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ekmap_core/qgs_symbology_parser/categories_symbol_parser.py | eKMap/ekmap-publisher-for-qgis | 4 | 173321 | from .symbology_parser import SymbologyParser
class CategoriesSymbolParser(SymbologyParser):
def parse(self, renderer, exporter):
styles = []
selectedProperty = renderer.classAttribute()
# Find else conditions:
otherValues = self.__getDetailOtherValues(renderer.dump())
elseFilter = self.__buildElseFilter(selectedProperty, otherValues)
pointDict = {}
pointFallback = ''
for category in renderer.categories():
# Check active
if not category.renderState():
continue
symbol = category.symbol()
styleLayers = self._wrapSymbolLayer(symbol, exporter)
# Get filter
currentFilter = self.__getFilter(selectedProperty, category)
# for styleLayer in styleLayers:
# styleLayer['layout']['visibility'] = 'visible'
if symbol.type() != 0: # Is LINE or POLYGON
if currentFilter is None:
currentFilter = elseFilter
# Apply filter for layers
for styleLayer in styleLayers:
styleLayer['filter'] = currentFilter
styles.extend(styleLayers)
else: # POINT
key = self.__extractIconKey(styleLayers)
if currentFilter is None:
pointFallback = key
else:
pointDict[key] = currentFilter
pointStyle = self.__parsePoint(pointDict, pointFallback)
if pointStyle is not None:
styles.append(pointStyle)
return styles
def __getDetailOtherValues(self, dump):
categoryDumps = dump.split('\n')
otherValues = []
for categoryDump in categoryDumps:
lineSplit = categoryDump.split('::')
if len(lineSplit) > 1 and lineSplit[0] != '':
otherValues.append(lineSplit[0])
return otherValues
def __buildElseFilter(self, selectedProperty, otherValues):
return [
"!",
[
"in",
[
"get",
selectedProperty
],
[
"literal",
otherValues
]
]
]
def __parseFilter(self, selectedProperty, value):
return [
"==",
[
"get",
selectedProperty,
],
value
]
def __getFilter(self, selectedProperty, category):
dummyInfos = category.dump().split('::')
if dummyInfos[0] != '':
return self.__parseFilter(selectedProperty, category.value())
else:
return None
def __extractIconKey(self, styleLayers):
# In case Symbol Icon, there is only one styleLayer
if len(styleLayers) > 0:
styleLayer = styleLayers[0]
key = styleLayer['layout']['icon-image']
return key
else:
return None
def __parsePoint(self, pointDict, pointDefault):
if len(pointDict) == 0:
return None
else:
case = []
case.append('case')
for key in pointDict:
case.append(pointDict[key]) # condition
case.append(key) # image name
case.append(pointDefault)
return {
'type': 'symbol',
'layout': {
'icon-image': case
}
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demos/instance_occlsegm/examples/synthetic2d/legacy/train_mrcnn.py | pazeshun/jsk_apc | 0 | 148928 | <reponame>pazeshun/jsk_apc
#!/usr/bin/env python
from __future__ import division
import argparse
import datetime
import os
import os.path as osp
import random
import socket
os.environ['MPLBACKEND'] = 'Agg' # NOQA
# import cv2 # FIXME: cv2 should be imported first to avoid segfault.
import chainer
from chainer import training
from chainer.training import extensions
import fcn
import numpy as np
import chainer_mask_rcnn as mrcnn
import contrib
here = osp.dirname(osp.abspath(__file__))
class MaskRcnnDataset(chainer.dataset.DatasetMixin):
def __init__(self, dataset):
self._dataset = dataset
self.fg_class_names = dataset.class_names[1:]
def __len__(self):
return len(self._dataset)
def get_example(self, i):
img, bboxes, labels, lbls = self._dataset.get_example(i)
bboxes = bboxes.astype(np.float32, copy=False)
labels = labels.astype(np.int32, copy=False)
labels -= 1 # 0: background -> 0: object_0
masks = (lbls == 1).astype(np.int32, copy=False)
return img, bboxes, labels, masks
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('dataset', choices=['real', 'synthetic'],
help='The dataset.')
parser.add_argument('--model', '-m',
choices=['vgg16', 'resnet50', 'resnet101'],
default='resnet50', help='Base model of Mask R-CNN.')
parser.add_argument('--pooling-func', '-pf',
choices=['pooling', 'align', 'resize'],
default='align', help='Pooling function.')
parser.add_argument('--gpu', '-g', type=int, help='GPU id.')
parser.add_argument('--multi-node', '-mn', action='store_true',
help='use multi node')
parser.add_argument('--max-epoch', type=float,
help='Epoch (default: 12.17)')
args = parser.parse_args()
if args.multi_node:
import chainermn
comm = chainermn.create_communicator('hierarchical')
device = comm.intra_rank
args.n_node = comm.inter_size
args.n_gpu = comm.size
chainer.cuda.get_device_from_id(device).use()
else:
args.n_node = 1
args.n_gpu = 1
chainer.cuda.get_device_from_id(args.gpu).use()
device = args.gpu
args.seed = 0
now = datetime.datetime.now()
args.timestamp = now.isoformat()
args.out = osp.join(here, 'logs/train_mrcnn',
now.strftime('%Y%m%d_%H%M%S'))
# 0.00125 * 8 = 0.01 in original
args.batch_size = 1 * args.n_gpu
args.lr = 0.00125 * args.batch_size
args.weight_decay = 0.0001
if args.max_epoch is None:
# (180e3 * 8) / len(coco_trainval)
args.max_epoch = (180e3 * 8) / 118287
# lr / 10 at 120k iteration with
# 160k iteration * 16 batchsize in original
args.step_size = [(120e3 / 180e3) * args.max_epoch,
(160e3 / 180e3) * args.max_epoch]
random.seed(args.seed)
np.random.seed(args.seed)
# Default Config
min_size = 600
max_size = 1000
anchor_scales = [4, 8, 16, 32]
proposal_creator_params = dict(
n_train_pre_nms=12000,
n_train_post_nms=2000,
n_test_pre_nms=6000,
n_test_post_nms=1000,
min_size=0,
)
# if args.dataset == 'voc':
# train_data = mrcnn.datasets.SBDInstanceSeg('train')
# test_data = mrcnn.datasets.VOC2012InstanceSeg('val')
# elif args.dataset == 'coco':
# train_data = chainer.datasets.ConcatenatedDataset(
# mrcnn.datasets.CocoInstanceSeg('train'),
# mrcnn.datasets.CocoInstanceSeg('valminusminival'),
# )
# test_data = mrcnn.datasets.CocoInstanceSeg('minival')
# train_data.class_names = test_data.class_names
# min_size = 800
# max_size = 1333
# else:
# raise ValueError
# instance_class_names = train_data.class_names[1:]
# train_data = mrcnn.datasets.MaskRcnnDataset(train_data)
# test_data = mrcnn.datasets.MaskRcnnDataset(test_data)
if args.dataset == 'real':
train_data = contrib.datasets.ARC2017RealInstancesDataset(
'train', aug='standard')
elif args.dataset == 'synthetic':
train_data = contrib.datasets.ARC2017SyntheticInstancesDataset(
do_aug=True, aug_level='all')
else:
raise ValueError
test_data = contrib.datasets.ARC2017RealInstancesDataset('test')
instance_class_names = train_data.class_names[1:]
train_data = MaskRcnnDataset(train_data)
test_data = MaskRcnnDataset(test_data)
if args.pooling_func == 'align':
pooling_func = mrcnn.functions.roi_align_2d
elif args.pooling_func == 'pooling':
pooling_func = chainer.functions.roi_pooling_2d
elif args.pooling_func == 'resize':
pooling_func = mrcnn.functions.crop_and_resize
else:
raise ValueError
if args.model == 'vgg16':
mask_rcnn = mrcnn.models.MaskRCNNVGG16(
n_fg_class=len(instance_class_names),
pretrained_model='imagenet',
pooling_func=pooling_func,
anchor_scales=anchor_scales,
proposal_creator_params=proposal_creator_params,
min_size=min_size,
max_size=max_size)
elif args.model in ['resnet50', 'resnet101']:
n_layers = int(args.model.lstrip('resnet'))
mask_rcnn = mrcnn.models.MaskRCNNResNet(
n_layers=n_layers,
n_fg_class=len(instance_class_names),
pretrained_model='imagenet',
pooling_func=pooling_func,
anchor_scales=anchor_scales,
proposal_creator_params=proposal_creator_params,
min_size=min_size,
max_size=max_size)
else:
raise ValueError
mask_rcnn.use_preset('evaluate')
model = mrcnn.models.MaskRCNNTrainChain(
mask_rcnn,
proposal_target_creator=mrcnn.utils.ProposalTargetCreator(
n_sample=512),
)
if args.multi_node or args.gpu >= 0:
model.to_gpu()
optimizer = chainer.optimizers.MomentumSGD(lr=args.lr, momentum=0.9)
if args.multi_node:
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(rate=args.weight_decay))
if args.model in ['resnet50', 'resnet101']:
model.mask_rcnn.extractor.mode = 'res3+'
mask_rcnn.extractor.conv1.disable_update()
mask_rcnn.extractor.bn1.disable_update()
mask_rcnn.extractor.res2.disable_update()
train_data = chainer.datasets.TransformDataset(
train_data, mrcnn.datasets.MaskRCNNTransform(mask_rcnn))
test_data = chainer.datasets.TransformDataset(
test_data, mrcnn.datasets.MaskRCNNTransform(mask_rcnn, train=False))
if args.multi_node:
if comm.rank != 0:
train_data = None
test_data = None
train_data = chainermn.scatter_dataset(train_data, comm, shuffle=True)
test_data = chainermn.scatter_dataset(test_data, comm)
train_iter = chainer.iterators.MultiprocessIterator(
train_data, batch_size=1, n_prefetch=4, shared_mem=10 ** 8)
test_iter = chainer.iterators.MultiprocessIterator(
test_data, batch_size=1, n_prefetch=4, shared_mem=10 ** 8,
repeat=False, shuffle=False)
updater = chainer.training.updater.StandardUpdater(
train_iter, optimizer, device=device,
converter=mrcnn.datasets.concat_examples)
trainer = training.Trainer(
updater, (args.max_epoch, 'epoch'), out=args.out)
trainer.extend(
extensions.ExponentialShift('lr', 0.1),
trigger=training.triggers.ManualScheduleTrigger(
args.step_size, 'epoch'))
eval_interval = 1, 'epoch'
log_interval = 20, 'iteration'
plot_interval = 0.1, 'epoch'
print_interval = 20, 'iteration'
evaluator = mrcnn.extensions.InstanceSegmentationVOCEvaluator(
test_iter, model.mask_rcnn, device=device,
use_07_metric=True, label_names=instance_class_names)
if args.multi_node:
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator, trigger=eval_interval)
if not args.multi_node or comm.rank == 0:
trainer.extend(
extensions.snapshot_object(model.mask_rcnn, 'snapshot_model.npz'),
trigger=training.triggers.MaxValueTrigger(
'validation/main/map', eval_interval))
args.git_hash = mrcnn.utils.git_hash()
args.hostname = socket.gethostname()
trainer.extend(fcn.extensions.ParamsReport(args.__dict__))
trainer.extend(
mrcnn.extensions.InstanceSegmentationVisReport(
test_iter, model.mask_rcnn,
label_names=instance_class_names),
trigger=eval_interval)
trainer.extend(chainer.training.extensions.observe_lr(),
trigger=log_interval)
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.PrintReport(
['iteration', 'epoch', 'elapsed_time', 'lr',
'main/loss',
'main/roi_loc_loss',
'main/roi_cls_loss',
'main/roi_mask_loss',
'main/rpn_loc_loss',
'main/rpn_cls_loss',
'validation/main/map']), trigger=print_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
# plot
assert extensions.PlotReport.available()
trainer.extend(
extensions.PlotReport([
'main/loss',
'main/roi_loc_loss',
'main/roi_cls_loss',
'main/roi_mask_loss',
'main/rpn_loc_loss',
'main/rpn_cls_loss',
], file_name='loss.png', trigger=plot_interval),
trigger=plot_interval,
)
trainer.extend(
extensions.PlotReport(
['validation/main/map'],
file_name='accuracy.png', trigger=plot_interval
),
trigger=eval_interval,
)
trainer.extend(extensions.dump_graph('main/loss'))
trainer.run()
if __name__ == '__main__':
main()
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sb/base/sb_config.py | xyhuangjinfu/pytools | 0 | 1613346 | import json
import os
class SBConfig:
def __init__(self):
config_file_path = os.path.split(os.path.realpath(__file__))[0] + '/sb_config.json'
f = open(config_file_path)
self.config = json.load(f)
if __name__ == '__main__':
print(os.path.split(os.path.realpath(__file__))[0])
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ClassFiles/OOP/Encapsulation.py | minefarmer/Comprehensive-Python | 0 | 100893 | <reponame>minefarmer/Comprehensive-Python
''' Encapsulation : Part 1
Encapsulation is the process of restricting access to methods and variables in a class in order to prevent direct data modification so that it prevents accidental data modification.
Encapsulation basically allows the internal representation of an object to be hidden from the view outside of the objects definition.
Public methods and variables can be accessed from anywhere within the program.
Private methods and variables are accessible from their own class.
Double underscore prefix before object name makes it private'
Encapsulation Part 2: 40
Encapsulation Part: 3 70
'''
# class Cars:
# def __init__(self,speed, color):
# self.speed = speed
# self.color = color
# def set_speed(self,value):
# self.speed = value
# def get_speed(self):
# return self.speed
# Encapsulation Part 2: 40
# class Cars:
# def __init__(self,speed, color):
# self.speed = speed
# self.color = color
# def set_speed(self,value):
# self.speed = value
# def get_speed(self):
# return self.speed
# ford = Cars(250,"green")
# nissan = Cars(300,"red")
# toyota = Cars(350, "blue")
# # ford.set_speed(450) # If I wanted to chang he value of the speed after the instantiantion, I can do that by using the name of the instance and the method.
# ford.speed = 500 # I can also access the speed variable directly without the method and change the value. I'm able to do this because there is no encapsulation in place.
# print(ford.get_speed()) # 500
# print(ford) # <__main__.Cars object at 0x000002AA04FC60A0>
# print(ford.color) # green
# Encapsulation Part: 3 70
class Cars:
def __init__(self,speed, color):
self.__speed = speed # The double underscore makes the variable 'speed' private. It is now difficult to change the value of the variable directly from outside the methods in the class.
self.__color = color
def set_speed(self,value):
self.__speed = value
def get_speed(self):
return self.__speed
ford = Cars(250,"green")
nissan = Cars(300,"red")
toyota = Cars(350, "blue")
# ford.set_speed(450) # If I wanted to chang he value of the speed after the instantiantion, I can do that by using the name of the instance and the method.
ford.speed = 500 # I can also access the speed variable directly without the method and change the value. I'm able to do this because there is no encapsulation in place.
# print(ford.get_speed()) # 250
# print(ford) # <__main__.Cars object at 0x000002AA04FC60A0>
# # print(ford.color) # Traceback (most recent call last):
# # # File "/home/rich/CarlsHub/Comprehensive-Python/ClassFiles/OOP/Encapsulation.py", line 92, in <module>
# # # print(ford.color) # green
# # # AttributeError: 'Cars' object has no attribute 'color'
# print(ford.__color)
print(ford.get_speed()) # 250
print(ford.__color) # Traceback (most recent call last):
# File "/home/rich/CarlsHub/Comprehensive-Python/ClassFiles/OOP/Encapsulation.py", line 100, in <module>
# print(ford.__color) # <__main__.Cars object at 0x000002AA04FC60A0>
# AttributeError: 'Cars' object has no attribute '__color'
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onmt/Utils.py | KTH1234/deep_summ | 0 | 49209 | <filename>onmt/Utils.py<gh_stars>0
import torch
from torch.autograd import Variable
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments), \
"Not all arguments have the same value: " + str(args)
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return (torch.arange(0, max_len)
.type_as(lengths)
.repeat(batch_size, 1)
.lt(lengths.unsqueeze(1)))
def use_gpu(opt):
return (hasattr(opt, 'gpuid') and len(opt.gpuid) > 0) or \
(hasattr(opt, 'gpu') and opt.gpu > -1)
def pad(tensor, length, pad_index=1):
if isinstance(tensor, Variable):
var = tensor
if length > var.size(0):
return torch.cat([var,
Variable(pad_index * torch.ones(length - var.size(0), *var.size()[1:])).cuda().type_as(var)])
else:
return var
else:
if length > tensor.size(0):
return torch.cat([tensor,
pad_index * torch.ones(length - tensor.size(0), *tensor.size()[1:]).cuda()])
else:
return tensor | [
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] |
python/worldkit/placer.py | Angew/WorldKit | 0 | 118329 | <reponame>Angew/WorldKit
import math
import pyglet
import random
import sys
import xml.etree.ElementTree as xmltree
class Point:
components = ('x', 'y')
dimension = 2
eps = 1e-6
def __init__(self, *args, **kwargs):
data = [0.0] * 2
for i in range(min(len(args), self.dimension)):
data[i] = args[i]
for c, v in kwargs.items():
data[components.index(c)] = v
self._data = tuple(data)
def isClose(self, rhs):
return self.close(self, rhs)
@classmethod
def close(Self, lhs, rhs):
if isinstance(lhs, Self) and isinstance(rhs, Self):
return all(map(lambda l, r: Self.close(l, r), lhs._data, rhs._data))
else:
return abs(lhs - rhs) < Self.eps
def normalisation(self, onZero = None):
norm = self.size()
if self.close(norm, 0.0):
if onZero is not None:
normalised = onZero
norm = 0.0
else:
raise ValueError("Zero-length vector cannot be normalised.")
else:
normalised = self / norm
return normalised, norm
def size(self):
return math.sqrt(self.size2())
def size2(self):
return sum([v ** 2 for v in self._data])
def __add__(self, rhs):
return Point(*map(lambda l, r: l + r, self._data, rhs._data))
def __eq__(self, rhs):
return all(map(lambda l, r: l == r, self._data, rhs._data))
def __mul__(self, rhs):
return Point(*map(lambda v: v * rhs, self._data))
def __rmul__(self, lhs):
return self * lhs
def __sub__(self, rhs):
return Point(*map(lambda l, r: l - r, self._data, rhs._data))
def __truediv__(self, rhs):
return Point(*map(lambda v: v / rhs, self._data))
# class Point:
# def __init__(self, x = 0.0, y = 0.0):
# self.x = x
# self.y = y
# def __getitem__(self, index):
# return getattr(self, self._attrName(index))
# def __setitem__(self, index, value):
# return setattr(self, self._attrName(index), value)
# def __add__(lhs, rhs):
# return Point(
# lhs.x + rhs.x,
# lhs.y + rhs.y
# )
# def __sub__(lhs, rhs):
# return Point(
# lhs.x - rhs.x,
# lhs.y - rhs.y
# )
# @staticmethod
# def _attrName(index);
# return {
# 0: 'x', 'x': 'x',
# 1: 'y', 'y': 'y'
# }[index]
class PlacerConfig:
class Village:
def __init__(self, node):
self.count = int(node.get('count', 1))
self.force = float(node.find('force').text)
def __init__(self, filePath):
root = xmltree.parse(filePath).getroot()
area = root.find('area')
self.areaRadius = float(area.find('radius').text)
self.villages = [Village(village) for village in root.findall('village')]
class Placer:
class Village:
def __init__(self, config):
self.force = config.force
self.position = None
def __init__(self, config):
self.areaRadius = config.areaRadius
self.villages = []
for v in config.villages:
self.villages.extend(createVillages(v))
for v in self.villages:
self.initialiseVillagePosition(v)
def run(self):
window = pyglet.window.Window(
caption = "WorldKit:Placer",
width = 512,
height = 512,
resizable = True
)
pyglet.clock.schedule(self.step, 1/60.0)
pyglet.app.run()
def step(self):
forces = {v : self.computeTotalForce(v) for v in self.villages}
def createVillages(self, config):
return [Village(config) for i in range(config.count)]
def initialiseVillagePosition(self, village):
if village.position is not None:
return
while True:
position = Point(
random.uniform(-self.areaRadius, self.areaRadius),
random.uniform(-self.areaRadius, self.areaRadius)
)
if self.areaContains(position):
break
village.position = position
def areaContains(self, point):
return point.size2() <= self.areaRadius ** 2
def computeTotalForce(self, subject):
force = Point()
for partner in self.villages:
if partner is subject:
continue
force += self.computeForce(source = partner, target = subject)
return force
def computeForce(self, source, target):
difference = target.position - source.position
difference, size = difference.normalisation(keepZero = True)
return difference * (self.areaRadius * 2 - size) * source.force
def printHelp(out = sys.stdout):
pass
def main(argv):
try:
arg = argv[1]
except:
arg = 'placement.xml'
if arg in {'-h', '--help'}:
printHelp()
return
Placer(PlacerConfig(argv[1])).run()
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tests/gen/test_tca_multithreading.py | PontusHultkrantz/tcapy | 0 | 116683 | <reponame>PontusHultkrantz/tcapy
"""Tests more involved functionality of TCA such as multithreading (and checks computations are
the same as single threaded - note that if there are gaps in market data, there could be differences) and also if there
are multiple requests made at same time (stress testing).
Note for these tests to work we need to have properly populated databases of market/trade/order data.
Alternatively, we can use CSV files for the market and trade/order data in this test, although this will diminish
the usefulness of the stress test.
"""
from __future__ import division, print_function
__author__ = 'saeedamen' # <NAME> / <EMAIL>
#
# Copyright 2017 Cuemacro Ltd. - http//www.cuemacro.com / @cuemacro
#
# See the License for the specific language governing permissions and limitations under the License.
#
try:
from pandas.testing import assert_frame_equal
except:
from pandas.util.testing import assert_frame_equal
import pandas as pd
import os
from tcapy.util.customexceptions import *
from tcapy.conf.constants import Constants
from tcapy.util.mediator import Mediator
constants = Constants()
tcapy_version = constants.tcapy_version
from tcapy.analysis.tcaengine import TCARequest, TCAEngineImpl
########################################################################################################################
# YOU MAY NEED TO CHANGE TESTING PARAMETERS IF YOUR DATABASE DOESN'T COVER THESE DATES
start_date = '02 May 2017';
finish_date = '05 Jun 2017'
# Can try longer time periods - will make tests slower, but will make tests more exhaustive
# start_date = '01 Jan 2016'; finish_date = '01 Mar 2018'
# Stress test for TCA analysis (larger dataset more likely to see any issues)
# multithreading_start_date = '01 May 2017'; multithreading_finish_date = '20 Jul 2017'
multithreading_start_date = '01 May 2017'; multithreading_finish_date = '02 Jul 2017'
large_start_date = '01 Jan 2016';
large_finish_date = '01 Apr 2016'
market_data_store = 'arctic-ncfx'
trade_data_store = 'ms_sql_server'
ticker = 'EURUSD'
valid_ticker_list = ['EURUSD', 'GBPUSD', 'AUDSEK'] # it should return gracefully if at least 1 ticker exists
missing_ticker = 'AUDSEK'
from collections import OrderedDict
trade_order_mapping = ['trade_df', 'order_df']
# start_date = '01 Oct 2017'; finish_date = '15 Nov 2017'; ticker = 'GBPUSD'
invalid_start_date = '02 May 2000';
invalid_finish_date = '05 May 2001'
# So we are not specifically testing the database of tcapy - can instead use CSV in the test harness folder
use_trade_test_csv = False
use_market_test_csv = False
dump_csv_output = True # Can slow down testing, but is useful for debugging purposes
# Do a stress test, calling several large TCA requests in quick succession
stress_test = True
########################################################################################################################
# you can change the test_data_harness_folder to one on your own machine with real data
folder = constants.test_data_harness_folder
trade_order_mapping = constants.test_trade_order_list
trade_df_name = trade_order_mapping[0] # usually 'trade_df'
order_df_name = trade_order_mapping[1] # usually 'order_df'
eps = 10 ** -3
if use_market_test_csv:
# only contains limited amount of EURUSD and USDJPY in Apr/Jun 2017
market_data_store = os.path.join(folder, 'small_test_market_df.csv.gz')
if use_trade_test_csv:
trade_data_store = 'csv'
trade_order_mapping = OrderedDict([(trade_df_name, os.path.join(folder, 'small_test_trade_df.csv')),
(order_df_name, os.path.join(folder, 'small_test_order_df.csv'))])
else:
# define your own trade order mapping
pass
# Test will only succeed if the gen specific version is set
assert constants.tcapy_version == 'gen'
def test_multithreading_full_basic_tca():
"""Tests if the trade/order and market data is identical for multithreading versus singlethreading for detailed,
aggregated and compliance. Note that we need a running Celery server for multithreading to work (as well as the
usual SQL and Arctic databases running, if the test_csv option has not been selected). Uses a very large data sample
"""
Mediator.get_volatile_cache().clear_cache() # clear cache to ensure all test code runs!
tca_request = TCARequest(start_date=multithreading_start_date, finish_date=multithreading_finish_date,
ticker=valid_ticker_list,
trade_data_store=trade_data_store,
market_data_store=market_data_store,
trade_order_mapping=trade_order_mapping)
tca_engine = TCAEngineImpl(version=tcapy_version)
tca_type = ['aggregated', 'detailed', 'compliance']
multithreading = [True, False]
#### Checked the executed prices match with single and multithreaded cases
for t in tca_type:
dict_list = []
for m in multithreading:
Mediator.get_volatile_cache(
version=tcapy_version).clear_cache() # Clear cache to ensure all test code runs!
tca_request.use_multithreading = m
tca_request.tca_type = t
dict_list.append(tca_engine.calculate_tca(tca_request=tca_request))
print("tca_request " + t)
for k in dict_list[0].keys():
multi_df = dict_list[0][k]
single_df = dict_list[1][k]
if isinstance(single_df, pd.DataFrame) and isinstance(multi_df, pd.DataFrame):
if 'executed_price' in single_df.columns and 'executed_price' in multi_df.columns:
print("tablecomputation " + k)
exec_multi = multi_df['executed_price'].dropna()
exec_single = single_df['executed_price'].dropna()
if dump_csv_output:
df = pd.DataFrame(exec_multi)
df1 = pd.DataFrame(exec_single)
df1.columns = [x + '_single' for x in df1.columns]
df = df.join(pd.DataFrame(df1), how='outer')
df.to_csv(k + "_test.csv")
df_large = single_df.join(multi_df, lsuffix='_single', rsuffix='_multi', how='outer')
df_large.to_csv(k + "_test_full.csv")
assert all(exec_multi - exec_single < eps)
# Only check trade/orders and not any of the other DataFrames returned
if 'id' in multi_df.columns and 'id' in single_df.columns:
# check we have unique IDs (we should have unique IDs for every event_type trade (but the id's will
# be the same for placements)
id_multi = multi_df['id']
id_single = single_df['id']
assert len(id_multi.index) == len(id_multi.index)
assert len(id_multi.unique()) == len(id_single.index)
assert len(id_multi.index) == len(id_single.unique())
def test_invalid_dates_missing_data_tca():
"""Tests if the trade/order and market data is identical for multithreading versus singlethreading for detailed,
aggregated and compliance. Note that we need a running Celery server for multithreading to work (as well as the
usual SQL and Arctic databases running, if the test_csv option has not been selected). Uses a very large data sample
"""
Mediator.get_volatile_cache().clear_cache() # clear cache to ensure all test code runs!
tca_request = TCARequest(start_date=large_start_date, finish_date=large_finish_date, ticker=valid_ticker_list,
trade_data_store=trade_data_store,
market_data_store=market_data_store,
trade_order_mapping=trade_order_mapping)
tca_engine = TCAEngineImpl(version=tcapy_version)
tca_type = ['detailed', 'aggregated', 'compliance']
multithreading = [True, False]
## test invalid dates
tca_request.start_date = invalid_start_date;
tca_request.finish_date = invalid_finish_date
for t in tca_type:
for m in multithreading:
Mediator.get_volatile_cache().clear_cache() # clear cache to ensure all test code runs!
tca_request.use_multithreading = m
tca_request.tca_type = t
exception_triggered = True
try:
dict_of_df_invalid = tca_engine.calculate_tca(tca_request=tca_request)
exception_triggered = False
except DataMissingException:
assert exception_triggered
## test a single valid ticker, but missing data (only one ticker)
tca_request.start_date = start_date;
tca_request.finish_date = finish_date;
tca_request.ticker = missing_ticker
for t in tca_type:
for m in multithreading:
Mediator.get_volatile_cache(
version=tcapy_version).clear_cache() # clear cache to ensure all test code runs!
tca_request.use_multithreading = m
tca_request.tca_type = t
exception_triggered = True
try:
dict_of_df_missing_ticker = tca_engine.calculate_tca(tca_request=tca_request)
exception_triggered = False
except DataMissingException:
assert exception_triggered
def test_stress_tca():
"""Makes several large TCARequests at the same time to stress test tcapy application and also to check it works
with paralle requests (note: you may need to reduce the length of the dataset if your machine has limited amounts of RAM).
It can be possible that when deployed on the web, several users might make simultaneous requests. Note, do not use
pylibmc, and instead use python-memcached, when using memcached as a result backend. pylibmc is not thread-safe so
will come undone if you end up making parallel requests.
"""
from tcapy.util.swim import Swim
if not (stress_test):
return
# Clear cache to ensure all test code runs!
Mediator.get_volatile_cache().clear_cache()
tca_request = TCARequest(start_date=large_start_date, finish_date=large_finish_date, ticker=['All'],
dummy_market=True,
trade_data_store=trade_data_store,
market_data_store=market_data_store,
trade_order_mapping=trade_order_mapping, use_multithreading=True, tca_type='aggregated')
# Kick off several simulanteous large TCA requests
request_no = 2
tca_request_list = []
for i in range(0, request_no):
tca_request_list.append(TCARequest(tca_request=tca_request))
tca_engine = TCAEngineImpl(version=tcapy_version)
swim = Swim(parallel_library='thread')
pool = swim.create_pool(thread_no=len(tca_request_list))
result = []
for i in range(0, len(tca_request_list)):
result.append(
pool.apply_async(tca_engine.calculate_tca,
args=(tca_request_list[i],)))
output = [p.get() for p in result]
swim.close_pool(pool, True)
assert len(output) == len(tca_request_list)
# Check that several DataFrames exist in the results
for trade_order_results_df_dict in output:
assert 'trade_df' in trade_order_results_df_dict.keys()
if __name__ == '__main__':
test_multithreading_full_basic_tca()
test_invalid_dates_missing_data_tca()
test_stress_tca()
# import pytest; pytest.main()
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] |
saleor/order/migrations/0072_django_price_2.py | elwoodxblues/saleor | 19 | 11987 | # Generated by Django 2.2.4 on 2019-08-14 09:13
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [("order", "0071_order_gift_cards")]
operations = [
migrations.RenameField(
model_name="order",
old_name="shipping_price_gross",
new_name="shipping_price_gross_amount",
),
migrations.RenameField(
model_name="order",
old_name="shipping_price_net",
new_name="shipping_price_net_amount",
),
migrations.RenameField(
model_name="order", old_name="total_gross", new_name="total_gross_amount"
),
migrations.RenameField(
model_name="order", old_name="total_net", new_name="total_net_amount"
),
migrations.RenameField(
model_name="orderline",
old_name="unit_price_gross",
new_name="unit_price_gross_amount",
),
migrations.RenameField(
model_name="orderline",
old_name="unit_price_net",
new_name="unit_price_net_amount",
),
migrations.AddField(
model_name="order",
name="currency",
field=models.CharField(
default=settings.DEFAULT_CURRENCY,
max_length=settings.DEFAULT_CURRENCY_CODE_LENGTH,
),
),
migrations.AddField(
model_name="orderline",
name="currency",
field=models.CharField(
default=settings.DEFAULT_CURRENCY,
max_length=settings.DEFAULT_CURRENCY_CODE_LENGTH,
),
),
]
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] |
tests/datatypes/test_datatypes.py | ambv/edgedb-python | 214 | 1614136 | #
# This source file is part of the EdgeDB open source project.
#
# Copyright 2016-present MagicStack Inc. and the EdgeDB authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import unittest
import edgedb
from edgedb.datatypes import datatypes as private
from edgedb import introspect
class TestRecordDesc(unittest.TestCase):
def test_recorddesc_1(self):
with self.assertRaisesRegex(TypeError, 'one to three positional'):
private._RecordDescriptor()
with self.assertRaisesRegex(TypeError, 'one to three positional'):
private._RecordDescriptor(t=1)
with self.assertRaisesRegex(TypeError, 'requires a tuple'):
private._RecordDescriptor(1)
with self.assertRaisesRegex(TypeError, 'requires a tuple'):
private._RecordDescriptor(('a',), 1)
with self.assertRaisesRegex(TypeError,
'the same length as the names tuple'):
private._RecordDescriptor(('a',), ())
private._RecordDescriptor(('a', 'b'))
with self.assertRaisesRegex(ValueError, f'more than {0x4000-1}'):
private._RecordDescriptor(('a',) * 20000)
def test_recorddesc_2(self):
rd = private._RecordDescriptor(
('a', 'b', 'c'),
(private._EDGE_POINTER_IS_LINKPROP,
0,
private._EDGE_POINTER_IS_LINK))
self.assertEqual(rd.get_pos('a'), 0)
self.assertEqual(rd.get_pos('b'), 1)
self.assertEqual(rd.get_pos('c'), 2)
self.assertTrue(rd.is_linkprop('a'))
self.assertFalse(rd.is_linkprop('b'))
self.assertFalse(rd.is_linkprop('c'))
self.assertFalse(rd.is_link('a'))
self.assertFalse(rd.is_link('b'))
self.assertTrue(rd.is_link('c'))
with self.assertRaises(LookupError):
rd.get_pos('z')
with self.assertRaises(LookupError):
rd.is_linkprop('z')
def test_recorddesc_3(self):
f = private.create_object_factory(
id={'property', 'implicit'},
lb='link-property',
c='property',
d='link',
)
o = f(1, 2, 3, 4)
desc = private.get_object_descriptor(o)
self.assertEqual(set(dir(desc)), set(('id', 'lb', 'c', 'd')))
self.assertTrue(desc.is_linkprop('lb'))
self.assertFalse(desc.is_linkprop('id'))
self.assertFalse(desc.is_linkprop('c'))
self.assertFalse(desc.is_linkprop('d'))
self.assertFalse(desc.is_link('lb'))
self.assertFalse(desc.is_link('id'))
self.assertFalse(desc.is_link('c'))
self.assertTrue(desc.is_link('d'))
self.assertFalse(desc.is_implicit('lb'))
self.assertTrue(desc.is_implicit('id'))
self.assertFalse(desc.is_implicit('c'))
self.assertFalse(desc.is_implicit('d'))
self.assertEqual(desc.get_pos('lb'), 1)
self.assertEqual(desc.get_pos('id'), 0)
self.assertEqual(desc.get_pos('c'), 2)
self.assertEqual(desc.get_pos('d'), 3)
def test_recorddesc_4(self):
f = private.create_object_factory(
id={'property', 'implicit'},
lb='link-property',
c='property',
d='link',
)
o = f(1, 2, 3, 4)
intro = introspect.introspect_object(o)
self.assertEqual(
intro.pointers,
(
('id', introspect.PointerKind.PROPERTY, True),
('c', introspect.PointerKind.PROPERTY, False),
('d', introspect.PointerKind.LINK, False),
)
)
# clear cache so that tests in refcount mode don't freak out.
introspect._introspect_object_desc.cache_clear()
class TestTuple(unittest.TestCase):
def test_tuple_empty_1(self):
t = edgedb.Tuple()
self.assertEqual(len(t), 0)
self.assertEqual(hash(t), hash(()))
self.assertEqual(repr(t), '()')
with self.assertRaisesRegex(IndexError, 'out of range'):
t[0]
def test_tuple_2(self):
t = edgedb.Tuple((1, 'a'))
self.assertEqual(len(t), 2)
self.assertEqual(hash(t), hash((1, 'a')))
self.assertEqual(repr(t), "(1, 'a')")
self.assertEqual(t[0], 1)
self.assertEqual(t[1], 'a')
with self.assertRaisesRegex(IndexError, 'out of range'):
t[2]
def test_tuple_3(self):
t = edgedb.Tuple((1, []))
t[1].append(t)
self.assertEqual(t[1], [t])
self.assertEqual(repr(t), '(1, [(...)])')
self.assertEqual(str(t), '(1, [(...)])')
def test_tuple_4(self):
with self.assertRaisesRegex(ValueError, f'more than {0x4000 - 1}'):
edgedb.Tuple([1] * 20000)
def test_tuple_freelist_1(self):
lst = []
for _ in range(5000):
lst.append(edgedb.Tuple((1,)))
for t in lst:
self.assertEqual(t[0], 1)
def test_tuple_5(self):
self.assertEqual(
edgedb.Tuple([1, 2, 3]),
edgedb.Tuple([1, 2, 3]))
self.assertNotEqual(
edgedb.Tuple([1, 2, 3]),
edgedb.Tuple([1, 3, 2]))
self.assertLess(
edgedb.Tuple([1, 2, 3]),
edgedb.Tuple([1, 3, 2]))
self.assertEqual(
edgedb.Tuple([]),
edgedb.Tuple([]))
self.assertEqual(
edgedb.Tuple([1]),
edgedb.Tuple([1]))
self.assertGreaterEqual(
edgedb.Tuple([1]),
edgedb.Tuple([1]))
self.assertNotEqual(
edgedb.Tuple([1]),
edgedb.Tuple([]))
self.assertGreater(
edgedb.Tuple([1]),
edgedb.Tuple([]))
self.assertNotEqual(
edgedb.Tuple([1]),
edgedb.Tuple([2]))
self.assertLess(
edgedb.Tuple([1]),
edgedb.Tuple([2]))
self.assertNotEqual(
edgedb.Tuple([1, 2]),
edgedb.Tuple([2, 2]))
self.assertNotEqual(
edgedb.Tuple([1, 1]),
edgedb.Tuple([2, 2, 1]))
def test_tuple_6(self):
self.assertEqual(
edgedb.Tuple([1, 2, 3]),
(1, 2, 3))
self.assertEqual(
(1, 2, 3),
edgedb.Tuple([1, 2, 3]))
self.assertNotEqual(
edgedb.Tuple([1, 2, 3]),
(1, 3, 2))
self.assertLess(
edgedb.Tuple([1, 2, 3]),
(1, 3, 2))
self.assertEqual(
edgedb.Tuple([]),
())
self.assertEqual(
edgedb.Tuple([1]),
(1,))
self.assertGreaterEqual(
edgedb.Tuple([1]),
(1,))
self.assertNotEqual(
edgedb.Tuple([1]),
())
self.assertGreater(
edgedb.Tuple([1]),
())
self.assertNotEqual(
edgedb.Tuple([1]),
(2,))
self.assertLess(
edgedb.Tuple([1]),
(2,))
self.assertNotEqual(
edgedb.Tuple([1, 2]),
(2, 2))
self.assertNotEqual(
edgedb.Tuple([1, 1]),
(2, 2, 1))
def test_tuple_7(self):
self.assertNotEqual(
edgedb.Tuple([1, 2, 3]),
123)
class TestNamedTuple(unittest.TestCase):
def test_namedtuple_empty_1(self):
with self.assertRaisesRegex(ValueError, 'at least one field'):
edgedb.NamedTuple()
def test_namedtuple_2(self):
t = edgedb.NamedTuple(a=1)
self.assertEqual(repr(t), "(a := 1)")
t = edgedb.NamedTuple(a=1, b='a')
self.assertEqual(set(dir(t)), {'a', 'b'})
self.assertEqual(repr(t), "(a := 1, b := 'a')")
self.assertEqual(t[0], 1)
self.assertEqual(t[1], 'a')
with self.assertRaisesRegex(IndexError, 'out of range'):
t[2]
self.assertEqual(len(t), 2)
self.assertEqual(hash(t), hash((1, 'a')))
self.assertEqual(t.a, 1)
self.assertEqual(t.b, 'a')
with self.assertRaises(AttributeError):
t.z
def test_namedtuple_3(self):
t = edgedb.NamedTuple(a=1, b=[])
t.b.append(t)
self.assertEqual(t.b, [t])
self.assertEqual(repr(t), '(a := 1, b := [(...)])')
self.assertEqual(str(t), '(a := 1, b := [(...)])')
