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libpython/ben/icu_tools.py | longnow/panlex-tools | 8 | 71349 | <reponame>longnow/panlex-tools<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import icu
def all_glyphs(prop):
return [c.encode('utf-16', 'surrogatepass').decode('utf-16') for c in icu.UnicodeSetIterator(icu.UnicodeSet(r'[:{}:]'.format(prop)))]
def all_cps(prop):
return list(map(glyph_cp, all_glyphs(prop)))
def glyph_name(glyph, default=''):
if len(glyph) > 1:
raise TypeError('glyph must be a string with length of 1')
elif len(glyph) == 0:
return default
else:
return icu.Char.charName(glyph)
def cp_glyph(cp, default=''):
try:
return chr(int(cp, 16))
except ValueError:
return default
def cp_name(cp, default=''):
return glyph_name(cp_glyph(cp), default)
def glyph_cp(glyph):
return hex(ord(glyph))[2:]
class Rbnf:
def __init__(self, tag="spellout", locale="", rules=""):
if rules:
self._rbnf = icu.RuleBasedNumberFormat(rules)
else:
if tag.lower() not in {"spellout", "duration", "ordinal", "numbering_system"}:
raise ValueError("tag must be 'spellout', 'duration', 'ordinal', or 'numbering_system'")
self._rbnf = icu.RuleBasedNumberFormat(getattr(icu.URBNFRuleSetTag, tag.upper()), icu.Locale(locale))
def format(self, number):
return self._rbnf.format(number)
@property
def ruleset(self):
return self._rbnf.getDefaultRuleSetName()
@ruleset.setter
def ruleset(self, ruleset_name):
try:
self._rbnf.setDefaultRuleSet(ruleset_name)
except icu.ICUError:
raise ValueError(f"{ruleset_name} not a valid ruleset. See {self.__class__.__name__}.rulesets() for valid rulesets.")
def rulesets(self):
return {self._rbnf.getRuleSetName(i) for i in range(self._rbnf.getNumberOfRuleSetNames())}
def rules(self):
return self._rbnf.getRules()
def parse(self, string):
try:
return self._rbnf.parse(string)
except icu.ICUError:
raise ValueError("unable to parse string")
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] |
tools/benchmark.py | openlabs/mongo-python-driver | 5 | 197346 | # Copyright 2009-2010 10gen, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MongoDB benchmarking suite."""
import time
import sys
sys.path[0:0] = [""]
import datetime
import cProfile
from pymongo import connection
from pymongo import ASCENDING
trials = 2
per_trial = 5000
batch_size = 100
small = {}
medium = {"integer": 5,
"number": 5.05,
"boolean": False,
"array": ["test", "benchmark"]
}
# this is similar to the benchmark data posted to the user list
large = {"base_url": "http://www.example.com/test-me",
"total_word_count": 6743,
"access_time": datetime.datetime.utcnow(),
"meta_tags": {"description": "i am a long description string",
"author": "<NAME>",
"dynamically_created_meta_tag": "who know\n what"
},
"page_structure": {"counted_tags": 3450,
"no_of_js_attached": 10,
"no_of_images": 6
},
"harvested_words": ["10gen", "web", "open", "source", "application",
"paas", "platform-as-a-service", "technology",
"helps", "developers", "focus", "building",
"mongodb", "mongo"] * 20
}
def setup_insert(db, collection, object):
db.drop_collection(collection)
def insert(db, collection, object):
for i in range(per_trial):
to_insert = object.copy()
to_insert["x"] = i
db[collection].insert(to_insert)
def insert_batch(db, collection, object):
for i in range(per_trial / batch_size):
db[collection].insert([object] * batch_size)
def find_one(db, collection, x):
for _ in range(per_trial):
db[collection].find_one({"x": x})
def find(db, collection, x):
for _ in range(per_trial):
for _ in db[collection].find({"x": x}):
pass
def timed(name, function, args=[], setup=None):
times = []
for _ in range(trials):
if setup:
setup(*args)
start = time.time()
function(*args)
times.append(time.time() - start)
best_time = min(times)
print "%s%d" % (name + (60 - len(name)) * ".", per_trial / best_time)
return best_time
def main():
connection._TIMEOUT = 60 # jack up the timeout
c = connection.Connection()
c.drop_database("benchmark")
db = c.benchmark
timed("insert (small, no index)", insert,
[db, 'small_none', small], setup_insert)
timed("insert (medium, no index)", insert,
[db, 'medium_none', medium], setup_insert)
timed("insert (large, no index)", insert,
[db, 'large_none', large], setup_insert)
db.small_index.create_index("x", ASCENDING)
timed("insert (small, indexed)", insert, [db, 'small_index', small])
db.medium_index.create_index("x", ASCENDING)
timed("insert (medium, indexed)", insert, [db, 'medium_index', medium])
db.large_index.create_index("x", ASCENDING)
timed("insert (large, indexed)", insert, [db, 'large_index', large])
timed("batch insert (small, no index)", insert_batch,
[db, 'small_bulk', small], setup_insert)
timed("batch insert (medium, no index)", insert_batch,
[db, 'medium_bulk', medium], setup_insert)
timed("batch insert (large, no index)", insert_batch,
[db, 'large_bulk', large], setup_insert)
timed("find_one (small, no index)", find_one,
[db, 'small_none', per_trial / 2])
timed("find_one (medium, no index)", find_one,
[db, 'medium_none', per_trial / 2])
timed("find_one (large, no index)", find_one,
[db, 'large_none', per_trial / 2])
timed("find_one (small, indexed)", find_one,
[db, 'small_index', per_trial / 2])
timed("find_one (medium, indexed)", find_one,
[db, 'medium_index', per_trial / 2])
timed("find_one (large, indexed)", find_one,
[db, 'large_index', per_trial / 2])
timed("find (small, no index)", find, [db, 'small_none', per_trial / 2])
timed("find (medium, no index)", find, [db, 'medium_none', per_trial / 2])
timed("find (large, no index)", find, [db, 'large_none', per_trial / 2])
timed("find (small, indexed)", find, [db, 'small_index', per_trial / 2])
timed("find (medium, indexed)", find, [db, 'medium_index', per_trial / 2])
timed("find (large, indexed)", find, [db, 'large_index', per_trial / 2])
# timed("find range (small, no index)", find,
# [db, 'small_none',
# {"$gt": per_trial / 4, "$lt": 3 * per_trial / 4}])
# timed("find range (medium, no index)", find,
# [db, 'medium_none',
# {"$gt": per_trial / 4, "$lt": 3 * per_trial / 4}])
# timed("find range (large, no index)", find,
# [db, 'large_none',
# {"$gt": per_trial / 4, "$lt": 3 * per_trial / 4}])
timed("find range (small, indexed)", find,
[db, 'small_index',
{"$gt": per_trial / 2, "$lt": per_trial / 2 + batch_size}])
timed("find range (medium, indexed)", find,
[db, 'medium_index',
{"$gt": per_trial / 2, "$lt": per_trial / 2 + batch_size}])
timed("find range (large, indexed)", find,
[db, 'large_index',
{"$gt": per_trial / 2, "$lt": per_trial / 2 + batch_size}])
if __name__ == "__main__":
# cProfile.run("main()")
main()
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] |
quantsbin/derivativepricing/namesnmapper.py | quantsbin/Quantsbin | 132 | 40746 | """
developed by Quantsbin - Jun'18
"""
from enum import Enum
class AssetClass(Enum):
EQOPTION = 'EqOption'
FXOPTION = 'FXOption'
FUTOPTION = 'FutOption'
COMOPTION = 'ComOption'
class DerivativeType(Enum):
VANILLA_OPTION = 'Vanilla Option'
class PricingModel(Enum):
BLACKSCHOLESMERTON = 'BSM'
BLACK76 = 'B76'
GK = 'GK'
MC_GBM = "MC_GBM"
MC_GBM_LSM = "MC_GBM_LSM"
BINOMIAL = "Binomial"
class UnderlyingParameters(Enum):
SPOT = "spot0"
VOLATILITY = "volatility"
PRICEDATE = "_pricing_date"
RF_RATE = "rf_rate"
CNV_YIELD = "cnv_yield"
COST_YIELD = "cost_yield"
UNEXPLAINED = "unexplained"
class RiskParameter(Enum):
DELTA = 'delta'
GAMMA = 'gamma'
THETA = 'theta'
VEGA = 'vega'
RHO = 'rho'
PHI = 'phi'
RHO_FOREIGN = 'rho_foreign'
RHO_CONV = 'rho_conv_yield'
class VanillaOptionType(Enum):
CALL = 'Call'
PUT = 'Put'
class ExpiryType(Enum):
AMERICAN = 'American'
EUROPEAN = 'European'
class UdlType(Enum):
INDEX = 'Index'
STOCK = 'Stock'
FX = 'Currency'
COMMODITY = 'Commodity'
FUTURES = 'Futures'
class DivType(Enum):
DISCRETE = 'Discrete'
YIELD = 'Yield'
OBJECT_MODEL = {
UdlType.STOCK.value: {ExpiryType.EUROPEAN.value: [PricingModel.BLACKSCHOLESMERTON.value, PricingModel.MC_GBM.value
, PricingModel.BINOMIAL.value],
ExpiryType.AMERICAN.value: [PricingModel.MC_GBM.value, PricingModel.BINOMIAL.value]}
, UdlType.FUTURES.value: {ExpiryType.EUROPEAN.value: [PricingModel.BLACK76.value, PricingModel.MC_GBM.value
, PricingModel.BINOMIAL.value],
ExpiryType.AMERICAN.value: [PricingModel.MC_GBM.value, PricingModel.BINOMIAL.value]}
, UdlType.FX.value: {ExpiryType.EUROPEAN.value: [PricingModel.GK.value, PricingModel.MC_GBM.value
, PricingModel.BINOMIAL.value],
ExpiryType.AMERICAN.value: [PricingModel.MC_GBM.value, PricingModel.BINOMIAL.value]}
, UdlType.COMMODITY.value: {ExpiryType.EUROPEAN.value: [PricingModel.GK.value, PricingModel.MC_GBM.value
, PricingModel.BINOMIAL.value],
ExpiryType.AMERICAN.value: [PricingModel.MC_GBM.value, PricingModel.BINOMIAL.value]}
}
DEFAULT_MODEL = {
UdlType.STOCK.value:
{DerivativeType.VANILLA_OPTION.value: {ExpiryType.EUROPEAN.value: PricingModel.BLACKSCHOLESMERTON.value,
ExpiryType.AMERICAN.value: PricingModel.BINOMIAL.value},
},
UdlType.FUTURES.value:
{DerivativeType.VANILLA_OPTION.value: {ExpiryType.EUROPEAN.value: PricingModel.BLACK76.value,
ExpiryType.AMERICAN.value: PricingModel.BINOMIAL.value},
},
UdlType.FX.value:
{DerivativeType.VANILLA_OPTION.value: {ExpiryType.EUROPEAN.value: PricingModel.GK.value,
ExpiryType.AMERICAN.value: PricingModel.BINOMIAL.value},
},
UdlType.COMMODITY.value:
{DerivativeType.VANILLA_OPTION.value: {ExpiryType.EUROPEAN.value: PricingModel.GK.value,
ExpiryType.AMERICAN.value: PricingModel.BINOMIAL.value},
}
}
IV_MODELS = [PricingModel.BLACKSCHOLESMERTON.value, PricingModel.BLACK76.value, PricingModel.GK.value]
ANALYTICAL_GREEKS = [PricingModel.BLACKSCHOLESMERTON.value, PricingModel.BLACK76.value, PricingModel.GK.value]
from . import pricingmodels as pm
MODEL_MAPPER = {
PricingModel.BLACKSCHOLESMERTON.value: pm.BSM,
PricingModel.BLACK76.value: pm.B76,
PricingModel.GK.value: pm.GK,
PricingModel.MC_GBM.value: pm.MonteCarloGBM,
PricingModel.BINOMIAL.value: pm.BinomialModel
}
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] |
blender/nodes/element/mpm.py | LewisOrton/taichi_elements_houdini | 1 | 190769 | from .. import base
class ElementsMpmSolverNode(base.BaseNode):
bl_idname = 'elements_mpm_solver_node'
bl_label = 'MPM Solver'
required_nodes = {
'Domain Object': [
'elements_source_object_node',
],
}
category = base.ELEMENT
def init(self, context):
self.width = 180.0
out = self.outputs.new('elements_struct_socket', 'MPM Solver')
out.text = 'Solver Settings'
domain_obj = self.inputs.new('elements_struct_socket', 'Domain Object')
domain_obj.text = 'Domain Object'
res = self.inputs.new('elements_integer_socket', 'Resolution')
res.text = 'Resolution'
res.default = 64
size = self.inputs.new('elements_float_socket', 'Size')
size.text = 'Size'
size.default = 10.0
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riglib/kinarmsocket.py | sgowda/brain-python-interface | 7 | 126558 | <reponame>sgowda/brain-python-interface<filename>riglib/kinarmsocket.py
import socket
import struct
import time
import numpy as np
class KinarmSocket(object):
''' Method for receiving UDP packets from Kinarm. The kinarm must be running the Dexterit-E task
called 'PK_send_UDP_kinarm', or any task with a 'UDP Send Binary' block which is located in the
xPC target tab of the Simulink library. Note: The normal 'UDP Send' input will NOT work -- packets
will not be sent since the Dexterit task computer does not receive them (the xPC machine does).
The socket port that packets are sent to is also configured in this UDP Send Binary block.
Data format is double --> binary byte packing with Byte alignment = 1 (From Matlab: "The byte alignment
field specifies how the data types are aligned. The possible values are: 1, 2, 4, and 8. The byte
alignment scheme is simple, and starts each element in the list of signals on a boundary specified
by the alignment relative to the start of the vector."")
Packet received are 3 x 50 matrix. Refer to Dexterit Manual (on desktop of Dexterit task computer) for
what the rows / columns refer to (page 71/82).
'''
def __init__(self, addr=('192.168.0.8', 9090)):
''' Self-IP address is: 192.168.0.8 set manually. Once fully migrating system over to BMI3d,
will need to adjust for new IPs'''
#Set up UDP socket (specificed by socket.SOCK_DGRAM. TCP woudl use socket.SOCK_STREAM)
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
#Free up port for use if you just ran task:
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
#Bind address
self.socket.bind(addr)
self.data_size = 1200 # 3 x 50 x 8 bytes (double)
self.remote_ip = '192.168.0.2'
def _test_connected(self):
try:
data, add = self.socket.recvfrom(1200)
unpacked_ = struct.unpack('150d', data)
assert len(unpacked_) == 150
self.connected = True
except:
print('Make sure Kinarm Task is running - error in recieving packets')
self.connected = False
def connect(self):
self._test_connected()
def get_data(self):
'''
A generator which yields packets as they are received
'''
assert self.connected, "Socket is not connected, cannot get data"
while self.connected:
packet, address = self.socket.recvfrom(self.data_size)
#Make sure packet is from correct address:
if address[0] == self.remote_ip:
arrival_ts = time.time()
data = np.array(struct.unpack('150d', packet))
if data.shape[0] == 150:
#reshape data into 3 x 50
kindata = data.reshape(50,3).T
#kindata = np.hstack((kindata, np.zeros((3, 1))))
#kindata[:, -1] = arrival_ts
yield kindata
def disconnect(self):
self.socket.close()
self.connected = False
def __del__(self):
self.disconnect()
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] |
ni-usb-datalogger/tests/data_stream.py | chapdbr/ni-usb-datalogger | 0 | 1611067 | <gh_stars>0
# Libraries
# For plotting app
import time
import dash
import dash_core_components as dcc
import dash_html_components as html
from collections import deque
import plotly.graph_objs as go
import logging
# Stop suppress dash server posts to console
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
# Define app vars
app = dash.Dash('sensor-data')
maxLineLength = 20
max_length = 20
times = deque(maxlen=max_length)
fx = deque(maxlen=max_length)
fy = deque(maxlen=max_length)
fz = deque(maxlen=max_length)
mx = deque(maxlen=max_length)
my = deque(maxlen=max_length)
mz = deque(maxlen=max_length)
data_dict = {"Force en X":fx,
"Force en Y":fy,
"Force en Z":fz,
"Moment en X":mx,
"Moment en Y":my,
"Moment en Z":mz}
def read_data():
with open('ATI45.txt', 'r') as log:
data_in = list(log)[-1]
data_out = data_in.split(",")
return data_out
def add_data_to_plot(data):
times.append(time.time())
fx.append(data[1])
fy.append(data[2])
fz.append(data[3])
#mx.append(data_tared_cal[3,0])
#my.append(data_tared_cal[4,0])
#mz.append(data_tared_cal[5,0])
return times, fx, fy, fz
def init_time():
global start_time
start_time = time.time()
return
# Initialize values
times, fx, fy, fz = add_data_to_plot(read_data())
# Define app layout
app.layout = html.Div([
html.Div([
html.H2('Sensor Data',
style={'float': 'left',
}),
]),
dcc.Dropdown(id='sensor-data-name', #dropdown menu
options=[{'label': s, 'value': s}
for s in data_dict.keys()],
value=['Force en X', 'Force en Y', 'Force en Z'], #default values at startup
multi=True
),
html.Div(children=html.Div(id='graphs'), className='row'),
dcc.Interval(
id='graph-update',
interval=500, #graph update interval in ms
n_intervals=0)
], className="container",style={'width':'98%','margin-left':10,'margin-right':10,'max-width':50000}) #maxwidth peut être enlevé si vu sur ordi
@app.callback(
dash.dependencies.Output('graphs','children'),
[dash.dependencies.Input('sensor-data-name', 'value')],
events=[dash.dependencies.Event('graph-update', 'interval')]
)
def update_graph(data_names):
graphs = []
data = read_data()
add_data_to_plot(data)
if len(data_names)>2:
class_choice = 'col s12 m6 l4'
elif len(data_names) == 2:
class_choice = 'col s12 m6 l6'
else:
class_choice = 'col s12'
for data_name in data_names:
data = go.Scatter(
x=list(times),
y=list(data_dict[data_name]),
name='Scatter',
fill="tozeroy",
fillcolor="#6897bb"
)
graphs.append(html.Div(dcc.Graph(
id=data_name,
animate=True,
figure={'data': [data],'layout' : go.Layout(xaxis=dict(range=[min(times),max(times)]),
yaxis=dict(range=[-100,100]),
margin={'l':50,'r':1,'t':45,'b':1},
title='{}'.format(data_name))}
), className=class_choice))
return graphs
external_css = ["https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.min.css"]
for css in external_css:
app.css.append_css({"external_url": css})
external_js = ['https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js']
for js in external_css:
app.scripts.append_script({'external_url': js})
if __name__ == '__main__':
app.run_server(debug=False) | [
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] |
saas/backend/common/redis.py | Canway-shiisa/bk-iam-saas | 0 | 1600155 | # -*- coding: utf-8 -*-
"""
TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-权限中心(BlueKing-IAM) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
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 django.core.cache import caches
from django_redis import get_redis_connection as get_django_cache_redis_connection
from .cache import CacheEnum
def make_redis_key(key: str) -> str:
"""
所有通过get_redis_connection获取原生Redis来使用Redis的，其最终key都需要避免与Django Cache冲突
所以这里提供了方法来协助生成避免Key冲突的，这样使用原生Redis时不需要关注与其他项目或者项目内Cache的key冲突
"""
cache = caches[CacheEnum.REDIS.value]
# 对于Cache来说version是有意义的，但对于原生使用Redis，没意义，为了避免与Cache Key冲突，这里可以使用任何version不可能去的值来代替，比如"raw"
return cache.make_key(key, version="raw")
def get_redis_connection():
"""
复用Django Cache其配置的Redis Cache的Redis Client Connection
这样可以不需要根据Redis配置来生成Redis Client Connection
Note：
这里返回的是原生Redis Connection，所以Django Cache里配置的KEY_PREFIX、TIMEOUT、VERSION都不会生效
所以使用时为了避免与Cache的key冲突，必须配合 make_redis_key方法一起使用
"""
return get_django_cache_redis_connection(alias=CacheEnum.REDIS.value)
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backend/pages/views.py | ranwise/djangochannel | 45 | 95901 | from django.shortcuts import render, get_object_or_404
from django.views.generic.base import View
from .models import Pages
class Page(View):
"""Вывод страниц"""
def get(self, request, slug=None):
if slug is not None:
page = get_object_or_404(Pages, slug=slug, published=True)
else:
page = get_object_or_404(Pages, slug__isnull=True, published=True)
return render(request, page.template, {"page": page})
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erudition/data_challenge/__init__.py | papsebestyen/erudition | 0 | 162178 | from invoke import Collection
from . import tasks
data_ns = Collection.from_module(tasks)
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sancho/model.py | el-assistant/sancho | 0 | 128366 | """
Entity modelling for the whole project is described here.
"""
from sancho.defaults import *
import tree_sitter
class Repo(NamedTuple):
path: str
full_name: str = None
class File(NamedTuple):
repo: Repo
full_path: str
class ASTnode(NamedTuple):
"""It is a node and a tree at the same time, since the children nodes are nested
in a tuple. So the root node is actually the whole tree. Field 'kind' is the
kind of the node as defined in tree-sitter respective grammar. 'start' and
'end' are the position of the parsed structure in the source text. 'content'
has the raw content of the parsed text. It is filled only for some kinds of
nodes. For example, we track the content of 'identifier' nodes, so that we
can enrich the AST with symbol references later.
"""
kind: str
start: int
end: int
children: Tuple["ASTnode"]
content: str = None
class ParsedText(NamedTuple):
"""
Contains the AST of a file, together with the source code and some metadata.
sitter_tree contains the AST in the format provided by tree-sitter package,
whereas tree contains the AST defined as a NamedTuple. Once tree is defined,
sitter_tree can be discarded.
"""
sitter_tree: tree_sitter.Tree = None
tree: ASTnode = None
text: bytes = None
path: str = None
class ASTnodeRowFormat(NamedTuple):
"""These are generated from ASTnode information.
This format is suitable for ingesting data through Neo4j LOAD CSV command.
"""
full_path: str
local_id: int
kind: str
parent_id: int = None # root parent_id is None
next_id: int = None
content: str = None
class ASTnodesTable(NamedTuple):
full_path: str
rows: list[ASTnodeRowFormat]
class FileRowFormat(NamedTuple):
full_path: str # unique id
is_dir: bool = False
parent_path: str = None # repo root has parent_path=None
# respective root ASTnode is matched through full_path
class FilesTable(NamedTuple):
repo: Repo
rows: list[FileRowFormat]
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erp/migrations/0033_auto_20200205_1625.py | Foohx/acceslibre | 8 | 148058 | <filename>erp/migrations/0033_auto_20200205_1625.py
# Generated by Django 3.0.3 on 2020-02-05 15:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('erp', '0032_auto_20200205_1431'),
]
operations = [
migrations.AlterModelOptions(
name='cheminement',
options={'verbose_name': 'Cheminement', 'verbose_name_plural': 'Cheminements'},
),
migrations.AddField(
model_name='accessibilite',
name='aide_humaine',
field=models.BooleanField(blank=True, help_text="Présence ou possibilité d'une aide humaine au déplacement", null=True),
),
migrations.AddField(
model_name='accessibilite',
name='ascenseur',
field=models.BooleanField(blank=True, help_text="Présence d'un ascenseur ou d'un élévateur", null=True, verbose_name='Ascenseur/élévateur'),
),
migrations.AddField(
model_name='accessibilite',
name='escalier_main_courante',
field=models.BooleanField(blank=True, help_text="Présence d'une main courante d'escalier", null=True, verbose_name='Main courante'),
),
migrations.AddField(
model_name='accessibilite',
name='escalier_marches',
field=models.PositiveSmallIntegerField(blank=True, help_text="Nombre de marches d'escalier. Indiquez 0 si pas d'escalier ou si présence d'un ascenseur/élévateur.", null=True, verbose_name="Marches d'escalier"),
),
migrations.AddField(
model_name='accessibilite',
name='escalier_reperage',
field=models.BooleanField(blank=True, help_text="Si marches contrastées, bande d'éveil ou nez de marche contrastés, indiquez “Oui”", null=True, verbose_name="Repérage de l'escalier"),
),
migrations.AddField(
model_name='accessibilite',
name='guidage_sonore',
field=models.BooleanField(blank=True, help_text="Présence d'un dispositif de guidage sonore", null=True, verbose_name='Système de guidage sonore'),
),
migrations.AddField(
model_name='accessibilite',
name='largeur_mini',
field=models.PositiveSmallIntegerField(blank=True, help_text='Largeur minimale du passage ou rétrécissement, en centimètres', null=True, verbose_name='Largeur minimale'),
),
migrations.AddField(
model_name='accessibilite',
name='rampe',
field=models.CharField(blank=True, choices=[(None, 'Inconnu'), ('aucune', 'Aucune'), ('fixe', 'Fixe'), ('amovible', 'Amovible')], help_text='Présence et type de rampe', max_length=20, null=True),
),
migrations.AddField(
model_name='accessibilite',
name='reperage_vitres',
field=models.BooleanField(blank=True, help_text="Présence d'un repérage sur les surfaces vitrées", null=True, verbose_name='Répérage surfaces vitrées'),
),
migrations.AlterField(
model_name='cheminement',
name='type',
field=models.CharField(choices=[('int_entree_batiment_vers_accueil', "Cheminement intérieur de l'entrée du bâtiment jusqu'à l'accueil"), ('ext_stationnement_vers_entree', "Cheminement extérieur de la place de stationnement de l'ERP à l'entrée"), ('ext_entree_parcelle_entree_vers_batiment', "Cheminement extérieur de l'entrée de la parcelle de terrain à l'entrée du bâtiment")], default='int_entree_batiment_vers_accueil', help_text='Type de circulation', max_length=100, verbose_name='Type'),
),
]
| [
1,
529,
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261,
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fem/fem.py | Pengeace/DGP-PDE-FEM | 7 | 9477 | <reponame>Pengeace/DGP-PDE-FEM
import numpy as np
import pyamg
from scipy import sparse
from scipy.spatial import Delaunay
from linsolver import sparse_solver
from triangulation.delaunay import delaunay
class Element:
def __init__(self, points, global_indexes, fem):
self.points = np.array(points)
self.global_indexes = global_indexes
self.fem = fem
self.reference_triangle = np.array([[0, 0], [1., 0], [0, 1.]])
self.reference_grad = np.array([[-1., -1], [1., 0], [0, 1.]])
def perform_calculation(self):
self._calculate_transform()
self._calculate_stiffness_matrix()
self._calulate_load_vector()
def _calculate_transform(self):
reference_coord = np.array([self.reference_triangle[:, 0], self.reference_triangle[:, 1], [1] * 3])
transformed_coord = np.array([self.points[:, 0], self.points[:, 1], [1] * 3])
trans = np.dot(transformed_coord, np.linalg.inv(reference_coord))
self.transform_matrix = trans[0:-1, 0:-1]
self.area = abs(np.linalg.det(self.transform_matrix)) / 2
def _calculate_stiffness_matrix(self):
transform_matrix_inv = np.linalg.inv(self.transform_matrix)
self.element_stiffness_matrix = np.zeros((3, 3))
for row in range(3):
for col in range(3):
part_u_left_grad = np.dot(np.dot(self.fem.A, transform_matrix_inv.T), self.reference_grad[row])
part_u_right_grad = np.dot(transform_matrix_inv.T, self.reference_grad[col])
part_u_grad = self.area * np.dot(part_u_left_grad, part_u_right_grad)
part_u = (self.area / 6.0) if row == col else (self.area / 12.0)
self.element_stiffness_matrix[row, col] = part_u_grad + self.fem.q * part_u
def _calulate_load_vector(self):
mean_f = np.mean([self.fem.get_func_value(x) for x in self.points])
self.element_load_vector = np.array([mean_f * self.area / 3] * 3)
class FiniteElement:
"""
Finite Element Method to solve the 2D Elliptic Partial Differentiation differential Equation with below form:
div(A grad(u)) + q u = func
"""
def __init__(self, points, boundaries, A, q, func, slow_solver=True):
self.points = np.array(points)
self.dirichlet_boundaries = np.array(boundaries)
self.A = A
self.q = q
self.f = func
self.slow_solver = slow_solver
self.triangles = []
self.point_num = len(points)
def solve(self):
if len(self.triangles) == 0:
self._get_mesh()
self._process_each_element()
self._calculate_global_stiffness_matrix()
self._calulate_global_load_vector()
self._deal_with_dirichlet_bound()
self._solve_linear_equations()
def update_border_and_func(self, boundaries, func):
self.dirichlet_boundaries = np.array(boundaries)
self.f = func
def get_func_value(self, x):
if isinstance(self.f, dict):
return self.f[tuple(x)]
else:
return self.f(x)
def _get_mesh(self):
if self.slow_solver:
self.triangles = delaunay(self.points)
else:
triangulation = Delaunay(self.points)
self.triangles = triangulation.simplices
def _process_each_element(self):
self.elements = []
for tri in self.triangles:
ele = Element(points=[self.points[v] for v in tri], global_indexes=tri, fem=self)
ele.perform_calculation()
self.elements.append(ele)
def _calculate_global_stiffness_matrix(self):
self.global_stiffness_matrix_row = []
self.global_stiffness_matrix_col = []
self.global_stiffness_matrix_data = []
boundary_indexes = set(self.dirichlet_boundaries[:, 0].astype('int'))
for ele in self.elements:
for row in range(3):
if ele.global_indexes[row] not in boundary_indexes:
for col in range(3):
self.global_stiffness_matrix_row.append(ele.global_indexes[row])
self.global_stiffness_matrix_col.append(ele.global_indexes[col])
self.global_stiffness_matrix_data.append(ele.element_stiffness_matrix[row, col])
def _calulate_global_load_vector(self):
self.global_load_vector = np.zeros(self.point_num)
for ele in self.elements:
for v in range(3):
self.global_load_vector[ele.global_indexes[v]] += ele.element_load_vector[v]
def _deal_with_dirichlet_bound(self):
for index, val in self.dirichlet_boundaries:
index = int(index)
self.global_stiffness_matrix_row.append(index)
self.global_stiffness_matrix_col.append(index)
self.global_stiffness_matrix_data.append(1)
self.global_load_vector[index] = val
def _solve_linear_equations(self):
if not self.slow_solver:
self.global_stiffness_matrix_csr = sparse.coo_matrix((self.global_stiffness_matrix_data, (
self.global_stiffness_matrix_row, self.global_stiffness_matrix_col))).tocsr()
self.solution = pyamg.solve(self.global_stiffness_matrix_csr, self.global_load_vector, verb=False,
tol=1e-10)
else:
global_stiffness_sparse = [np.array(self.global_stiffness_matrix_row),
np.array(self.global_stiffness_matrix_col),
np.array(self.global_stiffness_matrix_data)]
self.solution = sparse_solver.sparse_gauss_seidel(global_stiffness_sparse, self.global_load_vector,
sparse_input=True)
## these solver methods are for test
# self.global_stiffness = sparse.coo_matrix((self.global_stiffness_matrix_data, (
# self.global_stiffness_matrix_row, self.global_stiffness_matrix_col))).tocsr()
# self.solution = linsolver.jacobi(self.global_stiffness.toarray(), self.global_load_vector)
# self.solution = linsolver.gauss_seidel(self.global_stiffness.toarray(), self.global_load_vector)
# self.solution = sparse_solver.sparse_jacobi(self.global_stiffness.toarray(), self.global_load_vector, sparse_input=False)
# self.solution = sparse_solver.sparse_gauss_seidel(self.global_stiffness.toarray(), self.global_load_vector, sparse_input=False)
if isinstance(self.solution, str):
print("The inputs for linear solver have problems.")
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tests/stdlib/test_math.py | bozhiyeh/voc | 0 | 112129 | import unittest
from ..utils import TranspileTestCase
class MathModuleTests(TranspileTestCase):
def test_fabs(self):
self.assertCodeExecution("""
import math
print(math.fabs(10234))
print(math.fabs(0.0001))
print(math.fabs(0))
print(math.fabs(-1))
""")
@unittest.expectedFailure
def test_fail(self):
self.assertCodeExecution("""
import math
try:
print(math.fabs('12334'))
except Exception as e:
print(type(e), e)
""") | [
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deepfence_backend/init_scripts/reindex_sbom_artifacts.py | riddopic/ThreatMapper | 0 | 1608741 | import os
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk
import math
EL_HOST = "%s://%s:%s" % (os.getenv('ELASTICSEARCH_SCHEME', 'http'), os.environ['ELASTICSEARCH_HOST'], os.environ['ELASTICSEARCH_PORT'])
http_auth = None
CUSTOMER_UNIQUE_ID = os.getenv('CUSTOMER_UNIQUE_ID', None)
if 'ELASTICSEARCH_USER' in os.environ:
http_auth = (os.environ['ELASTICSEARCH_USER'],
os.environ['ELASTICSEARCH_PASSWORD'])
if http_auth:
EL_CLIENT = Elasticsearch([EL_HOST], http_auth=http_auth, timeout=300)
else:
EL_CLIENT = Elasticsearch([EL_HOST], timeout=300)
SBOM_INDEX = "sbom-cve-scan"
SBOM_ARTIFACT_INDEX = "sbom-artifact"
if CUSTOMER_UNIQUE_ID:
SBOM_INDEX += f"-{CUSTOMER_UNIQUE_ID}"
SBOM_ARTIFACT_INDEX += f"-{CUSTOMER_UNIQUE_ID}"
ARRAY_SIZE = 5
if EL_CLIENT.indices.exists(index=SBOM_INDEX) and EL_CLIENT.indices.exists(index=SBOM_ARTIFACT_INDEX):
sbom_count_array = EL_CLIENT.cat.count(SBOM_INDEX, params={"format": "json"})
sbom_count = 0
if sbom_count_array:
sbom_count = int(sbom_count_array[0]["count"])
if sbom_count > 0:
for i in range(0, math.ceil(sbom_count/ARRAY_SIZE)):
sbom_docs = EL_CLIENT.search(index=SBOM_INDEX, body={"query": {"match_all": {}}}, from_=i*ARRAY_SIZE, size=ARRAY_SIZE,
sort="scan_id.keyword:desc", _source=["scan_id", "node_id", "node_type",
"@timestamp", "time_stamp", "artifacts"])
if sbom_docs["hits"]["total"]["value"] > 0:
for sbom_doc in sbom_docs["hits"]["hits"]:
body = {
"query": {
"constant_score": {
"filter": {
"bool": {
"must": {
"terms": {
"scan_id.keyword": [sbom_doc["_source"]["scan_id"]]
}
}
}
}
}
}
}
sbom_artifact_res = EL_CLIENT.search(index=SBOM_ARTIFACT_INDEX, body=body, size=1)
if sbom_artifact_res.get("hits", {}).get("total", {}).get("value", -1) == 0:
source_doc = sbom_doc["_source"]
defaults = {
"scan_id": source_doc["scan_id"],
"node_id": source_doc["node_id"],
"node_type": source_doc["node_type"],
"masked": "false",
"@timestamp": source_doc["@timestamp"],
"time_stamp": source_doc["time_stamp"],
}
bulk_index_actions = []
for artifact in sbom_doc["_source"]["artifacts"]:
# print("Going through artifact: ", artifact["name"])
doc = {
**defaults,
"name": artifact["name"],
"version": artifact["version"],
"locations": artifact["locations"],
"licenses": artifact["licenses"],
"language": artifact["language"]
}
bulk_index_actions.append({
"_op_type": "index",
"_index": SBOM_ARTIFACT_INDEX,
"_source": doc
})
errors = bulk(EL_CLIENT, bulk_index_actions)
if errors:
print("Error while bulk processing artifacts for scan_id: ", source_doc["scan_id"])
print(errors)
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edi/ad.py | GaloisInc/adapt | 2 | 117656 | <reponame>GaloisInc/adapt
import argparse
import csv
import gzip
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import edi.simple.ad as ad
parser = argparse.ArgumentParser(description='Attribute value frequency')
parser.add_argument('--input', '-i',
help='input file', required=True)
parser.add_argument('--output', '-o',
help='output file', required=True)
parser.add_argument('--score', '-s',
help='avf or avc or rasp',
default='avf',
choices=['avf','avc','rasp'])
parser.add_argument('--mode', '-m',
help='batch or stream',
default='batch',
choices=['batch','stream'])
if __name__ == '__main__':
args = parser.parse_args()
if(args.mode == 'batch'):
ad.batch(args.input, args.output, args.score)
elif (args.mode == 'stream'):
ad.stream(args.input, args.output, args.score)
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CS303_Pro/AI_Project1/test1.py | zc-BEAR/Course_Repo | 0 | 133890 | <reponame>zc-BEAR/Course_Repo<filename>CS303_Pro/AI_Project1/test1.py
import numpy as np
COLOR_BLACK = 1
COLOR_WHITE = -1
COLOR_NONE = 0
class AI(object):
def __init__(self, chessboard_size, color, time_out):
self.chessboard_size = chessboard_size
self.color = color
self.time_out = time_out
self.candidate_list = []
def go(self, chessboard):
chessboard = np.array(chessboard)
self.candidate_list.clear()
changeLimit(countRound(chessboard))
len, list = getMove(self.color, chessboard)
list = BubbleSort(list, len)
for i in range(0, len):
self.candidate_list.append((list[i][0], list[i][1]))
if (len != 0):
list.append((list[0][0], list[0][1]))
findMove(chessboard, self.color, 1, Limit, -INF, INF)
self.candidate_list.append((resultX, resultY))
DIR = ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1))
Varr = [
[300, -20, 11, 5, 5, 10, -20, 300],
[-20, -80, 1, 1, 1, 2, -80, -20],
[11, 2, 4, 2, 2, 4, 2, 11],
[5, 1, 2, 1, 1, 2, 1, 5],
[5, 1, 2, 1, 1, 2, 1, 5],
[11, 2, 4, 2, 2, 4, 2, 11],
[-20, -80, 2, 1, 1, 2, -80, -20],
[300, -20, 11, 5, 5, 11, -20, 300]
]
BLACK = 1
WHITE = -1
INF = 1e8
Limit = 7
curr: int = 0
def countRound(board):
b, w = 0, 0
for i in range(0, 8):
for j in range(0, 8):
if (board[i][j] == BLACK):
b = b + 1
elif (board[i][j] == WHITE):
w = w + 1
return b + w
def changeLimit(curr: int):
global Limit
if curr <= 7:
Limit = 6
elif curr <= 15:
Limit = 6
elif curr <= 33:
Limit = 4
elif curr <= 44:
Limit = 5
elif curr <= 48:
Limit = 6
elif curr <= 50:
Limit = 6
elif curr < 52:
Limit = 6
elif curr < 54:
Limit = 7
else:
Limit = 20
def getV(color, board):
global Varr
global INF
now = 0
op = 0
opColor = -color
res = 0
for i in range(0, 8):
for j in range(0, 8):
if (board[i][j] == color):
res += Varr[i][j]
now = now + 1
elif (board[i][j] == opColor):
res -= Varr[i][j]
op = op + 1
if (now + op == 64 and now > op):
return INF + now - op
elif (now + op == 64 and now < op):
return -INF + now - op
elif (now + op == 64):
return 0
if (now == 0):
return -INF - op
if (op == 0):
return INF + now
return res
def getMove(color, board):
nxtMove = []
opColor = -color
numOfMove = 0
for i in range(0, 8):
for j in range(0, 8):
if (board[i][j] == 0):
change = 0
for d in range(0, 8):
if (change != 0):
break
nxtx = i + DIR[d][0]
nxty = j + DIR[d][1]
if (nxtx < 0 or nxtx >= 8 or nxty < 0 or nxty >= 8 or board[nxtx][nxty] != opColor):
continue
for k in range(1, 8):
nxtx = nxtx + DIR[d][0]
nxty = nxty + DIR[d][1]
if (nxtx < 0 or nxtx >= 8 or nxty < 0 or nxty >= 8 or board[nxtx][nxty] == 0):
break
if (board[nxtx][nxty] == color):
nxtMove.append([i, j])
change = 1
numOfMove = numOfMove + 1
break
if (change == 1):
break
return numOfMove, nxtMove
def Moves(board, color, x, y):
board[x][y] = color
opColor = -color
for i in range(0, 8):
nxtx = x + DIR[i][0]
nxty = y + DIR[i][1]
if nxtx < 0 or nxtx >= 8 or nxty < 0 or nxty >= 8 or board[nxtx][nxty] != opColor:
continue
for k in range(1, 8):
nxtx = nxtx + DIR[i][0]
nxty = nxty + DIR[i][1]
if (nxtx < 0 or nxtx >= 8 or nxty < 0 or nxty >= 8 or board[nxtx][nxty] == 0):
break
if (board[nxtx][nxty] == color):
while (nxtx != x or nxty != y):
nxtx = nxtx - DIR[i][0]
nxty = nxty - DIR[i][1]
board[nxtx][nxty] = color
return board
def getstable(color, board):
h, s, k1, k0 = [], [], [], []
for i in range(0, 8):
h.append(1)
s.append(1)
for i in range(0, 15):
k1.append(1)
k0.append(1)
for i in range(0, 8):
for j in range(0, 8):
if (board[i][j] == 0):
h[i] = 0
s[j] = 0
k1[i - j + 7] = 1
k0[i + j] = 1
stab, stb = 0, []
for i in range(0, 8):
L = []
for j in range(0, 8):
L.append(0)
if (i == 0 or i == 7 or j == 0 or j == 7):
if (i != 0 or i != 7 or j != 0 or j != 7):
continue
stab = stab + color * board[i][j]
stb[i][j] = abs(board[i][j])
elif (h[i] and s[j] and k1[i - j + 7] and k0[i + j]):
stab = stab + color * board[i][j]
stb.append(L)
for y in range(0, 8, 7):
if (h[y] == 1):
for x in range(1, 7):
stab = stab + color * board[y][x]
continue
for d in range(-1, 2, 2):
for X in range(1, 7):
x = X
if (d == 1):
x = 7 - X
if (board[y][x] == 0):
break
elif (board[y][x] == board[y][x + d]):
stb[y][x] = stb[y][x + d]
stab = stab + color * board[y][x]
elif (board[y][x] * board[y][x + d] == -1 and stb[y][x + d] == 1):
stb[y][x] = -1
elif (board[y][x] * board[y][x + d] == -1 and stb[y][x + d] == -1):
tem = x + d
while (stb[y][tem] != -1):
stb[y][tem] = 1
stab = stab + color * board[y][tem]
tem = tem + d
for y in range(0, 8, 7):
if (s[y] == 1):
for x in range(1, 7):
stab = stab + color * board[x][y]
continue
for d in range(-1, 2, 2):
for X in range(1, 7):
x = X
if (d == 1):
x = 7 - X
if (board[x][y] == 0):
break
if (board[x][y] == board[x + d][y]):
stb[x][y] = stb[x + d][y]
stab = stab + color * board[x][y]
elif (board[x][y] * board[x + d][y] == -1 and stb[x + d][y] == 1):
stb[x][y] = -1
elif (board[x][y] * board[x + d][y] == -1 and stb[x + d][y] == -1):
tem = x + d
while (stb[tem][y] != -1):
stb[tem][y] = 1
stab = stab + color * board[tem][y]
tem = tem + d
return stab
CurrentRound = 0
NumOfFind = 0
def Function(color, isMyTurn, board, movePower):
global NumOfFind
NumOfFind = NumOfFind + 1
if (isMyTurn == 0):
color = -color
movePower = -movePower
return getV(color, board) + movePower * 6 + getstable(color, board) * 20 + 6 * stableLine(color, board)
def stableLine(color, board):
ct = [0, 1, 2, 3, 4, 5, 6, 7, 8]
nowcolor = 0
nowcnt = 0
start = 1
res = 0
for i in range(0, 8):
if (start == 0 and board[i][0] == 0):
start = 1
elif (start == 1 and nowcnt != 0 and board[i][0] == 0):
res = res + ct[nowcnt] * color * nowcolor
start = 1
nowcnt = 0
nowcolor = 0
elif (nowcolor == 0 and start == 1 and board[i][0] != 0):
nowcolor = board[i][0]
nowcnt = nowcnt + 1
elif (start == 1 and board[i][0] != 0 and nowcolor != board[i][0]):
start = 0
elif (start == 1 and board[i][0] == nowcolor):
nowcnt = nowcnt + 1
if (start == 1):
res = res + ct[nowcnt] * color * nowcolor
return res
def Finished(board):
b, w = 0, 0
for i in range(0, 8):
for j in range(0, 8):
if (board[i][j] == BLACK):
b = b + 1
elif (board[i][j] == WHITE):
w = w + 1
if (b == 0 or w == 0 or b + w == 64):
return 1
return 0
def BubbleSort(moves, numOfMoves):
global Varr
for i in range(0, numOfMoves):
for j in range(i + 1, numOfMoves):
if (Varr[moves[i][0]][moves[i][1]] < Varr[moves[j][0]][moves[j][1]]):
moves[i], moves[j] = moves[j], moves[i]
return moves
MAX_FIND = 15000
resultX, resultY = -1, -1
def findMove(board, color, isMyTurn, round, alpha, beta):
global NumOfFind
global resultX
global resultY
if (round == 0 or Finished(board) == 1):
movePower, nxtMove = getMove(color, board)
return Function(color, isMyTurn, board, movePower)
if (round == Limit):
NumOfFind = 0
if (NumOfFind >= MAX_FIND):
if (isMyTurn == 1):
return alpha
else:
return beta
movePower, nxtMove = getMove(color, board)
nxtMove = BubbleSort(nxtMove, movePower)
opColor = -color
if (movePower == 0):
return findMove(board, opColor, 1 - isMyTurn, round - 1, alpha, beta)
elif (round == Limit):
resultX, resultY = nxtMove[0][0], nxtMove[0][1]
if (isMyTurn == 1):
for step in range(0, movePower):
newBoard = []
for i in range(0, 8):
L = []
for j in range(0, 8):
L.append(board[i][j])
newBoard.append(L)
newBoard = Moves(newBoard, color, nxtMove[step][0], nxtMove[step][1])
nxtV = findMove(newBoard, opColor, 1 - isMyTurn, round - 1, alpha, beta)
if (nxtV > alpha):
alpha = nxtV
if (round == Limit):
resultX = nxtMove[step][0]
resultY = nxtMove[step][1]
if (alpha >= beta):
return beta
else:
for step in range(0, movePower):
newBoard = []
for i in range(0, 8):
L = []
for j in range(0, 8):
L.append(board[i][j])
newBoard.append(L)
newBoard = Moves(newBoard, color, nxtMove[step][0], nxtMove[step][1])
nxtV = findMove(newBoard, opColor, 1 - isMyTurn, round - 1, alpha, beta)
if (nxtV < beta):
beta = nxtV
if (round == Limit):
resultX = nxtMove[step][0]
resultY = nxtMove[step][1]
if (alpha >= beta):
return alpha
del nxtMove
if (isMyTurn == 1):
return alpha
else:
return beta
#
# import datetime
# chessboard = []
# for i in range(0,8):
# L = []
# for j in range(0,8):
# L.append(0)
# chessboard.append(L)
# chessboard[3][4],chessboard[4][3] ,chessboard[3][3] ,chessboard[4][4] = BLACK,BLACK,WHITE,WHITE
# ai = AI(8,BLACK,10000000)
#
# for round in range (4,64):
# start=datetime.datetime.now()
# ai.go(chessboard)
# lens = len(ai.candidate_list)
# print(ai.candidate_list)
# print(round,Limit,NumOfFind)
# if( lens != 0 ):
# Moves(chessboard,ai.color,ai.candidate_list[lens-1][0],ai.candidate_list[lens-1][1])
# ai.color = -ai.color
# end=datetime.datetime.now()
# print(end-start)
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] |
livius/video/processing/visualization/slide_boundaries.py | papar22/livius | 1 | 73646 | """
Visualise the min/max function for the slides contrast
"""
def visualize_environment_changes_and_histogram_interpolation(path_to_video, path_to_json_file):
from video.processing.postProcessing import ContrastEnhancer
# Read the Json file
corr, boundaries, frame_ids = read_histogram_correlations_and_boundaries_from_json_file(path_to_json_file)
X = frame_ids
print X
print corr
print boundaries
# Extract the segments and boundaries from the Contrast Enhancer
contrast_enhancer = ContrastEnhancer(corr, boundaries)
segments = contrast_enhancer.segments
segment_boundaries = contrast_enhancer.segment_histogram_boundaries
plt.figure('Histogram correlations and boundaries')
# ## Plot the histogram correlations
subplot1 = plt.subplot(2, 1, 1)
subplot1.set_title('Histogram Correlations')
subplot1.set_ylim([0, 1])
# Plot everything red
subplot1.plot(X, corr, 'r')
# Plot segments green
for (start, end) in segments:
subplot1.plot(X[int(start):int(end)], corr[int(start):int(end)], 'g')
# ## Plot the histogram boundaries
# Get the histogram boundary for every second
seconds = range(len(X))
boundaries = map(contrast_enhancer.get_histogram_boundaries_at_time, seconds)
min_max = zip(*boundaries)
min_values = min_max[0]
max_values = min_max[1]
subplot2 = plt.subplot(2, 1, 2)
subplot2.set_title('Min and Max histogram boundaries used for color correction')
def get_non_segments(frame_ids, segments):
"""Extract times that do not belong to segments"""
non_segments = []
# If the segments don't start at 0, we have a non-segment from 0 to the first segment
if segments[0][0] > 0:
non_segments.append((0, segments[0][0]))
# Append all times between the segments
for i in range(len(segments) - 1):
non_segments.append((segments[i][1], segments[i + 1][0]))
# If the segments don't end at the last possible frame, we have a non-segment from the last segment to the end
if segments[-1][1] < len(frame_ids):
non_segments.append((segments[-1][1], len(frame_ids)))
return non_segments
non_segments = get_non_segments(X, segments)
# Print Segments as straight line
for (start, end) in segments:
x_range = X[int(start) : int(end)]
subplot2.plot(x_range, min_values[int(start) : int(end)], 'b')
subplot2.plot(x_range, max_values[int(start) : int(end)], 'g')
# Print dashes between the segments
for (start, end) in non_segments:
x_range = X[int(start) : int(end)]
if len(x_range) > 1:
# We can use dotted dash line style
subplot2.plot(x_range, min_values[int(start) : int(end)], 'b-.')
subplot2.plot(x_range, max_values[int(start) : int(end)], 'g-.')
else:
# We only have one point, draw it as a hline marker
subplot2.plot(x_range, min_values[int(start) : int(end)], 'b_')
subplot2.plot(x_range, max_values[int(start) : int(end)], 'g_')
plt.show()
plt.figure('Frames')
# ## Plot the start frames of each segment
video = VideoFileClip(path_to_video, audio=False)
max_pics_in_x = 4
x_count = 1
num_rows = (len(segments) / max_pics_in_x) + 1
frame_count = 1
# Adjust the segments. The Json file starts at frame 60, so we cannot take get_frame(0) for this
# but rather get_frame(2)
time_correction = X[0][1] / 30 # @todo(Stephan): This assumes 30fps, what to do with missing fps data?
adjusted_segments = map(lambda (x, y): (x + time_correction, y + time_correction), segments)
for (start, end) in adjusted_segments:
# Extract and resize
frame = video.get_frame(start)
print frame.shape
frame = cv2.resize(frame, dsize=(0, 0), fx=0.1, fy=0.1)
# Plotting
subplot = plt.subplot(num_rows, max_pics_in_x, frame_count)
subplot.set_title('t = ' + str(start))
# Remove axis description
subplot.get_xaxis().set_ticks([])
subplot.get_yaxis().set_ticks([])
subplot.imshow(frame)
frame_count += 1
plt.show()
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app.py | aws-samples/eks-configrules-with-cdk | 1 | 88746 | #!/usr/bin/env python3
from aws_cdk import App, Aspects
from eks_cis_cdk.eks_cis_cdk_stack import CdkConfigEksStack
from eks_cis_cdk.config_cis_cdk_stack import lambdaStack
# from config_cis_cdk.eks_cis_cdk_stack import lambdaStack
# from eks_cis_cdk.eks_test_stack import lambdaTestStack
############### Deployment Parameters ####################################################################
eks_admin_rolename = (
"Admin" # Set this to the Admin role in your AWS account, this is typically 'Admin'
)
trusted_registries = "111111111111.dkr.ecr.us-east-1.amazonaws.com,busybox"
##########################################################################################################
app = App()
eks_stack = CdkConfigEksStack(
app, "eksconfigexample", eks_admin_rolename=eks_admin_rolename
)
config_stack = lambdaStack(
app,
"configrules",
eks_lambda_role=eks_stack.lambda_role,
eks_cluster=eks_stack.cluster.cluster_name,
trusted_registries=trusted_registries,
)
app.synth()
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hard-gists/3a2a081e4f3089920fd8aecefecbe280/snippet.py | jjhenkel/dockerizeme | 21 | 29075 | <reponame>jjhenkel/dockerizeme<gh_stars>10-100
'''Trains a simple convnet on the MNIST dataset.
