id stringlengths 1 265 | text stringlengths 6 5.19M | dataset_id stringclasses 7 values |
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/3Di_cmd_client-0.0.3.tar.gz/3Di_cmd_client-0.0.3/cmd_client/commands/settings.py | from __future__ import annotations
from dataclasses import dataclass, field, asdict
from datetime import timedelta
from datetime import datetime
from enum import Enum
from pathlib import Path
import sys
from urllib.parse import urlparse
from functools import lru_cache
import click
import jwt
import yaml
from rich.prompt import Prompt
from openapi_client import Configuration, ApiClient, ApiException
from threedi_api_client.threedi_api_client import ThreediApiClient
from openapi_client.api.auth_api import AuthApi
from threedi_api_client.threedi_api_client import (
is_token_usable,
get_auth_token,
)
from cmd_client.console import console
from cmd_client.errors import ExitCodes
SCENARIO_DIR = Path(__file__).parent.parent / "scenarios"
REFRESH_TIME_DELTA = timedelta(hours=4).total_seconds()
EXPIRE_LEEWAY = -300
class EndpointOption(Enum):
localhost = "http://localhost:8000/v3.0"
staging = "https://api.staging.3di.live/v3.0"
production = "https://api.3di.live/v3.0"
@lru_cache()
def get_settings(endpoint):
return Settings(endpoint=endpoint)
@dataclass
class WebSocketSettings:
api_base_url: str
token: str
host: str = field(init=False)
proto: str = field(init=False)
api_version: str = field(init=False)
def __post_init__(self):
parsed_url = urlparse(self.api_base_url)
self.host = parsed_url.netloc
self.proto = "wss" if parsed_url.scheme == "https" else "ws"
self.api_version = parsed_url.path.lstrip("/")
self.token = (
self.token
if self.token.startswith("Bearer")
else f"Bearer {self.token}"
)
@dataclass
class CachedConfig:
username: str = ""
access: str = ""
refresh: str = ""
organisation_uuid: str = ""
result_folder: str = ""
@classmethod
def load_from_file(cls, file: Path) -> CachedConfig:
"""
Loads saved settings from 3di_config.yaml. See save method for available attributes.
"""
try:
with open(file, "r") as f:
cached_data = yaml.load(f, Loader=yaml.FullLoader)
return cls(**cached_data)
except OSError:
# settings file does not yet exist
return cls()
class Settings:
"""Settings that are saved (authentication) values between calls to the cli.
The default settings are saved in the file '3di_config.yaml' which lives in
the configuration folder of your operating system.
"""
app_name = "3di-scenario"
file_name = "3di_config.yaml"
def __init__(self, endpoint: str = ""):
self.endpoint = EndpointOption[endpoint].value
self.cached_config = CachedConfig.load_from_file(self.config_file)
self.websocket_settings = WebSocketSettings(
api_base_url=self.endpoint, token=self.access
)
@property
def username(self):
return self.cached_config.username
@property
def access(self):
return self.cached_config.access
@property
def refresh(self):
return self.cached_config.refresh
@property
def organisation_uuid(self):
return self.cached_config.organisation_uuid
@property
def result_folder(self):
return self.cached_config.result_folder
@property
def scenario_dir(self):
return SCENARIO_DIR
@property
def app_dir(self) -> Path:
_app_dir = Path(click.get_app_dir(app_name=self.app_name))
_app_dir.mkdir(parents=True, exist_ok=True)
return _app_dir
@property
def config_file(self) -> Path:
return self.app_dir / self.file_name
@property
def configuration(self):
configuration = Configuration(
host=self.endpoint,
username=f"{self.username}",
api_key={
"Authorization": f"{self.access}",
"refresh": f"{self.refresh}",
},
api_key_prefix={"Authorization": "Bearer"},
)
configuration.refresh_api_key_hook = refresh_api_key
return configuration
@property
def api_client(self) -> ThreediApiClient:
client = ApiClient(self.configuration)
return client
@property
def scenarios(self):
scenario_list = []
scenario_files = self.scenario_dir.glob("*.yaml")
for f in scenario_files:
with f.open("r") as y:
content = yaml.load_all(y, Loader=yaml.FullLoader)
try:
meta = next(content)["meta"]
meta["file"] = f
scenario_list.append(meta)
except KeyError:
raise AttributeError(
f"The scenario definition is missing the 'meta' section"
)
return scenario_list
def save(self):
with open(self.config_file, "w") as file:
yaml.dump(
asdict(self.cached_config),
default_flow_style=False,
stream=file,
)
@staticmethod
def save_settings():
ctx = click.get_current_context()
ctx.obj.save()
def do_refresh(token) -> bool:
try:
details = jwt.decode(
token,
options={"verify_signature": False},
leeway=EXPIRE_LEEWAY,
)
except (jwt.exceptions.ExpiredSignatureError, jwt.exceptions.DecodeError):
return True
expiry_dt = datetime.fromtimestamp(details["exp"])
sec_left = (expiry_dt - datetime.utcnow()).total_seconds()
return sec_left <= REFRESH_TIME_DELTA
def refresh_api_key(config: Configuration):
"""Refreshes the access key if its expired"""
api_key = config.api_key.get("Authorization")
if not do_refresh(api_key) and is_token_usable(api_key):
return
refresh_key = config.api_key.get("refresh")
if is_token_usable(refresh_key):
api_client = ApiClient(Configuration(config.host))
auth = AuthApi(api_client)
token = auth.auth_refresh_token_create(
{"refresh": config.api_key["refresh"]}
)
else:
console.rule(
f":key: Authentication required for {config.host}",
characters="*",
style="gold3",
)
settings: Settings = click.get_current_context().obj
kwargs = {}
try:
username = settings.username
if username:
kwargs.update({"default": username})
except AttributeError:
pass
username = Prompt.ask("Username", **kwargs)
password = Prompt.ask("Password", password=True)
try:
token = get_auth_token(username, password, config.host)
except ValueError as err:
console.print(f":collision: {err}", style="error")
sys.exit(ExitCodes.AUTHENTICATION_FAILED.value)
# Update the settings
settings.cached_config.username = username
settings.cached_config.access = token.access
settings.cached_config.refresh = token.refresh
config.api_key = {"Authorization": token.access, "refresh": token.refresh} | PypiClean |
/AstroCabTools-1.5.1.tar.gz/AstroCabTools-1.5.1/astrocabtools/mrs_subviz/src/viewers/centroidWavelengthSelection.py | import sys
import glob
import traceback
import numpy as np
from os.path import expanduser
from ..utils.basic_transformations import wavelength_to_slice
from PyQt5.QtWidgets import QDialog, QMessageBox, QSizePolicy, QFileDialog
from PyQt5.QtCore import Qt, pyqtSlot, pyqtSignal
from PyQt5 import QtGui
from PyQt5 import uic
import astrocabtools.mrs_subviz.src.viewers.centroidAreaSelection as centSelect
import astrocabtools.mrs_subviz.src.ui.ui_centroidWavelengthSelection
class CentroidWavelengthSelection(QDialog, astrocabtools.mrs_subviz.src.ui.ui_centroidWavelengthSelection.Ui_centroidWavelengthSelection):
def __init__(self, parent = None):
super(CentroidWavelengthSelection, self).__init__(parent)
self.setupUi(self)
self.subband = 0
self.allWavelengthButton.clicked.connect(lambda: self.send_range(True))
self.customWavelengthButton.clicked.connect(lambda: self.send_range(False))
self.initialWavelengthLineEdit.setValidator(QtGui.QDoubleValidator())
self.endWavelengthLineEdit.setValidator(QtGui.QDoubleValidator())
self.centSelect = centSelect.CentroidAreaSelection()
def send_range(self, allRange):
"""
Get the sum of the data from the range selected
:param bool allRange: If True, get the sum of all values of the cube, else, get the sum values of the range
"""
if allRange:
self.centSelect.get_data_from_centroid_wave(self.order, self.cubeParams, self.subband, np.mean(self.cubeParams.cubeModel.data[:], axis=0), self.additionalOrder)
self.centSelect.show()
self.centSelect.open()
else:
if float(self.initialWavelengthLineEdit.text()) < self.initRange or \
float(self.endWavelengthLineEdit.text()) > self.endRange:
self.range_warning()
else:
initialWaveTrans = wavelength_to_slice(float(self.initialWavelengthLineEdit.text()), self.cubeParams.cubeModel.meta.wcsinfo.crpix3, self.cubeParams.cubeModel.meta.wcsinfo.cdelt3, self.cubeParams.cubeModel.meta.wcsinfo.crval3)
endWaveTrans = wavelength_to_slice(float(self.endWavelengthLineEdit.text()), self.cubeParams.cubeModel.meta.wcsinfo.crpix3, self.cubeParams.cubeModel.meta.wcsinfo.cdelt3, self.cubeParams.cubeModel.meta.wcsinfo.crval3)
self.centSelect.get_data_from_centroid_wave(self.order, self.cubeParams, self.subband, np.mean(self.cubeParams.cubeModel.data[initialWaveTrans:endWaveTrans], axis=0), self.additionalOrder)
self.centSelect.show()
self.centSelect.open()
def get_data_from_sub_viz(self, initRange, endRange, subband, cubeParams, order=None, additionalOrder = None):
"""
Get data from the main window to initialize the values
:param float initRange: initial wavelength value
:param float endRange: end wavelength value
:param int subband: subband value
:param object cubeParams: cube data structured
:param str order: type of action to be done
:param str additionalOrder: extra action to be done after the centroid had been calculated
"""
self.initRange = initRange
self.endRange = endRange
self.initialWavelengthLineEdit.setText(str(initRange))
self.endWavelengthLineEdit.setText(str(endRange))
self.subband = 0
self.cubeParams = cubeParams
self.order = order
self.additionalOrder = additionalOrder
def range_warning(self):
warning = QMessageBox()
warning.setWindowTitle("Warning")
warning.setIcon(QMessageBox.Warning)
warning.setText("Range values out of the limits of the cube values")
warning.exec_()
def clear_data(self):
self.initialWavelengthLineEdit.setText('')
self.endWavelengthLineEdit.setText('')
self.initialRangeWavelengthLineEdit.setText('')
self.endRangeWavelengthLineEdit.setText('')
self.initRange = -1
self.endRange = -1
self.subband = 0
self.centSelect.clear_data()
self.close()
def closeEvent(self, event):
self.centSelect.close() | PypiClean |
/CADET-Process-0.7.3.tar.gz/CADET-Process-0.7.3/CADETProcess/stationarity.py | from addict import Dict
import numpy as np
from CADETProcess import log
from CADETProcess.dataStructure import StructMeta, UnsignedFloat
from CADETProcess import SimulationResults
from CADETProcess.comparison import Comparator
from CADETProcess.processModel import Inlet
__all__ = ['RelativeArea', 'NRMSE', 'StationarityEvaluator']
class CriterionBase(metaclass=StructMeta):
threshold = UnsignedFloat(default=1e-3)
def __str__(self):
return self.__class__.__name__
class RelativeArea(CriterionBase):
"""Class to evaluate difference in relative area as stationarity critereon."""
pass
class NRMSE(CriterionBase):
"""Class to evaluate NRMSE as stationarity critereon."""
pass
class StationarityEvaluator(Comparator):
"""Class for checking two succeding chromatograms for stationarity."""
valid_criteria = ['RelativeArea', 'NRMSE']
def __init__(
self,
criteria=None,
log_level='WARNING',
*args, **kwargs):
"""Initialize the stationarity evaluator.
Parameters
----------
criteria : List[CriterionBase], optional
List of criteria for stationarity evaluation, by default None
log_level : str, optional
The logging level, by default 'WARNING'
args : list
Additional arguments.
kwargs : dict
Additional keyword arguments.
"""
super().__init__(*args, **kwargs)
self.logger = log.get_logger('StationarityEvaluator', level=log_level)
self._criteria = []
@property
def criteria(self):
"""list: List of criteria."""
return self._criteria
def add_criterion(self, criterion):
"""Add a criterion to the list of criteria.
Parameters
----------
criterion : CriterionBase
Criterion to add to the list of criteria.
"""
if not isinstance(criterion, CriterionBase):
raise TypeError("Expected CriterionBase.")
self._criteria.append(criterion)
def assert_stationarity(self, simulation_results):
"""Check stationarity of two succeeding cycles.
Parameters
----------
simulation_results : SimulationResults
Results of current cycle.
Returns
-------
bool
True if stationarity is reached. False otherwise.
Raises
------
TypeError
If simulation_results is not a SimulationResults object.
"""
self._metrics = []
criteria = Dict()
if not isinstance(simulation_results, SimulationResults):
raise TypeError('Expcected SimulationResults')
stationarity = True
for unit, solution in simulation_results.solution_cycles.items():
if isinstance(simulation_results.process.flow_sheet[unit], Inlet):
continue
solution_previous = solution.outlet[-2]
solution_this = solution.outlet[-1]
self.add_reference(solution_previous, update=True, smooth=False)
for c in self.criteria:
metric = self.add_difference_metric(
str(c), unit, f'{unit}.outlet', smooth=False
)
criteria[unit][str(c)]['threshold'] = c.threshold
diff = metric.evaluate(solution_this)
criteria[unit][str(c)]['metric'] = diff
if not np.all(diff <= c.threshold):
s = False
stationarity = s
else:
s = True
criteria[unit][str(c)]['stationarity'] = s
self.logger.debug(f'Stationrity criteria: {criteria}')
return stationarity | PypiClean |
/FanFicFare-4.27.0.tar.gz/FanFicFare-4.27.0/fanficfare/mobihtml.py |
# Copyright(c) 2009 Andrew Chatham and Vijay Pandurangan
# Changes Copyright 2018 FanFicFare team
## This module is used by mobi.py exclusively.
## Renamed Jul 2018 to avoid conflict with other 'html' packages
from __future__ import absolute_import
import re
import logging
# py2 vs py3 transition
from .six.moves.urllib.parse import unquote
from .six import text_type as unicode
from .six import ensure_binary
# import bs4
# BeautifulSoup = bs4.BeautifulSoup
from bs4 import BeautifulSoup
logger = logging.getLogger(__name__)
class HtmlProcessor:
WHITESPACE_RE = re.compile(r'\s')
# Look for </blockquote <p>
#BAD_TAG_RE = re.compile(r'<[^>]+<', re.MULTILINE)
def __init__(self, html, unfill=0):
self.unfill = unfill
# html = self._ProcessRawHtml(html)
self._soup = BeautifulSoup(html,'html5lib')
# logger.debug(html)
## mobi format wants to find this <guide> tag inside <head>.
## html5lib, on the other hand, moved it to <body>. So we'll move
## it back.
guide = self._soup.find('guide')
if guide:
self._soup.head.append(guide)
# logger.debug(self._soup)
if self._soup.title.contents:
self.title = self._soup.title.contents[0]
else:
self.title = None
# Unnecessary with BS4
# def _ProcessRawHtml(self, html):
# new_html, count = HtmlProcessor.BAD_TAG_RE.subn('<', html)
# if count:
# print >>sys.stderr, 'Replaced %d bad tags' % count
# return new_html
def _StubInternalAnchors(self):
'''Replace each internal anchor with a fixed-size filepos anchor.
Looks for every anchor with <a href="#myanchor"> and replaces that
with <a filepos="00000000050">. Stores anchors in self._anchor_references'''
self._anchor_references = []
anchor_num = 0
# anchor links
anchorlist = self._soup.findAll('a', href=re.compile('^#'))
# treat reference tags like a tags for TOCTOP.
anchorlist.extend(self._soup.findAll('reference', href=re.compile('^#')))
for anchor in anchorlist:
self._anchor_references.append((anchor_num, anchor['href']))
anchor['filepos'] = '%.10d' % anchor_num
# logger.debug("Add anchor: %s %s"%((anchor_num, anchor)))
del anchor['href']
anchor_num += 1
def _ReplaceAnchorStubs(self):
# TODO: Browsers allow extra whitespace in the href names.
assembled_text = ensure_binary(unicode(self._soup))
# html5lib/bs4 creates close tags for <mbp:pagebreak>
assembled_text = assembled_text.replace(b'<mbp:pagebreak>',b'<mbp:pagebreak/>')
assembled_text = assembled_text.replace(b'</mbp:pagebreak>',b'')
del self._soup # shouldn't touch this anymore
for anchor_num, original_ref in self._anchor_references:
ref = unquote(original_ref[1:]) # remove leading '#'
# Find the position of ref in the utf-8 document.
# TODO(chatham): Using regexes and looking for name= would be better.
newpos = assembled_text.find(b'name="'+ensure_binary(ref)) # .encode('utf-8')
if newpos == -1:
logger.warning('Could not find anchor "%s"' % original_ref)
continue
# instead of somewhere slightly *after* the <a> tag pointed to,
# let's go right in front of it instead by looking for the page
# break before it.
newpos = assembled_text.rfind(b'<',0,newpos)
# logger.debug("Anchor Pos: %s %s '%s|%s'"%((anchor_num, newpos,assembled_text[newpos-15:newpos],assembled_text[newpos:newpos+15])))
old_filepos = b'filepos="%.10d"' % anchor_num
new_filepos = b'filepos="%.10d"' % newpos
assert assembled_text.find(old_filepos) != -1
assembled_text = assembled_text.replace(old_filepos, new_filepos, 1)
return assembled_text
def _FixPreTags(self):
'''Replace <pre> tags with HTML-ified text.'''
pres = self._soup.findAll('pre')
for pre in pres:
pre.replaceWith(self._FixPreContents(unicode(pre.contents[0])))
def _FixPreContents(self, text):
if self.unfill:
line_splitter = '\n\n'
line_joiner = '<p>'
else:
line_splitter = '\n'
line_joiner = '<br>'
lines = []
for line in text.split(line_splitter):
lines.append(self.WHITESPACE_RE.subn(' ', line)[0])
return line_joiner.join(lines)
def _RemoveUnsupported(self):
'''Remove any tags which the kindle cannot handle.'''
# TODO(chatham): <link> tags to script?
unsupported_tags = ('script', 'style')
for tag_type in unsupported_tags:
for element in self._soup.findAll(tag_type):
element.extract()
def RenameAnchors(self, prefix):
'''Rename every internal anchor to have the given prefix, then
return the contents of the body tag.'''
for anchor in self._soup.findAll('a', href=re.compile('^#')):
anchor['href'] = '#' + prefix + anchor['href'][1:]
for a in self._soup.findAll('a'):
if a.get('name'):
a['name'] = prefix + a['name']
# TODO(chatham): figure out how to fix this. sometimes body comes out
# as NoneType.
content = []
if self._soup.body is not None:
content = [unicode(c) for c in self._soup.body.contents]
return '\n'.join(content)
def CleanHtml(self):
# TODO(chatham): fix_html_br, fix_html
self._RemoveUnsupported()
self._StubInternalAnchors()
self._FixPreTags()
return self._ReplaceAnchorStubs()
if __name__ == '__main__':
FILE ='/tmp/documentation.html'
#FILE = '/tmp/multipre.html'
FILE = '/tmp/view.html'
d = open(FILE).read()
h = HtmlProcessor(d)
s = h.CleanHtml()
#print s | PypiClean |
/Electrum-CHI-3.3.8.tar.gz/Electrum-CHI-3.3.8/electrum_chi/electrum/coinchooser.py | from collections import defaultdict
from math import floor, log10
from typing import NamedTuple, List, Callable
from decimal import Decimal
from .bitcoin import sha256, COIN, TYPE_ADDRESS, is_address
from .transaction import Transaction, TxOutput
from .util import NotEnoughFunds
from .logging import Logger
# A simple deterministic PRNG. Used to deterministically shuffle a
# set of coins - the same set of coins should produce the same output.
# Although choosing UTXOs "randomly" we want it to be deterministic,
# so if sending twice from the same UTXO set we choose the same UTXOs
# to spend. This prevents attacks on users by malicious or stale
# servers.
class PRNG:
def __init__(self, seed):
self.sha = sha256(seed)
self.pool = bytearray()
def get_bytes(self, n):
while len(self.pool) < n:
self.pool.extend(self.sha)
self.sha = sha256(self.sha)
result, self.pool = self.pool[:n], self.pool[n:]
return result
def randint(self, start, end):
# Returns random integer in [start, end)
n = end - start
r = 0
p = 1
while p < n:
r = self.get_bytes(1)[0] + (r << 8)
p = p << 8
return start + (r % n)
def choice(self, seq):
return seq[self.randint(0, len(seq))]
def shuffle(self, x):
for i in reversed(range(1, len(x))):
# pick an element in x[:i+1] with which to exchange x[i]
j = self.randint(0, i+1)
x[i], x[j] = x[j], x[i]
class Bucket(NamedTuple):
desc: str
weight: int # as in BIP-141
value: int # in satoshis
effective_value: int # estimate of value left after subtracting fees. in satoshis
coins: List[dict] # UTXOs
min_height: int # min block height where a coin was confirmed
witness: bool # whether any coin uses segwit
class ScoredCandidate(NamedTuple):
penalty: float
tx: Transaction
buckets: List[Bucket]
def strip_unneeded(bkts, sufficient_funds):
'''Remove buckets that are unnecessary in achieving the spend amount'''
if sufficient_funds([], bucket_value_sum=0):
# none of the buckets are needed
return []
bkts = sorted(bkts, key=lambda bkt: bkt.value, reverse=True)
bucket_value_sum = 0
for i in range(len(bkts)):
bucket_value_sum += (bkts[i]).value
if sufficient_funds(bkts[:i+1], bucket_value_sum=bucket_value_sum):
return bkts[:i+1]
raise Exception("keeping all buckets is still not enough")
class CoinChooserBase(Logger):
enable_output_value_rounding = False
def __init__(self):
Logger.__init__(self)
def keys(self, coins):
raise NotImplementedError
def bucketize_coins(self, coins, *, fee_estimator_vb):
keys = self.keys(coins)
buckets = defaultdict(list)
for key, coin in zip(keys, coins):
buckets[key].append(coin)
# fee_estimator returns fee to be paid, for given vbytes.
# guess whether it is just returning a constant as follows.
constant_fee = fee_estimator_vb(2000) == fee_estimator_vb(200)
def make_Bucket(desc, coins):
witness = any(Transaction.is_segwit_input(coin, guess_for_address=True) for coin in coins)
# note that we're guessing whether the tx uses segwit based
# on this single bucket
weight = sum(Transaction.estimated_input_weight(coin, witness)
for coin in coins)
value = sum(coin['value'] for coin in coins)
min_height = min(coin['height'] for coin in coins)
# the fee estimator is typically either a constant or a linear function,
# so the "function:" effective_value(bucket) will be homomorphic for addition
# i.e. effective_value(b1) + effective_value(b2) = effective_value(b1 + b2)
if constant_fee:
effective_value = value
else:
# when converting from weight to vBytes, instead of rounding up,
# keep fractional part, to avoid overestimating fee
fee = fee_estimator_vb(Decimal(weight) / 4)
effective_value = value - fee
return Bucket(desc=desc,
weight=weight,
value=value,
effective_value=effective_value,
coins=coins,
min_height=min_height,
witness=witness)
return list(map(make_Bucket, buckets.keys(), buckets.values()))
def penalty_func(self, base_tx, *, tx_from_buckets) -> Callable[[List[Bucket]], ScoredCandidate]:
raise NotImplementedError
def _change_amounts(self, tx, count, fee_estimator_numchange) -> List[int]:
# Break change up if bigger than max_change
output_amounts = [o.value for o in tx.outputs()]
# Don't split change of less than 0.02 BTC
max_change = max(max(output_amounts) * 1.25, 0.02 * COIN)
# Use N change outputs
for n in range(1, count + 1):
# How much is left if we add this many change outputs?
change_amount = max(0, tx.get_fee() - fee_estimator_numchange(n))
if change_amount // n <= max_change:
break
# Get a handle on the precision of the output amounts; round our
# change to look similar
def trailing_zeroes(val):
s = str(val)
return len(s) - len(s.rstrip('0'))
zeroes = [trailing_zeroes(i) for i in output_amounts]
min_zeroes = min(zeroes)
max_zeroes = max(zeroes)
if n > 1:
zeroes = range(max(0, min_zeroes - 1), (max_zeroes + 1) + 1)
else:
# if there is only one change output, this will ensure that we aim
# to have one that is exactly as precise as the most precise output
zeroes = [min_zeroes]
# Calculate change; randomize it a bit if using more than 1 output
remaining = change_amount
amounts = []
while n > 1:
average = remaining / n
amount = self.p.randint(int(average * 0.7), int(average * 1.3))
precision = min(self.p.choice(zeroes), int(floor(log10(amount))))
amount = int(round(amount, -precision))
amounts.append(amount)
remaining -= amount
n -= 1
# Last change output. Round down to maximum precision but lose
# no more than 10**max_dp_to_round_for_privacy
# e.g. a max of 2 decimal places means losing 100 satoshis to fees
max_dp_to_round_for_privacy = 2 if self.enable_output_value_rounding else 0
N = int(pow(10, min(max_dp_to_round_for_privacy, zeroes[0])))
amount = (remaining // N) * N
amounts.append(amount)
assert sum(amounts) <= change_amount
return amounts
def _change_outputs(self, tx, change_addrs, fee_estimator_numchange, dust_threshold):
amounts = self._change_amounts(tx, len(change_addrs), fee_estimator_numchange)
assert min(amounts) >= 0
assert len(change_addrs) >= len(amounts)
assert all([isinstance(amt, int) for amt in amounts])
# If change is above dust threshold after accounting for the
# size of the change output, add it to the transaction.
amounts = [amount for amount in amounts if amount >= dust_threshold]
change = [TxOutput(TYPE_ADDRESS, addr, amount)
for addr, amount in zip(change_addrs, amounts)]
return change
def _construct_tx_from_selected_buckets(self, *, buckets, base_tx, change_addrs,
fee_estimator_w, dust_threshold, base_weight):
# make a copy of base_tx so it won't get mutated
tx = Transaction.from_io(base_tx.inputs()[:], base_tx.outputs()[:])
tx.add_inputs([coin for b in buckets for coin in b.coins])
tx_weight = self._get_tx_weight(buckets, base_weight=base_weight)
# change is sent back to sending address unless specified
if not change_addrs:
change_addrs = [tx.inputs()[0]['address']]
# note: this is not necessarily the final "first input address"
# because the inputs had not been sorted at this point
assert is_address(change_addrs[0])
# This takes a count of change outputs and returns a tx fee
output_weight = 4 * Transaction.estimated_output_size(change_addrs[0])
fee_estimator_numchange = lambda count: fee_estimator_w(tx_weight + count * output_weight)
change = self._change_outputs(tx, change_addrs, fee_estimator_numchange, dust_threshold)
tx.add_outputs(change)
return tx, change
def _get_tx_weight(self, buckets, *, base_weight) -> int:
"""Given a collection of buckets, return the total weight of the
resulting transaction.
base_weight is the weight of the tx that includes the fixed (non-change)
outputs and potentially some fixed inputs. Note that the change outputs
at this point are not yet known so they are NOT accounted for.
"""
total_weight = base_weight + sum(bucket.weight for bucket in buckets)
is_segwit_tx = any(bucket.witness for bucket in buckets)
if is_segwit_tx:
total_weight += 2 # marker and flag
# non-segwit inputs were previously assumed to have
# a witness of '' instead of '00' (hex)
# note that mixed legacy/segwit buckets are already ok
num_legacy_inputs = sum((not bucket.witness) * len(bucket.coins)
for bucket in buckets)
total_weight += num_legacy_inputs
return total_weight
def make_tx(self, coins, inputs, outputs, change_addrs, fee_estimator_vb,
dust_threshold):
"""Select unspent coins to spend to pay outputs. If the change is
greater than dust_threshold (after adding the change output to
the transaction) it is kept, otherwise none is sent and it is
added to the transaction fee.
`inputs` and `outputs` are guaranteed to be a subset of the
inputs and outputs of the resulting transaction.
`coins` are further UTXOs we can choose from.
Note: fee_estimator_vb expects virtual bytes
"""
# Deterministic randomness from coins
utxos = [c['prevout_hash'] + str(c['prevout_n']) for c in (coins)]
self.p = PRNG(''.join(sorted(utxos)))
# Copy the outputs so when adding change we don't modify "outputs"
base_tx = Transaction.from_io(inputs[:], outputs[:])
input_value = base_tx.input_value()
# Weight of the transaction with no inputs and no change
# Note: this will use legacy tx serialization as the need for "segwit"
# would be detected from inputs. The only side effect should be that the
# marker and flag are excluded, which is compensated in get_tx_weight()
# FIXME calculation will be off by this (2 wu) in case of RBF batching
base_weight = base_tx.estimated_weight()
spent_amount = base_tx.output_value()
def fee_estimator_w(weight):
return fee_estimator_vb(Transaction.virtual_size_from_weight(weight))
def sufficient_funds(buckets, *, bucket_value_sum):
'''Given a list of buckets, return True if it has enough
value to pay for the transaction'''
# assert bucket_value_sum == sum(bucket.value for bucket in buckets) # expensive!
total_input = input_value + bucket_value_sum
if total_input < spent_amount: # shortcut for performance
return False
# note re performance: so far this was constant time
# what follows is linear in len(buckets)
total_weight = self._get_tx_weight(buckets, base_weight=base_weight)
return total_input >= spent_amount + fee_estimator_w(total_weight)
def tx_from_buckets(buckets):
return self._construct_tx_from_selected_buckets(buckets=buckets,
base_tx=base_tx,
change_addrs=change_addrs,
fee_estimator_w=fee_estimator_w,
dust_threshold=dust_threshold,
base_weight=base_weight)
# Collect the coins into buckets
all_buckets = self.bucketize_coins(coins, fee_estimator_vb=fee_estimator_vb)
# Filter some buckets out. Only keep those that have positive effective value.
# Note that this filtering is intentionally done on the bucket level
# instead of per-coin, as each bucket should be either fully spent or not at all.
# (e.g. CoinChooserPrivacy ensures that same-address coins go into one bucket)
all_buckets = list(filter(lambda b: b.effective_value > 0, all_buckets))
# Choose a subset of the buckets
scored_candidate = self.choose_buckets(all_buckets, sufficient_funds,
self.penalty_func(base_tx, tx_from_buckets=tx_from_buckets))
tx = scored_candidate.tx
self.logger.info(f"using {len(tx.inputs())} inputs")
self.logger.info(f"using buckets: {[bucket.desc for bucket in scored_candidate.buckets]}")
return tx
def choose_buckets(self, buckets, sufficient_funds,
penalty_func: Callable[[List[Bucket]], ScoredCandidate]) -> ScoredCandidate:
raise NotImplemented('To be subclassed')
class CoinChooserRandom(CoinChooserBase):
def bucket_candidates_any(self, buckets, sufficient_funds):
'''Returns a list of bucket sets.'''
if not buckets:
raise NotEnoughFunds()
candidates = set()
# Add all singletons
for n, bucket in enumerate(buckets):
if sufficient_funds([bucket], bucket_value_sum=bucket.value):
candidates.add((n, ))
# And now some random ones
attempts = min(100, (len(buckets) - 1) * 10 + 1)
permutation = list(range(len(buckets)))
for i in range(attempts):
# Get a random permutation of the buckets, and
# incrementally combine buckets until sufficient
self.p.shuffle(permutation)
bkts = []
bucket_value_sum = 0
for count, index in enumerate(permutation):
bucket = buckets[index]
bkts.append(bucket)
bucket_value_sum += bucket.value
if sufficient_funds(bkts, bucket_value_sum=bucket_value_sum):
candidates.add(tuple(sorted(permutation[:count + 1])))
break
else:
# note: this assumes that the effective value of any bkt is >= 0
raise NotEnoughFunds()
candidates = [[buckets[n] for n in c] for c in candidates]
return [strip_unneeded(c, sufficient_funds) for c in candidates]
def bucket_candidates_prefer_confirmed(self, buckets, sufficient_funds):
"""Returns a list of bucket sets preferring confirmed coins.
Any bucket can be:
1. "confirmed" if it only contains confirmed coins; else
2. "unconfirmed" if it does not contain coins with unconfirmed parents
3. other: e.g. "unconfirmed parent" or "local"
This method tries to only use buckets of type 1, and if the coins there
are not enough, tries to use the next type but while also selecting
all buckets of all previous types.
"""
conf_buckets = [bkt for bkt in buckets if bkt.min_height > 0]
unconf_buckets = [bkt for bkt in buckets if bkt.min_height == 0]
other_buckets = [bkt for bkt in buckets if bkt.min_height < 0]
bucket_sets = [conf_buckets, unconf_buckets, other_buckets]
already_selected_buckets = []
already_selected_buckets_value_sum = 0
for bkts_choose_from in bucket_sets:
try:
def sfunds(bkts, *, bucket_value_sum):
bucket_value_sum += already_selected_buckets_value_sum
return sufficient_funds(already_selected_buckets + bkts,
bucket_value_sum=bucket_value_sum)
candidates = self.bucket_candidates_any(bkts_choose_from, sfunds)
break
except NotEnoughFunds:
already_selected_buckets += bkts_choose_from
already_selected_buckets_value_sum += sum(bucket.value for bucket in bkts_choose_from)
else:
raise NotEnoughFunds()
candidates = [(already_selected_buckets + c) for c in candidates]
return [strip_unneeded(c, sufficient_funds) for c in candidates]
def choose_buckets(self, buckets, sufficient_funds, penalty_func):
candidates = self.bucket_candidates_prefer_confirmed(buckets, sufficient_funds)
scored_candidates = [penalty_func(cand) for cand in candidates]
winner = min(scored_candidates, key=lambda x: x.penalty)
self.logger.info(f"Total number of buckets: {len(buckets)}")
self.logger.info(f"Num candidates considered: {len(candidates)}. "
f"Winning penalty: {winner.penalty}")
return winner
class CoinChooserPrivacy(CoinChooserRandom):
"""Attempts to better preserve user privacy.
First, if any coin is spent from a user address, all coins are.
Compared to spending from other addresses to make up an amount, this reduces
information leakage about sender holdings. It also helps to
reduce blockchain UTXO bloat, and reduce future privacy loss that
would come from reusing that address' remaining UTXOs.
Second, it penalizes change that is quite different to the sent amount.
Third, it penalizes change that is too big.
"""
def keys(self, coins):
return [coin['address'] for coin in coins]
def penalty_func(self, base_tx, *, tx_from_buckets):
min_change = min(o.value for o in base_tx.outputs()) * 0.75
max_change = max(o.value for o in base_tx.outputs()) * 1.33
def penalty(buckets) -> ScoredCandidate:
# Penalize using many buckets (~inputs)
badness = len(buckets) - 1
tx, change_outputs = tx_from_buckets(buckets)
change = sum(o.value for o in change_outputs)
# Penalize change not roughly in output range
if change == 0:
pass # no change is great!
elif change < min_change:
badness += (min_change - change) / (min_change + 10000)
# Penalize really small change; under 1 mBTC ~= using 1 more input
if change < COIN / 1000:
badness += 1
elif change > max_change:
badness += (change - max_change) / (max_change + 10000)
# Penalize large change; 5 BTC excess ~= using 1 more input
badness += change / (COIN * 5)
return ScoredCandidate(badness, tx, buckets)
return penalty
COIN_CHOOSERS = {
'Privacy': CoinChooserPrivacy,
}
def get_name(config):
kind = config.get('coin_chooser')
if not kind in COIN_CHOOSERS:
kind = 'Privacy'
return kind
def get_coin_chooser(config):
klass = COIN_CHOOSERS[get_name(config)]
coinchooser = klass()
coinchooser.enable_output_value_rounding = config.get('coin_chooser_output_rounding', False)
return coinchooser | PypiClean |
/Eskapade-1.0.0-py3-none-any.whl/eskapade/analysis/links/apply_func_to_df.py | import collections
from eskapade import process_manager, DataStore, Link, StatusCode
class ApplyFuncToDf(Link):
"""Apply functions to data-frame.
Applies one or more functions to a (grouped) dataframe column or an
entire dataframe. In the latter case, this can be done row wise or
column wise. The input dataframe will be overwritten.
"""
def __init__(self, **kwargs):
"""Initialize link instance.
:param str read_key: data-store input key
:param str store_key: data-store output key
:param list apply_funcs: functions to apply (list of dicts)
- 'func': function to apply
- 'colout' (string): output column
- 'colin' (string, optional): input column
- 'entire' (boolean, optional): apply to the entire dataframe?
- 'args' (tuple, optional): args for 'func'
- 'kwargs' (dict, optional): kwargs for 'func'
- 'groupby' (list, optional): column names to group by
- 'groupbyColout' (string) output column after the split-apply-combine combination
:param dict add_columns: columns to add to output (name, column)
"""
Link.__init__(self, kwargs.pop('name', 'apply_func_to_dataframe'))
# process keyword arguments
self._process_kwargs(kwargs, read_key='', store_key='', apply_funcs=[], add_columns=None)
self.check_extra_kwargs(kwargs)
def initialize(self):
"""Initialize the link."""
self.check_arg_vals('read_key')
if not self.apply_funcs:
self.logger.warning('No functions to apply')
return StatusCode.Success
def execute(self):
"""Execute the link."""
ds = process_manager.service(DataStore)
assert self.read_key in ds, 'key "{key}" not in DataStore.'.format(key=self.read_key)
df = ds[self.read_key]
for arr in self.apply_funcs:
# get func input
keys = list(arr.keys())
assert 'func' in keys, 'function input is insufficient.'
func = arr['func']
self.logger.debug('Applying function {function!s}.', function=func)
args = ()
kwargs = {}
if 'kwargs' in keys:
kwargs = arr['kwargs']
if 'args' in keys:
args = arr['args']
# apply func
if 'groupby' in keys:
groupby = arr['groupby']
if 'groupbyColout' in keys:
kwargs['groupbyColout'] = arr['groupbyColout']
df = self.groupbyapply(df, groupby, func, *args, **kwargs)
elif 'store_key' in keys:
if 'entire' in keys:
result = func(df, *args, **kwargs)
elif 'colin' in keys:
colin = arr['colin']
assert colin in df.columns
result = df[colin].apply(func, args=args, **kwargs)
else:
result = df.apply(func, args=args, **kwargs)
ds[arr['store_key']] = result
else:
assert 'colout' in keys, 'function input is insufficient'
colout = arr['colout']
if 'entire' in keys:
df[colout] = func(df, *args, **kwargs)
elif 'colin' in keys:
colin = arr['colin']
if isinstance(colin, list):
for c in colin:
assert c in df.columns
else:
assert colin in df.columns
df[colout] = df[colin].apply(func, args=args, **kwargs)
else:
df[colout] = df.apply(func, args=args, **kwargs)
# add columns
if self.add_columns is not None:
for k, v in self.add_columns.items():
df[k] = v
if self.store_key is None:
ds[self.read_key] = df
else:
ds[self.store_key] = df
return StatusCode.Success
def add_apply_func(self, func, out_column, in_column='', *args, **kwargs):
"""Add function to be applied to dataframe."""
# check inputs
if not isinstance(func, collections.Callable):
self.logger.fatal('Specified function object is not callable.')
raise AssertionError('functions in ApplyFuncToDf link must be callable objects')
if not isinstance(out_column, str) or not isinstance(in_column, str):
self.logger.fatal('Types of specified column names are "{in_type}" and "{out_type}."',
in_type=type(out_column).__name__, out_type=type(in_column).__name__)
raise TypeError('Column names in ApplyFuncToDf must be strings.')
if not out_column:
self.logger.fatal('No output column specified.')
raise RuntimeError('An output column must be specified to apply function in ApplyFuncToDf.')
# add function
if in_column == '':
self.apply_funcs.append({'func': func, 'colout': out_column, 'args': args, 'kwargs': kwargs})
else:
self.apply_funcs.append({'colin': in_column, 'func': func, 'colout': out_column, 'args': args,
'kwargs': kwargs})
def groupbyapply(self, df, groupby_columns, applyfunc, *args, **kwargs):
"""Apply groupby to dataframe."""
if 'groupbyColout' not in kwargs:
return df.groupby(groupby_columns).apply(applyfunc, *args, **kwargs).reset_index(drop=True)
else:
colout = kwargs['groupbyColout']
kwargs.pop('groupbyColout')
t = df.groupby(groupby_columns).apply(applyfunc, *args, **kwargs)
for _ in range(0, len(groupby_columns)):
t.index = t.index.droplevel()
df[colout] = t
return df | PypiClean |
/Flask-DB-0.3.2.tar.gz/Flask-DB-0.3.2/flask_db/cli.py | import subprocess
import os
import click
from flask import current_app
from flask.cli import with_appcontext
from sqlalchemy_utils import database_exists, create_database
from flask_db.init import generate_configs
DEFAULT_SEEDS_PATH = os.path.join("db", "seeds.py")
@click.group()
def db():
"""
Migrate and manage your SQL database.
"""
pass
@db.command()
@click.argument("path", default="db/")
@with_appcontext
def init(path):
"""
Generate Alembic config files and seeds.py.
"""
copied_files, existing_files = generate_configs(path,
current_app.import_name)
if copied_files is not None:
msg = f"""alembic.ini was created in the root of your project
{path} was created with your Alembic configs, versions/ directory and seeds.py
"""
click.echo(msg)
if existing_files:
click.echo("Also, you already had these files in your project:\n")
for file in existing_files:
click.echo(f" {file[0]}")
msg = """
Instead of aborting or erroring out, a new version of any existing files have
been created with a .new file extension in their respective directories.
Now you can diff them and decide on what to do next.
Chances are you'll want to use the .new version of any file but if you have
any custom Alembic configuration you may want to copy those changes over.
If you want to use the .new file as is you can delete your original file and
then rename the .new file by removing its .new file extension."""
click.echo(msg)
return None
@db.command()
@click.option("--with-testdb", is_flag=True,
help="Create a test DB in addition to your main DB?")
@click.pass_context
@with_appcontext
def reset(ctx, with_testdb):
"""
Drop, create and seed your database (careful in production).
"""
db = current_app.extensions["sqlalchemy"].db
db_uri = current_app.config["SQLALCHEMY_DATABASE_URI"]
if not database_exists(db_uri):
create_database(db_uri)
db.drop_all()
db.create_all()
if with_testdb:
db_uri = f"{db_uri}_test"
if not database_exists(db_uri):
create_database(db_uri)
ctx.invoke(seed)
return None
@db.command()
@with_appcontext
def seed():
"""
Seed the database with your custom records.
"""
seeds_path = current_app.config.get("FLASK_DB_SEEDS_PATH",
DEFAULT_SEEDS_PATH)
if os.path.isfile and os.path.exists(seeds_path):
exec(open(seeds_path).read())
else:
msg = f"""{seeds_path} does not exist
If you haven't done so already, run: flask db init
If you're using a custom init path (ie. not db/ (the default)) then you can
define a custom seeds path by setting FLASK_DB_SEEDS_PATH in your app's config.
For example if you did flask db init myapp/migrations then you would want
to set FLASK_DB_SEEDS_PATH = "myapp/migrations/seeds.py"."""
click.echo(msg)
return None
@db.command(context_settings=dict(ignore_unknown_options=True))
@click.argument("alembic_args", nargs=-1, type=click.UNPROCESSED)
@with_appcontext
def migrate(alembic_args):
"""Wrap the alembic CLI tool (defaults to running upgrade head)."""
if not alembic_args:
alembic_args = ("upgrade", "head")
cmdline = ["alembic"] + list(alembic_args)
subprocess.call(cmdline)
return None | PypiClean |
/LibML-0.1.03.tar.gz/LibML-0.1.03/libml/Classification/NaiveBayes.py | import numpy as np
import numpy.linalg as la
from sklearn.utils.extmath import safe_sparse_dot as safedot
class NaiveBayes():
def __init__(self, model):
"""
Initializes the Naive Bayes classifier for given model type
Parameters
----------
model : used to specify distribution of feature values. Multinomial for
categorical and Bernoulli for binary
"""
if model not in ['Bernoulli', 'Multinomial']:
raise Exception('Must specify correct model')
self.model = model
def compute_priors(self, y):
"""
Computes the prior probilities for each class
Parameters
----------
y : N x 1 vector where each observation is a class label corresponding
to the same index in X
"""
self.classes = np.array(np.unique(y), dtype = 'uint16') ## should not be any negative classes
self.priors = np.zeros(shape=self.classes.shape[0])
for i in self.classes:
self.priors[i] = np.sum(y == self.classes[i]) / y.shape[0]
def get_bernoulli_probabilities(self, X, y):
"""
Computes the class conditional probability of each element in all feature vectors
given that all elements are either 0 or 1
Parameters
----------
X : N x D matrix of 1 x D feature vectors
y : N x 1 vector where each observation is a class label corresponding
to the same index in X
"""
p_matrix = np.zeros(shape=(self.classes.shape[0], X.shape[1]))
for c in self.classes:
mask = y == c
elems = X[mask]
length = np.sum(mask)
p_matrix[c] = np.sum(elems, axis = 0) / length
# preventing value errors when calculating log likelihood
p_matrix[p_matrix == 0] = np.finfo('float').resolution
p_matrix[p_matrix == 1] = 1 - np.finfo('float').resolution
self.p_matrix = p_matrix
def get_bernoulli_likelihood(self, X):
"""
Calculates the likelihood of each observation in X being in a
class given that the features in X are either 0 or 1
Parameters
----------
X : N x D matrix of 1 x D feature vectors
"""
log_p = np.log(self.p_matrix)
log_p_not = np.log(1 - self.p_matrix)
a = safedot(1 - X, log_p_not.T)
b = safedot(X, log_p.T)
self.predictions = np.argmax(np.log(self.priors) + a + b, axis = 1)
def get_multinomial_probabilities(self, X, y):
"""
Calculates the class conditional probabilities for each feature given
that each one is has a multinomial distribution
Parameters
----------
X : N x D matrix of 1 x D feature vectors
y : N x 1 vector where each observation is a class label corresponding
to the same index in X
"""
num_class = self.classes.shape[0]
p_matrix = np.zeros(shape= (num_class, X.shape[1]))
for i in range(num_class):
p_matrix[i] = np.squeeze(np.asarray(X[y == i].sum(axis = 0) / np.sum(y == i)))
# Need log probability for dot product later, so cannot have zeros
p_matrix[p_matrix == 0] = np.finfo('float').resolution
self.l_p_matrix = np.log(p_matrix)
def get_multinomial_likelihood(self, X):
"""
Calculates the likelihood of each observation in X being in a
class given that the features in X are of a multinomial distribution
Parameters
----------
X : N x D matrix of 1 x D feature vectors
"""
ll = safedot(X, self.l_p_matrix.T)
l_prior = np.log(self.priors)
self.predictions = np.argmax(ll + l_prior, axis = 1)
def fit(self, X, y):
"""
Fits generative model that will be used for classification
Parameters
----------
X : N x D matrix of 1 x D feature vectors
y : N x 1 vector where each observation is a class label corresponding
to the same index in X
"""
self.compute_priors(y)
if self.model == 'Bernoulli':
if (np.unique(X).size > 2):
raise ValueError("Cannot use Bernoulli model for data that is not binary")
self.get_bernoulli_probabilities(X, y)
elif self.model == 'Multinomial':
self.get_multinomial_probabilities(X, y)
def classify(self, X):
"""
Classifies new feature vectors using the models generated by the fit() method
Parameters
----------
X : N x D matrix of 1 x D feature vectors
"""
if self.model == 'Bernoulli':
self.get_bernoulli_likelihood(X)
elif self.model == 'Multinomial':
self.get_multinomial_likelihood(X) | PypiClean |
/Flask_JSONRPC-2.2.2-py3-none-any.whl/flask_jsonrpc/types.py | import typing as t
from numbers import Real, Integral, Rational
from collections import OrderedDict, defaultdict
from collections.abc import Mapping
from typing_inspect import is_new_type # type: ignore
# Python 3.10+
try:
from types import NoneType, UnionType
except ImportError: # pragma: no cover
UnionType = None # type: ignore
NoneType = type(None) # type: ignore
# Python 3.8+
try:
from typing_extensions import Literal
except ImportError: # pragma: no cover
from typing import Literal # type: ignore # pylint: disable=C0412
# Python 3.8+
try:
from typing_extensions import Final
except ImportError: # pragma: no cover
from typing import Final # type: ignore # pylint: disable=C0412
# Python 3.5.4+ / 3.6.2+
try:
from typing import get_args # pylint: disable=C0412
except ImportError: # pragma: no cover
from typing_inspect import get_args # type: ignore # pylint: disable=C0412
# Python 3.5.4+ / 3.6.2+
try:
from typing import get_origin # pylint: disable=C0412
except ImportError: # pragma: no cover
from typing_inspect import get_origin # type: ignore # pylint: disable=C0412
# Python 3.5.4+ / 3.6.2+
try:
from typing_extensions import NoReturn # pylint: disable=C0412
except ImportError: # pragma: no cover
try:
from typing import NoReturn # pylint: disable=C0412
except ImportError:
NoReturn = None # type: ignore
class JSONRPCNewType:
def __init__(self, name: str, *types: t.Union[type, t.Tuple[t.Union[type, t.Tuple[type, ...]], ...]]) -> None:
self.name = name
self.types = types
def check_expected_type(self, expected_type: t.Any) -> bool:
return any(expected_type is tp for tp in self.types)
def check_expected_types(self, expected_types: t.Any) -> bool:
return all(any(expt_tp is tp for tp in self.types) for expt_tp in expected_types)
def check_type_var(self, expected_type: t.Any) -> bool: # pragma: no cover py3.6
bound_type = getattr(expected_type, '__bound__', None)
if bound_type is None:
expected_types = getattr(expected_type, '__constraints__', None)
if not expected_types:
return self is Object
return self.check_expected_types(expected_types)
return self.check_expected_type(bound_type)
def check_new_type(self, expected_type: t.Any) -> bool: # pragma: no cover py3.6
super_type = getattr(expected_type, '__supertype__', None)
return self.check_expected_type(super_type)
def check_union(self, expected_type: t.Any) -> bool:
expected_types = [expt_tp for expt_tp in get_args(expected_type) if expt_tp is not type(None)] # noqa: E721
return self.check_expected_types(expected_types)
def check_args_type(self, expected_type: t.Any) -> bool: # pragma: no cover py3.6
expected_types = get_args(expected_type)
return self.check_expected_types(expected_types)
def check_type(self, o: t.Any) -> bool: # pylint: disable=R0911
expected_type = o
if expected_type is t.Any:
return self is Object
if expected_type is None or expected_type is NoReturn:
expected_type = type(None)
if type(expected_type) is t.TypeVar: # pylint: disable=C0123
return self.check_type_var(expected_type)
if is_new_type(expected_type):
return self.check_new_type(expected_type)
origin_type = get_origin(expected_type)
if origin_type is not None:
if origin_type is t.Union or origin_type is UnionType:
return self.check_union(expected_type)
if origin_type is t.Tuple or origin_type is tuple:
return self is Array
if origin_type is Literal: # pragma: no cover py3.6
return self.check_args_type(expected_type)
if origin_type is Final: # pragma: no cover py3.6
return self.check_args_type(expected_type)
expected_type = origin_type
return self.check_expected_type(expected_type)
def __str__(self) -> str:
return self.name
String = JSONRPCNewType('String', str, bytes, bytearray)
Number = JSONRPCNewType('Number', int, float, Real, Rational, Integral)
Object = JSONRPCNewType('Object', dict, t.Dict, defaultdict, OrderedDict, Mapping)
Array = JSONRPCNewType('Array', list, set, t.Set, tuple, t.List, t.NamedTuple, frozenset, t.FrozenSet)
Boolean = JSONRPCNewType('Boolean', bool)
Null = JSONRPCNewType('Null', type(None), NoneType)
Types = [String, Number, Object, Array, Boolean, Null] | PypiClean |
/Flask_MonitoringDashboard-3.1.2-py3-none-any.whl/flask_monitoringdashboard/controllers/requests.py | import datetime
import numpy
from sqlalchemy import func, and_
from flask_monitoringdashboard.core.timezone import to_utc_datetime, to_local_datetime
from flask_monitoringdashboard.database import Request
from flask_monitoringdashboard.database.count_group import count_requests_per_day, get_value
from flask_monitoringdashboard.database.endpoint import get_endpoints, get_num_requests
from flask_monitoringdashboard.database.request import create_time_based_sample_criterion
def get_num_requests_data(session, start_date, end_date):
"""
:param session: session for the database
:param start_date: datetime object
:param end_date: datetime object and: end_date >= start_date
:return: a list of the number of requests for each endpoint and on which day
"""
numdays = (end_date - start_date).days + 1
days = [start_date + datetime.timedelta(days=i) for i in range(numdays)]
hits = count_requests_per_day(session, days)
endpoints = get_endpoints(session)
data = [
{'name': end.name, 'values': [get_value(hits_day, end.id) for hits_day in hits]}
for end in endpoints
]
return {'days': [d.strftime('%Y-%m-%d') for d in days], 'data': data}
def get_all_request_status_code_counts(session, endpoint_id):
"""
Gets all the request status code counts.
:param session: session for the database
:param endpoint_id: id for the endpoint
:return: A list of tuples in the form of `(status_code, count)`
"""
return (
session.query(Request.status_code, func.count(Request.status_code))
.filter(Request.endpoint_id == endpoint_id, Request.status_code.isnot(None))
.group_by(Request.status_code)
.all()
)
def get_status_code_distribution(session, endpoint_id):
"""
Gets the distribution of status codes returned by the given endpoint.
:param session: session for the database
:param endpoint_id: id for the endpoint
:return: A dict where the key is the status code and the value is the fraction of requests
that returned the status
code. Example: a return value of `{ 200: 0.92, 404: 0.08 }` means that status code 200 was
returned on 92% of the
requests. 8% of the requests returned a 404 status code.
"""
status_code_counts = get_all_request_status_code_counts(session, endpoint_id)
total_count = sum(frequency for (_, frequency) in status_code_counts)
return {status_code: frequency / total_count for (status_code, frequency) in status_code_counts}
def get_status_code_frequencies(session, endpoint_id, *criterion):
"""
Gets the frequencies of each status code.
:param session: session for the database
:param endpoint_id: id for the endpoint
:param criterion: Optional criteria used to file the requests.
:return: A dict where the key is the status code and the value is the fraction of requests that returned the status
code. Example: a return value of `{ 200: 105, 404: 3 }` means that status code 200 was returned 105 times and
404 was returned 3 times.
"""
status_code_counts = session.query(Request.status_code, func.count(Request.status_code)) \
.filter(Request.endpoint_id == endpoint_id, Request.status_code.isnot(None), *criterion) \
.group_by(Request.status_code).all()
return dict(status_code_counts)
def get_error_requests(session, endpoint_id, *criterion):
"""
Gets all requests that did not return a 200 status code.
:param session: session for the database
:param endpoint_id: ID of the endpoint to be queried
:param criterion: Optional criteria used to file the requests.
:return:
"""
criteria = and_(
Request.endpoint_id == endpoint_id,
Request.status_code.isnot(None),
Request.status_code >= 400,
Request.status_code <= 599,
)
return session.query(Request).filter(criteria, *criterion).all()
def get_status_code_frequencies_in_interval(session, endpoint_id, criterion):
return get_status_code_frequencies(session, endpoint_id, *criterion)
def get_hourly_load(session, endpoint_id, start_date, end_date):
"""
:param session: session for the database
:param endpoint_id: id for the endpoint
:param start_date: datetime object
:param end_date: datetime object and: end_date >= start_date
:return:
"""
numdays = (end_date - start_date).days + 1
# list of hours: 0:00 - 23:00
hours = ['0{}:00'.format(h) for h in range(0, 10)] + ['{}:00'.format(h) for h in range(10, 24)]
heatmap_data = numpy.zeros((len(hours), numdays))
start_datetime = to_utc_datetime(
datetime.datetime.combine(start_date, datetime.time(0, 0, 0, 0))
)
end_datetime = to_utc_datetime(datetime.datetime.combine(end_date, datetime.time(23, 59, 59)))
for time, count in get_num_requests(session, endpoint_id, start_datetime, end_datetime):
parsed_time = datetime.datetime.strptime(time, '%Y-%m-%d %H:%M:%S')
day_index = (parsed_time - start_datetime).days
hour_index = int(to_local_datetime(parsed_time).strftime('%H'))
heatmap_data[hour_index][day_index] = count
return {
'days': [
(start_date + datetime.timedelta(days=i)).strftime('%Y-%m-%d') for i in range(numdays)
],
"data": heatmap_data.tolist(),
} | PypiClean |
/GxSphinx-1.0.0.tar.gz/GxSphinx-1.0.0/doc/usage/restructuredtext/roles.rst | .. highlight:: rst
=====
Roles
=====
Sphinx uses interpreted text roles to insert semantic markup into documents.
They are written as ``:rolename:`content```.
.. note::
The default role (```content```) has no special meaning by default. You are
free to use it for anything you like, e.g. variable names; use the
:confval:`default_role` config value to set it to a known role -- the
:rst:role:`any` role to find anything or the :rst:role:`py:obj` role to find
Python objects are very useful for this.
See :doc:`/usage/restructuredtext/domains` for roles added by domains.
.. _xref-syntax:
Cross-referencing syntax
------------------------
Cross-references are generated by many semantic interpreted text roles.
Basically, you only need to write ``:role:`target```, and a link will be
created to the item named *target* of the type indicated by *role*. The link's
text will be the same as *target*.
There are some additional facilities, however, that make cross-referencing
roles more versatile:
* You may supply an explicit title and reference target, like in reST direct
hyperlinks: ``:role:`title <target>``` will refer to *target*, but the link
text will be *title*.
* If you prefix the content with ``!``, no reference/hyperlink will be created.
* If you prefix the content with ``~``, the link text will only be the last
component of the target. For example, ``:py:meth:`~Queue.Queue.get``` will
refer to ``Queue.Queue.get`` but only display ``get`` as the link text. This
does not work with all cross-reference roles, but is domain specific.
In HTML output, the link's ``title`` attribute (that is e.g. shown as a
tool-tip on mouse-hover) will always be the full target name.
.. _any-role:
Cross-referencing anything
^^^^^^^^^^^^^^^^^^^^^^^^^^
.. rst:role:: any
.. versionadded:: 1.3
This convenience role tries to do its best to find a valid target for its
reference text.
* First, it tries standard cross-reference targets that would be referenced
by :rst:role:`doc`, :rst:role:`ref` or :rst:role:`option`.
Custom objects added to the standard domain by extensions (see
:meth:`.Sphinx.add_object_type`) are also searched.
* Then, it looks for objects (targets) in all loaded domains. It is up to
the domains how specific a match must be. For example, in the Python
domain a reference of ``:any:`Builder``` would match the
``sphinx.builders.Builder`` class.
If none or multiple targets are found, a warning will be emitted. In the
case of multiple targets, you can change "any" to a specific role.
This role is a good candidate for setting :confval:`default_role`. If you
do, you can write cross-references without a lot of markup overhead. For
example, in this Python function documentation ::
.. function:: install()
This function installs a `handler` for every signal known by the
`signal` module. See the section `about-signals` for more information.
there could be references to a glossary term (usually ``:term:`handler```), a
Python module (usually ``:py:mod:`signal``` or ``:mod:`signal```) and a
section (usually ``:ref:`about-signals```).
The :rst:role:`any` role also works together with the
:mod:`~sphinx.ext.intersphinx` extension: when no local cross-reference is
found, all object types of intersphinx inventories are also searched.
Cross-referencing objects
^^^^^^^^^^^^^^^^^^^^^^^^^
These roles are described with their respective domains:
* :ref:`Python <python-roles>`
* :ref:`C <c-roles>`
* :ref:`C++ <cpp-roles>`
* :ref:`JavaScript <js-roles>`
* :ref:`ReST <rst-roles>`
.. _ref-role:
Cross-referencing arbitrary locations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. rst:role:: ref
To support cross-referencing to arbitrary locations in any document, the
standard reST labels are used. For this to work label names must be unique
throughout the entire documentation. There are two ways in which you can
refer to labels:
* If you place a label directly before a section title, you can reference to
it with ``:ref:`label-name```. For example::
.. _my-reference-label:
Section to cross-reference
--------------------------
This is the text of the section.
It refers to the section itself, see :ref:`my-reference-label`.
The ``:ref:`` role would then generate a link to the section, with the
link title being "Section to cross-reference". This works just as well
when section and reference are in different source files.
Automatic labels also work with figures. For example::
.. _my-figure:
.. figure:: whatever
Figure caption
In this case, a reference ``:ref:`my-figure``` would insert a reference
to the figure with link text "Figure caption".
The same works for tables that are given an explicit caption using the
:dudir:`table` directive.
* Labels that aren't placed before a section title can still be referenced,
but you must give the link an explicit title, using this syntax:
``:ref:`Link title <label-name>```.
.. note::
Reference labels must start with an underscore. When referencing a label,
the underscore must be omitted (see examples above).
Using :rst:role:`ref` is advised over standard reStructuredText links to
sections (like ```Section title`_``) because it works across files, when
section headings are changed, will raise warnings if incorrect, and works
for all builders that support cross-references.
Cross-referencing documents
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. versionadded:: 0.6
There is also a way to directly link to documents:
.. rst:role:: doc
Link to the specified document; the document name can be specified in
absolute or relative fashion. For example, if the reference
``:doc:`parrot``` occurs in the document ``sketches/index``, then the link
refers to ``sketches/parrot``. If the reference is ``:doc:`/people``` or
``:doc:`../people```, the link refers to ``people``.
If no explicit link text is given (like usual: ``:doc:`Monty Python members
</people>```), the link caption will be the title of the given document.
Referencing downloadable files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. versionadded:: 0.6
.. rst:role:: download
This role lets you link to files within your source tree that are not reST
documents that can be viewed, but files that can be downloaded.
When you use this role, the referenced file is automatically marked for
inclusion in the output when building (obviously, for HTML output only).
All downloadable files are put into a ``_downloads/<unique hash>/``
subdirectory of the output directory; duplicate filenames are handled.
An example::
See :download:`this example script <../example.py>`.
The given filename is usually relative to the directory the current source
file is contained in, but if it absolute (starting with ``/``), it is taken
as relative to the top source directory.
The ``example.py`` file will be copied to the output directory, and a
suitable link generated to it.
Not to show unavailable download links, you should wrap whole paragraphs that
have this role::
.. only:: builder_html
See :download:`this example script <../example.py>`.
Cross-referencing figures by figure number
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. versionadded:: 1.3
.. versionchanged:: 1.5
`numref` role can also refer sections.
And `numref` allows `{name}` for the link text.
.. rst:role:: numref
Link to the specified figures, tables, code-blocks and sections; the standard
reST labels are used. When you use this role, it will insert a reference to
the figure with link text by its figure number like "Fig. 1.1".
If an explicit link text is given (as usual: ``:numref:`Image of Sphinx (Fig.
%s) <my-figure>```), the link caption will serve as title of the reference.
As placeholders, `%s` and `{number}` get replaced by the figure
number and `{name}` by the figure caption.
If no explicit link text is given, the :confval:`numfig_format` setting is
used as fall-back default.
If :confval:`numfig` is ``False``, figures are not numbered,
so this role inserts not a reference but the label or the link text.
Cross-referencing other items of interest
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The following roles do possibly create a cross-reference, but do not refer to
objects:
.. rst:role:: envvar
An environment variable. Index entries are generated. Also generates a link
to the matching :rst:dir:`envvar` directive, if it exists.
.. rst:role:: token
The name of a grammar token (used to create links between
:rst:dir:`productionlist` directives).
.. rst:role:: keyword
The name of a keyword in Python. This creates a link to a reference label
with that name, if it exists.
.. rst:role:: option
A command-line option to an executable program. This generates a link to
a :rst:dir:`option` directive, if it exists.
The following role creates a cross-reference to a term in a
:ref:`glossary <glossary-directive>`:
.. rst:role:: term
Reference to a term in a glossary. A glossary is created using the
``glossary`` directive containing a definition list with terms and
definitions. It does not have to be in the same file as the ``term`` markup,
for example the Python docs have one global glossary in the ``glossary.rst``
file.
If you use a term that's not explained in a glossary, you'll get a warning
during build.
Math
----
.. rst:role:: math
Role for inline math. Use like this::
Since Pythagoras, we know that :math:`a^2 + b^2 = c^2`.
.. rst:role:: eq
Same as :rst:role:`math:numref`.
Other semantic markup
---------------------
The following roles don't do anything special except formatting the text in a
different style:
.. rst:role:: abbr
An abbreviation. If the role content contains a parenthesized explanation,
it will be treated specially: it will be shown in a tool-tip in HTML, and
output only once in LaTeX.
Example: ``:abbr:`LIFO (last-in, first-out)```.
.. versionadded:: 0.6
.. rst:role:: command
The name of an OS-level command, such as ``rm``.
.. rst:role:: dfn
Mark the defining instance of a term in the text. (No index entries are
generated.)
.. rst:role:: file
The name of a file or directory. Within the contents, you can use curly
braces to indicate a "variable" part, for example::
... is installed in :file:`/usr/lib/python2.{x}/site-packages` ...
In the built documentation, the ``x`` will be displayed differently to
indicate that it is to be replaced by the Python minor version.
.. rst:role:: guilabel
Labels presented as part of an interactive user interface should be marked
using ``guilabel``. This includes labels from text-based interfaces such as
those created using :mod:`curses` or other text-based libraries. Any label
used in the interface should be marked with this role, including button
labels, window titles, field names, menu and menu selection names, and even
values in selection lists.
.. versionchanged:: 1.0
An accelerator key for the GUI label can be included using an ampersand;
this will be stripped and displayed underlined in the output (example:
``:guilabel:`&Cancel```). To include a literal ampersand, double it.
.. rst:role:: kbd
Mark a sequence of keystrokes. What form the key sequence takes may depend
on platform- or application-specific conventions. When there are no
relevant conventions, the names of modifier keys should be spelled out, to
improve accessibility for new users and non-native speakers. For example,
an *xemacs* key sequence may be marked like ``:kbd:`C-x C-f```, but without
reference to a specific application or platform, the same sequence should be
marked as ``:kbd:`Control-x Control-f```.
.. rst:role:: mailheader
The name of an RFC 822-style mail header. This markup does not imply that
the header is being used in an email message, but can be used to refer to
any header of the same "style." This is also used for headers defined by
the various MIME specifications. The header name should be entered in the
same way it would normally be found in practice, with the camel-casing
conventions being preferred where there is more than one common usage. For
example: ``:mailheader:`Content-Type```.
.. rst:role:: makevar
The name of a :command:`make` variable.
.. rst:role:: manpage
A reference to a Unix manual page including the section, e.g.
``:manpage:`ls(1)```. Creates a hyperlink to an external site rendering the
manpage if :confval:`manpages_url` is defined.
.. rst:role:: menuselection
Menu selections should be marked using the ``menuselection`` role. This is
used to mark a complete sequence of menu selections, including selecting
submenus and choosing a specific operation, or any subsequence of such a
sequence. The names of individual selections should be separated by
``-->``.
For example, to mark the selection "Start > Programs", use this markup::
:menuselection:`Start --> Programs`
When including a selection that includes some trailing indicator, such as
the ellipsis some operating systems use to indicate that the command opens a
dialog, the indicator should be omitted from the selection name.
``menuselection`` also supports ampersand accelerators just like
:rst:role:`guilabel`.
.. rst:role:: mimetype
The name of a MIME type, or a component of a MIME type (the major or minor
portion, taken alone).
.. rst:role:: newsgroup
The name of a Usenet newsgroup.
.. todo:: Is this not part of the standard domain?
.. rst:role:: program
The name of an executable program. This may differ from the file name for
the executable for some platforms. In particular, the ``.exe`` (or other)
extension should be omitted for Windows programs.
.. rst:role:: regexp
A regular expression. Quotes should not be included.
.. rst:role:: samp
A piece of literal text, such as code. Within the contents, you can use
curly braces to indicate a "variable" part, as in :rst:role:`file`. For
example, in ``:samp:`print 1+{variable}```, the part ``variable`` would be
emphasized.
If you don't need the "variable part" indication, use the standard
````code```` instead.
.. versionchanged:: 1.8
Allowed to escape curly braces with backslash
There is also an :rst:role:`index` role to generate index entries.
The following roles generate external links:
.. rst:role:: pep
A reference to a Python Enhancement Proposal. This generates appropriate
index entries. The text "PEP *number*\ " is generated; in the HTML output,
this text is a hyperlink to an online copy of the specified PEP. You can
link to a specific section by saying ``:pep:`number#anchor```.
.. rst:role:: rfc
A reference to an Internet Request for Comments. This generates appropriate
index entries. The text "RFC *number*\ " is generated; in the HTML output,
this text is a hyperlink to an online copy of the specified RFC. You can
link to a specific section by saying ``:rfc:`number#anchor```.
Note that there are no special roles for including hyperlinks as you can use
the standard reST markup for that purpose.
.. _default-substitutions:
Substitutions
-------------
The documentation system provides three substitutions that are defined by
default. They are set in the build configuration file.
.. describe:: |release|
Replaced by the project release the documentation refers to. This is meant
to be the full version string including alpha/beta/release candidate tags,
e.g. ``2.5.2b3``. Set by :confval:`release`.
.. describe:: |version|
Replaced by the project version the documentation refers to. This is meant to
consist only of the major and minor version parts, e.g. ``2.5``, even for
version 2.5.1. Set by :confval:`version`.
.. describe:: |today|
Replaced by either today's date (the date on which the document is read), or
the date set in the build configuration file. Normally has the format
``April 14, 2007``. Set by :confval:`today_fmt` and :confval:`today`.
| PypiClean |
/LT2OpenCorpora-2.0.3.tar.gz/LT2OpenCorpora-2.0.3/bin/lt_plot.py | import os.path
import sys
import pydot
from unicodecsv import DictReader
import xml.etree.ElementTree as ET
sys.path.insert(0, ".")
import lt2opencorpora
if __name__ == '__main__':
# TODO: argparse
BASEPATH = os.path.dirname(lt2opencorpora.__file__)
graph = pydot.Dot(graph_type='digraph')
nodes_by_opencorpora = {}
nodes_by_LT = {}
tree = ET.parse(os.path.join(BASEPATH, 'open_corpora_tagset.xml'))
root = tree.getroot()
for child in root[0]:
node = pydot.Node(
"\n%s" % child.find("name").text, style="filled",
fillcolor="blanchedalmond")
node.ru_parent = child.attrib["parent"]
node.parent = ""
nodes_by_opencorpora[child.find("name").text] = node
with open(os.path.join(BASEPATH, "mapping.csv"), "r") as fp:
r = DictReader(fp)
for tag in r:
tag["opencorpora tags"] = (
tag["opencorpora tags"] or tag["name"])
name = "%s\n%s" % (tag["name"], tag["opencorpora tags"])
if tag["opencorpora tags"] in nodes_by_opencorpora:
node = nodes_by_opencorpora[tag["opencorpora tags"]]
node.set_name(name)
node.set_shape("doublecircle")
node.set("fillcolor", "palegreen")
else:
name = "%s\n" % (tag["name"])
node = pydot.Node(name, style="filled", fillcolor="skyblue")
node.ru_parent = ""
node.parent = tag["parent"]
nodes_by_LT[tag["name"]] = node
nodes_by_opencorpora[tag["opencorpora tags"]] = node
for k, node in nodes_by_opencorpora.iteritems():
graph.add_node(node)
if node.parent and node.parent != "aux":
graph.add_edge(pydot.Edge(node, nodes_by_LT[node.parent],
color="orange"))
if node.ru_parent:
graph.add_edge(
pydot.Edge(node, nodes_by_opencorpora[node.ru_parent],
color="blue"))
graph.write_png('mapping.png', prog="fdp") | PypiClean |
/Cocopot-0.2.tar.gz/Cocopot-0.2/cocopot/routing.py | import re
from .exceptions import BadRequest, NotFound, MethodNotAllowed
class RouteSyntaxError(Exception):
pass
class Router(object):
""" A Router is an ordered collection of route->endpoint pairs. It is used to
efficiently match WSGI requests against a number of routes and return
the first endpoint that satisfies the request. The endpoint may be anything,
usually a string, ID or callable object. A route consists of a path-rule
and a HTTP method.
The path-rule is either a static path (e.g. `/contact`) or a dynamic
path that contains wildcards (e.g. `/wiki/<page>`). The wildcard syntax
and details on the matching order are described in docs:`routing`.
"""
#: The current CPython regexp implementation does not allow more
#: than 99 matching groups per regular expression.
_MAX_GROUPS_PER_PATTERN = 99
def __init__(self, strict=False):
self.static_routes = {} # Search structure for static routes
self.dynamic_patterns = []
self.dynamic_routes = {}
#: If true, static routes are no longer checked first.
self.strict_order = strict
self.filters = {
'string': lambda: (r'[^/]+', None, None),
'int': lambda: (r'-?\d+', int, lambda x: str(int(x))),
'float': lambda: (r'-?[\d.]+', float, lambda x: str(float(x))),
'path': lambda: (r'.+?', None, None)
}
def add_filter(self, name, func):
""" Add a filter. The provided function is called with the configuration
string as parameter and must return a (regexp, to_python, to_url) tuple.
The first element is a string, the last two are callables or None. """
self.filters[name] = func
rule_syntax = re.compile('(?:<([a-zA-Z_]+:)?(?:([a-zA-Z_][a-zA-Z_0-9]*))>)')
def _itertokens(self, rule):
offset, prefix = 0, ''
for match in self.rule_syntax.finditer(rule):
prefix += rule[offset:match.start()]
g = match.groups()
if prefix:
yield prefix, None, None
if g[1] is None:
converter, variable = None, g[0]
else:
converter, variable = g[0], g[1]
converter = converter[:-1] if converter else 'string'
yield None, converter, variable
offset, prefix = match.end(), ''
if offset <= len(rule) or prefix:
yield prefix + rule[offset:], None, None
def add(self, rule, endpoint, methods=['GET'], defaults=None):
""" Add a new rule or replace the endpoint for an existing rule. """
pattern = '' # Regular expression pattern with named groups
filters = [] # Lists of wildcard input filters
builder = [] # Data structure for the URL builder
is_static = True
for key, converter, variable in self._itertokens(rule):
if converter:
is_static = False
mask, in_filter, out_filter = self.filters[converter]()
pattern += '(?P<%s>%s)' % (variable, mask)
if in_filter: filters.append((variable, in_filter))
builder.append((variable, out_filter or str))
elif key:
pattern += re.escape(key)
builder.append((None, key))
rule_args = dict(endpoint=endpoint, rule=rule, filters=filters,
builder=builder, pattern=pattern, defaults=defaults)
if is_static and not self.strict_order:
self.static_routes[rule] = dict([(m.upper(), rule_args)for m in methods])
return
try:
re_pattern = re.compile('^(%s)$' % pattern)
re_match = re_pattern.match
except re.error:
raise RouteSyntaxError("Could not add Route: %s (%s)" %
(rule, _e()))
rule_args['match'] = re_match
self.dynamic_patterns.append(re_pattern)
self.dynamic_routes[re_pattern] = dict([(m.upper(), rule_args)for m in methods])
def match(self, path, method='GET'):
""" Return a (endpoint, url_args) tuple or raise HTTPException(400/404/405). """
rule_args = self.static_routes.get(path)
url_args = {}
if not rule_args:
for re_pattern in self.dynamic_patterns:
matched = re_pattern.match(path)
if matched:
url_args = matched.groupdict()
rule_args = self.dynamic_routes[re_pattern]
break
if not rule_args:
raise NotFound("Not found: " + repr(path))
if method not in rule_args:
allow_header = ",".join(sorted(rule_args.keys()))
raise MethodNotAllowed("Method not allowed. Allowed methods: %s"%(allow_header))
args = rule_args[method]
filters = args.get('filters')
defaults = args.get('defaults') or {}
if filters:
for name, wildcard_filter in filters:
try:
url_args[name] = wildcard_filter(url_args[name])
except ValueError:
raise BadRequest('Path has wrong format.')
for k, v in defaults.items():
if k not in url_args:
url_args[k] = v
return args['endpoint'], url_args | PypiClean |
/AWSpider-0.3.2.12.tar.gz/AWSpider-0.3.2.12/awspider/servers/base.py | import cPickle
import hashlib
import inspect
import logging
import logging.handlers
import os
import time
from decimal import Decimal
from uuid import uuid4
from twisted.internet import reactor
from twisted.internet.threads import deferToThread
from twisted.internet.defer import Deferred, DeferredList, maybeDeferred
from ..aws import AmazonS3, AmazonSDB
from ..aws import sdb_now
from ..exceptions import DeleteReservationException
from ..pagegetter import PageGetter
from ..requestqueuer import RequestQueuer
from ..timeoffset import getTimeOffset
import pprint
PRETTYPRINTER = pprint.PrettyPrinter(indent=4)
LOGGER = logging.getLogger("main")
class ReservationCachingException(Exception):
pass
class BaseServer(object):
logging_handler = None
shutdown_trigger_id = None
uuid = uuid4().hex
start_time = time.time()
active_jobs = {}
reserved_arguments = [
"reservation_function_name",
"reservation_created",
"reservation_next_request",
"reservation_error",
"reservation_cache"]
functions = {}
reservation_fast_caches = {}
def __init__(self,
aws_access_key_id,
aws_secret_access_key,
aws_s3_http_cache_bucket=None,
aws_sdb_reservation_domain=None,
aws_s3_storage_bucket=None,
aws_s3_reservation_cache_bucket=None,
aws_sdb_coordination_domain=None,
max_simultaneous_requests=100,
max_requests_per_host_per_second=0,
max_simultaneous_requests_per_host=0,
log_file=None,
log_directory=None,
log_level="debug",
name=None,
time_offset=None,
port=8080):
if name == None:
name = "AWSpider Server UUID: %s" % self.uuid
self.port = port
self.time_offset = time_offset
self.name = name
self.start_deferred = Deferred()
self.rq = RequestQueuer(
max_simultaneous_requests=int(max_simultaneous_requests),
max_requests_per_host_per_second=int(max_requests_per_host_per_second),
max_simultaneous_requests_per_host=int(max_simultaneous_requests_per_host))
self.rq.setHostMaxRequestsPerSecond("127.0.0.1", 0)
self.rq.setHostMaxSimultaneousRequests("127.0.0.1", 0)
self.aws_s3_reservation_cache_bucket = aws_s3_reservation_cache_bucket
self.aws_access_key_id = aws_access_key_id
self.aws_secret_access_key = aws_secret_access_key
self.aws_s3_http_cache_bucket = aws_s3_http_cache_bucket
self.aws_s3_storage_bucket = aws_s3_storage_bucket
self.aws_sdb_reservation_domain = aws_sdb_reservation_domain
self.aws_sdb_coordination_domain = aws_sdb_coordination_domain
self.s3 = AmazonS3(
self.aws_access_key_id,
self.aws_secret_access_key,
rq=self.rq)
self.sdb = AmazonSDB(
self.aws_access_key_id,
self.aws_secret_access_key,
rq=self.rq)
self.pg = PageGetter(
self.s3,
self.aws_s3_http_cache_bucket,
rq=self.rq)
self._setupLogging(log_file, log_directory, log_level)
if self.name is not None:
LOGGER.info("Successfully loaded %s configuration." % self.name)
def _setupLogging(self, log_file, log_directory, log_level):
if log_directory is None:
self.logging_handler = logging.StreamHandler()
else:
self.logging_handler = logging.handlers.TimedRotatingFileHandler(
os.path.join(log_directory, log_file),
when='D',
interval=1)
log_format = "%(levelname)s: %(message)s %(pathname)s:%(lineno)d"
self.logging_handler.setFormatter(logging.Formatter(log_format))
LOGGER.addHandler(self.logging_handler)
log_level = log_level.lower()
log_levels = {
"debug":logging.DEBUG,
"info":logging.INFO,
"warning":logging.WARNING,
"error":logging.ERROR,
"critical":logging.CRITICAL
}
if log_level in log_levels:
LOGGER.setLevel(log_levels[log_level])
else:
LOGGER.setLevel(logging.DEBUG)
def start(self):
reactor.callWhenRunning(self._start)
return self.start_deferred
def start(self):
reactor.callWhenRunning(self._baseStart)
return self.start_deferred
def _baseStart(self):
LOGGER.critical("Checking S3 and SDB setup.")
deferreds = []
if self.aws_s3_reservation_cache_bucket is not None:
deferreds.append(
self.s3.checkAndCreateBucket(self.aws_s3_reservation_cache_bucket))
if self.aws_s3_http_cache_bucket is not None:
deferreds.append(
self.s3.checkAndCreateBucket(self.aws_s3_http_cache_bucket))
if self.aws_sdb_reservation_domain is not None:
deferreds.append(
self.sdb.checkAndCreateDomain(self.aws_sdb_reservation_domain))
if self.aws_s3_storage_bucket is not None:
deferreds.append(
self.s3.checkAndCreateBucket(self.aws_s3_storage_bucket))
if self.aws_sdb_coordination_domain is not None:
deferreds.append(
self.sdb.checkAndCreateDomain(self.aws_sdb_coordination_domain))
d = DeferredList(deferreds, consumeErrors=True)
d.addCallback(self._baseStartCallback)
def _baseStartCallback(self, data):
for row in data:
if row[0] == False:
d = self.shutdown()
d.addCallback(self._startHandleError, row[1])
return d
self.shutdown_trigger_id = reactor.addSystemEventTrigger(
'before',
'shutdown',
self.shutdown)
LOGGER.critical("Starting %s" % self.name)
self._baseStartCallback2(None)
def _baseStartCallback2(self, data):
self.start_deferred.callback(True)
def _startHandleError(self, data, error):
self.start_deferred.errback(error)
def shutdown(self):
LOGGER.debug("%s waiting for shutdown." % self.name)
d = Deferred()
reactor.callLater(0, self._waitForShutdown, d)
return d
def _waitForShutdown(self, shutdown_deferred):
if self.rq.getPending() > 0 or self.rq.getActive() > 0:
LOGGER.debug("%s waiting for shutdown." % self.name)
reactor.callLater(1, self._waitForShutdown, shutdown_deferred)
return
self.shutdown_trigger_id = None
LOGGER.debug("%s shut down." % self.name)
LOGGER.removeHandler(self.logging_handler)
shutdown_deferred.callback(True)
def getTimeOffset(self):
d = getTimeOffset()
d.addCallback(self._getTimeOffsetCallback)
d.addErrback(self._getTimeOffsetErrback)
return d
def _getTimeOffsetCallback(self, time_offset):
self.time_offset = time_offset
LOGGER.info("Got time offset for sync: %s" % self.time_offset)
def _getTimeOffsetErrback(self, error):
if self.time_offset is None:
message = "Could not get time offset for sync."
