>
#
#
# # $\texttt{GiRaFFEfood}$: Initial data for $\texttt{GiRaFFE}$
#
# ## Aligned Rotator
#
# $$\label{top}$$
#
# This module provides another initial data option for $\texttt{GiRaFFE}$. This is a flat-spacetime test with initial data $$A_{\phi} = \frac{\mu \varpi}{r^3},$$ where $\mu = B_p R_{\rm NS} / 2$, $R_{\rm NS}$ is the neutron star radius, and $\varpi = \sqrt{x^2+y^2}$ is the cylindrical radius. We let $A_r = A_\theta = 0$.
#
# Additionally, the drift velocity $v^i = \Omega \textbf{e}_z \times \textbf{r} = [ijk] \Omega \textbf{e}^j_z x^k$, where $[ijk]$ is the Levi-Civita permutation symbol and $\textbf{e}^i_z = (0,0,1)$.
# <a id='preliminaries'></a>
#
# ### Steps 0-1: Preliminaries
# $$\label{preliminaries}$$
#
# \[Back to [top](#top)\]
#
# Here, we will import the NRPy+ core modules and set the reference metric to Cartesian, set commonly used NRPy+ parameters, and set C parameters that will be set from outside the code eventually generated from these expressions. We will also set up a parameter to determine what initial data is set up, although it won't do much yet.
# Step 0: Import the NRPy+ core modules and set the reference metric to Cartesian
import NRPy_param_funcs as par
import indexedexp as ixp
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import reference_metric as rfm
par.set_parval_from_str("reference_metric::CoordSystem","Cartesian")
rfm.reference_metric()
# Step 1a: Set commonly used parameters.
thismodule = __name__
B_p_aligned_rotator,R_NS_aligned_rotator = par.Cparameters("REAL",thismodule,
# B_p_aligned_rotator = the intensity of the magnetic field and
# R_NS_aligned_rotator= "Neutron star" radius
["B_p_aligned_rotator","R_NS_aligned_rotator"],
[1e-5, 1.0])
# The angular velocity of the "neutron star"
Omega_aligned_rotator = par.Cparameters("REAL",thismodule,"Omega_aligned_rotator",1e3)
# <a id='step2'></a>
#
# ### Step 2: Set the vectors A in Spherical coordinates
# $$\label{step2}$$
#
# \[Back to [top](#top)\]
#
# We will first build the fundamental vector $A_i$ in spherical coordinates (see [Table 3](https://arxiv.org/pdf/1704.00599.pdf)). Note that we use reference_metric.py to set $r$ and $\theta$ in terms of Cartesian coordinates; this will save us a step later when we convert to Cartesian coordinates. So, we set
# \begin{align}
# A_{\phi} &= \frac{\mu \varpi}{r^3}, \\
# \end{align}
# with $\mu = B_p R_{\rm NS} / 2$, $R_{\rm NS}$ is the neutron star radius, and $\varpi = \sqrt{x^2+y^2}$
def GiRaFFEfood_NRPy_Aligned_Rotator():
r = rfm.xxSph[0]
varpi = sp.sqrt(rfm.xx_to_Cart[0]**2 + rfm.xx_to_Cart[1]**2)
mu = B_p_aligned_rotator * R_NS_aligned_rotator**3 / 2
ASphD = ixp.zerorank1()
ASphD[2] = mu * varpi**2 / (r**3) # The other components were already declared to be 0.
# <a id='step3'></a>
#
# ### Step 3: Use the Jacobian matrix to transform the vectors to Cartesian coordinates.
# $$\label{step3}$$
#
# \[Back to [top](#top)\]
#
# Now, we will use the coordinate transformation definitions provided by reference_metric.py to build the Jacobian
# $$
# \frac{\partial x_{\rm Sph}^j}{\partial x_{\rm Cart}^i},
# $$
# where $x_{\rm Sph}^j \in \{r,\theta,\phi\}$ and $x_{\rm Cart}^i \in \{x,y,z\}$. We would normally compute its inverse, but since none of the quantities we need to transform have upper indices, it is not necessary. Then, since $A_i$ and has one lower index, it will need to be multiplied by the Jacobian:
#
# $$
# A_i^{\rm Cart} = A_j^{\rm Sph} \frac{\partial x_{\rm Sph}^j}{\partial x_{\rm Cart}^i},
# $$
# Step 3: Use the Jacobian matrix to transform the vectors to Cartesian coordinates.
drrefmetric__dx_0UDmatrix = sp.Matrix([[sp.diff(rfm.xxSph[0],rfm.xx[0]), sp.diff(rfm.xxSph[0],rfm.xx[1]), sp.diff(rfm.xxSph[0],rfm.xx[2])],
[sp.diff(rfm.xxSph[1],rfm.xx[0]), sp.diff(rfm.xxSph[1],rfm.xx[1]), sp.diff(rfm.xxSph[1],rfm.xx[2])],
[sp.diff(rfm.xxSph[2],rfm.xx[0]), sp.diff(rfm.xxSph[2],rfm.xx[1]), sp.diff(rfm.xxSph[2],rfm.xx[2])]])
#dx__drrefmetric_0UDmatrix = drrefmetric__dx_0UDmatrix.inv() # We don't actually need this in this case.
global AD
AD = ixp.zerorank1(DIM=3)
for i in range(3):
for j in range(3):
AD[i] = drrefmetric__dx_0UDmatrix[(j,i)]*ASphD[j]
# <a id='step4'></a>
#
# ### Step 4: Calculate $v^i$
# $$\label{step4}$$
#
# \[Back to [top](#top)\]
#
# Here, we will calculate the drift velocity $v^i = \Omega \textbf{e}_z \times \textbf{r} = [ijk] \Omega \textbf{e}^j_z x^k$, where $[ijk]$ is the Levi-Civita permutation symbol and $\textbf{e}^i_z = (0,0,1)$. Conveniently, in flat space, the drift velocity reduces to the Valencia velocity because $\alpha = 1$ and $\beta^i = 0$.
# Step 4: Calculate v^i
LeviCivitaSymbolDDD = ixp.LeviCivitaSymbol_dim3_rank3()
import Min_Max_and_Piecewise_Expressions as noif
unit_zU = ixp.zerorank1()
unit_zU[2] = sp.sympify(1)
global ValenciavU
ValenciavU = ixp.zerorank1()
for i in range(3):
for j in range(3):
for k in range(3):
ValenciavU[i] += noif.coord_leq_bound(r,R_NS_aligned_rotator)*LeviCivitaSymbolDDD[i][j][k] * Omega_aligned_rotator * unit_zU[j] * rfm.xx[k]
# ### NRPy+ Module Code Validation
#
# \[Back to [top](#top)\]
#
# Here, as a code validation check, we verify agreement in the SymPy expressions for the $\texttt{GiRaFFE}$ Aligned Rotator initial data equations we intend to use between
# 1. this tutorial and
# 2. the NRPy+ [GiRaFFEfood_NRPy_Aligned_Rotator.py](../edit/GiRaFFEfood_NRPy/GiRaFFEfood_NRPy_Aligned_Rotator.py) module.
#
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farm/__init__.py | ViktorAlm/FARM | 2 | 68126 | import logging
import torch.multiprocessing as mp
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# reduce verbosity from transformers library
logging.getLogger('transformers.configuration_utils').setLevel(logging.WARNING)
# https://pytorch.org/docs/stable/multiprocessing.html#sharing-strategies
if "file_descriptor" in mp.get_all_sharing_strategies():
import resource
mp.set_sharing_strategy("file_descriptor")
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
# seting soft limit to hard limit (=rlimit[1]) minus a small amount to be safe
resource.setrlimit(resource.RLIMIT_NOFILE, (rlimit[1]-512, rlimit[1]))
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misago/misago/users/migrations/0003_bans_version_tracker.py | vascoalramos/misago-deployment | 2 | 1604943 | <filename>misago/misago/users/migrations/0003_bans_version_tracker.py<gh_stars>1-10
from django.db import migrations
class Migration(migrations.Migration):
"""Migration superseded by 0016"""
dependencies = [
("misago_users", "0002_users_settings"),
("misago_core", "0001_initial"),
]
operations = []
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notebooks/sports/interactive.py | callysto/lesson-plans | 2 | 123756 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 30 2021
@author: TinaL5, adapted from lisacao, <NAME>.
"""
# libraries
import random
################################ TEXT ENCODING
def soccer(text): # blocked out grey with emoji
rvr = "\033[1;35m ⚽️ : \033[1;0m" + text + "\033[1;0m"
return rvr
def task(text, check = False): # light blue with emoji option
if check == True:
blu = "\033[1;36m" + "✅ " + text + "\033[1;0m"
return blu
else:
blu = "\033[1;36m" + text + "\033[1;0m"
return blu
def bedazzle(text): # purple with randomized emoji
sparkle = [" 🌟 ", " 🎉 ", " 🥳 ", " 🤩 ", " 🏆 ", " 😎 "]
prl = "\033[1;35m" + random.choice(sparkle) + text + "\033[1;0m"
return prl
def normal(text): # plain black
nrl = "\033[1;0m" + text + "\033[1;0m"
return nrl
def code(text): # bright green
grn = "\033[1;32m" + text + "\033[1;0m"
return grn
def tryagain(text, exclaim = True): # red with default emoji
if exclaim == False:
red = "\033[1;31m" + text + "\033[1;0m"
return red
else:
red = "\033[1;31m" + "❗️ " + text + "\033[1;0m"
return red
# positive feedback for correct answers
def correct_answer(): # random positive reinforcement
compliment = ["Amazing work!", "Nice job!", "Right on!", "You're out of this world!", "Great stuff!", "You rock!", "I wish I were this good!", "Correct!", "That was great!", "You're picking up on this really well!"]
nice = print(bedazzle(random.choice(compliment)))
return nice
################################ CHALLENGE 1
# Question 1
def challenge1(): # interpretation of scatter plots
print(soccer(" Go back and look at the scatter plot Relationship between average ball possession (%) and total goals. The data points further from the trend line seem to tell a different story.\n"), task("Hover over the data points to find the team with average ball possession of 44% and 16 total goals. ", check = True))
def Q1a(): # Monchengladbach total goals
ans1 = str(input(normal("The team is: ")))
if ans1 == "Monchengladbach":
correct_answer()
else:
print(tryagain(" Make sure to locate ball possession on the x-axis and total goals on the y-axis."),tryagain(" The team name starts with an M.", exclaim = False))
Q1a()
#execute
Q1a()
################################ CHALLENGE 2
# Question 2
def challenge2(): # interpretation of scatter plots
print(soccer(" Go back and look at the scatter plot Relationship between average ball possession (%) and goal difference by team. Let's try to gain more insight into Monchengladbach who ties second with total goals of 16.\n"), task("Hover over the data points to determine Monchengladbach's goal difference. ", check = True))
def Q2a(): # Monchengladbach goal difference
ans1 = str(input(normal("Monchengladbach's goal difference is: ")))
if ans1 == "7":
correct_answer()
else:
print(tryagain(" Make sure to locate average ball possession of 44% on the x-axis.", exclaim = False))
Q2a()
#execute
Q2a()
################################ CHALLENGE 3
# Question 3
def challenge3(): # interpretation of scatter plots
print(soccer(" Looking at the scatter plot Relationship between average ball possession (%) and goal difference by team, try to find the team with the second lowest average ball possession of 41%, total goals of 10, and a goal difference of 7.\n"), task("Hover over the data points to locate the team. ", check = True))
def Q3a(): # Porto
ans1 = str(input(normal("The team is: ")))
if ans1 == "Porto":
correct_answer()
print('Porto only had 3 goals scored against them with their opponents possessing the ball 56% of the time and they were able to score 10 goals!')
else:
print(tryagain(" Make sure to locate average ball possession of 41% on the x-axis and goal difference of 7 on the y-axis. The team's name starts with a P.", exclaim = False))
Q3a()
#execute
Q3a() | [
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stocks/__init__.py | xBlynd/FlameCogs | 0 | 99092 | <filename>stocks/__init__.py
from .stocks import Stocks
def setup(bot):
bot.add_cog(Stocks(bot))
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models/tot_metrics.py | GU-DataLab/fairness-and-missing-values | 0 | 114270 | <gh_stars>0
# pred is the list of predicted labels, sensitive is the list of labels for sensitive attribute
# unprotected_vals is the label for unprotected group. e.g. 1 means unprivilege, 0 means privilege
# positive_pred is the favorable result in prediction task. e.g. get approved for a loan
def DIbinary(pred,sensitive,unprotected_vals,positive_pred):
unprotected_positive = 0.0
unprotected_negative = 0.0
protected_positive = 0.0
protected_negative = 0.0
for i in range(0, len(pred)):
protected_val = sensitive[i]
predicted_val = pred[i]
# when someone is in unprotected group
if protected_val in unprotected_vals:
if int(predicted_val) == int(positive_pred):
unprotected_positive += 1
else:
unprotected_negative += 1
# the person is in protected group ie male = 0
else:
if int(predicted_val) == int(positive_pred):
protected_positive += 1
else:
protected_negative += 1
protected_pos_percent = 0.0
if protected_positive + protected_negative > 0:
protected_pos_percent = protected_positive / (protected_positive + protected_negative)
unprotected_pos_percent = 0.0
if unprotected_positive + unprotected_negative > 0:
unprotected_pos_percent = unprotected_positive / \
(unprotected_positive + unprotected_negative)
return unprotected_pos_percent, protected_pos_percent
def CV(pred,sensitive,unprotected_vals,positive_pred):
protected_pos_percent, unprotected_pos_percent = DIbinary(pred, sensitive, unprotected_vals,positive_pred)
cv = unprotected_pos_percent -protected_pos_percent
return 1 - cv
# acutal is the list of actual labels, pred is the list of predicted labels.
# sensitive is the column of sensitive attribute, target_group is s in S = s
# positive_pred is the favorable result in prediction task. e.g. get approved for a loan
def calibration_pos(actual,pred,sensitive,target_group,positive_pred):
tot_pred_pos = 0
act_pos = 0
for act, pred_val, sens in zip(actual, pred,sensitive):
# the case S != s
if sens != target_group:
continue
else:
# Yhat = 1
if pred_val == positive_pred:
tot_pred_pos += 1
# the case both Yhat = 1 and Y = 1
if act == positive_pred:
act_pos +=1
if act_pos == 0 and tot_pred_pos ==0:
return 1
if tot_pred_pos == 0:
return 0
return act_pos/tot_pred_pos
from sklearn.metrics import confusion_matrix
# acutal is the list of actual labels, pred is the list of predicted labels.
# positive_pred is the favorable result in prediction task. e.g. get approved for a loan
def TNR(actual,pred,positive_pred):
classes = list(set(actual))
matrix = confusion_matrix(actual, pred, labels=classes)
TN = 0.0
allN = 0.0
for i in range(0, len(classes)):
trueval = classes[i]
if trueval == positive_pred:
continue
for j in range(0, len(classes)):
allN += matrix[i][j]
predval = classes[j]
if trueval == predval:
TN += matrix[i][j]
if allN == 0.0:
return 1.0
return TN / allN
from sklearn.metrics import recall_score
# acutal is the list of actual labels, pred is the list of predicted labels.
# positive_pred is the favorable result in prediction task. e.g. get approved for a loan
def TPR(actual,pred,positive_pred):
return recall_score(actual,pred,pos_label = positive_pred,average = 'binary')
# acutal is the list of actual labels, pred is the list of predicted labels.
# positive_pred is the favorable result in prediction task. e.g. get approved for a loan
def get_BCR(actual,pred,positive_pred):
tpr_val = TPR(actual,pred,positive_pred)
tnr_val = TNR(actual,pred,positive_pred)
bcr = (tpr_val + tnr_val) / 2.0
return bcr
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] |
ceilometer/notifier.py | aristanetworks/ceilometer | 2 | 70387 | <reponame>aristanetworks/ceilometer
#
# Copyright 2013 eNovance
#
# Author: <NAME> <<EMAIL>>
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from stevedore import extension
from ceilometer.openstack.common import context as req_context
from ceilometer.openstack.common.gettextutils import _
from ceilometer.openstack.common import log as logging
from ceilometer import pipeline
from ceilometer import transformer
LOG = logging.getLogger(__name__)
_notification_manager = None
_pipeline_manager = None
def _load_notification_manager():
global _notification_manager, _pipeline_manager
namespace = 'ceilometer.notification'
LOG.debug(_('loading notification handlers from %s'), namespace)
_notification_manager = extension.ExtensionManager(
namespace=namespace,
invoke_on_load=True,
invoke_args=(_pipeline_manager, )
)
if not list(_notification_manager):
LOG.warning(_('Failed to load any notification handlers for %s'),
namespace)
def _load_pipeline_manager():
global _pipeline_manager
_pipeline_manager = pipeline.setup_pipeline(
transformer.TransformerExtensionManager(
'ceilometer.transformer',
),
)
def notify(context, message):
"""Sends a notification as a meter using Ceilometer pipelines."""
if not _pipeline_manager:
_load_pipeline_manager()
if not _notification_manager:
_load_notification_manager()
_notification_manager.map_method(
'to_samples_and_publish',
context=context or req_context.get_admin_context(),
notification=message)
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] |
mongomail/main.py | terrabitz/MongoMail | 0 | 41314 | """
This file is for initializing the uWSGI application. It defines a post-fork command in
order to avoid an issue in uWSGI that prevents MongoEngine from lazily creating
mongo connections
"""
from uwsgidecorators import postfork
from mongomail.rest_app import app, db, connection
@postfork
def init_db():
db.init_app(app)
print("initializing db")
if not connection.get_api_keys():
print("No Key found")
key = connection.generate_api_key()
print("Generated key: " + key)
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] |
opt.py | ArmandB/RITnet | 0 | 40678 | from pprint import pprint
import argparse
def parse_args():
parser = argparse.ArgumentParser()
# Data input settings
parser.add_argument('--dataset', type=str, default='Semantic_Segmentation_Dataset/', help='name of dataset')
# Optimization: General
parser.add_argument('--bs', type=int, default = 8 )
parser.add_argument('--epochs', type=int,help='Number of epochs',default= 250)
parser.add_argument('--workers', type=int,help='Number of workers',default=4)
parser.add_argument('--model', help='model name',default='densenet')
parser.add_argument('--evalsplit', help='eval spolit',default='val')
parser.add_argument('--lr', type=float,default= 1e-3,help='Learning rate')
parser.add_argument('--save', help='save folder name',default='0try')
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--load', type=str, default='best_model.pkl', help='load checkpoint file name')
parser.add_argument('--resume', action='store_true', help='resume train from load chkpoint')
parser.add_argument('--test', action='store_true', help='test only')
parser.add_argument('--savemodel',action='store_true',help='checkpoint save the model')
parser.add_argument('--testrun', action='store_true', help='test run with few dataset')
parser.add_argument('--expname', type=str, default='info', help='extra explanation of the method')
parser.add_argument('--useGPU', type=str, default=True, help='Set it as False if GPU is unavailable')
# parse
args = parser.parse_args()
opt = vars(args)
pprint('parsed input parameters:')
pprint(opt)
return args
if __name__ == '__main__':
opt = parse_args()
print('opt[\'dataset\'] is ', opt.dataset)
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common/inputs/custom_reader.py | bhneo/FMRouting | 0 | 102134 | import os
import tensorflow_datasets as tfds
import tensorflow as tf
import numpy as np
from common.inputs.data_input import DataInfo
bxs_m2 = [[1, 1], [1, -1], [-1, 1], [-1, -1]]
def parse_multi_mnist1(serialized_example):
""" Data parsing function.
"""
features = tf.io.parse_single_example(serialized_example,
features={
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label_1': tf.io.FixedLenFeature([], tf.int64),
'label_2': tf.io.FixedLenFeature([], tf.int64),
'image_raw_1': tf.io.FixedLenFeature([], tf.string),
'image_raw_2': tf.io.FixedLenFeature([], tf.string),
'merged_raw': tf.io.FixedLenFeature([], tf.string),
})
# Decode 3 images
image_raw_1 = tf.io.decode_raw(features['image_raw_1'], tf.uint8)
image_raw_1 = tf.reshape(image_raw_1, shape=[36, 36, 1])
image_raw_2 = tf.io.decode_raw(features['image_raw_2'], tf.uint8)
image_raw_2 = tf.reshape(image_raw_2, shape=[36, 36, 1])
merged_raw = tf.io.decode_raw(features['merged_raw'], tf.uint8)
merged_raw = tf.reshape(merged_raw, shape=[36, 36, 1])
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image_raw_1 = tf.cast(image_raw_1, tf.float32) * (1. / 255)
image_raw_2 = tf.cast(image_raw_2, tf.float32) * (1. / 255)
merged_raw = tf.cast(merged_raw, tf.float32) * (1. / 255)
# Convert label from a scalar uint8 tensor to an int32 scalar.
label_1 = tf.one_hot(tf.cast(features['label_1'], tf.int32), 10)
label_2 = tf.one_hot(tf.cast(features['label_2'], tf.int32), 10)
label = label_1 + label_2
features = {'images': merged_raw,
'labels': label,
'recons_label': label_1,
'recons_image': image_raw_1,
'spare_label': label_2,
'spare_image': image_raw_2}
return features
def parse_multi_mnist(serialized_example):
""" Data parsing function.
"""
features = tf.io.parse_single_example(serialized_example,
features={
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64),
'label_1': tf.io.FixedLenFeature([], tf.int64),
'label_2': tf.io.FixedLenFeature([], tf.int64),
'image_raw_1': tf.io.FixedLenFeature([], tf.string),
'image_raw_2': tf.io.FixedLenFeature([], tf.string),
# 'merged_raw': tf.io.FixedLenFeature([], tf.string),
})
# Decode 3 images
image_raw_1 = tf.io.decode_raw(features['image_raw_1'], tf.uint8)
image_raw_1 = tf.reshape(image_raw_1, shape=[36, 36, 1])
image_raw_2 = tf.io.decode_raw(features['image_raw_2'], tf.uint8)
image_raw_2 = tf.reshape(image_raw_2, shape=[36, 36, 1])
merged_raw = tf.add(tf.cast(image_raw_1, tf.int32), tf.cast(image_raw_2, tf.int32))
merged_raw = tf.minimum(merged_raw, 255)
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image_raw_1 = tf.cast(image_raw_1, tf.float32) * (1. / 255)
image_raw_2 = tf.cast(image_raw_2, tf.float32) * (1. / 255)
merged_raw = tf.cast(merged_raw, tf.float32) * (1. / 255)
# Convert label from a scalar uint8 tensor to an int32 scalar.
label_1 = tf.one_hot(tf.cast(features['label_1'], tf.int32), 10)
label_2 = tf.one_hot(tf.cast(features['label_2'], tf.int32), 10)
label = label_1 + label_2
features = {'images': merged_raw,
'labels': label,
'recons_label': label_1,
'recons_image': image_raw_1,
'spare_label': label_2,
'spare_image': image_raw_2}
return features
def parse_aff_mnist(serialized_example):
""" Data parsing function.
"""
features = tf.io.parse_single_example(serialized_example,
features={'image': tf.io.FixedLenFeature([], tf.string),
'label': tf.io.FixedLenFeature([], tf.int64),
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'depth': tf.io.FixedLenFeature([], tf.int64)})
image = tf.io.decode_raw(features['image'], tf.uint8)
image = tf.reshape(image, shape=[40, 40, 1])
image = tf.cast(image, tf.float32) * (1. / 255)
label = tf.cast(features['label'], tf.int32)
label = tf.one_hot(label, 10)
return image, label
def build_parse(dataset):
if dataset == 'aff_mnist':
return parse_aff_mnist
elif dataset == 'shift_mnist':
return parse_aff_mnist
elif dataset == 'multi_mnist':
return parse_multi_mnist
elif dataset == 'multi_mnist1':
return parse_multi_mnist1
def get_dataset(name, data_path):
if name == 'aff_mnist':# 1920000/320000
train_files = os.path.join(data_path, "train_affnist.tfrecord")
test_files = os.path.join(data_path, "test_affnist.tfrecord")
train_parse_fun = build_parse('aff_mnist')
test_parse_fun = build_parse('aff_mnist')
info = DataInfo(tfds.features.FeaturesDict({'image': tfds.features.Image(shape=(40, 40, 1)),
'label': tfds.features.ClassLabel(num_classes=10)}),
{'train_examples': 1920000,
'test_examples': 320000})
elif name == 'shift_mnist':# 10140000/1690000
train_files = os.path.join(data_path, "train_6shifted_mnist.tfrecord")
test_files = os.path.join(data_path, "test_6shifted_mnist.tfrecord")
train_parse_fun = build_parse('shift_mnist')
test_parse_fun = build_parse('shift_mnist')
info = DataInfo(tfds.features.FeaturesDict({'image': tfds.features.Image(shape=(40, 40, 1)),
'label': tfds.features.ClassLabel(num_classes=10)}),
{'train_examples': 10140000,
'test_examples': 1690000})
# elif name == 'multi_mnist':# 6000000/1000000
# train_files = [os.path.join(data_path, 'train', "multitrain_6shifted_mnist.tfrecords-0000{}-of-00060".format(i)) for i
# in range(10)] + [os.path.join(data_path, 'train', "multitrain_6shifted_mnist.tfrecords-000{}-of-00060".format(i)) for i
# in range(10, 60)]
# test_files = [os.path.join(data_path, 'test', "multitest_6shifted_mnist.tfrecords-0000{}-of-00010".format(i)) for i in range(10)]
# train_parse_fun = build_parse('multi_mnist')
# test_parse_fun = build_parse('multi_mnist')
# info = DataInfo(tfds.features.FeaturesDict({'image': tfds.features.Image(shape=(36, 36, 1)),
# 'label': tfds.features.ClassLabel(num_classes=10)}),
# {'train_examples': 6000000,
# 'test_examples': 1000000})
elif name == 'multi_mnist':# 599999/100000
train_files = [os.path.join(data_path, "multitrain_6shifted_mnist.tfrecords-0000{}-of-00006".format(i)) for i
in range(6)]
test_files = [os.path.join(data_path, "multitest_6shifted_mnist.tfrecords-00000-of-00001")]
train_parse_fun = build_parse('multi_mnist1')
test_parse_fun = build_parse('multi_mnist1')
info = DataInfo(tfds.features.FeaturesDict({'image': tfds.features.Image(shape=(36, 36, 1)),
'label': tfds.features.ClassLabel(num_classes=10)}),
{'train_examples': 599999,
'test_examples': 100000})
else:
raise Exception('dataset note support!')
train = tf.data.TFRecordDataset(train_files)
test = tf.data.TFRecordDataset(test_files)
return train, test, train_parse_fun, test_parse_fun, info
def build_dataset(name, data_dir='data', batch_size=128, buffer_size=50000):
data_path = os.path.join(data_dir, name)
train, test, train_parse_fun, test_parse_fun, info = get_dataset(name, data_path)
if buffer_size > 0:
train = train.shuffle(buffer_size=buffer_size)
train = train.map(train_parse_fun,
num_parallel_calls=tf.data.experimental.AUTOTUNE)\
.batch(batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
test = test.map(test_parse_fun,
num_parallel_calls=tf.data.experimental.AUTOTUNE)\
.batch(batch_size)\
.prefetch(tf.data.experimental.AUTOTUNE)
return train, test, info
def count_data(name):
train, test, _ = build_dataset(name, '')
train_num = 0
for image, label in train:
train_num += image.shape[0]
test_num = 0
for image, label in test:
test_num += image.shape[0]
print('train num:', train_num)
print('test num:', test_num)
def count_multi_mnist():
train, test, _ = build_dataset('multi_mnist', '')
train_num = 0
for feature in train:
train_num += feature['images'].shape[0]
test_num = 0
for feature in test:
test_num += feature['images'].shape[0]
print('train num:', train_num)
print('test num:', test_num)
def view_data(name, img_stand=False):
train, test, _ = build_dataset(name, '')
for image, label in train:
if not img_stand:
image /= 255.
out_image(image, label)
break
for image, label in test:
if not img_stand:
image /= 255.
out_image(image, label)
break
def view_multi_mnist(img_stand=False):
train, test, _ = build_dataset('multi_mnist', '')
for features in train:
image = features['images']
label = features['labels']
recons_label = features['recons_label']
recons_image = features['recons_image']
spare_label = features['spare_label']
spare_image = features['spare_image']
image_final = tf.concat([recons_image, spare_image, image], axis=2)
if not img_stand:
image_final /= 255.
out_image(image_final, label)
break
def out_image(images, labels):
import matplotlib.pyplot as plt
plt.figure()
for i in range(16):
plt.subplot(4, 4, i+1)
plt.title(tf.argmax(labels[i]).numpy())
image = images[i, :, :, :]
if image.shape[-1] == 1:
image = np.squeeze(image, -1)
plt.imshow(image, cmap='gray')
else:
plt.imshow(image)
plt.subplots_adjust(hspace=0.5)
plt.show()
if __name__ == "__main__":
# view_data('aff_mnist')
# count_data('shift_mnist')
# view_multi_mnist()
count_multi_mnist()
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tests/test_add_option_backtrace.py | ponponon/loguru | 11,391 | 13505 | <reponame>ponponon/loguru<gh_stars>1000+
from loguru import logger
# See "test_catch_exceptions.py" for extended testing
def test_backtrace(writer):
logger.add(writer, format="{message}", backtrace=True)
try:
1 / 0
except Exception:
logger.exception("")
result_with = writer.read().strip()
logger.remove()
writer.clear()
logger.add(writer, format="{message}", backtrace=False)
try:
1 / 0
except Exception:
logger.exception("")
result_without = writer.read().strip()
assert len(result_with.splitlines()) > len(result_without.splitlines())
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trump_net/spec/persons.py | iandennismiller/trump-net | 0 | 1613410 | # -*- coding: utf-8 -*-
# trump-net (c) <NAME>
from situation import Person
donald_trump = Person.create(name="<NAME>")
donald_trump_jr = Person.create(name="<NAME>, Jr.")
fred_trump = Person.create(name="<NAME>")
fred_trump_jr = Person.create(name="<NAME>ump, Jr.")
ivana_trump = Person.create(name="<NAME>")
ivanka_trump = Person.create(name="<NAME>")
eric_trump = Person.create(name="<NAME>")
tiffany_trump = Person.create(name="<NAME>")
barron_trump = Person.create(name="<NAME>")
marla_maples = Person.create(name="<NAME>")
melania_trump = Person.create(name="<NAME>")
jared_kushner = Person.create(name="<NAME>")
charles_kushner = Person.create(name="<NAME>")
paul_manafort = Person.create(name="<NAME>")
steve_bannon = Person.create(name="<NAME>")
roger_stone = Person.create(name="<NAME>")
kelly_anne_conway = Person.create(name="<NAME>")
robert_mercer = Person.create(name="<NAME>")
rebekah_mercer = Person.create(name="<NAME>")
natalia_veselnetskaya = Person.create(name="<NAME>")
milo_yiannopoulos = Person.create(name="<NAME>")
dmitry_rybolovlev = Person.create(name="<NAME>")
artem_chaika = Person.create(name="<NAME>")
igor_chaika = Person.create(name="<NAME>")
yuri_chaika = Person.create(name="<NAME>")
rob_goldstone = Person.create(name="<NAME>")
aras_agalrov = Person.create(name="<NAME>")
emin_agalrov = Person.create(name="<NAME>")
dana_rohrbacher = Person.create(name="<NAME>")
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interview/leet/445_Add_Two_Numbers_II_stack.py | eroicaleo/LearningPython | 1 | 182305 | #!/usr/bin/env python3
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
from linklist import *
class Solution(object):
def addTwoNumbers(self, l1, l2):
"""
:type l1: ListNode
:type l2: ListNode
:rtype: ListNode
"""
import collections
stack1, stack2 = collections.deque(), collections.deque()
while l1:
stack1.append(l1.val)
l1 = l1.next
while l2:
stack2.append(l2.val)
l2 = l2.next
add1, head = 0, None
while stack1 or stack2:
val1 = stack1.pop() if stack1 else 0
val2 = stack2.pop() if stack2 else 0
summmation = (val1+val2+add1)
node = ListNode(summmation%10)
add1 = summmation//10
head, node.next = node, head
if add1:
node = ListNode(add1)
head, node.next = node, head
return head
sol = Solution()
l1 = linkListBuilder('[7,2,4,3]')
l2 = linkListBuilder('[5,6,4]')
l1 = linkListBuilder('[1]')
l2 = linkListBuilder('[9,9]')
traverse(l1)
traverse(l2)
traverse(sol.addTwoNumbers(l1, l2))
| [
1,
18787,
4855,
29914,
2109,
29914,
6272,
3017,
29941,
13,
13,
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368,
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2324,
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4622,
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29906,
29901,
13,
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5096,
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29889,
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29898,
29880,
29906,
29889,
791,
29897,
13,
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301,
29906,
353,
301,
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353,
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29900,
29892,
6213,
13,
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1550,
5096,
29896,
470,
5096,
29906,
29901,
13,
9651,
659,
29896,
353,
5096,
29896,
29889,
7323,
580,
565,
5096,
29896,
1683,
29871,
29900,
13,
9651,
659,
29906,
353,
5096,
29906,
29889,
7323,
580,
565,
5096,
29906,
1683,
29871,
29900,
13,
9651,
2533,
4317,
362,
353,
313,
791,
29896,
29974,
791,
29906,
29974,
1202,
29896,
29897,
13,
9651,
2943,
353,
2391,
4247,
29898,
2083,
4317,
362,
29995,
29896,
29900,
29897,
13,
9651,
788,
29896,
353,
2533,
4317,
362,
458,
29896,
29900,
13,
9651,
2343,
29892,
2943,
29889,
4622,
353,
2943,
29892,
2343,
13,
4706,
565,
788,
29896,
29901,
13,
9651,
2943,
353,
2391,
4247,
29898,
1202,
29896,
29897,
13,
9651,
2343,
29892,
2943,
29889,
4622,
353,
2943,
29892,
2343,
13,
4706,
736,
2343,
13,
13,
2929,
353,
24380,
580,
13,
29880,
29896,
353,
1544,
1293,
5627,
877,
29961,
29955,
29892,
29906,
29892,
29946,
29892,
29941,
29962,
1495,
13,
29880,
29906,
353,
1544,
1293,
5627,
877,
29961,
29945,
29892,
29953,
29892,
29946,
29962,
1495,
13,
29880,
29896,
353,
1544,
1293,
5627,
877,
29961,
29896,
29962,
1495,
13,
29880,
29906,
353,
1544,
1293,
5627,
877,
29961,
29929,
29892,
29929,
29962,
1495,
13,
3018,
3901,
29898,
29880,
29896,
29897,
13,
3018,
3901,
29898,
29880,
29906,
29897,
13,
3018,
3901,
29898,
2929,
29889,
1202,
13985,
29478,
29898,
29880,
29896,
29892,
301,
29906,
876,
13,
2
] |
veniq/patterns/classic_setter/classic_setter.py | lyriccoder/veniq | 23 | 169510 | from typing import List
from veniq.ast_framework import ASTNodeType, AST
from veniq.ast_framework.ast_node import ASTNode
class ClassicSetter:
"""
The method's name starts with set. There are attributes
assigning in the method. Also, asserts are ignored.