Does flat increment from <NAME> "Error-Driven Incremental Learning in Deep Convolutional
Neural Network for Large-Scale Image Classification"
Starts with just 3 classes, trains for 12 epochs then
incrementally trains the rest of the classes by reusing
the trained weights.
'''
from __future__ import print_function
import numpy as np
np.random.seed(1) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.utils import np_utils
def build_data(classes,total_classes,X_train_all,y_train_all,X_test_all,y_test_all):
train_ind = []
test_ind = []
for c in classes:
train_ind.extend(list(np.where(y_train_all==c)[0]))
test_ind.extend(list(np.where(y_test_all==c)[0]))
X_train = X_train_all[train_ind,:,:]
X_test = X_test_all[test_ind,:,:]
y_train_true = y_train_all[train_ind]
y_train = np.zeros(y_train_true.shape)
y_test_true = y_test_all[test_ind]
y_test = np.zeros(y_test_true.shape)
for i,c in enumerate(classes):
train_ind = list(np.where(y_train_true==c)[0])
test_ind = list(np.where(y_test_true==c)[0])
y_train[train_ind] = i
y_test[test_ind] = i
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, total_classes)
Y_test = np_utils.to_categorical(y_test, total_classes)
return X_train, Y_train, X_test, Y_test
def build_model(old_model=None):
model = Sequential()
if old_model is None:
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=(1, img_rows, img_cols)))
else:
weights = old_model.layers[0].get_weights()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',weights=weights,
input_shape=(1, img_rows, img_cols)))
model.add(Activation('relu'))
if old_model is None:
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
else:
weights = old_model.layers[2].get_weights()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,weights=weights))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Flatten())
if old_model is None:
model.add(Dense(128))
else:
weights = old_model.layers[7].get_weights()
model.add(Dense(128,weights=weights))
model.add(Activation('relu'))
model.add(Dropout(0.5))
return model
if __name__ == '__main__':
MODEL_TRAINED = False
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(X_train_all, y_train_all), (X_test_all, y_test_all) = mnist.load_data()
if not MODEL_TRAINED:
batch_size = 256
total_classes = 10
nb_epoch = 12
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
classes = [9,1,6]
X_train, Y_train, X_test, Y_test = build_data(classes,3,
X_train_all,y_train_all,X_test_all,y_test_all)
model1 = build_model()
model1.add(Dense(len(classes)))
model1.add(Activation('softmax'))
model1.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
model1.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
# Save this model for later interrogation
json_string = model1.to_json()
open('model1_incremental_architecture.json', 'w').write(json_string)
model1.save_weights('model1_incremental_weights.h5')
score = model1.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
# Now create a new model with all total_classes in the softmax layer. Copy over the weights to
# this new network and initialize the new class connections randomly.
model2 = build_model(old_model=model1)
model2.add(Dense(total_classes))
# Replace the corresponding weights of the new network with the previously trained class weights
weights = model2.layers[-1].get_weights()
old_weights = model1.layers[-2].get_weights() # Last dense layer is second to last layer
weights[0][:,-len(classes):] = old_weights[0]
weights[1][-len(classes):] = old_weights[1]
model2.layers[-1].set_weights(weights)
model2.add(Activation('softmax'))
model2.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
new_classes = [7, 0, 3, 5, 2, 8, 4]
class_mapping = new_classes[:]
class_mapping.extend(classes)
X_train, Y_train, X_test, Y_test = build_data(new_classes,10,
X_train_all,y_train_all,X_test_all,y_test_all)
model2.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
score = model2.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
# Save the incrementally trained model
json_string = model2.to_json()
open('model2_incremental_architecture.json', 'w').write(json_string)
model2.save_weights('model2_incremental_weights.h5')
X_test = X_test_all.reshape(X_test_all.shape[0], 1, img_rows, img_cols)
X_test = X_test.astype('float32')
X_test /= 255
# Convert class vectors to binary class matrices
# Note, that when a new image is presented to this network, the label of the image must be
# fed into class_mapping to get the "real" label of the output
y_test = np.array([class_mapping.index(c) for c in y_test_all])
Y_test = np_utils.to_categorical(y_test, total_classes)
score = model2.evaluate(X_test, Y_test, verbose=1)
print('Total Test score:', score[0])
print('Total Test accuracy:', score[1])
else:
# Load the incrementally trained model and test it
model = model_from_json(open('model2_incremental_architecture.json').read())
model.load_weights('model2_incremental_weights.h5')
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
classes = [7, 0, 3, 5, 2, 8, 4, 9, 1, 6]
X_train, Y_train, X_test, Y_test = build_data(classes,10,
X_train_all,y_train_all,X_test_all,y_test_all)
score = model.evaluate(X_test, Y_test, verbose=1)
print('Total Test score:', score[0])
print('Total Test accuracy:', score[1])
score = model.evaluate(X_train, Y_train, verbose=1)
print('Total Train score:', score[0])
print('Total Train accuracy:', score[1])
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mnist_pytorch/previewer.py | DewanshuHaswani/fastai | 0 | 79660 | import torch
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
from random import choice
BATCH_SIZE=64
# Load the mnist dataset
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"./data",
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BATCH_SIZE
)
train_data = train_loader.dataset.train_data
char = choice(train_data)
print(char)
plt.imshow(char.numpy())
plt.show()
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] |
src/pipeline/pipeline.py | GuiYuDaniel/CGC_of_Sn | 0 | 167493 | # -*- coding:utf8 -*-
"""
pipeline负责构建（不是执行）整体计算图：
（当前支持串行拓扑序调度）
1，构建顶层拓扑序（既检查了DAG性，又是串行执行顺序）
2，创建pipenodes
本工作目前只考虑单线程串行，所以这是个简略版pipeline，
暂不支持：微服务，并行异步计算
"""
import copy
from db.typing import PipeLineInfo
from pipeline.pipenode import PipeNode
from utils.config import Config
from utils.log import get_logger
from utils.utils import new_id
from utils.topo import (calc_dag, calc_topo_order)
logger = get_logger(__name__)
class PipeLine(object):
"""
负责检查并构建计算图，分配对应的pipe node
pipeline在新建后，是不随任务改变的
"""
def __init__(self, workflow_conf=None, ppl_id=None, ppl_name=None):
if workflow_conf is None:
# a blank ppl waiting for load
self.ppl_id = ppl_id if ppl_id and isinstance(ppl_id, str) else None
self.ppl_name = ppl_name if ppl_name and isinstance(ppl_name, str) else None
self.config = None
self.dag_dict = None # 计算图的有向无环图表达
self.topo_order_list = None # 序列化的计算顺序
self.node_id_dict = None # {name: ppd_id}
self.flags = None
else:
# new a real ppl
self.ppl_id = ppl_id if ppl_id and isinstance(ppl_id, str) else new_id()
self.ppl_name = ppl_name if ppl_name and isinstance(ppl_name, str) else self.ppl_id
self.config = Config()
self.dag_dict = calc_dag(workflow_conf)
self.topo_order_list = calc_topo_order(self.dag_dict)
self.node_id_dict = self._create_node_dict(workflow_conf)
self.flags = None
self.save_to_db()
def _from_dict(self, ppl_dict):
if not isinstance(ppl_dict, dict):
err_msg = "_from_dict need input dict but {} with {}".format(type(ppl_dict), ppl_dict)
logger.error(err_msg)
raise Exception(err_msg)
map_dict = {
"pipeline_id": "ppl_id",
"pipeline_name": "ppl_name",
"dag_dict": "dag_dict",
"topo_order_list": "topo_order_list",
"config": "config",
"node_id_dict": "node_id_dict",
"flags": "flags"
}
self_keys = self.__dict__.keys()
for map_key in map_dict:
# if ppl_dict.get(map_key) and map_dict.get(map_key) in self_keys:
if map_key in ppl_dict and map_dict.get(map_key) in self_keys:
self.__dict__[map_dict.get(map_key)] = ppl_dict.get(map_key)
def load_by_id(self, ppl_id):
"""成功则返回通过id装载起的类实例，失败则返回空实例"""
if not isinstance(ppl_id, str):
err_msg = "the ppl_id={} must be str, will return blank class".format(ppl_id)
logger.error(err_msg)
# 返回未经改动的实例
return self
flag, ppl_dict = PipeLineInfo().query_by_id(ppl_id)
if not flag:
err_msg = "query pipeline by ppl_id={} meet error with {}, will return blank class".format(
ppl_id, ppl_dict) # 此时ppl_dict是msg
logger.error(err_msg)
return self
if ppl_dict is False:
err_msg = "no pipeline in db by ppl_id={}, will return blank class".format(ppl_id)
logger.warning(err_msg)
return self
self._from_dict(ppl_dict)
return self
def _to_dict(self):
ppl_dict = {
"pipeline_id": self.ppl_id,
"pipeline_name": self.ppl_name,
"dag_dict": self.dag_dict,
"topo_order_list": self.topo_order_list,
"config": self.config,
"node_id_dict": self.node_id_dict,
"flags": self.flags
}
return ppl_dict
def save_to_db(self): # 不需要写try，因为db交互层已经写好了保护和log，只需要返回结果即可
ppl_dict = self._to_dict()
flag, msg = PipeLineInfo().insert(ppl_dict)
if not flag:
err_msg = "save ppl_dict={} to db meet error".format(ppl_dict)
logger.error(err_msg)
return flag, msg
def _create_node_dict(self, workflow_conf):
"""
这个函数一是补完node信息，并存入db；
另一个是返回{node_name: node_id}的字典存在于pipeline中作为索引
"""
# 补完prep_nodes添加进workflow_conf
padding_workflow_conf = copy.deepcopy(workflow_conf)
for single_node_id_dict in padding_workflow_conf:
node_name = single_node_id_dict.get("name")
single_node_id_dict["prep_nodes"] = self.dag_dict.get(node_name).get("prep_nodes")
# check
node_name_list = self.topo_order_list
if len(node_name_list) != len(padding_workflow_conf):
err_msg = "node_name_list={} and padding_workflow_conf={} must have same len".format(
node_name_list, padding_workflow_conf)
logger.error(err_msg)
raise Exception(err_msg)
node_id_dict = {}
# create nodes
for single_dict in padding_workflow_conf: # 结果的字典也是无序化的，依workflow字典初始化node没问题
if single_dict.get("name") not in node_name_list:
err_msg = "node_name={} not in topo_order_list={}, conf_dict={}".format(
single_dict.get("name"), node_name_list, single_dict)
logger.error(err_msg)
raise Exception(err_msg)
try:
ppn = PipeNode(conf=single_dict)
node_id_dict[single_dict.get("name")] = ppn.ppn_id
except Exception as e:
err_msg = "create pipenode with conf={} meet error".format(single_dict)
logger.error(err_msg)
raise Exception(e)
if len(node_name_list) != len(node_id_dict):
err_msg = "node_name_list={} and node_id_dict={} must have same len".format(node_name_list, node_id_dict)
logger.error(err_msg)
logger.debug("workflow_conf={}".format(workflow_conf))
raise Exception(err_msg)
return node_id_dict
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736,
2943,
29918,
333,
29918,
8977,
13,
2
] |
oscar/apps/dashboard/nav.py | endgame/django-oscar | 0 | 159483 | <gh_stars>0
from django.core.urlresolvers import reverse
_nodes = []
class Node(object):
def __init__(self, label, url_name=None, url_args=None, url_kwargs=None, access_fn=None):
self.label = label
self.url_name = url_name
self.url_args = url_args
self.url_kwargs = url_kwargs
self.access_fn = access_fn
self.children = []
@property
def is_heading(self):
return self.url_name is None
@property
def url(self):
return reverse(self.url_name, args=self.url_args, kwargs=self.url_kwargs)
def add_child(self, node):
self.children.append(node)
def is_visible(self, user):
if not self.access_fn:
return True
return self.access_fn(user)
def filter(self, user):
if not self.is_visible(user):
return None
node = Node(self.label, self.url_name, self.access_fn)
for child in self.children:
if child.is_visible(node):
node.add_child(child)
return node
def has_children(self):
return len(self.children) > 0
def register(node, display_order=5):
# We use tuples so we can sort later on. The lower the display order, the
# closer to the left the node appears.
_nodes.append((display_order, node))
def flush():
_nodes = []
def get_nodes(user):
nodes = []
for _, node in sorted(_nodes):
filtered_node = node.filter(user)
if filtered_node:
nodes.append(node)
return nodes
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4397,
29898,
3177,
29897,
13,
1678,
736,
7573,
13,
13,
13,
2
] |
main/admin.py | ravsimar-sodhi/SRIP-web-app | 0 | 72763 | from django.contrib import admin
from .models import LoggedCommit
from mentor.models import Mentor
def approve_status(modeladmin, request, queryset):
queryset.update(status = "APPROVED")
approve_status.short_description = "Mark selected Commits as approved"
class LoggedCommitAdmin(admin.ModelAdmin):
list_filter = ('status',)
list_display = ['user', 'commit_id','url', 'issue_points','toc', 'remark','status', 'project', 'evaluated_by', 'time_eval']
actions = [approve_status]
def get_queryset(self, request):
qs = super(admin.ModelAdmin, self).get_queryset(request)
if not request.user.is_superuser:
mentor = Mentor.objects.get(handle=request.user)
projects = mentor.project_set.all()
qs = qs.filter(project__in=projects)
return qs
admin.site.register(LoggedCommit, LoggedCommitAdmin)
| [
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1523,
2415,
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4522,
3192,
1523,
2415,
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29897,
13,
2
] |
setup.py | DecodEPFL/neurSLS | 0 | 161688 | #!/usr/bin/env python
import setuptools
import os
os.chmod("run.py", 0o744)
setuptools.setup(
name='neurSLS',
version='1.0',
url='https://github.com/DecodEPFL/neurSLS',
license='CC-BY-4.0 License',
author='<NAME>',
author_email='<EMAIL>',
description='Neural System Level Synthesis',
packages=setuptools.find_packages(),
install_requires=['torch>=1.7.1',
'numpy>=1.18.1',
'matplotlib>=3.1.3'],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
],
python_requires='>=3.8',
)
| [
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13,
2
] |
tensorflow_datasets/ranking/istella/istella.py | vanshhhhh/datasets | 3,380 | 110934 | <reponame>vanshhhhh/datasets
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors.
#
# 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.
"""istella dataset."""
import dataclasses
from typing import Optional
import tensorflow as tf
import tensorflow_datasets.public_api as tfds
from tensorflow_datasets.ranking.libsvm_ranking_parser import LibSVMRankingParser
_DESCRIPTION = """
The Istella datasets are three large-scale Learning-to-Rank datasets released by
Istella. Each dataset consists of query-document pairs represented as feature
vectors and corresponding relevance judgment labels.
The dataset contains three versions:
* `main` ("Istella LETOR"): Containing 10,454,629 query-document pairs.
* `s` ("Istella-S LETOR"): Containing 3,408,630 query-document pairs.
* `x` ("Istella-X LETOR"): Containing 26,791,447 query-document pairs.
You can specify whether to use the `main`, `s` or `x` version of the dataset as
follows:
```python
ds = tfds.load("istella/main")
ds = tfds.load("istella/s")
ds = tfds.load("istella/x")
```
If only `istella` is specified, the `istella/main` option is selected by
default:
```python
# This is the same as `tfds.load("istella/main")`
ds = tfds.load("istella")
```
"""
_CITATION = """
@article{10.1145/2987380,
author = {<NAME> Lucchese, <NAME>},
title = {Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees},
year = {2016},
publisher = {ACM},
address = {New York, NY, USA},
volume = {35},
number = {2},
issn = {1046-8188},
url = {https://doi.org/10.1145/2987380},
doi = {10.1145/2987380},
journal = {ACM Transactions on Information Systems},
articleno = {15},
numpages = {31},
}
"""
_URLS = {
"main": "http://library.istella.it/dataset/istella-letor.tar.gz",
"s": "http://library.istella.it/dataset/istella-s-letor.tar.gz",
"x": "http://quickrank.isti.cnr.it/istella-datasets-mirror/istella-X.tar.gz"
}
_FEATURE_NAMES = {n: f"feature_{n}" for n in range(1, 221)}
_LABEL_NAME = "label"
@dataclasses.dataclass
class IstellaConfig(tfds.core.BuilderConfig):
has_vali: bool = False
subdirectory: Optional[str] = None
class Istella(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for istella dataset."""
VERSION = tfds.core.Version("1.0.1")
RELEASE_NOTES = {
"1.0.0": "Initial release.",
"1.0.1": "Fix serialization to support float64."
}
# pytype: disable=wrong-keyword-args
BUILDER_CONFIGS = [
IstellaConfig(name="main", has_vali=False, subdirectory="full"),
IstellaConfig(name="s", has_vali=True, subdirectory="sample"),
IstellaConfig(name="x", has_vali=True, subdirectory=None)
]
# pytype: enable=wrong-keyword-args
def _info(self) -> tfds.core.DatasetInfo:
"""Returns the dataset metadata."""
encoding = tfds.features.Encoding.ZLIB
features = {
name: tfds.features.Tensor(
shape=(None,), dtype=tf.float64, encoding=encoding)
for name in _FEATURE_NAMES.values()
}
features[_LABEL_NAME] = tfds.features.Tensor(
shape=(None,), dtype=tf.float64, encoding=encoding)
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds.features.FeaturesDict(features),
homepage="http://quickrank.isti.cnr.it/istella-dataset/",
citation=_CITATION,
)
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(_URLS[self.builder_config.name])
# For some dataset configs, the data is in a subdirectory.
if self.builder_config.subdirectory is not None:
path = path / self.builder_config.subdirectory
splits = {
"train": self._generate_examples(path / "train.txt"),
"test": self._generate_examples(path / "test.txt")
}
# For some dataset configs, there is an additional validation split.
if self.builder_config.has_vali:
splits["vali"] = self._generate_examples(path / "vali.txt")
return splits
def _generate_examples(self, path):
"""Yields examples."""
# Istella datasets seems to be encoded as latin1 and not utf-8, so we have
# to read the file contents as bytes and manually decode it as latin1.
with tf.io.gfile.GFile(path, "rb") as f:
lines = map(lambda bytes_line: bytes_line.decode("latin1"), f)
yield from LibSVMRankingParser(lines, _FEATURE_NAMES, _LABEL_NAME)
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refocus/constant.py | harshkothari410/refocus-python | 0 | 1616041 | HTTPSTATUS = {
'OK': 200,
'CREATED': 201,
'BAD_REQUEST': 400,
'UNAUTHORIZED': 401,
'FORBIDDEN': 403,
'NOT_FOUND': 404,
'NOT_ALLOWED': 405,
}
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setup.py | Peder2911/dstk | 0 | 106087 | <reponame>Peder2911/dstk<gh_stars>0
import setuptools
setuptools.setup(
name="dstk",
version="0.0.1",
author="<NAME>",
author_email="<EMAIL>",
packages=setuptools.find_packages(),
scripts=["bin/dstk"],
url="http://pypi.python.org/pypi/dstk",
license="LICENSE",
description="Data science tookit",
long_description=open("README").read(),
install_requires=[
"pandas==1.0.1"
]
)
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monorun/datasets/pipelines/loading.py | minghanz/MonoRUn | 0 | 86484 | <reponame>minghanz/MonoRUn
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets import PIPELINES
@PIPELINES.register_module()
class LoadAnnotations3D(object):
def __init__(self,
with_bbox_3d=True,
with_coord_3d=True,
with_coord_2d=True,
with_truncation=False,
with_depth=False,
depth_mean=0.0,
depth_std=1.0):
self.with_bbox_3d = with_bbox_3d
self.with_coord_3d = with_coord_3d
self.with_coord_2d = with_coord_2d
self.with_truncation = with_truncation
self.with_depth = with_depth
self.depth_mean = depth_mean
self.depth_std = depth_std
@staticmethod
def _load_coord_3d(results):
assert results['img_shape'] == results['ori_shape']
h, w = results['img_shape'][:2]
oc_dict = mmcv.load(
osp.join(results['coord_3d_prefix'], results['ann_info']['coord_3d']))
gt_coords_3d = []
gt_coords_3d_mask = []
for i, bbox_3d in zip(results['ann_info']['object_ids'],
results['ann_info']['bboxes_3d']):
uv = np.round(oc_dict['uv_list'][i]).astype(np.int)
oc = oc_dict['oc_list'][i].astype(np.float32)
coord_3d = np.zeros((h, w, 3), dtype=np.float32)
coord_3d_mask = np.zeros((h, w, 1), dtype=np.float32)
coord_3d[uv[:, 1], uv[:, 0]] = oc
coord_3d_mask[uv[:, 1], uv[:, 0]] = 1.0
gt_coords_3d.append(coord_3d)
gt_coords_3d_mask.append(coord_3d_mask)
results['gt_coords_3d'] = gt_coords_3d
results['gt_coords_3d_mask'] = gt_coords_3d_mask
results['dense_fields'].append('gt_coords_3d')
results['dense_fields'].append('gt_coords_3d_mask')
return results
@staticmethod
def _load_bboxes_3d(results):
results['gt_bboxes_3d'] = results['ann_info']['bboxes_3d']
results['bbox_3d_fields'].append('gt_bboxes_3d')
results['gt_proj_r_mats'] = results['ann_info']['gt_proj_r_mats']
results['gt_proj_t_vecs'] = results['ann_info']['gt_proj_t_vecs']
return results
def _load_depth(self, results):
depth = mmcv.imread(
osp.join(results['depth_prefix'],
results['ann_info']['depth']),
flag='unchanged')[..., None] # (H, W, 1)
results['depth'] = (depth.astype(np.float32) - self.depth_mean) / self.depth_std
results['dense_fields'].append('depth')
return results
@staticmethod
def _gen_coord_2d(results):
assert results['img_shape'] == results['ori_shape']
h, w = results['img_shape'][:2]
coord_2d = np.mgrid[:h, :w].astype(np.float32)
coord_2d[[1, 0]] = coord_2d[[0, 1]] # to [u, v]
results['coord_2d'] = np.moveaxis(coord_2d, 0, -1) # (H, W, 2)
if 'dense_fields' in results:
results['dense_fields'].append('coord_2d')
else:
results['dense_fields'] = ['coord_2d']
return results
def __call__(self, results):
if 'ann_info' in results and 'cam_intrinsic' in results['ann_info']:
results['cam_intrinsic'] = results['ann_info']['cam_intrinsic']
elif 'img_info' in results and 'cam_intrinsic' in results['img_info']:
results['cam_intrinsic'] = results['img_info']['cam_intrinsic']
else:
raise ValueError('cam_intrinsic not found')
if self.with_bbox_3d:
results = self._load_bboxes_3d(results)
if self.with_coord_3d:
results = self._load_coord_3d(results)
if self.with_coord_2d:
results = self._gen_coord_2d(results)
if self.with_truncation:
results['truncation'] = results['ann_info']['truncation']
if self.with_depth:
results = self._load_depth(results)
return results
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] |
mouse_burrows/hpc/templates/pass3_single.py | david-zwicker/cv-mouse-burrows | 1 | 149371 | <reponame>david-zwicker/cv-mouse-burrows<filename>mouse_burrows/hpc/templates/pass3_single.py
#!/usr/bin/env python2
from __future__ import division
import sys
import logging
{ADD_PYTHON_PATHS} # @UndefinedVariable
from numpy import array # @UnusedImport
from mouse_burrows.algorithm.pass3 import ThirdPass
from mouse_burrows.hpc.project import process_trials
from video.io.backend_ffmpeg import FFmpegError
# configure basic logging, which will be overwritten later
logging.basicConfig()
# set specific parameters for this job
parameters = {SPECIFIC_PARAMETERS} # @UndefinedVariable
# set job parameters
job_id = sys.argv[1]
parameters.update({{
'base_folder': "{JOB_DIRECTORY}",
'logging/folder': ".",
'output/folder': ".",
'resources/pass3/job_id': job_id,
}})
# do the second pass scan
for trial in process_trials("{LOG_FILE}" % job_id, 10):
try:
pass3 = ThirdPass("{NAME}", parameters=parameters, read_data=True)
pass3.process()
except FFmpegError:
print('FFmpeg error occurred. Continue.')
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] |
autowrite/zmatrix.py | sjklipp/autochem_1219 | 0 | 151500 | """ z-matrix writers
"""
def write(syms, key_mat, name_mat, val_dct, mat_delim=' ', setval_sign='='):
""" write a z-matrix to a string
"""
mat_str = matrix_block(syms=syms, key_mat=key_mat, name_mat=name_mat,
delim=mat_delim)
setval_str = setval_block(val_dct=val_dct, setval_sign=setval_sign)
zma_str = '\n\n'.join((mat_str, setval_str))
return zma_str
def matrix_block(syms, key_mat, name_mat, delim=' '):
""" write the .zmat matrix block to a string
"""
def _line_string(row_idx):
line_str = '{:<2s} '.format(syms[row_idx])
keys = key_mat[row_idx]
names = name_mat[row_idx]
line_str += delim.join([
'{:>d}{}{:>5s} '.format(keys[col_idx], delim, names[col_idx])
for col_idx in range(min(row_idx, 3))])
return line_str
natms = len(syms)
mat_str = '\n'.join([_line_string(row_idx) for row_idx in range(natms)])
return mat_str
def setval_block(val_dct, setval_sign='='):
""" write the .zmat setval block to a string
"""
setval_str = '\n'.join([
'{:<5s}{}{:>11.6f}'.format(name, setval_sign, val)
for name, val in val_dct.items()])
return setval_str
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] |
src/dataAccess/Connection.py | mattmillr/utaka | 1 | 9175 | <filename>src/dataAccess/Connection.py
#Copyright 2009 Humanitarian International Services Group
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
'''
Created Aug 4, 2009
connection pool abstraction over previous Connection.py which is now SingleConnection.py
sets up module scope connection pool, currently with no size limit
pool for both connections with dictionary cursors and regular cursors
reconnects to db every x hours depending on config file
@author: Andrew
'''
from utaka.src.dataAccess.SingleConnection import Connection as SingleConnection
import utaka.src.Config as Config
import MySQLdb
import datetime
dcp = [SingleConnection(True)]
rcp = [SingleConnection(False)]
dbTimer = datetime.datetime.today()
dbTimeout = datetime.timedelta(hours = int(Config.get('database', 'connection_timeout_in_hours')))
class Connection:
def __init__(self, useDictCursor = False):
if len(dcp) > 0:
if useDictCursor:
self.innerConn = dcp.pop()
else:
self.innerConn = rcp.pop()
now = datetime.datetime.today()
if (now - dbTimeout) > self.innerConn.connectTime:
self.innerConn.close()
self.innerConn = SingleConnection(useDictCursor)
else:
self.innerConn = SingleConnection(useDictCursor)
def usingDictCursor(self):
return self.innerConn.usingDictCursor()
def executeStatement(self, statement, placeholder):
return self.innerConn.executeStatement(statement, placeholder)
def getRowCount(self):
return self.innerConn.rowcount()
def commit(self):
self.innerConn.commit()
def rollback(self):
self.innerConn.rollback()
def close(self):
self.commit()
self.__close_()
def cancelAndClose(self):
self.rollback()
self.__close_()
def __close_(self):
utakaLog = open('/var/www/html/utaka/utakaLog', 'a')
try:
if self.usingDictCursor():
utakaLog.write('Dictionary Database Connection Returned to Pool\r\n')
else:
utakaLog.write('Regular Database Connection Returned to Pool\r\n')
finally:
utakaLog.close()
if self.usingDictCursor():
dcp.append(self.innerConn)
else:
rcp.append(self.innerConn)
self.innerConn = None
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pyutils/timing.py | eltrompetero/maxent_fim | 0 | 168847 | # Module for timing.
# Author: <NAME>, <EMAIL>
from .fim import *
import timeit
n = 6
hJ = np.concatenate((np.zeros(n), np.random.normal(scale=.3, size=n*(n-1)//2)))
def example():
isingdkl = IsingFisherCurvatureMethod2(n, h=hJ[:n], J=hJ[n:])
isingdkl.maj_curvature(n_cpus=1)
| [
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BleVibrationDevice.py | Suitceyes-Project-Code/Vibration-Pattern-Player | 0 | 31723 | from bluepy.btle import UUID, Peripheral
from VestDeviceBase import VestDevice
class BleVestDevice(VestDevice):
def __init__(self, deviceAddr):
try:
self._peripheral = Peripheral(deviceAddr)
serviceUUID = UUID("713d0000-503e-4c75-ba94-3148f18d941e")
characteristicUUID = UUID("713d0003-503e-4c75-ba94-3148f18d941e")
s = self._peripheral.getServiceByUUID(serviceUUID)
self._characteristic = s.getCharacteristics(characteristicUUID)[0]
except Exception as e:
print("Error: " + str(e))
def __isValidState(self):
return self._peripheral.getState() == "conn"
def __write(self, byteArr):
self._peripheral.writeCharacteristic(self._characteristic.getHandle(), byteArr)
def set_pin(self, index, intensity):
"""Sets a pin to a given intensity.
index: an integer from 0 - 6
intensity: an integer from 0 - 255
"""
if self.__isValidState():
rList=[0,index,intensity]
self.__write(bytes(rList))
def set_frequency(self,frequency):
"""Sets the frequency of the entire vest.
frequency.
"""
if self.__isValidState():
rList=[4, frequency & (255), (frequency & (255 << 8)) >> 8, (frequency & (255 << 16)) >> 16, (frequency & (255 << 24)) >> 24]
b = bytes(rList)
self.__write(b)
def mute(self):
"""Stops all motors on the vest from vibrating"""
if self.__isValidState():
rList=[3]
self.__write(bytes(rList))
def set_motor(self,index,rotation):
"""
Sets a given motor index to a given target rotation.
"""
if self.__isValidState():
rList = [11,index,rotation]
self.__write(bytes(rList))
def set_motor_speed(self,speed):
"""
Changes how long it takes to move 1 degree per millisecond.
"""
if speed <= 0:
raise ValueError("speed must be greater than 0.")
rList = [12,speed]
self.__write(bytes(rList))
def set_pins_batched(self, values = dict):
for pin in values:
self.set_pin(pin, values[pin]) | [
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modred/tests/testera.py | shubhamKGIT/modred | 0 | 31290 | #!/usr/bin/env python
"""Test era module"""
import unittest
import os
from os.path import join
from shutil import rmtree
import numpy as np
from modred import era, parallel, util
from modred.py2to3 import range
def make_time_steps(num_steps, interval):
"""Helper function to find array of integer time steps.
Args:
num_steps: integer number of time steps to create.
interval: interval between pairs of time steps, as shown above.
Returns:
time_steps: array of integers, time steps [0 1 interval interval+1 ...]
"""
if num_steps % 2 != 0:
raise ValueError('num_steps, %d, must be even'%num_steps)
interval = int(interval)
time_steps = np.zeros(num_steps, dtype=int)
time_steps[::2] = interval*np.arange(num_steps/2)
time_steps[1::2] = 1 + interval*np.arange(num_steps/2)
return time_steps
@unittest.skipIf(parallel.is_distributed(), 'Only test ERA in serial')
class testERA(unittest.TestCase):
def setUp(self):
if not os.access('.', os.W_OK):
raise RuntimeError('Cannot write to current directory')
self.test_dir = 'files_ERA_DELETE_ME'
if not os.path.exists(self.test_dir):
os.mkdir(self.test_dir)
self.impulse_file_path = join(self.test_dir, 'impulse_input%03d.txt')
def tearDown(self):
"""Deletes all of the arrays created by the tests"""
rmtree(self.test_dir, ignore_errors=True)
<EMAIL>('Testing others')
def test_make_sampled_format(self):
"""
Test that can give time_values and outputs in either format.
First tests format [0, 1, P, P+1, ...] and if there is a wrong time
value.
Then tests [0, 1, 2, 3, ...] format.
"""
for num_inputs in [1, 3]:
for num_outputs in [1, 2, 4]:
for num_time_steps in [4, 10, 12]:
# Generate data
# P=2 format [0, 1, 2, 3, ...]
sample_interval = 2
dt_system = np.random.random()
dt_sample = sample_interval * dt_system
outputs = np.random.random(
(num_time_steps, num_outputs, num_inputs))
time_steps = make_time_steps(
num_time_steps, sample_interval)
time_values = time_steps * dt_system
# Compute using modred
my_ERA = era.ERA()
time_steps_computed, outputs_computed =\
era.make_sampled_format(time_values, outputs)
#self.assertEqual(dt_system_computed, dt_system)
# Reference values
num_time_steps_true = (num_time_steps - 1) * 2
time_steps_true = make_time_steps(num_time_steps_true, 1)
outputs_true = np.zeros(
(num_time_steps_true, num_outputs, num_inputs))
outputs_true[::2] = outputs[:-1]
outputs_true[1::2] = outputs[1:]
# Compare values
np.testing.assert_equal(
time_steps_computed, time_steps_true)
np.testing.assert_equal(outputs_computed, outputs_true)
# Test that if there is a wrong time value, get an error
time_values[num_time_steps // 2] = -1
self.assertRaises(
ValueError, era.make_sampled_format, time_values,
outputs)
<EMAIL>("testing others")
def test_assemble_Hankel(self):
""" Tests Hankel arrays are symmetric given
``[CB CAB CA**P CA**(P+1)B ...]``."""
rtol = 1e-10
atol = 1e-12
for num_inputs in [1,3]:
for num_outputs in [1, 2, 4]:
for sample_interval in [1]:
num_time_steps = 50
num_states = 5
A, B, C = util.drss(num_states, num_inputs, num_outputs)
time_steps = make_time_steps(
num_time_steps, sample_interval)
Markovs = util.impulse(A, B, C, time_steps[-1] + 1)
Markovs = Markovs[time_steps]
if sample_interval == 2:
time_steps, Markovs = era.make_sampled_format(
time_steps, Markovs)
my_ERA = era.ERA(verbosity=0)
my_ERA._set_Markovs(Markovs)
my_ERA._assemble_Hankel()
H = my_ERA.Hankel_array
Hp = my_ERA.Hankel_array2
for row in range(my_ERA.mc):
for col in range(my_ERA.mo):
np.testing.assert_equal(
H[row * num_outputs:(row + 1) * num_outputs,
col * num_inputs:(col + 1) * num_inputs],
H[col * num_outputs:(col + 1) * num_outputs,
row * num_inputs:(row + 1) * num_inputs])
np.testing.assert_equal(
Hp[row * num_outputs:(row + 1) * num_outputs,
col * num_inputs:(col + 1) * num_inputs],
Hp[col * num_outputs:(col + 1) * num_outputs,
row * num_inputs:(row + 1) * num_inputs])
np.testing.assert_allclose(
H[row * num_outputs:(row + 1) * num_outputs,
col * num_inputs:(col + 1) * num_inputs],
C.dot(
np.linalg.matrix_power(
A, time_steps[(row + col) * 2]).dot(
B)),
rtol=rtol, atol=atol)
np.testing.assert_allclose(
Hp[row * num_outputs:(row + 1) * num_outputs,
col * num_inputs:(col + 1) * num_inputs],
C.dot(
np.linalg.matrix_power(
A, time_steps[(row + col) * 2 + 1]).dot(
B)),
rtol=rtol, atol=atol)
<EMAIL>('testing others')
def test_compute_model(self):
"""
Test ROM Markov params similar to those given
- generates data
- assembles Hankel array
- computes SVD
- forms the ROM discrete arrays A, B, and C (D = 0)
- Tests Markov parameters from ROM are approx. equal to full plant's
"""
num_time_steps = 40
num_states_plant = 12
num_states_model = num_states_plant // 3
for num_inputs in [1, 3]:
for num_outputs in [1, 2]:
for sample_interval in [1, 2, 4]:
time_steps = make_time_steps(
num_time_steps, sample_interval)
A, B, C = util.drss(
num_states_plant, num_inputs, num_outputs)
my_ERA = era.ERA(verbosity=0)
Markovs = util.impulse(A, B, C, time_steps[-1] + 1)
Markovs = Markovs[time_steps]
if sample_interval == 2:
time_steps, Markovs =\
era.make_sampled_format(time_steps, Markovs)
num_time_steps = time_steps.shape[0]
A_path_computed = join(self.test_dir, 'A_computed.txt')
B_path_computed = join(self.test_dir, 'B_computed.txt')
C_path_computed = join(self.test_dir, 'C_computed.txt')
A, B, C = my_ERA.compute_model(Markovs, num_states_model)
my_ERA.put_model(
A_path_computed, B_path_computed, C_path_computed)
#sing_vals = my_ERA.sing_vals[:num_states_model]
# Flatten vecs into 2D X and Y arrays:
# [B AB A**PB A**(P+1)B ...]
#direct_vecs_flat = direct_vecs.swapaxes(0,1).reshape(
# (num_states_model,-1)))
# Exact grammians from Lyapunov eqn solve
#gram_cont = util.solve_Lyapunov(A, B*B.H)
#gram_obs = util.solve_Lyapunov(A.H, C.H*C)
#print(np.sort(np.linalg.eig(gram_cont)[0])[::-1])
#print(sing_vals)
#np.testing.assert_allclose(gram_cont.diagonal(),
# sing_vals, atol=.1, rtol=.1)
#np.testing.assert_allclose(gram_obs.diagonal(),
# sing_vals, atol=.1, rtol=.1)
#np.testing.assert_allclose(np.sort(np.linalg.eig(
# gram_cont)[0])[::-1], sing_vals,
# atol=.1, rtol=.1)
#np.testing.assert_allclose(np.sort(np.linalg.eig(
# gram_obs)[0])[::-1], sing_vals,
# atol=.1, rtol=.1)
# Check that the diagonals are largest entry on each row
#self.assertTrue((np.max(np.abs(gram_cont),axis=1) ==
# np.abs(gram_cont.diagonal())).all())
#self.assertTrue((np.max(np.abs(gram_obs),axis=1) ==
# np.abs(gram_obs.diagonal())).all())
# Check the ROM Markov params match the full plant's
Markovs_model = np.zeros(Markovs.shape)
for ti, tv in enumerate(time_steps):
Markovs_model[ti] = C.dot(
np.linalg.matrix_power(A, tv).dot(
B))
#print(
# 'Computing ROM Markov param at time step %d' % tv)
"""
import matplotlib.pyplot as PLT
for input_num in range(num_inputs):
PLT.figure()
PLT.hold(True)
for output_num in range(num_outputs):
PLT.plot(time_steps[:50],
# Markovs_model[:50, output_num,input_num], 'ko')
PLT.plot(time_steps[:50],Markovs[:50,
# output_num, input_num],'rx')
PLT.plot(time_steps_dense[:50],
# Markovs_dense[:50, output_num, input_num],'b--')
PLT.title('input %d to outputs'%input_num)
PLT.legend(['ROM','Plant','Dense plant'])
PLT.show()
"""
np.testing.assert_allclose(
Markovs_model.squeeze(), Markovs.squeeze(),
rtol=0.5, atol=0.5)
np.testing.assert_equal(
util.load_array_text(A_path_computed), A)
np.testing.assert_equal(
util.load_array_text(B_path_computed), B)
np.testing.assert_equal(
util.load_array_text(C_path_computed), C)
if __name__ == '__main__':
unittest.main()
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lectures/07-python-dictionaries/examples/gashlycrumb.py | mattmiller899/biosys-analytics | 4 | 23979 | <gh_stars>1-10
#!/usr/bin/env python3
"""dictionary lookup"""
import os
import sys
args = sys.argv[1:]
if len(args) != 1:
print('Usage: {} LETTER'.format(os.path.basename(sys.argv[0])))
sys.exit(1)
letter = args[0].upper()
lines = """
A is for Amy who fell down the stairs.
B is for Basil assaulted by bears.
C is for Clara who wasted away.
D is for Desmond thrown out of a sleigh.
E is for Ernest who choked on a peach.
F is for Fanny sucked dry by a leech.
G is for George smothered under a rug.
H is for Hector done in by a thug.
I is for Ida who drowned in a lake.
J is for James who took lye by mistake.
K is for Kate who was struck with an axe.
L is for Leo who choked on some tacks.
M is for Maud who was swept out to sea.
N is for Neville who died of ennui.
O is for Olive run through with an awl.
P is for Prue trampled flat in a brawl.
Q is for Quentin who sank on a mire.
R is for Rhoda consumed by a fire.
S is for Susan who perished of fits.
T is for Titus who flew into bits.
U is for Una who slipped down a drain.
V is for Victor squashed under a train.
W is for Winnie embedded in ice.
X is for Xerxes devoured by mice.
Y is for Yorick whose head was bashed in.
Z is for Zillah who drank too much gin.
""".strip().splitlines()
lookup = {}
for line in lines:
lookup[line[0]] = line
if letter in lookup:
print(lookup[letter])
else:
print('I do not know "{}"'.format(letter))
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monodepth/geometry/utils.py | vguizilini/packnet-sfm | 1 | 22232 | # Copyright 2020 Toyota Research Institute. All rights reserved.
"""
Geometry utilities
"""
import numpy as np
def invert_pose_numpy(T):
"""
'Invert' 4x4 extrinsic matrix
Parameters
----------
T: 4x4 matrix (world to camera)
Returns
-------
4x4 matrix (camera to world)
"""
Tc = np.copy(T)
R, t = Tc[:3, :3], Tc[:3, 3]
Tc[:3, :3], Tc[:3, 3] = R.T, - np.matmul(R.T, t)
return Tc
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quimb/tensor/tensor_3d.py | cnakhl/quimb | 0 | 106857 | <reponame>cnakhl/quimb<gh_stars>0
import functools
from numbers import Integral
from itertools import product, starmap
from collections import defaultdict
from autoray import do
from ..gen.rand import randn, seed_rand
from . import array_ops as ops
from .tensor_core import (
Tensor,
TensorNetwork,
oset,
tags_to_oset,
rand_uuid,
)
def gen_3d_bonds(Lx, Ly, Lz, steppers, coo_filter=None):
"""Convenience function for tiling pairs of bond coordinates on a 3D
lattice given a function like ``lambda i, j, k: (i + 1, j + 1, k + 1)``.
Parameters
----------
Lx : int
The number of x-slices.
Ly : int
The number of y-slices.
Lz : int
The number of z-slices.
steppers : callable or sequence of callable
Function(s) that take args ``(i, j, k)`` and generate another
coordinate, thus defining a bond.
coo_filter : callable
Function that takes args ``(i, j, k)`` and only returns ``True`` if
this is to be a valid starting coordinate.
Yields
------
bond : tuple[tuple[int, int, int], tuple[int, int, int]]
A pair of coordinates.
Examples
--------
Generate nearest neighbor bonds:
>>> for bond in gen_3d_bonds(2, 2, 2, [lambda i, j, k: (i + 1, j, k),
... lambda i, j, k: (i, j + 1, k),
... lambda i, j, k: (i, j, k + 1)]):
... print(bond)
((0, 0, 0), (1, 0, 0))
((0, 0, 0), (0, 1, 0))
((0, 0, 0), (0, 0, 1))
((0, 0, 1), (1, 0, 1))
((0, 0, 1), (0, 1, 1))
((0, 1, 0), (1, 1, 0))
((0, 1, 0), (0, 1, 1))
((0, 1, 1), (1, 1, 1))
((1, 0, 0), (1, 1, 0))
((1, 0, 0), (1, 0, 1))
((1, 0, 1), (1, 1, 1))
((1, 1, 0), (1, 1, 1))
"""
if callable(steppers):
steppers = (steppers,)
for i, j, k in product(range(Lx), range(Ly), range(Lz)):
if (coo_filter is None) or coo_filter(i, j, k):
for stepper in steppers:
i2, j2, k2 = stepper(i, j, k)
if (0 <= i2 < Lx) and (0 <= j2 < Ly) and (0 <= k2 < Lz):
yield (i, j, k), (i2, j2, k2)
class TensorNetwork3D(TensorNetwork):
_EXTRA_PROPS = (
'_site_tag_id',
'_x_tag_id',
'_y_tag_id',
'_z_tag_id',
'_Lx',
'_Ly',
'_Lz',
)
def _compatible_3d(self, other):
"""Check whether ``self`` and ``other`` are compatible 3D tensor
networks such that they can remain a 3D tensor network when combined.
"""
return (
isinstance(other, TensorNetwork3D) and
all(getattr(self, e) == getattr(other, e)
for e in TensorNetwork3D._EXTRA_PROPS)
)
def __and__(self, other):
new = super().__and__(other)
if self._compatible_3d(other):
new.view_as_(TensorNetwork3D, like=self)
return new
def __or__(self, other):
new = super().__or__(other)
if self._compatible_3d(other):
new.view_as_(TensorNetwork3D, like=self)
return new
@property
def Lx(self):
"""The number of x-slices.
"""
return self._Lx
@property
def Ly(self):
"""The number of y-slices.
"""
return self._Ly
@property
def Lz(self):
"""The number of z-slices.
"""
return self._Lz
@property
def nsites(self):
"""The total number of sites.
"""
return self._Lx * self._Ly * self._Lz
@property
def site_tag_id(self):
"""The string specifier for tagging each site of this 3D TN.
"""
return self._site_tag_id
def site_tag(self, i, j, k):
"""The name of the tag specifiying the tensor at site ``(i, j, k)``.
"""
if not isinstance(i, str):
i = i % self.Lx
if not isinstance(j, str):
j = j % self.Ly
if not isinstance(k, str):
k = k % self.Lz
return self.site_tag_id.format(i, j, k)
@property
def x_tag_id(self):
"""The string specifier for tagging each x-slice of this 3D TN.
"""
return self._x_tag_id
def x_tag(self, i):
if not isinstance(i, str):
i = i % self.Lx
return self.x_tag_id.format(i)
@property
def x_tags(self):
"""A tuple of all of the ``Lx`` different x-slice tags.
"""
return tuple(map(self.x_tag, range(self.Lx)))
@property
def y_tag_id(self):
"""The string specifier for tagging each y-slice of this 3D TN.
"""
return self._y_tag_id
def y_tag(self, j):
if not isinstance(j, str):
j = j % self.Ly
return self.y_tag_id.format(j)
@property
def y_tags(self):
"""A tuple of all of the ``Ly`` different y-slice tags.
"""
return tuple(map(self.y_tag, range(self.Ly)))
@property
def z_tag_id(self):
"""The string specifier for tagging each z-slice of this 3D TN.
"""
return self._z_tag_id
def z_tag(self, k):
if not isinstance(k, str):
k = k % self.Lz
return self.z_tag_id.format(k)
@property
def z_tags(self):
"""A tuple of all of the ``Lz`` different z-slice tags.
"""
return tuple(map(self.z_tag, range(self.Lz)))
@property
def site_tags(self):
"""All of the ``Lx * Ly`` site tags.
"""
return tuple(starmap(self.site_tag, self.gen_site_coos()))
def maybe_convert_coo(self, coo):
"""Check if ``coo`` is a tuple of three ints and convert to the
corresponding site tag if so.
"""
if not isinstance(coo, str):
try:
i, j, k = map(int, coo)
return self.site_tag(i, j, k)
except (ValueError, TypeError):
pass
return coo
def _get_tids_from_tags(self, tags, which='all'):
"""This is the function that lets coordinates such as ``(i, j, k)`` be
used for many 'tag' based functions.
"""
tags = self.maybe_convert_coo(tags)
return super()._get_tids_from_tags(tags, which=which)
def gen_site_coos(self):
"""Generate coordinates for all the sites in this 3D TN.
"""
return product(range(self.Lx), range(self.Ly), range(self.Lz))
def gen_bond_coos(self):
"""Generate pairs of coordinates for all the bonds in this 3D TN.
"""
return gen_3d_bonds(self.Lx, self.Ly, self.Lz, steppers=[
lambda i, j, k: (i + 1, j, k),
lambda i, j, k: (i, j + 1, k),
lambda i, j, k: (i, j, k + 1),
])
def __getitem__(self, key):
"""Key based tensor selection, checking for integer based shortcut.
"""
return super().__getitem__(self.maybe_convert_coo(key))
def __repr__(self):
"""Insert number of slices into standard print.
"""
s = super().__repr__()
extra = (f', Lx={self.Lx}, Ly={self.Ly}, Lz={self.Lz}, '
f'max_bond={self.max_bond()}')
s = f'{s[:-2]}{extra}{s[-2:]}'
return s
def __str__(self):
"""Insert number of slices into standard print.
"""
s = super().__repr__()
extra = (f', Lx={self.Lx}, Ly={self.Ly}, Lz={self.Lz}'
f'max_bond={self.max_bond()}')
s = f'{s[:-2]}{extra}{s[-2:]}'
return s
def flatten(self, fuse_multibonds=True, inplace=False):
"""Contract all tensors corresponding to each site into one.
"""
tn = self if inplace else self.copy()
for i, j, k in self.gen_site_coos():
tn ^= (i, j, k)
if fuse_multibonds:
tn.fuse_multibonds_()
return tn.view_as_(TensorNetwork3DFlat, like=self)
flatten_ = functools.partialmethod(flatten, inplace=True)
def is_lone_coo(where):
"""Check if ``where`` has been specified as a single coordinate triplet.
"""
return (len(where) == 3) and (isinstance(where[0], Integral))
class TensorNetwork3DVector(TensorNetwork3D,
TensorNetwork):
"""Mixin class for a 3D square lattice vector TN, i.e. one with a single
physical index per site.
"""
_EXTRA_PROPS = (
'_site_tag_id',
'_x_tag_id',
'_y_tag_id',
'_z_tag_id',
'_Lx',
'_Ly',
'_Lz',
'_site_ind_id',
)
@property
def site_ind_id(self):
return self._site_ind_id
def site_ind(self, i, j, k):
if not isinstance(i, str):
i = i % self.Lx
if not isinstance(j, str):
j = j % self.Ly
if not isinstance(k, str):
k = k % self.Lz
return self.site_ind_id.format(i, j, k)
def reindex_sites(self, new_id, where=None, inplace=False):
if where is None:
where = self.gen_site_coos()
return self.reindex(
{
self.site_ind(*coo): new_id.format(*coo) for coo in where
},
inplace=inplace
)
@site_ind_id.setter
def site_ind_id(self, new_id):
if self._site_ind_id != new_id:
self.reindex_sites(new_id, inplace=True)
self._site_ind_id = new_id
@property
def site_inds(self):
"""All of the site inds.
"""
return tuple(starmap(self.site_ind, self.gen_site_coos()))
def to_dense(self, *inds_seq, **contract_opts):
"""Return the dense ket version of this 3D vector, i.e. a ``qarray``
with shape (-1, 1).
"""
if not inds_seq:
# just use list of site indices
return do('reshape', TensorNetwork.to_dense(
self, self.site_inds, **contract_opts
), (-1, 1))
return TensorNetwork.to_dense(self, *inds_seq, **contract_opts)
def phys_dim(self, i=None, j=None, k=None):
"""Get the size of the physical indices / a specific physical index.
"""
if (i is not None) and (j is not None) and (k is not None):
pix = self.site_ind(i, j, k)
else:
# allow for when some physical indices might have been contracted
pix = next(iter(
ix for ix in self.site_inds if ix in self.ind_map
))
return self.ind_size(pix)
def gate(
self,
G,
where,
contract=False,
tags=None,
info=None,
inplace=False,
**compress_opts,
):
"""Apply a gate ``G`` to sites ``where``, preserving the outer site
inds.
"""
if is_lone_coo(where):
where = (where,)
else:
where = tuple(where)
inds = tuple(starmap(self.site_ind, where))
return super().gate_inds(
G, inds, contract=contract, tags=tags, info=info, inplace=inplace,
**compress_opts
)
gate_ = functools.partialmethod(gate, inplace=True)
class TensorNetwork3DFlat(TensorNetwork3D,
TensorNetwork):
"""Mixin class for a 3D square lattice tensor network with a single tensor
per site, for example, both PEPS and PEPOs.