LOGGER.critical(message)
raise Exception(message)
def callExposedFunction(self, func, kwargs, function_name, reservation_fast_cache=None, uuid=None):
if uuid is not None:
self.active_jobs[uuid] = True
if self.functions[function_name]["get_reservation_uuid"]:
kwargs["reservation_uuid"] = uuid
if self.functions[function_name]["check_reservation_fast_cache"] and \
reservation_fast_cache is not None:
kwargs["reservation_fast_cache"] = reservation_fast_cache
elif self.functions[function_name]["check_reservation_fast_cache"]:
kwargs["reservation_fast_cache"] = None
if self.functions[function_name]["check_reservation_cache"] and \
self.aws_s3_reservation_cache_bucket is not None:
d = self.getReservationCache(uuid)
d.addCallback(self._reservationCacheCallback,
func,
kwargs,
function_name,
uuid)
d.addErrback(self._reservationCacheErrback,
func,
kwargs,
function_name,
uuid)
return d
elif self.functions[function_name]["check_reservation_cache"]:
kwargs["reservation_cache"] = None
d = maybeDeferred(func, **kwargs)
d.addCallback(self._callExposedFunctionCallback, function_name, uuid)
d.addErrback(self._callExposedFunctionErrback, function_name, uuid)
return d
def _reservationCacheCallback(self, data, func, kwargs, function_name, uuid):
LOGGER.debug("Got reservation cache for %s" % uuid)
kwargs["reservation_cache"] = data
d = maybeDeferred(func, **kwargs)
d.addCallback(self._callExposedFunctionCallback, function_name, uuid)
d.addErrback(self._callExposedFunctionErrback, function_name, uuid)
return d
def _reservationCacheErrback(self, error, func, kwargs, function_name, uuid):
LOGGER.debug("Could not get reservation cache for %s" % uuid)
kwargs["reservation_cache"] = None
d = maybeDeferred(func, **kwargs)
d.addCallback(self._callExposedFunctionCallback, function_name, uuid)
d.addErrback(self._callExposedFunctionErrback, function_name, uuid)
return d
def _callExposedFunctionErrback(self, error, function_name, uuid):
if uuid is not None:
del self.active_jobs[uuid]
try:
error.raiseException()
except DeleteReservationException, e:
if uuid is not None:
self.deleteReservation(uuid)
message = """Error with %s, %s.\n%s
Reservation deleted at request of the function.""" % (
function_name,
uuid,
error)
LOGGER.error(message)
return
except:
pass
if uuid is None:
LOGGER.error("Error with %s.\n%s" % (function_name, error))
else:
LOGGER.error("Error with %s.\nUUID:%s\n%s" % (
function_name,
uuid,
error))
return error
def _callExposedFunctionCallback(self, data, function_name, uuid):
LOGGER.debug("Function %s returned successfully." % (function_name))
# If the UUID is None, this is a one-off type of thing.
if uuid is None:
return data
# If the data is None, there's nothing to store.
if data is None:
del self.active_jobs[uuid]
return None
# If we have an place to store the response on S3, do it.
if self.aws_s3_storage_bucket is not None:
LOGGER.debug("Putting result for %s, %s on S3." % (function_name, uuid))
pickled_data = cPickle.dumps(data)
d = self.s3.putObject(
self.aws_s3_storage_bucket,
uuid,
pickled_data,
content_type="text/plain",
gzip=True)
d.addCallback(self._exposedFunctionCallback2, data, uuid)
d.addErrback(self._exposedFunctionErrback2, data, function_name, uuid)
return d
return data
def _exposedFunctionErrback2(self, error, data, function_name, uuid):
del self.active_jobs[uuid]
LOGGER.error("Could not put results of %s, %s on S3.\n%s" % (function_name, uuid, error))
return data
def _exposedFunctionCallback2(self, s3_callback_data, data, uuid):
del self.active_jobs[uuid]
return data
def expose(self, *args, **kwargs):
return self.makeCallable(expose=True, *args, **kwargs)
def makeCallable(self, func, interval=0, name=None, expose=False):
argspec = inspect.getargspec(func)
# Get required / optional arguments
arguments = argspec[0]
if len(arguments) > 0 and arguments[0:1][0] == 'self':
arguments.pop(0)
kwarg_defaults = argspec[3]
if kwarg_defaults is None:
kwarg_defaults = []
required_arguments = arguments[0:len(arguments) - len(kwarg_defaults)]
optional_arguments = arguments[len(arguments) - len(kwarg_defaults):]
# Reservation cache is stored on S3
if "reservation_cache" in required_arguments:
del required_arguments[required_arguments.index("reservation_cache")]
check_reservation_cache = True
elif "reservation_cache" in optional_arguments:
del optional_arguments[optional_arguments.index("reservation_cache")]
check_reservation_cache = True
else:
check_reservation_cache = False
# Reservation fast cache is stored on SDB with the reservation
if "reservation_fast_cache" in required_arguments:
del required_arguments[required_arguments.index("reservation_fast_cache")]
check_reservation_fast_cache = True
elif "reservation_fast_cache" in optional_arguments:
del optional_arguments[optional_arguments.index("reservation_fast_cache")]
check_reservation_fast_cache = True
else:
check_reservation_fast_cache = False
# Indicates whether to send the reservation's UUID to the function
if "reservation_uuid" in required_arguments:
del required_arguments[required_arguments.index("reservation_uuid")]
get_reservation_uuid = True
elif "reservation_uuid" in optional_arguments:
del optional_arguments[optional_arguments.index("reservation_uuid")]
get_reservation_uuid = True
else:
get_reservation_uuid = False
# Get function name, usually class/method
if name is not None:
function_name = name
elif hasattr(func, "im_class"):
function_name = "%s/%s" % (func.im_class.__name__, func.__name__)
else:
function_name = func.__name__
function_name = function_name.lower()
# Make sure the function isn't using any reserved arguments.
for key in required_arguments:
if key in self.reserved_arguments:
message = "Required argument name '%s' used in function %s is reserved." % (key, function_name)
LOGGER.error(message)
raise Exception(message)
for key in optional_arguments:
if key in self.reserved_arguments:
message = "Optional argument name '%s' used in function %s is reserved." % (key, function_name)
LOGGER.error(message)
raise Exception(message)
# Make sure we don't already have a function with the same name.
if function_name in self.functions:
raise Exception("A method or function with the name %s is already callable." % function_name)
# Add it to our list of callable functions.
self.functions[function_name] = {
"function":func,
"interval":interval,
"required_arguments":required_arguments,
"optional_arguments":optional_arguments,
"check_reservation_cache":check_reservation_cache,
"check_reservation_fast_cache":check_reservation_fast_cache,
"get_reservation_uuid":get_reservation_uuid
}
LOGGER.info("Function %s is now callable." % function_name)
return function_name
def getPage(self, *args, **kwargs):
return self.pg.getPage(*args, **kwargs)
def setHostMaxRequestsPerSecond(self, *args, **kwargs):
return self.rq.setHostMaxRequestsPerSecond(*args, **kwargs)
def setHostMaxSimultaneousRequests(self, *args, **kwargs):
return self.rq.setHostMaxSimultaneousRequests(*args, **kwargs)
def deleteReservation(self, uuid, function_name="Unknown"):
LOGGER.info("Deleting reservation %s, %s." % (function_name, uuid))
deferreds = []
deferreds.append(self.sdb.delete(self.aws_sdb_reservation_domain, uuid))
deferreds.append(self.s3.deleteObject(self.aws_s3_storage_bucket, uuid))
d = DeferredList(deferreds)
d.addCallback(self._deleteReservationCallback, function_name, uuid)
d.addErrback(self._deleteReservationErrback, function_name, uuid)
return d
def _deleteReservationCallback(self, data, function_name, uuid):
LOGGER.info("Reservation %s, %s successfully deleted." % (function_name, uuid))
return True
def _deleteReservationErrback(self, error, function_name, uuid ):
LOGGER.error("Error deleting reservation %s, %s.\n%s" % (function_name, uuid, error))
return False
def deleteHTTPCache(self):
deferreds = []
if self.aws_s3_http_cache_bucket is not None:
deferreds.append(
self.s3.emptyBucket(self.aws_s3_http_cache_bucket))
if len(deferreds) > 0:
d = DeferredList(deferreds, consumeErrors=True)
d.addCallback(self._deleteHTTPCacheCallback)
return d
else:
return True
def _deleteHTTPCacheCallback(self, data):
deferreds = []
if self.aws_s3_http_cache_bucket is not None:
deferreds.append(
self.s3.deleteBucket(self.aws_s3_http_cache_bucket))
if len(deferreds) > 0:
d = DeferredList(deferreds, consumeErrors=True)
d.addCallback(self._deleteHTTPCacheCallback2)
return d
else:
return True
def _deleteHTTPCacheCallback2(self, data):
return True
def getServerData(self):
running_time = time.time() - self.start_time
cost = (self.sdb.box_usage * .14) * (60*60*24*30.4) / (running_time)
active_requests_by_host = self.rq.getActiveRequestsByHost()
pending_requests_by_host = self.rq.getPendingRequestsByHost()
data = {
"load_avg":[str(Decimal(str(x), 2)) for x in os.getloadavg()],
"running_time":running_time,
"cost":cost,
"active_requests_by_host":active_requests_by_host,
"pending_requests_by_host":pending_requests_by_host,
"active_requests":self.rq.getActive(),
"pending_requests":self.rq.getPending(),
"current_timestamp":sdb_now(offset=self.time_offset)
}
LOGGER.debug("Got server data:\n%s" % PRETTYPRINTER.pformat(data))
return data
def getReservationCache(self, uuid):
if self.aws_s3_reservation_cache_bucket is None:
raise ReservationCachingException("No reservation cache bucket is specified.")
d = self.s3.getObject(
self.aws_s3_reservation_cache_bucket,
uuid)
d.addCallback(self._getReservationCacheCallback)
return d
def _getReservationCacheCallback(self, data):
return cPickle.loads(data["response"])
def setReservationFastCache(self, uuid, data):
if not isinstance(data, str):
raise Exception("ReservationFastCache must be a string.")
if uuid is None:
return None
self.reservation_fast_caches[uuid] = data
def setReservationCache(self, uuid, data):
if uuid is None:
return None
if self.aws_s3_reservation_cache_bucket is None:
raise ReservationCachingException("No reservation cache bucket is specified.")
d = self.s3.putObject(
self.aws_s3_reservation_cache_bucket,
uuid,
cPickle.dumps(data))
return d | PypiClean |
/Dabo-0.9.16.tar.gz/Dabo-0.9.16/dabo/lib/xmltodict.py | import os
import string
import locale
import codecs
from xml.parsers import expat
# If we're in Dabo, get the default encoding.
import dabo
import dabo.lib.DesignerUtils as desUtil
from dabo.dLocalize import _
from dabo.lib.utils import resolvePath
from dabo.lib.utils import ustr
app = dabo.dAppRef
default_encoding = dabo.getEncoding()
# Normalize the names, as xml.sax running on Gtk will complain for some variations
deLow = default_encoding.lower()
if deLow in ("utf8", "utf-8"):
default_encoding = "utf-8"
if deLow in ("utf16", "utf-16"):
default_encoding = "utf-16"
# Python seems to need to compile code with \n linesep:
code_linesep = "\n"
eol = os.linesep
class Xml2Obj(object):
"""XML to Object"""
def __init__(self, encoding=None):
self.root = None
self.nodeStack = []
self.attsToSkip = []
self._inCode = False
self._mthdName = ""
self._mthdCode = ""
self._codeDict = None
self._inProp = False
self._propName = ""
self._propData = ""
self._propDict = None
self._currPropAtt = ""
self._currPropDict = None
if encoding is None:
self._encoding = dabo.getEncoding()
else:
self._encoding = encoding
def StartElement(self, name, attributes):
"""SAX start element even handler"""
if name == "code":
# This is code for the parent element
self._inCode = True
parent = self.nodeStack[-1]
if "code" not in parent:
parent["code"] = {}
self._codeDict = parent["code"]
elif name == "properties":
# These are the custom property definitions
self._inProp = True
self._propName = ""
self._propData = ""
parent = self.nodeStack[-1]
if "properties" not in parent:
parent["properties"] = {}
self._propDict = parent["properties"]
else:
if self._inCode:
self._mthdName = name #.encode()
elif self._inProp:
if self._propName:
# In the middle of a prop definition
self._currPropAtt = name #.encode()
else:
self._propName = name #.encode()
self._currPropDict = {}
self._currPropAtt = ""
else:
element = {"name": name} #.encode()}
if attributes:
for att in self.attsToSkip:
try:
del attributes[att]
except KeyError:
pass
# Unescape any escaped values
for kk, vv in attributes.items():
attributes[kk] = unescape(vv)
element["attributes"] = attributes
# Push element onto the stack and make it a child of parent
if len(self.nodeStack) > 0:
parent = self.nodeStack[-1]
if "children" not in parent:
parent["children"] = []
parent["children"].append(element)
else:
self.root = element
self.nodeStack.append(element)
def EndElement(self, name):
"""SAX end element event handler"""
if self._inCode:
if name == "code":
self._inCode = False
self._codeDict = None
else:
# End of an individual method
mth = self._mthdCode.strip()
if not mth.endswith("\n"):
mth += "\n"
self._codeDict[self._mthdName] = mth
self._mthdName = ""
self._mthdCode = ""
elif self._inProp:
if name == "properties":
self._inProp = False
self._propDict = None
elif name == self._propName:
# End of an individual prop definition
self._propDict[self._propName] = self._currPropDict
self._propName = ""
else:
# end of a property attribute
self._currPropDict[self._currPropAtt] = self._propData
self._propData = self._currPropAtt = ""
else:
self.nodeStack = self.nodeStack[:-1]
def CharacterData(self, data):
"""SAX character data event handler"""
if self._inCode or data.strip():
data = data.replace("<", "<").replace(">",">")
data = data #.encode()
if self._inCode:
if self._mthdCode:
self._mthdCode += data
else:
self._mthdCode = data
elif self._inProp:
self._propData += data
else:
element = self.nodeStack[-1]
if "cdata" not in element:
element["cdata"] = ""
element["cdata"] += data
def Parse(self, xml):
# Create a SAX parser
Parser = expat.ParserCreate()
# SAX event handlers
Parser.StartElementHandler = self.StartElement
Parser.EndElementHandler = self.EndElement
Parser.CharacterDataHandler = self.CharacterData
# Parse the XML File
ParserStatus = Parser.Parse(xml, 1)
return self.root
def ParseFromFile(self, filename):
return self.Parse(open(filename,"r").read())
def xmltodict(xml, attsToSkip=[], addCodeFile=False, encoding=None):
"""Given an xml string or file, return a Python dictionary."""
parser = Xml2Obj(encoding=encoding)
parser.attsToSkip = attsToSkip
if eol in xml and "<?xml" in xml:
isPath = False
else:
isPath = os.path.exists(xml)
errmsg = ""
if eol not in xml and isPath:
# argument was a file
xmlContent = codecs.open(xml, "r", encoding).read()
if isinstance(xmlContent, unicode):
xmlContent = xmlContent.encode(encoding)
try:
ret = parser.Parse(xmlContent)
except expat.ExpatError, e:
errmsg = _("The XML in '%(xml)s' is not well-formed and cannot be parsed: %(e)s") % locals()
else:
# argument must have been raw xml:
try:
ret = parser.Parse(xml)
except expat.ExpatError, e:
errmsg = _("An invalid XML string was encountered: %s") % e
if errmsg:
raise dabo.dException.XmlException(errmsg)
if addCodeFile and isPath:
# Get the associated code file, if any
codePth = "%s-code.py" % os.path.splitext(xml)[0]
if os.path.exists(codePth):
try:
codeContent = codecs.open(codePth, "r", encoding).read()
codeDict = desUtil.parseCodeFile(codeContent)
ret["importStatements"] = codeDict.pop("importStatements", "")
desUtil.addCodeToClassDict(ret, codeDict)
except StandardError, e:
print "Failed to parse code file:", e
return ret
def escQuote(val, noEscape=False, noQuote=False):
"""
Add surrounding quotes to the string, and escape
any illegal XML characters.
"""
val = ustr(val)
if noQuote:
qt = ''
else:
qt = '"'
slsh = "\\"
# val = val.replace(slsh, slsh+slsh)
if not noEscape:
val = escape(val)
return "%s%s%s" % (qt, val, qt)
def escape(val, escapeAmp=True):
"""Escape any characters that cannot be stored directly in XML."""
# First escape internal ampersands. We need to double them up due to a
# quirk in wxPython and the way it displays this character.
if escapeAmp:
val = val.replace("&", "&&")
else:
val = val.replace("&", "&")
# Escape any internal quotes
val = val.replace('"', '"').replace("'", "'")
# Escape any high-order characters
chars = []
for pos, char in enumerate(list(val)):
if ord(char) > 127:
chars.append("&#%s;" % ord(char))
else:
chars.append(char)
val = "".join(chars)
val = val.replace("<", "<").replace(">", ">")
return val
def unescape(val):
"""
Reverse the escape() process to re-create the original values. The parser
handles the XML conventions; we need to reverse the doubled-up ampersands.
"""
val = val.replace("&&", "&")
return val
def dicttoxml(dct, level=0, header=None, linesep=None):
"""
Given a Python dictionary, return an xml string.
The dictionary must be in the format returned by dicttoxml(), with keys
on "attributes", "code", "cdata", "name", and "children".
Send your own XML header, otherwise a default one will be used.
The linesep argument is a dictionary, with keys on levels, allowing the
developer to add extra whitespace depending on the level.
"""
att = ""
ret = ""
if "attributes" in dct:
for key, val in dct["attributes"].items():
# Some keys are already handled.
noEscape = key in ("sizerInfo",)
val = escQuote(val, noEscape)
att += " %s=%s" % (key, val)
ret += "%s<%s%s" % ("\t" * level, dct["name"], att)
if (("cdata" not in dct) and ("children" not in dct) and ("code" not in dct)
and ("properties" not in dct)):
ret += " />%s" % eol
else:
ret += ">"
if "cdata" in dct:
ret += "%s" % dct["cdata"].replace("<", "<").replace(">", ">")
if "code" in dct:
if len(dct["code"].keys()):
ret += "%s%s<code>%s" % (eol, "\t" * (level+1), eol)
methodTab = "\t" * (level+2)
for mthd, cd in dct["code"].items():
# Convert \n's in the code to eol:
cd = eol.join(cd.splitlines())
# Make sure that the code ends with a linefeed
if not cd.endswith(eol):
cd += eol
ret += "%s<%s><![CDATA[%s%s]]>%s%s</%s>%s" % (methodTab,
mthd, eol, cd, eol,
methodTab, mthd, eol)
ret += "%s</code>%s" % ("\t" * (level+1), eol)
if "properties" in dct:
if len(dct["properties"].keys()):
ret += "%s%s<properties>%s" % (eol, "\t" * (level+1), eol)
currTab = "\t" * (level+2)
for prop, val in dct["properties"].items():
ret += "%s<%s>%s" % (currTab, prop, eol)
for propItm, itmVal in val.items():
itmTab = "\t" * (level+3)
ret += "%s<%s>%s</%s>%s" % (itmTab, propItm, itmVal,
propItm, eol)
ret += "%s</%s>%s" % (currTab, prop, eol)
ret += "%s</properties>%s" % ("\t" * (level+1), eol)
if ("children" in dct) and dct["children"]:
ret += eol
for child in dct["children"]:
ret += dicttoxml(child, level+1, linesep=linesep)
indnt = ""
if ret.endswith(eol):
# Indent the closing tag
indnt = ("\t" * level)
ret += "%s</%s>%s" % (indnt, dct["name"], eol)
if linesep:
ret += linesep.get(level, "")
if level == 0:
if header is None:
header = '<?xml version="1.0" encoding="%s" standalone="no"?>%s' \
% (default_encoding, eol)
ret = header + ret
return ret
def flattenClassDict(cd, retDict=None):
"""
Given a dict containing a series of nested objects such as would
be created by restoring from a cdxml file, returns a dict with all classIDs
as keys, and a dict as the corresponding value. The dict value will have
keys for the attributes and/or code, depending on what was in the original
dict. The end result is to take a nested dict structure and return a flattened
dict with all objects at the top level.
"""
if retDict is None:
retDict = {}
atts = cd.get("attributes", {})
props = cd.get("properties", {})
kids = cd.get("children", [])
code = cd.get("code", {})
classID = atts.get("classID", "")
classFile = resolvePath(atts.get("designerClass", ""))
superclass = resolvePath(atts.get("superclass", ""))
superclassID = atts.get("superclassID", "")
if superclassID and os.path.exists(superclass):
# Get the superclass info
superCD = xmltodict(superclass, addCodeFile=True)
flattenClassDict(superCD, retDict)
if classID:
if os.path.exists(classFile):
# Get the class info
classCD = xmltodict(classFile, addCodeFile=True)
classAtts = classCD.get("attributes", {})
classProps = classCD.get("properties", {})
classCode = classCD.get("code", {})
classKids = classCD.get("children", [])
currDict = retDict.get(classID, {})
retDict[classID] = {"attributes": classAtts, "code": classCode,
"properties": classProps}
retDict[classID].update(currDict)
# Now update the child objects in the dict
for kid in classKids:
flattenClassDict(kid, retDict)
else:
# Not a file; most likely just a component in another class
currDict = retDict.get(classID, {})
retDict[classID] = {"attributes": atts, "code": code,
"properties": props}
retDict[classID].update(currDict)
if kids:
for kid in kids:
flattenClassDict(kid, retDict)
return retDict
def addInheritedInfo(src, super, updateCode=False):
"""
Called recursively on the class container structure, modifying
the attributes to incorporate superclass information. When the
'updateCode' parameter is True, superclass code is added to the
object's code
"""
atts = src.get("attributes", {})
props = src.get("properties", {})
kids = src.get("children", [])
code = src.get("code", {})
classID = atts.get("classID", "")
if classID:
superInfo = super.get(classID, {"attributes": {}, "code": {}, "properties": {}})
src["attributes"] = superInfo["attributes"].copy()
src["attributes"].update(atts)
src["properties"] = superInfo.get("properties", {}).copy()
src["properties"].update(props)
if updateCode:
src["code"] = superInfo["code"].copy()
src["code"].update(code)
if kids:
for kid in kids:
addInheritedInfo(kid, super, updateCode)
if __name__ == "__main__":
test_dict = {"name": "test", "attributes":{"path": "c:\\temp\\name",
"problemChars": "Welcome to <Jos\xc3\xa9's \ Stuff!>\xc2\xae".decode("latin-1")}}
print "test_dict:", test_dict
xml = dicttoxml(test_dict)
print "xml:", xml
test_dict2 = xmltodict(xml)
print "test_dict2:", test_dict2
print "same?:", test_dict == test_dict2 | PypiClean |
/Flask_Admin-1.6.1-py3-none-any.whl/flask_admin/contrib/appengine/view.py | import logging
from flask_admin.model import BaseModelView
from wtforms_appengine import db as wt_db
from wtforms_appengine import ndb as wt_ndb
from google.appengine.ext import db
from google.appengine.ext import ndb
from flask_wtf import Form
from flask_admin.model.form import create_editable_list_form
from .form import AdminModelConverter
class NdbModelView(BaseModelView):
"""
AppEngine NDB model scaffolding.
"""
def get_pk_value(self, model):
return model.key.urlsafe()
def scaffold_list_columns(self):
return sorted([k for (k, v) in self.model.__dict__.iteritems() if isinstance(v, ndb.Property)])
def scaffold_sortable_columns(self):
return [k for (k, v) in self.model.__dict__.iteritems() if isinstance(v, ndb.Property) and v._indexed]
def init_search(self):
return None
def is_valid_filter(self):
pass
def scaffold_filters(self):
# TODO: implement
pass
form_args = None
model_form_converter = AdminModelConverter
"""
Model form conversion class. Use this to implement custom field conversion logic.
For example::
class MyModelConverter(AdminModelConverter):
pass
class MyAdminView(ModelView):
model_form_converter = MyModelConverter
"""
def scaffold_form(self):
form_class = wt_ndb.model_form(
self.model(),
base_class=Form,
only=self.form_columns,
exclude=self.form_excluded_columns,
field_args=self.form_args,
converter=self.model_form_converter(),
)
return form_class
def scaffold_list_form(self, widget=None, validators=None):
form_class = wt_ndb.model_form(
self.model(),
base_class=Form,
only=self.column_editable_list,
field_args=self.form_args,
converter=self.model_form_converter(),
)
result = create_editable_list_form(Form, form_class, widget)
return result
def get_list(self, page, sort_field, sort_desc, search, filters,
page_size=None):
# TODO: implement filters (don't think search can work here)
q = self.model.query()
if sort_field:
order_field = getattr(self.model, sort_field)
if sort_desc:
order_field = -order_field
q = q.order(order_field)
if not page_size:
page_size = self.page_size
results = q.fetch(page_size, offset=page * page_size)
return q.count(), results
def get_one(self, urlsafe_key):
return ndb.Key(urlsafe=urlsafe_key).get()
def create_model(self, form):
try:
model = self.model()
form.populate_obj(model)
model.put()
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to create record. %(error)s',
# error=ex), 'error')
logging.exception('Failed to create record.')
return False
else:
self.after_model_change(form, model, True)
return model
def update_model(self, form, model):
try:
form.populate_obj(model)
model.put()
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to update record. %(error)s',
# error=ex), 'error')
logging.exception('Failed to update record.')
return False
else:
self.after_model_change(form, model, False)
return True
def delete_model(self, model):
try:
model.key.delete()
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to delete record. %(error)s',
# error=ex),
# 'error')
logging.exception('Failed to delete record.')
return False
else:
self.after_model_delete(model)
return True
class DbModelView(BaseModelView):
"""
AppEngine DB model scaffolding.
"""
def get_pk_value(self, model):
return str(model.key())
def scaffold_list_columns(self):
return sorted([k for (k, v) in self.model.__dict__.iteritems() if isinstance(v, db.Property)])
def scaffold_sortable_columns(self):
# We use getattr() because ReferenceProperty does not specify a 'indexed' field
return [k for (k, v) in self.model.__dict__.iteritems()
if isinstance(v, db.Property) and getattr(v, 'indexed', None)]
def init_search(self):
return None
def is_valid_filter(self):
pass
def scaffold_filters(self):
# TODO: implement
pass
def scaffold_form(self):
return wt_db.model_form(self.model())
def get_list(self, page, sort_field, sort_desc, search, filters):
# TODO: implement filters (don't think search can work here)
q = self.model.all()
if sort_field:
if sort_desc:
sort_field = "-" + sort_field
q.order(sort_field)
results = q.fetch(self.page_size, offset=page * self.page_size)
return q.count(), results
def get_one(self, encoded_key):
return db.get(db.Key(encoded=encoded_key))
def create_model(self, form):
try:
model = self.model()
form.populate_obj(model)
model.put()
return model
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to create record. %(error)s',
# error=ex), 'error')
logging.exception('Failed to create record.')
return False
def update_model(self, form, model):
try:
form.populate_obj(model)
model.put()
return True
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to update record. %(error)s',
# error=ex), 'error')
logging.exception('Failed to update record.')
return False
def delete_model(self, model):
try:
model.delete()
return True
except Exception as ex:
if not self.handle_view_exception(ex):
# flash(gettext('Failed to delete record. %(error)s',
# error=ex),
# 'error')
logging.exception('Failed to delete record.')
return False
def ModelView(model):
if issubclass(model, ndb.Model):
return NdbModelView(model)
elif issubclass(model, db.Model):
return DbModelView(model)
else:
raise ValueError("Unsupported model: %s" % model) | PypiClean |
/Bluebook-0.0.1.tar.gz/Bluebook-0.0.1/pylot/component/static/pylot/vendor/mdeditor/bower_components/codemirror/mode/meta.js | CodeMirror.modeInfo = [
{name: 'APL', mime: 'text/apl', mode: 'apl'},
{name: 'Asterisk', mime: 'text/x-asterisk', mode: 'asterisk'},
{name: 'C', mime: 'text/x-csrc', mode: 'clike'},
{name: 'C++', mime: 'text/x-c++src', mode: 'clike'},
{name: 'Cobol', mime: 'text/x-cobol', mode: 'cobol'},
{name: 'Java', mime: 'text/x-java', mode: 'clike'},
{name: 'C#', mime: 'text/x-csharp', mode: 'clike'},
{name: 'Scala', mime: 'text/x-scala', mode: 'clike'},
{name: 'Clojure', mime: 'text/x-clojure', mode: 'clojure'},
{name: 'CoffeeScript', mime: 'text/x-coffeescript', mode: 'coffeescript'},
{name: 'Common Lisp', mime: 'text/x-common-lisp', mode: 'commonlisp'},
{name: 'CSS', mime: 'text/css', mode: 'css'},
{name: 'D', mime: 'text/x-d', mode: 'd'},
{name: 'diff', mime: 'text/x-diff', mode: 'diff'},
{name: 'ECL', mime: 'text/x-ecl', mode: 'ecl'},
{name: 'Erlang', mime: 'text/x-erlang', mode: 'erlang'},
{name: 'Gas', mime: 'text/x-gas', mode: 'gas'},
{name: 'GitHub Flavored Markdown', mime: 'text/x-gfm', mode: 'gfm'},
{name: 'GO', mime: 'text/x-go', mode: 'go'},
{name: 'Groovy', mime: 'text/x-groovy', mode: 'groovy'},
{name: 'Haskell', mime: 'text/x-haskell', mode: 'haskell'},
{name: 'Haxe', mime: 'text/x-haxe', mode: 'haxe'},
{name: 'ASP.NET', mime: 'application/x-aspx', mode: 'htmlembedded'},
{name: 'Embedded Javascript', mime: 'application/x-ejs', mode: 'htmlembedded'},
{name: 'JavaServer Pages', mime: 'application/x-jsp', mode: 'htmlembedded'},
{name: 'HTML', mime: 'text/html', mode: 'htmlmixed'},
{name: 'HTTP', mime: 'message/http', mode: 'http'},
{name: 'Jade', mime: 'text/x-jade', mode: 'jade'},
{name: 'JavaScript', mime: 'text/javascript', mode: 'javascript'},
{name: 'JSON', mime: 'application/x-json', mode: 'javascript'},
{name: 'JSON', mime: 'application/json', mode: 'javascript'},
{name: 'TypeScript', mime: 'application/typescript', mode: 'javascript'},
{name: 'Jinja2', mime: 'jinja2', mode: 'jinja2'},
{name: 'LESS', mime: 'text/x-less', mode: 'less'},
{name: 'LiveScript', mime: 'text/x-livescript', mode: 'livescript'},
{name: 'Lua', mime: 'text/x-lua', mode: 'lua'},
{name: 'Markdown (GitHub-flavour)', mime: 'text/x-markdown', mode: 'markdown'},
{name: 'mIRC', mime: 'text/mirc', mode: 'mirc'},
{name: 'Nginx', mime: 'text/x-nginx-conf', mode: 'nginx'},
{name: 'NTriples', mime: 'text/n-triples', mode: 'ntriples'},
{name: 'OCaml', mime: 'text/x-ocaml', mode: 'ocaml'},
{name: 'Pascal', mime: 'text/x-pascal', mode: 'pascal'},
{name: 'Perl', mime: 'text/x-perl', mode: 'perl'},
{name: 'PHP', mime: 'text/x-php', mode: 'php'},
{name: 'PHP(HTML)', mime: 'application/x-httpd-php', mode: 'php'},
{name: 'Pig', mime: 'text/x-pig', mode: 'pig'},
{name: 'Plain Text', mime: 'text/plain', mode: 'null'},
{name: 'Properties files', mime: 'text/x-properties', mode: 'clike'},
{name: 'Python', mime: 'text/x-python', mode: 'python'},
{name: 'Cython', mime: 'text/x-cython', mode: 'python'},
{name: 'R', mime: 'text/x-rsrc', mode: 'r'},
{name: 'reStructuredText', mime: 'text/x-rst', mode: 'rst'},
{name: 'Ruby', mime: 'text/x-ruby', mode: 'ruby'},
{name: 'Rust', mime: 'text/x-rustsrc', mode: 'rust'},
{name: 'Sass', mime: 'text/x-sass', mode: 'sass'},
{name: 'Scheme', mime: 'text/x-scheme', mode: 'scheme'},
{name: 'SCSS', mime: 'text/x-scss', mode: 'css'},
{name: 'Shell', mime: 'text/x-sh', mode: 'shell'},
{name: 'Sieve', mime: 'application/sieve', mode: 'sieve'},
{name: 'Smalltalk', mime: 'text/x-stsrc', mode: 'smalltalk'},
{name: 'Smarty', mime: 'text/x-smarty', mode: 'smarty'},
{name: 'SmartyMixed', mime: 'text/x-smarty', mode: 'smartymixed'},
{name: 'SPARQL', mime: 'application/x-sparql-query', mode: 'sparql'},
{name: 'SQL', mime: 'text/x-sql', mode: 'sql'},
{name: 'MariaDB', mime: 'text/x-mariadb', mode: 'sql'},
{name: 'sTeX', mime: 'text/x-stex', mode: 'stex'},
{name: 'LaTeX', mime: 'text/x-latex', mode: 'stex'},
{name: 'Tcl', mime: 'text/x-tcl', mode: 'tcl'},
{name: 'TiddlyWiki ', mime: 'text/x-tiddlywiki', mode: 'tiddlywiki'},
{name: 'Tiki wiki', mime: 'text/tiki', mode: 'tiki'},
{name: 'VB.NET', mime: 'text/x-vb', mode: 'vb'},
{name: 'VBScript', mime: 'text/vbscript', mode: 'vbscript'},
{name: 'Velocity', mime: 'text/velocity', mode: 'velocity'},
{name: 'Verilog', mime: 'text/x-verilog', mode: 'verilog'},
{name: 'XML', mime: 'application/xml', mode: 'xml'},
{name: 'HTML', mime: 'text/html', mode: 'xml'},
{name: 'XQuery', mime: 'application/xquery', mode: 'xquery'},
{name: 'YAML', mime: 'text/x-yaml', mode: 'yaml'},
{name: 'Z80', mime: 'text/x-z80', mode: 'z80'}
]; | PypiClean |
/Heimdallr-0.2.7-py36-none-any.whl/heimdallr/utilities/server/gpu_utilities.py | from typing import List, Mapping, Sequence
import numpy
import pandas
from dash import html
from dash.dcc import Graph
from dash.html import Div, H3
from pandas import DataFrame
from plotly import graph_objs
from warg import Number
from heimdallr.configuration.heimdallr_config import (
DROP_COLUMNS,
INT_COLUMNS,
MB_COLUMNS,
PERCENT_COLUMNS,
)
from heimdallr.utilities.date_tools import timestamp_to_datetime
from heimdallr.utilities.publisher.unpacking import pull_gpu_info
MB_DIVISOR = int(1024**2)
__all__ = [
"to_overall_gpu_process_df",
"per_machine_per_device_pie_charts",
]
def to_overall_gpu_process_df(
gpu_stats: Mapping, sort_by_key="used_gpu_mem"
) -> DataFrame:
""" """
resulta = []
columns = []
if len(gpu_stats):
for k2, v2 in gpu_stats.items():
device_info = v2["devices"]
for device_i in device_info:
processes = device_i["processes"]
if len(processes) > 0:
columns = list(processes[0].keys())
df = pandas.DataFrame(data=processes)
df["machine"] = [k2] * len(processes)
resulta.append(df)
out_df = pandas.concat(resulta, sort=False)
out_df.sort_values(by=sort_by_key, axis=0, ascending=False, inplace=True)
if len(out_df) == 0:
return pandas.DataFrame()
k = columns
idx = ["machine", *k]
out_df = out_df[idx]
out_df.create_time = out_df.create_time.map(timestamp_to_datetime)
for c in INT_COLUMNS:
out_df[c] = out_df[c].astype(int)
for c in PERCENT_COLUMNS:
out_df[c] = numpy.round(out_df[c], 2)
for c in MB_COLUMNS:
out_df[c] = numpy.round(out_df[c] // MB_DIVISOR, 2)
out_cols = [c for c in out_df.columns if c not in DROP_COLUMNS]
out_df = out_df[out_cols]
return out_df
def per_machine_per_device_pie_charts(
gpu_stats: Mapping, keep_alive: Sequence[Number]
) -> List[html.Div]:
""" """
compute_machines = []
for machine_name, machine in gpu_stats.items():
machine_devices = []
devices = machine["devices"]
for i, d in enumerate(devices):
used = d["used"] // MB_DIVISOR
total = d["total"] // MB_DIVISOR
used_ratio = used / total
used_ratio = [used_ratio] + [1.0 - used_ratio]
hover_text = [f"used:{used:.0f}mb", f"free:{total - used:.0f}mb"]
machine_devices.append(
Div(
[
Graph(
id=f"gpu_stats_{machine_name}{i}",
figure={
"data": [
graph_objs.Pie(
values=used_ratio,
text=hover_text,
name="used ratio",
hole=0.4,
)
],
"layout": graph_objs.Layout(
title=f"{d['name']} cuda_idx:{d['id']}",
showlegend=False,
hovermode="closest",
),
},
style={
"width": "100%",
},
)
],
className="col",
)
)
compute_machines.append(
Div(
[
H3(
f"{machine_name}: {keep_alive[machine_name]} sec ago",
className="text-monospace",
style={"text-decoration": "underline"},
),
Div([*machine_devices], className="row"),
],
style={
"text-align": "center",
"width": "100%",
},
)
)
return compute_machines
if __name__ == "__main__":
infos = pull_gpu_info()
print(infos) | PypiClean |
/FrAG-1.1.0.tar.gz/FrAG-1.1.0/frag_pele/AdaptivePELE_repo/AdaptivePELE/freeEnergies/checkDetailedBalance.py | from __future__ import absolute_import, division, print_function, unicode_literals
import glob
import os
import argparse
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from scipy import linalg
FOLDER = "discretized"
CLUSTER_CENTERS = "clusterCenters.dat"
TRAJECTORY_MATCHING_PATTERN = "*.disctraj"
def parseArguments():
desc = "Program that analyses a column of data, printing different statistical values and a histogram if desired."
parser = argparse.ArgumentParser(description=desc)
parser.add_argument("-f", default='discretized', help="Folder with cluster contents")
parser.add_argument("-t", "--threshold", default=0, type=float, help="If Cij < threshold and Cji < threshold, Cij = Cji = 0")
parser.add_argument("-l", "--lagtime", default=1, type=int, help="Lagtime")
parser.add_argument("-p", "--printFigs", action="store_true")
args = parser.parse_args()
return args.f, args.threshold, args.lagtime, args.printFigs
def getNumberOfClusters(folder_name):
clusterCentersFilename = os.path.join(folder_name, CLUSTER_CENTERS)
clusterCentersFile = open(clusterCentersFilename, 'r')
clusterCenters = clusterCentersFile.readlines()
clusterCentersFile.close()
return len(clusterCenters)
def normalizePopulations(populations):
return populations / populations.sum()
def normalizeTransitions(transitions):
transitionsSum = transitions.sum(axis=1, dtype='float')
transitionsSum = np.transpose(transitionsSum)
normalizedPopulations = np.divide(transitions, transitionsSum[:, np.newaxis])
return normalizedPopulations
def computeCountsAndCountMatrix(trajectories, numberOfClusters, lag_time=1):
transitions = np.zeros((numberOfClusters, numberOfClusters))
populations = np.zeros(numberOfClusters)
for trajectoryFilename in trajectories:
dtraj = np.loadtxt(trajectoryFilename, dtype=int, ndmin=1)
# can be done much faster with sparse matrices (see runMarkovChain script)
for i in range(len(dtraj) - lag_time):
fromCluster = dtraj[i]
toCluster = dtraj[i + lag_time]
transitions[fromCluster][toCluster] += 1
populations[dtraj] += 1
return populations, transitions
def removeNoise(countMatrix, threshold):
for i in range(len(countMatrix)):
for j in range(i+1, len(countMatrix[i])):
if countMatrix[i][j] < threshold or countMatrix[j][i] < threshold:
countMatrix[i][j] = 0
countMatrix[j][i] = 0
return countMatrix
def computeCountsAndCountMatrixRemovingNoise(folder_name, countsThreshold, lag_time):
"""
Computes the number of counts per cluster (#times it's been visited)
and the count matrix, setting to 0 those counts below the threshold
"""
trajectoryMatchingPattern = os.path.join(folder_name, TRAJECTORY_MATCHING_PATTERN)
trajectories = glob.glob(trajectoryMatchingPattern)
nclusters = getNumberOfClusters(folder_name)
countsPerCluster, countMatrix = computeCountsAndCountMatrix(trajectories, nclusters, lag_time)
countMatrixWithoutNoise = removeNoise(countMatrix, countsThreshold)
countMatrixWithoutNoise += 1./nclusters # added pseudo counts to avoid nans in relative entropy calc
return countsPerCluster, countMatrixWithoutNoise
def computePopulationsAndTransitionProbabilities(folder, countsThreshold, lagtime):
counts, countMatrix = computeCountsAndCountMatrixRemovingNoise(folder, countsThreshold, lagtime)
# countMatrix = np.array(transitions)
# sparseCountMatrix = sparse.csr_matrix(countMatrix)
# sparseInString = sparseCountMatrix.__str__()
# outputFile = open("countMatrix.dat", "w")
# outputFile.write(sparseInString)
# outputFile.close()
populations = normalizePopulations(counts)
transitions = normalizeTransitions(countMatrix)
# for i in range(len(transitions)):
# for j in range(i+1, len(transitions[i])):
# if transitions[i][j] < probabilityThershold and transitions[j][i] < probabilityThershold:
# transitions[i][j] = 0
# transitions[j][i] = 0
return populations, transitions
def plotMatrix(figureNumber, titleString, matrix, cmap=''):
fig = plt.figure(figureNumber)
fig.set_facecolor('white')
ax = fig.add_subplot(111)
ax.set_aspect('equal')
plt.title(titleString, fontsize=20)
if cmap != '':
cax = plt.pcolor(matrix, alpha=0.9, cmap=cmap)
else:
cax = plt.pcolor(matrix, alpha=0.9)
# add colorbar if does not exist
# if exists, remove previous, and add new one
if len(plt.gcf().axes) > 1:
# if so, then the last axes must be the colorbar.
# we get its extent
pts = plt.gcf().axes[-1].get_position().get_points()
# and its label
label = plt.gcf().axes[-1].get_ylabel()
# and then remove the axes
plt.gcf().axes[-1].remove()
# then we draw a new axes a the extents of the old one
cax = plt.gcf().add_axes([pts[0][0], pts[0][1], pts[1][0]-pts[0][0], pts[1][1]-pts[0][1]])
# and add a colorbar to it
cbar = plt.colorbar(cax=cax)
cbar.ax.set_ylabel(label)
# unfortunately the aspect is different between the initial call to colorbar
# without cax argument. Try to reset it (but still it's somehow different)
cbar.ax.set_aspect(20)
else:
plt.colorbar(cax)
def barPlot(figureNumber, titleString, array):
fig = plt.figure(figureNumber)
fig.set_facecolor('white')
ax = fig.add_subplot(111)
plt.title(titleString)
ax.bar(np.arange(len(array)), array, width=1, alpha=0.4, color='b')
def getRelativeEntropy(goldenStationary, goldenT, T):
return np.dot(goldenStationary, goldenT*np.log(goldenT/T)).sum()
def main(folder, countsThreshold, lagtime, printFigs=False):
populations, transitions = computePopulationsAndTransitionProbabilities(folder, countsThreshold, lagtime)
if printFigs:
cmap = matplotlib.cm.coolwarm
cmap.set_bad('w', 1.)
matplotlib.rc('font', family='Times New Roman')
if printFigs:
# transitionsWithoutDiagonal = np.copy(transitions)
# for i in range(len(transitions)):
# transitionsWithoutDiagonal[i][i] = 0
# plotMatrix(6, r'$P_{ij}, P_{ii} = 0 \forall i$', transitionsWithoutDiagonal,cmap)
pass
# p_i * P_ij
detailedBalanceComponents = np.copy(transitions)
for i in range(len(detailedBalanceComponents)):
detailedBalanceComponents[i] = np.multiply(populations[i], transitions[i])
if printFigs:
# barPlot(1, 'Population', populations)
# plotMatrix(2, r'$P_{ij}$', transitions, cmap)
plotMatrix(3, r'$\pi_i P_{ij}$', detailedBalanceComponents, cmap)
# plt.savefig("db_flux.eps")
detailedBalanceComponentsAbsoluteDifference = np.absolute(detailedBalanceComponents - detailedBalanceComponents.T) / 2. # factor 2 to avoid the metric to go from 0 to 2, but from 0 to 1
detailedBalanceComponentsAverage = np.multiply(detailedBalanceComponents + detailedBalanceComponents.T, 0.5)
frobeniusAvg = linalg.norm(detailedBalanceComponentsAbsoluteDifference) / linalg.norm(detailedBalanceComponentsAverage)
print("|semidiff| / |average|", frobeniusAvg)
np.seterr(divide='ignore', invalid='ignore')
detailedbalanceComponentsRelativeDifference = np.divide(detailedBalanceComponentsAbsoluteDifference, detailedBalanceComponentsAverage)
maskedDetailedbalanceComponentsRelativeDifference = np.ma.array(detailedbalanceComponentsRelativeDifference, mask=np.isnan(detailedbalanceComponentsRelativeDifference))
if printFigs:
plotMatrix(4, r'$|F_{ij} - F_{ji}|$', detailedBalanceComponentsAbsoluteDifference, cmap)
plt.savefig("db_abs_diff.eps")
plotMatrix(5, r'$M(F)$', maskedDetailedbalanceComponentsRelativeDifference, cmap)
plt.savefig("db_frobenius.eps")
return frobeniusAvg
if __name__ == '__main__':
folder_path, counts_Threshold, lagTime, print_Figs = parseArguments()
main(folder_path, counts_Threshold, lagTime, print_Figs) | PypiClean |
/My_Learn_Messenger_Client-0.1.1.tar.gz/My_Learn_Messenger_Client-0.1.1/сhatclient/client/client_main_window.py | import base64
import json
from Cryptodome.Cipher import PKCS1_OAEP
from Cryptodome.PublicKey import RSA
from PyQt5.QtCore import pyqtSlot, Qt
from PyQt5.QtGui import QStandardItemModel, QStandardItem, QBrush, QColor
from PyQt5.QtWidgets import QMainWindow, qApp, QMessageBox
from chatclient.сhatclient.Log.client_log_config import client_log
from chatclient.сhatclient.Mainlib.exception import ServerError
from chatclient.сhatclient.Mainlib.variables import MESSAGE_TEXT, SENDER
from chatclient.сhatclient.client.add_contact_window import AddContactDialog
from chatclient.сhatclient.client.client_main_window_conv import Ui_MainClientWindow
from chatclient.сhatclient.client.del_contact_window import DelContactDialog
class ClientMainWindow(QMainWindow):
"""
Class is the main user window.
Contains all the main logic of the client module.
The window configuration is created in QTDesigner and loaded from
converted file main_window_conv.py
"""
def __init__(self, database, transport, keys):
super().__init__()
# основные переменные
self.database = database
self.transport = transport
# объект - дешифорвщик сообщений с предзагруженным ключём
self.decrypter = PKCS1_OAEP.new(keys)
# Загружаем конфигурацию окна из дизайнера
self.ui = Ui_MainClientWindow()
self.ui.setupUi(self)
# Кнопка "Выход"
self.ui.menu_exit.triggered.connect(qApp.exit)
# Кнопка отправить сообщение
self.ui.btn_send.clicked.connect(self.send_message)
# "добавить контакт"
self.ui.btn_add_contact.clicked.connect(self.add_contact_window)
self.ui.menu_add_contact.triggered.connect(self.add_contact_window)
# Удалить контакт
self.ui.btn_remove_contact.clicked.connect(self.delete_contact_window)
self.ui.menu_del_contact.triggered.connect(self.delete_contact_window)
# Дополнительные требующиеся атрибуты
self.contacts_model = None
self.history_model = None
self.messages = QMessageBox()
self.current_chat = None
self.current_chat_key = None
self.encryptor = None
self.ui.list_messages.setHorizontalScrollBarPolicy(
Qt.ScrollBarAlwaysOff)
self.ui.list_messages.setWordWrap(True)
# Даблклик по листу контактов отправляется в обработчик
self.ui.list_contacts.doubleClicked.connect(self.select_active_user)
self.clients_list_update()
self.set_disabled_input()
self.show()
def set_disabled_input(self):
"""
Method making input fields inactive
:return:
"""
# Надпись - получатель.
self.ui.label_new_message.setText(
'Для выбора получателя дважды кликните на нем в окне контактов.')
self.ui.text_message.clear()
if self.history_model:
self.history_model.clear()
# Поле ввода и кнопка отправки неактивны до выбора получателя.
self.ui.btn_clear.setDisabled(True)
self.ui.btn_send.setDisabled(True)
self.ui.text_message.setDisabled(True)
self.encryptor = None
self.current_chat = None
self.current_chat_key = None
def history_list_update(self):
"""
The filling method of the corresponding QListView
history of correspondence with the current interlocutor.
:return:
"""
# Получаем историю сортированную по дате
list = sorted(
self.database.get_history(
self.current_chat),
key=lambda item: item[3])
# Если модель не создана, создадим.
if not self.history_model:
self.history_model = QStandardItemModel()
self.ui.list_messages.setModel(self.history_model)
# Очистим от старых записей
self.history_model.clear()
# Берём не более 20 последних записей.
length = len(list)
start_index = 0
if length > 20:
start_index = length - 20
# Заполнение модели записями, так-же стоит разделить входящие
# и исходящие выравниванием и разным фоном.
# отображает только последие 20 сообщений
for i in range(start_index, length):
item = list[i]
if item[1] == 'in':
mess = QStandardItem(
f'Входящее от {item[3].replace(microsecond=0)}:\n {item[2]}')
mess.setEditable(False)
mess.setBackground(QBrush(QColor(255, 213, 213)))
mess.setTextAlignment(Qt.AlignLeft)
self.history_model.appendRow(mess)
else:
mess = QStandardItem(
f'Исходящее от {item[3].replace(microsecond=0)}:\n {item[2]}')
mess.setEditable(False)
mess.setTextAlignment(Qt.AlignRight)
mess.setBackground(QBrush(QColor(204, 255, 204)))
self.history_model.appendRow(mess)
self.ui.list_messages.scrollToBottom()
def select_active_user(self):
"""
Double click event handler method on the contact list
:return:
"""
# Выбранный пользователем (даблклик) находится в выделеном элементе в
# QListView
# online_users_list = ServerStorage.active_users_list() # получаем активных пользователей в
# # списке кортежей из базы сервера
# self.current_chat = self.ui.list_contacts.currentIndex().data()
# if self.current_chat in online_users_list.count(): # проверяем список активных пользователей
# self.set_active_user() # вызываем основную функцию
# else:
# self.messages.warning(
# self, 'Ошибка', 'Данный пользователь не в сети.')
self.current_chat = self.ui.list_contacts.currentIndex().data()
online_users_list = self.transport.online_users_list_request() # получаем активных пользователей в
# списке кортежей из базы сервера
if self.current_chat in online_users_list:
self.set_active_user() # вызываем основную функцию
else:
self.messages.warning(self, 'Ошибка', 'Данный пользователь не в сети.')
def set_active_user(self):
"""
A method for activating a chat with an interlocutor.
:return:
"""
# Запрашиваем публичный ключ пользователя и создаём объект шифрования
try:
self.current_chat_key = self.transport.key_request(
self.current_chat)
client_log.debug(f'Загружен открытый ключ для {self.current_chat}')
if self.current_chat_key:
self.encryptor = PKCS1_OAEP.new(
RSA.import_key(self.current_chat_key))
except (OSError, json.JSONDecodeError):
self.current_chat_key = None
self.encryptor = None
client_log.debug(f'Не удалось получить ключ для {self.current_chat}')
# Если ключа нет то ошибка, что не удалось начать чат с пользователем
if not self.current_chat_key:
self.messages.warning(
self, 'Ошибка', 'Для выбранного пользователя нет ключа шифрования.')
return
# Ставим надпись и активируем кнопки
self.ui.label_new_message.setText(
f'Введите сообщенние для {self.current_chat}:')
self.ui.btn_clear.setDisabled(False)
self.ui.btn_send.setDisabled(False)
self.ui.text_message.setDisabled(False)
# Заполняем окно историю сообщений по требуемому пользователю.
self.history_list_update()
def clients_list_update(self):
"""
Method for updating the contact list.
:return:
"""
contacts_list = self.database.get_contacts()
self.contacts_model = QStandardItemModel()
for i in sorted(contacts_list):
item = QStandardItem(i)
item.setEditable(False)
self.contacts_model.appendRow(item)
self.ui.list_contacts.setModel(self.contacts_model)
def add_contact_window(self):
"""
Method creating a window - a dialog for adding a contact
:return:
"""
global select_dialog
select_dialog = AddContactDialog(self.transport, self.database)
select_dialog.btn_ok.clicked.connect(
lambda: self.add_contact_action(select_dialog))
select_dialog.show()
def add_contact_action(self, item):
"""
Method to handle clicking the 'Add' button
:param item:
:return:
"""
new_contact = item.selector.currentText()
self.add_contact(new_contact)
item.close()
def add_contact(self, new_contact):
"""
Method adding contact to server and client BD.
After updating the databases, the contents of the window are also updated.
:param new_contact:
:return:
"""
try:
self.transport.add_contact(new_contact)
except ServerError as err:
self.messages.critical(self, 'Ошибка сервера', err.text)
except OSError as err:
if err.errno:
self.messages.critical(
self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
else:
self.database.add_contact(new_contact)
new_contact = QStandardItem(new_contact)
new_contact.setEditable(False)
self.contacts_model.appendRow(new_contact)
client_log.info(f'Успешно добавлен контакт {new_contact}')
self.messages.information(
self, 'Успех', 'Контакт успешно добавлен.')
def delete_contact_window(self):
"""
A method that creates a window for deleting a contact.
:return:
"""
global remove_dialog
remove_dialog = DelContactDialog(self.database)
remove_dialog.btn_ok.clicked.connect(
lambda: self.delete_contact(remove_dialog))
remove_dialog.show()
def delete_contact(self, item):
"""
Method for removing contact from server and client BD.
After updating the databases, the contents of the window are also updated.
:param item:
:return:
"""
selected = item.selector.currentText()
try:
self.transport.remove_contact(selected)
except ServerError as err:
self.messages.critical(self, 'Ошибка сервера', err.text)
except OSError as err:
if err.errno:
self.messages.critical(
self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
else:
self.database.del_contact(selected)
self.clients_list_update()
client_log.info(f'Успешно удалён контакт {selected}')
self.messages.information(self, 'Успех', 'Контакт успешно удалён.')
item.close()
# Если удалён активный пользователь, то деактивируем поля ввода.
if selected == self.current_chat:
self.current_chat = None
self.set_disabled_input()
def send_message(self):
"""
The function of sending a message to the current interlocutor.
Implements message encryption and sending.
"""
# Текст в поле, проверяем что поле не пустое затем забирается сообщение
# и поле очищается
message_text = self.ui.text_message.toPlainText()
self.ui.text_message.clear()
if not message_text:
return
# Шифруем сообщение ключом получателя и упаковываем в base64.
message_text_encrypted = self.encryptor.encrypt(
message_text.encode('utf8'))
message_text_encrypted_base64 = base64.b64encode(
message_text_encrypted)
try:
self.transport.send_message(
self.current_chat,
message_text_encrypted_base64.decode('ascii'))
pass
except ServerError as err:
self.messages.critical(self, 'Ошибка', err.text)
except OSError as err:
if err.errno:
self.messages.critical(
self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
self.messages.critical(self, 'Ошибка', 'Таймаут соединения!')
except (ConnectionResetError, ConnectionAbortedError):
self.messages.critical(
self, 'Ошибка', 'Потеряно соединение с сервером!')
self.close()
else:
self.database.save_message(self.current_chat, 'out', message_text)
client_log.debug(
f'Отправлено сообщение для {self.current_chat}: {message_text}')
self.history_list_update()
@pyqtSlot(dict)
def message(self, message):
"""
Slot handler of incoming messages, performs decryption
received messages and their saving in the message history.
Asks the user if the message is not from the current one
interlocutor. Changes the interlocutor if necessary.
:param message:
:return:
"""
# Получаем строку байтов
encrypted_message = base64.b64decode(message[MESSAGE_TEXT])
# Декодируем строку, при ошибке выдаём сообщение и завершаем функцию
try:
decrypted_message = self.decrypter.decrypt(encrypted_message)
except (ValueError, TypeError):
self.messages.warning(
self, 'Ошибка', 'Не удалось декодировать сообщение.')
return
# Сохраняем сообщение в базу и обновляем историю сообщений или
# открываем новый чат.
self.database.save_message(
self.current_chat,
'in',
decrypted_message.decode('utf8'))
sender = message[SENDER]
if sender == self.current_chat:
self.history_list_update()
else:
# Проверим есть ли такой пользователь у нас в контактах:
if self.database.check_contact(sender):
# Если есть, спрашиваем и желании открыть с ним чат и открываем
# при желании
if self.messages.question(
self,
'Новое сообщение',
f'Получено новое сообщение от {sender}, открыть чат с ним?',
QMessageBox.Yes,
QMessageBox.No) == QMessageBox.Yes:
self.current_chat = sender
self.set_active_user()
else:
print('NO')
# Раз нету,спрашиваем хотим ли добавить юзера в контакты.
if self.messages.question(
self,
'Новое сообщение',
f'Получено новое сообщение от {sender}.\n Данного пользователя нет в вашем контакт-листе.\n '
f'Добавить в контакты и открыть чат с ним?',
QMessageBox.Yes,
QMessageBox.No) == QMessageBox.Yes:
self.add_contact(sender)
self.current_chat = sender
# Нужно заново сохранить сообщение, иначе оно будет потеряно,
# т.к. на момент предыдущего вызова контакта не было.
self.database.save_message(
self.current_chat, 'in', decrypted_message.decode('utf8'))
self.set_active_user()
@pyqtSlot()
def connection_lost(self):
"""
Slot handler for lost connection to the server.
Shows a warning window and exits the application.
:return:
"""
self.messages.warning(
self,
'Сбой соединения',
'Потеряно соединение с сервером. ')
self.close()
@pyqtSlot()
def sig_205(self):
"""
Slot performing database update on server command
"""
if self.current_chat and not self.database.check_user(
self.current_chat):
self.messages.warning(
self,
'Сочувствую',
'К сожалению собеседник был удалён с сервера.')
self.set_disabled_input()
self.current_chat = None
self.clients_list_update()
def make_connection(self, trans_obj):
"""
Method for connecting signals and slots
:param trans_obj:
:return:
"""
trans_obj.new_message.connect(self.message)
trans_obj.connection_lost.connect(self.connection_lost)
trans_obj.message_205.connect(self.sig_205) | PypiClean |
/Distribution_G_B-0.1.tar.gz/Distribution_G_B-0.1/distributions/Gaussiandistribution.py | import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) a list of floats extracted from the data file
"""
def __init__(self, mu=0, sigma=1):
Distribution.__init__(self, mu, sigma)
def calculate_mean(self):
"""Function to calculate the mean of the data set.
Args:
None
Returns:
float: mean of the data set
"""
avg = 1.0 * sum(self.data) / len(self.data)
self.mean = avg
return self.mean
def calculate_stdev(self, sample=True):
"""Function to calculate the standard deviation of the data set.
Args:
sample (bool): whether the data represents a sample or population
Returns:
float: standard deviation of the data set
"""
if sample:
n = len(self.data) - 1
else:
n = len(self.data)
mean = self.calculate_mean()
sigma = 0
for d in self.data:
sigma += (d - mean) ** 2
sigma = math.sqrt(sigma / n)
self.stdev = sigma
return self.stdev
def plot_histogram(self):
"""Function to output a histogram of the instance variable data using
matplotlib pyplot library.
Args:
None
Returns:
None
"""
plt.hist(self.data)
plt.title('Histogram of Data')
plt.xlabel('data')
plt.ylabel('count')
def pdf(self, x):
"""Probability density function calculator for the gaussian distribution.
Args:
x (float): point for calculating the probability density function
Returns:
float: probability density function output
"""
return (1.0 / (self.stdev * math.sqrt(2*math.pi))) * math.exp(-0.5*((x - self.mean) / self.stdev) ** 2)
def plot_histogram_pdf(self, n_spaces = 50):
"""Function to plot the normalized histogram of the data and a plot of the
probability density function along the same range
Args:
n_spaces (int): number of data points
Returns:
list: x values for the pdf plot
list: y values for the pdf plot
"""
mu = self.mean
sigma = self.stdev
min_range = min(self.data)
max_range = max(self.data)
# calculates the interval between x values
interval = 1.0 * (max_range - min_range) / n_spaces
x = []
y = []
# calculate the x values to visualize
for i in range(n_spaces):
tmp = min_range + interval*i
x.append(tmp)
y.append(self.pdf(tmp))
# make the plots
fig, axes = plt.subplots(2,sharex=True)
fig.subplots_adjust(hspace=.5)
axes[0].hist(self.data, density=True)
axes[0].set_title('Normed Histogram of Data')
axes[0].set_ylabel('Density')
axes[1].plot(x, y)
axes[1].set_title('Normal Distribution for \n Sample Mean and Sample Standard Deviation')
axes[0].set_ylabel('Density')
plt.show()
return x, y
def __add__(self, other):
"""Function to add together two Gaussian distributions
Args:
other (Gaussian): Gaussian instance
Returns:
Gaussian: Gaussian distribution
"""
result = Gaussian()
result.mean = self.mean + other.mean
result.stdev = math.sqrt(self.stdev ** 2 + other.stdev ** 2)
return result
def __repr__(self):
"""Function to output the characteristics of the Gaussian instance
Args:
None
Returns:
string: characteristics of the Gaussian
"""
return "mean {}, standard deviation {}".format(self.mean, self.stdev) | PypiClean |
/IpfsML-1.0.2.tar.gz/IpfsML-1.0.2/nft_storage/model/get_response.py | import re # noqa: F401
import sys # noqa: F401
from nft_storage.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
from ..model_utils import OpenApiModel
from nft_storage.exceptions import ApiAttributeError
def lazy_import():
from nft_storage.model.nft import NFT
globals()['NFT'] = NFT
class GetResponse(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'ok': (bool,), # noqa: E501
'value': (NFT,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'ok': 'ok', # noqa: E501
'value': 'value', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
"""GetResponse - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
ok (bool): [optional] if omitted the server will use the default value of True # noqa: E501
value (NFT): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""GetResponse - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
ok (bool): [optional] if omitted the server will use the default value of True # noqa: E501
value (NFT): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.") | PypiClean |
/OASYS1-SYNED-1.0.45.tar.gz/OASYS1-SYNED-1.0.45/orangecontrib/syned/widgets/gui/ow_light_source.py | __author__ = 'labx'
import os, sys
from PyQt5.QtGui import QPalette, QColor, QFont
from PyQt5.QtWidgets import QMessageBox, QApplication
from PyQt5.QtCore import QRect
from orangewidget import gui
from orangewidget import widget
from orangewidget.settings import Setting
from oasys.widgets.widget import OWWidget
from oasys.widgets import gui as oasysgui
from oasys.widgets import congruence
from oasys.widgets.gui import ConfirmDialog
from syned.storage_ring.light_source import LightSource, ElectronBeam
from syned.beamline.beamline import Beamline
from syned.util.json_tools import load_from_json_file, load_from_json_url
class OWLightSource(OWWidget):
maintainer = "Luca Rebuffi"
maintainer_email = "luca.rebuffi(@at@)elettra.eu"
category = "Syned Light Sources"
keywords = ["data", "file", "load", "read"]
outputs = [{"name":"SynedData",
"type":Beamline,
"doc":"Syned Beamline",
"id":"data"}]
syned_file_name = Setting("Select *.json file")
source_name = Setting("Undefined")
electron_energy_in_GeV = Setting(2.0)
electron_energy_spread = Setting(0.001)
ring_current = Setting(0.4)
number_of_bunches = Setting(400)
moment_xx = Setting(0.0)
moment_xxp = Setting(0.0)
moment_xpxp = Setting(0.0)
moment_yy = Setting(0.0)
moment_yyp = Setting(0.0)
moment_ypyp = Setting(0.0)
electron_beam_size_h = Setting(0.0)
electron_beam_divergence_h = Setting(0.0)
electron_beam_size_v = Setting(0.0)
electron_beam_divergence_v = Setting(0.0)
electron_beam_emittance_h = Setting(0.0)
electron_beam_emittance_v = Setting(0.0)
electron_beam_beta_h = Setting(0.0)
electron_beam_beta_v = Setting(0.0)
electron_beam_alpha_h = Setting(0.0)
electron_beam_alpha_v = Setting(0.0)
electron_beam_eta_h = Setting(0.0)
electron_beam_eta_v = Setting(0.0)
electron_beam_etap_h = Setting(0.0)
electron_beam_etap_v = Setting(0.0)
type_of_properties = Setting(0)
want_main_area=0
MAX_WIDTH = 460
MAX_HEIGHT = 700
TABS_AREA_HEIGHT = 625
CONTROL_AREA_WIDTH = 450
def __init__(self):
super().__init__()
self.runaction = widget.OWAction("Send Data", self)
self.runaction.triggered.connect(self.send_data)
self.addAction(self.runaction)
button_box = oasysgui.widgetBox(self.controlArea, "", addSpace=False, orientation="horizontal")
button = gui.button(button_box, self, "Send Data", callback=self.send_data)
font = QFont(button.font())
font.setBold(True)
button.setFont(font)
palette = QPalette(button.palette()) # make a copy of the palette
palette.setColor(QPalette.ButtonText, QColor('Dark Blue'))
button.setPalette(palette) # assign new palette
button.setFixedHeight(45)
button = gui.button(button_box, self, "Reset Fields", callback=self.callResetSettings)
font = QFont(button.font())
font.setItalic(True)
button.setFont(font)
palette = QPalette(button.palette()) # make a copy of the palette
palette.setColor(QPalette.ButtonText, QColor('Dark Red'))
button.setPalette(palette) # assign new palette
button.setFixedHeight(45)
button.setFixedWidth(150)
gui.separator(self.controlArea)
geom = QApplication.desktop().availableGeometry()
self.setGeometry(QRect(round(geom.width()*0.05),
round(geom.height()*0.05),
round(min(geom.width()*0.98, self.MAX_WIDTH)),
round(min(geom.height()*0.95, self.MAX_HEIGHT))))
self.setMaximumHeight(self.geometry().height())
self.setMaximumWidth(self.geometry().width())
self.controlArea.setFixedWidth(self.CONTROL_AREA_WIDTH)
self.tabs_setting = oasysgui.tabWidget(self.controlArea)
self.tabs_setting.setFixedHeight(self.TABS_AREA_HEIGHT)
self.tabs_setting.setFixedWidth(self.CONTROL_AREA_WIDTH-5)
self.tab_sou = oasysgui.createTabPage(self.tabs_setting, "Light Source Setting")
box_json = oasysgui.widgetBox(self.tab_sou, "Read/Write File", addSpace=False, orientation="vertical")
file_box = oasysgui.widgetBox(box_json, "", addSpace=False, orientation="horizontal")
self.le_syned_file_name = oasysgui.lineEdit(file_box, self, "syned_file_name", "Syned File Name/URL",
labelWidth=150, valueType=str, orientation="horizontal")
gui.button(file_box, self, "...", callback=self.select_syned_file, width=25)
button_box = oasysgui.widgetBox(box_json, "", addSpace=False, orientation="horizontal")
button = gui.button(button_box, self, "Read Syned File", callback=self.read_syned_file)
button.setFixedHeight(25)
button = gui.button(button_box, self, "Write Syned File", callback=self.write_syned_file)
button.setFixedHeight(25)
oasysgui.lineEdit(self.tab_sou, self, "source_name", "Light Source Name", labelWidth=260, valueType=str, orientation="horizontal")
self.electron_beam_box = oasysgui.widgetBox(self.tab_sou, "Electron Beam/Machine Parameters", addSpace=False, orientation="vertical")
oasysgui.lineEdit(self.electron_beam_box, self, "electron_energy_in_GeV", "Energy [GeV]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.electron_beam_box, self, "electron_energy_spread", "Energy Spread", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.electron_beam_box, self, "ring_current", "Ring Current [A]", labelWidth=260, valueType=float, orientation="horizontal")
gui.comboBox(self.electron_beam_box, self, "type_of_properties", label="Electron Beam Properties", labelWidth=350,
items=["From 2nd Moments", "From Size/Divergence", "From Twiss parameters","Zero emittance"],
callback=self.set_TypeOfProperties,
sendSelectedValue=False, orientation="horizontal")
self.left_box_2_1 = oasysgui.widgetBox(self.electron_beam_box, "", addSpace=False, orientation="vertical", height=150)
oasysgui.lineEdit(self.left_box_2_1, self, "moment_xx", "<x x> [m^2]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_1, self, "moment_xxp", "<x x'> [m.rad]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_1, self, "moment_xpxp", "<x' x'> [rad^2]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_1, self, "moment_yy", "<y y> [m^2]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_1, self, "moment_yyp", "<y y'> [m.rad]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_1, self, "moment_ypyp", "<y' y'> [rad^2]", labelWidth=260, valueType=float, orientation="horizontal")
self.left_box_2_2 = oasysgui.widgetBox(self.electron_beam_box, "", addSpace=False, orientation="vertical", height=150)
oasysgui.lineEdit(self.left_box_2_2, self, "electron_beam_size_h", "Horizontal Beam Size \u03c3x [m]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_2, self, "electron_beam_size_v", "Vertical Beam Size \u03c3y [m]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_2, self, "electron_beam_divergence_h", "Horizontal Beam Divergence \u03c3'x [rad]", labelWidth=260, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_2, self, "electron_beam_divergence_v", "Vertical Beam Divergence \u03c3'y [rad]", labelWidth=260, valueType=float, orientation="horizontal")
self.left_box_2_3 = oasysgui.widgetBox(self.electron_beam_box, "", addSpace=False, orientation="horizontal",height=150)
self.left_box_2_3_l = oasysgui.widgetBox(self.left_box_2_3, "", addSpace=False, orientation="vertical")
self.left_box_2_3_r = oasysgui.widgetBox(self.left_box_2_3, "", addSpace=False, orientation="vertical")
oasysgui.lineEdit(self.left_box_2_3_l, self, "electron_beam_emittance_h", "\u03B5x [m.rad]",labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_l, self, "electron_beam_alpha_h", "\u03B1x", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_l, self, "electron_beam_beta_h", "\u03B2x [m]", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_l, self, "electron_beam_eta_h", "\u03B7x", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_l, self, "electron_beam_etap_h", "\u03B7'x", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_r, self, "electron_beam_emittance_v", "\u03B5y [m.rad]",labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_r, self, "electron_beam_alpha_v", "\u03B1y", labelWidth=75,valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_r, self, "electron_beam_beta_v", "\u03B2y [m]", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_r, self, "electron_beam_eta_v", "\u03B7y", labelWidth=75, valueType=float, orientation="horizontal")
oasysgui.lineEdit(self.left_box_2_3_r, self, "electron_beam_etap_v", "\u03B7'y", labelWidth=75, valueType=float, orientation="horizontal")
self.set_TypeOfProperties()
gui.rubber(self.controlArea)
def set_TypeOfProperties(self):
self.left_box_2_1.setVisible(self.type_of_properties == 0)
self.left_box_2_2.setVisible(self.type_of_properties == 1)
self.left_box_2_3.setVisible(self.type_of_properties == 2)
def check_data(self):
congruence.checkStrictlyPositiveNumber(self.electron_energy_in_GeV , "Energy")
congruence.checkStrictlyPositiveNumber(self.electron_energy_spread, "Energy Spread")
congruence.checkStrictlyPositiveNumber(self.ring_current, "Ring Current")
if self.type_of_properties == 0:
congruence.checkPositiveNumber(self.moment_xx , "Moment xx")
congruence.checkPositiveNumber(self.moment_xpxp , "Moment xpxp")
congruence.checkPositiveNumber(self.moment_yy , "Moment yy")
congruence.checkPositiveNumber(self.moment_ypyp , "Moment ypyp")
elif self.type_of_properties == 1:
congruence.checkPositiveNumber(self.electron_beam_size_h , "Horizontal Beam Size")
congruence.checkPositiveNumber(self.electron_beam_divergence_h , "Vertical Beam Size")
congruence.checkPositiveNumber(self.electron_beam_size_v , "Horizontal Beam Divergence")
congruence.checkPositiveNumber(self.electron_beam_divergence_v , "Vertical Beam Divergence")
elif self.type_of_properties == 2:
congruence.checkPositiveNumber(self.electron_beam_emittance_h, "Horizontal Beam Emittance")
congruence.checkPositiveNumber(self.electron_beam_emittance_v, "Vertical Beam Emittance")
congruence.checkNumber(self.electron_beam_alpha_h, "Horizontal Beam Alpha")
congruence.checkNumber(self.electron_beam_alpha_v, "Vertical Beam Alpha")
congruence.checkNumber(self.electron_beam_beta_h, "Horizontal Beam Beta")
congruence.checkNumber(self.electron_beam_beta_v, "Vertical Beam Beta")
congruence.checkNumber(self.electron_beam_eta_h, "Horizontal Beam Dispersion Eta")
congruence.checkNumber(self.electron_beam_eta_v, "Vertical Beam Dispersion Eta")
congruence.checkNumber(self.electron_beam_etap_h, "Horizontal Beam Dispersion Eta'")
congruence.checkNumber(self.electron_beam_etap_v, "Vertical Beam Dispersion Eta'")
self.check_magnetic_structure()
def send_data(self):
try:
self.check_data()
self.send("SynedData", Beamline(light_source=self.get_light_source()))
except Exception as e:
QMessageBox.critical(self, "Error", str(e.args[0]), QMessageBox.Ok)
self.setStatusMessage("")
self.progressBarFinished()
def get_light_source(self):
electron_beam = ElectronBeam(energy_in_GeV=self.electron_energy_in_GeV,
energy_spread=self.electron_energy_spread,
current=self.ring_current,
number_of_bunches=self.number_of_bunches)
if self.type_of_properties == 0:
electron_beam.set_moments_horizontal(self.moment_xx,self.moment_xxp,self.moment_xpxp)
electron_beam.set_moments_vertical(self.moment_yy, self.moment_yyp, self.moment_ypyp)
elif self.type_of_properties == 1:
electron_beam.set_sigmas_all(sigma_x=self.electron_beam_size_h,
sigma_y=self.electron_beam_size_v,
sigma_xp=self.electron_beam_divergence_h,
sigma_yp=self.electron_beam_divergence_v)
elif self.type_of_properties == 2:
electron_beam.set_twiss_horizontal(self.electron_beam_emittance_h,
self.electron_beam_alpha_h,
self.electron_beam_beta_h,
self.electron_beam_eta_h,
self.electron_beam_etap_h)
electron_beam.set_twiss_vertical(self.electron_beam_emittance_v,
self.electron_beam_alpha_v,
self.electron_beam_beta_v,
self.electron_beam_eta_v,
self.electron_beam_etap_v)
elif self.type_of_properties == 3:
electron_beam.set_moments_all(0,0,0,0,0,0)
return LightSource(name=self.source_name,
electron_beam = electron_beam,
magnetic_structure = self.get_magnetic_structure())
def check_magnetic_structure(self):
raise NotImplementedError("Shoudl be implemented in subclasses")
def get_magnetic_structure(self):
raise NotImplementedError("Shoudl be implemented in subclasses")
def callResetSettings(self):
if ConfirmDialog.confirmed(parent=self, message="Confirm Reset of the Fields?"):
try:
self.resetSettings()
except:
pass
def select_syned_file(self):
self.le_syned_file_name.setText(oasysgui.selectFileFromDialog(self, self.syned_file_name, "Open Syned File"))
def read_syned_file(self):
try:
congruence.checkEmptyString(self.syned_file_name, "Syned File Name/Url")
if (len(self.syned_file_name) > 7 and self.syned_file_name[:7] == "http://") or \
(len(self.syned_file_name) > 8 and self.syned_file_name[:8] == "https://"):
congruence.checkUrl(self.syned_file_name)
is_remote = True
else:
congruence.checkFile(self.syned_file_name)
is_remote = False
try:
if is_remote:
content = load_from_json_url(self.syned_file_name)
else:
content = load_from_json_file(self.syned_file_name)
if isinstance(content, LightSource):
self.populate_fields(content)
elif isinstance(content, Beamline) and not content._light_source is None:
self.populate_fields(content._light_source)
else:
raise Exception("json file must contain a SYNED LightSource")
except Exception as e:
raise Exception("Error reading SYNED LightSource from file: " + str(e))
except Exception as e:
QMessageBox.critical(self, "Error", str(e.args[0]), QMessageBox.Ok)
def populate_fields(self, light_source):
electron_beam = light_source._electron_beam
magnetic_structure = light_source._magnetic_structure
self.check_magnetic_structure_instance(magnetic_structure)
self.source_name = light_source._name
self.electron_energy_in_GeV = electron_beam._energy_in_GeV
self.electron_energy_spread = electron_beam._energy_spread
self.ring_current = electron_beam._current
self.number_of_bunches = electron_beam._number_of_bunches
self.type_of_properties = 0
self.moment_xx = electron_beam._moment_xx
self.moment_xxp = electron_beam._moment_xxp
self.moment_xpxp = electron_beam._moment_xpxp
self.moment_yy = electron_beam._moment_yy
self.moment_yyp = electron_beam._moment_yyp
self.moment_ypyp = electron_beam._moment_ypyp
x, xp, y, yp = electron_beam.get_sigmas_all()
self.electron_beam_size_h = x
self.electron_beam_size_v = y
self.electron_beam_divergence_h = xp
self.electron_beam_divergence_v = yp
self.populate_magnetic_structure(magnetic_structure)
self.set_TypeOfProperties()
def check_magnetic_structure_instance(self, magnetic_structure):
raise NotImplementedError()
def populate_magnetic_structure(self, magnetic_structure):
raise NotImplementedError()
def write_syned_file(self):
try:
congruence.checkDir(self.syned_file_name)
self.get_light_source().to_json(self.syned_file_name)
QMessageBox.information(self, "File Read", "JSON file correctly written to disk", QMessageBox.Ok)
except Exception as e:
QMessageBox.critical(self, "Error", str(e.args[0]), QMessageBox.Ok) | PypiClean |
/KegElements-0.9.1.tar.gz/KegElements-0.9.1/keg_elements/forms/__init__.py | import functools
import inspect
import logging
import warnings
from decimal import Decimal
from operator import attrgetter
from blazeutils.strings import case_cw2dash
import flask
from flask_wtf import FlaskForm as BaseForm
from keg.db import db
import sqlalchemy as sa
from markupsafe import Markup
from sqlalchemy_utils import ArrowType, get_class_by_table
import wtforms.fields
import wtforms.form
from wtforms.validators import InputRequired, Optional, StopValidation, NumberRange, AnyOf
from wtforms_alchemy import (
FormGenerator as FormGeneratorBase,
model_form_factory,
model_form_meta_factory,
)
from wtforms_components.fields import (
SelectField as SelectFieldBase,
SelectMultipleField as SelectMultipleFieldBase,
)
from keg_elements.db.columns import DBEnum
from keg_elements.db.utils import has_column
from keg_elements.extensions import lazy_gettext as _
from keg_elements.forms.validators import NumberScale
form_element = flask.Blueprint('form_element', __name__)
log = logging.getLogger(__name__)
def to_title_case(x):
""" underscore or dash to title case notation """
return x.replace('_', ' ').replace('-', ' ').title()
# sentinel
_not_given = ()
class FieldMeta(object):
"""Meta information for fields to override model-generated info.
Rather than forcing all-or-nothing acceptance of model-generated meta info from wtforms,
FieldMeta may be provided in the FieldsMeta nested class of the form to override specifics.
All modifications are applied to the field instance during the form generation process.
Example::
class PersonForm(ModelForm):
class Meta:
model = Person
class FieldsMeta:
name = FieldMeta('Full Name')
:param label_text: Force a label value.
:param description: Force a description value.
:param label_modifier: Callable to be called with the default label. label_text takes
precedence if both are provided. But, this modifier will apply to choices as well if
applicable and a choices_modifier is not given.
:param choices_modifier: Callable to be called with the label value for choices.
:param required: Force validators to be added/removed for requirement.
:param widget: Force a specific widget to be used for the field in render.
:param extra_validators: Add the given validators to those existing on the field.
:param coerce: Applies a specific coerce for field values. Applicable to select fields only.
:param default: Forces a default value on the field.
"""
def __init__(self, label_text=_not_given, description=_not_given, label_modifier=_not_given,
choices_modifier=_not_given, choices=None, required=_not_given, widget=_not_given,
extra_validators=tuple(), coerce=_not_given, default=_not_given):
self.label_text = label_text
self.label_modifier = label_modifier
self.description = description
self.choices_modifier = choices_modifier
self.choices = choices
self.required = required
self.widget = widget
self.extra_validators = extra_validators
self.coerce = coerce
self.default = default
assert self.required in (_not_given, False, True)
def apply_to_field(self, field):
# field is a wtforms.fields.core.UnboundField instance
self.apply_to_label(field)
self.apply_to_description(field)
self.apply_to_choices(field)
self.apply_required(field)
self.apply_widget(field)
self.apply_extra_validators(field)
self.apply_coerce(field)
self.apply_default(field)
def apply_to_label(self, field):
default_label = field.kwargs['label']
if self.label_text is not _not_given:
label_text = self.label_text
elif self.label_modifier is None:
label_text = default_label
elif self.label_modifier is _not_given:
label_text = to_title_case(default_label)
else:
label_text = self.label_modifier(default_label)
field.kwargs['label'] = self.modify_label(label_text)
def modify_label(self, label_text):
""" for subclasses to easily modify the final label text value """
return label_text
def apply_to_description(self, field):
default_description = field.kwargs.get('description')
if self.description is _not_given:
description = default_description
else:
description = self.description
field.kwargs['description'] = self.modify_description(description)
def modify_description(self, description):
""" for subclasses to easily modify the final label text value """
return description
def apply_to_choices(self, field):
default_choices = field.kwargs.get('choices', None)
if default_choices is None:
# this isn't a field that has choices
return
if self.choices_modifier is None:
modifier = None
elif self.choices_modifier is not _not_given:
modifier = self.choices_modifier
elif self.label_modifier is None:
# no choices modifier and the label modifier is explicit, so no label modifier
modifier = None
elif self.label_modifier is _not_given:
# title case to labels by default
modifier = to_title_case
else:
# a label modifier was given, use that since no choices modifier was given to override
modifier = self.label_modifier
if self.choices is not None:
choices = self.choices
elif modifier is None:
choices = default_choices
else:
choices = [(v, modifier(l)) for v, l in default_choices]
field.kwargs['choices'] = self.modify_choices(choices)
def modify_choices(self, choices):
return choices
def apply_coerce(self, field):
if self.coerce is _not_given:
return
if not issubclass(field.field_class, wtforms.SelectField):
raise ValueError('`coerce` argument may only be used for select fields')
field.kwargs['coerce'] = self.coerce
def apply_default(self, field):
if self.default is _not_given:
return
field.kwargs['default'] = self.default
def apply_required(self, field):
validators = field.kwargs.get('validators', [])
if self.required == _not_given:
# required value not given on FieldMeta, don't make any changes
pass
elif self.required:
# If a required validator isn't present, we need to add one.
req_val_test = lambda val: hasattr(val, 'field_flags') and 'required' in val.field_flags
if not list(filter(req_val_test, validators)):
validators.append(InputRequired())
# If an optional validator is present, we need to remove it.
not_opt_val_test = lambda val: not hasattr(val, 'field_flags') or \
'optional' not in val.field_flags
not_opt_validators = list(filter(not_opt_val_test, validators))
field.kwargs['validators'] = not_opt_validators
else:
# If an optional validator isn't present, we need to add one.
opt_val_test = lambda val: hasattr(val, 'field_flags') and 'optional' in val.field_flags
if not list(filter(opt_val_test, validators)):
validators.append(Optional())
# If a required validator is present, we need to remove it.
non_req_val_test = lambda val: not hasattr(val, 'field_flags') or \
'required' not in val.field_flags
not_req_validators = list(filter(non_req_val_test, validators))
field.kwargs['validators'] = not_req_validators
def apply_widget(self, field):
if self.widget != _not_given:
field.kwargs['widget'] = self.widget
def apply_extra_validators(self, field):
field.kwargs.setdefault('validators', [])
field.kwargs['validators'] += self.extra_validators
def select_coerce(es_pass_thru, coerce, value):
if es_pass_thru and value == '':
return value
if coerce is not _not_given:
return coerce(value)
# try coercing to int first. If not valid, fall back to default behavior
try:
return int(value)
except ValueError as e:
if 'invalid literal for int()' not in str(e):
raise
return str(value)
class SelectMixin:
def __init__(self, *args, **kwargs):
self.add_blank_choice = kwargs.pop('add_blank_choice', True)
coerce_arg = kwargs.pop('coerce', _not_given)
super().__init__(*args, **kwargs)
if self.add_blank_choice:
# If we are adding a blank choice, and it is selected, we want the value that comes back
# in .data to be None -> as if no value was selected.
#
# self.filters is a tuple, so have to do some extra work.
self.filters = [lambda x: None if x == '' else x] + list(self.filters)
self.coerce = functools.partial(select_coerce, self.add_blank_choice, coerce_arg)
def iter_choices(self):
if self.add_blank_choice:
yield ('', '', (self.coerce, False))
for value in super().iter_choices():
yield value
@property
def choice_values(self):
values = super().choice_values
if self.add_blank_choice:
return [''] + values
return values
@property
def selected_choice_label(self):
value_dict = dict(self.concrete_choices)
return value_dict.get(self.data)
class SelectField(SelectMixin, SelectFieldBase):
"""
Provides helpful features above wtforms_components SelectField which it is based on:
1) Adds a blank choice by default at the front of the choices. This results in your user
being forced to select something if the field is required, which avoids initial
defaulting of the first value in the field getting submitted.
2) The coerce function used for the choices will automatically convert to int if possible,
falling back to unicode if the value is not an integer.
"""
class SelectMultipleField(SelectMixin, SelectMultipleFieldBase):
"""
Provides helpful features above wtforms_components SelectMultipleField which it is
based on:
The coerce function used for the choices will automatically convert to int if possible,
falling back to unicode if the value is not an integer.
"""
def __init__(self, *args, **kwargs):
kwargs['add_blank_choice'] = kwargs.get('add_blank_choice', False)
super().__init__(*args, **kwargs)
class MultiCheckboxField(wtforms.fields.SelectMultipleField):
"""
A multiple-select, except displays a list of checkboxes.
"""
class RequiredBoolRadioField(wtforms.fields.RadioField):
"""A radio group field with true/false labels and a required validator.
:param true_label: Optional, defaults to Yes.
:param false_label: Optional, defaults to No.
:param validators: Optional. Any provided validators will be added to InputRequired.
"""
def __init__(self, *args, **kwargs):
true_label = kwargs.pop('true_label', _('Yes'))
false_label = kwargs.pop('false_label', _('No'))
def bool_coerce(val):
if val == u'True':
return True
if val == u'False':
return False
return val
kwargs['choices'] = [(True, true_label), (False, false_label)]
kwargs['coerce'] = bool_coerce
kwargs['validators'] = [InputRequired()] + kwargs.get('validators', [])
super(RequiredBoolRadioField, self).__init__(*args, **kwargs)
self.type = 'RadioField'
class RelationshipFieldBase:
"""Common base for single/multiple select fields that reference ORM relationships.