def test_namedtuple_4(self):
t1 = edgedb.NamedTuple(a=1, b='aaaa')
t2 = edgedb.Tuple((1, 'aaaa'))
t3 = (1, 'aaaa')
self.assertEqual(hash(t1), hash(t2))
self.assertEqual(hash(t1), hash(t3))
def test_namedtuple_5(self):
self.assertEqual(
edgedb.NamedTuple(a=1, b=2, c=3),
edgedb.NamedTuple(x=1, y=2, z=3))
self.assertNotEqual(
edgedb.NamedTuple(a=1, b=2, c=3),
edgedb.NamedTuple(a=1, c=3, b=2))
self.assertLess(
edgedb.NamedTuple(a=1, b=2, c=3),
edgedb.NamedTuple(a=1, b=3, c=2))
self.assertEqual(
edgedb.NamedTuple(a=1),
edgedb.NamedTuple(b=1))
self.assertEqual(
edgedb.NamedTuple(a=1),
edgedb.NamedTuple(a=1))
def test_namedtuple_6(self):
self.assertEqual(
edgedb.NamedTuple(a=1, b=2, c=3),
(1, 2, 3))
self.assertEqual(
(1, 2, 3),
edgedb.NamedTuple(a=1, b=2, c=3))
self.assertNotEqual(
edgedb.NamedTuple(a=1, b=2, c=3),
(1, 3, 2))
self.assertLess(
edgedb.NamedTuple(a=1, b=2, c=3),
(1, 3, 2))
self.assertEqual(
edgedb.NamedTuple(a=1),
(1,))
self.assertEqual(
edgedb.NamedTuple(a=1),
(1,))
def test_namedtuple_7(self):
self.assertNotEqual(
edgedb.NamedTuple(a=1, b=2, c=3),
1)
class TestObject(unittest.TestCase):
def test_object_1(self):
f = private.create_object_factory(
id='property',
lb='link-property',
c='property'
)
o = f(1, 2, 3)
self.assertEqual(repr(o), 'Object{id := 1, @lb := 2, c := 3}')
self.assertEqual(o.id, 1)
self.assertEqual(o.c, 3)
with self.assertRaises(AttributeError):
o.lb
with self.assertRaises(AttributeError):
o.z
with self.assertRaises(TypeError):
len(o)
with self.assertRaises(KeyError):
o[0]
with self.assertRaises(TypeError):
o['id']
self.assertEqual(set(dir(o)), {'id', 'c'})
def test_object_2(self):
f = private.create_object_factory(
id={'property', 'implicit'},
lb='link-property',
c='property'
)
o = f(1, 2, 3)
self.assertEqual(repr(o), 'Object{@lb := 2, c := 3}')
self.assertEqual(hash(o), hash(f(1, 2, 3)))
self.assertNotEqual(hash(o), hash(f(1, 2, 'aaaa')))
self.assertNotEqual(hash(o), hash((1, 2, 3)))
self.assertEqual(set(dir(o)), {'id', 'c'})
def test_object_3(self):
f = private.create_object_factory(id='property', c='link')
o = f(1, [])
o.c.append(o)
self.assertEqual(repr(o), 'Object{id := 1, c := [Object{...}]}')
with self.assertRaisesRegex(TypeError, 'unhashable'):
hash(o)
def test_object_4(self):
f = private.create_object_factory(
id={'property', 'implicit'},
lb='link-property',
c='property'
)
o1 = f(1, 'aa', 'ba')
o2 = f(1, 'ab', 'bb')
o3 = f(3, 'ac', 'bc')
self.assertEqual(o1, o2)
self.assertNotEqual(o1, o3)
self.assertLess(o1, o3)
self.assertGreater(o3, o2)
def test_object_5(self):
f = private.create_object_factory(
a='property',
lb='link-property',
c='property'
)
with self.assertRaisesRegex(ValueError, "without 'id' field"):
f(1, 2, 3)
def test_object_6(self):
User = private.create_object_factory(
id='property',
name='property',
)
u = User(1, 'user1')
with self.assertRaisesRegex(TypeError,
"property 'name' should be "
"accessed via dot notation"):
u['name']
def test_object_links_1(self):
O2 = private.create_object_factory(
id='property',
lb='link-property',
c='property'
)
O1 = private.create_object_factory(
id='property',
o2s='link'
)
o2_1 = O2(1, 'linkprop o2 1', 3)
o2_2 = O2(4, 'linkprop o2 2', 6)
o1 = O1(2, edgedb.Set((o2_1, o2_2)))
linkset = o1['o2s']
self.assertEqual(len(linkset), 2)
self.assertEqual(linkset, o1['o2s'])
self.assertEqual(hash(linkset), hash(o1['o2s']))
self.assertEqual(
repr(linkset),
"LinkSet(name='o2s', source_id=2, target_ids={1, 4})")
link1 = linkset[0]
self.assertIs(link1.source, o1)
self.assertIs(link1.target, o2_1)
self.assertEqual(
repr(link1),
"Link(name='o2s', source_id=2, target_id=1)")
self.assertEqual(set(dir(link1)), {'target', 'source', 'lb'})
link2 = linkset[1]
self.assertIs(link2.source, o1)
self.assertIs(link2.target, o2_2)
self.assertNotEqual(link1, link2)
self.assertEqual(list(linkset), [link1, link2])
self.assertEqual([link for link in linkset], [link1, link2])
self.assertNotEqual(link1, link2)
self.assertEqual(link1.lb, 'linkprop o2 1')
self.assertEqual(link2.lb, 'linkprop o2 2')
with self.assertRaises(AttributeError):
link2.aaaa
def test_object_links_2(self):
User = private.create_object_factory(
id='property',
friends='link',
enemies='link',
)
u1 = User(1, edgedb.Set([]), edgedb.Set([]))
u2 = User(2, edgedb.Set([]), edgedb.Set([]))
u3 = User(3, edgedb.Set([]), edgedb.Set([]))
u4 = User(4, edgedb.Set([u1, u2]), edgedb.Set([u1, u2]))
u5 = User(5, edgedb.Set([u1, u3]), edgedb.Set([u1, u2]))
self.assertNotEqual(u4['friends'], u4['enemies'])
self.assertNotEqual(u4['enemies'], u5['enemies'])
self.assertEqual(set(dir(u1)), {'id', 'friends', 'enemies'})
def test_object_links_3(self):
User = private.create_object_factory(
id='property',
friend='link',
)
u1 = User(1, None)
u2 = User(2, u1)
u3 = User(3, edgedb.Set([]))
self.assertEqual(set(dir(u2['friend'])), {'source', 'target'})
self.assertIs(u2['friend'].target, u1)
self.assertIsNone(u1['friend'])
self.assertEqual(len(u3['friend']), 0)
self.assertEqual(
repr(u3['friend']),
"LinkSet(name='friend', source_id=3, target_ids={})")
self.assertEqual(
repr(u2['friend']),
"Link(name='friend', source_id=2, target_id=1)")
def test_object_links_4(self):
User = private.create_object_factory(
id='property',
friend='link',
)
u = User(1, None)
with self.assertRaisesRegex(KeyError,
"link 'error_key' does not exist"):
u['error_key']
class TestSet(unittest.TestCase):
def test_set_1(self):
s = edgedb.Set(())
self.assertEqual(repr(s), 'Set{}')
s = edgedb.Set((1, 2, [], 'a'))
self.assertEqual(s[1], 2)
self.assertEqual(s[2], [])
self.assertEqual(len(s), 4)
with self.assertRaises(IndexError):
s[10]
with self.assertRaises(TypeError):
s[0] = 1
def test_set_2(self):
s = edgedb.Set((1, 2, 3000, 'a'))
self.assertEqual(repr(s), "Set{1, 2, 3000, 'a'}")
self.assertEqual(
hash(s),
hash(edgedb.Set((1, 2, sum([1000, 2000]), 'a'))))
self.assertNotEqual(
hash(s),
hash((1, 2, 3000, 'a')))
def test_set_3(self):
s = edgedb.Set(())
self.assertEqual(len(s), 0)
self.assertEqual(hash(s), hash(edgedb.Set(())))
self.assertNotEqual(hash(s), hash(()))
def test_set_4(self):
s = edgedb.Set(([],))
s[0].append(s)
self.assertEqual(repr(s), "Set{[Set{...}]}")
def test_set_5(self):
self.assertEqual(
edgedb.Set([1, 2, 3]),
edgedb.Set([3, 2, 1]))
self.assertEqual(
edgedb.Set([]),
edgedb.Set([]))
self.assertEqual(
edgedb.Set([1]),
edgedb.Set([1]))
self.assertNotEqual(
edgedb.Set([1]),
edgedb.Set([]))
self.assertNotEqual(
edgedb.Set([1]),
edgedb.Set([2]))
self.assertNotEqual(
edgedb.Set([1, 2]),
edgedb.Set([2, 2]))
self.assertNotEqual(
edgedb.Set([1, 1, 2]),
edgedb.Set([2, 2, 1]))
def test_set_6(self):
f = private.create_object_factory(
id={'property', 'implicit'},
lb='link-property',
c='property'
)
o1 = f(1, 'aa', edgedb.Set([1, 2, 3]))
o2 = f(1, 'ab', edgedb.Set([1, 2, 4]))
o3 = f(3, 'ac', edgedb.Set([5, 5, 5, 5]))
self.assertEqual(
edgedb.Set([o1, o2, o3]),
edgedb.Set([o2, o3, o1]))
self.assertEqual(
edgedb.Set([o1, o3]),
edgedb.Set([o2, o3]))
self.assertNotEqual(
edgedb.Set([o1, o1]),
edgedb.Set([o2, o3]))
def test_set_7(self):
self.assertEqual(
edgedb.Set([1, 2, 3]),
[1, 2, 3])
self.assertNotEqual(
edgedb.Set([1, 2, 3]),
[3, 2, 1])
self.assertNotEqual(
edgedb.Set([1, 2, 3]),
1)
def test_set_8(self):
s = edgedb.Set([1, 2, 3])
si = iter(s)
self.assertEqual(list(si), [1, 2, 3])
class TestArray(unittest.TestCase):
def test_array_empty_1(self):
t = edgedb.Array()
self.assertEqual(len(t), 0)
self.assertNotEqual(hash(t), hash(()))
with self.assertRaisesRegex(IndexError, 'out of range'):
t[0]
self.assertEqual(repr(t), "[]")
def test_array_2(self):
t = edgedb.Array((1, 'a'))
self.assertEqual(repr(t), "[1, 'a']")
self.assertEqual(str(t), "[1, 'a']")
self.assertEqual(len(t), 2)
self.assertEqual(hash(t), hash(edgedb.Array([1, 'a'])))
self.assertNotEqual(hash(t), hash(edgedb.Array([10, 'ab'])))
self.assertEqual(t[0], 1)
self.assertEqual(t[1], 'a')
with self.assertRaisesRegex(IndexError, 'out of range'):
t[2]
def test_array_3(self):
t = edgedb.Array((1, []))
t[1].append(t)
self.assertEqual(t[1], [t])
self.assertEqual(repr(t), '[1, [[...]]]')
def test_array_4(self):
self.assertEqual(
edgedb.Array([1, 2, 3]),
edgedb.Array([1, 2, 3]))
self.assertNotEqual(
edgedb.Array([1, 2, 3]),
edgedb.Array([1, 3, 2]))
self.assertLess(
edgedb.Array([1, 2, 3]),
edgedb.Array([1, 3, 2]))
self.assertEqual(
edgedb.Array([]),
edgedb.Array([]))
self.assertEqual(
edgedb.Array([1]),
edgedb.Array([1]))
self.assertGreaterEqual(
edgedb.Array([1]),
edgedb.Array([1]))
self.assertNotEqual(
edgedb.Array([1]),
edgedb.Array([]))
self.assertGreater(
edgedb.Array([1]),
edgedb.Array([]))
self.assertNotEqual(
edgedb.Array([1]),
edgedb.Array([2]))
self.assertLess(
edgedb.Array([1]),
edgedb.Array([2]))
self.assertNotEqual(
edgedb.Array([1, 2]),
edgedb.Array([2, 2]))
self.assertNotEqual(
edgedb.Array([1, 1]),
edgedb.Array([2, 2, 1]))
def test_array_5(self):
self.assertEqual(
edgedb.Array([1, 2, 3]),
[1, 2, 3])
self.assertEqual(
[1, 2, 3],
edgedb.Array([1, 2, 3]))
self.assertNotEqual(
[1, 2, 4],
edgedb.Array([1, 2, 3]))
self.assertNotEqual(
edgedb.Array([1, 2, 3]),
[1, 3, 2])
self.assertLess(
edgedb.Array([1, 2, 3]),
[1, 3, 2])
self.assertEqual(
edgedb.Array([]),
[])
self.assertEqual(
edgedb.Array([1]),
[1])
self.assertGreaterEqual(
edgedb.Array([1]),
[1])
self.assertNotEqual(
edgedb.Array([1]),
[])
self.assertGreater(
edgedb.Array([1]),
[])
self.assertNotEqual(
edgedb.Array([1]),
[2])
self.assertLess(
edgedb.Array([1]),
[2])
self.assertNotEqual(
edgedb.Array([1, 2]),
[2, 2])
self.assertNotEqual(
edgedb.Array([1, 1]),
[2, 2, 1])
def test_array_6(self):
self.assertNotEqual(
edgedb.Array([1, 2, 3]),
False)
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] |
find_offset.py | psdon/bufferoverflow_scripts | 0 | 120977 | <gh_stars>0
import argparse
from pwn import cyclic_metasploit_find
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-a", "--address", type=str, required=True, help="EIP hex value"
)
parser.add_argument(
"-c",
"--prepend_cmd",
type=str,
required=False,
default="",
help="EIP hex value",
)
args = parser.parse_args()
hex_val = int(args.address, 16)
offset = cyclic_metasploit_find(hex_val)
cmd = args.prepend_cmd
offset_eip = offset + len(cmd)
offset_esp = offset_eip + 4 # + 4 bytes after EIP
print(f"Offset of {args.address}: {offset}")
print(f"Offset of EIP + cmd len : {offset_eip}")
print(f"Offset of EIP + cmd len + 4 = ESP: {offset_esp}")
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13,
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666,
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310,
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13,
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13,
2
] |
sdk/ml/azure-ai-ml/azure/ai/ml/_schema/automl/image_vertical/image_limit_settings.py | dubiety/azure-sdk-for-python | 1 | 84261 | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from marshmallow import fields, post_load
from azure.ai.ml.constants import AutoMLConstants
from azure.ai.ml._schema import PatchedSchemaMeta
from azure.ai.ml._restclient.v2022_02_01_preview.models import ImageSweepLimitSettings
class ImageLimitsSchema(metaclass=PatchedSchemaMeta):
max_concurrent_trials = fields.Int()
max_trials = fields.Int()
timeout_minutes = fields.Int() # type duration
@post_load
def make(self, data, **kwargs):
from azure.ai.ml.automl import ImageLimitSettings
return ImageLimitSettings(**data)
class ImageSweepLimitSchema(metaclass=PatchedSchemaMeta):
max_concurrent_trials = fields.Int()
max_trials = fields.Int(data_key=AutoMLConstants.MAX_TRIALS_YAML)
@post_load
def make(self, data, **kwargs):
return ImageSweepLimitSettings(**data)
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2
] |
tests/test_general.py | MichaelRazum/cachier | 0 | 154719 | """Non-core-specific tests for cachier."""
import os
from cachier.core import (
MAX_WORKERS_ENVAR_NAME,
DEFAULT_MAX_WORKERS,
_max_workers,
_set_max_workers,
_get_executor
)
def test_max_workers():
"""Just call this function for coverage."""
try:
del os.environ[MAX_WORKERS_ENVAR_NAME]
except KeyError:
pass
assert _max_workers() == DEFAULT_MAX_WORKERS
def test_get_executor():
"""Just call this function for coverage."""
_get_executor()
_get_executor(False)
_get_executor(True)
def test_set_max_workers():
"""Just call this function for coverage."""
_set_max_workers(9)
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414,
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29929,
29897,
13,
13,
13,
2
] |
heroku_py/__init__.py | AnthonyAniobi/heroku-py | 5 | 49230 | <gh_stars>1-10
from .heroku_client import HerokuClient
__version__ = "1.2.0"
__all__ = ["HerokuClient"]
| [
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VideoSearchWithAutoMLVision/parser/ParserAPI/app.py | nikkiconley/media-services-video-indexer | 54 | 187556 | import os
from typing import Dict, List
import uvicorn
from dotenv import load_dotenv
from fastapi import FastAPI, Security, Depends, HTTPException
from fastapi.security.api_key import APIKeyHeader, APIKey
from impl import extractor
from pydantic import BaseModel
from starlette.status import HTTP_403_FORBIDDEN
load_dotenv()
app = FastAPI()
class Values(BaseModel):
values: List = []
class Value(Values):
recordId: str
data: Dict[str, str] = None
API_KEY = os.environ['KEY']
API_KEY_NAME = "Ocp-Apim-Subscription-Key"
api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)
async def get_api_key(
api_key_header: str = Security(api_key_header),
):
if api_key_header == API_KEY:
return api_key_header
else:
raise HTTPException(
status_code = HTTP_403_FORBIDDEN, detail = "Key not present"
)
@app.get('/api/healthcheck', status_code=200)
async def healthcheck():
return 'Ready'
@app.post('/api/parse')
def parse(values: Values, api_key: APIKey = Depends(get_api_key)): #
body = values.dict()
if not body:
return 'Expected text within body of request. No text found.', status.HTTP_400_BAD_REQUEST
else:
return extractor.parse(body)
# Remove these two lines below for non-debug/production mode
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=5000)
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13,
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13,
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13,
13,
13,
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1438,
1023,
3454,
2400,
363,
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8382,
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24601,
4464,
13,
361,
4770,
978,
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1275,
376,
1649,
3396,
1649,
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13,
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318,
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932,
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3495,
543,
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29900,
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29900,
29889,
29900,
613,
2011,
29922,
29945,
29900,
29900,
29900,
29897,
13,
2
] |
start_project/src/start_project.py | pdminimal/sandbox | 0 | 170619 | from fs import enums, errors, osfs
from self_print import SelfPrint
class StartProject:
def __init__(self, name, fs=None):
self.sp = SelfPrint(leading="- ")
self.name = name
if fs is None:
fs = osfs.OSFS(".")
self.fs = fs
def warning(self, text):
print("Warining: " + text)
def error(self, text):
print("Error: " + text)
raise IOError(text)
def generate(self):
# - investigating dir
try:
info = self.fs.getinfo(self.name, namespaces=["details"])
if info.type == enums.ResourceType.directory:
self.sp.print()
self.warning(f"Target directory '{self.name}' already exists.")
else:
self.error(
f"Target path '{self.name}' already exists and is not a directory."
)
except errors.ResourceNotFound:
# - generate dir
self.fs.makedir(self.name)
self.sp.print()
| [
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Discord Bots/Galaxy Studio/cogs/money/commands.py | LUNA761/Code-Archive | 1 | 1613932 | <filename>Discord Bots/Galaxy Studio/cogs/money/commands.py
import discord, time, asyncio, os, random, json
from discord.ext import commands, tasks
from discord.ext.commands import has_permissions, cooldown, MissingPermissions, check, Cog
from discord.utils import get
def is_wiggle_or_luna(ctx):
return ctx.author.id in [<PASSWORD>, 7<PASSWORD>]
class App(commands.Cog):
def __init__(self, client):
self.client = client
print("Money Commands Cog Running")
# <-- commands -->
@commands.command()
async def bal(self, ctx, user : discord.Member = None):
with open("data/money.json") as f:
money = json.load(f)
f.close()
if user == None:
user = ctx.author
try:
bux = str(money[str(user.id)])
except:
bux = 0
embed=discord.Embed(title="Boogie Bal", color=0x00ffff)
embed.add_field(name="Boogie Bucks:", value=f"${bux}", inline=False)
await ctx.send(embed=embed)
@commands.command()
@check(is_wiggle_or_luna)
async def give(self, ctx, user : discord.User = None, num : int = None):
if user == None or num == None:
embed=discord.Embed(title="Incorrect command usage!", description=".give @user {number}", color=0x00ffff)
await ctx.send(embed=embed)
return
with open("data/money.json") as f:
money = json.load(f)
f.close()
with open("data/bank-queue.json") as f:
queue = json.load(f)
f.close()
try:
bal = money[str(user.id)]
except:
bal = 0
bal += num
queue.append(-num)
money[str(user.id)] = bal
with open("data/money.json", "w+") as f:
json.dump(money, f)
f.close()
with open("data/bank-queue.json", "w+") as f:
json.dump(queue, f)
f.close()
await ctx.send(f"I have gave {user.mention} {str(num)} Boogie bucks!")
@give.error
async def give_error(self, ctx, error):
if isinstance(error, commands.CheckFailure):
await ctx.send(f"❌ Hey, {ctx.author.name} you can't do that!")
@commands.command()
@check(is_wiggle_or_luna)
async def take(self, ctx, user : discord.User = None, num : int = None):
if user == None or num == None:
embed=discord.Embed(title="Incorrect command usage!", description=".take @user {number}", color=0x00ffff)
await ctx.send(embed=embed)
return
with open("data/money.json") as f:
money = json.load(f)
f.close()
try:
bal = money[str(user.id)]
except:
bal = 0
bal -= num
queue.append(num)
money[str(user.id)] = bal
with open("data/money.json", "w+") as f:
json.dump(money, f)
f.close()
with open("data/bank-queue.json", "w+") as f:
json.dump(queue, f)
f.close()
await ctx.send(f"I have taken {str(num)} Boogie bucks from {user.mention}!")
@take.error
async def take_error(self, ctx, error):
if isinstance(error, commands.CheckFailure):
await ctx.send(f"❌ Hey, {ctx.author.name} you can't do that!")
@commands.command()
@check(is_wiggle_or_luna)
async def totalreset(self, ctx, num : int = None):
if num == None:
embed=discord.Embed(title="Incorrect command usage!", description=".totalreset {number}", color=0x00ffff)
await ctx.send(embed=embed)
return
with open("data/money.json") as f:
money = json.load(f)
f.close()
for user in money:
money[user] = num
with open("data/money.json", "w+") as f:
json.dump(money, f)
f.close()
await ctx.send(f"I have reset all Boogie Bucks!")
@take.error
async def take_error(self, ctx, error):
if isinstance(error, commands.CheckFailure):
await ctx.send(f"❌ Hey, {ctx.author.name} you can't do that!")
def setup(client):
client.add_cog(App(client))
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] |
new_pro/shorten/models.py | Lavkushmani/URL_SHORTNER | 1 | 185483 | from django.db import models
# Create your models here.
class bitly(models.Model):
user = models.CharField(max_length=15, null=True)
long_url = models.URLField(null=False)
shortcode = models.CharField(max_length=6,unique=True)
created_date = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
datewise = models.TextField()
def __str__(self):
return self.long_url
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python/tests/test_insertion_sort.py | bjgman12/data-structures-and-algorithms | 0 | 131371 | from code_challenges.insertion_sort.insertion_sort import insert_sort
def test_sorting_functionality():
test_list = [8,4,23,42,16,15]
actual = insert_sort(test_list)
expected = [4,8,15,16,23,42]
assert actual == expected
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testing/onQuest/longClusters/m67/OLD-analyseEBLSSTm67.py | andrewbowen19/ClusterEclipsingBinaries | 0 | 2429 | #########################
#########################
# Need to account for limit in input period
#########################
#########################
# Baseline M67 long script -- NO crowding
# New script copied from quest - want to take p and ecc from each population (all, obs, rec) and put them into separate file
# Doing this so we don't have to run analyse each time
# Can write separate script for p-ecc plots
# Quest paths in this version of script
import pandas as pd
import numpy as np
import os
from astropy.coordinates import SkyCoord
from astropy import units, constants
from astropy.modeling import models, fitting
import scipy.stats
from scipy.integrate import quad
#for Quest
import matplotlib
matplotlib.use('Agg')
doIndividualPlots = True
from matplotlib import pyplot as plt
def file_len(fname):
i = 0
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def getPhs(sigma, m1=1*units.solMass, m2=1*units.solMass, m3=0.5*units.solMass):
Phs = np.pi*constants.G/np.sqrt(2.)*(m1*m2/m3)**(3./2.)*(m1 + m2)**(-0.5)*sigma**(-3.)
return Phs.decompose().to(units.day)
#similar to field, but limiting by the hard-soft boundary
def fitRagfb():
x = [0.05, 0.1, 1, 8, 15] #estimates of midpoints in bins, and using this: https://sites.uni.edu/morgans/astro/course/Notes/section2/spectralmasses.html
y = [0.20, 0.35, 0.50, 0.70, 0.75]
init = models.PowerLaw1D(amplitude=0.5, x_0=1, alpha=-1.)
fitter = fitting.LevMarLSQFitter()
fit = fitter(init, x, y)
return fit
def RagNormal(x, cdf = False):
mean = 5.03
std = 2.28
if (cdf):
return scipy.stats.norm.cdf(x,mean,std)
return scipy.stats.norm.pdf(x,mean,std)
def saveHist(histAll, histObs, histRec, bin_edges, xtitle, fname, filters = ['u_', 'g_', 'r_', 'i_', 'z_', 'y_','all']):
c1 = '#5687A6' #Dali Blue (Andrew's AAS Poster)
c2 = '#A62B1F' #Dai Red
c3 = '#BF8A26' #Dali Beige
fig,ax1 = plt.subplots(figsize=(8,6), sharex=True)#can change to include cdf with ax1, ax2
histAll = np.insert(histAll,0,0)
histObs = np.insert(histObs,0,0)
for f in filters:
histRec[f] = np.insert(histRec[f],0,0)
#PDF
ax1.step(bin_edges, histAll/np.sum(histAll), color=c1)
ax1.step(bin_edges, histObs/np.sum(histObs), color=c2)
for f in filters:
lw = 1
if (f == 'all'):
lw = 0.5
ax1.step(bin_edges, histRec[f]/np.sum(histRec[f]), color=c3, linewidth=lw)
ax1.set_ylabel('PDF')
ax1.set_yscale('log')
ax1.set_title('Globular Clusters - Baseline', fontsize = 16)
ax1.set_xlabel(xtitle)
#CDF
#cdfAll = []
#cdfObs = []
#cdfRec = dict()
#for f in filters:
# cdfRec[f] = []
# for i in range(len(histAll)):
# cdfAll.append(np.sum(histAll[:i])/np.sum(histAll))
# for i in range(len(histObs)):
# cdfObs.append(np.sum(histObs[:i])/np.sum(histObs))
# for f in filters:
# for i in range(len(histRec[f])):
# cdfRec[f].append(np.sum(histRec[f][:i])/np.sum(histRec[f]))
#ax2.step(bin_edges, cdfAll, color=c1)
#ax2.step(bin_edges, cdfObs, color=c2)
#for f in filters:
# lw = 1
# if (f == 'all'):
# lw = 0.5
# ax2.step(bin_edges, cdfRec[f], color=c3, linewidth=lw)
#ax2.set_ylabel('CDF')
#ax2.set_xlabel(xtitle)
fig.subplots_adjust(hspace=0)
fig.savefig('./plots/' + fname+'.pdf',format='pdf', bbox_inches = 'tight')
#write to a text file
with open('./eblsst_files/' + fname+'.csv','w') as fl:
outline = 'binEdges,histAll,histObs'
for f in filters:
outline += ','+f+'histRec'
outline += '\n'
fl.write(outline)
for i in range(len(bin_edges)):
outline = str(bin_edges[i])+','+str(histAll[i])+','+str(histObs[i])
for f in filters:
outline += ','+str(histRec[f][i])
outline += '\n'
fl.write(outline)
if __name__ == "__main__":
filters = ['u_', 'g_', 'r_', 'i_', 'z_', 'y_', 'all']
#get the Raghavan binary fraction fit
fbFit= fitRagfb()
print(fbFit)
#to normalize
intAll, err = quad(RagNormal, -20, 20)
intCut, err = quad(RagNormal, -20, np.log10(365*10.))
intNorm = intCut/intAll
#cutoff in percent error for "recovered"
Pcut = 0.1
#assumed mean stellar mass
mMean = 0.5
#minimum number of lines to consider in file
Nlim = 3
if (doIndividualPlots):
fmass, axmass = plt.subplots()
fqrat, axqrat = plt.subplots()
fecc, axecc = plt.subplots()
flper, axlper = plt.subplots()
fdist, axdist = plt.subplots()
fmag, axmag = plt.subplots()
frad, axrad = plt.subplots()
#bins for all the histograms
Nbins = 25
mbins = np.arange(0,10, 0.1, dtype='float')
qbins = np.arange(0,1, 0.1, dtype='float')
ebins = np.arange(0, 1.05, 0.05, dtype='float')
lpbins = np.arange(-2, 10, 0.5, dtype='float')
dbins = np.arange(0, 40, 1, dtype='float')
magbins = np.arange(11, 25, 1, dtype='float')
rbins = np.arange(0, 100, 0.2, dtype='float')
#blanks for the histograms
#All
m1hAll = np.zeros_like(mbins)[1:]
qhAll = np.zeros_like(qbins)[1:]
ehAll = np.zeros_like(ebins)[1:]
lphAll = np.zeros_like(lpbins)[1:]
dhAll = np.zeros_like(dbins)[1:]
maghAll = np.zeros_like(magbins)[1:]
rhAll = np.zeros_like(rbins)[1:]
#Observable
m1hObs = np.zeros_like(mbins)[1:]
qhObs = np.zeros_like(qbins)[1:]
ehObs = np.zeros_like(ebins)[1:]
lphObs = np.zeros_like(lpbins)[1:]
dhObs = np.zeros_like(dbins)[1:]
maghObs = np.zeros_like(magbins)[1:]
rhObs = np.zeros_like(rbins)[1:]
#Recovered
m1hRec = dict()
qhRec = dict()
ehRec = dict()
lphRec = dict()
dhRec = dict()
maghRec = dict()
rhRec = dict()
for f in filters:
m1hRec[f] = np.zeros_like(mbins)[1:]
qhRec[f] = np.zeros_like(qbins)[1:]
ehRec[f] = np.zeros_like(ebins)[1:]
lphRec[f] = np.zeros_like(lpbins)[1:]
dhRec[f] = np.zeros_like(dbins)[1:]
maghRec[f] = np.zeros_like(magbins)[1:]
rhRec[f] = np.zeros_like(rbins)[1:]
RA = []
Dec = []
recFrac = []
recN = []
rawN = []
obsN = []
fileN = []
fileObsN = []
fileRecN = []
allNPrsa = []
obsNPrsa = []
recNPrsa = []
# Lists for period and eccentricity for Andrew's circularization plots
eccAll = []
eccObs = []
eccRec = []
pAll = []
pObs = []
pRec = []
# Using prsa dataframes for these lists because of period cutoff at 1000 days
# Dataframes to write to files later; 3 files for each sub-population - append everything to these
peccAll = pd.DataFrame(columns = ['e', 'p'])
peccObs = pd.DataFrame(columns = ['e', 'p'])
peccRec = pd.DataFrame(columns = ['e', 'p'])
#Read in all the data and make the histograms
d = "./input_files/"
files = os.listdir(d)
IDs = []
for i, f in enumerate(files):
print(round(i/len(files),4), f)
fl = file_len(d+f)
if (fl >= 4):
#read in the header
header = pd.read_csv(d+f, nrows=1)
######################
#NEED TO ACCOUNT FOR THE BINARY FRACTION when combining histograms
#####################
Nmult = header['clusterMass'][0]/mMean
#Nmult = 1.
RA.append(header['OpSimRA'])
Dec.append(header['OpSimDec'])
#read in rest of the file
data = pd.read_csv(d+f, header = 2).fillna(-999)
rF = 0.
rN = 0.
Nrec = 0.
Nobs = 0.
raN = 0.
obN = 0.
fiN = 0.
fioN = 0.
firN = 0.
NallPrsa = 0.
NobsPrsa = 0.
NrecPrsa = 0.
Nall = len(data.index)/intNorm ###is this correct? (and the only place I need to normalize?)
prsa = data.loc[(data['appMagMean_r'] <= 19.5) & (data['appMagMean_r'] > 15.8) & (data['p'] < 1000) & (data['p'] > 0.5)]
# Appending for Andrew
eccAll.append(prsa['e'].values)
pAll.append(prsa['p'].values)
NallPrsa = len(prsa.index)
if (Nall >= Nlim):
#create histograms
#All
m1hAll0, m1b = np.histogram(data["m1"], bins=mbins)
qhAll0, qb = np.histogram(data["m2"]/data["m1"], bins=qbins)
ehAll0, eb = np.histogram(data["e"], bins=ebins)
lphAll0, lpb = np.histogram(np.ma.log10(data["p"].values).filled(-999), bins=lpbins)
dhAll0, db = np.histogram(data["d"], bins=dbins)
maghAll0, magb = np.histogram(data["appMagMean_r"], bins=magbins)
rhAll0, rb = np.histogram(data["r2"]/data["r1"], bins=rbins)
if (doIndividualPlots):
axmass.step(m1b[0:-1], m1hAll0/np.sum(m1hAll0), color='black', alpha=0.1)
axqrat.step(qb[0:-1], qhAll0/np.sum(qhAll0), color='black', alpha=0.1)
axecc.step(eb[0:-1], ehAll0/np.sum(ehAll0), color='black', alpha=0.1)
axlper.step(lpb[0:-1], lphAll0/np.sum(lphAll0), color='black', alpha=0.1)
axdist.step(db[0:-1], dhAll0/np.sum(dhAll0), color='black', alpha=0.1)
axmag.step(magb[0:-1], maghAll0/np.sum(maghAll0), color='black', alpha=0.1)
axrad.step(rb[0:-1], rhAll0/np.sum(rhAll0), color='black', alpha=0.1)
#account for the binary fraction, as a function of mass
dm1 = np.diff(m1b)
m1val = m1b[:-1] + dm1/2.
fb = np.sum(m1hAll0/len(data.index)*fbFit(m1val))
#account for the hard-soft boundary
Phs = getPhs(header['clusterVdisp'].iloc[0]*units.km/units.s).to(units.day).value
fb *= RagNormal(np.log10(Phs), cdf = True)
print("fb, Phs = ", fb, Phs)
Nmult *= fb
m1hAll += m1hAll0/Nall*Nmult
qhAll += qhAll0/Nall*Nmult
ehAll += ehAll0/Nall*Nmult
lphAll += lphAll0/Nall*Nmult
dhAll += dhAll0/Nall*Nmult
maghAll += maghAll0/Nall*Nmult
rhAll += rhAll0/Nall*Nmult
#Obs
obs = data.loc[data['LSM_PERIOD'] != -999]
Nobs = len(obs.index)
prsaObs = data.loc[(data['appMagMean_r'] <= 19.5) & (data['appMagMean_r'] > 15.8) & (data['p'] < 1000) & (data['p'] >0.5) & (data['LSM_PERIOD'] != -999)]
NobsPrsa = len(prsaObs.index)
# Appending for Andrew's files
eccObs.append(prsaObs['e'].values)
pObs.append(prsaObs['p'].values)
if (Nobs >= Nlim):
m1hObs0, m1b = np.histogram(obs["m1"], bins=mbins)
qhObs0, qb = np.histogram(obs["m2"]/obs["m1"], bins=qbins)
ehObs0, eb = np.histogram(obs["e"], bins=ebins)
lphObs0, lpb = np.histogram(np.ma.log10(obs["p"].values).filled(-999), bins=lpbins)
dhObs0, db = np.histogram(obs["d"], bins=dbins)
maghObs0, magb = np.histogram(obs["appMagMean_r"], bins=magbins)
rhObs0, rb = np.histogram(obs["r2"]/obs["r1"], bins=rbins)
m1hObs += m1hObs0/Nall*Nmult
qhObs += qhObs0/Nall*Nmult
ehObs += ehObs0/Nall*Nmult
lphObs += lphObs0/Nall*Nmult
dhObs += dhObs0/Nall*Nmult
maghObs += maghObs0/Nall*Nmult
rhObs += rhObs0/Nall*Nmult
#Rec
recCombined = pd.DataFrame()
prsaRecCombined = pd.DataFrame()
for filt in filters:
key = filt+'LSS_PERIOD'
if (filt == 'all'):
key = 'LSM_PERIOD'
fullP = abs(data[key] - data['p'])/data['p']
halfP = abs(data[key] - 0.5*data['p'])/(0.5*data['p'])
twiceP = abs(data[key] - 2.*data['p'])/(2.*data['p'])
rec = data.loc[(data[key] != -999) & ( (fullP < Pcut) | (halfP < Pcut) | (twiceP < Pcut))]
prsaRec = data.loc[(data['appMagMean_r'] <= 19.5) & (data['appMagMean_r'] >15.8) & (data['p'] < 1000) & (data['p'] >0.5) & (data['LSM_PERIOD'] != -999) & ( (fullP < Pcut) | (halfP < Pcut) | (twiceP < Pcut))]
Nrec = len(rec.index)
#I'd like to account for all filters here to have more accurate numbers
recCombined = recCombined.append(rec)
prsaRecCombined = prsaRecCombined.append(prsaRec)
# Going to use prsaRecCombined for ecc-p plots to account for all filters
eccRec.append(prsaRec['e'].values)
pRec.append(prsaRec['p'].values)
if (filt == 'all'):
recCombined.drop_duplicates(inplace=True)
prsaRecCombined.drop_duplicates(inplace=True)
if (Nrec >= Nlim):
m1hRec0, m1b = np.histogram(rec["m1"], bins=mbins)
qhRec0, qb = np.histogram(rec["m2"]/rec["m1"], bins=qbins)
ehRec0, eb = np.histogram(rec["e"], bins=ebins)
lphRec0, lpb = np.histogram(np.ma.log10(rec["p"].values).filled(-999), bins=lpbins)
dhRec0, db = np.histogram(rec["d"], bins=dbins)
maghRec0, magb = np.histogram(rec["appMagMean_r"], bins=magbins)
rhRec0, rb = np.histogram(rec["r2"]/rec["r1"], bins=rbins)
m1hRec[filt] += m1hRec0/Nall*Nmult
qhRec[filt] += qhRec0/Nall*Nmult
ehRec[filt] += ehRec0/Nall*Nmult
lphRec[filt] += lphRec0/Nall*Nmult
dhRec[filt] += dhRec0/Nall*Nmult
maghRec[filt] += maghRec0/Nall*Nmult
rhRec[filt] += rhRec0/Nall*Nmult
#for the mollweide
if (filt == 'all'):
Nrec = len(recCombined.index)
rF = Nrec/Nall
rN = Nrec/Nall*Nmult
raN = Nmult
obN = Nobs/Nall*Nmult
fiN = Nall
fioN = Nobs
firN = Nrec
NrecPrsa = len(prsaRecCombined.index)
NrecPrsa = NrecPrsa/Nall*Nmult
NobsPrsa = NobsPrsa/Nall*Nmult
NallPrsa = NallPrsa/Nall*Nmult
recFrac.append(rF)
recN.append(rN)
rawN.append(raN)
obsN.append(obN)
fileN.append(fiN)
fileObsN.append(fioN)
fileRecN.append(firN)
allNPrsa.append(NallPrsa)
obsNPrsa.append(NobsPrsa)
recNPrsa.append(NrecPrsa)
#print(np.sum(lphRec), np.sum(recN), np.sum(lphRec)/np.sum(recN), np.sum(lphRec0), Nrec, np.sum(lphRec0)/Nrec, np.sum(lphObs), np.sum(obsN), np.sum(lphObs)/np.sum(obsN))
# Concatenating p and ecc lists
eccAll = np.concatenate(eccAll)
eccObs = np.concatenate(eccObs)
eccRec = np.concatenate(eccRec)
pAll = np.concatenate(pAll)
pObs = np.concatenate(pObs)
pRec = np.concatenate(pRec)
# print('Ecc lists:', eccAll, eccObs, eccRec)
# print('P lists:', pAll, pObs, pRec)
# Appending lists with all the p/ecc values to our dataframes
# All dataframe
peccAll['e'] = eccAll
peccAll['p'] = pAll
# Observable dataframe
peccObs['e'] = eccObs
peccObs['p'] = pObs
# Recovered dataframe
peccRec['e'] = eccRec
peccRec['p'] = pRec
# print('Final Dataframes:', peccAll, peccObs, peccRec)
# print(peccRec.columns)
# 3 letter code corresponds to scenario (OC/GC, baseline/colossus, crowding/no crowding)
peccAll.to_csv('./pecc/all-M67BN-ecc-p.csv', header = ['e', 'p'])
peccObs.to_csv('./pecc/obs-M67BN-ecc-p.csv', header = ['e', 'p'])
peccRec.to_csv('./pecc/rec-M67BN-ecc-p.csv', header = ['e', 'p'])
#plot and save the histograms
saveHist(m1hAll, m1hObs, m1hRec, m1b, 'm1 (Msolar)', 'EBLSST_m1hist')
saveHist(qhAll, qhObs, qhRec, qb, 'q (m2/m1)', 'EBLSST_qhist')
saveHist(ehAll, ehObs, ehRec, eb, 'e', 'EBLSST_ehist')
saveHist(lphAll, lphObs, lphRec, lpb, 'log(P [days])', 'EBLSST_lphist')
saveHist(dhAll, dhObs, dhRec, db, 'd (kpc)', 'EBLSST_dhist')
saveHist(maghAll, maghObs, maghRec, magb, 'mag', 'EBLSST_maghist')
saveHist(rhAll, rhObs, rhRec, rb, 'r2/r1', 'EBLSST_rhist')
#make the mollweide
coords = SkyCoord(RA, Dec, unit=(units.degree, units.degree),frame='icrs')
lGal = coords.galactic.l.wrap_at(180.*units.degree).degree
bGal = coords.galactic.b.wrap_at(180.*units.degree).degree
RAwrap = coords.ra.wrap_at(180.*units.degree).degree
Decwrap = coords.dec.wrap_at(180.*units.degree).degree
f, ax = plt.subplots(subplot_kw={'projection': "mollweide"}, figsize=(8,5))
ax.grid(True)
#ax.set_xlabel(r"$l$",fontsize=16)
#ax.set_ylabel(r"$b$",fontsize=16)
#mlw = ax.scatter(lGal.ravel()*np.pi/180., bGal.ravel()*np.pi/180., c=np.log10(np.array(recFrac)*100.), cmap='viridis_r', s = 4)
ax.set_xlabel("RA",fontsize=16)
ax.set_ylabel("Dec",fontsize=16)
mlw = ax.scatter(np.array(RAwrap).ravel()*np.pi/180., np.array(Decwrap).ravel()*np.pi/180., c=np.array(recFrac)*100., cmap='viridis_r', s = 4)
cbar = f.colorbar(mlw, shrink=0.7)
cbar.set_label(r'% recovered')
f.savefig('./plots/' + 'mollweide_pct.pdf',format='pdf', bbox_inches = 'tight')
f, ax = plt.subplots(subplot_kw={'projection': "mollweide"}, figsize=(8,5))
ax.grid(True)
#ax.set_xlabel(r"$l$",fontsize=16)
#ax.set_ylabel(r"$b$",fontsize=16)
#mlw = ax.scatter(lGal.ravel()*np.pi/180., bGal.ravel()*np.pi/180., c=np.log10(np.array(recN)), cmap='viridis_r', s = 4)
ax.set_xlabel("RA",fontsize=16)
ax.set_ylabel("Dec",fontsize=16)
mlw = ax.scatter(np.array(RAwrap).ravel()*np.pi/180., np.array(Decwrap).ravel()*np.pi/180., c=np.log10(np.array(recN)), cmap='viridis_r', s = 4)
cbar = f.colorbar(mlw, shrink=0.7)
cbar.set_label(r'log10(N) recovered')
f.savefig('./plots/' + 'mollweide_N.pdf',format='pdf', bbox_inches = 'tight')
if (doIndividualPlots):
fmass.savefig('./plots/' + 'massPDFall.pdf',format='pdf', bbox_inches = 'tight')
fqrat.savefig('./plots/' + 'qPDFall.pdf',format='pdf', bbox_inches = 'tight')
fecc.savefig('./plots/' + 'eccPDFall.pdf',format='pdf', bbox_inches = 'tight')
flper.savefig('./plots/' + 'lperPDFall.pdf',format='pdf', bbox_inches = 'tight')
fdist.savefig('./plots/' + 'distPDFall.pdf',format='pdf', bbox_inches = 'tight')
fmag.savefig('./plots/' + 'magPDFall.pdf',format='pdf', bbox_inches = 'tight')
frad.savefig('./plots/' + 'radPDFall.pdf',format='pdf', bbox_inches = 'tight')
print("###################")
print("number of binaries in input files (raw, log):",np.sum(fileN), np.log10(np.sum(fileN)))
print("number of binaries in tested with gatspy (raw, log):",np.sum(fileObsN), np.log10(np.sum(fileObsN)))
print("number of binaries in recovered with gatspy (raw, log):",np.sum(fileRecN), np.log10(np.sum(fileRecN)))
print("recovered/observable*100 with gatspy:",np.sum(fileRecN)/np.sum(fileObsN)*100.)
print("###################")
print("total in sample (raw, log):",np.sum(rawN), np.log10(np.sum(rawN)))
print("total observable (raw, log):",np.sum(obsN), np.log10(np.sum(obsN)))
print("total recovered (raw, log):",np.sum(recN), np.log10(np.sum(recN)))
print("recovered/observable*100:",np.sum(recN)/np.sum(obsN)*100.)
print("###################")
print("total in Prsa 15.8<r<19.5 P<1000d sample (raw, log):",np.sum(allNPrsa), np.log10(np.sum(allNPrsa)))
print("total observable in Prsa 15.8<r<19.5 P<1000d sample (raw, log):",np.sum(obsNPrsa), np.log10(np.sum(obsNPrsa)))
print("total recovered in Prsa 15.8<r<19.5 P<1000d sample (raw, log):",np.sum(recNPrsa), np.log10(np.sum(recNPrsa)))
print("Prsa 15.8<r<19.5 P<1000d rec/obs*100:",np.sum(recNPrsa)/np.sum(obsNPrsa)*100.)