"""
_EXTRA_PROPS = (
'_site_tag_id',
'_x_tag_id',
'_y_tag_id',
'_z_tag_id',
'_Lx',
'_Ly',
'_Lz',
)
def bond(self, coo1, coo2):
"""Get the name of the index defining the bond between sites at
``coo1`` and ``coo2``.
"""
b_ix, = self[coo1].bonds(self[coo2])
return b_ix
def bond_size(self, coo1, coo2):
"""Return the size of the bond between sites at ``coo1`` and ``coo2``.
"""
b_ix = self.bond(coo1, coo2)
return self[coo1].ind_size(b_ix)
class PEPS3D(TensorNetwork3DVector,
TensorNetwork3DFlat,
TensorNetwork3D,
TensorNetwork):
r"""Projected Entangled Pair States object (3D).
Parameters
----------
arrays : sequence of sequence of sequence of array
The core tensor data arrays.
shape : str, optional
Which order the dimensions of the arrays are stored in, the default
``'urfdlbp'`` stands for ('up', 'right', 'front', down', 'left',
'behind', 'physical') meaning (x+, y+, z+, x-, y-, z-, physical)
respectively. Arrays on the edge of lattice are assumed to be missing
the corresponding dimension.
tags : set[str], optional
Extra global tags to add to the tensor network.
site_ind_id : str, optional
String specifier for naming convention of site indices.
site_tag_id : str, optional
String specifier for naming convention of site tags.
x_tag_id : str, optional
String specifier for naming convention of x-slice tags.
y_tag_id : str, optional
String specifier for naming convention of y-slice tags.
z_tag_id : str, optional
String specifier for naming convention of z-slice tags.
"""
_EXTRA_PROPS = (
'_site_tag_id',
'_x_tag_id',
'_y_tag_id',
'_z_tag_id',
'_Lx',
'_Ly',
'_Lz',
'_site_ind_id',
)
def __init__(
self,
arrays,
*,
shape='urfdlbp',
tags=None,
site_ind_id='k{},{},{}',
site_tag_id='I{},{},{}',
x_tag_id='X{}',
y_tag_id='Y{}',
z_tag_id='Z{}',
**tn_opts
):
if isinstance(arrays, PEPS3D):
super().__init__(arrays)
return
tags = tags_to_oset(tags)
self._site_ind_id = site_ind_id
self._site_tag_id = site_tag_id
self._x_tag_id = x_tag_id
self._y_tag_id = y_tag_id
self._z_tag_id = z_tag_id
arrays = tuple(tuple(tuple(z for z in y) for y in x) for x in arrays)
self._Lx = len(arrays)
self._Ly = len(arrays[0])
self._Lz = len(arrays[0][0])
tensors = []
# cache for both creating and retrieving indices
ix = defaultdict(rand_uuid)
for i, j, k in self.gen_site_coos():
array = arrays[i][j][k]
# figure out if we need to transpose the arrays from some order
# other than up right front down left behind physical
array_order = shape
if i == self.Lx - 1:
array_order = array_order.replace('u', '')
if j == self.Ly - 1:
array_order = array_order.replace('r', '')
if k == self.Lz - 1:
array_order = array_order.replace('f', '')
if i == 0:
array_order = array_order.replace('d', '')
if j == 0:
array_order = array_order.replace('l', '')
if k == 0:
array_order = array_order.replace('b', '')
# allow convention of missing bonds to be singlet dimensions
if len(array.shape) != len(array_order):
array = do('squeeze', array)
transpose_order = tuple(
array_order.find(x) for x in 'urfdlbp' if x in array_order
)
if transpose_order != tuple(range(len(array_order))):
array = do('transpose', array, transpose_order)
# get the relevant indices corresponding to neighbours
inds = []
if 'u' in array_order:
inds.append(ix[(i, j, k), (i + 1, j, k)])
if 'r' in array_order:
inds.append(ix[(i, j, k), (i, j + 1, k)])
if 'f' in array_order:
inds.append(ix[(i, j, k), (i, j, k + 1)])
if 'd' in array_order:
inds.append(ix[(i - 1, j, k), (i, j, k)])
if 'l' in array_order:
inds.append(ix[(i, j - 1, k), (i, j, k)])
if 'b' in array_order:
inds.append(ix[(i, j, k - 1), (i, j, k)])
inds.append(self.site_ind(i, j, k))
# mix site, slice and global tags
ijk_tags = tags | oset((self.site_tag(i, j, k), self.x_tag(i),
self.y_tag(j), self.z_tag(k)))
# create the site tensor!
tensors.append(Tensor(data=array, inds=inds, tags=ijk_tags))
super().__init__(tensors, virtual=True, **tn_opts)
@classmethod
def from_fill_fn(
cls, fill_fn, Lx, Ly, Lz, bond_dim, phys_dim=2, **peps3d_opts
):
"""Create a 3D PEPS from a filling function with signature
``fill_fn(shape)``.
Parameters
----------
Lx : int
The number of x-slices.
Ly : int
The number of y-slices.
Lz : int
The number of z-slices.
bond_dim : int
The bond dimension.
physical : int, optional
The physical index dimension.
peps_opts
Supplied to :class:`~quimb.tensor.tensor_3d.PEPS3D`.
Returns
-------
psi : PEPS3D
"""
arrays = [[[None
for _ in range(Lz)]
for _ in range(Ly)]
for _ in range(Lx)]
for i, j, k in product(range(Lx), range(Ly), range(Lz)):
shape = []
if i != Lx - 1: # bond up
shape.append(bond_dim)
if j != Ly - 1: # bond right
shape.append(bond_dim)
if k != Lz - 1: # bond front
shape.append(bond_dim)
if i != 0: # bond down
shape.append(bond_dim)
if j != 0: # bond left
shape.append(bond_dim)
if k != 0: # bond behind
shape.append(bond_dim)
shape.append(phys_dim)
arrays[i][j][k] = fill_fn(shape)
return cls(arrays, **peps3d_opts)
@classmethod
def empty(
self, Lx, Ly, Lz, bond_dim, phys_dim=2, like='numpy', **peps3d_opts,
):
"""Create an empty 3D PEPS.
Parameters
----------
Lx : int
The number of x-slices.
Ly : int
The number of y-slices.
Lz : int
The number of z-slices.
bond_dim : int
The bond dimension.
physical : int, optional
The physical index dimension.
peps3d_opts
Supplied to :class:`~quimb.tensor.tensor_3d.PEPS3D`.
Returns
-------
psi : PEPS3D
See Also
--------
PEPS3D.from_fill_fn
"""
return self.from_fill_fn(
lambda shape: do("zeros", shape, like=like),
Lx, Ly, Lz, bond_dim, phys_dim, **peps3d_opts
)
@classmethod
def ones(
self, Lx, Ly, Lz, bond_dim, phys_dim=2, like='numpy', **peps3d_opts,
):
"""Create a 3D PEPS whose tensors are filled with ones.
Parameters
----------
Lx : int
The number of x-slices.
Ly : int
The number of y-slices.
Lz : int
The number of z-slices.
bond_dim : int
The bond dimension.
physical : int, optional
The physical index dimension.
peps3d_opts
Supplied to :class:`~quimb.tensor.tensor_3d.PEPS3D`.
Returns
-------
psi : PEPS3D
See Also
--------
PEPS3D.from_fill_fn
"""
return self.from_fill_fn(
lambda shape: do("ones", shape, like=like),
Lx, Ly, Lz, bond_dim, phys_dim, **peps3d_opts
)
@classmethod
def rand(
cls, Lx, Ly, Lz, bond_dim, phys_dim=2,
dtype='float64', seed=None, **peps3d_opts
):
"""Create a random (un-normalized) 3D PEPS.
Parameters
----------
Lx : int
The number of x-slices.
Ly : int
The number of y-slices.
Lz : int
The number of z-slices.
bond_dim : int
The bond dimension.
physical : int, optional
The physical index dimension.
dtype : dtype, optional
The dtype to create the arrays with, default is real double.
seed : int, optional
A random seed.
peps_opts
Supplied to :class:`~quimb.tensor.tensor_3d.PEPS3D`.
Returns
-------
psi : PEPS3D
See Also
--------
PEPS3D.from_fill_fn
"""
if seed is not None:
seed_rand(seed)
def fill_fn(shape):
return ops.sensibly_scale(ops.sensibly_scale(
randn(shape, dtype=dtype)))
return cls.from_fill_fn(
fill_fn, Lx, Ly, Lz, bond_dim, phys_dim, **peps3d_opts
)
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] |
widget/build_css_source.py | vesellov/callfeed.net | 1 | 84379 | import sys
sys.stdout.write('function CallFeedBuildCSS(settings) {\n')
sys.stdout.write(' var o = "";\n')
sys.stdout.write(' o+="<style type="text/css">\\n";\n')
for line in open(sys.argv[1]).read().splitlines():
l = line.rstrip()
if not l:
continue
l = l.replace('"', '"')
l = ' o+="%s\\n";\n' % (unicode(l, 'utf-8').encode('unicode-escape'))
sys.stdout.write(l)
sys.stdout.write(' o+="</style>\\n";\n')
sys.stdout.write(' for (var key in settings) if (settings.hasOwnProperty(key))\n')
sys.stdout.write(' if ((key.indexOf("flag_")==0)||(key.indexOf("text_")==0)||(key.indexOf("param_")==0)||(key.indexOf("color_")==0))\n')
sys.stdout.write(' o = o.replace(new RegExp("%\\\\("+key+"\\\\)s", "g"), unescape(settings[key]));\n')
sys.stdout.write(' return o;\n')
sys.stdout.write('}\n')
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13,
2
] |
Testing/publications/magazines/glossy/__init__.py | freder/PageBotExamples | 5 | 131084 | # -*- coding: UTF-8 -*-
# -----------------------------------------------------------------------------
#
# P A G E B O T
#
# Copyright (c) 2016+ <NAME> + <NAME>
# www.pagebot.io
# Licensed under MIT conditions
#
# Supporting DrawBot, www.drawbot.com
# Supporting Flat, xxyxyz.org/flat
# -----------------------------------------------------------------------------
#
# magazines/glossy/__init__.py
#
from pagebot.publications.magazines.basemagazine import BaseMagazine
class Glossy(BaseMagazine):
pass
| [
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13,
1678,
1209,
13,
2
] |
contrib/newsumt/setup.py | mjfwest/OpenMDAO-Framework | 69 | 68301 | <filename>contrib/newsumt/setup.py
import os.path
import setuptools
import sys
from numpy.distutils.core import setup
from numpy.distutils.misc_util import Configuration
sdkdir = os.environ.get('WindowsSdkDir')
if sys.platform == 'win32' and sdkdir:
# Update the ``library_dir_option`` function in MSVCCompiler
# to add quotes around /LIBPATH entries.
import types
def _lib_dir_option(self, dir):
return '/LIBPATH:"%s"' % dir
from distutils.msvc9compiler import MSVCCompiler
setattr(MSVCCompiler, 'library_dir_option',
types.MethodType(_lib_dir_option, None, MSVCCompiler))
include_dirs = [os.path.join(sdkdir,'Include')]
library_dirs = [os.path.join(sdkdir,'Lib')]
# make sure we have mt.exe available in path
path = os.environ['PATH'].split(';')
path.append(os.path.join(sdkdir,'bin'))
os.environ['PATH'] = ';'.join(path)
else:
include_dirs = []
library_dirs = []
config = Configuration(name='newsumt')
config.add_extension('newsumtinterruptible',
sources=['newsumt_interruptible.f',
'newsumt_interruptible.pyf'],
include_dirs=include_dirs,
library_dirs=library_dirs)
kwds = {'install_requires':['numpy'],
'version': '1.1.1',
'zip_safe': False,
'license': 'public domain',
# NOTE: we use 'url' here, but it really translates to 'home-page'
# in the metadata. Go figure.
'url': 'http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19790018640_1979018640.pdf',
'package_data': {'openmdao.main': ['*.html']},
}
kwds.update(config.todict())
setup(**kwds)
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13,
2
] |
CoquoBot/order_manager.py | Josef212/CoquoBot | 0 | 28716 | from order import Order
class OrderManager:
def __init__(self):
self.orders = {}
def user_has_any_order(self, chat_id: int, user: str) -> bool:
order = self.get_order(chat_id)
return order.user_has_any_order(user)
def get_order(self, id: int) -> Order:
if id not in self.orders:
self.orders[id] = Order()
return self.orders[id]
def reset_order(self, id: int) -> None:
self.get_order(id).reset() | [
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] |
resqpy/property/__init__.py | poc11/resqpy | 35 | 165006 | <reponame>poc11/resqpy
"""Collections of properties for grids, wellbore frames, grid connection sets etc."""
__all__ = [
'PropertyCollection', 'Property', 'WellLog', 'WellIntervalProperty', 'WellIntervalPropertyCollection',
'WellLogCollection', 'StringLookup', 'PropertyKind', 'GridPropertyCollection',
'property_over_time_series_from_collection', 'property_collection_for_keyword', 'infer_property_kind',
'write_hdf5_and_create_xml_for_active_property', 'reformat_column_edges_to_resqml_format',
'reformat_column_edges_from_resqml_format', 'same_property_kind', 'selective_version_of_collection',
'supported_local_property_kind_list', 'supported_property_kind_list', 'supported_facet_type_list',
'expected_facet_type_dict', 'create_transmisibility_multiplier_property_kind',
'property_kind_and_facet_from_keyword', 'guess_uom'
]
from .property_common import property_collection_for_keyword, \
property_over_time_series_from_collection, \
write_hdf5_and_create_xml_for_active_property, \
infer_property_kind, \
reformat_column_edges_to_resqml_format, \
reformat_column_edges_from_resqml_format, \
selective_version_of_collection, \
same_property_kind, \
supported_property_kind_list, \
supported_local_property_kind_list, \
supported_facet_type_list, \
expected_facet_type_dict, \
property_kind_and_facet_from_keyword, \
guess_uom
from .property_kind import PropertyKind, create_transmisibility_multiplier_property_kind
from .string_lookup import StringLookup
from .property_collection import PropertyCollection
from .grid_property_collection import GridPropertyCollection
from .property import Property
from .well_interval_property import WellIntervalProperty
from .well_interval_property_collection import WellIntervalPropertyCollection
from .well_log import WellLog
from .well_log_collection import WellLogCollection
# Set "module" attribute of all public objects to this path.
for _name in __all__:
_obj = eval(_name)
if hasattr(_obj, "__module__"):
_obj.__module__ = __name__
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Entry.py | yeweiasia/ater | 2 | 1613182 | import wx
from ui.MainWindowUI import MainWindowUI
from ConfigHolder import ConfigHolder
if __name__ == "__main__":
ConfigHolder("etc/ater.ini")
app = wx.App()
frame = MainWindowUI("Ater").Show()
app.MainLoop() | [
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lab2/text_recognizer/data/base_data_module.py | ronaldokun/fsdl-text-recognizer-2021-labs | 0 | 65683 | <gh_stars>0
"""Base DataModule class."""
from pathlib import Path
from typing import Collection, Dict, Optional, Tuple, Union
import argparse
import os
from torch.utils.data import ConcatDataset, DataLoader
import pytorch_lightning as pl
from text_recognizer import util
from text_recognizer.data.util import BaseDataset
def load_and_print_info(data_module_class) -> None:
"""Load EMNISTLines and print info."""
parser = argparse.ArgumentParser()
data_module_class.add_to_argparse(parser)
args = parser.parse_args()
dataset = data_module_class(args)
dataset.prepare_data()
dataset.setup()
print(dataset)
def _download_raw_dataset(metadata: Dict, dl_dirname: Path) -> Path:
dl_dirname.mkdir(parents=True, exist_ok=True)
filename = dl_dirname / metadata["filename"]
if filename.exists():
return filename
print(f"Downloading raw dataset from {metadata['url']} to {filename}...")
util.download_url(metadata["url"], filename)
print("Computing SHA-256...")
sha256 = util.compute_sha256(filename)
if sha256 != metadata["sha256"]:
raise ValueError("Downloaded data file SHA-256 does not match that listed in metadata document.")
return filename
BATCH_SIZE = 2048
NUM_WORKERS = os.cpu_count()
class BaseDataModule(pl.LightningDataModule):
"""
Base DataModule.
Learn more at https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html
"""
def __init__(self, args: argparse.Namespace = None) -> None:
super().__init__()
self.args = vars(args) if args is not None else {}
self.batch_size = self.args.get("batch_size", BATCH_SIZE)
self.num_workers = self.args.get("num_workers", NUM_WORKERS)
self.on_gpu = isinstance(self.args.get("gpus", None), (str, int))
# Make sure to set the variables below in subclasses
self.dims: Tuple[int, ...]
self.output_dims: Tuple[int, ...]
self.mapping: Collection
self.data_train: Union[BaseDataset, ConcatDataset]
self.data_val: Union[BaseDataset, ConcatDataset]
self.data_test: Union[BaseDataset, ConcatDataset]
@classmethod
def data_dirname(cls):
return Path(__file__).resolve().parents[3] / "data"
@staticmethod
def add_to_argparse(parser):
parser.add_argument(
"--batch_size", type=int, default=BATCH_SIZE, help="Number of examples to operate on per forward step."
)
parser.add_argument(
"--num_workers", type=int, default=NUM_WORKERS, help="Number of additional processes to load data."
)
return parser
def config(self):
"""Return important settings of the dataset, which will be passed to instantiate models."""
return {"input_dims": self.dims, "output_dims": self.output_dims, "mapping": self.mapping}
def prepare_data(self, *args, **kwargs) -> None:
"""
Use this method to do things that might write to disk or that need to be done only from a single GPU
in distributed settings (so don't set state `self.x = y`).
"""
def setup(self, stage: Optional[str] = None) -> None:
"""
Split into train, val, test, and set dims.
Should assign `torch Dataset` objects to self.data_train, self.data_val, and optionally self.data_test.
"""
def train_dataloader(self):
return DataLoader(
self.data_train,
shuffle=True,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.on_gpu,
)
def val_dataloader(self):
return DataLoader(
self.data_val,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.on_gpu,
)
def test_dataloader(self):
return DataLoader(
self.data_test,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.on_gpu,
)
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seq_predict/ac.py | dmorrill10/seq_predict | 0 | 72016 | """Arithmetic Coding
Functions for doing compression using arithmetic coding.
http://en.wikipedia.org/wiki/Arithmetic_coding
The functions and classes all need predictive models; see model.py
"""
import math
import itertools
from seq_predict import CTW
def grouper(n, iterable, fillvalue=None):
args = [iter(iterable)] * n
return itertools.zip_longest(*args, fillvalue=fillvalue)
def compress_bits(model, bits):
"""Compresses a stream of bits into another stream of bits.
Requires a prediction model.
"""
encoder = BinaryArithmeticEncoder(model)
for c in itertools.chain.from_iterable((encoder.encode(b) for b in bits)):
yield c
for c in encoder.flush():
yield c
def compress_bytes(model, bytes):
"""Compresses a stream of bytes into another steam of bytes.
Requires a prediction model.
"""
bits = ((m >> i) & 1 for m in bytes for i in range(8))
cbits = compress_bits(model, bits)
for c in (int(''.join(reversed(byte)), 2) for byte in grouper(8, (str(b) for b in cbits), '0')):
yield c
def decompress_bits(model, bits, msglen):
"""Decompresses a stream of bits into another stream of bits.
Requires the same prediction model (from its original state) that was
used for decompression and the number of bits in the message.
"""
decoder = BinaryArithmeticDecoder(model)
nbits = 0
for r in itertools.chain.from_iterable((decoder.decode(b) for b in bits)):
yield r
nbits += 1
for r in decoder.flush(msglen - nbits):
yield r
def decompress_bytes(model, bytes, msglen):
"""Decompresses a stream of bytes into another stream of bytes.
Requires the same prediction model (from its original state) that was
used for decompression and the number of bytes in the message.
"""
cbits = ((m >> i) & 1 for m in bytes for i in range(8))
bits = decompress_bits(model, cbits, msglen * 8)
for r in (int(''.join(reversed(byte)), 2) for byte in grouper(8, (str(b) for b in bits), '0')):
yield r
class BinaryArithmeticEncoder:
"""BinaryArithmeticEncoder
An arithmetic encoder for binary data sources. For the theory behind the encoder
see http://en.wikipedia.org/wiki/Arithmetic_coding.
>>> encoder = BinaryArithmeticEncoder(CTW(8))
See also: BinaryArithmeticDecoder, compress, and compress_bytes
"""
def __init__(self, model, num_bits = 32):
self.model = model
self.num_bits = num_bits
self._top = 2 ** self.num_bits
self._half = self._top // 2 # [0, self._half) is outputs the zero bit
self._1_4 = self._half // 2
self._3_4 = self._top - self._1_4
self.low = 0 # Interval is [self.low, self.high)
self.high = self._top
self.follow_bits = 0 # Opposing bits to follow the next output'd bit
self.history = []
def encode(self, symbol):
"""Encodes a symbol returning a sequence of coded bits.
The encoder is stateful and (since it is hopefully compressing the input) it will not
return output bits for each input symbol.
You will need to flush the encoder to get remaining coded bits after encoding the
complete sequence.
"""
output = []
# Find the split point
p_zero = self.model.predict(0, self.history)
split = self.low + max(1, int((self.high - self.low) * p_zero)) # 0-interval is [self.low, split)
# Update the model
self.model.update(symbol, self.history)
self.history.append(symbol)
# Update the range based on the observed symbol
if symbol:
self.low = split
else:
self.high = split
# If the range no longer overlaps the midpoint, the next bit is known
# also rescale the interval to get back precision
#
# If the range overlaps the midpoint but not the 1/4 or 3/4 points then
# we rescale the interval, but track this with follow bits. If the next
# bit to output is a 1, then we already know it's at the low end of the upper
# half, so we follow with a 0. Similarly if the next bit is a 0, then
# we already know it's at the high end of the lower half, so we follow
# with a 1.
# If this happens a second time before outputting any bit, then there will
# need to be 2 of these follow bits. So we track this by just incrementing
# a follow bit counter.
#
# This is in a loop because the new range may not overlap the new midpoint,
# allowing multiple bits to be determined
output = []
while True:
if self.high <= self._half:
output.append(0)
output.extend([1] * self.follow_bits) # Add the follow bits
self.follow_bits = 0
elif self.low >= self._half:
output.append(1)
output.extend([0] * self.follow_bits) # Add the follow bits
self.follow_bits = 0
self.low -= self._half
self.high -= self._half
elif self.low >= self._1_4 and self.high <= self._3_4:
self.follow_bits += 1
self.low -= self._1_4
self.high -= self._1_4
else:
break
self.low *= 2
self.high *= 2
return output
def flush(self):
"""Flushes any coded bits in the encoder. Typically called after the entire
sequence has been encoded.
"""
if self.low < self._1_4:
output = [0] + [1] * (self.follow_bits + 1)
else:
output = [1] + [0] * (self.follow_bits + 1)
return output
class BinaryArithmeticDecoder:
def __init__(self, model, num_bits = 32):
self.model = model
self.num_bits = num_bits
self._top = 2 ** self.num_bits
self._half = self._top // 2 # [0, self._half) outputs the zero bit
self._1_4 = self._half // 2
self._3_4 = self._top - self._1_4
self.low = 0
self.high = 1 # This ensures num_bits are read before decoding
self.value = 0
self.history = []
def decode(self, bit):
if self.high <= self._half:
pass
elif self.low >= self._half:
self.value -= self._half
self.low -= self._half
self.high -= self._half
elif self.low >= self._1_4 and self.high <= self._3_4:
self.value -= self._1_4
self.low -= self._1_4
self.high -= self._1_4
self.low *= 2
self.high *= 2
self.value *= 2
self.value += bit
output = []
while (self.low < self._half < self.high and
((self.low < self._1_4 or self.high > self._3_4))):
p_zero = self.model.predict(0, self.history)
split = self.low + max(1, int((self.high - self.low) * p_zero)) # 0-interval is [self.low, split)
symbol = 0 if self.value < split else 1
output.append(symbol)
self.model.update(symbol, self.history)
self.history.append(symbol)
if symbol:
self.low = split
else:
self.high = split
return output
def flush(self, nbits):
output = []
while len(output) < nbits:
output += self.decode(0)
return output[:nbits]
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src/files/trilio/get_pkgs.py | mskalka/charm-trilio-data-mover | 0 | 184991 | import cryptography
import libvirtmod
import cffi
import _cffi_backend
print(cryptography.__path__[0])
print(libvirtmod.__file__)
print(cffi.__path__[0])
print(_cffi_backend.__file__)
| [
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TwoPointers/Leetcode11.py | Rylie-W/LeetRecord | 0 | 9954 | class Solution:
def maxArea(self, height) -> int:
left=0
right=len(height)-1
res=min(height[left],height[right])*(right-left)
while right>left:
res=max(res,(right-left)*min(height[right],height[left]))
if height[left]<height[right]:
left+=1
else: right-=1
return res
if __name__ == '__main__':
sol=Solution()
# height = [1, 1]
height=[1,3,2,5,25,24,5]
print(sol.maxArea(height))
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] |
examples/pytorch/AxHyperOptimizationPTL/ax_hpo_mnist.py | PeterSulcs/mlflow | 10,351 | 81368 | <filename>examples/pytorch/AxHyperOptimizationPTL/ax_hpo_mnist.py
import argparse
import mlflow
from ax.service.ax_client import AxClient
from iris import IrisClassification
from iris_data_module import IrisDataModule
import pytorch_lightning as pl
def train_evaluate(params, max_epochs=100):
model = IrisClassification(**params)
dm = IrisDataModule()
dm.setup(stage="fit")
trainer = pl.Trainer(max_epochs=max_epochs)
mlflow.pytorch.autolog()
trainer.fit(model, dm)
trainer.test(datamodule=dm)
test_accuracy = trainer.callback_metrics.get("test_acc")
return test_accuracy
def model_training_hyperparameter_tuning(max_epochs, total_trials, params):
"""
This function takes input params max_epochs, total_trials, params
and creates a nested run in Mlflow. The parameters, metrics, model and summary are dumped into their
respective mlflow-run ids. The best parameters are dumped along with the baseline model.
:param max_epochs: Max epochs used for training the model. Type:int
:param total_trials: Number of ax-client experimental trials. Type:int
:param params: Model parameters. Type:dict
"""
with mlflow.start_run(run_name="Parent Run"):
train_evaluate(params=params, max_epochs=max_epochs)
ax_client = AxClient()
ax_client.create_experiment(
parameters=[
{"name": "lr", "type": "range", "bounds": [1e-3, 0.15], "log_scale": True},
{"name": "weight_decay", "type": "range", "bounds": [1e-4, 1e-3]},
{"name": "momentum", "type": "range", "bounds": [0.7, 1.0]},
],
objective_name="test_accuracy",
)
for i in range(total_trials):
with mlflow.start_run(nested=True, run_name="Trial " + str(i)) as child_run:
parameters, trial_index = ax_client.get_next_trial()
test_accuracy = train_evaluate(params=parameters, max_epochs=max_epochs)
# completion of trial
ax_client.complete_trial(trial_index=trial_index, raw_data=test_accuracy.item())
best_parameters, metrics = ax_client.get_best_parameters()
for param_name, value in best_parameters.items():
mlflow.log_param("optimum_" + param_name, value)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parent_parser=parser)
parser.add_argument(
"--total_trials",
default=3,
help="umber of trials to be run for the optimization experiment",
)
args = parser.parse_args()
if "max_epochs" in args:
max_epochs = args.max_epochs
else:
max_epochs = 100
params = {"lr": 0.1, "momentum": 0.9, "weight_decay": 0}
model_training_hyperparameter_tuning(
max_epochs=int(max_epochs), total_trials=int(args.total_trials), params=params
)
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] |
master/result_import_slp.py | dsolisherrera/excovery | 0 | 78653 | <filename>master/result_import_slp.py
import sys
#sys.path.append('/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7')
#sys.path.append('/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages')
import struct
import zlib
from exp_description import *
import pcapy
import datetime
import sqlite3
import json
import zlib
def init_db():
global conn
c = conn.cursor()
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=''")
if c.fetchone()==None:
# Create table
c.execute('''CREATE TABLE "run_ids" (
"runID" INTEGER PRIMARY KEY AUTOINCREMENT,
"run_identifier" TEXT
) ''' )
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='EEFiles'")
if c.fetchone()==None:
# Create table
c.execute('''CREATE TABLE "EEFiles" (
"ID" INTEGER PRIMARY KEY AUTOINCREMENT,
"File" BLOB)'''
)
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='Events'")
if c.fetchone()==None:
c.execute('''
CREATE TABLE "Events" (
"RunID" INTEGER,
"NodeID" INTEGER,
"CommonTime" TEXT,
"EventType" TEXT,
"Parameter" TEXT
) ''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='ExperimentInfo'")
if c.fetchone()==None:
c.execute('''
CREATE TABLE "ExperimentInfo" (
"ExpXML" TEXT,
"EEVersion" TEXT,
"Name" TEXT,
"Comment" TEXT
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='NodesIP'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "NodesIP" (
"NodeID" TEXT,
"IP_Address" TEXT
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='ExperimentMeasurements'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "ExperimentMeasurements" (
"ID" INTEGER PRIMARY KEY,
"NodeID" TEXT,
"Name" TEXT,
"Content" TEXT
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='ExtraRunMeasurements'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "ExtraRunMeasurements" (
"RunID" INTEGER,
"NodeID" INTEGER,
"Name" TEXT,
"Content" TEXT
)
''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='Links'")
if c.fetchone()==None:
c.execute('''
CREATE TABLE "Links" (
"RunID" INTEGER,
"NodeIP" TEXT,
"NeighborIP" TEXT,
"LQ" REAL,
"NLQ" REAL
) ''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='OLSR'")
if c.fetchone()==None:
c.execute('''
CREATE TABLE "OLSR" (
"RunID" INTEGER,
"NodeID" TEXT,
"OLSR" BLOB
) ''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='Logs'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "Logs" (
"NodeID" TEXT,
"Log" BLOB
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='PacketsBmf0'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "PacketsBmf0" (
"RunID" INTEGER,
"NodeID" INTEGER,
"CommonTime" TEXT,
"SrcNodeID" TEXT,
"Data" BLOB
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='PacketsWLAN2'")
if c.fetchone()==None:
c.execute('''CREATE TABLE "PacketsWLAN2" (
"RunID" INTEGER,
"NodeID" INTEGER,
"CommonTime" TEXT,
"SrcNodeID" TEXT,
"Data" BLOB
)''')
c.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='RunInfos'")
if c.fetchone()==None:
c.execute(''' CREATE TABLE "RunInfos" (
"RunID" INTEGER,
"NodeID" INTEGER,
"StartTime" TEXT,
"TimeDiff" TEXT
)''')
# c.execute('''CREATE INDEX "id" on measurements (runID ASC)''')
# Save (commit) the changes
conn.commit()
# We can also close the cursor if we are done with it
c.close()
class exp_event_list:
def __init__(self):
self.event_list = []
def load_from_file(self, file):
self.event_list = []
fd = open(file)
i = 0
event_list = []
for line in fd:
if i == 0 or line.split(',')[0] == "timestamp":
i = i +1
continue #skip the description line
try:
self.event_list.append(self.parse_line(line))
except:
print "Error in file ", file
raise()
#event_list.append("%s,%s" %(line,node_name))
fd.close()
return self.event_list
def parse_line(self, line):
'''
Each line consists of a timestamp,type,param
The timestamp is concerted into epoch value
'''
list = line.split(',')
dt, _, us = list[0].partition(".")
if _ != ".":
_, us = [".", "000000"]
list[0] = list[0] + ".000000"
dt= datetime.datetime.strptime(dt, "%Y-%m-%d %H:%M:%S")
us= int(us.rstrip("Z"), 10)
ret = dt + datetime.timedelta(microseconds=us)
## patch old event types:
if list[1]=="SD_init": list[1]="sd_init_done"
if list[1]=="SD_exit": list[1]="sd_exit"
if list[1]=="SD_start_search": list[1]="sd_start_search"
if list[1]=="SD_stop_search": list[1]="sd_stop_search"
if list[1]=="SD_publish": list[1]="sd_start_publish"
if list[1]=="SD_add": list[1]="sd_service_add"
return {'ts':ret, 'ts_string':list[0],'EventType':list[1], 'Parameter':list[2] }
#gt("2008-08-12T12:20:30.656234Z")
#datetime.datetime(2008, 8, 12, 12, 20, 30, 656234)
def get_packet_list(file, timediff,pos):
'''
returns an dict {'ts' commonTime, 'srcNode', 'data'}
'''
global exp
diff = datetime.timedelta(milliseconds=timediff)
p = pcapy.open_offline(file)
done = False
packet_list = []
while done==False:
try:
(hdr,data) = p.next()
curr_hdr = {}
curr_hdr['ts_s'],curr_hdr['ts_us'] = hdr.getts()
ts = datetime.datetime.fromtimestamp(curr_hdr['ts_s'])
ts = ts + datetime.timedelta(microseconds=curr_hdr['ts_us'])
ts = ts + diff
#ipaddr=struct.unpack('B',data[12:16])[0]
if pos == "bmp":
ip0=struct.unpack('B',data[12])[0]
ip1=struct.unpack('B',data[13])[0]
ip2=struct.unpack('B',data[14])[0]
ip3=struct.unpack('B',data[15])[0]
else:
ip0=struct.unpack('B',data[26])[0]
ip1=struct.unpack('B',data[27])[0]
ip2=struct.unpack('B',data[28])[0]
ip3=struct.unpack('B',data[29])[0]
ip = "%d.%d.%d.%d" % (ip0,ip1,ip2,ip3)
#node ID
node=exp.get_node_by_ip(ip)
try:
packet_list.append({'CommonTime':ts, 'SrcNodeID':node['real_id'], 'Data':data})
except TypeError:
print ">>> OOOOOPS!"
print ">>> Does one of you nodes have the wrong IP in the XML file?"
print ">>> Aborting ..."
continue
#print "Packet TS:",ts
except pcapy.PcapError:
done=True
return packet_list
def get_links_measurements(exp_dir, run_id, db_run_id , node, c,fmt='json'):
if fmt == 'txt':
start = 0
fd = open("%s/nodes/%s/links/%s.log" % (exp_dir,run_id, node['real_id']))
i = 0
for line in fd:
links = line.split()
if len(links) == 0 and start == 1:
break
for i in range(len(links)):
if links[i] == 'INFINITE':
links[i] = "infinity"
if start == 1:
c.execute("INSERT INTO Links (RunID, NodeIP, NeighborIP, LQ, NLQ) VALUES (?,?,?,?,?)", [db_run_id, links[0],links[1],links[3],links[4]])
if "Local" in links:
start = 1
# Need to find a proper way to save the text file
#c.execute("INSERT INTO OLSR (RunID,NodeID, OLSR) VALUES (?,?,?)",[db_run_id,node['real_id'], sqlite3.Binary(zlib.compress(fd.read()))])
fd.close()
else:
fd = open("%s/nodes/%s/links/%s.json" % (exp_dir,run_id, node['real_id']))
olsr = json.load(fd)
for link in olsr['links']:
c.execute("INSERT INTO Links (RunID, NodeIP, NeighborIP, LQ, NLQ) VALUES (?,?,?,?,?)", [db_run_id, link['localIP'],link['remoteIP'],link['linkQuality'],link['neighborLinkQuality']])
c.execute("INSERT INTO OLSR (RunID,NodeID, OLSR) VALUES (?,?,?)",[db_run_id,node['real_id'], sqlite3.Binary(zlib.compress(json.dumps(olsr)))])
fd.close()
def get_extra_run_measurements(exp_dir, run_id, node):
'''
'''
fd = open("%s/nodes/%s/routes/%s.log" % (exp_dir,run_id, node['real_id']))
i = 0
for line in fd:
routes = line
fd.close()
return routes
def get_log_file(exp_dir, node):
fd = open("%s/nodes/RPC-Server_%s.log" % (exp_dir, node['real_id']))
i = 0
log = fd.read()
log = zlib.compress(log)
fd.close()
return log
def run_dir_importer(exp_dir, run_identifier, db_run_id, node, c,olsr_fmt):
'''
'''
#get events
event_list = exp_event_list()
event_list.load_from_file("%s/nodes/%s/event_log_%s.log" % (exp_dir, run_identifier, node['real_id']))
time_diff = 0
#get time diff info
if node['abstract_id']!="":
fd = open("%s/master/%s/ping_t_%s" % (exp_dir,run_identifier, node['real_id']))
i = 0
offset_1 = 0
for line in fd:
if i == 3:
#fetch first offset
try:
offset_1 = int(line)
except ValueError:
last_run_identifier = run_identifier.split('_')
last_run_identifier[-1] = str(int(last_run_identifier[-1])-1)
last_run_identifier = '_'.join(last_run_identifier)
fd_last = open("%s/master/%s/ping_t_%s" % (exp_dir,last_run_identifier, node['real_id']))
i_last = 0
for line_last in fd_last:
if i_last == 3:
#fetch first offset
offset_1 = int(line_last)
if i_last == 5:
offset_2 = int(line_last)
break
i_last = i_last +1
fd_last.close()
break
if i == 5:
offset_2 = int(line)
break
i = i +1
fd.close()
# todo check calculation
time_diff = offset_1 #+ abs(offset_2))/2
#print "Time_diff=",time_diff
try:
c.execute("INSERT INTO RunInfos (RunID, NodeID, StartTime, TimeDiff) VALUES(?,?,?,?)",[db_run_id,node['real_id'],event_list.event_list[0]['ts_string'],time_diff])
except IndexError:
sys.stdout.flush()
sys.stdout.write("\n")
print ">>> OOOOOPS!"
print ">>> We have an index error."
print "db_run_id: %d" % db_run_id
print "node: %s" % node
print "node['real_id']: %s" % node['real_id']
print "event_list: %s" % event_list
print "event_list.event_list: %s" % event_list.event_list
print "event_list.event_list[0]: %s" % event_list.event_list[0]
print "event_list.event_list[0]['ts_string']: %s" % event_list.event_list[0]['ts_string']
print "time_diff: %d" % time_diff
raise()
for event in event_list.event_list:
diff = datetime.timedelta(milliseconds=time_diff)
commonTime = event['ts'] + diff
c.execute("INSERT INTO Events (RunID, NodeID, CommonTime, EventType, Parameter) VALUES (?,?,?,?,?)", [db_run_id,node['real_id'],commonTime, event['EventType'],event['Parameter']])
# parse packets
if node['abstract_id']!="":
cap = "%s/nodes/%s/capture/%s_bmf0.pcap" %(exp_dir, run_identifier,node['real_id'])
packet_list = get_packet_list(cap, time_diff,"bmp")
#print len(packet_list)
#print packet_list
for packet in packet_list:
c.execute("INSERT INTO PacketsBmf0 (RunID,NodeID,CommonTime, SrcNodeId, Data) VALUES (?,?,?,?,?)", [db_run_id,node['real_id'],packet['CommonTime'],packet['SrcNodeID'],sqlite3.Binary(packet['Data'])])
if node['abstract_id']!="":
cap = "%s/nodes/%s/capture/%s_wlan2.pcap" %(exp_dir, run_identifier,node['real_id'])
packet_list = get_packet_list(cap, time_diff,"wlan2")
#print len(packet_list)
#print packet_list
for packet in packet_list:
c.execute("INSERT INTO PacketsWLAN2 (RunID,NodeID,CommonTime, SrcNodeId, Data) VALUES (?,?,?,?,?)", [db_run_id,node['real_id'],packet['CommonTime'],packet['SrcNodeID'],sqlite3.Binary(packet['Data'])])
# ExtraRun measurements
routes = get_extra_run_measurements(exp_dir,run_identifier, node)
c.execute("INSERT INTO ExtraRunMeasurements (RunID, NodeID, Name, Content) VALUES (?,?,?,?)", [db_run_id, node['real_id'],"routes",routes])
# Topology/links measurements given by OLSR
get_links_measurements(exp_dir, run_identifier, db_run_id , node, c, fmt=olsr_fmt)
def get_topo(exp_dir, node, node2):
fd = open("%s/nodes/topology/traceroute_%s_%s" % (exp_dir, node['real_id'],node2['real_id']))
i = 0
s = fd.read()
#s = zlib.compress(s)
fd.close()
return s
def experiment_dir_importer(exp_dir,olsr_fmt):
global csv_file
global exp
global conn
c = conn.cursor()
#loop over run sequence
for run_number in range(exp.get_run_count()):
###### SETUP LEVELS OF ALL FACTORS FOR THIS RUN ########
exp.set_run_number(run_number)
run_definition = exp.get_run_identifier()
sys.stdout.write("\rImporting run ID %d" % run_number)
sys.stdout.flush()
c.execute("INSERT INTO run_ids (run_identifier) VALUES (?)", [run_definition])
run_id = c.lastrowid
# import measurements from nodes and master
# event lists, packets, topo, timediff
all_nodes = exp.get_all_spec_nodes()
for node in all_nodes:
#if node['real_id']=="t9-007":
run_dir_importer(exp_dir, run_definition, run_id, node, c,olsr_fmt)
sys.stdout.write("\r%d runs imported " % (run_number + 1))
sys.stdout.flush()
sys.stdout.write("\n")
conn.commit()
# get log files
nodenum = 0
for node in all_nodes:
nodenum = nodenum + 1
c.execute("INSERT INTO NodesIP (NodeID, IP_Address) VALUES (?,?)",[node['real_id'], node['real_ip']])
try:
sys.stdout.write("\rInserting log for node %d" % nodenum)
sys.stdout.flush()
log = get_log_file(exp_dir, node)
c.execute("INSERT INTO Logs (NodeID, Log) VALUES (?,?)",[node['real_id'], sqlite3.Binary(log)])
except:
sys.stdout.write(" ... no logfile")
sys.stdout.write("\rLogs for %d nodes inserted " % nodenum)
sys.stdout.flush()
sys.stdout.write("\n")
# get program files
conn.commit()
# get experiment measurements
for node in all_nodes:
for node2 in all_nodes:
if node!= node2:
if node['abstract_id']!="" and node2['abstract_id']!="":
topo = get_topo(exp_dir, node, node2)
c.execute("INSERT INTO ExperimentMeasurements (NodeID, Name, Content) VALUES (?,'hopcount',?)",[node['real_id'],topo])
# get experiment description
conn.commit()
if __name__ == '__main__':
global exp
global conn
parser = optparse.OptionParser(
description='Import Service Discovery Experiments.',
prog=os.path.basename(sys.argv[0]),
version='%s 0.0.1' % os.path.basename(sys.argv[0]),
)
parser.add_option('-e', metavar='experiment_dir', dest='experiment_dir', help='Name of the experiment')
parser.add_option('-x', metavar='exp_file', dest='exp_file', help='the abstract experiment description')
parser.add_option('-o', metavar='csv_file', dest='csv_file', help='the file to which the results are written')
parser.add_option('-d', metavar='database', dest='database', help='the file that should contain the database')
parser.add_option('--olsr', metavar='olsr_fmt', dest='olsr_fmt', help='the format in which the olsr data was generated')
options, arguments = parser.parse_args()
if options.experiment_dir==None:
print "Need experiment dir"
exit()
if options.csv_file!=None:
csv_file = options.csv_file
if options.database==None:
conn = sqlite3.connect('/tmp/example4')
else:
conn = sqlite3.connect(options.database)
if options.olsr_fmt!=None:
olsr_fmt = options.olsr_fmt
else:
olsr_fmt = 'json'
exp = experiment_description(options.exp_file)
#exp.summary()
init_db()
experiment_dir_importer(options.experiment_dir,olsr_fmt)
c = conn.cursor()
fd = open(options.exp_file)
xml = fd.read()
fd.close()
c.execute("INSERT INTO ExperimentInfo (ExpXML,EEVersion,Name,Comment) VALUES (?,'0.4',?,?)",[xml, exp.experiment_name,""])
conn.commit()
print "Creating indexes"
c.execute('''CREATE INDEX "events_index" on events (RunID ASC, NodeID ASC)''')
c.execute('''CREATE INDEX "packet_index" on packets (RunID ASC, NodeID ASC, SrcNodeID ASC)''')
c.execute('''CREATE INDEX "runinfo_index" on runinfos (RunID ASC, NodeID ASC)''')
conn.commit()
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cleanupRawFiles.py | porterad/photo-utils | 0 | 1606053 | #!/usr/bin/python3
import re
import os
import argparse
TRASH_DIR = 'Trash'
parser = argparse.ArgumentParser()
parser.add_argument("dir", help="directory to cleanup")
parser.add_argument("-e", "--extension", default="NEF", help="File extension to clean up. Files of this type without"
" a match of reference type are moved.")
parser.add_argument("-r", "--reference", default="JPG", help="reference file extension")
args = parser.parse_args()
# Using the case insensitive flag to match, for example, both .NEF and .nef
regex = re.compile(r"(.*)\." + args.extension + "$", re.I)
files = os.listdir(args.dir)
files_to_move = []
for filename in files:
match = regex.fullmatch(filename)
if match:
if str(match.groups()[0]) + '.' + args.reference not in files:
files_to_move.append(match.string)
files_to_move.sort()
if len(files_to_move) > 0:
trash_path = os.path.join(args.dir, TRASH_DIR)
if not os.path.exists(trash_path):
try:
os.mkdir(trash_path)
except FileExistsError as e:
print(e)
exit()
if os.path.isdir(trash_path):
for file_to_move in files_to_move:
print("moving " + file_to_move)
os.rename(os.path.join(args.dir, file_to_move), os.path.join(trash_path, file_to_move))
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create_dataset.py | phihes/dnp-create-paneldata | 0 | 168579 | import pandas as pd
import numpy as np
import time
import sys
import json
from jsmin import jsmin
from collections import Counter
import os.path
from xlrd.biffh import XLRDError
from aenum import IntEnum
import time
# set up logging (to console)
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('[%(levelname)s] %(message)s')
handler = logging.StreamHandler(stream=sys.stdout)
handler.setFormatter(formatter)
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
PATH_TO_SETTINGS = 'settings/settings.json'
DEFAULT_SETTINGS = dict(
# path to panel structure excel file
path_panel='settings/panel.xlsx',
# name of sheets in panel structure excel file
sheet_persons='persons',
sheet_panel_entities='panel_entities',
sheet_companies='companies',
sheet_groups='groups',
sheet_questions='data dictionary',
sheet_scales='scales',
# path to survey data - {} will be replaced with year (YYYY)
path_results='survey-results/results_{}.xlsx',
# path to resulting output file
path_output='output/dnp_panel-data_{}-{}',
# years to include
years=[2013, 2014, 2015, 2016, 2017, 2018, 2019],
# exclude deprecated variables
exclude_deprecated=True,
# exclude variables with personal information
exclude_personal=True,
# exclude variables with auxiliary information
exclude_auxiliary=True,
# exclude variables from special sections
exclude_special_sections=True,
# exclude din meta data
exclude_din_meta_data=True,
# exclude variables with less x years in which they were part of the questionnaire
exclude_years_min=0,
# allow missing observations: include panel entities which did not complete all specified years
allow_missing=True,
# save the results to an excel file
save_output=True
)
def load_excel_sheet(file, sheet=None, exit_on_error=True):
"""
Load an Excel sheet and return as Pandas dataframe with user friendly error-handling.
:param file: Path to excel file
:param sheet: Name of sheet to load. Default: None -> Loads first sheet.
:param exit_on_error: Whether to call exit(0) on exception.
:returns: Pandas DataFrame, or None in case of error.
"""
df = None
try:
if sheet is not None:
df = pd.read_excel(file, sheet_name=sheet)
else:
df = pd.read_excel(file)
except FileNotFoundError:
logger.error("Could not find file '{}'.".format(file))
if exit_on_error:
exit(0)
except pd.errors.EmptyDataError:
logger.error("File '{}' is empty.".format(file))
if exit_on_error:
exit(0)
except pd.errors.ParserError:
logger.error("File '{}' is broken.".format(file))
if exit_on_error:
exit(0)
except XLRDError:
logger.error("Sheet '{}' is missing in {}.".format(sheet, file))
if exit_on_error:
exit(0)
except Exception:
logger.error("There was an error while loading '{}'".format(file))
if exit_on_error:
exit(0)
return df
class DataSetType:
SINGLE_YEAR = 1
PANEL = 2
class SelectionReason(IntEnum):
"""
Reasons why a participant was / was not selected for sample;
Reason > 0 : selected, Reason <= 0 : not selected
"""
NOT_ENOUGH_QUESTIONS_ANSWERED = -1
NOT_SELECTED_OTHER_REPRESENTATIVE_WAS_SELECTED = 0
SELECTED_AS_COMPANY_REPRESENTATIVE = 1
SELECTED_AS_COMPANY_REPRESENTATIVE_NO_OTHERS_FOUND = 2
SELECTED_AS_INDUSTRY_REPRESENTATIVE = 3
class DataSetCreator(object):
dataset_extra_cols = {
'selected', 'algorithmic_selection',
'algorithmic_selection_comment', 'historic_selection',
'panel_entity_id', 'person_id', 'year', 'cat_position', 'email'
}
required_settings = ['path_panel', 'sheet_persons', 'sheet_panel_entities',
'sheet_companies', 'sheet_groups', 'sheet_questions',
'sheet_scales', 'sheet_selection', 'path_results',
'path_output', 'years', 'exclude_deprecated',
'exclude_personal', 'exclude_auxiliary',
'exclude_din_meta_data', 'exclude_special_sections',
'exclude_years_min', 'allow_missing', 'save_output',
'use_selection_history']
def __init__(self, settings=DEFAULT_SETTINGS):
# settings: dataframe
self.settings = settings
self.years = self.settings['years']
self._validate_settings()
# list people (per year) that can't be found when creating dataset
self.missing_persons = {y: set() for y in self.years}
# load data according to settings...
# data: dictionary year:dataframe per self.years
# questions, q_dict, scales: data structure dataframes
# participant database: dataframes
# selection: historic (who was in the samples of the past?), dataframes
logger.info("Loading data...")
self.data, self.questions, self.q_dict, self.scales, self.persons, self.companies,\
self.panel_entities, self.groups,\
self.selection = DataSetCreator._load_data(
settings=self.settings
)
# create lookup for persons
self.persons_lookup = (self.persons
.drop_duplicates(
subset=["email", "wave_added"],
keep="last"
)
.set_index(
["email", "wave_added"]
)
[["id", "panel_entity_id"]]
.rename(
columns={
"id": "person_id"
})
.to_dict(
orient='index'
)
)
logger.info("Mapping scales...")
self.data = self._map_scales(
data=self.data,
scales=self.scales,
q_dict=self.q_dict
)
logger.info("Generating panel data...")
self.panel_df, self.available_questions_per_year = self._make_panel_df()
# warn if persons couldn't be identified
for y, missing in self.missing_persons.items():
if len(missing) > 0:
logger.warning("Missing in {}: {} persons".format(y, len(missing))) # ", ".join(missing)))
def _validate_settings(self):
for s in self.required_settings:
if s not in self.settings.keys():
logger.error("Setting '{}' is missing.".format(s))
exit(0)
if not os.path.isfile(self.settings["path_panel"]):
logger.error("Panel file '{}' could not be found.".format(self.settings["path_panel"]))
exit(0)
outdir = "/".join(self.settings["path_output"].split("/")[:-1])
if not os.path.isdir(outdir):
logger.error("Can't find output directory '{}'.".format(outdir))
exit(0)
def _set_sheet_settings_attribute(self, attr_name):
"""
Sets an object attribute (=attr_name) to a df generate from the sheet
of the same name in the panel file ("panel.xlsx").
:param attr_name: Object attribute = sheet name to set
:return: None
"""
setattr(self, attr_name, load_excel_sheet(
self.settings['path_panel'],
self.settings['sheet_{}'.format(attr_name)]
))
def _get_relevant_questions(self, dataset_type=DataSetType.SINGLE_YEAR):
"""
Get a set of question names that should be included in dataset
according to settings: in/exclude deprecated, personal, auxiliary questions,
meta data, questions that appear in a minimum number of years, special sections, etc.