Handles one-to-many and many-to-many patterns.
Note, must use the wtforms-components fields as a base, because we depend on
lazy-loaded choices. At import time, a field's choices may not be fully available
in the data. In addition, when pairing the form with an existing record, we need
to ensure that the option from the record is present even if it would normally
be filtered (e.g. an inactive option).
"""
def __init__(self, label=None, orm_cls=None, label_attr=None, fk_attr='id',
query_filter=None, coerce=_not_given, **kwargs):
label = self.field_label_modifier(label)
self.orm_cls = orm_cls
self.label_attr = label_attr
if self.label_attr is None:
self.label_attr = self.get_best_label_attr()
if self.label_attr:
# compute this once and store on the object, rather than for each option
self.label_attr_name = self.compute_option_attr_name(self.label_attr)
self.fk_attr = fk_attr
if self.fk_attr:
self.fk_attr_name = self.compute_option_attr_name(self.fk_attr)
self.query_filter = query_filter
if not self.fk_attr and not coerce:
def coerce_to_orm_obj(value):
"""Coerce ID to relationship object."""
# If coming form formdata, we'll get a string ID.
if isinstance(value, str):
return self.orm_cls.get(value)
# If coming from object data, we'll get an ORM instance.
return value
coerce = coerce_to_orm_obj
super().__init__(label=label, choices=self.get_choices, coerce=coerce, **kwargs)
def compute_option_attr_name(self, base_attr):
"""To access the label value on an option in the result set, we need to know what
attribute to use. Based on ``label_attr``, which can be string, SA ORM attr,
SA hybrid attr, etc., determine a sane attribute."""
retval = base_attr
if isinstance(base_attr, sa.orm.InstrumentedAttribute):
retval = base_attr.name
else:
retval = getattr(base_attr, '__name__', str(base_attr))
return retval
def field_label_modifier(self, label):
"""Modifies the label to something more human-friendly.
One-to-many relationships often have a field name like "foo_id", which title cases
as "Foo Id". Form should only show "Foo", though, so we trim it that way here.
"""
if label.lower().endswith(' id'):
return label.rsplit(' ', 1)[0]
return label
def build_query(self):
query = self.query_base()
query = self.filter_query(query)
return query
def query_base(self):
orm_label_attr = self.label_attr
if isinstance(self.label_attr, str):
orm_label_attr = getattr(self.orm_cls, self.label_attr)
return self.orm_cls.query.order_by(orm_label_attr)
def get_data_filter(self):
if self.fk_attr:
return getattr(self.orm_cls, self.fk_attr_name) == self.data
else:
return self.orm_cls.id == self.data.id
def filter_query(self, query):
filter_terms = []
# Get supplied filters.
if callable(self.query_filter):
filter_terms.append(self.query_filter(self))
elif self.query_filter is not None:
filter_terms.append(self.query_filter)
# Having an existing value should filter the query.
if self.data is not None:
data_filter = self.get_data_filter()
if data_filter is not None and filter_terms:
filter_terms.append(data_filter)
# Apply filter terms with or_, or directly, depending on length.
if len(filter_terms) == 1:
query = query.filter(*filter_terms)
elif len(filter_terms) > 1:
query = query.filter(sa.sql.or_(*filter_terms))
return query
def get_best_label_attr(self):
if has_column(self.orm_cls, 'label'):
return 'label'
if has_column(self.orm_cls, 'name'):
return 'name'
return None
def get_option_label(self, obj):
if self.label_attr:
return getattr(obj, self.label_attr_name)
return str(obj)
def get_choices(self):
query = self.build_query()
def get_value(obj):
if self.fk_attr:
return str(getattr(obj, self.fk_attr_name))
return str(obj.id)
return [(get_value(obj), self.get_option_label(obj)) for obj in query]
@property
def choice_values(self):
# coerce values used for validation, because the data we're matching will
# be int type
return [self.coerce(v) for v in super().choice_values]
class RelationshipField(RelationshipFieldBase, SelectField):
"""SelectField for relationships.
Args:
orm_cls (class): Model class of the relationship attribute. Used to query
records for populating select options.
label_attr (str): Name of attribute on relationship class to use for select
option labels.
fk_attr (str): Optional name of foreign key column of ORM class. If set to
None, coerce values to instances of ORM class. Otherwise, coerce values to
the attribute of ORM class the foreign key belongs to. Default is 'id'.
query_filter (callable): Optional SA query filter criterion for querying select
options. Can be a function that returns a filter criterion. Function is
called with the RelationshipField instance it belongs to.
coerce (callable): Optional function used to coerce form values. By default,
if fk_attr is set to None, values are coerced to instances of ORM class.
Otherwise, the default select coersion is applied. Setting this overrides
default behavior.
kwargs: Passed to ``SelectField.__init__``.
Example::
class Bar(Model):
name = Column(Unicode(255))
foos = relationship('Foo', foreign_keys='foos.id')
class Foo(Model):
name = Column(Unicode(255))
bar_id = Column(sa.ForeignKey(Bar.id))
bar = relationship(Bar, foreign_keys=bar_id)
class FooForm(ModelForm):
bar_id = RelationshipField('Bar Label', Bar, 'name')
"""
class RelationshipMultipleField(RelationshipFieldBase, SelectMultipleField):
"""SelectMultipleField for relationships.
Args:
orm_cls (class): Model class of the relationship attribute. Used to query
records for populating select options.
label_attr (str): Name of attribute on relationship class to use for select
option labels.
query_filter (callable): Optional SA query filter criterion for querying select
options. Can be a function that returns a filter criterion. Function is
called with the RelationshipField instance it belongs to.
coerce (callable): Optional function used to coerce form values. By default,
values are coerced to instances of ORM class. Setting this overrides
default behavior.
kwargs: Passed to ``SelectMultipleField.__init__``.
Example::
class Bar(Model):
name = Column(Unicode(255))
foos = relationship('Foo', foreign_keys='foos.id')
class Foo(Model):
name = Column(Unicode(255))
bar_id = Column(sa.ForeignKey(Bar.id))
bar = relationship(Bar, foreign_keys=bar_id)
class BarForm(ModelForm):
foos = RelationshipMultipleField('Foos', Foo, 'name')
"""
def __init__(self, label, orm_cls, label_attr=None,
query_filter=None, coerce=_not_given, **kwargs):
coerce = coerce or self.coerce_to_int
super().__init__(label, orm_cls, label_attr, None, query_filter, coerce, **kwargs)
def get_data_filter(self):
if not self.data:
# Empty set should not add any options. Returning in_ in this case has
# undesirable results.
return
existing_ids = [obj.id for obj in self.data]
return self.orm_cls.id.in_(existing_ids)
@staticmethod
def coerce_to_int(value):
return int(value)
def coerce_to_obj(self, value):
if type(value) in [int, str]:
return self.orm_cls.get(int(value))
else:
return value
def iter_choices(self):
selected_ids = [v.id for v in self.data or []]
for value, label in self.choices():
selected = selected_ids is not None and self.coerce(value) in selected_ids
yield (value, label, selected)
def process_formdata(self, valuelist):
try:
self.data = list(self.coerce_to_obj(v) for v in valuelist)
except ValueError:
raise ValueError(self.gettext('Invalid Choice: could not coerce'))
def process_data(self, value):
try:
self.data = list(v for v in value)
except (ValueError, TypeError):
self.data = None
def pre_validate(self, form):
if self.data:
values = list(c[0] for c in self.choices())
for d in self.data:
if str(d.id) not in values:
raise ValueError(
self.gettext("'%(value)s' is not a valid choice for this field")
% dict(value=d)
)
class _TypeHintingTextInputBase(wtforms.widgets.TextInput):
def __init__(self, prefix=None, suffix=None):
self.prefix = prefix
self.suffix = suffix
super().__init__()
class TypeHintingTextInputB3(_TypeHintingTextInputBase):
"""
A text input widget with a prefix and/or suffix to hint at the expected type or units.
For use with bootstrap 3
"""
def __call__(self, field, **kwargs):
def make_addon(txt):
return Markup(
'<span class="input-group-addon">{}</span>'.format(wtforms.widgets.core.escape(txt))
)
return Markup(
'<div class="input-group">{pre}{field}{post}</div>'.format(
pre=make_addon(self.prefix) if self.prefix else '',
field=super().__call__(field, **kwargs).__html__(),
post=make_addon(self.suffix) if self.suffix else ''
)
)
class TypeHintingTextInputB4(_TypeHintingTextInputBase):
"""
A text input widget with a prefix and/or suffix to hint at the expected type or units.
For use with bootstrap 4
"""
def __call__(self, field, **kwargs):
def make_addon(txt, addon_type):
return Markup(
'<div class="input-group-{type}">'
' <span class="input-group-text">{txt}</span>'
"</div>".format(type=addon_type, txt=wtforms.widgets.core.escape(txt))
)
return Markup(
'<div class="input-group">{pre}{field}{post}</div>'.format(
pre=make_addon(self.prefix, "prepend") if self.prefix else "",
field=super().__call__(field, **kwargs).__html__(),
post=make_addon(self.suffix, "append") if self.suffix else "",
)
)
def _max_for_numeric(digits, scale):
return Decimal('{}.{}'.format('9' * (digits - scale), '9' * scale))
class FormGenerator(FormGeneratorBase):
"""Model form generator that applies field meta info, provides validators, etc.
Meta nested class directives (in addition to wtforms-alchemy):
- include_datetimes_with_default
- include_required_foreign_keys
Field class overrides:
- Use our SelectField instead of the WTForms default
- Use our RequiredBoolRadioField for non-nullable boolean fields
- Use RelationshipField for foreign key fields
Meta info modifiers:
- Use FieldsMeta.<field_name> if provided
- Falls back to FieldsMeta.__default__
- If none of the above, uses a blank FieldsMeta object, which will title case the label.
Validators:
- Applies range/scale numeric validators when applicable.
"""
def __init__(self, form_class):
super(FormGenerator, self).__init__(form_class)
self.fields_meta = getattr(self.form_class, 'FieldsMeta', None)
def skip_column(self, column):
# Verify the key is not also in exclude=[] so we don't break compatibility with forms
# that already manually excluded these fields
if (not self.meta.include_datetimes_with_default
and isinstance(column.type, ArrowType)
and column.default
and column.key not in self.meta.exclude):
return True
# include_foreign_keys will pull in all foreign keys on the object. If we want the
# form to include only required keys, we use include_required_foreign_keys.
include_required_fks = getattr(self.meta, 'include_required_foreign_keys', False)
if (include_required_fks and column.foreign_keys and column.nullable is False):
return False
return super().skip_column(column)
def get_field_class(self, column):
field_cls = super(FormGenerator, self).get_field_class(column)
if field_cls is SelectFieldBase:
return SelectField
is_required_boolean = (field_cls is wtforms.fields.BooleanField
and not column.nullable
and not column.default)
if is_required_boolean:
return RequiredBoolRadioField
return field_cls
def get_field_modifier(self, prop):
# is there an entry in FieldsMeta?
if hasattr(self.fields_meta, prop.key):
field_modifier = getattr(self.fields_meta, prop.key)
else:
field_modifier = getattr(self.fields_meta, '__default__', _not_given)
if field_modifier is _not_given:
field_modifier = FieldMeta
return field_modifier() if inspect.isclass(field_modifier) else field_modifier
def create_field(self, prop, column):
if column.foreign_keys:
foreign_key = next(iter(column.foreign_keys))
orm_cls = get_class_by_table(db.Model, foreign_key.column.table)
validators = self.create_validators(prop, column)
field = RelationshipField(
label=to_title_case(str(column.key)),
orm_cls=orm_cls,
validators=validators,
)
else:
field = super(FormGenerator, self).create_field(prop, column)
modifier = self.get_field_modifier(prop)
if modifier is not None:
modifier.apply_to_field(field)
return field
def create_validators(self, prop, column):
validators = super(FormGenerator, self).create_validators(prop, column)
if isinstance(column.type, sa.Numeric) and not isinstance(column.type, sa.Float):
if column.type.precision is None or column.type.scale is None:
raise ValueError('Numeric fields must specify precision and scale')
max_ = _max_for_numeric(column.type.precision, column.type.scale)
validators.append(NumberRange(min=-max_, max=max_))
validators.append(NumberScale(column.type.scale))
return validators
def length_validator(self, column):
if isinstance(column.type, sa.types.Enum):
return None
return super(FormGenerator, self).length_validator(column)
def select_field_kwargs(self, column):
enum_cls = getattr(column.type, 'enum_class', None)
if enum_cls and issubclass(enum_cls, DBEnum):
return {
'coerce': enum_cls.coerce,
'choices': enum_cls.form_options()
}
return super().select_field_kwargs(column)
def field_to_dict(field):
if isinstance(field, wtforms.fields.FormField):
return form_fields_to_dict(field)
if isinstance(field, wtforms.fields.FieldList):
return [field_to_dict(subfield) for subfield in field]
return {
'data': field.data,
'errors': field.errors,
'label': field.label.text,
'required': field.flags.required,
}
def form_fields_to_dict(form):
return dict((str(name), field_to_dict(field))
for name, field in form._fields.items())
___validator_creation_counter = 0
def form_validator(func=None, only_when_fields_valid=False):
"""Decorator used to mark a method as a form level validator.
:param only_when_fields_valid: Use to disable validator if form already has errors.
"""
if func is None:
return functools.partial(form_validator, only_when_fields_valid=only_when_fields_valid)
@functools.wraps(func)
def wrapper(form):
if not only_when_fields_valid or not form.errors:
return func(form)
global ___validator_creation_counter
wrapper.___form_validator = True
___validator_creation_counter += 1
wrapper.___creation_counter = ___validator_creation_counter
return wrapper
class Form(BaseForm):
"""Base form with a bunch of QoL improvements
:param _field_order: Relying on the default field ordering can lead to unintuitive forms. It is
possible to override this by adding the ``_field_order`` class attribute. Set this class
variable to a tuple or list of field names (addressable via Form._fields['name_of_field'])
and the form will render in that order. You must include all the fields, except CSRF.
Forgetting a field or adding one which doesn't exist will cause the form to raise a
``ValueError`` and the form will not be rendered.
class MyForm(Form):
_field_order = ('field1', 'field2',)
field1 = String('field1_label') # Note that we don't use the label in the ordering
field2 = String()
"""
_form_ident_enabled = True
_form_ident_strict = True
def __init__(self, *args, **kwargs):
super(Form, self).__init__(*args, **kwargs)
self._form_level_errors = []
self._errors = None
self.after_init(args, kwargs)
def __init_subclass__(cls):
cls.add_form_ident()
super().__init_subclass__()
def __iter__(self):
custom_field_order = getattr(self, '_field_order', None)
if custom_field_order is None:
return super().__iter__()
order = []
if hasattr(self, 'csrf_token'):
order.append('csrf_token')
if self._form_ident_enabled:
order.append(self._form_ident_key())
order.extend(list(custom_field_order))
declared = set(self._fields.keys())
ordered = set(order)
if declared != ordered:
not_ordered = declared - ordered
extra_ordered = ordered - declared
raise ValueError(
'Custom field ordering for {} is incorrect.'.format(self.__class__.__name__),
' Missing fields: {} '.format(not_ordered),
' Extra fields: {} '.format(extra_ordered),
)
return (self._fields[f] for f in order)
def after_init(self, args, kwargs):
"""Hook for providing customization on the form after fields are initialized."""
pass
def fields_todict(self):
"""Turns a form into dicts and lists with both data and errors for each field."""
return form_fields_to_dict(self)
def validate(self):
"""Applies validators and returns bool.
Methods decorated as form-level validators are run after WTForms generic validation.
"""
fields_valid = super(Form, self).validate()
form_validators = {}
# Traverse the MRO so we can get validators in parent classes.
# Do so in reverse order so child classes can override parents' validators.
# WTForms will not include the methods on form instances so we get them from the classes.
for cls in reversed(self.__class__.__mro__):
cls_validators = {
name: attr for name, attr in cls.__dict__.items()
if getattr(attr, '___form_validator', False)
}
form_validators.update(cls_validators)
for validator in sorted(form_validators.values(), key=attrgetter('___creation_counter')):
try:
validator(self)
except StopValidation as e:
if e.args and e.args[0]:
self.form_errors.append(e.args[0])
break
except ValueError as e:
self.form_errors.append(e.args[0])
return fields_valid and not self.form_errors
@property
def field_errors(self):
"""Field-level validator errors come from WTForms' errors."""
warnings.warn(
'WTForms has form-level validation now, use form.errors instead', DeprecationWarning, 2
)
return self.errors
@classmethod
def add_form_ident(cls):
# may need to clean up from a superclass init, so we have fresh config here
key = cls._form_ident_key()
if hasattr(cls, key):
setattr(cls, key, None)
if not cls._form_ident_enabled:
return
if key.startswith('_'):
raise Exception('Cannot start form ident name with "_", since WTForms will ignore')
validators = []
value = cls._form_ident_value()
if cls._form_ident_strict:
validators.append(AnyOf([value]))
setattr(
cls,
key,
wtforms.fields.HiddenField(
default=value,
validators=validators,
)
)
@classmethod
def _form_ident_key(cls):
"""Field name to embed as a hidden value for form identification. Default is keg_form_ident.
Note: this cannot start with an underscore, or WTForms will ignore the field.
"""
return 'keg_form_ident'
@classmethod
def _form_ident_value(cls):
"""Field value to embed for form identification. Default is class name converted to
dash notation."""
return case_cw2dash(cls.__name__)
BaseModelFormMeta = model_form_meta_factory()
class ModelFormMeta(BaseModelFormMeta):
"""Base model form metaclass that handles nested inheritance issues.
The default metaclass here will handle the nested Meta class. A form
subclass with a Meta nested class will treat the form's superclass' Meta
as a parent.
This metaclass does the same thing for FieldsMeta, allowing superclasses
to define a FieldsMeta that may reasonably be passed down to the subclass.
"""
def __init__(cls, *args, **kwargs):
bases = []
for class_ in cls.__mro__:
if 'FieldsMeta' in class_.__dict__:
bases.append(getattr(class_, 'FieldsMeta'))
if object not in bases:
bases.append(object)
cls.FieldsMeta = type('FieldsMeta', tuple(bases), {})
BaseModelFormMeta.__init__(cls, *args, **kwargs)
BaseModelForm = model_form_factory(Form, meta=ModelFormMeta, form_generator=FormGenerator)
class ModelForm(BaseModelForm):
"""Base model-generated form class that applies KegElements generator and meta."""
@classmethod
def get_session(cls):
return db.session | PypiClean |
/My-CountriesAPI-123456-1.0.tar.gz/My-CountriesAPI-123456-1.0/my_countries_api_123456/http/http_request.py | from my_countries_api_123456.api_helper import APIHelper
class HttpRequest(object):
"""Information about an HTTP Request including its method, headers,
parameters, URL, and Basic Auth details
Attributes:
http_method (HttpMethodEnum): The HTTP Method that this request should
perform when called.
headers (dict): A dictionary of headers (key : value) that should be
sent along with the request.
query_url (string): The URL that the request should be sent to.
parameters (dict): A dictionary of parameters that are to be sent along
with the request in the form body of the request
"""
def __init__(self,
http_method,
query_url,
headers=None,
query_parameters=None,
parameters=None,
files=None):
"""Constructor for the HttpRequest class
Args:
http_method (HttpMethodEnum): The HTTP Method.
query_url (string): The URL to send the request to.
headers (dict, optional): The headers for the HTTP Request.
query_parameters (dict, optional): Query parameters to add in the URL.
parameters (dict, optional): Form or body parameters to be included in the body.
files (dict, optional): Files to be sent with the request.
"""
self.http_method = http_method
self.query_url = query_url
self.headers = headers
self.query_parameters = query_parameters
self.parameters = parameters
self.files = files
def add_header(self, name, value):
""" Add a header to the HttpRequest.
Args:
name (string): The name of the header.
value (string): The value of the header.
"""
self.headers[name] = value
def add_parameter(self, name, value):
""" Add a parameter to the HttpRequest.
Args:
name (string): The name of the parameter.
value (string): The value of the parameter.
"""
self.parameters[name] = value
def add_query_parameter(self, name, value):
""" Add a query parameter to the HttpRequest.
Args:
name (string): The name of the query parameter.
value (string): The value of the query parameter.
"""
self.query_url = APIHelper.append_url_with_query_parameters(self.query_url,
{name:value})
self.query_url = APIHelper.clean_url(self.query_url) | PypiClean |
/DataTig-0.5.0.tar.gz/DataTig-0.5.0/datatig/models/type.py | import json
import os.path
from datatig.jsondeepreaderwriter import JSONDeepReaderWriter
from datatig.jsonschemabuilder import build_json_schema
from .field import FieldConfigModel
from .field_boolean import FieldBooleanConfigModel
from .field_date import FieldDateConfigModel
from .field_datetime import FieldDateTimeConfigModel
from .field_integer import FieldIntegerConfigModel
from .field_list_strings import FieldListStringsConfigModel
from .field_string import FieldStringConfigModel
from .field_url import FieldURLConfigModel
class TypeModel:
def __init__(self, siteconfig):
self._id = None
self._config = None
self._fields = {}
self._siteconfig = siteconfig
def load_from_config(self, config) -> None:
self._id = config.get("id")
self._config = config
self._fields = {}
for config in self._config.get("fields", []):
field_config: FieldConfigModel = FieldStringConfigModel()
if config.get("type") == "url":
field_config = FieldURLConfigModel()
elif config.get("type") == "list-strings":
field_config = FieldListStringsConfigModel()
elif config.get("type") == "date":
field_config = FieldDateConfigModel()
elif config.get("type") == "datetime":
field_config = FieldDateTimeConfigModel()
elif config.get("type") == "boolean":
field_config = FieldBooleanConfigModel()
elif config.get("type") == "integer":
field_config = FieldIntegerConfigModel()
field_config.load(config)
self._fields[field_config.get_id()] = field_config
def get_directory(self) -> str:
return self._config.get("directory")
def get_directory_in_git_repository(self) -> str:
return self._config.get("directory")
def get_guide_form_xlsx(self) -> str:
return self._config.get("guide_form_xlsx")
def get_list_fields(self) -> list:
return self._config.get("list_fields", []) # TODO add some sensible defaults
def get_json_schema_as_dict(self) -> dict:
if self._config.get("json_schema"):
with open(
os.path.join(
self._siteconfig.get_source_dir(), self._config.get("json_schema")
)
) as fp:
return json.load(fp)
else:
results = build_json_schema(self._fields.values())
return results.get_json_schema()
def get_json_schema_as_string(self) -> str:
return json.dumps(self.get_json_schema_as_dict())
def get_pretty_json_indent(self) -> int:
return self._config.get("pretty_json_indent", 4)
def get_default_format(self) -> str:
return self._config.get("default_format", "yaml")
def get_markdown_body_is_field(self) -> str:
return self._config.get("markdown_body_is_field", "body")
def get_new_item_json(self) -> dict:
out: JSONDeepReaderWriter = JSONDeepReaderWriter({})
for field in self._fields.values():
out.write(field.get_key(), field.get_new_item_json())
return out.get_json()
def get_new_item_json_as_string(self) -> str:
return json.dumps(self.get_new_item_json())
def get_id(self) -> str:
return self._id
def get_fields(self) -> dict:
return self._fields
def get_field(self, field_id) -> FieldConfigModel:
return self._fields.get(field_id) | PypiClean |
/AutoTransform-1.1.1a8-py3-none-any.whl/autotransform/change/base.py |
# @black_format
"""The base class and associated classes for Change components."""
from __future__ import annotations
from abc import abstractmethod
from enum import Enum
from typing import TYPE_CHECKING, ClassVar, List
from autotransform.batcher.base import Batch
from autotransform.util.component import ComponentFactory, ComponentImport, NamedComponent
if TYPE_CHECKING:
from autotransform.runner.base import Runner
from autotransform.schema.schema import AutoTransformSchema
class ChangeState(str, Enum):
"""A simple enum for the state of a given Change in code review or version
control systems."""
CLOSED = "closed"
MERGED = "merged"
OPEN = "open"
class ReviewState(str, Enum):
"""A simple enum for the review state of a given Change in code review or version
control systems."""
APPROVED = "approved"
CHANGES_REQUESTED = "changes_requested"
NEEDS_REVIEW = "needs_review"
class TestState(str, Enum):
"""A simple enum for the test state of a given Change in code review or version
control systems."""
FAILURE = "failure"
PENDING = "pending"
SUCCESS = "success"
class ChangeName(str, Enum):
"""A simple enum for mapping."""
GITHUB = "github"
class Change(NamedComponent):
"""The base for Change components. Used by AutoTransform to manage submissions to
code review and source control systems.
Attributes:
name (ClassVar[ChangeName]): The name of the Component.
"""
name: ClassVar[ChangeName]
@abstractmethod
def get_batch(self) -> Batch:
"""Gets the Batch that was used to produce the Change.
Returns:
Batch: The Batch used to produce the Change.
"""
@abstractmethod
def get_schema(self) -> AutoTransformSchema:
"""Gets the Schema that was used to produce the Change.
Returns:
AutoTransformSchema: The Schema used to produce the Change.
"""
def get_schema_name(self) -> str:
"""Gets the name of the Schema that produced the Change.
Returns:
str: The name of the Schema.
"""
return self.get_schema().config.schema_name
@abstractmethod
def get_state(self) -> ChangeState:
"""Gets the current state of the Change.
Returns:
ChangeState: The current state of the Change.
"""
@abstractmethod
def get_mergeable_state(self) -> str:
"""Gets the mergeable state of the Change.
Returns:
ChangeState: The mergeable state of the Change.
"""
@abstractmethod
def get_review_state(self) -> ReviewState:
"""Gets the current review state of the Change.
Returns:
ReviewState: The current review state of the Change.
"""
@abstractmethod
def get_test_state(self) -> TestState:
"""Gets the current test state of the Change.
Returns:
TestState: The current test state of the Change.
"""
@abstractmethod
def get_labels(self) -> List[str]:
"""Gets all labels for a Change.
Returns:
List[str]: The list of labels.
"""
@abstractmethod
def get_reviewers(self) -> List[str]:
"""Gets all reviewers for a Change.
Returns:
List[str]: The list of reviewers.
"""
@abstractmethod
def get_team_reviewers(self) -> List[str]:
"""Gets all team reviewers for a Change.
Returns:
List[str]: The list of team reviewers.
"""
@abstractmethod
def get_created_timestamp(self) -> int:
"""Returns the timestamp when the Change was created.
Returns:
int: The timestamp in seconds when the Change was created.
"""
@abstractmethod
def get_last_updated_timestamp(self) -> int:
"""Returns the timestamp when the Change was last updated.
Returns:
int: The timestamp in seconds when the Change was last updated.
"""
@abstractmethod
def abandon(self) -> bool:
"""Close out and abandon a Change, removing it from the code review
and/or version control system.
Returns:
bool: Whether the abandon was completed successfully.
"""
@abstractmethod
def add_labels(self, labels: List[str]) -> bool:
"""Adds labels to an outstanding Change.
Args:
labels (List[str]): The labels to add.
Returns:
bool: Whether the labels were added successfully.
"""
@abstractmethod
def add_reviewers(self, reviewers: List[str], team_reviewers: List[str]) -> bool:
"""Adds reviewers to an outstanding Change.
Args:
reviewers (List[str]): The reviewers to add.
team_reviewers (List[str]): Any team reviewers to add.
Returns:
bool: Whether the reviewers were added successfully.
"""
@abstractmethod
def comment(self, body: str) -> bool:
"""Comments on an outstanding Change.
Args:
body (str): The body of the comment.
Returns:
bool: Whether the comment was successful.
"""
@abstractmethod
def merge(self) -> bool:
"""Merges an approved change in to main.
Returns:
bool: Whether the merge was completed successfully.
"""
@abstractmethod
def remove_label(self, label: str) -> bool:
"""Removes a label from an outstanding Change.
Args:
label (str): The label to remove.
Returns:
bool: Whether the label was removed successfully.
"""
def update(self, runner: Runner) -> bool:
"""Update an outstanding Change against the latest state of the codebase.
Args:
runner (Runner): The Runner to use to update the Change.
Returns:
bool: Whether the update was completed successfully.
"""
runner.update(self)
return True
FACTORY = ComponentFactory(
{
ChangeName.GITHUB: ComponentImport(
class_name="GithubChange", module="autotransform.change.github"
),
},
Change, # type: ignore [type-abstract]
"change.json",
) | PypiClean |
/Flet_StoryBoard-1.4-py3-none-any.whl/fletsb/pages/settings.py | from flet import Page
import flet
import time
from .Settings.pages import page_settings_page
class SettingsPage:
def __init__(self, page:Page, main_class) -> None:
self.page : Page = page
self.main_class = main_class
#? Create a copy of things.
self.__last_keyboard_manager = main_class.manage_keyboard_commands
self.__last_controls = list(page.controls)
self.__lat_appbar = page.appbar
self.__last_on_resize_function = main_class.on_page_resize
#? For keyboard events
self.current_selected_page = None
self.page_opend = True
#? Hide all page controls
self.hide_all()
#? ReSet all page controls
page.on_keyboard_event = self.keyboard_keys_manager
page.on_resize = self.on_page_resize
page.controls.clear()
page.appbar = None
page.bgcolor = "black"
page.update()
main_row = flet.Row(alignment="center")
page.add(main_row)
#? pages browseing section
pages_browser_container = flet.Container(border_radius=12)
self.pages_broswer = flet.Column(width=page.width/3, height=page.height-50, scroll=True)
pages_browser_container.content = self.pages_broswer
main_row.controls.append(pages_browser_container)
page.update()
#? viewing selected page section
page_viewing_section_container = flet.Container(bgcolor="#1C1C1E", border_radius=12)
self.page_viewing_section = flet.Column(width=page.width-(page.width/3)-50, height=page.height-50, scroll=True)
page_viewing_section_container.content = self.page_viewing_section
main_row.controls.append(page_viewing_section_container)
page.update()
#? The title and back_btn
title = flet.Text(" Settings", size=28, weight="bold", color="white")
self.pages_broswer.controls.append(flet.Row([
flet.IconButton("CLOSE_ROUNDED", on_click=self.go_back, icon_color="white", width=30), title
], spacing=0))
self.pages_broswer.controls.append(flet.Text(""))
#? show all settings pages
for p in self.get_all_settings_pages():
self.pages_broswer.controls.append(page_navigator_frame_button(p, self.get_all_settings_pages()[p]["icon"], self.get_all_settings_pages()[p]["function"],self))
page.update()
def open_new_settings_page(self, control):
"""Open a new sub-page on settings page"""
pass
def route_to_page (self):
"""Use flet Route to a page"""
def hide_all (self):
page : Page = self.page
for i in range(10):
for c in page.controls:
c.opacity = c.opacity - 0.1
page.appbar.opacity = page.appbar.opacity - 0.1
c.update()
for ac in page.appbar.actions:
ac.opacity = ac.opacity - 0.1
ac.update()
page.appbar.leading.opacity = page.appbar.leading.opacity - 0.1
page.appbar.leading.update()
page.appbar.title.opacity = page.appbar.title.opacity - 0.1
page.appbar.title.update()
time.sleep(0.01)
def go_back (self, *e):
self.page_opend = False
page : Page = self.page
page.on_keyboard_event = self.__last_keyboard_manager
page.controls.clear()
page.bgcolor = "black"
page.appbar = self.__lat_appbar
for c in self.__last_controls:
c.opacity = 1.0
page.controls.append(c)
for ac in page.appbar.actions:
ac.opacity = 1.0
page.appbar.leading.opacity = 1.0
page.appbar.title.opacity = 1.0
page.on_resize = self.__last_on_resize_function
page.update()
def on_page_resize (self, e):
page = self.page
self.pages_broswer.width = page.width/3 - 50
self.pages_broswer.height = self.page.height - 50
self.page_viewing_section.width = page.width-(page.width/3-50)-50
self.page_viewing_section.height = page.height-50
self.page.update()
def get_all_settings_pages (self):
return {
"Pages" : {"icon":"PREVIEW_ROUNDED", "function":page_settings_page, "name":"Pages"},
"Editor" : {"icon":"SETTINGS_ROUNDED", "function":editor_page, "name":"Editor"}
}
def keyboard_keys_manager (self, e):
if self.page_opend == False: return
pass
#? Generate page button
def page_navigator_frame_button (name:str, icon, function, settings_class:SettingsPage, as_a_click=False):
def on_hov (e):
if str(e.data) == "true":
container.bgcolor = flet.colors.WHITE60
else:
container.bgcolor = None
container.update()
if e.control == settings_class.current_selected_page:
e.control.bgcolor = "blue"
e.control.update()
def on_click (e):
if settings_class.current_selected_page == None:
settings_class.current_selected_page = container
container.bgcolor = "blue"
if container.page == None:
settings_class.page.update()
else:
container.update()
else:
settings_class.current_selected_page.bgcolor = None
if settings_class.current_selected_page.page != None:
settings_class.current_selected_page.update()
settings_class.current_selected_page = container
container.bgcolor = "blue"
if container.page == None:
settings_class.page.update()
else:
container.update()
function(settings_class)
container = flet.Container(width=200, height=40, border_radius=12, expand=True,
on_hover=on_hov, on_click=on_click)
r = flet.Row([flet.Text(" ")], alignment="center", width=200)
container.content = r
ICON = flet.Icon(icon, size=20, color="white")
r.controls.append(ICON)
NAME = flet.Text(name, size=17, color="white", expand=True, text_align="left")
r.controls.append(NAME)
if as_a_click:
container.bgcolor = "blue"
on_click(container)
return flet.Row([container], alignment="center")
#? Pages
def editor_page (settings_class:SettingsPage):
v = settings_class.page_viewing_section
v.clean()
v.controls.append(flet.Text("soon", color="white"))
v.update() | PypiClean |
/Django-4.2.4.tar.gz/Django-4.2.4/django/contrib/auth/base_user.py | import unicodedata
import warnings
from django.conf import settings
from django.contrib.auth import password_validation
from django.contrib.auth.hashers import (
check_password,
is_password_usable,
make_password,
)
from django.db import models
from django.utils.crypto import get_random_string, salted_hmac
from django.utils.deprecation import RemovedInDjango51Warning
from django.utils.translation import gettext_lazy as _
class BaseUserManager(models.Manager):
@classmethod
def normalize_email(cls, email):
"""
Normalize the email address by lowercasing the domain part of it.
"""
email = email or ""
try:
email_name, domain_part = email.strip().rsplit("@", 1)
except ValueError:
pass
else:
email = email_name + "@" + domain_part.lower()
return email
def make_random_password(
self,
length=10,
allowed_chars="abcdefghjkmnpqrstuvwxyzABCDEFGHJKLMNPQRSTUVWXYZ23456789",
):
"""
Generate a random password with the given length and given
allowed_chars. The default value of allowed_chars does not have "I" or
"O" or letters and digits that look similar -- just to avoid confusion.
"""
warnings.warn(
"BaseUserManager.make_random_password() is deprecated.",
category=RemovedInDjango51Warning,
stacklevel=2,
)
return get_random_string(length, allowed_chars)
def get_by_natural_key(self, username):
return self.get(**{self.model.USERNAME_FIELD: username})
class AbstractBaseUser(models.Model):
password = models.CharField(_("password"), max_length=128)
last_login = models.DateTimeField(_("last login"), blank=True, null=True)
is_active = True
REQUIRED_FIELDS = []
# Stores the raw password if set_password() is called so that it can
# be passed to password_changed() after the model is saved.
_password = None
class Meta:
abstract = True
def __str__(self):
return self.get_username()
def save(self, *args, **kwargs):
super().save(*args, **kwargs)
if self._password is not None:
password_validation.password_changed(self._password, self)
self._password = None
def get_username(self):
"""Return the username for this User."""
return getattr(self, self.USERNAME_FIELD)
def clean(self):
setattr(self, self.USERNAME_FIELD, self.normalize_username(self.get_username()))
def natural_key(self):
return (self.get_username(),)
@property
def is_anonymous(self):
"""
Always return False. This is a way of comparing User objects to
anonymous users.
"""
return False
@property
def is_authenticated(self):
"""
Always return True. This is a way to tell if the user has been
authenticated in templates.
"""
return True
def set_password(self, raw_password):
self.password = make_password(raw_password)
self._password = raw_password
def check_password(self, raw_password):
"""
Return a boolean of whether the raw_password was correct. Handles
hashing formats behind the scenes.
"""
def setter(raw_password):
self.set_password(raw_password)
# Password hash upgrades shouldn't be considered password changes.
self._password = None
self.save(update_fields=["password"])
return check_password(raw_password, self.password, setter)
def set_unusable_password(self):
# Set a value that will never be a valid hash
self.password = make_password(None)
def has_usable_password(self):
"""
Return False if set_unusable_password() has been called for this user.
"""
return is_password_usable(self.password)
def get_session_auth_hash(self):
"""
Return an HMAC of the password field.
"""
return self._get_session_auth_hash()
def get_session_auth_fallback_hash(self):
for fallback_secret in settings.SECRET_KEY_FALLBACKS:
yield self._get_session_auth_hash(secret=fallback_secret)
def _get_session_auth_hash(self, secret=None):
key_salt = "django.contrib.auth.models.AbstractBaseUser.get_session_auth_hash"
return salted_hmac(
key_salt,
self.password,
secret=secret,
algorithm="sha256",
).hexdigest()
@classmethod
def get_email_field_name(cls):
try:
return cls.EMAIL_FIELD
except AttributeError:
return "email"
@classmethod
def normalize_username(cls, username):
return (
unicodedata.normalize("NFKC", username)
if isinstance(username, str)
else username
) | PypiClean |
/MJOLNIRGui-0.9.10.tar.gz/MJOLNIRGui-0.9.10/src/main/python/Views/Raw1DManager.py | import sys
sys.path.append('..')
try:
from MJOLNIRGui.src.main.python.MJOLNIR_Data import GuiDataFile,GuiDataSet
from MJOLNIRGui.src.main.python._tools import ProgressBarDecoratorArguments,loadUI
except ImportError:
from MJOLNIR_Data import GuiDataFile,GuiDataSet
from _tools import ProgressBarDecoratorArguments,loadUI
from os import path
from PyQt5 import QtWidgets,uic
import numpy as np
from MJOLNIR.Data import DataFile
def setupRaw1DCutSpinBoxes(self):
self.ui.Raw1D_Analyzer_spinBox.valueChanged.connect(self.raw1DCutAnalyzerSpinBoxChanged)
self.ui.Raw1D_Detector_spinBox.valueChanged.connect(self.raw1DCutDetectorSpinBoxChanged)
self.resetRaw1DCutSpinBoxes()
def resetRaw1DCutSpinBoxes(self):
self.ui.Raw1D_Analyzer_spinBox.setEnabled(False)
self.ui.Raw1D_Detector_spinBox.setEnabled(False)
self.ui.Raw1D_Analyzer_spinBox.setValue(0)
self.ui.Raw1D_Detector_spinBox.setValue(0)
self.ui.Raw1D_Analyzer_Original_label.setText('Original N/A')
self.ui.Raw1D_Detector_Original_label.setText('Original N/A')
EfEntry = '{:.2f}'.format(0).rjust(9,' ')
A4Entry = '{:+.2f}'.format(0).rjust(9,' ')
self.ui.Raw1D_Analyzer_label.setText('Analyzer number (Ef = {} meV)'.format(EfEntry))
self.ui.Raw1D_Detector_label.setText('Detector number (A4 = {} deg)'.format(A4Entry))
def updateRaw1DCutSpinBoxes(self,dfs=None):
if dfs is None:
dfs = self.DataFileModel.getCurrentDatafiles()
if dfs is None:
return self.resetRaw1DCutSpinBoxes()
if len(dfs)>1:
return self.resetRaw1DCutSpinBoxes()
df = dfs[0]
maxAnalyzer = DataFile.analyzerLimits[df.instrument]
maxDetector = DataFile.detectorLimits[df.instrument]
self.ui.Raw1D_Analyzer_spinBox.setEnabled(True)
self.ui.Raw1D_Detector_spinBox.setEnabled(True)
self.ui.Raw1D_Analyzer_spinBox.setMaximum(maxAnalyzer)
self.ui.Raw1D_Detector_spinBox.setMaximum(maxDetector)
self.ui.Raw1D_Analyzer_spinBox.setValue(df.analyzerSelection)
self.ui.Raw1D_Detector_spinBox.setValue(df.detectorSelection)
self.updateRaw1DCutLabels(dfs)
def updateRaw1DCutLabels(self,dfs=None):
if dfs is None:
dfs = self.DataFileModel.getCurrentDatafiles()
if dfs is None:
return self.resetRaw1DCutSpinBoxes()
if len(dfs)>1:
return self.resetRaw1DCutSpinBoxes()
df = dfs[0]
self.ui.Raw1D_Analyzer_Original_label.setText('Original {}'.format(df.analyzerSelectionOriginal))
self.ui.Raw1D_Detector_Original_label.setText('Original {}'.format(df.detectorSelectionOriginal))
binning = 1
calibrationIndex = list(df.possibleBinnings).index(binning) # Only binning 1 is used for raw plotting
if df.instrument == 'CAMEA':
EPrDetector = 8
detectors = 104
elif df.type == 'MultiFLEXX':
EPrDetector = 5
detectors = 31
elif df.type == 'FlatCone':
EPrDetector = 1
detectors = 31
elif df.type == 'Bambus' or df.instrument == 'Bambus':
EPrDetector = 1
detectors = 100
else:
totalDetectors = np.array(df.instrumentCalibrations[calibrationIndex][0].shape[:-1])
if len(totalDetectors) == 2:
detectors,EPrDetector = totalDetectors
else:
if np.mod(totalDetectors,31)==0: # either MultiFLEXX or FlatCone
EPrDetector = int(totalDetectors/31)
detectors = 31
else: # CAMEA
EPrDetector = 8
detectors = 104
instrumentCalibrationEf,instrumentCalibrationA4,_ = df.instrumentCalibrations[calibrationIndex]
instrumentCalibrationEf.shape = (-1,4)
instrumentCalibrationA4.shape = (-1)
analyzerValue = self.ui.Raw1D_Analyzer_spinBox.value()
detectorValue = self.ui.Raw1D_Detector_spinBox.value() #
idxDetector,_ = df.calcualteDataIndexFromDasel(detectorSelection=detectorValue,analyzerSelection=analyzerValue)
Ef = instrumentCalibrationEf[idxDetector,1]
A4 = instrumentCalibrationA4[idxDetector]
EfEntry = '{:.2f}'.format(Ef).rjust(9,' ')
A4Entry = '{:+.2f}'.format(A4).rjust(9,' ')
self.ui.Raw1D_Analyzer_label.setText('Analyzer number (Ef = {} meV)'.format(EfEntry))
self.ui.Raw1D_Detector_label.setText('Detector number (A4 = {} deg)'.format(A4Entry))
def raw1DCutAnalyzerSpinBoxChanged(self):
value = self.ui.Raw1D_Analyzer_spinBox.value()
dfs = self.DataFileModel.getCurrentDatafiles()
if dfs is None:
return None
if len(dfs)>1:
return None
df = dfs[0]
df.analyzerSelection = np.array(value)
self.updateRaw1DCutLabels(dfs=dfs)
def raw1DCutDetectorSpinBoxChanged(self):
value = self.ui.Raw1D_Detector_spinBox.value()
dfs = self.DataFileModel.getCurrentDatafiles()
if dfs is None:
return None
if len(dfs)>1:
return None
df = dfs[0]
df.detectorSelection = np.array(value)
self.updateRaw1DCutLabels(dfs=dfs)
@ProgressBarDecoratorArguments(runningText='Plotting raw 1D Data',completedText='Plotting Done')
def Raw1D_plot_button_function(self):
if not self.stateMachine.requireStateByName('Raw'):
return False
dataFiles = self.DataFileModel.getCurrentDatafiles()
if dataFiles is None:
ds = self.DataSetModel.getCurrentDataSet()
elif len(dataFiles) == 0: # No data files selected, use them all
ds = self.DataSetModel.getCurrentDataSet()
else:
ds = GuiDataSet(dataFiles)
if ds is None:
return False
ax = ds.plotRaw1D()
self.windows.append(ax.get_figure())
return True
Raw1DManagerBase, Raw1DManagerForm = loadUI('Raw1D.ui')
class Raw1DManager(Raw1DManagerBase, Raw1DManagerForm):
def __init__(self, parent=None, guiWindow=None):
super(Raw1DManager, self).__init__(parent)
self.setupUi(self)
self.guiWindow = guiWindow
self.initRaw1DManager()
def initRaw1DManager(self):
self.guiWindow.setupRaw1DCutSpinBoxes = lambda: setupRaw1DCutSpinBoxes(self.guiWindow)
self.guiWindow.resetRaw1DCutSpinBoxes = lambda: resetRaw1DCutSpinBoxes(self.guiWindow)
self.guiWindow.updateRaw1DCutSpinBoxes = lambda dfs=None: updateRaw1DCutSpinBoxes(self.guiWindow, dfs)
self.guiWindow.updateRaw1DCutLabels = lambda dfs=None: updateRaw1DCutLabels(self.guiWindow,dfs)
self.guiWindow.raw1DCutAnalyzerSpinBoxChanged = lambda: raw1DCutAnalyzerSpinBoxChanged(self.guiWindow)
self.guiWindow.raw1DCutDetectorSpinBoxChanged = lambda: raw1DCutDetectorSpinBoxChanged(self.guiWindow)
self.guiWindow.Raw1D_plot_button_function = lambda: Raw1D_plot_button_function(self.guiWindow)
for key,value in self.__dict__.items():
if 'Raw1D' in key:
self.guiWindow.ui.__dict__[key] = value
def setup(self):
self.guiWindow.ui.Raw1D_plot_button.clicked.connect(self.guiWindow.Raw1D_plot_button_function) | PypiClean |
/BioSAK-1.72.0.tar.gz/BioSAK-1.72.0/My_Python_scripts/comparative_genomics/COG_number_count.py |
import sys
__author__ = 'weizhisong'
usage = """
####################################################################
Usage:
python COG_number_count.py annotation_results.txt output.txt
COG_annotation_results format:
G_00005 yebN S COG1971 Predicted membrane protein
G_00009 PA1596 O COG0326 Molecular chaperone, HSP90 family
...
Author: Weizhi Song
####################################################################
"""
if len(sys.argv) != 3:
print (usage)
exit(1)
# Generate COG list for all genes:
input_file = sys.argv[1]
output_file = sys.argv[2]
annotation_results = open(input_file)
out = open(output_file, 'w')
all_cogs = []
for each_gene in annotation_results:
cog_category = each_gene.strip().split('\t')[2]
all_cogs.append(cog_category)
all_cogs_temp = []
for item in all_cogs:
if len(item) == 1:
all_cogs_temp.append(item)
else:
list(item)
all_cogs_temp += list(item)
Count_A = 'A' + '\t' + str(all_cogs_temp.count('A')) + '\n'
Count_B = 'B' + '\t' + str(all_cogs_temp.count('B')) + '\n'
Count_C = 'C' + '\t' + str(all_cogs_temp.count('C')) + '\n'
Count_D = 'D' + '\t' + str(all_cogs_temp.count('D')) + '\n'
Count_E = 'E' + '\t' + str(all_cogs_temp.count('E')) + '\n'
Count_F = 'F' + '\t' + str(all_cogs_temp.count('F')) + '\n'
Count_G = 'G' + '\t' + str(all_cogs_temp.count('G')) + '\n'
Count_H = 'H' + '\t' + str(all_cogs_temp.count('H')) + '\n'
Count_I = 'I' + '\t' + str(all_cogs_temp.count('I')) + '\n'
Count_J = 'J' + '\t' + str(all_cogs_temp.count('J')) + '\n'
Count_K = 'K' + '\t' + str(all_cogs_temp.count('K')) + '\n'
Count_L = 'L' + '\t' + str(all_cogs_temp.count('L')) + '\n'
Count_M = 'M' + '\t' + str(all_cogs_temp.count('M')) + '\n'
Count_N = 'N' + '\t' + str(all_cogs_temp.count('N')) + '\n'
Count_O = 'O' + '\t' + str(all_cogs_temp.count('O')) + '\n'
Count_P = 'P' + '\t' + str(all_cogs_temp.count('P')) + '\n'
Count_Q = 'Q' + '\t' + str(all_cogs_temp.count('Q')) + '\n'
Count_R = 'R' + '\t' + str(all_cogs_temp.count('R')) + '\n'
Count_S = 'S' + '\t' + str(all_cogs_temp.count('S')) + '\n'
Count_T = 'T' + '\t' + str(all_cogs_temp.count('T')) + '\n'
Count_U = 'U' + '\t' + str(all_cogs_temp.count('U')) + '\n'
Count_V = 'V' + '\t' + str(all_cogs_temp.count('V')) + '\n'
Count_W = 'W' + '\t' + str(all_cogs_temp.count('W')) + '\n'
Count_Y = 'Y' + '\t' + str(all_cogs_temp.count('Y')) + '\n'
Count_Z = 'Z' + '\t' + str(all_cogs_temp.count('Z')) + '\n'
in_sum = Count_A + Count_B + Count_C + Count_D + Count_E + Count_F + Count_G \
+ Count_H + Count_I + Count_J + Count_K + Count_L + Count_M + Count_N \
+ Count_O + Count_P + Count_Q + Count_R + Count_S + Count_T + Count_U \
+ Count_V + Count_W + Count_Y + Count_Z
print(in_sum)
out.write(in_sum)
out.close() | PypiClean |
/HEBO-0.3.2-py3-none-any.whl/hebo/design_space/design_space.py |
# This program is free software; you can redistribute it and/or modify it under
# the terms of the MIT license.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the MIT License for more details.
import pandas as pd
import torch
from torch import Tensor
from .numeric_param import NumericPara
from .integer_param import IntegerPara
from .pow_param import PowPara
from .categorical_param import CategoricalPara
from .bool_param import BoolPara
from .pow_integer_param import PowIntegerPara
from .int_exponent_param import IntExponentPara
from .step_int import StepIntPara
class DesignSpace:
def __init__(self):
self.para_types = {}
self.register_para_type('num', NumericPara)
self.register_para_type('pow', PowPara)
self.register_para_type('pow_int', PowIntegerPara)
self.register_para_type('int_exponent', IntExponentPara)
self.register_para_type('int', IntegerPara)
self.register_para_type('step_int', StepIntPara)
self.register_para_type('cat', CategoricalPara)
self.register_para_type('bool', BoolPara)
self.paras = {}
self.para_names = []
self.numeric_names = []
self.enum_names = []
@property
def num_paras(self):
return len(self.para_names)
@property
def num_numeric(self):
return len(self.numeric_names)
@property
def num_categorical(self):
return len(self.enum_names)
def parse(self, rec):
self.para_config = rec
self.paras = {}
self.para_names = []
for item in rec:
assert(item['type'] in self.para_types)
param = self.para_types[item['type']](item)
self.paras[param.name] = param
if param.is_categorical:
self.enum_names.append(param.name)
else:
self.numeric_names.append(param.name)
self.para_names = self.numeric_names + self.enum_names
return self
def register_para_type(self, type_name, para_class):
"""
User can define their specific parameter type and register the new type
using this function
"""
self.para_types[type_name] = para_class
def sample(self, num_samples = 1):
"""
df_suggest: suggested initial points
"""
df = pd.DataFrame(columns = self.para_names)
for c in df.columns:
df[c] = self.paras[c].sample(num_samples)
return df
def transform(self, data : pd.DataFrame) -> (Tensor, Tensor):
"""
input: pandas dataframe
output: xc and xe
transform data to be within [opt_lb, opt_ub]
"""
xc = data[self.numeric_names].values.astype(float).copy()
xe = data[self.enum_names].values.copy()
for i, name in enumerate(self.numeric_names):
xc[:, i] = self.paras[name].transform(xc[:, i])
for i, name in enumerate(self.enum_names):
xe[:, i] = self.paras[name].transform(xe[:, i])
return torch.FloatTensor(xc), torch.LongTensor(xe.astype(int))
def inverse_transform(self, x : Tensor, xe : Tensor) -> pd.DataFrame:
"""
input: x and xe
output: pandas dataframe
"""
with torch.no_grad():
inv_dict = {}
for i, name in enumerate(self.numeric_names):
inv_dict[name] = self.paras[name].inverse_transform(x.detach().double().numpy()[:, i])
for i, name in enumerate(self.enum_names):
inv_dict[name] = self.paras[name].inverse_transform(xe.detach().numpy()[:, i])
return pd.DataFrame(inv_dict)
@property
def opt_lb(self):
lb_numeric = [self.paras[p].opt_lb for p in self.numeric_names]
lb_enum = [self.paras[p].opt_lb for p in self.enum_names]
return torch.tensor(lb_numeric + lb_enum)
@property
def opt_ub(self):
ub_numeric = [self.paras[p].opt_ub for p in self.numeric_names]
ub_enum = [self.paras[p].opt_ub for p in self.enum_names]
return torch.tensor(ub_numeric + ub_enum)
def __eq__(self, other):
return self.__dict__ == other.__dict__ | PypiClean |
/DiscordPyExt-0.0.3-py3-none-any.whl/discordPyExt/components/ctxx.py | import discord
from discord.ext import commands
import typing
class Ctxl:
"""
class contains extended static methods for discord.ext.commands.Context
"""
@staticmethod
def has_role(ctx : discord.Interaction, bot : commands.Bot = None, *query_roles : typing.Union[discord.Role, int, str]) -> bool:
"""
check if the user has any role in guild
"""
uids = [role.id for role in ctx.user.roles]
for role in ctx.user.roles:
if isinstance(role, discord.Role) and role not in query_roles:
return False
if isinstance(role, int) and role not in uids:
return False
if isinstance(role, str) and int(role) not in uids:
return False
return True
@staticmethod
def has_any_role(ctx : discord.Interaction, bot : commands.Bot = None, *query_roles : typing.Union[discord.Role, int, str]) -> bool:
"""
check if the user has any role in guild
"""
uids = [role.id for role in ctx.user.roles]
for role in ctx.user.roles:
if isinstance(role, discord.Role) and role in query_roles:
return True
if isinstance(role, int) and role in uids:
return True
if isinstance(role, str) and int(role) in uids:
return True
return False
@staticmethod
def user_has_any_role(
user : discord.User,
*query_roles : typing.Union[discord.Role, int, str]
) -> bool:
"""
check if the user has any role in guild
"""
if not isinstance(user, discord.User):
raise TypeError("user must be discord.User")
uids = [role.id for role in user.roles]
for role in user.roles:
if isinstance(role, discord.Role) and role in query_roles:
return True
if isinstance(role, int) and role in uids:
return True
if isinstance(role, str) and int(role) in uids:
return True
return False
@staticmethod
def user_has_role(
user : discord.User,
*query_roles : typing.Union[discord.Role, int, str]
) -> bool:
"""
check if the user has any role in guild
"""
if not isinstance(user, discord.User):
raise TypeError("user must be discord.User")
uids = [role.id for role in user.roles]
for role in user.roles:
if isinstance(role, discord.Role) and role not in query_roles:
return False
if isinstance(role, int) and role not in uids:
return False
if isinstance(role, str) and int(role) not in uids:
return False
return True
@staticmethod
def has_permission(ctx : discord.Interaction, bot : commands.Bot = None, **kwargs) -> bool:
"""
check if the user has permission in guild
"""
return all(getattr(ctx.user.guild_permissions, name, None) == value for name, value in kwargs.items())
@staticmethod
def has_any_permission(ctx : discord.Interaction, bot : commands.Bot = None, **kwargs) -> bool:
"""
check if the user has any permission in guild
"""
return any(getattr(ctx.user.guild_permissions, name, None) == value for name, value in kwargs.items())
@staticmethod
def user_has_any_permission(
user : discord.Member,
**kwargs
) -> bool:
"""
check if the user has any permission in guild
"""
if not isinstance(user, discord.User):
raise TypeError("user must be discord.User")
return any(getattr(user.guild_permissions, name, None) == value for name, value in kwargs.items())
@staticmethod
def user_has_permission(
user : discord.Member,
**kwargs
) -> bool:
"""
check if the user has permission in guild
"""
if not isinstance(user, discord.User):
raise TypeError("user must be discord.User")
return all(getattr(user.guild_permissions, name, None) == value for name, value in kwargs.items())
class Ctxx:
"""
A wrapper for discord.Interaction
"""
def __init__(self, ctx:discord.Interaction) -> None:
self._ctx = ctx
def has_role(self, *query_roles : typing.Union[discord.Role, int, str]) -> bool:
"""
check if the user has any role in guild
"""
return Ctxl.has_role(self._ctx, *query_roles)
def has_any_role(self, *query_roles : typing.Union[discord.Role, int, str]) -> bool:
"""
check if the user has any role in guild
"""
return Ctxl.has_any_role(self._ctx, *query_roles)
def has_permission(self, **kwargs) -> bool:
"""
check if the user has permission in guild
"""
return Ctxl.has_permission(self._ctx, **kwargs)
def has_any_permission(self, **kwargs) -> bool:
"""
check if the user has any permission in guild
"""
return Ctxl.has_any_permission(self._ctx, **kwargs) | PypiClean |
/EnergyCapSdk-8.2304.4743.tar.gz/EnergyCapSdk-8.2304.4743/energycap/sdk/models/required_address_child_py3.py |
from msrest.serialization import Model
class RequiredAddressChild(Model):
"""RequiredAddressChild.
All required parameters must be populated in order to send to Azure.
:param country: Required. The address country <span
class='property-internal'>Required</span> <span
class='property-internal'>Must be between 0 and 64 characters</span>
:type country: str
:param postal_code: The postal code for the address
Required when the country is United States or Canada <span
class='property-internal'>Must be between 0 and 32 characters</span>
:type postal_code: str
:param address_type_id: The address type identifier
:type address_type_id: int
:param line1: The line 1 of the address <span
class='property-internal'>Must be between 0 and 100 characters</span>
:type line1: str
:param line2: The line 2 of the address <span
class='property-internal'>Must be between 0 and 100 characters</span>
:type line2: str
:param line3: The line 3 of the address <span
class='property-internal'>Must be between 0 and 100 characters</span>.
Default value: "Æ" .
:type line3: str
:param city: The city of the place <span class='property-internal'>Must be
between 0 and 100 characters</span>
:type city: str
:param state: The state of the place <span class='property-internal'>Must
be between 0 and 100 characters</span>
:type state: str
:param latitude: The latitude of the place
Required when the country is not United States or Canada <span
class='property-internal'>Must be between -90 and 90</span>
:type latitude: float
:param longitude: The longitude of the place
Required when the country is not United States or Canada <span
class='property-internal'>Must be between -180 and 180</span>
:type longitude: float
"""
_validation = {
'country': {'required': True, 'max_length': 64, 'min_length': 0},
'postal_code': {'max_length': 32, 'min_length': 0},
'line1': {'max_length': 100, 'min_length': 0},
'line2': {'max_length': 100, 'min_length': 0},
'line3': {'max_length': 100, 'min_length': 0},
'city': {'max_length': 100, 'min_length': 0},
'state': {'max_length': 100, 'min_length': 0},
'latitude': {'maximum': 90, 'minimum': -90},
'longitude': {'maximum': 180, 'minimum': -180},
}
_attribute_map = {
'country': {'key': 'country', 'type': 'str'},
'postal_code': {'key': 'postalCode', 'type': 'str'},
'address_type_id': {'key': 'addressTypeId', 'type': 'int'},
'line1': {'key': 'line1', 'type': 'str'},
'line2': {'key': 'line2', 'type': 'str'},
'line3': {'key': 'line3', 'type': 'str'},
'city': {'key': 'city', 'type': 'str'},
'state': {'key': 'state', 'type': 'str'},
'latitude': {'key': 'latitude', 'type': 'float'},
'longitude': {'key': 'longitude', 'type': 'float'},
}
def __init__(self, *, country: str, postal_code: str=None, address_type_id: int=None, line1: str=None, line2: str=None, line3: str="Æ", city: str=None, state: str=None, latitude: float=None, longitude: float=None, **kwargs) -> None:
super(RequiredAddressChild, self).__init__(**kwargs)
self.country = country
self.postal_code = postal_code
self.address_type_id = address_type_id
self.line1 = line1
self.line2 = line2
self.line3 = line3
self.city = city
self.state = state
self.latitude = latitude
self.longitude = longitude | PypiClean |
/AGouTI-1.0.3.tar.gz/AGouTI-1.0.3/agouti_pkg/processing_product.py | from agouti_pkg.miscallaneous import *
class ProcessingProduct(object):
"""Feature from the BED file. One line in BED corresponds\
to one ProcessingProduct"""
def __init__(self, coordinates, processing_product, score, strand,
bed_line, first_base_num, coords_outside_transcript="No"):
self.coordinates = coordinates
if (first_base_num == 0):
one_based_coordinates = (coordinates[0], coordinates[1]+1,
coordinates[2])
self.coordinates = one_based_coordinates
self.processing_product = processing_product
self.score = score
self.strand = strand
self.bed_line = bed_line
self.coords_outside_transcript = coords_outside_transcript
def convert_coordinates(self):
"""Converts coordinates into UCSC-like format
Returns:
str -- coordinates in UCSC-like format
"""
coord_formated = "{chr}:{start}-{end}".format(
chr=self.coordinates[0],
start=self.coordinates[1],
end=self.coordinates[2])
return coord_formated
def genomic_to_transcriptomic(self, database, featuretypes_from_db):
"""Converts genomic coordinates to transcriptomic coordinates of given\
mRNA. Require two args. One of them is a sqlite3 database created \
by gffutils, second is a generator object that yields featuretypes\
from database (as strings)
Arguments:
database {modules.database.Database} -- Database object
featuretypes_from_db {list} -- feature types in the database
Returns:
tuple -- first element is tuple of (5' utr len, cds len ,\
3' utr len), second is start coordinate of CDS within\
transcript
"""
transcript_id = self.coordinates[0]
mRNA = database.database[transcript_id]
three_prime_UTRs = get_three_prime_UTRs(mRNA,
list(featuretypes_from_db),
database)
five_prime_UTRs = get_five_prime_UTRs(mRNA, list(featuretypes_from_db),
database)
exons = get_exons(mRNA, database)
cds = get_cds(mRNA, list(featuretypes_from_db), database)
five_prime_utr_len = sum_features_length(five_prime_UTRs)
three_prime_utr_len = sum_features_length(three_prime_UTRs)
transcript_length = sum_features_length(exons)
cds_len = transcript_length - five_prime_utr_len - three_prime_utr_len
if len(cds) == 0:
cds_start = None
else:
cds_start = min(cds, key=attrgetter('start')).start
if (sum([cds_len, three_prime_utr_len, five_prime_utr_len]) !=
transcript_length and len(cds)):
left_side_of_cds_len = 0
for exon in exons:
if (cds_start >= exon.end):
left_side_of_cds_len += exon.end - exon.start
elif (cds_start < exon.end and cds_start > exon.start):
left_side_of_cds_len += cds_start - exon.start
five_prime_utr_len = left_side_of_cds_len
three_prime_utr_len = (transcript_length - five_prime_utr_len
- cds_len)
lengths_tuple = (five_prime_utr_len, cds_len,
three_prime_utr_len)
if lengths_tuple == (0, 0, 0):
lengths_tuple = (0, transcript_length, 0)
return lengths_tuple, cds_start
def check_overlapping_feature__position(self, lengths_dict, feature,
transcriptomic, offset=0):
"""Checks in which part of overlapping feature lies ProcessingProduct\
object
Arguments:
feature {Feature} -- overlapping feature
transcriptomic {bool} -- transcriptomic option (see help)
Keyword Arguments:
offset {int} -- offset option (see help) (default: {0})
Returns:
str -- localization within overlapping feature
"""
start, end = min(self.coordinates[1], self.coordinates[2]), max(self.coordinates[1], self.coordinates[2])
if (transcriptomic):
feature_length = sum(lengths_dict[feature.id])
else:
feature_length = max(feature.stop, feature.start) - min(feature.stop, feature.start)
pp_length = self.coordinates[2] - self.coordinates[1]
if (offset):
feature_start = offset
else:
feature_start = 0
if start > feature_length and end > feature_length:
loc = "downstream"
elif self.coords_outside_transcript == "both":
loc = "upstream"
elif (start < feature_start + (0.25 * feature_length)
and end < feature_start + (0.5 * feature_length)):
loc = "5 prime"
elif (start > feature_start + (0.25 * feature_length) and
end < feature_start + (0.75 * feature_length)):
loc = "middle"
elif (start > feature_start + (0.5 * feature_length) and
end > feature_start + (0.75 * feature_length)):
loc = "3 prime"
elif (start < feature_start + (0.25 * feature_length) and
end > feature_start + (0.75 * feature_length) and
pp_length < 0.9 * feature_length):
loc = "whole"
elif (start < feature_start + (0.25 * feature_length) and
end > feature_start + (0.75 * feature_length) and
pp_length >= 0.9 * feature_length):
loc = "full"
else:
loc = "other"
return loc | PypiClean |
/ESMValCore-2.9.0rc1.tar.gz/ESMValCore-2.9.0rc1/.github/pull_request_template.md | <!--
Thank you for contributing to our project!
Please do not delete this text completely, but read the text below and keep
items that seem relevant. If in doubt, just keep everything and add your
own text at the top, a reviewer will update the checklist for you.
-->
## Description
<!--
Please describe your changes here, especially focusing on why this pull
request makes ESMValCore better and what problem it solves.
Before you start, please read our contribution guidelines: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html
Please fill in the GitHub issue that is closed by this pull request,
e.g. Closes #1903
-->
Closes #issue_number
Link to documentation:
***
## [Before you get started](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#getting-started)
- [ ] [☝ Create an issue](https://github.com/ESMValGroup/ESMValCore/issues) to discuss what you are going to do
## [Checklist](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#checklist-for-pull-requests)
It is the responsibility of the author to make sure the pull request is ready to review. The icons indicate whether the item will be subject to the [🛠 Technical][1] or [🧪 Scientific][2] review.
<!-- The next two lines turn the 🛠 and 🧪 below into hyperlinks -->
[1]: https://docs.esmvaltool.org/en/latest/community/review.html#technical-review
[2]: https://docs.esmvaltool.org/en/latest/community/review.html#scientific-review
- [ ] [🧪][2] The new functionality is [relevant and scientifically sound](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#scientific-relevance)
- [ ] [🛠][1] This pull request has a [descriptive title and labels](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#pull-request-title-and-label)
- [ ] [🛠][1] Code is written according to the [code quality guidelines](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#code-quality)
- [ ] [🧪][2] and [🛠][1] [Documentation](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#documentation) is available
- [ ] [🛠][1] [Unit tests](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#tests) have been added
- [ ] [🛠][1] Changes are [backward compatible](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#backward-compatibility)
- [ ] [🛠][1] Any changed [dependencies have been added or removed](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#dependencies) correctly
- [ ] [🛠][1] The [list of authors](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#list-of-authors) is up to date
- [ ] [🛠][1] All [checks below this pull request](https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html#pull-request-checks) were successful
***
To help with the number pull requests:
- 🙏 We kindly ask you to [review](https://docs.esmvaltool.org/en/latest/community/review.html#review-of-pull-requests) two other [open pull requests](https://github.com/ESMValGroup/ESMValCore/pulls) in this repository
| PypiClean |
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/Nevow-0.14.5.tar.gz/Nevow-0.14.5/nevow/appserver.py | import cgi
import warnings
from collections import MutableMapping
from urllib import unquote
from zope.interface import implements, classImplements
import twisted.python.components as tpc
from twisted.web import server
try:
from twisted.web import http
except ImportError:
from twisted.protocols import http
from twisted.python import log
from twisted.internet import defer
from nevow import context
from nevow import inevow
from nevow import url
from nevow import flat
from nevow import stan
class _DictHeaders(MutableMapping):
"""
A C{dict}-like wrapper around L{Headers} to provide backwards compatibility
for L{twisted.web.http.Request.received_headers} and
L{twisted.web.http.Request.headers} which used to be plain C{dict}
instances.
@type _headers: L{Headers}
@ivar _headers: The real header storage object.
"""
def __init__(self, headers):
self._headers = headers
def __getitem__(self, key):
"""
Return the last value for header of C{key}.
"""
if self._headers.hasHeader(key):
return self._headers.getRawHeaders(key)[-1]
raise KeyError(key)
def __setitem__(self, key, value):
"""
Set the given header.
"""
self._headers.setRawHeaders(key, [value])
def __delitem__(self, key):
"""
Delete the given header.
"""
if self._headers.hasHeader(key):
self._headers.removeHeader(key)
else:
raise KeyError(key)
def __iter__(self):
"""
Return an iterator of the lowercase name of each header present.
"""
for k, v in self._headers.getAllRawHeaders():
yield k.lower()
def __len__(self):
"""
Return the number of distinct headers present.
"""
# XXX Too many _
return len(self._headers._rawHeaders)
# Extra methods that MutableMapping doesn't care about but that we do.
def copy(self):
"""
Return a C{dict} mapping each header name to the last corresponding
header value.
"""
return dict(self.items())
def has_key(self, key):
"""
Return C{True} if C{key} is a header in this collection, C{False}
otherwise.
"""
return key in self
class UninformativeExceptionHandler:
implements(inevow.ICanHandleException)
def renderHTTP_exception(self, ctx, reason):
request = inevow.IRequest(ctx)
log.err(reason)
request.write("<html><head><title>Internal Server Error</title></head>")
request.write("<body><h1>Internal Server Error</h1>An error occurred rendering the requested page. To see a more detailed error message, enable tracebacks in the configuration.</body></html>")
request.finishRequest( False )
def renderInlineException(self, context, reason):
log.err(reason)
return """<div style="border: 1px dashed red; color: red; clear: both">[[ERROR]]</div>"""
class DefaultExceptionHandler:
implements(inevow.ICanHandleException)
def renderHTTP_exception(self, ctx, reason):
log.err(reason)
request = inevow.IRequest(ctx)
request.setResponseCode(http.INTERNAL_SERVER_ERROR)
request.write("<html><head><title>Exception</title></head><body>")
from nevow import failure
result = failure.formatFailure(reason)
request.write(''.join(flat.flatten(result)))
request.write("</body></html>")
request.finishRequest( False )
def renderInlineException(self, context, reason):
from nevow import failure
formatted = failure.formatFailure(reason)
desc = str(reason)
return flat.serialize([
stan.xml("""<div style="border: 1px dashed red; color: red; clear: both" onclick="this.childNodes[1].style.display = this.childNodes[1].style.display == 'none' ? 'block': 'none'">"""),
desc,
stan.xml('<div style="display: none">'),
formatted,
stan.xml('</div></div>')
], context)
errorMarker = object()
def processingFailed(reason, request, ctx):
try:
handler = inevow.ICanHandleException(ctx)
handler.renderHTTP_exception(ctx, reason)
except:
request.setResponseCode(http.INTERNAL_SERVER_ERROR)
log.msg("Exception rendering error page:", isErr=1)
log.err()
log.err("Original exception:", isErr=1)
log.err(reason)
request.write("<html><head><title>Internal Server Error</title></head>")
request.write("<body><h1>Internal Server Error</h1>An error occurred rendering the requested page. Additionally, an error occurred rendering the error page.</body></html>")
request.finishRequest( False )
return errorMarker
def defaultExceptionHandlerFactory(ctx):
return DefaultExceptionHandler()
class NevowRequest(tpc.Componentized, server.Request):
"""
A Request subclass which does additional
processing if a form was POSTed. When a form is POSTed,
we create a cgi.FieldStorage instance using the data posted,
and set it as the request.fields attribute. This way, we can
get at information about filenames and mime-types of
files that were posted.
TODO: cgi.FieldStorage blocks while decoding the MIME.
Rewrite it to do the work in chunks, yielding from time to
time.
@ivar fields: C{None} or, if the HTTP method is B{POST}, a
L{cgi.FieldStorage} instance giving the content of the POST.
@ivar _lostConnection: A flag which keeps track of whether the response to
this request has been interrupted (for example, by the connection being
lost) or not. C{False} until this happens, C{True} afterwards.
@type _lostConnection: L{bool}
"""
implements(inevow.IRequest)
fields = None
_lostConnection = False
def __init__(self, *args, **kw):
server.Request.__init__(self, *args, **kw)
tpc.Componentized.__init__(self)
self.notifyFinish().addErrback(self._flagLostConnection)
def _flagLostConnection(self, error):
"""
Observe and record an error trying to deliver the response for this
request.
"""
self._lostConnection = True
def process(self):
# extra request parsing
if self.method == 'POST':
t = self.content.tell()
self.content.seek(0)
self.fields = cgi.FieldStorage(
self.content, _DictHeaders(self.requestHeaders),
environ={'REQUEST_METHOD': 'POST'})
self.content.seek(t)
# get site from channel
self.site = self.channel.site
# set various default headers
self.setHeader('server', server.version)
self.setHeader('date', server.http.datetimeToString())
self.setHeader('content-type', "text/html; charset=UTF-8")
# Resource Identification
self.prepath = []
self.postpath = map(unquote, self.path[1:].split('/'))
self.sitepath = []
self.deferred = defer.Deferred()
requestContext = context.RequestContext(parent=self.site.context, tag=self)
requestContext.remember( (), inevow.ICurrentSegments)
requestContext.remember(tuple(self.postpath), inevow.IRemainingSegments)
return self.site.getPageContextForRequestContext(
requestContext
).addErrback(
processingFailed, self, requestContext
).addCallback(
self.gotPageContext
)
def gotPageContext(self, pageContext):
if pageContext is not errorMarker:
return defer.maybeDeferred(
pageContext.tag.renderHTTP, pageContext
).addBoth(
self._cbSetLogger, pageContext
).addErrback(
processingFailed, self, pageContext
).addCallback(
self._cbFinishRender, pageContext
)
def finish(self):
self.deferred.callback("")
def finishRequest(self, success):
"""
Indicate the response to this request has been completely generated
(headers have been set, the response body has been completely written).
@param success: Indicate whether this response is considered successful
or not. Not used.
"""
if not self._lostConnection:
# Only bother doing the work associated with finishing if the
# connection is still there.
server.Request.finish(self)
def _cbFinishRender(self, html, ctx):
"""
Callback for the page rendering process having completed.
@param html: Either the content of the response body (L{bytes}) or a
marker that an exception occurred and has already been handled or
an object adaptable to L{IResource} to use to render the response.
"""
if self._lostConnection:
# No response can be sent at this point.
pass
elif isinstance(html, str):
self.write(html)
self.finishRequest( True )
elif html is errorMarker:
## Error webpage has already been rendered and finish called
pass
else:
res = inevow.IResource(html)
pageContext = context.PageContext(tag=res, parent=ctx)
return self.gotPageContext(pageContext)
return html
_logger = None
def _cbSetLogger(self, result, ctx):
try:
logger = ctx.locate(inevow.ILogger)
except KeyError:
pass
else:
self._logger = lambda : logger.log(ctx)
return result
session = None
def getSession(self, sessionInterface=None):
if self.session is not None:
self.session.touch()
if sessionInterface:
return sessionInterface(self.session)
return self.session
## temporary until things settle down with the new sessions
return server.Request.getSession(self, sessionInterface)
def URLPath(self):
return url.URL.fromContext(self)
def rememberRootURL(self, url=None):
"""
Remember the currently-processed part of the URL for later
recalling.
"""
if url is None:
return server.Request.rememberRootURL(self)
else:
self.appRootURL = url
def _warnHeaders(self, old, new):
"""
Emit a warning related to use of one of the deprecated C{headers} or
C{received_headers} attributes.
@param old: The name of the deprecated attribute to which the warning
pertains.
@param new: The name of the preferred attribute which replaces the old
attribute.
"""
warnings.warn(
category=DeprecationWarning,
message=(
"nevow.appserver.NevowRequest.%(old)s was deprecated in "
"Nevow 0.13.0: Please use nevow.appserver.NevowRequest."
"%(new)s instead." % dict(old=old, new=new)),
stacklevel=3)
@property
def headers(self):
"""
Transform the L{Headers}-style C{responseHeaders} attribute into a
deprecated C{dict}-style C{headers} attribute.
"""
self._warnHeaders("headers", "responseHeaders")
return _DictHeaders(self.responseHeaders)
@property
def received_headers(self):
"""
Transform the L{Headers}-style C{requestHeaders} attribute into a
deprecated C{dict}-style C{received_headers} attribute.
"""
self._warnHeaders("received_headers", "requestHeaders")
return _DictHeaders(self.requestHeaders)
def sessionFactory(ctx):
"""Given a RequestContext instance with a Request as .tag, return a session
"""
return ctx.tag.getSession()
requestFactory = lambda ctx: ctx.tag
class NevowSite(server.Site):
requestFactory = NevowRequest
def __init__(self, resource, *args, **kwargs):
resource.addSlash = True
server.Site.__init__(self, resource, *args, **kwargs)
self.context = context.SiteContext()
def remember(self, obj, inter=None):
"""Remember the given object for the given interfaces (or all interfaces
obj implements) in the site's context.
The site context is the parent of all other contexts. Anything
remembered here will be available throughout the site.
"""
self.context.remember(obj, inter)
def getPageContextForRequestContext(self, ctx):
"""Retrieve a resource from this site for a particular request. The
resource will be wrapped in a PageContext which keeps track
of how the resource was located.
"""
path = inevow.IRemainingSegments(ctx)
res = inevow.IResource(self.resource)
pageContext = context.PageContext(tag=res, parent=ctx)
return defer.maybeDeferred(res.locateChild, pageContext, path).addCallback(
self.handleSegment, ctx.tag, path, pageContext
)
def handleSegment(self, result, request, path, pageContext):
if result is errorMarker:
return errorMarker
newres, newpath = result
# If the child resource is None then display a 404 page
if newres is None:
from nevow.rend import FourOhFour
return context.PageContext(tag=FourOhFour(), parent=pageContext)
# If we got a deferred then we need to call back later, once the
# child is actually available.
if isinstance(newres, defer.Deferred):
return newres.addCallback(
lambda actualRes: self.handleSegment(
(actualRes, newpath), request, path, pageContext))
#
# FIX A GIANT LEAK. Is this code really useful anyway?
#
newres = inevow.IResource(newres)#, persist=True)
if newres is pageContext.tag:
assert not newpath is path, "URL traversal cycle detected when attempting to locateChild %r from resource %r." % (path, pageContext.tag)
assert len(newpath) < len(path), "Infinite loop impending..."
## We found a Resource... update the request.prepath and postpath
for x in xrange(len(path) - len(newpath)):
if request.postpath:
request.prepath.append(request.postpath.pop(0))
## Create a context object to represent this new resource
ctx = context.PageContext(tag=newres, parent=pageContext)
ctx.remember(tuple(request.prepath), inevow.ICurrentSegments)
ctx.remember(tuple(request.postpath), inevow.IRemainingSegments)
res = newres
path = newpath
if not path:
return ctx
return defer.maybeDeferred(
res.locateChild, ctx, path
).addErrback(
processingFailed, request, ctx
).addCallback(
self.handleSegment, request, path, ctx
)
def log(self, request):
if request._logger is None:
server.Site.log(self, request)
else:
request._logger()
## This should be moved somewhere else, it's cluttering up this module.
class OldResourceAdapter(object):
implements(inevow.IResource)
# This is required to properly handle the interaction between
# original.isLeaf and request.postpath, from which PATH_INFO is set in
# twcgi. Because we have no choice but to consume all elements in
# locateChild to terminate the recursion, we do so, but first save the
# length of prepath in real_prepath_len. Subsequently in renderHTTP, if
# real_prepath_len is not None, prepath is correct to the saved length and
# the extra segments moved to postpath. If real_prepath_len is None, then
# locateChild has never been called, so we know not the real length, so we
# do nothing, which is correct.
real_prepath_len = None
def __init__(self, original):
self.original = original
def __repr__(self):
return "<%s @ 0x%x adapting %r>" % (self.__class__.__name__, id(self), self.original)
def locateChild(self, ctx, segments):
request = inevow.IRequest(ctx)
if self.original.isLeaf:
self.real_prepath_len = len(request.prepath)
return self, ()
name = segments[0]
request.prepath.append(request.postpath.pop(0))
res = self.original.getChildWithDefault(name, request)
request.postpath.insert(0, request.prepath.pop())
if isinstance(res, defer.Deferred):
return res.addCallback(lambda res: (res, segments[1:]))
return res, segments[1:]
def _handle_NOT_DONE_YET(self, data, request):
if data == server.NOT_DONE_YET:
return request.deferred
else:
return data
def renderHTTP(self, ctx):
request = inevow.IRequest(ctx)
if self.real_prepath_len is not None:
request.postpath = request.prepath + request.postpath
request.prepath = request.postpath[:self.real_prepath_len]
del request.postpath[:self.real_prepath_len]
result = defer.maybeDeferred(self.original.render, request).addCallback(
self._handle_NOT_DONE_YET, request)
return result
def willHandle_notFound(self, request):
if hasattr(self.original, 'willHandle_notFound'):
return self.original.willHandle_notFound(request)
return False
def renderHTTP_notFound(self, ctx):
return self.original.renderHTTP_notFound(ctx)
from nevow import rend
NotFound = rend.NotFound
FourOhFour = rend.FourOhFour
classImplements(server.Session, inevow.ISession) | PypiClean |
/HorizonJPL-0.1.7.tar.gz/HorizonJPL-0.1.7/README.txt | NASA's JPL HORIOZON Ephemeris System API
===============
This Python API, is an effort towards opening NASA's PDS data sets to the public with a focus on ease of access. Thus creating, NASA's JPL Horizons On-Line Ephemeris System API
Background
-----------------
From http://en.wikipedia.org/wiki/Ephemeris:
For scientific uses, a modern planetary ephemeris comprises software that generates positions of planets and often of
their satellites, asteroids, or comets, at virtually any time desired by the user.
API Documentation
-----------------
https://docs.google.com/document/d/1g9q3ln9LVAATOZ15986HLTCaqcAj_Jd8e_jOGS3YWrE/pub
Resources
-----------------
Planetary Data System: http://pds.nasa.gov/
Jet Propulsion Labs: http://www.jpl.nasa.gov/
HORIZON User Manual: http://ssd.jpl.nasa.gov/?horizons_doc
Contributors
-----------------
Matthew Mihok (@mattmattmatt)
Dexter Jagula (@djagula)
Siddarth Kalra (@SiddarthKalra)
Tiago Moreira (@Kamots)
| PypiClean |
/DEW-ISI-0.0.1.tar.gz/DEW-ISI-0.0.1/NLP/nlp.py | import spacy
import sys
sys.path.append('../')
import globals
from spacy.lang.en.stop_words import STOP_WORDS
from HighLevelBehaviorLanguage.hlb import *
from NLP.nlplib import findEntities, findCondClauses, actionRelation
class nlpHandler():
sentences = []
dict = spacy.load('en')
splitchars = ['!', '.', '\n', '?']
submittedTriggers = []
submittedActors = []
timeindex = 0
def __init__(self):
for s in self.splitText(globals.nlp_help_str):
self.sentences.append(s)
def splitText(self, text):
# Split the text up based on whatever delimeters we picked above.
t = text
for d in self.splitchars:
t = t.replace(d, '|')
return(t.split('|'))
def nlpChanged(self, widget):
current_text = globals.app.getTextArea("NLP Input")
split_text = self.splitText(current_text)
if len(split_text) != len(self.sentences) and len(split_text) > 1:
if len(split_text) > len(self.sentences):
# We have a new sentence.
for sentence in split_text:
if sentence.strip() not in self.sentences and sentence.strip() != "":
print("New to process: %s" % sentence)
tokens = self.dict(sentence)
# Entities.
entities = findEntities(tokens, sub=True, obj=False)
actors = []
for ent in entities:
ent_phrase = str(ent).split()
print(ent_phrase)
ent_phrase = [x for x in ent_phrase if x.lower() not in STOP_WORDS]
ent_str = ' '.join(ent_phrase)
ent_str = ''.join(x for x in ent_str.title() if not x.isspace())
print(ent_str)
if ent_str not in actors:
actors.append(ent_str)
# Generate Behavior Suggestions
suggested_hlb = self.generate_behavior(sentence, tokens, actors=actors)
if suggested_hlb != None:
removetable = str.maketrans('', '', "'@#%")
suggested_hlb = suggested_hlb.translate(removetable)
print(suggested_hlb)
globals.app.setTextArea("behavior", suggested_hlb, callFunction=behaviorentered)
# We don't add actors ourselves. The code that extracts actors from behavior will
# end up doing what we want there.
#for ent_str in actors:
# if ent_str not in self.submittedActors and ent_str not in globals.actors:
# globals.app.setTextArea("actor",ent_str+'\n', callFunction=actorentered)
# self.submittedActors.append(ent_str)
elif len(split_text) < len(self.sentences):
print("Do not handle erasures yet.")