"""
suitable_nodes: List[ASTNodeType] = [
ASTNodeType.ASSERT_STATEMENT,
ASTNodeType.STATEMENT_EXPRESSION,
]
def _check_body_nodes(self, check_setter_body: List[ASTNode]) -> bool:
"""
Check whether nodes are agree with the following types
(in self.suitable_nodes) or not.
"""
for node in check_setter_body:
if node.node_type not in self.suitable_nodes:
return False
return True
def value(self, ast: AST) -> List[int]:
lines: List[int] = []
for node in ast.get_proxy_nodes(ASTNodeType.METHOD_DECLARATION):
method_name = node.name
if node.return_type is None and method_name.startswith('set') and \
self._check_body_nodes(node.body):
lines.append(node.line)
return sorted(lines)
| [
1,
515,
19229,
1053,
2391,
13,
3166,
6003,
29875,
29939,
29889,
579,
29918,
4468,
1053,
319,
1254,
4247,
1542,
29892,
319,
1254,
13,
3166,
6003,
29875,
29939,
29889,
579,
29918,
4468,
29889,
579,
29918,
3177,
1053,
319,
1254,
4247,
13,
13,
13,
1990,
24300,
22304,
29901,
13,
1678,
9995,
13,
1678,
450,
1158,
29915,
29879,
1024,
8665,
411,
731,
29889,
1670,
526,
8393,
13,
1678,
23188,
297,
278,
1158,
29889,
3115,
29892,
408,
643,
1372,
526,
17262,
29889,
13,
1678,
9995,
13,
1678,
13907,
29918,
18010,
29901,
2391,
29961,
28938,
4247,
1542,
29962,
353,
518,
13,
4706,
319,
1254,
4247,
1542,
29889,
22933,
20161,
29918,
19713,
13780,
29892,
13,
4706,
319,
1254,
4247,
1542,
29889,
19713,
13780,
29918,
5746,
15094,
13507,
29892,
13,
1678,
4514,
13,
13,
1678,
822,
903,
3198,
29918,
2587,
29918,
18010,
29898,
1311,
29892,
1423,
29918,
842,
357,
29918,
2587,
29901,
2391,
29961,
28938,
4247,
2314,
1599,
6120,
29901,
13,
4706,
9995,
13,
4706,
5399,
3692,
7573,
526,
8661,
411,
278,
1494,
4072,
13,
4706,
313,
262,
1583,
29889,
2146,
8270,
29918,
18010,
29897,
470,
451,
29889,
13,
4706,
9995,
13,
4706,
363,
2943,
297,
1423,
29918,
842,
357,
29918,
2587,
29901,
13,
9651,
565,
2943,
29889,
3177,
29918,
1853,
451,
297,
1583,
29889,
2146,
8270,
29918,
18010,
29901,
13,
18884,
736,
7700,
13,
4706,
736,
5852,
13,
13,
1678,
822,
995,
29898,
1311,
29892,
8717,
29901,
319,
1254,
29897,
1599,
2391,
29961,
524,
5387,
13,
4706,
3454,
29901,
2391,
29961,
524,
29962,
353,
5159,
13,
4706,
363,
2943,
297,
8717,
29889,
657,
29918,
14701,
29918,
18010,
29898,
28938,
4247,
1542,
29889,
2303,
4690,
13668,
29918,
2287,
6154,
1718,
8098,
1125,
13,
9651,
1158,
29918,
978,
353,
2943,
29889,
978,
13,
9651,
565,
2943,
29889,
2457,
29918,
1853,
338,
6213,
322,
1158,
29918,
978,
29889,
27382,
2541,
877,
842,
1495,
322,
320,
13,
462,
1678,
1583,
3032,
3198,
29918,
2587,
29918,
18010,
29898,
3177,
29889,
2587,
1125,
13,
18884,
3454,
29889,
4397,
29898,
3177,
29889,
1220,
29897,
13,
4706,
736,
12705,
29898,
9012,
29897,
13,
2
] |
BOJ2559.py | INYEONGKIM/BOJ | 2 | 91821 | n,k=map(int,input().split());a=[int(i) for i in input().split()];m=sum(a[:k]);s=m
for i in range(k,n):
s+=(a[i]-a[i-k])
if s>m:m=s
print(m)
| [
1,
302,
29892,
29895,
29922,
1958,
29898,
524,
29892,
2080,
2141,
5451,
3310,
29874,
11759,
524,
29898,
29875,
29897,
363,
474,
297,
1881,
2141,
5451,
580,
1385,
29885,
29922,
2083,
29898,
29874,
7503,
29895,
5691,
29879,
29922,
29885,
13,
1454,
474,
297,
3464,
29898,
29895,
29892,
29876,
1125,
13,
1678,
269,
29974,
7607,
29874,
29961,
29875,
29962,
29899,
29874,
29961,
29875,
29899,
29895,
2314,
13,
1678,
565,
269,
29958,
29885,
29901,
29885,
29922,
29879,
13,
2158,
29898,
29885,
29897,
13,
2
] |
test/test_cartesian.py | hwazni/discopy | 205 | 117 | from pytest import raises
from discopy.cartesian import *
def test_Box_repr():
f = Box('f', 1, 2, lambda x: (x, x))
assert "Box('f', 1, 2" in repr(f)
def test_Function_str():
f = Function(2, 1, lambda x, y: x + y)
assert 'Function(dom=2, cod=1,' in str(f)
def test_Function_call():
f = Swap(2, 1)
values = (2, 3)
with raises(TypeError) as err:
f(*values)
assert str(err.value) == messages.expected_input_length(f, values)
def test_Function_then():
f, g = Function(2, 1, lambda x, y: x + y), Function(1, 1, lambda x: x + 1)
assert Function.id(2).then(*(f, g))(20, 21) == 42
def test_Function_then_err():
f = Function(2, 1, lambda x, y: x + y)
g = (lambda x: x, )
with raises(TypeError) as err:
f >> g
assert str(err.value) == messages.type_err(Function, g)
g = Function.id(2)
with raises(AxiomError) as err:
f >> g
assert str(err.value) == messages.does_not_compose(f, g)
def test_Function_tensor():
assert Function.id(3)(1, 2, 3)\
== Function.id(0).tensor(*(3 * [Function.id(1)]))(1, 2, 3)
def test_Function_tensor_err():
f = Function(2, 1, lambda x, y: x + y)
g = (lambda x: x, )
with raises(TypeError) as err:
f @ g
assert str(err.value) == messages.type_err(Function, g)
| [
1,
515,
11451,
1688,
1053,
1153,
4637,
13,
3166,
2313,
2270,
29889,
13823,
18970,
1053,
334,
13,
13,
13,
1753,
1243,
29918,
3313,
29918,
276,
558,
7295,
13,
1678,
285,
353,
11773,
877,
29888,
742,
29871,
29896,
29892,
29871,
29906,
29892,
14013,
921,
29901,
313,
29916,
29892,
921,
876,
13,
1678,
4974,
376,
3313,
877,
29888,
742,
29871,
29896,
29892,
29871,
29906,
29908,
297,
2062,
29898,
29888,
29897,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
710,
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13,
1678,
285,
353,
6680,
29898,
29906,
29892,
29871,
29896,
29892,
14013,
921,
29892,
343,
29901,
921,
718,
343,
29897,
13,
1678,
4974,
525,
6678,
29898,
3129,
29922,
29906,
29892,
15234,
29922,
29896,
5501,
297,
851,
29898,
29888,
29897,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
4804,
7295,
13,
1678,
285,
353,
3925,
481,
29898,
29906,
29892,
29871,
29896,
29897,
13,
1678,
1819,
353,
313,
29906,
29892,
29871,
29941,
29897,
13,
1678,
411,
1153,
4637,
29898,
1542,
2392,
29897,
408,
4589,
29901,
13,
4706,
285,
10456,
5975,
29897,
13,
1678,
4974,
851,
29898,
3127,
29889,
1767,
29897,
1275,
7191,
29889,
9684,
29918,
2080,
29918,
2848,
29898,
29888,
29892,
1819,
29897,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
6098,
7295,
13,
1678,
285,
29892,
330,
353,
6680,
29898,
29906,
29892,
29871,
29896,
29892,
14013,
921,
29892,
343,
29901,
921,
718,
343,
511,
6680,
29898,
29896,
29892,
29871,
29896,
29892,
14013,
921,
29901,
921,
718,
29871,
29896,
29897,
13,
1678,
4974,
6680,
29889,
333,
29898,
29906,
467,
6098,
10456,
29898,
29888,
29892,
330,
876,
29898,
29906,
29900,
29892,
29871,
29906,
29896,
29897,
1275,
29871,
29946,
29906,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
6098,
29918,
3127,
7295,
13,
1678,
285,
353,
6680,
29898,
29906,
29892,
29871,
29896,
29892,
14013,
921,
29892,
343,
29901,
921,
718,
343,
29897,
13,
1678,
330,
353,
313,
2892,
921,
29901,
921,
29892,
1723,
13,
1678,
411,
1153,
4637,
29898,
1542,
2392,
29897,
408,
4589,
29901,
13,
4706,
285,
5099,
330,
13,
1678,
4974,
851,
29898,
3127,
29889,
1767,
29897,
1275,
7191,
29889,
1853,
29918,
3127,
29898,
6678,
29892,
330,
29897,
13,
1678,
330,
353,
6680,
29889,
333,
29898,
29906,
29897,
13,
1678,
411,
1153,
4637,
29898,
29909,
5389,
290,
2392,
29897,
408,
4589,
29901,
13,
4706,
285,
5099,
330,
13,
1678,
4974,
851,
29898,
3127,
29889,
1767,
29897,
1275,
7191,
29889,
13221,
29918,
1333,
29918,
19438,
29898,
29888,
29892,
330,
29897,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
20158,
7295,
13,
1678,
4974,
6680,
29889,
333,
29898,
29941,
5033,
29896,
29892,
29871,
29906,
29892,
29871,
29941,
2144,
13,
4706,
1275,
6680,
29889,
333,
29898,
29900,
467,
20158,
10456,
29898,
29941,
334,
518,
6678,
29889,
333,
29898,
29896,
4638,
876,
29898,
29896,
29892,
29871,
29906,
29892,
29871,
29941,
29897,
13,
13,
13,
1753,
1243,
29918,
6678,
29918,
20158,
29918,
3127,
7295,
13,
1678,
285,
353,
6680,
29898,
29906,
29892,
29871,
29896,
29892,
14013,
921,
29892,
343,
29901,
921,
718,
343,
29897,
13,
1678,
330,
353,
313,
2892,
921,
29901,
921,
29892,
1723,
13,
1678,
411,
1153,
4637,
29898,
1542,
2392,
29897,
408,
4589,
29901,
13,
4706,
285,
732,
330,
13,
1678,
4974,
851,
29898,
3127,
29889,
1767,
29897,
1275,
7191,
29889,
1853,
29918,
3127,
29898,
6678,
29892,
330,
29897,
13,
2
] |
src/Testing/ZopeTestCase/__init__.py | tseaver/Zope-RFA | 2 | 13378 | <filename>src/Testing/ZopeTestCase/__init__.py
##############################################################################
#
# Copyright (c) 2005 Zope Foundation and Contributors.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Names exported by the ZopeTestCase package
"""
import ZopeLite as Zope2
import utils
import layer
from ZopeLite import hasProduct
from ZopeLite import installProduct
from ZopeLite import hasPackage
from ZopeLite import installPackage
from ZopeLite import _print
from ZopeTestCase import folder_name
from ZopeTestCase import user_name
from ZopeTestCase import user_password
from ZopeTestCase import user_role
from ZopeTestCase import standard_permissions
from ZopeTestCase import ZopeTestCase
from ZopeTestCase import FunctionalTestCase
from PortalTestCase import portal_name
from PortalTestCase import PortalTestCase
from sandbox import Sandboxed
from functional import Functional
from base import TestCase
from base import app
from base import close
from warnhook import WarningsHook
from unittest import main
from zopedoctest import ZopeDocTestSuite
from zopedoctest import ZopeDocFileSuite
from zopedoctest import FunctionalDocTestSuite
from zopedoctest import FunctionalDocFileSuite
import zopedoctest as doctest
import transaction
import placeless
Zope = Zope2
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custom_components/waste_collection_schedule/waste_collection_schedule/wizard/stadtreinigung_hamburg.py | UBS-P/hacs_waste_collection_schedule | 142 | 13112 | #!/usr/bin/env python3
from html.parser import HTMLParser
import inquirer
import requests
# Parser for HTML input
class InputParser(HTMLParser):
def __init__(self, input_name):
super().__init__()
self._input_name = input_name
self._value = None
@property
def value(self):
return self._value
def handle_starttag(self, tag, attrs):
if tag == "input":
for attr in attrs:
if attr[0] == "name" and attr[1] == self._input_name:
for attr2 in attrs:
if attr2[0] == "value":
self._value = attr2[1]
break
break
# Parser for HTML option list
class OptionParser(HTMLParser):
def __init__(self, select_name):
super().__init__()
self._select_name = select_name
self._within_select = False
self._within_option = False
self._option_name = ""
self._option_value = "-1"
self._choices = []
@property
def choices(self):
return self._choices
def handle_starttag(self, tag, attrs):
if tag == "select":
for attr in attrs:
if attr[0] == "name" and attr[1] == self._select_name:
self._within_select = True
break
elif tag == "option" and self._within_select:
self._within_option = True
for attr in attrs:
if attr[0] == "value":
self._option_value = attr[1]
def handle_endtag(self, tag):
if tag == "select":
self._within_select = False
elif tag == "option":
if (
self._within_select
and self._within_option
and len(self._option_name) > 0
and self._option_value != ""
):
self._choices.append((self._option_name, self._option_value))
self._within_option = False
self._option_name = ""
self._option_value = "-1"
def handle_data(self, data):
if self._within_option:
self._option_name += data
def main():
# search for street
questions = [
inquirer.Text("strasse", message="Enter search string for street"),
# inquirer.Text("hausnummer", message="Enter search string for house number"),
]
answers = inquirer.prompt(questions)
answers["hausnummer"] = ""
answers["bestaetigung"] = "true"
answers["mode"] = "search"
r = requests.post(
"https://www.stadtreinigung.hamburg/privatkunden/abfuhrkalender/index.html",
data=answers,
)
# search for street
input_parser = InputParser(input_name="asId")
input_parser.feed(r.text)
if input_parser.value is not None:
answers["asId"] = input_parser.value
else:
# query returned a list of streets
parser = OptionParser(select_name="asId")
parser.feed(r.text)
questions = [
inquirer.List("asId", choices=parser.choices, message="Select street")
]
answers.update(inquirer.prompt(questions))
# search for building number
r = requests.post(
"https://www.stadtreinigung.hamburg/privatkunden/abfuhrkalender/index.html",
data=answers,
)
# parser HTML option list
parser = OptionParser(select_name="hnId")
parser.feed(r.text)
if len(parser.choices) == 0:
answers["hnId"] = ""
else:
questions = [
inquirer.List("hnId", choices=parser.choices, message="Select house number")
]
answers.update(inquirer.prompt(questions))
print("Copy the following statements into your configuration.yaml:\n")
print("# waste_collection_schedule source configuration")
print("waste_collection_schedule:")
print(" sources:")
print(" - name: stadtreinigung_hamburg")
print(" args:")
print(f" asId: {answers['asId']}")
print(f" hnId: {answers['hnId']}")
if __name__ == "__main__":
main()
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lib/tagnews/crimetype/tag.py | jherzberg/article-tagging | 44 | 158495 | <filename>lib/tagnews/crimetype/tag.py<gh_stars>10-100
import os
import pickle
import glob
import time
import pandas as pd
# not used explicitly, but this needs to be imported like this
# for unpickling to work.
from ..utils.model_helpers import LemmaTokenizer # noqa
"""
Contains the CrimeTags class that allows tagging of articles.
"""
MODEL_LOCATION = os.path.join(os.path.split(__file__)[0],
'models',
'binary_stemmed_logistic')
TAGS = ['OEMC', 'CPD', 'SAO', 'CCCC', 'CCJ', 'CCSP',
'CPUB', 'IDOC', 'DOMV', 'SEXA', 'POLB', 'POLM',
'GUNV', 'GLBTQ', 'JUVE', 'REEN', 'VIOL', 'BEAT',
'PROB', 'PARL', 'CPLY', 'DRUG', 'CPS', 'GANG', 'ILSP',
'HOMI', 'IPRA', 'CPBD', 'IMMG', 'ENVI', 'UNSPC',
'ILSC', 'ARSN', 'BURG', 'DUI', 'FRUD', 'ROBB', 'TASR']
def load_model(location=MODEL_LOCATION):
"""
Load a model from the given folder `location`.
There should be at least one file named model-TIME.pkl and
a file named vectorizer-TIME.pkl inside the folder.
The files with the most recent timestamp are loaded.
"""
models = glob.glob(os.path.join(location, 'model*.pkl'))
if not models:
raise RuntimeError(('No models to load. Run'
' "python -m tagnews.crimetype.models.'
'binary_stemmed_logistic.save_model"'))
model = models.pop()
while models:
model_time = time.strptime(model[-19:-4], '%Y%m%d-%H%M%S')
new_model_time = time.strptime(models[0][-19:-4], '%Y%m%d-%H%M%S')
if model_time < new_model_time:
model = models[0]
models = models[1:]
with open(model, 'rb') as f:
clf = pickle.load(f)
with open(os.path.join(location, 'vectorizer-' + model[-19:-4] + '.pkl'),
'rb') as f:
vectorizer = pickle.load(f)
return clf, vectorizer
class CrimeTags():
"""
CrimeTags let you tag articles. Neat!
"""
def __init__(self,
model_directory=MODEL_LOCATION,
clf=None,
vectorizer=None):
"""
Load a model from the given `model_directory`.
See `load_model` for more information.
Alternatively, the classifier and vectorizer can be
provided. If one is provided, then both must be provided.
"""
if clf is None and vectorizer is None:
self.clf, self.vectorizer = load_model(model_directory)
elif clf is None or vectorizer is None:
raise ValueError(('clf and vectorizer must both be None,'
' or both be not None'))
else:
self.clf, self.vectorizer = clf, vectorizer
def tagtext_proba(self, text):
"""
Compute the probability each tag applies to the given text.
inputs:
text: A python string.
returns:
pred_proba: A pandas series indexed by the tag name.
"""
x = self.vectorizer.transform([text])
y_hat = self.clf.predict_proba(x)
preds = pd.DataFrame(y_hat)
preds.columns = TAGS
preds = preds.T.iloc[:, 0].sort_values(ascending=False)
return preds
def tagtext(self, text, prob_thresh=0.5):
"""
Tag a string with labels.
inputs:
text: A python string.
prob_thresh: The threshold on probability at which point
the tag will be applied.
returns:
preds: A list of tags that have > prob_thresh probability
according to the model.
"""
preds = self.tagtext_proba(text)
return preds[preds > prob_thresh].index.values.tolist()
def relevant_proba(self, text):
"""
Outputs the probability that the given text is relevant.
This probability is computed naively as the maximum of
the probabilities each tag applies to the text.
A more nuanced method would compute a joint probability.
inputs:
text: A python string.
returns:
relevant_proba: Probability the text is relevant.
"""
return max(self.tagtext_proba(text))
def relevant(self, text, prob_thresh=0.05):
"""
Determines whether given text is relevant or not. Relevance
is defined as whether any tag has more than prob_thresh
chance of applying to the text according to the model.
inputs:
text: A python string.
prob_thresh: The threshold on probability that
determines relevance. If no tags have >=
prob_thresh of applying to the text, then
the text is not relevant.
returns:
relevant: Boolean. Is the text "relevant"?
"""
return len(self.tagtext(text, prob_thresh)) > 0
def get_contributions(self, text):
"""
Rank the words in the text by their contribution to each
category. This function assumes that clf has an attribute
`coef_` and that vectorizer has an attribute
`inverse_transform`.
inputs:
text: A python string.
returns:
contributions: Pandas panel keyed off [category, word].
Example:
>>> s = 'This is an article about drugs and gangs.'
>>> s += ' Written by the amazing <NAME>.'
>>> p = tagger.get_contributions(s)
>>> p['DRUG'].sort_values('weight', ascending=False)
weight
drug 5.549870
copyright 0.366905
gang 0.194773
this 0.124590
an -0.004484
article -0.052026
is -0.085534
about -0.154800
kevin -0.219028
rose -0.238296
and -0.316201
. -0.853208
"""
p = {}
vec = self.vectorizer.transform([text])
vec_inv = self.vectorizer.inverse_transform(vec)
for i, tag in enumerate(TAGS):
p[tag] = pd.DataFrame(
index=vec_inv,
data={'weight': self.clf.coef_[i, vec.nonzero()[1]]}
)
return pd.Panel(p)
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] |
FeiZhai/feizhaipro/apps/news/migrations/0007_auto_20181031_1327.py | FSen0/FeiZhai | 0 | 192520 | # Generated by Django 2.0.6 on 2018-10-31 05:27
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('news', '0006_auto_20181030_0943'),
]
operations = [
migrations.RemoveField(
model_name='comment',
name='create_time',
),
migrations.RemoveField(
model_name='comment',
name='is_delete',
),
migrations.RemoveField(
model_name='comment',
name='update_time',
),
]
| [
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Differential Equation Parameter Estimate/testrun1/fitting_post_processing.py | etjoa003/incipy | 0 | 71620 | import param_estimate as pe
import matplotlib.pyplot as plt
import numpy as np
import time
import pickle
filename = 'fitting_data'
save_data_series = [1,2,3]
for j in save_data_series:
pkl_file = open(filename+'_'+str(j)+'.par', 'rb')
SAVE_DATA = None
SAVE_DATA = pickle.load(pkl_file)
pkl_file.close()
fig = plt.figure(j)
ax = None
ax2 = None
ax = fig.add_subplot(211)
# ------------------- EXPT DATA ---------------------------------=
Xr, Yr = pe.load_data(plot_data=0)
ax.scatter(Xr,Yr,3,marker="x",c="g")
# ------------------- DATA FROM OPTIMIZATION ---------------------
color_scheme = np.linspace(0,1,len(SAVE_DATA))
for i in range(len(SAVE_DATA)):
data_store = SAVE_DATA[i]
n_set = data_store["n_set"]
t_set = data_store["t_set"]
p = data_store["p"]
mse_list = data_store["mse_list"]
print(" i = ", i)
print(" p = ", p )
if i==0:
this_label = "init curve"
else:
this_label = str(i)
ax.plot(t_set,n_set, color=(1-color_scheme[i],0,color_scheme[i]), label=this_label)
if i ==0:
this_title = "initial G,k1,k2,k3 = " + str(p)
plt.title(this_title)
ax2 = fig.add_subplot(212)
ax2.plot(range(len(mse_list)),mse_list)
ax.legend()
ax.set_xlabel("t")
ax.set_ylabel("n")
ax2.set_xlabel("i")
ax2.set_ylabel("MSE")
plt.show()
print("\nClosing Post-Processing Program...") | [
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12,
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4918,
29899,
7032,
292,
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2
] |
SmallTool/Web/UDPClient.py | ASC8384/- | 8 | 141354 | from socket import *
import time
serverName = '127.0.0.1'
serverPort = 12345
clientSocket = socket(AF_INET, SOCK_DGRAM)
now = 0
while(1):
message = input('Input lowercase sentence>\n')
message = message.encode('utf-8')
clientSocket.sendto(message, (serverName, serverPort))
modifiedMessage, serverAddress = clientSocket.recvfrom(2048)
sec = time.time_ns()
now += 1
print('Time(ns): {}'.format(sec))
print('No.{} is {}'.format(now, modifiedMessage.decode()), end = "\n\n")
| [
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config.py | weingxing/Netease-View | 2 | 1604426 | headers = {
'host': 'music.163.com',
'Referer': 'http://music.163.com/search/',
'User-Agent':
('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36')
}
# 浏览器抓包
cookies = {
'iuqxldmzr': '32',
'_ntes_nnid': '47a31c044187ff48f995fc10e593b78b,1615971340579',
'_ntes_nuid': '47a31c044187ff48f995fc10e593b78b',
'NMTID': '00OYwPk3p1J102uyEFbguri5i7SpswAAAF4P2Xuaw',
'WM_TID': 'LKnniVnKnBNAAFERBVJ7Kf9ziZMa+E+n',
'JSESSIONID-WYYY': 'Um92Sp7WbAwPE\K8ffeNQA/SFd9Yqnbsv0GaCNPUTJh2D+ZH8VTbDZa6GD+UBW/jmVDRUj5Wexduo8kWx5KWj7AT5qjdFdXvVvz+\+6QexXUZWMMCYltXG2EQF+7zgEoDMEF9jqinoQpU3fv2Qxh46\3v774Toshdvqn6MJA/cRTV9SW:1617175277426; WNMCID=luvhfq.1617173477749.01.0',
'WEVNSM': '1.0.0',
'WM_NI': 'nKY3Kf28ifAQL425HXspkWwFGYc+t7gHoz1rLtJqw+3v2+F1dTdjVFbBMTAchIM4uY8V/9SHbnMVO+SVOYiF5JmKlP3+ToCLWHIvf4MeR1t25XGbho7FL0ehVvFQgZaYMjI=',
'WM_NIKE': '<KEY>',
'MUSIC_U': '<KEY>',
'__csrf': '805b6f08367a5e62dc131d88f653b51e',
'ntes_kaola_ad': '1'
}
mongodb_url = 'mongodb://localhost:27017/'
# db_name = 'netease'
db_name = 'netease2'
col = 'ballad'
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29915,
13,
1054,
353,
525,
2135,
328,
29915,
13,
2
] |
tests/pyexp_tests.py | nguyentu1602/pyexp | 0 | 162242 | from nose.tools import *
import pyexp
def setup():
print("Setup.")
def teardown():
print("Tear down.")
def test_basic():
print("Ran.")