:param dataset_type: default: DataSetType.SINGLE_YEAR. If DataSetType.PANEL, exclude
special section questions
:return: Set of question names
"""
query = 'name != "NaN"'
query += ' and not deprecated' if self.settings['exclude_deprecated'] else ""
query += ' and not personal_data' if self.settings['exclude_personal'] else ""
query += ' and not auxiliary' if self.settings['exclude_auxiliary'] else ""
query += ' and not name.str.startswith("din_")' if self.settings['exclude_din_meta_data'] else ""
query += ' and num_years_observed >= {}'.format(self.settings['exclude_years_min'])
if dataset_type==DataSetType.PANEL:
query += ' and special_section_year == "NaN"' if self.settings[
'exclude_special_sections'] else ""
return set(self.questions.query(query, engine='python')['name'].tolist())
@staticmethod
def _load_data(settings):
"""
Load survey data files and additional settings from panel.xlsx.
:param settings:
:return: DataFrames (data, questions, q_dict, scales, persons,
companies, groups, panel_entities, selection)
"""
# load survey data
data = {y: load_excel_sheet(
settings['path_results'].format(y))
for y in settings['years']
}
# load sheets from panel.xlsx
questions = load_excel_sheet(settings['path_panel'], settings['sheet_questions'])
scales = load_excel_sheet(settings['path_panel'], settings['sheet_scales'])
persons = load_excel_sheet(settings['path_panel'], settings['sheet_persons'])
companies = load_excel_sheet(settings['path_panel'], settings['sheet_companies'])
groups = load_excel_sheet(settings['path_panel'], settings['sheet_groups'])
panel_entities = load_excel_sheet(settings['path_panel'], settings['sheet_panel_entities'])
selection = load_excel_sheet(settings['path_panel'], settings['sheet_selection'])
# group scale entries
scales = scales.groupby('scale')
# make sure deprecated works...
questions['deprecated'] = questions['deprecated'].astype('bool')
# save dict representation of questions
try:
q_dict = questions.set_index('name').to_dict(orient='index')
except ValueError:
logger.error("questions are not unique")
counter = Counter(questions['name'].tolist())
logger.error(["{}({})".format(i, counter[i]) for i in counter if counter[i] > 1])
exit(0)
except Exception as ex:
logger.error("Can not generate question dictionary, questions not properly loaded or defined")
raise ex
return data, questions, q_dict, scales, persons,\
companies, groups, panel_entities, selection
def _was_selected(self, email, year):
selected = np.nan
selection_col = "selection_{}".format(year)
# try all indices
for y in self.years:
try:
sel = self.selection.set_index("email_{}".format(y))
selected = True if sel.loc[email][selection_col] == 1 else False
break
except KeyError:
pass
# logger.warning("could not find {} in {}".format(email, y))
return selected
def _select_by_history(self, df):
return df.apply(lambda x: self._was_selected(x['email'], x['year']), axis=1)
@staticmethod
def _get_q_map(q, q_dict, scales):
"""
Map observations to desired values, using the corresponding
question's scale definition.
:param q: question name
:param q_dict: question dictionary (q->{...,scale: scalename})
:param scales: Pandas DataFrame
:return: dict {original_data_value: mapped_data_value}
"""
q_scale = q_dict[q]['scale']
q_map = {}
# if question is not associated with any scale, no mapping is necessary
if q_scale in scales.groups.keys():
for r in scales.get_group(q_scale).iterrows():
r = r[1]
for n in range(1, 50):
key_name = 'alternative_{}'.format(n)
if key_name in r.keys():
original = r['alternative_{}'.format(n)]
if not pd.isnull(original):
# cast all values to str to avoid type mismatches
q_map[str(original)] = str(r['value'])
return q_map
def identify(self, email, year):
"""
Get id of associated panel entity and person based on email and year of observation.
:param email: person's e-mail address
:param year: year of entry in participant DB
:return: {panel_entity_id:int value, person_id:int value}
"""
res = None
for i in range(year, min(self.persons.wave_added)-1, -1):
try:
res = self.persons_lookup[(email, i)]
break
except KeyError:
pass
if res is None:
self.missing_persons[year].add(email)
return {
'panel_entity_id': None,
'person_id': None
}
else:
return res
@staticmethod
def _select_company_representative(group):
"""
Algorithm for selecting DNP participants:
When there are multiple participants who answer for the same company in
one wave, a set of rules has to determine the one participant whose
answers to take into account.
Prerequisites: All participants need to be matched to a company.
Company names have to be cleaned / coded, considering a
threshold up to which organization subdivisions are
regarded as separate units.
:param group: A Pandas group of participants, where each participant represents
the same company.
:return: Pandas Series with bool selected
"""
# selected = []
# order by fraction of answered questions (most first)
# ranked = group.sort_values(['fill'], ascending=False)
fill = group["total_fill"]
max_fill = max(fill)
# de-select all participants with too much missing data
# group.loc[fill < min_fill, "selected"] = SelectionReason.NOT_ENOUGH_QUESTIONS_ANSWERED
# CASE A: only one participant in group
if len(group) == 1:
group.loc[
:, "selected"
] = SelectionReason.SELECTED_AS_COMPANY_REPRESENTATIVE_NO_OTHERS_FOUND
# CASE B: multiple potential representatives
else:
# find best candidate = first cand. with max fill and standardization position
candidates = (
group[(fill == max_fill) & group.std_position].index
if group[fill == max_fill].std_position.any()
else group[fill == max_fill].index
)
# the representative
group.loc[
group.index == candidates[0],
"selected"
] = SelectionReason.SELECTED_AS_COMPANY_REPRESENTATIVE
# the rest is not selected
group.loc[
pd.isnull(group.selected),
"selected"
] = SelectionReason.NOT_SELECTED_OTHER_REPRESENTATIVE_WAS_SELECTED
return group.selected
def _select(self, df):
"""
Select one participant from all participant groups in the data.
Calls DataSetCreator._select on each group. Appends selection reasons.
:param df: Pandas DataFrame
:return:
"""
participants = None
try:
participants = df[["panel_entity_id", "person_id", "year", "total_fill", "cat_position", "view"]].copy()
except Exception as ex:
logger.error("Data is missing for {}:".format(df['year'].unique()[0]))
logger.error(ex)
logger.info("Make sure that the following variables exist and have completely mapped scales:")
logger.info("email, cat_position, num_empl, num_turnover, view")
exit(0)
participants["selected"] = np.nan
participants.loc[
participants.total_fill < self.settings["min_fill"],
"selected"
] = SelectionReason.NOT_ENOUGH_QUESTIONS_ANSWERED
participants["std_position"] = participants['cat_position'].str.contains('standard', case=False)
selection = []
if self.settings["include_privates_in_representative_selection"]:
# group all participants that answered enough questions
# by their panel_entity_id and the year of participantion
for name, group in participants.groupby(['panel_entity_id', 'year']):
group_selection = self._select_company_representative(group.copy())
selection.append(group_selection)
else:
# select all participants with view=private & enough answered questions
participants.loc[
(participants.view == "private") &
(participants.total_fill >= self.settings["min_fill"]),
"selected"
] = SelectionReason.SELECTED_AS_INDUSTRY_REPRESENTATIVE
selection.append(participants[participants.view == "private"].selected)
# group all view=company participants that answered enough questions
# by their panel_entity_id and the year of participantion
remaining = participants[
(participants.view != "private") &
(pd.isnull(participants.selected))
].copy()
for name, group in remaining.groupby(['panel_entity_id', 'year']):
group_selection = DataSetCreator._select_company_representative(group)
selection.append(group_selection)
selected = pd.concat(selection)
return (
selected > 0,
selected.replace({r.value: r.name.lower() for r in SelectionReason})
)
@staticmethod
def get_fill(df):
"""
Get percentage of questions filled in by participants as Pandas Series.
:param df: Pandas DataFrame
:return: Pandas Series
"""
col_delta = set(df.columns).intersection(DataSetCreator.dataset_extra_cols)
return df.count(axis=1).divide(len(df.columns) - len(col_delta))
def make_dataset(self, data, selected_years):
"""
Prepare a dataset from given data for the specified years:
- select a sample of participants based on DataSetCreator.select()
- include historical selection if available
- drop variables that are not available in time-span
:param data: Pandas DataFrame (stacked panel data in long format with var 'year')
:param selected_years: list<int> of years
:return: dataset (Pandas DataFrame)
"""
# keep only observations for selected years
df = data[data['year'].isin(selected_years)].dropna(axis=1, how='all').copy()
# drop questions that are not required
try:
relevant_questions = self._get_relevant_questions(
DataSetType.SINGLE_YEAR if len(selected_years) == 1
else DataSetType.PANEL
)
drop_cols = set(df.columns) - relevant_questions - DataSetCreator.dataset_extra_cols
df = df.drop(drop_cols, axis=1)
except Exception as ex:
logger.error("An error occured when trying to drop irrelevant questions:")
raise ex
exit(0)
# calculate % of questions filled in
df['total_fill'] = DataSetCreator.get_fill(df)
logger.info("{}: {} participants dropped due to too many missings".format(
", ".join([str(y) for y in selected_years]),
len(df.total_fill[df.total_fill < self.settings["min_fill"]].index)
))
# create "selection" column
selection, selection_reason = self._select(df)
df['algorithmic_selection'] = selection
df['algorithmic_selection_comment'] = selection_reason
# create "historic selection" column
df['historic_selection'] = self._select_by_history(df)
# create merged selection column (history if available, otherwise alg. selection)
# TODO: review
# df['selected'] = df['historic_selection'].fillna(df['algorithmic_selection'])
df['selected'] = df['algorithmic_selection']
logger.info("{}: selected {}/{} participants".format(
", ".join([str(y) for y in selected_years]),
len(df.selected[df.selected > 0].index),
len(df.selected)
))
# drop email address
if self.settings['exclude_personal']:
df = df.drop(['email'], axis=1)
return df
@staticmethod
def _map_scales(data, scales, q_dict):
"""
Replaces all data values for questions that have defined scales.
A default mapped value can be set by setting the $default$ flag,
all original data values that can't be mapped will be mapped
to the default value.
:param data: dict{year:DataFrame} of survey data
:param scales:
:param q_dict:
:return:
"""
obs_map = {}
for y in data.keys():
for q in q_dict.keys():
obs_map[q_dict[q]['name_{}'.format(y)]] = DataSetCreator._get_q_map(q, q_dict, scales)
for y in data.keys():
for c in data[y].columns:
if c in obs_map.keys():
observations = data[y][c]
scale = q_dict[c] if c in q_dict.keys() else None
scale_map = obs_map[c]
default_map = {}
# if a default value is set,
# replace all original values for which no mapping exists
# with that default mapping value
if "$default$" in scale_map.keys():
# all values in the data that do not appear in map:
unmapped = set(observations.unique()) - set(scale_map.keys()) - {'', np.nan, 'nan'}
default_map = {str(k): scale_map["$default$"] for k in unmapped}
# replace values with mapped + default values
data[y][c] = observations.astype(str).replace({
**scale_map,
**default_map,
**{
'nan': np.nan,
'': np.nan
}
})
# if scale is in defined as numeric, cast column to numeric
# if c in q_dict.keys() and q_dict[c]['format'] == 'numeric':
if scale is not None and scale['format'] == 'numeric':
data[y][c] = pd.to_numeric(observations, errors='coerce')
return data
def _make_panel_df(self):
"""
Create panel DataFrame by stacking yearly data, adding year variable, and identifying
persons and panel entities by <year, email> information.
:return: panel (Pandas DataFrame), number of available questions per year (dict{year:num})
"""
panel_data = []
available_questions_per_year = {}
# iterate over columns (questions)
for y, d in self.data.items():
qs = {q_info['name_{}'.format(y)]: q_panel for q_panel, q_info in self.q_dict.items()}
found_qs = [k for k in qs.keys() if k in d.columns]
not_found_qs = list(set(qs.keys()) - set(found_qs))
available_questions_per_year[y] = [qs[f] for f in found_qs]
for _ in d.iterrows():
row = _[1]
panel_data.append({
**{'year': y},
**{qs[q]: row[q] for q in found_qs},
**{qs[nf_q]: np.nan for nf_q in not_found_qs}
})
# identify panel entities
panel_df = pd.DataFrame(panel_data).replace({'nan': np.nan})
# t = time.process_time()
ident = panel_df.apply(
lambda x: self.identify(x['email'], x['year']), axis=1).apply(pd.Series)
# logger.info("identifying took {} s".format(time.process_time() - t))
panel_df['panel_entity_id'] = ident['panel_entity_id']
panel_df['person_id'] = ident['person_id']
return panel_df, available_questions_per_year
def get_datasets(self, years=None):
if years is None:
years = self.years
datasets = {}
if len(years) == 1:
datasets = {
'{}'.format(years[0]): self.make_dataset(self.panel_df, years)
}
elif len(years) > 1:
datasets = {
'panel {}-{}'.format(years[0], years[-1]): self.make_dataset(self.panel_df, years),
**{'{}'.format(y): self.make_dataset(self.panel_df, [y]) for y in years}
}
return datasets
def to_excel(self):
# prepare datasets
logger.info("Preparing all datasets...")
datasets = self.get_datasets()
fn = self.settings['path_output'].format(min(self.years), max(self.years))
filename = '{}_{}.xlsx'.format(fn, time.strftime("%Y%m%d-%H%M%S"))
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
info_years = "the years {} to {}".format(
min(self.years), max(self.years)) if len(self.years) > 1 else "{}".format(self.years[0])
info = [
"German Standardization Panel",
"Deutsches Normungspanel (DNP)",
"",
"This data set contains pseudonymized survey data from " + info_years + ".",
"It was created automatically on " + time.strftime("%Y-%m-%d") + ".",
"The data structure is described in the sheets 'variables' and 'scales'.",
"Observations:"
]
for n, dat in datasets.items():
c_text = "Sheet {}: Selected observations: {}. Total: {}.".format(
n,
len(dat[dat['selected'] == True].index),
len(dat.index)
)
if "-" in n:
c_text += " Panel observations: "
selected = dat[dat['selected'] == True]
try:
selected_grouped = selected.groupby('panel_entity_id').agg({'year': pd.Series.nunique})
yearly = selected_grouped['year'].value_counts().to_dict()
c_text += ". ".join(["{} year(s): {}".format(y, c) for y, c in yearly.items()])
except KeyError:
logger.error(selected.groupby('panel_entity_id'))
info.append(c_text)
# write questions and scales
questions_drop_cols = set(self.questions.columns) - {
'name', 'question', 'label', 'scale', 'format'
}
scales_drop_cols = set(self.scales.obj.columns) - {'scale', 'value'}
# sheet 'info'
pd.DataFrame({'info': info}).to_excel(writer, sheet_name='info')
# sheet 'variables'
self.questions.drop(questions_drop_cols, axis=1).to_excel(writer, sheet_name='variables')
# sheet 'scales'
self.scales.obj.drop(scales_drop_cols, axis=1).to_excel(writer, sheet_name='scales')
# sheets yyyy
for n, dat in datasets.items():
dat.to_excel(writer, sheet_name=n)
# stack all years and put them in one sheet
years_dat = [d for y, d in datasets.items() if y.isdigit()]
# sheet 'data'
pd.concat(years_dat, sort=False).to_excel(writer, sheet_name="data")
logger.info("Saving to excel...")
try:
writer.save()
except FileNotFoundError:
logger.error("Could not save to {}".format(filename))
exit(0)
return filename
if __name__ == "__main__":
settings = DEFAULT_SETTINGS
try:
with open(PATH_TO_SETTINGS) as handle:
minified = jsmin(handle.read())
settings = json.loads(minified)
logger.info("Loaded custom settings from {}".format(PATH_TO_SETTINGS))
except Exception as e:
logger.error(e)
logger.info("Could not load custom settings, using default settings")
creator = DataSetCreator(settings=settings)
filename = creator.to_excel()
logger.info("Dataset saved to {}".format(filename)) | [
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] |
utils/phase0/minimal_ssz.py | CarlBeek/eth2.0-specs | 3 | 146044 | <reponame>CarlBeek/eth2.0-specs
from .hash_function import hash
BYTES_PER_CHUNK = 32
BYTES_PER_LENGTH_PREFIX = 4
ZERO_CHUNK = b'\x00' * BYTES_PER_CHUNK
def SSZType(fields):
class SSZObject():
def __init__(self, **kwargs):
for f in fields:
if f not in kwargs:
raise Exception("Missing constructor argument: %s" % f)
setattr(self, f, kwargs[f])
def __eq__(self, other):
return (
self.fields == other.fields and
self.serialize() == other.serialize()
)
def __hash__(self):
return int.from_bytes(self.hash_tree_root(), byteorder="little")
def __str__(self):
output = []
for field in self.fields:
output.append(f'{field}: {getattr(self, field)}')
return "\n".join(output)
def serialize(self):
return serialize_value(self, self.__class__)
def hash_tree_root(self):
return hash_tree_root(self, self.__class__)
SSZObject.fields = fields
return SSZObject
class Vector():
def __init__(self, items):
self.items = items
self.length = len(items)
def __getitem__(self, key):
return self.items[key]
def __setitem__(self, key, value):
self.items[key] = value
def __iter__(self):
return iter(self.items)
def __len__(self):
return self.length
def is_basic(typ):
return isinstance(typ, str) and (typ[:4] in ('uint', 'bool') or typ == 'byte')
def is_constant_sized(typ):
if is_basic(typ):
return True
elif isinstance(typ, list) and len(typ) == 1:
return is_constant_sized(typ[0])
elif isinstance(typ, list) and len(typ) == 2:
return False
elif isinstance(typ, str) and typ[:5] == 'bytes':
return len(typ) > 5
elif hasattr(typ, 'fields'):
for subtype in typ.fields.values():
if not is_constant_sized(subtype):
return False
return True
else:
raise Exception("Type not recognized")
def coerce_to_bytes(x):
if isinstance(x, str):
o = x.encode('utf-8')
assert len(o) == len(x)
return o
elif isinstance(x, bytes):
return x
else:
raise Exception("Expecting bytes")
def serialize_value(value, typ=None):
if typ is None:
typ = infer_type(value)
if isinstance(typ, str) and typ[:4] == 'uint':
length = int(typ[4:])
assert length in (8, 16, 32, 64, 128, 256)
return value.to_bytes(length // 8, 'little')
elif typ == 'bool':
assert value in (True, False)
return b'\x01' if value is True else b'\x00'
elif (isinstance(typ, list) and len(typ) == 1) or typ == 'bytes':
serialized_bytes = coerce_to_bytes(value) if typ == 'bytes' else b''.join([serialize_value(element, typ[0]) for element in value])
assert len(serialized_bytes) < 2**(8 * BYTES_PER_LENGTH_PREFIX)
serialized_length = len(serialized_bytes).to_bytes(BYTES_PER_LENGTH_PREFIX, 'little')
return serialized_length + serialized_bytes
elif isinstance(typ, list) and len(typ) == 2:
assert len(value) == typ[1]
return b''.join([serialize_value(element, typ[0]) for element in value])
elif isinstance(typ, str) and len(typ) > 5 and typ[:5] == 'bytes':
assert len(value) == int(typ[5:]), (value, int(typ[5:]))
return coerce_to_bytes(value)
elif hasattr(typ, 'fields'):
serialized_bytes = b''.join([serialize_value(getattr(value, field), subtype) for field, subtype in typ.fields.items()])
if is_constant_sized(typ):
return serialized_bytes
else:
assert len(serialized_bytes) < 2**(8 * BYTES_PER_LENGTH_PREFIX)
serialized_length = len(serialized_bytes).to_bytes(BYTES_PER_LENGTH_PREFIX, 'little')
return serialized_length + serialized_bytes
else:
print(value, typ)
raise Exception("Type not recognized")
def chunkify(bytez):
bytez += b'\x00' * (-len(bytez) % BYTES_PER_CHUNK)
return [bytez[i:i + 32] for i in range(0, len(bytez), 32)]
def pack(values, subtype):
return chunkify(b''.join([serialize_value(value, subtype) for value in values]))
def is_power_of_two(x):
return x > 0 and x & (x - 1) == 0
def merkleize(chunks):
tree = chunks[::]
while not is_power_of_two(len(tree)):
tree.append(ZERO_CHUNK)
tree = [ZERO_CHUNK] * len(tree) + tree
for i in range(len(tree) // 2 - 1, 0, -1):
tree[i] = hash(tree[i * 2] + tree[i * 2 + 1])
return tree[1]
def mix_in_length(root, length):
return hash(root + length.to_bytes(32, 'little'))
def infer_type(value):
if hasattr(value.__class__, 'fields'):
return value.__class__
elif isinstance(value, Vector):
return [infer_type(value[0]) if len(value) > 0 else 'uint64', len(value)]
elif isinstance(value, list):
return [infer_type(value[0])] if len(value) > 0 else ['uint64']
elif isinstance(value, (bytes, str)):
return 'bytes'
elif isinstance(value, int):
return 'uint64'
else:
raise Exception("Failed to infer type")
def hash_tree_root(value, typ=None):
if typ is None:
typ = infer_type(value)
if is_basic(typ):
return merkleize(pack([value], typ))
elif isinstance(typ, list) and len(typ) == 1 and is_basic(typ[0]):
return mix_in_length(merkleize(pack(value, typ[0])), len(value))
elif isinstance(typ, list) and len(typ) == 1 and not is_basic(typ[0]):
return mix_in_length(merkleize([hash_tree_root(element, typ[0]) for element in value]), len(value))
elif isinstance(typ, list) and len(typ) == 2 and is_basic(typ[0]):
assert len(value) == typ[1]
return merkleize(pack(value, typ[0]))
elif typ == 'bytes':
return mix_in_length(merkleize(chunkify(coerce_to_bytes(value))), len(value))
elif isinstance(typ, str) and typ[:5] == 'bytes' and len(typ) > 5:
assert len(value) == int(typ[5:])
return merkleize(chunkify(coerce_to_bytes(value)))
elif isinstance(typ, list) and len(typ) == 2 and not is_basic(typ[0]):
return merkleize([hash_tree_root(element, typ[0]) for element in value])
elif hasattr(typ, 'fields'):
return merkleize([hash_tree_root(getattr(value, field), subtype) for field, subtype in typ.fields.items()])
else:
raise Exception("Type not recognized")
def truncate(container):
field_keys = list(container.fields.keys())
truncated_fields = {
key: container.fields[key]
for key in field_keys[:-1]
}
truncated_class = SSZType(truncated_fields)
kwargs = {
field: getattr(container, field)
for field in field_keys[:-1]
}
return truncated_class(**kwargs)
def signed_root(container):
return hash_tree_root(truncate(container))
def serialize(ssz_object):
return getattr(ssz_object, 'serialize')()
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] |
data/app.d/crypto.py | hythloda/easy-crypto | 0 | 1616235 | from pycoingecko import CoinGeckoAPI
from deephaven.DBTimeUtils import secondsToTime, millisToTime
from deephaven.TableTools import merge
from deephaven import DynamicTableWriter
import deephaven.Types as dht
import jpy
from time import sleep, time
import pandas as pd
import threading
# secondsToSleep should be 10 or higher. If too fast, will hit request limit.
secondsToSleep = 60
# if getHistory = true, the days to pull
daysHistory = 90
# coins to get data
ids = ['bitcoin', 'ethereum', 'litecoin', 'dogecoin', 'tether', 'binancecoin', 'cardano', 'ripple', 'polkadot']
getLive = True
getHistory = True
# below this line there are no variables that need changed
########################################################################
def get_coingecko_table_historical(daysHistory = 90):
tableArray = []
cg = CoinGeckoAPI()
# get historical data
for id in ids:
coin_data_hist = cg.get_coin_market_chart_by_id(id, vs_currency = "usd", days = daysHistory)
sub = pd.DataFrame(coin_data_hist)
coin_query = "Coin = `{id}`".format(id=id)
tableArray.append(dataFrameToTable(sub).view("DateTime = millisToTime((long)prices_[i][0])", coin_query, "Price = prices_[i][1]", "MarketCap = market_caps_[i][1]", "TotalVolume = total_volumes_[i][1]").moveUpColumns("DateTime", "Coin"))
return merge(tableArray).selectDistinct("DateTime", "Coin", "Price", "MarketCap", "TotalVolume").sortDescending("DateTime", "Coin")
def get_coingecko_table_live(secondsToSleep = 60):
cg = CoinGeckoAPI()
# array to store tables for current and previous data
tableArray=[]
tableWriter = DynamicTableWriter(['Coin', 'DateTime', 'Price', 'MarketCap', 'TotalVolume'], [dht.string, dht.datetime, dht.double, dht.double, dht.double])
tableArray.append(tableWriter.getTable())
result = merge(tableArray).selectDistinct('DateTime', 'Coin', 'Price', 'MarketCap', 'TotalVolume').sortDescending('DateTime', 'Coin')
def thread_func():
cg.get_coins_markets(vs_currency = 'usd')
while True:
coin_data = cg.get_price(ids, vs_currencies = 'usd', include_market_cap = True, include_24hr_vol = True, include_24hr_change = False, include_last_updated_at = True)
for id in ids:
#Add new data to the dynamic table
tableWriter.logRow( id, secondsToTime(int(coin_data[id]['last_updated_at'])), float(coin_data[id]['usd']), coin_data[id]['usd_market_cap'], coin_data[id]['usd_24h_vol'])
sleep(secondsToSleep)
thread = threading.Thread(target = thread_func)
thread.start()
return result
if getHistory:
HistoricalCryptoTable = get_coingecko_table_historical(daysHistory)
if getLive:
LiveCryptoTable = get_coingecko_table_live(secondsToSleep)
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sparkdh/dataframe_column_functions.py | mmutiso/dea | 0 | 114924 | from pyspark.sql import functions
def clone_column(df, existing_column_name, new_column_name):
"""
Adds a column to the dataframe with a new name copying
all the values from the existing column
"""
df = df.withColumn(new_column_name, df[existing_column_name])
return df | [
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app.py | TimothyWillard/dash-multi-app-server | 0 | 185324 | <gh_stars>0
import dash
import dash_auth
from config import config as cfg
app = None
auth = None
if cfg["auth"]["required"]:
app = dash.Dash("auth")
auth = dash_auth.BasicAuth(app, cfg["auth"]["validPairs"])
else:
app = dash.Dash()
server = app.server
app.config.suppress_callback_exceptions = True
app.css.append_css({"external_url": "https://codepen.io/chriddyp/pen/bWLwgP.css"}) | [
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setup.py | FoxNerdSaysMoo/HomeAssistantAPI | 0 | 32436 | from setuptools import setup
from homeassistant_api import __version__
with open("README.md", "r") as f:
read = f.read()
setup(
name="HomeAssistant API",
url="https://github.com/GrandMoff100/HomeassistantAPI",
description="Python Wrapper for Homeassistant's REST API",
version=__version__,
keywords=['homeassistant', 'api', 'wrapper', 'client'],
author="GrandMoff100",
author_email="<EMAIL>",
packages=[
"homeassistant_api",
"homeassistant_api.models",
"homeassistant_api._async",
"homeassistant_api._async.models"
],
long_description=read,
long_description_content_type="text/markdown",
install_requires=["requests", "simplejson"],
extras_require={
"async": ["aiohttp"]
},
python_requires=">=3.6",
provides=["homeassistant_api"],
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Natural Language :: English",
"Operating System :: OS Independent",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Software Development :: Version Control :: Git"
]
)
| [
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lambda/apiSimpleAuth.py | mjuliana/staticformupload | 0 | 1616694 | <filename>lambda/apiSimpleAuth.py
import json
def lambda_handler(event, context):
if event["headers"]["authorization"] == "superSecret" :
response = {
"isAuthorized": True
}
else :
response = {
"isAuthorized": False
}
return response
| [
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posts/tests.py | Kolokol2002/Yatube | 0 | 104865 | <reponame>Kolokol2002/Yatube
import tempfile
from django.core import mail
from django.core.cache import cache
from django.test import Client, TestCase, override_settings
from posts.models import Follow, Group, Post, User
# User = get_user_model()
class ProfileTest(TestCase):
def setUp(self):
# создаём пользователя
self.client = Client()
self.user = User.objects.create_user(
username="sarah", email="<EMAIL>", password="<PASSWORD>"
)
# создаём пост от имени пользователя
self.post = Post.objects.create(
text="You're talking about things I haven't done yet in the past tense. It's driving me crazy!",
author=self.user)
def test_profile(self):
#После регистрации пользователя создается его персональная страница (profile)
response = self.client.get("/sarah/")
self.assertEqual(response.status_code, 200)
#После публикации поста новая запись появляется на главной странице сайта (index), на персональной странице
#пользователя (profile), и на отдельной странице поста (post)
# print(response.context)
self.assertEqual(response.context["post"], self.post)
response = self.client.get("")
self.assertEqual(response.context["post"], self.post)
def test_new_post(self):
#Авторизованный пользователь может опубликовать пост (new)
#Неавторизованный посетитель не может опубликовать пост (его редиректит на страницу входа)
self.client.login(username=self.user, password="<PASSWORD>")
response = self.client.get("/new/")
self.assertEqual(response.status_code, 200)
self.client.post('/new/',
data={
"text": "Тест для створення нового поста"
}
)
self.assertTrue(Post.objects.filter(text='Тест для створення нового поста',
author=self.user).exists()
)
self.client.logout()
response = self.client.get("/new/")
self.assertEqual(response.status_code, 302)
def test_edit_post(self):
#Авторизованный пользователь может отредактировать свой пост
# и его содержимое изменится на всех связанных страницах
self.client.login(username=self.user, password="<PASSWORD>")
response = self.client.get(f'/{self.user}/{self.post.id}/edit/')
self.assertEqual(response.status_code, 200)
self.client.post(f'/{self.user}/{self.post.id}/edit/',
data={
'text': 'Змінено для тесту',
}
)
self.assertTrue(Post.objects.filter(text='Змінено для тесту').exists())
class ErrorsTest(TestCase):
def test_404(self):
response = self.client.get("/test/test/test/test/")
self.assertEqual(response.status_code, 404)
class PostsImgTest(TestCase):
'''Тестирование возможности добавления изображений к публикациям'''
def setUp(self):
self.client = Client()
self.group = Group.objects.create(title='Test Group',
slug='testgroup',
description='A test group')
def test_img_upload(self):
user = User.objects.create_user(username="testUserImg",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test",
last_name="User")
self.client.login(username="testUserImg", password="*<PASSWORD>")
post = Post.objects.create(author=user,
text='Post with img',
group=self.group,
image='test-img.jpg')
urls = [f'/{user}/',
f'/{user.username}/{post.id}/',
f'/group/{self.group.slug}/',
"",]
for urls in urls:
response = self.client.get(urls)
self.assertContains(response, 'img_posts"', status_code=200)
def test_file_upload(self):
User.objects.create_user(username="testUserFile",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test",
last_name="User")
self.client.login(username="testUserFile", password="*<PASSWORD>")
with open('test-file.txt', 'rb') as fp:
response = self.client.post(
'/new/', {'group': 1, 'text': 'Test post', 'image': fp, })
self.assertFormError(response, 'form', 'image',
"Загрузите правильное изображение. "
"Файл, который вы загрузили, поврежден "
"или не является изображением.")
class CacheTest(TestCase):
''''Тестирование работы кеширования главной страницы'''
def setUp(self):
self.client = Client()
self.user = User.objects.create_user(username="testUser",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test",
last_name="User")
self.client.login(username="testUser", password="*<PASSWORD>")
Post.objects.create(text='A test post', author=self.user)
def test_index_cache(self):
response = self.client.get('/')
self.assertContains(response, 'A test post', status_code=200)
self.client.post('/new/', {'text': 'A test post 2'})
response = self.client.get('/')
self.assertNotContains(response, 'A test post 2', status_code=200)
cache.clear()
response = self.client.get('/')
self.assertContains(response, 'A test post 2', status_code=200)
class FollowTest(TestCase):
'''Тестирование подписок'''
def setUp(self):
self.client = Client()
self.user1 = User.objects.create_user(username="testUser1",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test1",
last_name="User1")
self.user2 = User.objects.create_user(username="testUser2",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test2",
last_name="User2")
self.user3 = User.objects.create_user(username="testUser3",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test3",
last_name="User3")
def test_follow(self):
self.client.login(username="testUser1", password="*<PASSWORD>")
response = self.client.get('/testUser2/follow')
self.assertRedirects(response, '/testUser2/')
follower = Follow.objects.get(user=self.user1, author=self.user2)
self.assertIsNotNone(follower)
response = self.client.get('/testUser2/unfollow')
self.assertRedirects(response, '/testUser2/')
follower = Follow.objects.filter(
user=self.user1, author=self.user2).first()
self.assertIsNone(follower)
def test_follow_posts(self):
self.client.login(username="testUser1", password="*<PASSWORD>")
self.client.get('/testUser2/follow')
Post.objects.create(author=self.user2, text='A test post')
response = self.client.get('/follow/')
self.assertContains(response, 'A test post', status_code=200)
self.client.login(username="testUser3", password="*<PASSWORD>")
response = self.client.get('/follow/')
self.assertNotContains(response, 'A test post', status_code=200)
class CommentsTest(TestCase):
'''Тестирование комментариев'''
def setUp(self):
self.client = Client()
self.user1 = User.objects.create_user(username="testUser1",
email="<EMAIL>",
password="*<PASSWORD>",
first_name="Test1",
last_name="User1")
Post.objects.create(author=self.user1, text='A test post')
def test_comments(self):
response = self.client.get('/testUser1/1/comment/')
self.assertRedirects(
response, '/auth/login/?next=/testUser1/1/comment/')
self.client.login(username="testUser1", password="*<PASSWORD>")
response = self.client.post(
'/testUser1/1/comment/', {'text': 'A test comment'})
self.assertRedirects(response, '/testUser1/1/')
response = self.client.get('/testUser1/1/')
self.assertContains(response, 'A test comment', status_code=200)
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] |
projectFile.py | waelbenamara/predef | 0 | 141524 | <filename>projectFile.py
from flask_website import app
app.run(debug=True)
#!/usr/bin/env python
from flask_website import app
from flask_website.search import update_documentation_index
with app.test_request_context():
update_documentation_index()
import os
_basedir = os.path.abspath(os.path.dirname(__file__))
DEBUG = False
SECRET_KEY = 'testkey'
DATABASE_URI = 'sqlite:///' + os.path.join(_basedir, 'flask-website.db')
DATABASE_CONNECT_OPTIONS = {}
ADMINS = frozenset(['http://lucumr.pocoo.org/'])
WHOOSH_INDEX = os.path.join(_basedir, 'flask-website.whoosh')
DOCUMENTATION_PATH = os.path.join(_basedir, '../flask/docs/_build/dirhtml')
del os
# flasky extensions. flasky pygments style based on tango style
from pygments.style import Style
from pygments.token import Keyword, Name, Comment, String, Error, \
Number, Operator, Generic, Whitespace, Punctuation, Other, Literal
class FlaskyStyle(Style):
background_color = "#f8f8f8"
default_style = ""
styles = {
# No corresponding class for the following:
#Text: "", # class: ''
Whitespace: "underline #f8f8f8", # class: 'w'
Error: "#a40000 border:#ef2929", # class: 'err'
Other: "#000000", # class 'x'
Comment: "italic #8f5902", # class: 'c'
Comment.Preproc: "noitalic", # class: 'cp'
Keyword: "bold #004461", # class: 'k'
Keyword.Constant: "bold #004461", # class: 'kc'
Keyword.Declaration: "bold #004461", # class: 'kd'
Keyword.Namespace: "bold #004461", # class: 'kn'
Keyword.Pseudo: "bold #004461", # class: 'kp'
Keyword.Reserved: "bold #004461", # class: 'kr'
Keyword.Type: "bold #004461", # class: 'kt'
Operator: "#582800", # class: 'o'
Operator.Word: "bold #004461", # class: 'ow' - like keywords
Punctuation: "bold #000000", # class: 'p'
# because special names such as Name.Class, Name.Function, etc.
# are not recognized as such later in the parsing, we choose them
# to look the same as ordinary variables.
Name: "#000000", # class: 'n'
Name.Attribute: "#c4a000", # class: 'na' - to be revised
Name.Builtin: "#004461", # class: 'nb'
Name.Builtin.Pseudo: "#3465a4", # class: 'bp'
Name.Class: "#000000", # class: 'nc' - to be revised
Name.Constant: "#000000", # class: 'no' - to be revised
Name.Decorator: "#888", # class: 'nd' - to be revised
Name.Entity: "#ce5c00", # class: 'ni'
Name.Exception: "bold #cc0000", # class: 'ne'
Name.Function: "#000000", # class: 'nf'
Name.Property: "#000000", # class: 'py'
Name.Label: "#f57900", # class: 'nl'
Name.Namespace: "#000000", # class: 'nn' - to be revised
Name.Other: "#000000", # class: 'nx'
Name.Tag: "bold #004461", # class: 'nt' - like a keyword
Name.Variable: "#000000", # class: 'nv' - to be revised
Name.Variable.Class: "#000000", # class: 'vc' - to be revised
Name.Variable.Global: "#000000", # class: 'vg' - to be revised
Name.Variable.Instance: "#000000", # class: 'vi' - to be revised
Number: "#990000", # class: 'm'
Literal: "#000000", # class: 'l'
Literal.Date: "#000000", # class: 'ld'
String: "#4e9a06", # class: 's'
String.Backtick: "#4e9a06", # class: 'sb'
String.Char: "#4e9a06", # class: 'sc'
String.Doc: "italic #8f5902", # class: 'sd' - like a comment
String.Double: "#4e9a06", # class: 's2'
String.Escape: "#4e9a06", # class: 'se'
String.Heredoc: "#4e9a06", # class: 'sh'
String.Interpol: "#4e9a06", # class: 'si'
String.Other: "#4e9a06", # class: 'sx'
String.Regex: "#4e9a06", # class: 'sr'
String.Single: "#4e9a06", # class: 's1'
String.Symbol: "#4e9a06", # class: 'ss'
Generic: "#000000", # class: 'g'
Generic.Deleted: "#a40000", # class: 'gd'
Generic.Emph: "italic #000000", # class: 'ge'
Generic.Error: "#ef2929", # class: 'gr'
Generic.Heading: "bold #000080", # class: 'gh'
Generic.Inserted: "#00A000", # class: 'gi'
Generic.Output: "#888", # class: 'go'
Generic.Prompt: "#745334", # class: 'gp'
Generic.Strong: "bold #000000", # class: 'gs'
Generic.Subheading: "bold #800080", # class: 'gu'
Generic.Traceback: "bold #a40000", # class: 'gt'
}
# -*- coding: utf-8 -*-
from urlparse import urlparse
from werkzeug import url_quote
from flask import Markup
class Extension(object):
def __init__(self, name, author, description,
github=None, gitlab=None, bitbucket=None, docs=None, website=None,
approved=False, notes=None):
self.name = name
self.author = author
self.description = Markup(description)
self.github = github
self.gitlab = gitlab
self.bitbucket = bitbucket
self.docs = docs
self.website = website
self.approved = approved
self.notes = notes
def to_json(self):
rv = vars(self).copy()
rv['description'] = unicode(rv['description'])
return rv
@property
def pypi(self):
return 'http://pypi.python.org/pypi/%s' % url_quote(self.name)
@property
def docserver(self):
if self.docs:
return urlparse(self.docs)[1]
# This list contains all extensions that were approved as well as those which
# passed listing.
extensions = [
Extension('Flask-OpenID', '<NAME>',
description='''
<p>Adds <a href="http://openid.net/">OpenID</a> support to Flask.
''',
github='mitsuhiko/flask-openid',
docs='http://pythonhosted.org/Flask-OpenID/',
notes='''
Short long description, missing tests.
'''
),
Extension('Flask-Babel', '<NAME>',
description='''
<p>Adds i18n/l10n support to Flask, based on
<a href=http://babel.edgewall.org/>babel</a> and
<a href=http://pytz.sourceforge.net/>pytz</a>.
''',
github='mitsuhiko/flask-babel',
docs='http://pythonhosted.org/Flask-Babel/',
approved=True,
notes='''
How to improve: add a better long description to the next release.
'''
),
Extension('Flask-SQLAlchemy', '<NAME>',
description='''
<p>Add <a href="http://www.sqlalchemy.org/">SQLAlchemy</a> support to Flask
with automatic configuration and helpers to simplify common web use cases.
Major features include:</p>
<ul>
<li>Handle configuring one or more database connections.</li>
<li>Set up sessions scoped to the request/response cycle.</li>
<li>Time queries and track model changes for debugging.</li>
</ul>
''',
github='mitsuhiko/flask-sqlalchemy',
docs='http://flask-sqlalchemy.pocoo.org/',
approved=True
),
Extension('Flask-Migrate', '<NAME>',
description='''
<p><a href="http://www.sqlalchemy.org/">SQLAlchemy</a> database
migrations for Flask applications using
<a href="https://alembic.readthedocs.org/">Alembic</a>. The
database operations are provided as command line arguments for
<a href="https://flask-script.readthedocs.org/">Flask-Script</a>.
''',
github='miguelgrinberg/flask-migrate',
docs='http://pythonhosted.org/Flask-Migrate/',
),
Extension('Flask-XML-RPC', '<NAME>',
description='''
<p>Adds <a href="http://www.xmlrpc.com/">XML-RPC</a> support to Flask.
''',
bitbucket='leafstorm/flask-xml-rpc',
docs='http://pythonhosted.org/Flask-XML-RPC/',
approved=True
),
Extension('Flask-CouchDB', '<NAME>',
description='''
<p>Adds <a href="http://couchdb.apache.org/">CouchDB</a> support to Flask.
''',
bitbucket='leafstorm/flask-couchdb',
docs='http://pythonhosted.org/Flask-CouchDB/',
approved=True,
notes='''
There is also Flask-CouchDBKit. Both are fine because they are
doing different things, but the latter is not yet approved.
'''
),
Extension('Flask-Uploads', '<NAME>',
description='''
<p>Flask-Uploads allows your application to flexibly and
efficiently handle file uploading and serving the uploaded files.
You can create different sets of uploads - one for document
attachments, one for photos, etc.
''',
github='maxcountryman/flask-uploads',
docs='https://flask-uploads.readthedocs.org/en/latest/',
approved=True
),
Extension('Flask-Themes', '<NAME>',
description='''
<p>Flask-Themes makes it easy for your application to support
a wide range of appearances.
''',
bitbucket='leafstorm/flask-themes',
docs='http://pythonhosted.org/Flask-Themes/',
approved=True
),
Extension('Flask-CouchDBKit', '<NAME>',
description='''
<p>Adds <a href="http://www.couchdbkit.org/">CouchDBKit</a> support to Flask.
''',
github='sirn/flask-couchdbkit',
docs='http://pythonhosted.org/Flask-CouchDBKit/'
),
Extension('Flask-Genshi', '<NAME>',
description='''
<p>Adds support for the <a href="http://genshi.edgewall.org/">Genshi</a>
templating language to Flask applications.
''',
github='dag/flask-genshi',
docs='http://pythonhosted.org/Flask-Genshi/',
approved=True,
notes='''
This is the first template engine extension. When others come
around it would be a good idea to decide on a common interface.
'''
),
Extension('Flask-Mail', '<NAME> (created by <NAME>)',
description='''
<p>Makes sending mails from Flask applications very easy and
has also support for unittesting.
''',
github='mattupstate/flask-mail',
docs='http://pythonhosted.org/Flask-Mail/',
approved=True
),
Extension('Flask-WTF', '<NAME> (created by <NAME>)',
description='''
<p>Flask-WTF offers simple integration with WTForms. This
integration includes optional CSRF handling for greater security.
''',
github='ajford/flask-wtf',
docs='http://pythonhosted.org/Flask-WTF/',
approved=True
),
Extension('Flask-Testing', u'<NAME> (created by <NAME>)',
description='''
<p>The Flask-Testing extension provides unit testing utilities for Flask.
''',
github='jarus/flask-testing',
docs='http://pythonhosted.org/Flask-Testing/',
approved=True
),
Extension('Flask-Script', '<NAME> (created by <NAME>)',
description='''
<p>The Flask-Script extension provides support for writing external
scripts in Flask. It uses argparse to parse command line arguments.
''',
github='techniq/flask-script',
docs='http://pythonhosted.org/Flask-Script/',
approved=True,
notes='''
Flask-Actions has some overlap. Consider that when approving
Flask-Actions or similar packages.
'''
),
Extension('flask-lesscss', '<NAME>',
description='''
<p>
A small Flask extension that makes it easy to use
<a href=http://lesscss.org/>LessCSS</a> with your
Flask application.
''',
docs='http://sjl.bitbucket.org/flask-lesscss/',
bitbucket='sjl/flask-lesscss',
notes='''
Broken package description, nonconforming package name, does not
follow standard API rules (init_lesscss instead of lesscss).
Considered for unlisting, improved version should release as
"Flask-LessCSS" with a conforming API and fixed packages indices,
as well as a testsuite.
'''
),
Extension('Flask-Creole', '<NAME>',
description='''
<p>Creole parser filters for Flask.
''',
docs='http://pythonhosted.org/Flask-Creole',
bitbucket='aafshar/flask-creole-main',
approved=True,
notes='''
Flask-Markdown and this should share API, consider that when
approving Flask-Markdown
'''
),
Extension('Flask-Cache', '<NAME>',
description='''
<p>Adds cache support to your Flask application.
''',
docs='http://pythonhosted.org/Flask-Cache',
github='thadeusb/flask-cache',
),
Extension('Flask-Principal', '<NAME>',
description='''
<p>Identity management for Flask.
''',
docs='http://pythonhosted.org/Flask-Principal',
github='mattupstate/flask-principal',
approved=False
),
Extension('Flask-Zen', '<NAME>',
description='''
<p>Flask-Zen allows you to use PyZen via Flask-Script commands.
''',
docs='http://pythonhosted.org/Flask-Zen/',
github='coderanger/flask-zen',
approved=False
),
Extension('Flask-Static-Compress', '<NAME>',
description='''
<p>Automatically minifies, combines, and versions your static CSS
and JavaScript assets. Like Django-Compressor for Flask.
''',
github='alanhamlett/flask-static-compress',
docs='https://github.com/alanhamlett/flask-static-compress',
approved=False
),
Extension('Flask-Assets', u'<NAME>',
description='''
<p>
Integrates the webassets library with Flask, adding support for
merging, minifying and compiling CSS and Javascript files.
''',
docs='http://elsdoerfer.name/docs/flask-assets/',
github='miracle2k/flask-assets',
approved=False
),
Extension('Flask-AutoIndex', '<NAME>',
description='''
<p>
An extension that generates an index page for your Flask
application automatically
''',
docs='http://pythonhosted.org/Flask-AutoIndex/',
github='sublee/flask-autoindex',
approved=False
),
Extension('Flask-Celery', '<NAME>',
description='''
<p>
Celery integration for Flask
''',
docs='http://ask.github.com/celery/',
github='ask/flask-celery',
approved=False
),
Extension('Flask-Cors', '<NAME>',
description='''
<p>
Cross Origin Resource Sharing (CORS) for flask
''',
docs='http://flask-cors.readthedocs.org/en/latest/',
github='wcdolphin/flask-cors',
approved=False
),
Extension('Frozen-Flask', '<NAME>',
description='''
<p>
Freezes a Flask application into a set of static files.
The result can be hosted without any server-side software
other than a traditional web server.
''',
docs='http://pythonhosted.org/Frozen-Flask/',
github='SimonSapin/Frozen-Flask',
approved=True
),
Extension('Flask-FlatPages', '<NAME>',
description='''
<p>
Provides flat static pages to a Flask application, based on text
files as opposed to a relational database.
''',
docs='http://pythonhosted.org/Flask-FlatPages/',
github='SimonSapin/Flask-FlatPages',
approved=True
),
Extension('Flask-FluidDB', '<NAME>',
description='''
<p>
FluidDB access for Flask.
''',
docs='http://pythonhosted.org/Flask-FluidDB/',
bitbucket='aafshar/flask-fluiddb-main',
approved=False
),
Extension('Flask-fillin', '<NAME>',
description='''
<p>The Flask-fillin extension provides simple utilities for testing your forms in Flask application..
''',
github='jarus/flask-fillin',
docs='http://pythonhosted.org/Flask-fillin/',
),
Extension('Flask-Gravatar', '<NAME>',
description='''
<p>
Small extension for Flask to make using Gravatar easy.
''',
docs='http://pythonhosted.org/Flask-Gravatar/',
github='zzzsochi/Flask-Gravatar',
approved=False
),
Extension('Flask-HTMLBuilder', '<NAME>',
description='''
<p>
Flask-HTMLBuilder is an extension that allows flexible and easy
Python-only generation of HTML snippets and full HTML documents
using a robust syntax.
''',
docs='http://majorz.github.com/flask-htmlbuilder/',
github='majorz/flask-htmlbuilder',
approved=False
),
Extension('Flask-MongoAlchemy', '<NAME>',
description='''
<p>
Add Flask support for MongoDB using MongoAlchemy.
''',
docs='http://pythonhosted.org/Flask-MongoAlchemy/',
github='cobrateam/flask-mongoalchemy',
approved=False
),
Extension('Flask-DebugToolbar', '<NAME>',
description='''
<p>
A port of the Django debug toolbar to Flask
''',
docs='https://github.com/mgood/flask-debugtoolbar',
github='mgood/flask-debugtoolbar',
approved=False
),
Extension('Flask-Login', '<NAME>',
description='''
<p>
Flask-Login provides user session management for Flask. It
handles the common tasks of logging in, logging out, and
remembering your users' sessions over extended periods of time.
''',
github='maxcountryman/flask-login',
docs='http://pythonhosted.org/Flask-Login/',
approved=True
),
Extension('Flask-Security', '<NAME>',
description='''
<p>
Flask-Security is an opinionated Flask extension which adds
basic security and authentication features to your Flask apps
quickly and easily.
''',
docs='https://pythonhosted.org/Flask-Security/',
github='mattupstate/flask-security'
),
Extension('Flask-Exceptional', '<NAME>',
description='''
<p>
Adds Exceptional support to Flask applications
''',
docs='http://pythonhosted.org/Flask-Exceptional/',
github='jzempel/flask-exceptional',
approved=True,
),
Extension('Flask-Bcrypt', '<NAME>',
description='''
<p>
Bcrypt support for hashing passwords
''',
docs='http://pythonhosted.org/Flask-Bcrypt/',
github='maxcountryman/flask-bcrypt',
approved=True,
),
Extension('Flask-MongoKit', '<NAME>',
description='''
<p>
Flask extension to better integrate MongoKit into Flask
''',
docs='http://pythonhosted.org/Flask-MongoKit/',
github='jarus/flask-mongokit'
),
Extension('Flask-GAE-Mini-Profiler', '<NAME>',
description='''
<p>
Flask integration of gae_mini_profiler for Google App Engine.
''',
docs='http://pythonhosted.org/Flask-GAE-Mini-Profiler',
github='passy/flask-gae-mini-profiler'
),
Extension('Flask-Admin', 'Flask-Admin team',
description='''
<p>Simple and extensible administrative interface framework for Flask
''',
docs='http://flask-admin.readthedocs.org/en/latest/index.html',
github='flask-admin/flask-admin'
),
Extension('Flask-ZODB', '<NAME>',
description='''
<p>
Use the ZODB with Flask
''',
docs='http://pythonhosted.org/Flask-ZODB/',
github='dag/flask-zodb',
approved=True
),
Extension('Flask-Peewee', '<NAME>',
description='''
<p>
Integrates Flask and the peewee orm
''',
docs='http://charlesleifer.com/docs/flask-peewee/index.html',
github='coleifer/flask-peewee',
approved=False
),
Extension('Flask-Lettuce', 'Daniel, <NAME>',
description='''
<p>
Add Lettuce support for Flask
''',
# docs='http://pythonhosted.org/Flask-Lettuce/',
github='dqminh/flask-lettuce',
approved=False
),
Extension('Flask-Sijax', '<NAME>',
description='''
<p>
Flask integration for Sijax,
a Python/jQuery library that makes AJAX easy to use
''',
docs='http://pythonhosted.org/Flask-Sijax/',
github='spantaleev/flask-sijax',
approved=False
),
Extension('Flask-Dashed', '<NAME>',
description='''
<p>
Flask-Dashed provides tools for building
simple and extensible admin interfaces.
''',
docs='http://jeanphix.github.com/Flask-Dashed/',
github='jeanphix/Flask-Dashed',
approved=False
),
Extension('Flask-SeaSurf', '<NAME>',
description='''
<p>
SeaSurf is a Flask extension for preventing
cross-site request forgery (CSRF).