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examples/pest_query.py | vpreston/jetyak-parsing | 0 | 189401 | <filename>examples/pest_query.py<gh_stars>0
#!/usr/env/python
'''
This is an example in which all CTD and Optode data is compiled for PEST deployments.
Maintainer: vpreston-at-{whoi, mit}-dot-edu
'''
import numpy as np
import jetyak
import sensors
import jviz
import shapefile
import matplotlib
import gpxpy
import pandas as pd
import copy
import gsw
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import mpl_toolkits.basemap as mb
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
from matplotlib.mlab import griddata
import utm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from matplotlib.ticker import LogFormatter
def lat2str(deg):
min = 60 * (deg - np.floor(deg))
deg = np.floor(deg)
dir = 'N'
if deg < 0:
if min != 0.0:
deg += 1.0
min -= 60.0
dir = 'S'
return ("%d$\degree$ %g'") % (np.abs(deg),np.abs(min))
def lon2str(deg):
min = 60 * (deg - np.floor(deg))
deg = np.floor(deg)
dir = 'E'
if deg < 0:
if min != 0.0:
deg += 1.0
min -= 60.0
dir = 'W'
return ("%d$\degree$ %g'") % (np.abs(deg),np.abs(min))
def calc_salinity(temp, conduc):
pressure = 0.1
salt = gsw.SP_from_C(conduc, temp, pressure)
return salt
if __name__ == '__main__':
####################################################
###### Instrument Data and Params ##################
####################################################
base_path = '/home/vpreston/Documents/field_work/pest/'
save_path = '/home/vpreston/Documents/IPP/jetyak_parsing/missions/pest/'
ctd_dirs = [base_path + 'ctd_all.txt']
optode_dirs = [base_path + 'op_all.txt']
gps_dirs = [base_path + '20190605-142802.gpx']
# ctd_dirs = [base_path + 'ctd.txt']
# optode_dirs = [base_path + 'op_morning.txt']
# gps_dirs = [base_path + '20190605-105758.gpx']
mission_name = 'PEST_ExternalLoggers_060519_all.csv'
trim_values = None
bounds = None
offset = 0.0
####################################################
###### Make a "mission" dataset ####################
####################################################
jy = jetyak.JetYak(trim_vals=trim_values, bounds=bounds, args=[offset])
jy.attach_sensor('ctd', ctd_dirs)
jy.attach_sensor('mini_optode', optode_dirs)
jy.attach_sensor('phone_gps', gps_dirs)
# Can now perform work with the sensors
jy.create_mission({'geoframe':'phone_gps'})
jy.save_mission(save_path, mission_name=mission_name)
# Also get the waypoints from the mission and save those
data = []
for point in jy.phone_gps.waypoints:
data.append([point.longitude, point.latitude, point.elevation, point.time, point.name])
pdf = pd.DataFrame(data, columns=['Longitude', 'Latitude', 'Elevation', 'Time', 'Name'])
# Set Times
data = copy.copy(pdf)
data.loc[:, 'Year'] = data.apply(lambda x: x['Time'].year, axis=1)
data.loc[:, 'Month'] = data.apply(lambda x: x['Time'].month, axis=1)
data.loc[:, 'Day'] = data.apply(lambda x: x['Time'].day, axis=1)
data.loc[:, 'Hour'] = data.apply(lambda x: x['Time'].hour, axis=1)
data.loc[:, 'Minute'] = data.apply(lambda x: x['Time'].minute, axis=1)
data.loc[:, 'Second'] = data.apply(lambda x: x['Time'].second, axis=1)
data = sensors.make_global_time(data)
# Query for Data Reading
m = jy.mission[0]
oxy = []
conduct = []
salt = []
ctdtemp = []
optemp = []
for lat, lon, time in zip(pdf.Latitude, pdf.Longitude, pdf.Julian_Date):
# temp = m[(m['phone_gps']['Latitude'] == lat) & (m['phone_gps']['Longitude'] == lon)]
temp = m[np.fabs(m.index - time) < 0.0007]
oxy.append(np.median(temp['mini_optode']['DO']))
conduct.append(np.median(temp['ctd']['Conductivity']))
salt.append(np.median(temp['ctd']['Salinity']))
ctdtemp.append(np.median(temp['ctd']['Temperature']))
optemp.append(np.median(temp['mini_optode']['Temperature']))
data.loc[:, 'DO'] = oxy
data.loc[:, 'Conductivity'] = conduct
data.loc[:, 'Salinity'] = salt
data.loc[:, 'CTD_Temperature'] = ctdtemp
data.loc[:, 'Op_Temperature'] = optemp
# print conduct
# print data.Name
pdf = data
pdf.to_csv(save_path+'waypoint_'+mission_name)
# plt.plot(m.index, m['ctd']['Temperature'], label='CTD')
# plt.plot(m.index, m['mini_optode']['Temperature'], label='Optode')
# plt.legend()
# plt.xlabel('Julian Date')
# plt.ylabel('Temeprature, C')
# plt.show()
# plt.close()
x_min = np.nanmin(m['phone_gps']['Latitude']) - 0.0001
x_max = np.nanmax(m['phone_gps']['Latitude']) + 0.0001
y_min = np.nanmin(m['phone_gps']['Longitude']) - 0.0001
y_max = np.nanmax(m['phone_gps']['Longitude']) + 0.0001
# y_max = -70.640268
# y_min = -70.641015
# x_min = 41.575445
# x_max = 41.576148
base = Basemap(llcrnrlon=y_min,
llcrnrlat=x_min,
urcrnrlon=y_max,
urcrnrlat=x_max,
resolution='l',
projection='cyl',
suppress_ticks=False)
# im = plt.imshow(plt.imread('/home/vpreston/Pictures/little_sipp.jpg'), extent=(y_min, y_max, x_min, x_max))
proj_lon, proj_lat = base(*(m['phone_gps']['Longitude'].values, m['phone_gps']['Latitude'].values))
path_lon, path_lat = base(*(pdf['Longitude'].values, pdf['Latitude'].values))
scat = base.scatter(path_lon,
path_lat,
zorder=5,
s=100.0,
alpha=1.0,
c=pdf['Conductivity'],
cmap='coolwarm',
lw=0)
base.scatter(proj_lon,
proj_lat,
zorder=3,
s=1.0,
alpha=1.0,
c='k')
ax = plt.gca()
def xformat(x, pos=None): return lon2str(x)
def yformat(x, pos=None): return lat2str(x)
ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(xformat))
ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(yformat))
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
plt.gcf().tight_layout(rect=(0, 0.11, 1, 1))
cbar = plt.colorbar(scat)
# cbar.set_label('Salinity, PSS')
cbar.set_label('Conductivity, mS/cm')
# cbar.set_label('DO, mg/l')
# cbar.set_label('Temperature, C')
# plt.legend()
plt.show()
plt.close()
scat = plt.scatter(pdf['DO'], pdf['Conductivity'], c=pdf['CTD_Temperature'], cmap='coolwarm')
cbar = plt.colorbar(scat)
cbar.set_label('Temperature, C')
plt.xlabel('DO, mg/l')
plt.ylabel('Conductivity, mS/cm')
plt.show()
plt.close()
####################################################
###### Make a PEST Version of Everything ###########
####################################################
#read in Noa's PEST file
pest_path = '/home/vpreston/Documents/field_work/pest/'
pixhawk = base_path + 'pix_log17.gpx'
suite = base_path + 'LOG99.txt'
pest = pd.read_table(suite, delimiter=',', header=0, engine='c')
pest.Year = pest.Year.values - 3
pest.Month = pest.Month.values - 5
pest.Day = pest.Day.values - 9
pest.Hour = pest.Hour.values - 11
pest = sensors.make_global_time(pest)
pest.loc[:, 'Salinity'] = pest.apply(lambda x: calc_salinity(x['Temperature'], x['Conductivity']/1000), axis=1)
print pest.Salinity.values
sal = [np.round(x, 3) for x in pest.Salinity.values]
np.savetxt('pest_salinity.txt', sal, fmt='%s')
plt.scatter(pest.index, pest['Salinity'])
plt.show()
plt.close()
pix = gpxpy.parse(open(pixhawk))
track = pix.tracks[0]
segment = track.segments[0]
data = []
for i, point in enumerate(segment.points):
data.append([point.longitude, point.latitude, point.elevation, point.time])
pixdf = pd.DataFrame(data, columns=['Longitude', 'Latitude', 'Elevation', 'Time'])
data = copy.copy(pixdf)
data.loc[:, 'Year'] = data.apply(lambda x: x['Time'].year, axis=1)
data.loc[:, 'Month'] = data.apply(lambda x: x['Time'].month, axis=1)
data.loc[:, 'Day'] = data.apply(lambda x: x['Time'].day, axis=1)
data.loc[:, 'Hour'] = data.apply(lambda x: x['Time'].hour, axis=1)
data.loc[:, 'Minute'] = data.apply(lambda x: x['Time'].minute, axis=1)
data.loc[:, 'Second'] = data.apply(lambda x: x['Time'].second, axis=1)
data = sensors.make_global_time(data)
pixdf = data
dfs = [pest.drop_duplicates(subset='Julian_Date', keep='last').set_index('Julian_Date'),
pixdf.drop_duplicates(subset='Julian_Date', keep='last').set_index('Julian_Date')]
temp = pd.concat(dfs, axis=1, keys=['pest', 'pix'])
inter_temp = temp.interpolate()
df_index = dfs[1].index
pest_df = inter_temp.loc[df_index]
print pest_df.head(3)
plt.scatter(pest_df['pix']['Longitude'], pest_df['pix']['Latitude'], c=pest_df['pest']['Salinity'], cmap='coolwarm')
plt.show()
plt.close() | [
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] |
Examples/tools/display_tremolo.py | apohl79/AudioTK | 10 | 137669 | <gh_stars>1-10
#!/usr/bin/env python
from ATK.Core import DoubleInPointerFilter, DoubleOutPointerFilter
from ATK.Tools import DoubleCachedSinusGeneratorFilter, DoubleApplyGainFilter
import matplotlib.pyplot as plt
sample_rate = 96000
import sys, os
sys.path.append(os.path.dirname(os.path.realpath(__file__))+"/..")
from display.compare_spec import plot_me
def filter(input):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
infilter.set_input_sampling_rate(sample_rate)
sinusfilter = DoubleCachedSinusGeneratorFilter(20, 5)
sinusfilter.set_input_sampling_rate(sample_rate)
sinusfilter.set_offset(0.5)
sinusfilter.set_volume(0.5)
gainfilter = DoubleApplyGainFilter(1)
gainfilter.set_input_sampling_rate(sample_rate)
gainfilter.set_input_port(0, infilter, 0)
gainfilter.set_input_port(1, sinusfilter, 0)
outfilter = DoubleOutPointerFilter(output, False)
outfilter.set_input_sampling_rate(sample_rate)
outfilter.set_input_port(0, gainfilter, 0)
outfilter.process(input.shape[1])
return output
if __name__ == "__main__":
import numpy as np
samples = 2000000
freq_max = 20000
t = np.arange(samples, dtype=np.float64).reshape(1, -1) / sample_rate
d = np.sin(np.pi * (sample_rate * freq_max / samples * (t + .1)) * t)
np.savetxt("input.txt", d)
out = filter(d)
np.savetxt("output.txt", d)
plt.figure()
plot_me((d[0], out[0]), sample_rate)
plt.gcf().suptitle("Delay")
plt.legend()
plt.show()
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] |
test_spmm.py | binfoo1993/torchdgl | 0 | 79108 | <reponame>binfoo1993/torchdgl
import argparse, time, math
import numpy as np
import networkx as nx
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.function as fn
from dgl.data import register_data_args
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
from dgl.data import RedditDataset
from torch_sparse import SparseTensor
from torch_geometric.nn import MessagePassing
from torch_sparse import matmul
from typing import Optional
th.classes.load_library("build/libadjmatrix.so")
AdjMatrix = th.classes.DGL.AdjMatrix
def do_spmm(adj: AdjMatrix,
op: str,
reduce: str,
ufeat : Optional[th.Tensor],
efeat : Optional[th.Tensor]):
return th.ops.DGL.GSpMM(adj, op, reduce, ufeat, efeat)
scripted_spmm = th.jit.script(do_spmm)
class GCNConv_pyg(MessagePassing):
def __init__(self):
super(GCNConv_pyg, self).__init__(aggr="add")
def forward(self, x, edge_index):
out = self.propagate(edge_index, x=x)
return out
def message(self, x_j):
return x_j
def message_and_aggregate(self, adj_t, x):
return matmul(adj_t, x, reduce=self.aggr)
pyg_spmm = GCNConv_pyg()
def run_dgl(g, features):
g.ndata["h"] = features
g.update_all(fn.copy_src(src="h", out="m"), fn.sum(msg="m", out="h"))
return g.ndata['h']
def run_pyg(edge_index, features):
return pyg_spmm(features, edge_index)
def run_script(adj, features):
return scripted_spmm(adj, "copy_lhs", "sum", features, None)
def main(args):
# load and preprocess dataset
if args.dataset == 'cora':
data = CoraGraphDataset()
elif args.dataset == 'citeseer':
data = CiteseerGraphDataset()
elif args.dataset == 'pubmed':
data = PubmedGraphDataset()
elif args.dataset == 'reddit':
data = RedditDataset()
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
g = data[0]
if args.gpu < 0:
cuda = False
else:
cuda = True
g = g.to(args.gpu)
features = g.ndata['feat']
in_feats = features.shape[1]
n_classes = data.num_classes
print("feature size: {}".format(in_feats))
# add self loop
g = dgl.remove_self_loop(g)
g = dgl.add_self_loop(g)
n_edges = g.number_of_edges()
src, dst = g.edges()
edge_index = SparseTensor(row=src,
col=dst,
sparse_sizes=(g.number_of_nodes(), g.number_of_nodes()))
adj = AdjMatrix(src, dst)
runtime = 0.0
n = 1
if args.impl == "dgl":
run_dgl(g, features)
if args.gpu >= 0:
th.cuda.synchronize()
th.cuda.nvtx.range_push("spmm start")
for _ in range(n):
start_run = time.perf_counter()
run_dgl(g, features)
if args.gpu >= 0:
th.cuda.synchronize()
runtime += time.perf_counter() - start_run
th.cuda.nvtx.range_pop()
elif args.impl == "pyg":
run_pyg(edge_index, features)
if args.gpu >= 0:
th.cuda.synchronize()
for _ in range(n):
start_run = time.perf_counter()
run_pyg(edge_index, features)
if args.gpu >= 0:
th.cuda.synchronize()
runtime += time.perf_counter() - start_run
else:
run_script(adj, features)
if args.gpu >= 0:
th.cuda.synchronize()
for _ in range(n):
start_run = time.perf_counter()
run_script(adj, features)
if args.gpu >= 0:
th.cuda.synchronize()
runtime += time.perf_counter() - start_run
#print('Time (ms): {:.3f}'.format(runtime*1e3/n))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GCN')
register_data_args(parser)
parser.add_argument("--impl", type=str, default="dgl",
help="use torch script or not")
parser.add_argument("--gpu", type=int, default=-1,
help="gpu")
args = parser.parse_args()
print(args)
main(args)
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Sample/PyWebApi.IIS/json_fmtr.py | DataBooster/PyWebApi | 6 | 39252 | # -*- coding: utf-8 -*-
"""json_fmtr.py
This module implements a MediaTypeFormatter with JSON response.
This module was originally shipped as an example code from https://github.com/DataBooster/PyWebApi, licensed under the MIT license.
Anyone who obtains a copy of this code is welcome to modify it for any purpose, and holds all rights to the modified part only.
The above license notice and permission notice shall be included in all copies or substantial portions of the Software.
"""
from jsonpickle import dumps
from pywebapi import MediaTypeFormatter
class JsonFormatter(MediaTypeFormatter):
"""description of class"""
@property
def supported_media_types(self):
return ['application/json', 'text/json']
def format(self, obj, media_type:str, **kwargs):
kwargs['unpicklable'] = kwargs.get('unpicklable', False)
return dumps(obj, **kwargs)
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web1.py | Riddhikshah21/whyec2 | 0 | 120007 | <filename>web1.py
from flask import Flask
from flask import request
import random
app = Flask(__name__)
@app.route("/")
def hello():
return "Hello World!!!!"
def randomNumber(rnum):
sumran=0
for i in range(int(rnum)):
sumran += random.randint(0,int(rnum))
return sumran
@app.route("/r")
def printRandom():
rnum = request.args.get('rnum')
print(rnum)
var1 =randomNumber(rnum)
return str(var1)
if __name__ == '__name__':
app.run()
app.run(port=5000)
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src/sima/simo/blueprints/simplewirecoupling.py | SINTEF/simapy | 0 | 124214 | <filename>src/sima/simo/blueprints/simplewirecoupling.py
#
# Generated with SimpleWireCouplingBlueprint
from dmt.blueprint import Blueprint
from dmt.dimension import Dimension
from dmt.attribute import Attribute
from dmt.enum_attribute import EnumAttribute
from dmt.blueprint_attribute import BlueprintAttribute
from .simplecoupling import SimpleCouplingBlueprint
class SimpleWireCouplingBlueprint(SimpleCouplingBlueprint):
""""""
def __init__(self, name="SimpleWireCoupling", package_path="sima/simo", description=""):
super().__init__(name,package_path,description)
self.attributes.append(Attribute("name","string","",default=""))
self.attributes.append(Attribute("description","string","",default=""))
self.attributes.append(Attribute("_id","string","",default=""))
self.attributes.append(BlueprintAttribute("scriptableValues","sima/sima/ScriptableValue","",True,Dimension("*")))
self.attributes.append(BlueprintAttribute("endPoint1","sima/simo/SIMOBodyPoint","",False))
self.attributes.append(BlueprintAttribute("endPoint2","sima/simo/SIMOBodyPoint","",False))
self.attributes.append(EnumAttribute("failureMode","sima/simo/ActivationFailureMode","Failure mode of coupling element"))
self.attributes.append(Attribute("failureTime","number","Earliest possible time of failure",default=0.0))
self.attributes.append(Attribute("breakingStrength","number","Breaking strength",default=0.0))
self.attributes.append(Attribute("ea","number","Wire cross section stiffness",default=0.0))
self.attributes.append(Attribute("length","number","Initial, unstretched wire length",default=0.0))
self.attributes.append(Attribute("damping","number","Material damping",default=0.0))
self.attributes.append(Attribute("flexibility","number","Connection flexibility",default=0.0))
self.attributes.append(BlueprintAttribute("guidePointSpecifications","sima/simo/GuidePointSpecification","",True,Dimension("*")))
self.attributes.append(Attribute("constantTensionControl","boolean","Enable constant tension control for this wire",default=True))
self.attributes.append(Attribute("tensionLevel","number","Desired tension level",default=0.0))
self.attributes.append(Attribute("tensionDeadband","number","Tension level deadband",default=0.0))
self.attributes.append(Attribute("maxSpeed","number","Max wire length rate of change",default=0.0))
self.attributes.append(Attribute("activationTime","number","Activation time for constant tension control",default=0.0))
self.attributes.append(Attribute("deactivationTime","number","Deactivation time for constant tension control. If value is less then or equal to activation time, controller will remain active throughout simulation",default=0.0)) | [
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heroes/heroes/apps.py | devrishik/Heroes | 0 | 106563 | <reponame>devrishik/Heroes
from django.apps import AppConfig
class HeroesConfig(AppConfig):
name = 'heroes.heroes'
verbose_name = "Heroes"
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29882,
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] |
visfbpic/static.py | georgeholt1/visfbpic | 1 | 119787 | <gh_stars>1-10
# Author: <NAME>
# License: MIT
# Version: 0.1.0
"""
Part of VISFBPIC.
Contains methods for creating static visualisations of the FBPIC data.
"""
import os
import sys
import importlib.util
import numpy as np
import matplotlib.pyplot as plt
plt.style.use(os.path.join(
os.path.split(__file__)[0], '_mpl_config', 'style.mplstyle'
))
def plot_plasma_profile(
n_file,
z_min,
z_max,
z_points=1000,
out_dir=None,
dpi=200,
figsize=(8, 4.5)
):
'''Create plot of plasma density profile.
Parameters
n_file : str
Path to file containing a function called `number_density`. The function
should take one argument: a value of z-position, and should return a
corresponding number density value. See `plasma_profile_example.py` for
an example.
z_min, z_max : float
Minimum and maximum values of the z-coordinate for the plasma profile.
z_points : int, optional
Number of points to sample the number density profile along z.
out_dir : str, optional
Path to output directory within which to save the animation. Defaults to
None, which shows the visualisation onscreen.
dpi : int, optional
Dots per inch resolution. Changing this parameter may result in a bad
plot layout. Defaults to 200.
figsize : tuple, optional
Figure size in the form (width, height). Changing this parameter may
results in a bad plot layout. Defaults to (8, 4.5).
'''
# initialise method from external file
spec = importlib.util.spec_from_file_location(
"plasma_profile",
n_file
)
number_density_mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(number_density_mod)
# generate data
z_array = np.linspace(z_min, z_max, z_points)
n_array = []
for z in z_array:
n_array.append(number_density_mod.number_density(z))
n_array = np.array(n_array)
n_profile_max_oom = int(np.floor(np.log10(n_array.max())))
# plotting
fig, ax = plt.subplots()
ax.plot(
z_array*1e3,
n_array/10**n_profile_max_oom,
c='C1'
)
ax.set_xlabel('$z$ (mm)')
ax.set_ylabel(r'$n_e$ ($\times 10 ^ {{{}}}$'.format(
n_profile_max_oom-6
) + ' cm$^{-3}$)')
if out_dir is None:
plt.show()
else:
fig.savefig(os.path.join(out_dir, "plasma_profile.png"))
# unit testing
if __name__ == "__main__":
plot_plasma_profile(
sys.argv[1],
float(sys.argv[2]),
float(sys.argv[3]),
out_dir=sys.argv[4],
) | [
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python/rhinoscripts/example_csv_loading.py | tasbolat1/hmv-s16 | 1 | 15414 | <gh_stars>1-10
"""Example code for importing a single rigid body trajectory into Rhino from a Optitrack CSV file.
Copyright (c) 2016, <NAME>. All rights reserved. Licensed under the terms
of the BSD 3-clause license as included in LICENSE.
Example code for generating a path of Rhino 'planes' (e.g. coordinate frame)
from a trajectory data file. The path is returned as a list of Plane objects.
Each plane is created using an origin vector and X and Y basis vectors. The
time stamps and Z basis vectors in the trajectory file are ignored.
"""
# Load the Rhino API.
import rhinoscriptsyntax as rs
# Make sure that the Python libraries also contained within this course package
# are on the load path. This adds the parent folder to the load path, assuming that this
# script is still located with the rhinoscripts/ subfolder of the Python library tree.
import sys, os
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
# Load the Optitrack CSV file parser module.
import optitrack.csv_reader as csv
from optitrack.geometry import *
# Find the path to the test data file located alongside the script.
filename = os.path.join( os.path.abspath(os.path.dirname(__file__)), "sample_optitrack_take.csv")
# Read the file.
take = csv.Take().readCSV(filename)
# Print out some statistics
print "Found rigid bodies:", take.rigid_bodies.keys()
# Process the first rigid body into a set of planes.
bodies = take.rigid_bodies.values()
# for now:
xaxis = [1,0,0]
yaxis = [0,1,0]
if len(bodies) > 0:
body = bodies[0]
for pos,rot in zip(body.positions, body.rotations):
if pos is not None and rot is not None:
xaxis, yaxis = quaternion_to_xaxis_yaxis(rot)
plane = rs.PlaneFromFrame(pos, xaxis, yaxis)
# create a visible plane, assuming units are in meters
rs.AddPlaneSurface( plane, 0.1, 0.1 )
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2
] |
models/attention_ensemble_diff_layers.py | tlatkowski/attention-ensemble-gene-expression | 0 | 35479 | import tensorflow as tf
from layers.attention_layers import attention_layer
from layers.common_layers import init_inputs
from layers.feed_forward_layers import feed_forward_diff_features, feed_forward_diff_layers
from utils.hyperparams import Hyperparams as hp
class AttentionBasedEnsembleNets:
def __init__(self, selection_methods, num_features, learning_rate=0.01):
with tf.name_scope('input'):
self.nn_inputs = init_inputs(num_features, selection_methods)
self.labels = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='labels')
with tf.name_scope('ff'):
nets = {'fisher': [200, 50], 'ttest': [200, 50], 'corr': [200, 50], 'random': [200, 50]}
feed_forward = feed_forward_diff_layers(self.nn_inputs, nets)
with tf.name_scope('output'):
out = attention_layer(feed_forward, attention_size=50)
logits = tf.layers.dense(out, units=1)
sig = tf.nn.sigmoid(logits)
predictions = tf.round(sig)
with tf.name_scope('train'):
self.loss = tf.losses.sigmoid_cross_entropy(
multi_class_labels=self.labels, logits=logits)
self.opt = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.loss)
with tf.name_scope('summaries'):
self.acc = tf.reduce_mean((predictions * self.labels) + ((1 - predictions) * (1 - self.labels)))
self.precision, precision_op = tf.metrics.precision(self.labels, predictions)
# summaries
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('accuracy', self.acc)
tf.summary.scalar('precision_op', precision_op)
self.merged_summary_op = tf.summary.merge_all()
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] |
kmip/services/results.py | ondrap/PyKMIP | 179 | 78665 | # Copyright (c) 2014 The Johns Hopkins University/Applied Physics Laboratory
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
class OperationResult(object):
def __init__(self,
result_status,
result_reason=None,
result_message=None):
self.result_status = result_status
if result_reason is not None:
self.result_reason = result_reason
else:
self.result_reason = None
if result_message is not None:
self.result_message = result_message
else:
self.result_message = None
class CreateResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
object_type=None,
uuid=None,
template_attribute=None):
super(CreateResult, self).__init__(
result_status, result_reason, result_message)
if object_type is not None:
self.object_type = object_type
else:
self.object_type = None
if uuid is not None:
self.uuid = uuid
else:
self.uuid = None
if template_attribute is not None:
self.template_attribute = template_attribute
else:
self.template_attribute = None
class CreateKeyPairResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
private_key_uuid=None,
public_key_uuid=None,
private_key_template_attribute=None,
public_key_template_attribute=None):
super(CreateKeyPairResult, self).__init__(
result_status, result_reason, result_message)
self.private_key_uuid = private_key_uuid
self.public_key_uuid = public_key_uuid
self.private_key_template_attribute = private_key_template_attribute
self.public_key_template_attribute = public_key_template_attribute
class ActivateResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
uuid=None):
super(ActivateResult, self).__init__(
result_status, result_reason, result_message)
if uuid is not None:
self.uuid = uuid
else:
self.uuid = None
class RegisterResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
uuid=None,
template_attribute=None):
super(RegisterResult, self).__init__(
result_status, result_reason, result_message)
if uuid is not None:
self.uuid = uuid
else:
self.uuid = None
if template_attribute is not None:
self.template_attribute = template_attribute
else:
self.template_attribute = None
class RekeyKeyPairResult(CreateKeyPairResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
private_key_uuid=None,
public_key_uuid=None,
private_key_template_attribute=None,
public_key_template_attribute=None):
super(RekeyKeyPairResult, self).__init__(
result_status, result_reason, result_message, private_key_uuid,
public_key_uuid, private_key_template_attribute,
public_key_template_attribute)
class GetResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
object_type=None,
uuid=None,
secret=None):
super(GetResult, self).__init__(
result_status, result_reason, result_message)
if object_type is not None:
self.object_type = object_type
else:
self.object_type = None
if uuid is not None:
self.uuid = uuid
else:
self.uuid = None
if secret is not None:
self.secret = secret
else:
self.secret = None
class GetAttributesResult(OperationResult):
def __init__(
self,
result_status,
result_reason=None,
result_message=None,
uuid=None,
attributes=None
):
super(GetAttributesResult, self).__init__(
result_status,
result_reason,
result_message
)
self.uuid = uuid
self.attributes = attributes
class GetAttributeListResult(OperationResult):
def __init__(
self,
result_status,
result_reason=None,
result_message=None,
uid=None,
names=None):
super(GetAttributeListResult, self).__init__(
result_status, result_reason, result_message)
self.uid = uid
self.names = names
class DestroyResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
uuid=None):
super(DestroyResult, self).__init__(
result_status, result_reason, result_message)
if uuid is not None:
self.uuid = uuid
else:
self.uuid = None
class LocateResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
uuids=None):
super(LocateResult, self).__init__(
result_status, result_reason, result_message)
self.uuids = uuids
class QueryResult(OperationResult):
"""
A container for the results of a Query operation.
Attributes:
result_status: The status of the Query operation (e.g., success or
failure).
result_reason: The reason for the operation status.
result_message: Extra information pertaining to the status reason.
operations: A list of Operations supported by the server.
object_types: A list of Object Types supported by the server.
vendor_identification:
server_information:
application_namespaces: A list of namespaces supported by the server.
extension_information: A list of extensions supported by the server.