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tests/benchmarks/benchmark_indiv.py | qanastek/EasyMCDM | 4 | 162262 | <gh_stars>1-10
import time
import matplotlib.pyplot as plt
from EasyMCDM.models.Electre import Electre
from EasyMCDM.models.Promethee import Promethee
from EasyMCDM.models.Pareto import Pareto
from EasyMCDM.models.WeightedSum import WeightedSum
from EasyMCDM.models.Irmo import Irmo
data = {}
indexes = [ 0, 1, 2, 3, 4, 5, 6 ]
weights = [0.14,0.14,0.14,0.14,0.14,0.14,0.14]
prefs = ["min","max","min","min","min","max","min"]
vetoes = [45, 29, 550, 6, 4.5, 4.5, 4.5]
indifference_threshold = 0.6
preference_thresholds = [20, 10, 200, 4, 2, 2, 7]
paddings = []
promethee_values = []
electre_values = []
weighted_values = []
pareto_values = []
nbindiv=2500
for i in range(10,nbindiv,100):
print(i)
paddings.append(i)
for j in range(i,i+100):
data["item_" + str(j)] = [4.0, 5.0, 18.0, 39.6, 6.0, 378.0, 31.2]
w = WeightedSum(data=data, verbose=False)
pa = Pareto(data=data, verbose=False)
pr = Promethee(data=data, verbose=False)
e = Electre(data=data, verbose=False)
start = time.time()
re = e.solve(weights, prefs, vetoes, indifference_threshold, preference_thresholds)
electre_values.append(time.time() - start)
start = time.time()
rpr = pr.solve(weights=weights, prefs=prefs)
promethee_values.append(time.time() - start)
start = time.time()
rpa = pa.solve(indexes=indexes, prefs=prefs)
pareto_values.append(time.time() - start)
start = time.time()
rw = w.solve(pref_indexes=indexes, prefs=prefs, weights=weights, target='min')
weighted_values.append(time.time() - start)
plt.plot(paddings, promethee_values, label="promethee")
plt.plot(paddings, electre_values, label="electre")
plt.plot(paddings, weighted_values, label="weighted")
plt.plot(paddings, pareto_values, label="pareto")
plt.legend()
plt.savefig("benchmark_nb_indiv.png")
plt.close()
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yezdi/parser/parser.py | ragsagar/yezdi | 1 | 3887 | from yezdi.lexer.token import TokenType
from yezdi.parser.ast import Program, Statement, Participant, Title, LineStatement
class Parser:
def __init__(self, lexer):
self.lexer = lexer
self.current_token = None
self.peek_token = None
self.next_token()
self.next_token()
self.participants = {}
def next_token(self):
self.current_token, self.peek_token = self.peek_token, self.lexer.next_token()
def parse_program(self):
program = Program()
while self.current_token.type != TokenType.EOF:
statement = self.parse_statement()
if statement:
program.statements.append(statement)
self.next_token()
return program
def parse_statement(self):
if self.current_token.type == TokenType.IDENTIFIER:
return self.parse_line_statement()
elif self.current_token.type == TokenType.TITLE:
return self.parse_title()
return None
def parse_line_statement(self):
participant_literal = self.current_token.literal
if not self.peek_token.type in [TokenType.SOLID_LINE, TokenType.DASHED_LINE]:
return None
self.next_token()
participant = Participant(participant_literal)
line = LineStatement(self.current_token.type)
line.set_source(participant)
if not self.expect_peek(TokenType.IDENTIFIER):
return None
target = Participant(self.current_token.literal)
line.set_target(target)
if not self.expect_peek(TokenType.COLON):
return None
if self.expect_peek(TokenType.IDENTIFIER):
line.set_info(self.current_token.literal)
if self.peek_token.type not in [TokenType.NEWLINE, TokenType.EOF]:
return None
statement = Statement(line)
return statement
def get_participant(self, value):
if value in self.participants:
return self.participants[value]
else:
participant = Participant(value)
self.participants[value] = participant
return participant
def expect_peek(self, token_type):
if self.peek_token.type == token_type:
self.next_token()
return True
else:
return False
def parse_title(self):
if not self.expect_peek(TokenType.IDENTIFIER):
return None
title = Title(self.current_token.literal)
return Statement(title)
class ParserError(Exception):
pass
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] |
fetch_data.py | Kartones/pcgw-game-engines | 0 | 111471 | import csv
from datetime import datetime
import json
from operator import itemgetter
from typing import cast, Dict, List, Tuple
from urllib.parse import quote_plus
import requests
class FetchPCGWData:
USER_AGENT = "PCGW-Game-Engines/0.2 (https://github.com/kartones/pcgw-game-engines)"
CSV_SEPARATOR = ","
CSV_QUOTE_CHAR = "\""
PAGE_SIZE = 500
ENGINE_PREFIX = "Engine:"
GAMES_CSV_HEADER_ROW = ["title", "engine", "release_date"]
# bad data
ENGINE_OUTLIERS = ["129401"]
@classmethod
def fetch_game_engines_list_to_csv(cls, list_output_filename: str) -> None:
count = 0
# must be json
engines_list_url = "https://www.pcgamingwiki.com/w/api.php?action=query&generator=categorymembers&gcmlimit={limit}&format=json&gcmtitle=Category:Engines".format( # NOQA: E501
limit=cls.PAGE_SIZE
)
headers = {
"User-Agent": cls.USER_AGENT
}
response = requests.get(engines_list_url, headers=headers)
engines_json = json.loads(response.text)["query"]["pages"]
with open(list_output_filename, "w", newline="") as csv_file_handle:
csv_writer = csv.writer(csv_file_handle, delimiter=cls.CSV_SEPARATOR, quotechar=cls.CSV_QUOTE_CHAR,
quoting=csv.QUOTE_ALL)
csv_writer.writerow(["id", "title"])
for key in (engines_json.keys() - cls.ENGINE_OUTLIERS):
csv_writer.writerow([engines_json[key]["pageid"], engines_json[key]["title"].lstrip(cls.ENGINE_PREFIX)])
count += 1
print("> Read {} engines ".format(count))
print("> Written data to '{}'".format(list_output_filename))
@classmethod
def fetch_games(cls, engines_list_filename: str, games_output_filename: str) -> None:
count = 0
games = [] # type: List[Tuple[str, str, str]]
engines = cls._load_engines_from_csv(engines_list_filename)
# Note: as saves each game of a certain engine, engines without games automatically filtered out
for engine in engines:
engine_games = data._fetch_games_per_engine(engine)
games += engine_games
print("> Read {count} games with engine '{engine}'".format(count=len(engine_games), engine=engine))
with open(games_output_filename, "w", newline="") as csv_file_handle:
csv_writer = csv.writer(csv_file_handle, delimiter=cls.CSV_SEPARATOR, quotechar=cls.CSV_QUOTE_CHAR,
quoting=csv.QUOTE_ALL)
csv_writer.writerow(cls.GAMES_CSV_HEADER_ROW)
for game in games:
csv_writer.writerow(game)
count += 1
print("> Read {} games".format(count))
print("> Written data to '{}'".format(games_output_filename))
@classmethod
def generalize_game_engines(cls, games_list_filename: str, generalized_games_output_filename: str) -> None:
count = 0
engine_mapping = cls._combined_engine_versions_map()
games = cls._load_games_from_csv(games_list_filename)
generalized_games = [(
game[0],
engine_mapping[game[1]] if game[1] in engine_mapping else game[1],
game[2],
) for game in games]
# to ease human reading
generalized_games = sorted(generalized_games, key=itemgetter(1, 0))
with open(generalized_games_output_filename, "w", newline="") as csv_file_handle:
csv_writer = csv.writer(csv_file_handle, delimiter=cls.CSV_SEPARATOR, quotechar=cls.CSV_QUOTE_CHAR,
quoting=csv.QUOTE_ALL)
csv_writer.writerow(cls.GAMES_CSV_HEADER_ROW)
for game in generalized_games:
csv_writer.writerow(game)
count += 1
print("> Checked {} games".format(count))
print("> Written data to '{}'".format(generalized_games_output_filename))
@staticmethod
def _combined_engine_versions_map() -> Dict[str, str]:
return {
"CryEngine V": "CryEngine",
"CryEngine (4th generation)": "CryEngine",
"CryEngine 2": "CryEngine",
"CryEngine 3": "CryEngine",
"Id Tech 1": "Id Tech",
"Id Tech 2": "Id Tech",
"Id Tech 3": "Id Tech",
"Id Tech 4": "Id Tech",
"Id Tech 5": "Id Tech",
"Id Tech 6": "Id Tech",
"Id Tech 7": "Id Tech",
"Quake engine": "Id Tech",
"QuakeWorld": "Id Tech",
"Frostbite 1.5": "Frostbite",
"Frostbite 2": "Frostbite",
"Frostbite 3": "Frostbite",
"SAGE 2.0": "SAGE",
"Unreal Engine 1": "Unreal Engine",
"Unreal Engine 2": "Unreal Engine",
"Unreal Engine 2.5": "Unreal Engine",
"Unreal Engine 3": "Unreal Engine",
"Unreal Engine 4": "Unreal Engine",
"Unreal Engine 5": "Unreal Engine",
"Gamebryo (TES Engine)": "Gamebryo",
"GEM 2": "GEM",
"GEM 3": "GEM",
"AGL 2": "AGL",
"AGL 3": "AGL",
"AGL 4": "AGL",
"The Sims 2 Engine": "The Sims Engine",
"The Sims 3 Engine": "The Sims Engine",
"Glacier 2": "Glacier",
"Avalanche Engine 3.0": "Avalanche Engine",
"Avalanche Engine 2.0": "Avalanche Engine",
"Ptero Engine II": "Ptero Engine",
"Ptero Engine III": "Ptero Engine",
"Refractor 2": "Refractor",
"Construct Classic": "Construct",
"Construct 2": "Construct",
"Construct 3": "Construct",
"Photex2": "Photex",
"Dunia 2": "Dunia",
"Source 2": "Source",
"Vicious Engine 2": "Vicious Engine",
"KiriKiri Z": "KiriKiri",
}
@classmethod
def _load_games_from_csv(cls, games_list_filename: str) -> List[Tuple[str, str, str]]:
games = [] # type: List[Tuple[str, str, str]]
with open(games_list_filename, newline="") as csv_file_handle:
csv_reader = csv.reader(csv_file_handle, delimiter=cls.CSV_SEPARATOR, quotechar=cls.CSV_QUOTE_CHAR)
for index, row in enumerate(csv_reader):
if (index == 0):
continue
games.append(cast(Tuple[str, str, str], tuple(row)))
return games
@classmethod
def _load_engines_from_csv(cls, engines_list_filename: str) -> List[str]:
engines = [] # type: List[str]
with open(engines_list_filename, newline="") as csv_file_handle:
csv_reader = csv.reader(csv_file_handle, delimiter=cls.CSV_SEPARATOR, quotechar=cls.CSV_QUOTE_CHAR)
for index, row in enumerate(csv_reader):
if (index == 0):
continue
engines.append(row[1])
return engines
@classmethod
def _fetch_games_per_engine_page(cls, engine_title: str, offset: int) -> List[Tuple[str, str, str]]:
# could be csv also, but we want to filter data
export_format = "json"
# [[Category:Games]] [[Uses engine::Engine:id Tech 2]]
games_per_engine_url = "https://www.pcgamingwiki.com/w/index.php?title=Special:Ask&q=[[Category%3AGames]]+[[Uses+engine%3A%3AEngine%3A{engine}]]&po=%3FRelease+date%23ISO%7C%2Border%3Dasc%7C%2Blimit%3D1%0D%0A&p=format%3D{format}&limit={limit}&offset={offset}".format( # NOQA: E501
engine=quote_plus(engine_title), format=export_format, limit=cls.PAGE_SIZE, offset=max(offset, 0)
)
headers = {
"User-Agent": cls.USER_AGENT
}
response = requests.get(games_per_engine_url, headers=headers)
if response.text:
games_json = json.loads(response.text)["results"]
else:
games_json = {}
games = [] # type: List[Tuple[str, str, str]]
for key in games_json.keys():
release_date = games_json[key]["printouts"]["Release date"]
games.append(
(games_json[key]["fulltext"],
engine_title,
datetime.fromtimestamp(int(release_date[0]["timestamp"])).strftime("%Y") if release_date else "")
)
return games
@classmethod
def _fetch_games_per_engine(cls, title: str) -> List[Tuple[str, str, str]]:
games = [] # type: List[Tuple[str, str, str]]
offset = 0
keep_reading = True
while keep_reading:
games_page = cls._fetch_games_per_engine_page(title, offset)
games += games_page
offset += cls.PAGE_SIZE
keep_reading = len(games_page) == cls.PAGE_SIZE
return games
if __name__ == "__main__":
engines_csv = "engines_list.csv"
games_csv = "games.csv"
generalized_games_csv = "games_generalized.csv"
data = FetchPCGWData()
data.fetch_game_engines_list_to_csv(engines_csv)
data.fetch_games(engines_csv, games_csv)
data.generalize_game_engines(games_csv, generalized_games_csv)
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] |
core/migrations/0008_auto_20200212_1114.py | StateArchivesOfNorthCarolina/ratom_server | 1 | 74311 | <gh_stars>1-10
# Generated by Django 2.2.10 on 2020-02-12 16:14
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('core', '0007_auto_20200206_0844'),
]
operations = [
migrations.RenameField(
model_name='historicalmessageaudit',
old_name='restrictions',
new_name='restriction',
),
migrations.RenameField(
model_name='messageaudit',
old_name='restrictions',
new_name='restriction',
),
]
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notebooks/tests/test_requirements.py | jagin/opencv-notebooks | 6 | 98659 | import cv2
import matplotlib
import jupyter
def test_opencv_version():
assert cv2.__version__ == '3.4.5'
def test_matplotlib_version():
assert matplotlib.__version__ == '3.0.2'
def test_jupyter_version():
assert jupyter.__version__ == '1.0.0' | [
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tests/classes/simple_wordbook.py | Jesse-Yung/jsonclasses | 50 | 79153 | <filename>tests/classes/simple_wordbook.py
from __future__ import annotations
from typing import Optional
from jsonclasses import jsonclass, types
@jsonclass
class SimpleWordbook:
name: Optional[str]
words: list[str] = types.nonnull.listof(str)
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] |
thinc/shims/__init__.py | justindujardin/thinc | 0 | 192770 | <filename>thinc/shims/__init__.py<gh_stars>0
from .shim import Shim
from .pytorch import PyTorchShim
from .tensorflow import keras_model_fns, TensorFlowShim, maybe_handshake_model
from .mxnet import MXNetShim
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] |
opendatatools/hedgefund/simu_agent.py | jjcc/OpenData | 1,179 | 44821 | from opendatatools.common import RestAgent, md5
from progressbar import ProgressBar
import json
import pandas as pd
import io
import hashlib
import time
index_map = {
'Barclay_Hedge_Fund_Index' : 'ghsndx',
'Convertible_Arbitrage_Index' : 'ghsca',
'Distressed_Securities_Index' : 'ghsds',
'Emerging_Markets_Index' : 'ghsem',
'Equity_Long_Bias_Index' : 'ghselb',
'Equity_Long_Short_Index' : 'ghsels',
'Equity_Market_Neutral_Index' : 'ghsemn',
'European_Equities_Index' : 'ghsee',
'Event_Driven_Index' : 'ghsed',
'Fixed_Income_Arbitrage_Index' : 'ghsfia',
'Fund_of_Funds_Index' : 'ghsfof',
'Global_Macro_Index' : 'ghsmc',
'Healthcare_&_Biotechnology_Index': 'ghsbio',
'Merger_Arbitrage_Index' : 'ghsma',
'Multi_Strategy_Index' : 'ghsms',
'Pacific_Rim_Equities_Index' : 'ghspre',
'Technology_Index' : 'ghstec',
}
class SimuAgent(RestAgent):
def __init__(self):
RestAgent.__init__(self)
self.user_info = None
self.df_fundlist = None
self.cookies = None
def login(self, username, password):
url = 'https://passport.simuwang.com/index.php?m=Passport&c=auth&a=login&type=login&name=%s&pass=%s&reme=1&rn=1' % (username, password)
self.add_headers({'Referer': 'https://dc.simuwang.com/'})
response = self.do_request(url)
if response is None:
return None, '登录失败'
jsonobj = json.loads(response)
suc = jsonobj['suc']
msg = jsonobj['msg']
if suc != 1:
return None, msg
self.cookies = self.get_cookies()
self.user_info = jsonobj['data']
return self.user_info, msg
def prepare_cookies(self, url):
response = self.do_request(url, None)
if response is not None:
cookies = self.get_cookies()
return cookies
else:
return None
def _get_rz_token(self, time):
mk = time * 158995555893
mtoken = md5(md5(str(mk))) + '.' + str(time)
return mtoken
def _get_fund_list_page(self, page_no):
url = 'https://dc.simuwang.com/ranking/get?page=%s&condition=fund_type:1,6,4,3,8,2;ret:9;rating_year:1;istiered:0;company_type:1;sort_name:profit_col2;sort_asc:desc;keyword:' % page_no
response = self.do_request(url)
if response is None:
return None, '获取数据失败', None
jsonobj = json.loads(response)
code = jsonobj['code']
msg = jsonobj['msg']
if code != 1000:
return None, msg, None
df = pd.DataFrame(jsonobj['data'])
pageinfo = jsonobj['pager']
return df, '', pageinfo
def load_data(self):
page_no = 1
df_list = []
df, msg, pageinfo = self._get_fund_list_page(page_no)
if df is None:
return None, msg
df_list.append(df)
page_count = pageinfo['pagecount']
process_bar = ProgressBar().start(max_value=page_count)
page_no = page_no + 1
while page_no <= page_count:
df, msg, pageinfo = self._get_fund_list_page(page_no)
if df is None:
return None, msg
df_list.append(df)
process_bar.update(page_no)
page_no = page_no + 1
self.df_fundlist = pd.concat(df_list)
return self.df_fundlist, ''
def get_fund_list(self):
if self.df_fundlist is None:
return None, '请先加载数据 load_data'
return self.df_fundlist, ''
def _get_sign(self, url, params):
str = url
for k,v in params.items():
str = str + k + params[k]
sha1 = hashlib.sha1()
sha1.update(str.encode('utf8'))
sign = sha1.hexdigest()
return sign
def _get_token(self, fund_id):
sign = self._get_sign('https://dc.simuwang.com/Api/getToken', {'id' : fund_id})
url = 'https://dc.simuwang.com/Api/getToken?id=%s&sign=%s' % (fund_id, sign)
self.add_headers({'Referer': 'https://dc.simuwang.com/'})
response = self.do_request(url)
if response is None:
return None, '获取数据失败'
jsonobj = json.loads(response)
code = jsonobj['code']
msg = jsonobj['message']
if code != 1000 :
return code, msg
self.cookies.update(self.get_cookies())
salt = jsonobj['data']
muid = self.user_info['userid']
#str = 'id%smuid%spage%s%s' % (fund_id, muid, page_no, salt)
str = '%s%s' % (fund_id, salt)
sha1 = hashlib.sha1()
sha1.update(str.encode('utf8'))
token = sha1.hexdigest()
return token, ''
def _get_fund_nav_page(self, fund_id, page_no):
muid = self.user_info['userid']
token, msg = self._get_token(fund_id)
if token is None:
return None, '获取token失败: ' + msg, ''
url = 'https://dc.simuwang.com/fund/getNavList.html'
self.add_headers({'Referer': 'https://dc.simuwang.com/product/%s.html' % fund_id})
data = {
'id' : fund_id,
'muid' : muid,
'page' : str(page_no),
'token': token,
}
response = self.do_request(url, param=data, cookies=self.cookies, encoding="utf8")
if response is None:
return None, '获取数据失败', ''
jsonobj = json.loads(response)
code = jsonobj['code']
msg = jsonobj['msg']
if code != 1000 :
return code, msg, ''
df = pd.DataFrame(jsonobj['data'])
pageinfo = jsonobj['pager']
return df, '', pageinfo
def _bit_encrypt(self, str, key):
cryText = ''
keyLen = len(key)
strLen = len(str)
for i in range(strLen):
k = i % keyLen
cryText = cryText + chr(ord(str[i]) - k)
return cryText
def _bit_encrypt2(self, str, key):
cryText = ''
keyLen = len(key)
strLen = len(str)
for i in range(strLen):
k = i % keyLen
cryText = cryText + chr(ord(str[i]) ^ ord(key[k]))
return cryText
def _decrypt_data(self, str, func, key):
# return self._bit_encrypt(str, 'cd0a8bee4c6b2f8a91ad5538dde2eb34')
# return self._bit_encrypt(str, '937ab03370497f2b4e8d0599ad25c44c')
# return self._bit_encrypt(str, '083975ce19392492bbccff21a52f1ace')
return func(str, key)
def _get_decrypt_info(self, fund_id):
url = 'https://dc.simuwang.com/product/%s.html' % fund_id
response = self.do_request(url, param=None, cookies=self.cookies, encoding="utf8")
if response is None:
return None, '获取数据失败', ''
if "String.fromCharCode(str.charCodeAt(i) - k)" in response:
decrypt_func = self._bit_encrypt
else:
decrypt_func = self._bit_encrypt2
if response.find("return xOrEncrypt(str, ")> 0:
tag = "return xOrEncrypt(str, "
else:
tag = "return bitEncrypt(str, "
pos = response.index(tag) + len(tag) + 1
key = response[pos:pos+32]
return decrypt_func, key
def get_fund_nav(self, fund_id, time_elapse = 0):
if self.user_info is None:
return None, '请先登录'
page_no = 1
df_list = []
df, msg, pageinfo = self._get_fund_nav_page(fund_id, page_no)
if df is None:
return None, msg
df_list.append(df)
page_count = pageinfo['pagecount']
page_no = page_no + 1
while page_no <= page_count:
try_times = 1
while try_times <= 3:
df, msg, pageinfo = self._get_fund_nav_page(fund_id, page_no)
if df is None:
if try_times > 3:
return None, msg
else:
try_times = try_times + 1
continue
else:
df_list.append(df)
break
page_no = page_no + 1
if time_elapse > 0:
time.sleep(time_elapse)
df_nav = pd.concat(df_list)
df_nav.drop('c', axis=1, inplace=True)
df_nav.rename(columns={'d': 'date', 'n': 'nav', 'cn' : 'accu_nav', 'cnw' : 'accu_nav_w'}, inplace=True)
# 这个网站搞了太多的小坑
func, key = self._get_decrypt_info(fund_id)
df_nav['nav'] = df_nav['nav'].apply(lambda x : self._decrypt_data(x, func, key))
df_nav['accu_nav'] = df_nav['accu_nav'].apply(lambda x : self._decrypt_data(x, func, key))
df_nav['accu_nav_w'] = df_nav['accu_nav_w'].apply(lambda x : self._decrypt_data(x, func, key))
#df_nav['nav'] = df_nav['nav'] - df_nav.index * 0.01 - 0.01
#df_nav['accu_nav'] = df_nav['accu_nav'].apply(lambda x: float(x) - 0.01)
#df_nav['accu_nav_w'] = df_nav['accu_nav_w'].apply(lambda x: float(x) - 0.02)
return df_nav, ''
class BarclayAgent(RestAgent):
def __init__(self):
RestAgent.__init__(self)
self.add_headers({'Referer': 'https://www.barclayhedge.com/research/indices/ghs/Equity_Long_Short_Index.html'})
self.add_headers({'Content - Type': 'application / x - www - form - urlencoded'})
def get_data(self, index):
prog_cod = index_map[index]
url = "https://www.barclayhedge.com/cgi-bin/barclay_stats/ghsndx.cgi"
param = {
'dump': 'excel',
'prog_cod': prog_cod,
}
response = self.do_request(url, param=param, method='POST', type='binary')
if response is not None:
excel = pd.ExcelFile(io.BytesIO(response))
df = excel.parse('Sheet1').dropna(how='all').copy().reset_index().drop(0)
df.columns = ['year', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'YTD']
df = df.set_index('year')
return df, ''
return None, "获取数据失败" | [
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] |
glue-codes/kitefast/preprocessor/src/main_set.py | OpenFAST/KiteFAST | 6 | 184957 | <reponame>OpenFAST/KiteFAST
#
# Copyright 2018 National Renewable Energy Laboratory
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from .iohandler import Output
class MainSet():
def __init__(self,
simulation_type,
mip_reference_frame,
components):
self.simulation_type = simulation_type
self.mip_reference_frame = mip_reference_frame
if self.simulation_type < 3:
self.fuselage = components[0]
self.starboard_wing = components[1]
self.port_wing = components[2]
self.vstab = components[3]
self.starboard_hstab = components[4]
self.port_hstab = components[5]
self.starboard_pylons = components[6] # this is [Pylon]
self.port_pylons = components[7] # this is [Pylon]
self.starboard_rotors = components[8] # this is [Rotor]
self.port_rotors = components[9] # this is [Rotor]
if self.simulation_type > 1:
self.platform = components[-1]
def export_set_file(self, output_directory):
output = Output("{}/KiteMain.set".format(output_directory))
output.write_line("# This file sets the reference id's and counters")
output.write_empty_line()
output.write_line("# *** Reference ***")
output.write_empty_line()
output.write_line("set: integer mip_rf = 10;")
output.write_line("{}".format(self.mip_reference_frame))
output.write_empty_line()
if self.simulation_type < 3:
output.write_line("# *** Fuselage ***")
output.write_line("set: integer {}_root_node = {};".format(self.fuselage.component_name, self.fuselage.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.fuselage.component_name, self.fuselage.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.fuselage.component_name, self.fuselage.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.fuselage.component_name, self.fuselage.beam_count))
output.write_empty_line()
output.write_line("# *** Wing ***")
output.write_line("set: integer {}_root_node = {};".format(self.starboard_wing.component_name, self.starboard_wing.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.starboard_wing.component_name, self.starboard_wing.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.starboard_wing.component_name, self.starboard_wing.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.starboard_wing.component_name, self.starboard_wing.beam_count))
output.write_empty_line()
output.write_line("set: integer {}_root_node = {};".format(self.port_wing.component_name, self.port_wing.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.port_wing.component_name, self.port_wing.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.port_wing.component_name, self.port_wing.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.port_wing.component_name, self.port_wing.beam_count))
output.write_empty_line()
output.write_line("# *** Horizontal Stabilizer ***")
output.write_line("set: integer {}_root_node = {};".format(self.starboard_hstab.component_name, self.starboard_hstab.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.starboard_hstab.component_name, self.starboard_hstab.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.starboard_hstab.component_name, self.starboard_hstab.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.starboard_hstab.component_name, self.starboard_hstab.beam_count))
output.write_empty_line()
output.write_line("set: integer {}_root_node = {};".format(self.port_hstab.component_name, self.port_hstab.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.port_hstab.component_name, self.port_hstab.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.port_hstab.component_name, self.port_hstab.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.port_hstab.component_name, self.port_hstab.beam_count))
output.write_empty_line()
output.write_line("# *** Vertical Stabilizer ***")
output.write_line("set: integer {}_root_node = {};".format(self.vstab.component_name, self.vstab.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(self.vstab.component_name, self.vstab.node_count))
output.write_line("set: integer {}_body_count = {};".format(self.vstab.component_name, self.vstab.body_count))
output.write_line("set: integer {}_beam_count = {};".format(self.vstab.component_name, self.vstab.beam_count))
output.write_empty_line()
output.write_line("# *** Pylons ***")
for pylon in self.starboard_pylons:
output.write_line("set: integer {}_root_node = {};".format(pylon.component_name, pylon.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(pylon.component_name, pylon.node_count))
output.write_line("set: integer {}_body_count = {};".format(pylon.component_name, pylon.body_count))
output.write_line("set: integer {}_beam_count = {};".format(pylon.component_name, pylon.beam_count))
for pylon in self.port_pylons:
output.write_line("set: integer {}_root_node = {};".format(pylon.component_name, pylon.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(pylon.component_name, pylon.node_count))
output.write_line("set: integer {}_body_count = {};".format(pylon.component_name, pylon.body_count))
output.write_line("set: integer {}_beam_count = {};".format(pylon.component_name, pylon.beam_count))
output.write_empty_line()
output.write_line("# *** Rotors ***")
for component in self.starboard_rotors + self.port_rotors:
output.write_line("set: integer {}_reference_frame = {};".format(component.component_name, component.mbdyn_ref_index))
output.write_line("set: integer {}_root_node = {};".format(component.component_name, component.mbdyn_ref_index))
output.write_line("set: integer {}_node_count = {};".format(component.component_name, component.node_count))
if self.simulation_type > 1:
output.write_line("# *** Platform ***")
output.write_line("set: integer {}_root_node = {};".format(self.platform.component_name, self.platform.mbdyn_ref_index))
output.write_empty_line()
output.write_line("# Initialize variables for reuse later")
output.write_line("set: integer current_beam = 0;")
output.write_line("set: real twist1 = 0.0;")
output.write_line("set: real twist2 = 0.0;")
output.write_line("set: integer beam_node1 = 0;")
output.write_line("set: integer beam_node2 = 0;")
output.write_line("set: integer beam_node3 = 0;")
output.write_line("set: real k1_11 = 0.0;")
output.write_line("set: real k1_12 = 0.0;")
output.write_line("set: real k1_13 = 0.0;")
output.write_line("set: real k1_14 = 0.0;")
output.write_line("set: real k1_15 = 0.0;")
output.write_line("set: real k1_16 = 0.0;")
output.write_line("set: real k1_22 = 0.0;")
output.write_line("set: real k1_23 = 0.0;")
output.write_line("set: real k1_24 = 0.0;")
output.write_line("set: real k1_25 = 0.0;")
output.write_line("set: real k1_26 = 0.0;")
output.write_line("set: real k1_33 = 0.0;")
output.write_line("set: real k1_34 = 0.0;")
output.write_line("set: real k1_35 = 0.0;")
output.write_line("set: real k1_36 = 0.0;")
output.write_line("set: real k1_44 = 0.0;")
output.write_line("set: real k1_45 = 0.0;")
output.write_line("set: real k1_46 = 0.0;")
output.write_line("set: real k1_55 = 0.0;")
output.write_line("set: real k1_56 = 0.0;")
output.write_line("set: real k1_66 = 0.0;")
output.write_line("set: real k2_11 = 0.0;")
output.write_line("set: real k2_12 = 0.0;")
output.write_line("set: real k2_13 = 0.0;")
output.write_line("set: real k2_14 = 0.0;")
output.write_line("set: real k2_15 = 0.0;")
output.write_line("set: real k2_16 = 0.0;")
output.write_line("set: real k2_22 = 0.0;")
output.write_line("set: real k2_23 = 0.0;")
output.write_line("set: real k2_24 = 0.0;")
output.write_line("set: real k2_25 = 0.0;")
output.write_line("set: real k2_26 = 0.0;")
output.write_line("set: real k2_33 = 0.0;")
output.write_line("set: real k2_34 = 0.0;")
output.write_line("set: real k2_35 = 0.0;")
output.write_line("set: real k2_36 = 0.0;")
output.write_line("set: real k2_44 = 0.0;")
output.write_line("set: real k2_45 = 0.0;")
output.write_line("set: real k2_46 = 0.0;")
output.write_line("set: real k2_55 = 0.0;")
output.write_line("set: real k2_56 = 0.0;")
output.write_line("set: real k2_66 = 0.0;")
output.end()
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code/reset.py | e4rthdog/raspberrypi-msfs-panel | 0 | 92087 | from pyky040 import pyky040
from gpiozero import *
from time import sleep
from RPLCD.i2c import CharLCD
lcd=CharLCD(i2c_expander='PCF8574', address=0x27, port=1,
cols=16, rows=2, dotsize=8,
charmap='A02',
auto_linebreaks=True,
backlight_enabled=True)
gled1=LED(21)
gled2=LED(16)
gled3=LED(25)
rled1=LED(20)
rled2=LED(12)
rled3=LED(24)
gled1.off()
gled2.off()
gled3.off()
rled1.off()
rled2.off()
rled3.off()
lcd.backlight_enabled=False
lcd.clear() | [
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SRTTimeShifter.py | Colin-Fredericks/hx-xml | 1 | 98615 | import sys
import os
import argparse
import re
from glob import glob
instructions = """
To use:
python3 SRTTimeShifter.py filename seconds (options)
Takes the given subtitle file or files, in SRT format,
and shifts the times in each by the given number of seconds.