''',
docs='http://pythonhosted.org/Flask-SeaSurf/',
github='maxcountryman/flask-seasurf',
approved=True,
),
Extension('Flask-PonyWhoosh', '<NAME> & <NAME>',
description='''
<p>
A full-text search engine using Pony ORM and Whoosh.
''',
docs='https://pythonhosted.org/Flask-PonyWhoosh/',
github='compiteing/flask-ponywhoosh',
),
Extension('Flask-PyMongo', '<NAME>',
description='''
<p>
Flask-PyMongo bridges Flask and PyMongo.
''',
docs='http://readthedocs.org/docs/flask-pymongo/',
github='dcrosta/flask-pymongo',
),
Extension('Flask-Raptor', '<NAME>',
description='''
<p>
Flask-Raptor provides support for adding raptors
to Flask instances.
''',
docs='http://pythonhosted.org/Flask-Raptor/',
github='dplepage/flask-raptor',
),
Extension('Flask-Shelve', '<NAME>',
description='''
<p>
Flask-Shelve bridges Flask and the Python standard library
`shelve` module, for very simple (slow) no-dependency key-value
storage.
''',
docs='http://pythonhosted.org/Flask-Shelve/',
github='jamesls/flask-shelve',
),
Extension('Flask-RESTful', 'Twilio API Team',
description='''
<p>Flask-RESTful provides the building blocks for creating a great REST API.
''',
docs='https://flask-restful.readthedocs.org/',
github='flask-restful/flask-restful',
approved=True
),
Extension('Flask-Restless', '<NAME>',
description='''
<p>Flask-Restless provides simple generation of ReSTful APIs for
database models defined using Flask-SQLAlchemy. The generated
APIs send and receive messages in JSON format.
''',
docs='http://readthedocs.org/docs/flask-restless/en/latest/',
github='jfinkels/flask-restless',
approved=True
),
Extension('Flask-Heroku', '<NAME>',
description='''
<p>Sets Flask configuration defaults for Heroku-esque environment variables
''',
github='kennethreitz/flask-heroku',
approved=False
),
Extension('Flask-Mako', '<NAME>, <NAME>',
description='''
<p>Allows for <a href="http://www.makotemplates.org/">Mako templates</a>
to be used instead of Jinja2
''',
github='benselme/flask-mako',
docs='http://pythonhosted.org/Flask-Mako/',
approved=False
),
Extension('Flask-WeasyPrint', '<NAME>',
description='''
<p>Make PDF with <a href="http://weasyprint.org/">WeasyPrint</a>
in your Flask app.
''',
docs='http://pythonhosted.org/Flask-WeasyPrint/',
github='SimonSapin/Flask-WeasyPrint',
),
Extension('Flask-Classy', 'Freedom Dumlao',
description='''
<p>Class based views for Flask.
''',
github='apiguy/flask-classy',
docs='http://pythonhosted.org/Flask-Classy/',
approved=False
),
Extension('Flask-WebTest', '<NAME>',
description='''
<p>Utilities for testing Flask applications with
<a href="http://webtest.readthedocs.org/en/latest/">WebTest</a>.
''',
github='aromanovich/flask-webtest',
docs='http://flask-webtest.readthedocs.org/',
approved=False
),
Extension('Flask-Misaka', '<NAME>',
description='''
A simple extension to integrate the
<a href="http://misaka.61924.nl/">Misaka</a> module for efficiently
parsing Markdown.
''',
docs='https://flask-misaka.readthedocs.org/en/latest/',
github='singingwolfboy/flask-misaka',
approved=True,
),
Extension('Flask-Dance', '<NAME>',
description='''
Doing the OAuth dance with style using Flask, requests, and oauthlib.
''',
docs='https://flask-dance.readthedocs.org/en/latest/',
github='singingwolfboy/flask-dance',
approved=True,
),
Extension('Flask-SSE', '<NAME>',
description='''
<a href="http://www.html5rocks.com/en/tutorials/eventsource/basics/">
Server Sent Events</a> for Flask.
''',
docs='https://flask-sse.readthedocs.org/en/latest/',
github='singingwolfboy/flask-sse',
approved=True,
),
Extension('Flask-Limiter', '<NAME>',
description='''
<p>Adds Ratelimiting support to Flask.
Supports a configurable storage backend with implementations for
in-memory, redis and memcache.
''',
github='alisaifee/flask-limiter',
docs='http://flask-limiter.readthedocs.org/en/latest/',
approved=False,
),
Extension('Flask-User', '<NAME>',
description='''
Customizable User Account Management for Flask:
Register, Confirm email, Login, Change username, Change password, Forgot password,
Role-based Authorization and Internationalization.
''',
github='lingthio/flask-user',
docs='http://flask-user.readthedocs.io/',
approved=True,
),
Extension('Flask-Via', 'SOON_, <NAME>',
description='''
<p>
Provides a clean, simple URL routing framework for growing Flask
Applications.
''',
docs='http://flask-via.soon.build',
github='thisissoon/Flask-Via',
),
Extension('Flask-QueryInspect', '<NAME>',
description='''
<p>Provides metrics on SQL queries (using SQLAlchemy) executed
for each request.</p>
''',
docs='https://github.com/noise/flask-queryinspect',
github='noise/flask-queryinspect',
),
Extension('Flask-Stormpath', '<NAME>',
description='''
<p>Add Stormpath user management, authentication,
and authorization to Flask.
''',
docs='http://flask-stormpath.readthedocs.org/en/latest/',
github='stormpath/stormpath-flask'
),
Extension('Flask-Ask', '<NAME>',
description='''
<p>
Flask-Ask makes it easy to write Amazon Echo apps with Flask and
the Alexa Skills Kit.
''',
docs='http://flask-ask.readthedocs.io/en/latest/',
github='johnwheeler/flask-ask'
),
Extension('Flask-Rest-JSONAPI', '<NAME>',
description='''
<p>
Build REST APIs following the
<a href="http://jsonapi.org/format/">JSONAPI</a>
specification with a powerful data layer system.
''',
docs='http://flask-rest-jsonapi.readthedocs.io/en/latest/',
github='miLibris/flask-rest-jsonapi'
),
Extension('Flask-SAML', '<NAME>',
description='''
<p>
Enable SAML authentication for your webapp.
''',
docs='https://flask-saml.readthedocs.io/en/latest/',
bitbucket='asecurityteam/flask_saml'
),
Extension('Flask-Snow', '<NAME>',
description='''
<p>
Flask-snow enables easy access to the REST API in the ServiceNow ITSM platform
with the help of the <a href="http://github.com/rbw0/pysnow">pysnow library</a>.
Additionally, it supports OAuth for a seamless authentication / authorization experience.
''',
docs='http://flask-snow.readthedocs.io/en/latest/',
github='rbw0/flask-snow'
),
Extension('flask-keycloak', '<NAME>',
description='''
<p>Flask extension for
<a href=https://www.keycloak.org>Keycloak</a> integration.
''',
docs='https://github.com/akhilputhiry/flask-keycloak/blob/master/README.md',
github='akhilputhiry/flask-keycloak',
),
Extension('Flask-MailboxValidator', 'MailboxValidator,
description='''
<p>
Flask-MailboxValidator provides an easy way to call the MailboxValidator API which validates if an email address is a valid one.
''',
docs='https://flask-mailboxvalidator.readthedocs.io/en/latest/',
github='MailboxValidator/Flask_MailboxValidator'
),
Extension('Flask-Dropzone', 'Grey Li',
description='''
<p>
Upload file in Flask application with <a href="http://dropzonejs.com">Dropzone.js</a>.
''',
docs='https://flask-dropzone.readthedocs.io/en/latest/',
github='greyli/flask-dropzone',
),
Extension('Flask-CKEditor', 'Grey Li',
description='''
<p>
<a href="https://ckeditor.com">CKEditor</a> integration for Flask, including image
upload, code syntax highlight and more.
''',
docs='https://flask-ckeditor.readthedocs.io/en/latest/',
github='greyli/flask-ckeditor',
),
Extension('Bootstrap-Flask', 'Grey Li',
description='''
<p>
<a href="https://getbootstrap.com/">Bootstrap 4</a> helper for Flask/Jinja2.
Based on Flask-Bootstrap, but lighter and better.
''',
docs='https://bootstrap-flask.readthedocs.io/en/latest/',
github='greyli/bootstrap-flask',
),
Extension('Flask-Avatars', 'Grey Li',
description='''
<p>
All avatar generators in one place. Including:
<ul>
<li>Default avatar</li>
<li>Gravatar</li>
<li>Robohash</li>
<li>Social media avatar</li>
<li>Identicon generation</li>
<li>Avatar crop</li>
</ul>
''',
docs='https://flask-avatars.readthedocs.io/en/latest/',
github='greyli/flask-avatars',
),
Extension('Flask-Share', 'Grey Li',
description='''
<p>
Create social share component in Jinja2 template based on
<a href="https://github.com/overtrue/share.js/">share.js</a>.
''',
docs='https://flask-share.readthedocs.io/en/latest/',
github='greyli/flask-share',
),
Extension('Flask-Praetorian', '<NAME>',
description='''
<p>
API security should be strong, simple, and precise like a Roman
Legionary. This package aims to provide that. Using JWT tokens as
implemented by PyJWT, flask-praetorian uses a very simple
interface to make sure that the users accessing your API’s
endpoints are provisioned with the correct roles for access.
''',
docs='http://flask-praetorian.readthedocs.io/en/latest/',
github='dusktreader/flask-praetorian',
),
Extension('Flask-MonitoringDashboard', '<NAME>',
description='''
<p>
Flask-MonitoringDashboard is an extension that offers 3 main functionalities with little effort from the
Flask developer:
<ol>
<li>Monitor and visualize the performance of a Flask application.</li>
<li>Monitor and visualize the test coverage.</li>
<li>Collect extra information about request outliers (requests which are slower than normal).</li>
</ol>
</p>
''',
docs='http://flask-monitoringdashboard.readthedocs.io/',
github='flask-dashboard/Flask-MonitoringDashboard'
),
Extension('Flask-Dramatiq', '<NAME>',
description='''
<p>Plugs <a href="https://dramatiq.io/">Dramatiq</a> task queue in your
Flask web app.
''',
docs='https://flask-dramatiq.readthedocs.io/',
gitlab='https://gitlab.com/bersace/flask-dramatiq',
),
Extension('Flask-Assistant', '<NAME>',
description='''
<p>
Framework for building conversational apps using
Flask and API.AI with integration for Google Assistant.
''',
docs='http://flask-assistant.readthedocs.io/en/latest/',
github='treethought/flask-assistant'
),
]
# This is a list of extensions that is currently rejected from listing and with
# that also not approved. If an extension ends up here it should improved to
# be listed.
unlisted = [
Extension('Flask-Actions', 'Young King',
description='''
<p>
Flask-actions provide some management comands for flask based
project.
''',
docs='http://pythonhosted.org/Flask-Actions/',
bitbucket='youngking/flask-actions',
approved=False,
notes='''
Rejected because of missing description in PyPI, formatting issues
with the documentation (missing headlines, scrollbars etc.) and a
general clash of functionality with the Flask-Script package.
Latter should not be a problem, but the documentation should
improve. For listing, the extension developer should probably
discuss the extension on the mailinglist with others.
Futhermore it also has an egg registered with an invalid filename.
'''
),
Extension('Flask-Jinja2Extender', '<NAME>',
description='''
<p>
''',
docs=None,
github='dcolish/flask-jinja2extender',
approved=False,
notes='''
Appears to be discontinued.
Usecase not obvious, hacky implementation, does not solve a problem
that could not be solved with Flask itself. I suppose it is to aid
other extensions, but that should be discussed on the mailinglist.
'''
),
Extension('Flask-Markdown', '<NAME>',
description='''
<p>
This is a small module to a markdown processing filter into your
flask.
''',
docs='http://pythonhosted.org/Flask-Markdown/',
github='dcolish/flask-markdown',
approved=False,
notes='''
Would be great for enlisting but it should follow the API of
Flask-Creole. Besides that, the docstrings are not valid rst (run
through rst2html to see the issue) and it is missing tests.
Otherwise fine :)
'''
),
Extension('flask-urls', '<NAME>',
description='''
<p>
A collection of URL-related functions for Flask applications.
''',
docs='http://sjl.bitbucket.org/flask-urls/',
bitbucket='sjl/flask-urls',
approved=False,
notes='''
Broken PyPI index and non-conforming extension name. Due to the
small featureset this was also delisted from the list. It was
there previously before the approval process was introduced.
'''
),
Extension('Flask-Coffee', '<NAME>',
description='''
<p>
Automatically compile CoffeeScript files while developing with
the Flask framework.
''',
docs=None,
approved=False,
notes='''
On the mailing list, author claims it's flask-lesscss with a
different label. No sphinx-based docs, just a blog post. No
publicly accessible repository -- requires login on
bettercodes.org.
'''
),
Extension('Flask-Solr', '<NAME>',
description='''
<p>
Add Solr support to Flask using pysolr.
''',
docs=None,
github='willowtreeapps/flask-solr',
notes='''
Fully exposes pysolr API in Flask extension pattern, and code is
production-ready. It lacks documentation and tests.
'''
),
Extension('flask-csrf', '<NAME>',
description='''
<p>A small Flask extension for adding
<a href=http://en.wikipedia.org/wiki/CSRF>CSRF</a> protection.
''',
docs='http://sjl.bitbucket.org/flask-csrf/',
bitbucket='sjl/flask-csrf',
notes='''
Unlisted because duplicates the Flask-SeaSurf extension.
'''
),
]
extensions.sort(key=lambda x: x.name.lower())
unlisted.sort(key=lambda x: x.name.lower())
from urlparse import urljoin
server = 'https://pypi.org/'
detail_path = '/project/Flask/%s/'
class Release(object):
def __init__(self, version):
self.version = version
def to_json(self):
return dict(version=self.version,
detail_url=self.detail_url)
@property
def detail_url(self):
return urljoin(server, detail_path % self.version)
releases = map(Release, [
'0.1',
'0.2',
'0.3',
'0.3.1',
'0.4',
'0.5',
'0.5.1',
'0.5.2',
'0.6',
'0.6.1',
'0.7',
'0.7.1',
'0.7.2',
'0.8',
'0.8.1',
'0.9',
'0.10',
'0.10.1',
'0.11',
'0.11.1',
'0.12',
'0.12.1',
'0.12.2',
'0.12.3',
'0.12.4',
'1.0',
'1.0.1',
'1.0.2',
])
# -*- coding: utf-8 -*-
from urlparse import urlparse
from flask import Markup
class Project(object):
def __init__(self, name, url, description, source=None):
self.name = name
self.url = url
self.description = Markup(description)
self.source = source
@property
def host(self):
if self.url is not None:
return urlparse(self.url)[1]
@property
def sourcehost(self):
if self.source is not None:
return urlparse(self.source)[1]
def to_json(self):
rv = vars(self).copy()
rv['description'] = unicode(rv['description'])
return rv
projects = {
'websites': [
Project('Flask Website', 'http://flask.pocoo.org/', '''
<p>
The website of the Flask microframework itself including the
mailinglist interface, snippet archive and extension registry.
'''),
Project('Brightonpy', 'http://brightonpy.org/', '''
<p>
The website of the Brighton Python User Group
''', source='http://github.com/j4mie/brightonpy.org/'),
Project(u's h o r e … software development', 'http://shore.be/', '''
<p>Corporate website of Shore Software Development.
'''),
Project(u'ROCKYOU.fm', 'https://www.rockyou.fm/', '''
<p>
ROCKYOU.fm is a german internet radio station and webzine
featuring mostly metal and hard rock. Since 2012 the DJs and
reporters provide their listeners with news, reviews, feature
shows and interviews.
'''),
Project(u'MetalSpy', 'https://www.metalspy.de/', '''
<p>
MetalSpy.de is the portfolio website of a german hobby
photographer featuring mainly photos of metal bands,
festivals, fantasy conventions and cosplay.
'''),
Project('ThadeusB\'s Website', 'http://thadeusb.com/', u'''
<p>
Personal website of ThadeusB.
'''),
Project('Blueslug', 'http://blueslug.com/', u'''
<p>
A flask-powered anti-social delicious clone
'''),
Project('DotShare', 'http://dotshare.it/', u'''
<p>
Socially driven website for sharing Linux/Unix dot files.
'''),
Project(
'sopython', 'http://sopython.com/',
'<p>Site of the Python chat room on Stack Overflow. '
'Includes OAuth authentication, a wiki, and a growing, '
'searchable list of "canonical" answers to Python '
'questions on SO.</p>',
source='https://github.com/sopython/sopython-site'
)
],
'apps': [
Project('hg-review', None, '''
<p>
hg-review is a code review system for Mercurial. It is available
GPL2 license.
''', source='http://bitbucket.org/sjl/hg-review/'),
Project('Ryshcate', None, '''
<p>
Ryshcate is a Flask powered pastebin with sourcecode
available.
''', source='http://bitbucket.org/leafstorm/ryshcate/'),
Project(u'Übersuggest Keyword Suggestion Tool', None, u'''
<p>
Übersuggest is a free tool that exploit the Google
suggest JSON API to get keyword ideas for your search marketing
campaign (PPC or SEO).
''', source='http://bitbucket.org/esaurito/ubersuggest'),
Project('Have they emailed me?', None, '''
<p>
A mini-site for checking Google's GMail feed with Oauth.
''', source='http://github.com/lincolnloop/emailed-me'),
Project('Remar.kZ', None, '''
<p>
Sometimes you use someone else's computer and find something
neat and interesting. Store it on Remar.kZ without having
to enter your credentials.
''', source='http://bitbucket.org/little_arhat/remarkz'),
Project('Domination', None, u'''
<p>
Domination is a clone of a well-known card game.
''', source='https://bitbucket.org/xoraxax/domination/'),
Project('jitviewer', None, '''
<p>
web-based tool to inspect the output of PyPy JIT log
''', source='https://bitbucket.org/pypy/jitviewer'),
Project('blohg', 'http://blohg.org/', '''
<p>
A mercurial based blog engine.
''', source='https://github.com/rafaelmartins/blohg'),
Project('pidsim-web', None, '''
<p>
PID Controller simulator.
''', source='https://github.com/rafaelmartins/pidsim-web'),
Project('HTTPBin', 'http://httpbin.org/', u'''
<p>
An HTTP request & response service.
''', source='https://github.com/kennethreitz/httpbin'),
Project('Flask-Pastebin', None, u'''
<p>
Pastebin app with Flask and a few extensions that does Facebook
connect as well as realtime push notifications with socket.io
and juggernaut.
''', source='https://github.com/mitsuhiko/flask-pastebin'),
Project('newsmeme', None, u'''
<p>
A hackernews/reddit clone written with Flask and
various Flask extensions.
''', source='https://bitbucket.org/danjac/newsmeme'),
Project('Indico', 'https://getindico.io/', u'''
<p>
Indico is a full-fledged meeting and conference management
system developed at CERN. It includes advanced features such
as integration with collaborative systems, a plugin system
and support for SSO-based authentication.
''', source='https://github.com/indico/indico/'),
Project('SayHello', 'http://sayhello.helloflask.com', u'''
<p>
A simple message board.
''', source='https://github.com/greyli/sayhello'),
Project('Bluelog', 'http://bluelog.helloflask.com', u'''
<p>
A blog engine that supports category and resource management.
''', source='https://github.com/greyli/bluelog'),
Project('Albumy', 'http://albumy.helloflask.com', u'''
<p>
A full-featured photo-sharing social networking.
''', source='https://github.com/greyli/albumy'),
Project('Todoism', 'http://todoism.helloflask.com', u'''
<p>
A to-do application implements as SPA, it supports
i18n (Flask-Babel) and provides web APIs.
''', source='https://github.com/greyli/todoism'),
Project('CatChat', 'http://catchat.helloflask.com', u'''
<p>
A chat room based on WebSocket (Flask-Socket-IO), fetured
with Markdown support and code syntax highlighting.
''', source='https://github.com/greyli/catchat'),
]
}
# order projects by name
for _category in projects.itervalues():
_category.sort(key=lambda x: x.name.lower())
del _category
from datetime import datetime
from sqlalchemy import create_engine, Column, Integer, String, DateTime, \
ForeignKey, event
from sqlalchemy.orm import scoped_session, sessionmaker, backref, relation
from sqlalchemy.ext.declarative import declarative_base
from werkzeug import cached_property, http_date
from flask import url_for, Markup
from flask_website import app, search
engine = create_engine(app.config['DATABASE_URI'],
convert_unicode=True,
**app.config['DATABASE_CONNECT_OPTIONS'])
db_session = scoped_session(sessionmaker(autocommit=False,
autoflush=False,
bind=engine))
def init_db():
Model.metadata.create_all(bind=engine)
Model = declarative_base(name='Model')
Model.query = db_session.query_property()
class User(Model):
__tablename__ = 'users'
id = Column('user_id', Integer, primary_key=True)
openid = Column('openid', String(200))
name = Column(String(200))
def __init__(self, name, openid):
self.name = name
self.openid = openid
def to_json(self):
return dict(name=self.name, is_admin=self.is_admin)
@property
def is_admin(self):
return self.openid in app.config['ADMINS']
def __eq__(self, other):
return type(self) is type(other) and self.id == other.id
def __ne__(self, other):
return not self.__eq__(other)
class Category(Model):
__tablename__ = 'categories'
id = Column('category_id', Integer, primary_key=True)
name = Column(String(50))
slug = Column(String(50))
def __init__(self, name):
self.name = name
self.slug = '-'.join(name.split()).lower()
def to_json(self):
return dict(name=self.name, slug=self.slug, count=self.count)
@cached_property
def count(self):
return self.snippets.count()
@property
def url(self):
return url_for('snippets.category', slug=self.slug)
class Snippet(Model, search.Indexable):
__tablename__ = 'snippets'
id = Column('snippet_id', Integer, primary_key=True)
author_id = Column(Integer, ForeignKey('users.user_id'))
category_id = Column(Integer, ForeignKey('categories.category_id'))
title = Column(String(200))
body = Column(String)
pub_date = Column(DateTime)
author = relation(User, backref=backref('snippets', lazy='dynamic'))
category = relation(Category, backref=backref('snippets', lazy='dynamic'))
search_document_kind = 'snippet'
def __init__(self, author, title, body, category):
self.author = author
self.title = title
self.body = body
self.category = category
self.pub_date = datetime.utcnow()
def to_json(self):
return dict(id=self.id, title=self.title,
body=unicode(self.rendered_body),
pub_date=http_date(self.pub_date),
comments=[c.to_json() for c in self.comments],
author=self.author.to_json(),
category=self.category.slug)
def get_search_document(self):
return dict(
id=unicode(self.id),
title=self.title,
keywords=[self.category.name],
content=self.body
)
@classmethod
def describe_search_result(cls, result):
obj = cls.query.get(int(result['id']))
if obj is not None:
text = obj.rendered_body.striptags()
return Markup(result.highlights('content', text=text)) or None
@property
def url(self):
return url_for('snippets.show', id=self.id)
@property
def rendered_body(self):
from flask_website.utils import format_creole
return format_creole(self.body)
class Comment(Model):
__tablename__ = 'comments'
id = Column('comment_id', Integer, primary_key=True)
snippet_id = Column(Integer, ForeignKey('snippets.snippet_id'))
author_id = Column(Integer, ForeignKey('users.user_id'))
title = Column(String(200))
text = Column(String)
pub_date = Column(DateTime)
snippet = relation(Snippet, backref=backref('comments', lazy=True))
author = relation(User, backref=backref('comments', lazy='dynamic'))
def __init__(self, snippet, author, title, text):
self.snippet = snippet
self.author = author
self.title = title
self.text = text
self.pub_date = datetime.utcnow()
def to_json(self):
return dict(author=self.author.to_json(),
title=self.title,
pub_date=http_date(self.pub_date),
text=unicode(self.rendered_text))
@property
def rendered_text(self):
from flask_website.utils import format_creole
return format_creole(self.text)
class OpenIDAssociation(Model):
__tablename__ = 'openid_associations'
id = Column('association_id', Integer, primary_key=True)
server_url = Column(String(1024))
handle = Column(String(255))
secret = Column(String(255))
issued = Column(Integer)
lifetime = Column(Integer)
assoc_type = Column(String(64))
class OpenIDUserNonce(Model):
__tablename__ = 'openid_user_nonces'
id = Column('user_nonce_id', Integer, primary_key=True)
server_url = Column(String(1024))
timestamp = Column(Integer)
salt = Column(String(40))
event.listen(db_session, 'after_flush', search.update_model_based_indexes)
from datetime import datetime
from flask import Flask, session, g, render_template
from flask_openid import OpenID
app = Flask(__name__)
app.config.from_object('websiteconfig')
from flask_website.openid_auth import DatabaseOpenIDStore
oid = OpenID(app, store_factory=DatabaseOpenIDStore)
@app.errorhandler(404)
def not_found(error):
return render_template('404.html'), 404
@app.before_request
def load_current_user():
g.user = User.query.filter_by(openid=session['openid']).first() \
if 'openid' in session else None
@app.teardown_request
def remove_db_session(exception):
db_session.remove()
@app.context_processor
def current_year():
return {'current_year': datetime.utcnow().year}
app.add_url_rule('/docs/', endpoint='docs.index', build_only=True)
app.add_url_rule('/docs/<path:page>/', endpoint='docs.show',
build_only=True)
app.add_url_rule('/docs/<version>/.latex/Flask.pdf', endpoint='docs.pdf',
build_only=True)
from flask_website.views import general
from flask_website.views import community
from flask_website.views import mailinglist
from flask_website.views import snippets
from flask_website.views import extensions
app.register_blueprint(general.mod)
app.register_blueprint(community.mod)
app.register_blueprint(mailinglist.mod)
app.register_blueprint(snippets.mod)
app.register_blueprint(extensions.mod)
from flask_website.database import User, db_session
from flask_website import utils
app.jinja_env.filters['datetimeformat'] = utils.format_datetime
app.jinja_env.filters['dateformat'] = utils.format_date
app.jinja_env.filters['timedeltaformat'] = utils.format_timedelta
app.jinja_env.filters['displayopenid'] = utils.display_openid
# -*- coding: utf-8 -*-
import os
import re
from flask import url_for, Markup
from flask_website import app
from flask_website.search import Indexable
_doc_body_re = re.compile(r'''(?smx)
<title>(.*?)</title>.*?
<div.*?class=".*?body.*?>(.*)
<div.*?class=".*?sphinxsidebar
''')
class DocumentationPage(Indexable):
search_document_kind = 'documentation'
def __init__(self, slug):
self.slug = slug
fn = os.path.join(app.config['DOCUMENTATION_PATH'],
slug, 'index.html')
with open(fn) as f:
contents = f.read().decode('utf-8')
title, text = _doc_body_re.search(contents).groups()
self.title = Markup(title).striptags().split(u'—')[0].strip()
self.text = Markup(text).striptags().strip().replace(u'¶', u'')
def get_search_document(self):
return dict(
id=unicode(self.slug),
title=self.title,
keywords=[],
content=self.text
)
@property
def url(self):
return url_for('docs.show', page=self.slug)
@classmethod
def describe_search_result(cls, result):
rv = cls(result['id'])
return Markup(result.highlights('content', text=rv.text)) or None
@classmethod
def iter_pages(cls):
base_folder = os.path.abspath(app.config['DOCUMENTATION_PATH'])
for dirpath, dirnames, filenames in os.walk(base_folder):
if 'index.html' in filenames:
slug = dirpath[len(base_folder) + 1:]
# skip the index page. useless
if slug:
yield DocumentationPage(slug)
import re
import creoleparser
from datetime import datetime, timedelta
from genshi import builder
from functools import wraps
from creoleparser.elements import PreBlock
from pygments import highlight
from pygments.formatters import HtmlFormatter
from pygments.lexers import get_lexer_by_name
from pygments.util import ClassNotFound
from flask import g, url_for, flash, abort, request, redirect, Markup
from flask_website.flaskystyle import FlaskyStyle # same as docs
pygments_formatter = HtmlFormatter(style=FlaskyStyle)
_ws_split_re = re.compile(r'(\s+)')
TIMEDELTA_UNITS = (
('year', 3600 * 24 * 365),
('month', 3600 * 24 * 30),
('week', 3600 * 24 * 7),
('day', 3600 * 24),
('hour', 3600),
('minute', 60),
('second', 1)
)
class CodeBlock(PreBlock):
def __init__(self):
super(CodeBlock, self).__init__('pre', ['{{{', '}}}'])
def _build(self, mo, element_store, environ):
lines = self.regexp2.sub(r'\1', mo.group(1)).splitlines()
if lines and lines[0].startswith('#!'):
try:
lexer = get_lexer_by_name(lines.pop(0)[2:].strip())
except ClassNotFound:
pass
else:
return Markup(highlight(u'\n'.join(lines), lexer,
pygments_formatter))
return builder.tag.pre(u'\n'.join(lines))
custom_dialect = creoleparser.create_dialect(creoleparser.creole10_base)
# hacky way to get rid of image support
custom_dialect.img = custom_dialect.no_wiki
custom_dialect.pre = CodeBlock()
_parser = creoleparser.Parser(
dialect=custom_dialect,
method='html'
)
def format_creole(text):
return Markup(_parser.render(text, encoding=None))
def split_lines_wrapping(text, width=74, threshold=82):
lines = text.splitlines()
if all(len(line) <= threshold for line in lines):
return lines
result = []
for line in lines:
if len(line) <= threshold:
result.append(line)
continue
line_width = 0
line_buffer = []
for piece in _ws_split_re.split(line):
line_width += len(piece)
if line_width > width:
result.append(u''.join(line_buffer))
line_buffer = []
if not piece.isspace():
line_buffer.append(piece)
line_width = len(piece)
else:
line_width = 0
else:
line_buffer.append(piece)
if line_buffer:
result.append(u''.join(line_buffer))
return result
def request_wants_json():
# we only accept json if the quality of json is greater than the
# quality of text/html because text/html is preferred to support
# browsers that accept on */*
best = request.accept_mimetypes \
.best_match(['application/json', 'text/html'])
return best == 'application/json' and \
request.accept_mimetypes[best] > request.accept_mimetypes['text/html']
def requires_login(f):
@wraps(f)
def decorated_function(*args, **kwargs):
if g.user is None:
flash(u'You need to be signed in for this page.')
return redirect(url_for('general.login', next=request.path))
return f(*args, **kwargs)
return decorated_function
def requires_admin(f):
@wraps(f)
def decorated_function(*args, **kwargs):
if not g.user.is_admin:
abort(401)
return f(*args, **kwargs)
return requires_login(decorated_function)
def format_datetime(dt):
return dt.strftime('%Y-%m-%d @ %H:%M')
def format_date(dt):
return dt.strftime('%Y-%m-%d')
def format_timedelta(delta, granularity='second', threshold=.85):
if isinstance(delta, datetime):
delta = datetime.utcnow() - delta
if isinstance(delta, timedelta):
seconds = int((delta.days * 86400) + delta.seconds)
else:
seconds = delta
for unit, secs_per_unit in TIMEDELTA_UNITS:
value = abs(seconds) / secs_per_unit
if value >= threshold or unit == granularity:
if unit == granularity and value > 0:
value = max(1, value)
value = int(round(value))
rv = u'%s %s' % (value, unit)
if value != 1:
rv += u's'
return rv
return u''
def display_openid(openid):
if not openid:
return ''
rv = openid
if rv.startswith(('http://', 'https://')):
rv = rv.split('/', 2)[-1]
return rv.rstrip('/')
from time import time
from openid.association import Association
from openid.store.interface import OpenIDStore
from openid.store import nonce
from flask_website.database import db_session, OpenIDAssociation, \
OpenIDUserNonce
class DatabaseOpenIDStore(OpenIDStore):
"""Implements the open store for the website using the database."""
def storeAssociation(self, server_url, association):
assoc = OpenIDAssociation(
server_url=server_url,
handle=association.handle,
secret=association.secret.encode('base64'),
issued=association.issued,
lifetime=association.lifetime,
assoc_type=association.assoc_type
)
db_session.add(assoc)
db_session.commit()
def getAssociation(self, server_url, handle=None):
q = OpenIDAssociation.query.filter_by(server_url=server_url)
if handle is not None:
q = q.filter_by(handle=handle)
result_assoc = None
for item in q.all():
assoc = Association(item.handle, item.secret.decode('base64'),
item.issued, item.lifetime, item.assoc_type)
if assoc.getExpiresIn() <= 0:
self.removeAssociation(server_url, assoc.handle)
else:
result_assoc = assoc
return result_assoc
def removeAssociation(self, server_url, handle):
try:
return OpenIDAssociation.query.filter(
(OpenIDAssociation.server_url == server_url) &
(OpenIDAssociation.handle == handle)
).delete()
finally:
db_session.commit()
def useNonce(self, server_url, timestamp, salt):
if abs(timestamp - time()) > nonce.SKEW:
return False
rv = OpenIDUserNonce.query.filter(
(OpenIDUserNonce.server_url == server_url) &
(OpenIDUserNonce.timestamp == timestamp) &
(OpenIDUserNonce.salt == salt)
).first()
if rv is not None:
return False
rv = OpenIDUserNonce(server_url=server_url, timestamp=timestamp,
salt=salt)
db_session.add(rv)
db_session.commit()
return True
def cleanupNonces(self):
try:
return OpenIDUserNonce.query.filter(
OpenIDUserNonce.timestamp <= int(time() - nonce.SKEW)
).delete()
finally:
db_session.commit()
def cleanupAssociations(self):
try:
return OpenIDAssociation.query.filter(
OpenIDAssociation.lifetime < int(time())
).delete()
finally:
db_session.commit()
# -*- coding: utf-8 -*-
import os
from whoosh import highlight, analysis, qparser
from whoosh.support.charset import accent_map
from flask import Markup
from flask_website import app
from werkzeug import import_string
def open_index():
from whoosh import index, fields as f
if os.path.isdir(app.config['WHOOSH_INDEX']):
return index.open_dir(app.config['WHOOSH_INDEX'])
os.mkdir(app.config['WHOOSH_INDEX'])
analyzer = analysis.StemmingAnalyzer() | analysis.CharsetFilter(accent_map)
schema = f.Schema(
url=f.ID(stored=True, unique=True),
id=f.ID(stored=True),
title=f.TEXT(stored=True, field_boost=2.0, analyzer=analyzer),
type=f.ID(stored=True),
keywords=f.KEYWORD(commas=True),
content=f.TEXT(analyzer=analyzer)
)
return index.create_in(app.config['WHOOSH_INDEX'], schema)
index = open_index()
class Indexable(object):
search_document_kind = None
def add_to_search_index(self, writer):
writer.add_document(url=unicode(self.url),
type=self.search_document_type,
**self.get_search_document())
@classmethod
def describe_search_result(cls, result):
return None
@property
def search_document_type(self):
cls = type(self)
return cls.__module__ + u'.' + cls.__name__
def get_search_document(self):
raise NotImplementedError()
def remove_from_search_index(self, writer):
writer.delete_by_term('url', unicode(self.url))
def highlight_all(result, field):
text = result[field]
return Markup(highlight.Highlighter(
fragmenter=highlight.WholeFragmenter(),
formatter=result.results.highlighter.formatter)
.highlight_hit(result, field, text=text)) or text
class SearchResult(object):
def __init__(self, result):
self.url = result['url']
self.title_text = result['title']
self.title = highlight_all(result, 'title')
cls = import_string(result['type'])
self.kind = cls.search_document_kind
self.description = cls.describe_search_result(result)
class SearchResultPage(object):
def __init__(self, results, page):
self.page = page
if results is None:
self.results = []
self.pages = 1
self.total = 0
else:
self.results = [SearchResult(r) for r in results]
self.pages = results.pagecount
self.total = results.total
def __iter__(self):
return iter(self.results)
def search(query, page=1, per_page=20):
with index.searcher() as s:
qp = qparser.MultifieldParser(['title', 'content'], index.schema)
q = qp.parse(unicode(query))
try:
result_page = s.search_page(q, page, pagelen=per_page)
except ValueError:
if page == 1:
return SearchResultPage(None, page)
return None
results = result_page.results
results.highlighter.fragmenter.maxchars = 512
results.highlighter.fragmenter.surround = 40
results.highlighter.formatter = highlight.HtmlFormatter('em',
classname='search-match', termclass='search-term',
between=u'<span class=ellipsis> … </span>')
return SearchResultPage(result_page, page)
def update_model_based_indexes(session, flush_context):
"""Called by a session event, updates the model based documents."""
to_delete = []
to_add = []
for model in session.new:
if isinstance(model, Indexable):
to_add.append(model)
for model in session.dirty:
if isinstance(model, Indexable):
to_delete.append(model)
to_add.append(model)
for model in session.dirty:
if isinstance(model, Indexable):
to_delete.append(model)
if not (to_delete or to_add):
return
writer = index.writer()
for model in to_delete:
model.remove_from_search_index(writer)
for model in to_add:
model.add_to_search_index(writer)
writer.commit()
def update_documentation_index():
from flask_website.docs import DocumentationPage
writer = index.writer()
for page in DocumentationPage.iter_pages():
page.remove_from_search_index(writer)
page.add_to_search_index(writer)
writer.commit()
def reindex_snippets():
from flask_website.database import Snippet
writer = index.writer()
for snippet in Snippet.query.all():
snippet.remove_from_search_index(writer)
snippet.add_to_search_index(writer)
writer.commit()
from __future__ import with_statement
from flask import Blueprint, render_template, redirect
mod = Blueprint('mailinglist', __name__, url_prefix='/mailinglist')
@mod.route('/')
def index():
return render_template('mailinglist/index.html')
@mod.route('/archive/', defaults={'page': 1})
@mod.route('/archive/page/<int:page>/')
def archive(page):
return redirect('http://librelist.com/browser/flask/')
@mod.route('/archive/<int:year>/<int:month>/<int:day>/<slug>/')
def show_thread(year, month, day, slug):
return redirect('http://librelist.com/browser/flask/%s/%s/%s/%s'
% (year, month, day, slug))
from flask import Blueprint, render_template, jsonify, redirect, \
url_for
from flask_website.utils import request_wants_json
from flask_website.listings.extensions import extensions, unlisted
mod = Blueprint('extensions', __name__, url_prefix='/extensions')
@mod.route('/')
def index():
if request_wants_json():
return jsonify(extensions=[ext.to_json() for ext in extensions],
unlisted_extensions=[ext.to_json() for ext in unlisted])
return render_template('extensions/index.html', extensions=extensions)
@mod.route('/creating/')
def creating():
return redirect(url_for('docs.show', page='extensiondev'), 301)
from flask import Blueprint, render_template, jsonify
from flask_website.utils import request_wants_json
from flask_website.listings.projects import projects
mod = Blueprint('community', __name__, url_prefix='/community')
@mod.route('/')
def index():
return render_template('community/index.html')
@mod.route('/irc/')
def irc():
return render_template('community/irc.html')
@mod.route('/badges/')
def badges():
return render_template('community/badges.html')
@mod.route('/poweredby/')
def poweredby():
if request_wants_json():
return jsonify((k, [p.to_json() for p in v])
for k, v in projects.iteritems())
return render_template('community/poweredby.html', projects=projects)
@mod.route('/logos/')
def logos():
return render_template('community/logos.html')
# -*- coding: utf-8 -*-
from flask import Blueprint, abort, flash, g, jsonify, redirect, \
render_template, request, url_for
from flask_website.utils import format_creole, request_wants_json, \
requires_admin, requires_login
from flask_website.database import Category, Comment, Snippet, db_session
mod = Blueprint('snippets', __name__, url_prefix='/snippets')
@mod.route('/')
def index():
return render_template('snippets/index.html',
categories=Category.query.order_by(Category.name).all())
@mod.route('/<int:id>/', methods=['GET', 'POST'])
def show(id):
snippet = Snippet.query.get(id)
if snippet is None:
abort(404)
if request_wants_json():
return jsonify(snippet=snippet.to_json())
if request.method == 'POST':
title = request.form['title']
text = request.form['text']
if text:
db_session.add(Comment(snippet, g.user, title, text))
db_session.commit()
flash(u'Your comment was added')
return redirect(snippet.url)
return render_template('snippets/show.html', snippet=snippet)
@mod.route('/comments/<int:id>/', methods=['GET', 'POST'])
@requires_admin
def edit_comment(id):
comment = Comment.query.get(id)
if comment is None:
abort(404)
snippet = comment.snippet
form = dict(title=comment.title, text=comment.text)
if request.method == 'POST':
if 'delete' in request.form:
db_session.delete(comment)
db_session.commit()
flash(u'Comment was deleted.')
return redirect(snippet.url)
elif 'cancel' in request.form:
return redirect(snippet.url)
form['title'] = request.form['title']
form['text'] = request.form['text']
if not form['text']:
flash(u'Error: comment text is required.')
else:
comment.title = form['title']
comment.text = form['text']
db_session.commit()
flash(u'Comment was updated.')
return redirect(snippet.url)
return render_template('snippets/edit_comment.html', form=form,
comment=comment)
@mod.route('/edit/<int:id>/', methods=['GET', 'POST'])
@requires_login
def edit(id):
snippet = Snippet.query.get(id)
if snippet is None:
abort(404)
if g.user is None or (not g.user.is_admin and snippet.author != g.user):
abort(401)
preview = None
form = dict(title=snippet.title, body=snippet.body,
category=snippet.category.id)
if request.method == 'POST':
form['title'] = request.form['title']
form['body'] = request.form['body']
form['category'] = request.form.get('category', type=int)
if 'preview' in request.form:
preview = format_creole(request.form['body'])
elif 'delete' in request.form:
for comment in snippet.comments:
db_session.delete(comment)
db_session.delete(snippet)
db_session.commit()
flash(u'Your snippet was deleted')
return redirect(url_for('snippets.index'))
else:
category_id = request.form.get('category', type=int)
if not form['body']:
flash(u'Error: you have to enter a snippet')
else:
category = Category.query.get(category_id)
if category is not None:
snippet.title = form['title']
snippet.body = form['body']
snippet.category = category
db_session.commit()
flash(u'Your snippet was modified')
return redirect(snippet.url)
return render_template('snippets/edit.html',
snippet=snippet, preview=preview, form=form,
categories=Category.query.order_by(Category.name).all())
@mod.route('/category/<slug>/')
def category(slug):
category = Category.query.filter_by(slug=slug).first()
if category is None:
abort(404)
snippets = category.snippets.order_by(Snippet.title).all()
if request_wants_json():
return jsonify(category=category.to_json(),
snippets=[s.id for s in snippets])
return render_template('snippets/category.html', category=category,
snippets=snippets)
from flask import Blueprint, render_template, session, redirect, url_for, \
request, flash, g, jsonify, abort
from flask_openid import COMMON_PROVIDERS
from flask_website import oid
from flask_website.search import search as perform_search
from flask_website.utils import requires_login, request_wants_json
from flask_website.database import db_session, User
from flask_website.listings.releases import releases
mod = Blueprint('general', __name__)
@mod.route('/')
def index():
if request_wants_json():
return jsonify(releases=[r.to_json() for r in releases])
return render_template(
'general/index.html',
latest_release=releases[-1],
# pdf link does not redirect, needs version
# docs version only includes major.minor
docs_pdf_version='.'.join(releases[-1].version.split('.', 2)[:2])
)
@mod.route('/search/')
def search():
q = request.args.get('q') or ''
page = request.args.get('page', type=int) or 1
results = None
if q:
results = perform_search(q, page=page)
if results is None:
abort(404)
return render_template('general/search.html', results=results, q=q)
@mod.route('/logout/')
def logout():
if 'openid' in session:
flash(u'Logged out')
del session['openid']
return redirect(request.referrer or url_for('general.index'))
@mod.route('/login/', methods=['GET', 'POST'])
@oid.loginhandler
def login():
if g.user is not None:
return redirect(url_for('general.index'))
if 'cancel' in request.form:
flash(u'Cancelled. The OpenID was not changed.')
return redirect(oid.get_next_url())
openid = request.values.get('openid')
if not openid:
openid = COMMON_PROVIDERS.get(request.args.get('provider'))
if openid:
return oid.try_login(openid, ask_for=['fullname', 'nickname'])
error = oid.fetch_error()
if error:
flash(u'Error: ' + error)
return render_template('general/login.html', next=oid.get_next_url())
@mod.route('/first-login/', methods=['GET', 'POST'])
def first_login():
if g.user is not None or 'openid' not in session:
return redirect(url_for('.login'))
if request.method == 'POST':
if 'cancel' in request.form:
del session['openid']
flash(u'Login was aborted')
return redirect(url_for('general.login'))
db_session.add(User(request.form['name'], session['openid']))
db_session.commit()
flash(u'Successfully created profile and logged in')
return redirect(oid.get_next_url())
return render_template('general/first_login.html',
next=oid.get_next_url(),
openid=session['openid'])
@mod.route('/profile/', methods=['GET', 'POST'])
@requires_login
def profile():
name = g.user.name
if request.method == 'POST':
name = request.form['name'].strip()
if not name:
flash(u'Error: a name is required')
else:
g.user.name = name
db_session.commit()
flash(u'User profile updated')
return redirect(url_for('.index'))
return render_template('general/profile.html', name=name)
@mod.route('/profile/change-openid/', methods=['GET', 'POST'])
@requires_login
@oid.loginhandler
def change_openid():
if request.method == 'POST':
if 'cancel' in request.form:
flash(u'Cancelled. The OpenID was not changed.')