# We have lost a sentence.
self.sentences = [s.strip() for s in split_text]
def generate_behavior(self, sentence, tokens, actors=[]):
# Pull out any conditional clauses.
cond_clauses = findCondClauses(tokens)
# Pull out each of these clauses to simplify the main sentence.
main_sen = sentence
for cond in cond_clauses:
main_sen = main_sen.replace(str(cond[0]), '', 1)
# Get the main action relation
print("Main sentence part: %s" % main_sen)
main_action_relation = actionRelation(main_sen)
# Try and parse the main conditional clause (if we have one)
# We only handle one clause. Others are ignored.
clause_action_relation = ()
clause_includes_time = False
if len(cond_clauses) > 0:
clause_action_relation = actionRelation(cond_clauses[0][0])
clause_action_dep = str(cond_clauses[0][1])
clause_inclues_time = cond_clauses[0][2]
#print(clause_action_relation)
if len(cond_clauses) > 1:
for cond in cond_clauses:
if cond_clauses[cond][2]:
clause_inclues_time = True
print("WARNING: We do not handle multiple conditional clauses in a sentence.")
# Get the main action.
try:
actor, action, object = main_action_relation[0]
except(ValueError, IndexError):
# We didn't have enough values to unpack, skip everything else and return.
print("WARNING: Did not extract any behavior from:\n\t%s"%(sentence))
print(main_action_relation)
return None
for objents in findEntities(self.dict(main_sen),sub=False, obj=True):
try:
print("OBJECT ENT: %s" % str(objents))
except Exception as e:
print(e)
main_hlb, main_trigger = self.hlbify(actor, action, object, actors=findEntities(self.dict(main_sen)), objects=findEntities(self.dict(main_sen),sub=False, obj=True))
#print("MAIN HLB: %s. TRIGGER: %s" % (main_hlb, main_trigger))
try:
#print("Have below for clause action relation:\n\t"),
#print(clause_action_relation)
cond_actor, cond_action, cond_object = clause_action_relation[0]
except(ValueError, IndexError):
stmt = "%s EMIT %s\n" % (main_hlb, main_trigger)
return(stmt)
cond_hlb, cond_trigger = self.hlbify(cond_actor, cond_action, cond_object, actors=findEntities(self.dict(cond_clauses[0][0])), objects=findEntities(self.dict(cond_clauses[0][0]),sub=False, obj=True))
if clause_action_dep == "<<STARTS BEFORE MAIN CLAUSE>>":
if clause_inclues_time:
main_hlb = "WHEN %s WAIT %d %s EMIT %s" % (cond_trigger,self.timeindex,main_hlb,main_trigger)
else:
main_hlb = "WHEN %s %s EMIT %s" % (cond_trigger,main_hlb,main_trigger)
cond_hlb = "%s EMIT %s" %(cond_hlb, cond_trigger)
elif clause_action_dep == "<<STARTS AFTER MAIN CLAUSE>>":
main_hlb = "%s EMIT %s" %(main_hlb, main_trigger)
if clause_inclues_time:
cond_hlb = "WHEN %s WAIT %d %s EMIT %s" % (main_trigger, self.timeindex, cond_hlb, cond_trigger)
else:
cond_hlb = "WHEN %s %s EMIT %s" % (main_trigger, cond_hlb, cond_trigger)
else:
# We want to start these at the same time??
cond_hlb = "WAIT X%d %s EMIT %s" % (self.timeindex, cond_hlb, cond_trigger)
main_hlb = "WAIT X%d %s EMIT %s" % (self.timeindex, main_hlb, main_trigger)
self.timeindex = self.timeindex + 1
return_stmt = ""
if cond_trigger not in self.submittedTriggers:
# We already have submitted the conditional action statement.
self.submittedTriggers.append(cond_trigger)
return_stmt = cond_hlb+'\n'
if main_trigger not in self.submittedTriggers:
self.submittedTriggers.append(main_trigger)
return_stmt = return_stmt + main_hlb+'\n'
if return_stmt != "":
return(return_stmt)
else:
return None
def hlbify(self, actor, action, object, actors=[], objects=[]):
action_type = self.expActionType(action)
shortest_actor_match = -1
shortest_object_match = -1
for act in actors:
a = ' '.join(x for x in str(act).split() if x.lower() not in STOP_WORDS)
a = ''.join(x for x in a.title() if not x.isspace())
# This isn't a good match - we need to figure out the modifiers from the text.
if actor.lower() in a.lower():
if shortest_actor_match < 0:
shortest_actor_match = len(a)
actor = a
elif shortest_actor_match <= len(a):
shortest_actor_match = len(a)
actor = a
for act in objects:
a = ' '.join(x for x in str(act).split() if x.lower() not in STOP_WORDS)
a = ''.join(x for x in a.title() if not x.isspace())
if object.lower() in a.lower():
if shortest_object_match < 0:
shortest_object_match = len(a)
object = a
elif shortest_object_match <= len(a):
shortest_object_match = len(a)
object = a
if object not in ["<<ITSELF>>"]:
script = action_type + object.title()
else:
script = action_type + actor.title()
return("%s %s" % (actor, script), "%sSig" %(script))
def baseForm(self, word):
tokens = self.dict(word)
for t in tokens:
return str(t.lemma_).lower().strip()
def expActionType(self, word):
base = self.baseForm(word)
if base in ['start', 'begin', 'boot', 'commence', 'deploy', 'kick', 'trigger', 'launch', 'run']:
return 'start'
if base in ['end', 'stop', 'finish', 'quit', 'die', 'close', 'exit', 'halt', 'destroy']:
return 'end'
if base in ['if', 'be', 'will', 'can', 'have']:
return 'check'
return base | PypiClean |
/GC-Flask-Blogging-1.1.2.tar.gz/GC-Flask-Blogging-1.1.2/flask_blogging/engine.py | try:
from builtins import object
except ImportError:
pass
from .processor import PostProcessor
from flask_principal import Principal, Permission, RoleNeed
from .signals import engine_initialised, post_processed, blueprint_created
from flask_fileupload import FlaskFileUpload
class BloggingEngine(object):
"""
The BloggingEngine is the class for initializing the blog support for your
web app. Here is an example usage:
.. code:: python
from flask import Flask
from flask_blogging import BloggingEngine, SQLAStorage
from sqlalchemy import create_engine
app = Flask(__name__)
db_engine = create_engine("sqlite:////tmp/sqlite.db")
meta = MetaData()
storage = SQLAStorage(db_engine, metadata=meta)
blog_engine = BloggingEngine(app, storage)
"""
def __init__(self, app=None, storage=None, post_processor=None,
extensions=None, cache=None, file_upload=None):
"""
:param app: Optional app to use
:type app: object
:param storage: The blog storage instance that implements the
``Storage`` class interface.
:type storage: object
:param post_processor: (optional) The post processor object. If none
provided, the default post processor is used.
:type post_processor: object
:param extensions: (optional) A list of markdown extensions to add to
post processing step.
:type extensions: list
:param cache: (Optional) A Flask-Cache object to enable caching
:type cache: Object
:param file_upload: (Optional) A FileUpload object from
flask_fileupload extension
:type file_upload: Object
:return:
"""
self.app = None
self.storage = storage
self.config = None
self.ffu = None
self.cache = cache
self._blogger_permission = None
self.post_processor = PostProcessor() if post_processor is None \
else post_processor
if extensions:
self.post_processor.set_custom_extensions(extensions)
self.user_callback = None
self.file_upload = file_upload
if app is not None and storage is not None:
self.init_app(app, storage)
self.principal = None
@classmethod
def _register_plugins(cls, app, config):
plugins = config.get("BLOGGING_PLUGINS")
if plugins:
for plugin in plugins:
lib = __import__(plugin, globals(), locals(), str("module"))
lib.register(app)
def init_app(self, app, storage=None, cache=None):
"""
Initialize the engine.
:param app: The app to use
:type app: Object
:param storage: The blog storage instance that implements the
:type storage: Object
:param cache: (Optional) A Flask-Cache object to enable caching
:type cache: Object
``Storage`` class interface.
"""
self.app = app
self.config = self.app.config
self.storage = storage or self.storage
self.cache = cache or self.cache
self._register_plugins(self.app, self.config)
from .views import create_blueprint
blog_app = create_blueprint(__name__, self)
# external urls
blueprint_created.send(self.app, engine=self, blueprint=blog_app)
self.app.register_blueprint(
blog_app, url_prefix=self.config.get("BLOGGING_URL_PREFIX"))
self.app.extensions["FLASK_BLOGGING_ENGINE"] = self # duplicate
self.app.extensions["blogging"] = self
self.principal = Principal(self.app)
engine_initialised.send(self.app, engine=self)
if self.config.get("BLOGGING_ALLOW_FILEUPLOAD", True):
self.ffu = self.file_upload or FlaskFileUpload(app)
@property
def blogger_permission(self):
if self._blogger_permission is None:
if self.config.get("BLOGGING_PERMISSIONS", False):
self._blogger_permission = Permission(RoleNeed(
self.config.get("BLOGGING_PERMISSIONNAME", "blogger")))
else:
self._blogger_permission = Permission()
return self._blogger_permission
def user_loader(self, callback):
"""
The decorator for loading the user.
:param callback: The callback function that can load a user given a
unicode ``user_id``.
:return: The callback function
"""
self.user_callback = callback
return callback
def is_user_blogger(self):
return self.blogger_permission.require().can()
def get_posts(self, count=10, offset=0, recent=True, tag=None,
user_id=None, include_draft=False, render=False):
posts = self.storage(count, offset, recent, tag, user_id,
include_draft)
for post in posts:
self.process_post(post, render=False)
def process_post(self, post, render=True):
"""
A high level view to create post processing.
:param post: Dictionary representing the post
:type post: dict
:param render: Choice if the markdown text has to be converted or not
:type render: bool
:return:
"""
post_processor = self.post_processor
post_processor.process(post, render)
try:
author = self.user_callback(post["user_id"])
except Exception:
raise Exception("No user_loader has been installed for this "
"BloggingEngine. Add one with the "
"'BloggingEngine.user_loader' decorator.")
if author is not None:
post["user_name"] = self.get_user_name(author)
post_processed.send(self.app, engine=self, post=post, render=render)
@classmethod
def get_user_name(cls, user):
user_name = user.get_name() if hasattr(user, "get_name") else str(user)
return user_name | PypiClean |
/MGLEX-0.2.1.tar.gz/MGLEX-0.2.1/doc/source/index.rst | MGLEX documentation
===================
Welcome to the MGLEX documentation! MGLEX (MetaGenome Likelihood Extractor) is a probablistic model implementation in Python 3 to extract genomes from metagenome assemblies using various features.
**Note**: This documentation is a stub, it will be extended with upcoming versions of MGLEX.
Contents
^^^^^^^^
.. toctree::
:maxdepth: 2
install
quickstart
data
help
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
| PypiClean |
/Create-Multi-Langs-0.1.1.tar.gz/Create-Multi-Langs-0.1.1/create_multi_langs/creater/go.py | from __future__ import absolute_import
from create_multi_langs.creater.base import CreaterBase
import os
from subprocess import call
from typing import NoReturn
from . import to_upper_without_underscore
class CreaterGo(CreaterBase):
@staticmethod
def from_csv_file(csv_file: str,
output_code_file: str,
naming_rule='ucc',
sep=','):
assert output_code_file.endswith(".go"), \
"go filename must ends with .go"
if naming_rule != "ucc":
print('[WARNING] the naming rule: `{}` might conflict to go format'.format(naming_rule)) # noqa: E501
creater = CreaterGo(
csv_file,
output_code_file,
template_path="data/go/template.tmpl",
naming_rule=naming_rule,
sep=sep,
)
return creater
@property
def lang_data_define(self) -> str:
return self._templater.key_value_lines(
self._reader.field_notes(),
double_quote_key=False,
double_quote_value=False,
split_punctuation=" string // ",
end_punctuation="",
n_indent=1,
)
@property
def lang_code_contents(self) -> str:
data = {}
for lang_code in self._reader.lang_codes():
data[to_upper_without_underscore(lang_code)] = lang_code
return self._templater.key_value_lines(
data,
double_quote_key=False,
double_quote_value=True,
split_punctuation=" LangCode = ",
end_punctuation="",
n_indent=1,
)
@property
def init_contents(self) -> str:
lines = []
for lang_code in self._reader.lang_codes():
head = '{spaces}table[{lang_code}] = LangData'.format(
spaces=self._templater.spaces(1),
lang_code=to_upper_without_underscore(lang_code),
) + '{'
lines.append(head)
data = self._templater.key_value_lines(
self._reader.field_values(lang_code),
double_quote_key=False,
double_quote_value=True,
split_punctuation=": ",
end_punctuation=",",
n_indent=2,
)
lines.append(data)
lines.append(self._templater.spaces(1) + '}')
return '\n'.join(lines)
@property
def package_name(self) -> str:
return os.path.splitext(os.path.basename(self._output))[0]
def __call__(self) -> NoReturn:
super().__call__()
return_code = call(["gofmt", "-w", self._output])
assert return_code == 0 | PypiClean |
/Gletscher-0.0.1.tar.gz/Gletscher-0.0.1/gletscher/aws.py |
import http.client
from datetime import datetime
import hashlib
import hmac
import json
import os
import socket
import stat
import logging
import uuid
import re
import time
from gletscher import hex, crypto
from gletscher.progressbar import ProgressBar
logger = logging.getLogger(__name__)
class GlacierJob(object):
def __init__(self, js):
self._js = js
def Id(self):
return self._js["JobId"]
def IsInventoryRetrieval(self):
return self._js["Action"] == "InventoryRetrieval"
def IsArchiveRetrieval(self):
return self._js["Action"] == "ArchiveRetrieval"
def CreationDate(self):
return datetime.strptime(
self._js["CreationDate"], "%Y-%m-%dT%H:%M:%S.%fZ")
def CompletedSuccessfully(self):
return self._js["Completed"] and self._js["StatusCode"] == "Succeeded"
def CompletionDate(self):
if not self.CompletedSuccessfully():
raise Exception("job did not complete successfully")
return datetime.strptime(
self._js["CompletionDate"], "%Y-%m-%dT%H:%M:%S.%fZ")
def ResultAge(self):
return datetime.utcnow() - self.CompletionDate()
def CreationAge(self):
return datetime.utcnow() - self.CreationDate()
def IsPending(self):
return not self._js["Completed"]
def GetArchiveId(self):
if not self.IsArchiveRetrieval():
raise Exception("job is not an archive retrieval")
return self._js["ArchiveId"]
def GetTreeHash(self):
if not self.IsArchiveRetrieval():
raise Exception("job is not an archive retrieval")
return hex.h2b(self._js["SHA256TreeHash"])
def __str__(self):
return json.dumps(self._js, indent=2, sort_keys=True)
def __repr__(self):
return self.__str__()
class GlacierArchive(object):
def __init__(self, js):
self._js = js
def GetBackupId(self):
description = self.GetDescriptionAsJSON()
return uuid.UUID(description["backup"])
def GetTreeHash(self):
return hex.h2b(self._js["SHA256TreeHash"])
def GetSize(self):
return int(self._js["Size"])
def GetCreationDate(self):
return datetime.strptime(
self._js["CreationDate"], "%Y-%m-%dT%H:%M:%S.%fZ")
def IsDataArchive(self):
description = self.GetDescriptionAsJSON()
return description["type"] == "data"
def GetId(self):
return self._js["ArchiveId"]
def GetDescriptionAsJSON(self):
return json.loads(self._js["ArchiveDescription"])
def __str__(self):
return json.dumps(self._js, indent=2, sort_keys=True)
def __repr__(self):
return self.__str__()
class GlacierClient(object):
@staticmethod
def FromConfig(config):
return GlacierClient(
config.aws_region(),
config.aws_account_id(),
config.vault_name(),
config.aws_access_key(),
config.aws_secret_access_key())
def __init__(self, aws_region, aws_account_id, vault_name, aws_access_key,
aws_secret_access_key):
self._aws_region = aws_region
self._aws_account_id = aws_account_id
self._vault_name = vault_name
self._aws_access_key = aws_access_key
self._aws_secret_access_key = aws_secret_access_key
self._upload_chunk_size = 8 * 1024 * 1024
self._host = "glacier.%s.amazonaws.com" % self._aws_region
def NewConnection(self):
return http.client.HTTPSConnection(self._host)
def _log_headers(self, response):
logger.debug("response: %d %s\n%s" % (
response.status, response.reason,
"\n".join(" %s: %s" % x for x in sorted(response.getheaders()))))
def _sha256(self, content):
digest = hashlib.sha256()
digest.update(content)
return digest.hexdigest()
def _compute_all_headers(
self, method, path, headers={}, query_string="", payload=b""):
headers = dict(headers)
full_date = datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
day = full_date[:8]
headers["Date"] = full_date
headers["Host"] = self._host
headers["x-amz-glacier-version"] = "2012-06-01"
sorted_header_key_list = sorted(
[x for x in headers.keys()], key=lambda x: x.lower())
canonical_headers = "".join(
"%s:%s\n" % (key.lower(), headers[key].strip())
for key in sorted_header_key_list)
signed_headers = ";".join(x.lower() for x in sorted_header_key_list)
canonical = "%s\n%s\n%s\n%s\n%s\n%s" % (
method, path, query_string, canonical_headers, signed_headers,
self._sha256(payload))
string_to_sign = "%s\n%s\n%s/%s/%s/%s\n%s" % (
"AWS4-HMAC-SHA256", full_date, day, self._aws_region, "glacier",
"aws4_request", self._sha256(str.encode(canonical)))
k_date = hmac.new(str.encode("AWS4" + self._aws_secret_access_key),
str.encode(day), digestmod=hashlib.sha256).digest()
k_region = hmac.new(k_date, str.encode(self._aws_region),
digestmod=hashlib.sha256).digest()
k_service = hmac.new(k_region, b"glacier",
digestmod=hashlib.sha256).digest()
k_signing = hmac.new(k_service, b"aws4_request",
digestmod=hashlib.sha256).digest()
signature = hmac.new(k_signing, str.encode(string_to_sign),
digestmod=hashlib.sha256).hexdigest()
headers["Authorization"] = (
"AWS4-HMAC-SHA256 Credential=%s/%s/%s/glacier/aws4_request,"
"SignedHeaders=%s,Signature=%s" % (
self._aws_access_key, day, self._aws_region,
signed_headers, signature))
return headers
def _initiateMultipartUpload(self, connection, part_size, description):
path = "/%d/vaults/%s/multipart-uploads" % (
self._aws_account_id, self._vault_name)
headers = {
"x-amz-part-size": "%d" % part_size,
"x-amz-archive-description": description,
}
headers = self._compute_all_headers("POST", path, headers=headers)
connection.request("POST", path, headers=headers)
response = connection.getresponse()
body = response.read()
assert response.status == http.client.CREATED, "%d: %s" % (
response.status, body)
self._log_headers(response)
return response.getheader("x-amz-multipart-upload-id")
def _uploadPart(self, connection, upload_id, payload, tree_hash, offset):
path = "/%d/vaults/%s/multipart-uploads/%s" % (
self._aws_account_id, self._vault_name, upload_id)
headers = {
"x-amz-sha256-tree-hash": tree_hash,
"x-amz-content-sha256": self._sha256(payload),
"Content-Range": "bytes %d-%d/*" % (
offset, offset + len(payload) - 1),
"Content-Length": "%d" % len(payload),
"Content-Type": "application/octet-stream"
}
sleep = 1
max_sleep = 120
while True:
try:
if not connection:
connection = self.NewConnection()
new_headers = self._compute_all_headers(
"PUT", path, headers=headers, payload=payload)
connection.request("PUT", path, body=payload, headers=new_headers)
response = connection.getresponse()
self._log_headers(response)
body = response.read()
if (response.status == http.client.NO_CONTENT
and response.getheader("x-amz-sha256-tree-hash") == tree_hash):
return connection
else:
logger.warning("unexpected response: %d / %s", response.status, body)
except ConnectionError as e:
logger.warning("ConnectionError: %s", e)
pass
except TimeoutError as e:
logger.warning("TimeoutError: %s", e)
pass
except socket.gaierror as e:
logger.warning("gaierror: %s", e)
logger.info("will re-try in %d seconds", sleep)
time.sleep(sleep)
sleep = min(sleep * 2, max_sleep)
connection = None
def _completeUpload(self, connection, upload_id, tree_hash, total_size):
path = "/%d/vaults/%s/multipart-uploads/%s" % (
self._aws_account_id, self._vault_name, upload_id)
headers = {
"x-amz-sha256-tree-hash": tree_hash,
"x-amz-archive-size": "%d" % total_size,
}
headers = self._compute_all_headers("POST", path, headers=headers)
connection.request("POST", path, headers=headers)
response = connection.getresponse()
body = response.read()
assert response.status == http.client.CREATED, "%d: %s" % (
response.status, body)
assert response.getheader("x-amz-sha256-tree-hash") == tree_hash, body
self._log_headers(response)
return response.getheader("x-amz-archive-id")
def _listPendingUploads(self, connection):
path = "/%d/vaults/%s/multipart-uploads" % (
self._aws_account_id, self._vault_name)
headers = {}
headers = self._compute_all_headers("GET", path, headers=headers)
connection.request("GET", path, headers=headers)
response = connection.getresponse()
assert response.status == http.client.OK, "%d: %s" % (
response.status, response.reason)
self._log_headers(response)
body = json.loads(bytes.decode(response.read()))
return body["UploadsList"]
def _listParts(self, connection, upload_id):
parts = []
marker = None
while True:
query_string = "limit=1000"
if marker:
query_string += "&marker=%s" % marker
path = "/%d/vaults/%s/multipart-uploads/%s" % (
self._aws_account_id, self._vault_name, upload_id)
headers = self._compute_all_headers(
"GET", path, query_string=query_string)
connection.request(
"GET", path + "?" + query_string, headers=headers)
response = connection.getresponse()
assert response.status == http.client.OK, "%d: %s\n---%s\n---" % (
response.status, response.reason, response.read())
self._log_headers(response)
body = json.loads(bytes.decode(response.read()))
parts += body["Parts"]
part_size = int(body["PartSizeInBytes"])
if body["Marker"]:
marker = body["Marker"]
else:
return part_size, parts
def _abortPendingUpload(self, connection, upload_id):
path = "/%d/vaults/%s/multipart-uploads/%s" % (
self._aws_account_id, self._vault_name, upload_id)
headers = {}
headers = self._compute_all_headers("DELETE", path, headers=headers)
connection.request("DELETE", path, headers=headers)
response = connection.getresponse()
body = response.read()
assert response.status == http.client.NO_CONTENT, "%d: %s" % (
response.status, body)
self._log_headers(response)
def _initiateInventoryRetrieval(self, connection):
path = "/%d/vaults/%s/jobs" % (self._aws_account_id, self._vault_name)
payload = str.encode(json.dumps(
{"Type": "inventory-retrieval", "Description": "test job",
"Format": "JSON"}))
headers = self._compute_all_headers("POST", path, payload=payload)
connection.request("POST", path, headers=headers, body=payload)
response = connection.getresponse()
self._log_headers(response)
response.read()
def _initiateArchiveRetrieval(self, connection, archive_id):
path = "/%d/vaults/%s/jobs" % (self._aws_account_id, self._vault_name)
payload = str.encode(json.dumps(
{"Type": "archive-retrieval", "Description": "test job",
"ArchiveId": archive_id}))
headers = self._compute_all_headers("POST", path, payload=payload)
connection.request("POST", path, headers=headers, body=payload)
response = connection.getresponse()
self._log_headers(response)
response.read()
def _listJobs(self, connection):
path = "/%d/vaults/%s/jobs" % (self._aws_account_id, self._vault_name)
headers = self._compute_all_headers("GET", path)
connection.request("GET", path, headers=headers)
response = connection.getresponse()
self._log_headers(response)
return [GlacierJob(js) for js in
json.loads(bytes.decode(response.read()))["JobList"]]
def _getJobOutput(self, connection, job_id, range=None):
path = "/%d/vaults/%s/jobs/%s/output" % (
self._aws_account_id, self._vault_name, job_id)
headers = {}
if range:
headers["Range"] = "bytes %d-%d" % range
headers = self._compute_all_headers("GET", path, headers=headers)
connection.request("GET", path, headers=headers)
response = connection.getresponse()
self._log_headers(response)
return response.read()
def _getInventory(self, connection, inventory_retrieval_job_id):
data = json.loads(bytes.decode(self._getJobOutput(connection, inventory_retrieval_job_id)))
return [GlacierArchive(js) for js in data["ArchiveList"]]
def _deleteArchive(self, connection, archive_id):
path = "/%d/vaults/%s/archives/%s" % (
self._aws_account_id, self._vault_name, archive_id)
headers = self._compute_all_headers("DELETE", path)
connection.request("DELETE", path, headers=headers)
response = connection.getresponse()
self._log_headers(response)
assert response.status == http.client.NO_CONTENT, "%d: %s" % (
response.status, response.reason)
def upload_file(self, file,
existing_tree_hasher=None, description=None, pending_upload=None):
logger.info("starting upload of %s", file)
assert description or pending_upload
f_stat = os.stat(file)
assert stat.S_ISREG(f_stat.st_mode), "must be a regular file: " + file
with open(file, "rb") as f:
connection = self.NewConnection()
if description:
description = json.dumps(description)
assert len(description) < 1024
pending_upload = self._initiateMultipartUpload(
connection, self._upload_chunk_size, description)
available_parts = set()
chunk_size = self._upload_chunk_size
else:
# TODO(patrick): handle more than 1000 parts (marker)
chunk_size, parts = self._listParts(connection, pending_upload)
available_parts = set()
for part in parts:
start, end = re.match(
"(\d+)-(\d+)", part["RangeInBytes"]).groups()
available_parts.add(
(int(start), int(end), hex.h2b(part["SHA256TreeHash"])))
logger.debug("available parts: %s", "\n".join(
["%d-%d:%s" % (a, b, hex.b2h(c))
for a, b, c in available_parts]))
total_size = f_stat.st_size
tree_hasher = crypto.TreeHasher()
progress_bar = ProgressBar("Transferring", total_size,
lambda x: "%0.2f MB" % (x / 1024 / 1024))
for start in range(0, total_size, chunk_size):
progress_bar.set_progress(start)
progress_bar.print()
end = min(start + chunk_size, total_size)
data = f.read(end - start)
tree_hasher.update(data)
tree_hash = tree_hasher.get_tree_hash(start, end)
if existing_tree_hasher:
existing_tree_hash = existing_tree_hasher.get_tree_hash(
start, end)
assert tree_hash == existing_tree_hash, \
"computed tree hash does not match expected hash"
logger.debug(
"uploading %s [%d,%d)", hex.b2h(tree_hash), start, end)
if (start, end - 1, tree_hash) in available_parts:
logger.debug(
"this part is already available - skipping upload")
continue
connection = self._uploadPart(
connection, pending_upload, data, hex.b2h(tree_hash), start)
progress_bar.complete()
tree_hash = tree_hasher.get_tree_hash()
archive_id = self._completeUpload(
connection, pending_upload, hex.b2h(tree_hash), total_size)
return archive_id, tree_hash
def find_pending_upload(self, backup_uuid, tree_hash):
connection = http.client.HTTPSConnection(self._host)
for pending_upload in self._listPendingUploads(connection):
description = json.loads(pending_upload["ArchiveDescription"])
their_uuid = uuid.UUID(description["backup"])
if their_uuid == backup_uuid:
their_tree_hash = hex.h2b(description["tree-hash"])
if their_tree_hash == tree_hash:
return pending_upload["MultipartUploadId"] | PypiClean |
/Infomericaclass-1.0.0.tar.gz/Infomericaclass-1.0.0/inf/examples/ethnicolr_app_contrib20xx-fl_reg.ipynb | ## Application: 2000/2010 Political Campaign Contributions by Race
Using ethnicolr, we look to answer three basic questions:
<ol>
<li>What proportion of contributions were made by blacks, whites, Hispanics, and Asians?
<li>What proportion of unique contributors were blacks, whites, Hispanics, and Asians?
<li>What proportion of total donations were given by blacks, whites, Hispanics, and Asians?
</ol>
```
import pandas as pd
df = pd.read_csv('/opt/names/fec_contrib/contribDB_2000.csv', nrows=100)
df.columns
from ethnicolr import pred_fl_reg_name, pred_fl_reg_ln
```
**Load and Subset on Individual Contributors**
```
df = pd.read_csv('/opt/names/fec_contrib/contribDB_2000.csv', usecols=['amount', 'contributor_type', 'contributor_lname', 'contributor_fname', 'contributor_name'])
sdf = df[df.contributor_type=='I'].copy()
sdf.fillna('', inplace=True)
rdf2000 = pred_fl_reg_name(sdf, 'contributor_lname', 'contributor_fname')
rdf2000['year'] = 2000
df = pd.read_csv('/opt/names/fec_contrib/contribDB_2010.csv.zip', usecols=['amount', 'contributor_type', 'contributor_lname', 'contributor_fname', 'contributor_name'])
sdf = df[df.contributor_type=='I'].copy()
sdf.fillna('', inplace=True)
rdf2010 = pred_fl_reg_name(sdf, 'contributor_lname', 'contributor_fname')
rdf2010['year'] = 2010
rdf = pd.concat([rdf2000, rdf2010])
rdf.head(20)
```
### What proportion of contributons were by blacks, whites, Hispanics, and Asians?
```
adf = rdf.groupby(['year', 'race']).agg({'contributor_lname': 'count'})
adf.unstack().apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
### What proportion of the donors were blacks, whites, Hispanics, and Asians?
```
udf = rdf.drop_duplicates(subset=['contributor_name']).copy()
gdf = udf.groupby(['year', 'race']).agg({'contributor_name': 'count'})
gdf.unstack().apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
### What proportion of the total donation was given by blacks, whites, Hispanics, and Asians?
```
bdf = rdf.groupby(['year', 'race']).agg({'amount': 'sum'})
bdf.unstack().apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
### What if we estimated by using probabilities for race rather than labels?
#### What proportion of contributons were by blacks, whites, Hispanics, and Asians?
```
rdf['white_count'] = rdf.nh_white
rdf['black_count'] = rdf.nh_black
rdf['asian_count'] = rdf.asian
rdf['hispanic_count'] = rdf.hispanic
gdf = rdf.groupby(['year']).agg({'white_count': 'sum', 'black_count': 'sum', 'asian_count': 'sum', 'hispanic_count': 'sum'})
gdf.apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
#### What proportion of the donors were blacks, whites, Hispanics, and Asians?
```
udf['white_count'] = udf.nh_white
udf['black_count'] = udf.nh_black
udf['asian_count'] = udf.asian
udf['hispanic_count'] = udf.hispanic
gdf = udf.groupby(['year']).agg({'white_count': 'sum', 'black_count': 'sum', 'asian_count': 'sum', 'hispanic_count': 'sum'})
gdf.apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
#### What proportion of the total donation was given by blacks, whites, Hispanics, and Asians?
```
rdf['white_amount'] = rdf.amount * rdf.nh_white
rdf['black_amount'] = rdf.amount * rdf.nh_black
rdf['api_amount'] = rdf.amount * rdf.asian
rdf['hispanic_amount'] = rdf.amount * rdf.hispanic
gdf = rdf.groupby(['year']).agg({'white_amount': 'sum', 'black_amount': 'sum', 'api_amount': 'sum', 'hispanic_amount': 'sum'}) / 10e6
gdf.style.format("{:0.2f}")
gdf.apply(lambda r: r / r.sum(), axis=1).style.format("{:.2%}")
```
| PypiClean |
/Impression-CMS-0.2.0.tar.gz/Impression-CMS-0.2.0/impression/themes/admin/static/js/plugins/dataTables/dataTables.bootstrap.js | $.extend(true, $.fn.dataTable.defaults, {
"sDom": "<'row'<'col-sm-6'l><'col-sm-6'f>r>" + "t" + "<'row'<'col-sm-6'i><'col-sm-6'p>>",
"oLanguage": {
"sLengthMenu": "_MENU_ records per page"
}
});
/* Default class modification */
$.extend($.fn.dataTableExt.oStdClasses, {
"sWrapper": "dataTables_wrapper form-inline",
"sFilterInput": "form-control input-sm",
"sLengthSelect": "form-control input-sm"
});
// In 1.10 we use the pagination renderers to draw the Bootstrap paging,
// rather than custom plug-in
if ($.fn.dataTable.Api) {
$.fn.dataTable.defaults.renderer = 'bootstrap';
$.fn.dataTable.ext.renderer.pageButton.bootstrap = function(settings, host, idx, buttons, page, pages) {
var api = new $.fn.dataTable.Api(settings);
var classes = settings.oClasses;
var lang = settings.oLanguage.oPaginate;
var btnDisplay, btnClass;
var attach = function(container, buttons) {
var i, ien, node, button;
var clickHandler = function(e) {
e.preventDefault();
if (e.data.action !== 'ellipsis') {
api.page(e.data.action).draw(false);
}
};
for (i = 0, ien = buttons.length; i < ien; i++) {
button = buttons[i];
if ($.isArray(button)) {
attach(container, button);
} else {
btnDisplay = '';
btnClass = '';
switch (button) {
case 'ellipsis':
btnDisplay = '…';
btnClass = 'disabled';
break;
case 'first':
btnDisplay = lang.sFirst;
btnClass = button + (page > 0 ?
'' : ' disabled');
break;
case 'previous':
btnDisplay = lang.sPrevious;
btnClass = button + (page > 0 ?
'' : ' disabled');
break;
case 'next':
btnDisplay = lang.sNext;
btnClass = button + (page < pages - 1 ?
'' : ' disabled');
break;
case 'last':
btnDisplay = lang.sLast;
btnClass = button + (page < pages - 1 ?
'' : ' disabled');
break;
default:
btnDisplay = button + 1;
btnClass = page === button ?
'active' : '';
break;
}
if (btnDisplay) {
node = $('<li>', {
'class': classes.sPageButton + ' ' + btnClass,
'aria-controls': settings.sTableId,
'tabindex': settings.iTabIndex,
'id': idx === 0 && typeof button === 'string' ? settings.sTableId + '_' + button : null
})
.append($('<a>', {
'href': '#'
})
.html(btnDisplay)
)
.appendTo(container);
settings.oApi._fnBindAction(
node, {
action: button
}, clickHandler
);
}
}
}
};
attach(
$(host).empty().html('<ul class="pagination"/>').children('ul'),
buttons
);
}
} else {
// Integration for 1.9-
$.fn.dataTable.defaults.sPaginationType = 'bootstrap';
/* API method to get paging information */
$.fn.dataTableExt.oApi.fnPagingInfo = function(oSettings) {
return {
"iStart": oSettings._iDisplayStart,
"iEnd": oSettings.fnDisplayEnd(),
"iLength": oSettings._iDisplayLength,
"iTotal": oSettings.fnRecordsTotal(),
"iFilteredTotal": oSettings.fnRecordsDisplay(),
"iPage": oSettings._iDisplayLength === -1 ? 0 : Math.ceil(oSettings._iDisplayStart / oSettings._iDisplayLength),
"iTotalPages": oSettings._iDisplayLength === -1 ? 0 : Math.ceil(oSettings.fnRecordsDisplay() / oSettings._iDisplayLength)
};
};
/* Bootstrap style pagination control */
$.extend($.fn.dataTableExt.oPagination, {
"bootstrap": {
"fnInit": function(oSettings, nPaging, fnDraw) {
var oLang = oSettings.oLanguage.oPaginate;
var fnClickHandler = function(e) {
e.preventDefault();
if (oSettings.oApi._fnPageChange(oSettings, e.data.action)) {
fnDraw(oSettings);
}
};
$(nPaging).append(
'<ul class="pagination">' +
'<li class="prev disabled"><a href="#">← ' + oLang.sPrevious + '</a></li>' +
'<li class="next disabled"><a href="#">' + oLang.sNext + ' → </a></li>' +
'</ul>'
);
var els = $('a', nPaging);
$(els[0]).bind('click.DT', {
action: "previous"
}, fnClickHandler);
$(els[1]).bind('click.DT', {
action: "next"
}, fnClickHandler);
},
"fnUpdate": function(oSettings, fnDraw) {
var iListLength = 5;
var oPaging = oSettings.oInstance.fnPagingInfo();
var an = oSettings.aanFeatures.p;
var i, ien, j, sClass, iStart, iEnd, iHalf = Math.floor(iListLength / 2);
if (oPaging.iTotalPages < iListLength) {
iStart = 1;
iEnd = oPaging.iTotalPages;
} else if (oPaging.iPage <= iHalf) {
iStart = 1;
iEnd = iListLength;
} else if (oPaging.iPage >= (oPaging.iTotalPages - iHalf)) {
iStart = oPaging.iTotalPages - iListLength + 1;
iEnd = oPaging.iTotalPages;
} else {
iStart = oPaging.iPage - iHalf + 1;
iEnd = iStart + iListLength - 1;
}
for (i = 0, ien = an.length; i < ien; i++) {
// Remove the middle elements
$('li:gt(0)', an[i]).filter(':not(:last)').remove();
// Add the new list items and their event handlers
for (j = iStart; j <= iEnd; j++) {
sClass = (j == oPaging.iPage + 1) ? 'class="active"' : '';
$('<li ' + sClass + '><a href="#">' + j + '</a></li>')
.insertBefore($('li:last', an[i])[0])
.bind('click', function(e) {
e.preventDefault();
oSettings._iDisplayStart = (parseInt($('a', this).text(), 10) - 1) * oPaging.iLength;
fnDraw(oSettings);
});
}
// Add / remove disabled classes from the static elements
if (oPaging.iPage === 0) {
$('li:first', an[i]).addClass('disabled');
} else {
$('li:first', an[i]).removeClass('disabled');
}
if (oPaging.iPage === oPaging.iTotalPages - 1 || oPaging.iTotalPages === 0) {
$('li:last', an[i]).addClass('disabled');
} else {
$('li:last', an[i]).removeClass('disabled');
}
}
}
}
});
}
/*
* TableTools Bootstrap compatibility
* Required TableTools 2.1+
*/
if ($.fn.DataTable.TableTools) {
// Set the classes that TableTools uses to something suitable for Bootstrap
$.extend(true, $.fn.DataTable.TableTools.classes, {
"container": "DTTT btn-group",
"buttons": {
"normal": "btn btn-default",
"disabled": "disabled"
},
"collection": {
"container": "DTTT_dropdown dropdown-menu",
"buttons": {
"normal": "",
"disabled": "disabled"
}
},
"print": {
"info": "DTTT_print_info modal"
},
"select": {
"row": "active"
}
});
// Have the collection use a bootstrap compatible dropdown
$.extend(true, $.fn.DataTable.TableTools.DEFAULTS.oTags, {
"collection": {
"container": "ul",
"button": "li",
"liner": "a"
}
});
} | PypiClean |
/BlueWhale3-Educational-0.4.1.tar.gz/BlueWhale3-Educational-0.4.1/doc/widgets/google-sheets.md | Google Sheets
=============
Read data from a Google Sheets spreadsheet.
**Outputs**
- Data: data set from the Google Sheets service.
Description
-----------
The widget reads data from the [Google Sheets service](https://docs.google.com/spreadsheets). To use the widget, click the Share button in a selected spreadsheet, copy the provided link and paste it into the widget's URL line. Press enter to load the data. To observe the data in real time, use the Reload function.
1. Enter the link to the spreadsheet. Press Enter to load the data. Set reload if you wish to observe the updates in real time.
2. Information on the data set: name and attributes.
3. If *Commit Automatically* is ticked, the data will be automatically communicated downstream. Alternatively, press *Commit*.
Example
-------
This widget is used for loading the data. We have used the link from the Google Sheets: [https://goo.gl/jChYki](https://goo.gl/jChYki). This is a fictional data on hamsters and rabbits, of which some have the disease and some don't. Use the **Data Table** to observe the loaded data in a spreadsheet.
| PypiClean |
/GNS-1.0-py3-none-any.whl/gns/prob_funcs.py | import numpy as np
import sys
import scipy.stats
from scipy.special import ive as ModifiedBessel, gamma as Gamma #for Kent distribution
try:
from scipy.special import logsumexp
except ImportError:
from scipy.misc import logsumexp
import inspect
#import custom modules
"""
NB scipy.stats.norm uses standard deviation, scipy.stats.multivariate_normal uses covariance!!
NB if x.shape = (m,1), then scipy.stats.rv_continuous.pdf(x) returns array shape (m, 1)
whereas scipy.stats.multivariate_normal.pdf(x) returns array shape (m,)
"""
###########PDF related functions
def fitPriors(priorParams):
"""
Only currently handles one dimensional (independent priors). Scipy.stats multivariate functions do not have built in inverse CDF methods, so if I want to consider multivariate priors I may have to write my own code.
Note scipy.stats.uniform takes parameters loc and scale where the boundaries are defined to be
loc and loc + scale, so scale = upper - lower bound.
Returns list of fitted prior objects length of nDims (one function for each parameter)
"""
priorFuncs = []
priorFuncsPpf = []
priorFuncsLogPdf = []
priorType = priorParams[0,:]
param1Vec = priorParams[1,:]
param2Vec = priorParams[2,:]
for i in range(len(priorType)):
if priorType[i] == 1:
priorFunc = scipy.stats.uniform(param1Vec[i], param2Vec[i] - param1Vec[i])
elif priorType[i] == 2:
priorFunc = scipy.stats.norm(param1Vec[i], param2Vec[i])
elif priorType[i] == 3:
priorFunc = scipy.stats.sine()
else:
print("priors other than uniform, Gaussian and sin not currently supported")
sys.exit(1)
priorFuncs.append(priorFunc)
return priorFuncs
def getPriorPdfs(priorObjs):
"""
Takes list of fitted prior objects, returns list of objects' .pdf() methods
"""
priorFuncsPdf = []
for obj in priorObjs:
priorFuncsPdf.append(obj.pdf)
return priorFuncsPdf
def getPriorLogPdfs(priorObjs):
"""
Takes list of fitted prior objects, returns list of objects' .logpdf() methods
"""
priorFuncsLogPdf = []
for obj in priorObjs:
priorFuncsLogPdf.append(obj.logpdf)
return priorFuncsLogPdf
def getPriorPpfs(priorObjs):
"""
Takes list of fitted prior objects, returns list of objects' .ppf() methods
"""
priorFuncsPpf = []
for obj in priorObjs:
priorFuncsPpf.append(obj.ppf)
return priorFuncsPpf
def invPrior(livePoints, priorFuncsPpf):
"""
take in array of livepoints each which has value isin[0,1], and has nDim dimensions. Output physical array of values corresponding to priors for each parameter dimension.
"""
livePointsPhys = np.zeros_like(livePoints)
for i in range(len(priorFuncsPpf)):
livePointsPhys[:,i] = priorFuncsPpf[i](livePoints[:,i])
return livePointsPhys
def priorFuncsProd(livePoint, priorFuncsPdf):
"""
calculates pdf of prior for each parameter dimension, then multiplies these together to get the pdf of the prior (i.e. the prior pdf assuming the parameters are independent)
Works in linear space, but can easily be adapted if this ever leads to underflow errors (consider sum of log(pi(theta))).
Only currently designed to work with one livepoint at a time
"""
livePointPriorValues = np.zeros_like(livePoint)
for i in range(len(priorFuncsPdf)):
livePointPriorValues[i] = priorFuncsPdf[i](livePoint[i])
priorProd = livePointPriorValues.prod()
return priorProd
def logPriorFuncsSum(livePoint, priorFuncsLogPdf):
"""
calculates logpdf of prior for each parameter dimension,
then adds these together to get the logpdf of the prior (i.e. the prior logpdf assuming the parameters are independent)
"""
livePointPriorValues = np.zeros_like(livePoint)
for i in range(len(priorLogFuncsPdf)):
livePointPriorLogValues[i] = priorFuncsLogPdf[i](livePoint[i])
logPriorSum = livePointPriorLogValues.sum()
return logPriorSum
class priorObjs:
"""
class with priorFuncsP*f as attributes required when creating an object, with methods
which evaluate these functions (and multiply together in case of pdf) whilst just requiring
livepoints as argument.
This is essentially a work around for the toys models so the prior and inverse prior functions can be called
with just livepoints as an argument, to fit to the way a user specified prior would be best called
"""
def __init__(self, priorFuncsPdf, priorFuncsLogPdf, priorFuncsPpf):
"""
get pdf and ppf methods of scipy.stats.* objects
"""
self.priorFuncsPdf = priorFuncsPdf
self.priorFuncsLogPdf = priorFuncsLogPdf
self.priorFuncsPpf = priorFuncsPpf
def invPrior(self, livePoints):
"""
Same as the function invPrior(), but uses
self.priorFuncsPpf rather than taking it as an argument
"""
livePointsPhys = np.zeros_like(livePoints)
for i in range(len(self.priorFuncsPpf)):
livePointsPhys[:,i] = self.priorFuncsPpf[i](livePoints[:,i])
return livePointsPhys
def priorFuncsProd(self, livePoint):
"""
Same as the function priorFuncsProd(), but uses
self.priorFuncsPdf rather than taking it as an argument
Only currently designed to work with one livepoint at a time.
Here we have opposite problem to LhoodObjs class. When using theoretical Z/ H functions,
parameter has shape (1, nDims) when it needs to have shape (nDims) to not cause an IndexError exception
#TODO: get this to work for more than one livepoint (for calculating theoretical posterior from grid)
"""
livePointPriorValues = np.zeros_like(livePoint)
for i in range(len(self.priorFuncsPdf)):
try:
livePointPriorValues[i] = self.priorFuncsPdf[i](livePoint[0,i])
except IndexError:
livePointPriorValues[i] = self.priorFuncsPdf[i](livePoint[i])
priorProd = livePointPriorValues.prod()
return priorProd
def logPriorFuncsSum(self, livePoint):
"""
Same as the function logPriorFuncsSum(), but uses
self.priorFuncsPdf rather than taking it as an argument.
Here we have opposite problem to LhoodObjs class. When using theoretical Z/ H functions,
parameter has shape (1, nDims) when it needs to have shape (nDims) to not cause an IndexError exception
#TODO: get this to work for more than one livepoint (for calculating theoretical posterior from grid)
"""
livePointPriorLogValues = np.zeros(len(self.priorFuncsLogPdf))
for i in range(len(self.priorFuncsLogPdf)):
try:
livePointPriorLogValues[i] = self.priorFuncsLogPdf[i](livePoint[0,i])
except IndexError:
livePointPriorLogValues[i] = self.priorFuncsLogPdf[i](livePoint[i])
logPriorSum = livePointPriorLogValues.sum()
return logPriorSum
class LhoodObjs:
"""
Class which is essentially defined to wrap around scipy.stats.continuous_rv inheriting classes
(e.g. scipy.stats.norm).
Main purpose of class is to have methods .pdf (.logpdf) which evaluate the .pdf methods of a list of
rv_continuous instances (specified by LhoodTypes) and multiply (add) together the resulting values.
Useful so that you can create an object whose .pdf method evaluates product of Lhoods and can still be used by
Lhood() (LLhood())
Note the outputted Lhood has shape (len(x[:]),), NOT AS IN CASE OF instances of single continuous_rv instances.
This is because the variable Lhoods is declared to have this shape.
"""
def __init__(self, mu, sigma, LhoodTypes, bounds = np.array([])):
"""
set values of attributes and call method which fits
Lhoods
"""
self.mu = mu
self.sigma = sigma
self.LhoodTypes = LhoodTypes
self.LhoodObjsList = []
self.bounds = bounds
self.fitLhoods()
def fitLhoods(self):
"""
Separately fits 1-d likelihood functions (normal or von Mises)
using each element of self.mu, and each diagonal element of self.sigma.
NB von Mises is parameterised by kappa = 1 / variance, normal is parameterised by standard deviation
"""
for i, LhoodType in enumerate(self.LhoodTypes):
if LhoodType == 'normal':
LhoodObj = scipy.stats.norm(loc = self.mu[i], scale = self.sigma[i,i])
elif LhoodType == 'von mises':
LhoodObj = scipy.stats.vonmises(loc = self.mu[i], kappa = 1. / self.sigma[i,i]) #got rid of sigma^2 on 21/01/18. n.b. this is variance not sigma for this case
elif LhoodType == 'truncated normal':
#scale of truncated normal affects bounds of truncation such that you must set bounds = bounds_true * 1 / scale
a = self.bounds[i,0] * 1. / self.sigma[i,i]
b = self.bounds[i,1] * 1. / self.sigma[i,i]
LhoodObj = scipy.stats.truncnorm(a = a, b = b, loc = self.mu[i], scale = self.sigma[i,i])
elif LhoodType == 'uniform':
LhoodObj = scipy.stats.uniform(loc = self.mu[i], scale = self.sigma[i,i] - self.mu[i])
elif LhoodType == 'kent':
self.mu = self.mu.reshape(3,3) #reshape gamma matrix to 3x3 here
g1 = self.mu[0,:]
g2 = self.mu[1,:]
g3 = self.mu[2,:]
kappa = self.sigma[0]
beta = self.sigma[1]
LhoodObj = KentDistribution(kappa, beta, g1, g2, g3)
elif LhoodType == 'kent sum':
g1s = self.mu[3*i]
g2s = self.mu[3*i+1]
g3s = self.mu[3*i+2]
kappas = self.sigma[2*i]
betas = self.sigma[2*i+1]
LhoodObj = KentDistributionSum(kappas, betas, g1s, g2s, g3s)
elif LhoodType == 'gauss kent sum': #bit messy but can't get consistent with for loop without using another index
LhoodObjsList2 = []
gaussLhoodObj = scipy.stats.multivariate_normal(self.mu[0], self.sigma[0])
LhoodObjsList2.append(gaussLhoodObj)
g1s = self.mu[1]
g2s = self.mu[2]
g3s = self.mu[3]
kappas = self.sigma[1]
betas = self.sigma[2]
kentLhoodObj = KentDistributionSum(kappas, betas, g1s, g2s, g3s)
LhoodObjsList2.append(kentLhoodObj)
LhoodObj = LhoodObjsList2 #so line outside for loop doesn't need to be changed
self.LhoodObjsList.append(LhoodObj)
def pdf(self, x):
"""
evaluates .pdf method of all Lhood objects in self.LhoodObjsList
and multiplies resulting values together.
It may be the case that x is a 1d array (n,) instead of a 2d array (m,n), this is what the
IndexError except statements are for. Could instead ensure that all Lhood vectors are
instead arrays, but I don't think it makes much difference in terms of efficiency or clarity of code.
Note this isn't in an issue in integrating functions, as they reshape each parameter point to (1,nDims)
TODO: find neater way for this to work in the case of trialing a point in the MH algorithm, i.e. when it has shape (nDims,)
"""
try:
Lhoods = np.array([1.]*len(x[:,0]))
except IndexError:
Lhoods = np.array([1.])
for i, LhoodObj in enumerate(self.LhoodObjsList):
if 'angles' in inspect.getargspec(LhoodObj.logpdf)[0]: #kent distribution requires two-dim array as argument
try:
Lhoods *= LhoodObj.pdf(x[:,2*i:2*i+2]) #assumes each pair (in 2nd dimension) corresponds to two arguments required for kent
except IndexError:
Lhoods *= LhoodObj.pdf(x[2*i:2*i+2])
else:
try:
Lhoods *= LhoodObj.pdf(x[:,i])
except IndexError:
Lhoods *= LhoodObj.pdf(x[i])
return Lhoods
def logpdf(self, x):
"""
evaluates .logpdf method of all Lhood objects in self.LhoodObjsList
and adds resulting values together.
It may be the case that x is a 1d array (n,) instead of a 2d array (m,n), this is what causes the IndexErrors. Could instead ensure that all Lhood vectors are
instead arrays, but I don't think it makes much difference in terms of efficiency or clarity of code.
Note this isn't in an issue in integrating functions, as they reshape each parameter point to (1,nDims)
TODO: find neater way for this to work in the case of trialing a point in the MH algorithm, i.e. when it has shape (nDims,).
This could be done by writing separate .pdf methods for when x is 1-d or 2-d
"""
#this is messy but sufficient for what we're doing. n.b. this is only for evaluating lhood, so doesn't affect actual implementation of gns itself
try:
LLhoods = np.zeros_like(x[:,0], dtype = np.float) #need to specify dtype or it uses int32
except IndexError:
LLhoods = np.array([0.])
for i, LhoodObj in enumerate(self.LhoodObjsList):
try:
if 'angles' in inspect.getargspec(LhoodObj.logpdf)[0]: #kent distribution requires two-dim array as argument
try:
LLhoods += LhoodObj.logpdf(x[:,2*i:2*i+2]) #assumes each pair (in 2nd dimension) corresponds to two arguments required for kent
except IndexError:
LLhoods += LhoodObj.logpdf(x[2*i:2*i+2])
else:
try:
LLhoods += LhoodObj.logpdf(x[:,i])
except IndexError:
LLhoods += LhoodObj.logpdf(x[i])
except AttributeError: #LhoodObj is in fact a list of LhoodObjs (gauss and sphere model)
#if 'multivariate_normal' in str(self.LhoodObjsList[0,0]): #this shouldn't be needed unless I try functions other than gaussian
#another hack for the multivariate norm, which requires more than one dimension of nDims
#p.s. this is a MASSIVE hack, requires gauss lhood obj to be before kent
gaussDim = 20
try:
# print "gauss nLive"
# print LhoodObj[0].logpdf(x[:,:gaussDim])
# print LhoodObj[0].logpdf(x[:,:gaussDim]).shape
LLhoods += LhoodObj[0].logpdf(x[:,0:gaussDim])
except IndexError:
# print "gauss trial"
# print LhoodObj[0].logpdf(x[:gaussDim])
# print LhoodObj[0].logpdf(x[:gaussDim]).shape
LLhoods += LhoodObj[0].logpdf(x[0:gaussDim])
try:
# print "kent nLive"
# print LhoodObj[1].logpdf(x[:,gaussDim:])
# print LhoodObj[1].logpdf(x[:,gaussDim:]).shape
LLhoods += LhoodObj[1].logpdf(x[:,gaussDim:])
except IndexError:
# print "kent trial"
# print LhoodObj[1].logpdf(x[gaussDim:])
# print LhoodObj[1].logpdf(x[gaussDim:]).shape
LLhoods += LhoodObj[1].logpdf(x[gaussDim:])
# import sys; sys.exit()
return LLhoods
def fitLhood(LhoodParams):
"""
fit lhood (without data) for parameters to make future evaluations much faster.
LLhoodType == 2 is multivariate Gaussian, applicable in most 'usual' circumstances
LLhoodType == 3 is the 1d von Mises distribution, a 'wrapped likelihood function defined on [-pi, pi].
Equivalent to having a likelihood defined on the unit circle and parameterised by theta isin [-pi, pi].
LLhoodType == 4 is the 2d (independent) von Mises distribution, a wrapped likelihood function defined on [-pi, pi] x [-pi, pi].
Equivalent to having a likelihood defined on the unit torus and parameterised by theta isin [-pi, pi] and phi isin [-pi, pi].
"""
LhoodType = LhoodParams[0]
if LhoodParams[0] < 11 or LhoodParams[0] == 16 or LhoodParams[0] == 17: #hack
mu = LhoodParams[1].reshape(-1)
else:
mu = LhoodParams[1]
sigma = LhoodParams[2]
if LhoodType == 2:
LhoodObj = scipy.stats.multivariate_normal(mu, sigma)
elif LhoodType == 3:
LhoodObj = scipy.stats.vonmises(loc = mu, kappa = np.reciprocal(sigma))
elif LhoodType == 4:
LhoodTypes = ['von mises', 'von mises']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 5:
LhoodTypes = ['uniform', 'truncated normal']
bounds = np.array([0., 0., 0., np.pi]).reshape(2,2)
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes, bounds)
elif LhoodType == 6:
LhoodTypes = ['von mises', 'truncated normal']
bounds = np.array([0., 0., -np.pi / 2., np.pi / 2.]).reshape(2,2)
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes, bounds)
elif LhoodType == 7:
LhoodTypes = ['von mises', 'truncated normal']
bounds = np.array([0., 0., 0., np.pi]).reshape(2,2)
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes, bounds)
elif LhoodType == 8: #four-torus
LhoodTypes = ['von mises', 'von mises', 'von mises', 'von mises']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 9: #six-torus
LhoodTypes = ['von mises', 'von mises', 'von mises', 'von mises', 'von mises', 'von mises']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 10: #Kent distribution
LhoodTypes = ['kent']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes) #mu is 3x3 gamma matrix, sigma is kappa and beta
elif LhoodType == 11: #Kent distribution sum
LhoodTypes = ['kent sum']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes) #mu is a list (3,) of list of gamma vectors, sigma is lists of kappas and betas
elif LhoodType == 12: #Kent distribution sum on multiple spheres
LhoodTypes = ['kent sum', 'kent sum', 'kent sum']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 13:
LhoodTypes = ['kent sum', 'kent sum', 'kent sum', 'kent sum', 'kent sum']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 14:
LhoodTypes = ['kent sum', 'kent sum', 'kent sum', 'kent sum', 'kent sum', 'kent sum']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 15:
LhoodTypes = ['gauss kent sum']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 16: #eight-torus
LhoodTypes = ['von mises', 'von mises', 'von mises', 'von mises', 'von mises', 'von mises', 'von mises', 'von mises']
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
elif LhoodType == 17: #eight-torus
LhoodTypes = ['von mises'] * 10
LhoodObj = LhoodObjs(mu, sigma, LhoodTypes)
return LhoodObj
def Lhood(LhoodObj):
"""
Returns .pdf method of LhoodObj
"""
return LhoodObj.pdf
def LLhood(LhoodObj):
"""
Returns .logpdf method of LhoodObj
"""
return LhoodObj.logpdf
def cartesian_from_spherical(phi, theta):
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)
return np.array([x, y, z])
def infinite_sum(f, i0 = 0, eps = 1e-8):
i = i0
total = 0.
while True:
diff = f(i)
if diff < eps * total:
return total
total += diff
i += 1
class KentDistribution():
"""
Originally written by Will Handley, edited by Kamran Javid.
Attempts to roughly follow style of scipy.stats functions
in terms of how the pdf is fitted and evaluated
"""
def __init__(self, kappa, beta, g1, g2, g3):
self.kappa = kappa
self.beta = beta
self.c = self.cCalc()
self.g1 = g1
self.g2 = g2
self.g3 = g3
def cCalc(self):
"""
Calculate normalisation coefficient c
"""
c = infinite_sum(lambda j: Gamma(j + 1. / 2.)/Gamma(j + 1) * self.beta**(2. * j) * (self.kappa / 2.)**(-2. * j - 1./2.) * ModifiedBessel(2. * j + 1. / 2., self.kappa))
return c
def logpdf(self, angles):
"""
Uses try and IndexError block for same reason as pdf and logpdf methods above
"""
if self.kappa < 0:
raise ValueError("KentDistribution: Parameter kappa ({}) must be >= 0".format(self.kappa))
elif self.beta < 0:
raise ValueError("KentDistribution: Parameter beta ({}) must be >= 0".format(self.beta))
elif 2 * self.beta > self.kappa:
raise ValueError("KentDistribution: Parameter beta ({}) must be <= kappa / 2 ({})".format(self.beta, self.kappa / 2.))
try:
x = cartesian_from_spherical(angles[:,0], angles[:,1])
except IndexError:
x = cartesian_from_spherical(angles[0], angles[1])
gx1 = x.transpose().dot(self.g1).transpose()
gx2 = x.transpose().dot(self.g2).transpose()
gx3 = x.transpose().dot(self.g3).transpose()
return - np.log(self.c) + self.kappa * gx1 + self.beta * (gx2**2. - gx3**2.)
def pdf(self, angles):
return np.exp(self.logpdf(angles))
class KentDistributionSum():
"""
Sum of Kent distribution pdfs.
"""
def __init__(self, kappas, betas, g1s, g2s, g3s):
self.kentList = []
for i in range(len(kappas)):
self.kentList.append(KentDistribution(kappas[i], betas[i], g1s[i], g2s[i], g3s[i]))
def logpdf(self, angles):
Lpdfs = np.array([kentObj.logpdf(angles) for kentObj in self.kentList])
LpdfSums = []
try:
LpdfSums = np.array([logsumexp(Lpdfs[:,i]) for i in range(len(Lpdfs[0,:]))])
except IndexError:
LpdfSums = logsumexp(Lpdfs)
return LpdfSums
def pdf(self, angles):
return np.exp(self.logpdf(angles)) | PypiClean |
/MergePythonSDK.ticketing-2.2.2-py3-none-any.whl/MergePythonSDK/crm/model/webhook_receiver.py | import re # noqa: F401
import sys # noqa: F401
from typing import (
Optional,
Union,
List,
Dict,
)
from MergePythonSDK.shared.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
OpenApiModel,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
from MergePythonSDK.shared.exceptions import ApiAttributeError
from MergePythonSDK.shared.model_utils import import_model_by_name
class WebhookReceiver(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
return (bool, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
defined_types = {
'event': (str,), # noqa: E501
'is_active': (bool,), # noqa: E501
'key': (str, none_type,), # noqa: E501
}
return defined_types
@cached_property
def discriminator():
return None
attribute_map = {
'event': 'event', # noqa: E501
'is_active': 'is_active', # noqa: E501
'key': 'key', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, event, is_active, *args, **kwargs): # noqa: E501
"""WebhookReceiver - a model defined in OpenAPI
Args:
event (str):
is_active (bool):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
key (str): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', True)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
for arg in args:
if isinstance(arg, dict):
kwargs.update(arg)
else:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.event = event
self.is_active = is_active
self.key = kwargs.get("key", None)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, event, is_active, *args, **kwargs): # noqa: E501
"""WebhookReceiver - a model defined in OpenAPI
Args:
event (str):
is_active (bool):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
key (str): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
for arg in args:
if isinstance(arg, dict):
kwargs.update(arg)
else:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.event: Union[str] = event
self.is_active: Union[bool] = is_active
self.key: Union[str] = kwargs.get("key", str()) | PypiClean |
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_2.provide("dojox.jq");
_2.require("dojo.NodeList-traverse");
_2.require("dojo.NodeList-manipulate");
_2.require("dojo.io.script");
(function(){
_2.config.ioPublish=true;
var _4="|img|meta|hr|br|input|";
function _5(_6,_7){
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_6=_6.replace(/<\s*(\w+)([^\/\>]*)\/\s*>/g,function(_8,_9,_a){
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return "<"+_9+_a+"></"+_9+">";
}else{
return _8;
}
});
return _2._toDom(_6,_7);
};
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if(_d!=-1){
if(_d==0){
_c=_c.substring(1);
}
_c=_c.replace(/-(\w)/g,function(_e,_f){
return _f.toUpperCase();
});
}
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};
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var _11=_2.global.jQuery;
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if(arg.nodeType==11){
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var _12=_2._NodeListCtor;
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var _13=arguments[1];
if(_13&&_13._is$){
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_13=_2.query(_13)[0];
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}
var nl=_2.query.call(this,arg,_13);
_2._NodeListCtor=_12;
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if(_2.isFunction(arg)){
$.ready(arg);
return $;
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if(arg==document||arg==window){
return $._wrap([arg],null,$);
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}); | PypiClean |
/Jakaria08_distributions-0.1.tar.gz/Jakaria08_distributions-0.1/jakaria08_distributions/Binomialdistribution.py | import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Binomial(Distribution):
""" Binomial distribution class for calculating and
visualizing a Binomial distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) a list of floats to be extracted from the data file
p (float) representing the probability of an event occurring
n (int) number of trials
TODO: Fill out all functions below
"""
def __init__(self, prob=.5, size=20):
self.n = size
self.p = prob
Distribution.__init__(self, self.calculate_mean(), self.calculate_stdev())
def calculate_mean(self):
"""Function to calculate the mean from p and n
Args:
None
Returns:
float: mean of the data set
"""
self.mean = self.p * self.n
return self.mean
def calculate_stdev(self):
"""Function to calculate the standard deviation from p and n.
Args:
None
Returns:
float: standard deviation of the data set
"""
self.stdev = math.sqrt(self.n * self.p * (1 - self.p))
return self.stdev
def replace_stats_with_data(self):
"""Function to calculate p and n from the data set
Args:
None
Returns:
float: the p value
float: the n value
"""
self.n = len(self.data)
self.p = 1.0 * sum(self.data) / len(self.data)
self.mean = self.calculate_mean()
self.stdev = self.calculate_stdev()
def plot_bar(self):
"""Function to output a histogram of the instance variable data using
matplotlib pyplot library.
Args:
None
Returns:
None
"""
plt.bar(x = ['0', '1'], height = [(1 - self.p) * self.n, self.p * self.n])
plt.title('Bar Chart of Data')
plt.xlabel('outcome')
plt.ylabel('count')
def pdf(self, k):
"""Probability density function calculator for the gaussian distribution.
Args:
x (float): point for calculating the probability density function
Returns:
float: probability density function output
"""
a = math.factorial(self.n) / (math.factorial(k) * (math.factorial(self.n - k)))
b = (self.p ** k) * (1 - self.p) ** (self.n - k)
return a * b
def plot_bar_pdf(self):
"""Function to plot the pdf of the binomial distribution
Args:
None
Returns:
list: x values for the pdf plot
list: y values for the pdf plot
"""
x = []
y = []
# calculate the x values to visualize
for i in range(self.n + 1):
x.append(i)
y.append(self.pdf(i))
# make the plots
plt.bar(x, y)
plt.title('Distribution of Outcomes')
plt.ylabel('Probability')
plt.xlabel('Outcome')
plt.show()
return x, y
def __add__(self, other):
"""Function to add together two Binomial distributions with equal p
Args:
other (Binomial): Binomial instance
Returns:
Binomial: Binomial distribution
"""
try:
assert self.p == other.p, 'p values are not equal'
except AssertionError as error:
raise
result = Binomial()
result.n = self.n + other.n
result.p = self.p
result.calculate_mean()
result.calculate_stdev()
return result
def __repr__(self):
"""Function to output the characteristics of the Binomial instance
Args:
None
Returns:
string: characteristics of the Gaussian
"""
return "mean {}, standard deviation {}, p {}, n {}".\
format(self.mean, self.stdev, self.p, self.n) | PypiClean |
/FreePyBX-1.0-RC1.tar.gz/FreePyBX-1.0-RC1/freepybx/public/js/dojo/query.js.uncompressed.js | define("dojo/query", ["./_base/kernel", "./has", "./dom", "./on", "./_base/array", "./_base/lang", "./selector/_loader", "./selector/_loader!default"],
function(dojo, has, dom, on, array, lang, loader, defaultEngine){
"use strict";
has.add("array-extensible", function(){
// test to see if we can extend an array (not supported in old IE)
return lang.delegate([], {length: 1}).length == 1 && !has("bug-for-in-skips-shadowed");
});
var ap = Array.prototype, aps = ap.slice, apc = ap.concat, forEach = array.forEach;
var tnl = function(/*Array*/ a, /*dojo.NodeList?*/ parent, /*Function?*/ NodeListCtor){
// summary:
// decorate an array to make it look like a `dojo.NodeList`.
// a:
// Array of nodes to decorate.
// parent:
// An optional parent NodeList that generated the current
// list of nodes. Used to call _stash() so the parent NodeList
// can be accessed via end() later.
// NodeListCtor:
// An optional constructor function to use for any
// new NodeList calls. This allows a certain chain of
// NodeList calls to use a different object than dojo.NodeList.
var nodeList = new (NodeListCtor || this._NodeListCtor || nl)(a);
return parent ? nodeList._stash(parent) : nodeList;
};
var loopBody = function(f, a, o){
a = [0].concat(aps.call(a, 0));
o = o || dojo.global;
return function(node){
a[0] = node;
return f.apply(o, a);
};
};
// adapters
var adaptAsForEach = function(f, o){
// summary:
// adapts a single node function to be used in the forEach-type
// actions. The initial object is returned from the specialized
// function.
// f: Function
// a function to adapt
// o: Object?
// an optional context for f
return function(){
this.forEach(loopBody(f, arguments, o));
return this; // Object
};
};
var adaptAsMap = function(f, o){
// summary:
// adapts a single node function to be used in the map-type
// actions. The return is a new array of values, as via `dojo.map`
// f: Function
// a function to adapt
// o: Object?
// an optional context for f
return function(){
return this.map(loopBody(f, arguments, o));
};
};
var adaptAsFilter = function(f, o){
// summary:
// adapts a single node function to be used in the filter-type actions
// f: Function
// a function to adapt
// o: Object?
// an optional context for f
return function(){
return this.filter(loopBody(f, arguments, o));
};
};
var adaptWithCondition = function(f, g, o){
// summary:
// adapts a single node function to be used in the map-type
// actions, behaves like forEach() or map() depending on arguments
// f: Function
// a function to adapt
// g: Function
// a condition function, if true runs as map(), otherwise runs as forEach()
// o: Object?
// an optional context for f and g
return function(){
var a = arguments, body = loopBody(f, a, o);
if(g.call(o || dojo.global, a)){
return this.map(body); // self
}
this.forEach(body);
return this; // self
};
};
var NodeList = function(array){
// summary:
// dojo.NodeList is an of Array-like object which adds syntactic
// sugar for chaining, common iteration operations, animation, and
// node manipulation. NodeLists are most often returned as the
// result of dojo.query() calls.
// description:
// dojo.NodeList instances provide many utilities that reflect
// core Dojo APIs for Array iteration and manipulation, DOM
// manipulation, and event handling. Instead of needing to dig up
// functions in the dojo.* namespace, NodeLists generally make the
// full power of Dojo available for DOM manipulation tasks in a
// simple, chainable way.
// example:
// create a node list from a node
// | new dojo.NodeList(dojo.byId("foo"));
// example:
// get a NodeList from a CSS query and iterate on it
// | var l = dojo.query(".thinger");
// | l.forEach(function(node, index, nodeList){
// | console.log(index, node.innerHTML);
// | });
// example:
// use native and Dojo-provided array methods to manipulate a
// NodeList without needing to use dojo.* functions explicitly:
// | var l = dojo.query(".thinger");
// | // since NodeLists are real arrays, they have a length
// | // property that is both readable and writable and
// | // push/pop/shift/unshift methods
// | console.log(l.length);
// | l.push(dojo.create("span"));
// |
// | // dojo's normalized array methods work too:
// | console.log( l.indexOf(dojo.byId("foo")) );
// | // ...including the special "function as string" shorthand
// | console.log( l.every("item.nodeType == 1") );
// |
// | // NodeLists can be [..] indexed, or you can use the at()
// | // function to get specific items wrapped in a new NodeList:
// | var node = l[3]; // the 4th element
// | var newList = l.at(1, 3); // the 2nd and 4th elements
// example:
// the style functions you expect are all there too:
// | // style() as a getter...
// | var borders = dojo.query(".thinger").style("border");
// | // ...and as a setter:
// | dojo.query(".thinger").style("border", "1px solid black");
// | // class manipulation
// | dojo.query("li:nth-child(even)").addClass("even");
// | // even getting the coordinates of all the items
// | var coords = dojo.query(".thinger").coords();
// example:
// DOM manipulation functions from the dojo.* namespace area also
// available:
// | // remove all of the elements in the list from their
// | // parents (akin to "deleting" them from the document)
// | dojo.query(".thinger").orphan();
// | // place all elements in the list at the front of #foo
// | dojo.query(".thinger").place("foo", "first");
// example:
// Event handling couldn't be easier. `dojo.connect` is mapped in,
// and shortcut handlers are provided for most DOM events:
// | // like dojo.connect(), but with implicit scope
// | dojo.query("li").connect("onclick", console, "log");
// |
// | // many common event handlers are already available directly:
// | dojo.query("li").onclick(console, "log");
// | var toggleHovered = dojo.hitch(dojo, "toggleClass", "hovered");
// | dojo.query("p")
// | .onmouseenter(toggleHovered)
// | .onmouseleave(toggleHovered);
// example:
// chainability is a key advantage of NodeLists:
// | dojo.query(".thinger")
// | .onclick(function(e){ /* ... */ })
// | .at(1, 3, 8) // get a subset
// | .style("padding", "5px")
// | .forEach(console.log);
var isNew = this instanceof nl && has("array-extensible");
if(typeof array == "number"){
array = Array(array);
}
var nodeArray = (array && "length" in array) ? array : arguments;
if(isNew || !nodeArray.sort){
// make sure it's a real array before we pass it on to be wrapped
var target = isNew ? this : [],
l = target.length = nodeArray.length;
for(var i = 0; i < l; i++){
target[i] = nodeArray[i];
}
if(isNew){
// called with new operator, this means we are going to use this instance and push
// the nodes on to it. This is usually much faster since the NodeList properties
// don't need to be copied (unless the list of nodes is extremely large).
return target;
}
nodeArray = target;
}
// called without new operator, use a real array and copy prototype properties,
// this is slower and exists for back-compat. Should be removed in 2.0.
lang._mixin(nodeArray, nlp);
nodeArray._NodeListCtor = function(array){
// call without new operator to preserve back-compat behavior
return nl(array);
};
return nodeArray;
};
var nl = NodeList, nlp = nl.prototype =
has("array-extensible") ? [] : {};// extend an array if it is extensible
// expose adapters and the wrapper as private functions
nl._wrap = nlp._wrap = tnl;
nl._adaptAsMap = adaptAsMap;
nl._adaptAsForEach = adaptAsForEach;
nl._adaptAsFilter = adaptAsFilter;
nl._adaptWithCondition = adaptWithCondition;
// mass assignment
// add array redirectors
forEach(["slice", "splice"], function(name){
var f = ap[name];
//Use a copy of the this array via this.slice() to allow .end() to work right in the splice case.
// CANNOT apply ._stash()/end() to splice since it currently modifies
// the existing this array -- it would break backward compatibility if we copy the array before
// the splice so that we can use .end(). So only doing the stash option to this._wrap for slice.
nlp[name] = function(){ return this._wrap(f.apply(this, arguments), name == "slice" ? this : null); };
});
// concat should be here but some browsers with native NodeList have problems with it
// add array.js redirectors
forEach(["indexOf", "lastIndexOf", "every", "some"], function(name){
var f = array[name];
nlp[name] = function(){ return f.apply(dojo, [this].concat(aps.call(arguments, 0))); };
});
/*===== var NodeList = dojo.NodeList; =====*/
lang.extend(NodeList, {
// copy the constructors
constructor: nl,
_NodeListCtor: nl,
toString: function(){
// Array.prototype.toString can't be applied to objects, so we use join
return this.join(",");
},
_stash: function(parent){
// summary:
// private function to hold to a parent NodeList. end() to return the parent NodeList.
//
// example:
// How to make a `dojo.NodeList` method that only returns the third node in
// the dojo.NodeList but allows access to the original NodeList by using this._stash:
// | dojo.extend(dojo.NodeList, {
// | third: function(){
// | var newNodeList = dojo.NodeList(this[2]);
// | return newNodeList._stash(this);
// | }
// | });
// | // then see how _stash applies a sub-list, to be .end()'ed out of
// | dojo.query(".foo")
// | .third()
// | .addClass("thirdFoo")
// | .end()
// | // access to the orig .foo list
// | .removeClass("foo")
// |
//
this._parent = parent;
return this; //dojo.NodeList
},
on: function(eventName, listener){
// summary:
// Listen for events on the nodes in the NodeList. Basic usage is:
// | query(".my-class").on("click", listener);
// This supports event delegation by using selectors as the first argument with the event names as
// pseudo selectors. For example:
// | dojo.query("#my-list").on("li:click", listener);
// This will listen for click events within <li> elements that are inside the #my-list element.
// Because on supports CSS selector syntax, we can use comma-delimited events as well:
// | dojo.query("#my-list").on("li button:mouseover, li:click", listener);
var handles = this.map(function(node){
return on(node, eventName, listener); // TODO: apply to the NodeList so the same selector engine is used for matches
});
handles.remove = function(){
for(var i = 0; i < handles.length; i++){
handles[i].remove();
}
};
return handles;
},
end: function(){
// summary:
// Ends use of the current `dojo.NodeList` by returning the previous dojo.NodeList
// that generated the current dojo.NodeList.
// description:
// Returns the `dojo.NodeList` that generated the current `dojo.NodeList`. If there
// is no parent dojo.NodeList, an empty dojo.NodeList is returned.
// example:
// | dojo.query("a")
// | .filter(".disabled")
// | // operate on the anchors that only have a disabled class
// | .style("color", "grey")
// | .end()
// | // jump back to the list of anchors
// | .style(...)
//
if(this._parent){
return this._parent;
}else{
//Just return empty list.
return new this._NodeListCtor(0);
}
},
// http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array#Methods
// FIXME: handle return values for #3244
// http://trac.dojotoolkit.org/ticket/3244
// FIXME:
// need to wrap or implement:
// join (perhaps w/ innerHTML/outerHTML overload for toString() of items?)
// reduce
// reduceRight
/*=====
slice: function(begin, end){
// summary:
// Returns a new NodeList, maintaining this one in place
// description:
// This method behaves exactly like the Array.slice method
// with the caveat that it returns a dojo.NodeList and not a
// raw Array. For more details, see Mozilla's (slice
// documentation)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:slice]
// begin: Integer
// Can be a positive or negative integer, with positive
// integers noting the offset to begin at, and negative
// integers denoting an offset from the end (i.e., to the left
// of the end)
// end: Integer?
// Optional parameter to describe what position relative to
// the NodeList's zero index to end the slice at. Like begin,
// can be positive or negative.
return this._wrap(a.slice.apply(this, arguments));
},
splice: function(index, howmany, item){
// summary:
// Returns a new NodeList, manipulating this NodeList based on
// the arguments passed, potentially splicing in new elements
// at an offset, optionally deleting elements
// description:
// This method behaves exactly like the Array.splice method
// with the caveat that it returns a dojo.NodeList and not a
// raw Array. For more details, see Mozilla's (splice
// documentation)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:splice]
// For backwards compatibility, calling .end() on the spliced NodeList
// does not return the original NodeList -- splice alters the NodeList in place.
// index: Integer
// begin can be a positive or negative integer, with positive
// integers noting the offset to begin at, and negative
// integers denoting an offset from the end (i.e., to the left
// of the end)
// howmany: Integer?
// Optional parameter to describe what position relative to
// the NodeList's zero index to end the slice at. Like begin,
// can be positive or negative.
// item: Object...?
// Any number of optional parameters may be passed in to be
// spliced into the NodeList
// returns:
// dojo.NodeList
return this._wrap(a.splice.apply(this, arguments));
},
indexOf: function(value, fromIndex){
// summary:
// see dojo.indexOf(). The primary difference is that the acted-on
// array is implicitly this NodeList
// value: Object:
// The value to search for.
// fromIndex: Integer?:
// The location to start searching from. Optional. Defaults to 0.
// description:
// For more details on the behavior of indexOf, see Mozilla's
// (indexOf
// docs)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:indexOf]
// returns:
// Positive Integer or 0 for a match, -1 of not found.
return d.indexOf(this, value, fromIndex); // Integer
},
lastIndexOf: function(value, fromIndex){
// summary:
// see dojo.lastIndexOf(). The primary difference is that the
// acted-on array is implicitly this NodeList
// description:
// For more details on the behavior of lastIndexOf, see
// Mozilla's (lastIndexOf
// docs)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:lastIndexOf]
// value: Object
// The value to search for.
// fromIndex: Integer?
// The location to start searching from. Optional. Defaults to 0.
// returns:
// Positive Integer or 0 for a match, -1 of not found.
return d.lastIndexOf(this, value, fromIndex); // Integer
},
every: function(callback, thisObject){
// summary:
// see `dojo.every()` and the (Array.every
// docs)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:every].
// Takes the same structure of arguments and returns as
// dojo.every() with the caveat that the passed array is
// implicitly this NodeList
// callback: Function: the callback
// thisObject: Object?: the context
return d.every(this, callback, thisObject); // Boolean
},
some: function(callback, thisObject){
// summary:
// Takes the same structure of arguments and returns as
// `dojo.some()` with the caveat that the passed array is
// implicitly this NodeList. See `dojo.some()` and Mozilla's
// (Array.some
// documentation)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:some].
// callback: Function: the callback
// thisObject: Object?: the context
return d.some(this, callback, thisObject); // Boolean
},
=====*/
concat: function(item){
// summary:
// Returns a new NodeList comprised of items in this NodeList
// as well as items passed in as parameters
// description:
// This method behaves exactly like the Array.concat method
// with the caveat that it returns a `dojo.NodeList` and not a
// raw Array. For more details, see the (Array.concat
// docs)[http://developer.mozilla.org/en/docs/Core_JavaScript_1.5_Reference:Global_Objects:Array:concat]
// item: Object?
// Any number of optional parameters may be passed in to be
// spliced into the NodeList
// returns:
// dojo.NodeList
//return this._wrap(apc.apply(this, arguments));
// the line above won't work for the native NodeList :-(
// implementation notes:
// 1) Native NodeList is not an array, and cannot be used directly
// in concat() --- the latter doesn't recognize it as an array, and
// does not inline it, but append as a single entity.
// 2) On some browsers (e.g., Safari) the "constructor" property is
// read-only and cannot be changed. So we have to test for both
// native NodeList and dojo.NodeList in this property to recognize
// the node list.
var t = lang.isArray(this) ? this : aps.call(this, 0),
m = array.map(arguments, function(a){
return a && !lang.isArray(a) &&
(typeof NodeList != "undefined" && a.constructor === NodeList || a.constructor === this._NodeListCtor) ?
aps.call(a, 0) : a;
});
return this._wrap(apc.apply(t, m), this); // dojo.NodeList
},
map: function(/*Function*/ func, /*Function?*/ obj){
// summary:
// see dojo.map(). The primary difference is that the acted-on
// array is implicitly this NodeList and the return is a
// dojo.NodeList (a subclass of Array)
///return d.map(this, func, obj, d.NodeList); // dojo.NodeList
return this._wrap(array.map(this, func, obj), this); // dojo.NodeList
},
forEach: function(callback, thisObj){
// summary:
// see `dojo.forEach()`. The primary difference is that the acted-on
// array is implicitly this NodeList. If you want the option to break out
// of the forEach loop, use every() or some() instead.
forEach(this, callback, thisObj);
// non-standard return to allow easier chaining
return this; // dojo.NodeList
},
filter: function(/*String|Function*/ filter){
// summary:
// "masks" the built-in javascript filter() method (supported
// in Dojo via `dojo.filter`) to support passing a simple
// string filter in addition to supporting filtering function
// objects.
// filter:
// If a string, a CSS rule like ".thinger" or "div > span".
// example:
// "regular" JS filter syntax as exposed in dojo.filter:
// | dojo.query("*").filter(function(item){
// | // highlight every paragraph
// | return (item.nodeName == "p");
// | }).style("backgroundColor", "yellow");
// example:
// the same filtering using a CSS selector
// | dojo.query("*").filter("p").styles("backgroundColor", "yellow");
var a = arguments, items = this, start = 0;
if(typeof filter == "string"){ // inline'd type check
items = query._filterResult(this, a[0]);
if(a.length == 1){
// if we only got a string query, pass back the filtered results
return items._stash(this); // dojo.NodeList
}
// if we got a callback, run it over the filtered items
start = 1;
}
return this._wrap(array.filter(items, a[start], a[start + 1]), this); // dojo.NodeList
},
instantiate: function(/*String|Object*/ declaredClass, /*Object?*/ properties){
// summary:
// Create a new instance of a specified class, using the
// specified properties and each node in the nodeList as a
// srcNodeRef.
// example:
// Grabs all buttons in the page and converts them to diji.form.Buttons.
// | var buttons = dojo.query("button").instantiate("dijit.form.Button", {showLabel: true});
var c = lang.isFunction(declaredClass) ? declaredClass : lang.getObject(declaredClass);
properties = properties || {};
return this.forEach(function(node){
new c(properties, node);
}); // dojo.NodeList
},
at: function(/*===== index =====*/){
// summary:
// Returns a new NodeList comprised of items in this NodeList
// at the given index or indices.
//
// index: Integer...
// One or more 0-based indices of items in the current
// NodeList. A negative index will start at the end of the
// list and go backwards.
//
// example:
// Shorten the list to the first, second, and third elements
// | dojo.query("a").at(0, 1, 2).forEach(fn);
//
// example:
// Retrieve the first and last elements of a unordered list:
// | dojo.query("ul > li").at(0, -1).forEach(cb);
//
// example:
// Do something for the first element only, but end() out back to
// the original list and continue chaining:
// | dojo.query("a").at(0).onclick(fn).end().forEach(function(n){
// | console.log(n); // all anchors on the page.
// | })
//
// returns:
// dojo.NodeList
var t = new this._NodeListCtor(0);
forEach(arguments, function(i){
if(i < 0){ i = this.length + i; }
if(this[i]){ t.push(this[i]); }
}, this);
return t._stash(this); // dojo.NodeList
}
});
/*=====
dojo.query = function(selector, context){
// summary:
// This modules provides DOM querying functionality. The module export is a function
// that can be used to query for DOM nodes by CSS selector and returns a dojo.NodeList
// representing the matching nodes.
//
// selector: String
// A CSS selector to search for.
// context: String|DomNode?
// An optional context to limit the searching scope. Only nodes under `context` will be
// scanned.