For time, use decimals, not frames (e.g. 2.5 seconds).
You can use negative times to shift backwards, if there's enough
padding at the start of the file.
Valid options:
-o Overwrite. Overwrites the old file rather than making a new one.
-h Help. Print this message.
Last update: March 15th 2018
"""
# Converts from miliseconds to hh:mm:ss,msec format
def msecToHMS(time):
# Make sure it's an integer.
time = int(float(time))
# Downconvert through hours. SRTs don't handle days.
msec = time % 1000
time -= msec
seconds = (time // 1000) % 60
time -= seconds * 1000
minutes = (time // 60 // 1000) % 60
time -= minutes * 60 * 1000
hours = (time // 1000 // 3600) % 24
# Make sure we get enough zeroes.
if int(msec) == 0:
msec = "000"
elif int(msec) < 10:
msec = "00" + str(msec)
elif int(msec) < 100:
msec = "0" + str(msec)
if int(seconds) == 0:
seconds = "00"
elif int(seconds) < 10:
seconds = "0" + str(seconds)
if int(minutes) == 0:
minutes = "00"
elif int(minutes) < 10:
minutes = "0" + str(minutes)
if int(hours) == 0:
hours = "00"
elif int(hours) < 10:
hours = "0" + str(hours)
# Send back a string
return str(hours) + ":" + str(minutes) + ":" + str(seconds) + "," + str(msec)
# Converts from hh:mm:ss,msec format to miliseconds
def HMSTomsec(timestring):
# Get hours, minutes, and seconds as individual strings
hours, minutes, seconds = timestring.split(":")
# Convert the comma in seconds to a decimal
seconds = seconds.replace(",", ".")
# Convert times to numbers
hours = int(hours)
minutes = int(minutes)
seconds = float(seconds)
# Convert everything to miliseconds and add them all up
msec = int(seconds * 1000) + minutes * 60000 + hours * 3600000
# Send back an integer
return msec
# Opens our input and output files.
def openFiles(name, seconds, args):
completed = False
print(name)
# Open the existing SRT file.
with open(name, "r") as inputFile:
# Open a new file to work with.
newname = name + ".new"
with open(newname, "w") as outputFile:
# With the files open, shift the times.
completed = shiftTimes(inputFile, outputFile, name, seconds, args)
# If we fail, we shouldn't leave a random file lying around.
if not completed:
os.remove(newname)
return completed, newname
# If we got here, we couldn't open the file I guess.
return False, "error.srt"
# Gets a list of dicts with all the entries from our original SRT file
def getSRTEntries(inFile):
SRTEntries = []
lastLine = ""
twoBack = ""
# Loop down the file, storing lines, until you find 'numbers --> numbers'
for index, line in enumerate(inFile):
entryData = {}
if re.search("\d\d --> \d\d", line):
# The line before that is the index.
try:
entryData["index"] = int(lastLine)
except ValueError:
# A missing blank line can cause this problem.
# Go one back to fix it.
line = lastLine
lastLine = twoBack
# That line is the timecode. Store it in miliseconds.
entryData["start"] = HMSTomsec(line.split(" --> ")[0])
entryData["end"] = HMSTomsec(line.split(" --> ")[1])
# Watch out for the end of the file.
try:
# The next line is text1, and the one after is text2 or maybe blank.
entryData["text1"] = str(inFile.readline())
# If text1 is blank, move on.
if entryData["text1"].strip() == "":
entryData["text2"] = ""
SRTEntries.append(entryData)
continue
entryData["text2"] = str(inFile.readline())
except StopIteration:
pass
# Once we've gotten our info, add it to the list.
SRTEntries.append(entryData)
twoBack = lastLine
lastLine = line
return SRTEntries
# Writes a standard SRT entry into our output files.
def writeEntry(outFile, entry, index):
outFile.write(str(index) + "\n")
outFile.write(
str(msecToHMS(entry["start"])) + " --> " + str(msecToHMS(entry["end"])) + "\n"
)
outFile.write(str(entry["text1"]))
if "".join(entry["text2"].split()) != "":
outFile.write(str(entry["text2"]))
outFile.write("\n")
# The core loop that calls the important stuff.
def shiftTimes(inFile, outFile, name, seconds, args):
# Get a list of all the entries.
SRTEntries = getSRTEntries(inFile)
removeEntry = False
blankEntry = None
# Go through the list and adjust times.
for i, entry in enumerate(SRTEntries):
# If we're going positive:
if seconds > 0 and i == 0:
# If there's already a blank 'padding' entry, extend it.
if entry["text1"].strip() == "":
entry["start"] = 0
entry["end"] += seconds * 1000
# If not, add a blank entry at 0.
else:
blankEntry = {
"start": 0,
"end": SRTEntries[i]["start"] + seconds * 1000,
"index": 0,
"text1": "",
"text2": "",
}
# Adjust the existing first entry.
entry["start"] += seconds * 1000
entry["end"] += seconds * 1000
# If we're going negative:
elif seconds < 0 and i == 0:
# Check to see if we can shrink the first entry enough.
# If we have enough time, shrink the first entry back.
# If not, stop and throw an error message.
# print('start: ' + str(entry['start']/1000.0))
# print('end: ' + str(entry['end']/1000.0))
# print(entry['start'] > abs(seconds*1000))
# print(entry['end'] > abs(seconds*1000))
# If the first entry starts at or after our time, we're all good.
if entry["start"] > abs(seconds * 1000):
entry["start"] += seconds * 1000
entry["end"] += seconds * 1000
elif entry["end"] == abs(seconds * 1000):
removeEntry = True
# We might still be good if our first entry is blank. We can shrink it back.
elif entry["end"] > abs(seconds * 1000) and entry["text1"].strip() == "":
entry["end"] += seconds * 1000
# But if not, we can't change this file.
else:
print(
"Cannot shift "
+ name
+ ". First subtitle is before "
+ str(-seconds)
+ " seconds."
)
return False
if i > 0:
entry["start"] += seconds * 1000
entry["end"] += seconds * 1000
if blankEntry:
SRTEntries.insert(0, blankEntry)
if removeEntry:
del SRTEntries[0]
# Write the file.
for i, entry in enumerate(SRTEntries):
writeEntry(outFile, entry, i)
return True
# Takes in arguments and runs the shifter on each file.
def SRTTimeShifter(args):
# Handle arguments and flags
parser = argparse.ArgumentParser(usage=instructions, add_help=False)
parser.add_argument("--help", "-h", action="store_true")
parser.add_argument("-o", action="store_true")
parser.add_argument("file_names", nargs="*")
parser.add_argument("seconds", action="store")
args = parser.parse_args(args)
# Replace arguments with wildcards with their expansion.
# If a string does not contain a wildcard, glob will return it as is.
# Mostly important if we run this on Windows systems.
file_names = list()
for arg in args.file_names:
file_names += glob(arg)
# If "seconds" is not a number, it's probably in the wrong place.
try:
seconds = float(args.seconds)
except ValueError:
# Probably fed arguments in wrong order.
sys.exit(instructions)
# If the filenames don't exist, say so and quit.
if file_names == []:
sys.exit("No file or directory found by that name.")
if args.help:
sys.exit(instructions)
fileCount = 0
# If we're not shifting anything, just return.
if seconds == 0:
print("Zero second shift - no files changed.")
return False
# Convert every file we're passed.
for name in file_names:
# Make sure single files exist.
if not os.path.exists(name):
print("File or directory not found: " + name)
return
# If it's just a file...
if os.path.isfile(name):
# Make sure this is an srt file (just check extension)
if name.lower().endswith(".srt"):
# Open that file and shift the times in that file
completed, newname = openFiles(name, seconds, args)
if completed:
# If we're not copying files, clean up the original.
if args.o:
os.remove(name)
else:
os.rename(name, name + ".old")
os.rename(newname, name)
fileCount += 1
# Finish by saying how many files we shifted.
if fileCount > 0:
plFiles = "file" if fileCount == 1 else "files"
plSeconds = "second" if seconds == 1 else "seconds"
print(
"Shifted "
+ str(fileCount)
+ " "
+ plFiles
+ " by "
+ str(seconds)
+ " "
+ plSeconds
+ "."
)
if __name__ == "__main__":
# This won't be run when the file is imported
SRTTimeShifter(sys.argv)
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vignettes/brute-force-interaction.py | koaning/sushigo | 3 | 92220 | import sushigo
import itertools as it
import random
POSSIBLE_CARDS = list(set([str(_) for _ in sushigo.deck.StandardDeck()]))
class CustomPlayer(sushigo.player.Player):
def __init__(self, order, name=None):
super(CustomPlayer, self).__init__()
self.name = 'custom-player'
if name:
self.name = name
self.order = order
if any([(_ not in order) for _ in POSSIBLE_CARDS]):
raise ValueError("forgot card type in OrderedPlayer init")
def act(self, reward, observation=None, action_space=None):
if not action_space:
raise ValueError("player received an empty set of actions")
# the player can get a notion of possible cards, give them an order
order = {j: i for i, j in enumerate(self.order)}
# the action space consists of objects now, which we may need to string-sort
ordered_actions = sorted(action_space, key = lambda _: order[str(_)])
return ordered_actions[-1]
def simulate(order, n_games = 10):
"""
This function simulates a game, assuming a player uses certain order.
"""
res = []
for _ in range(n_games):
p1 = sushigo.player.Player(name="p1")
p2 = CustomPlayer(name="custom", order = order)
players = [p1,p2]
game = sushigo.game.Game(players, deck=sushigo.deck.StandardDeck())
game.simulate_game()
res.append(game.did_player_win("custom"))
return sum(res)
def allow(perm):
"""
This function acts as a predicate for allowed permutations.
"""
# leaves us with 1108800/39916800 permutations
order = {j: i for i, j in enumerate([str(_) for _ in perm])}
if order['NigiriCard("egg")'] > order['NigiriCard("salmon")']:
return False
if order['NigiriCard("salmon")'] > order['NigiriCard("squid")']:
return False
if order['MakiCard("2")'] > order['MakiCard("3")']:
return False
if order['MakiCard("1")'] > order['MakiCard("2")']:
return False
return True
def rand_iterable(iter, prob=0.001, max_i = 100):
"""
Randomly selects some items in the iterable.
"""
i = 0
while i<max_i:
if random.random() < prob:
yield next(iter)
allowed_perms = (_ for _ in it.permutations(POSSIBLE_CARDS) if allow(_))
# if you want to sample, you can uncomment this line.
allowed_perms = rand_iterable(_ for _ in it.permutations(POSSIBLE_CARDS) if allow(_))
print("This will run for a while because brute force is stupid.")
for permutation in allowed_perms:
if not allow(permutation):
continue
n = 20
order = {j: i for i, j in enumerate([str(_) for _ in permutation])}
score = simulate(order, n)
print("card order:{}\nnumber of wins:{}/{}".format(permutation, score, n))
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spikeforest2/experimental/__init__.py | michaeljohnclancy/spikeforest2 | 26 | 55205 | from ._sort import sort | [
1,
515,
869,
29918,
6605,
1053,
2656,
2
] |
logger.py | vjdw/piper | 0 | 188451 | <reponame>vjdw/piper
class Logger:
def __init__(self, filepath):
self.filepath = filepath
def write(self, msg):
with open(self.filepath, "a") as logfile:
logfile.write("{}\n".format(msg))
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mesostat/visualization/mpl_timescale_bar.py | HelmchenLabSoftware/mesostat-dev | 0 | 105081 | <gh_stars>0
import numpy as np
from matplotlib.patches import Rectangle
from mesostat.visualization.mpl_colors import base_colors_rgb
def add_timescale_bar(fig, tNames, tPosts, tNow, colorscheme='tableau', colorNow='red', fontsize=14):
"""
:param fig: Existing figure. May have multiple subplots (intended)
:param tNames: Names of time intervals
:param tPosts: Time at which each time interval ends
:param tNow: Specific current time to be marked on the timebar
:param colorscheme: Color scheme name from which the time interval colors will be sampled
:param colorNow: Color of the marker for the current time
:param fontsize: Font size of the timebar labels
:return: Nothing
Produces a timescale bar, namely, a set of consecutive labeled rectangles denoting different time intervals.
Attaches the timescale bar at the bottom of existing figure. Intended use is to mark the timeframe of a video, when
multiple consecutive figures of the same shape are produced.
"""
# Get size of the original image in inches
wSrcInch, hSrcInch = fig.get_size_inches()
# Postulate size of the timebar in inches
wTrgInch = wSrcInch # Currently, width of timebar will be equal to the full figure width
hTrgInch = 0.3 # Currently, height of the timebar is fixed
# Find size of the timebar in pixels
wTrgPix = wTrgInch * fig.dpi
hTrgPix = hTrgInch * fig.dpi
# Find size of the timebar relative to the original image
wRel = wTrgInch / wSrcInch
hRel = hTrgInch / hSrcInch
# Add a new axis just below the existing figure
# Note: Positions and sizes must be specified relative to the original figure for this function
# Note: timebar is shifted down exactly by its height. In theory it should overlap with original image, in practice
# it does not, there is some extra margin introduced by matplotlib somewhere
ax = fig.add_axes([0, -hRel, wRel, hRel])
_make_time_legend(ax, tNames, tPosts, wTrgPix, hTrgPix, tNow,
colorscheme=colorscheme, colorNow=colorNow, fontsize=fontsize)
def _make_time_legend(ax, tNames, tPosts, wPix, hPix, tNow, colorscheme='tableau', colorNow='red', fontsize=14):
"""
:param ax: Axis where to plot the timebar
:param tNames: Names of time intervals
:param tPosts: Time at which each time interval ends
:param wPix: Width of timebar in pixels
:param hPix: Height of timebar in pixels
:param tNow: Specific current time to be marked on the timebar
:param colorscheme: Color scheme name from which the time interval colors will be sampled
:param colorNow: Color of the marker for the current time
:param fontsize: Font size of the timebar labels
:return: Nothing
Auxiliary procedure for add_timescale_bar to actually produce the timebar
"""
nBars = len(tNames)
epsTime2Pix = wPix / tPosts[-1]
# Rescale timeposts from time to timebar width. Add 0-timepost at the beginning
tPostsPix = np.hstack([[0], tPosts]) * epsTime2Pix
# Rescale current time to timebar width
tNowPix = tNow * epsTime2Pix
# Get color palette to sample rectangles
# basecolors = list(base_colors_rgb(key=colorscheme).values())
basecolors = base_colors_rgb(key=colorscheme)
# Plot all rectangles
for i in range(nBars):
bar = Rectangle((tPostsPix[i], 0), tPostsPix[i + 1] - tPostsPix[i], hPix, facecolor=basecolors[i], alpha=0.5)
ax.add_patch(bar)
# Label each rectangle. Each label is centered with respect to its rectangle.
ax.text((tPostsPix[i] + tPostsPix[i + 1]) // 2, hPix // 2, tNames[i],
fontsize=fontsize, ha='center', va='center')
ax.axvline(x=tNowPix, color=colorNow)
ax.set_axis_off()
ax.autoscale()
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2696,
580,
13,
1678,
4853,
29889,
1300,
14174,
744,
580,
13,
2
] |
data/compute_budget.py | minorchange/co2_monitor_hd | 0 | 100009 | import os, sys
import datetime
from dateutil.relativedelta import relativedelta
from data.read_data import read_budget
def get_remaining_paris_budget(df):
assert "co2_kt_total" in df.columns
budget_start_year, budget_start_value_kt = read_budget()
s_total = df["co2_kt_total"].dropna()
s_measured_since_budgetstart = s_total[s_total.index >= budget_start_year]
measured_co2kt_since_budgetstart = s_measured_since_budgetstart.sum()
last_measured_year = s_total.index.max()
latest_emissions_ktperyear = s_total[last_measured_year]
assert latest_emissions_ktperyear > -1
assert latest_emissions_ktperyear < 3000
seconds_per_year = 60 * 60 * 24 * 365.25
latest_emissions_ktpersecond = latest_emissions_ktperyear / seconds_per_year
last_measured_year_since_before_budgetstart = max(
last_measured_year, budget_start_year - 1
)
last_measured_second = datetime.datetime.strptime(
f"{last_measured_year_since_before_budgetstart}-12-31 23:59:59",
"%Y-%m-%d %H:%M:%S",
)
now = datetime.datetime.now()
seconds_since_last_measured_second = (now - last_measured_second).total_seconds()
estimated_emissions_kt = (
latest_emissions_ktpersecond * seconds_since_last_measured_second
)
total_emissions_kt = measured_co2kt_since_budgetstart + estimated_emissions_kt
remaining_budget_kt = budget_start_value_kt - total_emissions_kt
remaining_seconds = remaining_budget_kt / latest_emissions_ktpersecond
when_budget_is_depleted = now + relativedelta(seconds=+remaining_seconds)
return remaining_budget_kt, when_budget_is_depleted
if __name__ == "__main__":
from read_data import read_emissions
df_emissions = read_emissions()
remaining_budget_kt, when_budget_is_depleted = get_remaining_paris_budget(
df_emissions, trend
)
print(total_emissions_kt)
print(remaining_budget_kt)
print(remaining_budget_kt.year)
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13,
2
] |
loris/requests/meta_request.py | jpstroop/loris-redux | 7 | 81833 | #
# This helps us not have to pass so many things (caches, resolvers,
# transcoders...) around by letting us set class properties on the IIIFRequest
# at startup. Here's a basic example of how this pattern works:
#
# >>> class MyMeta(type): # Note we subclass type, not object
# ... _something = None
# ... def _get_something(self):
# ... return self._something
# ... def _set_something(self, value):
# ... self._something = value
# ... something = property(_get_something, _set_something)
# ...
# >>> class MyFoo(metaclass=MyMeta):
# ... pass
# >>> print(MyFoo.something)
# None
# >>> MyFoo.something = 'bar'
# >>> MyFoo.something
# 'bar'
#
class MetaRequest(type):
_compliance = None
_info_cache = None
_extractors = None
_app_configs = None
_transcoders = None
_resolvers = None
def _get_compliance(self):
return self._compliance
def _set_compliance(self, compliance):
self._compliance = compliance
compliance = property(_get_compliance, _set_compliance)
def _get_info_cache(self):
return self._info_cache
def _set_info_cache(self, info_cache):
self._info_cache = info_cache
info_cache = property(_get_info_cache, _set_info_cache)
def _get_extractors(self):
return self._extractors
def _set_extractors(self, extractors):
self._extractors = extractors
extractors = property(_get_extractors, _set_extractors)
def _get_app_configs(self):
return self._app_configs
def _set_app_configs(self, app_configs):
self._app_configs = app_configs
app_configs = property(_get_app_configs, _set_app_configs)
def _get_transcoders(self):
return self._transcoders
def _set_transcoders(self, transcoders):
self._transcoders = transcoders
transcoders = property(_get_transcoders, _set_transcoders)
def _get_resolvers(self):
return self._resolvers
def _set_resolvers(self, resolvers):
self._resolvers = resolvers
resolvers = property(_get_resolvers, _set_resolvers)
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] |
src/core/ServerBase.py | PyDO-Team/PyDO-Old | 1 | 149670 | import sys
from direct.showbase.ShowBase import ShowBase
from direct.directnotify import DirectNotifyGlobal
from panda3d.core import UniqueIdAllocator
from src.util.LogManager import LogManager
from src.dclass.DCManager import DCManager
from src.core.ConnectionManager import ConnectionManager
from src.message.MessageManager import MessageManager
from src.distributed.DistributedObjectManager import DistributedObjectManager
from src.interest.InterestManager import InterestManager
class ServerBase(ShowBase):
notify = DirectNotifyGlobal.directNotify.newCategory("ServerBase")
serverVersion = 'pcsv1.0.34.31'
def __init__(self):
ShowBase.__init__(self)
self.activeConnections = {}
maxChannels = self.config.GetInt('max-channel-id', 1000000)
self.channelAllocator = UniqueIdAllocator(0, 0+maxChannels-1)
self.configManager = None
self.logManager = LogManager()
self.dcManager = DCManager()
self.dcManager.readDCFile()
self.notify.warning(str(self.dcManager.dclassesByName))
self.connectionManager = ConnectionManager()
self.messageManager = MessageManager()
self.doManager = DistributedObjectManager()
self.interestManager = InterestManager() | [
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source/db/Entry.py | RobinSatterthwaite/BirdTable | 0 | 92285 |
from typing import Any
import pyodbc as odbc
from .Property import Property
class Entry(object):
def __init__(self, table_name: str):
self._delete = False
self._tableName = table_name
self._primaryKey = Property("pk")
self.properties = [self._primaryKey]
@property
def primaryKey(self) -> int:
return self._primaryKey.value
def delete(self) -> None:
self._delete = True
def _commitInsert(self, cursor: odbc.Cursor) -> None:
prop_names = ""
prop_value_tags = ""
prop_values = []
for prop in self.properties:
if prop.value != None:
if prop_names != "":
prop_names += ","
prop_value_tags += ","
prop_names += prop.name
prop_value_tags += "?"
prop_values.append(prop.value)
cursor.execute("INSERT INTO {0} ({1}) VALUES ({2})"
.format(self._tableName, prop_names, prop_value_tags), prop_values)
cursor.commit()
cursor.execute("SELECT LAST_INSERT_ID()")
self._primaryKey.value = cursor.fetchone()[0]
def _executeUpdate(self, cursor: odbc.Cursor) -> None:
prop_sets = ""
prop_values = []
for prop in self.properties:
if prop.updated:
if prop_sets != "":
prop_sets += ","
prop_sets += "{0}=?".format(prop.name)
prop_values.append(prop.value)
if len(prop_values) > 0:
prop_values.append(self.primaryKey)
cursor.execute("UPDATE {0} SET {1} WHERE pk=?"
.format(self._tableName, prop_sets), prop_values)
def _executeDelete(self, cursor: odbc.Cursor) -> None:
cursor.execute("DELETE FROM {0} WHERE pk=?"
.format(self._tableName), self.primaryKey)
def _finaliseCommit(self) -> None:
self._delete = False
for prop in self.properties:
prop.finaliseCommit()
def rollback(self) -> None:
self._delete = False
for prop in self.properties:
prop.rollback()
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] |
mrq/redishelpers.py | fredstro/mrq | 745 | 100624 | from future.builtins import range
from .utils import memoize
from . import context
def redis_key(name, *args):
prefix = context.get_current_config()["redis_prefix"]
if name == "known_subqueues":
return "%s:ksq:%s" % (prefix, args[0].root_id)
elif name == "queue":
return "%s:q:%s" % (prefix, args[0].id)
elif name == "started_jobs":
return "%s:s:started" % prefix
elif name == "paused_queues":
return "%s:s:paused" % prefix
elif name == "notify":
return "%s:notify:%s" % (prefix, args[0].root_id)
@memoize
def redis_zaddbyscore():
""" Increments multiple keys in a sorted set & returns them """
return context.connections.redis.register_script("""
local zset = KEYS[1]
local min = ARGV[1]
local max = ARGV[2]
local offset = ARGV[3]
local count = ARGV[4]
local score = ARGV[5]
local data = redis.call('zrangebyscore', zset, min, max, 'LIMIT', offset, count)
for i, member in pairs(data) do
redis.call('zadd', zset, score, member)
end
return data
""")
@memoize
def redis_zpopbyscore():
""" Pops multiple keys by score """
return context.connections.redis.register_script("""
local zset = KEYS[1]
local min = ARGV[1]
local max = ARGV[2]
local offset = ARGV[3]
local count = ARGV[4]
local data = redis.call('zrangebyscore', zset, min, max, 'LIMIT', offset, count)
if #data > 0 then
redis.call('zremrangebyrank', zset, 0, #data - 1)
end
return data
""")
@memoize
def redis_lpopsafe():
""" Safe version of LPOP that also adds the key in a "started" zset """
return context.connections.redis.register_script("""
local key = KEYS[1]
local zset_started = KEYS[2]
local count = ARGV[1]
local now = ARGV[2]
local left = ARGV[3]
local data = {}
local current = nil
for i=1, count do
if left == '1' then
current = redis.call('lpop', key)
else
current = redis.call('rpop', key)
end
if current == false then
return data
end
data[i] = current
redis.call('zadd', zset_started, now, current)
end
return data
""")
def redis_group_command(command, cnt, redis_key):
with context.connections.redis.pipeline(transaction=False) as pipe:
for _ in range(cnt):
getattr(pipe, command)(redis_key)
return [x for x in pipe.execute() if x]
| [
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Torneo/apps/equipo/migrations/0001_initial.py | Estuardiaz2611/Djangotuto2 | 0 | 125078 | <reponame>Estuardiaz2611/Djangotuto2
# Generated by Django 2.2.6 on 2019-10-02 17:34
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('liga', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Equipo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nombre_equipo', models.CharField(max_length=50)),
('siglas', models.CharField(max_length=5)),
('jugadores', models.IntegerField()),
('encargado', models.CharField(max_length=50)),
('telefono', models.CharField(max_length=9)),
('liga', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='liga.Liga')),
],
),
]
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] |
xero_python/finance/models/problem_type.py | dupski/xero-python | 0 | 171205 | <gh_stars>0
# coding: utf-8
"""
Xero Finance API
The Finance API is a collection of endpoints which customers can use in the course of a loan application, which may assist lenders to gain the confidence they need to provide capital. # noqa: E501
Contact: <EMAIL>
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
from enum import Enum
class ProblemType(Enum):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
allowed enum values
"""
NOTSET = "NotSet"
BANK_ACCOUNT_NOT_FOUND = "bank-account-not-found"
INTERNAL_ERROR = "internal-error"
INVALID_APPLICATION = "invalid-application"
INVALID_REQUEST = "invalid-request"
ORGANISATION_NOT_FOUND = "organisation-not-found"
ORGANISATION_OFFLINE = "organisation-offline"
REQUEST_TIMEOUT = "request-timeout"
SERVICE_UNAVAILABLE = "service-unavailable"
UNAUTHORIZED = "unauthorized"
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] |
scripts/printer.py | WIGGITYWHAT/Island_MST | 0 | 88976 | <gh_stars>0
"""Define a function that prints a progress bar of percent p completion."""
import sys
def print_progress(a, b):
"""Print a progress bar with %p finished."""