"""
def __init__(self,
result_status,
result_reason=None,
result_message=None,
operations=None,
object_types=None,
vendor_identification=None,
server_information=None,
application_namespaces=None,
extension_information=None):
super(QueryResult, self).__init__(
result_status, result_reason, result_message)
if operations is None:
self.operations = list()
else:
self.operations = operations
if object_types is None:
self.object_types = list()
else:
self.object_types = object_types
self.vendor_identification = vendor_identification
self.server_information = server_information
if application_namespaces is None:
self.application_namespaces = list()
else:
self.application_namespaces = application_namespaces
if extension_information is None:
self.extension_information = list()
else:
self.extension_information = extension_information
class DiscoverVersionsResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
protocol_versions=None):
super(DiscoverVersionsResult, self).__init__(
result_status, result_reason, result_message)
self.protocol_versions = protocol_versions
class RevokeResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
unique_identifier=None):
super(RevokeResult, self).__init__(
result_status, result_reason, result_message)
self.unique_identifier = unique_identifier
class MACResult(OperationResult):
def __init__(self,
result_status,
result_reason=None,
result_message=None,
uuid=None,
mac_data=None):
super(MACResult, self).__init__(
result_status,
result_reason,
result_message
)
self.uuid = uuid
self.mac_data = mac_data
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13,
4706,
565,
318,
5416,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
25118,
353,
6213,
13,
13,
4706,
565,
4472,
29918,
12715,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
6886,
29918,
12715,
353,
4472,
29918,
12715,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
6886,
29918,
12715,
353,
6213,
13,
13,
13,
1990,
6204,
2558,
20547,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
2024,
29918,
1989,
29918,
25118,
29922,
8516,
29892,
13,
462,
970,
29918,
1989,
29918,
25118,
29922,
8516,
29892,
13,
462,
2024,
29918,
1989,
29918,
6886,
29918,
12715,
29922,
8516,
29892,
13,
462,
970,
29918,
1989,
29918,
6886,
29918,
12715,
29922,
8516,
1125,
13,
4706,
2428,
29898,
4391,
2558,
20547,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
1583,
29889,
9053,
29918,
1989,
29918,
25118,
353,
2024,
29918,
1989,
29918,
25118,
13,
4706,
1583,
29889,
3597,
29918,
1989,
29918,
25118,
353,
970,
29918,
1989,
29918,
25118,
13,
4706,
1583,
29889,
9053,
29918,
1989,
29918,
6886,
29918,
12715,
353,
2024,
29918,
1989,
29918,
6886,
29918,
12715,
13,
4706,
1583,
29889,
3597,
29918,
1989,
29918,
6886,
29918,
12715,
353,
970,
29918,
1989,
29918,
6886,
29918,
12715,
13,
13,
13,
1990,
21775,
403,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
318,
5416,
29922,
8516,
1125,
13,
4706,
2428,
29898,
21786,
403,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
13,
4706,
565,
318,
5416,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
25118,
353,
6213,
13,
13,
13,
1990,
12577,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
318,
5416,
29922,
8516,
29892,
13,
462,
4472,
29918,
12715,
29922,
8516,
1125,
13,
4706,
2428,
29898,
15213,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
565,
318,
5416,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
25118,
353,
6213,
13,
13,
4706,
565,
4472,
29918,
12715,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
6886,
29918,
12715,
353,
4472,
29918,
12715,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
6886,
29918,
12715,
353,
6213,
13,
13,
13,
1990,
830,
1989,
2558,
20547,
3591,
29898,
4391,
2558,
20547,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
2024,
29918,
1989,
29918,
25118,
29922,
8516,
29892,
13,
462,
970,
29918,
1989,
29918,
25118,
29922,
8516,
29892,
13,
462,
2024,
29918,
1989,
29918,
6886,
29918,
12715,
29922,
8516,
29892,
13,
462,
970,
29918,
1989,
29918,
6886,
29918,
12715,
29922,
8516,
1125,
13,
4706,
2428,
29898,
1123,
1989,
2558,
20547,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29892,
2024,
29918,
1989,
29918,
25118,
29892,
13,
9651,
970,
29918,
1989,
29918,
25118,
29892,
2024,
29918,
1989,
29918,
6886,
29918,
12715,
29892,
13,
9651,
970,
29918,
1989,
29918,
6886,
29918,
12715,
29897,
13,
13,
13,
1990,
3617,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
1203,
29918,
1853,
29922,
8516,
29892,
13,
462,
318,
5416,
29922,
8516,
29892,
13,
462,
7035,
29922,
8516,
1125,
13,
4706,
2428,
29898,
2577,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
565,
1203,
29918,
1853,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
3318,
29918,
1853,
353,
1203,
29918,
1853,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
3318,
29918,
1853,
353,
6213,
13,
13,
4706,
565,
318,
5416,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
25118,
353,
6213,
13,
13,
4706,
565,
7035,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
19024,
353,
7035,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
19024,
353,
6213,
13,
13,
13,
1990,
3617,
15801,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
9651,
1583,
29892,
13,
9651,
1121,
29918,
4882,
29892,
13,
9651,
1121,
29918,
23147,
29922,
8516,
29892,
13,
9651,
1121,
29918,
4906,
29922,
8516,
29892,
13,
9651,
318,
5416,
29922,
8516,
29892,
13,
9651,
8393,
29922,
8516,
13,
268,
1125,
13,
4706,
2428,
29898,
2577,
15801,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
13,
9651,
1121,
29918,
23147,
29892,
13,
9651,
1121,
29918,
4906,
13,
4706,
1723,
13,
4706,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1583,
29889,
15697,
353,
8393,
13,
13,
13,
1990,
3617,
6708,
1293,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
9651,
1583,
29892,
13,
9651,
1121,
29918,
4882,
29892,
13,
9651,
1121,
29918,
23147,
29922,
8516,
29892,
13,
9651,
1121,
29918,
4906,
29922,
8516,
29892,
13,
9651,
318,
333,
29922,
8516,
29892,
13,
9651,
2983,
29922,
8516,
1125,
13,
4706,
2428,
29898,
2577,
6708,
1293,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
1583,
29889,
5416,
353,
318,
333,
13,
4706,
1583,
29889,
7039,
353,
2983,
13,
13,
13,
1990,
15435,
4727,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
318,
5416,
29922,
8516,
1125,
13,
4706,
2428,
29898,
14994,
4727,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
565,
318,
5416,
338,
451,
6213,
29901,
13,
9651,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
25118,
353,
6213,
13,
13,
13,
1990,
5976,
403,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
318,
29884,
4841,
29922,
8516,
1125,
13,
4706,
2428,
29898,
3524,
403,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
4706,
1583,
29889,
29884,
29884,
4841,
353,
318,
29884,
4841,
13,
13,
13,
1990,
13641,
3591,
29898,
10925,
3591,
1125,
13,
1678,
9995,
13,
1678,
319,
5639,
363,
278,
2582,
310,
263,
13641,
5858,
29889,
13,
13,
1678,
6212,
5026,
29901,
13,
4706,
1121,
29918,
4882,
29901,
450,
4660,
310,
278,
13641,
5858,
313,
29872,
29889,
29887,
1696,
2551,
470,
13,
9651,
10672,
467,
13,
4706,
1121,
29918,
23147,
29901,
450,
2769,
363,
278,
5858,
4660,
29889,
13,
4706,
1121,
29918,
4906,
29901,
7338,
336,
2472,
639,
2408,
292,
304,
278,
4660,
2769,
29889,
13,
4706,
6931,
29901,
319,
1051,
310,
6607,
800,
6969,
491,
278,
1923,
29889,
13,
4706,
1203,
29918,
8768,
29901,
319,
1051,
310,
4669,
28025,
6969,
491,
278,
1923,
29889,
13,
4706,
27042,
29918,
1693,
2450,
29901,
13,
4706,
1923,
29918,
19678,
29901,
13,
4706,
2280,
29918,
7039,
22459,
29901,
319,
1051,
310,
2983,
22459,
6969,
491,
278,
1923,
29889,
13,
4706,
6081,
29918,
19678,
29901,
319,
1051,
310,
17752,
6969,
491,
278,
1923,
29889,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
6931,
29922,
8516,
29892,
13,
462,
1203,
29918,
8768,
29922,
8516,
29892,
13,
462,
27042,
29918,
1693,
2450,
29922,
8516,
29892,
13,
462,
1923,
29918,
19678,
29922,
8516,
29892,
13,
462,
2280,
29918,
7039,
22459,
29922,
8516,
29892,
13,
462,
6081,
29918,
19678,
29922,
8516,
1125,
13,
4706,
2428,
29898,
3010,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
13,
4706,
565,
6931,
338,
6213,
29901,
13,
9651,
1583,
29889,
3372,
800,
353,
1051,
580,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
3372,
800,
353,
6931,
13,
13,
4706,
565,
1203,
29918,
8768,
338,
6213,
29901,
13,
9651,
1583,
29889,
3318,
29918,
8768,
353,
1051,
580,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
3318,
29918,
8768,
353,
1203,
29918,
8768,
13,
13,
4706,
1583,
29889,
19167,
29918,
1693,
2450,
353,
27042,
29918,
1693,
2450,
13,
4706,
1583,
29889,
2974,
29918,
19678,
353,
1923,
29918,
19678,
13,
13,
4706,
565,
2280,
29918,
7039,
22459,
338,
6213,
29901,
13,
9651,
1583,
29889,
6214,
29918,
7039,
22459,
353,
1051,
580,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
6214,
29918,
7039,
22459,
353,
2280,
29918,
7039,
22459,
13,
13,
4706,
565,
6081,
29918,
19678,
338,
6213,
29901,
13,
9651,
1583,
29889,
17588,
29918,
19678,
353,
1051,
580,
13,
4706,
1683,
29901,
13,
9651,
1583,
29889,
17588,
29918,
19678,
353,
6081,
29918,
19678,
13,
13,
13,
1990,
8565,
957,
29963,
414,
1080,
3591,
29898,
10925,
3591,
1125,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
9608,
29918,
26100,
29922,
8516,
1125,
13,
4706,
2428,
29898,
4205,
11911,
29963,
414,
1080,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
1121,
29918,
23147,
29892,
1121,
29918,
4906,
29897,
13,
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1583,
29889,
20464,
29918,
26100,
353,
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29918,
26100,
13,
13,
13,
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830,
5744,
3591,
29898,
10925,
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1125,
13,
13,
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822,
4770,
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12035,
1311,
29892,
13,
462,
1121,
29918,
4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
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462,
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29922,
8516,
29892,
13,
462,
5412,
29918,
25378,
29922,
8516,
1125,
13,
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2428,
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1123,
5744,
3591,
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13,
462,
1121,
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4882,
29892,
13,
462,
1121,
29918,
23147,
29922,
8516,
29892,
13,
462,
1121,
29918,
4906,
29922,
8516,
29892,
13,
462,
318,
5416,
29922,
8516,
29892,
13,
462,
5825,
29918,
1272,
29922,
8516,
1125,
13,
4706,
2428,
29898,
1529,
29907,
3591,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
1121,
29918,
4882,
29892,
13,
9651,
1121,
29918,
23147,
29892,
13,
9651,
1121,
29918,
4906,
13,
4706,
1723,
13,
4706,
1583,
29889,
25118,
353,
318,
5416,
13,
4706,
1583,
29889,
8628,
29918,
1272,
353,
5825,
29918,
1272,
13,
2
] |
ARC033/ARC033b.py | VolgaKurvar/AtCoder | 0 | 127167 | na, nb = map(int, input().split())
a = list(map(int, input().split()))
b = list(map(int, input().split()))
a = set(a)
b = set(b)
t = len(a & b)
t2 = len(a.union(b))
print(t/t2)
| [
1,
1055,
29892,
302,
29890,
353,
2910,
29898,
524,
29892,
1881,
2141,
5451,
3101,
13,
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353,
1051,
29898,
1958,
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524,
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1881,
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29890,
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13,
13,
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353,
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29874,
669,
289,
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13,
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29906,
353,
7431,
29898,
29874,
29889,
13094,
29898,
29890,
876,
13,
2158,
29898,
29873,
29914,
29873,
29906,
29897,
13,
2
] |
8_Mixins/post/views.py | LIkelion-at-KOREATECH/LikeLion_Django_Study_Summary | 28 | 1607694 | <gh_stars>10-100
# 데이터 처리 대상 : 모델, Serializer import 시키기
from .models import Post
from .serializer import PostSerializer
from rest_framework import generics
from rest_framework import mixins
class PostList(mixins.ListModelMixin,
mixins.CreateModelMixin,
generics.GenericAPIView):
# 쿼리셋 등록
queryset = Post.objects.all()
# Serializer 클래스 등록
serializer_class = PostSerializer
def get(self, request, *args, **kwargs):
return self.list(request, *args, **kwargs)
def post(self, request, *args, **kwargs):
return self.create(request, *args, **kwargs)
class PostDetail(mixins.RetrieveModelMixin,
mixins.UpdateModelMixin,
mixins.DestroyModelMixin,
generics.GenericAPIView):
# 쿼리셋 등록
queryset = Post.objects.all()
# Serializer 클래스 등록
serializer_class = PostSerializer
def get(self, request, *args, **kwargs):
return self.retrieve(request, *args, **kwargs)
def put(self, request, *args, **kwargs):
return self.update(request, *args, **kwargs)
def delete(self, request, *args, **kwargs):
return self.destroy(request, *args, **kwargs)
| [
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20524,
29898,
3827,
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334,
5085,
29892,
3579,
19290,
29897,
13,
2
] |
modules/utils/module_user.py | Fogapod/KiwiBot | 18 | 115772 | from objects.modulebase import ModuleBase
from objects.permissions import PermissionEmbedLinks, PermissionExternalEmojis
from utils.funcs import find_user, _get_last_user_message_timestamp
from datetime import datetime
import discord
STATUS_EMOTES = {
'online': '<:online:427209268240973854>',
'idle': '<:idle:427209268203094017>',
'dnd': '<:dnd:427209268043841537>',
'offline': '<:offline:427209267687194625>',
'invisible': '<:invisible:427209267687194625>'
}
ACTIVITY_MAP = {
discord.ActivityType.unknown: '',
discord.ActivityType.playing: 'Playing: ',
discord.ActivityType.streaming: 'Streaming: ',
discord.ActivityType.listening: 'Listening: ',
discord.ActivityType.watching: 'Watching: ',
discord.ActivityType.custom: '',
}
class Module(ModuleBase):
usage_doc = '{prefix}{aliases} [user]'
short_doc = 'Get information about given user'
name = 'user'
aliases = (name, 'userinfo')
category = 'Discord'
bot_perms = (PermissionEmbedLinks(), )
async def on_call(self, ctx, args, **flags):
if len(args) == 1:
user = ctx.author
else:
user = await find_user(args[1:], ctx.message)
if user is None:
return await ctx.warn('User not found')
e = discord.Embed(
title=str(user),
url=str(user.avatar_url),
colour=discord.Colour.gold()
)
e.set_thumbnail(url=user.avatar_url)
e.add_field(
name='registered', inline=False,
value=f'`{user.created_at.replace(microsecond=0)}` ({(datetime.now() - user.created_at).days} days ago)'
)
if isinstance(user, discord.Member):
# function can return members from different guild
if user.guild == ctx.guild:
e.title += ' (member)'
e.add_field(
name='member since', inline=False,
value=f'`{user.joined_at.replace(microsecond=0)}` ({(datetime.now() - user.joined_at).days} days)'
)
last_msg_ts = _get_last_user_message_timestamp(user.id, ctx.channel.id)
if last_msg_ts != datetime.fromtimestamp(0):
last_msg_ts = last_msg_ts.replace(microsecond=0)
e.add_field(
name='last message sent', inline=False,
value=f'`{last_msg_ts}`'
)
e.add_field(name='top role', value=user.top_role.mention)
e.add_field(name='total roles', value=len(user.roles))
if user.nick is not None:
e.add_field(name='nick', value=user.nick, inline=False)
external_emoji_perm = PermissionExternalEmojis().check(ctx.channel, self.bot.user)
activity = user.activity
if activity is None:
e.add_field(
name='status',
value=(STATUS_EMOTES[str(user.status)] if external_emoji_perm else '') + str(user.status)
)
else:
activity_state = ACTIVITY_MAP.get(activity.type, '')
emoji = ''
# thanks discord.py
if getattr(activity, 'emoji', None):
# activity has emoji
if activity.emoji.id:
# emoji is custom
if self.bot.get_emoji(activity.emoji.id):
# bot has access to emoji
emoji = activity.emoji
else:
# bot has no acces to emoji
emoji = '\N{THINKING FACE}'
else:
# emoji is standard
emoji = activity.emoji
activity_name = f'{emoji} {activity.name if activity.name else ""}'
e.add_field(
name='activity',
value=(STATUS_EMOTES[str(user.status)] if external_emoji_perm else '') + f'{activity_state}{activity_name}'
)
e.add_field(name='robot', value='yes' if user.bot else 'no')
e.set_footer(text=user.id)
await ctx.send(embed=e)
| [
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271,
467,
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apps/blog/templatetags/template_library.py | gurnitha/2022-django-blog-magazine-cbv | 0 | 183825 | <filename>apps/blog/templatetags/template_library.py
# apps/blog/templatetags/menu_tags.py
# Django modules
from django import template
# Locals
from apps.blog.models import Category
# Template library
register = template.Library()
# Register your template here
@register.inclusion_tag('shared/tpl/menu_tpl.html')
def show_menu():
# categories = Category.objects.all()
first_two_categories = Category.objects.order_by('id')[0:2]
# print(first_three_categories) # <-- :)
the_rest_categories = Category.objects.order_by('id')[2:10]
# print(the_rest_categories) # <-- :)
context = {
# 'menu_category':categories,
'first_two_categories':first_two_categories,
'the_rest_categories':the_rest_categories
}
return context
@register.inclusion_tag('shared/tpl/canvas_menu_tpl.html')
def show_canvas_menu():
first_two_categories_for_canvas_menu = Category.objects.order_by('id')[0:2]
# print(canvas_menu) # <-- :)
the_rest_categories_for_canvas_menu = Category.objects.order_by('id')[2:10]
# print(first_two_categories_for_canvas_menu) # <-- :)
context = {
'first_two_categories_for_canvas_menu':first_two_categories_for_canvas_menu,
'the_rest_categories_for_canvas_menu':the_rest_categories_for_canvas_menu
}
return context | [
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yolov5/temp.py | shuyansy/A-detection-and-recognition-pipeline-of-complex-meters-in-wild | 17 | 31295 | <gh_stars>10-100
import os
import cv2
import numpy as np
from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
# Load model
device='0'
weights='runs/train/exp6/weights/best.pt'
data='data/mydata.yaml'
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=False, data=data)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size([640], s=stride) # check image size
path='/home/sy/ocr/datasets/all_meter_image/'
img_dir=os.listdir(path)
for i in img_dir:
img=cv2.imread(path+i)
# Padded resize
img = letterbox(img, [640], stride=stride, auto=pt)[0]
# Convert
img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
img = np.ascontiguousarray(img)
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src/polyswarm/formatters/text.py | polyswarm/polyswarm-cli | 2 | 50641 | from __future__ import absolute_import, unicode_literals
import sys
import functools
import json
import click
from polyswarm.formatters import base
def is_grouped(fn):
@functools.wraps(fn)
def wrapper(self, text):
return self._depth*'\t'+fn(self, text)
return wrapper
class TextOutput(base.BaseOutput):
name = 'text'
def __init__(self, color=True, output=sys.stdout, **kwargs):
super(TextOutput, self).__init__(output)
self.color = color
self._depth = 0
self.color = color
def _get_score_format(self, score):
if score < 0.15:
return self._white
elif score < 0.4:
return self._yellow
else:
return self._red
def _output(self, output, write):
if write:
click.echo('\n'.join(output) + '\n', file=self.out)
else:
return output
def artifact(self, artifact, write=True):
output = []
output.append(self._blue('SHA256: {hash}'.format(hash=artifact.sha256)))
output.append(self._white('SHA1: {hash}'.format(hash=artifact.sha1)))
output.append(self._white('MD5: {hash}'.format(hash=artifact.md5)))
output.append(self._white('File type: mimetype: {mimetype}, extended_info: {extended_type}'.
format(mimetype=artifact.mimetype, extended_type=artifact.extended_type)))
h = artifact.metadata.hash
if 'ssdeep' in h:
output.append(self._white('SSDEEP: {}'.format(h['ssdeep'])))
if 'tlsh' in h:
output.append(self._white('TLSH: {}'.format(h['tlsh'])))
if 'authentihash' in h:
output.append(self._white('Authentihash: {}'.format(h['authentihash'])))
p = artifact.metadata.pefile
if 'imphash' in p:
output.append(self._white('Imphash: {}'.format(p['imphash'])))
output.append(self._white('First seen: {}'.format(artifact.first_seen)))
output.append(self._white('Last scanned: {}'.format(artifact.last_scanned)))
# Deprecated
output.append(self._white('Last seen: {}'.format(artifact.last_scanned)))
return self._output(output, write)
def artifact_instance(self, instance, write=True, timeout=False):
output = []
output.append(self._white('============================= Artifact Instance ============================='))
output.append(self._white('Scan permalink: {}'.format(instance.permalink)))
if instance.community == 'stream':
output.append(self._white('Detections: This artifact has not been scanned. You can trigger a scan now.'))
elif len(instance.valid_assertions) == 0 and instance.window_closed and not instance.failed:
output.append(self._white('Detections: No engines responded to this scan. You can trigger a rescan now.'))
elif len(instance.valid_assertions) > 0 and instance.window_closed and not instance.failed:
malicious = 'Detections: {}/{} engines reported malicious'\
.format(len(instance.malicious_assertions), len(instance.valid_assertions))
if len(instance.malicious_assertions) > 0:
output.append(self._red(malicious))
else:
output.append(self._white(malicious))
elif not instance.window_closed and not instance.failed:
output.append(self._white('Detections: This scan has not finished running yet.'))
else:
output.append(self._white('Detections: This scan has failed. Please try again.'))
self._open_group()
for assertion in instance.assertions:
if assertion.verdict is False:
output.append('%s: %s' % (self._green(assertion.engine_name), 'Clean'))
elif assertion.verdict is None or assertion.mask is False:
output.append('%s: %s' % (self._blue(assertion.engine_name), 'Engine chose not respond to this bounty.'))
else:
value = 'Malicious'
if assertion.metadata:
value += ', metadata: %s' % json.dumps(assertion.metadata, sort_keys=True)
output.append('%s: %s' % (self._red(assertion.engine_name), value))
self._close_group()
output.append(self._blue('Scan id: {}'.format(instance.id)))
output.extend(self.artifact(instance, write=False))
if instance.failed:
output.append(self._red('Status: Failed'))
elif instance.window_closed:
output.append(self._white('Status: Assertion window closed'))
elif instance.community == 'stream':
output.append(self._white('Status: This artifact has not been scanned. You can trigger a scan now.'))
elif timeout:
output.append(self._yellow('Status: Lookup timed-out, please retry'))
else:
output.append(self._white('Status: Running'))
if instance.type == 'URL':
output.append(self._white('URL: {}'.format(instance.filename)))
else:
output.append(self._white('Filename: {}'.format(instance.filename)))
output.append(self._white('Community: {}'.format(instance.community)))
output.append(self._white('Country: {}'.format(instance.country)))
if instance.polyscore is not None:
formatter = self._get_score_format(instance.polyscore)
output.append(formatter('PolyScore: {:.20f}'.format(instance.polyscore)))
return self._output(output, write)
def hunt(self, result, write=True):
output = []
output.append(self._blue('Hunt Id: {}'.format(result.id)))
if result.active is not None:
output.append(self._white('Active: {}'.format(result.active)))
if result.ruleset_name is not None:
output.append(self._white('Ruleset Name: {}'.format(result.ruleset_name)))
output.append(self._white('Created at: {}'.format(result.created)))
return self._output(output, write)
def hunt_deletion(self, result, write=True):
output = []
output.append(self._yellow('Successfully deleted Hunt:'))
output.extend(self.hunt(result, write=False))
return self._output(output, write)
def hunt_result(self, result, write=True):
output = []
output.append(self._white('Match on rule {name}'.format(name=result.rule_name) +
(', tags: {result_tags}'.format(
result_tags=result.tags) if result.tags != '' else '')))
output.extend(self.artifact_instance(result.artifact, write=False))
return self._output(output, write)
def ruleset(self, result, write=True, contents=False):
output = []
output.append(self._blue('Ruleset Id: {}'.format(result.id)))
output.append(self._white('Name: {}'.format(result.name)))
output.append(self._white('Description: {}'.format(result.description)))
output.append(self._white('Created at: {}'.format(result.created)))
output.append(self._white('Modified at: {}'.format(result.modified)))
if contents:
output.append(self._white('Contents:\n{}'.format(result.yara)))
return self._output(output, write)
def tag_link(self, result, write=True):
output = []
output.append(self._blue('SHA256: {}'.format(result.sha256)))
output.append(self._white('First seen: {}'.format(result.first_seen)))
output.append(self._white('Tags: {}'.format(result.tags)))
output.append(self._white('Families: {}'.format(result.families)))
output.append(self._white('Emerging: {}'.format(result.emerging)))
return self._output(output, write)
def family(self, result, write=True):
output = []
output.append(self._blue('Family: {}'.format(result.name)))
output.append(self._white('Emerging: {}'.format(result.emerging)))
return self._output(output, write)
def tag(self, result, write=True):
output = []
output.append(self._blue('Tag: {}'.format(result.name)))
return self._output(output, write)
def local_artifact(self, artifact, write=True):
output = []
output.append(self._white('Successfully downloaded artifact {} to {}'
.format(artifact.artifact_name, artifact.name)))
return self._output(output, write)
def _dfs_mapping_render(self, output, path, tree, depth=0):
tree_string = (' | ' * (depth - 1)) + ' +-' if depth > 0 else ''
current_path = '.'.join(path)
if not tree:
output.append(self._white(tree_string + current_path))
else:
if path:
output.append(self._white(tree_string + current_path))
for k, v in tree.items():
self._dfs_mapping_render(output, path + [k], v, depth=depth + 1)
def mapping(self, mapping, write=True):
output = []
output.append(self._white('============================= Mapping ============================='))
self._dfs_mapping_render(output, [], mapping.json)
return self._output(output, write)
def metadata(self, instance, write=True):
output = []
output.append(self._white('============================= Metadata ============================='))
output.append(self._blue('Artifact id: {}'.format(instance.id)))
output.append(self._white('Created: {}'.format(instance.created)))
if instance.sha256:
output.append(self._white('SHA256: {}'.format(instance.sha256)))
if instance.sha1:
output.append(self._white('SHA1: {}'.format(instance.sha1)))
if instance.md5:
output.append(self._white('MD5: {}'.format(instance.md5)))
if instance.ssdeep:
output.append(self._white('SSDEEP: {}'.format(instance.ssdeep)))
if instance.tlsh:
output.append(self._white('TLSH: {}'.format(instance.tlsh)))
if instance.first_seen:
output.append(self._white('First seen: {}'.format(instance.first_seen)))
if instance.last_scanned:
output.append(self._white('Last scanned: {}'.format(instance.last_scanned)))
# Deprecated
output.append(self._white('Last seen: {}'.format(instance.last_scanned)))
if instance.mimetype:
output.append(self._white('Mimetype: {}'.format(instance.mimetype)))
if instance.extended_mimetype:
output.append(self._white('Extended mimetype: {}'.format(instance.extended_mimetype)))
if instance.malicious:
output.append(self._white('Malicious: {}'.format(instance.malicious)))
if instance.benign:
output.append(self._white('Benign: {}'.format(instance.benign)))
if instance.total_detections:
output.append(self._white('Total detections: {}'.format(instance.total_detections)))
if instance.domains:
output.append(self._white('Domains:'))
self._open_group()
output.append(self._white('{}'.format(', '.join(instance.domains))))
self._close_group()
if instance.ipv4:
output.append(self._white('Ipv4:'))
self._open_group()
output.append(self._white('{}'.format(', '.join(instance.ipv4))))
self._close_group()
if instance.ipv6:
output.append(self._white('Ipv6:'))
self._open_group()
output.append(self._white('{}'.format(', '.join(instance.ipv6))))
self._close_group()
if instance.urls:
output.append(self._white('Urls:'))
self._open_group()
output.append(self._white('{}'.format(', '.join(instance.urls))))
self._close_group()
if instance.filenames:
output.append(self._white('Filenames:'))
self._open_group()
output.append(self._white('{}'.format(', '.join(instance.filenames))))
self._close_group()
return self._output(output, write)
def assertions(self, instance, write=True):
output = []
output.append(self._white('============================= Assertions Job ============================='))
output.append(self._blue('Assertions Job id: {}'.format(instance.id)))
output.append(self._white('Engine id: {}'.format(instance.engine_id)))
output.append(self._white('Created at: {}'.format(instance.created)))
output.append(self._white('Start date: {}'.format(instance.date_start)))
output.append(self._white('End date: {}'.format(instance.date_end)))
if instance.storage_path is not None:
output.append(self._white('Download: {}'.format(instance.storage_path)))
output.append(self._white('True Positive: {}'.format(instance.true_positive)))
output.append(self._white('True Negative: {}'.format(instance.true_negative)))
output.append(self._white('False Positive: {}'.format(instance.false_positive)))
output.append(self._white('False Negative: {}'.format(instance.false_negative)))
output.append(self._white('Suspicious: {}'.format(instance.suspicious)))
output.append(self._white('Unknown: {}'.format(instance.unknown)))
output.append(self._white('Total: {}'.format(instance.total)))
return self._output(output, write)
def votes(self, instance, write=True):
output = []
output.append(self._white('============================= Votes Job ============================='))
output.append(self._blue('Votes Job id: {}'.format(instance.id)))
output.append(self._white('Engine id: {}'.format(instance.engine_id)))
output.append(self._white('Created at: {}'.format(instance.created)))
output.append(self._white('Start date: {}'.format(instance.date_start)))
output.append(self._white('End date: {}'.format(instance.date_end)))
if instance.storage_path is not None:
output.append(self._white('Download: {}'.format(instance.storage_path)))
output.append(self._white('True Positive: {}'.format(instance.true_positive)))
output.append(self._white('True Negative: {}'.format(instance.true_negative)))
output.append(self._white('False Positive: {}'.format(instance.false_positive)))
output.append(self._white('False Negative: {}'.format(instance.false_negative)))
output.append(self._white('Suspicious: {}'.format(instance.suspicious)))
output.append(self._white('Unknown: {}'.format(instance.unknown)))
output.append(self._white('Total: {}'.format(instance.total)))
return self._output(output, write)
@is_grouped
def _white(self, text):
return click.style(text, fg='white')
@is_grouped
def _yellow(self, text):
return click.style(text, fg='yellow')
@is_grouped
def _red(self, text):
return click.style(text, fg='red')
@is_grouped
def _blue(self, text):
return click.style(text, fg='blue')
@is_grouped
def _green(self, text):
return click.style(text, fg='green')
def _open_group(self):
self._depth += 1
def _close_group(self):
self._depth -= 1
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examples/elementtree/pickle.py | Thhhza/sqlalchemy | 2 | 109050 | <filename>examples/elementtree/pickle.py<gh_stars>1-10
"""illustrates a quick and dirty way to persist an XML document expressed using ElementTree and pickle.
This is a trivial example using PickleType to marshal/unmarshal the ElementTree
document into a binary column. Compare to explicit.py which stores the individual components of the ElementTree
structure in distinct rows using two additional mapped entities. Note that the usage of both
styles of persistence are identical, as is the structure of the main Document class.
"""
from sqlalchemy import (create_engine, MetaData, Table, Column, Integer, String,
PickleType)
from sqlalchemy.orm import mapper, Session
import sys, os
from xml.etree import ElementTree
e = create_engine('sqlite://')
meta = MetaData()
# setup a comparator for the PickleType since it's a mutable
# element.
def are_elements_equal(x, y):
return x == y
# stores a top level record of an XML document.
# the "element" column will store the ElementTree document as a BLOB.
documents = Table('documents', meta,
Column('document_id', Integer, primary_key=True),
Column('filename', String(30), unique=True),
Column('element', PickleType(comparator=are_elements_equal))
)
meta.create_all(e)
# our document class. contains a string name,
# and the ElementTree root element.
class Document(object):
def __init__(self, name, element):
self.filename = name
self.element = element
# setup mapper.
mapper(Document, documents)
###### time to test ! #########
# get ElementTree document
filename = os.path.join(os.path.dirname(__file__), "test.xml")
doc = ElementTree.parse(filename)
# save to DB
session = Session(e)
session.add(Document("test.xml", doc))
session.commit()
# restore
document = session.query(Document).filter_by(filename="test.xml").first()
# print
document.element.write(sys.stdout)
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usermanagementorg/views_group_insert_user.py | shagun30/djambala-2 | 0 | 164681 | <reponame>shagun30/djambala-2<gh_stars>0
# -*- coding: utf-8 -*-
"""
/dms/usermanagementorg/views_group_insert_user.py
.. loescht User mit dem entsprechenden User-Namen
Django content Management System
<NAME>
<EMAIL>
Die Programme des dms-Systems koennen frei genutzt und den spezifischen
Beduerfnissen entsprechend angepasst werden.
0.01 23.04.2008 Beginn der Arbeit
0.02 24.04.2008 E-Mail versenden
0.03 30.04.2008 target_group_ids
"""
import string
import datetime
import random
from django.db import transaction
from django.http import HttpResponseRedirect
from django.shortcuts import render_to_response
from django import newforms as forms
from django.utils.safestring import mark_safe
from django.template.loader import get_template
from django.template import Context
from django.utils.translation import ugettext as _
from dms.auth.models import User
from dms.queries import get_site_url
from dms.queries import delete_user_by_username
from dms.queries import get_userfolder_org_id
from dms.queries import delete_group_by_id
from dms.queries import delete_users_in_group
from dms.queries import get_users_by_org_id
from dms.queries import get_users_by_group_id
from dms.queries import get_group_by_id
from dms.queries import get_user_by_email
from dms.queries import set_user_org
from dms.queries import set_user_group
from dms.roles import require_permission
from dms.mail import send_password
from dms.utils import get_tabbed_form
from dms.utils_form import get_item_vars_add
from dms.userregistration.utils import get_username
from dms.usermanagementorg.utils import get_groups
from dms.usermanagementorg.utils import create_member
from dms.usermanagementorg.help_form import help_form
from dms_ext.extension import * # dms-Funktionen ueberschreiben
# -----------------------------------------------------
def create_members(org_id, group_id, target_group_ids, fcontent):
""" legt mehrere User fuer org_id an und ordnet sie der group_id zu """
lines = string.splitfields(fcontent)
for line in lines:
if line.find('@') > 0:
line = line.replace('"', '')
items = string.splitfields(line.strip(), ';')
if len(items) == 4:
sex = items[0].strip()
first_name = items[1].strip()
last_name = items[2].strip()
email = items[3].strip()
title = ''
elif len(items) == 5:
sex = items[0].strip()
title = items[1].strip()
first_name = items[2].strip()
last_name = items[3].strip()
email = items[4].strip()
elif len(items) == 6:
sex = items[1].strip()
title = items[2].strip()
first_name = items[3].strip()
last_name = items[4].strip()
email = items[5].strip()
create_member(org_id, group_id, target_group_ids, sex, first_name, last_name, title_name, email)
# -----------------------------------------------------
@require_permission('perm_manage_user_new')
def usermanagementorg_group_insert_user(request, item_container):
""" nimmt User in Comunity auf und ergaenzt sie bei einer Basisgruppe """
org_id = get_userfolder_org_id(item_container)
def get_group_list(org_id):
""" liefert die vorhandenen Gruppen der Organisation org_id """
tList = get_template('app/usermanagementorg/group_list.html')
groups = get_groups(org_id, True)
return tList.render(Context({ 'groups': groups, }))
def get_group_names(org_id, primary_mode):
""" liefert die vorhandenen Gruppen der Organisation org_id """
groups = get_groups(org_id)
ret = []
for group in groups:
if ( primary_mode and group['is_primary'] ) or \
( not primary_mode and not group['is_primary'] ) :
ret.append((group['id'], group['description']))
return ret
group_items = get_group_list(org_id)
class DmsItemForm(forms.Form):
""" Elemente des Eingabeformulars """
group_names_primary = forms.ChoiceField(choices=get_group_names(org_id, True), widget=forms.RadioSelect() )
group_names = forms.MultipleChoiceField(required=False, choices=get_group_names(org_id, False),
widget=forms.CheckboxSelectMultiple() )
fname = forms.CharField(required=False, max_length=200,
widget=forms.FileInput(attrs={'size':40}) )
app_name = 'usermanagementorg'
my_title = _(u'Mitglieder aufnehmen')
show_errors = ( request.method == 'POST' )
if show_errors:
data = request.POST.copy()
else :
data = {}
# --- Formular mit Daten fuellen und zur Anzeige vorbereiten
f = DmsItemForm(data)
tabs = [ ('tab_group_user_insert', [ 'group_names_primary', 'group_names', 'fname' ]) ]
if request.method == 'POST' and not f.errors:
data = f.cleaned_data
create_members(org_id, data['group_names_primary'], data['group_names'],
request.FILES['fname']['content'])
return HttpResponseRedirect(get_site_url(item_container, 'index.html'))
else:
content = get_tabbed_form(tabs, help_form, app_name, f)
vars = get_item_vars_add(request, item_container, app_name, my_title, content, show_errors)
vars['next'] = './user_management.html?op=member_insert_group'
vars['is_file_form'] = True
return render_to_response('app/base_edit.html', vars)
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] |
house_robber.py | tusharsadhwani/leetcode | 6 | 119426 | class Solution:
def rob(self, nums: list[int]) -> int:
if len(nums) == 0:
return 0
max_loot: list[int] = [0 for _ in nums]
for index, num in enumerate(nums):
if index == 0:
max_loot[index] = num
elif index == 1:
max_loot[index] = max(max_loot[index-1], num)
else:
max_loot[index] = max(
max_loot[index-1],
num + max_loot[index-2]
)
return max_loot[-1]
tests = [
(
([1, 2, 3, 1],),
4,
),
(
([2, 7, 9, 3, 1],),
12,
),
]
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10353,
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29962,
13,
2
] |
registrobrepp/domain/eppcreatebrdomain.py | ivcmartello/registrobrepp | 0 | 77680 | <gh_stars>0
from eppy.doc import EppDoc
class EppCreateBrDomain(EppDoc):
_path = ('brdomain:create', )
def __init__(self, organization: str, flag1: str = None, flag2: str = None, flag3: str = None, autorenew: bool = False):
rpf = None
if flag1 or flag2 or flag3:
rpf = {'@flag1': flag1, '@flag2': flag2, '@flag3': flag3}
dct = {
'brdomain:create': {
'organization': organization,
'releaseProcessFlags': rpf,
'autoRenew': {'@active': '1' if autorenew else '0'}
}
}
super(EppCreateBrDomain, self).__init__(dct=self.annotate(dct))
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] |
universal_portfolio/util/process_data.py | jehung/universal_portfolio | 14 | 1615928 | import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
datapath = '../util/stock_dfs/'
def get_ticker(x):
return x.split('/')[-1].split('.')[0]
def ret(x, y):
return np.log(y/x)
def get_zscore(x):
return (x -x.mean())/x.std()
def make_inputs(filepath):
D = pd.read_csv(filepath).set_index('Date')
#D.index = pd.to_datetime(D.index,format='%Y-%m-%d') # Set the indix to a datetime
Res = pd.DataFrame()
ticker = get_ticker(filepath)
Res['c_2_o'] = get_zscore(ret(D.Open,D.Close))
Res['h_2_o'] = get_zscore(ret(D.Open,D.High))
Res['l_2_o'] = get_zscore(ret(D.Open,D.Low))
Res['c_2_h'] = get_zscore(ret(D.High,D.Close))
Res['h_2_l'] = get_zscore(ret(D.High,D.Low))
Res['c1_c0'] = ret(D.Close,D.Close.shift(-1)).fillna(0) #Tommorows return
Res['vol'] = get_zscore(D.Volume)
Res['ticker'] = ticker
return Res
def merge_all_data(datapath):
all = pd.DataFrame()
for f in os.listdir(datapath):
filepath = os.path.join(datapath,f)
if filepath.endswith('.csv'):
print(filepath)
Res = make_inputs(filepath)
all = all.append(Res)
return all
def embed(df, str):
"str: choice of return, class, multi_class"
pivot_columns = df.columns[:-1]
P = df.pivot_table(index=df.index, columns='ticker', values=pivot_columns) # Make a pivot table from the data
mi = P.columns.tolist()
new_ind = pd.Index(e[1] + '_' + e[0] for e in mi)
P.columns = new_ind
clean_and_flat = P.dropna(axis=1)
target_cols = list(filter(lambda x: 'c1_c0' in x, clean_and_flat.columns.values))
input_cols = list(filter(lambda x: 'c1_c0' not in x, clean_and_flat.columns.values))
inputDF = clean_and_flat[input_cols]
targetDF = clean_and_flat[target_cols]
TotalReturn = ((1 - np.exp(targetDF)).sum(axis=1)) / len(targetDF.columns) # If i put one dollar in each stock at the close, this is how much I'd get back
Labeled = pd.DataFrame()
Labeled['return'] = TotalReturn
Labeled['class'] = TotalReturn.apply(labeler, 1)
Labeled['multi_class'] = pd.qcut(TotalReturn, 11, labels=range(11))
pd.qcut(TotalReturn, 5).unique()
return inputDF, Labeled[str]
def labeler(x):
if x>0.0029:
return 1
if x<-0.00462:
return -1
else:
return 0
'''
if __name__ == "__main__":
all = merge_all_data(datapath)
inputdf, targetdf = embed(all)
labeled = process_target(targetdf)
print(inputdf.head())
print(labeled.head())
''' | [
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] |
ui/mext.py | szymonkaliski/nott | 25 | 15140 | <filename>ui/mext.py
# FIXME: fix all "happy paths coding" issues
import liblo
from threading import Thread
class Mext(object):
device = None
def __init__(self, device_port=5000):
self.device_receiver = liblo.ServerThread(device_port)
self.device_receiver.add_method("/monome/grid/key", "iii", self.on_grid_key)
self.device_receiver.add_method(
"/serialosc/device", "ssi", self.on_serialosc_device
)
self.device_receiver.start()
liblo.send(liblo.Address(12002), "/serialosc/list", "127.0.0.1", device_port)
def set_grid_key_callback(self, fn):
self.grid_key_callback = fn
def set_led_level(self, x, y, value):
Thread(
target=(
lambda: liblo.send(
self.device, "/monome/grid/led/level/set", x, y, value
)
)
).start()
def set_led_map(self, offset_x, offset_y, values):
Thread(
target=(
lambda: liblo.send(
self.device,
"/monome/grid/led/level/map",
offset_x,
offset_y,
*values
)
)
).start()
def on_grid_key(self, path, args):
x, y, edge = args
if self.grid_key_callback:
self.grid_key_callback(x, y, edge)
def on_serialosc_device(self, path, args):
_, sysId, port = args
self.device = liblo.Address(port)
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] |
tests/settings/test_custom_metrics.py | proknow/proknow-python | 2 | 27193 | import pytest
import re
from proknow import Exceptions
def test_create(app, custom_metric_generator):
pk = app.pk
# Verify returned CustomMetricItem
params, custom_metric = custom_metric_generator()
assert custom_metric.name == params["name"]
assert custom_metric.context == params["context"]
assert custom_metric.type == params["type"]
# Assert item can be found in query
custom_metrics = pk.custom_metrics.query()
for custom_metric in custom_metrics:
if custom_metric.name == params["name"]:
custom_metric_match = custom_metric
break
else:
custom_metric_match = None
assert custom_metric_match is not None
assert custom_metric_match.name == params["name"]
assert custom_metric_match.context == params["context"]
assert custom_metric_match.type == params["type"]
def test_create_failure(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator()
# Assert error is raised for duplicate custom metric
with pytest.raises(Exceptions.HttpError) as err_wrapper:
pk.custom_metrics.create(**params)
assert err_wrapper.value.status_code == 409
assert err_wrapper.value.body == 'Custom metric already exists with name "' + params["name"] + '"'
def test_delete(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator(do_not_mark=True)
# Verify custom metric was deleted successfully
custom_metric.delete()
for custom_metric in pk.custom_metrics.query():
if custom_metric.name == params["name"]:
match = custom_metric
break
else:
match = None
assert match is None
def test_delete_failure(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator(do_not_mark=True)
custom_metric.delete()
# Assert error is raised when attempting to delete protected custom metric
with pytest.raises(Exceptions.HttpError) as err_wrapper:
custom_metric.delete()
assert err_wrapper.value.status_code == 404
assert err_wrapper.value.body == 'Custom metric "' + custom_metric.id + '" not found'
def test_find(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator(name="Find Me")
expr = re.compile(r"ind M")
# Find with no args
found = pk.custom_metrics.find()
assert found is None
# Find using predicate
found = pk.custom_metrics.find(lambda ws: expr.search(ws.data["name"]) is not None)
assert found is not None
assert found.name == params["name"]
assert found.context == params["context"]
assert found.type == params["type"]
# Find using props
found = pk.custom_metrics.find(id=custom_metric.id, name=params["name"])
assert found is not None
assert found.name == params["name"]
assert found.context == params["context"]
assert found.type == params["type"]
# Find using both
found = pk.custom_metrics.find(lambda ws: expr.search(ws.data["name"]) is not None, id=custom_metric.id, name=params["name"])
assert found is not None
assert found.name == params["name"]
assert found.context == params["context"]
assert found.type == params["type"]
# Find failure
found = pk.custom_metrics.find(lambda ws: expr.search(ws.data["id"]) is not None)
assert found is None
found = pk.custom_metrics.find(id=custom_metric.id, name=params["name"].lower())
assert found is None
def test_query(app, custom_metric_generator):
pk = app.pk
params1, custom_metric1 = custom_metric_generator()
params2, custom_metric2 = custom_metric_generator()
# Verify test 1
for custom_metric in pk.custom_metrics.query():
if custom_metric.name == params1["name"]:
match = custom_metric
break
else:
match = None
assert match is not None
assert match.name == params1["name"]
assert match.context == params1["context"]
assert match.type == params1["type"]
# Verify test 2
for custom_metric in pk.custom_metrics.query():
if custom_metric.name == params2["name"]:
match = custom_metric
break
else:
match = None
assert match is not None
assert match.name == params2["name"]
assert match.context == params2["context"]
assert match.type == params2["type"]
def test_resolve(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator()
# Test resolve by id
resolved = pk.custom_metrics.resolve(custom_metric.id)
assert resolved is not None
assert resolved.name == params["name"]
assert resolved.context == params["context"]
assert resolved.type == params["type"]
# Test resolve by name
resolved = pk.custom_metrics.resolve(params["name"])
assert resolved is not None
assert resolved.name == params["name"]
assert resolved.context == params["context"]
assert resolved.type == params["type"]
def test_resolve_failure(app):
pk = app.pk
# Test resolve by id
with pytest.raises(Exceptions.CustomMetricLookupError) as err_wrapper:
pk.custom_metrics.resolve("00000000000000000000000000000000")
assert err_wrapper.value.message == "Custom metric with id `00000000000000000000000000000000` not found."
# Test resolve by name
with pytest.raises(Exceptions.CustomMetricLookupError) as err_wrapper:
pk.custom_metrics.resolve("My Metric")
assert err_wrapper.value.message == "Custom metric with name `My Metric` not found."
def test_resolve_by_id(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator()
resolved = pk.custom_metrics.resolve_by_id(custom_metric.id)
assert resolved is not None
assert resolved.name == params["name"]
assert resolved.context == params["context"]
assert resolved.type == params["type"]
def test_resolve_by_id_failure(app):
pk = app.pk
with pytest.raises(Exceptions.CustomMetricLookupError) as err_wrapper:
pk.custom_metrics.resolve_by_id("00000000000000000000000000000000")
assert err_wrapper.value.message == "Custom metric with id `00000000000000000000000000000000` not found."
def test_resolve_by_name(app, custom_metric_generator):
pk = app.pk
params, custom_metric = custom_metric_generator(name="custom-lower1")
resolved = pk.custom_metrics.resolve_by_name(params["name"])
assert resolved is not None
assert resolved.name == params["name"]
assert resolved.context == params["context"]
assert resolved.type == params["type"]
resolved = pk.custom_metrics.resolve_by_name(params["name"].upper())
assert resolved is not None
assert resolved.name == params["name"]
assert resolved.context == params["context"]
assert resolved.type == params["type"]
def test_resolve_by_name_failure(app):
pk = app.pk
with pytest.raises(Exceptions.CustomMetricLookupError) as err_wrapper:
pk.custom_metrics.resolve("My Custom Metric")
assert err_wrapper.value.message == "Custom metric with name `My Custom Metric` not found."
def test_update(app, custom_metric_generator):
pk = app.pk
resource_prefix = app.resource_prefix
params, custom_metric = custom_metric_generator()
# Verify custom metric was updated successfully
updated_name = resource_prefix + "Updated Custom Metric Name"
custom_metric.name = updated_name
custom_metric.context = "image_set"
custom_metric.save()
custom_metrics = pk.custom_metrics.query()
for custom_metric in custom_metrics:
if custom_metric.name == updated_name:
custom_metric_match = custom_metric
break
else:
custom_metric_match = None
assert custom_metric_match is not None
assert custom_metric_match.name == updated_name
assert custom_metric_match.context == "image_set"
assert custom_metric_match.type == params["type"]
def test_update_failure(app, custom_metric_generator):
pk = app.pk
params1, _ = custom_metric_generator()
params2, custom_metric = custom_metric_generator()
# Assert error is raised for duplicate workspace
with pytest.raises(Exceptions.HttpError) as err_wrapper:
custom_metric.name = params1["name"]
custom_metric.save()
assert err_wrapper.value.status_code == 409
assert err_wrapper.value.body == 'Custom metric already exists with name "' + params1["name"] + '"'
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2
] |
apps/libro/urls.py | ernestoaguaysol/mysite | 0 | 173249 | <reponame>ernestoaguaysol/mysite<filename>apps/libro/urls.py
from django.urls import path
from .views import crearAutor,listarAutor
urlpatterns = [
path('crear_autor/', crearAutor, name = 'crear_autor'),
path('listar_autor/', listarAutor, name = 'listar_autor')
] | [
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] |
app/viewsets.py | mvargas33/Tablero_digital | 1 | 167341 | from rest_framework import viewsets, status, serializers, mixins, filters
from rest_framework.exceptions import NotFound
from rest_framework.decorators import action
from rest_framework.response import Response
from .models import PostIt, Board, WorkIn, VoteIn
from django.core.exceptions import ObjectDoesNotExist
from django.contrib.auth.models import User
from .serializer import (
PostitSerializer,
UserSerializer,
BoardSerializer,
WorkInSerializer,
VoteInSerializer
)
from django_filters.rest_framework import DjangoFilterBackend
from rest_framework.permissions import IsAuthenticated
from .permissions import (
UserViewSetPermission,
WorkInViewSetPermission,
PostitViewSetPermission,
)
class NoUpdateModelViewSet(mixins.CreateModelMixin,
mixins.RetrieveModelMixin,
mixins.DestroyModelMixin,
mixins.ListModelMixin,
viewsets.GenericViewSet):
"""Similar to a ModelViewSet, but without update actions.
Used by WorkinViewSet.