//
// example:
// add an onclick handler to every submit button in the document
// which causes the form to be sent via Ajax instead:
// | define(["dojo/query"], function(query){
// | query("input[type='submit']").on("click", function(e){
// | dojo.stopEvent(e); // prevent sending the form
// | var btn = e.target;
// | dojo.xhrPost({
// | form: btn.form,
// | load: function(data){
// | // replace the form with the response
// | var div = dojo.doc.createElement("div");
// | dojo.place(div, btn.form, "after");
// | div.innerHTML = data;
// | dojo.style(btn.form, "display", "none");
// | }
// | });
// | });
//
// description:
// dojo/query is responsible for loading the appropriate query engine and wrapping
// its results with a `dojo.NodeList`. You can use dojo/query with a specific selector engine
// by using it as a plugin. For example, if you installed the sizzle package, you could
// use it as the selector engine with:
// | define("dojo/query!sizzle", function(query){
// | query("div")...
//
// The id after the ! can be a module id of the selector engine or one of the following values:
// | + acme: This is the default engine used by Dojo base, and will ensure that the full
// | Acme engine is always loaded.
// |
// | + css2: If the browser has a native selector engine, this will be used, otherwise a
// | very minimal lightweight selector engine will be loaded that can do simple CSS2 selectors
// | (by #id, .class, tag, and [name=value] attributes, with standard child or descendant (>)
// | operators) and nothing more.
// |
// | + css2.1: If the browser has a native selector engine, this will be used, otherwise the
// | full Acme engine will be loaded.
// |
// | + css3: If the browser has a native selector engine with support for CSS3 pseudo
// | selectors (most modern browsers except IE8), this will be used, otherwise the
// | full Acme engine will be loaded.
// |
// | + Or the module id of a selector engine can be used to explicitly choose the selector engine
//
// For example, if you are using CSS3 pseudo selectors in module, you can specify that
// you will need support them with:
// | define("dojo/query!css3", function(query){
// | query('#t > h3:nth-child(odd)')...
//
// You can also choose the selector engine/load configuration by setting the <FIXME:what is the configuration setting?>.
// For example:
// | <script data-dojo-config="query-selector:'css3'" src="dojo.js"></script>
//
return new dojo.NodeList(); // dojo.NodeList
};
=====*/
function queryForEngine(engine, NodeList){
var query = function(/*String*/ query, /*String|DOMNode?*/ root){
// summary:
// Returns nodes which match the given CSS selector, searching the
// entire document by default but optionally taking a node to scope
// the search by. Returns an instance of dojo.NodeList.
if(typeof root == "string"){
root = dom.byId(root);
if(!root){
return new NodeList([]);
}
}
var results = typeof query == "string" ? engine(query, root) : query.orphan ? query : [query];
if(results.orphan){
// already wrapped
return results;
}
return new NodeList(results);
};
query.matches = engine.match || function(node, selector, root){
// summary:
// Test to see if a node matches a selector
return query.filter([node], selector, root).length > 0;
};
// the engine provides a filtering function, use it to for matching
query.filter = engine.filter || function(nodes, selector, root){
// summary:
// Filters an array of nodes. Note that this does not guarantee to return a dojo.NodeList, just an array.
return query(selector, root).filter(function(node){
return array.indexOf(nodes, node) > -1;
});
};
if(typeof engine != "function"){
var search = engine.search;
engine = function(selector, root){
// Slick does it backwards (or everyone else does it backwards, probably the latter)
return search(root || document, selector);
};
}
return query;
}
var query = queryForEngine(defaultEngine, NodeList);
// the query that is returned from this module is slightly different than dojo.query,
// because dojo.query has to maintain backwards compatibility with returning a
// true array which has performance problems. The query returned from the module
// does not use true arrays, but rather inherits from Array, making it much faster to
// instantiate.
dojo.query = queryForEngine(defaultEngine, function(array){
// call it without the new operator to invoke the back-compat behavior that returns a true array
return NodeList(array);
});
query.load = /*===== dojo.query.load= ======*/ function(id, parentRequire, loaded, config){
// summary: can be used as AMD plugin to conditionally load new query engine
// example:
// | define(["dojo/query!custom"], function(qsa){
// | // loaded selector/custom.js as engine
// | qsa("#foobar").forEach(...);
// | });
loader.load(id, parentRequire, function(engine){
loaded(queryForEngine(engine, NodeList));
});
};
dojo._filterQueryResult = query._filterResult = function(nodes, selector, root){
return new NodeList(query.filter(nodes, selector, root));
};
dojo.NodeList = query.NodeList = NodeList;
return query;
}); | PypiClean |
/HousePredicition-0.0.1.tar.gz/HousePredicition-0.0.1/src/house_prediction/ingest_data.py | import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os.path
import argparse
import mlflow
import mlflow.sklearn
import logging
remote_server_uri = "http://0.0.0.0:5000" # set to your server URI
mlflow.set_tracking_uri(remote_server_uri)
parser = argparse.ArgumentParser()
parser.add_argument(
"outdir",
type=str,
help="Output dir for data",
nargs="?",
default="data/processed/",
)
parser.add_argument(
"--log-level",
default=logging.INFO,
type=lambda x: getattr(logging, x),
help="Configure the logging level.",
)
parser.add_argument(
"--no-console",
default=True,
type=bool,
help="Configure the logging console.",
)
parser.add_argument(
"saveLogs",
type=str,
help="Output dir for data",
nargs="?",
default="logs/ingest_data.log",
)
p = parser.parse_args()
# import data
housing = pd.read_csv("data/raw/housing.csv")
logger = logging.getLogger(__name__)
logger.setLevel(level=p.log_level)
sh = logging.StreamHandler()
file_handler = logging.FileHandler(p.saveLogs)
logger.addHandler(file_handler)
console = p.no_console
if console:
logger.addHandler(sh)
def data_prepration(housing):
housing["income_cat"] = pd.cut(
housing["median_income"],
bins=[0.0, 1.5, 3.0, 4.5, 6.0, np.inf],
labels=[1, 2, 3, 4, 5],
)
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing["income_cat"]):
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
for set_ in (strat_train_set, strat_test_set):
set_.drop("income_cat", axis=1, inplace=True)
housing = strat_train_set.drop("median_house_value", axis=1)
housing_labels = strat_train_set["median_house_value"].copy()
imputer = SimpleImputer(strategy="median")
housing_num = housing.drop("ocean_proximity", axis=1)
imputer.fit(housing_num)
X = imputer.transform(housing_num)
housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing.index)
housing_tr["rooms_per_household"] = (
housing_tr["total_rooms"] / housing_tr["households"]
)
housing_tr["bedrooms_per_room"] = (
housing_tr["total_bedrooms"] / housing_tr["total_rooms"]
)
housing_tr["population_per_household"] = (
housing_tr["population"] / housing_tr["households"]
)
housing_cat = housing[["ocean_proximity"]]
housing_prepared = housing_tr.join(pd.get_dummies(housing_cat, drop_first=True))
logger.info(f"Data Prepared")
return housing_prepared, housing_labels, strat_test_set
housing_prepared, housing_labels, strat_test_set = data_prepration(housing)
housing_prepared.to_csv(
os.path.join(p.outdir, "housing_prepared.csv"), header=True, index=False
)
housing_labels.to_csv(
os.path.join(p.outdir, "housing_labels.csv"), header=True, index=False
)
strat_test_set.to_csv(
os.path.join(p.outdir, "strat_test_set.csv"), header=True, index=False
)
experiment_name = "MLE_TRAINING"
try:
exp_id = mlflow.create_experiment(name=experiment_name)
except Exception as e:
exp_id = mlflow.get_experiment_by_name(experiment_name).experiment_id
with mlflow.start_run(experiment_id=exp_id):
mlflow.log_artifacts(p.outdir)
mlflow.log_artifact("data/raw/housing.csv") | PypiClean |
/GNS-1.0-py3-none-any.whl/gns/keeton_calculations.py | import numpy as np
import scipy
#import custom modules
from . import calculations
from . import tools
################Calculate Z moments and H a-posteri using Keeton's methods
def calcZMomentsKeeton(Lhoods, nLive, nest):
"""
calculate Z moments a-posteri with full list of Lhoods used in NS loop,
using equations given in Keeton
TODO: consider Keeton equations using trapezium rule
"""
EofZ = calcEofZKeeton(Lhoods, nLive, nest)
EofZ2 = calcEofZ2Keeton(Lhoods, nLive, nest)
return EofZ, EofZ2
def calcEofZKeeton(Lhoods, nLive, nest):
"""
Calculate first moment of Z from main NS loop.
According to paper, this is just E[Z] = 1. / nLive * sum_i^nest L_i * E[t]^i
"""
EoftArr = calculations.getEofftArr(calculations.EoftPowi, nLive, nest)
LEoft = Lhoods * EoftArr
return 1. / nLive * LEoft.sum()
def calcEofZ2Keeton(Lhoods, nLive, nest):
"""
Calculate second (raw) moment of Z from main NS loop (equation 22 Keeton)
"""
const = 2. / (nLive * (nLive + 1.))
summations = calcSums(Lhoods, nLive, nest)
return const * summations
def calcSums(Lhoods, nLive, nest):
"""
Calculate double summation in equation (22) of Keeton using two generator (yielding) functions.
First one creates array associated with index of inner sum (which is subsequently summed).
Second one creates array of summed inner sums, which is then multiplied by array of
L_k * E[t]^k terms to give outer summation terms.
Outer summation terms are added together to give total of double sum.
"""
EoftArr = calculations.getEofftArr(calculations.EoftPowi, nLive, nest)
LEoft = Lhoods * EoftArr
innerSums = np.fromiter(calcInnerSums(Lhoods, nLive, nest), dtype = float, count = nest)
outerSums = LEoft * innerSums
return outerSums.sum()
def calcInnerSums(Lhoods, nLive, nest):
"""
Second generator (yielding) function, which returns inner sum for outer index k
"""
for k in range(1, nest + 1):
Eoft2OverEoftArr = calculations.getEofftArr(calculations.Eoft2OverEoftPowi, nLive, k)
innerTerms = Lhoods[:k] * Eoft2OverEoftArr
innerSum = innerTerms.sum()
yield innerSum
def calcSumsLoop(Lhoods, nLive, nest):
"""
Calculate double summation in equation (22) of Keeton using double for loop (one for each summation).
Inefficient (I think) but easy
"""
total = 0.
for k in range(1, nest + 1):
innerSum = 0.
for i in range(1, k + 1):
innerSum += Lhoods[i-1] * calculations.Eoft2OverEoftPowi(nLive, i)
outerSum = Lhoods[k-1] * calculations.EoftPowi(nLive, k) * innerSum
total += outerSum
return total
def calcHKeeton(EofZ, Lhoods, nLive, nest):
"""
Calculate H from KL divergence equation transformed to LX space
as given in Keeton.
Note this calculates contribution to H from main NS loop
TODO: consider Keeton equations using trapezium rule
"""
sumTerms = Lhoods * np.log(Lhoods) * calculations.getEofftArr(calculations.EoftPowi, nLive, nest)
sumTerm = 1. / nLive * sumTerms.sum()
return 1. / EofZ * sumTerm - np.log(EofZ)
def calcZMomentsKeetonLog(LLhoods, nLive, nest):
"""
Calculate logs of Z moments based on Keeton's equations
"""
logEofZ = calcEofZKeetonLog(LLhoods, nLive, nest)
logEofZ2 = calcEofZ2KeetonLog(LLhoods, nLive, nest)
return logEofZ, logEofZ2
def calcEofZKeetonLog(LLhoods, nLive, nest):
"""
Calculate log of first moment of Z from main NS loop.
based on function calcEofZKeeton
"""
logEoftArr = np.log(calculations.getEofftArr(calculations.EoftPowi, nLive, nest))
logLEoft = LLhoods + logEoftArr
#return tools.logAddArr2(-np.inf, logLEoft) - np.log(nLive)
return scipy.misc.logsumexp(logLEoft) - np.log(nLive)
def calcEofZ2KeetonLog(LLhoods, nLive, nest):
"""
calculates logEofZ^2 based on Keeton's equations.
Based on functions calcSums, calcInnerSums and calcSumsLoop
"""
const = np.log(2. / (nLive * (nLive + 1.)))
logSums = calcLogSums(LLhoods, nLive, nest)
return const + logSums
def calcLogSums(LLhoods, nLive, nest):
"""
Based on calcSums function
"""
logEoftArr = np.log(calculations.getEofftArr(calculations.EoftPowi, nLive, nest))
logLEoft = LLhoods + logEoftArr
innerLogSums = np.fromiter(calcInnerLogSums(LLhoods, nLive, nest), dtype = float, count = nest)
outerLogSums = logLEoft + innerLogSums
#return tools.logAddArr2(-np.inf, outerLogSums)
return scipy.misc.logsumexp(outerLogSums)
def calcInnerLogSums(LLhoods, nLive, nest):
"""
based on calcInnerSums function
"""
for k in range(1, nest + 1):
logEoft2OverEoftArr = np.log(calculations.getEofftArr(calculations.Eoft2OverEoftPowi, nLive, k))
innerLogTerms = LLhoods[:k] + logEoft2OverEoftArr
#innerLogSum = tools.logAddArr2(-np.inf, innerLogTerms)
innerLogSum = scipy.misc.logsumexp(innerLogTerms)
yield innerLogSum
def calcLogSumsLoop(LLhoods, nLive, nest):
"""
Based on calcSumsLoop function
"""
total = -np.inf
for k in range(1, nest + 1):
innerLogSum = -np.inf
for i in range(1, k + 1):
innerLogSum = np.logaddexp(innerLogSum, LLhoods[i-1] + np.log(calculations.Eoft2OverEoftPowi(nLive, i)))
outerLogSum = LLhoods[k-1] + np.log(calculations.EoftPowi(nLive, k)) + innerLogSum
total = np.logaddexp(total, outerLogSum)
return total
def calcHKeetonLog(logEofZ, LLhoods, nLive, nest):
"""
Based on calcHKeeton() function
Doesn't actually work in log-space, this is to prevent numerical difficulties e.g.
log(negative number) associated with negative values of LLhoods and logZ (from low L, Z values)
"""
LovrZ = np.exp(LLhoods - logEofZ) #hopefully this shouldn't under / over flow
sumTerms = LovrZ * LLhoods * calculations.getEofftArr(calculations.EoftPowi, nLive, nest)
sumTerm = 1. / nLive * sumTerms.sum()
return sumTerm - logEofZ
def calcHKeetonLog2(logEofZ, LLhoods, nLive, nest):
"""
Based on calcHKeeton() function
Works in log-space, but will screw up for low values of
L and Z since log(L) ~ negative and log(negative) undefined
"""
logSumTerms = np.log(LLhoods) + LLhoods + np.log(calculations.getEofftArr(calculations.EoftPowi, nLive, nest))
#logSumTerm = np.log(1. / logEofZ) + np.log(1. / nLive) + tools.logAddArr2(-np.inf, logSumTerms)
logSumTerm = np.log(1. / logEofZ) + np.log(1. / nLive) + scipy.misc.logsumexp(logSumTerms)
maxTerm = max(logSumTerm, logEofZ)
logH = tools.logSubExp(logSumTerm, np.log(logEofZ), logSumTerm)
return np.exp(logH)
#return logH
def calcZMomentsFinalKeeton(finalLhoods, nLive, nest):
"""
calculate Z moments a-posteri with list of final Lhood points (ones remaining at termination of main loop),
using equations given in Keeton
TODO: consider Keeton equations using trapezium rule
TODO: consider different ways of handling final livepoints (i.e. average over X or max Lhood)
"""
EofZ = calcEofZFinalKeeton(finalLhoods, nLive, nest)
EofZ2 = calcEofZ2FinalKeeton(finalLhoods, nLive, nest)
return EofZ, EofZ2
def calcEofZFinalKeeton(finalLhoods, nLive, nest):
"""
Averages over Lhood, which I don't think is the correct thing to do as it doesn't correspond to a unique parameter vector value.
However this gives same value for Z as by averaging over X
TODO: consider other ways of getting final contribution from livepoints with Keeton's method
"""
LhoodAv = finalLhoods.mean()
EofFinalX = calculations.EoftPowi(nLive, nest)
return EofFinalX * LhoodAv
def calcEofZ2FinalKeeton(finalLhoods, nLive, nest):
"""
Averages over Lhood, which I don't think is the correct thing to do as it doesn't correspond to a unique parameter vector value.
TODO: consider other ways of getting final contribution from livepoints with Keeton's method
"""
LhoodAv = finalLhoods.mean()
EofFinalX2 = calculations.Eoft2Powi(nLive, nest)
return LhoodAv**2. * EofFinalX2
def calcEofZZFinalKeeton(Lhoods, finalLhoods, nLive, nest):
"""
Averages over Lhood for contribution from final points,
which I don't think is the correct thing to do as it doesn't correspond to a unique parameter vector value.
TODO: consider other ways of getting final contribution from livepoints with Keeton's method
"""
finalLhoodAv = finalLhoods.mean()
finalTerm = finalLhoodAv / (nLive + 1.) * calculations.EoftPowi(nLive, nest)
Eoft2OverEoftArr = calculations.getEofftArr(calculations.Eoft2OverEoftPowi, nLive, nest)
loopTerms = Lhoods * Eoft2OverEoftArr
loopTerm = loopTerms.sum()
return finalTerm * loopTerm
def calcHTotalKeeton(EofZ, Lhoods, nLive, nest, finalLhoods):
"""
Calculates total value of H based on KL divergence equation transformed to
LX space as given in Keeton.
Uses H function used to calculate loop H value (but with total Z), and adapts
final result to give HTotal
Note EofZ corresponds to total EofZ
TODO: consider Keeton equations using trapezium rule
"""
LAv = finalLhoods.mean()
HPartial = calcHKeeton(EofZ, Lhoods, nLive, nest)
return HPartial + 1. / EofZ * LAv * np.log(LAv) * calculations.EoftPowi(nLive, nest) #n.b. the 2nd contribution isn't HFinal, as we are dividing by EofZTotal not EofZFinal
def calcZMomentsFinalKeetonLog(finalLLhoods, nLive, nest):
"""
Calculate log of moments of Z from contribution
after main NS loop
"""
logEofZ = calcEofZFinalKeetonLog(finalLLhoods, nLive, nest)
logEofZ2 = calcEofZ2FinalKeetonLog(finalLLhoods, nLive, nest)
return logEofZ, logEofZ2
def calcEofZFinalKeetonLog(finalLLhoods, nLive, nest):
"""
Based on function calcEofZFinalKeeton().
"""
#logLhoodAv = tools.logAddArr2(-np.inf, finalLLhoods) - np.log(nLive)
logLhoodAv = scipy.misc.logsumexp(finalLLhoods) - np.log(nLive)
logEofFinalX = np.log(calculations.EoftPowi(nLive, nest))
return logLhoodAv + logEofFinalX
def calcEofZ2FinalKeetonLog(finalLLhoods, nLive, nest):
"""
Based on function calcEofZ2FinalKeetonLog()
"""
#logLhoodAv = tools.logAddArr2(-np.inf, finalLLhoods) - np.log(nLive)
logLhoodAv = scipy.misc.logsumexp(finalLLhoods) - np.log(nLive)
logEofFinalX2 = np.log(calculations.Eoft2Powi(nLive, nest))
return 2. * logLhoodAv + logEofFinalX2
def calcEofZZFinalKeetonLog(LLhoods, finalLLhoods, nLive, nest):
"""
Based on function calcEofZZFinalKeeton()
"""
#logFinalLhoodAv = tools.logAddArr2(-np.inf, finalLLhoods) - np.log(nLive)
logFinalLhoodAv = scipy.misc.logsumexp(finalLLhoods) - np.log(nLive)
finalTerm = logFinalLhoodAv - np.log(nLive + 1.) + np.log(calculations.EoftPowi(nLive, nest))
logEoft2OverEoftArr = np.log(calculations.getEofftArr(calculations.Eoft2OverEoftPowi, nLive, nest))
loopTerms = LLhoods + logEoft2OverEoftArr
#loopTerm = tools.logAddArr2(-np.inf, loopTerms)
loopTerm = scipy.misc.logsumexp(loopTerms)
return finalTerm + loopTerm
def calcHTotalKeetonLog(logEofZ, LLhoods, nLive, nest, finalLLhoods):
"""
Based on function calcHTotalKeeton()
Again mainly doesn't work in log-space to avoid undefined
function evaluations
"""
#logFinalLhoodAv = tools.logAddArr2(-np.inf, finalLLhoods) - np.log(nLive)
logFinalLhoodAv = scipy.misc.logsumexp(finalLLhoods) - np.log(nLive)
HPartial = calcHKeetonLog(logEofZ, LLhoods, nLive, nest)
LAvOverZ = np.exp(logFinalLhoodAv - logEofZ)
HPartial2 = LAvOverZ * logFinalLhoodAv * calculations.EoftPowi(nLive, nest)
return HPartial + HPartial2
def calcHTotalKeetonLog2(logEofZ, LLhoods, nLive, nest, finalLLhoods):
"""
Based on function calcHTotalKeeton()
Again mainly works in log-space so can mess up for
small L, Z
"""
#logFinalLhoodAv = tools.logAddArr2(-np.inf, finalLLhoods) - np.log(nLive)
logFinalLhoodAv = scipy.misc.logsumexp(finalLLhoods) - np.log(nLive)
HPartial = calcHKeetonLog2(logEofZ, LLhoods, nLive, nest)
#logHPartial = calcHKeetonLog2(logEofZ, LLhoods, nLive, nest)
logHPartial2 = - logEofZ + logFinalLhoodAv + np.log(logFinalLhoodAv) + np.log(calculations.EoftPowi(nLive, nest))
H = HPartial + np.exp(logHPartial2)
return H
#logH = np.logaddexp(logHPartial, logHPartial2)
#return logH
#Functions for combining contributions from main NS loop and termination ('final' quantities) for estimate or Z and its error
def getEofZTotalKeeton(EofZ, EofZFinal):
"""
get total from NS loop and final contributions
"""
return EofZ + EofZFinal
def getEofZ2TotalKeeton(EofZ2, EofZ2Final, EofZZFinal):
"""
get total from NS loop and final contributions
"""
return EofZ2 + EofZ2Final + 2. * EofZZFinal
def getVarTotalKeeton(varZ, varZFinal, EofZ, EofZFinal, EofZZFinal):
"""
Get total variance from NS loop and final contributions.
For recursive method, since E[ZLive] = E[ZTot] etc.,
and assuming that the recurrence relations account for the covariance between
Z and ZFinal, this is just varZFinal.
For Keeton's method, have to explicitly account for correlation as expectations for Z and ZLive are essentially calculated independently
TODO: check if recurrence relations of Z and ZFinal properly account for correlation between two ANSWER: they do not
"""
return varZ + varZFinal + 2. * (EofZZFinal - EofZ * EofZFinal)
def getVarTotalKeetonLog(logVarZ, logVarZFinal, logEofZ, logEofZFinal, logEofZZFinal):
"""
Get log of total variance based on Keeton's equations
"""
#positiveTerms = tools.logAddArr2(-np.inf, np.array([logVarZ, logVarZFinal, np.log(2.) + logEofZZFinal]))
positiveTerms = scipy.misc.logsumexp(np.array([logVarZ, logVarZFinal, np.log(2.) + logEofZZFinal]))
return tools.logSubExp(positiveTerms, np.log(2.) + logEofZ + logEofZFinal, positiveTerms)
def getEofZTotalKeetonLog(logEofZ, logEofZFinal):
"""
Get log of EofZ total from loop and final contributions
"""
return np.logaddexp(logEofZ, logEofZFinal)
def getEofZ2TotalKeetonLog(logEofZ2, logEofZ2Final, logEofZZFinal):
"""
Get log of EofZ^2 total from loop and final contributions
"""
return scipy.misc.logsumexp([logEofZ2, logEofZ2Final, np.log(2.) + logEofZZFinal]) | PypiClean |
/Djblets-3.3.tar.gz/Djblets-3.3/djblets/privacy/consent/views.py |
from functools import wraps
from django.conf import settings
from django.core.exceptions import ImproperlyConfigured
from django.http import HttpResponseRedirect
from django.utils.decorators import method_decorator
from djblets.privacy.consent import (Consent,
get_consent_requirements_registry,
get_consent_tracker)
from djblets.privacy.consent.common import PolicyConsentRequirement
_CONSENT_REDIRECT_SETTING = 'DJBLETS_PRIVACY_PENDING_CONSENT_REDIRECT_URL'
def check_pending_consent(view):
"""A decorator for ensuring the user has no pending consent requirements.
If the user does, they will be redirected to
``settings.DJBLETS_PRIVACY_PENDING_CONSENT_REDIRECT_URL``.
Args:
view (callable):
The view to decorate
Returns:
callable:
The decorated view.
"""
@wraps(view)
def decorated(request, *args, **kwargs):
user = request.user
if user.is_authenticated:
pending_requirements = \
get_consent_tracker().get_pending_consent_requirements(user)
policy_requirement = \
get_consent_requirements_registry().get_consent_requirement(
PolicyConsentRequirement.requirement_id)
if (pending_requirements or
(policy_requirement is not None and
(policy_requirement.get_consent(user) !=
Consent.GRANTED))):
redirect_url = getattr(settings, _CONSENT_REDIRECT_SETTING,
None)
if redirect_url is None:
raise ImproperlyConfigured(
'settings.%s must be set.' % _CONSENT_REDIRECT_SETTING
)
if callable(redirect_url):
redirect_url = redirect_url(request)
return HttpResponseRedirect(redirect_url)
return view(request, *args, **kwargs)
return decorated
class CheckPendingConsentMixin(object):
"""A view mixin for ensuring the user has no pending consent requirements.
If the user does, they will be redirected to
``settings.DJBLETS_PRIVACY_PENDING_CONSENT_REDIRECT_URL``
This mixin requires the use of
:py:class:`~djblets.views.generic.base.PrePostDispatchViewMixin`.
"""
@method_decorator(check_pending_consent)
def pre_dispatch(self, *args, **kwargs):
"""Dispatch the request.
Args:
*args (tuple):
Positional arguments from the URL resolver.
**kwargs (dict):
Keyword arguments from the URL resolver.
Returns:
django.http.HttpResponse:
Either a redirect or ``None``.
"""
return super(CheckPendingConsentMixin, self).pre_dispatch(
*args, **kwargs) | PypiClean |
/JsonTest-1.4.tar.gz/JsonTest-1.4/README.md | # JsonTest
`JsonTest` is a tiny metaclass designed for automatically generating tests based off JSON files. Originally built for testing [`ElasticQuery`](https://github.com/Fizzadar/ElasticQuery). Install with `pip install jsontest`.
## Synopsis
```py
from jsontest import JsonTest
class MyTests(TestCase):
# Set the metaclass to JsonTest
__metaclass__ = JsonTest
# Tell JsonTest where to find our JSON files
jsontest_files = path.join('tests', 'filters')
# Optional prefix for the test names
jsontest_prefix = 'test_'
# Define a function to run against each file
def jsontest_function(self, test_name, test_data):
print(test_name, test_data)
self.assertFalse(False)
```
| PypiClean |
/MapProxy-1.16.0.tar.gz/MapProxy-1.16.0/doc/inspire.rst | .. _inpire:
.. highlight:: yaml
INSPIRE View Service
====================
MapProxy can act as an INSPIRE View Service. A View Service is a WMS 1.3.0 with an extended capabilities document.
.. versionadded:: 1.8.1
INSPIRE Metadata
----------------
A View Service can either link to an existing metadata document or it can embed the service and layer metadata.
These two options are described as Scenario 1 and 2 in the Technical Guidance document.
Linked Metadata
^^^^^^^^^^^^^^^
Scenario 1 uses links to existing INSPIRE Discovery Services (CSW). You can link to metadata documents for the service and each layer.
For services you need to use the ``inspire_md`` block inside ``services.wms`` with ``type: linked``.
For example::
services:
wms:
md:
title: Example INSPIRE View Service
inspire_md:
type: linked
metadata_url:
media_type: application/vnd.iso.19139+xml
url: http://example.org/csw/doc
languages:
default: eng
The View Services specification uses the WMS 1.3.0 extended capabilities for the layers metadata.
Refer to the :ref:`layers metadata documentation<layer_metadata>`.
For example::
layers:
- name: example_layer
title: Example Layer
md:
metadata:
- url: http://example.org/csw/layerdoc
type: ISO19115:2003
format: text/xml
Embedded Metadata
^^^^^^^^^^^^^^^^^
Scenario 2 embeds the metadata directly into the capabilities document.
Some metadata elements are mapped to an equivalent element in the WMS capabilities. The Resource Title is set with the normal `title` option for example. Other elements need to be configured inside the ``inspire_md`` block with ``type: embedded``.
Here is a full example::
services:
wms:
md:
title: Example INSPIRE View Service
abstract: This is an example service with embedded INSPIRE metadata.
online_resource: http://example.org/
contact:
person: Your Name Here
position: Technical Director
organization: Acme Inc.
address: Fakestreet 123
city: Somewhere
postcode: 12345
country: Germany
phone: +49(0)000-000000-0
fax: +49(0)000-000000-0
email: info@example.org
access_constraints: constraints
fees: 'None'
keyword_list:
- vocabulary: GEMET
keywords: [Orthoimagery]
inspire_md:
type: embedded
resource_locators:
- url: http://example.org/metadata
media_type: application/vnd.iso.19139+xml
temporal_reference:
date_of_creation: 2015-05-01
metadata_points_of_contact:
- organisation_name: Acme Inc.
email: acme@example.org
conformities:
- title:
COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services
date_of_publication: 2010-12-08
uris:
- OJ:L:2010:323:0011:0102:EN:PDF
resource_locators:
- url: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:323:0011:0102:EN:PDF
media_type: application/pdf
degree: notEvaluated
mandatory_keywords:
- infoMapAccessService
- humanGeographicViewer
keywords:
- title: GEMET - INSPIRE themes
date_of_last_revision: 2008-06-01
keyword_value: Orthoimagery
metadata_date: 2015-07-23
metadata_url:
media_type: application/vnd.iso.19139+xml
url: http://example.org/csw/doc
You can express all dates as either ``date_of_creation``, ``date_of_publication`` or ``date_of_last_revision``.
The View Services specification uses the WMS 1.3.0 extended capabilities for the layers metadata.
Refer to the :ref:`layers metadata documentation<layer_metadata>` for all available options.
For example::
layers:
- name: example_layer
title: Example Layer
legendurl: http://example.org/example_legend.png
md:
abstract: Some abstract
keyword_list:
- vocabulary: GEMET
keywords: [Orthoimagery]
metadata:
- url: http://example.org/csw/layerdoc
type: ISO19115:2003
format: text/xml
identifier:
- url: http://www.example.org
name: example.org
value: "http://www.example.org#cf3c8572-601f-4f47-a922-6c67d388d220"
Languages
---------
A View Service always needs to indicate the language of the layer names, abstracts, map labels, etc..
You can only configure a single language as MapProxy does not support multi-lingual configurations.
You need to set the default language as a `ISO 639-2/alpha-3 <https://www.loc.gov/standards/iso639-2/php/code_list.php>`_ code:
::
inspire_md:
languages:
default: eng
....
| PypiClean |
/Dts-OpenFisca-Core-34.8.0.tar.gz/Dts-OpenFisca-Core-34.8.0/openfisca_web_api/loader/parameters.py |
from openfisca_core.parameters import Parameter, ParameterNode, Scale
def build_api_values_history(values_history):
api_values_history = {}
for value_at_instant in values_history.values_list:
api_values_history[value_at_instant.instant_str] = value_at_instant.value
return api_values_history
def get_value(date, values):
candidates = sorted([
(start_date, value)
for start_date, value in values.items()
if start_date <= date # dates are lexicographically ordered and can be sorted
], reverse = True)
if candidates:
return candidates[0][1]
else:
return None
def build_api_scale(scale, value_key_name):
# preprocess brackets for a scale with 'rates' or 'amounts'
brackets = [{
'thresholds': build_api_values_history(bracket.threshold),
'values': build_api_values_history(getattr(bracket, value_key_name))
} for bracket in scale.brackets]
dates = set(sum(
[list(bracket['thresholds'].keys())
+ list(bracket['values'].keys()) for bracket in brackets],
[])) # flatten the dates and remove duplicates
# We iterate on all dates as we need to build the whole scale for each of them
api_scale = {}
for date in dates:
for bracket in brackets:
threshold_value = get_value(date, bracket['thresholds'])
if threshold_value is not None:
rate_or_amount_value = get_value(date, bracket['values'])
api_scale[date] = api_scale.get(date) or {}
api_scale[date][threshold_value] = rate_or_amount_value
# Handle stopped parameters: a parameter is stopped if its first bracket is stopped
latest_date_first_threshold = max(brackets[0]['thresholds'].keys())
latest_value_first_threshold = brackets[0]['thresholds'][latest_date_first_threshold]
if latest_value_first_threshold is None:
api_scale[latest_date_first_threshold] = None
return api_scale
def build_source_url(absolute_file_path, country_package_metadata):
relative_path = absolute_file_path.replace(country_package_metadata['location'], '')
return '{}/blob/{}{}'.format(
country_package_metadata['repository_url'],
country_package_metadata['version'],
relative_path
)
def build_api_parameter(parameter, country_package_metadata):
api_parameter = {
'description': getattr(parameter, "description", None),
'id': parameter.name,
'metadata': parameter.metadata
}
if parameter.file_path:
api_parameter['source'] = build_source_url(parameter.file_path, country_package_metadata)
if isinstance(parameter, Parameter):
if parameter.documentation:
api_parameter['documentation'] = parameter.documentation.strip()
api_parameter['values'] = build_api_values_history(parameter)
elif isinstance(parameter, Scale):
if 'rate' in parameter.brackets[0].children:
api_parameter['brackets'] = build_api_scale(parameter, 'rate')
elif 'amount' in parameter.brackets[0].children:
api_parameter['brackets'] = build_api_scale(parameter, 'amount')
elif isinstance(parameter, ParameterNode):
if parameter.documentation:
api_parameter['documentation'] = parameter.documentation.strip()
api_parameter['subparams'] = {
child_name: {
'description': child.description,
}
for child_name, child in parameter.children.items()
}
return api_parameter
def build_parameters(tax_benefit_system, country_package_metadata):
return {
parameter.name.replace('.', '/'): build_api_parameter(parameter, country_package_metadata)
for parameter in tax_benefit_system.parameters.get_descendants()
} | PypiClean |
/MDTools-0.0.2.tar.gz/MDTools-0.0.2/mdtools/metadata.py | from collections import OrderedDict as OD
from lxml import etree
from copy import deepcopy
class Metadata(object):
"""
Generic class to hold metadata nodes. Nodes can be added by simply calling Metadata.add_node(node_object)
Alternatively nodes can be input with a file parser.
Attributes
----------
fname: string
The path and name of a flat file that is going to be parsed
root: int
The root node of the xml tree, this is set automatically by the file parser, but must be set manually for all other types of object creation
etree_nodes: OrderedDict
A dictionary of exported nodes. After nodes have been exported into etree elements they are stored here. They can be accessed by UID key.
nodes: Dictionary
A dictionary of all the nodes, all of which are of class MD_node, and can be accessed by UID key.
tree: lxml etree
The xml etree of the entire Metadata object
tree_struct: Dictionary
Holds the tree structure as a dictionary in the form {Parent_node: [child_node1, child_node2...child_noden]}
"""
def __init__(self, fname = ""):
# The file name to parse when we get there
self.fname = fname
# the root node ID
self.root = 0
# Holds exported nodes as individual etree elements
self.etree_nodes = OD()
# Dictionary of nodes referenced by UID
self.nodes = {}
if fname:
self.ingest()
# Holds the etree from parsed input
self.tree = None
# Holds tree strucure
self.tree_struct = {}
def add_node(self,MD_node):
"""
Just adds a node to the dictionary
"""
self.nodes[MD_node.UID] = MD_node
def ingest(self):
"""
Parse a flat file into a metadata object. Takes a file specified by the creation of the metadata object and populates nodes. Format is as follows.
The following three headers are a requirement and must be specified in order:
Parent_ID: <ID or None for root node>
Node_ID: <ID for the node>
Node_Name: <Name of the node>
The remaining entries are free form and specified as:
<element> : <value>
If a given element has attributes they can be specified with a "-'' character
<element> : <value> -- <attribute name> : <attribute value> -- <attribute name> : <attribute value> etc...
"""
node_header = OD()
node_data = []
# Keep track of the starting node
ncount = 0
nodedict = OD()
f = open(self.fname,"r")
for lines in f:
# Split the string into components
# Run through some if statements to capture headers
spltstr = lines.split(":",1)
## clean up the string
spltstr = map(lambda x: x.rstrip().strip(),spltstr)
if "Parent_ID" in spltstr[0]:
if ncount > 0:
### Create a new node
new_node = MD_node(node_header["Node_ID"], name = node_header["Node_Name"], parent = node_header["Parent_ID"], data = node_data , node_attr=node_header["Node_attrib"])
nodedict[int(node_header["Node_ID"])] = new_node
### Reset data if a new parent id is encountered, denoting a new node
node_header = OD()
node_data = []
ncount += 1
### Capture the none case
try:
node_header["Parent_ID"] = int(spltstr[1])
except ValueError:
node_header["Parent_ID"] = spltstr[1]
elif "Node_ID" in spltstr[0]:
node_header["Node_ID"] = int(spltstr[1])
if node_header["Parent_ID"] == "None" or node_header["Parent_ID"] == "none":
node_root = int(spltstr[1])
elif "Node_Name" in spltstr[0]:
### Automatically root the xml tree
node_header["Node_Name"] = spltstr[1].rstrip().strip()
if "--" in spltstr[1]:
subsplt = spltstr[1].split("--")
node_header["Node_Name"] = subsplt[0].rstrip().strip()
attr_dict = OD()
for j in subsplt[1:]:
asplit = j.split(":",1)
stext = map(lambda x: x.rstrip().strip(),asplit)
attr_dict[stext[0]] = stext[1]
node_header["Node_attrib"] = attr_dict
else:
node_header["Node_attrib"] = OD()
elif len(spltstr) > 1:
### Create temporary element
tmp_el = MD_element()
### Check for attributes
if "--" in spltstr[1]:
attr_dict = OD()
subsplt = spltstr[1].split("--")
tmp_el.tag = spltstr[0]
tmp_el.text = subsplt[0]
for j in subsplt[1:]:
asplit = j.split(":",1)
asplit = map(lambda x: x.rstrip().strip(),asplit)
attr_dict[asplit[0]]= asplit[1]
tmp_el.attr = attr_dict
node_data.append(tmp_el)
else:
tmp_el.tag = spltstr[0]
tmp_el.text = spltstr[1]
node_data.append(tmp_el)
f.close()
### Write the last node
new_node = MD_node(node_header["Node_ID"], name = node_header["Node_Name"], parent = node_header["Parent_ID"], data = node_data , node_attr=node_header["Node_attrib"])
nodedict[int(node_header["Node_ID"])] =new_node
self.nodes = nodedict
self.root = node_root
def create_tree(self):
"""
Creates a dictionary of nodes. Nodes are added arbitrarily to a dictionary, without structure. This will create a parent child type structure and store it in self.tree_struct
"""
## Loop through node dictionary and create tree.
for k,v in self.nodes.iteritems():
## Get parent UID's
## Lot's of ints and strings to convert between types for proper comparisons
pid = v.parent
if any(pid == j for j in self.tree_struct.keys()) and not any(k == j for j in self.tree_struct[pid]) and v.UID != self.root:
self.tree_struct[pid].append(k)
elif pid and v.UID != self.root:
self.tree_struct[pid] = [k]
def traverse(self):
r"""
Traverse the metadata tree in a depth first post order.
"""
pstack = [0]
cnode = self.root
### Get list of all nodes to ensure I visit them all.
## make copy of the tree structure you can prune
node_dict = deepcopy(self.tree_struct)
nodes_list = self.list_from_vals(node_dict)
### Loop over all nodes
while nodes_list:
#check if a node has children
childs = self.has_child(cnode,node_dict)
if childs:
# If it does then the current
parent = cnode
cnode = childs[0]
if parent not in pstack:
pstack.append(parent)
else:
## Remove from the master list
nodes_list.remove(cnode)
### Remove from the dictionary
node_dict[pstack[-1]].remove(cnode)
self.export_node(cnode)
### check if the parent stack needs to adjusted.
### if so pop off value
if not node_dict[pstack[-1]]:
pstack.pop()
cnode = pstack[-1]
### Adjust for exporting root
self.export_node(self.root)
def has_child(self, nuid, node_dict):
r"""
Determine if a given node ID has children, used in tree traversal
Parameters
---------------
nuid:
The uid (within the metadata structure) to check if it has any children
nodes_dict: dict
The dictionary containing the metadata tree structure
Returns
---------
list
A list of child nodes for a given parent node
"""
if any(nuid == k for k in node_dict.keys()):
if isinstance(node_dict[nuid],int):
return([node_dict[nuid]])
else:
return(node_dict[nuid])
else:
return(False)
def list_from_vals(self,d):
'''
Flatten a list.
'''
out = []
for i in d.values():
for j in i:
out.append(j)
return(out)
def export_node(self,nuid):
"""
Export a given node as an ordered dictionary, in a format readable to the Metadata class
Parameters
-------------
nuid: int
The node ID to export
Modifies
--------
self.etree_nodes
adds exported node to the dictionary of etree nodes, with node ID as the dictionary key
"""
nroot = etree.Element(self.nodes[nuid].name)
for k in self.nodes[nuid].data:
etree.SubElement(nroot,k.tag,attrib = k.attr).text=k.text
### Add attributes to root node
for k,v in self.nodes[nuid].node_attr.iteritems():
nroot.set(k,v)
### add in child nodes
for k in self.tree_struct.keys():
if(k == nuid):
for j in self.tree_struct[nuid]:
nroot.append(self.etree_nodes[j])
#for j in self.tree_struct[nuid].values():
#print j
self.etree_nodes[nuid] = nroot
def create_xml(self):
'''
Create an xml etree that can be print out.
Modifies
----------
self.tree
Creates a full tree as an etree object, can be used for printing and writing purposes.
'''
self.traverse()
self.tree = self.etree_nodes[self.root]
def print_xml(self):
'''
Pretty print Metadata object, just calles lxml pretty print method. If you want more fancy print methods, use the etree.tostring method on Metadata.tree
'''
print(etree.tostring(self.tree,encoding="UTF-8",pretty_print=True,xml_declaration=True))
class MD_node(object):
"""
A node class that contains all the data about that node. A node is a top level xml container tag, which may contain child xml nodes.
Attributes
-----------------
UID: int
The unique identifier of a node
name: string
The name of a node
parent: int
The UID of a parent node
data: list
A list of data elements, must be of type MD_element
node_attr: OrderedDict
A dictionary of node attributes in
"""
def __init__(self,UID, name, parent = None, data = [], node_attr = OD()):
self.UID = UID
self.name = name
self.parent = parent
self.data = data
self.node_attr = node_attr
class MD_element(object):
"""
Simple container for elements of a node.
Attributes
------------
tag: str
The name of the XML element e.g. foo for the xml tag <foo>
text: str
The text value of the element, e.g. bar for the text in <foo> bar </foo>
attr: OrderedDict
A dictionary of attributes where keys are attributes and values are attribute values
"""
def __init__(self, tag = "", text = "" , attr = OD()):
self.tag = tag
self.text = text
self.attr = attr | PypiClean |
/Authlog-1.0.0.tar.gz/Authlog-1.0.0/authlog/decorators.py | from __future__ import absolute_import
import logging
# from datetime import datetime, timedelta
try:
from django.urls import reverse, NoReverseMatch
except:
from django.core.urlresolvers import reverse, NoReverseMatch
from authlog import models
import authlog
log = logging.getLogger(authlog.AUTHLOG_LOGGER)
log.info('AUTHLOG: BEGIN LOG')
log.info('Using version ' + authlog.get_version())
def hide_passwd(key, value):
if key == 'password':
return key, '*********'
else:
return key, value
def query2str(items):
return '\n'.join(['%s=%s' % (hide_passwd(k, v)) for k, v in items])
def get_tracked_models():
return [tuple([i.lower() for i in j.split('.')]) for j in authlog.AUTHLOG_TRACKED_MODELS]
def watch_login(func):
"""
Used to decorate the django.contrib.admin.site.login method.
"""
def decorated_login(request, *args, **kwargs):
response = func(request, *args, **kwargs)
if func.__name__ == 'decorated_login':
# if we're dealing with this function itself, don't bother checking
# for invalid login attempts. I suppose there's a bunch of
# recursion going on here that used to cause one failed login
# attempt to generate 10+ failed access attempt records (with 3
# failed attempts each supposedly)
return response
if request.method == 'POST':
# see if the login was successful
login_unsuccessful = (
response and
not response.has_header('location') and
response.status_code != 302
)
if login_check_request(request, login_unsuccessful):
return response
return response
if hasattr(func, 'is_decorated_login'):
return func
decorated_login.is_decorated_login = True
return decorated_login
def login_check_request(request, login_unsuccessful):
ip = request.META.get('REMOTE_ADDR', '')[:255]
ip_forward = request.META.get('HTTP_X_FORWARDED_FOR', '')[:255]
path = request.META.get('PATH_INFO', '<unknown>')[:255]
accept = request.META.get('HTTP_ACCEPT', '<unknown>')[:255]
ua = request.META.get('HTTP_USER_AGENT', '<unknown>')[:255]
# time = datetime.now()
get = query2str(list(request.GET.items()))
if authlog.AUTHLOG_SAVE_LOGIN_POST_DATA:
post = query2str(list(request.POST.items()))
else:
post = 'POST data was submitted'
if login_unsuccessful:
login_status = "Fail"
user = 'None'
if authlog.AUTHLOG_SAVE_BAD_LOGINS:
access = models.Access.objects.create(
user=user,
user_agent=ua,
ip_address=ip,
ip_forward=ip_forward,
get_data=get,
post_data=post,
http_accept=accept,
path_info=path,
)
return_status = False
else:
login_status = "Pass"
user = request.user
if authlog.AUTHLOG_SAVE_GOOD_LOGINS:
access = models.Access.objects.create(
user=user.username,
user_agent=ua,
ip_address=ip,
ip_forward=ip_forward,
get_data=get,
post_data=post,
http_accept=accept,
path_info=path,
)
return_status = True
log.info('AUTHLOG: Login %s : ip : %s : ip_forward : %s : path : %s : user : %s ' % (
login_status, ip, ip_forward or "None", path, user))
return return_status
class ActionInfo(object):
def __init__(self, url=None, atype=None):
self.url = url
self.atype = atype
def is_complete(self):
if self.url and self.atype:
return True
else:
return False
def watch_view(func):
def decorated_view(request_func, *args, **kwargs):
response = func(request_func, *args, **kwargs)
posted = False
if func.__name__ == 'decorated_view':
return response
try:
request = args[0]
except:
request = None # should never get here
if not request:
try:
request = kwargs.get('request', None)
except:
request = None
user, ip, path, accept, ua, get, post = ('None', '', '<unknown>', '<unknown>', '<unknown>', '', '')
current_path = '/'
else:
current_path = request.path_info
user = request.user
ip = request.META.get('REMOTE_ADDR', '')[:255]
ip_forward = request.META.get('HTTP_X_FORWARDED_FOR', '')[:255]
path = request.META.get('PATH_INFO', '<unknown>')[:255]
accept = request.META.get('HTTP_ACCEPT', '<unknown>')[:255]
ua = request.META.get('HTTP_USER_AGENT', '<unknown>')[:255]
get = query2str(list(request.GET.items()))
post = query2str(list(request.POST.items()))
if request.method == 'POST':
posted = True
if posted and not authlog.AUTHLOG_SAVE_VIEW_POST_DATA:
post = "POST data was submitted"
tracked_models = get_tracked_models()
# tracked_urls = []
action = ActionInfo()
for tmodel in tracked_models:
try:
app = tmodel[0]
model = tmodel[1]
except IndexError:
pass # should only get here if someone does not specify correct tracked models
raise NameError("You settings file does not have a properly formated list " +
"of AUTHLOG_TRACKED_MODELS. Entries must be formatted app.model")
try:
first_arg = args[1]
except IndexError:
first_arg = None
try:
if first_arg:
if current_path == reverse('admin:%s_%s_change' % (app, model), args=(first_arg,)):
action.url = current_path
action.atype = models.ACTION_TYPE['SUBMIT_CHANGE'] if posted else models.ACTION_TYPE['VIEW_CHANGE']
break
elif current_path == reverse('admin:%s_%s_delete' % (app, model), args=(first_arg,)):
action.url = current_path
action.atype = models.ACTION_TYPE['SUBMIT_DELETE'] if posted else models.ACTION_TYPE['VIEW_DELETE']
break
elif current_path == reverse('admin:%s_%s_history' % (app, model), args=(first_arg,)):
action.url = current_path
action.atype = models.ACTION_TYPE['VIEW_HISTORY']
break
else:
if current_path == reverse('admin:%s_%s_changelist' % (app, model),):
action.url = current_path
action.atype = models.ACTION_TYPE['VIEW_LIST']
break
elif current_path == reverse('admin:%s_%s_add' % (app, model),):
action.url = current_path
action.atype = models.ACTION_TYPE['VIEW_ADD'] if posted else models.ACTION_TYPE['SUBMIT_ADD']
break
except NoReverseMatch:
# pass # should only get here if tracked_models is wrong (no view associated)
raise NoReverseMatch
if action.is_complete():
models.AccessPage.objects.create(
user=user.username, user_agent=ua, ip_address=ip, ip_forward=ip_forward,
get_data=get, post_data=post, http_accept=accept,
path_info=action.url, action_type=action.atype,
)
return response
# if hasattr(func, 'is_decorated_view'):
# return func
# decorated_view.is_decorated_view = True
return decorated_view | PypiClean |
/CL3d-1.0.10-py3-none-win32.whl/CL/res/dark_rc.py |
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.13.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
\x00\x00\x0b\xb9\
\x3c\
\x3f\x78\x6d\x6c\x20\x76\x65\x72\x73\x69\x6f\x6e\x3d\x22\x31\x2e\
\x30\x22\x20\x65\x6e\x63\x6f\x64\x69\x6e\x67\x3d\x22\x55\x54\x46\
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"
qt_resource_struct_v2 = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources() | PypiClean |
/FileStandardInput-0.2.tar.gz/FileStandardInput-0.2/README.rst | FILE STANDARD INPUT
=====================
**A Standard File Input Reader where you can provide custom input syntax instead of input() or raw_input() to get input from File instead of command line.**
USAGE
---------
To use this simply import file input like
*from FileStandardInput import FileInput*
Then initiate input class with the file path like
*input = FileInput.input('filepath')*
then substitute *input.file_input()* instead of standard input.
Author = "Ashik Vetrivelu <ashik@cepheuen.com>"
| PypiClean |
/Nuitka-1.8.tar.gz/Nuitka-1.8/nuitka/tree/ReformulationImportStatements.py | # spell-checker: ignore fromlist,asname
from nuitka.importing.ImportResolving import resolveModuleName
from nuitka.nodes.ConstantRefNodes import makeConstantRefNode
from nuitka.nodes.FutureSpecs import FutureSpec
from nuitka.nodes.GlobalsLocalsNodes import ExpressionBuiltinGlobals
from nuitka.nodes.ImportNodes import (
ExpressionBuiltinImport,
ExpressionImportName,
StatementImportStar,
makeExpressionImportModuleFixed,
)
from nuitka.nodes.NodeMakingHelpers import mergeStatements
from nuitka.nodes.StatementNodes import StatementsSequence
from nuitka.nodes.VariableAssignNodes import makeStatementAssignmentVariable
from nuitka.nodes.VariableNameNodes import StatementAssignmentVariableName
from nuitka.nodes.VariableRefNodes import ExpressionTempVariableRef
from nuitka.nodes.VariableReleaseNodes import makeStatementReleaseVariable
from nuitka.PythonVersions import python_version
from nuitka.utils.ModuleNames import ModuleName
from .ReformulationTryFinallyStatements import makeTryFinallyStatement
from .SyntaxErrors import raiseSyntaxError
from .TreeHelpers import makeStatementsSequenceOrStatement, mangleName
# For checking afterwards, if __future__ imports really were at the beginning
# of the file.
_future_import_nodes = []
def checkFutureImportsOnlyAtStart(body):
# Check if a __future__ imports really were at the beginning of the file.
for node in body:
if node in _future_import_nodes:
_future_import_nodes.remove(node)
else:
if _future_import_nodes:
raiseSyntaxError(
"""\
from __future__ imports must occur at the beginning of the file""",
_future_import_nodes[0].source_ref.atColumnNumber(
_future_import_nodes[0].col_offset
),
)
def _handleFutureImport(provider, node, source_ref):
# Don't allow future imports in functions or classes.
if not provider.isCompiledPythonModule():
raiseSyntaxError(
"""\
from __future__ imports must occur at the beginning of the file""",
source_ref.atColumnNumber(node.col_offset),
)
for import_desc in node.names:
object_name, _local_name = import_desc.name, import_desc.asname
_enableFutureFeature(node=node, object_name=object_name, source_ref=source_ref)
# Remember it for checks to be applied once module is complete, e.g. if
# they are all at module start.
node.source_ref = source_ref
_future_import_nodes.append(node)
_future_specs = []
def pushFutureSpec():
_future_specs.append(FutureSpec())
def getFutureSpec():
return _future_specs[-1]
def popFutureSpec():
del _future_specs[-1]
def _enableFutureFeature(node, object_name, source_ref):
future_spec = _future_specs[-1]
if object_name == "unicode_literals":
future_spec.enableUnicodeLiterals()
elif object_name == "absolute_import":
future_spec.enableAbsoluteImport()
elif object_name == "division":
future_spec.enableFutureDivision()
elif object_name == "print_function":
future_spec.enableFuturePrint()
elif object_name == "barry_as_FLUFL" and python_version >= 0x300:
future_spec.enableBarry()
elif object_name == "generator_stop":
future_spec.enableGeneratorStop()
elif object_name == "braces":
raiseSyntaxError("not a chance", source_ref.atColumnNumber(node.col_offset))
elif object_name in ("nested_scopes", "generators", "with_statement"):
# These are enabled in all cases already.
pass
elif object_name == "annotations" and python_version >= 0x370:
future_spec.enableFutureAnnotations()
else:
raiseSyntaxError(
"future feature %s is not defined" % object_name,
source_ref.atColumnNumber(node.col_offset),
)
def _resolveImportModuleName(module_name):
if module_name:
module_name = resolveModuleName(ModuleName(module_name)).asString()
return module_name
def buildImportFromNode(provider, node, source_ref):
# "from .. import .." statements. This may trigger a star import, or
# multiple names being looked up from the given module variable name.
# This is pretty complex.
# pylint: disable=too-many-branches,too-many-locals,too-many-statements
module_name = node.module if node.module is not None else ""
module_name = _resolveImportModuleName(module_name)
level = node.level
# Use default level under some circumstances.
if level == -1:
level = None
elif level == 0 and not _future_specs[-1].isAbsoluteImport():
level = None
if level is not None:
level_obj = makeConstantRefNode(level, source_ref, True)
else:
level_obj = None
# Importing from "__future__" module may enable flags to the parser,
# that we need to know about, handle that.
if module_name == "__future__":
_handleFutureImport(provider, node, source_ref)
target_names = []
import_names = []
# Mapping imported "fromlist" to assigned "fromlist" if any, handling the
# star case as well.
for import_desc in node.names:
object_name, local_name = import_desc.name, import_desc.asname
if object_name == "*":
target_names.append(None)
assert local_name is None
else:
target_names.append(local_name if local_name is not None else object_name)
import_names.append(object_name)
# Star imports get special treatment.
if None in target_names:
# More than "*" is a syntax error in Python, need not care about this at
# all, it's only allowed value for import list in this case.
assert target_names == [None]
# Python3 made it so that these can only occur on the module level,
# so this a syntax error if not there. For Python2 it is OK to
# occur everywhere though.
if not provider.isCompiledPythonModule() and python_version >= 0x300:
raiseSyntaxError(
"import * only allowed at module level",
source_ref.atColumnNumber(node.col_offset),
)
if provider.isCompiledPythonModule():
import_globals = ExpressionBuiltinGlobals(source_ref)
import_locals = ExpressionBuiltinGlobals(source_ref)
else:
import_globals = ExpressionBuiltinGlobals(source_ref)
import_locals = makeConstantRefNode({}, source_ref, True)
return StatementImportStar(
target_scope=provider.getLocalsScope(),
module=ExpressionBuiltinImport(
name=makeConstantRefNode(module_name, source_ref, True),
globals_arg=import_globals,
locals_arg=import_locals,
fromlist=makeConstantRefNode(("*",), source_ref, True),
level=level_obj,
source_ref=source_ref,
),
source_ref=source_ref,
)
else:
if module_name == "__future__":
imported_from_module = makeExpressionImportModuleFixed(
module_name="__future__", source_ref=source_ref
)
else:
imported_from_module = ExpressionBuiltinImport(
name=makeConstantRefNode(module_name, source_ref, True),
globals_arg=ExpressionBuiltinGlobals(source_ref),
locals_arg=makeConstantRefNode(None, source_ref, True),
fromlist=makeConstantRefNode(tuple(import_names), source_ref, True),
level=level_obj,
source_ref=source_ref,
)
# If we have multiple names to import, consider each.
multi_names = len(target_names) > 1
statements = []
if multi_names:
tmp_import_from = provider.allocateTempVariable(
temp_scope=provider.allocateTempScope("import_from"), name="module"
)
statements.append(
makeStatementAssignmentVariable(
variable=tmp_import_from,
source=imported_from_module,
source_ref=source_ref,
)
)
imported_from_module = ExpressionTempVariableRef(
variable=tmp_import_from, source_ref=source_ref
)
import_statements = []
first = True
for target_name, import_name in zip(target_names, import_names):
# Make a clone of the variable reference, if we are going to use
# another one.
if not first:
imported_from_module = imported_from_module.makeClone()
first = False
import_statements.append(
StatementAssignmentVariableName(
provider=provider,
variable_name=mangleName(target_name, provider),
source=ExpressionImportName(
module=imported_from_module,
import_name=import_name,
level=0,
source_ref=source_ref,
),
source_ref=source_ref,
)
)
# Release the temporary module value as well.
if multi_names:
statements.append(
makeTryFinallyStatement(
provider=provider,
tried=import_statements,
final=(
makeStatementReleaseVariable(
variable=tmp_import_from, source_ref=source_ref
),
),
source_ref=source_ref,
)
)
else:
statements.extend(import_statements)
# Note: Each import is sequential. It can succeed, and the failure of a
# later one is not undoing previous ones. We can therefore have a
# sequence of imports that each only import one thing therefore.
return StatementsSequence(
statements=mergeStatements(statements), source_ref=source_ref
)
def buildImportModulesNode(provider, node, source_ref):
# Import modules statement. As described in the Developer Manual, these
# statements can be treated as several ones.
import_names = [
(import_desc.name, import_desc.asname) for import_desc in node.names
]
import_nodes = []
for import_desc in import_names:
module_name, local_name = import_desc
module_top_name = module_name.split(".")[0]
# Note: The "level" of import is influenced by the future absolute
# imports.
level = (
makeConstantRefNode(0, source_ref, True)
if _future_specs[-1].isAbsoluteImport()
else None
)
module_name = _resolveImportModuleName(module_name)
# TODO: Go to fixed node directly, avoiding the optimization for the
# node to do it, with absolute imports we can use makeExpressionImportModuleFixed
# instead.
import_node = ExpressionBuiltinImport(
name=makeConstantRefNode(module_name, source_ref, True),
globals_arg=ExpressionBuiltinGlobals(source_ref),
locals_arg=makeConstantRefNode(None, source_ref, True),
fromlist=makeConstantRefNode(None, source_ref, True),
level=level,
source_ref=source_ref,
)
if local_name:
# If is gets a local name, the real name must be used as a
# temporary value only, being looked up recursively.
for import_name in module_name.split(".")[1:]:
import_node = ExpressionImportName(
module=import_node,
import_name=import_name,
# TODO: Does level make sense at all, should be removed.
level=0,
source_ref=source_ref,
)
# If a name was given, use the one provided, otherwise the import gives
# the top level package name given for assignment of the imported
# module.
import_nodes.append(
StatementAssignmentVariableName(
provider=provider,
variable_name=mangleName(
local_name if local_name is not None else module_top_name, provider
),
source=import_node,
source_ref=source_ref,
)
)
# Note: Each import is sequential. It will potentially succeed, and the
# failure of a later one is not changing that one bit . We can therefore
# have a sequence of imports that only import one thing therefore.
return makeStatementsSequenceOrStatement(
statements=import_nodes, source_ref=source_ref
) | PypiClean |
/OASYS1-ESRF-Extensions-0.0.69.tar.gz/OASYS1-ESRF-Extensions-0.0.69/orangecontrib/esrf/xoppy/widgets/extension/Scintillator.py | import sys
import numpy
import xraylib
from PyQt5.QtWidgets import QApplication, QMessageBox, QSizePolicy
from orangewidget import gui
from orangewidget.settings import Setting
from oasys.widgets import gui as oasysgui, congruence
from oasys.widgets.exchange import DataExchangeObject
from xoppylib.power.xoppy_calc_power import xoppy_calc_power
from oasys.widgets.exchange import DataExchangeObject
from orangecontrib.xoppy.widgets.gui.ow_xoppy_widget import XoppyWidget
import scipy.constants as codata
class Scintillator(XoppyWidget):
name = "Scintillator"
id = "orange.widgets.dataxpower"
description = "Power Absorbed and Transmitted by Optical Elements"
icon = "icons/id19_scintillator.png"
priority = 4
category = ""
keywords = ["xoppy", "power", "scintillator"]
inputs = [("ExchangeData", DataExchangeObject, "acceptExchangeData")]
SOURCE = Setting(2)
SCINTILLATOR = Setting(2)
THICK = Setting(500.0)
#DENS = Setting('?')
ENER_MIN = Setting(10000.0)
ENER_MAX = Setting(200000.0)
ENER_N = Setting(2000)
SOURCE_FILE = Setting("?")
CORRECTION_FACTOR = Setting(0.1)
FILE_DUMP = 0
def build_gui(self):
self.leftWidgetPart.setSizePolicy(QSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding))
self.leftWidgetPart.setMaximumWidth(self.CONTROL_AREA_WIDTH + 20)
self.leftWidgetPart.updateGeometry()
box = oasysgui.widgetBox(self.controlArea, self.name + " Input Parameters", orientation="vertical", width=self.CONTROL_AREA_WIDTH-10)
idx = -1
# widget index 1
idx += 1
box1 = gui.widgetBox(box)
self.box_source = gui.comboBox(box1, self, "SOURCE",
label=self.unitLabels()[idx], addSpace=False,
items=['From Oasys wire', 'Normalized to 1',
'From external file. '],
valueType=int, orientation="horizontal", labelWidth=150)
self.show_at(self.unitFlags()[idx], box1)
# widget index 2
idx += 1
box1 = gui.widgetBox(box)
self.box_source = gui.comboBox(box1, self, "SCINTILLATOR",
label=self.unitLabels()[idx], addSpace=False,
items=['Diamond','Gadox','LuAg','GGG','LSO','YAG','CWO'],
valueType=int, orientation="horizontal", labelWidth=150)
self.show_at(self.unitFlags()[idx], box1)
# widget index 3
idx += 1
box1 = gui.widgetBox(box)
oasysgui.lineEdit(box1, self, "THICK",
label=self.unitLabels()[idx], addSpace=False,
valueType=float, orientation="horizontal", labelWidth=150)
self.show_at(self.unitFlags()[idx], box1)
# widget index 4
# idx += 1
#box1 = gui.widgetBox(box)
#oasysgui.lineEdit(box1, self, "DENS",
#label=self.unitLabels()[idx], addSpace=False,
#valueType=float, orientation="horizontal", labelWidth=150)
#self.show_at(self.unitFlags()[idx], box1)
# widget index 5
idx += 1
box1 = gui.widgetBox(box)
oasysgui.lineEdit(box1, self, "ENER_MIN",
label=self.unitLabels()[idx], addSpace=False,
valueType=float, orientation="horizontal", labelWidth=250)
self.show_at(self.unitFlags()[idx], box1)
# widget index 6
idx += 1
box1 = gui.widgetBox(box)
oasysgui.lineEdit(box1, self, "ENER_MAX",
label=self.unitLabels()[idx], addSpace=False,
valueType=float, orientation="horizontal", labelWidth=250)
self.show_at(self.unitFlags()[idx], box1)
# widget index 7
idx += 1
box1 = gui.widgetBox(box)
oasysgui.lineEdit(box1, self, "ENER_N",
label=self.unitLabels()[idx], addSpace=False,
valueType=int, orientation="horizontal", labelWidth=250)
self.show_at(self.unitFlags()[idx], box1)
# widget index 8 *********** File Browser ******************
idx += 1
box1 = gui.widgetBox(box)
file_box_id = oasysgui.widgetBox(box1, "", addSpace=False, orientation="horizontal")
self.file_id = oasysgui.lineEdit(file_box_id, self, "SOURCE_FILE", self.unitLabels()[idx],
labelWidth=100, valueType=str, orientation="horizontal")
gui.button(file_box_id, self, "...", callback=self.select_input_file, width=25)
self.show_at(self.unitFlags()[idx], box1)
# widget index 9
idx += 1
box1 = gui.widgetBox(box)
oasysgui.lineEdit(box1, self, "CORRECTION_FACTOR",
label=self.unitLabels()[idx], addSpace=False,
valueType=float, orientation="horizontal", labelWidth=150)
self.show_at(self.unitFlags()[idx], box1)
#widget index 10
idx += 1
box1 = gui.widgetBox(box)
gui.separator(box1, height=7)
gui.comboBox(box1, self, "FILE_DUMP",
label=self.unitLabels()[idx], addSpace=False,
items=['No', 'Yes (Scinti.spec)'],
valueType=int, orientation="horizontal", labelWidth=250)
self.show_at(self.unitFlags()[idx], box1)
self.input_spectrum = None
def select_input_file(self):
self.file_id.setText(oasysgui.selectFileFromDialog(self, self.SOURCE_FILE,
"Open 2-columns file with spectral power",
file_extension_filter="ascii dat (*.dat *.txt *spec)"))
def unitLabels(self):
return ['Input beam:', 'Scintillator','Thickness [microns]',
#Density g/cm^3,
'From energy [eV]: ',
'To energy [eV]:',
'Energy points: ',
'File with input beam spectral power:', 'Correction factor',"Dump file"]
def unitFlags(self):
return ['True','True','True',
#True,
'self.SOURCE == 1',
'self.SOURCE == 1',
'self.SOURCE == 1',
'self.SOURCE == 2',
'True','True']
def get_help_name(self):
return 'Scintillator'
def selectFile(self):
self.le_source_file.setText(oasysgui.selectFileFromDialog(self, self.SOURCE_FILE, "Open Source File", file_extension_filter="*.*"))
def acceptExchangeData(self, exchangeData):
self.input_spectrum = None
self.SOURCE = 0
# self.box_source.setCurrentIndex(self.SOURCE)
try:
if not exchangeData is None:
if exchangeData.get_program_name() == "XOPPY":
no_bandwidth = False
if exchangeData.get_widget_name() =="UNDULATOR_FLUX" :
# self.SOURCE_FILE = "xoppy_undulator_flux"
no_bandwidth = True
index_flux = 2
elif exchangeData.get_widget_name() == "BM" :
if exchangeData.get_content("is_log_plot") == 1:
raise Exception("Logaritmic X scale of Xoppy Energy distribution not supported")
if exchangeData.get_content("calculation_type") == 0 and exchangeData.get_content("psi") == 0:
# self.SOURCE_FILE = "xoppy_bm_flux"
no_bandwidth = True
index_flux = 6
else:
raise Exception("Xoppy result is not an Flux vs Energy distribution integrated in Psi")
elif exchangeData.get_widget_name() =="XWIGGLER" :
# self.SOURCE_FILE = "xoppy_xwiggler_flux"
no_bandwidth = True
index_flux = 2
elif exchangeData.get_widget_name() =="WS" :
# self.SOURCE_FILE = "xoppy_xwiggler_flux"
no_bandwidth = True
index_flux = 2
elif exchangeData.get_widget_name() =="XTUBES" :
# self.SOURCE_FILE = "xoppy_xtubes_flux"
index_flux = 1
no_bandwidth = True
elif exchangeData.get_widget_name() =="XTUBE_W" :
# self.SOURCE_FILE = "xoppy_xtube_w_flux"
index_flux = 1
no_bandwidth = True
elif exchangeData.get_widget_name() =="BLACK_BODY" :
# self.SOURCE_FILE = "xoppy_black_body_flux"
no_bandwidth = True
index_flux = 2
elif exchangeData.get_widget_name() =="UNDULATOR_RADIATION" :
# self.SOURCE_FILE = "xoppy_undulator_radiation"
no_bandwidth = True
index_flux = 1
elif exchangeData.get_widget_name() =="POWER" :
# self.SOURCE_FILE = "xoppy_undulator_power"
no_bandwidth = True
index_flux = -1
elif exchangeData.get_widget_name() =="POWER3D" :
# self.SOURCE_FILE = "xoppy_power3d"
no_bandwidth = True
index_flux = 1
else:
raise Exception("Xoppy Source not recognized")
# self.SOURCE_FILE += "_" + str(id(self)) + ".dat"
spectrum = exchangeData.get_content("xoppy_data")
if exchangeData.get_widget_name() =="UNDULATOR_RADIATION" or \
exchangeData.get_widget_name() =="POWER3D":
[p, e, h, v ] = spectrum
tmp = p.sum(axis=2).sum(axis=1)*(h[1]-h[0])*(v[1]-v[0])*codata.e*1e3
spectrum = numpy.vstack((e,p.sum(axis=2).sum(axis=1)*(h[1]-h[0])*(v[1]-v[0])*
codata.e*1e3))
self.input_spectrum = spectrum
else:
if not no_bandwidth:
spectrum[:,index_flux] /= 0.001*spectrum[:,0]
self.input_spectrum = numpy.vstack((spectrum[:,0],spectrum[:,index_flux]))
self.process_showers()
self.compute()
except Exception as exception:
QMessageBox.critical(self, "Error",
str(exception),
QMessageBox.Ok)
#raise exception
def check_fields(self):
self.CORRECTION_FACTOR = congruence.checkPositiveNumber(self.CORRECTION_FACTOR, "Correction Factor")
self.THICK = congruence.checkStrictlyPositiveNumber(self.THICK, "Thickness")
#self.DENS = congruence.checkStrictlyPositiveNumber(self.DENS, "Density")
if self.SOURCE == 1:
self.ENER_MIN = congruence.checkPositiveNumber(self.ENER_MIN, "Energy from")
self.ENER_MAX = congruence.checkStrictlyPositiveNumber(self.ENER_MAX, "Energy to")
congruence.checkLessThan(self.ENER_MIN, self.ENER_MAX, "Energy from", "Energy to")
self.NPOINTS = congruence.checkStrictlyPositiveNumber(self.ENER_N, "Energy Points")
elif self.SOURCE == 2:
congruence.checkFile(self.SOURCE_FILE)
def do_xoppy_calculation(self):
return self.xoppy_calc_xpower()
def extract_data_from_xoppy_output(self, calculation_output):
return calculation_output
def get_data_exchange_widget_name(self):
return "POWER"
def getTitles(self):
return ['Input Beam','Total CS','Mu','Transmitivity','Absorption after correction','Intensity','Intensity after correction']
def getXTitles(self):
return ["Energy [eV]","Energy [eV]","Energy [eV]","Energy [eV]","Energy [eV]","Energy [eV]", 'Energy [eV]']
def getYTitles(self):
return ["Source",'Total CS cm^2/g','Mu cm^-1','Transmitivity','Absorption',"Intensity", 'Intensity']
def getVariablesToPlot(self):
return [(0, 1),(0, 2),(0, 3),(0, 4),(0, 5),(0, 6),(0,7)]
def getLogPlot(self):
return [(False,False),(False, True),(False, True),(False, False),(False, False),(False,False),(False,False)]
def formula(self):
if self.SCINTILLATOR == 0:
F='C'
if self.SCINTILLATOR == 1:
F='Gd2O2S'
if self.SCINTILLATOR == 2:
F='Al5Lu3O12'
if self.SCINTILLATOR == 3:
F='Gd3Ga5O12'
if self.SCINTILLATOR == 4:
F='Lu2SiO5'
if self.SCINTILLATOR == 5:
F='Y3Al5O12'
if self.SCINTILLATOR == 6:
F='CdWO4'
return F
def density(self):
if self.SCINTILLATOR == 0:
D=3.508
if self.SCINTILLATOR == 1:
D=7.32/2 #50% powder
if self.SCINTILLATOR == 2:
D=6.76
if self.SCINTILLATOR == 3:
D=7.08
if self.SCINTILLATOR == 4:
D=7.4
if self.SCINTILLATOR == 5:
D=4.55
if self.SCINTILLATOR == 6:
D=7.9
return D
def correction(self,E,F):
L=[]
if self.CORRECTION_FACTOR==0:
return(F)
else :
for k in range(len(E)):
L.append(E[k]/1000 *self.CORRECTION_FACTOR*F[k])
return(L)
def xoppy_calc_xpower(self):
substance=[self.formula()]
thick=[self.THICK*1e-3]
dens=[self.density()]
#dens=[self.DENS]
flags=[0]
if self.SOURCE == 0:
if self.input_spectrum is None:
raise Exception("No input beam")
else:
energies = self.input_spectrum[0,:].copy()
source = self.input_spectrum[1,:].copy()
elif self.SOURCE == 1:
energies = numpy.linspace(self.ENER_MIN,self.ENER_MAX,self.ENER_N)
source = numpy.ones(energies.size)
tmp = numpy.vstack( (energies,source))
self.input_spectrum = source
elif self.SOURCE == 2:
if self.SOURCE == 2: source_file = self.SOURCE_FILE
try:
tmp = numpy.loadtxt(source_file)
energies = tmp[:,0]
source = tmp[:,1]
self.input_spectrum = source
except:
print("Error loading file %s "%(source_file))
raise
if self.FILE_DUMP == 0:
output_file = None
else:
output_file = "Scinti.spec"
out_dictionary = xoppy_calc_power(energies=energies, source=source, substance=substance,
flags=flags, dens=dens, thick=thick, angle=[], roughness=[], material_constants_library=xraylib)
L=[]
for k in range (5):
L.append((out_dictionary['data'][k]).tolist())
L.append(self.correction(L[0],(out_dictionary['data'][5]).tolist()))
L.append((out_dictionary['data'][6]).tolist())
L.append(List_Product([L[5],L[1]]))
out_dictionary['data']=numpy.array(L)
try:
(out_dictionary["info"])
except:
pass
# send exchange
calculated_data = DataExchangeObject("XOPPY", self.get_data_exchange_widget_name())
try:
calculated_data.add_content("xoppy_data", out_dictionary["data"].T)
calculated_data.add_content("plot_x_col", 0)
calculated_data.add_content("plot_y_col", -1)
except:
pass
try:
calculated_data.add_content("labels", out_dictionary["labels"])
except:
pass
try:
calculated_data.add_content("info", out_dictionary["info"])
except:
pass
return calculated_data
def List_Product(list):
L = []
l = 1
for k in range(len(list[0])):
for i in range(len(list)):
l = l * list[i][k]
L.append(l)
l = 1
return (L)
if __name__ == "__main__":
from oasys.widgets.exchange import DataExchangeObject
input_data_type = "POWER"
if input_data_type == "POWER":
# create fake UNDULATOR_FLUX xoppy exchange data
e = numpy.linspace(1000.0, 10000.0, 100)
source = e/10
received_data = DataExchangeObject("XOPPY", "POWER")
received_data.add_content("xoppy_data", numpy.vstack((e,e,source)).T)
received_data.add_content("xoppy_code", "US")
elif input_data_type == "POWER3D":
# create unulator_radiation xoppy exchange data
from xoppylib.sources.xoppy_undulators import xoppy_calc_undulator_radiation
e, h, v, p, code = xoppy_calc_undulator_radiation(ELECTRONENERGY=6.04,ELECTRONENERGYSPREAD=0.001,ELECTRONCURRENT=0.2,\
ELECTRONBEAMSIZEH=0.000395,ELECTRONBEAMSIZEV=9.9e-06,\
ELECTRONBEAMDIVERGENCEH=1.05e-05,ELECTRONBEAMDIVERGENCEV=3.9e-06,\
PERIODID=0.018,NPERIODS=222,KV=1.68,DISTANCE=30.0,
SETRESONANCE=0,HARMONICNUMBER=1,
GAPH=0.001,GAPV=0.001,\
HSLITPOINTS=41,VSLITPOINTS=41,METHOD=0,
PHOTONENERGYMIN=7000,PHOTONENERGYMAX=8100,PHOTONENERGYPOINTS=20,
USEEMITTANCES=1)
received_data = DataExchangeObject("XOPPY", "POWER3D")
received_data = DataExchangeObject("XOPPY", "UNDULATOR_RADIATION")
received_data.add_content("xoppy_data", [p, e, h, v])
received_data.add_content("xoppy_code", code)
app = QApplication(sys.argv)
w = Scintillator()
w.acceptExchangeData(received_data)
w.show()
app.exec()
w.saveSettings() | PypiClean |
/DeePyMoD-20.11b0.tar.gz/DeePyMoD-20.11b0/docs/index.md | Documentation page for the Deep learning based Model Discovery package DeepMoD. DeePyMoD is a PyTorch-based implementation of the DeepMoD algorithm for model discovery of PDEs and ODEs.[github.com/PhIMaL/DeePyMoD](https://github.com/PhIMaL/DeePyMoD). This work is based on two papers: The original DeepMoD paper [arXiv:1904.09406](http://arxiv.org/abs/1904.09406), presenting the foundation of this neural network driven model discovery and a follow-up paper (Will be released soon) describing a modular plug and play framework.
## Summary
DeepMoD is a modular model discovery framewrok aimed at discovering the ODE/PDE underlying a spatio-temporal dataset. Essentially the framework is comprised of four components:
* Function approximator, e.g. a neural network to represent the dataset,
* Function library on which the model discovery is performed,
* Constraint function that constrains the neural network with the obtained solution
* Sparsity selection algorithm.
| PypiClean |
/DomiKnowS-0.533.tar.gz/DomiKnowS-0.533/domiknows/program/callbackprogram.py | from itertools import repeat
from typing import Callable, List
from dataclasses import dataclass
from ..utils import consume, entuple
from .model.base import Mode
from .program import LearningBasedProgram
class ProgramStorageCallback():
def __init__(self, program, fn) -> None:
self.program = program
self.fn = fn
self.storage = tuple()
def __call__(self):
self.storage = self.fn(self.program, *entuple(self.storage))
def hook(callbacks, *args, **kwargs):
if callbacks:
consume(callback(*args, **kwargs) for callback in callbacks)
class CallbackProgram(LearningBasedProgram):
def default_before_train_step(self):
if self.opt is not None:
self.opt.zero_grad()
def default_after_train_step(self, output=None):
loss, *_ = output
if self.opt and loss:
loss.backward()
self.opt.step()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.before_train = None
self.after_train = None
self.before_train_epoch = None
self.after_train_epoch = None
self.before_train_step = [self.default_before_train_step]
self.after_train_step = [self.default_after_train_step]
self.before_test = None
self.after_test = None
self.before_test_epoch = None
self.after_test_epoch = None
self.before_test_step = None
self.after_test_step = None
def train(self, *args, **kwargs):
hook(self.before_train)
super().train(*args, **kwargs)
hook(self.after_train)
def train_pure_epoch(self, dataset):
self.model.mode(Mode.TRAIN)
self.model.reset()
for data_item in dataset:
loss, metric, *output = self.model(data_item)
yield (loss, metric, *output[:1])
def train_epoch(self, dataset):
hook(self.before_train_epoch)
for _, output in zip(
map(hook, repeat(self.before_train_step)),
self.train_pure_epoch(dataset),
):
hook(self.after_train_step, output)
yield output
hook(self.after_train_epoch)
def test(self, *args, **kwargs):
hook(self.before_test)
super().test(*args, **kwargs)
hook(self.after_test)
def test_epoch(self, dataset):
hook(self.before_test_epoch)
for _, output in zip(
map(hook, repeat(self.before_test_step)),
super().test_epoch(dataset),
):
hook(self.after_test_step, output)
yield output
hook(self.after_test_epoch) | PypiClean |
/EFGs-0.8.4.tar.gz/EFGs-0.8.4/README.rst | EFGs (Extended functional groups)
=======================================================
.. image:: https://img.shields.io/pypi/v/EFGs.svg
:target: https://pypi.python.org/pypi/EFGs
:alt: Latest PyPI version
Extended Functional Groups
----------------------------
Extended functional group is a generalized version of traditional functional group and it also contains chemical groups that formed by only carbon atoms. It is inspired by `Peter Ertl`_'s work:
Ertl, P. An algorithm to identify functional groups in organic molecules. *J Cheminform* **9**, 36 (2017)
.. _Peter Ertl: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0225-z
Built based on that, we also induced the idea that a moelcule should be fully covered by 'Functional Groups'.
The philosophy of EFG (Extended functional group) is to do fragmentation on molecules so that all fragments of the molecule are chemical valid. To do that, we:
1. **Identify aromatic structures.** If two atoms shared the same aromatic ring system, they would be merged.
2. **Identify special substructures**:
* Mark all heteroatoms in a molecule
* Mark ‘special’ carbon atoms (carbon atoms with double/triple bonds, acetal carbons and three-membered heterocycles.)
* Merge all connected marked atoms to a single functional group
3. **Identify simple carbon chains**: sp3 carbons connected by two or more hydrogens
4. **Other single atoms** The number of single atoms can be significantly reduced by defining subclasses and merging some of them together. All atoms are classified by their aromaticity, degree and formal charge and recorded as element symbol followed by three number corresponding to above properties. For example, Hydrogen (𝐻2) would be H010, methyl group would be C010.
.. image:: image.png
In order to alleviate the imbalance distribution of different EFGs, we proposed an iterative way to selectively decompose large functional groups:
1. Set a cut-off value α (0<α<1)
2. Collect sparse functional groups whose rankings are behind top α in frequency distribution
3. Further decompose collected functional groups:
* a. Neighboring small functional groups which would be merged before would not be merged anymore unless they have shared atom(s).
* b. (If i. is not applicable) Cut all single bonds
4. Repeat previous steps until the number of functional groups does not change.
For most molecular datasets, this method is able to describe > 99% molecules with < 1% number of EFGs.
Requirements
^^^^^^^^^^^^
rdkit >= 2019.03
Installation
------------
1. To install from source (with latest version):
.. code:: bash
$ git clone https://github.com/HelloJocelynLu/EFGs.git
$ cd EFGs/
$ python setup.py install
2. Install from pip:
.. code:: bash
$ pip install EFGs
Usage
-----
See *Tutorial.ipynb* in Examples/ folder for detailed examples.
*mol2frag* is the core function to do the fragmentation.
Licence
-------
MIT Licence.
Authors
-------
`EFGs` was written by `Jocelyn Lu <jl8570@nyu.edu>`_.
Reference
----------
Lu, J. N.; Xia, S.; Lu, J. Y.; Zhang, Y. K., Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning. J. Chem. Inf. Model. 2021
| PypiClean |
/NREL_reV-0.8.1-py3-none-any.whl/reV/supply_curve/cli_supply_curve.py | import logging
from warnings import warn
from gaps.cli import as_click_command, CLICommandFromClass
from gaps.pipeline import parse_previous_status
from reV.supply_curve.supply_curve import SupplyCurve
from reV.utilities.exceptions import PipelineError
from reV.utilities import ModuleName
logger = logging.getLogger(__name__)
def _preprocessor(config, out_dir):
"""Preprocess supply curve config user input.
Parameters
----------
config : dict
User configuration file input as (nested) dict.
out_dir : str
Path to output file directory.
Returns
-------
dict
Updated config file.