p = float(a) / b
toolbar_len = 50
num_done = int(toolbar_len*p)
num_remain = toolbar_len - num_done
sys.stdout.flush()
sys.stdout.write("\r[{}{}] %{} {}/{}".format("="*num_done,
" "*num_remain, int(p*100), a, b))
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] |
code/src/main.py | ChaofWang/AWSRN | 162 | 35359 | <filename>code/src/main.py<gh_stars>100-1000
import torch
import utility
import data
import model
import loss
from option import args
from trainer import Trainer
def print_network(net):
num_params = 0
for param in net.parameters():
num_params += param.numel()
print(net)
print('Total number of parameters: %d' % num_params)
def print_setting(net, args):
print('init this train:')
print_network(net)
print('training model:', args.model)
print('scale:', args.scale)
print('resume from ', args.resume)
print('output patch size', args.patch_size)
print('model setting: n_resblocks:', args.n_resblocks,
'n_feats:', args.n_feats, 'block_feats:', args.block_feats)
print('optimization setting: ', args.optimizer)
print('total epochs:', args.epochs)
print('lr:', args.lr, 'lr_decay at:', args.decay_type, 'decay gamma:', args.gamma)
print('train loss:', args.loss)
print('save_name:', args.save)
torch.manual_seed(args.seed)
checkpoint = utility.checkpoint(args)
if checkpoint.ok:
loader = data.Data(args)
model = model.Model(args, checkpoint)
print_setting(model, args)
loss = loss.Loss(args, checkpoint) if not args.test_only else None
t = Trainer(args, loader, model, loss, checkpoint)
while not t.terminate():
t.train()
t.test()
checkpoint.done()
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tests/test_cif_io.py | dkratzert/FinalCif | 13 | 142169 | import unittest
from pathlib import Path
from cif.cif_file_io import CifContainer
from tests.test_utils import current_file_path
class CifFileCRCTestCase(unittest.TestCase):
def setUp(self) -> None:
current_file_path()
self.cif = CifContainer(Path('tests/examples/1979688.cif'))
def test_calc_crc(self):
self.assertEqual(20714, self.cif.calc_checksum(self.cif['_shelx_hkl_file']))
class CifFileCRClargerTestCase(unittest.TestCase):
def setUp(self) -> None:
current_file_path()
self.cif = CifContainer(Path('test-data/DK_Zucker2_0m.cif'))
def test_calc_crc(self):
self.assertEqual(26780, self.cif.calc_checksum(self.cif['_shelx_hkl_file']))
class CifFileTestCase(unittest.TestCase):
def setUp(self) -> None:
current_file_path()
self.cif = CifContainer(Path('tests/examples/1979688.cif'))
def test_calc_crc(self):
self.assertEqual(3583, self.cif.calc_checksum('hello world'))
def test_res_crc(self):
self.assertEqual(17612, self.cif.res_checksum_calcd)
def test_hkl_crc(self):
self.assertEqual(20714, self.cif.hkl_checksum_calcd)
def test_res_crc_without_res(self):
self.assertEqual(0, CifContainer(Path('test-data/1000006.cif')).res_checksum_calcd)
def test_get_unknown_value_from_key(self):
self.assertEqual('', self.cif['_chemical_melting_point'])
def test_get_known_value_from_key(self):
self.assertEqual('702.70', self.cif['_chemical_formula_weight'])
def test_get_spgr(self):
self.assertEqual('P 21 21 2', self.cif.space_group)
def test_symmops(self):
self.assertEqual(['x, y, z', '-x, -y, z', '-x+1/2, y+1/2, -z', 'x+1/2, -y+1/2, -z'], self.cif.symmops)
def test_symmops_from_spgr(self):
self.assertEqual(['x,y,z', '-x,-y,z', 'x+1/2,-y+1/2,-z', '-x+1/2,y+1/2,-z'], self.cif.symmops_from_spgr)
def test_centrosymm(self):
self.assertEqual(False, self.cif.is_centrosymm)
c = CifContainer(Path('test-data/DK_ML7-66-final.cif'))
self.assertEqual(True, c.is_centrosymm)
def test_ishydrogen(self):
self.assertEqual(True, self.cif.ishydrogen('H18a'))
self.assertEqual(True, self.cif.ishydrogen('H18A'))
self.assertEqual(False, self.cif.ishydrogen('C2'))
self.assertEqual(False, self.cif.ishydrogen('c2'))
def test_cell(self):
expected = [round(x, 8) for x in (19.678, 37.02290000000001, 4.772, 90.0, 90.0, 90.0, 3476.576780226401)]
actual = [round(y, 8) for y in self.cif.cell]
self.assertEqual(expected, actual)
def test_natoms(self):
self.assertEqual(94, self.cif.natoms())
self.assertEqual(52, self.cif.natoms(without_h=True))
def test_checksum_tests(self):
self.assertEqual(True, self.cif.test_hkl_checksum())
self.assertEqual(True, self.cif.test_res_checksum())
def test_checksum_test_without_checksum(self):
self.assertEqual(True, CifContainer('test-data/1000006.cif').test_res_checksum())
self.assertEqual(True, CifContainer('test-data/1000006.cif').test_hkl_checksum())
if __name__ == '__main__':
unittest.main()
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mvt/datasets/detection2ds/__init__.py | JackMing1986/MultipleVsiualTasks | 2 | 196826 | <reponame>JackMing1986/MultipleVsiualTasks
from .det_base import DetBaseDataset
from .voc import VOCDataset
from .coco import CocoDataset
from .det_retail import DetRetailDataset
from .det_retail_one import DetRetailOneDataset
__all__ = [
"DetBaseDataset",
"VOCDataset",
"CocoDataset",
"DetRetailDataset",
"DetRetailOneDataset",
]
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config.py | Maziar110/nostratheus | 1 | 180036 | # This dictionary is to define metrics that we should extract data from and then
# their exposed name as predicted metric
metrics = {
'actual_metric_name1': 'actual_metric_name1_predict',
'actual_metric_name2': 'actual_metric_name2_predict'
}
#
prom_url = 'http://localhost/'
expose_port = 8000
# interval in days
interval = 30
# chunk size in hour
chunk_size = 24 | [
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train/train.py | sjtu-tcloud/Tiny-OFA | 0 | 38863 | <reponame>sjtu-tcloud/Tiny-OFA
import argparse
import torch.distributed as dist
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import test # import test.py to get mAP after each epoch
from models import *
from utils.dataset import *
from utils.utils import *
from mymodel import *
wdir = 'weights' + os.sep # weights dir
last = wdir + 'last.pt'
best = wdir + 'best.pt'
test_best = wdir + 'test_best.pt'
results_file = 'results.txt'
# Hyperparameters (results68: 59.9 [email protected] yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.225, # iou training threshold
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.937, # SGD momentum
'weight_decay': 0.000484, # optimizer weight decay
'fl_gamma': 0.5, # focal loss gamma
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 1.98, # image rotation (+/- deg)
'translate': 0.05, # image translation (+/- fraction)
'scale': 0.05, # image scale (+/- gain)
'shear': 0.641} # image shear (+/- deg)
# Overwrite hyp with hyp*.txt (optional)
f = glob.glob('hyp*.txt')
if f:
print('Using %s' % f[0])
for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
hyp[k] = v
def train():
cfg = opt.cfg
data = opt.data
img_size, img_size_test = opt.img_size if len(opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
# Initialize
init_seeds()
if opt.multi_scale:
img_sz_min = round(img_size / 32 / 1.5)
img_sz_max = round(img_size / 32* 1.5)
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
# Configure run
# data_dict = parse_data_cfg(data)
train_path = '../data/data_training'
test_path = '../data/data_test'
nc = 1
# Remove previous results
for f in glob.glob('*_batch*.png') + glob.glob(results_file):
os.remove(f)
# Initialize model
# model = Darknet(cfg, arc=opt.arc).to(device)
# model = UltraNetFloat640().to(device)
# model = TempNet().to(device)
# model = TempNetDW().to(device)
# model = TempNetQua().to(device)
# model = SqueezeNetQua().to(device)
# model = UltraNet().to(device)
model = TinyUltraNet().to(device)
# Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2 += [v] # biases
elif 'Conv2d.weight' in k:
pg1 += [v] # apply weight_decay
else:
pg0 += [v] # all else
if opt.adam:
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
optimizer.param_groups[2]['lr'] *= 2.0 # bias lr
del pg0, pg1, pg2
start_epoch = 0
best_fitness = 0.0
test_best_iou = 0.0
# attempt_download(weights)
# 加载权重
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.99 + 0.01 # cosine https://arxiv.org/pdf/1812.01187.pdf
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1)
scheduler.last_epoch = start_epoch
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, '.-', label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_images=opt.cache_images,
single_cls=opt.single_cls)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size * 2,
hyp=hyp,
rect=False,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size * 2,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
nb = len(dataloader)
prebias = start_epoch == 0
model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
model.gr = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 loss ratio
# Prebias
if prebias:
ne = max(round(30 / nb), 3) # number of prebias epochs
ps = np.interp(epoch, [0, ne], [0.1, hyp['lr0'] * 2]), \
np.interp(epoch, [0, ne], [0.9, hyp['momentum']]) # prebias settings (lr=0.1, momentum=0.9)
if epoch == ne:
# print_model_biases(model)
prebias = False
# Bias optimizer settings
optimizer.param_groups[2]['lr'] = ps[0]
if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
optimizer.param_groups[2]['momentum'] = ps[1]
mloss = torch.zeros(4).to(device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Plot images with bounding boxes
if ni < 1:
f = 'train_batch%g.png' % i # filename
plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
if tb_writer:
tb_writer.add_image(f, cv2.imread(f)[:, :, ::-1], dataformats='HWC')
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 16-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
loss.backward()
# Optimize accumulated gradient
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
results = test.test(cfg,
data,
batch_size=batch_size * 2,
img_size=img_size_test,
model=model,
conf_thres=0.001, # 0.001 if opt.evolve or (final_epoch and is_coco) else 0.01,
iou_thres=0.6,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader)
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * len(results) % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))
# Write Tensorboard results
if tb_writer:
x = list(mloss) + list(results)
titles = ['GIoU', 'Objectness', 'Classification', 'Train loss',
'iou', 'loss', 'Giou loss', 'obj loss']
for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
test_iou = results[0]
if test_iou > test_best_iou:
test_best_iou = test_iou
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if best_fitness == fi:
torch.save(chkpt, best)
if test_iou == test_best_iou:
torch.save(chkpt, test_best)
# Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, 'last%s.pt' % n, 'best%s.pt' % n
os.rename('results.txt', fresults)
os.rename(wdir + 'last.pt', wdir + flast) if os.path.exists(wdir + 'last.pt') else None
os.rename(wdir + 'best.pt', wdir + fbest) if os.path.exists(wdir + 'best.pt') else None
if opt.bucket: # save to cloud
os.system('gsutil cp %s gs://%s/results' % (fresults, opt.bucket))
os.system('gsutil cp %s gs://%s/weights' % (wdir + flast, opt.bucket))
# os.system('gsutil cp %s gs://%s/weights' % (wdir + fbest, opt.bucket))
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=500) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64
parser.add_argument('--accumulate', type=int, default=4, help='batches to accumulate before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-tiny-1cls_1.cfg', help='*.cfg path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
parser.add_argument('--img-size', nargs='+', type=int, default=[320], help='train and test image-sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--weights', type=str, default='', help='initial weights path')
parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # default, uCE, uBCE
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--var', type=float, help='debug variable')
opt = parser.parse_args()
opt.weights = last if opt.resume else opt.weights
print(opt)
device = torch_utils.select_device(opt.device, batch_size=opt.batch_size)
# scale hyp['obj'] by img_size (evolved at 320)
# hyp['obj'] *= opt.img_size[0] / 320.
tb_writer = None
if not opt.evolve: # Train normally
try:
# Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
from torch.utils.tensorboard import SummaryWriter
tb_writer = SummaryWriter()
except:
pass
train() # train normally
else: # Evolve hyperparameters (optional)
opt.notest, opt.nosave = True, True # only test/save final epoch
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
for _ in range(1): # generations to evolve
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt('evolve.txt', ndmin=2)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() # weights
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
method, mp, s = 3, 0.9, 0.2 # method, mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
ng = len(g)
if method == 1:
v = (npr.randn(ng) * npr.random() * g * s + 1) ** 2.0
elif method == 2:
v = (npr.randn(ng) * npr.random(ng) * g * s + 1) ** 2.0
elif method == 3:
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
# v = (g * (npr.random(ng) < mp) * npr.randn(ng) * s + 1) ** 2.0
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = x[i + 7] * v[i] # mutate
# Clip to limits
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])
# Train mutation
results = train()
# Write mutation results
print_mutation(hyp, results, opt.bucket)
# Plot results
# plot_evolution_results(hyp)
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app/models/dbmodel.py | hashtrip/api | 1 | 77269 | from datetime import datetime
from typing import Optional
from pydantic import BaseModel, Field
from bson import ObjectId
from .util import ObjID
class DateTimeModelMixin(BaseModel):
created_at: Optional[datetime] = Field(None, alias="createdAt")
updated_at: Optional[datetime] = Field(None, alias="updatedAt")
class DBModelMixin(DateTimeModelMixin):
id: Optional[ObjID] = Field(None, alias="_id")
class Config:
allow_population_by_field_name = True
json_encoders = {
ObjectId: lambda x: str(x),
}
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validate.py | saravanabalagi/vae_collection | 0 | 128473 | <gh_stars>0
from experiment import VAEXperiment
import argparse
import yaml
from utils import get_model_module
import torch
import torch.backends.cudnn as cudnn
import pytorch_lightning as pl
from pytorch_lightning.loggers import TestTubeLogger
import numpy as np
import os
parser = argparse.ArgumentParser()
parser.add_argument('ckpt_path', help='path to checkpoint file')
args = parser.parse_args()
ckpt_path = args.ckpt_path
exp_path = os.path.dirname(os.path.dirname(ckpt_path))
# load config
with open(os.path.join(exp_path, 'config.yaml'), 'r') as file:
try:
config = yaml.safe_load(file)
except yaml.YAMLError as exc:
print(exc)
tt_logger = TestTubeLogger(
save_dir=config['logging_params']['save_dir'],
name=config['logging_params']['name'],
debug=False,
create_git_tag=False,
)
# For reproducibility
torch.manual_seed(config['logging_params']['manual_seed'])
np.random.seed(config['logging_params']['manual_seed'])
cudnn.deterministic = True
cudnn.benchmark = False
model_module = get_model_module(exp_path, config)
model = model_module(**config['model_params'])
experiment = VAEXperiment.load_from_checkpoint(ckpt_path, vae_model=model, params=config)
trainer = pl.Trainer(default_root_dir=f"{tt_logger.save_dir}",
logger=tt_logger,
**config['trainer_params'])
trainer.validate(experiment)
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] |
post/api/views.py | agiledesign2/drf-blog-post | 0 | 96961 | <gh_stars>0
from django.contrib.auth.models import User
from django.shortcuts import get_object_or_404
from rest_framework import generics, permissions
from rest_framework import viewsets
from rest_framework.decorators import api_view
from rest_framework.response import Response
from rest_framework.reverse import reverse
#from rest_framework.filters import SearchFilter, OrderingFilter
from django.db.models import F
from .permissions import IsOwnerOrReadOnly
from post.models import Post
from .serializers import *
from .pagination import *
class PostList(generics.ListCreateAPIView):
queryset = Post.objects.all()
permission_classes = (permissions.IsAuthenticatedOrReadOnly, permissions.IsAdminUser)
serializer_class = PostListSerializer
name = 'post'
filter_fields = (
'title',
)
search_fields = (
'^title',
)
ordering_fields = (
'title',
)
def get_queryset(self):
if self.request.user.is_staff:
return Post.objects.all()
else:
return Post.objects.published()
def get_serializer_class(self):
if self.request.method == 'POST':
return PostCreateSerializer
else:
return PostListSerializer
def list(self, request):
#super.(PostList, self).list()
# Note the use of `get_queryset()` instead of `self.queryset`
queryset = self.get_queryset()
context={'request': request}
serializer = PostListSerializer(queryset, many=True, context=context)
return Response(serializer.data)
def perform_create(self, serializer):
serializer.save(author=self.request.user)
class PostDetail(generics.RetrieveUpdateDestroyAPIView):
queryset = Post.objects.all()
permission_classes = (permissions.IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly)
serializer_class = PostDetailSerializer
lookup_field = 'slug'
name = 'post-detail'
filter_fields = (
'title',
)
search_fields = (
'^title',
)
ordering_fields = (
'title',
)
def get_serializer_class(self):
if self.request.method == 'PUT':
return PostCreateSerializer
else:
return PostListSerializer
def retrieve(self, request, *args, **kwargs):
instance = self.get_object()
Post.objects.filter(pk=instance.id).update(views_count=F('views_count') + 1)
instance.refresh_from_db()
serializer = self.get_serializer(instance)
return Response(serializer.data)
"""
class UserViewSet(viewsets.ReadOnlyModelViewSet):
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [SearchFilter, OrderingFilter]
search_fields = ["username"]
pagination_class = UserPageNumberPagination
""" | [
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tests/test_line.py | themiwi/ggplot | 1,133 | 118617 | <gh_stars>1000+
from ggplot import *
import pandas as pd
import numpy as np
import random
x = np.arange(100)
random.shuffle(x)
df = pd.DataFrame({
'x': x,
'y': np.arange(100)
})
print ggplot(df, aes(x='x', y='y')) + geom_line()
print ggplot(df, aes(x='x', y='y')) + geom_path()
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modules/templates/WACOP/config.py | mswdresden/AsylumEden | 1 | 22018 | # -*- coding: utf-8 -*-
from collections import OrderedDict
from gluon import current
from gluon.storage import Storage
def config(settings):
"""
Template for WA-COP + CAD Cloud Integration
"""
T = current.T
# =========================================================================
# System Settings
#
settings.base.system_name = T("Sahana: Washington Common Operating Picture (WA-COP)")
settings.base.system_name_short = T("Sahana")
# Prepop default
settings.base.prepopulate += ("WACOP", "default/users", "WACOP/Demo")
# Theme (folder to use for views/layout.html)
settings.base.theme = "WACOP"
settings.ui.social_buttons = True
# -------------------------------------------------------------------------
# Self-Registration and User Profile
#
# Users can self-register
settings.security.self_registration = False
# Users need to verify their email
settings.auth.registration_requires_verification = True
# Users need to be approved
settings.auth.registration_requires_approval = True
settings.auth.registration_requests_organisation = True
settings.auth.registration_organisation_required = True
# Approval emails get sent to all admins
settings.mail.approver = "ADMIN"
settings.auth.registration_link_user_to = {"staff": T("Staff")}
settings.auth.registration_link_user_to_default = ["staff"]
settings.auth.registration_roles = {"organisation_id": ["USER"],
}
settings.auth.show_utc_offset = False
settings.auth.show_link = False
# -------------------------------------------------------------------------
# Security Policy
#
settings.security.policy = 7 # Apply Controller, Function and Table ACLs
settings.security.map = True
# -------------------------------------------------------------------------
# L10n (Localization) settings
#
settings.L10n.languages = OrderedDict([
("en", "English"),
("es", "Español"),
])
# Default Language
settings.L10n.default_language = "en"
# Default timezone for users
settings.L10n.utc_offset = "-0800"
# Unsortable 'pretty' date format
settings.L10n.date_format = "%b %d %Y"
# Number formats (defaults to ISO 31-0)
# Decimal separator for numbers (defaults to ,)
settings.L10n.decimal_separator = "."
# Thousands separator for numbers (defaults to space)
settings.L10n.thousands_separator = ","
# Default Country Code for telephone numbers
settings.L10n.default_country_code = 1
# Enable this to change the label for 'Mobile Phone'
settings.ui.label_mobile_phone = "Cell Phone"
# Enable this to change the label for 'Postcode'
settings.ui.label_postcode = "ZIP Code"
settings.msg.require_international_phone_numbers = False
# PDF to Letter
settings.base.paper_size = T("Letter")
# Uncomment this to Translate CMS Series Names
# - we want this on when running s3translate but off in normal usage as we use the English names to lookup icons in render_posts
#settings.L10n.translate_cms_series = True
# Uncomment this to Translate Location Names
#settings.L10n.translate_gis_location = True
# Has scalability issues, but should be OK with our number of records
settings.search.dates_auto_range = True
# -------------------------------------------------------------------------
# GIS settings
#
# Restrict the Location Selector to just certain countries
settings.gis.countries = ("US",)
# Levels for the LocationSelector
levels = ("L1", "L2", "L3")
# Uncomment to pass Addresses imported from CSV to a Geocoder to try and automate Lat/Lon
#settings.gis.geocode_imported_addresses = "google"
# Until we add support to S3LocationSelector to set dropdowns from LatLons
settings.gis.check_within_parent_boundaries = False
# GeoNames username
settings.gis.geonames_username = "mcop"
# Uncomment to hide Layer Properties tool
#settings.gis.layer_properties = False
# Uncomment to display the Map Legend as a floating DIV
settings.gis.legend = "float"
# Uncomment to prevent showing LatLon in Location Represents
settings.gis.location_represent_address_only = "icon"
# Resources which can be directly added to the main map
settings.gis.poi_create_resources = None
# -------------------------------------------------------------------------
# Modules
#
settings.modules = OrderedDict([
# Core modules which shouldn't be disabled
("default", Storage(
name_nice = "Home",
restricted = False, # Use ACLs to control access to this module
access = None, # All Users (inc Anonymous) can see this module in the default menu & access the controller
module_type = None # This item is not shown in the menu
)),
("admin", Storage(
name_nice = "Administration",
#description = "Site Administration",
restricted = True,
access = "|1|", # Only Administrators can see this module in the default menu & access the controller
module_type = None # This item is handled separately for the menu
)),
("appadmin", Storage(
name_nice = "Administration",
#description = "Site Administration",
restricted = True,
module_type = None # No Menu
)),
# ("errors", Storage(
# name_nice = "Ticket Viewer",
# #description = "Needed for Breadcrumbs",
# restricted = False,
# module_type = None # No Menu
# )),
("sync", Storage(
name_nice = "Synchronization",
#description = "Synchronization",
restricted = True,
access = "|1|", # Only Administrators can see this module in the default menu & access the controller
module_type = None # This item is handled separately for the menu
)),
#("translate", Storage(
# name_nice = "Translation Functionality",
# #description = "Selective translation of strings based on module.",
# module_type = None,
#)),
("gis", Storage(
name_nice = "Map",
#description = "Situation Awareness & Geospatial Analysis",
restricted = True,
module_type = 1, # 1st item in the menu
)),
("pr", Storage(
name_nice = "Persons",
description = "Central point to record details on People",
restricted = True,
access = "|1|", # Only Administrators can see this module in the default menu (access to controller is possible to all still)
module_type = None
)),
("org", Storage(
name_nice = "Organizations",
#description = 'Lists "who is doing what & where". Allows relief agencies to coordinate their activities',
restricted = True,
module_type = 10
)),
# All modules below here should be possible to disable safely
("hrm", Storage(
name_nice = "Contacts",
#description = "Human Resources Management",
restricted = True,
module_type = None,
)),
("cms", Storage(
name_nice = "Content Management",
restricted = True,
module_type = 10,
)),
("event", Storage(
name_nice = "Events",
restricted = True,
module_type = 2,
)),
("fire", Storage(
name_nice = "Fire",
restricted = True,
module_type = None,
)),
("police", Storage(
name_nice = "Police",
restricted = True,
module_type = None,
)),
("project", Storage(
name_nice = "Tasks",
restricted = True,
module_type = None,
)),
("doc", Storage(
name_nice = "Documents",
#description = "A library of digital resources, such as photos, documents and reports",
restricted = True,
module_type = None,
)),
("stats", Storage(
name_nice = "Statistics",
restricted = True,
module_type = None
)),
])
# -------------------------------------------------------------------------
# CMS Content Management
#
settings.cms.bookmarks = True
settings.cms.richtext = True
settings.cms.show_tags = True
# -------------------------------------------------------------------------
def cms_post_onaccept(form):
"""
Handle Tags in Create / Update forms
"""
post_id = form.vars.id
db = current.db
s3db = current.s3db
ttable = s3db.cms_tag
ltable = s3db.cms_tag_post
# Delete all existing tags for this post
db(ltable.post_id == post_id).delete()
# Add these tags
tags = current.request.post_vars.get("tags")
if not tags:
return
tags = tags.split(",")
tag_ids = db(ttable.name.belongs(tags)).select(ttable.id,
ttable.name).as_dict(key="name")
for tag in tags:
row = tag_ids.get("tag")
if row:
tag_id = row.get("id")
else:
tag_id = ttable.insert(name=tag)
ltable.insert(post_id = post_id,
tag_id = tag_id,
)
# -------------------------------------------------------------------------
def customise_cms_post_resource(r, tablename):
db = current.db
s3db = current.s3db
table = s3db.cms_post
table.priority.readable = table.priority.writable = True
table.series_id.readable = table.series_id.writable = True
table.status_id.readable = table.status_id.writable = True
method = r.method
if method in ("create", "update"):
# Custom Form
from s3 import S3SQLCustomForm, S3SQLInlineComponent
crud_fields = [(T("Type"), "series_id"),
(T("Priority"), "priority"),
(T("Status"), "status_id"),
(T("Title"), "title"),
(T("Text"), "body"),
(T("Location"), "location_id"),
# Tags are added client-side
S3SQLInlineComponent("document",
name = "file",
label = T("Files"),
fields = [("", "file"),
#"comments",
],
),
]
if r.tablename != "event_incident":
if r.tablename == "event_event":
from gluon import IS_EMPTY_OR
from s3 import IS_ONE_OF
itable = s3db.event_incident
query = (itable.event_id == r.id) & \
(itable.closed == False) & \
(itable.deleted == False)
set = db(query)
f = s3db.event_post.incident_id
f.requires = IS_EMPTY_OR(
IS_ONE_OF(set, "event_incident.id",
f.represent,
orderby="event_incident.name",
sort=True))
crud_fields.insert(0, S3SQLInlineComponent("incident_post",
fields = [("", "incident_id")],
label = T("Incident"),
multiple = False,
))
crud_form = S3SQLCustomForm(*crud_fields
)
# Client support for Tags
appname = r.application
s3 = current.response.s3
scripts_append = s3.scripts.append
if s3.debug:
scripts_append("/%s/static/scripts/tag-it.js" % appname)
else:
scripts_append("/%s/static/scripts/tag-it.min.js" % appname)
scripts_append("/%s/static/themes/WACOP/js/update_tags.js" % appname)
if method == "create":
s3.jquery_ready.append('''wacop_update_tags("")''')
elif method == "update":
ttable = s3db.cms_tag
ltable = s3db.cms_tag_post
if r.tablename == "cms_post":
post_id = r.id
else:
post_id = r.component.id
query = (ltable.post_id == post_id) & \
(ltable.tag_id == ttable.id)
tags = db(query).select(ttable.name)
tags = [tag.name for tag in tags]
tags = ",".join(tags)
s3.jquery_ready.append('''wacop_update_tags("%s")''' % tags)
# Processing Tags
default = s3db.get_config(tablename, "onaccept")
if isinstance(default, list):
onaccept = default
onaccept.append(cms_post_onaccept)
else:
onaccept = [default, cms_post_onaccept]
s3db.configure(tablename,
crud_form = crud_form,
onaccept = onaccept,
)
elif method in ("custom", "datalist", "filter"):
# dataList configuration
from templates.WACOP.controllers import cms_post_list_layout
s3 = current.response.s3
s3.dl_no_header = True
s3db.configure(tablename,
list_fields = ["series_id",
"priority",
"status_id",
"date",
"title",
"body",
"created_by",
"tag.name",
"document.file",
"comment.id",
#"comment.body", # Extra fields come in unsorted, so can't match up to records
#"comment.created_by",
#"comment.created_on",
],
list_layout = cms_post_list_layout,
# Default
#orderby = "cms_post.date desc",
)
if method in ("custom", "filter"):
# Filter Widgets
from s3 import S3DateFilter, \
S3LocationFilter, \
S3OptionsFilter, \
S3TextFilter
if method == "filter":
# Apply filter_vars
get_vars = r.get_vars
for k, v in get_vars.iteritems():
# We only expect a maximum of 1 of these, no need to append
from s3 import FS
s3.filter = (FS(k) == v)
date_filter = S3DateFilter("date",
# If we introduce an end_date on Posts:
#["date", "end_date"],
label = "",
#hide_time = True,
slider = True,
clear_text = "X",
)
date_filter.input_labels = {"ge": "Start Time/Date", "le": "End Time/Date"}
from templates.WACOP.controllers import text_filter_formstyle
filter_widgets = [S3TextFilter(["body",
],
formstyle = text_filter_formstyle,
label = T("Search"),
_placeholder = T("Enter search term…"),
),
S3OptionsFilter("series_id",
label = "",
noneSelectedText = "Type", # T() added in widget
no_opts = "",
),
S3OptionsFilter("priority",
label = "",
noneSelectedText = "Priority", # T() added in widget
no_opts = "",
),
S3OptionsFilter("status_id",
label = "",
noneSelectedText = "Status", # T() added in widget
no_opts = "",
),
S3OptionsFilter("created_by$organisation_id",
label = "",
noneSelectedText = "Source", # T() added in widget
no_opts = "",
),
S3OptionsFilter("tag_post.tag_id",
label = "",
noneSelectedText = "Tag", # T() added in widget
no_opts = "",
),
date_filter,
]
if r.tablename == "event_event" or \
(method == "filter" and get_vars.get("event_post.event_id")):
# Event Profile
filter_widgets.insert(1, S3OptionsFilter("incident_post.incident_id",
label = "",
noneSelectedText = "Incident", # T() added in widget
no_opts = "",
))
user = current.auth.user
if user:
filter_widgets.insert(1, S3OptionsFilter("bookmark.user_id",
label = "",
options = {"*": T("All"),
user.id: T("My Bookmarks"),
},
cols = 2,
multiple = False,
table = False,
))
s3db.configure(tablename,
filter_widgets = filter_widgets,
)
settings.customise_cms_post_resource = customise_cms_post_resource
# -------------------------------------------------------------------------
# Event/Incident Management
#
settings.event.incident_teams_tab = "Units"
# Uncomment to preserve linked Incidents when an Event is deleted
settings.event.cascade_delete_incidents = False
# -------------------------------------------------------------------------
def customise_event_event_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod
db = current.db
s3db = current.s3db
# Virtual Fields
etable = s3db.event_event
#append = etable._virtual_methods.append
def event_name(row):
return A(row["event_event.name"],
_href = URL(c="event", f="event",
args=[row["event_event.id"], "custom"],
extension = "", # ensure no .aadata
),
)
#append(Field.Method("name_click", event_name))
etable.name_click = s3_fieldmethod("name_click",
event_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
represent = lambda v: v,
)
def event_status(row):
if row["event_event.exercise"]:
status = T("Testing")
elif not row["event_event.end_date"]:
status = T("Open")
else:
status = T("Closed")
return status
#append(Field.Method("status", event_status))
etable.status = s3_fieldmethod("status", event_status)
itable = s3db.event_incident
def event_incidents(row):
query = (itable.event_id == row["event_event.id"]) & \
(itable.deleted == False)
incidents = db(query).count()
return incidents
#append(Field.Method("incidents", event_incidents))
etable.incidents = s3_fieldmethod("incidents", event_incidents)
ertable = s3db.event_team
def event_resources(row):
query = (ertable.event_id == row["event_event.id"]) & \
(ertable.deleted == False)
resources = db(query).count()
return resources
#append(Field.Method("resources", event_resources))
etable.resources = s3_fieldmethod("resources", event_resources)
ettable = s3db.event_tag
ttable = s3db.cms_tag
def event_tags(row):
query = (ettable.event_id == row["event_event.id"]) & \
(ettable.deleted == False) & \
(ettable.tag_id == ttable.id)
tags = db(query).select(ttable.name)
if tags:
tags = [t.name for t in tags]
tags = ", ".join(tags)
return tags
else:
return current.messages["NONE"]
#append(Field.Method("tags", event_tags))
etable.tags = s3_fieldmethod("tags", event_tags)
list_fields = [(T("Name"), "name_click"),
(T("Status"), "status"),
(T("Zero Hour"), "start_date"),
(T("Closed"), "end_date"),
(T("City"), "location.location_id.L3"),
(T("State"), "location.location_id.L1"),
(T("Tags"), "tags"),
(T("Incidents"), "incidents"),
(T("Resources"), "resources"),
]
s3db.configure(tablename,
extra_fields = ("name",
"end_date",
"exercise",
),
list_fields = list_fields,