"""
class PostitViewSet(viewsets.ModelViewSet):
"""Postits related to the current authenticated user."""
permission_classes = [IsAuthenticated, PostitViewSetPermission]
filterset_fields = ['title', 'section', 'status', 'board', 'id']
def get_serializer_class(self):
class NewPostItSerializer(PostitSerializer):
# The user can only see boards related to him.
board = serializers.PrimaryKeyRelatedField(
queryset=Board.objects.filter(workin__user=self.request.user))
return NewPostItSerializer
def get_queryset(self):
# Return only postits related to the current user.
return PostIt.objects.filter(board__workin__user=self.request.user)
def perform_update(self, serializer):
# Overrides perform_update() to reset the votes of the post-it.
postit = serializer.save()
postit.reset_votes()
postit.save()
def vote_postit(self, request, vote):
# Method for voting. Only team leaders can vote, only if the post-it is
# open and the leader hasn't voted yet.
postit = self.get_object()
querySet = WorkIn.objects.filter(board=postit.board, user=request.user)
if not querySet.exists() or not querySet[0].is_leader:
self.permission_denied(
request, 'El usuario actual no es líder de equipo en el tablero de este post-it')
elif not postit.status == 'O':
self.permission_denied(
request, message='El post it no está en estado abierto.')
user_team = querySet[0].team
already_voted_msg = 'El líder de equipo ya votó.'
if user_team == 'S':
if postit.stakeholders_vote != 2:
self.permission_denied(request, message=already_voted_msg)
postit.stakeholders_vote = vote
elif user_team == 'D':
if postit.developers_vote != 2:
self.permission_denied(request, message=already_voted_msg)
postit.developers_vote = vote
if postit.developers_vote == 1 and postit.stakeholders_vote == 1:
postit.status = 'A'
elif postit.developers_vote + postit.stakeholders_vote <= 1:
postit.status = 'R'
postit.save()
serializer = self.get_serializer(postit)
return Response(serializer.data)
@action(detail=True, methods=['get', 'post'])
def reject(self, request, pk=None):
return self.vote_postit(request, 0)
@action(detail=True, methods=['get', 'post'])
def approve(self, request, pk=None):
return self.vote_postit(request, 1)
class BoardViewSet(viewsets.ModelViewSet):
"""Boards related to the current authenticated user."""
permission_classes = [IsAuthenticated]
serializer_class = BoardSerializer
filterset_fields = ['name', 'id']
def get_queryset(self):
# Return only boards related to the current user.
return Board.objects.filter(workin__user=self.request.user)
def create(self, request, *args, **kwargs):
response = super().create(request, *args, **kwargs)
if response.status_code == status.HTTP_201_CREATED:
newBoard = Board.objects.get(id=response.data['id'])
newBoard.project_leader = self.request.user
newBoard.save()
WorkIn.objects.create(
user=request.user, board_id=response.data['id'])
return response
@action(detail=True, methods=['post'])
def add_collaborator(self, request, pk=None):
board = self.get_object()
email = request.data['email']
try:
user = User.objects.get(username=email)
except ObjectDoesNotExist:
raise NotFound(detail='No existe un usuario con ese email.')
workIn, created = WorkIn.objects.get_or_create(board=board, user=user)
if not created:
return Response({'detail': 'El usuario ya colabora en este tablero.'})
serializer = WorkInSerializer(workIn)
return Response(serializer.data, status=status.HTTP_201_CREATED)
@action(methods=['delete'], detail=False)
def delete(self, request):
user_id = request.user.id
project_leader = request.data['project_leader']
if (user_id == project_leader):
id = request.data['id']
board = Board.objects.get(id=id)
board.delete()
return Response(status=status.HTTP_200_OK)
else:
self.permission_denied(
request, message='El usuario actual no es el líder de proyecto.')
class WorkInViewSet(NoUpdateModelViewSet):
"""WorkIn relations related to the current authenticated user and his
boards."""
permission_classes = [IsAuthenticated, WorkInViewSetPermission]
serializer_class = WorkInSerializer
filterset_fields = ['user', 'is_leader', 'board', 'team', 'id']
def get_serializer_class(self):
class NewWorkInSerializer(WorkInSerializer):
# The user can only see boards related to him.
board = serializers.PrimaryKeyRelatedField(
queryset=Board.objects.filter(workin__user=self.request.user))
return NewWorkInSerializer
def get_queryset(self):
# Return only instances of WorkIn for boards related to the current user,
# this includes the WorkIn instances related to collaborators,
# and prefetch user for the WorkInSerializer.
return WorkIn.objects.filter(
board__workin__user=self.request.user).prefetch_related('user')
@action(detail=True, methods=['get', 'put', 'post'])
def pass_leader_privileges(self, request, pk=None):
# Passes leader privileges from the current user to the user with
# primary key pk.
user_workin = self.get_object()
target_workin = WorkIn.objects.get(
user=request.data['user']['id'], board=user_workin.board)
if not user_workin.is_leader:
self.permission_denied(
request, message='El usuario actual no es lider.')
elif target_workin.team != user_workin.team:
self.permission_denied(
request, message='Los usuarios son de equipos diferentes.')
target_workin.is_leader = True
user_workin.is_leader = False
target_workin.save()
user_workin.save()
serializer = self.get_serializer(target_workin)
return Response(serializer.data)
@action(detail=True, methods=['post'])
def select_team(self, request, pk=None):
# Selects the team for the current user in WorkIn relation.
workIn = self.get_object()
team = request.data['team']
workIn.team = team
serializer = self.get_serializer(workIn)
boardWorkin = WorkIn.objects.filter(board=workIn.board)
leaderExist = False
for val in boardWorkin:
if val.is_leader and val.team == team:
leaderExist = True
break
if not leaderExist:
workIn.is_leader = True
workIn.save()
return Response(serializer.data)
@action(detail=True, methods=['delete'])
def delete(self, request, pk=None):
# Selects the team for the current user in WorkIn relation.
user_id = request.user.id
delete_user = request.data['delete_user']
board_id = request.data['board']['id']
board = Board.objects.get(id=board_id)
workin = WorkIn.objects.get(user=delete_user, board=board_id)
print(workin)
if (board.project_leader.id == user_id and user_id != delete_user and workin.is_leader == False):
workin.delete()
return Response(status=status.HTTP_200_OK)
else:
self.permission_denied(
request, message='El usuario actual no es el líder de proyecto.')
class UserViewSet(viewsets.ModelViewSet):
permission_classes = [UserViewSetPermission]
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [DjangoFilterBackend, filters.SearchFilter]
filterset_fields = ['username']
search_fields = ['username']
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1678,
2740,
29918,
9621,
353,
6024,
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2033,
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2
] |
testing/example.py | gndctrl2mjrtm/dkeras | 195 | 93559 | # Ideas
"""
server = dkeras.DataServer()
model1.link(model3)
model1.postprocess = lambda z: np.float16(z)
server = model1 + model2 + model3
server.add(camera1, dest=('m1', 'm2'))
server.add_address('192.168.1.42')
"""
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] |
tests/test_models.py | mgierm/djangocms_semantic_ui | 3 | 88550 | # -*- coding: utf-8 -*-
from django.test import TestCase
from djangocms_semantic_ui.models import Segment
class SegmentTestCase(TestCase):
def setUp(self):
pass
def test_models(self):
segment = Segment.objects.create(
label='Test',
color='green',
inverted_color=False,
type_segment='raised'
)
self.assertEqual("green", str(segment))
self.assertEqual("Test green <raised>", segment.get_short_description())
self.assertEqual("green raised", segment.get_classes())
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django_covid19/spider/nCoV/spiders/dxy.py | zhangguoyuanshuai/Python-Covid19API | 0 | 1611140 | """丁香园数据源"""
import json
import scrapy
import logging
from scrapy.selector import Selector
from .. import items
from django.core.cache import cache
from django.utils.timezone import datetime, make_aware
logger = logging.getLogger()
PROVINCE_CODES = {
"黑龙江": "HLJ",
"香港": "XG",
"青海": "QH",
"陕西": "SX", # 同山西
"重庆": "CQ",
"辽宁": "LN",
"贵州": "GZ",
"西藏": "XZ",
"福建": "FJ",
"甘肃": "GS",
"澳门": "AM",
"湖南": "HN",
"湖北": "HB",
"海南": "HN-2",
"浙江": "ZJ",
"河南": "HN-1",
"河北": "HB-1",
"江西": "JX",
"江苏": "JS",
"新疆": "XJ",
"广西": "GX",
"广东": "GD",
"山西": "SX-1",
"山东": "SD",
"安徽": "AH",
"宁夏": "NX",
"天津": "TJ",
"四川": "SC",
"吉林": "JL",
"台湾": "TW",
"北京": "BJ",
"内蒙古": "NMG",
"云南": "YN",
"上海": "SH"
}
COUNTRY_CODE_FIX = {
'Princess': 'PRINCESS',
'Saint Martin': 'MAF',
'Sint Maarten': 'SXM'
}
class DXYSpider(scrapy.Spider):
name = "dxy"
allowed_domains = ["ncov.dxy.cn", "file1.dxycdn.com"]
start_urls = [
"http://ncov.dxy.cn/ncovh5/view/pneumonia",
]
countryCode = 'CHN'
def parse(self, response):
object_id = self.object_id
spider_id = cache.get('running_spider_id')
if object_id != spider_id:
logger.info('Spider is running.')
self.crawled = 0
return
sel = Selector(response)
scripts = sel.xpath('//script')
# 判断是否需要保存抓取的数据
statistics = self.get_dict(scripts, '#getStatisticsService')
create_time = make_aware(
datetime.fromtimestamp(statistics['createTime'] / 1000.0))
modify_time = make_aware(
datetime.fromtimestamp(statistics['modifyTime'] / 1000.0))
qs = items.StatisticsItem.django_model.objects.all().order_by('-id')
if qs.count() > 1 and qs[0].modifyTime == modify_time:
logger.info('Data does not change.')
self.crawled = 0
return
# 统计信息
statistics = self.explain_statistics(statistics)
statistics['createTime'] = create_time
statistics['modifyTime'] = modify_time
# 国内数据
provinces = self.get_list(scripts, '#getAreaStat')
for province in provinces:
cities = province.pop('cities', [])
province.pop('locationId')
yield scrapy.Request(
province['statisticsData'],
callback=self.parse_province_statistics_data,
meta={
'province': province,
'cities': cities
}
)
# 时间线事件，id=“getTimelineService2” 为英文内容
timelines = self.get_list(scripts, '#getTimelineService1')
result = []
for item in timelines:
timeline = {}
for key in ('title', 'summary', 'infoSource', 'sourceUrl',
'pubDate', 'pubDateStr'):
timeline[key] = item.get(key)
result.append(timeline)
statistics['timelines'] = json.dumps(result)
# 建议，id=“#getIndexRecommendList2” 为英文内容
recommends = self.get_list(
scripts, '#getIndexRecommendListundefined')
result = []
for item in recommends:
recommend = {}
for key in ('title', 'linkUrl', 'imgUrl', 'countryType',
'contentType', 'recordStatus', 'sort'):
recommend[key] = item.get(key)
result.append(recommend)
statistics['recommends'] = json.dumps(result)
# WHO 文章
item = self.get_dict(scripts, '#fetchWHOArticle')
article = {}
for key in ('title', 'linkUrl', 'imgUrl'):
article[key] = item.get(key)
statistics['WHOArticle'] = json.dumps(article)
# wiki
wiki_result = self.get_dict(scripts, '#getWikiList')
wikis = wiki_result['result']
result = []
for item in wikis:
wiki = {}
for key in ('title', 'linkUrl', 'imgUrl', 'description'):
wiki[key] = item.get(key)
result.append(wiki)
statistics['wikis'] = json.dumps(result)
# 购物指南
guides = self.get_list(scripts, '#fetchGoodsGuide')
result = []
for item in guides:
guide = {}
for key in ('categoryName', 'title', 'recordStatus',
'contentImgUrls'):
guide[key] = item.get(key)
result.append(guide)
statistics['goodsGuides'] = json.dumps(result)
# 辟谣与防护
rumors = self.get_list(scripts, '#getIndexRumorList')
result = []
for item in rumors:
rumor = {}
for key in ('title', 'mainSummary', 'summary', 'body',
'sourceUrl', 'score', 'rumorType'):
rumor[key] = item.get(key)
result.append(rumor)
statistics['rumors'] = json.dumps(result)
yield statistics
# 国外数据
countries = self.get_list(
scripts, '#getListByCountryTypeService2true')
for country in countries:
country.pop('id', None)
country['countryName'] = country.pop('provinceName', None)
country['provinceName'] = ''
country.pop('countryType')
country.pop('cityName')
country.pop('provinceId')
country.pop('provinceName')
country.pop('provinceShortName')
country.pop('modifyTime', None)
country.pop('createTime', None)
country['incrVo'] = json.dumps(country.get('incrVo', {}))
statistics_data = country.get('statisticsData')
if statistics_data:
yield scrapy.Request(
statistics_data,
callback=self.parse_country_statistics_data,
meta={
'country': country
}
)
else:
yield items.CountryItem(dailyData=[], **country)
self.crawled = 1 # 代表爬虫已爬取数据
def parse_province_statistics_data(self, response):
countryCode = self.countryCode
meta = response.meta
cities = meta['cities']
province = meta['province']
provinceName = province['provinceShortName']
provinceCode = PROVINCE_CODES[provinceName]
result = json.loads(response.text)
dailyData = result['data']
for item in dailyData:
item['countryCode'] = countryCode
item['provinceName'] = provinceName
item['provinceCode'] = provinceCode
data = json.dumps(dailyData)
province['provinceName'] = provinceName
province_args = {}
provice_fieldnames = [f.name for f in \
items.ProvinceItem.django_model._meta.get_fields()]
for fieldname in provice_fieldnames:
value = province.get(fieldname)
if value is not None:
province_args[fieldname] = value
province_args['dailyData'] = data
province_args['dailyUrl'] = province.get('statisticsData')
province_args['countryCode'] = countryCode
province_args['provinceCode'] = provinceCode
yield items.ProvinceItem(**province_args)
for city in cities:
city.pop('locationId')
yield items.CityItem(
countryCode=countryCode,
provinceCode=provinceCode,
provinceName=provinceName,
**city
)
def parse_country_statistics_data(self, response):
result = json.loads(response.text)
dailyData = result['data']
meta = response.meta
country = meta['country']
country.pop('locationId')
country['countryCode'] = countryCode \
= country.pop('countryShortCode')
countryCode = COUNTRY_CODE_FIX.get(countryCode, countryCode)
for item in dailyData:
item['countryCode'] = countryCode
item['countryName'] = country['countryName']
dailyData = json.dumps(dailyData)
yield items.CountryItem(dailyData=dailyData, **country)
def explain_statistics(self, data):
statistics = data['globalStatistics']
instance = {}
item = {}
for key in (
'currentConfirmedCount', 'curedCount', 'confirmedCount',
'seriousCount', 'suspectedCount', 'deadCount',
'currentConfirmedIncr', 'curedIncr', 'confirmedIncr',
'suspectedIncr', 'deadIncr'):
item[key] = statistics.get(key, 0)
instance['globalStatistics'] = json.dumps(item)
statistics = data['foreignStatistics']
item = {}
for key in (
'currentConfirmedCount', 'curedCount', 'confirmedCount',
'seriousCount', 'suspectedCount', 'deadCount',
'currentConfirmedIncr', 'curedIncr', 'confirmedIncr',
'suspectedIncr', 'deadIncr'):
item[key] = statistics.get(key, 0)
instance['internationalStatistics'] = json.dumps(item)
statistics = data
item = {}
for key in (
'currentConfirmedCount', 'curedCount', 'confirmedCount',
'seriousCount', 'suspectedCount', 'deadCount',
'currentConfirmedIncr', 'curedIncr', 'confirmedIncr',
'suspectedIncr', 'deadIncr'):
item[key] = statistics.get(key, 0)
instance['domesticStatistics'] = json.dumps(item)
# Remark and Note
remarks = []
for key in ('remark1', 'remark2', 'remark3', 'remark4', 'remark5'):
remark = data.get(key)
if remark:
remarks.append(remark)
notes = []
for key in ('note1', 'note2', 'note3'):
note = data.get(key)
if note:
notes.append(note)
instance['remarks'] = json.dumps(remarks)
instance['notes'] = json.dumps(notes)
instance['generalRemark'] = data.get('generalRemark')
return items.StatisticsItem(**instance)
def get_list(self, scripts, data_id):
ret = scripts.css(data_id).re(r'(\[.+\])')
return json.loads(ret[0])
def get_dict(self, scripts, data_id):
ret = scripts.css(data_id).re(r'\=\s*(\{.+\})\}catch\(e\)\{\}')
return json.loads(ret[0])
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src/utility/get_data.py | mtsu-data-science/non-profit-categorization | 1 | 150359 | import pandas as pd
import os
import urllib.request
def download_project_maslow_files():
"""The purpose of this function is to handle getting the nonprofit.txt and nonprofit_text.txt
files. This functin will create a data directory, add a .gitignore file so
source control does not pick up the txt files, and then check to see
if either nonprofit.txt and nonprofit_text.txt have been downloaded yet.
If not, it will proceed to download the files.
"""
if os.path.isfile("data/nonprofit.txt") is False:
print("Downloading nonprofit.txt...")
urllib.request.urlretrieve("https://mtsu-dsi-non-profit-categorization.s3.amazonaws.com/tax-data-descriptions/nonprofit.txt", 'data/nonprofit.txt')
if os.path.isfile("data/nonprofit_text.txt") is False:
print("Downloading nonprofit_text.txt...")
urllib.request.urlretrieve("https://mtsu-dsi-non-profit-categorization.s3.amazonaws.com/tax-data-descriptions/nonprofit_text.txt", 'data/nonprofit_text.txt')
def get_non_profit_df():
"""This function will first run the download_project_maslow_files() to check to
see if the files have been downloaded to the `data/` folder. If not,
it will download the files.
Then, it will read in the file and return a dataframe with the nonprofit
"""
download_project_maslow_files()
col_types = {
"nonprofit_id": "Int64",
"reporting_year": "Int64",
"ein": "Int64",
"businessname": "str",
"phone": "str",
"address1": "str",
"address2": "str",
"city": "str",
"stabbrv": "str",
"zip": "str"
}
df = pd.read_csv("data/nonprofit.txt", sep = "|", dtype=col_types)
return df
def get_non_profit_text_df():
"""This function will first run the download_project_maslow_files() to check to
see if the files have been downloaded to the `data/` folder. If not,
it will download the files.
Then, it will read in the file and return a dataframe with the nonprofit_text
"""
download_project_maslow_files()
col_types = {
"nonprofit_text_id": "Int64",
"reporting_year": "Int64",
"nonprofit_id": "Int64",
"grouptype": "str",
"description": "str"
}
df = pd.read_csv("data/nonprofit_text.txt", sep = "|", encoding="cp1252", dtype=col_types)
return df | [
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molecule/centos/tests/test_icinga_satclient.py | dverhelst/icinga2 | 3 | 195202 | import os
import testinfra.utils.ansible_runner
testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(
os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('icinga2satclient')
def test_sat_client_config(host):
""" basic configuration checks
"""
with host.sudo():
# Check if zone is configured
file = host.file('/etc/icinga2/zones.conf')
assert file.exists
assert file.user == 'icinga'
assert file.group == 'icinga'
assert file.mode == 0o644
assert file.contains('Generated by Icinga 2 node setup commands')
# Check if satellite endpoint is configured
endpoints = host.run('icinga2 object list --type endpoint')
assert "-icinga2-satellite' of type 'Endpoint':" in endpoints.stdout
# Check if config from master is included
zones = host.run('icinga2 object list --type zone')
assert "Object 'global-templates' of type 'Zone'" in zones.stdout
assert "Object 'director-global' of type 'Zone'" in zones.stdout
assert "Object 'master' of type 'Zone'" in zones.stdout
def test_sat_client_connection(host):
""" Check if client is connected to icinga
"""
with host.sudo():
connections = list(host.socket('tcp://5665')._iter_sockets(False))
# >>> print(connections)
# [(u'tcp', u'10.244.3.219', 51716, u'10.244.5.174', 5665), (u'tcp', u'10.244.3.219', 22, u'10.244.4.0', 33686)]
assert [x for x in connections if x[4] == 5665]
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] |
project_template.py | dvillega/scripts | 0 | 175242 | <gh_stars>0
#!/usr/bin/env python
"""
Creates a base directory structure ala Project Template (from R)
author - <NAME>
2013/08/19
"""
import argparse
import errno
import os
import sys
def make_sure_path_exists(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
parser = argparse.ArgumentParser(description='Create a data analysis directory structure')
parser.add_argument('project_name',help='Name of the project we are creating')
args = parser.parse_args()
project_name = args.project_name
cwd = os.getcwd()
target = cwd + '/' + project_name
# Check if we are going to overwrite a directory
current_dirs = os.listdir(cwd)
if args.project_name in current_dirs:
print "Current project name exists in dir"
# Confirm location to create template
ans = raw_input("Confirm creating " + project_name + " project in " + cwd + ": y/[n]\n")
if ans.lower() != 'y':
print "Aborting"
sys.exit(1)
# Create directory structure
dirs = ['cache','config','data','diagnostics',
'doc','figures','lib','logs','munge',
'profiling','reports','scratch','src',
'tests']
make_sure_path_exists(target)
for elem in dirs:
create_dir = target + '/' + elem
print "Creating " + create_dir
make_sure_path_exists(create_dir)
# Create subdirs
make_sure_path_exists(target + '/figures/exploratory')
make_sure_path_exists(target + '/figures/final')
print 'Writing README'
fh1 = open(target + '/README.md','w')
fh1.write('README File for ' + project_name)
fh1.close()
print 'Writing TODO'
fh2 = open(target + '/TODO.md','w')
fh2.write('TODO File for ' + project_name)
fh2.close()
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] |
setup.py | akhiilkasare/end-to-end-macine-learning-pipeline-with-mlops-tools | 0 | 176040 | <reponame>akhiilkasare/end-to-end-macine-learning-pipeline-with-mlops-tools
from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='End to End Machine Learning Pipeline with MLOps tools',
author='<NAME>',
license='MIT',
)
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main/migrations/0005_auto_20180603_0449.py | OpenHumans/jupyter-gallery | 10 | 115732 | # Generated by Django 2.0.6 on 2018-06-03 04:49
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('main', '0004_auto_20180601_2005'),
]
operations = [
migrations.AlterField(
model_name='sharednotebook',
name='created_at',
field=models.DateTimeField(default='2018-05-27 04:49:28+00:00'),
),
migrations.AlterField(
model_name='sharednotebook',
name='updated_at',
field=models.DateTimeField(default='2018-05-27 04:49:28+00:00'),
),
]
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] |
tests/ea/mutation/mutator/test_mutate.py | stevenbennett96/stk | 21 | 41317 | from tests.utilities import is_equivalent
def test_mutate(case_data):
"""
Test :meth:`mutate`.
Parameters
----------
case_data : :class:`.CaseData`
A test case. Holds the mutator to test and the correct mutation
record.
Returns
-------
None : :class:`NoneType`
"""
_test_mutate(
mutator=case_data.mutator,
record=case_data.record,
mutation_record=case_data.mutation_record,
)
def _test_mutate(mutator, record, mutation_record):
"""
Test :meth:`mutate`.
Parameters
----------
mutator : :class:`.MoleculeMutator`
The mutator to test.
record : :class:`.MoleculeRecord`
The molecule to mutate.
mutation_record : :class:`.MutationRecord`
The correct mutation record.
Returns
-------
None : :class:`NoneType`
"""
result = mutator.mutate(record)
assert (
result.get_mutator_name() == mutation_record.get_mutator_name()
)
is_equivalent(
result.get_molecule_record().get_molecule(),
mutation_record.get_molecule_record().get_molecule(),
)
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] |
tests/test_general.py | haku-kiro/flaskCRUD | 0 | 44677 | """
General unit tests
"""
class TestClass:
def test_base(self):
assert 42 == 42
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1,
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29906,
1275,
29871,
29946,
29906,
13,
2
] |
fourtynine.py | glennandreph/learnpython | 1 | 37106 | def my_function_with_args(username, greeting):
print("Hello, %s , From My Function! I wish you %s" %(username, greeting))
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] |
micropub/renderer/element.py | HakierGrzonzo/microPub | 0 | 189501 | <reponame>HakierGrzonzo/microPub
from bs4 import BeautifulSoup as Soup
from bs4 import Tag
from micropub.renderer.text import Text
class Element:
def __init__(self, tag, renderer, parent = None):
self.renderer = renderer
self.parent = parent
self.tag = tag
self.children = list()
self.style = self.renderer.resolveStyle(self)
self.display = self.style.get("display", "inline")
for child in tag.children:
if isinstance(child, Tag):
self.children.append(Element(child, renderer, self))
else:
self.children.append(Text(child, self))
def render(self, force_inline = False):
if self.display != "inline" and not force_inline:
# handle block elements
contents = list()
lastParagraph = list()
for child in self.children:
if isinstance(child, Text):
lastParagraph.append(child)
elif child.display == "inline":
childContent = child.render(force_inline)
lastParagraph += childContent
else:
childContent = child.render(force_inline)
if len(lastParagraph) > 0:
contents.append(lastParagraph)
lastParagraph = list()
contents.append(childContent)
if len(lastParagraph) > 0:
contents.append(lastParagraph)
return contents
else:
# handle inline elements
contents = list()
for child in self.children:
if isinstance(child, Text):
contents.append(child)
else:
childContent = child.render(force_inline = True)
contents += childContent
return contents
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] |
unit_test.py | ksaper/CloudShell_LDAP_Integration | 0 | 78044 | from ldap_user_mgmt import ldapimport
test_runner = ldapimport()
error_log = []
t_user = 'unit_test_user'
user_created = 0
# Get user list from CloudShell
try:
master_list = test_runner.load_cloudshell_users()
print 'CloudShell User List'
print master_list
except:
error_log.append('Fail user list CloudShell')
print 'Unable to get user list from CloudShell'
# check whitelist
print 'The white list: '
for name in test_runner.configs['qs_whitelist']:
print name
# check is_admin
try:
if test_runner.is_admin('admin'):
print '"admin" is an admin'
else:
print('Error "admin" is not an admin')
error_log.append('"admin" not flagged as admin')
except:
error_log.append('Error on is_admin check with "admin"')
print 'Unable to validate "admin" as an admin'
# create a new user
try:
test_runner.create_cloudshell_user(user_name=t_user,
password='<PASSWORD>',
email='<EMAIL>')
user_created = 1
except:
error_log.append('Fail user creation')
print 'Unable to create user'
if user_created == 1:
# validate is_active
try:
check = test_runner.is_active(t_user)
if check:
print 'Test User is active'
else:
error_log.append('Test user was not created in an active state')
except:
error_log.append('Unable to check active status')
print 'Unable to check active status'
# check make_inactive & make_active
try:
test_runner.make_cloudshell_user_inactive(t_user)
try:
test_runner.make_cloudshell_user_active(t_user)
except:
error_log.append('Unable to make user active')
print 'Error making user active'
except error_log.append('Unable to make user inactive'):
print 'Error making user inactive'
# assign to user group
try:
test_runner.assign_cloudshell_usergroup([t_user], test_runner.configs['new_user_default_group'])
except:
error_log.append('Unable to assign user to usergroups')
print 'Error assigning user to a usergroup'
# remove user from usergroup
try:
test_runner.remove_cloudshell_usergroup([t_user], test_runner.configs['new_user_default_group'])
except:
error_log.append('Unable to remove user from usergroup')
print 'Error removing user from usergroup'
try:
test_runner._delete_cloudshell_user(t_user)
except:
error_log.append('Unable to delete the test user: ' + t_user)
print 'Error deleting the test user account'
# end if statment
# get user details from admin
try:
details = test_runner.get_cloudshell_user_detail('admin')
print details.Name
except:
error_log.append('Unable to get user detail for admin')
print 'Error get_cloudshell_user_details'
# try the ldap user lists
for each in test_runner.configs['ldap_import_DN']:
try:
list = test_runner.ldap_query(test_runner.configs["ldap_connection"],
test_runner.configs["ldap_username"],
test_runner.configs["ldap_password"],
each,
test_runner.configs["ldap_use_auth"])
print '\nLDAP user list from ' + each
for name in list:
print name
except:
error_log.append('Unable to get LDAP query, ' + each)
print 'Error on Group ' + each
raise
# try the ldap subgroups
for each in test_runner.configs['ldap_subgroups']:
try:
list = test_runner.ldap_query(test_runner.configs["ldap_connection"],
test_runner.configs["ldap_username"],
test_runner.configs["ldap_password"],
each,
test_runner.configs["ldap_use_auth"])
print '\nLDAP subgroup user list from ' + each
for name in list:
print name
except:
error_log.append('Unable to get LDAP subgroup query, ' + each)
print 'Error on subgroup ' + each
# last thing, print out error_log
print '\n>> Unit test complete'
if len(error_log) > 0:
print 'Errors during run:'
for error in error_log:
print '- ' + error
else:
print 'No Errors found'
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create_bq_table.py | timurk1234/bqstream | 0 | 133463 | #!/usr/bin/env python
import webapp2
from bqloader import BQLoader
class MainHandler(webapp2.RequestHandler):
def get(self):
bq_loader = BQLoader()
bq_loader.create_table()
self.response.write('ok')
app = webapp2.WSGIApplication([
('/tasks/create_bq_table', MainHandler)
], debug=True) | [
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deepchem/feat/molecule_featurizers/coulomb_matrices.py | deloragaskins/deepchem | 3,782 | 13183 | """
Generate coulomb matrices for molecules.
See Montavon et al., _New Journal of Physics_ __15__ (2013) 095003.
"""
import numpy as np
from typing import Any, List, Optional
from deepchem.utils.typing import RDKitMol
from deepchem.utils.data_utils import pad_array
from deepchem.feat.base_classes import MolecularFeaturizer
class CoulombMatrix(MolecularFeaturizer):
"""Calculate Coulomb matrices for molecules.
Coulomb matrices provide a representation of the electronic structure of
a molecule. For a molecule with `N` atoms, the Coulomb matrix is a
`N X N` matrix where each element gives the strength of the
electrostatic interaction between two atoms. The method is described
in more detail in [1]_.
Examples
--------
>>> import deepchem as dc
>>> featurizers = dc.feat.CoulombMatrix(max_atoms=23)
>>> input_file = 'deepchem/feat/tests/data/water.sdf' # really backed by water.sdf.csv
>>> tasks = ["atomization_energy"]
>>> loader = dc.data.SDFLoader(tasks, featurizer=featurizers)
>>> dataset = loader.create_dataset(input_file)
References
----------
.. [1] Montavon, Grégoire, et al. "Learning invariant representations of
molecules for atomization energy prediction." Advances in neural information
processing systems. 2012.
Note
----
This class requires RDKit to be installed.
"""
def __init__(self,
max_atoms: int,
remove_hydrogens: bool = False,
randomize: bool = False,
upper_tri: bool = False,
n_samples: int = 1,
seed: Optional[int] = None):
"""Initialize this featurizer.
Parameters
----------
max_atoms: int
The maximum number of atoms expected for molecules this featurizer will
process.
remove_hydrogens: bool, optional (default False)
If True, remove hydrogens before processing them.
randomize: bool, optional (default False)
If True, use method `randomize_coulomb_matrices` to randomize Coulomb matrices.
upper_tri: bool, optional (default False)
Generate only upper triangle part of Coulomb matrices.
n_samples: int, optional (default 1)
If `randomize` is set to True, the number of random samples to draw.
seed: int, optional (default None)
Random seed to use.
"""
self.max_atoms = int(max_atoms)
self.remove_hydrogens = remove_hydrogens
self.randomize = randomize
self.upper_tri = upper_tri
self.n_samples = n_samples
if seed is not None:
seed = int(seed)
self.seed = seed
def _featurize(self, datapoint: RDKitMol, **kwargs) -> np.ndarray:
"""
Calculate Coulomb matrices for molecules. If extra randomized
matrices are generated, they are treated as if they are features
for additional conformers.
Since Coulomb matrices are symmetric, only the (flattened) upper
triangular portion is returned.
Parameters
----------
datapoint: rdkit.Chem.rdchem.Mol
RDKit Mol object
Returns
-------
np.ndarray
The coulomb matrices of the given molecule.
The default shape is `(num_confs, max_atoms, max_atoms)`.
If num_confs == 1, the shape is `(max_atoms, max_atoms)`.
"""
if 'mol' in kwargs:
datapoint = kwargs.get("mol")
raise DeprecationWarning(
'Mol is being phased out as a parameter, please pass "datapoint" instead.'
)
features = self.coulomb_matrix(datapoint)
if self.upper_tri:
features = [f[np.triu_indices_from(f)] for f in features]
features = np.asarray(features)
if features.shape[0] == 1:
# `(1, max_atoms, max_atoms)` -> `(max_atoms, max_atoms)`
features = np.squeeze(features, axis=0)
return features
def coulomb_matrix(self, mol: RDKitMol) -> np.ndarray:
"""
Generate Coulomb matrices for each conformer of the given molecule.
Parameters
----------
mol: rdkit.Chem.rdchem.Mol
RDKit Mol object
Returns
-------
np.ndarray
The coulomb matrices of the given molecule
"""
try:
from rdkit import Chem
from rdkit.Chem import AllChem
except ModuleNotFoundError:
raise ImportError("This class requires RDKit to be installed.")
# Check whether num_confs >=1 or not
num_confs = len(mol.GetConformers())
if num_confs == 0:
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, AllChem.ETKDG())
if self.remove_hydrogens:
mol = Chem.RemoveHs(mol)
n_atoms = mol.GetNumAtoms()
z = [atom.GetAtomicNum() for atom in mol.GetAtoms()]
rval = []
for conf in mol.GetConformers():
d = self.get_interatomic_distances(conf)
m = np.outer(z, z) / d
m[range(n_atoms), range(n_atoms)] = 0.5 * np.array(z)**2.4
if self.randomize:
for random_m in self.randomize_coulomb_matrix(m):
random_m = pad_array(random_m, self.max_atoms)
rval.append(random_m)
else:
m = pad_array(m, self.max_atoms)
rval.append(m)
return np.asarray(rval)
def randomize_coulomb_matrix(self, m: np.ndarray) -> List[np.ndarray]:
"""Randomize a Coulomb matrix as decribed in [1]_:
1. Compute row norms for M in a vector row_norms.
2. Sample a zero-mean unit-variance noise vector e with dimension
equal to row_norms.
3. Permute the rows and columns of M with the permutation that
sorts row_norms + e.
Parameters
----------
m: np.ndarray
Coulomb matrix.
Returns
-------
List[np.ndarray]
List of the random coulomb matrix
References
----------
.. [1] Montavon et al., New Journal of Physics, 15, (2013), 095003
"""
rval = []
row_norms = np.asarray([np.linalg.norm(row) for row in m], dtype=float)
rng = np.random.RandomState(self.seed)
for i in range(self.n_samples):
e = rng.normal(size=row_norms.size)
p = np.argsort(row_norms + e)
new = m[p][:, p] # permute rows first, then columns
rval.append(new)
return rval
@staticmethod
def get_interatomic_distances(conf: Any) -> np.ndarray:
"""
Get interatomic distances for atoms in a molecular conformer.
Parameters
----------
conf: rdkit.Chem.rdchem.Conformer
Molecule conformer.
Returns
-------
np.ndarray
The distances matrix for all atoms in a molecule
"""
n_atoms = conf.GetNumAtoms()
coords = [
# Convert AtomPositions from Angstrom to bohr (atomic units)
conf.GetAtomPosition(i).__idiv__(0.52917721092) for i in range(n_atoms)
]
d = np.zeros((n_atoms, n_atoms), dtype=float)
for i in range(n_atoms):
for j in range(i):
d[i, j] = coords[i].Distance(coords[j])
d[j, i] = d[i, j]
return d
class CoulombMatrixEig(CoulombMatrix):
"""Calculate the eigenvalues of Coulomb matrices for molecules.
This featurizer computes the eigenvalues of the Coulomb matrices for provided
molecules. Coulomb matrices are described in [1]_.
Examples
--------
>>> import deepchem as dc
>>> featurizers = dc.feat.CoulombMatrixEig(max_atoms=23)
>>> input_file = 'deepchem/feat/tests/data/water.sdf' # really backed by water.sdf.csv
>>> tasks = ["atomization_energy"]
>>> loader = dc.data.SDFLoader(tasks, featurizer=featurizers)
>>> dataset = loader.create_dataset(input_file)
References
----------
.. [1] Montavon, Grégoire, et al. "Learning invariant representations of
molecules for atomization energy prediction." Advances in neural information
processing systems. 2012.
"""
def __init__(self,
max_atoms: int,
remove_hydrogens: bool = False,
randomize: bool = False,
n_samples: int = 1,
seed: Optional[int] = None):
"""Initialize this featurizer.
Parameters
----------
max_atoms: int
The maximum number of atoms expected for molecules this featurizer will
process.
remove_hydrogens: bool, optional (default False)
If True, remove hydrogens before processing them.
randomize: bool, optional (default False)
If True, use method `randomize_coulomb_matrices` to randomize Coulomb matrices.
n_samples: int, optional (default 1)
If `randomize` is set to True, the number of random samples to draw.
seed: int, optional (default None)
Random seed to use.
"""
self.max_atoms = int(max_atoms)
self.remove_hydrogens = remove_hydrogens
self.randomize = randomize
self.n_samples = n_samples
if seed is not None:
seed = int(seed)
self.seed = seed
def _featurize(self, datapoint: RDKitMol, **kwargs) -> np.ndarray:
"""
Calculate eigenvalues of Coulomb matrix for molecules. Eigenvalues
are returned sorted by absolute value in descending order and padded
by max_atoms.
Parameters
----------
datapoint: rdkit.Chem.rdchem.Mol
RDKit Mol object
Returns
-------
np.ndarray
The eigenvalues of Coulomb matrix for molecules.
The default shape is `(num_confs, max_atoms)`.
If num_confs == 1, the shape is `(max_atoms,)`.
"""
if 'mol' in kwargs:
datapoint = kwargs.get("mol")
raise DeprecationWarning(
'Mol is being phased out as a parameter, please pass "datapoint" instead.'
)
cmat = self.coulomb_matrix(datapoint)
features_list = []
for f in cmat:
w, v = np.linalg.eig(f)
w_abs = np.abs(w)
sortidx = np.argsort(w_abs)
sortidx = sortidx[::-1]
w = w[sortidx]
f = pad_array(w, self.max_atoms)
features_list.append(f)
features = np.asarray(features_list)
if features.shape[0] == 1:
# `(1, max_atoms)` -> `(max_atoms,)`
features = np.squeeze(features, axis=0)
return features
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] |
multiuploader/management/commands/clean_uploads.py | SharmaVinayKumar/django-multiuploader | 5 | 32904 | from __future__ import print_function, unicode_literals
import os
from datetime import timedelta
import multiuploader.default_settings as DEFAULTS
from django.conf import settings
from django.core.management.base import BaseCommand
from django.utils.timezone import now
from multiuploader.models import MultiuploaderFile
class Command(BaseCommand):
help = 'Clean all temporary attachments loaded to MultiuploaderFile model'
def handle(self, *args, **options):
expiration_time = getattr(settings, "MULTIUPLOADER_FILE_EXPIRATION_TIME",
DEFAULTS.MULTIUPLOADER_FILE_EXPIRATION_TIME)
time_threshold = now() - timedelta(seconds=expiration_time)
for attach in MultiuploaderFile.objects.filter(upload_date__lt=time_threshold):
try:
os.remove(attach.file.path)
except Exception as ex:
print(ex)
MultiuploaderFile.objects.filter(upload_date__lt=time_threshold).delete()
print("Cleaning temporary upload files complete")
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] |
Python/ConkyServerStatus/conkyserverstatus.py | abergman/API | 12 | 168107 | <filename>Python/ConkyServerStatus/conkyserverstatus.py<gh_stars>10-100
#!/usr/bin/python
# -*- coding: utf-8 -*-
# coding: UTF-8
import json, codecs, os, sys, datetime
import xml.etree.ElementTree as ET
import httplib2
import ConfigParser
config = ConfigParser.ConfigParser()
config.readfp(open('glesys.conf'))
__APIKEY__ = config.get('ConkyServerStatus', 'apikey')
__APIUSER__ = config.get('ConkyServerStatus', 'apiuser')
__APISERVER__ = config.get('ConkyServerStatus', 'apiserver')
__APIURL__ = config.get('ConkyServerStatus', 'apiurl')
#print __APIKEY__, __APISERVER__, __APIUSER__
#print __APIURL__
# For some reason ssl_certification is failing at the moment..
try:
h = httplib2.Http(disable_ssl_certificate_validation=True)
h.add_credentials(__APIUSER__, __APIKEY__)
resp, content = h.request(__APIURL__+"/serverid/"+__APISERVER__)
except:
print "No connection"
exit
# Bugfix for conky exec, python does not know which encoding to use and
# will wrongly default to ascii
sys.stdout = codecs.getwriter('utf-8')(sys.stdout)
def printMetric(metricNode, metricName):
usage = float(metricNode.find("usage").text)
max = float(metricNode.find("max").text)
print metricName+": $alignr", usage, " / ", max, metricNode.find("unit").text[0:2]
print "${execbar echo \""+ str(usage * 100 / max) + "\"}"
tree = ET.fromstring(content)
for server in tree.findall("server"):
print "Server name: $alignr", __APISERVER__
print "State: $alignr", server.find("state").text
uptime = int(server.find("uptime").find("current").text)
d = datetime.timedelta(seconds=uptime)
print "Uptime: $alignr", d
print
cpu = server.find("cpu")
print "CPUs: $alignr", cpu.find("usage").text + " / " + cpu.find("max").text, cpu.find("unit").text
memory = server.find("memory")
printMetric(memory, "Memory")
transfer = server.find("transfer")
printMetric(transfer, "Transfer")
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] |
test/module/test_null_values.py | dmorel/cfn-python-lint | 1 | 68112 | <reponame>dmorel/cfn-python-lint<gh_stars>1-10
"""
Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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.