return redirect(oid.get_next_url())
openid = request.values.get('openid')
if not openid:
openid = COMMON_PROVIDERS.get(request.args.get('provider'))
if openid:
return oid.try_login(openid)
error = oid.fetch_error()
if error:
flash(u'Error: ' + error)
return render_template('general/change_openid.html',
next=oid.get_next_url())
@oid.after_login
def create_or_login(resp):
session['openid'] = resp.identity_url
user = g.user or User.query.filter_by(openid=resp.identity_url).first()
if user is None:
return redirect(url_for('.first_login', next=oid.get_next_url(),
name=resp.fullname or resp.nickname))
if user.openid != resp.identity_url:
user.openid = resp.identity_url
db_session.commit()
flash(u'OpenID identity changed')
else:
flash(u'Successfully signed in')
return redirect(oid.get_next_url())
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13,
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543,
1124,
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12332,
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264,
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1013,
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13,
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275,
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5813,
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13,
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13,
9651,
319,
2560,
6081,
304,
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278,
13,
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529,
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2822,
543,
1124,
597,
26737,
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29953,
29896,
29929,
29906,
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29889,
12938,
29914,
1013,
29924,
275,
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829,
29874,
29958,
3883,
363,
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13,
9651,
13755,
4485,
3204,
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13,
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6629,
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13,
4706,
10561,
2433,
991,
597,
1579,
1278,
29899,
26737,
8245,
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949,
386,
287,
12332,
29889,
990,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
2976,
292,
29893,
4369,
19415,
29914,
1579,
1278,
29899,
26737,
8245,
742,
13,
4706,
23454,
29922,
5574,
29892,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
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1278,
29899,
29928,
749,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
1938,
292,
278,
438,
6444,
17948,
411,
3114,
773,
2379,
1278,
29892,
7274,
29892,
322,
288,
5150,
1982,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
991,
597,
1579,
1278,
29899,
29881,
749,
29889,
949,
386,
287,
12332,
29889,
990,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
2976,
292,
29893,
4369,
19415,
29914,
1579,
1278,
29899,
29881,
749,
742,
13,
4706,
23454,
29922,
5574,
29892,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
29903,
1660,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29874,
2822,
543,
1124,
597,
1636,
29889,
1420,
29945,
307,
4684,
29889,
510,
29914,
264,
29914,
12631,
29879,
29914,
3696,
4993,
29914,
6500,
1199,
29914,
1013,
13,
9651,
5656,
28048,
28488,
829,
29874,
29958,
363,
2379,
1278,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
991,
597,
1579,
1278,
29899,
29879,
344,
29889,
949,
386,
287,
12332,
29889,
990,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
2976,
292,
29893,
4369,
19415,
29914,
1579,
1278,
29899,
29879,
344,
742,
13,
4706,
23454,
29922,
5574,
29892,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
29931,
19657,
742,
12801,
5813,
29958,
742,
13,
795,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
2528,
29879,
390,
14830,
326,
11407,
2304,
304,
2379,
1278,
29889,
13,
9651,
18601,
29879,
263,
17127,
519,
8635,
14998,
411,
20240,
363,
13,
9651,
297,
29899,
14834,
29892,
29825,
322,
2626,
8173,
29889,
13,
4706,
6629,
742,
13,
4706,
18546,
2433,
5711,
29874,
1607,
29872,
29914,
1579,
1278,
29899,
2576,
1524,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
2576,
1524,
29889,
949,
386,
287,
12332,
29889,
990,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
23454,
29922,
8824,
29892,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
2659,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
8701,
13902,
4911,
16535,
15057,
363,
2379,
1278,
29901,
13,
9651,
12577,
29892,
10811,
3568,
4876,
29892,
19130,
29892,
10726,
8952,
29892,
10726,
4800,
29892,
383,
990,
327,
4800,
29892,
13,
9651,
1528,
280,
29899,
6707,
13361,
2133,
322,
4623,
2133,
29889,
13,
4706,
6629,
742,
13,
4706,
18546,
2433,
1847,
386,
601,
29914,
1579,
1278,
29899,
1792,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
1792,
29889,
949,
386,
287,
12332,
29889,
601,
29914,
742,
13,
4706,
23454,
29922,
5574,
29892,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
29963,
423,
742,
525,
6156,
1164,
3383,
529,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
13,
18884,
9133,
2247,
263,
5941,
29892,
2560,
3988,
21398,
6890,
363,
15678,
2379,
1278,
13,
18884,
2401,
5795,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
6071,
29889,
578,
265,
29889,
4282,
742,
13,
4706,
18546,
2433,
1366,
275,
578,
265,
29914,
8754,
1278,
29899,
29963,
423,
742,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
3010,
797,
21494,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
1184,
29894,
2247,
21556,
373,
3758,
9365,
313,
4746,
3758,
2499,
305,
6764,
29897,
8283,
13,
632,
363,
1269,
2009,
21106,
29886,
29958,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
991,
597,
3292,
29889,
510,
29914,
1217,
895,
29914,
1579,
1278,
29899,
1972,
1144,
1103,
742,
13,
4706,
18546,
2433,
1217,
895,
29914,
1579,
1278,
29899,
1972,
1144,
1103,
742,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
855,
555,
2084,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
2528,
24444,
2084,
1404,
10643,
29892,
10760,
29892,
13,
9651,
322,
28733,
304,
2379,
1278,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
303,
555,
2084,
29889,
949,
386,
287,
12332,
29889,
990,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
303,
555,
2084,
29914,
303,
555,
2084,
29899,
1579,
1278,
29915,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
29909,
808,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
13,
795,
2379,
1278,
29899,
29909,
808,
3732,
372,
4780,
304,
2436,
16631,
382,
1859,
11446,
411,
2379,
1278,
322,
13,
795,
278,
4827,
29874,
4971,
6090,
26240,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
1278,
29889,
949,
386,
287,
12332,
29889,
601,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
29926,
6547,
29893,
354,
7367,
29914,
1579,
1278,
29899,
1278,
29915,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
15078,
29899,
7249,
8787,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
13,
795,
8878,
16759,
23649,
1494,
278,
13,
795,
529,
29874,
2822,
543,
1124,
597,
3126,
2754,
29889,
990,
29914,
4830,
29914,
1013,
7249,
8787,
829,
29874,
29958,
13,
795,
21992,
411,
263,
13988,
848,
7546,
1788,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
5060,
29899,
3126,
2754,
29889,
949,
386,
287,
12332,
29889,
601,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
2460,
14868,
3780,
29914,
1579,
1278,
29899,
5060,
29899,
3126,
2754,
29915,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
8132,
1988,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
13,
795,
1174,
519,
16698,
1988,
10760,
363,
596,
1856,
932,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
991,
597,
1579,
1278,
29899,
29879,
8807,
29889,
949,
386,
287,
12332,
29889,
601,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
2586,
21454,
2433,
559,
4672,
14318,
29914,
1579,
1278,
29918,
29879,
8807,
29915,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
8754,
1278,
29899,
29903,
3707,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
13,
795,
2379,
1278,
29899,
29879,
3707,
28936,
4780,
2130,
304,
278,
16759,
3450,
297,
278,
6692,
10454,
306,
9375,
29924,
7481,
13,
795,
411,
278,
1371,
310,
278,
529,
29874,
2822,
543,
1124,
597,
3292,
29889,
510,
29914,
6050,
29893,
29900,
29914,
29886,
952,
3707,
1013,
29886,
952,
3707,
3489,
829,
29874,
15513,
13,
795,
19814,
29892,
372,
11286,
438,
6444,
363,
263,
409,
314,
2222,
10760,
847,
28733,
7271,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
1124,
597,
1579,
1278,
29899,
29879,
3707,
29889,
949,
386,
287,
12332,
29889,
601,
29914,
264,
29914,
12333,
29914,
742,
13,
4706,
18546,
2433,
6050,
29893,
29900,
29914,
1579,
1278,
29899,
29879,
3707,
29915,
13,
1678,
10353,
13,
1678,
7338,
2673,
877,
1579,
1278,
29899,
1989,
15126,
557,
742,
12801,
5813,
29958,
742,
13,
4706,
6139,
2433,
4907,
13,
9651,
529,
29886,
29958,
8754,
1278,
6081,
363,
13,
9651,
529,
29874,
2822,
29922,
991,
597,
1636,
29889,
1989,
15126,
557,
29889,
990,
29958,
2558,
15126,
557,
829,
29874,
29958,
13465,
29889,
13,
4706,
6629,
742,
13,
4706,
10561,
2433,
991,
597,
3292,
29889,
510,
29914,
19426,
309,
649,
29882,
16129,
29914,
1579,
1278,
29899,
1989,
15126,
557,
29914,
10054,
29914,
6207,
29914,
16310,
2303,
29889,
3487,
742,
13,
4706,
18546,
2433,
19426,
309,
649,
29882,
16129,
29914,
1579,
1278,
29899,
1989,
15126,
557,
742,
13,
1678,
10353,
1678,
13,
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13,
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304,
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13,
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4485,
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13,
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13,
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13,
13,
29937,
1797,
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491,
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13,
1454,
903,
7320,
297,
9279,
29889,
1524,
5975,
7295,
13,
1678,
903,
7320,
29889,
6605,
29898,
1989,
29922,
2892,
921,
29901,
921,
29889,
978,
29889,
13609,
3101,
13,
6144,
903,
7320,
13,
13,
3166,
12865,
1053,
12865,
13,
3166,
4576,
284,
305,
6764,
1053,
1653,
29918,
10599,
29892,
12481,
29892,
8102,
29892,
1714,
29892,
12315,
29892,
320,
13,
268,
19358,
2558,
29892,
1741,
13,
3166,
4576,
284,
305,
6764,
29889,
555,
1053,
16505,
287,
29918,
7924,
29892,
4867,
28107,
29892,
1250,
999,
29892,
8220,
13,
3166,
4576,
284,
305,
6764,
29889,
1062,
29889,
311,
16544,
1230,
1053,
7669,
1230,
29918,
3188,
13,
13,
3166,
23085,
13289,
1053,
22152,
29918,
6799,
29892,
1732,
29918,
1256,
13,
13,
3166,
29784,
1053,
3142,
29918,
1454,
29892,
4485,
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13,
3166,
29784,
29918,
22942,
1053,
623,
29892,
2740,
13,
13,
10599,
353,
1653,
29918,
10599,
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932,
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2917,
1839,
25832,
27982,
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15551,
7464,
13,
462,
539,
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29918,
2523,
356,
29922,
5574,
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13,
462,
539,
3579,
932,
29889,
2917,
1839,
25832,
27982,
29918,
6007,
8186,
1783,
29918,
14094,
27946,
11287,
13,
2585,
29918,
7924,
353,
16505,
287,
29918,
7924,
29898,
7924,
28107,
29898,
6921,
15060,
29922,
8824,
29892,
13,
462,
462,
308,
1120,
974,
29880,
1878,
29922,
8824,
29892,
13,
462,
462,
308,
7868,
29922,
10599,
876,
13,
13,
1753,
2069,
29918,
2585,
7295,
13,
1678,
8125,
29889,
19635,
29889,
3258,
29918,
497,
29898,
5355,
29922,
10599,
29897,
13,
13,
13,
3195,
353,
7669,
1230,
29918,
3188,
29898,
978,
2433,
3195,
1495,
13,
3195,
29889,
1972,
353,
4833,
29918,
7924,
29889,
1972,
29918,
6799,
580,
13,
13,
13,
1990,
4911,
29898,
3195,
1125,
13,
1678,
4770,
3891,
2435,
420,
1649,
353,
525,
7193,
29915,
13,
1678,
1178,
353,
12481,
877,
1792,
29918,
333,
742,
8102,
29892,
7601,
29918,
1989,
29922,
5574,
29897,
13,
1678,
1722,
333,
353,
12481,
877,
3150,
333,
742,
1714,
29898,
29906,
29900,
29900,
876,
13,
1678,
1024,
353,
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1231,
29898,
29906,
29900,
29900,
876,
13,
13,
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822,
4770,
2344,
12035,
1311,
29892,
1024,
29892,
1722,
333,
1125,
13,
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1583,
29889,
978,
353,
1024,
13,
4706,
1583,
29889,
3150,
333,
353,
1722,
333,
13,
13,
1678,
822,
304,
29918,
3126,
29898,
1311,
1125,
13,
4706,
736,
9657,
29898,
978,
29922,
1311,
29889,
978,
29892,
338,
29918,
6406,
29922,
1311,
29889,
275,
29918,
6406,
29897,
13,
13,
1678,
732,
6799,
13,
1678,
822,
338,
29918,
6406,
29898,
1311,
1125,
13,
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736,
1583,
29889,
3150,
333,
297,
623,
29889,
2917,
1839,
3035,
16173,
29903,
2033,
13,
13,
1678,
822,
4770,
1837,
12035,
1311,
29892,
916,
1125,
13,
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736,
1134,
29898,
1311,
29897,
338,
1134,
29898,
1228,
29897,
322,
1583,
29889,
333,
1275,
916,
29889,
333,
13,
13,
1678,
822,
4770,
484,
12035,
1311,
29892,
916,
1125,
13,
4706,
736,
451,
1583,
17255,
1837,
12035,
1228,
29897,
13,
13,
13,
1990,
17943,
29898,
3195,
1125,
13,
1678,
4770,
3891,
2435,
420,
1649,
353,
525,
20683,
29915,
13,
1678,
1178,
353,
12481,
877,
7320,
29918,
333,
742,
8102,
29892,
7601,
29918,
1989,
29922,
5574,
29897,
13,
1678,
1024,
353,
12481,
29898,
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] |
vfoot/graphics/__init__.py | filipecn/vfoot | 0 | 17476 | import glfw
import OpenGL.GL as gl
import imgui
from imgui.integrations.glfw import GlfwRenderer
def app(render):
imgui.create_context()
window = impl_glfw_init()
impl = GlfwRenderer(window)
while not glfw.window_should_close(window):
glfw.poll_events()
impl.process_inputs()
gl.glClearColor(.2, .5, .2, 0.6)
gl.glClear(gl.GL_COLOR_BUFFER_BIT)
imgui.new_frame()
render()
imgui.render()
impl.render(imgui.get_draw_data())
glfw.swap_buffers(window)
impl.shutdown()
glfw.terminate()
def impl_glfw_init():
width, height = 1280, 720
window_name = "minimal ImGui/GLFW3 example"
if not glfw.init():
print("Could not initialize OpenGL context")
exit(1)
# OS X supports only forward-compatible core profiles from 3.2
glfw.window_hint(glfw.CONTEXT_VERSION_MAJOR, 3)
glfw.window_hint(glfw.CONTEXT_VERSION_MINOR, 3)
glfw.window_hint(glfw.OPENGL_PROFILE, glfw.OPENGL_CORE_PROFILE)
glfw.window_hint(glfw.OPENGL_FORWARD_COMPAT, gl.GL_TRUE)
# Create a windowed mode window and its OpenGL context
window = glfw.create_window(
int(width), int(height), window_name, None, None
)
glfw.make_context_current(window)
if not window:
glfw.terminate()
print("Could not initialize Window")
exit(1)
return window
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] |
gbot/libs/helper.py | dgw/goshu | 5 | 15422 | #!/usr/bin/env python3
# Goshu IRC Bot
# written by <NAME> <<EMAIL>>
# licensed under the ISC license
"""extends several builtin functions and provides helper functions
The default Python library is extensive and well-stocked. There are some
times however, you wish a small task was taken care of for you. This module
if chock full of little extensions and helper functions I've needed while
writing Goshu.
Small, interesting, self-contained functions that can probably be reused
elsewhere.
"""
import collections.abc
import datetime
import imp
import json
import os
import re
import string
import sys
import urllib.parse
from girc.formatting import escape
from http_status import Status
from pyquery import PyQuery as pq
import importlib
import requests
import xml.sax.saxutils as saxutils
import yaml
valid_filename_chars = string.ascii_letters + string.digits + '#._- '
def true_or_false(in_str):
"""Returns True/False if string represents it, else None."""
in_str = in_str.lower()
if in_str.startswith(('true', 'y', '1', 'on')):
return True
elif in_str.startswith(('false', 'n', '0', 'off')):
return False
else:
return None
def split_num(line, chars=' ', maxsplits=1, empty=''):
"""/lazy/ wrapper, to stop us having to bounds-check when splitting.
Arguments:
line -- line to split
chars -- character(s) to split line on
maxsplits -- how many split items are returned
empty -- character to put in place of nothing
Returns:
line.split(chars, items); return value is padded until `maxsplits + 1` number of values
are present"""
line = line.split(chars, maxsplits)
while len(line) <= maxsplits:
line.append(empty)
return line
def is_ok(func, prompt, blank='', clearline=False):
"""Prompt the user for yes/no and returns True/False
Arguments:
prompt -- Prompt for the user
blank -- If True, a blank response will return True, ditto for False, the default ''
will not accept blank responses and ask until the user gives an appropriate
response
Returns:
True if user accepts, False if user does not"""
while True:
ok = func(prompt).lower().strip()
if len(ok) > 0:
if ok[0] == 'y' or ok[0] == 't' or ok[0] == '1': # yes, true, 1
return True
elif ok[0] == 'n' or ok[0] == 'f' or ok[0] == '0': # no, false, 0
return False
else:
if blank is True:
return True
elif blank is False:
return False
def bytes_to_str(bytes, base=2, precision=0):
"""Convert number of bytes to a human-readable format
Arguments:
bytes -- number of bytes
base -- base 2 'regular' multiplexer, or base 10 'storage' multiplexer
precision -- number of decimal places to output
Returns:
Human-readable string such as '1.32M'
"""
if base == 2:
multiplexer = 1024
elif base == 10:
multiplexer = 1000
else:
return None # raise error
precision_string = '%.' + str(precision) + 'f'
mebi_convert = True
if bytes >= (multiplexer ** 4):
terabytes = float(bytes / (multiplexer ** 4))
output = (precision_string % terabytes) + 'T'
elif bytes >= (multiplexer ** 3):
gigabytes = float(bytes / (multiplexer ** 3))
output = (precision_string % gigabytes) + 'G'
elif bytes >= (multiplexer ** 2):
megabytes = float(bytes / (multiplexer ** 2))
output = (precision_string % megabytes) + 'M'
elif bytes >= (multiplexer ** 1):
kilobytes = float(bytes / (multiplexer ** 1))
output = (precision_string % kilobytes) + 'K'
else:
output = (precision_string % float(bytes)) + 'B'
mebi_convert = False
# mebibytes and gibibytes all those weird HDD manufacturer terms
if base == 10 and mebi_convert:
num, base = output[:-1], output[-1]
output = num + base.lower() + 'B'
return output
def time_metric(secs=60, mins=0):
"""Returns user-readable string representing given number of seconds."""
if mins:
secs += (mins * 60)
time = ''
for metric_secs, metric_char in [[7 * 24 * 60 * 60, 'w'],
[24 * 60 * 60, 'd'],
[60 * 60, 'h'],
[60, 'm']]:
if secs > metric_secs:
time += '{}{}'.format(int(secs / metric_secs), metric_char)
secs -= int(secs / metric_secs) * metric_secs
if secs > 0:
time += '{}s'.format(secs)
return time
def metric(num, metric_list=[[10 ** 9, 'B'], [10 ** 6, 'M'], [10 ** 3, 'k']], additive=False):
"""Returns user-readable string representing given value.
Arguments:
num is the base value we're converting.
metric_list is the list of data we're working off.
additive is whether we add the various values together, or separate them.
Return:
a string such as 345K or 23w6d2h53s"""
output = ''
for metric_count, metric_char in metric_list:
if num > metric_count:
if additive:
format_str = '{}{}'
else:
format_str = '{:.1f}{}'
num = (num / metric_count)
if not additive:
num = float(num)
output += format_str.format(num, metric_char)
if not additive:
break
# just in case no output
if output == '':
output = str(num)
return output
def get_url(url, **kwargs):
"""Gets a url, handles all the icky requests stuff."""
try:
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
r = requests.get(url, **kwargs)
r.status = Status(r.status_code)
if not r.ok:
return 'HTTP Error - {code} {name} - {description}'.format(**{
'code': r.status.code,
'name': r.status.name,
'description': r.status.description
})
except requests.exceptions.Timeout:
return 'Connection timed out'
except requests.exceptions.RequestException as x:
return '{}'.format(x.__class__.__name__)
return r
def format_extract(format_json, input_element, format=None, debug=False, fail='Failure'):
if not format:
if 'format' in format_json:
format = format_json['format']
else:
return 'No format for format_extract()'
if 'debug' in format_json:
debug = format_json['debug']
# format-specific settings
if format == 'json':
input_element = json.loads(input_element)
retrieve = json_return
elif format == 'xml':
# ignore xml namespaces
input_element = input_element.replace(' xmlns:', ' xmlnamespace:')
input_element = input_element.replace(' xmlns=', ' xmlnamespace=')
retrieve = xml_return
# format extraction - format kwargs
format_dict = {}
if 'response_dict' in format_json:
for name in format_json['response_dict']:
try:
if isinstance(format_json['response_dict'][name], collections.abc.Callable):
try:
format_dict[name] = format_json['response_dict'][name](format_json,
input_element)
except BaseException as x:
if debug:
return 'Unknown failure: {}'.format(x)
else:
return 'Code error'
else:
format_dict[name] = retrieve(input_element,
format_json['response_dict'][name])
if format_dict[name] is None:
return fail
except KeyError:
if debug:
return 'Fail on {}'.format(name)
else:
return fail
except IndexError:
if debug:
return 'Fail on {}'.format(name)
else:
return fail
try:
return format_json['response'].format(**format_dict)
except KeyError:
if debug:
return 'Fail on format() key'
else:
return fail
except IndexError:
if debug:
return 'Fail on format() index'
else:
return fail
def xml_return(input_xml, selector):
pq_xml = pq(input_xml)
if selector[0] == 'text':
return selector[1]
elif selector[0] == 'text.escape':
return escape(selector[1])
elif selector[0] == 'jquery':
return pq_xml(selector[1]).text()
elif selector[0] == 'jquery.attr':
return pq_xml(selector[1]).attr(selector[2])
def json_return(input_json, selector):
if selector[0] == 'text':
return selector[1]
elif selector[0] == 'text.escape':
return escape(selector[1])
elif selector[0] == 'json.lower':
if len(selector) > 2:
default = selector[2]
else:
default = ""
return str(json_element(input_json, selector[1], default=default)).lower()
elif selector[0] == 'json.quote_plus':
if len(selector) > 2:
default = selector[2]
else:
default = ""
return urllib.parse.quote_plus(str(json_element(input_json, selector[1],
default=default)))
elif selector[0] == 'json.num.metric':
if len(selector) > 2:
default = selector[2]
else:
default = 0
return metric(int(json_element(input_json, selector[1], default=default)))
elif selector[0] == 'json.datetime.fromtimestamp':
if len(selector) > 2:
default = selector[2]
else:
default = 0
ts = json_element(input_json, selector[1], default=default)
return datetime.datetime.fromtimestamp(ts).strftime(selector[2])
elif selector[0] == 'json.dict.returntrue':
keys = []
json_dict = json_element(input_json, selector[1])
for key in json_dict:
if json_dict[key]:
keys.append(key)
return selector[2].join(keys)
# before general json
else:
if len(selector) > 2:
default = selector[2]
else:
default = None
return escape(str(json_element(input_json, selector[1], default=default)))
def json_element(input_dict, query, default=None):
"""Runs through a data structure and returns the selected element."""
for element in query:
is_list_index = isinstance(element, int) and isinstance(input_dict, (list, tuple))
if is_list_index or element in input_dict:
input_dict = input_dict[element]
else:
return default
return input_dict
def filename_escape(unsafe, replace_char='_', valid_chars=valid_filename_chars):
"""Escapes a string to provide a safe local filename
Arguments:
unsafe -- Unsafe string to escape
replace_char -- Character to replace unsafe characters with
valid_chars -- Valid filename characters
Returns:
Safe local filename string
"""
if not unsafe:
return ''
safe = ''
for character in unsafe:
if character in valid_chars:
safe += character
else:
safe += replace_char
return safe
_unescape_map = {
''': "'",
''': "'",
'"': "'",
}
def html_unescape(input):
"""Turns any html-escaped characters back to their normal equivalents."""
output = saxutils.unescape(input)
for char in _unescape_map.keys():
output = output.replace(char, _unescape_map[char])
return output
def utf8_bom(input):
"""Strips BOM from a utf8 string, because open() leaves it in for some reason."""
output = input.replace('\ufeff', '')
return output
class JsonHandler:
def __init__(self, base, folder, attr=None, callback_name=None, ext=None, yaml=False):
if ext:
self.pattern = [x.format(ext) for x in ['*.{}.yaml', '*.{}.json', '*_{}.py']]
else:
self.pattern = ['*.yaml', '*.json', '*.py']
self.base = base
self.attr = attr
self.folder = folder
self.ext = ext
self.callback_name = callback_name
self.yaml = yaml
self.reload()
def spread_new_json(self, new_json):
if self.attr:
setattr(self.base, self.attr, new_json)
if self.callback_name:
getattr(self.base, self.callback_name, None)(new_json)
def reload(self):
new_json = {}
if not os.path.exists(self.folder):
self.spread_new_json(new_json)
return
# loading
folders_to_scan = [self.folder]
# loading list of folders that contain modules
for f in os.listdir(self.folder):
if f == 'disabled':
continue
full_name = os.path.join(self.folder, f)
if os.path.isdir(full_name):
folders_to_scan.append(full_name)
# loading actual modules
for folder in folders_to_scan:
for f in os.listdir(folder):
full_name = os.path.join(folder, f)
if os.path.isfile(full_name):
(extname, ext) = os.path.splitext(full_name)
if ext.lower() not in ['.json', '.yaml']:
continue
# check for loader-specific extension
if self.ext:
name, ext = os.path.splitext(extname)
pyfile = '{}_{}'.format('.'.join(name.split(os.sep)), self.ext)
# not really our module
if ext != os.extsep + self.ext:
continue
else:
name, ext = extname, ''
pyfile = '.'.join(name[2:].split(os.sep))
# NOTE: this is static, and that is bad
pyfile = pyfile.lstrip('..modules.')
# py file
if self.yaml:
try:
module = importlib.import_module(pyfile)
imp.reload(module) # so reloading works
# we should capture this and output errors to stderr
except:
pass
# yaml / json
with open(full_name, encoding='utf-8') as js_f:
if self.yaml:
try:
info = yaml.load(js_f.read(), Loader=yaml.FullLoader)
# we should capture this and output errors to stderr
except Exception as ex:
print('failed to load YAML file', full_name, ':', ex)
continue
else:
info = json.loads(js_f.read())
# set module name and info
if 'name' not in info:
new_name = name.split('/')[-1].split('\\')[-1]
info['name'] = [new_name]
new_json[info['name'][0]] = info
# set info on base object and / or call callback
self.spread_new_json(new_json)
# timedelta functions
_td_str_map = [
('d', 'days'),
('h', 'hours'),
('m', 'minutes'),
('s', 'seconds'),
]
_str_td = r''
for istr, td in _td_str_map:
_str_td += r'\s*(?:(?P<' + td + r'>[0-9]+)\s*' + istr + r')?'
_TD_STR_REGEX = re.compile(_str_td)
def timedelta_to_string(delta):
"""Converts a timedelta dict to a string."""
td_string = ''
for istr, td in _td_str_map:
if td in delta:
td_string += str(delta[td])
td_string += istr
return td_string
def string_to_timedelta(td_string):
"""Converts a string to a timedelta dict."""
match = _TD_STR_REGEX.match(td_string)
delta = {}
for istr, td in _td_str_map:
if match.group(td):
if '.' in match.group(td):
val = float(match.group(td))
else:
val = int(match.group(td))
delta[td] = val
return delta
# path
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
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teams/api/views/teams_views.py | Onlynfk/Freshdesk-CRM-Platform | 0 | 174964 | <filename>teams/api/views/teams_views.py
from rest_framework import generics
from rest_framework.permissions import IsAuthenticated
from teams.api.serializers.teams_serializers import TeamSerializer
from teams.models import Teams
class TeamAPIList(generics.ListAPIView):
permission_classes = [IsAuthenticated]
queryset = Teams.objects.all().order_by('-created_on')
serializer_class = TeamSerializer
class CreateTeam(generics.CreateAPIView):
permission_classes = [IsAuthenticated]
queryset = Teams.objects.all()
serializer_class = TeamSerializer
class UpdateTeam(generics.UpdateAPIView):
permission_classes = [IsAuthenticated]
queryset = Teams.objects.all()
serializer_class = TeamSerializer
class RetrieveTeam(generics.RetrieveAPIView):
permission_classes = [IsAuthenticated]
queryset = Teams.objects.all()
serializer_class = TeamSerializer
class DestroyTeam(generics.DestroyAPIView):
permission_classes = [IsAuthenticated]
queryset = Teams.objects.all()
serializer_class = TeamSerializer
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examples/vanilla/multiple_defaults_vs_ar.py | pfistfl/openml-defaults | 1 | 63638 | import arff
import argparse
import json
import logging
import numpy as np
import openmlcontrib
import openmldefaults
import os
import pandas as pd
# SSHFS NEMO FREIBURG:
# sshfs <EMAIL>:/rigel/home/jv2657/experiments ~/habanero_experiments
#
# SSHFS GRACE LEIDEN:
# ssh -f -N -L 1233:grace.liacs.nl:22 <EMAIL>
# sshfs -p 1233 vanrijn@localhost:/home/vanrijn/experiments ~/grace_experiments
def parse_args():
metadata_file_text_classification = os.path.expanduser('../../data/text_classification.arff')
parser = argparse.ArgumentParser(description='Creates an ARFF file')
parser.add_argument('--output_directory', type=str, help='directory to store output',
default=os.path.expanduser('~/experiments/openml-defaults/at_vs_ar/'))
parser.add_argument('--task_idx', type=int)
parser.add_argument('--metadata_files', type=str, nargs='+', default=[metadata_file_text_classification])
parser.add_argument('--scoring', type=str, default='missclassification_rate')
parser.add_argument('--search_space_identifier', type=str, default='ferreira')
parser.add_argument('--minimize', action='store_true', default=True)
parser.add_argument('--normalize_base', type=str, default=None)
parser.add_argument('--normalize_a3r', type=str, default=None)
parser.add_argument('--a3r_r', type=int, default=2)
parser.add_argument('--aggregate', type=str, choices=openmldefaults.experiments.AGGREGATES, default='sum')
parser.add_argument('--n_defaults', type=int, default=384)
parser.add_argument('--n_estimators', type=int, default=64)
parser.add_argument('--minimum_evals', type=int, default=128)
parser.add_argument('--random_iterations', type=int, default=1)
parser.add_argument('--run_on_surrogates', action='store_true', default=True)
parser.add_argument('--task_limit', type=int, default=None, help='For speed')
parser.add_argument('--task_id_column', default='dataset', type=str)
parser.add_argument('--override_parameters', type=str)
args_ = parser.parse_args()
return args_
def run(args):
root = logging.getLogger()
root.setLevel(logging.INFO)
task_ids = None
for arff_file in args.metadata_files:
with open(arff_file, 'r') as fp:
df = openmlcontrib.meta.arff_to_dataframe(arff.load(fp), None)
if task_ids is None:
task_ids = np.sort(np.unique(df[args.task_id_column].values))
else:
task_ids = np.sort(np.unique(np.append(task_ids, df[args.task_id_column].values)))
logging.info('Task ids: %s' % task_ids)
if args.task_idx is None:
task_ids_to_process = task_ids
else:
task_ids_to_process = [task_ids[args.task_idx]]
# run random search
for random_seed in range(args.random_iterations):
for task_id in task_ids_to_process:
openmldefaults.experiments.run_vanilla_surrogates_on_task(
task_id=task_id,
models=[openmldefaults.models.AverageRankDefaults(), openmldefaults.models.ActiveTestingDefaults()],
use_surrogates=False,
random_seed=random_seed,
search_space_identifier=args.search_space_identifier,
metadata_files=args.metadata_files,
scoring=args.scoring,
minimize_measure=args.minimize,
n_defaults=args.n_defaults,
aggregate=args.aggregate,
a3r_r=args.a3r_r,
normalize_base=args.normalize_base,
normalize_a3r=args.normalize_a3r,
surrogate_n_estimators=args.n_estimators,
surrogate_minimum_evals=args.minimum_evals,
runtime_column='runtime',
consider_a3r=True,
evaluate_on_surrogate=args.run_on_surrogates,
task_limit=args.task_limit,
output_directory=args.output_directory,
task_id_column=args.task_id_column,
skip_row_check=True,
override_parameters=json.loads(args.override_parameters) if args.override_parameters else None
)
if __name__ == '__main__':
pd.options.mode.chained_assignment = 'raise'
run(parse_args())
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exps/algos-v2/bohb.py | PeterouZh/AutoDL-Projects | 0 | 86761 | ##################################################
# Copyright (c) <NAME> [GitHub D-X-Y], 2020 #
###################################################################
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
# required to install hpbandster ##################################
# pip install hpbandster ##################################
###################################################################
# OMP_NUM_THREADS=4 python exps/algos-v2/bohb.py --search_space tss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
# OMP_NUM_THREADS=4 python exps/algos-v2/bohb.py --search_space sss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
###################################################################
import os, sys, time, random, argparse, collections
from copy import deepcopy
from pathlib import Path
import torch
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config
from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_201_api import NASBench201API, NASBench301API
from models import CellStructure, get_search_spaces
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
from hpbandster.optimizers.bohb import BOHB
import hpbandster.core.nameserver as hpns
from hpbandster.core.worker import Worker
def get_topology_config_space(search_space, max_nodes=4):
cs = ConfigSpace.ConfigurationSpace()
#edge2index = {}
for i in range(1, max_nodes):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space))
return cs
def get_size_config_space(search_space):
cs = ConfigSpace.ConfigurationSpace()
for ilayer in range(search_space['numbers']):
node_str = 'layer-{:}'.format(ilayer)
cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space['candidates']))
return cs
def config2topology_func(max_nodes=4):
def config2structure(config):
genotypes = []
for i in range(1, max_nodes):
xlist = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
op_name = config[node_str]
xlist.append((op_name, j))
genotypes.append( tuple(xlist) )
return CellStructure( genotypes )
return config2structure
def config2size_func(search_space):
def config2structure(config):
channels = []
for ilayer in range(search_space['numbers']):
node_str = 'layer-{:}'.format(ilayer)
channels.append(str(config[node_str]))
return ':'.join(channels)
return config2structure
class MyWorker(Worker):
def __init__(self, *args, convert_func=None, dataset=None, api=None, **kwargs):
super().__init__(*args, **kwargs)
self.convert_func = convert_func
self._dataset = dataset
self._api = api
self.total_times = []
self.trajectory = []
def compute(self, config, budget, **kwargs):
arch = self.convert_func( config )
accuracy, latency, time_cost, total_time = self._api.simulate_train_eval(arch, self._dataset, iepoch=int(budget)-1, hp='12')
self.trajectory.append((accuracy, arch))
self.total_times.append(total_time)
return ({'loss': 100 - accuracy,
'info': self._api.query_index_by_arch(arch)})
def main(xargs, api):
torch.set_num_threads(4)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
logger.log('{:} use api : {:}'.format(time_string(), api))
api.reset_time()
search_space = get_search_spaces(xargs.search_space, 'nas-bench-301')
if xargs.search_space == 'tss':
cs = get_topology_config_space(search_space)
config2structure = config2topology_func()
else:
cs = get_size_config_space(search_space)
config2structure = config2size_func(search_space)
hb_run_id = '0'
NS = hpns.NameServer(run_id=hb_run_id, host='localhost', port=0)
ns_host, ns_port = NS.start()
num_workers = 1
workers = []
for i in range(num_workers):
w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataset=xargs.dataset, api=api, run_id=hb_run_id, id=i)
w.run(background=True)
workers.append(w)
start_time = time.time()
bohb = BOHB(configspace=cs, run_id=hb_run_id,
eta=3, min_budget=1, max_budget=12,
nameserver=ns_host,
nameserver_port=ns_port,
num_samples=xargs.num_samples,
random_fraction=xargs.random_fraction, bandwidth_factor=xargs.bandwidth_factor,
ping_interval=10, min_bandwidth=xargs.min_bandwidth)
results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
# print('There are {:} runs.'.format(len(results.get_all_runs())))
# workers[0].total_times
# workers[0].trajectory
current_best_index = []
for idx in range(len(workers[0].trajectory)):
trajectory = workers[0].trajectory[:idx+1]
arch = max(trajectory, key=lambda x: x[0])[1]
current_best_index.append(api.query_index_by_arch(arch))
best_arch = max(workers[0].trajectory, key=lambda x: x[0])[1]
logger.log('Best found configuration: {:} within {:.3f} s'.format(best_arch, workers[0].total_times[-1]))
info = api.query_info_str_by_arch(best_arch, '200' if xargs.search_space == 'tss' else '90')
logger.log('{:}'.format(info))
logger.log('-'*100)
logger.close()
return logger.log_dir, current_best_index, workers[0].total_times
if __name__ == '__main__':
parser = argparse.ArgumentParser("BOHB: Robust and Efficient Hyperparameter Optimization at Scale")
parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
# general arg
parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).')
parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.')
# BOHB
parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function')
parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE')
parser.add_argument('--num_samples', default=64, type=int, nargs='?', help='number of samples for the acquisition function')
parser.add_argument('--random_fraction', default=.33, type=float, nargs='?', help='fraction of random configurations')
parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth')
parser.add_argument('--n_iters', default=300, type=int, nargs='?', help='number of iterations for optimization method')
# log
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
parser.add_argument('--rand_seed', type=int, default=-1, help='manual seed')
args = parser.parse_args()
if args.search_space == 'tss':
api = NASBench201API(verbose=False)
elif args.search_space == 'sss':
api = NASBench301API(verbose=False)
else:
raise ValueError('Invalid search space : {:}'.format(args.search_space))
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'BOHB')
print('save-dir : {:}'.format(args.save_dir))
if args.rand_seed < 0:
save_dir, all_info = None, collections.OrderedDict()
for i in range(args.loops_if_rand):
print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand))
args.rand_seed = random.randint(1, 100000)
save_dir, all_archs, all_total_times = main(args, api)
all_info[i] = {'all_archs': all_archs,
'all_total_times': all_total_times}
save_path = save_dir / 'results.pth'
print('save into {:}'.format(save_path))
torch.save(all_info, save_path)
else:
main(args, api)
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accounts/models.py | olamijinadebayo/hood-watch | 0 | 175888 | <reponame>olamijinadebayo/hood-watch<filename>accounts/models.py
from django.db import models
from django.contrib import auth
# Create your models here.
class User(auth.models.User, auth.models.PermissionsMixin):
'''
This will initialize the user class which inherits classes
from the django.contrib class
'''
def __str__(self):
'''
This is a string reperesentation of each user
'''
return "@{}".format(self.username)
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tests/bitly/*REPL* [python].py | goldsborough/lnk | 3 | 27166 | Python 3.5.0 (default, Sep 14 2015, 02:37:27)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> a = [{'a': 1}, {'b': 2}]
>>> sorted(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unorderable types: dict() < dict()
>>> b = [{'b': 2}, {'a': 1}] | [
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src/stk/molecular/topology_graphs/topology_graph/topology_graph/__init__.py | stevenbennett96/stk | 21 | 104231 | <reponame>stevenbennett96/stk
from .topology_graph import * # noqa
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http_shadow/__init__.py | abador/http-shadow | 0 | 13391 | <gh_stars>0
from .backend import Backend
from .thread import HttpPool
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Translocations_Detector/mAP/Scalable_mean_avg_precision.py | dariodematties/NANOPORE_TRANSLOCATIOS | 0 | 97749 | <filename>Translocations_Detector/mAP/Scalable_mean_avg_precision.py
# Mean average precision (mAP) computation
# Source from this amazing tutorial by <NAME>
# https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/object_detection/metrics/mean_avg_precision.py
# But I modified it to make it scalable
import sys
import torch
from collections import Counter
sys.path.append('./mAP')
from Scalable_IoU import intersection_over_union
def mean_average_precision(
device, pred_segments, true_segments, iou_threshold=0.5, seg_format="mix", num_classes=1
):
"""
Calculates mean average precision
Parameters:
device (str): the device used in the run
pred_segments (list): list of lists containing all bsegments with each bsegments
specified as [train_idx, class_prediction, prob_score, x1, x2]
true_segments (list): Similar as pred_segments except all the correct ones
iou_threshold (float): threshold where predicted bsegments is correct
segment_format (str): mix/midpoint/extremes, if (left,w) or (center,w) or (left,right) are respectively used to specify bsegments
num_classes (int): number of classes
Returns:
float: mAP value across all classes given a specific IoU threshold
"""
# list storing all AP for respective classes
average_precisions = []
# used for numerical stability later on
epsilon = 1e-6
for c in range(num_classes):
detections = []
ground_truths = []
# Go through all predictions and targets,
# and only add the ones that belong to the
# current class c
for detection in pred_segments:
if detection[1] == c:
detections.append(detection)
for true_segment in true_segments:
if true_segment[1] == c:
ground_truths.append(true_segment)
# find the amount of bsegments for each training example
# Counter here finds how many ground truth bsegments we get
# for each training example, so let's say wind 0 has 3,
# wind 1 has 5 then we will obtain a dictionary with:
# amount_bsegments = {0:3, 1:5}
amount_bsegments = Counter([gt[0] for gt in ground_truths])
# We then go through each key, val in this dictionary
# and convert to the following (w.r.t same example):
# ammount_bsegments = {0:torch.tensor[0,0,0], 1:torch.tensor[0,0,0,0,0]}
for key, val in amount_bsegments.items():
amount_bsegments[key] = torch.zeros(val)
# sort by segment probabilities which is index 2
detections.sort(key=lambda x: x[2], reverse=True)
TP = torch.zeros((len(detections)))
FP = torch.zeros((len(detections)))
total_true_bsegments = len(ground_truths)
# If none exists for this class then we can safely skip
if total_true_bsegments == 0:
continue
ground_truths = torch.tensor(ground_truths)
for detection_idx, detection in enumerate(detections):
detection = torch.tensor(detection)
# Only take out the ground_truths that have the same
# training idx as detection
loc = detection[0] == ground_truths[:,0]
ground_truth_wind = ground_truths[loc]
num_gts = ground_truth_wind.shape[0]
best_iou = 0
if num_gts > 0:
iou = intersection_over_union(
torch.unsqueeze(detection[3:],0).to(device),
ground_truth_wind[:,3:].to(device),
segment_format=seg_format,
)
best_iou = torch.max(iou)
best_gt_idx = torch.argmax(iou)
if best_iou > iou_threshold:
# only detect ground truth detection once
if amount_bsegments[int(detection[0])][best_gt_idx] == 0:
# true positive and add this bounding segment to seen
TP[detection_idx] = 1
amount_bsegments[int(detection[0])][best_gt_idx] = 1
else:
FP[detection_idx] = 1
# if IOU is lower then the detection is a false positive
else:
FP[detection_idx] = 1
TP_cumsum = torch.cumsum(TP, dim=0)
FP_cumsum = torch.cumsum(FP, dim=0)
recalls = TP_cumsum / (total_true_bsegments + epsilon)
precisions = TP_cumsum / (TP_cumsum + FP_cumsum + epsilon)
precisions = torch.cat((torch.tensor([1]).type(torch.float32), precisions))
recalls = torch.cat((torch.tensor([0]).type(torch.float32), recalls))
# torch.trapz for numerical integration
average_precisions.append(torch.trapz(precisions, recalls))
return sum(average_precisions) / len(average_precisions)
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] |
script/hassfest/translations.py | KingofTown/core | 0 | 124204 | """Validate integration translation files."""
import json
from typing import Dict
import voluptuous as vol
from voluptuous.humanize import humanize_error
from .model import Integration
def data_entry_schema(*, require_title: bool, require_step_title: bool):
"""Generate a data entry schema."""
step_title_class = vol.Required if require_step_title else vol.Optional
data_entry_schema = {
vol.Optional("flow_title"): str,
vol.Required("step"): {
str: {
step_title_class("title"): str,
vol.Optional("description"): str,
vol.Optional("data"): {str: str},
}
},
vol.Optional("error"): {str: str},
vol.Optional("abort"): {str: str},
vol.Optional("create_entry"): {str: str},
}
if require_title:
data_entry_schema[vol.Required("title")] = str
return data_entry_schema
STRINGS_SCHEMA = vol.Schema(
{
vol.Optional("title"): str,
vol.Optional("config"): data_entry_schema(
require_title=False, require_step_title=True
),
vol.Optional("options"): data_entry_schema(
require_title=False, require_step_title=False
),
vol.Optional("device_automation"): {
vol.Optional("action_type"): {str: str},
vol.Optional("condition_type"): {str: str},
vol.Optional("trigger_type"): {str: str},
vol.Optional("trigger_subtype"): {str: str},
},
vol.Optional("state"): {str: str},
}
)
AUTH_SCHEMA = vol.Schema(
{
vol.Optional("mfa_setup"): {
str: data_entry_schema(require_title=True, require_step_title=True)
}
}
)
ONBOARDING_SCHEMA = vol.Schema({vol.Required("area"): {str: str}})
def validate_translation_file(integration: Integration):
"""Validate translation files for integration."""
strings_file = integration.path / "strings.json"
if not strings_file.is_file():
return
strings = json.loads(strings_file.read_text())
if integration.domain == "auth":
schema = AUTH_SCHEMA
elif integration.domain == "onboarding":
schema = ONBOARDING_SCHEMA
else:
schema = STRINGS_SCHEMA
try:
schema(strings)
except vol.Invalid as err:
integration.add_error(
"translations", f"Invalid strings.json: {humanize_error(strings, err)}"
)
def validate(integrations: Dict[str, Integration], config):
"""Handle JSON files inside integrations."""
for integration in integrations.values():
validate_translation_file(integration)
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python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py | SmirnovKol/Paddle | 0 | 139468 | <reponame>SmirnovKol/Paddle
# copyright (c) 2018 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
from __future__ import print_function
import os
import numpy as np
import random
import shutil
import time
import unittest
import copy
import logging
import paddle.nn as nn
import paddle
import paddle.fluid as fluid
from paddle.fluid.contrib.slim.quantization import *
from paddle.fluid.log_helper import get_logger
from paddle.dataset.common import download
from paddle.fluid.framework import _test_eager_guard
from imperative_test_utils import fix_model_dict, ImperativeLenet, ImperativeLinearBn
from imperative_test_utils import ImperativeLinearBn_hook
_logger = get_logger(__name__,
logging.INFO,
fmt='%(asctime)s-%(levelname)s: %(message)s')
class TestFuseLinearBn(unittest.TestCase):
"""
Fuse the linear and bn layers, and then quantize the model.
"""
def test_fuse(self):
model = ImperativeLinearBn()
model_h = ImperativeLinearBn_hook()
inputs = paddle.randn((3, 10), dtype="float32")
config = PTQConfig(AbsmaxQuantizer(), AbsmaxQuantizer())
ptq = ImperativePTQ(config)
f_l = [['linear', 'bn']]
quant_model = ptq.quantize(model, fuse=True, fuse_list=f_l)
quant_h = ptq.quantize(model_h, fuse=True, fuse_list=f_l)
for name, layer in quant_model.named_sublayers():
if name in f_l:
assert not (isinstance(layer, nn.BatchNorm1D)
or isinstance(layer, nn.BatchNorm2D))
out = model(inputs)
out_h = model_h(inputs)
out_quant = quant_model(inputs)
out_quant_h = quant_h(inputs)
cos_sim_func = nn.CosineSimilarity(axis=0)
print('fuse linear+bn', cos_sim_func(out.flatten(),
out_quant.flatten()))
print(cos_sim_func(out_h.flatten(), out_quant_h.flatten()))
class TestImperativePTQ(unittest.TestCase):
"""
"""
@classmethod
def setUpClass(cls):
timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
cls.root_path = os.path.join(os.getcwd(), "imperative_ptq_" + timestamp)
cls.save_path = os.path.join(cls.root_path, "model")
cls.download_path = 'dygraph_int8/download'
cls.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
cls.download_path)
cls.lenet_url = "https://paddle-inference-dist.cdn.bcebos.com/int8/unittest_model_data/lenet_pretrained.tar.gz"
cls.lenet_md5 = "953b802fb73b52fae42896e3c24f0afb"
seed = 1
np.random.seed(seed)
paddle.static.default_main_program().random_seed = seed
paddle.static.default_startup_program().random_seed = seed
@classmethod
def tearDownClass(cls):
try:
pass
# shutil.rmtree(cls.root_path)
except Exception as e:
print("Failed to delete {} due to {}".format(cls.root_path, str(e)))
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
cmd = 'mkdir {0} && tar xf {1} -C {0}'.format(
target_folder, zip_path)
os.system(cmd)
def download_model(self, data_url, data_md5, folder_name):
download(data_url, self.download_path, data_md5)
file_name = data_url.split('/')[-1]
zip_path = os.path.join(self.cache_folder, file_name)
print('Data is downloaded at {0}'.format(zip_path))
data_cache_folder = os.path.join(self.cache_folder, folder_name)
self.cache_unzipping(data_cache_folder, zip_path)
return data_cache_folder
def set_vars(self):
config = PTQConfig(AbsmaxQuantizer(), AbsmaxQuantizer())
self.ptq = ImperativePTQ(config)
self.batch_num = 10
self.batch_size = 10
self.eval_acc_top1 = 0.95
# the input, output and weight thresholds of quantized op
self.gt_thresholds = {
'conv2d_0': [[1.0], [0.37673383951187134], [0.10933732241392136]],
'batch_norm2d_0': [[0.37673383951187134], [0.44249194860458374]],
're_lu_0': [[0.44249194860458374], [0.25804123282432556]],
'max_pool2d_0': [[0.25804123282432556], [0.25804123282432556]],
'linear_0': [[1.7058950662612915], [14.405526161193848],
[0.4373355209827423]],
'add_0': [[1.7058950662612915, 0.0], [1.7058950662612915]],
}
def model_test(self, model, batch_num=-1, batch_size=8):
model.eval()
test_reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=batch_size)
eval_acc_top1_list = []
for batch_id, data in enumerate(test_reader()):
x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array([x[1]
for x in data]).astype('int64').reshape(-1, 1)
img = paddle.to_tensor(x_data)
label = paddle.to_tensor(y_data)
out = model(img)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
eval_acc_top1_list.append(float(acc_top1.numpy()))
if batch_id % 50 == 0:
_logger.info("Test | At step {}: acc1 = {:}, acc5 = {:}".format(
batch_id, acc_top1.numpy(), acc_top5.numpy()))
if batch_num > 0 and batch_id + 1 >= batch_num:
break
eval_acc_top1 = sum(eval_acc_top1_list) / len(eval_acc_top1_list)
return eval_acc_top1
def program_test(self, program_path, batch_num=-1, batch_size=8):
exe = paddle.static.Executor(paddle.CPUPlace())
[inference_program, feed_target_names, fetch_targets
] = (paddle.static.load_inference_model(program_path, exe))
test_reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=batch_size)
top1_correct_num = 0.
total_num = 0.
for batch_id, data in enumerate(test_reader()):
img = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
label = np.array([x[1] for x in data]).astype('int64')
feed = {feed_target_names[0]: img}
results = exe.run(inference_program,
feed=feed,
fetch_list=fetch_targets)
pred = np.argmax(results[0], axis=1)
top1_correct_num += np.sum(np.equal(pred, label))
total_num += len(img)
if total_num % 50 == 49:
_logger.info("Test | Test num {}: acc1 = {:}".format(
total_num, top1_correct_num / total_num))
if batch_num > 0 and batch_id + 1 >= batch_num:
break
return top1_correct_num / total_num
def func_ptq(self):
start_time = time.time()
self.set_vars()
# Load model
params_path = self.download_model(self.lenet_url, self.lenet_md5,
"lenet")
params_path += "/lenet_pretrained/lenet.pdparams"
model = ImperativeLenet()
model_state_dict = paddle.load(params_path)
model.set_state_dict(model_state_dict)
# Quantize, calibrate and save
quant_model = self.ptq.quantize(model)
before_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
input_spec = [
paddle.static.InputSpec(shape=[None, 1, 28, 28], dtype='float32')
]
self.ptq.save_quantized_model(model=quant_model,
path=self.save_path,
input_spec=input_spec)
print('Quantized model saved in {%s}' % self.save_path)
after_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
paddle.enable_static()
infer_acc_top1 = self.program_test(self.save_path, self.batch_num,
self.batch_size)
paddle.disable_static()
# Check
print('Before converted acc_top1: %s' % before_acc_top1)
print('After converted acc_top1: %s' % after_acc_top1)
print('Infer acc_top1: %s' % infer_acc_top1)
self.assertTrue(after_acc_top1 >= self.eval_acc_top1,
msg="The test acc {%f} is less than {%f}." %
(after_acc_top1, self.eval_acc_top1))
self.assertTrue(infer_acc_top1 >= after_acc_top1,
msg='The acc is lower after converting model.')
end_time = time.time()
print("total time: %ss \n" % (end_time - start_time))
def test_ptq(self):
with _test_eager_guard():
self.func_ptq()
self.func_ptq()
class TestImperativePTQfuse(TestImperativePTQ):
def func_ptq(self):
start_time = time.time()
self.set_vars()
# Load model
params_path = self.download_model(self.lenet_url, self.lenet_md5,
"lenet")
params_path += "/lenet_pretrained/lenet.pdparams"
model = ImperativeLenet()
model_state_dict = paddle.load(params_path)
model.set_state_dict(model_state_dict)
# Quantize, calibrate and save
f_l = [['features.0', 'features.1'], ['features.4', 'features.5']]
quant_model = self.ptq.quantize(model, fuse=True, fuse_list=f_l)
for name, layer in quant_model.named_sublayers():
if name in f_l:
assert not (isinstance(layer, nn.BatchNorm1D)
or isinstance(layer, nn.BatchNorm2D))
before_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
input_spec = [
paddle.static.InputSpec(shape=[None, 1, 28, 28], dtype='float32')
]
self.ptq.save_quantized_model(model=quant_model,
path=self.save_path,
input_spec=input_spec)
print('Quantized model saved in {%s}' % self.save_path)
after_acc_top1 = self.model_test(quant_model, self.batch_num,
self.batch_size)
paddle.enable_static()
infer_acc_top1 = self.program_test(self.save_path, self.batch_num,
self.batch_size)
paddle.disable_static()
# Check
print('Before converted acc_top1: %s' % before_acc_top1)
print('After converted acc_top1: %s' % after_acc_top1)
print('Infer acc_top1: %s' % infer_acc_top1)
#Check whether the quant_model is correct after converting.
#The acc of quantized model should be higher than 0.95.
self.assertTrue(after_acc_top1 >= self.eval_acc_top1,
msg="The test acc {%f} is less than {%f}." %
(after_acc_top1, self.eval_acc_top1))
#Check the saved infer_model.The acc of infer model
#should not be lower than the one of dygraph model.
self.assertTrue(infer_acc_top1 >= after_acc_top1,
msg='The acc is lower after converting model.')
end_time = time.time()
print("total time: %ss \n" % (end_time - start_time))
def test_ptq(self):
with _test_eager_guard():
self.func_ptq()
self.func_ptq()
class TestImperativePTQHist(TestImperativePTQ):
def set_vars(self):
config = PTQConfig(HistQuantizer(), AbsmaxQuantizer())
self.ptq = ImperativePTQ(config)
self.batch_num = 10
self.batch_size = 10
self.eval_acc_top1 = 0.98
self.gt_thresholds = {
'conv2d_0': [[0.99853515625], [0.35732391771364225],
[0.10933732241392136]],
'batch_norm2d_0': [[0.35732391771364225], [0.4291427868761275]],
're_lu_0': [[0.4291427868761275], [0.2359918110742001]],
'max_pool2d_0': [[0.2359918110742001], [0.25665526917146053]],
'linear_0': [[1.7037603475152991], [14.395224522473026],
[0.4373355209827423]],
'add_0': [[1.7037603475152991, 0.0], [1.7037603475152991]],
}
class TestImperativePTQKL(TestImperativePTQ):
def set_vars(self):
config = PTQConfig(KLQuantizer(), PerChannelAbsmaxQuantizer())
self.ptq = ImperativePTQ(config)
self.batch_num = 10
self.batch_size = 10
self.eval_acc_top1 = 1.0
conv2d_1_wt_thresholds = [
0.18116560578346252, 0.17079241573810577, 0.1702047884464264,
0.179476797580719, 0.1454375684261322, 0.22981858253479004
]
self.gt_thresholds = {
'conv2d_0': [[0.99267578125], [0.37695913558696836]],
'conv2d_1': [[0.19189296757394914], [0.24514256547263358],
[conv2d_1_wt_thresholds]],
'batch_norm2d_0': [[0.37695913558696836], [0.27462541429440535]],
're_lu_0': [[0.27462541429440535], [0.19189296757394914]],
'max_pool2d_0': [[0.19189296757394914], [0.19189296757394914]],
'linear_0': [[1.2839322163611087], [8.957185942414352]],
'add_0': [[1.2839322163611087, 0.0], [1.2839322163611087]],
}
if __name__ == '__main__':
unittest.main()
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] |
examples/test_buckcol.py | Tarheel-Formal-Methods/kaa-optimize | 0 | 71187 | from models.buckcol import BuckCol_UnitBox
from kaa.experi_init import *
from kaa.timer import Timer
def test_box_BuckCol():
num_steps = 4
model = BuckCol_UnitBox()
experi_input = dict(model=model,
strat=None,
label=f"BuckCol Box Reachable Set",
num_steps=num_steps)
experi = PhasePlotExperiment(experi_input)
experi.execute(0, 1, plot_border_traj=True)
Timer.generate_stats()
def test_sliding_equal_BuckCol():
num_steps = 60
model = BuckCol_UnitBox()
test_equal_sliding_strat(model, num_steps)
def test_max_sliding_lin_strat_BuckCol():
num_steps = 20
model = BuckCol_UnitBox()
test_max_sliding_lin_strat(model, num_steps)
def test_pca_dominant_BuckCol():
num_steps = 20
model = BuckCol_UnitBox()
use_supp = True
use_pregen = False
num_trajs = 5000
pca_strat = SlidingPCAStrat(model, lifespan=5)
lin_strat = SlidingLinStrat(model, lifespan=15)
experi_input = dict(model=model,
strat=MultiStrategy(pca_strat, lin_strat),
label=f"SlidingPCA Size 15, SlidingLin Size 5",
supp_mode = use_supp,
pregen_mode = use_pregen,
num_trajs=num_trajs,
num_steps=num_steps-1,
max_steps=num_steps)
if use_supp:
file_identifier = "(SUPP)"
elif use_pregen:
file_identifier = f"(PREGEN: {num_trajs})"
else:
file_identifier = "(RAND)"
experi = PhasePlotExperiment(experi_input)
experi.execute(0, 1, plot_border_traj=True)
Timer.generate_stats()
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destiny_timelost/constants.py | dmfigol/destiny-timelost | 0 | 125118 | <gh_stars>0
GOOGLE_SHEET_URL = (
"https://sheets.googleapis.com/v4/spreadsheets/{sheet_id}/values/{table_name}"
)
LOGGING_DICT = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {"standard": {"format": "%(message)s"}},
"handlers": {
"file": {
"level": "INFO",
"class": "logging.handlers.RotatingFileHandler",
"filename": "app.log",
"maxBytes": 1024 * 1024 * 5,
"backupCount": 5,
"formatter": "standard",
},
"console": {
"level": "INFO",
"class": "logging.StreamHandler",
"stream": "ext://sys.stdout",
"formatter": "standard",
},
},
"loggers": {},
"root": {"handlers": ["file"], "level": "INFO"},
}
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HW1/main.py | mapa17/LearningFromData | 0 | 43721 | <filename>HW1/main.py
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 20 18:28:50 2015
@author: <NAME> , <EMAIL>
Homework Assignment from https://work.caltech.edu/telecourse.html
Simple Perceptron Learning Model Algorithm
"""
import sys
import errno
import random
def main(argv):
if len(argv) < 2:
print('Please specify the number of random Training sample Points N')
print('Usage %s N' % argv[0])
sys.exit(errno.EINVAL)
trainingData = createTestData(N)
def createTestData(N):
#Sample N random values in the range x =[-1,1], y=[-1,1], and asign them two either
#group 0, or 1 depending if the are left of x = 0 or right of it.
coordinates = [(-1.0 + random.random()*2.0, -1.0 + random.random()*2.0) for x in range(N)]
points = [ (p[0], p[1], 0) if p[0] < 0.0 else (p[0], p[1], 1) for p in coordinates]
return points
if __name__ == '__main__':
main(sys.argv) | [
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umlawuli/urls.py | mrdvince/dgenie | 0 | 186739 | <filename>umlawuli/urls.py
from django.urls import include, path
from . import views
urlpatterns = [
path("", include("common.urls")),
path("admins/", views.admin_api_view, name="admins"),
path("users/", views.users_api_view, name="users"),
]
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110.py | whoophee/DCP | 2 | 62020 | <reponame>whoophee/DCP<filename>110.py
# This problem was asked by Facebook.
# Given a binary tree, return all paths from the root to leaves.
# For example, given the tree
# 1
# / \
# 2 3
# / \
# 4 5
# it should return [[1, 2], [1, 3, 4], [1, 3, 5]].
####
class Node:
def __init__(self, val = None, left = None, right = None):
self.val = val
self.left = left
self.right = right
l1 = Node(5)
l2 = Node(3)
l3 = Node(7)
l4 = Node(4)
m1 = Node(2, l1, l2)
m2 = Node(1, l3, l4)
root = Node(6, m1, m2)
####
def paths(root):
# if no node, it does not contribute to the path
if not root:
return []
# if leaf node, return node as is
if not root.left and not root.right:
return [[root.val]]
# generate paths to the left and right of current node
p = paths(root.left) + paths(root.right)
# prepend current value to generated paths
p = [[root.val] + p[i] for i in range(len(p))]
return p
####
print(paths(root))
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] |
example2.py | jwllee/pygame-menu | 0 | 154333 | # coding=utf-8
"""
pygame-menu
https://github.com/ppizarror/pygame-menu
EXAMPLE 2
Game menu with 3 difficulty options.