orderby = "event_event.name",
)
settings.customise_event_event_resource = customise_event_event_resource
# -------------------------------------------------------------------------
def customise_event_event_controller(**attr):
s3db = current.s3db
s3 = current.response.s3
# Modify Components
s3db.add_components("event_event",
# Events have just a single Location
event_event_location = {"joinby": "event_id",
"multiple": False,
},
# Incidents are linked to Events, not created from them
# - not a link table though, so can't change the actuation
#event_incident = {"joinby": "event_id",
# },
)
# Custom Browse
from templates.WACOP.controllers import event_Browse, event_Profile
set_method = s3db.set_method
set_method("event", "event",
method = "browse",
action = event_Browse)
# Custom Profile
set_method("event", "event",
method = "custom",
action = event_Profile)
# Custom prep
standard_prep = s3.prep
def custom_prep(r):
# Call standard postp
if callable(standard_prep):
result = standard_prep(r)
cname = r.component_name
if not cname:
f = s3db.event_event.event_type_id
f.readable = f.writable = False
elif cname == "task":
from gluon import IS_EMPTY_OR
from s3 import IS_ONE_OF, S3SQLCustomForm, S3SQLInlineComponent
itable = s3db.event_incident
query = (itable.event_id == r.id) & \
(itable.closed == False) & \
(itable.deleted == False)
set = current.db(query)
f = s3db.event_task.incident_id
f.requires = IS_EMPTY_OR(
IS_ONE_OF(set, "event_incident.id",
f.represent,
orderby="event_incident.name",
sort=True))
crud_form = S3SQLCustomForm(
S3SQLInlineComponent("incident",
fields = [("", "incident_id")],
label = T("Incident"),
multiple = False,
filterby = dict(field = "event_id",
options = r.id,
)
),
"name",
"description",
"source",
"priority",
"pe_id",
"date_due",
"status",
"comments",
)
r.component.configure(crud_form = crud_form,
)
elif r.representation == "popup" and r.get_vars.get("view"):
# Popups for lists in Parent Event of Incident Screen or Event Profile header
# No Title since this is on the Popup
s3.crud_strings["event_event"].title_display = ""
# No create button & Tweak list_fields
if cname == "incident":
list_fields = ["date",
"name",
"incident_type_id",
]
elif cname == "group":
list_fields = ["incident_id",
"group_id",
"status_id",
]
elif cname == "post":
list_fields = ["date",
"series_id",
"priority",
"status_id",
"body",
]
else:
# Shouldn't get here but want to avoid crashes
list_fields = []
r.component.configure(insertable = False,
list_fields = list_fields,
)
return True
s3.prep = custom_prep
# Custom rheader tabs
attr = dict(attr)
attr["rheader"] = wacop_event_rheader
return attr
settings.customise_event_event_controller = customise_event_event_controller
# -------------------------------------------------------------------------
def customise_event_incident_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod
s3db = current.s3db
# Virtual Fields
itable = s3db.event_incident
#append = itable._virtual_methods.append
def incident_name(row):
return A(row["event_incident.name"],
_href = URL(c="event", f="incident",
args=[row["event_incident.id"], "custom"],
extension = "", # ensure no .aadata
),
)
#append(Field.Method("name_click", incident_name))
itable.name_click = s3_fieldmethod("name_click",
incident_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
represent = lambda v: v,
)
def incident_status(row):
if row["event_incident.exercise"]:
status = T("Testing")
elif not row["event_incident.end_date"]:
status = T("Open")
else:
status = T("Closed")
return status
#append(Field.Method("status", incident_status))
itable.status = s3_fieldmethod("status", incident_status)
if r.method == "browse" or r.get_vars.get("browse"):
# Incident Browse
db = current.db
ertable = s3db.event_team
def incident_resources(row):
query = (ertable.event_id == row["event_incident.id"]) & \
(ertable.deleted == False)
resources = db(query).count()
return resources
#append(Field.Method("resources", incident_resources))
itable.resources = s3_fieldmethod("resources", incident_resources)
ettable = s3db.event_tag
ttable = s3db.cms_tag
def incident_tags(row):
query = (ettable.incident_id == row["event_incident.id"]) & \
(ettable.deleted == False) & \
(ettable.tag_id == ttable.id)
tags = db(query).select(ttable.name)
if tags:
tags = [t.name for t in tags]
tags = ", ".join(tags)
return tags
else:
return current.messages["NONE"]
#append(Field.Method("tags", incident_tags))
itable.tags = s3_fieldmethod("tags", incident_tags)
list_fields = [(T("Name"), "name_click"),
(T("Status"), "status"),
(T("Type"), "incident_type_id"),
(T("Zero Hour"), "date"),
(T("Closed"), "end_date"),
(T("City"), "location.location_id.L3"),
(T("State"), "location.location_id.L1"),
(T("Tags"), "tags"),
(T("Resources"), "resources"),
(T("Event"), "event_id"),
]
else:
# Homepage or Event Profile
list_fields = [(T("Name"), "name_click"),
(T("Status"), "status"),
(T("Type"), "incident_type_id"),
"location_id",
(T("Start"), "date"),
]
s3db.configure(tablename,
extra_fields = ("name",
"end_date",
"exercise",
),
list_fields = list_fields,
orderby = "event_incident.name",
)
settings.customise_event_incident_resource = customise_event_incident_resource
# -------------------------------------------------------------------------
def customise_event_incident_controller(**attr):
s3db = current.s3db
response = current.response
s3 = response.s3
# Load normal model to be able to override configuration
table = s3db.event_incident
table.event_id.readable = table.event_id.writable = True
# Custom Browse
from templates.WACOP.controllers import incident_Browse, incident_Profile
set_method = s3db.set_method
set_method("event", "incident",
method = "browse",
action = incident_Browse)
# Custom Profile
set_method("event", "incident",
method = "custom",
action = incident_Profile)
#s3.crud_strings["event_incident"].title_list = T("Browse Incidents")
# Custom prep
standard_prep = s3.prep
def custom_prep(r):
# Call standard postp
if callable(standard_prep):
result = standard_prep(r)
if r.method == "assign":
current.menu.main = ""
elif r.component_name == "task":
from s3 import S3SQLCustomForm
crud_form = S3SQLCustomForm("name",
"description",
"source",
"priority",
"pe_id",
"date_due",
"status",
"comments",
)
r.component.configure(crud_form = crud_form,
)
elif r.representation == "popup":
if not r.component:
if r.get_vars.get("set_event"):
# Popup just used to link to Event
#s3.crud_strings["event_incident"].title_update = T("Add to Event")
from s3 import S3SQLCustomForm
crud_form = S3SQLCustomForm("event_id",
)
s3db.configure("event_incident",
crud_form = crud_form,
)
#elif r.component_name == "post":
# from s3 import S3SQLCustomForm
# crud_form = S3SQLCustomForm("body",
# )
# s3db.configure("cms_post",
# crud_form = crud_form,
# )
return True
s3.prep = custom_prep
# Custom postp
standard_postp = s3.postp
def custom_postp(r, output):
# Call standard postp
if callable(standard_postp):
output = standard_postp(r, output)
if r.interactive and isinstance(output, dict):
if r.method == "assign":
# No Top Menu
current.menu.main = ""
# Custom View to waste less space inside popup
import os
response.view = os.path.join(r.folder,
"modules", "templates",
"WACOP", "views",
"assign.html")
#elif r.component_name == "post":
# # Add Tags - no, do client-side
# output["form"].append()
#else:
# # Summary or Profile pages
# # Additional styles
# s3.external_stylesheets += ["https://cdn.knightlab.com/libs/timeline3/latest/css/timeline.css",
# "https://fonts.googleapis.com/css?family=Merriweather:400,700|Source+Sans+Pro:400,700",
# ]
#if r.method == "summary":
# # Open the Custom profile page instead of the normal one
# from gluon import URL
# from s3 import S3CRUD
# custom_url = URL(args = ["[id]", "custom"])
# S3CRUD.action_buttons(r,
# read_url=custom_url,
# update_url=custom_url)
# # System-wide Alert
# from templates.WACOP.controllers import custom_WACOP
# custom = custom_WACOP()
# output["system_wide"] = custom._system_wide_html()
return output
s3.postp = custom_postp
# Custom rheader tabs
#attr = dict(attr)
#attr["rheader"] = wacop_event_rheader
attr["rheader"] = None
# No sidebar menu
current.menu.options = None
return attr
settings.customise_event_incident_controller = customise_event_incident_controller
# -------------------------------------------------------------------------
def customise_event_human_resource_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod
s3db = current.s3db
# Virtual Fields
# Always used from either the Event or Incident context
f = r.function
record_id = r.id
ehrtable = s3db.event_human_resource
hr_represent = ehrtable.human_resource_id.represent
def hr_name(row):
hr_id = row["event_human_resource.human_resource_id"]
return A(hr_represent(hr_id),
_href = URL(c="event", f=f,
args=[record_id, "human_resource", hr_id, "profile"],
),
)
ehrtable.name_click = s3_fieldmethod("name_click",
hr_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
# @ToDo: Bulk lookups
represent = lambda v: v,
)
s3db.configure(tablename,
#crud_form = crud_form,
extra_fields = ("human_resource_id",
),
list_fields = [(T("Name"), "name_click"),
(T("Title"), "human_resource_id$job_title_id"),
"human_resource_id$organisation_id",
(T("Email"), "human_resource_id$person_id$email.value"),
(T("Phone"), "human_resource_id$person_id$phone.value"),
"status",
(T("Notes"), "comments"),
],
orderby = "event_human_resource.human_resource_id",
)
settings.customise_event_human_resource_resource = customise_event_human_resource_resource
# -------------------------------------------------------------------------
def customise_event_organisation_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod
s3db = current.s3db
# Virtual Fields
# Always used from either the Event or Incident context
f = r.function
record_id = r.id
eotable = s3db.event_organisation
org_represent = eotable.organisation_id.represent
def org_name(row):
organisation_id = row["event_organisation.organisation_id"]
return A(org_represent(organisation_id),
_href = URL(c="event", f=f,
args=[record_id, "organisation", organisation_id, "profile"],
),
)
eotable.name_click = s3_fieldmethod("name_click",
org_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
# @ToDo: Bulk lookups
represent = lambda v: v,
)
s3db.configure(tablename,
#crud_form = crud_form,
extra_fields = ("organisation_id",
),
list_fields = [(T("Name"), "name_click"),
"status",
"comments",
],
orderby = "event_organisation.organisation_id",
)
settings.customise_event_organisation_resource = customise_event_organisation_resource
# -------------------------------------------------------------------------
def customise_event_team_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod, S3SQLCustomForm
s3db = current.s3db
ertable = s3db.event_team
#sertable.group_id.label = T("Resource")
# Form
# @ToDo: Have both Team & Event_Team in 1 form
crud_form = S3SQLCustomForm("incident_id",
"group_id",
"status_id",
)
# Virtual Fields
# Always used from either the Event or Incident context
f = r.function
record_id = r.id
group_represent = ertable.group_id.represent
def team_name(row):
group_id = row["event_team.group_id"]
return A(group_represent(group_id),
_href = URL(c="event", f=f,
args=[record_id, "group", group_id, "profile"],
extension = "", # ensure no .aadata
),
)
ertable.name_click = s3_fieldmethod("name_click",
team_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
# @ToDo: Bulk lookups
represent = lambda v: v,
)
s3db.configure(tablename,
crud_form = crud_form,
extra_fields = ("group_id",
),
list_fields = [(T("Name"), "name_click"),
"status_id",
],
orderby = "pr_group.name",
)
settings.customise_event_team_resource = customise_event_team_resource
# -------------------------------------------------------------------------
def customise_pr_group_resource(r, tablename):
s3db = current.s3db
current.response.s3.crud_strings[tablename] = Storage(
label_create = T("Create Resource"),
title_display = T("Resource Details"),
title_list = T("Resources"),
title_update = T("Edit Resource"),
label_list_button = T("List Resources"),
label_delete_button = T("Delete Resource"),
msg_record_created = T("Resource added"),
msg_record_modified = T("Resource updated"),
msg_record_deleted = T("Resource deleted"),
msg_list_empty = T("No Resources currently registered"))
field = s3db.pr_group.status_id
field.readable = field.writable = True
from s3 import S3SQLCustomForm
crud_form = S3SQLCustomForm((T("Name"), "name"),
"status_id",
"comments",
)
list_fields = [(T("Name"), "name"),
"status_id",
"comments",
]
s3db.configure(tablename,
crud_form = crud_form,
list_fields = list_fields,
)
settings.customise_pr_group_resource = customise_pr_group_resource
# -------------------------------------------------------------------------
def customise_pr_person_controller(**attr):
# Custom Profile
from templates.WACOP.controllers import person_Dashboard
current.s3db.set_method("pr", "person",
method = "dashboard",
action = person_Dashboard)
# No sidebar menu
current.menu.options = None
return attr
settings.customise_pr_person_controller = customise_pr_person_controller
# -------------------------------------------------------------------------
def customise_project_task_resource(r, tablename):
from gluon import A, URL
from s3 import s3_fieldmethod
s3db = current.s3db
# Virtual Fields
# Always used from either the Event or Incident context
f = r.function
record_id = r.id
def task_name(row):
return A(row["project_task.name"],
_href = URL(c="event", f=f,
args=[record_id, "task", row["project_task.id"], "profile"],
),
)
s3db.project_task.name_click = s3_fieldmethod("name_click",
task_name,
# over-ride the default represent of s3_unicode to prevent HTML being rendered too early
represent = lambda v: v,
)
s3db.configure(tablename,
#crud_form = crud_form,
extra_fields = ("name",
),
list_fields = ["status",
(T("Description"), "name_click"),
(T("Created"), "created_on"),
(T("Due"), "date_due"),
],
orderby = "project_task.date_due",
)
settings.customise_project_task_resource = customise_project_task_resource
# =============================================================================
def wacop_event_rheader(r, tabs=[]):
""" EVENT custom resource headers """
if r.representation != "html":
# Resource headers only used in interactive views
return None
from s3 import s3_rheader_resource, S3ResourceHeader
tablename, record = s3_rheader_resource(r)
if tablename != r.tablename:
resource = current.s3db.resource(tablename, id=record.id)
else:
resource = r.resource
rheader = None
rheader_fields = []
if record:
T = current.T
if tablename == "event_event":
if not tabs:
tabs = [(T("Event Details"), None),
(T("Incidents"), "incident"),
(T("Units"), "group"),
(T("Tasks"), "task"),
(T("Updates"), "post"),
]
rheader_fields = [["name"],
["start_date"],
["comments"],
]
elif tablename == "event_incident":
if not tabs:
tabs = [(T("Incident Details"), None),
(T("Units"), "group"),
(T("Tasks"), "task"),
(T("Updates"), "post"),
]
rheader_fields = [["name"],
["date"],
["comments"],
]
rheader = S3ResourceHeader(rheader_fields, tabs)(r,
table=resource.table,
record=record,
)
return rheader
# END =========================================================================
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SNACC_Mapper/trend_node.py | emariwileslee/SNACC | 0 | 97369 | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 7 09:58:55 2021
@author: emari
"""
import numpy as np
import pandas as pd
class node():
def __init__(self):
self.parent_node = ""
self.child_connections = [np.array([],dtype=object),np.array([],dtype=object),np.array([],dtype=object),np.array([],dtype=object),np.array([],dtype=object)]
self.username = ""
self.connection_type = ""
self.bio = ""
self.captions = []
self.total_likes = 0
self.total_followers = 0
self.total_following = 0
self.post_date = ""
self.root_post_url = ""
self.profile_img_url = ""
#This is selector for profile image
##react-root > section > main > div > header > div > div > span > img
def printNode(self):
print("parent node: ",self.parent_node)
print("username: ",self.username)
print("connection_type: ",self.connection_type)
print("total_likes: ",self.total_likes)
print("total_followers: ",self.total_followers)
print("total_following: ",self.total_following)
class nodeClassifier():
def __init__(self,output_location):
self.node_list = []
self.output_location = output_location
self.node_df = pd.DataFrame(columns=["parent_node","username","connection_type","bio","captions","total_likes","total_followers","total_following","profile_img_url","root_post_url"])
def addNode(self,node):#(self,parent_node,username,connection_type):
#nodeBuffer = [parent_node,username,connection_type]
nodeBuffer = [node.parent_node,node.username,node.connection_type,node.bio,'foo',node.total_likes,node.total_followers,node.total_following,node.profile_img_url,node.root_post_url]
self.node_df = self.node_df.append(pd.Series(nodeBuffer,index=self.node_df.columns),ignore_index=True)
self.node_list.append(node)
#self.exportNode(node)
#print(self.node_df)
def printNetwork(self):
print(self.node_df)
#assume that the parent_node has already been added
#add the child node to the db
#find the parent node
#create the connection from the "parent" to the username
def exportNetwork(self):
self.node_df.to_csv(self.output_location+"\output.csv",index=False) | [
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test users/tcp_service_test.py | Tapawingo/FreeTakServer | 27 | 124667 | import unittest
from test import support
import socket
import test_data
import time
from lxml import etree
import uuid
import asyncio
from common_testing_tools import TCPClient
class TCPServiceTests(unittest.TestCase):
def setUp(self):
""" setup method to establish two sockets.
this method is run prior to any other tests running and will create the basic sockets
required for further testing.
"""
self.client_socket_a = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.client_socket_b = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
def test_old_clientinformation_sent(self):
""" this method tests that most recent geo information is shared upon connection of new client
test procedure:
client A conn -> FTS
client A send geo update -> FTS
client B conn -> FTS
client B <-updated client A geo Information FTS
"""
client_a_object = TCPClient(ip='127.0.0.1', port=15777) # establish client A connection
time.sleep(1)
cot = test_data.TestCoTClient(uid= client_a_object.clientObj.uid).generate_cot()
client_a_object.send_specific_data(cot=cot)
client_b_object = TCPClient(ip='127.0.0.1', port=15777)
data = client_b_object.receive_specific_data(10000)
self.assertIn(cot, data)
def test_clientinformation_is_not_sent_issue(self):
""" this method tests that client data is not resent after disconnections
test procedure:
client A conn-> FTS
client A clientinfo-> FTS
client A close-> FTS
client B conn-> FTS
client B clientinfo-> FTS
client B <-otherclientinfo FTS
client B otherclientinfo != clientAinfo
client B close-> FTS
"""
client_a_object = self.connect_client_to_server(sock = self.client_socket_a, ip = '127.0.0.1', port = 15777, uid = '245397c8-69f7-4f68-979e-685ff7ee1451') # establish client A connection
time.sleep(1)
self.client_socket_a.close()
time.sleep(5) # if this delay is reduced the test fails as FTS doesn't have time to process the changes
client_b_object = self.connect_client_to_server(sock = self.client_socket_b, ip = '127.0.0.1', port = 15777) # establish client B connection
time.sleep(1)
# receive data and assert client_a_data not in resent data
start = time.time()
delay = 3 # 10 second delay
while time.time() < start+delay:
data = b''
self.client_socket_b.settimeout(0.1)
while b'</event>' not in data:
try:
data += self.client_socket_b.recv(1)
except socket.timeout:
break
if data == b'':
break
cot = etree.fromstring(data)
self.assertNotEqual(client_a_object.uid, cot.get('uid'))
self.client_socket_b.close() # disconnect final socket
def test_simple_client_connection(self):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(('127.0.0.1', 15777))
client_object = test_data.TestCoTClient()
sock.send(client_object.generate_cot())
sock.close()
def connect_client_to_server(self, sock: socket.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM), ip: str = '127.0.0.1', port: int = 15777, uid: str = str(uuid.uuid4())) -> test_data.TestCoTClient:
"""this method is used to connect a client to the server and send a basic connection message
"""
sock.connect((ip, port))
client_object = test_data.TestCoTClient(uid=uid)
sock.send(client_object.generate_cot())
return client_object
if __name__ == '__main__':
unittest.main()
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program/tests/integration_tests_output/graph/graph.py | alienbri/audaces-perps | 0 | 20814 | <filename>program/tests/integration_tests_output/graph/graph.py
import yaml
import matplotlib.pyplot as plt
import math
from jsonmerge import merge
from datetime import datetime
import plotly as ply
import pandas as pd
import plotly.express as px
TRANSFORM = False
PLOT_MEMORY = True
NB_INSTRUCTIONS = 1000
f_value_props = {
# [Color, MinOffset, MaxOffset]
"total_collateral": ["", 0, 1],
"total_fee_balance": ["", 0, 1],
"rebalancing_funds": ["#99cc99", 0, 0.5], #
# "rebalanced_v_coin": ["", 0, 1],
"v_coin_amount": ["", 0, 1],
"v_pc_amount": ["", 0, 1],
"open_shorts_v_coin": ["", 0, 1],
"open_longs_v_coin": ["", 0, 1], #
"insurance_fund": ["#808080", 0.2, 1.2],
"market_price": ["#008080", 0.5, 1.5],
"oracle_price": ["#99cc99", 0.5, 1.5],
"equilibrium_price": ["#ff8000", 0.5, 1], #
# "signer_nonce",
# "market_symbol",
# "oracle_address",
# "admin_address",
# "vault_address",
# "quote_decimals",
# "coin_decimals",
# "total_user_balances",
# "last_funding_timestamp",
# "last_recording_timestamp",
# "funding_samples_offset",
# "funding_samples",
# "funding_history_offset",
# "funding_history",
# "funding_balancing_factors",
# "number_of_instances",
}
m_value_props = {
"gc_list_lengths",
"page_full_ratios",
"longs_depths",
"shorts_depths"
}
market_state_line_header = "INFO - MarketDataPoint"
date_time = datetime.now().strftime("%d-%m-%Y_%H-%M-%S")
infile = open("../log/output.log")
outfile = open(
"../log/formatted_output_{}.log".format(date_time), "a")
market_data_json = []
for line in infile:
if (market_state_line_header in line) or ("DEBUG - Program" in line) or ("DEBUG - tx error:" in line) or ("INFO - Tree:" in line) or ("INFO - Initial Conditions:" in line) or ("INFO - Seed for this run:" in line):
outfile.write(line)
if market_state_line_header in line:
market_state_datapoint_str = line[len(
market_state_line_header):].replace("Instance", "").replace("PageInfo", "") # Stripping header
line_json = yaml.load(market_state_datapoint_str)
market_data_json.append(line_json)
# Extract
market_data = {}
value_names = list(f_value_props.keys())
for key in market_data_json[0]:
if key in value_names:
market_data[key] = [data_point[key] for data_point in market_data_json]
# Normalize
if TRANSFORM:
max_per_value = [max(market_data[key]) for key in value_names]
min_per_value = [min(market_data[key]) for key in value_names]
max_per_value[value_names.index(
"market_price")] = max_per_value[value_names.index("oracle_price")]
min_per_value[value_names.index(
"market_price")] = min_per_value[value_names.index("oracle_price")]
scaled_market_data = [[((1 - f_value_props[value_names[i]][1]) * (data_value_point - min_per_value[i]) / abs((max_per_value[i] / f_value_props[value_names[i]][2]) - min_per_value[i])) + f_value_props[value_names[i]][1] for data_value_point in market_data[value_names[i]]]
for i in range(len(value_names))]
else:
max_per_value = [max(market_data[key]) for key in value_names]
total_max = max(max_per_value)
scaling_factors = [int(round(math.log10(total_max / value_max)))
if value_max != 0 else 1 for value_max in max_per_value]
scaled_market_data = [[(10 ** scaling_factors[i]) * data_value_point for data_value_point in market_data[value_names[i]]]
for i in range(len(value_names))]
# Plotting
if PLOT_MEMORY:
nb_lines = min(len(market_data_json), NB_INSTRUCTIONS)
df = pd.DataFrame(market_data_json)
print(df.columns)
print(df.shape)
df["shorts_depths"] = [k[0] for k in df["shorts_depths"]]
df["longs_depths"] = [k[0] for k in df["longs_depths"]]
df["gc_list_lengths"] = [k[0] for k in df["gc_list_lengths"]]
for k in range(len(df["page_full_ratios"][0][0])):
df[f"page_{k}_full_ratio"] = [l[0][k] for l in df["page_full_ratios"]]
df.drop("page_full_ratios", axis=1)
df = df.stack().reset_index()
print(df)
fig = px.line(df, x="level_0", y=0, color="level_1")
fig.show()
# print([len(m["page_full_ratios"]) for m in market_data_json])
page_full_ratios = [
market_data_json[i]["page_full_ratios"][0] for i in range(nb_lines)]
longs_depths = [
market_data_json[i]["longs_depths"] for i in range(nb_lines)
]
shorts_depths = [
market_data_json[i]["shorts_depths"] for i in range(nb_lines)
]
for k in range(len(market_data_json[0]["page_full_ratios"][0])):
plt.plot([page_full_ratios[i][k] for i in range(nb_lines)], label=(
"page_full_ratios for page " + str(k)))
plt.plot()
gc_list_lenghts = [
market_data_json[i]["gc_list_lengths"][0] for i in range(nb_lines)] # TODO Mult instances
# plt.plot([gc_list_lenghts[i] for i in range(nb_lines)], label=(
# "gc_list_length"))
plt.plot(longs_depths, label=("longs_depths"))
plt.plot(shorts_depths, label=("shorts_depths"))
elif TRANSFORM:
for (i, key) in enumerate(value_names):
if f_value_props[key][0] != "":
plt.plot(scaled_market_data[i][:NB_INSTRUCTIONS], label=(
key + " x1e"), color=f_value_props[key][0])
else:
plt.plot(scaled_market_data[i][:NB_INSTRUCTIONS], label=(
key + " x1e"))
else:
for (i, key) in enumerate(value_names):
if f_value_props[key][0] != "":
plt.plot(scaled_market_data[i], label=(
key + " x1e" + str(scaling_factors[i])), color=f_value_props[key][0])
else:
plt.plot(scaled_market_data[i], label=(
key + " x1e"))
plt.legend(prop={'size': 15})
plt.show() # block=False)
# plt.savefig("../log/graph_{}.png".format(date_time), dpi=440)
# gc_list_lengths: [0], page_full_ratios: [[], [0.0, 0.0, 0.0, 0.0, 0.0]]
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2
] |
larc/templates/common.py | lowlandresearch/larc | 0 | 58341 | <gh_stars>0
import re
from pathlib import Path
import calendar
import jinja2
import toolz.curried as _
from .. import common as __
from .. import logging
from .. import images
from .. import markdown
log = logging.new_log(__name__)
@_.curry
def find_files(start, filename):
start_path = Path(start).resolve()
log.info(f'Finding image {filename}')
if Path(start_path, filename).exists():
path = Path(start_path, filename)
log.info(f' found: {path}')
return [path]
file_re = re.compile(
filename.lower().replace('.', '\\.').replace('*', '.*')
)
log.info(f' searching with re: {file_re.pattern}')
paths = _.pipe(
__.walk(start_path),
filter(lambda p: file_re.search(p.name.lower())),
_.partial(sorted, key=lambda p: p.name),
tuple,
)
if not paths:
log.error(
f'Could not find any filenames meeting the search string'
f' "{filename}" looking in directory: {start_path}'
)
return paths
def pluralize(number, singular='', plural='s'):
if number == 1:
return singular
return plural
def md_to_html(text):
return markdown.markdown(text)
def long_date(text):
import datetime
import dateutil.parser
try:
if isinstance(text, datetime.datetime):
dt = text
else:
dt = dateutil.parser.parse(text)
return dt.strftime('%B %d, %Y')
except ValueError as error:
log.error(
'Error in long_date:\n'
f'error: {error}\n'
f'for text: {repr(text)}\n'
'exception:', exc_info=True
)
def year(text):
return _.pipe(
text,
__.maybe_dt,
__.deref('year'),
)
def month(text):
return _.pipe(
text,
__.maybe_dt,
__.deref('month'),
lambda m: calendar.month_name[m],
)
def add_filters(env: jinja2.Environment):
env.filters['pluralize'] = pluralize
env.filters['md_to_html'] = md_to_html
env.filters['long_date'] = long_date
env.filters['year'] = year
env.filters['month'] = month
env.filters['b64encode'] = __.b64encode_str
return env
@_.curry
def make_figure(im_type, filename, caption, images, *, style: dict = None):
style = _.pipe(
_.pipe(
style.items(),
__.vmap(lambda k, v: f"{k}: {v}"),
';'.join,
),
'style="{}"'.format,
) if style else ''
figure = {'type': im_type, 'filename': Path(filename).name,
'search': filename, 'caption': caption,
'images': images, 'style': style}
return get_env().get_template('figure.html.j2').render(figure=figure)
@_.curry
def figure_function(im_type, bytes_function, caption, filename, start='.',
*, style: dict = None):
return _.pipe(
filename,
find_files(start),
map(bytes_function),
map(__.b64encode_str),
tuple,
make_figure(im_type, filename, caption, style=style),
)
png = figure_function('png', images.common.png_bytes)
jpeg = figure_function('jpeg', images.common.jpeg_bytes)
def table(data):
return get_env().get_template('table.html.j2').render(data=data)
def add_functions(env: jinja2.Environment):
env.globals['png'] = png
env.globals['jpeg'] = jpeg
env.globals['table'] = table
return env
get_env = _.compose_left(
jinja2.Environment,
add_filters,
add_functions,
)
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] |
models/ACT/__init__.py | for-ai/ACT | 18 | 149687 | <reponame>for-ai/ACT
__all__ = ["act"]
from .act import *
| [
1,
529,
276,
1112,
420,
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1454,
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627,
3108,
13,
13,
3166,
869,
627,
1053,
334,
13,
2
] |
setup.py | Willd14469/cj8-patient-panthers | 1 | 17420 | import sys
from setuptools import setup
required_packages = ["boombox", "Pillow", "PyYAML", "rich"]
win_packages = ["keyboard"]
unix_packages = ["pynput"]
WIN = "win32"
LINUX = "linux"
MACOS = "darwin"
if sys.platform == WIN:
required_packages += win_packages
elif sys.platform in (LINUX, MACOS):
required_packages += unix_packages
setup(
name="pantheras_box",
version="0.1.0",
packages=[
"pantheras_box",
"pantheras_box.story",
"pantheras_box.sounds",
"pantheras_box.backend",
"pantheras_box.frontend",
"pantheras_box.keyboard_handlers",
],
url="",
license="MIT",
author="<NAME>",
author_email="",
description="Pantheras box TUI game.",
install_requires=required_packages,
entry_points={
"console_scripts": [
"pantheras-box = pantheras_box.run:run_game",
],
},
package_data={"": ["**/*.txt", "**/*.yaml", "**/*.png", "**/*.wav"]},
include_package_data=True,
)
| [
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] |
src/dataset.py | hrkz/SubgridTransportNN | 5 | 183141 | <filename>src/dataset.py
import torch
import numpy as np
import h5py
import matplotlib
import matplotlib.pyplot as plt
import glob
plt.rcParams.update({'mathtext.fontset':'cm'})
class SubgridDataset(torch.utils.data.Dataset):
def __init__(self, device, path, samples, size, x, y, established=0):
self.device = device
self.path = path
self.samples = samples - established
self.size = size
self.established = established
self.x = x
self.y = y
self.inputs = torch.zeros(self.samples, len(x), size, size, size, dtype=torch.float32)
self.labels = torch.zeros(self.samples, len(y), size, size, size, dtype=torch.float32)
for i, f in enumerate(x):
for j, c in enumerate(sorted(glob.glob(path + f + '_*'))):
if j < established:
# dynamic not established
continue
elif j >= samples:
break
# transform
p = j - established
file = h5py.File(c, 'r')
dset = file[f]
self.inputs[p][i] = torch.from_numpy(dset[()])
for i, f in enumerate(y):
for j, c in enumerate(sorted(glob.glob(path + f + '_*'))):
if j < established:
# dynamic not established
continue
elif j >= samples:
break
# transform
p = j - established
#to_pytorch(self.labels[p][i], f, c)
file = h5py.File(c, 'r')
dset = file[f]
self.labels[p][i] = torch.from_numpy(dset[()])
def plot(self, ylabels, plot_samples=5, slice=5):
features_inputs = self.inputs.shape[1]
features_labels = self.labels.shape[1]
features = features_inputs + features_labels
fig, ax = plt.subplots(
nrows=features,
ncols=plot_samples + 1,
figsize=(plot_samples * 2.5, features * 2.5),
constrained_layout=True,
gridspec_kw={"width_ratios": np.append(np.repeat(1, plot_samples), 0.05)}
)
fig.suptitle(r'Cut in dataset throughout the simulation of $\mathcal{S}$')
max_input, _ = self.inputs[:,:,slice].flatten(start_dim=2).max(2)[0].max(0)
min_input, _ = self.inputs[:,:,slice].flatten(start_dim=2).min(2)[0].min(0)
max_label, _ = self.labels[:,:,slice].flatten(start_dim=2).max(2)[0].max(0)
min_label, _ = self.labels[:,:,slice].flatten(start_dim=2).min(2)[0].min(0)
for j in range(plot_samples):
sample = int(
(j) *
((self.samples - 1) /
(plot_samples - 1) ))
off = len(self.x)
for i, f in enumerate(self.x):
c = ax[i, j].contourf(self.inputs[sample][i][slice], 100, cmap='bwr', vmin=min_input[i], vmax=max_input[i])
if j < 1:
# add label
ax[i, j].set_ylabel(ylabels[i], fontsize=20.0)
fig.colorbar(c, cax=ax[i, plot_samples])
for i, f in enumerate(self.y):
l = ax[i + off, j].contourf(self.labels[sample][i][slice], 100, cmap='bwr', vmin=min_label[0], vmax=max_label[0])
if j < 1:
# add label
ax[i + off, j].set_ylabel(ylabels[i + off], fontsize=20.0)
fig.colorbar(l, cax=ax[i + off, plot_samples])
ax[i + off, j].set_xlabel(r'$\mathcal{S}_{' + str((self.established + sample + 1) * 500) + r'}$', fontsize=20.0)
#fig.savefig('dataset.png')
def __len__(self):
return self.samples
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
inputs = self.inputs[idx]
labels = self.labels[idx]
return (inputs.to(self.device), labels.to(self.device))
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] |
tf_quant_finance/volatility/black_scholes.py | 0xflotus/tf-quant-finance | 2 | 156770 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://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.