"""
if config.get("sc_points") == 'PIPELINE':
sc_points = parse_previous_status(out_dir, ModuleName.SUPPLY_CURVE)
if not sc_points:
raise PipelineError('Could not parse "sc_points" from previous '
'pipeline jobs.')
config["sc_points"] = sc_points[0]
logger.info('Supply curve using the following '
'pipeline input for sc_points: {}'
.format(config["sc_points"]))
if config.get("simple"):
no_effect = [key for key in ['avail_cap_frac', 'line_limited']
if key in config]
if no_effect:
msg = ('The following key(s) have no effect when running '
'supply curve with "simple=True": "{}". To silence this '
'warning, please remove them from the config'
.format(', '.join(no_effect)))
logger.warning(msg)
warn(msg)
return config
sc_command = CLICommandFromClass(SupplyCurve, method="run",
name=str(ModuleName.SUPPLY_CURVE),
add_collect=False, split_keys=None,
config_preprocessor=_preprocessor)
main = as_click_command(sc_command)
if __name__ == '__main__':
try:
main(obj={})
except Exception:
logger.exception('Error running reV Supply Curve CLI.')
raise | PypiClean |
/DicksonUI-2.4.5.tar.gz/DicksonUI-2.4.5/dicksonui/jslib.py | lib = '"object"!=typeof JSON&&(JSON={}),function(){"use strict";var rx_one=/^[\\],:{}\\s]*$/,rx_two=/\\\\(?:["\\\\\\/bfnrt]|u[0-9a-fA-F]{4})/g,rx_three=/"[^"\\\\\\n\\r]*"|true|false|null|-?\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d+)?/g,rx_four=/(?:^|:|,)(?:\\s*\\[)+/g,rx_escapable=/[\\\\"\\u0000-\\u001f\\u007f-\\u009f\\u00ad\\u0600-\\u0604\\u070f\\u17b4\\u17b5\\u200c-\\u200f\\u2028-\\u202f\\u2060-\\u206f\\ufeff\\ufff0-\\uffff]/g,rx_dangerous=/[\\u0000\\u00ad\\u0600-\\u0604\\u070f\\u17b4\\u17b5\\u200c-\\u200f\\u2028-\\u202f\\u2060-\\u206f\\ufeff\\ufff0-\\uffff]/g,gap,indent,meta,rep;function f(e){return e<10?"0"+e:e}function this_value(){return this.valueOf()}function quote(e){return rx_escapable.lastIndex=0,rx_escapable.test(e)?\'"\'+e.replace(rx_escapable,function(e){var t=meta[e];return"string"==typeof t?t:"\\\\u"+("0000"+e.charCodeAt(0).toString(16)).slice(-4)})+\'"\':\'"\'+e+\'"\'}function str(e,t){var 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isFinite(this.valueOf())?this.getUTCFullYear()+"-"+f(this.getUTCMonth()+1)+"-"+f(this.getUTCDate())+"T"+f(this.getUTCHours())+":"+f(this.getUTCMinutes())+":"+f(this.getUTCSeconds())+"Z":null},Boolean.prototype.toJSON=this_value,Number.prototype.toJSON=this_value,String.prototype.toJSON=this_value),meta={"\\b":"\\\\b","\\t":"\\\\t","\\n":"\\\\n","\\f":"\\\\f","\\r":"\\\\r",\'"\':\'\\\\"\',"\\\\":"\\\\\\\\"},JSON.stringify=function(e,t,n){var r;if(gap="",indent="","number"==typeof n)for(r=0;r<n;r+=1)indent+=" ";else"string"==typeof n&&(indent=n);if(rep=t,t&&"function"!=typeof t&&("object"!=typeof t||"number"!=typeof t.length))throw new Error("JSON.stringify");return str("",{"":e})},"function"!=typeof JSON.parse&&(JSON.parse=function(text,reviver){var j;function walk(e,t){var n,r,o=e[t];if(o&&"object"==typeof o)for(n in o)Object.prototype.hasOwnProperty.call(o,n)&&(void 0!==(r=walk(o,n))?o[n]=r:delete o[n]);return reviver.call(e,t,o)}if(text=String(text),rx_dangerous.lastIndex=0,rx_dangerous.test(text)&&(text=text.replace(rx_dangerous,function(e){return"\\\\u"+("0000"+e.charCodeAt(0).toString(16)).slice(-4)})),rx_one.test(text.replace(rx_two,"@").replace(rx_three,"]").replace(rx_four,"")))return j=eval("("+text+")"),"function"==typeof reviver?walk({"":j},""):j;throw new SyntaxError("JSON.parse")})}(),"function"!=typeof JSON.decycle&&function(){JSON.decycle=function(e,t){"use strict";var n=[],r=(t=void 0!==t&&t,[]);return function e(o,a){var u,i,f;if(t&&"object"==typeof o&&null!==o&&"nodeType"in o)return function(e){var t="";switch(e.nodeType){case e.ELEMENT_NODE:t=e.nodeName.toLowerCase(),e.id.length?t+="#"+e.id:(e.className.length&&(t+="."+e.className.replace(/ /,".")),"textContent"in 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value&&(Array.isArray(value)?value.forEach(function(element,i){if("object"==typeof element&&null!==element){var path=element.$ref;"string"==typeof path&&px.test(path)?value[i]=eval(path):rez(element)}}):Object.keys(value).forEach(function(name){var item=value[name];if("object"==typeof item&&null!==item){var path=item.$ref;"string"==typeof path&&px.test(path)?value[name]=eval(path):rez(item)}}))}($),$});var env_index=0,sock=SignalPy(window.location.host+window.location.pathname+"/Hub");function setsock(){sock=SignalPy(window.location.host+window.location.pathname+"/Hub")}function dicksonui_event_handler(e){var t={};for(x in e)t[x]=e[x];t.target=e.target.id,t.currentTarget=e.currentTarget.id,sd={},sd.data=t,sd.ftarget=t.target+"."+e.type,sock.send(JSON.stringify(JSON.decycle(sd)))}sock.onerror=function(e){try{setTimeout(setsock,2)}catch{}},sock.onclose=function(e){try{setTimeout(setsock,2)}catch{}},sock.onmessage=function(e){s=document.createElement("script"),s.id="dicksonuienvironment"+env_index.toString(),s.innerHTML=e.data+\'document.head.removeChild(document.getElementById("\'+s.id+\'"));\',document.head.appendChild(s)};\n' | PypiClean |
/Cohen-0.7.4.tar.gz/Cohen-0.7.4/coherence/backends/feed_storage.py |
# Copyright 2009, Dominik Ruf <dominikruf at googlemail dot com>
from coherence.backend import BackendItem
from coherence.backend import BackendStore
from coherence.upnp.core import DIDLLite
from coherence.upnp.core.utils import ReverseProxyUriResource
from xml.etree.ElementTree import ElementTree
import urllib
import httplib
from urlparse import urlsplit
try:
import feedparser
except:
raise ImportError("""
This backend depends on the feedparser module.
You can get it at http://www.feedparser.org/.""")
MIME_TYPES_EXTENTION_MAPPING = {'mp3': 'audio/mpeg', }
ROOT_CONTAINER_ID = 0
AUDIO_ALL_CONTAINER_ID = 51
AUDIO_ARTIST_CONTAINER_ID = 52
AUDIO_ALBUM_CONTAINER_ID = 53
VIDEO_FOLDER_CONTAINER_ID = 54
class RedirectingReverseProxyUriResource(ReverseProxyUriResource):
def render(self, request):
self.uri = self.follow_redirect(self.uri)
self.resetUri(self.uri)
return ReverseProxyUriResource.render(self, request)
def follow_redirect(self, uri):
netloc, path, query, fragment = urlsplit(uri)[1:]
conn = httplib.HTTPConnection(netloc)
conn.request('HEAD', '%s?%s#%s' % (path, query, fragment))
res = conn.getresponse()
if(res.status == 301 or res.status == 302):
return self.follow_redirect(res.getheader('location'))
else:
return uri
class FeedStorageConfigurationException(Exception):
pass
class FeedContainer(BackendItem):
def __init__(self, parent_id, id, title):
BackendItem.__init__(self)
self.id = id
self.parent_id = parent_id
self.name = title
self.mimetype = 'directory'
self.item = DIDLLite.Container(self.id, self.parent_id, self.name)
self.children = []
def get_children(self, start=0, end=0):
"""returns all the chidlren of this container"""
if end != 0:
return self.children[start:end]
return self.children[start:]
def get_child_count(self):
"""returns the number of children in this container"""
return len(self.children)
class FeedEnclosure(BackendItem):
def __init__(self, store, parent, id, title, enclosure):
BackendItem.__init__(self)
self.store = store
self.parent = parent
self.external_id = id
self.name = title
self.location = RedirectingReverseProxyUriResource(enclosure.url.encode('latin-1'))
# doing this because some (Fraunhofer Podcast) feeds say there mime type is audio/x-mpeg
# which at least my XBOX doesn't like
ext = enclosure.url.rsplit('.', 1)[0]
if ext in MIME_TYPES_EXTENTION_MAPPING:
mime_type = MIME_TYPES_EXTENTION_MAPPING[ext]
else:
mime_type = enclosure.type
if(enclosure.type.startswith('audio')):
self.item = DIDLLite.AudioItem(id, parent, self.name)
elif(enclosure.type.startswith('video')):
self.item = DIDLLite.VideoItem(id, parent, self.name)
elif(enclosure.type.startswith('image')):
self.item = DIDLLite.ImageItem(id, parent, self.name)
res = DIDLLite.Resource("%s%d" % (store.urlbase, id), 'http-get:*:%s:*' % mime_type)
self.item.res.append(res)
class FeedStore(BackendStore):
"""a general feed store"""
logCategory = 'feed_store'
implements = ['MediaServer']
def __init__(self, server, **kwargs):
BackendStore.__init__(self, server, **kwargs)
self.name = kwargs.get('name', 'Feed Store')
self.urlbase = kwargs.get('urlbase', '')
if(len(self.urlbase) > 0 and
self.urlbase[len(self.urlbase) - 1] != '/'):
self.urlbase += '/'
self.feed_urls = kwargs.get('feed_urls')
self.opml_url = kwargs.get('opml_url')
if(not(self.feed_urls or self.opml_url)):
raise FeedStorageConfigurationException("either feed_urls or opml_url has to be set")
if(self.feed_urls and self.opml_url):
raise FeedStorageConfigurationException("only feed_urls OR opml_url can be set")
self.server = server
self.refresh = int(kwargs.get('refresh', 1)) * (60 * 60) # TODO: not used yet
self.store = {}
self.wmc_mapping = {'4': str(AUDIO_ALL_CONTAINER_ID), # all tracks
'7': str(AUDIO_ALBUM_CONTAINER_ID), # all albums
'6': str(AUDIO_ARTIST_CONTAINER_ID), # all artists
'15': str(VIDEO_FOLDER_CONTAINER_ID), # all videos
}
self.store[ROOT_CONTAINER_ID] = FeedContainer(-1, ROOT_CONTAINER_ID, self.name)
self.store[AUDIO_ALL_CONTAINER_ID] = FeedContainer(-1, AUDIO_ALL_CONTAINER_ID, 'AUDIO_ALL_CONTAINER')
self.store[AUDIO_ALBUM_CONTAINER_ID] = FeedContainer(-1, AUDIO_ALBUM_CONTAINER_ID, 'AUDIO_ALBUM_CONTAINER')
self.store[VIDEO_FOLDER_CONTAINER_ID] = FeedContainer(-1, VIDEO_FOLDER_CONTAINER_ID, 'VIDEO_FOLDER_CONTAINER')
try:
self._update_data()
except Exception, e:
self.error('error while updateing the feed contant for %s: %s', self.name, str(e))
self.init_completed()
def get_by_id(self, id):
"""returns the item according to the DIDLite id"""
if isinstance(id, basestring):
id = id.split('@', 1)
id = id[0]
try:
return self.store[int(id)]
except (ValueError, KeyError):
self.info("can't get item %d from %s feed storage", int(id), self.name)
return None
def _update_data(self):
"""get the feed xml, parse it, etc."""
feed_urls = []
if(self.opml_url):
tree = ElementTree(file=urllib.urlopen(self.opml_url))
body = tree.find('body')
for outline in body.findall('outline'):
feed_urls.append(outline.attrib['url'])
if(self.feed_urls):
feed_urls = self.feed_urls.split()
container_id = 100
item_id = 1001
for feed_url in feed_urls:
netloc, path, query, fragment = urlsplit(feed_url)[1:]
conn = httplib.HTTPConnection(netloc)
conn.request('HEAD', '%s?%s#%s' % (path, query, fragment))
res = conn.getresponse()
if res.status >= 400:
self.warning('error getting %s status code: %d', feed_url, res.status)
continue
fp_dict = feedparser.parse(feed_url)
name = fp_dict.feed.title
self.store[container_id] = FeedContainer(ROOT_CONTAINER_ID, container_id, name)
self.store[ROOT_CONTAINER_ID].children.append(self.store[container_id])
self.store[VIDEO_FOLDER_CONTAINER_ID].children.append(self.store[container_id])
self.store[AUDIO_ALBUM_CONTAINER_ID].children.append(self.store[container_id])
for item in fp_dict.entries:
for enclosure in item.enclosures:
self.store[item_id] = FeedEnclosure(self, container_id, item_id, '%04d - %s' % (item_id, item.title), enclosure)
self.store[container_id].children.append(self.store[item_id])
if enclosure.type.startswith('audio'):
self.store[AUDIO_ALL_CONTAINER_ID].children.append(self.store[item_id])
if not isinstance(self.store[container_id].item, DIDLLite.MusicAlbum):
self.store[container_id].item = DIDLLite.MusicAlbum(container_id, AUDIO_ALBUM_CONTAINER_ID, name)
item_id += 1
if container_id <= 1000:
container_id += 1
else:
raise Exception('to many containers') | PypiClean |
/AstroCabTools-1.5.1.tar.gz/AstroCabTools-1.5.1/astrocabtools/cube_ans/src/viewers/canvas_interaction/spectrumCanvas/mplInteraction.py | import numpy as np
import weakref
from pubsub import pub
import matplotlib.pyplot as _plt
from matplotlib.patches import Rectangle
from ....models.rectangleSpectrum import rectangle_spectrum
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
class MplInteraction(object):
def __init__(self, figure):
"""Initializer
:param Figure figure: The matplotlib figure to attach the behavior to.
:param Canvas FigureCanvas: The matplotlib PyQT canvas to allow focus after declaration
"""
self._fig_ref = weakref.ref(figure)
self.canvas = FigureCanvas(figure)
self._rectangle_interactions = None
self._line_position = None
self._rectangle_yAxis_limits = [None, None]
self._range_wavelength_limits = [0.0,0.0]
self._cids_zoom = []
self._cids_pan = []
self._cids_rectangle_creation = []
self._cids_rectangle_interaction = []
self._cids_callback_zoom = {}
self._cids_callback_pan = {}
self._cids_callback_rectangle_creation = {}
self._cids_callback_rectangle_interaction = {}
self._cids = []
self._rectangleStats = rectangle_spectrum(0)
def __del__(self):
self.disconnect()
def _add_connection(self, event_name, callback):
"""Called to add a connection to an event of the figure
:param str event_name: The matplotlib event name to connect to.
:param callback: The callback to register to this event.
"""
cid = self.canvas.mpl_connect(event_name, callback)
self._cids.append(cid)
def _add_connection_zoom(self, event_name, callback):
"""Called to add a connection of type zoom to an event of the figure
:param str event_name: The matplotlib event name to connect to.
:param callback: The callback to register to this event.
"""
#cid = self.canvas.mpl_connect(event_name, callback)
#self._cids_zoom.append(cid)
self._cids_callback_zoom[event_name] = callback
def _add_connection_pan(self, event_name, callback):
"""Called to add a connection of type pan to an event of the figure
:param str event_name: The matplotlib event name to connect to.
:param callback: The callback to register to this event.
"""
#cid = self.canvas.mpl_connect(event_name, callback)
#self._cids_pan.append(cid)
self._cids_callback_pan[event_name] = callback
def _add_connection_rectangle_creation(self, event_name, callback):
"""Called to add a connection of type rectangle to an event of the figure
:param str event_name: The matplotlib event name to connect to.
:param callback: The callback to register to this event.
"""
self._cids_callback_rectangle_creation[event_name] = callback
def _add_connection_rectangle_interaction(self, event_name, callback):
"""Called to add a connection of type rectangle to an event of the figure
:param str event_name: The matplotlib event name to connect to.
:param callback: The callback to register to this event.
"""
self._cids_callback_rectangle_interaction[event_name] = callback
def disconnect_zoom(self):
"""
Disconnect all zoom events and disable the rectangle selector
"""
if self._fig_ref is not None:
figure = self._fig_ref()
if figure is not None:
for cid in self._cids_zoom:
figure.canvas.mpl_disconnect(cid)
self._cids_zoom.clear()
def disconnect_pan(self):
"""
Disconnect all pan events
"""
if self._fig_ref is not None:
figure = self._fig_ref()
if figure is not None:
for cid in self._cids_pan:
figure.canvas.mpl_disconnect(cid)
self._cids_pan.clear()
def disconnect_rectangle_creation(self):
"""
Disconnect all rectangle events
"""
if self._fig_ref is not None:
figure = self._fig_ref()
if figure is not None:
for cid in self._cids_rectangle_creation:
figure.canvas.mpl_disconnect(cid)
self._cids_rectangle_creation.clear()
def disconnect_rectangle_interaction(self):
"""
Disconnect all rectangle events
"""
if self._fig_ref is not None:
figure = self._fig_ref()
if figure is not None:
for cid in self._cids_rectangle_interaction:
figure.canvas.mpl_disconnect(cid)
self._cids_rectangle_interaction.clear()
def disconnect(self):
"""Disconnect interaction from Figure."""
if self._fig_ref is not None:
figure = self._fig_ref()
if figure is not None:
for cid in self._cids:
figure.canvas.mpl_disconnect(cid)
self._fig_ref = None
def connect_zoom(self):
"""
Assign all callback zoom events to the mpl
"""
for event_name, callback in self._cids_callback_zoom.items():
cid = self.canvas.mpl_connect(event_name, callback)
self._cids_zoom.append(cid)
def connect_pan(self):
"""
Assign all callback pan events to the mpl
"""
for event_name, callback in self._cids_callback_pan.items():
cid = self.canvas.mpl_connect(event_name, callback)
self._cids_pan.append(cid)
def connect_rectangle_creation(self):
"""
Assign all callback rectangle events to the mpl
"""
for event_name, callback in self._cids_callback_rectangle_creation.items():
cid = self.canvas.mpl_connect(event_name, callback)
self._cids_rectangle_creation.append(cid)
def connect_rectangle_interaction(self):
"""
Assign all callback rectangle events to the mpl
"""
for event_name, callback in self._cids_callback_rectangle_interaction.items():
cid = self.canvas.mpl_connect(event_name, callback)
self._cids_rectangle_interaction.append(cid)
def draw_line_position(self, x):
"""Set parametes (x positions and height) of the line that will
move along x_axis according to the current wavelength and the wavelength yAxis limits
:param float x: current wavelength selected
"""
if self._line_position is not None:
for ax in self.figure.axes:
yAxis_limits = self._rectangle_yAxis_limits
segments = []
segments.append(tuple(zip((x,x), (yAxis_limits[0], yAxis_limits[1]))))
self._line_position.set_segments(segments)
else:
for ax in self.figure.axes:
yAxis_limits = self._rectangle_yAxis_limits
self._line_position = ax.vlines(x=x, ymin = yAxis_limits[0], ymax= yAxis_limits[1])
self._draw()
def set_rectangle_yAxis_limits(self, ymin, ymax):
"""Pass the spectrum min and max values into an object to use it as the height
of the range rectangle figure
:param float ymin: minimum flux value of the spectrum
:param float ymax: maximum flux value of the spectrum
"""
self._rectangle_yAxis_limits[0] = ymin
self._rectangle_yAxis_limits[1] = ymax
def update_rectangle_yAxis_limits_on_range_update(self, yAxis_range_limits):
"""Set the height of the rectangle every time a rectangle is created or is moved
:param tuple yAxis_range_limits: tuple with the bottom and top "y" limits
"""
if isinstance(self._rectangle_interactions, Rectangle):
self._rectangle_interactions.set_height(abs(yAxis_range_limits[0]*0.9 - \
yAxis_range_limits[1]*1.1))
self._rectangle_interactions.set_y(yAxis_range_limits[0]*0.9)
self._draw()
def update_rectangle_yAxis_limits_on_spectrum_update(self, yAxis_range_limits):
"""Set the height of the rectangle every time the spectrum data changes to
adapt it based on the max and min values of the spectrum
"""
if isinstance(self._rectangle_interactions, Rectangle):
self._rectangle_interactions.set_height(abs(yAxis_range_limits[0]*0.9 - \
yAxis_range_limits[1]*1.1))
self._rectangle_interactions.set_y(yAxis_range_limits[0]*0.9)
self._draw()
def set_initial_limits(self, xlim, ylim):
""" Set initial limits of the representation to use it when the zoom reset
button is pushed
:param tuple xlim: limit values on x axis
:param tuple ylim: limit values on y axis
"""
self._spectrum_initial_xlim = xlim
self._spectrum_initial_ylim = ylim
def zoom_reset(self):
for ax in self.figure.axes:
ax.set_xlim((self._spectrum_initial_xlim[0],\
self._spectrum_initial_xlim[1]))
ax.set_ylim((self._spectrum_initial_ylim[0],\
self._spectrum_initial_ylim[1]))
self._draw()
def clear_elements_axes(self):
self._rectangle_interactions = None
self._line_position = None
@property
def figure(self):
"""The Figure this interaction is connected to or
None if not connected."""
return self._fig_ref() if self._fig_ref is not None else None
def _axes_to_update(self, event):
"""Returns two sets of Axes to update according to event.
:param MouseEvent event: Matplotlib event to consider
:return: Axes for which to update xlimits and ylimits
:rtype: 2-tuple of set (xaxes, yaxes)
"""
x_axes, y_axes = set(), set()
for ax in self.figure.axes:
if ax.contains(event)[0]:
x_axes.add(ax)
y_axes.add(ax)
return x_axes, y_axes
def _draw(self):
"""Conveninent method to redraw the figure"""
self.canvas.draw() | PypiClean |
/GradientDR-0.1.3.4-py3-none-any.whl/GDR/optimizer/spectral.py | from warnings import warn
import numpy as np
import scipy.sparse
import scipy.sparse.csgraph
from sklearn.manifold import SpectralEmbedding
from sklearn.metrics import pairwise_distances
from sklearn.metrics.pairwise import _VALID_METRICS as SKLEARN_PAIRWISE_VALID_METRICS
def component_layout(
data,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
):
"""Provide a layout relating the separate connected components. This is done
by taking the centroid of each component and then performing a spectral embedding
of the centroids.
Parameters
----------
data: array of shape (n_samples, n_features)
The source data -- required so we can generate centroids for each
connected component of the graph.
n_components: int
The number of distinct components to be layed out.
component_labels: array of shape (n_samples)
For each vertex in the graph the label of the component to
which the vertex belongs.
dim: int
The chosen embedding dimension.
metric: string or callable (optional, default 'euclidean')
The metric used to measure distances among the source data points.
Returns
-------
component_embedding: array of shape (n_components, dim)
The ``dim``-dimensional embedding of the ``n_components``-many
connected components.
"""
if data is None:
# We don't have data to work with; just guess
return np.random.random(size=(n_components, dim)) * 10.0
component_centroids = np.empty((n_components, data.shape[1]), dtype=np.float64)
if metric == "precomputed":
# cannot compute centroids from precomputed distances
# instead, compute centroid distances using linkage
distance_matrix = np.zeros((n_components, n_components), dtype=np.float64)
linkage = np.mean
for c_i in range(n_components):
dm_i = data[component_labels == c_i]
for c_j in range(c_i + 1, n_components):
dist = linkage(dm_i[:, component_labels == c_j])
distance_matrix[c_i, c_j] = dist
distance_matrix[c_j, c_i] = dist
else:
for label in range(n_components):
component_centroids[label] = data[component_labels == label].mean(axis=0)
distance_matrix = pairwise_distances(
component_centroids, metric=metric
)
affinity_matrix = np.exp(-(distance_matrix ** 2))
component_embedding = SpectralEmbedding(
n_components=dim, affinity="precomputed", random_state=random_state
).fit_transform(affinity_matrix)
component_embedding /= component_embedding.max()
return component_embedding
def multi_component_layout(
data,
graph,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
):
"""Specialised layout algorithm for dealing with graphs with many connected components.
This will first find relative positions for the components by spectrally embedding
their centroids, then spectrally embed each individual connected component positioning
them according to the centroid embeddings. This provides a decent embedding of each
component while placing the components in good relative positions to one another.
Parameters
----------
data: array of shape (n_samples, n_features)
The source data -- required so we can generate centroids for each
connected component of the graph.
graph: sparse matrix
The adjacency matrix of the graph to be emebdded.
n_components: int
The number of distinct components to be layed out.
component_labels: array of shape (n_samples)
For each vertex in the graph the label of the component to
which the vertex belongs.
dim: int
The chosen embedding dimension.
metric: string or callable (optional, default 'euclidean')
The metric used to measure distances among the source data points.
Returns
-------
embedding: array of shape (n_samples, dim)
The initial embedding of ``graph``.
"""
result = np.empty((graph.shape[0], dim), dtype=np.float32)
if n_components > 2 * dim:
meta_embedding = component_layout(
data,
n_components,
component_labels,
dim,
random_state,
metric=metric,
)
else:
k = int(np.ceil(n_components / 2.0))
base = np.hstack([np.eye(k), np.zeros((k, dim - k))])
meta_embedding = np.vstack([base, -base])[:n_components]
for label in range(n_components):
component_graph = graph.tocsr()[component_labels == label, :].tocsc()
component_graph = component_graph[:, component_labels == label].tocoo()
distances = pairwise_distances([meta_embedding[label]], meta_embedding)
data_range = distances[distances > 0.0].min() / 2.0
if component_graph.shape[0] < 2 * dim or component_graph.shape[0] <= dim + 1:
result[component_labels == label] = (
random_state.uniform(
low=-data_range,
high=data_range,
size=(component_graph.shape[0], dim),
)
+ meta_embedding[label]
)
continue
diag_data = np.asarray(component_graph.sum(axis=0))
# standard Laplacian
# D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0])
# L = D - graph
# Normalized Laplacian
I = scipy.sparse.identity(component_graph.shape[0], dtype=np.float64)
D = scipy.sparse.spdiags(
1.0 / np.sqrt(diag_data),
0,
component_graph.shape[0],
component_graph.shape[0],
)
L = I - D * component_graph * D
k = dim + 1
num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(component_graph.shape[0])))
try:
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
L,
k,
which="SM",
ncv=num_lanczos_vectors,
tol=1e-4,
v0=np.ones(L.shape[0]),
maxiter=graph.shape[0] * 5,
)
order = np.argsort(eigenvalues)[1:k]
component_embedding = eigenvectors[:, order]
expansion = data_range / np.max(np.abs(component_embedding))
component_embedding *= expansion
result[component_labels == label] = (
component_embedding + meta_embedding[label]
)
except scipy.sparse.linalg.ArpackError:
warn(
"WARNING: spectral initialisation failed! The eigenvector solver\n"
"failed. This is likely due to too small an eigengap. Consider\n"
"adding some noise or jitter to your data.\n\n"
"Falling back to random initialisation!"
)
result[component_labels == label] = (
random_state.uniform(
low=-data_range,
high=data_range,
size=(component_graph.shape[0], dim),
)
+ meta_embedding[label]
)
return result
def spectral_layout(data, graph, dim, random_state, metric="euclidean"):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
Parameters
----------
data: array of shape (n_samples, n_features)
The source data
graph: sparse matrix
The (weighted) adjacency matrix of the graph as a sparse matrix.
dim: int
The dimension of the space into which to embed.
random_state: numpy RandomState or equivalent
A state capable being used as a numpy random state.
Returns
-------
embedding: array of shape (n_vertices, dim)
The spectral embedding of the graph.
"""
n_samples = graph.shape[0]
n_components, labels = scipy.sparse.csgraph.connected_components(graph)
if n_components > 1:
return multi_component_layout(
data,
graph,
n_components,
labels,
dim,
random_state,
metric=metric,
)
diag_data = np.asarray(graph.sum(axis=0))
I = scipy.sparse.identity(graph.shape[0], dtype=np.float64)
D = scipy.sparse.spdiags(
1.0 / np.sqrt(diag_data), 0, graph.shape[0], graph.shape[0]
)
L = I - D * graph * D
k = dim + 1
num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(graph.shape[0])))
try:
if L.shape[0] < 2000000:
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
L,
k,
which="SM",
ncv=num_lanczos_vectors,
tol=1e-4,
v0=np.ones(L.shape[0]),
maxiter=graph.shape[0] * 5,
)
else:
eigenvalues, eigenvectors = scipy.sparse.linalg.lobpcg(
L, random_state.normal(size=(L.shape[0], k)), largest=False, tol=1e-8
)
order = np.argsort(eigenvalues)[1:k]
return eigenvectors[:, order]
except scipy.sparse.linalg.ArpackError:
warn(
"WARNING: spectral initialisation failed! The eigenvector solver\n"
"failed. This is likely due to too small an eigengap. Consider\n"
"adding some noise or jitter to your data.\n\n"
"Falling back to random initialisation!"
)
return random_state.uniform(low=-10.0, high=10.0, size=(graph.shape[0], dim)) | PypiClean |
/gramaddict-3.2.5.tar.gz/gramaddict-3.2.5/CONTRIBUTING.md | # Contributing to GramAddict
:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
The following is a set of guidelines for contributing to GramAddict and its associated repos, which are hosted in the [GramAddict Organization](https://github.com/gramaddict) on GitHub. These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request.
#### Table Of Contents
- [I don't want to read this whole thing, I just have a question!!!](#i-dont-want-to-read-this-whole-thing-i-just-have-a-question)
- [Code of Conduct](#code-of-conduct)
- [How Can I Contribute?](#how-can-i-contribute)
- [Reporting Bugs](#reporting-bugs)
- [Suggesting Enhancements](#suggesting-enhancements)
- [Your First Code Contribution](#your-first-code-contribution)
- [Pull Requests](#pull-requests)
- [Styleguides](#styleguides)
- [Git Commit Messages](#git-commit-messages)
- [Python Styleguide](#javascript-styleguide)
<br />
## I don't want to read this whole thing I just have a question!!!
> **Note:** Please don't file an issue to ask a question. You may not get any assistance and if you do get any, you would have gotten faster results by using the resources below.
We have a detailed FAQ and docs website where you can learn how to use GramAddict: [https://docs.gramaddict.org/](https://docs.gramaddict.org/)
If you can't find the answer or want to chat with other people using GramAddict; you can join the official GramAddict discord server. Here there are many active members of the community - as well as the development team - who chime in with helpful advice if you have questions.
- [Discord Server](https://discord.com/channels/771481743471017994) - If you've never joined before, use the [Invite Link](https://discord.com/invite/NK8PNEFGFF)
> **Note:** Even though Discord is a chat service, sometimes it takes several hours for community members to respond — please be patient!
- Use the `#general` channel for general questions or discussion about GramAddict
- Use the `#community-support` channel for help with issues or questions about running the bot
- Use the `#development` channel for questions or discussion about writing or contributing to GramAddict packages
- Use the `#lobby` channel for creating a ticket to share a crash report
- There are other channels available as well, check the channel list
<br />
## Code of Conduct
This project and everyone participating in it is governed by the [GramAddict Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please report unacceptable behavior via the [Discord Server](https://discord.com/invite/NK8PNEFGFF). Please direct them to any of the project owners via DM.
<br />
## How Can I Contribute?
### Reporting Bugs
This section guides you through submitting a bug report for GramAddict. Following these guidelines helps maintainers and the community understand your report :pencil:, reproduce the behavior :computer: :computer:, and find related reports :mag_right:.
Before creating bug reports, please check [this list](#before-submitting-a-bug-report) as you might find out that you don't need to create one. When you are creating a bug report, please [include as many details as possible](#how-do-i-submit-a-good-bug-report). Fill out [the required template](https://github.com/gramaddict/bot/blob/master/.github/ISSUE_TEMPLATE/bug_report.md), the information it asks for helps us resolve issues faster.
> **Note:** If you find a **Closed** issue that seems like it is the same thing that you're experiencing, open a new issue and include a link to the original issue in the body of your new one.
#### Before Submitting A Bug Report
* **Check the [docs](https://docs.gramaddict.org).** You might be able to find the cause of the problem and fix things yourself. Most importantly, check if you can reproduce the problem [in the latest version of GramAddict](https://github.com/GramAddict/bot/releases/latest).
* **Check the [FAQs on the docs site](https://docs.gramaddict.org/#/?id=faq)** for a list of common questions and problems.
* **Perform a [cursory search](https://github.com/search?q=+is%3Aissue+user%3Agramaddict)** to see if the problem has already been reported. If it has **and the issue is still open**, add a comment to the existing issue instead of opening a new one.
#### How Do I Submit A (Good) Bug Report?
Bugs are tracked as [GitHub issues](https://guides.github.com/features/issues/). After you've determined [you have a valid bug report](#before-submitting-a-bug-report) and there is not an existing issue open for it; create an issue on the associated repository and provide the following information by filling in [the template](https://github.com/gramaddict/bot/blob/master/.github/ISSUE_TEMPLATE/bug_report.md).
Explain the problem and include additional details to help maintainers reproduce the problem:
* **Use a clear and descriptive title** for the issue to identify the problem.
* **Describe the exact steps which reproduce the problem** in as many details as possible. For example, start by explaining how you started GramAddict, e.g. which command exactly you used in the terminal, or how you started GramAddict otherwise. When listing steps, **don't just say what you did, but explain how you did it**. For example, provide the arguments you ran it with.
* **Provide specific examples to demonstrate the steps**. Include links to files or GitHub projects, or copy/pasteable snippets, which you use in those examples. If you're providing snippets in the issue, use [Markdown code blocks](https://help.github.com/articles/markdown-basics/#multiple-lines).
* **Describe the behavior you observed after following the steps** and point out what exactly is the problem with that behavior.
* **Explain which behavior you expected to see instead and why.**
* **If you're reporting that GramAddict crashed**, please open a ticket on discord, upload the crash file there, and then provide the ticket number in the issue.
* **If the problem wasn't triggered by a specific action**, describe what you were doing before the problem happened and share more information using the guidelines below.
* **Specify the name and version of the OS you're using.**
* **Specify the model of phone/tablet or name and version number of the emulator you are using.**
* **Specify the version of Instagram you are running.**
Provide more context by answering these questions:
* **Can you reproduce the problem in every time** or was it a temporary issue with another open dialogue on your device?
* **Did the problem start happening recently** (e.g. after updating to a new version of GramAddict) or was this always a problem?
* If the problem started happening recently, **can you reproduce the problem in an older version of GramAddict?** What's the most recent version in which the problem doesn't happen? You can download older versions of GramAddict from [the releases page](https://github.com/gramaddict/bot/releases)
Include details about your configuration and environment:
* **Which version of GramAddict are you using?** You can get the exact version by checking the log when you run the script.
* **What's the name and version of the OS you're using**?
### Suggesting Enhancements
This section guides you through submitting an enhancement suggestion for GramAddict, including completely new features and minor improvements to existing functionality. Following these guidelines helps maintainers and the community understand your suggestion :pencil: and find related suggestions :mag_right:.
Before creating enhancement suggestions, please check [this list](#before-submitting-an-enhancement-suggestion) as you might find out that you don't need to create one. When you are creating an enhancement suggestion, please [include as many details as possible](#how-do-i-submit-a-good-enhancement-suggestion). Fill in [the template](https://github.com/gramaddict/bot/blob/master/.github/ISSUE_TEMPLATE/feature_request.md), including the steps that you imagine you would take if the feature you're requesting existed.
#### Before Submitting An Enhancement Suggestion
* **Check the [docs](https://doc.gramaddict.org).** for tips — you might discover that the enhancement is already available. Most importantly, check if you're using [in the latest version of GramAddict](https://github.com/GramAddict/bot/releases/latest).
* **Perform a [cursory search](https://github.com/search?q=+is%3Aissue+user%3Agramaddict)** to see if the enhancement has already been suggested. If it has, add a comment to the existing issue instead of opening a new one.
#### How Do I Submit A (Good) Enhancement Suggestion?
Enhancement suggestions are tracked as [GitHub issues](https://guides.github.com/features/issues/). After you've determined your enhancement suggestion is valid, create an issue on the associated repository and provide the following information:
* **Use a clear and descriptive title** for the issue to identify the suggestion.
* **Provide a step-by-step description of the suggested enhancement** in as many details as possible.
* **Provide specific examples to demonstrate the steps**. Include copy/pasteable snippets which you use in those examples, as [Markdown code blocks](https://help.github.com/articles/markdown-basics/#multiple-lines).
* **Describe the current behavior** and **explain which behavior you expected to see instead** and why.
* **Include screenshots and animated GIFs** which help you demonstrate the steps or point out the part of GramAddict which the suggestion is related to. You can use [this tool](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [this tool](https://github.com/colinkeenan/silentcast) or [this tool](https://github.com/GNOME/byzanz) on Linux.
* **Explain why this enhancement would be useful** to most GramAddict users.
* **Specify which version of GramAddict you're using.** You can get the exact version by checking the log when you run the script.
* **Specify the name and version of the OS you're using.**
* **Specify the model of phone/tablet or name and version number of the emulator you are using.**
* **Specify the version of Instagram you are running.**
### Your First Code Contribution
Unsure where to begin contributing to GramAddict? You can start by looking for `beginner` and `help-wanted` issues:
* [Beginner issues][beginner] - issues which should only require a few lines of code, and a test or two.
* [Help wanted issues][help-wanted] - issues which should be a bit more involved than `beginner` issues.
Both issue lists are sorted by total number of comments. While not perfect, number of comments is a reasonable proxy for impact a given change will have.
If you want to read about using GramAddict or developing packages in GramAddict, the [GramAddict Docs](https://doc.gramaddict.org) are available to assist with every aspect of GramAddict.
### Making Changes
* Create a topic branch from where you want to base your work.
* This is almost always the develop branch.
* To quickly create a topic branch based on develop, run `git checkout -b
feature-mybranch develop`. Please avoid working directly on the
`develop` branch.
* Make commits using the [styleguides](#styleguides)
* Make sure to [Blacken](https://github.com/psf/black) your code before committing
* Make sure your commit messages are in the proper format. If the commit addresses an issue filed in the GitHub, end the first line of the commit with the issue number prefaced by a #.
Example:
```
:cat2: Fixing an encoding bug with the logging system #31
- Without utf-8 encoding, certain logs cannot be written and will cause an exception
```
### Pull Requests
The process described here has several goals:
- Maintain GramAddict's quality
- Fix problems that are important to users
- Engage the community in working toward the best possible GramAddict
- Enable a sustainable system for GramAddict's maintainers to review contributions
Please follow these steps to have your contribution considered by the maintainers:
1. Follow all instructions in [the template](.github/PULL_REQUEST_TEMPLATE.md)
2. Follow the [styleguides](#styleguides)
3. After you submit your pull request, verify that all [status checks](https://help.github.com/articles/about-status-checks/) are passing <details><summary>What if the status checks are failing?</summary>If a status check is failing, and you believe that the failure is unrelated to your change, please leave a comment on the pull request explaining why you believe the failure is unrelated. A maintainer will re-run the status check for you. If we conclude that the failure was a false positive, then we will open an issue to track that problem with our status check suite.</details>
While the prerequisites above must be satisfied prior to having your pull request reviewed, the reviewer(s) may ask you to complete additional design work, tests, or other changes before your pull request can be ultimately accepted.
<br />
## Styleguides
### Git Commit Messages
* Use the present tense ("Add feature" not "Added feature")
* Use the imperative mood ("Move cursor to..." not "Moves cursor to...")
* Limit the first line to 72 characters or less
* Reference issues and pull requests liberally after the first line
* Consider starting the commit message with an applicable emoji:
* :cat2: `:cat2:` when fixing or improving existing code
* :racehorse: `:racehorse:` when improving performance
* :memo: `:memo:` when writing docs
* :penguin: `:penguin:` when fixing something on Linux
* :apple: `:apple:` when fixing something on macOS
* :checkered_flag: `:checkered_flag:` when fixing something on Windows
* :bug: `:bug:` when fixing a bug
* :fire: `:fire:` when removing code or files
* :green_heart: `:green_heart:` when fixing the CI build
* :white_check_mark: `:white_check_mark:` when adding tests
* :lock: `:lock:` when dealing with security
* :arrow_up: `:arrow_up:` when upgrading dependencies
* :arrow_down: `:arrow_down:` when downgrading dependencies
* :gift: `:gift:` when adding a new feature
* :rage: `:rage:` when fixing something the linter complained about
### Python Styleguide
All Python code is linted with [Black](https://github.com/psf/black) using the default settings. Your code will not be accepted if it is not blackened.
| PypiClean |
/LFake-18.9.0.tar.gz/LFake-18.9.0/lfake/providers/internet/ru_RU/__init__.py | from .. import Provider as InternetProvider
class Provider(InternetProvider):
user_name_formats = (
"{{last_name_female}}.{{first_name_female}}",
"{{last_name_male}}.{{first_name_male}}",
"{{last_name_male}}.{{first_name_male}}",
"{{first_name_male}}.{{last_name_male}}",
"{{first_name}}##",
"{{first_name}}_##",
"?{{last_name}}",
"{{first_name}}{{year}}",
"{{first_name}}_{{year}}",
)
email_formats = (
"{{user_name}}@{{free_email_domain}}",
"{{user_name}}@{{domain_name}}",
)
free_email_domains = (
"gmail.com",
"yahoo.com",
"hotmail.com",
"mail.ru",
"yandex.ru",
"rambler.ru",
)
tlds = ("ru", "com", "biz", "info", "net", "org", "edu")
replacements = (
("А", "a"),
("Б", "b"),
("В", "v"),
("Г", "g"),
("Д", "d"),
("Е", "e"),
("Ё", "e"),
("Ж", "zh"),
("З", "z"),
("И", "i"),
("Й", ""),
("К", "k"),
("Л", "l"),
("М", "m"),
("Н", "n"),
("О", "o"),
("П", "p"),
("Р", "r"),
("С", "s"),
("Т", "t"),
("У", "u"),
("Ф", "f"),
("Х", "h"),
("Ц", "ts"),
("Ч", "ch"),
("Ш", "sh"),
("Щ", "shch"),
("Ъ", ""),
("Ы", "i"),
("Ь", ""),
("Э", "e"),
("Ю", "yu"),
("Я", "ya"),
("а", "a"),
("б", "b"),
("в", "v"),
("г", "g"),
("д", "d"),
("е", "e"),
("ё", "e"),
("ж", "zh"),
("з", "z"),
("и", "i"),
("й", ""),
("к", "k"),
("л", "l"),
("м", "m"),
("н", "n"),
("о", "o"),
("п", "p"),
("р", "r"),
("с", "s"),
("т", "t"),
("у", "u"),
("ф", "f"),
("х", "h"),
("ц", "ts"),
("ч", "ch"),
("ш", "sh"),
("щ", "shch"),
("ъ", ""),
("ы", "i"),
("ь", ""),
("э", "e"),
("ю", "ju"),
("я", "ja"),
) | PypiClean |
/DI_engine-0.4.9-py3-none-any.whl/ding/policy/ngu.py | from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple
import torch
import copy
from ding.torch_utils import Adam, to_device
from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, q_nstep_td_error_with_rescale, get_nstep_return_data, \
get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .base_policy import Policy
@POLICY_REGISTRY.register('ngu')
class NGUPolicy(Policy):
r"""
Overview:
Policy class of NGU. The corresponding paper is `never give up: learning directed exploration strategies`.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str dqn | RL policy register name, refer to | This arg is optional,
| registry ``POLICY_REGISTRY`` | a placeholder
2 ``cuda`` bool False | Whether to use cuda for network | This arg can be diff-
| erent from modes
3 ``on_policy`` bool False | Whether the RL algorithm is on-policy
| or off-policy
4 ``priority`` bool False | Whether use priority(PER) | Priority sample,
| update priority
5 | ``priority_IS`` bool False | Whether use Importance Sampling Weight
| ``_weight`` | to correct biased update. If True,
| priority must be True.
6 | ``discount_`` float 0.997, | Reward's future discount factor, aka. | May be 1 when sparse
| ``factor`` [0.95, 0.999] | gamma | reward env
7 ``nstep`` int 3, | N-step reward discount sum for target
[3, 5] | q_value estimation
8 ``burnin_step`` int 2 | The timestep of burnin operation,
| which is designed to RNN hidden state
| difference caused by off-policy
9 | ``learn.update`` int 1 | How many updates(iterations) to train | This args can be vary
| ``per_collect`` | after collector's one collection. Only | from envs. Bigger val
| valid in serial training | means more off-policy
10 | ``learn.batch_`` int 64 | The number of samples of an iteration
| ``size``
11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration.
| ``_rate``
12 | ``learn.value_`` bool True | Whether use value_rescale function for
| ``rescale`` | predicted value
13 | ``learn.target_`` int 100 | Frequence of target network update. | Hard(assign) update
| ``update_freq``
14 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some
| ``done`` | calculation. | fake termination env
15 ``collect.n_sample`` int [8, 128] | The number of training samples of a | It varies from
| call of collector. | different envs
16 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1
| ``_len``
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='ngu',
# (bool) Whether to use cuda for network.
cuda=False,
# (bool) Whether the RL algorithm is on-policy or off-policy.
on_policy=False,
# (bool) Whether use priority(priority sample, IS weight, update priority)
priority=True,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=True,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (float) Reward's future discount factor, aka. gamma.
discount_factor=0.997,
# (int) N-step reward for target q_value estimation
nstep=5,
# (int) the timestep of burnin operation, which is designed to RNN hidden state difference
# caused by off-policy
burnin_step=20,
# (int) <learn_unroll_len> is the total length of [sequence sample] minus
# the length of burnin part in [sequence sample],
# i.e., <sequence sample length> = <unroll_len> = <burnin_step> + <learn_unroll_len>
learn_unroll_len=80, # set this key according to the episode length
learn=dict(
update_per_collect=1,
batch_size=64,
learning_rate=0.0001,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (float type) target_update_theta: Used for soft update of the target network,
# aka. Interpolation factor in polyak averaging for target networks.
target_update_theta=0.001,
# (bool) whether use value_rescale function for predicted value
value_rescale=True,
ignore_done=False,
),
collect=dict(
# NOTE: It is important that set key traj_len_inf=True here,
# to make sure self._traj_len=INF in serial_sample_collector.py.
# In sequence-based policy, for each collect_env,
# we want to collect data of length self._traj_len=INF
# unless the episode enters the 'done' state.
# In each collect phase, we collect a total of <n_sample> sequence samples.
n_sample=32,
traj_len_inf=True,
# `env_num` is used in hidden state, should equal to that one in env config.
# User should specify this value in user config.
env_num=None,
),
eval=dict(
# `env_num` is used in hidden state, should equal to that one in env config.
# User should specify this value in user config.
env_num=None,
),
other=dict(
eps=dict(
type='exp',
start=0.95,
end=0.05,
decay=10000,
),
replay_buffer=dict(replay_buffer_size=10000, ),
),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'ngu', ['ding.model.template.ngu']
def _init_learn(self) -> None:
r"""
Overview:
Init the learner model of R2D2Policy
Arguments:
.. note::
The _init_learn method takes the argument from the self._cfg.learn in the config file
- learning_rate (:obj:`float`): The learning rate fo the optimizer
- gamma (:obj:`float`): The discount factor
- nstep (:obj:`int`): The num of n step return
- value_rescale (:obj:`bool`): Whether to use value rescaled loss in algorithm
- burnin_step (:obj:`int`): The num of step of burnin
"""
self._priority = self._cfg.priority
self._priority_IS_weight = self._cfg.priority_IS_weight
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
self._burnin_step = self._cfg.burnin_step
self._value_rescale = self._cfg.learn.value_rescale
self._target_model = copy.deepcopy(self._model)
# here we should not adopt the 'assign' mode of target network here because the reset bug
# self._target_model = model_wrap(
# self._target_model,
# wrapper_name='target',
# update_type='assign',
# update_kwargs={'freq': self._cfg.learn.target_update_freq}
# )
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='momentum',
update_kwargs={'theta': self._cfg.learn.target_update_theta}
)
self._target_model = model_wrap(
self._target_model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, save_prev_state=True
)
self._learn_model = model_wrap(
self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, save_prev_state=True
)
self._learn_model = model_wrap(self._learn_model, wrapper_name='argmax_sample')
self._learn_model.reset()
self._target_model.reset()
def _data_preprocess_learn(self, data: List[Dict[str, Any]]) -> dict:
r"""
Overview:
Preprocess the data to fit the required data format for learning
Arguments:
- data (:obj:`List[Dict[str, Any]]`): the data collected from collect function
Returns:
- data (:obj:`Dict[str, Any]`): the processed data, including at least \
['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']
- data_info (:obj:`dict`): the data info, such as replay_buffer_idx, replay_unique_id
"""
# data preprocess
data = timestep_collate(data)
if self._cuda:
data = to_device(data, self._device)
if self._priority_IS_weight:
assert self._priority, "Use IS Weight correction, but Priority is not used."
if self._priority and self._priority_IS_weight:
data['weight'] = data['IS']
else:
data['weight'] = data.get('weight', None)
bs = self._burnin_step
# data['done'], data['weight'], data['value_gamma'] is used in def _forward_learn() to calculate
# the q_nstep_td_error, should be length of [self._sequence_len-self._burnin_step]
ignore_done = self._cfg.learn.ignore_done
if ignore_done:
data['done'] = [None for _ in range(self._sequence_len - bs - self._nstep)]
else:
data['done'] = data['done'][bs:].float() # for computation of online model self._learn_model
# NOTE that after the proprocessing of get_nstep_return_data() in _get_train_sample
# the data['done'] [t] is already the n-step done
# if the data don't include 'weight' or 'value_gamma' then fill in None in a list
# with length of [self._sequence_len-self._burnin_step],
# below is two different implementation ways
if 'value_gamma' not in data:
data['value_gamma'] = [None for _ in range(self._sequence_len - bs)]
else:
data['value_gamma'] = data['value_gamma'][bs:]
if 'weight' not in data:
data['weight'] = [None for _ in range(self._sequence_len - bs)]
else:
data['weight'] = data['weight'] * torch.ones_like(data['done'])
# every timestep in sequence has same weight, which is the _priority_IS_weight in PER
# the burnin_nstep_obs is used to calculate the init hidden state of rnn for the calculation of the q_value,
# target_q_value, and target_q_action
data['burnin_nstep_obs'] = data['obs'][:bs + self._nstep]
data['burnin_nstep_action'] = data['action'][:bs + self._nstep]
data['burnin_nstep_reward'] = data['reward'][:bs + self._nstep]
data['burnin_nstep_beta'] = data['beta'][:bs + self._nstep]
# split obs into three parts 'burnin_obs' [0:bs], 'main_obs' [bs:bs+nstep], 'target_obs' [bs+nstep:]
# data['burnin_obs'] = data['obs'][:bs]
data['main_obs'] = data['obs'][bs:-self._nstep]
data['target_obs'] = data['obs'][bs + self._nstep:]
# data['burnin_action'] = data['action'][:bs]
data['main_action'] = data['action'][bs:-self._nstep]
data['target_action'] = data['action'][bs + self._nstep:]
# data['burnin_reward'] = data['reward'][:bs]
data['main_reward'] = data['reward'][bs:-self._nstep]
data['target_reward'] = data['reward'][bs + self._nstep:]
# data['burnin_beta'] = data['beta'][:bs]
data['main_beta'] = data['beta'][bs:-self._nstep]
data['target_beta'] = data['beta'][bs + self._nstep:]
# Note that Must be here after the previous slicing operation
data['action'] = data['action'][bs:-self._nstep]
data['reward'] = data['reward'][bs:-self._nstep]
return data
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Acquire the data, calculate the loss and optimize learner model.
Arguments:
- data (:obj:`dict`): Dict type data, including at least \
['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']
Returns:
- info_dict (:obj:`Dict[str, Any]`): Including cur_lr and total_loss
- cur_lr (:obj:`float`): Current learning rate
- total_loss (:obj:`float`): The calculated loss
"""
# forward
data = self._data_preprocess_learn(data)
self._learn_model.train()
self._target_model.train()
# use the hidden state in timestep=0
self._learn_model.reset(data_id=None, state=data['prev_state'][0])
self._target_model.reset(data_id=None, state=data['prev_state'][0])
if len(data['burnin_nstep_obs']) != 0:
with torch.no_grad():
inputs = {
'obs': data['burnin_nstep_obs'],
'action': data['burnin_nstep_action'],
'reward': data['burnin_nstep_reward'],
'beta': data['burnin_nstep_beta'],
'enable_fast_timestep': True
}
tmp = self._learn_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
)
tmp_target = self._target_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
)
inputs = {
'obs': data['main_obs'],
'action': data['main_action'],
'reward': data['main_reward'],
'beta': data['main_beta'],
'enable_fast_timestep': True
}
self._learn_model.reset(data_id=None, state=tmp['saved_state'][0])
q_value = self._learn_model.forward(inputs)['logit']
self._learn_model.reset(data_id=None, state=tmp['saved_state'][1])
self._target_model.reset(data_id=None, state=tmp_target['saved_state'][1])
next_inputs = {
'obs': data['target_obs'],
'action': data['target_action'],
'reward': data['target_reward'],
'beta': data['target_beta'],
'enable_fast_timestep': True
}
with torch.no_grad():
target_q_value = self._target_model.forward(next_inputs)['logit']
# argmax_action double_dqn
target_q_action = self._learn_model.forward(next_inputs)['action']
action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight']
value_gamma = [
None for _ in range(self._sequence_len - self._burnin_step)
] # NOTE this is important, because we use diffrent gamma according to their beta in NGU alg.
# T, B, nstep -> T, nstep, B
reward = reward.permute(0, 2, 1).contiguous()
loss = []
td_error = []
self._gamma = [self.index_to_gamma[int(i)] for i in data['main_beta'][0]] # T, B -> B, e.g. 75,64 -> 64
# reward torch.Size([4, 5, 64])
for t in range(self._sequence_len - self._burnin_step - self._nstep):
# here t=0 means timestep <self._burnin_step> in the original sample sequence, we minus self._nstep
# because for the last <self._nstep> timestep in the sequence, we don't have their target obs
td_data = q_nstep_td_data(
q_value[t], target_q_value[t], action[t], target_q_action[t], reward[t], done[t], weight[t]
)
if self._value_rescale:
l, e = q_nstep_td_error_with_rescale(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])
loss.append(l)
td_error.append(e.abs())
else:
l, e = q_nstep_td_error(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])
loss.append(l)
td_error.append(e.abs())
loss = sum(loss) / (len(loss) + 1e-8)
# using the mixture of max and mean absolute n-step TD-errors as the priority of the sequence
td_error_per_sample = 0.9 * torch.max(
torch.stack(td_error), dim=0
)[0] + (1 - 0.9) * (torch.sum(torch.stack(td_error), dim=0) / (len(td_error) + 1e-8))
# td_error shape list(<self._sequence_len-self._burnin_step-self._nstep>, B),
# for example, (75,64)
# torch.sum(torch.stack(td_error), dim=0) can also be replaced with sum(td_error)
# update
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
# after update
self._target_model.update(self._learn_model.state_dict())
# the information for debug
batch_range = torch.arange(action[0].shape[0])
q_s_a_t0 = q_value[0][batch_range, action[0]]
target_q_s_a_t0 = target_q_value[0][batch_range, target_q_action[0]]
return {
'cur_lr': self._optimizer.defaults['lr'],
'total_loss': loss.item(),
'priority': td_error_per_sample.abs().tolist(),
# the first timestep in the sequence, may not be the start of episode
'q_s_taken-a_t0': q_s_a_t0.mean().item(),
'target_q_s_max-a_t0': target_q_s_a_t0.mean().item(),
'q_s_a-mean_t0': q_value[0].mean().item(),
}
def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:
self._learn_model.reset(data_id=data_id)
def _state_dict_learn(self) -> Dict[str, Any]:
return {
'model': self._learn_model.state_dict(),
'target_model': self._target_model.state_dict(),
'optimizer': self._optimizer.state_dict(),
}
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
self._learn_model.load_state_dict(state_dict['model'])
self._target_model.load_state_dict(state_dict['target_model'])
self._optimizer.load_state_dict(state_dict['optimizer'])
def _init_collect(self) -> None:
r"""
Overview:
Collect mode init method. Called by ``self.__init__``.
Init traj and unroll length, collect model.
"""
assert 'unroll_len' not in self._cfg.collect, "ngu use default <unroll_len = learn_unroll_len + burnin_step>"
self._nstep = self._cfg.nstep
self._burnin_step = self._cfg.burnin_step
self._gamma = self._cfg.discount_factor
self._sequence_len = self._cfg.learn_unroll_len + self._cfg.burnin_step
self._unroll_len = self._sequence_len
self._collect_model = model_wrap(
self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True
)
self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample')
self._collect_model.reset()
self.index_to_gamma = { # NOTE
i: 1 - torch.exp(
(
(self._cfg.collect.env_num - 1 - i) * torch.log(torch.tensor(1 - 0.997)) +
i * torch.log(torch.tensor(1 - 0.99))
) / (self._cfg.collect.env_num - 1)
)
for i in range(self._cfg.collect.env_num)
}
# NOTE: for NGU policy collect phase
self.beta_index = {
i: torch.randint(0, self._cfg.collect.env_num, [1])
for i in range(self._cfg.collect.env_num)
}
# epsilon=0.4, alpha=9
self.eps = {i: 0.4 ** (1 + 8 * i / (self._cfg.collect.env_num - 1)) for i in range(self._cfg.collect.env_num)}
def _forward_collect(self, data: dict) -> dict:
r"""
Overview:
Collect output according to eps_greedy plugin
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs'].
Returns:
- data (:obj:`dict`): The collected data
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
obs = data['obs']
prev_action = data['prev_action'].long()
prev_reward_extrinsic = data['prev_reward_extrinsic']
beta_index = default_collate(list(self.beta_index.values()))
if len(data_id) != self._cfg.collect.env_num:
# in case, some env is in reset state and only return part data
beta_index = beta_index[data_id]
if self._cuda:
obs = to_device(obs, self._device)
beta_index = to_device(beta_index, self._device)
prev_action = to_device(prev_action, self._device)
prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)
# TODO(pu): add prev_reward_intrinsic to network input,
# reward uses some kind of embedding instead of 1D value
data = {
'obs': obs,
'prev_action': prev_action,
'prev_reward_extrinsic': prev_reward_extrinsic,
'beta': beta_index
}
self._collect_model.eval()
with torch.no_grad():
output = self._collect_model.forward(data, data_id=data_id, eps=self.eps, inference=True)
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _reset_collect(self, data_id: Optional[List[int]] = None) -> None:
self._collect_model.reset(data_id=data_id)
# NOTE: for NGU policy, in collect phase, each episode, we sample a new beta for each env
if data_id is not None:
self.beta_index[data_id[0]] = torch.randint(0, self._cfg.collect.env_num, [1])
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple, env_id) -> dict:
r"""
Overview:
Generate dict type transition data from inputs.
Arguments:
- obs (:obj:`Any`): Env observation
- model_output (:obj:`dict`): Output of collect model, including at least ['action', 'prev_state']
- timestep (:obj:`namedtuple`): Output after env step, including at least ['reward', 'done'] \
(here 'obs' indicates obs after env step).
Returns:
- transition (:obj:`dict`): Dict type transition data.
"""
if hasattr(timestep, 'null'):
transition = {
'beta': self.beta_index[env_id],
'obs': obs['obs'], # NOTE: input obs including obs, prev_action, prev_reward_extrinsic
'action': model_output['action'],
'prev_state': model_output['prev_state'],
'reward': timestep.reward,
'done': timestep.done,
'null': timestep.null,
}
else:
transition = {
'beta': self.beta_index[env_id],
'obs': obs['obs'], # NOTE: input obs including obs, prev_action, prev_reward_extrinsic
'action': model_output['action'],
'prev_state': model_output['prev_state'],
'reward': timestep.reward,
'done': timestep.done,
'null': False,
}
return transition
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
r"""
Overview:
Get the trajectory and the n step return data, then sample from the n_step return data
Arguments:
- data (:obj:`list`): The trajectory's cache
Returns:
- samples (:obj:`dict`): The training samples generated
"""
data = get_nstep_return_data(data, self._nstep, gamma=self.index_to_gamma[int(data[0]['beta'])].item())
return get_train_sample(data, self._sequence_len)
def _init_eval(self) -> None:
r"""
Overview:
Evaluate mode init method. Called by ``self.__init__``.
Init eval model with argmax strategy.
"""
self._eval_model = model_wrap(self._model, wrapper_name='hidden_state', state_num=self._cfg.eval.env_num)
self._eval_model = model_wrap(self._eval_model, wrapper_name='argmax_sample')
self._eval_model.reset()
# NOTE: for NGU policy eval phase
# beta_index = 0 -> beta is approximately 0
self.beta_index = {i: torch.tensor([0]) for i in range(self._cfg.eval.env_num)}
def _forward_eval(self, data: dict) -> dict:
r"""
Overview:
Forward function of collect mode, similar to ``self._forward_collect``.
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs'].
Returns:
- output (:obj:`dict`): Dict type data, including at least inferred action according to input obs.
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
obs = data['obs']
prev_action = data['prev_action'].long()
prev_reward_extrinsic = data['prev_reward_extrinsic']
beta_index = default_collate(list(self.beta_index.values()))
if len(data_id) != self._cfg.collect.env_num:
# in case, some env is in reset state and only return part data
beta_index = beta_index[data_id]
if self._cuda:
obs = to_device(obs, self._device)
beta_index = to_device(beta_index, self._device)
prev_action = to_device(prev_action, self._device)
prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)
# TODO(pu): add prev_reward_intrinsic to network input,
# reward uses some kind of embedding instead of 1D value
data = {
'obs': obs,
'prev_action': prev_action,
'prev_reward_extrinsic': prev_reward_extrinsic,
'beta': beta_index
}
self._eval_model.eval()
with torch.no_grad():
output = self._eval_model.forward(data, data_id=data_id, inference=True)
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _reset_eval(self, data_id: Optional[List[int]] = None) -> None:
self._eval_model.reset(data_id=data_id)
def _monitor_vars_learn(self) -> List[str]:
return super()._monitor_vars_learn() + [
'total_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0'
] | PypiClean |
/Congo-0.0.1.tar.gz/Congo-0.0.1/portfolio/component/static/portfolio/vendor/mdeditor/bower_components/codemirror/mode/jade/jade.js | CodeMirror.defineMode("jade", function () {
var symbol_regex1 = /^(?:~|!|%|\^|\*|\+|=|\\|:|;|,|\/|\?|&|<|>|\|)/;
var open_paren_regex = /^(\(|\[)/;
var close_paren_regex = /^(\)|\])/;
var keyword_regex1 = /^(if|else|return|var|function|include|doctype|each)/;
var keyword_regex2 = /^(#|{|}|\.)/;
var keyword_regex3 = /^(in)/;
var html_regex1 = /^(html|head|title|meta|link|script|body|br|div|input|span|a|img)/;
var html_regex2 = /^(h1|h2|h3|h4|h5|p|strong|em)/;
return {
startState: function () {
return {
inString: false,
stringType: "",
beforeTag: true,
justMatchedKeyword: false,
afterParen: false
};
},
token: function (stream, state) {
//check for state changes
if (!state.inString && ((stream.peek() == '"') || (stream.peek() == "'"))) {
state.stringType = stream.peek();
stream.next(); // Skip quote
state.inString = true; // Update state
}
//return state
if (state.inString) {
if (stream.skipTo(state.stringType)) { // Quote found on this line
stream.next(); // Skip quote
state.inString = false; // Clear flag
} else {
stream.skipToEnd(); // Rest of line is string
}
state.justMatchedKeyword = false;
return "string"; // Token style
} else if (stream.sol() && stream.eatSpace()) {
if (stream.match(keyword_regex1)) {
state.justMatchedKeyword = true;
stream.eatSpace();
return "keyword";
}
if (stream.match(html_regex1) || stream.match(html_regex2)) {
state.justMatchedKeyword = true;
return "variable";
}
} else if (stream.sol() && stream.match(keyword_regex1)) {
state.justMatchedKeyword = true;
stream.eatSpace();
return "keyword";
} else if (stream.sol() && (stream.match(html_regex1) || stream.match(html_regex2))) {
state.justMatchedKeyword = true;
return "variable";
} else if (stream.eatSpace()) {
state.justMatchedKeyword = false;
if (stream.match(keyword_regex3) && stream.eatSpace()) {
state.justMatchedKeyword = true;
return "keyword";
}
} else if (stream.match(symbol_regex1)) {
state.justMatchedKeyword = false;
return "atom";
} else if (stream.match(open_paren_regex)) {
state.afterParen = true;
state.justMatchedKeyword = true;
return "def";
} else if (stream.match(close_paren_regex)) {
state.afterParen = false;
state.justMatchedKeyword = true;
return "def";
} else if (stream.match(keyword_regex2)) {
state.justMatchedKeyword = true;
return "keyword";
} else if (stream.eatSpace()) {
state.justMatchedKeyword = false;
} else {
stream.next();
if (state.justMatchedKeyword) {
return "property";
} else if (state.afterParen) {
return "property";
}
}
return null;
}
};
});
CodeMirror.defineMIME('text/x-jade', 'jade'); | PypiClean |
/Mezzanine-6.0.0.tar.gz/Mezzanine-6.0.0/mezzanine/generic/managers.py | from django_comments.managers import CommentManager as DjangoCM
from mezzanine.conf import settings
from mezzanine.core.managers import CurrentSiteManager
class CommentManager(CurrentSiteManager, DjangoCM):
"""
Provides filter for restricting comments that are not approved
if ``COMMENTS_UNAPPROVED_VISIBLE`` is set to ``False``.
"""
def visible(self):
"""
Return the comments that are visible based on the
``COMMENTS_XXX_VISIBLE`` settings. When these settings
are set to ``True``, the relevant comments are returned
that shouldn't be shown, and are given placeholders in
the template ``generic/includes/comment.html``.
"""
visible = self.all()
if not settings.COMMENTS_UNAPPROVED_VISIBLE:
visible = visible.filter(is_public=True)
if not settings.COMMENTS_REMOVED_VISIBLE:
visible = visible.filter(is_removed=False)
return visible
def count_queryset(self):
"""
Called from ``CommentsField.related_items_changed`` to store
the comment count against an item each time a comment is saved.
"""
return self.visible().count()
class KeywordManager(CurrentSiteManager):
def get_by_natural_key(self, value):
"""
Provides natural key method.
"""
return self.get(value=value)
def get_or_create_iexact(self, **kwargs):
"""
Case insensitive title version of ``get_or_create``. Also
allows for multiple existing results.
"""
lookup = dict(**kwargs)
try:
lookup["title__iexact"] = lookup.pop("title")
except KeyError:
pass
try:
return self.filter(**lookup)[0], False
except IndexError:
return self.create(**kwargs), True
def delete_unused(self, keyword_ids=None):
"""
Removes all instances that are not assigned to any object. Limits
processing to ``keyword_ids`` if given.
"""
if keyword_ids is None:
keywords = self.all()
else:
keywords = self.filter(id__in=keyword_ids)
keywords.filter(assignments__isnull=True).delete() | PypiClean |
/BlueWhale3-3.31.3.tar.gz/BlueWhale3-3.31.3/Orange/evaluation/performance_curves.py | import numpy as np
class Curves:
# names of scores are standard acronyms, pylint: disable=invalid-name
"""
Computation of performance curves (ca, f1, precision, recall and the rest
of the zoo) from test results.
The class works with binary classes. Attribute `probs` contains ordered
probabilities and all curves represent performance statistics if an
instance is classified as positive if it equals or exceeds the threshold
in `probs`, that is, `sensitivity[i]` is the sensitivity of the classifier
that classifies an instances as positive if the probability of being
positive is at least `probs[i]`.
Class can be constructed by giving `probs` and `ytrue`, or from test
results (see :obj:`Curves.from_results`). The latter removes instances
with missing class values or predicted probabilities.
The class treats all results as obtained from a single run instead of
computing separate curves and fancy averaging.
Arguments:
probs (np.ndarray): vector of predicted probabilities
ytrue (np.ndarray): corresponding true classes
Attributes:
probs (np.ndarray): ordered vector of predicted probabilities
ytrue (np.ndarray): corresponding true classes
tot (int): total number of data instances
p (int): number of real positive instances
n (int): number of real negative instances
tp (np.ndarray): number of true positives (property computed from `tn`)
fp (np.ndarray): number of false positives (property computed from `tn`)
tn (np.ndarray): number of true negatives (property computed from `tn`)
fn (np.ndarray): number of false negatives (precomputed, not a property)
"""
def __init__(self, ytrue, probs):
sortind = np.argsort(probs)
self.probs = np.hstack((probs[sortind], [1]))
self.ytrue = ytrue[sortind]
self.fn = np.hstack(([0], np.cumsum(self.ytrue)))
self.tot = len(probs)
self.p = self.fn[-1]
self.n = self.tot - self.p
@classmethod
def from_results(cls, results, target_class=None, model_index=None):
"""
Construct an instance of `Curves` from test results.
Args:
results (:obj:`Orange.evaluation.testing.Results`): test results
target_class (int): target class index; if the class is binary,
this defaults to `1`, otherwise it must be given
model_index (int): model index; if there is only one model, this
argument can be omitted
Returns:
curves (:obj:`Curves`)
"""
if model_index is None:
if results.probabilities.shape[0] != 1:
raise ValueError("Argument 'model_index' is required when "
"there are multiple models")
model_index = 0
if target_class is None:
if results.probabilities.shape[2] != 2:
raise ValueError("Argument 'target_class' is required when the "
"class is not binary")
target_class = 1
actual = results.actual
probs = results.probabilities[model_index, :, target_class]
nans = np.isnan(actual) + np.isnan(probs)
if nans.any():
actual = actual[~nans]
probs = probs[~nans]
return cls(actual == target_class, probs)
@property
def tn(self):
return np.arange(self.tot + 1) - self.fn
@property
def tp(self):
return self.p - self.fn
@property
def fp(self):
return self.n - self.tn
def ca(self):
"""Classification accuracy curve"""
return (self.tp + self.tn) / self.tot
def f1(self):
"""F1 curve"""
return 2 * self.tp / (2 * self.tp + self.fp + self.fn)
def sensitivity(self):
"""Sensitivity curve"""
return self.tp / self.p
def specificity(self):
"""Specificity curve"""
return self.tn / self.n
def precision(self):
"""
Precision curve
The last element represents precision at threshold 1. Unless such
a probability appears in the data, the precision at this point is
undefined. To avoid this, we copy the previous value to the last.
"""
tp_fp = np.arange(self.tot, -1, -1)
tp_fp[-1] = 1 # avoid division by zero
prec = self.tp / tp_fp
prec[-1] = prec[-2]
return prec
def recall(self):
"""Recall curve"""
return self.sensitivity()
def ppv(self):
"""PPV curve; see the comment at :obj:`precision`"""
return self.precision()
def npv(self):
"""
NPV curve
The first value is undefined (no negative instances). To avoid this,
we copy the second value into the first.
"""
tn_fn = np.arange(self.tot + 1)
tn_fn[0] = 1 # avoid division by zero
npv = self.tn / tn_fn
npv[0] = npv[1]
return npv
def fpr(self):
"""FPR curve"""
return self.fp / self.n
def tpr(self):
"""TPR curve"""
return self.sensitivity() | PypiClean |
/KratosRANSApplication-9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/KratosMultiphysics/RANSApplication/formulations/monolithic_vms/monolithic_k_omega_sst_rans_formulation.py | import KratosMultiphysics as Kratos
import KratosMultiphysics.RANSApplication as KratosRANS
# import formulation interface
from KratosMultiphysics.RANSApplication.formulations.rans_formulation import RansFormulation
# import formulations
from KratosMultiphysics.RANSApplication.formulations.incompressible_potential_flow import IncompressiblePotentialFlowRansFormulation
from KratosMultiphysics.RANSApplication.formulations.turbulence_models.k_omega_sst_rans_formulation import KOmegaSSTRansFormulation
from KratosMultiphysics.RANSApplication.formulations.monolithic_vms.monolithic_velocity_pressure_rans_formulation import MonolithicVelocityPressureRansFormulation
class MonolithicKOmegaSSTRansFormulation(RansFormulation):
def __init__(self, model_part, settings):
super().__init__(model_part, settings)
default_settings = Kratos.Parameters(r'''
{
"formulation_name": "monolithic_k_omega_sst",
"incompressible_potential_flow_initialization_settings": {},
"monolithic_flow_solver_settings": {},
"k_omega_sst_solver_settings": {},
"max_iterations": 1
}''')
settings.ValidateAndAssignDefaults(default_settings)
if (not settings["incompressible_potential_flow_initialization_settings"].IsEquivalentTo(
Kratos.Parameters("{}"))):
self.incompressible_potential_flow_formulation = IncompressiblePotentialFlowRansFormulation(model_part, settings["incompressible_potential_flow_initialization_settings"])
self.AddRansFormulation(self.incompressible_potential_flow_formulation)
self.monolithic_formulation = MonolithicVelocityPressureRansFormulation(model_part, settings["monolithic_flow_solver_settings"])
self.AddRansFormulation(self.monolithic_formulation)
self.k_omega_sst_formulation = KOmegaSSTRansFormulation(model_part, settings["k_omega_sst_solver_settings"])
self.AddRansFormulation(self.k_omega_sst_formulation)
self.SetMaxCouplingIterations(settings["max_iterations"].GetInt())
def SetConstants(self, settings):
self.k_omega_sst_formulation.SetConstants(settings)
def Initialize(self):
super().Initialize()
if (self.monolithic_formulation.ElementHasNodalProperties()):
nut_nodal_update_process = KratosRANS.RansNutNodalUpdateProcess(
self.GetBaseModelPart().GetModel(),
self.GetBaseModelPart().Name,
self.k_omega_sst_formulation.echo_level)
self.k_omega_sst_formulation.AddProcess(nut_nodal_update_process) | PypiClean |
/Djblets-3.3.tar.gz/Djblets-3.3/docs/releasenotes/0.9.rst | =========================
Djblets 0.9 Release Notes
=========================
**Release date**: October 28, 2015
This release contains all bug fixes and features found in Djblets version
:doc:`0.8.23 <0.8.23>`.
Installation
============
To install this release, run the following::
$ sudo easy_install \
-f http://downloads.reviewboard.org/releases/Djblets/0.9/ \
-U Djblets
Compatibility Changes
=====================
* Added initial support for Django 1.8.
This release of Djblets is experimentally compatible with Django 1.8. For
the time being we still recommend using Django 1.6, but would appreciate bug
reports if you do hit problems using newer versions.
* Upgraded to jQuery 1.11.
This release of jQuery offers performance and feature improvements we'll be
relying upon.
JavaScript
==========
* Added a function for returning the best Gravatar for the display, given a
Gravatar URL.
:js:func:`Djblets.getGravatarForDisplay` will take a Gravatar URL and
return a new URL containing the Gravatar best suited for the display's
pixel ratio multiplier. For instance, when running on a Retina display,
the size of the Gravatar will be doubled.
* :js:func:`$.getExtents()` now returns fractional values.
Previously, only integer values were returned, which were not always an
accurate representation of the extents.
djblets.cache
=============
.. currentmodule:: djblets.cache
* Enhanced the :py:func:`~djblets.cache.backend.cache_memoize` implementation
to be usable with generators.
When trying to cache data that's being created via a generator, the new
:py:func:`~djblets.cache.backend.cache_memoize_iter` method will populate
the cache without gathering up everything into a list first. Pulling data
out of the cache will also operate as an iterator rather than preprocessing
everything at once.
Caching large data has also been optimized for speed.
djblets.datagrid
================
* Added a responsive UI mode for datagrids.
When the datagrid is rendered on a small (<= 720px) screen, it now goes into
a mobile mode. In this mode, the contents of the datagrid become more
vertical in order to make better use of the space without making text too
small.
* Added an :py:class:`~djblets.datagrid.grids.AlphanumericDataGrid` subclass
for paginating lists of results alphanumerically.
This can be used like any other datagrid, but will handle pagination based
on the first letter, number, or symbol of the value of the given field.
This was built for use when paginating through lists of users, but can be
used for many other cases.
Patch by Ryan Done.
* Datagrids can now build their own paginators by implementing
:py:meth:`~djblets.datagrid.grids.DataGrid.build_paginator`. This is
particularly useful when creating a datagrid that integrates with Haystack.
* Datagrids can now work with simpler QuerySet-like objects.
Datagrids are now less tightly bound to Django's
:py:class:`~django.db.models.query.QuerySet`, and can now use QuerySet-like
objects such as Haystack's :py:class:`SearchQuerySet`.
* Added various blocks to the datagrid templates for better extensibility.
Rather than just having a big blank white spot, datagrids can now show some
custom HTML by overriding the ``datagrid_empty`` block.
The new ``datagrid_titlebox`` block can be used to provide additional
content before, after, or in place of the titlebox.
Finally, the ``paginator`` block wraps the paginator, allowing it to be
removed or replaced.
* The column customization menu now has an animated sliding effect when
showing or dismissing the menu.
* Optimized database queries, leading to faster datagrids.
* Improved performance when setting up the datagrid.
* Improved positioning and scrolling of datagrid menus.
* Fixed numerous display bugs and made some small visual tweaks.
This includes fixes for inconsistencies between the column headers and
the colmn data, jumps in the display of those headers when first rendering
the datagrid, and alignment issues when dragging columns.
* Fixed problems with pagination links and the ``gridonly`` query string
parameter. (:bug:`3794`)
Patch by Griffin Myers.
* Fixed :py:class:`~djblets.datagrid.grids.CheckboxColumn` to not render links
around the checkbox.
djblets.extensions
==================
.. currentmodule:: djblets.extensions
* Extension state is now properly cleaned up when the process ends.
Previously, we let the Python garbage collector handle the cleanup of
extension state, but this would result in some errors when using
certain hooks, such as :py:class:`~djblets.extensions.hooks.SignalHook`. We
now handle the cleanup manually.
* Added sandboxing to :py:meth:`TemplateHook.applies_to()
<djblets.extensions.hooks.TemplateHook.applies_to>` in the
:py:func:`{% template_hook_point %}
<djblets.extensions.templatetags.djblets_extensions.template_hook_point>`
template tag.
If this function ever raises an exception for any reason, the exception
will be caught, and the failure information logged.
Patch by Justin Maillet.
djblets.mail
============
.. currentmodule:: djblets.mail
* Added :py:class:`djblets.mail.message.EmailMessage`.
This provides a number of additional benefits over Django's default e-mail
support:
* Built-in support for a number of sender/recipient-related headers
(:mailheader:`Sender`, :mailheader:`X-Sender`, :mailheader:`In-Reply-To`,
:mailheader:`References`, and :mailheader:`Reply-To`)
* Auto-generated status headers (:mailheader:`Auto-Submitted`)
* Disabling of auto-responses (:mailheader:`X-Auto-Response-Suppress`)
* Ability to set multiple headers with the same name but different values.
* Easier support for setting HTML content.
* Convenience methods for working with recipients.
djblets.markdown
================
The new :py:mod:`djblets.markdown` module contains a bunch of useful utilities
for dealing with Markdown text, including:
* Functions for escaping content for direct inclusion in a Markdown document,
and unescaping pre-escaped content.
* A WYSIWYG-style renderer, which outputs rendered Markdown that looks as
close as possible to the source text.
* A variation of the WISYWIG-style renderer for use in e-mails. This contains
works the same way, but uses inline styles instead of requiring an
external CSS file.
These functions were previously part of the Review Board codebase, but are
useful beyond Review Board and have been moved here.
djblets.testing
===============
* Added a mixin for pre-compiling fixtures to reduce test times.
The new :py:class:`~djblets.testing.testcases.FixturesCompilerMixin` can be
mixed into a test case to compile each listed fixture up-front,
significantly reducing the time needed for tests to run.
djblets.util
============
.. currentmodule:: djblets.util
* Improved performance of the
:py:func:`{% crop_image %}
<djblets.util.templatetags.djblets_images.crop_image>` and
:py:func:`{% thumbnail %}
<djblets.util.templatetags.djblets_images.thumbnail>` template tags.
These template tags no longer write to a temporary file before writing to
storage, and instead write to storage directly. This will improve
performance when working with storage backends, like Amazon S3.
* Added height-for-width sizing to the
:py:func:`~djblets.util.templatetags.djblets_images.thumbnail` template tag.
When being used from Python, the thumbnail tag can now be passed a 2-tuple
instead of a string to represent the size. If the height is omitted, it will
be calculated to preserve the image's aspect ratio.
* Added a new
:py:func:`~djblets.util.templatetags.djblets_utils.querystring_with` tag to
help with building links.
When creating links that modify query parameters, it's often useful to
build a new query that contains all of the previous parameters but with the
value of one of them changed. This new tag helps with that.
* Functions decorated by :py:func:`~djblets.util.decorators.simple_decorator`
and :py:func:`~djblets.util.decorators.basictag` now show up in Sphinx
documentation.
They were previously missing the correct :py:attr:`__module__` value,
resulting in them being filtered out by Sphinx.
djblets.webapi
==============
.. currentmodule:: djblets.webapi
* Added support for returning only certain fields or links in the API.
API resources now support a couple new query arguments for limiting the
results in a payload, in order to reduce database queries and payload
sizes.
The ``?only-fields=`` query argument limits the returned fields in the
payload to the comma-separated list of field names. If the value is
blank, then no fields will be returned, leaving only links.
Likewise, the ``?only-links=`` query argument limits the returned links in
the payload. It behaves exactly like ``?only-fields=``.
Resources that inject custom fields into the payload outside of
:py:meth:`WebAPIResource.serialize_object
<djblets.webapi.resources.base.WebAPIResource.serialize_object>` can call
:py:meth:`~djblets.webapi.resources.base.WebAPIResource.get_only_fields` and
:py:meth:`~djblets.webapi.resources.base.WebAPIResource.get_only_links` to
determine whether to include specific fields.
* Links can now be serialized by subclasses.
:py:class:`~djblets.webapi.resources.base.WebAPIResource` subclasses can now
provide a :samp:`serialize_<linkname>_link()` function that will take an
object and serialize a link for it. This is useful for links that need to
contain additional metadata about the link would be helpful to consumers.
* Added support for generating and using API tokens for authentication.
API tokens are a safer way of authenticating with an API, without needing
to supply a username or password to a service or script. These tokens
can be created and deleted at any time without affecting the user's account.
Tokens can also have an access policy assigned, which will limit what
operations can be performed on what parts of the API tree.
Consumers need to use the
:py:class:`~djblets.webapi.auth.backends.api_tokens.TokenAuthBackendMixin`
Django authentication backend, the
:py:class:`~djblets.webapi.auth.backends.api_tokens.WebAPITokenAuthBackend`
API authentication backend, and the
:py:class:`~djblets.webapi.resources.mixins.api_tokens.ResourceAPITokenMixin`
mixin for resource subclasses in order to accept API tokens. They must also
define a model for storing API token data, inheriting from
:py:class:`~djblets.webapi.models.BaseWebAPIToken`.
* Added a mixin for helping with range-based database queries.
The new
:py:class:`~djblets.webapi.resources.mixins.queries.APIQueryUtilsMixin` can
be used in a resource subclass to help with range-based queries (``<``,
``<=``, ``>``, ``>=``) coming from the caller, translating them into an
appropriate database query.
* Added an :py:class:`~djblets.webapi.resources.mixins.forms.UpdateFormMixin`
for connecting a :py:class:`~djblets.webapi.resources.base.WebAPIResource`
subclass to a Django :py:class:`~django.forms.ModelForm`.
This mixin provides a
:py:meth:`~djblets.webapi.resources.mixins.forms.UpdateFormMixin.create_form`
method that resources can use to create forms for either creating or
updating model instances, giving all the advantages of form validation and
consistent instance creation.
* Added resource-bound utility functions for retrieving the URL to an item
or list resource, given a set of URL arguments.
Each :py:class:`~djblets.webapi.resources.base.WebAPIResource` class now
provides
:py:meth:`~djblets.webapi.resources.base.WebAPIResource.get_list_url`,
:py:meth:`~djblets.webapi.resources.base.WebAPIResource.get_item_url`, and
:py:meth:`~djblets.webapi.resources.base.WebAPIResource.build_resource_url`
functions that can generate URLs suitable for the resource, given arguments
for the URL.
* Added a class for handling the registration and lookup of API resources.
:py:class:`~djblets.webapi.resources.registry.ResourcesRegistry` makes
it easy to lazily register model-to-resource mappings and to handle lookups
of resources without running into import loops or other problems. It's
meant to be subclassed and populated.
* Added utilities for writing unit tests for APIs.
The new :py:mod:`djblets.webapi.testing` module includes decorators and
mixins for writing complete and comprehensive test suites for an API.
That consists of helpers for docstrings and functions for performing API
requests and validating results,
* Added a new :py:data:`~djblets.webapi.errors.DUPLICATE_ITEM` error code.
This is a generic error code that can be used to indicate error states when
duplicate constraints are violated.
Patch by Vincent Le.
* :py:func:`~djblets.webapi.decorators.webapi_request_fields` now passes all
parsed arguments from the caller to the function as a
``parsed_request_fields`` dictionary.
* Reorganized the authentication and resources code.
The :py:mod:`djblets.webapi.auth` and :py:mod:`djblets.webapi.resources`
modules have been split into multiple modules in preparation for adding some
new features. The old names still work, but will show a
:py:exc:`DeprecationWarning`.
Miscellaneous
=============
* Replaced spinners with FontAwesome_.
The old animated GIF spinner has been replaced with the spinner in the icon
font FontAwesome_.
.. _FontAwesome: http://fortawesome.github.io/Font-Awesome/
Changes Since 0.9 RC 1
======================
djblets.mail
------------
* Ensure all e-mail headers are bytes, rather than unicode. This fixes
:py:exc:`UnicodeDecodeErrors <UnicodeDecodeError>` when sending a message
that contains non-ASCII headers.
djblets.webapi
--------------
* Fixed encoding for API resources using the ``expand`` parameter that would
create a circular reference.
This had previously been fixed, but regressed with the addition of field
limiting.