License:
-------------------------------------------------------------------------------
The MIT License (MIT)
Copyright 2017-2019 <NAME>. @ppizarror
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software
is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-------------------------------------------------------------------------------
"""
# Import libraries
from random import randrange
import os
# Import pygame
import pygame
import pygameMenu
ABOUT = ['pygameMenu {0}'.format(pygameMenu.__version__),
'Author: {0}'.format(pygameMenu.__author__),
pygameMenu.locals.PYGAMEMENU_TEXT_NEWLINE,
'Email: {0}'.format(pygameMenu.__email__)]
COLOR_BACKGROUND = (128, 0, 128)
COLOR_BLACK = (0, 0, 0)
COLOR_WHITE = (255, 255, 255)
FPS = 60.0
MENU_BACKGROUND_COLOR = (228, 55, 36)
WINDOW_SIZE = (640, 480)
# -----------------------------------------------------------------------------
# Init pygame
# -----------------------------------------------------------------------------
pygame.init()
os.environ['SDL_VIDEO_CENTERED'] = '1'
# Create pygame screen and objects
surface = pygame.display.set_mode(WINDOW_SIZE)
pygame.display.set_caption('PygameMenu Example 2')
clock = pygame.time.Clock()
dt = 1 / FPS
# Global variables
DIFFICULTY = ['EASY']
# -----------------------------------------------------------------------------
# Methods
# -----------------------------------------------------------------------------
def change_difficulty(value, d):
"""
Change difficulty of the game.
:param value: Tuple containing the data of the selected object
:type value: tuple
:param d: Optional parameter passed as argument to add_selector
:type d: basestring
:return: None
"""
selected, index = value
print('Selected difficulty: "{0}" ({1}) at index {2}'.format(selected, d, index))
DIFFICULTY[0] = d
def random_color():
"""
Return random color.
:return: Color tuple
:rtype: tuple
"""
return randrange(0, 255), randrange(0, 255), randrange(0, 255)
def play_function(difficulty, font):
"""
Main game function.
:param difficulty: Difficulty of the game
:type difficulty: basestring
:param font: Pygame font
:type font: pygame._font.Font
:return: None
"""
difficulty = difficulty[0]
assert isinstance(difficulty, str)
if difficulty == 'EASY':
f = font.render('Playing as baby', 1, COLOR_WHITE)
elif difficulty == 'MEDIUM':
f = font.render('Playing as normie', 1, COLOR_WHITE)
elif difficulty == 'HARD':
f = font.render('Playing as god', 1, COLOR_WHITE)
else:
raise Exception('Unknown difficulty {0}'.format(difficulty))
# Draw random color and text
bg_color = random_color()
f_width = f.get_size()[0]
# Reset main menu and disable
# You also can set another menu, like a 'pause menu', or just use the same
# main_menu as the menu that will check all your input.
main_menu.disable()
main_menu.reset(1)
while True:
# Clock tick
clock.tick(60)
# Application events
playevents = pygame.event.get()
for e in playevents:
if e.type == pygame.QUIT:
exit()
elif e.type == pygame.KEYDOWN:
if e.key == pygame.K_ESCAPE and main_menu.is_disabled():
main_menu.enable()
# Quit this function, then skip to loop of main-menu on line 260
return
# Pass events to main_menu
main_menu.mainloop(playevents)
# Continue playing
surface.fill(bg_color)
surface.blit(f, ((WINDOW_SIZE[0] - f_width) / 2, WINDOW_SIZE[1] / 2))
pygame.display.flip()
def main_background():
"""
Function used by menus, draw on background while menu is active.
:return: None
"""
surface.fill(COLOR_BACKGROUND)
# -----------------------------------------------------------------------------
# Create menus
# -----------------------------------------------------------------------------
# Play menu
play_menu = pygameMenu.Menu(surface,
bgfun=main_background,
color_selected=COLOR_WHITE,
font=pygameMenu.fonts.FONT_BEBAS,
font_color=COLOR_BLACK,
font_size=30,
menu_alpha=100,
menu_color=MENU_BACKGROUND_COLOR,
menu_height=int(WINDOW_SIZE[1] * 0.6),
menu_width=int(WINDOW_SIZE[0] * 0.7),
onclose=pygameMenu.events.PYGAME_MENU_DISABLE_CLOSE,
option_shadow=False,
title='Play menu',
window_height=WINDOW_SIZE[1],
window_width=WINDOW_SIZE[0]
)
# When pressing return -> play(DIFFICULTY[0], font)
play_menu.add_option('Start',
play_function,
DIFFICULTY,
pygame.font.Font(pygameMenu.fonts.FONT_FRANCHISE, 30))
play_menu.add_selector('Select difficulty',
[('1 - Easy', 'EASY'),
('2 - Medium', 'MEDIUM'),
('3 - Hard', 'HARD')],
onchange=change_difficulty)
play_menu.add_option('Return to main menu', pygameMenu.events.PYGAME_MENU_BACK)
# About menu
about_menu = pygameMenu.TextMenu(surface,
bgfun=main_background,
color_selected=COLOR_WHITE,
font=pygameMenu.fonts.FONT_BEBAS,
font_color=COLOR_BLACK,
font_size_title=30,
font_title=pygameMenu.fonts.FONT_8BIT,
menu_color=MENU_BACKGROUND_COLOR,
menu_color_title=COLOR_WHITE,
menu_height=int(WINDOW_SIZE[1] * 0.6),
menu_width=int(WINDOW_SIZE[0] * 0.6),
onclose=pygameMenu.events.PYGAME_MENU_DISABLE_CLOSE,
option_shadow=False,
text_color=COLOR_BLACK,
text_fontsize=20,
title='About',
window_height=WINDOW_SIZE[1],
window_width=WINDOW_SIZE[0]
)
for m in ABOUT:
about_menu.add_line(m)
about_menu.add_line(pygameMenu.locals.PYGAMEMENU_TEXT_NEWLINE)
about_menu.add_option('Return to menu', pygameMenu.events.PYGAME_MENU_BACK)
# Main menu
main_menu = pygameMenu.Menu(surface,
bgfun=main_background,
color_selected=COLOR_WHITE,
font=pygameMenu.fonts.FONT_BEBAS,
font_color=COLOR_BLACK,
font_size=30,
menu_alpha=100,
menu_color=MENU_BACKGROUND_COLOR,
menu_height=int(WINDOW_SIZE[1] * 0.6),
menu_width=int(WINDOW_SIZE[0] * 0.6),
onclose=pygameMenu.events.PYGAME_MENU_DISABLE_CLOSE,
option_shadow=False,
title='Main menu',
window_height=WINDOW_SIZE[1],
window_width=WINDOW_SIZE[0]
)
main_menu.add_option('Play', play_menu)
main_menu.add_option('About', about_menu)
main_menu.add_option('Quit', pygameMenu.events.PYGAME_MENU_EXIT)
# -----------------------------------------------------------------------------
# Main loop
# -----------------------------------------------------------------------------
while True:
# Tick
clock.tick(60)
# Paint background
main_background()
# Application events
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
exit()
# Main menu
main_menu.mainloop(events)
# Flip surface
pygame.display.flip()
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] |
textpreprocess/__init__.py | dreamvrutik/textclean | 0 | 94692 | <reponame>dreamvrutik/textclean<gh_stars>0
from textpreprocess.process import process
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DS_Scripts/functionsLoadCSV.py | SoulfulArt/Mapa_Vivo_Producao_Brasil | 0 | 90742 | <reponame>SoulfulArt/Mapa_Vivo_Producao_Brasil
#These are functions and variables that support script loadCSV.py
from os import remove
from os import path
#function that organizes numerical data because some files use , as thousand
#separator or , as decimal separator
def simplifyNumber (df):
df["Producao"] = df["Producao"].astype(str).str.replace(',','.')
df["AreaH"] = df["AreaH"].astype(str).str.replace(',','.')
df["Valor"] = df["Valor"].astype(str).str.replace(',','.')
df["Producao"] = df["Producao"].astype(float)
df["AreaH"] = df["AreaH"].astype(float)
df["Valor"] = df["Valor"].astype(float)
#Rendimento is a function of producao and area
df["Rendimento"] = (df["Producao"]/df["AreaH"])
df["Rendimento"] = round(df["Rendimento"],2)
return df
'''Function that replace letter with accent for letter without accent it can generate
problems in finding cities that are the same but has different accents from the
datasets. Other transformations that are specific of the data that is used in this
project such as '/' for ' e ' were identified as an important substituion to help
data manipulation through the project.'''
def simplifyText (pdSeries):
#it's better to work with homogenous casing
pdSeries = pdSeries.str.lower()
#cities that have name changes
pdSeries = pdSeries.str.replace('fortaleza do tabocão','tabocão')
pdSeries = pdSeries.str.replace('augusto severo','campo grande')
#problems with accent in Portuguese
pdSeries = pdSeries.str.replace('á','a')
pdSeries = pdSeries.str.replace('ã','a')
pdSeries = pdSeries.str.replace('â','a')
pdSeries = pdSeries.str.replace('é','e')
pdSeries = pdSeries.str.replace('ê','e')
pdSeries = pdSeries.str.replace('í','i')
pdSeries = pdSeries.str.replace('ó','o')
pdSeries = pdSeries.str.replace('ô','o')
pdSeries = pdSeries.str.replace('õ','o')
pdSeries = pdSeries.str.replace('ú','u')
pdSeries = pdSeries.str.replace('û','u')
pdSeries = pdSeries.str.replace('ü','u')
pdSeries = pdSeries.str.replace('j','g')
pdSeries = pdSeries.str.replace('-','')
pdSeries = pdSeries.str.replace('y','i') #old portuguese had y
#problems related to Portugese language
pdSeries = pdSeries.str.replace('za','sa') #Izabel x Isabel
pdSeries = pdSeries.str.replace('zo','so') #Brazopolis x Braspolis
pdSeries = pdSeries.str.replace('ze','se') #Euzebia x Eusebia
pdSeries = pdSeries.str.replace('reo','reu') #poxoreu x poxoreo
pdSeries = pdSeries.str.replace('tho','to') #thome x thome
pdSeries = pdSeries.str.replace('tomaz','tomas') #thomaz x thomas
pdSeries = pdSeries.str.replace('thi','ti') #thiago x tiago
pdSeries = pdSeries.str.replace('luiz','luis') #luiz x luis
pdSeries = pdSeries.str.replace('florinea','florinia') #florinea x florinia
pdSeries = pdSeries.str.replace(' moz',' mos') # moz x mos (porto)
pdSeries = pdSeries.str.replace(' luz',' lus') # santa luz x lus
pdSeries = pdSeries.str.replace('cruz','crus') #vera cruz x crus
pdSeries = pdSeries.str.replace('das artes','') #embu das artes x embu
pdSeries = pdSeries.str.replace('terezinha','teresinha') #terezinha x teresinha
#articles
pdSeries = pdSeries.str.replace(' de ','dx')
pdSeries = pdSeries.str.replace(' da ','dx')
pdSeries = pdSeries.str.replace(' do ','dx')
pdSeries = pdSeries.str.replace(' das ','dx')
pdSeries = pdSeries.str.replace(' dos ','dx')
#separator / and e
pdSeries = pdSeries.str.replace('/','e')
pdSeries = pdSeries.str.replace(' ','')
#some cities are comming with state initials
pdSeries = pdSeries.str.replace('(ac)','', regex = False)
pdSeries = pdSeries.str.replace('(al)','', regex = False)
pdSeries = pdSeries.str.replace('(ap)','', regex = False)
pdSeries = pdSeries.str.replace('(am)','', regex = False)
pdSeries = pdSeries.str.replace('(ba)','', regex = False)
pdSeries = pdSeries.str.replace('(ce)','', regex = False)
pdSeries = pdSeries.str.replace('(df)','', regex = False)
pdSeries = pdSeries.str.replace('(es)','', regex = False)
pdSeries = pdSeries.str.replace('(go)','', regex = False)
pdSeries = pdSeries.str.replace('(ma)','', regex = False)
pdSeries = pdSeries.str.replace('(mt)','', regex = False)
pdSeries = pdSeries.str.replace('(ms)','', regex = False)
pdSeries = pdSeries.str.replace('(mg)','', regex = False)
pdSeries = pdSeries.str.replace('(pa)','', regex = False)
pdSeries = pdSeries.str.replace('(pb)','', regex = False)
pdSeries = pdSeries.str.replace('(pr)','', regex = False)
pdSeries = pdSeries.str.replace('(pe)','', regex = False)
pdSeries = pdSeries.str.replace('(pi)','', regex = False)
pdSeries = pdSeries.str.replace('(rj)','', regex = False)
pdSeries = pdSeries.str.replace('(rg)','', regex = False)
pdSeries = pdSeries.str.replace('(rn)','', regex = False)
pdSeries = pdSeries.str.replace('(rs)','', regex = False)
pdSeries = pdSeries.str.replace('(ro)','', regex = False)
pdSeries = pdSeries.str.replace('(rr)','', regex = False)
pdSeries = pdSeries.str.replace('(sc)','', regex = False)
pdSeries = pdSeries.str.replace('(sp)','', regex = False)
pdSeries = pdSeries.str.replace('(se)','', regex = False)
pdSeries = pdSeries.str.replace('(to)','', regex = False)
return pdSeries
#Function cleanDataCSV clens data from a DataFrame
def cleanDataCSV (df):
#Valor Produção (Moeda em Real) must have number values
df = df[df['Valor Produção (Moeda em Real)']!='...']
df = df[df['Valor Produção (Moeda em Real)']!='..']
df = df[df['Valor Produção (Moeda em Real)']!='-']
df = df[df['Nome Lavoura']!='Total']
#Qtd.Produzida can't be NaN, but Valor can
df = df[df["Qtd.Produzida"].notna()]
df = df.reset_index(drop = True)
return df
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forherobj_app/urls.py | chaitphani/ForHerObj-repo | 0 | 35506 | <filename>forherobj_app/urls.py
from django.urls import path
from . import views
urlpatterns = [
path('', views.home, name='home'),
path('login', views.login_fun, name='login'),
path('signup', views.signup, name='signup'),
path('logout', views.logout, name='logout'),
] | [
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robovat/simulation/camera/__init__.py | leobxpan/robovat | 62 | 1606064 | from robovat.simulation.camera.bullet_camera import BulletCamera
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core/api_shortcode.py | royrogers/operative-framework | 5 | 104044 | <reponame>royrogers/operative-framework
#!/usr/bin/env python
import sys
menu_list_api = {
'list_module': 'API_listmodule',
'use_module': 'API_startmodule',
'requirement_module': 'API_requirementModule'
}
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src/telliot_core/queries/snapshot.py | tellor-io/telliot-core | 9 | 48753 | <gh_stars>1-10
import logging
from dataclasses import dataclass
from telliot_core.dtypes.value_type import ValueType
from telliot_core.queries.abi_query import AbiQuery
logger = logging.getLogger(__name__)
@dataclass
class Snapshot(AbiQuery):
"""Returns the result for a given option ID (a specific proposal) on Snapshot.
An array of values representing the amount of votes (uints) for each vote option should be returned
Attributes:
proposal_id:
Specifies the requested data a of a valid proposal on Snapshot.
see https://docs.snapshot.org/graphql-api for reference
"""
proposal_id: str
#: ABI used for encoding/decoding parameters
abi = [{"name": "proposal_id", "type": "string"}]
@property
def value_type(self) -> ValueType:
"""Data type returned for a Snapshot query.
- `uint256[]`: variable-length array of 256-bit values with 18 decimals of precision
- `packed`: false
"""
return ValueType(abi_type="uint256[]", packed=False)
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samaf/model/EvaluationSinhalaSongsDataset.py | pasinducw/scs-4224-fyp | 1 | 129689 | import os
import math
import torch
import numpy as np
class EvaluationSinhalaSongsDataset(torch.utils.data.Dataset):
def __init__(self, root_dir, trim_seconds=10, indexing=False):
self.dir = root_dir
self.feature_list = sorted(os.listdir(self.dir))
self.trim_seconds = trim_seconds
self.indexing = indexing
self.divisor = 1 if indexing else 10
length = len(self.feature_list)
block_1 = math.floor((length/5.0) * 0.6) * 5 # 60% of originals
block_2 = math.floor((length/5.0) * 0.8) * 5 # 80% of originals
start_index = block_1
end_index = block_2
self.feature_list = self.feature_list[start_index:end_index]
def __len__(self):
return len(self.feature_list) * self.divisor
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
file_id = idx // self.divisor
song_id = self.feature_list[file_id].split(".")[0]
offset_id = idx % self.divisor
song_path = os.path.join(self.dir, self.feature_list[file_id])
_, features = np.load(song_path, allow_pickle=True)
# mfccs = torch.from_numpy(np.load(song_path))
trim_frames = self.trim_seconds * 100
# mfccs = mfccs[:, :trim_frames] # trim song to given number of seconds
all_mfccs = []
features = [features[offset_id]]
for mfccs in features:
# trim song to given number of seconds
mfccs = torch.from_numpy(mfccs[:, :trim_frames])
# converting to shape [M_number_of_mfcc_coefficients, I_MFCC_blocks, T_number_of_steps]
mfccs = mfccs.view(13, -1, 100)
# converting to shape [I_MFCC_blocks, T_number_of_steps, M_number_of_mfcc_coefficients]
mfccs = mfccs.permute(1, 2, 0)
all_mfccs.append(mfccs)
return int(song_id), torch.cat(all_mfccs).float()
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scripts/Data_eda.py | abimur-123/Mentalhealth_project | 1 | 54420 | #!/usr/bin/env python
# coding: utf-8
# # Data EDA
# Author - <NAME>
# Written - December 2017
# This script take cleansed data from a csv file and writes figures to a results folder
# Parameters required -
# 1. Cleansed csv file
# 2. Codebook csv file
# 3. Destination folder for figures
import pandas as pd
import numpy as np
import sys
import os
import matplotlib as mpl
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
#%matplotlib inline
sns.set_style("dark")
def plotBar(var, data,title,filename,directory):
'''
This function takes in a pandas data frame and a column and prints/saves count of instances in a categorical variable
Users can specify where this file needs to be saved
Args: var - variable to plot
data - input data
title - Graph title
filename - File name for plot
directory - Folder location for plot
'''
bar_df = data[[var]].groupby([var]).size().reset_index(name='count')
bar_plot = sns.barplot(x = var,y = "count",data = bar_df)
plt.ylabel("count")
plt.xlabel(" ")
plt.title("{0}".format(title))
fig = bar_plot.get_figure()
if(filename is not None):
di = directory
if not os.path.exists(di):#checks if data directory exists, creates one otherwise
os.makedirs(di)
fig.savefig(di + "/" + filename + ".png")
def plot_data(cleansed_file,codebook_src,results_dest):
'''
This function takes in a cleansed file,the codebook and saves images of bar plots to the specified folder
Args: cleansed_file - file with cleansed data
codebook_src - variable codebook
results_dest - destination specifying where the images need to be stored
'''
# Read cleansed data and codebook
clean_fn = cleansed_file
di = clean_fn.rsplit('/',1)[0]
if not os.path.exists(di):#checks if data directory exists, creates one otherwise
os.makedirs(di)
df = pd.read_csv(clean_fn, encoding='latin-1')
code_fn = codebook_src
di = code_fn.rsplit('/',1)[0]
if not os.path.exists(di): #checks if data directory exists, creates one otherwise
os.makedirs(di)
cb = pd.read_csv(code_fn, encoding='latin-1')
a = cb.loc[cb['Old_names'] == 'Have you been diagnosed with a mental health condition by a medical professional?','New_names']
plotBar(a.values[0],df,"Have you been diagnosed for mental disorder by a professional?","diag_prof",results_dest)
b = cb.loc[cb['Old_names'] == 'Do you currently have a mental health disorder?','New_names']
plotBar(b.values[0],df,"Do you have mental disorder?","have_disorder",results_dest)
c = cb.loc[cb['Old_names'] == 'What is your gender?','New_names']
plotBar(c.values[0],df,"Gender breakdown","gender_breakdown",results_dest)
d = cb.loc[cb['Old_names'] == 'Does your employer provide mental health benefits as part of healthcare coverage?','New_names']
plotBar(d.values[0],df,"Does your employee provide Mental health benefits?","employee_benefits",results_dest)
e = cb.loc[cb['Old_names'] == 'Would you feel comfortable discussing a mental health disorder with your coworkers?','New_names']
plotBar(e.values[0],df,"Comfortable with discussing about mental health condition with coworkers?","coworker_comfort",results_dest)
f = cb.loc[cb['Old_names'] == 'Do you feel that your employer takes mental health as seriously as physical health?','New_names']
plotBar(f.values[0],df,"Do you feel that your employer takes mental health as seriously as physical health?","mental_physical",results_dest)
g = cb.loc[cb['Old_names'] == 'Does your employer offer resources to learn more about mental health concerns and options for seeking help?','New_names']
plotBar(g.values[0],df,"Does employee offer resources to learn about mental health?","emp_resources",results_dest)
h = cb.loc[cb['Old_names'] == 'Do you think that discussing a mental health disorder with your employer would have negative consequences?','New_names']
plotBar(h.values[0],df,"Do you think discussing mental health would have a negative consequence?","disc_negative",results_dest)
i = cb.loc[cb['Old_names'] == 'Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources provided by your employer?','New_names']
plotBar(i.values[0],df,"Anonymity when one opens up about mental health?","anonymity",results_dest)
if __name__ == '__main__':
cleansed_file = sys.argv[1] if len(sys.argv) > 1 else 'data/cleansed_data.csv'
codebook_src = sys.argv[2] if len(sys.argv) > 2 else 'docs/codebook.csv'
results_dest = sys.argv[3] if len(sys.argv) > 3 else 'results/figures'
plot_data(cleansed_file,codebook_src,results_dest)
print("End of program")
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] |
time_series_example.py | techBeck03/intersight-python-utils | 2 | 191319 | import logging
from pprint import pformat
import traceback
import intersight.api.telemetry_api
import intersight.model.telemetry_druid_data_source
import intersight.model.telemetry_druid_period_granularity
import intersight.model.telemetry_druid_query_context
import intersight.model.telemetry_druid_time_series_request
import credentials
FORMAT = '%(asctime)-15s [%(levelname)s] [%(filename)s:%(lineno)s] %(message)s'
logging.basicConfig(format=FORMAT, level=logging.DEBUG)
logger = logging.getLogger('openapi')
def get_time_series(api_client):
"""Query Druid time series"""
# Create an instance of the API telemetry service.
api_instance = intersight.api.telemetry_api.TelemetryApi(api_client)
logger.info("Query 'ucs_ether_port_stat' time series")
req = intersight.model.telemetry_druid_time_series_request.TelemetryDruidTimeSeriesRequest(
query_type="timeseries",
data_source=intersight.model.telemetry_druid_data_source.TelemetryDruidDataSource(
type="table",
name="ucs_ether_port_stat",
),
intervals=[
"2021-01-01T00:00:00.000Z/2021-01-15T00:00:00.000Z",
],
granularity=intersight.model.telemetry_druid_period_granularity.TelemetryDruidPeriodGranularity(
type="period",
period="P1D",
),
context=intersight.model.telemetry_druid_query_context.TelemetryDruidQueryContext(
timeout=30,
query_id="ucs_ether_port_stat-QueryIdentifier",
),
)
api_response = api_instance.query_telemetry_time_series(
telemetry_druid_time_series_request=req,
)
logger.info(pformat(api_response))
##########################
logger.info("Query 'device_connector' time series")
req = intersight.model.telemetry_druid_time_series_request.TelemetryDruidTimeSeriesRequest(
query_type="timeseries",
data_source=intersight.model.telemetry_druid_data_source.TelemetryDruidDataSource(
type="table",
name="device_connector",
),
intervals=[
"2021-01-01T00:00:00.000Z/2021-01-15T00:00:00.000Z",
],
granularity=intersight.model.telemetry_druid_period_granularity.TelemetryDruidPeriodGranularity(
type="period",
period="P1D",
),
context=intersight.model.telemetry_druid_query_context.TelemetryDruidQueryContext(
timeout=30,
query_id="device_connector-QueryIdentifier",
),
)
api_response = api_instance.query_telemetry_time_series(
telemetry_druid_time_series_request=req,
)
logger.info(pformat(api_response))
##########################
logger.info("Query 'PSU stat' time series")
req = intersight.model.telemetry_druid_time_series_request.TelemetryDruidTimeSeriesRequest(
aggregations=[
intersight.model.telemetry_druid_aggregator.TelemetryDruidAggregator(
field_name="sumEnergyConsumed",
type="doubleSum",
name="energyConsumed",
field_names=["sumEnergyConsumed"]
),
],
query_type="timeseries",
data_source=intersight.model.telemetry_druid_data_source.TelemetryDruidDataSource(
type="table",
name="psu_stat",
),
intervals=[
"2021-01-01T00:00:00.000Z/2021-01-15T00:00:00.000Z",
],
granularity=intersight.model.telemetry_druid_period_granularity.TelemetryDruidPeriodGranularity(
type="period",
period="P1D",
),
context=intersight.model.telemetry_druid_query_context.TelemetryDruidQueryContext(
timeout=30,
query_id="psu_stat-QueryIdentifier",
),
)
api_response = api_instance.query_telemetry_time_series(
telemetry_druid_time_series_request=req,
)
logger.info(pformat(api_response))
def main():
# Configure API key settings for authentication
api_client = credentials.config_credentials()
try:
# Get example time series data
get_time_series(api_client)
except intersight.OpenApiException as e:
logger.error("Exception when calling API: %s\n" % e)
traceback.print_exc()
if __name__ == "__main__":
main()
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] |
year_3/comppi_1/managers/views.py | honchardev/KPI | 0 | 33363 | <gh_stars>0
import json
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import User
from django.http import JsonResponse
from django.shortcuts import get_object_or_404
from django.utils.decorators import method_decorator
from django.views.decorators.csrf import csrf_exempt
from .models import ManagersModelsJSONEncoder
from .models import PubOrgAdmin, PubOrgRegistrant
@method_decorator(csrf_exempt, name='dispatch')
def apilogin(req):
if req.method == 'POST':
req_params = req.body.decode("utf-8")
login_fields = json.loads(req_params)
username = login_fields.get('username')
password = login_fields.get('password')
auth_user = authenticate(username=username, password=password)
if auth_user is not None:
login(req, auth_user)
json_positive_response = {'status': True}
return JsonResponse(json_positive_response)
json_negative_response = {'status': False}
return JsonResponse(json_negative_response)
json_err_response = {'status': 405, 'descr': 'method not allowed'}
return JsonResponse(json_err_response)
@method_decorator(csrf_exempt, name='dispatch')
def apilogout(req):
if req.method == 'POST':
if req.user.is_authenticated:
saved_user = req.user
else:
saved_user = None
logout(req)
json_logout_state_response = {
'logged_out_user': saved_user,
'logged_out': not req.user.is_authenticated
}
return JsonResponse(json_logout_state_response, safe=False, encoder=ManagersModelsJSONEncoder)
json_err_response = {'status': 405, 'descr': 'method not allowed'}
return JsonResponse(json_err_response)
@method_decorator(csrf_exempt, name='dispatch')
def apimanageadmin(req):
try:
current_user_instance = req.user
current_admin_instance = get_object_or_404(PubOrgAdmin, user=req.user.id)
except Exception as e:
json_err_response = {'status': 404, 'descr': 'Some exception: {0}'.format(e)}
return JsonResponse(json_err_response)
if req.method == 'GET':
json_get_response = {
'user_inst': current_user_instance,
'admin_inst': current_admin_instance
}
return JsonResponse(json_get_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'POST':
req_params = req.body.decode("utf-8")
post_fields = json.loads(req_params)
username = post_fields.get('username')
password = post_fields.get('password')
doc_code = post_fields.get('doc_code')
credentials = post_fields.get('credentials')
# Create and save User instance.
new_user_inst = User(username=username, password=password)
new_user_inst.save()
# Create and save PubOrgAdmin instance.
new_puborgadmin_inst = PubOrgAdmin(
user=new_user_inst.pk,
doc_code=doc_code,
credentials=credentials
)
new_puborgadmin_inst.save()
# Send resulting json response.
json_post_response = {
'new_user_inst': new_user_inst,
'new_admin_inst': new_puborgadmin_inst,
}
return JsonResponse(json_post_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'PUT':
req_params = req.body.decode("utf-8")
put_fields = json.loads(req_params)
admin_id = put_fields.get('admin_id')
doc_code = put_fields.get('doc_code')
credentials = put_fields.get('credentials')
# Update and save PubOrgAdmin instance.
admin_to_update = PubOrgAdmin.objects.get(pk=admin_id)
admin_to_update.doc_code = doc_code
admin_to_update.credentials = credentials
admin_to_update.save()
# Send resulting json response.
json_put_response = {
'upd_admin_inst': admin_to_update
}
return JsonResponse(json_put_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'DELETE':
req_params = req.body.decode("utf-8")
delete_fields = json.loads(req_params)
admin_id = delete_fields.get('admin_id')
admin_instance = get_object_or_404(PubOrgAdmin, pk=admin_id)
admin_user_instance = get_object_or_404(User, pk=admin_instance.user.pk)
admin_instance.delete()
admin_user_instance.delete()
json_delete_response = {
'deleted_id': admin_id,
}
return JsonResponse(json_delete_response)
json_err_response = {'status': 405, 'descr': 'method not allowed'}
return JsonResponse(json_err_response)
@method_decorator(csrf_exempt, name='dispatch')
def apimanageregistrant(req):
try:
current_user_instance = req.user
current_registrant_instance = get_object_or_404(PubOrgRegistrant, user=req.user.id)
except Exception as e:
json_err_response = {'status': 404, 'descr': 'Some exception: {0}'.format(e)}
return JsonResponse(json_err_response)
if req.method == 'GET':
json_get_response = {
'user_inst': current_user_instance,
'registrant_inst': current_registrant_instance
}
return JsonResponse(json_get_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'POST':
req_params = req.body.decode("utf-8")
post_fields = json.loads(req_params)
username = post_fields.get('username')
password = post_fields.get('password')
hired_by = post_fields.get('hired_by')
hired_order_code = post_fields.get('hired_order_code')
doc_code = post_fields.get('doc_code')
credentials = post_fields.get('credentials')
# Create and save User instance.
new_user_inst = User(username=username, password=password)
new_user_inst.save()
# Create and save PubOrgRegistrant instance.
new_puborgregistrant_inst = PubOrgRegistrant(
user=new_user_inst.pk,
hired_by=hired_by,
hired_order_code=hired_order_code,
doc_code=doc_code,
credentials=credentials
)
new_puborgregistrant_inst.save()
# Send resulting json response.
json_post_response = {
'new_user_inst': new_user_inst,
'new_registrant_inst': new_puborgregistrant_inst,
}
return JsonResponse(json_post_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'PUT':
req_params = req.body.decode("utf-8")
put_fields = json.loads(req_params)
registrant_id = put_fields.get('registrant_id')
hired_by = put_fields.get('hired_by')
hired_order_code = put_fields.get('hired_order_code')
doc_code = put_fields.get('doc_code')
credentials = put_fields.get('credentials')
# Update and save PubOrgRegistrant instance.
registrant_to_update = PubOrgRegistrant.objects.get(pk=registrant_id)
registrant_to_update.hired_by = hired_by
registrant_to_update.hired_order_code = hired_order_code
registrant_to_update.doc_code = doc_code
registrant_to_update.credentials = credentials
registrant_to_update.save()
# Send resulting json response.
json_put_response = {
'upd_registrant_inst': registrant_to_update
}
return JsonResponse(json_put_response, safe=False, encoder=ManagersModelsJSONEncoder)
elif req.method == 'DELETE':
req_params = req.body.decode("utf-8")
delete_fields = json.loads(req_params)
registrant_id = delete_fields.get('registrant_id')
registrant_instance = get_object_or_404(PubOrgRegistrant, pk=registrant_id)
registrant_user_instance = get_object_or_404(User, pk=registrant_instance.user.pk)
registrant_instance.delete()
registrant_user_instance.delete()
json_delete_response = {
'deleted_id': registrant_id,
}
return JsonResponse(json_delete_response)
json_err_response = {'status': 405, 'descr': 'method not allowed'}
return JsonResponse(json_err_response)
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13,
2
] |
utils/cloudmusic_dao.py | awesome-archive/MusicTaster | 45 | 75255 | # coding=utf-8
"""
负责进行云音乐的数据库相关操作
Created by jayvee on 16/12/24.
"""
from utils.db_utils import get_db_inst
from utils.logger_utils import data_process_logger
class CloudMusicDAO:
def __init__(self, db_name, collection_name):
self.db_name = db_name
self.collection_name = collection_name
self.db_inst = get_db_inst(self.db_name, self.collection_name)
def save_unique_item(self, data_obj, primary_key='userId', is_overwrite=False, is_inform=False):
"""
存储数据对象,并避免重复存储
Args:
data_obj:
primary_key:
is_overwrite:
Returns:
"""
find_result = self.db_inst.find_one({primary_key: data_obj[primary_key]}, {primary_key: 1})
# is_exist = user_dbinst.find({'userId': userinfo['userId']}).count() != 0
# print find_result.count()
if not find_result:
self.db_inst.insert(data_obj)
elif is_overwrite:
self.db_inst.update({primary_key: data_obj[primary_key]}, data_obj)
if is_inform:
data_process_logger.warn(
'overwrite item %s in %s' % (data_obj[primary_key], self.collection_name))
else:
if is_inform:
data_process_logger.warn(
'Item %s exist! in %s' % (data_obj[primary_key], self.collection_name))
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1989,
1402,
1583,
29889,
10855,
29918,
978,
876,
13,
2
] |
button/take_screen.py | PitPietro/gpiozero-pyqt5 | 0 | 15382 | import os
# from signal import pause
from gpiozero import Button
from datetime import datetime
def take_screen():
screen_btn = Button(2)
while True:
if screen_btn.is_pressed:
timestamp = datetime.now()
cmd = "scrot -u d 5 $n {}.png".format('screen_' + str(timestamp))
os.system(cmd)
#screen_btn.when_pressed=os.system(cmd)
#pause()
take_screen()
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] |
labscript_devices/chipfpgaui.py | specialforcea/labscript_suite | 0 | 176715 | <reponame>specialforcea/labscript_suite
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'chipfpga.ui'
#
# Created by: PyQt4 UI code generator 4.11.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8("Form"))
Form.resize(663, 635)
Form.setMaximumSize(QtCore.QSize(15777191, 16777215))
self.gridLayout = QtGui.QGridLayout(Form)
self.gridLayout.setObjectName(_fromUtf8("gridLayout"))
self.scrollArea = QtGui.QScrollArea(Form)
self.scrollArea.setWidgetResizable(True)
self.scrollArea.setObjectName(_fromUtf8("scrollArea"))
self.scrollAreaWidgetContents_2 = QtGui.QWidget()
self.scrollAreaWidgetContents_2.setGeometry(QtCore.QRect(0, 0, 643, 310))
self.scrollAreaWidgetContents_2.setObjectName(_fromUtf8("scrollAreaWidgetContents_2"))
self.gridLayout_6 = QtGui.QGridLayout(self.scrollAreaWidgetContents_2)
self.gridLayout_6.setObjectName(_fromUtf8("gridLayout_6"))
self.tab = QtGui.QTabWidget(self.scrollAreaWidgetContents_2)
self.tab.setObjectName(_fromUtf8("tab"))
self.load_tab = QtGui.QWidget()
self.load_tab.setObjectName(_fromUtf8("load_tab"))
self.gridLayout_3 = QtGui.QGridLayout(self.load_tab)
self.gridLayout_3.setObjectName(_fromUtf8("gridLayout_3"))
self.load_table = QtGui.QTableWidget(self.load_tab)
self.load_table.setObjectName(_fromUtf8("load_table"))
self.load_table.setColumnCount(0)
self.load_table.setRowCount(0)
self.gridLayout_3.addWidget(self.load_table, 0, 0, 1, 1)
self.tab.addTab(self.load_tab, _fromUtf8(""))
self.read_table_tab = QtGui.QWidget()
self.read_table_tab.setObjectName(_fromUtf8("read_table_tab"))
self.gridLayout_2 = QtGui.QGridLayout(self.read_table_tab)
self.gridLayout_2.setObjectName(_fromUtf8("gridLayout_2"))
self.read_table = QtGui.QTableWidget(self.read_table_tab)
self.read_table.setObjectName(_fromUtf8("read_table"))
self.read_table.setColumnCount(0)
self.read_table.setRowCount(0)
self.gridLayout_2.addWidget(self.read_table, 0, 0, 1, 1)
self.tab.addTab(self.read_table_tab, _fromUtf8(""))
self.read_graph_tab = QtGui.QWidget()
self.read_graph_tab.setObjectName(_fromUtf8("read_graph_tab"))
self.verticalLayout = QtGui.QVBoxLayout(self.read_graph_tab)
self.verticalLayout.setObjectName(_fromUtf8("verticalLayout"))
self.read_graph = QtGui.QGraphicsView(self.read_graph_tab)
self.read_graph.setObjectName(_fromUtf8("read_graph"))
self.verticalLayout.addWidget(self.read_graph)
self.tab.addTab(self.read_graph_tab, _fromUtf8(""))
self.gridLayout_6.addWidget(self.tab, 0, 0, 1, 1)
self.scrollArea.setWidget(self.scrollAreaWidgetContents_2)
self.gridLayout.addWidget(self.scrollArea, 0, 1, 1, 1)
self.scrollArea_2 = QtGui.QScrollArea(Form)
sizePolicy = QtGui.QSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.scrollArea_2.sizePolicy().hasHeightForWidth())
self.scrollArea_2.setSizePolicy(sizePolicy)
self.scrollArea_2.setWidgetResizable(True)
self.scrollArea_2.setObjectName(_fromUtf8("scrollArea_2"))
self.scrollAreaWidgetContents = QtGui.QWidget()
self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 643, 297))
self.scrollAreaWidgetContents.setObjectName(_fromUtf8("scrollAreaWidgetContents"))
self.gridLayout_7 = QtGui.QGridLayout(self.scrollAreaWidgetContents)
self.gridLayout_7.setObjectName(_fromUtf8("gridLayout_7"))
self.file_dir_Edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.file_dir_Edit.setObjectName(_fromUtf8("file_dir_Edit"))
self.gridLayout_7.addWidget(self.file_dir_Edit, 1, 1, 1, 1)
self.read_status_edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.read_status_edit.setObjectName(_fromUtf8("read_status_edit"))
self.gridLayout_7.addWidget(self.read_status_edit, 3, 1, 1, 1)
self.byte_to_read_edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.byte_to_read_edit.setObjectName(_fromUtf8("byte_to_read_edit"))
self.gridLayout_7.addWidget(self.byte_to_read_edit, 5, 1, 1, 1)
self.Load_Button = QtGui.QPushButton(self.scrollAreaWidgetContents)
self.Load_Button.setObjectName(_fromUtf8("Load_Button"))
self.gridLayout_7.addWidget(self.Load_Button, 1, 0, 1, 1)
self.Write_Button = QtGui.QPushButton(self.scrollAreaWidgetContents)
self.Write_Button.setObjectName(_fromUtf8("Write_Button"))
self.gridLayout_7.addWidget(self.Write_Button, 3, 0, 1, 1)
self.write_status_edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.write_status_edit.setObjectName(_fromUtf8("write_status_edit"))
self.gridLayout_7.addWidget(self.write_status_edit, 5, 2, 1, 1)
self.Read_button = QtGui.QPushButton(self.scrollAreaWidgetContents)
self.Read_button.setObjectName(_fromUtf8("Read_button"))
self.gridLayout_7.addWidget(self.Read_button, 5, 0, 1, 1)
self.correct_button = QtGui.QPushButton(self.scrollAreaWidgetContents)
self.correct_button.setObjectName(_fromUtf8("correct_button"))
self.gridLayout_7.addWidget(self.correct_button, 7, 0, 1, 1)
self.correct_byte_edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.correct_byte_edit.setObjectName(_fromUtf8("correct_byte_edit"))
self.gridLayout_7.addWidget(self.correct_byte_edit, 7, 1, 1, 1)
self.write_status = QtGui.QLabel(self.scrollAreaWidgetContents)
self.write_status.setObjectName(_fromUtf8("write_status"))
self.gridLayout_7.addWidget(self.write_status, 2, 1, 1, 1, QtCore.Qt.AlignBottom)
self.read_status = QtGui.QLabel(self.scrollAreaWidgetContents)
self.read_status.setObjectName(_fromUtf8("read_status"))
self.gridLayout_7.addWidget(self.read_status, 4, 2, 1, 1, QtCore.Qt.AlignBottom)
self.table_dir = QtGui.QLabel(self.scrollAreaWidgetContents)
self.table_dir.setMaximumSize(QtCore.QSize(308, 48))
self.table_dir.setObjectName(_fromUtf8("table_dir"))
self.gridLayout_7.addWidget(self.table_dir, 0, 1, 1, 1, QtCore.Qt.AlignBottom)
self.correct_value_edit = QtGui.QLineEdit(self.scrollAreaWidgetContents)
self.correct_value_edit.setObjectName(_fromUtf8("correct_value_edit"))
self.gridLayout_7.addWidget(self.correct_value_edit, 7, 2, 1, 1)
self.bytes_to_read = QtGui.QLabel(self.scrollAreaWidgetContents)
self.bytes_to_read.setObjectName(_fromUtf8("bytes_to_read"))
self.gridLayout_7.addWidget(self.bytes_to_read, 4, 1, 1, 1, QtCore.Qt.AlignBottom)
self.correct_byte = QtGui.QLabel(self.scrollAreaWidgetContents)
self.correct_byte.setObjectName(_fromUtf8("correct_byte"))
self.gridLayout_7.addWidget(self.correct_byte, 6, 1, 1, 1, QtCore.Qt.AlignBottom)
self.corret_value = QtGui.QLabel(self.scrollAreaWidgetContents)
self.corret_value.setObjectName(_fromUtf8("corret_value"))
self.gridLayout_7.addWidget(self.corret_value, 6, 2, 1, 1, QtCore.Qt.AlignBottom)
self.Load_Button.raise_()
self.Load_Button.raise_()
self.Write_Button.raise_()
self.Read_button.raise_()
self.byte_to_read_edit.raise_()
self.read_status_edit.raise_()
self.file_dir_Edit.raise_()
self.correct_button.raise_()
self.correct_byte_edit.raise_()
self.correct_value_edit.raise_()
self.write_status_edit.raise_()
self.table_dir.raise_()
self.write_status.raise_()
self.read_status.raise_()
self.bytes_to_read.raise_()
self.correct_byte.raise_()
self.corret_value.raise_()
self.scrollArea_2.setWidget(self.scrollAreaWidgetContents)
self.gridLayout.addWidget(self.scrollArea_2, 1, 1, 1, 1)
self.retranslateUi(Form)
self.tab.setCurrentIndex(0)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
Form.setWindowTitle(_translate("Form", "Form", None))
self.tab.setTabText(self.tab.indexOf(self.load_tab), _translate("Form", "Loaded table", None))
self.tab.setTabText(self.tab.indexOf(self.read_table_tab), _translate("Form", "Read table", None))
self.tab.setTabText(self.tab.indexOf(self.read_graph_tab), _translate("Form", "Read graph", None))
self.Load_Button.setText(_translate("Form", "Load", None))
self.Write_Button.setText(_translate("Form", "Write", None))
self.Read_button.setText(_translate("Form", "Read", None))
self.correct_button.setText(_translate("Form", "Correct", None))
self.write_status.setText(_translate("Form", "Write status", None))
self.read_status.setText(_translate("Form", "Read status", None))
self.table_dir.setText(_translate("Form", "Table file directory", None))
self.bytes_to_read.setText(_translate("Form", "bytes to read", None))
self.correct_byte.setText(_translate("Form", "correct byte", None))
self.corret_value.setText(_translate("Form", "correct_value", None))
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lib/raven/transport/eventlet.py | lightopa/Aiopa-Battles | 1 | 141491 | """
raven.transport.eventlet
~~~~~~~~~~~~~~~~~~~~~~~~
:copyright: (c) 2010-2012 by the Sentry Team, see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
from __future__ import absolute_import
import sys
from raven.transport.http import HTTPTransport
try:
import eventlet
try:
from eventlet.green import urllib2 as eventlet_urllib2
except ImportError:
from eventlet.green.urllib import request as eventlet_urllib2
has_eventlet = True
except:
has_eventlet = False
class EventletHTTPTransport(HTTPTransport):
scheme = ['eventlet+http', 'eventlet+https']
def __init__(self, parsed_url, pool_size=100, **kwargs):
if not has_eventlet:
raise ImportError('EventletHTTPTransport requires eventlet.')
super(EventletHTTPTransport, self).__init__(parsed_url, **kwargs)
# remove the eventlet+ from the protocol, as it is not a real protocol
self._url = self._url.split('+', 1)[-1]
def _send_payload(self, payload):
req = eventlet_urllib2.Request(self._url, headers=payload[1])
try:
if sys.version_info < (2, 6):
response = eventlet_urllib2.urlopen(req, payload[0]).read()
else:
response = eventlet_urllib2.urlopen(req, payload[0],
self.timeout).read()
return response
except Exception as err:
return err
def send(self, data, headers):
"""
Spawn an async request to a remote webserver.
"""
eventlet.spawn(self._send_payload, (data, headers))
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pycollo/quadrature.py | NoNotCar/pycollo | 3 | 64208 | """Computation of quadrature points and weights for different schemes.
Attributes
----------
DEFAULT_COLLOCATION_POINTS_MAX : int
Constant default limitation on the maximum number of collocation points
per mesh section that a user can specify. The value of 20 has been chosen
as above this the algorithms that are used for evaluating the orthogonal
polynomials become numerically unstable and raise a warning.
DEFAULT_COLLOCATION_POINTS_MIN : int
Constant default limitation on the minimum number of collocation points
per mesh section that a user can specify. The value of 2 has been chosen
as this is the smallest possible value that still makes logical sense (i.e.
a mesh section cannot have fewer than two nodes).
GAUSS : str
Keyword identifier for Legendre-Gauss quadrature method.
LOBATTO : str
Keyword identifier for Legendre-Gauss-Lobatto quadrature method.
RADAU : str
Keyword identifier for Legendre-Gauss-Radau quadrature method.