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 json
from cfnlint import RulesCollection # pylint: disable=E0401
from cfnlint.core import DEFAULT_RULESDIR
import cfnlint.decode.cfn_yaml # pylint: disable=E0401
import cfnlint.decode.cfn_json # pylint: disable=E0401
from testlib.testcase import BaseTestCase
class TestNulls(BaseTestCase):
"""Test Null Value Parsing """
def setUp(self):
""" SetUp template object"""
self.rules = RulesCollection()
rulesdirs = [DEFAULT_RULESDIR]
for rulesdir in rulesdirs:
self.rules.extend(
RulesCollection.create_from_directory(rulesdir))
def test_success_run(self):
"""Test success run"""
filename = 'test/fixtures/templates/good/generic.yaml'
try:
cfnlint.decode.cfn_yaml.load(filename)
except cfnlint.decode.cfn_yaml.CfnParseError:
assert(False)
return
assert(True)
def test_fail_json_run(self):
"""Test failure run"""
def test_fail_run(self):
"""Test failure run"""
filename = 'test/fixtures/templates/bad/null_values.json'
try:
with open(filename) as fp:
json.load(fp, cls=cfnlint.decode.cfn_json.CfnJSONDecoder)
except cfnlint.decode.cfn_json.JSONDecodeError as err:
self.assertIn("Null Error \"EbsOptimized\"", err.msg)
return
assert(False)
def test_fail_yaml_run(self):
"""Test failure run"""
filename = 'test/fixtures/templates/bad/null_values.yaml'
try:
cfnlint.decode.cfn_yaml.load(filename)
except cfnlint.decode.cfn_yaml.CfnParseError:
assert(True)
return
assert(False)
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german_experiment.py | laurensalvarez/Fooling-LIME-SHAP | 55 | 164317 | <filename>german_experiment.py<gh_stars>10-100
"""
The experiment MAIN for GERMAN.
"""
import warnings
warnings.filterwarnings('ignore')
from adversarial_models import *
from utils import *
from get_data import *
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import lime
import lime.lime_tabular
import shap
from sklearn.cluster import KMeans
from copy import deepcopy
# Set up experiment parameters
params = Params("model_configurations/experiment_params.json")
X, y, cols = get_and_preprocess_german(params)
features = [c for c in X]
gender_indc = features.index('Gender')
loan_rate_indc = features.index('LoanRateAsPercentOfIncome')
X = X.values
xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=0.1)
ss = StandardScaler().fit(xtrain)
xtrain = ss.transform(xtrain)
xtest = ss.transform(xtest)
mean_lrpi = np.mean(xtrain[:,loan_rate_indc])
categorical = ['Gender', 'ForeignWorker', 'Single', 'HasTelephone','CheckingAccountBalance_geq_0','CheckingAccountBalance_geq_200','SavingsAccountBalance_geq_100','SavingsAccountBalance_geq_500','MissedPayments','NoCurrentLoan','CriticalAccountOrLoansElsewhere','OtherLoansAtBank','OtherLoansAtStore','HasCoapplicant','HasGuarantor','OwnsHouse','RentsHouse','Unemployed','YearsAtCurrentJob_lt_1','YearsAtCurrentJob_geq_4','JobClassIsSkilled']
categorical = [features.index(c) for c in categorical]
###
## The models f and psi for GERMAN. We discriminate based on gender for f and consider loan rate % income for explanation
#
# the biased model
class racist_model_f:
# Decision rule: classify negative outcome if female
def predict(self,X):
return np.array([params.negative_outcome if x[gender_indc] < 0 else params.positive_outcome for x in X])
def predict_proba(self, X):
return one_hot_encode(self.predict(X))
def score(self, X,y):
return np.sum(self.predict(X)==y) / len(X)
# the display model with one unrelated feature
class innocuous_model_psi:
# Decision rule: classify according to loan rate indc
def predict_proba(self, X):
return one_hot_encode(np.array([params.negative_outcome if x[loan_rate_indc] > mean_lrpi else params.positive_outcome for x in X]))
##
###
def experiment_main():
"""
Run through experiments for LIME/SHAP on GERMAN.
* This may take some time given that we iterate through every point in the test set
* We print out the rate at which features occur in the top three features
"""
print ('---------------------')
print ("Beginning LIME GERMAN Experiments....")
print ("(These take some time to run because we have to generate explanations for every point in the test set) ")
print ('---------------------')
# Train the adversarial model for LIME with f and psi
adv_lime = Adversarial_Lime_Model(racist_model_f(), innocuous_model_psi()).train(xtrain, ytrain, feature_names=features, perturbation_multiplier=30, categorical_features=categorical)
adv_explainer = lime.lime_tabular.LimeTabularExplainer(xtrain, feature_names=adv_lime.get_column_names(), discretize_continuous=False, categorical_features=categorical)
explanations = []
for i in range(xtest.shape[0]):
explanations.append(adv_explainer.explain_instance(xtest[i], adv_lime.predict_proba).as_list())
# Display Results
print ("LIME Ranks and Pct Occurances (1 corresponds to most important feature) for one unrelated feature:")
print (experiment_summary(explanations, features))
print ("Fidelity:", round(adv_lime.fidelity(xtest),2))
print ('---------------------')
print ('Beginning SHAP GERMAN Experiments....')
print ('---------------------')
#Setup SHAP
background_distribution = KMeans(n_clusters=10,random_state=0).fit(xtrain).cluster_centers_
adv_shap = Adversarial_Kernel_SHAP_Model(racist_model_f(), innocuous_model_psi()).train(xtrain, ytrain,
feature_names=features, background_distribution=background_distribution, rf_estimators=100, n_samples=5e4)
adv_kerenel_explainer = shap.KernelExplainer(adv_shap.predict, background_distribution,)
explanations = adv_kerenel_explainer.shap_values(xtest)
# format for display
formatted_explanations = []
for exp in explanations:
formatted_explanations.append([(features[i], exp[i]) for i in range(len(exp))])
print ("SHAP Ranks and Pct Occurances one unrelated features:")
print (experiment_summary(formatted_explanations, features))
print ("Fidelity:",round(adv_shap.fidelity(xtest),2))
print ('---------------------')
if __name__ == "__main__":
experiment_main()
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] |
mytrading/configs.py | joeledwardson/betfair-browser | 3 | 59020 | from typing import List, Dict, Optional
from myutils.registrar import Registrar
from myutils import dictionaries
import os, yaml
from os import path
import json
from .exceptions import FeatureConfigException
reg_plots = Registrar()
reg_features = Registrar()
KEY_SAMPLE = 'smp'
KEY_AVERAGE = 'avg'
KEY_TICKS = 'tck'
KEY_MAX_DIF = 'mdf'
KEY_COMPARE = 'cmp'
class ConfigGenerator:
CONFIG_SPEC = {
'name': {
'type': str
},
'kwargs': {
'type': dict
}
}
def __init__(self, cfg_dir: str, out_dir, reg: Registrar):
self._cfg_dir = path.abspath(path.expandvars(cfg_dir))
if not path.isdir(self._cfg_dir):
raise FeatureConfigException(f'configuration dir "{self._cfg_dir}" is not a directory')
self._out_dir = path.abspath(path.expandvars(out_dir))
if not path.isdir(self._out_dir):
raise FeatureConfigException(f'output dir "{self._out_dir}" is not a directory')
self._reg = reg
def reload(self):
_, _, filenames = next(os.walk(self._cfg_dir))
for fn in filenames:
p_in = path.join(self._cfg_dir, fn)
with open(p_in, 'r') as f:
data = f.read()
file_cfg = yaml.load(data, yaml.FullLoader)
dictionaries.validate_config(file_cfg, self.CONFIG_SPEC)
reg_nm = file_cfg['name']
reg_kwargs = file_cfg['kwargs']
ftr_cfg = self._reg[reg_nm](**reg_kwargs)
p_out = path.join(self._out_dir, fn)
with open(p_out, 'w') as f:
f.write(yaml.dump(ftr_cfg, sort_keys=False))
p_out_json = path.splitext(p_out)[0] + '.json'
with open(p_out_json, 'w') as f:
f.write(json.dumps(ftr_cfg, indent=2, sort_keys=False))
def _plot_procs_lad(ftr_key, lad_key):
"""return processors to add ladder feature of price sizes to back/lay feature"""
return [
{
'name': 'prc_ftrstodf',
'kwargs': {
'ftr_keys': {
'y': ftr_key,
'text': lad_key
}
}
}, {
'name': 'prc_dffmtps',
'kwargs': {
'df_col': 'text'
}
}, {
'name': 'prc_dftodict'
}
]
def _plot_colorscale(color_0, color_1) -> Dict:
"""`chart_args` argument to set colorscale with lines+markers"""
return {
'mode': 'lines+markers',
'line_color': 'black',
'marker': {
'colorscale': [
[0, color_0],
[1, color_1]
],
'cmid': 0,
}
}
def _ftr_smooth(sample_ms, cache_count):
"""sub-features config for sampling and then moving average"""
return {
KEY_SAMPLE: {
'name': 'RFSample',
'kwargs': {
'periodic_ms': sample_ms,
'cache_count': cache_count,
'sub_features_config': {
KEY_AVERAGE: {
'name': 'RFMvAvg'
}
}
}
}
}
def _ftr_tick(sub_features_config=None):
"""sub-feature converting parent to tick"""
return {
'name': 'RunnerFeatureSub',
'kwargs': {
'value_processors_config': [{
'name': 'value_processor_to_tick',
}],
'sub_features_config': sub_features_config,
},
}
def _ftr_tvlad(window_s, sampling_ms, cache_count):
"""traded volume `TVLad` feature sub-config for creating max/min values over window, sampling then moving avg"""
return {
'cache_secs': window_s,
'cache_insidewindow': False,
'sub_features_config': {
'dif': {
'name': 'RFTVLadDif',
'kwargs': {
'sub_features_config': {
'max': {
'name': 'RFTVLadMax',
'kwargs': {
'sub_features_config': _ftr_smooth(sampling_ms, cache_count)
}
},
'min': {
'name': 'RFTVLadMin',
'kwargs': {
'sub_features_config': _ftr_smooth(sampling_ms, cache_count)
}
}
}
}
}
}
}
@reg_features.register_element
def feature_configs_spike(
n_ladder_elements,
n_wom_ticks,
ltp_window_width_s,
ltp_window_sampling_ms,
ltp_window_sampling_count,
spread_sampling_ms,
spread_sampling_count,
) -> Dict[str, Dict]:
"""
Get a dict of default runner features, where each entry is a dictionary of:
- key: feature usage name
- value: dict of
- 'name': class name of feature
- 'kwargs': dict of constructor arguments used when creating feature
"""
def ltp_win_kwargs(sample_ms, cache_count):
d = {
'sub_features_config': {
'smp': {
'name': 'RFSample',
'kwargs': {
'periodic_ms': sample_ms,
'cache_count': cache_count,
'sub_features_config': {
'avg': {
'name': 'RFMvAvg'
}
}
}
}
}
}
return d
return {
'best back': {
'name': 'RFBck',
},
'best lay': {
'name': 'RFLay',
},
'back ladder': {
'name': 'RFLadBck',
'kwargs': {
'n_elements': n_ladder_elements,
}
},
'lay ladder': {
'name': 'RFLadLay',
'kwargs': {
'n_elements': n_ladder_elements,
}
},
'wom': {
'name': 'RFWOM',
'kwargs': {
'wom_ticks': n_wom_ticks
},
},
'tvlad': {
'name': 'RFTVLad',
'kwargs': {
'cache_secs': ltp_window_width_s,
'cache_insidewindow': False,
'sub_features_config': {
'dif': {
'name': 'RFTVLadDif',
'kwargs': {
'sub_features_config': {
'max': {
'name': 'RFTVLadMax',
'kwargs': ltp_win_kwargs(ltp_window_sampling_ms, ltp_window_sampling_count)
},
'min': {
'name': 'RFTVLadMin',
'kwargs': ltp_win_kwargs(ltp_window_sampling_ms, ltp_window_sampling_count)
},
'spread': {
'name': 'RFTVLadSpread'
}
}
}
}
}
}
},
'ltp': {
'name': 'RFLTP',
},
'tv': {
'name': 'RFTVTot',
},
'spread': {
'name': 'RFLadSprd',
'kwargs': {
'sub_features_config': {
'smp': {
'name': 'RFSample',
'kwargs': {
'periodic_ms': spread_sampling_ms,
'cache_count': spread_sampling_count,
'sub_features_config': {
'avg': {
'name': 'RFMvAvg'
}
}
}
}
}
}
}
}
@reg_plots.register_element
def plot_configs_spike(
ltp_diff_opacity,
ltp_diff_s,
tv_width_ms
):
IGNORE = [
'back ladder',
'lay ladder',
'spread',
'spread.smp',
'spread.smp.avg',
'wom',
'tvlad',
'tvlad.dif',
'tvlad.dif.max',
'tvlad.dif.max.smp',
'tvlad.dif',
'tvlad.dif.spread',
'tvlad.dif.min',
'tvlad.dif.min.smp',
]
return {
k: {
'ignore': True
} for k in IGNORE
} | {
'best back': {
'value_processors': _plot_procs_lad('best back', 'back ladder'),
},
'best lay': {
'value_processors': _plot_procs_lad('best lay', 'lay ladder')
},
'ltp': {
'chart_args': {
'mode': 'lines+markers'
},
'value_processors': [{
'name': 'prc_ftrstodf',
'kwargs': {
'ftr_keys': {
'y': 'ltp',
'text': 'tv',
}
}
}, {
'name': 'prc_dffillna'
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text',
'fmt_spec': 'Traded Volume: £{0:.2f}'
}
}, {
'name': 'prc_dftodict',
}],
},
'tv': {
'chart': 'Bar',
'chart_args': {
'marker': {
'colorscale': [
[0, 'rgb(250,50,50)'],
[1, 'rgb(50,250,50)']
], # default plotly colours go white, so use a green to red scale
'cmid': 0, # with grey 0 scale
},
'opacity': ltp_diff_opacity,
'width': tv_width_ms, # 1 seconds width of bars
'offset': 0, # end of bar to be aligned with timestamp
},
'trace_args': {
'secondary_y': True
},
'value_processors': [{
'name': 'prc_dfdiff'
}, {
'name': 'prc_getftr',
'keys': {
'key_out': 'key_1'
},
'kwargs': {
'ftr_key': 'wom',
}
}, {
'name': 'prc_buftodf',
'kwargs': {
'buf_cfg': {
'y': 'key_0',
'text': 'key_1'
},
}
}, {
'name': 'prc_dftypes',
'kwargs': {
'dtypes': {
'y': 'float',
'text': 'float',
}
}
}, {
'name': 'prc_resmp',
'kwargs': {
'n_seconds': tv_width_ms/1000,
'agg_function': {
'y': 'sum',
'text': 'mean',
}
}
}, {
'name': 'prc_dfcp',
'kwargs': {
'col_src': 'text',
'col_out': 'marker_color'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text',
'fmt_spec': 'Weight of Money: £{0:.2f}'
},
}, {
'name': 'prc_dftodict'
}],
},
'tvlad.dif.max.smp.avg': {
'rename': 'ltp max'
},
'tvlad.dif.min.smp.avg': {
'rename': 'ltp min'
}
}
@reg_features.register_element
def feature_configs_smooth(
spread_sampling_ms,
spread_sampling_count,
wom_ticks,
ltp_window_width_s,
ltp_window_sampling_ms,
ltp_window_sampling_count,
ladder_sampling_ms,
ladder_sampling_count,
ltp_sampling_ms,
ltp_sampling_count,
n_ladder_elements,
diff_s,
split_sum_s,
):
def side_kwargs(diff_s, ladder_sampling_ms, ladder_sampling_count) -> Dict:
return {
'cache_secs': diff_s,
'cache_insidewindow': False,
'sub_features_config': {
KEY_TICKS: {
'name': 'RFTick',
'kwargs': {
'cache_secs': diff_s,
'cache_insidewindow': False,
'sub_features_config': {
KEY_MAX_DIF: {
'name': 'RFMaxDif'
},
KEY_SAMPLE: {
'name': 'RFSample',
'kwargs': {
'periodic_ms': ladder_sampling_ms,
'cache_count': ladder_sampling_count,
'sub_features_config': {
KEY_AVERAGE: {
'name': 'RFMvAvg',
'kwargs': {
'cache_secs': diff_s,
'sub_features_config': {
KEY_COMPARE: {
'name': 'RFDif'
}
}
}
}
}
}
}
}
}
},
KEY_SAMPLE: {
'name': 'RFSample',
'kwargs': {
'periodic_ms': ladder_sampling_ms,
'cache_count': ladder_sampling_count,
'sub_features_config': {
KEY_AVERAGE: {
'name': 'RFMvAvg'
}
}
}
}
}
}
return {
'spread': {
'name': 'RFLadSprd',
'kwargs': {
'sub_features_config': _ftr_smooth(
spread_sampling_ms,
spread_sampling_count
)
}
},
'lay': {
'name': 'RFLay',
'kwargs': side_kwargs(diff_s, ladder_sampling_ms, ladder_sampling_count)
},
'bck': {
'name': 'RFBck',
'kwargs': side_kwargs(diff_s, ladder_sampling_ms, ladder_sampling_count)
},
'ltp': {
'name': 'RFLTP',
'kwargs': {
'sub_features_config': _ftr_smooth(
ltp_sampling_ms,
ltp_sampling_count
),
}
},
'tvlad': {
'name': 'RFTVLad',
'kwargs': _ftr_tvlad(
ltp_window_width_s,
ltp_window_sampling_ms,
ltp_window_sampling_count
)
},
'bcklad': {
'name': 'RFLadBck',
'kwargs': {
'n_elements': n_ladder_elements,
}
},
'laylad': {
'name': 'RFLadLay',
'kwargs': {
'n_elements': n_ladder_elements,
}
},
'wom': {
'name': 'RFWOM',
'kwargs': {
'wom_ticks': wom_ticks
},
},
'split': {
'name': 'RFBkSplit',
'kwargs': {
'cache_secs': split_sum_s,
'cache_insidewindow': False,
'sub_features_config': {
'sum': {
'name': 'RFSum'
},
'tot': {
'name': 'RFIncSum'
}
}
}
},
'tv': {
'name': 'RFTVTot',
},
}
@reg_plots.register_element
def plot_configs_smooth(bar_width_ms, tv_opacity):
IGNORE_LIST = [
'bcklad',
'laylad',
'wom',
'spread',
'spread.smp',
'spread.smp.avg',
'tv',
'bck.smp',
'lay.smp',
'bck.tck',
'lay.tck',
'bck.tck.mdf',
'lay.tck.mdf',
'bck.tck.smp',
'lay.tck.smp',
'bck.tck.smp.avg',
'lay.tck.smp.avg',
'bck.tck.smp.avg.cmp',
'lay.tck.smp.avg.cmp',
'ltp.smp',
'tvlad',
'tvlad.dif',
'tvlad.dif.max',
'tvlad.dif.max.smp',
'tvlad.dif',
'tvlad.dif.min',
'tvlad.dif.min.smp',
]
def prcs_ltp(ltp, tv, spread, split) -> List[Dict]:
return [{
'name': 'prc_ftrstodf',
'kwargs': {
'ftr_keys': {
'y': ltp,
'tv_text': tv,
'spread_text': spread,
'split_text': split,
'marker_color': 'wom',
'wom_text': 'wom',
}
}
}, {
'name': 'prc_dffillna',
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'spread_text',
'fmt_spec': 'Spread: {0}'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'tv_text',
'fmt_spec': 'Traded Volume: £{0:.2f}'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'split_text',
'fmt_spec': 'Book split: £{0:.2f}'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'wom_text',
'fmt_spec': 'WOM: £{0:.2f}'
}
}, {
'name': 'prc_dftxtjoin',
'kwargs': {
'dest_col': 'text',
'src_cols': [
'tv_text',
'spread_text',
'split_text',
'wom_text'
],
}
}, {
'name': 'prc_dfdrop',
'kwargs': {
'cols': [
'tv_text',
'spread_text',
'split_text',
'wom_text'
]
}
}, {
'name': 'prc_dftodict',
}]
def prcs_tvbar(tv, bar_width_ms):
return [{
'name': 'prc_getftr',
'keys': {
'key_out': 'key_tv'
},
'kwargs': {
'ftr_key': tv
}
}, {
'name': 'prc_dfdiff',
'keys': {
'key_in': 'key_tv',
'key_out': 'key_tv'
}
}, {
'name': 'prc_buftodf',
'kwargs': {
'buf_cfg': {
'y': 'key_0',
'text': 'key_tv'
}
}
}, {
'name': 'prc_resmp',
'kwargs': {
'n_seconds': int(bar_width_ms / 1000),
'agg_function': {
'y': 'sum',
'text': 'sum'
}
}
}, {
'name': 'prc_dfcp',
'kwargs': {
'col_src': 'text',
'col_out': 'marker_color'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text',
'fmt_spec': 'Traded volume: £{0:.2f}'
}
}, {
'name': 'prc_dftodict'
}]
def smooth_value_processors(ftr_src, ftr_tks, ftr_cmp, ftr_dif) -> List[Dict]:
return [{
'name': 'prc_ftrstodf',
'kwargs': {
'ftr_keys': {
'y': ftr_src,
'marker_color': ftr_cmp,
'text_ticks': ftr_tks,
'text_tick_comp': ftr_cmp,
'text_max_diff': ftr_dif
}
},
}, {
'name': 'prc_dffillna',
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text_ticks',
'fmt_spec': 'Tick: {0:.2f}'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text_tick_comp',
'fmt_spec': 'Tick difference: {0:.2f}'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text_max_diff',
'fmt_spec': 'Max tick difference: {0:.2f}',
}
}, {
'name': 'prc_dftxtjoin',
'kwargs': {
'dest_col': 'text',
'src_cols': [
'text_ticks',
'text_tick_comp',
'text_max_diff'
],
}
}, {
'name': 'prc_dfdrop',
'kwargs': {
'cols': [
'text_ticks',
'text_tick_comp',
'text_max_diff'
]
}
}, {
'name': 'prc_dftodict',
}]
return {
f: {
'ignore': True
} for f in IGNORE_LIST
} | {
'tvlad.dif.max.smp.avg': {
'rename': 'ltp max'
},
'tvlad.dif.min.smp.avg': {
'rename': 'ltp min'
},
'bck': {
'chart_args': {
'visible': 'legendonly',
},
'value_processors': _plot_procs_lad('bck', 'bcklad'),
},
'lay': {
'chart_args': {
'visible': 'legendonly',
},
'value_processors': _plot_procs_lad('lay', 'laylad'),
},
'ltp': {
'value_processors': prcs_ltp(
ltp='ltp',
tv='tv',
spread='spread',
split='split.sum',
),
'chart_args': {
'mode': 'lines+markers',
'visible': 'legendonly',
'line_color': 'black',
'marker': {
'colorscale': [
[0, 'rgb(255,0,0)'],
[1, 'rgb(0,255,0)']
],
'cmid': 0,
}
},
},
'split': {
'chart': 'Bar',
'chart_args': {
'marker': { # default plotly colours go white, so use a green to red scale
'colorscale': [
[0, 'rgb(250,50,50)'],
[1, 'rgb(50,250,50)']
],
'cmid': 0, # with grey 0 scale
},
'opacity': tv_opacity,
'width': bar_width_ms, # 1 seconds width of bars
'offset': 0, # end of bar to be aligned with timestamp
},
'trace_args': {
'secondary_y': True
},
'value_processors': prcs_tvbar('tv', bar_width_ms),
},
'split.sum': {
'chart_args': {
'visible': 'legendonly',
},
'trace_args': {
'secondary_y': True
},
},
'split.tot': {
# 'trace_args': {
# 'secondary_y': True
# },
'ignore': True
},
'ltp.smp.avg': {
'chart_args': _plot_colorscale(
color_0='rgb(255,255,0)',
color_1='rgb(0,0,255)' # yellow to blue scale
),
'value_processors': [{
'name': 'prc_ftrstodf',
'kwargs': {
'ftr_keys': {
'y': 'ltp.smp.avg',
'text': 'split.sum',
},
},
}, {
'name': 'prc_dffillna'
}, {
'name': 'prc_dfcp',
'kwargs': {
'col_src': 'text',
'col_out': 'marker_color'
}
}, {
'name': 'prc_dffmtstr',
'kwargs': {
'df_col': 'text',
'fmt_spec': 'Book split: £{0:.2f}'
}
}, {
'name': 'prc_dftodict',
}],
'rename': 'ltp smoothed'
},
'bck.smp.avg': {
# use red to green scale
'chart_args': _plot_colorscale(
color_0='rgb(255,0,0)',
color_1='rgb(0,255,0)',
),
'value_processors': smooth_value_processors(
ftr_src='bck.smp.avg',
ftr_tks='bck.tck.smp.avg',
ftr_cmp='bck.tck.smp.avg.cmp',
ftr_dif='bck.tck.mdf',
),
'rename': 'back smoothed'
},
'lay.smp.avg': {
# use red to green scale
'chart_args': _plot_colorscale(
color_0='rgb(255,0,0)',
color_1='rgb(0,255,0)',
),
'value_processors': smooth_value_processors(
ftr_src='lay.smp.avg',
ftr_tks='lay.tck.smp.avg',
ftr_cmp='lay.tck.smp.avg.cmp',
ftr_dif='lay.tck.mdf',
),
'rename': 'lay smoothed'
},
}
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] |
simulation/model/sim_model.py | fladdimir/csa-simulation-based-sc-forecast | 2 | 75393 | from typing import Any, Dict, List
from model.blocks.delivery import Delivery
from model.blocks.order_creation import OrderCreation
from model.blocks.order_ingest import OrderIngest
from model.blocks.order_material import OrderMaterial
from model.blocks.production import Production
from model.blocks.wait_for_material import WaitForMaterial
from model.blocks.wait_for_sop import WaitForSop
from simpy.core import Environment
class Model:
def __init__(self, env: Environment):
self.env = env
self.model_components: Any
self.model_graph_names: Dict[str, List[str]]
#!resources+components (generated)
self.customer = OrderCreation(
self.env, "customer", xy=(41, 43), ways={"ingest": [(59, 43), (117, 43)]}
)
self.delivery = Delivery(self.env, "delivery", xy=(457, 214), ways={})
self.ingest = OrderIngest(
self.env,
"ingest",
xy=(167, 43),
ways={"order_material": [(217, 43), (277, 43)]},
)
self.order_material = OrderMaterial(
self.env,
"order_material",
xy=(327, 43),
ways={"wait_for_material": [(377, 43), (437, 43)]},
)
self.wait_for_material = WaitForMaterial(
self.env,
"wait_for_material",
xy=(487, 43),
ways={
"wait_for_sop": [
(537, 43),
(567, 43),
(567, 134),
(87, 134),
(87, 214),
(117, 214),
]
},
)
self.wait_for_sop = WaitForSop(
self.env,
"wait_for_sop",
xy=(167, 214),
ways={"production": [(217, 214), (277, 214)]},
)
self.production = Production(
self.env,
"production",
xy=(327, 214),
ways={"delivery": [(377, 214), (439, 214)]},
)
#!model (generated)
self.model_components = {
"customer": self.customer,
"delivery": self.delivery,
"ingest": self.ingest,
"order_material": self.order_material,
"wait_for_material": self.wait_for_material,
"wait_for_sop": self.wait_for_sop,
"production": self.production,
}
self.model_graph_names = {
"customer": ["ingest"],
"delivery": [],
"ingest": ["order_material"],
"order_material": ["wait_for_material"],
"wait_for_material": ["wait_for_sop"],
"wait_for_sop": ["production"],
"production": ["delivery"],
}
# translate model_graph_names into corresponding objects
self.model_graph = {
self.model_components[name]: [
self.model_components[nameSucc]
for nameSucc in self.model_graph_names[name]
]
for name in self.model_graph_names
}
for component in self.model_graph:
component.successors = self.model_graph[component]
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tests/functional/modules/test_zos_tso_command.py | IBM/zos-core-collection-ftp | 4 | 23698 | from __future__ import absolute_import, division, print_function
__metaclass__ = type
import os
import sys
import warnings
import ansible.constants
import ansible.errors
import ansible.utils
import pytest
from pprint import pprint
# The positive path test
def test_zos_tso_command_listuser(ansible_adhoc):
hosts = ansible_adhoc(inventory='localhost', connection='local')
print('--- hosts.all ---')
pprint(hosts.all)
pprint(hosts.all.options)
pprint(vars(hosts.all.options['inventory_manager']))
pprint(hosts.all.options['inventory_manager']._inventory.hosts)
hosts.all.options['inventory_manager']._inventory.hosts
results = hosts.localhost.zos_tso_command(commands=["LU"])
print('--- results.contacted ---')
pprint(results.contacted)
for result in results.contacted.values():
assert result.get("output")[0].get("rc") == 0
assert result.get("changed") is True
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deeposlandia/datagen.py | Oslandia/Streetscene_CNN | 53 | 140452 | """Main method to generate new datasets
Example of program call:
* generate 64*64 pixel images from Shapes dataset, 10000 images in the training
set, 100 in the validation set, 1000 in the testing set::
python deeposlandia/datagen.py -D shapes -s 64 -t 10000 -v 100 -T 1000
"""
import argparse
import os
import daiquiri
import pandas as pd
from deeposlandia import config, utils
from deeposlandia.datasets import AVAILABLE_DATASETS, GEOGRAPHIC_DATASETS
from deeposlandia.datasets.mapillary import MapillaryDataset
from deeposlandia.datasets.aerial import AerialDataset
from deeposlandia.datasets.shapes import ShapeDataset
from deeposlandia.datasets.tanzania import TanzaniaDataset
logger = daiquiri.getLogger(__name__)
def main(args):
# Data path and repository management
input_folder = utils.prepare_input_folder(args.datapath, args.dataset)
prepro_folder = utils.prepare_preprocessed_folder(
args.datapath, args.dataset, args.image_size
)
if (
args.dataset in GEOGRAPHIC_DATASETS
and (args.nb_training_image > 0 or args.nb_validation_image > 0)
and args.nb_tiles_per_image is None
):
raise ValueError(
"The amount of tiles per image must be specified for "
f"the {args.dataset} dataset, if training and/or validation images "
"are required. See 'deepo datagen -h' for more details."
)
# Dataset creation
if args.dataset == "mapillary":
config_path = os.path.join(input_folder, "config_aggregate.json")
train_dataset = MapillaryDataset(args.image_size, config_path)
validation_dataset = MapillaryDataset(args.image_size, config_path)
test_dataset = MapillaryDataset(args.image_size, config_path)
elif args.dataset == "shapes":
train_dataset = ShapeDataset(args.image_size)
validation_dataset = ShapeDataset(args.image_size)
test_dataset = ShapeDataset(args.image_size)
os.makedirs(
os.path.join(prepro_folder["testing"], "labels"), exist_ok=True
)
elif args.dataset == "aerial":
train_dataset = AerialDataset(args.image_size)
validation_dataset = AerialDataset(args.image_size)
test_dataset = AerialDataset(args.image_size)
elif args.dataset == "tanzania":
train_dataset = TanzaniaDataset(args.image_size)
validation_dataset = TanzaniaDataset(args.image_size)
test_dataset = TanzaniaDataset(args.image_size)
else:
raise ValueError(
f"Unsupported dataset type. Please choose amongst {AVAILABLE_DATASETS}"
)
# Dataset populating/loading
# (depends on the existence of a specification file)
if args.nb_training_image > 0:
if os.path.isfile(prepro_folder["training_config"]):
train_dataset.load(
prepro_folder["training_config"], args.nb_training_image
)
else:
logger.info(
"No existing configuration file for this dataset. Create %s.",
prepro_folder["training_config"],
)
input_image_dir = os.path.join(input_folder, "training")
train_dataset.populate(
prepro_folder["training"],
input_image_dir,
nb_images=args.nb_training_image,
nb_processes=int(config.get("running", "processes")),
nb_tiles_per_image=args.nb_tiles_per_image,
)
train_dataset.save(prepro_folder["training_config"])
if args.nb_validation_image > 0:
if os.path.isfile(prepro_folder["validation_config"]):
validation_dataset.load(
prepro_folder["validation_config"], args.nb_validation_image
)
else:
logger.info(
"No existing configuration file for this dataset. Create %s.",
prepro_folder["validation_config"],
)
input_image_dir = os.path.join(input_folder, "validation")
validation_dataset.populate(
prepro_folder["validation"],
input_image_dir,
nb_images=args.nb_validation_image,
nb_processes=int(config.get("running", "processes")),
nb_tiles_per_image=args.nb_tiles_per_image,
)
validation_dataset.save(prepro_folder["validation_config"])
if args.nb_testing_image > 0:
if os.path.isfile(prepro_folder["testing_config"]):
test_dataset.load(
prepro_folder["testing_config"], args.nb_testing_image
)
else:
logger.info(
"No existing configuration file for this dataset. Create %s.",
prepro_folder["testing_config"],
)
input_image_dir = os.path.join(input_folder, "testing")
test_dataset.populate(
prepro_folder["testing"],
input_image_dir,
nb_images=args.nb_testing_image,
labelling=False,
nb_processes=int(config.get("running", "processes")),
)
test_dataset.save(prepro_folder["testing_config"])
glossary = pd.DataFrame(train_dataset.labels)
glossary["popularity"] = train_dataset.get_label_popularity()
logger.info("Data glossary:\n%s", glossary)
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Section 12/12.4-download-img.py | magnusmel/The-Modern-Python-Challenge-v | 3 | 189256 | import requests
def get_image(url, filename):
target = requests.get(url)
ext = '.png'
open(filename + ext, 'wb').write(target.content)
print(f"{filename}{ext} downloaded")
url = "https://www.python.org/static/img/[email protected]"
get_image(url,'py-logo')
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] |
examples/tornado/myapp/__init__.py | s-shin/wswrapper | 2 | 26895 | # -*- coding: utf-8 -*-
def setup_argparser(parser):
"""コマンドパーサーのセットアップ。
パーサーは共有されるので、被らないように上手く調整すること。
:param parser: ``argparse.ArgumentParser`` のインスタンス。
"""
pass
def setup_app(args):
"""コマンドパース後のセットアップ。
:param args: ``parser.arg_parse()`` の戻り値。
"""
pass
def on_open(client):
"""WebSocketのコネクションが成立した時に呼ばれる。
"""
pass
def on_close(client):
"""WebSocketのコネクションが切れた時に呼ばれる。
"""
pass
def on_setup(client, data):
client.emit("print", "Hello world!")
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] |
python/pointcloud/lidaroverview.py | NLeSC/pointcloud-benchmark | 9 | 158016 | <filename>python/pointcloud/lidaroverview.py
#!/usr/bin/env python
################################################################################
# Created by <NAME> #
# <EMAIL> #
################################################################################
import os, optparse, psycopg2, multiprocessing, logging
from pointcloud import utils, postgresops, lasops
def runChild(childId, childrenQueue, connectionString, dbtable, srid):
kill_received = False
connection = psycopg2.connect(connectionString)
cursor = connection.cursor()
while not kill_received:
job = None
try:
# This call will patiently wait until new job is available
job = childrenQueue.get()
except:
# if there is an error we will quit the loop
kill_received = True
if job == None:
kill_received = True
else:
[identifier, inputFile,] = job
(_, count, minX, minY, minZ, maxX, maxY, maxZ, scaleX, scaleY, scaleZ, offsetX, offsetY, offsetZ) = lasops.getPCFileDetails(inputFile)
insertStatement = """INSERT INTO """ + dbtable + """(id,filepath,num,scalex,scaley,scalez,offsetx,offsety,offsetz,geom) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, ST_MakeEnvelope(%s, %s, %s, %s, %s));"""
insertArgs = [identifier, inputFile, int(count), float(scaleX), float(scaleY), float(scaleZ), float(offsetX), float(offsetY), float(offsetZ), float(minX), float(minY), float(maxX), float(maxY), int(srid)]
logging.info(cursor.mogrify(insertStatement, insertArgs))
cursor.execute(insertStatement, insertArgs)
connection.commit()
cursor.close()
connection.close()
def run(inputFolder, numcores, dbname, dbuser, dbpass, dbhost, dbport, createdb, dbtable, srid):
opts = 0
childrenQueue = multiprocessing.Queue()
ifiles = utils.getFiles(inputFolder)
for i in range(len(ifiles)):
childrenQueue.put([i, ifiles[i]])
for i in range(int(numcores)): #we add as many None jobs as numWorkers to tell them to terminate (queue is FIFO)
childrenQueue.put(None)
clineConString = postgresops.getConnectString(dbname, dbuser, dbpass, dbhost, dbport, True)
psycopgConString = postgresops.getConnectString(dbname, dbuser, dbpass, dbhost, dbport, False)
if createdb:
os.system('dropdb ' + clineConString)
os.system('createdb ' + clineConString)
connection = psycopg2.connect(psycopgConString)
cursor = connection.cursor()
if createdb:
cursor.execute('CREATE EXTENSION postgis')
connection.commit()
q = """
CREATE TABLE """ + dbtable + """ (
id integer,
filepath text,
num integer,
scalex double precision,
scaley double precision,
scalez double precision,
offsetx double precision,
offsety double precision,
offsetz double precision,
geom public.geometry(Geometry,""" + str(srid) + """)
)"""
logging.info(cursor.mogrify(q))
cursor.execute(q)
connection.commit()
# q = "select addgeometrycolumn('" + dbtable + "','geom',28992,'POLYGON',2)"
# logging.info(cursor.mogrify(q))
# cursor.execute(q)
# connection.commit()
print 'numcores',numcores
children = []
# We start numcores children processes
for i in range(int(numcores)):
children.append(multiprocessing.Process(target=runChild,
args=(i, childrenQueue, psycopgConString, dbtable, srid)))
children[-1].start()
# wait for all children to finish their execution
for i in range(int(numcores)):
children[i].join()
q = "create index ON " + dbtable + " using GIST (geom)"
logging.info(cursor.mogrify(q))
cursor.execute(q)
connection.commit()
old_isolation_level = connection.isolation_level
connection.set_isolation_level(0)
q = "VACUUM FULL ANALYZE " + dbtable
logging.info(cursor.mogrify(q))
cursor.execute(q)
connection.commit()
connection.set_isolation_level(old_isolation_level)
cursor.close()
def main(opts):
run(opts.input, opts.cores, opts.dbname, opts.dbuser, opts.dbpass, opts.dbhost, opts.dbport, opts.create, opts.dbtable, opts.srid)
if __name__ == "__main__":
usage = 'Usage: %prog [options]'
description = "Creates a table with geometries describing the areas of which the LAS files contain points."
op = optparse.OptionParser(usage=usage, description=description)
op.add_option('-i','--input',default='',help='Input folder where to find the LAS files',type='string')
op.add_option('-s','--srid',default='',help='SRID',type='string')
op.add_option('-x','--create',default=False,help='Creates the database',action='store_true')
op.add_option('-n','--dbname',default='',help='Postgres DB name where to store the geometries',type='string')
op.add_option('-u','--dbuser',default='',help='DB user',type='string')
op.add_option('-p','--dbpass',default='',help='DB pass',type='string')
op.add_option('-m','--dbhost',default='',help='DB host',type='string')
op.add_option('-r','--dbport',default='',help='DB port',type='string')
op.add_option('-t','--dbtable',default='',help='DB table',type='string')
op.add_option('-c','--cores',default='',help='Number of used processes',type='string')
(opts, args) = op.parse_args()
main(opts)
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] |
paccmann_proteomics/data/datasets/seq_clf.py | xueeinstein/paccmann_proteomics | 28 | 35753 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from transformers import PreTrainedTokenizer, RobertaTokenizer, RobertaTokenizerFast, XLMRobertaTokenizer
from transformers.data.datasets import GlueDataset
from transformers.data.datasets import GlueDataTrainingArguments
from transformers.data.processors.glue import glue_convert_examples_to_features
from transformers.data.processors.utils import InputFeatures
from loguru import logger
from ..processors.seq_clf import seq_clf_output_modes, seq_clf_processors, seq_clf_tasks_num_labels
class Split(Enum):
train = 'train'
dev = 'dev'
test = 'test'
class SeqClfDataset(GlueDataset):
"""
Why this class even exists?
`class GlueDataset(Dataset)` has a constructor `def __init__()` with
`processor = glue_processors[args.task_name]()`, however I want to expand `glue_processors`
with protein clf task names. The line `processor = glue_processors[args.task_name]()` in parent
class doesn't accomodate this.