* Fixed lookups for resources when using deferred models (such as those
returned by
:py:meth:`QuerySet.only <django.db.models.query.QuerySet.only>`).
Contributors
============
* Barret Rennie
* Christian Hammond
* David Trowbridge
* Griffin Myers
* Justin Maillet
* Ryan Done
* Vincent Le
| PypiClean |
/MaterialDjango-0.2.5.tar.gz/MaterialDjango-0.2.5/materialdjango/static/materialdjango/components/bower_components/prism/components/prism-nginx.js | Prism.languages.nginx = Prism.languages.extend('clike', {
'comment': {
pattern: /(^|[^"{\\])#.*/,
lookbehind: true
},
'keyword': /\b(?:CONTENT_|DOCUMENT_|GATEWAY_|HTTP_|HTTPS|if_not_empty|PATH_|QUERY_|REDIRECT_|REMOTE_|REQUEST_|SCGI|SCRIPT_|SERVER_|http|events|accept_mutex|accept_mutex_delay|access_log|add_after_body|add_before_body|add_header|addition_types|aio|alias|allow|ancient_browser|ancient_browser_value|auth|auth_basic|auth_basic_user_file|auth_http|auth_http_header|auth_http_timeout|autoindex|autoindex_exact_size|autoindex_localtime|break|charset|charset_map|charset_types|chunked_transfer_encoding|client_body_buffer_size|client_body_in_file_only|client_body_in_single_buffer|client_body_temp_path|client_body_timeout|client_header_buffer_size|client_header_timeout|client_max_body_size|connection_pool_size|create_full_put_path|daemon|dav_access|dav_methods|debug_connection|debug_points|default_type|deny|devpoll_changes|devpoll_events|directio|directio_alignment|disable_symlinks|empty_gif|env|epoll_events|error_log|error_page|expires|fastcgi_buffer_size|fastcgi_buffers|fastcgi_busy_buffers_size|fastcgi_cache|fastcgi_cache_bypass|fastcgi_cache_key|fastcgi_cache_lock|fastcgi_cache_lock_timeout|fastcgi_cache_methods|fastcgi_cache_min_uses|fastcgi_cache_path|fastcgi_cache_purge|fastcgi_cache_use_stale|fastcgi_cache_valid|fastcgi_connect_timeout|fastcgi_hide_header|fastcgi_ignore_client_abort|fastcgi_ignore_headers|fastcgi_index|fastcgi_intercept_errors|fastcgi_keep_conn|fastcgi_max_temp_file_size|fastcgi_next_upstream|fastcgi_no_cache|fastcgi_param|fastcgi_pass|fastcgi_pass_header|fastcgi_read_timeout|fastcgi_redirect_errors|fastcgi_send_timeout|fastcgi_split_path_info|fastcgi_store|fastcgi_store_access|fastcgi_temp_file_write_size|fastcgi_temp_path|flv|geo|geoip_city|geoip_country|google_perftools_profiles|gzip|gzip_buffers|gzip_comp_level|gzip_disable|gzip_http_version|gzip_min_length|gzip_proxied|gzip_static|gzip_types|gzip_vary|if|if_modified_since|ignore_invalid_headers|image_filter|image_filter_buffer|image_filter_jpeg_quality|image_filter_sharpen|image_filter_transparency|imap_capabilities|imap_client_buffer|include|index|internal|ip_hash|keepalive|keepalive_disable|keepalive_requests|keepalive_timeout|kqueue_changes|kqueue_events|large_client_header_buffers|limit_conn|limit_conn_log_level|limit_conn_zone|limit_except|limit_rate|limit_rate_after|limit_req|limit_req_log_level|limit_req_zone|limit_zone|lingering_close|lingering_time|lingering_timeout|listen|location|lock_file|log_format|log_format_combined|log_not_found|log_subrequest|map|map_hash_bucket_size|map_hash_max_size|master_process|max_ranges|memcached_buffer_size|memcached_connect_timeout|memcached_next_upstream|memcached_pass|memcached_read_timeout|memcached_send_timeout|merge_slashes|min_delete_depth|modern_browser|modern_browser_value|mp4|mp4_buffer_size|mp4_max_buffer_size|msie_padding|msie_refresh|multi_accept|open_file_cache|open_file_cache_errors|open_file_cache_min_uses|open_file_cache_valid|open_log_file_cache|optimize_server_names|override_charset|pcre_jit|perl|perl_modules|perl_require|perl_set|pid|pop3_auth|pop3_capabilities|port_in_redirect|post_action|postpone_output|protocol|proxy|proxy_buffer|proxy_buffer_size|proxy_buffering|proxy_buffers|proxy_busy_buffers_size|proxy_cache|proxy_cache_bypass|proxy_cache_key|proxy_cache_lock|proxy_cache_lock_timeout|proxy_cache_methods|proxy_cache_min_uses|proxy_cache_path|proxy_cache_use_stale|proxy_cache_valid|proxy_connect_timeout|proxy_cookie_domain|proxy_cookie_path|proxy_headers_hash_bucket_size|proxy_headers_hash_max_size|proxy_hide_header|proxy_http_version|proxy_ignore_client_abort|proxy_ignore_headers|proxy_intercept_errors|proxy_max_temp_file_size|proxy_method|proxy_next_upstream|proxy_no_cache|proxy_pass|proxy_pass_error_message|proxy_pass_header|proxy_pass_request_body|proxy_pass_request_headers|proxy_read_timeout|proxy_redirect|proxy_redirect_errors|proxy_send_lowat|proxy_send_timeout|proxy_set_body|proxy_set_header|proxy_ssl_session_reuse|proxy_store|proxy_store_access|proxy_temp_file_write_size|proxy_temp_path|proxy_timeout|proxy_upstream_fail_timeout|proxy_upstream_max_fails|random_index|read_ahead|real_ip_header|recursive_error_pages|request_pool_size|reset_timedout_connection|resolver|resolver_timeout|return|rewrite|root|rtsig_overflow_events|rtsig_overflow_test|rtsig_overflow_threshold|rtsig_signo|satisfy|satisfy_any|secure_link_secret|send_lowat|send_timeout|sendfile|sendfile_max_chunk|server|server_name|server_name_in_redirect|server_names_hash_bucket_size|server_names_hash_max_size|server_tokens|set|set_real_ip_from|smtp_auth|smtp_capabilities|so_keepalive|source_charset|split_clients|ssi|ssi_silent_errors|ssi_types|ssi_value_length|ssl|ssl_certificate|ssl_certificate_key|ssl_ciphers|ssl_client_certificate|ssl_crl|ssl_dhparam|ssl_engine|ssl_prefer_server_ciphers|ssl_protocols|ssl_session_cache|ssl_session_timeout|ssl_verify_client|ssl_verify_depth|starttls|stub_status|sub_filter|sub_filter_once|sub_filter_types|tcp_nodelay|tcp_nopush|timeout|timer_resolution|try_files|types|types_hash_bucket_size|types_hash_max_size|underscores_in_headers|uninitialized_variable_warn|upstream|use|user|userid|userid_domain|userid_expires|userid_name|userid_p3p|userid_path|userid_service|valid_referers|variables_hash_bucket_size|variables_hash_max_size|worker_connections|worker_cpu_affinity|worker_priority|worker_processes|worker_rlimit_core|worker_rlimit_nofile|worker_rlimit_sigpending|working_directory|xclient|xml_entities|xslt_entities|xslt_stylesheet|xslt_types)\b/i
});
Prism.languages.insertBefore('nginx', 'keyword', {
'variable': /\$[a-z_]+/i
}); | PypiClean |
/Mopidy-WebLibrary-1.0.0.tar.gz/Mopidy-WebLibrary-1.0.0/mopidy_weblibrary/static/vendors/jquery_fileupload/jquery.fileupload-process.js |
;(function (factory) {
'use strict';
if (typeof define === 'function' && define.amd) {
// Register as an anonymous AMD module:
define([
'jquery',
'./jquery.fileupload'
], factory);
} else if (typeof exports === 'object') {
// Node/CommonJS:
factory(
require('jquery'),
require('./jquery.fileupload')
);
} else {
// Browser globals:
factory(
window.jQuery
);
}
}(function ($) {
'use strict';
var originalAdd = $.blueimp.fileupload.prototype.options.add;
// The File Upload Processing plugin extends the fileupload widget
// with file processing functionality:
$.widget('blueimp.fileupload', $.blueimp.fileupload, {
options: {
// The list of processing actions:
processQueue: [
/*
{
action: 'log',
type: 'debug'
}
*/
],
add: function (e, data) {
var $this = $(this);
data.process(function () {
return $this.fileupload('process', data);
});
originalAdd.call(this, e, data);
}
},
processActions: {
/*
log: function (data, options) {
console[options.type](
'Processing "' + data.files[data.index].name + '"'
);
}
*/
},
_processFile: function (data, originalData) {
var that = this,
dfd = $.Deferred().resolveWith(that, [data]),
chain = dfd.promise();
this._trigger('process', null, data);
$.each(data.processQueue, function (i, settings) {
var func = function (data) {
if (originalData.errorThrown) {
return $.Deferred()
.rejectWith(that, [originalData]).promise();
}
return that.processActions[settings.action].call(
that,
data,
settings
);
};
chain = chain.then(func, settings.always && func);
});
chain
.done(function () {
that._trigger('processdone', null, data);
that._trigger('processalways', null, data);
})
.fail(function () {
that._trigger('processfail', null, data);
that._trigger('processalways', null, data);
});
return chain;
},
// Replaces the settings of each processQueue item that
// are strings starting with an "@", using the remaining
// substring as key for the option map,
// e.g. "@autoUpload" is replaced with options.autoUpload:
_transformProcessQueue: function (options) {
var processQueue = [];
$.each(options.processQueue, function () {
var settings = {},
action = this.action,
prefix = this.prefix === true ? action : this.prefix;
$.each(this, function (key, value) {
if ($.type(value) === 'string' &&
value.charAt(0) === '@') {
settings[key] = options[
value.slice(1) || (prefix ? prefix +
key.charAt(0).toUpperCase() + key.slice(1) : key)
];
} else {
settings[key] = value;
}
});
processQueue.push(settings);
});
options.processQueue = processQueue;
},
// Returns the number of files currently in the processsing queue:
processing: function () {
return this._processing;
},
// Processes the files given as files property of the data parameter,
// returns a Promise object that allows to bind callbacks:
process: function (data) {
var that = this,
options = $.extend({}, this.options, data);
if (options.processQueue && options.processQueue.length) {
this._transformProcessQueue(options);
if (this._processing === 0) {
this._trigger('processstart');
}
$.each(data.files, function (index) {
var opts = index ? $.extend({}, options) : options,
func = function () {
if (data.errorThrown) {
return $.Deferred()
.rejectWith(that, [data]).promise();
}
return that._processFile(opts, data);
};
opts.index = index;
that._processing += 1;
that._processingQueue = that._processingQueue.then(func, func)
.always(function () {
that._processing -= 1;
if (that._processing === 0) {
that._trigger('processstop');
}
});
});
}
return this._processingQueue;
},
_create: function () {
this._super();
this._processing = 0;
this._processingQueue = $.Deferred().resolveWith(this)
.promise();
}
});
})); | PypiClean |
/Flask-Injector-0.15.0.tar.gz/Flask-Injector-0.15.0/flask_injector/__init__.py | import functools
from inspect import ismethod
from typing import Any, Callable, cast, Dict, get_type_hints, Iterable, List, TypeVar, Union
import flask
try:
from flask_restful import Api as FlaskRestfulApi
from flask_restful.utils import unpack as flask_response_unpack
except ImportError:
FlaskRestfulApi = None
try:
import flask_restx
from flask_restx.utils import unpack as flask_response_unpack # noqa
except ImportError:
flask_restx = None
from injector import Binder, Injector, inject
from flask import Config, Request
from werkzeug.local import Local, LocalManager, LocalProxy
from werkzeug.wrappers import Response
from injector import Module, Provider, Scope, ScopeDecorator, singleton
__author__ = 'Alec Thomas <alec@swapoff.org>'
__version__ = '0.15.0'
__all__ = ['request', 'RequestScope', 'Config', 'Request', 'FlaskInjector']
T = TypeVar('T', LocalProxy, Callable)
def instance_method_wrapper(im: T) -> T:
@functools.wraps(im)
def wrapper(*args: Any, **kwargs: Any) -> Any:
return im(*args, **kwargs)
return wrapper # type: ignore
def wrap_fun(fun: T, injector: Injector) -> T:
if isinstance(fun, LocalProxy):
return fun # type: ignore
if ismethod(fun):
fun = instance_method_wrapper(fun)
# Important: this block needs to stay here so it's executed *before* the
# hasattr(fun, '__call__') block below - otherwise things may crash.
if hasattr(fun, '__bindings__'):
return wrap_function(fun, injector)
if hasattr(fun, 'view_class'):
return wrap_class_based_view(fun, injector)
if hasattr(fun, '__call__') and not isinstance(fun, type):
try:
type_hints = get_type_hints(fun)
except (AttributeError, TypeError):
# Some callables aren't introspectable with get_type_hints,
# let's assume they don't have anything to inject. The exception
# types handled here are what I encountered so far.
# It used to be AttributeError, then https://github.com/python/typing/pull/314
# changed it to TypeError.
wrap_it = False
except NameError:
wrap_it = True
else:
type_hints.pop('return', None)
wrap_it = type_hints != {}
if wrap_it:
return wrap_fun(inject(fun), injector)
return fun
def wrap_function(fun: Callable, injector: Injector) -> Callable:
@functools.wraps(fun)
def wrapper(*args: Any, **kwargs: Any) -> Any:
return injector.call_with_injection(callable=fun, args=args, kwargs=kwargs)
return wrapper
def wrap_class_based_view(fun: Callable, injector: Injector) -> Callable:
cls = cast(Any, fun).view_class
name = fun.__name__
closure_contents = (c.cell_contents for c in cast(Any, fun).__closure__)
fun_closure = dict(zip(fun.__code__.co_freevars, closure_contents))
try:
class_kwargs = fun_closure['class_kwargs']
except KeyError:
# Most likely flask_restful resource, we'll see in a second
flask_restful_api = fun_closure['self']
# flask_restful wraps ResourceClass.as_view() result in its own wrapper
# the as_view() result is available under 'resource' name in this closure
fun = fun_closure['resource']
fun_closure = {}
class_kwargs = {}
# if the lines above succeeded we're quite sure it's flask_restful resource
else:
flask_restful_api = None
class_args = fun_closure.get('class_args')
assert not class_args, 'Class args are not supported, use kwargs instead'
if flask_restful_api and flask_restx and isinstance(flask_restful_api, flask_restx.Api):
# This is flask_restplus' (before it forked into flask_restx) add_resource
# implementation:
#
# def add_resource(self, resource, *urls, **kwargs):
# (...)
# args = kwargs.pop('resource_class_args', [])
# if isinstance(args, tuple):
# args = list(args)
# args.insert(0, self)
# kwargs['resource_class_args'] = args
#
# super(Api, self).add_resource(resource, *urls, **kwargs)
#
# Correspondingly, flask_restx.Resource's constructor expects
# flask_restx.Api instance in its first argument; since we detected
# that we're very likely dealing with flask_restx we'll provide the Api
# instance as keyword argument instead (it'll work just as well unless
# flask_restx changes the name of the parameter; also
# Injector.create_object doesn't support extra positional arguments anyway).
if 'api' in class_kwargs:
raise AssertionError('api keyword argument is reserved')
class_kwargs['api'] = flask_restful_api
# This section is flask.views.View.as_view code modified to make the injection
# possible without relying on modifying view function in place
# Copyright (c) 2014 by Armin Ronacher and Flask contributors, see Flask
# license for details
def view(*args: Any, **kwargs: Any) -> Any:
self = injector.create_object(cls, additional_kwargs=class_kwargs)
return self.dispatch_request(*args, **kwargs)
if cls.decorators:
view.__name__ = name
view.__module__ = cls.__module__
for decorator in cls.decorators:
view = decorator(view)
# We attach the view class to the view function for two reasons:
# first of all it allows us to easily figure out what class-based
# view this thing came from, secondly it's also used for instantiating
# the view class so you can actually replace it with something else
# for testing purposes and debugging.
cast(Any, view).view_class = cls
view.__name__ = name
view.__doc__ = cls.__doc__
view.__module__ = cls.__module__
cast(Any, view).methods = cls.methods
fun = view
if flask_restful_api:
return wrap_flask_restful_resource(fun, flask_restful_api, injector)
return fun
def wrap_flask_restful_resource(
fun: Callable, flask_restful_api: FlaskRestfulApi, injector: Injector
) -> Callable:
"""
This is needed because of how flask_restful views are registered originally.
:type flask_restful_api: :class:`flask_restful.Api`
"""
# The following fragment of code is copied from flask_restful project
"""
Copyright (c) 2013, Twilio, Inc.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
- Neither the name of the Twilio, Inc. nor the names of its contributors may be
used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
@functools.wraps(fun)
def wrapper(*args: Any, **kwargs: Any) -> Any:
resp = fun(*args, **kwargs)
if isinstance(resp, Response): # There may be a better way to test
return resp
data, code, headers = flask_response_unpack(resp)
return flask_restful_api.make_response(data, code, headers=headers)
# end of flask_restful code
return wrapper
class CachedProviderWrapper(Provider):
def __init__(self, old_provider: Provider) -> None:
self._old_provider = old_provider
self._cache = {} # type: Dict[int, Any]
def get(self, injector: Injector) -> Any:
key = id(injector)
try:
return self._cache[key]
except KeyError:
instance = self._cache[key] = self._old_provider.get(injector)
return instance
class RequestScope(Scope):
"""A scope whose object lifetime is tied to a request.
@request
class Session:
pass
"""
# We don't want to assign here, just provide type hints
if False:
_local_manager = None # type: LocalManager
_locals = None # type: Any
def cleanup(self) -> None:
self._local_manager.cleanup()
def prepare(self) -> None:
self._locals.scope = {}
def configure(self) -> None:
self._locals = Local()
self._local_manager = LocalManager([self._locals])
self.prepare()
def get(self, key: Any, provider: Provider) -> Any:
try:
return self._locals.scope[key]
except KeyError:
new_provider = self._locals.scope[key] = CachedProviderWrapper(provider)
return new_provider
request = ScopeDecorator(RequestScope)
_ModuleT = Union[Callable[[Binder], Any], Module]
class FlaskInjector:
def __init__(
self,
app: flask.Flask,
modules: Iterable[_ModuleT] = [],
injector: Injector = None,
request_scope_class: type = RequestScope,
) -> None:
"""Initializes Injector for the application.
.. note::
Needs to be called *after* all views, signal handlers, template globals
and context processors are registered.
:param app: Application to configure
:param modules: Configuration for newly created :class:`injector.Injector`
:param injector: Injector to initialize app with, if not provided
a new instance will be created.
:type app: :class:`flask.Flask`
:type modules: Iterable of configuration modules
:rtype: :class:`injector.Injector`
"""
if not injector:
injector = Injector()
modules = list(modules)
modules.insert(0, FlaskModule(app=app, request_scope_class=request_scope_class))
for module in modules:
injector.binder.install(module)
for container in (
app.view_functions,
app.before_request_funcs,
app.after_request_funcs,
app.teardown_request_funcs,
app.template_context_processors,
app.jinja_env.globals,
app.error_handler_spec,
):
process_dict(container, injector)
# This is to make sure that mypy sees a non-nullable variable
# in the closures below, otherwise it'd complain that injector
# union may not have get attribute
injector_not_null = injector
def reset_request_scope_before(*args: Any, **kwargs: Any) -> None:
injector_not_null.get(request_scope_class).prepare()
def global_reset_request_scope_after(*args: Any, **kwargs: Any) -> None:
blueprint = flask.request.blueprint
# If current blueprint has teardown_request_funcs associated with it we know there may be
# a some teardown request handlers we need to inject into, so we can't reset the scope just yet.
# We'll leave it to blueprint_reset_request_scope_after to do the job which we know will run
# later and we know it'll run after any teardown_request handlers we may want to inject into.
if blueprint is None or blueprint not in app.teardown_request_funcs:
injector_not_null.get(request_scope_class).cleanup()
def blueprint_reset_request_scope_after(*args: Any, **kwargs: Any) -> None:
# If we got here we truly know this is the last teardown handler, which means we can reset the
# scope unconditionally.
injector_not_null.get(request_scope_class).cleanup()
app.before_request_funcs.setdefault(None, []).insert(0, reset_request_scope_before)
# We're accessing Flask internals here as the app.teardown_request decorator appends to a list of
# handlers but Flask itself reverses the list when it executes them. To allow injecting request-scoped
# dependencies into teardown_request handlers we need to run our teardown_request handler after them.
# Also see https://github.com/alecthomas/flask_injector/issues/42 where it was reported.
# Secondly, we need to handle blueprints. Flask first executes non-blueprint teardown handlers in
# reverse order and only then executes blueprint-associated teardown handlers in reverse order,
# which means we can't just set on non-blueprint teardown handler, but we need to set both.
# In non-blueprint teardown handler we check if a blueprint handler will run – if so, we do nothing
# there and leave it to the blueprint teardown handler.
#
# We need the None key to be present in the dictionary so that the dictionary iteration always yields
# None as well. We *always* have to set the global teardown request.
app.teardown_request_funcs.setdefault(None, []).insert(0, global_reset_request_scope_after)
for bp, functions in app.teardown_request_funcs.items():
if bp is not None:
functions.insert(0, blueprint_reset_request_scope_after)
self.injector = injector_not_null
self.app = app
def process_dict(d: Dict, injector: Injector) -> None:
for key, value in d.items():
if isinstance(value, LocalProxy):
# We need this no-op here, because if we have a LocalProxy and try to use isinstance() on it
# we'll get a RuntimeError
pass
elif isinstance(value, list):
process_list(value, injector)
elif hasattr(value, '__call__'):
d[key] = wrap_fun(value, injector)
elif isinstance(value, dict):
process_dict(value, injector)
def process_list(l: List, injector: Injector) -> None:
# This function mutates the l parameter
l[:] = [wrap_fun(fun, injector) for fun in l]
class FlaskModule(Module):
def __init__(self, app: flask.Flask, request_scope_class: type = RequestScope) -> None:
self.app = app
self.request_scope_class = request_scope_class
def configure(self, binder: Binder) -> None:
binder.bind(flask.Flask, to=self.app, scope=singleton)
binder.bind(Config, to=self.app.config, scope=singleton)
binder.bind(Request, to=lambda: flask.request) | PypiClean |
/Congo-0.0.1.tar.gz/Congo-0.0.1/portfolio/ext/mailer.py | import warnings
import ses_mailer
import flask_mail
from six.moves.urllib.parse import urlparse
class Mailer(object):
"""
A simple wrapper to switch between SES-Mailer and Flask-Mail based on config
"""
mail = None
provider = None
app = None
def init_app(self, app):
self.app = app
provider = app.config.get("MAILER_PROVIDER", None)
if provider:
self.provider = provider.upper()
if self.provider == "SES":
class _App(object):
config = {
"SES_AWS_ACCESS_KEY": app.config.get("MAILER_SES_ACCESS_KEY"),
"SES_AWS_SECRET_KEY": app.config.get("MAILER_SES_SECRET_KEY"),
"SES_SENDER": app.config.get("MAILER_SENDER"),
"SES_REPLY_TO": app.config.get("MAILER_REPLY_TO"),
"SES_TEMPLATE": app.config.get("MAILER_TEMPLATE"),
"SES_TEMPLATE_CONTEXT": app.config.get("MAILER_TEMPLATE_CONTEXT")
}
_app = _App()
self.mail = ses_mailer.Mail(app=_app)
elif self.provider == "SMTP":
uri = app.config.get("MAILER_SMTP_URI", None)
if uri is None:
raise ValueError("<Portfolio:Component:Mailer: MAILER_SMTP_URI is empty'")
parse_uri = urlparse(uri)
if "smtp" not in parse_uri.scheme:
raise ValueError("<Portfolio:Component:Mailer: MAILER_SMTP_URI must start with 'smtp://'")
class _App(object):
config = {
"MAIL_SERVER": parse_uri.hostname,
"MAIL_USERNAME": parse_uri.username,
"MAIL_PASSWORD": parse_uri.password,
"MAIL_PORT": parse_uri.port,
"MAIL_USE_TLS": True if "tls" in parse_uri.scheme else False,
"MAIL_USE_SSL": True if "ssl" in parse_uri.scheme else False,
"MAIL_DEFAULT_SENDER": app.config.get("MAILER_SENDER"),
"TESTING": app.config.get("TESTING"),
"DEBUG": app.config.get("DEBUG")
}
debug = app.config.get("DEBUG")
testing = app.config.get("TESTING")
_app = _App()
self.mail = flask_mail.Mail(app=_app)
else:
raise warnings.warn("<Portfolio:Component:Mailer invalid provider '%s'>" % provider)
def send(self, to, subject, body, reply_to=None, **kwargs):
"""
Send simple message
"""
if self.provider == "SES":
self.mail.send(to=to,
subject=subject,
body=body,
reply_to=reply_to,
**kwargs)
elif self.provider == "SMTP":
print body
msg = flask_mail.Message(recipients=[to] if not isinstance(to, list) else to,
subject=subject,
body=body,
reply_to=reply_to,
sender=self.app.config.get("MAILER_SENDER"))
self.mail.send(msg)
def send_template(self, template, to, reply_to=None, **context):
"""
Send Template message
"""
if self.provider == "SES":
self.mail.send_template(template=template,
to=to,
reply_to=reply_to,
**context)
elif self.provider == "SMTP":
_template = self.app.config.get("MAILER_TEMPLATE", None)
template_context = self.app.config.get("MAILER_TEMPLATER_CONTEXT")
ses_mail = ses_mailer.Mail(template=_template,
template_context=template_context)
data = ses_mail.parse_template(template=template, **context)
msg = flask_mail.Message(recipients=[to] if not isinstance(to, list) else to,
subject=data["subject"],
body=data["body"],
reply_to=reply_to,
sender=self.app.config.get("MAILER_SENDER")
)
self.mail.send(msg) | PypiClean |
/JT_Techfield-0.0.11-py3-none-any.whl/JT/Gradients.py | import numpy as np
class Gradient:
def __init__(self):
self.change = 0
def Evaluate(self, grad, learning_rate):
return grad * learning_rate
class Momentum(Gradient):
def __init__(self, velocity_rate = 0.9):
self.velocity_rate = velocity_rate
self.velocity = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.velocity = self.velocity_rate * self.velocity + learning_rate * grad
return self.velocity
class Ada_Grad(Gradient):
def __init__(self, adaptive_rate = 1, epsilon = 1e-8):
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.ada_grad = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.ada_grad = self.adaptive_rate * self.ada_grad + grad ** 2
learning_rate = learning_rate / np.sqrt(self.ada_grad + self.epsilon)
return learning_rate * grad
class Momentum_Ada_Grad(Gradient):
def __init__(self, velocity_rate = 0.9, adaptive_rate = 1, epsilon = 1e-8):
self.velocity_rate = velocity_rate
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.velocity = 0
self.ada_grad = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.ada_grad = self.adaptive_rate * self.ada_grad + grad ** 2
learning_rate = learning_rate / np.sqrt(self.ada_grad + self.epsilon)
self.velocity = self.velocity_rate * self.velocity + learning_rate * grad
return self.velocity
class RMS_Prop(Gradient):
def __init__(self, adaptive_rate = 0.5, epsilon = 1e-8):
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.grad = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.grad = self.adaptive_rate * self.grad + (1 - self.adaptive_rate) * grad ** 2
learning_rate = learning_rate / np.sqrt(self.grad + self.epsilon)
return learning_rate * grad
class Momentum_RMS_Prop(Gradient):
def __init__(self, velocity_rate = 0.9, adaptive_rate = 0.5, epsilon = 1e-8):
self.velocity_rate = velocity_rate
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.velocity = 0
self.grad = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.grad = self.adaptive_rate * self.grad + (1 - self.adaptive_rate) * grad ** 2
learning_rate = learning_rate / np.sqrt(self.grad + self.epsilon)
self.velocity = self.velocity_rate * self.velocity + learning_rate * grad
return self.velocity
class Nesterov_Momentum(Gradient):
def __init__(self, velocity_rate = 0.9):
self.velocity_rate = velocity_rate
self.velocity = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
out = -self.change
self.change = self.velocity_rate * self.change + learning_rate * grad
return out + self.change * 2
class Nesterov_RMS_Prop(Gradient):
def __init__(self, velocity_rate = 0.9, adaptive_rate = 0.5, epsilon = 1e-8):
self.velocity_rate = velocity_rate
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.velocity = 0
self.grad = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
out = -self.change
self.grad = self.adaptive_rate * self.grad + (1 - self.adaptive_rate) * grad ** 2
learning_rate = learning_rate / np.sqrt(self.grad + self.epsilon)
self.change = self.velocity_rate * self.change + learning_rate * grad
return out + self.change * 2
class Adam(Gradient):
def __init__(self, velocity_rate = 0.9, adaptive_rate = 0.5, epsilon = 1e-8):
self.velocity_rate = velocity_rate
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.velocity = 0
self.grad = 0
self.i = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.i += 1
self.grad = self.adaptive_rate * self.grad + (1 - self.adaptive_rate) * grad ** 2
grad_hat = self.grad / (1 + self.adaptive_rate ** self.i)
learning_rate = learning_rate / np.sqrt(grad_hat + self.epsilon)
self.velocity = self.velocity_rate * self.velocity + (1 - self.velocity_rate) * grad
velocity_hat = self.velocity / (1 + self.velocity_rate ** self.i)
return learning_rate * velocity_hat
class Nesterov_Adam(Gradient):
def __init__(self, velocity_rate = 0.9, adaptive_rate = 0.5, epsilon = 1e-8):
self.velocity_rate = velocity_rate
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.velocity = 0
self.grad = 0
self.out = 0
self.i = 0
self.change = 0
def Evaluate(self, grad, learning_rate):
self.i += 1
out = -self.change
self.grad = self.adaptive_rate * self.grad + (1 - self.adaptive_rate) * grad ** 2
grad_hat = self.grad / (1 + self.adaptive_rate ** self.i)
learning_rate = learning_rate / np.sqrt(grad_hat + self.epsilon)
self.velocity = self.velocity_rate * self.velocity + (1 - self.velocity_rate) * grad
velocity_hat = self.velocity / (1 + self.velocity_rate ** self.i)
self.change = learning_rate * velocity_hat
return out + self.change * 2
class Nesterov_Ada_Grad(Gradient):
def __init__(self, adaptive_rate = 1, epsilon = 1e-8):
self.adaptive_rate = adaptive_rate
self.epsilon = epsilon
self.ada_grad = 0
def Evaluate(self, grad, learning_rate):
out = -self.change
self.ada_grad = self.adaptive_rate * self.ada_grad + grad ** 2
learning_rate = learning_rate / np.sqrt(self.ada_grad + self.epsilon)
self.change = self.velocity_rate * self.change + learning_rate * grad
return out + self.change * 2 | PypiClean |
/OOPyGame-0.0.5-py3-none-any.whl/PyGameUI/colors.py | TABLEAU_COLORS = {
'blue': '#1f77b4',
'orange': '#ff7f0e',
'green': '#2ca02c',
'red': '#d62728',
'purple': '#9467bd',
'brown': '#8c564b',
'pink': '#e377c2',
'gray': '#7f7f7f',
'olive': '#bcbd22',
'cyan': '#17becf',
}
CSS4_COLORS = {
'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkgrey': '#A9A9A9',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkslategrey': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dimgrey': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'grey': '#808080',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgray': '#D3D3D3',
'lightgreen': '#90EE90',
'lightgrey': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightslategrey': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'rebeccapurple': '#663399',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#F4A460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'slategrey': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}
# Define functions to convert colors
XKCD_COLORS = {
'cloudy blue': '#acc2d9',
'dark pastel green': '#56ae57',
'dust': '#b2996e',
'electric lime': '#a8ff04',
'fresh green': '#69d84f',
'light eggplant': '#894585',
'nasty green': '#70b23f',
'really light blue': '#d4ffff',
'tea': '#65ab7c',
'warm purple': '#952e8f',
'yellowish tan': '#fcfc81',
'cement': '#a5a391',
'dark grass green': '#388004',
'dusty teal': '#4c9085',
'grey teal': '#5e9b8a',
'macaroni and cheese': '#efb435',
'pinkish tan': '#d99b82',
'spruce': '#0a5f38',
'strong blue': '#0c06f7',
'toxic green': '#61de2a',
'windows blue': '#3778bf',
'blue blue': '#2242c7',
'blue with a hint of purple': '#533cc6',
'booger': '#9bb53c',
'bright sea green': '#05ffa6',
'dark green blue': '#1f6357',
'deep turquoise': '#017374',
'green teal': '#0cb577',
'strong pink': '#ff0789',
'bland': '#afa88b',
'deep aqua': '#08787f',
'lavender pink': '#dd85d7',
'light moss green': '#a6c875',
'light seafoam green': '#a7ffb5',
'olive yellow': '#c2b709',
'pig pink': '#e78ea5',
'deep lilac': '#966ebd',
'desert': '#ccad60',
'dusty lavender': '#ac86a8',
'purpley grey': '#947e94',
'purply': '#983fb2',
'candy pink': '#ff63e9',
'light pastel green': '#b2fba5',
'boring green': '#63b365',
'kiwi green': '#8ee53f',
'light grey green': '#b7e1a1',
'orange pink': '#ff6f52',
'tea green': '#bdf8a3',
'very light brown': '#d3b683',
'egg shell': '#fffcc4',
'eggplant purple': '#430541',
'powder pink': '#ffb2d0',
'reddish grey': '#997570',
'baby shit brown': '#ad900d',
'liliac': '#c48efd',
'stormy blue': '#507b9c',
'ugly brown': '#7d7103',
'custard': '#fffd78',
'darkish pink': '#da467d',
'deep brown': '#410200',
'greenish beige': '#c9d179',
'manilla': '#fffa86',
'off blue': '#5684ae',
'battleship grey': '#6b7c85',
'browny green': '#6f6c0a',
'bruise': '#7e4071',
'kelley green': '#009337',
'sickly yellow': '#d0e429',
'sunny yellow': '#fff917',
'azul': '#1d5dec',
'darkgreen': '#054907',
'green/yellow': '#b5ce08',
'lichen': '#8fb67b',
'light light green': '#c8ffb0',
'pale gold': '#fdde6c',
'sun yellow': '#ffdf22',
'tan green': '#a9be70',
'burple': '#6832e3',
'butterscotch': '#fdb147',
'toupe': '#c7ac7d',
'dark cream': '#fff39a',
'indian red': '#850e04',
'light lavendar': '#efc0fe',
'poison green': '#40fd14',
'baby puke green': '#b6c406',
'bright yellow green': '#9dff00',
'charcoal grey': '#3c4142',
'squash': '#f2ab15',
'cinnamon': '#ac4f06',
'light pea green': '#c4fe82',
'radioactive green': '#2cfa1f',
'raw sienna': '#9a6200',
'baby purple': '#ca9bf7',
'cocoa': '#875f42',
'light royal blue': '#3a2efe',
'orangeish': '#fd8d49',
'rust brown': '#8b3103',
'sand brown': '#cba560',
'swamp': '#698339',
'tealish green': '#0cdc73',
'burnt siena': '#b75203',
'camo': '#7f8f4e',
'dusk blue': '#26538d',
'fern': '#63a950',
'old rose': '#c87f89',
'pale light green': '#b1fc99',
'peachy pink': '#ff9a8a',
'rosy pink': '#f6688e',
'light bluish green': '#76fda8',
'light bright green': '#53fe5c',
'light neon green': '#4efd54',
'light seafoam': '#a0febf',
'tiffany blue': '#7bf2da',
'washed out green': '#bcf5a6',
'browny orange': '#ca6b02',
'nice blue': '#107ab0',
'sapphire': '#2138ab',
'greyish teal': '#719f91',
'orangey yellow': '#fdb915',
'parchment': '#fefcaf',
'straw': '#fcf679',
'very dark brown': '#1d0200',
'terracota': '#cb6843',
'ugly blue': '#31668a',
'clear blue': '#247afd',
'creme': '#ffffb6',
'foam green': '#90fda9',
'grey/green': '#86a17d',
'light gold': '#fddc5c',
'seafoam blue': '#78d1b6',
'topaz': '#13bbaf',
'violet pink': '#fb5ffc',
'wintergreen': '#20f986',
'yellow tan': '#ffe36e',
'dark fuchsia': '#9d0759',
'indigo blue': '#3a18b1',
'light yellowish green': '#c2ff89',
'pale magenta': '#d767ad',
'rich purple': '#720058',
'sunflower yellow': '#ffda03',
'green/blue': '#01c08d',
'leather': '#ac7434',
'racing green': '#014600',
'vivid purple': '#9900fa',
'dark royal blue': '#02066f',
'hazel': '#8e7618',
'muted pink': '#d1768f',
'booger green': '#96b403',
'canary': '#fdff63',
'cool grey': '#95a3a6',
'dark taupe': '#7f684e',
'darkish purple': '#751973',
'true green': '#089404',
'coral pink': '#ff6163',
'dark sage': '#598556',
'dark slate blue': '#214761',
'flat blue': '#3c73a8',
'mushroom': '#ba9e88',
'rich blue': '#021bf9',
'dirty purple': '#734a65',
'greenblue': '#23c48b',
'icky green': '#8fae22',
'light khaki': '#e6f2a2',
'warm blue': '#4b57db',
'dark hot pink': '#d90166',
'deep sea blue': '#015482',
'carmine': '#9d0216',
'dark yellow green': '#728f02',
'pale peach': '#ffe5ad',
'plum purple': '#4e0550',
'golden rod': '#f9bc08',
'neon red': '#ff073a',
'old pink': '#c77986',
'very pale blue': '#d6fffe',
'blood orange': '#fe4b03',
'grapefruit': '#fd5956',
'sand yellow': '#fce166',
'clay brown': '#b2713d',
'dark blue grey': '#1f3b4d',
'flat green': '#699d4c',
'light green blue': '#56fca2',
'warm pink': '#fb5581',
'dodger blue': '#3e82fc',
'gross green': '#a0bf16',
'ice': '#d6fffa',
'metallic blue': '#4f738e',
'pale salmon': '#ffb19a',
'sap green': '#5c8b15',
'algae': '#54ac68',
'bluey grey': '#89a0b0',
'greeny grey': '#7ea07a',
'highlighter green': '#1bfc06',
'light light blue': '#cafffb',
'light mint': '#b6ffbb',
'raw umber': '#a75e09',
'vivid blue': '#152eff',
'deep lavender': '#8d5eb7',
'dull teal': '#5f9e8f',
'light greenish blue': '#63f7b4',
'mud green': '#606602',
'pinky': '#fc86aa',
'red wine': '#8c0034',
'shit green': '#758000',
'tan brown': '#ab7e4c',
'darkblue': '#030764',
'rosa': '#fe86a4',
'lipstick': '#d5174e',
'pale mauve': '#fed0fc',
'claret': '#680018',
'dandelion': '#fedf08',
'orangered': '#fe420f',
'poop green': '#6f7c00',
'ruby': '#ca0147',
'dark': '#1b2431',
'greenish turquoise': '#00fbb0',
'pastel red': '#db5856',
'piss yellow': '#ddd618',
'bright cyan': '#41fdfe',
'dark coral': '#cf524e',
'algae green': '#21c36f',
'darkish red': '#a90308',
'reddy brown': '#6e1005',
'blush pink': '#fe828c',
'camouflage green': '#4b6113',
'lawn green': '#4da409',
'putty': '#beae8a',
'vibrant blue': '#0339f8',
'dark sand': '#a88f59',
'purple/blue': '#5d21d0',
'saffron': '#feb209',
'twilight': '#4e518b',
'warm brown': '#964e02',
'bluegrey': '#85a3b2',
'bubble gum pink': '#ff69af',
'duck egg blue': '#c3fbf4',
'greenish cyan': '#2afeb7',
'petrol': '#005f6a',
'royal': '#0c1793',
'butter': '#ffff81',
'dusty orange': '#f0833a',
'off yellow': '#f1f33f',
'pale olive green': '#b1d27b',
'orangish': '#fc824a',
'leaf': '#71aa34',
'light blue grey': '#b7c9e2',
'dried blood': '#4b0101',
'lightish purple': '#a552e6',
'rusty red': '#af2f0d',
'lavender blue': '#8b88f8',
'light grass green': '#9af764',
'light mint green': '#a6fbb2',
'sunflower': '#ffc512',
'velvet': '#750851',
'brick orange': '#c14a09',
'lightish red': '#fe2f4a',
'pure blue': '#0203e2',
'twilight blue': '#0a437a',
'violet red': '#a50055',
'yellowy brown': '#ae8b0c',
'carnation': '#fd798f',
'muddy yellow': '#bfac05',
'dark seafoam green': '#3eaf76',
'deep rose': '#c74767',
'dusty red': '#b9484e',
'grey/blue': '#647d8e',
'lemon lime': '#bffe28',
'purple/pink': '#d725de',
'brown yellow': '#b29705',
'purple brown': '#673a3f',
'wisteria': '#a87dc2',
'banana yellow': '#fafe4b',
'lipstick red': '#c0022f',
'water blue': '#0e87cc',
'brown grey': '#8d8468',
'vibrant purple': '#ad03de',
'baby green': '#8cff9e',
'barf green': '#94ac02',
'eggshell blue': '#c4fff7',
'sandy yellow': '#fdee73',
'cool green': '#33b864',
'pale': '#fff9d0',
'blue/grey': '#758da3',
'hot magenta': '#f504c9',
'greyblue': '#77a1b5',
'purpley': '#8756e4',
'baby shit green': '#889717',
'brownish pink': '#c27e79',
'dark aquamarine': '#017371',
'diarrhea': '#9f8303',
'light mustard': '#f7d560',
'pale sky blue': '#bdf6fe',
'turtle green': '#75b84f',
'bright olive': '#9cbb04',
'dark grey blue': '#29465b',
'greeny brown': '#696006',
'lemon green': '#adf802',
'light periwinkle': '#c1c6fc',
'seaweed green': '#35ad6b',
'sunshine yellow': '#fffd37',
'ugly purple': '#a442a0',
'medium pink': '#f36196',
'puke brown': '#947706',
'very light pink': '#fff4f2',
'viridian': '#1e9167',
'bile': '#b5c306',
'faded yellow': '#feff7f',
'very pale green': '#cffdbc',
'vibrant green': '#0add08',
'bright lime': '#87fd05',
'spearmint': '#1ef876',
'light aquamarine': '#7bfdc7',
'light sage': '#bcecac',
'yellowgreen': '#bbf90f',
'baby poo': '#ab9004',
'dark seafoam': '#1fb57a',
'deep teal': '#00555a',
'heather': '#a484ac',
'rust orange': '#c45508',
'dirty blue': '#3f829d',
'fern green': '#548d44',
'bright lilac': '#c95efb',
'weird green': '#3ae57f',
'peacock blue': '#016795',
'avocado green': '#87a922',
'faded orange': '#f0944d',
'grape purple': '#5d1451',
'hot green': '#25ff29',
'lime yellow': '#d0fe1d',
'mango': '#ffa62b',
'shamrock': '#01b44c',
'bubblegum': '#ff6cb5',
'purplish brown': '#6b4247',
'vomit yellow': '#c7c10c',
'pale cyan': '#b7fffa',
'key lime': '#aeff6e',
'tomato red': '#ec2d01',
'lightgreen': '#76ff7b',
'merlot': '#730039',
'night blue': '#040348',
'purpleish pink': '#df4ec8',
'apple': '#6ecb3c',
'baby poop green': '#8f9805',
'green apple': '#5edc1f',
'heliotrope': '#d94ff5',
'yellow/green': '#c8fd3d',
'almost black': '#070d0d',
'cool blue': '#4984b8',
'leafy green': '#51b73b',
'mustard brown': '#ac7e04',
'dusk': '#4e5481',
'dull brown': '#876e4b',
'frog green': '#58bc08',
'vivid green': '#2fef10',
'bright light green': '#2dfe54',
'fluro green': '#0aff02',
'kiwi': '#9cef43',
'seaweed': '#18d17b',
'navy green': '#35530a',
'ultramarine blue': '#1805db',
'iris': '#6258c4',
'pastel orange': '#ff964f',
'yellowish orange': '#ffab0f',
'perrywinkle': '#8f8ce7',
'tealish': '#24bca8',
'dark plum': '#3f012c',
'pear': '#cbf85f',
'pinkish orange': '#ff724c',
'midnight purple': '#280137',
'light urple': '#b36ff6',
'dark mint': '#48c072',
'greenish tan': '#bccb7a',
'light burgundy': '#a8415b',
'turquoise blue': '#06b1c4',
'ugly pink': '#cd7584',
'sandy': '#f1da7a',
'electric pink': '#ff0490',
'muted purple': '#805b87',
'mid green': '#50a747',
'greyish': '#a8a495',
'neon yellow': '#cfff04',
'banana': '#ffff7e',
'carnation pink': '#ff7fa7',
'tomato': '#ef4026',
'sea': '#3c9992',
'muddy brown': '#886806',
'turquoise green': '#04f489',
'buff': '#fef69e',
'fawn': '#cfaf7b',
'muted blue': '#3b719f',
'pale rose': '#fdc1c5',
'dark mint green': '#20c073',
'amethyst': '#9b5fc0',
'blue/green': '#0f9b8e',
'chestnut': '#742802',
'sick green': '#9db92c',
'pea': '#a4bf20',
'rusty orange': '#cd5909',
'stone': '#ada587',
'rose red': '#be013c',
'pale aqua': '#b8ffeb',
'deep orange': '#dc4d01',
'earth': '#a2653e',
'mossy green': '#638b27',
'grassy green': '#419c03',
'pale lime green': '#b1ff65',
'light grey blue': '#9dbcd4',
'pale grey': '#fdfdfe',
'asparagus': '#77ab56',
'blueberry': '#464196',
'purple red': '#990147',
'pale lime': '#befd73',
'greenish teal': '#32bf84',
'caramel': '#af6f09',
'deep magenta': '#a0025c',
'light peach': '#ffd8b1',
'milk chocolate': '#7f4e1e',
'ocher': '#bf9b0c',
'off green': '#6ba353',
'purply pink': '#f075e6',
'lightblue': '#7bc8f6',
'dusky blue': '#475f94',
'golden': '#f5bf03',
'light beige': '#fffeb6',
'butter yellow': '#fffd74',
'dusky purple': '#895b7b',
'french blue': '#436bad',
'ugly yellow': '#d0c101',
'greeny yellow': '#c6f808',
'orangish red': '#f43605',
'shamrock green': '#02c14d',
'orangish brown': '#b25f03',
'tree green': '#2a7e19',
'deep violet': '#490648',
'gunmetal': '#536267',
'blue/purple': '#5a06ef',
'cherry': '#cf0234',
'sandy brown': '#c4a661',
'warm grey': '#978a84',
'dark indigo': '#1f0954',
'midnight': '#03012d',
'bluey green': '#2bb179',
'grey pink': '#c3909b',
'soft purple': '#a66fb5',
'blood': '#770001',
'brown red': '#922b05',
'medium grey': '#7d7f7c',
'berry': '#990f4b',
'poo': '#8f7303',
'purpley pink': '#c83cb9',
'light salmon': '#fea993',
'snot': '#acbb0d',
'easter purple': '#c071fe',
'light yellow green': '#ccfd7f',
'dark navy blue': '#00022e',
'drab': '#828344',
'light rose': '#ffc5cb',
'rouge': '#ab1239',
'purplish red': '#b0054b',
'slime green': '#99cc04',
'baby poop': '#937c00',
'irish green': '#019529',
'pink/purple': '#ef1de7',
'dark navy': '#000435',
'greeny blue': '#42b395',
'light plum': '#9d5783',
'pinkish grey': '#c8aca9',
'dirty orange': '#c87606',
'rust red': '#aa2704',
'pale lilac': '#e4cbff',
'orangey red': '#fa4224',
'primary blue': '#0804f9',
'kermit green': '#5cb200',
'brownish purple': '#76424e',
'murky green': '#6c7a0e',
'wheat': '#fbdd7e',
'very dark purple': '#2a0134',
'bottle green': '#044a05',
'watermelon': '#fd4659',
'deep sky blue': '#0d75f8',
'fire engine red': '#fe0002',
'yellow ochre': '#cb9d06',
'pumpkin orange': '#fb7d07',
'pale olive': '#b9cc81',
'light lilac': '#edc8ff',
'lightish green': '#61e160',
'carolina blue': '#8ab8fe',
'mulberry': '#920a4e',
'shocking pink': '#fe02a2',
'auburn': '#9a3001',
'bright lime green': '#65fe08',
'celadon': '#befdb7',
'pinkish brown': '#b17261',
'poo brown': '#885f01',
'bright sky blue': '#02ccfe',
'celery': '#c1fd95',
'dirt brown': '#836539',
'strawberry': '#fb2943',
'dark lime': '#84b701',
'copper': '#b66325',
'medium brown': '#7f5112',
'muted green': '#5fa052',
"robin's egg": '#6dedfd',
'bright aqua': '#0bf9ea',
'bright lavender': '#c760ff',
'ivory': '#ffffcb',
'very light purple': '#f6cefc',
'light navy': '#155084',
'pink red': '#f5054f',
'olive brown': '#645403',
'poop brown': '#7a5901',
'mustard green': '#a8b504',
'ocean green': '#3d9973',
'very dark blue': '#000133',
'dusty green': '#76a973',
'light navy blue': '#2e5a88',
'minty green': '#0bf77d',
'adobe': '#bd6c48',
'barney': '#ac1db8',
'jade green': '#2baf6a',
'bright light blue': '#26f7fd',
'light lime': '#aefd6c',
'dark khaki': '#9b8f55',
'orange yellow': '#ffad01',
'ocre': '#c69c04',
'maize': '#f4d054',
'faded pink': '#de9dac',
'british racing green': '#05480d',
'sandstone': '#c9ae74',
'mud brown': '#60460f',
'light sea green': '#98f6b0',
'robin egg blue': '#8af1fe',
'aqua marine': '#2ee8bb',
'dark sea green': '#11875d',
'soft pink': '#fdb0c0',
'orangey brown': '#b16002',
'cherry red': '#f7022a',
'burnt yellow': '#d5ab09',
'brownish grey': '#86775f',
'camel': '#c69f59',
'purplish grey': '#7a687f',
'marine': '#042e60',
'greyish pink': '#c88d94',
'pale turquoise': '#a5fbd5',
'pastel yellow': '#fffe71',
'bluey purple': '#6241c7',
'canary yellow': '#fffe40',
'faded red': '#d3494e',
'sepia': '#985e2b',
'coffee': '#a6814c',
'bright magenta': '#ff08e8',
'mocha': '#9d7651',
'ecru': '#feffca',
'purpleish': '#98568d',
'cranberry': '#9e003a',
'darkish green': '#287c37',
'brown orange': '#b96902',
'dusky rose': '#ba6873',
'melon': '#ff7855',
'sickly green': '#94b21c',
'silver': '#c5c9c7',
'purply blue': '#661aee',
'purpleish blue': '#6140ef',
'hospital green': '#9be5aa',
'shit brown': '#7b5804',
'mid blue': '#276ab3',
'amber': '#feb308',
'easter green': '#8cfd7e',
'soft blue': '#6488ea',
'cerulean blue': '#056eee',
'golden brown': '#b27a01',
'bright turquoise': '#0ffef9',
'red pink': '#fa2a55',
'red purple': '#820747',
'greyish brown': '#7a6a4f',
'vermillion': '#f4320c',
'russet': '#a13905',
'steel grey': '#6f828a',
'lighter purple': '#a55af4',
'bright violet': '#ad0afd',
'prussian blue': '#004577',
'slate green': '#658d6d',
'dirty pink': '#ca7b80',
'dark blue green': '#005249',
'pine': '#2b5d34',
'yellowy green': '#bff128',
'dark gold': '#b59410',
'bluish': '#2976bb',
'darkish blue': '#014182',
'dull red': '#bb3f3f',
'pinky red': '#fc2647',
'bronze': '#a87900',
'pale teal': '#82cbb2',
'military green': '#667c3e',
'barbie pink': '#fe46a5',
'bubblegum pink': '#fe83cc',
'pea soup green': '#94a617',
'dark mustard': '#a88905',
'shit': '#7f5f00',
'medium purple': '#9e43a2',
'very dark green': '#062e03',
'dirt': '#8a6e45',
'dusky pink': '#cc7a8b',
'red violet': '#9e0168',
'lemon yellow': '#fdff38',
'pistachio': '#c0fa8b',
'dull yellow': '#eedc5b',
'dark lime green': '#7ebd01',
'denim blue': '#3b5b92',
'teal blue': '#01889f',
'lightish blue': '#3d7afd',
'purpley blue': '#5f34e7',
'light indigo': '#6d5acf',
'swamp green': '#748500',
'brown green': '#706c11',
'dark maroon': '#3c0008',
'hot purple': '#cb00f5',
'dark forest green': '#002d04',
'faded blue': '#658cbb',
'drab green': '#749551',
'light lime green': '#b9ff66',
'snot green': '#9dc100',
'yellowish': '#faee66',
'light blue green': '#7efbb3',
'bordeaux': '#7b002c',
'light mauve': '#c292a1',
'ocean': '#017b92',
'marigold': '#fcc006',
'muddy green': '#657432',
'dull orange': '#d8863b',
'steel': '#738595',
'electric purple': '#aa23ff',
'fluorescent green': '#08ff08',
'yellowish brown': '#9b7a01',
'blush': '#f29e8e',
'soft green': '#6fc276',
'bright orange': '#ff5b00',
'lemon': '#fdff52',
'purple grey': '#866f85',
'acid green': '#8ffe09',
'pale lavender': '#eecffe',
'violet blue': '#510ac9',
'light forest green': '#4f9153',
'burnt red': '#9f2305',
'khaki green': '#728639',
'cerise': '#de0c62',
'faded purple': '#916e99',
'apricot': '#ffb16d',
'dark olive green': '#3c4d03',
'grey brown': '#7f7053',
'green grey': '#77926f',
'true blue': '#010fcc',
'pale violet': '#ceaefa',
'periwinkle blue': '#8f99fb',
'light sky blue': '#c6fcff',
'blurple': '#5539cc',
'green brown': '#544e03',
'bluegreen': '#017a79',
'bright teal': '#01f9c6',
'brownish yellow': '#c9b003',
'pea soup': '#929901',
'forest': '#0b5509',
'barney purple': '#a00498',
'ultramarine': '#2000b1',
'purplish': '#94568c',
'puke yellow': '#c2be0e',
'bluish grey': '#748b97',
'dark periwinkle': '#665fd1',
'dark lilac': '#9c6da5',
'reddish': '#c44240',
'light maroon': '#a24857',
'dusty purple': '#825f87',
'terra cotta': '#c9643b',
'avocado': '#90b134',
'marine blue': '#01386a',
'teal green': '#25a36f',
'slate grey': '#59656d',
'lighter green': '#75fd63',
'electric green': '#21fc0d',
'dusty blue': '#5a86ad',
'golden yellow': '#fec615',
'bright yellow': '#fffd01',
'light lavender': '#dfc5fe',
'umber': '#b26400',
'poop': '#7f5e00',
'dark peach': '#de7e5d',
'jungle green': '#048243',
'eggshell': '#ffffd4',
'denim': '#3b638c',
'yellow brown': '#b79400',
'dull purple': '#84597e',
'chocolate brown': '#411900',
'wine red': '#7b0323',
'neon blue': '#04d9ff',
'dirty green': '#667e2c',
'light tan': '#fbeeac',
'ice blue': '#d7fffe',
'cadet blue': '#4e7496',
'dark mauve': '#874c62',
'very light blue': '#d5ffff',
'grey purple': '#826d8c',
'pastel pink': '#ffbacd',
'very light green': '#d1ffbd',
'dark sky blue': '#448ee4',
'evergreen': '#05472a',
'dull pink': '#d5869d',
'aubergine': '#3d0734',
'mahogany': '#4a0100',
'reddish orange': '#f8481c',
'deep green': '#02590f',
'vomit green': '#89a203',
'purple pink': '#e03fd8',
'dusty pink': '#d58a94',
'faded green': '#7bb274',
'camo green': '#526525',
'pinky purple': '#c94cbe',
'pink purple': '#db4bda',
'brownish red': '#9e3623',
'dark rose': '#b5485d',
'mud': '#735c12',
'brownish': '#9c6d57',
'emerald green': '#028f1e',
'pale brown': '#b1916e',
'dull blue': '#49759c',
'burnt umber': '#a0450e',
'medium green': '#39ad48',
'clay': '#b66a50',
'light aqua': '#8cffdb',
'light olive green': '#a4be5c',
'brownish orange': '#cb7723',
'dark aqua': '#05696b',
'purplish pink': '#ce5dae',
'dark salmon': '#c85a53',
'greenish grey': '#96ae8d',
'jade': '#1fa774',
'ugly green': '#7a9703',
'dark beige': '#ac9362',
'emerald': '#01a049',
'pale red': '#d9544d',
'light magenta': '#fa5ff7',
'sky': '#82cafc',
'light cyan': '#acfffc',
'yellow orange': '#fcb001',
'reddish purple': '#910951',
'reddish pink': '#fe2c54',
'orchid': '#c875c4',
'dirty yellow': '#cdc50a',
'orange red': '#fd411e',
'deep red': '#9a0200',
'orange brown': '#be6400',
'cobalt blue': '#030aa7',
'neon pink': '#fe019a',
'rose pink': '#f7879a',
'greyish purple': '#887191',
'raspberry': '#b00149',
'aqua green': '#12e193',
'salmon pink': '#fe7b7c',
'tangerine': '#ff9408',
'brownish green': '#6a6e09',
'red brown': '#8b2e16',
'greenish brown': '#696112',
'pumpkin': '#e17701',
'pine green': '#0a481e',
'charcoal': '#343837',
'baby pink': '#ffb7ce',
'cornflower': '#6a79f7',
'blue violet': '#5d06e9',
'chocolate': '#3d1c02',
'greyish green': '#82a67d',
'scarlet': '#be0119',
'green yellow': '#c9ff27',
'dark olive': '#373e02',
'sienna': '#a9561e',
'pastel purple': '#caa0ff',
'terracotta': '#ca6641',
'aqua blue': '#02d8e9',
'sage green': '#88b378',
'blood red': '#980002',
'deep pink': '#cb0162',
'grass': '#5cac2d',
'moss': '#769958',
'pastel blue': '#a2bffe',
'bluish green': '#10a674',
'green blue': '#06b48b',
'dark tan': '#af884a',
'greenish blue': '#0b8b87',
'pale orange': '#ffa756',
'vomit': '#a2a415',
'forrest green': '#154406',
'dark lavender': '#856798',
'dark violet': '#34013f',
'purple blue': '#632de9',
'dark cyan': '#0a888a',
'olive drab': '#6f7632',
'pinkish': '#d46a7e',
'cobalt': '#1e488f',
'neon purple': '#bc13fe',
'light turquoise': '#7ef4cc',
'apple green': '#76cd26',
'dull green': '#74a662',
'wine': '#80013f',
'powder blue': '#b1d1fc',
'off white': '#ffffe4',
'electric blue': '#0652ff',
'dark turquoise': '#045c5a',
'blue purple': '#5729ce',
'azure': '#069af3',
'bright red': '#ff000d',
'pinkish red': '#f10c45',
'cornflower blue': '#5170d7',
'light olive': '#acbf69',
'grape': '#6c3461',
'greyish blue': '#5e819d',
'purplish blue': '#601ef9',
'yellowish green': '#b0dd16',
'greenish yellow': '#cdfd02',
'medium blue': '#2c6fbb',
'dusty rose': '#c0737a',
'light violet': '#d6b4fc',
'midnight blue': '#020035',
'bluish purple': '#703be7',
'red orange': '#fd3c06',
'dark magenta': '#960056',
'greenish': '#40a368',
'ocean blue': '#03719c',
'coral': '#fc5a50',
'cream': '#ffffc2',
'reddish brown': '#7f2b0a',
'burnt sienna': '#b04e0f',
'brick': '#a03623',
'sage': '#87ae73',
'grey green': '#789b73',
'white': '#ffffff',
"robin's egg blue": '#98eff9',
'moss green': '#658b38',
'steel blue': '#5a7d9a',
'eggplant': '#380835',
'light yellow': '#fffe7a',
'leaf green': '#5ca904',
'light grey': '#d8dcd6',
'puke': '#a5a502',
'pinkish purple': '#d648d7',
'sea blue': '#047495',
'pale purple': '#b790d4',
'slate blue': '#5b7c99',
'blue grey': '#607c8e',
'hunter green': '#0b4008',
'fuchsia': '#ed0dd9',
'crimson': '#8c000f',
'pale yellow': '#ffff84',
'ochre': '#bf9005',
'mustard yellow': '#d2bd0a',
'light red': '#ff474c',
'cerulean': '#0485d1',
'pale pink': '#ffcfdc',
'deep blue': '#040273',
'rust': '#a83c09',
'light teal': '#90e4c1',
'slate': '#516572',
'goldenrod': '#fac205',
'dark yellow': '#d5b60a',
'dark grey': '#363737',
'army green': '#4b5d16',
'grey blue': '#6b8ba4',
'seafoam': '#80f9ad',
'puce': '#a57e52',
'spring green': '#a9f971',
'dark orange': '#c65102',
'sand': '#e2ca76',
'pastel green': '#b0ff9d',
'mint': '#9ffeb0',
'light orange': '#fdaa48',
'bright pink': '#fe01b1',
'chartreuse': '#c1f80a',
'deep purple': '#36013f',
'dark brown': '#341c02',
'taupe': '#b9a281',
'pea green': '#8eab12',
'puke green': '#9aae07',
'kelly green': '#02ab2e',
'seafoam green': '#7af9ab',
'blue green': '#137e6d',
'khaki': '#aaa662',
'burgundy': '#610023',
'dark teal': '#014d4e',
'brick red': '#8f1402',
'royal purple': '#4b006e',
'plum': '#580f41',
'mint green': '#8fff9f',
'gold': '#dbb40c',
'baby blue': '#a2cffe',
'yellow green': '#c0fb2d',
'bright purple': '#be03fd',
'dark red': '#840000',
'pale blue': '#d0fefe',
'grass green': '#3f9b0b',
'navy': '#01153e',
'aquamarine': '#04d8b2',
'burnt orange': '#c04e01',
'neon green': '#0cff0c',
'bright blue': '#0165fc',
'rose': '#cf6275',
'light pink': '#ffd1df',
'mustard': '#ceb301',
'indigo': '#380282',
'lime': '#aaff32',
'sea green': '#53fca1',
'periwinkle': '#8e82fe',
'dark pink': '#cb416b',
'olive green': '#677a04',
'peach': '#ffb07c',
'pale green': '#c7fdb5',
'light brown': '#ad8150',
'hot pink': '#ff028d',
'black': '#000000',
'lilac': '#cea2fd',
'navy blue': '#001146',
'royal blue': '#0504aa',
'beige': '#e6daa6',
'salmon': '#ff796c',
'olive': '#6e750e',
'maroon': '#650021',
'bright green': '#01ff07',
'dark purple': '#35063e',
'mauve': '#ae7181',
'forest green': '#06470c',
'aqua': '#13eac9',
'cyan': '#00ffff',
'tan': '#d1b26f',
'dark blue': '#00035b',
'lavender': '#c79fef',
'turquoise': '#06c2ac',
'dark green': '#033500',
'violet': '#9a0eea',
'light purple': '#bf77f6',
'lime green': '#89fe05',
'grey': '#929591',
'sky blue': '#75bbfd',
'yellow': '#ffff14',
'magenta': '#c20078',
'light green': '#96f97b',
'orange': '#f97306',
'teal': '#029386',
'light blue': '#95d0fc',
'red': '#e50000',
'brown': '#653700',
'pink': '#ff81c0',
'blue': '#0343df',
'green': '#15b01a',
'purple': '#7e1e9c'}
COLOR_Spaces = [
TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS
]
def strtuple2color(str_val:str):
try:
values = str_val.strip()[1:-1].split(',')
if len(values)!=3:
return None
r = int(values[0])
g = int(values[1])
b = int(values[2])
return (r,g,b)
except:
return None
def hex2color(hex_val:str):
try:
if len(hex_val)==7:
r = int('0x'+hex_val[1:3], base=16)
g = int('0x'+hex_val[3:5], base=16)
b = int('0x'+hex_val[5:7], base=16)
elif len(hex_val)==4:
r = int('0x'+hex_val[1:2]+hex_val[1:2], base=16)
g = int('0x'+hex_val[2:3]+hex_val[2:3], base=16)
b = int('0x'+hex_val[3:4]+hex_val[3:4], base=16)
return (r,g,b)
except:
return None
def str2rgb(color_string:str):
for color_space in COLOR_Spaces:
try:
return hex2color(color_space[color_string])
except:
pass
return None
def get_color(color_string:str):
color = hex2color(color_string)
if color is None:
color = str2rgb(color_string)
if color is None:
return strtuple2color(color_string)
return color | PypiClean |
/MAnorm-1.3.0.tar.gz/MAnorm-1.3.0/manorm/region/parsers.py | import logging
from manorm.exceptions import FileFormatError
logger = logging.getLogger(__name__)
def is_track_header(line):
"""Returns if the line is a header line used in genome tracks/browers."""
line = line.strip()
if line.startswith('#') or line.startswith('track') or line.startswith(
'browser'):
return True
else:
return False
def is_comment_header(line):
"""Returns if the line is a comment header line."""
line = line.strip()
if line.startswith('#'):
return True
else:
return False
def is_macs_header(line):
"""Returns if the line is a header line used in MACS/MACS2 xls."""
line = line.strip()
if line.startswith('#') or line.split('\t')[0] == 'chr':
return True
else:
return False
class RegionParser:
"""Base class for region file parsers."""
def __init__(self, format):
self.format = format
@staticmethod
def _is_header(line):
"""Abstract method to check if a line is a header line."""
raise NotImplementedError
@staticmethod
def _parse_line(line):
"""Abstract method to parse a line."""
raise NotImplementedError
def parse(self, path):
"""Read genomic regions from the given file."""
with open(path, 'r') as fin:
line_num = 0
expect_header = True
for line in fin:
line_num += 1
line = line.strip()
if not line: # skip empty lines
continue
if expect_header:
if self._is_header(line):
logger.debug(
f"Detected header at line {line_num}: {line!r}")
continue
else:
expect_header = False
try:
yield self._parse_line(line)
except (IndexError, ValueError, TypeError):
raise FileFormatError(format=self.format,
line_num=line_num, line=line)
class BedRegionParser(RegionParser):
"""Region parser for the BED format."""
def __init__(self):
super().__init__('BED')
@staticmethod
def _is_header(line):
return is_track_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
summit = None
return chrom, start, end, summit
class Bed3SummitRegionParser(RegionParser):
"""Region parser for the BED3-summit format."""
def __init__(self):
super().__init__(format='BED3-summit')
@staticmethod
def _is_header(line):
return is_comment_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
summit = int(fields[3])
return chrom, start, end, summit
class MacsRegionParser(RegionParser):
"""Region parser for the MACS-xls format."""
def __init__(self):
super().__init__(format='MACS-xls')
@staticmethod
def _is_header(line):
return is_macs_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1]) - 1 # coordinates are 1-based in MACS xls
end = int(fields[2])
summit = int(fields[4]) + start # relative summit pos for MACS1
return chrom, start, end, summit
class Macs2RegionParser(RegionParser):
"""Region parser for the MACS2-xls format."""
def __init__(self):
super().__init__(format='MACS2-xls')
@staticmethod
def _is_header(line):
return is_macs_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1]) - 1 # coordinates are 1-based in MACS2 xls
end = int(fields[2])
summit = int(fields[4]) - 1 # absolute summit pos for MACS2
return chrom, start, end, summit
class NarrowPeakRegionParser(RegionParser):
"""Region parser for the NarrowPeak format."""
def __init__(self):
super().__init__(format='NarrowPeak')
@staticmethod
def _is_header(line):
return is_track_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
# https://genome.ucsc.edu/FAQ/FAQformat.html#format12
summit = int(fields[9])
if summit == -1:
summit = None
else:
summit = start + summit
return chrom, start, end, summit
class BroadPeakRegionParser(RegionParser):
"""Region parser for the BroadPeak format."""
def __init__(self):
super().__init__(format='BroadPeak')
@staticmethod
def _is_header(line):
return is_track_header(line)
@staticmethod
def _parse_line(line):
fields = line.strip().split('\t')
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
# https://genome.ucsc.edu/FAQ/FAQformat.html#format13
summit = None
return chrom, start, end, summit
def get_region_parser(format):
"""Get proper region parser for the given format.
Parameters
----------
format : str
File format (case-insensitive).
Returns
-------
Corresponding region parser.
"""
format = format.lower()
if format == 'bed':
return BedRegionParser
elif format == 'bed3-summit':
return Bed3SummitRegionParser
elif format == 'macs':
return MacsRegionParser
elif format == 'macs2':
return Macs2RegionParser
elif format == 'narrowpeak':
return NarrowPeakRegionParser
elif format == 'broadpeak':
return BroadPeakRegionParser
else:
raise ValueError(f"unknown region file format: {format!r}") | PypiClean |
/DLTA-AI-1.1.tar.gz/DLTA-AI-1.1/DLTA_AI_app/mmdetection/mmdet/core/utils/misc.py | from functools import partial
import numpy as np
import torch
from six.moves import map, zip
from ..mask.structures import BitmapMasks, PolygonMasks
def multi_apply(func, *args, **kwargs):
"""Apply function to a list of arguments.
Note:
This function applies the ``func`` to multiple inputs and
map the multiple outputs of the ``func`` into different
list. Each list contains the same type of outputs corresponding
to different inputs.
Args:
func (Function): A function that will be applied to a list of
arguments
Returns:
tuple(list): A tuple containing multiple list, each list contains \
a kind of returned results by the function
"""
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
return tuple(map(list, zip(*map_results)))
def unmap(data, count, inds, fill=0):
"""Unmap a subset of item (data) back to the original set of items (of size
count)"""
if data.dim() == 1:
ret = data.new_full((count, ), fill)
ret[inds.type(torch.bool)] = data
else:
new_size = (count, ) + data.size()[1:]
ret = data.new_full(new_size, fill)
ret[inds.type(torch.bool), :] = data
return ret
def mask2ndarray(mask):
"""Convert Mask to ndarray..
Args:
mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
torch.Tensor or np.ndarray): The mask to be converted.
Returns:
np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
"""
if isinstance(mask, (BitmapMasks, PolygonMasks)):
mask = mask.to_ndarray()
elif isinstance(mask, torch.Tensor):
mask = mask.detach().cpu().numpy()
elif not isinstance(mask, np.ndarray):
raise TypeError(f'Unsupported {type(mask)} data type')
return mask
def flip_tensor(src_tensor, flip_direction):
"""flip tensor base on flip_direction.
Args:
src_tensor (Tensor): input feature map, shape (B, C, H, W).
flip_direction (str): The flipping direction. Options are
'horizontal', 'vertical', 'diagonal'.
Returns:
out_tensor (Tensor): Flipped tensor.
"""
assert src_tensor.ndim == 4
valid_directions = ['horizontal', 'vertical', 'diagonal']
assert flip_direction in valid_directions
if flip_direction == 'horizontal':
out_tensor = torch.flip(src_tensor, [3])
elif flip_direction == 'vertical':
out_tensor = torch.flip(src_tensor, [2])
else:
out_tensor = torch.flip(src_tensor, [2, 3])
return out_tensor
def select_single_mlvl(mlvl_tensors, batch_id, detach=True):
"""Extract a multi-scale single image tensor from a multi-scale batch
tensor based on batch index.
Note: The default value of detach is True, because the proposal gradient
needs to be detached during the training of the two-stage model. E.g
Cascade Mask R-CNN.
Args:
mlvl_tensors (list[Tensor]): Batch tensor for all scale levels,
each is a 4D-tensor.
batch_id (int): Batch index.
detach (bool): Whether detach gradient. Default True.
Returns:
list[Tensor]: Multi-scale single image tensor.
"""
assert isinstance(mlvl_tensors, (list, tuple))
num_levels = len(mlvl_tensors)
if detach:
mlvl_tensor_list = [
mlvl_tensors[i][batch_id].detach() for i in range(num_levels)
]
else:
mlvl_tensor_list = [
mlvl_tensors[i][batch_id] for i in range(num_levels)
]
return mlvl_tensor_list
def filter_scores_and_topk(scores, score_thr, topk, results=None):
"""Filter results using score threshold and topk candidates.
Args:
scores (Tensor): The scores, shape (num_bboxes, K).
score_thr (float): The score filter threshold.
topk (int): The number of topk candidates.
results (dict or list or Tensor, Optional): The results to
which the filtering rule is to be applied. The shape
of each item is (num_bboxes, N).
Returns:
tuple: Filtered results
- scores (Tensor): The scores after being filtered, \
shape (num_bboxes_filtered, ).
- labels (Tensor): The class labels, shape \
(num_bboxes_filtered, ).
- anchor_idxs (Tensor): The anchor indexes, shape \
(num_bboxes_filtered, ).
- filtered_results (dict or list or Tensor, Optional): \
The filtered results. The shape of each item is \
(num_bboxes_filtered, N).
"""
valid_mask = scores > score_thr
scores = scores[valid_mask]
valid_idxs = torch.nonzero(valid_mask)
num_topk = min(topk, valid_idxs.size(0))
# torch.sort is actually faster than .topk (at least on GPUs)
scores, idxs = scores.sort(descending=True)
scores = scores[:num_topk]
topk_idxs = valid_idxs[idxs[:num_topk]]
keep_idxs, labels = topk_idxs.unbind(dim=1)
filtered_results = None
if results is not None:
if isinstance(results, dict):
filtered_results = {k: v[keep_idxs] for k, v in results.items()}
elif isinstance(results, list):
filtered_results = [result[keep_idxs] for result in results]
elif isinstance(results, torch.Tensor):
filtered_results = results[keep_idxs]
else:
raise NotImplementedError(f'Only supports dict or list or Tensor, '
f'but get {type(results)}.')
return scores, labels, keep_idxs, filtered_results
def center_of_mass(mask, esp=1e-6):
"""Calculate the centroid coordinates of the mask.
Args:
mask (Tensor): The mask to be calculated, shape (h, w).
esp (float): Avoid dividing by zero. Default: 1e-6.
Returns:
tuple[Tensor]: the coordinates of the center point of the mask.
- center_h (Tensor): the center point of the height.
- center_w (Tensor): the center point of the width.
"""
h, w = mask.shape
grid_h = torch.arange(h, device=mask.device)[:, None]
grid_w = torch.arange(w, device=mask.device)
normalizer = mask.sum().float().clamp(min=esp)
center_h = (mask * grid_h).sum() / normalizer
center_w = (mask * grid_w).sum() / normalizer
return center_h, center_w
def generate_coordinate(featmap_sizes, device='cuda'):
"""Generate the coordinate.
Args:
featmap_sizes (tuple): The feature to be calculated,
of shape (N, C, W, H).
device (str): The device where the feature will be put on.
Returns:
coord_feat (Tensor): The coordinate feature, of shape (N, 2, W, H).
"""
x_range = torch.linspace(-1, 1, featmap_sizes[-1], device=device)
y_range = torch.linspace(-1, 1, featmap_sizes[-2], device=device)
y, x = torch.meshgrid(y_range, x_range)
y = y.expand([featmap_sizes[0], 1, -1, -1])
x = x.expand([featmap_sizes[0], 1, -1, -1])
coord_feat = torch.cat([x, y], 1)
return coord_feat | PypiClean |
/logic/text_prompts.py | mainMessage = """
------------Expense tracker---------------
0. Help
1. Add string
2. Find month
3. Display all
4. Display grouped data
5. Currencies
6. Local convertor
7. Clear data
8. Quit
------------------------------------------
"""
helpMessage = """
----Description for all commands here:----
0. Help
1. Add a new value for finance array
2. Find all items based on month in array
3. Display all items in array
4. Display grouped data by months
5. List of all available currencies \n at Forex exchange at current moment
6. Local currency convertor
7. Clear all data from array
8. Quit app
9. Credits
------------------------------------------
"""
creditsMessage = """
------------------------------------------
Expense tracker python CLI app made by
Vladimir Kobranov
https://github.com/VladimirKobranov
------------------------------------------
Release Date: 2023-07-10
Version Number: 1.4.0
Release Notes:
--added conversion at Forex Exchange Market
--added local currency convertor
--added input checks
--added program name title
--added delete string functionality
--overhaul, fixes
--packed to pip
------------------------------------------
"""
availableCurrencies = """
---------------------------------------------------------
Available currencies:
EUR - Euro Member Countries CHF - Switzerland Franc
IDR - Indonesia Rupiah KRW - Korea (South) Won
BGN - Bulgaria Lev CNY - China Yuan Renminbi
ILS - Israel Shekel TRY - Turkey Lira
GBP - United Kingdom Pound HRK - Croatia Kuna
DKK - Denmark Krone NZD - New Zealand Dollar
CAD - Canada Dollar THB - Thailand Baht
JPY - Japan Yen USD - United States Dollar
HUF - Hungary Forint NOK - Norway Krone
RON - Romania New Leu RUB - Russia Ruble --not available
MYR - Malaysia Ringgit INR - India Rupee
SEK - Sweden Krona MXN - Mexico Peso
SGD - Singapore Dollar CZK - Czech Republic Koruna
HKD - Hong Kong Dollar BRL - Brazil Real
AUD - Australia Dollar PLN - Poland Zloty
PHP - Philippines Peso ZAR - South Africa Rand
---------------------------------------------------------
"""
currency_list = ['EUR', 'IDR', 'BGN', 'ILS', 'GBP', 'DKK', 'CAD', 'JPY', 'HUF', 'RON',
'MYR', 'SEK', 'SGD', 'HKD', 'AUD', 'CHF', 'KRW', 'CNY', 'TRY', 'HRK',
'NZD', 'THB', 'USD', 'NOK', 'RUB', 'INR', 'MXN', 'CZK', 'BRL', 'PLN', 'PHP', 'ZAR'] | PypiClean |
/Imaginary-0.0.5.tar.gz/Imaginary-0.0.5/imaginary/iimaginary.py |
from zope.interface import Interface, Attribute
class ITelnetService(Interface):
"""
Really lame tag interface used by the Mantissa offering system to uniquely
identify a powerup that runs a telnet server.
"""
class ISSHService(Interface):
"""
Really lame tag interface used by the Mantissa offering system to uniquely
identify a powerup that runs an ssh server.
"""
class IThingType(Interface):
"""
Plugin interface for kinds of objects which can be created in the realm.
"""
type = Attribute("Name of this type of object.")
def getType():
"""
Return a two-argument callable which will be invoked with C{name},
C{description} to create a new instance of this type. Should return an
L{IThing} provider.
"""
class ILinkContributor(Interface):
"""
A powerup interface which can add more connections between objects in the
world graph.
All ILinkContributors which are powered up on a particular Thing will be
given a chance to add to the L{IThing.link} method's return value.
"""
def links():
"""
Return a C{dict} mapping names of connections to C{IThings}.
"""
class IDescriptionContributor(Interface):
"""
A powerup interface which can add text to the description of an object.
All IDescriptionContributors which are powered up on a particular Object
will be given a chance to add to the output of its C{conceptualize} method.
"""
def conceptualize():
"""
Return an IConcept provider.
"""
class IThing(Interface):
"""
A thing in the world. It has a location and and might be relocateable.
"""
location = Attribute("An IThing which contains this IThing")
def moveTo(where, arrivalEventFactory=None):
"""
Change this things location to the new location, if possible.
@type where: L{IThing} provider.
@param where: The new location to be moved to.
@type arrivalEventFactory: A callable which takes a single
argument, the thing being moved, and returns an event.
@param arrivalEventFactory: Will be called to produce the
event to be broadcast to the new location upon arrival of this
thing. If not specified (or None), no event will be broadcast.
"""
def findProviders(interface, distance):
"""
Retrieve all game objects which provide C{interface} within C{distance}.
@return: A generator of providers of C{interface}.
"""
def proxiedThing(thing, interface, distance):
"""
Given an L{IThing} provider, return a provider of L{interface} as it is
accessible from C{self}. Any necessary proxies will be applied.
"""
def knownAs(name):
"""
Return a boolean indicating whether this thing might reasonably be
called C{name}.
@type name: C{unicode}
"""
class IActor(Interface):
hitpoints = Attribute("L{Points} instance representing hit points")
experience = Attribute("C{int} representing experience")
level = Attribute("C{int} representing player's level")
def send(event):
"""Describe something to the actor.
@type event: L{IConcept} provider
@param event: Something that will be described to the actor.
"""
def getIntelligence():
"""
Return the current intelligence associated with this actor, be it
ephemeral or enduring.
@rtype: L{IEventObserver}.
"""
def setEphemeralIntelligence(intelligence):
"""
Set the intelligence for this actor to an ephemeral L{IEventObserver}.
@type intelligence: L{IEventObserver} provider.
"""
def setEnduringIntelligence(intelligence):
"""
Set the intelligence for this actor to a persistent L{IEventObserver}.
@type intelligence: L{IEventObserver} provider.
"""
class IManipulator(Interface):
"""
An L{IManipulator} provider is an actor who can perform direct
manipulations of a world's environment (or at least, try to).
"""
def setIllumination(candelas):
"""
Attempt to set the ambient illumination this L{IManipulator}'s
location.
@param candelas: the desired ambient illumination value of the location
in candelas.
@type candelas: L{int}
@return: The previous ambient light level (in candelas)
@raise imaginary.eimaginary.ActionFailure: if the action cannot be
completed (for example, if this L{IManipulator} doesn't have
permission to change the lighting in its location).
"""
class IEventObserver(Interface):
def prepare(concept):
"""
Capture the given concept in a callable which will describe something
to this observer.
The callable will be invoked when it is entirely certain that the
concept is congruent with game reality. For example, a concept for an
arrow striking its target might be prepared but the resulting callable
will not be invoked until the combat game system decides the arrow
really is striking its target.
This two-phase process is also used to deal with events occurring
during transactions. While the event will be prepared immediately
during the execution of an action, the callable resulting from the
preparation will not be invoked until the transaction has completed.
If the transaction fails with an exception, then the callables will not
be invoked.
@type concept: L{IConcept} provider
@param concept: Something that will be described to the actor.
@return: a 0-argument callable which will deliver the given concept to
this observer.
"""
class ITransactionalEventBroadcaster(Interface):
"""
A thing which mediates the deadly side-effects of event broadcast by
holding things back until a transaction has been successfully committed or
is being reverted.
"""
def addEvent(event):
"""
Add an event which will be broadcast when the transaction is committed
successfully.
"""
def addRevertEvent(event):
"""
Add an event which will be broadcast when the transaction is reverted.
"""
class IContainer(Interface):
"""
An object which can contain other objects.
"""
capacity = Attribute("""
The maximum weight this container is capable of holding.
""")
# lid = Attribute("""
# A reference to an L{IThing} which serves as this containers lid, or
# C{None} if there is no lid.
# """)
closed = Attribute("""
A boolean indicating whether this container is closed.
""")
def add(object):
"""
Place the given object into this container.
@type object: L{IThing}
@param object: The world object to be added to this container. Its
C{location} attribute will be updated if it is successfully added.
@raise DoesntFit: If there is no room for C{object} in this container.
@raise Closed: If this container is not currently open.
"""
def remove(object):
"""
Remove the given object from this container.
@type object: L{IThing}
@param object: The world object which is currently in this container
which is to be removed. If it is successfully removed, its C{location}
attribute will be set to C{None}.
@raise ValueError: If C{object} is not within this container.
@raise Closed: If this container is not currently open.
"""
def contains(other):
"""
@returns: True if other is in me. And by 'in', I mean 'IN'! (And
by 'IN' he means to any arbitrarily deeply nested distance)
"""
def getContents():
"""
@returns: An iterable of the direct contents of this container.
"""
def getExits():
"""
@return: an L{axiom.store.ItemQuery} of the exits leading out of this
container.
"""
def getExitNames():
"""
@return: an L{axiom.store.AttributeQuery} of the names of the exits
leading out of this container.
"""
def getExitNamed(name, default=None):
"""
@return: The L{imaginary.objects.Exit} with the given name, or default
if none is found.
@raise KeyError: When an exit with the given name is not found and no
default was passed.
"""
class IConcept(Interface):
"""
This represents a concept which can be expressed in English.
"""
def plaintext(observer):
"""
@param observer: the IThing provider who is asking to learn about this
concept, or None. This comes from the argument to 'express'.
"""
def capitalizeConcept():
"""
Make this concept CAPITALISERD!
Oh man this is retarded.
XXX fix it or something pleeeeeeeaaaaaaaaaasssssssssseeeeeeeeee
deletedeletedletedletledeltetledleltellxceltedlelt
"""
class IProxy(Interface):
"""
| > look
| [ Nuclear Reactor Core ]
| High-energy particles are wizzing around here at a fantastic rate. You can
| feel the molecules in your body splitting apart as neutrons bombard the
| nuclei of their constituent atoms. In a few moments you will be dead.
| There is a radiation suit on the floor.
| > take radiation suit
| You take the radiation suit.
| Your internal organs hurt a lot.
| > wear radiation suit
| You wear the radiation suit.
| You start to feel better.