"""
__all__ = []
import numpy as np
import scipy.interpolate as interpolate
from pyproprop import Options
GAUSS = "gauss"
LOBATTO = "lobatto"
RADAU = "radau"
QUADRATURES = Options((GAUSS, LOBATTO, RADAU), default=LOBATTO,
unsupported=GAUSS)
DEFAULT_COLLOCATION_POINTS_MIN = 4
DEFAULT_COLLOCATION_POINTS_MAX = 10
class Quadrature:
"""Class for quadrature schemes including weights and points."""
def __init__(self, backend):
self.backend = backend
self._polynomials = {}
self._quadrature_points = {}
self._quadrature_weights = {}
self._butcher_arrays = {}
self._D_matrices = {}
self._A_matrices = {}
self._W_matrices = {}
self._D_index_arrays = {}
self._A_index_arrays = {}
@property
def settings(self):
return self.backend.ocp.settings
@property
def backend(self):
return self._backend
@backend.setter
def backend(self, backend):
self._backend = backend
self.order_range = list(range(
self.settings.collocation_points_min, self.settings.collocation_points_max))
if self.settings.quadrature_method == LOBATTO:
self.quadrature_generator = self.lobatto_generator
elif self.settings.quadrature_method == RADAU:
self.quadrature_generator = self.radau_generator
elif self.settings.quadrature_method == GAUSS:
self.quadrature_generator = self.gauss_generator
def _retrive_or_generate_dict_value(self, quad_dict, order):
try:
quad_dict[order]
except KeyError:
self.quadrature_generator(order)
return quad_dict[order]
def polynomials(self, order):
return self._retrive_or_generate_dict_value(self._polynomials, order)
def quadrature_point(self, order, *, domain=None):
points = self._retrive_or_generate_dict_value(
self._quadrature_points, order)
if domain:
stretch = 0.5 * (domain[1] - domain[0])
scale = 0.5 * (domain[0] + domain[1])
return stretch * points + scale
else:
return points
def quadrature_weight(self, order):
return self._retrive_or_generate_dict_value(self._quadrature_weights, order)
def butcher_array(self, order):
return self._retrive_or_generate_dict_value(self._butcher_arrays, order)
def D_matrix(self, order):
return self._retrive_or_generate_dict_value(self._D_matrices, order)
def A_matrix(self, order):
return self._retrive_or_generate_dict_value(self._A_matrices, order)
def W_matrix(self, order):
return self._retrive_or_generate_dict_value(self._W_matrices, order)
def D_index_array(self, order):
return self._retrive_or_generate_dict_value(self._D_index_arrays, order)
def A_index_array(self, order):
return self._retrive_or_generate_dict_value(self._A_index_arrays, order)
def radau_generator(self, order):
coefficients = [0] * (order - 2)
coefficients.extend([1, 1])
legendre_polynomial = np.polynomial.legendre.Legendre(coefficients)
self._polynomials.update({order: legendre_polynomial})
radau_points = legendre_polynomial.roots()
radau_points = np.concatenate([radau_points, np.array([0])])
self._quadrature_points.update({order: radau_points})
coefficients = [0] * (order - 2)
coefficients.extend([1])
legendre_polynomial = np.polynomial.legendre.Legendre(coefficients)
radau_weights = [2 / (order - 1)**2]
radau_weights = np.array(
radau_weights + [(1 - x) / ((order - 1)**2 * (legendre_polynomial(x)**2))
for x in radau_points[1:-1]])
radau_weights = np.concatenate([radau_weights, np.array([0])])
self._quadrature_weights.update({order: radau_weights})
butcher_points = self.quadrature_point(order, domain=[0, 1])
butcher_array = np.zeros((order, order))
butcher_array[-1, :] = radau_weights / 2
if order > 2:
A_row = (order + 1) * (order - 2)
A_col = order * (order - 2)
A = np.zeros((A_row, A_col))
b = np.zeros(A_row)
for k in range(order - 2):
for j in range(order):
row = j + k * order
for i in range(order - 2):
col = i + j * (order - 2)
A[row, col] = radau_weights[i + 1] * \
butcher_points[i + 1]**k
b[row] = (radau_weights[j] / (k + 1)) * (1 - butcher_points[j]
** (k + 1)) - radau_weights[-1] * radau_weights[j]
del_row = []
for i, row in enumerate(A):
if np.count_nonzero(row) == 0:
del_row.append(i)
A = np.delete(A, del_row, axis=0)
b = np.delete(b, del_row, axis=0)
a = np.linalg.solve(A, b)
butcher_array[1:-1, :] = a.reshape(order - 2, -1, order='F')
self._butcher_arrays.update({order: butcher_array})
D_left = np.ones((order - 1, 1), dtype=int)
D_right = np.diag(-1 * np.ones((order - 1, ), dtype=int))
D_matrix = np.hstack([D_left, D_right])
self._D_matrices.update({order: D_matrix})
A_matrix = self.butcher_array(order)[1:, :]
self._A_matrices.update({order: A_matrix})
A_index_array = np.array(range(A_matrix.size), dtype=int)
self._A_index_arrays.update({order: A_index_array})
D_num_row, D_num_col = D_matrix.shape
D_rows = np.array(range(D_num_row), dtype=int)
D_left = D_rows * D_num_col
D_right = D_rows * (D_num_col + 1) + 1
D_index_array = np.concatenate((D_left, D_right))
D_index_array.sort()
self._D_index_arrays.update({order: D_index_array})
# print(f'x: {radau_points}')
# print(f'w: {radau_weights}, {sum(radau_weights)}')
# print(f"a: {butcher_array}")
# print(f"A: {D_matrix}")
# print(f"I: {A_matrix}")
# input()
def lobatto_generator(self, order):
num_interior_points = order - 1
coefficients = [0] * (num_interior_points)
coefficients.append(1)
legendre_polynomial = np.polynomial.legendre.Legendre(coefficients)
self._polynomials.update({order: legendre_polynomial})
lobatto_points = legendre_polynomial.deriv().roots()
lobatto_points = np.insert(lobatto_points, 0, -1, axis=0)
lobatto_points = np.append(lobatto_points, 1)
self._quadrature_points.update({order: lobatto_points})
lobatto_weights = np.array(
[1 / (order * (order - 1) * (legendre_polynomial(x)**2)) for x in lobatto_points])
self._quadrature_weights.update({order: lobatto_weights})
butcher_points = self.quadrature_point(order, domain=[0, 1])
# print(f'x\': {butcher_points}')
butcher_array = np.zeros((order, order))
butcher_array[-1, :] = lobatto_weights
if order > 2:
A_row = (order + 1) * (order - 2)
A_col = order * (order - 2)
A = np.zeros((A_row, A_col))
b = np.zeros(A_row)
for k in range(order - 2):
# print(f'k: {k}')
for j in range(order):
# print(f'j: {j}')
row = j + k * order
# print(f'row: {row}')
for i in range(order - 2):
# print(f'i: {i}')
col = i + j * (order - 2)
# print(f'col: {col}')
A[row, col] = lobatto_weights[i + 1] * \
butcher_points[i + 1]**k
# print(f'A: {lobatto_weights[i+1] * butcher_points[i+1]**k}')
b[row] = (lobatto_weights[j] / (k + 1)) * (1 - butcher_points[j]
** (k + 1)) - lobatto_weights[-1] * lobatto_weights[j]
# print(f'b: {(lobatto_weights[j]/(k+1))*(1 - butcher_points[j]**(k+1)) - lobatto_weights[-1]*lobatto_weights[j]}\n')
del_row = []
for i, row in enumerate(A):
if np.count_nonzero(row) == 0:
del_row.append(i)
A = np.delete(A, del_row, axis=0)
b = np.delete(b, del_row, axis=0)
a = np.linalg.solve(A, b)
# print(f'A: {A}')
# print(f'b: {b}')
# print(f'a: {a}')
butcher_array[1:-1, :] = a.reshape(order - 2, -1, order='F')
self._butcher_arrays.update({order: butcher_array})
D_left = np.ones((num_interior_points, 1), dtype=int)
D_right = np.diag(-1 * np.ones((num_interior_points, ), dtype=int))
D_matrix = np.hstack([D_left, D_right])
self._D_matrices.update({order: D_matrix})
A_matrix = self.butcher_array(order)[1:, :]
self._A_matrices.update({order: A_matrix})
A_index_array = np.array(range(A_matrix.size), dtype=int)
self._A_index_arrays.update({order: A_index_array})
D_num_row, D_num_col = D_matrix.shape
D_rows = np.array(range(D_num_row), dtype=int)
D_left = D_rows * D_num_col
D_right = D_rows * (D_num_col + 1) + 1
D_index_array = np.concatenate((D_left, D_right))
D_index_array.sort()
self._D_index_arrays.update({order: D_index_array})
# print(f'x: {lobatto_points}')
# print(f'w: {lobatto_weights}, {sum(lobatto_weights)}')
# print(f"a: {butcher_array}")
# print(f"A: {D_matrix}")
# print(f"I: {A_matrix}")
# input()
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2
] |
mocket/utils.py | hectorcanto/python-mocket | 0 | 21204 | <reponame>hectorcanto/python-mocket<filename>mocket/utils.py
import binascii
import io
import os
import ssl
from .compat import decode_from_bytes, encode_to_bytes
SSL_PROTOCOL = ssl.PROTOCOL_SSLv23
class MocketSocketCore(io.BytesIO):
def write(self, content):
super(MocketSocketCore, self).write(content)
from mocket import Mocket
if Mocket.r_fd and Mocket.w_fd:
os.write(Mocket.w_fd, content)
def wrap_ssl_socket(
cls,
sock,
context,
keyfile=None,
certfile=None,
server_side=False,
cert_reqs=ssl.CERT_NONE,
ssl_version=SSL_PROTOCOL,
ca_certs=None,
do_handshake_on_connect=True,
suppress_ragged_eofs=True,
ciphers=None,
):
return cls(
sock=sock,
keyfile=keyfile,
certfile=certfile,
server_side=server_side,
cert_reqs=cert_reqs,
ssl_version=ssl_version,
ca_certs=ca_certs,
do_handshake_on_connect=do_handshake_on_connect,
suppress_ragged_eofs=suppress_ragged_eofs,
ciphers=ciphers,
_context=context,
)
def hexdump(binary_string):
r"""
>>> hexdump(b"bar foobar foo") == decode_from_bytes(encode_to_bytes("62 61 72 20 66 6F 6F 62 61 72 20 66 6F 6F"))
True
"""
bs = decode_from_bytes(binascii.hexlify(binary_string).upper())
return " ".join(a + b for a, b in zip(bs[::2], bs[1::2]))
def hexload(string):
r"""
>>> hexload("62 61 72 20 66 6F 6F 62 61 72 20 66 6F 6F") == encode_to_bytes("bar foobar foo")
True
"""
string_no_spaces = "".join(string.split())
return encode_to_bytes(binascii.unhexlify(string_no_spaces))
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] |
scripts/hyperdrive/widgets.py | timlehr/hyperdrive | 55 | 127829 |
# -----------------------------------------------------------------------------
# This source file has been developed within the scope of the
# Technical Director course at Filmakademie Baden-Wuerttemberg.
# http://technicaldirector.de
#
# Written by <NAME>
# Copyright (c) 2019 Animationsinstitut of <NAME>
# -----------------------------------------------------------------------------
from __future__ import division
from abc import abstractmethod
from random import randint
import logging
import pymel.core as pm
from collections import OrderedDict
from Qt import QtWidgets, QtGui, QtCore
from maya.app.general.mayaMixin import MayaQWidgetDockableMixin
from . import HdPoseNode, utils
from .models import HdCacheTableModel, MayaNodeTableModel
from .utils import logger
log = logger.get_logger(__name__)
class HdManagerWidget(MayaQWidgetDockableMixin, QtWidgets.QWidget):
"""Main Hyperdrive Manager window. Dockable in Maya main window.
"""
def __init__(self, parent, *args, **kwargs):
super(HdManagerWidget, self).__init__(parent, *args, **kwargs)
utils.mayautil.load_hyperdrive_plugin()
self._cache_tab = None
self._whitelist_tab = None
self._blacklist_tab = None
self._controller_widget = None
self._settings_widget = None
self._setup_ui()
self._update_rig_select_cbx()
def _setup_ui(self):
self.setWindowTitle("Hyperdrive")
self._tabwidget = QtWidgets.QTabWidget()
self._layout = QtWidgets.QVBoxLayout()
self._rig_layout = QtWidgets.QHBoxLayout()
self._rig_select_cbx = QtWidgets.QComboBox()
self._rig_select_cbx.currentIndexChanged.connect(self._on_rig_selection_changed)
self._add_rig_btn = QtWidgets.QPushButton("Add Rig")
self._add_rig_btn.clicked.connect(self._on_add_rig)
self._rig_layout.addWidget(self._rig_select_cbx)
self._rig_layout.addWidget(self._add_rig_btn)
self._layout.addLayout(self._rig_layout)
self._layout.addWidget(self._tabwidget)
self.setLayout(self._layout)
self.installEventFilter(self)
def update_ui(self, pose_node=None):
self._tabwidget.clear()
if pose_node:
self._cache_tab = CacheWidget(pose_node)
self._whitelist_tab = WhitelistWidget(pose_node)
self._blacklist_tab = BlacklistWidget(pose_node)
self._controller_widget = CtrlAttrsWidget(pose_node)
self._settings_widget = SettingsWidget(pose_node)
self._tabwidget.addTab(self._cache_tab, "Caches")
self._tabwidget.addTab(self._controller_widget, "Controls")
self._tabwidget.addTab(self._whitelist_tab, "Whitelist")
self._tabwidget.addTab(self._blacklist_tab, "Blacklist")
self._tabwidget.addTab(self._settings_widget, "Settings")
def _on_add_rig(self):
text, accepted = QtWidgets.QInputDialog.getText(self,
"Add Rig to Hyperdrive",
"Please enter a unique ID for your rig. e.g. 'Chewie_v009'",
QtWidgets.QLineEdit.Normal, "")
if accepted:
if not text:
rand_number = "{}".format(randint(0, 999999))
rig_tag = "RigTag_{}".format(rand_number.zfill(6))
else:
rig_tag = text
self._create_pose_node(rig_tag)
def _update_rig_select_cbx(self, active_pose_node=None):
current_pose_node = self._rig_select_cbx.currentData() if self._rig_select_cbx.count() > 0 else None
self._rig_select_cbx.clear()
pose_nodes = HdPoseNode.get_all()
if not pose_nodes:
self._rig_select_cbx.addItem("- No configured rigs -", None)
else:
for pose_node in pose_nodes:
self._rig_select_cbx.addItem(pose_node.rig_tag, pose_node)
if active_pose_node in pose_nodes:
self._rig_select_cbx.setCurrentIndex(pose_nodes.index(active_pose_node))
elif current_pose_node in pose_nodes:
self._rig_select_cbx.setCurrentIndex(pose_nodes.index(current_pose_node))
def _create_pose_node(self, rig_tag):
node = HdPoseNode.create(rig_tag)
self._update_rig_select_cbx(node)
def _on_rig_selection_changed(self, index):
pose_node = self._rig_select_cbx.itemData(index)
self.update_ui(pose_node)
def eventFilter(self, object, event):
catch = [QtCore.QEvent.WindowActivate, QtCore.QEvent.FocusIn]
if event.type() in catch:
pose_node = self._rig_select_cbx.currentData()
if not pose_node or not pose_node.exists():
self._update_rig_select_cbx()
return super(HdManagerWidget, self).eventFilter(object, event)
class CacheWidget(QtWidgets.QWidget):
def __init__(self, pose_node):
super(CacheWidget, self).__init__()
self._pose_node = pose_node
self._setup_ui()
self.update_ui()
def _setup_ui(self):
mapping = [
{"display_attr": "display_name", "header_str": "Node"},
{"display_attr": "display_mem_size", "header_str": "Memory Size",
"edit_attr": "max_cache_mem_size", "editor": SpinboxEditor(min_value=64, max_value=64000)},
]
self._layout = QtWidgets.QVBoxLayout()
self._layout.setContentsMargins(0, 0, 0, 0)
self.setLayout(self._layout)
self._node_table_view = QtWidgets.QTableView()
self._node_table_view.setItemDelegateForColumn(1, mapping[1]["editor"])
self._node_table_model = HdCacheTableModel(self._pose_node, mapping)
self._node_table_view.setModel(self._node_table_model)
self._node_table_view.horizontalHeader().setStretchLastSection(True)
self._node_table_view.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)
self._node_table_view.verticalHeader().hide()
self._btn_box = QtWidgets.QHBoxLayout()
self._add_cache_node_btn = QtWidgets.QPushButton("Add Mesh")
self._remove_cache_node_btn = QtWidgets.QPushButton("Remove Mesh")
self._clear_cache_btn = QtWidgets.QPushButton("Clear Cache")
self._add_cache_node_btn.clicked.connect(self._on_btn_clicked)
self._remove_cache_node_btn.clicked.connect(self._on_btn_clicked)
self._clear_cache_btn.clicked.connect(self._on_btn_clicked)
self._btn_box.addWidget(self._add_cache_node_btn)
self._btn_box.addWidget(self._remove_cache_node_btn)
self._btn_box.addWidget(self._clear_cache_btn)
self._layout.addWidget(self._node_table_view)
self._layout.addLayout(self._btn_box)
self.installEventFilter(self)
self.setContentsMargins(0, 0, 0, 0)
def _on_btn_clicked(self):
sender = self.sender()
if sender == self._add_cache_node_btn:
log.debug("Add cache node")
self.add_meshes()
elif sender == self._remove_cache_node_btn:
log.debug("Remove cache node")
selected = self._get_selected_nodes()
for node in selected:
node.delete()
elif sender == self._clear_cache_btn:
log.debug("Clear cache")
selected = self._get_selected_nodes()
if not selected:
log.info("Select at least one cache node to clear.")
for node in selected:
node.clear_cache()
self.update_ui()
def _get_selected_nodes(self):
indexes = self._node_table_view.selectionModel().selectedRows()
return [index.data(role=QtCore.Qt.UserRole) for index in indexes]
def add_meshes(self):
shapes = []
for node in pm.selected():
shape = node
if shape.nodeType() == 'transform':
shape = node.getShape()
if shape.nodeType() == 'mesh':
shapes.append(node)
else:
log.warning("'{}' is not a valid mesh node. Skip.".format(shape))
self._pose_node.add_meshes(*shapes)
def update_ui(self):
self._node_table_model.refresh()
def eventFilter(self, object, event):
catch = [QtCore.QEvent.WindowActivate,
QtCore.QEvent.FocusIn]
if event.type() in catch:
self.update_ui()
return super(CacheWidget, self).eventFilter(object, event)
class NodelistWidget(QtWidgets.QWidget):
_add_btn_text = "Add"
_remove_btn_text = "Remove"
def __init__(self, pose_node):
super(NodelistWidget, self).__init__()
self._pose_node = pose_node
self._setup_ui()
self.update_ui()
def _setup_ui(self):
self._layout = QtWidgets.QVBoxLayout()
self._layout.setContentsMargins(0, 0, 0, 0)
self.setLayout(self._layout)
self._node_table_view = QtWidgets.QTableView()
self._node_table_model = self._get_table_model()
self._node_table_view.setModel(self._node_table_model)
self._node_table_view.horizontalHeader().setStretchLastSection(True)
self._node_table_view.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)
self._node_table_view.verticalHeader().hide()
self._btn_box = QtWidgets.QHBoxLayout()
self._add_btn = QtWidgets.QPushButton(self._add_btn_text)
self._remove_btn = QtWidgets.QPushButton(self._remove_btn_text)
self._refresh_btn = QtWidgets.QPushButton("Refresh")
self._add_btn.clicked.connect(self._on_btn_clicked)
self._remove_btn.clicked.connect(self._on_btn_clicked)
self._refresh_btn.clicked.connect(self._on_btn_clicked)
self._btn_box.addWidget(self._add_btn)
self._btn_box.addWidget(self._remove_btn)
self._btn_box.addWidget(self._refresh_btn)
self._layout.addWidget(self._node_table_view)
self._layout.addLayout(self._btn_box)
self.setContentsMargins(0, 0, 0, 0)
def _on_btn_clicked(self):
sender = self.sender()
if sender == self._add_btn:
log.debug("Add Node")
self.add_nodes()
elif sender == self._remove_btn:
log.debug("Remove Node")
nodes = self._get_selected_nodes()
self.remove_nodes(nodes)
elif sender == self._refresh_btn:
log.debug("Refresh UI")
self.update_ui()
def _get_selected_nodes(self):
return [] # TODO: Fix remove. Currently crashes for some index access reason.
"""
indexes = self._node_table_view.selectionModel().selectedRows()
return [index.data(role=QtCore.Qt.UserRole) for index in indexes]"""
@abstractmethod
def _get_table_model(self):
raise NotImplementedError
@abstractmethod
def add_nodes(self):
raise NotImplementedError
@abstractmethod
def remove_nodes(self, selected):
raise NotImplementedError
def update_ui(self):
self._node_table_model.refresh()
def showEvent(self, event):
val = super(NodelistWidget, self).showEvent(event)
self.update_ui()
return val
class CtrlAttrsWidget(NodelistWidget):
_add_btn_text = "Add Control Attrs."
_remove_btn_text = "Remove Attrs."
def _get_table_model(self):
mapping = [
{"display_attr": "name", "header_str": "Controller Attribute Name"}
]
return MayaNodeTableModel(self._pose_node, "get_controller_attrs", mapping)
def add_nodes(self):
for node in pm.selected():
t = node.listAttr(st='translate*', keyable=True)
self._pose_node.add_controller_attrs(*t)
s = node.listAttr(st='scale*', keyable=True)
self._pose_node.add_controller_attrs(*s)
r = node.listAttr(st='rotate*', keyable=True)
self._pose_node.add_controller_attrs(*r)
def remove_nodes(self, selected):
self._pose_node.remove_controller_attrs(*selected)
class WhitelistWidget(NodelistWidget):
_add_btn_text = "Add Node"
_remove_btn_text = "Remove Node"
def _get_table_model(self):
mapping = [
{"display_attr": "name", "header_str": "Node Name"}
]
return MayaNodeTableModel(self._pose_node, "get_whitelisted_nodes", mapping)
def add_nodes(self):
self._pose_node.add_whitelisted_nodes(*pm.selected())
def remove_nodes(self, selected):
self._pose_node.remove_whitelisted_nodes(*selected)
class BlacklistWidget(NodelistWidget):
_add_btn_text = "Add Node"
_remove_btn_text = "Remove Node"
def _get_table_model(self):
mapping = [
{"display_attr": "name", "header_str": "Node Name"}
]
return MayaNodeTableModel(self._pose_node, "get_blacklisted_nodes", mapping)
def add_nodes(self):
self._pose_node.add_blacklisted_nodes(*pm.selected())
def remove_nodes(self, selected):
self._pose_node.remove_blacklisted_nodes(*selected)
class SettingsWidget(QtWidgets.QWidget):
rig_tag_changed = QtCore.Signal(str)
_log_level_mapping = OrderedDict([
("Debug", (3, logging.DEBUG)),
("Info", (2, logging.INFO)),
("Warning", (1, logging.WARNING)),
("Critical", (0, logging.CRITICAL))
])
def __init__(self, pose_node):
super(SettingsWidget, self).__init__()
self._pose_node = pose_node
self._setup_ui()
def _setup_ui(self):
self._layout = QtWidgets.QFormLayout()
self.setLayout(self._layout)
self._rig_tag_edit = QtWidgets.QLineEdit()
self._log_level_cbx = QtWidgets.QComboBox()
self._rig_tag_edit.installEventFilter(self)
self._rig_tag_edit.setText(self._pose_node.rig_tag)
self._log_level_cbx.addItems(self._log_level_mapping.keys())
self._log_level_cbx.currentIndexChanged.connect(self._on_log_level_changed)
self._hd_bypass_chx = QtWidgets.QCheckBox("Disable Hyperdrive for '{}'".format(self._pose_node.rig_tag))
self._hd_bypass_chx.setChecked(not self._pose_node.enabled)
self._hd_bypass_chx.stateChanged.connect(self._on_bypass_changed)
self._layout.addRow("<b>Rig Tag:</b>", self._rig_tag_edit)
self._layout.addRow("<b>Log Level:</b>", self._log_level_cbx)
self._layout.addRow("<b>Bypass:</b>", self._hd_bypass_chx)
def _on_bypass_changed(self, checked):
self._pose_node.enabled = not checked
def _on_log_level_changed(self, index):
current_text = self._log_level_cbx.itemText(index)
data = self._log_level_mapping[current_text]
pm.other.hdLog("-verbosity", data[0])
logger.set_log_level(data[1])
def _on_rig_tag_edited(self):
text = self._rig_tag_edit.text()
if text:
self._pose_node.rig_tag = text
self.rig_tag_changed.emit(text)
def eventFilter(self, object, event):
catch = [QtCore.QEvent.KeyRelease]
if event.type() in catch:
self._on_rig_tag_edited()
return super(SettingsWidget, self).eventFilter(object, event)
class SpinboxEditor(QtWidgets.QStyledItemDelegate):
"""ComboBox model instance editor."""
def __init__(self, **kwargs):
self._display_attr = kwargs.get("display_attr") or "display_str"
self._none_item = kwargs.get("none_valid") if kwargs.get("none_valid") is not None else False
self._deco_pos = kwargs.get("deco_pos", QtWidgets.QStyleOptionViewItem.Right)
self._deco_size = kwargs.get("deco_size", QtCore.QSize(12, 12))
self._min_value = kwargs.get("min_value")
self._max_value = kwargs.get("max_value")
super(SpinboxEditor, self).__init__()
def initStyleOption(self, option, index):
super(SpinboxEditor, self).initStyleOption(option, index)
if self._deco_pos:
option.decorationPosition = self._deco_pos
if self._deco_size:
option.decorationSize = self._deco_size
def createEditor(self, parent, option, index):
widget = QtWidgets.QSpinBox(parent)
if self._min_value:
widget.setMinimum(self._min_value)
if self._max_value:
widget.setMaximum(self._max_value)
return widget
def setEditorData(self, editor, index):
edit_value = index.data(role=QtCore.Qt.EditRole)
editor.setValue(edit_value)
def setModelData(self, editor, model, index):
if not index.isValid():
return False
model.setData(index, editor.value(), role=QtCore.Qt.EditRole)
return True
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swipe.py | amagrabi/preference-learning | 1 | 176154 |
import datetime
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
import shutil
import sys
import time
import pandas as pd
import pynder
import urllib.request
from pynput import keyboard
from pylab import rcParams
from config import FACEBOOK_AUTH_TOKEN, FACEBOOK_ID, RATING_THRESHOLD
# Iterable needed to store ratings from keypresses
RATINGS = []
# Directory structure
DF_PATH = 'data/df_swipes.p'
IMG_PATH_LIKES = 'data/images/likes'
IMG_PATH_DISLIKES = 'data/images/dislikes'
IMG_PATH_NEARBY = 'data/images/nearby'
# Create image directories
IMG_PATHS = [IMG_PATH_LIKES, IMG_PATH_DISLIKES, IMG_PATH_NEARBY]
for path in IMG_PATHS:
if not os.path.exists(path):
os.makedirs(path)
def rate_photos(event):
sys.stdout.flush()
rating_set = [str(i) for i in list(range(1,10))]
rating_set.append('escape')
if event.key in rating_set:
RATINGS.append(event.key)
plt.close(fig)
def show_images(filenames, fig, user=None, session=None, pause=0.0,
figure_pos_x=500, figure_pos_y=0):
# plt.style.use('dark_background')
thismanager = plt.get_current_fig_manager()
thismanager.window.wm_geometry(f'+{figure_pos_x}+{figure_pos_y}')
for j, photo in enumerate(filenames):
a = fig.add_subplot(1, len(filenames), j + 1)
a.axis('off')
a.set_facecolor('black')
img = mpimg.imread(filenames[j])
plt.imshow(img)
title = ''
if user:
title += f'Name: {user.name}, \n'
title += f'Age: {user.age} \n'
title += f'Distance: {user.distance_km} \n'
fig.text(0.01, 0.1, f'Bio: {user.bio}', bbox={'facecolor': 'white', 'alpha': 1, 'edgecolor': 'none', 'pad': 1})
if session:
title += f' Likes remaining: {session.likes_remaining}'
plt.suptitle(title)
fig.canvas.mpl_connect('key_release_event', rate_photos)
plt.show()
if pause:
time.sleep(pause)
def add_data_to_df(df, path, user, liked, rating):
new_data = pd.DataFrame({
'name': user.name,
'id': user.id,
'liked': liked,
'rating': rating,
'age': user.age,
'bio': user.bio,
'photos': [list(user.get_photos())],
'photos_instagram': [user.instagram_photos],
'common_likes': [user.common_likes],
'common_like_count': len(user.common_likes),
'common_interests': [user._data.get('common_interests', [])],
'common_interests_count': len(user._data.get('common_interests', [])),
'common_friends': [user._data.get('common_friends', [])],
'common_friend_count': len(user._data.get('common_friends', [])),
'connection_count': [user.common_connections],
'jobs': [user.jobs],
'schools': [user.schools],
'distance_km': user.distance_km,
'distance_mi': user.distance_mi,
'ping_time': user._data['ping_time'],
'teasers': [user._data['teasers']],
'gender': user.gender,
'birth_date': user.birth_date,
'instagram_name': user.instagram_username,
'content_hash': user._data.get('content_hash', None),
'datetime': datetime.datetime.isoformat(datetime.datetime.now())
})
df = df.append(new_data, ignore_index=True)
df.to_pickle(path)
return df
if __name__ == "__main__":
reset = False
if reset:
if os.path.exists(IMG_PATH_LIKES):
shutil.rmtree(IMG_PATH_LIKES)
os.makedirs(IMG_PATH_LIKES)
if os.path.exists(IMG_PATH_DISLIKES):
shutil.rmtree(IMG_PATH_DISLIKES)
os.makedirs(IMG_PATH_DISLIKES)
if os.path.exists(IMG_PATH_NEARBY):
shutil.rmtree(IMG_PATH_NEARBY)
os.makedirs(IMG_PATH_NEARBY)
if os.path.exists(DF_PATH):
os.remove(DF_PATH)
# Load DataFrame
if os.path.exists(DF_PATH):
df = pd.read_pickle(DF_PATH)
else:
# columns = [
# 'name', 'id', 'liked', 'age', 'bio', 'photos', 'photos_instagram',
# 'common_likes', 'common_like_count', 'common_friends_count', 'common_friends_count',
# 'jobs', 'schools', 'distance_km', 'distance_mi',
# 'ping_time', 'teasers', 'gender', 'birth_date'
# ]
df = pd.DataFrame()
session = pynder.Session(FACEBOOK_AUTH_TOKEN)
# Get nearby users
liked = False
abort = False
while True:
print(f'Starting session...')
print(f'Likes remaining: {session.likes_remaining}')
print(f'Super likes remaining: {session.super_likes_remaining}')
for i, user in enumerate(session.nearby_users(limit=1)):
# print(f'User {i}: {user.name}')
# Download user photos to nearby folder
photo_filenames = []
for j, photo in enumerate(user.photos):
photo_filename = os.path.join(IMG_PATH_NEARBY, f'{user.id}_{str(j).zfill(4)}.jpg')
urllib.request.urlretrieve(photo, photo_filename)
photo_filenames.append(photo_filename)
# Show images
rcParams['figure.figsize'] = 10, 5
fig = plt.figure()
show_images(photo_filenames, fig, user=user, session=session, pause=0.0)
# Process rating
rating = RATINGS.pop()
if rating == 'escape':
abort = True
break
elif int(rating) < RATING_THRESHOLD:
liked = False
df = add_data_to_df(df=df, path=DF_PATH, user=user, liked=liked, rating=int(rating))
for photo in photo_filenames:
filepath, file_extension = os.path.splitext(photo)
new_filename = os.path.basename(filepath) + f'_{str(rating).zfill(2)}' + file_extension
new_filepath = os.path.join(IMG_PATH_DISLIKES, new_filename)
shutil.copyfile(photo, new_filepath)
# user.dislike()
resp = session._api.dislike(user.id)
print(f'{user.name} was disliked ({int(rating)})')
else:
liked = True
df = add_data_to_df(df=df, path=DF_PATH, user=user, liked=liked, rating=int(rating))
for photo in photo_filenames:
filepath, file_extension = os.path.splitext(photo)
new_filename = os.path.basename(filepath) + f'_{str(rating).zfill(2)}' + file_extension
new_filepath = os.path.join(IMG_PATH_LIKES, new_filename)
shutil.copyfile(photo, new_filepath)
# user.like()
resp = session._api.like(user.id)
print(f'{user.name} was LIKED ({int(rating)})')
print(f'{resp}')
if abort:
break
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] |
DiffGCN.py | BGUCompSci/DiffGCNs.py | 4 | 101632 | import torch
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import to_dense_adj, contains_self_loops, contains_isolated_nodes
from torch_cluster import knn_graph
import torch.nn as nn
from torch_geometric.nn.inits import reset
from torch.nn import Sequential as Seq, Linear as Lin, ReLU, BatchNorm1d as BN
from torch.nn import functional as F
from message_passing2 import MessagePassing2
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from torch.nn.init import kaiming_normal
import os.path as osp
from torch_geometric.nn.pool import graclus, avg_pool_x
import torch_sparse
from mgpool import mgpool
currPath = osp.dirname(osp.realpath(__file__))
epsilon = 1e-20
class mySequential(nn.Sequential):
def forward(self, *inputs):
for module in self._modules.values():
if type(inputs) == tuple:
x, pos, edge_index, batch, k = inputs
inputs = module(x, pos, edge_index)
res = inputs
inputs = (inputs, pos, edge_index, batch, k)
else:
res = module(inputs)
return res
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def MLP(channels, batch_norm=True, relu=False):
return Seq(*[
Seq(nn.Linear(channels[i - 1], channels[i]), BN(channels[i]) if batch_norm else Seq(),
nn.ReLU() if relu else Seq())
for i in range(1, len(channels))
])
def CreateTransposedAuxGraph(edge_index):
source = edge_index[0, :]
target = edge_index[1, :]
new_edge_index = torch.zeros_like(edge_index)
new_edge_index[0, :] = target
new_edge_index[1, :] = source
return new_edge_index
def CreateAuxGraph(edge_index, pos_i, pos_j, original_vertex_features, pos):
# Build new auxiliary graph:
num_graph_verts = original_vertex_features.shape[0]
new_verts = torch.arange(num_graph_verts,
num_graph_verts + len(edge_index[0, :])) # Add edges in G as vertices in aux G
new_vertex_pos = torch.cat(
[pos, torch.zeros(len(new_verts), pos.shape[1]).to(device)]
, dim=0)
new_vertex_features = torch.cat(
[original_vertex_features, torch.zeros(len(new_verts), original_vertex_features.shape[1]).to(device)]
, dim=0)
# Compute needed components:
sources = edge_index[0, :]
targets = edge_index[1, :]
edge_indices = num_graph_verts + torch.arange(0, len(edge_index[0, :]))
edge_indices = edge_indices.to(device)
new_source_edges = torch.stack((sources, edge_indices), dim=0)
new_target_edges = torch.stack((targets, edge_indices), dim=0)
new_vertex_pos[num_graph_verts:, :] = (pos_i + pos_j) / 2
# Build graph from components:
edge_index_aux = torch.cat([new_target_edges, new_source_edges], dim=1)
assert (not contains_self_loops(edge_index_aux))
assert (not contains_isolated_nodes(edge_index_aux))
return edge_index_aux, new_vertex_features, new_vertex_pos
class EdgeAggr(MessagePassing):
def __init__(self, in_planes, aggr='add', **kwargs):
super(EdgeAggr, self).__init__(aggr=aggr, **kwargs)
# self.bn = torch.nn.BatchNorm1d(num_features=3*in_planes)
self.aggr = aggr
def forward(self, x, edge_index, pos):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
return self.propagate(edge_index, x=x, pos=pos)
def message(self, x_i, x_j, pos_i, pos_j, x):
dist = torch.pow(pos_i - pos_j, 2)
dist = torch.sum(dist, dim=1) + epsilon
subs = pos_i - pos_j
sx = subs[:, 0]
sy = subs[:, 1]
sz = subs[:, 2]
dx = x_j * sx[:, None]
dy = x_j * sy[:, None]
dz = x_j * sz[:, None]
derivs = torch.cat([dx, dy, dz], dim=1)
return derivs
class VertToEdge(MessagePassing2):
def __init__(self, aggr='add', **kwargs):
super(VertToEdge, self).__init__(aggr=aggr, **kwargs)
self.reset_parameters()
def reset_parameters(self):
a = 10
def forward(self, x, edge_index, pos):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
return self.propagate(edge_index, x=x, pos=pos)
def message(self, pos_i, pos_j, edge_index, x, pos):
edge_index_aux, new_vertex_features, new_vertex_pos = CreateAuxGraph(edge_index, pos_i, pos_j, x, pos)
return edge_index_aux, new_vertex_features, new_vertex_pos
class AvgTranspose(MessagePassing):
def __init__(self, aggr='mean', **kwargs):
super(AvgTranspose, self).__init__(aggr=aggr, **kwargs)
self.aggr = aggr
def forward(self, x, edge_index):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
return self.propagate(edge_index, x=x)
def message(self, x_j):
return x_j
class GraphTranspose(MessagePassing):
def __init__(self, aggr='add', **kwargs):
super(GraphTranspose, self).__init__(aggr=aggr, **kwargs)
self.aggr = aggr
def forward(self, x, edge_index, pos):
""""""
x = x.unsqueeze(-1) if x.dim() == 1 else x
return self.propagate(edge_index, x=x, pos=pos)
def message(self, x_j, pos_i, pos_j):
pos_i = pos_i - pos_j
feature_size = int(x_j.shape[1] / 3)
d2x = x_j[:, 0:feature_size] * pos_i[:, 0][:, None]
d2y = x_j[:, feature_size: 2 * feature_size] * pos_i[:, 1][:, None]
d2z = x_j[:, 2 * feature_size:] * pos_i[:, 2][:, None]
pos_i = torch.pow(pos_i, 2)
pos_i = torch.sum(pos_i, dim=1) + epsilon
return torch.cat([d2x, d2y, d2z], dim=1)
class DiffGCNLayer(MessagePassing):
def __init__(self, in_planes, out_planes, relu=True, **kwargs):
super(DiffGCNLayer, self).__init__()
self.VtoE = VertToEdge(aggr='mean')
self.EdgeAggr = EdgeAggr(in_planes=in_planes, aggr='mean')
self.avgTrans = AvgTranspose(aggr='mean')
self.GraphTrans = GraphTranspose(aggr='mean')
self.combine = MLP([3 * in_planes, out_planes], relu=relu)
# if in_planes != out_planes:
# self.shortcut = MLP([in_planes, out_planes], relu=False)
self.in_planes = in_planes
self.out_planes = out_planes
def forward(self, x, pos, edge_index, batch=None):
# Create aux graph:
edge_index_aux, new_vertex_features, new_vertex_pos = self.VtoE(x, edge_index, pos)
edge_index_aux = edge_index_aux.to(device)
edge_index_aux = edge_index_aux.to(device)
new_vertex_features = new_vertex_features.to(device)
new_vertex_pos = new_vertex_pos.to(device)
# Gradient via local aggregation:
edge_aggr = self.EdgeAggr(new_vertex_features, edge_index_aux, new_vertex_pos)
# Create transposed graph (flipped edges):
transposed_edge_index_aux = CreateTransposedAuxGraph(edge_index_aux)
# Calculate Gradient term:
gradAggr = self.avgTrans(edge_aggr, transposed_edge_index_aux)
gradAggr = gradAggr[0:x.shape[0], :]
# Calculate Laplacian term:
laplacianAggr = self.GraphTrans(edge_aggr, transposed_edge_index_aux, new_vertex_pos)
laplacianAggr = laplacianAggr[0:x.shape[0], :]
return F.relu(self.combine(gradAggr))
if self.out_planes == self.in_planes:
return F.relu(self.combine(torch.cat([x, gradAggr, laplacianAggr], dim=1)))
else:
return F.relu(self.combine(torch.cat([x, gradAggr, laplacianAggr], dim=1)))
class DiffGCNBlock(MessagePassing):
def __init__(self, in_planes, out_planes, k, blocks=3, pool=False, **kwargs):
super(DiffGCNBlock, self).__init__()
self.blocks = blocks
self.k = k
self.openLayer = DiffGCNLayer(in_planes, out_planes, relu=False)
self.layers = self._make_layer(self.openLayer, out_planes, self.blocks)
self.pool = pool
def _make_layer(self, openLayer, planes, blocks):
layers = [openLayer]
for i in range(1, blocks):
layers.append(DiffGCNLayer(planes, planes, relu=False))
return mySequential(*layers)
def forward(self, x, pos, batch=None, edge_index=None):
if edge_index is None:
edge_index = knn_graph(x, self.k, batch, loop=False)
edge_index = edge_index.to(device)
if self.pool:
new_adj, new_feat, new_pos, new_batch, index, values, origsize, newsize = mgpool(x, pos, edge_index, batch)
return self.layers(new_feat, new_pos, new_adj, new_batch, self.k), new_pos, new_batch, (
index, values, origsize, newsize)
else:
new_pos = pos
new_batch = batch
new_feat = x
new_adj = edge_index
return self.layers(new_feat, new_pos, new_adj, new_batch, self.k), new_pos, new_batch
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7750,
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] |
down.py | mostafabasuni/video_downloader_gui | 0 | 127700 | <gh_stars>0
from posixpath import split
import threading
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.uic import loadUiType
from pytube import YouTube
from pytube import Playlist
from pytube.cli import on_progress
from threading import Thread
import os
from os import path
import sys
import urllib.request
Form_Class,_ = loadUiType(path.join(path.dirname(__file__),"DownLoad.ui")) # Form_Class اسم اختياري للتطبيق
class MainApp(QMainWindow, Form_Class):
def __init__(self, parent=None):
super(MainApp, self).__init__(parent)
QMainWindow.__init__(self)
self.setupUi(self)
self.handel_Ui()
self.handel_button()
def handel_Ui(self):
self.setWindowTitle('Mostafa Downloader')
self.setFixedSize(750,290)
def handel_button(self):
self.pushButton.clicked.connect(self.Download)
self.pushButton_2.clicked.connect(self.handel_browse)
self.pushButton_3.clicked.connect(self.Save_Browse)
self.pushButton_4.clicked.connect(self.Video_Download)
self.pushButton_5.clicked.connect(self.Save_Browse)
self.pushButton_6.clicked.connect(self.Play_List_Download)
self.pushButton_7.clicked.connect(self.Save_Browse)
self.pushButton_8.clicked.connect(self.Select_From_Playlist)
def handel_browse(self):
# save_place = QFileDialog.getSaveFileName(self, caption='Save As', directory='.', filter='All File(*.*)')
# text = str(save_place)
# name = (text[2:].split(',')[0].replace("'",''))
# self.lineEdit_2.setText(name)
save_location = QFileDialog.getExistingDirectory(self, "Save Location")
self.lineEdit_2.setText(save_location)
def handel_progress(self, blocknum, blocksize, totalsize):
read = blocknum * blocksize
percent = int(read * 100 / totalsize)
self.progressBar.setValue(percent)
QApplication.processEvents() # to solve "Not Responding" proplem
def Download(self):
url = self.lineEdit.text()
file_name = url.split("/")[-1]
self.lineEdit_3.setText(file_name)
location = self.lineEdit_2.text()
save_location = location + "/" + self.lineEdit_3.text()
exists = os.path.exists(save_location)
if not exists:
try:
urllib.request.urlretrieve(url, save_location , self.handel_progress)
except Exception:
QMessageBox.warning(self, 'Download Error', 'Download Faild')
return
QMessageBox.information(self, 'Download Completed', 'Download Finished')
self.progressBar.setValue(0)
self.lineEdit.setText('')
self.lineEdit_2.setText('')
self.lineEdit_3.setText('')
else:
QMessageBox.warning(self, 'Download Notation', 'This File Already Exist')
def Save_Browse(self):
save_location = QFileDialog.getExistingDirectory(self, "Save Location")
self.lineEdit_13.setText(save_location)
self.lineEdit_5.setText(save_location)
self.lineEdit_7.setText(save_location)
def on_Progress_Video(self, stream, chunk, bytes_remaining):
total_size = stream.filesize
bytes_downloaded = total_size - bytes_remaining
percentage_of_completion = bytes_downloaded / total_size * 100
self.progressBar_2.setValue(percentage_of_completion)
QApplication.processEvents()
def on_Progress_Playlist(self, stream, chunk, bytes_remaining):
total_size = stream.filesize
bytes_downloaded = total_size - bytes_remaining
percentage_of_completion = bytes_downloaded / total_size * 100
self.progressBar_3.setValue(percentage_of_completion)
QApplication.processEvents()
def on_Progress_Select_From_Playlist(self, stream, chunk, bytes_remaining):
total_size = stream.filesize
bytes_downloaded = total_size - bytes_remaining
percentage_of_completion = bytes_downloaded / total_size * 100
self.progressBar_4.setValue(percentage_of_completion)
QApplication.processEvents()
def Video_Download(self):
url = self.lineEdit_4.text()
save_location = self.lineEdit_5.text()
try:
# quality = self.comboBox.currentText()
yt = YouTube(url) #, on_progress_callback=on_progress)
yt.register_on_progress_callback(self.on_Progress_Video) # (video, chunk_size, video.filesize))
# video = yt.streams.order_by('resolution')
# video = yt.streams.get_highest_resolution()
video = yt.streams.filter(progressive = True, file_extension = "mp4").last() #order_by('resolution').last()
video.download(save_location)
except Exception:
QMessageBox.warning(self, 'Download Error', 'Download Faild')
return
QMessageBox.information(self, 'Download Completed', 'Video Download Finished')
self.progressBar_2.setValue(0)
self.lineEdit_4.setText('')
self.lineEdit_5.setText('')
def Play_List_Download(self):
url_playlist = self.lineEdit_6.text()
save_location = self.lineEdit_7.text()
try:
pl = Playlist(url_playlist)
l = len(pl.videos)
self.lineEdit_8.setText(str(l))
i = 1
for video in (pl.videos):
self.lineEdit_9.setText(str(i))
video.register_on_progress_callback(self.on_Progress_Playlist)
i += 1
video.streams.filter(progressive = True, file_extension = "mp4").order_by('resolution').get_highest_resolution().download(save_location)
self.progressBar_3.setValue(0)
except Exception:
QMessageBox.warning(self, 'Download Error', 'Download Faild')
return
QMessageBox.information(self, 'Download Completed', 'Playlist Download Finished')
self.lineEdit_6.setText('')
self.lineEdit_7.setText('')
self.lineEdit_8.setText('')
self.lineEdit_9.setText('')
def Select_From_Playlist(self):
url_playlist = self.lineEdit_12.text()
save_location = self.lineEdit_13.text()
start = int(self.lineEdit_11.text())
end = int(self.lineEdit_10.text())
try:
pl = Playlist(url_playlist)
for video in (pl.videos[start-1:end]):
video.register_on_progress_callback(self.on_Progress_Select_From_Playlist)
video.streams.filter(progressive = True, file_extension = "mp4").order_by('resolution').get_lowest_resolution().download(save_location)
self.progressBar_4.setValue(0)
except Exception:
QMessageBox.warning(self, 'Download Error', 'Download Faild')
return
QMessageBox.information(self, 'Download Completed', 'Selected Playlist Download Finished')
self.lineEdit_10.setText('')
self.lineEdit_11.setText('')
self.lineEdit_12.setText('')
self.lineEdit_13.setText('')
def main():
app = QApplication(sys.argv)
window = MainApp()
window.show()
app.exec_()
if __name__ == '__main__':
main()
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] |
showspectralImage.py | guanguanboy/MyHybridSNNew | 0 | 69485 | <gh_stars>0
import keras
from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization
from keras.layers import Dropout, Input
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
from operator import truediv
from plotly.offline import init_notebook_mode
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import os
import spectral
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
## GLOBAL VARIABLES
dataset = 'IP'
test_ratio = 0.7
windowSize = 25
def loadData(name):
data_path = os.path.join(os.getcwd(), 'data')
if name == 'IP':
data = sio.loadmat(os.path.join(data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected']
labels = sio.loadmat(os.path.join(data_path, 'Indian_pines_gt.mat'))['indian_pines_gt']
elif name == 'SA':
data = sio.loadmat(os.path.join(data_path, 'Salinas_corrected.mat'))['salinas_corrected']
labels = sio.loadmat(os.path.join(data_path, 'Salinas_gt.mat'))['salinas_gt']
elif name == 'PU':
data = sio.loadmat(os.path.join(data_path, 'PaviaU.mat'))['paviaU']
labels = sio.loadmat(os.path.join(data_path, 'PaviaU_gt.mat'))['paviaU_gt']
return data, labels
X, y = loadData(dataset)
print(X.shape, y.shape)
view = spectral.imshow(X, (29, 19, 9))
#view = spectral.imshow(X, (1, 2, 3))
print(view)
plt.show()
#show grandtruth
gt_view = spectral.imshow(classes=y)
plt.show()
#show both the data and the class value
img = X
gt = y
view = spectral.imshow(img, (30, 20, 10), classes=gt)
view.set_display_mode('overlay')
view.class_alpha = 0.5
plt.show()
#saving rgb image files
spectral.save_rgb('rgb.jpg', img, [29, 19, 9])
spectral.save_rgb('gt.jpg', gt, colors=spectral.spy_colors)
#spectral.view_cube(img, spectral.bands=[29, 19, 9])
spectral.view_cube(img,bands=[29, 19, 9])
plt.show()
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] |
tests/unit/test_base_adapter.py | norton120/snowshu | 0 | 59927 | <gh_stars>0
import pytest
from snowshu.adapters import BaseSQLAdapter
from tests.common import rand_string
from snowshu.core.models.credentials import Credentials, USER, PASSWORD, HOST, ACCOUNT, SCHEMA, DATABASE, ROLE
def rand_creds(args) -> Credentials:
kwargs = dict(zip(args, [rand_string(10) for _ in range(len(args))]))
return Credentials(**kwargs)
class TestAdapter(BaseSQLAdapter):
REQUIRED_CREDENTIALS = (USER, PASSWORD, HOST)
ALLOWED_CREDENTIALS = (ACCOUNT, SCHEMA)
DATA_TYPE_MAPPINGS = dict()
MATERIALIZATION_MAPPINGS = dict()
def test_sets_credentials():
base = TestAdapter()
with pytest.raises(KeyError):
base.credentials = rand_creds((HOST,))
with pytest.raises(KeyError):
base.credentials = rand_creds((USER, PASSWORD, HOST, DATABASE,))
base.credentials = rand_creds((USER, PASSWORD, HOST,))
base.credentials = rand_creds((USER, PASSWORD, HOST, ACCOUNT,))
def test_default_conn_string():
base = TestAdapter()
base.dialect = 'postgres'
base.REQUIRED_CREDENTIALS = (USER, PASSWORD, DATABASE, HOST)
base.ALLOWED_CREDENTIALS = (ROLE, SCHEMA, ACCOUNT)
creds = rand_creds((USER, PASSWORD, HOST, DATABASE, ACCOUNT,))
base.credentials = creds
assert base._build_conn_string(
) == f'postgres://{creds.user}:{creds.password}@{creds.host}/{creds.database}?account={creds.account}'
| [
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] |
model/constants.py | JIMENOFONSECA/HBLM-USA | 1 | 147507 | <filename>model/constants.py<gh_stars>1-10
'''
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
MODEL_NAME = "HBLM-USA 1.0"
samples = 100
response_variable = "LOG_SITE_ENERGY_kWh_yr"
predictor_variables = ["LOG_THERMAL_ENERGY_kWh_yr", "CLUSTER_LOG_SITE_EUI_kWh_m2yr"]
random_state = 170
n_clusters = 5
storey_height_m = 3
air_density_kgm3 = 1.202
hours_of_day = 24
COP_cooling = 3.3
COP_heating = 3.0
RH_base_cooling_perc = 60
RH_base_heating_perc = 30
T_base_cooling_C = 18.5
T_base_heating_C = 18.5
ACH_Commercial = 6.0
ACH_Residential = 4.0
ZONE_NAMES = {"Hot-humid": ["1A", "2A", "3A"],
"Hot-dry": ["2B", "3B"],
"Hot-marine": ["3C"],
"Mixed-humid": ["4A"],
"Mixed-dry": ["4B"],
"Mixed-marine": ["4C"],
"Cold-humid": ["5A", "6A"],
"Cold-dry": ["5B", "6B", "7"]} | [
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3919,
24972,
9818,
6093,
13,
20656,
29950,
24125,
6323,
315,
4590,
29979,
22789,
3912,
379,
5607,
8032,
29903,
20700,
17705,
6181,
15842,
13764,
29979,
315,
4375,
7833,
29892,
21330,
1529,
1692,
29903,
6323,
438,
29911,
4448,
13,
5265,
2882,
6227,
11937,
29892,
12317,
2544,
4448,
2672,
13764,
319,
9838,
8079,
8707,
29911,
4717,
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323,
8476,
6323,
438,
29911,
4448,
22119,
1660,
29892,
9033,
3235,
4214,
3895,
29892,
13,
12015,
8079,
6323,
2672,
8707,
8186,
9838,
22659,
6093,
7791,
7818,
12982,
1525,
6323,
6093,
501,
1660,
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438,
29911,
4448,
5012,
1964,
4214,
29903,
2672,
6093,
13,
6156,
7818,
12982,
1525,
29889,
13,
12008,
13,
13,
20387,
29931,
29918,
5813,
353,
376,
29950,
13367,
29924,
29899,
27019,
29871,
29896,
29889,
29900,
29908,
13,
27736,
353,
29871,
29896,
29900,
29900,
13,
5327,
29918,
11918,
353,
376,
14480,
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29918,
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29885,
29906,
4316,
3108,
13,
8172,
29918,
3859,
353,
29871,
29896,
29955,
29900,
13,
29876,
29918,
695,
504,
414,
353,
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29945,
13,
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29918,
3545,
29918,
29885,
353,
29871,
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13,
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21518,
537,
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9415,
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29941,
353,
29871,
29896,
29889,
29906,
29900,
29906,
13,
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2470,
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974,
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3250,
353,
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29906,
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13,
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1111,
324,
292,
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13,
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354,
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29900,
13,
29934,
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3188,
29918,
1111,
324,
292,
29918,
546,
29883,
353,
29871,
29953,
29900,
13,
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29950,
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354,
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29918,
546,
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353,
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13,
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3188,
29918,
1111,
324,
292,
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353,
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13,
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13,
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1523,
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29953,
29889,
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13,
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29950,
29918,
1666,
1693,
616,
353,
29871,
29946,
29889,
29900,
13,
29999,
12413,
29918,
5813,
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8853,
28917,
29899,
16063,
333,
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6796,
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29909,
613,
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29909,
613,
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12436,
13,
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376,
28917,
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29933,
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13,
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6796,
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29924,
11925,
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6796,
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29933,
12436,
13,
795,
376,
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24556,
1115,
6796,
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13,
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1025,
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333,
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613,
376,
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13,
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1025,
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719,
1115,
6796,
29945,
29933,
613,
376,
29953,
29933,
613,
376,
29955,
3108,
29913,
2
] |
Subsets and Splits