"""
args: GlueDataTrainingArguments
output_mode: str
features: List[InputFeatures]
def __init__(
self,
args: GlueDataTrainingArguments,
tokenizer: PreTrainedTokenizer,
limit_length: Optional[int] = None,
mode: Union[str, Split] = Split.train,
cache_dir: Optional[str] = None,
):
self.args = args
self.processor = seq_clf_processors[args.task_name]()
self.output_mode = seq_clf_output_modes[args.task_name]
if isinstance(mode, str):
try:
mode = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name')
# Load data features from cache or dataset file
cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,
'cached_{}_{}_{}_{}'.format(
mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name,
),
)
label_list = self.processor.get_labels()
if args.task_name in ['mnli', 'mnli-mm'] and tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
label_list[1], label_list[2] = label_list[2], label_list[1]
self.label_list = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + '.lock'
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
start = time.time()
self.features = torch.load(cached_features_file)
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]', time.time() - start
)
else:
logger.info(f'Creating features from dataset file at {args.data_dir}')
if mode == Split.dev:
examples = self.processor.get_dev_examples(args.data_dir)
elif mode == Split.test:
examples = self.processor.get_test_examples(args.data_dir)
else:
examples = self.processor.get_train_examples(args.data_dir)
if limit_length is not None:
examples = examples[:limit_length]
# Load a data file into a list of ``InputFeatures``
self.features = glue_convert_examples_to_features(
examples,
tokenizer,
max_length=args.max_seq_length,
label_list=label_list,
output_mode=self.output_mode,
)
start = time.time()
torch.save(self.features, cached_features_file)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
'Saving features into cached file %s [took %.3f s]', cached_features_file, time.time() - start
)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
def get_labels(self):
return self.label_list
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1643,
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13,
2
] |
lianjia_fetch/proxy/mutilprocess_fetch_proxy.py | lxyangfan/lianjia_fetch | 0 | 77446 | <filename>lianjia_fetch/proxy/mutilprocess_fetch_proxy.py
# ! -*- encoding:utf8 -*-
import multiprocessing as MP
from multiprocessing import Manager, Queue, Value, Lock
import time
def task(url, v):
print 'Url is:', url
v.value += 1
return url
def apply_pool_test():
url = 'http://sh.lianjia.com/d'
useful_proxy_num = Value('i', 0)
pool_size = MP.cpu_count() * 2
print 'Pool count', pool_size
# init process pool
pool = MP.Pool(processes=pool_size)
# mark the start time
start_time = time.time()
# task num is 100, and thread num is 8
for i in xrange(100):
pool.apply_async(task, ('{0}{1}'.format(url, i),useful_proxy_num,))
pool.close()
pool.join()
# computing elapsed time
end_time = time.time()
print 'Time used: {0}'.format(end_time - start_time)
print 'Useful proxy num, ', useful_proxy_num.value
def pool_map_test():
urls = [
'https://www.baidu.com',
'http://www.meituan.com/',
'http://blog.csdn.net/',
'http://xxxyxxx.net'
]
print 'Cpu count', MP.cpu_count()
pool_size = MP.cpu_count() * 2
print 'Pool count', pool_size
pool = MP.Pool(processes=pool_size)
pool_outputs = pool.map(task, urls)
pool.close()
pool.join()
print 'Pool :', pool_outputs
if __name__ == '__main__':
apply_pool_test() | [
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] |
ThinkPython/chap9/ex9.py | sokolowskik/Tutorials | 0 | 22353 | <gh_stars>0
def is_reverse(i, j):
"""
Convert 2-digit numbers to strings and check if they are palindromic.
If one of the numbers has less then 2 digits, fill with zeros.
"""
str_i = str(i)
str_j = str(j)
if len(str_i) < 2:
str_i = str_i.zfill(2)
if len(str_j) < 2:
str_j = str_j.zfill(2)
return str_j[::-1] == str_i
age_diff = 15
d_age = 0
while age_diff <= 50:
reversible = 0
for d_age in range(0,80):
m_age = d_age + age_diff
if is_reverse(d_age, m_age):
reversible += 1
if reversible == 6:
print 'The daughter is', d_age, 'years old'
if reversible == 8:
print 'At the 8th time the daughter will be', d_age, 'years old and the mother will be', m_age, 'years old'
break
d_age += 1
age_diff += 1
| [
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] |
websauna/utils/psql.py | highPriestLOL/websauna | 286 | 165264 | <reponame>highPriestLOL/websauna
"""PostgreSQL helpers."""
# Use pgcrypto, instead of uuid-ossp
# Also create a function uuid_generate_v4 for backward compatibility
UUID_SUPPORT_STMT = """CREATE EXTENSION IF NOT EXISTS "pgcrypto";
CREATE OR REPLACE FUNCTION uuid_generate_v4()
RETURNS uuid
AS '
BEGIN
RETURN gen_random_uuid();
END'
LANGUAGE 'plpgsql';
"""
| [
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cardboard/cards/sets/saviors_of_kamigawa.py | Julian/cardboard | 5 | 71238 | <reponame>Julian/cardboard
from cardboard import types
from cardboard.ability import (
AbilityNotImplemented, spell, activated, triggered, static
)
from cardboard.cards import card, common, keywords, match
@card("Hidetsugu's Second Rite")
def hidetsugus_second_rite(card, abilities):
def hidetsugus_second_rite():
return AbilityNotImplemented
return hidetsugus_second_rite,
@card("Kitsune Dawnblade")
def kitsune_dawnblade(card, abilities):
def kitsune_dawnblade():
return AbilityNotImplemented
def kitsune_dawnblade():
return AbilityNotImplemented
return kitsune_dawnblade, kitsune_dawnblade,
@card("Measure of Wickedness")
def measure_of_wickedness(card, abilities):
def measure_of_wickedness():
return AbilityNotImplemented
def measure_of_wickedness():
return AbilityNotImplemented
return measure_of_wickedness, measure_of_wickedness,
@card("Pithing Needle")
def pithing_needle(card, abilities):
def pithing_needle():
return AbilityNotImplemented
def pithing_needle():
return AbilityNotImplemented
return pithing_needle, pithing_needle,
@card("Kuon, Ogre Ascendant")
def kuon_ogre_ascendant(card, abilities):
def kuon_ogre_ascendant():
return AbilityNotImplemented
def kuon_ogre_ascendant():
return AbilityNotImplemented
return kuon_ogre_ascendant, kuon_ogre_ascendant,
@card("Kaho, Minamo Historian")
def kaho_minamo_historian(card, abilities):
def kaho_minamo_historian():
return AbilityNotImplemented
def kaho_minamo_historian():
return AbilityNotImplemented
return kaho_minamo_historian, kaho_minamo_historian,
@card("Inner-Chamber Guard")
def innerchamber_guard(card, abilities):
def innerchamber_guard():
return AbilityNotImplemented
return innerchamber_guard,
@card("Nikko-Onna")
def nikkoonna(card, abilities):
def nikkoonna():
return AbilityNotImplemented
def nikkoonna():
return AbilityNotImplemented
return nikkoonna, nikkoonna,
@card("Oppressive Will")
def oppressive_will(card, abilities):
def oppressive_will():
return AbilityNotImplemented
return oppressive_will,
@card("Soramaro, First to Dream")
def soramaro_first_to_dream(card, abilities):
def soramaro_first_to_dream():
return AbilityNotImplemented
def soramaro_first_to_dream():
return AbilityNotImplemented
def soramaro_first_to_dream():
return AbilityNotImplemented
return soramaro_first_to_dream, soramaro_first_to_dream, soramaro_first_to_dream,
@card("Kashi-Tribe Elite")
def kashitribe_elite(card, abilities):
def kashitribe_elite():
return AbilityNotImplemented
def kashitribe_elite():
return AbilityNotImplemented
return kashitribe_elite, kashitribe_elite,
@card("Arashi, the Sky Asunder")
def arashi_the_sky_asunder(card, abilities):
def arashi_the_sky_asunder():
return AbilityNotImplemented
def arashi_the_sky_asunder():
return AbilityNotImplemented
return arashi_the_sky_asunder, arashi_the_sky_asunder,
@card("Rune-Tail's Essence")
def runetails_essence(card, abilities):
def runetails_essence():
return AbilityNotImplemented
def runetails_essence():
return AbilityNotImplemented
return runetails_essence, runetails_essence,
@card("Captive Flame")
def captive_flame(card, abilities):
def captive_flame():
return AbilityNotImplemented
return captive_flame,
@card("Shinen of Life's Roar")
def shinen_of_lifes_roar(card, abilities):
def shinen_of_lifes_roar():
return AbilityNotImplemented
def shinen_of_lifes_roar():
return AbilityNotImplemented
return shinen_of_lifes_roar, shinen_of_lifes_roar,
@card("Oboro Envoy")
def oboro_envoy(card, abilities):
def oboro_envoy():
return AbilityNotImplemented
def oboro_envoy():
return AbilityNotImplemented
return oboro_envoy, oboro_envoy,
@card("Shifting Borders")
def shifting_borders(card, abilities):
def shifting_borders():
return AbilityNotImplemented
def shifting_borders():
return AbilityNotImplemented
return shifting_borders, shifting_borders,
@card("Presence of the Wise")
def presence_of_the_wise(card, abilities):
def presence_of_the_wise():
return AbilityNotImplemented
return presence_of_the_wise,
@card("Curtain of Light")
def curtain_of_light(card, abilities):
def curtain_of_light():
return AbilityNotImplemented
def curtain_of_light():
return AbilityNotImplemented
def curtain_of_light():
return AbilityNotImplemented
return curtain_of_light, curtain_of_light, curtain_of_light,
@card("Endless Swarm")
def endless_swarm(card, abilities):
def endless_swarm():
return AbilityNotImplemented
def endless_swarm():
return AbilityNotImplemented
return endless_swarm, endless_swarm,
@card("Sokenzan Spellblade")
def sokenzan_spellblade(card, abilities):
def sokenzan_spellblade():
return AbilityNotImplemented
def sokenzan_spellblade():
return AbilityNotImplemented
return sokenzan_spellblade, sokenzan_spellblade,
@card("Hand of Cruelty")
def hand_of_cruelty(card, abilities):
def hand_of_cruelty():
return AbilityNotImplemented
def hand_of_cruelty():
return AbilityNotImplemented
return hand_of_cruelty, hand_of_cruelty,
@card("Neverending Torment")
def neverending_torment(card, abilities):
def neverending_torment():
return AbilityNotImplemented
def neverending_torment():
return AbilityNotImplemented
return neverending_torment, neverending_torment,
@card("Sekki, Seasons' Guide")
def sekki_seasons_guide(card, abilities):
def sekki_seasons_guide():
return AbilityNotImplemented
def sekki_seasons_guide():
return AbilityNotImplemented
def sekki_seasons_guide():
return AbilityNotImplemented
return sekki_seasons_guide, sekki_seasons_guide, sekki_seasons_guide,
@card("Kagemaro's Clutch")
def kagemaros_clutch(card, abilities):
def kagemaros_clutch():
return AbilityNotImplemented
def kagemaros_clutch():
return AbilityNotImplemented
return kagemaros_clutch, kagemaros_clutch,
@card("Shinen of Fear's Chill")
def shinen_of_fears_chill(card, abilities):
def shinen_of_fears_chill():
return AbilityNotImplemented
def shinen_of_fears_chill():
return AbilityNotImplemented
return shinen_of_fears_chill, shinen_of_fears_chill,
@card("Adamaro, First to Desire")
def adamaro_first_to_desire(card, abilities):
def adamaro_first_to_desire():
return AbilityNotImplemented
return adamaro_first_to_desire,
@card("Celestial Kirin")
def celestial_kirin(card, abilities):
def celestial_kirin():
return AbilityNotImplemented
def celestial_kirin():
return AbilityNotImplemented
return celestial_kirin, celestial_kirin,
@card("Ideas Unbound")
def ideas_unbound(card, abilities):
def ideas_unbound():
return AbilityNotImplemented
return ideas_unbound,
@card("Godo's Irregulars")
def godos_irregulars(card, abilities):
def godos_irregulars():
return AbilityNotImplemented
return godos_irregulars,
@card("Kitsune Loreweaver")
def kitsune_loreweaver(card, abilities):
def kitsune_loreweaver():
return AbilityNotImplemented
return kitsune_loreweaver,
@card("Promise of Bunrei")
def promise_of_bunrei(card, abilities):
def promise_of_bunrei():
return AbilityNotImplemented
return promise_of_bunrei,
@card("Nightsoil Kami")
def nightsoil_kami(card, abilities):
def nightsoil_kami():
return AbilityNotImplemented
return nightsoil_kami,
@card("Kagemaro, First to Suffer")
def kagemaro_first_to_suffer(card, abilities):
def kagemaro_first_to_suffer():
return AbilityNotImplemented
def kagemaro_first_to_suffer():
return AbilityNotImplemented
return kagemaro_first_to_suffer, kagemaro_first_to_suffer,
@card("Homura, Human Ascendant")
def homura_human_ascendant(card, abilities):
def homura_human_ascendant():
return AbilityNotImplemented
def homura_human_ascendant():
return AbilityNotImplemented
def homura_human_ascendant():
return AbilityNotImplemented
return homura_human_ascendant, homura_human_ascendant, homura_human_ascendant,
@card("Erayo's Essence")
def erayos_essence(card, abilities):
def erayos_essence():
return AbilityNotImplemented
def erayos_essence():
return AbilityNotImplemented
return erayos_essence, erayos_essence,
@card("Eternal Dominion")
def eternal_dominion(card, abilities):
def eternal_dominion():
return AbilityNotImplemented
def eternal_dominion():
return AbilityNotImplemented
return eternal_dominion, eternal_dominion,
@card("Kami of the Tended Garden")
def kami_of_the_tended_garden(card, abilities):
def kami_of_the_tended_garden():
return AbilityNotImplemented
def kami_of_the_tended_garden():
return AbilityNotImplemented
return kami_of_the_tended_garden, kami_of_the_tended_garden,
@card("Oboro, Palace in the Clouds")
def oboro_palace_in_the_clouds(card, abilities):
def oboro_palace_in_the_clouds():
return AbilityNotImplemented
def oboro_palace_in_the_clouds():
return AbilityNotImplemented
return oboro_palace_in_the_clouds, oboro_palace_in_the_clouds,
@card("Rally the Horde")
def rally_the_horde(card, abilities):
def rally_the_horde():
return AbilityNotImplemented
return rally_the_horde,
@card("Sasaya, Orochi Ascendant")
def sasaya_orochi_ascendant(card, abilities):
def sasaya_orochi_ascendant():
return AbilityNotImplemented
def sasaya_orochi_ascendant():
return AbilityNotImplemented
return sasaya_orochi_ascendant, sasaya_orochi_ascendant,
@card("Minamo Scrollkeeper")
def minamo_scrollkeeper(card, abilities):
def minamo_scrollkeeper():
return AbilityNotImplemented
def minamo_scrollkeeper():
return AbilityNotImplemented
return minamo_scrollkeeper, minamo_scrollkeeper,
@card("Footsteps of the Goryo")
def footsteps_of_the_goryo(card, abilities):
def footsteps_of_the_goryo():
return AbilityNotImplemented
return footsteps_of_the_goryo,
@card("Cut the Earthly Bond")
def cut_the_earthly_bond(card, abilities):
def cut_the_earthly_bond():
return AbilityNotImplemented
return cut_the_earthly_bond,
@card("Dosan's Oldest Chant")
def dosans_oldest_chant(card, abilities):
def dosans_oldest_chant():
return AbilityNotImplemented
def dosans_oldest_chant():
return AbilityNotImplemented
return dosans_oldest_chant, dosans_oldest_chant,
@card("Ghost-Lit Warder")
def ghostlit_warder(card, abilities):
def ghostlit_warder():
return AbilityNotImplemented
def ghostlit_warder():
return AbilityNotImplemented
return ghostlit_warder, ghostlit_warder,
@card("Fiddlehead Kami")
def fiddlehead_kami(card, abilities):
def fiddlehead_kami():
return AbilityNotImplemented
return fiddlehead_kami,
@card("Dense Canopy")
def dense_canopy(card, abilities):
def dense_canopy():
return AbilityNotImplemented
return dense_canopy,
@card("Elder Pine of Jukai")
def elder_pine_of_jukai(card, abilities):
def elder_pine_of_jukai():
return AbilityNotImplemented
def elder_pine_of_jukai():
return AbilityNotImplemented
return elder_pine_of_jukai, elder_pine_of_jukai,
@card("Iname as One")
def iname_as_one(card, abilities):
def iname_as_one():
return AbilityNotImplemented
def iname_as_one():
return AbilityNotImplemented
return iname_as_one, iname_as_one,
@card("Rune-Tail, Kitsune Ascendant")
def runetail_kitsune_ascendant(card, abilities):
def runetail_kitsune_ascendant():
return AbilityNotImplemented
def runetail_kitsune_ascendant():
return AbilityNotImplemented
return runetail_kitsune_ascendant, runetail_kitsune_ascendant,
@card("Gnat Miser")
def gnat_miser(card, abilities):
def gnat_miser():
return AbilityNotImplemented
return gnat_miser,
@card("Akki Underling")
def akki_underling(card, abilities):
def akki_underling():
return AbilityNotImplemented
return akki_underling,
@card("Kami of Empty Graves")
def kami_of_empty_graves(card, abilities):
def kami_of_empty_graves():
return AbilityNotImplemented
return kami_of_empty_graves,
@card("Kami of the Crescent Moon")
def kami_of_the_crescent_moon(card, abilities):
def kami_of_the_crescent_moon():
return AbilityNotImplemented
return kami_of_the_crescent_moon,
@card("Twincast")
def twincast(card, abilities):
def twincast():
return AbilityNotImplemented
return twincast,
@card("<NAME>")
def deathknell_kami(card, abilities):
def deathknell_kami():
return AbilityNotImplemented
def deathknell_kami():
return AbilityNotImplemented
def deathknell_kami():
return AbilityNotImplemented
return deathknell_kami, deathknell_kami, deathknell_kami,
@card("Sakashima the Impostor")
def sakashima_the_impostor(card, abilities):
def sakashima_the_impostor():
return AbilityNotImplemented
return sakashima_the_impostor,
@card("<NAME>")
def promised_kannushi(card, abilities):
def promised_kannushi():
return AbilityNotImplemented
return promised_kannushi,
@card("Homura's Essence")
def homuras_essence(card, abilities):
def homuras_essence():
return AbilityNotImplemented
def homuras_essence():
return AbilityNotImplemented
return homuras_essence, homuras_essence,
@card("<NAME>")
def sokenzan_renegade(card, abilities):
def sokenzan_renegade():
return AbilityNotImplemented
def sokenzan_renegade():
return AbilityNotImplemented
return sokenzan_renegade, sokenzan_renegade,
@card("Overwhelming Intellect")
def overwhelming_intellect(card, abilities):
def overwhelming_intellect():
return AbilityNotImplemented
return overwhelming_intellect,
@card("Descendant of Kiyomaro")
def descendant_of_kiyomaro(card, abilities):
def descendant_of_kiyomaro():
return AbilityNotImplemented
return descendant_of_kiyomaro,
@card("Dreamcatcher")
def dreamcatcher(card, abilities):
def dreamcatcher():
return AbilityNotImplemented
return dreamcatcher,
@card("Kemuri-Onna")
def kemurionna(card, abilities):
def kemurionna():
return AbilityNotImplemented
def kemurionna():
return AbilityNotImplemented
return kemurionna, kemurionna,
@card("Oni of Wild Places")
def oni_of_wild_places(card, abilities):
def oni_of_wild_places():
return AbilityNotImplemented
def oni_of_wild_places():
return AbilityNotImplemented
return oni_of_wild_places, oni_of_wild_places,
@card("One with Nothing")
def one_with_nothing(card, abilities):
def one_with_nothing():
return AbilityNotImplemented
return one_with_nothing,
@card("Soratami Cloud Chariot")
def soratami_cloud_chariot(card, abilities):
def soratami_cloud_chariot():
return AbilityNotImplemented
def soratami_cloud_chariot():
return AbilityNotImplemented
return soratami_cloud_chariot, soratami_cloud_chariot,
@card("Spiraling Embers")
def spiraling_embers(card, abilities):
def spiraling_embers():
return AbilityNotImplemented
return spiraling_embers,
@card("Akuta, Born of Ash")
def akuta_born_of_ash(card, abilities):
def akuta_born_of_ash():
return AbilityNotImplemented
def akuta_born_of_ash():
return AbilityNotImplemented
return akuta_born_of_ash, akuta_born_of_ash,
@card("Feral Lightning")
def feral_lightning(card, abilities):
def feral_lightning():
return AbilityNotImplemented
return feral_lightning,
@card("Trusted Advisor")
def trusted_advisor(card, abilities):
def trusted_advisor():
return AbilityNotImplemented
def trusted_advisor():
return AbilityNotImplemented
return trusted_advisor, trusted_advisor,
@card("Death<NAME>")
def deathmask_nezumi(card, abilities):
def deathmask_nezumi():
return AbilityNotImplemented
return deathmask_nezumi,
@card("Path of Anger's Flame")
def path_of_angers_flame(card, abilities):
def path_of_angers_flame():
return AbilityNotImplemented
return path_of_angers_flame,
@card("Rushing-Tide Zubera")
def rushingtide_zubera(card, abilities):
def rushingtide_zubera():
return AbilityNotImplemented
return rushingtide_zubera,
@card("Descendant of Masumaro")
def descendant_of_masumaro(card, abilities):
def descendant_of_masumaro():
return AbilityNotImplemented
return descendant_of_masumaro,
@card("Kitsune Bonesetter")
def kitsune_bonesetter(card, abilities):
def kitsune_bonesetter():
return AbilityNotImplemented
return kitsune_bonesetter,
@card("Ashes of the Fallen")
def ashes_of_the_fallen(card, abilities):
def ashes_of_the_fallen():
return AbilityNotImplemented
def ashes_of_the_fallen():
return AbilityNotImplemented
return ashes_of_the_fallen, ashes_of_the_fallen,
@card("Ghost-Lit Nourisher")
def ghostlit_nourisher(card, abilities):
def ghostlit_nourisher():
return AbilityNotImplemented
def ghostlit_nourisher():
return AbilityNotImplemented
return ghostlit_nourisher, ghostlit_nourisher,
@card("Eiganjo Free-Riders")
def eiganjo_freeriders(card, abilities):
def eiganjo_freeriders():
return AbilityNotImplemented
def eiganjo_freeriders():
return AbilityNotImplemented
return eiganjo_freeriders, eiganjo_freeriders,
@card("Kiyomaro, First to Stand")
def kiyomaro_first_to_stand(card, abilities):
def kiyomaro_first_to_stand():
return AbilityNotImplemented
def kiyomaro_first_to_stand():
return AbilityNotImplemented
def kiyomaro_first_to_stand():
return AbilityNotImplemented
return kiyomaro_first_to_stand, kiyomaro_first_to_stand, kiyomaro_first_to_stand,
@card("Evermind")
def evermind(card, abilities):
def evermind():
return AbilityNotImplemented
def evermind():
return AbilityNotImplemented
def evermind():
return AbilityNotImplemented
def evermind():
return AbilityNotImplemented
return evermind, evermind, evermind, evermind,
@card("Bounteous Kirin")
def bounteous_kirin(card, abilities):
def bounteous_kirin():
return AbilityNotImplemented
def bounteous_kirin():
return AbilityNotImplemented
return bounteous_kirin, bounteous_kirin,
@card("Enduring Ideal")
def enduring_ideal(card, abilities):
def enduring_ideal():
return AbilityNotImplemented
def enduring_ideal():
return AbilityNotImplemented
return enduring_ideal, enduring_ideal,
@card("Iv<NAME>")
def ivory_crane_netsuke(card, abilities):
def ivory_crane_netsuke():
return AbilityNotImplemented
return ivory_crane_netsuke,
@card("Blood Clock")
def blood_clock(card, abilities):
def blood_clock():
return AbilityNotImplemented
return blood_clock,
@card("Infernal Kirin")
def infernal_kirin(card, abilities):
def infernal_kirin():
return AbilityNotImplemented
def infernal_kirin():
return AbilityNotImplemented
return infernal_kirin, infernal_kirin,
@card("<NAME>, Truth Seeker")
def michiko_konda_truth_seeker(card, abilities):
def michiko_konda_truth_seeker():
return AbilityNotImplemented
return michiko_konda_truth_seeker,
@card("Manriki-Gusari")
def manrikigusari(card, abilities):
def manrikigusari():
return AbilityNotImplemented
def manrikigusari():
return AbilityNotImplemented
return manrikigusari, manrikigusari,
@card("Masumaro, First to Live")
def masumaro_first_to_live(card, abilities):
def masumaro_first_to_live():
return AbilityNotImplemented
return masumaro_first_to_live,
@card("Cowed by Wisdom")
def cowed_by_wisdom(card, abilities):
def cowed_by_wisdom():
return AbilityNotImplemented
def cowed_by_wisdom():
return AbilityNotImplemented
return cowed_by_wisdom, cowed_by_wisdom,
@card("Choice of Damnations")
def choice_of_damnations(card, abilities):
def choice_of_damnations():
return AbilityNotImplemented
return choice_of_damnations,
@card("Skull Collector")
def skull_collector(card, abilities):
def skull_collector():
return AbilityNotImplemented
def skull_collector():
return AbilityNotImplemented
return skull_collector, skull_collector,
@card("Meishin, the Mind Cage")
def meishin_the_mind_cage(card, abilities):
def meishin_the_mind_cage():
return AbilityNotImplemented
return meishin_the_mind_cage,
@card("Shinen of Stars' Light")
def shinen_of_stars_light(card, abilities):
def shinen_of_stars_light():
return AbilityNotImplemented
def shinen_of_stars_light():
return AbilityNotImplemented
return shinen_of_stars_light, shinen_of_stars_light,
@card("Hand of Honor")
def hand_of_honor(card, abilities):
def hand_of_honor():
return AbilityNotImplemented
def hand_of_honor():
return AbilityNotImplemented
return hand_of_honor, hand_of_honor,
@card("Pure Intentions")
def pure_intentions(card, abilities):
def pure_intentions():
return AbilityNotImplemented
def pure_intentions():
return AbilityNotImplemented
return pure_intentions, pure_intentions,
@card("Murmurs from Beyond")
def murmurs_from_beyond(card, abilities):
def murmurs_from_beyond():
return AbilityNotImplemented
return murmurs_from_beyond,
@card("Hail of Arrows")
def hail_of_arrows(card, abilities):
def hail_of_arrows():
return AbilityNotImplemented
return hail_of_arrows,
@card("Seek the Horizon")
def seek_the_horizon(card, abilities):
def seek_the_horizon():
return AbilityNotImplemented
return seek_the_horizon,
@card("Gaze of Adamaro")
def gaze_of_adamaro(card, abilities):
def gaze_of_adamaro():
return AbilityNotImplemented
return gaze_of_adamaro,
@card("Reki, the History of Kamigawa")
def reki_the_history_of_kamigawa(card, abilities):
def reki_the_history_of_kamigawa():
return AbilityNotImplemented
return reki_the_history_of_kamigawa,
@card("Kuon's Essence")
def kuons_essence(card, abilities):
def kuons_essence():
return AbilityNotImplemented
def kuons_essence():
return AbilityNotImplemented
return kuons_essence, kuons_essence,
@card("Charge Across the Araba")
def charge_across_the_araba(card, abilities):
def charge_across_the_araba():
return AbilityNotImplemented
return charge_across_the_araba,
@card("Death Denied")
def death_denied(card, abilities):
def death_denied():
return AbilityNotImplemented
return death_denied,
@card("Ghost-Lit Raider")
def ghostlit_raider(card, abilities):
def ghostlit_raider():
return AbilityNotImplemented
def ghostlit_raider():
return AbilityNotImplemented
return ghostlit_raider, ghostlit_raider,
@card("Thoughts of Ruin")
def thoughts_of_ruin(card, abilities):
def thoughts_of_ruin():
return AbilityNotImplemented
return thoughts_of_ruin,
@card("Kataki, War's Wage")
def kataki_wars_wage(card, abilities):
def kataki_wars_wage():
return AbilityNotImplemented
return kataki_wars_wage,
@card("O-Naginata")
def onaginata(card, abilities):
def onaginata():
return AbilityNotImplemented
def onaginata():
return AbilityNotImplemented
def onaginata():
return AbilityNotImplemented
return onaginata, onaginata, onaginata,
@card("B<NAME>")
def briarknit_kami(card, abilities):
def briarknit_kami():
return AbilityNotImplemented
return briarknit_kami,
@card("Glitterfang")
def glitterfang(card, abilities):
def glitterfang():
return AbilityNotImplemented
def glitterfang():
return AbilityNotImplemented
return glitterfang, glitterfang,
@card("Reverence")
def reverence(card, abilities):
def reverence():
return AbilityNotImplemented
return reverence,
@card("Plow Through Reito")
def plow_through_reito(card, abilities):
def plow_through_reito():
return AbilityNotImplemented
return plow_through_reito,
@card("Undying Flames")
def undying_flames(card, abilities):
def undying_flames():
return AbilityNotImplemented
def undying_flames():
return AbilityNotImplemented
return undying_flames, undying_flames,
@card("Sunder from Within")
def sunder_from_within(card, abilities):
def sunder_from_within():
return AbilityNotImplemented
return sunder_from_within,
@card("Bar<NAME>")
def barrel_down_sokenzan(card, abilities):
def barrel_down_sokenzan():
return AbilityNotImplemented
return barrel_down_sokenzan,
@card("Shape Stealer")
def shape_stealer(card, abilities):
def shape_stealer():
return AbilityNotImplemented
return shape_stealer,
@card("<NAME>")
def razorjaw_oni(card, abilities):
def razorjaw_oni():
return AbilityNotImplemented
return razorjaw_oni,
@card("Sink into Takenuma")
def sink_into_takenuma(card, abilities):
def sink_into_takenuma():
return AbilityNotImplemented
return sink_into_takenuma,
@card("Ghost-Lit Redeemer")
def ghostlit_redeemer(card, abilities):
def ghostlit_redeemer():
return AbilityNotImplemented
def ghostlit_redeemer():
return AbilityNotImplemented
return ghostlit_redeemer, ghostlit_redeemer,
@card("Inner Fire")
def inner_fire(card, abilities):
def inner_fire():
return AbilityNotImplemented
return inner_fire,
@card("Moonwing Moth")
def moonwing_moth(card, abilities):
def moonwing_moth():
return AbilityNotImplemented
def moonwing_moth():
return AbilityNotImplemented
return moonwing_moth, moonwing_moth,
@card("Kiku's Shadow")
def kikus_shadow(card, abilities):
def kikus_shadow():
return AbilityNotImplemented
return kikus_shadow,
@card("Locust Miser")
def locust_miser(card, abilities):
def locust_miser():
return AbilityNotImplemented
return locust_miser,
@card("Sakura-Tribe Scout")
def sakuratribe_scout(card, abilities):
def sakuratribe_scout():
return AbilityNotImplemented
return sakuratribe_scout,
@card("Rending Vines")
def rending_vines(card, abilities):
def rending_vines():
return AbilityNotImplemented
def rending_vines():
return AbilityNotImplemented
return rending_vines, rending_vines,
@card("Stampeding Serow")
def stampeding_serow(card, abilities):
def stampeding_serow():
return AbilityNotImplemented
def stampeding_serow():
return AbilityNotImplemented
return stampeding_serow, stampeding_serow,
@card("Skyfire Kirin")
def skyfire_kirin(card, abilities):
def skyfire_kirin():
return AbilityNotImplemented
def skyfire_kirin():
return AbilityNotImplemented
return skyfire_kirin, skyfire_kirin,
@card("Cloudhoof Kirin")
def cloudhoof_kirin(card, abilities):
def cloudhoof_kirin():
return AbilityNotImplemented
def cloudhoof_kirin():
return AbilityNotImplemented
return cloudhoof_kirin, cloudhoof_kirin,
@card("Spiritual Visit")
def spiritual_visit(card, abilities):
def spiritual_visit():
return AbilityNotImplemented
def spiritual_visit():
return AbilityNotImplemented
return spiritual_visit, spiritual_visit,
@card("Into the Fray")
def into_the_fray(card, abilities):
def into_the_fray():
return AbilityNotImplemented
def into_the_fray():
return AbilityNotImplemented
return into_the_fray, into_the_fray,
@card("Ghost-Lit Stalker")
def ghostlit_stalker(card, abilities):
def ghostlit_stalker():
return AbilityNotImplemented
def ghostlit_stalker():
return AbilityNotImplemented
return ghostlit_stalker, ghostlit_stalker,
@card("Ronin Cavekeeper")
def ronin_cavekeeper(card, abilities):
def ronin_cavekeeper():
return AbilityNotImplemented
return ronin_cavekeeper,
@card("<NAME>")
def araba_mothrider(card, abilities):
def araba_mothrider():
return AbilityNotImplemented
def araba_mothrider():
return AbilityNotImplemented
return araba_mothrider, araba_mothrider,
@card("Okina Nightwatch")
def okina_nightwatch(card, abilities):
def okina_nightwatch():
return AbilityNotImplemented
return okina_nightwatch,
@card("Ayumi, the Last Visitor")
def ayumi_the_last_visitor(card, abilities):
def ayumi_the_last_visitor():
return AbilityNotImplemented
return ayumi_the_last_visitor,
@card("Haru-Onna")
def haruonna(card, abilities):
def haruonna():
return AbilityNotImplemented
def haruonna():
return AbilityNotImplemented
return haruonna, haruonna,
@card("Molting Skin")
def molting_skin(card, abilities):
def molting_skin():
return AbilityNotImplemented
return molting_skin,
@card("Freed from the Real")
def freed_from_the_real(card, abilities):
def freed_from_the_real():
return AbilityNotImplemented
def freed_from_the_real():
return AbilityNotImplemented
def freed_from_the_real():
return AbilityNotImplemented
return freed_from_the_real, freed_from_the_real, freed_from_the_real,
@card("Inner Calm, Outer Strength")
def inner_calm_outer_strength(card, abilities):
def inner_calm_outer_strength():
return AbilityNotImplemented
return inner_calm_outer_strength,
@card("Raving Oni-Slave")
def raving_onislave(card, abilities):
def raving_onislave():
return AbilityNotImplemented
return raving_onislave,
@card("Seed the Land")
def seed_the_land(card, abilities):
def seed_the_land():
return AbilityNotImplemented
return seed_the_land,
@card("Descendant of Soramaro")
def descendant_of_soramaro(card, abilities):
def descendant_of_soramaro():
return AbilityNotImplemented
return descendant_of_soramaro,
@card("Maga, Traitor to Mortals")
def maga_traitor_to_mortals(card, abilities):
def maga_traitor_to_mortals():
return AbilityNotImplemented
def maga_traitor_to_mortals():
return AbilityNotImplemented
return maga_traitor_to_mortals, maga_traitor_to_mortals,
@card("Kiri-Onna")
def kirionna(card, abilities):
def kirionna():
return AbilityNotImplemented
def kirionna():
return AbilityNotImplemented
return kirionna, kirionna,
@card("Iizuka the Ruthless")
def iizuka_the_ruthless(card, abilities):
def iizuka_the_ruthless():
return AbilityNotImplemented
def iizuka_the_ruthless():
return AbilityNotImplemented
return iizuka_the_ruthless, iizuka_the_ruthless,
@card("Yuki-Onna")
def yukionna(card, abilities):
def yukionna():
return AbilityNotImplemented
def yukionna():
return AbilityNotImplemented
return yukionna, yukionna,
@card("Death of a Thousand Stings")
def death_of_a_thousand_stings(card, abilities):
def death_of_a_thousand_stings():
return AbilityNotImplemented
def death_of_a_thousand_stings():
return AbilityNotImplemented
return death_of_a_thousand_stings, death_of_a_thousand_stings,
@card("Pain's Reward")
def pains_reward(card, abilities):
def pains_reward():
return AbilityNotImplemented
return pains_reward,
@card("Scroll of Origins")
def scroll_of_origins(card, abilities):
def scroll_of_origins():
return AbilityNotImplemented
return scroll_of_origins,
@card("Jiwari, the Earth Aflame")
def jiwari_the_earth_aflame(card, abilities):
def jiwari_the_earth_aflame():
return AbilityNotImplemented
def jiwari_the_earth_aflame():
return AbilityNotImplemented
return jiwari_the_earth_aflame, jiwari_the_earth_aflame,
@card("<NAME>")
def oboro_breezecaller(card, abilities):
def oboro_breezecaller():
return AbilityNotImplemented
def oboro_breezecaller():
return AbilityNotImplemented
return oboro_breezecaller, oboro_breezecaller,
@card("Sasaya's Essence")
def sasayas_essence(card, abilities):
def sasayas_essence():
return AbilityNotImplemented
def sasayas_essence():
return AbilityNotImplemented
return sasayas_essence, sasayas_essence,
@card("Tomb of Urami")
def tomb_of_urami(card, abilities):
def tomb_of_urami():
return AbilityNotImplemented
def tomb_of_urami():
return AbilityNotImplemented
return tomb_of_urami, tomb_of_urami,
@card("Shinen of Flight's Wings")
def shinen_of_flights_wings(card, abilities):
def shinen_of_flights_wings():
return AbilityNotImplemented
def shinen_of_flights_wings():
return AbilityNotImplemented
return shinen_of_flights_wings, shinen_of_flights_wings,
@card("Torii Watchward")
def torii_watchward(card, abilities):
def torii_watchward():
return AbilityNotImplemented
def torii_watchward():
return AbilityNotImplemented
return torii_watchward, torii_watchward,
@card("Akki Drillmaster")
def akki_drillmaster(card, abilities):
def akki_drillmaster():
return AbilityNotImplemented
return akki_drillmaster,
@card("Secretkeeper")
def secretkeeper(card, abilities):
def secretkeeper():
return AbilityNotImplemented
return secretkeeper,
@card("<NAME>")
def ebony_owl_netsuke(card, abilities):
def ebony_owl_netsuke():
return AbilityNotImplemented
return ebony_owl_netsuke,
@card("Moonbow Illusionist")
def moonbow_illusionist(card, abilities):
def moonbow_illusionist():
return AbilityNotImplemented
def moonbow_illusionist():
return AbilityNotImplemented
return moonbow_illusionist, moonbow_illusionist,
@card("Kuro's Taken")
def kuros_taken(card, abilities):
def kuros_taken():
return AbilityNotImplemented
def kuros_taken():
return AbilityNotImplemented
return kuros_taken, kuros_taken,
@card("Burning-Eye Zubera")
def burningeye_zubera(card, abilities):
def burningeye_zubera():
return AbilityNotImplemented
return burningeye_zubera,
@card("AEther Shockwave")
def aether_shockwave(card, abilities):
def aether_shockwave():
return AbilityNotImplemented
return aether_shockwave,
@card("Exile into Darkness")
def exile_into_darkness(card, abilities):
def exile_into_darkness():
return AbilityNotImplemented
def exile_into_darkness():
return AbilityNotImplemented
return exile_into_darkness, exile_into_darkness,
@card("Wine of Blood and Iron")
def wine_of_blood_and_iron(card, abilities):
def wine_of_blood_and_iron():
return AbilityNotImplemented
return wine_of_blood_and_iron,
@card("Matsu-Tribe Birdstalker")
def matsutribe_birdstalker(card, abilities):
def matsutribe_birdstalker():
return AbilityNotImplemented
def matsutribe_birdstalker():
return AbilityNotImplemented
return matsutribe_birdstalker, matsutribe_birdstalker,
@card("Shinen of Fury's Fire")
def shinen_of_furys_fire(card, abilities):
def shinen_of_furys_fire():
return AbilityNotImplemented
def shinen_of_furys_fire():
return AbilityNotImplemented
return shinen_of_furys_fire, shinen_of_furys_fire,
@card("Miren, the Moaning Well")
def miren_the_moaning_well(card, abilities):
def miren_the_moaning_well():
return AbilityNotImplemented
def miren_the_moaning_well():
return AbilityNotImplemented
return miren_the_moaning_well, miren_the_moaning_well,
@card("Erayo, Soratami Ascendant")
def erayo_soratami_ascendant(card, abilities):
def erayo_soratami_ascendant():
return AbilityNotImplemented
def erayo_soratami_ascendant():
return AbilityNotImplemented
def erayo_soratami_ascendant():
return AbilityNotImplemented
return erayo_soratami_ascendant, erayo_soratami_ascendant, erayo_soratami_ascendant,
@card("Mikokoro, Center of the Sea")
def mikokoro_center_of_the_sea(card, abilities):
def mikokoro_center_of_the_sea():
return AbilityNotImplemented
def mikokoro_center_of_the_sea():
return AbilityNotImplemented
return mikokoro_center_of_the_sea, mikokoro_center_of_the_sea, | [
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] |
apps/blog/migrations/0005_blogentry_is_active.py | deniskrumko/deniskrumko | 2 | 87584 | # Generated by Django 2.0.5 on 2018-09-03 09:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0004_auto_20180828_2007'),
]
operations = [
migrations.AddField(
model_name='blogentry',
name='is_active',