"""Black Scholes prices of a batch of European options."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow_probability as tfp
def option_price(forwards,
strikes,
volatilities,
expiries,
discount_factors=None,
is_call_options=None,
dtype=None,
name=None):
"""Computes the Black Scholes price for a batch of European options.
## References:
[1] Hull, <NAME>., Options, Futures and Other Derivatives. Pearson, 2018.
[2] Wikipedia contributors. Black-Scholes model. Available at:
https://en.wikipedia.org/w/index.php?title=Black%E2%80%93Scholes_model
Args:
forwards: A real `Tensor` of any shape. The current forward prices to
expiry.
strikes: A real `Tensor` of the same shape and dtype as `forwards`. The
strikes of the options to be priced.
volatilities: A real `Tensor` of same shape and dtype as `forwards`. The
volatility to expiry.
expiries: A real `Tensor` of same shape and dtype as `forwards`. The expiry
for each option. The units should be such that `expiry * volatility**2` is
dimensionless.
discount_factors: A real `Tensor` of same shape and dtype as the `forwards`.
The discount factors to expiry (i.e. e^(-rT)). If not specified, no
discounting is applied (i.e. the undiscounted option price is returned).
Default value: None, interpreted as discount factors = 1.
is_call_options: A boolean `Tensor` of a shape compatible with `forwards`.
Indicates whether to compute the price of a call (if True) or a put (if
False). If not supplied, it is assumed that every element is a call.
dtype: Optional `tf.DType`. If supplied, the dtype to be used for conversion
of any supplied non-`Tensor` arguments to `Tensor`.
Default value: None which maps to the default dtype inferred by TensorFlow
(float32).
name: str. The name for the ops created by this function.
Default value: None which is mapped to the default name `option_price`.
Returns:
option_prices: A `Tensor` of the same shape as `forwards`. The Black
Scholes price of the options.
#### Examples
```python
forwards = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0])
volatilities = np.array([0.0001, 102.0, 2.0, 0.1, 0.4])
expiries = 1.0
computed_prices = option_price(
forwards,
strikes,
volatilities,
expiries,
dtype=tf.float64)
# Expected print output of computed prices:
# [ 0. 2. 2.04806848 1.00020297 2.07303131]
```
"""
with tf.compat.v1.name_scope(
name,
default_name='option_price',
values=[
forwards, strikes, volatilities, expiries, discount_factors,
is_call_options
]):
forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')
strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')
volatilities = tf.convert_to_tensor(
volatilities, dtype=dtype, name='volatilities')
expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')
if discount_factors is None:
discount_factors = 1
discount_factors = tf.convert_to_tensor(
discount_factors, dtype=dtype, name='discount_factors')
normal = tfp.distributions.Normal(
loc=tf.zeros([], dtype=forwards.dtype), scale=1)
sqrt_var = volatilities * tf.math.sqrt(expiries)
d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var
d2 = d1 - sqrt_var
undiscounted_calls = forwards * normal.cdf(d1) - strikes * normal.cdf(d2)
if is_call_options is None:
return discount_factors * undiscounted_calls
undiscounted_forward = forwards - strikes
undiscounted_puts = undiscounted_calls - undiscounted_forward
return discount_factors * tf.where(is_call_options, undiscounted_calls,
undiscounted_puts)
def binary_price(forwards,
strikes,
volatilities,
expiries,
discount_factors=None,
is_call_options=None,
dtype=None,
name=None):
"""Computes the Black Scholes price for a batch of European binary options.
The binary call (resp. put) option priced here is that which pays off a unit
of cash if the underlying asset has a value greater (resp. smaller) than the
strike price at expiry. Hence the binary option price is the discounted
probability that the asset will end up higher (resp. lower) than the
strike price at expiry.
## References:
[1] <NAME>., Options, Futures and Other Derivatives. Pearson, 2018.
[2] Wikipedia contributors. Binary option. Available at:
https://en.wikipedia.org/w/index.php?title=Binary_option
Args:
forwards: A real `Tensor` of any shape. The current forward prices to
expiry.
strikes: A real `Tensor` of the same shape and dtype as `forwards`. The
strikes of the options to be priced.
volatilities: A real `Tensor` of same shape and dtype as `forwards`. The
volatility to expiry.
expiries: A real `Tensor` of same shape and dtype as `forwards`. The expiry
for each option. The units should be such that `expiry * volatility**2` is
dimensionless.
discount_factors: A real `Tensor` of same shape and dtype as the `forwards`.
The discount factors to expiry (i.e. e^(-rT)). If not specified, no
discounting is applied (i.e. the undiscounted option price is returned).
Default value: None, interpreted as discount factors = 1.
is_call_options: A boolean `Tensor` of a shape compatible with `forwards`.
Indicates whether to compute the price of a call (if True) or a put (if
False). If not supplied, it is assumed that every element is a call.
dtype: Optional `tf.DType`. If supplied, the dtype to be used for conversion
of any supplied non-`Tensor` arguments to `Tensor`.
Default value: None which maps to the default dtype inferred by TensorFlow
(float32).
name: str. The name for the ops created by this function.
Default value: None which is mapped to the default name `binary_price`.
Returns:
option_prices: A `Tensor` of the same shape as `forwards`. The Black
Scholes price of the binary options with unit of cash payoff.
#### Examples
```python
forwards = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0])
volatilities = np.array([0.0001, 102.0, 2.0, 0.1, 0.4])
expiries = 1.0
prices = binary_price(forwards, strikes, volatilities, expiries,
dtype=tf.float64)
# Expected print output of prices:
# [0. 0. 0.15865525 0.99764937 0.85927418]
```
"""
with tf.compat.v1.name_scope(
name,
default_name='binary_price',
values=[
forwards, strikes, volatilities, expiries, discount_factors,
is_call_options
]):
forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')
strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')
volatilities = tf.convert_to_tensor(
volatilities, dtype=dtype, name='volatilities')
expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')
if is_call_options is None:
is_call_options = True
if discount_factors is None:
discount_factors = 1
discount_factors = tf.convert_to_tensor(
discount_factors, dtype=dtype, name='discount_factors')
sqrt_var = volatilities * tf.math.sqrt(expiries)
d2 = (tf.math.log(forwards / strikes) - sqrt_var * sqrt_var / 2) / sqrt_var
one = tf.ones_like(forwards)
d2_signs = tf.where(is_call_options, one, -one)
normal = tfp.distributions.Normal(
loc=tf.zeros([], dtype=forwards.dtype), scale=1)
return discount_factors * normal.cdf(d2_signs * d2)
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] |
tests/test_index_db.py | tos-kamiya/d2vg | 3 | 1611616 | <filename>tests/test_index_db.py
from typing import *
import unittest
import contextlib
from itertools import zip_longest
import os
import tempfile
import numpy as np
from d2vg import index_db
@contextlib.contextmanager
def back_to_curdir():
curdir = os.getcwd()
try:
yield
finally:
os.chdir(curdir)
def touch(file_name: str):
with open(file_name, "w") as outp:
print("", end="", file=outp)
class IndexDbTest(unittest.TestCase):
def test_has(self):
pos_vecs: List[index_db.PosVec] = [
(0, 1, np.array([2, 3], dtype=np.float32)),
(1, 2, np.array([3, 4], dtype=np.float32)),
]
with tempfile.TemporaryDirectory() as tempdir:
with back_to_curdir():
os.chdir(tempdir)
file_a = os.path.join("a")
touch(file_a)
file_index_db = "index_db"
db = index_db.open(file_index_db, 1)
self.assertFalse(db.has(file_a))
db.store(file_a, pos_vecs)
self.assertTrue(db.has(file_a))
# path is normalized, so './a' is same as 'a'
dot_file_a = os.path.join(os.curdir, file_a)
self.assertTrue(db.has(dot_file_a))
# file name with absolute path is not stored
abs_file_a = os.path.abspath(file_a)
self.assertFalse(db.has(abs_file_a))
def test_lookup(self):
pos_vecs: List[index_db.PosVec] = [
(0, 1, np.array([2, 3], dtype=np.float32)),
(1, 2, np.array([3, 4], dtype=np.float32)),
]
with tempfile.TemporaryDirectory() as tempdir:
with back_to_curdir():
os.chdir(tempdir)
file_a = os.path.join("a")
touch(file_a)
file_index_db = "index_db"
db = index_db.open(file_index_db, 1)
db.store(file_a, pos_vecs)
self.assertTrue(db.has(file_a))
act = db.lookup(file_a)
for a, e in zip_longest(act, pos_vecs):
self.assertEqual(a[0], e[0])
self.assertEqual(a[1], e[1])
for a2i, e2i in zip_longest(a[2], e[2]):
self.assertEqual(a2i, e2i)
if __name__ == "__main__":
unittest.main()
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1678,
443,
27958,
29889,
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580,
13,
2
] |
pyz3r/exceptions.py | mgius/pyz3r | 0 | 37416 | <filename>pyz3r/exceptions.py
class alttprException(Exception):
pass
class alttprFailedToRetrieve(Exception):
pass
class alttprFailedToGenerate(Exception):
pass
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tests/core_symbol.py | cubetrain/CubeTrain | 0 | 46385 | <gh_stars>0
CORE_SYMBOL='SEAT'
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] |
dog.py | AmjadNoor/ReconDog | 1 | 62436 | <gh_stars>1-10
#!/usr/bin/env python3
import sys
import requests
import argparse
from core.hq import hq
from core.extractor import extractor
from core.colors import white, green, end, red, yellow, run
parser = argparse.ArgumentParser()
parser.add_argument('-t', help='target', dest='target')
parser.add_argument('-c', help='choice', dest='choice')
parser.add_argument('--domains', help='stdin type: domain', dest='domains', action='store_true')
parser.add_argument('--ips', help='stdin type: ip', dest='ips', action='store_true')
args = parser.parse_args()
ips = args.ips
target = args.target
choice = args.choice
domains = args.domains
data = False
if ips or domains:
data = sys.stdin.readlines()
arged = False
if target and choice:
arged = True
if sys.version_info < (3, 0):
input = raw_input
def banner():
print ('''%s
_____ ____
| __ |___ ___ ___ ___ %s|\_/|%s | \ ___ ___
| -| -_| _| . | | %s|. .|%s | | | . | . |
|__|__|___|___|___|_|_| %s \_/ %s |____/|___|_ |
|___| v2.0%s''' % (white, red, white, red, white, red, white, end))
def menu():
print('''
%s1.%s Censys
%s2.%s NS lookup
%s3.%s Port scan
%s4.%s Detect CMS
%s5.%s Whois lookup
%s6.%s Detect honeypot
%s7.%s Find subdomains
%s8.%s Reverse IP lookup
%s9.%s Detect technologies
%s0.%s All''' % (white, end, white, end, white, end, white, end, white, end, white, end, white, end, white, end, white, end, white, end))
def dog(choice, target):
if not args.target:
banner()
if arged:
hq(choice, target)
else:
while True:
menu()
result = False
choice = input('\033[1;91m>>\033[0m ')
hq(choice)
if data:
kind = 'domain'
if ips:
kind = 'ip'
targets = extractor(data, kind)
if choice:
for target in targets:
print ('%s %s' % (run, target))
hq(choice, target)
print (red + ('-' * 60) + end)
else:
for target in targets:
sys.stdout.write(target + '\n')
else:
try:
dog(choice, target)
except KeyboardInterrupt:
quit('')
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examples/plugin_log_task_usage/setup.py | kinow/miniwdl | 2 | 122223 | <reponame>kinow/miniwdl
from setuptools import setup
setup(
name='miniwdl_log_task_usage',
version='0.0.1',
description='miniwdl task plugin to log container cpu+mem usage',
author='<NAME>',
py_modules=["miniwdl_log_task_usage"],
python_requires='>=3.6',
setup_requires=['reentry'],
reentry_register=True,
entry_points={
'miniwdl.plugin.task': ['log_task_usage = miniwdl_log_task_usage:main'],
}
)
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tests/test_machine.py | learnitall/foremanlite | 0 | 131900 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Test components of foremanlite.machine
The tests in here are pretty basic and non-exhaustive.
Could use more work to get truly full coverage.
"""
import re
import typing as t
import pytest
from hypothesis import given
from hypothesis import strategies as st
from foremanlite.machine import (
Arch,
Mac,
Machine,
MachineGroup,
MachineGroupSet,
MachineSelector,
SelectorMatchStr,
get_uuid,
)
from .conftest import machine_strategy, two_unique_machines_strategy
class TestMachine:
"""Test functionality of foremanlite.machine.Machine"""
@staticmethod
@given(st.sampled_from([a.value for a in Arch]))
def test_str_arch_gets_cased_to_enum(arch_str: str):
"""Test the Arch attr gets casted to enum, even if str given."""
assert isinstance(arch_str, str) # quick sanity check
machine = Machine(
name="name",
mac=Mac("mac"),
provision=True,
arch=arch_str,
)
assert isinstance(machine.arch, Arch)
assert machine.arch.value == arch_str
assert machine.arch == Arch(arch_str)
@staticmethod
@given(two_unique_machines_strategy())
def test_hash_is_different_for_two_different_machines(
machines: t.Tuple[Machine, Machine]
):
"""Test the hash of a machine is unique to all its attrs."""
machine_one, machine_two = machines
assert hash(machine_one) != hash(machine_two)
for key, value in machine_one.dict().items():
setattr(machine_two, key, value)
assert hash(machine_one) == hash(machine_two)
@staticmethod
@given(two_unique_machines_strategy())
def test_hash_is_different_than_uuid(machines: t.Tuple[Machine, Machine]):
"""
Test that the hash of a machine is distinct from its uuid.
uuid is based off of mac and arch only, hash is based off of
all attributes.
"""
machine_one, machine_two = machines
hash_one = hash(machine_one)
uuid_one = get_uuid(machine=machine_one)
machine_one.provision = machine_two.provision
machine_one.name = machine_two.name
assert hash(machine_one) != hash_one
assert get_uuid(machine=machine_one) == uuid_one
class TestGetUUID:
"""Test functionality of foremanlite.machine.get_uuid"""
@staticmethod
@given(machine_strategy())
def test_get_uuid_returns_same_if_machine_or_arch_and_mac(
machine: Machine,
):
"""Test get_uuid returns same uuid for all kwarg options."""
assert get_uuid(machine=machine) == get_uuid(
mac=machine.mac, arch=machine.arch
)
@staticmethod
@given(two_unique_machines_strategy())
def test_get_uuid_keys_off_of_arch_and_mac(
machines: t.Tuple[Machine, Machine]
):
"""Test get_uuid returns same uuid for same arch and mac."""
machine_one, machine_two = machines
assert get_uuid(machine=machine_one) != get_uuid(machine=machine_two)
machine_two.arch = machine_one.arch
machine_two.mac = machine_one.mac
assert get_uuid(machine=machine_one) == get_uuid(machine=machine_two)
class TestMachineSelector:
"""Test functionality of foremanlite.machine.MachineSelector."""
@staticmethod
@given(two_unique_machines_strategy())
def test_exact_machine_selector_matches_attributes_exactly(
machines: t.Tuple[Machine, Machine]
):
"""Test exact MachineSelector matches Machines by exact attributes."""
machine_one, machine_two = machines
for key, value in machine_one.dict().items():
selector = MachineSelector(type="exact", attr=key, val=value)
assert selector.matches(machine_one)
assert not selector.matches(machine_two)
@staticmethod
@given(two_unique_machines_strategy())
def test_regex_machine_selector_matches_attributes_with_regex_str(
machines: t.Tuple[Machine, Machine]
):
"""Test regex MachineSelector matches Machines with regex strings."""
machine_one, machine_two = machines
for key, value in machine_one.dict().items():
if isinstance(value, Arch):
value = value.value
value = str(value)
selector = MachineSelector(
type="regex", attr=key, val=f"^{re.escape(value)}$"
)
assert selector.matches(machine_one)
assert not selector.matches(machine_two)
class TestSelectorMatchStr:
"""Test functionality of foremanlite.machine.SelectorMatchStr."""
@staticmethod
@given(st.text(alphabet=st.characters(blacklist_characters=["{", "}"])))
def test_test_method_raises_value_error_on_bad_template_output(
bad_jinja_str: str,
):
"""Check that test raises ValueError on bad template output."""
with pytest.raises(ValueError):
assert SelectorMatchStr(exp=bad_jinja_str).test({})
@staticmethod
@given(st.booleans())
def test_test_method_is_not_case_sensitive(bool_val: bool):
"""Check that test is not case sensitive to expected boolean output"""
bool_val_str = str(bool_val)
checks = [
bool_val_str.upper(),
bool_val_str.capitalize(),
bool_val_str.lower(),
bool_val_str.swapcase(),
]
for check in checks:
assert SelectorMatchStr(exp=check).test({}) == bool_val
@staticmethod
@given(st.from_regex(re.compile(r"^\s*\{\}\s*$")))
def test_test_method_is_not_sensitive_to_whitespace(bool_val_str: str):
"""Check that test is not sensitive to whitespace surrounding output"""
for expected in [True, False]:
exp = bool_val_str.format(str(expected))
assert SelectorMatchStr(exp=exp).test({}) == expected
@staticmethod
@given(
bool_vals=st.lists(st.booleans(), min_size=4, max_size=4),
machines=two_unique_machines_strategy(),
)
def test_test_method_can_substitute_in_names_and_eval_exp(
bool_vals: t.List[bool],
machines: t.Tuple[Machine, Machine],
monkeypatch,
):
"""Check that test can substitute selector results into an exp."""
# Don't know how to dynamically generate these,
# so just use a hardcoded one
exp = (
"{% if (name and provision) or arch or mac %} true "
"{% else %} false {% endif %}"
)
selector_results = dict(
zip(["name", "provision", "arch", "mac"], bool_vals)
)
def mock_test(_, selector_values: t.Dict[str, bool]):
"""Mock test method to check expected input"""
assert selector_values == selector_results
monkeypatch.setattr(SelectorMatchStr, "test", mock_test)
selectors = []
machine_one, machine_two = machines
for selector_name, bool_val in selector_results.items():
selector = MachineSelector(
type="exact",
name=selector_name,
attr=selector_name,
val=getattr(
machine_one if bool_val else machine_two, selector_name
),
)
selectors.append(selector)
SelectorMatchStr(exp=exp).apply(machine_one, selectors)
class TestMachineGroupSet:
"""Test functionality of foremanlite.machine.MachineGroupSet"""
@staticmethod
@given(two_unique_machines_strategy())
def test_filter_returns_correct_set_of_group_membership(
machines: t.Tuple[Machine, Machine]
):
"""Test filter returns the groups a machine is a part of."""
machine_one, machine_two = machines
machine_one_attrs = machine_one.dict().items()
machine_two_attrs = machine_two.dict().items()
both_attrs = [*machine_one_attrs, *machine_two_attrs]
groups = [
MachineGroup(
name="group_one",
selectors=[
MachineSelector(type="exact", attr=attr, val=val)
for attr, val in machine_one_attrs
],
),
MachineGroup(
name="group_two",
selectors=[
MachineSelector(type="exact", attr=attr, val=val)
for attr, val in machine_two_attrs
],
),
MachineGroup(
name="group_three",
selectors=[
MachineSelector(
name=str(i), type="exact", attr=attr, val=val
)
for i, (attr, val) in enumerate(both_attrs)
],
# will not match since these are all and-ed together
match_str=SelectorMatchStr(
exp="{% if "
+ " and ".join(map(str, range(len(both_attrs))))
+ "%} True {% else %} False {% endif %}"
),
),
]
machine_group_set = MachineGroupSet(groups=groups)
one_groups = machine_group_set.filter(machine_one)
two_groups = machine_group_set.filter(machine_two)
assert len(one_groups) == 1
assert len(two_groups) == 1
assert one_groups[0].name == "group_one"
assert two_groups[0].name == "group_two"
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solutions/solution165.py | Satily/leetcode_python_solution | 3 | 174011 | from itertools import zip_longest
class Solution:
def compareVersion(self, version1: str, version2: str) -> int:
for v1n, v2n in zip_longest(map(int, version1.split('.')), map(int, version2.split('.')), fillvalue=0):
if v1n > v2n:
return 1
elif v1n < v2n:
return -1
return 0
if __name__ == '__main__':
print(Solution().compareVersion(version1="1.01", version2="1.001"))
print(Solution().compareVersion(version1="1.0", version2="1.0.0"))
print(Solution().compareVersion(version1="0.1", version2="1.1"))
print(Solution().compareVersion(version1="1.0.1", version2="1"))
print(Solution().compareVersion(version1="7.5.2.4", version2="7.5.3"))
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bzl/opam.bzl | obazl/orocksdb | 0 | 54666 | PACKAGES = {
"ctypes": ["0.17.1", ["ctypes.foreign"]],
"ctypes-foreign": ["0.4.0"], # WARNING: requires libffi-dev
}
opam = struct(
version = "2.0",
switches = {
"mina-0.1.0": struct(
default = True,
compiler = "4.07.1",
packages = PACKAGES
),
"4.07.1": struct(
compiler = "4.07.1",
packages = PACKAGES
)
}
)
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] |
question_bank/find-majority-element-lcci/find-majority-element-lcci.py | yatengLG/leetcode-python | 9 | 126055 | # -*- coding: utf-8 -*-
# @Author : LG
"""
执行用时：48 ms, 在所有 Python3 提交中击败了83.86% 的用户
内存消耗：14.8 MB, 在所有 Python3 提交中击败了62.52%
解题思路：
对拼消耗，摩尔投票
时间复杂度为 O(N)，空间复杂度为 O(1)
不同数字一一对拼消耗，如果有剩余，则存在主要元素(人数最多，且，人数在一半以上)
具体实现见代码注释
"""
class Solution:
def majorityElement(self, nums: List[int]) -> int:
n = len(nums)
major = nums[0] # 记录当前元素
count = 0 # 当前元素的出现次数统计
for i in range(n):
if nums[i] == major: # 如果与当前元素相同，则次数+1
count += 1
else: # 不同，则对拼消耗，次数减一
count -= 1
if count == 0 and i < n-1: # 如果当前元素被消耗殆尽，当前元素指向下一个元素
major = nums[i+1]
if count > 0: # 如果当前元素的统计次数大于0，则存在主要元素
return major
else: # 否则，不存在
return -1 | [
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Happy vs Sad Face Classification/happy_vs_sad.py | DhruvAwasthi/ModelsCollection | 2 | 170353 | <reponame>DhruvAwasthi/ModelsCollection
#!/usr/bin/env python
# coding: utf-8
# Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad.
# Create a convolutional neural network that trains to 100% accuracy on these images, which cancels training upon hitting training accuracy of >.999
#
# Hint -- it will work best with 3 convolutional layers.
# In[1]:
import tensorflow as tf
import os
import zipfile
from os import path, getcwd, chdir
get_ipython().system('wget --no-check-certificate "https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip" -O "/tmp/happy-or-sad.zip"')
zip_ref = zipfile.ZipFile("/tmp/happy-or-sad.zip", 'r')
zip_ref.extractall("/tmp/h-or-s")
zip_ref.close()
# In[18]:
def train_happy_sad_model():
DESIRED_ACCURACY = 0.999
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, log={}):
if (log.get('acc')>DESIRED_ACCURACY):
print("\nAccuracy achieved {:.1f}% so cancelling training.".format(DESIRED_ACCURACY))
self.model.stop_training = True
callbacks = myCallback()
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(8, (3, 3), activation='relu', input_shape=(150, 150, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(4, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(4, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(8, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.summary()
from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
# Please use a target_size of 150 X 150.
train_generator = train_datagen.flow_from_directory(
'/tmp/h-or-s',
target_size=(150, 150),
batch_size=128,
class_mode='binary')
history = model.fit_generator(train_generator, steps_per_epoch=8, epochs=15, verbose=1, callbacks=[callbacks])
# model fitting
return history.history['acc'][-1]
# In[19]:
train_happy_sad_model()
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ncbi/patric_add_taxonomy.py | johned0/EdwardsLab | 0 | 29416 | """
Add the taxonomy to the patric metadata file
"""
import os
import sys
import argparse
from taxon import get_taxonomy_db, get_taxonomy
c = get_taxonomy_db()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Append taxonomy to the patric metadata file. This adds it at column 67")
parser.add_argument('-f', help='patric metadata file', required=True)
parser.add_argument('-o', help='output file', required=True)
parser.add_argument('-c', help='taxonomy ID column', required=True, type=int)
parser.add_argument('-t', help='taxonomy directory (or we will use default)')
parser.add_argument('-v', help='verbose output', action="store_true")
args = parser.parse_args()
want = ['superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']
# read the file once to figure out the longest line
maxp=0
with open(args.f, 'r', encoding='utf-8') as f:
for l in f:
p = l.strip().split("\t")
if len(p) > maxp:
maxp = len(p)
with open(args.o, 'w', encoding='utf-8') as out:
with open(args.f, 'r', encoding='utf-8') as f:
for l in f:
p = l.strip().split("\t")
while (len(p) < maxp):
p.append("")
if l.startswith("genome_id"):
out.write("{}\t{}\n".format(l.strip(), "\t".join(want)))
continue
tid = p[args.c]
level = {}
t, n = get_taxonomy(tid, c)
while t and t.parent > 1 and t.parent != 131567:
# 131567 is cellular organisms
if t.rank in want:
level[t.rank] = n.scientific_name
t, n = get_taxonomy(t.parent, c)
for w in want:
if w in level:
p.append(level[w])
else:
p.append("")
out.write("\t".join(map(str, p)))
out.write("\n")
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] |
test/unittests/chemical/building_blocks/aminoacids.py | Alexhuszagh/XLDiscoverer | 0 | 31328 | <filename>test/unittests/chemical/building_blocks/aminoacids.py
'''
Unittests/Chemical/Building_Blocks/aminoacids
_____________________________________________
Unit tests for aminoacid lookups.
:copyright: (c) 2015 The Regents of the University of California.
:license: GNU GPL, see licenses/GNU GPLv3.txt for more details.
'''
# load modules/submodules
import unittest
from xldlib.chemical.building_blocks import aminoacids
# CASES
# -----
class AminoAcidTest(unittest.TestCase):
'''Test aminoacid lookups'''
def test_mixed_case(self):
'''Test mixed-case lookups produce the same object'''
lower = aminoacids.AMINOACIDS['a']
upper = aminoacids.AMINOACIDS['A']
self.assertEquals(id(lower), id(upper))
self.assertEquals(lower, upper)
assert 'a' in aminoacids.AMINOACIDS
assert 'A' in aminoacids.AMINOACIDS
# TESTS
# -----
def add_tests(suite):
'''Add tests to the unittest suite'''
suite.addTest(AminoAcidTest('test_mixed_case'))
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students/K33402/Beresnev_Andrey/practical2.1/django_project_beresnev/project_first_app/urls.py | agentofknowledge/ITMO_ICT_WebDevelopment_2020-2021 | 4 | 58521 | from django.urls import path
from . import views
urlpatterns = [
path('owner/<int:owner_id>', views.index),
]
| [
1,
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pyroomacoustics/experimental/tests/test_deconvolution.py | HemaZ/pyroomacoustics | 1 | 12796 | <filename>pyroomacoustics/experimental/tests/test_deconvolution.py<gh_stars>1-10
from unittest import TestCase
import numpy as np
from scipy.signal import fftconvolve
import pyroomacoustics as pra
# fix seed for repeatability
np.random.seed(0)
h_len = 30
x_len = 1000
SNR = 1000. # decibels
h_lp = np.fft.irfft(np.ones(5), n=h_len)
h_rand = np.random.randn(h_len)
h_hann = pra.hann(h_len, flag='symmetric')
x = np.random.randn(x_len)
noise = np.random.randn(x_len + h_len - 1)
def generate_signals(SNR, x, h, noise):
''' run convolution '''
# noise standard deviation
sigma_noise = 10**(-SNR / 20.)
y = fftconvolve(x, h)
y += sigma_noise * noise
return y, sigma_noise
class TestDeconvolution(TestCase):
def test_deconvolve_hann_noiseless(self):
h = h_hann
h_len = h_hann.shape[0]
SNR = 1000.
tol = 1e-7
y, sigma_noise = generate_signals(SNR, x, h, noise)
h_hat = pra.experimental.deconvolve(y, x, length=h_len)
rmse = np.sqrt(np.linalg.norm(h_hat - h)**2 / h_len)
print('rmse=', rmse, '(tol=', tol, ')')
self.assertTrue(rmse < tol)
def test_wiener_deconvolve_hann_noiseless(self):
h = h_hann
h_len = h_hann.shape[0]
SNR = 1000.
tol = 1e-7
y, sigma_noise = generate_signals(SNR, x, h, noise)
h_hat = pra.experimental.wiener_deconvolve(y, x, length=h_len, noise_variance=sigma_noise**2)
rmse = np.sqrt(np.linalg.norm(h_hat - h)**2 / h_len)
print('rmse=', rmse, '(tol=', tol, ')')
self.assertTrue(rmse < tol)
if __name__ == '__main__':
import matplotlib.pyplot as plt
h = h_hann
y, sigma_noise = generate_signals(SNR, x, h, noise)
h_hat1 = pra.experimental.deconvolve(y, x, length=h_len)
res1 = np.linalg.norm(y - fftconvolve(x, h_hat1))**2 / y.shape[0]
mse1 = np.linalg.norm(h_hat1 - h)**2 / h_len
h_hat2 = pra.experimental.wiener_deconvolve(y, x, length=h_len, noise_variance=sigma_noise**2, let_n_points=15)
res2 = np.linalg.norm(y - fftconvolve(x, h_hat2))**2 / y.shape[0]
mse2 = np.linalg.norm(h_hat2 - h)**2 / h_len
print('MSE naive: rmse=', np.sqrt(mse1), ' res=', pra.dB(res1, power=True))
print('MSE Wiener: rmse=', np.sqrt(mse2), ' res=', pra.dB(res1, power=True))
plt.plot(h)
plt.plot(h_hat1)
plt.plot(h_hat2)
plt.legend(['Original', 'Naive', 'Wiener'])
plt.show()
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] |
src/django_perf_rec/settings.py | adamchainz/django-perf-rec | 147 | 28167 | import sys
from typing import Any
from django.conf import settings
if sys.version_info >= (3, 8):
from typing import Literal
ModeType = Literal["once", "none", "all"]
else:
ModeType = str
class Settings:
defaults = {"HIDE_COLUMNS": True, "MODE": "once"}
def get_setting(self, key: str) -> Any:
try:
return settings.PERF_REC[key]
except (AttributeError, KeyError):
return self.defaults.get(key, None)
@property
def HIDE_COLUMNS(self) -> bool:
return self.get_setting("HIDE_COLUMNS")
@property
def MODE(self) -> ModeType:
return self.get_setting("MODE")
perf_rec_settings = Settings()
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build/lib/ruleset/ripper.py | ts01174755/wittgenstein | 64 | 147060 | <filename>build/lib/ruleset/ripper.py
"""
Implementation of the RIPPERk algorithm for growing classification rulesets.
See https://www.let.rug.nl/nerbonne/teach/learning/cohen95fast.pdf
"""
# Author: <NAME> <<EMAIL>>
# License: MIT
import pandas as pd
import copy
import math
import warnings
from ruleset import base
from .base import Cond, Rule, Ruleset
from .base import rnd, fit_bins, bin_transform, score_accuracy
class RIPPER:
""" Class for generating ruleset classification models.