That is to say, a factory for objects which take the place of elements in
the result of L{IThing.findProviders} for the purpose of altering their
behavior in some manner due to a particular property of the path in the
game object graph through which the original element would have been found.
Another example to consider is that of a pair of sunglasses worn by a
player: these might power up that player for IProxy so as to be able to
proxy IVisible in such a way as to reduce glaring light.
"""
# XXX: Perhaps add 'distance' here, so Fog can be implemented as an
# IVisibility proxy which reduces the distance a observer can see.
def proxy(iface, facet):
"""
Proxy C{facet} which provides C{iface}.
@param facet: A candidate for inclusion in the set of objects returned
by findProviders.
@return: Either a provider of C{iface} or C{None}. If C{None} is
returned, then the object will not be returned from findProviders.
"""
class ILocationProxy(Interface):
"""
Similar to L{IProxy}, except the pathway between the observer and the
target is not considered: instead, all targets are wrapped by all
ILocationProxy providers on their location.
"""
def proxy(iface, facet):
"""
Proxy C{facet} which provides C{iface}.
@param facet: A candidate B{contained by the location on which this is
a powerup} for inclusion in the set of objects returned by
findProviders.
@return: Either a provider of C{iface} or C{None}. If C{None} is
returned, then the object will not be returned from findProviders.
"""
class IVisible(Interface):
"""
A thing which can be seen.
"""
def visualize():
"""
Return an IConcept which represents the visible aspects of this
visible thing.
"""
class ILightSource(Interface):
"""
Powerup interface for things which emit photons in measurable quantities.
"""
candelas = Attribute("""
The luminous intensity in candelas.
See U{http://en.wikipedia.org/wiki/Candela}.
""")
####### Below here is new, experimental stuff, which doesn't really work yet.
class IThingPowerUp(Interface):
"""
Utility super-interface of all interfaces which are designed to be used as
arguments to powerUp for Thing.
Objects which provide this interface must also provide IItem, obviously, as
only Items can be Powerups.
"""
class IClothingWearer(IThingPowerUp):
"""
A person who can wear clothing.
"""
def putOn(garment):
"""
Put on an article of clothing.
@param garment: An article of clothing.
@type garment: L{IClothing} provider
@raise: L{TooBulky}, if the new article of clothing will not fit
because this wearer is already wearing a bulkier article of clothing in
that slot.
"""
def takeOff(garment):
"""
Remove an article of clothing.
@param garment: An article of clothing that this wearer is wearing.
@raise: L{InaccessibleGarment}: if the article of clothing is either
not being worn, or is being worn beneath another article of clothing
which must be removed first.
"""
class IClothing(IThingPowerUp):
"""
A piece of clothing which can be worn by an L{IClothingWearer}.
"""
garmentSlots = Attribute(
"""
A list of unicode strings that describe the parts of the body where
this article of clothing can be worn, taken from the list of constants
in L{imaginary.garments.GARMENT_SLOTS}.
""")
bulk = Attribute(
"""
An integer, 1 or greater, abstractly describing how thick this garment
is. A bulkier garment cannot be worn over a less bulky one.
""")
class IDescriptor(IThingPowerUp):
"""
I provide a portion of a Thing's description.
Install IDescribable powerUps on Thing to influence how it will be shown to
the user.
"""
def conceptualize():
"""
Return an object adaptable to the IConcept for the language of an
observer.
""" | PypiClean |
/GenIce-1.0.11.tar.gz/GenIce-1.0.11/genice/lattices/Struct77.py | pairs="""
183 8
28 152
92 195
107 48
18 197
9 63
133 150
167 194
69 169
52 25
57 212
61 26
216 82
209 97
171 176
20 18
59 199
140 119
113 40
47 103
226 73
202 209
27 131
42 4
66 82
122 124
76 104
76 105
130 209
14 86
97 54
104 60
77 106
191 199
15 92
204 31
165 67
126 223
128 213
44 217
157 206
225 41
168 103
110 179
111 177
1 180
26 210
113 58
28 222
19 52
135 217
42 141
77 62
224 170
32 0
87 189
204 55
12 21
102 150
22 13
146 35
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13 23
112 199
39 147
32 226
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22 165
58 200
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93 2
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121 174
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225 222
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215 203
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162 223
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1 55
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20 181
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18 142
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211 207
101 37
28 97
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19 135
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161 156
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43 65
38 220
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117 179
63 51
37 62
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37 173
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110 166
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27 168
106 26
32 11
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213 211
207 16
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159 55
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158 56
15 198
130 221
66 94
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24 182
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73 168
59 145
0 145
118 199
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207 159
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131 47
153 216
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52 212
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203 116
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122 92
86 224
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114 196
41 97
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134 144
216 4
75 179
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113 156
5 28
223 69
30 193
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109 156
94 87
165 173
46 162
20 71
117 36
211 69
197 210
29 11
172 167
151 53
21 45
55 152
127 222
80 169
149 205
122 227
29 74
36 47
5 56
15 159
162 217
34 99
10 83
150 67
100 54
76 175
96 123
44 80
140 206
110 218
190 86
154 7
8 10
164 155
31 176
57 218
40 144
38 141
187 65
74 136
61 56
14 82
12 34
178 54
3 34
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129 6
13 83
153 72
105 17
207 195
161 42
60 210
93 132
128 195
160 189
125 61
76 206
7 182
31 202
121 65
201 147
75 220
147 206
202 152
66 25
57 135
103 2
168 187
128 180
167 186
214 115
191 89
186 147
153 176
83 173
151 227
137 56
75 85
213 214
101 194
177 185
3 174
118 50
95 65
1 127
34 134
148 189
196 91
70 54
79 47
120 216
170 78
161 82
98 146
23 159
93 91
158 152
123 62
204 46
203 81
138 184
158 227
5 70
63 210
111 85
148 4
68 35
95 146
19 115
166 80
224 221
0 33
171 155
29 203
182 191
220 200
140 51
143 219
111 200
139 6
64 205
145 2
33 132
10 221
138 94
138 95
160 174
108 136
84 108
148 72
90 36
87 78
192 50
14 213
96 219
110 58
186 119
184 3
205 40
171 127
45 192
102 41
32 157
13 224
84 125
96 8
93 177
105 73
139 163
226 175
185 68
24 186
6 30
111 166
64 99
120 170
22 128
1 164
215 48
182 219
133 22
37 137
66 109
18 0
36 118
29 175
139 215
223 16
194 81
201 64
70 9
24 183
149 58
122 222
49 27
149 117
137 198
43 103
189 46
87 31
153 94
49 105
77 71
132 90
70 108
79 68
163 108
160 35
95 72
204 16
179 79
176 225
178 102
142 136
143 137
49 33
45 39
7 112
133 14
162 35
113 141
53 102
68 85
109 188
39 116
96 198
187 144
78 4
23 190
24 116
151 225
60 17
59 17
8 67
52 42
"""
waters="""
0.9501 0.5 0.92024
0.4501 0.5 0.07976
0.02495 0.681 0.03988
0.13126 0.19392 0.75655
0.33852 0.5 0.65205
0.62996 0.5 0.96762
0.75 0.125 0.66898
0.86875 0.19392 0.24345
0.6748 0.0 0.40575
0.71245 0.319 0.84394
0.63121 0.19392 0.452
0.86868 0.5 0.72017
0.02846 0.375 0.6212
0.52846 0.375 0.3788
0.44219 0.875 0.47171
0.56876 0.125 0.1568
0.40981 0.0 0.05176
0.90981 0.0 0.94824
0.87004 0.5 0.96762
0.35474 0.319 0.34456
0.8547 0.681 0.0355
0.02846 0.625 0.6212
0.52846 0.625 0.3788
0.4846 0.319 0.28578
0.82521 0.0 0.40575
0.36879 0.19392 0.548
0.75 0.125 0.02279
0.06876 0.125 0.84321
0.5499 0.5 0.92024
0.85474 0.681 0.65544
0.75 0.19392 0.54447
0.43125 0.125 0.84321
0.94875 0.5 0.76139
0.97505 0.319 0.96012
0.10988 0.31892 0.65165
0.25 0.125 0.97721
0.039 0.0 0.16681
0.70506 0.5 0.22068
0.18281 0.31892 0.46803
0.94219 0.875 0.5283
0.13121 0.80608 0.548
0.51541 0.681 0.71422
0.29358 0.5 0.52677
0.14531 0.681 0.96451
0.3251 0.5 0.1571
0.0 0.75 0.5
0.3547 0.319 0.96451
0.09019 0.0 0.05176
0.68281 0.68108 0.53198
0.00351 0.194 0.83132
0.01541 0.681 0.28578
0.81116 0.194 0.73371
0.31719 0.31892 0.46803
0.5779 0.0 0.72859
0.63132 0.5 0.72017
0.47505 0.319 0.03988
0.64531 0.319 0.0355
0.25 0.125 0.33102
0.14526 0.681 0.34456
0.9172 0.806 0.02987
0.84731 0.0 0.9094
0.69024 0.194 0.95491
0.68286 0.68108 0.16203
0.78755 0.319 0.84394
0.05781 0.125 0.47171
0.18286 0.68108 0.83797
0.32521 0.0 0.59425
0.63121 0.80608 0.452
0.19024 0.194 0.04509
0.34871 0.0 0.2232
0.6749 0.5 0.8429
0.81715 0.68108 0.16203
0.31715 0.68108 0.83797
0.00351 0.806 0.83132
0.81116 0.806 0.73371
0.18885 0.194 0.26629
0.92211 0.0 0.72859
0.75 0.80608 0.14828
0.39012 0.31892 0.65165
0.15269 0.0 0.0906
0.28755 0.681 0.15606
0.79358 0.5 0.47323
0.36879 0.80608 0.548
0.60988 0.31892 0.34835
0.69024 0.806 0.95491
0.21245 0.319 0.15606
0.44219 0.125 0.47171
0.36875 0.19392 0.75655
0.9172 0.194 0.02987
0.89012 0.68108 0.34835
0.99649 0.194 0.16868
0.05126 0.5 0.23861
0.56876 0.875 0.1568
0.0499 0.5 0.07976
0.32381 0.0 0.71165
0.25 0.80608 0.85172
0.67619 0.0 0.28835
0.55126 0.5 0.76139
0.19024 0.806 0.04509
0.13121 0.19392 0.548
0.64526 0.319 0.65544
0.79494 0.5 0.22068
0.57186 0.806 0.65137
0.08281 0.806 0.97014
0.84871 0.0 0.7768
0.961 0.0 0.83319
0.75 0.875 0.02279
0.70642 0.5 0.47323
0.71245 0.681 0.84394
0.25 0.0 0.53591
0.18885 0.806 0.26629
0.1749 0.5 0.1571
0.93125 0.125 0.1568
0.18281 0.68108 0.46803
0.07186 0.194 0.34864
0.36868 0.5 0.27983
0.86879 0.80608 0.452
0.0779 0.0 0.27142
0.99649 0.806 0.16868
0.81719 0.31892 0.53198
0.47154 0.625 0.6212
0.20506 0.5 0.77932
0.58281 0.806 0.02987
0.63126 0.80608 0.24345
0.64531 0.681 0.0355
0.65269 0.0 0.9094
0.30976 0.806 0.04509
0.47505 0.681 0.03988
0.4846 0.681 0.28578
0.68885 0.194 0.73371
0.57186 0.194 0.65137
0.08281 0.194 0.97014
0.02495 0.319 0.03988
0.5 0.75 0.5
0.16148 0.5 0.65205
0.31116 0.194 0.26629
0.78755 0.681 0.84394
0.68286 0.31892 0.16203
0.25 0.0 0.7707
0.75 0.875 0.66898
0.85474 0.319 0.65544
0.20642 0.5 0.52677
0.8251 0.5 0.8429
0.75 0.19392 0.14828
0.10988 0.68108 0.65165
0.97505 0.681 0.96012
0.25 0.875 0.97721
0.94219 0.125 0.5283
0.29494 0.5 0.77932
0.07186 0.806 0.34864
0.55781 0.875 0.5283
0.539 0.0 0.83319
0.52495 0.319 0.96012
0.36875 0.80608 0.75655
0.81715 0.31892 0.16203
0.3547 0.681 0.96451
0.25 0.80608 0.45553
0.9846 0.319 0.71422
0.58281 0.194 0.02987
0.50351 0.194 0.16868
0.25 0.19392 0.85172
0.31719 0.68108 0.46803
0.30976 0.194 0.04509
0.68885 0.806 0.73371
0.37004 0.5 0.03238
0.60988 0.68108 0.34835
0.21245 0.681 0.15606
0.89012 0.31892 0.34835
0.06876 0.875 0.84321
0.31116 0.806 0.26629
0.47154 0.375 0.6212
0.4172 0.806 0.97014
0.97154 0.375 0.3788
0.66148 0.5 0.34795
0.18286 0.31892 0.83797
0.92814 0.806 0.65137
0.43125 0.875 0.84321
0.12996 0.5 0.03238
0.64526 0.681 0.65544
0.1513 0.0 0.2232
0.44875 0.5 0.23861
0.80976 0.806 0.95491
0.82381 0.0 0.28835
0.75 0.0 0.4641
0.17619 0.0 0.71165
0.14531 0.319 0.96451
0.86879 0.19392 0.452
0.13126 0.80608 0.75655
0.1748 0.0 0.59425
0.31715 0.31892 0.83797
0.42814 0.194 0.34864
0.86875 0.80608 0.24345
0.97154 0.625 0.3788
0.68281 0.31892 0.53198
0.83852 0.5 0.34795
0.50351 0.806 0.16868
0.01541 0.319 0.28578
0.8547 0.319 0.0355
0.63126 0.19392 0.24345
0.93125 0.875 0.1568
0.13132 0.5 0.27983
0.0 0.25 0.5
0.49649 0.194 0.83132
0.81719 0.68108 0.53198
0.4172 0.194 0.97014
0.05781 0.875 0.47171
0.92814 0.194 0.65137
0.461 0.0 0.16681
0.6513 0.0 0.7768
0.51541 0.319 0.71422
0.80976 0.194 0.95491
0.42211 0.0 0.27142
0.25 0.19392 0.45553
0.42814 0.806 0.34864
0.35474 0.681 0.34456
0.75 0.80608 0.54447
0.39012 0.68108 0.65165
0.28755 0.319 0.15606
0.25 0.875 0.33102
0.75 0.0 0.2293
0.14526 0.319 0.34456
0.55781 0.125 0.5283
0.52495 0.681 0.96012
0.34731 0.0 0.0906
0.5 0.25 0.5
0.49649 0.806 0.83132
0.9846 0.681 0.71422
0.59019 0.0 0.94824
"""
coord= "relative"
cages="""
15 -0.5436 0.0 -0.3482
16 0.07025 -0.5 -0.16699
14 -0.42433 -0.5 -0.4684
12 -0.4002 -0.27599 -0.15952
12 -0.66117 0.0 -0.11199
12 0.25 0.0 0.15534
12 -0.0998 -0.27599 -0.15952
15 -0.75 0.5 -0.02252
14 0.42433 -0.5 0.4684
12 0.4002 0.27599 0.15952
12 0.0 0.0 0.0
14 -0.25 -0.27567 -0.65339
12 0.0998 -0.27599 0.15952
15 0.75 0.5 0.02252
15 0.25 0.5 0.33189
14 0.25 0.27567 0.65339
12 -0.15558 0.0 -0.41842
12 0.15558 0.0 0.41842
12 -0.25 0.0 -0.15534
12 0.4002 -0.27599 0.15952
15 -0.0436 0.0 0.3482
12 0.0998 0.27599 0.15952
12 0.66117 0.0 0.11199
12 -0.16117 0.0 0.11199
14 -0.07567 0.5 -0.4684
12 -0.34442 0.0 -0.41842
12 -0.0998 0.27599 -0.15952
16 0.57025 0.5 0.16699
12 0.34442 0.0 0.41842
16 -0.07025 -0.5 0.16699
15 -0.25 0.5 -0.33189
14 -0.25 0.27567 -0.65339
12 -0.4002 0.27599 -0.15952
12 0.5 0.0 0.0
12 0.16117 0.0 -0.11199
14 0.07567 0.5 0.4684
16 -0.57025 0.5 -0.16699
15 0.0436 0.0 -0.3482
14 0.25 -0.27567 0.65339
15 0.5436 0.0 0.3482
"""
bondlen = 3
cell = """
41.49002717228765 14.6803698879515 24.33425022050341
"""
density = 0.45979888522587886
from genice.cell import cellvectors
cell = cellvectors(a=41.49002717228765,
b=14.6803698879515,
c=24.33425022050341) | PypiClean |
/NeodroidVision-0.3.0-py36-none-any.whl/neodroidvision/utilities/torch_utilities/transforms/interpolate.py |
__author__ = "heider"
__doc__ = r"""
Created on 5/5/22
"""
import math
import random
import warnings
from PIL import Image
from torchvision.transforms.functional import resized_crop
_pil_interpolation_to_str = {
Image.NEAREST: "PIL.Image.NEAREST",
Image.BILINEAR: "PIL.Image.BILINEAR",
Image.BICUBIC: "PIL.Image.BICUBIC",
Image.LANCZOS: "PIL.Image.LANCZOS",
Image.HAMMING: "PIL.Image.HAMMING",
Image.BOX: "PIL.Image.BOX",
}
def _pil_interp(method):
if method == "bicubic":
return Image.BICUBIC
elif method == "lanczos":
return Image.LANCZOS
elif method == "hamming":
return Image.HAMMING
else:
# default bilinear, do we want to allow nearest?
return Image.BILINEAR
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
class RandomResizedCropAndInterpolationWithTwoPic:
"""Crop the given PIL Image to random size and aspect ratio with random interpolation.
A crop of random size (default: of 0.08 to 1.0) of the original size and a random
aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.
Args:
size: expected output size of each edge
scale: range of size of the origin size cropped
ratio: range of aspect ratio of the origin aspect ratio cropped
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(
self,
size,
second_size=None,
scale=(0.08, 1.0),
ratio=(3.0 / 4.0, 4.0 / 3.0),
interpolation="bilinear",
second_interpolation="lanczos",
):
if isinstance(size, tuple):
self.size = size
else:
self.size = (size, size)
if second_size is not None:
if isinstance(second_size, tuple):
self.second_size = second_size
else:
self.second_size = (second_size, second_size)
else:
self.second_size = None
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("range should be of kind (min, max)")
if interpolation == "random":
self.interpolation = _RANDOM_INTERPOLATION
else:
self.interpolation = _pil_interp(interpolation)
self.second_interpolation = _pil_interp(second_interpolation)
self.scale = scale
self.ratio = ratio
@staticmethod
def get_params(img, scale, ratio):
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image): Image to be cropped.
scale (tuple): range of size of the origin size cropped
ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
area = img.size[0] * img.size[1]
for attempt in range(10):
target_area = random.uniform(*scale) * area
log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if w <= img.size[0] and h <= img.size[1]:
i = random.randint(0, img.size[1] - h)
j = random.randint(0, img.size[0] - w)
return i, j, h, w
# Fallback to central crop
in_ratio = img.size[0] / img.size[1]
if in_ratio < min(ratio):
w = img.size[0]
h = int(round(w / min(ratio)))
elif in_ratio > max(ratio):
h = img.size[1]
w = int(round(h * max(ratio)))
else: # whole image
w = img.size[0]
h = img.size[1]
i = (img.size[1] - h) // 2
j = (img.size[0] - w) // 2
return i, j, h, w
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped and resized.
Returns:
PIL Image: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
if isinstance(self.interpolation, (tuple, list)):
interpolation = random.choice(self.interpolation)
else:
interpolation = self.interpolation
if self.second_size is None:
return resized_crop(img, i, j, h, w, self.size, interpolation)
else:
return resized_crop(
img, i, j, h, w, self.size, interpolation
), resized_crop(
img, i, j, h, w, self.second_size, self.second_interpolation
)
def __repr__(self):
if isinstance(self.interpolation, (tuple, list)):
interpolate_str = " ".join(
[_pil_interpolation_to_str[x] for x in self.interpolation]
)
else:
interpolate_str = _pil_interpolation_to_str[self.interpolation]
format_string = self.__class__.__name__ + "(size={0}".format(self.size)
format_string += ", scale={0}".format(tuple(round(s, 4) for s in self.scale))
format_string += ", ratio={0}".format(tuple(round(r, 4) for r in self.ratio))
format_string += ", interpolation={0}".format(interpolate_str)
if self.second_size is not None:
format_string += ", second_size={0}".format(self.second_size)
format_string += ", second_interpolation={0}".format(
_pil_interpolation_to_str[self.second_interpolation]
)
format_string += ")"
return format_string | PypiClean |
/Bottlechest-0.7.1-cp34-cp34m-macosx_10_9_x86_64.whl/bottlechest/src/template/template.py | "Turn templates into Cython pyx files."
import os.path
def template(funcs, bits, header):
"Convert template dictionary `func` to a pyx file."
codes = []
for func in funcs: #supports multiple functions in a single file
codes.append("# %s bit version\n" % str(bits))
codes.append(func['main'])
select = Selector(func['name'])
for key in func['templates']:
f = func['templates'][key]
code = subtemplate(name=func['name'],
top=f['top'],
loop=f['loop'],
axisNone=f['axisNone'],
dtypes=f['dtypes'],
force_output_dtype=f['force_output_dtype'],
reuse_non_nan_func=f['reuse_non_nan_func'],
is_reducing_function=func['is_reducing_function'],
cdef_output=func['cdef_output'],
select=select,
bits=bits)
codes.append(code)
if 'sparse' in f:
code = sparsetemplate(name=func['name'],
template=f['sparse'],
dtypes=f['dtypes'],
select=select)
codes.append(code)
codes.append('\n' + str(select))
if 'slow' in func:
if func['slow'] is not None:
slow = func['slow']
code1 = slow_selector(slow['name'])
code2 = slow_functions(slow['name'],
slow['signature'],
slow['func'])
codes.append(code2)
codes.append(code1)
modpath = os.path.dirname(__file__)
fid = open(os.path.join(modpath, '..', func['pyx_file']) % str(bits), 'w')
fid.write(header)
fid.write(''.join(codes))
fid.close()
def sparsetemplate(name, template, dtypes, select):
funcs = []
for dtype in dtypes:
func = template + "\n\n"
func = func.replace('SPARSE', 'NAME_NDIMd_DTYPE_axisAXIS')
func = func.replace('NAME', name)
func = func.replace('DTYPE', dtype)
func = func.replace('NDIM', '0')
func = func.replace('AXIS', "None")
funcs.append(func)
select.append(0, dtype, None)
return ''.join(funcs)
def subtemplate(name, top, loop, axisNone, dtypes, force_output_dtype,
reuse_non_nan_func, is_reducing_function, cdef_output, select,
bits):
"Assemble template"
ndims = sorted(loop.keys())
funcs = []
for ndim in ndims:
if axisNone:
axes = [None]
else:
axes = list(range(ndim))
for dtype in dtypes:
for axis in axes:
if reuse_non_nan_func:
select.append(ndim, dtype, axis, True)
else:
# Code template
func = top
# loop
if force_output_dtype is not False:
ydtype = force_output_dtype
else:
ydtype = dtype
func += loop_cdef(ndim, ydtype, axis, is_reducing_function,
cdef_output)
func += looper(loop[ndim], ndim, axis)
# name, ndim, dtype, axis
func = func.replace('NAME', name)
func = func.replace('NDIM', str(ndim))
func = func.replace('DTYPE', dtype)
func = func.replace('AXIS', str(axis))
funcs.append(func)
select.append(ndim, dtype, axis)
return ''.join(funcs)
def looper(loop, ndim, axis):
"""
Given loop template, expand index markers for given `ndim` and `axis`.
Parameters
----------
loop : str
Code of loop where the following template markers will be expanded
(example given is for 3d input, similarly for other nd):
================= =================================================
INDEXALL Replace with i0, i1, i2
INDEXPOP If axis=1, e.g., replace with i0, i2
INDEXN If N=1, e.g., replace with 1
INDEXREPLACE|exp| If exp = 'k - window' and axis=1, e.g., replace
with i0, k - window, i2
NREPLACE|exp| If exp = 'n - window' and axis=1, e.g., replace
with n0, n - window, n2
================= =================================================
ndim : int
Number of dimensions in the loop.
axis : {int, None}
Axis over which the loop is evaluated.
Returns
-------
code : str
Code for the loop with templated index markers expanded.
Examples
--------
Make a 3d loop template:
>>> loop = '''
.... for iINDEX0 in range(nINDEX0):
.... for iINDEX1 in range(nINDEX1):
.... amin = MAXDTYPE
.... for iINDEX2 in range(nINDEX2):
.... ai = a[INDEXALL]
.... if ai <= amin:
.... amin = ai
.... y[INDEXPOP] = amin
.... '''
Import the looper function:
>>> from bottlechest.src.template.template import looper
Make a loop over axis=0:
>>> print(looper(loop, ndim=3, axis=0))
for i1 in range(n1):
for i2 in range(n2):
amin = MAXDTYPE
for i0 in range(n0):
ai = a[i0, i1, i2]
if ai <= amin:
amin = ai
y[i1, i2] = amin
Make a loop over axis=1:
>>> print(looper(loop, ndim=3, axis=1))
for i0 in range(n0):
for i2 in range(n2):
amin = MAXDTYPE
for i1 in range(n1):
ai = a[i0, i1, i2]
if ai <= amin:
amin = ai
y[i0, i2] = amin
Make a loop over axis=2:
>>> print(looper(loop, ndim=3, axis=2))
for i0 in range(n0):
for i1 in range(n1):
amin = MAXDTYPE
for i2 in range(n2):
ai = a[i0, i1, i2]
if ai <= amin:
amin = ai
y[i0, i1] = amin
"""
if ndim < 1:
raise ValueError("ndim(=%d) must be and integer greater than 0" % ndim)
if axis is not None:
if axis < 0:
raise ValueError("`axis` must be a non-negative integer or None")
elif axis >= ndim:
raise ValueError("`axis` must be less then `ndim`")
# INDEXALL
INDEXALL = ', '.join('i' + str(i) for i in range(ndim))
code = loop.replace('INDEXALL', INDEXALL)
# INDEXPOP
idx = list(range(ndim))
if axis is not None:
idx.pop(axis)
INDEXPOP = ', '.join(['i' + str(i) for i in idx])
code = code.replace('INDEXPOP', INDEXPOP)
# INDEXN
idx = list(range(ndim))
if axis is not None:
idxpop = idx.pop(axis)
idx.append(idxpop)
for i, j in enumerate(idx):
code = code.replace('INDEX%d' % i, '%d' % j)
# INDEXREPLACE|x|
mark = 'INDEXREPLACE|'
nreplace = code.count(mark)
if (nreplace > 0) and (axis is None):
raise ValueError("`INDEXREPLACE` cannot be used when axis is None.")
while mark in code:
idx0 = code.index(mark)
idx1 = idx0 + len(mark)
idx2 = idx1 + code[idx1:].index('|')
if (idx0 >= idx1) or (idx1 >= idx2):
raise RuntimeError("Parsing error or poorly formatted input.")
replacement = code[idx1:idx2]
idx = ['i' + str(i) for i in range(ndim)]
idx[axis] = replacement
idx = ', '.join(idx)
code = code[:idx0] + idx + code[idx2+1:]
# NREPLACE|x|
mark = 'NREPLACE|'
nreplace = code.count(mark)
# TODO: reuse while loop above, only difference is 'i' --> 'n'
while mark in code:
idx0 = code.index(mark)
idx1 = idx0 + len(mark)
idx2 = idx1 + code[idx1:].index('|')
if (idx0 >= idx1) or (idx1 >= idx2):
raise RuntimeError("Parsing error or poorly formatted input.")
replacement = code[idx1:idx2]
idx = ['n' + str(i) for i in range(ndim)]
idx[axis] = replacement
idx = ', '.join(idx)
code = code[:idx0] + idx + code[idx2+1:]
return code
def loop_cdef(ndim, dtype, axis, is_reducing_function, cdef_output=True):
"""
String of code that initializes variables needed in a for loop.
The output string contains code for: index array counters, one for each
dimension (cdef Py_size_t i0, i1, i2, ....); the length along each
dimension of the input array, `a` (cdef Py_ssize_t n0 = a.shape[0],...);
the initialized, empty output array, `y`.
Parameters
----------
ndim = int
Number of dimensions.
dtype : str
The data type of the output. Used for initilizing the empty output
array, `y`.
axis : {int, None}
If `is_reducing_function` is True then remove the dimension given
by `axis` when initializing the output array, `y`.
is_reducing_function : bool
If True then remove the dimension given by `axis` when initializing
the output array, `y`.
cdef_output : bool, optional
If False then only initialize indices (i) and shapes (n). If True
(default) then also intialized output array `y`.
Returns
-------
cdefs : str
String of code to use to initialize variables needed for loop.
Examples
--------
Define parameters:
>>> ndim = 3
>>> dtype = 'float64'
>>> axis = 1
>>> is_reducing_function = True
Import loop_cdef:
>>> from bottlechest.src.template.template import loop_cdef
Make loop initialization code:
>>> print(loop_cdef(ndim, dtype, axis, is_reducing_function))
cdef Py_ssize_t i0, i1, i2
cdef np.npy_intp *dim
dim = PyArray_DIMS(a)
Py_ssize_t n0 = dim[0]
Py_ssize_t n1 = dim[1]
Py_ssize_t n2 = dim[2]
cdef np.npy_intp *dims = [n0, n2]
cdef np.ndarray[np.float64_t, ndim=2] y = PyArray_EMPTY(2, dims,
NPY_float64, 0)
Repeat, but this time make the output non-reducing:
>>> is_reducing_function = False
>>> print(loop_cdef(ndim, dtype, axis, is_reducing_function))
cdef Py_ssize_t i0, i1, i2
cdef np.npy_intp *dim
dim = PyArray_DIMS(a)
Py_ssize_t n0 = dim[0]
Py_ssize_t n1 = dim[1]
Py_ssize_t n2 = dim[2]
cdef np.npy_intp *dims = [n0, n1, n2]
cdef np.ndarray[np.float64_t, ndim=3] y = PyArray_EMPTY(3, dims,
NPY_float64, 0)
"""
if ndim < 1:
raise ValueError("ndim(=%d) must be and integer greater than 0" % ndim)
if axis is not None:
if axis < 0:
raise ValueError("`axis` must be a non-negative integer or None")
elif axis >= ndim:
raise ValueError("`axis` must be less then `ndim`")
tab = ' '
cdefs = []
# cdef loop indices
idx = ', '.join('i'+str(i) for i in range(ndim))
cdefs.append(tab + 'cdef Py_ssize_t ' + idx)
# Length along each dimension
cdefs.append(tab + "cdef np.npy_intp *dim")
cdefs.append(tab + "dim = PyArray_DIMS(a)")
for dim in range(ndim):
cdefs.append(tab + "cdef Py_ssize_t n%d = dim[%d]" % (dim, dim))
if not cdef_output:
return '\n'.join(cdefs) + '\n'
# cdef initialize output
if is_reducing_function:
if (ndim > 1) and (axis is not None):
idx = list(range(ndim))
del idx[axis]
ns = ', '.join(['n'+str(i) for i in idx])
cdefs.append("%scdef np.npy_intp *dims = [%s]" % (tab, ns))
if dtype == 'bool':
y = "%scdef np.ndarray[np.uint8_t, ndim=%d, cast=True] "
y += "y = PyArray_EMPTY(%d, dims,"
y += "\n\t\tNPY_BOOL, 0)"
cdefs.append(y % (tab, ndim-1, ndim-1))
else:
y = "%scdef np.ndarray[np.%s_t, ndim=%d] "
y += "y = PyArray_EMPTY(%d, dims,"
y += "\n\t\tNPY_%s, 0)"
cdefs.append(y % (tab, dtype, ndim-1, ndim-1, dtype))
else:
idx = list(range(ndim))
ns = ', '.join('n'+str(i) for i in idx)
cdefs.append("%scdef np.npy_intp *dims = [%s]" % (tab, ns))
if dtype == 'bool':
y = "%scdef np.ndarray[np.uint8_t, ndim=%d, cast=True] "
y += "y = PyArray_EMPTY(%d, dims,"
y += "\n\t\tNPY_BOOL, 0)"
cdefs.append(y % (tab, ndim, ndim))
else:
y = "%scdef np.ndarray[np.%s_t, ndim=%d] "
y += "y = PyArray_EMPTY(%d, dims,"
y += "\n\t\tNPY_%s, 0)"
cdefs.append(y % (tab, dtype, ndim, ndim, dtype))
return '\n'.join(cdefs) + '\n'
class Selector(object):
"String of code for dictionary that maps dtype to cython function."
def __init__(self, name):
self.name = name
self.data = []
def append(self, ndim, dtype, axis, reuse=False):
self.data.append((ndim, dtype, axis, reuse))
def __str__(self):
fmt = "%s_dict[(%s, NPY_%s, %s)] = %s_%sd_%s_axis%s"
src = []
src.append("cdef dict %s_dict = {}" % self.name)
for ndim, dtype, axis, reuse in self.data:
name = self.name
if reuse:
name = name.replace('nan', '')
if (ndim == 1) and (axis is None):
tup = (self.name, str(ndim), str(dtype), str(0),
name, str(ndim), str(dtype), str(axis))
src.append(fmt % tup)
tup = (self.name, str(ndim), str(dtype), str(axis),
name, str(ndim), str(dtype), str(axis))
src.append(fmt % tup)
return '\n'.join(src)
def slow_selector(name, maxaxis=32):
"String of code for slow function mapping dictionary."
axes = list(range(maxaxis+1)) + [None]
src = ['\n']
src.append("cdef dict %s_slow_dict = {}" % name)
fmt = "%s_slow_dict[%s] = %s_slow_axis%s"
for axis in axes:
tup = 2 * (name, str(axis))
src.append(fmt % tup)
return '\n'.join(src)
def slow_functions(name, signature, func, maxaxis=32):
"String of code for slow functions."
axes = list(range(maxaxis+1)) + [None]
tab = ' '
sig = "def %s_slow_axis%s(%s):"
doc = '%s"Unaccelerated (slow) %s along axis %s."'
function = "%sreturn %s\n"
src = ['\n']
for axis in axes:
axis = str(axis)
# signature
code = sig % (name, axis, signature)
code = code.replace('AXIS', axis)
src.append(code)
# docstring
code = doc % (tab, name, axis)
code = code.replace('AXIS', axis)
src.append(code)
# function
code = function % (tab, func)
code = code.replace('AXIS', axis)
src.append(code)
return '\n'.join(src) | PypiClean |
/Biblioteca_RIT-3.0.0-py3-none-any.whl/BibliotecaRIT/Sources/controladoras/ControladoraExtracaoDados.py | from BibliotecaRIT.Sources.Requisicao import Requisicao
from BibliotecaRIT.Sources.entidades.Comentario import Comentario
from BibliotecaRIT.Sources.entidades.Projeto import Projeto
from BibliotecaRIT.Sources.entidades.Topico import Topico
from BibliotecaRIT.Sources.estrategias.extracao.FiltroExtracaoIssuesAbertasFechadas import FiltroExtracaoIssuesAbertasFechadas
from BibliotecaRIT.Sources.estrategias.extracao.FiltroExtracaoIssuesAbertas import FiltroExtracaoIssuesAbertas
from BibliotecaRIT.Sources.estrategias.extracao.FiltroExtracaoIssuesFechadas import FiltroExtracaoIssuesFechadas
class ControladoraExtracaoDados:
_requisicao = Requisicao
_filtroExtracao = FiltroExtracaoIssuesAbertas
@classmethod
def setFiltroExtracaoPorTipoIssue(cls,tipoIssue):
if tipoIssue == 2:
cls._filtroExtracao=FiltroExtracaoIssuesFechadas
elif tipoIssue == 3:
cls._filtroExtracao =FiltroExtracaoIssuesAbertasFechadas
@classmethod
def numeroPaginas(cls,usuario, repositorio):
url = cls._filtroExtracao.urlNumeroPaginas(usuario,repositorio)
response = cls._requisicao.request(url=url)
if response.links.keys():
return int(response.links['last']['url'].partition("&page=")[-1])
else:
return 1
@classmethod
def requisicaoIssuesPorPagina(cls,usuario, repositorio, numeroPagina) -> Projeto:
url = cls._filtroExtracao.urlRequisicaoIssuesPorPagina(usuario,repositorio,numeroPagina)
response = cls._requisicao.request(url=url).json()
topicos=[]
if response is not None:
dadosIssues = response
for j in range(len(dadosIssues)):
# Inicializando uma Issue
topico = Topico( dadosIssues[j]['created_at'], dadosIssues[j]['title'], dadosIssues[j]['body'], dadosIssues[j]['number'] ,dadosIssues[j]['html_url'],dadosIssues[j]['id'])
# Requisitando os Comentários de cada Issue
dadosComentarios = cls.__requisicaoComentariosPorIssue(usuario, repositorio, dadosIssues[j]['number'])
comentarios = []
for k in range(len(dadosComentarios)):
# Inserindo os Dados dos Comentários da Issue
comentario = Comentario(dadosComentarios[k]['id'], dadosComentarios[k]['user']['login'], dadosComentarios[k]['body'], dadosComentarios[k]['created_at'])
comentarios.append(comentario)
# Inserindo Dados da Issue
# Instanciando os Comentários na Issue, e Salvando a Issue na Lista
topico.inserirComentarios(comentarios)
topicos.append(topico)
return Projeto(usuario, repositorio, topicos)
@classmethod
def __requisicaoComentariosPorIssue(cls, usuario, repositorio, numeroIssue):
url = 'https://api.github.com/repos/'+usuario+'/'+repositorio+'/issues/'+str(numeroIssue)+'/comments'
response = cls._requisicao.request(url=url).json()
if response is not None:
return response | PypiClean |
/CombCov-0.6.5.tar.gz/CombCov-0.6.5/demo/mesh_tiling.py | import logging
from collections import deque, namedtuple
from itertools import chain, combinations, product
from combcov import CombCov, Rule
from permuta import Av, MeshPatt, Perm, PermSet
from permuta.misc import flatten, ordered_set_partitions
logger = logging.getLogger("MeshTiling")
class MockAvCoPatts:
def __init__(self, av_patts, co_patts):
self.base_perm_set = Av(av_patts)
if isinstance(co_patts, (Perm, MeshPatt)):
self.filter = lambda perm: perm.contains(co_patts)
elif all(isinstance(patt, (Perm, MeshPatt)) for patt in co_patts):
self.filter = lambda perm: any(perm.contains(patt)
for patt in co_patts)
else:
raise ValueError("Variable 'co_patts' not as expected: "
"'{}'".format(co_patts))
def of_length(self, size):
return filter(self.filter, self.base_perm_set.of_length(size))
class Cell(namedtuple('Cell', ['obstructions', 'requirements'])):
__slots__ = ()
def is_empty(self):
return any(obstruction == Perm((0,)) or (
isinstance(obstruction, MeshPatt) and
obstruction.pattern == Perm((0,)) and
obstruction.shading in Utils.equivalent_shadings
) for obstruction in self.obstructions
)
def is_point(self):
return self.obstructions == frozenset({Perm((0, 1)), Perm((1, 0))}) \
and self.requirements == frozenset({Perm((0,))})
def is_anything(self):
return self.obstructions == frozenset() \
and self.requirements == frozenset()
def is_avoiding(self):
return len(self.obstructions) > 0
def is_containing(self):
return len(self.requirements) > 0
def flip(self):
return Cell(self.requirements, self.obstructions)
def get_permclass(self):
if self.is_empty():
return Av(Perm((0,)))
elif self.is_point():
return PermSet(1)
elif self.is_anything():
return PermSet()
elif self.is_avoiding() and not self.is_containing():
return Av(self.obstructions)
else:
return MockAvCoPatts(self.obstructions, self.requirements)
def __repr__(self):
if self.is_empty():
return " "
elif self.is_point():
return "o"
elif self.is_anything():
return "S"
else:
Avs = ", ".join(repr(p) for p in Utils.sorted(self.obstructions))
Cos = ", ".join(repr(p) for p in Utils.sorted(self.requirements))
if self.is_avoiding() and not self.is_containing():
return "Av({})".format(Avs)
elif self.is_containing() and not self.is_avoiding():
return "Co({})".format(Cos)
else:
return "Av({}) and Co({})".format(Avs, Cos)
def __str__(self):
if self.is_empty() or self.is_point() or self.is_anything():
return repr(self)
else:
# String representation of mesh patts are a (2N + 1) x (2N + 1)
# matrix where N is the length of the underlying permutation
height = 1 + 2 * max(len(patt) for patt in chain(
self.obstructions, self.requirements))
Av_strings = [
Utils.pad_string_to_rectangle(
str(patt if isinstance(patt, MeshPatt) else
MeshPatt(patt, [])), 1 + 2 * len(patt), height
).split("\n") for patt in Utils.sorted(self.obstructions)
]
Co_strings = [
Utils.pad_string_to_rectangle(
str(patt if isinstance(patt, MeshPatt) else
MeshPatt(patt, [])), 1 + 2 * len(patt), height
).split("\n") for patt in Utils.sorted(self.requirements)
]
lines = ["" for _ in range(height)]
for row in range(height):
middle_row = (row == (height - 1) / 2)
if middle_row:
prefix, delim, postfix = "{}( ", " , ", " )"
else:
prefix, delim, postfix = " ", " ", " "
if self.is_avoiding():
lines[row] += prefix.format("Av") + delim.join(
av_str[row] for av_str in Av_strings) + postfix
if self.is_containing():
if self.is_avoiding():
lines[row] += " and " if middle_row else " "
lines[row] += prefix.format("Co") + delim.join(
co_str[row] for co_str in Co_strings) + postfix
return "\n".join(line for line in lines)
class MeshTiling(Rule):
empty_cell = Cell(frozenset({Perm((0,))}), frozenset())
point_cell = Cell(frozenset({Perm((0, 1)), Perm((1, 0))}),
frozenset({Perm((0,))}))
anything_cell = Cell(frozenset(), frozenset())
def __init__(self, cells={}):
# Sane and fast-running default values, overwrite as needed
self.MAX_COLUMN_DIMENSION = 2
self.MAX_ROW_DIMENSION = 2
self.MAX_ACTIVE_CELLS = 3
# Clean empty rows and columns and save cells with shifted coordinates
Xs = set(x for (x, y) in cells if not cells[(x, y)].is_empty())
Ys = set(y for (x, y) in cells if not cells[(x, y)].is_empty())
self.columns, self.rows = max(1, len(Xs)), max(1, len(Ys))
compression_dict = {
'col': {old: new for (new, old) in enumerate(sorted(Xs))},
'row': {old: new for (new, old) in enumerate(sorted(Ys))}
}
self.cells = {}
self.tiling = [self.empty_cell] * (self.columns * self.rows)
for (old_col, old_row), cell in cells.items():
if not cell.is_empty():
col = compression_dict['col'][old_col]
row = compression_dict['row'][old_row]
self.cells[(col, row)] = cell
self.tiling[
self.convert_coordinates_to_linear_number(col, row)] = cell
def convert_linear_number_to_coordinates(self, number):
# Linear number = (column, row)
# -----------------------------------
# | 3 = (0,1) | 4 = (1,1) | 5 = (2,1) |
# |-----------+-----------+-----------|
# | 0 = (0,0) | 1 = (1,0) | 2 = (2,0) |
# -----------------------------------
if number < 0 or number >= self.columns * self.rows:
raise IndexError
else:
col = number % self.columns
row = number // self.columns
return (col, row)
def get_obstructions_lists(self):
for cell in self.cells.values():
if not cell.is_empty() and not cell.is_point():
yield cell.obstructions
def get_requirements_lists(self):
for cell in self.cells.values():
if not (cell.is_empty() or cell.is_point() or cell.is_anything()):
yield cell.requirements
def convert_coordinates_to_linear_number(self, col, row):
if col < 0 or col >= self.columns or row < 0 or row >= self.rows:
raise IndexError
else:
return row * self.columns + col
def get_elmnts(self, of_size):
# Return permutations of length 'of_size' on a MeshTiling like this:
#
# ------------------------
# | | o | |
# |----------+---+---------|
# | Av(31#2) | | Av(1#2) |
# ------------------------
#
# The following code was shamelessly ported and adapted from
# PermutaTriangle/grids repo, grids/Tilings.py file
w = self.columns
h = self.rows
tiling = self.tiling
def permute(arr, perm):
res = [None] * len(arr)
for i in range(len(arr)):
res[i] = arr[perm[i]]
return res
def count_assignments(at, left):
if at == len(self):
# base case in recursion
if left == 0:
yield []
else:
if tiling[at].is_point():
# one point in cell
if left > 0:
for ass in count_assignments(at + 1, left - 1):
yield [1] + ass
elif tiling[at].is_empty():
# no point in cell
for ass in count_assignments(at + 1, left):
yield [0] + ass
else:
for cur in range(left + 1):
for ass in count_assignments(at + 1, left - cur):
yield [cur] + ass
elmnts_list = []
for count_ass in count_assignments(0, of_size):
cntz = [[0 for j in range(w)] for i in range(h)]
for i, k in enumerate(count_ass):
(col, row) = self.convert_linear_number_to_coordinates(i)
cntz[row][col] = k
rowcnt = [sum(cntz[ro][co] for co in range(w)) for ro in range(h)]
colcnt = [sum(cntz[ro][co] for ro in range(h)) for co in range(w)]
for colpart in product(*[
ordered_set_partitions(
range(colcnt[col]), [
cntz[row][col] for row in range(h)
]
) for col in range(w)]):
scolpart = [[sorted(colpart[i][j]) for j in range(h)] for i in
range(w)]
for rowpart in product(*[
ordered_set_partitions(range(rowcnt[row]),
[cntz[row][col] for col in
range(w)]) for row in range(h)]):
srowpart = [[sorted(rowpart[i][j]) for j in range(w)] for i
in range(h)]
for perm_ass in product(
*[s.get_permclass().of_length(cnt) for
cnt, s in zip(count_ass, tiling)]):
arr = [[[] for j in range(w)] for i in range(h)]
for i, perm in enumerate(perm_ass):
(col,
row) = self.convert_linear_number_to_coordinates(
i)
arr[row][col] = perm
res = [[None] * colcnt[col] for col in range(w)]
cumul = 0
for row in range(h):
for col in range(w):
for idx, val in zip(scolpart[col][row],
permute(srowpart[row][col],
arr[row][col])):
res[col][idx] = cumul + val
cumul += rowcnt[row]
elmnts_list.append(Perm(flatten(res)))
return elmnts_list
def get_subrules(self):
# Subrules are MeshTilings of sizes ranging from 1 x 1 to 3 x 3
# (adjustable with self.MAX_COLUMN_DIMENSION and self.MAX_ROW_DIMENSION
# variables). Each cell contains a mix of requirements (Perms) and
# obstructions (MeshPatts) where the obstructions are sub mesh patterns
# of any of the obstructions in the root object.
logger.info(
"About to generate subrules with up to {} active cells "
"and dimensions up to {}x{}".format(
self.MAX_ACTIVE_CELLS, self.MAX_COLUMN_DIMENSION,
self.MAX_ROW_DIMENSION
)
)
perms, mesh_patts = set(), set()
for obstructions_list in chain(
self.get_obstructions_lists(), self.get_requirements_lists()):
for obstruction in obstructions_list:
n = len(obstruction)
if isinstance(obstruction, Perm):
for l in range(n):
perms.update(
Perm.to_standard((obstruction[i] for i in indices))
for indices in combinations(range(n), l + 1)
)
elif isinstance(obstruction, MeshPatt):
for l in range(n):
for indices in combinations(range(n), l + 1):
mesh_patt = obstruction.sub_mesh_pattern(indices)
if len(mesh_patt.shading) > 0:
mesh_patts.add(mesh_patt)
else:
# A mesh patt without shading is just a perm
perms.add(mesh_patt.pattern)
else:
raise ValueError(
"[ERROR] obstruction '{}' is neither a MeshPatt "
"or Perm!".format(obstruction))
origin_cell = self.tiling[0]
cell_choices = {self.point_cell, self.anything_cell}
cell_choices.add(
origin_cell if origin_cell.is_avoiding() else origin_cell.flip()
)
for patt in Utils.clean_patts(perms, mesh_patts):
av_cell = Cell(frozenset({patt}), frozenset())
if not av_cell.is_empty():
cell_choices.add(av_cell)
logger.info("{} cell choices: {}".format(
len(cell_choices), cell_choices))
subrules = 1
yield MeshTiling() # always include the empty rule
for (dim_col, dim_row) in product(
range(1, self.columns + self.MAX_COLUMN_DIMENSION),
range(1, self.rows + self.MAX_ROW_DIMENSION)
):
nr_of_cells = dim_col * dim_row
for how_many_active_cells in range(min(dim_col, dim_row),
self.MAX_ACTIVE_CELLS + 1):
for active_cells in product(
cell_choices, repeat=how_many_active_cells):
for combination in combinations(
range(nr_of_cells), how_many_active_cells):
cells = {}
for i, cell_index in enumerate(combination):
c = cell_index % dim_col
r = cell_index // dim_col
cells[(c, r)] = active_cells[i]
subrules += 1
yield MeshTiling(cells)
logger.info("Generated in total {} subrules ".format(subrules))
def get_dimension(self):
return (self.columns, self.rows)
def _key(self):
return frozenset(self.cells.items()),
def __len__(self):
return self.columns * self.rows
def __repr__(self):
return "({}x{}) MeshTiling [{}]".format(
self.columns, self.rows,
", ".join(repr(cell) for cell in self.tiling))
def __str__(self):
unpadded_tiling_strings = [str(cell) for cell in self.tiling]
col_widths = [
max(max(len(line) for line in unpadded_tiling_strings[
self.convert_coordinates_to_linear_number(col, row)
].split("\n")) for row in range(self.rows)) + 2
for col in range(self.columns)
]
row_heights = [
max(len(unpadded_tiling_strings[
self.convert_coordinates_to_linear_number(col, row)
].split("\n")) for col in range(self.columns))
for row in range(self.rows)
]
padded_tiling_strings = []
for i in range(len(unpadded_tiling_strings)):
tiling_string = unpadded_tiling_strings[i]
col, row = self.convert_linear_number_to_coordinates(i)
width, height = col_widths[col], row_heights[row]
padded_tiling_strings.append(
Utils.pad_string_to_rectangle(tiling_string, width, height))
top_bottom_lines = " " + "-".join("-" * l for l in col_widths) + " \n"
middle_lines = "|" + "+".join("-" * l for l in col_widths) + "|\n"
cell_multilines = []
for row in reversed(range(self.rows)):
cell_lines = ""
for cell_row in range(row_heights[row]):
line = ""
for col in range(self.columns):
col_width = col_widths[col]
i = self.convert_coordinates_to_linear_number(col, row)
cell_strings = padded_tiling_strings[i].split("\n")
line += "|" + cell_strings[cell_row].center(col_width)
cell_lines += line + "|\n"
cell_multilines.append(cell_lines)
return "\n" + \
top_bottom_lines + \
middle_lines.join(line for line in cell_multilines) + \
top_bottom_lines
class Utils:
# See https://github.com/PermutaTriangle/CombCov/issues/28
equivalent_shadings = {
frozenset(),
frozenset([(0, 0)]),
frozenset([(0, 1)]),
frozenset([(1, 1)]),
frozenset([(1, 0)]),
frozenset([(0, 0), (0, 1)]),
frozenset([(1, 1), (0, 1)]),
frozenset([(1, 1), (1, 0)]),
frozenset([(0, 0), (1, 0)]),
}
@staticmethod
def clean_patts(perms, mesh_patts):
unique_patts = dict()
for patt in chain(sorted(perms), sorted(mesh_patts)):
perms_from_cell = set()
cell = Cell(frozenset({patt}), frozenset())
for length in range(min(7, 2 * len(patt) + 1)):
perms_from_cell.update(cell.get_permclass().of_length(length))
perms_from_cell = frozenset(perms_from_cell)
if perms_from_cell not in unique_patts:
unique_patts[perms_from_cell] = patt
return set(unique_patts.values())
@staticmethod
def pad_string_to_rectangle(string, width, height):
lines = string.split("\n")
if len(lines) > height or any(len(line) > width for line in lines):
height_needed = len(lines)
width_needed = max(len(line) for line in lines)
raise ValueError(
"Input string cannot be padded inside a WxH = {}x{} rectangle "
"and needs at least a {}x{} rectangle".format(
width, height, width_needed, height_needed
)
)
new_lines = deque()
for line in lines:
new_lines.append(line.center(width))
empty_line = " " * width
for padding in range(height - len(lines)):
if (padding % 2) == 1:
new_lines.append(empty_line)
else:
new_lines.appendleft(empty_line)
return "\n".join(line for line in new_lines)
@staticmethod
def sorted(mixed_patts):
perms = filter(lambda perm: isinstance(perm, Perm), mixed_patts)
mpatts = filter(lambda mpatt: isinstance(mpatt, MeshPatt), mixed_patts)
yield from chain(sorted(perms), sorted(mpatts))
def main():
logging.getLogger().setLevel(logging.INFO)
perm = Perm((2, 0, 1))
mesh_patt = MeshPatt(perm, ((2, 0), (2, 1), (2, 2), (2, 3)))
mesh_tiling = MeshTiling({
(0, 0): Cell(
obstructions=frozenset({mesh_patt}),
requirements=frozenset()
),
})
mesh_tiling.MAX_COLUMN_DIMENSION = 3
mesh_tiling.MAX_ROW_DIMENSION = 2
mesh_tiling.MAX_ACTIVE_CELLS = 3
max_elmnt_size = 5
comb_cov = CombCov(mesh_tiling, max_elmnt_size)
comb_cov.print_outcome()
if __name__ == "__main__":
main() | PypiClean |
/GxSphinx-1.0.0.tar.gz/GxSphinx-1.0.0/sphinx/environment/collectors/toctree.py | from typing import Any, Dict, List, Set, Tuple, TypeVar
from typing import cast
from docutils import nodes
from docutils.nodes import Element, Node
from sphinx import addnodes
from sphinx.application import Sphinx
from sphinx.environment import BuildEnvironment
from sphinx.environment.adapters.toctree import TocTree
from sphinx.environment.collectors import EnvironmentCollector
from sphinx.locale import __
from sphinx.transforms import SphinxContentsFilter
from sphinx.util import url_re, logging
if False:
# For type annotation
from typing import Type # for python3.5.1
N = TypeVar('N')
logger = logging.getLogger(__name__)
class TocTreeCollector(EnvironmentCollector):
def clear_doc(self, app: Sphinx, env: BuildEnvironment, docname: str) -> None:
env.tocs.pop(docname, None)
env.toc_secnumbers.pop(docname, None)
env.toc_fignumbers.pop(docname, None)
env.toc_num_entries.pop(docname, None)
env.toctree_includes.pop(docname, None)
env.glob_toctrees.discard(docname)
env.numbered_toctrees.discard(docname)
for subfn, fnset in list(env.files_to_rebuild.items()):
fnset.discard(docname)
if not fnset:
del env.files_to_rebuild[subfn]
def merge_other(self, app: Sphinx, env: BuildEnvironment, docnames: Set[str],
other: BuildEnvironment) -> None:
for docname in docnames:
env.tocs[docname] = other.tocs[docname]
env.toc_num_entries[docname] = other.toc_num_entries[docname]
if docname in other.toctree_includes:
env.toctree_includes[docname] = other.toctree_includes[docname]
if docname in other.glob_toctrees:
env.glob_toctrees.add(docname)
if docname in other.numbered_toctrees:
env.numbered_toctrees.add(docname)
for subfn, fnset in other.files_to_rebuild.items():
env.files_to_rebuild.setdefault(subfn, set()).update(fnset & set(docnames))
def process_doc(self, app: Sphinx, doctree: nodes.document) -> None:
"""Build a TOC from the doctree and store it in the inventory."""
docname = app.env.docname
numentries = [0] # nonlocal again...
def traverse_in_section(node: Element, cls: "Type[N]") -> List[N]:
"""Like traverse(), but stay within the same section."""
result = [] # type: List[N]
if isinstance(node, cls):
result.append(node)
for child in node.children:
if isinstance(child, nodes.section):
continue
elif isinstance(child, nodes.Element):
result.extend(traverse_in_section(child, cls))
return result
def build_toc(node: Element, depth: int = 1) -> nodes.bullet_list:
entries = [] # type: List[Element]
for sectionnode in node:
# find all toctree nodes in this section and add them
# to the toc (just copying the toctree node which is then
# resolved in self.get_and_resolve_doctree)
if isinstance(sectionnode, nodes.section):
title = sectionnode[0]
# copy the contents of the section title, but without references
# and unnecessary stuff
visitor = SphinxContentsFilter(doctree)
title.walkabout(visitor)
nodetext = visitor.get_entry_text()
if not numentries[0]:
# for the very first toc entry, don't add an anchor
# as it is the file's title anyway
anchorname = ''
else:
anchorname = '#' + sectionnode['ids'][0]
numentries[0] += 1
# make these nodes:
# list_item -> compact_paragraph -> reference
reference = nodes.reference(
'', '', internal=True, refuri=docname,
anchorname=anchorname, *nodetext)
para = addnodes.compact_paragraph('', '', reference)
item = nodes.list_item('', para) # type: Element
sub_item = build_toc(sectionnode, depth + 1)
if sub_item:
item += sub_item
entries.append(item)
elif isinstance(sectionnode, addnodes.only):
onlynode = addnodes.only(expr=sectionnode['expr'])
blist = build_toc(sectionnode, depth)
if blist:
onlynode += blist.children
entries.append(onlynode)
elif isinstance(sectionnode, nodes.Element):
for toctreenode in traverse_in_section(sectionnode,
addnodes.toctree):
item = toctreenode.copy()
entries.append(item)
# important: do the inventory stuff
TocTree(app.env).note(docname, toctreenode)
if entries:
return nodes.bullet_list('', *entries)
return None
toc = build_toc(doctree)
if toc:
app.env.tocs[docname] = toc
else:
app.env.tocs[docname] = nodes.bullet_list('')
app.env.toc_num_entries[docname] = numentries[0]
def get_updated_docs(self, app: Sphinx, env: BuildEnvironment) -> List[str]:
return self.assign_section_numbers(env) + self.assign_figure_numbers(env)
def assign_section_numbers(self, env: BuildEnvironment) -> List[str]:
"""Assign a section number to each heading under a numbered toctree."""
# a list of all docnames whose section numbers changed
rewrite_needed = []
assigned = set() # type: Set[str]
old_secnumbers = env.toc_secnumbers
env.toc_secnumbers = {}
def _walk_toc(node: Element, secnums: Dict, depth: int, titlenode: nodes.title = None) -> None: # NOQA
# titlenode is the title of the document, it will get assigned a
# secnumber too, so that it shows up in next/prev/parent rellinks
for subnode in node.children:
if isinstance(subnode, nodes.bullet_list):
numstack.append(0)
_walk_toc(subnode, secnums, depth - 1, titlenode)
numstack.pop()
titlenode = None
elif isinstance(subnode, nodes.list_item):
_walk_toc(subnode, secnums, depth, titlenode)
titlenode = None
elif isinstance(subnode, addnodes.only):
# at this stage we don't know yet which sections are going
# to be included; just include all of them, even if it leads
# to gaps in the numbering
_walk_toc(subnode, secnums, depth, titlenode)
titlenode = None
elif isinstance(subnode, addnodes.compact_paragraph):
numstack[-1] += 1
reference = cast(nodes.reference, subnode[0])
if depth > 0:
number = list(numstack)
secnums[reference['anchorname']] = tuple(numstack)
else:
number = None
secnums[reference['anchorname']] = None
reference['secnumber'] = number
if titlenode:
titlenode['secnumber'] = number
titlenode = None
elif isinstance(subnode, addnodes.toctree):
_walk_toctree(subnode, depth)
def _walk_toctree(toctreenode: addnodes.toctree, depth: int) -> None:
if depth == 0:
return
for (title, ref) in toctreenode['entries']:
if url_re.match(ref) or ref == 'self':
# don't mess with those
continue
elif ref in assigned:
logger.warning(__('%s is already assigned section numbers '
'(nested numbered toctree?)'), ref,
location=toctreenode, type='toc', subtype='secnum')
elif ref in env.tocs:
secnums = {} # type: Dict[str, Tuple[int, ...]]
env.toc_secnumbers[ref] = secnums
assigned.add(ref)
_walk_toc(env.tocs[ref], secnums, depth, env.titles.get(ref))
if secnums != old_secnumbers.get(ref):
rewrite_needed.append(ref)
for docname in env.numbered_toctrees:
assigned.add(docname)
doctree = env.get_doctree(docname)
for toctreenode in doctree.traverse(addnodes.toctree):
depth = toctreenode.get('numbered', 0)
if depth:
# every numbered toctree gets new numbering
numstack = [0]
_walk_toctree(toctreenode, depth)
return rewrite_needed
def assign_figure_numbers(self, env: BuildEnvironment) -> List[str]:
"""Assign a figure number to each figure under a numbered toctree."""
rewrite_needed = []
assigned = set() # type: Set[str]
old_fignumbers = env.toc_fignumbers
env.toc_fignumbers = {}
fignum_counter = {} # type: Dict[str, Dict[Tuple[int, ...], int]]
def get_figtype(node: Node) -> str:
for domain in env.domains.values():
figtype = domain.get_enumerable_node_type(node)
if figtype:
return figtype
return None
def get_section_number(docname: str, section: nodes.section) -> Tuple[int, ...]:
anchorname = '#' + section['ids'][0]
secnumbers = env.toc_secnumbers.get(docname, {})
if anchorname in secnumbers:
secnum = secnumbers.get(anchorname)
else:
secnum = secnumbers.get('')
return secnum or tuple()
def get_next_fignumber(figtype: str, secnum: Tuple[int, ...]) -> Tuple[int, ...]:
counter = fignum_counter.setdefault(figtype, {})
secnum = secnum[:env.config.numfig_secnum_depth]
counter[secnum] = counter.get(secnum, 0) + 1
return secnum + (counter[secnum],)
def register_fignumber(docname: str, secnum: Tuple[int, ...],
figtype: str, fignode: Element) -> None:
env.toc_fignumbers.setdefault(docname, {})
fignumbers = env.toc_fignumbers[docname].setdefault(figtype, {})
figure_id = fignode['ids'][0]
fignumbers[figure_id] = get_next_fignumber(figtype, secnum)
def _walk_doctree(docname: str, doctree: Element, secnum: Tuple[int, ...]) -> None:
for subnode in doctree.children:
if isinstance(subnode, nodes.section):
next_secnum = get_section_number(docname, subnode)
if next_secnum:
_walk_doctree(docname, subnode, next_secnum)
else:
_walk_doctree(docname, subnode, secnum)
elif isinstance(subnode, addnodes.toctree):
for title, subdocname in subnode['entries']:
if url_re.match(subdocname) or subdocname == 'self':
# don't mess with those
continue
_walk_doc(subdocname, secnum)
elif isinstance(subnode, nodes.Element):
figtype = get_figtype(subnode)
if figtype and subnode['ids']:
register_fignumber(docname, secnum, figtype, subnode)
_walk_doctree(docname, subnode, secnum)
def _walk_doc(docname: str, secnum: Tuple[int, ...]) -> None:
if docname not in assigned:
assigned.add(docname)
doctree = env.get_doctree(docname)
_walk_doctree(docname, doctree, secnum)
if env.config.numfig:
_walk_doc(env.config.master_doc, tuple())
for docname, fignums in env.toc_fignumbers.items():
if fignums != old_fignumbers.get(docname):
rewrite_needed.append(docname)
return rewrite_needed
def setup(app: Sphinx) -> Dict[str, Any]:
app.add_env_collector(TocTreeCollector)
return {
'version': 'builtin',
'parallel_read_safe': True,
'parallel_write_safe': True,
} | PypiClean |
/Dgram-1.0.0.tar.gz/Dgram-1.0.0/dgram/storage/redis_storage.py | from dgram.storage.base_storage import StateStorageBase, StateContext
import json
redis_installed = True
try:
from redis import Redis, ConnectionPool
except:
redis_installed = False
class StateRedisStorage(StateStorageBase):
"""
This class is for Redis storage.
This will work only for states.
To use it, just pass this class to:
dgram(storage=StateRedisStorage())
"""
def __init__(self, host='localhost', port=6379, db=0, password=None, prefix='dgram_'):
super().__init__()
self.redis = ConnectionPool(host=host, port=port, db=db, password=password)
#self.con = Redis(connection_pool=self.redis) -> use this when necessary
#
# {chat_id: {user_id: {'state': None, 'data': {}}, ...}, ...}
self.prefix = prefix
if not redis_installed:
raise Exception("Redis is not installed. Install it via 'pip install redis'")
def get_record(self, key):
"""
Function to get record from database.
It has nothing to do with states.
Made for backward compatibility
"""
connection = Redis(connection_pool=self.redis)
result = connection.get(self.prefix+str(key))
connection.close()
if result: return json.loads(result)
return
def set_record(self, key, value):
"""
Function to set record to database.
It has nothing to do with states.
Made for backward compatibility
"""
connection = Redis(connection_pool=self.redis)
connection.set(self.prefix+str(key), json.dumps(value))
connection.close()
return True
def delete_record(self, key):
"""
Function to delete record from database.
It has nothing to do with states.
Made for backward compatibility
"""
connection = Redis(connection_pool=self.redis)
connection.delete(self.prefix+str(key))
connection.close()
return True
def set_state(self, chat_id, user_id, state):
"""
Set state for a particular user in a chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if hasattr(state, 'name'):
state = state.name
if response:
if user_id in response:
response[user_id]['state'] = state
else:
response[user_id] = {'state': state, 'data': {}}
else:
response = {user_id: {'state': state, 'data': {}}}
self.set_record(chat_id, response)
return True
def delete_state(self, chat_id, user_id):
"""
Delete state for a particular user in a chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
del response[user_id]
if user_id == str(chat_id):
self.delete_record(chat_id)
return True
else: self.set_record(chat_id, response)
return True
return False
def get_value(self, chat_id, user_id, key):
"""
Get value for a data of a user in a chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
if key in response[user_id]['data']:
return response[user_id]['data'][key]
return None
def get_state(self, chat_id, user_id):
"""
Get state of a user in a chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
return response[user_id]['state']
return None
def get_data(self, chat_id, user_id):
"""
Get data of particular user in a particular chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
return response[user_id]['data']
return None
def reset_data(self, chat_id, user_id):
"""
Reset data of a user in a chat.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
response[user_id]['data'] = {}
self.set_record(chat_id, response)
return True
def set_data(self, chat_id, user_id, key, value):
"""
Set data without interactive data.
"""
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
response[user_id]['data'][key] = value
self.set_record(chat_id, response)
return True
return False
def get_interactive_data(self, chat_id, user_id):
"""
Get Data in interactive way.
You can use with() with this function.
"""
return StateContext(self, chat_id, user_id)
def save(self, chat_id, user_id, data):
response = self.get_record(chat_id)
user_id = str(user_id)
if response:
if user_id in response:
response[user_id]['data'] = dict(data, **response[user_id]['data'])
self.set_record(chat_id, response)
return True | PypiClean |
/Homevee_Dev-0.0.0.0-py3-none-any.whl/Homevee/CloudConnection_NEW.py | import json
import socket
import ssl
import time
import traceback
from _thread import start_new_thread
from urllib.parse import urlencode
from urllib.request import Request, urlopen
from Homevee.API import API
from Homevee.Helper import Logger, translations
from Homevee.Utils.Constants import END_OF_MESSAGE
from Homevee.Utils.Database import Database
from .Helper.helper_functions import remote_control_enabled
CLOUD_URL = "test-cloud.homevee.de"
#CLOUD_URL = "premium.cloud.homevee.de"
CLOUD_PORT = 7778
#Verbindung zu Cloud herstellen
#Auf Befehl warten, ausführen und Ergebnis zurück an Server senden
class CloudConnection():
def __init__(self):
self.cloud_url = CLOUD_URL
self.cloud_port = CLOUD_PORT
def start_connection_loop(self):
start_new_thread(self.connection_loop, ())
def connection_loop(self):
db = Database.get_database_con()
REMOTE_ID, ACCESS_TOKEN = self.get_remote_data(db)
if REMOTE_ID is None or ACCESS_TOKEN is None:
REMOTE_ID, ACCESS_TOKEN = self.register_to_cloud(db)
# Logger.log("NO CLOUD CONNECTION CREDENTIALS")
print(translations.translate('your_remote_id_is') + REMOTE_ID)
while (True):
self.do_connection()
def do_connection(self):
db = Database.get_database_con()
conn = None
if (not remote_control_enabled(db)):
# Logger.log("REMOTE CONTROL NOT ENABLED")
# time.sleep(5 * 60) # 5 Minuten warten
time.sleep(10)
return
cloud_to_use = self.get_cloud_to_use(db)
cloud_to_use = None
if cloud_to_use is not None:
#Logger.log("using cloud: " + cloud_to_use)
pass
else:
cloud_to_use = CLOUD_URL
#Logger.log("using default cloud: " + cloud_to_use)
pass
try:
conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# conn.settimeout(5*60)
# conn.connect((CLOUD_URL, CLOUD_PORT))
# Logger.log("Connected")
'''
conn = ssl.wrap_socket(conn,
ca_certs=SSL_FULLCHAIN,
cert_reqs=ssl.CERT_REQUIRED,
ssl_version=ssl.PROTOCOL_SSLv23)
'''
# SSL aktivieren
conn = ssl.wrap_socket(conn)
# Logger.log("Wrapping SSL")
# Logger.log(("Connecting to cloud ("+CLOUD_URL+")..."))
conn.connect((cloud_to_use, CLOUD_PORT))
start_new_thread(self.keep_connection_alive, (conn, 60))
# print ("SSL connected")
# Logger.log("Connection successful!")
REMOTE_ID, ACCESS_TOKEN = self.get_remote_data(db)
data_dict = {'access_token': ACCESS_TOKEN, 'remote_id': REMOTE_ID}
data = json.dumps(data_dict) + END_OF_MESSAGE
len_send = conn.send(bytes(data, 'utf-8'))
while 1:
try:
data = conn.recv(8192).decode('utf-8')
except:
data = None
if data is None or data == "":
db.close()
conn.close()
return
Logger.log("")
Logger.log(("Received from Cloud: " + data))
data_parts = data.split(END_OF_MESSAGE)
for data_part in data_parts:
if data_part is None or data_part == "":
continue
Logger.log("")
Logger.log(data_part)
data_dict = json.loads(data_part)
if 'result' in data_dict:
Logger.log(data_dict)
if (data_dict['result'] == "error"):
Logger.log("couldn't connect to cloud")
time.sleep(60) # 1 Minute warten
else:
#Logger.log("Processing data:")
#Logger.log(data_dict)
try:
client_id = data_dict['client_id']
data_dict = json.loads(data_dict['msg'])
msg = API().process_data(data_dict, db)
# start_time = time.time()
# compressed_message = compression.compress_string(msg)
# end_time = time.time()
# print "uncompressed: " + str(len(msg))
# print "compressed: " + str(len(compressed_message)) + ", time: " + str(end_time - start_time)
# compressed_message = compressed_message.decode('iso-8859-1').encode('utf8')
data = {}
data['msg'] = msg
data['client_id'] = client_id
data = json.dumps(data)
if data is None:
data = {'status': 'error'}
data['client_id'] = client_id
data = json.dumps(data)
# else:
# data['client_id'] = client_id
# data['msg'] = json.dumps(msg)
# data = json.dumps(data)
data_to_send = data + END_OF_MESSAGE
len_send = conn.send(data_to_send.encode('utf-8'))
Logger.log(("Sent to remote Server (" + data_dict['action'] + ")(client: " + str(
client_id) + "): " + data))
except Exception as e:
#if(Logger.IS_DEBUG):
# traceback.print_exc()
if 'status' in data_dict:
if data_dict['status'] == 'connectionok':
#Logger.log('connectionok')
pass
if data_dict['status'] == 'nocredentials':
# Credentials wrong
# deleting them from db
with db:
# Logger.log("wrongcredentials")
time.sleep(60) # 1 Minute warten
# cur = db.cursor()
# Database.delete("DELETE FROM SERVER_DATA WHERE KEY IN ('REMOTE_ID', 'REMOTE_ACCESS_TOKEN')")
except:
# do not print errors
if(Logger.IS_DEBUG):
traceback.print_exc()
time.sleep(5)
if conn is not None:
conn.close()
db.close()
def keep_connection_alive(self, conn, time):
while True:
try:
# Send some data every minute
time.sleep(time)
Logger.log("sending ping")
conn.send(1)
except:
break
# Returns a tuple: (REMOTE_ID, REMOTE_ACCESS_TOKEN)
def get_remote_data(self, db):
# Database.delete("DELETE FROM SERVER_DATA WHERE KEY IN ('REMOTE_ACCESS_TOKEN', 'REMOTE_ID')", {}, db)
remote_id = Database.get_server_data("REMOTE_ID", db)
remote_access_token = Database.get_server_data("REMOTE_ACCESS_TOKEN", db)
return (remote_id, remote_access_token)
def get_cloud_to_use(self, db):
try:
REMOTE_ID, ACCESS_TOKEN = self.get_remote_data(db)
url = 'https://cloud.homevee.de/server-api.php' # Set destination URL here
post_fields = {'action': 'getcloudtouse',
'remoteid': REMOTE_ID,
'accesstoken': ACCESS_TOKEN} # Set POST fields here
# Logger.log(url+"?action=getcloudtouse&remoteid="+REMOTE_ID+"&accesstoken="+ACCESS_TOKEN)
request = Request(url, urlencode(post_fields).encode())
response = urlopen(request).read().decode()
data = json.loads(response)
return data['cloud']
except:
return None
def register_to_cloud(self, db):
url = 'https://cloud.homevee.de/server-api.php' # Set destination URL here
post_fields = {'action': 'generateremoteid'} # Set POST fields here
request = Request(url, urlencode(post_fields).encode())
response = urlopen(request).read().decode()
data = json.loads(response)
remote_id = data['remote_id']
access_token = data['access_token']
# Logger.log("Deine Remote-ID lautet: "+remote_id)
# save remote id
Database.set_server_data("REMOTE_ID", remote_id, db)
Database.set_server_data("REMOTE_ACCESS_TOKEN", access_token, db)
return remote_id, access_token | PypiClean |
/Azure-Sentinel-Utilities-0.6.5.tar.gz/Azure-Sentinel-Utilities-0.6.5/SentinelWidgets/widget_view_helper.py | import os
import ipywidgets as widgets
from IPython.display import HTML
# pylint: disable-msg=R0904
# pylint: disable-msg=E0602
class WidgetViewHelper():
""" This classes provides helper methods for UI controls and components. """
def __init__(self):
self.variable = None
def set_env(self, env_dir, var):
""" Set notebook environmental variable """
val = None
if var in env_dir.keys():
val = env_dir[var]
self.variable = var
user_input = widgets.Text(value=val, description=var + ': ')
user_input.observe(self.save_env, 'value')
display(user_input)
def save_env(self, val):
""" Save notebook environmental variable """
os.environ[self.variable] = val.new
@staticmethod
def set_env1(reset, env_dir, env_dict):
""" Set notebook environmental variables """
for key in env_dict.keys():
user_input = ''
if not reset and key in env_dir:
env_dict[key] = env_dir[key]
print(key + '=' + env_dir[key])
else:
user_input = widgets.Text(description=key + ': ')
os.environ[key] = user_input.value
env_dict[key] = user_input.value
return env_dict
@staticmethod
def select_vm(compute):
""" Select a VM """
vm_names = sorted(list(vm.name for vm in list(compute.get_vm_list())))
return widgets.Dropdown(options=vm_names, value=vm_names[0], description='VM:')
@staticmethod
def select_managed_disk(compute, vm_name):
""" Select a managed disk """
disk_list = compute.get_vm_disk_names(vm_name)
return widgets.Dropdown(options=disk_list, value=disk_list[0], description='Disk:')
@staticmethod
def select_account_creation():
""" Create a new account or use existing account """
storage_account_creation = ['Creating new account', 'Using exist account']
return widgets.Dropdown(options=storage_account_creation,
value=storage_account_creation[0],
description='Storage Account Creation:')
@staticmethod
def select_blob_container_creation():
""" Create a new container or use existing container """
blob_container_creation = ['Creating new container', 'Using exist container']
return widgets.Dropdown(options=blob_container_creation,
value=blob_container_creation[0],
description='Blob Container Creation:')
@staticmethod
def select_os():
""" Select Windows or Linux """
os_type_list = ['Windows', 'Linux']
return widgets.Dropdown(options=os_type_list, value=os_type_list[0], description='OS Type:')
@staticmethod
def check_storage_account_name_availability(storage):
""" Check if a storage account name is available to use """
# usert input storage account name
storage_account_name = input('Storage Account Name:')
name_availability = storage.is_storage_account_name_available(storage_account_name)
return storage_account_name if name_availability.name_available else None
@staticmethod
def create_storage_account_and_get_key(storage,
storage_account_name,
resource_group_for_storage):
""" Create storage account """
storage_location = input('Storage Location:')
storage.create_storage_account_async(
storage_account_name,
resource_group_for_storage,
**{'storage_location' : storage_location})
return storage.get_storage_account_key(storage_account_name, resource_group_for_storage)
@staticmethod
def select_storage_account(storage, resource_group_for_storage):
""" Select a storage account """
storage_account_list = storage.get_storage_account_names(resource_group_for_storage)
return widgets.Dropdown(options=storage_account_list,
value=storage_account_list[0],
description='Existing Storage Accounts:')
@staticmethod
def select_blob_container(storage, resource_group_for_storage, storage_account_name):
""" Select a blob container """
blob_container_list = storage.get_container_name_list(resource_group_for_storage,
storage_account_name,
None)
return widgets.Dropdown(options=blob_container_list,
value=blob_container_list[0],
description='Blob Containers:')
@staticmethod
def select_log_analytics_workspace(loganalytics):
""" Select a LA workspace """
workspace_name_list = loganalytics.get_workspace_name_list()
return widgets.Dropdown(options=workspace_name_list,
value=workspace_name_list[0],
description='Workspace:')
@staticmethod
def select_multiple_tables(anomaly_lookup):
""" Select data tables """
table_list = anomaly_lookup.query_table_list()
tables = sorted(table_list.SentinelTableName.tolist())
return widgets.SelectMultiple(options=tables,
row=len(tables),
value=[],
description='Tables:')
@staticmethod
def generate_upload_container_path(storage, os_type, sas_expiration_in_days):
""" Generate a upload container path """
sas_url = storage.generate_blob_container_sas_url(sas_expiration_in_days)
upload_container_path = storage.build_upload_container_path(os_type, sas_url)
return upload_container_path
@staticmethod
# pylint: disable=line-too-long
def get_vm_extension_properties(os_type, upload_container_path, user_id=None):
""" Get VM extensions properties """
if os_type == 'Windows':
command_to_execute = 'powershell -File installNotebookExtension.ps1 "{0}" >> out.txt'.format(upload_container_path)
file_list = ['https://sentinelnotebooks.blob.core.windows.net/piwindowsstorage/installNotebookExtension.ps1',
'https://sentinelnotebooks.blob.core.windows.net/piwindowsstorage/piextension.zip']
elif os_type == 'Linux':
command_to_execute = './piondemand.sh "' + upload_container_path + '"'
file_list = ['https://sentinelnotebooks.blob.core.windows.net/pilinuxstorage/release/ondemand/stable/piondemand.sh',
'https://sentinelnotebooks.blob.core.windows.net/pilinuxstorage/release/ondemand/stable/pilinux.ondemand.tar.bz2']
elif os_type == 'DSVM':
command_to_execute = './azureforensics.sh {0}'.format(user_id)
file_list = ['https://sentinelnotebooks.blob.core.windows.net/forensicsnotebooks/azureforensics.sh',
'https://sentinelnotebooks.blob.core.windows.net/forensicsnotebooks/vhdexplorer.tar']
return command_to_execute, file_list
@staticmethod
def define_int_progress_bar():
""" Define a progress bar """
return widgets.IntProgress(value=0,
min=0,
max=10,
step=1,
description='Loading:',
bar_style='success',
orientation='horizontal',
position='top')
@staticmethod
# pylint: disable=line-too-long
def copy_to_clipboard(url, text_body, label_text):
""" Copy text to Clipboard """
html_str = (
"""<!DOCTYPE html>
<html><body style="height:20px">
<input id="sentinel_text_for_copy" type="text" readonly style="font-weight: bold; border: none; max-height:10px; width:1px;" size = '"""
+ str(len(text_body))
+ """' value='"""
+ text_body
+ """'>
<button style="border: 2px solid #4CAF50;" onclick="sentinel_copy()">""" + label_text + """</button>
<script>
function sentinel_copy() {
var copyText = document.getElementById("sentinel_text_for_copy");
copyText.select();
document.execCommand("copy");
}
</script>
</body></html>"""
)
return html_str
@staticmethod
# pylint: disable=line-too-long
def construct_url_for_log_analytics_logs(tenant_domain,
subscription_id,
resource_group,
workspace_name):
""" Generate URL for LA logs """
return 'https://portal.azure.com/#blade/Microsoft_Azure_Security_Insights/MainMenuBlade/7/subscriptionId/{0}/resourceGroup/{1}/workspaceName/{2}'.format(subscription_id, resource_group, workspace_name)
@staticmethod
# pylint: disable=undefined-variable
def display_html(inner_html):
""" Display HTML """
display(HTML(inner_html))
@staticmethod
def pick_start_and_end_date():
""" Pick dates """
start_date = widgets.DatePicker(description='Pick a start date', disabled=False)
end_date = widgets.DatePicker(description='Pick a end date', disabled=False)
# pylint: disable=undefined-variable
display(start_date)
# pylint: disable=undefined-variable
display(end_date)
return start_date, end_date
@staticmethod
# pylint: disable=line-too-long
# pylint: disable=undefined-variable
def select_multiple_items(label, item_name):
""" Select multiple items """
label_item = widgets.Label(value=label)
items = widgets.Textarea(value='', placeholder='One per line: \n 0x7ae3 \n 0x7ae6', description=item_name, disabled=False, rows=5)
display(label_item)
display(items)
return items | PypiClean |
/Netzob-2.0.0.tar.gz/Netzob-2.0.0/src/netzob/Export/WiresharkDissector/CodeBuffer.py |
#+---------------------------------------------------------------------------+
#| 01001110 01100101 01110100 01111010 01101111 01100010 |
#| |
#| Netzob : Inferring communication protocols |
#+---------------------------------------------------------------------------+
#| Copyright (C) 2012 AMOSSYS |
#| This program is free software: you can redistribute it and/or modify |
#| it under the terms of the GNU General Public License as published by |
#| the Free Software Foundation, either version 3 of the License, or |
#| (at your option) any later version. |
#| |
#| This program is distributed in the hope that it will be useful, |
#| but WITHOUT ANY WARRANTY; without even the implied warranty of |
#| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
#| GNU General Public License for more details. |
#| |
#| You should have received a copy of the GNU General Public License |
#| along with this program. If not, see <http://www.gnu.org/licenses/>. |
#+---------------------------------------------------------------------------+
#| @url : http://www.netzob.org |
#| @contact : contact@netzob.org |
#| @sponsors : Amossys, http://www.amossys.fr |
#| Supélec, http://www.rennes.supelec.fr/ren/rd/cidre/ |
#+---------------------------------------------------------------------------+
#+---------------------------------------------------------------------------+
#| Standard library imports |
#+---------------------------------------------------------------------------+
from io import StringIO
class CodeBuffer(StringIO):
"""
StringIO class with indentation capabilities
Used to write indent-based blocks of code (Python, LUA...)
Use like this:
> buffer = CodeBuffer()
> buffer << "rabbits eats"
> with buffer.new_block("carrots, but also"):
> buffer << "hay,"
> buffer << "vegetables,"
> buffer << "and grass."
> print buffer
< rabbits eats
< carrots, but also
< hay,
< vegetables,
< and grass.
"""
INDENT_SIZE = 2
def __init__(self, *args, **kwargs):
StringIO.__init__(self, *args, **kwargs)
self._stack = [self.new_block()]
def __lshift__(self, elm):
self._stack[-1].write(elm)
return self
def enter(self, elm):
self._stack.append(elm)
def exit(self):
self._stack.pop()
def write(self, s):
indent_s = ' ' * ((len(self._stack) - 1) * self.INDENT_SIZE) + s + '\n'
StringIO.write(self, indent_s)
def new_block(self, data=None):
cb = CodeBlock(self)
if data is not None:
self << data
return cb
class LUACodeBuffer(CodeBuffer):
def exit(self):
self << "end"
CodeBuffer.exit(self)
class CodeBlock(object):
def __init__(self, buf):
self._buf = buf
def __enter__(self):
return self._buf.enter(self)
def __exit__(self, exception, data, tb):
self._buf.exit()
def write(self, data):
self._buf.write(data) | PypiClean |
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