field=models.BooleanField(default=True, verbose_name='Is active'),
),
]
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2
] |
tools/ghc-paths/defs.bzl | guibou/rules_haskell | 222 | 197238 | <filename>tools/ghc-paths/defs.bzl
load("@bazel_skylib//lib:paths.bzl", "paths")
def _add_data_impl(ctx):
(_, extension) = paths.split_extension(ctx.executable.executable.path)
executable = ctx.actions.declare_file(
ctx.label.name + extension,
)
ctx.actions.symlink(
output = executable,
target_file = ctx.executable.executable,
is_executable = True,
)
runfiles = ctx.runfiles(files = [executable, ctx.executable.executable] + ctx.files.data)
runfiles = runfiles.merge(ctx.attr.executable[DefaultInfo].default_runfiles)
for data_dep in ctx.attr.data:
runfiles = runfiles.merge(data_dep[DefaultInfo].default_runfiles)
return [DefaultInfo(
executable = executable,
files = depset(direct = [executable]),
runfiles = runfiles,
)]
add_data = rule(
_add_data_impl,
attrs = {
"executable": attr.label(
executable = True,
cfg = "target",
doc = "Create a symlink to this executable",
),
"data": attr.label_list(
allow_files = True,
doc = "Add these data files to the executable's runfiles",
),
},
executable = True,
doc = "Creates a new target for the given executable with additional runfiles.",
)
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] |
record_types.py | droberson/greylost | 13 | 140771 | """ DNS Record Types """
# https://en.wikipedia.org/wiki/List_of_DNS_record_types
DNS_RECORD_TYPES = {
1: "A",
28: "AAAA",
18: "AFSDB",
42: "APL",
257: "CAA",
60: "CDNSKEY",
59: "CDS",
37: "CERT",
5: "CNAME",
62: "CSYNC",
49: "DHCID",
32769: "DLV",
39: "DNAME",
48: "DNSKEY",
43: "DS",
55: "HIP",
45: "IPSECKEY",
25: "KEY",
36: "KX",
29: "LOC",
15: "MX",
35: "NAPTR",
2: "NS",
47: "NSEC",
50: "NSEC3",
51: "NSEC3PARAM",
61: "OPENPGPKEY",
12: "PTR",
46: "RRSIG",
17: "RP",
24: "SIG",
53: "SMIMEA",
6: "SOA",
33: "SRV",
44: "SSHFP",
32768: "TA",
249: "TKEY",
52: "TLSA",
250: "TSIG",
16: "TXT",
256: "URI",
255: "*",
252: "AXFR",
251: "IXFR",
41: "OPT",
# Obsolete record types
3: "MD",
4: "MF",
254: "MAILA",
7: "MB",
8: "MG",
9: "MR",
14: "MINFO",
253: "MAILB",
11: "WKS",
#32: "NB", # now assigned to NIMLOC
#33: "NBTSTAT", # now assigned to SRV
10: "NULL",
38: "A6",
13: "HINFO",
19: "X25",
20: "ISDN",
21: "RT",
22: "NSAP",
23: "NSAP-PTR",
26: "PX",
31: "EID",
32: "NIMLOC", # duplicate id: NB
34: "ATMA",
40: "SINK",
27: "GPOS",
100: "UINFO",
101: "UID",
102: "GID",
103: "UNSPEC",
99: "SPF",
56: "NINFO",
57: "RKEY",
58: "TALINK",
104: "NID",
105: "L32",
106: "L64",
107: "LP",
108: "EUI48",
109: "EUI64",
259: "DOA",
}
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13,
2
] |
tests/integration/test_jwt_authorization.py | ONSdigital/ras-frontstage | 8 | 130934 | <reponame>ONSdigital/ras-frontstage<filename>tests/integration/test_jwt_authorization.py
import unittest
from unittest import mock
from uuid import uuid4
from jose import JWTError
from frontstage import app
from frontstage.common.authorisation import jwt_authorization
from frontstage.common.session import Session
from frontstage.exceptions.exceptions import JWTValidationError
valid_jwt = (
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"jc2NzQwMDAuMH0.m94R50EPIKTJmE6gf6PvCmCq8ZpYwwV8PHSqsJh5fnI"
)
expired_jwt = (
"<KEY>"
"<KEY>"
"<KEY>"
"GFydHlfaWQiOiJkYjAzNmZkNy1jZTE3LTQwYzItYThmYy05MzJlN2MyMjgzOTcifQ.ro95XUJ2gqgz7ecF2r3guSi-kh4wI_XYTgUF"
"8IZFHDA"
)
no_expiry_jwt = (
"<KEY>"
"<KEY>"
)
class TestJWTAuthorization(unittest.TestCase):
def setUp(self):
self.app = app.test_client()
self.app.testing = True
self.session = Session.from_party_id("test")
def tearDown(self):
self.session.delete_session()
@staticmethod
def decorator_test(request):
@jwt_authorization(request)
def test_function(session):
pass
test_function()
def test_jwt_authorization_success(self):
self.session.encoded_jwt_token = valid_jwt
self.session.session_key = str(uuid4())
self.session.save()
request = mock.MagicMock(cookies={"authorization": self.session.session_key})
# If this function runs without exceptions the test is considered passed
self.decorator_test(request)
def test_jwt_authorization_expired_jwt(self):
self.session.encoded_jwt_token = expired_jwt
self.session.session_key = str(uuid4())
self.session.save()
request = mock.MagicMock(cookies={"authorization": self.session.session_key})
with self.assertRaises(JWTValidationError):
self.decorator_test(request)
def test_jwt_authorization_no_expiry(self):
self.session.encoded_jwt_token = no_expiry_jwt
self.session.session_key = str(uuid4())
self.session.save()
request = mock.MagicMock(cookies={"authorization": self.session.session_key})
with self.assertRaises(JWTValidationError):
self.decorator_test(request)
@mock.patch("frontstage.common.authorisation.decode")
def test_jwt_authorization_decode_failure(self, mock_decode):
self.session.encoded_jwt_token = valid_jwt
self.session.session_key = str(uuid4())
self.session.save()
request = mock.MagicMock(cookies={"authorization": self.session.session_key})
mock_decode.side_effect = JWTError
with self.assertRaises(JWTValidationError):
self.decorator_test(request)
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] |
komoog/paths.py | benmaier/komoog | 2 | 49658 | # -*- coding: utf-8 -*-
"""
Path handling
"""
import pathlib
from pathlib import Path
import simplejson as json
customdir = Path.home() / ".komoog"
def _prepare():
customdir.mkdir(exist_ok=True)
cred_file = customdir / "komoot.json"
if not cred_file.exists():
data = {
"email" : "",
"password" : "",
"clientid" : "",
}
with open(cred_file,'w') as f:
credentials = json.dump(data,f)
def get_credentials():
"""
Returns credentials for komoot login in structure
.. code:: python
{
"email" : "",
"password" : "",
"clientid" : ""
}
from the file ``~/.komoog/komoot.json``
"""
_prepare()
cred_file = customdir / "komoot.json"
with open(cred_file,'r') as f:
credentials = json.load(f)
assert(all([ k in credentials.keys() for k in ["email", "password", "clientid"]]))
assert(not any([ credentials[k] == '' for k in ["email", "password", "clientid"]]))
return credentials
if __name__ == "__main__":
get_credentials()
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] |
GettingStarted/gettingstarted.py | rohitp934/roadtoadatascientist | 0 | 11960 | #importing necessary modules
from sklearn.linear_model import Perceptron
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import numpy as np
# Data and labels
Xtrain = [[182, 80, 34], [176, 70, 33], [161, 60, 28], [154, 55, 27], [166, 63, 30], [189, 90, 36], [175, 63, 28], [177, 71, 30], [159, 52, 27], [171, 72, 32], [181, 85, 34]]
Ytrain = ['male', 'male', 'female', 'female', 'male', 'male', 'female', 'female', 'female', 'male', 'male']
Xval = [[163, 62, 28], [182, 80, 35], [150, 50, 24], [160, 57, 27], [175, 62, 30], [183, 67, 32], [177, 64, 29], [164, 62, 29], [157, 53, 23], [170, 73, 32], [169, 59, 29]]
Yval = ['female', 'male', 'female', 'female', 'male', 'male', 'female', 'female',
'female', 'male', 'female']
# initializing the ML models
knn = KNeighborsClassifier()
perceptron = Perceptron()
# Fitting the models
knn.fit(Xtrain, Ytrain)
perceptron.fit(Xtrain, Ytrain)
# Testing using our input data
pred_knn = knn.predict(Xval)
acc_knn = accuracy_score(Yval, pred_knn) * 100
print(f'Accuracy for knn: {acc_knn}')
pred_perceptron = perceptron.predict(Xval)
acc_perceptron = accuracy_score(Yval, pred_perceptron) * 100
print(f'Accuracy for perceptron: {acc_perceptron}')
# The best classifier out of the two models
index = np.argmax([acc_knn, acc_perceptron])
#argmax function assigns the index of the maximum value to the variable
classifiers = {0: 'KNN', 1:'PER'}
print(f'Best gender classifier is {classifiers[index]}')
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hard-gists/2787060/snippet.py | jjhenkel/dockerizeme | 21 | 172782 | <reponame>jjhenkel/dockerizeme<filename>hard-gists/2787060/snippet.py<gh_stars>10-100
# -*- coding:UTF8 -*-
#1.概念：
# 正则表达式（或 RE）是一种小型的、高度专业化的编程语言，
# （在Python中）它内嵌在Python中，并通过 re 模块实现。使用这个小型语言，
# 你可以为想要匹配的相应字符串集指定规则；该字符串集可能包含英文语句、email
# 地址、TeX命令或任何你想搞定的东西。然后你可以问诸如“这个字符串匹配
# 该模式吗？”或“在这个字符串中是否有部分匹配该模式呢？”。你也可以使用 RE
# 以各种方式来修改或分割字符串。
#
# 正则表达式语言相对小型和受限（功能有限），因此并非所有字符串处理都能用
# 正则表达式完成。当然也有些任务可以用正则表达式完成，不过最终表达式会变
# 得异常复杂。碰到这些情形时，编写 Python 代码进行处理可能反而更好；尽管
# Python 代码比一个精巧的正则表达式要慢些，但它更易理解。
#
#2.在正则表达式中， 如下的字符是具有特殊含义的
# . (所有字符) ^ $ *(0-N次) +(1-N次) ? (0-1次) { } [ ] \ | ( )
# 1)."[" 和 "]"。它们常用来指定一个字符类别，所谓字符类别就是你想匹配的一个字符集
# 2).其它地方的"^"只会简单匹配 "^"字符本身。例[^5] 将匹配除 "5" 之外的任意字符。
# 3).反斜杠后面可以加不同的字符以表示不同特殊意义。它也可以用于取消所有的元字符
#
#3.RE 函数用法:
# findall(rule , target [,flag] ) 在目标字符串中查找符合规则的字符串。
# match() 决定 RE 是否在字符串刚开始的位置匹配
# search() 扫描字符串，找到这个 RE 匹配的位置
# findall() 找到 RE 匹配的所有子串，并把它们作为一个列表返回
# finditer() 找到 RE 匹配的所有子串，并把它们作为一个迭代器返回
# group() 返回被 RE 匹配的字符串
# start() 返回匹配开始的位置
# end() 返回匹配结束的位置
# span() 返回一个元组包含匹配 (开始,结束) 的位置
# compile( rule [,flag] )将正则规则编译成一个Pattern对象，以供接下来使用第一个参数
#
# 是规则式，第二个参数是规则选项。(使用compile加速)
#
#4 : 含义:
# 预定义转义字符集： “\d” “\w” “\s” 等等，它们是以字符’\’开头，后面接一个特定
#
#字符的形式,用来指示一个预定义好的含义
#
# ‘^’ 和’$’ 匹配字符串开头和结尾
# ‘.’ 匹配所有字符 除\n以外
# ‘\d’ 匹配数字
# ‘\D’ 匹配非数字
# ‘\w’ 匹配字母和数字
# ‘\W’ 匹配非英文字母和数字
# ‘\s’ 匹配间隔符
# ‘\S’ 匹配非间隔符
# ‘\A’ 匹配字符串开头
# ‘\Z’ 匹配字符串结尾
# ‘\b’ 只用以匹配单词的词首和词尾。单词被定义为一个字母数字序列，因此词尾就
#
# 是用空白符或非字母数字符来标示的。(退格)
# ‘\B’，它正好同 \b 相反，只在当前位置不在单词边界时匹配。
#5.前向界定与后向界定:
# ‘(?<=…)’ 前向界定:括号中’…’代表你希望匹配的字符串的前面应该出现的字符串。
# ‘(?=…)’后向界定 :括号中的’…’代表你希望匹配的字符串后面应该出现的字符串
# ‘(?<!..)’前向非界定 :只有当你希望的字符串前面不是’…’的内容时才匹配
# ‘(?!...)’后向非界定 :只有当你希望的字符串后面不跟着’…’内容时才匹配。
#6.组的基本知识:
# ‘(‘’)’ 无命名组 [a-z]+(\d+)[a-z]+
# ‘(?P<name>…)’ 命名组 (?P<g1>[a-z]+)\d+(?P=g1)
# ‘(?P=name)’ 调用已匹配的命名组
# ‘\number’通过序号调用已匹配的组正则式中的每个组都有一个序号，序号是按组
#
#从左到右，从1开始的数字，你可以通过下面的形式来调用已匹配的组
# ( r"(\d+)([a-z]+)(\d+)(\2)(\1)" )
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import rhinoscriptsyntax as rs
# 正则表达式
import re
str1 = "abc \\ 123 456"
print re.findall("\\\\",str1) # 不用r和用r的区
print re.findall(r"\d\Z",str1) # 用"r"来定义规则字符串
p = re.compile('(a)b')
m = p.match('ab')
print m.group()
s = "aaa1 22 gg 333 ccc 4444 pppp 55555 666"
print re.findall(r"\b\d{3}\b",s)
print re.findall(r"\b\d{2,4}\b",s)
s2 = "aaa111aaa , bbb222 , 333ccc"
print re.findall( r"(?<=[a-z]+)\d+(?=[a-z]+)",s2 )
print re.findall( r"\d+(?=[a-z]+)",s2 )
## 目标 前面是a-z 1-多次、中间数字1-9 1-多次
print re.findall(r"\d+(?!\w+)",s2)
#无命名组
print re.findall(r"[a-z]+(\d+)[a-z]+",s2) # 只返回()里面的
s3 = 'aaa111aaa,bbb222,333ccc,444ddd444,555eee666,fff777ggg,hhh888hhh'
print re.findall(r"([a-z]+)\d+([a-z]+)",s3) #返回括号里面的
#‘(?P<name>…)’ 命名组
print re.findall( r"(?P<g1>[a-z]+)\d+(?P=g1)",s3 ) #找出被中间夹有数字的前后同样的字母
print re.findall(r"([a-z]+)\d+\1",s3)
s4 = "111aaa222aaa111,333bbb444bb33"
print re.findall( r"(\d+)([a-z]+)(\d+)(\2)(\1)", s4 ) #数字、字母、数字、字母、数字相对称
print re.compile(r"(\d+)([a-z]+)(\d+)(\2)(\1)").findall(s4)
#compile( rule [,flag] ) 使用compile加速
s5 = "111,222,aaa,bbb,ccc333,444ddd"
print re.compile(r"\d+\b").findall(s5) # \退格 匹配一个位于开头的数字，没有使用M选项
s6 = "123 456\n789 012\n345 678"
print re.compile(r"^\d+",re.M).findall(s6) # 匹配位于(M/多行)开头的数字
rcm=re.compile(r"\d+$")# 对于’$’来说，没有使用M选项，它将匹配最后一个行尾的数字，即’678’，加上以后，就能匹配三个行尾的数字456 012和678了.
print re.compile(r"\d+$",re.M).findall(s6) #
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instagram/admin.py | James19stack/instagram-copy_cat | 0 | 4868 | <reponame>James19stack/instagram-copy_cat<filename>instagram/admin.py
from django.contrib import admin
from .models import Images,Comments,Profile
# Register your models here.
class CommentInline(admin.TabularInline):
model=Comments
extra=3
class ImageInline(admin.ModelAdmin):
fieldsets=[
(None,{'fields':['image']}),
(None,{'fields':['image_name']}),
(None,{'fields':['image_caption']}),
(None,{'fields':['likes']}),
]
inlines=[CommentInline]
admin.site.site_header='InstaPost Admin'
admin.site.site_title='InstaPost Admin Dashboard'
admin.site.register(Images,ImageInline)
admin.site.register(Profile)
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tests/sentry/event_manager/interfaces/test_debug_meta.py | uandco/sentry | 2 | 67931 | <filename>tests/sentry/event_manager/interfaces/test_debug_meta.py
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import pytest
from sentry.models import Event
from sentry.event_manager import EventManager
@pytest.fixture
def make_debug_meta_snapshot(insta_snapshot):
def inner(data):
mgr = EventManager(data={"debug_meta": data})
mgr.normalize()
evt = Event(data=mgr.get_data())
interface = evt.interfaces.get('debug_meta')
insta_snapshot({
'errors': evt.data.get('errors'),
'to_json': interface and interface.to_json()
})
return inner
@pytest.mark.parametrize('input', [
{},
{"images": None},
# TODO(markus): Should eventually generate {"images": [None]}
{"images": [None]},
])
def test_null_values(make_debug_meta_snapshot, input):
make_debug_meta_snapshot(input)
def test_apple_behavior(make_debug_meta_snapshot):
image_name = (
'/var/containers/Bundle/Application/'
'B33C37A8-F933-4B6B-9FFA-152282BFDF13/SentryTest.app/SentryTest'
)
make_debug_meta_snapshot(
{
"images": [
{
"type": "apple",
"cpu_subtype": 0,
"uuid": "C05B4DDD-69A7-3840-A649-32180D341587",
"image_vmaddr": 4294967296,
"image_addr": '0x100020000',
"cpu_type": 16777228,
"image_size": 32768,
"name": image_name,
}
],
"sdk_info": {
"sdk_name": "iOS",
"version_major": 9,
"version_minor": 3,
"version_patchlevel": 0
}
}
)
def test_apple_behavior_with_arch(make_debug_meta_snapshot):
image_name = (
'/var/containers/Bundle/Application/'
'B33C37A8-F933-4B6B-9FFA-152282BFDF13/SentryTest.app/SentryTest'
)
make_debug_meta_snapshot(
{
"images": [
{
"type": "apple",
"arch": "x86_64",
"cpu_subtype": 0,
"uuid": "C05B4DDD-69A7-3840-A649-32180D341587",
"image_vmaddr": 4294967296,
"image_addr": '0x100020000',
"cpu_type": 16777228,
"image_size": 32768,
"name": image_name,
}
],
"sdk_info": {
"sdk_name": "iOS",
"version_major": 9,
"version_minor": 3,
"version_patchlevel": 0
}
}
)
def test_symbolic_behavior(make_debug_meta_snapshot):
make_debug_meta_snapshot(
{
"images": [
{
"type": "symbolic",
"id": "3249d99d-0c40-4931-8610-f4e4fb0b6936-1",
"image_addr": 2752512,
"image_size": 36864,
"name": "C:\\projects\\breakpad-tools\\windows\\Release\\crash.exe",
}
],
"sdk_info": {
"sdk_name": "Windows",
"version_major": 10,
"version_minor": 0,
"version_patchlevel": 14393
}
}
)
def test_symbolic_behavior_with_arch(make_debug_meta_snapshot):
make_debug_meta_snapshot(
{
"images": [
{
"type": "symbolic",
"arch": "x86",
"id": "3249d99d-0c40-4931-8610-f4e4fb0b6936-1",
"image_addr": 2752512,
"image_size": 36864,
"name": "C:\\projects\\breakpad-tools\\windows\\Release\\crash.exe",
}
],
"sdk_info": {
"sdk_name": "Windows",
"version_major": 10,
"version_minor": 0,
"version_patchlevel": 14393
}
}
)
def test_proguard_behavior(make_debug_meta_snapshot):
make_debug_meta_snapshot(
{
"images": [{
"type": "proguard",
"uuid": "C05B4DDD-69A7-3840-A649-32180D341587",
}]
}
)
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wsgi.py | nadeengamage/flaskee | 0 | 41200 | """
The Flaskee is an Open Source project for Microservices.
Develop By <NAME> | https://nadeengamage.com | <EMAIL>
"""
from werkzeug.serving import run_simple
from werkzeug.middleware.dispatcher import DispatcherMiddleware
from flaskee import api
app = api.create_app()
application = DispatcherMiddleware(app)
if __name__ == "__main__":
run_simple('0.0.0.0', 5000, application, use_reloader=True, use_debugger=True)
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] |
flink-libraries/flink-streaming-python/src/test/python/org/apache/flink/streaming/python/api/utils/pygeneratorbase.py | Silven1117/flink | 9 | 115211 | ################################################################################
# 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 org.apache.flink.streaming.api.functions.source import SourceFunction
class PyGeneratorBase(SourceFunction):
def __init__(self, num_iters=7000):
self._running = True
self._num_iters = num_iters
def run(self, ctx):
counter = 0
while self._running and counter < self._num_iters:
self.do(ctx)
counter += 1
def do(self, ctx):
pass
def cancel(self):
self._running = False
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] |
divedownload.py | AmoghRRao/Game-Recommendation-System | 0 | 105365 | <filename>divedownload.py<gh_stars>0
from google_drive_downloader import GoogleDriveDownloader as gdd
gdd.download_file_from_google_drive(file_id='14LXxwldPTV5K1EmrDr-HQc1svBhMKDvT',
dest_path='./similarity.pkl',
unzip=False)
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] |
lib/clean_npy.py | L0SG/Liver_segmentation | 0 | 198041 | import skimage
from skimage.measure import label, regionprops
import matplotlib.pyplot as plt
import cv2
import numpy as np
import os
from imageio import imwrite
# following previous labeling method
lesion_class_label = 0
def convert(warped_mask):
bboxes = []
if warped_mask.max() == 1:
mask_contour, _ = cv2.findContours(warped_mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
label_slice = label(warped_mask)
props = regionprops(label_slice)
for prop in props:
# additionally construct bbox coordinates format matching miccai2018 notations
x_start, y_start, x_end, y_end = prop.bbox[1], prop.bbox[0], prop.bbox[3], prop.bbox[2]
# use [x_min, y_min, x_max, y_max] type
coordinate = [x_start, y_start, x_end, y_end, lesion_class_label]
# append zero class label for lesion
coordinate.append(lesion_class_label)
bboxes.append(coordinate)
# print(len(props))
else:
bboxes.append(None)
return bboxes
if __name__ == '__main__':
metadata_path = os.path.join('/home/eunji/hdd/eunji/Data/liver_year1_dataset_extended_1904_preprocessed/ml_ready_phaselabel', "metadata.txt")
src_path = '/home/eunji/hdd/eunji/Data/liver_year1_dataset_extended_1904_preprocessed/ml_ready_phaselabel_align'
out_path = '/home/eunji/hdd/eunji/Data/liver_year1_dataset_extended_1904_preprocessed/ml_ready_phaselabel_align_clean'
data = []
with open(metadata_path, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.strip().split("|")
data.append(line[0])
os.makedirs(out_path, exist_ok=True)
for i in range(len(data)):
os.makedirs(os.path.join(out_path, data[i].split('/')[0]), exist_ok=True)
datapoint_name_relative = data[i]
datapoint_name_ct = datapoint_name_relative + '_ct.npy'
datapoint_name_mask = datapoint_name_relative + '_mask.npy'
datapoint_name_bbox = datapoint_name_relative + '_bbox.npy'
if os.path.isfile(os.path.join(src_path, datapoint_name_ct)):
ct_data = np.load(os.path.join(src_path, datapoint_name_ct))
mask_data = np.load(os.path.join(src_path, datapoint_name_mask))
orig_bbox = np.load(
os.path.join('/home/eunji/hdd/eunji/Data/liver_year1_dataset_extended_1904_preprocessed/ml_ready_phaselabel',
datapoint_name_bbox))
else:
continue
ct_data[ct_data < 0] = 0
ct_data[ct_data > 1] = 1
mask_data[mask_data > 0.5] = 1
mask_data[mask_data < 1] = 0
bbox_data = convert(mask_data)
if (len(bbox_data) != orig_bbox.shape[0]):
print("{}: {} -> {}".format(data[i], orig_bbox.shape[0], len(bbox_data)))
np.save(os.path.join(out_path, datapoint_name_ct), ct_data)
np.save(os.path.join(out_path, datapoint_name_mask), mask_data)
np.save(os.path.join(out_path, datapoint_name_bbox), bbox_data)
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] |
logs/on_member_unban.py | DiscordHackers/BetterBot-1.0.2.2 | 0 | 123586 | import disnake as discord
from disnake.ext import commands
from datetime import datetime
from api.server import base, main
class OnMemberUnBan(commands.Cog):
def __init__(self, client):
self.client = client
@commands.Cog.listener()
async def on_member_unban(self, guild, user):
if base.guild(guild)[6] is not None:
async for event in guild.audit_logs(limit = 1, action = discord.AuditLogAction.unban):
if getattr(event.target, "id", None) != user.id:
continue
try:
embed = discord.Embed(description = main.get_lang(guild, "MEMBER_UNBAN").format(user.name, user.mention), timestamp = datetime.utcnow(), color = 0x85ea8a)
embed.add_field(name = main.get_lang(guild, "MODERATOR"), value=f'**{event.user.name}** ({event.user.mention})', inline = True)
embed.set_footer(text = main.get_lang(guild, "MEMBER_ID").format(user.id), icon_url = user.avatar.url)
await self.client.get_channel(int(base.guild(guild)[6])).send(embed = embed)
except:
embed = discord.Embed(description = main.get_lang(guild, "MEMBER_UNBAN").format(user.name, user.mention), timestamp = datetime.utcnow(), color = 0x85ea8a)
embed.add_field(name = main.get_lang(guild, "MODERATOR"), value=f'**{event.user.name}** ({event.user.mention})', inline = True)
embed.set_footer(text = main.get_lang(guild, "MEMBER_ID").format(user.id))
await self.client.get_channel(int(base.guild(guild)[6])).send(embed = embed)
def setup(client):
client.add_cog(OnMemberUnBan(client)) | [
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] |
scripts/example_basic_classifier.py | nicolasigor/sleep-stage | 0 | 199018 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import matplotlib.pyplot as plt
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
sys.path.append('..')
from libs.data.mass import Mass
from libs.data.utils import power_spectrum
def plot_confusion_matrix(y_true, y_pred,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
classes = unique_labels(y_true, y_pred)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
if __name__ == '__main__':
dataset = Mass(load_checkpoint=True)
subject_id = 1
original_fs = dataset.fs
page_duration = dataset.page_duration
signal_names = dataset.get_signal_names()
stage2int_dict = {
'?': 2,
'W': 1,
'R': 0,
'N1': -1,
'N2': -2,
'N3': -3
}
# Get database
output_fs = 100
x_train, y_train = dataset.get_subset_data(
subject_id_list=dataset.get_train_ids(),
output_fs=output_fs, border_duration=0, ignore_unknown=True)
x_test, y_test = dataset.get_subset_data(
subject_id_list=dataset.get_test_ids(),
output_fs=output_fs, border_duration=0, ignore_unknown=True)
x_train = np.concatenate(x_train, axis=0)
y_train = np.concatenate(y_train, axis=0)
x_test = np.concatenate(x_test, axis=0)
y_test = np.concatenate(y_test, axis=0)
print('%d segments in train set. %d segments in test set'
% (y_train.size, y_test.size))
# Check class frequency
print('')
print('Training set')
values, counts = np.unique(y_train, return_counts=True)
for value, count in zip(values, counts):
print('%s: %d segments (%1.2f %% of total)'
% (value, count, 100*count/y_train.size))
print('')
print('Test set')
values, counts = np.unique(y_test, return_counts=True)
for value, count in zip(values, counts):
print('%s: %d segments (%1.2f %% of total)'
% (value, count, 100 * count / y_test.size))
print('')
# Compute simple features using FFT
print('Computing Training set features')
x_train_features = []
for i in range(x_train.shape[0]):
example_feats = []
for chn in range(x_train.shape[2]):
single_channel = x_train[i, :, chn]
power, freq = power_spectrum(single_channel, output_fs)
delta_idx = np.where((freq >= 0) & (freq <= 4))[0]
theta_idx = np.where((freq >= 4) & (freq <= 7.5))[0]
alpha_idx = np.where((freq >= 7.5) & (freq <= 15.5))[0]
beta_idx = np.where((freq >= 15.5) & (freq <= 31))[0]
gamma_idx = np.where(freq >= 31)[0]
example_feats.append([
power[delta_idx].sum(),
power[theta_idx].sum(),
power[alpha_idx].sum(),
power[beta_idx].sum(),
power[gamma_idx].sum()
])
example_feats = np.concatenate(example_feats).flatten()
x_train_features.append(example_feats)
x_train_features = np.stack(x_train_features, axis=0)
print('Computing Test set features')
x_test_features = []
for i in range(x_test.shape[0]):
example_feats = []
for chn in range(x_test.shape[2]):
single_channel = x_test[i, :, chn]
power, freq = power_spectrum(single_channel, output_fs)
delta_idx = np.where((freq >= 0) & (freq <= 4))[0]
theta_idx = np.where((freq >= 4) & (freq <= 7.5))[0]
alpha_idx = np.where((freq >= 7.5) & (freq <= 15.5))[0]
beta_idx = np.where((freq >= 15.5) & (freq <= 31))[0]
gamma_idx = np.where(freq >= 31)[0]
example_feats.append([
power[delta_idx].sum(),
power[theta_idx].sum(),
power[alpha_idx].sum(),
power[beta_idx].sum(),
power[gamma_idx].sum()
])
example_feats = np.concatenate(example_feats).flatten()
x_test_features.append(example_feats)
x_test_features = np.stack(x_test_features, axis=0)
# Train a simple classifier to solve the task
x_train_features, y_train = shuffle(
x_train_features, y_train, random_state=0)
clf = RandomForestClassifier(
n_estimators=50, max_depth=4, class_weight="balanced", random_state=0)
clf = clf.fit(x_train_features, y_train)
# Predict on test data
y_hat_test = clf.predict(x_test_features)
# Evaluate performance
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plot_confusion_matrix(y_test, y_hat_test,
title='Confusion matrix, without normalization')
plt.show()
# Plot normalized confusion matrix
plot_confusion_matrix(y_test, y_hat_test, normalize=True,
title='Normalized confusion matrix')
plt.show()
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1891,
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4636,
1495,
13,
1678,
14770,
29889,
4294,
580,
13,
13,
2
] |
jexam/argparser.py | chrispyles/jexam | 1 | 22825 | <reponame>chrispyles/jexam
#################################
##### jExam Argument Parser #####
#################################
import argparse
def get_parser():
"""
Creates and returns the argument parser for jExam
Returns:
``argparse.ArgumentParser``: the argument parser for jExam
"""
parser = argparse.ArgumentParser()
parser.add_argument("master", type=str, help="Path to exam master notebook")
parser.add_argument("result", nargs="?", default="dist", help="Path at which to write output notebooks")
parser.add_argument("-f", "--format", type=str, default="otter", help="Name of autograder format; 'otter' or 'ok'")
parser.add_argument("-s", "--seed", type=int, default=None, help="Random seed for NumPy to run before execution")
parser.add_argument("-q", "--quiet", default=False, action="store_true", help="Run without printing status")
return parser
| [
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13,
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13,
2
] |
abc/abc189/abc189d-1.py | c-yan/atcoder | 1 | 70082 | N, *S = open(0).read().split()
t = 1
f = 1
for s in S:
if s == 'AND':
f = t + f * 2
elif s == 'OR':
t = t * 2 + f
print(t)
| [
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formato_python/calendario_formato.py | kurokirasama/webies | 0 | 1616107 | <gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# In[18]:
get_ipython().system('pip install unidecode')
# In[37]:
file = open("calendario/calendario.txt", "r")
for line in file:
datos = line.split(" : ")
info = datos[0].replace("iÃ","í").replace("³","o").replace("Ã","í").replace("í©","e").replace("í¡","a")
fecha = datos[1].replace("\n","")
print(f"<tr><td>{info}</td><td>{fecha}</td></tr>")
# In[ ]:
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tests/unit/injector/test_injector.py | pauleveritt/predico | 0 | 75358 | <filename>tests/unit/injector/test_injector.py
from dataclasses import dataclass, field
import pytest
from predico.field_types import injected
from predico.injector import inject, InvalidInjectable
@dataclass
class Shoe:
size: int = 77
@dataclass
class Athlete:
shoe: Shoe = Shoe()
def test_injector_props():
""" Create instance based on data from passed-in props """
shoe = Shoe(size=55)
props = dict(shoe=shoe)
injectables = dict()
athlete = inject(props, injectables, Athlete)
assert 55 == athlete.shoe.size
def test_injector_injected():
""" Create instance from data based on injectables """
shoe = Shoe(size=66)
props = dict()
injectables = {Shoe.__name__: shoe}
athlete = inject(props, injectables, Athlete)
assert 66 == athlete.shoe.size
def test_injector_injected_double():
""" Tell the injector to hand attribute of another injectable """
@dataclass
class InjectedAthlete:
shoe_size: int = injected(Shoe, attr='size')
shoe = Shoe(size=88)
props = dict()
injectables = {Shoe.__name__: shoe}
athlete = inject(props, injectables, InjectedAthlete)
assert 88 == athlete.shoe_size
def test_injector_injected_callable():
""" Tell the injector to hand call result of another injectable """
@dataclass
class CallableShoe:
size: int
def __call__(self):
return self.size + 5
@dataclass
class InjectedAthlete:
shoe_size: int = injected(CallableShoe)
shoe = CallableShoe(size=70)
props = dict()
injectables = {CallableShoe.__name__: shoe}
athlete = inject(props, injectables, InjectedAthlete)
assert 75 == athlete.shoe_size
def test_injector_injectedattr_missing_class():
""" Ask for a class not registered as injectable """
class Jersey:
pass
@dataclass
class InjectedAthlete:
shoe_size: int = injected(Jersey, attr='size')
shoe = Shoe(size=88)
props = dict()
injectables = {Shoe.__name__: shoe}
with pytest.raises(InvalidInjectable) as exc:
inject(props, injectables, InjectedAthlete)
expected = 'Invalid injected type Jersey requested from type'
assert expected == str(exc.value)
def test_injector_fielddefault():
props = dict()
injectables = dict()
athlete = inject(props, injectables, Athlete)
assert 77 == athlete.shoe.size
def test_injector_precedence():
# When both props and injectable, choose props
shoe = Shoe(size=55)
props = dict(shoe=shoe)
shoe = Shoe(size=66)
injectables = {Shoe.__name__: shoe}
athlete = inject(props, injectables, Athlete)
assert 55 == athlete.shoe.size
def test_injector_defaultvalue():
# Field has a default value which should be used instead of
# injection
default_shoesize = Shoe(size=34523)
@dataclass
class DefaultValueAthlete:
shoe: Shoe = default_shoesize
props = dict()
injectables = dict()
athlete = inject(props, injectables, DefaultValueAthlete)
assert 34523 == athlete.shoe.size
def test_injector_defaultfactory():
# Field has a default value which should be used instead of
# injection
@dataclass
class DefaultValueAthlete:
shoe: Shoe = field(default_factory=Shoe)
props = dict()
injectables = dict()
athlete = inject(props, injectables, DefaultValueAthlete)
assert 77 == athlete.shoe.size
def test_injector_failure():
# Dataclass wants a value, doesn't have a default, and it
# isn't in props or injector
@dataclass
class AgeAthlete:
age: int # Note that this field is required
props = dict()
injectables = dict()
with pytest.raises(TypeError):
inject(props, injectables, AgeAthlete)
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336,
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11030,
29892,
11658,
1849,
29892,
16767,
29909,
386,
2810,
29897,
13,
2
] |
dialog/apps.py | asmadotgh/neural_chat_web | 26 | 149973 | <reponame>asmadotgh/neural_chat_web
from django.apps import AppConfig
class DialogConfig(AppConfig):
name = 'dialog'
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tech_project/lib/python2.7/site-packages/djangocms_picture/migrations/0007_fix_alignment.py | priyamshah112/Project-Descripton-Blog | 0 | 121481 | <reponame>priyamshah112/Project-Descripton-Blog<filename>tech_project/lib/python2.7/site-packages/djangocms_picture/migrations/0007_fix_alignment.py
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
from djangocms_picture.models import PICTURE_ALIGNMENT
class Migration(migrations.Migration):
dependencies = [
('djangocms_picture', '0006_remove_null_values'),
]
operations = [
migrations.AlterField(
model_name='picture',
name='alignment',
field=models.CharField(blank=True, help_text='Aligns the image according to the selected option.', max_length=255, verbose_name='Alignment', choices=PICTURE_ALIGNMENT),
),
]
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] |
music_site/mongoServices/services.py | UVG-Teams/music-space | 0 | 24391 | from pymongo import MongoClient
client = MongoClient()
db = client.music_space
def print_collection(collection):
print("/" * 75)
for x in db[collection].find():
print(x)
print("/" * 75)
def save_sales_on_mongo(collection, data):
for i in range(len(data['sales'])):
data['sales'][i]['total'] = float(data['sales'][i]['total'])
# db[collection].insert_one({ 'sales': data['sales'] })
if data['sales'] != []:
db[collection].insert_many( data['sales'] )
def save_recommendations_on_mongo(collection, data):
db[collection].insert_one({ 'rec': data })
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] |
pc-policy-status.py | sgordon46/pc-toolbox | 0 | 1611028 | from __future__ import print_function
try:
input = raw_input
except NameError:
pass
import argparse
import pc_lib_general
import pc_lib_api
# --Execution Block-- #
# --Parse command line arguments-- #
parser = argparse.ArgumentParser(prog='rltoolbox')
parser.add_argument(
'-u',
'--username',
type=str,
help='*Required* - Prisma Cloud API Access Key ID that you want to set to access your Prisma Cloud account.')
parser.add_argument(
'-p',
'--password',
type=str,
help='*Required* - Prisma Cloud API Secret Key that you want to set to access your Prisma Cloud account.')
parser.add_argument(
'-url',
'--uiurl',
type=str,
help='*Required* - Base URL used in the UI for connecting to Prisma Cloud. '
'Formatted as app.prismacloud.io or app2.prismacloud.io or app.eu.prismacloud.io, etc. '
'You can also input the api version of the URL if you know it and it will be passed through.')
parser.add_argument(
'-y',
'--yes',
action='store_true',
help='(Optional) - Override user input for verification (auto answer for yes).')
parser.add_argument(
'policytype',
type=str,
choices=['config', 'network', 'audit_event', 'anomaly', 'all'],
help='Policy type to enable/disable.')
parser.add_argument(
'status',
type=str,
choices=['enable', 'disable'],
help='Policy status to change the policy types to (enable or disable).')
args = parser.parse_args()
# --End parse command line arguments-- #
# --Main-- #
# Get login details worked out
pc_settings = pc_lib_general.pc_login_get(args.username, args.password, args.uiurl)
# Verification (override with -y)
if not args.yes:
print()
print('Ready to excute commands aginst your Prisma Cloud tenant.')
verification_response = str(input('Would you like to continue (y or yes to continue)?'))
continue_response = {'yes', 'y'}
print()
if verification_response not in continue_response:
pc_lib_general.pc_exit_error(400, 'Verification failed due to user response. Exiting...')
# Sort out API Login
print('API - Getting authentication token...', end='')
pc_settings = pc_lib_api.pc_jwt_get(pc_settings)
print('Done.')
print('API - Getting list of Policies...', end='')
pc_settings, response_package = pc_lib_api.api_policy_list_get(pc_settings)
policy_list_old = response_package['data']
print('Done.')
print('Filter policy list for indicated policy types of ' + args.policytype + '...', end='')
policy_type = args.policytype.lower()
policy_list_filtered = []
if args.status.lower() == "enable":
policy_enabled = True
policy_enabled_str = "true"
else:
policy_enabled = False
policy_enabled_str = "false"
for policy_old in policy_list_old:
if policy_old['enabled'] is not policy_enabled:
if policy_type == "all":
policy_list_filtered.append(policy_old)
elif policy_old['policyType'] == policy_type:
policy_list_filtered.append(policy_old)
print('Done.')
print('API - Updating policy statuses...')
for policy_update in policy_list_filtered:
print('Updating policy: ' + policy_update['name'])
pc_settings, response_package = pc_lib_api.api_policy_status_update(pc_settings, policy_update['policyId'], policy_enabled_str)
print('Done.')
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] |
natlas-agent/config.py | m4rcu5/natlas | 0 | 1801 | import os
from dotenv import load_dotenv
class Config:
# Current Version
NATLAS_VERSION = "0.6.10"
BASEDIR = os.path.abspath(os.path.dirname(__file__))
load_dotenv(os.path.join(BASEDIR, '.env'))
def get_int(self, varname):
tmp = os.environ.get(varname)
if tmp:
return int(tmp)
return None
def get_bool(self, varname):
tmp = os.environ.get(varname)
if tmp and tmp.upper() == "TRUE":
return True
if tmp and tmp.upper() == "FALSE":
return False
return None
def __init__(self):
# url of server to get/submit work from/to
self.server = os.environ.get('NATLAS_SERVER_ADDRESS') or 'http://127.0.0.1:5000'
# ignore warnings about SSL connections
# you shouldn't ignore ssl warnings, but I'll give you the option
# Instead, you should put the trusted CA certificate bundle on the agent and use the REQUESTS_CA_BUNDLE env variable
self.ignore_ssl_warn = self.get_bool('NATLAS_IGNORE_SSL_WARN') or False
# maximum number of threads to utilize
self.max_threads = self.get_int('NATLAS_MAX_THREADS') or 3
# Are we allowed to scan local addresses?
# By default, agents protect themselves from scanning their local network
self.scan_local = self.get_bool('NATLAS_SCAN_LOCAL') or False
# default time to wait for the server to respond
self.request_timeout = self.get_int('NATLAS_REQUEST_TIMEOUT') or 15 # seconds
# Maximum value for exponential backoff of requests, 5 minutes default
self.backoff_max = self.get_int('NATLAS_BACKOFF_MAX') or 300 # seconds
# Base value to begin the exponential backoff
self.backoff_base = self.get_int('NATLAS_BACKOFF_BASE') or 1 # seconds
# Maximum number of times to retry submitting data before giving up
# This is useful if a thread is submitting data that the server doesn't understand for some reason
self.max_retries = self.get_int('NATLAS_MAX_RETRIES') or 10
# Identification string that identifies the agent that performed any given scan
# Used for database lookup and stored in scan output
self.agent_id = os.environ.get("NATLAS_AGENT_ID") or None
# Authentication token that agents can use to talk to the server API
# Only needed if the server is configured to require agent authentication
self.auth_token = os.environ.get("NATLAS_AGENT_TOKEN") or None
# Optionally save files that failed to upload
self.save_fails = self.get_bool("NATLAS_SAVE_FAILS") or False
# Allow version overrides for local development
# Necessary to test versioned host data templates before release
self.version_override = os.environ.get("NATLAS_VERSION_OVERRIDE") or None
self.sentry_dsn = os.environ.get("SENTRY_DSN") or None
if self.version_override:
self.NATLAS_VERSION = self.version_override
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] |
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