See Cohen (1995): https://www.let.rug.nl/nerbonne/teach/learning/cohen95fast.pdf
"""
def __init__(self, k=2, prune_size=.33, dl_allowance=64, verbosity=0):
""" Creates a new RIPPER object.
args:
k (optional): number of RIPPERk optimization iterations (default=2)
prune_size (optional): proportion of training set to be used for pruning (defailt=.33)
dl_allowance (optional): terminate Ruleset grow phase early if a Ruleset description length is encountered
that is more than this amount above the lowest description length so far encountered.
(default=64 bits)
verbosity (optional): output information about the training process (default=0)
1: Show results of each major phase
2: Show Ruleset grow/optimization steps
3: Show Ruleset grow/optimization calculations
4: Show Rule grow/prune steps
5: Show Rule grow/prune calculations
"""
self.prune_size = prune_size
self.dl_allowance = dl_allowance
self.k = k
self.verbosity = verbosity
def __str__(self):
""" Returns string representation of a RIPPER object. """
fitstr = f'with fit ruleset' if hasattr(self,'ruleset_') else '(unfit)'
return f'<RIPPER object {fitstr} (k={self.k}, prune_size={self.prune_size}, dl_allowance={self.dl_allowance})>'
__repr__ = __str__
def fit(self, df, y=None, class_feat=None, pos_class=None, n_discretize_bins=None, random_state=None):
""" Fit a Ruleset model using a training DataFrame.
args:
df <DataFrame>: categorical training dataset
y: <iterable>: class labels corresponding to df rows. Parameter y or class_feat (see next) must be provided.
class_feat: column name of class feature (Use if class feature is still in df.)
pos_class (optional): name of positive class. If not provided, defaults to class of first training example.
n_discretize_bins (optional): try to fit apparent numeric attributes into n_discretize_bins discrete bins.
Pass None to disable auto-discretization and treat values as categorical. (default=None)
random_state: (optional) random state to allow for repeatable results
"""
################
# Stage 0: Setup
################
# Set up trainset, set class feature name, and set pos class name
df, self.class_feat, self.pos_class = base.trainset_classfeat_posclass(df, y=y, class_feat=class_feat, pos_class=pos_class)
# Precalculate rule df lookup
#self._set_theory_dl_lookup(df, verbosity=self.verbosity)
# Anything to discretize?
numeric_feats = base.find_numeric_feats(df, min_unique=n_discretize_bins, ignore_feats=[self.class_feat])
if numeric_feats:
if n_discretize_bins is not None:
if self.verbosity==1:
print(f'binning data...\n')
elif self.verbosity>=2:
print(f'binning features {numeric_feats}...')
self.bin_transformer_ = fit_bins(df, n_bins=n_discretize_bins, output=False, ignore_feats=[self.class_feat], verbosity=self.verbosity)
binned_df = bin_transform(df, self.bin_transformer_)
else:
n_unique_values = sum([len(u) for u in [df[f].unique() for f in numeric_feats]])
warnings.warn(f'Optional param n_discretize_bins=None, but there are apparent numeric features: {numeric_feats}. \n Treating {n_unique_values} numeric values as nominal', RuntimeWarning)
binned_df=None
else:
binned_df=None
# Split df into pos, neg classes
pos_df, neg_df = base.pos_neg_split(df, self.class_feat, self.pos_class) if binned_df is None else base.pos_neg_split(binned_df, self.class_feat, self.pos_class)
pos_df = pos_df.drop(self.class_feat,axis=1)
neg_df = neg_df.drop(self.class_feat,axis=1)
# Collect possible conds
self._set_possible_conds(df)
###############################
# Stage 1: Grow initial Ruleset
###############################
self.ruleset_ = Ruleset()
self.ruleset_ = self._grow_ruleset(pos_df, neg_df,
prune_size=self.prune_size, dl_allowance=self.dl_allowance,
random_state=random_state)
if self.verbosity >= 1:
print()
print('GREW INITIAL RULESET:')
self.ruleset_.out_pretty()
print()
###########################
# Stage 2: Optimize Ruleset
###########################
for iter in range(self.k):
# Create new but reproducible random_state (if applicable)
iter_random_state = random_state+100 if random_state is not None else None
# Run optimization iteration
if self.verbosity>=1: print(f'optimization run {iter+1} of {self.k}')
newset = self._optimize_ruleset(self.ruleset_, pos_df, neg_df, prune_size=self.prune_size, random_state=iter_random_state)
if self.verbosity>=1:
print()
print('OPTIMIZED RULESET:')
if self.verbosity>=2: print(f'iteration {iter+1} of {self.k}\n modified rules {[i for i in range(len(self.ruleset_.rules)) if self.ruleset_.rules[i]!= newset.rules[i]]}')
newset.out_pretty()
print()
self.ruleset_ = newset
#############################################
# Stage 3: Cover any last remaining positives
#############################################
pos_remaining, neg_remaining = base.pos_neg_split(df, self.class_feat, self.pos_class)
pos_remaining = pos_remaining.drop(self.class_feat,axis=1)
neg_remaining = neg_remaining.drop(self.class_feat,axis=1)
pos_remaining, neg_remaining = base.rm_covered(self.ruleset_, pos_remaining, neg_remaining)
if len(pos_remaining)>=1:
if self.verbosity>=2:
print(f'{len(pos_remaining)} pos left. Growing final rules...')
newset = self._grow_ruleset(pos_remaining, neg_remaining, initial_ruleset=self.ruleset_,
prune_size=self.prune_size, dl_allowance=self.dl_allowance,
random_state=random_state)
if self.verbosity>=1:
print('GREW FINAL RULES')
newset.out_pretty()
print()
self.ruleset_ = newset
else:
if self.verbosity>=1: print('All pos covered\n')
#################################################
# Stage 4: Remove any rules that don't improve dl
#################################################
if self.verbosity>=2: print('Optimizing dl...')
mdl_subset, _ = _rs_total_bits(self.ruleset_, self.ruleset_.possible_conds, pos_df, neg_df,
bestsubset_dl=True, ret_bestsubset=True, verbosity=self.verbosity)
self.ruleset_ = mdl_subset
if self.verbosity>=1:
print('FINAL RULESET:')
self.ruleset_.out_pretty()
print()
def predict(self, X_df, give_reasons=False):
""" Predict classes of data using a RIPPER-fit model.
args:
X_df <DataFrame>: examples to make predictions on.
give_reasons (optional) <bool>: whether to provide reasons for each prediction made.
returns:
list of <bool> values corresponding to examples. True indicates positive predicted class; False non-positive class.
If give_reasons is True, returns a tuple that contains the above list of predictions
and a list of the corresponding reasons for each prediction;
for each positive prediction, gives a list of all the covering Rules, for negative predictions, an empty list.
"""
if not hasattr(self, 'ruleset_'):
raise AttributeError('You should fit a RIPPER object before making predictions with it.')
else:
return self.ruleset_.predict(X_df, give_reasons=give_reasons)
def score(self, X, y, score_function=score_accuracy):
""" Test performance of a RIPPER-fit model.
X: <DataFrame> of independent attributes
y: <DataFrame> or <iterable> of matching dependent target values
score_function (optional): function that takes two parameters: actuals <iterable<bool>>, predictions <iterable<bool>>,
containing class values. (default=accuracy)
this parameter is intended to be compatible with sklearn's scoring functions:
https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics
"""
predictions = self.predict(X)
if type(y)==pd.core.frame.DataFrame:
actuals = [yi==self.pos_class for yi in y.tolist()]
else:
actuals = [yi==self.pos_class for yi in y]
return score_function(actuals, predictions)
def _set_theory_dl_lookup(self, df, size=15, verbosity=0):
""" Precalculate rule theory dls for various-sized rules. """
self.dl_dict = {}
temp = Ruleset()
temp._set_possible_conds(df, df)
possible_conds = temp.possible_conds
for n in range(1, size+1):
rule = Rule([Cond('_','_')]*n)
dl = _r_theory_bits(rule, possible_conds, bits_dict=None, verbosity=verbosity)
self.dl_dict[n] = dl
if verbosity>=2:
print(f'updated dl for rule size {n}: {dl}')
def _grow_ruleset(self, pos_df, neg_df, prune_size, dl_allowance, initial_ruleset=None, random_state=None):
""" Grow a Ruleset with pruning. """
pos_remaining = pos_df.copy()
neg_remaining = neg_df.copy()
if initial_ruleset is None:
ruleset = Ruleset()
ruleset._set_possible_conds(pos_df, neg_df)
else:
ruleset = copy.deepcopy(initial_ruleset)
ruleset_dl = None
mdl = None # Minimum encountered description length (in bits)
dl_diff = 0
if self.verbosity>=2:
print('growing ruleset...')
print()
while len(pos_remaining) > 0 and dl_diff <= self.dl_allowance:
# Grow-prune split remaining uncovered examples
pos_growset, pos_pruneset = base.df_shuffled_split(pos_remaining, (1-prune_size), random_state=random_state)
neg_growset, neg_pruneset = base.df_shuffled_split(neg_remaining, (1-prune_size), random_state=random_state)
if self.verbosity>=2:
print(f'pos_growset {len(pos_growset)} pos_pruneset {len(pos_pruneset)}')
print(f'neg_growset {len(neg_growset)} neg_pruneset {len(neg_pruneset)}')
if len(pos_growset)==0: break # Probably safe, but a little dicey to only check pos_growset.
# Grow Rule
grown_rule = base.grow_rule(pos_growset, neg_growset, ruleset.possible_conds, verbosity=self.verbosity)
if grown_rule.isempty(): break # Generated an empty rule b/c no good conds exist
# Prune Rule
pruned_rule = base.prune_rule(grown_rule, _RIPPER_growphase_prune_metric, pos_pruneset, neg_pruneset, verbosity=self.verbosity)
# Add rule; calculate new description length
ruleset.add(pruned_rule) # Unlike IREP, IREP*/RIPPER stopping condition is inclusive: "After each rule is added, the total description length of the rule set and examples is computed."
if self.verbosity>=2:
print(f"updated ruleset: {ruleset.truncstr(direction='right')}")
print()
if ruleset_dl is None: # First Rule to be added
rule_dl = _r_theory_bits(pruned_rule, ruleset.possible_conds, verbosity=self.verbosity)
theory_dl = rule_dl
data_dl = _exceptions_bits(ruleset, pos_df, neg_df, verbosity=self.verbosity)
ruleset_dl = theory_dl + data_dl
mdl = ruleset_dl
else:
rule_dl = _r_theory_bits(pruned_rule, ruleset.possible_conds, verbosity=self.verbosity)
theory_dl += rule_dl
data_dl = _exceptions_bits(ruleset, pos_df, neg_df, verbosity=self.verbosity)
ruleset_dl = theory_dl + data_dl
dl_diff = ruleset_dl - mdl
if self.verbosity>=3:
print(f'rule dl: {rnd(rule_dl)}')
print(f'updated theory dl: {rnd(theory_dl)}')
print(f'exceptions: {rnd(data_dl)}')
print(f'total dl: {rnd(ruleset_dl)}')
if dl_diff<=self.dl_allowance:
print(f'mdl {rnd(mdl)} (diff {rnd(dl_diff)} <= {rnd(self.dl_allowance)})')
else:
print(f'mdl {rnd(mdl)} dl-halt: diff {rnd(dl_diff)} exceeds allowance ({rnd(self.dl_allowance)})')
mdl = ruleset_dl if ruleset_dl<mdl else mdl
# Remove covered examples
pos_remaining, neg_remaining = base.rm_covered(pruned_rule, pos_remaining, neg_remaining)
if self.verbosity>=3:
print(f'examples remaining: {len(pos_remaining)} pos, {len(neg_remaining)} neg')
print()
return ruleset
def _optimize_ruleset(self, ruleset, pos_df, neg_df, prune_size, random_state=None):
""" Optimization phase. """
if self.verbosity>=2:
print('optimizing ruleset...')
print()
pos_remaining = pos_df.copy()
neg_remaining = neg_df.copy()
original_ruleset = copy.deepcopy(ruleset)
if self.verbosity>=4: print('calculate original ruleset potential dl...')
original_dl = _rs_total_bits(original_ruleset, original_ruleset.possible_conds, pos_df, neg_df, bestsubset_dl=True, verbosity=self.verbosity)
if self.verbosity>=3:
print(f'original ruleset potential dl: {rnd(original_dl)}')
print()
new_ruleset = copy.deepcopy(ruleset)
for i, rule in enumerate(original_ruleset.rules):
pos_growset, pos_pruneset = base.df_shuffled_split(pos_remaining, (1-prune_size), random_state=random_state)
neg_growset, neg_pruneset = base.df_shuffled_split(neg_remaining, (1-prune_size), random_state=random_state)
if len(pos_growset) == 0: break # Possible where optimization run > 1
# Create alternative rules
if self.verbosity>=4: print(f'creating replacement for {i} of {len(original_ruleset.rules)}: {ruleset.rules[i]}')
g_replacement = base.grow_rule(pos_growset, neg_growset, original_ruleset.possible_conds, initial_rule=Rule(), verbosity=self.verbosity)
replacement_ruleset = Ruleset(base.i_replaced(original_ruleset.rules, i, g_replacement))
pr_replacement = base.prune_rule(g_replacement, _RIPPER_optimization_prune_metric, pos_pruneset, neg_pruneset, eval_index_on_ruleset=(i,replacement_ruleset), verbosity=self.verbosity)
replacement_ruleset = Ruleset(base.i_replaced(original_ruleset.rules, i, pr_replacement))
if self.verbosity>=3:
print(f'grew replacement {g_replacement}')
print(f'pruned replacement is {pr_replacement}')
if self.verbosity>=3: print(f'creating revision for {i} of {len(original_ruleset.rules)}: {ruleset.rules[i]}')
g_revision = base.grow_rule(pos_growset, neg_growset, original_ruleset.possible_conds, initial_rule=ruleset.rules[i], verbosity=self.verbosity)
revision_ruleset = Ruleset(base.i_replaced(original_ruleset.rules, i, g_revision))
pr_revision = base.prune_rule(g_revision, _RIPPER_optimization_prune_metric, pos_pruneset, neg_pruneset, eval_index_on_ruleset=(i,revision_ruleset), verbosity=self.verbosity)
revision_ruleset = Ruleset(base.i_replaced(original_ruleset.rules, i, pr_revision))
if self.verbosity>=3:
print(f'grew revision {g_replacement}')
print(f'pruned revision is {pr_replacement}')
print()
# Calculate alternative Rulesets' respective lowest potential dls to identify the best version
if self.verbosity>=3: print(f'calculate potential dl for ds with replacement {pr_replacement}')
replacement_dl = _rs_total_bits(replacement_ruleset, original_ruleset.possible_conds, pos_df, neg_df, bestsubset_dl=True, verbosity=self.verbosity)\
if pr_replacement!=rule else original_dl
if self.verbosity>=3: print(f'calculate potential dl for ds with revision {pr_revision}')
revision_dl = _rs_total_bits(revision_ruleset, original_ruleset.possible_conds, pos_df, neg_df, bestsubset_dl=True, verbosity=self.verbosity)\
if pr_revision!=rule else original_dl
best_rule = [rule, pr_replacement, pr_revision][base.argmin([original_dl, replacement_dl, revision_dl])]
if self.verbosity>=2:
print(f'\nrule {i+1} of {len(original_ruleset.rules)}')
rep_str = pr_replacement.__str__() if pr_replacement!=rule else 'unchanged'
rev_str = pr_revision.__str__() if pr_revision!=rule else 'unchanged'
best_str = best_rule.__str__() if best_rule!=rule else 'unchanged'
if self.verbosity==2:
print(f'original: {rule}')
print(f'replacement: {rep_str}')
print(f'revision: {rev_str}')
print(f'*best: {best_str}')
print()
else:
print(f'original: {rule}) | {rnd(original_dl)} bits')
print(f'replacement: {rep_str} | {rnd(replacement_dl)} bits')
print(f'revision: {rev_str} | {rnd(revision_dl)} bits')
print(f'*best: {best_str} | {rnd(min([replacement_dl, revision_dl, original_dl]))} bits')
print()
new_ruleset.rules[i] = best_rule
# Remove covered examples
pos_remaining, neg_remaining = base.rm_covered(rule, pos_remaining, neg_remaining)
if self.verbosity>=3:
print(f'examples remaining: {len(pos_remaining)} pos, {len(neg_remaining)} neg')
print()
# If there are no pos data remaining to train optimization (could happen if optimization run >1), keep remaining rules the same
if len(pos_remaining)==0: break
return new_ruleset
def _set_possible_conds(self, df):
self.possible_conds = []
for feat in df.columns.values:
for val in df[feat].unique():
self.possible_conds.append(Cond(feat, val))
#### HELPER Class #####
class RulesetStats:
# This class is not used in the current implementation but could come in handy for future optimization
# by storing and retreiving calculations that may be repeated.
# Haven't incorporated it because there are bigger fish to fry, optimization-wise.
def __init__(self):
self.subset_dls = []
self.ruleset = Ruleset()
self.dl = 0
def update(self, ruleset, possible_conds, pos_df, neg_df, verbosity=0):
# Find first mismatching rule index
# If there is no mismatch, return
# If there is a mismatch, update self.ruleset and wipe subsequent dls
index = 0
while index < len(self.ruleset) and \
index < len(ruleset) and \
self.ruleset[index] == ruleset[index]:
index += 1
if index == len(ruleset) and index == len(self.ruleset):
if verbosity>=4: print(f'not updating stats -- no ruleset change found')
return
if verbosity>=4: print(f'updating stats from index {index}')
self.ruleset.rules[index:] = ruleset[index:]
self.subset_dls = self.subset_dls[:index]
# Beginning with index, update subset dls
for i in range(index, len(ruleset)):
rule = ruleset[i]
subset = Ruleset(ruleset.rules[:i+1])
subset_dl = _rs_total_bits(subset, possible_conds, pos_df, neg_df, verbosity=verbosity)
self.subset_dls.append(subset_dl)
self.dl = self.subset_dls[-1]
def dl_change(self, index):
return self.subset_dls[index] - self.subset_dls[index-1]
def potential_dl_stats(self, possible_conds, pos_df, neg_df,
ret_ruleset=True, ret_dl=False, verbosity=0):
if not any((ret_ruleset, ret_dl)):
raise ValueError('method dl_pruned_ruleset called without any return values specified')
tempStats = copy.deepcopy(self)
i = len(tempStats.ruleset) - 1
while i > 0:
if tempStats.dl_change(i) > 0:
if verbosity>=4: print(f'rule {i} raised dl -- removing')
tempStats.update(Ruleset(tempStats.ruleset[:i]+tempStats.ruleset[i+1:]), possible_conds, pos_df, neg_df)
if verbosity>=4: print(f'new ruleset is {tempStats.ruleset}')
i -= 1
return tempStats
###################################
##### RIPPER-specific Metrics #####
###################################
def _RIPPER_growphase_prune_metric(rule, pos_pruneset, neg_pruneset):
""" RIPPER/IREP* prune metric.
Returns the prune value of a candidate Rule.
Cohen's formula is (p-n) / (p+n).
Unclear from the paper how they handle divzero (where p+n=0), so I Laplaced it.
Weka's solution was to modify the formula to (p+1)/(p+n+2), but the (non-NaN) values I got appeared closer to those of the original formula.
"""
# I imagine Weka's is 1/2 because that's closer to a 50-50 class distribution?
p = rule.num_covered(pos_pruneset)
n = rule.num_covered(neg_pruneset)
return (p - n + 1) / (p + n + 1)
def _RIPPER_optimization_prune_metric(rule, pos_pruneset, neg_pruneset):
return base.accuracy(rule, pos_pruneset, neg_pruneset)
def _r_theory_bits(rule, possible_conds, bits_dict=None, verbosity=0):
""" Returns description length (in bits) for a single Rule. """
if hasattr(rule, 'dl'):
return rule.dl
else:
if type(rule) != Rule:
raise TypeError(f'param rule in _r_theory_bits should be type Rule')
k = len(rule.conds) # Number of rule conditions
n = len(possible_conds) # Number of possible conditions
pr = k/n
S = k*math.log2(1/pr) + (n-k)*math.log2((1/(1-pr))) # S(n, k, pr)
K = math.log2(k) # Number bits need to send integer k
rule_dl = 0.5*(K + S) # Divide by 2 a la Quinlan. Cohen: "to adjust for possible redundency in attributes"
if verbosity>=5: print(f'rule theory bits| {rule} k {k} n {n} pr {rnd(pr)}: {rnd(rule_dl)} bits')
#rule.dl = rule_dl
return rule_dl
def _rs_theory_bits(ruleset, possible_conds, pos_df, neg_df, verbosity=0):
""" Returns theory description length (in bits) for a Ruleset. """
if type(ruleset) != Ruleset:
raise TypeError(f'param ruleset in _rs_theory_bits should be type Ruleset')
""" Returns sum of theory bits for each Rule in ruleset """
total = 0
for rule in ruleset.rules:
total += _r_theory_bits(rule, possible_conds, verbosity=verbosity)
#total += rule_bits(rule, possible_conds, rem_pos, rem_neg, verbosity=verbosity)
#rem_pos, rem_neg = base.rm_covered(rule, rem_pos, rem_neg)
if verbosity>=5: print(f'ruleset theory bits| {rnd(total)}')
#ruleset.dl = total
return total
def _exceptions_bits(ruleset, pos_df, neg_df, verbosity=0):
""" Returns description length (in bits) for exceptions to a Ruleset's coverage. """
if type(ruleset) != Ruleset:
raise TypeError(f'to avoid double-counting, _exceptions_bits should calculate exceptions over entire set of rules with type Ruleset')
N = len(pos_df) + len(neg_df) # Total number of examples
p = ruleset.num_covered(pos_df) + ruleset.num_covered(neg_df) # Total number of examples classified as positive = total covered
fp = ruleset.num_covered(neg_df) # Number false positives = negatives covered by the ruleset
fn = len(pos_df) - ruleset.num_covered(pos_df) # Number false negatives = positives not covered by the ruleset
exceptions_dl = math.log2(base.nCr(p,fp)) + math.log2(base.nCr((N-p),fn))
if verbosity>=5: print(f'exceptions_bits| {ruleset.truncstr()}: \n N {N} p {p} fp {fp} fn {fn}: exceptions_bits {rnd(exceptions_dl)}')
return exceptions_dl
def _rs_total_bits(ruleset, possible_conds, pos_df, neg_df, bestsubset_dl=False, ret_bestsubset=False, verbosity=0):
""" Returns total description length (in bits) of ruleset -- the sum of its theory dl and exceptions dl.
bestsubset_dl (optional, <bool>): whether to return estimated minimum possible dl were all rules that increase dl to be removed
ret_bestsubset (optional): whether to return the best subset that was found. Return format will be (<Ruleset>,dl).
"""
# The RIPPER paper is brief and unclear w/r how to evaluate a ruleset for best potential dl.
# 1) Do you reevaluate already-visited rules or evaluate each rule independently of one another?
# Weka's source code comments that you are not supposed to, and that this is "bizarre."
# Perhaps not recursing so -- and getting a possibly sub-optimal mdl -- could be viewed as a greedy time-saver?
# After all, this is supposed to be an iterative algorithm, it could optimize more times with future k's,
# and it's not like we're performing an exhaustive search of every possible combination anyways.
# 2) In what order are you supposed to evaluate? FIFO or LIFO?
# Footnote 7 suggests optimization is done FIFO; the previous page suggests IREP* final dl reduction is done LIFO;
# and context suggests dl reduction should be performed the same way both times.
# I chose to greedy for #1, and FIFO for #2 but may choose differently in a future version if it seems more appropriate.
# In any case, RIPPER's strong performance on the test sets vs. RandomForest suggests it may not matter all that much.
if type(ruleset) != Ruleset:
raise TypeError(f'param ruleset in _rs_total_bits should be type Ruleset')
if ret_bestsubset and not bestsubset_dl:
raise ValueError(f'bestsubset_dl must be True in order to return bestsubset_dl')
if not bestsubset_dl:
theory_bits = _rs_theory_bits(ruleset, possible_conds, pos_df, neg_df, verbosity=verbosity)
data_bits = _exceptions_bits(ruleset, pos_df, neg_df, verbosity=verbosity)
if verbosity>=3: print(f'total ruleset bits | {rnd(theory_bits + data_bits)}')
return theory_bits + data_bits
else:
# Collect the dl of each subset
subset_dls = []
theory_dl = 0
if verbosity>=5: print(f'find best potential dl for {ruleset}:')
for i, rule in enumerate(ruleset.rules): # Separating theory and exceptions dls in this way means you don't have to recalculate theory each time
subset = Ruleset(ruleset.rules[:i+1])
rule_theory_dl = _r_theory_bits(rule, possible_conds, verbosity=verbosity)
theory_dl += rule_theory_dl
exceptions_dl = _exceptions_bits(subset, pos_df, neg_df, verbosity=verbosity)
subset_dls.append(theory_dl + exceptions_dl)
if verbosity>=5: print(f'subset 0-{i} | dl: {rnd(subset_dls[i])}')
# Build up the best Ruleset and calculate the mdl
mdl_ruleset = Ruleset()
for i, rule, in enumerate(ruleset.rules):
if i==0 or subset_dls[i] <= subset_dls[i-1]: # Rule i does not worsen the dl
mdl_ruleset.add(rule)
if verbosity>=5:
print(f'subset dls: {[(i,rnd(dl)) for i,dl in enumerate(subset_dls)]}')
print(f'best potential ruleset: {mdl_ruleset}')
mdl = _rs_total_bits(mdl_ruleset, possible_conds, pos_df, neg_df, bestsubset_dl=False, verbosity=0) # About to print value below
if verbosity>=5:
print(f'best potential dl was {rnd(mdl)}')
print()
if not ret_bestsubset:
return mdl
else:
return (mdl_ruleset, mdl)
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cmdb/dao.py | bopopescu/dbsupport | 2 | 105045 | # -*- coding: UTF-8 -*-
import pymysql
pymysql.install_as_MySQLdb()
import MySQLdb
from django.db import connection
class cmdbDao(object):
def getDbInstanceList(self, search_key, offset, limit):
cursor = connection.cursor()
if search_key == "" or search_key is None:
sql = "select ch.id, ch.intranet_ip_addr, cdi.* from cmdb_db_instance cdi inner join cmdb_host ch on cdi.host = ch.id limit %d, %d" % (offset, limit)
else:
sql = "select * from cmdb_db_instance cdi inner join cmdb_host ch on cdi.host = ch.id where limit %d, %d" % (offset, limit)
cursor.execute(sql)
result = cursor.fetchall()
return result
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EX 11/GUI.py | Fallinqqq/ITP-100-01-Course-Projects | 0 | 172810 | <filename>EX 11/GUI.py
linz = '---' * 30
# This is the Procedual way of creating a GUI
'''import tkinter
def main():
#creates the main window
main_window = tkinter.Tk()
# Enters the Tkinter main loop
tkinter.mainloop()
# Calls the main function
main()
print(linz)'''
linz = '---' * 30
# This is the Module way of creating a GUI
"""import tkinter
class MYHGUI:
def init(self):
self.main_window = tkinter.Tk()
tkinter.mainloop()
my_gui = MYHGUI()"""
print(linz)
# ---------------------------------------------------
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TTBenchmark/check_benchmark.py | yuqil725/benchmark_lib | 0 | 8856 | def check_difference():
pass
def update_benchmark():
pass
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grortir/externals/__init__.py | wojtekPi/grortir | 0 | 72849 | <filename>grortir/externals/__init__.py<gh_stars>0
"""Package contains modified external modules."""
# pylint: skip-file
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onmt/modules/Samplers.py | d-ataman/lmm | 9 | 159604 | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import pad_packed_sequence as unpack
import onmt
from onmt.Utils import aeq
import torch.distributions as tdist
class ReparametrisationSampler(nn.Module):
"""
Reparametrization Sampler for the Variational Autoencoder.
Args:
num_latent_vars: The number of latent variables
latent_dim: dimension of the latent variable vector
hidden_sze: number of hidden units at each layer
num_affixes: number of hidden layers in the inference network
"""
def __init__(self, num_vars=1, latent_dim, hidden_size, num_affixes):
self.num_vars = num_vars
self.latent_dim = latent_dim
self.hidden_size = hidden_size
self.num_affixes = num_affixes
super(ReparametrisationSampler, self).__init__()
def get_reparametrisation_sampler(self, num_vars=1, latent_dim, hidden_size, num_affixes):
"""
Method for calling the sampler.
Right now only the Diagonal Gaussian distribution is supported.
"""
return DiagonalGaussianSampler(num_vars, latent_dim, hidden_size, num_affixes)
class DiagonalGaussianSampler(ReparametrisationSampler):
"""
The inference network based on MLP to learn the parameters of a diagonal
Gaussian distribution and predict samples from it given an input.
"""
def __init__(self, latent_dim, hidden_size):
super(DiagonalGaussianSampler, self).__init__()
self.latent_dim = latent_dim
self.hidden_size = hidden_size
self.mu = nn.ModuleList(nn.Linear(self.hidden_size, self.hidden_size, bias=True),
torch.nn.ReLU(),
nn.Linear(self.hidden_size, self.latent_dim, bias=True))
self.sigma = nn.ModuleList(nn.Linear(self.hidden_size, self.hidden_size, bias=True),
torch.nn.ReLU(),
nn.Linear(self.hidden_size, self.latent_dim, bias=True))
def sample_value(self, mean, variance):
"""
Produce a sample from the inferred Gaussian distribution.
:param mean: The mean of the Gaussian.
:param scale: The scale parameter of this Gaussian.
:return: A random Gaussian vector.
"""
N = tdist.Normal(torch.tensor([0.0]), torch.tensor([1.0]))
e = N.sample(sample_shape=self.latent_dim)
return mean + variance * e
def forward(self, X):
"""
Method for passing the input to the inference network
"""
mean = self.mu(X)
variance = self.sigma(X)
z = sample_value(self, mean, variance)
return z
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] |
aibox/utils.py | Aakash-kaushik/aibox | 0 | 83293 | # from https://gist.github.com/yashbonde/62df9d16858a43775c22a6af00a8d707
import os
import io
from PIL import Image
def fetch(url):
# efficient loading of URLS
import os, tempfile, hashlib, requests
fp = os.path.join(tempfile.gettempdir(), hashlib.md5(url.encode("utf-8")).hexdigest())
if os.path.isfile(fp) and os.stat(fp).st_size > 0:
with open(fp, "rb") as f:
dat = f.read()
else:
dat = requests.get(url).content
with open(fp + ".tmp", "wb") as f:
f.write(dat)
os.rename(fp + ".tmp", fp)
return dat
def get_image(file_path_or_url):
if os.path.exists(file_path_or_url):
return Image.open(file_path_or_url)
else:
return Image.open(io.BytesIO(fetch(file_path_or_url)))
def folder(x):
# get the folder of this file path
import os
return os.path.split(os.path.abspath(x))[0]
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